diff --git a/.gitattributes b/.gitattributes
index af93fe8174a5e0ca1106871e189e57ec5bdf4aaa..8df7db2b7ea9de84d88c2940389c0e4c03fd0f24 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -385,3 +385,23 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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 .venv/lib/python3.12/site-packages/scipy/special/cython_special.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+.venv/lib/python3.12/site-packages/scipy/stats/_ansari_swilk_statistics.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
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+.venv/lib/python3.12/site-packages/scipy/stats/_qmc_cy.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
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+.venv/lib/python3.12/site-packages/scipy/stats/_stats_pythran.cpython-312-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
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+.venv/lib/python3.12/site-packages/scipy.libs/libgfortran-040039e1-0352e75f.so.5.0.0 filter=lfs diff=lfs merge=lfs -text
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+.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/__pycache__/fastjsonschema_validations.cpython-312.pyc filter=lfs diff=lfs merge=lfs -text
diff --git a/.venv/lib/python3.12/site-packages/scipy-1.18.1.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/scipy-1.18.1.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy-1.18.1.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/scipy-1.18.1.dist-info/LICENSE.txt b/.venv/lib/python3.12/site-packages/scipy-1.18.1.dist-info/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..99828059c9523a0c94c7850a92843c0dfc9db548
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy-1.18.1.dist-info/LICENSE.txt
@@ -0,0 +1,937 @@
+Copyright (c) 2001-2002 Enthought, Inc. 2003, SciPy Developers.
+All rights reserved.
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+Redistribution and use in source and binary forms, with or without
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+Description: bundled in OpenBLAS
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diff --git a/.venv/lib/python3.12/site-packages/scipy-1.18.1.dist-info/METADATA b/.venv/lib/python3.12/site-packages/scipy-1.18.1.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..62f0e6fdb345de3adb6e17829e4cc31f4d583709
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy-1.18.1.dist-info/METADATA
@@ -0,0 +1,1092 @@
+Metadata-Version: 2.1
+Name: scipy
+Version: 1.18.1
+Summary: Fundamental algorithms for scientific computing in Python
+Maintainer-Email: SciPy Developers <scipy-dev@python.org>
+License: Copyright (c) 2001-2002 Enthought, Inc. 2003, SciPy Developers.
+         All rights reserved.
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+         Redistribution and use in source and binary forms, with or without
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+           MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+           GNU General Public License for more details.
+           
+           Under Section 7 of GPL version 3, you are granted additional
+           permissions described in the GCC Runtime Library Exception, version
+           3.1, as published by the Free Software Foundation.
+           
+           You should have received a copy of the GNU General Public License and
+           a copy of the GCC Runtime Library Exception along with this program;
+           see the files COPYING3 and COPYING.RUNTIME respectively.  If not, see
+           <http://www.gnu.org/licenses/>.
+         
+         ----
+         
+         Full text of license texts referred to above follows (that they are
+         listed below does not necessarily imply the conditions apply to the
+         present binary release):
+         
+         ----
+         
+         GCC RUNTIME LIBRARY EXCEPTION
+         
+         Version 3.1, 31 March 2009
+         
+         Copyright (C) 2009 Free Software Foundation, Inc. <http://fsf.org/>
+         
+         Everyone is permitted to copy and distribute verbatim copies of this
+         license document, but changing it is not allowed.
+         
+         This GCC Runtime Library Exception ("Exception") is an additional
+         permission under section 7 of the GNU General Public License, version
+         3 ("GPLv3"). It applies to a given file (the "Runtime Library") that
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+         the file is governed by GPLv3 along with this Exception.
+         
+         When you use GCC to compile a program, GCC may combine portions of
+         certain GCC header files and runtime libraries with the compiled
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+         non-GPL (including proprietary) programs to use, in this way, the
+         header files and runtime libraries covered by this Exception.
+         
+         0. Definitions.
+         
+         A file is an "Independent Module" if it either requires the Runtime
+         Library for execution after a Compilation Process, or makes use of an
+         interface provided by the Runtime Library, but is not otherwise based
+         on the Runtime Library.
+         
+         "GCC" means a version of the GNU Compiler Collection, with or without
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+         
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+         
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+         
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+         
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+         
+         1. Grant of Additional Permission.
+         
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+         all Target Code was generated by Eligible Compilation Processes. You
+         may then convey such a combination under terms of your choice,
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+         
+         2. No Weakening of GCC Copyleft.
+         
+         The availability of this Exception does not imply any general
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+         
+         ----
+         
+                             GNU GENERAL PUBLIC LICENSE
+                                Version 3, 29 June 2007
+         
+          Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+          Everyone is permitted to copy and distribute verbatim copies
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+         
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+         available, or (2) arrange to deprive yourself of the benefit of the
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+         
+           If, pursuant to or in connection with a single transaction or
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+         
+           A patent license is "discriminatory" if it does not include within
+         the scope of its coverage, prohibits the exercise of, or is
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+         or that patent license was granted, prior to 28 March 2007.
+         
+           Nothing in this License shall be construed as excluding or limiting
+         any implied license or other defenses to infringement that may
+         otherwise be available to you under applicable patent law.
+         
+           12. No Surrender of Others' Freedom.
+         
+           If conditions are imposed on you (whether by court order, agreement or
+         otherwise) that contradict the conditions of this License, they do not
+         excuse you from the conditions of this License.  If you cannot convey a
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+         not convey it at all.  For example, if you agree to terms that obligate you
+         to collect a royalty for further conveying from those to whom you convey
+         the Program, the only way you could satisfy both those terms and this
+         License would be to refrain entirely from conveying the Program.
+         
+           13. Use with the GNU Affero General Public License.
+         
+           Notwithstanding any other provision of this License, you have
+         permission to link or combine any covered work with a work licensed
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+         combination as such.
+         
+           14. Revised Versions of this License.
+         
+           The Free Software Foundation may publish revised and/or new versions of
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+         be similar in spirit to the present version, but may differ in detail to
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+         
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+         
+           If the Program specifies that a proxy can decide which future
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+           15. Disclaimer of Warranty.
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+         EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
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+         
+           17. Interpretation of Sections 15 and 16.
+         
+           If the disclaimer of warranty and limitation of liability provided
+         above cannot be given local legal effect according to their terms,
+         reviewing courts shall apply local law that most closely approximates
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+         Program, unless a warranty or assumption of liability accompanies a
+         copy of the Program in return for a fee.
+         
+                              END OF TERMS AND CONDITIONS
+         
+                     How to Apply These Terms to Your New Programs
+         
+           If you develop a new program, and you want it to be of the greatest
+         possible use to the public, the best way to achieve this is to make it
+         free software which everyone can redistribute and change under these terms.
+         
+           To do so, attach the following notices to the program.  It is safest
+         to attach them to the start of each source file to most effectively
+         state the exclusion of warranty; and each file should have at least
+         the "copyright" line and a pointer to where the full notice is found.
+         
+             <one line to give the program's name and a brief idea of what it does.>
+             Copyright (C) <year>  <name of author>
+         
+             This program is free software: you can redistribute it and/or modify
+             it under the terms of the GNU General Public License as published by
+             the Free Software Foundation, either version 3 of the License, or
+             (at your option) any later version.
+         
+             This program is distributed in the hope that it will be useful,
+             but WITHOUT ANY WARRANTY; without even the implied warranty of
+             MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+             GNU General Public License for more details.
+         
+             You should have received a copy of the GNU General Public License
+             along with this program.  If not, see <http://www.gnu.org/licenses/>.
+         
+         Also add information on how to contact you by electronic and paper mail.
+         
+           If the program does terminal interaction, make it output a short
+         notice like this when it starts in an interactive mode:
+         
+             <program>  Copyright (C) <year>  <name of author>
+             This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+             This is free software, and you are welcome to redistribute it
+             under certain conditions; type `show c' for details.
+         
+         The hypothetical commands `show w' and `show c' should show the appropriate
+         parts of the General Public License.  Of course, your program's commands
+         might be different; for a GUI interface, you would use an "about box".
+         
+           You should also get your employer (if you work as a programmer) or school,
+         if any, to sign a "copyright disclaimer" for the program, if necessary.
+         For more information on this, and how to apply and follow the GNU GPL, see
+         <http://www.gnu.org/licenses/>.
+         
+           The GNU General Public License does not permit incorporating your program
+         into proprietary programs.  If your program is a subroutine library, you
+         may consider it more useful to permit linking proprietary applications with
+         the library.  If this is what you want to do, use the GNU Lesser General
+         Public License instead of this License.  But first, please read
+         <http://www.gnu.org/philosophy/why-not-lgpl.html>.
+         
+         
+         Name: libquadmath
+         Files: scipy.libs/libquadmath*.so
+         Description: dynamically linked to files compiled with gcc
+         Availability: https://gcc.gnu.org/git/?p=gcc.git;a=tree;f=libquadmath
+         License: LGPL-2.1-or-later
+         
+             GCC Quad-Precision Math Library
+             Copyright (C) 2010-2019 Free Software Foundation, Inc.
+             Written by Francois-Xavier Coudert  <fxcoudert@gcc.gnu.org>
+         
+             This file is part of the libquadmath library.
+             Libquadmath is free software; you can redistribute it and/or
+             modify it under the terms of the GNU Library General Public
+             License as published by the Free Software Foundation; either
+             version 2.1 of the License, or (at your option) any later version.
+         
+             Libquadmath is distributed in the hope that it will be useful,
+             but WITHOUT ANY WARRANTY; without even the implied warranty of
+             MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+             Lesser General Public License for more details.
+             https://www.gnu.org/licenses/old-licenses/lgpl-2.1.html
+         
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Programming Language :: C
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Programming Language :: Python :: 3.15
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Topic :: Scientific/Engineering
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: Unix
+Classifier: Operating System :: MacOS
+Project-URL: homepage, https://scipy.org/
+Project-URL: documentation, https://docs.scipy.org/doc/scipy/
+Project-URL: source, https://github.com/scipy/scipy
+Project-URL: download, https://github.com/scipy/scipy/releases
+Project-URL: tracker, https://github.com/scipy/scipy/issues
+Requires-Python: >=3.12
+Requires-Dist: numpy<2.8,>=2.0.0
+Provides-Extra: test
+Requires-Dist: pytest>=8.0.0; extra == "test"
+Requires-Dist: pytest-cov; extra == "test"
+Requires-Dist: pytest-timeout; extra == "test"
+Requires-Dist: pytest-xdist; extra == "test"
+Requires-Dist: asv; extra == "test"
+Requires-Dist: mpmath; extra == "test"
+Requires-Dist: gmpy2; extra == "test"
+Requires-Dist: threadpoolctl; extra == "test"
+Requires-Dist: scikit-umfpack; extra == "test"
+Requires-Dist: pooch; extra == "test"
+Requires-Dist: hypothesis>=6.30; extra == "test"
+Requires-Dist: array-api-strict>=2.3.1; extra == "test"
+Requires-Dist: Cython; extra == "test"
+Requires-Dist: meson; extra == "test"
+Requires-Dist: ninja; sys_platform != "emscripten" and extra == "test"
+Requires-Dist: scipy-doctest>=2.0.0; extra == "test"
+Provides-Extra: doc
+Requires-Dist: sphinx<8.2.0,>=5.0.0; extra == "doc"
+Requires-Dist: intersphinx_registry; extra == "doc"
+Requires-Dist: pydata-sphinx-theme>=0.15.2; extra == "doc"
+Requires-Dist: sphinx-copybutton; extra == "doc"
+Requires-Dist: sphinx-design>=0.4.0; extra == "doc"
+Requires-Dist: matplotlib>=3.5; extra == "doc"
+Requires-Dist: numpydoc; extra == "doc"
+Requires-Dist: jupytext; extra == "doc"
+Requires-Dist: myst-nb>=1.2.0; extra == "doc"
+Requires-Dist: pooch; extra == "doc"
+Requires-Dist: jupyterlite-sphinx>=0.19.1; extra == "doc"
+Requires-Dist: jupyterlite-pyodide-kernel; extra == "doc"
+Requires-Dist: linkify-it-py; extra == "doc"
+Requires-Dist: tabulate; extra == "doc"
+Provides-Extra: dev
+Requires-Dist: click<8.3.0; extra == "dev"
+Requires-Dist: spin; extra == "dev"
+Requires-Dist: mypy==1.19.1; extra == "dev"
+Requires-Dist: pyrefly==0.63.0; extra == "dev"
+Requires-Dist: typing_extensions; extra == "dev"
+Requires-Dist: types-psutil; extra == "dev"
+Requires-Dist: pycodestyle; extra == "dev"
+Requires-Dist: ruff>=0.12.0; extra == "dev"
+Requires-Dist: cython-lint>=0.12.2; extra == "dev"
+Description-Content-Type: text/x-rst
+
+.. image:: https://raw.githubusercontent.com/scipy/scipy/main/doc/source/_static/logo.svg
+  :target: https://scipy.org
+  :width: 110
+  :height: 110
+  :align: left
+
+.. image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A
+  :target: https://numfocus.org
+
+.. image:: https://img.shields.io/pypi/dm/scipy.svg?label=Pypi%20downloads
+  :target: https://pypi.org/project/scipy/
+
+.. image:: https://img.shields.io/conda/dn/conda-forge/scipy.svg?label=Conda%20downloads
+  :target: https://anaconda.org/conda-forge/scipy
+
+.. image:: https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg
+  :target: https://stackoverflow.com/questions/tagged/scipy
+
+.. image:: https://img.shields.io/badge/DOI-10.1038%2Fs41592--019--0686--2-blue.svg
+  :target: https://www.nature.com/articles/s41592-019-0686-2
+
+.. image:: https://insights.linuxfoundation.org/api/badge/health-score?project=scipy
+  :target: https://insights.linuxfoundation.org/project/scipy
+
+SciPy (pronounced "Sigh Pie") is an open-source software for mathematics,
+science, and engineering. It includes modules for statistics, optimization,
+integration, linear algebra, Fourier transforms, signal and image processing,
+ODE solvers, and more.
+
+- **Website:** https://scipy.org
+- **Documentation:** https://docs.scipy.org/doc/scipy/
+- **Development version of the documentation:** https://scipy.github.io/devdocs
+- **SciPy development forum:** https://discuss.scientific-python.org/c/contributor/scipy
+- **Stack Overflow:** https://stackoverflow.com/questions/tagged/scipy
+- **Source code:** https://github.com/scipy/scipy
+- **Contributing:** https://scipy.github.io/devdocs/dev/index.html
+- **Bug reports:** https://github.com/scipy/scipy/issues
+- **Code of Conduct:** https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html
+- **Report a security vulnerability:** via Tidelift, as explained in
+  `our docs on Security <https://scipy.github.io/devdocs/tutorial/security.html>`__
+- **Citing in your work:** https://www.scipy.org/citing-scipy/
+
+SciPy is built to work with
+NumPy arrays, and provides many user-friendly and efficient numerical routines,
+such as routines for numerical integration and optimization. Together, they
+run on all popular operating systems, are quick to install, and are free of
+charge. NumPy and SciPy are easy to use, but powerful enough to be depended
+upon by some of the world's leading scientists and engineers. If you need to
+manipulate numbers on a computer and display or publish the results, give
+SciPy a try!
+
+For the installation instructions, see `our install
+guide <https://scipy.org/install/>`__.
+
+
+Call for Contributions
+----------------------
+
+We appreciate and welcome contributions. Small improvements or fixes are always appreciated; issues labeled as "good
+first issue" may be a good starting point. Have a look at `our contributing
+guide <https://scipy.github.io/devdocs/dev/index.html>`__.
+
+Writing code isn’t the only way to contribute to SciPy. You can also:
+
+- review pull requests
+- triage issues
+- develop tutorials, presentations, and other educational materials
+- maintain and improve `our website <https://github.com/scipy/scipy.org>`__
+- develop graphic design for our brand assets and promotional materials
+- help with outreach and onboard new contributors
+- write grant proposals and help with other fundraising efforts
+
+If you’re unsure where to start or how your skills fit in, reach out! You can
+ask on the `forum <https://discuss.scientific-python.org/c/contributor/scipy>`__
+or here, on GitHub, by leaving a comment on a relevant issue that is already
+open.
+
+If you are new to contributing to open source, `this
+guide <https://opensource.guide/how-to-contribute/>`__ helps explain why, what,
+and how to get involved.
diff --git a/.venv/lib/python3.12/site-packages/scipy-1.18.1.dist-info/RECORD b/.venv/lib/python3.12/site-packages/scipy-1.18.1.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..5082e484303a0d490d9616a12da46ac1f4eb4290
--- /dev/null
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\ No newline at end of file
diff --git a/.venv/lib/python3.12/site-packages/scipy.libs/libgfortran-040039e1-0352e75f.so.5.0.0 b/.venv/lib/python3.12/site-packages/scipy.libs/libgfortran-040039e1-0352e75f.so.5.0.0
new file mode 100644
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+++ b/.venv/lib/python3.12/site-packages/scipy.libs/libgfortran-040039e1-0352e75f.so.5.0.0
@@ -0,0 +1,3 @@
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new file mode 100644
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@@ -0,0 +1,3 @@
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new file mode 100644
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diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ellip_harm.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ellip_harm.py
new file mode 100644
index 0000000000000000000000000000000000000000..68526bc31e7a6caedae7de1cb8f033f9b221062a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ellip_harm.py
@@ -0,0 +1,288 @@
+#
+# Tests for the Ellipsoidal Harmonic Function,
+# Distributed under the same license as SciPy itself.
+#
+import warnings
+
+import numpy as np
+from numpy.testing import assert_equal, assert_allclose, assert_
+from scipy.special._testutils import assert_func_equal
+from scipy.special import ellip_harm, ellip_harm_2, ellip_normal
+from scipy.integrate import IntegrationWarning
+from numpy import sqrt, pi
+
+
+def test_ellip_potential():
+    def change_coefficient(lambda1, mu, nu, h2, k2):
+        x = sqrt(lambda1**2*mu**2*nu**2/(h2*k2))
+        y = sqrt((lambda1**2 - h2)*(mu**2 - h2)*(h2 - nu**2)/(h2*(k2 - h2)))
+        z = sqrt((lambda1**2 - k2)*(k2 - mu**2)*(k2 - nu**2)/(k2*(k2 - h2)))
+        return x, y, z
+
+    def solid_int_ellip(lambda1, mu, nu, n, p, h2, k2):
+        return (ellip_harm(h2, k2, n, p, lambda1)*ellip_harm(h2, k2, n, p, mu)
+               * ellip_harm(h2, k2, n, p, nu))
+
+    def solid_int_ellip2(lambda1, mu, nu, n, p, h2, k2):
+        return (ellip_harm_2(h2, k2, n, p, lambda1)
+                * ellip_harm(h2, k2, n, p, mu)*ellip_harm(h2, k2, n, p, nu))
+
+    def summation(lambda1, mu1, nu1, lambda2, mu2, nu2, h2, k2):
+        tol = 1e-8
+        sum1 = 0
+        for n in range(20):
+            xsum = 0
+            for p in range(1, 2*n+2):
+                xsum += (4*pi*(solid_int_ellip(lambda2, mu2, nu2, n, p, h2, k2)
+                    * solid_int_ellip2(lambda1, mu1, nu1, n, p, h2, k2)) /
+                    (ellip_normal(h2, k2, n, p)*(2*n + 1)))
+            if abs(xsum) < 0.1*tol*abs(sum1):
+                break
+            sum1 += xsum
+        return sum1, xsum
+
+    def potential(lambda1, mu1, nu1, lambda2, mu2, nu2, h2, k2):
+        x1, y1, z1 = change_coefficient(lambda1, mu1, nu1, h2, k2)
+        x2, y2, z2 = change_coefficient(lambda2, mu2, nu2, h2, k2)
+        res = sqrt((x2 - x1)**2 + (y2 - y1)**2 + (z2 - z1)**2)
+        return 1/res
+
+    pts = [
+        (120, sqrt(19), 2, 41, sqrt(17), 2, 15, 25),
+        (120, sqrt(16), 3.2, 21, sqrt(11), 2.9, 11, 20),
+       ]
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore", "The occurrence of roundoff error", IntegrationWarning)
+        warnings.filterwarnings(
+            "ignore", "The maximum number of subdivisions", IntegrationWarning)
+
+        for p in pts:
+            err_msg = repr(p)
+            exact = potential(*p)
+            result, last_term = summation(*p)
+            assert_allclose(exact, result, atol=0, rtol=1e-8, err_msg=err_msg)
+            assert_(abs(result - exact) < 10*abs(last_term), err_msg)
+
+
+def test_ellip_norm():
+
+    def G01(h2, k2):
+        return 4*pi
+
+    def G11(h2, k2):
+        return 4*pi*h2*k2/3
+
+    def G12(h2, k2):
+        return 4*pi*h2*(k2 - h2)/3
+
+    def G13(h2, k2):
+        return 4*pi*k2*(k2 - h2)/3
+
+    def G22(h2, k2):
+        res = (2*(h2**4 + k2**4) - 4*h2*k2*(h2**2 + k2**2) + 6*h2**2*k2**2 +
+        sqrt(h2**2 + k2**2 - h2*k2)*(-2*(h2**3 + k2**3) + 3*h2*k2*(h2 + k2)))
+        return 16*pi/405*res
+
+    def G21(h2, k2):
+        res = (2*(h2**4 + k2**4) - 4*h2*k2*(h2**2 + k2**2) + 6*h2**2*k2**2
+        + sqrt(h2**2 + k2**2 - h2*k2)*(2*(h2**3 + k2**3) - 3*h2*k2*(h2 + k2)))
+        return 16*pi/405*res
+
+    def G23(h2, k2):
+        return 4*pi*h2**2*k2*(k2 - h2)/15
+
+    def G24(h2, k2):
+        return 4*pi*h2*k2**2*(k2 - h2)/15
+
+    def G25(h2, k2):
+        return 4*pi*h2*k2*(k2 - h2)**2/15
+
+    def G32(h2, k2):
+        res = (16*(h2**4 + k2**4) - 36*h2*k2*(h2**2 + k2**2) + 46*h2**2*k2**2
+        + sqrt(4*(h2**2 + k2**2) - 7*h2*k2)*(-8*(h2**3 + k2**3) +
+        11*h2*k2*(h2 + k2)))
+        return 16*pi/13125*k2*h2*res
+
+    def G31(h2, k2):
+        res = (16*(h2**4 + k2**4) - 36*h2*k2*(h2**2 + k2**2) + 46*h2**2*k2**2
+        + sqrt(4*(h2**2 + k2**2) - 7*h2*k2)*(8*(h2**3 + k2**3) -
+        11*h2*k2*(h2 + k2)))
+        return 16*pi/13125*h2*k2*res
+
+    def G34(h2, k2):
+        res = (6*h2**4 + 16*k2**4 - 12*h2**3*k2 - 28*h2*k2**3 + 34*h2**2*k2**2
+        + sqrt(h2**2 + 4*k2**2 - h2*k2)*(-6*h2**3 - 8*k2**3 + 9*h2**2*k2 +
+                                            13*h2*k2**2))
+        return 16*pi/13125*h2*(k2 - h2)*res
+
+    def G33(h2, k2):
+        res = (6*h2**4 + 16*k2**4 - 12*h2**3*k2 - 28*h2*k2**3 + 34*h2**2*k2**2
+        + sqrt(h2**2 + 4*k2**2 - h2*k2)*(6*h2**3 + 8*k2**3 - 9*h2**2*k2 -
+        13*h2*k2**2))
+        return 16*pi/13125*h2*(k2 - h2)*res
+
+    def G36(h2, k2):
+        res = (16*h2**4 + 6*k2**4 - 28*h2**3*k2 - 12*h2*k2**3 + 34*h2**2*k2**2
+        + sqrt(4*h2**2 + k2**2 - h2*k2)*(-8*h2**3 - 6*k2**3 + 13*h2**2*k2 +
+        9*h2*k2**2))
+        return 16*pi/13125*k2*(k2 - h2)*res
+
+    def G35(h2, k2):
+        res = (16*h2**4 + 6*k2**4 - 28*h2**3*k2 - 12*h2*k2**3 + 34*h2**2*k2**2
+        + sqrt(4*h2**2 + k2**2 - h2*k2)*(8*h2**3 + 6*k2**3 - 13*h2**2*k2 -
+        9*h2*k2**2))
+        return 16*pi/13125*k2*(k2 - h2)*res
+
+    def G37(h2, k2):
+        return 4*pi*h2**2*k2**2*(k2 - h2)**2/105
+
+    known_funcs = {(0, 1): G01, (1, 1): G11, (1, 2): G12, (1, 3): G13,
+                   (2, 1): G21, (2, 2): G22, (2, 3): G23, (2, 4): G24,
+                   (2, 5): G25, (3, 1): G31, (3, 2): G32, (3, 3): G33,
+                   (3, 4): G34, (3, 5): G35, (3, 6): G36, (3, 7): G37}
+
+    def _ellip_norm(n, p, h2, k2):
+        func = known_funcs[n, p]
+        return func(h2, k2)
+    _ellip_norm = np.vectorize(_ellip_norm)
+
+    def ellip_normal_known(h2, k2, n, p):
+        return _ellip_norm(n, p, h2, k2)
+
+    # generate both large and small h2 < k2 pairs
+    np.random.seed(1234)
+    h2 = np.random.pareto(0.5, size=1)
+    k2 = h2 * (1 + np.random.pareto(0.5, size=h2.size))
+
+    points = []
+    for n in range(4):
+        for p in range(1, 2*n+2):
+            points.append((h2, k2, np.full(h2.size, n), np.full(h2.size, p)))
+    points = np.array(points)
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore", "The occurrence of roundoff error", IntegrationWarning)
+        assert_func_equal(ellip_normal, ellip_normal_known, points, rtol=1e-12)
+
+
+def test_ellip_harm_2():
+
+    def I1(h2, k2, s):
+        res = (ellip_harm_2(h2, k2, 1, 1, s)/(3 * ellip_harm(h2, k2, 1, 1, s))
+        + ellip_harm_2(h2, k2, 1, 2, s)/(3 * ellip_harm(h2, k2, 1, 2, s)) +
+        ellip_harm_2(h2, k2, 1, 3, s)/(3 * ellip_harm(h2, k2, 1, 3, s)))
+        return res
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore", "The occurrence of roundoff error", IntegrationWarning)
+        assert_allclose(I1(5, 8, 10), 1/(10*sqrt((100-5)*(100-8))),
+                        atol=1.5e-7, rtol=0)
+
+        # Values produced by code from arXiv:1204.0267
+        assert_allclose(ellip_harm_2(5, 8, 2, 1, 10), 0.00108056853382,
+                        atol=1.5e-7, rtol=0)
+        assert_allclose(ellip_harm_2(5, 8, 2, 2, 10), 0.00105820513809,
+                        atol=1.5e-7, rtol=0)
+        assert_allclose(ellip_harm_2(5, 8, 2, 3, 10), 0.00106058384743,
+                        atol=1.5e-7, rtol=0)
+        assert_allclose(ellip_harm_2(5, 8, 2, 4, 10), 0.00106774492306,
+                        atol=1.5e-7, rtol=0)
+        assert_allclose(ellip_harm_2(5, 8, 2, 5, 10), 0.00107976356454,
+                        atol=1.5e-7, rtol=0)
+
+
+def test_ellip_harm():
+
+    def E01(h2, k2, s):
+        return 1
+
+    def E11(h2, k2, s):
+        return s
+
+    def E12(h2, k2, s):
+        return sqrt(abs(s*s - h2))
+
+    def E13(h2, k2, s):
+        return sqrt(abs(s*s - k2))
+
+    def E21(h2, k2, s):
+        return s*s - 1/3*((h2 + k2) + sqrt(abs((h2 + k2)*(h2 + k2)-3*h2*k2)))
+
+    def E22(h2, k2, s):
+        return s*s - 1/3*((h2 + k2) - sqrt(abs((h2 + k2)*(h2 + k2)-3*h2*k2)))
+
+    def E23(h2, k2, s):
+        return s * sqrt(abs(s*s - h2))
+
+    def E24(h2, k2, s):
+        return s * sqrt(abs(s*s - k2))
+
+    def E25(h2, k2, s):
+        return sqrt(abs((s*s - h2)*(s*s - k2)))
+
+    def E31(h2, k2, s):
+        return s*s*s - (s/5)*(2*(h2 + k2) + sqrt(4*(h2 + k2)*(h2 + k2) -
+        15*h2*k2))
+
+    def E32(h2, k2, s):
+        return s*s*s - (s/5)*(2*(h2 + k2) - sqrt(4*(h2 + k2)*(h2 + k2) -
+        15*h2*k2))
+
+    def E33(h2, k2, s):
+        return sqrt(abs(s*s - h2))*(s*s - 1/5*((h2 + 2*k2) + sqrt(abs((h2 +
+        2*k2)*(h2 + 2*k2) - 5*h2*k2))))
+
+    def E34(h2, k2, s):
+        return sqrt(abs(s*s - h2))*(s*s - 1/5*((h2 + 2*k2) - sqrt(abs((h2 +
+        2*k2)*(h2 + 2*k2) - 5*h2*k2))))
+
+    def E35(h2, k2, s):
+        return sqrt(abs(s*s - k2))*(s*s - 1/5*((2*h2 + k2) + sqrt(abs((2*h2
+        + k2)*(2*h2 + k2) - 5*h2*k2))))
+
+    def E36(h2, k2, s):
+        return sqrt(abs(s*s - k2))*(s*s - 1/5*((2*h2 + k2) - sqrt(abs((2*h2
+        + k2)*(2*h2 + k2) - 5*h2*k2))))
+
+    def E37(h2, k2, s):
+        return s * sqrt(abs((s*s - h2)*(s*s - k2)))
+
+    assert_equal(ellip_harm(5, 8, 1, 2, 2.5, 1, 1),
+    ellip_harm(5, 8, 1, 2, 2.5))
+
+    known_funcs = {(0, 1): E01, (1, 1): E11, (1, 2): E12, (1, 3): E13,
+                   (2, 1): E21, (2, 2): E22, (2, 3): E23, (2, 4): E24,
+                   (2, 5): E25, (3, 1): E31, (3, 2): E32, (3, 3): E33,
+                   (3, 4): E34, (3, 5): E35, (3, 6): E36, (3, 7): E37}
+
+    point_ref = []
+
+    def ellip_harm_known(h2, k2, n, p, s):
+        for i in range(h2.size):
+            func = known_funcs[(int(n[i]), int(p[i]))]
+            point_ref.append(func(h2[i], k2[i], s[i]))
+        return point_ref
+
+    rng = np.random.RandomState(1234)
+    h2 = rng.pareto(0.5, size=30)
+    k2 = h2*(1 + rng.pareto(0.5, size=h2.size))
+    s = rng.pareto(0.5, size=h2.size)
+    points = []
+    for i in range(h2.size):
+        for n in range(4):
+            for p in range(1, 2*n+2):
+                points.append((h2[i], k2[i], n, p, s[i]))
+    points = np.array(points)
+    assert_func_equal(ellip_harm, ellip_harm_known, points, rtol=1e-12)
+
+
+def test_ellip_harm_invalid_p():
+    # Regression test. This should return nan.
+    n = 4
+    # Make p > 2*n + 1.
+    p = 2*n + 2
+    result = ellip_harm(0.5, 2.0, n, p, 0.2)
+    assert np.isnan(result)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_erfinv.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_erfinv.py
new file mode 100644
index 0000000000000000000000000000000000000000..98739b93fc6ad75a41a7b80107ee696453b12a09
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_erfinv.py
@@ -0,0 +1,89 @@
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+import pytest
+
+import scipy.special as sc
+
+
+class TestInverseErrorFunction:
+    def test_compliment(self):
+        # Test erfcinv(1 - x) == erfinv(x)
+        x = np.linspace(-1, 1, 101)
+        assert_allclose(sc.erfcinv(1 - x), sc.erfinv(x), rtol=0, atol=1e-15)
+
+    def test_literal_values(self):
+        # The expected values were calculated with mpmath:
+        #
+        #   import mpmath
+        #   mpmath.mp.dps = 200
+        #   for y in [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:
+        #       x = mpmath.erfinv(y)
+        #       print(x)
+        #
+        y = np.array([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
+        actual = sc.erfinv(y)
+        expected = [
+            0.0,
+            0.08885599049425769,
+            0.1791434546212917,
+            0.2724627147267543,
+            0.37080715859355795,
+            0.4769362762044699,
+            0.5951160814499948,
+            0.7328690779592167,
+            0.9061938024368233,
+            1.1630871536766743,
+        ]
+        assert_allclose(actual, expected, rtol=0, atol=1e-15)
+
+    @pytest.mark.parametrize(
+        'f, x, y',
+        [
+            (sc.erfinv, -1, -np.inf),
+            (sc.erfinv, 0, 0),
+            (sc.erfinv, 1, np.inf),
+            (sc.erfinv, -100, np.nan),
+            (sc.erfinv, 100, np.nan),
+            (sc.erfcinv, 0, np.inf),
+            (sc.erfcinv, 1, -0.0),
+            (sc.erfcinv, 2, -np.inf),
+            (sc.erfcinv, -100, np.nan),
+            (sc.erfcinv, 100, np.nan),
+        ],
+        ids=[
+            'erfinv at lower bound',
+            'erfinv at midpoint',
+            'erfinv at upper bound',
+            'erfinv below lower bound',
+            'erfinv above upper bound',
+            'erfcinv at lower bound',
+            'erfcinv at midpoint',
+            'erfcinv at upper bound',
+            'erfcinv below lower bound',
+            'erfcinv above upper bound',
+        ]
+    )
+    def test_domain_bounds(self, f, x, y):
+        assert_equal(f(x), y)
+
+    def test_erfinv_asympt(self):
+        # regression test for gh-12758: erfinv(x) loses precision at small x
+        # expected values precomputed with mpmath:
+        # >>> mpmath.mp.dps = 100
+        # >>> expected = [float(mpmath.erfinv(t)) for t in x]
+        x = np.array([1e-20, 1e-15, 1e-14, 1e-10, 1e-8, 0.9e-7, 1.1e-7, 1e-6])
+        expected = np.array([8.86226925452758e-21,
+                             8.862269254527581e-16,
+                             8.86226925452758e-15,
+                             8.862269254527581e-11,
+                             8.86226925452758e-09,
+                             7.97604232907484e-08,
+                             9.74849617998037e-08,
+                             8.8622692545299e-07])
+        assert_allclose(sc.erfinv(x), expected,
+                        rtol=1e-15)
+
+        # also test the roundtrip consistency
+        assert_allclose(sc.erf(sc.erfinv(x)),
+                        x,
+                        rtol=5e-15)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_exponential_integrals.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_exponential_integrals.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6081580b338fbf1c1eb39a846047ee0ea684e66
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_exponential_integrals.py
@@ -0,0 +1,123 @@
+import pytest
+
+import numpy as np
+from numpy.testing import assert_allclose
+import scipy.special as sc
+
+
+class TestExp1:
+
+    def test_branch_cut(self):
+        assert np.isnan(sc.exp1(-1))
+        assert sc.exp1(complex(-1, 0)).imag == (
+            -sc.exp1(complex(-1, -0.0)).imag
+        )
+
+        assert_allclose(
+            sc.exp1(complex(-1, 0)),
+            sc.exp1(-1 + 1e-20j),
+            atol=0,
+            rtol=1e-15
+        )
+        assert_allclose(
+            sc.exp1(complex(-1, -0.0)),
+            sc.exp1(-1 - 1e-20j),
+            atol=0,
+            rtol=1e-15
+        )
+
+    def test_834(self):
+        # Regression test for #834
+        a = sc.exp1(-complex(19.9999990))
+        b = sc.exp1(-complex(19.9999991))
+        assert_allclose(a.imag, b.imag, atol=0, rtol=1e-15)
+
+
+class TestScaledExp1:
+
+    @pytest.mark.parametrize('x, expected', [(0, 0), (np.inf, 1)])
+    def test_limits(self, x, expected):
+        y = sc._ufuncs._scaled_exp1(x)
+        assert y == expected
+
+    # The expected values were computed with mpmath, e.g.:
+    #
+    #   from mpmath import mp
+    #   mp.dps = 80
+    #   x = 1e-25
+    #   print(float(x*mp.exp(x)*np.expint(1, x)))
+    #
+    # prints 5.698741165994961e-24
+    #
+    # The method used to compute _scaled_exp1 changes at x=1
+    # and x=1250, so values at those inputs, and values just
+    # above and below them, are included in the test data.
+    @pytest.mark.parametrize('x, expected',
+                             [(1e-25, 5.698741165994961e-24),
+                              (0.1, 0.20146425447084518),
+                              (0.9995, 0.5962509885831002),
+                              (1.0, 0.5963473623231941),
+                              (1.0005, 0.5964436833238044),
+                              (2.5, 0.7588145912149602),
+                              (10.0, 0.9156333393978808),
+                              (100.0, 0.9901942286733019),
+                              (500.0, 0.9980079523802055),
+                              (1000.0, 0.9990019940238807),
+                              (1249.5, 0.9992009578306811),
+                              (1250.0, 0.9992012769377913),
+                              (1250.25, 0.9992014363957858),
+                              (2000.0, 0.9995004992514963),
+                              (1e4, 0.9999000199940024),
+                              (1e10, 0.9999999999),
+                              (1e15, 0.999999999999999),
+                              ])
+    def test_scaled_exp1(self, x, expected):
+        y = sc._ufuncs._scaled_exp1(x)
+        assert_allclose(y, expected, rtol=2e-15)
+
+
+class TestExpi:
+
+    @pytest.mark.parametrize('result', [
+        sc.expi(complex(-1, 0)),
+        sc.expi(complex(-1, -0.0)),
+        sc.expi(-1)
+    ])
+    def test_branch_cut(self, result):
+        desired = -0.21938393439552027368  # Computed using Mpmath
+        assert_allclose(result, desired, atol=0, rtol=1e-14)
+
+    def test_near_branch_cut(self):
+        lim_from_above = sc.expi(-1 + 1e-20j)
+        lim_from_below = sc.expi(-1 - 1e-20j)
+        assert_allclose(
+            lim_from_above.real,
+            lim_from_below.real,
+            atol=0,
+            rtol=1e-15
+        )
+        assert_allclose(
+            lim_from_above.imag,
+            -lim_from_below.imag,
+            atol=0,
+            rtol=1e-15
+        )
+
+    def test_continuity_on_positive_real_axis(self):
+        assert_allclose(
+            sc.expi(complex(1, 0)),
+            sc.expi(complex(1, -0.0)),
+            atol=0,
+            rtol=1e-15
+        )
+    
+    @pytest.mark.parametrize('x, expected', [(0, -np.inf), (np.inf, np.inf)]) 
+    def test_limits(self, x, expected):
+        y = sc.expi(x)
+        assert y == expected
+
+
+class TestExpn:
+
+    def test_out_of_domain(self):
+        assert all(np.isnan([sc.expn(-1, 1.0), sc.expn(1, -1.0)]))
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_extending.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_extending.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ecaf51545e006e37a451a08f356ce0392a3159c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_extending.py
@@ -0,0 +1,28 @@
+import os
+import platform
+import sysconfig
+
+import pytest
+
+from scipy._lib._testutils import IS_EDITABLE,_test_cython_extension, cython
+from scipy.special import beta, gamma
+
+
+@pytest.mark.fail_slow(40)
+# essential per https://github.com/scipy/scipy/pull/20487#discussion_r1567057247
+@pytest.mark.skipif(IS_EDITABLE,
+                    reason='Editable install cannot find .pxd headers.')
+@pytest.mark.skipif((platform.system() == 'Windows' and
+                     sysconfig.get_config_var('Py_GIL_DISABLED')),
+                    reason='gh-22039')
+@pytest.mark.skipif(platform.machine() in ["wasm32", "wasm64"],
+                    reason="Can't start subprocess")
+@pytest.mark.skipif(cython is None, reason="requires cython")
+def test_cython(tmp_path):
+    srcdir = os.path.dirname(os.path.dirname(__file__))
+    extensions, extensions_cpp = _test_cython_extension(tmp_path, srcdir)
+    # actually test the cython c-extensions
+    assert extensions.cy_beta(0.5, 0.1) == beta(0.5, 0.1)
+    assert extensions.cy_gamma(0.5 + 1.0j) == gamma(0.5 + 1.0j)
+    assert extensions_cpp.cy_beta(0.5, 0.1) == beta(0.5, 0.1)
+    assert extensions_cpp.cy_gamma(0.5 + 1.0j) == gamma(0.5 + 1.0j)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_faddeeva.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_faddeeva.py
new file mode 100644
index 0000000000000000000000000000000000000000..8868f66c47ce0d4bbb21c78435a6c89d44065252
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_faddeeva.py
@@ -0,0 +1,85 @@
+import pytest
+
+import numpy as np
+from numpy.testing import assert_allclose
+import scipy.special as sc
+from scipy.special._testutils import FuncData
+
+
+class TestVoigtProfile:
+
+    @pytest.mark.parametrize('x, sigma, gamma', [
+        (np.nan, 1, 1),
+        (0, np.nan, 1),
+        (0, 1, np.nan),
+        (1, np.nan, 0),
+        (np.nan, 1, 0),
+        (1, 0, np.nan),
+        (np.nan, 0, 1),
+        (np.nan, 0, 0)
+    ])
+    def test_nan(self, x, sigma, gamma):
+        assert np.isnan(sc.voigt_profile(x, sigma, gamma))
+
+    @pytest.mark.parametrize('x, desired', [
+        (-np.inf, 0),
+        (np.inf, 0)
+    ])
+    def test_inf(self, x, desired):
+        assert sc.voigt_profile(x, 1, 1) == desired
+
+    def test_against_mathematica(self):
+        # Results obtained from Mathematica by computing
+        #
+        # PDF[VoigtDistribution[gamma, sigma], x]
+        #
+        points = np.array([
+            [-7.89, 45.06, 6.66, 0.0077921073660388806401],
+            [-0.05, 7.98, 24.13, 0.012068223646769913478],
+            [-13.98, 16.83, 42.37, 0.0062442236362132357833],
+            [-12.66, 0.21, 6.32, 0.010052516161087379402],
+            [11.34, 4.25, 21.96, 0.0113698923627278917805],
+            [-11.56, 20.40, 30.53, 0.0076332760432097464987],
+            [-9.17, 25.61, 8.32, 0.011646345779083005429],
+            [16.59, 18.05, 2.50, 0.013637768837526809181],
+            [9.11, 2.12, 39.33, 0.0076644040807277677585],
+            [-43.33, 0.30, 45.68, 0.0036680463875330150996]
+        ])
+        FuncData(
+            sc.voigt_profile,
+            points,
+            (0, 1, 2),
+            3,
+            atol=0,
+            rtol=1e-15
+        ).check()
+
+    def test_symmetry(self):
+        x = np.linspace(0, 10, 20)
+        assert_allclose(
+            sc.voigt_profile(x, 1, 1),
+            sc.voigt_profile(-x, 1, 1),
+            rtol=1e-15,
+            atol=0
+        )
+
+    @pytest.mark.parametrize('x, sigma, gamma, desired', [
+        (0, 0, 0, np.inf),
+        (1, 0, 0, 0)
+    ])
+    def test_corner_cases(self, x, sigma, gamma, desired):
+        assert sc.voigt_profile(x, sigma, gamma) == desired
+
+    @pytest.mark.parametrize('sigma1, gamma1, sigma2, gamma2', [
+        (0, 1, 1e-16, 1),
+        (1, 0, 1, 1e-16),
+        (0, 0, 1e-16, 1e-16)
+    ])
+    def test_continuity(self, sigma1, gamma1, sigma2, gamma2):
+        x = np.linspace(1, 10, 20)
+        assert_allclose(
+            sc.voigt_profile(x, sigma1, gamma1),
+            sc.voigt_profile(x, sigma2, gamma2),
+            rtol=1e-16,
+            atol=1e-16
+        )
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_gamma.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_gamma.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e3fbd17dddeed73d311566a930f52899e3b9db6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_gamma.py
@@ -0,0 +1,12 @@
+import numpy as np
+import scipy.special as sc
+
+
+class TestRgamma:
+
+    def test_gh_11315(self):
+        assert sc.rgamma(-35) == 0
+
+    def test_rgamma_zeros(self):
+        x = np.array([0, -10, -100, -1000, -10000])
+        assert np.all(sc.rgamma(x) == 0)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_gammainc.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_gammainc.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5a0ffb65d6deebf77fe82ebee2dca61f2067c59
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_gammainc.py
@@ -0,0 +1,152 @@
+import pytest
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_array_equal
+
+import scipy.special as sc
+from scipy.special._testutils import FuncData
+
+
+INVALID_POINTS = [
+    (1, -1),
+    (0, 0),
+    (-1, 1),
+    (np.nan, 1),
+    (1, np.nan)
+]
+
+
+class TestGammainc:
+
+    @pytest.mark.parametrize('a, x', INVALID_POINTS)
+    def test_domain(self, a, x):
+        assert np.isnan(sc.gammainc(a, x))
+
+    def test_a_eq_0_x_gt_0(self):
+        assert sc.gammainc(0, 1) == 1
+
+    @pytest.mark.parametrize('a, x, desired', [
+        (np.inf, 1, 0),
+        (np.inf, 0, 0),
+        (np.inf, np.inf, np.nan),
+        (1, np.inf, 1)
+    ])
+    def test_infinite_arguments(self, a, x, desired):
+        result = sc.gammainc(a, x)
+        if np.isnan(desired):
+            assert np.isnan(result)
+        else:
+            assert result == desired
+
+    @pytest.mark.parametrize("x", [-np.inf, -1.0, -0.0, 0.0, np.inf, np.nan])
+    def test_a_nan(self, x):
+        assert np.isnan(sc.gammainc(np.nan, x))
+
+    @pytest.mark.parametrize("a", [-np.inf, -1.0, -0.0, 0.0, np.inf, np.nan])
+    def test_x_nan(self, a):
+        assert np.isnan(sc.gammainc(a, np.nan))
+
+    def test_infinite_limits(self):
+        # Test that large arguments converge to the hard-coded limits
+        # at infinity.
+        assert_allclose(
+            sc.gammainc(1000, 100),
+            sc.gammainc(np.inf, 100),
+            atol=1e-200,  # Use `atol` since the function converges to 0.
+            rtol=0
+        )
+        assert sc.gammainc(100, 1000) == sc.gammainc(100, np.inf)
+
+    def test_x_zero(self):
+        a = np.arange(1, 10)
+        assert_array_equal(sc.gammainc(a, 0), 0)
+
+    def test_limit_check(self):
+        result = sc.gammainc(1e-10, 1)
+        limit = sc.gammainc(0, 1)
+        assert np.isclose(result, limit)
+
+    def gammainc_line(self, x):
+        # The line a = x where a simpler asymptotic expansion (analog
+        # of DLMF 8.12.15) is available.
+        c = np.array([-1/3, -1/540, 25/6048, 101/155520,
+                      -3184811/3695155200, -2745493/8151736420])
+        res = 0
+        xfac = 1
+        for ck in c:
+            res -= ck*xfac
+            xfac /= x
+        res /= np.sqrt(2*np.pi*x)
+        res += 0.5
+        return res
+
+    def test_line(self):
+        x = np.logspace(np.log10(25), 300, 500)
+        a = x
+        dataset = np.vstack((a, x, self.gammainc_line(x))).T
+        FuncData(sc.gammainc, dataset, (0, 1), 2, rtol=1e-11).check()
+
+    def test_roundtrip(self):
+        a = np.logspace(-5, 10, 100)
+        x = np.logspace(-5, 10, 100)
+
+        y = sc.gammaincinv(a, sc.gammainc(a, x))
+        assert_allclose(x, y, rtol=1e-10)
+
+
+class TestGammaincc:
+
+    @pytest.mark.parametrize('a, x', INVALID_POINTS)
+    def test_domain(self, a, x):
+        assert np.isnan(sc.gammaincc(a, x))
+
+    def test_a_eq_0_x_gt_0(self):
+        assert sc.gammaincc(0, 1) == 0
+
+    @pytest.mark.parametrize('a, x, desired', [
+        (np.inf, 1, 1),
+        (np.inf, 0, 1),
+        (np.inf, np.inf, np.nan),
+        (1, np.inf, 0)
+    ])
+    def test_infinite_arguments(self, a, x, desired):
+        result = sc.gammaincc(a, x)
+        if np.isnan(desired):
+            assert np.isnan(result)
+        else:
+            assert result == desired
+
+    @pytest.mark.parametrize("x", [-np.inf, -1.0, -0.0, 0.0, np.inf, np.nan])
+    def test_a_nan(self, x):
+        assert np.isnan(sc.gammaincc(np.nan, x))
+
+    @pytest.mark.parametrize("a", [-np.inf, -1.0, -0.0, 0.0, np.inf, np.nan])
+    def test_x_nan(self, a):
+        assert np.isnan(sc.gammaincc(a, np.nan))
+
+    def test_infinite_limits(self):
+        # Test that large arguments converge to the hard-coded limits
+        # at infinity.
+        assert sc.gammaincc(1000, 100) == sc.gammaincc(np.inf, 100)
+        assert_allclose(
+            sc.gammaincc(100, 1000),
+            sc.gammaincc(100, np.inf),
+            atol=1e-200,  # Use `atol` since the function converges to 0.
+            rtol=0
+        )
+
+    def test_limit_check(self):
+        result = sc.gammaincc(1e-10,1)
+        limit = sc.gammaincc(0,1)
+        assert np.isclose(result, limit)
+
+    def test_x_zero(self):
+        a = np.arange(1, 10)
+        assert_array_equal(sc.gammaincc(a, 0), 1)
+
+    def test_roundtrip(self):
+        a = np.logspace(-5, 10, 100)
+        x = np.logspace(-5, 10, 100)
+
+        y = sc.gammainccinv(a, sc.gammaincc(a, x))
+        assert_allclose(x, y, rtol=1e-14)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_gen_harmonic.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_gen_harmonic.py
new file mode 100644
index 0000000000000000000000000000000000000000..0896142d571ae1d3815ab2eb99a775d52b7d9169
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_gen_harmonic.py
@@ -0,0 +1,99 @@
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+import pytest
+from scipy.special._ufuncs import _gen_harmonic, _normalized_gen_harmonic
+
+
+#
+# In the following tests, reference values were computed with mpmath.
+#
+
+@pytest.mark.parametrize('typ', [np.int32, np.int64, np.float64])
+@pytest.mark.parametrize(
+    'n, a, ref',
+    [(8, 9.0, 1.0020083884212339),
+     (1000, 2.5, 1.3414661912046497),
+     (10, 1.5, 1.9953364933456017),
+     (10000, 1.25, 4.1951168257387765),
+     (10000,1.00001, 9.787182620770265),
+     (80, 1.000002, 4.965460167788836),
+     (75, 1 + 1e-12, 4.901355630543771),
+     (100, 1 + 1e-14, 5.187377517639515),
+     (100, 1 + 8e-16, 5.187377517639611),
+     (100, 1.0, 5.187377517639621),
+     (7, 1.0, 2.592857142857143),
+     (8000, 1.0, 9.564474984261423),
+     (5, 1 - 1e-12, 2.2833333333347143),
+     (25000, 1 - 1e-12, 10.703866768669737),
+     (1000, 0.995, 7.6058022857089975),
+     (1000, 0.75, 19.055178975831392),
+     (10000, 0.25, 1332.5700547197382),
+     (5, 1e-8, 4.999999952125083),
+     (15, 1e-16, 14.999999999999996),
+     (100, 0.0, 100.0),
+     (4, -1.0, 10.0),
+     (75, -1.5, 19811.38815892374)]
+)
+def test_gen_harmonic(typ, n, a, ref):
+    h = _gen_harmonic(typ(n), a)
+    assert_allclose(h, ref, rtol=5e-15)
+
+
+@pytest.mark.parametrize('typ', [np.int32, np.int64, np.float64])
+@pytest.mark.parametrize(
+    'n, a, ref',
+    [(10, np.inf, 1.0),
+     (1, np.nan, 1.0),
+     (1, -np.inf, 1.0),
+     (3, np.nan, np.nan),
+     (-3, 1.0, np.nan)]
+)
+def test_gen_harmonic_exact_cases(typ, n, a, ref):
+    h = _gen_harmonic(typ(n), a)
+    assert_equal(h, ref)
+
+
+def test_gen_harmonic_n_nan():
+    h = _gen_harmonic(np.nan, 0.75)
+    assert_equal(h, np.nan)
+
+
+@pytest.mark.parametrize('typ', [np.int32, np.int64, np.float64])
+@pytest.mark.parametrize(
+    'j, k, n, a, ref',
+    [(400, 5000, 5000, 10.0, 4.2821759663214485e-25),
+     (400, 5000, 5000, 3.5, 1.11086549102426e-07),
+     (1, 2, 3, 1.5, 0.8755176866163012),
+     (300, 500, 500, 1 + 1e-14, 0.07559343891632035),
+     (1500, 2500, 3000, 1 - 1e-12, 0.05957291246371843),
+     (10, 12, 16, 0.5, 0.13601665344521513),
+     (16, 16, 20, 0.125, 0.04583107002260924),
+     (10, 12, 16, -0.5, 0.22359306724308234),
+     (1, 8000, 10000, -1.5, 0.5724512895513029)]
+)
+def test_normalized_gen_harmonic(typ, j, k, n, a, ref):
+    h = _normalized_gen_harmonic(typ(j), typ(k), typ(n), a)
+    assert_allclose(h, ref, 5e-15)
+
+
+@pytest.mark.parametrize('typ', [np.int32, np.int64, np.float64])
+@pytest.mark.parametrize(
+    'j, k, n, a, ref',
+    [(1, 1, 1, 0.5, 1.0),
+     (1, 1, 1, np.nan, 1.0),
+     (1, 2, 5, np.nan, np.nan),
+     (1, 2, 1, 1.25, np.nan),
+     (1, 2, 3, np.inf, 1.0),
+     (2, 3, 4, np.inf, 0.0),
+     (1, 1, 10, -np.inf, 0.0),
+     (2, 3, 4, -np.inf, np.nan),
+     (3, 6, 8, 0.0, 0.5)]
+)
+def test_normalized_gen_harmonic_exact_cases(typ, j, k, n, a, ref):
+    h = _normalized_gen_harmonic(typ(j), typ(k), typ(n), a)
+    assert_equal(h, ref)
+
+
+def test_normalized_gen_harmonic_input_nan():
+    h = _normalized_gen_harmonic(1.0, np.nan, 10.0, 1.05)
+    assert_equal(h, np.nan)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_hyp2f1.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_hyp2f1.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e51a48f91440583ddaa88ffe09d48e4376055cf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_hyp2f1.py
@@ -0,0 +1,2566 @@
+"""Tests for hyp2f1 for complex values.
+
+Author: Albert Steppi, with credit to Adam Kullberg (FormerPhycisist) for
+the implementation of mp_hyp2f1 below, which modifies mpmath's hyp2f1 to
+return the same branch as scipy's on the standard branch cut.
+"""
+
+import sys
+import pytest
+import numpy as np
+from typing import NamedTuple
+from numpy.testing import assert_allclose
+
+from scipy.special import hyp2f1
+from scipy.special._testutils import check_version, MissingModule
+
+
+try:
+    import mpmath
+except ImportError:
+    mpmath = MissingModule("mpmath")
+
+
+def mp_hyp2f1(a, b, c, z):
+    """Return mpmath hyp2f1 calculated on same branch as scipy hyp2f1.
+
+    For most values of a,b,c mpmath returns the x - 0j branch of hyp2f1 on the
+    branch cut x=(1,inf) whereas scipy's hyp2f1 calculates the x + 0j branch.
+    Thus, to generate the right comparison values on the branch cut, we
+    evaluate mpmath.hyp2f1 at x + 1e-15*j.
+
+    The exception to this occurs when c-a=-m in which case both mpmath and
+    scipy calculate the x + 0j branch on the branch cut. When this happens
+    mpmath.hyp2f1 will be evaluated at the original z point.
+    """
+    on_branch_cut = z.real > 1.0 and abs(z.imag) < 1.0e-15
+    cond1 = abs(c - a - round(c - a)) < 1.0e-15 and round(c - a) <= 0
+    cond2 = abs(c - b - round(c - b)) < 1.0e-15 and round(c - b) <= 0
+    # Make sure imaginary part is *exactly* zero
+    if on_branch_cut:
+        z = z.real + 0.0j
+    if on_branch_cut and not (cond1 or cond2):
+        z_mpmath = z.real + 1.0e-15j
+    else:
+        z_mpmath = z
+    return complex(mpmath.hyp2f1(a, b, c, z_mpmath))
+
+
+class Hyp2f1TestCase(NamedTuple):
+    a: float
+    b: float
+    c: float
+    z: complex
+    expected: complex
+    rtol: float
+
+
+class TestHyp2f1:
+    """Tests for hyp2f1 for complex values.
+
+    Expected values for test cases were computed using mpmath. See
+    `scipy.special._precompute.hyp2f1_data`. The verbose style of specifying
+    test cases is used for readability and to make it easier to mark individual
+    cases as expected to fail. Expected failures are used to highlight cases
+    where improvements are needed. See
+    `scipy.special._precompute.hyp2f1_data.make_hyp2f1_test_cases` for a
+    function to generate the boilerplate for the test cases.
+
+    Assertions have been added to each test to ensure that the test cases match
+    the situations that are intended. A final test `test_test_hyp2f1` checks
+    that the expected values in the test cases actually match what is computed
+    by mpmath. This test is marked slow even though it isn't particularly slow
+    so that it won't run by default on continuous integration builds.
+    """
+    @pytest.mark.parametrize(
+        "hyp2f1_test_case",
+        [
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=0.5,
+                    b=0.2,
+                    c=-10,
+                    z=0.2 + 0.2j,
+                    expected=np.inf + 0j,
+                    rtol=0
+                )
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=0.5,
+                    b=0.2,
+                    c=-10,
+                    z=0 + 0j,
+                    expected=1 + 0j,
+                    rtol=0
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=0.5,
+                    b=0,
+                    c=-10,
+                    z=0.2 + 0.2j,
+                    expected=1 + 0j,
+                    rtol=0
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=0.5,
+                    b=0,
+                    c=0,
+                    z=0.2 + 0.2j,
+                    expected=1 + 0j,
+                    rtol=0,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=0.5,
+                    b=0.2,
+                    c=0,
+                    z=0.2 + 0.2j,
+                    expected=np.inf + 0j,
+                    rtol=0,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=0.5,
+                    b=0.2,
+                    c=0,
+                    z=0 + 0j,
+                    expected=np.nan + 0j,
+                    rtol=0,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=0.5,
+                    b=-5,
+                    c=-10,
+                    z=0.2 + 0.2j,
+                    expected=(1.0495404166666666+0.05708208333333334j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=0.5,
+                    b=-10,
+                    c=-10,
+                    z=0.2 + 0.2j,
+                    expected=(1.092966013125+0.13455014673750001j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-10,
+                    b=-20,
+                    c=-10,
+                    z=0.2 + 0.2j,
+                    expected=(-0.07712512000000005+0.12752814080000005j),
+                    rtol=1e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-1,
+                    b=3.2,
+                    c=-1,
+                    z=0.2 + 0.2j,
+                    expected=(1.6400000000000001+0.6400000000000001j),
+                    rtol=1e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-2,
+                    b=1.2,
+                    c=-4,
+                    z=1 + 0j,
+                    expected=1.8200000000000001 + 0j,
+                    rtol=1e-15,
+                ),
+            ),
+        ]
+    )
+    def test_c_non_positive_int(self, hyp2f1_test_case):
+        a, b, c, z, expected, rtol = hyp2f1_test_case
+        assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
+
+    @pytest.mark.parametrize(
+        "hyp2f1_test_case",
+        [
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=0.5,
+                    b=0.2,
+                    c=1.5,
+                    z=1 + 0j,
+                    expected=1.1496439092239847 + 0j,
+                    rtol=1e-15
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=12.3,
+                    b=8.0,
+                    c=20.31,
+                    z=1 + 0j,
+                    expected=69280986.75273195 + 0j,
+                    rtol=1e-15
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=290.2,
+                    b=321.5,
+                    c=700.1,
+                    z=1 + 0j,
+                    expected=1.3396562400934e117 + 0j,
+                    rtol=1e-12,
+                ),
+            ),
+            # Note that here even mpmath produces different results for
+            # results that should be equivalent.
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=9.2,
+                    b=621.5,
+                    c=700.1,
+                    z=(1+0j),
+                    expected=(952726652.4158565+0j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=621.5,
+                    b=9.2,
+                    c=700.1,
+                    z=(1+0j),
+                    expected=(952726652.4160284+0j),
+                    rtol=5e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-101.2,
+                    b=-400.4,
+                    c=-172.1,
+                    z=(1+0j),
+                    expected=(2.2253618341394838e+37+0j),
+                    rtol=1e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-400.4,
+                    b=-101.2,
+                    c=-172.1,
+                    z=(1+0j),
+                    expected=(2.2253618341394838e+37+0j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=172.5,
+                    b=-201.3,
+                    c=151.2,
+                    z=(1+0j),
+                    expected=(7.072266653650905e-135+0j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-201.3,
+                    b=172.5,
+                    c=151.2,
+                    z=(1+0j),
+                    expected=(7.072266653650905e-135+0j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-102.1,
+                    b=-20.3,
+                    c=1.3,
+                    z=1 + 0j,
+                    expected=2.7899070752746906e22 + 0j,
+                    rtol=3e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-202.6,
+                    b=60.3,
+                    c=1.5,
+                    z=1 + 0j,
+                    expected=-1.3113641413099326e-56 + 0j,
+                    rtol=1e-12,
+                ),
+            ),
+        ],
+    )
+    def test_unital_argument(self, hyp2f1_test_case):
+        """Tests for case z = 1, c - a - b > 0.
+
+        Expected answers computed using mpmath.
+        """
+        a, b, c, z, expected, rtol = hyp2f1_test_case
+        assert z == 1 and c - a - b > 0  # Tests the test
+        assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
+
+    @pytest.mark.parametrize(
+        "hyp2f1_test_case",
+        [
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=0.5,
+                    b=0.2,
+                    c=1.3,
+                    z=-1 + 0j,
+                    expected=0.9428846409614143 + 0j,
+                    rtol=1e-15),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=12.3,
+                    b=8.0,
+                    c=5.300000000000001,
+                    z=-1 + 0j,
+                    expected=-4.845809986595704e-06 + 0j,
+                    rtol=1e-15
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=221.5,
+                    b=90.2,
+                    c=132.3,
+                    z=-1 + 0j,
+                    expected=2.0490488728377282e-42 + 0j,
+                    rtol=1e-7,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-102.1,
+                    b=-20.3,
+                    c=-80.8,
+                    z=-1 + 0j,
+                    expected=45143784.46783885 + 0j,
+                    rtol=1e-7,
+                ),
+                marks=pytest.mark.xfail(
+                    condition=sys.maxsize < 2**32,
+                    reason="Fails on 32 bit.",
+                )
+            ),
+        ],
+    )
+    def test_special_case_z_near_minus_1(self, hyp2f1_test_case):
+        """Tests for case z ~ -1, c ~ 1 + a - b
+
+        Expected answers computed using mpmath.
+        """
+        a, b, c, z, expected, rtol = hyp2f1_test_case
+        assert abs(1 + a - b - c) < 1e-15 and abs(z + 1) < 1e-15
+        assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
+
+    @pytest.mark.parametrize(
+        "hyp2f1_test_case",
+        [
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-4,
+                    b=2.02764642551431,
+                    c=1.0561196186065624,
+                    z=(0.9473684210526314-0.10526315789473695j),
+                    expected=(0.0031961077109535375-0.0011313924606557173j),
+                    rtol=1e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-8,
+                    b=-7.937789122896016,
+                    c=-15.964218273004214,
+                    z=(2-0.10526315789473695j),
+                    expected=(0.005543763196412503-0.0025948879065698306j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-8,
+                    b=8.095813935368371,
+                    c=4.0013768449590685,
+                    z=(0.9473684210526314-0.10526315789473695j),
+                    expected=(-0.0003054674127221263-9.261359291755414e-05j),
+                    rtol=1e-10,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-4,
+                    b=-3.956227226099288,
+                    c=-3.9316537064827854,
+                    z=(1.1578947368421053-0.3157894736842106j),
+                    expected=(-0.0020809502580892937-0.0041877333232365095j),
+                    rtol=5e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.02764642551431,
+                    b=-4,
+                    c=2.050308316530781,
+                    z=(0.9473684210526314-0.10526315789473695j),
+                    expected=(0.0011282435590058734+0.0002027062303465851j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=-8,
+                    c=-15.964218273004214,
+                    z=(1.3684210526315788+0.10526315789473673j),
+                    expected=(-9.134907719238265e-05-0.00040219233987390723j),
+                    rtol=5e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=-4,
+                    c=4.0013768449590685,
+                    z=(0.9473684210526314-0.10526315789473695j),
+                    expected=(-0.000519013062087489-0.0005855883076830948j),
+                    rtol=5e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-10000,
+                    b=2.2,
+                    c=93459345.3,
+                    z=(2+2j),
+                    expected=(0.9995292071559088-0.00047047067522659253j),
+                    rtol=1e-12,
+                ),
+            ),
+        ]
+    )
+    def test_a_b_negative_int(self, hyp2f1_test_case):
+        a, b, c, z, expected, rtol = hyp2f1_test_case
+        assert a == int(a) and a < 0 or b == int(b) and b < 0  # Tests the test
+        assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
+
+    @pytest.mark.parametrize(
+        "hyp2f1_test_case",
+        [
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.5,
+                    b=-0.9629749245209605,
+                    c=-15.5,
+                    z=(1.1578947368421053-1.1578947368421053j),
+                    expected=(0.9778506962676361+0.044083801141231616j),
+                    rtol=3e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.5,
+                    b=-3.9316537064827854,
+                    c=1.5,
+                    z=(0.9473684210526314-0.10526315789473695j),
+                    expected=(4.0793167523167675-10.11694246310966j),
+                    rtol=6e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.5,
+                    b=-0.9629749245209605,
+                    c=2.5,
+                    z=(1.1578947368421053-0.10526315789473695j),
+                    expected=(-2.9692999501916915+0.6394599899845594j),
+                    rtol=1e-11,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.5,
+                    b=-0.9629749245209605,
+                    c=-15.5,
+                    z=(1.5789473684210522-1.1578947368421053j),
+                    expected=(0.9493076367106102-0.04316852977183447j),
+                    rtol=1e-11,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.9220024191881196,
+                    b=-0.5,
+                    c=-15.5,
+                    z=(0.5263157894736841+0.10526315789473673j),
+                    expected=(0.9844377175631795-0.003120587561483841j),
+                    rtol=1e-10,
+                ),
+            ),
+        ],
+    )
+    def test_a_b_neg_int_after_euler_hypergeometric_transformation(
+        self, hyp2f1_test_case
+    ):
+        a, b, c, z, expected, rtol = hyp2f1_test_case
+        assert (  # Tests the test
+            (abs(c - a - int(c - a)) < 1e-15 and c - a < 0) or
+            (abs(c - b - int(c - b)) < 1e-15 and c - b < 0)
+        )
+        assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
+
+    @pytest.mark.parametrize(
+        "hyp2f1_test_case",
+        [
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.9220024191881196,
+                    b=-0.9629749245209605,
+                    c=-15.963511401609862,
+                    z=(0.10526315789473673-0.3157894736842106j),
+                    expected=(0.9941449585778349+0.01756335047931358j),
+                    rtol=1e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.0272592605282642,
+                    b=-0.9629749245209605,
+                    c=-15.963511401609862,
+                    z=(0.5263157894736841+0.5263157894736841j),
+                    expected=(1.0388722293372104-0.09549450380041416j),
+                    rtol=5e-11,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.02764642551431,
+                    b=1.0561196186065624,
+                    c=-7.93846038215665,
+                    z=(0.10526315789473673+0.7368421052631575j),
+                    expected=(2.1948378809826434+24.934157235172222j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.02764642551431,
+                    b=16.088264119063613,
+                    c=8.031683612216888,
+                    z=(0.3157894736842106-0.736842105263158j),
+                    expected=(-0.4075277891264672-0.06819344579666956j),
+                    rtol=2e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=2.050308316530781,
+                    c=8.031683612216888,
+                    z=(0.7368421052631575-0.10526315789473695j),
+                    expected=(2.833535530740603-0.6925373701408158j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.02764642551431,
+                    b=2.050308316530781,
+                    c=4.078873014294075,
+                    z=(0.10526315789473673-0.3157894736842106j),
+                    expected=(1.005347176329683-0.3580736009337313j),
+                    rtol=5e-16,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.9220024191881196,
+                    b=-0.9629749245209605,
+                    c=-15.963511401609862,
+                    z=(0.3157894736842106-0.5263157894736843j),
+                    expected=(0.9824353641135369+0.029271018868990268j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.9220024191881196,
+                    b=-0.9629749245209605,
+                    c=-159.63511401609862,
+                    z=(0.3157894736842106-0.5263157894736843j),
+                    expected=(0.9982436200365834+0.002927268199671111j),
+                    rtol=1e-7,
+                ),
+                marks=pytest.mark.xfail(reason="Poor convergence.")
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.02764642551431,
+                    b=16.088264119063613,
+                    c=8.031683612216888,
+                    z=(0.5263157894736841-0.5263157894736843j),
+                    expected=(-0.6906825165778091+0.8176575137504892j),
+                    rtol=5e-13,
+                ),
+            ),
+        ]
+    )
+    def test_region1(self, hyp2f1_test_case):
+        """|z| < 0.9 and real(z) >= 0."""
+        a, b, c, z, expected, rtol = hyp2f1_test_case
+        assert abs(z) < 0.9 and z.real >= 0  # Tests the test
+        assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
+
+    @pytest.mark.parametrize(
+        "hyp2f1_test_case",
+        [
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.02764642551431,
+                    b=1.0561196186065624,
+                    c=4.078873014294075,
+                    z=(-0.3157894736842106+0.7368421052631575j),
+                    expected=(0.7751915029081136+0.24068493258607315j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.087593263474208,
+                    b=16.088264119063613,
+                    c=2.0397202577726152,
+                    z=(-0.9473684210526316-0.3157894736842106j),
+                    expected=(6.564549348474962e-07+1.6761570598334562e-06j),
+                    rtol=5e-09,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.0272592605282642,
+                    b=2.050308316530781,
+                    c=16.056809865262608,
+                    z=(-0.10526315789473695-0.10526315789473695j),
+                    expected=(0.9862043298997204-0.013293151372712681j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=8.077282662161238,
+                    c=16.056809865262608,
+                    z=(-0.3157894736842106-0.736842105263158j),
+                    expected=(0.16163826638754716-0.41378530376373734j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.02764642551431,
+                    b=2.050308316530781,
+                    c=-0.906685989801748,
+                    z=(-0.5263157894736843+0.3157894736842106j),
+                    expected=(-6.256871535165936+0.13824973858225484j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.02764642551431,
+                    b=8.077282662161238,
+                    c=-3.9924618758357022,
+                    z=(-0.9473684210526316-0.3157894736842106j),
+                    expected=(75.54672526086316+50.56157041797548j),
+                    rtol=5e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.087593263474208,
+                    b=8.077282662161238,
+                    c=-1.9631175993998025,
+                    z=(-0.5263157894736843+0.5263157894736841j),
+                    expected=(282.0602536306534-82.31597306936214j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.095813935368371,
+                    b=-3.9316537064827854,
+                    c=8.031683612216888,
+                    z=(-0.5263157894736843-0.10526315789473695j),
+                    expected=(5.179603735575851+1.4445374002099813j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=-7.949900487447654,
+                    c=1.0651378143226575,
+                    z=(-0.3157894736842106-0.9473684210526316j),
+                    expected=(2317.623517606141-269.51476321010324j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.087593263474208,
+                    b=-1.92872979730171,
+                    c=2.0397202577726152,
+                    z=(-0.736842105263158-0.3157894736842106j),
+                    expected=(29.179154096175836+22.126690357535043j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.095813935368371,
+                    b=-3.9316537064827854,
+                    c=-15.963511401609862,
+                    z=(-0.736842105263158-0.10526315789473695j),
+                    expected=(0.20820247892032057-0.04763956711248794j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.0272592605282642,
+                    b=-15.964218273004214,
+                    c=-1.9631175993998025,
+                    z=(-0.3157894736842106-0.5263157894736843j),
+                    expected=(-157471.63920142158+991294.0587828817j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.095813935368371,
+                    b=-7.949900487447654,
+                    c=-7.93846038215665,
+                    z=(-0.10526315789473695-0.10526315789473695j),
+                    expected=(0.30765349653210194-0.2979706363594157j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.956227226099288,
+                    b=1.0561196186065624,
+                    c=8.031683612216888,
+                    z=(-0.9473684210526316-0.10526315789473695j),
+                    expected=(1.6787607400597109+0.10056620134616838j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=16.088264119063613,
+                    c=4.078873014294075,
+                    z=(-0.5263157894736843-0.736842105263158j),
+                    expected=(7062.07842506049-12768.77955655703j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=16.088264119063613,
+                    c=2.0397202577726152,
+                    z=(-0.3157894736842106+0.7368421052631575j),
+                    expected=(54749.216391029935-23078.144720887536j),
+                    rtol=2e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.956227226099288,
+                    b=1.0561196186065624,
+                    c=-0.906685989801748,
+                    z=(-0.10526315789473695-0.10526315789473695j),
+                    expected=(1.21521766411428-4.449385173946672j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.980848054962111,
+                    b=4.0013768449590685,
+                    c=-1.9631175993998025,
+                    z=(-0.736842105263158+0.5263157894736841j),
+                    expected=(19234693144.196907+1617913967.7294445j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-1.9214641416286231,
+                    b=1.0561196186065624,
+                    c=-15.963511401609862,
+                    z=(-0.5263157894736843+0.3157894736842106j),
+                    expected=(0.9345201094534371+0.03745712558992195j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=-0.9629749245209605,
+                    c=2.0397202577726152,
+                    z=(-0.10526315789473695+0.10526315789473673j),
+                    expected=(0.605732446296829+0.398171533680972j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-1.9214641416286231,
+                    b=-15.964218273004214,
+                    c=2.0397202577726152,
+                    z=(-0.10526315789473695-0.5263157894736843j),
+                    expected=(-9.753761888305416-4.590126012666959j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.956227226099288,
+                    b=-1.92872979730171,
+                    c=2.0397202577726152,
+                    z=(-0.10526315789473695+0.3157894736842106j),
+                    expected=(0.45587226291120714+1.0694545265819797j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.9220024191881196,
+                    b=-7.949900487447654,
+                    c=-0.906685989801748,
+                    z=(-0.736842105263158+0.3157894736842106j),
+                    expected=(12.334808243233418-76.26089051819054j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.9220024191881196,
+                    b=-7.949900487447654,
+                    c=-15.963511401609862,
+                    z=(-0.5263157894736843+0.10526315789473673j),
+                    expected=(1.2396019687632678-0.047507973161146286j),
+                    rtol=1e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.980848054962111,
+                    b=-0.9629749245209605,
+                    c=-0.906685989801748,
+                    z=(-0.3157894736842106-0.5263157894736843j),
+                    expected=(97.7889554372208-18.999754543400016j),
+                    rtol=5e-13,
+                ),
+            ),
+        ]
+    )
+    def test_region2(self, hyp2f1_test_case):
+        """|z| < 1 and real(z) < 0."""
+        a, b, c, z, expected, rtol = hyp2f1_test_case
+        assert abs(z) < 1 and z.real < 0  # Tests the test
+        assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
+
+    @pytest.mark.parametrize(
+        "hyp2f1_test_case",
+        [
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.25,
+                    b=-3.75,
+                    c=-3.5,
+                    z=(0.5263157894736841+0.7368421052631575j),
+                    expected=(-1279.4894322256655-2302.914821389276j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.75,
+                    b=8.25,
+                    c=-1.5,
+                    z=(0.9473684210526314+0.3157894736842106j),
+                    expected=(-8889.452798586273-11961.162305065242j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.25,
+                    b=2.25,
+                    c=-1.5,
+                    z=(0.5263157894736841-0.736842105263158j),
+                    expected=(-236.58971357952055-238.5228224781136j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.75,
+                    b=-7.75,
+                    c=-15.5,
+                    z=(0.5263157894736841+0.7368421052631575j),
+                    expected=(0.8116076584352279-0.29360565398246036j),
+                    rtol=5e-16,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.25,
+                    b=4.25,
+                    c=-0.5,
+                    z=(0.5263157894736841-0.736842105263158j),
+                    expected=(-28.119407485189985+98.89858821348005j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.75,
+                    b=2.25,
+                    c=1.5,
+                    z=(0.5263157894736841+0.7368421052631575j),
+                    expected=(0.5311049067450484-0.9434347326448517j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.25,
+                    b=-15.75,
+                    c=-7.5,
+                    z=(0.9473684210526314+0.10526315789473673j),
+                    expected=(1262084.378141873+1775569.6338380123j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.75,
+                    b=-7.75,
+                    c=-15.5,
+                    z=(0.5263157894736841-0.736842105263158j),
+                    expected=(-0.009810480794804165+0.3648997569257999j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.25,
+                    b=2.25,
+                    c=-3.5,
+                    z=(0.5263157894736841-0.736842105263158j),
+                    expected=(585660.8815535795-33646.68398590896j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=-3.9316537064827854,
+                    c=-15.963511401609862,
+                    z=(0.9473684210526314-0.10526315789473695j),
+                    expected=(181899621848365.2-173207123998705.7j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.75,
+                    b=8.25,
+                    c=-0.5,
+                    z=(0.9473684210526314-0.10526315789473695j),
+                    expected=(0.04271686244952705-0.14087902824639406j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=-1.92872979730171,
+                    c=-0.906685989801748,
+                    z=(0.9473684210526314-0.3157894736842106j),
+                    expected=(-449.5119088817207+320.1423128036188j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.956227226099288,
+                    b=1.0561196186065624,
+                    c=8.031683612216888,
+                    z=(0.9473684210526314-0.10526315789473695j),
+                    expected=(0.6361479738012501+0.028575620091205088j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.25,
+                    b=16.25,
+                    c=16.5,
+                    z=(0.5263157894736841+0.7368421052631575j),
+                    expected=(-0.9038811840552261-1.5356250756164884j),
+                    rtol=1e-8,
+                ),
+                marks=pytest.mark.xfail(
+                    reason="Unhandled parameters."
+                )
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.25,
+                    b=-1.75,
+                    c=-1.5,
+                    z=(0.9473684210526314+0.3157894736842106j),
+                    expected=(653.0109150415394-4554.162605155542j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.75,
+                    b=-3.75,
+                    c=4.5,
+                    z=(0.9473684210526314-0.10526315789473695j),
+                    expected=(118.7009859241035-34.18713648654642j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.25,
+                    b=-15.75,
+                    c=-3.5,
+                    z=(0.5263157894736841+0.7368421052631575j),
+                    expected=(-540204.4774526551+4970059.109251281j),
+                    rtol=1e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.25,
+                    b=-15.75,
+                    c=-0.5,
+                    z=(0.5263157894736841-0.736842105263158j),
+                    expected=(2253490.972258385+3318620.683390017j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.25,
+                    b=-7.75,
+                    c=-7.5,
+                    z=(0.9473684210526314+0.3157894736842106j),
+                    expected=(-46159826.46716958-17880663.82218242j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.9220024191881196,
+                    b=-7.949900487447654,
+                    c=-7.93846038215665,
+                    z=(0.9473684210526314-0.10526315789473695j),
+                    expected=(0.07116833581404514+0.11823358038036977j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.087593263474208,
+                    b=4.0013768449590685,
+                    c=-7.93846038215665,
+                    z=(0.7368421052631575+0.5263157894736841j),
+                    expected=(4.7724909620664006e+17-6.039064078946702e+16j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.25,
+                    b=-7.75,
+                    c=1.5,
+                    z=(0.9473684210526314-0.10526315789473695j),
+                    expected=(0.0188022179759303+0.002921737281641378j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.0272592605282642,
+                    b=1.0561196186065624,
+                    c=-7.93846038215665,
+                    z=(0.7368421052631575-0.5263157894736843j),
+                    expected=(-9203.462928334846+12390.110518017136j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.75,
+                    b=-15.75,
+                    c=8.5,
+                    z=(0.7368421052631575+0.5263157894736841j),
+                    expected=(6.468457061368628+24.190040684917374j),
+                    rtol=6e-16,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.02764642551431,
+                    b=16.088264119063613,
+                    c=2.0397202577726152,
+                    z=(0.7368421052631575+0.5263157894736841j),
+                    expected=(2408.3451340186543-4275.257316636014j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.75,
+                    b=-7.75,
+                    c=8.5,
+                    z=(0.7368421052631575-0.5263157894736843j),
+                    expected=(4.1379984626381345-5.183654781039423j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.25,
+                    b=-7.75,
+                    c=-0.5,
+                    z=(0.5263157894736841+0.7368421052631575j),
+                    expected=(-81177.775295738+56079.73286548954j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=2.050308316530781,
+                    c=-0.906685989801748,
+                    z=(0.9473684210526314+0.3157894736842106j),
+                    expected=(1192868.5068926765+3624210.8182139914j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=-1.92872979730171,
+                    c=8.031683612216888,
+                    z=(0.5263157894736841+0.7368421052631575j),
+                    expected=(1.8286341846195202+1.9295255682312178j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.0272592605282642,
+                    b=1.0561196186065624,
+                    c=16.056809865262608,
+                    z=(0.7368421052631575-0.5263157894736843j),
+                    expected=(1.0514645669696452-0.0430834059440128j),
+                    rtol=5e-10,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.087593263474208,
+                    b=-15.964218273004214,
+                    c=2.0397202577726152,
+                    z=(0.5263157894736841+0.7368421052631575j),
+                    expected=(541983.236432269+288200.2043029435j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.25,
+                    b=8.25,
+                    c=1.5,
+                    z=(0.5263157894736841-0.736842105263158j),
+                    expected=(-10.931988086039945+1.9136272843579096j),
+                    rtol=1e-15,
+                ),
+            ),
+        ]
+    )
+    def test_region3(self, hyp2f1_test_case):
+        """0.9 <= |z| <= 1 and |1 - z| < 0.9."""
+        a, b, c, z, expected, rtol = hyp2f1_test_case
+        assert 0.9 <= abs(z) <= 1 and abs(1 - z) < 0.9  # Tests the test
+        assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
+
+    @pytest.mark.parametrize(
+        "hyp2f1_test_case",
+        [
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.25,
+                    b=4.25,
+                    c=2.5,
+                    z=(0.4931034482758623-0.7965517241379311j),
+                    expected=(38.41207903409937-30.510151276075792j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.0,
+                    b=16.087593263474208,
+                    c=16.088264119063613,
+                    z=(0.5689655172413794-0.7965517241379311j),
+                    expected=(-0.6667857912761286-1.0206224321443573j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.0,
+                    b=1.0272592605282642,
+                    c=-7.949900487447654,
+                    z=(0.4931034482758623-0.7965517241379311j),
+                    expected=(1679024.1647997478-2748129.775857212j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=16.0,
+                    c=-7.949900487447654,
+                    z=(0.4931034482758623-0.7965517241379311j),
+                    expected=(424747226301.16986-1245539049327.2856j),
+                    rtol=1e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.02764642551431,
+                    b=-15.964218273004214,
+                    c=4.0,
+                    z=(0.4931034482758623-0.7965517241379311j),
+                    expected=(-0.0057826199201757595+0.026359861999025885j),
+                    rtol=5e-06,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.02764642551431,
+                    b=-0.9629749245209605,
+                    c=2.0397202577726152,
+                    z=(0.5689655172413794-0.7965517241379311j),
+                    expected=(0.4671901063492606+0.7769632229834897j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.0,
+                    b=-3.956227226099288,
+                    c=-7.949900487447654,
+                    z=(0.4931034482758623+0.7965517241379312j),
+                    expected=(0.9422283708145973+1.3476905754773343j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.0,
+                    b=-15.980848054962111,
+                    c=-15.964218273004214,
+                    z=(0.4931034482758623-0.7965517241379311j),
+                    expected=(0.4168719497319604-0.9770953555235625j),
+                    rtol=5e-10,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.5,
+                    b=16.088264119063613,
+                    c=2.5,
+                    z=(0.5689655172413794+0.7965517241379312j),
+                    expected=(1.279096377550619-2.173827694297929j),
+                    rtol=5e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-1.9214641416286231,
+                    b=4.0013768449590685,
+                    c=2.0397202577726152,
+                    z=(0.4931034482758623+0.7965517241379312j),
+                    expected=(-2.071520656161738-0.7846098268395909j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.9220024191881196,
+                    b=8.0,
+                    c=-0.9629749245209605,
+                    z=(0.5689655172413794-0.7965517241379311j),
+                    expected=(-7.740015495862889+3.386766435696699j),
+                    rtol=5e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-1.9214641416286231,
+                    b=16.088264119063613,
+                    c=-7.93846038215665,
+                    z=(0.4931034482758623+0.7965517241379312j),
+                    expected=(-6318.553685853241-7133.416085202879j),
+                    rtol=5e-9,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.980848054962111,
+                    b=-3.9316537064827854,
+                    c=16.056809865262608,
+                    z=(0.5689655172413794+0.7965517241379312j),
+                    expected=(-0.8854577905547399+8.135089099967278j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-1.9214641416286231,
+                    b=-0.9629749245209605,
+                    c=4.078873014294075,
+                    z=(0.4931034482758623+0.7965517241379312j),
+                    expected=(1.224291301521487+0.36014711766402485j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.75,
+                    b=-0.75,
+                    c=-1.5,
+                    z=(0.4931034482758623+0.7965517241379312j),
+                    expected=(-1.5765685855028473-3.9399766961046323j),
+                    rtol=1e-3,
+                ),
+                marks=pytest.mark.xfail(
+                    reason="Unhandled parameters."
+                )
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.980848054962111,
+                    b=-1.92872979730171,
+                    c=-7.93846038215665,
+                    z=(0.5689655172413794-0.7965517241379311j),
+                    expected=(56.794588688231194+4.556286783533971j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.5,
+                    b=4.5,
+                    c=2.050308316530781,
+                    z=(0.5689655172413794+0.7965517241379312j),
+                    expected=(-4.251456563455306+6.737837111569671j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.5,
+                    b=8.5,
+                    c=-1.92872979730171,
+                    z=(0.4931034482758623-0.7965517241379311j),
+                    expected=(2177143.9156599627-3313617.2748088865j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.5,
+                    b=-1.5,
+                    c=4.0013768449590685,
+                    z=(0.4931034482758623-0.7965517241379311j),
+                    expected=(0.45563554481603946+0.6212000158060831j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.5,
+                    b=-7.5,
+                    c=-15.964218273004214,
+                    z=(0.4931034482758623+0.7965517241379312j),
+                    expected=(61.03201617828073-37.185626416756214j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.5,
+                    b=16.5,
+                    c=4.0013768449590685,
+                    z=(0.4931034482758623+0.7965517241379312j),
+                    expected=(-33143.425963520735+20790.608514722644j),
+                    rtol=1e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.5,
+                    b=4.5,
+                    c=-0.9629749245209605,
+                    z=(0.5689655172413794+0.7965517241379312j),
+                    expected=(30.778600270824423-26.65160354466787j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.5,
+                    b=-3.5,
+                    c=16.088264119063613,
+                    z=(0.5689655172413794-0.7965517241379311j),
+                    expected=(1.0629792615560487-0.08308454486044772j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.5,
+                    b=-7.5,
+                    c=-0.9629749245209605,
+                    z=(0.4931034482758623-0.7965517241379311j),
+                    expected=(17431.571802591767+3553.7129767034507j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.25,
+                    b=8.25,
+                    c=16.5,
+                    z=(0.11379310344827598+0.9482758620689657j),
+                    expected=(0.4468600750211926+0.7313214934036885j),
+                    rtol=1e-3,
+                ),
+                marks=pytest.mark.xfail(
+                    reason="Unhandled parameters."
+                )
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.25,
+                    b=16.25,
+                    c=4.5,
+                    z=(0.3413793103448277+0.8724137931034486j),
+                    expected=(-3.905704438293991+3.693347860329299j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.25,
+                    b=4.25,
+                    c=-0.5,
+                    z=(0.11379310344827598-0.9482758620689655j),
+                    expected=(-40.31777941834244-89.89852492432011j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.0272592605282642,
+                    b=8.0,
+                    c=-15.964218273004214,
+                    z=(0.11379310344827598-0.9482758620689655j),
+                    expected=(52584.347773055284-109197.86244309516j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.095813935368371,
+                    b=-15.964218273004214,
+                    c=16.056809865262608,
+                    z=(0.03793103448275881+0.9482758620689657j),
+                    expected=(-1.187733570412592-1.5147865053584582j),
+                    rtol=5e-10,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=-3.9316537064827854,
+                    c=1.0651378143226575,
+                    z=(0.26551724137931054+0.9482758620689657j),
+                    expected=(13.077494677898947+35.071599628224966j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=-3.5,
+                    c=-3.5,
+                    z=(0.26551724137931054+0.8724137931034486j),
+                    expected=(-0.5359656237994614-0.2344483936591811j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.25,
+                    b=-3.75,
+                    c=-1.5,
+                    z=(0.26551724137931054+0.9482758620689657j),
+                    expected=(1204.8114871663133+64.41022826840198j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-1.9214641416286231,
+                    b=16.0,
+                    c=4.0013768449590685,
+                    z=(0.03793103448275881-0.9482758620689655j),
+                    expected=(-9.85268872413994+7.011107558429154j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=16.0,
+                    c=4.0013768449590685,
+                    z=(0.3413793103448277-0.8724137931034484j),
+                    expected=(528.5522951158454-1412.21630264791j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.5,
+                    b=1.0561196186065624,
+                    c=-7.5,
+                    z=(0.4172413793103451+0.8724137931034486j),
+                    expected=(133306.45260685298+256510.7045225382j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=8.077282662161238,
+                    c=-15.963511401609862,
+                    z=(0.3413793103448277-0.8724137931034484j),
+                    expected=(-0.998555715276967+2.774198742229889j),
+                    rtol=5e-11,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.75,
+                    b=-0.75,
+                    c=1.5,
+                    z=(0.11379310344827598-0.9482758620689655j),
+                    expected=(2.072445019723025-2.9793504811373515j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.5,
+                    b=-1.92872979730171,
+                    c=1.5,
+                    z=(0.11379310344827598-0.9482758620689655j),
+                    expected=(-41.87581944176649-32.52980303527139j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.75,
+                    b=-15.75,
+                    c=-0.5,
+                    z=(0.11379310344827598-0.9482758620689655j),
+                    expected=(-3729.6214864209774-30627.510509112635j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.956227226099288,
+                    b=-15.964218273004214,
+                    c=-0.906685989801748,
+                    z=(0.03793103448275881+0.9482758620689657j),
+                    expected=(-131615.07820609974+145596.13384245415j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.5,
+                    b=16.5,
+                    c=16.088264119063613,
+                    z=(0.26551724137931054+0.8724137931034486j),
+                    expected=(0.18981844071070744+0.7855036242583742j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.5,
+                    b=8.5,
+                    c=-3.9316537064827854,
+                    z=(0.11379310344827598-0.9482758620689655j),
+                    expected=(110224529.2376068+128287212.04290268j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.5,
+                    b=-7.5,
+                    c=4.0013768449590685,
+                    z=(0.3413793103448277-0.8724137931034484j),
+                    expected=(0.2722302180888523-0.21790187837266162j),
+                    rtol=1.2e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.5,
+                    b=-7.5,
+                    c=-15.964218273004214,
+                    z=(0.11379310344827598-0.9482758620689655j),
+                    expected=(-2.8252338010989035+2.430661949756161j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.5,
+                    b=16.5,
+                    c=4.0013768449590685,
+                    z=(0.03793103448275881+0.9482758620689657j),
+                    expected=(-20.604894257647945+74.5109432558078j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.5,
+                    b=8.5,
+                    c=-0.9629749245209605,
+                    z=(0.3413793103448277+0.8724137931034486j),
+                    expected=(-2764422.521269463-3965966.9965808876j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-1.5,
+                    b=-0.5,
+                    c=1.0561196186065624,
+                    z=(0.26551724137931054+0.9482758620689657j),
+                    expected=(1.2262338560994905+0.6545051266925549j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.5,
+                    b=-15.5,
+                    c=-7.949900487447654,
+                    z=(0.4172413793103451-0.8724137931034484j),
+                    expected=(-2258.1590330318213+8860.193389158803j),
+                    rtol=1.4e-10,
+                ),
+            ),
+        ]
+    )
+    def test_region4(self, hyp2f1_test_case):
+        """0.9 <= |z| <= 1 and |1 - z| >= 1.
+
+        This region is unhandled by of the standard transformations and
+        needs special care.
+        """
+        a, b, c, z, expected, rtol = hyp2f1_test_case
+        assert 0.9 <= abs(z) <= 1 and abs(1 - z) >= 0.9  # Tests the test
+        assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
+
+    @pytest.mark.parametrize(
+        "hyp2f1_test_case",
+        [
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.5,
+                    b=16.088264119063613,
+                    c=8.5,
+                    z=(0.6448275862068968+0.8724137931034486j),
+                    expected=(0.018601324701770394-0.07618420586062377j),
+                    rtol=5e-08,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.25,
+                    b=4.25,
+                    c=4.5,
+                    z=(0.6448275862068968-0.8724137931034484j),
+                    expected=(-1.391549471425551-0.118036604903893j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.02764642551431,
+                    b=2.050308316530781,
+                    c=-1.9631175993998025,
+                    z=(0.6448275862068968+0.8724137931034486j),
+                    expected=(-2309.178768155151-1932.7247727595172j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.087593263474208,
+                    b=1.0,
+                    c=-15.964218273004214,
+                    z=(0.6448275862068968+0.8724137931034486j),
+                    expected=(85592537010.05054-8061416766688.324j),
+                    rtol=2e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.095813935368371,
+                    b=-0.5,
+                    c=1.5,
+                    z=(0.6448275862068968+0.8724137931034486j),
+                    expected=(1.2334498208515172-2.1639498536219732j),
+                    rtol=5e-11,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.087593263474208,
+                    b=-15.964218273004214,
+                    c=4.0,
+                    z=(0.6448275862068968+0.8724137931034486j),
+                    expected=(102266.35398605966-44976.97828737755j),
+                    rtol=1e-3,
+                ),
+                marks=pytest.mark.xfail(
+                    reason="Unhandled parameters."
+                )
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.0,
+                    b=-3.956227226099288,
+                    c=-15.964218273004214,
+                    z=(0.6448275862068968-0.8724137931034484j),
+                    expected=(-2.9590030930007236-4.190770764773225j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=-15.5,
+                    c=-7.5,
+                    z=(0.5689655172413794-0.8724137931034484j),
+                    expected=(-112554838.92074208+174941462.9202412j),
+                    rtol=5e-05,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.980848054962111,
+                    b=2.050308316530781,
+                    c=1.0,
+                    z=(0.6448275862068968-0.8724137931034484j),
+                    expected=(3.7519882374080145+7.360753798667486j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=2.050308316530781,
+                    c=4.0,
+                    z=(0.6448275862068968-0.8724137931034484j),
+                    expected=(0.000181132943964693+0.07742903103815582j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=4.0013768449590685,
+                    c=-1.9631175993998025,
+                    z=(0.5689655172413794+0.8724137931034486j),
+                    expected=(386338.760913596-386166.51762171905j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.980848054962111,
+                    b=8.0,
+                    c=-1.92872979730171,
+                    z=(0.6448275862068968+0.8724137931034486j),
+                    expected=(1348667126.3444858-2375132427.158893j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.5,
+                    b=-0.9629749245209605,
+                    c=4.5,
+                    z=(0.5689655172413794+0.8724137931034486j),
+                    expected=(1.428353429538678+0.6472718120804372j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=-0.9629749245209605,
+                    c=2.0397202577726152,
+                    z=(0.5689655172413794-0.8724137931034484j),
+                    expected=(3.1439267526119643-3.145305240375117j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-1.9214641416286231,
+                    b=-15.964218273004214,
+                    c=-7.93846038215665,
+                    z=(0.6448275862068968-0.8724137931034484j),
+                    expected=(75.27467675681773+144.0946946292215j),
+                    rtol=1e-07,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.75,
+                    b=-7.75,
+                    c=-7.5,
+                    z=(0.5689655172413794+0.8724137931034486j),
+                    expected=(-0.3699450626264222+0.8732812475910993j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.5,
+                    b=16.5,
+                    c=1.0561196186065624,
+                    z=(0.5689655172413794-0.8724137931034484j),
+                    expected=(5.5361025821300665-2.4709693474656285j),
+                    rtol=5e-09,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.5,
+                    b=8.5,
+                    c=-3.9316537064827854,
+                    z=(0.6448275862068968-0.8724137931034484j),
+                    expected=(-782805.6699207705-537192.581278909j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.5,
+                    b=-15.5,
+                    c=1.0561196186065624,
+                    z=(0.6448275862068968+0.8724137931034486j),
+                    expected=(12.345113400639693-14.993248992902007j),
+                    rtol=0.0005,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.5,
+                    b=-0.5,
+                    c=-15.964218273004214,
+                    z=(0.6448275862068968+0.8724137931034486j),
+                    expected=(23.698109392667842+97.15002033534108j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.5,
+                    b=16.5,
+                    c=4.0013768449590685,
+                    z=(0.6448275862068968-0.8724137931034484j),
+                    expected=(1115.2978631811834+915.9212658718577j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.5,
+                    b=16.5,
+                    c=-0.9629749245209605,
+                    z=(0.6448275862068968+0.8724137931034486j),
+                    expected=(642077722221.6489+535274495398.21027j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.5,
+                    b=-3.5,
+                    c=4.0013768449590685,
+                    z=(0.5689655172413794+0.8724137931034486j),
+                    expected=(-5.689219222945697+16.877463062787143j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.5,
+                    b=-1.5,
+                    c=-0.9629749245209605,
+                    z=(0.5689655172413794-0.8724137931034484j),
+                    expected=(-44.32070290703576+1026.9127058617403j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.25,
+                    b=2.25,
+                    c=4.5,
+                    z=(0.11379310344827598-1.024137931034483j),
+                    expected=(-0.021965227124574663+0.009908300237809064j),
+                    rtol=1e-3,
+                ),
+                marks=pytest.mark.xfail(
+                    reason="Unhandled parameters."
+                )
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.02764642551431,
+                    b=1.5,
+                    c=16.5,
+                    z=(0.26551724137931054+1.024137931034483j),
+                    expected=(1.0046072901244183+0.19945500134119992j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.087593263474208,
+                    b=1.0,
+                    c=-3.9316537064827854,
+                    z=(0.3413793103448277+0.9482758620689657j),
+                    expected=(21022.30133421465+49175.98317370489j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=16.088264119063613,
+                    c=-1.9631175993998025,
+                    z=(0.4172413793103451-0.9482758620689655j),
+                    expected=(-7024239.358547302+2481375.02681063j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.25,
+                    b=-15.75,
+                    c=1.5,
+                    z=(0.18965517241379315+1.024137931034483j),
+                    expected=(92371704.94848-403546832.548352j),
+                    rtol=5e-06,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.5,
+                    b=-7.949900487447654,
+                    c=8.5,
+                    z=(0.26551724137931054-1.024137931034483j),
+                    expected=(1.9335109845308265+5.986542524829654j),
+                    rtol=5e-10,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.095813935368371,
+                    b=-1.92872979730171,
+                    c=-7.93846038215665,
+                    z=(0.4931034482758623+0.8724137931034486j),
+                    expected=(-122.52639696039328-59.72428067512221j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.25,
+                    b=-1.75,
+                    c=-1.5,
+                    z=(0.4931034482758623+0.9482758620689657j),
+                    expected=(-90.40642053579428+50.50649180047921j),
+                    rtol=5e-08,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.5,
+                    b=8.077282662161238,
+                    c=16.5,
+                    z=(0.4931034482758623+0.9482758620689657j),
+                    expected=(-0.2155745818150323-0.564628986876639j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.9220024191881196,
+                    b=1.0561196186065624,
+                    c=8.031683612216888,
+                    z=(0.4172413793103451-0.9482758620689655j),
+                    expected=(0.9503140488280465+0.11574960074292677j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.75,
+                    b=2.25,
+                    c=-15.5,
+                    z=(0.4172413793103451+0.9482758620689657j),
+                    expected=(0.9285862488442175+0.8203699266719692j),
+                    rtol=5e-13,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.75,
+                    b=4.25,
+                    c=-15.5,
+                    z=(0.3413793103448277-0.9482758620689655j),
+                    expected=(-1.0509834850116921-1.1145522325486075j),
+                    rtol=1.1e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=-0.9629749245209605,
+                    c=2.0397202577726152,
+                    z=(0.4931034482758623-0.9482758620689655j),
+                    expected=(2.88119116536769-3.4249933450696806j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.5,
+                    b=-15.964218273004214,
+                    c=16.5,
+                    z=(0.18965517241379315+1.024137931034483j),
+                    expected=(199.65868451496038+347.79384207302877j),
+                    rtol=5e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.75,
+                    b=-15.75,
+                    c=-3.5,
+                    z=(0.4931034482758623-0.8724137931034484j),
+                    expected=(-208138312553.07013+58631611809.026955j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=-15.5,
+                    c=-7.5,
+                    z=(0.3413793103448277+0.9482758620689657j),
+                    expected=(-23032.90519856288-18256.94050457296j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.5,
+                    b=1.5,
+                    c=1.0561196186065624,
+                    z=(0.4931034482758623-0.8724137931034484j),
+                    expected=(1.507342459587056+1.2332023580148403j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=2.5,
+                    b=4.5,
+                    c=-3.9316537064827854,
+                    z=(0.4172413793103451+0.9482758620689657j),
+                    expected=(7044.766127108853-40210.365567285575j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.5,
+                    b=-1.5,
+                    c=1.0561196186065624,
+                    z=(0.03793103448275881+1.024137931034483j),
+                    expected=(0.2725347741628333-2.247314875514784j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.5,
+                    b=-1.5,
+                    c=-7.949900487447654,
+                    z=(0.26551724137931054+1.024137931034483j),
+                    expected=(-11.250200011017546+12.597393659160472j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.5,
+                    b=8.5,
+                    c=16.088264119063613,
+                    z=(0.26551724137931054+1.024137931034483j),
+                    expected=(-0.18515160890991517+0.7959014164484782j),
+                    rtol=2e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.5,
+                    b=16.5,
+                    c=-3.9316537064827854,
+                    z=(0.3413793103448277-1.024137931034483j),
+                    expected=(998246378.8556538+1112032928.103645j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-1.5,
+                    b=-3.5,
+                    c=2.050308316530781,
+                    z=(0.03793103448275881+1.024137931034483j),
+                    expected=(0.5527670397711952+2.697662715303637j),
+                    rtol=1.2e-15,       # rtol bumped from 1e-15 in gh18414
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-15.5,
+                    b=-1.5,
+                    c=-0.9629749245209605,
+                    z=(0.4931034482758623-0.8724137931034484j),
+                    expected=(55.396931662136886+968.467463806326j),
+                    rtol=5e-14,
+                ),
+            ),
+        ]
+    )
+    def test_region5(self, hyp2f1_test_case):
+        """1 < |z| < 1.1 and |1 - z| >= 0.9 and real(z) >= 0"""
+        a, b, c, z, expected, rtol = hyp2f1_test_case
+        assert 1 < abs(z) < 1.1 and abs(1 - z) >= 0.9 and z.real >= 0
+        assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
+
+    @pytest.mark.parametrize(
+        "hyp2f1_test_case",
+        [
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.095813935368371,
+                    b=4.0013768449590685,
+                    c=4.078873014294075,
+                    z=(-0.9473684210526316+0.5263157894736841j),
+                    expected=(-0.0018093573941378783+0.003481887377423739j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.087593263474208,
+                    b=2.050308316530781,
+                    c=1.0651378143226575,
+                    z=(-0.736842105263158-0.736842105263158j),
+                    expected=(-0.00023401243818780545-1.7983496305603562e-05j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.0272592605282642,
+                    b=8.077282662161238,
+                    c=4.078873014294075,
+                    z=(-0.5263157894736843-0.9473684210526316j),
+                    expected=(0.22359773002226846-0.24092487123993353j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.0272592605282642,
+                    b=2.050308316530781,
+                    c=-15.963511401609862,
+                    z=(-0.9473684210526316-0.5263157894736843j),
+                    expected=(1.191573745740011+0.14347394589721466j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=4.0013768449590685,
+                    c=-15.963511401609862,
+                    z=(-0.9473684210526316-0.5263157894736843j),
+                    expected=(31.822620756901784-66.09094396747611j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=8.077282662161238,
+                    c=-7.93846038215665,
+                    z=(-0.9473684210526316+0.5263157894736841j),
+                    expected=(207.16750179245952+34.80478274924269j),
+                    rtol=5e-12,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=8.095813935368371,
+                    b=-7.949900487447654,
+                    c=8.031683612216888,
+                    z=(-0.736842105263158+0.7368421052631575j),
+                    expected=(-159.62429364277145+9.154224290644898j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.0272592605282642,
+                    b=-1.92872979730171,
+                    c=16.056809865262608,
+                    z=(-0.9473684210526316+0.5263157894736841j),
+                    expected=(1.121122351247184-0.07170260470126685j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=16.087593263474208,
+                    b=-0.9629749245209605,
+                    c=16.056809865262608,
+                    z=(-0.9473684210526316+0.5263157894736841j),
+                    expected=(1.9040596681316053-0.4951799449960107j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.0272592605282642,
+                    b=-1.92872979730171,
+                    c=-0.906685989801748,
+                    z=(-0.9473684210526316-0.5263157894736843j),
+                    expected=(-14.496623497780739-21.897524523299875j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=4.080187217753502,
+                    b=-3.9316537064827854,
+                    c=-3.9924618758357022,
+                    z=(-0.5263157894736843-0.9473684210526316j),
+                    expected=(36.33473466026878+253.88728442029577j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.0272592605282642,
+                    b=-15.964218273004214,
+                    c=-0.906685989801748,
+                    z=(-0.9473684210526316+0.5263157894736841j),
+                    expected=(1505052.5653144997-50820766.81043443j),
+                    rtol=1e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.956227226099288,
+                    b=4.0013768449590685,
+                    c=1.0651378143226575,
+                    z=(-0.5263157894736843+0.9473684210526314j),
+                    expected=(-127.79407519260877-28.69899444941112j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-1.9214641416286231,
+                    b=8.077282662161238,
+                    c=16.056809865262608,
+                    z=(-0.9473684210526316-0.5263157894736843j),
+                    expected=(2.0623331933754976+0.741234463565458j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.956227226099288,
+                    b=8.077282662161238,
+                    c=2.0397202577726152,
+                    z=(-0.9473684210526316+0.5263157894736841j),
+                    expected=(30.729193458862525-292.5700835046965j),
+                    rtol=1e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-1.9214641416286231,
+                    b=1.0561196186065624,
+                    c=-1.9631175993998025,
+                    z=(-0.5263157894736843-0.9473684210526316j),
+                    expected=(1.1285917906203495-0.735264575450189j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.9220024191881196,
+                    b=1.0561196186065624,
+                    c=-3.9924618758357022,
+                    z=(-0.736842105263158+0.7368421052631575j),
+                    expected=(0.6356474446678052-0.02429663008952248j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-1.9214641416286231,
+                    b=16.088264119063613,
+                    c=-7.93846038215665,
+                    z=(-0.736842105263158+0.7368421052631575j),
+                    expected=(0.4718880510273174+0.655083067736377j),
+                    rtol=1e-11,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-7.937789122896016,
+                    b=-3.9316537064827854,
+                    c=16.056809865262608,
+                    z=(-0.9473684210526316+0.5263157894736841j),
+                    expected=(-0.14681550942352714+0.16092206364265146j),
+                    rtol=5e-11,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.9220024191881196,
+                    b=-15.964218273004214,
+                    c=1.0651378143226575,
+                    z=(-0.5263157894736843+0.9473684210526314j),
+                    expected=(-6.436835190526225+22.883156700606182j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-0.9220024191881196,
+                    b=-7.949900487447654,
+                    c=4.078873014294075,
+                    z=(-0.9473684210526316-0.5263157894736843j),
+                    expected=(-0.7505682955068583-1.1026583264249945j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.956227226099288,
+                    b=-3.9316537064827854,
+                    c=-7.93846038215665,
+                    z=(-0.9473684210526316-0.5263157894736843j),
+                    expected=(3.6247814989198166+2.596041360148318j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.956227226099288,
+                    b=-15.964218273004214,
+                    c=-1.9631175993998025,
+                    z=(-0.5263157894736843-0.9473684210526316j),
+                    expected=(-59537.65287927933-669074.4342539902j),
+                    rtol=5e-15,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=-3.956227226099288,
+                    b=-15.964218273004214,
+                    c=-1.9631175993998025,
+                    z=(-0.9473684210526316-0.5263157894736843j),
+                    expected=(-433084.9970266166+431088.393918521j),
+                    rtol=5e-14,
+                ),
+            ),
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1,
+                    b=1,
+                    c=4,
+                    z=(3 + 4j),
+                    expected=(0.49234384000963544+0.6051340616612397j),
+                    rtol=5e-14,
+                ),
+            ),
+        ]
+    )
+    def test_region6(self, hyp2f1_test_case):
+        """|z| > 1 but not in region 5."""
+        a, b, c, z, expected, rtol = hyp2f1_test_case
+        assert (
+            abs(z) > 1 and
+            not (1 < abs(z) < 1.1 and abs(1 - z) >= 0.9 and z.real >= 0)
+        )
+        assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
+
+
+    @pytest.mark.parametrize(
+        "hyp2f1_test_case",
+        [
+            # Broke when fixing gamma pole behavior in gh-21827
+            pytest.param(
+                Hyp2f1TestCase(
+                    a=1.3,
+                    b=-0.2,
+                    c=0.3,
+                    z=-2.1,
+                    expected=1.8202169687521206,
+                    rtol=5e-15,
+                ),
+            ),
+        ]
+    )
+    def test_miscellaneous(self, hyp2f1_test_case ):
+        a, b, c, z, expected, rtol = hyp2f1_test_case
+        assert_allclose(hyp2f1(a, b, c, z), expected, rtol=rtol)
+
+    @pytest.mark.slow
+    @check_version(mpmath, "1.0.0")
+    def test_test_hyp2f1(self):
+        """Test that expected values match what is computed by mpmath.
+
+        This gathers the parameters for the test cases out of the pytest marks.
+        The parameters are a, b, c, z, expected, rtol, where expected should
+        be the value of hyp2f1(a, b, c, z) computed with mpmath. The test
+        recomputes hyp2f1(a, b, c, z) using mpmath and verifies that expected
+        actually is the correct value. This allows the data for the tests to
+        live within the test code instead of an external datafile, while
+        avoiding having to compute the results with mpmath during the test,
+        except for when slow tests are being run.
+        """
+        test_methods = [
+            test_method for test_method in dir(self)
+            if test_method.startswith('test') and
+            # Filter properties and attributes (futureproofing).
+            callable(getattr(self, test_method)) and
+            # Filter out this test
+            test_method != 'test_test_hyp2f1'
+        ]
+        for test_method in test_methods:
+            params = self._get_test_parameters(getattr(self, test_method))
+            for a, b, c, z, expected, _ in params:
+                assert_allclose(mp_hyp2f1(a, b, c, z), expected, rtol=2.25e-16)
+
+    def _get_test_parameters(self, test_method):
+        """Get pytest.mark parameters for a test in this class."""
+        return [
+            case.values[0] for mark in test_method.pytestmark
+            if mark.name == 'parametrize'
+            for case in mark.args[1]
+        ]
+
+class TestHyp2f1ExtremeInputs:
+
+    @pytest.mark.parametrize("a", [1.0, 2.0, 3.0, -np.inf, np.inf])
+    @pytest.mark.parametrize("b", [3.0, 4.0, 5.0, -np.inf, np.inf])
+    @pytest.mark.parametrize("c", [3.0, 5.0, 6.0, 7.0])
+    @pytest.mark.parametrize("z", [4.0 + 1.0j])
+    def test_inf_a_b(self, a, b, c, z):
+        if np.any(np.isinf(np.asarray([a, b]))):
+            assert(np.isnan(hyp2f1(a, b, c, z)))
+
+    def test_large_a_b(self):
+        assert(np.isnan(hyp2f1(10**7, 1.0, 3.0, 4.0 + 1.0j)))
+        assert(np.isnan(hyp2f1(-10**7, 1.0, 3.0, 4.0 + 1.0j)))
+
+        assert(np.isnan(hyp2f1(1.0, 10**7, 3.0, 4.0 + 1.0j)))
+        assert(np.isnan(hyp2f1(1.0, -10**7, 3.0, 4.0 + 1.0j)))
+
+        # Already correct in main but testing for surety
+        assert(np.isnan(hyp2f1(np.inf, 1.0, 3.0, 4.0)))
+        assert(np.isnan(hyp2f1(1.0, np.inf, 3.0, 4.0)))
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_hypergeometric.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_hypergeometric.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ad092491905ae66c39cd881836054aef3dc6f6e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_hypergeometric.py
@@ -0,0 +1,234 @@
+import pytest
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+import scipy.special as sc
+
+
+class TestHyperu:
+
+    def test_negative_x(self):
+        a, b, x = np.meshgrid(
+            [-1, -0.5, 0, 0.5, 1],
+            [-1, -0.5, 0, 0.5, 1],
+            np.linspace(-100, -1, 10),
+        )
+        assert np.all(np.isnan(sc.hyperu(a, b, x)))
+
+    def test_special_cases(self):
+        assert sc.hyperu(0, 1, 1) == 1.0
+
+    @pytest.mark.parametrize('a', [0.5, 1, np.nan])
+    @pytest.mark.parametrize('b', [1, 2, np.nan])
+    @pytest.mark.parametrize('x', [0.25, 3, np.nan])
+    def test_nan_inputs(self, a, b, x):
+        assert np.isnan(sc.hyperu(a, b, x)) == np.any(np.isnan([a, b, x]))
+
+    @pytest.mark.parametrize(
+        'a,b,x,expected',
+        [(0.21581740448533887, 1.0, 1e-05, 3.6030558839391325),
+         (0.21581740448533887, 1.0, 0.00021544346900318823, 2.8783254988948976),
+         (0.21581740448533887, 1.0, 0.004641588833612777, 2.154928216691109),
+         (0.21581740448533887, 1.0, 0.1, 1.446546638718792),
+         (0.0030949064301273865, 1.0, 1e-05, 1.0356696454116199),
+         (0.0030949064301273865, 1.0, 0.00021544346900318823, 1.0261510362481985),
+         (0.0030949064301273865, 1.0, 0.004641588833612777, 1.0166326903402296),
+         (0.0030949064301273865, 1.0, 0.1, 1.0071174207698674),
+         (0.1509924314279033, 1.0, 1e-05, 2.806173846998948),
+         (0.1509924314279033, 1.0, 0.00021544346900318823, 2.3092158526816124),
+         (0.1509924314279033, 1.0, 0.004641588833612777, 1.812905980588048),
+         (0.1509924314279033, 1.0, 0.1, 1.3239738117634872),
+         (-0.010678995342969011, 1.0, 1e-05, 0.8775194903781114),
+         (-0.010678995342969011, 1.0, 0.00021544346900318823, 0.9101008998540128),
+         (-0.010678995342969011, 1.0, 0.004641588833612777, 0.9426854294058609),
+         (-0.010678995342969011, 1.0, 0.1, 0.9753065150174902),
+         (-0.06556622211831487, 1.0, 1e-05, 0.26435429752668904),
+         (-0.06556622211831487, 1.0, 0.00021544346900318823, 0.4574756033875781),
+         (-0.06556622211831487, 1.0, 0.004641588833612777, 0.6507121093358457),
+         (-0.06556622211831487, 1.0, 0.1, 0.8453129788602187),
+         (-0.21628242470175185, 1.0, 1e-05, -1.2318314201114489),
+         (-0.21628242470175185, 1.0, 0.00021544346900318823, -0.6704694233529538),
+         (-0.21628242470175185, 1.0, 0.004641588833612777, -0.10795098653682857),
+         (-0.21628242470175185, 1.0, 0.1, 0.4687227684115524)]
+    )
+    def test_gh_15650_mp(self, a, b, x, expected):
+        # See https://github.com/scipy/scipy/issues/15650
+        # b == 1, |a| < 0.25, 0 < x < 1
+        #
+        # This purpose of this test is to check the accuracy of results
+        # in the region that was impacted by gh-15650.
+        #
+        # Reference values computed with mpmath using the script:
+        #
+        # import itertools as it
+        # import numpy as np
+        #
+        # from mpmath import mp
+        #
+        # rng = np.random.default_rng(1234)
+        #
+        # cases = []
+        # for a, x in it.product(
+        #         np.random.uniform(-0.25, 0.25, size=6),
+        #         np.logspace(-5, -1, 4),
+        # ):
+        #     with mp.workdps(100):
+        #         cases.append((float(a), 1.0, float(x), float(mp.hyperu(a, 1.0, x))))
+        assert_allclose(sc.hyperu(a, b, x), expected, rtol=1e-13)
+
+    def test_gh_15650_sanity(self):
+        # The purpose of this test is to sanity check hyperu in the region that
+        # was impacted by gh-15650 by making sure there are no excessively large
+        # results, as were reported there.
+        a = np.linspace(-0.5, 0.5, 500)
+        x = np.linspace(1e-6, 1e-1, 500)
+        a, x = np.meshgrid(a, x)
+        results = sc.hyperu(a, 1.0, x)
+        assert np.all(np.abs(results) < 1e3)
+
+
+class TestHyp1f1:
+
+    @pytest.mark.parametrize('a, b, x', [
+        (np.nan, 1, 1),
+        (1, np.nan, 1),
+        (1, 1, np.nan)
+    ])
+    def test_nan_inputs(self, a, b, x):
+        assert np.isnan(sc.hyp1f1(a, b, x))
+
+    def test_poles(self):
+        assert_equal(sc.hyp1f1(1, [0, -1, -2, -3, -4], 0.5), np.inf)
+
+    @pytest.mark.parametrize('a, b, x, result', [
+        (-1, 1, 0.5, 0.5),
+        (1, 1, 0.5, 1.6487212707001281468),
+        (2, 1, 0.5, 2.4730819060501922203),
+        (1, 2, 0.5, 1.2974425414002562937),
+        (-10, 1, 0.5, -0.38937441413785204475)
+    ])
+    def test_special_cases(self, a, b, x, result):
+        # Hit all the special case branches at the beginning of the
+        # function. Desired answers computed using Mpmath.
+        assert_allclose(sc.hyp1f1(a, b, x), result, atol=0, rtol=1e-15)
+
+    @pytest.mark.parametrize('a, b, x, result', [
+        (1, 1, 0.44, 1.5527072185113360455),
+        (-1, 1, 0.44, 0.55999999999999999778),
+        (100, 100, 0.89, 2.4351296512898745592),
+        (-100, 100, 0.89, 0.40739062490768104667),
+        (1.5, 100, 59.99, 3.8073513625965598107),
+        (-1.5, 100, 59.99, 0.25099240047125826943)
+    ])
+    def test_geometric_convergence(self, a, b, x, result):
+        # Test the region where we are relying on the ratio of
+        #
+        # (|a| + 1) * |x| / |b|
+        #
+        # being small. Desired answers computed using Mpmath
+        assert_allclose(sc.hyp1f1(a, b, x), result, atol=0, rtol=1e-15)
+
+    @pytest.mark.parametrize('a, b, x, result', [
+        (-1, 1, 1.5, -0.5),
+        (-10, 1, 1.5, 0.41801777430943080357),
+        (-25, 1, 1.5, 0.25114491646037839809),
+        (-50, 1, 1.5, -0.25683643975194756115),
+        (-80, 1, 1.5, -0.24554329325751503601),
+        (-150, 1, 1.5, -0.173364795515420454496),
+    ])
+    def test_a_negative_integer(self, a, b, x, result):
+        # Desired answers computed using Mpmath.
+        assert_allclose(sc.hyp1f1(a, b, x), result, atol=0, rtol=2e-14)
+
+    @pytest.mark.parametrize('a, b, x, expected', [
+        (0.01, 150, -4, 0.99973683897677527773),        # gh-3492
+        (1, 5, 0.01, 1.0020033381011970966),            # gh-3593
+        (50, 100, 0.01, 1.0050126452421463411),         # gh-3593
+        (1, 0.3, -1e3, -7.011932249442947651455e-04),   # gh-14149
+        (1, 0.3, -1e4, -7.001190321418937164734e-05),   # gh-14149
+        (9, 8.5, -350, -5.224090831922378361082e-20),   # gh-17120
+        (9, 8.5, -355, -4.595407159813368193322e-20),   # gh-17120
+        (75, -123.5, 15, 3.425753920814889017493e+06),
+    ])
+    def test_assorted_cases(self, a, b, x, expected):
+        # Expected values were computed with mpmath.hyp1f1(a, b, x).
+        assert_allclose(sc.hyp1f1(a, b, x), expected, atol=0, rtol=1e-14)
+
+    def test_a_neg_int_and_b_equal_x(self):
+        # This is a case where the Boost wrapper will call hypergeometric_pFq
+        # instead of hypergeometric_1F1.  When we use a version of Boost in
+        # which https://github.com/boostorg/math/issues/833 is fixed, this
+        # test case can probably be moved into test_assorted_cases.
+        # The expected value was computed with mpmath.hyp1f1(a, b, x).
+        a = -10.0
+        b = 2.5
+        x = 2.5
+        expected = 0.0365323664364104338721
+        computed = sc.hyp1f1(a, b, x)
+        assert_allclose(computed, expected, atol=0, rtol=1e-13)
+
+    @pytest.mark.parametrize('a, b, x, desired', [
+        (-1, -2, 2, 2),
+        (-1, -4, 10, 3.5),
+        (-2, -2, 1, 2.5)
+    ])
+    def test_gh_11099(self, a, b, x, desired):
+        # All desired results computed using Mpmath
+        assert sc.hyp1f1(a, b, x) == desired
+
+    @pytest.mark.parametrize('a', [-3, -2])
+    def test_x_zero_a_and_b_neg_ints_and_a_ge_b(self, a):
+        assert sc.hyp1f1(a, -3, 0) == 1
+
+    # In the following tests with complex z, the reference values
+    # were computed with mpmath.hyp1f1(a, b, z), and verified with
+    # Wolfram Alpha Hypergeometric1F1(a, b, z), except for the
+    # case a=0.1, b=1, z=7-24j, where Wolfram Alpha reported
+    # "Standard computation time exceeded".  That reference value
+    # was confirmed in an online Matlab session, with the commands
+    #
+    #  > format long
+    #  > hypergeom(0.1, 1, 7-24i)
+    #  ans =
+    #   -3.712349651834209 + 4.554636556672912i
+    #
+    @pytest.mark.parametrize(
+        'a, b, z, ref',
+        [(-0.25, 0.5, 1+2j, 1.1814553180903435-1.2792130661292984j),
+         (0.25, 0.5, 1+2j, 0.24636797405707597+1.293434354945675j),
+         (25, 1.5, -2j, -516.1771262822523+407.04142751922024j),
+         (12, -1.5, -10+20j, -5098507.422706547-1341962.8043508842j),
+         pytest.param(
+             10, 250, 10-15j, 1.1985998416598884-0.8613474402403436j,
+             marks=pytest.mark.xfail,
+         ),
+         pytest.param(
+             0.1, 1, 7-24j, -3.712349651834209+4.554636556672913j,
+             marks=pytest.mark.xfail,
+         )
+         ],
+    )
+    def test_complex_z(self, a, b, z, ref):
+        h = sc.hyp1f1(a, b, z)
+        assert_allclose(h, ref, rtol=4e-15)
+
+    # The "legacy edge cases" mentioned in the comments in the following
+    # tests refers to the behavior of hyp1f1(a, b, x) when b is a nonpositive
+    # integer.  In some subcases, the behavior of SciPy does not match that
+    # of Boost (1.81+), mpmath and Mathematica (via Wolfram Alpha online).
+    # If the handling of these edges cases is changed to agree with those
+    # libraries, these test will have to be updated.
+
+    @pytest.mark.parametrize('b', [0, -1, -5])
+    def test_legacy_case1(self, b):
+        # Test results of hyp1f1(0, n, x) for n <= 0.
+        # This is a legacy edge case.
+        # Boost (versions greater than 1.80), Mathematica (via Wolfram Alpha
+        # online) and mpmath all return 1 in this case, but SciPy's hyp1f1
+        # returns inf.
+        assert_equal(sc.hyp1f1(0, b, [-1.5, 0, 1.5]), [np.inf, np.inf, np.inf])
+
+    def test_legacy_case2(self):
+        # This is a legacy edge case.
+        # In software such as boost (1.81+), mpmath and Mathematica,
+        # the value is 1.
+        assert sc.hyp1f1(-4, -3, 0) == np.inf
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_iv_ratio.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_iv_ratio.py
new file mode 100644
index 0000000000000000000000000000000000000000..dafc35982c4d4869ead2136e74466f1c44c64556
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_iv_ratio.py
@@ -0,0 +1,249 @@
+# This file contains unit tests for iv_ratio() and related functions.
+
+import pytest
+import numpy as np
+from numpy.testing import assert_equal, assert_allclose
+from scipy.special._ufuncs import (  # type: ignore[attr-defined]
+    _iv_ratio as iv_ratio,
+    _iv_ratio_c as iv_ratio_c,
+)
+
+
+class TestIvRatio:
+
+    @pytest.mark.parametrize('v,x,r', [
+        (0.5, 0.16666666666666666, 0.16514041292462933),
+        (0.5, 0.3333333333333333, 0.32151273753163434),
+        (0.5, 0.5, 0.46211715726000974),
+        (0.5, 0.6666666666666666, 0.5827829453479101),
+        (0.5, 0.8333333333333335, 0.6822617902381698),
+        (1, 0.3380952380952381, 0.1666773049170313),
+        (1, 0.7083333333333333, 0.33366443586989925),
+        (1, 1.1666666666666667, 0.5023355231537423),
+        (1, 1.8666666666666665, 0.674616572252164),
+        (1, 3.560606060606061, 0.844207659503163),
+        (2.34, 0.7975238095238094, 0.16704903081553285),
+        (2.34, 1.7133333333333334, 0.3360215931268845),
+        (2.34, 2.953333333333333, 0.50681909317803),
+        (2.34, 5.0826666666666656, 0.6755252698800679),
+        (2.34, 10.869696969696973, 0.8379351104498762),
+        (56.789, 19.46575238095238, 0.1667020505391409),
+        (56.789, 42.55008333333333, 0.33353809996933026),
+        (56.789, 75.552, 0.5003932381177826),
+        (56.789, 135.76026666666667, 0.6670528221946127),
+        (56.789, 307.8642424242425, 0.8334999441460798),
+    ])
+    def test_against_reference_values(self, v, x, r):
+        """The reference values are computed using mpmath as follows.
+
+        from mpmath import mp
+        mp.dps = 100
+
+        def iv_ratio_mp(v, x):
+            return mp.besseli(v, x) / mp.besseli(v - 1, x)
+
+        def _sample(n, *, v):
+            '''Return n positive real numbers x such that iv_ratio(v, x) are
+            roughly evenly spaced over (0, 1).  The formula is taken from [1].
+
+            [1] Banerjee A., Dhillon, I. S., Ghosh, J., Sra, S. (2005).
+                "Clustering on the Unit Hypersphere using von Mises-Fisher
+                Distributions."  Journal of Machine Learning Research,
+                6(46):1345-1382.
+            '''
+            r = np.arange(1, n+1) / (n+1)
+            return r * (2*v-r*r) / (1-r*r)
+
+        for v in (0.5, 1, 2.34, 56.789):
+            xs = _sample(5, v=v)
+            for x in xs:
+                print(f"({v}, {x}, {float(iv_ratio_mp(v,x))}),")
+        """
+        assert_allclose(iv_ratio(v, x), r, rtol=4e-16, atol=0)
+
+    @pytest.mark.parametrize('v,x,r', [
+        (1, np.inf, 1),
+        (np.inf, 1, 0),
+    ])
+    def test_inf(self, v, x, r):
+        """If exactly one of v or x is inf and the other is within domain,
+        should return 0 or 1 accordingly."""
+        assert_equal(iv_ratio(v, x), r)
+
+    @pytest.mark.parametrize('v', [0.49, -np.inf, np.nan, np.inf])
+    @pytest.mark.parametrize('x', [-np.finfo(float).smallest_normal,
+                                   -np.finfo(float).smallest_subnormal,
+                                   -np.inf, np.nan, np.inf])
+    def test_nan(self, v, x):
+        """If at least one argument is out of domain, or if v = x = inf,
+        the function should return nan."""
+        assert_equal(iv_ratio(v, x), np.nan)
+
+    @pytest.mark.parametrize('v', [0.5, 1, np.finfo(float).max, np.inf])
+    def test_zero_x(self, v):
+        """If x is +/-0.0, return x to ensure iv_ratio is an odd function."""
+        assert_equal(iv_ratio(v, 0.0), 0.0)
+        assert_equal(iv_ratio(v, -0.0), -0.0)
+
+    @pytest.mark.parametrize('v,x', [
+        (1, np.finfo(float).smallest_normal),
+        (1, np.finfo(float).smallest_subnormal),
+        (1, np.finfo(float).smallest_subnormal*2),
+        (1e20, 123),
+        (np.finfo(float).max, 1),
+        (np.finfo(float).max, np.sqrt(np.finfo(float).max)),
+    ])
+    def test_tiny_x(self, v, x):
+        """If x is much less than v, the bounds
+
+                    x                                 x
+        --------------------------- <= R <= -----------------------
+        v-0.5+sqrt(x**2+(v+0.5)**2)         v-1+sqrt(x**2+(v+1)**2)
+
+        collapses to R ~= x/2v.  Test against this asymptotic expression.
+        """
+        assert_equal(iv_ratio(v, x), (0.5*x)/v)
+
+    @pytest.mark.parametrize('v,x', [
+        (1, 1e16),
+        (1e20, 1e40),
+        (np.sqrt(np.finfo(float).max), np.finfo(float).max),
+    ])
+    def test_huge_x(self, v, x):
+        """If x is much greater than v, the bounds
+
+                    x                                 x
+        --------------------------- <= R <= ---------------------------
+        v-0.5+sqrt(x**2+(v+0.5)**2)         v-0.5+sqrt(x**2+(v-0.5)**2)
+
+        collapses to R ~= 1.  Test against this asymptotic expression.
+        """
+        assert_equal(iv_ratio(v, x), 1.0)
+
+    @pytest.mark.parametrize('v,x', [
+        (np.finfo(float).max, np.finfo(float).max),
+        (np.finfo(float).max / 3, np.finfo(float).max),
+        (np.finfo(float).max, np.finfo(float).max / 3),
+    ])
+    def test_huge_v_x(self, v, x):
+        """If both x and v are very large, the bounds
+
+                    x                                 x
+        --------------------------- <= R <= -----------------------
+        v-0.5+sqrt(x**2+(v+0.5)**2)         v-1+sqrt(x**2+(v+1)**2)
+
+        collapses to R ~= x/(v+sqrt(x**2+v**2).  Test against this asymptotic
+        expression, and in particular that no numerical overflow occurs during
+        intermediate calculations.
+        """
+        t = x / v
+        expected = t / (1 + np.hypot(1, t))
+        assert_allclose(iv_ratio(v, x), expected, rtol=4e-16, atol=0)
+
+
+class TestIvRatioC:
+
+    @pytest.mark.parametrize('v,x,r', [
+        (0.5, 0.16666666666666666, 0.8348595870753707),
+        (0.5, 0.3333333333333333, 0.6784872624683657),
+        (0.5, 0.5, 0.5378828427399902),
+        (0.5, 0.6666666666666666, 0.4172170546520899),
+        (0.5, 0.8333333333333335, 0.3177382097618302),
+        (1, 0.3380952380952381, 0.8333226950829686),
+        (1, 0.7083333333333333, 0.6663355641301008),
+        (1, 1.1666666666666667, 0.4976644768462577),
+        (1, 1.8666666666666665, 0.325383427747836),
+        (1, 3.560606060606061, 0.155792340496837),
+        (2.34, 0.7975238095238094, 0.8329509691844672),
+        (2.34, 1.7133333333333334, 0.6639784068731155),
+        (2.34, 2.953333333333333, 0.49318090682197),
+        (2.34, 5.0826666666666656, 0.3244747301199321),
+        (2.34, 10.869696969696973, 0.16206488955012377),
+        (56.789, 19.46575238095238, 0.8332979494608591),
+        (56.789, 42.55008333333333, 0.6664619000306697),
+        (56.789, 75.552, 0.4996067618822174),
+        (56.789, 135.76026666666667, 0.3329471778053873),
+        (56.789, 307.8642424242425, 0.16650005585392025),
+    ])
+    def test_against_reference_values(self, v, x, r):
+        """The reference values are one minus those of TestIvRatio."""
+        assert_allclose(iv_ratio_c(v, x), r, rtol=1e-15, atol=0)
+
+    @pytest.mark.parametrize('v,x,r', [
+        (1, np.inf, 0),
+        (np.inf, 1, 1),
+    ])
+    def test_inf(self, v, x, r):
+        """If exactly one of v or x is inf and the other is within domain,
+        should return 0 or 1 accordingly."""
+        assert_equal(iv_ratio_c(v, x), r)
+
+    @pytest.mark.parametrize('v', [0.49, -np.inf, np.nan, np.inf])
+    @pytest.mark.parametrize('x', [-np.finfo(float).smallest_normal,
+                                   -np.finfo(float).smallest_subnormal,
+                                   -np.inf, np.nan, np.inf])
+    def test_nan(self, v, x):
+        """If at least one argument is out of domain, or if v = x = inf,
+        the function should return nan."""
+        assert_equal(iv_ratio_c(v, x), np.nan)
+
+    @pytest.mark.parametrize('v', [0.5, 1, np.finfo(float).max, np.inf])
+    def test_zero_x(self, v):
+        """If x is +/-0.0, return 1."""
+        assert_equal(iv_ratio_c(v, 0.0), 1.0)
+        assert_equal(iv_ratio_c(v, -0.0), 1.0)
+
+    @pytest.mark.parametrize('v,x', [
+        (1, np.finfo(float).smallest_normal),
+        (1, np.finfo(float).smallest_subnormal),
+        (1, np.finfo(float).smallest_subnormal*2),
+        (1e20, 123),
+        (np.finfo(float).max, 1),
+        (np.finfo(float).max, np.sqrt(np.finfo(float).max)),
+    ])
+    def test_tiny_x(self, v, x):
+        """If x is much less than v, the bounds
+
+                    x                                 x
+        --------------------------- <= R <= -----------------------
+        v-0.5+sqrt(x**2+(v+0.5)**2)         v-1+sqrt(x**2+(v+1)**2)
+
+        collapses to 1-R ~= 1-x/2v.  Test against this asymptotic expression.
+        """
+        assert_equal(iv_ratio_c(v, x), 1.0-(0.5*x)/v)
+
+    @pytest.mark.parametrize('v,x', [
+        (1, 1e16),
+        (1e20, 1e40),
+        (np.sqrt(np.finfo(float).max), np.finfo(float).max),
+    ])
+    def test_huge_x(self, v, x):
+        """If x is much greater than v, the bounds
+
+                    x                                 x
+        --------------------------- <= R <= ---------------------------
+        v-0.5+sqrt(x**2+(v+0.5)**2)         v-0.5+sqrt(x**2+(v-0.5)**2)
+
+        collapses to 1-R ~= (v-0.5)/x.  Test against this asymptotic expression.
+        """
+        assert_allclose(iv_ratio_c(v, x), (v-0.5)/x, rtol=1e-15, atol=0)
+
+    @pytest.mark.parametrize('v,x', [
+        (np.finfo(float).max, np.finfo(float).max),
+        (np.finfo(float).max / 3, np.finfo(float).max),
+        (np.finfo(float).max, np.finfo(float).max / 3),
+    ])
+    def test_huge_v_x(self, v, x):
+        """If both x and v are very large, the bounds
+
+                    x                                 x
+        --------------------------- <= R <= -----------------------
+        v-0.5+sqrt(x**2+(v+0.5)**2)         v-1+sqrt(x**2+(v+1)**2)
+
+        collapses to 1 - R ~= 1 - x/(v+sqrt(x**2+v**2).  Test against this
+        asymptotic expression, and in particular that no numerical overflow
+        occurs during intermediate calculations.
+        """
+        t = x / v
+        expected = 1 - t / (1 + np.hypot(1, t))
+        assert_allclose(iv_ratio_c(v, x), expected, rtol=4e-16, atol=0)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_kolmogorov.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_kolmogorov.py
new file mode 100644
index 0000000000000000000000000000000000000000..ade38115706808809890e1eba3b938562b0af6e6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_kolmogorov.py
@@ -0,0 +1,491 @@
+import itertools
+
+import numpy as np
+from numpy.testing import assert_
+from scipy.special._testutils import FuncData
+
+from scipy.special import kolmogorov, kolmogi, smirnov, smirnovi
+from scipy.special._ufuncs import (_kolmogc, _kolmogci, _kolmogp,
+                                   _smirnovc, _smirnovci, _smirnovp)
+
+_rtol = 1e-10
+
+class TestSmirnov:
+    def test_nan(self):
+        assert_(np.isnan(smirnov(1, np.nan)))
+
+    def test_basic(self):
+        dataset = [(1, 0.1, 0.9),
+                   (1, 0.875, 0.125),
+                   (2, 0.875, 0.125 * 0.125),
+                   (3, 0.875, 0.125 * 0.125 * 0.125)]
+
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_x_equals_0(self):
+        dataset = [(n, 0, 1) for n in itertools.chain(range(2, 20), range(1010, 1020))]
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_x_equals_1(self):
+        dataset = [(n, 1, 0) for n in itertools.chain(range(2, 20), range(1010, 1020))]
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_x_equals_0point5(self):
+        dataset = [(1, 0.5, 0.5),
+                   (2, 0.5, 0.25),
+                   (3, 0.5, 0.166666666667),
+                   (4, 0.5, 0.09375),
+                   (5, 0.5, 0.056),
+                   (6, 0.5, 0.0327932098765),
+                   (7, 0.5, 0.0191958707681),
+                   (8, 0.5, 0.0112953186035),
+                   (9, 0.5, 0.00661933257355),
+                   (10, 0.5, 0.003888705)]
+
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_equals_1(self):
+        x = np.linspace(0, 1, 101, endpoint=True)
+        dataset = np.column_stack([[1]*len(x), x, 1-x])
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_equals_2(self):
+        x = np.linspace(0.5, 1, 101, endpoint=True)
+        p = np.power(1-x, 2)
+        n = np.array([2] * len(x))
+        dataset = np.column_stack([n, x, p])
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_equals_3(self):
+        x = np.linspace(0.7, 1, 31, endpoint=True)
+        p = np.power(1-x, 3)
+        n = np.array([3] * len(x))
+        dataset = np.column_stack([n, x, p])
+        FuncData(
+            smirnov, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, -1] = 1 - dataset[:, -1]
+        FuncData(
+            _smirnovc, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_large(self):
+        # test for large values of n
+        # Probabilities should go down as n goes up
+        x = 0.4
+        pvals = np.array([smirnov(n, x) for n in range(400, 1100, 20)])
+        dfs = np.diff(pvals)
+        assert_(np.all(dfs <= 0), msg=f'Not all diffs negative {dfs}')
+
+
+class TestSmirnovi:
+    def test_nan(self):
+        assert_(np.isnan(smirnovi(1, np.nan)))
+
+    def test_basic(self):
+        dataset = [(1, 0.4, 0.6),
+                   (1, 0.6, 0.4),
+                   (1, 0.99, 0.01),
+                   (1, 0.01, 0.99),
+                   (2, 0.125 * 0.125, 0.875),
+                   (3, 0.125 * 0.125 * 0.125, 0.875),
+                   (10, 1.0 / 16 ** 10, 1 - 1.0 / 16)]
+
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_x_equals_0(self):
+        dataset = [(n, 0, 1) for n in itertools.chain(range(2, 20), range(1010, 1020))]
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_x_equals_1(self):
+        dataset = [(n, 1, 0) for n in itertools.chain(range(2, 20), range(1010, 1020))]
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_equals_1(self):
+        pp = np.linspace(0, 1, 101, endpoint=True)
+        # dataset = np.array([(1, p, 1-p) for p in pp])
+        dataset = np.column_stack([[1]*len(pp), pp, 1-pp])
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_equals_2(self):
+        x = np.linspace(0.5, 1, 101, endpoint=True)
+        p = np.power(1-x, 2)
+        n = np.array([2] * len(x))
+        dataset = np.column_stack([n, p, x])
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_n_equals_3(self):
+        x = np.linspace(0.7, 1, 31, endpoint=True)
+        p = np.power(1-x, 3)
+        n = np.array([3] * len(x))
+        dataset = np.column_stack([n, p, x])
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_round_trip(self):
+        def _sm_smi(n, p):
+            return smirnov(n, smirnovi(n, p))
+
+        def _smc_smci(n, p):
+            return _smirnovc(n, _smirnovci(n, p))
+
+        dataset = [(1, 0.4, 0.4),
+                   (1, 0.6, 0.6),
+                   (2, 0.875, 0.875),
+                   (3, 0.875, 0.875),
+                   (3, 0.125, 0.125),
+                   (10, 0.999, 0.999),
+                   (10, 0.0001, 0.0001)]
+
+        dataset = np.asarray(dataset)
+        FuncData(
+            _sm_smi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        FuncData(
+            _smc_smci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_x_equals_0point5(self):
+        dataset = [(1, 0.5, 0.5),
+                   (2, 0.5, 0.366025403784),
+                   (2, 0.25, 0.5),
+                   (3, 0.5, 0.297156508177),
+                   (4, 0.5, 0.255520481121),
+                   (5, 0.5, 0.234559536069),
+                   (6, 0.5, 0.21715965898),
+                   (7, 0.5, 0.202722580034),
+                   (8, 0.5, 0.190621765256),
+                   (9, 0.5, 0.180363501362),
+                   (10, 0.5, 0.17157867006)]
+
+        dataset = np.asarray(dataset)
+        FuncData(
+            smirnovi, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+        dataset[:, 1] = 1 - dataset[:, 1]
+        FuncData(
+            _smirnovci, dataset, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+
+class TestSmirnovp:
+    def test_nan(self):
+        assert_(np.isnan(_smirnovp(1, np.nan)))
+
+    def test_basic(self):
+        # Check derivative at endpoints
+        n1_10 = np.arange(1, 10)
+        dataset0 = np.column_stack([n1_10,
+                                    np.full_like(n1_10, 0),
+                                    np.full_like(n1_10, -1)])
+        FuncData(
+            _smirnovp, dataset0, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+        n2_10 = np.arange(2, 10)
+        dataset1 = np.column_stack([n2_10,
+                                    np.full_like(n2_10, 1.0),
+                                    np.full_like(n2_10, 0)])
+        FuncData(
+            _smirnovp, dataset1, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_oneminusoneovern(self):
+        # Check derivative at x=1-1/n
+        n = np.arange(1, 20)
+        x = 1.0/n
+        xm1 = 1-1.0/n
+        pp1 = -n * x**(n-1)
+        pp1 -= (1-np.sign(n-2)**2) * 0.5  # n=2, x=0.5, 1-1/n = 0.5, need to adjust
+        dataset1 = np.column_stack([n, xm1, pp1])
+        FuncData(
+            _smirnovp, dataset1, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_oneovertwon(self):
+        # Check derivative at x=1/2n  (Discontinuous at x=1/n, so check at x=1/2n)
+        n = np.arange(1, 20)
+        x = 1.0/2/n
+        pp = -(n*x+1) * (1+x)**(n-2)
+        dataset0 = np.column_stack([n, x, pp])
+        FuncData(
+            _smirnovp, dataset0, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_oneovern(self):
+        # Check derivative at x=1/n
+        # (Discontinuous at x=1/n, hard to tell if x==1/n, only use n=power of 2)
+        n = 2**np.arange(1, 10)
+        x = 1.0/n
+        pp = -(n*x+1) * (1+x)**(n-2) + 0.5
+        dataset0 = np.column_stack([n, x, pp])
+        FuncData(
+            _smirnovp, dataset0, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+    def test_oneovernclose(self):
+        # Check derivative at x=1/n
+        # (Discontinuous at x=1/n, test on either side: x=1/n +/- 2epsilon)
+        n = np.arange(3, 20)
+
+        x = 1.0/n - 2*np.finfo(float).eps
+        pp = -(n*x+1) * (1+x)**(n-2)
+        dataset0 = np.column_stack([n, x, pp])
+        FuncData(
+            _smirnovp, dataset0, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+        x = 1.0/n + 2*np.finfo(float).eps
+        pp = -(n*x+1) * (1+x)**(n-2) + 1
+        dataset1 = np.column_stack([n, x, pp])
+        FuncData(
+            _smirnovp, dataset1, (0, 1), 2, rtol=_rtol
+        ).check(dtypes=[int, float, float])
+
+
+class TestKolmogorov:
+    def test_nan(self):
+        assert_(np.isnan(kolmogorov(np.nan)))
+
+    def test_basic(self):
+        dataset = [(0, 1.0),
+                   (0.5, 0.96394524366487511),
+                   (0.8275735551899077, 0.5000000000000000),
+                   (1, 0.26999967167735456),
+                   (2, 0.00067092525577969533)]
+
+        dataset = np.asarray(dataset)
+        FuncData(kolmogorov, dataset, (0,), 1, rtol=_rtol).check()
+
+    def test_linspace(self):
+        x = np.linspace(0, 2.0, 21)
+        dataset = [1.0000000000000000, 1.0000000000000000, 0.9999999999994950,
+                   0.9999906941986655, 0.9971923267772983, 0.9639452436648751,
+                   0.8642827790506042, 0.7112351950296890, 0.5441424115741981,
+                   0.3927307079406543, 0.2699996716773546, 0.1777181926064012,
+                   0.1122496666707249, 0.0680922218447664, 0.0396818795381144,
+                   0.0222179626165251, 0.0119520432391966, 0.0061774306344441,
+                   0.0030676213475797, 0.0014636048371873, 0.0006709252557797]
+
+        dataset_c = [0.0000000000000000, 6.609305242245699e-53, 5.050407338670114e-13,
+                     9.305801334566668e-06, 0.0028076732227017, 0.0360547563351249,
+                     0.1357172209493958, 0.2887648049703110, 0.4558575884258019,
+                     0.6072692920593457, 0.7300003283226455, 0.8222818073935988,
+                     0.8877503333292751, 0.9319077781552336, 0.9603181204618857,
+                     0.9777820373834749, 0.9880479567608034, 0.9938225693655559,
+                     0.9969323786524203, 0.9985363951628127, 0.9993290747442203]
+
+        dataset = np.column_stack([x, dataset])
+        FuncData(kolmogorov, dataset, (0,), 1, rtol=_rtol).check()
+        dataset_c = np.column_stack([x, dataset_c])
+        FuncData(_kolmogc, dataset_c, (0,), 1, rtol=_rtol).check()
+
+    def test_linspacei(self):
+        p = np.linspace(0, 1.0, 21, endpoint=True)
+        dataset = [np.inf, 1.3580986393225507, 1.2238478702170823,
+                   1.1379465424937751, 1.0727491749396481, 1.0191847202536859,
+                   0.9730633753323726, 0.9320695842357622, 0.8947644549851197,
+                   0.8601710725555463, 0.8275735551899077, 0.7964065373291559,
+                   0.7661855555617682, 0.7364542888171910, 0.7067326523068980,
+                   0.6764476915028201, 0.6448126061663567, 0.6105590999244391,
+                   0.5711732651063401, 0.5196103791686224, 0.0000000000000000]
+
+        dataset_c = [0.0000000000000000, 0.5196103791686225, 0.5711732651063401,
+                     0.6105590999244391, 0.6448126061663567, 0.6764476915028201,
+                     0.7067326523068980, 0.7364542888171910, 0.7661855555617682,
+                     0.7964065373291559, 0.8275735551899077, 0.8601710725555463,
+                     0.8947644549851196, 0.9320695842357622, 0.9730633753323727,
+                     1.0191847202536859, 1.0727491749396481, 1.1379465424937754,
+                     1.2238478702170825, 1.3580986393225509, np.inf]
+
+        dataset = np.column_stack([p[1:], dataset[1:]])
+        FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
+        dataset_c = np.column_stack([p[:-1], dataset_c[:-1]])
+        FuncData(_kolmogci, dataset_c, (0,), 1, rtol=_rtol).check()
+
+    def test_smallx(self):
+        epsilon = 0.1 ** np.arange(1, 14)
+        x = np.array([0.571173265106, 0.441027698518, 0.374219690278, 0.331392659217,
+                      0.300820537459, 0.277539353999, 0.259023494805, 0.243829561254,
+                      0.231063086389, 0.220135543236, 0.210641372041, 0.202290283658,
+                      0.19487060742])
+
+        dataset = np.column_stack([x, 1-epsilon])
+        FuncData(kolmogorov, dataset, (0,), 1, rtol=_rtol).check()
+
+    def test_round_trip(self):
+        def _ki_k(_x):
+            return kolmogi(kolmogorov(_x))
+
+        def _kci_kc(_x):
+            return _kolmogci(_kolmogc(_x))
+
+        x = np.linspace(0.0, 2.0, 21, endpoint=True)
+        # Exclude 0.1, 0.2.  0.2 almost makes succeeds, but 0.1 has no chance.
+        x02 = x[(x == 0) | (x > 0.21)]
+        dataset02 = np.column_stack([x02, x02])
+        FuncData(_ki_k, dataset02, (0,), 1, rtol=_rtol).check()
+
+        dataset = np.column_stack([x, x])
+        FuncData(_kci_kc, dataset, (0,), 1, rtol=_rtol).check()
+
+
+class TestKolmogi:
+    def test_nan(self):
+        assert_(np.isnan(kolmogi(np.nan)))
+
+    def test_basic(self):
+        dataset = [(1.0, 0),
+                   (0.96394524366487511, 0.5),
+                   (0.9, 0.571173265106),
+                   (0.5000000000000000, 0.8275735551899077),
+                   (0.26999967167735456, 1),
+                   (0.00067092525577969533, 2)]
+
+        dataset = np.asarray(dataset)
+        FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
+
+    def test_smallpcdf(self):
+        epsilon = 0.5 ** np.arange(1, 55, 3)
+        # kolmogi(1-p) == _kolmogci(p) if  1-(1-p) == p, but not necessarily otherwise
+        # Use epsilon s.t. 1-(1-epsilon)) == epsilon,
+        # so can use same x-array for both results
+
+        x = np.array([0.8275735551899077, 0.5345255069097583, 0.4320114038786941,
+                      0.3736868442620478, 0.3345161714909591, 0.3057833329315859,
+                      0.2835052890528936, 0.2655578150208676, 0.2506869966107999,
+                      0.2380971058736669, 0.2272549289962079, 0.2177876361600040,
+                      0.2094254686862041, 0.2019676748836232, 0.1952612948137504,
+                      0.1891874239646641, 0.1836520225050326, 0.1785795904846466])
+
+        dataset = np.column_stack([1-epsilon, x])
+        FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
+
+        dataset = np.column_stack([epsilon, x])
+        FuncData(_kolmogci, dataset, (0,), 1, rtol=_rtol).check()
+
+    def test_smallpsf(self):
+        epsilon = 0.5 ** np.arange(1, 55, 3)
+        # kolmogi(p) == _kolmogci(1-p) if  1-(1-p) == p, but not necessarily otherwise
+        # Use epsilon s.t. 1-(1-epsilon)) == epsilon,
+        # so can use same x-array for both results
+
+        x = np.array([0.8275735551899077, 1.3163786275161036, 1.6651092133663343,
+                      1.9525136345289607, 2.2027324540033235, 2.4272929437460848,
+                      2.6327688477341593, 2.8233300509220260, 3.0018183401530627,
+                      3.1702735084088891, 3.3302184446307912, 3.4828258153113318,
+                      3.6290214150152051, 3.7695513262825959, 3.9050272690877326,
+                      4.0359582187082550, 4.1627730557884890, 4.2858371743264527])
+
+        dataset = np.column_stack([epsilon, x])
+        FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
+
+        dataset = np.column_stack([1-epsilon, x])
+        FuncData(_kolmogci, dataset, (0,), 1, rtol=_rtol).check()
+
+    def test_round_trip(self):
+        def _k_ki(_p):
+            return kolmogorov(kolmogi(_p))
+
+        p = np.linspace(0.1, 1.0, 10, endpoint=True)
+        dataset = np.column_stack([p, p])
+        FuncData(_k_ki, dataset, (0,), 1, rtol=_rtol).check()
+
+
+class TestKolmogp:
+    def test_nan(self):
+        assert_(np.isnan(_kolmogp(np.nan)))
+
+    def test_basic(self):
+        dataset = [(0.000000, -0.0),
+                   (0.200000, -1.532420541338916e-10),
+                   (0.400000, -0.1012254419260496),
+                   (0.600000, -1.324123244249925),
+                   (0.800000, -1.627024345636592),
+                   (1.000000, -1.071948558356941),
+                   (1.200000, -0.538512430720529),
+                   (1.400000, -0.2222133182429472),
+                   (1.600000, -0.07649302775520538),
+                   (1.800000, -0.02208687346347873),
+                   (2.000000, -0.005367402045629683)]
+
+        dataset = np.asarray(dataset)
+        FuncData(_kolmogp, dataset, (0,), 1, rtol=_rtol).check()
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_lambertw.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_lambertw.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac26dfb9ff65dbfd94740220a95a16c902b4be3d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_lambertw.py
@@ -0,0 +1,109 @@
+#
+# Tests for the lambertw function,
+# Adapted from the MPMath tests [1] by Yosef Meller, mellerf@netvision.net.il
+# Distributed under the same license as SciPy itself.
+#
+# [1] mpmath source code, Subversion revision 992
+#     http://code.google.com/p/mpmath/source/browse/trunk/mpmath/tests/test_functions2.py?spec=svn994&r=992
+
+import pytest
+import numpy as np
+from numpy.testing import assert_, assert_equal, assert_allclose
+from scipy.special import lambertw
+from numpy import nan, inf, pi, e, isnan, log, r_, array, complex128
+
+from scipy.special._testutils import FuncData
+
+
+def test_values():
+    assert_(isnan(lambertw(nan)))
+    assert_equal(lambertw(inf,1).real, inf)
+    assert_equal(lambertw(inf,1).imag, 2*pi)
+    assert_equal(lambertw(-inf,1).real, inf)
+    assert_equal(lambertw(-inf,1).imag, 3*pi)
+
+    assert_equal(lambertw(1.), lambertw(1., 0))
+
+    data = [
+        (0,0, 0),
+        (0+0j,0, 0),
+        (inf,0, inf),
+        (0,-1, -inf),
+        (0,1, -inf),
+        (0,3, -inf),
+        (e,0, 1),
+        (1,0, 0.567143290409783873),
+        (-pi/2,0, 1j*pi/2),
+        (-log(2)/2,0, -log(2)),
+        (0.25,0, 0.203888354702240164),
+        (-0.25,0, -0.357402956181388903),
+        (-1./10000,0, -0.000100010001500266719),
+        (-0.25,-1, -2.15329236411034965),
+        (0.25,-1, -3.00899800997004620-4.07652978899159763j),
+        (-0.25,-1, -2.15329236411034965),
+        (0.25,1, -3.00899800997004620+4.07652978899159763j),
+        (-0.25,1, -3.48973228422959210+7.41405453009603664j),
+        (-4,0, 0.67881197132094523+1.91195078174339937j),
+        (-4,1, -0.66743107129800988+7.76827456802783084j),
+        (-4,-1, 0.67881197132094523-1.91195078174339937j),
+        (1000,0, 5.24960285240159623),
+        (1000,1, 4.91492239981054535+5.44652615979447070j),
+        (1000,-1, 4.91492239981054535-5.44652615979447070j),
+        (1000,5, 3.5010625305312892+29.9614548941181328j),
+        (3+4j,0, 1.281561806123775878+0.533095222020971071j),
+        (-0.4+0.4j,0, -0.10396515323290657+0.61899273315171632j),
+        (3+4j,1, -0.11691092896595324+5.61888039871282334j),
+        (3+4j,-1, 0.25856740686699742-3.85211668616143559j),
+        (-0.5,-1, -0.794023632344689368-0.770111750510379110j),
+        (-1./10000,1, -11.82350837248724344+6.80546081842002101j),
+        (-1./10000,-1, -11.6671145325663544),
+        (-1./10000,-2, -11.82350837248724344-6.80546081842002101j),
+        (-1./100000,4, -14.9186890769540539+26.1856750178782046j),
+        (-1./100000,5, -15.0931437726379218666+32.5525721210262290086j),
+        ((2+1j)/10,0, 0.173704503762911669+0.071781336752835511j),
+        ((2+1j)/10,1, -3.21746028349820063+4.56175438896292539j),
+        ((2+1j)/10,-1, -3.03781405002993088-3.53946629633505737j),
+        ((2+1j)/10,4, -4.6878509692773249+23.8313630697683291j),
+        (-(2+1j)/10,0, -0.226933772515757933-0.164986470020154580j),
+        (-(2+1j)/10,1, -2.43569517046110001+0.76974067544756289j),
+        (-(2+1j)/10,-1, -3.54858738151989450-6.91627921869943589j),
+        (-(2+1j)/10,4, -4.5500846928118151+20.6672982215434637j),
+        (pi,0, 1.073658194796149172092178407024821347547745350410314531),
+
+        # Former bug in generated branch,
+        (-0.5+0.002j,0, -0.78917138132659918344 + 0.76743539379990327749j),
+        (-0.5-0.002j,0, -0.78917138132659918344 - 0.76743539379990327749j),
+        (-0.448+0.4j,0, -0.11855133765652382241 + 0.66570534313583423116j),
+        (-0.448-0.4j,0, -0.11855133765652382241 - 0.66570534313583423116j),
+    ]
+    data = array(data, dtype=complex128)
+
+    def w(x, y):
+        return lambertw(x, y.real.astype(int))
+    with np.errstate(all='ignore'):
+        FuncData(w, data, (0,1), 2, rtol=1e-10, atol=1e-13).check()
+
+
+def test_ufunc():
+    assert_allclose(lambertw(r_[0., e, 1.]), r_[0., 1., 0.567143290409783873],
+                    atol=1.5e-7, rtol=0)
+
+
+def test_lambertw_ufunc_loop_selection():
+    # see https://github.com/scipy/scipy/issues/4895
+    dt = np.dtype(np.complex128)
+    assert_equal(lambertw(0, 0, 0).dtype, dt)
+    assert_equal(lambertw([0], 0, 0).dtype, dt)
+    assert_equal(lambertw(0, [0], 0).dtype, dt)
+    assert_equal(lambertw(0, 0, [0]).dtype, dt)
+    assert_equal(lambertw([0], [0], [0]).dtype, dt)
+
+
+@pytest.mark.parametrize('z', [1e-316, -2e-320j, -5e-318+1e-320j])
+def test_lambertw_subnormal_k0(z):
+    # Verify that subnormal inputs are handled correctly on
+    # the branch k=0 (regression test for gh-16291).
+    w = lambertw(z)
+    # For values this small, we can be sure that numerically,
+    # lambertw(z) is z.
+    assert w == z
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_legendre.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_legendre.py
new file mode 100644
index 0000000000000000000000000000000000000000..7625313ef258de23307c6c4a432069f5c4cd0cd1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_legendre.py
@@ -0,0 +1,1421 @@
+import math
+import warnings
+
+import numpy as np
+
+import pytest
+from numpy.testing import assert_equal, assert_allclose
+
+from scipy import special
+from scipy.special import (legendre_p, legendre_p_all, assoc_legendre_p,
+    assoc_legendre_p_all, sph_legendre_p, sph_legendre_p_all)
+
+
+# Base polynomials come from Abrahmowitz and Stegan
+class TestLegendre:
+    def test_legendre(self):
+        leg0 = special.legendre(0)
+        leg1 = special.legendre(1)
+        leg2 = special.legendre(2)
+        leg3 = special.legendre(3)
+        leg4 = special.legendre(4)
+        leg5 = special.legendre(5)
+        assert_equal(leg0.c, [1])
+        assert_equal(leg1.c, [1,0])
+        assert_allclose(leg2.c, np.array([3, 0, -1])/2.0,
+                        atol=1.5e-13, rtol=0)
+        assert_allclose(leg3.c, np.array([5, 0, -3, 0])/2.0,
+                        atol=1.5e-7, rtol=0)
+        assert_allclose(leg4.c, np.array([35, 0, -30, 0, 3])/8.0,
+                        atol=1.5e-7, rtol=0)
+        assert_allclose(leg5.c, np.array([63, 0, -70, 0, 15, 0])/8.0,
+                        atol=1.5e-7, rtol=0)
+
+class TestLegendreP:
+    @pytest.mark.parametrize("shape", [(10,), (4, 9), (3, 5, 7)])
+    def test_ode(self, shape):
+        rng = np.random.default_rng(1234)
+
+        n = rng.integers(0, 100, shape)
+        x = rng.uniform(-1, 1, shape)
+
+        p, p_jac, p_hess = legendre_p(n, x, diff_n=2)
+
+        assert p.shape == shape
+        assert p_jac.shape == p.shape
+        assert p_hess.shape == p_jac.shape
+
+        err = (1 - x * x) * p_hess - 2 * x * p_jac + n * (n + 1) * p
+        np.testing.assert_allclose(err, 0, atol=1e-10)
+
+    @pytest.mark.parametrize("n_max", [1, 2, 4, 8, 16, 32])
+    @pytest.mark.parametrize("x_shape", [(10,), (4, 9), (3, 5, 7)])
+    def test_all_ode(self, n_max, x_shape):
+        rng = np.random.default_rng(1234)
+
+        x = rng.uniform(-1, 1, x_shape)
+        p, p_jac, p_hess = legendre_p_all(n_max, x, diff_n=2)
+
+        n = np.arange(n_max + 1)
+        n = np.expand_dims(n, axis = tuple(range(1, x.ndim + 1)))
+
+        assert p.shape == (len(n),) + x.shape
+        assert p_jac.shape == p.shape
+        assert p_hess.shape == p_jac.shape
+
+        err = (1 - x * x) * p_hess - 2 * x * p_jac + n * (n + 1) * p
+        np.testing.assert_allclose(err, 0, atol=1e-10)
+
+
+class TestAssocLegendreP:
+    def test_assoc_legendre_gh23101(self):
+        z = np.array([-1, -.5, 0, .5, 1])
+        expected = assoc_legendre_p_1_0(z)
+        result = assoc_legendre_p(1, 0, z)
+        assert_allclose(np.squeeze(result), expected)
+
+        expected = assoc_legendre_p_3_0(z)
+        result = assoc_legendre_p(3, 0, z)
+        assert_allclose(np.squeeze(result), expected)
+
+    @pytest.mark.parametrize("shape", [(10,), (4, 9), (3, 5, 7, 10)])
+    @pytest.mark.parametrize("m_max", [5, 4])
+    @pytest.mark.parametrize("n_max", [7, 10])
+    def test_lpmn(self, shape, n_max, m_max):
+        rng = np.random.default_rng(1234)
+
+        x = rng.uniform(-0.99, 0.99, shape)
+        p_all, p_all_jac, p_all_hess = \
+            assoc_legendre_p_all(n_max, m_max, x, diff_n=2)
+
+        n = np.arange(n_max + 1)
+        n = np.expand_dims(n, axis = tuple(range(1, x.ndim + 2)))
+
+        m = np.concatenate([np.arange(m_max + 1), np.arange(-m_max, 0)])
+        m = np.expand_dims(m, axis = (0,) + tuple(range(2, x.ndim + 2)))
+
+        x = np.expand_dims(x, axis = (0, 1))
+        p, p_jac, p_hess = assoc_legendre_p(n, m, x, diff_n=2)
+
+        np.testing.assert_allclose(p, p_all)
+        np.testing.assert_allclose(p_jac, p_all_jac)
+        np.testing.assert_allclose(p_hess, p_all_hess)
+
+    @pytest.mark.parametrize("shape", [(10,), (4, 9), (3, 5, 7, 10)])
+    @pytest.mark.parametrize("norm", [True, False])
+    def test_ode(self, shape, norm):
+        rng = np.random.default_rng(1234)
+
+        n = rng.integers(0, 10, shape)
+        m = rng.integers(-10, 10, shape)
+        x = rng.uniform(-1, 1, shape)
+
+        p, p_jac, p_hess = assoc_legendre_p(n, m, x, norm=norm, diff_n=2)
+
+        assert p.shape == shape
+        assert p_jac.shape == p.shape
+        assert p_hess.shape == p_jac.shape
+
+        np.testing.assert_allclose((1 - x * x) * p_hess,
+            2 * x * p_jac - (n * (n + 1) - m * m / (1 - x * x)) * p,
+            rtol=1e-05, atol=1e-08)
+
+    @pytest.mark.parametrize("shape", [(10,), (4, 9), (3, 5, 7)])
+    def test_all(self, shape):
+        rng = np.random.default_rng(1234)
+
+        n_max = 20
+        m_max = 20
+
+        x = rng.uniform(-0.99, 0.99, shape)
+
+        p, p_jac, p_hess = assoc_legendre_p_all(n_max, m_max, x, diff_n=2)
+
+        m = np.concatenate([np.arange(m_max + 1), np.arange(-m_max, 0)])
+        n = np.arange(n_max + 1)
+
+        n = np.expand_dims(n, axis = tuple(range(1, x.ndim + 2)))
+        m = np.expand_dims(m, axis = (0,) + tuple(range(2, x.ndim + 2)))
+        np.testing.assert_allclose((1 - x * x) * p_hess,
+            2 * x * p_jac - (n * (n + 1) - m * m / (1 - x * x)) * p,
+            rtol=1e-05, atol=1e-08)
+
+    @pytest.mark.parametrize("shape", [(10,), (4, 9), (3, 5, 7)])
+    @pytest.mark.parametrize("norm", [True, False])
+    def test_specific(self, shape, norm):
+        rng = np.random.default_rng(1234)
+
+        x = rng.uniform(-0.99, 0.99, shape)
+
+        p, p_jac = assoc_legendre_p_all(4, 4, x, norm=norm, diff_n=1)
+
+        np.testing.assert_allclose(p[0, 0],
+            assoc_legendre_p_0_0(x, norm=norm))
+        np.testing.assert_allclose(p[0, 1], 0)
+        np.testing.assert_allclose(p[0, 2], 0)
+        np.testing.assert_allclose(p[0, 3], 0)
+        np.testing.assert_allclose(p[0, 4], 0)
+        np.testing.assert_allclose(p[0, -3], 0)
+        np.testing.assert_allclose(p[0, -2], 0)
+        np.testing.assert_allclose(p[0, -1], 0)
+
+        np.testing.assert_allclose(p[1, 0],
+            assoc_legendre_p_1_0(x, norm=norm))
+        np.testing.assert_allclose(p[1, 1],
+            assoc_legendre_p_1_1(x, norm=norm))
+        np.testing.assert_allclose(p[1, 2], 0)
+        np.testing.assert_allclose(p[1, 3], 0)
+        np.testing.assert_allclose(p[1, 4], 0)
+        np.testing.assert_allclose(p[1, -4], 0)
+        np.testing.assert_allclose(p[1, -3], 0)
+        np.testing.assert_allclose(p[1, -2], 0)
+        np.testing.assert_allclose(p[1, -1],
+            assoc_legendre_p_1_m1(x, norm=norm))
+
+        np.testing.assert_allclose(p[2, 0],
+            assoc_legendre_p_2_0(x, norm=norm))
+        np.testing.assert_allclose(p[2, 1],
+            assoc_legendre_p_2_1(x, norm=norm))
+        np.testing.assert_allclose(p[2, 2],
+            assoc_legendre_p_2_2(x, norm=norm))
+        np.testing.assert_allclose(p[2, 3], 0)
+        np.testing.assert_allclose(p[2, 4], 0)
+        np.testing.assert_allclose(p[2, -4], 0)
+        np.testing.assert_allclose(p[2, -3], 0)
+        np.testing.assert_allclose(p[2, -2],
+            assoc_legendre_p_2_m2(x, norm=norm))
+        np.testing.assert_allclose(p[2, -1],
+            assoc_legendre_p_2_m1(x, norm=norm))
+
+        np.testing.assert_allclose(p[3, 0],
+            assoc_legendre_p_3_0(x, norm=norm))
+        np.testing.assert_allclose(p[3, 1],
+            assoc_legendre_p_3_1(x, norm=norm))
+        np.testing.assert_allclose(p[3, 2],
+            assoc_legendre_p_3_2(x, norm=norm))
+        np.testing.assert_allclose(p[3, 3],
+            assoc_legendre_p_3_3(x, norm=norm))
+        np.testing.assert_allclose(p[3, 4], 0)
+        np.testing.assert_allclose(p[3, -4], 0)
+        np.testing.assert_allclose(p[3, -3],
+            assoc_legendre_p_3_m3(x, norm=norm))
+        np.testing.assert_allclose(p[3, -2],
+            assoc_legendre_p_3_m2(x, norm=norm))
+        np.testing.assert_allclose(p[3, -1],
+            assoc_legendre_p_3_m1(x, norm=norm))
+
+        np.testing.assert_allclose(p[4, 0],
+            assoc_legendre_p_4_0(x, norm=norm))
+        np.testing.assert_allclose(p[4, 1],
+            assoc_legendre_p_4_1(x, norm=norm))
+        np.testing.assert_allclose(p[4, 2],
+            assoc_legendre_p_4_2(x, norm=norm))
+        np.testing.assert_allclose(p[4, 3],
+            assoc_legendre_p_4_3(x, norm=norm))
+        np.testing.assert_allclose(p[4, 4],
+            assoc_legendre_p_4_4(x, norm=norm))
+        np.testing.assert_allclose(p[4, -4],
+            assoc_legendre_p_4_m4(x, norm=norm))
+        np.testing.assert_allclose(p[4, -3],
+            assoc_legendre_p_4_m3(x, norm=norm))
+        np.testing.assert_allclose(p[4, -2],
+            assoc_legendre_p_4_m2(x, norm=norm))
+        np.testing.assert_allclose(p[4, -1],
+            assoc_legendre_p_4_m1(x, norm=norm))
+
+        np.testing.assert_allclose(p_jac[0, 0],
+            assoc_legendre_p_0_0_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[0, 1], 0)
+        np.testing.assert_allclose(p_jac[0, 2], 0)
+        np.testing.assert_allclose(p_jac[0, 3], 0)
+        np.testing.assert_allclose(p_jac[0, 4], 0)
+        np.testing.assert_allclose(p_jac[0, -4], 0)
+        np.testing.assert_allclose(p_jac[0, -3], 0)
+        np.testing.assert_allclose(p_jac[0, -2], 0)
+        np.testing.assert_allclose(p_jac[0, -1], 0)
+
+        np.testing.assert_allclose(p_jac[1, 0],
+            assoc_legendre_p_1_0_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[1, 1],
+            assoc_legendre_p_1_1_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[1, 2], 0)
+        np.testing.assert_allclose(p_jac[1, 3], 0)
+        np.testing.assert_allclose(p_jac[1, 4], 0)
+        np.testing.assert_allclose(p_jac[1, -4], 0)
+        np.testing.assert_allclose(p_jac[1, -3], 0)
+        np.testing.assert_allclose(p_jac[1, -2], 0)
+        np.testing.assert_allclose(p_jac[1, -1],
+            assoc_legendre_p_1_m1_jac(x, norm=norm))
+
+        np.testing.assert_allclose(p_jac[2, 0],
+            assoc_legendre_p_2_0_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[2, 1],
+            assoc_legendre_p_2_1_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[2, 2],
+            assoc_legendre_p_2_2_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[2, 3], 0)
+        np.testing.assert_allclose(p_jac[2, 4], 0)
+        np.testing.assert_allclose(p_jac[2, -4], 0)
+        np.testing.assert_allclose(p_jac[2, -3], 0)
+        np.testing.assert_allclose(p_jac[2, -2],
+            assoc_legendre_p_2_m2_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[2, -1],
+            assoc_legendre_p_2_m1_jac(x, norm=norm))
+
+        np.testing.assert_allclose(p_jac[3, 0],
+            assoc_legendre_p_3_0_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[3, 1],
+            assoc_legendre_p_3_1_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[3, 2],
+            assoc_legendre_p_3_2_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[3, 3],
+            assoc_legendre_p_3_3_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[3, 4], 0)
+        np.testing.assert_allclose(p_jac[3, -4], 0)
+        np.testing.assert_allclose(p_jac[3, -3],
+            assoc_legendre_p_3_m3_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[3, -2],
+            assoc_legendre_p_3_m2_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[3, -1],
+            assoc_legendre_p_3_m1_jac(x, norm=norm))
+
+        np.testing.assert_allclose(p_jac[4, 0],
+            assoc_legendre_p_4_0_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[4, 1],
+            assoc_legendre_p_4_1_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[4, 2],
+            assoc_legendre_p_4_2_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[4, 3],
+            assoc_legendre_p_4_3_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[4, 4],
+            assoc_legendre_p_4_4_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[4, -4],
+            assoc_legendre_p_4_m4_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[4, -3],
+            assoc_legendre_p_4_m3_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[4, -2],
+            assoc_legendre_p_4_m2_jac(x, norm=norm))
+        np.testing.assert_allclose(p_jac[4, -1],
+            assoc_legendre_p_4_m1_jac(x, norm=norm))
+
+    @pytest.mark.parametrize("m_max", [7])
+    @pytest.mark.parametrize("n_max", [10])
+    @pytest.mark.parametrize("x", [1, -1])
+    def test_all_limits(self, m_max, n_max, x):
+        p, p_jac = assoc_legendre_p_all(n_max, m_max, x, diff_n=1)
+
+        n = np.arange(n_max + 1)
+
+        np.testing.assert_allclose(p_jac[:, 0],
+            pow(x, n + 1) * n * (n + 1) / 2)
+        np.testing.assert_allclose(p_jac[:, 1],
+            np.where(n >= 1, pow(x, n) * np.inf, 0))
+        np.testing.assert_allclose(p_jac[:, 2],
+            np.where(n >= 2, -pow(x, n + 1) * (n + 2) * (n + 1) * n * (n - 1) / 4, 0))
+        np.testing.assert_allclose(p_jac[:, -2],
+            np.where(n >= 2, -pow(x, n + 1) / 4, 0))
+        np.testing.assert_allclose(p_jac[:, -1],
+            np.where(n >= 1, -pow(x, n) * np.inf, 0))
+
+        for m in range(3, m_max + 1):
+            np.testing.assert_allclose(p_jac[:, m], 0)
+            np.testing.assert_allclose(p_jac[:, -m], 0)
+
+    @pytest.mark.parametrize("n", np.arange(0, 20))
+    def test_assoc_legendre_p_norm_m0(self, n):
+        # gh 24099
+        m = 0
+        z = np.linspace(-1, 1, 101)
+        p = assoc_legendre_p(n, m, z, norm=False)
+        p_norm = assoc_legendre_p(n, m, z, norm=True)
+        factor = np.sqrt((2 * n + 1) / 2)
+        np.testing.assert_allclose(p_norm, factor * p)
+
+    @pytest.mark.parametrize("n", np.arange(0, 20))
+    def test_assoc_legendre_p_all_norm_m0(self, n):
+        # gh 24099
+        m = 0
+        z = np.linspace(-1, 1, 101)
+        p_all = assoc_legendre_p_all(n, m, z, norm=False)[0, : n + 1, 0, :]
+        p_norm_all = assoc_legendre_p_all(n, m, z, norm=True)[0, : n + 1, 0, :]
+        factor = np.sqrt((2 * np.arange(n + 1) + 1) / 2)
+        factor = factor[:, np.newaxis]
+        np.testing.assert_allclose(p_norm_all, factor * p_all)
+
+
+class TestMultiAssocLegendreP:
+    @pytest.mark.parametrize("shape", [(1000,), (4, 9), (3, 5, 7)])
+    @pytest.mark.parametrize("branch_cut", [2, 3])
+    @pytest.mark.parametrize("z_min, z_max", [(-10 - 10j, 10 + 10j),
+        (-1, 1), (-10j, 10j)])
+    @pytest.mark.parametrize("norm", [True, False])
+    def test_specific(self, shape, branch_cut, z_min, z_max, norm):
+        rng = np.random.default_rng(1234)
+
+        z = rng.uniform(z_min.real, z_max.real, shape) + \
+            1j * rng.uniform(z_min.imag, z_max.imag, shape)
+
+        p, p_jac = assoc_legendre_p_all(4, 4,
+            z, branch_cut=branch_cut, norm=norm, diff_n=1)
+
+        np.testing.assert_allclose(p[0, 0],
+            assoc_legendre_p_0_0(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[0, 1], 0)
+        np.testing.assert_allclose(p[0, 2], 0)
+        np.testing.assert_allclose(p[0, 3], 0)
+        np.testing.assert_allclose(p[0, 4], 0)
+        np.testing.assert_allclose(p[0, -4], 0)
+        np.testing.assert_allclose(p[0, -3], 0)
+        np.testing.assert_allclose(p[0, -2], 0)
+        np.testing.assert_allclose(p[0, -1], 0)
+
+        np.testing.assert_allclose(p[1, 0],
+            assoc_legendre_p_1_0(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[1, 1],
+            assoc_legendre_p_1_1(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[1, 2], 0)
+        np.testing.assert_allclose(p[1, 3], 0)
+        np.testing.assert_allclose(p[1, 4], 0)
+        np.testing.assert_allclose(p[1, -4], 0)
+        np.testing.assert_allclose(p[1, -3], 0)
+        np.testing.assert_allclose(p[1, -2], 0)
+        np.testing.assert_allclose(p[1, -1],
+            assoc_legendre_p_1_m1(z, branch_cut=branch_cut, norm=norm))
+
+        np.testing.assert_allclose(p[2, 0],
+            assoc_legendre_p_2_0(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[2, 1],
+            assoc_legendre_p_2_1(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[2, 2],
+            assoc_legendre_p_2_2(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[2, 3], 0)
+        np.testing.assert_allclose(p[2, 4], 0)
+        np.testing.assert_allclose(p[2, -4], 0)
+        np.testing.assert_allclose(p[2, -3], 0)
+        np.testing.assert_allclose(p[2, -2],
+            assoc_legendre_p_2_m2(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[2, -1],
+            assoc_legendre_p_2_m1(z, branch_cut=branch_cut, norm=norm))
+
+        np.testing.assert_allclose(p[3, 0],
+            assoc_legendre_p_3_0(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[3, 1],
+            assoc_legendre_p_3_1(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[3, 2],
+            assoc_legendre_p_3_2(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[3, 3],
+            assoc_legendre_p_3_3(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[3, 4], 0)
+        np.testing.assert_allclose(p[3, -4], 0)
+        np.testing.assert_allclose(p[3, -3],
+            assoc_legendre_p_3_m3(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[3, -2],
+            assoc_legendre_p_3_m2(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[3, -1],
+            assoc_legendre_p_3_m1(z, branch_cut=branch_cut, norm=norm))
+
+        np.testing.assert_allclose(p[4, 0],
+            assoc_legendre_p_4_0(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[4, 1],
+            assoc_legendre_p_4_1(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[4, 2],
+            assoc_legendre_p_4_2(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[4, 3],
+            assoc_legendre_p_4_3(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[4, 4],
+            assoc_legendre_p_4_4(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[4, -4],
+            assoc_legendre_p_4_m4(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[4, -3],
+            assoc_legendre_p_4_m3(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[4, -2],
+            assoc_legendre_p_4_m2(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p[4, -1],
+            assoc_legendre_p_4_m1(z, branch_cut=branch_cut, norm=norm))
+
+        np.testing.assert_allclose(p_jac[0, 0],
+            assoc_legendre_p_0_0_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[0, 1], 0)
+        np.testing.assert_allclose(p_jac[0, 2], 0)
+        np.testing.assert_allclose(p_jac[0, 3], 0)
+        np.testing.assert_allclose(p_jac[0, 4], 0)
+        np.testing.assert_allclose(p_jac[0, -4], 0)
+        np.testing.assert_allclose(p_jac[0, -3], 0)
+        np.testing.assert_allclose(p_jac[0, -2], 0)
+        np.testing.assert_allclose(p_jac[0, -1], 0)
+
+        np.testing.assert_allclose(p_jac[1, 0],
+            assoc_legendre_p_1_0_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[1, 1],
+            assoc_legendre_p_1_1_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[1, 2], 0)
+        np.testing.assert_allclose(p_jac[1, 3], 0)
+        np.testing.assert_allclose(p_jac[1, 4], 0)
+        np.testing.assert_allclose(p_jac[1, -4], 0)
+        np.testing.assert_allclose(p_jac[1, -3], 0)
+        np.testing.assert_allclose(p_jac[1, -2], 0)
+        np.testing.assert_allclose(p_jac[1, -1],
+            assoc_legendre_p_1_m1_jac(z, branch_cut=branch_cut, norm=norm))
+
+        np.testing.assert_allclose(p_jac[2, 0],
+            assoc_legendre_p_2_0_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[2, 1],
+            assoc_legendre_p_2_1_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[2, 2],
+            assoc_legendre_p_2_2_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[2, 3], 0)
+        np.testing.assert_allclose(p_jac[2, 4], 0)
+        np.testing.assert_allclose(p_jac[2, -4], 0)
+        np.testing.assert_allclose(p_jac[2, -3], 0)
+        np.testing.assert_allclose(p_jac[2, -2],
+            assoc_legendre_p_2_m2_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[2, -1],
+            assoc_legendre_p_2_m1_jac(z, branch_cut=branch_cut, norm=norm))
+
+        np.testing.assert_allclose(p_jac[3, 0],
+            assoc_legendre_p_3_0_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[3, 1],
+            assoc_legendre_p_3_1_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[3, 2],
+            assoc_legendre_p_3_2_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[3, 3],
+            assoc_legendre_p_3_3_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[3, 4], 0)
+        np.testing.assert_allclose(p_jac[3, -4], 0)
+        np.testing.assert_allclose(p_jac[3, -3],
+            assoc_legendre_p_3_m3_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[3, -2],
+            assoc_legendre_p_3_m2_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[3, -1],
+            assoc_legendre_p_3_m1_jac(z, branch_cut=branch_cut, norm=norm))
+
+        np.testing.assert_allclose(p_jac[4, 0],
+            assoc_legendre_p_4_0_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[4, 1],
+            assoc_legendre_p_4_1_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[4, 2],
+            assoc_legendre_p_4_2_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[4, 3],
+            assoc_legendre_p_4_3_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[4, 4],
+            assoc_legendre_p_4_4_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[4, -4],
+            assoc_legendre_p_4_m4_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[4, -3],
+            assoc_legendre_p_4_m3_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[4, -2],
+            assoc_legendre_p_4_m2_jac(z, branch_cut=branch_cut, norm=norm))
+        np.testing.assert_allclose(p_jac[4, -1],
+            assoc_legendre_p_4_m1_jac(z, branch_cut=branch_cut, norm=norm))
+
+class TestSphLegendreP:
+    @pytest.mark.parametrize("shape", [(10,), (4, 9), (3, 5, 7)])
+    def test_specific(self, shape):
+        rng = np.random.default_rng(1234)
+
+        theta = rng.uniform(-np.pi, np.pi, shape)
+
+        p, p_jac = sph_legendre_p_all(4, 4, theta, diff_n=1)
+
+        np.testing.assert_allclose(p[0, 0],
+            sph_legendre_p_0_0(theta))
+        np.testing.assert_allclose(p[0, 1], 0)
+        np.testing.assert_allclose(p[0, 2], 0)
+        np.testing.assert_allclose(p[0, 3], 0)
+        np.testing.assert_allclose(p[0, 4], 0)
+        np.testing.assert_allclose(p[0, -3], 0)
+        np.testing.assert_allclose(p[0, -2], 0)
+        np.testing.assert_allclose(p[0, -1], 0)
+
+        np.testing.assert_allclose(p[1, 0],
+            sph_legendre_p_1_0(theta))
+        np.testing.assert_allclose(p[1, 1],
+            sph_legendre_p_1_1(theta))
+        np.testing.assert_allclose(p[1, 2], 0)
+        np.testing.assert_allclose(p[1, 3], 0)
+        np.testing.assert_allclose(p[1, 4], 0)
+        np.testing.assert_allclose(p[1, -4], 0)
+        np.testing.assert_allclose(p[1, -3], 0)
+        np.testing.assert_allclose(p[1, -2], 0)
+        np.testing.assert_allclose(p[1, -1],
+            sph_legendre_p_1_m1(theta))
+
+        np.testing.assert_allclose(p[2, 0],
+            sph_legendre_p_2_0(theta))
+        np.testing.assert_allclose(p[2, 1],
+            sph_legendre_p_2_1(theta))
+        np.testing.assert_allclose(p[2, 2],
+            sph_legendre_p_2_2(theta))
+        np.testing.assert_allclose(p[2, 3], 0)
+        np.testing.assert_allclose(p[2, 4], 0)
+        np.testing.assert_allclose(p[2, -4], 0)
+        np.testing.assert_allclose(p[2, -3], 0)
+        np.testing.assert_allclose(p[2, -2],
+            sph_legendre_p_2_m2(theta))
+        np.testing.assert_allclose(p[2, -1],
+            sph_legendre_p_2_m1(theta))
+
+        np.testing.assert_allclose(p[3, 0],
+            sph_legendre_p_3_0(theta))
+        np.testing.assert_allclose(p[3, 1],
+            sph_legendre_p_3_1(theta))
+        np.testing.assert_allclose(p[3, 2],
+            sph_legendre_p_3_2(theta))
+        np.testing.assert_allclose(p[3, 3],
+            sph_legendre_p_3_3(theta))
+        np.testing.assert_allclose(p[3, 4], 0)
+        np.testing.assert_allclose(p[3, -4], 0)
+        np.testing.assert_allclose(p[3, -3],
+            sph_legendre_p_3_m3(theta))
+        np.testing.assert_allclose(p[3, -2],
+            sph_legendre_p_3_m2(theta))
+        np.testing.assert_allclose(p[3, -1],
+            sph_legendre_p_3_m1(theta))
+
+        np.testing.assert_allclose(p[4, 0],
+            sph_legendre_p_4_0(theta))
+        np.testing.assert_allclose(p[4, 1],
+            sph_legendre_p_4_1(theta))
+        np.testing.assert_allclose(p[4, 2],
+            sph_legendre_p_4_2(theta))
+        np.testing.assert_allclose(p[4, 3],
+            sph_legendre_p_4_3(theta))
+        np.testing.assert_allclose(p[4, 4],
+            sph_legendre_p_4_4(theta))
+        np.testing.assert_allclose(p[4, -4],
+            sph_legendre_p_4_m4(theta))
+        np.testing.assert_allclose(p[4, -3],
+            sph_legendre_p_4_m3(theta))
+        np.testing.assert_allclose(p[4, -2],
+            sph_legendre_p_4_m2(theta))
+        np.testing.assert_allclose(p[4, -1],
+            sph_legendre_p_4_m1(theta))
+
+        np.testing.assert_allclose(p_jac[0, 0],
+            sph_legendre_p_0_0_jac(theta))
+        np.testing.assert_allclose(p_jac[0, 1], 0)
+        np.testing.assert_allclose(p_jac[0, 2], 0)
+        np.testing.assert_allclose(p_jac[0, 3], 0)
+        np.testing.assert_allclose(p_jac[0, 4], 0)
+        np.testing.assert_allclose(p_jac[0, -3], 0)
+        np.testing.assert_allclose(p_jac[0, -2], 0)
+        np.testing.assert_allclose(p_jac[0, -1], 0)
+
+        np.testing.assert_allclose(p_jac[1, 0],
+            sph_legendre_p_1_0_jac(theta))
+        np.testing.assert_allclose(p_jac[1, 1],
+            sph_legendre_p_1_1_jac(theta))
+        np.testing.assert_allclose(p_jac[1, 2], 0)
+        np.testing.assert_allclose(p_jac[1, 3], 0)
+        np.testing.assert_allclose(p_jac[1, 4], 0)
+        np.testing.assert_allclose(p_jac[1, -4], 0)
+        np.testing.assert_allclose(p_jac[1, -3], 0)
+        np.testing.assert_allclose(p_jac[1, -2], 0)
+        np.testing.assert_allclose(p_jac[1, -1],
+            sph_legendre_p_1_m1_jac(theta))
+
+        np.testing.assert_allclose(p_jac[2, 0],
+            sph_legendre_p_2_0_jac(theta))
+        np.testing.assert_allclose(p_jac[2, 1],
+            sph_legendre_p_2_1_jac(theta))
+        np.testing.assert_allclose(p_jac[2, 2],
+            sph_legendre_p_2_2_jac(theta))
+        np.testing.assert_allclose(p_jac[2, 3], 0)
+        np.testing.assert_allclose(p_jac[2, 4], 0)
+        np.testing.assert_allclose(p_jac[2, -4], 0)
+        np.testing.assert_allclose(p_jac[2, -3], 0)
+        np.testing.assert_allclose(p_jac[2, -2],
+            sph_legendre_p_2_m2_jac(theta))
+        np.testing.assert_allclose(p_jac[2, -1],
+            sph_legendre_p_2_m1_jac(theta))
+
+        np.testing.assert_allclose(p_jac[3, 0],
+            sph_legendre_p_3_0_jac(theta))
+        np.testing.assert_allclose(p_jac[3, 1],
+            sph_legendre_p_3_1_jac(theta))
+        np.testing.assert_allclose(p_jac[3, 2],
+            sph_legendre_p_3_2_jac(theta))
+        np.testing.assert_allclose(p_jac[3, 3],
+            sph_legendre_p_3_3_jac(theta))
+        np.testing.assert_allclose(p_jac[3, 4], 0)
+        np.testing.assert_allclose(p_jac[3, -4], 0)
+        np.testing.assert_allclose(p_jac[3, -3],
+            sph_legendre_p_3_m3_jac(theta))
+        np.testing.assert_allclose(p_jac[3, -2],
+            sph_legendre_p_3_m2_jac(theta))
+        np.testing.assert_allclose(p_jac[3, -1],
+            sph_legendre_p_3_m1_jac(theta))
+
+        np.testing.assert_allclose(p_jac[4, 0],
+            sph_legendre_p_4_0_jac(theta))
+        np.testing.assert_allclose(p_jac[4, 1],
+            sph_legendre_p_4_1_jac(theta))
+        np.testing.assert_allclose(p_jac[4, 2],
+            sph_legendre_p_4_2_jac(theta))
+        np.testing.assert_allclose(p_jac[4, 3],
+            sph_legendre_p_4_3_jac(theta))
+        np.testing.assert_allclose(p_jac[4, 4],
+            sph_legendre_p_4_4_jac(theta))
+        np.testing.assert_allclose(p_jac[4, -4],
+            sph_legendre_p_4_m4_jac(theta))
+        np.testing.assert_allclose(p_jac[4, -3],
+            sph_legendre_p_4_m3_jac(theta))
+        np.testing.assert_allclose(p_jac[4, -2],
+            sph_legendre_p_4_m2_jac(theta))
+        np.testing.assert_allclose(p_jac[4, -1],
+            sph_legendre_p_4_m1_jac(theta))
+
+    @pytest.mark.parametrize("shape", [(10,), (4, 9), (3, 5, 7, 10)])
+    def test_ode(self, shape):
+        rng = np.random.default_rng(1234)
+
+        n = rng.integers(0, 10, shape)
+        m = rng.integers(-10, 10, shape)
+        theta = rng.uniform(-np.pi, np.pi, shape)
+
+        p, p_jac, p_hess = sph_legendre_p(n, m, theta, diff_n=2)
+
+        assert p.shape == shape
+        assert p_jac.shape == p.shape
+        assert p_hess.shape == p_jac.shape
+
+        np.testing.assert_allclose(np.sin(theta) * p_hess, -np.cos(theta) * p_jac
+            - (n * (n + 1) * np.sin(theta) - m * m / np.sin(theta)) * p,
+            rtol=1e-05, atol=1e-08)
+
+class TestLegendreFunctions:
+    """
+    @pytest.mark.parametrize("m_max", [3])
+    @pytest.mark.parametrize("n_max", [5])
+    @pytest.mark.parametrize("z", [-1])
+    def test_clpmn_all_limits(self, m_max, n_max, z):
+        rng = np.random.default_rng(1234)
+
+        type = 2
+
+        p, p_jac = special.clpmn_all(m_max, n_max, type, z, diff_n=1)
+
+        n = np.arange(n_max + 1)
+
+        np.testing.assert_allclose(p_jac[0], pow(z, n + 1) * n * (n + 1) / 2)
+        np.testing.assert_allclose(p_jac[1], np.where(n >= 1, pow(z, n) * np.inf, 0))
+        np.testing.assert_allclose(p_jac[2], np.where(n >= 2,
+            -pow(z, n + 1) * (n + 2) * (n + 1) * n * (n - 1) / 4, 0))
+        np.testing.assert_allclose(p_jac[-2], np.where(n >= 2, -pow(z, n + 1) / 4, 0))
+        np.testing.assert_allclose(p_jac[-1], np.where(n >= 1, -pow(z, n) * np.inf, 0))
+
+        for m in range(3, m_max + 1):
+            np.testing.assert_allclose(p_jac[m], 0)
+            np.testing.assert_allclose(p_jac[-m], 0)
+    """
+
+    def test_lpmv(self):
+        lp = special.lpmv(0, 2, .5)
+        assert_allclose(lp, -0.125, atol=1.5e-7, rtol=0)
+        lp = special.lpmv(0, 40, .001)
+        assert_allclose(lp, 0.1252678976534484, atol=1.5e-7, rtol=0)
+
+        # XXX: this is outside the domain of the current implementation,
+        #      so ensure it returns a NaN rather than a wrong answer.
+        with np.errstate(all='ignore'):
+            lp = special.lpmv(-1,-1,.001)
+        assert lp != 0 or np.isnan(lp)
+
+    def test_lqmn(self):
+        lqmnf = special.lqmn(0, 2, .5)
+        lqf = special.lqn(2, .5)
+        assert_allclose(lqmnf[0][0], lqf[0], atol=1.5e-4, rtol=0)
+        assert_allclose(lqmnf[1][0], lqf[1], atol=1.5e-4, rtol=0)
+
+    def test_lqmn_gt1(self):
+        """algorithm for real arguments changes at 1.0001
+           test against analytical result for m=2, n=1
+        """
+        x0 = 1.0001
+        delta = 0.00002
+        for x in (x0-delta, x0+delta):
+            lq = special.lqmn(2, 1, x)[0][-1, -1]
+            expected = 2/(x*x-1)
+            assert_allclose(lq, expected, atol=1.5e-7, rtol=0)
+
+    def test_lqmn_shape(self):
+        a, b = special.lqmn(4, 4, 1.1)
+        assert_equal(a.shape, (5, 5))
+        assert_equal(b.shape, (5, 5))
+
+        a, b = special.lqmn(4, 0, 1.1)
+        assert_equal(a.shape, (5, 1))
+        assert_equal(b.shape, (5, 1))
+
+    def test_lqn(self):
+        lqf = special.lqn(2, .5)
+        assert_allclose(lqf, (np.array([0.5493, -0.7253, -0.8187]),
+                              np.array([1.3333, 1.216, -0.8427])),
+                        atol=1.5e-4, rtol=0)
+
+    @pytest.mark.parametrize("function", [special.lqn])
+    @pytest.mark.parametrize("n", [1, 2, 4, 8, 16, 32])
+    @pytest.mark.parametrize("z_complex", [False, True])
+    @pytest.mark.parametrize("z_inexact", [False, True])
+    @pytest.mark.parametrize(
+        "input_shape",
+        [
+            (), (1, ), (2, ), (2, 1), (1, 2), (2, 2), (2, 2, 1), (2, 2, 2)
+        ]
+    )
+    def test_array_inputs_lxn(self, function, n, z_complex, z_inexact, input_shape):
+        """Tests for correct output shapes."""
+        rng = np.random.default_rng(1234)
+        if z_inexact:
+            z = rng.integers(-3, 3, size=input_shape)
+        else:
+            z = rng.uniform(-1, 1, size=input_shape)
+
+        if z_complex:
+            z = 1j * z + 0.5j * z
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=DeprecationWarning)
+            P_z, P_d_z = function(n, z)
+        assert P_z.shape == (n + 1, ) + input_shape
+        assert P_d_z.shape == (n + 1, ) + input_shape
+
+    @pytest.mark.parametrize("function", [special.lqmn])
+    @pytest.mark.parametrize(
+        "m,n",
+        [(0, 1), (1, 2), (1, 4), (3, 8), (11, 16), (19, 32)]
+    )
+    @pytest.mark.parametrize("z_inexact", [False, True])
+    @pytest.mark.parametrize(
+        "input_shape", [
+            (), (1, ), (2, ), (2, 1), (1, 2), (2, 2), (2, 2, 1)
+        ]
+    )
+    def test_array_inputs_lxmn(self, function, m, n, z_inexact, input_shape):
+        """Tests for correct output shapes and dtypes."""
+        rng = np.random.default_rng(1234)
+        if z_inexact:
+            z = rng.integers(-3, 3, size=input_shape)
+        else:
+            z = rng.uniform(-1, 1, size=input_shape)
+
+        P_z, P_d_z = function(m, n, z)
+        assert P_z.shape == (m + 1, n + 1) + input_shape
+        assert P_d_z.shape == (m + 1, n + 1) + input_shape
+
+    @pytest.mark.parametrize("function", [special.lqmn])
+    @pytest.mark.parametrize(
+        "m,n",
+        [(0, 1), (1, 2), (1, 4), (3, 8), (11, 16), (19, 32)]
+    )
+    @pytest.mark.parametrize(
+        "input_shape", [
+            (), (1, ), (2, ), (2, 1), (1, 2), (2, 2), (2, 2, 1)
+        ]
+    )
+    def test_array_inputs_clxmn(self, function, m, n, input_shape):
+        """Tests for correct output shapes and dtypes."""
+        rng = np.random.default_rng(1234)
+        z = rng.uniform(-1, 1, size=input_shape)
+        z = 1j * z + 0.5j * z
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=DeprecationWarning)
+            P_z, P_d_z = function(m, n, z)
+
+        assert P_z.shape == (m + 1, n + 1) + input_shape
+        assert P_d_z.shape == (m + 1, n + 1) + input_shape
+
+def assoc_legendre_factor(n, m, norm):
+    if norm:
+        return (math.sqrt((2 * n + 1) *
+            math.factorial(n - m) / (2 * math.factorial(n + m))))
+
+    return 1
+
+def assoc_legendre_p_0_0(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(0, 0, norm)
+
+    return np.full_like(z, fac)
+
+def assoc_legendre_p_1_0(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(1, 0, norm)
+
+    return fac * z
+
+def assoc_legendre_p_1_1(z, *, branch_cut=2, norm=False):
+    branch_sign = np.where(branch_cut == 3, np.where(np.signbit(np.real(z)), 1, -1), -1)
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(1, 1, norm)
+
+    w = np.sqrt(np.where(branch_cut == 3, z * z - 1, 1 - z * z))
+
+    return branch_cut_sign * branch_sign * fac * w
+
+def assoc_legendre_p_1_m1(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(1, -1, norm)
+
+    return (-branch_cut_sign * fac *
+        assoc_legendre_p_1_1(z, branch_cut=branch_cut) / 2)
+
+def assoc_legendre_p_2_0(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(2, 0, norm)
+
+    return fac * (3 * z * z - 1) / 2
+
+def assoc_legendre_p_2_1(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(2, 1, norm)
+
+    return (3 * fac * z *
+        assoc_legendre_p_1_1(z, branch_cut=branch_cut))
+
+def assoc_legendre_p_2_2(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(2, 2, norm)
+
+    return 3 * branch_cut_sign * fac * (1 - z * z)
+
+def assoc_legendre_p_2_m2(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(2, -2, norm)
+
+    return branch_cut_sign * fac * (1 - z * z) / 8
+
+def assoc_legendre_p_2_m1(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(2, -1, norm)
+
+    return (-branch_cut_sign * fac * z *
+        assoc_legendre_p_1_1(z, branch_cut=branch_cut) / 2)
+
+def assoc_legendre_p_3_0(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(3, 0, norm)
+
+    return fac * (5 * z * z - 3) * z / 2
+
+def assoc_legendre_p_3_1(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(3, 1, norm)
+
+    return (3 * fac * (5 * z * z - 1) *
+        assoc_legendre_p_1_1(z, branch_cut=branch_cut) / 2)
+
+def assoc_legendre_p_3_2(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(3, 2, norm)
+
+    return 15 * branch_cut_sign * fac * (1 - z * z) * z
+
+def assoc_legendre_p_3_3(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(3, 3, norm)
+
+    return (15 * branch_cut_sign * fac * (1 - z * z) *
+        assoc_legendre_p_1_1(z, branch_cut=branch_cut))
+
+def assoc_legendre_p_3_m3(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(3, -3, norm)
+
+    return (fac * (z * z - 1) *
+        assoc_legendre_p_1_1(z, branch_cut=branch_cut) / 48)
+
+def assoc_legendre_p_3_m2(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(3, -2, norm)
+
+    return branch_cut_sign * fac * (1 - z * z) * z / 8
+
+def assoc_legendre_p_3_m1(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(3, -1, norm)
+
+    return (branch_cut_sign * fac * (1 - 5 * z * z) *
+        assoc_legendre_p_1_1(z, branch_cut=branch_cut) / 8)
+
+def assoc_legendre_p_4_0(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(4, 0, norm)
+
+    return fac * ((35 * z * z - 30) * z * z + 3) / 8
+
+def assoc_legendre_p_4_1(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(4, 1, norm)
+
+    return (5 * fac * (7 * z * z - 3) * z *
+       assoc_legendre_p_1_1(z, branch_cut=branch_cut) / 2)
+
+def assoc_legendre_p_4_2(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(4, 2, norm)
+
+    return 15 * branch_cut_sign * fac * ((8 - 7 * z * z) * z * z - 1) / 2
+
+def assoc_legendre_p_4_3(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(4, 3, norm)
+
+    return (105 * branch_cut_sign * fac * (1 - z * z) * z *
+        assoc_legendre_p_1_1(z, branch_cut=branch_cut))
+
+def assoc_legendre_p_4_4(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(4, 4, norm)
+
+    return 105 * fac * np.square(z * z - 1)
+
+def assoc_legendre_p_4_m4(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(4, -4, norm)
+
+    return fac * np.square(z * z - 1) / 384
+
+def assoc_legendre_p_4_m3(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(4, -3, norm)
+
+    return (fac * (z * z - 1) * z *
+        assoc_legendre_p_1_1(z, branch_cut=branch_cut) / 48)
+
+def assoc_legendre_p_4_m2(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(4, -2, norm)
+
+    return branch_cut_sign * fac * ((8 - 7 * z * z) * z * z - 1) / 48
+
+def assoc_legendre_p_4_m1(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(4, -1, norm)
+
+    return (branch_cut_sign * fac * (3 - 7 * z * z) * z *
+        assoc_legendre_p_1_1(z, branch_cut=branch_cut) / 8)
+
+def assoc_legendre_p_1_1_jac_div_z(z, branch_cut=2):
+    branch_sign = np.where(branch_cut == 3, np.where(np.signbit(np.real(z)), 1, -1), -1)
+
+    out11_div_z = (-branch_sign /
+        np.sqrt(np.where(branch_cut == 3, z * z - 1, 1 - z * z)))
+
+    return out11_div_z
+
+def assoc_legendre_p_0_0_jac(z, *, branch_cut=2, norm=False):
+    return np.zeros_like(z)
+
+def assoc_legendre_p_1_0_jac(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(1, 0, norm)
+
+    return np.full_like(z, fac)
+
+def assoc_legendre_p_1_1_jac(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(1, 1, norm)
+
+    return (fac * z *
+        assoc_legendre_p_1_1_jac_div_z(z, branch_cut=branch_cut))
+
+def assoc_legendre_p_1_m1_jac(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(1, -1, norm)
+
+    return (-branch_cut_sign * fac * z *
+        assoc_legendre_p_1_1_jac_div_z(z, branch_cut=branch_cut) / 2)
+
+def assoc_legendre_p_2_0_jac(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(2, 0, norm)
+
+    return 3 * fac * z
+
+def assoc_legendre_p_2_1_jac(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(2, 1, norm)
+
+    return (3 * fac * (2 * z * z - 1) *
+        assoc_legendre_p_1_1_jac_div_z(z, branch_cut=branch_cut))
+
+def assoc_legendre_p_2_2_jac(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(2, 2, norm)
+
+    return -6 * branch_cut_sign * fac * z
+
+def assoc_legendre_p_2_m1_jac(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(2, -1, norm)
+
+    return (branch_cut_sign * fac * (1 - 2 * z * z) *
+        assoc_legendre_p_1_1_jac_div_z(z, branch_cut=branch_cut) / 2)
+
+def assoc_legendre_p_2_m2_jac(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(2, -2, norm)
+
+    return -branch_cut_sign * fac * z / 4
+
+def assoc_legendre_p_3_0_jac(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(3, 0, norm)
+
+    return 3 * fac * (5 * z * z - 1) / 2
+
+def assoc_legendre_p_3_1_jac(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(3, 1, norm)
+
+    return (3 * fac * (15 * z * z - 11) * z *
+        assoc_legendre_p_1_1_jac_div_z(z, branch_cut=branch_cut) / 2)
+
+def assoc_legendre_p_3_2_jac(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(3, 2, norm)
+
+    return 15 * branch_cut_sign * fac * (1 - 3 * z * z)
+
+def assoc_legendre_p_3_3_jac(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(3, 3, norm)
+
+    return (45 * branch_cut_sign * fac * (1 - z * z) * z *
+        assoc_legendre_p_1_1_jac_div_z(z, branch_cut=branch_cut))
+
+def assoc_legendre_p_3_m3_jac(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(3, -3, norm)
+
+    return (fac * (z * z - 1) * z *
+        assoc_legendre_p_1_1_jac_div_z(z, branch_cut=branch_cut) / 16)
+
+def assoc_legendre_p_3_m2_jac(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(3, -2, norm)
+
+    return branch_cut_sign * fac * (1 - 3 * z * z) / 8
+
+def assoc_legendre_p_3_m1_jac(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(3, -1, norm)
+
+    return (branch_cut_sign * fac * (11 - 15 * z * z) * z *
+        assoc_legendre_p_1_1_jac_div_z(z, branch_cut=branch_cut) / 8)
+
+def assoc_legendre_p_4_0_jac(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(4, 0, norm)
+
+    return 5 * fac * (7 * z * z - 3) * z / 2
+
+def assoc_legendre_p_4_1_jac(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(4, 1, norm)
+
+    return (5 * fac * ((28 * z * z - 27) * z * z + 3) *
+        assoc_legendre_p_1_1_jac_div_z(z, branch_cut=branch_cut) / 2)
+
+def assoc_legendre_p_4_2_jac(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(4, 2, norm)
+
+    return 30 * branch_cut_sign * fac * (4 - 7 * z * z) * z
+
+def assoc_legendre_p_4_3_jac(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(4, 3, norm)
+
+    return (105 * branch_cut_sign * fac * ((5 - 4 * z * z) * z * z - 1) *
+        assoc_legendre_p_1_1_jac_div_z(z, branch_cut=branch_cut))
+
+def assoc_legendre_p_4_4_jac(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(4, 4, norm)
+
+    return 420 * fac * (z * z - 1) * z
+
+def assoc_legendre_p_4_m4_jac(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(4, -4, norm)
+
+    return fac * (z * z - 1) * z / 96
+
+def assoc_legendre_p_4_m3_jac(z, *, branch_cut=2, norm=False):
+    fac = assoc_legendre_factor(4, -3, norm)
+
+    return (fac * ((4 * z * z - 5) * z * z + 1) *
+        assoc_legendre_p_1_1_jac_div_z(z, branch_cut=branch_cut) / 48)
+
+def assoc_legendre_p_4_m2_jac(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(4, -2, norm)
+
+    return branch_cut_sign * fac * (4 - 7 * z * z) * z / 12
+
+def assoc_legendre_p_4_m1_jac(z, *, branch_cut=2, norm=False):
+    branch_cut_sign = np.where(branch_cut == 3, -1, 1)
+    fac = assoc_legendre_factor(4, -1, norm)
+
+    return (branch_cut_sign * fac * ((27 - 28 * z * z) * z * z - 3) *
+        assoc_legendre_p_1_1_jac_div_z(z, branch_cut=branch_cut) / 8)
+
+def sph_legendre_factor(n, m):
+    return assoc_legendre_factor(n, m, norm=True) / np.sqrt(2 * np.pi)
+
+def sph_legendre_p_0_0(theta):
+    fac = sph_legendre_factor(0, 0)
+
+    return np.full_like(theta, fac)
+
+def sph_legendre_p_1_0(theta):
+    fac = sph_legendre_factor(1, 0)
+
+    return fac * np.cos(theta)
+
+def sph_legendre_p_1_1(theta):
+    fac = sph_legendre_factor(1, 1)
+
+    return -fac * np.abs(np.sin(theta))
+
+def sph_legendre_p_1_m1(theta):
+    fac = sph_legendre_factor(1, -1)
+
+    return fac * np.abs(np.sin(theta)) / 2
+
+def sph_legendre_p_2_0(theta):
+    fac = sph_legendre_factor(2, 0)
+
+    return fac * (3 * np.square(np.cos(theta)) - 1) / 2
+
+def sph_legendre_p_2_1(theta):
+    fac = sph_legendre_factor(2, 1)
+
+    return -3 * fac * np.abs(np.sin(theta)) * np.cos(theta)
+
+def sph_legendre_p_2_2(theta):
+    fac = sph_legendre_factor(2, 2)
+
+    return 3 * fac * (1 - np.square(np.cos(theta)))
+
+def sph_legendre_p_2_m2(theta):
+    fac = sph_legendre_factor(2, -2)
+
+    return fac * (1 - np.square(np.cos(theta))) / 8
+
+def sph_legendre_p_2_m1(theta):
+    fac = sph_legendre_factor(2, -1)
+
+    return fac * np.cos(theta) * np.abs(np.sin(theta)) / 2
+
+def sph_legendre_p_3_0(theta):
+    fac = sph_legendre_factor(3, 0)
+
+    return (fac * (5 * np.square(np.cos(theta)) - 3) *
+        np.cos(theta) / 2)
+
+def sph_legendre_p_3_1(theta):
+    fac = sph_legendre_factor(3, 1)
+
+    return (-3 * fac * (5 * np.square(np.cos(theta)) - 1) *
+        np.abs(np.sin(theta)) / 2)
+
+def sph_legendre_p_3_2(theta):
+    fac = sph_legendre_factor(3, 2)
+
+    return (-15 * fac * (np.square(np.cos(theta)) - 1) *
+        np.cos(theta))
+
+def sph_legendre_p_3_3(theta):
+    fac = sph_legendre_factor(3, 3)
+
+    return -15 * fac * np.power(np.abs(np.sin(theta)), 3)
+
+def sph_legendre_p_3_m3(theta):
+    fac = sph_legendre_factor(3, -3)
+
+    return fac * np.power(np.abs(np.sin(theta)), 3) / 48
+
+def sph_legendre_p_3_m2(theta):
+    fac = sph_legendre_factor(3, -2)
+
+    return (-fac * (np.square(np.cos(theta)) - 1) *
+        np.cos(theta) / 8)
+
+def sph_legendre_p_3_m1(theta):
+    fac = sph_legendre_factor(3, -1)
+
+    return (fac * (5 * np.square(np.cos(theta)) - 1) *
+        np.abs(np.sin(theta)) / 8)
+
+def sph_legendre_p_4_0(theta):
+    fac = sph_legendre_factor(4, 0)
+
+    return (fac * (35 * np.square(np.square(np.cos(theta))) -
+        30 * np.square(np.cos(theta)) + 3) / 8)
+
+def sph_legendre_p_4_1(theta):
+    fac = sph_legendre_factor(4, 1)
+
+    return (-5 * fac * (7 * np.square(np.cos(theta)) - 3) *
+        np.cos(theta) * np.abs(np.sin(theta)) / 2)
+
+def sph_legendre_p_4_2(theta):
+    fac = sph_legendre_factor(4, 2)
+
+    return (-15 * fac * (7 * np.square(np.cos(theta)) - 1) *
+        (np.square(np.cos(theta)) - 1) / 2)
+
+def sph_legendre_p_4_3(theta):
+    fac = sph_legendre_factor(4, 3)
+
+    return -105 * fac * np.power(np.abs(np.sin(theta)), 3) * np.cos(theta)
+
+def sph_legendre_p_4_4(theta):
+    fac = sph_legendre_factor(4, 4)
+
+    return 105 * fac * np.square(np.square(np.cos(theta)) - 1)
+
+def sph_legendre_p_4_m4(theta):
+    fac = sph_legendre_factor(4, -4)
+
+    return fac * np.square(np.square(np.cos(theta)) - 1) / 384
+
+def sph_legendre_p_4_m3(theta):
+    fac = sph_legendre_factor(4, -3)
+
+    return (fac * np.power(np.abs(np.sin(theta)), 3) *
+        np.cos(theta) / 48)
+
+def sph_legendre_p_4_m2(theta):
+    fac = sph_legendre_factor(4, -2)
+
+    return (-fac * (7 * np.square(np.cos(theta)) - 1) *
+        (np.square(np.cos(theta)) - 1) / 48)
+
+def sph_legendre_p_4_m1(theta):
+    fac = sph_legendre_factor(4, -1)
+
+    return (fac * (7 * np.square(np.cos(theta)) - 3) *
+        np.cos(theta) * np.abs(np.sin(theta)) / 8)
+
+def sph_legendre_p_0_0_jac(theta):
+    return np.zeros_like(theta)
+
+def sph_legendre_p_1_0_jac(theta):
+    fac = sph_legendre_factor(1, 0)
+
+    return -fac * np.sin(theta)
+
+def sph_legendre_p_1_1_jac(theta):
+    fac = sph_legendre_factor(1, 1)
+
+    return -fac * np.cos(theta) * (2 * np.heaviside(np.sin(theta), 1) - 1)
+
+def sph_legendre_p_1_m1_jac(theta):
+    fac = sph_legendre_factor(1, -1)
+
+    return fac * np.cos(theta) * (2 * np.heaviside(np.sin(theta), 1) - 1) / 2
+
+def sph_legendre_p_2_0_jac(theta):
+    fac = sph_legendre_factor(2, 0)
+
+    return -3 * fac * np.cos(theta) * np.sin(theta)
+
+def sph_legendre_p_2_1_jac(theta):
+    fac = sph_legendre_factor(2, 1)
+
+    return (3 * fac * (-np.square(np.cos(theta)) *
+        (2 * np.heaviside(np.sin(theta), 1) - 1) +
+        np.abs(np.sin(theta)) * np.sin(theta)))
+
+def sph_legendre_p_2_2_jac(theta):
+    fac = sph_legendre_factor(2, 2)
+
+    return 6 * fac * np.sin(theta) * np.cos(theta)
+
+def sph_legendre_p_2_m2_jac(theta):
+    fac = sph_legendre_factor(2, -2)
+
+    return fac * np.sin(theta) * np.cos(theta) / 4
+
+def sph_legendre_p_2_m1_jac(theta):
+    fac = sph_legendre_factor(2, -1)
+
+    return (-fac * (-np.square(np.cos(theta)) *
+        (2 * np.heaviside(np.sin(theta), 1) - 1) +
+        np.abs(np.sin(theta)) * np.sin(theta)) / 2)
+
+def sph_legendre_p_3_0_jac(theta):
+    fac = sph_legendre_factor(3, 0)
+
+    return 3 * fac * (1 - 5 * np.square(np.cos(theta))) * np.sin(theta) / 2
+
+def sph_legendre_p_3_1_jac(theta):
+    fac = sph_legendre_factor(3, 1)
+
+    return (3 * fac * (11 - 15 * np.square(np.cos(theta))) * np.cos(theta) *
+        (2 * np.heaviside(np.sin(theta), 1) - 1) / 2)
+
+def sph_legendre_p_3_2_jac(theta):
+    fac = sph_legendre_factor(3, 2)
+
+    return 15 * fac * (3 * np.square(np.cos(theta)) - 1) * np.sin(theta)
+
+def sph_legendre_p_3_3_jac(theta):
+    fac = sph_legendre_factor(3, 3)
+
+    return -45 * fac * np.abs(np.sin(theta)) * np.sin(theta) * np.cos(theta)
+
+def sph_legendre_p_3_m3_jac(theta):
+    fac = sph_legendre_factor(3, -3)
+
+    return fac * np.abs(np.sin(theta)) * np.sin(theta) * np.cos(theta) / 16
+
+def sph_legendre_p_3_m2_jac(theta):
+    fac = sph_legendre_factor(3, -2)
+
+    return fac * (3 * np.square(np.cos(theta)) - 1) * np.sin(theta) / 8
+
+def sph_legendre_p_3_m1_jac(theta):
+    fac = sph_legendre_factor(3, -1)
+
+    return (-fac * (11 - 15 * np.square(np.cos(theta))) *
+        np.cos(theta) *
+        (2 * np.heaviside(np.sin(theta), 1) - 1) / 8)
+
+def sph_legendre_p_4_0_jac(theta):
+    fac = sph_legendre_factor(4, 0)
+
+    return (-5 * fac * (7 * np.square(np.cos(theta)) - 3) *
+        np.sin(theta) * np.cos(theta) / 2)
+
+def sph_legendre_p_4_1_jac(theta):
+    fac = sph_legendre_factor(4, 1)
+
+    return (5 * fac * (-3 + 27 * np.square(np.cos(theta)) -
+        28 * np.square(np.square(np.cos(theta)))) *
+        (2 * np.heaviside(np.sin(theta), 1) - 1) / 2)
+
+def sph_legendre_p_4_2_jac(theta):
+    fac = sph_legendre_factor(4, 2)
+
+    return (30 * fac * (7 * np.square(np.cos(theta)) - 4) *
+        np.sin(theta) * np.cos(theta))
+
+def sph_legendre_p_4_3_jac(theta):
+    fac = sph_legendre_factor(4, 3)
+
+    return (-105 * fac * (4 * np.square(np.cos(theta)) - 1) *
+        np.abs(np.sin(theta)) * np.sin(theta))
+
+def sph_legendre_p_4_4_jac(theta):
+    fac = sph_legendre_factor(4, 4)
+
+    return (-420 * fac * (np.square(np.cos(theta)) - 1) *
+        np.sin(theta) * np.cos(theta))
+
+def sph_legendre_p_4_m4_jac(theta):
+    fac = sph_legendre_factor(4, -4)
+
+    return (-fac * (np.square(np.cos(theta)) - 1) *
+        np.sin(theta) * np.cos(theta) / 96)
+
+def sph_legendre_p_4_m3_jac(theta):
+    fac = sph_legendre_factor(4, -3)
+
+    return (fac * (4 * np.square(np.cos(theta)) - 1) *
+        np.abs(np.sin(theta)) * np.sin(theta) / 48)
+
+def sph_legendre_p_4_m2_jac(theta):
+    fac = sph_legendre_factor(4, -2)
+
+    return (fac * (7 * np.square(np.cos(theta)) - 4) * np.sin(theta) *
+        np.cos(theta) / 12)
+
+def sph_legendre_p_4_m1_jac(theta):
+    fac = sph_legendre_factor(4, -1)
+
+    return (-fac * (-3 + 27 * np.square(np.cos(theta)) -
+        28 * np.square(np.square(np.cos(theta)))) *
+        (2 * np.heaviside(np.sin(theta), 1) - 1) / 8)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_log1mexp.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_log1mexp.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed8b1958eabd973704c32e9be157397c7b3472e3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_log1mexp.py
@@ -0,0 +1,85 @@
+import numpy as np
+import pytest
+
+from numpy.testing import assert_allclose, assert_equal
+
+from scipy.special._ufuncs import _log1mexp
+
+# # Test cases generated with the script
+#
+# import numpy as np
+
+# from mpmath import mp
+
+
+# def mp_log1mexp(x):
+#     with mp.workdps(324):
+#         return float(mp.log(mp.one - mp.exp(x)))
+
+# X = np.concat([-np.logspace(-1, -300, 20), np.linspace(-745, -1, 20)])
+
+# cases = [(float(x), mp_log1mexp(x)) for x in X]
+
+@pytest.mark.parametrize(
+    "x,expected",
+    [
+        (-0.1, -2.3521684610440907),
+        (-1.8329807108324374e-17, -38.538003135374026),
+        (-3.359818286283788e-33, -74.773421177754),
+        (-6.1584821106602796e-49, -111.00883922013399),
+        (-1.1288378916846929e-64, -147.24425726251397),
+        (-2.0691380811148324e-80, -183.47967530489393),
+        (-3.792690190732269e-96, -219.71509334727392),
+        (-6.951927961775534e-112, -255.95051138965394),
+        (-1.2742749857031425e-127, -292.1859294320339),
+        (-2.3357214690901785e-143, -328.42134747441384),
+        (-4.281332398719571e-159, -364.6567655167938),
+        (-7.847599703514559e-175, -400.8921835591739),
+        (-1.4384498882876776e-190, -437.1276016015538),
+        (-2.6366508987304307e-206, -473.3630196439338),
+        (-4.832930238571653e-222, -509.59843768631384),
+        (-8.858667904100796e-238, -545.8338557286938),
+        (-1.623776739188744e-253, -582.0692737710738),
+        (-2.9763514416312156e-269, -618.3046918134538),
+        (-5.455594781168782e-285, -654.5401098558336),
+        (-1e-300, -690.7755278982137),
+        (-745.0, -5e-324),
+        (-705.8421052631579, -2.8619931451743316e-307),
+        (-666.6842105263158, -2.9021923726875757e-290),
+        (-627.5263157894738, -2.9429562339405562e-273),
+        (-588.3684210526316, -2.9842926597143714e-256),
+        (-549.2105263157895, -3.0262096921839423e-239),
+        (-510.0526315789474, -3.0687154864846747e-222),
+        (-470.89473684210526, -3.1118183122979086e-205),
+        (-431.7368421052632, -3.155526555459449e-188),
+        (-392.5789473684211, -3.1998487195921207e-171),
+        (-353.42105263157896, -3.2447934277596653e-154),
+        (-314.2631578947369, -3.2903694241438367e-137),
+        (-275.1052631578948, -3.3365855757467166e-120),
+        (-235.94736842105266, -3.3834508741152875e-103),
+        (-196.78947368421052, -3.4309744370903894e-86),
+        (-157.63157894736844, -3.4791655105810003e-69),
+        (-118.47368421052636, -3.528033470363468e-52),
+        (-79.31578947368428, -3.577587823905024e-35),
+        (-40.157894736842195, -3.627838212213697e-18),
+        (-1.0, -0.4586751453870819),
+    ]
+)
+def test_log1mexp(x, expected):
+    observed = _log1mexp(x)
+    assert_allclose(observed, expected, rtol=1e-15, atol=1e-300)
+
+
+@pytest.mark.parametrize("x", [1.1, 1e10, np.inf])
+def test_log1mexp_out_of_domain(x):
+    observed = _log1mexp(x)
+    assert np.isnan(observed)
+
+
+@pytest.mark.parametrize(
+    "x,expected",
+    [(-np.inf, -0.0), (0.0, -np.inf), (-0.0, -np.inf), (np.nan, np.nan)]
+)
+def test_log1mexp_extreme(x, expected):
+    observed = _log1mexp(x)
+    assert_equal(expected, observed)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_loggamma.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_loggamma.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fcb5a20037de46df939895d38fbe5fe6b85c9aa
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_loggamma.py
@@ -0,0 +1,70 @@
+import numpy as np
+from numpy.testing import assert_allclose, assert_
+
+from scipy.special._testutils import FuncData
+from scipy.special import gamma, gammaln, loggamma
+
+
+def test_identities1():
+    # test the identity exp(loggamma(z)) = gamma(z)
+    x = np.array([-99.5, -9.5, -0.5, 0.5, 9.5, 99.5])
+    y = x.copy()
+    x, y = np.meshgrid(x, y)
+    z = (x + 1J*y).flatten()
+    dataset = np.vstack((z, gamma(z))).T
+
+    def f(z):
+        return np.exp(loggamma(z))
+
+    FuncData(f, dataset, 0, 1, rtol=1e-14, atol=1e-14).check()
+
+
+def test_identities2():
+    # test the identity loggamma(z + 1) = log(z) + loggamma(z)
+    x = np.array([-99.5, -9.5, -0.5, 0.5, 9.5, 99.5])
+    y = x.copy()
+    x, y = np.meshgrid(x, y)
+    z = (x + 1J*y).flatten()
+    dataset = np.vstack((z, np.log(z) + loggamma(z))).T
+
+    def f(z):
+        return loggamma(z + 1)
+
+    FuncData(f, dataset, 0, 1, rtol=1e-14, atol=1e-14).check()
+
+
+def test_complex_dispatch_realpart():
+    # Test that the real parts of loggamma and gammaln agree on the
+    # real axis.
+    x = np.r_[-np.logspace(10, -10), np.logspace(-10, 10)] + 0.5
+
+    dataset = np.vstack((x, gammaln(x))).T
+
+    def f(z):
+        z = np.array(z, dtype='complex128')
+        return loggamma(z).real
+
+    FuncData(f, dataset, 0, 1, rtol=1e-14, atol=1e-14).check()
+
+
+def test_real_dispatch():
+    x = np.logspace(-10, 10) + 0.5
+    dataset = np.vstack((x, gammaln(x))).T
+
+    FuncData(loggamma, dataset, 0, 1, rtol=1e-14, atol=1e-14).check()
+    assert_(loggamma(0) == np.inf)
+    assert_(np.isnan(loggamma(-1)))
+
+
+def test_gh_6536():
+    z = loggamma(complex(-3.4, +0.0))
+    zbar = loggamma(complex(-3.4, -0.0))
+    assert_allclose(z, zbar.conjugate(), rtol=1e-15, atol=0)
+
+
+def test_branch_cut():
+    # Make sure negative zero is treated correctly
+    x = -np.logspace(300, -30, 100)
+    z = np.asarray([complex(x0, 0.0) for x0 in x])
+    zbar = np.asarray([complex(x0, -0.0) for x0 in x])
+    assert_allclose(z, zbar.conjugate(), rtol=1e-15, atol=0)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_logit.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_logit.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae8dd8851d8932dcbbda68c7f11edbe2daafcf00
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_logit.py
@@ -0,0 +1,161 @@
+import numpy as np
+from numpy.testing import assert_equal, assert_allclose
+from scipy.special import logit, expit, log_expit
+
+
+class TestLogit:
+
+    def check_logit_out(self, a, expected):
+        actual = logit(a)
+        assert_equal(actual.dtype, a.dtype)
+        rtol = 16*np.finfo(a.dtype).eps
+        assert_allclose(actual, expected, rtol=rtol)
+
+    def test_float32(self):
+        a = np.concatenate((np.linspace(0, 1, 10, dtype=np.float32),
+                            [np.float32(0.0001), np.float32(0.49999),
+                             np.float32(0.50001)]))
+        # Expected values computed with mpmath from float32 inputs, e.g.
+        #   from mpmath import mp
+        #   mp.dps = 200
+        #   a = np.float32(1/9)
+        #   print(np.float32(mp.log(a) - mp.log1p(-a)))
+        # prints `-2.0794415`.
+        expected = np.array([-np.inf, -2.0794415, -1.2527629, -6.9314712e-01,
+                             -2.2314353e-01,  2.2314365e-01,  6.9314724e-01,
+                             1.2527630, 2.0794415, np.inf,
+                             -9.2102404, -4.0054321e-05, 4.0054321e-05],
+                            dtype=np.float32)
+        self.check_logit_out(a, expected)
+
+    def test_float64(self):
+        a = np.concatenate((np.linspace(0, 1, 10, dtype=np.float64),
+                            [1e-8, 0.4999999999999, 0.50000000001]))
+        # Expected values computed with mpmath.
+        expected = np.array([-np.inf,
+                             -2.079441541679836,
+                             -1.252762968495368,
+                             -0.6931471805599454,
+                             -0.22314355131420985,
+                             0.22314355131420985,
+                             0.6931471805599452,
+                             1.2527629684953674,
+                             2.0794415416798353,
+                             np.inf,
+                             -18.420680733952366,
+                             -3.999023334699814e-13,
+                             4.000000330961484e-11])
+        self.check_logit_out(a, expected)
+
+    def test_nan(self):
+        expected = np.array([np.nan]*4)
+        with np.errstate(invalid='ignore'):
+            actual = logit(np.array([-3., -2., 2., 3.]))
+
+        assert_equal(expected, actual)
+
+
+class TestExpit:
+    def check_expit_out(self, dtype, expected):
+        a = np.linspace(-4, 4, 10)
+        a = np.array(a, dtype=dtype)
+        actual = expit(a)
+        assert_allclose(actual, expected, atol=1.5e-7, rtol=0)
+        assert_equal(actual.dtype, np.dtype(dtype))
+
+    def test_float32(self):
+        expected = np.array([0.01798621, 0.04265125,
+                            0.09777259, 0.20860852,
+                            0.39068246, 0.60931754,
+                            0.79139149, 0.9022274,
+                            0.95734876, 0.98201376], dtype=np.float32)
+        self.check_expit_out('f4', expected)
+
+    def test_float64(self):
+        expected = np.array([0.01798621, 0.04265125,
+                            0.0977726, 0.20860853,
+                            0.39068246, 0.60931754,
+                            0.79139147, 0.9022274,
+                            0.95734875, 0.98201379])
+        self.check_expit_out('f8', expected)
+
+    def test_large(self):
+        for dtype in (np.float32, np.float64, np.longdouble):
+            for n in (88, 89, 709, 710, 11356, 11357):
+                n = np.array(n, dtype=dtype)
+                assert_allclose(expit(n), 1.0, atol=1e-20)
+                assert_allclose(expit(-n), 0.0, atol=1e-20)
+                assert_equal(expit(n).dtype, dtype)
+                assert_equal(expit(-n).dtype, dtype)
+
+
+class TestLogExpit:
+
+    def test_large_negative(self):
+        x = np.array([-10000.0, -750.0, -500.0, -35.0])
+        y = log_expit(x)
+        assert_equal(y, x)
+
+    def test_large_positive(self):
+        x = np.array([750.0, 1000.0, 10000.0])
+        y = log_expit(x)
+        # y will contain -0.0, and -0.0 is used in the expected value,
+        # but assert_equal does not check the sign of zeros, and I don't
+        # think the sign is an essential part of the test (i.e. it would
+        # probably be OK if log_expit(1000) returned 0.0 instead of -0.0).
+        assert_equal(y, np.array([-0.0, -0.0, -0.0]))
+
+    def test_basic_float64(self):
+        x = np.array([-32, -20, -10, -3, -1, -0.1, -1e-9,
+                      0, 1e-9, 0.1, 1, 10, 100, 500, 710, 725, 735])
+        y = log_expit(x)
+        #
+        # Expected values were computed with mpmath:
+        #
+        #   import mpmath
+        #
+        #   mpmath.mp.dps = 100
+        #
+        #   def mp_log_expit(x):
+        #       return -mpmath.log1p(mpmath.exp(-x))
+        #
+        #   expected = [float(mp_log_expit(t)) for t in x]
+        #
+        expected = [-32.000000000000014, -20.000000002061153,
+                    -10.000045398899218, -3.048587351573742,
+                    -1.3132616875182228, -0.7443966600735709,
+                    -0.6931471810599453, -0.6931471805599453,
+                    -0.6931471800599454, -0.6443966600735709,
+                    -0.3132616875182228, -4.539889921686465e-05,
+                    -3.720075976020836e-44, -7.124576406741286e-218,
+                    -4.47628622567513e-309, -1.36930634e-315,
+                    -6.217e-320]
+
+        # When tested locally, only one value in y was not exactly equal to
+        # expected.  That was for x=1, and the y value differed from the
+        # expected by 1 ULP.  For this test, however, I'll use rtol=1e-15.
+        assert_allclose(y, expected, rtol=1e-15)
+
+    def test_basic_float32(self):
+        x = np.array([-32, -20, -10, -3, -1, -0.1, -1e-9,
+                      0, 1e-9, 0.1, 1, 10, 100], dtype=np.float32)
+        y = log_expit(x)
+        #
+        # Expected values were computed with mpmath:
+        #
+        #   import mpmath
+        #
+        #   mpmath.mp.dps = 100
+        #
+        #   def mp_log_expit(x):
+        #       return -mpmath.log1p(mpmath.exp(-x))
+        #
+        #   expected = [np.float32(mp_log_expit(t)) for t in x]
+        #
+        expected = np.array([-32.0, -20.0, -10.000046, -3.0485873,
+                             -1.3132616, -0.7443967, -0.6931472,
+                             -0.6931472, -0.6931472, -0.64439666,
+                             -0.3132617, -4.5398898e-05, -3.8e-44],
+                            dtype=np.float32)
+
+        assert_allclose(y, expected, rtol=5e-7)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_logsumexp.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_logsumexp.py
new file mode 100644
index 0000000000000000000000000000000000000000..51196a3d5e3ec3376b4311c374d13bec37f37f00
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_logsumexp.py
@@ -0,0 +1,481 @@
+import itertools as it
+import math
+import pytest
+
+import numpy as np
+
+from scipy._lib._array_api import (is_array_api_strict, make_xp_test_case,
+                                   xp_default_dtype, xp_device)
+from scipy._lib._array_api_no_0d import (xp_assert_equal, xp_assert_close,
+                                         xp_assert_less)
+
+from scipy.special import log_softmax, logsumexp, softmax
+from scipy.special._logsumexp import _wrap_radians
+
+
+dtypes = ['float32', 'float64', 'int32', 'int64', 'complex64', 'complex128']
+integral_dtypes = ['int32', 'int64']
+
+
+@pytest.mark.uses_xp_capabilities(False, reason="private")
+def test_wrap_radians(xp):
+    x = xp.asarray([-math.pi-1, -math.pi, -1, -1e-300,
+                    0, 1e-300, 1, math.pi, math.pi+1])
+    ref = xp.asarray([math.pi-1, math.pi, -1, -1e-300,
+                    0, 1e-300, 1, math.pi, -math.pi+1])
+    res = _wrap_radians(x, xp=xp)
+    xp_assert_close(res, ref, atol=0)
+
+
+# numpy warning filters don't work for dask (dask/dask#3245)
+# (also we should not expect the numpy warning filter to work for any Array API
+# library)
+@pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning")
+@pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning")
+@pytest.mark.filterwarnings("ignore:overflow encountered:RuntimeWarning")
+@make_xp_test_case(logsumexp)
+class TestLogSumExp:
+    def test_logsumexp(self, xp):
+        # Test with zero-size array
+        a = xp.asarray([])
+        desired = xp.asarray(-xp.inf)
+        xp_assert_equal(logsumexp(a), desired)
+
+        # Test whether logsumexp() function correctly handles large inputs.
+        a = xp.arange(200., dtype=xp.float64)
+        desired = xp.log(xp.sum(xp.exp(a)))
+        xp_assert_close(logsumexp(a), desired)
+
+        # Now test with large numbers
+        b = xp.asarray([1000., 1000.])
+        desired = xp.asarray(1000.0 + math.log(2.0))
+        xp_assert_close(logsumexp(b), desired)
+
+        n = 1000
+        b = xp.full((n,), 10000)
+        desired = xp.asarray(10000.0 + math.log(n))
+        xp_assert_close(logsumexp(b), desired)
+
+        x = xp.asarray([1e-40] * 1000000)
+        logx = xp.log(x)
+        X = xp.stack([x, x])
+        logX = xp.stack([logx, logx])
+        xp_assert_close(xp.exp(logsumexp(logX)), xp.sum(X))
+        xp_assert_close(xp.exp(logsumexp(logX, axis=0)), xp.sum(X, axis=0))
+        xp_assert_close(xp.exp(logsumexp(logX, axis=1)), xp.sum(X, axis=1))
+
+        # Handling special values properly
+        inf = xp.asarray([xp.inf])
+        nan = xp.asarray([xp.nan])
+        xp_assert_equal(logsumexp(inf), inf[0])
+        xp_assert_equal(logsumexp(-inf), -inf[0])
+        xp_assert_equal(logsumexp(nan), nan[0])
+        xp_assert_equal(logsumexp(xp.asarray([-xp.inf, -xp.inf])), -inf[0])
+
+        # Handling an array with different magnitudes on the axes
+        a = xp.asarray([[1e10, 1e-10],
+                        [-1e10, -np.inf]])
+        ref = xp.asarray([1e10, -1e10])
+        xp_assert_close(logsumexp(a, axis=-1), ref)
+
+        # Test keeping dimensions
+        ref = xp.expand_dims(ref, axis=-1)
+        xp_assert_close(logsumexp(a, axis=-1, keepdims=True), ref)
+
+        # Test multiple axes
+        xp_assert_close(logsumexp(a, axis=(-1, -2)), xp.asarray(1e10))
+
+    def test_logsumexp_b(self, xp):
+        a = xp.arange(200., dtype=xp.float64)
+        b = xp.arange(200., 0., -1.)
+        desired = xp.log(xp.sum(b*xp.exp(a)))
+        xp_assert_close(logsumexp(a, b=b), desired)
+
+        a = xp.asarray([1000, 1000])
+        b = xp.asarray([1.2, 1.2])
+        desired = xp.asarray(1000 + math.log(2 * 1.2))
+        xp_assert_close(logsumexp(a, b=b), desired)
+
+        x = xp.asarray([1e-40] * 100000)
+        b = xp.linspace(1, 1000, 100000)
+        logx = xp.log(x)
+        X = xp.stack((x, x))
+        logX = xp.stack((logx, logx))
+        B = xp.stack((b, b))
+        xp_assert_close(xp.exp(logsumexp(logX, b=B)), xp.sum(B * X))
+        xp_assert_close(xp.exp(logsumexp(logX, b=B, axis=0)), xp.sum(B * X, axis=0))
+        xp_assert_close(xp.exp(logsumexp(logX, b=B, axis=1)), xp.sum(B * X, axis=1))
+
+    def test_logsumexp_sign(self, xp):
+        a = xp.asarray([1, 1, 1])
+        b = xp.asarray([1, -1, -1])
+
+        r, s = logsumexp(a, b=b, return_sign=True)
+        xp_assert_close(r, xp.asarray(1.))
+        xp_assert_equal(s, xp.asarray(-1.))
+
+    def test_logsumexp_sign_zero(self, xp):
+        a = xp.asarray([1, 1])
+        b = xp.asarray([1, -1])
+
+        r, s = logsumexp(a, b=b, return_sign=True)
+        assert not xp.isfinite(r)
+        assert not xp.isnan(r)
+        assert r < 0
+        assert s == 0
+
+    def test_logsumexp_sign_shape(self, xp):
+        a = xp.ones((1, 2, 3, 4))
+        b = xp.ones_like(a)
+
+        r, s = logsumexp(a, axis=2, b=b, return_sign=True)
+        assert r.shape == s.shape == (1, 2, 4)
+
+        r, s = logsumexp(a, axis=(1, 3), b=b, return_sign=True)
+        assert r.shape == s.shape == (1,3)
+
+    def test_logsumexp_complex_sign(self, xp):
+        a = xp.asarray([1 + 1j, 2 - 1j, -2 + 3j])
+
+        r, s = logsumexp(a, return_sign=True)
+
+        expected_sumexp = xp.sum(xp.exp(a))
+        # This is the numpy>=2.0 convention for np.sign
+        expected_sign = expected_sumexp / xp.abs(expected_sumexp)
+
+        xp_assert_close(s, expected_sign)
+        xp_assert_close(s * xp.exp(r), expected_sumexp)
+
+    def test_logsumexp_shape(self, xp):
+        a = xp.ones((1, 2, 3, 4))
+        b = xp.ones_like(a)
+
+        r = logsumexp(a, axis=2, b=b)
+        assert r.shape == (1, 2, 4)
+
+        r = logsumexp(a, axis=(1, 3), b=b)
+        assert r.shape == (1, 3)
+
+    def test_logsumexp_b_zero(self, xp):
+        a = xp.asarray([1, 10000])
+        b = xp.asarray([1, 0])
+
+        xp_assert_close(logsumexp(a, b=b), xp.asarray(1.))
+
+    def test_logsumexp_b_shape(self, xp):
+        a = xp.zeros((4, 1, 2, 1))
+        b = xp.ones((3, 1, 5))
+
+        logsumexp(a, b=b)
+
+    @pytest.mark.parametrize('arg', (1, [1, 2, 3]))
+    def test_xp_invalid_input(self, arg):
+        assert logsumexp(arg) == logsumexp(np.asarray(np.atleast_1d(arg)))
+
+    def test_array_like(self):
+        a = [1000, 1000]
+        desired = np.asarray(1000.0 + math.log(2.0))
+        xp_assert_close(logsumexp(a), desired)
+
+    @pytest.mark.parametrize('dtype', dtypes)
+    def test_dtypes_a(self, dtype, xp):
+        dtype = getattr(xp, dtype)
+        a = xp.asarray([1000., 1000.], dtype=dtype)
+        desired_dtype = (xp.asarray(1.).dtype if xp.isdtype(dtype, 'integral')
+                         else dtype)  # true for all libraries tested
+        desired = xp.asarray(1000.0 + math.log(2.0), dtype=desired_dtype)
+        xp_assert_close(logsumexp(a), desired)
+
+    @pytest.mark.parametrize('dtype_a', dtypes)
+    @pytest.mark.parametrize('dtype_b', dtypes)
+    def test_dtypes_ab(self, dtype_a, dtype_b, xp):
+        xp_dtype_a = getattr(xp, dtype_a)
+        xp_dtype_b = getattr(xp, dtype_b)
+        a = xp.asarray([2, 1], dtype=xp_dtype_a)
+        b = xp.asarray([1, -1], dtype=xp_dtype_b)
+        if is_array_api_strict(xp):
+            # special-case for `TypeError: array_api_strict.float32 and
+            # and array_api_strict.int64 cannot be type promoted together`
+            xp_float_dtypes = [dtype for dtype in [xp_dtype_a, xp_dtype_b]
+                               if not xp.isdtype(dtype, 'integral')]
+            if len(xp_float_dtypes) < 2:  # at least one is integral
+                xp_float_dtypes.append(xp.asarray(1.).dtype)
+            desired_dtype = xp.result_type(*xp_float_dtypes)
+        else:
+            desired_dtype = xp.result_type(xp_dtype_a, xp_dtype_b)
+            if xp.isdtype(desired_dtype, 'integral'):
+               desired_dtype = xp_default_dtype(xp)
+        desired = xp.asarray(math.log(math.exp(2) - math.exp(1)), dtype=desired_dtype)
+        xp_assert_close(logsumexp(a, b=b), desired)
+
+    def test_gh18295(self, xp):
+        # gh-18295 noted loss of precision when real part of one element is much
+        # larger than the rest. Check that this is resolved.
+        a = xp.asarray([0.0, -40.0])
+        res = logsumexp(a)
+        ref = xp.logaddexp(a[0], a[1])
+        xp_assert_close(res, ref)
+
+    @pytest.mark.parametrize('dtype', ['complex64', 'complex128'])
+    def test_gh21610(self, xp, dtype):
+        # gh-21610 noted that `logsumexp` could return imaginary components
+        # outside the range (-pi, pi]. Check that this is resolved.
+        # While working on this, I noticed that all other tests passed even
+        # when the imaginary component of the result was zero. This suggested
+        # the need of a stronger test with imaginary dtype.
+        rng = np.random.default_rng(324984329582349862)
+        dtype = getattr(xp, dtype)
+        shape = (10, 100)
+        x = rng.uniform(1, 40, shape) + 1.j * rng.uniform(1, 40, shape)
+        x = xp.asarray(x, dtype=dtype)
+
+        res = logsumexp(x, axis=1)
+        ref = xp.log(xp.sum(xp.exp(x), axis=1))
+        max = xp.full_like(xp.imag(res), xp.pi)
+        xp_assert_less(xp.abs(xp.imag(res)), max)
+        xp_assert_close(res, ref)
+
+        out, sgn = logsumexp(x, return_sign=True, axis=1)
+        ref = xp.sum(xp.exp(x), axis=1)
+        xp_assert_less(xp.abs(xp.imag(sgn)), max)
+        xp_assert_close(out, xp.real(xp.log(ref)))
+        xp_assert_close(sgn, ref/xp.abs(ref))
+
+    def test_gh21709_small_imaginary(self, xp):
+        # Test that `logsumexp` does not lose relative precision of
+        # small imaginary components
+        x = xp.asarray([0, 0.+2.2204460492503132e-17j])
+        res = logsumexp(x)
+        # from mpmath import mp
+        # mp.dps = 100
+        # x, y = mp.mpc(0), mp.mpc('0', '2.2204460492503132e-17')
+        # ref = complex(mp.log(mp.exp(x) + mp.exp(y)))
+        ref = xp.asarray(0.6931471805599453+1.1102230246251566e-17j)
+        xp_assert_close(xp.real(res), xp.real(ref))
+        xp_assert_close(xp.imag(res), xp.imag(ref), atol=0, rtol=1e-15)
+
+
+    @pytest.mark.parametrize('x,y', list(it.product(
+        [
+            -np.inf,
+            np.inf,
+            complex(-np.inf, 0.),
+            complex(-np.inf, -0.),
+            complex(-np.inf, np.inf),
+            complex(-np.inf, -np.inf),
+            complex(np.inf, 0.),
+            complex(np.inf, -0.),
+            complex(np.inf, np.inf),
+            complex(np.inf, -np.inf),
+            # Phase in each quadrant.
+            complex(-np.inf, 0.7533),
+            complex(-np.inf, 2.3562),
+            complex(-np.inf, 3.9270),
+            complex(-np.inf, 5.4978),
+            complex(np.inf, 0.7533),
+            complex(np.inf, 2.3562),
+            complex(np.inf, 3.9270),
+            complex(np.inf, 5.4978),
+        ], repeat=2)
+    ))
+    def test_gh22601_infinite_elements(self, x, y, xp):
+        # Test that `logsumexp` does reasonable things in the presence of
+        # real and complex infinities.
+        res = logsumexp(xp.asarray([x, y]))
+        ref = xp.log(xp.sum(xp.exp(xp.asarray([x, y]))))
+        xp_assert_equal(res, ref)
+
+    def test_no_writeback(self, xp):
+        """Test that logsumexp doesn't accidentally write back to its parameters."""
+        a = xp.asarray([5., 4.])
+        b = xp.asarray([3., 2.])
+        logsumexp(a)
+        logsumexp(a, b=b)
+        xp_assert_equal(a, xp.asarray([5., 4.]))
+        xp_assert_equal(b, xp.asarray([3., 2.]))
+
+    @pytest.mark.parametrize("x_raw", [1.0, 1.0j, []])
+    def test_device(self, x_raw, xp, devices):
+        """Test input device propagation to output."""
+        for d in devices:
+            x = xp.asarray(x_raw, device=d)
+            assert xp_device(logsumexp(x)) == xp_device(x)
+            assert xp_device(logsumexp(x, b=x)) == xp_device(x)
+
+    def test_gh22903(self, xp):
+        # gh-22903 reported that `logsumexp` produced NaN where the weight associated
+        # with the max magnitude element was negative and `return_sign=False`, even if
+        # the net result should be the log of a positive number.
+
+        # result is log of positive number
+        a = xp.asarray([3.06409428, 0.37251854, 3.87471931])
+        b = xp.asarray([1.88190708, 2.84174795, -0.85016884])
+        xp_assert_close(logsumexp(a, b=b), logsumexp(a, b=b, return_sign=True)[0])
+
+        # result is log of negative number
+        b = xp.asarray([1.88190708, 2.84174795, -3.85016884])
+        xp_assert_close(logsumexp(a, b=b), xp.asarray(xp.nan))
+
+    @pytest.mark.parametrize("a, b, sign_ref",
+                             [([np.inf], None, 1.),
+                              ([np.inf], [-1.], -1.)])
+    def test_gh23548(self, xp, a, b, sign_ref):
+        # gh-23548 reported that `logsumexp` with `return_sign=True` returned a sign
+        # of NaN with infinite reals
+        a, b = xp.asarray(a), xp.asarray(b) if b is not None else None
+        val, sign = logsumexp(a, b=b, return_sign=True)
+        assert xp.isinf(val)
+        xp_assert_equal(sign, xp.asarray(sign_ref))
+
+
+@make_xp_test_case(softmax)
+class TestSoftmax:
+    def test_softmax_fixtures(self, xp):
+        xp_assert_close(softmax(xp.asarray([1000., 0., 0., 0.])),
+                        xp.asarray([1., 0., 0., 0.]), rtol=1e-13)
+        xp_assert_close(softmax(xp.asarray([1., 1.])),
+                        xp.asarray([.5, .5]), rtol=1e-13)
+        xp_assert_close(softmax(xp.asarray([0., 1.])),
+                        xp.asarray([1., np.e])/(1 + np.e),
+                        rtol=1e-13)
+
+        # Expected value computed using mpmath (with mpmath.mp.dps = 200) and then
+        # converted to float.
+        x = xp.arange(4, dtype=xp.float64)
+        expected = xp.asarray([0.03205860328008499,
+                               0.08714431874203256,
+                               0.23688281808991013,
+                               0.6439142598879722], dtype=xp.float64)
+
+        xp_assert_close(softmax(x), expected, rtol=1e-13)
+
+        # Translation property.  If all the values are changed by the same amount,
+        # the softmax result does not change.
+        xp_assert_close(softmax(x + 100), expected, rtol=1e-13)
+
+        # When axis=None, softmax operates on the entire array, and preserves
+        # the shape.
+        xp_assert_close(softmax(xp.reshape(x, (2, 2))),
+                        xp.reshape(expected, (2, 2)), rtol=1e-13)
+
+    def test_softmax_multi_axes(self, xp):
+        xp_assert_close(softmax(xp.asarray([[1000., 0.], [1000., 0.]]), axis=0),
+                        xp.asarray([[.5, .5], [.5, .5]]), rtol=1e-13)
+        xp_assert_close(softmax(xp.asarray([[1000., 0.], [1000., 0.]]), axis=1),
+                        xp.asarray([[1., 0.], [1., 0.]]), rtol=1e-13)
+
+        # Expected value computed using mpmath (with mpmath.mp.dps = 200) and then
+        # converted to float.
+        x = xp.asarray([[-25.,   0.,  25.,  50.],
+                        [  1., 325., 749., 750.]])
+        expected = xp.asarray([[2.678636961770877e-33,
+                                1.9287498479371314e-22,
+                                1.3887943864771144e-11,
+                                0.999999999986112],
+                               [0.0,
+                                1.9444526359919372e-185,
+                                0.2689414213699951,
+                                0.7310585786300048]])
+        xp_assert_close(softmax(x, axis=1), expected, rtol=1e-13)
+        xp_assert_close(softmax(x.T, axis=0), expected.T, rtol=1e-13)
+
+        # 3-d input, with a tuple for the axis.
+        x3d = xp.reshape(x, (2, 2, 2))
+        xp_assert_close(softmax(x3d, axis=(1, 2)),
+                        xp.reshape(expected, (2, 2, 2)), rtol=1e-13)
+
+    @pytest.mark.xfail_xp_backends("array_api_strict", reason="int->float promotion")
+    def test_softmax_int_array(self, xp):
+        xp_assert_close(softmax(xp.asarray([1000, 0, 0, 0])),
+                        xp.asarray([1., 0., 0., 0.]), rtol=1e-13)
+
+    def test_softmax_scalar(self):
+        xp_assert_close(softmax(1000), np.asarray(1.), rtol=1e-13)
+
+    def test_softmax_array_like(self):
+        xp_assert_close(softmax([1000, 0, 0, 0]),
+                        np.asarray([1., 0., 0., 0.]), rtol=1e-13)
+
+
+@make_xp_test_case(log_softmax)
+class TestLogSoftmax:
+    def test_log_softmax_basic(self, xp):
+        xp_assert_close(log_softmax(xp.asarray([1000., 1.])),
+                        xp.asarray([0., -999.]), rtol=1e-13)
+
+    @pytest.mark.xfail_xp_backends("array_api_strict", reason="int->float promotion")
+    def test_log_softmax_int_array(self, xp):
+        xp_assert_close(log_softmax(xp.asarray([1000, 1])),
+                        xp.asarray([0., -999.]), rtol=1e-13)
+
+    def test_log_softmax_scalar(self):
+        xp_assert_close(log_softmax(1.0), 0.0, rtol=1e-13)
+
+    def test_log_softmax_array_like(self):
+        xp_assert_close(log_softmax([1000, 1]),
+                        np.asarray([0., -999.]), rtol=1e-13)
+
+    @staticmethod
+    def data_1d(xp):
+        x = xp.arange(4, dtype=xp.float64)
+        # Expected value computed using mpmath (with mpmath.mp.dps = 200)
+        expect = [-3.4401896985611953,
+                  -2.4401896985611953,
+                  -1.4401896985611953,
+                  -0.44018969856119533]
+        return x, xp.asarray(expect, dtype=xp.float64)
+
+    @staticmethod
+    def data_2d(xp):
+        x = xp.reshape(xp.arange(8, dtype=xp.float64), (2, 4))
+
+        # Expected value computed using mpmath (with mpmath.mp.dps = 200)
+        expect = [[-3.4401896985611953,
+                   -2.4401896985611953,
+                   -1.4401896985611953,
+                   -0.44018969856119533],
+                  [-3.4401896985611953,
+                   -2.4401896985611953,
+                   -1.4401896985611953,
+                   -0.44018969856119533]]
+        return x, xp.asarray(expect, dtype=xp.float64)
+
+    @pytest.mark.parametrize("offset", [0, 100])
+    def test_log_softmax_translation(self, offset, xp):
+        # Translation property.  If all the values are changed by the same amount,
+        # the softmax result does not change.
+        x, expect = self.data_1d(xp)
+        x += offset
+        xp_assert_close(log_softmax(x), expect, rtol=1e-13)
+
+    def test_log_softmax_noneaxis(self, xp):
+        # When axis=None, softmax operates on the entire array, and preserves
+        # the shape.
+        x, expect = self.data_1d(xp)
+        x = xp.reshape(x, (2, 2))
+        expect = xp.reshape(expect, (2, 2))
+        xp_assert_close(log_softmax(x), expect, rtol=1e-13)
+
+    @pytest.mark.parametrize('axis_2d, expected_2d', [
+        (0, np.log(0.5) * np.ones((2, 2))),
+        (1, [[0., -999.], [0., -999.]]),
+    ])
+    def test_axes(self, axis_2d, expected_2d, xp):
+        x = xp.asarray([[1000., 1.], [1000., 1.]])
+        xp_assert_close(log_softmax(x, axis=axis_2d),
+                        xp.asarray(expected_2d, dtype=x.dtype), rtol=1e-13)
+
+    def test_log_softmax_2d_axis1(self, xp):
+        x, expect = self.data_2d(xp)
+        xp_assert_close(log_softmax(x, axis=1), expect, rtol=1e-13)
+
+    def test_log_softmax_2d_axis0(self, xp):
+        x, expect = self.data_2d(xp)
+        xp_assert_close(log_softmax(x.T, axis=0), expect.T, rtol=1e-13)
+
+    def test_log_softmax_3d(self, xp):
+        # 3D input, with a tuple for the axis.
+        x, expect = self.data_2d(xp)
+        x = xp.reshape(x, (2, 2, 2))
+        expect = xp.reshape(expect, (2, 2, 2))
+        xp_assert_close(log_softmax(x, axis=(1, 2)), expect, rtol=1e-13)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_mpmath.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_mpmath.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a2c5d9874eb1f80c211a2bfa1bcbf06502ac84b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_mpmath.py
@@ -0,0 +1,2160 @@
+"""
+Test SciPy functions versus mpmath, if available.
+
+"""
+import numpy as np
+from numpy.testing import assert_, assert_allclose
+from numpy import pi
+import pytest
+import itertools
+
+from scipy._external.packaging_version import version
+
+import scipy.special as sc
+from scipy.special._testutils import (
+    MissingModule, check_version, FuncData,
+    assert_func_equal)
+from scipy.special._mptestutils import (
+    Arg, FixedArg, ComplexArg, IntArg, assert_mpmath_equal,
+    nonfunctional_tooslow, trace_args, time_limited, exception_to_nan,
+    inf_to_nan)
+from scipy.special._ufuncs import (
+    _sinpi, _cospi, _lgam1p, _lanczos_sum_expg_scaled, _log1pmx,
+    _igam_fac)
+
+try:
+    import mpmath
+except ImportError:
+    mpmath = MissingModule('mpmath')
+
+pytestmark = pytest.mark.thread_unsafe(
+    reason=("mpmath gmpy2 backend is not thread-safe, "
+            "see https://github.com/mpmath/mpmath/issues/974"))
+
+# ------------------------------------------------------------------------------
+# expi
+# ------------------------------------------------------------------------------
+
+@check_version(mpmath, '0.10')
+def test_expi_complex():
+    dataset = []
+    for r in np.logspace(-99, 2, 10):
+        for p in np.linspace(0, 2*np.pi, 30):
+            z = r*np.exp(1j*p)
+            dataset.append((z, complex(mpmath.ei(z))))
+    dataset = np.array(dataset, dtype=np.cdouble)
+
+    FuncData(sc.expi, dataset, 0, 1).check()
+
+
+# ------------------------------------------------------------------------------
+# expn
+# ------------------------------------------------------------------------------
+
+@check_version(mpmath, '0.19')
+def test_expn_large_n():
+    # Test the transition to the asymptotic regime of n.
+    dataset = []
+    for n in [50, 51]:
+        for x in np.logspace(0, 4, 200):
+            with mpmath.workdps(100):
+                dataset.append((n, x, float(mpmath.expint(n, x))))
+    dataset = np.asarray(dataset)
+
+    FuncData(sc.expn, dataset, (0, 1), 2, rtol=1e-13).check()
+
+# ------------------------------------------------------------------------------
+# hyp0f1
+# ------------------------------------------------------------------------------
+
+
+@check_version(mpmath, '0.19')
+def test_hyp0f1_gh5764():
+    # Do a small and somewhat systematic test that runs quickly
+    dataset = []
+    axis = [-99.5, -9.5, -0.5, 0.5, 9.5, 99.5]
+    for v in axis:
+        for x in axis:
+            for y in axis:
+                z = x + 1j*y
+                # mpmath computes the answer correctly at dps ~ 17 but
+                # fails for 20 < dps < 120 (uses a different method);
+                # set the dps high enough that this isn't an issue
+                with mpmath.workdps(120):
+                    res = complex(mpmath.hyp0f1(v, z))
+                dataset.append((v, z, res))
+    dataset = np.array(dataset)
+
+    FuncData(lambda v, z: sc.hyp0f1(v.real, z), dataset, (0, 1), 2,
+             rtol=1e-13).check()
+
+
+@check_version(mpmath, '0.19')
+def test_hyp0f1_gh_1609():
+    # this is a regression test for gh-1609
+    vv = np.linspace(150, 180, 21)
+    af = sc.hyp0f1(vv, 0.5)
+    mf = np.array([mpmath.hyp0f1(v, 0.5) for v in vv])
+    assert_allclose(af, mf.astype(float), rtol=1e-12)
+
+
+# ------------------------------------------------------------------------------
+# hyperu
+# ------------------------------------------------------------------------------
+
+@check_version(mpmath, '1.1.0')
+def test_hyperu_around_0():
+    dataset = []
+    # DLMF 13.2.14-15 test points.
+    for n in np.arange(-5, 5):
+        for b in np.linspace(-5, 5, 20):
+            a = -n
+            dataset.append((a, b, 0, float(mpmath.hyperu(a, b, 0))))
+            a = -n + b - 1
+            dataset.append((a, b, 0, float(mpmath.hyperu(a, b, 0))))
+    # DLMF 13.2.16-22 test points.
+    for a in [-10.5, -1.5, -0.5, 0, 0.5, 1, 10]:
+        for b in [-1.0, -0.5, 0, 0.5, 1, 1.5, 2, 2.5]:
+            dataset.append((a, b, 0, float(mpmath.hyperu(a, b, 0))))
+    dataset = np.array(dataset)
+
+    FuncData(sc.hyperu, dataset, (0, 1, 2), 3, rtol=1e-15, atol=5e-13).check()
+
+
+# ------------------------------------------------------------------------------
+# hyp2f1
+# ------------------------------------------------------------------------------
+
+@check_version(mpmath, '1.0.0')
+def test_hyp2f1_strange_points():
+    pts = [
+        (2, -1, -1, 0.7),  # expected: 2.4
+        (2, -2, -2, 0.7),  # expected: 3.87
+    ]
+    pts += list(itertools.product([2, 1, -0.7, -1000], repeat=4))
+    pts = [
+        (a, b, c, x) for a, b, c, x in pts
+        if b == c and round(b) == b and b < 0 and b != -1000
+    ]
+    kw = dict(eliminate=True)
+    dataset = [p + (float(mpmath.hyp2f1(*p, **kw)),) for p in pts]
+    dataset = np.array(dataset, dtype=np.float64)
+
+    FuncData(sc.hyp2f1, dataset, (0,1,2,3), 4, rtol=1e-10).check()
+
+
+@check_version(mpmath, '0.13')
+def test_hyp2f1_real_some_points():
+    pts = [
+        (1, 2, 3, 0),
+        (1./3, 2./3, 5./6, 27./32),
+        (1./4, 1./2, 3./4, 80./81),
+        (2,-2, -3, 3),
+        (2, -3, -2, 3),
+        (2, -1.5, -1.5, 3),
+        (1, 2, 3, 0),
+        (0.7235, -1, -5, 0.3),
+        (0.25, 1./3, 2, 0.999),
+        (0.25, 1./3, 2, -1),
+        (2, 3, 5, 0.99),
+        (3./2, -0.5, 3, 0.99),
+        (2, 2.5, -3.25, 0.999),
+        (-8, 18.016500331508873, 10.805295997850628, 0.90875647507000001),
+        (-10, 900, -10.5, 0.99),
+        (-10, 900, 10.5, 0.99),
+        (-1, 2, 1, 1.0),
+        (-1, 2, 1, -1.0),
+        (-3, 13, 5, 1.0),
+        (-3, 13, 5, -1.0),
+        (0.5, 1 - 270.5, 1.5, 0.999**2),  # from issue 1561
+    ]
+    dataset = [p + (float(mpmath.hyp2f1(*p)),) for p in pts]
+    dataset = np.array(dataset, dtype=np.float64)
+
+    with np.errstate(invalid='ignore'):
+        FuncData(sc.hyp2f1, dataset, (0,1,2,3), 4, rtol=1e-10).check()
+
+
+@check_version(mpmath, '0.14')
+def test_hyp2f1_some_points_2():
+    # Taken from mpmath unit tests -- this point failed for mpmath 0.13 but
+    # was fixed in their SVN since then
+    pts = [
+        (112, (51,10), (-9,10), -0.99999),
+        (10,-900,10.5,0.99),
+        (10,-900,-10.5,0.99),
+    ]
+
+    def fev(x):
+        if isinstance(x, tuple):
+            return float(x[0]) / x[1]
+        else:
+            return x
+
+    dataset = [tuple(map(fev, p)) + (float(mpmath.hyp2f1(*p)),) for p in pts]
+    dataset = np.array(dataset, dtype=np.float64)
+
+    FuncData(sc.hyp2f1, dataset, (0,1,2,3), 4, rtol=1e-10).check()
+
+
+@check_version(mpmath, '0.13')
+def test_hyp2f1_real_some():
+    dataset = []
+    for a in [-10, -5, -1.8, 1.8, 5, 10]:
+        for b in [-2.5, -1, 1, 7.4]:
+            for c in [-9, -1.8, 5, 20.4]:
+                for z in [-10, -1.01, -0.99, 0, 0.6, 0.95, 1.5, 10]:
+                    try:
+                        v = float(mpmath.hyp2f1(a, b, c, z))
+                    except Exception:
+                        continue
+                    dataset.append((a, b, c, z, v))
+    dataset = np.array(dataset, dtype=np.float64)
+
+    with np.errstate(invalid='ignore'):
+        FuncData(sc.hyp2f1, dataset, (0,1,2,3), 4, rtol=1e-9,
+                 ignore_inf_sign=True).check()
+
+
+@check_version(mpmath, '0.12')
+@pytest.mark.slow
+def test_hyp2f1_real_random():
+    npoints = 500
+    dataset = np.zeros((npoints, 5), np.float64)
+
+    np.random.seed(1234)
+    dataset[:, 0] = np.random.pareto(1.5, npoints)
+    dataset[:, 1] = np.random.pareto(1.5, npoints)
+    dataset[:, 2] = np.random.pareto(1.5, npoints)
+    dataset[:, 3] = 2*np.random.rand(npoints) - 1
+
+    dataset[:, 0] *= (-1)**np.random.randint(2, npoints)
+    dataset[:, 1] *= (-1)**np.random.randint(2, npoints)
+    dataset[:, 2] *= (-1)**np.random.randint(2, npoints)
+
+    for ds in dataset:
+        if mpmath.__version__ < '0.14':
+            # mpmath < 0.14 fails for c too much smaller than a, b
+            if abs(ds[:2]).max() > abs(ds[2]):
+                ds[2] = abs(ds[:2]).max()
+        ds[4] = float(mpmath.hyp2f1(*tuple(ds[:4])))
+
+    FuncData(sc.hyp2f1, dataset, (0, 1, 2, 3), 4, rtol=1e-9).check()
+
+
+# ------------------------------------------------------------------------------
+# erf (complex)
+# ------------------------------------------------------------------------------
+
+@pytest.mark.slow()
+@check_version(mpmath, '0.14')
+def test_erf_complex():
+    # need to increase mpmath precision for this test
+    old_dps, old_prec = mpmath.mp.dps, mpmath.mp.prec
+    try:
+        mpmath.mp.dps = 70
+        x1, y1 = np.meshgrid(np.linspace(-10, 1, 31), np.linspace(-10, 1, 11))
+        x2, y2 = np.meshgrid(np.logspace(-80, .8, 31), np.logspace(-80, .8, 11))
+        points = np.r_[x1.ravel(),x2.ravel()] + 1j*np.r_[y1.ravel(), y2.ravel()]
+
+        assert_func_equal(sc.erf, lambda x: complex(mpmath.erf(x)), points,
+                          vectorized=False, rtol=1e-13)
+        assert_func_equal(sc.erfc, lambda x: complex(mpmath.erfc(x)), points,
+                          vectorized=False, rtol=1e-13)
+    finally:
+        mpmath.mp.dps, mpmath.mp.prec = old_dps, old_prec
+
+
+# ------------------------------------------------------------------------------
+# lpmv
+# ------------------------------------------------------------------------------
+
+@check_version(mpmath, '0.15')
+def test_lpmv():
+    pts = []
+    for x in [-0.99, -0.557, 1e-6, 0.132, 1]:
+        pts.extend([
+            (1, 1, x),
+            (1, -1, x),
+            (-1, 1, x),
+            (-1, -2, x),
+            (1, 1.7, x),
+            (1, -1.7, x),
+            (-1, 1.7, x),
+            (-1, -2.7, x),
+            (1, 10, x),
+            (1, 11, x),
+            (3, 8, x),
+            (5, 11, x),
+            (-3, 8, x),
+            (-5, 11, x),
+            (3, -8, x),
+            (5, -11, x),
+            (-3, -8, x),
+            (-5, -11, x),
+            (3, 8.3, x),
+            (5, 11.3, x),
+            (-3, 8.3, x),
+            (-5, 11.3, x),
+            (3, -8.3, x),
+            (5, -11.3, x),
+            (-3, -8.3, x),
+            (-5, -11.3, x),
+        ])
+
+    def mplegenp(nu, mu, x):
+        if mu == int(mu) and x == 1:
+            # mpmath 0.17 gets this wrong
+            if mu == 0:
+                return 1
+            else:
+                return 0
+        return mpmath.legenp(nu, mu, x)
+
+    dataset = [p + (mplegenp(p[1], p[0], p[2]),) for p in pts]
+    dataset = np.array(dataset, dtype=np.float64)
+
+    def evf(mu, nu, x):
+        return sc.lpmv(mu.astype(int), nu, x)
+
+    with np.errstate(invalid='ignore'):
+        FuncData(evf, dataset, (0,1,2), 3, rtol=1e-10, atol=1e-14).check()
+
+
+# ------------------------------------------------------------------------------
+# beta
+# ------------------------------------------------------------------------------
+
+@pytest.mark.slow
+@check_version(mpmath, '0.15')
+def test_beta():
+    np.random.seed(1234)
+
+    b = np.r_[np.logspace(-200, 200, 4),
+              np.logspace(-10, 10, 4),
+              np.logspace(-1, 1, 4),
+              np.arange(-10, 11, 1),
+              np.arange(-10, 11, 1) + 0.5,
+              -1, -2.3, -3, -100.3, -10003.4]
+    a = b
+
+    ab = np.array(np.broadcast_arrays(a[:,None], b[None,:])).reshape(2, -1).T
+
+    old_dps, old_prec = mpmath.mp.dps, mpmath.mp.prec
+    try:
+        mpmath.mp.dps = 400
+
+        assert_func_equal(sc.beta,
+                          lambda a, b: float(mpmath.beta(a, b)),
+                          ab,
+                          vectorized=False,
+                          rtol=1e-10,
+                          ignore_inf_sign=True)
+
+        assert_func_equal(
+            sc.betaln,
+            lambda a, b: float(mpmath.log(abs(mpmath.beta(a, b)))),
+            ab,
+            vectorized=False,
+            rtol=1e-10)
+    finally:
+        mpmath.mp.dps, mpmath.mp.prec = old_dps, old_prec
+
+
+# ------------------------------------------------------------------------------
+# loggamma
+# ------------------------------------------------------------------------------
+
+LOGGAMMA_TAYLOR_RADIUS = 0.2
+
+
+@check_version(mpmath, '0.19')
+def test_loggamma_taylor_transition():
+    # Make sure there isn't a big jump in accuracy when we move from
+    # using the Taylor series to using the recurrence relation.
+
+    r = LOGGAMMA_TAYLOR_RADIUS + np.array([-0.1, -0.01, 0, 0.01, 0.1])
+    theta = np.linspace(0, 2*np.pi, 20)
+    r, theta = np.meshgrid(r, theta)
+    dz = r*np.exp(1j*theta)
+    z = np.r_[1 + dz, 2 + dz].flatten()
+
+    dataset = [(z0, complex(mpmath.loggamma(z0))) for z0 in z]
+    dataset = np.array(dataset)
+
+    FuncData(sc.loggamma, dataset, 0, 1, rtol=5e-14).check()
+
+
+@check_version(mpmath, '0.19')
+def test_loggamma_taylor():
+    # Test around the zeros at z = 1, 2.
+
+    r = np.logspace(-16, np.log10(LOGGAMMA_TAYLOR_RADIUS), 10)
+    theta = np.linspace(0, 2*np.pi, 20)
+    r, theta = np.meshgrid(r, theta)
+    dz = r*np.exp(1j*theta)
+    z = np.r_[1 + dz, 2 + dz].flatten()
+
+    dataset = [(z0, complex(mpmath.loggamma(z0))) for z0 in z]
+    dataset = np.array(dataset)
+
+    FuncData(sc.loggamma, dataset, 0, 1, rtol=5e-14).check()
+
+
+# ------------------------------------------------------------------------------
+# rgamma
+# ------------------------------------------------------------------------------
+
+@check_version(mpmath, '0.19')
+@pytest.mark.slow
+def test_rgamma_zeros():
+    # Test around the zeros at z = 0, -1, -2, ...,  -169. (After -169 we
+    # get values that are out of floating point range even when we're
+    # within 0.1 of the zero.)
+
+    # Can't use too many points here or the test takes forever.
+    dx = np.r_[-np.logspace(-1, -13, 3), 0, np.logspace(-13, -1, 3)]
+    dy = dx.copy()
+    dx, dy = np.meshgrid(dx, dy)
+    dz = dx + 1j*dy
+    zeros = np.arange(0, -170, -1).reshape(1, 1, -1)
+    z = (zeros + np.dstack((dz,)*zeros.size)).flatten()
+    with mpmath.workdps(100):
+        dataset = [(z0, complex(mpmath.rgamma(z0))) for z0 in z]
+
+    dataset = np.array(dataset)
+    FuncData(sc.rgamma, dataset, 0, 1, rtol=1e-12).check()
+
+
+# ------------------------------------------------------------------------------
+# digamma
+# ------------------------------------------------------------------------------
+
+@check_version(mpmath, '0.19')
+@pytest.mark.slow
+def test_digamma_roots():
+    # Test the special-cased roots for digamma.
+    root = mpmath.findroot(mpmath.digamma, 1.5)
+    roots = [float(root)]
+    root = mpmath.findroot(mpmath.digamma, -0.5)
+    roots.append(float(root))
+    roots = np.array(roots)
+
+    # If we test beyond a radius of 0.24 mpmath will take forever.
+    dx = np.r_[-0.24, -np.logspace(-1, -15, 10), 0, np.logspace(-15, -1, 10), 0.24]
+    dy = dx.copy()
+    dx, dy = np.meshgrid(dx, dy)
+    dz = dx + 1j*dy
+    z = (roots + np.dstack((dz,)*roots.size)).flatten()
+    with mpmath.workdps(30):
+        dataset = [(z0, complex(mpmath.digamma(z0))) for z0 in z]
+
+    dataset = np.array(dataset)
+    FuncData(sc.digamma, dataset, 0, 1, rtol=1e-14).check()
+
+
+@check_version(mpmath, '0.19')
+def test_digamma_negreal():
+    # Test digamma around the negative real axis. Don't do this in
+    # TestSystematic because the points need some jiggering so that
+    # mpmath doesn't take forever.
+
+    digamma = exception_to_nan(mpmath.digamma)
+
+    x = -np.logspace(300, -30, 100)
+    y = np.r_[-np.logspace(0, -3, 5), 0, np.logspace(-3, 0, 5)]
+    x, y = np.meshgrid(x, y)
+    z = (x + 1j*y).flatten()
+
+    with mpmath.workdps(40):
+        dataset = [(z0, complex(digamma(z0))) for z0 in z]
+    dataset = np.asarray(dataset)
+
+    FuncData(sc.digamma, dataset, 0, 1, rtol=1e-13).check()
+
+
+@check_version(mpmath, '0.19')
+def test_digamma_boundary():
+    # Check that there isn't a jump in accuracy when we switch from
+    # using the asymptotic series to the reflection formula.
+
+    x = -np.logspace(300, -30, 100)
+    y = np.array([-6.1, -5.9, 5.9, 6.1])
+    x, y = np.meshgrid(x, y)
+    z = (x + 1j*y).flatten()
+
+    with mpmath.workdps(30):
+        dataset = [(z0, complex(mpmath.digamma(z0))) for z0 in z]
+    dataset = np.asarray(dataset)
+
+    FuncData(sc.digamma, dataset, 0, 1, rtol=1e-13).check()
+
+
+# ------------------------------------------------------------------------------
+# gammainc
+# ------------------------------------------------------------------------------
+
+@check_version(mpmath, '0.19')
+@pytest.mark.slow
+def test_gammainc_boundary():
+    # Test the transition to the asymptotic series.
+    small = 20
+    a = np.linspace(0.5*small, 2*small, 50)
+    x = a.copy()
+    a, x = np.meshgrid(a, x)
+    a, x = a.flatten(), x.flatten()
+    with mpmath.workdps(100):
+        dataset = [(a0, x0, float(mpmath.gammainc(a0, b=x0, regularized=True)))
+                   for a0, x0 in zip(a, x)]
+    dataset = np.array(dataset)
+
+    FuncData(sc.gammainc, dataset, (0, 1), 2, rtol=1e-12).check()
+
+
+# ------------------------------------------------------------------------------
+# spence
+# ------------------------------------------------------------------------------
+
+@check_version(mpmath, '0.19')
+@pytest.mark.slow
+def test_spence_circle():
+    # The trickiest region for spence is around the circle |z - 1| = 1,
+    # so test that region carefully.
+
+    def spence(z):
+        return complex(mpmath.polylog(2, 1 - z))
+
+    r = np.linspace(0.5, 1.5)
+    theta = np.linspace(0, 2*pi)
+    z = (1 + np.outer(r, np.exp(1j*theta))).flatten()
+    dataset = np.asarray([(z0, spence(z0)) for z0 in z])
+
+    FuncData(sc.spence, dataset, 0, 1, rtol=1e-14).check()
+
+
+# ------------------------------------------------------------------------------
+# sinpi and cospi
+# ------------------------------------------------------------------------------
+
+@check_version(mpmath, '0.19')
+def test_sinpi_zeros():
+    eps = np.finfo(float).eps
+    dx = np.r_[-np.logspace(0, -13, 3), 0, np.logspace(-13, 0, 3)]
+    dy = dx.copy()
+    dx, dy = np.meshgrid(dx, dy)
+    dz = dx + 1j*dy
+    zeros = np.arange(-100, 100, 1).reshape(1, 1, -1)
+    z = (zeros + np.dstack((dz,)*zeros.size)).flatten()
+    dataset = np.asarray([(z0, complex(mpmath.sinpi(z0)))
+                          for z0 in z])
+    FuncData(_sinpi, dataset, 0, 1, rtol=2*eps).check()
+
+
+@check_version(mpmath, '0.19')
+def test_cospi_zeros():
+    eps = np.finfo(float).eps
+    dx = np.r_[-np.logspace(0, -13, 3), 0, np.logspace(-13, 0, 3)]
+    dy = dx.copy()
+    dx, dy = np.meshgrid(dx, dy)
+    dz = dx + 1j*dy
+    zeros = (np.arange(-100, 100, 1) + 0.5).reshape(1, 1, -1)
+    z = (zeros + np.dstack((dz,)*zeros.size)).flatten()
+    dataset = np.asarray([(z0, complex(mpmath.cospi(z0)))
+                          for z0 in z])
+
+    FuncData(_cospi, dataset, 0, 1, rtol=2*eps).check()
+
+
+# ------------------------------------------------------------------------------
+# ellipj
+# ------------------------------------------------------------------------------
+
+@check_version(mpmath, '0.19')
+def test_dn_quarter_period():
+    def dn(u, m):
+        return sc.ellipj(u, m)[2]
+
+    def mpmath_dn(u, m):
+        return float(mpmath.ellipfun("dn", u=u, m=m))
+
+    m = np.linspace(0, 1, 20)
+    du = np.r_[-np.logspace(-1, -15, 10), 0, np.logspace(-15, -1, 10)]
+    dataset = []
+    for m0 in m:
+        u0 = float(mpmath.ellipk(m0))
+        for du0 in du:
+            p = u0 + du0
+            dataset.append((p, m0, mpmath_dn(p, m0)))
+    dataset = np.asarray(dataset)
+
+    FuncData(dn, dataset, (0, 1), 2, rtol=1e-10).check()
+
+
+# ------------------------------------------------------------------------------
+# Wright Omega
+# ------------------------------------------------------------------------------
+
+def _mpmath_wrightomega(z, dps):
+    with mpmath.workdps(dps):
+        z = mpmath.mpc(z)
+        unwind = mpmath.ceil((z.imag - mpmath.pi)/(2*mpmath.pi))
+        res = mpmath.lambertw(mpmath.exp(z), unwind)
+    return res
+
+
+@pytest.mark.slow
+@check_version(mpmath, '0.19')
+def test_wrightomega_branch():
+    x = -np.logspace(10, 0, 25)
+    picut_above = [np.nextafter(np.pi, np.inf)]
+    picut_below = [np.nextafter(np.pi, -np.inf)]
+    npicut_above = [np.nextafter(-np.pi, np.inf)]
+    npicut_below = [np.nextafter(-np.pi, -np.inf)]
+    for i in range(50):
+        picut_above.append(np.nextafter(picut_above[-1], np.inf))
+        picut_below.append(np.nextafter(picut_below[-1], -np.inf))
+        npicut_above.append(np.nextafter(npicut_above[-1], np.inf))
+        npicut_below.append(np.nextafter(npicut_below[-1], -np.inf))
+    y = np.hstack((picut_above, picut_below, npicut_above, npicut_below))
+    x, y = np.meshgrid(x, y)
+    z = (x + 1j*y).flatten()
+
+    dataset = np.asarray([(z0, complex(_mpmath_wrightomega(z0, 25)))
+                          for z0 in z])
+
+    FuncData(sc.wrightomega, dataset, 0, 1, rtol=1e-8).check()
+
+
+@pytest.mark.slow
+@check_version(mpmath, '0.19')
+def test_wrightomega_region1():
+    # This region gets less coverage in the TestSystematic test
+    x = np.linspace(-2, 1)
+    y = np.linspace(1, 2*np.pi)
+    x, y = np.meshgrid(x, y)
+    z = (x + 1j*y).flatten()
+
+    dataset = np.asarray([(z0, complex(_mpmath_wrightomega(z0, 25)))
+                          for z0 in z])
+
+    FuncData(sc.wrightomega, dataset, 0, 1, rtol=1e-15).check()
+
+
+@pytest.mark.slow
+@check_version(mpmath, '0.19')
+def test_wrightomega_region2():
+    # This region gets less coverage in the TestSystematic test
+    x = np.linspace(-2, 1)
+    y = np.linspace(-2*np.pi, -1)
+    x, y = np.meshgrid(x, y)
+    z = (x + 1j*y).flatten()
+
+    dataset = np.asarray([(z0, complex(_mpmath_wrightomega(z0, 25)))
+                          for z0 in z])
+
+    FuncData(sc.wrightomega, dataset, 0, 1, rtol=1e-15).check()
+
+
+# ------------------------------------------------------------------------------
+# lambertw
+# ------------------------------------------------------------------------------
+
+@pytest.mark.slow
+@check_version(mpmath, '0.19')
+def test_lambertw_smallz():
+    x, y = np.linspace(-1, 1, 25), np.linspace(-1, 1, 25)
+    x, y = np.meshgrid(x, y)
+    z = (x + 1j*y).flatten()
+
+    dataset = np.asarray([(z0, complex(mpmath.lambertw(z0)))
+                          for z0 in z])
+
+    FuncData(sc.lambertw, dataset, 0, 1, rtol=1e-13).check()
+
+
+# ------------------------------------------------------------------------------
+# Systematic tests
+# ------------------------------------------------------------------------------
+
+HYPERKW = dict(maxprec=200, maxterms=200)
+
+
+@pytest.mark.slow
+@check_version(mpmath, '0.17')
+class TestSystematic:
+
+    def test_airyai(self):
+        # oscillating function, limit range
+        assert_mpmath_equal(lambda z: sc.airy(z)[0],
+                            mpmath.airyai,
+                            [Arg(-1e8, 1e8)],
+                            rtol=1e-5)
+        assert_mpmath_equal(lambda z: sc.airy(z)[0],
+                            mpmath.airyai,
+                            [Arg(-1e3, 1e3)])
+
+    def test_airyai_complex(self):
+        assert_mpmath_equal(lambda z: sc.airy(z)[0],
+                            mpmath.airyai,
+                            [ComplexArg()])
+
+    def test_airyai_prime(self):
+        # oscillating function, limit range
+        assert_mpmath_equal(lambda z: sc.airy(z)[1], lambda z:
+                            mpmath.airyai(z, derivative=1),
+                            [Arg(-1e8, 1e8)],
+                            rtol=1e-5)
+        assert_mpmath_equal(lambda z: sc.airy(z)[1], lambda z:
+                            mpmath.airyai(z, derivative=1),
+                            [Arg(-1e3, 1e3)])
+
+    def test_airyai_prime_complex(self):
+        assert_mpmath_equal(lambda z: sc.airy(z)[1], lambda z:
+                            mpmath.airyai(z, derivative=1),
+                            [ComplexArg()])
+
+    def test_airybi(self):
+        # oscillating function, limit range
+        assert_mpmath_equal(lambda z: sc.airy(z)[2], lambda z:
+                            mpmath.airybi(z),
+                            [Arg(-1e8, 1e8)],
+                            rtol=1e-5)
+        assert_mpmath_equal(lambda z: sc.airy(z)[2], lambda z:
+                            mpmath.airybi(z),
+                            [Arg(-1e3, 1e3)])
+
+    def test_airybi_complex(self):
+        assert_mpmath_equal(lambda z: sc.airy(z)[2], lambda z:
+                            mpmath.airybi(z),
+                            [ComplexArg()])
+
+    def test_airybi_prime(self):
+        # oscillating function, limit range
+        assert_mpmath_equal(lambda z: sc.airy(z)[3], lambda z:
+                            mpmath.airybi(z, derivative=1),
+                            [Arg(-1e8, 1e8)],
+                            rtol=1e-5)
+        assert_mpmath_equal(lambda z: sc.airy(z)[3], lambda z:
+                            mpmath.airybi(z, derivative=1),
+                            [Arg(-1e3, 1e3)])
+
+    def test_airybi_prime_complex(self):
+        assert_mpmath_equal(lambda z: sc.airy(z)[3], lambda z:
+                            mpmath.airybi(z, derivative=1),
+                            [ComplexArg()])
+
+    def test_bei(self):
+        assert_mpmath_equal(sc.bei,
+                            exception_to_nan(lambda z: mpmath.bei(0, z, **HYPERKW)),
+                            [Arg(-1e3, 1e3)])
+
+    def test_ber(self):
+        assert_mpmath_equal(sc.ber,
+                            exception_to_nan(lambda z: mpmath.ber(0, z, **HYPERKW)),
+                            [Arg(-1e3, 1e3)])
+
+    def test_bernoulli(self):
+        assert_mpmath_equal(lambda n: sc.bernoulli(int(n))[int(n)],
+                            lambda n: float(mpmath.bernoulli(int(n))),
+                            [IntArg(0, 13000)],
+                            rtol=1e-9, n=13000)
+
+    def test_besseli(self):
+        assert_mpmath_equal(
+            sc.iv,
+            exception_to_nan(lambda v, z: mpmath.besseli(v, z, **HYPERKW)),
+            [Arg(-1e100, 1e100), Arg()],
+            atol=1e-270,
+        )
+
+    def test_besseli_complex(self):
+        assert_mpmath_equal(
+            lambda v, z: sc.iv(v.real, z),
+            exception_to_nan(lambda v, z: mpmath.besseli(v, z, **HYPERKW)),
+            [Arg(-1e100, 1e100), ComplexArg()],
+        )
+
+    def test_besselj(self):
+        assert_mpmath_equal(
+            sc.jv,
+            exception_to_nan(lambda v, z: mpmath.besselj(v, z, **HYPERKW)),
+            [Arg(-1e100, 1e100), Arg(-1e3, 1e3)],
+            ignore_inf_sign=True,
+        )
+
+        # loss of precision at large arguments due to oscillation
+        assert_mpmath_equal(
+            sc.jv,
+            exception_to_nan(lambda v, z: mpmath.besselj(v, z, **HYPERKW)),
+            [Arg(-1e100, 1e100), Arg(-1e8, 1e8)],
+            ignore_inf_sign=True,
+            rtol=1e-5,
+        )
+
+    def test_besselj_complex(self):
+        assert_mpmath_equal(
+            lambda v, z: sc.jv(v.real, z),
+            exception_to_nan(lambda v, z: mpmath.besselj(v, z, **HYPERKW)),
+            [Arg(), ComplexArg()]
+        )
+
+    def test_besselk(self):
+        assert_mpmath_equal(
+            sc.kv,
+            mpmath.besselk,
+            [Arg(-200, 200), Arg(0, np.inf)],
+            nan_ok=False,
+            rtol=1e-12,
+        )
+
+    def test_besselk_int(self):
+        assert_mpmath_equal(
+            sc.kn,
+            mpmath.besselk,
+            [IntArg(-200, 200), Arg(0, np.inf)],
+            nan_ok=False,
+            rtol=1e-12,
+        )
+
+    def test_besselk_complex(self):
+        assert_mpmath_equal(
+            lambda v, z: sc.kv(v.real, z),
+            exception_to_nan(lambda v, z: mpmath.besselk(v, z, **HYPERKW)),
+            [Arg(-1e100, 1e100), ComplexArg()],
+        )
+
+    def test_bessely(self):
+        def mpbessely(v, x):
+            r = float(mpmath.bessely(v, x, **HYPERKW))
+            if abs(r) > 1e305:
+                # overflowing to inf a bit earlier is OK
+                r = np.inf * np.sign(r)
+            if abs(r) == 0 and x == 0:
+                # invalid result from mpmath, point x=0 is a divergence
+                return np.nan
+            return r
+        assert_mpmath_equal(
+            sc.yv,
+            exception_to_nan(mpbessely),
+            [Arg(-1e100, 1e100), Arg(-1e8, 1e8)],
+            n=5000,
+        )
+
+    def test_bessely_complex(self):
+        def mpbessely(v, x):
+            r = complex(mpmath.bessely(v, x, **HYPERKW))
+            if abs(r) > 1e305:
+                # overflowing to inf a bit earlier is OK
+                with np.errstate(invalid='ignore'):
+                    r = np.inf * np.sign(r)
+            return r
+        assert_mpmath_equal(
+            lambda v, z: sc.yv(v.real, z),
+            exception_to_nan(mpbessely),
+            [Arg(), ComplexArg()],
+            n=15000,
+        )
+
+    def test_bessely_int(self):
+        def mpbessely(v, x):
+            r = float(mpmath.bessely(v, x))
+            if abs(r) == 0 and x == 0:
+                # invalid result from mpmath, point x=0 is a divergence
+                return np.nan
+            return r
+        assert_mpmath_equal(
+            lambda v, z: sc.yn(int(v), z),
+            exception_to_nan(mpbessely),
+            [IntArg(-1000, 1000), Arg(-1e8, 1e8)],
+        )
+
+    def test_beta(self):
+        bad_points = []
+
+        def beta(a, b, nonzero=False):
+            if a < -1e12 or b < -1e12:
+                # Function is defined here only at integers, but due
+                # to loss of precision this is numerically
+                # ill-defined. Don't compare values here.
+                return np.nan
+            if (a < 0 or b < 0) and (abs(float(a + b)) % 1) == 0:
+                # close to a zero of the function: mpmath and scipy
+                # will not round here the same, so the test needs to be
+                # run with an absolute tolerance
+                if nonzero:
+                    bad_points.append((float(a), float(b)))
+                    return np.nan
+            return mpmath.beta(a, b)
+
+        assert_mpmath_equal(
+            sc.beta,
+            lambda a, b: beta(a, b, nonzero=True),
+            [Arg(), Arg()],
+            dps=400,
+            ignore_inf_sign=True,
+        )
+
+        assert_mpmath_equal(
+            sc.beta,
+            beta,
+            np.array(bad_points),
+            dps=400,
+            ignore_inf_sign=True,
+            atol=1e-11,
+        )
+
+    def test_betainc(self):
+        assert_mpmath_equal(
+            sc.betainc,
+            time_limited()(
+                exception_to_nan(
+                    lambda a, b, x: mpmath.betainc(a, b, 0, x, regularized=True)
+                )
+            ),
+            [Arg(), Arg(), Arg()],
+        )
+
+    def test_betaincc(self):
+        assert_mpmath_equal(
+            sc.betaincc,
+            time_limited()(
+                exception_to_nan(
+                    lambda a, b, x: mpmath.betainc(a, b, x, 1, regularized=True)
+                )
+            ),
+            [Arg(), Arg(), Arg()],
+            dps=400,
+        )
+
+    def test_binom(self):
+        bad_points = []
+
+        def binomial(n, k, nonzero=False):
+            if abs(k) > 1e8*(abs(n) + 1):
+                # The binomial is rapidly oscillating in this region,
+                # and the function is numerically ill-defined. Don't
+                # compare values here.
+                return np.nan
+            if n < k and abs(float(n-k) - np.round(float(n-k))) < 1e-15:
+                # close to a zero of the function: mpmath and scipy
+                # will not round here the same, so the test needs to be
+                # run with an absolute tolerance
+                if nonzero:
+                    bad_points.append((float(n), float(k)))
+                    return np.nan
+            return mpmath.binomial(n, k)
+
+        assert_mpmath_equal(
+            sc.binom,
+            lambda n, k: binomial(n, k, nonzero=True),
+            [Arg(), Arg()],
+            dps=400,
+        )
+
+        assert_mpmath_equal(
+            sc.binom,
+            binomial,
+            np.array(bad_points),
+            dps=400,
+            atol=1e-14,
+        )
+
+    def test_chebyt_int(self):
+        assert_mpmath_equal(
+            lambda n, x: sc.eval_chebyt(int(n), x),
+            exception_to_nan(lambda n, x: mpmath.chebyt(n, x, **HYPERKW)),
+            [IntArg(), Arg()],
+            dps=50,
+        )
+
+    @pytest.mark.xfail(run=False, reason="some cases in hyp2f1 not fully accurate")
+    def test_chebyt(self):
+        assert_mpmath_equal(
+            sc.eval_chebyt,
+            lambda n, x: time_limited()(
+                exception_to_nan(mpmath.chebyt)
+            )(n, x, **HYPERKW),
+            [Arg(-101, 101), Arg()],
+            n=10000,
+        )
+
+    def test_chebyu_int(self):
+        assert_mpmath_equal(
+            lambda n, x: sc.eval_chebyu(int(n), x),
+            exception_to_nan(lambda n, x: mpmath.chebyu(n, x, **HYPERKW)),
+            [IntArg(), Arg()],
+            dps=50,
+        )
+
+    @pytest.mark.xfail(run=False, reason="some cases in hyp2f1 not fully accurate")
+    def test_chebyu(self):
+        assert_mpmath_equal(
+            sc.eval_chebyu,
+            lambda n, x: time_limited()(
+                exception_to_nan(mpmath.chebyu)
+            )(n, x, **HYPERKW),
+            [Arg(-101, 101), Arg()],
+        )
+
+    def test_chi(self):
+        def chi(x):
+            return sc.shichi(x)[1]
+        assert_mpmath_equal(chi, mpmath.chi, [Arg()])
+        # check asymptotic series cross-over
+        assert_mpmath_equal(chi, mpmath.chi, [FixedArg([88 - 1e-9, 88, 88 + 1e-9])])
+
+    def test_chi_complex(self):
+        def chi(z):
+            return sc.shichi(z)[1]
+        # chi oscillates as Im[z] -> +- inf, so limit range
+        assert_mpmath_equal(
+            chi,
+            mpmath.chi,
+            [ComplexArg(complex(-np.inf, -1e8), complex(np.inf, 1e8))],
+            rtol=1e-12,
+        )
+
+    def test_ci(self):
+        def ci(x):
+            return sc.sici(x)[1]
+        # oscillating function: limit range
+        assert_mpmath_equal(ci, mpmath.ci, [Arg(-1e8, 1e8)])
+
+    def test_ci_complex(self):
+        def ci(z):
+            return sc.sici(z)[1]
+        # ci oscillates as Re[z] -> +- inf, so limit range
+        assert_mpmath_equal(
+            ci,
+            mpmath.ci,
+            [ComplexArg(complex(-1e8, -np.inf), complex(1e8, np.inf))],
+            rtol=1e-8,
+        )
+
+    def test_cospi(self):
+        eps = np.finfo(float).eps
+        assert_mpmath_equal(_cospi, mpmath.cospi, [Arg()], nan_ok=False, rtol=2*eps)
+
+    def test_cospi_complex(self):
+        assert_mpmath_equal(
+            _cospi,
+            mpmath.cospi,
+            [ComplexArg()],
+            nan_ok=False,
+            rtol=1e-13,
+        )
+
+    def test_digamma(self):
+        assert_mpmath_equal(
+            sc.digamma,
+            exception_to_nan(mpmath.digamma),
+            [Arg()],
+            rtol=1e-12,
+            dps=50,
+        )
+
+    def test_digamma_complex(self):
+        # Test on a cut plane because mpmath will hang. See
+        # test_digamma_negreal for tests on the negative real axis.
+        def param_filter(z):
+            return np.where((z.real < 0) & (np.abs(z.imag) < 1.12), False, True)
+
+        assert_mpmath_equal(
+            sc.digamma,
+            exception_to_nan(mpmath.digamma),
+            [ComplexArg()],
+            rtol=1e-13,
+            dps=40,
+            param_filter=param_filter
+        )
+
+    def test_e1(self):
+        assert_mpmath_equal(
+            sc.exp1,
+            mpmath.e1,
+            [Arg()],
+            rtol=1e-14,
+        )
+
+    def test_e1_complex(self):
+        # E_1 oscillates as Im[z] -> +- inf, so limit range
+        assert_mpmath_equal(
+            sc.exp1,
+            mpmath.e1,
+            [ComplexArg(complex(-np.inf, -1e8), complex(np.inf, 1e8))],
+            rtol=1e-11,
+        )
+
+        # Check cross-over region
+        assert_mpmath_equal(
+            sc.exp1,
+            mpmath.e1,
+            (np.linspace(-50, 50, 171)[:, None]
+             + np.r_[0, np.logspace(-3, 2, 61), -np.logspace(-3, 2, 11)]*1j).ravel(),
+            rtol=1e-11,
+        )
+        assert_mpmath_equal(
+            sc.exp1,
+            mpmath.e1,
+            (np.linspace(-50, -35, 10000) + 0j),
+            rtol=1e-11,
+        )
+
+    def test_exprel(self):
+        assert_mpmath_equal(
+            sc.exprel,
+            lambda x: mpmath.expm1(x)/x if x != 0 else mpmath.mpf('1.0'),
+            [Arg(a=-np.log(np.finfo(np.float64).max),
+                 b=np.log(np.finfo(np.float64).max))],
+        )
+        assert_mpmath_equal(
+            sc.exprel,
+            lambda x: mpmath.expm1(x)/x if x != 0 else mpmath.mpf('1.0'),
+            np.array([1e-12, 1e-24, 0, 1e12, 1e24, np.inf]),
+            rtol=1e-11,
+        )
+        assert_(np.isinf(sc.exprel(np.inf)))
+        assert_(sc.exprel(-np.inf) == 0)
+
+    def test_expm1_complex(self):
+        # Oscillates as a function of Im[z], so limit range to avoid loss of precision
+        assert_mpmath_equal(
+            sc.expm1,
+            mpmath.expm1,
+            [ComplexArg(complex(-np.inf, -1e7), complex(np.inf, 1e7))],
+        )
+
+    def test_log1p_complex(self):
+        assert_mpmath_equal(
+            sc.log1p,
+            lambda x: mpmath.log(x+1),
+            [ComplexArg()],
+            dps=60,
+        )
+
+    def test_log1pmx(self):
+        assert_mpmath_equal(
+            _log1pmx,
+            lambda x: mpmath.log(x + 1) - x,
+            [Arg()],
+            dps=60,
+            rtol=1e-14,
+        )
+
+    def test_ei(self):
+        assert_mpmath_equal(sc.expi, mpmath.ei, [Arg()], rtol=1e-11)
+
+    def test_ei_complex(self):
+        # Ei oscillates as Im[z] -> +- inf, so limit range
+        assert_mpmath_equal(
+            sc.expi,
+            mpmath.ei,
+            [ComplexArg(complex(-np.inf, -1e8), complex(np.inf, 1e8))],
+            rtol=1e-9,
+        )
+
+    def test_ellipe(self):
+        assert_mpmath_equal(sc.ellipe, mpmath.ellipe, [Arg(b=1.0)])
+
+    def test_ellipeinc(self):
+        assert_mpmath_equal(sc.ellipeinc, mpmath.ellipe, [Arg(-1e3, 1e3), Arg(b=1.0)])
+
+    def test_ellipeinc_largephi(self):
+        assert_mpmath_equal(sc.ellipeinc, mpmath.ellipe, [Arg(), Arg()])
+
+    def test_ellipf(self):
+        assert_mpmath_equal(sc.ellipkinc, mpmath.ellipf, [Arg(-1e3, 1e3), Arg()])
+
+    def test_ellipf_largephi(self):
+        assert_mpmath_equal(sc.ellipkinc, mpmath.ellipf, [Arg(), Arg()])
+
+    def test_ellipk(self):
+        assert_mpmath_equal(sc.ellipk, mpmath.ellipk, [Arg(b=1.0)])
+        assert_mpmath_equal(
+            sc.ellipkm1,
+            lambda m: mpmath.ellipk(1 - m),
+            [Arg(a=0.0)],
+            dps=400,
+        )
+
+    def test_ellipkinc(self):
+        def ellipkinc(phi, m):
+            return mpmath.ellippi(0, phi, m)
+        assert_mpmath_equal(
+            sc.ellipkinc,
+            ellipkinc,
+            [Arg(-1e3, 1e3), Arg(b=1.0)],
+            ignore_inf_sign=True,
+        )
+
+    def test_ellipkinc_largephi(self):
+        def ellipkinc(phi, m):
+            return mpmath.ellippi(0, phi, m)
+        assert_mpmath_equal(
+            sc.ellipkinc,
+            ellipkinc,
+            [Arg(), Arg(b=1.0)],
+            ignore_inf_sign=True,
+        )
+
+    def test_ellipfun_sn(self):
+        def sn(u, m):
+            # mpmath doesn't get the zero at u = 0--fix that
+            if u == 0:
+                return 0
+            else:
+                return mpmath.ellipfun("sn", u=u, m=m)
+
+        # Oscillating function --- limit range of first argument; the
+        # loss of precision there is an expected numerical feature
+        # rather than an actual bug
+        assert_mpmath_equal(
+            lambda u, m: sc.ellipj(u, m)[0],
+            sn,
+            [Arg(-1e6, 1e6), Arg(a=0, b=1)],
+            rtol=1e-8,
+        )
+
+    def test_ellipfun_cn(self):
+        # see comment in ellipfun_sn
+        assert_mpmath_equal(
+            lambda u, m: sc.ellipj(u, m)[1],
+            lambda u, m: mpmath.ellipfun("cn", u=u, m=m),
+            [Arg(-1e6, 1e6), Arg(a=0, b=1)],
+            rtol=1e-8,
+        )
+
+    def test_ellipfun_dn(self):
+        # see comment in ellipfun_sn
+        assert_mpmath_equal(
+            lambda u, m: sc.ellipj(u, m)[2],
+            lambda u, m: mpmath.ellipfun("dn", u=u, m=m),
+            [Arg(-1e6, 1e6), Arg(a=0, b=1)],
+            rtol=1e-8,
+        )
+
+    def test_erf(self):
+        assert_mpmath_equal(sc.erf, lambda z: mpmath.erf(z), [Arg()])
+
+    def test_erf_complex(self):
+        assert_mpmath_equal(sc.erf, lambda z: mpmath.erf(z), [ComplexArg()], n=200)
+
+    def test_erfc(self):
+        assert_mpmath_equal(
+            sc.erfc,
+            exception_to_nan(lambda z: mpmath.erfc(z)),
+            [Arg()],
+            rtol=1e-13,
+        )
+
+    def test_erfc_complex(self):
+        assert_mpmath_equal(
+            sc.erfc,
+            exception_to_nan(lambda z: mpmath.erfc(z)),
+            [ComplexArg()],
+            n=200,
+        )
+
+    def test_erfi(self):
+        assert_mpmath_equal(sc.erfi, mpmath.erfi, [Arg()], n=200)
+
+    def test_erfi_complex(self):
+        assert_mpmath_equal(sc.erfi, mpmath.erfi, [ComplexArg()], n=200)
+
+    def test_ndtr(self):
+        assert_mpmath_equal(
+            sc.ndtr,
+            exception_to_nan(lambda z: mpmath.ncdf(z)),
+            [Arg()],
+            n=200,
+        )
+
+    def test_ndtr_complex(self):
+        assert_mpmath_equal(
+            sc.ndtr,
+            lambda z: mpmath.erfc(-z/np.sqrt(2.))/2.,
+            [ComplexArg(a=complex(-10000, -10000), b=complex(10000, 10000))],
+            n=400,
+        )
+
+    def test_log_ndtr(self):
+        assert_mpmath_equal(
+            sc.log_ndtr,
+            exception_to_nan(lambda z: mpmath.log(mpmath.ncdf(z))),
+            [Arg()], n=600, dps=300, rtol=1e-13,
+        )
+
+    def test_log_ndtr_complex(self):
+        assert_mpmath_equal(
+            sc.log_ndtr,
+            exception_to_nan(lambda z: mpmath.log(mpmath.erfc(-z/np.sqrt(2.))/2.)),
+            [ComplexArg(a=complex(-10000, -100), b=complex(10000, 100))],
+            n=200, dps=300,
+        )
+
+    def test_eulernum(self):
+        assert_mpmath_equal(
+            lambda n: sc.euler(n)[-1],
+            mpmath.eulernum,
+            [IntArg(1, 10000)],
+            n=10000,
+        )
+
+    def test_expint(self):
+        assert_mpmath_equal(
+            sc.expn,
+            mpmath.expint,
+            [IntArg(0, 200), Arg(0, np.inf)],
+            rtol=1e-13,
+            dps=160,
+        )
+
+    def test_fresnels(self):
+        def fresnels(x):
+            return sc.fresnel(x)[0]
+        assert_mpmath_equal(fresnels, mpmath.fresnels, [Arg()])
+
+    def test_fresnelc(self):
+        def fresnelc(x):
+            return sc.fresnel(x)[1]
+        assert_mpmath_equal(fresnelc, mpmath.fresnelc, [Arg()])
+
+    def test_gamma(self):
+        assert_mpmath_equal(sc.gamma, exception_to_nan(mpmath.gamma), [Arg()])
+
+    def test_gamma_complex(self):
+        assert_mpmath_equal(
+            sc.gamma,
+            exception_to_nan(mpmath.gamma),
+            [ComplexArg()],
+            rtol=5e-13,
+        )
+
+    def test_gammainc(self):
+        # Larger arguments are tested in test_data.py:test_local
+        assert_mpmath_equal(
+            sc.gammainc,
+            lambda z, b: mpmath.gammainc(z, b=b, regularized=True),
+            [Arg(0, 1e4, inclusive_a=False), Arg(0, 1e4)],
+            nan_ok=False,
+            rtol=1e-11,
+        )
+
+    def test_gammaincc(self):
+        # Larger arguments are tested in test_data.py:test_local
+        assert_mpmath_equal(
+            sc.gammaincc,
+            lambda z, a: mpmath.gammainc(z, a=a, regularized=True),
+            [Arg(0, 1e4, inclusive_a=False), Arg(0, 1e4)],
+            nan_ok=False,
+            rtol=1e-11,
+        )
+
+    def test_gammaln(self):
+        # The real part of loggamma is log(|gamma(z)|).
+        def f(z):
+            return mpmath.loggamma(z).real
+
+        assert_mpmath_equal(sc.gammaln, exception_to_nan(f), [Arg()])
+
+    @pytest.mark.xfail(run=False)
+    def test_gegenbauer(self):
+        assert_mpmath_equal(
+            sc.eval_gegenbauer,
+            exception_to_nan(mpmath.gegenbauer),
+            [Arg(-1e3, 1e3), Arg(), Arg()],
+        )
+
+    def test_gegenbauer_int(self):
+        # Redefine functions to deal with numerical + mpmath issues
+        def gegenbauer(n, a, x):
+            # Avoid overflow at large `a` (mpmath would need an even larger
+            # dps to handle this correctly, so just skip this region)
+            if abs(a) > 1e100:
+                return np.nan
+
+            # Deal with n=0, n=1 correctly; mpmath 0.17 doesn't do these
+            # always correctly
+            if n == 0:
+                r = 0.0 if a == 0.0 else 1.0
+            elif n == 1:
+                r = 2*a*x
+            else:
+                r = mpmath.gegenbauer(n, a, x)
+
+            # Mpmath 0.17 gives wrong results (spurious zero) in some cases, so
+            # compute the value by perturbing the result
+            if float(r) == 0 and a < -1 and float(a) == int(float(a)):
+                r = mpmath.gegenbauer(n, a + mpmath.mpf('1e-50'), x)
+                if abs(r) < mpmath.mpf('1e-50'):
+                    r = mpmath.mpf('0.0')
+
+            # Differing overflow thresholds in scipy vs. mpmath
+            if abs(r) > 1e270:
+                return np.inf
+            return r
+
+        def sc_gegenbauer(n, a, x):
+            r = sc.eval_gegenbauer(int(n), a, x)
+            # Differing overflow thresholds in scipy vs. mpmath
+            if abs(r) > 1e270:
+                return np.inf
+            return r
+        assert_mpmath_equal(
+            sc_gegenbauer,
+            exception_to_nan(gegenbauer),
+            [IntArg(0, 100), Arg(-1e9, 1e9), Arg()],
+            dps=100, ignore_inf_sign=True, rtol=1e-6,
+        )
+
+        # Check the small-x expansion
+        assert_mpmath_equal(
+            sc_gegenbauer,
+            exception_to_nan(gegenbauer),
+            [IntArg(0, 100), Arg(), FixedArg(np.logspace(-30, -4, 30))],
+            dps=100, ignore_inf_sign=True,
+        )
+
+    @pytest.mark.xfail(run=False)
+    def test_gegenbauer_complex(self):
+        assert_mpmath_equal(
+            lambda n, a, x: sc.eval_gegenbauer(int(n), a.real, x),
+            exception_to_nan(mpmath.gegenbauer),
+            [IntArg(0, 100), Arg(), ComplexArg()],
+        )
+
+    @nonfunctional_tooslow
+    def test_gegenbauer_complex_general(self):
+        assert_mpmath_equal(
+            lambda n, a, x: sc.eval_gegenbauer(n.real, a.real, x),
+            exception_to_nan(mpmath.gegenbauer),
+            [Arg(-1e3, 1e3), Arg(), ComplexArg()],
+        )
+
+    def test_hankel1(self):
+        assert_mpmath_equal(
+            sc.hankel1,
+            exception_to_nan(lambda v, x: mpmath.hankel1(v, x, **HYPERKW)),
+            [Arg(-1e20, 1e20), Arg()],
+        )
+
+    def test_hankel2(self):
+        assert_mpmath_equal(
+            sc.hankel2,
+            exception_to_nan(lambda v, x: mpmath.hankel2(v, x, **HYPERKW)),
+            [Arg(-1e20, 1e20), Arg()],
+        )
+
+    @pytest.mark.xfail(run=False, reason="issues at intermediately large orders")
+    def test_hermite(self):
+        assert_mpmath_equal(
+            lambda n, x: sc.eval_hermite(int(n), x),
+            exception_to_nan(mpmath.hermite),
+            [IntArg(0, 10000), Arg()],
+        )
+
+    # hurwitz: same as zeta
+
+    def test_hyp0f1(self):
+        # mpmath reports no convergence unless maxterms is large enough
+        KW = dict(maxprec=400, maxterms=1500)
+        # n=500 (non-xslow default) fails for one bad point
+        assert_mpmath_equal(
+            sc.hyp0f1,
+            lambda a, x: mpmath.hyp0f1(a, x, **KW),
+            [Arg(-1e7, 1e7), Arg(0, 1e5)],
+            n=5000,
+        )
+        # NB: The range of the second parameter ("z") is limited from below
+        # because of an overflow in the intermediate calculations. The way
+        # for fix it is to implement an asymptotic expansion for Bessel J
+        # (similar to what is implemented for Bessel I here).
+
+    def test_hyp0f1_complex(self):
+        assert_mpmath_equal(
+            lambda a, z: sc.hyp0f1(a.real, z),
+            exception_to_nan(lambda a, x: mpmath.hyp0f1(a, x, **HYPERKW)),
+            [Arg(-10, 10), ComplexArg(complex(-120, -120), complex(120, 120))],
+        )
+        # NB: The range of the first parameter ("v") are limited by an overflow
+        # in the intermediate calculations. Can be fixed by implementing an
+        # asymptotic expansion for Bessel functions for large order.
+
+    def test_hyp1f1(self):
+        def mpmath_hyp1f1(a, b, x):
+            try:
+                return mpmath.hyp1f1(a, b, x)
+            except ZeroDivisionError:
+                return np.inf
+
+        assert_mpmath_equal(
+            sc.hyp1f1,
+            mpmath_hyp1f1,
+            [Arg(-50, 50), Arg(1, 50, inclusive_a=False), Arg(-50, 50)],
+            n=500,
+            nan_ok=False,
+        )
+
+    @pytest.mark.xfail(run=False)
+    def test_hyp1f1_complex(self):
+        assert_mpmath_equal(
+            inf_to_nan(lambda a, b, x: sc.hyp1f1(a.real, b.real, x)),
+            exception_to_nan(lambda a, b, x: mpmath.hyp1f1(a, b, x, **HYPERKW)),
+            [Arg(-1e3, 1e3), Arg(-1e3, 1e3), ComplexArg()],
+            n=2000,
+        )
+
+    @nonfunctional_tooslow
+    def test_hyp2f1_complex(self):
+        # SciPy's hyp2f1 seems to have performance and accuracy problems
+        assert_mpmath_equal(
+            lambda a, b, c, x: sc.hyp2f1(a.real, b.real, c.real, x),
+            exception_to_nan(lambda a, b, c, x: mpmath.hyp2f1(a, b, c, x, **HYPERKW)),
+            [Arg(-1e2, 1e2), Arg(-1e2, 1e2), Arg(-1e2, 1e2), ComplexArg()],
+            n=10,
+        )
+
+    @pytest.mark.xfail(run=False)
+    def test_hyperu(self):
+        assert_mpmath_equal(
+            sc.hyperu,
+            exception_to_nan(lambda a, b, x: mpmath.hyperu(a, b, x, **HYPERKW)),
+            [Arg(), Arg(), Arg()],
+        )
+
+    @pytest.mark.xfail_on_32bit("mpmath issue gh-342: "
+                                "unsupported operand mpz, long for pow")
+    def test_igam_fac(self):
+        def mp_igam_fac(a, x):
+            return mpmath.power(x, a)*mpmath.exp(-x)/mpmath.gamma(a)
+
+        assert_mpmath_equal(
+            _igam_fac,
+            mp_igam_fac,
+            [Arg(0, 1e14, inclusive_a=False), Arg(0, 1e14)],
+            rtol=1e-10,
+            dps=29,
+        )
+
+    def test_j0(self):
+        # The Bessel function at large arguments is j0(x) ~ cos(x + phi)/sqrt(x)
+        # and at large arguments the phase of the cosine loses precision.
+        #
+        # This is numerically expected behavior, so we compare only up to
+        # 1e8 = 1e15 * 1e-7
+        assert_mpmath_equal(sc.j0, mpmath.j0, [Arg(-1e3, 1e3)])
+        assert_mpmath_equal(sc.j0, mpmath.j0, [Arg(-1e8, 1e8)], rtol=1e-5)
+
+    def test_j1(self):
+        # See comment in test_j0
+        assert_mpmath_equal(sc.j1, mpmath.j1, [Arg(-1e3, 1e3)])
+        assert_mpmath_equal(sc.j1, mpmath.j1, [Arg(-1e8, 1e8)], rtol=1e-5)
+
+    @pytest.mark.xfail(run=False)
+    def test_jacobi(self):
+        assert_mpmath_equal(
+            sc.eval_jacobi,
+            exception_to_nan(lambda a, b, c, x: mpmath.jacobi(a, b, c, x, **HYPERKW)),
+            [Arg(), Arg(), Arg(), Arg()],
+        )
+        assert_mpmath_equal(
+            lambda n, b, c, x: sc.eval_jacobi(int(n), b, c, x),
+            exception_to_nan(lambda a, b, c, x: mpmath.jacobi(a, b, c, x, **HYPERKW)),
+            [IntArg(), Arg(), Arg(), Arg()],
+        )
+
+    def test_jacobi_int(self):
+        # Redefine functions to deal with numerical + mpmath issues
+        def jacobi(n, a, b, x):
+            # Mpmath does not handle n=0 case always correctly
+            if n == 0:
+                return 1.0
+            return mpmath.jacobi(n, a, b, x)
+        assert_mpmath_equal(
+            lambda n, a, b, x: sc.eval_jacobi(int(n), a, b, x),
+            lambda n, a, b, x: exception_to_nan(jacobi)(n, a, b, x, **HYPERKW),
+            [IntArg(), Arg(), Arg(), Arg()],
+            n=4095,
+            dps=50,
+        )
+
+    def test_kei(self):
+        def kei(x):
+            if x == 0:
+                # work around mpmath issue at x=0
+                return -pi/4
+            return exception_to_nan(mpmath.kei)(0, x, **HYPERKW)
+        assert_mpmath_equal(sc.kei, kei, [Arg(-1e30, 1e30)], n=1000)
+
+    def test_ker(self):
+        assert_mpmath_equal(
+            sc.ker,
+            exception_to_nan(lambda x: mpmath.ker(0, x, **HYPERKW)),
+            [Arg(-1e30, 1e30)],
+            n=1000,
+        )
+
+    @nonfunctional_tooslow
+    def test_laguerre(self):
+        assert_mpmath_equal(
+            trace_args(sc.eval_laguerre),
+            lambda n, x: exception_to_nan(mpmath.laguerre)(n, x, **HYPERKW),
+            [Arg(), Arg()],
+        )
+
+    def test_laguerre_int(self):
+        assert_mpmath_equal(
+            lambda n, x: sc.eval_laguerre(int(n), x),
+            lambda n, x: exception_to_nan(mpmath.laguerre)(n, x, **HYPERKW),
+            [IntArg(), Arg()],
+            n=20000,
+        )
+
+    @pytest.mark.xfail_on_32bit("see gh-3551 for bad points")
+    def test_lambertw_real(self):
+        assert_mpmath_equal(
+            lambda x, k: sc.lambertw(x, int(k.real)),
+            lambda x, k: mpmath.lambertw(x, int(k.real)),
+            [ComplexArg(-np.inf, np.inf), IntArg(0, 10)],
+            rtol=1e-13, nan_ok=False,
+        )
+
+    def test_lanczos_sum_expg_scaled(self):
+        maxgamma = 171.624376956302725
+        e = np.exp(1)
+        g = 6.024680040776729583740234375
+
+        def gamma(x):
+            with np.errstate(over='ignore'):
+                fac = ((x + g - 0.5)/e)**(x - 0.5)
+                if fac != np.inf:
+                    res = fac*_lanczos_sum_expg_scaled(x)
+                else:
+                    fac = ((x + g - 0.5)/e)**(0.5*(x - 0.5))
+                    res = fac*_lanczos_sum_expg_scaled(x)
+                    res *= fac
+            return res
+
+        assert_mpmath_equal(
+            gamma,
+            mpmath.gamma,
+            [Arg(0, maxgamma, inclusive_a=False)],
+            rtol=1e-13,
+        )
+
+    @nonfunctional_tooslow
+    def test_legendre(self):
+        assert_mpmath_equal(sc.eval_legendre, mpmath.legendre, [Arg(), Arg()])
+
+    def test_legendre_int(self):
+        assert_mpmath_equal(
+            lambda n, x: sc.eval_legendre(int(n), x),
+            lambda n, x: exception_to_nan(mpmath.legendre)(n, x, **HYPERKW),
+            [IntArg(), Arg()],
+            n=20000,
+        )
+
+        # Check the small-x expansion
+        assert_mpmath_equal(
+            lambda n, x: sc.eval_legendre(int(n), x),
+            lambda n, x: exception_to_nan(mpmath.legendre)(n, x, **HYPERKW),
+            [IntArg(), FixedArg(np.logspace(-30, -4, 20))],
+        )
+
+    @pytest.mark.xfail(run=False, reason="apparently picks wrong function at |z| > 1")
+    def test_legenq(self):
+        def lqnm(n, m, z):
+            return sc.lqmn(m, n, z)[0][-1,-1]
+
+        def legenq(n, m, z):
+            if abs(z) < 1e-15:
+                # mpmath has bad performance here
+                return np.nan
+            return exception_to_nan(mpmath.legenq)(n, m, z, type=2)
+
+        assert_mpmath_equal(
+            lqnm,
+            legenq,
+            [IntArg(0, 100), IntArg(0, 100), Arg()],
+        )
+
+    @nonfunctional_tooslow
+    def test_legenq_complex(self):
+        def lqnm(n, m, z):
+            return sc.lqmn(int(m.real), int(n.real), z)[0][-1,-1]
+
+        def legenq(n, m, z):
+            if abs(z) < 1e-15:
+                # mpmath has bad performance here
+                return np.nan
+            return exception_to_nan(mpmath.legenq)(int(n.real), int(m.real), z, type=2)
+
+        assert_mpmath_equal(
+            lqnm,
+            legenq,
+            [IntArg(0, 100), IntArg(0, 100), ComplexArg()],
+            n=100,
+        )
+
+    def test_lgam1p(self):
+        def param_filter(x):
+            # Filter the poles
+            return np.where((np.floor(x) == x) & (x <= 0), False, True)
+
+        def mp_lgam1p(z):
+            # The real part of loggamma is log(|gamma(z)|)
+            return mpmath.loggamma(1 + z).real
+
+        assert_mpmath_equal(
+            _lgam1p,
+            mp_lgam1p,
+            [Arg()],
+            rtol=1e-13,
+            dps=100,
+            param_filter=param_filter,
+        )
+
+    def test_loggamma(self):
+        def mpmath_loggamma(z):
+            try:
+                res = mpmath.loggamma(z)
+            except ValueError:
+                res = complex(np.nan, np.nan)
+            return res
+
+        assert_mpmath_equal(
+            sc.loggamma,
+            mpmath_loggamma,
+            [ComplexArg()],
+            nan_ok=False,
+            distinguish_nan_and_inf=False,
+            rtol=5e-14,
+        )
+
+    @pytest.mark.xfail(run=False)
+    def test_pcfd(self):
+        def pcfd(v, x):
+            return sc.pbdv(v, x)[0]
+        assert_mpmath_equal(
+            pcfd,
+            exception_to_nan(lambda v, x: mpmath.pcfd(v, x, **HYPERKW)),
+            [Arg(), Arg()],
+        )
+
+    @pytest.mark.xfail(run=False, reason="it's not the same as the mpmath function --- "
+                                         "maybe different definition?")
+    def test_pcfv(self):
+        def pcfv(v, x):
+            return sc.pbvv(v, x)[0]
+        assert_mpmath_equal(
+            pcfv,
+            lambda v, x: time_limited()(exception_to_nan(mpmath.pcfv))(v, x, **HYPERKW),
+            [Arg(), Arg()],
+            n=1000,
+        )
+
+    def test_pcfw(self):
+        def pcfw(a, x):
+            return sc.pbwa(a, x)[0]
+
+        def dpcfw(a, x):
+            return sc.pbwa(a, x)[1]
+
+        def mpmath_dpcfw(a, x):
+            return mpmath.diff(mpmath.pcfw, (a, x), (0, 1))
+
+        # The Zhang and Jin implementation only uses Taylor series and
+        # is thus accurate in only a very small range.
+        assert_mpmath_equal(
+            pcfw,
+            mpmath.pcfw,
+            [Arg(-5, 5), Arg(-5, 5)],
+            rtol=2e-8,
+            n=100,
+        )
+
+        assert_mpmath_equal(
+            dpcfw,
+            mpmath_dpcfw,
+            [Arg(-5, 5), Arg(-5, 5)],
+            rtol=2e-9,
+            n=100,
+        )
+
+    @pytest.mark.xfail(run=False,
+                       reason="issues at large arguments (atol OK, rtol not) "
+                              "and <eps-close to z=0")
+    def test_polygamma(self):
+        assert_mpmath_equal(
+            sc.polygamma,
+            time_limited()(exception_to_nan(mpmath.polygamma)),
+            [IntArg(0, 1000), Arg()],
+        )
+
+    def test_rgamma(self):
+        assert_mpmath_equal(
+            sc.rgamma,
+            mpmath.rgamma,
+            [Arg(-8000, np.inf)],
+            n=5000,
+            nan_ok=False,
+            ignore_inf_sign=True,
+        )
+
+    def test_rgamma_complex(self):
+        assert_mpmath_equal(
+            sc.rgamma,
+            exception_to_nan(mpmath.rgamma),
+            [ComplexArg()],
+            rtol=5e-13,
+        )
+
+    @pytest.mark.xfail(reason=("see gh-3551 for bad points on 32 bit "
+                               "systems and gh-8095 for another bad "
+                               "point"))
+    def test_rf(self):
+        if version.parse(mpmath.__version__) >= version.Version("1.0.0"):
+            # no workarounds needed
+            mppoch = mpmath.rf
+        else:
+            def mppoch(a, m):
+                # deal with cases where the result in double precision
+                # hits exactly a non-positive integer, but the
+                # corresponding extended-precision mpf floats don't
+                if float(a + m) == int(a + m) and float(a + m) <= 0:
+                    a = mpmath.mpf(a)
+                    m = int(a + m) - a
+                return mpmath.rf(a, m)
+
+        assert_mpmath_equal(sc.poch, mppoch, [Arg(), Arg()], dps=400)
+
+    def test_sinpi(self):
+        eps = np.finfo(float).eps
+        assert_mpmath_equal(
+            _sinpi,
+            mpmath.sinpi,
+            [Arg()],
+            nan_ok=False,
+            rtol=2*eps,
+        )
+
+    def test_sinpi_complex(self):
+        assert_mpmath_equal(
+            _sinpi,
+            mpmath.sinpi,
+            [ComplexArg()],
+            nan_ok=False,
+            rtol=2e-14,
+        )
+
+    def test_shi(self):
+        def shi(x):
+            return sc.shichi(x)[0]
+        assert_mpmath_equal(shi, mpmath.shi, [Arg()])
+        # check asymptotic series cross-over
+        assert_mpmath_equal(shi, mpmath.shi, [FixedArg([88 - 1e-9, 88, 88 + 1e-9])])
+
+    def test_shi_complex(self):
+        def shi(z):
+            return sc.shichi(z)[0]
+        # shi oscillates as Im[z] -> +- inf, so limit range
+        assert_mpmath_equal(
+            shi,
+            mpmath.shi,
+            [ComplexArg(complex(-np.inf, -1e8), complex(np.inf, 1e8))],
+            rtol=1e-12,
+        )
+
+    def test_si(self):
+        def si(x):
+            return sc.sici(x)[0]
+        assert_mpmath_equal(si, mpmath.si, [Arg()])
+
+    def test_si_complex(self):
+        def si(z):
+            return sc.sici(z)[0]
+        # si oscillates as Re[z] -> +- inf, so limit range
+        assert_mpmath_equal(
+            si,
+            mpmath.si,
+            [ComplexArg(complex(-1e8, -np.inf), complex(1e8, np.inf))],
+            rtol=1e-12,
+        )
+
+    def test_spence(self):
+        # mpmath uses a different convention for the dilogarithm
+        def dilog(x):
+            return mpmath.polylog(2, 1 - x)
+        # Spence has a branch cut on the negative real axis
+        assert_mpmath_equal(
+            sc.spence,
+            exception_to_nan(dilog),
+            [Arg(0, np.inf)],
+            rtol=1e-14,
+        )
+
+    def test_spence_complex(self):
+        def dilog(z):
+            return mpmath.polylog(2, 1 - z)
+        assert_mpmath_equal(
+            sc.spence,
+            exception_to_nan(dilog),
+            [ComplexArg()],
+            rtol=1e-14,
+        )
+
+    def test_struveh(self):
+        assert_mpmath_equal(
+            sc.struve,
+            exception_to_nan(mpmath.struveh),
+            [Arg(-1e4, 1e4), Arg(0, 1e4)],
+            rtol=5e-10,
+        )
+
+    def test_struvel(self):
+        def mp_struvel(v, z):
+            if v < 0 and z < -v and abs(v) > 1000:
+                # larger DPS needed for correct results
+                old_dps = mpmath.mp.dps
+                try:
+                    mpmath.mp.dps = 500
+                    return mpmath.struvel(v, z)
+                finally:
+                    mpmath.mp.dps = old_dps
+            return mpmath.struvel(v, z)
+
+        assert_mpmath_equal(
+            sc.modstruve,
+            exception_to_nan(mp_struvel),
+            [Arg(-1e4, 1e4), Arg(0, 1e4)],
+            rtol=5e-10,
+            ignore_inf_sign=True,
+        )
+
+    def test_wrightomega_real(self):
+        def mpmath_wrightomega_real(x):
+            return mpmath.lambertw(mpmath.exp(x), mpmath.mpf('-0.5'))
+
+        # For x < -1000 the Wright Omega function is just 0 to double
+        # precision, and for x > 1e21 it is just x to double
+        # precision.
+        assert_mpmath_equal(
+            sc.wrightomega,
+            mpmath_wrightomega_real,
+            [Arg(-1000, 1e21)],
+            rtol=5e-15,
+            atol=0,
+            nan_ok=False,
+        )
+
+    def test_wrightomega(self):
+        assert_mpmath_equal(
+            sc.wrightomega,
+            lambda z: _mpmath_wrightomega(z, 25),
+            [ComplexArg()],
+            rtol=1e-14,
+            nan_ok=False,
+        )
+
+    def test_hurwitz_zeta(self):
+        assert_mpmath_equal(
+            sc.zeta,
+            exception_to_nan(mpmath.zeta),
+            [Arg(a=1, b=1e10, inclusive_a=False), Arg(a=0, inclusive_a=False)],
+        )
+
+    def test_riemann_zeta(self):
+        assert_mpmath_equal(
+            sc.zeta,
+            lambda x: mpmath.zeta(x) if x != 1 else mpmath.inf,
+            [Arg(-100, 100)],
+            nan_ok=False,
+            rtol=5e-13,
+        )
+
+    def test_zetac(self):
+        assert_mpmath_equal(
+            sc.zetac,
+            lambda x: mpmath.zeta(x) - 1 if x != 1 else mpmath.inf,
+            [Arg(-100, 100)],
+            nan_ok=False,
+            dps=45,
+            rtol=5e-13,
+        )
+
+    def test_boxcox(self):
+
+        def mp_boxcox(x, lmbda):
+            x = mpmath.mp.mpf(x)
+            lmbda = mpmath.mp.mpf(lmbda)
+            if lmbda == 0:
+                return mpmath.mp.log(x)
+            else:
+                return mpmath.mp.powm1(x, lmbda) / lmbda
+
+        assert_mpmath_equal(
+            sc.boxcox,
+            exception_to_nan(mp_boxcox),
+            [Arg(a=0, inclusive_a=False), Arg()],
+            n=200,
+            dps=60,
+            rtol=1e-13,
+        )
+
+    def test_boxcox1p(self):
+
+        def mp_boxcox1p(x, lmbda):
+            x = mpmath.mp.mpf(x)
+            lmbda = mpmath.mp.mpf(lmbda)
+            one = mpmath.mp.mpf(1)
+            if lmbda == 0:
+                return mpmath.mp.log(one + x)
+            else:
+                return mpmath.mp.powm1(one + x, lmbda) / lmbda
+
+        assert_mpmath_equal(
+            sc.boxcox1p,
+            exception_to_nan(mp_boxcox1p),
+            [Arg(a=-1, inclusive_a=False), Arg()],
+            n=200,
+            dps=60,
+            rtol=1e-13,
+        )
+
+    def test_spherical_jn(self):
+        def mp_spherical_jn(n, z):
+            arg = mpmath.mpmathify(z)
+            out = (mpmath.besselj(n + mpmath.mpf(1)/2, arg) /
+                   mpmath.sqrt(2*arg/mpmath.pi))
+            if arg.imag == 0:
+                return out.real
+            else:
+                return out
+
+        assert_mpmath_equal(
+            lambda n, z: sc.spherical_jn(int(n), z),
+            exception_to_nan(mp_spherical_jn),
+            [IntArg(0, 200), Arg(-1e8, 1e8)],
+            dps=300,
+            # underflow of `spherical_jn` is a bit premature; see gh-21629
+            param_filter=(None, lambda z: np.abs(z) > 1e-20),
+        )
+
+    def test_spherical_jn_complex(self):
+        def mp_spherical_jn(n, z):
+            arg = mpmath.mpmathify(z)
+            out = (mpmath.besselj(n + mpmath.mpf(1)/2, arg) /
+                   mpmath.sqrt(2*arg/mpmath.pi))
+            if arg.imag == 0:
+                return out.real
+            else:
+                return out
+
+        assert_mpmath_equal(
+            lambda n, z: sc.spherical_jn(int(n.real), z),
+            exception_to_nan(mp_spherical_jn),
+            [IntArg(0, 200), ComplexArg()]
+        )
+
+    def test_spherical_yn(self):
+        def mp_spherical_yn(n, z):
+            arg = mpmath.mpmathify(z)
+            out = (mpmath.bessely(n + mpmath.mpf(1)/2, arg) /
+                   mpmath.sqrt(2*arg/mpmath.pi))
+            if arg.imag == 0:
+                return out.real
+            else:
+                return out
+
+        assert_mpmath_equal(
+            lambda n, z: sc.spherical_yn(int(n), z),
+            exception_to_nan(mp_spherical_yn),
+            [IntArg(0, 200), Arg(-1e10, 1e10)],
+            dps=100,
+        )
+
+    def test_spherical_yn_complex(self):
+        def mp_spherical_yn(n, z):
+            arg = mpmath.mpmathify(z)
+            out = (mpmath.bessely(n + mpmath.mpf(1)/2, arg) /
+                   mpmath.sqrt(2*arg/mpmath.pi))
+            if arg.imag == 0:
+                return out.real
+            else:
+                return out
+
+        assert_mpmath_equal(
+            lambda n, z: sc.spherical_yn(int(n.real), z),
+            exception_to_nan(mp_spherical_yn),
+            [IntArg(0, 200), ComplexArg()],
+        )
+
+    def test_spherical_in(self):
+        def mp_spherical_in(n, z):
+            arg = mpmath.mpmathify(z)
+            out = (mpmath.besseli(n + mpmath.mpf(1)/2, arg) /
+                   mpmath.sqrt(2*arg/mpmath.pi))
+            if arg.imag == 0:
+                return out.real
+            else:
+                return out
+
+        assert_mpmath_equal(
+            lambda n, z: sc.spherical_in(int(n), z),
+            exception_to_nan(mp_spherical_in),
+            [IntArg(0, 200), Arg()],
+            dps=200,
+            atol=10**(-278),
+        )
+
+    def test_spherical_in_complex(self):
+        def mp_spherical_in(n, z):
+            arg = mpmath.mpmathify(z)
+            out = (mpmath.besseli(n + mpmath.mpf(1)/2, arg) /
+                   mpmath.sqrt(2*arg/mpmath.pi))
+            if arg.imag == 0:
+                return out.real
+            else:
+                return out
+
+        assert_mpmath_equal(
+            lambda n, z: sc.spherical_in(int(n.real), z),
+            exception_to_nan(mp_spherical_in),
+            [IntArg(0, 200), ComplexArg()],
+        )
+
+    def test_spherical_kn(self):
+        def mp_spherical_kn(n, z):
+            arg = mpmath.mpmathify(z)
+            out = (mpmath.besselk(n + mpmath.mpf(1)/2, arg) /
+                   mpmath.sqrt(2*arg/mpmath.pi))
+            if mpmath.mpmathify(z).imag == 0:
+                return out.real
+            else:
+                return out
+
+        assert_mpmath_equal(
+            lambda n, z: sc.spherical_kn(int(n), z),
+            exception_to_nan(mp_spherical_kn),
+            [IntArg(0, 150), Arg()],
+            dps=100,
+        )
+
+    @pytest.mark.xfail(run=False,
+                       reason="Accuracy issues near z = -1 inherited from kv.")
+    def test_spherical_kn_complex(self):
+        def mp_spherical_kn(n, z):
+            arg = mpmath.mpmathify(z)
+            out = (mpmath.besselk(n + mpmath.mpf(1)/2, arg) /
+                   mpmath.sqrt(2*arg/mpmath.pi))
+            if arg.imag == 0:
+                return out.real
+            else:
+                return out
+
+        assert_mpmath_equal(
+            lambda n, z: sc.spherical_kn(int(n.real), z),
+            exception_to_nan(mp_spherical_kn),
+            [IntArg(0, 200), ComplexArg()],
+            dps=200,
+        )
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_nan_inputs.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_nan_inputs.py
new file mode 100644
index 0000000000000000000000000000000000000000..76af3032b62484b630713631e23d95110161b759
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_nan_inputs.py
@@ -0,0 +1,72 @@
+"""Test how the ufuncs in special handle nan inputs.
+
+"""
+import warnings
+
+from collections.abc import Callable
+
+import numpy as np
+from numpy.testing import assert_array_equal, assert_
+import pytest
+import scipy.special as sc
+
+
+KNOWNFAILURES: dict[str, Callable] = {}
+
+POSTPROCESSING: dict[str, Callable] = {}
+
+
+def _get_ufuncs():
+    ufuncs = []
+    ufunc_names = []
+    for name in sorted(sc.__dict__):
+        obj = sc.__dict__[name]
+        if not isinstance(obj, np.ufunc):
+            continue
+        msg = KNOWNFAILURES.get(obj)
+        if msg is None:
+            ufuncs.append(obj)
+            ufunc_names.append(name)
+        else:
+            fail = pytest.mark.xfail(run=False, reason=msg)
+            ufuncs.append(pytest.param(obj, marks=fail))
+            ufunc_names.append(name)
+    return ufuncs, ufunc_names
+
+
+UFUNCS, UFUNC_NAMES = _get_ufuncs()
+
+
+@pytest.mark.parametrize("func", UFUNCS, ids=UFUNC_NAMES)
+def test_nan_inputs(func):
+    args = (np.nan,)*func.nin
+    with warnings.catch_warnings():
+        # Ignore warnings about unsafe casts from legacy wrappers
+        warnings.filterwarnings(
+            "ignore",
+            "floating point number truncated to an integer",
+            RuntimeWarning
+        )
+        try:
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore", DeprecationWarning)
+                res = func(*args)
+        except TypeError:
+            # One of the arguments doesn't take real inputs
+            return
+    if func in POSTPROCESSING:
+        res = POSTPROCESSING[func](*res)
+
+    msg = f"got {res} instead of nan"
+    assert_array_equal(np.isnan(res), True, err_msg=msg)
+
+
+def test_legacy_cast():
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore",
+            "floating point number truncated to an integer",
+            RuntimeWarning
+        )
+        res = sc.bdtrc(np.nan, 1, 0.5)
+        assert_(np.isnan(res))
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ndtr.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ndtr.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba9b689b34384585cc65204000febcb99c910d55
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ndtr.py
@@ -0,0 +1,77 @@
+import numpy as np
+from numpy.testing import assert_equal, assert_allclose
+import scipy.special as sc
+
+
+def test_ndtr():
+    assert_equal(sc.ndtr(0), 0.5)
+    assert_allclose(sc.ndtr(1), 0.8413447460685429)
+
+
+class TestNdtri:
+
+    def test_zero(self):
+        assert sc.ndtri(0.5) == 0.0
+
+    def test_asymptotes(self):
+        assert_equal(sc.ndtri([0.0, 1.0]), [-np.inf, np.inf])
+
+    def test_outside_of_domain(self):
+        assert all(np.isnan(sc.ndtri([-1.5, 1.5])))
+
+
+class TestLogNdtr:
+
+    # The expected values in these tests were computed with mpmath:
+    #
+    #   def log_ndtr_mp(x):
+    #       return mpmath.log(mpmath.ncdf(x))
+    #
+
+    def test_log_ndtr_moderate_le8(self):
+        x = np.array([-0.75, -0.25, 0, 0.5, 1.5, 2.5, 3, 4, 5, 7, 8])
+        expected = np.array([-1.4844482299196562,
+                             -0.9130617648111351,
+                             -0.6931471805599453,
+                             -0.3689464152886564,
+                             -0.06914345561223398,
+                             -0.006229025485860002,
+                             -0.0013508099647481938,
+                             -3.167174337748927e-05,
+                             -2.866516129637636e-07,
+                             -1.279812543886654e-12,
+                             -6.220960574271786e-16])
+        y = sc.log_ndtr(x)
+        assert_allclose(y, expected, rtol=1e-14)
+
+    def test_log_ndtr_values_8_16(self):
+        x = np.array([8.001, 8.06, 8.15, 8.5, 10, 12, 14, 16])
+        expected = [-6.170639424817055e-16,
+                    -3.814722443652823e-16,
+                    -1.819621363526629e-16,
+                    -9.479534822203318e-18,
+                    -7.619853024160525e-24,
+                    -1.776482112077679e-33,
+                    -7.7935368191928e-45,
+                    -6.388754400538087e-58]
+        y = sc.log_ndtr(x)
+        assert_allclose(y, expected, rtol=5e-14)
+
+    def test_log_ndtr_values_16_31(self):
+        x = np.array([16.15, 20.3, 21.4, 26.2, 30.9])
+        expected = [-5.678084565148492e-59,
+                    -6.429244467698346e-92,
+                    -6.680402412553295e-102,
+                    -1.328698078458869e-151,
+                    -5.972288641838264e-210]
+        y = sc.log_ndtr(x)
+        assert_allclose(y, expected, rtol=2e-13)
+
+    def test_log_ndtr_values_gt31(self):
+        x = np.array([31.6, 32.8, 34.9, 37.1])
+        expected = [-1.846036234858162e-219,
+                    -2.9440539964066835e-236,
+                    -3.71721649450857e-267,
+                    -1.4047119663106221e-301]
+        y = sc.log_ndtr(x)
+        assert_allclose(y, expected, rtol=3e-13)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ndtri_exp.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ndtri_exp.py
new file mode 100644
index 0000000000000000000000000000000000000000..82a9fbd3bcda117770e00018facda3f56630a6bc
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ndtri_exp.py
@@ -0,0 +1,94 @@
+import pytest
+import numpy as np
+from numpy.testing import assert_equal, assert_allclose
+from scipy.special import log_ndtr, ndtri_exp
+from scipy.special._testutils import assert_func_equal
+
+
+def log_ndtr_ndtri_exp(y):
+    return log_ndtr(ndtri_exp(y))
+
+
+@pytest.fixture(scope="class")
+def uniform_random_points():
+    random_state = np.random.RandomState(1234)
+    points = random_state.random_sample(1000)
+    return points
+
+
+class TestNdtriExp:
+    """Tests that ndtri_exp is sufficiently close to an inverse of log_ndtr.
+
+    We have separate tests for the five intervals (-inf, -10),
+    [-10, -2), [-2, -0.14542), [-0.14542, -1e-6), and [-1e-6, 0).
+    ndtri_exp(y) is computed in three different ways depending on if y
+    is in (-inf, -2), [-2, log(1 - exp(-2))], or [log(1 - exp(-2), 0).
+    Each of these intervals is given its own test with two additional tests
+    for handling very small values and values very close to zero.
+    """
+
+    @pytest.mark.parametrize(
+        "test_input", [-1e1, -1e2, -1e10, -1e20, -np.finfo(float).max]
+    )
+    def test_very_small_arg(self, test_input, uniform_random_points):
+        scale = test_input
+        points = scale * (0.5 * uniform_random_points + 0.5)
+        assert_func_equal(
+            log_ndtr_ndtri_exp,
+            lambda y: y, points,
+            rtol=1e-14,
+            nan_ok=True
+        )
+
+    @pytest.mark.parametrize(
+        "interval,expected_rtol",
+        [
+            ((-10, -2), 1e-14),
+            ((-2, -0.14542), 1e-12),
+            ((-0.14542, -1e-6), 1e-10),
+            ((-1e-6, 0), 1e-6),
+        ],
+    )
+    def test_in_interval(self, interval, expected_rtol, uniform_random_points):
+        left, right = interval
+        points = (right - left) * uniform_random_points + left
+        assert_func_equal(
+            log_ndtr_ndtri_exp,
+            lambda y: y, points,
+            rtol=expected_rtol,
+            nan_ok=True
+        )
+
+    def test_extreme(self):
+        # bigneg is not quite the largest negative double precision value.
+        # Here's why:
+        # The round-trip calculation
+        #    y = ndtri_exp(bigneg)
+        #    bigneg2 = log_ndtr(y)
+        # where bigneg is a very large negative value, would--with infinite
+        # precision--result in bigneg2 == bigneg.  When bigneg is large enough,
+        # y is effectively equal to -sqrt(2)*sqrt(-bigneg), and log_ndtr(y) is
+        # effectively -(y/sqrt(2))**2.  If we use bigneg = np.finfo(float).min,
+        # then by construction, the theoretical value is the most negative
+        # finite value that can be represented with 64 bit float point.  This
+        # means tiny changes in how the computation proceeds can result in the
+        # return value being -inf.  (E.g. changing the constant representation
+        # of 1/sqrt(2) from 0.7071067811865475--which is the value returned by
+        # 1/np.sqrt(2)--to 0.7071067811865476--which is the most accurate 64
+        # bit floating point representation of 1/sqrt(2)--results in the
+        # round-trip that starts with np.finfo(float).min returning -inf.  So
+        # we'll move the bigneg value a few ULPs towards 0 to avoid this
+        # sensitivity.
+        # Use the reduce method to apply nextafter four times.
+        bigneg = np.nextafter.reduce([np.finfo(float).min, 0, 0, 0, 0])
+        # tinyneg is approx. -2.225e-308.
+        tinyneg = -np.finfo(float).tiny
+        x = np.array([tinyneg, bigneg])
+        result = log_ndtr_ndtri_exp(x)
+        assert_allclose(result, x, rtol=1e-12)
+
+    def test_asymptotes(self):
+        assert_equal(ndtri_exp([-np.inf, 0.0]), [-np.inf, np.inf])
+
+    def test_outside_domain(self):
+        assert np.isnan(ndtri_exp(1.0))
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_orthogonal.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_orthogonal.py
new file mode 100644
index 0000000000000000000000000000000000000000..130d52073b3da0a27fc7a15ca18506ca9094942f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_orthogonal.py
@@ -0,0 +1,823 @@
+import pytest
+from pytest import raises as assert_raises
+
+import numpy as np
+from numpy import array, sqrt
+from numpy.testing import assert_equal, assert_allclose
+
+from scipy import integrate
+import scipy.special as sc
+from scipy.special import gamma
+import scipy.special._orthogonal as orth
+
+
+class TestCheby:
+    def test_chebyc(self):
+        C0 = orth.chebyc(0)
+        C1 = orth.chebyc(1)
+        with np.errstate(all='ignore'):
+            C2 = orth.chebyc(2)
+            C3 = orth.chebyc(3)
+            C4 = orth.chebyc(4)
+            C5 = orth.chebyc(5)
+
+        assert_allclose(C0.c, [2], atol=1.5e-13, rtol=0)
+        assert_allclose(C1.c, [1, 0], atol=1.5e-13, rtol=0)
+        assert_allclose(C2.c, [1, 0, -2], atol=1.5e-13, rtol=0)
+        assert_allclose(C3.c, [1, 0, -3, 0], atol=1.5e-13, rtol=0)
+        assert_allclose(C4.c, [1, 0, -4, 0, 2], atol=1.5e-13, rtol=0)
+        assert_allclose(C5.c, [1, 0, -5, 0, 5, 0],atol=1.5e-13, rtol=0)
+
+    def test_chebys(self):
+        S0 = orth.chebys(0)
+        S1 = orth.chebys(1)
+        S2 = orth.chebys(2)
+        S3 = orth.chebys(3)
+        S4 = orth.chebys(4)
+        S5 = orth.chebys(5)
+        assert_allclose(S0.c, [1], atol=1.5e-13, rtol=0)
+        assert_allclose(S1.c, [1, 0], atol=1.5e-13, rtol=0)
+        assert_allclose(S2.c, [1, 0, -1], atol=1.5e-13, rtol=0)
+        assert_allclose(S3.c, [1, 0, -2, 0], atol=1.5e-13, rtol=0)
+        assert_allclose(S4.c, [1, 0, -3, 0, 1], atol=1.5e-13, rtol=0)
+        assert_allclose(S5.c, [1, 0, -4, 0, 3, 0], atol=1.5e-13, rtol=0)
+
+    def test_chebyt(self):
+        T0 = orth.chebyt(0)
+        T1 = orth.chebyt(1)
+        T2 = orth.chebyt(2)
+        T3 = orth.chebyt(3)
+        T4 = orth.chebyt(4)
+        T5 = orth.chebyt(5)
+        assert_allclose(T0.c, [1], atol=1.5e-13, rtol=0)
+        assert_allclose(T1.c, [1, 0], atol=1.5e-13, rtol=0)
+        assert_allclose(T2.c, [2, 0, -1], atol=1.5e-13, rtol=0)
+        assert_allclose(T3.c, [4, 0, -3, 0], atol=1.5e-13, rtol=0)
+        assert_allclose(T4.c, [8, 0, -8, 0, 1], atol=1.5e-13, rtol=0)
+        assert_allclose(T5.c, [16, 0, -20, 0, 5, 0], atol=1.5e-13, rtol=0)
+
+    def test_chebyu(self):
+        U0 = orth.chebyu(0)
+        U1 = orth.chebyu(1)
+        U2 = orth.chebyu(2)
+        U3 = orth.chebyu(3)
+        U4 = orth.chebyu(4)
+        U5 = orth.chebyu(5)
+        assert_allclose(U0.c, [1], atol=1.5e-13, rtol=0)
+        assert_allclose(U1.c, [2, 0], atol=1.5e-13, rtol=0)
+        assert_allclose(U2.c, [4, 0, -1], atol=1.5e-13, rtol=0)
+        assert_allclose(U3.c, [8, 0, -4, 0], atol=1.5e-13, rtol=0)
+        assert_allclose(U4.c, [16, 0, -12, 0, 1], atol=1.5e-13, rtol=0)
+        assert_allclose(U5.c, [32, 0, -32, 0, 6, 0], atol=1.5e-13, rtol=0)
+
+
+class TestGegenbauer:
+
+    def test_gegenbauer(self):
+        a = 5*np.random.random() - 0.5
+        if np.any(a == 0):
+            a = -0.2
+        Ca0 = orth.gegenbauer(0,a)
+        Ca1 = orth.gegenbauer(1,a)
+        Ca2 = orth.gegenbauer(2,a)
+        Ca3 = orth.gegenbauer(3,a)
+        Ca4 = orth.gegenbauer(4,a)
+        Ca5 = orth.gegenbauer(5,a)
+
+        assert_allclose(Ca0.c, array([1]), atol=1.5e-13, rtol=0)
+        assert_allclose(Ca1.c, array([2*a, 0]), atol=1.5e-13, rtol=0)
+        assert_allclose(Ca2.c, array([2*a*(a + 1), 0, -a]),
+                        atol=1.5e-13, rtol=0)
+        assert_allclose(Ca3.c, array([4*sc.poch(a, 3), 0,-6*a*(a + 1), 0])/3.0,
+                        atol=1.5e-11, rtol=0)
+        assert_allclose(Ca4.c, array([4*sc.poch(a, 4), 0, -12*sc.poch(a, 3),
+                                      0, 3*a*(a + 1)])/6.0,
+                        atol=1.5e-11, rtol=0)
+        assert_allclose(Ca5.c, array([4*sc.poch(a, 5), 0, -20*sc.poch(a, 4),
+                                      0, 15*sc.poch(a, 3), 0])/15.0,
+                        atol=1.5e-11, rtol=0)
+
+    @pytest.mark.parametrize('a', [0, 1])
+    def test_n_zero_gh8888(self, a):
+        # gh-8888 reported that gegenbauer(0, 0) returns NaN polynomial
+        Cn0 = orth.gegenbauer(0, a)
+        assert_equal(Cn0.c, np.asarray([1.]))
+
+    def test_valid_alpha(self):
+        # Check input validation of `alpha`
+        message = '`alpha` must be a finite number greater...'
+        with pytest.raises(ValueError, match=message):
+            orth.gegenbauer(0, np.nan)
+        with pytest.raises(ValueError, match=message):
+            orth.gegenbauer(1, -0.5)
+        with pytest.raises(ValueError, match=message):
+            orth.gegenbauer(2, -np.inf)
+
+
+class TestHermite:
+    def test_hermite(self):
+        H0 = orth.hermite(0)
+        H1 = orth.hermite(1)
+        H2 = orth.hermite(2)
+        H3 = orth.hermite(3)
+        H4 = orth.hermite(4)
+        H5 = orth.hermite(5)
+        assert_allclose(H0.c, [1], atol=1.5e-13, rtol=0)
+        assert_allclose(H1.c, [2, 0], atol=1.5e-13, rtol=0)
+        assert_allclose(H2.c, [4, 0, -2], atol=1.5e-13, rtol=0)
+        assert_allclose(H3.c, [8, 0, -12, 0], atol=1.5e-13, rtol=0)
+        assert_allclose(H4.c, [16, 0, -48, 0, 12], atol=1.5e-12, rtol=0)
+        assert_allclose(H5.c, [32, 0, -160, 0, 120, 0], atol=1.5e-12, rtol=0)
+
+    def test_hermitenorm(self):
+        # He_n(x) = 2**(-n/2) H_n(x/sqrt(2))
+        psub = np.poly1d([1.0/sqrt(2),0])
+        H0 = orth.hermitenorm(0)
+        H1 = orth.hermitenorm(1)
+        H2 = orth.hermitenorm(2)
+        H3 = orth.hermitenorm(3)
+        H4 = orth.hermitenorm(4)
+        H5 = orth.hermitenorm(5)
+        he0 = orth.hermite(0)(psub)
+        he1 = orth.hermite(1)(psub) / sqrt(2)
+        he2 = orth.hermite(2)(psub) / 2.0
+        he3 = orth.hermite(3)(psub) / (2*sqrt(2))
+        he4 = orth.hermite(4)(psub) / 4.0
+        he5 = orth.hermite(5)(psub) / (4.0*sqrt(2))
+
+        assert_allclose(H0.c, he0.c, atol=1.5e-13, rtol=0)
+        assert_allclose(H1.c, he1.c, atol=1.5e-13, rtol=0)
+        assert_allclose(H2.c, he2.c, atol=1.5e-13, rtol=0)
+        assert_allclose(H3.c, he3.c, atol=1.5e-13, rtol=0)
+        assert_allclose(H4.c, he4.c, atol=1.5e-13, rtol=0)
+        assert_allclose(H5.c, he5.c, atol=1.5e-13, rtol=0)
+
+
+class TestShLegendre:
+    def test_sh_legendre(self):
+        # P*_n(x) = P_n(2x-1)
+        psub = np.poly1d([2,-1])
+        Ps0 = orth.sh_legendre(0)
+        Ps1 = orth.sh_legendre(1)
+        Ps2 = orth.sh_legendre(2)
+        Ps3 = orth.sh_legendre(3)
+        Ps4 = orth.sh_legendre(4)
+        Ps5 = orth.sh_legendre(5)
+        pse0 = orth.legendre(0)(psub)
+        pse1 = orth.legendre(1)(psub)
+        pse2 = orth.legendre(2)(psub)
+        pse3 = orth.legendre(3)(psub)
+        pse4 = orth.legendre(4)(psub)
+        pse5 = orth.legendre(5)(psub)
+        assert_allclose(Ps0.c, pse0.c, atol=1.5e-13, rtol=0)
+        assert_allclose(Ps1.c, pse1.c, atol=1.5e-13, rtol=0)
+        assert_allclose(Ps2.c, pse2.c, atol=1.5e-13, rtol=0)
+        assert_allclose(Ps3.c, pse3.c, atol=1.5e-13, rtol=0)
+        assert_allclose(Ps4.c, pse4.c, atol=1.5e-12, rtol=0)
+        assert_allclose(Ps5.c, pse5.c, atol=1.5e-12, rtol=0)
+
+
+class TestShChebyt:
+    def test_sh_chebyt(self):
+        # T*_n(x) = T_n(2x-1)
+        psub = np.poly1d([2,-1])
+        Ts0 = orth.sh_chebyt(0)
+        Ts1 = orth.sh_chebyt(1)
+        Ts2 = orth.sh_chebyt(2)
+        Ts3 = orth.sh_chebyt(3)
+        Ts4 = orth.sh_chebyt(4)
+        Ts5 = orth.sh_chebyt(5)
+        tse0 = orth.chebyt(0)(psub)
+        tse1 = orth.chebyt(1)(psub)
+        tse2 = orth.chebyt(2)(psub)
+        tse3 = orth.chebyt(3)(psub)
+        tse4 = orth.chebyt(4)(psub)
+        tse5 = orth.chebyt(5)(psub)
+        assert_allclose(Ts0.c, tse0.c, atol=1.5e-13, rtol=0)
+        assert_allclose(Ts1.c, tse1.c, atol=1.5e-13, rtol=0)
+        assert_allclose(Ts2.c, tse2.c, atol=1.5e-13, rtol=0)
+        assert_allclose(Ts3.c, tse3.c, atol=1.5e-13, rtol=0)
+        assert_allclose(Ts4.c, tse4.c, atol=1.5e-12, rtol=0)
+        assert_allclose(Ts5.c, tse5.c, atol=1.5e-12, rtol=0)
+
+
+class TestShChebyu:
+    def test_sh_chebyu(self):
+        # U*_n(x) = U_n(2x-1)
+        psub = np.poly1d([2,-1])
+        Us0 = orth.sh_chebyu(0)
+        Us1 = orth.sh_chebyu(1)
+        Us2 = orth.sh_chebyu(2)
+        Us3 = orth.sh_chebyu(3)
+        Us4 = orth.sh_chebyu(4)
+        Us5 = orth.sh_chebyu(5)
+        use0 = orth.chebyu(0)(psub)
+        use1 = orth.chebyu(1)(psub)
+        use2 = orth.chebyu(2)(psub)
+        use3 = orth.chebyu(3)(psub)
+        use4 = orth.chebyu(4)(psub)
+        use5 = orth.chebyu(5)(psub)
+        assert_allclose(Us0.c, use0.c, atol=1.5e-13, rtol=0)
+        assert_allclose(Us1.c, use1.c, atol=1.5e-13, rtol=0)
+        assert_allclose(Us2.c, use2.c, atol=1.5e-13, rtol=0)
+        assert_allclose(Us3.c, use3.c, atol=1.5e-13, rtol=0)
+        assert_allclose(Us4.c, use4.c, atol=1.5e-12, rtol=0)
+        assert_allclose(Us5.c, use5.c, atol=1.5e-11, rtol=0)
+
+
+class TestShJacobi:
+    def test_sh_jacobi(self):
+        # G^(p,q)_n(x) = n! gamma(n+p)/gamma(2*n+p) * P^(p-q,q-1)_n(2*x-1)
+        def conv(n, p):
+            return gamma(n + 1) * gamma(n + p) / gamma(2 * n + p)
+        psub = np.poly1d([2,-1])
+        q = 4 * np.random.random()
+        p = q-1 + 2*np.random.random()
+        # print("shifted jacobi p,q = ", p, q)
+        G0 = orth.sh_jacobi(0,p,q)
+        G1 = orth.sh_jacobi(1,p,q)
+        G2 = orth.sh_jacobi(2,p,q)
+        G3 = orth.sh_jacobi(3,p,q)
+        G4 = orth.sh_jacobi(4,p,q)
+        G5 = orth.sh_jacobi(5,p,q)
+        ge0 = orth.jacobi(0,p-q,q-1)(psub) * conv(0,p)
+        ge1 = orth.jacobi(1,p-q,q-1)(psub) * conv(1,p)
+        ge2 = orth.jacobi(2,p-q,q-1)(psub) * conv(2,p)
+        ge3 = orth.jacobi(3,p-q,q-1)(psub) * conv(3,p)
+        ge4 = orth.jacobi(4,p-q,q-1)(psub) * conv(4,p)
+        ge5 = orth.jacobi(5,p-q,q-1)(psub) * conv(5,p)
+
+        assert_allclose(G0.c, ge0.c, atol=1.5e-13, rtol=0)
+        assert_allclose(G1.c, ge1.c, atol=1.5e-13, rtol=0)
+        assert_allclose(G2.c, ge2.c, atol=1.5e-13, rtol=0)
+        assert_allclose(G3.c, ge3.c, atol=1.5e-13, rtol=0)
+        assert_allclose(G4.c, ge4.c, atol=1.5e-13, rtol=0)
+        assert_allclose(G5.c, ge5.c, atol=1.5e-13, rtol=0)
+
+
+class TestCall:
+    def test_call(self):
+        poly = []
+        for n in range(5):
+            poly.extend([x.strip() for x in
+                (f"""
+                orth.jacobi({n},0.3,0.9)
+                orth.sh_jacobi({n},0.3,0.9)
+                orth.genlaguerre({n},0.3)
+                orth.laguerre({n})
+                orth.hermite({n})
+                orth.hermitenorm({n})
+                orth.gegenbauer({n},0.3)
+                orth.chebyt({n})
+                orth.chebyu({n})
+                orth.chebyc({n})
+                orth.chebys({n})
+                orth.sh_chebyt({n})
+                orth.sh_chebyu({n})
+                orth.legendre({n})
+                orth.sh_legendre({n})
+                """).split()])
+        with np.errstate(all='ignore'):
+            for pstr in poly:
+                p = eval(pstr)
+                assert_allclose(p(0.315), np.poly1d(p.coef)(0.315),
+                                atol=1.5e-7, rtol=0, err_msg=pstr)
+
+
+class TestGenlaguerre:
+    def test_regression(self):
+        assert_equal(orth.genlaguerre(1, 1, monic=False)(0), 2.)
+        assert_equal(orth.genlaguerre(1, 1, monic=True)(0), -2.)
+        assert_equal(orth.genlaguerre(1, 1, monic=False), np.poly1d([-1, 2]))
+        assert_equal(orth.genlaguerre(1, 1, monic=True), np.poly1d([1, -2]))
+
+
+def verify_gauss_quad(root_func, eval_func, weight_func, a, b, N,
+                      rtol=1e-15, atol=5e-14):
+    # this test is copied from numpy's TestGauss in test_hermite.py
+    x, w, mu = root_func(N, True)
+
+    n = np.arange(N, dtype=np.dtype("long"))
+    v = eval_func(n[:,np.newaxis], x)
+    vv = np.dot(v*w, v.T)
+    vd = 1 / np.sqrt(vv.diagonal())
+    vv = vd[:, np.newaxis] * vv * vd
+    assert_allclose(vv, np.eye(N), rtol, atol)
+
+    # check that the integral of 1 is correct
+    assert_allclose(w.sum(), mu, rtol, atol)
+
+    # compare the results of integrating a function with quad.
+    def f(x):
+        return x ** 3 - 3 * x ** 2 + x - 2
+    resI = integrate.quad(lambda x: f(x)*weight_func(x), a, b)
+    resG = np.vdot(f(x), w)
+    rtol = 1e-6 if 1e-6 < resI[1] else resI[1] * 10
+    assert_allclose(resI[0], resG, rtol=rtol)
+
+def test_roots_jacobi():
+    def rf(a, b):
+        return lambda n, mu: sc.roots_jacobi(n, a, b, mu)
+    def ef(a, b):
+        return lambda n, x: sc.eval_jacobi(n, a, b, x)
+    def wf(a, b):
+        return lambda x: (1 - x) ** a * (1 + x) ** b
+
+    vgq = verify_gauss_quad
+    vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1., 1., 5)
+    vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1., 1.,
+        25, atol=1e-12)
+    vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1., 1.,
+        100, atol=1e-11)
+
+    vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1., 1., 5)
+    vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1., 1., 25, atol=1.5e-13)
+    vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1., 1., 100, atol=2e-12)
+
+    vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1., 1., 5, atol=2e-13)
+    vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1., 1., 25, atol=2e-13)
+    vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1., 1., 100, atol=1e-12)
+
+    vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1., 1., 5)
+    vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1., 1., 25, atol=1e-13)
+    vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1., 1., 100, atol=3e-13)
+
+    vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1., 1., 5)
+    vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1., 1., 25,
+        atol=1.1e-14)
+    vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1., 1.,
+        100, atol=1e-13)
+
+    vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1., 1., 5, atol=1e-13)
+    vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1., 1., 25, atol=2e-13)
+    vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1., 1.,
+        100, atol=1e-11)
+
+    vgq(rf(1., 658.), ef(1., 658.), wf(1., 658.), -1., 1., 5, atol=2e-13)
+    vgq(rf(1., 658.), ef(1., 658.), wf(1., 658.), -1., 1., 25, atol=1e-12)
+    vgq(rf(1., 658.), ef(1., 658.), wf(1., 658.), -1., 1., 100, atol=1e-11)
+    vgq(rf(1., 658.), ef(1., 658.), wf(1., 658.), -1., 1., 250, atol=1e-11)
+
+    vgq(rf(511., 511.), ef(511., 511.), wf(511., 511.), -1., 1., 5,
+        atol=1e-12)
+    vgq(rf(511., 511.), ef(511., 511.), wf(511., 511.), -1., 1., 25,
+        atol=1e-11)
+    vgq(rf(511., 511.), ef(511., 511.), wf(511., 511.), -1., 1., 100,
+        atol=1e-10)
+
+    vgq(rf(511., 512.), ef(511., 512.), wf(511., 512.), -1., 1., 5,
+        atol=1e-12)
+    vgq(rf(511., 512.), ef(511., 512.), wf(511., 512.), -1., 1., 25,
+        atol=1e-11)
+    vgq(rf(511., 512.), ef(511., 512.), wf(511., 512.), -1., 1., 100,
+        atol=1e-10)
+
+    vgq(rf(1000., 500.), ef(1000., 500.), wf(1000., 500.), -1., 1., 5,
+        atol=1e-12)
+    vgq(rf(1000., 500.), ef(1000., 500.), wf(1000., 500.), -1., 1., 25,
+        atol=1e-11)
+    vgq(rf(1000., 500.), ef(1000., 500.), wf(1000., 500.), -1., 1., 100,
+        atol=1e-10)
+
+    vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1., 1., 5)
+    vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1., 1., 25,
+        atol=1e-13)
+    vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1., 1., 100,
+        atol=1e-13)
+
+    # when alpha == beta == 0, P_n^{a,b}(x) == P_n(x)
+    xj, wj = sc.roots_jacobi(6, 0.0, 0.0)
+    xl, wl = sc.roots_legendre(6)
+    assert_allclose(xj, xl, 1e-14, 1e-14)
+    assert_allclose(wj, wl, 1e-14, 1e-14)
+
+    # when alpha == beta != 0, P_n^{a,b}(x) == C_n^{alpha+0.5}(x)
+    xj, wj = sc.roots_jacobi(6, 4.0, 4.0)
+    xc, wc = sc.roots_gegenbauer(6, 4.5)
+    assert_allclose(xj, xc, 1e-14, 1e-14)
+    assert_allclose(wj, wc, 1e-14, 1e-14)
+
+    x, w = sc.roots_jacobi(5, 2, 3, False)
+    y, v, m = sc.roots_jacobi(5, 2, 3, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(wf(2,3), -1, 1)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_jacobi, 0, 1, 1)
+    assert_raises(ValueError, sc.roots_jacobi, 3.3, 1, 1)  # type: ignore[call-overload]
+    assert_raises(ValueError, sc.roots_jacobi, 3, -2, 1)
+    assert_raises(ValueError, sc.roots_jacobi, 3, 1, -2)
+    assert_raises(ValueError, sc.roots_jacobi, 3, -2, -2)
+
+def test_roots_sh_jacobi():
+    def rf(a, b):
+        return lambda n, mu: sc.roots_sh_jacobi(n, a, b, mu)
+    def ef(a, b):
+        return lambda n, x: sc.eval_sh_jacobi(n, a, b, x)
+    def wf(a, b):
+        return lambda x: (1.0 - x) ** (a - b) * x ** (b - 1.0)
+
+    vgq = verify_gauss_quad
+    vgq(rf(-0.5, 0.25), ef(-0.5, 0.25), wf(-0.5, 0.25), 0., 1., 5)
+    vgq(rf(-0.5, 0.25), ef(-0.5, 0.25), wf(-0.5, 0.25), 0., 1.,
+        25, atol=1e-12)
+    vgq(rf(-0.5, 0.25), ef(-0.5, 0.25), wf(-0.5, 0.25), 0., 1.,
+        100, atol=1e-11)
+
+    vgq(rf(0.5, 0.5), ef(0.5, 0.5), wf(0.5, 0.5), 0., 1., 5)
+    vgq(rf(0.5, 0.5), ef(0.5, 0.5), wf(0.5, 0.5), 0., 1., 25, atol=1e-13)
+    vgq(rf(0.5, 0.5), ef(0.5, 0.5), wf(0.5, 0.5), 0., 1., 100, atol=1e-12)
+
+    vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), 0., 1., 5)
+    vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), 0., 1., 25, atol=1.5e-13)
+    vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), 0., 1., 100, atol=2e-12)
+
+    vgq(rf(2, 0.9), ef(2, 0.9), wf(2, 0.9), 0., 1., 5)
+    vgq(rf(2, 0.9), ef(2, 0.9), wf(2, 0.9), 0., 1., 25, atol=1e-13)
+    vgq(rf(2, 0.9), ef(2, 0.9), wf(2, 0.9), 0., 1., 100, atol=1e-12)
+
+    vgq(rf(27.3, 18.24), ef(27.3, 18.24), wf(27.3, 18.24), 0., 1., 5)
+    vgq(rf(27.3, 18.24), ef(27.3, 18.24), wf(27.3, 18.24), 0., 1., 25)
+    vgq(rf(27.3, 18.24), ef(27.3, 18.24), wf(27.3, 18.24), 0., 1.,
+        100, atol=1e-13)
+
+    vgq(rf(47.1, 0.2), ef(47.1, 0.2), wf(47.1, 0.2), 0., 1., 5, atol=1e-12)
+    vgq(rf(47.1, 0.2), ef(47.1, 0.2), wf(47.1, 0.2), 0., 1., 25, atol=1e-11)
+    vgq(rf(47.1, 0.2), ef(47.1, 0.2), wf(47.1, 0.2), 0., 1., 100, atol=1e-10)
+
+    vgq(rf(68.9, 2.25), ef(68.9, 2.25), wf(68.9, 2.25), 0., 1., 5, atol=3.5e-14)
+    vgq(rf(68.9, 2.25), ef(68.9, 2.25), wf(68.9, 2.25), 0., 1., 25, atol=2e-13)
+    vgq(rf(68.9, 2.25), ef(68.9, 2.25), wf(68.9, 2.25), 0., 1.,
+        100, atol=1e-12)
+
+    x, w = sc.roots_sh_jacobi(5, 3, 2, False)
+    y, v, m = sc.roots_sh_jacobi(5, 3, 2, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(wf(3,2), 0, 1)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_sh_jacobi, 0, 1, 1)
+    assert_raises(ValueError, sc.roots_sh_jacobi, 3.3, 1, 1)  # type: ignore[call-overload]
+    assert_raises(ValueError, sc.roots_sh_jacobi, 3, 1, 2)    # p - q <= -1
+    assert_raises(ValueError, sc.roots_sh_jacobi, 3, 2, -1)   # q <= 0
+    assert_raises(ValueError, sc.roots_sh_jacobi, 3, -2, -1)  # both
+
+def test_roots_hermite():
+    rootf = sc.roots_hermite
+    evalf = sc.eval_hermite
+    weightf = orth.hermite(5).weight_func
+
+    verify_gauss_quad(rootf, evalf, weightf, -np.inf, np.inf, 5)
+    verify_gauss_quad(rootf, evalf, weightf, -np.inf, np.inf, 25, atol=1e-13)
+    verify_gauss_quad(rootf, evalf, weightf, -np.inf, np.inf, 100, atol=1e-12)
+
+    # Golub-Welsch branch
+    x, w = sc.roots_hermite(5, False)
+    y, v, m = sc.roots_hermite(5, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(weightf, -np.inf, np.inf)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    # Asymptotic branch (switch over at n >= 150)
+    x, w = sc.roots_hermite(200, False)
+    y, v, m = sc.roots_hermite(200, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+    assert_allclose(sum(v), m, 1e-14, 1e-14)
+
+    assert_raises(ValueError, sc.roots_hermite, 0)
+    assert_raises(ValueError, sc.roots_hermite, 3.3)  # type: ignore[call-overload]
+
+def test_roots_hermite_asy():
+    # Recursion for Hermite functions
+    def hermite_recursion(n, nodes):
+        H = np.zeros((n, nodes.size))
+        H[0,:] = np.pi**(-0.25) * np.exp(-0.5*nodes**2)
+        if n > 1:
+            H[1,:] = sqrt(2.0) * nodes * H[0,:]
+            for k in range(2, n):
+                H[k,:] = sqrt(2.0/k) * nodes * H[k-1,:] - sqrt((k-1.0)/k) * H[k-2,:]
+        return H
+
+    # This tests only the nodes
+    def test(N, rtol=1e-15, atol=1e-14):
+        x, w = orth._roots_hermite_asy(N)
+        H = hermite_recursion(N+1, x)
+        assert_allclose(H[-1,:], np.zeros(N), rtol, atol)
+        assert_allclose(sum(w), sqrt(np.pi), rtol, atol)
+
+    test(150, atol=1e-12)
+    test(151, atol=1e-12)
+    test(300, atol=1e-12)
+    test(301, atol=1e-12)
+    test(500, atol=1e-12)
+    test(501, atol=1e-12)
+    test(999, atol=1e-12)
+    test(1000, atol=1e-12)
+    test(2000, atol=1e-12)
+    test(5000, atol=1e-12)
+
+def test_roots_hermitenorm():
+    rootf = sc.roots_hermitenorm
+    evalf = sc.eval_hermitenorm
+    weightf = orth.hermitenorm(5).weight_func
+
+    verify_gauss_quad(rootf, evalf, weightf, -np.inf, np.inf, 5)
+    verify_gauss_quad(rootf, evalf, weightf, -np.inf, np.inf, 25, atol=1e-13)
+    verify_gauss_quad(rootf, evalf, weightf, -np.inf, np.inf, 100, atol=1e-12)
+
+    x, w = sc.roots_hermitenorm(5, False)
+    y, v, m = sc.roots_hermitenorm(5, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(weightf, -np.inf, np.inf)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_hermitenorm, 0)
+    assert_raises(ValueError, sc.roots_hermitenorm, 3.3)  # type: ignore[call-overload]
+
+def test_roots_gegenbauer():
+    def rootf(a):
+        return lambda n, mu: sc.roots_gegenbauer(n, a, mu)
+    def evalf(a):
+        return lambda n, x: sc.eval_gegenbauer(n, a, x)
+    def weightf(a):
+        return lambda x: (1 - x ** 2) ** (a - 0.5)
+
+    vgq = verify_gauss_quad
+    vgq(rootf(-0.25), evalf(-0.25), weightf(-0.25), -1., 1., 5)
+    vgq(rootf(-0.25), evalf(-0.25), weightf(-0.25), -1., 1., 25, atol=1e-12)
+    vgq(rootf(-0.25), evalf(-0.25), weightf(-0.25), -1., 1., 100, atol=1e-11)
+
+    vgq(rootf(0.1), evalf(0.1), weightf(0.1), -1., 1., 5)
+    vgq(rootf(0.1), evalf(0.1), weightf(0.1), -1., 1., 25, atol=1e-13)
+    vgq(rootf(0.1), evalf(0.1), weightf(0.1), -1., 1., 100, atol=1e-12)
+
+    vgq(rootf(1), evalf(1), weightf(1), -1., 1., 5)
+    vgq(rootf(1), evalf(1), weightf(1), -1., 1., 25, atol=1e-13)
+    vgq(rootf(1), evalf(1), weightf(1), -1., 1., 100, atol=1e-12)
+
+    vgq(rootf(10), evalf(10), weightf(10), -1., 1., 5)
+    vgq(rootf(10), evalf(10), weightf(10), -1., 1., 25, atol=1e-13)
+    vgq(rootf(10), evalf(10), weightf(10), -1., 1., 100, atol=1e-12)
+
+    vgq(rootf(50), evalf(50), weightf(50), -1., 1., 5, atol=1e-13)
+    vgq(rootf(50), evalf(50), weightf(50), -1., 1., 25, atol=1e-12)
+    vgq(rootf(50), evalf(50), weightf(50), -1., 1., 100, atol=1e-11)
+
+    # Alpha=170 is where the approximation used in roots_gegenbauer changes
+    vgq(rootf(170), evalf(170), weightf(170), -1., 1., 5, atol=1e-13)
+    vgq(rootf(170), evalf(170), weightf(170), -1., 1., 25, atol=1e-12)
+    vgq(rootf(170), evalf(170), weightf(170), -1., 1., 100, atol=1e-11)
+    vgq(rootf(170.5), evalf(170.5), weightf(170.5), -1., 1., 5, atol=1.25e-13)
+    vgq(rootf(170.5), evalf(170.5), weightf(170.5), -1., 1., 25, atol=1e-12)
+    vgq(rootf(170.5), evalf(170.5), weightf(170.5), -1., 1., 100, atol=1e-11)
+
+    # Test for failures, e.g. overflows, resulting from large alphas
+    vgq(rootf(238), evalf(238), weightf(238), -1., 1., 5, atol=1e-13)
+    vgq(rootf(238), evalf(238), weightf(238), -1., 1., 25, atol=1e-12)
+    vgq(rootf(238), evalf(238), weightf(238), -1., 1., 100, atol=1e-11)
+    vgq(rootf(512.5), evalf(512.5), weightf(512.5), -1., 1., 5, atol=1e-12)
+    vgq(rootf(512.5), evalf(512.5), weightf(512.5), -1., 1., 25, atol=1e-11)
+    vgq(rootf(512.5), evalf(512.5), weightf(512.5), -1., 1., 100, atol=1e-10)
+
+    # this is a special case that the old code supported.
+    # when alpha = 0, the gegenbauer polynomial is uniformly 0. but it goes
+    # to a scaled down copy of T_n(x) there.
+    vgq(rootf(0), sc.eval_chebyt, weightf(0), -1., 1., 5)
+    vgq(rootf(0), sc.eval_chebyt, weightf(0), -1., 1., 25)
+    vgq(rootf(0), sc.eval_chebyt, weightf(0), -1., 1., 100, atol=1e-12)
+
+    x, w = sc.roots_gegenbauer(5, 2, False)
+    y, v, m = sc.roots_gegenbauer(5, 2, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(weightf(2), -1, 1)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_gegenbauer, 0, 2)
+    assert_raises(ValueError, sc.roots_gegenbauer, 3.3, 2)  # type: ignore[call-overload]
+    assert_raises(ValueError, sc.roots_gegenbauer, 3, -.75)
+
+def test_roots_chebyt():
+    weightf = orth.chebyt(5).weight_func
+    verify_gauss_quad(sc.roots_chebyt, sc.eval_chebyt, weightf, -1., 1., 5)
+    verify_gauss_quad(sc.roots_chebyt, sc.eval_chebyt, weightf, -1., 1., 25)
+    verify_gauss_quad(sc.roots_chebyt, sc.eval_chebyt, weightf, -1., 1., 100,
+                      atol=1e-12)
+
+    x, w = sc.roots_chebyt(5, False)
+    y, v, m = sc.roots_chebyt(5, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(weightf, -1, 1)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_chebyt, 0)
+    assert_raises(ValueError, sc.roots_chebyt, 3.3)  # type: ignore[call-overload]
+
+def test_chebyt_symmetry():
+    x, w = sc.roots_chebyt(21)
+    pos, neg = x[:10], x[11:]
+    assert_equal(neg, -pos[::-1])
+    assert_equal(x[10], 0)
+
+def test_roots_chebyu():
+    weightf = orth.chebyu(5).weight_func
+    verify_gauss_quad(sc.roots_chebyu, sc.eval_chebyu, weightf, -1., 1., 5)
+    verify_gauss_quad(sc.roots_chebyu, sc.eval_chebyu, weightf, -1., 1., 25)
+    verify_gauss_quad(sc.roots_chebyu, sc.eval_chebyu, weightf, -1., 1., 100)
+
+    x, w = sc.roots_chebyu(5, False)
+    y, v, m = sc.roots_chebyu(5, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(weightf, -1, 1)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_chebyu, 0)
+    assert_raises(ValueError, sc.roots_chebyu, 3.3)  # type: ignore[call-overload]
+
+def test_roots_chebyc():
+    weightf = orth.chebyc(5).weight_func
+    verify_gauss_quad(sc.roots_chebyc, sc.eval_chebyc, weightf, -2., 2., 5)
+    verify_gauss_quad(sc.roots_chebyc, sc.eval_chebyc, weightf, -2., 2., 25)
+    verify_gauss_quad(sc.roots_chebyc, sc.eval_chebyc, weightf, -2., 2., 100,
+                      atol=1e-12)
+
+    x, w = sc.roots_chebyc(5, False)
+    y, v, m = sc.roots_chebyc(5, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(weightf, -2, 2)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_chebyc, 0)
+    assert_raises(ValueError, sc.roots_chebyc, 3.3)  # type: ignore[call-overload]
+
+def test_roots_chebys():
+    weightf = orth.chebys(5).weight_func
+    verify_gauss_quad(sc.roots_chebys, sc.eval_chebys, weightf, -2., 2., 5)
+    verify_gauss_quad(sc.roots_chebys, sc.eval_chebys, weightf, -2., 2., 25)
+    verify_gauss_quad(sc.roots_chebys, sc.eval_chebys, weightf, -2., 2., 100)
+
+    x, w = sc.roots_chebys(5, False)
+    y, v, m = sc.roots_chebys(5, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(weightf, -2, 2)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_chebys, 0)
+    assert_raises(ValueError, sc.roots_chebys, 3.3)  # type: ignore[call-overload]
+
+def test_roots_sh_chebyt():
+    weightf = orth.sh_chebyt(5).weight_func
+    verify_gauss_quad(sc.roots_sh_chebyt, sc.eval_sh_chebyt, weightf, 0., 1., 5)
+    verify_gauss_quad(sc.roots_sh_chebyt, sc.eval_sh_chebyt, weightf, 0., 1., 25)
+    verify_gauss_quad(sc.roots_sh_chebyt, sc.eval_sh_chebyt, weightf, 0., 1.,
+                      100, atol=1e-13)
+
+    x, w = sc.roots_sh_chebyt(5, False)
+    y, v, m = sc.roots_sh_chebyt(5, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(weightf, 0, 1)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_sh_chebyt, 0)
+    assert_raises(ValueError, sc.roots_sh_chebyt, 3.3)  # type: ignore[call-overload]
+
+def test_roots_sh_chebyu():
+    weightf = orth.sh_chebyu(5).weight_func
+    verify_gauss_quad(sc.roots_sh_chebyu, sc.eval_sh_chebyu, weightf, 0., 1., 5)
+    verify_gauss_quad(sc.roots_sh_chebyu, sc.eval_sh_chebyu, weightf, 0., 1., 25)
+    verify_gauss_quad(sc.roots_sh_chebyu, sc.eval_sh_chebyu, weightf, 0., 1.,
+                      100, atol=1e-13)
+
+    x, w = sc.roots_sh_chebyu(5, False)
+    y, v, m = sc.roots_sh_chebyu(5, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(weightf, 0, 1)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_sh_chebyu, 0)
+    assert_raises(ValueError, sc.roots_sh_chebyu, 3.3)  # type: ignore[call-overload]
+
+def test_roots_legendre():
+    weightf = orth.legendre(5).weight_func
+    verify_gauss_quad(sc.roots_legendre, sc.eval_legendre, weightf, -1., 1., 5)
+    verify_gauss_quad(sc.roots_legendre, sc.eval_legendre, weightf, -1., 1.,
+                      25, atol=1e-13)
+    verify_gauss_quad(sc.roots_legendre, sc.eval_legendre, weightf, -1., 1.,
+                      100, atol=1e-12)
+
+    x, w = sc.roots_legendre(5, False)
+    y, v, m = sc.roots_legendre(5, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(weightf, -1, 1)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_legendre, 0)
+    assert_raises(ValueError, sc.roots_legendre, 3.3)  # type: ignore[call-overload]
+
+def test_roots_sh_legendre():
+    weightf = orth.sh_legendre(5).weight_func
+    verify_gauss_quad(sc.roots_sh_legendre, sc.eval_sh_legendre, weightf, 0., 1., 5)
+    verify_gauss_quad(sc.roots_sh_legendre, sc.eval_sh_legendre, weightf, 0., 1.,
+                      25, atol=1e-13)
+    verify_gauss_quad(sc.roots_sh_legendre, sc.eval_sh_legendre, weightf, 0., 1.,
+                      100, atol=1e-12)
+
+    x, w = sc.roots_sh_legendre(5, False)
+    y, v, m = sc.roots_sh_legendre(5, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(weightf, 0, 1)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_sh_legendre, 0)
+    assert_raises(ValueError, sc.roots_sh_legendre, 3.3)  # type: ignore[call-overload]
+
+def test_roots_laguerre():
+    weightf = orth.laguerre(5).weight_func
+    verify_gauss_quad(sc.roots_laguerre, sc.eval_laguerre, weightf, 0., np.inf, 5)
+    verify_gauss_quad(sc.roots_laguerre, sc.eval_laguerre, weightf, 0., np.inf,
+                      25, atol=1e-13)
+    verify_gauss_quad(sc.roots_laguerre, sc.eval_laguerre, weightf, 0., np.inf,
+                      100, atol=1e-12)
+
+    x, w = sc.roots_laguerre(5, False)
+    y, v, m = sc.roots_laguerre(5, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(weightf, 0, np.inf)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_laguerre, 0)
+    assert_raises(ValueError, sc.roots_laguerre, 3.3)  # type: ignore[call-overload]
+
+def test_roots_genlaguerre():
+    def rootf(a):
+        return lambda n, mu: sc.roots_genlaguerre(n, a, mu)
+    def evalf(a):
+        return lambda n, x: sc.eval_genlaguerre(n, a, x)
+    def weightf(a):
+        return lambda x: x ** a * np.exp(-x)
+
+    vgq = verify_gauss_quad
+    vgq(rootf(-0.5), evalf(-0.5), weightf(-0.5), 0., np.inf, 5)
+    vgq(rootf(-0.5), evalf(-0.5), weightf(-0.5), 0., np.inf, 25, atol=1e-13)
+    vgq(rootf(-0.5), evalf(-0.5), weightf(-0.5), 0., np.inf, 100, atol=1e-12)
+
+    vgq(rootf(0.1), evalf(0.1), weightf(0.1), 0., np.inf, 5)
+    vgq(rootf(0.1), evalf(0.1), weightf(0.1), 0., np.inf, 25, atol=1e-13)
+    vgq(rootf(0.1), evalf(0.1), weightf(0.1), 0., np.inf, 100, atol=1.6e-13)
+
+    vgq(rootf(1), evalf(1), weightf(1), 0., np.inf, 5)
+    vgq(rootf(1), evalf(1), weightf(1), 0., np.inf, 25, atol=1e-13)
+    vgq(rootf(1), evalf(1), weightf(1), 0., np.inf, 100, atol=1.03e-13)
+
+    vgq(rootf(10), evalf(10), weightf(10), 0., np.inf, 5)
+    vgq(rootf(10), evalf(10), weightf(10), 0., np.inf, 25, atol=1e-13)
+    vgq(rootf(10), evalf(10), weightf(10), 0., np.inf, 100, atol=1e-12)
+
+    vgq(rootf(50), evalf(50), weightf(50), 0., np.inf, 5)
+    vgq(rootf(50), evalf(50), weightf(50), 0., np.inf, 25, atol=1e-13)
+    vgq(rootf(50), evalf(50), weightf(50), 0., np.inf, 100, rtol=1e-14, atol=2e-13)
+
+    x, w = sc.roots_genlaguerre(5, 2, False)
+    y, v, m = sc.roots_genlaguerre(5, 2, True)
+    assert_allclose(x, y, 1e-14, 1e-14)
+    assert_allclose(w, v, 1e-14, 1e-14)
+
+    muI, muI_err = integrate.quad(weightf(2.), 0., np.inf)
+    assert_allclose(m, muI, rtol=muI_err)
+
+    assert_raises(ValueError, sc.roots_genlaguerre, 0, 2)
+    assert_raises(ValueError, sc.roots_genlaguerre, 3.3, 2)  # type: ignore[call-overload]
+    assert_raises(ValueError, sc.roots_genlaguerre, 3, -1.1)
+
+
+def test_gh_6721():
+    # Regression test for gh_6721. This should not raise.
+    sc.chebyt(65)(0.2)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_orthogonal_eval.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_orthogonal_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..b88027d4cf531c4dc0c5ad9c80e2a5c264f06844
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_orthogonal_eval.py
@@ -0,0 +1,383 @@
+import numpy as np
+from numpy.testing import assert_, assert_allclose
+import pytest
+
+from scipy.special import _ufuncs, gammasgn
+import scipy.special._orthogonal as orth
+from scipy.special._testutils import FuncData
+
+
+def test_eval_chebyt():
+    n = np.arange(0, 10000, 7, dtype=np.dtype("long"))
+    x = 2*np.random.rand() - 1
+    v1 = np.cos(n*np.arccos(x))
+    v2 = _ufuncs.eval_chebyt(n, x)
+    assert_(np.allclose(v1, v2, rtol=1e-15))
+
+
+def test_eval_chebyt_gh20129():
+    # https://github.com/scipy/scipy/issues/20129
+    assert _ufuncs.eval_chebyt(7, 2 + 0j) == 5042.0
+
+
+def test_eval_genlaguerre_restriction():
+    # check it returns nan for alpha <= -1
+    assert_(np.isnan(_ufuncs.eval_genlaguerre(0, -1, 0)))
+    assert_(np.isnan(_ufuncs.eval_genlaguerre(0.1, -1, 0)))
+
+
+def test_warnings():
+    # ticket 1334
+    with np.errstate(all='raise'):
+        # these should raise no fp warnings
+        _ufuncs.eval_legendre(1, 0)
+        _ufuncs.eval_laguerre(1, 1)
+        _ufuncs.eval_gegenbauer(1, 1, 0)
+
+
+class TestPolys:
+    """
+    Check that the eval_* functions agree with the constructed polynomials
+
+    """
+
+    def check_poly(self, func, cls, param_ranges=(), x_range=(), nn=10,
+                   nparam=10, nx=10, rtol=1e-8):
+        rng = np.random.RandomState(1234)
+
+        dataset = []
+        for n in np.arange(nn):
+            params = [a + (b-a)*rng.rand(nparam) for a,b in param_ranges]
+            params = np.asarray(params).T
+            if not param_ranges:
+                params = [0]
+            for p in params:
+                if param_ranges:
+                    p = (n,) + tuple(p)
+                else:
+                    p = (n,)
+                x = x_range[0] + (x_range[1] - x_range[0])*rng.rand(nx)
+                x[0] = x_range[0]  # always include domain start point
+                x[1] = x_range[1]  # always include domain end point
+                poly = np.poly1d(cls(*p).coef)
+                z = np.c_[np.tile(p, (nx,1)), x, poly(x)]
+                dataset.append(z)
+
+        dataset = np.concatenate(dataset, axis=0)
+
+        def polyfunc(*p):
+            p = (p[0].astype(np.dtype("long")),) + p[1:]
+            return func(*p)
+
+        with np.errstate(all='raise'):
+            ds = FuncData(polyfunc, dataset, list(range(len(param_ranges)+2)), -1,
+                          rtol=rtol)
+            ds.check()
+
+    def test_jacobi(self):
+        self.check_poly(_ufuncs.eval_jacobi, orth.jacobi,
+                        param_ranges=[(-0.99, 10), (-0.99, 10)],
+                        x_range=[-1, 1], rtol=1e-5)
+
+    def test_sh_jacobi(self):
+        self.check_poly(_ufuncs.eval_sh_jacobi, orth.sh_jacobi,
+                        param_ranges=[(1, 10), (0, 1)], x_range=[0, 1],
+                        rtol=1e-5)
+
+    def test_gegenbauer(self):
+        self.check_poly(_ufuncs.eval_gegenbauer, orth.gegenbauer,
+                        param_ranges=[(-0.499, 10)], x_range=[-1, 1],
+                        rtol=1e-7)
+
+    def test_chebyt(self):
+        self.check_poly(_ufuncs.eval_chebyt, orth.chebyt,
+                        param_ranges=[], x_range=[-1, 1])
+
+    def test_chebyu(self):
+        self.check_poly(_ufuncs.eval_chebyu, orth.chebyu,
+                        param_ranges=[], x_range=[-1, 1])
+
+    def test_chebys(self):
+        self.check_poly(_ufuncs.eval_chebys, orth.chebys,
+                        param_ranges=[], x_range=[-2, 2])
+
+    def test_chebyc(self):
+        self.check_poly(_ufuncs.eval_chebyc, orth.chebyc,
+                        param_ranges=[], x_range=[-2, 2])
+
+    def test_sh_chebyt(self):
+        with np.errstate(all='ignore'):
+            self.check_poly(_ufuncs.eval_sh_chebyt, orth.sh_chebyt,
+                            param_ranges=[], x_range=[0, 1])
+
+    def test_sh_chebyu(self):
+        self.check_poly(_ufuncs.eval_sh_chebyu, orth.sh_chebyu,
+                        param_ranges=[], x_range=[0, 1])
+
+    def test_legendre(self):
+        self.check_poly(_ufuncs.eval_legendre, orth.legendre,
+                        param_ranges=[], x_range=[-1, 1])
+
+    def test_sh_legendre(self):
+        with np.errstate(all='ignore'):
+            self.check_poly(_ufuncs.eval_sh_legendre, orth.sh_legendre,
+                            param_ranges=[], x_range=[0, 1])
+
+    def test_genlaguerre(self):
+        self.check_poly(_ufuncs.eval_genlaguerre, orth.genlaguerre,
+                        param_ranges=[(-0.99, 10)], x_range=[0, 100])
+
+    def test_laguerre(self):
+        self.check_poly(_ufuncs.eval_laguerre, orth.laguerre,
+                        param_ranges=[], x_range=[0, 100])
+
+    def test_hermite(self):
+        self.check_poly(_ufuncs.eval_hermite, orth.hermite,
+                        param_ranges=[], x_range=[-100, 100])
+
+    def test_hermitenorm(self):
+        self.check_poly(_ufuncs.eval_hermitenorm, orth.hermitenorm,
+                        param_ranges=[], x_range=[-100, 100])
+
+
+class TestRecurrence:
+    """
+    Check that the eval_* functions sig='ld->d' and 'dd->d' agree.
+
+    """
+
+    def check_poly(self, func, param_ranges=(), x_range=(), nn=10,
+                   nparam=10, nx=10, rtol=1e-8):
+        rng = np.random.default_rng(1234)
+
+        dataset = []
+        for n in np.arange(nn):
+            params = [a + (b-a)*rng.random(nparam) for a,b in param_ranges]
+            params = np.asarray(params).T
+            if not param_ranges:
+                params = [0]
+            for p in params:
+                if param_ranges:
+                    p = (n,) + tuple(p)
+                else:
+                    p = (n,)
+                x = x_range[0] + (x_range[1] - x_range[0])*rng.random(nx)
+                x[0] = x_range[0]  # always include domain start point
+                x[1] = x_range[1]  # always include domain end point
+                kw = dict(sig=(len(p)+1)*'d'+'->d')
+                z = np.c_[np.tile(p, (nx,1)), x, func(*(p + (x,)), **kw)]
+                dataset.append(z)
+
+        dataset = np.concatenate(dataset, axis=0)
+
+        def polyfunc(*p):
+            p0 = p[0].astype(np.intp)
+            p = (p0,) + p[1:]
+            p0_type_char = p0.dtype.char
+            kw = dict(sig=p0_type_char + (len(p)-1)*'d' + '->d')
+            return func(*p, **kw)
+
+        with np.errstate(all='raise'):
+            ds = FuncData(polyfunc, dataset, list(range(len(param_ranges)+2)), -1,
+                          rtol=rtol)
+            ds.check()
+
+    def test_jacobi(self):
+        self.check_poly(_ufuncs.eval_jacobi,
+                        param_ranges=[(-0.99, 10), (-0.99, 10)],
+                        x_range=[-1, 1])
+
+    def test_sh_jacobi(self):
+        self.check_poly(_ufuncs.eval_sh_jacobi,
+                        param_ranges=[(1, 10), (0, 1)], x_range=[0, 1])
+
+    def test_gegenbauer(self):
+        self.check_poly(_ufuncs.eval_gegenbauer,
+                        param_ranges=[(-0.499, 10)], x_range=[-1, 1])
+
+    def test_chebyt(self):
+        self.check_poly(_ufuncs.eval_chebyt,
+                        param_ranges=[], x_range=[-1, 1])
+
+    def test_chebyu(self):
+        self.check_poly(_ufuncs.eval_chebyu,
+                        param_ranges=[], x_range=[-1, 1])
+
+    def test_chebys(self):
+        self.check_poly(_ufuncs.eval_chebys,
+                        param_ranges=[], x_range=[-2, 2])
+
+    def test_chebyc(self):
+        self.check_poly(_ufuncs.eval_chebyc,
+                        param_ranges=[], x_range=[-2, 2])
+
+    def test_sh_chebyt(self):
+        self.check_poly(_ufuncs.eval_sh_chebyt,
+                        param_ranges=[], x_range=[0, 1])
+
+    def test_sh_chebyu(self):
+        self.check_poly(_ufuncs.eval_sh_chebyu,
+                        param_ranges=[], x_range=[0, 1])
+
+    def test_legendre(self):
+        self.check_poly(_ufuncs.eval_legendre,
+                        param_ranges=[], x_range=[-1, 1])
+
+    def test_sh_legendre(self):
+        self.check_poly(_ufuncs.eval_sh_legendre,
+                        param_ranges=[], x_range=[0, 1])
+
+    def test_genlaguerre(self):
+        self.check_poly(_ufuncs.eval_genlaguerre,
+                        param_ranges=[(-0.99, 10)], x_range=[0, 100])
+
+    def test_laguerre(self):
+        self.check_poly(_ufuncs.eval_laguerre,
+                        param_ranges=[], x_range=[0, 100])
+
+    def test_hermite(self):
+        v = _ufuncs.eval_hermite(70, 1.0)
+        a = -1.457076485701412e60
+        assert_allclose(v, a)
+
+
+def test_hermite_domain():
+    # Regression test for gh-11091.
+    assert np.isnan(_ufuncs.eval_hermite(-1, 1.0))
+    assert np.isnan(_ufuncs.eval_hermitenorm(-1, 1.0))
+
+
+@pytest.mark.parametrize("n", [0, 1, 2])
+@pytest.mark.parametrize("x", [0, 1, np.nan])
+def test_hermite_nan(n, x):
+    # Regression test for gh-11369.
+    assert np.isnan(_ufuncs.eval_hermite(n, x)) == np.any(np.isnan([n, x]))
+    assert np.isnan(_ufuncs.eval_hermitenorm(n, x)) == np.any(np.isnan([n, x]))
+
+
+@pytest.mark.parametrize('n', [0, 1, 2, 3.2])
+@pytest.mark.parametrize('alpha', [1, np.nan])
+@pytest.mark.parametrize('x', [2, np.nan])
+def test_genlaguerre_nan(n, alpha, x):
+    # Regression test for gh-11361.
+    nan_laguerre = np.isnan(_ufuncs.eval_genlaguerre(n, alpha, x))
+    nan_arg = np.any(np.isnan([n, alpha, x]))
+    assert nan_laguerre == nan_arg
+
+
+@pytest.mark.parametrize('n', [0, 1, 2, 3.2, np.nan])
+@pytest.mark.parametrize('alpha', [0.0, 1, np.nan])
+@pytest.mark.parametrize('x', [1e-6, 2, np.nan])
+def test_gegenbauer_nan(n, alpha, x):
+    # Regression test for gh-11370.
+    nan_gegenbauer = np.isnan(_ufuncs.eval_gegenbauer(n, alpha, x))
+    nan_arg = np.any(np.isnan([n, alpha, x]))
+    assert nan_gegenbauer == nan_arg
+
+@pytest.mark.parametrize("n", np.arange(10))
+@pytest.mark.parametrize("alpha", [-0.45, -0.25, -0.125, 0.0, 0.25, 1.0, 2.0])
+@pytest.mark.parametrize("x", [-np.inf, np.inf])
+def test_gegenbauer_infinity(n, alpha, x):
+    # gh-11713 - check correct handling of x = +inf and x = -inf
+    if alpha == 0.0:
+        expected = 0.0
+    elif n == 0.0:
+        expected = 1.0
+    else:
+        # sign of leading coefficient: 2^n * Gamma(n+alpha) / (n! Gamma(alpha))
+        lead_sign = gammasgn(n + alpha) * gammasgn(alpha)
+        expected = lead_sign * (np.sign(x) ** n) * np.inf
+    assert_allclose(_ufuncs.eval_gegenbauer(int(n), alpha, x), expected, rtol=1e-10)
+    assert_allclose(_ufuncs.eval_gegenbauer(float(n), alpha, x), expected, rtol=1e-10)
+
+@pytest.mark.parametrize(
+    "n, expected",
+    [
+        (0, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),
+        (1, [-2.0, -1.8, -1.6, -1.4, -1.2, -1.0, -0.8, -0.6, -0.4, -0.2, 0.0]),
+        (2, [3.0, 2.16, 1.44, 0.84, 0.36, 0.0, -0.24, -0.36, -0.36, -0.24, 0.0]),
+        (3, [-4.0, -1.98, -0.64, 0.14, 0.48, 0.5, 0.32, 0.06, -0.16, -0.22, 0.0]),
+        (
+            4,
+            [5.0, 1.332, -0.288, -0.658, -0.408, 0.0, 0.272, 0.282, 0.072, -0.148, 0.0],
+        ),
+        (
+            5,
+            [
+                -6.0,
+                -0.43308,
+                0.79104,
+                0.36876,
+                -0.21312,
+                -0.375,
+                -0.14208,
+                0.15804,
+                0.19776,
+                -0.04812,
+                0.0,
+            ],
+        ),
+    ],
+)
+def test_jacobi_alpha_minus_one_beta_plus_one(n, expected):
+    # gh-7001 - expected values were computed with mathematica
+    x = np.linspace(-1.0, 1.0, 11)
+    a, b = -1, 1  # alpha, beta
+    assert_allclose(_ufuncs.eval_jacobi(n, a, b, x), expected, rtol=1e-10, atol=1e-14)
+    assert_allclose(
+        _ufuncs.eval_jacobi(float(n), a, b, x), expected, rtol=1e-10, atol=1e-14
+    )
+
+
+@pytest.mark.parametrize(
+    "n, expected",
+    [
+        (0, [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),
+        (1, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),
+        (2, [0.0, -0.09, -0.16, -0.21, -0.24, -0.25, -0.24, -0.21, -0.16, -0.09, 0.0]),
+        (
+            3,
+            [0.0, 0.144, 0.192, 0.168, 0.096, 0.0, -0.096, -0.168, -0.192, -0.144, 0.0],
+        ),
+        (
+            4,
+            [
+                0.0,
+                -0.1485,
+                -0.096,
+                0.0315,
+                0.144,
+                0.1875,
+                0.144,
+                0.0315,
+                -0.096,
+                -0.1485,
+                0.0,
+            ],
+        ),
+        (
+            5,
+            [
+                0.0,
+                0.10656,
+                -0.04608,
+                -0.15792,
+                -0.13056,
+                0.0,
+                0.13056,
+                0.15792,
+                0.04608,
+                -0.10656,
+                0.0,
+            ],
+        ),
+    ],
+)
+def test_jacobi_alpha_minus_one_beta_minus_one(n, expected):
+    # gh-7001 - expected values were computed with mathematica
+    x = np.linspace(-1.0, 1.0, 11)
+    a, b = -1, -1  # alpha, beta
+    assert_allclose(_ufuncs.eval_jacobi(n, a, b, x), expected, rtol=1e-10, atol=1e-14)
+    assert_allclose(
+        _ufuncs.eval_jacobi(float(n), a, b, x), expected, rtol=1e-10, atol=1e-14
+    )
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_owens_t.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_owens_t.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d15aead25302023c5f07d8392c0931995764ced
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_owens_t.py
@@ -0,0 +1,53 @@
+import numpy as np
+from numpy.testing import assert_equal, assert_allclose
+
+import scipy.special as sc
+
+
+def test_symmetries():
+    np.random.seed(1234)
+    a, h = np.random.rand(100), np.random.rand(100)
+    assert_equal(sc.owens_t(h, a), sc.owens_t(-h, a))
+    assert_equal(sc.owens_t(h, a), -sc.owens_t(h, -a))
+
+
+def test_special_cases():
+    assert_equal(sc.owens_t(5, 0), 0)
+    assert_allclose(sc.owens_t(0, 5), 0.5*np.arctan(5)/np.pi,
+                    rtol=5e-14)
+    # Target value is 0.5*Phi(5)*(1 - Phi(5)) for Phi the CDF of the
+    # standard normal distribution
+    assert_allclose(sc.owens_t(5, 1), 1.4332574485503512543e-07,
+                    rtol=5e-14)
+
+
+def test_nans():
+    assert_equal(sc.owens_t(20, np.nan), np.nan)
+    assert_equal(sc.owens_t(np.nan, 20), np.nan)
+    assert_equal(sc.owens_t(np.nan, np.nan), np.nan)
+
+
+def test_infs():
+    h, a = 0, np.inf
+    # T(0, a) = 1/2π * arctan(a)
+    res = 1/(2*np.pi) * np.arctan(a)
+    assert_allclose(sc.owens_t(h, a), res, rtol=5e-14)
+    assert_allclose(sc.owens_t(h, -a), -res, rtol=5e-14)
+
+    h = 1
+    # Refer Owens T function definition in Wikipedia
+    # https://en.wikipedia.org/wiki/Owen%27s_T_function
+    # Value approximated through Numerical Integration
+    # using scipy.integrate.quad
+    # quad(lambda x: 1/(2*pi)*(exp(-0.5*(1*1)*(1+x*x))/(1+x*x)), 0, inf)
+    res = 0.07932762696572854
+    assert_allclose(sc.owens_t(h, np.inf), res, rtol=5e-14)
+    assert_allclose(sc.owens_t(h, -np.inf), -res, rtol=5e-14)
+
+    assert_equal(sc.owens_t(np.inf, 1), 0)
+    assert_equal(sc.owens_t(-np.inf, 1), 0)
+
+    assert_equal(sc.owens_t(np.inf, np.inf), 0)
+    assert_equal(sc.owens_t(-np.inf, np.inf), 0)
+    assert_equal(sc.owens_t(np.inf, -np.inf), -0.0)
+    assert_equal(sc.owens_t(-np.inf, -np.inf), -0.0)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_pcf.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_pcf.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8c42aa688081fb58f79ad2c8ea932d03b33523b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_pcf.py
@@ -0,0 +1,24 @@
+"""Tests for parabolic cylinder functions.
+
+"""
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+import scipy.special as sc
+
+
+def test_pbwa_segfault():
+    # Regression test for https://github.com/scipy/scipy/issues/6208.
+    #
+    # Data generated by mpmath.
+    #
+    w = 1.02276567211316867161
+    wp = -0.48887053372346189882
+    assert_allclose(sc.pbwa(0, 0), (w, wp), rtol=1e-13, atol=0)
+
+
+def test_pbwa_nan():
+    # Check that NaN's are returned outside of the range in which the
+    # implementation is accurate.
+    pts = [(-6, -6), (-6, 6), (6, -6), (6, 6)]
+    for p in pts:
+        assert_equal(sc.pbwa(*p), (np.nan, np.nan))
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_pdtr.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_pdtr.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2159e6bd3acd60f4bdc98fc9ce0f6d44ac76485
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_pdtr.py
@@ -0,0 +1,48 @@
+import numpy as np
+import scipy.special as sc
+from numpy.testing import assert_allclose, assert_array_equal
+
+
+class TestPdtr:
+    def test(self):
+        val = sc.pdtr(0, 1)
+        assert_allclose(val, np.exp(-1), atol=1.5e-7, rtol=0)
+
+    def test_m_zero(self):
+        val = sc.pdtr([0, 1, 2], 0)
+        assert_allclose(val, [1, 1, 1], atol=1.5e-7, rtol=0)
+
+    def test_rounding(self):
+        double_val = sc.pdtr([0.1, 1.1, 2.1], 1.0)
+        int_val = sc.pdtr([0, 1, 2], 1.0)
+        assert_array_equal(double_val, int_val)
+
+    def test_inf(self):
+        val = sc.pdtr(np.inf, 1.0)
+        assert_allclose(val, 1.0, atol=1.5e-7, rtol=0)
+
+    def test_domain(self):
+        val = sc.pdtr(-1.1, 1.0)
+        assert np.isnan(val)
+
+class TestPdtrc:
+    def test_value(self):
+        val = sc.pdtrc(0, 1)
+        assert_allclose(val, 1 - np.exp(-1), atol=1.5e-7, rtol=0)
+
+    def test_m_zero(self):
+        val = sc.pdtrc([0, 1, 2], 0.0)
+        assert_array_equal(val, [0, 0, 0])
+
+    def test_rounding(self):
+        double_val = sc.pdtrc([0.1, 1.1, 2.1], 1.0)
+        int_val = sc.pdtrc([0, 1, 2], 1.0)
+        assert_array_equal(double_val, int_val)
+
+    def test_inf(self):
+        val = sc.pdtrc(np.inf, 1.0)
+        assert_allclose(val, 0.0, atol=1.5e-7, rtol=0)
+
+    def test_domain(self):
+        val = sc.pdtrc(-1.1, 1.0)
+        assert np.isnan(val)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_powm1.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_powm1.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d809963f64ddaedf6b59de80dcd5f7ca8fa18a9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_powm1.py
@@ -0,0 +1,65 @@
+import pytest
+import numpy as np
+from numpy.testing import assert_allclose
+from scipy.special import powm1
+
+
+# Expected values were computed with mpmath, e.g.
+#
+#   >>> import mpmath
+#   >>> mpmath.np.dps = 200
+#   >>> print(float(mpmath.powm1(2.0, 1e-7))
+#   6.931472045825965e-08
+#
+powm1_test_cases = [
+    (1.25, 0.75, 0.18217701125396976, 1e-15),
+    (2.0, 1e-7, 6.931472045825965e-08, 1e-15),
+    (25.0, 5e-11, 1.6094379125636148e-10, 1e-15),
+    (0.99996, 0.75, -3.0000150002530058e-05, 1e-15),
+    (0.9999999999990905, 20, -1.81898940353014e-11, 1e-15),
+    (-1.25, 751.0, -6.017550852453444e+72, 2e-15)
+]
+
+
+@pytest.mark.parametrize('x, y, expected, rtol', powm1_test_cases)
+def test_powm1(x, y, expected, rtol):
+    p = powm1(x, y)
+    assert_allclose(p, expected, rtol=rtol)
+
+
+@pytest.mark.parametrize('x, y, expected',
+                         [(0.0, 0.0, 0.0),
+                          (0.0, -1.5, np.inf),
+                          (0.0, 1.75, -1.0),
+                          (-1.5, 2.0, 1.25),
+                          (-1.5, 3.0, -4.375),
+                          (np.nan, 0.0, 0.0),
+                          (1.0, np.nan, 0.0),
+                          (1.0, np.inf, 0.0),
+                          (1.0, -np.inf, 0.0),
+                          (np.inf, 7.5, np.inf),
+                          (np.inf, -7.5, -1.0),
+                          (3.25, np.inf, np.inf),
+                          (np.inf, np.inf, np.inf),
+                          (np.inf, -np.inf, -1.0),
+                          (np.inf, 0.0, 0.0),
+                          (-np.inf, 0.0, 0.0),
+                          (-np.inf, 2.0, np.inf),
+                          (-np.inf, 3.0, -np.inf),
+                          (-1.0, float(2**53 - 1), -2.0)])
+def test_powm1_exact_cases(x, y, expected):
+    # Test cases where we have an exact expected value.
+    p = powm1(x, y)
+    assert p == expected
+
+
+@pytest.mark.parametrize('x, y',
+                         [(-1.25, 751.03),
+                          (-1.25, np.inf),
+                          (np.nan, np.nan),
+                          (-np.inf, -np.inf),
+                          (-np.inf, 2.5)])
+def test_powm1_return_nan(x, y):
+    # Test cases where the expected return value is nan.
+    p = powm1(x, y)
+    assert np.isnan(p)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_precompute_expn_asy.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_precompute_expn_asy.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b6c6cba21d5c1d5fdfab879153ba8f125e98d5f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_precompute_expn_asy.py
@@ -0,0 +1,24 @@
+from numpy.testing import assert_equal
+
+from scipy.special._testutils import check_version, MissingModule
+from scipy.special._precompute.expn_asy import generate_A
+
+try:
+    import sympy
+    from sympy import Poly
+except ImportError:
+    sympy = MissingModule("sympy")
+
+
+@check_version(sympy, "1.0")
+def test_generate_A():
+    # Data from DLMF 8.20.5
+    x = sympy.symbols('x')
+    Astd = [Poly(1, x),
+            Poly(1, x),
+            Poly(1 - 2*x),
+            Poly(1 - 8*x + 6*x**2)]
+    Ares = generate_A(len(Astd))
+
+    for p, q in zip(Astd, Ares):
+        assert_equal(p, q)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_precompute_gammainc.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_precompute_gammainc.py
new file mode 100644
index 0000000000000000000000000000000000000000..86278b065a58bdde3fcde32a2f7ec0d53d17077d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_precompute_gammainc.py
@@ -0,0 +1,112 @@
+import pytest
+
+from scipy.special._testutils import MissingModule, check_version
+from scipy.special._mptestutils import (
+    Arg, IntArg, mp_assert_allclose, assert_mpmath_equal)
+from scipy.special._precompute.gammainc_asy import (
+    compute_g, compute_alpha, compute_d)
+from scipy.special._precompute.gammainc_data import gammainc, gammaincc
+
+try:
+    import sympy
+except ImportError:
+    sympy = MissingModule('sympy')
+
+try:
+    import mpmath as mp
+except ImportError:
+    mp = MissingModule('mpmath')
+
+
+pytestmark = pytest.mark.thread_unsafe(
+    reason=("mpmath gmpy2 backend is not thread-safe, "
+            "see https://github.com/mpmath/mpmath/issues/974"))
+
+@check_version(mp, '0.19')
+def test_g():
+    # Test data for the g_k. See DLMF 5.11.4.
+    with mp.workdps(30):
+        g = [mp.mpf(1), mp.mpf(1)/12, mp.mpf(1)/288,
+             -mp.mpf(139)/51840, -mp.mpf(571)/2488320,
+             mp.mpf(163879)/209018880, mp.mpf(5246819)/75246796800]
+        mp_assert_allclose(compute_g(7), g)
+
+
+@pytest.mark.slow
+@check_version(mp, '0.19')
+@check_version(sympy, '0.7')
+@pytest.mark.xfail_on_32bit("rtol only 2e-11, see gh-6938")
+def test_alpha():
+    # Test data for the alpha_k. See DLMF 8.12.14.
+    with mp.workdps(30):
+        alpha = [mp.mpf(0), mp.mpf(1), mp.mpf(1)/3, mp.mpf(1)/36,
+                 -mp.mpf(1)/270, mp.mpf(1)/4320, mp.mpf(1)/17010,
+                 -mp.mpf(139)/5443200, mp.mpf(1)/204120]
+        mp_assert_allclose(compute_alpha(9), alpha)
+
+
+@pytest.mark.xslow
+@check_version(mp, '0.19')
+@check_version(sympy, '0.7')
+def test_d():
+    # Compare the d_{k, n} to the results in appendix F of [1].
+    #
+    # Sources
+    # -------
+    # [1] DiDonato and Morris, Computation of the Incomplete Gamma
+    #     Function Ratios and their Inverse, ACM Transactions on
+    #     Mathematical Software, 1986.
+
+    with mp.workdps(50):
+        dataset = [(0, 0, -mp.mpf('0.333333333333333333333333333333')),
+                   (0, 12, mp.mpf('0.102618097842403080425739573227e-7')),
+                   (1, 0, -mp.mpf('0.185185185185185185185185185185e-2')),
+                   (1, 12, mp.mpf('0.119516285997781473243076536700e-7')),
+                   (2, 0, mp.mpf('0.413359788359788359788359788360e-2')),
+                   (2, 12, -mp.mpf('0.140925299108675210532930244154e-7')),
+                   (3, 0, mp.mpf('0.649434156378600823045267489712e-3')),
+                   (3, 12, -mp.mpf('0.191111684859736540606728140873e-7')),
+                   (4, 0, -mp.mpf('0.861888290916711698604702719929e-3')),
+                   (4, 12, mp.mpf('0.288658297427087836297341274604e-7')),
+                   (5, 0, -mp.mpf('0.336798553366358150308767592718e-3')),
+                   (5, 12, mp.mpf('0.482409670378941807563762631739e-7')),
+                   (6, 0, mp.mpf('0.531307936463992223165748542978e-3')),
+                   (6, 12, -mp.mpf('0.882860074633048352505085243179e-7')),
+                   (7, 0, mp.mpf('0.344367606892377671254279625109e-3')),
+                   (7, 12, -mp.mpf('0.175629733590604619378669693914e-6')),
+                   (8, 0, -mp.mpf('0.652623918595309418922034919727e-3')),
+                   (8, 12, mp.mpf('0.377358774161109793380344937299e-6')),
+                   (9, 0, -mp.mpf('0.596761290192746250124390067179e-3')),
+                   (9, 12, mp.mpf('0.870823417786464116761231237189e-6'))]
+        d = compute_d(10, 13)
+        res = [d[k][n] for k, n, std in dataset]
+        std = [x[2] for x in dataset]
+        mp_assert_allclose(res, std)
+
+
+@check_version(mp, '0.19')
+def test_gammainc():
+    # Quick check that the gammainc in
+    # special._precompute.gammainc_data agrees with mpmath's
+    # gammainc.
+    assert_mpmath_equal(gammainc,
+                        lambda a, x: mp.gammainc(a, b=x, regularized=True),
+                        [Arg(0, 100, inclusive_a=False), Arg(0, 100)],
+                        nan_ok=False, rtol=1e-17, n=50, dps=50)
+
+
+@pytest.mark.xslow
+@check_version(mp, '0.19')
+def test_gammaincc():
+    # Check that the gammaincc in special._precompute.gammainc_data
+    # agrees with mpmath's gammainc.
+    assert_mpmath_equal(lambda a, x: gammaincc(a, x, dps=1000),
+                        lambda a, x: mp.gammainc(a, a=x, regularized=True),
+                        [Arg(20, 100), Arg(20, 100)],
+                        nan_ok=False, rtol=1e-17, n=50, dps=1000)
+
+    # Test the fast integer path
+    assert_mpmath_equal(gammaincc,
+                        lambda a, x: mp.gammainc(a, a=x, regularized=True),
+                        [IntArg(1, 100), Arg(0, 100)],
+                        nan_ok=False, rtol=1e-17, n=50, dps=50)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_precompute_utils.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_precompute_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..89616b92329691ca76039fe11a7e08f7f3db1150
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_precompute_utils.py
@@ -0,0 +1,36 @@
+import pytest
+
+from scipy.special._testutils import MissingModule, check_version
+from scipy.special._mptestutils import mp_assert_allclose
+from scipy.special._precompute.utils import lagrange_inversion
+
+try:
+    import sympy
+except ImportError:
+    sympy = MissingModule('sympy')
+
+try:
+    import mpmath as mp
+except ImportError:
+    mp = MissingModule('mpmath')
+
+
+@pytest.mark.slow
+@check_version(sympy, '0.7')
+@check_version(mp, '0.19')
+class TestInversion:
+    @pytest.mark.xfail_on_32bit("rtol only 2e-9, see gh-6938")
+    def test_log(self):
+        with mp.workdps(30):
+            logcoeffs = mp.taylor(lambda x: mp.log(1 + x), 0, 10)
+            expcoeffs = mp.taylor(lambda x: mp.exp(x) - 1, 0, 10)
+            invlogcoeffs = lagrange_inversion(logcoeffs)
+            mp_assert_allclose(invlogcoeffs, expcoeffs)
+
+    @pytest.mark.xfail_on_32bit("rtol only 1e-15, see gh-6938")
+    def test_sin(self):
+        with mp.workdps(30):
+            sincoeffs = mp.taylor(mp.sin, 0, 10)
+            asincoeffs = mp.taylor(mp.asin, 0, 10)
+            invsincoeffs = lagrange_inversion(sincoeffs)
+            mp_assert_allclose(invsincoeffs, asincoeffs, atol=1e-30)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_round.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_round.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba28b7c1cff95327df0ed69086dd92b93399c3c4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_round.py
@@ -0,0 +1,18 @@
+import numpy as np
+import pytest
+
+from scipy.special import _test_internal
+
+
+@pytest.mark.fail_slow(20)
+@pytest.mark.skipif(not _test_internal.have_fenv(), reason="no fenv()")
+def test_add_round_up():
+    rng = np.random.RandomState(1234)
+    _test_internal.test_add_round(10**5, 'up', rng)
+
+
+@pytest.mark.fail_slow(20)
+@pytest.mark.skipif(not _test_internal.have_fenv(), reason="no fenv()")
+def test_add_round_down():
+    rng = np.random.RandomState(1234)
+    _test_internal.test_add_round(10**5, 'down', rng)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_sf_error.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_sf_error.py
new file mode 100644
index 0000000000000000000000000000000000000000..337bd0c65ebd712bfda1385072c15a47e8a495c2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_sf_error.py
@@ -0,0 +1,153 @@
+import sys
+import warnings
+
+import numpy as np
+from numpy.testing import assert_, assert_equal, HAS_REFCOUNT
+import pytest
+from pytest import raises as assert_raises
+
+import scipy.special as sc
+from scipy.special._ufuncs import _sf_error_test_function
+
+_sf_error_code_map = {
+    # skip 'ok'
+    'singular': 1,
+    'underflow': 2,
+    'overflow': 3,
+    'slow': 4,
+    'loss': 5,
+    'no_result': 6,
+    'domain': 7,
+    'arg': 8,
+    'other': 9,
+    'memory': 10,
+}
+
+_sf_error_actions = [
+    'ignore',
+    'warn',
+    'raise'
+]
+
+
+def _check_action(fun, args, action):
+    # TODO: special expert should correct
+    # the coercion at the true location?
+    args = np.asarray(args, dtype=np.dtype("long"))
+    if action == 'warn':
+        with pytest.warns(sc.SpecialFunctionWarning):
+            fun(*args)
+    elif action == 'raise':
+        with assert_raises(sc.SpecialFunctionError):
+            fun(*args)
+    else:
+        # action == 'ignore', make sure there are no warnings/exceptions
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+            fun(*args)
+
+
+def test_geterr():
+    err = sc.geterr()
+    for key, value in err.items():
+        assert_(key in _sf_error_code_map)
+        assert_(value in _sf_error_actions)
+
+
+def test_seterr():
+    entry_err = sc.geterr()
+    try:
+        for category, error_code in _sf_error_code_map.items():
+            for action in _sf_error_actions:
+                geterr_olderr = sc.geterr()
+                seterr_olderr = sc.seterr(**{category: action})
+                assert_(geterr_olderr == seterr_olderr)
+                newerr = sc.geterr()
+                assert_(newerr[category] == action)
+                geterr_olderr.pop(category)
+                newerr.pop(category)
+                assert_(geterr_olderr == newerr)
+                _check_action(_sf_error_test_function, (error_code,), action)
+    finally:
+        sc.seterr(**entry_err)
+
+
+@pytest.mark.thread_unsafe(reason="module refcounts are not stable in multiple threads")
+@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
+def test_sf_error_special_refcount():
+    # Regression test for gh-16233.
+    # Check that the reference count of scipy.special is not increased
+    # when a SpecialFunctionError is raised.
+    refcount_before = sys.getrefcount(sc)
+    with sc.errstate(all='raise'):
+        with pytest.raises(sc.SpecialFunctionError, match='domain error'):
+            sc.ndtri(2.0)
+    refcount_after = sys.getrefcount(sc)
+    assert refcount_after == refcount_before
+
+
+def test_errstate_pyx_basic():
+    olderr = sc.geterr()
+    with sc.errstate(singular='raise'):
+        with assert_raises(sc.SpecialFunctionError):
+            sc.loggamma(0)
+    assert_equal(olderr, sc.geterr())
+
+
+def test_errstate_c_basic():
+    olderr = sc.geterr()
+    with sc.errstate(domain='raise'):
+        with assert_raises(sc.SpecialFunctionError):
+            sc.spence(-1)
+    assert_equal(olderr, sc.geterr())
+
+
+def test_errstate_cpp_basic():
+    olderr = sc.geterr()
+    with sc.errstate(underflow='raise'):
+        with assert_raises(sc.SpecialFunctionError):
+            sc.wrightomega(-1000)
+    assert_equal(olderr, sc.geterr())
+
+
+def test_errstate_cpp_scipy_special():
+    olderr = sc.geterr()
+    with sc.errstate(singular='raise'):
+        with assert_raises(sc.SpecialFunctionError):
+            sc.lambertw(0, 1)
+    assert_equal(olderr, sc.geterr())
+
+
+def test_errstate_cpp_alt_ufunc_machinery():
+    olderr = sc.geterr()
+    with sc.errstate(singular='raise'):
+        with assert_raises(sc.SpecialFunctionError):
+            sc.gammaln(0)
+    assert_equal(olderr, sc.geterr())
+
+
+def test_errstate():
+    for category, error_code in _sf_error_code_map.items():
+        for action in _sf_error_actions:
+            olderr = sc.geterr()
+            with sc.errstate(**{category: action}):
+                _check_action(_sf_error_test_function, (error_code,), action)
+            assert_equal(olderr, sc.geterr())
+
+
+def test_errstate_all_but_one():
+    olderr = sc.geterr()
+    with sc.errstate(all='raise', singular='ignore'):
+        sc.gammaln(0)
+        with assert_raises(sc.SpecialFunctionError):
+            sc.spence(-1.0)
+    assert_equal(olderr, sc.geterr())
+
+
+def test_check_overflow_message():
+    # Regression test for a bug where the overflow and underflow
+    # messages were switched.
+    with pytest.raises(sc.SpecialFunctionError, match="overflow"):
+        with sc.errstate(all='raise'):
+            # This should trigger an overflow:
+            sc.yn(3, 1e-105)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_sici.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_sici.py
new file mode 100644
index 0000000000000000000000000000000000000000..d33c1795641ba74f777fcfdcffe80d86463477e3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_sici.py
@@ -0,0 +1,36 @@
+import numpy as np
+
+import scipy.special as sc
+from scipy.special._testutils import FuncData
+
+
+def test_sici_consistency():
+    # Make sure the implementation of sici for real arguments agrees
+    # with the implementation of sici for complex arguments.
+
+    # On the negative real axis Cephes drops the imaginary part in ci
+    def sici(x):
+        si, ci = sc.sici(x + 0j)
+        return si.real, ci.real
+    
+    x = np.r_[-np.logspace(8, -30, 200), 0, np.logspace(-30, 8, 200)]
+    si, ci = sc.sici(x)
+    dataset = np.column_stack((x, si, ci))
+    FuncData(sici, dataset, 0, (1, 2), rtol=1e-12).check()
+
+
+def test_shichi_consistency():
+    # Make sure the implementation of shichi for real arguments agrees
+    # with the implementation of shichi for complex arguments.
+
+    # On the negative real axis Cephes drops the imaginary part in chi
+    def shichi(x):
+        shi, chi = sc.shichi(x + 0j)
+        return shi.real, chi.real
+
+    # Overflow happens quickly, so limit range
+    x = np.r_[-np.logspace(np.log10(700), -30, 200), 0,
+              np.logspace(-30, np.log10(700), 200)]
+    shi, chi = sc.shichi(x)
+    dataset = np.column_stack((x, shi, chi))
+    FuncData(shichi, dataset, 0, (1, 2), rtol=1e-14).check()
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_specfun.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_specfun.py
new file mode 100644
index 0000000000000000000000000000000000000000..f36fd2915be8c8d14138493c34dfd5733a2a1e6d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_specfun.py
@@ -0,0 +1,48 @@
+"""
+Various made-up tests to hit different branches of the code in specfun.c
+"""
+
+import numpy as np
+from numpy.testing import assert_allclose
+from scipy import special
+
+
+def test_cva2_cv0_branches():
+    res, resp = special.mathieu_cem([40, 129], [13, 14], [30, 45])
+    assert_allclose(res, np.array([-0.3741211, 0.74441928]))
+    assert_allclose(resp, np.array([-37.02872758, -86.13549877]))
+
+    res, resp = special.mathieu_sem([40, 129], [13, 14], [30, 45])
+    assert_allclose(res, np.array([0.92955551, 0.66771207]))
+    assert_allclose(resp, np.array([-14.91073448, 96.02954185]))
+
+
+def test_chgm_branches():
+    res = special.eval_genlaguerre(-3.2, 3, 2.5)
+    assert_allclose(res, -0.7077721935779854)
+
+
+def test_hygfz_branches():
+    """(z == 1.0) && (c-a-b > 0.0)"""
+    res = special.hyp2f1(1.5, 2.5, 4.5, 1.+0.j)
+    assert_allclose(res, 10.30835089459151+0j)
+    """(cabs(z+1) < eps) && (fabs(c-a+b - 1.0) < eps)"""
+    res = special.hyp2f1(5+5e-16, 2, 2, -1.0 + 5e-16j)
+    assert_allclose(res, 0.031249999999999986+3.9062499999999994e-17j)
+
+
+def test_pro_rad1():
+    # https://github.com/scipy/scipy/issues/21058
+    # Reference values taken from WolframAlpha
+    # SpheroidalS1(1, 1, 30, 1.1)
+    # SpheroidalS1Prime(1, 1, 30, 1.1)
+    res = special.pro_rad1(1, 1, 30, 1.1)
+    assert_allclose(res, (0.009657872296166435, 3.253369651472877), rtol=2e-5)
+
+def test_pro_rad2():
+    # https://github.com/scipy/scipy/issues/21461
+    # Reference values taken from WolframAlpha
+    # SpheroidalS2(0, 0, 3, 1.02)
+    # SpheroidalS2Prime(0, 0, 3, 1.02)
+    res = special.pro_rad2(0, 0, 3, 1.02)
+    assert_allclose(res, (-0.35089596858528077, 13.652764213480872), rtol=10e-10)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_spence.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_spence.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbb26ac281dff81ea71b30318731065fe5a78f94
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_spence.py
@@ -0,0 +1,32 @@
+import numpy as np
+from numpy import sqrt, log, pi
+from scipy.special._testutils import FuncData
+from scipy.special import spence
+
+
+def test_consistency():
+    # Make sure the implementation of spence for real arguments
+    # agrees with the implementation of spence for imaginary arguments.
+
+    x = np.logspace(-30, 300, 200)
+    dataset = np.vstack((x + 0j, spence(x))).T
+    FuncData(spence, dataset, 0, 1, rtol=1e-14).check()
+
+
+def test_special_points():
+    # Check against known values of Spence's function.
+
+    phi = (1 + sqrt(5))/2
+    dataset = [(1, 0),
+               (2, -pi**2/12),
+               (0.5, pi**2/12 - log(2)**2/2),
+               (0, pi**2/6),
+               (-1, pi**2/4 - 1j*pi*log(2)),
+               ((-1 + sqrt(5))/2, pi**2/15 - log(phi)**2),
+               ((3 - sqrt(5))/2, pi**2/10 - log(phi)**2),
+               (phi, -pi**2/15 + log(phi)**2/2),
+               # Corrected from Zagier, "The Dilogarithm Function"
+               ((3 + sqrt(5))/2, -pi**2/10 - log(phi)**2)]
+
+    dataset = np.asarray(dataset)
+    FuncData(spence, dataset, 0, 1, rtol=1e-14).check()
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_spfun_stats.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_spfun_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..7dd4164bbab3ba88765617148dc7ea488bf81c65
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_spfun_stats.py
@@ -0,0 +1,61 @@
+import numpy as np
+from numpy.testing import (assert_array_equal, assert_array_almost_equal_nulp,
+                           assert_allclose)
+from pytest import raises as assert_raises
+
+from scipy.special import gammaln, multigammaln  # type:ignore[attr-defined]
+
+
+class TestMultiGammaLn:
+
+    def test1(self):
+        # A test of the identity
+        #     Gamma_1(a) = Gamma(a)
+        np.random.seed(1234)
+        a = np.abs(np.random.randn())
+        assert_array_equal(multigammaln(a, 1), gammaln(a))
+
+    def test2(self):
+        # A test of the identity
+        #     Gamma_2(a) = sqrt(pi) * Gamma(a) * Gamma(a - 0.5)
+        a = np.array([2.5, 10.0])
+        result = multigammaln(a, 2)
+        expected = np.log(np.sqrt(np.pi)) + gammaln(a) + gammaln(a - 0.5)
+        assert_allclose(result, expected, atol=1.5e-7, rtol=0)
+
+    def test_bararg(self):
+        assert_raises(ValueError, multigammaln, 0.5, 1.2)
+
+
+def _check_multigammaln_array_result(a, d):
+    # Test that the shape of the array returned by multigammaln
+    # matches the input shape, and that all the values match
+    # the value computed when multigammaln is called with a scalar.
+    result = multigammaln(a, d)
+    assert_array_equal(a.shape, result.shape)
+    a1 = a.ravel()
+    result1 = result.ravel()
+    for i in range(a.size):
+        assert_array_almost_equal_nulp(result1[i], multigammaln(a1[i], d))
+
+
+def test_multigammaln_array_arg():
+    # Check that the array returned by multigammaln has the correct
+    # shape and contains the correct values.  The cases have arrays
+    # with several different shapes.
+    # The cases include a regression test for ticket #1849
+    # (a = np.array([2.0]), an array with a single element).
+    np.random.seed(1234)
+
+    cases = [
+        # a, d
+        (np.abs(np.random.randn(3, 2)) + 5, 5),
+        (np.abs(np.random.randn(1, 2)) + 5, 5),
+        (np.arange(10.0, 18.0).reshape(2, 2, 2), 3),
+        (np.array([2.0]), 3),
+        (np.float64(2.0), 3),
+    ]
+
+    for a, d in cases:
+        _check_multigammaln_array_result(a, d)
+
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_sph_harm.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_sph_harm.py
new file mode 100644
index 0000000000000000000000000000000000000000..117f0938379082125f97d738d27364f7821eaefc
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_sph_harm.py
@@ -0,0 +1,48 @@
+import numpy as np
+import pytest
+
+from numpy.testing import assert_allclose
+import scipy.special as sc
+
+class TestSphHarm:
+    @pytest.mark.slow
+    def test_p(self):
+        m_max = 20
+        n_max = 10
+
+        theta = np.linspace(0, np.pi)
+        phi = np.linspace(0, 2*np.pi)
+        theta, phi = np.meshgrid(theta, phi)
+
+        y, y_jac, y_hess = sc.sph_harm_y_all(n_max, m_max, theta, phi, diff_n=2)
+        p, p_jac, p_hess = sc.sph_legendre_p_all(n_max, m_max, theta, diff_n=2)
+
+        m = np.concatenate([np.arange(m_max + 1), np.arange(-m_max, 0)])
+        m = np.expand_dims(m, axis=(0,)+tuple(range(2,theta.ndim+2)))
+
+        assert_allclose(y, p * np.exp(1j * m * phi))
+
+        assert_allclose(y_jac[..., 0], p_jac * np.exp(1j * m * phi))
+        assert_allclose(y_jac[..., 1], 1j * m * p * np.exp(1j * m * phi))
+
+        assert_allclose(y_hess[..., 0, 0], p_hess * np.exp(1j * m * phi))
+        assert_allclose(y_hess[..., 0, 1], 1j * m * p_jac * np.exp(1j * m * phi))
+        assert_allclose(y_hess[..., 1, 0], y_hess[..., 0, 1])
+        assert_allclose(y_hess[..., 1, 1], -m * m * p * np.exp(1j * m * phi))
+
+    @pytest.mark.parametrize("n_max", [7, 10, 50])
+    @pytest.mark.parametrize("m_max", [1, 4, 5, 9, 14])
+    def test_all(self, n_max, m_max):
+        theta = np.linspace(0, np.pi)
+        phi = np.linspace(0, 2 * np.pi)
+
+        n = np.arange(n_max + 1)
+        n = np.expand_dims(n, axis=tuple(range(1,theta.ndim+2)))
+
+        m = np.concatenate([np.arange(m_max + 1), np.arange(-m_max, 0)])
+        m = np.expand_dims(m, axis=(0,)+tuple(range(2,theta.ndim+2)))
+
+        y_actual = sc.sph_harm_y_all(n_max, m_max, theta, phi)
+        y_desired = sc.sph_harm_y(n, m, theta, phi)
+
+        np.testing.assert_allclose(y_actual, y_desired, rtol=1e-05)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_spherical_bessel.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_spherical_bessel.py
new file mode 100644
index 0000000000000000000000000000000000000000..8444a4c16aa9e3b9ffc204f824a3f136a030f2be
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_spherical_bessel.py
@@ -0,0 +1,407 @@
+#
+# Tests of spherical Bessel functions.
+#
+import warnings
+
+import numpy as np
+from numpy.testing import assert_allclose
+import pytest
+from numpy import sin, cos, sinh, cosh, exp, inf, nan, r_, pi
+
+from scipy.special import spherical_jn, spherical_yn, spherical_in, spherical_kn
+from scipy.integrate import quad
+
+
+class TestSphericalJn:
+    def test_spherical_jn_exact(self):
+        # https://dlmf.nist.gov/10.49.E3
+        # Note: exact expression is numerically stable only for small
+        # n or z >> n.
+        x = np.array([0.12, 1.23, 12.34, 123.45, 1234.5])
+        assert_allclose(spherical_jn(2, x),
+                        (-1/x + 3/x**3)*sin(x) - 3/x**2*cos(x))
+
+    def test_spherical_jn_recurrence_complex(self):
+        # https://dlmf.nist.gov/10.51.E1
+        n = np.array([1, 2, 3, 7, 12])
+        x = 1.1 + 1.5j
+        assert_allclose(spherical_jn(n - 1, x) + spherical_jn(n + 1, x),
+                        (2*n + 1)/x*spherical_jn(n, x))
+
+    def test_spherical_jn_recurrence_real(self):
+        # https://dlmf.nist.gov/10.51.E1
+        n = np.array([1, 2, 3, 7, 12])
+        x = 0.12
+        assert_allclose(spherical_jn(n - 1, x) + spherical_jn(n + 1,x),
+                        (2*n + 1)/x*spherical_jn(n, x))
+
+    def test_spherical_jn_inf_real(self):
+        # https://dlmf.nist.gov/10.52.E3
+        n = 6
+        x = np.array([-inf, inf])
+        assert_allclose(spherical_jn(n, x), np.array([0, 0]))
+
+    def test_spherical_jn_inf_complex(self):
+        # https://dlmf.nist.gov/10.52.E3
+        n = 7
+        x = np.array([-inf + 0j, inf + 0j, inf*(1+1j)])
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "invalid value encountered in multiply", RuntimeWarning)
+            assert_allclose(spherical_jn(n, x), np.array([0, 0, inf*(1+1j)]))
+
+    def test_spherical_jn_large_arg_1(self):
+        # https://github.com/scipy/scipy/issues/2165
+        # Reference value computed using mpmath, via
+        # besselj(n + mpf(1)/2, z)*sqrt(pi/(2*z))
+        assert_allclose(spherical_jn(2, 3350.507), -0.00029846226538040747)
+
+    def test_spherical_jn_large_arg_2(self):
+        # https://github.com/scipy/scipy/issues/1641
+        # Reference value computed using mpmath, via
+        # besselj(n + mpf(1)/2, z)*sqrt(pi/(2*z))
+        assert_allclose(spherical_jn(2, 10000), 3.0590002633029811e-05)
+
+    def test_spherical_jn_at_zero(self):
+        # https://dlmf.nist.gov/10.52.E1
+        # But note that n = 0 is a special case: j0 = sin(x)/x -> 1
+        n = np.array([0, 1, 2, 5, 10, 100])
+        x = 0
+        assert_allclose(spherical_jn(n, x), np.array([1, 0, 0, 0, 0, 0]))
+
+
+class TestSphericalYn:
+    def test_spherical_yn_exact(self):
+        # https://dlmf.nist.gov/10.49.E5
+        # Note: exact expression is numerically stable only for small
+        # n or z >> n.
+        x = np.array([0.12, 1.23, 12.34, 123.45, 1234.5])
+        assert_allclose(spherical_yn(2, x),
+                        (1/x - 3/x**3)*cos(x) - 3/x**2*sin(x))
+
+    def test_spherical_yn_recurrence_real(self):
+        # https://dlmf.nist.gov/10.51.E1
+        n = np.array([1, 2, 3, 7, 12])
+        x = 0.12
+        assert_allclose(spherical_yn(n - 1, x) + spherical_yn(n + 1,x),
+                        (2*n + 1)/x*spherical_yn(n, x))
+
+    def test_spherical_yn_recurrence_complex(self):
+        # https://dlmf.nist.gov/10.51.E1
+        n = np.array([1, 2, 3, 7, 12])
+        x = 1.1 + 1.5j
+        assert_allclose(spherical_yn(n - 1, x) + spherical_yn(n + 1, x),
+                        (2*n + 1)/x*spherical_yn(n, x))
+
+    def test_spherical_yn_inf_real(self):
+        # https://dlmf.nist.gov/10.52.E3
+        n = 6
+        x = np.array([-inf, inf])
+        assert_allclose(spherical_yn(n, x), np.array([0, 0]))
+
+    def test_spherical_yn_inf_complex(self):
+        # https://dlmf.nist.gov/10.52.E3
+        n = 7
+        x = np.array([-inf + 0j, inf + 0j, inf*(1+1j)])
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "invalid value encountered in multiply", RuntimeWarning)
+            assert_allclose(spherical_yn(n, x), np.array([0, 0, inf*(1+1j)]))
+
+    def test_spherical_yn_at_zero(self):
+        # https://dlmf.nist.gov/10.52.E2
+        n = np.array([0, 1, 2, 5, 10, 100])
+        x = 0
+        assert_allclose(spherical_yn(n, x), np.full(n.shape, -inf))
+
+    def test_spherical_yn_at_zero_complex(self):
+        # Consistently with numpy:
+        # >>> -np.cos(0)/0
+        # -inf
+        # >>> -np.cos(0+0j)/(0+0j)
+        # (-inf + nan*j)
+        n = np.array([0, 1, 2, 5, 10, 100])
+        x = 0 + 0j
+        assert_allclose(spherical_yn(n, x), np.full(n.shape, nan))
+
+
+class TestSphericalJnYnCrossProduct:
+    def test_spherical_jn_yn_cross_product_1(self):
+        # https://dlmf.nist.gov/10.50.E3
+        n = np.array([1, 5, 8])
+        x = np.array([0.1, 1, 10])
+        left = (spherical_jn(n + 1, x) * spherical_yn(n, x) -
+                spherical_jn(n, x) * spherical_yn(n + 1, x))
+        right = 1/x**2
+        assert_allclose(left, right)
+
+    def test_spherical_jn_yn_cross_product_2(self):
+        # https://dlmf.nist.gov/10.50.E3
+        n = np.array([1, 5, 8])
+        x = np.array([0.1, 1, 10])
+        left = (spherical_jn(n + 2, x) * spherical_yn(n, x) -
+                spherical_jn(n, x) * spherical_yn(n + 2, x))
+        right = (2*n + 3)/x**3
+        assert_allclose(left, right)
+
+
+class TestSphericalIn:
+    def test_spherical_in_exact(self):
+        # https://dlmf.nist.gov/10.49.E9
+        x = np.array([0.12, 1.23, 12.34, 123.45])
+        assert_allclose(spherical_in(2, x),
+                        (1/x + 3/x**3)*sinh(x) - 3/x**2*cosh(x))
+
+    def test_spherical_in_recurrence_real(self):
+        # https://dlmf.nist.gov/10.51.E4
+        n = np.array([1, 2, 3, 7, 12])
+        x = 0.12
+        assert_allclose(spherical_in(n - 1, x) - spherical_in(n + 1,x),
+                        (2*n + 1)/x*spherical_in(n, x))
+
+    def test_spherical_in_recurrence_complex(self):
+        # https://dlmf.nist.gov/10.51.E1
+        n = np.array([1, 2, 3, 7, 12])
+        x = 1.1 + 1.5j
+        assert_allclose(spherical_in(n - 1, x) - spherical_in(n + 1,x),
+                        (2*n + 1)/x*spherical_in(n, x))
+
+    def test_spherical_in_inf_real(self):
+        # https://dlmf.nist.gov/10.52.E3
+        n = 5
+        x = np.array([-inf, inf])
+        assert_allclose(spherical_in(n, x), np.array([-inf, inf]))
+
+    def test_spherical_in_inf_complex(self):
+        # https://dlmf.nist.gov/10.52.E5
+        # Ideally, i1n(n, 1j*inf) = 0 and i1n(n, (1+1j)*inf) = (1+1j)*inf, but
+        # this appears impossible to achieve because C99 regards any complex
+        # value with at least one infinite  part as a complex infinity, so
+        # 1j*inf cannot be distinguished from (1+1j)*inf.  Therefore, nan is
+        # the correct return value.
+        n = 7
+        x = np.array([-inf + 0j, inf + 0j, inf*(1+1j)])
+        assert_allclose(spherical_in(n, x), np.array([-inf, inf, nan]))
+
+    def test_spherical_in_at_zero(self):
+        # https://dlmf.nist.gov/10.52.E1
+        # But note that n = 0 is a special case: i0 = sinh(x)/x -> 1
+        n = np.array([0, 1, 2, 5, 10, 100])
+        x = 0
+        assert_allclose(spherical_in(n, x), np.array([1, 0, 0, 0, 0, 0]))
+
+
+class TestSphericalKn:
+    def test_spherical_kn_exact(self):
+        # https://dlmf.nist.gov/10.49.E13
+        x = np.array([0.12, 1.23, 12.34, 123.45])
+        assert_allclose(spherical_kn(2, x),
+                        pi/2*exp(-x)*(1/x + 3/x**2 + 3/x**3))
+
+    def test_spherical_kn_recurrence_real(self):
+        # https://dlmf.nist.gov/10.51.E4
+        n = np.array([1, 2, 3, 7, 12])
+        x = 0.12
+        assert_allclose(
+            (-1)**(n - 1)*spherical_kn(n - 1, x) - (-1)**(n + 1)*spherical_kn(n + 1,x),
+            (-1)**n*(2*n + 1)/x*spherical_kn(n, x)
+        )
+
+    def test_spherical_kn_recurrence_complex(self):
+        # https://dlmf.nist.gov/10.51.E4
+        n = np.array([1, 2, 3, 7, 12])
+        x = 1.1 + 1.5j
+        assert_allclose(
+            (-1)**(n - 1)*spherical_kn(n - 1, x) - (-1)**(n + 1)*spherical_kn(n + 1,x),
+            (-1)**n*(2*n + 1)/x*spherical_kn(n, x)
+        )
+
+    def test_spherical_kn_inf_real(self):
+        # https://dlmf.nist.gov/10.52.E6
+        n = 5
+        x = np.array([-inf, inf])
+        assert_allclose(spherical_kn(n, x), np.array([-inf, 0]))
+
+    def test_spherical_kn_inf_complex(self):
+        # https://dlmf.nist.gov/10.52.E6
+        # The behavior at complex infinity depends on the sign of the real
+        # part: if Re(z) >= 0, then the limit is 0; if Re(z) < 0, then it's
+        # z*inf.  This distinction cannot be captured, so we return nan.
+        n = 7
+        x = np.array([-inf + 0j, inf + 0j, inf*(1+1j)])
+        assert_allclose(spherical_kn(n, x), np.array([-inf, 0, nan]))
+
+    def test_spherical_kn_at_zero(self):
+        # https://dlmf.nist.gov/10.52.E2
+        n = np.array([0, 1, 2, 5, 10, 100])
+        x = 0
+        assert_allclose(spherical_kn(n, x), np.full(n.shape, inf))
+
+    def test_spherical_kn_at_zero_complex(self):
+        # https://dlmf.nist.gov/10.52.E2
+        n = np.array([0, 1, 2, 5, 10, 100])
+        x = 0 + 0j
+        assert_allclose(spherical_kn(n, x), np.full(n.shape, nan))
+
+
+class SphericalDerivativesTestCase:
+    def fundamental_theorem(self, n, a, b):
+        integral, tolerance = quad(lambda z: self.df(n, z), a, b)
+        assert_allclose(integral,
+                        self.f(n, b) - self.f(n, a),
+                        atol=tolerance)
+
+    @pytest.mark.slow
+    def test_fundamental_theorem_0(self):
+        self.fundamental_theorem(0, 3.0, 15.0)
+
+    @pytest.mark.slow
+    def test_fundamental_theorem_7(self):
+        self.fundamental_theorem(7, 0.5, 1.2)
+
+
+class TestSphericalJnDerivatives(SphericalDerivativesTestCase):
+    def f(self, n, z):
+        return spherical_jn(n, z)
+
+    def df(self, n, z):
+        return spherical_jn(n, z, derivative=True)
+
+    def test_spherical_jn_d_zero(self):
+        n = np.array([0, 1, 2, 3, 7, 15])
+        assert_allclose(spherical_jn(n, 0, derivative=True),
+                        np.array([0, 1/3, 0, 0, 0, 0]))
+
+
+class TestSphericalYnDerivatives(SphericalDerivativesTestCase):
+    def f(self, n, z):
+        return spherical_yn(n, z)
+
+    def df(self, n, z):
+        return spherical_yn(n, z, derivative=True)
+
+
+class TestSphericalInDerivatives(SphericalDerivativesTestCase):
+    def f(self, n, z):
+        return spherical_in(n, z)
+
+    def df(self, n, z):
+        return spherical_in(n, z, derivative=True)
+
+    def test_spherical_in_d_zero(self):
+        n = np.array([0, 1, 2, 3, 7, 15])
+        spherical_in(n, 0, derivative=False)
+        assert_allclose(spherical_in(n, 0, derivative=True),
+                        np.array([0, 1/3, 0, 0, 0, 0]))
+
+
+class TestSphericalKnDerivatives(SphericalDerivativesTestCase):
+    def f(self, n, z):
+        return spherical_kn(n, z)
+
+    def df(self, n, z):
+        return spherical_kn(n, z, derivative=True)
+
+
+class TestSphericalOld:
+    # These are tests from the TestSpherical class of test_basic.py,
+    # rewritten to use spherical_* instead of sph_* but otherwise unchanged.
+
+    def test_sph_in(self):
+        # This test reproduces test_basic.TestSpherical.test_sph_in.
+        i1n = np.empty((2,2))
+        x = 0.2
+
+        i1n[0][0] = spherical_in(0, x)
+        i1n[0][1] = spherical_in(1, x)
+        i1n[1][0] = spherical_in(0, x, derivative=True)
+        i1n[1][1] = spherical_in(1, x, derivative=True)
+
+        inp0 = (i1n[0][1])
+        inp1 = (i1n[0][0] - 2.0/0.2 * i1n[0][1])
+        assert_allclose(i1n[0], np.array([1.0066800127054699381,
+                                          0.066933714568029540839]),
+                        atol=1.5e-12, rtol=0.0)
+        assert_allclose(i1n[1], [inp0, inp1], atol=1.5e-12, rtol=0)
+
+    def test_sph_in_kn_order0(self):
+        x = 1.
+        sph_i0 = np.empty((2,))
+        sph_i0[0] = spherical_in(0, x)
+        sph_i0[1] = spherical_in(0, x, derivative=True)
+        sph_i0_expected = np.array([np.sinh(x)/x,
+                                    np.cosh(x)/x-np.sinh(x)/x**2])
+        assert_allclose(r_[sph_i0], sph_i0_expected, atol=1.5e-7, rtol=0)
+
+        sph_k0 = np.empty((2,))
+        sph_k0[0] = spherical_kn(0, x)
+        sph_k0[1] = spherical_kn(0, x, derivative=True)
+        sph_k0_expected = np.array([0.5*pi*exp(-x)/x,
+                                    -0.5*pi*exp(-x)*(1/x+1/x**2)])
+        assert_allclose(r_[sph_k0], sph_k0_expected, atol=1.5e-7, rtol=0)
+
+    def test_sph_jn(self):
+        s1 = np.empty((2,3))
+        x = 0.2
+
+        s1[0][0] = spherical_jn(0, x)
+        s1[0][1] = spherical_jn(1, x)
+        s1[0][2] = spherical_jn(2, x)
+        s1[1][0] = spherical_jn(0, x, derivative=True)
+        s1[1][1] = spherical_jn(1, x, derivative=True)
+        s1[1][2] = spherical_jn(2, x, derivative=True)
+
+        s10 = -s1[0][1]
+        s11 = s1[0][0]-2.0/0.2*s1[0][1]
+        s12 = s1[0][1]-3.0/0.2*s1[0][2]
+        assert_allclose(s1[0], [0.99334665397530607731,
+                                0.066400380670322230863,
+                                0.0026590560795273856680],
+                        atol=1.5e-12, rtol=0)
+        assert_allclose(s1[1], [s10, s11, s12], atol=1.5e-12, rtol=0)
+
+    def test_sph_kn(self):
+        kn = np.empty((2,3))
+        x = 0.2
+
+        kn[0][0] = spherical_kn(0, x)
+        kn[0][1] = spherical_kn(1, x)
+        kn[0][2] = spherical_kn(2, x)
+        kn[1][0] = spherical_kn(0, x, derivative=True)
+        kn[1][1] = spherical_kn(1, x, derivative=True)
+        kn[1][2] = spherical_kn(2, x, derivative=True)
+
+        kn0 = -kn[0][1]
+        kn1 = -kn[0][0]-2.0/0.2*kn[0][1]
+        kn2 = -kn[0][1]-3.0/0.2*kn[0][2]
+        assert_allclose(kn[0], [6.4302962978445670140,
+                                38.581777787067402086,
+                                585.15696310385559829],
+                        atol=1.5e-12, rtol=0)
+        assert_allclose(kn[1], [kn0, kn1, kn2], atol=1.5e-9, rtol=0)
+
+    def test_sph_yn(self):
+        sy1 = spherical_yn(2, 0.2)
+        sy2 = spherical_yn(0, 0.2)
+        # previous values in the system
+        assert_allclose(sy1, -377.52483, atol=1.5e-5, rtol=0)
+        assert_allclose(sy2, -4.9003329, atol=1.5e-5, rtol=0)
+        sphpy = (spherical_yn(0, 0.2) - 2*spherical_yn(2, 0.2))/3
+        sy3 = spherical_yn(1, 0.2, derivative=True)
+        # compare correct derivative val. (correct =-system val).
+        assert_allclose(sy3, sphpy, atol=1.5e-4, rtol=0)
+
+
+@pytest.mark.parametrize('derivative', [False, True])
+@pytest.mark.parametrize('fun', [spherical_jn, spherical_in,
+                                 spherical_yn, spherical_kn])
+def test_negative_real_gh14582(derivative, fun):
+    # gh-14582 reported that the spherical Bessel functions did not work
+    # with negative real argument `z`. Check that this is resolved.
+    rng = np.random.default_rng(3598435982345987234)
+    size = 25
+    n = rng.integers(0, 10, size=size)
+    z = rng.standard_normal(size=size)
+    res = fun(n, z, derivative=derivative)
+    ref = fun(n, z+0j, derivative=derivative)
+    assert_allclose(res, ref.real)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_support_alternative_backends.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_support_alternative_backends.py
new file mode 100644
index 0000000000000000000000000000000000000000..34d239e4e8f8a619b59ef79f5ce2e4842e5c41a0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_support_alternative_backends.py
@@ -0,0 +1,404 @@
+from functools import partial
+import pickle
+
+import pytest
+from hypothesis import given, strategies
+import hypothesis.extra.numpy as npst
+from packaging import version
+
+from scipy import special
+from scipy.special._support_alternative_backends import _special_funcs
+from scipy._lib._array_api_no_0d import xp_assert_close
+from scipy._lib._array_api import (is_cupy, is_dask, is_jax, is_torch,
+                                   make_xp_pytest_param, make_xp_test_case,
+                                   get_native_namespace_name)
+from scipy._external.array_api_compat import numpy as np
+import scipy._external.array_api_extra as xpx
+
+
+# Run all tests in this module in the Array API CI, including those without
+# the xp fixture
+pytestmark = pytest.mark.array_api_backends
+
+lazy_xp_modules = [special]
+
+
+def _skip_or_tweak_alternative_backends(xp, nfo, dtypes, int_only):
+    """Skip tests for specific intersections of scipy.special functions 
+    vs. backends vs. dtypes vs. devices.
+    Also suggest bespoke tweaks.
+
+    Returns
+    -------
+    positive_only : list[bool]
+        Whether you should exclusively test positive inputs.
+    dtypes : list[str]
+        dtype strings 'float64', 'int32', 'int64', etc. with integer types
+        mapped to the type of the NumPy default int.
+    """
+    f_name = nfo.name
+    if isinstance(nfo.positive_only, dict):
+        positive_only = nfo.positive_only.get(get_native_namespace_name(xp), False)
+    else:
+        positive_only = nfo.positive_only
+    if isinstance(positive_only, bool):
+        positive_only = [positive_only]*nfo.n_args
+
+    dtypes = [np.intp.__name__ if dtype == "intp" else dtype for dtype in dtypes]
+
+    if f_name in {'betaincinv'} and is_cupy(xp):
+        pytest.xfail("CuPy uses different convention for out of domain input.")
+
+    if not any('int' in dtype for dtype in dtypes):
+        return positive_only, dtypes
+
+    # Integer-specific issues from this point onwards
+
+    if f_name in {'gamma', 'gammasgn'} and is_cupy(xp):
+        # CuPy has not yet updated gamma pole behavior to match
+        # https://github.com/scipy/scipy/pull/21827.
+        positive_only = [True]
+
+    if f_name in {'poch'} and is_jax(xp):
+        # Jax uses a different convention at gamma poles.
+        positive_only = [True, True]
+
+    if f_name == 'multigammaln':
+        pytest.skip("multigammaln raises for out of domain inputs.")
+
+    if ((is_torch(xp) and f_name in {'gammainc', 'gammaincc'})
+        or (is_cupy(xp) and f_name in {'stdtr', 'i0e', 'i1e'})
+        or (is_jax(xp) and f_name in {'stdtr', 'ndtr', 'ndtri', 'log_ndtr', 'hyp1f1',
+                                      'hyp2f1', 'spence', 'kl_div'})
+    ):
+        pytest.skip(f"`{f_name}` does not support integer types")
+
+    # int/float mismatched args support is sketchy
+    if (any('float' in dtype for dtype in dtypes)
+        and ((is_torch(xp) and f_name in ('rel_entr', 'xlogy', 'polygamma',
+                                          'zeta', 'xlog1py'))
+             or (is_jax(xp) and f_name in ('gammainc', 'gammaincc', 'expn',
+                                           'rel_entr', 'xlogy', 'betaln',
+                                           'polygamma', 'zeta', 'poch',
+                                           'xlog1py')))
+    ):
+        pytest.xfail("dtypes do not match")
+
+    if (is_torch(xp) and xpx.default_dtype(xp) == xp.float32):
+        # On PyTorch with float32 default dtype, all ints are promoted
+        # to float32, but when falling back to NumPy/SciPy int64 is promoted
+        # instead to float64. Integer only parameters essentially do not
+        # participate in determination of the result type in PyTorch with
+        # float32 default dtype, but will impact the output dtype as if
+        # they were float64 when falling back to NumPy/SciPy.
+        if not nfo.torch_native:
+            pytest.xfail("dtypes do not match")
+
+    return positive_only, dtypes
+
+
+@pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+@pytest.mark.filterwarnings("ignore:overflow encountered:RuntimeWarning")
+@pytest.mark.parametrize('shapes', [[(0,)]*4, [tuple()]*4, [(10,)]*4,
+                                    [(10,), (11, 1), (12, 1, 1), (13, 1, 1, 1)]])
+@pytest.mark.parametrize('base_dtype', ['float32', 'float64', 'intp'])
+@pytest.mark.parametrize(
+    'func,nfo', [make_xp_pytest_param(i.wrapper, i) for i in _special_funcs])
+def test_support_alternative_backends(xp, func, nfo, base_dtype, shapes):
+    int_only = nfo.int_only
+    if int_only is None:
+        int_only = (False, ) * nfo.n_args
+        dtypes = (base_dtype, ) * nfo.n_args
+    else:
+        dtypes = tuple(
+            'intp' if needs_int else base_dtype for needs_int in int_only
+        )
+
+    positive_only, dtypes = _skip_or_tweak_alternative_backends(
+        xp, nfo, dtypes, int_only
+    )
+
+    dtypes_np = [getattr(np, dtype) for dtype in dtypes]
+    dtypes_xp = [getattr(xp, dtype) for dtype in dtypes]
+
+    shapes = shapes[:nfo.n_args]
+    rng = np.random.default_rng(984254252920492019)
+    args_np = []
+
+    # Handle cases where there's an argument which only takes scalar values.
+    python_int_only = nfo.python_int_only
+    if isinstance(python_int_only, dict):
+        python_int_only = python_int_only.get(get_native_namespace_name(xp))
+    scalar_or_0d_only = nfo.scalar_or_0d_only
+    if isinstance(scalar_or_0d_only, dict):
+        scalar_or_0d_only = scalar_or_0d_only.get(get_native_namespace_name(xp))
+
+    test_large_ints = nfo.test_large_ints
+    if isinstance(nfo.test_large_ints, dict):
+        test_large_ints = test_large_ints.get(get_native_namespace_name(xp), False)
+
+    if python_int_only is None:
+        python_int_only = [False] * nfo.n_args
+    if scalar_or_0d_only is None:
+        scalar_or_0d_only = [False] * nfo.n_args
+
+    no_shape = [
+        cond1 or cond2 for cond1, cond2 in zip(python_int_only, scalar_or_0d_only)
+    ]
+
+    shapes = [shape if not cond else None for shape, cond in zip(shapes, no_shape)]
+
+    for dtype, dtype_np, shape, needs_python_int in zip(
+            dtypes, dtypes_np, shapes, python_int_only
+    ):
+        if 'int' in dtype and test_large_ints:
+            iinfo = np.iinfo(dtype_np)
+            rand = partial(rng.integers, iinfo.min, iinfo.max + 1)
+        elif 'int' in dtype:
+            rand = partial(rng.integers, -20, 21)
+        else:
+            rand = rng.standard_normal
+        val = rand(size=shape, dtype=dtype_np)
+        if needs_python_int:
+            # The logic above for determining shapes guarantees that
+            # shape will be None in the above line when a Python int is required,
+            # so this can safely be converted to an int.
+            val = int(val)
+        args_np.append(val)
+
+    args_np = [
+        np.abs(arg) if cond else arg for arg, cond in zip(args_np, positive_only)
+    ]
+
+    args_xp = [
+        xp.asarray(arg, dtype=dtype_xp) if not needs_python_int
+        else arg
+        for arg, dtype_xp, needs_python_int
+        in zip(args_np, dtypes_xp, python_int_only)
+    ]
+
+    args_np = [
+        np.asarray(arg, dtype=dtype_np) if not needs_python_int
+        else arg
+        for arg, dtype_np, needs_python_int
+        in zip(args_np, dtypes_np, python_int_only)
+    ]
+
+    if is_dask(xp):
+        # We're using map_blocks to dispatch the function to Dask.
+        # This is the correct thing to do IF all tested functions are elementwise;
+        # otherwise the output would change depending on chunking.
+        # Try to trigger bugs related to having multiple chunks.
+        args_xp = [arg.rechunk(5) for arg in args_xp]
+
+    res = nfo.wrapper(*args_xp)  # Also wrapped by lazy_xp_function
+    ref = nfo.func(*args_np)  # Unwrapped ufunc
+    if (
+            is_torch(xp)
+            and xpx.default_dtype(xp) == xp.float32
+            and "float64" not in dtypes
+    ):
+        # int64 promotes like float32 on torch with default dtype = float32
+        # cast reference if needed
+        ref = np.float32(ref)
+    # When dtype_np is integer, the output dtype can be float
+    atol = 0 if ref.dtype.kind in 'iu' else 10 * np.finfo(ref.dtype).eps
+    rtol = None
+    if is_torch(xp) and func.__name__ == 'j1':
+        # If we end up needing more function/backend specific tolerance
+        # adjustments, this should be factored out properly.
+        atol = 1e-7
+        rtol = 1e-5
+    xp_assert_close(
+        res, xp.asarray(ref), rtol=rtol, atol=atol, check_0d=nfo.produces_0d
+    )
+
+
+@pytest.mark.parametrize(
+    'func, nfo',
+    [make_xp_pytest_param(i.wrapper, i) for i in _special_funcs if i.n_args >= 2])
+@pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+def test_support_alternative_backends_mismatched_dtypes(xp, func, nfo):
+    """Test mix-n-match of int and float arguments"""
+    if func.__name__ in {'expn', 'polygamma', 'multigammaln', 'bdtr', 'bdtrc', 'bdtri',
+                         'nbdtr', 'nbdtrc', 'nbdtri', 'pdtri'}:
+        pytest.skip(f"dtypes for {func.__name__} make it a bad fit for this test.")
+    dtypes = ['intp', 'float32', 'float64', 'float64'][:nfo.n_args]
+
+    positive_only, dtypes = _skip_or_tweak_alternative_backends(
+        xp, nfo, dtypes, (False,)*nfo.n_args
+    )
+    dtypes_np = [getattr(np, dtype) for dtype in dtypes]
+    dtypes_xp = [getattr(xp, dtype) for dtype in dtypes]
+
+    rng = np.random.default_rng(984254252920492019)
+    iinfo = np.iinfo(np.intp)
+    if nfo.test_large_ints:
+        randint = partial(rng.integers, iinfo.min, iinfo.max + 1)
+    else:
+        randint = partial(rng.integers, -20, 21)
+    args_np = [
+        randint(size=1, dtype=np.int64),
+        rng.standard_normal(size=1, dtype=np.float32),
+        rng.standard_normal(size=1, dtype=np.float64),
+        rng.standard_normal(size=1, dtype=np.float64),
+    ][:nfo.n_args]
+    args_np = [
+        np.abs(arg) if cond else arg for arg, cond in zip(args_np, positive_only)
+    ]
+
+    args_xp = [xp.asarray(arg, dtype=dtype_xp)
+               for arg, dtype_xp in zip(args_np, dtypes_xp)]
+    args_np = [np.asarray(arg, dtype=dtype_np)
+               for arg, dtype_np in zip(args_np, dtypes_np)]
+
+    res = nfo.wrapper(*args_xp)  # Also wrapped by lazy_xp_function
+    ref = nfo.func(*args_np)  # Unwrapped ufunc
+    if (
+            is_torch(xp)
+            and xpx.default_dtype(xp) == xp.float32
+            and "float64" not in dtypes
+    ):
+        # int64 promotes like float32 on torch with default dtype = float32
+        # cast reference if needed
+        ref = np.float32(ref)
+
+    atol = 10 * np.finfo(ref.dtype).eps
+    xp_assert_close(res, xp.asarray(ref), atol=atol)
+
+
+@pytest.mark.xslow
+@given(data=strategies.data())
+@pytest.mark.fail_slow(5)
+@pytest.mark.parametrize(
+    'func,nfo', [make_xp_pytest_param(nfo.wrapper, nfo) for nfo in _special_funcs])
+@pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+@pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+@pytest.mark.filterwarnings("ignore:overflow encountered:RuntimeWarning:dask")
+@pytest.mark.filterwarnings(
+    "ignore:overflow encountered:RuntimeWarning:array_api_strict"
+)
+def test_support_alternative_backends_hypothesis(xp, func, nfo, data):
+    if func.__name__ in {'expn', 'polygamma', 'multigammaln', 'bdtr', 'bdtrc', 'bdtri',
+                         'nbdtr', 'nbdtrc', 'nbdtri', 'pdtri'}:
+        pytest.skip(f"dtypes for {func.__name__} make it a bad fit for this test.")
+    dtype = data.draw(strategies.sampled_from(['float32', 'float64', 'intp']))
+    positive_only, dtypes = _skip_or_tweak_alternative_backends(
+        xp, nfo, [dtype], (False,)*nfo.n_args
+    )
+    dtype_np = getattr(np, dtypes[0])
+    dtype_xp = getattr(xp, dtypes[0])
+
+    elements = {'allow_subnormal': False}
+    # Most failures are due to NaN or infinity; uncomment to suppress them
+    # elements['allow_infinity'] = False
+    # elements['allow_nan'] = False
+    if any(positive_only):
+        elements['min_value'] = 0
+
+    shapes, _ = data.draw(
+        npst.mutually_broadcastable_shapes(num_shapes=nfo.n_args))
+    args_np = [data.draw(npst.arrays(dtype_np, shape, elements=elements))
+               for shape in shapes]
+
+    args_xp = [xp.asarray(arg, dtype=dtype_xp) for arg in args_np]
+    args_np = [np.asarray(arg, dtype=dtype_np) for arg in args_np]
+
+    res = nfo.wrapper(*args_xp)  # Also wrapped by lazy_xp_function
+    ref = nfo.func(*args_np)  # Unwrapped ufunc
+    if (
+            is_torch(xp)
+            and xpx.default_dtype(xp) == xp.float32
+            and dtype != "float64"
+    ):
+        # int64 promotes like float32 on torch with default dtype = float32
+        # cast reference if needed
+        ref = np.float32(ref)
+
+    # When dtype_np is integer, the output dtype can be float
+    atol = 0 if ref.dtype.kind in 'iu' else 10 * np.finfo(ref.dtype).eps
+    xp_assert_close(res, xp.asarray(ref), atol=atol)
+
+
+@pytest.mark.filterwarnings("ignore:numpy.core is deprecated:DeprecationWarning")
+@pytest.mark.parametrize("func", [nfo.wrapper for nfo in _special_funcs])
+def test_pickle(func):
+    roundtrip = pickle.loads(pickle.dumps(func))
+    assert roundtrip is func
+
+
+@pytest.mark.parametrize("func", [nfo.wrapper for nfo in _special_funcs])
+def test_repr(func):
+    assert func.__name__ in repr(func)
+    assert "locals" not in repr(func)
+
+
+@pytest.mark.skipif(
+    version.parse(np.__version__) < version.parse("2.2"),  # type:ignore[attr-defined]
+    reason="Can't update ufunc __doc__ when SciPy is compiled vs. NumPy < 2.2")
+@pytest.mark.parametrize('func', [nfo.wrapper for nfo in _special_funcs])
+def test_doc(func):
+    """xp_capabilities updates the docstring in place. 
+    Make sure it does so exactly once, including when SCIPY_ARRAY_API is not set.
+    """
+    match = "has experimental support for Python Array API"
+    assert func.__doc__.count(match) == 1
+
+
+@pytest.mark.parametrize(
+    'func,n_args,int_only,is_ufunc',
+    [(nfo.wrapper, nfo.n_args, nfo.int_only, nfo.is_ufunc)
+     for nfo in _special_funcs]
+)
+def test_ufunc_kwargs(func, n_args, int_only, is_ufunc):
+    """Test that numpy-specific out= and dtype= keyword arguments
+    of ufuncs still work when SCIPY_ARRAY_API is set.
+    """
+    if not is_ufunc:
+        pytest.skip(f"{func.__name__} is not a ufunc.")
+    if int_only is None:
+        int_only = (False, ) * n_args
+    # out=
+    args = [
+        np.asarray([.1, .2]) if not needs_int
+        else np.asarray([1, 2])
+        for needs_int in int_only
+    ]
+    out = np.empty(2)
+    y = func(*args, out=out)
+    xp_assert_close(y, out)
+
+    # out= with out.dtype != args.dtype
+    out = np.empty(2, dtype=np.float32)
+    y = func(*args, out=out)
+    xp_assert_close(y, out)
+
+    if func.__name__ in {"bdtr", "bdtrc", "bdtri"}:
+        # The below function evaluation will trigger a deprecation warning
+        # with dtype=np.float32. This will go away if the trigger is actually
+        # pulled on the deprecation.
+        return
+
+    # dtype=
+    y = func(*args, dtype=np.float32)
+    assert y.dtype == np.float32
+
+
+@make_xp_test_case(special.chdtr)
+def test_chdtr_gh21311(xp):
+    # the edge case behavior of generic chdtr was not right; see gh-21311
+    # be sure to test at least these cases
+    # should add `np.nan` into the mix when gh-21317 is resolved
+    x = np.asarray([-np.inf, -1., 0., 1., np.inf])
+    v = x.reshape(-1, 1)
+    ref = special.chdtr(v, x)
+    res = special.chdtr(xp.asarray(v), xp.asarray(x))
+    xp_assert_close(res, xp.asarray(ref))
+
+
+@make_xp_test_case(special.fdtrc)
+def test_mixed_arrays_and_python_scalars(xp):
+    # Tests that the delegation infrastructure respects NEP50.
+    res = special.fdtrc(1.1, 2., xp.asarray(1., dtype=xp.float32))
+    ref = xp.asarray(0.4349004, dtype=xp.float32)
+    xp_assert_close(res, ref)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_trig.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_trig.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5f8f79f8d2cac51bf9476f04f13df2bfac587a5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_trig.py
@@ -0,0 +1,76 @@
+import warnings
+
+import pytest
+import numpy as np
+from numpy.testing import assert_equal, assert_allclose
+
+from scipy.special._ufuncs import _sinpi as sinpi
+from scipy.special._ufuncs import _cospi as cospi
+
+
+def test_integer_real_part():
+    x = np.arange(-100, 101)
+    y = np.hstack((-np.linspace(310, -30, 10), np.linspace(-30, 310, 10)))
+    x, y = np.meshgrid(x, y)
+    z = x + 1j*y
+    # In the following we should be *exactly* right
+    res = sinpi(z)
+    assert_equal(res.real, 0.0)
+    res = cospi(z)
+    assert_equal(res.imag, 0.0)
+
+
+def test_half_integer_real_part():
+    x = np.arange(-100, 101) + 0.5
+    y = np.hstack((-np.linspace(310, -30, 10), np.linspace(-30, 310, 10)))
+    x, y = np.meshgrid(x, y)
+    z = x + 1j*y
+    # In the following we should be *exactly* right
+    res = sinpi(z)
+    assert_equal(res.imag, 0.0)
+    res = cospi(z)
+    assert_equal(res.real, 0.0)
+
+
+@pytest.mark.skip("Temporary skip while gh-19526 is being resolved")
+def test_intermediate_overlow():
+    # Make sure we avoid overflow in situations where cosh/sinh would
+    # overflow but the product with sin/cos would not
+    sinpi_pts = [complex(1 + 1e-14, 227),
+                 complex(1e-35, 250),
+                 complex(1e-301, 445)]
+    # Data generated with mpmath
+    sinpi_std = [complex(-8.113438309924894e+295, -np.inf),
+                 complex(1.9507801934611995e+306, np.inf),
+                 complex(2.205958493464539e+306, np.inf)]
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore", "invalid value encountered in multiply", RuntimeWarning)
+        for p, std in zip(sinpi_pts, sinpi_std):
+            res = sinpi(p)
+            assert_allclose(res.real, std.real)
+            assert_allclose(res.imag, std.imag)
+
+    # Test for cosine, less interesting because cos(0) = 1.
+    p = complex(0.5 + 1e-14, 227)
+    std = complex(-8.113438309924894e+295, -np.inf)
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore", "invalid value encountered in multiply", RuntimeWarning)
+        res = cospi(p)
+        assert_allclose(res.real, std.real)
+        assert_allclose(res.imag, std.imag)
+
+
+def test_zero_sign():
+    y = sinpi(-0.0)
+    assert y == 0.0
+    assert np.signbit(y)
+
+    y = sinpi(0.0)
+    assert y == 0.0
+    assert not np.signbit(y)
+
+    y = cospi(0.5)
+    assert y == 0.0
+    assert not np.signbit(y)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ufunc_infra.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ufunc_infra.py
new file mode 100644
index 0000000000000000000000000000000000000000..30b08cde365de9c115935d1100001f8ba1349276
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ufunc_infra.py
@@ -0,0 +1,34 @@
+import numpy as np
+import pytest
+
+from scipy.special import mathieu_sem
+# from scipy.special._ufuncs import _mathieu_sem
+
+
+@pytest.mark.parametrize("m_shape,q_shape,x_shape,where_shape", [
+    ((5, 1), (1, 1), (1, 10), ()),
+    ((2, 3), (2, 3), (2, 3), ()),
+    ((1, 5), (1, 5), (10, 5), ()),
+    ((5, 1), (1, 1), (1, 10), (1, 10)),
+    ((2, 3), (2, 3), (2, 3), (2, 3)),
+    ((1, 5), (1, 5), (10, 5), (10, 5)),
+    ((5, 1), (1, 1), (1, 10), (5, 1)),
+])
+def test_out(m_shape, q_shape, x_shape, where_shape):
+    rng = np.random.default_rng(1234)
+
+    m = rng.integers(1, 20, m_shape)
+    q = rng.uniform(0, 10, q_shape)
+    x = rng.uniform(0, 90, x_shape)
+
+    if where_shape == ():
+        batch_shape = np.broadcast_shapes(m_shape, q_shape, x_shape)
+        where = True
+    else:
+        where = rng.choice([True, False], size=where_shape)
+        batch_shape = np.broadcast_shapes(m_shape, q_shape, x_shape, where_shape)
+
+    out0 = np.empty(batch_shape)
+    out1 = np.empty(batch_shape)
+    res0, res1 = mathieu_sem(m, q, x, out=(out0, out1), where=where)
+    assert res0 is out0 and res1 is out1
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ufunc_signatures.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ufunc_signatures.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4fdb02a855797923a792dfce7747a10f6b407db
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_ufunc_signatures.py
@@ -0,0 +1,119 @@
+"""Test that all ufuncs have float32-preserving signatures.
+
+This was once guaranteed through the code generation script for
+generating ufuncs, `scipy/special/_generate_pyx.py`. Starting with
+gh-20260, SciPy developers have begun moving to generate ufuncs
+through direct use of the NumPy C API (through C++). Existence of
+float32 preserving signatures must now be tested since it is no
+longer guaranteed.
+"""
+
+import numpy as np
+import pytest
+import scipy.special._ufuncs
+import scipy.special._gufuncs
+import scipy.special as special
+
+from numpy.testing import assert_equal
+
+
+# Single precision is not implemented for these ufuncs;
+# floating point inputs must be float64.
+exceptions = ['_gen_harmonic', '_normalized_gen_harmonic']
+
+_ufuncs = []
+for funcname in dir(scipy.special._ufuncs):
+    if funcname not in exceptions:
+        _ufuncs.append(getattr(scipy.special._ufuncs, funcname))
+for funcname in dir(scipy.special._gufuncs):
+    _ufuncs.append(getattr(scipy.special._gufuncs, funcname))
+
+# Not all module members are actually ufuncs
+_ufuncs = [func for func in _ufuncs if isinstance(func, np.ufunc)]
+
+@pytest.mark.parametrize("ufunc", _ufuncs)
+def test_ufunc_signatures(ufunc):
+
+    # From _generate_pyx.py
+    # "Don't add float32 versions of ufuncs with integer arguments, as this
+    # can lead to incorrect dtype selection if the integer arguments are
+    # arrays, but float arguments are scalars.
+    # This may be a NumPy bug, but we need to work around it.
+    # cf. gh-4895, https://github.com/numpy/numpy/issues/5895"
+    types = set(sig for sig in ufunc.types
+                if not ("l" in sig or "i" in sig or "q" in sig or "p" in sig))
+
+    # Generate the full expanded set of signatures which should exist. There
+    # should be matching float and double versions of any existing signature.
+    expanded_types = set()
+    for sig in types:
+        expanded_types.update(
+            [sig.replace("d", "f").replace("D", "F"),
+             sig.replace("f", "d").replace("F", "D")]
+        )
+    assert types == expanded_types
+
+
+def _get_nan_val(typecode):
+    return {
+        "f": np.asarray(np.nan, dtype=np.float32),
+        "d": np.asarray(np.nan, dtype=np.float64),
+        "g": np.asarray(np.nan, dtype=np.longdouble),
+        "F": np.asarray(np.nan + np.nan*1j, dtype=np.complex64),
+        "D": np.asarray(np.nan + np.nan*1j, dtype=np.complex128),
+        "G": np.asarray(np.nan + np.nan*1j, dtype=np.clongdouble),
+    }[typecode]
+
+skips = {
+    # These hit https://github.com/scipy/xsf/issues/94
+    "eval_chebyc",
+    "eval_chebys",
+    "eval_chebyt",
+    "eval_chebyu",
+    "eval_gegenbauer",
+    "eval_genlaguerre",
+    "eval_jacobi",
+    "eval_laguerre",
+    "eval_legendre",
+    "eval_sh_chebyt",
+    "eval_sh_chebyu",
+    "eval_sh_jacobi",
+    "eval_sh_legendre",
+    "hyp1f1",
+    "hyp2f1",
+    # warns if called with noninteger values of some args
+    "bdtr",
+    "bdtrc",
+    "bdtri",
+}
+_multi_arg_public_ufuncs = [
+    ufunc for ufunc in _ufuncs if ufunc.nargs - ufunc.nout > 1
+    and ufunc.__name__ not in skips and ufunc.__name__ in special.__all__
+]
+
+
+@pytest.mark.parametrize("ufunc", _multi_arg_public_ufuncs)
+def test_nep50(ufunc):
+    # Test that functions with multiple arguments respect nep50 promotion rules.
+    rng = np.random.default_rng(1234)
+    # As in test_ufunc_signatures, filter out signatures involving integers.
+    types = set(sig for sig in ufunc.types
+                if not ("l" in sig or "i" in sig or "q" in sig or "p" in sig))
+    for sig in types:
+        input_types, output_types = sig.split("->")
+        # since we only care about dtypes and not values here, just use an appropriately
+        # typed nan for each argument.
+        args = [_get_nan_val(typecode) for typecode in input_types]
+        # swap out a random one of the nans with an appropriately typed numpy scalar.
+        idx = rng.choice(len(args))
+        args[idx] = float("nan") if np.isrealobj(args[idx]) else complex("nan")
+        result = ufunc(*args)
+        result = [result] if len(output_types) == 1 else result
+        result = np.asarray(result)
+        
+        # Test that the output is an appropriately typed nan. This also implicitly
+        # tests that ufuncs propagate nans correctly.
+        assert_equal(
+            result, np.asarray([_get_nan_val(typecode) for typecode in output_types]),
+            strict=True
+        )
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_wright_bessel.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_wright_bessel.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef30163f55c90850fe32ca600eb47547e4fa5e03
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_wright_bessel.py
@@ -0,0 +1,205 @@
+# Reference MPMATH implementation:
+#
+# import mpmath
+# from mpmath import nsum
+#
+# def Wright_Series_MPMATH(a, b, z, dps=50, method='r+s+e', steps=[1000]):
+#    """Compute Wright' generalized Bessel function as Series.
+#
+#    This uses mpmath for arbitrary precision.
+#    """
+#    with mpmath.workdps(dps):
+#        res = nsum(lambda k: z**k/mpmath.fac(k) * mpmath.rgamma(a*k+b),
+#                          [0, mpmath.inf],
+#                          tol=dps, method=method, steps=steps
+#                          )
+#
+#    return res
+
+from itertools import product
+
+import pytest
+import numpy as np
+from numpy.testing import assert_equal, assert_allclose
+
+import scipy.special as sc
+from scipy.special import log_wright_bessel, loggamma, rgamma, wright_bessel
+
+
+@pytest.mark.parametrize('a', [0, 1e-6, 0.1, 0.5, 1, 10])
+@pytest.mark.parametrize('b', [0, 1e-6, 0.1, 0.5, 1, 10])
+def test_wright_bessel_zero(a, b):
+    """Test at x = 0."""
+    assert_equal(wright_bessel(a, b, 0.), rgamma(b))
+    assert_allclose(log_wright_bessel(a, b, 0.), -loggamma(b))
+
+
+@pytest.mark.parametrize('b', [0, 1e-6, 0.1, 0.5, 1, 10])
+@pytest.mark.parametrize('x', [0, 1e-6, 0.1, 0.5, 1])
+def test_wright_bessel_iv(b, x):
+    """Test relation of wright_bessel and modified bessel function iv.
+
+    iv(z) = (1/2*z)**v * Phi(1, v+1; 1/4*z**2).
+    See https://dlmf.nist.gov/10.46.E2
+    """
+    if x != 0:
+        v = b - 1
+        wb = wright_bessel(1, v + 1, x**2 / 4.)
+        # Note: iv(v, x) has precision of less than 1e-12 for some cases
+        # e.g v=1-1e-6 and x=1e-06)
+        assert_allclose(np.power(x / 2., v) * wb,
+                        sc.iv(v, x),
+                        rtol=1e-11, atol=1e-11)
+
+
+@pytest.mark.parametrize('a', [0, 1e-6, 0.1, 0.5, 1, 10])
+@pytest.mark.parametrize('b', [1, 1 + 1e-3, 2, 5, 10])
+@pytest.mark.parametrize('x', [0, 1e-6, 0.1, 0.5, 1, 5, 10, 100])
+def test_wright_functional(a, b, x):
+    """Test functional relation of wright_bessel.
+
+    Phi(a, b-1, z) = a*z*Phi(a, b+a, z) + (b-1)*Phi(a, b, z)
+
+    Note that d/dx Phi(a, b, x) = Phi(a, b-1, x)
+    See Eq. (22) of
+    B. Stankovic, On the Function of E. M. Wright,
+    Publ. de l' Institut Mathematique, Beograd,
+    Nouvelle S`er. 10 (1970), 113-124.
+    """
+    assert_allclose(wright_bessel(a, b - 1, x),
+                    a * x * wright_bessel(a, b + a, x)
+                    + (b - 1) * wright_bessel(a, b, x),
+                    rtol=1e-8, atol=1e-8)
+
+
+# grid of rows [a, b, x, value, accuracy] that do not reach 1e-11 accuracy
+# see output of:
+# cd scipy/scipy/_precompute
+# python wright_bessel_data.py
+grid_a_b_x_value_acc = np.array([
+    [0.1, 100.0, 709.7827128933841, 8.026353022981087e+34, 2e-8],
+    [0.5, 10.0, 709.7827128933841, 2.680788404494657e+48, 9e-8],
+    [0.5, 10.0, 1000.0, 2.005901980702872e+64, 1e-8],
+    [0.5, 100.0, 1000.0, 3.4112367580445246e-117, 6e-8],
+    [1.0, 20.0, 100000.0, 1.7717158630699857e+225, 3e-11],
+    [1.0, 100.0, 100000.0, 1.0269334596230763e+22, np.nan],
+    [1.0000000000000222, 20.0, 100000.0, 1.7717158630001672e+225, 3e-11],
+    [1.0000000000000222, 100.0, 100000.0, 1.0269334595866202e+22, np.nan],
+    [1.5, 0.0, 500.0, 15648961196.432373, 3e-11],
+    [1.5, 2.220446049250313e-14, 500.0, 15648961196.431465, 3e-11],
+    [1.5, 1e-10, 500.0, 15648961192.344728, 3e-11],
+    [1.5, 1e-05, 500.0, 15648552437.334162, 3e-11],
+    [1.5, 0.1, 500.0, 12049870581.10317, 2e-11],
+    [1.5, 20.0, 100000.0, 7.81930438331405e+43, 3e-9],
+    [1.5, 100.0, 100000.0, 9.653370857459075e-130, np.nan],
+    ])
+
+
+@pytest.mark.xfail
+@pytest.mark.parametrize(
+    'a, b, x, phi',
+    grid_a_b_x_value_acc[:, :4].tolist())
+def test_wright_data_grid_failures(a, b, x, phi):
+    """Test cases of test_data that do not reach relative accuracy of 1e-11"""
+    assert_allclose(wright_bessel(a, b, x), phi, rtol=1e-11)
+
+
+@pytest.mark.parametrize(
+    'a, b, x, phi, accuracy',
+    grid_a_b_x_value_acc.tolist())
+def test_wright_data_grid_less_accurate(a, b, x, phi, accuracy):
+    """Test cases of test_data that do not reach relative accuracy of 1e-11
+
+    Here we test for reduced accuracy or even nan.
+    """
+    if np.isnan(accuracy):
+        assert np.isnan(wright_bessel(a, b, x))
+    else:
+        assert_allclose(wright_bessel(a, b, x), phi, rtol=accuracy)
+
+
+@pytest.mark.parametrize(
+    'a, b, x',
+    list(
+        product([0, 0.1, 0.5, 1.5, 5, 10], [1, 2], [1e-3, 1, 1.5, 5, 10])
+    )
+)
+def test_log_wright_bessel_same_as_wright_bessel(a, b, x):
+    """Test that log_wright_bessel equals log of wright_bessel."""
+    assert_allclose(
+        log_wright_bessel(a, b, x),
+        np.log(wright_bessel(a, b, x)),
+        rtol=1e-8,
+    )
+
+
+# Computed with, see also mp_wright_bessel from wright_bessel_data.py:
+#
+# from functools import lru_cache
+# import mpmath as mp
+#
+# @lru_cache(maxsize=1_000_000)
+# def rgamma_cached(x, dps):
+#     with mp.workdps(dps):
+#         return mp.rgamma(x)
+#
+# def mp_log_wright_bessel(a, b, x, dps=100, maxterms=10_000, method="d"):
+#     """Compute log of Wright's generalized Bessel function as Series with mpmath."""
+#     with mp.workdps(dps):
+#         a, b, x = mp.mpf(a), mp.mpf(b), mp.mpf(x)
+#         res = mp.nsum(lambda k: x**k / mp.fac(k)
+#                       * rgamma_cached(a * k + b, dps=dps),
+#                       [0, mp.inf],
+#                       tol=dps, method=method, steps=[maxterms]
+#                       )
+#         return mp.log(res)
+#
+# Sometimes, one needs to set maxterms as high as 1_00_000 to get accurate results for
+# phi.
+# At the end of the day, we can only hope that results are correct for very large x,
+# e.g. by the asymptotic series, as there is no way to produce those in "exact"
+# arithmetic.
+# Note: accuracy = np.nan means log_wright_bessel returns nan.
+@pytest.mark.parametrize(
+    'a, b, x, phi, accuracy',
+    [
+        (0, 0, 0, -np.inf, 1e-11),
+        (0, 0, 1, -np.inf, 1e-11),
+        (0, 1, 1.23, 1.23, 1e-11),
+        (0, 1, 1e50, 1e50, 1e-11),
+        (1e-5, 0, 700, 695.0421608273609, 1e-11),
+        (1e-5, 0, 1e3, 995.40052566540066, 1e-11),
+        (1e-5, 100, 1e3, 640.8197935670078, 1e-11),
+        (1e-3, 0, 1e4, 9987.2229532297262, 1e-11),
+        (1e-3, 0, 1e5, 99641.920687169507, 1e-11),
+        (1e-3, 0, 1e6, 994118.55560054416, 1e-11),  # maxterms=1_000_000
+        (1e-3, 10, 1e5, 99595.47710802537, 1e-11),
+        (1e-3, 50, 1e5, 99401.240922855647, 1e-3),
+        (1e-3, 100, 1e5, 99143.465191656527, np.nan),
+        (0.5, 0, 1e5, 4074.1112442197941, 1e-11),
+        (0.5, 0, 1e7, 87724.552120038896, 1e-11),
+        (0.5, 100, 1e5, 3350.3928746306163, np.nan),
+        (0.5, 100, 1e7, 86696.109975301719, 1e-11),
+        (1, 0, 1e5, 634.06765787997266, 1e-11),
+        (1, 0, 1e8, 20003.339639312035, 1e-11),
+        (1.5, 0, 1e5, 197.01777556071194, 1e-11),
+        (1.5, 0, 1e8, 3108.987414395706, 1e-11),
+        (1.5, 100, 1e8, 2354.8915946283275, np.nan),
+        (5, 0, 1e5, 9.8980480013203547, 1e-11),
+        (5, 0, 1e8, 33.642337258687465, 1e-11),
+        (5, 0, 1e12, 157.53704288117429, 1e-11),
+        (5, 100, 1e5, -359.13419630792148, 1e-11),
+        (5, 100, 1e12, -337.07722086995229, 1e-4),
+        (5, 100, 1e20, 2588.2471229986845, 2e-6),
+        (100, 0, 1e5, -347.62127990460517, 1e-11),
+        (100, 0, 1e20, -313.08250350969449, 1e-11),
+        (100, 100, 1e5, -359.1342053695754, 1e-11),
+        (100, 100, 1e20, -359.1342053695754, 1e-11),
+    ]
+)
+def test_log_wright_bessel(a, b, x, phi, accuracy):
+    """Test for log_wright_bessel, in particular for large x."""
+    if np.isnan(accuracy):
+        assert np.isnan(log_wright_bessel(a, b, x))
+    else:
+        assert_allclose(log_wright_bessel(a, b, x), phi, rtol=accuracy)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_wrightomega.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_wrightomega.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1a93ca007e42fea3a0dec634c51b37f03effa9e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_wrightomega.py
@@ -0,0 +1,117 @@
+import pytest
+import numpy as np
+from numpy.testing import assert_, assert_equal, assert_allclose
+
+import scipy.special as sc
+from scipy.special._testutils import assert_func_equal
+
+
+def test_wrightomega_nan():
+    pts = [complex(np.nan, 0),
+           complex(0, np.nan),
+           complex(np.nan, np.nan),
+           complex(np.nan, 1),
+           complex(1, np.nan)]
+    for p in pts:
+        res = sc.wrightomega(p)
+        assert_(np.isnan(res.real))
+        assert_(np.isnan(res.imag))
+
+
+def test_wrightomega_inf_branch():
+    pts = [complex(-np.inf, np.pi/4),
+           complex(-np.inf, -np.pi/4),
+           complex(-np.inf, 3*np.pi/4),
+           complex(-np.inf, -3*np.pi/4)]
+    expected_results = [complex(0.0, 0.0),
+                        complex(0.0, -0.0),
+                        complex(-0.0, 0.0),
+                        complex(-0.0, -0.0)]
+    for p, expected in zip(pts, expected_results):
+        res = sc.wrightomega(p)
+        # We can't use assert_equal(res, expected) because in older versions of
+        # numpy, assert_equal doesn't check the sign of the real and imaginary
+        # parts when comparing complex zeros. It does check the sign when the
+        # arguments are *real* scalars.
+        assert_equal(res.real, expected.real)
+        assert_equal(res.imag, expected.imag)
+
+
+def test_wrightomega_inf():
+    pts = [complex(np.inf, 10),
+           complex(-np.inf, 10),
+           complex(10, np.inf),
+           complex(10, -np.inf)]
+    for p in pts:
+        assert_equal(sc.wrightomega(p), p)
+
+
+def test_wrightomega_singular():
+    pts = [complex(-1.0, np.pi),
+           complex(-1.0, -np.pi)]
+    for p in pts:
+        res = sc.wrightomega(p)
+        assert_equal(res, -1.0)
+        assert_(np.signbit(res.imag) == np.bool_(False))
+
+
+@pytest.mark.parametrize('x, desired', [
+    (-np.inf, 0),
+    (np.inf, np.inf),
+])
+def test_wrightomega_real_infinities(x, desired):
+    assert sc.wrightomega(x) == desired
+
+
+def test_wrightomega_real_nan():
+    assert np.isnan(sc.wrightomega(np.nan))
+
+
+def test_wrightomega_real_series_crossover():
+    desired_error = 2 * np.finfo(float).eps
+    crossover = 1e20
+    x_before_crossover = np.nextafter(crossover, -np.inf)
+    x_after_crossover = np.nextafter(crossover, np.inf)
+    # Computed using Mpmath
+    desired_before_crossover = 99999999999999983569.948
+    desired_after_crossover = 100000000000000016337.948
+    assert_allclose(
+        sc.wrightomega(x_before_crossover),
+        desired_before_crossover,
+        atol=0,
+        rtol=desired_error,
+    )
+    assert_allclose(
+        sc.wrightomega(x_after_crossover),
+        desired_after_crossover,
+        atol=0,
+        rtol=desired_error,
+    )
+
+
+def test_wrightomega_exp_approximation_crossover():
+    desired_error = 2 * np.finfo(float).eps
+    crossover = -50
+    x_before_crossover = np.nextafter(crossover, np.inf)
+    x_after_crossover = np.nextafter(crossover, -np.inf)
+    # Computed using Mpmath
+    desired_before_crossover = 1.9287498479639314876e-22
+    desired_after_crossover = 1.9287498479639040784e-22
+    assert_allclose(
+        sc.wrightomega(x_before_crossover),
+        desired_before_crossover,
+        atol=0,
+        rtol=desired_error,
+    )
+    assert_allclose(
+        sc.wrightomega(x_after_crossover),
+        desired_after_crossover,
+        atol=0,
+        rtol=desired_error,
+    )
+
+
+def test_wrightomega_real_versus_complex():
+    x = np.linspace(-500, 500, 1001)
+    results = sc.wrightomega(x + 0j).real
+    assert_func_equal(sc.wrightomega, results, x, atol=0, rtol=1e-14)
diff --git a/.venv/lib/python3.12/site-packages/scipy/special/tests/test_zeta.py b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_zeta.py
new file mode 100644
index 0000000000000000000000000000000000000000..988f9a716196fb8f65a92a0a2e894038c2474cfa
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/special/tests/test_zeta.py
@@ -0,0 +1,301 @@
+import scipy
+import scipy.special as sc
+import sys
+import numpy as np
+import pytest
+
+from numpy.testing import assert_equal, assert_allclose
+
+
+def test_zeta():
+    assert_allclose(sc.zeta(2,2), np.pi**2/6 - 1, rtol=1e-12)
+
+
+def test_zetac():
+    # Expected values in the following were computed using Wolfram
+    # Alpha's `Zeta[x] - 1`
+    x = [-2.1, 0.8, 0.9999, 9, 50, 75]
+    desired = [
+        -0.9972705002153750,
+        -5.437538415895550,
+        -10000.42279161673,
+        0.002008392826082214,
+        8.881784210930816e-16,
+        2.646977960169853e-23,
+    ]
+    assert_allclose(sc.zetac(x), desired, rtol=1e-12)
+
+
+def test_zetac_special_cases():
+    assert sc.zetac(np.inf) == 0
+    assert np.isnan(sc.zetac(-np.inf))
+    assert sc.zetac(0) == -1.5
+    assert sc.zetac(1.0) == np.inf
+
+    assert_equal(sc.zetac([-2, -50, -100]), -1)
+
+
+def test_riemann_zeta_special_cases():
+    assert np.isnan(sc.zeta(np.nan))
+    assert sc.zeta(np.inf) == 1
+    assert sc.zeta(0) == -0.5
+
+    # Riemann zeta is zero add negative even integers.
+    assert_equal(sc.zeta([-2, -4, -6, -8, -10]), 0)
+
+    assert_allclose(sc.zeta(2), np.pi**2/6, rtol=1e-12)
+    assert_allclose(sc.zeta(4), np.pi**4/90, rtol=1e-12)
+
+
+def test_riemann_zeta_avoid_overflow():
+    s = -260.00000000001
+    desired = -5.6966307844402683127e+297  # Computed with Mpmath
+    assert_allclose(sc.zeta(s), desired, atol=0, rtol=5e-14)
+
+
+@pytest.mark.parametrize(
+    "z, desired, rtol",
+    [
+        ## Test cases taken from mpmath with the script:
+
+        # import numpy as np
+        # import scipy.stats as stats
+
+        # from mpmath import mp
+
+        # # seed = np.random.SeedSequence().entropy
+        # seed = 154689806791763421822480125722191067828
+        # rng = np.random.default_rng(seed)
+        # default_rtol = 1e-13
+
+        # # A small point in each quadrant outside of the critical strip
+        # cases = []
+        # for x_sign, y_sign in [1, 1], [1, -1], [-1, 1], [-1, -1]:
+        #     x = x_sign * rng.uniform(2, 8)
+        #     y = y_sign * rng.uniform(2, 8)
+        #     z = x + y*1j
+        #     reference = complex(mp.zeta(z))
+        #     cases.append((z, reference, default_rtol))
+
+        # # Moderately large imaginary part in each quadrant outside of critical strip
+        # for x_sign, y_sign in [1, 1], [1, -1], [-1, 1], [-1, -1]:
+        #     x = x_sign * rng.uniform(2, 8)
+        #     y = y_sign * rng.uniform(50, 80)
+        #     z = x + y*1j
+        #     reference = complex(mp.zeta(z))
+        #     cases.append((z, reference, default_rtol))
+
+        # # points in critical strip
+        # x = rng.uniform(0.0, 1.0, size=5)
+        # y = np.exp(rng.uniform(0, 5, size=5))
+        # z = x + y*1j
+        # for t in z:
+        #     reference = complex(mp.zeta(t))
+        #     cases.append((complex(t), reference, default_rtol))
+        # z = x - y*1j
+        # for t in z:
+        #     reference = complex(mp.zeta(t))
+        #     cases.append((complex(t), reference, default_rtol))
+
+        # # Near small trivial zeros
+        # x = np.array([-2, -4, -6, -8])
+        # y = np.array([1e-15, -1e-15])
+        # x, y = np.meshgrid(x, y)
+        # x, y = x.ravel(), y.ravel()
+        # z = x + y*1j
+        # for t in z:
+        #     reference = complex(mp.zeta(t))
+        #     cases.append((complex(t), reference, 1e-7))
+
+        # # Some other points near real axis
+        # x = np.array([-0.5, 0, 0.2, 0.75])
+        # y = np.array([1e-15, -1e-15])
+        # x, y = np.meshgrid(x, y)
+        # x, y = x.ravel(), y.ravel()
+        # z = x + y*1j
+        # for t in z:
+        #     reference = complex(mp.zeta(t))
+        #     cases.append((complex(t), reference, 1e-7))
+
+        # # Moderately large real part
+        # x = np.array([49.33915930750887, 50.55805244181687])
+        # y = rng.uniform(20, 100, size=3)
+        # x, y = np.meshgrid(x, y)
+        # x, y = x.ravel(), y.ravel()
+        # z = x + y*1j
+        # for t in z:
+        #     reference = complex(mp.zeta(t))
+        #     cases.append((complex(t), reference, default_rtol))
+
+        # # Very large imaginary part
+        # x = np.array([0.5, 34.812847097948854, 50.55805244181687])
+        # y = np.array([1e6, -1e6])
+        # x, y = np.meshgrid(x, y)
+        # x, y = x.ravel(), y.ravel()
+        # z = x + y*1j
+        # for t in z:
+        #     reference = complex(mp.zeta(t))
+        #     rtol = 1e-7 if t.real == 0.5 else default_rtol
+        #     cases.append((complex(t), reference, rtol))
+        #
+        # # Naive implementation of reflection formula suffers internal overflow
+        # x = -rng.uniform(200, 300, 3)
+        # y = np.array([rng.uniform(10, 30), -rng.uniform(10, 30)])
+        # x, y = np.meshgrid(x, y)
+        # x, y = x.ravel(), y.ravel()
+        # z = x + y*1j
+        # for t in z:
+        #     reference = complex(mp.zeta(t))
+        #     cases.append((complex(t), reference, default_rtol))
+        #
+        # A small point in each quadrant outside of the critical strip
+        ((3.12838509346655+7.111085974836645j),
+         (1.0192654793474945+0.08795174413289127j),
+         1e-13),
+        ((7.06791362314716-7.219497492626728j),
+         (1.0020740683598117-0.006752725913243711j),
+         1e-13),
+        ((-6.806227077655519+2.724411451005281j),
+         (0.06312488213559667-0.061641496333765956j),
+         1e-13),
+        ((-3.0170751511621026-6.3686522550665945j),
+         (-0.10330747857150148-1.214541994832571j),
+         1e-13),
+        # Moderately large imaginary part in each quadrant outside of critical strip
+        ((6.133994402212294+60.03091448000761j),
+         (0.9885701843417336+0.009636925981078128j),
+         1e-13),
+        ((6.17268142822657-64.74883149743795j),
+         (1.0080474225840865+0.012032804974965354j),
+         1e-13),
+        ((-3.462191939791879+76.16258975567534j),
+         (18672.072070850158+2908.5104826247184j),
+         1e-13),
+        ((-6.955735216531752-74.75791554155748j),
+         (-77672258.72276545+71625206.0401107j),
+         1e-13),
+        # Points in critical strip
+        ((0.4088038289823922+1.4596830498094384j),
+         (0.3032837969400845-0.47272237994110344j),
+         1e-13),
+        ((0.9673493951209633+4.918968547259143j),
+         (0.7488756907431944+0.17281553371482428j),
+         1e-13),
+        ((0.8692482679977754+66.6142398421354j),
+         (0.5831942469552066-0.26848904799062334j),
+         1e-13),
+        ((0.42771847720003764+21.783747851715468j),
+         (0.4767032638444329+0.6898148744603123j),
+         1e-13),
+        ((0.20479494678428956+33.17656449538932j),
+         (-0.6983038977487848+0.18060923618150224j),
+         1e-13),
+        ((0.4088038289823922-1.4596830498094384j),
+         (0.3032837969400845+0.47272237994110344j),
+         1e-13),
+        ((0.9673493951209633-4.918968547259143j),
+         (0.7488756907431944-0.17281553371482428j),
+         1e-13),
+        ((0.8692482679977754-66.6142398421354j),
+         (0.5831942469552066+0.26848904799062334j),
+         1e-13),
+        ((0.42771847720003764-21.783747851715468j),
+         (0.4767032638444329-0.6898148744603123j),
+         1e-13),
+        ((0.20479494678428956-33.17656449538932j),
+         (-0.6983038977487848-0.18060923618150224j),
+         1e-13),
+        # Near small trivial zeros
+        ((-2+1e-15j), (3.288175809370978e-32-3.0448457058393275e-17j), 1e-07),
+        ((-4+1e-15j), (-2.868707923051182e-33+7.983811450268625e-18j), 1e-07),
+        ((-6+1e-15j), (-1.7064292323640116e-34-5.8997591435159376e-18j), 1e-07),
+        ((-8+1e-15j), (2.5060859548261706e-33+8.316161985602247e-18j), 1e-07),
+        ((-2-1e-15j), (3.288175809371319e-32+3.0448457058393275e-17j), 1e-07),
+        ((-4-1e-15j), (-2.8687079230520114e-33-7.983811450268625e-18j), 1e-07),
+        ((-6-1e-15j), (-1.70642923235801e-34+5.8997591435159376e-18j), 1e-07),
+        ((-8-1e-15j), (2.5060859548253293e-33-8.316161985602247e-18j), 1e-07),
+        # Some other points near real axis
+        ((-0.5+1e-15j), (-0.20788622497735457-3.608543395999408e-16j), 1e-07),
+        (1e-15j, (-0.5-9.189384239689193e-16j), 1e-07),
+        ((0.2+1e-15j), (-0.7339209248963406-1.4828001150329085e-15j), 1e-07),
+        ((0.75+1e-15j), (-3.4412853869452227-1.5924832114302393e-14j), 1e-13),
+        ((-0.5-1e-15j), (-0.20788622497735457+3.608543395999408e-16j), 1e-07),
+        (-1e-15j, (-0.5+9.189387416062746e-16j), 1e-07),
+        ((0.2-1e-15j), (-0.7339209248963406+1.4828004007675122e-15j), 1e-07),
+        ((0.75-1e-15j), (-3.4412853869452227+1.5924831974403957e-14j), 1e-13),
+        # Moderately large real part
+        ((49.33915930750887+53.213478698903955j),
+         (1.0000000000000009+1.0212452494616078e-15j),
+         1e-13),
+        ((50.55805244181687+53.213478698903955j),
+         (1.0000000000000004+4.387394180390787e-16j),
+         1e-13),
+        ((49.33915930750887+40.6366015728302j),
+         (0.9999999999999986-1.502268709924849e-16j),
+         1e-13),
+        ((50.55805244181687+40.6366015728302j),
+         (0.9999999999999994-6.453929613571651e-17j),
+         1e-13),
+        ((49.33915930750887+85.83555435273925j),
+         (0.9999999999999987-2.7014400611995846e-16j),
+         1e-13),
+        ((50.55805244181687+85.83555435273925j),
+         (0.9999999999999994-1.160571605555322e-16j),
+         1e-13),
+        # Very large imaginary part
+        ((0.5+1e6j), (0.0760890697382271+2.805102101019299j), 1e-07),
+        ((34.812847097948854+1e6j),
+         (1.0000000000102545+3.150848654056419e-11j),
+         1e-13),
+        ((50.55805244181687+1e6j),
+         (1.0000000000000002+5.736517078070873e-16j),
+         1e-13),
+        ((0.5-1e6j), (0.0760890697382271-2.805102101019299j), 1e-07),
+        ((34.812847097948854-1e6j),
+         (1.0000000000102545-3.150848654056419e-11j),
+         1e-13),
+        ((50.55805244181687-1e6j),
+         (1.0000000000000002-5.736517078070873e-16j),
+         1e-13),
+        ((-294.86605461349745+13.992648136816397j), (-np.inf+np.inf*1j), 1e-13),
+        ((-294.86605461349745-16.147667799398363j), (np.inf-np.inf*1j), 1e-13),
+    ]
+)
+def test_riemann_zeta_complex(z, desired, rtol):
+    assert_allclose(sc.zeta(z), desired, rtol=rtol)
+
+
+# Some of the test cases below fail for intel compilers
+cpp_compiler = scipy.__config__.CONFIG["Compilers"]["c++"]["name"]
+gcc_linux = cpp_compiler == "gcc" and sys.platform == "linux"
+clang_macOS = cpp_compiler == "clang" and sys.platform == "darwin"
+
+
+@pytest.mark.skipif(
+    not (gcc_linux or clang_macOS),
+    reason="Underflow may not be avoided on other platforms",
+)
+@pytest.mark.parametrize(
+    "z, desired, rtol",
+    [
+        # Test cases generated as part of same script for
+        # test_riemann_zeta_complex. These cases are split off because
+        # they fail on some platforms.
+        #
+        # Naive implementation of reflection formula suffers internal overflow
+        ((-217.40285743524163+13.992648136816397j),
+         (-6.012818500554211e+249-1.926943776932387e+250j),
+         5e-13,),
+        ((-237.71710702931668+13.992648136816397j),
+         (-8.823803086106129e+281-5.009074181335139e+281j),
+         1e-13,),
+        ((-217.40285743524163-16.147667799398363j),
+         (-5.111612904844256e+251-4.907132127666742e+250j),
+         5e-13,),
+        ((-237.71710702931668-16.147667799398363j),
+         (-1.3256112779883167e+283-2.253002003455494e+283j),
+         5e-13,),
+    ],
+)
+def test_riemann_zeta_complex_avoid_underflow(z, desired, rtol):
+    assert_allclose(sc.zeta(z), desired, rtol=rtol)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/__init__.py b/.venv/lib/python3.12/site-packages/scipy/stats/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e67ecbe12091add03d50815fe8307e746af3b85b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/__init__.py
@@ -0,0 +1,644 @@
+"""
+.. _statsrefmanual:
+
+==========================================
+Statistical functions (:mod:`scipy.stats`)
+==========================================
+
+.. currentmodule:: scipy.stats
+
+This module contains a large number of probability distributions,
+summary and frequency statistics, correlation functions and statistical
+tests, masked statistics, kernel density estimation, quasi-Monte Carlo
+functionality, and more.
+
+Statistics is a very large area, and there are topics that are out of scope
+for SciPy and are covered by other packages. Some of the most important ones
+are:
+
+- `statsmodels <https://www.statsmodels.org/stable/index.html>`__:
+  regression, linear models, time series analysis, extensions to topics
+  also covered by ``scipy.stats``.
+- `Pandas <https://pandas.pydata.org/>`__: tabular data, time series
+  functionality, interfaces to other statistical languages.
+- `PyMC <https://docs.pymc.io/>`__: Bayesian statistical
+  modeling, probabilistic machine learning.
+- `scikit-learn <https://scikit-learn.org/>`__: classification, regression,
+  model selection.
+- `Seaborn <https://seaborn.pydata.org/>`__: statistical data visualization.
+- `rpy2 <https://rpy2.github.io/>`__: Python to R bridge.
+
+
+Probability distributions
+=========================
+
+Each univariate distribution is an instance of a subclass of `rv_continuous`
+(`rv_discrete` for discrete distributions):
+
+.. autosummary::
+   :toctree: generated/
+
+   rv_continuous
+   rv_discrete
+   rv_histogram
+
+Continuous distributions
+------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   alpha             -- Alpha
+   anglit            -- Anglit
+   arcsine           -- Arcsine
+   argus             -- Argus
+   beta              -- Beta
+   betaprime         -- Beta Prime
+   bradford          -- Bradford
+   burr              -- Burr (Type III)
+   burr12            -- Burr (Type XII)
+   cauchy            -- Cauchy
+   chi               -- Chi
+   chi2              -- Chi-squared
+   cosine            -- Cosine
+   crystalball       -- Crystalball
+   dgamma            -- Double Gamma
+   dpareto_lognorm   -- Double Pareto Lognormal
+   dweibull          -- Double Weibull
+   erlang            -- Erlang
+   expon             -- Exponential
+   exponnorm         -- Exponentially Modified Normal
+   exponweib         -- Exponentiated Weibull
+   exponpow          -- Exponential Power
+   f                 -- F (Snecdor F)
+   fatiguelife       -- Fatigue Life (Birnbaum-Saunders)
+   fisk              -- Fisk
+   foldcauchy        -- Folded Cauchy
+   foldnorm          -- Folded Normal
+   genlogistic       -- Generalized Logistic
+   gennorm           -- Generalized normal
+   genpareto         -- Generalized Pareto
+   genexpon          -- Generalized Exponential
+   genextreme        -- Generalized Extreme Value
+   gausshyper        -- Gauss Hypergeometric
+   gamma             -- Gamma
+   gengamma          -- Generalized gamma
+   genhalflogistic   -- Generalized Half Logistic
+   genhyperbolic     -- Generalized Hyperbolic
+   geninvgauss       -- Generalized Inverse Gaussian
+   gibrat            -- Gibrat
+   gompertz          -- Gompertz (Truncated Gumbel)
+   gumbel_r          -- Right Sided Gumbel, Log-Weibull, Fisher-Tippett, Extreme Value Type I
+   gumbel_l          -- Left Sided Gumbel, etc.
+   halfcauchy        -- Half Cauchy
+   halflogistic      -- Half Logistic
+   halfnorm          -- Half Normal
+   halfgennorm       -- Generalized Half Normal
+   hypsecant         -- Hyperbolic Secant
+   invgamma          -- Inverse Gamma
+   invgauss          -- Inverse Gaussian
+   invweibull        -- Inverse Weibull
+   irwinhall         -- Irwin-Hall
+   jf_skew_t         -- Jones and Faddy Skew-T
+   johnsonsb         -- Johnson SB
+   johnsonsu         -- Johnson SU
+   kappa4            -- Kappa 4 parameter
+   kappa3            -- Kappa 3 parameter
+   ksone             -- Distribution of Kolmogorov-Smirnov one-sided test statistic
+   kstwo             -- Distribution of Kolmogorov-Smirnov two-sided test statistic
+   kstwobign         -- Limiting Distribution of scaled Kolmogorov-Smirnov two-sided test statistic.
+   landau            -- Landau
+   laplace           -- Laplace
+   laplace_asymmetric    -- Asymmetric Laplace
+   levy              -- Levy
+   levy_l
+   levy_stable
+   logistic          -- Logistic
+   loggamma          -- Log-Gamma
+   loglaplace        -- Log-Laplace (Log Double Exponential)
+   lognorm           -- Log-Normal
+   loguniform        -- Log-Uniform
+   lomax             -- Lomax (Pareto of the second kind)
+   maxwell           -- Maxwell
+   mielke            -- Mielke's Beta-Kappa
+   moyal             -- Moyal
+   nakagami          -- Nakagami
+   ncx2              -- Non-central chi-squared
+   ncf               -- Non-central F
+   nct               -- Non-central Student's T
+   norm              -- Normal (Gaussian)
+   norminvgauss      -- Normal Inverse Gaussian
+   pareto            -- Pareto
+   pearson3          -- Pearson type III
+   powerlaw          -- Power-function
+   powerlognorm      -- Power log normal
+   powernorm         -- Power normal
+   rdist             -- R-distribution
+   rayleigh          -- Rayleigh
+   rel_breitwigner   -- Relativistic Breit-Wigner
+   rice              -- Rice
+   recipinvgauss     -- Reciprocal Inverse Gaussian
+   semicircular      -- Semicircular
+   skewcauchy        -- Skew Cauchy
+   skewnorm          -- Skew normal
+   studentized_range    -- Studentized Range
+   t                 -- Student's T
+   trapezoid         -- Trapezoidal
+   triang            -- Triangular
+   truncexpon        -- Truncated Exponential
+   truncnorm         -- Truncated Normal
+   truncpareto       -- Truncated Pareto
+   truncweibull_min  -- Truncated minimum Weibull distribution
+   tukeylambda       -- Tukey-Lambda
+   uniform           -- Uniform
+   vonmises          -- Von-Mises (Circular)
+   vonmises_line     -- Von-Mises (Line)
+   wald              -- Wald
+   weibull_min       -- Minimum Weibull (see Frechet)
+   weibull_max       -- Maximum Weibull (see Frechet)
+   wrapcauchy        -- Wrapped Cauchy
+
+The ``fit`` method of the univariate continuous distributions uses
+maximum likelihood estimation to fit the distribution to a data set.
+The ``fit`` method can accept regular data or *censored data*.
+Censored data is represented with instances of the `CensoredData`
+class.
+
+.. autosummary::
+   :toctree: generated/
+
+   CensoredData
+
+
+Multivariate distributions
+--------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   multivariate_normal    -- Multivariate normal distribution
+   matrix_normal          -- Matrix normal distribution
+   dirichlet              -- Dirichlet
+   dirichlet_multinomial  -- Dirichlet multinomial distribution
+   wishart                -- Wishart
+   invwishart             -- Inverse Wishart
+   multinomial            -- Multinomial distribution
+   special_ortho_group    -- SO(N) group
+   ortho_group            -- O(N) group
+   unitary_group          -- U(N) group
+   random_correlation     -- random correlation matrices
+   multivariate_t         -- Multivariate t-distribution
+   multivariate_hypergeom -- Multivariate hypergeometric distribution
+   normal_inverse_gamma   -- Normal-inverse-gamma distribution
+   random_table           -- Distribution of random tables with given marginals
+   uniform_direction      -- Uniform distribution on S(N-1)
+   vonmises_fisher        -- Von Mises-Fisher distribution
+   matrix_t               -- Matrix variate t distribution
+
+`scipy.stats.multivariate_normal` methods accept instances
+of the following class to represent the covariance.
+
+.. autosummary::
+   :toctree: generated/
+
+   Covariance             -- Representation of a covariance matrix
+
+
+Discrete distributions
+----------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   bernoulli                -- Bernoulli
+   betabinom                -- Beta-Binomial
+   betanbinom               -- Beta-Negative Binomial
+   binom                    -- Binomial
+   boltzmann                -- Boltzmann (Truncated Discrete Exponential)
+   dlaplace                 -- Discrete Laplacian
+   geom                     -- Geometric
+   hypergeom                -- Hypergeometric
+   logser                   -- Logarithmic (Log-Series, Series)
+   nbinom                   -- Negative Binomial
+   nchypergeom_fisher       -- Fisher's Noncentral Hypergeometric
+   nchypergeom_wallenius    -- Wallenius's Noncentral Hypergeometric
+   nhypergeom               -- Negative Hypergeometric
+   planck                   -- Planck (Discrete Exponential)
+   poisson                  -- Poisson
+   poisson_binom            -- Poisson Binomial
+   randint                  -- Discrete Uniform
+   skellam                  -- Skellam
+   yulesimon                -- Yule-Simon
+   zipf                     -- Zipf (Zeta)
+   zipfian                  -- Zipfian
+
+
+An overview of statistical functions is given below.  Many of these functions
+have a similar version in `scipy.stats.mstats` which work for masked arrays.
+
+Summary statistics
+==================
+
+.. autosummary::
+   :toctree: generated/
+
+   describe          -- Descriptive statistics
+   gmean             -- Geometric mean
+   hmean             -- Harmonic mean
+   pmean             -- Power mean
+   kurtosis          -- Fisher or Pearson kurtosis
+   mode              -- Modal value
+   moment            -- Central moment
+   lmoment
+   expectile         -- Expectile
+   skew              -- Skewness
+   kstat             --
+   kstatvar          --
+   tmean             -- Truncated arithmetic mean
+   tvar              -- Truncated variance
+   tmin              --
+   tmax              --
+   tstd              --
+   tsem              --
+   variation         -- Coefficient of variation
+   rankdata
+   tiecorrect
+   trim_mean
+   gstd              -- Geometric Standard Deviation
+   iqr
+   sem
+   bayes_mvs
+   mvsdist
+   entropy
+   differential_entropy
+   median_abs_deviation
+
+Frequency statistics
+====================
+
+.. autosummary::
+   :toctree: generated/
+
+   quantile
+   estimated_cdf
+   cumfreq
+   relfreq
+   percentileofscore
+   scoreatpercentile
+
+.. autosummary::
+   :toctree: generated/
+
+   binned_statistic     -- Compute a binned statistic for a set of data.
+   binned_statistic_2d  -- Compute a 2-D binned statistic for a set of data.
+   binned_statistic_dd  -- Compute a d-D binned statistic for a set of data.
+
+.. _hypotests:
+
+Hypothesis Tests and related functions
+======================================
+SciPy has many functions for performing hypothesis tests that return a
+test statistic and a p-value, and several of them return confidence intervals
+and/or other related information.
+
+The headings below are based on common uses of the functions within, but due to
+the wide variety of statistical procedures, any attempt at coarse-grained
+categorization will be imperfect. Also, note that tests within the same heading
+are not interchangeable in general (e.g. many have different distributional
+assumptions).
+
+One Sample Tests / Paired Sample Tests
+--------------------------------------
+One sample tests are typically used to assess whether a single sample was
+drawn from a specified distribution or a distribution with specified properties
+(e.g. zero mean).
+
+.. autosummary::
+   :toctree: generated/
+
+   ttest_1samp
+   binomtest
+   quantile_test
+   skewtest
+   kurtosistest
+   normaltest
+   jarque_bera
+   shapiro
+   anderson
+   cramervonmises
+   ks_1samp
+   goodness_of_fit
+   chisquare
+   power_divergence
+
+Paired sample tests are often used to assess whether two samples were drawn
+from the same distribution; they differ from the independent sample tests below
+in that each observation in one sample is treated as paired with a
+closely-related observation in the other sample (e.g. when environmental
+factors are controlled between observations within a pair but not among pairs).
+They can also be interpreted or used as one-sample tests (e.g. tests on the
+mean or median of *differences* between paired observations).
+
+.. autosummary::
+   :toctree: generated/
+
+   ttest_rel
+   wilcoxon
+
+Association/Correlation Tests
+-----------------------------
+
+These tests are often used to assess whether there is a relationship (e.g.
+linear) between paired observations in multiple samples or among the
+coordinates of multivariate observations.
+
+.. autosummary::
+   :toctree: generated/
+
+   linregress
+   pearsonr
+   spearmanrho
+   pointbiserialr
+   kendalltau
+   chatterjeexi
+   weightedtau
+   somersd
+   siegelslopes
+   theilslopes
+   page_trend_test
+   multiscale_graphcorr
+   spearmanr
+
+These association tests and are to work with samples in the form of contingency
+tables. Supporting functions are available in `scipy.stats.contingency`.
+
+.. autosummary::
+   :toctree: generated/
+
+   chi2_contingency
+   fisher_exact
+   barnard_exact
+   boschloo_exact
+
+Independent Sample Tests
+------------------------
+Independent sample tests are typically used to assess whether multiple samples
+were independently drawn from the same distribution or different distributions
+with a shared property (e.g. equal means).
+
+Some tests are specifically for comparing two samples.
+
+.. autosummary::
+   :toctree: generated/
+
+   ttest_ind_from_stats
+   poisson_means_test
+   ttest_ind
+   mannwhitneyu
+   bws_test
+   ranksums
+   brunnermunzel
+   mood
+   ansari
+   cramervonmises_2samp
+   epps_singleton_2samp
+   ks_2samp
+   kstest
+
+Others are generalized to multiple samples.
+
+.. autosummary::
+   :toctree: generated/
+
+   f_oneway
+   tukey_hsd
+   dunnett
+   kruskal
+   alexandergovern
+   fligner
+   levene
+   bartlett
+   median_test
+   friedmanchisquare
+   anderson_ksamp
+
+Resampling and Monte Carlo Methods
+----------------------------------
+The following functions can reproduce the p-value and confidence interval
+results of most of the functions above, and often produce accurate results in a
+wider variety of conditions. They can also be used to perform hypothesis tests
+and generate confidence intervals for custom statistics. This flexibility comes
+at the cost of greater computational requirements and stochastic results.
+
+.. autosummary::
+   :toctree: generated/
+
+   monte_carlo_test
+   permutation_test
+   bootstrap
+   power
+
+Instances of the following object can be passed into some hypothesis test
+functions to perform a resampling or Monte Carlo version of the hypothesis
+test.
+
+.. autosummary::
+   :toctree: generated/
+
+   MonteCarloMethod
+   PermutationMethod
+   BootstrapMethod
+
+Multiple Hypothesis Testing and Meta-Analysis
+---------------------------------------------
+These functions are for assessing the results of individual tests as a whole.
+Functions for performing specific multiple hypothesis tests (e.g. post hoc
+tests) are listed above.
+
+.. autosummary::
+   :toctree: generated/
+
+   combine_pvalues
+   false_discovery_control
+
+Random Variables
+================
+
+.. autosummary::
+   :toctree: generated/
+
+   make_distribution
+   Normal
+   Logistic
+   Uniform
+   Binomial
+   Mixture
+   order_statistic
+   truncate
+   abs
+   exp
+   log
+
+Other statistical functionality
+===============================
+.. toctree::
+   :maxdepth: 1
+
+   stats.qmc
+   stats.contingency
+   stats.mstats
+   stats.sampling
+
+Transformations
+---------------
+
+.. autosummary::
+   :toctree: generated/
+
+   boxcox
+   boxcox_normmax
+   boxcox_llf
+   yeojohnson
+   yeojohnson_normmax
+   yeojohnson_llf
+   obrientransform
+   sigmaclip
+   trimboth
+   trim1
+   zmap
+   zscore
+   gzscore
+
+Statistical distances
+---------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   wasserstein_distance
+   wasserstein_distance_nd
+   energy_distance
+
+Fitting / Survival Analysis
+---------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   fit
+   ecdf
+   logrank
+
+Directional statistical functions
+---------------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   directional_stats
+   circmean
+   circvar
+   circstd
+
+Sensitivity Analysis
+--------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   sobol_indices
+
+Plot-tests
+----------
+
+.. autosummary::
+   :toctree: generated/
+
+   ppcc_max
+   ppcc_plot
+   probplot
+   boxcox_normplot
+   yeojohnson_normplot
+
+Univariate and multivariate kernel density estimation
+-----------------------------------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   gaussian_kde
+
+Warnings / Errors used in :mod:`scipy.stats`
+--------------------------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   DegenerateDataWarning
+   ConstantInputWarning
+   NearConstantInputWarning
+   FitError
+
+Result classes used in :mod:`scipy.stats`
+-----------------------------------------
+
+.. warning::
+
+    These classes are private, but they are included here because instances
+    of them are returned by other statistical functions. User import and
+    instantiation is not supported.
+
+.. toctree::
+   :maxdepth: 2
+
+   stats._result_classes
+
+"""  # noqa: E501
+
+from ._warnings_errors import (ConstantInputWarning, NearConstantInputWarning,
+                               DegenerateDataWarning, FitError)
+from ._stats_py import *
+from ._variation import variation
+from .distributions import *
+from ._morestats import *
+from ._multicomp import *
+from ._binomtest import binomtest
+from ._binned_statistic import *
+from ._kde import gaussian_kde
+from . import mstats
+from . import qmc
+from ._multivariate import *
+from . import contingency
+from .contingency import chi2_contingency
+from ._censored_data import CensoredData
+from ._resampling import (bootstrap, monte_carlo_test, permutation_test, power,
+                          MonteCarloMethod, PermutationMethod, BootstrapMethod)
+from ._entropy import *
+from ._hypotests import *
+from ._page_trend_test import page_trend_test
+from ._mannwhitneyu import mannwhitneyu
+from ._bws_test import bws_test
+from ._fit import fit, goodness_of_fit
+from ._covariance import Covariance
+from ._sensitivity_analysis import *
+from ._survival import *
+from ._distribution_infrastructure import (
+    make_distribution, Mixture, order_statistic, truncate, exp, log, abs
+)
+from ._new_distributions import Normal, Logistic, Uniform, Binomial
+from ._mgc import multiscale_graphcorr
+from ._correlation import chatterjeexi, spearmanrho, theilslopes, siegelslopes
+from ._quantile import quantile, estimated_cdf
+
+
+# Deprecated namespaces, to be removed in v2.0.0
+from . import (
+    biasedurn, kde, morestats, mstats_basic, mstats_extras, mvn, stats
+)
+
+
+__all__ = [s for s in dir() if not s.startswith("_")]  # Remove dunders.
+
+from scipy._lib._testutils import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_ansari_swilk_statistics.cpython-312-x86_64-linux-gnu.so b/.venv/lib/python3.12/site-packages/scipy/stats/_ansari_swilk_statistics.cpython-312-x86_64-linux-gnu.so
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+version https://git-lfs.github.com/spec/v1
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+size 108400
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_axis_nan_policy.py b/.venv/lib/python3.12/site-packages/scipy/stats/_axis_nan_policy.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b47171d2667e066af9a6e74c6a0fb739f8d9480
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_axis_nan_policy.py
@@ -0,0 +1,725 @@
+# Many scipy.stats functions support `axis` and `nan_policy` parameters.
+# When the two are combined, it can be tricky to get all the behavior just
+# right. This file contains utility functions useful for scipy.stats functions
+# that support `axis` and `nan_policy`, including a decorator that
+# automatically adds `axis` and `nan_policy` arguments to a function.
+
+import math
+import warnings
+import numpy as np
+from functools import wraps
+from scipy._lib._array_api import xp_ravel
+from scipy._lib._docscrape import FunctionDoc, Parameter
+from scipy._lib._util import _contains_nan, AxisError, _get_nan
+from scipy._lib._array_api import (array_namespace, is_numpy, xp_size, xp_copy,
+                                   xp_promote, is_dask, is_jax, xp_capabilities)
+import scipy._external.array_api_extra as xpx
+
+import inspect
+
+too_small_1d_not_omit = (
+    "One or more sample arguments is too small; all "
+    "returned values will be NaN. "
+    "See documentation for sample size requirements.")
+
+too_small_1d_omit = (
+    "After omitting NaNs, one or more sample arguments "
+    "is too small; all returned values will be NaN. "
+    "See documentation for sample size requirements.")
+
+too_small_nd_not_omit = (
+    "All axis-slices of one or more sample arguments are "
+    "too small; all elements of returned arrays will be NaN. "
+    "See documentation for sample size requirements.")
+
+too_small_nd_omit = (
+    "After omitting NaNs, one or more axis-slices of one "
+    "or more sample arguments is too small; corresponding "
+    "elements of returned arrays will be NaN. "
+    "See documentation for sample size requirements.")
+
+class SmallSampleWarning(RuntimeWarning):
+    pass
+
+
+def _broadcast_arrays(arrays, axis=None, xp=None):
+    """
+    Broadcast shapes of arrays, ignoring incompatibility of specified axes
+    """
+    arrays = tuple(arrays)
+    if not arrays:
+        return arrays
+    xp = array_namespace(*arrays) if xp is None else xp
+    arrays = [xp.asarray(arr) for arr in arrays]
+    shapes = [arr.shape for arr in arrays]
+    new_shapes = _broadcast_shapes(shapes, axis)
+    if axis is None:
+        new_shapes = [new_shapes]*len(arrays)
+    return [xp.broadcast_to(array, new_shape)
+            for array, new_shape in zip(arrays, new_shapes)]
+
+
+def _broadcast_shapes(shapes, axis=None):
+    """
+    Broadcast shapes, ignoring incompatibility of specified axes
+    """
+    if not shapes:
+        return shapes
+
+    # input validation
+    if axis is not None:
+        axis = np.atleast_1d(axis)
+        message = '`axis` must be an integer, a tuple of integers, or `None`.'
+        try:
+            with np.errstate(invalid='ignore'):
+                axis_int = axis.astype(int)
+        except ValueError as e:
+            raise AxisError(message) from e
+        if not np.array_equal(axis_int, axis):
+            raise AxisError(message)
+        axis = axis_int
+
+    # First, ensure all shapes have same number of dimensions by prepending 1s.
+    n_dims = max([len(shape) for shape in shapes])
+    new_shapes = np.ones((len(shapes), n_dims), dtype=int)
+    for row, shape in zip(new_shapes, shapes):
+        row[len(row)-len(shape):] = shape  # can't use negative indices (-0:)
+
+    # Remove the shape elements of the axes to be ignored, but remember them.
+    if axis is not None:
+        axis[axis < 0] = n_dims + axis[axis < 0]
+        axis = np.sort(axis)
+        if axis[-1] >= n_dims or axis[0] < 0:
+            message = (f"`axis` is out of bounds "
+                       f"for array of dimension {n_dims}")
+            raise AxisError(message)
+
+        if len(np.unique(axis)) != len(axis):
+            raise AxisError("`axis` must contain only distinct elements")
+
+        removed_shapes = new_shapes[:, axis]
+        new_shapes = np.delete(new_shapes, axis, axis=1)
+
+    # If arrays are broadcastable, shape elements that are 1 may be replaced
+    # with a corresponding non-1 shape element. Assuming arrays are
+    # broadcastable, that final shape element can be found with:
+    new_shape = np.max(new_shapes, axis=0)
+    # except in case of an empty array:
+    new_shape *= new_shapes.all(axis=0)
+
+    # Among all arrays, there can only be one unique non-1 shape element.
+    # Therefore, if any non-1 shape element does not match what we found
+    # above, the arrays must not be broadcastable after all.
+    if np.any(~((new_shapes == 1) | (new_shapes == new_shape))):
+        raise ValueError("Array shapes are incompatible for broadcasting.")
+
+    if axis is not None:
+        # Add back the shape elements that were ignored
+        new_axis = axis - np.arange(len(axis))
+        new_shapes = [tuple(np.insert(new_shape, new_axis, removed_shape))
+                      for removed_shape in removed_shapes]
+        return new_shapes
+    else:
+        return tuple(new_shape)
+
+
+def _broadcast_array_shapes_remove_axis(arrays, axis=None):
+    """
+    Broadcast shapes of arrays, dropping specified axes
+
+    Given a sequence of arrays `arrays` and an integer or tuple `axis`, find
+    the shape of the broadcast result after consuming/dropping `axis`.
+    In other words, return output shape of a typical hypothesis test on
+    `arrays` vectorized along `axis`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats._axis_nan_policy import _broadcast_array_shapes_remove_axis
+    >>> a = np.zeros((5, 2, 1))
+    >>> b = np.zeros((9, 3))
+    >>> _broadcast_array_shapes_remove_axis((a, b), 1)
+    (5, 3)
+    """
+    # Note that here, `axis=None` means do not consume/drop any axes - _not_
+    # ravel arrays before broadcasting.
+    shapes = [arr.shape for arr in arrays]
+    return _broadcast_shapes_remove_axis(shapes, axis)
+
+
+def _broadcast_shapes_remove_axis(shapes, axis=None):
+    """
+    Broadcast shapes, dropping specified axes
+
+    Same as _broadcast_array_shapes_remove_axis, but given a sequence
+    of array shapes `shapes` instead of the arrays themselves.
+    """
+    shapes = _broadcast_shapes(shapes, axis)
+    shape = shapes[0]
+    if axis is not None:
+        shape = np.delete(shape, axis)
+    return tuple(shape)
+
+
+@xp_capabilities()
+def _broadcast_concatenate(arrays, axis, paired=False, xp=None):
+    """Concatenate arrays along an axis with broadcasting."""
+    xp = array_namespace(*arrays) if xp is None else xp
+    arrays = _broadcast_arrays(arrays, axis if not paired else None, xp=xp)
+    res = xp.concat(arrays, axis=axis)
+    return res
+
+
+def _remove_nans(samples, paired, xp=None):
+    "Remove nans from paired or unpaired 1D samples"
+    # potential optimization: don't copy arrays that don't contain nans
+    xp = array_namespace(*samples)
+    if not paired:
+        return [sample[~xp.isnan(sample)] for sample in samples]
+
+    # for paired samples, we need to remove the whole pair when any part
+    # has a nan
+    nans = xp.isnan(samples[0])
+    for sample in samples[1:]:
+        nans = nans | xp.isnan(sample)
+    not_nans = ~nans
+    return [sample[not_nans] for sample in samples]
+
+
+def _remove_sentinel(samples, paired, sentinel):
+    "Remove sentinel values from paired or unpaired 1D samples"
+    # could consolidate with `_remove_nans`, but it's not quite as simple as
+    # passing `sentinel=np.nan` because `(np.nan == np.nan) is False`
+
+    # potential optimization: don't copy arrays that don't contain sentinel
+    if not paired:
+        return [sample[sample != sentinel] for sample in samples]
+
+    # for paired samples, we need to remove the whole pair when any part
+    # has a nan
+    sentinels = (samples[0] == sentinel)
+    for sample in samples[1:]:
+        sentinels = sentinels | (sample == sentinel)
+    not_sentinels = ~sentinels
+    return [sample[not_sentinels] for sample in samples]
+
+
+def _masked_arrays_2_sentinel_arrays(samples):
+    # masked arrays in `samples` are converted to regular arrays, and values
+    # corresponding with masked elements are replaced with a sentinel value
+
+    # return without modifying arrays if none have a mask
+    has_mask = False
+    for sample in samples:
+        mask = getattr(sample, 'mask', False)
+        has_mask = has_mask or np.any(mask)
+    if not has_mask:
+        return samples, None  # None means there is no sentinel value
+
+    # Choose a sentinel value. We can't use `np.nan`, because sentinel (masked)
+    # values are always omitted, but there are different nan policies.
+    dtype = np.result_type(*samples)
+    dtype = dtype if np.issubdtype(dtype, np.number) else np.float64
+    for i in range(len(samples)):
+        # Things get more complicated if the arrays are of different types.
+        # We could have different sentinel values for each array, but
+        # the purpose of this code is convenience, not efficiency.
+        samples[i] = samples[i].astype(dtype, copy=False)
+
+    inexact = np.issubdtype(dtype, np.inexact)
+    info = np.finfo if inexact else np.iinfo
+    max_possible, min_possible = info(dtype).max, info(dtype).min
+    nextafter = np.nextafter if inexact else (lambda x, _: x - 1)
+
+    sentinel = max_possible
+    # For simplicity, min_possible/np.infs are not candidate sentinel values
+    while sentinel > min_possible:
+        for sample in samples:
+            if np.any(sample == sentinel):  # choose a new sentinel value
+                sentinel = nextafter(sentinel, -np.inf)
+                break
+        else:  # when sentinel value is OK, break the while loop
+            break
+    else:
+        message = ("This function replaces masked elements with sentinel "
+                   "values, but the data contains all distinct values of this "
+                   "data type. Consider promoting the dtype to `np.float64`.")
+        raise ValueError(message)
+
+    # replace masked elements with sentinel value
+    out_samples = []
+    for sample in samples:
+        mask = getattr(sample, 'mask', None)
+        if mask is not None:  # turn all masked arrays into sentinel arrays
+            mask = np.broadcast_to(mask, sample.shape)
+            sample = sample.data.copy() if np.any(mask) else sample.data
+            sample = np.asarray(sample)  # `sample.data` could be a memoryview?
+            sample[mask] = sentinel
+        out_samples.append(sample)
+
+    return out_samples, sentinel
+
+
+def _check_empty_inputs(samples, axis, xp=None):
+    """
+    Check for empty sample; return appropriate output for a vectorized hypotest
+    """
+    xp = array_namespace(*samples) if xp is None else xp
+    # if none of the samples are empty, we need to perform the test
+    if not any(xp_size(sample) == 0 for sample in samples):
+        return None
+    # otherwise, the statistic and p-value will be either empty arrays or
+    # arrays with NaNs. Produce the appropriate array and return it.
+    output_shape = _broadcast_array_shapes_remove_axis(samples, axis)
+    NaN = _get_nan(*samples)
+    output = xp.full(output_shape, xp.nan, dtype=NaN.dtype)
+    return output
+
+
+def _add_reduced_axes(res, reduced_axes, keepdims, xp=np):
+    """
+    Add reduced axes back to all the arrays in the result object
+    if keepdims = True.
+    """
+    return ([xpx.expand_dims(output, axis=reduced_axes)
+             if not isinstance(output, int) else output for output in res]
+            if keepdims else res)
+
+
+# Standard docstring / signature entries for `axis`, `nan_policy`, `keepdims`
+_name = 'axis'
+_desc = (
+    """If an int, the axis of the input along which to compute the statistic.
+The statistic of each axis-slice (e.g. row) of the input will appear in a
+corresponding element of the output.
+If ``None``, the input will be raveled before computing the statistic."""
+    .split('\n'))
+
+
+def _get_axis_params(default_axis=0, _name=_name, _desc=_desc):  # bind NOW
+    _type = f"int or None, default: {default_axis}"
+    _axis_parameter_doc = Parameter(_name, _type, _desc)
+    _axis_parameter = inspect.Parameter(_name,
+                                        inspect.Parameter.KEYWORD_ONLY,
+                                        default=default_axis)
+    return _axis_parameter_doc, _axis_parameter
+
+
+_name = 'nan_policy'
+_type = "{'propagate', 'omit', 'raise'}"
+_desc = (
+    """Defines how to handle input NaNs.
+
+- ``propagate``: if a NaN is present in the axis slice (e.g. row) along
+  which the  statistic is computed, the corresponding entry of the output
+  will be NaN.
+- ``omit``: NaNs will be omitted when performing the calculation.
+  If insufficient data remains in the axis slice along which the
+  statistic is computed, the corresponding entry of the output will be
+  NaN.
+- ``raise``: if a NaN is present, a ``ValueError`` will be raised."""
+    .split('\n'))
+_nan_policy_parameter_doc = Parameter(_name, _type, _desc)
+_nan_policy_parameter = inspect.Parameter(_name,
+                                          inspect.Parameter.KEYWORD_ONLY,
+                                          default='propagate')
+
+_name = 'keepdims'
+_type = "bool, default: False"
+_desc = (
+    """If this is set to True, the axes which are reduced are left
+in the result as dimensions with size one. With this option,
+the result will broadcast correctly against the input array."""
+    .split('\n'))
+_keepdims_parameter_doc = Parameter(_name, _type, _desc)
+_keepdims_parameter = inspect.Parameter(_name,
+                                        inspect.Parameter.KEYWORD_ONLY,
+                                        default=False)
+
+_standard_note_addition = (
+    """\nBeginning in SciPy 1.9, ``np.matrix`` inputs (not recommended for new
+code) are converted to ``np.ndarray`` before the calculation is performed. In
+this case, the output will be a scalar or ``np.ndarray`` of appropriate shape
+rather than a 2D ``np.matrix``. Similarly, while masked elements of masked
+arrays are ignored, the output will be a scalar or ``np.ndarray`` rather than a
+masked array with ``mask=False``.""").split('\n')
+
+
+def _axis_nan_policy_factory(tuple_to_result, default_axis=0,
+                             n_samples=1, paired=False,
+                             result_to_tuple=None, too_small=0,
+                             n_outputs=2, kwd_samples=(), override=None):
+    """Factory for a wrapper that adds axis/nan_policy params to a function.
+
+    Parameters
+    ----------
+    tuple_to_result : callable
+        Callable that returns an object of the type returned by the function
+        being wrapped (e.g. the namedtuple or dataclass returned by a
+        statistical test) provided the separate components (e.g. statistic,
+        pvalue).
+    default_axis : int, default: 0
+        The default value of the axis argument. Standard is 0 except when
+        backwards compatibility demands otherwise (e.g. `None`).
+    n_samples : int or callable, default: 1
+        The number of data samples accepted by the function
+        (e.g. `mannwhitneyu`), a callable that accepts a dictionary of
+        parameters passed into the function and returns the number of data
+        samples (e.g. `wilcoxon`), or `None` to indicate an arbitrary number
+        of samples (e.g. `kruskal`).
+    paired : {False, True}
+        Whether the function being wrapped treats the samples as paired (i.e.
+        corresponding elements of each sample should be considered as different
+        components of the same sample.)
+    result_to_tuple : callable, optional
+        Function that unpacks the results of the function being wrapped into
+        a tuple. This is essentially the inverse of `tuple_to_result`. Default
+        is `None`, which is appropriate for statistical tests that return a
+        statistic, pvalue tuple (rather than, e.g., a non-iterable dataclass).
+    too_small : int or callable, default: 0
+        The largest unacceptably small sample for the function being wrapped.
+        For example, some functions require samples of size two or more or they
+        raise an error. This argument prevents the error from being raised when
+        input is not 1D and instead places a NaN in the corresponding element
+        of the result. If callable, it must accept a list of samples, axis,
+        and a dictionary of keyword arguments passed to the wrapper function as
+        arguments and return a bool indicating whether the samples passed are
+        too small.
+    n_outputs : int or callable, default: 2
+        The number of outputs produced by the function given 1d sample(s). For
+        example, hypothesis tests that return a namedtuple or result object
+        with attributes ``statistic`` and ``pvalue`` use the default
+        ``n_outputs=2``; summary statistics with scalar output use
+        ``n_outputs=1``. Alternatively, may be a callable that accepts a
+        dictionary of arguments passed into the wrapped function and returns
+        the number of outputs corresponding with those arguments.
+    kwd_samples : sequence, default: ()
+        The names of keyword parameters that should be treated as samples. For
+        example, `gmean` accepts as its first argument a sample `a` but
+        also `weights` as a fourth, optional keyword argument. In this case, we
+        use `n_samples=1` and kwd_samples=['weights'].
+    override : dict, default: {'vectorization': False, 'nan_propagation': True}
+        Pass a dictionary with ``'vectorization': True`` to ensure that the
+        decorator overrides the function's behavior for multimensional input.
+        Use ``'nan_propagation': False`` to ensure that the decorator does not
+        override the function's behavior for ``nan_policy='propagate'``.
+    """
+    # Specify which existing behaviors the decorator must override
+    temp = override or {}
+    override = {'vectorization': False,
+                'nan_propagation': True}
+    override.update(temp)
+
+    if result_to_tuple is None:
+        def result_to_tuple(res, _):
+            return res
+
+    if not callable(too_small):
+        def is_too_small(samples, *ts_args, axis=-1, **ts_kwargs):
+            for sample in samples:
+                if sample.shape[axis] <= too_small:
+                    return True
+            return False
+    else:
+        is_too_small = too_small
+
+    def axis_nan_policy_decorator(hypotest_fun_in):
+        @wraps(hypotest_fun_in)
+        def axis_nan_policy_wrapper(*args, _no_deco=False, **kwds):
+
+            if _no_deco:  # for testing, decorator does nothing
+                return hypotest_fun_in(*args, **kwds)
+
+            # For now, skip the decorator entirely if using array API. In the future,
+            # we'll probably want to use it for `keepdims`, `axis` tuples, etc.
+            if len(args) == 0:  # extract sample from `kwds` if there are no `args`
+                used_kwd_samples = list(set(kwds).intersection(set(kwd_samples)))
+                temp = used_kwd_samples[:1]
+            else:
+                temp = args[0]
+
+            if is_dask(array_namespace(temp)):
+                msg = ("Use of `nan_policy` and `keepdims` "
+                       "is incompatible with lazy arrays.")
+                if 'nan_policy' in kwds or 'keepdims' in kwds:
+                    raise NotImplementedError(msg)
+                return hypotest_fun_in(*args, **kwds)
+
+            # We need to be flexible about whether position or keyword
+            # arguments are used, but we need to make sure users don't pass
+            # both for the same parameter. To complicate matters, some
+            # functions accept samples with *args, and some functions already
+            # accept `axis` and `nan_policy` as positional arguments.
+            # The strategy is to make sure that there is no duplication
+            # between `args` and `kwds`, combine the two into `kwds`, then
+            # the samples, `nan_policy`, and `axis` from `kwds`, as they are
+            # dealt with separately.
+
+            # Check for intersection between positional and keyword args
+            params = list(inspect.signature(hypotest_fun_in).parameters)
+            if n_samples is None:
+                # Give unique names to each positional sample argument
+                # Note that *args can't be provided as a keyword argument
+                params = [f"arg{i}" for i in range(len(args))] + params[1:]
+
+            # raise if there are too many positional args
+            maxarg = (np.inf if inspect.getfullargspec(hypotest_fun_in).varargs
+                      else len(inspect.getfullargspec(hypotest_fun_in).args))
+            if len(args) > maxarg:  # let the function raise the right error
+                hypotest_fun_in(*args, **kwds)
+
+            # raise if multiple values passed for same parameter
+            d_args = dict(zip(params, args))
+            intersection = set(d_args) & set(kwds)
+            if intersection:  # let the function raise the right error
+                hypotest_fun_in(*args, **kwds)
+
+            # Consolidate other positional and keyword args into `kwds`
+            kwds.update(d_args)
+
+            # rename avoids UnboundLocalError
+            if callable(n_samples):
+                # Future refactoring idea: no need for callable n_samples.
+                # Just replace `n_samples` and `kwd_samples` with a single
+                # list of the names of all samples, and treat all of them
+                # as `kwd_samples` are treated below.
+                n_samp = n_samples(kwds)
+            else:
+                n_samp = n_samples or len(args)
+
+            # get the number of outputs
+            n_out = n_outputs  # rename to avoid UnboundLocalError
+            if callable(n_out):
+                n_out = n_out(kwds)
+
+            # If necessary, rearrange function signature: accept other samples
+            # as positional args right after the first n_samp args
+            kwd_samp = [name for name in kwd_samples
+                        if kwds.get(name, None) is not None]
+            n_kwd_samp = len(kwd_samp)
+            if not kwd_samp:
+                hypotest_fun_out = hypotest_fun_in
+            else:
+                def hypotest_fun_out(*samples, **kwds):
+                    new_kwds = dict(zip(kwd_samp, samples[n_samp:]))
+                    kwds.update(new_kwds)
+                    return hypotest_fun_in(*samples[:n_samp], **kwds)
+
+            # Extract the things we need here
+            try:  # if something is missing
+                samples = [kwds.pop(param) for param in (params[:n_samp] + kwd_samp)]
+                xp = array_namespace(*samples)
+                samples = xp_promote(*samples, xp=xp)
+                samples = (samples,) if not isinstance(samples, tuple) else samples
+                samples = [xpx.atleast_nd(sample, ndim=1) for sample in samples]
+            except KeyError:  # let the function raise the right error
+                # might need to revisit this if required arg is not a "sample"
+                hypotest_fun_in(*args, **kwds)
+            vectorized = True if 'axis' in params else False
+            vectorized = vectorized and not override['vectorization']
+            axis = kwds.pop('axis', default_axis)
+            nan_policy = kwds.pop('nan_policy', 'propagate')
+            keepdims = kwds.pop("keepdims", False)
+            del args  # avoid the possibility of passing both `args` and `kwds`
+
+            # convert masked arrays to regular arrays with sentinel values
+            sentinel = None
+            if is_numpy(xp):
+                samples, sentinel = _masked_arrays_2_sentinel_arrays(samples)
+
+            # standardize to always work along last axis
+            reduced_axes = axis
+            if axis is None:
+                if samples:
+                    # when axis=None, take the maximum of all dimensions since
+                    # all the dimensions are reduced.
+                    n_dims = max([xp.asarray(sample).ndim for sample in samples])
+                    reduced_axes = tuple(range(n_dims))
+                samples = [xp_ravel(sample) for sample in samples]
+            else:
+                # don't ignore any axes when broadcasting if paired
+                samples = _broadcast_arrays(samples, axis=axis if not paired else None)
+                axis = (axis,) if np.isscalar(axis) else axis
+                n_axes = len(axis)
+                # move all axes in `axis` to the end to be raveled
+                samples = [xp.moveaxis(sample, axis, tuple(range(-len(axis), 0)))
+                           for sample in samples]
+                shapes = [sample.shape for sample in samples]
+                # New shape is unchanged for all axes _not_ in `axis`
+                # At the end, we append the product of the shapes of the axes
+                # in `axis`. Appending -1 doesn't work for zero-size arrays!
+                new_shapes = [shape[:-n_axes] + (math.prod(shape[-n_axes:]),)
+                              for shape in shapes]
+                samples = [xp.reshape(sample, new_shape)
+                           for sample, new_shape in zip(samples, new_shapes)]
+            axis = -1  # work over the last axis
+
+            NaN = _get_nan(*samples) if samples else xp.nan
+
+            # if axis is not needed, just handle nan_policy and return
+            ndims = np.array([sample.ndim for sample in samples])  # NumPy OK for ndims
+            if np.all(ndims <= 1):
+                # Addresses nan_policy == "raise"
+                if nan_policy != 'propagate' or override['nan_propagation']:
+                    contains_nan = [_contains_nan(sample, nan_policy)
+                                    for sample in samples]
+                else:
+                    # Behave as though there are no NaNs (even if there are)
+                    contains_nan = [False] * len(samples)
+
+                # To give JAX most benefits of the `_axis_nan_policy` decorator without
+                # value-dependent branching, the decorator will always treat JAX arrays
+                # as if there are no NaNs. Typically, this means that NaNs will
+                # multidimensional naturally. However, there may be cases in which this
+                # doesn't produce the desired result, so for now, JAX users should
+                # treat all functions as though there is no explicit`nan_policy`.
+                # Future work tracked in gh-14651.
+                any_contains_nan = not is_jax(xp) and any(contains_nan)
+                # Addresses nan_policy == "propagate"
+                if any_contains_nan and (nan_policy == 'propagate'
+                                         and override['nan_propagation']):
+                    res = xp.full(n_out, xp.nan, dtype=NaN.dtype)
+                    res = _add_reduced_axes(res, reduced_axes, keepdims)
+                    return tuple_to_result(*res)
+
+                # Addresses nan_policy == "omit"
+                too_small_msg = too_small_1d_not_omit
+                if any_contains_nan and nan_policy == 'omit':
+                    # consider passing in contains_nan
+                    samples = _remove_nans(samples, paired)
+                    too_small_msg = too_small_1d_omit
+
+                if sentinel:
+                    samples = _remove_sentinel(samples, paired, sentinel)
+
+                if is_too_small(samples, kwds):
+                    warnings.warn(too_small_msg, SmallSampleWarning, stacklevel=2)
+                    res = xp.full(n_out, xp.nan, dtype=NaN.dtype)
+                    res = _add_reduced_axes(res, reduced_axes, keepdims)
+                    return tuple_to_result(*res)
+
+                res = hypotest_fun_out(*samples, **kwds)
+                res = result_to_tuple(res, n_out)
+                res = _add_reduced_axes(res, reduced_axes, keepdims)
+                return tuple_to_result(*res)
+
+            # check for empty input
+            empty_output = _check_empty_inputs(samples, axis, xp=xp)
+            # only return empty output if zero sized input is too small.
+            if (
+                empty_output is not None
+                and (is_too_small(samples, kwds) or xp_size(empty_output) == 0)
+            ):
+                if is_too_small(samples, kwds) and xp_size(empty_output) != 0:
+                    warnings.warn(too_small_nd_not_omit, SmallSampleWarning,
+                                  stacklevel=2)
+                res = [xp_copy(empty_output) for i in range(n_out)]
+                res = _add_reduced_axes(res, reduced_axes, keepdims)
+                return tuple_to_result(*res)
+
+            if not is_numpy(xp) and 'nan_policy' in kwds:
+                msg = ("Use of `nan_policy` is incompatible with multidimensional "
+                       "non-NumPy arrays.")
+                raise NotImplementedError(msg)
+
+            if not is_numpy(xp):
+                res = hypotest_fun_out(*samples, axis=axis, **kwds)
+                res = result_to_tuple(res, n_out)
+                res = _add_reduced_axes(res, reduced_axes, keepdims, xp=xp)
+                return tuple_to_result(*res)
+
+            # otherwise, concatenate all samples along axis, remembering where
+            # each separate sample begins
+            lengths = np.array([sample.shape[axis] for sample in samples])
+            split_indices = np.cumsum(lengths)
+            x = _broadcast_concatenate(samples, axis, paired=paired)
+
+            # Addresses nan_policy == "raise"
+            if nan_policy != 'propagate' or override['nan_propagation']:
+                contains_nan = _contains_nan(x, nan_policy)
+            else:
+                contains_nan = False  # behave like there are no NaNs
+
+            if vectorized and not contains_nan and not sentinel:
+                res = hypotest_fun_out(*samples, axis=axis, **kwds)
+                res = result_to_tuple(res, n_out)
+                res = _add_reduced_axes(res, reduced_axes, keepdims)
+                return tuple_to_result(*res)
+
+            # Addresses nan_policy == "omit"
+            if contains_nan and nan_policy == 'omit':
+                def hypotest_fun(x):
+                    samples = np.split(x, split_indices)[:n_samp+n_kwd_samp]
+                    samples = _remove_nans(samples, paired)
+                    if sentinel:
+                        samples = _remove_sentinel(samples, paired, sentinel)
+                    if is_too_small(samples, kwds):
+                        warnings.warn(too_small_nd_omit, SmallSampleWarning,
+                                      stacklevel=4)
+                        return np.full(n_out, NaN)
+                    return result_to_tuple(hypotest_fun_out(*samples, **kwds), n_out)
+
+            # Addresses nan_policy == "propagate"
+            elif (contains_nan and nan_policy == 'propagate'
+                  and override['nan_propagation']):
+                def hypotest_fun(x):
+                    if np.isnan(x).any():
+                        return np.full(n_out, NaN)
+
+                    samples = np.split(x, split_indices)[:n_samp+n_kwd_samp]
+                    if sentinel:
+                        samples = _remove_sentinel(samples, paired, sentinel)
+                    if is_too_small(samples, kwds):
+                        return np.full(n_out, NaN)
+                    return result_to_tuple(hypotest_fun_out(*samples, **kwds), n_out)
+
+            else:
+                def hypotest_fun(x):
+                    samples = np.split(x, split_indices)[:n_samp+n_kwd_samp]
+                    if sentinel:
+                        samples = _remove_sentinel(samples, paired, sentinel)
+                    if is_too_small(samples, kwds):
+                        return np.full(n_out, NaN)
+                    return result_to_tuple(hypotest_fun_out(*samples, **kwds), n_out)
+
+            x = np.moveaxis(x, axis, 0)
+            res = np.apply_along_axis(hypotest_fun, axis=0, arr=x)
+            res = _add_reduced_axes(res, reduced_axes, keepdims)
+            return tuple_to_result(*res)
+
+        _axis_parameter_doc, _axis_parameter = _get_axis_params(default_axis)
+        doc = FunctionDoc(axis_nan_policy_wrapper)
+        parameter_names = [param.name for param in doc['Parameters']]
+        if 'axis' in parameter_names:
+            doc['Parameters'][parameter_names.index('axis')] = (
+                _axis_parameter_doc)
+        else:
+            doc['Parameters'].append(_axis_parameter_doc)
+        if 'nan_policy' in parameter_names:
+            doc['Parameters'][parameter_names.index('nan_policy')] = (
+                _nan_policy_parameter_doc)
+        else:
+            doc['Parameters'].append(_nan_policy_parameter_doc)
+        if 'keepdims' in parameter_names:
+            doc['Parameters'][parameter_names.index('keepdims')] = (
+                _keepdims_parameter_doc)
+        else:
+            doc['Parameters'].append(_keepdims_parameter_doc)
+        doc['Notes'] += _standard_note_addition
+        doc = str(doc).split("\n", 1)[1].lstrip(" \n")  # remove signature
+        axis_nan_policy_wrapper.__doc__ = str(doc)
+
+        sig = inspect.signature(axis_nan_policy_wrapper)
+        parameters = sig.parameters
+        parameter_list = list(parameters.values())
+        if 'axis' not in parameters:
+            parameter_list.append(_axis_parameter)
+        if 'nan_policy' not in parameters:
+            parameter_list.append(_nan_policy_parameter)
+        if 'keepdims' not in parameters:
+            parameter_list.append(_keepdims_parameter)
+        sig = sig.replace(parameters=parameter_list)
+        axis_nan_policy_wrapper.__signature__ = sig
+
+        return axis_nan_policy_wrapper
+    return axis_nan_policy_decorator
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_biasedurn.cpython-312-x86_64-linux-gnu.so b/.venv/lib/python3.12/site-packages/scipy/stats/_biasedurn.cpython-312-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..2b70ffc0545dc2504bd21df2bd9065da93e4ad57
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_biasedurn.cpython-312-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49b1fadb555b9f5e80d93f2ab5b4d9910c47319ef83fb94cbbbfebef9abf0c74
+size 239568
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_biasedurn.pxd b/.venv/lib/python3.12/site-packages/scipy/stats/_biasedurn.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..92785f08dbec30a4db286fcb85b42d7221e2228e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_biasedurn.pxd
@@ -0,0 +1,27 @@
+# Declare the class with cdef
+cdef extern from "biasedurn/stocc.h" nogil:
+    cdef cppclass CFishersNCHypergeometric:
+        CFishersNCHypergeometric(int, int, int, double, double) except +
+        int mode()
+        double mean()
+        double variance()
+        double probability(int x)
+        double moments(double * mean, double * var)
+
+    cdef cppclass CWalleniusNCHypergeometric:
+        CWalleniusNCHypergeometric() except +
+        CWalleniusNCHypergeometric(int, int, int, double, double) except +
+        int mode()
+        double mean()
+        double variance()
+        double probability(int x)
+        double moments(double * mean, double * var)
+
+    cdef cppclass StochasticLib3:
+        StochasticLib3(int seed) except +
+        double Random() except +
+        void SetAccuracy(double accur)
+        int FishersNCHyp (int n, int m, int N, double odds) except +
+        int WalleniusNCHyp (int n, int m, int N, double odds) except +
+        double(*next_double)()
+        double(*next_normal)(const double m, const double s)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_binned_statistic.py b/.venv/lib/python3.12/site-packages/scipy/stats/_binned_statistic.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a11a1caa4bc48d2e6a0a42793f79635a3e58d36
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_binned_statistic.py
@@ -0,0 +1,806 @@
+import builtins
+from warnings import catch_warnings, simplefilter
+import numpy as np
+from operator import index
+from collections import namedtuple
+
+from scipy._lib._array_api import xp_capabilities
+
+__all__ = ['binned_statistic',
+           'binned_statistic_2d',
+           'binned_statistic_dd']
+
+
+BinnedStatisticResult = namedtuple('BinnedStatisticResult',
+                                   ('statistic', 'bin_edges', 'binnumber'))
+
+
+@xp_capabilities(np_only=True)
+def binned_statistic(x, values, statistic='mean',
+                     bins=10, range=None):
+    """
+    Compute a binned statistic for one or more sets of data.
+
+    This is a generalization of a histogram function.  A histogram divides
+    the space into bins, and returns the count of the number of points in
+    each bin.  This function allows the computation of the sum, mean, median,
+    or other statistic of the values (or set of values) within each bin.
+
+    Parameters
+    ----------
+    x : (N,) array_like
+        A sequence of values to be binned.
+    values : (N,) array_like or list of (N,) array_like
+        The data on which the statistic will be computed.  This must be
+        the same shape as `x`, or a set of sequences - each the same shape as
+        `x`.  If `values` is a set of sequences, the statistic will be computed
+        on each independently.
+    statistic : str or callable, optional
+        The statistic to compute (default is 'mean').
+        The following statistics are available:
+
+        * 'mean' : compute the mean of values for points within each bin.
+          Empty bins will be represented by NaN.
+        * 'std' : compute the standard deviation within each bin. This
+          is implicitly calculated with ddof=0.
+        * 'median' : compute the median of values for points within each
+          bin. Empty bins will be represented by NaN.
+        * 'count' : compute the count of points within each bin.  This is
+          identical to an unweighted histogram.  `values` array is not
+          referenced.
+        * 'sum' : compute the sum of values for points within each bin.
+          This is identical to a weighted histogram.
+        * 'min' : compute the minimum of values for points within each bin.
+          Empty bins will be represented by NaN.
+        * 'max' : compute the maximum of values for point within each bin.
+          Empty bins will be represented by NaN.
+        * function : a user-defined function which takes a 1D array of
+          values, and outputs a single numerical statistic. This function
+          will be called on the values in each bin.  Empty bins will be
+          represented by function([]), or NaN if this returns an error.
+
+    bins : int or sequence of scalars, optional
+        If `bins` is an int, it defines the number of equal-width bins in the
+        given range (10 by default).  If `bins` is a sequence, it defines the
+        bin edges, including the rightmost edge, allowing for non-uniform bin
+        widths.  Values in `x` that are smaller than lowest bin edge are
+        assigned to bin number 0, values beyond the highest bin are assigned to
+        ``bins[-1]``.  If the bin edges are specified, the number of bins will
+        be, (nx = len(bins)-1).
+    range : (float, float) or [(float, float)], optional
+        The lower and upper range of the bins.  If not provided, range
+        is simply ``(x.min(), x.max())``.  Values outside the range are
+        ignored.
+
+    Returns
+    -------
+    statistic : array
+        The values of the selected statistic in each bin.
+    bin_edges : array of dtype float
+        Return the bin edges ``(length(statistic)+1)``.
+    binnumber: 1-D ndarray of ints
+        Indices of the bins (corresponding to `bin_edges`) in which each value
+        of `x` belongs.  Same length as `values`.  A binnumber of `i` means the
+        corresponding value is between (bin_edges[i-1], bin_edges[i]).
+
+    See Also
+    --------
+    numpy.digitize, numpy.histogram, binned_statistic_2d, binned_statistic_dd
+
+    Notes
+    -----
+    All but the last (righthand-most) bin is half-open.  In other words, if
+    `bins` is ``[1, 2, 3, 4]``, then the first bin is ``[1, 2)`` (including 1,
+    but excluding 2) and the second ``[2, 3)``.  The last bin, however, is
+    ``[3, 4]``, which *includes* 4.
+
+    .. versionadded:: 0.11.0
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+
+    First some basic examples:
+
+    Create two evenly spaced bins in the range of the given sample, and sum the
+    corresponding values in each of those bins:
+
+    >>> values = [1.0, 1.0, 2.0, 1.5, 3.0]
+    >>> stats.binned_statistic([1, 1, 2, 5, 7], values, 'sum', bins=2)
+    BinnedStatisticResult(statistic=array([4. , 4.5]),
+            bin_edges=array([1., 4., 7.]), binnumber=array([1, 1, 1, 2, 2]))
+
+    Multiple arrays of values can also be passed.  The statistic is calculated
+    on each set independently:
+
+    >>> values = [[1.0, 1.0, 2.0, 1.5, 3.0], [2.0, 2.0, 4.0, 3.0, 6.0]]
+    >>> stats.binned_statistic([1, 1, 2, 5, 7], values, 'sum', bins=2)
+    BinnedStatisticResult(statistic=array([[4. , 4.5],
+           [8. , 9. ]]), bin_edges=array([1., 4., 7.]),
+           binnumber=array([1, 1, 1, 2, 2]))
+
+    >>> stats.binned_statistic([1, 2, 1, 2, 4], np.arange(5), statistic='mean',
+    ...                        bins=3)
+    BinnedStatisticResult(statistic=array([1., 2., 4.]),
+            bin_edges=array([1., 2., 3., 4.]),
+            binnumber=array([1, 2, 1, 2, 3]))
+
+    As a second example, we now generate some random data of sailing boat speed
+    as a function of wind speed, and then determine how fast our boat is for
+    certain wind speeds:
+
+    >>> rng = np.random.default_rng()
+    >>> windspeed = 8 * rng.random(500)
+    >>> boatspeed = .3 * windspeed**.5 + .2 * rng.random(500)
+    >>> bin_means, bin_edges, binnumber = stats.binned_statistic(windspeed,
+    ...                 boatspeed, statistic='median', bins=[1,2,3,4,5,6,7])
+    >>> plt.figure()
+    >>> plt.plot(windspeed, boatspeed, 'b.', label='raw data')
+    >>> plt.hlines(bin_means, bin_edges[:-1], bin_edges[1:], colors='g', lw=5,
+    ...            label='binned statistic of data')
+    >>> plt.legend()
+
+    Now we can use ``binnumber`` to select all datapoints with a windspeed
+    below 1:
+
+    >>> low_boatspeed = boatspeed[binnumber == 0]
+
+    As a final example, we will use ``bin_edges`` and ``binnumber`` to make a
+    plot of a distribution that shows the mean and distribution around that
+    mean per bin, on top of a regular histogram and the probability
+    distribution function:
+
+    >>> x = np.linspace(0, 5, num=500)
+    >>> x_pdf = stats.maxwell.pdf(x)
+    >>> samples = stats.maxwell.rvs(size=10000)
+
+    >>> bin_means, bin_edges, binnumber = stats.binned_statistic(x, x_pdf,
+    ...         statistic='mean', bins=25)
+    >>> bin_width = (bin_edges[1] - bin_edges[0])
+    >>> bin_centers = bin_edges[1:] - bin_width/2
+
+    >>> plt.figure()
+    >>> plt.hist(samples, bins=50, density=True, histtype='stepfilled',
+    ...          alpha=0.2, label='histogram of data')
+    >>> plt.plot(x, x_pdf, 'r-', label='analytical pdf')
+    >>> plt.hlines(bin_means, bin_edges[:-1], bin_edges[1:], colors='g', lw=2,
+    ...            label='binned statistic of data')
+    >>> plt.plot((binnumber - 0.5) * bin_width, x_pdf, 'g.', alpha=0.5)
+    >>> plt.legend(fontsize=10)
+    >>> plt.show()
+
+    """
+    try:
+        N = len(bins)
+    except TypeError:
+        N = 1
+
+    if N != 1:
+        bins = [np.asarray(bins, float)]
+
+    if range is not None:
+        if len(range) == 2:
+            range = [range]
+
+    medians, edges, binnumbers = binned_statistic_dd(
+        [x], values, statistic, bins, range)
+
+    return BinnedStatisticResult(medians, edges[0], binnumbers)
+
+
+BinnedStatistic2dResult = namedtuple('BinnedStatistic2dResult',
+                                     ('statistic', 'x_edge', 'y_edge',
+                                      'binnumber'))
+
+
+@xp_capabilities(np_only=True)
+def binned_statistic_2d(x, y, values, statistic='mean',
+                        bins=10, range=None, expand_binnumbers=False):
+    """
+    Compute a bidimensional binned statistic for one or more sets of data.
+
+    This is a generalization of a histogram2d function.  A histogram divides
+    the space into bins, and returns the count of the number of points in
+    each bin.  This function allows the computation of the sum, mean, median,
+    or other statistic of the values (or set of values) within each bin.
+
+    Parameters
+    ----------
+    x : (N,) array_like
+        A sequence of values to be binned along the first dimension.
+    y : (N,) array_like
+        A sequence of values to be binned along the second dimension.
+    values : (N,) array_like or list of (N,) array_like
+        The data on which the statistic will be computed.  This must be
+        the same shape as `x`, or a list of sequences - each with the same
+        shape as `x`.  If `values` is such a list, the statistic will be
+        computed on each independently.
+    statistic : str or callable, optional
+        The statistic to compute (default is 'mean').
+        The following statistics are available:
+
+        * 'mean' : compute the mean of values for points within each bin.
+          Empty bins will be represented by NaN.
+        * 'std' : compute the standard deviation within each bin. This
+          is implicitly calculated with ddof=0.
+        * 'median' : compute the median of values for points within each
+          bin. Empty bins will be represented by NaN.
+        * 'count' : compute the count of points within each bin.  This is
+          identical to an unweighted histogram.  `values` array is not
+          referenced.
+        * 'sum' : compute the sum of values for points within each bin.
+          This is identical to a weighted histogram.
+        * 'min' : compute the minimum of values for points within each bin.
+          Empty bins will be represented by NaN.
+        * 'max' : compute the maximum of values for point within each bin.
+          Empty bins will be represented by NaN.
+        * function : a user-defined function which takes a 1D array of
+          values, and outputs a single numerical statistic. This function
+          will be called on the values in each bin.  Empty bins will be
+          represented by function([]), or NaN if this returns an error.
+
+    bins : int or [int, int] or array_like or [array, array], optional
+        The bin specification:
+
+        * the number of bins for the two dimensions (nx = ny = bins),
+        * the number of bins in each dimension (nx, ny = bins),
+        * the bin edges for the two dimensions (x_edge = y_edge = bins),
+        * the bin edges in each dimension (x_edge, y_edge = bins).
+
+        If the bin edges are specified, the number of bins will be,
+        (nx = len(x_edge)-1, ny = len(y_edge)-1).
+
+    range : (2,2) array_like, optional
+        The leftmost and rightmost edges of the bins along each dimension
+        (if not specified explicitly in the `bins` parameters):
+        [[xmin, xmax], [ymin, ymax]]. All values outside of this range will be
+        considered outliers and not tallied in the histogram.
+    expand_binnumbers : bool, optional
+        'False' (default): the returned `binnumber` is a shape (N,) array of
+        linearized bin indices.
+        'True': the returned `binnumber` is 'unraveled' into a shape (2,N)
+        ndarray, where each row gives the bin numbers in the corresponding
+        dimension.
+        See the `binnumber` returned value, and the `Examples` section.
+
+        .. versionadded:: 0.17.0
+
+    Returns
+    -------
+    statistic : (nx, ny) ndarray
+        The values of the selected statistic in each two-dimensional bin.
+    x_edge : (nx + 1) ndarray
+        The bin edges along the first dimension.
+    y_edge : (ny + 1) ndarray
+        The bin edges along the second dimension.
+    binnumber : (N,) array of ints or (2,N) ndarray of ints
+        This assigns to each element of `sample` an integer that represents the
+        bin in which this observation falls.  The representation depends on the
+        `expand_binnumbers` argument.  See `Notes` for details.
+
+
+    See Also
+    --------
+    numpy.digitize, numpy.histogram2d, binned_statistic, binned_statistic_dd
+
+    Notes
+    -----
+    Binedges:
+    All but the last (righthand-most) bin is half-open.  In other words, if
+    `bins` is ``[1, 2, 3, 4]``, then the first bin is ``[1, 2)`` (including 1,
+    but excluding 2) and the second ``[2, 3)``.  The last bin, however, is
+    ``[3, 4]``, which *includes* 4.
+
+    `binnumber`:
+    This returned argument assigns to each element of `sample` an integer that
+    represents the bin in which it belongs.  The representation depends on the
+    `expand_binnumbers` argument. If 'False' (default): The returned
+    `binnumber` is a shape (N,) array of linearized indices mapping each
+    element of `sample` to its corresponding bin (using row-major ordering).
+    Note that the returned linearized bin indices are used for an array with
+    extra bins on the outer binedges to capture values outside of the defined
+    bin bounds.
+    If 'True': The returned `binnumber` is a shape (2,N) ndarray where
+    each row indicates bin placements for each dimension respectively.  In each
+    dimension, a binnumber of `i` means the corresponding value is between
+    (D_edge[i-1], D_edge[i]), where 'D' is either 'x' or 'y'.
+
+    .. versionadded:: 0.11.0
+
+    Examples
+    --------
+    >>> from scipy import stats
+
+    Calculate the counts with explicit bin-edges:
+
+    >>> x = [0.1, 0.1, 0.1, 0.6]
+    >>> y = [2.1, 2.6, 2.1, 2.1]
+    >>> binx = [0.0, 0.5, 1.0]
+    >>> biny = [2.0, 2.5, 3.0]
+    >>> ret = stats.binned_statistic_2d(x, y, None, 'count', bins=[binx, biny])
+    >>> ret.statistic
+    array([[2., 1.],
+           [1., 0.]])
+
+    The bin in which each sample is placed is given by the `binnumber`
+    returned parameter.  By default, these are the linearized bin indices:
+
+    >>> ret.binnumber
+    array([5, 6, 5, 9])
+
+    The bin indices can also be expanded into separate entries for each
+    dimension using the `expand_binnumbers` parameter:
+
+    >>> ret = stats.binned_statistic_2d(x, y, None, 'count', bins=[binx, biny],
+    ...                                 expand_binnumbers=True)
+    >>> ret.binnumber
+    array([[1, 1, 1, 2],
+           [1, 2, 1, 1]])
+
+    Which shows that the first three elements belong in the xbin 1, and the
+    fourth into xbin 2; and so on for y.
+
+    """
+
+    # This code is based on np.histogram2d
+    try:
+        N = len(bins)
+    except TypeError:
+        N = 1
+
+    if N != 1 and N != 2:
+        xedges = yedges = np.asarray(bins, float)
+        bins = [xedges, yedges]
+
+    medians, edges, binnumbers = binned_statistic_dd(
+        [x, y], values, statistic, bins, range,
+        expand_binnumbers=expand_binnumbers)
+
+    return BinnedStatistic2dResult(medians, edges[0], edges[1], binnumbers)
+
+
+BinnedStatisticddResult = namedtuple('BinnedStatisticddResult',
+                                     ('statistic', 'bin_edges',
+                                      'binnumber'))
+
+
+def _bincount(x, weights):
+    if np.iscomplexobj(weights):
+        a = np.bincount(x, np.real(weights))
+        b = np.bincount(x, np.imag(weights))
+        z = a + b*1j
+
+    else:
+        z = np.bincount(x, weights)
+    return z
+
+
+@xp_capabilities(np_only=True)
+def binned_statistic_dd(sample, values, statistic='mean',
+                        bins=10, range=None, expand_binnumbers=False,
+                        binned_statistic_result=None):
+    """
+    Compute a multidimensional binned statistic for a set of data.
+
+    This is a generalization of a histogramdd function.  A histogram divides
+    the space into bins, and returns the count of the number of points in
+    each bin.  This function allows the computation of the sum, mean, median,
+    or other statistic of the values within each bin.
+
+    Parameters
+    ----------
+    sample : array_like
+        Data to histogram passed as a sequence of N arrays of length D, or
+        as an (N,D) array.
+    values : (N,) array_like or list of (N,) array_like
+        The data on which the statistic will be computed.  This must be
+        the same shape as `sample`, or a list of sequences - each with the
+        same shape as `sample`.  If `values` is such a list, the statistic
+        will be computed on each independently.
+    statistic : str or callable, optional
+        The statistic to compute (default is 'mean').
+        The following statistics are available:
+
+        * 'mean' : compute the mean of values for points within each bin.
+          Empty bins will be represented by NaN.
+        * 'median' : compute the median of values for points within each
+          bin. Empty bins will be represented by NaN.
+        * 'count' : compute the count of points within each bin.  This is
+          identical to an unweighted histogram.  `values` array is not
+          referenced.
+        * 'sum' : compute the sum of values for points within each bin.
+          This is identical to a weighted histogram.
+        * 'std' : compute the standard deviation within each bin. This
+          is implicitly calculated with ddof=0. If the number of values
+          within a given bin is 0 or 1, the computed standard deviation value
+          will be 0 for the bin.
+        * 'min' : compute the minimum of values for points within each bin.
+          Empty bins will be represented by NaN.
+        * 'max' : compute the maximum of values for point within each bin.
+          Empty bins will be represented by NaN.
+        * function : a user-defined function which takes a 1D array of
+          values, and outputs a single numerical statistic. This function
+          will be called on the values in each bin.  Empty bins will be
+          represented by function([]), or NaN if this returns an error.
+
+    bins : sequence or positive int, optional
+        The bin specification must be in one of the following forms:
+
+        * A sequence of arrays describing the bin edges along each dimension.
+        * The number of bins for each dimension (nx, ny, ... = bins).
+        * The number of bins for all dimensions (nx = ny = ... = bins).
+
+    range : sequence, optional
+        A sequence of lower and upper bin edges to be used if the edges are
+        not given explicitly in `bins`. Defaults to the minimum and maximum
+        values along each dimension.
+    expand_binnumbers : bool, optional
+        'False' (default): the returned `binnumber` is a shape (N,) array of
+        linearized bin indices.
+        'True': the returned `binnumber` is 'unraveled' into a shape (D,N)
+        ndarray, where each row gives the bin numbers in the corresponding
+        dimension.
+        See the `binnumber` returned value, and the `Examples` section of
+        `binned_statistic_2d`.
+    binned_statistic_result : binnedStatisticddResult
+        Result of a previous call to the function in order to reuse bin edges
+        and bin numbers with new values and/or a different statistic.
+        To reuse bin numbers, `expand_binnumbers` must have been set to False
+        (the default)
+
+        .. versionadded:: 0.17.0
+
+    Returns
+    -------
+    statistic : ndarray, shape(nx1, nx2, nx3,...)
+        The values of the selected statistic in each two-dimensional bin.
+    bin_edges : list of ndarrays
+        A list of D arrays describing the (nxi + 1) bin edges for each
+        dimension.
+    binnumber : (N,) array of ints or (D,N) ndarray of ints
+        This assigns to each element of `sample` an integer that represents the
+        bin in which this observation falls.  The representation depends on the
+        `expand_binnumbers` argument.  See `Notes` for details.
+
+
+    See Also
+    --------
+    numpy.digitize, numpy.histogramdd, binned_statistic, binned_statistic_2d
+
+    Notes
+    -----
+    Binedges:
+    All but the last (righthand-most) bin is half-open in each dimension.  In
+    other words, if `bins` is ``[1, 2, 3, 4]``, then the first bin is
+    ``[1, 2)`` (including 1, but excluding 2) and the second ``[2, 3)``.  The
+    last bin, however, is ``[3, 4]``, which *includes* 4.
+
+    `binnumber`:
+    This returned argument assigns to each element of `sample` an integer that
+    represents the bin in which it belongs.  The representation depends on the
+    `expand_binnumbers` argument. If 'False' (default): The returned
+    `binnumber` is a shape (N,) array of linearized indices mapping each
+    element of `sample` to its corresponding bin (using row-major ordering).
+    If 'True': The returned `binnumber` is a shape (D,N) ndarray where
+    each row indicates bin placements for each dimension respectively.  In each
+    dimension, a binnumber of `i` means the corresponding value is between
+    (bin_edges[D][i-1], bin_edges[D][i]), for each dimension 'D'.
+
+    .. versionadded:: 0.11.0
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+    >>> from mpl_toolkits.mplot3d import Axes3D
+
+    Take an array of 600 (x, y) coordinates as an example.
+    `binned_statistic_dd` can handle arrays of higher dimension `D`. But a plot
+    of dimension `D+1` is required.
+
+    >>> mu = np.array([0., 1.])
+    >>> sigma = np.array([[1., -0.5],[-0.5, 1.5]])
+    >>> multinormal = stats.multivariate_normal(mu, sigma)
+    >>> data = multinormal.rvs(size=600, random_state=235412)
+    >>> data.shape
+    (600, 2)
+
+    Create bins and count how many arrays fall in each bin:
+
+    >>> N = 60
+    >>> x = np.linspace(-3, 3, N)
+    >>> y = np.linspace(-3, 4, N)
+    >>> ret = stats.binned_statistic_dd(data, np.arange(600), bins=[x, y],
+    ...                                 statistic='count')
+    >>> bincounts = ret.statistic
+
+    Set the volume and the location of bars:
+
+    >>> dx = x[1] - x[0]
+    >>> dy = y[1] - y[0]
+    >>> x, y = np.meshgrid(x[:-1]+dx/2, y[:-1]+dy/2)
+    >>> z = 0
+
+    >>> bincounts = bincounts.ravel()
+    >>> x = x.ravel()
+    >>> y = y.ravel()
+
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111, projection='3d')
+    >>> with np.errstate(divide='ignore'):   # silence random axes3d warning
+    ...     ax.bar3d(x, y, z, dx, dy, bincounts)
+
+    Reuse bin numbers and bin edges with new values:
+
+    >>> ret2 = stats.binned_statistic_dd(data, -np.arange(600),
+    ...                                  binned_statistic_result=ret,
+    ...                                  statistic='mean')
+    """
+    known_stats = ['mean', 'median', 'count', 'sum', 'std', 'min', 'max']
+    if not callable(statistic) and statistic not in known_stats:
+        raise ValueError(f'invalid statistic {statistic!r}')
+
+    try:
+        bins = index(bins)
+    except TypeError:
+        # bins is not an integer
+        pass
+    # If bins was an integer-like object, now it is an actual Python int.
+
+    # For context: see gh-23751 and linked issues/PRs
+    if np.isnan(sample).any():
+        message = ("The sample on which `values` is to be binned contains at least one "
+                   "NaN, and the meaning is ambiguous. Consider removing or replacing "
+                   "with numerical outliers.")
+        raise ValueError(message)
+
+    # `Ndim` is the number of dimensions (e.g. `2` for `binned_statistic_2d`)
+    # `Dlen` is the length of elements along each dimension.
+    # This code is based on np.histogramdd
+    try:
+        # `sample` is an ND-array.
+        Dlen, Ndim = sample.shape
+    except (AttributeError, ValueError):
+        # `sample` is a sequence of 1D arrays.
+        sample = np.atleast_2d(sample).T
+        Dlen, Ndim = sample.shape
+
+    # Store initial shape of `values` to preserve it in the output
+    values = np.asarray(values)
+    input_shape = list(values.shape)
+    # Make sure that `values` is 2D to iterate over rows
+    values = np.atleast_2d(values)
+    Vdim, Vlen = values.shape
+
+    # Make sure `values` match `sample`
+    if statistic != 'count' and Vlen != Dlen:
+        raise AttributeError('The number of `values` elements must match the '
+                             'length of each `sample` dimension.')
+
+    try:
+        M = len(bins)
+        if M != Ndim:
+            raise AttributeError('The dimension of bins must be equal '
+                                 'to the dimension of the sample x.')
+    except TypeError:
+        bins = Ndim * [bins]
+
+    if binned_statistic_result is None:
+        nbin, edges, dedges = _bin_edges(sample, bins, range)
+        binnumbers = _bin_numbers(sample, nbin, edges, dedges)
+    else:
+        edges = binned_statistic_result.bin_edges
+        nbin = np.array([len(edges[i]) + 1 for i in builtins.range(Ndim)])
+        # +1 for outlier bins
+        dedges = [np.diff(edges[i]) for i in builtins.range(Ndim)]
+        binnumbers = binned_statistic_result.binnumber
+
+    # Avoid overflow with double precision. Complex `values` -> `complex128`.
+    result_type = np.result_type(values, np.float64)
+    result = np.empty([Vdim, nbin.prod()], dtype=result_type)
+
+    if statistic in {'mean', np.mean}:
+        result.fill(np.nan)
+        flatcount = _bincount(binnumbers, None)
+        a = flatcount.nonzero()
+        for vv in builtins.range(Vdim):
+            flatsum = _bincount(binnumbers, values[vv])
+            result[vv, a] = flatsum[a] / flatcount[a]
+    elif statistic in {'std', np.std}:
+        result.fill(np.nan)
+        flatcount = _bincount(binnumbers, None)
+        a = flatcount.nonzero()
+        for vv in builtins.range(Vdim):
+            flatsum = _bincount(binnumbers, values[vv])
+            delta = values[vv] - flatsum[binnumbers] / flatcount[binnumbers]
+            std = np.sqrt(
+                _bincount(binnumbers, delta*np.conj(delta))[a] / flatcount[a]
+            )
+            result[vv, a] = std
+        result = np.real(result)
+    elif statistic == 'count':
+        result = np.empty([Vdim, nbin.prod()], dtype=np.float64)
+        result.fill(0)
+        flatcount = _bincount(binnumbers, None)
+        a = np.arange(len(flatcount))
+        result[:, a] = flatcount[np.newaxis, :]
+    elif statistic in {'sum', np.sum}:
+        result.fill(0)
+        for vv in builtins.range(Vdim):
+            flatsum = _bincount(binnumbers, values[vv])
+            a = np.arange(len(flatsum))
+            result[vv, a] = flatsum
+    elif statistic in {'median', np.median}:
+        result.fill(np.nan)
+        for vv in builtins.range(Vdim):
+            i = np.lexsort((values[vv], binnumbers))
+            _, j, counts = np.unique(binnumbers[i],
+                                     return_index=True, return_counts=True)
+            mid = j + (counts - 1) / 2
+            mid_a = values[vv, i][np.floor(mid).astype(int)]
+            mid_b = values[vv, i][np.ceil(mid).astype(int)]
+            medians = (mid_a + mid_b) / 2
+            result[vv, binnumbers[i][j]] = medians
+    elif statistic in {'min', np.min}:
+        result.fill(np.nan)
+        for vv in builtins.range(Vdim):
+            i = np.argsort(values[vv])[::-1]  # Reversed so the min is last
+            result[vv, binnumbers[i]] = values[vv, i]
+    elif statistic in {'max', np.max}:
+        result.fill(np.nan)
+        for vv in builtins.range(Vdim):
+            i = np.argsort(values[vv])
+            result[vv, binnumbers[i]] = values[vv, i]
+    elif callable(statistic):
+        with np.errstate(invalid='ignore'), catch_warnings():
+            simplefilter("ignore", RuntimeWarning)
+            try:
+                null = statistic([])
+            except Exception:
+                null = np.nan
+        if np.iscomplexobj(null):
+            result = result.astype(np.complex128)
+        result.fill(null)
+        try:
+            _calc_binned_statistic(
+                Vdim, binnumbers, result, values, statistic
+            )
+        except ValueError:
+            result = result.astype(np.complex128)
+            _calc_binned_statistic(
+                Vdim, binnumbers, result, values, statistic
+            )
+
+    # Shape into a proper matrix
+    result = result.reshape(np.append(Vdim, nbin))
+
+    # Remove outliers (indices 0 and -1 for each bin-dimension).
+    core = tuple([slice(None)] + Ndim * [slice(1, -1)])
+    result = result[core]
+
+    # Unravel binnumbers into an ndarray, each row the bins for each dimension
+    if expand_binnumbers and Ndim > 1:
+        binnumbers = np.asarray(np.unravel_index(binnumbers, nbin))
+
+    if np.any(result.shape[1:] != nbin - 2):
+        raise RuntimeError('Internal Shape Error')
+
+    # Reshape to have output (`result`) match input (`values`) shape
+    result = result.reshape(input_shape[:-1] + list(nbin-2))
+
+    return BinnedStatisticddResult(result, edges, binnumbers)
+
+
+def _calc_binned_statistic(Vdim, bin_numbers, result, values, stat_func):
+    unique_bin_numbers = np.unique(bin_numbers)
+    for vv in builtins.range(Vdim):
+        bin_map = _create_binned_data(bin_numbers, unique_bin_numbers,
+                                      values, vv)
+        for i in unique_bin_numbers:
+            stat = stat_func(np.array(bin_map[i]))
+            if np.iscomplexobj(stat) and not np.iscomplexobj(result):
+                raise ValueError("The statistic function returns complex ")
+            result[vv, i] = stat
+
+
+def _create_binned_data(bin_numbers, unique_bin_numbers, values, vv):
+    """ Create hashmap of bin ids to values in bins
+    key: bin number
+    value: list of binned data
+    """
+    bin_map = dict()
+    for i in unique_bin_numbers:
+        bin_map[i] = []
+    for i in builtins.range(len(bin_numbers)):
+        bin_map[bin_numbers[i]].append(values[vv, i])
+    return bin_map
+
+
+def _bin_edges(sample, bins=None, range=None):
+    """ Create edge arrays
+    """
+    Dlen, Ndim = sample.shape
+
+    nbin = np.empty(Ndim, int)    # Number of bins in each dimension
+    edges = Ndim * [None]         # Bin edges for each dim (will be 2D array)
+    dedges = Ndim * [None]        # Spacing between edges (will be 2D array)
+
+    # Preserve sample floating point precision in bin edges
+    edges_dtype = (sample.dtype if np.issubdtype(sample.dtype, np.floating)
+                   else float)
+
+    # Select range for each dimension
+    # Used only if number of bins is given.
+    if range is None:
+        sample = sample.astype(float, copy=True)
+        sample[np.isinf(sample)] = np.nan
+        smin = np.atleast_1d(np.nanmin(sample, axis=0))
+        smax = np.atleast_1d(np.nanmax(sample, axis=0))
+    else:
+        if len(range) != Ndim:
+            raise ValueError(
+                f"range given for {len(range)} dimensions; {Ndim} required")
+        smin = np.empty(Ndim)
+        smax = np.empty(Ndim)
+        for i in builtins.range(Ndim):
+            if range[i][1] < range[i][0]:
+                raise ValueError(
+                    f"In {f'dimension {i + 1} of ' if Ndim > 1 else ''}range,"
+                    " start must be <= stop")
+            smin[i], smax[i] = range[i]
+
+    # Make sure the bins have a finite width.
+    for i in builtins.range(len(smin)):
+        if smin[i] == smax[i]:
+            smin[i] = smin[i] - .5
+            smax[i] = smax[i] + .5
+
+    # Create edge arrays
+    for i in builtins.range(Ndim):
+        if np.isscalar(bins[i]):
+            nbin[i] = bins[i] + 2  # +2 for outlier bins
+            edges[i] = np.linspace(smin[i], smax[i], nbin[i] - 1,
+                                   dtype=edges_dtype)
+        else:
+            edges[i] = np.asarray(bins[i], edges_dtype)
+            nbin[i] = len(edges[i]) + 1  # +1 for outlier bins
+        dedges[i] = np.diff(edges[i])
+
+    nbin = np.asarray(nbin)
+
+    return nbin, edges, dedges
+
+
+def _bin_numbers(sample, nbin, edges, dedges):
+    """Compute the bin number each sample falls into, in each dimension
+    """
+    Dlen, Ndim = sample.shape
+
+    sampBin = [
+        np.digitize(sample[:, i], edges[i])
+        for i in range(Ndim)
+    ]
+
+    # Using `digitize`, values that fall on an edge are put in the right bin.
+    # For the rightmost bin, we want values equal to the right
+    # edge to be counted in the last bin, and not as an outlier.
+    for i in range(Ndim):
+        # Find the rounding precision
+        dedges_min = dedges[i].min()
+        if dedges_min == 0:
+            raise ValueError('The smallest edge difference is numerically 0.')
+        decimal = int(-np.log10(dedges_min)) + 6
+        # Find which points are on the rightmost edge.
+        on_edge = np.where((sample[:, i] >= edges[i][-1]) &
+                           (np.around(sample[:, i], decimal) ==
+                            np.around(edges[i][-1], decimal)))[0]
+        # Shift these points one bin to the left.
+        sampBin[i][on_edge] -= 1
+
+    # Compute the sample indices in the flattened statistic matrix.
+    binnumbers = np.ravel_multi_index(sampBin, nbin)
+
+    return binnumbers
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_binomtest.py b/.venv/lib/python3.12/site-packages/scipy/stats/_binomtest.py
new file mode 100644
index 0000000000000000000000000000000000000000..746be06a07c949902d9173bc8fe8eb6cb1cbeece
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_binomtest.py
@@ -0,0 +1,417 @@
+import numpy as np
+import scipy._external.array_api_extra as xpx
+from scipy._lib._array_api import (xp_capabilities, array_namespace, xp_promote, is_jax,
+                                   is_lazy_array)
+from scipy.optimize.elementwise import find_root
+from scipy.special import ndtri
+from scipy.special import _ufuncs as scu
+from ._common import ConfidenceInterval
+
+
+class BinomTestResult:
+    """
+    Result of `scipy.stats.binomtest`.
+
+    Attributes
+    ----------
+    k : int
+        The number of successes (copied from `binomtest` input).
+    n : int
+        The number of trials (copied from `binomtest` input).
+    alternative : str
+        Indicates the alternative hypothesis specified in the input
+        to `binomtest`.  It will be one of ``'two-sided'``, ``'greater'``,
+        or ``'less'``.
+    statistic: float
+        The estimate of the proportion of successes.
+    pvalue : float
+        The p-value of the hypothesis test.
+
+    """
+    def __init__(self, k, n, alternative, statistic, pvalue, xp):
+        lazy = is_lazy_array(k)
+        self.k = int(k) if not lazy and statistic.ndim == 0 and not xp.isnan(k) else k
+        self.n = int(n) if not lazy and statistic.ndim == 0 and not xp.isnan(n) else n
+        self.alternative = alternative
+        self.statistic = statistic
+        self.pvalue = pvalue
+        self._xp = xp
+
+        # add alias for backward compatibility
+        self.proportion_estimate = statistic
+
+    def __repr__(self):
+        s = ("BinomTestResult("
+             f"k={self.k}, "
+             f"n={self.n}, "
+             f"alternative={self.alternative!r}, "
+             f"statistic={self.statistic}, "
+             f"pvalue={self.pvalue})")
+        return s
+
+    def proportion_ci(self, confidence_level=0.95, method='exact'):
+        """
+        Compute the confidence interval for ``statistic``.
+
+        Parameters
+        ----------
+        confidence_level : float, optional
+            Confidence level for the computed confidence interval
+            of the estimated proportion. Default is 0.95.
+        method : {'exact', 'wilson', 'wilsoncc'}, optional
+            Selects the method used to compute the confidence interval
+            for the estimate of the proportion:
+
+            'exact' :
+                Use the Clopper-Pearson exact method [1]_.
+            'wilson' :
+                Wilson's method, without continuity correction ([2]_, [3]_).
+            'wilsoncc' :
+                Wilson's method, with continuity correction ([2]_, [3]_).
+
+            Default is ``'exact'``.
+
+        Returns
+        -------
+        ci : ``ConfidenceInterval`` object
+            The object has attributes ``low`` and ``high`` that hold the
+            lower and upper bounds of the confidence interval.
+
+        References
+        ----------
+        .. [1] C. J. Clopper and E. S. Pearson, The use of confidence or
+               fiducial limits illustrated in the case of the binomial,
+               Biometrika, Vol. 26, No. 4, pp 404-413 (Dec. 1934).
+        .. [2] E. B. Wilson, Probable inference, the law of succession, and
+               statistical inference, J. Amer. Stat. Assoc., 22, pp 209-212
+               (1927).
+        .. [3] Robert G. Newcombe, Two-sided confidence intervals for the
+               single proportion: comparison of seven methods, Statistics
+               in Medicine, 17, pp 857-872 (1998).
+
+        Examples
+        --------
+        >>> from scipy.stats import binomtest
+        >>> result = binomtest(k=7, n=50, p=0.1)
+        >>> result.statistic
+        0.14
+        >>> result.proportion_ci()
+        ConfidenceInterval(low=0.05819170033997342, high=0.26739600249700846)
+        """
+        if method not in ('exact', 'wilson', 'wilsoncc'):
+            raise ValueError(f"method ('{method}') must be one of 'exact', "
+                             "'wilson' or 'wilsoncc'.")
+        if not (0 <= confidence_level <= 1):
+            raise ValueError(f'confidence_level ({confidence_level}) must be in '
+                             'the interval [0, 1].')
+
+        xp = self._xp
+        k, n, confidence_level = xp_promote(
+            self.k, self.n, confidence_level,
+            xp=xp, force_floating=True,
+        )
+        if method == 'exact':
+            low, high = _binom_exact_conf_int(
+                k, n, confidence_level, self.alternative, xp=xp)
+        else:
+            # method is 'wilson' or 'wilsoncc'
+            correction = method == 'wilsoncc'
+            low, high = _binom_wilson_conf_int(
+                k, n, confidence_level, self.alternative, correction, xp=xp)
+        low = low[()] if low.ndim == 0 else low
+        high = high[()] if high.ndim == 0 else high
+        return ConfidenceInterval(low=low, high=high)
+
+
+def _binom_exact_conf_int(k, n, confidence_level, alternative, *, xp):
+    """
+    Compute the estimate and confidence interval for the binomial test.
+
+    Returns proportion, prop_low, prop_high
+    """
+    init = (xp.zeros_like(k), xp.ones_like(k))
+    args = (k, n)
+    alpha = ((1 - confidence_level) / 2 if alternative == 'two-sided'
+             else 1 - confidence_level)
+
+    # I think using the private methods here is fine, since we will only evaluate with
+    # valid `p`, `k`, and `n` (or all NaNs). One exception is when `k=0` and
+    # `binom._sf(k-1, n, p)`: evaluates to NaN, but that's not a problem because
+    # `plow` has a special case for `k=0` below.
+    plow = (xp.zeros_like(k) if alternative == 'less' else
+            find_root(lambda p, k, n: _SimpleBinomial(n, p).sf(k-1) - alpha,
+                      init, args=args).x)
+    phigh = (xp.ones_like(k) if alternative == 'greater' else
+             find_root(lambda p, k, n: _SimpleBinomial(n, p).cdf(k) - alpha,
+                       init, args=args).x)
+
+    plow = xp.where(k == 0, 0.0, plow)
+    phigh = xp.where(k == n, 1.0, phigh)
+    return plow, phigh
+
+
+def _binom_wilson_conf_int(k, n, confidence_level, alternative, correction, *, xp):
+    # This function assumes that the arguments have already been validated.
+    # In particular, `alternative` must be one of 'two-sided', 'less' or
+    # 'greater'.
+    p = k / n
+    if alternative == 'two-sided':
+        z = ndtri(0.5 + 0.5*confidence_level)
+    else:
+        z = ndtri(confidence_level)
+
+    # For reference, the formulas implemented here are from
+    # Newcombe (1998) (ref. [3] in the proportion_ci docstring).
+    denom = 2*(n + z**2)
+    center = (2*n*p + z**2)/denom
+    q = 1 - p
+
+    if correction:
+        with np.errstate(divide='ignore', invalid='ignore'):
+            dlo = (1 + z*xp.sqrt(z**2 - 2 - 1/n + 4*p*(n*q + 1)))/denom
+            dhi = (1 + z*xp.sqrt(z**2 + 2 - 1/n + 4*p*(n*q - 1)))/denom
+    else:
+        delta = z / denom * xp.sqrt(4*n*p*q + z**2)
+        dlo, dhi = delta, delta
+
+    lo = xp.where((k == 0) | (alternative == 'less'), 0.0, center - dlo)
+    hi = xp.where((k == n) | (alternative == 'greater'), 1.0, center + dhi)
+    return lo, hi
+
+
+@xp_capabilities(
+    skip_backends=[('dask.array', "")], cpu_only=True,
+    reason="binomial distribution ufuncs only available for NumPy",
+    extra_note="`alternative='two-sided'` is incompatible with JAX arrays.",
+)
+def binomtest(k, n, p=0.5, alternative='two-sided'):
+    """
+    Perform a test that the probability of success is p.
+
+    The binomial test [1]_ is a test of the null hypothesis that the
+    probability of success in a Bernoulli experiment is `p`.
+
+    Details of the test can be found in many texts on statistics, such
+    as section 24.5 of [2]_.
+
+    The documentation is written as though the function accepts and returns Python
+    scalars, but the function is vectorized to work elementwise with NumPy arrays.
+
+    Parameters
+    ----------
+    k : int
+        The number of successes.
+    n : int
+        The number of trials.
+    p : float, optional
+        The hypothesized probability of success, i.e. the expected
+        proportion of successes.  The value must be in the interval
+        ``0 <= p <= 1``. The default value is ``p = 0.5``.
+    alternative : {'two-sided', 'greater', 'less'}, optional
+        Indicates the alternative hypothesis. The default value is
+        'two-sided'.
+
+    Returns
+    -------
+    result : `~scipy.stats._result_classes.BinomTestResult` instance
+        The return value is an object with the following attributes:
+
+        k : int
+            The number of successes (copied from `binomtest` input).
+        n : int
+            The number of trials (copied from `binomtest` input).
+        alternative : str
+            Indicates the alternative hypothesis specified in the input
+            to `binomtest`.  It will be one of ``'two-sided'``, ``'greater'``,
+            or ``'less'``.
+        statistic : float
+            The estimate of the proportion of successes.
+        pvalue : float
+            The p-value of the hypothesis test.
+
+        The object has the following methods:
+
+        proportion_ci(confidence_level=0.95, method='exact') :
+            Compute the confidence interval for ``statistic``.
+
+    Notes
+    -----
+    .. versionadded:: 1.7.0
+
+    References
+    ----------
+    .. [1] Binomial test, https://en.wikipedia.org/wiki/Binomial_test
+    .. [2] Jerrold H. Zar, Biostatistical Analysis (fifth edition),
+           Prentice Hall, Upper Saddle River, New Jersey USA (2010)
+
+    Examples
+    --------
+    >>> from scipy.stats import binomtest
+
+    A car manufacturer claims that no more than 10% of their cars are unsafe.
+    15 cars are inspected for safety, 3 were found to be unsafe. Test the
+    manufacturer's claim:
+
+    >>> result = binomtest(3, n=15, p=0.1, alternative='greater')
+    >>> result.pvalue
+    0.18406106910639114
+
+    The null hypothesis cannot be rejected at the 5% level of significance
+    because the returned p-value is greater than the critical value of 5%.
+
+    The test statistic is equal to the estimated proportion, which is simply
+    ``3/15``:
+
+    >>> result.statistic
+    0.2
+
+    We can use the `proportion_ci()` method of the result to compute the
+    confidence interval of the estimate:
+
+    >>> result.proportion_ci(confidence_level=0.95)
+    ConfidenceInterval(low=0.05684686759024681, high=1.0)
+
+    """
+    xp = array_namespace(k, n, p)
+    k, n, p = xp_promote(k, n, p, force_floating=True, broadcast=True, xp=xp)
+    k_valid = (k >= 0) & (k <= n) & (k == xp.floor(k))
+    n_valid = (n >= 1) & (n == xp.floor(n))
+    p_valid = (p >= 0) & (p <= 1)
+    valid = k_valid & n_valid & p_valid
+    k = xp.where(valid, k, xp.nan)
+    n = xp.where(valid, n, xp.nan)
+    p = xp.where(valid, p, xp.nan)
+
+    if alternative not in ('two-sided', 'less', 'greater'):
+        raise ValueError(f"alternative ('{alternative}') not recognized; \n"
+                         "must be 'two-sided', 'less' or 'greater'")
+
+    B = _SimpleBinomial(n, p)
+    if alternative == 'less':
+        pval = B.cdf(k)
+    elif alternative == 'greater':
+        pval = B.sf(k - 1)
+    else:
+        if is_jax(xp):
+            message = "`alternative='two-sided'` is incompatible with JAX arrays."
+            raise ValueError(message)
+
+        # alternative is 'two-sided'
+        d = B.pmf(k)
+        rerr = 1 + 1e-7
+
+        def k_lt_pn(d, k, p, n):
+            B = _SimpleBinomial(n, p)
+            ix = _binary_search_for_binom_tst(lambda x1: -B.pmf(x1), -d*rerr,
+                                              xp.ceil(p * n), n, xp=xp)
+            # y is the number of terms between mode and n that are <= d*rerr.
+            # ix gave us the first term where a(ix) <= d*rerr < a(ix-1)
+            # if the first equality doesn't hold, y=n-ix. Otherwise, we
+            # need to include ix as well as the equality holds. Note that
+            # the equality will hold in very very rare situations due to rerr.
+            y = n - ix + xp.asarray(d*rerr == B.pmf(ix), dtype=ix.dtype)
+            pval = B.cdf(k) + B.sf(n - y)
+            return pval
+
+        def k_gte_pn(d, k, p, n):
+            B = _SimpleBinomial(n, p)
+            ix = _binary_search_for_binom_tst(B.pmf, d*rerr,
+                                              xp.zeros_like(n), xp.floor(p * n), xp=xp)
+            # y is the number of terms between 0 and mode that are <= d*rerr.
+            # we need to add a 1 to account for the 0 index.
+            # For comparing this with old behavior, see
+            # tst_binary_srch_for_binom_tst method in test_morestats.
+            y = ix + 1
+            pval = B.cdf(y-1) + B.sf(k-1)
+            return pval
+
+        pval = xpx.apply_where(k < p*n, (d, k, p, n), k_lt_pn,  k_gte_pn)
+        # xp.minimum(1.0, pval) but for data-apis/array-api-compat#271
+        pval = xp.minimum(xp.asarray(1.0, dtype=pval.dtype), pval)
+
+    statistic = xp.where(valid, k/n, xp.nan)
+    pval = xp.where(valid, pval, xp.nan)
+    if statistic.ndim == 0:
+        k, n, statistic, pval = k[()], n[()], statistic[()], pval[()]
+
+    result = BinomTestResult(k=k, n=n, alternative=alternative,
+                             statistic=statistic, pvalue=pval, xp=xp)
+    return result
+
+
+def _binary_search_for_binom_tst(a, d, lo, hi, *, xp):
+    """
+    Conducts an implicit binary search on a function specified by `a`.
+
+    Meant to be used on the binomial PMF for the case of two-sided tests
+    to obtain the value on the other side of the mode where the tail
+    probability should be computed. The values on either side of
+    the mode are always in order, meaning binary search is applicable.
+
+    Parameters
+    ----------
+    a : callable
+      The function over which to perform binary search. Its values
+      for inputs lo and hi should be in ascending order.
+    d : float
+      The value to search.
+    lo : int
+      The lower end of range to search.
+    hi : int
+      The higher end of the range to search.
+
+    Returns
+    -------
+    int
+      The index, i between lo and hi
+      such that a(i)<=d<a(i+1)
+    """
+    d = xp.asarray(d, copy=True)
+    lo = xp.asarray(lo, copy=True)
+    hi = xp.asarray(hi, copy=True)
+    while xp.any(lo < hi):
+        mid = lo + (hi-lo)//2
+        midval = a(mid)
+
+        i_lt = midval < d
+        lo = xpx.at(lo)[i_lt].set(mid[i_lt] + 1)
+
+        i_gt = midval > d
+        hi = xpx.at(hi)[i_gt].set(mid[i_gt] - 1)
+
+        i_eq = (midval == d)
+        mid_i_eq = mid[i_eq]
+        lo = xpx.at(lo)[i_eq].set(mid_i_eq)
+        hi = xpx.at(hi)[i_eq].set(mid_i_eq)
+
+    return xp.where(a(lo) <= d, lo, lo-1)
+
+
+class _SimpleBinomial:
+    # A very simple, array-API compatible binomial distribution for use in
+    # hypothesis tests. May be replaced by new infrastructure Binomial
+    # distribution in due time.
+    def __init__(self, n, p, *, xp=None):
+        xp = array_namespace(n, p) if xp is None else xp
+        self.n = n
+        self.p = p
+        self.xp = xp
+
+    def f(self, x, fun):
+        xp = self.xp
+        return xpx.lazy_apply(fun, x, self.n, self.p, as_numpy=True, xp=xp)
+
+    def cdf(self, x):
+        return self.xp.where(x >= 0, self.f(self.xp.floor(x), scu._binom_cdf), 0.0)
+
+    def sf(self, x):
+        return self.xp.where(x >= 0, self.f(self.xp.floor(x), scu._binom_sf), 1.0)
+
+    def ppf(self, x):
+        return self.f(x, scu._binom_ppf)
+
+    def isf(self, x):
+        return self.f(x, scu._binom_isf)
+
+    def pmf(self, x):
+        return self.f(self.xp.floor(x), scu._binom_pmf)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_bws_test.py b/.venv/lib/python3.12/site-packages/scipy/stats/_bws_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..786b5ebe57ada5aa0bda7dc583494004769daf3f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_bws_test.py
@@ -0,0 +1,204 @@
+import numpy as np
+from functools import partial
+
+from scipy import stats
+from scipy.stats._axis_nan_policy import _broadcast_arrays
+from scipy._external import array_api_extra as xpx
+from scipy._lib._array_api import (xp_capabilities, array_namespace, xp_ravel,
+                                   xp_result_type)
+
+
+def _bws_input_validation(x, y, alternative, axis, method):
+    ''' Input validation and standardization for bws test'''
+    xp = array_namespace(x, y)
+    x, y = xp.asarray(x), xp.asarray(y)
+    x, y = xpx.atleast_nd(x, ndim=1), xpx.atleast_nd(y, ndim=1)
+
+    axis_none = axis is None
+    ndim = max(x.ndim, y.ndim)
+    if axis_none:
+        x = xp_ravel(x, xp=xp)
+        y = xp_ravel(y, xp=xp)
+        axis = 0
+    elif np.iterable(axis) or int(axis) != axis:
+        message = "`axis` must be an integer or None."
+        raise ValueError(message)
+    elif (axis >= ndim) or (axis < -ndim):
+        message = "`axis` is not compatible with the shapes of the inputs."
+        raise ValueError(message)
+    axis = int(axis)
+
+    x, y = _broadcast_arrays((x, y), axis=axis, xp=xp)
+    nx, ny = x.shape[-1], y.shape[-1]
+    if nx < 2 or ny < 2:
+        raise ValueError('`x` and `y` must contain at least two observations each.')
+
+    x, y = xp.moveaxis(x, axis, -1), xp.moveaxis(y, axis, -1)
+    z = stats.rankdata(xp.concat((x, y), axis=-1), axis=-1)
+    z = xp.astype(z, xp_result_type(x, y, force_floating=True, xp=xp))
+    x, y = z[..., :x.shape[-1]], z[..., x.shape[-1]:]
+    x, y = xp.moveaxis(x, -1, axis), xp.moveaxis(y, -1, axis)
+
+    alternatives = {'two-sided', 'less', 'greater'}
+    alternative = alternative.lower()
+    if alternative not in alternatives:
+        raise ValueError(f'`alternative` must be one of {alternatives}.')
+
+    method = stats.PermutationMethod() if method is None else method
+    if not isinstance(method, stats.PermutationMethod):
+        raise ValueError('`method` must be an instance of '
+                         '`scipy.stats.PermutationMethod`')
+
+    return x, y, alternative, axis, method, xp
+
+
+def _bws_statistic(x, y, alternative, axis, xp):
+    '''Compute the BWS test statistic for two independent samples'''
+    # Public function currently does not accept `axis`, but `permutation_test`
+    # uses `axis` to make vectorized call.
+
+    Ri, Hj = xp.sort(x, axis=axis), xp.sort(y, axis=axis)
+    n, m = Ri.shape[axis], Hj.shape[axis]
+    i, j = xp.arange(1, n+1, dtype=Ri.dtype), xp.arange(1, m+1, dtype=Hj.dtype)
+
+    Bx_num = Ri - (m + n)/n * i
+    By_num = Hj - (m + n)/m * j
+
+    if alternative == 'two-sided':
+        Bx_num = xpx.at(Bx_num)[...].multiply(Bx_num)
+        By_num = xpx.at(By_num)[...].multiply(By_num)
+    else:
+        Bx_num = xpx.at(Bx_num)[...].multiply(xp.abs(Bx_num))
+        By_num = xpx.at(By_num)[...].multiply(xp.abs(By_num))
+
+    Bx_den = i/(n+1) * (1 - i/(n+1)) * m*(m+n)/n
+    By_den = j/(m+1) * (1 - j/(m+1)) * n*(m+n)/m
+
+    Bx = 1/n * xp.sum(Bx_num/Bx_den, axis=axis)
+    By = 1/m * xp.sum(By_num/By_den, axis=axis)
+
+    B = (Bx + By) / 2 if alternative == 'two-sided' else (Bx - By) / 2
+
+    return B
+
+
+@xp_capabilities(skip_backends=[('dask.array', 'no rankdata')])
+def bws_test(x, y, *, alternative="two-sided", axis=0, method=None):
+    r'''Perform the Baumgartner-Weiss-Schindler test on two independent samples.
+
+    The Baumgartner-Weiss-Schindler (BWS) test is a nonparametric test of
+    the null hypothesis that the distribution underlying sample `x`
+    is the same as the distribution underlying sample `y`. Unlike
+    the Kolmogorov-Smirnov, Wilcoxon, and Cramer-Von Mises tests,
+    the BWS test weights the integral by the variance of the difference
+    in cumulative distribution functions (CDFs), emphasizing the tails of the
+    distributions, which increases the power of the test in many applications.
+
+    Parameters
+    ----------
+    x, y : array-like
+        Arrays of samples. Shapes must be broadcastable except along `axis`.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        Let *F(u)* and *G(u)* be the cumulative distribution functions of the
+        distributions underlying `x` and `y`, respectively. Then the following
+        alternative hypotheses are available:
+
+        * 'two-sided': the distributions are not equal, i.e. *F(u) ≠ G(u)* for
+          at least one *u*.
+        * 'less': the distribution underlying `x` is stochastically less than
+          the distribution underlying `y`, i.e. *F(u) >= G(u)* for all *u*.
+        * 'greater': the distribution underlying `x` is stochastically greater
+          than the distribution underlying `y`, i.e. *F(u) <= G(u)* for all
+          *u*.
+
+        Under a more restrictive set of assumptions, the alternative hypotheses
+        can be expressed in terms of the locations of the distributions;
+        see [2] section 5.1.
+    axis : int or None, default: 0
+        Axis along which the quantiles are computed.
+        ``None`` ravels both `x` and `y` before performing the calculation.
+    method : PermutationMethod, optional
+        Configures the method used to compute the p-value. The default is
+        the default `PermutationMethod` object.
+
+    Returns
+    -------
+    res : PermutationTestResult
+        An object with attributes:
+
+        statistic : float
+            The observed test statistic of the data.
+        pvalue : float
+            The p-value for the given alternative.
+        null_distribution : ndarray
+            The values of the test statistic generated under the null hypothesis.
+
+    See Also
+    --------
+    scipy.stats.wilcoxon, scipy.stats.mannwhitneyu, scipy.stats.ttest_ind
+
+    Notes
+    -----
+    When ``alternative=='two-sided'``, the statistic is defined by the
+    equations given in [1]_ Section 2. This statistic is not appropriate for
+    one-sided alternatives; in that case, the statistic is the *negative* of
+    that given by the equations in [1]_ Section 2. Consequently, when the
+    distribution of the first sample is stochastically greater than that of the
+    second sample, the statistic will tend to be positive.
+
+    References
+    ----------
+    .. [1] Neuhäuser, M. (2005). Exact Tests Based on the
+           Baumgartner-Weiss-Schindler Statistic: A Survey. Statistical Papers,
+           46(1), 1-29.
+    .. [2] Fay, M. P., & Proschan, M. A. (2010). Wilcoxon-Mann-Whitney or t-test?
+           On assumptions for hypothesis tests and multiple interpretations of
+           decision rules. Statistics surveys, 4, 1.
+
+    Examples
+    --------
+    We follow the example of table 3 in [1]_: Fourteen children were divided
+    randomly into two groups. Their ranks at performing a specific tests are
+    as follows.
+
+    >>> import numpy as np
+    >>> x = [1, 2, 3, 4, 6, 7, 8]
+    >>> y = [5, 9, 10, 11, 12, 13, 14]
+
+    We use the BWS test to assess whether there is a statistically significant
+    difference between the two groups.
+    The null hypothesis is that there is no difference in the distributions of
+    performance between the two groups. We decide that a significance level of
+    1% is required to reject the null hypothesis in favor of the alternative
+    that the distributions are different.
+    Since the number of samples is very small, we can compare the observed test
+    statistic against the *exact* distribution of the test statistic under the
+    null hypothesis.
+
+    >>> from scipy.stats import bws_test
+    >>> res = bws_test(x, y)
+    >>> print(res.statistic)
+    5.132167152575315
+
+    This agrees with :math:`B = 5.132` reported in [1]_. The *p*-value produced
+    by `bws_test` also agrees with :math:`p = 0.0029` reported in [1]_.
+
+    >>> print(res.pvalue)
+    0.002913752913752914
+
+    Because the p-value is below our threshold of 1%, we take this as evidence
+    against the null hypothesis in favor of the alternative that there is a
+    difference in performance between the two groups.
+    '''
+
+    x, y, alternative, axis, method, xp = _bws_input_validation(x, y, alternative,
+                                                                axis, method)
+    bws_statistic = partial(_bws_statistic, alternative=alternative, xp=xp)
+
+    permutation_alternative = 'less' if alternative == 'less' else 'greater'
+    res = stats.permutation_test((x, y), bws_statistic,
+                                 alternative=permutation_alternative, axis=axis,
+                                 **method._asdict())
+
+    return res
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_censored_data.py b/.venv/lib/python3.12/site-packages/scipy/stats/_censored_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..14c90aa3edb8c5303642a9b36a3323fe6fbcac2c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_censored_data.py
@@ -0,0 +1,468 @@
+from types import GenericAlias
+import numpy as np
+
+
+def _validate_1d(a, name, allow_inf=False):
+    if np.ndim(a) != 1:
+        raise ValueError(f'`{name}` must be a one-dimensional sequence.')
+    if np.isnan(a).any():
+        raise ValueError(f'`{name}` must not contain nan.')
+    if not allow_inf and np.isinf(a).any():
+        raise ValueError(f'`{name}` must contain only finite values.')
+
+
+def _validate_interval(interval):
+    interval = np.asarray(interval)
+    if interval.shape == (0,):
+        # The input was a sequence with length 0.
+        interval = interval.reshape((0, 2))
+    if interval.ndim != 2 or interval.shape[-1] != 2:
+        raise ValueError('`interval` must be a two-dimensional array with '
+                         'shape (m, 2), where m is the number of '
+                         'interval-censored values, but got shape '
+                         f'{interval.shape}')
+
+    if np.isnan(interval).any():
+        raise ValueError('`interval` must not contain nan.')
+    if np.isinf(interval).all(axis=1).any():
+        raise ValueError('In each row in `interval`, both values must not'
+                         ' be infinite.')
+    if (interval[:, 0] > interval[:, 1]).any():
+        raise ValueError('In each row of `interval`, the left value must not'
+                         ' exceed the right value.')
+
+    uncensored_mask = interval[:, 0] == interval[:, 1]
+    left_mask = np.isinf(interval[:, 0])
+    right_mask = np.isinf(interval[:, 1])
+    interval_mask = np.isfinite(interval).all(axis=1) & ~uncensored_mask
+
+    uncensored2 = interval[uncensored_mask, 0]
+    left2 = interval[left_mask, 1]
+    right2 = interval[right_mask, 0]
+    interval2 = interval[interval_mask]
+
+    return uncensored2, left2, right2, interval2
+
+
+def _validate_x_censored(x, censored):
+    x = np.asarray(x)
+    if x.ndim != 1:
+        raise ValueError('`x` must be one-dimensional.')
+    censored = np.asarray(censored)
+    if censored.ndim != 1:
+        raise ValueError('`censored` must be one-dimensional.')
+    if (~np.isfinite(x)).any():
+        raise ValueError('`x` must not contain nan or inf.')
+    if censored.size != x.size:
+        raise ValueError('`x` and `censored` must have the same length.')
+    return x, censored.astype(bool)
+
+
+class CensoredData:
+    """
+    Instances of this class represent censored data.
+
+    Instances may be passed to the ``fit`` method of continuous
+    univariate SciPy distributions for maximum likelihood estimation.
+    The *only* method of the univariate continuous distributions that
+    understands `CensoredData` is the ``fit`` method.  An instance of
+    `CensoredData` can not be passed to methods such as ``pdf`` and
+    ``cdf``.
+
+    An observation is said to be *censored* when the precise value is unknown,
+    but it has a known upper and/or lower bound.  The conventional terminology
+    is:
+
+    * left-censored: an observation is below a certain value but it is
+      unknown by how much.
+    * right-censored: an observation is above a certain value but it is
+      unknown by how much.
+    * interval-censored: an observation lies somewhere on an interval between
+      two values.
+
+    Left-, right-, and interval-censored data can be represented by
+    `CensoredData`.
+
+    For convenience, the class methods ``left_censored`` and
+    ``right_censored`` are provided to create a `CensoredData`
+    instance from a single one-dimensional array of measurements
+    and a corresponding boolean array to indicate which measurements
+    are censored.  The class method ``interval_censored`` accepts two
+    one-dimensional arrays that hold the lower and upper bounds of the
+    intervals.
+
+    Parameters
+    ----------
+    uncensored : array_like, 1D
+        Uncensored observations.
+    left : array_like, 1D
+        Left-censored observations.
+    right : array_like, 1D
+        Right-censored observations.
+    interval : array_like, 2D, with shape (m, 2)
+        Interval-censored observations.  Each row ``interval[k, :]``
+        represents the interval for the kth interval-censored observation.
+
+    Notes
+    -----
+    In the input array `interval`, the lower bound of the interval may
+    be ``-inf``, and the upper bound may be ``inf``, but at least one must be
+    finite. When the lower bound is ``-inf``, the row represents a left-
+    censored observation, and when the upper bound is ``inf``, the row
+    represents a right-censored observation.  If the length of an interval
+    is 0 (i.e. ``interval[k, 0] == interval[k, 1]``, the observation is
+    treated as uncensored.  So one can represent all the types of censored
+    and uncensored data in ``interval``, but it is generally more convenient
+    to use `uncensored`, `left` and `right` for uncensored, left-censored and
+    right-censored observations, respectively.
+
+    Examples
+    --------
+    In the most general case, a censored data set may contain values that
+    are left-censored, right-censored, interval-censored, and uncensored.
+    For example, here we create a data set with five observations.  Two
+    are uncensored (values 1 and 1.5), one is a left-censored observation
+    of 0, one is a right-censored observation of 10 and one is
+    interval-censored in the interval [2, 3].
+
+    >>> import numpy as np
+    >>> from scipy.stats import CensoredData
+    >>> data = CensoredData(uncensored=[1, 1.5], left=[0], right=[10],
+    ...                     interval=[[2, 3]])
+    >>> print(data)
+    CensoredData(5 values: 2 not censored, 1 left-censored,
+    1 right-censored, 1 interval-censored)
+
+    Equivalently,
+
+    >>> data = CensoredData(interval=[[1, 1],
+    ...                               [1.5, 1.5],
+    ...                               [-np.inf, 0],
+    ...                               [10, np.inf],
+    ...                               [2, 3]])
+    >>> print(data)
+    CensoredData(5 values: 2 not censored, 1 left-censored,
+    1 right-censored, 1 interval-censored)
+
+    A common case is to have a mix of uncensored observations and censored
+    observations that are all right-censored (or all left-censored). For
+    example, consider an experiment in which six devices are started at
+    various times and left running until they fail.  Assume that time is
+    measured in hours, and the experiment is stopped after 30 hours, even
+    if all the devices have not failed by that time.  We might end up with
+    data such as this::
+
+        Device  Start-time  Fail-time  Time-to-failure
+           1         0         13           13
+           2         2         24           22
+           3         5         22           17
+           4         8         23           15
+           5        10        ***          >20
+           6        12        ***          >18
+
+    Two of the devices had not failed when the experiment was stopped;
+    the observations of the time-to-failure for these two devices are
+    right-censored.  We can represent this data with
+
+    >>> data = CensoredData(uncensored=[13, 22, 17, 15], right=[20, 18])
+    >>> print(data)
+    CensoredData(6 values: 4 not censored, 2 right-censored)
+
+    Alternatively, we can use the method `CensoredData.right_censored` to
+    create a representation of this data.  The time-to-failure observations
+    are put the list ``ttf``.  The ``censored`` list indicates which values
+    in ``ttf`` are censored.
+
+    >>> ttf = [13, 22, 17, 15, 20, 18]
+    >>> censored = [False, False, False, False, True, True]
+
+    Pass these lists to `CensoredData.right_censored` to create an
+    instance of `CensoredData`.
+
+    >>> data = CensoredData.right_censored(ttf, censored)
+    >>> print(data)
+    CensoredData(6 values: 4 not censored, 2 right-censored)
+
+    If the input data is interval censored and already stored in two
+    arrays, one holding the low end of the intervals and another
+    holding the high ends, the class method ``interval_censored`` can
+    be used to create the `CensoredData` instance.
+
+    This example creates an instance with four interval-censored values.
+    The intervals are [10, 11], [0.5, 1], [2, 3], and [12.5, 13.5].
+
+    >>> a = [10, 0.5, 2, 12.5]  # Low ends of the intervals
+    >>> b = [11, 1.0, 3, 13.5]  # High ends of the intervals
+    >>> data = CensoredData.interval_censored(low=a, high=b)
+    >>> print(data)
+    CensoredData(4 values: 0 not censored, 4 interval-censored)
+
+    Finally, we create and censor some data from the `weibull_min`
+    distribution, and then fit `weibull_min` to that data. We'll assume
+    that the location parameter is known to be 0.
+
+    >>> from scipy.stats import weibull_min
+    >>> rng = np.random.default_rng()
+
+    Create the random data set.
+
+    >>> x = weibull_min.rvs(2.5, loc=0, scale=30, size=250, random_state=rng)
+    >>> x[x > 40] = 40  # Right-censor values greater or equal to 40.
+
+    Create the `CensoredData` instance with the `right_censored` method.
+    The censored values are those where the value is 40.
+
+    >>> data = CensoredData.right_censored(x, x == 40)
+    >>> print(data)
+    CensoredData(250 values: 215 not censored, 35 right-censored)
+
+    35 values have been right-censored.
+
+    Fit `weibull_min` to the censored data.  We expect to shape and scale
+    to be approximately 2.5 and 30, respectively.
+
+    >>> weibull_min.fit(data, floc=0)
+    (2.3575922823897315, 0, 30.40650074451254)
+
+    """
+
+    # generic type compatibility with scipy-stubs
+    __class_getitem__: classmethod = classmethod(GenericAlias)
+
+    def __init__(self, uncensored=None, *, left=None, right=None,
+                 interval=None):
+        if uncensored is None:
+            uncensored = []
+        if left is None:
+            left = []
+        if right is None:
+            right = []
+        if interval is None:
+            interval = np.empty((0, 2))
+
+        _validate_1d(uncensored, 'uncensored')
+        _validate_1d(left, 'left')
+        _validate_1d(right, 'right')
+        uncensored2, left2, right2, interval2 = _validate_interval(interval)
+
+        self._uncensored = np.concatenate((uncensored, uncensored2))
+        self._left = np.concatenate((left, left2))
+        self._right = np.concatenate((right, right2))
+        # Note that by construction, the private attribute _interval
+        # will be a 2D array that contains only finite values representing
+        # intervals with nonzero but finite length.
+        self._interval = interval2
+
+    def __repr__(self):
+        uncensored_str = " ".join(np.array_repr(self._uncensored).split())
+        left_str = " ".join(np.array_repr(self._left).split())
+        right_str = " ".join(np.array_repr(self._right).split())
+        interval_str = " ".join(np.array_repr(self._interval).split())
+        return (f"CensoredData(uncensored={uncensored_str}, left={left_str}, "
+                f"right={right_str}, interval={interval_str})")
+
+    def __str__(self):
+        num_nc = len(self._uncensored)
+        num_lc = len(self._left)
+        num_rc = len(self._right)
+        num_ic = len(self._interval)
+        n = num_nc + num_lc + num_rc + num_ic
+        parts = [f'{num_nc} not censored']
+        if num_lc > 0:
+            parts.append(f'{num_lc} left-censored')
+        if num_rc > 0:
+            parts.append(f'{num_rc} right-censored')
+        if num_ic > 0:
+            parts.append(f'{num_ic} interval-censored')
+        return f'CensoredData({n} values: ' + ', '.join(parts) + ')'
+
+    # This is not a complete implementation of the arithmetic operators.
+    # All we need is subtracting a scalar and dividing by a scalar.
+
+    def __sub__(self, other):
+        return CensoredData(uncensored=self._uncensored - other,
+                            left=self._left - other,
+                            right=self._right - other,
+                            interval=self._interval - other)
+
+    def __truediv__(self, other):
+        return CensoredData(uncensored=self._uncensored / other,
+                            left=self._left / other,
+                            right=self._right / other,
+                            interval=self._interval / other)
+
+    def __len__(self):
+        """
+        The number of values (censored and not censored).
+        """
+        return (len(self._uncensored) + len(self._left) + len(self._right)
+                + len(self._interval))
+
+    def num_censored(self):
+        """
+        Number of censored values.
+
+        Returns
+        -------
+        int
+            The number of censored values.
+        """
+        return len(self._left) + len(self._right) + len(self._interval)
+
+    @classmethod
+    def right_censored(cls, x, censored):
+        """
+        Create a `CensoredData` instance of right-censored data.
+
+        Parameters
+        ----------
+        x : array_like
+            `x` is the array of observed data or measurements.
+            `x` must be a one-dimensional sequence of finite numbers.
+        censored : array_like of bool
+            `censored` must be a one-dimensional sequence of boolean
+            values.  If ``censored[k]`` is True, the corresponding value
+            in `x` is right-censored.  That is, the value ``x[k]``
+            is the lower bound of the true (but unknown) value.
+
+        Returns
+        -------
+        data : `CensoredData`
+            An instance of `CensoredData` that represents the
+            collection of uncensored and right-censored values.
+
+        Examples
+        --------
+        >>> from scipy.stats import CensoredData
+
+        Two uncensored values (4 and 10) and two right-censored values
+        (24 and 25).
+
+        >>> data = CensoredData.right_censored([4, 10, 24, 25],
+        ...                                    [False, False, True, True])
+        >>> data
+        CensoredData(uncensored=array([ 4., 10.]),
+        left=array([], dtype=float64), right=array([24., 25.]),
+        interval=array([], shape=(0, 2), dtype=float64))
+        >>> print(data)
+        CensoredData(4 values: 2 not censored, 2 right-censored)
+        """
+        x, censored = _validate_x_censored(x, censored)
+        return cls(uncensored=x[~censored], right=x[censored])
+
+    @classmethod
+    def left_censored(cls, x, censored):
+        """
+        Create a `CensoredData` instance of left-censored data.
+
+        Parameters
+        ----------
+        x : array_like
+            `x` is the array of observed data or measurements.
+            `x` must be a one-dimensional sequence of finite numbers.
+        censored : array_like of bool
+            `censored` must be a one-dimensional sequence of boolean
+            values.  If ``censored[k]`` is True, the corresponding value
+            in `x` is left-censored.  That is, the value ``x[k]``
+            is the upper bound of the true (but unknown) value.
+
+        Returns
+        -------
+        data : `CensoredData`
+            An instance of `CensoredData` that represents the
+            collection of uncensored and left-censored values.
+
+        Examples
+        --------
+        >>> from scipy.stats import CensoredData
+
+        Two uncensored values (0.12 and 0.033) and two left-censored values
+        (both 1e-3).
+
+        >>> data = CensoredData.left_censored([0.12, 0.033, 1e-3, 1e-3],
+        ...                                   [False, False, True, True])
+        >>> data
+        CensoredData(uncensored=array([0.12 , 0.033]),
+        left=array([0.001, 0.001]), right=array([], dtype=float64),
+        interval=array([], shape=(0, 2), dtype=float64))
+        >>> print(data)
+        CensoredData(4 values: 2 not censored, 2 left-censored)
+        """
+        x, censored = _validate_x_censored(x, censored)
+        return cls(uncensored=x[~censored], left=x[censored])
+
+    @classmethod
+    def interval_censored(cls, low, high):
+        """
+        Create a `CensoredData` instance of interval-censored data.
+
+        This method is useful when all the data is interval-censored, and
+        the low and high ends of the intervals are already stored in
+        separate one-dimensional arrays.
+
+        Parameters
+        ----------
+        low : array_like
+            The one-dimensional array containing the low ends of the
+            intervals.
+        high : array_like
+            The one-dimensional array containing the high ends of the
+            intervals.
+
+        Returns
+        -------
+        data : `CensoredData`
+            An instance of `CensoredData` that represents the
+            collection of censored values.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> from scipy.stats import CensoredData
+
+        ``a`` and ``b`` are the low and high ends of a collection of
+        interval-censored values.
+
+        >>> a = [0.5, 2.0, 3.0, 5.5]
+        >>> b = [1.0, 2.5, 3.5, 7.0]
+        >>> data = CensoredData.interval_censored(low=a, high=b)
+        >>> print(data)
+        CensoredData(4 values: 0 not censored, 4 interval-censored)
+        """
+        _validate_1d(low, 'low', allow_inf=True)
+        _validate_1d(high, 'high', allow_inf=True)
+        if len(low) != len(high):
+            raise ValueError('`low` and `high` must have the same length.')
+        interval = np.column_stack((low, high))
+        uncensored, left, right, interval = _validate_interval(interval)
+        return cls(uncensored=uncensored, left=left, right=right,
+                   interval=interval)
+
+    def _uncensor(self):
+        """
+        This function is used when a non-censored version of the data
+        is needed to create a rough estimate of the parameters of a
+        distribution via the method of moments or some similar method.
+        The data is "uncensored" by taking the given endpoints as the
+        data for the left- or right-censored data, and the mean for the
+        interval-censored data.
+        """
+        data = np.concatenate((self._uncensored, self._left, self._right,
+                               self._interval.mean(axis=1)))
+        return data
+
+    def _supported(self, a, b):
+        """
+        Return a subset of self containing the values that are in
+        (or overlap with) the interval (a, b).
+        """
+        uncensored = self._uncensored
+        uncensored = uncensored[(a < uncensored) & (uncensored < b)]
+        left = self._left
+        left = left[a < left]
+        right = self._right
+        right = right[right < b]
+        interval = self._interval
+        interval = interval[(a < interval[:, 1]) & (interval[:, 0] < b)]
+        return CensoredData(uncensored, left=left, right=right,
+                            interval=interval)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_common.py b/.venv/lib/python3.12/site-packages/scipy/stats/_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..4011d425cc4afea3c7ee8937526b13f1f92b0850
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_common.py
@@ -0,0 +1,5 @@
+from collections import namedtuple
+
+
+ConfidenceInterval = namedtuple("ConfidenceInterval", ["low", "high"])
+ConfidenceInterval. __doc__ = "Class for confidence intervals."
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_constants.py b/.venv/lib/python3.12/site-packages/scipy/stats/_constants.py
new file mode 100644
index 0000000000000000000000000000000000000000..b539ce8146ebdbc8e08c66143461b04d742804f2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_constants.py
@@ -0,0 +1,42 @@
+"""
+Statistics-related constants.
+
+"""
+import numpy as np
+
+
+# The smallest representable positive number such that 1.0 + _EPS != 1.0.
+_EPS = np.finfo(float).eps
+
+# The largest [in magnitude] usable floating value.
+_XMAX = np.finfo(float).max
+
+# The log of the largest usable floating value; useful for knowing
+# when exp(something) will overflow
+_LOGXMAX = np.log(_XMAX)
+
+# The smallest [in magnitude] usable (i.e. not subnormal) double precision
+# floating value.
+_XMIN = np.finfo(float).tiny
+
+# The log of the smallest [in magnitude] usable (i.e not subnormal)
+# double precision floating value.
+_LOGXMIN = np.log(_XMIN)
+
+# -special.psi(1)
+_EULER = 0.577215664901532860606512090082402431042
+
+# special.zeta(3, 1)  Apery's constant
+_ZETA3 = 1.202056903159594285399738161511449990765
+
+# sqrt(pi)
+_SQRT_PI = 1.772453850905516027298167483341145182798
+
+# sqrt(2/pi)
+_SQRT_2_OVER_PI = 0.7978845608028654
+
+# log(pi)
+_LOG_PI = 1.1447298858494002
+
+# log(sqrt(2/pi))
+_LOG_SQRT_2_OVER_PI = -0.22579135264472744
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_continued_fraction.py b/.venv/lib/python3.12/site-packages/scipy/stats/_continued_fraction.py
new file mode 100644
index 0000000000000000000000000000000000000000..4966b1c99ed092ba3578aa7ca15b5ba8ea4bc6a9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_continued_fraction.py
@@ -0,0 +1,387 @@
+import numpy as np
+
+from scipy._lib._array_api import (
+    array_namespace, xp_ravel, xp_copy, xp_promote
+)
+import scipy._lib._elementwise_iterative_method as eim
+from scipy._lib._util import _RichResult
+from scipy import special
+
+# Todo:
+# Avoid special-casing key 'n' in _lib._elementwise_iterative_method::_check_termination
+# Rearrange termination condition to allow absolute and relative tolerances?
+# Interpret/return |f_n - f_{n-1}| as an error estimate?
+# Return gracefully for size=0 arrays
+
+def _logaddexp(x, y, xp=None):
+    # logaddexp that supports complex numbers
+    xp = array_namespace(x, y) if xp is None else xp
+    x, y = xp.broadcast_arrays(x, y)
+    xy = xp.stack((x, y), axis=0)
+    return special.logsumexp(xy, axis=0)
+
+
+def _continued_fraction_iv(a, b, args, tolerances, maxiter, log):
+    # Input validation for `_continued_fraction`
+
+    if not callable(a) or not callable(b):
+        raise ValueError('`a` and `b` must be callable.')
+
+    if not np.iterable(args):
+        args = (args,)
+
+    # Call each callable once to determine namespace and dtypes
+    a0, b0 = a(0, *args), b(0, *args)
+    xp = array_namespace(a0, b0, *args)
+    a0, b0, *args = xp_promote(a0, b0, *args, force_floating=True, broadcast=True,
+                               xp=xp)
+    shape, dtype = a0.shape, a0.dtype
+    a0, b0, *args = (xp_ravel(arg) for arg in (a0, b0) + tuple(args))
+
+    tolerances = {} if tolerances is None else tolerances
+    eps = tolerances.get('eps', None)
+    tiny = tolerances.get('tiny', None)
+
+    # tolerances are floats, not arrays, so it's OK to use NumPy
+    message = ('`eps` and `tiny` must be (or represent the logarithm of) '
+               'finite, positive, real scalars.')
+    tols = np.asarray([eps if eps is not None else 1,
+                       tiny if tiny is not None else 1])
+    not_real = (not np.issubdtype(tols.dtype, np.number)
+                or np.issubdtype(tols.dtype, np.complexfloating))
+    not_positive = np.any(tols <= 0) if not log else False
+    not_finite = not np.all(np.isfinite(tols))
+    not_scalar = tols.shape != (2,)
+    if not_real or not_positive or not_finite or not_scalar:
+        raise ValueError(message)
+
+    maxiter_int = int(maxiter)
+    if maxiter != maxiter_int or maxiter < 0:
+        raise ValueError('`maxiter` must be a non-negative integer.')
+
+    if not isinstance(log, bool):
+        raise ValueError('`log` must be boolean.')
+
+    return a, b, args, eps, tiny, maxiter, log, a0, b0, shape, dtype, xp
+
+
+def _continued_fraction(a, b, *, args=(), tolerances=None, maxiter=100, log=False):
+    r"""Evaluate a generalized continued fraction numerically.
+
+    `_continued_fraction` iteratively evaluates convergents of a continued fraction
+    given coefficients returned by callables `a` and `b`. Iteration terminates when
+    `maxiter` terms have been evaluated or a termination criterion controlled by
+    `tolerances` is satisfied, and the final convergent is returned as the ``f``
+    attribute of the result object.
+
+    This function works elementwise when `args` contains (broadcastable) arrays.
+
+    Parameters
+    ----------
+    a, b: callable
+        Functions that provide the *numerator* and *denominator* coefficients of
+        the continued fraction, respectively.
+
+        The signature of each must be::
+
+            a(n: int, *argsj) -> ndarray
+
+        where ``n`` is the coefficient number and ``argsj`` is a tuple, which may
+        contain an arbitrary number of arrays of any shape. `a` and `b` must be
+        elementwise functions: each scalar element ``a(n, *argsj)[i]`` must equal
+        ``a(n, *[argj[i] for argj in argsj])`` for valid indices ``i``.
+        `a` and `b` must not mutate the arrays in ``argsj``.
+
+        The result shape is the broadcasted shape of ``a(0, *args)`` and
+        ``b(0, *args)``. The dtype used throughout computation is the result dtype
+        of these terms if it is a float, and the default float of the array library
+        otherwise. The numerical value of ``a(0, *args)`` is ignored, and
+        the value of the leading term ``b(0, *args)`` is the so-called "integer"
+        part of the continued fraction (although it need not be integral).
+
+    args : tuple of array_like, optional
+        Additional positional *array* arguments to be passed to `a` and `b`. Arrays
+        must be broadcastable with one another. If the coefficient callables
+        require additional arguments that are not broadcastable with one
+        another, wrap them with callables `a` and `b` such that `a` and `b` accept
+        only ``n`` and broadcastable array arguments.
+    tolerances : dictionary of floats, optional
+        Tolerances and numerical thresholds used by the algorithm. Currently,
+        valid keys of the dictionary are:
+
+        - ``eps`` - the convergence threshold of Lentz' algorithm
+        - ``tiny`` - not strictly a "tolerance", but a very small positive number
+          used to avoid division by zero
+
+        The default `eps` is the precision of the appropriate dtype, and the default
+        `tiny` is the precision squared. [1]_ advises that ``eps`` is "as small as
+        you like", but for most purposes, it should not be set smaller than the default
+        because it may prevent convergence of the algorithm. [1]_ also advises that
+        ``tiny`` should be less than typical values of ``eps * b(n)``, so the default
+        is a good choice unless the :math:`b_n` are very small. See [1]_ for details.
+    maxiter : int, default: 100
+        The maximum number of iterations of the algorithm to perform.
+    log : bool, default: False
+        If True, `a` and `b` return the (natural) logarithm of the terms, `tolerances`
+        contains the logarithm of the tolerances, and the result object reports the
+        logarithm of the convergent.
+
+    Returns
+    -------
+    res : _RichResult
+        An object similar to an instance of `scipy.optimize.OptimizeResult` with the
+        following attributes. The descriptions are written as though the values will
+        be scalars; however, if `f` returns an array, the outputs will be
+        arrays of the same shape.
+
+        success : bool array
+            ``True`` where the algorithm terminated successfully (status ``0``);
+            ``False`` otherwise.
+        status : int array
+            An integer representing the exit status of the algorithm.
+
+            - ``0`` : The algorithm converged to the specified tolerances.
+            - ``-2`` : The maximum number of iterations was reached.
+            - ``-3`` : A non-finite value was encountered.
+
+        f : float array
+            The convergent which satisfied a termination criterion.
+        nit : int array
+            The number of iterations of the algorithm that were performed.
+        nfev : int array
+            The number of terms that were evaluated.
+
+    Notes
+    -----
+    A generalized continued fraction is an expression of the form
+
+    .. math::
+
+        b_0 + \frac{a_1}{b_1 + \frac{a_2}{b_2 + \frac{a_3}{b_3 + \cdots}}}
+
+    Successive "convergents" approximate the infinitely recursive continued fraction
+    with a finite number of terms :math:`a_n` and :math:`b_n`, which are provided
+    by callables `a` and `b`, respectively. This implementation follows the modified
+    Lentz algorithm ([1]_, [2]_) to evaluate successive convergents until a
+    termination condition is satisfied.
+
+    References
+    ----------
+    .. [1] Press, William H., and Saul A. Teukolsky. "Evaluating continued fractions
+           and computing exponential integrals." Computers in Physics 2.5 (1988): 88-89.
+    .. [2] Lentz's algorithm. Wikipedia.
+           https://en.wikipedia.org/wiki/Lentz%27s_algorithm
+    .. [3] Continued fraction. Wikipedia.
+           https://en.wikipedia.org/wiki/Continued_fraction
+    .. [4] Generalized continued fraction. Wikipedia.
+           https://en.wikipedia.org/wiki/Generalized_continued_fraction
+
+    Examples
+    --------
+    The "simple continued fraction" of :math:`\pi` is given in [3]_ as
+
+    .. math::
+
+        3 + \frac{1}{7 + \frac{1}{15 + \frac{1}{1 + \cdots}}}
+
+    where the :math:`b_n` terms follow no obvious pattern:
+
+    >>> b = [3, 7, 15, 1, 292, 1, 1, 1, 2, 1, 3, 1]
+
+    and the :math:`a_n` terms are all :math:`1`.
+    In this case, all the terms have been precomputed, so we call `_continued_fraction`
+    with simple callables which simply return the precomputed coefficients:
+
+    >>> import numpy as np
+    >>> from scipy.special._continued_fraction import _continued_fraction
+    >>> res = _continued_fraction(a=lambda n: 1, b=lambda n: b[n], maxiter=len(b) - 1)
+    >>> (res.f - np.pi) / np.pi
+    np.float64(7.067899292141148e-15)
+
+    A generalized continued fraction for :math:`\pi` is given by:
+
+    .. math::
+
+        3 + \frac{1^2}{6 + \frac{3^2}{6 + \frac{5^2}{6 + \cdots}}}
+
+    We define the coefficient callables as:
+
+    >>> def a(n):
+    ...     return (2*n - 1)**2
+    >>>
+    >>> def b(n):
+    ...     if n == 0:
+    ...         return 3
+    ...     else:
+    ...         return 6
+
+    Then the continued fraction can be evaluated as:
+
+    >>> res = _continued_fraction(a, b)
+    >>> res
+         success: False
+          status: -2
+               f: 3.1415924109719846
+             nit: 100
+            nfev: 101
+
+    Note that the requested tolerance was not reached within the (default)
+    maximum number of iterations because it converges very slowly.
+    An expression that converges more rapidly is expressed as the difference
+    between two continued fractions. We will compute both of them in one
+    vectorized call to `_continued_fraction`.
+
+    >>> u, v = 5, 239
+    >>>
+    >>> def a(n, a1, _):
+    ...     # The shape of the output must be the shape of the arguments
+    ...     shape = a1.shape
+    ...     if n == 0:
+    ...         return np.zeros(shape)
+    ...     elif n == 1:
+    ...         return a1
+    ...     else:
+    ...         return np.full(shape, (n-1)**2)
+    >>>
+    >>> def b(n, _, uv):
+    ...     shape = uv.shape
+    ...     if  n == 0:
+    ...         return np.zeros(shape)
+    ...     return np.full(shape, (2*n - 1)*uv)
+    >>>
+    >>> res = _continued_fraction(a, b, args=([16, 4], [u, v]))
+    >>> res
+         success: [ True  True]
+          status: [0 0]
+               f: [ 3.158e+00  1.674e-02]
+             nit: [10  4]
+            nfev: [11  5]
+
+    Note that the second term converged in fewer than half the number of iterations
+    as the first. The approximation of :math:`\pi` is the difference between the two:
+
+    >>> pi = res.f[0] - res.f[1]
+    >>> (pi - np.pi) / np.pi
+    np.float64(2.8271597168564594e-16)
+
+    If it is more efficient to compute the :math:`a_n` and :math:`b_n` terms together,
+    consider instantiating a class with a method that computes both terms and stores
+    the results in an attribute. Separate methods of the class retrieve the
+    coefficients, and these methods are passed to `_continued_fraction` as arguments
+    `a` and `b`. Similarly,if the coefficients can be computed recursively in terms of
+    previous coefficients, use a class to maintain state between callable evaluations.
+
+    """
+
+    res = _continued_fraction_iv(a, b, args, tolerances, maxiter, log)
+    a, b, args, eps, tiny, maxiter, log, a0, b0, shape, dtype, xp = res
+    callback = None  # don't want to test it, but easy to add later
+
+    # The EIM framework was designed for the case in where there would
+    # be only one callable, and all arguments of the callable would be
+    # arrays. We're going a bit beyond that here, since we have two callables,
+    # and the first argument is an integer (the number of the term). Rather
+    # than complicate the framework, we wrap the user-provided callables to
+    # make this problem fit within the existing framework.
+
+    def a(n, *args, a=a):
+        n = int(xp.real(xp_ravel(n))[0])
+        return a(n, *args)
+
+    def b(n, *args, b=b):
+        n = int(xp.real(xp_ravel(n))[0])
+        return b(n, *args)
+
+    def func(n, *args):
+        return xp.stack((a(n, *args), b(n, *args)), axis=-1)
+
+    status = xp.full_like(a0, eim._EINPROGRESS, dtype=xp.int32)  # in progress
+    nit, nfev = 0, 1  # one function evaluation (per function) performed above
+    maxiter = 100 if maxiter is None else maxiter
+
+    # Quotations describing the algorithm are from [1]_
+    # "... as small as you like, say eps"
+    if eps is None:
+        eps = xp.finfo(dtype).eps if not log else np.log(xp.finfo(dtype).eps)
+
+    # "The parameter tiny should be less than typical values of eps |b_n|"
+    if tiny is None:
+        tiny = xp.finfo(dtype).eps**2 if not log else 2*np.log(xp.finfo(dtype).eps)
+
+    # "Set f0 and C0 to the value b0 or to tiny if b0=0. Set D0 = 0.
+    zero = -xp.inf if log else 0
+    fn = xp.where(b0 == zero, tiny, b0)
+    Cnm1 = xp_copy(fn)
+    Dnm1 = xp.full_like(fn, zero)
+
+    CnDn = xp.full_like(fn, xp.inf)
+
+    work = _RichResult(n=0, fn=fn, Cnm1=Cnm1, Dnm1=Dnm1, CnDn=CnDn,
+                       eps=eps, tiny=tiny,
+                       nit=nit, nfev=nfev, status=status)
+    res_work_pairs = [('status', 'status'), ('f', 'fn'),
+                      ('nit', 'nit'), ('nfev', 'nfev')]
+
+    def pre_func_eval(work):
+        work.n = xp.reshape(xp.asarray(work.n + 1), (-1,))
+        return work.n
+
+    def post_func_eval(n, ab, work):
+        an, bn = ab[..., 0], ab[..., 1]
+
+        zero = 0 if not log else -xp.inf
+
+        # "Set D_n = 1/(b_n + a_n D_{n-1}) or 1/tiny, if the denominator vanishes"
+        denominator = (bn + an*work.Dnm1 if not log
+                       else _logaddexp(bn, an + work.Dnm1, xp=xp))
+        denominator[denominator == zero] = tiny
+        Dn = (1/denominator if not log
+              else -denominator)
+
+        # "Set C_n = b_n + a_n / C_{n-1} (or =tiny, if the expression vanishes)"
+        Cn = (bn + an / work.Cnm1 if not log
+              else _logaddexp(bn, an - work.Cnm1, xp=xp))
+        Cn[Cn == zero] = tiny
+
+        # "and set f_n = f_{n-1} C_n D_n"
+        work.CnDn = (Cn * Dn if not log
+                     else Cn + Dn)
+        work.fn = (work.fn * work.CnDn if not log
+                   else work.fn + work.CnDn)
+
+
+        work.Cnm1, work.Dnm1 = Cn, Dn
+
+    def check_termination(work):
+        # Check for all terminal conditions and record statuses.
+        stop = xp.zeros_like(work.CnDn, dtype=xp.bool)
+
+        # "You quit when |D_n C_n - 1| is as small as you like, say eps"
+        pij = xp.full_like(work.CnDn, xp.pi*1j) if log else None
+        residual = (xp.abs(work.CnDn - 1) if not log
+                    else xp.real(_logaddexp(work.CnDn, pij, xp=xp)))
+        i = residual < work.eps
+        work.status[i] = eim._ECONVERGED
+        stop[i] = True
+
+        # If function value is NaN, report failure.
+        i = (~xp.isfinite(work.fn) if not log
+             else ~(xp.isfinite(work.fn) | (work.fn == -xp.inf)))
+        work.status[i] = eim._EVALUEERR
+        stop[i] = True
+
+        return stop
+
+    def post_termination_check(work):
+        pass
+
+    def customize_result(res, shape):
+        # Only needed pre-NEP 50
+        res['f'] = xp.asarray(res['f'], dtype=dtype)
+        res['f'] = res['f'][()] if res['f'].ndim == 0 else res['f']
+        return shape
+
+    return eim._loop(work, callback, shape, maxiter, func, args, dtype,
+                     pre_func_eval, post_func_eval, check_termination,
+                     post_termination_check, customize_result, res_work_pairs,
+                     xp=xp)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_continuous_distns.py b/.venv/lib/python3.12/site-packages/scipy/stats/_continuous_distns.py
new file mode 100644
index 0000000000000000000000000000000000000000..98029b3ec326e29bb33ce9832232b89ab31e2641
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_continuous_distns.py
@@ -0,0 +1,12628 @@
+#
+# Author:  Travis Oliphant  2002-2011 with contributions from
+#          SciPy Developers 2004-2011
+#
+import warnings
+from collections.abc import Iterable
+from functools import wraps, cached_property
+import ctypes
+import operator
+
+import numpy as np
+from numpy.polynomial import Polynomial
+from scipy.interpolate import BSpline
+from scipy._lib.doccer import (extend_notes_in_docstring,
+                               replace_notes_in_docstring,
+                               inherit_docstring_from)
+from scipy._lib._ccallback import LowLevelCallable
+from scipy import optimize
+from scipy import integrate
+import scipy.special as sc
+
+import scipy.special._ufuncs as scu
+from scipy._lib._util import _lazyselect
+import scipy._external.array_api_extra as xpx
+from scipy._lib._array_api import xp_promote
+
+from . import _stats
+from ._tukeylambda_stats import (tukeylambda_variance as _tlvar,
+                                 tukeylambda_kurtosis as _tlkurt)
+from ._distn_infrastructure import (_vectorize_rvs_over_shapes,
+    get_distribution_names, _kurtosis, _isintegral,
+    rv_continuous, _skew, _get_fixed_fit_value, _check_shape, _ShapeInfo)
+from ._ksstats import kolmogn, kolmognp, kolmogni
+from ._constants import (_XMIN, _LOGXMIN, _EULER, _ZETA3, _SQRT_PI,
+                         _SQRT_2_OVER_PI, _LOG_PI, _LOG_SQRT_2_OVER_PI)
+from ._censored_data import CensoredData
+from scipy.optimize import root_scalar
+from scipy.stats._warnings_errors import FitError
+import scipy.stats as stats
+
+def _remove_optimizer_parameters(kwds):
+    """
+    Remove the optimizer-related keyword arguments 'loc', 'scale' and
+    'optimizer' from `kwds`.  Then check that `kwds` is empty, and
+    raise `TypeError("Unknown arguments: %s." % kwds)` if it is not.
+
+    This function is used in the fit method of distributions that override
+    the default method and do not use the default optimization code.
+
+    `kwds` is modified in-place.
+    """
+    kwds.pop('loc', None)
+    kwds.pop('scale', None)
+    kwds.pop('optimizer', None)
+    kwds.pop('method', None)
+    if kwds:
+        raise TypeError(f"Unknown arguments: {kwds}.")
+
+
+def _call_super_mom(fun):
+    # If fit method is overridden only for MLE and doesn't specify what to do
+    # if method == 'mm' or with censored data, this decorator calls the generic
+    # implementation.
+    @wraps(fun)
+    def wrapper(self, data, *args, **kwds):
+        method = kwds.get('method', 'mle').lower()
+        censored = isinstance(data, CensoredData)
+        if method == 'mm' or (censored and data.num_censored() > 0):
+            return super(type(self), self).fit(data, *args, **kwds)
+        else:
+            if censored:
+                # data is an instance of CensoredData, but actually holds
+                # no censored values, so replace it with the array of
+                # uncensored values.
+                data = data._uncensored
+            return fun(self, data, *args, **kwds)
+
+    return wrapper
+
+
+def _get_left_bracket(fun, rbrack, lbrack=None):
+    # find left bracket for `root_scalar`. A guess for lbrack may be provided.
+    lbrack = lbrack or rbrack - 1
+    diff = rbrack - lbrack
+
+    # if there is no sign change in `fun` between the brackets, expand
+    # rbrack - lbrack until a sign change occurs
+    def interval_contains_root(lbrack, rbrack):
+        # return true if the signs disagree.
+        return np.sign(fun(lbrack)) != np.sign(fun(rbrack))
+
+    while not interval_contains_root(lbrack, rbrack):
+        diff *= 2
+        lbrack = rbrack - diff
+
+        msg = ("The solver could not find a bracket containing a "
+               "root to an MLE first order condition.")
+        if np.isinf(lbrack):
+            raise FitSolverError(msg)
+
+    return lbrack
+
+
+class ksone_gen(rv_continuous):
+    r"""Kolmogorov-Smirnov one-sided test statistic distribution.
+
+    This is the distribution of the one-sided Kolmogorov-Smirnov (KS)
+    statistics :math:`D_n^+` and :math:`D_n^-`
+    for a finite sample size ``n >= 1`` (the shape parameter).
+
+    %(before_notes)s
+
+    See Also
+    --------
+    kstwobign, kstwo, kstest
+
+    Notes
+    -----
+    :math:`D_n^+` and :math:`D_n^-` are given by
+
+    .. math::
+
+        D_n^+ &= \text{sup}_x (F_n(x) - F(x)),\\
+        D_n^- &= \text{sup}_x (F(x) - F_n(x)),\\
+
+    where :math:`F` is a continuous CDF and :math:`F_n` is an empirical CDF.
+    `ksone` describes the distribution under the null hypothesis of the KS test
+    that the empirical CDF corresponds to :math:`n` i.i.d. random variates
+    with CDF :math:`F`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Birnbaum, Z. W. and Tingey, F.H. "One-sided confidence contours
+       for probability distribution functions", The Annals of Mathematical
+       Statistics, 22(4), pp 592-596 (1951).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import ksone
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+
+    Display the probability density function (``pdf``):
+
+    >>> n = 1e+03
+    >>> x = np.linspace(ksone.ppf(0.01, n),
+    ...                 ksone.ppf(0.99, n), 100)
+    >>> ax.plot(x, ksone.pdf(x, n),
+    ...         'r-', lw=5, alpha=0.6, label='ksone pdf')
+
+    Alternatively, the distribution object can be called (as a function)
+    to fix the shape, location and scale parameters. This returns a "frozen"
+    RV object holding the given parameters fixed.
+
+    Freeze the distribution and display the frozen ``pdf``:
+
+    >>> rv = ksone(n)
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+
+    Check accuracy of ``cdf`` and ``ppf``:
+
+    >>> vals = ksone.ppf([0.001, 0.5, 0.999], n)
+    >>> np.allclose([0.001, 0.5, 0.999], ksone.cdf(vals, n))
+    True
+
+    """
+    def _argcheck(self, n):
+        return (n >= 1) & (n == np.round(n))
+
+    def _shape_info(self):
+        return [_ShapeInfo("n", True, (1, np.inf), (True, False))]
+
+    def _pdf(self, x, n):
+        return -scu._smirnovp(n, x)
+
+    def _cdf(self, x, n):
+        return scu._smirnovc(n, x)
+
+    def _sf(self, x, n):
+        return sc.smirnov(n, x)
+
+    def _ppf(self, q, n):
+        return scu._smirnovci(n, q)
+
+    def _isf(self, q, n):
+        return sc.smirnovi(n, q)
+
+
+ksone = ksone_gen(a=0.0, b=1.0, name='ksone')
+
+
+class kstwo_gen(rv_continuous):
+    r"""Kolmogorov-Smirnov two-sided test statistic distribution.
+
+    This is the distribution of the two-sided Kolmogorov-Smirnov (KS)
+    statistic :math:`D_n` for a finite sample size ``n >= 1``
+    (the shape parameter).
+
+    %(before_notes)s
+
+    See Also
+    --------
+    kstwobign, ksone, kstest
+
+    Notes
+    -----
+    :math:`D_n` is given by
+
+    .. math::
+
+        D_n = \text{sup}_x |F_n(x) - F(x)|
+
+    where :math:`F` is a (continuous) CDF and :math:`F_n` is an empirical CDF.
+    `kstwo` describes the distribution under the null hypothesis of the KS test
+    that the empirical CDF corresponds to :math:`n` i.i.d. random variates
+    with CDF :math:`F`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Simard, R., L'Ecuyer, P. "Computing the Two-Sided
+       Kolmogorov-Smirnov Distribution",  Journal of Statistical Software,
+       Vol 39, 11, 1-18 (2011).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import kstwo
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+
+    Display the probability density function (``pdf``):
+
+    >>> n = 10
+    >>> x = np.linspace(kstwo.ppf(0.01, n),
+    ...                 kstwo.ppf(0.99, n), 100)
+    >>> ax.plot(x, kstwo.pdf(x, n),
+    ...         'r-', lw=5, alpha=0.6, label='kstwo pdf')
+
+    Alternatively, the distribution object can be called (as a function)
+    to fix the shape, location and scale parameters. This returns a "frozen"
+    RV object holding the given parameters fixed.
+
+    Freeze the distribution and display the frozen ``pdf``:
+
+    >>> rv = kstwo(n)
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+
+    Check accuracy of ``cdf`` and ``ppf``:
+
+    >>> vals = kstwo.ppf([0.001, 0.5, 0.999], n)
+    >>> np.allclose([0.001, 0.5, 0.999], kstwo.cdf(vals, n))
+    True
+
+    """
+    def _argcheck(self, n):
+        return (n >= 1) & (n == np.round(n))
+
+    def _shape_info(self):
+        return [_ShapeInfo("n", True, (1, np.inf), (True, False))]
+
+    def _get_support(self, n):
+        return (0.5/(n if not isinstance(n, Iterable) else np.asanyarray(n)),
+                1.0)
+
+    def _pdf(self, x, n):
+        return kolmognp(n, x)
+
+    def _cdf(self, x, n):
+        return kolmogn(n, x)
+
+    def _sf(self, x, n):
+        return kolmogn(n, x, cdf=False)
+
+    def _ppf(self, q, n):
+        return kolmogni(n, q, cdf=True)
+
+    def _isf(self, q, n):
+        return kolmogni(n, q, cdf=False)
+
+
+# Use the pdf, (not the ppf) to compute moments
+kstwo = kstwo_gen(momtype=0, a=0.0, b=1.0, name='kstwo')
+
+
+class kstwobign_gen(rv_continuous):
+    r"""Limiting distribution of scaled Kolmogorov-Smirnov two-sided test statistic.
+
+    This is the asymptotic distribution of the two-sided Kolmogorov-Smirnov
+    statistic :math:`\sqrt{n} D_n` that measures the maximum absolute
+    distance of the theoretical (continuous) CDF from the empirical CDF.
+    (see `kstest`).
+
+    %(before_notes)s
+
+    See Also
+    --------
+    ksone, kstwo, kstest
+
+    Notes
+    -----
+    :math:`\sqrt{n} D_n` is given by
+
+    .. math::
+
+        D_n = \text{sup}_x |F_n(x) - F(x)|
+
+    where :math:`F` is a continuous CDF and :math:`F_n` is an empirical CDF.
+    `kstwobign`  describes the asymptotic distribution (i.e. the limit of
+    :math:`\sqrt{n} D_n`) under the null hypothesis of the KS test that the
+    empirical CDF corresponds to i.i.d. random variates with CDF :math:`F`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Feller, W. "On the Kolmogorov-Smirnov Limit Theorems for Empirical
+       Distributions",  Ann. Math. Statist. Vol 19, 177-189 (1948).
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        return -scu._kolmogp(x)
+
+    def _cdf(self, x):
+        return scu._kolmogc(x)
+
+    def _sf(self, x):
+        return sc.kolmogorov(x)
+
+    def _ppf(self, q):
+        return scu._kolmogci(q)
+
+    def _isf(self, q):
+        return sc.kolmogi(q)
+
+
+kstwobign = kstwobign_gen(a=0.0, name='kstwobign')
+
+
+## Normal distribution
+
+# loc = mu, scale = std
+# Keep these implementations out of the class definition so they can be reused
+# by other distributions.
+_norm_pdf_C = np.sqrt(2*np.pi)
+_norm_pdf_logC = np.log(_norm_pdf_C)
+
+
+def _norm_pdf(x):
+    return np.exp(-x**2/2.0) / _norm_pdf_C
+
+
+def _norm_logpdf(x):
+    return -x**2 / 2.0 - _norm_pdf_logC
+
+
+def _norm_cdf(x):
+    return sc.ndtr(x)
+
+
+def _norm_logcdf(x):
+    return sc.log_ndtr(x)
+
+
+def _norm_ppf(q):
+    return sc.ndtri(q)
+
+
+def _norm_sf(x):
+    return _norm_cdf(-x)
+
+
+def _norm_logsf(x):
+    return _norm_logcdf(-x)
+
+
+def _norm_isf(q):
+    return -_norm_ppf(q)
+
+
+class norm_gen(rv_continuous):
+    r"""A normal continuous random variable.
+
+    The location (``loc``) keyword specifies the mean.
+    The scale (``scale``) keyword specifies the standard deviation.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `norm` is:
+
+    .. math::
+
+        f(x) = \frac{\exp(-x^2/2)}{\sqrt{2\pi}}
+
+    for a real number :math:`x`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return random_state.standard_normal(size)
+
+    def _pdf(self, x):
+        # norm.pdf(x) = exp(-x**2/2)/sqrt(2*pi)
+        return _norm_pdf(x)
+
+    def _logpdf(self, x):
+        return _norm_logpdf(x)
+
+    def _cdf(self, x):
+        return _norm_cdf(x)
+
+    def _logcdf(self, x):
+        return _norm_logcdf(x)
+
+    def _sf(self, x):
+        return _norm_sf(x)
+
+    def _logsf(self, x):
+        return _norm_logsf(x)
+
+    def _ppf(self, q):
+        return _norm_ppf(q)
+
+    def _isf(self, q):
+        return _norm_isf(q)
+
+    def _stats(self):
+        return 0.0, 1.0, 0.0, 0.0
+
+    def _entropy(self):
+        return 0.5*(np.log(2*np.pi)+1)
+
+    @_call_super_mom
+    @replace_notes_in_docstring(rv_continuous, notes="""\
+        For the normal distribution, method of moments and maximum likelihood
+        estimation give identical fits, and explicit formulas for the estimates
+        are available.
+        This function uses these explicit formulas for the maximum likelihood
+        estimation of the normal distribution parameters, so the
+        `optimizer` and `method` arguments are ignored.\n\n""")
+    def fit(self, data, **kwds):
+        floc = kwds.pop('floc', None)
+        fscale = kwds.pop('fscale', None)
+
+        _remove_optimizer_parameters(kwds)
+
+        if floc is not None and fscale is not None:
+            # This check is for consistency with `rv_continuous.fit`.
+            # Without this check, this function would just return the
+            # parameters that were given.
+            raise ValueError("All parameters fixed. There is nothing to "
+                             "optimize.")
+
+        data = np.asarray(data)
+
+        if not np.isfinite(data).all():
+            raise ValueError("The data contains non-finite values.")
+
+        if floc is None:
+            loc = data.mean()
+        else:
+            loc = floc
+
+        if fscale is None:
+            scale = np.sqrt(((data - loc)**2).mean())
+        else:
+            scale = fscale
+
+        return loc, scale
+
+    def _munp(self, n):
+        """
+        @returns Moments of standard normal distribution for integer n >= 0
+
+        See eq. 16 of https://arxiv.org/abs/1209.4340v2
+        """
+        if n == 0:
+            return 1.
+        if n % 2 == 0:
+            return sc.factorial2(int(n) - 1)
+        else:
+            return 0.
+
+
+norm = norm_gen(name='norm')
+
+
+class alpha_gen(rv_continuous):
+    r"""An alpha continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `alpha` ([1]_, [2]_) is:
+
+    .. math::
+
+        f(x, a) = \frac{1}{x^2 \Phi(a) \sqrt{2\pi}} *
+                  \exp(-\frac{1}{2} (a-1/x)^2)
+
+    where :math:`\Phi` is the normal CDF, :math:`x > 0`, and :math:`a > 0`.
+
+    `alpha` takes ``a`` as a shape parameter.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Johnson, Kotz, and Balakrishnan, "Continuous Univariate
+           Distributions, Volume 1", Second Edition, John Wiley and Sons,
+           p. 173 (1994).
+    .. [2] Anthony A. Salvia, "Reliability applications of the Alpha
+           Distribution", IEEE Transactions on Reliability, Vol. R-34,
+           No. 3, pp. 251-252 (1985).
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, a):
+        # alpha.pdf(x, a) = 1/(x**2*Phi(a)*sqrt(2*pi)) * exp(-1/2 * (a-1/x)**2)
+        return 1.0/(x**2)/_norm_cdf(a)*_norm_pdf(a-1.0/x)
+
+    def _logpdf(self, x, a):
+        return -2*np.log(x) + _norm_logpdf(a-1.0/x) - np.log(_norm_cdf(a))
+
+    def _cdf(self, x, a):
+        return _norm_cdf(a-1.0/x) / _norm_cdf(a)
+
+    def _ppf(self, q, a):
+        return 1.0/np.asarray(a - _norm_ppf(q*_norm_cdf(a)))
+
+    def _stats(self, a):
+        return [np.inf]*2 + [np.nan]*2
+
+
+alpha = alpha_gen(a=0.0, name='alpha')
+
+
+class anglit_gen(rv_continuous):
+    r"""An anglit continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `anglit` is:
+
+    .. math::
+
+        f(x) = \sin(2x + \pi/2) = \cos(2x)
+
+    for :math:`-\pi/4 \le x \le \pi/4`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # anglit.pdf(x) = sin(2*x + \pi/2) = cos(2*x)
+        return np.cos(2*x)
+
+    def _cdf(self, x):
+        return np.sin(x+np.pi/4)**2.0
+
+    def _sf(self, x):
+        return np.cos(x + np.pi / 4) ** 2.0
+
+    def _ppf(self, q):
+        return np.arcsin(np.sqrt(q))-np.pi/4
+
+    def _stats(self):
+        return 0.0, np.pi*np.pi/16-0.5, 0.0, -2*(np.pi**4 - 96)/(np.pi*np.pi-8)**2
+
+    def _entropy(self):
+        return 1-np.log(2)
+
+
+anglit = anglit_gen(a=-np.pi/4, b=np.pi/4, name='anglit')
+
+
+class arcsine_gen(rv_continuous):
+    r"""An arcsine continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `arcsine` is:
+
+    .. math::
+
+        f(x) = \frac{1}{\pi \sqrt{x (1-x)}}
+
+    for :math:`0 < x < 1`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # arcsine.pdf(x) = 1/(pi*sqrt(x*(1-x)))
+        with np.errstate(divide='ignore'):
+            return 1.0/np.pi/np.sqrt(x*(1-x))
+
+    def _cdf(self, x):
+        return 2.0/np.pi*np.arcsin(np.sqrt(x))
+
+    def _ppf(self, q):
+        return np.sin(np.pi/2.0*q)**2.0
+
+    def _stats(self):
+        mu = 0.5
+        mu2 = 1.0/8
+        g1 = 0
+        g2 = -3.0/2.0
+        return mu, mu2, g1, g2
+
+    def _entropy(self):
+        return -0.24156447527049044468
+
+
+arcsine = arcsine_gen(a=0.0, b=1.0, name='arcsine')
+
+
+class FitDataError(ValueError):
+    """Raised when input data is inconsistent with fixed parameters."""
+    # This exception is raised by, for example, beta_gen.fit when both floc
+    # and fscale are fixed and there are values in the data not in the open
+    # interval (floc, floc+fscale).
+    def __init__(self, distr, lower, upper):
+        self.args = (
+            "Invalid values in `data`.  Maximum likelihood "
+            f"estimation with {distr!r} requires that {lower!r} < "
+            f"(x - loc)/scale  < {upper!r} for each x in `data`.",
+        )
+
+
+class FitSolverError(FitError):
+    """
+    Raised when a solver fails to converge while fitting a distribution.
+    """
+    # This exception is raised by, for example, beta_gen.fit when
+    # optimize.fsolve returns with ier != 1.
+    def __init__(self, mesg):
+        emsg = "Solver for the MLE equations failed to converge: "
+        emsg += mesg.replace('\n', '')
+        self.args = (emsg,)
+
+
+def _beta_mle_a(a, b, n, s1):
+    # The zeros of this function give the MLE for `a`, with
+    # `b`, `n` and `s1` given.  `s1` is the sum of the logs of
+    # the data. `n` is the number of data points.
+    psiab = sc.psi(a + b)
+    func = s1 - n * (-psiab + sc.psi(a))
+    return func
+
+
+def _beta_mle_ab(theta, n, s1, s2):
+    # Zeros of this function are critical points of
+    # the maximum likelihood function.  Solving this system
+    # for theta (which contains a and b) gives the MLE for a and b
+    # given `n`, `s1` and `s2`.  `s1` is the sum of the logs of the data,
+    # and `s2` is the sum of the logs of 1 - data.  `n` is the number
+    # of data points.
+    a, b = theta
+    psiab = sc.psi(a + b)
+    func = [s1 - n * (-psiab + sc.psi(a)),
+            s2 - n * (-psiab + sc.psi(b))]
+    return func
+
+
+class beta_gen(rv_continuous):
+    r"""A beta continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `beta` is:
+
+    .. math::
+
+        f(x, a, b) = \frac{\Gamma(a+b) x^{a-1} (1-x)^{b-1}}
+                          {\Gamma(a) \Gamma(b)}
+
+    for :math:`0 <= x <= 1`, :math:`a > 0`, :math:`b > 0`, where
+    :math:`\Gamma` is the gamma function (`scipy.special.gamma`).
+
+    `beta` takes :math:`a` and :math:`b` as shape parameters.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``pdf``, ``cdf``, ``ppf``, ``sf`` and ``isf``
+    methods. [1]_
+
+    Maximum likelihood estimates of parameters are only available when the location and
+    scale are fixed. When either of these parameters is free, ``beta.fit`` resorts to
+    numerical optimization, but this problem is unbounded: the location and scale may be
+    chosen to make the minimum and maximum elements of the data coincide with the
+    endpoints of the support, and the shape parameters may be chosen to make the PDF at
+    these points infinite. For best results, pass ``floc`` and ``fscale`` keyword
+    arguments to fix the location and scale, or use `scipy.stats.fit` with
+    ``method='mse'``.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ia, ib]
+
+    def _rvs(self, a, b, size=None, random_state=None):
+        return random_state.beta(a, b, size)
+
+    def _pdf(self, x, a, b):
+        #                     gamma(a+b) * x**(a-1) * (1-x)**(b-1)
+        # beta.pdf(x, a, b) = ------------------------------------
+        #                              gamma(a)*gamma(b)
+        with np.errstate(over='ignore'):
+            return scu._beta_pdf(x, a, b)
+
+    def _logpdf(self, x, a, b):
+        lPx = sc.xlog1py(b - 1.0, -x) + sc.xlogy(a - 1.0, x)
+        lPx -= sc.betaln(a, b)
+        return lPx
+
+    def _cdf(self, x, a, b):
+        return sc.betainc(a, b, x)
+
+    def _sf(self, x, a, b):
+        return sc.betaincc(a, b, x)
+
+    def _isf(self, x, a, b):
+        return sc.betainccinv(a, b, x)
+
+    def _ppf(self, q, a, b):
+        return scu._beta_ppf(q, a, b)
+
+    def _stats(self, a, b):
+        a_plus_b = a + b
+        _beta_mean = a/a_plus_b
+        _beta_variance = a*b / (a_plus_b**2 * (a_plus_b + 1))
+        _beta_skewness = ((2 * (b - a) * np.sqrt(a_plus_b + 1)) /
+                          ((a_plus_b + 2) * np.sqrt(a * b)))
+        _beta_kurtosis_excess_n = 6 * ((a - b)**2 * (a_plus_b + 1) -
+                                       a * b * (a_plus_b + 2))
+        _beta_kurtosis_excess_d = a * b * (a_plus_b + 2) * (a_plus_b + 3)
+        _beta_kurtosis_excess = _beta_kurtosis_excess_n / _beta_kurtosis_excess_d
+        return (
+            _beta_mean,
+            _beta_variance,
+            _beta_skewness,
+            _beta_kurtosis_excess)
+
+    def _fitstart(self, data):
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+
+        g1 = _skew(data)
+        g2 = _kurtosis(data)
+
+        def func(x):
+            a, b = x
+            sk = 2*(b-a)*np.sqrt(a + b + 1) / (a + b + 2) / np.sqrt(a*b)
+            ku = a**3 - a**2*(2*b-1) + b**2*(b+1) - 2*a*b*(b+2)
+            ku /= a*b*(a+b+2)*(a+b+3)
+            ku *= 6
+            return [sk-g1, ku-g2]
+        a, b = optimize.fsolve(func, (1.0, 1.0))
+        return super()._fitstart(data, args=(a, b))
+
+    @_call_super_mom
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        In the special case where `method="MLE"` and
+        both `floc` and `fscale` are given, a
+        `ValueError` is raised if any value `x` in `data` does not satisfy
+        `floc < x < floc + fscale`.\n\n""")
+    def fit(self, data, *args, **kwds):
+        # Override rv_continuous.fit, so we can more efficiently handle the
+        # case where floc and fscale are given.
+
+        floc = kwds.get('floc', None)
+        fscale = kwds.get('fscale', None)
+
+        if floc is None or fscale is None:
+            # do general fit
+            return super().fit(data, *args, **kwds)
+
+        # We already got these from kwds, so just pop them.
+        kwds.pop('floc', None)
+        kwds.pop('fscale', None)
+
+        f0 = _get_fixed_fit_value(kwds, ['f0', 'fa', 'fix_a'])
+        f1 = _get_fixed_fit_value(kwds, ['f1', 'fb', 'fix_b'])
+
+        _remove_optimizer_parameters(kwds)
+
+        if f0 is not None and f1 is not None:
+            # This check is for consistency with `rv_continuous.fit`.
+            raise ValueError("All parameters fixed. There is nothing to "
+                             "optimize.")
+
+        # Special case: loc and scale are constrained, so we are fitting
+        # just the shape parameters.  This can be done much more efficiently
+        # than the method used in `rv_continuous.fit`.  (See the subsection
+        # "Two unknown parameters" in the section "Maximum likelihood" of
+        # the Wikipedia article on the Beta distribution for the formulas.)
+
+        if not np.isfinite(data).all():
+            raise ValueError("The data contains non-finite values.")
+
+        # Normalize the data to the interval [0, 1].
+        data = (np.ravel(data) - floc) / fscale
+        if np.any(data <= 0) or np.any(data >= 1):
+            raise FitDataError("beta", lower=floc, upper=floc + fscale)
+
+        xbar = data.mean()
+
+        if f0 is not None or f1 is not None:
+            # One of the shape parameters is fixed.
+
+            if f0 is not None:
+                # The shape parameter a is fixed, so swap the parameters
+                # and flip the data.  We always solve for `a`.  The result
+                # will be swapped back before returning.
+                b = f0
+                data = 1 - data
+                xbar = 1 - xbar
+            else:
+                b = f1
+
+            # Initial guess for a.  Use the formula for the mean of the beta
+            # distribution, E[x] = a / (a + b), to generate a reasonable
+            # starting point based on the mean of the data and the given
+            # value of b.
+            a = b * xbar / (1 - xbar)
+
+            # Compute the MLE for `a` by solving _beta_mle_a.
+            theta, info, ier, mesg = optimize.fsolve(
+                _beta_mle_a, a,
+                args=(b, len(data), np.log(data).sum()),
+                full_output=True
+            )
+            if ier != 1:
+                raise FitSolverError(mesg=mesg)
+            a = theta[0]
+
+            if f0 is not None:
+                # The shape parameter a was fixed, so swap back the
+                # parameters.
+                a, b = b, a
+
+        else:
+            # Neither of the shape parameters is fixed.
+
+            # s1 and s2 are used in the extra arguments passed to _beta_mle_ab
+            # by optimize.fsolve.
+            s1 = np.log(data).sum()
+            s2 = sc.log1p(-data).sum()
+
+            # Use the "method of moments" to estimate the initial
+            # guess for a and b.
+            fac = xbar * (1 - xbar) / data.var(ddof=0) - 1
+            a = xbar * fac
+            b = (1 - xbar) * fac
+
+            # Compute the MLE for a and b by solving _beta_mle_ab.
+            theta, info, ier, mesg = optimize.fsolve(
+                _beta_mle_ab, [a, b],
+                args=(len(data), s1, s2),
+                full_output=True
+            )
+            if ier != 1:
+                raise FitSolverError(mesg=mesg)
+            a, b = theta
+
+        return a, b, floc, fscale
+
+    def _entropy(self, a, b):
+        def regular(a, b):
+            return (sc.betaln(a, b) - (a - 1) * sc.psi(a) -
+                    (b - 1) * sc.psi(b) + (a + b - 2) * sc.psi(a + b))
+
+        def asymptotic_ab_large(a, b):
+            sum_ab = a + b
+            log_term = 0.5 * (
+                np.log(2*np.pi) + np.log(a) + np.log(b) - 3*np.log(sum_ab) + 1
+            )
+            t1 = 110/sum_ab + 20*sum_ab**-2.0 + sum_ab**-3.0 - 2*sum_ab**-4.0
+            t2 = -50/a - 10*a**-2.0 - a**-3.0 + a**-4.0
+            t3 = -50/b - 10*b**-2.0 - b**-3.0 + b**-4.0
+            return log_term + (t1 + t2 + t3) / 120
+
+        def asymptotic_b_large(a, b):
+            sum_ab = a + b
+            t1 = sc.gammaln(a) - (a - 1) * sc.psi(a)
+            t2 = (
+                - 1/(2*b) + 1/(12*b) - b**-2.0/12 - b**-3.0/120 + b**-4.0/120
+                + b**-5.0/252 - b**-6.0/252 + 1/sum_ab - 1/(12*sum_ab)
+                + sum_ab**-2.0/6 + sum_ab**-3.0/120 - sum_ab**-4.0/60
+                - sum_ab**-5.0/252 + sum_ab**-6.0/126
+            )
+            log_term = sum_ab*np.log1p(a/b) + np.log(b) - 2*np.log(sum_ab)
+            return t1 + t2 + log_term
+
+        def asymptotic_a_large(a, b):
+            return asymptotic_b_large(b, a)
+
+        def threshold_large(v):
+            j = np.floor(np.log10(v))
+            d = np.floor(v / 10 ** j) + 2
+            return xpx.apply_where(v != 1.0, (d, j), lambda d_, j_: d_ * 10**(7 + j_),
+                                   fill_value=1000)
+
+        threshold_a = threshold_large(a)
+        threshold_b = threshold_large(b)
+        return _lazyselect([(a >= 4.96e6) & (b >= 4.96e6),
+                            (a <= 4.9e6) & (b - a >= 1e6) & (b >= threshold_a),
+                            (b <= 4.9e6) & (a - b >= 1e6) & (a >= threshold_b),
+                            (a < 4.9e6) & (b < 4.9e6)
+                           ],
+                           [asymptotic_ab_large, asymptotic_b_large,
+                            asymptotic_a_large, regular],
+                           [a, b]
+        )
+
+
+beta = beta_gen(a=0.0, b=1.0, name='beta')
+
+
+class betaprime_gen(rv_continuous):
+    r"""A beta prime continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `betaprime` is:
+
+    .. math::
+
+        f(x, a, b) = \frac{x^{a-1} (1+x)^{-a-b}}{\beta(a, b)}
+
+    for :math:`x >= 0`, :math:`a > 0`, :math:`b > 0`, where
+    :math:`\beta(a, b)` is the beta function (see `scipy.special.beta`).
+
+    `betaprime` takes ``a`` and ``b`` as shape parameters.
+
+    The distribution is related to the `beta` distribution as follows:
+    If :math:`X` follows a beta distribution with parameters :math:`a, b`,
+    then :math:`Y = X/(1-X)` has a beta prime distribution with
+    parameters :math:`a, b` ([1]_).
+
+    The beta prime distribution is a reparametrized version of the
+    F distribution.  The beta prime distribution with shape parameters
+    ``a`` and ``b`` and ``scale = s`` is equivalent to the F distribution
+    with parameters ``d1 = 2*a``, ``d2 = 2*b`` and ``scale = (a/b)*s``.
+    For example,
+
+    >>> from scipy.stats import betaprime, f
+    >>> x = [1, 2, 5, 10]
+    >>> a = 12
+    >>> b = 5
+    >>> betaprime.pdf(x, a, b, scale=2)
+    array([0.00541179, 0.08331299, 0.14669185, 0.03150079])
+    >>> f.pdf(x, 2*a, 2*b, scale=(a/b)*2)
+    array([0.00541179, 0.08331299, 0.14669185, 0.03150079])
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Beta prime distribution, Wikipedia,
+           https://en.wikipedia.org/wiki/Beta_prime_distribution
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ia, ib]
+
+    def _rvs(self, a, b, size=None, random_state=None):
+        u1 = gamma.rvs(a, size=size, random_state=random_state)
+        u2 = gamma.rvs(b, size=size, random_state=random_state)
+        return u1 / u2
+
+    def _pdf(self, x, a, b):
+        # betaprime.pdf(x, a, b) = x**(a-1) * (1+x)**(-a-b) / beta(a, b)
+        return np.exp(self._logpdf(x, a, b))
+
+    def _logpdf(self, x, a, b):
+        return sc.xlogy(a - 1.0, x) - sc.xlog1py(a + b, x) - sc.betaln(a, b)
+
+    def _cdf(self, x, a, b):
+        # note: f2 is the direct way to compute the cdf if the relationship
+        # to the beta distribution is used.
+        # however, for very large x, x/(1+x) == 1. since the distribution
+        # has very fat tails if b is small, this can cause inaccurate results
+        # use the following relationship of the incomplete beta function:
+        # betainc(x, a, b) = 1 - betainc(1-x, b, a)
+        # see gh-17631
+        return xpx.apply_where(
+            x > 1, (x, a, b),
+            lambda x_, a_, b_: beta._sf(1 / (1 + x_), b_, a_),
+            lambda x_, a_, b_: beta._cdf(x_ / (1 + x_), a_, b_))
+
+    def _sf(self, x, a, b):
+        return xpx.apply_where(
+            x > 1, (x, a, b),
+            lambda x_, a_, b_: beta._cdf(1 / (1 + x_), b_, a_),
+            lambda x_, a_, b_: beta._sf(x_ / (1 + x_), a_, b_))
+
+    def _ppf(self, p, a, b):
+        p, a, b = np.broadcast_arrays(p, a, b)
+        # By default, compute the ppf by solving the following:
+        # p = beta._cdf(x/(1+x), a, b). This implies x = r/(1-r) with
+        # r = beta._ppf(p, a, b). This can cause numerical issues if r is
+        # very close to 1. In that case, invert the alternative expression of
+        # the cdf: p = beta._sf(1/(1+x), b, a).
+        r = stats.beta._ppf(p, a, b)
+        with np.errstate(divide='ignore'):
+            out = r / (1 - r)
+        rnear1 = r > 0.9999
+        if np.isscalar(r):
+            if rnear1:
+                out = 1/stats.beta._isf(p, b, a) - 1
+        else:
+            out[rnear1] = 1/stats.beta._isf(p[rnear1], b[rnear1], a[rnear1]) - 1
+        return out
+
+    def _munp(self, n, a, b):
+        return xpx.apply_where(
+            b > n, (a, b),
+            lambda a, b: np.prod([(a+i-1)/(b-i) for i in range(1, int(n)+1)], axis=0),
+            fill_value=np.inf)
+
+
+betaprime = betaprime_gen(a=0.0, name='betaprime')
+
+
+class bradford_gen(rv_continuous):
+    r"""A Bradford continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `bradford` is:
+
+    .. math::
+
+        f(x, c) = \frac{c}{\log(1+c) (1+cx)}
+
+    for :math:`0 <= x <= 1` and :math:`c > 0`.
+
+    `bradford` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # bradford.pdf(x, c) = c / (k * (1+c*x))
+        return c / (c*x + 1.0) / sc.log1p(c)
+
+    def _cdf(self, x, c):
+        return sc.log1p(c*x) / sc.log1p(c)
+
+    def _ppf(self, q, c):
+        return sc.expm1(q * sc.log1p(c)) / c
+
+    def _stats(self, c, moments='mv'):
+        k = np.log(1.0+c)
+        mu = (c-k)/(c*k)
+        mu2 = ((c+2.0)*k-2.0*c)/(2*c*k*k)
+        g1 = None
+        g2 = None
+        if 's' in moments:
+            g1 = np.sqrt(2)*(12*c*c-9*c*k*(c+2)+2*k*k*(c*(c+3)+3))
+            g1 /= np.sqrt(c*(c*(k-2)+2*k))*(3*c*(k-2)+6*k)
+        if 'k' in moments:
+            g2 = (c**3*(k-3)*(k*(3*k-16)+24)+12*k*c*c*(k-4)*(k-3) +
+                  6*c*k*k*(3*k-14) + 12*k**3)
+            g2 /= 3*c*(c*(k-2)+2*k)**2
+        return mu, mu2, g1, g2
+
+    def _entropy(self, c):
+        k = np.log(1+c)
+        return k/2.0 - np.log(c/k)
+
+
+bradford = bradford_gen(a=0.0, b=1.0, name='bradford')
+
+
+class burr_gen(rv_continuous):
+    r"""A Burr (Type III) continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    fisk : a special case of either `burr` or `burr12` with ``d=1``
+    burr12 : Burr Type XII distribution
+    mielke : Mielke Beta-Kappa / Dagum distribution
+
+    Notes
+    -----
+    The probability density function for `burr` is:
+
+    .. math::
+
+        f(x; c, d) = c d \frac{x^{-c - 1}}
+                              {{(1 + x^{-c})}^{d + 1}}
+
+    for :math:`x >= 0` and :math:`c, d > 0`.
+
+    `burr` takes ``c`` and ``d`` as shape parameters for :math:`c` and
+    :math:`d`.
+
+    This is the PDF corresponding to the third CDF given in Burr's list;
+    specifically, it is equation (11) in Burr's paper [1]_. The distribution
+    is also commonly referred to as the Dagum distribution [2]_. If the
+    parameter :math:`c < 1` then the mean of the distribution does not
+    exist and if :math:`c < 2` the variance does not exist [2]_.
+    The PDF is finite at the left endpoint :math:`x = 0` if :math:`c * d >= 1`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Burr, I. W. "Cumulative frequency functions", Annals of
+       Mathematical Statistics, 13(2), pp 215-232 (1942).
+    .. [2] https://en.wikipedia.org/wiki/Dagum_distribution
+    .. [3] Kleiber, Christian. "A guide to the Dagum distributions."
+       Modeling Income Distributions and Lorenz Curves  pp 97-117 (2008).
+
+    %(example)s
+
+    """
+    # Do not set _support_mask to rv_continuous._open_support_mask
+    # Whether the left-hand endpoint is suitable for pdf evaluation is dependent
+    # on the values of c and d: if c*d >= 1, the pdf is finite, otherwise infinite.
+
+    def _shape_info(self):
+        ic = _ShapeInfo("c", False, (0, np.inf), (False, False))
+        id = _ShapeInfo("d", False, (0, np.inf), (False, False))
+        return [ic, id]
+
+    def _pdf(self, x, c, d):
+        # burr.pdf(x, c, d) = c * d * x**(-c-1) * (1+x**(-c))**(-d-1)
+        output = xpx.apply_where(
+            x == 0, (x, c, d),
+            lambda x_, c_, d_: c_ * d_ * (x_**(c_*d_-1)) / (1 + x_**c_),
+            lambda x_, c_, d_: (c_ * d_ * (x_ ** (-c_ - 1.0)) /
+                                ((1 + x_ ** (-c_)) ** (d_ + 1.0))))
+        return output[()] if output.ndim == 0 else output
+
+    def _logpdf(self, x, c, d):
+        output = xpx.apply_where(
+            x == 0, (x, c, d),
+            lambda x_, c_, d_: (np.log(c_) + np.log(d_) + sc.xlogy(c_*d_ - 1, x_)
+                                - (d_+1) * sc.log1p(x_**(c_))),
+            lambda x_, c_, d_: (np.log(c_) + np.log(d_)
+                                + sc.xlogy(-c_ - 1, x_)
+                                - sc.xlog1py(d_+1, x_**(-c_))))
+        return output[()] if output.ndim == 0 else output
+
+    def _cdf(self, x, c, d):
+        return (1 + x**(-c))**(-d)
+
+    def _logcdf(self, x, c, d):
+        return sc.log1p(x**(-c)) * (-d)
+
+    def _sf(self, x, c, d):
+        return np.exp(self._logsf(x, c, d))
+
+    def _logsf(self, x, c, d):
+        return np.log1p(- (1 + x**(-c))**(-d))
+
+    def _ppf(self, q, c, d):
+        return (q**(-1.0/d) - 1)**(-1.0/c)
+
+    def _isf(self, q, c, d):
+        _q = sc.xlog1py(-1.0 / d, -q)
+        return sc.expm1(_q) ** (-1.0 / c)
+
+    def _stats(self, c, d):
+        nc = np.arange(1, 5).reshape(4,1) / c
+        # ek is the kth raw moment, e1 is the mean e2-e1**2 variance etc.
+        e1, e2, e3, e4 = sc.beta(d + nc, 1. - nc) * d
+        mu = np.where(c > 1.0, e1, np.nan)
+        mu2_if_c = e2 - mu**2
+        mu2 = np.where(c > 2.0, mu2_if_c, np.nan)
+        g1 = xpx.apply_where(
+            c > 3.0, (e1, e2, e3, mu2_if_c),
+            lambda e1, e2, e3, mu2_if_c: ((e3 - 3*e2*e1 + 2*e1**3)
+                                           / np.sqrt((mu2_if_c)**3)),
+            fill_value=np.nan)
+        g2 = xpx.apply_where(
+            c > 4.0, (e1, e2, e3, e4, mu2_if_c),
+            lambda e1, e2, e3, e4, mu2_if_c: (
+                ((e4 - 4*e3*e1 + 6*e2*e1**2 - 3*e1**4) / mu2_if_c**2) - 3),
+            fill_value=np.nan)
+        if np.ndim(c) == 0:
+            return mu.item(), mu2.item(), g1.item(), g2.item()
+        return mu, mu2, g1, g2
+
+    def _munp(self, n, c, d):
+        def __munp(n, c, d):
+            nc = 1. * n / c
+            return d * sc.beta(1.0 - nc, d + nc)
+        n, c, d = np.asarray(n), np.asarray(c), np.asarray(d)
+        return xpx.apply_where((c > n) & (n == n) & (d == d),
+                               (n, c, d), __munp, fill_value=np.nan)
+
+
+burr = burr_gen(a=0.0, name='burr')
+
+
+class burr12_gen(rv_continuous):
+    r"""A Burr (Type XII) continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    fisk : a special case of either `burr` or `burr12` with ``d=1``
+    burr : Burr Type III distribution
+
+    Notes
+    -----
+    The probability density function for `burr12` is:
+
+    .. math::
+
+        f(x; c, d) = c d \frac{x^{c-1}}
+                              {(1 + x^c)^{d + 1}}
+
+    for :math:`x >= 0` and :math:`c, d > 0`.
+
+    `burr12` takes ``c`` and ``d`` as shape parameters for :math:`c`
+    and :math:`d`.
+
+    This is the PDF corresponding to the twelfth CDF given in Burr's list;
+    specifically, it is equation (20) in Burr's paper [1]_.
+
+    %(after_notes)s
+
+    The Burr type 12 distribution is also sometimes referred to as
+    the Singh-Maddala distribution from NIST [2]_.
+
+    References
+    ----------
+    .. [1] Burr, I. W. "Cumulative frequency functions", Annals of
+       Mathematical Statistics, 13(2), pp 215-232 (1942).
+
+    .. [2] https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/b12pdf.htm
+
+    .. [3] "Burr distribution",
+       https://en.wikipedia.org/wiki/Burr_distribution
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        ic = _ShapeInfo("c", False, (0, np.inf), (False, False))
+        id = _ShapeInfo("d", False, (0, np.inf), (False, False))
+        return [ic, id]
+
+    def _pdf(self, x, c, d):
+        # burr12.pdf(x, c, d) = c * d * x**(c-1) * (1+x**(c))**(-d-1)
+        return np.exp(self._logpdf(x, c, d))
+
+    def _logpdf(self, x, c, d):
+        return np.log(c) + np.log(d) + sc.xlogy(c - 1, x) + sc.xlog1py(-d-1, x**c)
+
+    def _cdf(self, x, c, d):
+        return -sc.expm1(self._logsf(x, c, d))
+
+    def _logcdf(self, x, c, d):
+        return sc.log1p(-(1 + x**c)**(-d))
+
+    def _sf(self, x, c, d):
+        return np.exp(self._logsf(x, c, d))
+
+    def _logsf(self, x, c, d):
+        return sc.xlog1py(-d, x**c)
+
+    def _ppf(self, q, c, d):
+        # The following is an implementation of
+        #   ((1 - q)**(-1.0/d) - 1)**(1.0/c)
+        # that does a better job handling small values of q.
+        return sc.expm1(-1/d * sc.log1p(-q))**(1/c)
+
+    def _isf(self, p, c, d):
+        return sc.expm1(-1/d * np.log(p))**(1/c)
+
+    def _munp(self, n, c, d):
+        def moment_if_exists(n, c, d):
+            nc = 1. * n / c
+            return d * sc.beta(1.0 + nc, d - nc)
+
+        return xpx.apply_where(c * d > n, (n, c, d), moment_if_exists,
+                               fill_value=np.nan)
+
+
+burr12 = burr12_gen(a=0.0, name='burr12')
+
+
+class fisk_gen(burr_gen):
+    r"""A Fisk continuous random variable.
+
+    The Fisk distribution is also known as the log-logistic distribution.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    burr
+
+    Notes
+    -----
+    The probability density function for `fisk` is:
+
+    .. math::
+
+        f(x, c) = \frac{c x^{c-1}}
+                       {(1 + x^c)^2}
+
+    for :math:`x >= 0` and :math:`c > 0`.
+
+    Please note that the above expression can be transformed into the following
+    one, which is also commonly used:
+
+    .. math::
+
+        f(x, c) = \frac{c x^{-c-1}}
+                       {(1 + x^{-c})^2}
+
+    `fisk` takes ``c`` as a shape parameter for :math:`c`.
+
+    `fisk` is a special case of `burr` or `burr12` with ``d=1``.
+
+    Suppose ``X`` is a logistic random variable with location ``l``
+    and scale ``s``. Then ``Y = exp(X)`` is a Fisk (log-logistic)
+    random variable with ``scale = exp(l)`` and shape ``c = 1/s``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # fisk.pdf(x, c) = c * x**(-c-1) * (1 + x**(-c))**(-2)
+        return burr._pdf(x, c, 1.0)
+
+    def _cdf(self, x, c):
+        return burr._cdf(x, c, 1.0)
+
+    def _sf(self, x, c):
+        return burr._sf(x, c, 1.0)
+
+    def _logpdf(self, x, c):
+        # fisk.pdf(x, c) = c * x**(-c-1) * (1 + x**(-c))**(-2)
+        return burr._logpdf(x, c, 1.0)
+
+    def _logcdf(self, x, c):
+        return burr._logcdf(x, c, 1.0)
+
+    def _logsf(self, x, c):
+        return burr._logsf(x, c, 1.0)
+
+    def _ppf(self, x, c):
+        return burr._ppf(x, c, 1.0)
+
+    def _isf(self, q, c):
+        return burr._isf(q, c, 1.0)
+
+    def _munp(self, n, c):
+        return burr._munp(n, c, 1.0)
+
+    def _stats(self, c):
+        return burr._stats(c, 1.0)
+
+    def _entropy(self, c):
+        return 2 - np.log(c)
+
+
+fisk = fisk_gen(a=0.0, name='fisk')
+
+
+class cauchy_gen(rv_continuous):
+    r"""A Cauchy continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `cauchy` is
+
+    .. math::
+
+        f(x) = \frac{1}{\pi (1 + x^2)}
+
+    for a real number :math:`x`.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``ppf`` and ``isf`` methods. [1]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # cauchy.pdf(x) = 1 / (pi * (1 + x**2))
+        with np.errstate(over='ignore'):
+            return 1.0/np.pi/(1.0+x*x)
+
+    def _logpdf(self, x):
+        # The formulas
+        #     log(1/(pi*(1 + x**2))) = -log(pi) - log(1 + x**2)
+        #                            = -log(pi) - log(x**2*(1 + 1/x**2))
+        #                            = -log(pi) - (2log(|x|) + log1p(1/x**2))
+        # are used here.
+        absx = np.abs(x)
+        # In the following apply_where, `f1` provides better precision than `f2`
+        # for small and moderate x, while `f2` avoids the overflow that can
+        # occur with absx**2.
+        return xpx.apply_where(
+            absx < 1, absx,
+            lambda absx: -_LOG_PI - np.log1p(absx**2),
+            lambda absx: (-_LOG_PI - (2*np.log(absx) + np.log1p((1/absx)**2))))
+
+    def _cdf(self, x):
+        return np.arctan2(1, -x)/np.pi
+
+    def _ppf(self, q):
+        return scu._cauchy_ppf(q, 0, 1)
+
+    def _sf(self, x):
+        return np.arctan2(1, x)/np.pi
+
+    def _isf(self, q):
+        return scu._cauchy_isf(q, 0, 1)
+
+    def _stats(self):
+        return np.nan, np.nan, np.nan, np.nan
+
+    def _entropy(self):
+        return np.log(4*np.pi)
+
+    def _fitstart(self, data, args=None):
+        # Initialize ML guesses using quartiles instead of moments.
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        p25, p50, p75 = np.percentile(data, [25, 50, 75])
+        return p50, (p75 - p25)/2
+
+
+cauchy = cauchy_gen(name='cauchy')
+
+
+class chi_gen(rv_continuous):
+    r"""A chi continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `chi` is:
+
+    .. math::
+
+        f(x, k) = \frac{1}{2^{k/2-1} \Gamma \left( k/2 \right)}
+                   x^{k-1} \exp \left( -x^2/2 \right)
+
+    for :math:`x >= 0` and :math:`k > 0` (degrees of freedom, denoted ``df``
+    in the implementation). :math:`\Gamma` is the gamma function
+    (`scipy.special.gamma`).
+
+    Special cases of `chi` are:
+
+    - ``chi(1, loc, scale)`` is equivalent to `halfnorm`
+    - ``chi(2, 0, scale)`` is equivalent to `rayleigh`
+    - ``chi(3, 0, scale)`` is equivalent to `maxwell`
+
+    `chi` takes ``df`` as a shape parameter.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("df", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, df, size=None, random_state=None):
+        return np.sqrt(chi2.rvs(df, size=size, random_state=random_state))
+
+    def _pdf(self, x, df):
+        #                   x**(df-1) * exp(-x**2/2)
+        # chi.pdf(x, df) =  -------------------------
+        #                   2**(df/2-1) * gamma(df/2)
+        return np.exp(self._logpdf(x, df))
+
+    def _logpdf(self, x, df):
+        l = np.log(2) - .5*np.log(2)*df - sc.gammaln(.5*df)
+        return l + sc.xlogy(df - 1., x) - .5*x**2
+
+    def _cdf(self, x, df):
+        return sc.gammainc(.5*df, .5*x**2)
+
+    def _sf(self, x, df):
+        return sc.gammaincc(.5*df, .5*x**2)
+
+    def _ppf(self, q, df):
+        return np.sqrt(2*sc.gammaincinv(.5*df, q))
+
+    def _isf(self, q, df):
+        return np.sqrt(2*sc.gammainccinv(.5*df, q))
+
+    def _stats(self, df):
+        # poch(df/2, 1/2) = gamma(df/2 + 1/2) / gamma(df/2)
+        mu = np.sqrt(2) * sc.poch(0.5 * df, 0.5)
+        mu2 = df - mu*mu
+        g1 = (2*mu**3.0 + mu*(1-2*df))/np.asarray(np.power(mu2, 1.5))
+        g2 = 2*df*(1.0-df)-6*mu**4 + 4*mu**2 * (2*df-1)
+        g2 /= np.asarray(mu2**2.0)
+        return mu, mu2, g1, g2
+
+    def _entropy(self, df):
+
+        def regular_formula(df):
+            return (sc.gammaln(.5 * df)
+                    + 0.5 * (df - np.log(2) - (df - 1) * sc.digamma(0.5 * df)))
+
+        def asymptotic_formula(df):
+            return (0.5 + np.log(np.pi)/2 - (df**-1)/6 - (df**-2)/6
+                    - 4/45*(df**-3) + (df**-4)/15)
+
+        return xpx.apply_where(df < 300, df, regular_formula, asymptotic_formula)
+
+
+chi = chi_gen(a=0.0, name='chi')
+
+
+class chi2_gen(rv_continuous):
+    r"""A chi-squared continuous random variable.
+
+    For the noncentral chi-square distribution, see `ncx2`.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    ncx2
+
+    Notes
+    -----
+    The probability density function for `chi2` is:
+
+    .. math::
+
+        f(x, k) = \frac{1}{2^{k/2} \Gamma \left( k/2 \right)}
+                   x^{k/2-1} \exp \left( -x/2 \right)
+
+    for :math:`x > 0`  and :math:`k > 0` (degrees of freedom, denoted ``df``
+    in the implementation).
+
+    `chi2` takes ``df`` as a shape parameter.
+
+    The chi-squared distribution is a special case of the gamma
+    distribution, with gamma parameters ``a = df/2``, ``loc = 0`` and
+    ``scale = 2``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("df", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, df, size=None, random_state=None):
+        return random_state.chisquare(df, size)
+
+    def _pdf(self, x, df):
+        # chi2.pdf(x, df) = 1 / (2*gamma(df/2)) * (x/2)**(df/2-1) * exp(-x/2)
+        return np.exp(self._logpdf(x, df))
+
+    def _logpdf(self, x, df):
+        return sc.xlogy(df/2.-1, x) - x/2. - sc.gammaln(df/2.) - (np.log(2)*df)/2.
+
+    def _cdf(self, x, df):
+        return sc.chdtr(df, x)
+
+    def _sf(self, x, df):
+        return sc.chdtrc(df, x)
+
+    def _isf(self, p, df):
+        return sc.chdtri(df, p)
+
+    def _ppf(self, p, df):
+        return 2*sc.gammaincinv(df/2, p)
+
+    def _stats(self, df):
+        mu = df
+        mu2 = 2*df
+        g1 = 2*np.sqrt(2.0/df)
+        g2 = 12.0/df
+        return mu, mu2, g1, g2
+
+    def _entropy(self, df):
+        half_df = 0.5 * df
+
+        def regular_formula(half_df):
+            return (half_df + np.log(2) + sc.gammaln(half_df) +
+                    (1 - half_df) * sc.psi(half_df))
+
+        def asymptotic_formula(half_df):
+            # plug in the above formula the following asymptotic
+            # expansions:
+            # ln(gamma(a)) ~ (a - 0.5) * ln(a) - a + 0.5 * ln(2 * pi) +
+            #                 1/(12 * a) - 1/(360 * a**3)
+            # psi(a) ~ ln(a) - 1/(2 * a) - 1/(3 * a**2) + 1/120 * a**4)
+            c = np.log(2) + 0.5*(1 + np.log(2*np.pi))
+            h = 0.5/half_df
+            return (h*(-2/3 + h*(-1/3 + h*(-4/45 + h/7.5))) +
+                    0.5*np.log(half_df) + c)
+
+        return xpx.apply_where(half_df < 125, half_df,
+                               regular_formula, asymptotic_formula)
+
+
+chi2 = chi2_gen(a=0.0, name='chi2')
+
+
+class cosine_gen(rv_continuous):
+    r"""A cosine continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The cosine distribution is an approximation to the normal distribution.
+    The probability density function for `cosine` is:
+
+    .. math::
+
+        f(x) = \frac{1}{2\pi} (1+\cos(x))
+
+    for :math:`-\pi \le x \le \pi`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # cosine.pdf(x) = 1/(2*pi) * (1+cos(x))
+        return 1.0/2/np.pi*(1+np.cos(x))
+
+    def _logpdf(self, x):
+        c = np.cos(x)
+        return xpx.apply_where(c != -1, c,
+                               lambda c: np.log1p(c) - np.log(2*np.pi),
+                               fill_value=-np.inf)
+
+    def _cdf(self, x):
+        return scu._cosine_cdf(x)
+
+    def _sf(self, x):
+        return scu._cosine_cdf(-x)
+
+    def _ppf(self, p):
+        return scu._cosine_invcdf(p)
+
+    def _isf(self, p):
+        return -scu._cosine_invcdf(p)
+
+    def _stats(self):
+        v = (np.pi * np.pi / 3.0) - 2.0
+        k = -6.0 * (np.pi**4 - 90) / (5.0 * (np.pi * np.pi - 6)**2)
+        return 0.0, v, 0.0, k
+
+    def _entropy(self):
+        return np.log(4*np.pi)-1.0
+
+
+cosine = cosine_gen(a=-np.pi, b=np.pi, name='cosine')
+
+
+class dgamma_gen(rv_continuous):
+    r"""A double gamma continuous random variable.
+
+    The double gamma distribution is also known as the reflected gamma
+    distribution [1]_.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `dgamma` is:
+
+    .. math::
+
+        f(x, a) = \frac{1}{2\Gamma(a)} |x|^{a-1} \exp(-|x|)
+
+    for a real number :math:`x` and :math:`a > 0`. :math:`\Gamma` is the
+    gamma function (`scipy.special.gamma`).
+
+    `dgamma` takes ``a`` as a shape parameter for :math:`a`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Johnson, Kotz, and Balakrishnan, "Continuous Univariate
+           Distributions, Volume 1", Second Edition, John Wiley and Sons
+           (1994).
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, a, size=None, random_state=None):
+        u = random_state.uniform(size=size)
+        gm = gamma.rvs(a, size=size, random_state=random_state)
+        return gm * np.where(u >= 0.5, 1, -1)
+
+    def _pdf(self, x, a):
+        # dgamma.pdf(x, a) = 1 / (2*gamma(a)) * abs(x)**(a-1) * exp(-abs(x))
+        ax = abs(x)
+        return 1.0/(2*sc.gamma(a))*ax**(a-1.0) * np.exp(-ax)
+
+    def _logpdf(self, x, a):
+        ax = abs(x)
+        return sc.xlogy(a - 1.0, ax) - ax - np.log(2) - sc.gammaln(a)
+
+    def _cdf(self, x, a):
+        return np.where(x > 0,
+                        0.5 + 0.5*sc.gammainc(a, x),
+                        0.5*sc.gammaincc(a, -x))
+
+    def _sf(self, x, a):
+        return np.where(x > 0,
+                        0.5*sc.gammaincc(a, x),
+                        0.5 + 0.5*sc.gammainc(a, -x))
+
+    def _entropy(self, a):
+        return stats.gamma._entropy(a) - np.log(0.5)
+
+    def _ppf(self, q, a):
+        return np.where(q > 0.5,
+                        sc.gammaincinv(a, 2*q - 1),
+                        -sc.gammainccinv(a, 2*q))
+
+    def _isf(self, q, a):
+        return np.where(q > 0.5,
+                        -sc.gammaincinv(a, 2*q - 1),
+                        sc.gammainccinv(a, 2*q))
+
+    def _stats(self, a):
+        mu2 = a*(a+1.0)
+        return 0.0, mu2, 0.0, (a+2.0)*(a+3.0)/mu2-3.0
+
+
+dgamma = dgamma_gen(name='dgamma')
+
+
+class dpareto_lognorm_gen(rv_continuous):
+    r"""A double Pareto lognormal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `dpareto_lognorm` is:
+
+    .. math::
+
+        f(x, \mu, \sigma, \alpha, \beta) =
+        \frac{\alpha \beta}{(\alpha + \beta) x}
+        \phi\left( \frac{\log x - \mu}{\sigma} \right)
+        \left( R(y_1) + R(y_2) \right)
+
+    where :math:`R(t) = \frac{1 - \Phi(t)}{\phi(t)}`,
+    :math:`\phi` and :math:`\Phi` are the normal PDF and CDF, respectively,
+    :math:`y_1 = \alpha \sigma - \frac{\log x - \mu}{\sigma}`,
+    and :math:`y_2 = \beta \sigma + \frac{\log x - \mu}{\sigma}`
+    for real numbers :math:`x` and :math:`\mu`, :math:`\sigma > 0`,
+    :math:`\alpha > 0`, and :math:`\beta > 0` [1]_.
+
+    `dpareto_lognorm` takes
+    ``u`` as a shape parameter for :math:`\mu`,
+    ``s`` as a shape parameter for :math:`\sigma`,
+    ``a`` as a shape parameter for :math:`\alpha`, and
+    ``b`` as a shape parameter for :math:`\beta`.
+
+    A random variable :math:`X` distributed according to the PDF above
+    can be represented as :math:`X = U \frac{V_1}{V_2}` where :math:`U`,
+    :math:`V_1`, and :math:`V_2` are independent, :math:`U` is lognormally
+    distributed such that :math:`\log U \sim N(\mu, \sigma^2)`, and
+    :math:`V_1` and :math:`V_2` follow Pareto distributions with parameters
+    :math:`\alpha` and :math:`\beta`, respectively [2]_.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Hajargasht, Gholamreza, and William E. Griffiths. "Pareto-lognormal
+           distributions: Inequality, poverty, and estimation from grouped income
+           data." Economic Modelling 33 (2013): 593-604.
+    .. [2] Reed, William J., and Murray Jorgensen. "The double Pareto-lognormal
+           distribution - a new parametric model for size distributions."
+           Communications in Statistics - Theory and Methods 33.8 (2004): 1733-1753.
+
+    %(example)s
+
+    """
+    _logphi = norm._logpdf
+    _logPhi = norm._logcdf
+    _logPhic = norm._logsf
+    _phi = norm._pdf
+    _Phi = norm._cdf
+    _Phic = norm._sf
+
+    def _R(self, z):
+        return self._Phic(z) / self._phi(z)
+
+    def _logR(self, z):
+        return self._logPhic(z) - self._logphi(z)
+
+    def _shape_info(self):
+        return [_ShapeInfo("u", False, (-np.inf, np.inf), (False, False)),
+                _ShapeInfo("s", False, (0, np.inf), (False, False)),
+                _ShapeInfo("a", False, (0, np.inf), (False, False)),
+                _ShapeInfo("b", False, (0, np.inf), (False, False))]
+
+    def _argcheck(self, u, s, a, b):
+        return (s > 0) & (a > 0) & (b > 0)
+
+    def _rvs(self, u, s, a, b, size=None, random_state=None):
+        # From [1] after Equation (12): "To generate pseudo-random
+        # deviates from the dPlN distribution, one can exponentiate
+        # pseudo-random deviates from NL generated using (6)."
+        Z = random_state.normal(u, s, size=size)
+        E1 = random_state.standard_exponential(size=size)
+        E2 = random_state.standard_exponential(size=size)
+        return np.exp(Z + E1 / a - E2 / b)
+
+    def _logpdf(self, x, u, s, a, b):
+        with np.errstate(invalid='ignore', divide='ignore'):
+            log_y, m = np.log(x), u  # compare against [1] Eq. 1
+            z = (log_y - m) / s
+            x1 = a * s - z
+            x2 = b * s + z
+            out = np.asarray(np.log(a) + np.log(b) - np.log(a + b) - log_y)
+            out += self._logphi(z)
+            out += np.logaddexp(self._logR(x1), self._logR(x2))
+        out[(x == 0) | np.isinf(x)] = -np.inf
+        return out[()]
+
+    def _logcdf(self, x, u, s, a, b):
+        with np.errstate(invalid='ignore', divide='ignore'):
+            log_y, m = np.log(x), u  # compare against [1] Eq. 2
+            z = (log_y - m) / s
+            x1 = a * s - z
+            x2 = b * s + z
+            t1 = self._logPhi(z)
+            t2 = self._logphi(z)
+            t3 = (np.log(b) + self._logR(x1))
+            t4 = (np.log(a) + self._logR(x2))
+            t1, t2, t3, t4, one = np.broadcast_arrays(t1, t2, t3, t4, 1)
+            # t3 can be smaller than t4, so we have to consider log of negative number
+            # This would be much simpler, but `return_sign` is available, so use it?
+            # t5 =  sc.logsumexp([t3, t4 + np.pi*1j])
+            t5, sign =  sc.logsumexp([t3, t4], b=[one, -one], axis=0, return_sign=True)
+            temp = [t1, t2 + t5 - np.log(a + b)]
+            out = np.asarray(sc.logsumexp(temp, b=[one, -one*sign], axis=0))
+        out[x == 0] = -np.inf
+        return out[()]
+
+    def _logsf(self, x, u, s, a, b):
+        return scu._log1mexp(self._logcdf(x, u, s, a, b))
+
+    # Infrastructure doesn't seem to do this, so...
+
+    def _pdf(self, x, u, s, a, b):
+        return np.exp(self._logpdf(x, u, s, a, b))
+
+    def _cdf(self, x, u, s, a, b):
+        return np.exp(self._logcdf(x, u, s, a, b))
+
+    def _sf(self, x, u, s, a, b):
+        return np.exp(self._logsf(x, u, s, a, b))
+
+    def _munp(self, n, u, s, a, b):
+        m, k = u, float(n)  # compare against [1] Eq. 6
+        out = (a * b) / ((a - k) * (b + k)) * np.exp(k * m + k ** 2 * s ** 2 / 2)
+        out = np.asarray(out)
+        out[a <= k] = np.nan
+        return out
+
+
+dpareto_lognorm = dpareto_lognorm_gen(a=0, name='dpareto_lognorm')
+
+
+class dweibull_gen(rv_continuous):
+    r"""A double Weibull continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `dweibull` is given by
+
+    .. math::
+
+        f(x, c) = c / 2 |x|^{c-1} \exp(-|x|^c)
+
+    for a real number :math:`x` and :math:`c > 0`.
+
+    `dweibull` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, c, size=None, random_state=None):
+        u = random_state.uniform(size=size)
+        w = weibull_min.rvs(c, size=size, random_state=random_state)
+        return w * (np.where(u >= 0.5, 1, -1))
+
+    def _pdf(self, x, c):
+        # dweibull.pdf(x, c) = c / 2 * abs(x)**(c-1) * exp(-abs(x)**c)
+        ax = abs(x)
+        Px = c / 2.0 * ax**(c-1.0) * np.exp(-ax**c)
+        return Px
+
+    def _logpdf(self, x, c):
+        ax = abs(x)
+        return np.log(c) - np.log(2.0) + sc.xlogy(c - 1.0, ax) - ax**c
+
+    def _cdf(self, x, c):
+        Cx1 = 0.5 * np.exp(-abs(x)**c)
+        return np.where(x > 0, 1 - Cx1, Cx1)
+
+    def _ppf(self, q, c):
+        fac = 2. * np.where(q <= 0.5, q, 1. - q)
+        fac = np.power(-np.log(fac), 1.0 / c)
+        return np.where(q > 0.5, fac, -fac)
+
+    def _sf(self, x, c):
+        half_weibull_min_sf = 0.5 * stats.weibull_min._sf(np.abs(x), c)
+        return np.where(x > 0, half_weibull_min_sf, 1 - half_weibull_min_sf)
+
+    def _isf(self, q, c):
+        double_q = 2. * np.where(q <= 0.5, q, 1. - q)
+        weibull_min_isf = stats.weibull_min._isf(double_q, c)
+        return np.where(q > 0.5, -weibull_min_isf, weibull_min_isf)
+
+    def _munp(self, n, c):
+        return (1 - (n % 2)) * sc.gamma(1.0 + 1.0 * n / c)
+
+    # since we know that all odd moments are zeros, return them at once.
+    # returning Nones from _stats makes the public stats call _munp
+    # so overall we're saving one or two gamma function evaluations here.
+    def _stats(self, c):
+        return 0, None, 0, None
+
+    def _entropy(self, c):
+        h = stats.weibull_min._entropy(c) - np.log(0.5)
+        return h
+
+
+dweibull = dweibull_gen(name='dweibull')
+
+
+class expon_gen(rv_continuous):
+    r"""An exponential continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `expon` is:
+
+    .. math::
+
+        f(x) = \exp(-x)
+
+    for :math:`x \ge 0`.
+
+    %(after_notes)s
+
+    A common parameterization for `expon` is in terms of the rate parameter
+    ``lambda``, such that ``pdf = lambda * exp(-lambda * x)``. This
+    parameterization corresponds to using ``scale = 1 / lambda``.
+
+    The exponential distribution is a special case of the gamma
+    distributions, with gamma shape parameter ``a = 1``.
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return random_state.standard_exponential(size)
+
+    def _pdf(self, x):
+        # expon.pdf(x) = exp(-x)
+        return np.exp(-x)
+
+    def _logpdf(self, x):
+        return -x
+
+    def _cdf(self, x):
+        return -sc.expm1(-x)
+
+    def _ppf(self, q):
+        return -sc.log1p(-q)
+
+    def _sf(self, x):
+        return np.exp(-x)
+
+    def _logsf(self, x):
+        return -x
+
+    def _isf(self, q):
+        return -np.log(q)
+
+    def _stats(self):
+        return 1.0, 1.0, 2.0, 6.0
+
+    def _entropy(self):
+        return 1.0
+
+    @_call_super_mom
+    @replace_notes_in_docstring(rv_continuous, notes="""\
+        When `method='MLE'`,
+        this function uses explicit formulas for the maximum likelihood
+        estimation of the exponential distribution parameters, so the
+        `optimizer`, `loc` and `scale` keyword arguments are
+        ignored.\n\n""")
+    def fit(self, data, *args, **kwds):
+        if len(args) > 0:
+            raise TypeError("Too many arguments.")
+
+        floc = kwds.pop('floc', None)
+        fscale = kwds.pop('fscale', None)
+
+        _remove_optimizer_parameters(kwds)
+
+        if floc is not None and fscale is not None:
+            # This check is for consistency with `rv_continuous.fit`.
+            raise ValueError("All parameters fixed. There is nothing to "
+                             "optimize.")
+
+        data = np.asarray(data)
+
+        if not np.isfinite(data).all():
+            raise ValueError("The data contains non-finite values.")
+
+        data_min = data.min()
+
+        if floc is None:
+            # ML estimate of the location is the minimum of the data.
+            loc = data_min
+        else:
+            loc = floc
+            if data_min < loc:
+                # There are values that are less than the specified loc.
+                raise FitDataError("expon", lower=floc, upper=np.inf)
+
+        if fscale is None:
+            # ML estimate of the scale is the shifted mean.
+            scale = data.mean() - loc
+        else:
+            scale = fscale
+
+        # We expect the return values to be floating point, so ensure it
+        # by explicitly converting to float.
+        return float(loc), float(scale)
+
+
+expon = expon_gen(a=0.0, name='expon')
+
+
+class exponnorm_gen(rv_continuous):
+    r"""An exponentially modified Normal continuous random variable.
+
+    Also known as the exponentially modified Gaussian distribution [1]_.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `exponnorm` is:
+
+    .. math::
+
+        f(x, K) = \frac{1}{2K} \exp\left(\frac{1}{2 K^2} - x / K \right)
+                  \text{erfc}\left(-\frac{x - 1/K}{\sqrt{2}}\right)
+
+    where :math:`x` is a real number and :math:`K > 0`.
+
+    It can be thought of as the sum of a standard normal random variable
+    and an independent exponentially distributed random variable with rate
+    ``1/K``.
+
+    %(after_notes)s
+
+    An alternative parameterization of this distribution (for example, in
+    the Wikipedia article [1]_) involves three parameters, :math:`\mu`,
+    :math:`\lambda` and :math:`\sigma`.
+
+    In the present parameterization this corresponds to having ``loc`` and
+    ``scale`` equal to :math:`\mu` and :math:`\sigma`, respectively, and
+    shape parameter :math:`K = 1/(\sigma\lambda)`.
+
+    .. versionadded:: 0.16.0
+
+    References
+    ----------
+    .. [1] Exponentially modified Gaussian distribution, Wikipedia,
+           https://en.wikipedia.org/wiki/Exponentially_modified_Gaussian_distribution
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("K", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, K, size=None, random_state=None):
+        expval = random_state.standard_exponential(size) * K
+        gval = random_state.standard_normal(size)
+        return expval + gval
+
+    def _pdf(self, x, K):
+        return np.exp(self._logpdf(x, K))
+
+    def _logpdf(self, x, K):
+        u = (-x + 1.0 / K) / np.sqrt(2)
+        def logpdf_erfcx(x, K, u):
+            erfcx_term = np.log(sc.erfcx(u))
+            return -np.log(2) - 0.5 * (x ** 2) - np.log(K) + erfcx_term
+        def logpdf_default(x, K, u):
+            invK = 1.0 / K
+            exparg = invK * (-x + 0.5 * invK)
+            return exparg + _norm_logcdf(x - invK) - np.log(K)
+        use_erfcx = np.logical_and(u >= 0, K < 1)
+        return xpx.apply_where(
+            use_erfcx, (x, K, u), logpdf_erfcx, logpdf_default)
+
+    def _cdf(self, x, K):
+        u = (-x + 1.0 / K) / np.sqrt(2)
+        def cdf_erfcx(x, K, u):
+            return _norm_cdf(x) - 0.5 * np.exp(-0.5 * x ** 2) * sc.erfcx(u)
+        def cdf_default(x, K, u):
+            invK = 1.0 / K
+            expval = invK * (0.5 * invK - x)
+            logprod = expval + _norm_logcdf(x - invK)
+            return _norm_cdf(x) - np.exp(logprod)
+        use_erfcx = np.logical_and(u >= 0, K < 1)
+        return xpx.apply_where(
+            use_erfcx, (x, K, u), cdf_erfcx, cdf_default)
+
+    def _sf(self, x, K):
+        u = (-x + 1.0 / K) / np.sqrt(2)
+        def sf_erfcx(x, K, u):
+            return _norm_cdf(-x) + 0.5 * np.exp(-0.5 * x ** 2) * sc.erfcx(u)
+        def sf_default(x, K, u):
+            invK = 1.0 / K
+            expval = invK * (0.5 * invK - x)
+            logprod = expval + _norm_logcdf(x - invK)
+            return _norm_cdf(-x) + np.exp(logprod)
+        use_erfcx = np.logical_and(u >= 0, K < 1)
+        return xpx.apply_where(
+            use_erfcx, (x, K, u), sf_erfcx, sf_default)
+
+    def _stats(self, K):
+        K2 = K * K
+        opK2 = 1.0 + K2
+        skw = 2 * K**3 * opK2**(-1.5)
+        krt = 6.0 * K2 * K2 * opK2**(-2)
+        return K, opK2, skw, krt
+
+exponnorm = exponnorm_gen(name='exponnorm')
+
+
+def _pow1pm1(x, y):
+    """
+    Compute (1 + x)**y - 1.
+
+    Uses expm1 and xlog1py to avoid loss of precision when
+    (1 + x)**y is close to 1.
+
+    Note that the inverse of this function with respect to x is
+    ``_pow1pm1(x, 1/y)``.  That is, if
+
+        t = _pow1pm1(x, y)
+
+    then
+
+        x = _pow1pm1(t, 1/y)
+    """
+    return np.expm1(sc.xlog1py(y, x))
+
+
+class exponweib_gen(rv_continuous):
+    r"""An exponentiated Weibull continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    weibull_min, numpy.random.Generator.weibull
+
+    Notes
+    -----
+    The probability density function for `exponweib` is:
+
+    .. math::
+
+        f(x, a, c) = a c [1-\exp(-x^c)]^{a-1} \exp(-x^c) x^{c-1}
+
+    and its cumulative distribution function is:
+
+    .. math::
+
+        F(x, a, c) = [1-\exp(-x^c)]^a
+
+    for :math:`x > 0`, :math:`a > 0`, :math:`c > 0`.
+
+    `exponweib` takes :math:`a` and :math:`c` as shape parameters:
+
+    * :math:`a` is the exponentiation parameter,
+      with the special case :math:`a=1` corresponding to the
+      (non-exponentiated) Weibull distribution `weibull_min`.
+    * :math:`c` is the shape parameter of the non-exponentiated Weibull law.
+
+    %(after_notes)s
+
+    References
+    ----------
+    https://en.wikipedia.org/wiki/Exponentiated_Weibull_distribution
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ic = _ShapeInfo("c", False, (0, np.inf), (False, False))
+        return [ia, ic]
+
+    def _pdf(self, x, a, c):
+        # exponweib.pdf(x, a, c) =
+        #     a * c * (1-exp(-x**c))**(a-1) * exp(-x**c)*x**(c-1)
+        return np.exp(self._logpdf(x, a, c))
+
+    def _logpdf(self, x, a, c):
+        negxc = -x**c
+        exm1c = -sc.expm1(negxc)
+        logp = (np.log(a) + np.log(c) + sc.xlogy(a - 1.0, exm1c) +
+                negxc + sc.xlogy(c - 1.0, x))
+        return logp
+
+    def _cdf(self, x, a, c):
+        exm1c = -sc.expm1(-x**c)
+        return exm1c**a
+
+    def _ppf(self, q, a, c):
+        return (-sc.log1p(-q**(1.0/a)))**np.asarray(1.0/c)
+
+    def _sf(self, x, a, c):
+        return -_pow1pm1(-np.exp(-x**c), a)
+
+    def _isf(self, p, a, c):
+        return (-np.log(-_pow1pm1(-p, 1/a)))**(1/c)
+
+
+exponweib = exponweib_gen(a=0.0, name='exponweib')
+
+
+class exponpow_gen(rv_continuous):
+    r"""An exponential power continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `exponpow` is:
+
+    .. math::
+
+        f(x, b) = b x^{b-1} \exp(1 + x^b - \exp(x^b))
+
+    for :math:`x \ge 0`, :math:`b > 0`.  Note that this is a different
+    distribution from the exponential power distribution that is also known
+    under the names "generalized normal" or "generalized Gaussian".
+
+    `exponpow` takes ``b`` as a shape parameter for :math:`b`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    http://www.math.wm.edu/~leemis/chart/UDR/PDFs/Exponentialpower.pdf
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("b", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, b):
+        # exponpow.pdf(x, b) = b * x**(b-1) * exp(1 + x**b - exp(x**b))
+        return np.exp(self._logpdf(x, b))
+
+    def _logpdf(self, x, b):
+        xb = x**b
+        f = 1 + np.log(b) + sc.xlogy(b - 1.0, x) + xb - np.exp(xb)
+        return f
+
+    def _cdf(self, x, b):
+        return -sc.expm1(-sc.expm1(x**b))
+
+    def _sf(self, x, b):
+        return np.exp(-sc.expm1(x**b))
+
+    def _isf(self, x, b):
+        return (sc.log1p(-np.log(x)))**(1./b)
+
+    def _ppf(self, q, b):
+        return pow(sc.log1p(-sc.log1p(-q)), 1.0/b)
+
+
+exponpow = exponpow_gen(a=0.0, name='exponpow')
+
+
+class fatiguelife_gen(rv_continuous):
+    r"""A fatigue-life (Birnbaum-Saunders) continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `fatiguelife` is:
+
+    .. math::
+
+        f(x, c) = \frac{x+1}{2c\sqrt{2\pi x^3}} \exp(-\frac{(x-1)^2}{2x c^2})
+
+    for :math:`x >= 0` and :math:`c > 0`.
+
+    `fatiguelife` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] "Birnbaum-Saunders distribution",
+           https://en.wikipedia.org/wiki/Birnbaum-Saunders_distribution
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, c, size=None, random_state=None):
+        z = random_state.standard_normal(size)
+        x = 0.5*c*z
+        x2 = x*x
+        t = 1.0 + 2*x2 + 2*x*np.sqrt(1 + x2)
+        return t
+
+    def _pdf(self, x, c):
+        # fatiguelife.pdf(x, c) =
+        #     (x+1) / (2*c*sqrt(2*pi*x**3)) * exp(-(x-1)**2/(2*x*c**2))
+        return np.exp(self._logpdf(x, c))
+
+    def _logpdf(self, x, c):
+        return (np.log(x+1) - (x-1)**2 / (2.0*x*c**2) - np.log(2*c) -
+                0.5*(np.log(2*np.pi) + 3*np.log(x)))
+
+    def _cdf(self, x, c):
+        return _norm_cdf(1.0 / c * (np.sqrt(x) - 1.0/np.sqrt(x)))
+
+    def _ppf(self, q, c):
+        tmp = c * _norm_ppf(q)
+        return 0.25 * (tmp + np.sqrt(tmp**2 + 4))**2
+
+    def _sf(self, x, c):
+        return _norm_sf(1.0 / c * (np.sqrt(x) - 1.0/np.sqrt(x)))
+
+    def _isf(self, q, c):
+        tmp = -c * _norm_ppf(q)
+        return 0.25 * (tmp + np.sqrt(tmp**2 + 4))**2
+
+    def _stats(self, c):
+        # NB: the formula for kurtosis in wikipedia seems to have an error:
+        # it's 40, not 41. At least it disagrees with the one from Wolfram
+        # Alpha.  And the latter one, below, passes the tests, while the wiki
+        # one doesn't So far I didn't have the guts to actually check the
+        # coefficients from the expressions for the raw moments.
+        c2 = c*c
+        mu = c2 / 2.0 + 1.0
+        den = 5.0 * c2 + 4.0
+        mu2 = c2*den / 4.0
+        g1 = 4 * c * (11*c2 + 6.0) / np.power(den, 1.5)
+        g2 = 6 * c2 * (93*c2 + 40.0) / den**2.0
+        return mu, mu2, g1, g2
+
+
+fatiguelife = fatiguelife_gen(a=0.0, name='fatiguelife')
+
+
+class foldcauchy_gen(rv_continuous):
+    r"""A folded Cauchy continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `foldcauchy` is:
+
+    .. math::
+
+        f(x, c) = \frac{1}{\pi (1+(x-c)^2)} + \frac{1}{\pi (1+(x+c)^2)}
+
+    for :math:`x \ge 0` and :math:`c \ge 0`.
+
+    `foldcauchy` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(example)s
+
+    """
+    def _argcheck(self, c):
+        return c >= 0
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (True, False))]
+
+    def _rvs(self, c, size=None, random_state=None):
+        return abs(cauchy.rvs(loc=c, size=size,
+                              random_state=random_state))
+
+    def _pdf(self, x, c):
+        # foldcauchy.pdf(x, c) = 1/(pi*(1+(x-c)**2)) + 1/(pi*(1+(x+c)**2))
+        return 1.0/np.pi*(1.0/(1+(x-c)**2) + 1.0/(1+(x+c)**2))
+
+    def _cdf(self, x, c):
+        return 1.0/np.pi*(np.arctan(x-c) + np.arctan(x+c))
+
+    def _sf(self, x, c):
+        # 1 - CDF(x, c) = 1 - (atan(x - c) + atan(x + c))/pi
+        #               = ((pi/2 - atan(x - c)) + (pi/2 - atan(x + c)))/pi
+        #               = (acot(x - c) + acot(x + c))/pi
+        #               = (atan2(1, x - c) + atan2(1, x + c))/pi
+        return (np.arctan2(1, x - c) + np.arctan2(1, x + c))/np.pi
+
+    def _stats(self, c):
+        return np.inf, np.inf, np.nan, np.nan
+
+
+foldcauchy = foldcauchy_gen(a=0.0, name='foldcauchy')
+
+
+class f_gen(rv_continuous):
+    r"""An F continuous random variable.
+
+    For the noncentral F distribution, see `ncf`.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    ncf
+
+    Notes
+    -----
+    The F distribution with :math:`df_1 > 0` and :math:`df_2 > 0` degrees of freedom is
+    the distribution of the ratio of two independent chi-squared distributions with
+    :math:`df_1` and :math:`df_2` degrees of freedom, after rescaling by
+    :math:`df_2 / df_1`.
+
+    The probability density function for `f` is:
+
+    .. math::
+
+        f(x, df_1, df_2) = \frac{df_2^{df_2/2} df_1^{df_1/2} x^{df_1 / 2-1}}
+                                {(df_2+df_1 x)^{(df_1+df_2)/2}
+                                 B(df_1/2, df_2/2)}
+
+    for :math:`x > 0`.
+
+    `f` accepts shape parameters ``dfn`` and ``dfd`` for :math:`df_1`, the degrees of
+    freedom of the chi-squared distribution in the numerator, and :math:`df_2`, the
+    degrees of freedom of the chi-squared distribution in the denominator, respectively.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        idfn = _ShapeInfo("dfn", False, (0, np.inf), (False, False))
+        idfd = _ShapeInfo("dfd", False, (0, np.inf), (False, False))
+        return [idfn, idfd]
+
+    def _rvs(self, dfn, dfd, size=None, random_state=None):
+        return random_state.f(dfn, dfd, size)
+
+    def _pdf(self, x, dfn, dfd):
+        #                      df2**(df2/2) * df1**(df1/2) * x**(df1/2-1)
+        # F.pdf(x, df1, df2) = --------------------------------------------
+        #                      (df2+df1*x)**((df1+df2)/2) * B(df1/2, df2/2)
+        return np.exp(self._logpdf(x, dfn, dfd))
+
+    def _logpdf(self, x, dfn, dfd):
+        n = 1.0 * dfn
+        m = 1.0 * dfd
+        lPx = (m/2 * np.log(m) + n/2 * np.log(n) + sc.xlogy(n/2 - 1, x)
+               - (((n+m)/2) * np.log(m + n*x) + sc.betaln(n/2, m/2)))
+        return lPx
+
+    def _cdf(self, x, dfn, dfd):
+        return sc.fdtr(dfn, dfd, x)
+
+    def _sf(self, x, dfn, dfd):
+        return sc.fdtrc(dfn, dfd, x)
+
+    def _ppf(self, q, dfn, dfd):
+        return sc.fdtri(dfn, dfd, q)
+
+    def _stats(self, dfn, dfd):
+        v1, v2 = 1. * dfn, 1. * dfd
+        v2_2, v2_4, v2_6, v2_8 = v2 - 2., v2 - 4., v2 - 6., v2 - 8.
+
+        mu = xpx.apply_where(
+            v2 > 2, (v2, v2_2),
+            lambda v2, v2_2: v2 / v2_2,
+            fill_value=np.inf)
+
+        mu2 = xpx.apply_where(
+            v2 > 4, (v1, v2, v2_2, v2_4),
+            lambda v1, v2, v2_2, v2_4:
+            2 * v2 * v2 * (v1 + v2_2) / (v1 * v2_2**2 * v2_4),
+            fill_value=np.inf)
+
+        g1 = xpx.apply_where(
+            v2 > 6, (v1, v2_2, v2_4, v2_6),
+            lambda v1, v2_2, v2_4, v2_6:
+            (2 * v1 + v2_2) / v2_6 * np.sqrt(v2_4 / (v1 * (v1 + v2_2))),
+            fill_value=np.nan)
+        g1 *= np.sqrt(8.)
+
+        g2 = xpx.apply_where(
+            v2 > 8, (g1, v2_6, v2_8),
+            lambda g1, v2_6, v2_8: (8 + g1 * g1 * v2_6) / v2_8,
+            fill_value=np.nan)
+        g2 *= 3. / 2.
+
+        return mu, mu2, g1, g2
+
+    def _entropy(self, dfn, dfd):
+        # the formula found in literature is incorrect. This one yields the
+        # same result as numerical integration using the generic entropy
+        # definition. This is also tested in tests/test_conntinous_basic
+        half_dfn = 0.5 * dfn
+        half_dfd = 0.5 * dfd
+        half_sum = 0.5 * (dfn + dfd)
+
+        return (np.log(dfd) - np.log(dfn) + sc.betaln(half_dfn, half_dfd) +
+                (1 - half_dfn) * sc.psi(half_dfn) - (1 + half_dfd) *
+                sc.psi(half_dfd) + half_sum * sc.psi(half_sum))
+
+
+f = f_gen(a=0.0, name='f')
+
+
+## Folded Normal
+##   abs(Z) where (Z is normal with mu=L and std=S so that c=abs(L)/S)
+##
+##  note: regress docs have scale parameter correct, but first parameter
+##    he gives is a shape parameter A = c * scale
+
+##  Half-normal is folded normal with shape-parameter c=0.
+
+class foldnorm_gen(rv_continuous):
+    r"""A folded normal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `foldnorm` is:
+
+    .. math::
+
+        f(x, c) = \sqrt{2/\pi} cosh(c x) \exp(-\frac{x^2+c^2}{2})
+
+    for :math:`x \ge 0` and :math:`c \ge 0`.
+
+    `foldnorm` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _argcheck(self, c):
+        return c >= 0
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (True, False))]
+
+    def _rvs(self, c, size=None, random_state=None):
+        return abs(random_state.standard_normal(size) + c)
+
+    def _pdf(self, x, c):
+        # foldnormal.pdf(x, c) = sqrt(2/pi) * cosh(c*x) * exp(-(x**2+c**2)/2)
+        return _norm_pdf(x + c) + _norm_pdf(x-c)
+
+    def _cdf(self, x, c):
+        sqrt_two = np.sqrt(2)
+        return 0.5 * (sc.erf((x - c)/sqrt_two) + sc.erf((x + c)/sqrt_two))
+
+    def _sf(self, x, c):
+        return _norm_sf(x - c) + _norm_sf(x + c)
+
+    def _stats(self, c):
+        # Regina C. Elandt, Technometrics 3, 551 (1961)
+        # https://www.jstor.org/stable/1266561
+        #
+        c2 = c*c
+        expfac = np.exp(-0.5*c2) / np.sqrt(2.*np.pi)
+
+        mu = 2.*expfac + c * sc.erf(c/np.sqrt(2))
+        mu2 = c2 + 1 - mu*mu
+
+        g1 = 2. * (mu*mu*mu - c2*mu - expfac)
+        g1 /= np.power(mu2, 1.5)
+
+        g2 = c2 * (c2 + 6.) + 3 + 8.*expfac*mu
+        g2 += (2. * (c2 - 3.) - 3. * mu**2) * mu**2
+        g2 = g2 / mu2**2.0 - 3.
+
+        return mu, mu2, g1, g2
+
+
+foldnorm = foldnorm_gen(a=0.0, name='foldnorm')
+
+
+class weibull_min_gen(rv_continuous):
+    r"""Weibull minimum continuous random variable.
+
+    The Weibull Minimum Extreme Value distribution, from extreme value theory
+    (Fisher-Gnedenko theorem), is also often simply called the Weibull
+    distribution. It arises as the limiting distribution of the rescaled
+    minimum of iid random variables.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    weibull_max, numpy.random.Generator.weibull, exponweib
+
+    Notes
+    -----
+    The probability density function for `weibull_min` is:
+
+    .. math::
+
+        f(x, c) = c x^{c-1} \exp(-x^c)
+
+    for :math:`x > 0`, :math:`c > 0`.
+
+    `weibull_min` takes ``c`` as a shape parameter for :math:`c`.
+    (named :math:`k` in Wikipedia article and :math:`a` in
+    ``numpy.random.weibull``).  Special shape values are :math:`c=1` and
+    :math:`c=2` where Weibull distribution reduces to the `expon` and
+    `rayleigh` distributions respectively.
+
+    Suppose ``X`` is an exponentially distributed random variable with
+    scale ``s``. Then ``Y = X**k`` is `weibull_min` distributed with shape
+    ``c = 1/k`` and scale ``s**k``.
+
+    %(after_notes)s
+
+    References
+    ----------
+    https://en.wikipedia.org/wiki/Weibull_distribution
+
+    https://en.wikipedia.org/wiki/Fisher-Tippett-Gnedenko_theorem
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # weibull_min.pdf(x, c) = c * x**(c-1) * exp(-x**c)
+        return c*pow(x, c-1)*np.exp(-pow(x, c))
+
+    def _logpdf(self, x, c):
+        return np.log(c) + sc.xlogy(c - 1, x) - pow(x, c)
+
+    def _cdf(self, x, c):
+        return -sc.expm1(-pow(x, c))
+
+    def _ppf(self, q, c):
+        return pow(-sc.log1p(-q), 1.0/c)
+
+    def _sf(self, x, c):
+        return np.exp(self._logsf(x, c))
+
+    def _logsf(self, x, c):
+        return -pow(x, c)
+
+    def _isf(self, q, c):
+        return (-np.log(q))**(1/c)
+
+    def _munp(self, n, c):
+        return sc.gamma(1.0+n*1.0/c)
+
+    def _entropy(self, c):
+        return -_EULER / c - np.log(c) + _EULER + 1
+
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        If ``method='mm'``, parameters fixed by the user are respected, and the
+        remaining parameters are used to match distribution and sample moments
+        where possible. For example, if the user fixes the location with
+        ``floc``, the parameters will only match the distribution skewness and
+        variance to the sample skewness and variance; no attempt will be made
+        to match the means or minimize a norm of the errors.
+        \n\n""")
+    def fit(self, data, *args, **kwds):
+
+        if isinstance(data, CensoredData):
+            if data.num_censored() == 0:
+                data = data._uncensor()
+            else:
+                return super().fit(data, *args, **kwds)
+
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        # this extracts fixed shape, location, and scale however they
+        # are specified, and also leaves them in `kwds`
+        data, fc, floc, fscale = _check_fit_input_parameters(self, data,
+                                                             args, kwds)
+        method = kwds.get("method", "mle").lower()
+
+        # See https://en.wikipedia.org/wiki/Weibull_distribution#Moments for
+        # moment formulas.
+        def skew(c):
+            gamma1 = sc.gamma(1+1/c)
+            gamma2 = sc.gamma(1+2/c)
+            gamma3 = sc.gamma(1+3/c)
+            num = 2 * gamma1**3 - 3*gamma1*gamma2 + gamma3
+            den = (gamma2 - gamma1**2)**(3/2)
+            return num/den
+
+        # For c in [1e2, 3e4], population skewness appears to approach
+        # asymptote near -1.139, but past c > 3e4, skewness begins to vary
+        # wildly, and MoM won't provide a good guess. Get out early.
+        s = stats.skew(data)
+        max_c = 1e4
+        s_min = skew(max_c)
+        if s < s_min and method != "mm" and fc is None and not args:
+            return super().fit(data, *args, **kwds)
+
+        # If method is method of moments, we don't need the user's guesses.
+        # Otherwise, extract the guesses from args and kwds.
+        if method == "mm":
+            c, loc, scale = None, None, None
+        else:
+            c = args[0] if len(args) else None
+            loc = kwds.pop('loc', None)
+            scale = kwds.pop('scale', None)
+
+        if fc is None and c is None:  # not fixed and no guess: use MoM
+            # Solve for c that matches sample distribution skewness to sample
+            # skewness.
+            # we start having numerical issues with `weibull_min` with
+            # parameters outside this range - and not just in this method.
+            # We could probably improve the situation by doing everything
+            # in the log space, but that is for another time.
+            c = root_scalar(lambda c: skew(c) - s, bracket=[0.02, max_c],
+                            method='bisect').root
+        elif fc is not None:  # fixed: use it
+            c = fc
+
+        if fscale is None and scale is None:
+            v = np.var(data)
+            scale = np.sqrt(v / (sc.gamma(1+2/c) - sc.gamma(1+1/c)**2))
+        elif fscale is not None:
+            scale = fscale
+
+        if floc is None and loc is None:
+            m = np.mean(data)
+            loc = m - scale*sc.gamma(1 + 1/c)
+        elif floc is not None:
+            loc = floc
+
+        if method == 'mm':
+            return c, loc, scale
+        else:
+            # At this point, parameter "guesses" may equal the fixed parameters
+            # in kwds. No harm in passing them as guesses, too.
+            return super().fit(data, c, loc=loc, scale=scale, **kwds)
+
+
+weibull_min = weibull_min_gen(a=0.0, name='weibull_min')
+
+
+class truncweibull_min_gen(rv_continuous):
+    r"""A doubly truncated Weibull minimum continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    weibull_min, truncexpon
+
+    Notes
+    -----
+    The probability density function for `truncweibull_min` is:
+
+    .. math::
+
+        f(x, a, b, c) = \frac{c x^{c-1} \exp(-x^c)}{\exp(-a^c) - \exp(-b^c)}
+
+    for :math:`a < x <= b`, :math:`0 \le a < b` and :math:`c > 0`.
+
+    `truncweibull_min` takes :math:`a`, :math:`b`, and :math:`c` as shape
+    parameters.
+
+    Notice that the truncation values, :math:`a` and :math:`b`, are defined in
+    standardized form:
+
+    .. math::
+
+        a = (u_l - loc)/scale
+        b = (u_r - loc)/scale
+
+    where :math:`u_l` and :math:`u_r` are the specific left and right
+    truncation values, respectively. In other words, the support of the
+    distribution becomes :math:`(a*scale + loc) < x <= (b*scale + loc)` when
+    :math:`loc` and/or :math:`scale` are provided.
+
+    %(after_notes)s
+
+    References
+    ----------
+
+    .. [1] Rinne, H. "The Weibull Distribution: A Handbook". CRC Press (2009).
+
+    %(example)s
+
+    """
+    def _argcheck(self, c, a, b):
+        return (a >= 0.) & (b > a) & (c > 0.)
+
+    def _shape_info(self):
+        ic = _ShapeInfo("c", False, (0, np.inf), (False, False))
+        ia = _ShapeInfo("a", False, (0, np.inf), (True, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ic, ia, ib]
+
+    def _fitstart(self, data):
+        # Arbitrary, but default a=b=c=1 is not valid
+        return super()._fitstart(data, args=(1, 0, 1))
+
+    def _get_support(self, c, a, b):
+        return a, b
+
+    def _pdf(self, x, c, a, b):
+        denum = (np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return (c * pow(x, c-1) * np.exp(-pow(x, c))) / denum
+
+    def _logpdf(self, x, c, a, b):
+        logdenum = np.log(np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return np.log(c) + sc.xlogy(c - 1, x) - pow(x, c) - logdenum
+
+    def _cdf(self, x, c, a, b):
+        num = (np.exp(-pow(a, c)) - np.exp(-pow(x, c)))
+        denum = (np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return num / denum
+
+    def _logcdf(self, x, c, a, b):
+        lognum = np.log(np.exp(-pow(a, c)) - np.exp(-pow(x, c)))
+        logdenum = np.log(np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return lognum - logdenum
+
+    def _sf(self, x, c, a, b):
+        num = (np.exp(-pow(x, c)) - np.exp(-pow(b, c)))
+        denum = (np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return num / denum
+
+    def _logsf(self, x, c, a, b):
+        lognum = np.log(np.exp(-pow(x, c)) - np.exp(-pow(b, c)))
+        logdenum = np.log(np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return lognum - logdenum
+
+    def _isf(self, q, c, a, b):
+        return pow(
+            -np.log((1 - q) * np.exp(-pow(b, c)) + q * np.exp(-pow(a, c))), 1/c
+            )
+
+    def _ppf(self, q, c, a, b):
+        return pow(
+            -np.log((1 - q) * np.exp(-pow(a, c)) + q * np.exp(-pow(b, c))), 1/c
+            )
+
+    def _munp(self, n, c, a, b):
+        gamma_fun = sc.gamma(n/c + 1.) * (
+            sc.gammainc(n/c + 1., pow(b, c)) - sc.gammainc(n/c + 1., pow(a, c))
+            )
+        denum = (np.exp(-pow(a, c)) - np.exp(-pow(b, c)))
+        return gamma_fun / denum
+
+
+truncweibull_min = truncweibull_min_gen(name='truncweibull_min')
+truncweibull_min._support = ('a', 'b')
+
+
+class weibull_max_gen(rv_continuous):
+    r"""Weibull maximum continuous random variable.
+
+    The Weibull Maximum Extreme Value distribution, from extreme value theory
+    (Fisher-Gnedenko theorem), is the limiting distribution of rescaled
+    maximum of iid random variables. This is the distribution of -X
+    if X is from the `weibull_min` function.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    weibull_min
+
+    Notes
+    -----
+    The probability density function for `weibull_max` is:
+
+    .. math::
+
+        f(x, c) = c (-x)^{c-1} \exp(-(-x)^c)
+
+    for :math:`x < 0`, :math:`c > 0`.
+
+    `weibull_max` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    https://en.wikipedia.org/wiki/Weibull_distribution
+
+    https://en.wikipedia.org/wiki/Fisher-Tippett-Gnedenko_theorem
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # weibull_max.pdf(x, c) = c * (-x)**(c-1) * exp(-(-x)**c)
+        return c*pow(-x, c-1)*np.exp(-pow(-x, c))
+
+    def _logpdf(self, x, c):
+        return np.log(c) + sc.xlogy(c-1, -x) - pow(-x, c)
+
+    def _cdf(self, x, c):
+        return np.exp(-pow(-x, c))
+
+    def _logcdf(self, x, c):
+        return -pow(-x, c)
+
+    def _sf(self, x, c):
+        return -sc.expm1(-pow(-x, c))
+
+    def _ppf(self, q, c):
+        return -pow(-np.log(q), 1.0/c)
+
+    def _munp(self, n, c):
+        val = sc.gamma(1.0+n*1.0/c)
+        if int(n) % 2:
+            sgn = -1
+        else:
+            sgn = 1
+        return sgn * val
+
+    def _entropy(self, c):
+        return -_EULER / c - np.log(c) + _EULER + 1
+
+
+weibull_max = weibull_max_gen(b=0.0, name='weibull_max')
+
+
+class genlogistic_gen(rv_continuous):
+    r"""A generalized logistic continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `genlogistic` is:
+
+    .. math::
+
+        f(x, c) = c \frac{\exp(-x)}
+                         {(1 + \exp(-x))^{c+1}}
+
+    for real :math:`x` and :math:`c > 0`. In literature, different
+    generalizations of the logistic distribution can be found. This is the type 1
+    generalized logistic distribution according to [1]_. It is also referred to
+    as the skew-logistic distribution [2]_.
+
+    `genlogistic` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Johnson et al. "Continuous Univariate Distributions", Volume 2,
+           Wiley. 1995.
+    .. [2] "Generalized Logistic Distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Generalized_logistic_distribution
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # genlogistic.pdf(x, c) = c * exp(-x) / (1 + exp(-x))**(c+1)
+        return np.exp(self._logpdf(x, c))
+
+    def _logpdf(self, x, c):
+        # Two mathematically equivalent expressions for log(pdf(x, c)):
+        #     log(pdf(x, c)) = log(c) - x - (c + 1)*log(1 + exp(-x))
+        #                    = log(c) + c*x - (c + 1)*log(1 + exp(x))
+        mult = -(c - 1) * (x < 0) - 1
+        absx = np.abs(x)
+        return np.log(c) + mult*absx - (c+1) * sc.log1p(np.exp(-absx))
+
+    def _cdf(self, x, c):
+        Cx = (1+np.exp(-x))**(-c)
+        return Cx
+
+    def _logcdf(self, x, c):
+        return -c * np.log1p(np.exp(-x))
+
+    def _ppf(self, q, c):
+        return -np.log(sc.powm1(q, -1.0/c))
+
+    def _sf(self, x, c):
+        return -sc.expm1(self._logcdf(x, c))
+
+    def _isf(self, q, c):
+        return self._ppf(1 - q, c)
+
+    def _stats(self, c):
+        mu = _EULER + sc.psi(c)
+        mu2 = np.pi*np.pi/6.0 + sc.zeta(2, c)
+        g1 = -2*sc.zeta(3, c) + 2*_ZETA3
+        g1 /= np.power(mu2, 1.5)
+        g2 = np.pi**4/15.0 + 6*sc.zeta(4, c)
+        g2 /= mu2**2.0
+        return mu, mu2, g1, g2
+
+    def _entropy(self, c):
+        return xpx.apply_where(
+            c < 8e6, c,
+            lambda c: -np.log(c) + sc.psi(c + 1) + _EULER + 1,
+            # asymptotic expansion: psi(c) ~ log(c) - 1 / (2 * c)
+            # a = -log(c) + psi(c + 1)
+            #   = -log(c) + psi(c) + 1 / c
+            #   ~ -log(c) + log(c) - 1 / (2 * c) + 1 / c
+            #   = 1 / (2 * c)
+            lambda c: 1 / (2 * c) + _EULER + 1)
+
+
+genlogistic = genlogistic_gen(name='genlogistic')
+
+
+class genpareto_gen(rv_continuous):
+    r"""A generalized Pareto continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `genpareto` is:
+
+    .. math::
+
+        f(x, c) = (1 + c x)^{-1 - 1/c}
+
+    defined for :math:`x \ge 0` if :math:`c \ge 0`, and for
+    :math:`0 \le x \le -1/c` if :math:`c < 0`.
+
+    `genpareto` takes ``c`` as a shape parameter for :math:`c`.
+
+    For :math:`c=0`, `genpareto` reduces to the exponential
+    distribution, `expon`:
+
+    .. math::
+
+        f(x, 0) = \exp(-x)
+
+    For :math:`c=-1`, `genpareto` is uniform on ``[0, 1]``:
+
+    .. math::
+
+        f(x, -1) = 1
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _argcheck(self, c):
+        return np.isfinite(c)
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (-np.inf, np.inf), (False, False))]
+
+    def _get_support(self, c):
+        c = np.asarray(c)
+        a = np.broadcast_arrays(self.a, c)[0].copy()
+        b = xpx.apply_where(c < 0, c, lambda c: -1. / c,
+                            fill_value=np.inf)
+        return a, b
+
+    def _pdf(self, x, c):
+        # genpareto.pdf(x, c) = (1 + c * x)**(-1 - 1/c)
+        return np.exp(self._logpdf(x, c))
+
+    def _logpdf(self, x, c):
+        return xpx.apply_where((x == x) & (c != 0), (x, c),
+                               lambda x, c: -sc.xlog1py(c + 1., c*x) / c,
+                               fill_value=-x)
+
+    def _cdf(self, x, c):
+        return -sc.inv_boxcox1p(-x, -c)
+
+    def _sf(self, x, c):
+        return sc.inv_boxcox(-x, -c)
+
+    def _logsf(self, x, c):
+        return xpx.apply_where((x == x) & (c != 0), (x, c),
+                               lambda x, c: -sc.log1p(c*x) / c,
+                               fill_value=-x)
+
+    def _ppf(self, q, c):
+        return -sc.boxcox1p(-q, -c)
+
+    def _isf(self, q, c):
+        return -sc.boxcox(q, -c)
+
+    def _stats(self, c, moments='mv'):
+        m, v, s, k = None, None, None, None
+
+        if 'm' in moments:
+            m = xpx.apply_where(c < 1, c,
+                                lambda xi: 1 / (1 - xi),
+                                fill_value=np.inf)
+
+        if 'v' in moments:
+            v = xpx.apply_where(c < 1/2, c,
+                                lambda xi: 1 / (1 - xi)**2 / (1 - 2 * xi),
+                                fill_value=np.nan)
+
+        if 's' in moments:
+            s = xpx.apply_where(
+                c < 1/3, c,
+                lambda xi: 2 * (1 + xi) * np.sqrt(1 - 2*xi) / (1 - 3*xi),
+                fill_value=np.nan)
+
+        if 'k' in moments:
+            k = xpx.apply_where(
+                c < 1/4, c,
+                lambda xi: 3 * (1 - 2*xi) * (2*xi**2 + xi + 3)
+                           / (1 - 3*xi) / (1 - 4*xi) - 3,
+                fill_value=np.nan)
+
+        return m, v, s, k
+
+    def _munp(self, n, c):
+        def __munp(c):
+            val = 0.0
+            k = np.arange(0, n + 1)
+            for ki, cnk in zip(k, sc.comb(n, k)):
+                val = val + cnk * (-1) ** ki / (1.0 - c * ki)
+            return np.where(c * n < 1, val * (-1.0 / c) ** n, np.inf)
+
+        return xpx.apply_where(c != 0, c, __munp, fill_value=sc.gamma(n + 1))
+
+    def _entropy(self, c):
+        return 1. + c
+
+
+genpareto = genpareto_gen(a=0.0, name='genpareto')
+
+
+class genexpon_gen(rv_continuous):
+    r"""A generalized exponential continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `genexpon` is:
+
+    .. math::
+
+        f(x, a, b, c) = (a + b (1 - \exp(-c x)))
+                        \exp(-a x - b x + \frac{b}{c}  (1-\exp(-c x)))
+
+    for :math:`x \ge 0`, :math:`a, b, c > 0`.
+
+    `genexpon` takes :math:`a`, :math:`b` and :math:`c` as shape parameters.
+
+    %(after_notes)s
+
+    References
+    ----------
+    H.K. Ryu, "An Extension of Marshall and Olkin's Bivariate Exponential
+    Distribution", Journal of the American Statistical Association, 1993.
+
+    N. Balakrishnan, Asit P. Basu (editors), *The Exponential Distribution:
+    Theory, Methods and Applications*, Gordon and Breach, 1995.
+    ISBN 10: 2884491929
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        ic = _ShapeInfo("c", False, (0, np.inf), (False, False))
+        return [ia, ib, ic]
+
+    def _pdf(self, x, a, b, c):
+        # genexpon.pdf(x, a, b, c) = (a + b * (1 - exp(-c*x))) * \
+        #                            exp(-a*x - b*x + b/c * (1-exp(-c*x)))
+        return (a + b*(-sc.expm1(-c*x)))*np.exp((-a-b)*x +
+                                                b*(-sc.expm1(-c*x))/c)
+
+    def _logpdf(self, x, a, b, c):
+        return np.log(a+b*(-sc.expm1(-c*x))) + (-a-b)*x+b*(-sc.expm1(-c*x))/c
+
+    def _cdf(self, x, a, b, c):
+        return -sc.expm1((-a-b)*x + b*(-sc.expm1(-c*x))/c)
+
+    def _ppf(self, p, a, b, c):
+        s = a + b
+        t = (b - c*np.log1p(-p))/s
+        return (t + sc.lambertw(-b/s * np.exp(-t)).real)/c
+
+    def _sf(self, x, a, b, c):
+        return np.exp((-a-b)*x + b*(-sc.expm1(-c*x))/c)
+
+    def _isf(self, p, a, b, c):
+        s = a + b
+        t = (b - c*np.log(p))/s
+        return (t + sc.lambertw(-b/s * np.exp(-t)).real)/c
+
+
+genexpon = genexpon_gen(a=0.0, name='genexpon')
+
+
+class genextreme_gen(rv_continuous):
+    r"""A generalized extreme value continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    gumbel_r
+
+    Notes
+    -----
+    For :math:`c=0`, `genextreme` is equal to `gumbel_r` with
+    probability density function
+
+    .. math::
+
+        f(x) = \exp(-\exp(-x)) \exp(-x),
+
+    where :math:`-\infty < x < \infty`.
+
+    For :math:`c \ne 0`, the probability density function for `genextreme` is:
+
+    .. math::
+
+        f(x, c) = \exp(-(1-c x)^{1/c}) (1-c x)^{1/c-1},
+
+    where :math:`-\infty < x \le 1/c` if :math:`c > 0` and
+    :math:`1/c \le x < \infty` if :math:`c < 0`.
+
+    Note that several sources and software packages use the opposite
+    convention for the sign of the shape parameter :math:`c`.
+
+    `genextreme` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _argcheck(self, c):
+        return np.isfinite(c)
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (-np.inf, np.inf), (False, False))]
+
+    def _get_support(self, c):
+        c = np.asarray(c)
+        _b = np.where(c > 0, 1.0 / np.maximum(c, _XMIN), np.inf)
+        _a = np.where(c < 0, 1.0 / np.minimum(c, -_XMIN), -np.inf)
+        return _a, _b
+
+    def _loglogcdf(self, x, c):
+        # Returns log(-log(cdf(x, c)))
+        return xpx.apply_where(
+            (x == x) & (c != 0), (x, c),
+            lambda x, c: sc.log1p(-c*x)/c,
+            fill_value=-x)
+
+    def _pdf(self, x, c):
+        # genextreme.pdf(x, c) =
+        #     exp(-exp(-x))*exp(-x),                    for c==0
+        #     exp(-(1-c*x)**(1/c))*(1-c*x)**(1/c-1),    for x \le 1/c, c > 0
+        return np.exp(self._logpdf(x, c))
+
+    def _logpdf(self, x, c):
+        # Suppress warnings 0 * inf
+        cx = xpx.apply_where((x == x) & (c != 0), (c, x),
+                             operator.mul, fill_value=0.0)
+        logex2 = sc.log1p(-cx)
+        logpex2 = self._loglogcdf(x, c)
+        pex2 = np.exp(logpex2)
+        # Handle special cases
+        np.putmask(logpex2, (c == 0) & (x == -np.inf), 0.0)
+        logpdf = xpx.apply_where(
+            ~((cx == 1) | (cx == -np.inf)),
+            (pex2, logpex2, logex2),
+            lambda pex2, lpex2, lex2: -pex2 + lpex2 - lex2,
+            fill_value=-np.inf)
+        np.putmask(logpdf, (c == 1) & (x == 1), 0.0)
+        return logpdf
+
+    def _logcdf(self, x, c):
+        return -np.exp(self._loglogcdf(x, c))
+
+    def _cdf(self, x, c):
+        return np.exp(self._logcdf(x, c))
+
+    def _sf(self, x, c):
+        return -sc.expm1(self._logcdf(x, c))
+
+    def _ppf(self, q, c):
+        x = -np.log(-np.log(q))
+        return xpx.apply_where(
+            (x == x) & (c != 0), (x, c),
+            lambda x, c: -sc.expm1(-c * x) / c,
+            fill_value=x)
+
+    def _isf(self, q, c):
+        x = -np.log(-sc.log1p(-q))
+        return xpx.apply_where(
+            (x == x) & (c != 0), (x, c),
+            lambda x, c: -sc.expm1(-c * x) / c,
+            fill_value=x)
+
+    def _stats(self, c):
+        def g(n):
+            return sc.gamma(n * c + 1)
+        g1 = g(1)
+        g2 = g(2)
+        g3 = g(3)
+        g4 = g(4)
+        g2mg12 = np.where(abs(c) < 1e-7, (c*np.pi)**2.0/6.0, g2-g1**2.0)
+        def gam2k_f(c):
+            return sc.expm1(sc.gammaln(2.0*c+1.0)-2*sc.gammaln(c + 1.0))/c**2.0
+        gam2k = xpx.apply_where(abs(c) >= 1e-7, c, gam2k_f, fill_value=np.pi**2.0/6.0)
+        eps = 1e-14
+        def gamk_f(c):
+            return sc.expm1(sc.gammaln(c + 1))/c
+        gamk = xpx.apply_where(abs(c) >= eps, c, gamk_f, fill_value=-_EULER)
+
+        # mean
+        m = np.where(c < -1.0, np.nan, -gamk)
+
+        # variance
+        v = np.where(c < -0.5, np.nan, g1**2.0*gam2k)
+
+        # skewness
+        def sk1_eval(c, *args):
+            def sk1_eval_f(c, g1, g2, g3, g2mg12):
+                return np.sign(c)*(-g3 + (g2 + 2*g2mg12)*g1)/g2mg12**1.5
+            return xpx.apply_where(c >= -1./3, (c, *args),
+                                   sk1_eval_f, fill_value=np.nan)
+
+        sk_fill = 12*np.sqrt(6)*_ZETA3/np.pi**3
+        args = (g1, g2, g3, g2mg12)
+        sk = xpx.apply_where(abs(c) > eps**0.29, (c, *args),
+                             sk1_eval, fill_value=sk_fill)
+
+        # kurtosis
+        def ku1_eval(c, *args):
+            def ku1_eval_f(g1, g2, g3, g4, g2mg12):
+                return (g4 + (-4*g3 + 3*(g2 + g2mg12)*g1)*g1)/g2mg12**2 - 3
+            return xpx.apply_where(c >= -1./4, args, ku1_eval_f, fill_value=np.nan)
+
+        args = (g1, g2, g3, g4, g2mg12)
+        ku = xpx.apply_where(abs(c) > eps**0.23, (c, *args),
+                             ku1_eval, fill_value=12.0/5.0)
+
+        return m, v, sk, ku
+
+    def _fitstart(self, data):
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        # This is better than the default shape of (1,).
+        g = _skew(data)
+        if g < 0:
+            a = 0.5
+        else:
+            a = -0.5
+        return super()._fitstart(data, args=(a,))
+
+    def _munp(self, n, c):
+        k = np.arange(0, n+1)
+        k = np.reshape(k, (-1,) + (1,)*c.ndim)
+        vals = 1.0/c**n * np.sum(
+            sc.comb(n, k) * (-1)**k * sc.gamma(c*k + 1),
+            axis=0)
+        return np.where(c*n > -1, vals, np.inf)
+
+    def _entropy(self, c):
+        return _EULER*(1 - c) + 1
+
+
+genextreme = genextreme_gen(name='genextreme')
+
+
+def _digammainv(y):
+    """Inverse of the digamma function (real positive arguments only).
+
+    This function is used in the `fit` method of `gamma_gen`.
+    The function uses either optimize.fsolve or optimize.newton
+    to solve `sc.digamma(x) - y = 0`.  There is probably room for
+    improvement, but currently it works over a wide range of y:
+
+    >>> import numpy as np
+    >>> rng = np.random.default_rng()
+    >>> y = 64*rng.standard_normal(1000000)
+    >>> y.min(), y.max()
+    (-311.43592651416662, 351.77388222276869)
+    >>> x = [_digammainv(t) for t in y]
+    >>> np.abs(sc.digamma(x) - y).max()
+    1.1368683772161603e-13
+
+    """
+    _em = 0.5772156649015328606065120
+
+    def func(x):
+        return sc.digamma(x) - y
+
+    if y > -0.125:
+        x0 = np.exp(y) + 0.5
+        if y < 10:
+            # Some experimentation shows that newton reliably converges
+            # must faster than fsolve in this y range.  For larger y,
+            # newton sometimes fails to converge.
+            value = optimize.newton(func, x0, tol=1e-10)
+            return value
+    elif y > -3:
+        x0 = np.exp(y/2.332) + 0.08661
+    else:
+        x0 = 1.0 / (-y - _em)
+
+    value, info, ier, mesg = optimize.fsolve(func, x0, xtol=1e-11,
+                                             full_output=True)
+    if ier != 1:
+        raise RuntimeError(f"_digammainv: fsolve failed, y = {y!r}")
+
+    return value[0]
+
+
+## Gamma (Use MATLAB and MATHEMATICA (b=theta=scale, a=alpha=shape) definition)
+
+## gamma(a, loc, scale)  with a an integer is the Erlang distribution
+## gamma(1, loc, scale)  is the Exponential distribution
+## gamma(df/2, 0, 2) is the chi2 distribution with df degrees of freedom.
+
+class gamma_gen(rv_continuous):
+    r"""A gamma continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    erlang, expon
+
+    Notes
+    -----
+    The probability density function for `gamma` is:
+
+    .. math::
+
+        f(x, a) = \frac{x^{a-1} e^{-x}}{\Gamma(a)}
+
+    for :math:`x \ge 0`, :math:`a > 0`. Here :math:`\Gamma(a)` refers to the
+    gamma function.
+
+    `gamma` takes ``a`` as a shape parameter for :math:`a`.
+
+    When :math:`a` is an integer, `gamma` reduces to the Erlang
+    distribution, and when :math:`a=1` to the exponential distribution.
+
+    Gamma distributions are sometimes parameterized with two variables,
+    with a probability density function of:
+
+    .. math::
+
+        f(x, \alpha, \beta) =
+        \frac{\beta^\alpha x^{\alpha - 1} e^{-\beta x }}{\Gamma(\alpha)}
+
+    Note that this parameterization is equivalent to the above, with
+    ``scale = 1 / beta``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, a, size=None, random_state=None):
+        return random_state.standard_gamma(a, size)
+
+    def _pdf(self, x, a):
+        # gamma.pdf(x, a) = x**(a-1) * exp(-x) / gamma(a)
+        return np.exp(self._logpdf(x, a))
+
+    def _logpdf(self, x, a):
+        return sc.xlogy(a-1.0, x) - x - sc.gammaln(a)
+
+    def _cdf(self, x, a):
+        return sc.gammainc(a, x)
+
+    def _sf(self, x, a):
+        return sc.gammaincc(a, x)
+
+    def _ppf(self, q, a):
+        return sc.gammaincinv(a, q)
+
+    def _isf(self, q, a):
+        return sc.gammainccinv(a, q)
+
+    def _stats(self, a):
+        return a, a, 2.0/np.sqrt(a), 6.0/a
+
+    def _munp(self, n, a):
+        return sc.poch(a, n)
+
+    def _entropy(self, a):
+
+        def regular_formula(a):
+            return sc.psi(a) * (1-a) + a + sc.gammaln(a)
+
+        def asymptotic_formula(a):
+            # plug in above formula the expansions:
+            # psi(a) ~ ln(a) - 1/2a - 1/12a^2 + 1/120a^4
+            # gammaln(a) ~ a * ln(a) - a - 1/2 * ln(a) + 1/2 ln(2 * pi) +
+            #              1/12a - 1/360a^3
+            return (0.5 * (1. + np.log(2*np.pi) + np.log(a)) - 1/(3 * a)
+                    - (a**-2.)/12 - (a**-3.)/90 + (a**-4.)/120)
+
+        return xpx.apply_where(a < 250, a, regular_formula, asymptotic_formula)
+
+    def _fitstart(self, data):
+        # The skewness of the gamma distribution is `2 / np.sqrt(a)`.
+        # We invert that to estimate the shape `a` using the skewness
+        # of the data.  The formula is regularized with 1e-8 in the
+        # denominator to allow for degenerate data where the skewness
+        # is close to 0.
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        sk = _skew(data)
+        a = 4 / (1e-8 + sk**2)
+        return super()._fitstart(data, args=(a,))
+
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        When the location is fixed by using the argument `floc`
+        and `method='MLE'`, this
+        function uses explicit formulas or solves a simpler numerical
+        problem than the full ML optimization problem.  So in that case,
+        the `optimizer`, `loc` and `scale` arguments are ignored.
+        \n\n""")
+    def fit(self, data, *args, **kwds):
+        floc = kwds.get('floc', None)
+        method = kwds.get('method', 'mle')
+
+        if (isinstance(data, CensoredData) or
+                floc is None and method.lower() != 'mm'):
+            # loc is not fixed or we're not doing standard MLE.
+            # Use the default fit method.
+            return super().fit(data, *args, **kwds)
+
+        # We already have this value, so just pop it from kwds.
+        kwds.pop('floc', None)
+
+        f0 = _get_fixed_fit_value(kwds, ['f0', 'fa', 'fix_a'])
+        fscale = kwds.pop('fscale', None)
+
+        _remove_optimizer_parameters(kwds)
+
+        if f0 is not None and floc is not None and fscale is not None:
+            # This check is for consistency with `rv_continuous.fit`.
+            # Without this check, this function would just return the
+            # parameters that were given.
+            raise ValueError("All parameters fixed. There is nothing to "
+                             "optimize.")
+
+        # Fixed location is handled by shifting the data.
+        data = np.asarray(data)
+
+        if not np.isfinite(data).all():
+            raise ValueError("The data contains non-finite values.")
+
+        # Use explicit formulas for mm (gh-19884)
+        if method.lower() == 'mm':
+            m1 = np.mean(data)
+            m2 = np.var(data)
+            m3 = np.mean((data - m1) ** 3)
+            a, loc, scale = f0, floc, fscale
+            # Three unknowns
+            if a is None and loc is None and scale is None:
+                scale = m3 / (2 * m2)
+            # Two unknowns
+            if loc is None and scale is None:
+                scale = np.sqrt(m2 / a)
+            if a is None and scale is None:
+                scale = m2 / (m1 - loc)
+            if a is None and loc is None:
+                a = m2 / (scale ** 2)
+            # One unknown
+            if a is None:
+                a = (m1 - loc) / scale
+            if loc is None:
+                loc = m1 - a * scale
+            if scale is None:
+                scale = (m1 - loc) / a
+            return a, loc, scale
+
+        # Special case: loc is fixed.
+
+        # NB: data == loc is ok if a >= 1; the below check is more strict.
+        if np.any(data <= floc):
+            raise FitDataError("gamma", lower=floc, upper=np.inf)
+
+        if floc != 0:
+            # Don't do the subtraction in-place, because `data` might be a
+            # view of the input array.
+            data = data - floc
+        xbar = data.mean()
+
+        # Three cases to handle:
+        # * shape and scale both free
+        # * shape fixed, scale free
+        # * shape free, scale fixed
+
+        if fscale is None:
+            # scale is free
+            if f0 is not None:
+                # shape is fixed
+                a = f0
+            else:
+                # shape and scale are both free.
+                # The MLE for the shape parameter `a` is the solution to:
+                # np.log(a) - sc.digamma(a) - np.log(xbar) +
+                #                             np.log(data).mean() = 0
+                s = np.log(xbar) - np.log(data).mean()
+                aest = (3-s + np.sqrt((s-3)**2 + 24*s)) / (12*s)
+                xa = aest*(1-0.4)
+                xb = aest*(1+0.4)
+                a = optimize.brentq(lambda a: np.log(a) - sc.digamma(a) - s,
+                                    xa, xb, disp=0)
+
+            # The MLE for the scale parameter is just the data mean
+            # divided by the shape parameter.
+            scale = xbar / a
+        else:
+            # scale is fixed, shape is free
+            # The MLE for the shape parameter `a` is the solution to:
+            # sc.digamma(a) - np.log(data).mean() + np.log(fscale) = 0
+            c = np.log(data).mean() - np.log(fscale)
+            a = _digammainv(c)
+            scale = fscale
+
+        return a, floc, scale
+
+
+gamma = gamma_gen(a=0.0, name='gamma')
+
+
+class erlang_gen(gamma_gen):
+    """An Erlang continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    gamma
+
+    Notes
+    -----
+    The Erlang distribution is a special case of the Gamma distribution, with
+    the shape parameter `a` an integer.  Note that this restriction is not
+    enforced by `erlang`. It will, however, generate a warning the first time
+    a non-integer value is used for the shape parameter.
+
+    Refer to `gamma` for examples.
+
+    """
+
+    def _argcheck(self, a):
+        allint = np.all(np.floor(a) == a)
+        if not allint:
+            # An Erlang distribution shouldn't really have a non-integer
+            # shape parameter, so warn the user.
+            message = ('The shape parameter of the erlang distribution '
+                       f'has been given a non-integer value {a!r}.')
+            warnings.warn(message, RuntimeWarning, stacklevel=3)
+        return a > 0
+
+    def _shape_info(self):
+        return [_ShapeInfo("a", True, (1, np.inf), (True, False))]
+
+    def _fitstart(self, data):
+        # Override gamma_gen_fitstart so that an integer initial value is
+        # used.  (Also regularize the division, to avoid issues when
+        # _skew(data) is 0 or close to 0.)
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        a = int(4.0 / (1e-8 + _skew(data)**2))
+        return super(gamma_gen, self)._fitstart(data, args=(a,))
+
+    # Trivial override of the fit method, so we can monkey-patch its
+    # docstring.
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        The Erlang distribution is generally defined to have integer values
+        for the shape parameter.  This is not enforced by the `erlang` class.
+        When fitting the distribution, it will generally return a non-integer
+        value for the shape parameter.  By using the keyword argument
+        `f0=<integer>`, the fit method can be constrained to fit the data to
+        a specific integer shape parameter.""")
+    def fit(self, data, *args, **kwds):
+        return super().fit(data, *args, **kwds)
+
+
+erlang = erlang_gen(a=0.0, name='erlang')
+
+
+class gengamma_gen(rv_continuous):
+    r"""A generalized gamma continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    gamma, halfgennorm, invgamma, weibull_min
+
+    Notes
+    -----
+    The probability density function for `gengamma` is ([1]_, [2]_):
+
+    .. math::
+
+        f(x, a, c) = \frac{|c| x^{c a-1} \exp(-x^c)}{\Gamma(a)}
+
+    for :math:`x \ge 0`, :math:`a > 0`, and :math:`c \ne 0`.
+    :math:`\Gamma` is the gamma function (`scipy.special.gamma`).
+
+    `gengamma` takes :math:`a` and :math:`c` as shape parameters.
+
+    The SciPy distribution `halfgennorm` is a special case of
+    `gengamma`: ``halfgennorm(beta) = gengamma(a=1/beta, c=beta)``.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] E.W. Stacy, "A Generalization of the Gamma Distribution",
+           Annals of Mathematical Statistics, Vol 33(3), pp. 1187--1192.
+    .. [2] "Generalized gamma distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Generalized_gamma_distribution
+
+    %(example)s
+
+    """
+    def _argcheck(self, a, c):
+        return (a > 0) & (c != 0)
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ic = _ShapeInfo("c", False, (-np.inf, np.inf), (False, False))
+        return [ia, ic]
+
+    def _pdf(self, x, a, c):
+        return np.exp(self._logpdf(x, a, c))
+
+    def _logpdf(self, x, a, c):
+        return xpx.apply_where(
+            (x != 0) | (c > 0), (x, c, a),
+            lambda x, c, a: (np.log(abs(c)) + sc.xlogy(c*a - 1, x)
+                             - x**c - sc.gammaln(a)),
+            fill_value=-np.inf)
+
+    def _cdf(self, x, a, c):
+        xc = x**c
+        val1 = sc.gammainc(a, xc)
+        val2 = sc.gammaincc(a, xc)
+        return np.where(c > 0, val1, val2)
+
+    def _rvs(self, a, c, size=None, random_state=None):
+        r = random_state.standard_gamma(a, size=size)
+        return r**(1./c)
+
+    def _sf(self, x, a, c):
+        xc = x**c
+        val1 = sc.gammainc(a, xc)
+        val2 = sc.gammaincc(a, xc)
+        return np.where(c > 0, val2, val1)
+
+    def _ppf(self, q, a, c):
+        val1 = sc.gammaincinv(a, q)
+        val2 = sc.gammainccinv(a, q)
+        return np.where(c > 0, val1, val2)**(1.0/c)
+
+    def _isf(self, q, a, c):
+        val1 = sc.gammaincinv(a, q)
+        val2 = sc.gammainccinv(a, q)
+        return np.where(c > 0, val2, val1)**(1.0/c)
+
+    def _munp(self, n, a, c):
+        # Pochhammer symbol: sc.pocha,n) = gamma(a+n)/gamma(a)
+        return sc.poch(a, n*1.0/c)
+
+    def _entropy(self, a, c):
+        def regular(a, c):
+            val = sc.psi(a)
+            A = a * (1 - val) + val / c
+            B = sc.gammaln(a) - np.log(abs(c))
+            h = A + B
+            return h
+
+        def asymptotic(a, c):
+            # using asymptotic expansions for gammaln and psi (see gh-18093)
+            return (norm._entropy() - np.log(a)/2
+                    - np.log(np.abs(c)) + (a**-1.)/6 - (a**-3.)/90
+                    + (np.log(a) - (a**-1.)/2 - (a**-2.)/12 + (a**-4.)/120)/c)
+
+        return xpx.apply_where(a >= 200, (a, c), asymptotic, regular)
+
+
+gengamma = gengamma_gen(a=0.0, name='gengamma')
+
+
+class genhalflogistic_gen(rv_continuous):
+    r"""A generalized half-logistic continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `genhalflogistic` is:
+
+    .. math::
+
+        f(x, c) = \frac{2 (1 - c x)^{1/(c-1)}}{[1 + (1 - c x)^{1/c}]^2}
+
+    for :math:`0 \le x \le 1/c`, and :math:`c > 0`.
+
+    `genhalflogistic` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _get_support(self, c):
+        return self.a, 1.0/c
+
+    def _pdf(self, x, c):
+        # genhalflogistic.pdf(x, c) =
+        #    2 * (1-c*x)**(1/c-1) / (1+(1-c*x)**(1/c))**2
+        limit = 1.0/c
+        tmp = np.asarray(1-c*x)
+        tmp0 = tmp**(limit-1)
+        tmp2 = tmp0*tmp
+        return 2*tmp0 / (1+tmp2)**2
+
+    def _cdf(self, x, c):
+        limit = 1.0/c
+        tmp = np.asarray(1-c*x)
+        tmp2 = tmp**(limit)
+        return (1.0-tmp2) / (1+tmp2)
+
+    def _ppf(self, q, c):
+        return 1.0/c*(1-((1.0-q)/(1.0+q))**c)
+
+    def _entropy(self, c):
+        return 2 - (2*c+1)*np.log(2)
+
+
+genhalflogistic = genhalflogistic_gen(a=0.0, name='genhalflogistic')
+
+
+class genhyperbolic_gen(rv_continuous):
+    r"""A generalized hyperbolic continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    t, norminvgauss, geninvgauss, laplace, cauchy
+
+    Notes
+    -----
+    The probability density function for `genhyperbolic` is:
+
+    .. math::
+
+        f(x, p, a, b) =
+            \frac{(a^2 - b^2)^{p/2}}
+            {\sqrt{2\pi}a^{p-1/2}
+            K_p\Big(\sqrt{a^2 - b^2}\Big)}
+            e^{bx} \times \frac{K_{p - 1/2}
+            (a \sqrt{1 + x^2})}
+            {(\sqrt{1 + x^2})^{1/2 - p}}
+
+    for :math:`x, p \in ( - \infty; \infty)`,
+    :math:`|b| < a` if :math:`p \ge 0`,
+    :math:`|b| \le a` if :math:`p < 0`.
+    :math:`K_{p}(.)` denotes the modified Bessel function of the second
+    kind and order :math:`p` (`scipy.special.kv`)
+
+    `genhyperbolic` takes ``p`` as a tail parameter,
+    ``a`` as a shape parameter,
+    ``b`` as a skewness parameter.
+
+    %(after_notes)s
+
+    The original parameterization of the Generalized Hyperbolic Distribution
+    is found in [1]_ as follows
+
+    .. math::
+
+        f(x, \lambda, \alpha, \beta, \delta, \mu) =
+           \frac{(\gamma/\delta)^\lambda}{\sqrt{2\pi}K_\lambda(\delta \gamma)}
+           e^{\beta (x - \mu)} \times \frac{K_{\lambda - 1/2}
+           (\alpha \sqrt{\delta^2 + (x - \mu)^2})}
+           {(\sqrt{\delta^2 + (x - \mu)^2} / \alpha)^{1/2 - \lambda}}
+
+    for :math:`x \in ( - \infty; \infty)`,
+    :math:`\gamma := \sqrt{\alpha^2 - \beta^2}`,
+    :math:`\lambda, \mu \in ( - \infty; \infty)`,
+    :math:`\delta \ge 0, |\beta| < \alpha` if :math:`\lambda \ge 0`,
+    :math:`\delta > 0, |\beta| \le \alpha` if :math:`\lambda < 0`.
+
+    The location-scale-based parameterization implemented in
+    SciPy is based on [2]_, where :math:`a = \alpha\delta`,
+    :math:`b = \beta\delta`, :math:`p = \lambda`,
+    :math:`scale=\delta` and :math:`loc=\mu`
+
+    Moments are implemented based on [3]_ and [4]_.
+
+    For the distributions that are a special case such as Student's t,
+    it is not recommended to rely on the implementation of genhyperbolic.
+    To avoid potential numerical problems and for performance reasons,
+    the methods of the specific distributions should be used.
+
+    References
+    ----------
+    .. [1] O. Barndorff-Nielsen, "Hyperbolic Distributions and Distributions
+       on Hyperbolae", Scandinavian Journal of Statistics, Vol. 5(3),
+       pp. 151-157, 1978. https://www.jstor.org/stable/4615705
+
+    .. [2] Eberlein E., Prause K. (2002) The Generalized Hyperbolic Model:
+        Financial Derivatives and Risk Measures. In: Geman H., Madan D.,
+        Pliska S.R., Vorst T. (eds) Mathematical Finance - Bachelier
+        Congress 2000. Springer Finance. Springer, Berlin, Heidelberg.
+        :doi:`10.1007/978-3-662-12429-1_12`
+
+    .. [3] Scott, David J, Würtz, Diethelm, Dong, Christine and Tran,
+       Thanh Tam, (2009), Moments of the generalized hyperbolic
+       distribution, MPRA Paper, University Library of Munich, Germany,
+       https://EconPapers.repec.org/RePEc:pra:mprapa:19081.
+
+    .. [4] E. Eberlein and E. A. von Hammerstein. Generalized hyperbolic
+       and inverse Gaussian distributions: Limiting cases and approximation
+       of processes. FDM Preprint 80, April 2003. University of Freiburg.
+       https://freidok.uni-freiburg.de/fedora/objects/freidok:7974/datastreams/FILE1/content
+
+    %(example)s
+
+    """
+
+    def _argcheck(self, p, a, b):
+        return (np.logical_and(np.abs(b) < a, p >= 0)
+                | np.logical_and(np.abs(b) <= a, p < 0))
+
+    def _shape_info(self):
+        ip = _ShapeInfo("p", False, (-np.inf, np.inf), (False, False))
+        ia = _ShapeInfo("a", False, (0, np.inf), (True, False))
+        ib = _ShapeInfo("b", False, (-np.inf, np.inf), (False, False))
+        return [ip, ia, ib]
+
+    def _fitstart(self, data):
+        # Arbitrary, but the default p = a = b = 1 is not valid; the
+        # distribution requires |b| < a if p >= 0.
+        return super()._fitstart(data, args=(1, 1, 0.5))
+
+    def _logpdf(self, x, p, a, b):
+        # kve instead of kv works better for large values of p
+        # and smaller values of sqrt(a^2  - b^2)
+        @np.vectorize
+        def _logpdf_single(x, p, a, b):
+            return _stats.genhyperbolic_logpdf(x, p, a, b)
+
+        return _logpdf_single(x, p, a, b)
+
+    def _pdf(self, x, p, a, b):
+        # kve instead of kv works better for large values of p
+        # and smaller values of sqrt(a^2  - b^2)
+        @np.vectorize
+        def _pdf_single(x, p, a, b):
+            return _stats.genhyperbolic_pdf(x, p, a, b)
+
+        return _pdf_single(x, p, a, b)
+
+    # np.vectorize isn't currently designed to be used as a decorator,
+    # so use a lambda instead.  This allows us to decorate the function
+    # with `np.vectorize` and still provide the `otypes` parameter.
+    @lambda func: np.vectorize(func, otypes=[np.float64])
+    @staticmethod
+    def _integrate_pdf(x0, x1, p, a, b):
+        """
+        Integrate the pdf of the genhyberbolic distribution from x0 to x1.
+        This is a private function used by _cdf() and _sf() only; either x0
+        will be -inf or x1 will be inf.
+        """
+        user_data = np.array([p, a, b], float).ctypes.data_as(ctypes.c_void_p)
+        llc = LowLevelCallable.from_cython(_stats, '_genhyperbolic_pdf',
+                                           user_data)
+        d = np.sqrt((a + b)*(a - b))
+        mean = b/d * sc.kv(p + 1, d) / sc.kv(p, d)
+        epsrel = 1e-10
+        epsabs = 0
+        if x0 < mean < x1:
+            # If the interval includes the mean, integrate over the two
+            # intervals [x0, mean] and [mean, x1] and add. If we try to do
+            # the integral in one call of quad and the non-infinite endpoint
+            # is far in the tail, quad might return an incorrect result
+            # because it does not "see" the peak of the PDF.
+            intgrl = (integrate.quad(llc, x0, mean,
+                                     epsrel=epsrel, epsabs=epsabs)[0]
+                      + integrate.quad(llc, mean, x1,
+                                       epsrel=epsrel, epsabs=epsabs)[0])
+        else:
+            intgrl = integrate.quad(llc, x0, x1,
+                                    epsrel=epsrel, epsabs=epsabs)[0]
+        if np.isnan(intgrl):
+            msg = ("Infinite values encountered in scipy.special.kve. "
+                   "Values replaced by NaN to avoid incorrect results.")
+            warnings.warn(msg, RuntimeWarning, stacklevel=3)
+        return max(0.0, min(1.0, intgrl))
+
+    def _cdf(self, x, p, a, b):
+        return self._integrate_pdf(-np.inf, x, p, a, b)
+
+    def _sf(self, x, p, a, b):
+        return self._integrate_pdf(x, np.inf, p, a, b)
+
+    def _rvs(self, p, a, b, size=None, random_state=None):
+        # note: X = b * V + sqrt(V) * X  has a
+        # generalized hyperbolic distribution
+        # if X is standard normal and V is
+        # geninvgauss(p = p, b = t2, loc = loc, scale = t3)
+        t1 = np.float_power(a, 2) - np.float_power(b, 2)
+        # b in the GIG
+        t2 = np.float_power(t1, 0.5)
+        # scale in the GIG
+        t3 = np.float_power(t1, - 0.5)
+        gig = geninvgauss.rvs(
+            p=p,
+            b=t2,
+            scale=t3,
+            size=size,
+            random_state=random_state
+            )
+        normst = norm.rvs(size=size, random_state=random_state)
+
+        return b * gig + np.sqrt(gig) * normst
+
+    def _stats(self, p, a, b):
+        # https://mpra.ub.uni-muenchen.de/19081/1/MPRA_paper_19081.pdf
+        # https://freidok.uni-freiburg.de/fedora/objects/freidok:7974/datastreams/FILE1/content
+        # standardized moments
+        p, a, b = np.broadcast_arrays(p, a, b)
+        t1 = np.float_power(a, 2) - np.float_power(b, 2)
+        t1 = np.float_power(t1, 0.5)
+        t2 = np.float_power(1, 2) * np.float_power(t1, - 1)
+        integers = np.linspace(0, 4, 5)
+        # make integers perpendicular to existing dimensions
+        integers = integers.reshape(integers.shape + (1,) * p.ndim)
+        b0, b1, b2, b3, b4 = sc.kv(p + integers, t1)
+        r1, r2, r3, r4 = (b / b0 for b in (b1, b2, b3, b4))
+
+        m = b * t2 * r1
+        v = (
+            t2 * r1 + np.float_power(b, 2) * np.float_power(t2, 2) *
+            (r2 - np.float_power(r1, 2))
+        )
+        m3e = (
+            np.float_power(b, 3) * np.float_power(t2, 3) *
+            (r3 - 3 * b2 * b1 * np.float_power(b0, -2) +
+             2 * np.float_power(r1, 3)) +
+            3 * b * np.float_power(t2, 2) *
+            (r2 - np.float_power(r1, 2))
+        )
+        s = m3e * np.float_power(v, - 3 / 2)
+        m4e = (
+            np.float_power(b, 4) * np.float_power(t2, 4) *
+            (r4 - 4 * b3 * b1 * np.float_power(b0, - 2) +
+             6 * b2 * np.float_power(b1, 2) * np.float_power(b0, - 3) -
+             3 * np.float_power(r1, 4)) +
+            np.float_power(b, 2) * np.float_power(t2, 3) *
+            (6 * r3 - 12 * b2 * b1 * np.float_power(b0, - 2) +
+             6 * np.float_power(r1, 3)) +
+            3 * np.float_power(t2, 2) * r2
+        )
+        k = m4e * np.float_power(v, -2) - 3
+
+        return m, v, s, k
+
+
+genhyperbolic = genhyperbolic_gen(name='genhyperbolic')
+
+
+class gompertz_gen(rv_continuous):
+    r"""A Gompertz (or truncated Gumbel) continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `gompertz` is:
+
+    .. math::
+
+        f(x, c) = c \exp(x) \exp(-c (e^x-1))
+
+    for :math:`x \ge 0`, :math:`c > 0`.
+
+    `gompertz` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # gompertz.pdf(x, c) = c * exp(x) * exp(-c*(exp(x)-1))
+        return np.exp(self._logpdf(x, c))
+
+    def _logpdf(self, x, c):
+        return np.log(c) + x - c * sc.expm1(x)
+
+    def _cdf(self, x, c):
+        return -sc.expm1(-c * sc.expm1(x))
+
+    def _ppf(self, q, c):
+        return sc.log1p(-1.0 / c * sc.log1p(-q))
+
+    def _sf(self, x, c):
+        return np.exp(-c * sc.expm1(x))
+
+    def _isf(self, p, c):
+        return sc.log1p(-np.log(p)/c)
+
+    def _entropy(self, c):
+        return 1.0 - np.log(c) - sc._ufuncs._scaled_exp1(c)/c
+
+
+gompertz = gompertz_gen(a=0.0, name='gompertz')
+
+
+def _average_with_log_weights(x, logweights):
+    x = np.asarray(x)
+    logweights = np.asarray(logweights)
+    maxlogw = logweights.max()
+    weights = np.exp(logweights - maxlogw)
+    return np.average(x, weights=weights)
+
+
+class gumbel_r_gen(rv_continuous):
+    r"""A right-skewed Gumbel continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    gumbel_l, gompertz, genextreme
+
+    Notes
+    -----
+    The probability density function for `gumbel_r` is:
+
+    .. math::
+
+        f(x) = \exp(-(x + e^{-x}))
+
+    for real :math:`x`.
+
+    The Gumbel distribution is sometimes referred to as a type I Fisher-Tippett
+    distribution.  It is also related to the extreme value distribution,
+    log-Weibull and Gompertz distributions.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # gumbel_r.pdf(x) = exp(-(x + exp(-x)))
+        return np.exp(self._logpdf(x))
+
+    def _logpdf(self, x):
+        return -x - np.exp(-x)
+
+    def _cdf(self, x):
+        return np.exp(-np.exp(-x))
+
+    def _logcdf(self, x):
+        return -np.exp(-x)
+
+    def _ppf(self, q):
+        return -np.log(-np.log(q))
+
+    def _sf(self, x):
+        return -sc.expm1(-np.exp(-x))
+
+    def _isf(self, p):
+        return -np.log(-np.log1p(-p))
+
+    def _stats(self):
+        return _EULER, np.pi*np.pi/6.0, 12*np.sqrt(6)/np.pi**3 * _ZETA3, 12.0/5
+
+    def _entropy(self):
+        # https://en.wikipedia.org/wiki/Gumbel_distribution
+        return _EULER + 1.
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+
+        # By the method of maximum likelihood, the estimators of the
+        # location and scale are the roots of the equations defined in
+        # `func` and the value of the expression for `loc` that follows.
+        # The first `func` is a first order derivative of the log-likelihood
+        # equation and the second is from Source: Statistical Distributions,
+        # 3rd Edition. Evans, Hastings, and Peacock (2000), Page 101.
+
+        def get_loc_from_scale(scale):
+            return -scale * (sc.logsumexp(-data / scale) - np.log(len(data)))
+
+        if fscale is not None:
+            # if the scale is fixed, the location can be analytically
+            # determined.
+            scale = fscale
+            loc = get_loc_from_scale(scale)
+        else:
+            # A different function is solved depending on whether the location
+            # is fixed.
+            if floc is not None:
+                loc = floc
+
+                # equation to use if the location is fixed.
+                # note that one cannot use the equation in Evans, Hastings,
+                # and Peacock (2000) (since it assumes that the derivative
+                # w.r.t. the log-likelihood is zero). however, it is easy to
+                # derive the MLE condition directly if loc is fixed
+                def func(scale):
+                    term1 = (loc - data) * np.exp((loc - data) / scale) + data
+                    term2 = len(data) * (loc + scale)
+                    return term1.sum() - term2
+            else:
+
+                # equation to use if both location and scale are free
+                def func(scale):
+                    sdata = -data / scale
+                    wavg = _average_with_log_weights(data, logweights=sdata)
+                    return data.mean() - wavg - scale
+
+            # set brackets for `root_scalar` to use when optimizing over the
+            # scale such that a root is likely between them. Use user supplied
+            # guess or default 1.
+            brack_start = kwds.get('scale', 1)
+            lbrack, rbrack = brack_start / 2, brack_start * 2
+
+            # if a root is not between the brackets, iteratively expand them
+            # until they include a sign change, checking after each bracket is
+            # modified.
+            def interval_contains_root(lbrack, rbrack):
+                # return true if the signs disagree.
+                return (np.sign(func(lbrack)) !=
+                        np.sign(func(rbrack)))
+            while (not interval_contains_root(lbrack, rbrack)
+                   and (lbrack > 0 or rbrack < np.inf)):
+                lbrack /= 2
+                rbrack *= 2
+
+            res = optimize.root_scalar(func, bracket=(lbrack, rbrack),
+                                       rtol=1e-14, xtol=1e-14)
+            scale = res.root
+            loc = floc if floc is not None else get_loc_from_scale(scale)
+        return loc, scale
+
+
+gumbel_r = gumbel_r_gen(name='gumbel_r')
+
+
+class gumbel_l_gen(rv_continuous):
+    r"""A left-skewed Gumbel continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    gumbel_r, gompertz, genextreme
+
+    Notes
+    -----
+    The probability density function for `gumbel_l` is:
+
+    .. math::
+
+        f(x) = \exp(x - e^x)
+
+    for real :math:`x`.
+
+    The Gumbel distribution is sometimes referred to as a type I Fisher-Tippett
+    distribution.  It is also related to the extreme value distribution,
+    log-Weibull and Gompertz distributions.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # gumbel_l.pdf(x) = exp(x - exp(x))
+        return np.exp(self._logpdf(x))
+
+    def _logpdf(self, x):
+        return x - np.exp(x)
+
+    def _cdf(self, x):
+        return -sc.expm1(-np.exp(x))
+
+    def _ppf(self, q):
+        return np.log(-sc.log1p(-q))
+
+    def _logsf(self, x):
+        return -np.exp(x)
+
+    def _sf(self, x):
+        return np.exp(-np.exp(x))
+
+    def _isf(self, x):
+        return np.log(-np.log(x))
+
+    def _stats(self):
+        return -_EULER, np.pi*np.pi/6.0, \
+               -12*np.sqrt(6)/np.pi**3 * _ZETA3, 12.0/5
+
+    def _entropy(self):
+        return _EULER + 1.
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        # The fit method of `gumbel_r` can be used for this distribution with
+        # small modifications. The process to do this is
+        # 1. pass the sign negated data into `gumbel_r.fit`
+        #    - if the location is fixed, it should also be negated.
+        # 2. negate the sign of the resulting location, leaving the scale
+        #    unmodified.
+        # `gumbel_r.fit` holds necessary input checks.
+
+        if kwds.get('floc') is not None:
+            kwds['floc'] = -kwds['floc']
+        loc_r, scale_r, = gumbel_r.fit(-np.asarray(data), *args, **kwds)
+        return -loc_r, scale_r
+
+
+gumbel_l = gumbel_l_gen(name='gumbel_l')
+
+
+class halfcauchy_gen(rv_continuous):
+    r"""A Half-Cauchy continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `halfcauchy` is:
+
+    .. math::
+
+        f(x) = \frac{2}{\pi (1 + x^2)}
+
+    for :math:`x \ge 0`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # halfcauchy.pdf(x) = 2 / (pi * (1 + x**2))
+        return 2.0/np.pi/(1.0+x*x)
+
+    def _logpdf(self, x):
+        return np.log(2.0/np.pi) - sc.log1p(x*x)
+
+    def _cdf(self, x):
+        return 2.0/np.pi*np.arctan(x)
+
+    def _ppf(self, q):
+        return np.tan(np.pi/2*q)
+
+    def _sf(self, x):
+        return 2.0/np.pi * np.arctan2(1, x)
+
+    def _isf(self, p):
+        return 1.0/np.tan(np.pi*p/2)
+
+    def _stats(self):
+        return np.inf, np.inf, np.nan, np.nan
+
+    def _entropy(self):
+        return np.log(2*np.pi)
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+
+        # location is independent from the scale
+        data_min = np.min(data)
+        if floc is not None:
+            if data_min < floc:
+                # There are values that are less than the specified loc.
+                raise FitDataError("halfcauchy", lower=floc, upper=np.inf)
+            loc = floc
+        else:
+            # if not provided, location MLE is the minimal data point
+            loc = data_min
+
+        # find scale
+        def find_scale(loc, data):
+            shifted_data = data - loc
+            n = data.size
+            shifted_data_squared = np.square(shifted_data)
+
+            def fun_to_solve(scale):
+                denominator = scale**2 + shifted_data_squared
+                return 2 * np.sum(shifted_data_squared/denominator) - n
+
+            small = np.finfo(1.0).tiny**0.5  # avoid underflow
+            res = root_scalar(fun_to_solve, bracket=(small, np.max(shifted_data)))
+            return res.root
+
+        if fscale is not None:
+            scale = fscale
+        else:
+            scale = find_scale(loc, data)
+
+        return loc, scale
+
+
+halfcauchy = halfcauchy_gen(a=0.0, name='halfcauchy')
+
+
+class halflogistic_gen(rv_continuous):
+    r"""A half-logistic continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `halflogistic` is:
+
+    .. math::
+
+        f(x) = \frac{ 2 e^{-x} }{ (1+e^{-x})^2 }
+             = \frac{1}{2} \text{sech}(x/2)^2
+
+    for :math:`x \ge 0`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Asgharzadeh et al (2011). "Comparisons of Methods of Estimation for the
+           Half-Logistic Distribution". Selcuk J. Appl. Math. 93-108.
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # halflogistic.pdf(x) = 2 * exp(-x) / (1+exp(-x))**2
+        #                     = 1/2 * sech(x/2)**2
+        return np.exp(self._logpdf(x))
+
+    def _logpdf(self, x):
+        return np.log(2) - x - 2. * sc.log1p(np.exp(-x))
+
+    def _cdf(self, x):
+        return np.tanh(x/2.0)
+
+    def _ppf(self, q):
+        return 2*np.arctanh(q)
+
+    def _sf(self, x):
+        return 2 * sc.expit(-x)
+
+    def _isf(self, q):
+        return xpx.apply_where(q < 0.5, q,
+                               lambda q: -sc.logit(0.5 * q),
+                               lambda q: 2*np.arctanh(1 - q))
+
+    def _munp(self, n):
+        if n == 0:
+            return 1  # otherwise returns NaN
+        if n == 1:
+            return 2*np.log(2)
+        if n == 2:
+            return np.pi*np.pi/3.0
+        if n == 3:
+            return 9*_ZETA3
+        if n == 4:
+            return 7*np.pi**4 / 15.0
+        return 2*(1-pow(2.0, 1-n))*sc.gamma(n+1)*sc.zeta(n, 1)
+
+    def _entropy(self):
+        return 2-np.log(2)
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+
+        def find_scale(data, loc):
+            # scale is solution to a fix point problem ([1] 2.6)
+            # use approximate MLE as starting point ([1] 3.1)
+            n_observations = data.shape[0]
+            sorted_data = np.sort(data, axis=0)
+            p = np.arange(1, n_observations + 1)/(n_observations + 1)
+            q = 1 - p
+            pp1 = 1 + p
+            alpha = p - 0.5 * q * pp1 * np.log(pp1 / q)
+            beta = 0.5 * q * pp1
+            sorted_data = sorted_data - loc
+            B = 2 * np.sum(alpha[1:] * sorted_data[1:])
+            C = 2 * np.sum(beta[1:] * sorted_data[1:]**2)
+            # starting guess
+            scale = ((B + np.sqrt(B**2 + 8 * n_observations * C))
+                    /(4 * n_observations))
+
+            # relative tolerance of fix point iterator
+            rtol = 1e-8
+            relative_residual = 1
+            shifted_mean = sorted_data.mean()  # y_mean - y_min
+
+            # find fix point by repeated application of eq. (2.6)
+            # simplify as
+            # exp(-x) / (1 + exp(-x)) = 1 / (1 + exp(x))
+            #                         = expit(-x))
+            while relative_residual > rtol:
+                sum_term = sorted_data * sc.expit(-sorted_data/scale)
+                scale_new = shifted_mean - 2/n_observations * sum_term.sum()
+                relative_residual = abs((scale - scale_new)/scale)
+                scale = scale_new
+            return scale
+
+        # location is independent from the scale
+        data_min = np.min(data)
+        if floc is not None:
+            if data_min < floc:
+                # There are values that are less than the specified loc.
+                raise FitDataError("halflogistic", lower=floc, upper=np.inf)
+            loc = floc
+        else:
+            # if not provided, location MLE is the minimal data point
+            loc = data_min
+
+        # scale depends on location
+        scale = fscale if fscale is not None else find_scale(data, loc)
+
+        return loc, scale
+
+
+halflogistic = halflogistic_gen(a=0.0, name='halflogistic')
+
+
+class halfnorm_gen(rv_continuous):
+    r"""A half-normal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `halfnorm` is:
+
+    .. math::
+
+        f(x) = \sqrt{2/\pi} \exp(-x^2 / 2)
+
+    for :math:`x >= 0`.
+
+    `halfnorm` is a special case of `chi` with ``df=1``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return abs(random_state.standard_normal(size=size))
+
+    def _pdf(self, x):
+        # halfnorm.pdf(x) = sqrt(2/pi) * exp(-x**2/2)
+        return np.sqrt(2.0/np.pi)*np.exp(-x*x/2.0)
+
+    def _logpdf(self, x):
+        return 0.5 * np.log(2.0/np.pi) - x*x/2.0
+
+    def _cdf(self, x):
+        return sc.erf(x / np.sqrt(2))
+
+    def _ppf(self, q):
+        return _norm_ppf((1+q)/2.0)
+
+    def _sf(self, x):
+        return 2 * _norm_sf(x)
+
+    def _isf(self, p):
+        return _norm_isf(p/2)
+
+    def _stats(self):
+        return (np.sqrt(2.0/np.pi),
+                1-2.0/np.pi,
+                np.sqrt(2)*(4-np.pi)/(np.pi-2)**1.5,
+                8*(np.pi-3)/(np.pi-2)**2)
+
+    def _entropy(self):
+        return 0.5*np.log(np.pi/2.0)+0.5
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+
+        data_min = np.min(data)
+
+        if floc is not None:
+            if data_min < floc:
+                # There are values that are less than the specified loc.
+                raise FitDataError("halfnorm", lower=floc, upper=np.inf)
+            loc = floc
+        else:
+            loc = data_min
+
+        if fscale is not None:
+            scale = fscale
+        else:
+            scale = stats.moment(data, order=2, center=loc)**0.5
+
+        return loc, scale
+
+
+halfnorm = halfnorm_gen(a=0.0, name='halfnorm')
+
+
+class hypsecant_gen(rv_continuous):
+    r"""A hyperbolic secant continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `hypsecant` is:
+
+    .. math::
+
+        f(x) = \frac{1}{\pi} \text{sech}(x)
+
+    for a real number :math:`x`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # hypsecant.pdf(x) = 1/pi * sech(x)
+        return 1.0/(np.pi*np.cosh(x))
+
+    def _cdf(self, x):
+        return 2.0/np.pi*np.arctan(np.exp(x))
+
+    def _ppf(self, q):
+        return np.log(np.tan(np.pi*q/2.0))
+
+    def _sf(self, x):
+        return 2.0/np.pi*np.arctan(np.exp(-x))
+
+    def _isf(self, q):
+        return -np.log(np.tan(np.pi*q/2.0))
+
+    def _stats(self):
+        return 0, np.pi*np.pi/4, 0, 2
+
+    def _entropy(self):
+        return np.log(2*np.pi)
+
+
+hypsecant = hypsecant_gen(name='hypsecant')
+
+
+class gausshyper_gen(rv_continuous):
+    r"""A Gauss hypergeometric continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `gausshyper` is:
+
+    .. math::
+
+        f(x, a, b, c, z) = C x^{a-1} (1-x)^{b-1} (1+zx)^{-c}
+
+    for :math:`0 \le x \le 1`, :math:`a,b > 0`, :math:`c` a real number,
+    :math:`z > -1`, and :math:`C = \frac{1}{B(a, b) F[2, 1](c, a; a+b; -z)}`.
+    :math:`F[2, 1]` is the Gauss hypergeometric function
+    `scipy.special.hyp2f1`.
+
+    `gausshyper` takes :math:`a`, :math:`b`, :math:`c` and :math:`z` as shape
+    parameters.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Armero, C., and M. J. Bayarri. "Prior Assessments for Prediction in
+           Queues." *Journal of the Royal Statistical Society*. Series D (The
+           Statistician) 43, no. 1 (1994): 139-53. :doi:`10.2307/2348939`.
+
+    %(example)s
+
+    """
+
+    def _argcheck(self, a, b, c, z):
+        # z > -1 per gh-10134
+        return (a > 0) & (b > 0) & (c == c) & (z > -1)
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        ic = _ShapeInfo("c", False, (-np.inf, np.inf), (False, False))
+        iz = _ShapeInfo("z", False, (-1, np.inf), (False, False))
+        return [ia, ib, ic, iz]
+
+    def _pdf(self, x, a, b, c, z):
+        normalization_constant = sc.beta(a, b) * sc.hyp2f1(c, a, a + b, -z)
+        return (1./normalization_constant * x**(a - 1.) * (1. - x)**(b - 1.0)
+                / (1.0 + z*x)**c)
+
+    def _munp(self, n, a, b, c, z):
+        fac = sc.beta(n+a, b) / sc.beta(a, b)
+        num = sc.hyp2f1(c, a+n, a+b+n, -z)
+        den = sc.hyp2f1(c, a, a+b, -z)
+        return fac*num / den
+
+
+gausshyper = gausshyper_gen(a=0.0, b=1.0, name='gausshyper')
+
+
+class invgamma_gen(rv_continuous):
+    r"""An inverted gamma continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `invgamma` is:
+
+    .. math::
+
+        f(x, a) = \frac{x^{-a-1}}{\Gamma(a)} \exp(-\frac{1}{x})
+
+    for :math:`x >= 0`, :math:`a > 0`. :math:`\Gamma` is the gamma function
+    (`scipy.special.gamma`).
+
+    `invgamma` takes ``a`` as a shape parameter for :math:`a`.
+
+    `invgamma` is a special case of `gengamma` with ``c=-1``, and it is a
+    different parameterization of the scaled inverse chi-squared distribution.
+    Specifically, if the scaled inverse chi-squared distribution is
+    parameterized with degrees of freedom :math:`\nu` and scaling parameter
+    :math:`\tau^2`, then it can be modeled using `invgamma` with
+    ``a=`` :math:`\nu/2` and ``scale=`` :math:`\nu \tau^2/2`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, a):
+        # invgamma.pdf(x, a) = x**(-a-1) / gamma(a) * exp(-1/x)
+        return np.exp(self._logpdf(x, a))
+
+    def _logpdf(self, x, a):
+        return -(a+1) * np.log(x) - sc.gammaln(a) - 1.0/x
+
+    def _cdf(self, x, a):
+        return sc.gammaincc(a, 1.0 / x)
+
+    def _ppf(self, q, a):
+        return 1.0 / sc.gammainccinv(a, q)
+
+    def _sf(self, x, a):
+        return sc.gammainc(a, 1.0 / x)
+
+    def _isf(self, q, a):
+        return 1.0 / sc.gammaincinv(a, q)
+
+    def _stats(self, a, moments='mvsk'):
+        m1 = xpx.apply_where(a > 1, a,
+                             lambda x: 1. / (x - 1.),
+                             fill_value=np.inf)
+        m2 = xpx.apply_where(a > 2, a,
+                             lambda x: 1. / (x - 1.)**2 / (x - 2.),
+                             fill_value=np.inf)
+
+        g1, g2 = None, None
+        if 's' in moments:
+            g1 = xpx.apply_where(a > 3, a,
+                                 lambda x: 4. * np.sqrt(x - 2.) / (x - 3.),
+                                 fill_value=np.nan)
+        if 'k' in moments:
+            g2 = xpx.apply_where(a > 4, a,
+                                 lambda x: 6. * (5. * x - 11.) / (x - 3.) / (x - 4.),
+                                 fill_value=np.nan)
+
+        return m1, m2, g1, g2
+
+    def _entropy(self, a):
+        def regular(a):
+            h = a - (a + 1.0) * sc.psi(a) + sc.gammaln(a)
+            return h
+
+        def asymptotic(a):
+            # gammaln(a) ~ a * ln(a) - a - 0.5 * ln(a) + 0.5 * ln(2 * pi)
+            # psi(a) ~ ln(a) - 1 / (2 * a)
+            h = ((1 - 3*np.log(a) + np.log(2) + np.log(np.pi))/2
+                 + 2/3*a**-1. + a**-2./12 - a**-3./90 - a**-4./120)
+            return h
+
+        h = xpx.apply_where(a >= 200, a, asymptotic, regular)
+        return h
+
+
+invgamma = invgamma_gen(a=0.0, name='invgamma')
+
+
+class invgauss_gen(rv_continuous):
+    r"""An inverse Gaussian continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `invgauss` is:
+
+    .. math::
+
+        f(x; \mu) = \frac{1}{\sqrt{2 \pi x^3}}
+                    \exp\left(-\frac{(x-\mu)^2}{2 \mu^2 x}\right)
+
+    for :math:`x \ge 0` and :math:`\mu > 0`.
+
+    `invgauss` takes ``mu`` as a shape parameter for :math:`\mu`.
+
+    %(after_notes)s
+
+    A common shape-scale parameterization of the inverse Gaussian distribution
+    has density
+
+    .. math::
+
+        f(x; \nu, \lambda) = \sqrt{\frac{\lambda}{2 \pi x^3}}
+                    \exp\left( -\frac{\lambda(x-\nu)^2}{2 \nu^2 x}\right)
+
+    Using ``nu`` for :math:`\nu` and ``lam`` for :math:`\lambda`, this
+    parameterization is equivalent to the one above with ``mu = nu/lam``,
+    ``loc = 0``, and ``scale = lam``.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``ppf`` and ``isf`` methods. [1]_
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return [_ShapeInfo("mu", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, mu, size=None, random_state=None):
+        return random_state.wald(mu, 1.0, size=size)
+
+    def _pdf(self, x, mu):
+        # invgauss.pdf(x, mu) =
+        #                  1 / sqrt(2*pi*x**3) * exp(-(x-mu)**2/(2*x*mu**2))
+        return 1.0/np.sqrt(2*np.pi*x**3.0)*np.exp(-1.0/(2*x)*(x/mu - 1)**2)
+
+    def _logpdf(self, x, mu):
+        return -0.5*np.log(2*np.pi) - 1.5*np.log(x) - (x/mu - 1)**2/(2*x)
+
+    # approach adapted from equations in
+    # https://journal.r-project.org/archive/2016-1/giner-smyth.pdf,
+    # not R code. see gh-13616
+
+    def _logcdf(self, x, mu):
+        fac = 1 / np.sqrt(x)
+        a = _norm_logcdf(fac * (x/mu - 1))
+        b = 2 / mu + _norm_logcdf(-fac * (x/mu + 1))
+        return a + np.log1p(np.exp(b - a))
+
+    def _logsf(self, x, mu):
+        fac = 1 / np.sqrt(x)
+        a = _norm_logsf(fac * (x/mu - 1))
+        b = 2 / mu + _norm_logcdf(-fac * (x/mu + 1))
+        return a + np.log1p(-np.exp(b - a))
+
+    def _sf(self, x, mu):
+        return np.exp(self._logsf(x, mu))
+
+    def _cdf(self, x, mu):
+        return np.exp(self._logcdf(x, mu))
+
+    def _ppf(self, x, mu):
+        with np.errstate(divide='ignore', over='ignore', invalid='ignore'):
+            x, mu = np.broadcast_arrays(x, mu)
+            ppf = np.asarray(scu._invgauss_ppf(x, mu, 1))
+            i_wt = x > 0.5  # "wrong tail" - sometimes too inaccurate
+            ppf[i_wt] = scu._invgauss_isf(1-x[i_wt], mu[i_wt], 1)
+            i_nan = np.isnan(ppf)
+            ppf[i_nan] = super()._ppf(x[i_nan], mu[i_nan])
+        return ppf
+
+    def _isf(self, x, mu):
+        with np.errstate(divide='ignore', over='ignore', invalid='ignore'):
+            x, mu = np.broadcast_arrays(x, mu)
+            isf = scu._invgauss_isf(x, mu, 1)
+            i_wt = x > 0.5  # "wrong tail" - sometimes too inaccurate
+            isf[i_wt] = scu._invgauss_ppf(1-x[i_wt], mu[i_wt], 1)
+            i_nan = np.isnan(isf)
+            isf[i_nan] = super()._isf(x[i_nan], mu[i_nan])
+        return isf
+
+    def _stats(self, mu):
+        return mu, mu**3.0, 3*np.sqrt(mu), 15*mu
+
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        method = kwds.get('method', 'mle')
+
+        if (isinstance(data, CensoredData) or isinstance(self, wald_gen)
+                or method.lower() == 'mm'):
+            return super().fit(data, *args, **kwds)
+
+        data, fshape_s, floc, fscale = _check_fit_input_parameters(self, data,
+                                                                   args, kwds)
+        '''
+        Source: Statistical Distributions, 3rd Edition. Evans, Hastings,
+        and Peacock (2000), Page 121. Their shape parameter is equivalent to
+        SciPy's with the conversion `fshape_s = fshape / scale`.
+
+        MLE formulas are not used in 3 conditions:
+
+        - `loc` is not fixed
+        - `mu` is fixed
+
+        These cases fall back on the superclass fit method.
+
+        - `loc` is fixed but translation results in negative data raises
+          a `FitDataError`.
+        '''
+        if floc is None or fshape_s is not None:
+            return super().fit(data, *args, **kwds)
+        elif np.any(data - floc < 0):
+            raise FitDataError("invgauss", lower=0, upper=np.inf)
+        else:
+            data = data - floc
+            fshape_n = np.mean(data)
+            if fscale is None:
+                fscale = len(data) / (np.sum(data ** -1 - fshape_n ** -1))
+            fshape_s = fshape_n / fscale
+        return fshape_s, floc, fscale
+
+    def _entropy(self, mu):
+        """
+        Ref.: https://moser-isi.ethz.ch/docs/papers/smos-2012-10.pdf (eq. 9)
+        """
+        # a = log(2*pi*e*mu**3)
+        #   = 1 + log(2*pi) + 3 * log(mu)
+        a = 1. + np.log(2 * np.pi) + 3 * np.log(mu)
+        # b = exp(2/mu) * exp1(2/mu)
+        #   = _scaled_exp1(2/mu) / (2/mu)
+        r = 2/mu
+        b = sc._ufuncs._scaled_exp1(r)/r
+        return 0.5 * a - 1.5 * b
+
+
+invgauss = invgauss_gen(a=0.0, name='invgauss')
+
+
+class geninvgauss_gen(rv_continuous):
+    r"""A Generalized Inverse Gaussian continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `geninvgauss` is:
+
+    .. math::
+
+        f(x, p, b) = x^{p-1} \exp(-b (x + 1/x) / 2) / (2 K_p(b))
+
+    where ``x > 0``, `p` is a real number and ``b > 0``\([1]_).
+    :math:`K_p` is the modified Bessel function of second kind of order `p`
+    (`scipy.special.kv`).
+
+    %(after_notes)s
+
+    The inverse Gaussian distribution `stats.invgauss(mu)` is a special case of
+    `geninvgauss` with ``p = -1/2``, ``b = 1 / mu`` and ``scale = mu``.
+
+    Generating random variates is challenging for this distribution. The
+    implementation is based on [2]_.
+
+    References
+    ----------
+    .. [1] O. Barndorff-Nielsen, P. Blaesild, C. Halgreen, "First hitting time
+       models for the generalized inverse gaussian distribution",
+       Stochastic Processes and their Applications 7, pp. 49--54, 1978.
+
+    .. [2] W. Hoermann and J. Leydold, "Generating generalized inverse Gaussian
+       random variates", Statistics and Computing, 24(4), p. 547--557, 2014.
+
+    %(example)s
+
+    """
+    def _argcheck(self, p, b):
+        return (p == p) & (b > 0)
+
+    def _shape_info(self):
+        ip = _ShapeInfo("p", False, (-np.inf, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ip, ib]
+
+    def _logpdf(self, x, p, b):
+        # kve instead of kv works better for large values of b
+        # warn if kve produces infinite values and replace by nan
+        # otherwise c = -inf and the results are often incorrect
+        def logpdf_single(x, p, b):
+            return _stats.geninvgauss_logpdf(x, p, b)
+
+        logpdf_single = np.vectorize(logpdf_single, otypes=[np.float64])
+
+        z = logpdf_single(x, p, b)
+        if np.isnan(z).any():
+            msg = ("Infinite values encountered in scipy.special.kve(p, b). "
+                   "Values replaced by NaN to avoid incorrect results.")
+            warnings.warn(msg, RuntimeWarning, stacklevel=3)
+        return z
+
+    def _pdf(self, x, p, b):
+        # relying on logpdf avoids overflow of x**(p-1) for large x and p
+        return np.exp(self._logpdf(x, p, b))
+
+    def _cdf(self, x, p, b):
+        _a, _b = self._get_support(p, b)
+
+        def _cdf_single(x, p, b):
+            user_data = np.array([p, b], float).ctypes.data_as(ctypes.c_void_p)
+            llc = LowLevelCallable.from_cython(_stats, '_geninvgauss_pdf',
+                                               user_data)
+
+            return integrate.quad(llc, _a, x)[0]
+
+        _cdf_single = np.vectorize(_cdf_single, otypes=[np.float64])
+
+        return _cdf_single(x, p, b)
+
+    def _logquasipdf(self, x, p, b):
+        # log of the quasi-density (w/o normalizing constant) used in _rvs
+        return xpx.apply_where(x > 0, (x, p, b),
+                               lambda x, p, b: (p - 1)*np.log(x) - b*(x + 1/x)/2,
+                               fill_value=-np.inf)
+
+    def _rvs(self, p, b, size=None, random_state=None):
+        # if p and b are scalar, use _rvs_scalar, otherwise need to create
+        # output by iterating over parameters
+        if np.isscalar(p) and np.isscalar(b):
+            out = self._rvs_scalar(p, b, size, random_state)
+        elif p.size == 1 and b.size == 1:
+            out = self._rvs_scalar(p.item(), b.item(), size, random_state)
+        else:
+            # When this method is called, size will be a (possibly empty)
+            # tuple of integers.  It will not be None; if `size=None` is passed
+            # to `rvs()`, size will be the empty tuple ().
+
+            p, b = np.broadcast_arrays(p, b)
+            # p and b now have the same shape.
+
+            # `shp` is the shape of the blocks of random variates that are
+            # generated for each combination of parameters associated with
+            # broadcasting p and b.
+            # bc is a tuple the same length as size.  The values
+            # in bc are bools.  If bc[j] is True, it means that
+            # entire axis is filled in for a given combination of the
+            # broadcast arguments.
+            shp, bc = _check_shape(p.shape, size)
+
+            # `numsamples` is the total number of variates to be generated
+            # for each combination of the input arguments.
+            numsamples = int(np.prod(shp))
+
+            # `out` is the array to be returned.  It is filled in the
+            # loop below.
+            out = np.empty(size)
+
+            it = np.nditer([p, b],
+                           flags=['multi_index'],
+                           op_flags=[['readonly'], ['readonly']])
+            while not it.finished:
+                # Convert the iterator's multi_index into an index into the
+                # `out` array where the call to _rvs_scalar() will be stored.
+                # Where bc is True, we use a full slice; otherwise we use the
+                # index value from it.multi_index.  len(it.multi_index) might
+                # be less than len(bc), and in that case we want to align these
+                # two sequences to the right, so the loop variable j runs from
+                # -len(size) to 0.  This doesn't cause an IndexError, as
+                # bc[j] will be True in those cases where it.multi_index[j]
+                # would cause an IndexError.
+                idx = tuple((it.multi_index[j] if not bc[j] else slice(None))
+                            for j in range(-len(size), 0))
+                out[idx] = self._rvs_scalar(it[0], it[1], numsamples,
+                                            random_state).reshape(shp)
+                it.iternext()
+
+        if size == ():
+            out = out.item()
+        return out
+
+    def _rvs_scalar(self, p, b, numsamples, random_state):
+        # following [2], the quasi-pdf is used instead of the pdf for the
+        # generation of rvs
+        invert_res = False
+        if not numsamples:
+            numsamples = 1
+        if p < 0:
+            # note: if X is geninvgauss(p, b), then 1/X is geninvgauss(-p, b)
+            p = -p
+            invert_res = True
+        m = self._mode(p, b)
+
+        # determine method to be used following [2]
+        ratio_unif = True
+        if p >= 1 or b > 1:
+            # ratio of uniforms with mode shift below
+            mode_shift = True
+        elif b >= min(0.5, 2 * np.sqrt(1 - p) / 3):
+            # ratio of uniforms without mode shift below
+            mode_shift = False
+        else:
+            # new algorithm in [2]
+            ratio_unif = False
+
+        # prepare sampling of rvs
+        size1d = tuple(np.atleast_1d(numsamples))
+        N = np.prod(size1d)  # number of rvs needed, reshape upon return
+        x = np.zeros(N)
+        simulated = 0
+
+        if ratio_unif:
+            # use ratio of uniforms method
+            if mode_shift:
+                a2 = -2 * (p + 1) / b - m
+                a1 = 2 * m * (p - 1) / b - 1
+                # find roots of x**3 + a2*x**2 + a1*x + m (Cardano's formula)
+                p1 = a1 - a2**2 / 3
+                q1 = 2 * a2**3 / 27 - a2 * a1 / 3 + m
+                phi = np.arccos(-q1 * np.sqrt(-27 / p1**3) / 2)
+                s1 = -np.sqrt(-4 * p1 / 3)
+                root1 = s1 * np.cos(phi / 3 + np.pi / 3) - a2 / 3
+                root2 = -s1 * np.cos(phi / 3) - a2 / 3
+                # root3 = s1 * np.cos(phi / 3 - np.pi / 3) - a2 / 3
+
+                # if g is the quasipdf, rescale: g(x) / g(m) which we can write
+                # as exp(log(g(x)) - log(g(m))). This is important
+                # since for large values of p and b, g cannot be evaluated.
+                # denote the rescaled quasipdf by h
+                lm = self._logquasipdf(m, p, b)
+                d1 = self._logquasipdf(root1, p, b) - lm
+                d2 = self._logquasipdf(root2, p, b) - lm
+                # compute the bounding rectangle w.r.t. h. Note that
+                # np.exp(0.5*d1) = np.sqrt(g(root1)/g(m)) = np.sqrt(h(root1))
+                vmin = (root1 - m) * np.exp(0.5 * d1)
+                vmax = (root2 - m) * np.exp(0.5 * d2)
+                umax = 1  # umax = sqrt(h(m)) = 1
+
+                def logqpdf(x):
+                    return self._logquasipdf(x, p, b) - lm
+
+                c = m
+            else:
+                # ratio of uniforms without mode shift
+                # compute np.sqrt(quasipdf(m))
+                umax = np.exp(0.5*self._logquasipdf(m, p, b))
+                xplus = ((1 + p) + np.sqrt((1 + p)**2 + b**2))/b
+                vmin = 0
+                # compute xplus * np.sqrt(quasipdf(xplus))
+                vmax = xplus * np.exp(0.5 * self._logquasipdf(xplus, p, b))
+                c = 0
+
+                def logqpdf(x):
+                    return self._logquasipdf(x, p, b)
+
+            if vmin >= vmax:
+                raise ValueError("vmin must be smaller than vmax.")
+            if umax <= 0:
+                raise ValueError("umax must be positive.")
+
+            i = 1
+            while simulated < N:
+                k = N - simulated
+                # simulate uniform rvs on [0, umax] and [vmin, vmax]
+                u = umax * random_state.uniform(size=k)
+                v = random_state.uniform(size=k)
+                v = vmin + (vmax - vmin) * v
+                rvs = v / u + c
+                # rewrite acceptance condition u**2 <= pdf(rvs) by taking logs
+                accept = (2*np.log(u) <= logqpdf(rvs))
+                num_accept = np.sum(accept)
+                if num_accept > 0:
+                    x[simulated:(simulated + num_accept)] = rvs[accept]
+                    simulated += num_accept
+
+                if (simulated == 0) and (i*N >= 50000):
+                    msg = ("Not a single random variate could be generated "
+                           f"in {i*N} attempts. Sampling does not appear to "
+                           "work for the provided parameters.")
+                    raise RuntimeError(msg)
+                i += 1
+        else:
+            # use new algorithm in [2]
+            x0 = b / (1 - p)
+            xs = np.max((x0, 2 / b))
+            k1 = np.exp(self._logquasipdf(m, p, b))
+            A1 = k1 * x0
+            if x0 < 2 / b:
+                k2 = np.exp(-b)
+                if p > 0:
+                    A2 = k2 * ((2 / b)**p - x0**p) / p
+                else:
+                    A2 = k2 * np.log(2 / b**2)
+            else:
+                k2, A2 = 0, 0
+            k3 = xs**(p - 1)
+            A3 = 2 * k3 * np.exp(-xs * b / 2) / b
+            A = A1 + A2 + A3
+
+            # [2]: rejection constant is < 2.73; so expected runtime is finite
+            while simulated < N:
+                k = N - simulated
+                h, rvs = np.zeros(k), np.zeros(k)
+                # simulate uniform rvs on [x1, x2] and [0, y2]
+                u = random_state.uniform(size=k)
+                v = A * random_state.uniform(size=k)
+                cond1 = v <= A1
+                cond2 = np.logical_not(cond1) & (v <= A1 + A2)
+                cond3 = np.logical_not(cond1 | cond2)
+                # subdomain (0, x0)
+                rvs[cond1] = x0 * v[cond1] / A1
+                h[cond1] = k1
+                # subdomain (x0, 2 / b)
+                if p > 0:
+                    rvs[cond2] = (x0**p + (v[cond2] - A1) * p / k2)**(1 / p)
+                else:
+                    rvs[cond2] = b * np.exp((v[cond2] - A1) * np.exp(b))
+                h[cond2] = k2 * rvs[cond2]**(p - 1)
+                # subdomain (xs, infinity)
+                z = np.exp(-xs * b / 2) - b * (v[cond3] - A1 - A2) / (2 * k3)
+                rvs[cond3] = -2 / b * np.log(z)
+                h[cond3] = k3 * np.exp(-rvs[cond3] * b / 2)
+                # apply rejection method
+                accept = (np.log(u * h) <= self._logquasipdf(rvs, p, b))
+                num_accept = sum(accept)
+                if num_accept > 0:
+                    x[simulated:(simulated + num_accept)] = rvs[accept]
+                    simulated += num_accept
+
+        rvs = np.reshape(x, size1d)
+        if invert_res:
+            rvs = 1 / rvs
+        return rvs
+
+    def _mode(self, p, b):
+        # distinguish cases to avoid catastrophic cancellation (see [2])
+        if p < 1:
+            return b / (np.sqrt((p - 1)**2 + b**2) + 1 - p)
+        else:
+            return (np.sqrt((1 - p)**2 + b**2) - (1 - p)) / b
+
+    def _munp(self, n, p, b):
+        num = sc.kve(p + n, b)
+        denom = sc.kve(p, b)
+        inf_vals = np.isinf(num) | np.isinf(denom)
+        if inf_vals.any():
+            msg = ("Infinite values encountered in the moment calculation "
+                   "involving scipy.special.kve. Values replaced by NaN to "
+                   "avoid incorrect results.")
+            warnings.warn(msg, RuntimeWarning, stacklevel=3)
+            m = np.full_like(num, np.nan, dtype=np.float64)
+            m[~inf_vals] = num[~inf_vals] / denom[~inf_vals]
+        else:
+            m = num / denom
+        return m
+
+
+geninvgauss = geninvgauss_gen(a=0.0, name="geninvgauss")
+
+
+@np.vectorize(otypes=[np.float64])
+def _norminvgauss_quadrature(x, y, a, b):
+    # gh-23196 reported that the norminvgauss CDF would drop to zero in the far right
+    # tail. The SF had a similar problem, dropping to zero at the far left.
+    # This fixes the bug by using 1 - SF to compute the CDF in the right tail and
+    # 1 - CDF to compute the SF in the left tail. The mean is guaranteed to be beyond
+    # the median, so there is no loss of precision due to subtractive cancellation.
+    mean = b / np.sqrt((a + b) * (a - b))
+    if np.isneginf(x) and y > abs(mean):
+        return 1 - integrate.quad(norminvgauss._pdf, y, np.inf, args=(a, b))[0]
+    if np.isposinf(y) and x < -abs(mean):
+        return 1 - integrate.quad(norminvgauss._pdf, -np.inf, x, args=(a, b))[0]
+    else:
+        res = integrate.quad(norminvgauss._pdf, x, y, args=(a, b))[0]
+    return np.clip(res, 0, 1)
+
+
+class norminvgauss_gen(rv_continuous):
+    r"""A Normal Inverse Gaussian continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `norminvgauss` is:
+
+    .. math::
+
+        f(x, a, b) = \frac{a \, K_1(a \sqrt{1 + x^2})}{\pi \sqrt{1 + x^2}} \,
+                     \exp(\sqrt{a^2 - b^2} + b x)
+
+    where :math:`x` is a real number, the parameter :math:`a` is the tail
+    heaviness and :math:`b` is the asymmetry parameter satisfying
+    :math:`a > 0` and :math:`|b| <= a`.
+    :math:`K_1` is the modified Bessel function of second kind
+    (`scipy.special.k1`).
+
+    %(after_notes)s
+
+    A normal inverse Gaussian random variable `Y` with parameters `a` and `b`
+    can be expressed as a normal mean-variance mixture:
+    ``Y = b * V + sqrt(V) * X`` where `X` is ``norm(0,1)`` and `V` is
+    ``invgauss(mu=1/sqrt(a**2 - b**2))``. This representation is used
+    to generate random variates.
+
+    Another common parametrization of the distribution (see Equation 2.1 in
+    [2]_) is given by the following expression of the pdf:
+
+    .. math::
+
+        g(x, \alpha, \beta, \delta, \mu) =
+        \frac{\alpha\delta K_1\left(\alpha\sqrt{\delta^2 + (x - \mu)^2}\right)}
+        {\pi \sqrt{\delta^2 + (x - \mu)^2}} \,
+        e^{\delta \sqrt{\alpha^2 - \beta^2} + \beta (x - \mu)}
+
+    In SciPy, this corresponds to
+    :math:`a=\alpha \delta, b=\beta \delta, \text{loc}=\mu, \text{scale}=\delta`.
+
+    References
+    ----------
+    .. [1] O. Barndorff-Nielsen, "Hyperbolic Distributions and Distributions on
+           Hyperbolae", Scandinavian Journal of Statistics, Vol. 5(3),
+           pp. 151-157, 1978.
+
+    .. [2] O. Barndorff-Nielsen, "Normal Inverse Gaussian Distributions and
+           Stochastic Volatility Modelling", Scandinavian Journal of
+           Statistics, Vol. 24, pp. 1-13, 1997.
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _argcheck(self, a, b):
+        return (a > 0) & (np.absolute(b) < a)
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (-np.inf, np.inf), (False, False))
+        return [ia, ib]
+
+    def _fitstart(self, data):
+        # Arbitrary, but the default a = b = 1 is not valid; the distribution
+        # requires |b| < a.
+        return super()._fitstart(data, args=(1, 0.5))
+
+    def _pdf(self, x, a, b):
+        gamma = np.sqrt((a + b) * (a - b))
+        fac1 = a / np.pi
+        sq = np.hypot(1, x)  # reduce overflows
+        return fac1 * sc.k1e(a * sq) * np.exp(b*x - a*sq + gamma) / sq
+
+    def _cdf(self, x, a, b):
+        return _norminvgauss_quadrature(-np.inf, x, a, b)
+
+    def _sf(self, x, a, b):
+        return _norminvgauss_quadrature(x, np.inf, a, b)
+
+    def _isf(self, q, a, b):
+        def _isf_scalar(q, a, b):
+
+            def eq(x, a, b, q):
+                # Solve eq(x, a, b, q) = 0 to obtain isf(x, a, b) = q.
+                return self._sf(x, a, b) - q
+
+            # Find a bracketing interval for the root.
+            # Start at the mean, and grow the length of the interval
+            # by 2 each iteration until there is a sign change in eq.
+            xm = self.mean(a, b)
+            em = eq(xm, a, b, q)
+            if em == 0:
+                # Unlikely, but might as well check.
+                return xm
+            if em > 0:
+                delta = 1
+                left = xm
+                right = xm + delta
+                while eq(right, a, b, q) > 0:
+                    delta = 2*delta
+                    right = xm + delta
+            else:
+                # em < 0
+                delta = 1
+                right = xm
+                left = xm - delta
+                while eq(left, a, b, q) < 0:
+                    delta = 2*delta
+                    left = xm - delta
+            result = optimize.brentq(eq, left, right, args=(a, b, q),
+                                     xtol=self.xtol)
+            return result
+
+        if np.isscalar(q):
+            return _isf_scalar(q, a, b)
+        else:
+            result = []
+            for (q0, a0, b0) in zip(q, a, b):
+                result.append(_isf_scalar(q0, a0, b0))
+            return np.array(result)
+
+    def _rvs(self, a, b, size=None, random_state=None):
+        # note: X = b * V + sqrt(V) * X is norminvgaus(a,b) if X is standard
+        # normal and V is invgauss(mu=1/sqrt(a**2 - b**2))
+        gamma = np.sqrt((a + b) * (a - b))
+        ig = invgauss.rvs(mu=1/gamma, size=size, random_state=random_state)
+        return b * ig + np.sqrt(ig) * norm.rvs(size=size,
+                                               random_state=random_state)
+
+    def _stats(self, a, b):
+        gamma = np.sqrt((a + b) * (a - b))
+        mean = b / gamma
+        variance = a**2 / gamma**3
+        skewness = 3.0 * b / (a * np.sqrt(gamma))
+        kurtosis = 3.0 * (1 + 4 * b**2 / a**2) / gamma
+        return mean, variance, skewness, kurtosis
+
+
+norminvgauss = norminvgauss_gen(name="norminvgauss")
+
+
+class invweibull_gen(rv_continuous):
+    """An inverted Weibull continuous random variable.
+
+    This distribution is also known as the Fréchet distribution or the
+    type II extreme value distribution.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `invweibull` is:
+
+    .. math::
+
+        f(x, c) = c x^{-c-1} \\exp(-x^{-c})
+
+    for :math:`x > 0`, :math:`c > 0`.
+
+    `invweibull` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    F.R.S. de Gusmao, E.M.M Ortega and G.M. Cordeiro, "The generalized inverse
+    Weibull distribution", Stat. Papers, vol. 52, pp. 591-619, 2011.
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # invweibull.pdf(x, c) = c * x**(-c-1) * exp(-x**(-c))
+        xc1 = np.power(x, -c - 1.0)
+        xc2 = np.power(x, -c)
+        xc2 = np.exp(-xc2)
+        return c * xc1 * xc2
+
+    def _cdf(self, x, c):
+        xc1 = np.power(x, -c)
+        return np.exp(-xc1)
+
+    def _sf(self, x, c):
+        return -np.expm1(-x**-c)
+
+    def _ppf(self, q, c):
+        return np.power(-np.log(q), -1.0/c)
+
+    def _isf(self, p, c):
+        return (-np.log1p(-p))**(-1/c)
+
+    def _munp(self, n, c):
+        return sc.gamma(1 - n / c)
+
+    def _entropy(self, c):
+        return 1+_EULER + _EULER / c - np.log(c)
+
+    def _fitstart(self, data, args=None):
+        # invweibull requires c > 1 for the first moment to exist, so use 2.0
+        args = (2.0,) if args is None else args
+        return super()._fitstart(data, args=args)
+
+
+invweibull = invweibull_gen(a=0, name='invweibull')
+
+
+class jf_skew_t_gen(rv_continuous):
+    r"""Jones and Faddy skew-t distribution.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `jf_skew_t` is:
+
+    .. math::
+
+        f(x; a, b) = C_{a,b}^{-1}
+                    \left(1+\frac{x}{\left(a+b+x^2\right)^{1/2}}\right)^{a+1/2}
+                    \left(1-\frac{x}{\left(a+b+x^2\right)^{1/2}}\right)^{b+1/2}
+
+    for real numbers :math:`a>0` and :math:`b>0`, where
+    :math:`C_{a,b} = 2^{a+b-1}B(a,b)(a+b)^{1/2}`, and :math:`B` denotes the
+    beta function (`scipy.special.beta`).
+
+    When :math:`a<b`, the distribution is negatively skewed, and when
+    :math:`a>b`, the distribution is positively skewed. If :math:`a=b`, then
+    we recover the `t` distribution with :math:`2a` degrees of freedom.
+
+    `jf_skew_t` takes :math:`a` and :math:`b` as shape parameters.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] M.C. Jones and M.J. Faddy. "A skew extension of the t distribution,
+           with applications" *Journal of the Royal Statistical Society*.
+           Series B (Statistical Methodology) 65, no. 1 (2003): 159-174.
+           :doi:`10.1111/1467-9868.00378`
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ia, ib]
+
+    def _pdf(self, x, a, b):
+        c = 2 ** (a + b - 1) * sc.beta(a, b) * np.sqrt(a + b)
+        d1 = (1 + x / np.sqrt(a + b + x ** 2)) ** (a + 0.5)
+        d2 = (1 - x / np.sqrt(a + b + x ** 2)) ** (b + 0.5)
+        return d1 * d2 / c
+
+    def _rvs(self, a, b, size=None, random_state=None):
+        d1 = random_state.beta(a, b, size)
+        d2 = (2 * d1 - 1) * np.sqrt(a + b)
+        d3 = 2 * np.sqrt(d1 * (1 - d1))
+        return d2 / d3
+
+    def _cdf(self, x, a, b):
+        y = (1 + x / np.sqrt(a + b + x ** 2)) * 0.5
+        return sc.betainc(a, b, y)
+
+    def _sf(self, x, a, b):
+        y = (1 + x / np.sqrt(a + b + x ** 2)) * 0.5
+        return sc.betaincc(a, b, y)
+
+    def _ppf(self, q, a, b):
+        d1 = beta.ppf(q, a, b)
+        d2 = (2 * d1 - 1) * np.sqrt(a + b)
+        d3 = 2 * np.sqrt(d1 * (1 - d1))
+        return d2 / d3
+
+    def _munp(self, n, a, b):
+        """Returns the n-th moment(s) where all the following hold:
+
+        - n >= 0
+        - a > n / 2
+        - b > n / 2
+
+        The result is np.nan in all other cases.
+        """
+        def nth_moment(n_k, a_k, b_k):
+            """Computes E[T^(n_k)] where T is skew-t distributed with
+            parameters a_k and b_k.
+            """
+            num = (a_k + b_k) ** (0.5 * n_k)
+            denom = 2 ** n_k * sc.beta(a_k, b_k)
+
+            indices = np.arange(n_k + 1)
+            sgn = np.where(indices % 2 > 0, -1, 1)
+            d = sc.beta(a_k + 0.5 * n_k - indices, b_k - 0.5 * n_k + indices)
+            sum_terms = sc.comb(n_k, indices) * sgn * d
+
+            return num / denom * sum_terms.sum()
+
+        nth_moment_valid = (a > 0.5 * n) & (b > 0.5 * n) & (n >= 0)
+        return xpx.apply_where(
+            nth_moment_valid,
+            (n, a, b),
+            np.vectorize(nth_moment, otypes=[np.float64]),
+            fill_value=np.nan,
+        )
+
+
+jf_skew_t = jf_skew_t_gen(name='jf_skew_t')
+
+
+class johnsonsb_gen(rv_continuous):
+    r"""A Johnson SB continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    johnsonsu
+
+    Notes
+    -----
+    The probability density function for `johnsonsb` is:
+
+    .. math::
+
+        f(x, a, b) = \frac{b}{x(1-x)}  \phi(a + b \log \frac{x}{1-x} )
+
+    where :math:`x`, :math:`a`, and :math:`b` are real scalars; :math:`b > 0`
+    and :math:`x \in [0,1]`.  :math:`\phi` is the pdf of the normal
+    distribution.
+
+    `johnsonsb` takes :math:`a` and :math:`b` as shape parameters.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _argcheck(self, a, b):
+        return (b > 0) & (a == a)
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (-np.inf, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ia, ib]
+
+    def _pdf(self, x, a, b):
+        # johnsonsb.pdf(x, a, b) = b / (x*(1-x)) * phi(a + b * log(x/(1-x)))
+        trm = _norm_pdf(a + b*sc.logit(x))
+        return b*1.0/(x*(1-x))*trm
+
+    def _cdf(self, x, a, b):
+        return _norm_cdf(a + b*sc.logit(x))
+
+    def _ppf(self, q, a, b):
+        return sc.expit(1.0 / b * (_norm_ppf(q) - a))
+
+    def _sf(self, x, a, b):
+        return _norm_sf(a + b*sc.logit(x))
+
+    def _isf(self, q, a, b):
+        return sc.expit(1.0 / b * (_norm_isf(q) - a))
+
+
+johnsonsb = johnsonsb_gen(a=0.0, b=1.0, name='johnsonsb')
+
+
+class johnsonsu_gen(rv_continuous):
+    r"""A Johnson SU continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    johnsonsb
+
+    Notes
+    -----
+    The probability density function for `johnsonsu` is:
+
+    .. math::
+
+        f(x, a, b) = \frac{b}{\sqrt{x^2 + 1}}
+                     \phi(a + b \log(x + \sqrt{x^2 + 1}))
+
+    where :math:`x`, :math:`a`, and :math:`b` are real scalars; :math:`b > 0`.
+    :math:`\phi` is the pdf of the normal distribution.
+
+    `johnsonsu` takes :math:`a` and :math:`b` as shape parameters.
+
+    The first four central moments are calculated according to the formulas
+    in [1]_.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Taylor Enterprises. "Johnson Family of Distributions".
+       https://variation.com/wp-content/distribution_analyzer_help/hs126.htm
+
+    %(example)s
+
+    """
+    def _argcheck(self, a, b):
+        return (b > 0) & (a == a)
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (-np.inf, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ia, ib]
+
+    def _pdf(self, x, a, b):
+        # johnsonsu.pdf(x, a, b) = b / sqrt(x**2 + 1) *
+        #                          phi(a + b * log(x + sqrt(x**2 + 1)))
+        x2 = x*x
+        trm = _norm_pdf(a + b * np.arcsinh(x))
+        return b*1.0/np.sqrt(x2+1.0)*trm
+
+    def _cdf(self, x, a, b):
+        return _norm_cdf(a + b * np.arcsinh(x))
+
+    def _ppf(self, q, a, b):
+        return np.sinh((_norm_ppf(q) - a) / b)
+
+    def _sf(self, x, a, b):
+        return _norm_sf(a + b * np.arcsinh(x))
+
+    def _isf(self, x, a, b):
+        return np.sinh((_norm_isf(x) - a) / b)
+
+    def _stats(self, a, b, moments='mv'):
+        # Naive implementation of first and second moment to address gh-18071.
+        # https://variation.com/wp-content/distribution_analyzer_help/hs126.htm
+        # Numerical improvements left to future enhancements.
+        mu, mu2, g1, g2 = None, None, None, None
+
+        bn2 = b**-2.
+        expbn2 = np.exp(bn2)
+        a_b = a / b
+
+        if 'm' in moments:
+            mu = -expbn2**0.5 * np.sinh(a_b)
+        if 'v' in moments:
+            mu2 = 0.5*sc.expm1(bn2)*(expbn2*np.cosh(2*a_b) + 1)
+        if 's' in moments:
+            t1 = expbn2**.5 * sc.expm1(bn2)**0.5
+            t2 = 3*np.sinh(a_b)
+            t3 = expbn2 * (expbn2 + 2) * np.sinh(3*a_b)
+            denom = np.sqrt(2) * (1 + expbn2 * np.cosh(2*a_b))**(3/2)
+            g1 = -t1 * (t2 + t3) / denom
+        if 'k' in moments:
+            t1 = 3 + 6*expbn2
+            t2 = 4*expbn2**2 * (expbn2 + 2) * np.cosh(2*a_b)
+            t3 = expbn2**2 * np.cosh(4*a_b)
+            t4 = -3 + 3*expbn2**2 + 2*expbn2**3 + expbn2**4
+            denom = 2*(1 + expbn2*np.cosh(2*a_b))**2
+            g2 = (t1 + t2 + t3*t4) / denom - 3
+        return mu, mu2, g1, g2
+
+
+johnsonsu = johnsonsu_gen(name='johnsonsu')
+
+
+class landau_gen(rv_continuous):
+    r"""A Landau continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    :ref:`landau_energy_loss` : Extended example in a particle physics context.
+
+    Notes
+    -----
+    The probability density function for `landau` ([1]_, [2]_) is:
+
+    .. math::
+
+        f(x) = \frac{1}{\pi}\int_0^\infty \exp(-t \log t - xt)\sin(\pi t) dt
+
+    for a real number :math:`x`.
+
+    %(after_notes)s
+
+    Often (e.g. [2]_), the Landau distribution is parameterized in terms of a
+    location parameter :math:`\mu` and scale parameter :math:`c`, the latter of
+    which *also* introduces a location shift. If ``mu`` and ``c`` are used to
+    represent these parameters, this corresponds with SciPy's parameterization
+    with ``loc = mu + 2*c / np.pi * np.log(c)`` and ``scale = c``.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``pdf``, ``cdf``, ``ppf``, ``sf`` and ``isf``
+    methods. [1]_
+
+    References
+    ----------
+    .. [1] Landau, L. (1944). "On the energy loss of fast particles by
+           ionization". J. Phys. (USSR). 8: 201.
+    .. [2] "Landau Distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Landau_distribution
+    .. [3] Chambers, J. M., Mallows, C. L., & Stuck, B. (1976).
+           "A method for simulating stable random variables."
+           Journal of the American Statistical Association, 71(354), 340-344.
+    .. [4] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+    .. [5] Yoshimura, T. "Numerical Evaluation and High Precision Approximation
+           Formula for Landau Distribution".
+           :doi:`10.36227/techrxiv.171822215.53612870/v2`
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _entropy(self):
+        # Computed with mpmath - see gh-19145
+        return 2.37263644000448182
+
+    def _pdf(self, x):
+        return scu._landau_pdf(x, 0, 1)
+
+    def _cdf(self, x):
+        return scu._landau_cdf(x, 0, 1)
+
+    def _sf(self, x):
+        return scu._landau_sf(x, 0, 1)
+
+    def _ppf(self, p):
+        return scu._landau_ppf(p, 0, 1)
+
+    def _isf(self, p):
+        return scu._landau_isf(p, 0, 1)
+
+    def _stats(self):
+        return np.nan, np.nan, np.nan, np.nan
+
+    def _munp(self, n):
+        return np.nan if n > 0 else 1
+
+    def _fitstart(self, data, args=None):
+        # Initialize ML guesses using quartiles instead of moments.
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        p25, p50, p75 = np.percentile(data, [25, 50, 75])
+        return p50, (p75 - p25)/2
+
+    def _rvs(self, size=None, random_state=None):
+        # Method from https://www.jstor.org/stable/2285309 Eq. 2.4
+        pi_2 = np.pi / 2
+        U = random_state.uniform(-np.pi / 2, np.pi / 2, size=size)
+        W = random_state.standard_exponential(size=size)
+        S = 2 / np.pi * ((pi_2 + U) * np.tan(U)
+                         - np.log((pi_2 * W * np.cos(U)) / (pi_2 + U)))
+        return S
+
+
+landau = landau_gen(name='landau')
+
+
+class laplace_gen(rv_continuous):
+    r"""A Laplace continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `laplace` is
+
+    .. math::
+
+        f(x) = \frac{1}{2} \exp(-|x|)
+
+    for a real number :math:`x`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return random_state.laplace(0, 1, size=size)
+
+    def _pdf(self, x):
+        # laplace.pdf(x) = 1/2 * exp(-abs(x))
+        return 0.5*np.exp(-abs(x))
+
+    def _cdf(self, x):
+        with np.errstate(over='ignore'):
+            return np.where(x > 0, 1.0 - 0.5*np.exp(-x), 0.5*np.exp(x))
+
+    def _sf(self, x):
+        # By symmetry...
+        return self._cdf(-x)
+
+    def _ppf(self, q):
+        return np.where(q > 0.5, -np.log(2*(1-q)), np.log(2*q))
+
+    def _isf(self, q):
+        # By symmetry...
+        return -self._ppf(q)
+
+    def _stats(self):
+        return 0, 2, 0, 3
+
+    def _entropy(self):
+        return np.log(2)+1
+
+    @_call_super_mom
+    @replace_notes_in_docstring(rv_continuous, notes="""\
+        This function uses explicit formulas for the maximum likelihood
+        estimation of the Laplace distribution parameters, so the keyword
+        arguments `loc`, `scale`, and `optimizer` are ignored.\n\n""")
+    def fit(self, data, *args, **kwds):
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+
+        # Source: Statistical Distributions, 3rd Edition. Evans, Hastings,
+        # and Peacock (2000), Page 124
+
+        if floc is None:
+            floc = np.median(data)
+
+        if fscale is None:
+            fscale = (np.sum(np.abs(data - floc))) / len(data)
+
+        return floc, fscale
+
+
+laplace = laplace_gen(name='laplace')
+
+
+class laplace_asymmetric_gen(rv_continuous):
+    r"""An asymmetric Laplace continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    laplace : Laplace distribution
+
+    Notes
+    -----
+    The probability density function for `laplace_asymmetric` is
+
+    .. math::
+
+       f(x, \kappa) &= \frac{1}{\kappa+\kappa^{-1}}\exp(-x\kappa),\quad x\ge0\\
+                    &= \frac{1}{\kappa+\kappa^{-1}}\exp(x/\kappa),\quad x<0\\
+
+    for :math:`-\infty < x < \infty`, :math:`\kappa > 0`.
+
+    `laplace_asymmetric` takes ``kappa`` as a shape parameter for
+    :math:`\kappa`. For :math:`\kappa = 1`, it is identical to a
+    Laplace distribution.
+
+    %(after_notes)s
+
+    Note that the scale parameter of some references is the reciprocal of
+    SciPy's ``scale``. For example, :math:`\lambda = 1/2` in the
+    parameterization of [1]_ is equivalent to ``scale = 2`` with
+    `laplace_asymmetric`.
+
+    References
+    ----------
+    .. [1] "Asymmetric Laplace distribution", Wikipedia
+            https://en.wikipedia.org/wiki/Asymmetric_Laplace_distribution
+
+    .. [2] Kozubowski TJ and Podgórski K. A Multivariate and
+           Asymmetric Generalization of Laplace Distribution,
+           Computational Statistics 15, 531--540 (2000).
+           :doi:`10.1007/PL00022717`
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("kappa", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, kappa):
+        return np.exp(self._logpdf(x, kappa))
+
+    def _logpdf(self, x, kappa):
+        kapinv = 1/kappa
+        lPx = x * np.where(x >= 0, -kappa, kapinv)
+        lPx -= np.log(kappa+kapinv)
+        return lPx
+
+    def _cdf(self, x, kappa):
+        kapinv = 1/kappa
+        kappkapinv = kappa+kapinv
+        return np.where(x >= 0,
+                        1 - np.exp(-x*kappa)*(kapinv/kappkapinv),
+                        np.exp(x*kapinv)*(kappa/kappkapinv))
+
+    def _sf(self, x, kappa):
+        kapinv = 1/kappa
+        kappkapinv = kappa+kapinv
+        return np.where(x >= 0,
+                        np.exp(-x*kappa)*(kapinv/kappkapinv),
+                        1 - np.exp(x*kapinv)*(kappa/kappkapinv))
+
+    def _ppf(self, q, kappa):
+        kapinv = 1/kappa
+        kappkapinv = kappa+kapinv
+        return np.where(q >= kappa/kappkapinv,
+                        -np.log((1 - q)*kappkapinv*kappa)*kapinv,
+                        np.log(q*kappkapinv/kappa)*kappa)
+
+    def _isf(self, q, kappa):
+        kapinv = 1/kappa
+        kappkapinv = kappa+kapinv
+        return np.where(q <= kapinv/kappkapinv,
+                        -np.log(q*kappkapinv*kappa)*kapinv,
+                        np.log((1 - q)*kappkapinv/kappa)*kappa)
+
+    def _stats(self, kappa):
+        kapinv = 1/kappa
+        mn = kapinv - kappa
+        var = kapinv*kapinv + kappa*kappa
+        g1 = 2.0*(1-np.power(kappa, 6))/np.power(1+np.power(kappa, 4), 1.5)
+        g2 = 6.0*(1+np.power(kappa, 8))/np.power(1+np.power(kappa, 4), 2)
+        return mn, var, g1, g2
+
+    def _entropy(self, kappa):
+        return 1 + np.log(kappa+1/kappa)
+
+
+laplace_asymmetric = laplace_asymmetric_gen(name='laplace_asymmetric')
+
+
+def _check_fit_input_parameters(dist, data, args, kwds):
+    if not isinstance(data, CensoredData):
+        data = np.asarray(data)
+
+    floc = kwds.get('floc', None)
+    fscale = kwds.get('fscale', None)
+
+    num_shapes = len(dist.shapes.split(",")) if dist.shapes else 0
+    fshape_keys = []
+    fshapes = []
+
+    # user has many options for fixing the shape, so here we standardize it
+    # into 'f' + the number of the shape.
+    # Adapted from `_reduce_func` in `_distn_infrastructure.py`:
+    if dist.shapes:
+        shapes = dist.shapes.replace(',', ' ').split()
+        for j, s in enumerate(shapes):
+            key = 'f' + str(j)
+            names = [key, 'f' + s, 'fix_' + s]
+            val = _get_fixed_fit_value(kwds, names)
+            fshape_keys.append(key)
+            fshapes.append(val)
+            if val is not None:
+                kwds[key] = val
+
+    # determine if there are any unknown arguments in kwds
+    known_keys = {'loc', 'scale', 'optimizer', 'method',
+                  'floc', 'fscale', *fshape_keys}
+    unknown_keys = set(kwds).difference(known_keys)
+    if unknown_keys:
+        raise TypeError(f"Unknown keyword arguments: {unknown_keys}.")
+
+    if len(args) > num_shapes:
+        raise TypeError("Too many positional arguments.")
+
+    if None not in {floc, fscale, *fshapes}:
+        # This check is for consistency with `rv_continuous.fit`.
+        # Without this check, this function would just return the
+        # parameters that were given.
+        raise RuntimeError("All parameters fixed. There is nothing to "
+                           "optimize.")
+
+    uncensored = data._uncensor() if isinstance(data, CensoredData) else data
+    if not np.isfinite(uncensored).all():
+        raise ValueError("The data contains non-finite values.")
+
+    return (data, *fshapes, floc, fscale)
+
+
+class levy_gen(rv_continuous):
+    r"""A Levy continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    levy_stable, levy_l
+
+    Notes
+    -----
+    The probability density function for `levy` is:
+
+    .. math::
+
+        f(x) = \frac{1}{\sqrt{2\pi x^3}} \exp\left(-\frac{1}{2x}\right)
+
+    for :math:`x > 0`.
+
+    This is the same as the Levy-stable distribution with :math:`a=1/2` and
+    :math:`b=1`.
+
+    %(after_notes)s
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import levy
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+
+    Calculate the first four moments:
+
+    >>> mean, var, skew, kurt = levy.stats(moments='mvsk')
+
+    Display the probability density function (``pdf``):
+
+    >>> # `levy` is very heavy-tailed.
+    >>> # To show a nice plot, let's cut off the upper 40 percent.
+    >>> a, b = levy.ppf(0), levy.ppf(0.6)
+    >>> x = np.linspace(a, b, 100)
+    >>> ax.plot(x, levy.pdf(x),
+    ...        'r-', lw=5, alpha=0.6, label='levy pdf')
+
+    Alternatively, the distribution object can be called (as a function)
+    to fix the shape, location and scale parameters. This returns a "frozen"
+    RV object holding the given parameters fixed.
+
+    Freeze the distribution and display the frozen ``pdf``:
+
+    >>> rv = levy()
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+
+    Check accuracy of ``cdf`` and ``ppf``:
+
+    >>> vals = levy.ppf([0.001, 0.5, 0.999])
+    >>> np.allclose([0.001, 0.5, 0.999], levy.cdf(vals))
+    True
+
+    Generate random numbers:
+
+    >>> r = levy.rvs(size=1000)
+
+    And compare the histogram:
+
+    >>> # manual binning to ignore the tail
+    >>> bins = np.concatenate((np.linspace(a, b, 20), [np.max(r)]))
+    >>> ax.hist(r, bins=bins, density=True, histtype='stepfilled', alpha=0.2)
+    >>> ax.set_xlim([x[0], x[-1]])
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # levy.pdf(x) = 1 / (x * sqrt(2*pi*x)) * exp(-1/(2*x))
+        return 1 / np.sqrt(2*np.pi*x) / x * np.exp(-1/(2*x))
+
+    def _cdf(self, x):
+        # Equivalent to 2*norm.sf(np.sqrt(1/x))
+        return sc.erfc(np.sqrt(0.5 / x))
+
+    def _sf(self, x):
+        return sc.erf(np.sqrt(0.5 / x))
+
+    def _ppf(self, q):
+        # Equivalent to 1.0/(norm.isf(q/2)**2) or 0.5/(erfcinv(q)**2)
+        val = _norm_isf(q/2)
+        return 1.0 / (val * val)
+
+    def _isf(self, p):
+        return 1/(2*sc.erfinv(p)**2)
+
+    def _stats(self):
+        return np.inf, np.inf, np.nan, np.nan
+
+
+levy = levy_gen(a=0.0, name="levy")
+
+
+class levy_l_gen(rv_continuous):
+    r"""A left-skewed Levy continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    levy, levy_stable
+
+    Notes
+    -----
+    The probability density function for `levy_l` is:
+
+    .. math::
+        f(x) = \frac{1}{|x| \sqrt{2\pi |x|}} \exp{ \left(-\frac{1}{2|x|} \right)}
+
+    for :math:`x < 0`.
+
+    This is the same as the Levy-stable distribution with :math:`a=1/2` and
+    :math:`b=-1`.
+
+    %(after_notes)s
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import levy_l
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+
+    Calculate the first four moments:
+
+    >>> mean, var, skew, kurt = levy_l.stats(moments='mvsk')
+
+    Display the probability density function (``pdf``):
+
+    >>> # `levy_l` is very heavy-tailed.
+    >>> # To show a nice plot, let's cut off the lower 40 percent.
+    >>> a, b = levy_l.ppf(0.4), levy_l.ppf(1)
+    >>> x = np.linspace(a, b, 100)
+    >>> ax.plot(x, levy_l.pdf(x),
+    ...        'r-', lw=5, alpha=0.6, label='levy_l pdf')
+
+    Alternatively, the distribution object can be called (as a function)
+    to fix the shape, location and scale parameters. This returns a "frozen"
+    RV object holding the given parameters fixed.
+
+    Freeze the distribution and display the frozen ``pdf``:
+
+    >>> rv = levy_l()
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+
+    Check accuracy of ``cdf`` and ``ppf``:
+
+    >>> vals = levy_l.ppf([0.001, 0.5, 0.999])
+    >>> np.allclose([0.001, 0.5, 0.999], levy_l.cdf(vals))
+    True
+
+    Generate random numbers:
+
+    >>> r = levy_l.rvs(size=1000)
+
+    And compare the histogram:
+
+    >>> # manual binning to ignore the tail
+    >>> bins = np.concatenate(([np.min(r)], np.linspace(a, b, 20)))
+    >>> ax.hist(r, bins=bins, density=True, histtype='stepfilled', alpha=0.2)
+    >>> ax.set_xlim([x[0], x[-1]])
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        # levy_l.pdf(x) = 1 / (abs(x) * sqrt(2*pi*abs(x))) * exp(-1/(2*abs(x)))
+        ax = abs(x)
+        return 1/np.sqrt(2*np.pi*ax)/ax*np.exp(-1/(2*ax))
+
+    def _cdf(self, x):
+        ax = abs(x)
+        return 2 * _norm_cdf(1 / np.sqrt(ax)) - 1
+
+    def _sf(self, x):
+        ax = abs(x)
+        return 2 * _norm_sf(1 / np.sqrt(ax))
+
+    def _ppf(self, q):
+        val = _norm_ppf((q + 1.0) / 2)
+        return -1.0 / (val * val)
+
+    def _isf(self, p):
+        return -1/_norm_isf(p/2)**2
+
+    def _stats(self):
+        return np.inf, np.inf, np.nan, np.nan
+
+
+levy_l = levy_l_gen(b=0.0, name="levy_l")
+
+
+class logistic_gen(rv_continuous):
+    r"""A logistic (or Sech-squared) continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `logistic` is:
+
+    .. math::
+
+        f(x) = \frac{\exp(-x)}
+                    {(1+\exp(-x))^2}
+
+    `logistic` is a special case of `genlogistic` with ``c=1``.
+
+    Remark that the survival function (``logistic.sf``) is equal to the
+    Fermi-Dirac distribution describing fermionic statistics.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return random_state.logistic(size=size)
+
+    def _pdf(self, x):
+        # logistic.pdf(x) = exp(-x) / (1+exp(-x))**2
+        return np.exp(self._logpdf(x))
+
+    def _logpdf(self, x):
+        y = -np.abs(x)
+        return y - 2. * sc.log1p(np.exp(y))
+
+    def _cdf(self, x):
+        return sc.expit(x)
+
+    def _logcdf(self, x):
+        return sc.log_expit(x)
+
+    def _ppf(self, q):
+        return sc.logit(q)
+
+    def _sf(self, x):
+        return sc.expit(-x)
+
+    def _logsf(self, x):
+        return sc.log_expit(-x)
+
+    def _isf(self, q):
+        return -sc.logit(q)
+
+    def _stats(self):
+        return 0, np.pi*np.pi/3.0, 0, 6.0/5.0
+
+    def _entropy(self):
+        # https://en.wikipedia.org/wiki/Logistic_distribution
+        return 2.0
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+        n = len(data)
+
+        # rv_continuous provided guesses
+        loc, scale = self._fitstart(data)
+        # these are trumped by user-provided guesses
+        loc, scale = kwds.get('loc', loc), kwds.get('scale', scale)
+
+        # the maximum likelihood estimators `a` and `b` of the location and
+        # scale parameters are roots of the two equations described in `func`.
+        # Source: Statistical Distributions, 3rd Edition. Evans, Hastings, and
+        # Peacock (2000), Page 130
+
+        def dl_dloc(loc, scale=fscale):
+            c = (data - loc) / scale
+            return np.sum(sc.expit(c)) - n/2
+
+        def dl_dscale(scale, loc=floc):
+            c = (data - loc) / scale
+            return np.sum(c*np.tanh(c/2)) - n
+
+        def func(params):
+            loc, scale = params
+            return dl_dloc(loc, scale), dl_dscale(scale, loc)
+
+        if fscale is not None and floc is None:
+            res = optimize.root(dl_dloc, (loc,))
+            loc = res.x[0]
+            scale = fscale
+        elif floc is not None and fscale is None:
+            res = optimize.root(dl_dscale, (scale,))
+            scale = res.x[0]
+            loc = floc
+        else:
+            res = optimize.root(func, (loc, scale))
+            loc, scale = res.x
+
+        # Note: gh-18176 reported data for which the reported MLE had
+        # `scale < 0`. To fix the bug, we return abs(scale). This is OK because
+        # `dl_dscale` and `dl_dloc` are even and odd functions of `scale`,
+        # respectively, so if `-scale` is a solution, so is `scale`.
+        scale = abs(scale)
+        return ((loc, scale) if res.success
+                else super().fit(data, *args, **kwds))
+
+
+logistic = logistic_gen(name='logistic')
+
+
+class loggamma_gen(rv_continuous):
+    r"""A log gamma continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `loggamma` is:
+
+    .. math::
+
+        f(x, c) = \frac{\exp(c x - \exp(x))}
+                       {\Gamma(c)}
+
+    for all :math:`x, c > 0`. Here, :math:`\Gamma` is the
+    gamma function (`scipy.special.gamma`).
+
+    `loggamma` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, c, size=None, random_state=None):
+        # Use the property of the gamma distribution Gamma(c)
+        #    Gamma(c) ~ Gamma(c + 1)*U**(1/c),
+        # where U is uniform on [0, 1]. (See, e.g.,
+        # G. Marsaglia and W.W. Tsang, "A simple method for generating gamma
+        # variables", https://doi.org/10.1145/358407.358414)
+        # So
+        #    log(Gamma(c)) ~ log(Gamma(c + 1)) + log(U)/c
+        # Generating a sample with this formulation is a bit slower
+        # than the more obvious log(Gamma(c)), but it avoids loss
+        # of precision when c << 1.
+        return (np.log(random_state.gamma(c + 1, size=size))
+                + np.log(random_state.uniform(size=size))/c)
+
+    def _pdf(self, x, c):
+        # loggamma.pdf(x, c) = exp(c*x-exp(x)) / gamma(c)
+        return np.exp(c*x-np.exp(x)-sc.gammaln(c))
+
+    def _logpdf(self, x, c):
+        return c*x - np.exp(x) - sc.gammaln(c)
+
+    def _cdf(self, x, c):
+        # This function is gammainc(c, exp(x)), where gammainc(c, z) is
+        # the regularized incomplete gamma function.
+        # The first term in a series expansion of gamminc(c, z) is
+        # z**c/Gamma(c+1); see 6.5.29 of Abramowitz & Stegun (and refer
+        # back to 6.5.1, 6.5.2 and 6.5.4 for the relevant notation).
+        # This can also be found in the wikipedia article
+        # https://en.wikipedia.org/wiki/Incomplete_gamma_function.
+        # Here we use that formula when x is sufficiently negative that
+        # exp(x) will result in subnormal numbers and lose precision.
+        # We evaluate the log of the expression first to allow the possible
+        # cancellation of the terms in the division, and then exponentiate.
+        # That is,
+        #     exp(x)**c/Gamma(c+1) = exp(log(exp(x)**c/Gamma(c+1)))
+        #                          = exp(c*x - gammaln(c+1))
+        return xpx.apply_where(
+            x < _LOGXMIN, (x, c),
+            lambda x, c: np.exp(c*x - sc.gammaln(c+1)),
+            lambda x, c: sc.gammainc(c, np.exp(x)))
+
+    def _ppf(self, q, c):
+        # The expression used when g < _XMIN inverts the one term expansion
+        # given in the comments of _cdf().
+        g = sc.gammaincinv(c, q)
+        return xpx.apply_where(
+            g < _XMIN, (g, q, c),
+            lambda g, q, c: (np.log(q) + sc.gammaln(c+1))/c,
+            lambda g, q, c: np.log(g))
+
+    def _sf(self, x, c):
+        # See the comments for _cdf() for how x < _LOGXMIN is handled.
+        return xpx.apply_where(
+            x < _LOGXMIN, (x, c),
+            lambda x, c: -np.expm1(c*x - sc.gammaln(c+1)),
+            lambda x, c: sc.gammaincc(c, np.exp(x)))
+
+    def _isf(self, q, c):
+        # The expression used when g < _XMIN inverts the complement of
+        # the one term expansion given in the comments of _cdf().
+        g = sc.gammainccinv(c, q)
+        return xpx.apply_where(
+            g < _XMIN, (g, q, c),
+            lambda g, q, c: (np.log1p(-q) + sc.gammaln(c+1))/c,
+            lambda g, q, c: np.log(g))
+
+    def _stats(self, c):
+        # See, for example, "A Statistical Study of Log-Gamma Distribution", by
+        # Ping Shing Chan (thesis, McMaster University, 1993).
+        mean = sc.digamma(c)
+        var = sc.polygamma(1, c)
+        skewness = sc.polygamma(2, c) / np.power(var, 1.5)
+        excess_kurtosis = sc.polygamma(3, c) / (var*var)
+        return mean, var, skewness, excess_kurtosis
+
+    def _entropy(self, c):
+        def regular(c):
+            h = sc.gammaln(c) - c * sc.digamma(c) + c
+            return h
+
+        def asymptotic(c):
+            # using asymptotic expansions for gammaln and psi (see gh-18093)
+            term = -0.5*np.log(c) + c**-1./6 - c**-3./90 + c**-5./210
+            h = norm._entropy() + term
+            return h
+
+        return xpx.apply_where(c >= 45, c, asymptotic, regular)
+
+
+loggamma = loggamma_gen(name='loggamma')
+
+
+class loglaplace_gen(rv_continuous):
+    r"""A log-Laplace continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `loglaplace` is:
+
+    .. math::
+
+        f(x, c) = \begin{cases}\frac{c}{2} x^{ c-1}  &\text{for } 0 < x < 1\\
+                               \frac{c}{2} x^{-c-1}  &\text{for } x \ge 1
+                  \end{cases}
+
+    for :math:`c > 0`.
+
+    `loglaplace` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    Suppose a random variable ``X`` follows the Laplace distribution with
+    location ``a`` and scale ``b``.  Then ``Y = exp(X)`` follows the
+    log-Laplace distribution with ``c = 1 / b`` and ``scale = exp(a)``.
+
+    References
+    ----------
+    T.J. Kozubowski and K. Podgorski, "A log-Laplace growth rate model",
+    The Mathematical Scientist, vol. 28, pp. 49-60, 2003.
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # loglaplace.pdf(x, c) = c / 2 * x**(c-1),   for 0 < x < 1
+        #                      = c / 2 * x**(-c-1),  for x >= 1
+        cd2 = c/2.0
+        c = np.where(x < 1, c, -c)
+        return cd2*x**(c-1)
+
+    def _cdf(self, x, c):
+        return np.where(x < 1, 0.5*x**c, 1-0.5*x**(-c))
+
+    def _sf(self, x, c):
+        return np.where(x < 1, 1 - 0.5*x**c, 0.5*x**(-c))
+
+    def _ppf(self, q, c):
+        return np.where(q < 0.5, (2.0*q)**(1.0/c), (2*(1.0-q))**(-1.0/c))
+
+    def _isf(self, q, c):
+        return np.where(q > 0.5, (2.0*(1.0 - q))**(1.0/c), (2*q)**(-1.0/c))
+
+    def _munp(self, n, c):
+        with np.errstate(divide='ignore'):
+            c2, n2 = c**2, n**2
+            return np.where(n2 < c2, c2 / (c2 - n2), np.inf)
+
+    def _entropy(self, c):
+        return np.log(2.0/c) + 1.0
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        data, fc, floc, fscale = _check_fit_input_parameters(self, data,
+                                                             args, kwds)
+
+        # Specialize MLE only when location is known.
+        if floc is None:
+            return super(type(self), self).fit(data, *args, **kwds)
+
+        # Raise an error if any observation has zero likelihood.
+        if np.any(data <= floc):
+            raise FitDataError("loglaplace", lower=floc, upper=np.inf)
+
+        # Remove location from data.
+        if floc != 0:
+            data = data - floc
+
+        # When location is zero, the log-Laplace distribution is related to
+        # the Laplace distribution in that if X ~ Laplace(loc=a, scale=b),
+        # then Y = exp(X) ~ LogLaplace(c=1/b, loc=0, scale=exp(a)).  It can
+        # be shown that the MLE for Y is the same as the MLE for X = ln(Y).
+        # Therefore, we reuse the formulas from laplace.fit() and transform
+        # the result back into log-laplace's parameter space.
+        a, b = laplace.fit(np.log(data),
+                           floc=np.log(fscale) if fscale is not None else None,
+                           fscale=1/fc if fc is not None else None,
+                           method='mle')
+        loc = floc
+        scale = np.exp(a) if fscale is None else fscale
+        c = 1 / b if fc is None else fc
+        return c, loc, scale
+
+loglaplace = loglaplace_gen(a=0.0, name='loglaplace')
+
+
+def _lognorm_logpdf(x, s):
+    return xpx.apply_where(
+        x != 0, (x, s),
+        lambda x, s: (-np.log(x)**2 / (2 * s**2)
+                      - np.log(s * x * np.sqrt(2 * np.pi))),
+        fill_value=-np.inf)
+
+
+class lognorm_gen(rv_continuous):
+    r"""A lognormal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `lognorm` is:
+
+    .. math::
+
+        f(x, s) = \frac{1}{s x \sqrt{2\pi}}
+                  \exp\left(-\frac{\log^2(x)}{2s^2}\right)
+
+    for :math:`x > 0`, :math:`s > 0`.
+
+    `lognorm` takes ``s`` as a shape parameter for :math:`s`.
+
+    %(after_notes)s
+
+    Suppose a normally distributed random variable ``X`` has  mean ``mu`` and
+    standard deviation ``sigma``. Then ``Y = exp(X)`` is lognormally
+    distributed with ``s = sigma`` and ``scale = exp(mu)``.
+
+    %(example)s
+
+    The logarithm of a log-normally distributed random variable is
+    normally distributed:
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy import stats
+    >>> fig, ax = plt.subplots(1, 1)
+    >>> mu, sigma = 2, 0.5
+    >>> X = stats.norm(loc=mu, scale=sigma)
+    >>> Y = stats.lognorm(s=sigma, scale=np.exp(mu))
+    >>> x = np.linspace(*X.interval(0.999))
+    >>> y = Y.rvs(size=10000)
+    >>> ax.plot(x, X.pdf(x), label='X (pdf)')
+    >>> ax.hist(np.log(y), density=True, bins=x, label='log(Y) (histogram)')
+    >>> ax.legend()
+    >>> plt.show()
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return [_ShapeInfo("s", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, s, size=None, random_state=None):
+        return np.exp(s * random_state.standard_normal(size))
+
+    def _pdf(self, x, s):
+        # lognorm.pdf(x, s) = 1 / (s*x*sqrt(2*pi)) * exp(-1/2*(log(x)/s)**2)
+        return np.exp(self._logpdf(x, s))
+
+    def _logpdf(self, x, s):
+        return _lognorm_logpdf(x, s)
+
+    def _cdf(self, x, s):
+        return _norm_cdf(np.log(x) / s)
+
+    def _logcdf(self, x, s):
+        return _norm_logcdf(np.log(x) / s)
+
+    def _ppf(self, q, s):
+        return np.exp(s * _norm_ppf(q))
+
+    def _sf(self, x, s):
+        return _norm_sf(np.log(x) / s)
+
+    def _logsf(self, x, s):
+        return _norm_logsf(np.log(x) / s)
+
+    def _isf(self, q, s):
+        return np.exp(s * _norm_isf(q))
+
+    def _stats(self, s):
+        p = np.exp(s*s)
+        mu = np.sqrt(p)
+        mu2 = p*(p-1)
+        g1 = np.sqrt(p-1)*(2+p)
+        g2 = np.polyval([1, 2, 3, 0, -6.0], p)
+        return mu, mu2, g1, g2
+
+    def _entropy(self, s):
+        return 0.5 * (1 + np.log(2*np.pi) + 2 * np.log(s))
+
+    @_call_super_mom
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        When `method='MLE'` and
+        the location parameter is fixed by using the `floc` argument,
+        this function uses explicit formulas for the maximum likelihood
+        estimation of the log-normal shape and scale parameters, so the
+        `optimizer`, `loc` and `scale` keyword arguments are ignored.
+        If the location is free, a likelihood maximum is found by
+        setting its partial derivative w.r.t. location to 0, and
+        solving by substituting the analytical expressions of shape
+        and scale (or provided parameters).
+        See, e.g., equation 3.1 in
+        A. Clifford Cohen & Betty Jones Whitten (1980)
+        Estimation in the Three-Parameter Lognormal Distribution,
+        Journal of the American Statistical Association, 75:370, 399-404
+        https://doi.org/10.2307/2287466
+        \n\n""")
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        parameters = _check_fit_input_parameters(self, data, args, kwds)
+        data, fshape, floc, fscale = parameters
+        data_min = np.min(data)
+
+        def get_shape_scale(loc):
+            # Calculate maximum likelihood scale and shape with analytical
+            # formulas unless provided by the user
+            if fshape is None or fscale is None:
+                lndata = np.log(data - loc)
+            scale = fscale or np.exp(lndata.mean())
+            shape = fshape or np.sqrt(np.mean((lndata - np.log(scale))**2))
+            return shape, scale
+
+        def dL_dLoc(loc):
+            # Derivative of (positive) LL w.r.t. loc
+            shape, scale = get_shape_scale(loc)
+            shifted = data - loc
+            return np.sum((1 + np.log(shifted/scale)/shape**2)/shifted)
+
+        def ll(loc):
+            # (Positive) log-likelihood
+            shape, scale = get_shape_scale(loc)
+            return -self.nnlf((shape, loc, scale), data)
+
+        if floc is None:
+            # The location must be less than the minimum of the data.
+            # Back off a bit to avoid numerical issues.
+            spacing = np.spacing(data_min)
+            rbrack = data_min - spacing
+
+            # Find the right end of the bracket by successive doubling of the
+            # distance to data_min. We're interested in a maximum LL, so the
+            # slope dL_dLoc_rbrack should be negative at the right end.
+            # optimization for later: share shape, scale
+            dL_dLoc_rbrack = dL_dLoc(rbrack)
+            ll_rbrack = ll(rbrack)
+            delta = 2 * spacing  # 2 * (data_min - rbrack)
+            while dL_dLoc_rbrack >= -1e-6:
+                rbrack = data_min - delta
+                dL_dLoc_rbrack = dL_dLoc(rbrack)
+                delta *= 2
+
+            if not np.isfinite(rbrack) or not np.isfinite(dL_dLoc_rbrack):
+                # If we never find a negative slope, either we missed it or the
+                # slope is always positive. It's usually the latter,
+                # which means
+                # loc = data_min - spacing
+                # But sometimes when shape and/or scale are fixed there are
+                # other issues, so be cautious.
+                return super().fit(data, *args, **kwds)
+
+            # Now find the left end of the bracket. Guess is `rbrack-1`
+            # unless that is too small of a difference to resolve. Double
+            # the size of the interval until the left end is found.
+            lbrack = np.minimum(np.nextafter(rbrack, -np.inf), rbrack-1)
+            dL_dLoc_lbrack = dL_dLoc(lbrack)
+            delta = 2 * (rbrack - lbrack)
+            while (np.isfinite(lbrack) and np.isfinite(dL_dLoc_lbrack)
+                   and np.sign(dL_dLoc_lbrack) == np.sign(dL_dLoc_rbrack)):
+                lbrack = rbrack - delta
+                dL_dLoc_lbrack = dL_dLoc(lbrack)
+                delta *= 2
+
+            # I don't recall observing this, but just in case...
+            if not np.isfinite(lbrack) or not np.isfinite(dL_dLoc_lbrack):
+                return super().fit(data, *args, **kwds)
+
+            # If we have a valid bracket, find the root
+            res = root_scalar(dL_dLoc, bracket=(lbrack, rbrack))
+            if not res.converged:
+                return super().fit(data, *args, **kwds)
+
+            # If the slope was positive near the minimum of the data,
+            # the maximum LL could be there instead of at the root. Compare
+            # the LL of the two points to decide.
+            ll_root = ll(res.root)
+            loc = res.root if ll_root > ll_rbrack else data_min-spacing
+
+        else:
+            if floc >= data_min:
+                raise FitDataError("lognorm", lower=0., upper=np.inf)
+            loc = floc
+
+        shape, scale = get_shape_scale(loc)
+        if not (self._argcheck(shape) and scale > 0):
+            return super().fit(data, *args, **kwds)
+        return shape, loc, scale
+
+
+lognorm = lognorm_gen(a=0.0, name='lognorm')
+
+
+class gibrat_gen(rv_continuous):
+    r"""A Gibrat continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `gibrat` is:
+
+    .. math::
+
+        f(x) = \frac{1}{x \sqrt{2\pi}} \exp(-\frac{1}{2} (\log(x))^2)
+
+    for :math:`x >= 0`.
+
+    `gibrat` is a special case of `lognorm` with ``s=1``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return np.exp(random_state.standard_normal(size))
+
+    def _pdf(self, x):
+        # gibrat.pdf(x) = 1/(x*sqrt(2*pi)) * exp(-1/2*(log(x))**2)
+        return np.exp(self._logpdf(x))
+
+    def _logpdf(self, x):
+        return _lognorm_logpdf(x, 1.0)
+
+    def _cdf(self, x):
+        return _norm_cdf(np.log(x))
+
+    def _ppf(self, q):
+        return np.exp(_norm_ppf(q))
+
+    def _sf(self, x):
+        return _norm_sf(np.log(x))
+
+    def _isf(self, p):
+        return np.exp(_norm_isf(p))
+
+    def _stats(self):
+        p = np.e
+        mu = np.sqrt(p)
+        mu2 = p * (p - 1)
+        g1 = np.sqrt(p - 1) * (2 + p)
+        g2 = np.polyval([1, 2, 3, 0, -6.0], p)
+        return mu, mu2, g1, g2
+
+    def _entropy(self):
+        return 0.5 * np.log(2 * np.pi) + 0.5
+
+
+gibrat = gibrat_gen(a=0.0, name='gibrat')
+
+
+class maxwell_gen(rv_continuous):
+    r"""A Maxwell continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    A special case of a `chi` distribution,  with ``df=3``, ``loc=0.0``,
+    and given ``scale = a``, where ``a`` is the parameter used in the
+    Mathworld description [1]_.
+
+    The probability density function for `maxwell` is:
+
+    .. math::
+
+        f(x) = \sqrt{2/\pi}x^2 \exp(-x^2/2)
+
+    for :math:`x >= 0`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] http://mathworld.wolfram.com/MaxwellDistribution.html
+
+    %(example)s
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return chi.rvs(3.0, size=size, random_state=random_state)
+
+    def _pdf(self, x):
+        # maxwell.pdf(x) = sqrt(2/pi)x**2 * exp(-x**2/2)
+        return _SQRT_2_OVER_PI*x*x*np.exp(-x*x/2.0)
+
+    def _logpdf(self, x):
+        # Allow x=0 without 'divide by zero' warnings
+        with np.errstate(divide='ignore'):
+            return _LOG_SQRT_2_OVER_PI + 2*np.log(x) - 0.5*x*x
+
+    def _cdf(self, x):
+        return sc.gammainc(1.5, x*x/2.0)
+
+    def _ppf(self, q):
+        return np.sqrt(2*sc.gammaincinv(1.5, q))
+
+    def _sf(self, x):
+        return sc.gammaincc(1.5, x*x/2.0)
+
+    def _isf(self, q):
+        return np.sqrt(2*sc.gammainccinv(1.5, q))
+
+    def _stats(self):
+        val = 3*np.pi-8
+        return (2*np.sqrt(2.0/np.pi),
+                3-8/np.pi,
+                np.sqrt(2)*(32-10*np.pi)/val**1.5,
+                (-12*np.pi*np.pi + 160*np.pi - 384) / val**2.0)
+
+    def _entropy(self):
+        return _EULER + 0.5*np.log(2*np.pi)-0.5
+
+
+maxwell = maxwell_gen(a=0.0, name='maxwell')
+
+
+class mielke_gen(rv_continuous):
+    r"""A Mielke Beta-Kappa / Dagum continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `mielke` is:
+
+    .. math::
+
+        f(x, k, s) = \frac{k x^{k-1}}{(1+x^s)^{1+k/s}}
+
+    for :math:`x > 0` and :math:`k, s > 0`. The distribution is sometimes
+    called Dagum distribution ([2]_). It was already defined in [3]_, called
+    a Burr Type III distribution (`burr` with parameters ``c=s`` and
+    ``d=k/s``).
+
+    `mielke` takes ``k`` and ``s`` as shape parameters.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Mielke, P.W., 1973 "Another Family of Distributions for Describing
+           and Analyzing Precipitation Data." J. Appl. Meteor., 12, 275-280
+    .. [2] Dagum, C., 1977 "A new model for personal income distribution."
+           Economie Appliquee, 33, 327-367.
+    .. [3] Burr, I. W. "Cumulative frequency functions", Annals of
+           Mathematical Statistics, 13(2), pp 215-232 (1942).
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        ik = _ShapeInfo("k", False, (0, np.inf), (False, False))
+        i_s = _ShapeInfo("s", False, (0, np.inf), (False, False))
+        return [ik, i_s]
+
+    def _pdf(self, x, k, s):
+        return k*x**(k-1.0) / (1.0+x**s)**(1.0+k*1.0/s)
+
+    def _logpdf(self, x, k, s):
+        # Allow x=0 without 'divide by zero' warnings.
+        with np.errstate(divide='ignore'):
+            return np.log(k) + np.log(x)*(k - 1) - np.log1p(x**s)*(1 + k/s)
+
+    def _cdf(self, x, k, s):
+        return x**k / (1.0+x**s)**(k*1.0/s)
+
+    def _ppf(self, q, k, s):
+        qsk = pow(q, s*1.0/k)
+        return pow(qsk/(1.0-qsk), 1.0/s)
+
+    def _munp(self, n, k, s):
+        def nth_moment(n, k, s):
+            # n-th moment is defined for -k < n < s
+            return sc.gamma((k+n)/s)*sc.gamma(1-n/s)/sc.gamma(k/s)
+
+        return xpx.apply_where(n < s, (n, k, s), nth_moment, fill_value=np.inf)
+
+
+mielke = mielke_gen(a=0.0, name='mielke')
+
+
+class kappa4_gen(rv_continuous):
+    r"""Kappa 4 parameter distribution.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for kappa4 is:
+
+    .. math::
+
+        f(x, h, k) = (1 - k x)^{1/k - 1} (1 - h (1 - k x)^{1/k})^{1/h-1}
+
+    if :math:`h` and :math:`k` are not equal to 0.
+
+    If :math:`h` or :math:`k` are zero then the pdf can be simplified:
+
+    :math:`h = 0` and :math:`k \neq 0`::
+
+        kappa4.pdf(x, h, k) = (1.0 - k*x)**(1.0/k - 1.0)*
+                              exp(-(1.0 - k*x)**(1.0/k))
+
+    :math:`h \neq 0` and :math:`k = 0`::
+
+        kappa4.pdf(x, h, k) = exp(-x)*(1.0 - h*exp(-x))**(1.0/h - 1.0)
+
+    :math:`h = 0` and :math:`k = 0`::
+
+        kappa4.pdf(x, h, k) = exp(-x)*exp(-exp(-x))
+
+    kappa4 takes :math:`h` and :math:`k` as shape parameters.
+
+    The kappa4 distribution returns other distributions when certain
+    :math:`h` and :math:`k` values are used.
+
+    +------+-------------+----------------+------------------+
+    | h    | k=0.0       | k=1.0          | -inf<=k<=inf     |
+    +======+=============+================+==================+
+    | -1.0 | Logistic    |                | Generalized      |
+    |      |             |                | Logistic(1)      |
+    |      |             |                |                  |
+    |      | logistic(x) |                |                  |
+    +------+-------------+----------------+------------------+
+    |  0.0 | Gumbel      | Reverse        | Generalized      |
+    |      |             | Exponential(2) | Extreme Value    |
+    |      |             |                |                  |
+    |      | gumbel_r(x) |                | genextreme(x, k) |
+    +------+-------------+----------------+------------------+
+    |  1.0 | Exponential | Uniform        | Generalized      |
+    |      |             |                | Pareto           |
+    |      |             |                |                  |
+    |      | expon(x)    | uniform(x)     | genpareto(x, -k) |
+    +------+-------------+----------------+------------------+
+
+    (1) There are at least five generalized logistic distributions.
+        Four are described here:
+        https://en.wikipedia.org/wiki/Generalized_logistic_distribution
+        The "fifth" one is the one kappa4 should match which currently
+        isn't implemented in scipy:
+        https://en.wikipedia.org/wiki/Talk:Generalized_logistic_distribution
+        https://www.mathwave.com/help/easyfit/html/analyses/distributions/gen_logistic.html
+    (2) This distribution is currently not in scipy.
+
+    References
+    ----------
+    J.C. Finney, "Optimization of a Skewed Logistic Distribution With Respect
+    to the Kolmogorov-Smirnov Test", A Dissertation Submitted to the Graduate
+    Faculty of the Louisiana State University and Agricultural and Mechanical
+    College, (August, 2004),
+    https://digitalcommons.lsu.edu/gradschool_dissertations/3672
+
+    J.R.M. Hosking, "The four-parameter kappa distribution". IBM J. Res.
+    Develop. 38 (3), 25 1-258 (1994).
+
+    B. Kumphon, A. Kaew-Man, P. Seenoi, "A Rainfall Distribution for the Lampao
+    Site in the Chi River Basin, Thailand", Journal of Water Resource and
+    Protection, vol. 4, 866-869, (2012).
+    :doi:`10.4236/jwarp.2012.410101`
+
+    C. Winchester, "On Estimation of the Four-Parameter Kappa Distribution", A
+    Thesis Submitted to Dalhousie University, Halifax, Nova Scotia, (March
+    2000).
+    http://www.nlc-bnc.ca/obj/s4/f2/dsk2/ftp01/MQ57336.pdf
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _argcheck(self, h, k):
+        shape = np.broadcast_arrays(h, k)[0].shape
+        return np.full(shape, fill_value=True)
+
+    def _shape_info(self):
+        ih = _ShapeInfo("h", False, (-np.inf, np.inf), (False, False))
+        ik = _ShapeInfo("k", False, (-np.inf, np.inf), (False, False))
+        return [ih, ik]
+
+    def _get_support(self, h, k):
+        condlist = [np.logical_and(h > 0, k > 0),
+                    np.logical_and(h > 0, k == 0),
+                    np.logical_and(h > 0, k < 0),
+                    np.logical_and(h <= 0, k > 0),
+                    np.logical_and(h <= 0, k == 0),
+                    np.logical_and(h <= 0, k < 0)]
+
+        def f0(h, k):
+            return (1.0 - np.float_power(h, -k))/k
+
+        def f1(h, k):
+            return np.log(h)
+
+        def f3(h, k):
+            a = np.empty(np.shape(h))
+            a[:] = -np.inf
+            return a
+
+        def f5(h, k):
+            return 1.0/k
+
+        _a = _lazyselect(condlist,
+                         [f0, f1, f0, f3, f3, f5],
+                         [h, k],
+                         default=np.nan)
+
+        def f0(h, k):
+            return 1.0/k
+
+        def f1(h, k):
+            a = np.empty(np.shape(h))
+            a[:] = np.inf
+            return a
+
+        _b = _lazyselect(condlist,
+                         [f0, f1, f1, f0, f1, f1],
+                         [h, k],
+                         default=np.nan)
+        return _a, _b
+
+    def _pdf(self, x, h, k):
+        # kappa4.pdf(x, h, k) = (1.0 - k*x)**(1.0/k - 1.0)*
+        #                       (1.0 - h*(1.0 - k*x)**(1.0/k))**(1.0/h-1)
+        return np.exp(self._logpdf(x, h, k))
+
+    def _logpdf(self, x, h, k):
+        condlist = [np.logical_and(h != 0, k != 0),
+                    np.logical_and(h == 0, k != 0),
+                    np.logical_and(h != 0, k == 0),
+                    np.logical_and(h == 0, k == 0)]
+
+        def f0(x, h, k):
+            '''pdf = (1.0 - k*x)**(1.0/k - 1.0)*(
+                      1.0 - h*(1.0 - k*x)**(1.0/k))**(1.0/h-1.0)
+               logpdf = ...
+            '''
+            return (sc.xlog1py(1.0/k - 1.0, -k*x) +
+                    sc.xlog1py(1.0/h - 1.0, -h*(1.0 - k*x)**(1.0/k)))
+
+        def f1(x, h, k):
+            '''pdf = (1.0 - k*x)**(1.0/k - 1.0)*np.exp(-(
+                      1.0 - k*x)**(1.0/k))
+               logpdf = ...
+            '''
+            return sc.xlog1py(1.0/k - 1.0, -k*x) - (1.0 - k*x)**(1.0/k)
+
+        def f2(x, h, k):
+            '''pdf = np.exp(-x)*(1.0 - h*np.exp(-x))**(1.0/h - 1.0)
+               logpdf = ...
+            '''
+            return -x + sc.xlog1py(1.0/h - 1.0, -h*np.exp(-x))
+
+        def f3(x, h, k):
+            '''pdf = np.exp(-x-np.exp(-x))
+               logpdf = ...
+            '''
+            return -x - np.exp(-x)
+
+        return _lazyselect(condlist,
+                           [f0, f1, f2, f3],
+                           [x, h, k],
+                           default=np.nan)
+
+    def _cdf(self, x, h, k):
+        return np.exp(self._logcdf(x, h, k))
+
+    def _logcdf(self, x, h, k):
+        condlist = [np.logical_and(h != 0, k != 0),
+                    np.logical_and(h == 0, k != 0),
+                    np.logical_and(h != 0, k == 0),
+                    np.logical_and(h == 0, k == 0)]
+
+        def f0(x, h, k):
+            '''cdf = (1.0 - h*(1.0 - k*x)**(1.0/k))**(1.0/h)
+               logcdf = ...
+            '''
+            return (1.0/h)*sc.log1p(-h*(1.0 - k*x)**(1.0/k))
+
+        def f1(x, h, k):
+            '''cdf = np.exp(-(1.0 - k*x)**(1.0/k))
+               logcdf = ...
+            '''
+            return -(1.0 - k*x)**(1.0/k)
+
+        def f2(x, h, k):
+            '''cdf = (1.0 - h*np.exp(-x))**(1.0/h)
+               logcdf = ...
+            '''
+            return (1.0/h)*sc.log1p(-h*np.exp(-x))
+
+        def f3(x, h, k):
+            '''cdf = np.exp(-np.exp(-x))
+               logcdf = ...
+            '''
+            return -np.exp(-x)
+
+        return _lazyselect(condlist,
+                           [f0, f1, f2, f3],
+                           [x, h, k],
+                           default=np.nan)
+
+    def _ppf(self, q, h, k):
+        condlist = [np.logical_and(h != 0, k != 0),
+                    np.logical_and(h == 0, k != 0),
+                    np.logical_and(h != 0, k == 0),
+                    np.logical_and(h == 0, k == 0)]
+
+        def f0(q, h, k):
+            return 1.0/k*(1.0 - ((1.0 - (q**h))/h)**k)
+
+        def f1(q, h, k):
+            return 1.0/k*(1.0 - (-np.log(q))**k)
+
+        def f2(q, h, k):
+            '''ppf = -np.log((1.0 - (q**h))/h)
+            '''
+            return -sc.log1p(-(q**h)) + np.log(h)
+
+        def f3(q, h, k):
+            return -np.log(-np.log(q))
+
+        return _lazyselect(condlist,
+                           [f0, f1, f2, f3],
+                           [q, h, k],
+                           default=np.nan)
+
+    def _get_stats_info(self, h, k):
+        condlist = [
+            np.logical_and(h < 0, k >= 0),
+            k < 0,
+        ]
+
+        def f0(h, k):
+            return (-1.0/h*k).astype(int)
+
+        def f1(h, k):
+            return (-1.0/k).astype(int)
+
+        return _lazyselect(condlist, [f0, f1], [h, k], default=5)
+
+    def _stats(self, h, k):
+        maxr = self._get_stats_info(h, k)
+        outputs = [None if np.any(r < maxr) else np.nan for r in range(1, 5)]
+        return outputs[:]
+
+    def _mom1_sc(self, m, *args):
+        maxr = self._get_stats_info(args[0], args[1])
+        if m >= maxr:
+            return np.nan
+        return integrate.quad(self._mom_integ1, 0, 1, args=(m,)+args)[0]
+
+
+kappa4 = kappa4_gen(name='kappa4')
+
+
+class kappa3_gen(rv_continuous):
+    r"""Kappa 3 parameter distribution.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `kappa3` is:
+
+    .. math::
+
+        f(x, a) = a (a + x^a)^{-(a + 1)/a}
+
+    for :math:`x > 0` and :math:`a > 0`.
+
+    `kappa3` takes ``a`` as a shape parameter for :math:`a`.
+
+    References
+    ----------
+    P.W. Mielke and E.S. Johnson, "Three-Parameter Kappa Distribution Maximum
+    Likelihood and Likelihood Ratio Tests", Methods in Weather Research,
+    701-707, (September, 1973),
+    :doi:`10.1175/1520-0493(1973)101<0701:TKDMLE>2.3.CO;2`
+
+    B. Kumphon, "Maximum Entropy and Maximum Likelihood Estimation for the
+    Three-Parameter Kappa Distribution", Open Journal of Statistics, vol 2,
+    415-419 (2012), :doi:`10.4236/ojs.2012.24050`
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, a):
+        # kappa3.pdf(x, a) = a*(a + x**a)**(-(a + 1)/a),     for x > 0
+        return a*(a + x**a)**(-1.0/a-1)
+
+    def _cdf(self, x, a):
+        return x*(a + x**a)**(-1.0/a)
+
+    def _sf(self, x, a):
+        x, a = np.broadcast_arrays(x, a)  # some code paths pass scalars
+        sf = super()._sf(x, a)
+
+        # When the SF is small, another formulation is typically more accurate.
+        # However, it blows up for large `a`, so use it only if it also returns
+        # a small value of the SF.
+        cutoff = 0.01
+        i = sf < cutoff
+        sf2 = -sc.expm1(sc.xlog1py(-1.0 / a[i], a[i] * x[i]**-a[i]))
+        i2 = sf2 > cutoff
+        sf2[i2] = sf[i][i2]  # replace bad values with original values
+
+        sf[i] = sf2
+        return sf
+
+    def _ppf(self, q, a):
+        return (a/(q**-a - 1.0))**(1.0/a)
+
+    def _isf(self, q, a):
+        lg = sc.xlog1py(-a, -q)
+        denom = sc.expm1(lg)
+        return (a / denom)**(1.0 / a)
+
+    def _stats(self, a):
+        outputs = [None if np.any(i < a) else np.nan for i in range(1, 5)]
+        return outputs[:]
+
+    def _mom1_sc(self, m, *args):
+        if np.any(m >= args[0]):
+            return np.nan
+        return integrate.quad(self._mom_integ1, 0, 1, args=(m,)+args)[0]
+
+
+kappa3 = kappa3_gen(a=0.0, name='kappa3')
+
+
+class moyal_gen(rv_continuous):
+    r"""A Moyal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `moyal` is:
+
+    .. math::
+
+        f(x) = \exp(-(x + \exp(-x))/2) / \sqrt{2\pi}
+
+    for a real number :math:`x`.
+
+    %(after_notes)s
+
+    This distribution has utility in high-energy physics and radiation
+    detection. It describes the energy loss of a charged relativistic
+    particle due to ionization of the medium [1]_. It also provides an
+    approximation for the Landau distribution. For an in depth description
+    see [2]_. For additional description, see [3]_.
+
+    References
+    ----------
+    .. [1] J.E. Moyal, "XXX. Theory of ionization fluctuations",
+           The London, Edinburgh, and Dublin Philosophical Magazine
+           and Journal of Science, vol 46, 263-280, (1955).
+           :doi:`10.1080/14786440308521076` (gated)
+    .. [2] G. Cordeiro et al., "The beta Moyal: A useful skew distribution",
+           International Journal of Research and Reviews in Applied Sciences,
+           vol 10, 171-192, (2012).
+           https://www.arpapress.com/files/volumes/vol10issue2/ijrras_10_2_02.pdf
+    .. [3] C. Walck, "Handbook on Statistical Distributions for
+           Experimentalists; International Report SUF-PFY/96-01", Chapter 26,
+           University of Stockholm: Stockholm, Sweden, (2007).
+           http://www.stat.rice.edu/~dobelman/textfiles/DistributionsHandbook.pdf
+
+    .. versionadded:: 1.1.0
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        u1 = gamma.rvs(a=0.5, scale=2, size=size,
+                       random_state=random_state)
+        return -np.log(u1)
+
+    def _pdf(self, x):
+        return np.exp(-0.5 * (x + np.exp(-x))) / np.sqrt(2*np.pi)
+
+    def _cdf(self, x):
+        return sc.erfc(np.exp(-0.5 * x) / np.sqrt(2))
+
+    def _sf(self, x):
+        return sc.erf(np.exp(-0.5 * x) / np.sqrt(2))
+
+    def _ppf(self, x):
+        return -np.log(2 * sc.erfcinv(x)**2)
+
+    def _stats(self):
+        mu = np.log(2) + np.euler_gamma
+        mu2 = np.pi**2 / 2
+        g1 = 28 * np.sqrt(2) * sc.zeta(3) / np.pi**3
+        g2 = 4.
+        return mu, mu2, g1, g2
+
+    def _munp(self, n):
+        if n == 1.0:
+            return np.log(2) + np.euler_gamma
+        elif n == 2.0:
+            return np.pi**2 / 2 + (np.log(2) + np.euler_gamma)**2
+        elif n == 3.0:
+            tmp1 = 1.5 * np.pi**2 * (np.log(2)+np.euler_gamma)
+            tmp2 = (np.log(2)+np.euler_gamma)**3
+            tmp3 = 14 * sc.zeta(3)
+            return tmp1 + tmp2 + tmp3
+        elif n == 4.0:
+            tmp1 = 4 * 14 * sc.zeta(3) * (np.log(2) + np.euler_gamma)
+            tmp2 = 3 * np.pi**2 * (np.log(2) + np.euler_gamma)**2
+            tmp3 = (np.log(2) + np.euler_gamma)**4
+            tmp4 = 7 * np.pi**4 / 4
+            return tmp1 + tmp2 + tmp3 + tmp4
+        else:
+            # return generic for higher moments
+            # return rv_continuous._mom1_sc(self, n, b)
+            return self._mom1_sc(n)
+
+
+moyal = moyal_gen(name="moyal")
+
+
+class nakagami_gen(rv_continuous):
+    r"""A Nakagami continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `nakagami` is:
+
+    .. math::
+
+        f(x, \nu) = \frac{2 \nu^\nu}{\Gamma(\nu)} x^{2\nu-1} \exp(-\nu x^2)
+
+    for :math:`x >= 0`, :math:`\nu > 0`. The distribution was introduced in
+    [2]_, see also [1]_ for further information.
+
+    `nakagami` takes ``nu`` as a shape parameter for :math:`\nu`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] "Nakagami distribution", Wikipedia
+           https://en.wikipedia.org/wiki/Nakagami_distribution
+    .. [2] M. Nakagami, "The m-distribution - A general formula of intensity
+           distribution of rapid fading", Statistical methods in radio wave
+           propagation, Pergamon Press, 1960, 3-36.
+           :doi:`10.1016/B978-0-08-009306-2.50005-4`
+
+    %(example)s
+
+    """
+    def _argcheck(self, nu):
+        return nu > 0
+
+    def _shape_info(self):
+        return [_ShapeInfo("nu", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, nu):
+        return np.exp(self._logpdf(x, nu))
+
+    def _logpdf(self, x, nu):
+        # nakagami.pdf(x, nu) = 2 * nu**nu / gamma(nu) *
+        #                       x**(2*nu-1) * exp(-nu*x**2)
+        return (np.log(2) + sc.xlogy(nu, nu) - sc.gammaln(nu) +
+                sc.xlogy(2*nu - 1, x) - nu*x**2)
+
+    def _cdf(self, x, nu):
+        return sc.gammainc(nu, nu*x*x)
+
+    def _ppf(self, q, nu):
+        return np.sqrt(1.0/nu*sc.gammaincinv(nu, q))
+
+    def _sf(self, x, nu):
+        return sc.gammaincc(nu, nu*x*x)
+
+    def _isf(self, p, nu):
+        return np.sqrt(1/nu * sc.gammainccinv(nu, p))
+
+    def _stats(self, nu):
+        mu = sc.poch(nu, 0.5)/np.sqrt(nu)
+        mu2 = 1.0-mu*mu
+        g1 = mu * (1 - 4*nu*mu2) / 2.0 / nu / np.power(mu2, 1.5)
+        g2 = -6*mu**4*nu + (8*nu-2)*mu**2-2*nu + 1
+        g2 /= nu*mu2**2.0
+        return mu, mu2, g1, g2
+
+    def _entropy(self, nu):
+        shape = np.shape(nu)
+        # because somehow this isn't taken care of by the infrastructure...
+        nu = np.atleast_1d(nu)
+        A = sc.gammaln(nu)
+        B = nu - (nu - 0.5) * sc.digamma(nu)
+        C = -0.5 * np.log(nu) - np.log(2)
+        h = A + B + C
+        # This is the asymptotic sum of A and B (see gh-17868)
+        norm_entropy = stats.norm._entropy()
+        # Above, this is lost to rounding error for large nu, so use the
+        # asymptotic sum when the approximation becomes accurate
+        i = nu > 5e4  # roundoff error ~ approximation error
+        # -1 / (12 * nu) is the O(1/nu) term; see gh-17929
+        h[i] = C[i] + norm_entropy - 1/(12*nu[i])
+        return h.reshape(shape)[()]
+
+    def _rvs(self, nu, size=None, random_state=None):
+        # this relationship can be found in [1] or by a direct calculation
+        return np.sqrt(random_state.standard_gamma(nu, size=size) / nu)
+
+    def _fitstart(self, data, args=None):
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        if args is None:
+            args = (1.0,) * self.numargs
+        # Analytical justified estimates
+        # see: https://docs.scipy.org/doc/scipy/reference/tutorial/stats/continuous_nakagami.html
+        loc = np.min(data)
+        scale = np.sqrt(np.sum((data - loc)**2) / len(data))
+        return args + (loc, scale)
+
+
+nakagami = nakagami_gen(a=0.0, name="nakagami")
+
+
+# The function name ncx2 is an abbreviation for noncentral chi squared.
+def _ncx2_log_pdf(x, df, nc):
+    # We use (xs**2 + ns**2)/2 = (xs - ns)**2/2  + xs*ns, and include the
+    # factor of exp(-xs*ns) into the ive function to improve numerical
+    # stability at large values of xs. See also `rice.pdf`.
+    df2 = df/2.0 - 1.0
+    xs, ns = np.sqrt(x), np.sqrt(nc)
+    res = sc.xlogy(df2/2.0, x/nc) - 0.5*(xs - ns)**2
+    corr = sc.ive(df2, xs*ns) / 2.0
+    # Return res + np.log(corr) avoiding np.log(0)
+    return xpx.apply_where(
+        corr > 0,
+        (res, corr),
+        lambda r, c: r + np.log(c),
+        fill_value=-np.inf)
+
+
+class ncx2_gen(rv_continuous):
+    r"""A non-central chi-squared continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `ncx2` is:
+
+    .. math::
+
+        f(x, k, \lambda) = \frac{1}{2} \exp(-(\lambda+x)/2)
+            (x/\lambda)^{(k-2)/4}  I_{(k-2)/2}(\sqrt{\lambda x})
+
+    for :math:`x >= 0`, :math:`k > 0` and :math:`\lambda \ge 0`.
+    :math:`k` specifies the degrees of freedom (denoted ``df`` in the
+    implementation) and :math:`\lambda` is the non-centrality parameter
+    (denoted ``nc`` in the implementation). :math:`I_\nu` denotes the
+    modified Bessel function of first order of degree :math:`\nu`
+    (`scipy.special.iv`).
+
+    `ncx2` takes ``df`` and ``nc`` as shape parameters.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``pdf``, ``cdf``, ``ppf``, ``sf`` and ``isf``
+    methods. [1]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    def _argcheck(self, df, nc):
+        return (df > 0) & np.isfinite(df) & (nc >= 0)
+
+    def _shape_info(self):
+        idf = _ShapeInfo("df", False, (0, np.inf), (False, False))
+        inc = _ShapeInfo("nc", False, (0, np.inf), (True, False))
+        return [idf, inc]
+
+    def _rvs(self, df, nc, size=None, random_state=None):
+        return random_state.noncentral_chisquare(df, nc, size)
+
+    def _logpdf(self, x, df, nc):
+        return xpx.apply_where(nc != 0, (x, df, nc), _ncx2_log_pdf,
+                               lambda x, df, _: chi2._logpdf(x, df))
+
+    def _pdf(self, x, df, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return xpx.apply_where(nc != 0, (x, df, nc), scu._ncx2_pdf,
+                                   lambda x, df, _: chi2._pdf(x, df))
+
+    def _cdf(self, x, df, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return xpx.apply_where(nc != 0, (x, df, nc), sc.chndtr,
+                                   lambda x, df, _: chi2._cdf(x, df))
+
+    def _ppf(self, q, df, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return xpx.apply_where(nc != 0, (q, df, nc), sc.chndtrix,
+                                   lambda x, df, _: chi2._ppf(x, df))
+
+    def _sf(self, x, df, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return xpx.apply_where(nc != 0, (x, df, nc), scu._ncx2_sf,
+                                   lambda x, df, _: chi2._sf(x, df))
+
+    def _isf(self, x, df, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return xpx.apply_where(nc != 0, (x, df, nc), scu._ncx2_isf,
+                                   lambda x, df, _: chi2._isf(x, df))
+
+    def _stats(self, df, nc):
+        _ncx2_mean = df + nc
+        def k_plus_cl(k, l, c):
+            return k + c*l
+        _ncx2_variance =  2.0 * k_plus_cl(df, nc, 2.0)
+        _ncx2_skewness = (np.sqrt(8.0) * k_plus_cl(df, nc, 3) /
+                          np.sqrt(k_plus_cl(df, nc, 2.0)**3))
+        _ncx2_kurtosis_excess = (12.0 * k_plus_cl(df, nc, 4.0) /
+                                 k_plus_cl(df, nc, 2.0)**2)
+        return (
+            _ncx2_mean,
+            _ncx2_variance,
+            _ncx2_skewness,
+            _ncx2_kurtosis_excess,
+        )
+
+
+ncx2 = ncx2_gen(a=0.0, name='ncx2')
+
+
+class ncf_gen(rv_continuous):
+    r"""A non-central F distribution continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    scipy.stats.f : Fisher distribution
+
+    Notes
+    -----
+    The probability density function for `ncf` is:
+
+    .. math::
+
+        f(x, n_1, n_2, \lambda) =
+            \exp\left(\frac{\lambda}{2} +
+                      \lambda n_1 \frac{x}{2(n_1 x + n_2)}
+                \right)
+            n_1^{n_1/2} n_2^{n_2/2} x^{n_1/2 - 1} \\
+            (n_2 + n_1 x)^{-(n_1 + n_2)/2}
+            \gamma(n_1/2) \gamma(1 + n_2/2) \\
+            \frac{L^{\frac{n_1}{2}-1}_{n_2/2}
+                \left(-\lambda n_1 \frac{x}{2(n_1 x + n_2)}\right)}
+            {B(n_1/2, n_2/2)
+                \gamma\left(\frac{n_1 + n_2}{2}\right)}
+
+    for :math:`n_1, n_2 > 0`, :math:`\lambda \ge 0`.  Here :math:`n_1` is the
+    degrees of freedom in the numerator, :math:`n_2` the degrees of freedom in
+    the denominator, :math:`\lambda` the non-centrality parameter,
+    :math:`\gamma` is the logarithm of the Gamma function, :math:`L_n^k` is a
+    generalized Laguerre polynomial and :math:`B` is the beta function.
+
+    `ncf` takes ``dfn``, ``dfd`` and ``nc`` as shape parameters. If ``nc=0``,
+    the distribution becomes equivalent to the Fisher distribution.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``pdf``, ``cdf``, ``ppf``, ``stats``, ``sf`` and
+    ``isf`` methods. [1]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    def _argcheck(self, dfn, dfd, nc):
+        return (dfn > 0) & (dfd > 0) & (nc >= 0)
+
+    def _shape_info(self):
+        idf1 = _ShapeInfo("dfn", False, (0, np.inf), (False, False))
+        idf2 = _ShapeInfo("dfd", False, (0, np.inf), (False, False))
+        inc = _ShapeInfo("nc", False, (0, np.inf), (True, False))
+        return [idf1, idf2, inc]
+
+    def _rvs(self, dfn, dfd, nc, size=None, random_state=None):
+        return random_state.noncentral_f(dfn, dfd, nc, size)
+
+    def _pdf(self, x, dfn, dfd, nc):
+        return scu._ncf_pdf(x, dfn, dfd, nc)
+
+    def _cdf(self, x, dfn, dfd, nc):
+        return sc.ncfdtr(dfn, dfd, nc, x)
+
+    def _ppf(self, q, dfn, dfd, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return sc.ncfdtri(dfn, dfd, nc, q)
+
+    def _sf(self, x, dfn, dfd, nc):
+        return scu._ncf_sf(x, dfn, dfd, nc)
+
+    def _isf(self, x, dfn, dfd, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return scu._ncf_isf(x, dfn, dfd, nc)
+
+    # # Produces bogus values as written - maybe it's close, though?
+    # def _munp(self, n, dfn, dfd, nc):
+    #     val = (dfn * 1.0/dfd)**n
+    #     term = sc.gammaln(n+0.5*dfn) + sc.gammaln(0.5*dfd-n) - sc.gammaln(dfd*0.5)
+    #     val *= np.exp(-nc / 2.0+term)
+    #     val *= sc.hyp1f1(n+0.5*dfn, 0.5*dfn, 0.5*nc)
+    #     return val
+
+    def _stats(self, dfn, dfd, nc, moments='mv'):
+        mu = scu._ncf_mean(dfn, dfd, nc)
+        mu2 = scu._ncf_variance(dfn, dfd, nc)
+        g1 = scu._ncf_skewness(dfn, dfd, nc) if 's' in moments else None
+        g2 = scu._ncf_kurtosis_excess(  # isn't really excess kurtosis!
+            dfn, dfd, nc) - 3 if 'k' in moments else None
+        # Mathematica: Kurtosis[NoncentralFRatioDistribution[27, 27, 0.415784417992261]]
+        return mu, mu2, g1, g2
+
+
+ncf = ncf_gen(a=0.0, name='ncf')
+
+
+class t_gen(rv_continuous):
+    r"""A Student's t continuous random variable.
+
+    For the noncentral t distribution, see `nct`.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    nct
+
+    Notes
+    -----
+    The probability density function for `t` is:
+
+    .. math::
+
+        f(x, \nu) = \frac{\Gamma((\nu+1)/2)}
+                        {\sqrt{\pi \nu} \Gamma(\nu/2)}
+                    (1+x^2/\nu)^{-(\nu+1)/2}
+
+    where :math:`x` is a real number and the degrees of freedom parameter
+    :math:`\nu` (denoted ``df`` in the implementation) satisfies
+    :math:`\nu > 0`. :math:`\Gamma` is the gamma function
+    (`scipy.special.gamma`).
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("df", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, df, size=None, random_state=None):
+        return random_state.standard_t(df, size=size)
+
+    def _pdf(self, x, df):
+        return xpx.apply_where(
+            df == np.inf, (x, df),
+            lambda x, df: norm._pdf(x),
+            lambda x, df: np.exp(self._logpdf(x, df)))
+
+    def _logpdf(self, x, df):
+
+        def t_logpdf(x, df):
+            return (np.log(sc.poch(0.5 * df, 0.5))
+                    - 0.5 * (np.log(df) + np.log(np.pi))
+                    - (df + 1)/2*np.log1p(x * x/df))
+
+        def norm_logpdf(x, df):
+            return norm._logpdf(x)
+
+        return xpx.apply_where(df == np.inf, (x, df), norm_logpdf, t_logpdf)
+
+    def _cdf(self, x, df):
+        return sc.stdtr(df, x)
+
+    def _sf(self, x, df):
+        return sc.stdtr(df, -x)
+
+    def _ppf(self, q, df):
+        return sc.stdtrit(df, q)
+
+    def _isf(self, q, df):
+        return -sc.stdtrit(df, q)
+
+    def _stats(self, df):
+        # infinite df -> normal distribution (0.0, 1.0, 0.0, 0.0)
+        infinite_df = np.isposinf(df)
+
+        mu = np.where(df > 1, 0.0, np.inf)
+
+        condlist = ((df > 1) & (df <= 2),
+                    (df > 2) & np.isfinite(df),
+                    infinite_df)
+        choicelist = (lambda df: np.broadcast_to(np.inf, df.shape),
+                      lambda df: df / (df-2.0),
+                      lambda df: np.broadcast_to(1, df.shape))
+        mu2 = _lazyselect(condlist, choicelist, (df,), np.nan)
+
+        g1 = np.where(df > 3, 0.0, np.nan)
+
+        condlist = ((df > 2) & (df <= 4),
+                    (df > 4) & np.isfinite(df),
+                    infinite_df)
+        choicelist = (lambda df: np.broadcast_to(np.inf, df.shape),
+                      lambda df: 6.0 / (df-4.0),
+                      lambda df: np.broadcast_to(0, df.shape))
+        g2 = _lazyselect(condlist, choicelist, (df,), np.nan)
+
+        return mu, mu2, g1, g2
+
+    def _entropy(self, df):
+
+        def regular(df):
+            half = df/2
+            half1 = (df + 1)/2
+            return (half1*(sc.digamma(half1) - sc.digamma(half))
+                    + np.log(np.sqrt(df)*sc.beta(half, 0.5)))
+
+        def asymptotic(df):
+            # Formula from Wolfram Alpha:
+            # "asymptotic expansion (d+1)/2 * (digamma((d+1)/2) - digamma(d/2))
+            #  + log(sqrt(d) * beta(d/2, 1/2))"
+            h = (norm._entropy() + 1/df + (df**-2.)/4 - (df**-3.)/6
+                 - (df**-4.)/8 + 3/10*(df**-5.) + (df**-6.)/4)
+            return h
+
+        return xpx.apply_where(df >= 100, df, asymptotic, regular)
+
+
+t = t_gen(name='t')
+
+
+class nct_gen(rv_continuous):
+    r"""A non-central Student's t continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    If :math:`Y` is a standard normal random variable and :math:`V` is
+    an independent chi-square random variable (`chi2`) with :math:`k` degrees
+    of freedom, then
+
+    .. math::
+
+        X = \frac{Y + c}{\sqrt{V/k}}
+
+    has a non-central Student's t distribution on the real line.
+    The degrees of freedom parameter :math:`k` (denoted ``df`` in the
+    implementation) satisfies :math:`k > 0` and the noncentrality parameter
+    :math:`c` (denoted ``nc`` in the implementation) is a real number.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``pdf``, ``cdf``, ``ppf``, ``sf`` and ``isf``
+    methods. [1]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    def _argcheck(self, df, nc):
+        return (df > 0) & (nc == nc)
+
+    def _shape_info(self):
+        idf = _ShapeInfo("df", False, (0, np.inf), (False, False))
+        inc = _ShapeInfo("nc", False, (-np.inf, np.inf), (False, False))
+        return [idf, inc]
+
+    def _rvs(self, df, nc, size=None, random_state=None):
+        n = norm.rvs(loc=nc, size=size, random_state=random_state)
+        c2 = chi2.rvs(df, size=size, random_state=random_state)
+        return n * np.sqrt(df) / np.sqrt(c2)
+
+    def _pdf(self, x, df, nc):
+        return scu._nct_pdf(x, df, nc)
+
+    def _cdf(self, x, df, nc):
+        return sc.nctdtr(df, nc, x)
+
+    def _ppf(self, q, df, nc):
+        return sc.nctdtrit(df, nc, q)
+
+    def _sf(self, x, df, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return np.clip(scu._nct_sf(x, df, nc), 0, 1)
+
+    def _isf(self, x, df, nc):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return scu._nct_isf(x, df, nc)
+
+    def _stats(self, df, nc, moments='mv'):
+        mu = scu._nct_mean(df, nc)
+        mu2 = scu._nct_variance(df, nc)
+        g1 = scu._nct_skewness(df, nc) if 's' in moments else None
+        g2 = scu._nct_kurtosis_excess(df, nc) if 'k' in moments else None
+        return mu, mu2, g1, g2
+
+
+nct = nct_gen(name="nct")
+
+
+class pareto_gen(rv_continuous):
+    r"""A Pareto continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `pareto` is:
+
+    .. math::
+
+        f(x, b) = \frac{b}{x^{b+1}}
+
+    for :math:`x \ge 1`, :math:`b > 0`.
+
+    `pareto` takes ``b`` as a shape parameter for :math:`b`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("b", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, b):
+        # pareto.pdf(x, b) = b / x**(b+1)
+        return b * x**(-b-1)
+
+    def _cdf(self, x, b):
+        return 1 - x**(-b)
+
+    def _ppf(self, q, b):
+        return pow(1-q, -1.0/b)
+
+    def _sf(self, x, b):
+        return x**(-b)
+
+    def _isf(self, q, b):
+        return np.power(q, -1.0 / b)
+
+    def _stats(self, b, moments='mv'):
+        mu, mu2, g1, g2 = None, None, None, None
+        if 'm' in moments:
+            mask = b > 1
+            bt = np.extract(mask, b)
+            mu = np.full(np.shape(b), fill_value=np.inf)
+            np.place(mu, mask, bt / (bt-1.0))
+        if 'v' in moments:
+            mask = b > 2
+            bt = np.extract(mask, b)
+            mu2 = np.full(np.shape(b), fill_value=np.inf)
+            np.place(mu2, mask, bt / (bt-2.0) / (bt-1.0)**2)
+        if 's' in moments:
+            mask = b > 3
+            bt = np.extract(mask, b)
+            g1 = np.full(np.shape(b), fill_value=np.nan)
+            vals = 2 * (bt + 1.0) * np.sqrt(bt - 2.0) / ((bt - 3.0) * np.sqrt(bt))
+            np.place(g1, mask, vals)
+        if 'k' in moments:
+            mask = b > 4
+            bt = np.extract(mask, b)
+            g2 = np.full(np.shape(b), fill_value=np.nan)
+            vals = (6.0*np.polyval([1.0, 1.0, -6, -2], bt) /
+                    np.polyval([1.0, -7.0, 12.0, 0.0], bt))
+            np.place(g2, mask, vals)
+        return mu, mu2, g1, g2
+
+    def _entropy(self, b):
+        return 1 + 1.0/b - np.log(b)
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        parameters = _check_fit_input_parameters(self, data, args, kwds)
+        data, fshape, floc, fscale = parameters
+
+        # ensure that any fixed parameters don't violate constraints of the
+        # distribution before continuing.
+        if floc is not None and np.min(data) - floc < (fscale or 0):
+            raise FitDataError("pareto", lower=1, upper=np.inf)
+
+        ndata = data.shape[0]
+
+        def get_shape(scale, location):
+            # The first-order necessary condition on `shape` can be solved in
+            # closed form
+            return ndata / np.sum(np.log((data - location) / scale))
+
+        if floc is fscale is None:
+            # The support of the distribution is `(x - loc)/scale > 0`.
+            # The method of Lagrange multipliers turns this constraint
+            # into an equation that can be solved numerically.
+            # See gh-12545 for details.
+
+            def dL_dScale(shape, scale):
+                # The partial derivative of the log-likelihood function w.r.t.
+                # the scale.
+                return ndata * shape / scale
+
+            def dL_dLocation(shape, location):
+                # The partial derivative of the log-likelihood function w.r.t.
+                # the location.
+                return (shape + 1) * np.sum(1 / (data - location))
+
+            def fun_to_solve(scale):
+                # optimize the scale by setting the partial derivatives
+                # w.r.t. location and scale equal and solving.
+                location = np.min(data) - scale
+                shape = fshape or get_shape(scale, location)
+                return dL_dLocation(shape, location) - dL_dScale(shape, scale)
+
+            def interval_contains_root(lbrack, rbrack):
+                # return true if the signs disagree.
+                return (np.sign(fun_to_solve(lbrack)) !=
+                        np.sign(fun_to_solve(rbrack)))
+
+            # set brackets for `root_scalar` to use when optimizing over the
+            # scale such that a root is likely between them. Use user supplied
+            # guess or default 1.
+            brack_start = float(kwds.get('scale', 1))
+            lbrack, rbrack = brack_start / 2, brack_start * 2
+            # if a root is not between the brackets, iteratively expand them
+            # until they include a sign change, checking after each bracket is
+            # modified.
+            while (not interval_contains_root(lbrack, rbrack)
+                   and (lbrack > 0 or rbrack < np.inf)):
+                lbrack /= 2
+                rbrack *= 2
+            res = root_scalar(fun_to_solve, bracket=[lbrack, rbrack])
+            if res.converged:
+                scale = res.root
+                loc = np.min(data) - scale
+                shape = fshape or get_shape(scale, loc)
+
+                # The Pareto distribution requires that its parameters satisfy
+                # the condition `fscale + floc <= min(data)`. However, to
+                # avoid numerical issues, we require that `fscale + floc`
+                # is strictly less than `min(data)`. If this condition
+                # is not satisfied, reduce the scale with `np.nextafter` to
+                # ensure that data does not fall outside of the support.
+                if not (scale + loc) < np.min(data):
+                    scale = np.min(data) - loc
+                    scale = np.nextafter(scale, 0)
+                return shape, loc, scale
+            else:
+                return super().fit(data, **kwds)
+        elif floc is None:
+            loc = np.min(data) - fscale
+        else:
+            loc = floc
+        # Source: Evans, Hastings, and Peacock (2000), Statistical
+        # Distributions, 3rd. Ed., John Wiley and Sons. Page 149.
+        scale = fscale or np.min(data) - loc
+        shape = fshape or get_shape(scale, loc)
+        return shape, loc, scale
+
+
+pareto = pareto_gen(a=1.0, name="pareto")
+
+
+class lomax_gen(rv_continuous):
+    r"""A Lomax (Pareto of the second kind) continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `lomax` is:
+
+    .. math::
+
+        f(x, c) = \frac{c}{(1+x)^{c+1}}
+
+    for :math:`x \ge 0`, :math:`c > 0`.
+
+    `lomax` takes ``c`` as a shape parameter for :math:`c`.
+
+    `lomax` is a special case of `pareto` with ``loc=-1.0``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # lomax.pdf(x, c) = c / (1+x)**(c+1)
+        return c*1.0/(1.0+x)**(c+1.0)
+
+    def _logpdf(self, x, c):
+        return np.log(c) - (c+1)*sc.log1p(x)
+
+    def _cdf(self, x, c):
+        return -sc.expm1(-c*sc.log1p(x))
+
+    def _sf(self, x, c):
+        return np.exp(-c*sc.log1p(x))
+
+    def _logsf(self, x, c):
+        return -c*sc.log1p(x)
+
+    def _ppf(self, q, c):
+        return sc.expm1(-sc.log1p(-q)/c)
+
+    def _isf(self, q, c):
+        return q**(-1.0 / c) - 1
+
+    def _stats(self, c):
+        mu, mu2, g1, g2 = pareto.stats(c, loc=-1.0, moments='mvsk')
+        return mu, mu2, g1, g2
+
+    def _entropy(self, c):
+        return 1+1.0/c-np.log(c)
+
+
+lomax = lomax_gen(a=0.0, name="lomax")
+
+
+class pearson3_gen(rv_continuous):
+    r"""
+    A pearson type III continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `pearson3` is:
+
+    .. math::
+
+        f(x, \kappa) = \frac{|\beta|}{\Gamma(\alpha)}
+                       (\beta (x - \zeta))^{\alpha - 1}
+                       \exp(-\beta (x - \zeta))
+
+    where:
+
+    .. math::
+
+            \beta = \frac{2}{\kappa}
+
+            \alpha = \beta^2 = \frac{4}{\kappa^2}
+
+            \zeta = -\frac{\alpha}{\beta} = -\beta
+
+    :math:`\Gamma` is the gamma function (`scipy.special.gamma`).
+    Pass the skew :math:`\kappa` into `pearson3` as the shape parameter
+    ``skew``.
+
+    %(after_notes)s
+
+    References
+    ----------
+    R.W. Vogel and D.E. McMartin, "Probability Plot Goodness-of-Fit and
+    Skewness Estimation Procedures for the Pearson Type 3 Distribution", Water
+    Resources Research, Vol.27, 3149-3158 (1991).
+
+    L.R. Salvosa, "Tables of Pearson's Type III Function", Ann. Math. Statist.,
+    Vol.1, 191-198 (1930).
+
+    "Using Modern Computing Tools to Fit the Pearson Type III Distribution to
+    Aviation Loads Data", Office of Aviation Research (2003).
+
+    %(example)s
+    """
+    def _preprocess(self, x, skew):
+        # The real 'loc' and 'scale' are handled in the calling pdf(...). The
+        # local variables 'loc' and 'scale' within pearson3._pdf are set to
+        # the defaults just to keep them as part of the equations for
+        # documentation.
+        loc = 0.0
+        scale = 1.0
+
+        # If skew is small, return _norm_pdf. The divide between pearson3
+        # and norm was found by brute force and is approximately a skew of
+        # 0.000016.  No one, I hope, would actually use a skew value even
+        # close to this small.
+        norm2pearson_transition = 0.000016
+
+        ans, x, skew = np.broadcast_arrays(1.0, x, skew)
+        ans = ans.copy()
+
+        # mask is True where skew is small enough to use the normal approx.
+        mask = np.absolute(skew) < norm2pearson_transition
+        invmask = ~mask
+
+        beta = 2.0 / (skew[invmask] * scale)
+        alpha = (scale * beta)**2
+        zeta = loc - alpha / beta
+
+        transx = beta * (x[invmask] - zeta)
+        return ans, x, transx, mask, invmask, beta, alpha, zeta
+
+    def _argcheck(self, skew):
+        # The _argcheck function in rv_continuous only allows positive
+        # arguments.  The skew argument for pearson3 can be zero (which I want
+        # to handle inside pearson3._pdf) or negative.  So just return True
+        # for all skew args.
+        return np.isfinite(skew)
+
+    def _shape_info(self):
+        return [_ShapeInfo("skew", False, (-np.inf, np.inf), (False, False))]
+
+    def _stats(self, skew):
+        m = 0.0
+        v = 1.0
+        s = skew
+        k = 1.5*skew**2
+        return m, v, s, k
+
+    def _pdf(self, x, skew):
+        # pearson3.pdf(x, skew) = abs(beta) / gamma(alpha) *
+        #     (beta * (x - zeta))**(alpha - 1) * exp(-beta*(x - zeta))
+        # Do the calculation in _logpdf since helps to limit
+        # overflow/underflow problems
+        ans = np.exp(self._logpdf(x, skew))
+        if ans.ndim == 0:
+            if np.isnan(ans):
+                return 0.0
+            return ans
+        ans[np.isnan(ans)] = 0.0
+        return ans
+
+    def _logpdf(self, x, skew):
+        #   PEARSON3 logpdf                           GAMMA logpdf
+        #   np.log(abs(beta))
+        # + (alpha - 1)*np.log(beta*(x - zeta))          + (a - 1)*np.log(x)
+        # - beta*(x - zeta)                           - x
+        # - sc.gammalnalpha)                              - sc.gammalna)
+        ans, x, transx, mask, invmask, beta, alpha, _ = (
+            self._preprocess(x, skew))
+
+        ans[mask] = np.log(_norm_pdf(x[mask]))
+        # use logpdf instead of _logpdf to fix issue mentioned in gh-12640
+        # (_logpdf does not return correct result for alpha = 1)
+        ans[invmask] = np.log(abs(beta)) + gamma.logpdf(transx, alpha)
+        return ans
+
+    def _cdf(self, x, skew):
+        ans, x, transx, mask, invmask, _, alpha, _ = (
+            self._preprocess(x, skew))
+
+        ans[mask] = _norm_cdf(x[mask])
+
+        skew = np.broadcast_to(skew, invmask.shape)
+        invmask1a = np.logical_and(invmask, skew > 0)
+        invmask1b = skew[invmask] > 0
+        # use cdf instead of _cdf to fix issue mentioned in gh-12640
+        # (_cdf produces NaNs for inputs outside support)
+        ans[invmask1a] = gamma.cdf(transx[invmask1b], alpha[invmask1b])
+
+        # The gamma._cdf approach wasn't working with negative skew.
+        # Note that multiplying the skew by -1 reflects about x=0.
+        # So instead of evaluating the CDF with negative skew at x,
+        # evaluate the SF with positive skew at -x.
+        invmask2a = np.logical_and(invmask, skew < 0)
+        invmask2b = skew[invmask] < 0
+        # gamma._sf produces NaNs when transx < 0, so use gamma.sf
+        ans[invmask2a] = gamma.sf(transx[invmask2b], alpha[invmask2b])
+
+        return ans
+
+    def _sf(self, x, skew):
+        ans, x, transx, mask, invmask, _, alpha, _ = (
+            self._preprocess(x, skew))
+
+        ans[mask] = _norm_sf(x[mask])
+
+        skew = np.broadcast_to(skew, invmask.shape)
+        invmask1a = np.logical_and(invmask, skew > 0)
+        invmask1b = skew[invmask] > 0
+        ans[invmask1a] = gamma.sf(transx[invmask1b], alpha[invmask1b])
+
+        invmask2a = np.logical_and(invmask, skew < 0)
+        invmask2b = skew[invmask] < 0
+        ans[invmask2a] = gamma.cdf(transx[invmask2b], alpha[invmask2b])
+
+        return ans
+
+    def _rvs(self, skew, size=None, random_state=None):
+        skew = np.broadcast_to(skew, size)
+        ans, _, _, mask, invmask, beta, alpha, zeta = (
+            self._preprocess([0], skew))
+
+        nsmall = mask.sum()
+        nbig = mask.size - nsmall
+        ans[mask] = random_state.standard_normal(nsmall)
+        ans[invmask] = random_state.standard_gamma(alpha, nbig)/beta + zeta
+
+        if size == ():
+            ans = ans[0]
+        return ans
+
+    def _ppf(self, q, skew):
+        ans, q, _, mask, invmask, beta, alpha, zeta = (
+            self._preprocess(q, skew))
+        ans[mask] = _norm_ppf(q[mask])
+        q = q[invmask]
+        q[beta < 0] = 1 - q[beta < 0]  # for negative skew; see gh-17050
+        ans[invmask] = sc.gammaincinv(alpha, q)/beta + zeta
+        return ans
+
+    @_call_super_mom
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        Note that method of moments (`method='MM'`) is not
+        available for this distribution.\n\n""")
+    def fit(self, data, *args, **kwds):
+        if kwds.get("method", None) == 'MM':
+            raise NotImplementedError("Fit `method='MM'` is not available for "
+                                      "the Pearson3 distribution. Please try "
+                                      "the default `method='MLE'`.")
+        else:
+            return super(type(self), self).fit(data, *args, **kwds)
+
+
+pearson3 = pearson3_gen(name="pearson3")
+
+
+class powerlaw_gen(rv_continuous):
+    r"""A power-function continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    pareto
+
+    Notes
+    -----
+    The probability density function for `powerlaw` is:
+
+    .. math::
+
+        f(x, a) = a x^{a-1}
+
+    for :math:`0 \le x \le 1`, :math:`a > 0`.
+
+    `powerlaw` takes ``a`` as a shape parameter for :math:`a`.
+
+    %(after_notes)s
+
+    For example, the support of `powerlaw` can be adjusted from the default
+    interval ``[0, 1]`` to the interval ``[c, c+d]`` by setting ``loc=c`` and
+    ``scale=d``. For a power-law distribution with infinite support, see
+    `pareto`. For a power-law distribution described by PDF:
+
+    .. math::
+
+        f(x; a, l, h) = \frac{a}{h^a - l^2} x^{a-1}
+
+    with :math:`a \neq 0` and :math:`0 < l < x < h`, see `truncpareto`.
+
+    `powerlaw` is a special case of `beta` with ``b=1``.
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, a):
+        # powerlaw.pdf(x, a) = a * x**(a-1)
+        return a*x**(a-1.0)
+
+    def _logpdf(self, x, a):
+        return np.log(a) + sc.xlogy(a - 1, x)
+
+    def _cdf(self, x, a):
+        return x**(a*1.0)
+
+    def _logcdf(self, x, a):
+        return a*np.log(x)
+
+    def _ppf(self, q, a):
+        return pow(q, 1.0/a)
+
+    def _sf(self, p, a):
+        return -sc.powm1(p, a)
+
+    def _munp(self, n, a):
+        # The following expression is correct for all real n (provided a > 0).
+        return a / (a + n)
+
+    def _stats(self, a):
+        return (a / (a + 1.0),
+                a / (a + 2.0) / (a + 1.0) ** 2,
+                -2.0 * ((a - 1.0) / (a + 3.0)) * np.sqrt((a + 2.0) / a),
+                6 * np.polyval([1, -1, -6, 2], a) / (a * (a + 3.0) * (a + 4)))
+
+    def _entropy(self, a):
+        return 1 - 1.0/a - np.log(a)
+
+    def _support_mask(self, x, a):
+        return (super()._support_mask(x, a)
+                & ((x != 0) | (a >= 1)))
+
+    @_call_super_mom
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        Notes specifically for ``powerlaw.fit``: If the location is a free
+        parameter and the value returned for the shape parameter is less than
+        one, the true maximum likelihood approaches infinity. This causes
+        numerical difficulties, and the resulting estimates are approximate.
+        \n\n""")
+    def fit(self, data, *args, **kwds):
+        # Summary of the strategy:
+        #
+        # 1) If the scale and location are fixed, return the shape according
+        #    to a formula.
+        #
+        # 2) If the scale is fixed, there are two possibilities for the other
+        #    parameters - one corresponding with shape less than one, and
+        #    another with shape greater than one. Calculate both, and return
+        #    whichever has the better log-likelihood.
+        #
+        # At this point, the scale is known to be free.
+        #
+        # 3) If the location is fixed, return the scale and shape according to
+        #    formulas (or, if the shape is fixed, the fixed shape).
+        #
+        # At this point, the location and scale are both free. There are
+        # separate equations depending on whether the shape is less than one or
+        # greater than one.
+        #
+        # 4a) If the shape is less than one, there are formulas for shape,
+        #     location, and scale.
+        # 4b) If the shape is greater than one, there are formulas for shape
+        #     and scale, but there is a condition for location to be solved
+        #     numerically.
+        #
+        # If the shape is fixed and less than one, we use 4a.
+        # If the shape is fixed and greater than one, we use 4b.
+        # If the shape is also free, we calculate fits using both 4a and 4b
+        # and choose the one that results a better log-likelihood.
+        #
+        # In many cases, the use of `np.nextafter` is used to avoid numerical
+        # issues.
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        if len(np.unique(data)) == 1:
+            return super().fit(data, *args, **kwds)
+
+        data, fshape, floc, fscale = _check_fit_input_parameters(self, data,
+                                                                 args, kwds)
+        penalized_nllf_args = [data, (self._fitstart(data),)]
+        penalized_nllf = self._reduce_func(penalized_nllf_args, {})[1]
+
+        # ensure that any fixed parameters don't violate constraints of the
+        # distribution before continuing. The support of the distribution
+        # is `0 < (x - loc)/scale < 1`.
+        if floc is not None:
+            if not data.min() > floc:
+                raise FitDataError('powerlaw', 0, 1)
+            if fscale is not None and not data.max() <= floc + fscale:
+                raise FitDataError('powerlaw', 0, 1)
+
+        if fscale is not None:
+            if fscale <= 0:
+                raise ValueError("Negative or zero `fscale` is outside the "
+                                 "range allowed by the distribution.")
+            if fscale <= np.ptp(data):
+                msg = "`fscale` must be greater than the range of data."
+                raise ValueError(msg)
+
+        def get_shape(data, loc, scale):
+            # The first-order necessary condition on `shape` can be solved in
+            # closed form. It can be used no matter the assumption of the
+            # value of the shape.
+            N = len(data)
+            return - N / (np.sum(np.log(data - loc)) - N*np.log(scale))
+
+        def get_scale(data, loc):
+            # analytical solution for `scale` based on the location.
+            # It can be used no matter the assumption of the value of the
+            # shape.
+            return data.max() - loc
+
+        # 1) The location and scale are both fixed. Analytically determine the
+        # shape.
+        if fscale is not None and floc is not None:
+            return get_shape(data, floc, fscale), floc, fscale
+
+        # 2) The scale is fixed. There are two possibilities for the other
+        # parameters. Choose the option with better log-likelihood.
+        if fscale is not None:
+            # using `data.min()` as the optimal location
+            loc_lt1 = np.nextafter(data.min(), -np.inf)
+            shape_lt1 = fshape or get_shape(data, loc_lt1, fscale)
+            ll_lt1 = penalized_nllf((shape_lt1, loc_lt1, fscale), data)
+
+            # using `data.max() - scale` as the optimal location
+            loc_gt1 = np.nextafter(data.max() - fscale, np.inf)
+            shape_gt1 = fshape or get_shape(data, loc_gt1, fscale)
+            ll_gt1 = penalized_nllf((shape_gt1, loc_gt1, fscale), data)
+
+            if ll_lt1 < ll_gt1:
+                return shape_lt1, loc_lt1, fscale
+            else:
+                return shape_gt1, loc_gt1, fscale
+
+        # 3) The location is fixed. Return the analytical scale and the
+        # analytical (or fixed) shape.
+        if floc is not None:
+            scale = get_scale(data, floc)
+            shape = fshape or get_shape(data, floc, scale)
+            return shape, floc, scale
+
+        # 4) Location and scale are both free
+        # 4a) Use formulas that assume `shape <= 1`.
+
+        def fit_loc_scale_w_shape_lt_1():
+            loc = np.nextafter(data.min(), -np.inf)
+            if np.abs(loc) < np.finfo(loc.dtype).tiny:
+                loc = np.sign(loc) * np.finfo(loc.dtype).tiny
+            scale = np.nextafter(get_scale(data, loc), np.inf)
+            shape = fshape or get_shape(data, loc, scale)
+            return shape, loc, scale
+
+        # 4b) Fit under the assumption that `shape > 1`. The support
+        # of the distribution is `(x - loc)/scale <= 1`. The method of Lagrange
+        # multipliers turns this constraint into the condition that
+        # dL_dScale - dL_dLocation must be zero, which is solved numerically.
+        # (Alternatively, substitute the constraint into the objective
+        # function before deriving the likelihood equation for location.)
+
+        def dL_dScale(data, shape, scale):
+            # The partial derivative of the log-likelihood function w.r.t.
+            # the scale.
+            return -data.shape[0] * shape / scale
+
+        def dL_dLocation(data, shape, loc):
+            # The partial derivative of the log-likelihood function w.r.t.
+            # the location.
+            return (shape - 1) * np.sum(1 / (loc - data))  # -1/(data-loc)
+
+        def dL_dLocation_star(loc):
+            # The derivative of the log-likelihood function w.r.t.
+            # the location, given optimal shape and scale
+            scale = np.nextafter(get_scale(data, loc), -np.inf)
+            shape = fshape or get_shape(data, loc, scale)
+            return dL_dLocation(data, shape, loc)
+
+        def fun_to_solve(loc):
+            # optimize the location by setting the partial derivatives
+            # w.r.t. location and scale equal and solving.
+            scale = np.nextafter(get_scale(data, loc), -np.inf)
+            shape = fshape or get_shape(data, loc, scale)
+            return (dL_dScale(data, shape, scale)
+                    - dL_dLocation(data, shape, loc))
+
+        def fit_loc_scale_w_shape_gt_1():
+            # set brackets for `root_scalar` to use when optimizing over the
+            # location such that a root is likely between them.
+            rbrack = np.nextafter(data.min(), -np.inf)
+
+            # if the sign of `dL_dLocation_star` is positive at rbrack,
+            # we're not going to find the root we're looking for
+            delta = (data.min() - rbrack)
+            while dL_dLocation_star(rbrack) > 0:
+                rbrack = data.min() - delta
+                delta *= 2
+
+            def interval_contains_root(lbrack, rbrack):
+                # Check if the interval (lbrack, rbrack) contains the root.
+                return (np.sign(fun_to_solve(lbrack))
+                        != np.sign(fun_to_solve(rbrack)))
+
+            lbrack = rbrack - 1
+
+            # if the sign doesn't change between the brackets, move the left
+            # bracket until it does. (The right bracket remains fixed at the
+            # maximum permissible value.)
+            i = 1.0
+            while (not interval_contains_root(lbrack, rbrack)
+                   and lbrack != -np.inf):
+                lbrack = (data.min() - i)
+                i *= 2
+
+            root = optimize.root_scalar(fun_to_solve, bracket=(lbrack, rbrack))
+
+            loc = np.nextafter(root.root, -np.inf)
+            scale = np.nextafter(get_scale(data, loc), np.inf)
+            shape = fshape or get_shape(data, loc, scale)
+            return shape, loc, scale
+
+        # Shape is fixed - choose 4a or 4b accordingly.
+        if fshape is not None and fshape <= 1:
+            return fit_loc_scale_w_shape_lt_1()
+        elif fshape is not None and fshape > 1:
+            return fit_loc_scale_w_shape_gt_1()
+
+        # Shape is free
+        fit_shape_lt1 = fit_loc_scale_w_shape_lt_1()
+        ll_lt1 = self.nnlf(fit_shape_lt1, data)
+
+        fit_shape_gt1 = fit_loc_scale_w_shape_gt_1()
+        ll_gt1 = self.nnlf(fit_shape_gt1, data)
+
+        if ll_lt1 <= ll_gt1 and fit_shape_lt1[0] <= 1:
+            return fit_shape_lt1
+        elif ll_lt1 > ll_gt1 and fit_shape_gt1[0] > 1:
+            return fit_shape_gt1
+        else:
+            return super().fit(data, *args, **kwds)
+
+
+powerlaw = powerlaw_gen(a=0.0, b=1.0, name="powerlaw")
+
+
+class powerlognorm_gen(rv_continuous):
+    r"""A power log-normal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `powerlognorm` is:
+
+    .. math::
+
+        f(x, c, s) = \frac{c}{x s} \phi(\log(x)/s)
+                     (\Phi(-\log(x)/s))^{c-1}
+
+    where :math:`\phi` is the normal pdf, and :math:`\Phi` is the normal cdf,
+    and :math:`x > 0`, :math:`s, c > 0`.
+
+    `powerlognorm` takes :math:`c` and :math:`s` as shape parameters.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        ic = _ShapeInfo("c", False, (0, np.inf), (False, False))
+        i_s = _ShapeInfo("s", False, (0, np.inf), (False, False))
+        return [ic, i_s]
+
+    def _pdf(self, x, c, s):
+        return np.exp(self._logpdf(x, c, s))
+
+    def _logpdf(self, x, c, s):
+        return (np.log(c) - np.log(x) - np.log(s) +
+                _norm_logpdf(np.log(x) / s) +
+                _norm_logcdf(-np.log(x) / s) * (c - 1.))
+
+    def _cdf(self, x, c, s):
+        return -sc.expm1(self._logsf(x, c, s))
+
+    def _ppf(self, q, c, s):
+        return self._isf(1 - q, c, s)
+
+    def _sf(self, x, c, s):
+        return np.exp(self._logsf(x, c, s))
+
+    def _logsf(self, x, c, s):
+        return _norm_logcdf(-np.log(x) / s) * c
+
+    def _isf(self, q, c, s):
+        return np.exp(-_norm_ppf(q**(1/c)) * s)
+
+
+powerlognorm = powerlognorm_gen(a=0.0, name="powerlognorm")
+
+
+class powernorm_gen(rv_continuous):
+    r"""A power normal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `powernorm` is:
+
+    .. math::
+
+        f(x, c) = c \phi(x) (\Phi(-x))^{c-1}
+
+    where :math:`\phi` is the normal pdf, :math:`\Phi` is the normal cdf,
+    :math:`x` is any real, and :math:`c > 0` [1]_.
+
+    `powernorm` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] NIST Engineering Statistics Handbook, Section 1.3.6.6.13,
+           https://www.itl.nist.gov/div898/handbook//eda/section3/eda366d.htm
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, c):
+        # powernorm.pdf(x, c) = c * phi(x) * (Phi(-x))**(c-1)
+        return c*_norm_pdf(x) * (_norm_cdf(-x)**(c-1.0))
+
+    def _logpdf(self, x, c):
+        return np.log(c) + _norm_logpdf(x) + (c-1)*_norm_logcdf(-x)
+
+    def _cdf(self, x, c):
+        return -sc.expm1(self._logsf(x, c))
+
+    def _ppf(self, q, c):
+        return -_norm_ppf(pow(1.0 - q, 1.0 / c))
+
+    def _sf(self, x, c):
+        return np.exp(self._logsf(x, c))
+
+    def _logsf(self, x, c):
+        return c * _norm_logcdf(-x)
+
+    def _isf(self, q, c):
+        return -_norm_ppf(np.exp(np.log(q) / c))
+
+
+powernorm = powernorm_gen(name='powernorm')
+
+
+class rdist_gen(rv_continuous):
+    r"""An R-distributed (symmetric beta) continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `rdist` is:
+
+    .. math::
+
+        f(x, c) = \frac{(1-x^2)^{c/2-1}}{B(1/2, c/2)}
+
+    for :math:`-1 \le x \le 1`, :math:`c > 0`. `rdist` is also called the
+    symmetric beta distribution: if B has a `beta` distribution with
+    parameters (c/2, c/2), then X = 2*B - 1 follows a R-distribution with
+    parameter c.
+
+    `rdist` takes ``c`` as a shape parameter for :math:`c`.
+
+    This distribution includes the following distribution kernels as
+    special cases::
+
+        c = 2:  uniform
+        c = 3:  `semicircular`
+        c = 4:  Epanechnikov (parabolic)
+        c = 6:  quartic (biweight)
+        c = 8:  triweight
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, np.inf), (False, False))]
+
+    # use relation to the beta distribution for pdf, cdf, etc
+    def _pdf(self, x, c):
+        return np.exp(self._logpdf(x, c))
+
+    def _logpdf(self, x, c):
+        return -np.log(2) + beta._logpdf((x + 1)/2, c/2, c/2)
+
+    def _cdf(self, x, c):
+        return beta._cdf((x + 1)/2, c/2, c/2)
+
+    def _sf(self, x, c):
+        return beta._sf((x + 1)/2, c/2, c/2)
+
+    def _ppf(self, q, c):
+        return 2*beta._ppf(q, c/2, c/2) - 1
+
+    def _rvs(self, c, size=None, random_state=None):
+        return 2 * random_state.beta(c/2, c/2, size) - 1
+
+    def _munp(self, n, c):
+        numerator = (1 - (n % 2)) * sc.beta((n + 1.0) / 2, c / 2.0)
+        return numerator / sc.beta(1. / 2, c / 2.)
+
+
+rdist = rdist_gen(a=-1.0, b=1.0, name="rdist")
+
+
+class rayleigh_gen(rv_continuous):
+    r"""A Rayleigh continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `rayleigh` is:
+
+    .. math::
+
+        f(x) = x \exp(-x^2/2)
+
+    for :math:`x \ge 0`.
+
+    `rayleigh` is a special case of `chi` with ``df=2``.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return chi.rvs(2, size=size, random_state=random_state)
+
+    def _pdf(self, r):
+        # rayleigh.pdf(r) = r * exp(-r**2/2)
+        return np.exp(self._logpdf(r))
+
+    def _logpdf(self, r):
+        return np.log(r) - 0.5 * r * r
+
+    def _cdf(self, r):
+        return -sc.expm1(-0.5 * r**2)
+
+    def _ppf(self, q):
+        return np.sqrt(-2 * sc.log1p(-q))
+
+    def _sf(self, r):
+        return np.exp(self._logsf(r))
+
+    def _logsf(self, r):
+        return -0.5 * r * r
+
+    def _isf(self, q):
+        return np.sqrt(-2 * np.log(q))
+
+    def _stats(self):
+        val = 4 - np.pi
+        return (np.sqrt(np.pi/2),
+                val/2,
+                2*(np.pi-3)*np.sqrt(np.pi)/val**1.5,
+                6*np.pi/val-16/val**2)
+
+    def _entropy(self):
+        return _EULER/2.0 + 1 - 0.5*np.log(2)
+
+    @_call_super_mom
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        Notes specifically for ``rayleigh.fit``: If the location is fixed with
+        the `floc` parameter, this method uses an analytical formula to find
+        the scale.  Otherwise, this function uses a numerical root finder on
+        the first order conditions of the log-likelihood function to find the
+        MLE.  Only the (optional) `loc` parameter is used as the initial guess
+        for the root finder; the `scale` parameter and any other parameters
+        for the optimizer are ignored.\n\n""")
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+        data, floc, fscale = _check_fit_input_parameters(self, data,
+                                                         args, kwds)
+
+        def scale_mle(loc):
+            # Source: Statistical Distributions, 3rd Edition. Evans, Hastings,
+            # and Peacock (2000), Page 175
+            return (np.sum((data - loc) ** 2) / (2 * len(data))) ** .5
+
+        def loc_mle(loc):
+            # This implicit equation for `loc` is used when
+            # both `loc` and `scale` are free.
+            xm = data - loc
+            s1 = xm.sum()
+            s2 = (xm**2).sum()
+            s3 = (1/xm).sum()
+            return s1 - s2/(2*len(data))*s3
+
+        def loc_mle_scale_fixed(loc, scale=fscale):
+            # This implicit equation for `loc` is used when
+            # `scale` is fixed but `loc` is not.
+            xm = data - loc
+            return xm.sum() - scale**2 * (1/xm).sum()
+
+        if floc is not None:
+            # `loc` is fixed, analytically determine `scale`.
+            if np.any(data - floc <= 0):
+                raise FitDataError("rayleigh", lower=1, upper=np.inf)
+            else:
+                return floc, scale_mle(floc)
+
+        # Account for user provided guess of `loc`.
+        loc0 = kwds.get('loc')
+        if loc0 is None:
+            # Use _fitstart to estimate loc; ignore the returned scale.
+            loc0 = self._fitstart(data)[0]
+
+        fun = loc_mle if fscale is None else loc_mle_scale_fixed
+        rbrack = np.nextafter(np.min(data), -np.inf)
+        lbrack = _get_left_bracket(fun, rbrack)
+        res = optimize.root_scalar(fun, bracket=(lbrack, rbrack))
+        if not res.converged:
+            raise FitSolverError(res.flag)
+        loc = res.root
+        scale = fscale or scale_mle(loc)
+        return loc, scale
+
+
+rayleigh = rayleigh_gen(a=0.0, name="rayleigh")
+
+
+class reciprocal_gen(rv_continuous):
+    r"""A loguniform or reciprocal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for this class is:
+
+    .. math::
+
+        f(x, a, b) = \frac{1}{x \log(b/a)}
+
+    for :math:`a \le x \le b`, :math:`b > a > 0`. This class takes
+    :math:`a` and :math:`b` as shape parameters.
+
+    %(after_notes)s
+
+    %(example)s
+
+    This doesn't show the equal probability of ``0.01``, ``0.1`` and
+    ``1``. This is best when the x-axis is log-scaled:
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+    >>> ax.hist(np.log10(r))
+    >>> ax.set_ylabel("Frequency")
+    >>> ax.set_xlabel("Value of random variable")
+    >>> ax.xaxis.set_major_locator(plt.FixedLocator([-2, -1, 0]))
+    >>> ticks = ["$10^{{ {} }}$".format(i) for i in [-2, -1, 0]]
+    >>> ax.set_xticklabels(ticks)  # doctest: +SKIP
+    >>> plt.show()
+
+    This random variable will be log-uniform regardless of the base chosen for
+    ``a`` and ``b``. Let's specify with base ``2`` instead:
+
+    >>> rvs = %(name)s(2**-2, 2**0).rvs(size=1000)
+
+    Values of ``1/4``, ``1/2`` and ``1`` are equally likely with this random
+    variable.  Here's the histogram:
+
+    >>> fig, ax = plt.subplots(1, 1)
+    >>> ax.hist(np.log2(rvs))
+    >>> ax.set_ylabel("Frequency")
+    >>> ax.set_xlabel("Value of random variable")
+    >>> ax.xaxis.set_major_locator(plt.FixedLocator([-2, -1, 0]))
+    >>> ticks = ["$2^{{ {} }}$".format(i) for i in [-2, -1, 0]]
+    >>> ax.set_xticklabels(ticks)  # doctest: +SKIP
+    >>> plt.show()
+
+    """
+    def _argcheck(self, a, b):
+        return (a > 0) & (b > a)
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (0, np.inf), (False, False))
+        ib = _ShapeInfo("b", False, (0, np.inf), (False, False))
+        return [ia, ib]
+
+    def _fitstart(self, data):
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        # Reasonable, since support is [a, b]
+        return super()._fitstart(data, args=(np.min(data), np.max(data)))
+
+    def _get_support(self, a, b):
+        return a, b
+
+    def _pdf(self, x, a, b):
+        # reciprocal.pdf(x, a, b) = 1 / (x*(log(b) - log(a)))
+        return np.exp(self._logpdf(x, a, b))
+
+    def _logpdf(self, x, a, b):
+        return -np.log(x) - np.log(np.log(b) - np.log(a))
+
+    def _cdf(self, x, a, b):
+        return (np.log(x)-np.log(a)) / (np.log(b) - np.log(a))
+
+    def _ppf(self, q, a, b):
+        return np.exp(np.log(a) + q*(np.log(b) - np.log(a)))
+
+    def _munp(self, n, a, b):
+        if n == 0:
+            return 1.0
+        t1 = 1 / (np.log(b) - np.log(a)) / n
+        t2 = np.real(np.exp(_log_diff(n * np.log(b), n*np.log(a))))
+        return t1 * t2
+
+    def _entropy(self, a, b):
+        return 0.5*(np.log(a) + np.log(b)) + np.log(np.log(b) - np.log(a))
+
+    fit_note = """\
+        `loguniform`/`reciprocal` is over-parameterized. `fit` automatically
+         fixes `scale` to 1 unless `fscale` is provided by the user.\n\n"""
+
+    @extend_notes_in_docstring(rv_continuous, notes=fit_note)
+    def fit(self, data, *args, **kwds):
+        fscale = kwds.pop('fscale', 1)
+        return super().fit(data, *args, fscale=fscale, **kwds)
+
+    # Details related to the decision of not defining
+    # the survival function for this distribution can be
+    # found in the PR: https://github.com/scipy/scipy/pull/18614
+
+
+loguniform = reciprocal_gen(name="loguniform")
+reciprocal = reciprocal_gen(name="reciprocal")
+loguniform._support = ('a', 'b')
+reciprocal._support = ('a', 'b')
+
+
+class rice_gen(rv_continuous):
+    r"""A Rice continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `rice` is:
+
+    .. math::
+
+        f(x, b) = x \exp(- \frac{x^2 + b^2}{2}) I_0(x b)
+
+    for :math:`x >= 0`, :math:`b > 0`. :math:`I_0` is the modified Bessel
+    function of order zero (`scipy.special.i0`).
+
+    `rice` takes ``b`` as a shape parameter for :math:`b`.
+
+    %(after_notes)s
+
+    The Rice distribution describes the length, :math:`r`, of a 2-D vector with
+    components :math:`(U+u, V+v)`, where :math:`U, V` are constant, :math:`u,
+    v` are independent Gaussian random variables with standard deviation
+    :math:`s`.  Let :math:`R = \sqrt{U^2 + V^2}`. Then the pdf of :math:`r` is
+    ``rice.pdf(x, R/s, scale=s)``.
+
+    %(example)s
+
+    """
+    def _argcheck(self, b):
+        return b >= 0
+
+    def _shape_info(self):
+        return [_ShapeInfo("b", False, (0, np.inf), (True, False))]
+
+    def _rvs(self, b, size=None, random_state=None):
+        # https://en.wikipedia.org/wiki/Rice_distribution
+        t = b/np.sqrt(2) + random_state.standard_normal(size=(2,) + size)
+        return np.sqrt((t*t).sum(axis=0))
+
+    def _cdf(self, x, b):
+        return sc.chndtr(np.square(x), 2, np.square(b))
+
+    def _ppf(self, q, b):
+        return np.sqrt(sc.chndtrix(q, 2, np.square(b)))
+
+    def _pdf(self, x, b):
+        # rice.pdf(x, b) = x * exp(-(x**2+b**2)/2) * I[0](x*b)
+        #
+        # We use (x**2 + b**2)/2 = ((x-b)**2)/2 + xb.
+        # The factor of np.exp(-xb) is then included in the i0e function
+        # in place of the modified Bessel function, i0, improving
+        # numerical stability for large values of xb.
+        return x * np.exp(-(x-b)*(x-b)/2.0) * sc.i0e(x*b)
+
+    def _munp(self, n, b):
+        nd2 = n/2.0
+        n1 = 1 + nd2
+        b2 = b*b/2.0
+        return (2.0**(nd2) * np.exp(-b2) * sc.gamma(n1) *
+                sc.hyp1f1(n1, 1, b2))
+
+
+rice = rice_gen(a=0.0, name="rice")
+
+class irwinhall_gen(rv_continuous):
+    r"""An Irwin-Hall (Uniform Sum) continuous random variable.
+
+    An `Irwin-Hall <https://en.wikipedia.org/wiki/Irwin-Hall_distribution/>`_
+    continuous random variable is the sum of :math:`n` independent
+    standard uniform random variables [1]_ [2]_.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    Applications include `Rao's Spacing Test
+    <https://jammalam.faculty.pstat.ucsb.edu/html/favorite/test.htm>`_,
+    a more powerful alternative to the Rayleigh test
+    when the data are not unimodal, and radar [3]_.
+
+    Conveniently, the pdf and cdf are the :math:`n`-fold convolution of
+    the ones for the standard uniform distribution, which is also the
+    definition of the cardinal B-splines of degree :math:`n-1`
+    having knots evenly spaced from :math:`1` to :math:`n` [4]_ [5]_.
+
+    The Bates distribution, which represents the *mean* of statistically
+    independent, uniformly distributed random variables, is simply the
+    Irwin-Hall distribution scaled by :math:`1/n`. For example, the frozen
+    distribution ``bates = irwinhall(10, scale=1/10)`` represents the
+    distribution of the mean of 10 uniformly distributed random variables.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] P. Hall, "The distribution of means for samples of size N drawn
+            from a population in which the variate takes values between 0 and 1,
+            all such values being equally probable",
+            Biometrika, Volume 19, Issue 3-4, December 1927, Pages 240-244,
+            :doi:`10.1093/biomet/19.3-4.240`.
+    .. [2] J. O. Irwin, "On the frequency distribution of the means of samples
+            from a population having any law of frequency with finite moments,
+            with special reference to Pearson's Type II,
+            Biometrika, Volume 19, Issue 3-4, December 1927, Pages 225-239,
+            :doi:`0.1093/biomet/19.3-4.225`.
+    .. [3] K. Buchanan, T. Adeyemi, C. Flores-Molina, S. Wheeland and D. Overturf,
+            "Sidelobe behavior and bandwidth characteristics
+            of distributed antenna arrays,"
+            2018 United States National Committee of
+            URSI National Radio Science Meeting (USNC-URSI NRSM),
+            Boulder, CO, USA, 2018, pp. 1-2.
+            https://www.usnc-ursi-archive.org/nrsm/2018/papers/B15-9.pdf.
+    .. [4] Amos Ron, "Lecture 1: Cardinal B-splines and convolution operators", p. 1
+            https://pages.cs.wisc.edu/~deboor/887/lec1new.pdf.
+    .. [5] Trefethen, N. (2012, July). B-splines and convolution. Chebfun.
+            Retrieved April 30, 2024, from http://www.chebfun.org/examples/approx/BSplineConv.html.
+
+    %(example)s
+    """  # noqa: E501
+
+    @replace_notes_in_docstring(rv_continuous, notes="""\
+        Raises a ``NotImplementedError`` for the Irwin-Hall distribution because
+        the generic `fit` implementation is unreliable and no custom implementation
+        is available. Consider using `scipy.stats.fit`.\n\n""")
+    def fit(self, data, *args, **kwds):
+        fit_notes = ("The generic `fit` implementation is unreliable for this "
+                     "distribution, and no custom implementation is available. "
+                     "Consider using `scipy.stats.fit`.")
+        raise NotImplementedError(fit_notes)
+
+    def _argcheck(self, n):
+        return (n > 0) & _isintegral(n) & np.isrealobj(n)
+
+    def _get_support(self, n):
+        return 0, n
+
+    def _shape_info(self):
+        return [_ShapeInfo("n", True, (1, np.inf), (True, False))]
+
+    def _munp(self, order, n):
+        # see https://link.springer.com/content/pdf/10.1007/s10959-020-01050-9.pdf
+        # page 640, with m=n, j=n+order
+        def vmunp(order, n):
+            n = np.asarray(n, dtype=np.int64)
+            return (sc.stirling2(n+order, n, exact=True)
+                    / sc.comb(n+order, n, exact=True))
+
+        # exact rationals, but we convert to float anyway
+        return np.vectorize(vmunp, otypes=[np.float64])(order, n)
+
+    @staticmethod
+    def _cardbspl(n):
+        t = np.arange(n+1)
+        return BSpline.basis_element(t)
+
+    def _pdf(self, x, n):
+        def vpdf(x, n):
+            return self._cardbspl(n)(x)
+        return np.vectorize(vpdf, otypes=[np.float64])(x, n)
+
+    def _cdf(self, x, n):
+        def vcdf(x, n):
+            return self._cardbspl(n).antiderivative()(x)
+        return np.vectorize(vcdf, otypes=[np.float64])(x, n)
+
+    def _sf(self, x, n):
+        def vsf(x, n):
+            return self._cardbspl(n).antiderivative()(n-x)
+        return np.vectorize(vsf, otypes=[np.float64])(x, n)
+
+    def _rvs(self, n, size=None, random_state=None, *args):
+        @_vectorize_rvs_over_shapes
+        def _rvs1(n, size=None, random_state=None):
+            n = np.floor(n).astype(int)
+            usize = (n,) if size is None else (n, *size)
+            return random_state.uniform(size=usize).sum(axis=0)
+        return _rvs1(n, size=size, random_state=random_state)
+
+    def _stats(self, n):
+        # mgf = ((exp(t) - 1)/t)**n
+        # m'th derivative follows from the generalized Leibniz rule
+        # Moments follow directly from the definition as the sum of n iid unif(0,1)
+        # and the summation rules for moments of a sum of iid random variables
+        # E(IH((n))) = n*E(U(0,1)) = n/2
+        # Var(IH((n))) = n*Var(U(0,1)) = n/12
+        # Skew(IH((n))) = Skew(U(0,1))/sqrt(n) = 0
+        # Kurt(IH((n))) = Kurt(U(0,1))/n = -6/(5*n) -- Fisher's excess kurtosis
+        # See e.g. https://en.wikipedia.org/wiki/Irwin%E2%80%93Hall_distribution
+
+        return n/2, n/12, 0, -6/(5*n)
+
+
+irwinhall = irwinhall_gen(name="irwinhall")
+irwinhall._support = (0.0, 'n')
+
+
+class recipinvgauss_gen(rv_continuous):
+    r"""A reciprocal inverse Gaussian continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `recipinvgauss` is:
+
+    .. math::
+
+        f(x, \mu) = \frac{1}{\sqrt{2\pi x}}
+                    \exp\left(\frac{-(1-\mu x)^2}{2\mu^2x}\right)
+
+    for :math:`x \ge 0`.
+
+    `recipinvgauss` takes ``mu`` as a shape parameter for :math:`\mu`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("mu", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, mu):
+        # recipinvgauss.pdf(x, mu) =
+        #                     1/sqrt(2*pi*x) * exp(-(1-mu*x)**2/(2*x*mu**2))
+        return np.exp(self._logpdf(x, mu))
+
+    def _logpdf(self, x, mu):
+        return xpx.apply_where(
+            x > 0, (x, mu),
+            lambda x, mu: (-(1 - mu*x)**2.0 / (2*x*mu**2.0)
+                           - 0.5*np.log(2*np.pi*x)),
+            fill_value=-np.inf)
+
+    def _cdf(self, x, mu):
+        trm1 = 1.0/mu - x
+        trm2 = 1.0/mu + x
+        isqx = 1.0/np.sqrt(x)
+        return _norm_cdf(-isqx*trm1) - np.exp(2.0/mu)*_norm_cdf(-isqx*trm2)
+
+    def _sf(self, x, mu):
+        trm1 = 1.0/mu - x
+        trm2 = 1.0/mu + x
+        isqx = 1.0/np.sqrt(x)
+        return _norm_cdf(isqx*trm1) + np.exp(2.0/mu)*_norm_cdf(-isqx*trm2)
+
+    def _rvs(self, mu, size=None, random_state=None):
+        return 1.0/random_state.wald(mu, 1.0, size=size)
+
+
+recipinvgauss = recipinvgauss_gen(a=0.0, name='recipinvgauss')
+
+
+class semicircular_gen(rv_continuous):
+    r"""A semicircular continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    rdist
+
+    Notes
+    -----
+    The probability density function for `semicircular` is:
+
+    .. math::
+
+        f(x) = \frac{2}{\pi} \sqrt{1-x^2}
+
+    for :math:`-1 \le x \le 1`.
+
+    The distribution is a special case of `rdist` with ``c = 3``.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] "Wigner semicircle distribution",
+           https://en.wikipedia.org/wiki/Wigner_semicircle_distribution
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _pdf(self, x):
+        return 2.0/np.pi*np.sqrt(1-x*x)
+
+    def _logpdf(self, x):
+        return np.log(2/np.pi) + 0.5*sc.log1p(-x*x)
+
+    def _cdf(self, x):
+        return 0.5+1.0/np.pi*(x*np.sqrt(1-x*x) + np.arcsin(x))
+
+    def _ppf(self, q):
+        return rdist._ppf(q, 3)
+
+    def _rvs(self, size=None, random_state=None):
+        # generate values uniformly distributed on the area under the pdf
+        # (semi-circle) by randomly generating the radius and angle
+        r = np.sqrt(random_state.uniform(size=size))
+        a = np.cos(np.pi * random_state.uniform(size=size))
+        return r * a
+
+    def _stats(self):
+        return 0, 0.25, 0, -1.0
+
+    def _entropy(self):
+        return 0.64472988584940017414
+
+
+semicircular = semicircular_gen(a=-1.0, b=1.0, name="semicircular")
+
+
+class skewcauchy_gen(rv_continuous):
+    r"""A skewed Cauchy random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    cauchy : Cauchy distribution
+
+    Notes
+    -----
+
+    The probability density function for `skewcauchy` is:
+
+    .. math::
+
+        f(x) = \frac{1}{\pi \left(\frac{x^2}{\left(a\, \text{sign}(x) + 1
+                                                   \right)^2} + 1 \right)}
+
+    for a real number :math:`x` and skewness parameter :math:`-1 < a < 1`.
+
+    When :math:`a=0`, the distribution reduces to the usual Cauchy
+    distribution.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] "Skewed generalized *t* distribution", Wikipedia
+       https://en.wikipedia.org/wiki/Skewed_generalized_t_distribution#Skewed_Cauchy_distribution
+
+    %(example)s
+
+    """
+    def _argcheck(self, a):
+        return np.abs(a) < 1
+
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (-1.0, 1.0), (False, False))]
+
+    def _pdf(self, x, a):
+        return 1 / (np.pi * (x**2 / (a * np.sign(x) + 1)**2 + 1))
+
+    def _cdf(self, x, a):
+        return np.where(x <= 0,
+                        (1 - a) / 2 + (1 - a) / np.pi * np.arctan(x / (1 - a)),
+                        (1 - a) / 2 + (1 + a) / np.pi * np.arctan(x / (1 + a)))
+
+    def _ppf(self, x, a):
+        i = x < self._cdf(0, a)
+        return np.where(i,
+                        np.tan(np.pi / (1 - a) * (x - (1 - a) / 2)) * (1 - a),
+                        np.tan(np.pi / (1 + a) * (x - (1 - a) / 2)) * (1 + a))
+
+    def _stats(self, a, moments='mvsk'):
+        return np.nan, np.nan, np.nan, np.nan
+
+    def _fitstart(self, data):
+        # Use 0 as the initial guess of the skewness shape parameter.
+        # For the location and scale, estimate using the median and
+        # quartiles.
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        p25, p50, p75 = np.percentile(data, [25, 50, 75])
+        return 0.0, p50, (p75 - p25)/2
+
+
+skewcauchy = skewcauchy_gen(name='skewcauchy')
+
+
+class skewnorm_gen(rv_continuous):
+    r"""A skew-normal random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The pdf is::
+
+        skewnorm.pdf(x, a) = 2 * norm.pdf(x) * norm.cdf(a*x)
+
+    `skewnorm` takes a real number :math:`a` as a skewness parameter
+    When ``a = 0`` the distribution is identical to a normal distribution
+    (`norm`). `rvs` implements the method of [1]_.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of ``cdf``, ``ppf`` and ``isf`` methods. [2]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] A. Azzalini and A. Capitanio (1999). Statistical applications of
+        the multivariate skew-normal distribution. J. Roy. Statist. Soc.,
+        B 61, 579-602. :arxiv:`0911.2093`
+    .. [2] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+
+    """
+    def _argcheck(self, a):
+        return np.isfinite(a)
+
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (-np.inf, np.inf), (False, False))]
+
+    def _pdf(self, x, a):
+        return xpx.apply_where(
+            a == 0, (x, a),
+            lambda x, a: _norm_pdf(x),
+            lambda x, a: 2.*_norm_pdf(x)*_norm_cdf(a*x))
+
+    def _logpdf(self, x, a):
+        return xpx.apply_where(
+            a == 0, (x, a),
+            lambda x, a: _norm_logpdf(x),
+            lambda x, a: np.log(2)+_norm_logpdf(x)+_norm_logcdf(a*x))
+
+    def _cdf(self, x, a):
+        a = np.atleast_1d(a)
+        cdf = scu._skewnorm_cdf(x, 0.0, 1.0, a)
+        # for some reason, a isn't broadcasted if some of x are invalid
+        a = np.broadcast_to(a, cdf.shape)
+        # Boost is not accurate in left tail when a > 0
+        i_small_cdf = (cdf < 1e-6) & (a > 0)
+        cdf[i_small_cdf] = super()._cdf(x[i_small_cdf], a[i_small_cdf])
+        return np.clip(cdf, 0, 1)
+
+    def _ppf(self, x, a):
+        return scu._skewnorm_ppf(x, 0.0, 1.0, a)
+
+    def _sf(self, x, a):
+        # Boost's SF is implemented this way. Use whatever customizations
+        # we made in the _cdf.
+        return self._cdf(-x, -a)
+
+    def _isf(self, x, a):
+        return scu._skewnorm_isf(x, 0.0, 1.0, a)
+
+    def _rvs(self, a, size=None, random_state=None):
+        u0 = random_state.normal(size=size)
+        v = random_state.normal(size=size)
+        d = a/np.sqrt(1 + a**2)
+        u1 = d*u0 + v*np.sqrt(1 - d**2)
+        return np.where(u0 >= 0, u1, -u1)
+
+    def _stats(self, a, moments='mvsk'):
+        output = [None, None, None, None]
+        const = np.sqrt(2/np.pi) * a/np.sqrt(1 + a**2)
+
+        if 'm' in moments:
+            output[0] = const
+        if 'v' in moments:
+            output[1] = 1 - const**2
+        if 's' in moments:
+            output[2] = ((4 - np.pi)/2) * (const/np.sqrt(1 - const**2))**3
+        if 'k' in moments:
+            output[3] = (2*(np.pi - 3)) * (const**4/(1 - const**2)**2)
+
+        return output
+
+    # For odd order, the each noncentral moment of the skew-normal distribution
+    # with location 0 and scale 1 can be expressed as a polynomial in delta,
+    # where delta = a/sqrt(1 + a**2) and `a` is the skew-normal shape
+    # parameter.  The dictionary _skewnorm_odd_moments defines those
+    # polynomials for orders up to 19.  The dict is implemented as a cached
+    # property to reduce the impact of the creation of the dict on import time.
+    @cached_property
+    def _skewnorm_odd_moments(self):
+        skewnorm_odd_moments = {
+            1: Polynomial([1]),
+            3: Polynomial([3, -1]),
+            5: Polynomial([15, -10, 3]),
+            7: Polynomial([105, -105, 63, -15]),
+            9: Polynomial([945, -1260, 1134, -540, 105]),
+            11: Polynomial([10395, -17325, 20790, -14850, 5775, -945]),
+            13: Polynomial([135135, -270270, 405405, -386100, 225225, -73710,
+                            10395]),
+            15: Polynomial([2027025, -4729725, 8513505, -10135125, 7882875,
+                            -3869775, 1091475, -135135]),
+            17: Polynomial([34459425, -91891800, 192972780, -275675400,
+                            268017750, -175429800, 74220300, -18378360,
+                            2027025]),
+            19: Polynomial([654729075, -1964187225, 4714049340, -7856748900,
+                            9166207050, -7499623950, 4230557100, -1571349780,
+                            346621275, -34459425]),
+        }
+        return skewnorm_odd_moments
+
+    def _munp(self, order, a):
+        if order % 2:
+            if order > 19:
+                raise NotImplementedError("skewnorm noncentral moments not "
+                                          "implemented for odd orders greater "
+                                          "than 19.")
+            # Use the precomputed polynomials that were derived from the
+            # moment generating function.
+            delta = a/np.sqrt(1 + a**2)
+            return (delta * self._skewnorm_odd_moments[order](delta**2)
+                    * _SQRT_2_OVER_PI)
+        else:
+            # For even order, the moment is just (order-1)!!, where !! is the
+            # notation for the double factorial; for an odd integer m, m!! is
+            # m*(m-2)*...*3*1.
+            # We could use special.factorial2, but we know the argument is odd,
+            # so avoid the overhead of that function and compute the result
+            # directly here.
+            return sc.gamma((order + 1)/2) * 2**(order/2) / _SQRT_PI
+
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        If ``method='mm'``, parameters fixed by the user are respected, and the
+        remaining parameters are used to match distribution and sample moments
+        where possible. For example, if the user fixes the location with
+        ``floc``, the parameters will only match the distribution skewness and
+        variance to the sample skewness and variance; no attempt will be made
+        to match the means or minimize a norm of the errors.
+        Note that the maximum possible skewness magnitude of a
+        `scipy.stats.skewnorm` distribution is approximately 0.9952717; if the
+        magnitude of the data's sample skewness exceeds this, the returned
+        shape parameter ``a`` will be infinite.
+        \n\n""")
+    def fit(self, data, *args, **kwds):
+        if kwds.pop("superfit", False):
+            return super().fit(data, *args, **kwds)
+        if isinstance(data, CensoredData):
+            if data.num_censored() == 0:
+                data = data._uncensor()
+            else:
+                return super().fit(data, *args, **kwds)
+
+        # this extracts fixed shape, location, and scale however they
+        # are specified, and also leaves them in `kwds`
+        data, fa, floc, fscale = _check_fit_input_parameters(self, data,
+                                                             args, kwds)
+        method = kwds.get("method", "mle").lower()
+
+        # See https://en.wikipedia.org/wiki/Skew_normal_distribution for
+        # moment formulas.
+        def skew_d(d):  # skewness in terms of delta
+            return (4-np.pi)/2 * ((d * np.sqrt(2 / np.pi))**3
+                                  / (1 - 2*d**2 / np.pi)**(3/2))
+
+        def d_skew(skew):  # delta in terms of skewness
+            s_23 = np.abs(skew)**(2/3)
+            return np.sign(skew) * np.sqrt(
+                np.pi/2 * s_23 / (s_23 + ((4 - np.pi)/2)**(2/3))
+            )
+
+        # If method is method of moments, we don't need the user's guesses.
+        # Otherwise, extract the guesses from args and kwds.
+        if method == "mm":
+            a, loc, scale = None, None, None
+        else:
+            a = args[0] if len(args) else None
+            loc = kwds.pop('loc', None)
+            scale = kwds.pop('scale', None)
+
+        if fa is None and a is None:  # not fixed and no guess: use MoM
+            # Solve for a that matches sample distribution skewness to sample
+            # skewness.
+            s = stats.skew(data)
+            if method == 'mle':
+                # For MLE initial conditions, clip skewness to a large but
+                # reasonable value in case the data skewness is out-of-range.
+                s = np.clip(s, -0.99, 0.99)
+            else:
+                s_max = skew_d(1)
+                s = np.clip(s, -s_max, s_max)
+            d = d_skew(s)
+            with np.errstate(divide='ignore'):
+                a = np.sqrt(np.divide(d**2, (1-d**2)))*np.sign(s)
+        else:
+            a = fa if fa is not None else a
+            d = a / np.sqrt(1 + a**2)
+
+        if fscale is None and scale is None:
+            v = np.var(data)
+            scale = np.sqrt(v / (1 - 2*d**2/np.pi))
+        elif fscale is not None:
+            scale = fscale
+
+        if floc is None and loc is None:
+            m = np.mean(data)
+            loc = m - scale*d*np.sqrt(2/np.pi)
+        elif floc is not None:
+            loc = floc
+
+        if method == 'mm':
+            return a, loc, scale
+        else:
+            # At this point, parameter "guesses" may equal the fixed parameters
+            # in kwds. No harm in passing them as guesses, too.
+            return super().fit(data, a, loc=loc, scale=scale, **kwds)
+
+
+skewnorm = skewnorm_gen(name='skewnorm')
+
+
+class trapezoid_gen(rv_continuous):
+    r"""
+    A trapezoidal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The trapezoidal distribution can be represented with an up-sloping line
+    from ``loc`` to ``(loc + c*scale)``, then constant to ``(loc + d*scale)``
+    and then downsloping from ``(loc + d*scale)`` to ``(loc+scale)``.  This
+    defines the trapezoid base from ``loc`` to ``(loc+scale)`` and the flat
+    top from ``c`` to ``d`` proportional to the position along the base
+    with ``0 <= c <= d <= 1``.  When ``c=d``, this is equivalent to `triang`
+    with the same values for `loc`, `scale` and `c`.
+    The method of [1]_ is used for computing moments.
+
+    `trapezoid` takes :math:`c` and :math:`d` as shape parameters.
+
+    %(after_notes)s
+
+    The standard form is in the range [0, 1] with c the mode.
+    The location parameter shifts the start to `loc`.
+    The scale parameter changes the width from 1 to `scale`.
+
+    References
+    ----------
+    .. [1] Kacker, R.N. and Lawrence, J.F. (2007). Trapezoidal and triangular
+       distributions for Type B evaluation of standard uncertainty.
+       Metrologia 44, 117-127. :doi:`10.1088/0026-1394/44/2/003`
+
+    %(example)s
+    """
+    def _argcheck(self, c, d):
+        return (c >= 0) & (c <= 1) & (d >= 0) & (d <= 1) & (d >= c)
+
+    def _shape_info(self):
+        ic = _ShapeInfo("c", False, (0, 1.0), (True, True))
+        id = _ShapeInfo("d", False, (0, 1.0), (True, True))
+        return [ic, id]
+
+    def _pdf(self, x, c, d):
+        u = 2 / (d-c+1)
+
+        return _lazyselect([x < c,
+                            (c <= x) & (x <= d),
+                            x > d],
+                           [lambda x, c, d, u: u * x / c,
+                            lambda x, c, d, u: u,
+                            lambda x, c, d, u: u * (1-x) / (1-d)],
+                           (x, c, d, u))
+
+    def _cdf(self, x, c, d):
+        return _lazyselect([x < c,
+                            (c <= x) & (x <= d),
+                            x > d],
+                           [lambda x, c, d: x**2 / c / (d-c+1),
+                            lambda x, c, d: (c + 2 * (x-c)) / (d-c+1),
+                            lambda x, c, d: 1-((1-x) ** 2
+                                               / (d-c+1) / (1-d))],
+                           (x, c, d))
+
+    def _ppf(self, q, c, d):
+        qc, qd = self._cdf(c, c, d), self._cdf(d, c, d)
+        condlist = [q < qc, q <= qd, q > qd]
+        choicelist = [np.sqrt(q * c * (1 + d - c)),
+                      0.5 * q * (1 + d - c) + 0.5 * c,
+                      1 - np.sqrt((1 - q) * (d - c + 1) * (1 - d))]
+        return np.select(condlist, choicelist)
+
+    def _munp(self, n, c, d):
+        # Using the parameterization from Kacker, 2007, with
+        # a=bottom left, c=top left, d=top right, b=bottom right, then
+        #     E[X^n] = h/(n+1)/(n+2) [(b^{n+2}-d^{n+2})/(b-d)
+        #                             - ((c^{n+2} - a^{n+2})/(c-a)]
+        # with h = 2/((b-a) - (d-c)). The corresponding parameterization
+        # in scipy, has a'=loc, c'=loc+c*scale, d'=loc+d*scale, b'=loc+scale,
+        # which for standard form reduces to a'=0, b'=1, c'=c, d'=d.
+        # Substituting into E[X^n] gives the bd' term as (1 - d^{n+2})/(1 - d)
+        # and the ac' term as c^{n-1} for the standard form. The bd' term has
+        # numerical difficulties near d=1, so replace (1 - d^{n+2})/(1-d)
+        # with expm1((n+2)*log(d))/(d-1).
+        # Testing with n=18 for c=(1e-30,1-eps) shows that this is stable.
+        # We still require an explicit test for d=1 to prevent divide by zero,
+        # and now a test for d=0 to prevent log(0).
+        ab_term = c**(n+1)
+        dc_term = _lazyselect(
+            [d == 0.0, (0.0 < d) & (d < 1.0), d == 1.0],
+            [lambda d: 1.0,
+             lambda d: np.expm1((n+2) * np.log(d)) / (d-1.0),
+             lambda d: n+2],
+            [d])
+        val = 2.0 / (1.0+d-c) * (dc_term - ab_term) / ((n+1) * (n+2))
+        return val
+
+    def _entropy(self, c, d):
+        # Using the parameterization from Wikipedia (van Dorp, 2003)
+        # with a=bottom left, c=top left, d=top right, b=bottom right
+        # gives a'=loc, b'=loc+c*scale, c'=loc+d*scale, d'=loc+scale,
+        # which for loc=0, scale=1 is a'=0, b'=c, c'=d, d'=1.
+        # Substituting into the entropy formula from Wikipedia gives
+        # the following result.
+        return 0.5 * (1.0-d+c) / (1.0+d-c) + np.log(0.5 * (1.0+d-c))
+
+    def _fitstart(self, data, args=None):
+        # Arbitrary, but c=d=1 fails due to being on edge of bounds
+        if args is None:
+            args = (0.33, 0.66)
+        return super()._fitstart(data, args=args)
+
+
+trapezoid = trapezoid_gen(a=0.0, b=1.0, name="trapezoid")
+
+
+class triang_gen(rv_continuous):
+    r"""A triangular continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The triangular distribution can be represented with an up-sloping line from
+    ``loc`` to ``(loc + c*scale)`` and then downsloping for ``(loc + c*scale)``
+    to ``(loc + scale)``.
+
+    `triang` takes ``c`` as a shape parameter for :math:`0 \le c \le 1`.
+
+    %(after_notes)s
+
+    The standard form is in the range [0, 1] with c the mode.
+    The location parameter shifts the start to `loc`.
+    The scale parameter changes the width from 1 to `scale`.
+
+    %(example)s
+
+    """
+    def _rvs(self, c, size=None, random_state=None):
+        return random_state.triangular(0, c, 1, size)
+
+    def _argcheck(self, c):
+        return (c >= 0) & (c <= 1)
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, 1.0), (True, True))]
+
+    def _pdf(self, x, c):
+        # 0: edge case where c=0
+        # 1: generalised case for x < c, don't use x <= c, as it doesn't cope
+        #    with c = 0.
+        # 2: generalised case for x >= c, but doesn't cope with c = 1
+        # 3: edge case where c=1
+        r = _lazyselect([c == 0,
+                         x < c,
+                         (x >= c) & (c != 1),
+                         c == 1],
+                        [lambda x, c: 2 - 2 * x,
+                         lambda x, c: 2 * x / c,
+                         lambda x, c: 2 * (1 - x) / (1 - c),
+                         lambda x, c: 2 * x],
+                        (x, c))
+        return r
+
+    def _cdf(self, x, c):
+        r = _lazyselect([c == 0,
+                         x < c,
+                         (x >= c) & (c != 1),
+                         c == 1],
+                        [lambda x, c: 2*x - x*x,
+                         lambda x, c: x * x / c,
+                         lambda x, c: (x*x - 2*x + c) / (c-1),
+                         lambda x, c: x * x],
+                        (x, c))
+        return r
+
+    def _ppf(self, q, c):
+        return np.where(q < c, np.sqrt(c * q), 1-np.sqrt((1-c) * (1-q)))
+
+    def _stats(self, c):
+        return ((c+1.0)/3.0,
+                (1.0-c+c*c)/18,
+                np.sqrt(2)*(2*c-1)*(c+1)*(c-2) / (5*np.power((1.0-c+c*c), 1.5)),
+                -3.0/5.0)
+
+    def _entropy(self, c):
+        return 0.5-np.log(2)
+
+
+triang = triang_gen(a=0.0, b=1.0, name="triang")
+
+
+class truncexpon_gen(rv_continuous):
+    r"""A truncated exponential continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `truncexpon` is:
+
+    .. math::
+
+        f(x, b) = \frac{\exp(-x)}{1 - \exp(-b)}
+
+    for :math:`0 <= x <= b`.
+
+    `truncexpon` takes ``b`` as a shape parameter for :math:`b`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("b", False, (0, np.inf), (False, False))]
+
+    def _get_support(self, b):
+        return self.a, b
+
+    def _pdf(self, x, b):
+        # truncexpon.pdf(x, b) = exp(-x) / (1-exp(-b))
+        return np.exp(-x)/(-sc.expm1(-b))
+
+    def _logpdf(self, x, b):
+        return -x - np.log(-sc.expm1(-b))
+
+    def _cdf(self, x, b):
+        return sc.expm1(-x)/sc.expm1(-b)
+
+    def _ppf(self, q, b):
+        return -sc.log1p(q*sc.expm1(-b))
+
+    def _sf(self, x, b):
+        return (np.exp(-b) - np.exp(-x))/sc.expm1(-b)
+
+    def _isf(self, q, b):
+        return -np.log(np.exp(-b) - q * sc.expm1(-b))
+
+    def _munp(self, n, b):
+        # wrong answer with formula, same as in continuous.pdf
+        # return sc.gamman+1)-sc.gammainc1+n, b)
+        if n == 1:
+            return (1-(b+1)*np.exp(-b))/(-sc.expm1(-b))
+        elif n == 2:
+            return 2*(1-0.5*(b*b+2*b+2)*np.exp(-b))/(-sc.expm1(-b))
+        else:
+            # return generic for higher moments
+            return super()._munp(n, b)
+
+    def _entropy(self, b):
+        eB = np.exp(b)
+        return np.log(eB-1)+(1+eB*(b-1.0))/(1.0-eB)
+
+
+truncexpon = truncexpon_gen(a=0.0, name='truncexpon')
+truncexpon._support = (0.0, 'b')
+
+
+# logsumexp trick for log(p + q) with only log(p) and log(q)
+def _log_sum(log_p, log_q):
+    return sc.logsumexp([log_p, log_q], axis=0)
+
+
+# same as above, but using -exp(x) = exp(x + πi)
+def _log_diff(log_p, log_q):
+    return sc.logsumexp([log_p, log_q+np.pi*1j], axis=0)
+
+
+def _log_gauss_mass(a, b):
+    """Log of Gaussian probability mass within an interval"""
+    a, b = np.broadcast_arrays(a, b)
+
+    # Calculations in right tail are inaccurate, so we'll exploit the
+    # symmetry and work only in the left tail
+    case_left = b <= 0
+    case_right = a > 0
+    case_central = ~(case_left | case_right)
+
+    def mass_case_left(a, b):
+        return _log_diff(_norm_logcdf(b), _norm_logcdf(a))
+
+    def mass_case_right(a, b):
+        return mass_case_left(-b, -a)
+
+    def mass_case_central(a, b):
+        # Previously, this was implemented as:
+        # left_mass = mass_case_left(a, 0)
+        # right_mass = mass_case_right(0, b)
+        # return _log_sum(left_mass, right_mass)
+        # Catastrophic cancellation occurs as np.exp(log_mass) approaches 1.
+        # Correct for this with an alternative formulation.
+        # We're not concerned with underflow here: if only one term
+        # underflows, it was insignificant; if both terms underflow,
+        # the result can't accurately be represented in logspace anyway
+        # because sc.log1p(x) ~ x for small x.
+        return sc.log1p(-_norm_cdf(a) - _norm_cdf(-b))
+
+    # _lazyselect not working; don't care to debug it
+    out = np.full_like(a, fill_value=np.nan, dtype=np.complex128)
+    if a[case_left].size:
+        out[case_left] = mass_case_left(a[case_left], b[case_left])
+    if a[case_right].size:
+        out[case_right] = mass_case_right(a[case_right], b[case_right])
+    if a[case_central].size:
+        out[case_central] = mass_case_central(a[case_central], b[case_central])
+    return np.real(out)  # discard ~0j
+
+
+class truncnorm_gen(rv_continuous):
+    r"""A truncated normal continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    This distribution is the normal distribution centered on ``loc`` (default
+    0), with standard deviation ``scale`` (default 1), and truncated at ``a``
+    and ``b`` *standard deviations* from ``loc``. For arbitrary ``loc`` and
+    ``scale``, ``a`` and ``b`` are *not* the abscissae at which the shifted
+    and scaled distribution is truncated.
+
+    .. note::
+        If ``a_trunc`` and ``b_trunc`` are the abscissae at which we wish
+        to truncate the distribution (as opposed to the number of standard
+        deviations from ``loc``), then we can calculate the distribution
+        parameters ``a`` and ``b`` as follows::
+
+            a, b = (a_trunc - loc) / scale, (b_trunc - loc) / scale
+
+        This is a common point of confusion. For additional clarification,
+        please see the example below.
+
+    %(example)s
+
+    In the examples above, ``loc=0`` and ``scale=1``, so the plot is truncated
+    at ``a`` on the left and ``b`` on the right. However, suppose we were to
+    produce the same histogram with ``loc = 1`` and ``scale=0.5``.
+
+    >>> loc, scale = 1, 0.5
+    >>> rv = truncnorm(a, b, loc=loc, scale=scale)
+    >>> x = np.linspace(truncnorm.ppf(0.01, a, b),
+    ...                 truncnorm.ppf(0.99, a, b), 100)
+    >>> r = rv.rvs(size=1000)
+
+    >>> fig, ax = plt.subplots(1, 1)
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+    >>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
+    >>> ax.set_xlim(a, b)
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+
+    Note that the distribution is no longer appears to be truncated at
+    abscissae ``a`` and ``b``. That is because the *standard* normal
+    distribution is first truncated at ``a`` and ``b``, *then* the resulting
+    distribution is scaled by ``scale`` and shifted by ``loc``. If we instead
+    want the shifted and scaled distribution to be truncated at ``a`` and
+    ``b``, we need to transform these values before passing them as the
+    distribution parameters.
+
+    >>> a_transformed, b_transformed = (a - loc) / scale, (b - loc) / scale
+    >>> rv = truncnorm(a_transformed, b_transformed, loc=loc, scale=scale)
+    >>> x = np.linspace(truncnorm.ppf(0.01, a, b),
+    ...                 truncnorm.ppf(0.99, a, b), 100)
+    >>> r = rv.rvs(size=10000)
+
+    >>> fig, ax = plt.subplots(1, 1)
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+    >>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
+    >>> ax.set_xlim(a-0.1, b+0.1)
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+    """
+
+    def _argcheck(self, a, b):
+        return a < b
+
+    def _shape_info(self):
+        ia = _ShapeInfo("a", False, (-np.inf, np.inf), (True, False))
+        ib = _ShapeInfo("b", False, (-np.inf, np.inf), (False, True))
+        return [ia, ib]
+
+    def _fitstart(self, data):
+        # Reasonable, since support is [a, b]
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        return super()._fitstart(data, args=(np.min(data), np.max(data)))
+
+    def _get_support(self, a, b):
+        return a, b
+
+    def _pdf(self, x, a, b):
+        return np.exp(self._logpdf(x, a, b))
+
+    def _logpdf(self, x, a, b):
+        return _norm_logpdf(x) - _log_gauss_mass(a, b)
+
+    def _cdf(self, x, a, b):
+        return np.exp(self._logcdf(x, a, b))
+
+    def _logcdf(self, x, a, b):
+        x, a, b = np.broadcast_arrays(x, a, b)
+        logcdf = np.asarray(_log_gauss_mass(a, x) - _log_gauss_mass(a, b))
+        i = logcdf > -0.1  # avoid catastrophic cancellation
+        if np.any(i):
+            logcdf[i] = np.log1p(-np.exp(self._logsf(x[i], a[i], b[i])))
+        return logcdf
+
+    def _sf(self, x, a, b):
+        return np.exp(self._logsf(x, a, b))
+
+    def _logsf(self, x, a, b):
+        x, a, b = np.broadcast_arrays(x, a, b)
+        logsf = np.asarray(_log_gauss_mass(x, b) - _log_gauss_mass(a, b))
+        i = logsf > -0.1  # avoid catastrophic cancellation
+        if np.any(i):
+            logsf[i] = np.log1p(-np.exp(self._logcdf(x[i], a[i], b[i])))
+        return logsf
+
+    def _entropy(self, a, b):
+        A = _norm_cdf(a)
+        B = _norm_cdf(b)
+        Z = B - A
+        C = np.log(np.sqrt(2 * np.pi * np.e) * Z)
+        D = (a * _norm_pdf(a) - b * _norm_pdf(b)) / (2 * Z)
+        h = C + D
+        return h
+
+    def _ppf(self, q, a, b):
+        q, a, b = np.broadcast_arrays(q, a, b)
+
+        case_left = a < 0
+        case_right = ~case_left
+
+        def ppf_left(q, a, b):
+            log_Phi_x = _log_sum(_norm_logcdf(a),
+                                 np.log(q) + _log_gauss_mass(a, b))
+            return sc.ndtri_exp(log_Phi_x)
+
+        def ppf_right(q, a, b):
+            log_Phi_x = _log_sum(_norm_logcdf(-b),
+                                 np.log1p(-q) + _log_gauss_mass(a, b))
+            return -sc.ndtri_exp(log_Phi_x)
+
+        out = np.empty_like(q)
+
+        q_left = q[case_left]
+        q_right = q[case_right]
+
+        if q_left.size:
+            out[case_left] = ppf_left(q_left, a[case_left], b[case_left])
+        if q_right.size:
+            out[case_right] = ppf_right(q_right, a[case_right], b[case_right])
+
+        return out
+
+    def _isf(self, q, a, b):
+        # Mostly copy-paste of _ppf, but I think this is simpler than combining
+        q, a, b = np.broadcast_arrays(q, a, b)
+
+        case_left = b < 0
+        case_right = ~case_left
+
+        def isf_left(q, a, b):
+            log_Phi_x = _log_diff(_norm_logcdf(b),
+                                  np.log(q) + _log_gauss_mass(a, b))
+            return sc.ndtri_exp(np.real(log_Phi_x))
+
+        def isf_right(q, a, b):
+            log_Phi_x = _log_diff(_norm_logcdf(-a),
+                                  np.log1p(-q) + _log_gauss_mass(a, b))
+            return -sc.ndtri_exp(np.real(log_Phi_x))
+
+        out = np.empty_like(q)
+
+        q_left = q[case_left]
+        q_right = q[case_right]
+
+        if q_left.size:
+            out[case_left] = isf_left(q_left, a[case_left], b[case_left])
+        if q_right.size:
+            out[case_right] = isf_right(q_right, a[case_right], b[case_right])
+
+        return out
+
+    def _munp(self, n, a, b):
+        def n_th_moment(n, a, b):
+            """
+            Returns n-th moment. Defined only if n >= 0.
+            Function cannot broadcast due to the loop over n
+            """
+            ab = np.asarray([a, b])
+            pA, pB = self._pdf(ab, a, b)
+            probs = np.asarray([pA, -pB])
+            cond = probs != 0
+            moments = [0, 1]
+            for k in range(1, n+1):
+                # a or b might be infinite, and the corresponding pdf value
+                # is 0 in that case, but nan is returned for the
+                # multiplication.  However, as b->infinity,  pdf(b)*b**k -> 0.
+                # So it is safe to use xpx.apply_where to avoid the nan.
+                vals = xpx.apply_where(cond, (probs, ab),
+                                       lambda x, y: x * y**(k-1),
+                                       fill_value=0)
+                mk = np.sum(vals) + (k-1) * moments[-2]
+                moments.append(mk)
+            return moments[-1]
+
+        return xpx.apply_where((n >= 0) & (a == a) & (b == b), (n, a, b),
+                               np.vectorize(n_th_moment, otypes=[np.float64]),
+                               fill_value=np.nan)
+
+    def _stats(self, a, b, moments='mv'):
+        pA, pB = self.pdf(np.array([a, b]), a, b)
+
+        def _truncnorm_stats_scalar(a, b, pA, pB):
+            ab = np.asarray([a, b])
+            m1 = pA - pB
+            mu = m1
+            # use xpx.apply_where to avoid nan (See detailed comment in _munp)
+            probs = np.asarray([pA, -pB])
+            cond = probs != 0
+            vals = xpx.apply_where(cond, (probs, ab), lambda x, y: x*y,
+                                   fill_value=0)
+            m2 = 1 + np.sum(vals)
+            vals = xpx.apply_where(cond, (probs, ab - mu), lambda x, y: x*y,
+                                   fill_value=0)
+            # mu2 = m2 - mu**2, but not as numerically stable as:
+            # mu2 = (a-mu)*pA - (b-mu)*pB + 1
+            mu2 = 1 + np.sum(vals)
+            vals = xpx.apply_where(cond, (probs, ab), lambda x, y: x*y**2,
+                                   fill_value=0)
+            m3 = 2*m1 + np.sum(vals)
+            vals = xpx.apply_where(cond, (probs, ab), lambda x, y: x*y**3,
+                                   fill_value=0)
+            m4 = 3*m2 + np.sum(vals)
+
+            mu3 = m3 + m1 * (-3*m2 + 2*m1**2)
+            g1 = mu3 / np.power(mu2, 1.5)
+            mu4 = m4 + m1*(-4*m3 + 3*m1*(2*m2 - m1**2))
+            g2 = mu4 / mu2**2 - 3
+            return mu, mu2, g1, g2
+
+        _truncnorm_stats = np.vectorize(_truncnorm_stats_scalar)
+        return _truncnorm_stats(a, b, pA, pB)
+
+
+truncnorm = truncnorm_gen(name='truncnorm', momtype=1)
+truncnorm._support = ('a', 'b')
+
+
+class truncpareto_gen(rv_continuous):
+    r"""An upper truncated Pareto continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    pareto : Pareto distribution
+
+    Notes
+    -----
+    The probability density function for `truncpareto` is:
+
+    .. math::
+
+        f(x, b, c) = \frac{b}{1 - c^{-b}} \frac{1}{x^{b+1}}
+
+    for :math:`b \neq 0`, :math:`c > 1` and :math:`1 \le x \le c`.
+
+    `truncpareto` takes `b` and `c` as shape parameters for :math:`b` and
+    :math:`c`.
+
+    Notice that the upper truncation value :math:`c` is defined in
+    standardized form so that random values of an unscaled, unshifted variable
+    are within the range ``[1, c]``.
+    If ``u_r`` is the upper bound to a scaled and/or shifted variable,
+    then ``c = (u_r - loc) / scale``. In other words, the support of the
+    distribution becomes ``(scale + loc) <= x <= (c*scale + loc)`` when
+    `scale` and/or `loc` are provided.
+
+    The ``fit`` method assumes that :math:`b` is positive; it does not produce
+    good results when the data is more consistent with negative :math:`b`.
+
+    `truncpareto` can also be used to model a general power law distribution
+    with PDF:
+
+    .. math::
+
+        f(x; a, l, h) = \frac{a}{h^a - l^a} x^{a-1}
+
+    for :math:`a \neq 0` and :math:`0 < l < x < h`. Suppose :math:`a`,
+    :math:`l`, and :math:`h` are represented in code as ``a``, ``l``, and
+    ``h``, respectively. In this case, use `truncpareto` with parameters
+    ``b = -a``, ``c = h / l``, ``scale = l``, and ``loc = 0``.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Burroughs, S. M., and Tebbens S. F.
+        "Upper-truncated power laws in natural systems."
+        Pure and Applied Geophysics 158.4 (2001): 741-757.
+
+    %(example)s
+
+    """
+
+    def _shape_info(self):
+        ib = _ShapeInfo("b", False, (-np.inf, np.inf), (False, False))
+        ic = _ShapeInfo("c", False, (1.0, np.inf), (False, False))
+        return [ib, ic]
+
+    def _argcheck(self, b, c):
+        return (b != 0.) & (c > 1.)
+
+    def _get_support(self, b, c):
+        return self.a, c
+
+    def _pdf(self, x, b, c):
+        # here and below, avoid int to negative int power
+        x, b, c = xp_promote(x, b, c, force_floating=True, xp=np)
+        return b * x**-(b+1) / (1 - 1/c**b)
+
+    def _logpdf(self, x, b, c):
+        x, b, c = xp_promote(x, b, c, force_floating=True, xp=np)
+        return xpx.apply_where(b > 0, (x, b, c), self._logpdf_pos_b, super()._logpdf)
+
+    def _logpdf_pos_b(self, x, b, c):
+        return np.log(b) - np.log(-np.expm1(-b*np.log(c))) - (b+1)*np.log(x)
+
+    def _cdf(self, x, b, c):
+        x, b, c = xp_promote(x, b, c, force_floating=True, xp=np)
+        return (1 - x**-b) / (1 - 1/c**b)
+
+    def _logcdf(self, x, b, c):
+        x, b, c = xp_promote(x, b, c, force_floating=True, xp=np)
+        return xpx.apply_where(b > 0, (x, b, c), self._logcdf_pos_b, super()._logcdf)
+
+    def _logcdf_pos_b(self, x, b, c):
+        return np.log1p(-x**-b) - np.log1p(-1/c**b)
+
+    def _ppf(self, q, b, c):
+        q, b, c = xp_promote(q, b, c, force_floating=True, xp=np)
+        return pow(1 - (1 - 1/c**b)*q, -1/b)
+
+    def _sf(self, x, b, c):
+        x, b, c = xp_promote(x, b, c, force_floating=True, xp=np)
+        return (x**-b - 1/c**b) / (1 - 1/c**b)
+
+    def _logsf(self, x, b, c):
+        x, b, c = xp_promote(x, b, c, force_floating=True, xp=np)
+        return xpx.apply_where(b > 0, (x, b, c), self._logsf_pos_b, super()._logsf)
+
+    def _logsf_pos_b(self, x, b, c):
+        return np.log(x**-b - 1/c**b) - np.log1p(-1/c**b)
+
+    def _isf(self, q, b, c):
+        q, b, c = xp_promote(q, b, c, force_floating=True, xp=np)
+        return pow(1/c**b + (1 - 1/c**b)*q, -1/b)
+
+    def _entropy(self, b, c):
+        return -(np.log(b/(1 - 1/c**b))
+                 + (b+1)*(np.log(c)/(c**b - 1) - 1/b))
+
+    def _munp(self, n, b, c):
+        n, b, c = xp_promote(n, b, c, force_floating=True, xp=np)
+        if (n == b).all():
+            return b*np.log(c) / (1 - 1/c**b)
+        else:
+            return b / (b-n) * (c**b - c**n) / (c**b - 1)
+
+    def _fitstart(self, data):
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        b, loc, scale = pareto.fit(data)
+        c = (max(data) - loc)/scale
+        return b, c, loc, scale
+
+    @_call_super_mom
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        if kwds.pop("superfit", False):
+            return super().fit(data, *args, **kwds)
+
+        def log_mean(x):
+            return np.mean(np.log(x))
+
+        def harm_mean(x):
+            return 1/np.mean(1/x)
+
+        def get_b(c, loc, scale):
+            u = (data-loc)/scale
+            harm_m = harm_mean(u)
+            log_m = log_mean(u)
+            quot = (harm_m-1)/log_m
+            return (1 - (quot-1) / (quot - (1 - 1/c)*harm_m/np.log(c)))/log_m
+
+        def get_c(loc, scale):
+            return (mx - loc)/scale
+
+        def get_loc(fc, fscale):
+            if fscale:  # (fscale and fc) or (fscale and not fc)
+                loc = mn - fscale
+                return loc
+            if fc:
+                loc = (fc*mn - mx)/(fc - 1)
+                return loc
+
+        def get_scale(loc):
+            return mn - loc
+
+        # Functions used for optimisation; partial derivatives of
+        # the Lagrangian, set to equal 0.
+
+        def dL_dLoc(loc, b_=None):
+            # Partial derivative wrt location.
+            # Optimised upon when no parameters, or only b, are fixed.
+            scale = get_scale(loc)
+            c = get_c(loc, scale)
+            b = get_b(c, loc, scale) if b_ is None else b_
+            harm_m = harm_mean((data - loc)/scale)
+            return 1 - (1 + (c - 1)/(c**(b+1) - c)) * (1 - 1/(b+1)) * harm_m
+
+        def dL_dB(b, logc, logm):
+            # Partial derivative wrt b.
+            # Optimised upon whenever at least one parameter but b is fixed,
+            # and b is free.
+            return b - np.log1p(b*logc / (1 - b*logm)) / logc
+
+        def fallback(data, *args, **kwargs):
+            # Should any issue arise, default to the general fit method.
+            return super(truncpareto_gen, self).fit(data, *args, **kwargs)
+
+        parameters = _check_fit_input_parameters(self, data, args, kwds)
+        data, fb, fc, floc, fscale = parameters
+        mn, mx = data.min(), data.max()
+        mn_inf = np.nextafter(mn, -np.inf)
+
+        if (fb is not None
+                and fc is not None
+                and floc is not None
+                and fscale is not None):
+            raise ValueError("All parameters fixed."
+                             "There is nothing to optimize.")
+        elif fc is None and floc is None and fscale is None:
+            if fb is None:
+                def cond_b(loc):
+                    # b is positive only if this function is positive
+                    scale = get_scale(loc)
+                    c = get_c(loc, scale)
+                    harm_m = harm_mean((data - loc)/scale)
+                    return (1 + 1/(c-1)) * np.log(c) / harm_m - 1
+
+                # This gives an upper bound on loc allowing for a positive b.
+                # Iteratively look for a bracket for root_scalar.
+                mn_inf = np.nextafter(mn, -np.inf)
+                rbrack = mn_inf
+                i = 0
+                lbrack = rbrack - 1
+                while ((lbrack > -np.inf)
+                       and (cond_b(lbrack)*cond_b(rbrack) >= 0)):
+                    i += 1
+                    lbrack = rbrack - np.power(2., i)
+                if not lbrack > -np.inf:
+                    return fallback(data, *args, **kwds)
+                res = root_scalar(cond_b, bracket=(lbrack, rbrack))
+                if not res.converged:
+                    return fallback(data, *args, **kwds)
+
+                # Determine the MLE for loc.
+                # Iteratively look for a bracket for root_scalar.
+                rbrack = res.root - 1e-3  # grad_loc is numerically ill-behaved
+                lbrack = rbrack - 1
+                i = 0
+                while ((lbrack > -np.inf)
+                       and (dL_dLoc(lbrack)*dL_dLoc(rbrack) >= 0)):
+                    i += 1
+                    lbrack = rbrack - np.power(2., i)
+                if not lbrack > -np.inf:
+                    return fallback(data, *args, **kwds)
+                res = root_scalar(dL_dLoc, bracket=(lbrack, rbrack))
+                if not res.converged:
+                    return fallback(data, *args, **kwds)
+                loc = res.root
+                scale = get_scale(loc)
+                c = get_c(loc, scale)
+                b = get_b(c, loc, scale)
+
+                std_data = (data - loc)/scale
+                # The expression of b relies on b being bounded above.
+                up_bound_b = min(1/log_mean(std_data),
+                                 1/(harm_mean(std_data)-1))
+                if not (b < up_bound_b):
+                    return fallback(data, *args, **kwds)
+            else:
+                # We know b is positive (or a FitError will be triggered)
+                # so we let loc get close to min(data).
+                rbrack = mn_inf
+                lbrack = mn_inf - 1
+                i = 0
+                # Iteratively look for a bracket for root_scalar.
+                while (lbrack > -np.inf
+                       and (dL_dLoc(lbrack, fb)
+                            * dL_dLoc(rbrack, fb) >= 0)):
+                    i += 1
+                    lbrack = rbrack - 2**i
+                if not lbrack > -np.inf:
+                    return fallback(data, *args, **kwds)
+                res = root_scalar(dL_dLoc, (fb,),
+                                  bracket=(lbrack, rbrack))
+                if not res.converged:
+                    return fallback(data, *args, **kwds)
+                loc = res.root
+                scale = get_scale(loc)
+                c = get_c(loc, scale)
+                b = fb
+        else:
+            # At least one of the parameters determining the support is fixed;
+            # the others then have analytical expressions from the constraints.
+            # The completely determined case (fixed c, loc and scale)
+            # has to be checked for not overflowing the support.
+            # If not fixed, b has to be determined numerically.
+            loc = floc if floc is not None else get_loc(fc, fscale)
+            scale = fscale or get_scale(loc)
+            c = fc or get_c(loc, scale)
+
+            # Unscaled, translated values should be positive when the location
+            # is fixed. If it is not the case, we end up with negative `scale`
+            # and `c`, which would trigger a FitError before exiting the
+            # method.
+            if floc is not None and data.min() - floc < 0:
+                raise FitDataError("truncpareto", lower=1, upper=c)
+
+            # Standardised values should be within the distribution support
+            # when all parameters controlling it are fixed. If it not the case,
+            # `fc` is overridden by `c` determined from `floc` and `fscale` when
+            # raising the exception.
+            if fc and (floc is not None) and fscale:
+                if data.max() > fc*fscale + floc:
+                    raise FitDataError("truncpareto", lower=1,
+                                       upper=get_c(loc, scale))
+
+            # The other constraints should be automatically satisfied
+            # from the analytical expressions of the parameters.
+            # If fc or fscale are respectively less than one or less than 0,
+            # a FitError is triggered before exiting the method.
+
+            if fb is None:
+                std_data = (data - loc)/scale
+                logm = log_mean(std_data)
+                logc = np.log(c)
+                # Condition for a positive root to exist.
+                if not (2*logm < logc):
+                    return fallback(data, *args, **kwds)
+
+                lbrack = 1/logm + 1/(logm - logc)
+                rbrack = np.nextafter(1/logm, 0)
+                try:
+                    res = root_scalar(dL_dB, (logc, logm),
+                                      bracket=(lbrack, rbrack))
+                    # we should then never get there
+                    if not res.converged:
+                        return fallback(data, *args, **kwds)
+                    b = res.root
+                except ValueError:
+                    b = rbrack
+            else:
+                b = fb
+
+        # The distribution requires that `scale+loc <= data <= c*scale+loc`.
+        # To avoid numerical issues, some tuning may be necessary.
+        # We adjust `scale` to satisfy the lower bound, and we adjust
+        # `c` to satisfy the upper bound.
+        if not (scale+loc) < mn:
+            if fscale:
+                loc = np.nextafter(loc, -np.inf)
+            else:
+                scale = get_scale(loc)
+                scale = np.nextafter(scale, 0)
+        if not (c*scale+loc) > mx:
+            c = get_c(loc, scale)
+            c = np.nextafter(c, np.inf)
+
+        if not (np.all(self._argcheck(b, c)) and (scale > 0)):
+            return fallback(data, *args, **kwds)
+
+        params_override = b, c, loc, scale
+        if floc is None and fscale is None:
+            # Based on testing in gh-16782, the following methods are only
+            # reliable if either `floc` or `fscale` are provided. They are
+            # fast, though, so might as well see if they are better than the
+            # generic method.
+            params_super = fallback(data, *args, **kwds)
+            nllf_override = self.nnlf(params_override, data)
+            nllf_super = self.nnlf(params_super, data)
+            if nllf_super < nllf_override:
+                return params_super
+
+        return params_override
+
+
+truncpareto = truncpareto_gen(a=1.0, name='truncpareto')
+truncpareto._support = (1.0, 'c')
+
+
+class tukeylambda_gen(rv_continuous):
+    r"""A Tukey-Lamdba continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    A flexible distribution, able to represent and interpolate between the
+    following distributions:
+
+    - Cauchy                (:math:`lambda = -1`)
+    - logistic              (:math:`lambda = 0`)
+    - approx Normal         (:math:`lambda = 0.14`)
+    - uniform from -1 to 1  (:math:`lambda = 1`)
+
+    `tukeylambda` takes a real number :math:`lambda` (denoted ``lam``
+    in the implementation) as a shape parameter.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _argcheck(self, lam):
+        return np.isfinite(lam)
+
+    def _shape_info(self):
+        return [_ShapeInfo("lam", False, (-np.inf, np.inf), (False, False))]
+
+    def _get_support(self, lam):
+        b = xpx.apply_where(lam > 0, lam,
+                            lambda lam: 1/lam,
+                            fill_value=np.inf)
+        return -b, b
+
+    def _pdf(self, x, lam):
+        Fx = np.asarray(sc.tklmbda(x, lam))
+        Px = Fx**(lam-1.0) + (np.asarray(1-Fx))**(lam-1.0)
+        with np.errstate(divide='ignore'):
+            Px = 1.0/np.asarray(Px)
+            return np.where((lam <= 0) | (abs(x) < 1.0/np.asarray(lam)), Px, 0.0)
+
+    def _cdf(self, x, lam):
+        return sc.tklmbda(x, lam)
+
+    def _ppf(self, q, lam):
+        return sc.boxcox(q, lam) - sc.boxcox1p(-q, lam)
+
+    def _stats(self, lam):
+        return 0, _tlvar(lam), 0, _tlkurt(lam)
+
+    def _entropy(self, lam):
+        @np.vectorize
+        def entropy_1d(lam):
+            def integ(p):
+                return np.log(pow(p, lam - 1) + pow(1 - p, lam - 1))
+            return integrate.quad(integ, 0, 1)[0]
+        return entropy_1d(lam)
+
+
+tukeylambda = tukeylambda_gen(name='tukeylambda')
+
+
+class FitUniformFixedScaleDataError(FitDataError):
+    def __init__(self, ptp, fscale):
+        self.args = (
+            "Invalid values in `data`.  Maximum likelihood estimation with "
+            "the uniform distribution and fixed scale requires that "
+            f"np.ptp(data) <= fscale, but np.ptp(data) = {ptp} and "
+            f"fscale = {fscale}."
+        )
+
+
+class uniform_gen(rv_continuous):
+    r"""A uniform continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `uniform` is:
+
+    .. math::
+
+        f(x) = \begin{cases}
+                 1  & \text{for } 0 \le x \le 1 \\
+                 0  & \text{otherwise}
+               \end{cases}
+
+    In the standard form, the distribution is uniform on ``[0, 1]``. Using
+    the parameters ``loc`` and ``scale``, one obtains the uniform distribution
+    on ``[loc, loc + scale]``.
+
+    References
+    ----------
+    .. [1] "Continuous uniform distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Continuous_uniform_distribution
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return random_state.uniform(0.0, 1.0, size)
+
+    def _pdf(self, x):
+        return 1.0*(x == x)
+
+    def _cdf(self, x):
+        return x
+
+    def _ppf(self, q):
+        return q
+
+    def _stats(self):
+        return 0.5, 1.0/12, 0, -1.2
+
+    def _entropy(self):
+        return 0.0
+
+    @_call_super_mom
+    def fit(self, data, *args, **kwds):
+        """
+        Maximum likelihood estimate for the location and scale parameters.
+
+        `uniform.fit` uses only the following parameters.  Because exact
+        formulas are used, the parameters related to optimization that are
+        available in the `fit` method of other distributions are ignored
+        here.  The only positional argument accepted is `data`.
+
+        Parameters
+        ----------
+        data : array_like
+            Data to use in calculating the maximum likelihood estimate.
+        floc : float, optional
+            Hold the location parameter fixed to the specified value.
+        fscale : float, optional
+            Hold the scale parameter fixed to the specified value.
+
+        Returns
+        -------
+        loc, scale : float
+            Maximum likelihood estimates for the location and scale.
+
+        Notes
+        -----
+        An error is raised if `floc` is given and any values in `data` are
+        less than `floc`, or if `fscale` is given and `fscale` is less
+        than ``data.max() - data.min()``.  An error is also raised if both
+        `floc` and `fscale` are given.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> from scipy.stats import uniform
+
+        We'll fit the uniform distribution to `x`:
+
+        >>> x = np.array([2, 2.5, 3.1, 9.5, 13.0])
+
+        For a uniform distribution MLE, the location is the minimum of the
+        data, and the scale is the maximum minus the minimum.
+
+        >>> loc, scale = uniform.fit(x)
+        >>> loc
+        2.0
+        >>> scale
+        11.0
+
+        If we know the data comes from a uniform distribution where the support
+        starts at 0, we can use ``floc=0``:
+
+        >>> loc, scale = uniform.fit(x, floc=0)
+        >>> loc
+        0.0
+        >>> scale
+        13.0
+
+        Alternatively, if we know the length of the support is 12, we can use
+        ``fscale=12``:
+
+        >>> loc, scale = uniform.fit(x, fscale=12)
+        >>> loc
+        1.5
+        >>> scale
+        12.0
+
+        In that last example, the support interval is [1.5, 13.5].  This
+        solution is not unique.  For example, the distribution with ``loc=2``
+        and ``scale=12`` has the same likelihood as the one above.  When
+        `fscale` is given and it is larger than ``data.max() - data.min()``,
+        the parameters returned by the `fit` method center the support over
+        the interval ``[data.min(), data.max()]``.
+
+        """
+        if len(args) > 0:
+            raise TypeError("Too many arguments.")
+
+        floc = kwds.pop('floc', None)
+        fscale = kwds.pop('fscale', None)
+
+        _remove_optimizer_parameters(kwds)
+
+        if floc is not None and fscale is not None:
+            # This check is for consistency with `rv_continuous.fit`.
+            raise ValueError("All parameters fixed. There is nothing to "
+                             "optimize.")
+
+        data = np.asarray(data)
+
+        if not np.isfinite(data).all():
+            raise ValueError("The data contains non-finite values.")
+
+        # MLE for the uniform distribution
+        # --------------------------------
+        # The PDF is
+        #
+        #     f(x, loc, scale) = {1/scale  for loc <= x <= loc + scale
+        #                        {0        otherwise}
+        #
+        # The likelihood function is
+        #     L(x, loc, scale) = (1/scale)**n
+        # where n is len(x), assuming loc <= x <= loc + scale for all x.
+        # The log-likelihood is
+        #     l(x, loc, scale) = -n*log(scale)
+        # The log-likelihood is maximized by making scale as small as possible,
+        # while keeping loc <= x <= loc + scale.   So if neither loc nor scale
+        # are fixed, the log-likelihood is maximized by choosing
+        #     loc = x.min()
+        #     scale = np.ptp(x)
+        # If loc is fixed, it must be less than or equal to x.min(), and then
+        # the scale is
+        #     scale = x.max() - loc
+        # If scale is fixed, it must not be less than np.ptp(x).  If scale is
+        # greater than np.ptp(x), the solution is not unique.  Note that the
+        # likelihood does not depend on loc, except for the requirement that
+        # loc <= x <= loc + scale.  All choices of loc for which
+        #     x.max() - scale <= loc <= x.min()
+        # have the same log-likelihood.  In this case, we choose loc such that
+        # the support is centered over the interval [data.min(), data.max()]:
+        #     loc = x.min() = 0.5*(scale - np.ptp(x))
+
+        if fscale is None:
+            # scale is not fixed.
+            if floc is None:
+                # loc is not fixed, scale is not fixed.
+                loc = data.min()
+                scale = np.ptp(data)
+            else:
+                # loc is fixed, scale is not fixed.
+                loc = floc
+                scale = data.max() - loc
+                if data.min() < loc:
+                    raise FitDataError("uniform", lower=loc, upper=loc + scale)
+        else:
+            # loc is not fixed, scale is fixed.
+            ptp = np.ptp(data)
+            if ptp > fscale:
+                raise FitUniformFixedScaleDataError(ptp=ptp, fscale=fscale)
+            # If ptp < fscale, the ML estimate is not unique; see the comments
+            # above.  We choose the distribution for which the support is
+            # centered over the interval [data.min(), data.max()].
+            loc = data.min() - 0.5*(fscale - ptp)
+            scale = fscale
+
+        # We expect the return values to be floating point, so ensure it
+        # by explicitly converting to float.
+        return float(loc), float(scale)
+
+
+uniform = uniform_gen(a=0.0, b=1.0, name='uniform')
+
+
+class vonmises_gen(rv_continuous):
+    r"""A Von Mises continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    scipy.stats.vonmises_fisher : Von-Mises Fisher distribution on a
+                                  hypersphere
+
+    Notes
+    -----
+    The probability density function for `vonmises` and `vonmises_line` is:
+
+    .. math::
+
+        f(x, \kappa) = \frac{ \exp(\kappa \cos(x)) }{ 2 \pi I_0(\kappa) }
+
+    for :math:`-\pi \le x \le \pi`, :math:`\kappa \ge 0`. :math:`I_0` is the
+    modified Bessel function of order zero (`scipy.special.i0`).
+
+    `vonmises` is a circular distribution which does not restrict the
+    distribution to a fixed interval. Currently, there is no circular
+    distribution framework in SciPy. The ``cdf`` is implemented such that
+    ``cdf(x + 2*np.pi) == cdf(x) + 1``.
+
+    `vonmises_line` is the same distribution, defined on :math:`[-\pi, \pi]`
+    on the real line. This is a regular (i.e. non-circular) distribution.
+
+    Note about distribution parameters: `vonmises` and `vonmises_line` take
+    ``kappa`` as a shape parameter (concentration) and ``loc`` as the location
+    (circular mean). A ``scale`` parameter is accepted but does not have any
+    effect.
+
+    References
+    ----------
+    .. [1] Mardia, K. V. and Jupp, P. E. *Directional Statistics*.
+           John Wiley & Sons, 1999, p. 36.
+    .. [2] "von Mises distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Von_Mises_distribution
+
+    Examples
+    --------
+    Import the necessary modules.
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy.stats import vonmises
+
+    Define distribution parameters.
+
+    >>> loc = 0.5 * np.pi  # circular mean
+    >>> kappa = 1  # concentration
+
+    Compute the probability density at ``x=0`` via the ``pdf`` method.
+
+    >>> vonmises.pdf(0, loc=loc, kappa=kappa)
+    0.12570826359722018
+
+    Verify that the percentile function ``ppf`` inverts the cumulative
+    distribution function ``cdf`` up to floating point accuracy.
+
+    >>> x = 1
+    >>> cdf_value = vonmises.cdf(x, loc=loc, kappa=kappa)
+    >>> ppf_value = vonmises.ppf(cdf_value, loc=loc, kappa=kappa)
+    >>> x, cdf_value, ppf_value
+    (1, 0.31489339900904967, 1.0000000000000004)
+
+    Draw 1000 random variates by calling the ``rvs`` method.
+
+    >>> sample_size = 1000
+    >>> sample = vonmises(loc=loc, kappa=kappa).rvs(sample_size)
+
+    Plot the von Mises density on a Cartesian and polar grid to emphasize
+    that it is a circular distribution.
+
+    >>> fig = plt.figure(figsize=(12, 6))
+    >>> left = plt.subplot(121)
+    >>> right = plt.subplot(122, projection='polar')
+    >>> x = np.linspace(-np.pi, np.pi, 500)
+    >>> vonmises_pdf = vonmises.pdf(x, loc=loc, kappa=kappa)
+    >>> ticks = [0, 0.15, 0.3]
+
+    The left image contains the Cartesian plot.
+
+    >>> left.plot(x, vonmises_pdf)
+    >>> left.set_yticks(ticks)
+    >>> number_of_bins = int(np.sqrt(sample_size))
+    >>> left.hist(sample, density=True, bins=number_of_bins)
+    >>> left.set_title("Cartesian plot")
+    >>> left.set_xlim(-np.pi, np.pi)
+    >>> left.grid(True)
+
+    The right image contains the polar plot.
+
+    >>> right.plot(x, vonmises_pdf, label="PDF")
+    >>> right.set_yticks(ticks)
+    >>> right.hist(sample, density=True, bins=number_of_bins,
+    ...            label="Histogram")
+    >>> right.set_title("Polar plot")
+    >>> right.legend(bbox_to_anchor=(0.15, 1.06))
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("kappa", False, (0, np.inf), (True, False))]
+
+    def _argcheck(self, kappa):
+        return kappa >= 0
+
+    def _rvs(self, kappa, size=None, random_state=None):
+        return random_state.vonmises(0.0, kappa, size=size)
+
+    @inherit_docstring_from(rv_continuous)
+    def rvs(self, *args, **kwds):
+        rvs = super().rvs(*args, **kwds)
+        return np.mod(rvs + np.pi, 2*np.pi) - np.pi
+
+    def _pdf(self, x, kappa):
+        # vonmises.pdf(x, kappa) = exp(kappa * cos(x)) / (2*pi*I[0](kappa))
+        #                        = exp(kappa * (cos(x) - 1)) /
+        #                          (2*pi*exp(-kappa)*I[0](kappa))
+        #                        = exp(kappa * cosm1(x)) / (2*pi*i0e(kappa))
+        return np.exp(kappa*sc.cosm1(x)) / (2*np.pi*sc.i0e(kappa))
+
+    def _logpdf(self, x, kappa):
+        # vonmises.pdf(x, kappa) = exp(kappa * cosm1(x)) / (2*pi*i0e(kappa))
+        return kappa * sc.cosm1(x) - np.log(2*np.pi) - np.log(sc.i0e(kappa))
+
+    def _cdf(self, x, kappa):
+        return _stats.von_mises_cdf(kappa, x)
+
+    def _stats_skip(self, kappa):
+        return 0, None, 0, None
+
+    def _entropy(self, kappa):
+        # vonmises.entropy(kappa) = -kappa * I[1](kappa) / I[0](kappa) +
+        #                           log(2 * np.pi * I[0](kappa))
+        #                         = -kappa * I[1](kappa) * exp(-kappa) /
+        #                           (I[0](kappa) * exp(-kappa)) +
+        #                           log(2 * np.pi *
+        #                           I[0](kappa) * exp(-kappa) / exp(-kappa))
+        #                         = -kappa * sc.i1e(kappa) / sc.i0e(kappa) +
+        #                           log(2 * np.pi * i0e(kappa)) + kappa
+        return (-kappa * sc.i1e(kappa) / sc.i0e(kappa) +
+                np.log(2 * np.pi * sc.i0e(kappa)) + kappa)
+
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        The default limits of integration are endpoints of the interval
+        of width ``2*pi`` centered at `loc` (e.g. ``[-pi, pi]`` when
+        ``loc=0``).\n\n""")
+    def expect(self, func=None, args=(), loc=0, scale=1, lb=None, ub=None,
+               conditional=False, **kwds):
+        _a, _b = -np.pi, np.pi
+
+        if lb is None:
+            lb = loc + _a
+        if ub is None:
+            ub = loc + _b
+
+        return super().expect(func, args, loc,
+                              scale, lb, ub, conditional, **kwds)
+
+    @_call_super_mom
+    @extend_notes_in_docstring(rv_continuous, notes="""\
+        Fit data is assumed to represent angles and will be wrapped onto the
+        unit circle. `f0` and `fscale` are ignored; the returned shape is
+        always the maximum likelihood estimate and the scale is always
+        1. Initial guesses are ignored.\n\n""")
+    def fit(self, data, *args, **kwds):
+        if kwds.pop('superfit', False):
+            return super().fit(data, *args, **kwds)
+
+        data, fshape, floc, fscale = _check_fit_input_parameters(self, data,
+                                                                 args, kwds)
+        if self.a == -np.pi:
+            # vonmises line case, here the default fit method will be used
+            return super().fit(data, *args, **kwds)
+
+        # wrap data to interval [0, 2*pi]
+        data = np.mod(data, 2 * np.pi)
+
+        def find_mu(data):
+            return stats.circmean(data)
+
+        def find_kappa(data, loc):
+            # Usually, sources list the following as the equation to solve for
+            # the MLE of the shape parameter:
+            # r = I[1](kappa)/I[0](kappa), where r = mean resultant length
+            # This is valid when the location is the MLE of location.
+            # More generally, when the location may be fixed at an arbitrary
+            # value, r should be defined as follows:
+            r = np.sum(np.cos(loc - data))/len(data)
+            # See gh-18128 for more information.
+
+            # The function r[0](kappa) := I[1](kappa)/I[0](kappa) is monotonic
+            # increasing from r[0](0) = 0 to r[0](+inf) = 1.  The partial
+            # derivative of the log likelihood function with respect to kappa
+            # is monotonic decreasing in kappa.
+            if r == 1:
+                # All observations are (almost) equal to the mean.  Return
+                # some large kappa such that r[0](kappa) = 1.0 numerically.
+                return 1e16
+            elif r > 0:
+                def solve_for_kappa(kappa):
+                    return sc.i1e(kappa)/sc.i0e(kappa) - r
+
+                # The bounds of the root of r[0](kappa) = r are derived from
+                # selected bounds of r[0](x) given in [1, Eq. 11 & 16].  See
+                # gh-20102 for details.
+                #
+                # [1] Amos, D. E. (1973).  Computation of Modified Bessel
+                #     Functions and Their Ratios.  Mathematics of Computation,
+                #     28(125): 239-251.
+                lower_bound = r/(1-r)/(1+r)
+                upper_bound = 2*lower_bound
+
+                # The bounds are violated numerically for certain values of r,
+                # where solve_for_kappa evaluated at the bounds have the same
+                # sign.  This indicates numerical imprecision of i1e()/i0e().
+                # Return the violated bound in this case as it's more accurate.
+                if solve_for_kappa(lower_bound) >= 0:
+                    return lower_bound
+                elif solve_for_kappa(upper_bound) <= 0:
+                    return upper_bound
+                else:
+                    root_res = root_scalar(solve_for_kappa, method="brentq",
+                                           bracket=(lower_bound, upper_bound))
+                    return root_res.root
+            else:
+                # if the provided floc is very far from the circular mean,
+                # the mean resultant length r can become negative.
+                # In that case, the equation
+                # I[1](kappa)/I[0](kappa) = r does not have a solution.
+                # The maximum likelihood kappa is then 0 which practically
+                # results in the uniform distribution on the circle. As
+                # vonmises is defined for kappa > 0, return instead the
+                # smallest floating point value.
+                # See gh-18190 for more information
+                return np.finfo(float).tiny
+
+        # location likelihood equation has a solution independent of kappa
+        loc = floc if floc is not None else find_mu(data)
+        # shape likelihood equation depends on location
+        shape = fshape if fshape is not None else find_kappa(data, loc)
+
+        loc = np.mod(loc + np.pi, 2 * np.pi) - np.pi  # ensure in [-pi, pi]
+        return shape, loc, 1  # scale is not handled
+
+
+vonmises = vonmises_gen(name='vonmises')
+vonmises_line = vonmises_gen(a=-np.pi, b=np.pi, name='vonmises_line')
+
+
+class wald_gen(invgauss_gen):
+    r"""A Wald continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `wald` is:
+
+    .. math::
+
+        f(x) = \frac{1}{\sqrt{2\pi x^3}} \exp(- \frac{ (x-1)^2 }{ 2x })
+
+    for :math:`x >= 0`.
+
+    `wald` is a special case of `invgauss` with ``mu=1``.
+
+    %(after_notes)s
+
+    %(example)s
+    """
+    _support_mask = rv_continuous._open_support_mask
+
+    def _shape_info(self):
+        return []
+
+    def _rvs(self, size=None, random_state=None):
+        return random_state.wald(1.0, 1.0, size=size)
+
+    def _pdf(self, x):
+        # wald.pdf(x) = 1/sqrt(2*pi*x**3) * exp(-(x-1)**2/(2*x))
+        return invgauss._pdf(x, 1.0)
+
+    def _cdf(self, x):
+        return invgauss._cdf(x, 1.0)
+
+    def _sf(self, x):
+        return invgauss._sf(x, 1.0)
+
+    def _ppf(self, x):
+        return invgauss._ppf(x, 1.0)
+
+    def _isf(self, x):
+        return invgauss._isf(x, 1.0)
+
+    def _logpdf(self, x):
+        return invgauss._logpdf(x, 1.0)
+
+    def _logcdf(self, x):
+        return invgauss._logcdf(x, 1.0)
+
+    def _logsf(self, x):
+        return invgauss._logsf(x, 1.0)
+
+    def _stats(self):
+        return 1.0, 1.0, 3.0, 15.0
+
+    def _entropy(self):
+        return invgauss._entropy(1.0)
+
+
+wald = wald_gen(a=0.0, name="wald")
+
+
+class wrapcauchy_gen(rv_continuous):
+    r"""A wrapped Cauchy continuous random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `wrapcauchy` is:
+
+    .. math::
+
+        f(x, c) = \frac{1-c^2}{2\pi (1+c^2 - 2c \cos(x))}
+
+    for :math:`0 \le x \le 2\pi`, :math:`0 < c < 1`.
+
+    `wrapcauchy` takes ``c`` as a shape parameter for :math:`c`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _argcheck(self, c):
+        return (c > 0) & (c < 1)
+
+    def _shape_info(self):
+        return [_ShapeInfo("c", False, (0, 1), (False, False))]
+
+    def _pdf(self, x, c):
+        # wrapcauchy.pdf(x, c) = (1-c**2) / (2*pi*(1+c**2-2*c*cos(x)))
+        return (1.0-c*c)/(2*np.pi*(1+c*c-2*c*np.cos(x)))
+
+    def _cdf(self, x, c):
+
+        def f1(x, cr):
+            # CDF for 0 <= x < pi
+            return 1/np.pi * np.arctan(cr*np.tan(x/2))
+
+        def f2(x, cr):
+            # CDF for pi <= x <= 2*pi
+            return 1 - 1/np.pi * np.arctan(cr*np.tan((2*np.pi - x)/2))
+
+        cr = (1 + c)/(1 - c)
+        return xpx.apply_where(x < np.pi, (x, cr), f1, f2)
+
+    def _ppf(self, q, c):
+        val = (1.0-c)/(1.0+c)
+        rcq = 2*np.arctan(val*np.tan(np.pi*q))
+        rcmq = 2*np.pi-2*np.arctan(val*np.tan(np.pi*(1-q)))
+        return np.where(q < 1.0/2, rcq, rcmq)
+
+    def _entropy(self, c):
+        return np.log(2*np.pi*(1-c*c))
+
+    def _fitstart(self, data):
+        # Use 0.5 as the initial guess of the shape parameter.
+        # For the location and scale, use the minimum and
+        # peak-to-peak/(2*pi), respectively.
+        if isinstance(data, CensoredData):
+            data = data._uncensor()
+        return 0.5, np.min(data), np.ptp(data)/(2*np.pi)
+
+    @inherit_docstring_from(rv_continuous)
+    def rvs(self, *args, **kwds):
+        rvs = super().rvs(*args, **kwds)
+        return np.mod(rvs, 2*np.pi)
+
+wrapcauchy = wrapcauchy_gen(a=0.0, b=2*np.pi, name='wrapcauchy')
+
+
+class gennorm_gen(rv_continuous):
+    r"""A (symmetric) generalized normal continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    laplace : Laplace distribution
+    norm : normal distribution
+
+    Notes
+    -----
+    The (symmetric) generalized normal distribution is also known as the
+    Subbotin distribution, exponential power distribution, and generalized
+    error distribution [1]_.
+
+    The probability density function for `gennorm` is [1]_:
+
+    .. math::
+
+        f(x, \beta) = \frac{\beta}{2 \Gamma(1/\beta)} \exp(-|x|^\beta),
+
+    where :math:`x` is a real number, :math:`\beta > 0` and
+    :math:`\Gamma` is the gamma function (`scipy.special.gamma`).
+
+    `gennorm` takes ``beta`` as a shape parameter for :math:`\beta`.
+    For :math:`\beta = 1`, it is identical to a Laplace distribution.
+    For :math:`\beta = 2`, it is identical to a normal distribution
+    (with ``scale=1/sqrt(2)``).
+
+    References
+    ----------
+
+    .. [1] "Generalized normal distribution, Version 1",
+           https://en.wikipedia.org/wiki/Generalized_normal_distribution#Version_1
+
+    .. [2] Nardon, Martina, and Paolo Pianca. "Simulation techniques for
+           generalized Gaussian densities." Journal of Statistical
+           Computation and Simulation 79.11 (2009): 1317-1329
+
+    .. [3] Wicklin, Rick. "Simulate data from a generalized Gaussian
+           distribution" in The DO Loop blog, September 21, 2016,
+           https://blogs.sas.com/content/iml/2016/09/21/simulate-generalized-gaussian-sas.html
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("beta", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, beta):
+        return np.exp(self._logpdf(x, beta))
+
+    def _logpdf(self, x, beta):
+        return np.log(0.5*beta) - sc.gammaln(1.0/beta) - abs(x)**beta
+
+    def _cdf(self, x, beta):
+        c = 0.5 * np.sign(x)
+        # evaluating (.5 + c) first prevents numerical cancellation
+        return (0.5 + c) - c * sc.gammaincc(1.0/beta, abs(x)**beta)
+
+    def _ppf(self, x, beta):
+        c = np.sign(x - 0.5)
+        # evaluating (1. + c) first prevents numerical cancellation
+        return c * sc.gammainccinv(1.0/beta, (1.0 + c) - 2.0*c*x)**(1.0/beta)
+
+    def _sf(self, x, beta):
+        return self._cdf(-x, beta)
+
+    def _isf(self, x, beta):
+        return -self._ppf(x, beta)
+
+    def _munp(self, n, beta):
+        if n == 0:
+            return 1.
+        if n % 2 == 0:
+            c1, cn = sc.gammaln([1.0/beta, (n + 1.0)/beta])
+            return np.exp(cn - c1)
+        else:
+            return 0.
+
+    def _stats(self, beta):
+        c1, c3, c5 = sc.gammaln([1.0/beta, 3.0/beta, 5.0/beta])
+        return 0., np.exp(c3 - c1), 0., np.exp(c5 + c1 - 2.0*c3) - 3.
+
+    def _entropy(self, beta):
+        return 1. / beta - np.log(.5 * beta) + sc.gammaln(1. / beta)
+
+    def _rvs(self, beta, size=None, random_state=None):
+        # see [2]_ for the algorithm
+        # see [3]_ for reference implementation in SAS
+        z = random_state.gamma(1/beta, size=size)
+        y = z ** (1/beta)
+        # convert y to array to ensure masking support
+        y = np.asarray(y)
+        mask = random_state.random(size=y.shape) < 0.5
+        y[mask] = -y[mask]
+        return y
+
+
+gennorm = gennorm_gen(name='gennorm')
+
+
+class halfgennorm_gen(rv_continuous):
+    r"""The upper half of a generalized normal continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    gengamma : generalized gamma distribution
+    gennorm : generalized normal distribution
+    expon : exponential distribution
+    halfnorm : half normal distribution
+
+    Notes
+    -----
+    The probability density function for `halfgennorm` is:
+
+    .. math::
+
+        f(x, \beta) = \frac{\beta}{\Gamma(1/\beta)} \exp(-|x|^\beta)
+
+    for :math:`x, \beta > 0`. :math:`\Gamma` is the gamma function
+    (`scipy.special.gamma`).
+
+    `halfgennorm` takes ``beta`` as a shape parameter for :math:`\beta`.
+    For :math:`\beta = 1`, it is identical to an exponential distribution.
+    For :math:`\beta = 2`, it is identical to a half normal distribution
+    (with ``scale=1/sqrt(2)``).
+
+    `halfgennorm` is the upper half of a generalized normal continuous
+    random variable [1]_.
+
+    `halfgennorm` is a special case of the generalized gamma distribution [2]_,
+    which is implemented in SciPy as `gengamma`:
+    ``halfgennorm(beta) = gengamma(a=1/beta, c=beta)``.
+
+    References
+    ----------
+
+    .. [1] "Generalized normal distribution, Version 1",  Wikipedia,
+           https://en.wikipedia.org/wiki/Generalized_normal_distribution#Version_1
+    .. [2] "Generalized gamma distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Generalized_gamma_distribution
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("beta", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, beta):
+        #                                 beta
+        # halfgennorm.pdf(x, beta) =  -------------  exp(-|x|**beta)
+        #                             gamma(1/beta)
+        return np.exp(self._logpdf(x, beta))
+
+    def _logpdf(self, x, beta):
+        return np.log(beta) - sc.gammaln(1.0/beta) - x**beta
+
+    def _cdf(self, x, beta):
+        return sc.gammainc(1.0/beta, x**beta)
+
+    def _ppf(self, x, beta):
+        return sc.gammaincinv(1.0/beta, x)**(1.0/beta)
+
+    def _sf(self, x, beta):
+        return sc.gammaincc(1.0/beta, x**beta)
+
+    def _isf(self, x, beta):
+        return sc.gammainccinv(1.0/beta, x)**(1.0/beta)
+
+    def _munp(self, n, beta):
+        return sc.poch(1/beta, n/beta)
+
+    def _entropy(self, beta):
+        return 1.0/beta - np.log(beta) + sc.gammaln(1.0/beta)
+
+
+halfgennorm = halfgennorm_gen(a=0, name='halfgennorm')
+
+
+class crystalball_gen(rv_continuous):
+    r"""
+    Crystalball distribution.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `crystalball` is:
+
+    .. math::
+
+        f(x, \beta, m) =  \begin{cases}
+                            N \exp(-x^2 / 2),  &\text{for } x > -\beta\\
+                            N A (B - x)^{-m}  &\text{for } x \le -\beta
+                          \end{cases}
+
+    where :math:`A = (m / |\beta|)^m  \exp(-\beta^2 / 2)`,
+    :math:`B = m/|\beta| - |\beta|` and :math:`N` is a normalisation constant.
+
+    `crystalball` takes :math:`\beta > 0` and :math:`m > 1` as shape
+    parameters.  :math:`\beta` defines the point where the pdf changes
+    from a power-law to a Gaussian distribution.  :math:`m` is the power
+    of the power-law tail.
+
+    %(after_notes)s
+
+    .. versionadded:: 0.19.0
+
+    References
+    ----------
+    .. [1] "Crystal Ball Function",
+           https://en.wikipedia.org/wiki/Crystal_Ball_function
+
+    %(example)s
+    """
+    def _argcheck(self, beta, m):
+        """
+        Shape parameter bounds are m > 1 and beta > 0.
+        """
+        return (m > 1) & (beta > 0)
+
+    def _shape_info(self):
+        ibeta = _ShapeInfo("beta", False, (0, np.inf), (False, False))
+        im = _ShapeInfo("m", False, (1, np.inf), (False, False))
+        return [ibeta, im]
+
+    def _fitstart(self, data):
+        # Arbitrary, but the default m=1 is not valid
+        return super()._fitstart(data, args=(1, 1.5))
+
+    def _pdf(self, x, beta, m):
+        """
+        Return PDF of the crystalball function.
+
+                                            --
+                                           | exp(-x**2 / 2),  for x > -beta
+        crystalball.pdf(x, beta, m) =  N * |
+                                           | A * (B - x)**(-m), for x <= -beta
+                                            --
+        """
+        N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) +
+                   _norm_pdf_C * _norm_cdf(beta))
+
+        def rhs(x, beta, m):
+            return np.exp(-x**2 / 2)
+
+        def lhs(x, beta, m):
+            return ((m/beta)**m * np.exp(-beta**2 / 2.0) *
+                    (m/beta - beta - x)**(-m))
+
+        return N * xpx.apply_where(x > -beta, (x, beta, m), rhs, lhs)
+
+    def _logpdf(self, x, beta, m):
+        """
+        Return the log of the PDF of the crystalball function.
+        """
+        N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) +
+                   _norm_pdf_C * _norm_cdf(beta))
+
+        def rhs(x, beta, m):
+            return -x**2/2
+
+        def lhs(x, beta, m):
+            return m*np.log(m/beta) - beta**2/2 - m*np.log(m/beta - beta - x)
+
+        return np.log(N) + xpx.apply_where(x > -beta, (x, beta, m), rhs, lhs)
+
+    def _cdf(self, x, beta, m):
+        """
+        Return CDF of the crystalball function
+        """
+        N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) +
+                   _norm_pdf_C * _norm_cdf(beta))
+
+        def rhs(x, beta, m):
+            return ((m/beta) * np.exp(-beta**2 / 2.0) / (m-1) +
+                    _norm_pdf_C * (_norm_cdf(x) - _norm_cdf(-beta)))
+
+        def lhs(x, beta, m):
+            return ((m/beta)**m * np.exp(-beta**2 / 2.0) *
+                    (m/beta - beta - x)**(-m+1) / (m-1))
+
+        return N * xpx.apply_where(x > -beta, (x, beta, m), rhs, lhs)
+
+    def _sf(self, x, beta, m):
+        """
+        Survival function of the crystalball distribution.
+        """
+
+        def rhs(x, beta, m):
+            # M is the same as 1/N used elsewhere.
+            M = m/beta/(m - 1)*np.exp(-beta**2/2) + _norm_pdf_C*_norm_cdf(beta)
+            return _norm_pdf_C*_norm_sf(x)/M
+
+        def lhs(x, beta, m):
+            # Default behavior is OK in the left tail of the SF.
+            return 1 - self._cdf(x, beta, m)
+
+        return xpx.apply_where(x > -beta, (x, beta, m), rhs, lhs)
+
+    def _ppf(self, p, beta, m):
+        N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) +
+                   _norm_pdf_C * _norm_cdf(beta))
+        pbeta = N * (m/beta) * np.exp(-beta**2/2) / (m - 1)
+
+        def ppf_less(p, beta, m):
+            eb2 = np.exp(-beta**2/2)
+            C = (m/beta) * eb2 / (m-1)
+            N = 1/(C + _norm_pdf_C * _norm_cdf(beta))
+            return (m/beta - beta -
+                    ((m - 1)*(m/beta)**(-m)/eb2*p/N)**(1/(1-m)))
+
+        def ppf_greater(p, beta, m):
+            eb2 = np.exp(-beta**2/2)
+            C = (m/beta) * eb2 / (m-1)
+            N = 1/(C + _norm_pdf_C * _norm_cdf(beta))
+            return _norm_ppf(_norm_cdf(-beta) + (1/_norm_pdf_C)*(p/N - C))
+
+        return xpx.apply_where(p < pbeta, (p, beta, m), ppf_less, ppf_greater)
+
+    def _munp(self, n, beta, m):
+        """
+        Returns the n-th non-central moment of the crystalball function.
+        """
+        N = 1.0 / (m/beta / (m-1) * np.exp(-beta**2 / 2.0) +
+                   _norm_pdf_C * _norm_cdf(beta))
+
+        def n_th_moment(n, beta, m):
+            """
+            Returns n-th moment. Defined only if n+1 < m
+            Function cannot broadcast due to the loop over n
+            """
+            A = (m/beta)**m * np.exp(-beta**2 / 2.0)
+            B = m/beta - beta
+            rhs = (2**((n-1)/2.0) * sc.gamma((n+1)/2) *
+                   (1.0 + (-1)**n * sc.gammainc((n+1)/2, beta**2 / 2)))
+            lhs = np.zeros(rhs.shape)
+            for k in range(int(n) + 1):
+                lhs += (sc.binom(n, k) * B**(n-k) * (-1)**k / (m - k - 1) *
+                        (m/beta)**(-m + k + 1))
+            return A * lhs + rhs
+
+        return N * xpx.apply_where(n + 1 < m, (n, beta, m),
+                                   np.vectorize(n_th_moment, otypes=[np.float64]),
+                                   fill_value=np.inf)
+
+
+crystalball = crystalball_gen(name='crystalball', longname="A Crystalball Function")
+
+
+def _argus_phi(chi):
+    """
+    Utility function for the argus distribution used in the pdf, sf and
+    moment calculation.
+    Note that for all x > 0:
+    gammainc(1.5, x**2/2) = 2 * (_norm_cdf(x) - x * _norm_pdf(x) - 0.5).
+    This can be verified directly by noting that the cdf of Gamma(1.5) can
+    be written as erf(sqrt(x)) - 2*sqrt(x)*exp(-x)/sqrt(Pi).
+    We use gammainc instead of the usual definition because it is more precise
+    for small chi.
+    """
+    return sc.gammainc(1.5, chi**2/2) / 2
+
+
+class argus_gen(rv_continuous):
+    r"""
+    Argus distribution.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability density function for `argus` is:
+
+    .. math::
+
+        f(x, \chi) = \frac{\chi^3}{\sqrt{2\pi} \Psi(\chi)} x \sqrt{1-x^2}
+                     \exp(-\chi^2 (1 - x^2)/2)
+
+    for :math:`0 < x < 1` and :math:`\chi > 0`, where
+
+    .. math::
+
+        \Psi(\chi) = \Phi(\chi) - \chi \phi(\chi) - 1/2
+
+    with :math:`\Phi` and :math:`\phi` being the CDF and PDF of a standard
+    normal distribution, respectively.
+
+    `argus` takes :math:`\chi` as shape a parameter. Details about sampling
+    from the ARGUS distribution can be found in [2]_.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] "ARGUS distribution",
+           https://en.wikipedia.org/wiki/ARGUS_distribution
+    .. [2] Christoph Baumgarten "Random variate generation by fast numerical
+           inversion in the varying parameter case." Research in Statistics,
+           vol. 1, 2023. :doi:`10.1080/27684520.2023.2279060`
+
+    .. versionadded:: 0.19.0
+
+    %(example)s
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("chi", False, (0, np.inf), (False, False))]
+
+    def _logpdf(self, x, chi):
+        # for x = 0 or 1, logpdf returns -np.inf
+        with np.errstate(divide='ignore'):
+            y = 1.0 - x*x
+            A = 3*np.log(chi) - _norm_pdf_logC - np.log(_argus_phi(chi))
+            return A + np.log(x) + 0.5*np.log1p(-x*x) - chi**2 * y / 2
+
+    def _pdf(self, x, chi):
+        return np.exp(self._logpdf(x, chi))
+
+    def _cdf(self, x, chi):
+        return 1.0 - self._sf(x, chi)
+
+    def _sf(self, x, chi):
+        return _argus_phi(chi * np.sqrt((1 - x)*(1 + x))) / _argus_phi(chi)
+
+    def _rvs(self, chi, size=None, random_state=None):
+        chi = np.asarray(chi)
+        if chi.size == 1:
+            out = self._rvs_scalar(chi, numsamples=size,
+                                   random_state=random_state)
+        else:
+            shp, bc = _check_shape(chi.shape, size)
+            numsamples = int(np.prod(shp))
+            out = np.empty(size)
+            it = np.nditer([chi],
+                           flags=['multi_index'],
+                           op_flags=[['readonly']])
+            while not it.finished:
+                idx = tuple((it.multi_index[j] if not bc[j] else slice(None))
+                            for j in range(-len(size), 0))
+                r = self._rvs_scalar(it[0], numsamples=numsamples,
+                                     random_state=random_state)
+                out[idx] = r.reshape(shp)
+                it.iternext()
+
+        if size == ():
+            out = out[()]
+        return out
+
+    def _rvs_scalar(self, chi, numsamples=None, random_state=None):
+        # if chi <= 1.8:
+        # use rejection method, see Devroye:
+        # Non-Uniform Random Variate Generation, 1986, section II.3.2.
+        # write: PDF f(x) = c * g(x) * h(x), where
+        # h is [0,1]-valued and g is a density
+        # we use two ways to write f
+        #
+        # Case 1:
+        # write g(x) = 3*x*sqrt(1-x**2), h(x) = exp(-chi**2 (1-x**2) / 2)
+        # If X has a distribution with density g its ppf G_inv is given by:
+        # G_inv(u) = np.sqrt(1 - u**(2/3))
+        #
+        # Case 2:
+        # g(x) = chi**2 * x * exp(-chi**2 * (1-x**2)/2) / (1 - exp(-chi**2 /2))
+        # h(x) = sqrt(1 - x**2), 0 <= x <= 1
+        # one can show that
+        # G_inv(u) = np.sqrt(2*np.log(u*(np.exp(chi**2/2)-1)+1))/chi
+        #          = np.sqrt(1 + 2*np.log(np.exp(-chi**2/2)*(1-u)+u)/chi**2)
+        # the latter expression is used for precision with small chi
+        #
+        # In both cases, the inverse cdf of g can be written analytically, and
+        # we can apply the rejection method:
+        #
+        # REPEAT
+        #    Generate U uniformly distributed on [0, 1]
+        #    Generate X with density g (e.g. via inverse transform sampling:
+        #    X = G_inv(V) with V uniformly distributed on [0, 1])
+        # UNTIL X <= h(X)
+        # RETURN X
+        #
+        # We use case 1 for chi <= 0.5 as it maintains precision for small chi
+        # and case 2 for 0.5 < chi <= 1.8 due to its speed for moderate chi.
+        #
+        # if chi > 1.8:
+        # use relation to the Gamma distribution: if X is ARGUS with parameter
+        # chi), then Y = chi**2 * (1 - X**2) / 2 has density proportional to
+        # sqrt(u) * exp(-u) on [0, chi**2 / 2], i.e. a Gamma(3/2) distribution
+        # conditioned on [0, chi**2 / 2]). Therefore, to sample X from the
+        # ARGUS distribution, we sample Y from the gamma distribution, keeping
+        # only samples on [0, chi**2 / 2], and apply the inverse
+        # transformation X = (1 - 2*Y/chi**2)**(1/2). Since we only
+        # look at chi > 1.8, gamma(1.5).cdf(chi**2/2) is large enough such
+        # Y falls in the interval [0, chi**2 / 2] with a high probability:
+        # stats.gamma(1.5).cdf(1.8**2/2) = 0.644...
+        #
+        # The points to switch between the different methods are determined
+        # by a comparison of the runtime of the different methods. However,
+        # the runtime is platform-dependent. The implemented values should
+        # ensure a good overall performance and are supported by an analysis
+        # of the rejection constants of different methods.
+
+        size1d = tuple(np.atleast_1d(numsamples))
+        N = int(np.prod(size1d))
+        x = np.zeros(N)
+        simulated = 0
+        chi2 = chi * chi
+        if chi <= 0.5:
+            d = -chi2 / 2
+            while simulated < N:
+                k = N - simulated
+                u = random_state.uniform(size=k)
+                v = random_state.uniform(size=k)
+                z = v**(2/3)
+                # acceptance condition: u <= h(G_inv(v)). This simplifies to
+                accept = (np.log(u) <= d * z)
+                num_accept = np.sum(accept)
+                if num_accept > 0:
+                    # we still need to transform z=v**(2/3) to X = G_inv(v)
+                    rvs = np.sqrt(1 - z[accept])
+                    x[simulated:(simulated + num_accept)] = rvs
+                    simulated += num_accept
+        elif chi <= 1.8:
+            echi = np.exp(-chi2 / 2)
+            while simulated < N:
+                k = N - simulated
+                u = random_state.uniform(size=k)
+                v = random_state.uniform(size=k)
+                z = 2 * np.log(echi * (1 - v) + v) / chi2
+                # as in case one, simplify u <= h(G_inv(v)) and then transform
+                # z to the target distribution X = G_inv(v)
+                accept = (u**2 + z <= 0)
+                num_accept = np.sum(accept)
+                if num_accept > 0:
+                    rvs = np.sqrt(1 + z[accept])
+                    x[simulated:(simulated + num_accept)] = rvs
+                    simulated += num_accept
+        else:
+            # conditional Gamma for chi > 1.8
+            while simulated < N:
+                k = N - simulated
+                g = random_state.standard_gamma(1.5, size=k)
+                accept = (g <= chi2 / 2)
+                num_accept = np.sum(accept)
+                if num_accept > 0:
+                    x[simulated:(simulated + num_accept)] = g[accept]
+                    simulated += num_accept
+            x = np.sqrt(1 - 2 * x / chi2)
+
+        return np.reshape(x, size1d)
+
+    def _stats(self, chi):
+        # need to ensure that dtype is float
+        # otherwise the mask below does not work for integers
+        chi = np.asarray(chi, dtype=float)
+        phi = _argus_phi(chi)
+        m = np.sqrt(np.pi/8) * chi * sc.ive(1, chi**2/4) / phi
+        # compute second moment, use Taylor expansion for small chi (<= 0.1)
+        mu2 = np.empty_like(chi)
+        mask = chi > 0.1
+        c = chi[mask]
+        mu2[mask] = 1 - 3 / c**2 + c * _norm_pdf(c) / phi[mask]
+        c = chi[~mask]
+        coef = [-358/65690625, 0, -94/1010625, 0, 2/2625, 0, 6/175, 0, 0.4]
+        mu2[~mask] = np.polyval(coef, c)
+        return m, mu2 - m**2, None, None
+
+
+argus = argus_gen(name='argus', longname="An Argus Function", a=0.0, b=1.0)
+
+
+class rv_histogram(rv_continuous):
+    """
+    Generates a distribution given by a histogram.
+    This is useful to generate a template distribution from a binned
+    datasample.
+
+    As a subclass of the `rv_continuous` class, `rv_histogram` inherits from it
+    a collection of generic methods (see `rv_continuous` for the full list),
+    and implements them based on the properties of the provided binned
+    datasample.
+
+    Parameters
+    ----------
+    histogram : tuple of array_like
+        Tuple containing two array_like objects.
+        The first containing the content of n bins,
+        the second containing the (n+1) bin boundaries.
+        In particular, the return value of `numpy.histogram` is accepted.
+
+    density : bool, optional
+        If False, assumes the histogram is proportional to counts per bin;
+        otherwise, assumes it is proportional to a density.
+        For constant bin widths, these are equivalent, but the distinction
+        is important when bin widths vary (see Notes).
+        If None (default), sets ``density=True`` for backwards compatibility,
+        but warns if the bin widths are variable. Set `density` explicitly
+        to silence the warning.
+
+        .. versionadded:: 1.10.0
+
+    Notes
+    -----
+    When a histogram has unequal bin widths, there is a distinction between
+    histograms that are proportional to counts per bin and histograms that are
+    proportional to probability density over a bin. If `numpy.histogram` is
+    called with its default ``density=False``, the resulting histogram is the
+    number of counts per bin, so ``density=False`` should be passed to
+    `rv_histogram`. If `numpy.histogram` is called with ``density=True``, the
+    resulting histogram is in terms of probability density, so ``density=True``
+    should be passed to `rv_histogram`. To avoid warnings, always pass
+    ``density`` explicitly when the input histogram has unequal bin widths.
+
+    There are no additional shape parameters except for the loc and scale.
+    The pdf is defined as a stepwise function from the provided histogram.
+    The cdf is a linear interpolation of the pdf.
+
+    .. versionadded:: 0.19.0
+
+    Examples
+    --------
+
+    Create a scipy.stats distribution from a numpy histogram
+
+    >>> import scipy.stats
+    >>> import numpy as np
+    >>> data = scipy.stats.norm.rvs(size=100000, loc=0, scale=1.5,
+    ...                             random_state=123)
+    >>> hist = np.histogram(data, bins=100)
+    >>> hist_dist = scipy.stats.rv_histogram(hist, density=False)
+
+    Behaves like an ordinary scipy rv_continuous distribution
+
+    >>> hist_dist.pdf(1.0)
+    0.20538577847618705
+    >>> hist_dist.cdf(2.0)
+    0.90818568543056499
+
+    PDF is zero above (below) the highest (lowest) bin of the histogram,
+    defined by the max (min) of the original dataset
+
+    >>> hist_dist.pdf(np.max(data))
+    0.0
+    >>> hist_dist.cdf(np.max(data))
+    1.0
+    >>> hist_dist.pdf(np.min(data))
+    7.7591907244498314e-05
+    >>> hist_dist.cdf(np.min(data))
+    0.0
+
+    PDF and CDF follow the histogram
+
+    >>> import matplotlib.pyplot as plt
+    >>> X = np.linspace(-5.0, 5.0, 100)
+    >>> fig, ax = plt.subplots()
+    >>> ax.set_title("PDF from Template")
+    >>> ax.hist(data, density=True, bins=100)
+    >>> ax.plot(X, hist_dist.pdf(X), label='PDF')
+    >>> ax.plot(X, hist_dist.cdf(X), label='CDF')
+    >>> ax.legend()
+    >>> fig.show()
+
+    """
+    _support_mask = rv_continuous._support_mask
+
+    def __init__(self, histogram, *args, density=None, **kwargs):
+        """
+        Create a new distribution using the given histogram
+
+        Parameters
+        ----------
+        histogram : tuple of array_like
+            Tuple containing two array_like objects.
+            The first containing the content of n bins,
+            the second containing the (n+1) bin boundaries.
+            In particular, the return value of np.histogram is accepted.
+        density : bool, optional
+            If False, assumes the histogram is proportional to counts per bin;
+            otherwise, assumes it is proportional to a density.
+            For constant bin widths, these are equivalent.
+            If None (default), sets ``density=True`` for backward
+            compatibility, but warns if the bin widths are variable. Set
+            `density` explicitly to silence the warning.
+        """
+        self._histogram = histogram
+        self._density = density
+        if len(histogram) != 2:
+            raise ValueError("Expected length 2 for parameter histogram")
+        self._hpdf = np.asarray(histogram[0])
+        self._hbins = np.asarray(histogram[1])
+        if len(self._hpdf) + 1 != len(self._hbins):
+            raise ValueError("Number of elements in histogram content "
+                             "and histogram boundaries do not match, "
+                             "expected n and n+1.")
+        self._hbin_widths = self._hbins[1:] - self._hbins[:-1]
+        bins_vary = not np.allclose(self._hbin_widths, self._hbin_widths[0])
+        if density is None and bins_vary:
+            message = ("Bin widths are not constant. Assuming `density=True`."
+                       "Specify `density` explicitly to silence this warning.")
+            warnings.warn(message, RuntimeWarning, stacklevel=2)
+            density = True
+        elif not density:
+            self._hpdf = self._hpdf / self._hbin_widths
+
+        self._hpdf = self._hpdf / float(np.sum(self._hpdf * self._hbin_widths))
+        self._hcdf = np.cumsum(self._hpdf * self._hbin_widths)
+        self._hpdf = np.hstack([0.0, self._hpdf, 0.0])
+        self._hcdf = np.hstack([0.0, self._hcdf])
+        # Set support
+        kwargs['a'] = self.a = self._hbins[0]
+        kwargs['b'] = self.b = self._hbins[-1]
+        super().__init__(*args, **kwargs)
+
+    def _pdf(self, x):
+        """
+        PDF of the histogram
+        """
+        return self._hpdf[np.searchsorted(self._hbins, x, side='right')]
+
+    def _cdf(self, x):
+        """
+        CDF calculated from the histogram
+        """
+        return np.interp(x, self._hbins, self._hcdf)
+
+    def _ppf(self, x):
+        """
+        Percentile function calculated from the histogram
+        """
+        return np.interp(x, self._hcdf, self._hbins)
+
+    def _munp(self, n):
+        """Compute the n-th non-central moment."""
+        integrals = (self._hbins[1:]**(n+1) - self._hbins[:-1]**(n+1)) / (n+1)
+        return np.sum(self._hpdf[1:-1] * integrals)
+
+    def _entropy(self):
+        """Compute entropy of distribution"""
+        hpdf = self._hpdf[1:-1]
+        res = xpx.apply_where(hpdf > 0.0, hpdf, np.log, fill_value=0.0)
+        return -np.sum(hpdf * res * self._hbin_widths)
+
+    def _updated_ctor_param(self):
+        """
+        Set the histogram as additional constructor argument
+        """
+        dct = super()._updated_ctor_param()
+        dct['histogram'] = self._histogram
+        dct['density'] = self._density
+        return dct
+
+
+class studentized_range_gen(rv_continuous):
+    r"""A studentized range continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    t: Student's t distribution
+
+    Notes
+    -----
+    The probability density function for `studentized_range` is:
+
+    .. math::
+
+         f(x; k, \nu) = \frac{k(k-1)\nu^{\nu/2}}{\Gamma(\nu/2)
+                        2^{\nu/2-1}} \int_{0}^{\infty} \int_{-\infty}^{\infty}
+                        s^{\nu} e^{-\nu s^2/2} \phi(z) \phi(sx + z)
+                        [\Phi(sx + z) - \Phi(z)]^{k-2} \,dz \,ds
+
+    for :math:`x ≥ 0`, :math:`k > 1`, and :math:`\nu > 0`.
+
+    `studentized_range` takes ``k`` for :math:`k` and ``df`` for :math:`\nu`
+    as shape parameters.
+
+    When :math:`\nu` exceeds 100,000, an asymptotic approximation (infinite
+    degrees of freedom) is used to compute the cumulative distribution
+    function [4]_ and probability distribution function.
+
+    %(after_notes)s
+
+    References
+    ----------
+
+    .. [1] "Studentized range distribution",
+           https://en.wikipedia.org/wiki/Studentized_range_distribution
+    .. [2] Batista, Ben Dêivide, et al. "Externally Studentized Normal Midrange
+           Distribution." Ciência e Agrotecnologia, vol. 41, no. 4, 2017, pp.
+           378-389., :doi:`10.1590/1413-70542017414047716`.
+    .. [3] Harter, H. Leon. "Tables of Range and Studentized Range." The Annals
+           of Mathematical Statistics, vol. 31, no. 4, 1960, pp. 1122-1147.
+           JSTOR, www.jstor.org/stable/2237810. Accessed 18 Feb. 2021.
+    .. [4] Lund, R. E., and J. R. Lund. "Algorithm AS 190: Probabilities and
+           Upper Quantiles for the Studentized Range." Journal of the Royal
+           Statistical Society. Series C (Applied Statistics), vol. 32, no. 2,
+           1983, pp. 204-210. JSTOR, www.jstor.org/stable/2347300. Accessed 18
+           Feb. 2021.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import studentized_range
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+
+    Display the probability density function (``pdf``):
+
+    >>> k, df = 3, 10
+    >>> x = np.linspace(studentized_range.ppf(0.01, k, df),
+    ...                 studentized_range.ppf(0.99, k, df), 100)
+    >>> ax.plot(x, studentized_range.pdf(x, k, df),
+    ...         'r-', lw=5, alpha=0.6, label='studentized_range pdf')
+
+    Alternatively, the distribution object can be called (as a function)
+    to fix the shape, location and scale parameters. This returns a "frozen"
+    RV object holding the given parameters fixed.
+
+    Freeze the distribution and display the frozen ``pdf``:
+
+    >>> rv = studentized_range(k, df)
+    >>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+
+    Check accuracy of ``cdf`` and ``ppf``:
+
+    >>> vals = studentized_range.ppf([0.001, 0.5, 0.999], k, df)
+    >>> np.allclose([0.001, 0.5, 0.999], studentized_range.cdf(vals, k, df))
+    True
+
+    Rather than using (``studentized_range.rvs``) to generate random variates,
+    which is very slow for this distribution, we can approximate the inverse
+    CDF using an interpolator, and then perform inverse transform sampling
+    with this approximate inverse CDF.
+
+    This distribution has an infinite but thin right tail, so we focus our
+    attention on the leftmost 99.9 percent.
+
+    >>> a, b = studentized_range.ppf([0, .999], k, df)
+    >>> a, b
+    0, 7.41058083802274
+
+    >>> from scipy.interpolate import interp1d
+    >>> rng = np.random.default_rng()
+    >>> xs = np.linspace(a, b, 50)
+    >>> cdf = studentized_range.cdf(xs, k, df)
+    # Create an interpolant of the inverse CDF
+    >>> ppf = interp1d(cdf, xs, fill_value='extrapolate')
+    # Perform inverse transform sampling using the interpolant
+    >>> r = ppf(rng.uniform(size=1000))
+
+    And compare the histogram:
+
+    >>> ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
+    >>> ax.legend(loc='best', frameon=False)
+    >>> plt.show()
+
+    """
+
+    def _argcheck(self, k, df):
+        return (k > 1) & (df > 0)
+
+    def _shape_info(self):
+        ik = _ShapeInfo("k", False, (1, np.inf), (False, False))
+        idf = _ShapeInfo("df", False, (0, np.inf), (False, False))
+        return [ik, idf]
+
+    def _fitstart(self, data):
+        # Default is k=1, but that is not a valid value of the parameter.
+        return super()._fitstart(data, args=(2, 1))
+
+    def _munp(self, K, k, df):
+        cython_symbol = '_studentized_range_moment'
+        _a, _b = self._get_support()
+        # all three of these are used to create a numpy array so they must
+        # be the same shape.
+
+        def _single_moment(K, k, df):
+            log_const = _stats._studentized_range_pdf_logconst(k, df)
+            arg = [K, k, df, log_const]
+            usr_data = np.array(arg, float).ctypes.data_as(ctypes.c_void_p)
+
+            llc = LowLevelCallable.from_cython(_stats, cython_symbol, usr_data)
+
+            ranges = [(-np.inf, np.inf), (0, np.inf), (_a, _b)]
+            opts = dict(epsabs=1e-11, epsrel=1e-12)
+
+            return integrate.nquad(llc, ranges=ranges, opts=opts)[0]
+
+        ufunc = np.frompyfunc(_single_moment, 3, 1)
+        return np.asarray(ufunc(K, k, df), dtype=np.float64)[()]
+
+    def _pdf(self, x, k, df):
+
+        def _single_pdf(q, k, df):
+            # The infinite form of the PDF is derived from the infinite
+            # CDF.
+            if df < 100000:
+                cython_symbol = '_studentized_range_pdf'
+                log_const = _stats._studentized_range_pdf_logconst(k, df)
+                arg = [q, k, df, log_const]
+                usr_data = np.array(arg, float).ctypes.data_as(ctypes.c_void_p)
+                ranges = [(-np.inf, np.inf), (0, np.inf)]
+
+            else:
+                cython_symbol = '_studentized_range_pdf_asymptotic'
+                arg = [q, k]
+                usr_data = np.array(arg, float).ctypes.data_as(ctypes.c_void_p)
+                ranges = [(-np.inf, np.inf)]
+
+            llc = LowLevelCallable.from_cython(_stats, cython_symbol, usr_data)
+            opts = dict(epsabs=1e-11, epsrel=1e-12)
+            return integrate.nquad(llc, ranges=ranges, opts=opts)[0]
+
+        ufunc = np.frompyfunc(_single_pdf, 3, 1)
+        return np.asarray(ufunc(x, k, df), dtype=np.float64)[()]
+
+    def _cdf(self, x, k, df):
+
+        def _single_cdf(q, k, df):
+            # "When the degrees of freedom V are infinite the probability
+            # integral takes [on a] simpler form," and a single asymptotic
+            # integral is evaluated rather than the standard double integral.
+            # (Lund, Lund, page 205)
+            if df < 100000:
+                cython_symbol = '_studentized_range_cdf'
+                log_const = _stats._studentized_range_cdf_logconst(k, df)
+                arg = [q, k, df, log_const]
+                usr_data = np.array(arg, float).ctypes.data_as(ctypes.c_void_p)
+                ranges = [(-np.inf, np.inf), (0, np.inf)]
+
+            else:
+                cython_symbol = '_studentized_range_cdf_asymptotic'
+                arg = [q, k]
+                usr_data = np.array(arg, float).ctypes.data_as(ctypes.c_void_p)
+                ranges = [(-np.inf, np.inf)]
+
+            llc = LowLevelCallable.from_cython(_stats, cython_symbol, usr_data)
+            opts = dict(epsabs=1e-11, epsrel=1e-12)
+            return integrate.nquad(llc, ranges=ranges, opts=opts)[0]
+
+        ufunc = np.frompyfunc(_single_cdf, 3, 1)
+
+        # clip p-values to ensure they are in [0, 1].
+        return np.clip(np.asarray(ufunc(x, k, df), dtype=np.float64)[()], 0, 1)
+
+
+studentized_range = studentized_range_gen(name='studentized_range', a=0,
+                                          b=np.inf)
+
+
+class rel_breitwigner_gen(rv_continuous):
+    r"""A relativistic Breit-Wigner random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    cauchy: Cauchy distribution, also known as the Breit-Wigner distribution.
+
+    Notes
+    -----
+
+    The probability density function for `rel_breitwigner` is
+
+    .. math::
+
+        f(x, \rho) = \frac{k}{(x^2 - \rho^2)^2 + \rho^2}
+
+    where
+
+    .. math::
+        k = \frac{2\sqrt{2}\rho^2\sqrt{\rho^2 + 1}}
+            {\pi\sqrt{\rho^2 + \rho\sqrt{\rho^2 + 1}}}
+
+    The relativistic Breit-Wigner distribution is used in high energy physics
+    to model resonances [1]_. It gives the uncertainty in the invariant mass,
+    :math:`M` [2]_, of a resonance with characteristic mass :math:`M_0` and
+    decay-width :math:`\Gamma`, where :math:`M`, :math:`M_0` and :math:`\Gamma`
+    are expressed in natural units. In SciPy's parametrization, the shape
+    parameter :math:`\rho` is equal to :math:`M_0/\Gamma` and takes values in
+    :math:`(0, \infty)`.
+
+    Equivalently, the relativistic Breit-Wigner distribution is said to give
+    the uncertainty in the center-of-mass energy :math:`E_{\text{cm}}`. In
+    natural units, the speed of light :math:`c` is equal to 1 and the invariant
+    mass :math:`M` is equal to the rest energy :math:`Mc^2`. In the
+    center-of-mass frame, the rest energy is equal to the total energy [3]_.
+
+    %(after_notes)s
+
+    :math:`\rho = M/\Gamma` and :math:`\Gamma` is the scale parameter. For
+    example, if one seeks to model the :math:`Z^0` boson with :math:`M_0
+    \approx 91.1876 \text{ GeV}` and :math:`\Gamma \approx 2.4952\text{ GeV}`
+    [4]_ one can set ``rho=91.1876/2.4952`` and ``scale=2.4952``.
+
+    To ensure a physically meaningful result when using the `fit` method, one
+    should set ``floc=0`` to fix the location parameter to 0.
+
+    References
+    ----------
+    .. [1] Relativistic Breit-Wigner distribution, Wikipedia,
+           https://en.wikipedia.org/wiki/Relativistic_Breit-Wigner_distribution
+    .. [2] Invariant mass, Wikipedia,
+           https://en.wikipedia.org/wiki/Invariant_mass
+    .. [3] Center-of-momentum frame, Wikipedia,
+           https://en.wikipedia.org/wiki/Center-of-momentum_frame
+    .. [4] M. Tanabashi et al. (Particle Data Group) Phys. Rev. D 98, 030001 -
+           Published 17 August 2018
+
+    %(example)s
+
+    """
+    def _argcheck(self, rho):
+        return rho > 0
+
+    def _shape_info(self):
+        return [_ShapeInfo("rho", False, (0, np.inf), (False, False))]
+
+    def _pdf(self, x, rho):
+        # C = k / rho**2
+        C = np.sqrt(
+            2 * (1 + 1/rho**2) / (1 + np.sqrt(1 + 1/rho**2))
+        ) * 2 / np.pi
+        with np.errstate(over='ignore'):
+            return C / (((x - rho)*(x + rho)/rho)**2 + 1)
+
+    def _cdf(self, x, rho):
+        # C = k / (2 * rho**2) / np.sqrt(1 + 1/rho**2)
+        C = np.sqrt(2/(1 + np.sqrt(1 + 1/rho**2)))/np.pi
+        result = (
+            np.sqrt(-1 + 1j/rho)
+            * np.arctan(x/np.sqrt(-rho*(rho + 1j)))
+        )
+        result = C * 2 * np.imag(result)
+        # Sometimes above formula produces values greater than 1.
+        return np.clip(result, None, 1)
+
+    def _munp(self, n, rho):
+        if n == 0:
+            return 1.
+        if n == 1:
+            # C = k / (2 * rho)
+            C = np.sqrt(
+                2 * (1 + 1/rho**2) / (1 + np.sqrt(1 + 1/rho**2))
+            ) / np.pi * rho
+            return C * (np.pi/2 + np.arctan(rho))
+        if n == 2:
+            # C = pi * k / (4 * rho)
+            C = np.sqrt(
+                (1 + 1/rho**2) / (2 * (1 + np.sqrt(1 + 1/rho**2)))
+            ) * rho
+            result = (1 - rho * 1j) / np.sqrt(-1 - 1j/rho)
+            return 2 * C * np.real(result)
+        else:
+            return np.inf
+
+    def _stats(self, rho):
+        # Returning None from stats makes public stats use _munp.
+        # nan values will be omitted from public stats. Skew and
+        # kurtosis are actually infinite.
+        return None, None, np.nan, np.nan
+
+    @inherit_docstring_from(rv_continuous)
+    def fit(self, data, *args, **kwds):
+        # Override rv_continuous.fit to better handle case where floc is set.
+        data, _, floc, fscale = _check_fit_input_parameters(
+            self, data, args, kwds
+        )
+
+        censored = isinstance(data, CensoredData)
+        if censored:
+            if data.num_censored() == 0:
+                # There are no censored values in data, so replace the
+                # CensoredData instance with a regular array.
+                data = data._uncensored
+                censored = False
+
+        if floc is None or censored:
+            return super().fit(data, *args, **kwds)
+
+        if fscale is None:
+            # The interquartile range approximates the scale parameter gamma.
+            # The median approximates rho * gamma.
+            p25, p50, p75 = np.quantile(data - floc, [0.25, 0.5, 0.75])
+            scale_0 = p75 - p25
+            rho_0 = p50 / scale_0
+            if not args:
+                args = [rho_0]
+            if "scale" not in kwds:
+                kwds["scale"] = scale_0
+        else:
+            M_0 = np.median(data - floc)
+            rho_0 = M_0 / fscale
+            if not args:
+                args = [rho_0]
+        return super().fit(data, *args, **kwds)
+
+
+rel_breitwigner = rel_breitwigner_gen(a=0.0, name="rel_breitwigner")
+
+
+# Collect names of classes and objects in this module.
+pairs = list(globals().copy().items())
+_distn_names, _distn_gen_names = get_distribution_names(pairs, rv_continuous)
+
+__all__ = _distn_names + _distn_gen_names + ['rv_histogram']
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_correlation.py b/.venv/lib/python3.12/site-packages/scipy/stats/_correlation.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c72e5eac9300911c8b6281eceb9ca91d3b03a4c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_correlation.py
@@ -0,0 +1,731 @@
+import numpy as np
+import math
+from scipy import stats, special
+from scipy._external import array_api_extra as xpx
+from scipy._lib._array_api import (xp_capabilities, array_namespace, xp_promote,
+                                   is_numpy, _share_masks, _count_nonmasked, is_marray)
+from scipy.stats._stats_py import (_SimpleNormal, SignificanceResult, _get_pvalue,
+                                   _rankdata)
+from scipy.stats._axis_nan_policy import _axis_nan_policy_factory
+from scipy.stats._stats_mstats_common import (TheilslopesResult, _n_samples_optional_x,
+                                              SiegelslopesResult)
+
+
+__all__ = ['chatterjeexi', 'spearmanrho', 'theilslopes', 'siegelslopes']
+
+
+def _xi_statistic(x, y, y_continuous, xp):
+    # Compute xi correlation statistic
+
+    # `axis=-1` is guaranteed by _axis_nan_policy decorator
+    n = x.shape[-1]
+
+    # "Rearrange the data as (X(1), Y(1)), . . . ,(X(n), Y(n))
+    # such that X(1) ≤ ··· ≤ X(n)"
+    j = xp.argsort(x, axis=-1)
+    j, y = xp.broadcast_arrays(j, y)
+    y = xp.take_along_axis(y, j, axis=-1)
+
+    # "Let ri be the rank of Y(i), that is, the number of j such that Y(j) ≤ Y(i)"
+    r = stats.rankdata(y, method='max', axis=-1)
+    # " additionally define li to be the number of j such that Y(j) ≥ Y(i)"
+    # Could probably compute this from r, but that can be an enhancement
+    l = stats.rankdata(-y, method='max', axis=-1)
+
+    num = xp.sum(xp.abs(xp.diff(r, axis=-1)), axis=-1)
+    if y_continuous:  # [1] Eq. 1.1
+        statistic = 1 - 3 * num / (n ** 2 - 1)
+    else:  # [1] Eq. 1.2
+        den = 2 * xp.sum((n - l) * l, axis=-1)
+        statistic = 1 - n * num / den
+
+    return statistic, r, l
+
+
+def _xi_std(r, l, y_continuous, xp):
+    # Compute asymptotic standard deviation of xi under null hypothesis of independence
+
+    # `axis=-1` is guaranteed by _axis_nan_policy decorator
+    n = r.shape[-1]
+
+    # "Suppose that X and Y are independent and Y is continuous. Then
+    # √n·ξn(X, Y) → N(0, 2/5) in distribution as n → ∞"
+    if y_continuous:  # [1] Theorem 2.1
+        return xp.asarray(math.sqrt(2 / 5) / math.sqrt(n), dtype=r.dtype)
+
+    # "Suppose that X and Y are independent. Then √n·ξn(X, Y)
+    # converges to N(0, τ²) in distribution as n → ∞
+    # [1] Eq. 2.2 and surrounding math
+    i = xp.arange(1, n + 1, dtype=r.dtype)
+    u = xp.sort(r, axis=-1)
+    v = xp.cumulative_sum(u, axis=-1)
+    an = 1 / n**4 * xp.sum((2*n - 2*i + 1) * u**2, axis=-1)
+    bn = 1 / n**5 * xp.sum((v + (n - i)*u)**2, axis=-1)
+    cn = 1 / n**3 * xp.sum((2*n - 2*i + 1) * u, axis=-1)
+    dn = 1 / n**3 * xp.sum((l * (n - l)), axis=-1)
+    tau2 = (an - 2*bn + cn**2) / dn**2
+
+    return xp.sqrt(tau2) / math.sqrt(n)
+
+
+def _chatterjeexi_iv(y_continuous, method):
+    # Input validation for `chatterjeexi`
+    # x, y, `axis` input validation taken care of by decorator
+
+    if y_continuous not in {True, False}:
+        raise ValueError('`y_continuous` must be boolean.')
+
+    if not isinstance(method, stats.PermutationMethod):
+        method = method.lower()
+        message = "`method` must be 'asymptotic' or a `PermutationMethod` instance."
+        if method != 'asymptotic':
+            raise ValueError(message)
+
+    return y_continuous, method
+
+
+def _unpack(res, _):
+    return res.statistic, res.pvalue
+
+
+@xp_capabilities(skip_backends=[('dask.array', 'no take_along_axis')])
+@_axis_nan_policy_factory(SignificanceResult, paired=True, n_samples=2,
+                          result_to_tuple=_unpack, n_outputs=2, too_small=1)
+def chatterjeexi(x, y, *, axis=0, y_continuous=False, method='asymptotic'):
+    r"""Compute the xi correlation and perform a test of independence.
+
+    The xi correlation coefficient is a measure of association between two
+    variables; the value tends to be close to zero when the variables are
+    independent and close to 1 when there is a strong association. Unlike
+    other correlation coefficients, the xi correlation is effective even
+    when the association is not monotonic.
+
+    Parameters
+    ----------
+    x, y : array-like
+        The samples: corresponding observations of the independent and
+        dependent variable. The (N-d) arrays must be broadcastable.
+    axis : int, default: 0
+        Axis along which to perform the test.
+    y_continuous : bool, default: False
+        Whether `y` is assumed to be drawn from a continuous distribution.
+        If `y` is drawn from a continuous distribution, results are valid
+        whether this is assumed or not, but enabling this assumption will
+        result in faster computation and typically produce similar results.
+    method : 'asymptotic' or `PermutationMethod` instance, optional
+        Selects the method used to calculate the *p*-value.
+        Default is 'asymptotic'. The following options are available.
+
+        * ``'asymptotic'``: compares the standardized test statistic
+          against the normal distribution.
+        * `PermutationMethod` instance. In this case, the p-value
+          is computed using `permutation_test` with the provided
+          configuration options and other appropriate settings.
+
+    Returns
+    -------
+    res : SignificanceResult
+        An object containing attributes:
+
+        statistic : float
+            The xi correlation statistic.
+        pvalue : float
+            The associated *p*-value: the probability of a statistic at least as
+            high as the observed value under the null hypothesis of independence.
+
+    See Also
+    --------
+    scipy.stats.pearsonr, scipy.stats.spearmanr, scipy.stats.kendalltau
+
+    Notes
+    -----
+    There is currently no special handling of ties in `x`; they are broken arbitrarily
+    by the implementation. [1]_ recommends: "if there are ties among the Xi's, then
+    choose an increasing rearrangement as above by breaking ties uniformly at random."
+    This is easily accomplished by adding a small amount of random noise to `x`; see
+    examples.
+
+    [1]_ notes that the statistic is not symmetric in `x` and `y` *by design*:
+    "...we may want to understand if :math:`Y` is a function :math:`X`, and not just
+    if one of the variables is a function of the other." See [1]_ Remark 1.
+
+    References
+    ----------
+    .. [1] Chatterjee, Sourav. "A new coefficient of correlation." Journal of
+           the American Statistical Association 116.536 (2021): 2009-2022.
+           :doi:`10.1080/01621459.2020.1758115`.
+
+    Examples
+    --------
+    Generate perfectly correlated data, and observe that the xi correlation is
+    nearly 1.0.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng(348932549825235)
+    >>> x = rng.uniform(0, 10, size=100)
+    >>> y = np.sin(x)
+    >>> res = stats.chatterjeexi(x, y)
+    >>> res.statistic
+    np.float64(0.9012901290129013)
+
+    The probability of observing such a high value of the statistic under the
+    null hypothesis of independence is very low.
+
+    >>> res.pvalue
+    np.float64(2.2206974648177804e-46)
+
+    As noise is introduced, the correlation coefficient decreases.
+
+    >>> noise = rng.normal(scale=[[0.1], [0.5], [1]], size=(3, 100))
+    >>> res = stats.chatterjeexi(x, y + noise, axis=-1)
+    >>> res.statistic
+    array([0.79507951, 0.41824182, 0.16651665])
+
+    Because the distribution of `y` is continuous, it is valid to pass
+    ``y_continuous=True``. The statistic is identical, and the p-value
+    (not shown) is only slightly different.
+
+    >>> stats.chatterjeexi(x, y + noise, y_continuous=True, axis=-1).statistic
+    array([0.79507951, 0.41824182, 0.16651665])
+
+    Consider a case in which there are ties in `x`.
+
+    >>> x = rng.integers(10, size=1000)
+    >>> y = rng.integers(10, size=1000)
+
+    [1]_ recommends breaking the ties uniformly at random.
+
+    >>> d = rng.uniform(1e-5, size=x.size)
+    >>> res = stats.chatterjeexi(x + d, y)
+    >>> res.statistic
+    -0.029919991638798438
+
+    Since this gives a randomized estimate of the statistic, [1]_ also suggests
+    considering the average over all possibilities of breaking ties. This is
+    computationally infeasible when there are many ties, but a randomized estimate of
+    *this* quantity can be obtained by considering many random possibilities of breaking
+    ties.
+
+    >>> d = rng.uniform(1e-5, size=(9999, x.size))
+    >>> res = stats.chatterjeexi(x + d, y, axis=1)
+    >>> np.mean(res.statistic)
+    0.001186895213756626
+
+    """
+    xp = array_namespace(x, y)
+
+    # x, y, `axis` input validation taken care of by decorator
+    # In fact, `axis` is guaranteed to be -1
+    y_continuous, method = _chatterjeexi_iv(y_continuous, method)
+    x, y = xp_promote(x, y, force_floating=True, xp=xp)
+
+    # A highly negative statistic is possible, e.g.
+    # x = np.arange(100.), y = (x % 2 == 0)
+    # Unclear whether we should expose `alternative`, though.
+    alternative = 'greater'
+
+    if method == 'asymptotic':
+        xi, r, l = _xi_statistic(x, y, y_continuous, xp=xp)
+        std = _xi_std(r, l, y_continuous, xp=xp)
+        norm = _SimpleNormal()
+        pvalue = _get_pvalue(xi / std, norm, alternative=alternative, xp=xp)
+    elif isinstance(method, stats.PermutationMethod):
+        res = stats.permutation_test(
+            # Could be faster if we just permuted the ranks; for now, keep it simple.
+            data=(y,),
+            statistic=lambda y, axis: _xi_statistic(x, y, y_continuous, xp=xp)[0],
+            alternative=alternative, permutation_type='pairings', **method._asdict(),
+            axis=-1)  # `axis=-1` is guaranteed by _axis_nan_policy decorator
+
+        xi, pvalue = res.statistic, res.pvalue
+
+    xi = xi[()] if xi.ndim == 0 else xi
+    pvalue = pvalue[()] if pvalue.ndim == 0 else pvalue
+    return SignificanceResult(xi, pvalue)
+
+
+@xp_capabilities(cpu_only=True, exceptions=['jax.numpy'], marray=True,
+    skip_backends=[('dask.array', 'not supported by rankdata (take_along_axis)')],
+    extra_note='Only the default `method` is compatible with MArray input.'
+)
+@_axis_nan_policy_factory(SignificanceResult, paired=True, n_samples=2,
+                          result_to_tuple=_unpack, n_outputs=2, too_small=1)
+def spearmanrho(x, y, /, *, alternative='two-sided', method=None, axis=0):
+    r"""Calculate a Spearman rho correlation coefficient with associated p-value.
+
+    The Spearman rank-order correlation coefficient is a nonparametric measure
+    of the monotonicity of the relationship between two datasets.
+    Like other correlation coefficients, it varies between -1 and +1 with 0
+    implying no correlation. Coefficients of -1 or +1 are associated with an exact
+    monotonic relationship.  Positive correlations indicate that as `x` increases,
+    so does `y`; negative correlations indicate that as `x` increases, `y` decreases.
+    The p-value is the probability of an uncorrelated system producing datasets
+    with a Spearman correlation at least as extreme as the one computed from the
+    observed dataset.
+
+    Parameters
+    ----------
+    x, y : array-like
+        The samples: corresponding observations of the independent and
+        dependent variable. The (N-d) arrays must be broadcastable.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        The following options are available:
+
+        * 'two-sided': the correlation is nonzero
+        * 'less': the correlation is negative (less than zero)
+        * 'greater':  the correlation is positive (greater than zero)
+
+    method : ResamplingMethod, optional
+        Defines the method used to compute the p-value. If `method` is an
+        instance of `PermutationMethod`/`MonteCarloMethod`, the p-value is
+        computed using
+        `scipy.stats.permutation_test`/`scipy.stats.monte_carlo_test` with the
+        provided configuration options and other appropriate settings.
+        Otherwise, the p-value is computed using an asymptotic approximation of
+        the null distribution.
+    axis : int or None, optional
+        If axis=0 (default), then each column represents a variable, with
+        observations in the rows. If axis=1, the relationship is transposed:
+        each row represents a variable, while the columns contain observations.
+        If axis=None, then both arrays will be raveled.
+        Like other `scipy.stats` functions, `axis` is interpreted after the
+        arrays are broadcasted.
+
+    Returns
+    -------
+    res : SignificanceResult
+        An object containing attributes:
+
+        statistic : floating point array or NumPy scalar
+            Spearman correlation coefficient
+        pvalue : floating point array NumPy scalar
+            The p-value - the probabilitiy of realizing such an extreme statistic
+            value under the null hypothesis that two samples have no ordinal
+            correlation. See `alternative` above for alternative hypotheses.
+
+    Warns
+    -----
+    `~scipy.stats.ConstantInputWarning`
+        Raised if an input is a constant array.  The correlation coefficient
+        is not defined in this case, so ``np.nan`` is returned.
+
+    Notes
+    -----
+    `spearmanrho` was created to make improvements to SciPy's implementation of
+    the Spearman correlation test without making backward-incompatible changes
+    to `spearmanr`. Advantages of `spearmanrho` over `spearmanr` include:
+
+    - `spearmanrho` follows standard array broadcasting rules.
+    - `spearmanrho` is compatible with some non-NumPy arrays.
+    - `spearmanrho` can compute exact p-values, even in the presence of ties,
+      when an appropriate instance of `PermutationMethod` is provided via the
+      `method` argument.
+
+    References
+    ----------
+    .. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
+       Probability and Statistics Tables and Formulae. Chapman & Hall: New
+       York. 2000.
+       Section  14.7
+    .. [2] Kendall, M. G. and Stuart, A. (1973).
+       The Advanced Theory of Statistics, Volume 2: Inference and Relationship.
+       Griffin. 1973.
+       Section 31.18
+
+    Examples
+    --------
+    Univariate samples, approximate p-value.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> x = [1, 2, 3, 4, 5]
+    >>> y = [5, 6, 7, 8, 7]
+    >>> res = stats.spearmanrho(x, y)
+    >>> res.statistic
+    np.float64(0.8207826816681233)
+    >>> res.pvalue
+    np.float64(0.08858700531354405)
+
+    Univariate samples, exact p-value.
+
+    >>> res = stats.spearmanrho(x, y, method=stats.PermutationMethod())
+    >>> res.statistic
+    np.float64(0.8207826816681233)
+    >>> res.pvalue
+    np.float64(0.13333333333333333)
+
+    Batch of univariate samples, one vectorized call.
+
+    >>> rng = np.random.default_rng(98145152315484)
+    >>> x2 = rng.standard_normal((2, 100))
+    >>> y2 = rng.standard_normal((2, 100))
+    >>> res = stats.spearmanrho(x2, y2, axis=-1)
+    >>> res.statistic
+    array([ 0.16585659, -0.12151215])
+    >>> res.pvalue
+    array([0.0991155 , 0.22846869])
+
+    Bivariate samples using standard broadcasting rules.
+
+    >>> res = stats.spearmanrho(x2[np.newaxis, :], x2[:, np.newaxis], axis=-1)
+    >>> res.statistic
+    array([[ 1.        , -0.14670267],
+           [-0.14670267,  1.        ]])
+    >>> res.pvalue
+    array([[0.        , 0.14526128],
+           [0.14526128, 0.        ]])
+
+    """
+    xp = array_namespace(x, y)
+    x, y = _share_masks(x, y, xp=xp)
+    rx = stats.rankdata(x, axis=axis)
+    ry = stats.rankdata(y, axis=axis)
+    res = stats.pearsonr(rx, ry, method=method, alternative=alternative, axis=axis)
+    return SignificanceResult(res.statistic, res.pvalue)
+
+
+@xp_capabilities(skip_backends=[("dask.array", "no take_along_axis"),
+                                ("jax.numpy", "non-concrete boolean indexing")],
+                 marray=True)
+@_axis_nan_policy_factory(TheilslopesResult, default_axis=None, n_outputs=4,
+                          n_samples=_n_samples_optional_x,
+                          result_to_tuple=lambda x, _: tuple(x), paired=True,
+                          too_small=1)
+def theilslopes(y, x=None, alpha=0.95, method='separate', *, axis=None):
+    r"""
+    Computes the Theil-Sen estimator for a set of points (x, y).
+
+    `theilslopes` implements a method for robust linear regression.  It
+    computes the slope as the median of all slopes between paired values.
+
+    Parameters
+    ----------
+    y : array_like
+        Dependent variable.
+    x : array_like or None, optional
+        Independent variable. If None, use ``arange(len(y))`` instead.
+    alpha : float, optional
+        Confidence degree between 0 and 1. Default is 95% confidence.
+        Note that `alpha` is symmetric around 0.5, i.e. both 0.1 and 0.9 are
+        interpreted as "find the 90% confidence interval".
+    method : {'joint', 'separate'}, optional
+        Method to be used for computing estimate for intercept.
+        Following methods are supported,
+
+        * 'joint': Uses ``np.median(y - slope * x)`` as intercept.
+        * 'separate': Uses ``np.median(y) - slope * np.median(x)``
+                      as intercept.
+
+        The default is 'separate'.
+
+        .. versionadded:: 1.8.0
+
+    axis : int or tuple of ints, default: None
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    result : ``TheilslopesResult`` instance
+        The return value is an object with the following attributes:
+
+        slope : float
+            Theil slope.
+        intercept : float
+            Intercept of the Theil line.
+        low_slope : float
+            Lower bound of the confidence interval on `slope`.
+        high_slope : float
+            Upper bound of the confidence interval on `slope`.
+
+    See Also
+    --------
+    siegelslopes : a similar technique using repeated medians
+
+    Notes
+    -----
+    The implementation of `theilslopes` follows [1]_. The intercept is
+    not defined in [1]_, and here it is defined as ``median(y) -
+    slope*median(x)``, which is given in [3]_. Other definitions of
+    the intercept exist in the literature such as  ``median(y - slope*x)``
+    in [4]_. The approach to compute the intercept can be determined by the
+    parameter ``method``. A confidence interval for the intercept is not
+    given as this question is not addressed in [1]_.
+
+    For compatibility with older versions of SciPy, the return value acts
+    like a ``namedtuple`` of length 4, with fields ``slope``, ``intercept``,
+    ``low_slope``, and ``high_slope``, so one can continue to write::
+
+        slope, intercept, low_slope, high_slope = theilslopes(y, x)
+
+    References
+    ----------
+    .. [1] P.K. Sen, "Estimates of the regression coefficient based on
+           Kendall's tau", J. Am. Stat. Assoc., Vol. 63, pp. 1379-1389, 1968.
+    .. [2] H. Theil, "A rank-invariant method of linear and polynomial
+           regression analysis I, II and III",  Nederl. Akad. Wetensch., Proc.
+           53:, pp. 386-392, pp. 521-525, pp. 1397-1412, 1950.
+    .. [3] W.L. Conover, "Practical nonparametric statistics", 2nd ed.,
+           John Wiley and Sons, New York, pp. 493.
+    .. [4] https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+
+    >>> x = np.linspace(-5, 5, num=150)
+    >>> y = x + np.random.normal(size=x.size)
+    >>> y[11:15] += 10  # add outliers
+    >>> y[-5:] -= 7
+
+    Compute the slope, intercept and 90% confidence interval.  For comparison,
+    also compute the least-squares fit with `linregress`:
+
+    >>> res = stats.theilslopes(y, x, 0.90, method='separate')
+    >>> lsq_res = stats.linregress(x, y)
+
+    Plot the results. The Theil-Sen regression line is shown in red, with the
+    dashed red lines illustrating the confidence interval of the slope (note
+    that the dashed red lines are not the confidence interval of the regression
+    as the confidence interval of the intercept is not included). The green
+    line shows the least-squares fit for comparison.
+
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111)
+    >>> ax.plot(x, y, 'b.')
+    >>> ax.plot(x, res[1] + res[0] * x, 'r-')
+    >>> ax.plot(x, res[1] + res[2] * x, 'r--')
+    >>> ax.plot(x, res[1] + res[3] * x, 'r--')
+    >>> ax.plot(x, lsq_res[1] + lsq_res[0] * x, 'g-')
+    >>> plt.show()
+
+    """
+    return _robust_slopes(y, x=x, alpha=alpha, method=method, pfun='theilslopes')
+
+
+@xp_capabilities(skip_backends=[("dask.array", "no take_along_axis"),
+                                ("jax.numpy", "quantile needs lazy nan_policy")],
+                 marray=True)
+@_axis_nan_policy_factory(SiegelslopesResult, default_axis=None, n_outputs=2,
+                          n_samples=_n_samples_optional_x,
+                          result_to_tuple=lambda x, _: tuple(x), paired=True,
+                          too_small=1)
+def siegelslopes(y, x=None, method='hierarchical', *, axis=None):
+    r"""
+    Computes the Siegel estimator for a set of points (x, y).
+
+    `siegelslopes` implements a method for robust linear regression
+    using repeated medians (see [1]_) to fit a line to the points (x, y).
+    The method is robust to outliers with an asymptotic breakdown point
+    of 50%.
+
+    Parameters
+    ----------
+    y : array_like
+        Dependent variable.
+    x : array_like or None, optional
+        Independent variable. If None, use ``arange(len(y))`` instead.
+    method : {'hierarchical', 'separate'}
+        If 'hierarchical', estimate the intercept using the estimated
+        slope ``slope`` (default option).
+        If 'separate', estimate the intercept independent of the estimated
+        slope. See Notes for details.
+    axis : int or tuple of ints, default: None
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    result : ``SiegelslopesResult`` instance
+        The return value is an object with the following attributes:
+
+        slope : float
+            Estimate of the slope of the regression line.
+        intercept : float
+            Estimate of the intercept of the regression line.
+
+    See Also
+    --------
+    theilslopes : a similar technique without repeated medians
+
+    Notes
+    -----
+    With ``n = len(y)``, compute ``m_j`` as the median of
+    the slopes of the lines from the point ``(x[j], y[j])`` to all other ``n-1`` points.
+    ``slope`` is then the median of all slopes ``m_j``.
+    Two ways are given to estimate the intercept in [1]_ which can be chosen
+    via the parameter ``method``.
+    The hierarchical approach uses the estimated slope ``slope``
+    and computes ``intercept`` as the median of ``y - slope*x``.
+    The other approach estimates the intercept separately as follows: for
+    each point ``(x[j], y[j])``, compute the intercepts of all the ``n-1``
+    lines through the remaining points and take the median ``i_j``.
+    ``intercept`` is the median of the ``i_j``.
+
+    The implementation computes `n` times the median of a vector of size `n`
+    which can be slow for large vectors. There are more efficient algorithms
+    (see [2]_) which are not implemented here.
+
+    For compatibility with older versions of SciPy, the return value acts
+    like a ``namedtuple`` of length 2, with fields ``slope`` and
+    ``intercept``, so one can continue to write::
+
+        slope, intercept = siegelslopes(y, x)
+
+    References
+    ----------
+    .. [1] A. Siegel, "Robust Regression Using Repeated Medians",
+           Biometrika, Vol. 69, pp. 242-244, 1982.
+
+    .. [2] A. Stein and M. Werman, "Finding the repeated median regression
+           line", Proceedings of the Third Annual ACM-SIAM Symposium on
+           Discrete Algorithms, pp. 409-413, 1992.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+
+    >>> x = np.linspace(-5, 5, num=150)
+    >>> y = x + np.random.normal(size=x.size)
+    >>> y[11:15] += 10  # add outliers
+    >>> y[-5:] -= 7
+
+    Compute the slope and intercept.  For comparison, also compute the
+    least-squares fit with `linregress`:
+
+    >>> res = stats.siegelslopes(y, x)
+    >>> lsq_res = stats.linregress(x, y)
+
+    Plot the results. The Siegel regression line is shown in red. The green
+    line shows the least-squares fit for comparison.
+
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111)
+    >>> ax.plot(x, y, 'b.')
+    >>> ax.plot(x, res[1] + res[0] * x, 'r-')
+    >>> ax.plot(x, lsq_res[1] + lsq_res[0] * x, 'g-')
+    >>> plt.show()
+
+    """
+    return _robust_slopes(y, x=x, method=method, pfun='siegelslopes')
+
+
+def _robust_slopes(y, *, x, alpha=None, method, pfun):
+    other_method = 'joint' if pfun == 'theilslopes' else 'hierarchical'
+    if method not in {other_method, 'separate'}:
+        raise ValueError(f"method must be either '{other_method}' or 'separate'. "
+                         f"'{method}' is invalid.")
+
+    xp = array_namespace(y, x)
+    y, x = xp_promote(y, x, force_floating=True, xp=xp)
+    x = xp.arange(y.shape[-1], dtype=y.dtype) if x is None else x
+    y, x = xp.broadcast_arrays(y, x)
+    x, y = _share_masks(x, y, xp=xp)
+
+    if x.shape[-1] < 2 or y.shape[-1] < 2:  # only needed by test_axis_nan_policy
+        raise ValueError("`x` and `y` must have length at least 2.")
+
+    # Compute sorted slopes only when deltax > 0
+    deltax = x[..., :, xp.newaxis] - x[..., xp.newaxis, :]
+    deltay = y[..., :, xp.newaxis] - y[..., xp.newaxis, :]
+
+    if pfun == 'theilslopes':
+        i = xp.astype(xp.triu(xp.ones(deltax.shape[-2:]), k=1), xp.bool)
+        if is_numpy(xp):
+            deltax, deltay = deltax[..., i], deltay[..., i]
+        else:
+            # With array API, mask must be sole index, so we need to broadcast it.
+            # Indexing ravels the array, so we need to reshape the results.
+            i = xp.broadcast_to(i, deltax.shape)
+            deltax, deltay = deltax[i], deltay[i]
+            deltax = xp.reshape(deltax, (*i.shape[:-2], -1))
+            deltay = xp.reshape(deltay, (*i.shape[:-2], -1))
+
+    # Use `.data` to avoid indexing with masked indices. If masked elements of deltax=0,
+    # they can safely be set to xp.nan, too - they're masked, after all.
+    deltax0 = (deltax.data == 0) if is_marray(xp) else (deltax == 0)
+    deltax = xpx.at(deltax)[deltax0].set(xp.nan)
+    slopes = deltay / deltax
+
+    if is_marray(xp):
+        def nanmedian(x, axis):  # use mask to ignore NaNs
+            x_nans_masked = xp.asarray(x.data, mask=x.mask | x._xp.isnan(x.data))
+            return stats.quantile(x_nans_masked, 0.5, axis=axis)
+    else:
+        def nanmedian(x, axis):  # use nan_policy to ignore NaNs
+            return stats.quantile(x, 0.5, axis=axis, nan_policy='omit')
+
+    def median(x, axis): return stats.quantile(x, 0.5, axis=axis)
+
+    # `theilslopes` is median of all slopes. Indexing with `i` above has already raveled
+    # all the slopes, so we only need to take the median along the last axis.
+    # `siegelslope` is a median of medians: we take the median of slopes from point i
+    # to all other points, then the median of those medians.
+    # The slope is NaN wherever the two points are the same, and those don't contribute,
+    # hence the first median omits NaNs. NaNs in the input are propagated by the
+    # `_axis_nan_policy` decorator.
+    medslope = (median(nanmedian(slopes, axis=-1), axis=-1) if pfun == 'siegelslopes'
+                else nanmedian(slopes, axis=-1))
+
+    if method in {'joint', 'hierarchical'}:
+        medinter = median(y - medslope[..., np.newaxis] * x, axis=-1)
+    elif pfun == 'theilslopes':
+        medinter = median(y, axis=-1) - medslope * median(x, axis=-1)
+    else:
+        # Calculate pairwise intercepts given each point (row i) and the slope to each
+        # other point (column j). Then calculate the median of (row) medians.
+        intercepts = y[..., :, xp.newaxis] - slopes*x[..., :, xp.newaxis]
+        medinter = median(nanmedian(intercepts, axis=-1), axis=-1)
+
+    if pfun == 'siegelslopes':
+        return SiegelslopesResult(slope=medslope[()], intercept=medinter[()])
+
+    # Now compute confidence intervals
+    if alpha > 0.5:
+        alpha = 1. - alpha
+
+    z = float(special.ndtri(alpha / 2.))
+    # This implements (2.6) from Sen (1968)
+    # we don't actually need ranks, so an enhancement could be to have
+    # `rankdata` return only the third output. In the meantime, use the
+    # least expensive `method`.
+    _, _, nxreps = _rankdata(x, method='min', return_ties=True)
+    _, _, nyreps = _rankdata(y, method='min', return_ties=True)
+    nt = xp.count_nonzero(xp.isfinite(slopes), axis=-1, keepdims=True)  # N in Sen 1968
+    nt = xp.asarray(nt, dtype=y.dtype)
+    ny = _count_nonmasked(y, keepdims=True, axis=-1)                    # n in Sen 1968
+    # Equation 2.6 in Sen (1968):
+    sigsq = 1/18. * (
+        ny * (ny-1) * (2*ny+5)
+        - xp.sum(nxreps * (nxreps-1) * (2*nxreps + 5), axis=-1, keepdims=True)
+        - xp.sum(nyreps * (nyreps-1) * (2*nyreps + 5), axis=-1, keepdims=True))
+    # Find the confidence interval indices in `slopes`
+    sigma = xp.sqrt(xp.maximum(sigsq, xp.zeros_like(sigsq)))
+    Ru = xp.minimum(xp.astype(xp.round((nt - z*sigma)/2.), xp.int64),
+                    xp.astype(nt, xp.int64)-1)
+    Rl = xp.maximum(xp.astype(xp.round((nt + z*sigma)/2.), xp.int64) - 1,
+                    xp.asarray(0, dtype=xp.int64))
+    R = xp.concat((xpx.atleast_nd(Rl, ndim=1), xpx.atleast_nd(Ru, ndim=1)), axis=-1)
+    slopes = xp.sort(slopes, axis=-1)
+    delta = xp.take_along_axis(slopes, R, axis=-1)
+    i_nan = xp.broadcast_to(sigsq < 0, delta.shape)
+    delta = xpx.at(delta)[i_nan].set(xp.nan)
+
+    slope = medslope[()] if medslope.ndim == 0 else medslope
+    intercept = medinter[()] if medinter.ndim == 0 else medinter
+    low_slope = delta[..., 0]
+    high_slope = delta[..., 1]
+    low_slope = low_slope[()] if low_slope.ndim == 0 else low_slope
+    high_slope = high_slope[()] if high_slope.ndim == 0 else high_slope
+    return TheilslopesResult(slope=slope, intercept=intercept,
+                             low_slope=low_slope, high_slope=high_slope)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_covariance.py b/.venv/lib/python3.12/site-packages/scipy/stats/_covariance.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2133b9f3278d19fbb94fd6b8ba74876078dc6dd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_covariance.py
@@ -0,0 +1,672 @@
+from functools import cached_property
+from types import GenericAlias
+
+import numpy as np
+from scipy import linalg
+from scipy.stats import _multivariate
+
+
+__all__ = ["Covariance"]
+
+
+class Covariance:
+    """
+    Representation of a covariance matrix.
+
+    Calculations involving covariance matrices (e.g. data whitening,
+    multivariate normal function evaluation) are often performed more
+    efficiently using a decomposition of the covariance matrix instead of the
+    covariance matrix itself. This class allows the user to construct an
+    object representing a covariance matrix using any of several
+    decompositions and perform calculations using a common interface.
+
+    .. note::
+
+        The `Covariance` class cannot be instantiated directly. Instead, use
+        one of the factory methods (e.g. `Covariance.from_diagonal`).
+
+    Examples
+    --------
+    The `Covariance` class is used by calling one of its
+    factory methods to create a `Covariance` object, then pass that
+    representation of the `Covariance` matrix as a shape parameter of a
+    multivariate distribution.
+
+    For instance, the multivariate normal distribution can accept an array
+    representing a covariance matrix:
+
+    >>> from scipy import stats
+    >>> import numpy as np
+    >>> d = [1, 2, 3]
+    >>> A = np.diag(d)  # a diagonal covariance matrix
+    >>> x = [4, -2, 5]  # a point of interest
+    >>> dist = stats.multivariate_normal(mean=[0, 0, 0], cov=A)
+    >>> dist.pdf(x)
+    4.9595685102808205e-08
+
+    but the calculations are performed in a very generic way that does not
+    take advantage of any special properties of the covariance matrix. Because
+    our covariance matrix is diagonal, we can use ``Covariance.from_diagonal``
+    to create an object representing the covariance matrix, and
+    `multivariate_normal` can use this to compute the probability density
+    function more efficiently.
+
+    >>> cov = stats.Covariance.from_diagonal(d)
+    >>> dist = stats.multivariate_normal(mean=[0, 0, 0], cov=cov)
+    >>> dist.pdf(x)
+    4.9595685102808205e-08
+
+    """
+
+    # generic type compatibility with scipy-stubs
+    __class_getitem__: classmethod = classmethod(GenericAlias)
+
+    def __init__(self):
+        message = ("The `Covariance` class cannot be instantiated directly. "
+                   "Please use one of the factory methods "
+                   "(e.g. `Covariance.from_diagonal`).")
+        raise NotImplementedError(message)
+
+    @staticmethod
+    def from_diagonal(diagonal):
+        r"""
+        Return a representation of a covariance matrix from its diagonal.
+
+        Parameters
+        ----------
+        diagonal : array_like
+            The diagonal elements of a diagonal matrix.
+
+        Returns
+        -------
+        Covariance
+            A `Covariance` object representing `diagonal`.
+
+        Notes
+        -----
+        Let the diagonal elements of a diagonal covariance matrix :math:`D` be
+        stored in the vector :math:`d`.
+
+        When all elements of :math:`d` are strictly positive, whitening of a
+        data point :math:`x` is performed by computing
+        :math:`x \cdot d^{-1/2}`, where the inverse square root can be taken
+        element-wise.
+        :math:`\log\det{D}` is calculated as :math:`-2 \sum(\log{d})`,
+        where the :math:`\log` operation is performed element-wise.
+
+        This `Covariance` class supports singular covariance matrices. When
+        computing ``_log_pdet``, non-positive elements of :math:`d` are
+        ignored. Whitening is not well defined when the point to be whitened
+        does not lie in the span of the columns of the covariance matrix. The
+        convention taken here is to treat the inverse square root of
+        non-positive elements of :math:`d` as zeros.
+
+        Examples
+        --------
+        Prepare a symmetric positive definite covariance matrix ``A`` and a
+        data point ``x``.
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> rng = np.random.default_rng()
+        >>> n = 5
+        >>> A = np.diag(rng.random(n))
+        >>> x = rng.random(size=n)
+
+        Extract the diagonal from ``A`` and create the `Covariance` object.
+
+        >>> d = np.diag(A)
+        >>> cov = stats.Covariance.from_diagonal(d)
+
+        Compare the functionality of the `Covariance` object against a
+        reference implementations.
+
+        >>> res = cov.whiten(x)
+        >>> ref = np.diag(d**-0.5) @ x
+        >>> np.allclose(res, ref)
+        True
+        >>> res = cov.log_pdet
+        >>> ref = np.linalg.slogdet(A)[-1]
+        >>> np.allclose(res, ref)
+        True
+
+        """
+        return CovViaDiagonal(diagonal)
+
+    @staticmethod
+    def from_precision(precision, covariance=None):
+        r"""
+        Return a representation of a covariance from its precision matrix.
+
+        Parameters
+        ----------
+        precision : array_like
+            The precision matrix; that is, the inverse of a square, symmetric,
+            positive definite covariance matrix.
+        covariance : array_like, optional
+            The square, symmetric, positive definite covariance matrix. If not
+            provided, this may need to be calculated (e.g. to evaluate the
+            cumulative distribution function of
+            `scipy.stats.multivariate_normal`) by inverting `precision`.
+
+        Returns
+        -------
+        Covariance
+            A `Covariance` object representing the covariance matrix
+            corresponding to `precision`.
+
+        Notes
+        -----
+        Let the covariance matrix be :math:`A`, its precision matrix be
+        :math:`P = A^{-1}`, and :math:`L` be the lower Cholesky factor such
+        that :math:`L L^T = P`.
+        Whitening of a data point :math:`x` is performed by computing
+        :math:`x^T L`. :math:`\log\det{A}` is calculated as
+        :math:`-2tr(\log{L})`, where the :math:`\log` operation is performed
+        element-wise.
+
+        This `Covariance` class does not support singular covariance matrices
+        because the precision matrix does not exist for a singular covariance
+        matrix.
+
+        Examples
+        --------
+        Prepare a symmetric positive definite precision matrix ``P`` and a
+        data point ``x``. (If the precision matrix is not already available,
+        consider the other factory methods of the `Covariance` class.)
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> rng = np.random.default_rng()
+        >>> n = 5
+        >>> P = rng.random(size=(n, n))
+        >>> P = P @ P.T  # a precision matrix must be positive definite
+        >>> x = rng.random(size=n)
+
+        Create the `Covariance` object.
+
+        >>> cov = stats.Covariance.from_precision(P)
+
+        Compare the functionality of the `Covariance` object against
+        reference implementations.
+
+        >>> res = cov.whiten(x)
+        >>> ref = x @ np.linalg.cholesky(P)
+        >>> np.allclose(res, ref)
+        True
+        >>> res = cov.log_pdet
+        >>> ref = -np.linalg.slogdet(P)[-1]
+        >>> np.allclose(res, ref)
+        True
+
+        """
+        return CovViaPrecision(precision, covariance)
+
+    @staticmethod
+    def from_cholesky(cholesky):
+        r"""
+        Representation of a covariance provided via the (lower) Cholesky factor.
+
+        Parameters
+        ----------
+        cholesky : array_like
+            The lower triangular Cholesky factor of the covariance matrix.
+
+        Returns
+        -------
+        Covariance
+            A `Covariance` object representing the covariance matrix
+            corresponding to `cholesky`.
+
+        Notes
+        -----
+        Let the covariance matrix be :math:`A` and :math:`L` be the lower
+        Cholesky factor such that :math:`L L^T = A`.
+        Whitening of a data point :math:`x` is performed by computing
+        :math:`L^{-1} x`. :math:`\log\det{A}` is calculated as
+        :math:`2tr(\log{L})`, where the :math:`\log` operation is performed
+        element-wise.
+
+        This `Covariance` class does not support singular covariance matrices
+        because the Cholesky decomposition does not exist for a singular
+        covariance matrix.
+
+        Examples
+        --------
+        Prepare a symmetric positive definite covariance matrix ``A`` and a
+        data point ``x``.
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> rng = np.random.default_rng()
+        >>> n = 5
+        >>> A = rng.random(size=(n, n))
+        >>> A = A @ A.T  # make the covariance symmetric positive definite
+        >>> x = rng.random(size=n)
+
+        Perform the Cholesky decomposition of ``A`` and create the
+        `Covariance` object.
+
+        >>> L = np.linalg.cholesky(A)
+        >>> cov = stats.Covariance.from_cholesky(L)
+
+        Compare the functionality of the `Covariance` object against
+        reference implementation.
+
+        >>> from scipy.linalg import solve_triangular
+        >>> res = cov.whiten(x)
+        >>> ref = solve_triangular(L, x, lower=True)
+        >>> np.allclose(res, ref)
+        True
+        >>> res = cov.log_pdet
+        >>> ref = np.linalg.slogdet(A)[-1]
+        >>> np.allclose(res, ref)
+        True
+
+        """
+        return CovViaCholesky(cholesky)
+
+    @staticmethod
+    def from_eigendecomposition(eigendecomposition):
+        r"""
+        Representation of a covariance provided via eigendecomposition.
+
+        Parameters
+        ----------
+        eigendecomposition : sequence
+            A sequence (nominally a tuple) containing the eigenvalue and
+            eigenvector arrays as computed by `scipy.linalg.eigh` or
+            `numpy.linalg.eigh`.
+
+        Returns
+        -------
+        Covariance
+            A `Covariance` object representing the covariance matrix
+            corresponding to `eigendecomposition`.
+
+        Notes
+        -----
+        Let the covariance matrix be :math:`A`, let :math:`V` be matrix of
+        eigenvectors, and let :math:`W` be the diagonal matrix of eigenvalues
+        such that `V W V^T = A`.
+
+        When all of the eigenvalues are strictly positive, whitening of a
+        data point :math:`x` is performed by computing
+        :math:`x^T (V W^{-1/2})`, where the inverse square root can be taken
+        element-wise.
+        :math:`\log\det{A}` is calculated as  :math:`tr(\log{W})`,
+        where the :math:`\log` operation is performed element-wise.
+
+        This `Covariance` class supports singular covariance matrices. When
+        computing ``_log_pdet``, non-positive eigenvalues are ignored.
+        Whitening is not well defined when the point to be whitened
+        does not lie in the span of the columns of the covariance matrix. The
+        convention taken here is to treat the inverse square root of
+        non-positive eigenvalues as zeros.
+
+        Examples
+        --------
+        Prepare a symmetric positive definite covariance matrix ``A`` and a
+        data point ``x``.
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> rng = np.random.default_rng()
+        >>> n = 5
+        >>> A = rng.random(size=(n, n))
+        >>> A = A @ A.T  # make the covariance symmetric positive definite
+        >>> x = rng.random(size=n)
+
+        Perform the eigendecomposition of ``A`` and create the `Covariance`
+        object.
+
+        >>> w, v = np.linalg.eigh(A)
+        >>> cov = stats.Covariance.from_eigendecomposition((w, v))
+
+        Compare the functionality of the `Covariance` object against
+        reference implementations.
+
+        >>> res = cov.whiten(x)
+        >>> ref = x @ (v @ np.diag(w**-0.5))
+        >>> np.allclose(res, ref)
+        True
+        >>> res = cov.log_pdet
+        >>> ref = np.linalg.slogdet(A)[-1]
+        >>> np.allclose(res, ref)
+        True
+
+        """
+        return CovViaEigendecomposition(eigendecomposition)
+
+    def whiten(self, x):
+        """
+        Perform a whitening transformation on data.
+
+        "Whitening" ("white" as in "white noise", in which each frequency has
+        equal magnitude) transforms a set of random variables into a new set of
+        random variables with unit-diagonal covariance. When a whitening
+        transform is applied to a sample of points distributed according to
+        a multivariate normal distribution with zero mean, the covariance of
+        the transformed sample is approximately the identity matrix.
+
+        Parameters
+        ----------
+        x : array_like
+            An array of points. The last dimension must correspond with the
+            dimensionality of the space, i.e., the number of columns in the
+            covariance matrix.
+
+        Returns
+        -------
+        x_ : array_like
+            The transformed array of points.
+
+        References
+        ----------
+        .. [1] "Whitening Transformation". Wikipedia.
+               https://en.wikipedia.org/wiki/Whitening_transformation
+        .. [2] Novak, Lukas, and Miroslav Vorechovsky. "Generalization of
+               coloring linear transformation". Transactions of VSB 18.2
+               (2018): 31-35. :doi:`10.31490/tces-2018-0013`
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> rng = np.random.default_rng()
+        >>> n = 3
+        >>> A = rng.random(size=(n, n))
+        >>> cov_array = A @ A.T  # make matrix symmetric positive definite
+        >>> precision = np.linalg.inv(cov_array)
+        >>> cov_object = stats.Covariance.from_precision(precision)
+        >>> x = rng.multivariate_normal(np.zeros(n), cov_array, size=(10000))
+        >>> x_ = cov_object.whiten(x)
+        >>> np.cov(x_, rowvar=False)  # near-identity covariance
+        array([[0.97862122, 0.00893147, 0.02430451],
+               [0.00893147, 0.96719062, 0.02201312],
+               [0.02430451, 0.02201312, 0.99206881]])
+
+        """
+        return self._whiten(np.asarray(x))
+
+    def colorize(self, x):
+        """
+        Perform a colorizing transformation on data.
+
+        "Colorizing" ("color" as in "colored noise", in which different
+        frequencies may have different magnitudes) transforms a set of
+        uncorrelated random variables into a new set of random variables with
+        the desired covariance. When a coloring transform is applied to a
+        sample of points distributed according to a multivariate normal
+        distribution with identity covariance and zero mean, the covariance of
+        the transformed sample is approximately the covariance matrix used
+        in the coloring transform.
+
+        Parameters
+        ----------
+        x : array_like
+            An array of points. The last dimension must correspond with the
+            dimensionality of the space, i.e., the number of columns in the
+            covariance matrix.
+
+        Returns
+        -------
+        x_ : array_like
+            The transformed array of points.
+
+        References
+        ----------
+        .. [1] "Whitening Transformation". Wikipedia.
+               https://en.wikipedia.org/wiki/Whitening_transformation
+        .. [2] Novak, Lukas, and Miroslav Vorechovsky. "Generalization of
+               coloring linear transformation". Transactions of VSB 18.2
+               (2018): 31-35. :doi:`10.31490/tces-2018-0013`
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> rng = np.random.default_rng(1638083107694713882823079058616272161)
+        >>> n = 3
+        >>> A = rng.random(size=(n, n))
+        >>> cov_array = A @ A.T  # make matrix symmetric positive definite
+        >>> cholesky = np.linalg.cholesky(cov_array)
+        >>> cov_object = stats.Covariance.from_cholesky(cholesky)
+        >>> x = rng.multivariate_normal(np.zeros(n), np.eye(n), size=(10000))
+        >>> x_ = cov_object.colorize(x)
+        >>> cov_data = np.cov(x_, rowvar=False)
+        >>> np.allclose(cov_data, cov_array, rtol=3e-2)
+        True
+        """
+        return self._colorize(np.asarray(x))
+
+    @property
+    def log_pdet(self):
+        """
+        Log of the pseudo-determinant of the covariance matrix
+        """
+        return np.array(self._log_pdet, dtype=float)[()]
+
+    @property
+    def rank(self):
+        """
+        Rank of the covariance matrix
+        """
+        return np.array(self._rank, dtype=int)[()]
+
+    @property
+    def covariance(self):
+        """
+        Explicit representation of the covariance matrix
+        """
+        return self._covariance
+
+    @property
+    def shape(self):
+        """
+        Shape of the covariance array
+        """
+        return self._shape
+
+    def _validate_matrix(self, A, name):
+        A = np.atleast_2d(A)
+        m, n = A.shape[-2:]
+        if m != n or A.ndim != 2 or not (np.issubdtype(A.dtype, np.integer) or
+                                         np.issubdtype(A.dtype, np.floating)):
+            message = (f"The input `{name}` must be a square, "
+                       "two-dimensional array of real numbers.")
+            raise ValueError(message)
+        return A
+
+    def _validate_vector(self, A, name):
+        A = np.atleast_1d(A)
+        if A.ndim != 1 or not (np.issubdtype(A.dtype, np.integer) or
+                               np.issubdtype(A.dtype, np.floating)):
+            message = (f"The input `{name}` must be a one-dimensional array "
+                       "of real numbers.")
+            raise ValueError(message)
+        return A
+
+
+class CovViaPrecision(Covariance):
+
+    __class_getitem__ = None  # type:ignore[assignment]
+
+    def __init__(self, precision, covariance=None):
+        precision = self._validate_matrix(precision, 'precision')
+        if covariance is not None:
+            covariance = self._validate_matrix(covariance, 'covariance')
+            message = "`precision.shape` must equal `covariance.shape`."
+            if precision.shape != covariance.shape:
+                raise ValueError(message)
+
+        self._chol_P = np.linalg.cholesky(precision)
+        self._log_pdet = -2*np.log(np.diag(self._chol_P)).sum(axis=-1)
+        self._rank = precision.shape[-1]  # must be full rank if invertible
+        self._precision = precision
+        self._cov_matrix = covariance
+        self._shape = precision.shape
+        self._allow_singular = False
+
+    def _whiten(self, x):
+        return x @ self._chol_P
+
+    @cached_property
+    def _covariance(self):
+        n = self._shape[-1]
+        return (linalg.cho_solve((self._chol_P, True), np.eye(n))
+                if self._cov_matrix is None else self._cov_matrix)
+
+    def _colorize(self, x):
+        m = x.T.shape[0]
+        res = linalg.solve_triangular(self._chol_P.T, x.T.reshape(m, -1), lower=False)
+        return res.reshape(x.T.shape).T
+
+
+def _dot_diag(x, d):
+    # If d were a full diagonal matrix, x @ d would always do what we want.
+    # Special treatment is needed for n-dimensional `d` in which each row
+    # includes only the diagonal elements of a covariance matrix.
+    return x * d if x.ndim < 2 else x * np.expand_dims(d, -2)
+
+
+class CovViaDiagonal(Covariance):
+
+    def __init__(self, diagonal):
+        diagonal = self._validate_vector(diagonal, 'diagonal')
+
+        i_zero = diagonal <= 0
+        positive_diagonal = np.array(diagonal, dtype=np.float64)
+
+        positive_diagonal[i_zero] = 1  # ones don't affect determinant
+        self._log_pdet = np.sum(np.log(positive_diagonal), axis=-1)
+
+        psuedo_reciprocals = 1 / np.sqrt(positive_diagonal)
+        psuedo_reciprocals[i_zero] = 0
+
+        self._sqrt_diagonal = np.sqrt(diagonal)
+        self._LP = psuedo_reciprocals
+        self._rank = positive_diagonal.shape[-1] - i_zero.sum(axis=-1)
+        self._covariance = np.apply_along_axis(np.diag, -1, diagonal)
+        self._i_zero = i_zero
+        self._shape = self._covariance.shape
+        self._allow_singular = True
+
+    def _whiten(self, x):
+        return _dot_diag(x, self._LP)
+
+    def _colorize(self, x):
+        return _dot_diag(x, self._sqrt_diagonal)
+
+    def _support_mask(self, x):
+        """
+        Check whether x lies in the support of the distribution.
+        """
+        return ~np.any(_dot_diag(x, self._i_zero), axis=-1)
+
+
+class CovViaCholesky(Covariance):
+
+    __class_getitem__ = None  # type:ignore[assignment]
+
+    def __init__(self, cholesky):
+        L = self._validate_matrix(cholesky, 'cholesky')
+
+        self._factor = L
+        self._log_pdet = 2*np.log(np.diag(self._factor)).sum(axis=-1)
+        self._rank = L.shape[-1]  # must be full rank for cholesky
+        self._shape = L.shape
+        self._allow_singular = False
+
+    @cached_property
+    def _covariance(self):
+        return self._factor @ self._factor.T
+
+    def _whiten(self, x):
+        m = x.T.shape[0]
+        res = linalg.solve_triangular(self._factor, x.T.reshape(m, -1), lower=True)
+        return res.reshape(x.T.shape).T
+
+    def _colorize(self, x):
+        return x @ self._factor.T
+
+
+class CovViaEigendecomposition(Covariance):
+
+    __class_getitem__ = None  # type:ignore[assignment]
+
+    def __init__(self, eigendecomposition):
+        eigenvalues, eigenvectors = eigendecomposition
+        eigenvalues = self._validate_vector(eigenvalues, 'eigenvalues')
+        eigenvectors = self._validate_matrix(eigenvectors, 'eigenvectors')
+        message = ("The shapes of `eigenvalues` and `eigenvectors` "
+                   "must be compatible.")
+        try:
+            eigenvalues = np.expand_dims(eigenvalues, -2)
+            eigenvectors, eigenvalues = np.broadcast_arrays(eigenvectors,
+                                                            eigenvalues)
+            eigenvalues = eigenvalues[..., 0, :]
+        except ValueError:
+            raise ValueError(message)
+
+        i_zero = eigenvalues <= 0
+        positive_eigenvalues = np.array(eigenvalues, dtype=np.float64)
+
+        positive_eigenvalues[i_zero] = 1  # ones don't affect determinant
+        self._log_pdet = np.sum(np.log(positive_eigenvalues), axis=-1)
+
+        psuedo_reciprocals = 1 / np.sqrt(positive_eigenvalues)
+        psuedo_reciprocals[i_zero] = 0
+
+        self._LP = eigenvectors * psuedo_reciprocals
+        self._LA = eigenvectors * np.sqrt(eigenvalues)
+        self._rank = positive_eigenvalues.shape[-1] - i_zero.sum(axis=-1)
+        self._w = eigenvalues
+        self._v = eigenvectors
+        self._shape = eigenvectors.shape
+        self._null_basis = eigenvectors * i_zero
+        # This is only used for `_support_mask`, not to decide whether
+        # the covariance is singular or not.
+        self._eps = _multivariate._eigvalsh_to_eps(eigenvalues) * 10**3
+        self._allow_singular = True
+
+    def _whiten(self, x):
+        return x @ self._LP
+
+    def _colorize(self, x):
+        return x @ self._LA.T
+
+    @cached_property
+    def _covariance(self):
+        return (self._v * self._w) @ self._v.T
+
+    def _support_mask(self, x):
+        """
+        Check whether x lies in the support of the distribution.
+        """
+        residual = np.linalg.norm(x @ self._null_basis, axis=-1)
+        in_support = residual < self._eps
+        return in_support
+
+
+class CovViaPSD(Covariance):
+    """
+    Representation of a covariance provided via an instance of _PSD
+    """
+
+    __class_getitem__ = None  # type:ignore[assignment]
+
+    def __init__(self, psd):
+        self._LP = psd.U
+        self._log_pdet = psd.log_pdet
+        self._rank = psd.rank
+        self._covariance = psd._M
+        self._shape = psd._M.shape
+        self._psd = psd
+        self._allow_singular = False  # by default
+
+    def _whiten(self, x):
+        return x @ self._LP
+
+    def _support_mask(self, x):
+        return self._psd._support_mask(x)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_crosstab.py b/.venv/lib/python3.12/site-packages/scipy/stats/_crosstab.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd016a11627968c5fa8361c439c9c55a60a592c2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_crosstab.py
@@ -0,0 +1,198 @@
+import numpy as np
+from scipy.sparse import coo_array
+from scipy._lib._bunch import _make_tuple_bunch
+
+
+CrosstabResult = _make_tuple_bunch(
+    "CrosstabResult", ["elements", "count"]
+)
+
+
+def crosstab(*args, levels=None, sparse=False):
+    """
+    Return table of counts for each possible unique combination in ``*args``.
+
+    When ``len(args) > 1``, the array computed by this function is
+    often referred to as a *contingency table* [1]_.
+
+    The arguments must be sequences with the same length.  The second return
+    value, `count`, is an integer array with ``len(args)`` dimensions.  If
+    `levels` is None, the shape of `count` is ``(n0, n1, ...)``, where ``nk``
+    is the number of unique elements in ``args[k]``.
+
+    Parameters
+    ----------
+    *args : sequences
+        A sequence of sequences whose unique aligned elements are to be
+        counted.  The sequences in args must all be the same length.
+    levels : sequence, optional
+        If `levels` is given, it must be a sequence that is the same length as
+        `args`.  Each element in `levels` is either a sequence or None.  If it
+        is a sequence, it gives the values in the corresponding sequence in
+        `args` that are to be counted.  If any value in the sequences in `args`
+        does not occur in the corresponding sequence in `levels`, that value
+        is ignored and not counted in the returned array `count`.  The default
+        value of `levels` for ``args[i]`` is ``np.unique(args[i])``
+    sparse : bool, optional
+        If True, return a sparse array.  The array will be an instance of
+        the `scipy.sparse.coo_array` class.
+        Default is False.
+
+    Returns
+    -------
+    res : CrosstabResult
+        An object containing the following attributes:
+
+        elements : tuple of numpy.ndarrays.
+            Tuple of length ``len(args)`` containing the arrays of elements
+            that are counted in `count`.  These can be interpreted as the
+            labels of the corresponding dimensions of `count`. If `levels` was
+            given, then if ``levels[i]`` is not None, ``elements[i]`` will
+            hold the values given in ``levels[i]``.
+        count : numpy.ndarray or scipy.sparse.coo_array
+            Counts of the unique elements in ``zip(*args)``, stored in an
+            array. Also known as a *contingency table* when ``len(args) > 1``.
+
+    See Also
+    --------
+    numpy.unique
+
+    Notes
+    -----
+    .. versionadded:: 1.7.0
+
+    References
+    ----------
+    .. [1] "Contingency table", http://en.wikipedia.org/wiki/Contingency_table
+
+    Examples
+    --------
+    >>> from scipy.stats.contingency import crosstab
+
+    Given the lists `a` and `x`, create a contingency table that counts the
+    frequencies of the corresponding pairs.
+
+    >>> a = ['A', 'B', 'A', 'A', 'B', 'B', 'A', 'A', 'B', 'B']
+    >>> x = ['X', 'X', 'X', 'Y', 'Z', 'Z', 'Y', 'Y', 'Z', 'Z']
+    >>> res = crosstab(a, x)
+    >>> avals, xvals = res.elements
+    >>> avals
+    array(['A', 'B'], dtype='<U1')
+    >>> xvals
+    array(['X', 'Y', 'Z'], dtype='<U1')
+    >>> res.count
+    array([[2, 3, 0],
+           [1, 0, 4]])
+
+    So ``('A', 'X')`` occurs twice, ``('A', 'Y')`` occurs three times, etc.
+
+    Higher dimensional contingency tables can be created.
+
+    >>> p = [0, 0, 0, 0, 1, 1, 1, 0, 0, 1]
+    >>> res = crosstab(a, x, p)
+    >>> res.count
+    array([[[2, 0],
+            [2, 1],
+            [0, 0]],
+           [[1, 0],
+            [0, 0],
+            [1, 3]]])
+    >>> res.count.shape
+    (2, 3, 2)
+
+    The values to be counted can be set by using the `levels` argument.
+    It allows the elements of interest in each input sequence to be
+    given explicitly instead finding the unique elements of the sequence.
+
+    For example, suppose one of the arguments is an array containing the
+    answers to a survey question, with integer values 1 to 4.  Even if the
+    value 1 does not occur in the data, we want an entry for it in the table.
+
+    >>> q1 = [2, 3, 3, 2, 4, 4, 2, 3, 4, 4, 4, 3, 3, 3, 4]  # 1 does not occur.
+    >>> q2 = [4, 4, 2, 2, 2, 4, 1, 1, 2, 2, 4, 2, 2, 2, 4]  # 3 does not occur.
+    >>> options = [1, 2, 3, 4]
+    >>> res = crosstab(q1, q2, levels=(options, options))
+    >>> res.count
+    array([[0, 0, 0, 0],
+           [1, 1, 0, 1],
+           [1, 4, 0, 1],
+           [0, 3, 0, 3]])
+
+    If `levels` is given, but an element of `levels` is None, the unique values
+    of the corresponding argument are used. For example,
+
+    >>> res = crosstab(q1, q2, levels=(None, options))
+    >>> res.elements
+    [array([2, 3, 4]), [1, 2, 3, 4]]
+    >>> res.count
+    array([[1, 1, 0, 1],
+           [1, 4, 0, 1],
+           [0, 3, 0, 3]])
+
+    If we want to ignore the pairs where 4 occurs in ``q2``, we can
+    give just the values [1, 2] to `levels`, and the 4 will be ignored:
+
+    >>> res = crosstab(q1, q2, levels=(None, [1, 2]))
+    >>> res.elements
+    [array([2, 3, 4]), [1, 2]]
+    >>> res.count
+    array([[1, 1],
+           [1, 4],
+           [0, 3]])
+
+    Finally, let's repeat the first example, but return a sparse array:
+
+    >>> res = crosstab(a, x, sparse=True)
+    >>> res.count
+    <COOrdinate sparse array of dtype 'int64'
+        with 4 stored elements and shape (2, 3)>
+    >>> res.count.toarray()
+    array([[2, 3, 0],
+           [1, 0, 4]])
+
+    """
+    nargs = len(args)
+    if nargs == 0:
+        raise TypeError("At least one input sequence is required.")
+
+    len0 = len(args[0])
+    if not all(len(a) == len0 for a in args[1:]):
+        raise ValueError("All input sequences must have the same length.")
+
+    if levels is None:
+        # Call np.unique with return_inverse=True on each argument.
+        actual_levels, indices = zip(*[np.unique(a, return_inverse=True)
+                                       for a in args])
+    else:
+        # `levels` is not None...
+        if len(levels) != nargs:
+            raise ValueError('len(levels) must equal the number of input '
+                             'sequences')
+
+        args = [np.asarray(arg) for arg in args]
+        mask = np.zeros((nargs, len0), dtype=np.bool_)
+        inv = np.zeros((nargs, len0), dtype=np.intp)
+        actual_levels = []
+        for k, (levels_list, arg) in enumerate(zip(levels, args)):
+            if levels_list is None:
+                levels_list, inv[k, :] = np.unique(arg, return_inverse=True)
+                mask[k, :] = True
+            else:
+                q = arg == np.asarray(levels_list).reshape(-1, 1)
+                mask[k, :] = np.any(q, axis=0)
+                qnz = q.T.nonzero()
+                inv[k, qnz[0]] = qnz[1]
+            actual_levels.append(levels_list)
+
+        mask_all = mask.all(axis=0)
+        indices = tuple(inv[:, mask_all])
+
+    if sparse:
+        count = coo_array((np.ones(len(indices[0]), dtype=int), indices))
+        count.sum_duplicates()
+    else:
+        shape = [len(u) for u in actual_levels]
+        count = np.zeros(shape, dtype=int)
+        np.add.at(count, indices, 1)
+
+    return CrosstabResult(actual_levels, count)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_discrete_distns.py b/.venv/lib/python3.12/site-packages/scipy/stats/_discrete_distns.py
new file mode 100644
index 0000000000000000000000000000000000000000..07b02cf7283c33fa1e9d133d7500dc99bff6df27
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_discrete_distns.py
@@ -0,0 +1,2105 @@
+#
+# Author:  Travis Oliphant  2002-2011 with contributions from
+#          SciPy Developers 2004-2011
+#
+from functools import partial
+from types import MethodType
+
+from scipy import special
+from scipy.special import entr, logsumexp, betaln, gammaln as gamln
+import scipy.special._ufuncs as scu
+from scipy.special._spfun_stats import _poisson_binom_pmf, _poisson_binom_cdf
+from scipy._lib._util import rng_integers
+import scipy._external.array_api_extra as xpx
+from scipy.interpolate import interp1d
+
+from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, sinh
+
+import numpy as np
+
+from ._distn_infrastructure import (rv_discrete, get_distribution_names,
+                                    _vectorize_rvs_over_shapes,
+                                    _ShapeInfo, _isintegral,
+                                    rv_discrete_frozen)
+from ._biasedurn import (_PyFishersNCHypergeometric,
+                         _PyWalleniusNCHypergeometric,
+                         _PyStochasticLib3)
+
+
+class binom_gen(rv_discrete):
+    r"""
+    A binomial discrete random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    hypergeom, nbinom, nhypergeom
+
+    Notes
+    -----
+    The probability mass function for `binom` is:
+
+    .. math::
+
+       f(k) = \binom{n}{k} p^k (1-p)^{n-k}
+
+    for :math:`k \in \{0, 1, \dots, n\}`, :math:`0 \leq p \leq 1`
+
+    `binom` takes :math:`n` and :math:`p` as shape parameters,
+    where :math:`p` is the probability of a single success
+    and :math:`1-p` is the probability of a single failure.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``pmf``, ``cdf``, ``sf``, ``ppf`` and ``isf``
+    methods. [1]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("n", True, (0, np.inf), (True, False)),
+                _ShapeInfo("p", False, (0, 1), (True, True))]
+
+    def _rvs(self, n, p, size=None, random_state=None):
+        if not np.all(n == np.floor(n)):
+            raise ValueError("`n` must be integral.")
+        return random_state.binomial(np.asarray(n, dtype=int), p, size)
+
+    def _argcheck(self, n, p):
+        return (n >= 0) & _isintegral(n) & (p >= 0) & (p <= 1)
+
+    def _get_support(self, n, p):
+        return self.a, n
+
+    def _logpmf(self, x, n, p):
+        k = floor(x)
+        combiln = (gamln(n+1) - (gamln(k+1) + gamln(n-k+1)))
+        return combiln + special.xlogy(k, p) + special.xlog1py(n-k, -p)
+
+    def _pmf(self, x, n, p):
+        # binom.pmf(k) = choose(n, k) * p**k * (1-p)**(n-k)
+        return scu._binom_pmf(x, n, p)
+
+    def _cdf(self, x, n, p):
+        k = floor(x)
+        return scu._binom_cdf(k, n, p)
+
+    def _sf(self, x, n, p):
+        k = floor(x)
+        return scu._binom_sf(k, n, p)
+
+    def _isf(self, x, n, p):
+        return scu._binom_isf(x, n, p)
+
+    def _ppf(self, q, n, p):
+        return scu._binom_ppf(q, n, p)
+
+    def _stats(self, n, p, moments='mv'):
+        mu = n * p
+        var = mu - n * np.square(p)
+        g1, g2 = None, None
+        if 's' in moments:
+            pq = p - np.square(p)
+            npq_sqrt = np.sqrt(n * pq)
+            t1 = np.reciprocal(npq_sqrt)
+            t2 = (2.0 * p) / npq_sqrt
+            g1 = t1 - t2
+        if 'k' in moments:
+            pq = p - np.square(p)
+            npq = n * pq
+            t1 = np.reciprocal(npq)
+            t2 = 6.0/n
+            g2 = t1 - t2
+        return mu, var, g1, g2
+
+
+binom = binom_gen(name='binom')
+
+
+class bernoulli_gen(binom_gen):
+    r"""A Bernoulli discrete random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability mass function for `bernoulli` is:
+
+    .. math::
+
+       f(k) = \begin{cases}1-p  &\text{if } k = 0\\
+                           p    &\text{if } k = 1\end{cases}
+
+    for :math:`k` in :math:`\{0, 1\}`, :math:`0 \leq p \leq 1`
+
+    `bernoulli` takes :math:`p` as shape parameter,
+    where :math:`p` is the probability of a single success
+    and :math:`1-p` is the probability of a single failure.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("p", False, (0, 1), (True, True))]
+
+    def _rvs(self, p, size=None, random_state=None):
+        return binom_gen._rvs(self, 1, p, size=size, random_state=random_state)
+
+    def _argcheck(self, p):
+        return (p >= 0) & (p <= 1)
+
+    def _get_support(self, p):
+        # Overrides binom_gen._get_support!x
+        return self.a, self.b
+
+    def _logpmf(self, x, p):
+        return binom._logpmf(x, 1, p)
+
+    def _pmf(self, x, p):
+        # bernoulli.pmf(k) = 1-p  if k = 0
+        #                  = p    if k = 1
+        return binom._pmf(x, 1, p)
+
+    def _cdf(self, x, p):
+        return binom._cdf(x, 1, p)
+
+    def _sf(self, x, p):
+        return binom._sf(x, 1, p)
+
+    def _isf(self, x, p):
+        return binom._isf(x, 1, p)
+
+    def _ppf(self, q, p):
+        return binom._ppf(q, 1, p)
+
+    def _stats(self, p):
+        return binom._stats(1, p)
+
+    def _entropy(self, p):
+        return entr(p) + entr(1-p)
+
+
+bernoulli = bernoulli_gen(b=1, name='bernoulli')
+
+
+class betabinom_gen(rv_discrete):
+    r"""
+    A beta-binomial discrete random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    beta, binom
+
+    Notes
+    -----
+    The beta-binomial distribution is a binomial distribution with a
+    probability of success `p` that follows a beta distribution.
+
+    The probability mass function for `betabinom` is:
+
+    .. math::
+
+       f(k) = \binom{n}{k} \frac{B(k + a, n - k + b)}{B(a, b)}
+
+    for :math:`k \in \{0, 1, \dots, n\}`, :math:`n \geq 0`, :math:`a > 0`,
+    :math:`b > 0`, where :math:`B(a, b)` is the beta function.
+
+    `betabinom` takes :math:`n`, :math:`a`, and :math:`b` as shape parameters.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Beta-binomial_distribution
+
+    .. versionadded:: 1.4.0
+
+    %(example)s
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("n", True, (0, np.inf), (True, False)),
+                _ShapeInfo("a", False, (0, np.inf), (False, False)),
+                _ShapeInfo("b", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, n, a, b, size=None, random_state=None):
+        p = random_state.beta(a, b, size)
+        if not np.all(n == np.floor(n)):
+            raise ValueError("`n` must be integral.")
+        return random_state.binomial(np.asarray(n, dtype=int), p, size)
+
+    def _get_support(self, n, a, b):
+        return 0, n
+
+    def _argcheck(self, n, a, b):
+        return (n >= 0) & _isintegral(n) & (a > 0) & (b > 0)
+
+    def _logpmf(self, x, n, a, b):
+        k = floor(x)
+        combiln = -log(n + 1) - betaln(n - k + 1, k + 1)
+        return combiln + betaln(k + a, n - k + b) - betaln(a, b)
+
+    def _pmf(self, x, n, a, b):
+        return exp(self._logpmf(x, n, a, b))
+
+    def _stats(self, n, a, b, moments='mv'):
+        e_p = a / (a + b)
+        e_q = 1 - e_p
+        mu = n * e_p
+        var = n * (a + b + n) * e_p * e_q / (a + b + 1)
+        g1, g2 = None, None
+        if 's' in moments:
+            g1 = 1.0 / sqrt(var)
+            g1 *= (a + b + 2 * n) * (b - a)
+            g1 /= (a + b + 2) * (a + b)
+        if 'k' in moments:
+            g2 = (a + b).astype(e_p.dtype)
+            g2 *= (a + b - 1 + 6 * n)
+            g2 += 3 * a * b * (n - 2)
+            g2 += 6 * n ** 2
+            g2 -= 3 * e_p * b * n * (6 - n)
+            g2 -= 18 * e_p * e_q * n ** 2
+            g2 *= (a + b) ** 2 * (1 + a + b)
+            g2 /= (n * a * b * (a + b + 2) * (a + b + 3) * (a + b + n))
+            g2 -= 3
+        return mu, var, g1, g2
+
+
+betabinom = betabinom_gen(name='betabinom')
+
+
+class nbinom_gen(rv_discrete):
+    r"""
+    A negative binomial discrete random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    hypergeom, binom, nhypergeom
+
+    Notes
+    -----
+    Negative binomial distribution describes a sequence of i.i.d. Bernoulli
+    trials, repeated until a predefined, non-random number of successes occurs.
+
+    The probability mass function of the number of failures for `nbinom` is:
+
+    .. math::
+
+       f(k) = \binom{k+n-1}{n-1} p^n (1-p)^k
+
+    for :math:`k \ge 0`, :math:`0 < p \leq 1`
+
+    `nbinom` takes :math:`n` and :math:`p` as shape parameters where :math:`n`
+    is the number of successes, :math:`p` is the probability of a single
+    success, and :math:`1-p` is the probability of a single failure.
+
+    Another common parameterization of the negative binomial distribution is
+    in terms of the mean number of failures :math:`\mu` to achieve :math:`n`
+    successes. The mean :math:`\mu` is related to the probability of success
+    as
+
+    .. math::
+
+       p = \frac{n}{n + \mu}
+
+    The number of successes :math:`n` may also be specified in terms of a
+    "dispersion", "heterogeneity", or "aggregation" parameter :math:`\alpha`,
+    which relates the mean :math:`\mu` to the variance :math:`\sigma^2`,
+    e.g. :math:`\sigma^2 = \mu + \alpha \mu^2`. Regardless of the convention
+    used for :math:`\alpha`,
+
+    .. math::
+
+       p &= \frac{\mu}{\sigma^2} \\
+       n &= \frac{\mu^2}{\sigma^2 - \mu}
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``pmf``, ``cdf``, ``sf``, ``ppf``, ``isf``
+    and ``stats`` methods. [1]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    %(example)s
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("n", True, (0, np.inf), (True, False)),
+                _ShapeInfo("p", False, (0, 1), (True, True))]
+
+    def _rvs(self, n, p, size=None, random_state=None):
+        return random_state.negative_binomial(n, p, size)
+
+    def _argcheck(self, n, p):
+        return (n > 0) & (p > 0) & (p <= 1)
+
+    def _pmf(self, x, n, p):
+        # nbinom.pmf(k) = choose(k+n-1, n-1) * p**n * (1-p)**k
+        return scu._nbinom_pmf(x, n, p)
+
+    def _logpmf(self, x, n, p):
+        coeff = gamln(n+x) - gamln(x+1) - gamln(n)
+        return coeff + n*log(p) + special.xlog1py(x, -p)
+
+    def _cdf(self, x, n, p):
+        k = floor(x)
+        return scu._nbinom_cdf(k, n, p)
+
+    def _logcdf(self, x, n, p):
+        k = floor(x)
+        k, n, p = np.broadcast_arrays(k, n, p)
+        cdf = self._cdf(k, n, p)
+        cond = cdf > 0.5
+        def f1(k, n, p):
+            return np.log1p(-special.betainc(k + 1, n, 1 - p))
+
+        # do calc in place
+        logcdf = cdf
+        with np.errstate(divide='ignore'):
+            logcdf[cond] = f1(k[cond], n[cond], p[cond])
+            logcdf[~cond] = np.log(cdf[~cond])
+        return logcdf
+
+    def _sf(self, x, n, p):
+        k = floor(x)
+        return scu._nbinom_sf(k, n, p)
+
+    def _isf(self, x, n, p):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return scu._nbinom_isf(x, n, p)
+
+    def _ppf(self, q, n, p):
+        with np.errstate(over='ignore'):  # see gh-17432
+            return scu._nbinom_ppf(q, n, p)
+
+    def _stats(self, n, p):
+        return (
+            scu._nbinom_mean(n, p),
+            scu._nbinom_variance(n, p),
+            scu._nbinom_skewness(n, p),
+            scu._nbinom_kurtosis_excess(n, p),
+        )
+
+
+nbinom = nbinom_gen(name='nbinom')
+
+
+class betanbinom_gen(rv_discrete):
+    r"""
+    A beta-negative-binomial discrete random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    betabinom : Beta binomial distribution
+
+    Notes
+    -----
+    The beta-negative-binomial distribution is a negative binomial
+    distribution with a probability of success `p` that follows a
+    beta distribution.
+
+    The probability mass function for `betanbinom` is:
+
+    .. math::
+
+       f(k) = \binom{n + k - 1}{k} \frac{B(a + n, b + k)}{B(a, b)}
+
+    for :math:`k \ge 0`, :math:`n \geq 0`, :math:`a > 0`,
+    :math:`b > 0`, where :math:`B(a, b)` is the beta function.
+
+    `betanbinom` takes :math:`n`, :math:`a`, and :math:`b` as shape parameters.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Beta_negative_binomial_distribution
+
+    .. versionadded:: 1.12.0
+
+    %(example)s
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("n", True, (0, np.inf), (True, False)),
+                _ShapeInfo("a", False, (0, np.inf), (False, False)),
+                _ShapeInfo("b", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, n, a, b, size=None, random_state=None):
+        p = random_state.beta(a, b, size)
+        return random_state.negative_binomial(n, p, size)
+
+    def _argcheck(self, n, a, b):
+        return (n >= 0) & _isintegral(n) & (a > 0) & (b > 0)
+
+    def _logpmf(self, x, n, a, b):
+        k = floor(x)
+        combiln = -np.log(n + k) - betaln(n, k + 1)
+        return combiln + betaln(a + n, b + k) - betaln(a, b)
+
+    def _pmf(self, x, n, a, b):
+        return exp(self._logpmf(x, n, a, b))
+
+    def _stats(self, n, a, b, moments='mv'):
+        # reference: Wolfram Alpha input
+        # BetaNegativeBinomialDistribution[a, b, n]
+        def mean(n, a, b):
+            return n * b / (a - 1.)
+        mu = xpx.apply_where(a > 1, (n, a, b), mean, fill_value=np.inf)
+        def var(n, a, b):
+            return (n * b * (n + a - 1.) * (a + b - 1.)
+                    / ((a - 2.) * (a - 1.)**2.))
+        var = xpx.apply_where(a > 2, (n, a, b), var, fill_value=np.inf)
+        g1, g2 = None, None
+        def skew(n, a, b):
+            return ((2 * n + a - 1.) * (2 * b + a - 1.)
+                    / (a - 3.) / sqrt(n * b * (n + a - 1.) * (b + a - 1.)
+                    / (a - 2.)))
+        if 's' in moments:
+            g1 = xpx.apply_where(a > 3, (n, a, b), skew, fill_value=np.inf)
+        def kurtosis(n, a, b):
+            term = (a - 2.)
+            term_2 = ((a - 1.)**2. * (a**2. + a * (6 * b - 1.)
+                      + 6. * (b - 1.) * b)
+                      + 3. * n**2. * ((a + 5.) * b**2. + (a + 5.)
+                      * (a - 1.) * b + 2. * (a - 1.)**2)
+                      + 3 * (a - 1.) * n
+                      * ((a + 5.) * b**2. + (a + 5.) * (a - 1.) * b
+                      + 2. * (a - 1.)**2.))
+            denominator = ((a - 4.) * (a - 3.) * b * n
+                           * (a + b - 1.) * (a + n - 1.))
+            # Wolfram Alpha uses Pearson kurtosis, so we subtract 3 to get
+            # scipy's Fisher kurtosis
+            return term * term_2 / denominator - 3.
+        if 'k' in moments:
+            g2 = xpx.apply_where(a > 4, (n, a, b), kurtosis, fill_value=np.inf)
+        return mu, var, g1, g2
+
+
+betanbinom = betanbinom_gen(name='betanbinom')
+
+
+class geom_gen(rv_discrete):
+    r"""A geometric discrete random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    planck
+
+    Notes
+    -----
+    The probability mass function for `geom` is:
+
+    .. math::
+
+        f(k) = (1-p)^{k-1} p
+
+    for :math:`k \ge 1`, :math:`0 < p \leq 1`
+
+    `geom` takes :math:`p` as shape parameter,
+    where :math:`p` is the probability of a single success
+    and :math:`1-p` is the probability of a single failure.
+
+    Note that when drawing random samples, the probability of observations that exceed
+    ``np.iinfo(np.int64).max`` increases rapidly as $p$ decreases below $10^{-17}$. For
+    $p < 10^{-20}$, almost all observations would exceed the maximum ``int64``; however,
+    the output dtype is always ``int64``, so these values are clipped to the maximum.
+
+    %(after_notes)s
+
+    %(example)s
+    """
+
+    def _shape_info(self):
+        return [_ShapeInfo("p", False, (0, 1), (True, True))]
+
+    def _rvs(self, p, size=None, random_state=None):
+        res = random_state.geometric(p, size=size)
+        # RandomState.geometric can wrap around to negative values; make behavior
+        # consistent with Generator.geometric by replacing with maximum integer.
+        max_int = np.iinfo(res.dtype).max
+        return np.where(res < 0, max_int, res)
+
+    def _argcheck(self, p):
+        return (p <= 1) & (p > 0)
+
+    def _pmf(self, k, p):
+        return np.power(1-p, k-1) * p
+
+    def _logpmf(self, k, p):
+        return special.xlog1py(k - 1, -p) + log(p)
+
+    def _cdf(self, x, p):
+        k = floor(x)
+        return -expm1(log1p(-p)*k)
+
+    def _sf(self, x, p):
+        return np.exp(self._logsf(x, p))
+
+    def _logsf(self, x, p):
+        k = floor(x)
+        return k*log1p(-p)
+
+    def _ppf(self, q, p):
+        vals = ceil(log1p(-q) / log1p(-p))
+        temp = self._cdf(vals-1, p)
+        return np.where((temp >= q) & (vals > 0), vals-1, vals)
+
+    def _stats(self, p):
+        mu = 1.0/p
+        qr = 1.0-p
+        var = qr / p / p
+        g1 = (2.0-p) / sqrt(qr)
+        g2 = np.polyval([1, -6, 6], p)/(1.0-p)
+        return mu, var, g1, g2
+
+    def _entropy(self, p):
+        return -np.log(p) - np.log1p(-p) * (1.0-p) / p
+
+
+geom = geom_gen(a=1, name='geom', longname="A geometric")
+
+
+class hypergeom_gen(rv_discrete):
+    r"""A hypergeometric discrete random variable.
+
+    The hypergeometric distribution models drawing objects from a bin.
+    `M` is the total number of objects, `n` is total number of Type I objects.
+    The random variate represents the number of Type I objects in `N` drawn
+    without replacement from the total population.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    nhypergeom, binom, nbinom
+
+    Notes
+    -----
+    The symbols used to denote the shape parameters (`M`, `n`, and `N`) are not
+    universally accepted.  See the Examples for a clarification of the
+    definitions used here.
+
+    The probability mass function is defined as,
+
+    .. math:: p(k, M, n, N) = \frac{\binom{n}{k} \binom{M - n}{N - k}}
+                                   {\binom{M}{N}}
+
+    for :math:`k \in [\max(0, N - M + n), \min(n, N)]`, where the binomial
+    coefficients are defined as,
+
+    .. math:: \binom{n}{k} \equiv \frac{n!}{k! (n - k)!}.
+
+    This distribution uses routines from the Boost Math C++ library for
+    the computation of the ``pmf``, ``cdf``, ``sf`` and ``stats`` methods. [1]_
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] The Boost Developers. "Boost C++ Libraries". https://www.boost.org/.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import hypergeom
+    >>> import matplotlib.pyplot as plt
+
+    Suppose we have a collection of 20 animals, of which 7 are dogs.  Then if
+    we want to know the probability of finding a given number of dogs if we
+    choose at random 12 of the 20 animals, we can initialize a frozen
+    distribution and plot the probability mass function:
+
+    >>> [M, n, N] = [20, 7, 12]
+    >>> rv = hypergeom(M, n, N)
+    >>> x = np.arange(0, n+1)
+    >>> pmf_dogs = rv.pmf(x)
+
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111)
+    >>> ax.plot(x, pmf_dogs, 'bo')
+    >>> ax.vlines(x, 0, pmf_dogs, lw=2)
+    >>> ax.set_xlabel('# of dogs in our group of chosen animals')
+    >>> ax.set_ylabel('hypergeom PMF')
+    >>> plt.show()
+
+    Instead of using a frozen distribution we can also use `hypergeom`
+    methods directly.  To for example obtain the cumulative distribution
+    function, use:
+
+    >>> prb = hypergeom.cdf(x, M, n, N)
+
+    And to generate random numbers:
+
+    >>> R = hypergeom.rvs(M, n, N, size=10)
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("M", True, (0, np.inf), (True, False)),
+                _ShapeInfo("n", True, (0, np.inf), (True, False)),
+                _ShapeInfo("N", True, (0, np.inf), (True, False))]
+
+    def _rvs(self, M, n, N, size=None, random_state=None):
+        return random_state.hypergeometric(n, M-n, N, size=size)
+
+    def _get_support(self, M, n, N):
+        return np.maximum(N-(M-n), 0), np.minimum(n, N)
+
+    def _argcheck(self, M, n, N):
+        cond = (M > 0) & (n >= 0) & (N >= 0)
+        cond &= (n <= M) & (N <= M)
+        cond &= _isintegral(M) & _isintegral(n) & _isintegral(N)
+        return cond
+
+    def _logpmf(self, k, M, n, N):
+        tot, good = M, n
+        bad = tot - good
+        result = (betaln(good+1, 1) + betaln(bad+1, 1) + betaln(tot-N+1, N+1) -
+                  betaln(k+1, good-k+1) - betaln(N-k+1, bad-N+k+1) -
+                  betaln(tot+1, 1))
+        return result
+
+    def _pmf(self, k, M, n, N):
+        return scu._hypergeom_pmf(k, n, N, M)
+
+    def _cdf(self, k, M, n, N):
+        return scu._hypergeom_cdf(k, n, N, M)
+
+    def _stats(self, M, n, N):
+        M, n, N = 1. * M, 1. * n, 1. * N
+        m = M - n
+
+        # Boost kurtosis_excess doesn't return the same as the value
+        # computed here.
+        g2 = M * (M + 1) - 6. * N * (M - N) - 6. * n * m
+        g2 *= (M - 1) * M * M
+        g2 += 6. * n * N * (M - N) * m * (5. * M - 6)
+        g2 /= n * N * (M - N) * m * (M - 2.) * (M - 3.)
+        return (
+            scu._hypergeom_mean(n, N, M),
+            scu._hypergeom_variance(n, N, M),
+            scu._hypergeom_skewness(n, N, M),
+            g2,
+        )
+
+    def _entropy(self, M, n, N):
+        k = np.r_[N - (M - n):min(n, N) + 1]
+        vals = self.pmf(k, M, n, N)
+        return np.sum(entr(vals), axis=0)
+
+    def _sf(self, k, M, n, N):
+        return scu._hypergeom_sf(k, n, N, M)
+
+    def _logsf(self, k, M, n, N):
+        res = []
+        for quant, tot, good, draw in zip(*np.broadcast_arrays(k, M, n, N)):
+            if (quant + 0.5) * (tot + 0.5) < (good - 0.5) * (draw - 0.5):
+                # Less terms to sum if we calculate log(1-cdf)
+                res.append(log1p(-exp(self.logcdf(quant, tot, good, draw))))
+            else:
+                # Integration over probability mass function using logsumexp
+                k2 = np.arange(quant + 1, draw + 1)
+                res.append(logsumexp(self._logpmf(k2, tot, good, draw)))
+        return np.asarray(res)
+
+    def _logcdf(self, k, M, n, N):
+        res = []
+        for quant, tot, good, draw in zip(*np.broadcast_arrays(k, M, n, N)):
+            if (quant + 0.5) * (tot + 0.5) > (good - 0.5) * (draw - 0.5):
+                # Less terms to sum if we calculate log(1-sf)
+                res.append(log1p(-exp(self.logsf(quant, tot, good, draw))))
+            else:
+                # Integration over probability mass function using logsumexp
+                k2 = np.arange(0, quant + 1)
+                res.append(logsumexp(self._logpmf(k2, tot, good, draw)))
+        return np.asarray(res)
+
+
+hypergeom = hypergeom_gen(name='hypergeom')
+
+
+class nhypergeom_gen(rv_discrete):
+    r"""A negative hypergeometric discrete random variable.
+
+    Consider a box containing :math:`M` balls:, :math:`n` red and
+    :math:`M-n` blue. We randomly sample balls from the box, one
+    at a time and *without* replacement, until we have picked :math:`r`
+    blue balls. `nhypergeom` is the distribution of the number of
+    red balls :math:`k` we have picked.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    hypergeom, binom, nbinom
+
+    Notes
+    -----
+    The symbols used to denote the shape parameters (`M`, `n`, and `r`) are not
+    universally accepted. See the Examples for a clarification of the
+    definitions used here.
+
+    The probability mass function is defined as,
+
+    .. math:: f(k; M, n, r) = \frac{{{k+r-1}\choose{k}}{{M-r-k}\choose{n-k}}}
+                                   {{M \choose n}}
+
+    for :math:`k \in [0, n]`, :math:`n \in [0, M]`, :math:`r \in [0, M-n]`,
+    and the binomial coefficient is:
+
+    .. math:: \binom{n}{k} \equiv \frac{n!}{k! (n - k)!}.
+
+    It is equivalent to observing :math:`k` successes in :math:`k+r-1`
+    samples with :math:`k+r`'th sample being a failure. The former
+    can be modelled as a hypergeometric distribution. The probability
+    of the latter is simply the number of failures remaining
+    :math:`M-n-(r-1)` divided by the size of the remaining population
+    :math:`M-(k+r-1)`. This relationship can be shown as:
+
+    .. math:: NHG(k;M,n,r) = HG(k;M,n,k+r-1)\frac{(M-n-(r-1))}{(M-(k+r-1))}
+
+    where :math:`NHG` is probability mass function (PMF) of the
+    negative hypergeometric distribution and :math:`HG` is the
+    PMF of the hypergeometric distribution.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Negative Hypergeometric Distribution on Wikipedia
+           https://en.wikipedia.org/wiki/Negative_hypergeometric_distribution
+
+    .. [2] Negative Hypergeometric Distribution from
+           http://www.math.wm.edu/~leemis/chart/UDR/PDFs/Negativehypergeometric.pdf
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import nhypergeom
+    >>> import matplotlib.pyplot as plt
+
+    Suppose we have a collection of 20 animals, of which 7 are dogs.
+    Then if we want to know the probability of finding a given number
+    of dogs (successes) in a sample with exactly 12 animals that
+    aren't dogs (failures), we can initialize a frozen distribution
+    and plot the probability mass function:
+
+    >>> M, n, r = [20, 7, 12]
+    >>> rv = nhypergeom(M, n, r)
+    >>> x = np.arange(0, n+2)
+    >>> pmf_dogs = rv.pmf(x)
+
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111)
+    >>> ax.plot(x, pmf_dogs, 'bo')
+    >>> ax.vlines(x, 0, pmf_dogs, lw=2)
+    >>> ax.set_xlabel('# of dogs in our group with given 12 failures')
+    >>> ax.set_ylabel('nhypergeom PMF')
+    >>> plt.show()
+
+    Instead of using a frozen distribution we can also use `nhypergeom`
+    methods directly.  To for example obtain the probability mass
+    function, use:
+
+    >>> prb = nhypergeom.pmf(x, M, n, r)
+
+    And to generate random numbers:
+
+    >>> R = nhypergeom.rvs(M, n, r, size=10)
+
+    To verify the relationship between `hypergeom` and `nhypergeom`, use:
+
+    >>> from scipy.stats import hypergeom, nhypergeom
+    >>> M, n, r = 45, 13, 8
+    >>> k = 6
+    >>> nhypergeom.pmf(k, M, n, r)
+    0.06180776620271643
+    >>> hypergeom.pmf(k, M, n, k+r-1) * (M - n - (r-1)) / (M - (k+r-1))
+    0.06180776620271644
+    """
+
+    def _shape_info(self):
+        return [_ShapeInfo("M", True, (0, np.inf), (True, False)),
+                _ShapeInfo("n", True, (0, np.inf), (True, False)),
+                _ShapeInfo("r", True, (0, np.inf), (True, False))]
+
+    def _get_support(self, M, n, r):
+        return 0, n
+
+    def _argcheck(self, M, n, r):
+        cond = (n >= 0) & (n <= M) & (r >= 0) & (r <= M-n)
+        cond &= _isintegral(M) & _isintegral(n) & _isintegral(r)
+        return cond
+
+    def _rvs(self, M, n, r, size=None, random_state=None):
+
+        @_vectorize_rvs_over_shapes
+        def _rvs1(M, n, r, size, random_state):
+            # invert cdf by calculating all values in support, scalar M, n, r
+            a, b = self.support(M, n, r)
+            ks = np.arange(a, b+1)
+            cdf = self.cdf(ks, M, n, r)
+            ppf = interp1d(cdf, ks, kind='next', fill_value='extrapolate')
+            rvs = ppf(random_state.uniform(size=size)).astype(int)
+            if size is None:
+                return rvs.item()
+            return rvs
+
+        return _rvs1(M, n, r, size=size, random_state=random_state)
+
+    def _logpmf(self, k, M, n, r):
+        return xpx.apply_where(
+            (r != 0) | (k != 0), (k, M, n, r),
+            lambda k, M, n, r:
+                (-betaln(k+1, r) + betaln(k+r, 1)
+                 - betaln(n-k+1, M-r-n+1) + betaln(M-r-k+1, 1)
+                 + betaln(n+1, M-n+1) - betaln(M+1, 1)),
+            fill_value=0.0)
+
+    def _pmf(self, k, M, n, r):
+        # same as the following but numerically more precise
+        # return comb(k+r-1, k) * comb(M-r-k, n-k) / comb(M, n)
+        return exp(self._logpmf(k, M, n, r))
+
+    def _stats(self, M, n, r):
+        # Promote the datatype to at least float
+        # mu = rn / (M-n+1)
+        M, n, r = 1.*M, 1.*n, 1.*r
+        mu = r*n / (M-n+1)
+
+        var = r*(M+1)*n / ((M-n+1)*(M-n+2)) * (1 - r / (M-n+1))
+
+        # The skew and kurtosis are mathematically
+        # intractable so return `None`. See [2]_.
+        g1, g2 = None, None
+        return mu, var, g1, g2
+
+
+nhypergeom = nhypergeom_gen(name='nhypergeom')
+
+
+# FIXME: Fails _cdfvec
+class logser_gen(rv_discrete):
+    r"""A Logarithmic (Log-Series, Series) discrete random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability mass function for `logser` is:
+
+    .. math::
+
+        f(k) = - \frac{p^k}{k \log(1-p)}
+
+    for :math:`k \ge 1`, :math:`0 < p < 1`
+
+    `logser` takes :math:`p` as shape parameter,
+    where :math:`p` is the probability of a single success
+    and :math:`1-p` is the probability of a single failure.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+
+    def _shape_info(self):
+        return [_ShapeInfo("p", False, (0, 1), (True, True))]
+
+    def _rvs(self, p, size=None, random_state=None):
+        # looks wrong for p>0.5, too few k=1
+        # trying to use generic is worse, no k=1 at all
+        return random_state.logseries(p, size=size)
+
+    def _argcheck(self, p):
+        return (p > 0) & (p < 1)
+
+    def _pmf(self, k, p):
+        # logser.pmf(k) = - p**k / (k*log(1-p))
+        return -np.power(p, k) * 1.0 / k / special.log1p(-p)
+
+    def _sf(self, k, p):
+        tiny = 1e-100
+        # Ideally, this is the unregularized beta function with `b=0`. We don't have
+        # an unregularized beta function (yet, although we could get it from Boost),
+        # and neither technically support `b=0` - despite the function being accurate
+        # for `b` super close to zero. See https://github.com/scipy/scipy/issues/3890.
+        return -special.betainc(k+1, tiny, p) * special.beta(k+1, tiny) / np.log1p(-p)
+
+    def _stats(self, p):
+        r = special.log1p(-p)
+        mu = p / (p - 1.0) / r
+        mu2p = -p / r / (p - 1.0)**2
+        var = mu2p - mu*mu
+        mu3p = -p / r * (1.0+p) / (1.0 - p)**3
+        mu3 = mu3p - 3*mu*mu2p + 2*mu**3
+        g1 = mu3 / np.power(var, 1.5)
+
+        mu4p = -p / r * (
+            1.0 / (p-1)**2 - 6*p / (p - 1)**3 + 6*p*p / (p-1)**4)
+        mu4 = mu4p - 4*mu3p*mu + 6*mu2p*mu*mu - 3*mu**4
+        g2 = mu4 / var**2 - 3.0
+        return mu, var, g1, g2
+
+
+logser = logser_gen(a=1, name='logser', longname='A logarithmic')
+
+
+class poisson_gen(rv_discrete):
+    r"""A Poisson discrete random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability mass function for `poisson` is:
+
+    .. math::
+
+        f(k) = \exp(-\mu) \frac{\mu^k}{k!}
+
+    for :math:`k \ge 0`.
+
+    `poisson` takes :math:`\mu \geq 0` as shape parameter.
+    When :math:`\mu = 0`, the ``pmf`` method
+    returns ``1.0`` at quantile :math:`k = 0`.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+
+    def _shape_info(self):
+        return [_ShapeInfo("mu", False, (0, np.inf), (True, False))]
+
+    # Override rv_discrete._argcheck to allow mu=0.
+    def _argcheck(self, mu):
+        return mu >= 0
+
+    def _rvs(self, mu, size=None, random_state=None):
+        return random_state.poisson(mu, size)
+
+    def _logpmf(self, k, mu):
+        Pk = special.xlogy(k, mu) - gamln(k + 1) - mu
+        return Pk
+
+    def _pmf(self, k, mu):
+        # poisson.pmf(k) = exp(-mu) * mu**k / k!
+        return exp(self._logpmf(k, mu))
+
+    def _cdf(self, x, mu):
+        k = floor(x)
+        return special.pdtr(k, mu)
+
+    def _sf(self, x, mu):
+        k = floor(x)
+        return special.pdtrc(k, mu)
+
+    def _ppf(self, q, mu):
+        vals = ceil(special.pdtrik(q, mu))
+        vals1 = np.maximum(vals - 1, 0)
+        temp = special.pdtr(vals1, mu)
+        return np.where(temp >= q, vals1, vals)
+
+    def _stats(self, mu):
+        var = mu
+        tmp = np.asarray(mu)
+        mu_nonzero = tmp > 0
+        g1 = xpx.apply_where(mu_nonzero, tmp, lambda x: sqrt(1.0/x), fill_value=np.inf)
+        g2 = xpx.apply_where(mu_nonzero, tmp, lambda x: 1.0/x, fill_value=np.inf)
+        return mu, var, g1, g2
+
+
+poisson = poisson_gen(name="poisson", longname='A Poisson')
+
+
+class planck_gen(rv_discrete):
+    r"""A Planck discrete exponential random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    geom
+
+    Notes
+    -----
+    The probability mass function for `planck` is:
+
+    .. math::
+
+        f(k) = (1-\exp(-\lambda)) \exp(-\lambda k)
+
+    for :math:`k \ge 0` and :math:`\lambda > 0`.
+
+    `planck` takes :math:`\lambda` as shape parameter. The Planck distribution
+    can be written as a geometric distribution (`geom`) with
+    :math:`p = 1 - \exp(-\lambda)` shifted by ``loc = -1``.
+
+    %(after_notes)s
+
+    %(example)s
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("lambda_", False, (0, np.inf), (False, False))]
+
+    def _argcheck(self, lambda_):
+        return lambda_ > 0
+
+    def _pmf(self, k, lambda_):
+        return -expm1(-lambda_)*exp(-lambda_*k)
+
+    def _cdf(self, x, lambda_):
+        k = floor(x)
+        return -expm1(-lambda_*(k+1))
+
+    def _sf(self, x, lambda_):
+        return exp(self._logsf(x, lambda_))
+
+    def _logsf(self, x, lambda_):
+        k = floor(x)
+        return -lambda_*(k+1)
+
+    def _ppf(self, q, lambda_):
+        vals = ceil(-1.0/lambda_ * log1p(-q)-1)
+        vals1 = (vals-1).clip(*(self._get_support(lambda_)))
+        temp = self._cdf(vals1, lambda_)
+        return np.where(temp >= q, vals1, vals)
+
+    def _rvs(self, lambda_, size=None, random_state=None):
+        # use relation to geometric distribution for sampling
+        p = -expm1(-lambda_)
+        return random_state.geometric(p, size=size) - 1.0
+
+    def _stats(self, lambda_):
+        mu = 1/expm1(lambda_)
+        var = exp(-lambda_)/(expm1(-lambda_))**2
+        g1 = 2*cosh(lambda_/2.0)
+        g2 = 4+2*cosh(lambda_)
+        return mu, var, g1, g2
+
+    def _entropy(self, lambda_):
+        C = -expm1(-lambda_)
+        return lambda_*exp(-lambda_)/C - log(C)
+
+
+planck = planck_gen(a=0, name='planck', longname='A discrete exponential ')
+
+
+class boltzmann_gen(rv_discrete):
+    r"""A Boltzmann (Truncated Discrete Exponential) random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability mass function for `boltzmann` is:
+
+    .. math::
+
+        f(k) = (1-\exp(-\lambda)) \exp(-\lambda k) / (1-\exp(-\lambda N))
+
+    for :math:`k = 0,..., N-1`.
+
+    `boltzmann` takes :math:`\lambda > 0` and :math:`N > 0` as shape parameters.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("lambda_", False, (0, np.inf), (False, False)),
+                _ShapeInfo("N", True, (0, np.inf), (False, False))]
+
+    def _argcheck(self, lambda_, N):
+        return (lambda_ > 0) & (N > 0) & _isintegral(N)
+
+    def _get_support(self, lambda_, N):
+        return self.a, N - 1
+
+    def _pmf(self, k, lambda_, N):
+        # boltzmann.pmf(k) =
+        #               (1-exp(-lambda_)*exp(-lambda_*k)/(1-exp(-lambda_*N))
+        fact = (1-exp(-lambda_))/(1-exp(-lambda_*N))
+        return fact*exp(-lambda_*k)
+
+    def _cdf(self, x, lambda_, N):
+        k = floor(x)
+        return (1-exp(-lambda_*(k+1)))/(1-exp(-lambda_*N))
+
+    def _ppf(self, q, lambda_, N):
+        qnew = q*(1-exp(-lambda_*N))
+        vals = ceil(-1.0/lambda_ * log(1-qnew)-1)
+        vals1 = (vals-1).clip(0.0, np.inf)
+        temp = self._cdf(vals1, lambda_, N)
+        return np.where(temp >= q, vals1, vals)
+
+    def _stats(self, lambda_, N):
+        z = exp(-lambda_)
+        zN = exp(-lambda_*N)
+        mu = z/(1.0-z)-N*zN/(1-zN)
+        var = z/(1.0-z)**2 - N*N*zN/(1-zN)**2
+        trm = (1-zN)/(1-z)
+        trm2 = (z*trm**2 - N*N*zN)
+        g1 = z*(1+z)*trm**3 - N**3*zN*(1+zN)
+        g1 = g1 / trm2**(1.5)
+        g2 = z*(1+4*z+z*z)*trm**4 - N**4 * zN*(1+4*zN+zN*zN)
+        g2 = g2 / trm2 / trm2
+        return mu, var, g1, g2
+
+
+boltzmann = boltzmann_gen(name='boltzmann', a=0,
+                          longname='A truncated discrete exponential ')
+
+
+class randint_gen(rv_discrete):
+    r"""A uniform discrete random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability mass function for `randint` is:
+
+    .. math::
+
+        f(k) = \frac{1}{\texttt{high} - \texttt{low}}
+
+    for :math:`k \in \{\texttt{low}, \dots, \texttt{high} - 1\}`.
+
+    `randint` takes :math:`\texttt{low}` and :math:`\texttt{high}` as shape
+    parameters.
+
+    %(after_notes)s
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import randint
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+
+    Calculate the first four moments:
+
+    >>> low, high = 7, 31
+    >>> mean, var, skew, kurt = randint.stats(low, high, moments='mvsk')
+
+    Display the probability mass function (``pmf``):
+
+    >>> x = np.arange(low - 5, high + 5)
+    >>> ax.plot(x, randint.pmf(x, low, high), 'bo', ms=8, label='randint pmf')
+    >>> ax.vlines(x, 0, randint.pmf(x, low, high), colors='b', lw=5, alpha=0.5)
+
+    Alternatively, the distribution object can be called (as a function) to
+    fix the shape and location. This returns a "frozen" RV object holding the
+    given parameters fixed.
+
+    Freeze the distribution and display the frozen ``pmf``:
+
+    >>> rv = randint(low, high)
+    >>> ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-',
+    ...           lw=1, label='frozen pmf')
+    >>> ax.legend(loc='lower center')
+    >>> plt.show()
+
+    Check the relationship between the cumulative distribution function
+    (``cdf``) and its inverse, the percent point function (``ppf``):
+
+    >>> q = np.arange(low, high)
+    >>> p = randint.cdf(q, low, high)
+    >>> np.allclose(q, randint.ppf(p, low, high))
+    True
+
+    Generate random numbers:
+
+    >>> r = randint.rvs(low, high, size=1000)
+
+    """
+
+    def _shape_info(self):
+        return [_ShapeInfo("low", True, (-np.inf, np.inf), (False, False)),
+                _ShapeInfo("high", True, (-np.inf, np.inf), (False, False))]
+
+    def _argcheck(self, low, high):
+        return (high > low) & _isintegral(low) & _isintegral(high)
+
+    def _get_support(self, low, high):
+        return low, high-1
+
+    def _pmf(self, k, low, high):
+        # randint.pmf(k) = 1./(high - low)
+        p = np.ones_like(k) / (np.asarray(high, dtype=np.int64) - low)
+        return np.where((k >= low) & (k < high), p, 0.)
+
+    def _cdf(self, x, low, high):
+        k = floor(x)
+        return (k - low + 1.) / (high - low)
+
+    def _ppf(self, q, low, high):
+        vals = ceil(q * (high - low) + low) - 1
+        vals1 = (vals - 1).clip(low, high)
+        temp = self._cdf(vals1, low, high)
+        return np.where(temp >= q, vals1, vals)
+
+    def _stats(self, low, high):
+        m2, m1 = np.asarray(high), np.asarray(low)
+        mu = (m2 + m1 - 1.0) / 2
+        d = m2 - m1
+        var = (d*d - 1) / 12.0
+        g1 = 0.0
+        g2 = -6.0/5.0 * (d*d + 1.0) / (d*d - 1.0)
+        return mu, var, g1, g2
+
+    def _rvs(self, low, high, size=None, random_state=None):
+        """An array of *size* random integers >= ``low`` and < ``high``."""
+        if np.asarray(low).size == 1 and np.asarray(high).size == 1:
+            # no need to vectorize in that case
+            return rng_integers(random_state, low, high, size=size)
+
+        if size is not None:
+            # NumPy's RandomState.randint() doesn't broadcast its arguments.
+            # Use `broadcast_to()` to extend the shapes of low and high
+            # up to size.  Then we can use the numpy.vectorize'd
+            # randint without needing to pass it a `size` argument.
+            low = np.broadcast_to(low, size)
+            high = np.broadcast_to(high, size)
+        randint = np.vectorize(partial(rng_integers, random_state),
+                               otypes=[np.dtype(int)])
+        return randint(low, high)
+
+    def _entropy(self, low, high):
+        return log(high - low)
+
+
+randint = randint_gen(name='randint', longname='A discrete uniform '
+                      '(random integer)')
+
+
+# FIXME: problems sampling.
+class zipf_gen(rv_discrete):
+    r"""A Zipf (Zeta) discrete random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    zipfian
+
+    Notes
+    -----
+    The probability mass function for `zipf` is:
+
+    .. math::
+
+        f(k, a) = \frac{1}{\zeta(a) k^a}
+
+    for :math:`k \ge 1`, :math:`a > 1`.
+
+    `zipf` takes :math:`a > 1` as shape parameter. :math:`\zeta` is the
+    Riemann zeta function (`scipy.special.zeta`)
+
+    The Zipf distribution is also known as the zeta distribution, which is
+    a special case of the Zipfian distribution (`zipfian`).
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] "Zeta Distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Zeta_distribution
+
+    %(example)s
+
+    Confirm that `zipf` is the large `n` limit of `zipfian`.
+
+    >>> import numpy as np
+    >>> from scipy.stats import zipf, zipfian
+    >>> k = np.arange(11)
+    >>> np.allclose(zipf.pmf(k, a), zipfian.pmf(k, a, n=10000000))
+    True
+
+    """
+
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (1, np.inf), (False, False))]
+
+    def _rvs(self, a, size=None, random_state=None):
+        return random_state.zipf(a, size=size)
+
+    def _argcheck(self, a):
+        return a > 1
+
+    def _pmf(self, k, a):
+        k = k.astype(np.float64)
+        # zipf.pmf(k, a) = 1/(zeta(a) * k**a)
+        Pk = 1.0 / special.zeta(a, 1) * k**-a
+        return Pk
+
+    def _munp(self, n, a):
+        return xpx.apply_where(
+            a > n + 1, (a, n),
+            lambda a, n: special.zeta(a - n, 1) / special.zeta(a, 1),
+            fill_value=np.inf)
+
+
+zipf = zipf_gen(a=1, name='zipf', longname='A Zipf')
+
+
+class zipfian_gen(rv_discrete):
+    r"""A Zipfian discrete random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    zipf
+
+    Notes
+    -----
+    The probability mass function for `zipfian` is:
+
+    .. math::
+
+        f(k, a, n) = \frac{1}{H_{n,a} k^a}
+
+    for :math:`k \in \{1, 2, \dots, n-1, n\}`, :math:`a \ge 0`,
+    :math:`n \in \{1, 2, 3, \dots\}`.
+
+    `zipfian` takes :math:`a` and :math:`n` as shape parameters.
+    :math:`H_{n,a}` is the :math:`n`:sup:`th` generalized harmonic
+    number of order :math:`a`.
+
+    The SciPy implementation of this distribution requires :math:`1 \le n \le 2^{53}`.
+    For larger values of :math:`n`, the `zipfian` methods (`pmf`, `cdf`, `mean`, etc.)
+    will return `nan`.
+
+    When :math:`a > 1`, the Zipfian distribution reduces to the Zipf (zeta)
+    distribution as :math:`n \rightarrow \infty`.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] "Zipf's Law", Wikipedia, https://en.wikipedia.org/wiki/Zipf's_law
+    .. [2] Larry Leemis, "Zipf Distribution", Univariate Distribution
+           Relationships. http://www.math.wm.edu/~leemis/chart/UDR/PDFs/Zipf.pdf
+
+    %(example)s
+
+    Confirm that `zipfian` reduces to `zipf` for large `n`, ``a > 1``.
+
+    >>> import numpy as np
+    >>> from scipy.stats import zipf, zipfian
+    >>> k = np.arange(11)
+    >>> np.allclose(zipfian.pmf(k, a=3.5, n=10000000), zipf.pmf(k, a=3.5))
+    True
+
+    """
+
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (True, False)),
+                _ShapeInfo("n", True, (0, np.inf), (False, False))]
+
+    def _argcheck(self, a, n):
+        # The upper bound on n is for practical numerical reasons.  The numerical
+        # methods for computing the PMF, CDF and SF involve sums over range(1, n+1)
+        # when a <= 1, so there is no way they can be computed for extremely large
+        # n--even 2**53 is ridiculously large for those calculations.  The bound is
+        # also required to ensure that the loops compute the sums correctly when
+        # the inputs are double precision instead of integers.
+        #
+        # n may be an integer or a float, but the value must be an integer in the
+        # range 1 <= n <= 2**53.  The expression below clips n to the accepted range
+        # before attempting to cast to integer to avoid warnings generated by an
+        # input such as n=1e100.  The extra `np.asarray()` wrapper avoids the error
+        # that arises with an input such as `n=2**100`.
+        # upper limit changed in gh-24119 because of overflow on windows, linux32 bit
+        return ((a >= 0) &
+                (n == np.asarray(np.clip(n, 1, 2**31 - 1)).astype(dtype=np.int64)))
+
+    def _get_support(self, a, n):
+        return 1, np.floor(n)
+
+    def _pmf(self, k, a, n):
+        k = np.floor(k)
+        n = np.floor(n)
+        return scu._normalized_gen_harmonic(k, k, n, a)
+
+    def _cdf(self, k, a, n):
+        k = np.floor(k)
+        n = np.floor(n)
+        return scu._normalized_gen_harmonic(1, k, n, a)
+
+    def _sf(self, k, a, n):
+        k = np.floor(k)
+        n = np.floor(n)
+        return scu._normalized_gen_harmonic(k + 1, n, n, a)
+
+    def _stats(self, a, n):
+        n = np.floor(n)
+        # see http://www.math.wm.edu/~leemis/chart/UDR/PDFs/Zipf.pdf
+        Hna = scu._gen_harmonic(n, a)
+        Hna1 = scu._gen_harmonic(n, a-1)
+        Hna2 = scu._gen_harmonic(n, a-2)
+        Hna3 = scu._gen_harmonic(n, a-3)
+        Hna4 = scu._gen_harmonic(n, a-4)
+        mu1 = Hna1/Hna
+        mu2n = (Hna2*Hna - Hna1**2)
+        mu2d = Hna**2
+        mu2 = mu2n / mu2d
+        g1 = (Hna3/Hna - 3*Hna1*Hna2/Hna**2 + 2*Hna1**3/Hna**3)/mu2**(3/2)
+        g2 = (Hna**3*Hna4 - 4*Hna**2*Hna1*Hna3 + 6*Hna*Hna1**2*Hna2
+              - 3*Hna1**4) / mu2n**2
+        g2 -= 3
+        return mu1, mu2, g1, g2
+
+
+zipfian = zipfian_gen(a=1, name='zipfian', longname='A Zipfian')
+
+
+class dlaplace_gen(rv_discrete):
+    r"""A  Laplacian discrete random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    The probability mass function for `dlaplace` is:
+
+    .. math::
+
+        f(k) = \tanh(a/2) \exp(-a |k|)
+
+    for integers :math:`k` and :math:`a > 0`.
+
+    `dlaplace` takes :math:`a` as shape parameter.
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+
+    def _shape_info(self):
+        return [_ShapeInfo("a", False, (0, np.inf), (False, False))]
+
+    def _pmf(self, k, a):
+        # dlaplace.pmf(k) = tanh(a/2) * exp(-a*abs(k))
+        return tanh(a/2.0) * exp(-a * abs(k))
+
+    def _cdf(self, x, a):
+        k = floor(x)
+
+        def f1(k, a):
+            return 1.0 - exp(-a * k) / (exp(a) + 1)
+
+        def f2(k, a):
+            return exp(a * (k + 1)) / (exp(a) + 1)
+
+        return xpx.apply_where(k >= 0, (k, a), f1, f2)
+
+    def _ppf(self, q, a):
+        const = 1 + exp(a)
+        vals = ceil(np.where(q < 1.0 / (1 + exp(-a)),
+                             log(q*const) / a - 1,
+                             -log((1-q) * const) / a))
+        vals1 = vals - 1
+        return np.where(self._cdf(vals1, a) >= q, vals1, vals)
+
+    def _stats(self, a):
+        ea = exp(a)
+        mu2 = 2.*ea/(ea-1.)**2
+        mu4 = 2.*ea*(ea**2+10.*ea+1.) / (ea-1.)**4
+        return 0., mu2, 0., mu4/mu2**2 - 3.
+
+    def _entropy(self, a):
+        return a / sinh(a) - log(tanh(a/2.0))
+
+    def _rvs(self, a, size=None, random_state=None):
+        # The discrete Laplace is equivalent to the two-sided geometric
+        # distribution with PMF:
+        #   f(k) = (1 - alpha)/(1 + alpha) * alpha^abs(k)
+        #   Reference:
+        #     https://www.sciencedirect.com/science/
+        #     article/abs/pii/S0378375804003519
+        # Furthermore, the two-sided geometric distribution is
+        # equivalent to the difference between two iid geometric
+        # distributions.
+        #   Reference (page 179):
+        #     https://pdfs.semanticscholar.org/61b3/
+        #     b99f466815808fd0d03f5d2791eea8b541a1.pdf
+        # Thus, we can leverage the following:
+        #   1) alpha = e^-a
+        #   2) probability_of_success = 1 - alpha (Bernoulli trial)
+        probOfSuccess = -np.expm1(-np.asarray(a))
+        x = random_state.geometric(probOfSuccess, size=size)
+        y = random_state.geometric(probOfSuccess, size=size)
+        return x - y
+
+
+dlaplace = dlaplace_gen(a=-np.inf,
+                        name='dlaplace', longname='A discrete Laplacian')
+
+
+class poisson_binom_gen(rv_discrete):
+    r"""A Poisson Binomial discrete random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    binom
+
+    Notes
+    -----
+    The probability mass function for `poisson_binom` is:
+
+    .. math::
+
+     f(k; p_1, p_2, ..., p_n) = \sum_{A \in F_k} \prod_{i \in A} p_i \prod_{j \in A^C} 1 - p_j
+
+    where :math:`k \in \{0, 1, \dots, n-1, n\}`, :math:`F_k` is the set of all
+    subsets of :math:`k` integers that can be selected :math:`\{0, 1, \dots, n-1, n\}`,
+    and :math:`A^C` is the complement of a set :math:`A`.
+
+    `poisson_binom` accepts a single array argument ``p`` for shape parameters
+    :math:`0 ≤ p_i ≤ 1`, where the last axis corresponds with the index :math:`i` and
+    any others are for batch dimensions. Broadcasting behaves according to the usual
+    rules except that the last axis of ``p`` is ignored. Instances of this class do
+    not support serialization/unserialization.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] "Poisson binomial distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Poisson_binomial_distribution
+    .. [2] Biscarri, William, Sihai Dave Zhao, and Robert J. Brunner. "A simple and
+           fast method for computing the Poisson binomial distribution function".
+           Computational Statistics & Data Analysis 122 (2018) 92-100.
+           :doi:`10.1016/j.csda.2018.01.007`
+
+    %(example)s
+
+    """  # noqa: E501
+
+    _parse_args_rvs: MethodType
+    _parse_args_stats: MethodType
+    _parse_args: MethodType
+
+    def _shape_info(self):
+        # message = 'Fitting is not implemented for this distribution."
+        # raise NotImplementedError(message)
+        return []
+
+    def _argcheck(self, *args):
+        p = np.stack(args, axis=0)
+        conds = (0 <= p) & (p <= 1)
+        return np.all(conds, axis=0)
+
+    def _rvs(self, *args, size=None, random_state=None):
+        # convenient to work along the last axis here to avoid interference with `size`
+        p = np.stack(args, axis=-1)
+        # Size passed by the user is the *shape of the returned array*, so it won't
+        # contain the length of the last axis of p.
+        size = (p.shape if size is None else
+                (size, 1) if np.isscalar(size) else tuple(size) + (1,))
+        size = np.broadcast_shapes(p.shape, size)
+        return bernoulli._rvs(p, size=size, random_state=random_state).sum(axis=-1)
+
+    def _get_support(self, *args):
+        return 0, len(args)
+
+    def _pmf(self, k, *args):
+        k = np.atleast_1d(k).astype(np.int64)
+        k, *args = np.broadcast_arrays(k, *args)
+        p = np.stack(args, dtype=np.float64, axis=-1)
+        return _poisson_binom_pmf(k, p)
+
+    def _cdf(self, k, *args):
+        k = np.atleast_1d(k).astype(np.int64)
+        k, *args = np.broadcast_arrays(k, *args)
+        p = np.stack(args, dtype=np.float64, axis=-1)
+        return _poisson_binom_cdf(k, p)
+
+    def _stats(self, *args, **kwds):
+        p = np.stack(args, axis=0)
+        mean = np.sum(p, axis=0)
+        var = np.sum(p * (1-p), axis=0)
+        return (mean, var, None, None)
+
+    def __call__(self, *args, **kwds):
+        return poisson_binomial_frozen(self, *args, **kwds)
+
+
+poisson_binom = poisson_binom_gen(name='poisson_binom', longname='A Poisson binomial',
+                                  shapes='p')
+
+# The _parse_args methods don't work with vector-valued shape parameters, so we rewrite
+# them. Note that `p` is accepted as an array with the index `i` of `p_i` corresponding
+# with the last axis; we return it as a tuple (p_1, p_2, ..., p_n) so that it looks
+# like `n` scalar (or arrays of scalar-valued) shape parameters to the infrastructure.
+
+def _parse_args_rvs(self, p, loc=0, size=None):
+    return tuple(np.moveaxis(p, -1, 0)), loc, 1.0, size
+
+def _parse_args_stats(self, p, loc=0, moments='mv'):
+    return tuple(np.moveaxis(p, -1, 0)), loc, 1.0, moments
+
+def _parse_args(self, p, loc=0):
+    return tuple(np.moveaxis(p, -1, 0)), loc, 1.0
+
+# The infrastructure manually binds these methods to the instance, so
+# we can only override them by manually binding them, too.
+_pb_obj, _pb_cls = poisson_binom, poisson_binom_gen  # shorter names (for PEP8)
+poisson_binom._parse_args_rvs = _parse_args_rvs.__get__(_pb_obj, _pb_cls)
+poisson_binom._parse_args_stats = _parse_args_stats.__get__(_pb_obj, _pb_cls)
+poisson_binom._parse_args = _parse_args.__get__(_pb_obj, _pb_cls)
+
+class poisson_binomial_frozen(rv_discrete_frozen):
+    # copied from rv_frozen; we just need to bind the `_parse_args` methods
+    def __init__(self, dist, *args, **kwds):                        # verbatim
+        self.args = args                                            # verbatim
+        self.kwds = kwds                                            # verbatim
+
+        # create a new instance                                     # verbatim
+        self.dist = dist.__class__(**dist._updated_ctor_param())    # verbatim
+
+        # Here is the only modification
+        self.dist._parse_args_rvs = _parse_args_rvs.__get__(_pb_obj, _pb_cls)
+        self.dist._parse_args_stats = _parse_args_stats.__get__(_pb_obj, _pb_cls)
+        self.dist._parse_args = _parse_args.__get__(_pb_obj, _pb_cls)
+
+        shapes, _, _ = self.dist._parse_args(*args, **kwds)         # verbatim
+        self.a, self.b = self.dist._get_support(*shapes)            # verbatim
+
+    def expect(self, func=None, lb=None, ub=None, conditional=False, **kwds):
+        a, loc, scale = self.dist._parse_args(*self.args, **self.kwds)
+        # Here's the modification: we pass all args (including `loc`) into the `args`
+        # parameter of `expect` so the shape only goes through `_parse_args` once.
+        return self.dist.expect(func, self.args, loc, lb, ub, conditional, **kwds)
+
+
+class skellam_gen(rv_discrete):
+    r"""A  Skellam discrete random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+    Probability distribution of the difference of two correlated or
+    uncorrelated Poisson random variables.
+
+    Let :math:`k_1` and :math:`k_2` be two Poisson-distributed r.v. with
+    expected values :math:`\lambda_1` and :math:`\lambda_2`. Then,
+    :math:`k_1 - k_2` follows a Skellam distribution with parameters
+    :math:`\mu_1 = \lambda_1 - \rho \sqrt{\lambda_1 \lambda_2}` and
+    :math:`\mu_2 = \lambda_2 - \rho \sqrt{\lambda_1 \lambda_2}`, where
+    :math:`\rho` is the correlation coefficient between :math:`k_1` and
+    :math:`k_2`. If the two Poisson-distributed r.v. are independent then
+    :math:`\rho = 0`.
+
+    Parameters :math:`\mu_1` and :math:`\mu_2` must be strictly positive.
+
+    `skellam` takes :math:`\mu_1` and :math:`\mu_2` as shape parameters.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Skellam, J. G. "The Frequency Distribution of the Difference
+           Between Two Poisson Variates Belonging to Different Populations."
+           *Journal of the Royal Statistical Society* 109, no. 3 (1946): 296-296.
+           :doi:`10.2307/2981372`
+    .. [2] "Skellam distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Skellam_distribution
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("mu1", False, (0, np.inf), (False, False)),
+                _ShapeInfo("mu2", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, mu1, mu2, size=None, random_state=None):
+        n = size
+        return (random_state.poisson(mu1, n) -
+                random_state.poisson(mu2, n))
+
+    def _pmf(self, x, mu1, mu2):
+        with np.errstate(over='ignore'):  # see gh-17432
+            px = np.where(x < 0,
+                          scu._ncx2_pdf(2*mu2, 2*(1-x), 2*mu1)*2,
+                          scu._ncx2_pdf(2*mu1, 2*(1+x), 2*mu2)*2)
+            # ncx2.pdf() returns nan's for extremely low probabilities
+        return px
+
+    def _cdf(self, x, mu1, mu2):
+        x = floor(x)
+        with np.errstate(over='ignore'):  # see gh-17432
+            px = np.where(x < 0,
+                          special.chndtr(2*mu2, -2*x, 2*mu1),
+                          scu._ncx2_sf(2*mu1, 2*(x+1), 2*mu2))
+        return px
+
+    def _stats(self, mu1, mu2):
+        mean = mu1 - mu2
+        var = mu1 + mu2
+        g1 = mean / sqrt((var)**3)
+        g2 = 1 / var
+        return mean, var, g1, g2
+
+
+skellam = skellam_gen(a=-np.inf, name="skellam", longname='A Skellam')
+
+
+class yulesimon_gen(rv_discrete):
+    r"""A Yule-Simon discrete random variable.
+
+    %(before_notes)s
+
+    Notes
+    -----
+
+    The probability mass function for the `yulesimon` is:
+
+    .. math::
+
+        f(k) =  \alpha B(k, \alpha+1)
+
+    for :math:`k=1,2,3,...`, where :math:`\alpha>0`.
+    Here :math:`B` refers to the `scipy.special.beta` function.
+
+    The sampling of random variates is based on pg 553, Section 6.3 of [1]_.
+    Our notation maps to the referenced logic via :math:`\alpha=a-1`.
+
+    For details see the wikipedia entry [2]_.
+
+    References
+    ----------
+    .. [1] Devroye, Luc. "Non-uniform Random Variate Generation",
+         (1986) Springer, New York.
+
+    .. [2] https://en.wikipedia.org/wiki/Yule-Simon_distribution
+
+    %(after_notes)s
+
+    %(example)s
+
+    """
+    def _shape_info(self):
+        return [_ShapeInfo("alpha", False, (0, np.inf), (False, False))]
+
+    def _rvs(self, alpha, size=None, random_state=None):
+        E1 = random_state.standard_exponential(size)
+        E2 = random_state.standard_exponential(size)
+        ans = ceil(-E1 / log1p(-exp(-E2 / alpha)))
+        return ans
+
+    def _pmf(self, x, alpha):
+        return alpha * special.beta(x, alpha + 1)
+
+    def _argcheck(self, alpha):
+        return (alpha > 0)
+
+    def _logpmf(self, x, alpha):
+        return log(alpha) + special.betaln(x, alpha + 1)
+
+    def _cdf(self, x, alpha):
+        return 1 - x * special.beta(x, alpha + 1)
+
+    def _sf(self, x, alpha):
+        return x * special.beta(x, alpha + 1)
+
+    def _logsf(self, x, alpha):
+        return log(x) + special.betaln(x, alpha + 1)
+
+    def _stats(self, alpha):
+        mu = np.where(alpha <= 1, np.inf, alpha / (alpha - 1))
+        mu2 = np.where(alpha > 2,
+                       alpha**2 / ((alpha - 2.0) * (alpha - 1)**2),
+                       np.inf)
+        mu2 = np.where(alpha <= 1, np.nan, mu2)
+        g1 = np.where(alpha > 3,
+                      sqrt(alpha - 2) * (alpha + 1)**2 / (alpha * (alpha - 3)),
+                      np.inf)
+        g1 = np.where(alpha <= 2, np.nan, g1)
+        g2 = np.where(alpha > 4,
+                      alpha + 3 + ((11 * alpha**3 - 49 * alpha - 22) /
+                                   (alpha * (alpha - 4) * (alpha - 3))),
+                      np.inf)
+        g2 = np.where(alpha <= 2, np.nan, g2)
+        return mu, mu2, g1, g2
+
+
+yulesimon = yulesimon_gen(name='yulesimon', a=1)
+
+
+class _nchypergeom_gen(rv_discrete):
+    r"""A noncentral hypergeometric discrete random variable.
+
+    For subclassing by nchypergeom_fisher_gen and nchypergeom_wallenius_gen.
+
+    """
+
+    rvs_name = None
+    dist = None
+
+    def _shape_info(self):
+        return [_ShapeInfo("M", True, (0, np.inf), (True, False)),
+                _ShapeInfo("n", True, (0, np.inf), (True, False)),
+                _ShapeInfo("N", True, (0, np.inf), (True, False)),
+                _ShapeInfo("odds", False, (0, np.inf), (False, False))]
+
+    def _get_support(self, M, n, N, odds):
+        N, m1, n = M, n, N  # follow Wikipedia notation
+        m2 = N - m1
+        x_min = np.maximum(0, n - m2)
+        x_max = np.minimum(n, m1)
+        return x_min, x_max
+
+    def _argcheck(self, M, n, N, odds):
+        M, n = np.asarray(M), np.asarray(n),
+        N, odds = np.asarray(N), np.asarray(odds)
+        cond1 = (~np.isnan(M)) & (M.astype(int) == M) & (M >= 0)
+        cond2 = (~np.isnan(n)) & (n.astype(int) == n) & (n >= 0)
+        cond3 = (~np.isnan(N)) & (N.astype(int) == N) & (N >= 0)
+        cond4 = odds > 0
+        cond5 = N <= M
+        cond6 = n <= M
+        return cond1 & cond2 & cond3 & cond4 & cond5 & cond6
+
+    def _rvs(self, M, n, N, odds, size=None, random_state=None):
+
+        @_vectorize_rvs_over_shapes
+        def _rvs1(M, n, N, odds, size, random_state):
+            if np.isnan(M) | np.isnan(n) | np.isnan(N):
+                return np.full(size, np.nan)
+            length = np.prod(size)
+            urn = _PyStochasticLib3()
+            rv_gen = getattr(urn, self.rvs_name)
+            rvs = rv_gen(N, n, M, odds, length, random_state)
+            rvs = rvs.reshape(size)
+            return rvs
+
+        return _rvs1(M, n, N, odds, size=size, random_state=random_state)
+
+    def _pmf(self, x, M, n, N, odds):
+
+        x, M, n, N, odds = np.broadcast_arrays(x, M, n, N, odds)
+        if x.size == 0:  # np.vectorize doesn't work with zero size input
+            return np.empty_like(x)
+
+        @np.vectorize
+        def _pmf1(x, M, n, N, odds):
+            if np.isnan(x) | np.isnan(M) | np.isnan(n) | np.isnan(N):
+                return np.nan
+            urn = self.dist(N, n, M, odds, 1e-12)
+            return urn.probability(x)
+
+        return _pmf1(x, M, n, N, odds)
+
+    def _stats(self, M, n, N, odds, moments='mv'):
+
+        @np.vectorize
+        def _moments1(M, n, N, odds):
+            if np.isnan(M) | np.isnan(n) | np.isnan(N):
+                return np.nan, np.nan
+            urn = self.dist(N, n, M, odds, 1e-12)
+            return urn.moments()
+
+        m, v = (_moments1(M, n, N, odds) if ("m" in moments or "v" in moments)
+                else (None, None))
+        s, k = None, None
+        return m, v, s, k
+
+
+class nchypergeom_fisher_gen(_nchypergeom_gen):
+    r"""A Fisher's noncentral hypergeometric discrete random variable.
+
+    Fisher's noncentral hypergeometric distribution models drawing objects of
+    two types from a bin. `M` is the total number of objects, `n` is the
+    number of Type I objects, and `odds` is the odds ratio: the odds of
+    selecting a Type I object rather than a Type II object when there is only
+    one object of each type.
+    The random variate represents the number of Type I objects drawn if we
+    take a handful of objects from the bin at once and find out afterwards
+    that we took `N` objects.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    nchypergeom_wallenius, hypergeom, nhypergeom
+
+    Notes
+    -----
+    Let mathematical symbols :math:`N`, :math:`n`, and :math:`M` correspond
+    with parameters `N`, `n`, and `M` (respectively) as defined above.
+
+    The probability mass function is defined as
+
+    .. math::
+
+        p(x; M, n, N, \omega) =
+        \frac{\binom{n}{x}\binom{M - n}{N-x}\omega^x}{P_0},
+
+    for
+    :math:`x \in [x_l, x_u]`,
+    :math:`M \in {\mathbb N}`,
+    :math:`n \in [0, M]`,
+    :math:`N \in [0, M]`,
+    :math:`\omega > 0`,
+    where
+    :math:`x_l = \max(0, N - (M - n))`,
+    :math:`x_u = \min(N, n)`,
+
+    .. math::
+
+        P_0 = \sum_{y=x_l}^{x_u} \binom{n}{y}\binom{M - n}{N-y}\omega^y,
+
+    and the binomial coefficients are defined as
+
+    .. math:: \binom{n}{k} \equiv \frac{n!}{k! (n - k)!}.
+
+    `nchypergeom_fisher` uses the BiasedUrn package by Agner Fog with
+    permission for it to be distributed under SciPy's license.
+
+    The symbols used to denote the shape parameters (`N`, `n`, and `M`) are not
+    universally accepted; they are chosen for consistency with `hypergeom`.
+
+    Note that Fisher's noncentral hypergeometric distribution is distinct
+    from Wallenius' noncentral hypergeometric distribution, which models
+    drawing a pre-determined `N` objects from a bin one by one.
+    When the odds ratio is unity, however, both distributions reduce to the
+    ordinary hypergeometric distribution.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Agner Fog, "Biased Urn Theory".
+           https://cran.r-project.org/web/packages/BiasedUrn/vignettes/UrnTheory.pdf
+
+    .. [2] "Fisher's noncentral hypergeometric distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Fisher's_noncentral_hypergeometric_distribution
+
+    %(example)s
+
+    """
+
+    rvs_name = "rvs_fisher"
+    dist = _PyFishersNCHypergeometric
+
+
+nchypergeom_fisher = nchypergeom_fisher_gen(
+    name='nchypergeom_fisher',
+    longname="A Fisher's noncentral hypergeometric")
+
+
+class nchypergeom_wallenius_gen(_nchypergeom_gen):
+    r"""A Wallenius' noncentral hypergeometric discrete random variable.
+
+    Wallenius' noncentral hypergeometric distribution models drawing objects of
+    two types from a bin. `M` is the total number of objects, `n` is the
+    number of Type I objects, and `odds` is the odds ratio: the odds of
+    selecting a Type I object rather than a Type II object when there is only
+    one object of each type.
+    The random variate represents the number of Type I objects drawn if we
+    draw a pre-determined `N` objects from a bin one by one.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    nchypergeom_fisher, hypergeom, nhypergeom
+
+    Notes
+    -----
+    Let mathematical symbols :math:`N`, :math:`n`, and :math:`M` correspond
+    with parameters `N`, `n`, and `M` (respectively) as defined above.
+
+    The probability mass function is defined as
+
+    .. math::
+
+        p(x; N, n, M) = \binom{n}{x} \binom{M - n}{N-x}
+        \int_0^1 \left(1-t^{\omega/D}\right)^x\left(1-t^{1/D}\right)^{N-x} dt
+
+    for
+    :math:`x \in [x_l, x_u]`,
+    :math:`M \in {\mathbb N}`,
+    :math:`n \in [0, M]`,
+    :math:`N \in [0, M]`,
+    :math:`\omega > 0`,
+    where
+    :math:`x_l = \max(0, N - (M - n))`,
+    :math:`x_u = \min(N, n)`,
+
+    .. math::
+
+        D = \omega(n - x) + ((M - n)-(N-x)),
+
+    and the binomial coefficients are defined as
+
+    .. math:: \binom{n}{k} \equiv \frac{n!}{k! (n - k)!}.
+
+    `nchypergeom_wallenius` uses the BiasedUrn package by Agner Fog with
+    permission for it to be distributed under SciPy's license.
+
+    The symbols used to denote the shape parameters (`N`, `n`, and `M`) are not
+    universally accepted; they are chosen for consistency with `hypergeom`.
+
+    Note that Wallenius' noncentral hypergeometric distribution is distinct
+    from Fisher's noncentral hypergeometric distribution, which models
+    take a handful of objects from the bin at once, finding out afterwards
+    that `N` objects were taken.
+    When the odds ratio is unity, however, both distributions reduce to the
+    ordinary hypergeometric distribution.
+
+    %(after_notes)s
+
+    References
+    ----------
+    .. [1] Agner Fog, "Biased Urn Theory".
+           https://cran.r-project.org/web/packages/BiasedUrn/vignettes/UrnTheory.pdf
+
+    .. [2] "Wallenius' noncentral hypergeometric distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Wallenius'_noncentral_hypergeometric_distribution
+
+    %(example)s
+
+    """
+
+    rvs_name = "rvs_wallenius"
+    dist = _PyWalleniusNCHypergeometric
+
+
+nchypergeom_wallenius = nchypergeom_wallenius_gen(
+    name='nchypergeom_wallenius',
+    longname="A Wallenius' noncentral hypergeometric")
+
+
+# Collect names of classes and objects in this module.
+pairs = list(globals().copy().items())
+_distn_names, _distn_gen_names = get_distribution_names(pairs, rv_discrete)
+
+__all__ = _distn_names + _distn_gen_names
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_distn_infrastructure.py b/.venv/lib/python3.12/site-packages/scipy/stats/_distn_infrastructure.py
new file mode 100644
index 0000000000000000000000000000000000000000..457b9ddb511d4dcba37e375a77c1a8a1dedd6554
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_distn_infrastructure.py
@@ -0,0 +1,4274 @@
+#
+# Author:  Travis Oliphant  2002-2011 with contributions from
+#          SciPy Developers 2004-2011
+#
+from scipy._lib._util import getfullargspec_no_self as _getfullargspec
+
+import sys
+import keyword
+import re
+import types
+import warnings
+from itertools import zip_longest
+
+from scipy._lib import doccer
+from scipy._lib._docscrape import FunctionDoc
+from ._distr_params import distcont, distdiscrete
+from scipy._lib._util import check_random_state
+import scipy._external.array_api_extra as xpx
+
+from scipy.special import comb, entr
+
+
+# for root finding for continuous distribution ppf, and maximum likelihood
+# estimation
+from scipy import optimize
+
+# for functions of continuous distributions (e.g. moments, entropy, cdf)
+from scipy import integrate
+
+# to approximate the pdf of a continuous distribution given its cdf
+from scipy.stats._finite_differences import _derivative
+
+# for scipy.stats.entropy. Attempts to import just that function or file
+# have cause import problems
+from scipy import stats
+
+from numpy import (arange, putmask, ones, shape, ndarray, zeros, floor,
+                   logical_and, log, sqrt, place, argmax, vectorize, asarray,
+                   nan, inf, isinf, empty)
+
+import numpy as np
+from ._constants import _XMAX, _LOGXMAX
+from ._censored_data import CensoredData
+from scipy.stats._warnings_errors import FitError
+
+# These are the docstring parts used for substitution in specific
+# distribution docstrings
+
+docheaders = {'methods': """\nMethods\n-------\n""",
+              'notes': """\nNotes\n-----\n""",
+              'examples': """\nExamples\n--------\n"""}
+
+_doc_rvs = """\
+rvs(%(shapes)s, loc=0, scale=1, size=1, random_state=None)
+    Random variates.
+"""
+_doc_pdf = """\
+pdf(x, %(shapes)s, loc=0, scale=1)
+    Probability density function.
+"""
+_doc_logpdf = """\
+logpdf(x, %(shapes)s, loc=0, scale=1)
+    Log of the probability density function.
+"""
+_doc_pmf = """\
+pmf(k, %(shapes)s, loc=0, scale=1)
+    Probability mass function.
+"""
+_doc_logpmf = """\
+logpmf(k, %(shapes)s, loc=0, scale=1)
+    Log of the probability mass function.
+"""
+_doc_cdf = """\
+cdf(x, %(shapes)s, loc=0, scale=1)
+    Cumulative distribution function.
+"""
+_doc_logcdf = """\
+logcdf(x, %(shapes)s, loc=0, scale=1)
+    Log of the cumulative distribution function.
+"""
+_doc_sf = """\
+sf(x, %(shapes)s, loc=0, scale=1)
+    Survival function  (also defined as ``1 - cdf``, but `sf` is sometimes more accurate).
+"""  # noqa: E501
+_doc_logsf = """\
+logsf(x, %(shapes)s, loc=0, scale=1)
+    Log of the survival function.
+"""
+_doc_ppf = """\
+ppf(q, %(shapes)s, loc=0, scale=1)
+    Percent point function (inverse of ``cdf`` --- percentiles).
+"""
+_doc_isf = """\
+isf(q, %(shapes)s, loc=0, scale=1)
+    Inverse survival function (inverse of ``sf``).
+"""
+_doc_moment = """\
+moment(order, %(shapes)s, loc=0, scale=1)
+    Non-central moment of the specified order.
+"""
+_doc_stats = """\
+stats(%(shapes)s, loc=0, scale=1, moments='mv')
+    Mean('m'), variance('v'), skew('s'), and/or kurtosis('k').
+"""
+_doc_entropy = """\
+entropy(%(shapes)s, loc=0, scale=1)
+    (Differential) entropy of the RV.
+"""
+_doc_fit = """\
+fit(data)
+    Parameter estimates for generic data.
+    See `scipy.stats.rv_continuous.fit <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.fit.html#scipy.stats.rv_continuous.fit>`__ for detailed documentation of the
+    keyword arguments.
+"""  # noqa: E501
+_doc_expect = """\
+expect(func, args=(%(shapes_)s), loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds)
+    Expected value of a function (of one argument) with respect to the distribution.
+"""  # noqa: E501
+_doc_expect_discrete = """\
+expect(func, args=(%(shapes_)s), loc=0, lb=None, ub=None, conditional=False)
+    Expected value of a function (of one argument) with respect to the distribution.
+"""
+_doc_median = """\
+median(%(shapes)s, loc=0, scale=1)
+    Median of the distribution.
+"""
+_doc_mean = """\
+mean(%(shapes)s, loc=0, scale=1)
+    Mean of the distribution.
+"""
+_doc_var = """\
+var(%(shapes)s, loc=0, scale=1)
+    Variance of the distribution.
+"""
+_doc_std = """\
+std(%(shapes)s, loc=0, scale=1)
+    Standard deviation of the distribution.
+"""
+_doc_interval = """\
+interval(confidence, %(shapes)s, loc=0, scale=1)
+    Confidence interval with equal areas around the median.
+"""
+_doc_allmethods = ''.join([docheaders['methods'], _doc_rvs, _doc_pdf,
+                           _doc_logpdf, _doc_cdf, _doc_logcdf, _doc_sf,
+                           _doc_logsf, _doc_ppf, _doc_isf, _doc_moment,
+                           _doc_stats, _doc_entropy, _doc_fit,
+                           _doc_expect, _doc_median,
+                           _doc_mean, _doc_var, _doc_std, _doc_interval])
+
+_doc_default_longsummary = """\
+As an instance of the `rv_continuous` class, `%(name)s` object inherits from it
+a collection of generic methods (see below for the full list),
+and completes them with details specific for this particular distribution.
+"""
+
+_doc_default_frozen_note = """
+Alternatively, the object may be called (as a function) to fix the shape,
+location, and scale parameters returning a "frozen" continuous RV object:
+
+rv = %(name)s(%(shapes)s, loc=0, scale=1)
+    - Frozen RV object with the same methods but holding the given shape,
+      location, and scale fixed.
+"""
+_doc_default_example = """\
+Examples
+--------
+>>> import numpy as np
+>>> from scipy.stats import %(name)s
+>>> import matplotlib.pyplot as plt
+>>> fig, ax = plt.subplots(1, 1)
+
+Get the support:
+
+%(set_vals_stmt)s
+>>> lb, ub = %(name)s.support(%(shapes)s)
+
+Calculate the first four moments:
+
+>>> mean, var, skew, kurt = %(name)s.stats(%(shapes)s, moments='mvsk')
+
+Display the probability density function (``pdf``):
+
+>>> x = np.linspace(%(name)s.ppf(0.01, %(shapes)s),
+...                 %(name)s.ppf(0.99, %(shapes)s), 100)
+>>> ax.plot(x, %(name)s.pdf(x, %(shapes)s),
+...        'r-', lw=5, alpha=0.6, label='%(name)s pdf')
+
+Alternatively, the distribution object can be called (as a function)
+to fix the shape, location and scale parameters. This returns a "frozen"
+RV object holding the given parameters fixed.
+
+Freeze the distribution and display the frozen ``pdf``:
+
+>>> rv = %(name)s(%(shapes)s)
+>>> ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
+
+Check accuracy of ``cdf`` and ``ppf``:
+
+>>> vals = %(name)s.ppf([0.001, 0.5, 0.999], %(shapes)s)
+>>> np.allclose([0.001, 0.5, 0.999], %(name)s.cdf(vals, %(shapes)s))
+True
+
+Generate random numbers:
+
+>>> r = %(name)s.rvs(%(shapes)s, size=1000)
+
+And compare the histogram:
+
+>>> ax.hist(r, density=True, bins='auto', histtype='stepfilled', alpha=0.2)
+>>> ax.set_xlim([x[0], x[-1]])
+>>> ax.legend(loc='best', frameon=False)
+>>> plt.show()
+
+"""
+
+_doc_default_locscale = """\
+The probability density above is defined in the "standardized" form. To shift
+and/or scale the distribution use the ``loc`` and ``scale`` parameters.
+Specifically, ``%(name)s.pdf(x, %(shapes)s, loc, scale)`` is identically
+equivalent to ``%(name)s.pdf(y, %(shapes)s) / scale`` with
+``y = (x - loc) / scale``. Note that shifting the location of a distribution
+does not make it a "noncentral" distribution; noncentral generalizations of
+some distributions are available in separate classes.
+"""
+
+_doc_default = ''.join([_doc_default_longsummary,
+                        _doc_allmethods,
+                        '\n',
+                        _doc_default_example])
+
+_doc_default_before_notes = ''.join([_doc_default_longsummary,
+                                     _doc_allmethods])
+
+docdict = {
+    'rvs': _doc_rvs,
+    'pdf': _doc_pdf,
+    'logpdf': _doc_logpdf,
+    'cdf': _doc_cdf,
+    'logcdf': _doc_logcdf,
+    'sf': _doc_sf,
+    'logsf': _doc_logsf,
+    'ppf': _doc_ppf,
+    'isf': _doc_isf,
+    'stats': _doc_stats,
+    'entropy': _doc_entropy,
+    'fit': _doc_fit,
+    'moment': _doc_moment,
+    'expect': _doc_expect,
+    'interval': _doc_interval,
+    'mean': _doc_mean,
+    'std': _doc_std,
+    'var': _doc_var,
+    'median': _doc_median,
+    'allmethods': _doc_allmethods,
+    'longsummary': _doc_default_longsummary,
+    'frozennote': _doc_default_frozen_note,
+    'example': _doc_default_example,
+    'default': _doc_default,
+    'before_notes': _doc_default_before_notes,
+    'after_notes': _doc_default_locscale
+}
+
+# Reuse common content between continuous and discrete docs, change some
+# minor bits.
+docdict_discrete = docdict.copy()
+
+docdict_discrete['pmf'] = _doc_pmf
+docdict_discrete['logpmf'] = _doc_logpmf
+docdict_discrete['expect'] = _doc_expect_discrete
+_doc_disc_methods = ['rvs', 'pmf', 'logpmf', 'cdf', 'logcdf', 'sf', 'logsf',
+                     'ppf', 'isf', 'stats', 'entropy', 'expect', 'median',
+                     'mean', 'var', 'std', 'interval']
+for obj in _doc_disc_methods:
+    docdict_discrete[obj] = docdict_discrete[obj].replace(', scale=1', '')
+
+_doc_disc_methods_err_varname = ['cdf', 'logcdf', 'sf', 'logsf']
+for obj in _doc_disc_methods_err_varname:
+    docdict_discrete[obj] = docdict_discrete[obj].replace('(x, ', '(k, ')
+
+docdict_discrete.pop('pdf')
+docdict_discrete.pop('logpdf')
+
+_doc_allmethods = ''.join([docdict_discrete[obj] for obj in _doc_disc_methods])
+docdict_discrete['allmethods'] = docheaders['methods'] + _doc_allmethods
+
+docdict_discrete['longsummary'] = _doc_default_longsummary.replace(
+    'rv_continuous', 'rv_discrete')
+
+_doc_default_frozen_note = """
+Alternatively, the object may be called (as a function) to fix the shape and
+location parameters returning a "frozen" discrete RV object:
+
+rv = %(name)s(%(shapes)s, loc=0)
+    - Frozen RV object with the same methods but holding the given shape and
+      location fixed.
+"""
+docdict_discrete['frozennote'] = _doc_default_frozen_note
+
+_doc_default_discrete_example = """\
+Examples
+--------
+>>> import numpy as np
+>>> from scipy.stats import %(name)s
+>>> import matplotlib.pyplot as plt
+>>> fig, ax = plt.subplots(1, 1)
+
+Get the support:
+
+%(set_vals_stmt)s
+>>> lb, ub = %(name)s.support(%(shapes)s)
+
+Calculate the first four moments:
+
+>>> mean, var, skew, kurt = %(name)s.stats(%(shapes)s, moments='mvsk')
+
+Display the probability mass function (``pmf``):
+
+>>> x = np.arange(%(name)s.ppf(0.01, %(shapes)s),
+...               %(name)s.ppf(0.99, %(shapes)s))
+>>> ax.plot(x, %(name)s.pmf(x, %(shapes)s), 'bo', ms=8, label='%(name)s pmf')
+>>> ax.vlines(x, 0, %(name)s.pmf(x, %(shapes)s), colors='b', lw=5, alpha=0.5)
+
+Alternatively, the distribution object can be called (as a function)
+to fix the shape and location. This returns a "frozen" RV object holding
+the given parameters fixed.
+
+Freeze the distribution and display the frozen ``pmf``:
+
+>>> rv = %(name)s(%(shapes)s)
+>>> ax.vlines(x, 0, rv.pmf(x), colors='k', linestyles='-', lw=1,
+...         label='frozen pmf')
+>>> ax.legend(loc='best', frameon=False)
+>>> plt.show()
+
+Check accuracy of ``cdf`` and ``ppf``:
+
+>>> prob = %(name)s.cdf(x, %(shapes)s)
+>>> np.allclose(x, %(name)s.ppf(prob, %(shapes)s))
+True
+
+Generate random numbers:
+
+>>> r = %(name)s.rvs(%(shapes)s, size=1000)
+"""
+
+
+_doc_default_discrete_locscale = """\
+The probability mass function above is defined in the "standardized" form.
+To shift distribution use the ``loc`` parameter.
+Specifically, ``%(name)s.pmf(k, %(shapes)s, loc)`` is identically
+equivalent to ``%(name)s.pmf(k - loc, %(shapes)s)``.
+"""
+
+docdict_discrete['example'] = _doc_default_discrete_example
+docdict_discrete['after_notes'] = _doc_default_discrete_locscale
+
+_doc_default_before_notes = ''.join([docdict_discrete['longsummary'],
+                                     docdict_discrete['allmethods']])
+docdict_discrete['before_notes'] = _doc_default_before_notes
+
+_doc_default_disc = ''.join([docdict_discrete['longsummary'],
+                             docdict_discrete['allmethods'],
+                             docdict_discrete['frozennote'],
+                             docdict_discrete['example']])
+docdict_discrete['default'] = _doc_default_disc
+
+# clean up all the separate docstring elements, we do not need them anymore
+for obj in [s for s in dir() if s.startswith('_doc_')]:
+    exec('del ' + obj)
+del obj
+
+
+def _moment(data, n, mu=None):
+    if mu is None:
+        mu = data.mean()
+    return ((data - mu)**n).mean()
+
+
+def _moment_from_stats(n, mu, mu2, g1, g2, moment_func, args):
+    if (n == 0):
+        return 1.0
+    elif (n == 1):
+        if mu is None:
+            val = moment_func(1, *args)
+        else:
+            val = mu
+    elif (n == 2):
+        if mu2 is None or mu is None:
+            val = moment_func(2, *args)
+        else:
+            val = mu2 + mu*mu
+    elif (n == 3):
+        if g1 is None or mu2 is None or mu is None:
+            val = moment_func(3, *args)
+        else:
+            mu3 = g1 * np.power(mu2, 1.5)  # 3rd central moment
+            val = mu3+3*mu*mu2+mu*mu*mu  # 3rd non-central moment
+    elif (n == 4):
+        if g1 is None or g2 is None or mu2 is None or mu is None:
+            val = moment_func(4, *args)
+        else:
+            mu4 = (g2+3.0)*(mu2**2.0)  # 4th central moment
+            mu3 = g1*np.power(mu2, 1.5)  # 3rd central moment
+            val = mu4+4*mu*mu3+6*mu*mu*mu2+mu*mu*mu*mu
+    else:
+        val = moment_func(n, *args)
+
+    return val
+
+
+def _skew(data):
+    """
+    skew is third central moment / variance**(1.5)
+    """
+    data = np.ravel(data)
+    mu = data.mean()
+    m2 = ((data - mu)**2).mean()
+    m3 = ((data - mu)**3).mean()
+    return m3 / np.power(m2, 1.5)
+
+
+def _kurtosis(data):
+    """Fisher's excess kurtosis is fourth central moment / variance**2 - 3."""
+    data = np.ravel(data)
+    mu = data.mean()
+    m2 = ((data - mu)**2).mean()
+    m4 = ((data - mu)**4).mean()
+    return m4 / m2**2 - 3
+
+def _vectorize_rvs_over_shapes(_rvs1):
+    """Decorator that vectorizes _rvs method to work on ndarray shapes"""
+    # _rvs1 must be a _function_ that accepts _scalar_ args as positional
+    # arguments, `size` and `random_state` as keyword arguments.
+    # _rvs1 must return a random variate array with shape `size`. If `size` is
+    # None, _rvs1 must return a scalar.
+    # When applied to _rvs1, this decorator broadcasts ndarray args
+    # and loops over them, calling _rvs1 for each set of scalar args.
+    # For usage example, see _nchypergeom_gen
+    def _rvs(*args, size, random_state):
+        _rvs1_size, _rvs1_indices = _check_shape(args[0].shape, size)
+
+        size = np.array(size)
+        _rvs1_size = np.array(_rvs1_size)
+        _rvs1_indices = np.array(_rvs1_indices)
+
+        if np.all(_rvs1_indices):  # all args are scalars
+            return _rvs1(*args, size, random_state)
+
+        out = np.empty(size)
+
+        # out.shape can mix dimensions associated with arg_shape and _rvs1_size
+        # Sort them to arg_shape + _rvs1_size for easy indexing of dimensions
+        # corresponding with the different sets of scalar args
+        j0 = np.arange(out.ndim)
+        j1 = np.hstack((j0[~_rvs1_indices], j0[_rvs1_indices]))
+        out = np.moveaxis(out, j1, j0)
+
+        for i in np.ndindex(*size[~_rvs1_indices]):
+            # arg can be squeezed because singleton dimensions will be
+            # associated with _rvs1_size, not arg_shape per _check_shape
+            out[i] = _rvs1(*[np.squeeze(arg)[i] for arg in args],
+                           _rvs1_size, random_state)
+
+        return np.moveaxis(out, j0, j1)  # move axes back before returning
+    return _rvs
+
+
+def _fit_determine_optimizer(optimizer):
+    if not callable(optimizer) and isinstance(optimizer, str):
+        if not optimizer.startswith('fmin_'):
+            optimizer = "fmin_"+optimizer
+        if optimizer == 'fmin_':
+            optimizer = 'fmin'
+        try:
+            optimizer = getattr(optimize, optimizer)
+        except AttributeError as e:
+            raise ValueError(f"{optimizer} is not a valid optimizer") from e
+    return optimizer
+
+def _isintegral(x):
+    return x == np.round(x)
+
+def _sum_finite(x):
+    """
+    For a 1D array x, return a tuple containing the sum of the
+    finite values of x and the number of nonfinite values.
+
+    This is a utility function used when evaluating the negative
+    loglikelihood for a distribution and an array of samples.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats._distn_infrastructure import _sum_finite
+    >>> tot, nbad = _sum_finite(np.array([-2, -np.inf, 5, 1]))
+    >>> tot
+    4.0
+    >>> nbad
+    1
+    """
+    finite_x = np.isfinite(x)
+    bad_count = finite_x.size - np.count_nonzero(finite_x)
+    return np.sum(x[finite_x]), bad_count
+
+
+# Frozen RV class
+class rv_frozen:
+
+    # generic type compatibility with scipy-stubs
+    __class_getitem__: classmethod = classmethod(types.GenericAlias)
+
+    def __init__(self, dist, *args, **kwds):
+        self.args = args
+        self.kwds = kwds
+
+        # create a new instance
+        self.dist = dist.__class__(**dist._updated_ctor_param())
+
+        shapes, _, _ = self.dist._parse_args(*args, **kwds)
+        self.a, self.b = self.dist._get_support(*shapes)
+
+    @property
+    def random_state(self):
+        return self.dist._random_state
+
+    @random_state.setter
+    def random_state(self, seed):
+        self.dist._random_state = check_random_state(seed)
+
+    def cdf(self, x):
+        return self.dist.cdf(x, *self.args, **self.kwds)
+
+    def logcdf(self, x):
+        return self.dist.logcdf(x, *self.args, **self.kwds)
+
+    def ppf(self, q):
+        return self.dist.ppf(q, *self.args, **self.kwds)
+
+    def isf(self, q):
+        return self.dist.isf(q, *self.args, **self.kwds)
+
+    def rvs(self, size=None, random_state=None):
+        kwds = self.kwds.copy()
+        kwds.update({'size': size, 'random_state': random_state})
+        return self.dist.rvs(*self.args, **kwds)
+
+    def sf(self, x):
+        return self.dist.sf(x, *self.args, **self.kwds)
+
+    def logsf(self, x):
+        return self.dist.logsf(x, *self.args, **self.kwds)
+
+    def stats(self, moments='mv'):
+        kwds = self.kwds.copy()
+        kwds.update({'moments': moments})
+        return self.dist.stats(*self.args, **kwds)
+
+    def median(self):
+        return self.dist.median(*self.args, **self.kwds)
+
+    def mean(self):
+        return self.dist.mean(*self.args, **self.kwds)
+
+    def var(self):
+        return self.dist.var(*self.args, **self.kwds)
+
+    def std(self):
+        return self.dist.std(*self.args, **self.kwds)
+
+    def moment(self, order=None):
+        return self.dist.moment(order, *self.args, **self.kwds)
+
+    def entropy(self):
+        return self.dist.entropy(*self.args, **self.kwds)
+
+    def interval(self, confidence=None):
+        return self.dist.interval(confidence, *self.args, **self.kwds)
+
+    def expect(self, func=None, lb=None, ub=None, conditional=False, **kwds):
+        # expect method only accepts shape parameters as positional args
+        # hence convert self.args, self.kwds, also loc/scale
+        # See the .expect method docstrings for the meaning of
+        # other parameters.
+        a, loc, scale = self.dist._parse_args(*self.args, **self.kwds)
+        if isinstance(self.dist, rv_discrete):
+            return self.dist.expect(func, a, loc, lb, ub, conditional, **kwds)
+        else:
+            return self.dist.expect(func, a, loc, scale, lb, ub,
+                                    conditional, **kwds)
+
+    def support(self):
+        return self.dist.support(*self.args, **self.kwds)
+
+
+class rv_discrete_frozen(rv_frozen):
+
+    def pmf(self, k):
+        return self.dist.pmf(k, *self.args, **self.kwds)
+
+    def logpmf(self, k):  # No error
+        return self.dist.logpmf(k, *self.args, **self.kwds)
+
+
+class rv_continuous_frozen(rv_frozen):
+
+    def pdf(self, x):
+        return self.dist.pdf(x, *self.args, **self.kwds)
+
+    def logpdf(self, x):
+        return self.dist.logpdf(x, *self.args, **self.kwds)
+
+
+def argsreduce(cond, *args):
+    """Clean arguments to:
+
+    1. Ensure all arguments are iterable (arrays of dimension at least one
+    2. If cond != True and size > 1, ravel(args[i]) where ravel(condition) is
+       True, in 1D.
+
+    Return list of processed arguments.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats._distn_infrastructure import argsreduce
+    >>> rng = np.random.default_rng()
+    >>> A = rng.random((4, 5))
+    >>> B = 2
+    >>> C = rng.random((1, 5))
+    >>> cond = np.ones(A.shape)
+    >>> [A1, B1, C1] = argsreduce(cond, A, B, C)
+    >>> A1.shape
+    (4, 5)
+    >>> B1.shape
+    (1,)
+    >>> C1.shape
+    (1, 5)
+    >>> cond[2,:] = 0
+    >>> [A1, B1, C1] = argsreduce(cond, A, B, C)
+    >>> A1.shape
+    (15,)
+    >>> B1.shape
+    (1,)
+    >>> C1.shape
+    (15,)
+
+    """
+    # some distributions assume arguments are iterable.
+    newargs = np.atleast_1d(*args)
+
+    # np.atleast_1d returns an array if only one argument, or a list of arrays
+    # if more than one argument.
+    if not isinstance(newargs, (list | tuple)):
+        newargs = (newargs,)
+
+    if np.all(cond):
+        # broadcast arrays with cond
+        *newargs, cond = np.broadcast_arrays(*newargs, cond)
+        return [arg.ravel() for arg in newargs]
+
+    s = cond.shape
+    # np.extract returns flattened arrays, which are not broadcastable together
+    # unless they are either the same size or size == 1.
+    return [(arg if np.size(arg) == 1
+            else np.extract(cond, np.broadcast_to(arg, s)))
+            for arg in newargs]
+
+
+parse_arg_template = """
+def _parse_args(self, %(shape_arg_str)s %(locscale_in)s):
+    return (%(shape_arg_str)s), %(locscale_out)s
+
+def _parse_args_rvs(self, %(shape_arg_str)s %(locscale_in)s, size=None):
+    return self._argcheck_rvs(%(shape_arg_str)s %(locscale_out)s, size=size)
+
+def _parse_args_stats(self, %(shape_arg_str)s %(locscale_in)s, moments='mv'):
+    return (%(shape_arg_str)s), %(locscale_out)s, moments
+"""
+
+
+class rv_generic:
+    """Class which encapsulates common functionality between rv_discrete
+    and rv_continuous.
+
+    """
+
+    def __init__(self, seed=None):
+        super().__init__()
+
+        # figure out if _stats signature has 'moments' keyword
+        sig = _getfullargspec(self._stats)
+        self._stats_has_moments = ((sig.varkw is not None) or
+                                   ('moments' in sig.args) or
+                                   ('moments' in sig.kwonlyargs))
+        self._random_state = check_random_state(seed)
+
+    @property
+    def random_state(self):
+        """Get or set the generator object for generating random variates.
+
+        If `random_state` is None (or `np.random`), the
+        `numpy.random.RandomState` singleton is used.
+        If `random_state` is an int, a new ``RandomState`` instance is used,
+        seeded with `random_state`.
+        If `random_state` is already a ``Generator`` or ``RandomState``
+        instance, that instance is used.
+
+        """
+        return self._random_state
+
+    @random_state.setter
+    def random_state(self, seed):
+        self._random_state = check_random_state(seed)
+
+    def __setstate__(self, state):
+        try:
+            self.__dict__.update(state)
+            # attaches the dynamically created methods on each instance.
+            # if a subclass overrides rv_generic.__setstate__, or implements
+            # it's own _attach_methods, then it must make sure that
+            # _attach_argparser_methods is called.
+            self._attach_methods()
+        except ValueError:
+            # reconstitute an old pickle scipy<1.6, that contains
+            # (_ctor_param, random_state) as state
+            self._ctor_param = state[0]
+            self._random_state = state[1]
+            self.__init__()
+
+    def _attach_methods(self):
+        """Attaches dynamically created methods to the rv_* instance.
+
+        This method must be overridden by subclasses, and must itself call
+         _attach_argparser_methods. This method is called in __init__ in
+         subclasses, and in __setstate__
+        """
+        raise NotImplementedError
+
+    def _attach_argparser_methods(self):
+        """
+        Generates the argument-parsing functions dynamically and attaches
+        them to the instance.
+
+        Should be called from `_attach_methods`, typically in __init__ and
+        during unpickling (__setstate__)
+        """
+        ns = {}
+        exec(self._parse_arg_template, ns)
+        # NB: attach to the instance, not class
+        for name in ['_parse_args', '_parse_args_stats', '_parse_args_rvs']:
+            setattr(self, name, types.MethodType(ns[name], self))
+
+    def _construct_argparser(
+            self, meths_to_inspect, locscale_in, locscale_out):
+        """Construct the parser string for the shape arguments.
+
+        This method should be called in __init__ of a class for each
+        distribution. It creates the `_parse_arg_template` attribute that is
+        then used by `_attach_argparser_methods` to dynamically create and
+        attach the `_parse_args`, `_parse_args_stats`, `_parse_args_rvs`
+        methods to the instance.
+
+        If self.shapes is a non-empty string, interprets it as a
+        comma-separated list of shape parameters.
+
+        Otherwise inspects the call signatures of `meths_to_inspect`
+        and constructs the argument-parsing functions from these.
+        In this case also sets `shapes` and `numargs`.
+        """
+
+        if self.shapes:
+            # sanitize the user-supplied shapes
+            if not isinstance(self.shapes, str):
+                raise TypeError('shapes must be a string.')
+
+            shapes = self.shapes.replace(',', ' ').split()
+
+            for field in shapes:
+                if keyword.iskeyword(field):
+                    raise SyntaxError('keywords cannot be used as shapes.')
+                if not re.match('^[_a-zA-Z][_a-zA-Z0-9]*$', field):
+                    raise SyntaxError(
+                        'shapes must be valid python identifiers')
+        else:
+            # find out the call signatures (_pdf, _cdf etc), deduce shape
+            # arguments. Generic methods only have 'self, x', any further args
+            # are shapes.
+            shapes_list = []
+            for meth in meths_to_inspect:
+                shapes_args = _getfullargspec(meth)  # NB does not contain self
+                args = shapes_args.args[1:]       # peel off 'x', too
+
+                if args:
+                    shapes_list.append(args)
+
+                    # *args or **kwargs are not allowed w/automatic shapes
+                    if shapes_args.varargs is not None:
+                        raise TypeError(
+                            '*args are not allowed w/out explicit shapes')
+                    if shapes_args.varkw is not None:
+                        raise TypeError(
+                            '**kwds are not allowed w/out explicit shapes')
+                    if shapes_args.kwonlyargs:
+                        raise TypeError(
+                            'kwonly args are not allowed w/out explicit shapes')
+                    if shapes_args.defaults is not None:
+                        raise TypeError('defaults are not allowed for shapes')
+
+            if shapes_list:
+                shapes = shapes_list[0]
+
+                # make sure the signatures are consistent
+                for item in shapes_list:
+                    if item != shapes:
+                        raise TypeError('Shape arguments are inconsistent.')
+            else:
+                shapes = []
+
+        # have the arguments, construct the method from template
+        shapes_str = ', '.join(shapes) + ', ' if shapes else ''  # NB: not None
+        dct = dict(shape_arg_str=shapes_str,
+                   locscale_in=locscale_in,
+                   locscale_out=locscale_out,
+                   )
+
+        # this string is used by _attach_argparser_methods
+        self._parse_arg_template = parse_arg_template % dct
+
+        self.shapes = ', '.join(shapes) if shapes else None
+        if not hasattr(self, 'numargs'):
+            # allows more general subclassing with *args
+            self.numargs = len(shapes)
+
+    def _construct_doc(self, docdict, shapes_vals=None):
+        """Construct the instance docstring with string substitutions."""
+        if sys.flags.optimize > 1:
+            # if run with -OO, docstrings are stripped
+            # see https://docs.python.org/3/using/cmdline.html#cmdoption-OO
+            return
+        tempdict = docdict.copy()
+        tempdict['name'] = self.name or 'distname'
+        tempdict['shapes'] = self.shapes or ''
+
+        if shapes_vals is None:
+            shapes_vals = ()
+        try:
+            vals = ', '.join(f'{val:.3g}' for val in shapes_vals)
+        except TypeError:
+            vals = ', '.join(f'{val}' for val in shapes_vals)
+        tempdict['vals'] = vals
+
+        tempdict['shapes_'] = self.shapes or ''
+        if self.shapes and self.numargs == 1:
+            tempdict['shapes_'] += ','
+
+        if self.shapes:
+            tempdict['set_vals_stmt'] = f'>>> {self.shapes} = {vals}'
+        else:
+            tempdict['set_vals_stmt'] = ''
+
+        if self.shapes is None:
+            # remove shapes from call parameters if there are none
+            for item in ['default', 'before_notes']:
+                tempdict[item] = tempdict[item].replace(
+                    "\n%(shapes)s : array_like\n    shape parameters", "")
+        for i in range(2):
+            if self.shapes is None:
+                # necessary because we use %(shapes)s in two forms (w w/o ", ")
+                self.__doc__ = self.__doc__.replace("%(shapes)s, ", "")
+            try:
+                self.__doc__ = doccer.docformat(self.__doc__, tempdict)
+            except TypeError as e:
+                raise Exception("Unable to construct docstring for "
+                                f"distribution \"{self.name}\": {repr(e)}") from e
+
+        # correct for empty shapes
+        self.__doc__ = self.__doc__.replace('(, ', '(').replace(', )', ')')
+
+    def _construct_default_doc(self, longname=None,
+                               docdict=None, discrete='continuous'):
+        """Construct instance docstring from the default template."""
+        if sys.flags.optimize > 1:
+            # if run with -OO, docstrings are stripped
+            # see https://docs.python.org/3/using/cmdline.html#cmdoption-OO
+            return
+        if longname is None:
+            longname = 'A'
+        self.__doc__ = ''.join([f'{longname} {discrete} random variable.',
+                                '\n\n%(before_notes)s\n', docheaders['notes'],
+                                '\n%(example)s'])
+        self._construct_doc(docdict)
+
+    def freeze(self, *args, **kwds):
+        """Freeze the distribution for the given arguments.
+
+        Parameters
+        ----------
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution.  Should include all
+            the non-optional arguments, may include ``loc`` and ``scale``.
+
+        Returns
+        -------
+        rv_frozen : rv_frozen instance
+            The frozen distribution.
+
+        """
+        if isinstance(self, rv_continuous):
+            return rv_continuous_frozen(self, *args, **kwds)
+        else:
+            return rv_discrete_frozen(self, *args, **kwds)
+
+    def __call__(self, *args, **kwds):
+        return self.freeze(*args, **kwds)
+    __call__.__doc__ = freeze.__doc__
+
+    # The actual calculation functions (no basic checking need be done)
+    # If these are defined, the others won't be looked at.
+    # Otherwise, the other set can be defined.
+    def _stats(self, *args, **kwds):
+        return None, None, None, None
+
+    # Noncentral moments (also known as the moment about the origin).
+    # Expressed in LaTeX, munp would be $\mu'_{n}$, i.e. "mu-sub-n-prime".
+    # The primed mu is a widely used notation for the noncentral moment.
+    def _munp(self, n, *args):
+        # Silence floating point warnings from integration.
+        with np.errstate(all='ignore'):
+            vals = self.generic_moment(n, *args)
+        return vals
+
+    def _argcheck_rvs(self, *args, **kwargs):
+        # Handle broadcasting and size validation of the rvs method.
+        # Subclasses should not have to override this method.
+        # The rule is that if `size` is not None, then `size` gives the
+        # shape of the result (integer values of `size` are treated as
+        # tuples with length 1; i.e. `size=3` is the same as `size=(3,)`.)
+        #
+        # `args` is expected to contain the shape parameters (if any), the
+        # location and the scale in a flat tuple (e.g. if there are two
+        # shape parameters `a` and `b`, `args` will be `(a, b, loc, scale)`).
+        # The only keyword argument expected is 'size'.
+        size = kwargs.get('size', None)
+        all_bcast = np.broadcast_arrays(*args)
+
+        def squeeze_left(a):
+            while a.ndim > 0 and a.shape[0] == 1:
+                a = a[0]
+            return a
+
+        # Eliminate trivial leading dimensions.  In the convention
+        # used by numpy's random variate generators, trivial leading
+        # dimensions are effectively ignored.  In other words, when `size`
+        # is given, trivial leading dimensions of the broadcast parameters
+        # in excess of the number of dimensions  in size are ignored, e.g.
+        #   >>> np.random.normal([[1, 3, 5]], [[[[0.01]]]], size=3)
+        #   array([ 1.00104267,  3.00422496,  4.99799278])
+        # If `size` is not given, the exact broadcast shape is preserved:
+        #   >>> np.random.normal([[1, 3, 5]], [[[[0.01]]]])
+        #   array([[[[ 1.00862899,  3.00061431,  4.99867122]]]])
+        #
+        all_bcast = [squeeze_left(a) for a in all_bcast]
+        bcast_shape = all_bcast[0].shape
+        bcast_ndim = all_bcast[0].ndim
+
+        if size is None:
+            size_ = bcast_shape
+        else:
+            size_ = tuple(np.atleast_1d(size))
+
+        # Check compatibility of size_ with the broadcast shape of all
+        # the parameters.  This check is intended to be consistent with
+        # how the numpy random variate generators (e.g. np.random.normal,
+        # np.random.beta) handle their arguments.   The rule is that, if size
+        # is given, it determines the shape of the output.  Broadcasting
+        # can't change the output size.
+
+        # This is the standard broadcasting convention of extending the
+        # shape with fewer dimensions with enough dimensions of length 1
+        # so that the two shapes have the same number of dimensions.
+        ndiff = bcast_ndim - len(size_)
+        if ndiff < 0:
+            bcast_shape = (1,)*(-ndiff) + bcast_shape
+        elif ndiff > 0:
+            size_ = (1,)*ndiff + size_
+
+        # This compatibility test is not standard.  In "regular" broadcasting,
+        # two shapes are compatible if for each dimension, the lengths are the
+        # same or one of the lengths is 1.  Here, the length of a dimension in
+        # size_ must not be less than the corresponding length in bcast_shape.
+        ok = all([bcdim == 1 or bcdim == szdim
+                  for (bcdim, szdim) in zip(bcast_shape, size_)])
+        if not ok:
+            raise ValueError("size does not match the broadcast shape of "
+                             f"the parameters. {size}, {size_}, {bcast_shape}")
+
+        param_bcast = all_bcast[:-2]
+        loc_bcast = all_bcast[-2]
+        scale_bcast = all_bcast[-1]
+
+        return param_bcast, loc_bcast, scale_bcast, size_
+
+    # These are the methods you must define (standard form functions)
+    # NB: generic _pdf, _logpdf, _cdf are different for
+    # rv_continuous and rv_discrete hence are defined in there
+    def _argcheck(self, *args):
+        """Default check for correct values on args and keywords.
+
+        Returns condition array of 1's where arguments are correct and
+         0's where they are not.
+
+        """
+        cond = 1
+        for arg in args:
+            cond = logical_and(cond, (asarray(arg) > 0))
+        return cond
+
+    def _get_support(self, *args, **kwargs):
+        """Return the support of the (unscaled, unshifted) distribution.
+
+        *Must* be overridden by distributions which have support dependent
+        upon the shape parameters of the distribution.  Any such override
+        *must not* set or change any of the class members, as these members
+        are shared amongst all instances of the distribution.
+
+        Parameters
+        ----------
+        arg1, arg2, ... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+
+        Returns
+        -------
+        a, b : numeric (float, or int or +/-np.inf)
+            end-points of the distribution's support for the specified
+            shape parameters.
+        """
+        return self.a, self.b
+
+    def _support_mask(self, x, *args):
+        a, b = self._get_support(*args)
+        with np.errstate(invalid='ignore'):
+            return (a <= x) & (x <= b)
+
+    def _open_support_mask(self, x, *args):
+        a, b = self._get_support(*args)
+        with np.errstate(invalid='ignore'):
+            return (a < x) & (x < b)
+
+    def _rvs(self, *args, size=None, random_state=None):
+        # This method must handle size being a tuple, and it must
+        # properly broadcast *args and size.  size might be
+        # an empty tuple, which means a scalar random variate is to be
+        # generated.
+
+        # Use basic inverse cdf algorithm for RV generation as default.
+        U = random_state.uniform(size=size)
+        Y = self._ppf(U, *args)
+        return Y
+
+    def _logcdf(self, x, *args):
+        with np.errstate(divide='ignore'):
+            return log(self._cdf(x, *args))
+
+    def _sf(self, x, *args):
+        return 1.0-self._cdf(x, *args)
+
+    def _logsf(self, x, *args):
+        with np.errstate(divide='ignore'):
+            return log(self._sf(x, *args))
+
+    def _ppf(self, q, *args):
+        return self._ppfvec(q, *args)
+
+    def _isf(self, q, *args):
+        return self._ppf(1.0-q, *args)  # use correct _ppf for subclasses
+
+    # These are actually called, and should not be overwritten if you
+    # want to keep error checking.
+    def rvs(self, *args, **kwds):
+        """Random variates of given type.
+
+        Parameters
+        ----------
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+        loc : array_like, optional
+            Location parameter (default=0).
+        scale : array_like, optional
+            Scale parameter (default=1).
+        size : int or tuple of ints, optional
+            Defining number of random variates (default is 1).
+        random_state : {None, int, `numpy.random.Generator`,
+                        `numpy.random.RandomState`}, optional
+
+            If `random_state` is None (or `np.random`), the
+            `numpy.random.RandomState` singleton is used.
+            If `random_state` is an int, a new ``RandomState`` instance is
+            used, seeded with `random_state`.
+            If `random_state` is already a ``Generator`` or ``RandomState``
+            instance, that instance is used.
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random variates of given `size`.
+
+        """
+        discrete = kwds.pop('discrete', None)
+        rndm = kwds.pop('random_state', None)
+        args, loc, scale, size = self._parse_args_rvs(*args, **kwds)
+        cond = logical_and(self._argcheck(*args), (scale >= 0))
+        if not np.all(cond):
+            message = ("Domain error in arguments. The `scale` parameter must "
+                       "be positive for all distributions, and many "
+                       "distributions have restrictions on shape parameters. "
+                       f"Please see the `scipy.stats.{self.name}` "
+                       "documentation for details.")
+            raise ValueError(message)
+
+        if np.all(scale == 0):
+            return loc*ones(size, 'd')
+
+        # extra gymnastics needed for a custom random_state
+        if rndm is not None:
+            random_state_saved = self._random_state
+            random_state = check_random_state(rndm)
+        else:
+            random_state = self._random_state
+
+        vals = self._rvs(*args, size=size, random_state=random_state)
+
+        vals = vals * scale + loc
+
+        # do not forget to restore the _random_state
+        if rndm is not None:
+            self._random_state = random_state_saved
+
+        # Cast to int if discrete
+        if discrete and not isinstance(self, rv_sample):
+            if size == ():
+                vals = int(vals)
+            else:
+                vals = vals.astype(np.int64)
+
+        return vals
+
+    def stats(self, *args, **kwds):
+        """Some statistics of the given RV.
+
+        Parameters
+        ----------
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            location parameter (default=0)
+        scale : array_like, optional
+            scale parameter (default=1)
+        moments : str, optional
+            composed of letters ['mvsk'] defining which moments to compute:
+            'm' = mean,
+            'v' = variance,
+            's' = (Fisher's) skew,
+            'k' = (Fisher's) kurtosis.
+            (default is 'mv')
+
+        Returns
+        -------
+        stats : sequence
+            of requested moments.
+
+        """
+        args, loc, scale, moments = self._parse_args_stats(*args, **kwds)
+        # scale = 1 by construction for discrete RVs
+        loc, scale = map(asarray, (loc, scale))
+        args = tuple(map(asarray, args))
+        cond = self._argcheck(*args) & (scale > 0) & (loc == loc)
+        output = []
+        default = np.full(shape(cond), fill_value=self.badvalue)
+
+        # Use only entries that are valid in calculation
+        if np.any(cond):
+            goodargs = argsreduce(cond, *(args+(scale, loc)))
+            scale, loc, goodargs = goodargs[-2], goodargs[-1], goodargs[:-2]
+
+            if self._stats_has_moments:
+                mu, mu2, g1, g2 = self._stats(*goodargs,
+                                              **{'moments': moments})
+            else:
+                mu, mu2, g1, g2 = self._stats(*goodargs)
+
+            if 'm' in moments:
+                if mu is None:
+                    mu = self._munp(1, *goodargs)
+                out0 = default.copy()
+                place(out0, cond, mu * scale + loc)
+                output.append(out0)
+
+            if 'v' in moments:
+                if mu2 is None:
+                    mu2p = self._munp(2, *goodargs)
+                    if mu is None:
+                        mu = self._munp(1, *goodargs)
+                    # if mean is inf then var is also inf
+                    with np.errstate(invalid='ignore'):
+                        mu2 = np.where(~np.isinf(mu), mu2p - mu**2, np.inf)
+                out0 = default.copy()
+                place(out0, cond, mu2 * scale * scale)
+                output.append(out0)
+
+            if 's' in moments:
+                if g1 is None:
+                    mu3p = self._munp(3, *goodargs)
+                    if mu is None:
+                        mu = self._munp(1, *goodargs)
+                    if mu2 is None:
+                        mu2p = self._munp(2, *goodargs)
+                        with np.errstate(invalid='ignore'):
+                            mu2 = mu2p - mu * mu
+                    with np.errstate(invalid='ignore'):
+                        mu3 = (-mu*mu - 3*mu2)*mu + mu3p
+                        g1 = mu3 / np.power(mu2, 1.5)
+                out0 = default.copy()
+                place(out0, cond, g1)
+                output.append(out0)
+
+            if 'k' in moments:
+                if g2 is None:
+                    mu4p = self._munp(4, *goodargs)
+                    if mu is None:
+                        mu = self._munp(1, *goodargs)
+                    if mu2 is None:
+                        mu2p = self._munp(2, *goodargs)
+                        with np.errstate(invalid='ignore'):
+                            mu2 = mu2p - mu * mu
+                    if g1 is None:
+                        mu3 = None
+                    else:
+                        # (mu2**1.5) breaks down for nan and inf
+                        mu3 = g1 * np.power(mu2, 1.5)
+                    if mu3 is None:
+                        mu3p = self._munp(3, *goodargs)
+                        with np.errstate(invalid='ignore'):
+                            mu3 = (-mu * mu - 3 * mu2) * mu + mu3p
+                    with np.errstate(invalid='ignore'):
+                        mu4 = ((-mu**2 - 6*mu2) * mu - 4*mu3)*mu + mu4p
+                        g2 = mu4 / mu2**2.0 - 3.0
+                out0 = default.copy()
+                place(out0, cond, g2)
+                output.append(out0)
+        else:  # no valid args
+            output = [default.copy() for _ in moments]
+
+        output = [out[()] for out in output]
+        if len(output) == 1:
+            return output[0]
+        else:
+            return tuple(output)
+
+    def entropy(self, *args, **kwds):
+        """Differential entropy of the RV.
+
+        Parameters
+        ----------
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+        loc : array_like, optional
+            Location parameter (default=0).
+        scale : array_like, optional
+            Scale parameter (default=1).
+
+        Notes
+        -----
+        Entropy is defined base `e`:
+
+        >>> import numpy as np
+        >>> from scipy.stats._distn_infrastructure import rv_discrete
+        >>> drv = rv_discrete(values=((0, 1), (0.5, 0.5)))
+        >>> np.allclose(drv.entropy(), np.log(2.0))
+        True
+
+        """
+        args, loc, scale = self._parse_args(*args, **kwds)
+        # NB: for discrete distributions scale=1 by construction in _parse_args
+        loc, scale = map(asarray, (loc, scale))
+        args = tuple(map(asarray, args))
+        cond0 = self._argcheck(*args) & (scale > 0) & (loc == loc)
+        output = zeros(shape(cond0), 'd')
+        place(output, (1-cond0), self.badvalue)
+        goodargs = argsreduce(cond0, scale, *args)
+        goodscale = goodargs[0]
+        goodargs = goodargs[1:]
+        place(output, cond0, self.vecentropy(*goodargs) + log(goodscale))
+        return output[()]
+
+    def moment(self, order, *args, **kwds):
+        """non-central moment of distribution of specified order.
+
+        Parameters
+        ----------
+        order : int, order >= 1
+            Order of moment.
+        arg1, arg2, arg3,... : float
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+        loc : array_like, optional
+            location parameter (default=0)
+        scale : array_like, optional
+            scale parameter (default=1)
+
+        """
+        n = order
+        shapes, loc, scale = self._parse_args(*args, **kwds)
+        args = np.broadcast_arrays(*(*shapes, loc, scale))
+        *shapes, loc, scale = args
+
+        i0 = np.logical_and(self._argcheck(*shapes), scale > 0)
+        i1 = np.logical_and(i0, loc == 0)
+        i2 = np.logical_and(i0, loc != 0)
+
+        args = argsreduce(i0, *shapes, loc, scale)
+        *shapes, loc, scale = args
+
+        if (floor(n) != n):
+            raise ValueError("Moment must be an integer.")
+        if (n < 0):
+            raise ValueError("Moment must be positive.")
+        mu, mu2, g1, g2 = None, None, None, None
+        if (n > 0) and (n < 5):
+            if self._stats_has_moments:
+                mdict = {'moments': {1: 'm', 2: 'v', 3: 'vs', 4: 'mvsk'}[n]}
+            else:
+                mdict = {}
+            mu, mu2, g1, g2 = self._stats(*shapes, **mdict)
+        val = np.empty(loc.shape)  # val needs to be indexed by loc
+        val[...] = _moment_from_stats(n, mu, mu2, g1, g2, self._munp, shapes)
+
+        # Convert to transformed  X = L + S*Y
+        # E[X^n] = E[(L+S*Y)^n] = L^n sum(comb(n, k)*(S/L)^k E[Y^k], k=0...n)
+        result = zeros(i0.shape)
+        place(result, ~i0, self.badvalue)
+
+        if i1.any():
+            res1 = scale[loc == 0]**n * val[loc == 0]
+            place(result, i1, res1)
+
+        if i2.any():
+            mom = [mu, mu2, g1, g2]
+            arrs = [i for i in mom if i is not None]
+            idx = [i for i in range(4) if mom[i] is not None]
+            if any(idx):
+                arrs = argsreduce(loc != 0, *arrs)
+                j = 0
+                for i in idx:
+                    mom[i] = arrs[j]
+                    j += 1
+            mu, mu2, g1, g2 = mom
+            args = argsreduce(loc != 0, *shapes, loc, scale, val)
+            *shapes, loc, scale, val = args
+
+            res2 = zeros(loc.shape, dtype='d')
+            fac = scale / loc
+            for k in range(n):
+                valk = _moment_from_stats(k, mu, mu2, g1, g2, self._munp,
+                                          shapes)
+                res2 += comb(n, k, exact=True)*fac**k * valk
+            res2 += fac**n * val
+            res2 *= loc**n
+            place(result, i2, res2)
+
+        return result[()]
+
+    def median(self, *args, **kwds):
+        """Median of the distribution.
+
+        Parameters
+        ----------
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            Location parameter, Default is 0.
+        scale : array_like, optional
+            Scale parameter, Default is 1.
+
+        Returns
+        -------
+        median : float
+            The median of the distribution.
+
+        See Also
+        --------
+        rv_discrete.ppf
+            Inverse of the CDF
+
+        """
+        return self.ppf(0.5, *args, **kwds)
+
+    def mean(self, *args, **kwds):
+        """Mean of the distribution.
+
+        Parameters
+        ----------
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            location parameter (default=0)
+        scale : array_like, optional
+            scale parameter (default=1)
+
+        Returns
+        -------
+        mean : float
+            the mean of the distribution
+
+        """
+        kwds['moments'] = 'm'
+        res = self.stats(*args, **kwds)
+        if isinstance(res, ndarray) and res.ndim == 0:
+            return res[()]
+        return res
+
+    def var(self, *args, **kwds):
+        """Variance of the distribution.
+
+        Parameters
+        ----------
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            location parameter (default=0)
+        scale : array_like, optional
+            scale parameter (default=1)
+
+        Returns
+        -------
+        var : float
+            the variance of the distribution
+
+        """
+        kwds['moments'] = 'v'
+        res = self.stats(*args, **kwds)
+        if isinstance(res, ndarray) and res.ndim == 0:
+            return res[()]
+        return res
+
+    def std(self, *args, **kwds):
+        """Standard deviation of the distribution.
+
+        Parameters
+        ----------
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            location parameter (default=0)
+        scale : array_like, optional
+            scale parameter (default=1)
+
+        Returns
+        -------
+        std : float
+            standard deviation of the distribution
+
+        """
+        kwds['moments'] = 'v'
+        res = sqrt(self.stats(*args, **kwds))
+        return res
+
+    def interval(self, confidence, *args, **kwds):
+        """Confidence interval with equal areas around the median.
+
+        Parameters
+        ----------
+        confidence : array_like of float
+            Probability that an rv will be drawn from the returned range.
+            Each value should be in the range [0, 1].
+        arg1, arg2, ... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+        loc : array_like, optional
+            location parameter, Default is 0.
+        scale : array_like, optional
+            scale parameter, Default is 1.
+
+        Returns
+        -------
+        a, b : ndarray of float
+            end-points of range that contain ``100 * alpha %`` of the rv's
+            possible values.
+
+        Notes
+        -----
+        This is implemented as ``ppf([p_tail, 1-p_tail])``, where
+        ``ppf`` is the inverse cumulative distribution function and
+        ``p_tail = (1-confidence)/2``. Suppose ``[c, d]`` is the support of a
+        discrete distribution; then ``ppf([0, 1]) == (c-1, d)``. Therefore,
+        when ``confidence=1`` and the distribution is discrete, the left end
+        of the interval will be beyond the support of the distribution.
+        For discrete distributions, the interval will limit the probability
+        in each tail to be less than or equal to ``p_tail`` (usually
+        strictly less).
+
+        """
+        alpha = confidence
+
+        alpha = asarray(alpha)
+        if np.any((alpha > 1) | (alpha < 0)):
+            raise ValueError("alpha must be between 0 and 1 inclusive")
+        q1 = (1.0-alpha)/2
+        q2 = (1.0+alpha)/2
+        a = self.ppf(q1, *args, **kwds)
+        b = self.ppf(q2, *args, **kwds)
+        return a, b
+
+    def support(self, *args, **kwargs):
+        """Support of the distribution.
+
+        Parameters
+        ----------
+        arg1, arg2, ... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+        loc : array_like, optional
+            location parameter, Default is 0.
+        scale : array_like, optional
+            scale parameter, Default is 1.
+
+        Returns
+        -------
+        a, b : array_like
+            end-points of the distribution's support.
+
+        """
+        args, loc, scale = self._parse_args(*args, **kwargs)
+        arrs = np.broadcast_arrays(*args, loc, scale)
+        args, loc, scale = arrs[:-2], arrs[-2], arrs[-1]
+        cond = self._argcheck(*args) & (scale > 0)
+        _a, _b = self._get_support(*args)
+        if cond.all():
+            return _a * scale + loc, _b * scale + loc
+        elif cond.ndim == 0:
+            return self.badvalue, self.badvalue
+        # promote bounds to at least float to fill in the badvalue
+        _a, _b = np.asarray(_a).astype('d'), np.asarray(_b).astype('d')
+        out_a, out_b = _a * scale + loc, _b * scale + loc
+        place(out_a, 1-cond, self.badvalue)
+        place(out_b, 1-cond, self.badvalue)
+        return out_a, out_b
+
+    def nnlf(self, theta, x):
+        """Negative loglikelihood function.
+        Notes
+        -----
+        This is ``-sum(log pdf(x, theta), axis=0)`` where `theta` are the
+        parameters (including loc and scale).
+        """
+        loc, scale, args = self._unpack_loc_scale(theta)
+        if not self._argcheck(*args) or scale <= 0:
+            return inf
+        x = (asarray(x)-loc) / scale
+        n_log_scale = len(x) * log(scale)
+        if np.any(~self._support_mask(x, *args)):
+            return inf
+        return self._nnlf(x, *args) + n_log_scale
+
+    def _nnlf(self, x, *args):
+        return -np.sum(self._logpxf(x, *args), axis=0)
+
+    def _nlff_and_penalty(self, x, args, log_fitfun):
+        # negative log fit function
+        cond0 = ~self._support_mask(x, *args)
+        n_bad = np.count_nonzero(cond0, axis=0)
+        if n_bad > 0:
+            x = argsreduce(~cond0, x)[0]
+        logff = log_fitfun(x, *args)
+        finite_logff = np.isfinite(logff)
+        n_bad += np.sum(~finite_logff, axis=0)
+        if n_bad > 0:
+            penalty = n_bad * log(_XMAX) * 100
+            return -np.sum(logff[finite_logff], axis=0) + penalty
+        return -np.sum(logff, axis=0)
+
+    def _penalized_nnlf(self, theta, x):
+        """Penalized negative loglikelihood function.
+        i.e., - sum (log pdf(x, theta), axis=0) + penalty
+        where theta are the parameters (including loc and scale)
+        """
+        loc, scale, args = self._unpack_loc_scale(theta)
+        if not self._argcheck(*args) or scale <= 0:
+            return inf
+        x = asarray((x-loc) / scale)
+        n_log_scale = len(x) * log(scale)
+        return self._nlff_and_penalty(x, args, self._logpxf) + n_log_scale
+
+    def _penalized_nlpsf(self, theta, x):
+        """Penalized negative log product spacing function.
+        i.e., - sum (log (diff (cdf (x, theta))), axis=0) + penalty
+        where theta are the parameters (including loc and scale)
+        Follows reference [1] of scipy.stats.fit
+        """
+        loc, scale, args = self._unpack_loc_scale(theta)
+        if not self._argcheck(*args) or scale <= 0:
+            return inf
+        x = (np.sort(x) - loc)/scale
+
+        def log_psf(x, *args):
+            x, lj = np.unique(x, return_counts=True)  # fast for sorted x
+            cdf_data = self._cdf(x, *args) if x.size else []
+            if not (x.size and 1 - cdf_data[-1] <= 0):
+                cdf = np.concatenate(([0], cdf_data, [1]))
+                lj = np.concatenate((lj, [1]))
+            else:
+                cdf = np.concatenate(([0], cdf_data))
+            # here we could use logcdf w/ logsumexp trick to take differences,
+            # but in the context of the method, it seems unlikely to matter
+            return lj * np.log(np.diff(cdf) / lj)
+
+        return self._nlff_and_penalty(x, args, log_psf)
+
+
+class _ShapeInfo:
+    def __init__(self, name, integrality=False, domain=(-np.inf, np.inf),
+                 inclusive=(True, True)):
+        self.name = name
+        self.integrality = integrality
+        self.endpoints = domain
+        self.inclusive = inclusive
+
+        domain = list(domain)
+        if np.isfinite(domain[0]) and not inclusive[0]:
+            domain[0] = np.nextafter(domain[0], np.inf)
+        if np.isfinite(domain[1]) and not inclusive[1]:
+            domain[1] = np.nextafter(domain[1], -np.inf)
+        self.domain = domain
+
+
+def _get_fixed_fit_value(kwds, names):
+    """
+    Given names such as ``['f0', 'fa', 'fix_a']``, check that there is
+    at most one non-None value in `kwds` associated with those names.
+    Return that value, or None if none of the names occur in `kwds`.
+    As a side effect, all occurrences of those names in `kwds` are
+    removed.
+    """
+    vals = [(name, kwds.pop(name)) for name in names if name in kwds]
+    if len(vals) > 1:
+        repeated = [name for name, val in vals]
+        raise ValueError("fit method got multiple keyword arguments to "
+                         "specify the same fixed parameter: " +
+                         ', '.join(repeated))
+    return vals[0][1] if vals else None
+
+
+#  continuous random variables: implement maybe later
+#
+#  hf  --- Hazard Function (PDF / SF)
+#  chf  --- Cumulative hazard function (-log(SF))
+#  psf --- Probability sparsity function (reciprocal of the pdf) in
+#                units of percent-point-function (as a function of q).
+#                Also, the derivative of the percent-point function.
+
+
+class rv_continuous(rv_generic):
+    """A generic continuous random variable class meant for subclassing.
+
+    `rv_continuous` is a base class to construct specific distribution classes
+    and instances for continuous random variables. It cannot be used
+    directly as a distribution.
+
+    Parameters
+    ----------
+    momtype : int, optional
+        The type of generic moment calculation to use: 0 for pdf, 1 (default)
+        for ppf.
+    a : float, optional
+        Lower bound of the support of the distribution, default is minus
+        infinity.
+    b : float, optional
+        Upper bound of the support of the distribution, default is plus
+        infinity.
+    xtol : float, optional
+        The tolerance for fixed point calculation for generic ppf.
+    badvalue : float, optional
+        The value in a result arrays that indicates a value that for which
+        some argument restriction is violated, default is np.nan.
+    name : str, optional
+        The name of the instance. This string is used to construct the default
+        example for distributions.
+    longname : str, optional
+        This string is used as part of the first line of the docstring returned
+        when a subclass has no docstring of its own. Note: `longname` exists
+        for backwards compatibility, do not use for new subclasses.
+    shapes : str, optional
+        The shape of the distribution. For example ``"m, n"`` for a
+        distribution that takes two integers as the two shape arguments for all
+        its methods. If not provided, shape parameters will be inferred from
+        the signature of the private methods, ``_pdf`` and ``_cdf`` of the
+        instance.
+    seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+        singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used,
+        seeded with `seed`.
+        If `seed` is already a ``Generator`` or ``RandomState`` instance then
+        that instance is used.
+
+    Attributes
+    ----------
+    a, b : float, optional
+        Lower/upper bound of the support of the unshifted/unscaled distribution.
+        This value is unaffected by the `loc` and `scale` parameters.
+        To calculate the support of the shifted/scaled distribution,
+        use the `support` method.
+
+    Methods
+    -------
+    rvs
+    pdf
+    logpdf
+    cdf
+    logcdf
+    sf
+    logsf
+    ppf
+    isf
+    moment
+    stats
+    entropy
+    expect
+    median
+    mean
+    std
+    var
+    interval
+    __call__
+    fit
+    fit_loc_scale
+    nnlf
+    support
+
+    Notes
+    -----
+    Public methods of an instance of a distribution class (e.g., ``pdf``,
+    ``cdf``) check their arguments and pass valid arguments to private,
+    computational methods (``_pdf``, ``_cdf``). For ``pdf(x)``, ``x`` is valid
+    if it is within the support of the distribution.
+    Whether a shape parameter is valid is decided by an ``_argcheck`` method
+    (which defaults to checking that its arguments are strictly positive.)
+
+    **Subclassing**
+
+    New random variables can be defined by subclassing the `rv_continuous` class
+    and re-defining at least the ``_pdf`` or the ``_cdf`` method (normalized
+    to location 0 and scale 1).
+
+    If positive argument checking is not correct for your RV
+    then you will also need to re-define the ``_argcheck`` method.
+
+    For most of the scipy.stats distributions, the support interval doesn't
+    depend on the shape parameters. ``x`` being in the support interval is
+    equivalent to ``self.a <= x <= self.b``.  If either of the endpoints of
+    the support do depend on the shape parameters, then
+    i) the distribution must implement the ``_get_support`` method; and
+    ii) those dependent endpoints must be omitted from the distribution's
+    call to the ``rv_continuous`` initializer.
+
+    Correct, but potentially slow defaults exist for the remaining
+    methods but for speed and/or accuracy you can over-ride::
+
+      _logpdf, _cdf, _logcdf, _ppf, _rvs, _isf, _sf, _logsf
+
+    The default method ``_rvs`` relies on the inverse of the cdf, ``_ppf``,
+    applied to a uniform random variate. In order to generate random variates
+    efficiently, either the default ``_ppf`` needs to be overwritten (e.g.
+    if the inverse cdf can expressed in an explicit form) or a sampling
+    method needs to be implemented in a custom ``_rvs`` method.
+
+    If possible, you should override ``_isf``, ``_sf`` or ``_logsf``.
+    The main reason would be to improve numerical accuracy: for example,
+    the survival function ``_sf`` is computed as ``1 - _cdf`` which can
+    result in loss of precision if ``_cdf(x)`` is close to one.
+
+    **Methods that can be overwritten by subclasses**
+    ::
+
+      _rvs
+      _pdf
+      _cdf
+      _sf
+      _ppf
+      _isf
+      _stats
+      _munp
+      _entropy
+      _argcheck
+      _get_support
+
+    There are additional (internal and private) generic methods that can
+    be useful for cross-checking and for debugging, but might work in all
+    cases when directly called.
+
+    A note on ``shapes``: subclasses need not specify them explicitly. In this
+    case, `shapes` will be automatically deduced from the signatures of the
+    overridden methods (`pdf`, `cdf` etc).
+    If, for some reason, you prefer to avoid relying on introspection, you can
+    specify ``shapes`` explicitly as an argument to the instance constructor.
+
+
+    **Frozen Distributions**
+
+    Normally, you must provide shape parameters (and, optionally, location and
+    scale parameters to each call of a method of a distribution.
+
+    Alternatively, the object may be called (as a function) to fix the shape,
+    location, and scale parameters returning a "frozen" continuous RV object:
+
+    rv = generic(<shape(s)>, loc=0, scale=1)
+        `rv_frozen` object with the same methods but holding the given shape,
+        location, and scale fixed
+
+    **Statistics**
+
+    Statistics are computed using numerical integration by default.
+    For speed you can redefine this using ``_stats``:
+
+    - take shape parameters and return mu, mu2, g1, g2
+    - If you can't compute one of these, return it as None
+    - Can also be defined with a keyword argument ``moments``, which is a
+      string composed of "m", "v", "s", and/or "k".
+      Only the components appearing in string should be computed and
+      returned in the order "m", "v", "s", or "k"  with missing values
+      returned as None.
+
+    Alternatively, you can override ``_munp``, which takes ``n`` and shape
+    parameters and returns the n-th non-central moment of the distribution.
+
+    **Deepcopying / Pickling**
+
+    If a distribution or frozen distribution is deepcopied (pickled/unpickled,
+    etc.), any underlying random number generator is deepcopied with it. An
+    implication is that if a distribution relies on the singleton RandomState
+    before copying, it will rely on a copy of that random state after copying,
+    and ``np.random.seed`` will no longer control the state.
+
+    Examples
+    --------
+    To create a new Gaussian distribution, we would do the following:
+
+    >>> from scipy.stats import rv_continuous
+    >>> class gaussian_gen(rv_continuous):
+    ...     "Gaussian distribution"
+    ...     def _pdf(self, x):
+    ...         return np.exp(-x**2 / 2.) / np.sqrt(2.0 * np.pi)
+    >>> gaussian = gaussian_gen(name='gaussian')
+
+    ``scipy.stats`` distributions are *instances*, so here we subclass
+    `rv_continuous` and create an instance. With this, we now have
+    a fully functional distribution with all relevant methods automagically
+    generated by the framework.
+
+    Note that above we defined a standard normal distribution, with zero mean
+    and unit variance. Shifting and scaling of the distribution can be done
+    by using ``loc`` and ``scale`` parameters: ``gaussian.pdf(x, loc, scale)``
+    essentially computes ``y = (x - loc) / scale`` and
+    ``gaussian._pdf(y) / scale``.
+
+    """
+
+    _support: tuple[float | str, float | str]  # not required
+
+    def __init__(self, momtype=1, a=None, b=None, xtol=1e-14,
+                 badvalue=None, name=None, longname=None,
+                 shapes=None, seed=None):
+
+        super().__init__(seed)
+
+        # save the ctor parameters, cf generic freeze
+        self._ctor_param = dict(
+            momtype=momtype, a=a, b=b, xtol=xtol,
+            badvalue=badvalue, name=name, longname=longname,
+            shapes=shapes, seed=seed)
+
+        if badvalue is None:
+            badvalue = nan
+        if name is None:
+            name = 'Distribution'
+        self.badvalue = badvalue
+        self.name = name
+        self.a = a
+        self.b = b
+        if a is None:
+            self.a = -inf
+        if b is None:
+            self.b = inf
+        self.xtol = xtol
+        self.moment_type = momtype
+        self.shapes = shapes
+
+        self._construct_argparser(meths_to_inspect=[self._pdf, self._cdf],
+                                  locscale_in='loc=0, scale=1',
+                                  locscale_out='loc, scale')
+        self._attach_methods()
+
+        if longname is None:
+            if name[0] in ['aeiouAEIOU']:
+                hstr = "An "
+            else:
+                hstr = "A "
+            longname = hstr + name
+
+        if sys.flags.optimize < 2:
+            # Skip adding docstrings if interpreter is run with -OO
+            if self.__doc__ is None:
+                self._construct_default_doc(longname=longname,
+                                            docdict=docdict,
+                                            discrete='continuous')
+            else:
+                dct = dict(distcont)
+                self._construct_doc(docdict, dct.get(self.name))
+
+    def __getstate__(self):
+        dct = self.__dict__.copy()
+
+        # these methods will be remade in __setstate__
+        # _random_state attribute is taken care of by rv_generic
+        attrs = ["_parse_args", "_parse_args_stats", "_parse_args_rvs",
+                 "_cdfvec", "_ppfvec", "vecentropy", "generic_moment"]
+        [dct.pop(attr, None) for attr in attrs]
+        return dct
+
+    def _attach_methods(self):
+        """
+        Attaches dynamically created methods to the rv_continuous instance.
+        """
+        # _attach_methods is responsible for calling _attach_argparser_methods
+        self._attach_argparser_methods()
+
+        # nin correction
+        self._ppfvec = vectorize(self._ppf_single, otypes='d')
+        self._ppfvec.nin = self.numargs + 1
+        self.vecentropy = vectorize(self._entropy, otypes='d')
+        self._cdfvec = vectorize(self._cdf_single, otypes='d')
+        self._cdfvec.nin = self.numargs + 1
+
+        if self.moment_type == 0:
+            self.generic_moment = vectorize(self._mom0_sc, otypes='d')
+        else:
+            self.generic_moment = vectorize(self._mom1_sc, otypes='d')
+        # Because of the *args argument of _mom0_sc, vectorize cannot count the
+        # number of arguments correctly.
+        self.generic_moment.nin = self.numargs + 1
+
+    def _updated_ctor_param(self):
+        """Return the current version of _ctor_param, possibly updated by user.
+
+        Used by freezing.
+        Keep this in sync with the signature of __init__.
+        """
+        dct = self._ctor_param.copy()
+        dct['a'] = self.a
+        dct['b'] = self.b
+        dct['xtol'] = self.xtol
+        dct['badvalue'] = self.badvalue
+        dct['name'] = self.name
+        dct['shapes'] = self.shapes
+        return dct
+
+    def _ppf_to_solve(self, x, q, *args):
+        return self.cdf(*(x, )+args)-q
+
+    def _ppf_single(self, q, *args):
+        factor = 10.
+        left, right = self._get_support(*args)
+
+        if np.isinf(left):
+            left = min(-factor, right)
+            while self._ppf_to_solve(left, q, *args) > 0.:
+                left, right = left * factor, left
+            # left is now such that cdf(left) <= q
+            # if right has changed, then cdf(right) > q
+
+        if np.isinf(right):
+            right = max(factor, left)
+            while self._ppf_to_solve(right, q, *args) < 0.:
+                left, right = right, right * factor
+            # right is now such that cdf(right) >= q
+
+        return optimize.brentq(self._ppf_to_solve,
+                               left, right, args=(q,)+args, xtol=self.xtol)
+
+    # moment from definition
+    def _mom_integ0(self, x, m, *args):
+        return x**m * self.pdf(x, *args)
+
+    def _mom0_sc(self, m, *args):
+        _a, _b = self._get_support(*args)
+        return integrate.quad(self._mom_integ0, _a, _b,
+                              args=(m,)+args)[0]
+
+    # moment calculated using ppf
+    def _mom_integ1(self, q, m, *args):
+        return (self.ppf(q, *args))**m
+
+    def _mom1_sc(self, m, *args):
+        return integrate.quad(self._mom_integ1, 0, 1, args=(m,)+args)[0]
+
+    def _pdf(self, x, *args):
+        return _derivative(self._cdf, x, dx=1e-5, args=args, order=5)
+
+    # Could also define any of these
+    def _logpdf(self, x, *args):
+        p = self._pdf(x, *args)
+        with np.errstate(divide='ignore'):
+            return log(p)
+
+    def _logpxf(self, x, *args):
+        # continuous distributions have PDF, discrete have PMF, but sometimes
+        # the distinction doesn't matter. This lets us use `_logpxf` for both
+        # discrete and continuous distributions.
+        return self._logpdf(x, *args)
+
+    def _cdf_single(self, x, *args):
+        _a, _b = self._get_support(*args)
+        return integrate.quad(self._pdf, _a, x, args=args)[0]
+
+    def _cdf(self, x, *args):
+        return self._cdfvec(x, *args)
+
+    def _logcdf(self, x, *args):
+        median = self._ppf(0.5, *args)
+        with np.errstate(divide='ignore'):
+            return xpx.apply_where(
+                x < median, (x,) + args,
+                lambda x, *args: np.log(self._cdf(x, *args)),
+                lambda x, *args: np.log1p(-self._sf(x, *args)))
+
+    def _logsf(self, x, *args):
+        median = self._ppf(0.5, *args)
+        with np.errstate(divide='ignore'):
+            return xpx.apply_where(
+                x > median, (x,) + args,
+                lambda x, *args: np.log(self._sf(x, *args)),
+                lambda x, *args: np.log1p(-self._cdf(x, *args)))
+
+    # generic _argcheck, _sf, _ppf, _isf, _rvs are defined
+    # in rv_generic
+
+    def pdf(self, x, *args, **kwds):
+        """Probability density function at x of the given RV.
+
+        Parameters
+        ----------
+        x : array_like
+            quantiles
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            location parameter (default=0)
+        scale : array_like, optional
+            scale parameter (default=1)
+
+        Returns
+        -------
+        pdf : ndarray
+            Probability density function evaluated at x
+
+        """
+        args, loc, scale = self._parse_args(*args, **kwds)
+        x, loc, scale = map(asarray, (x, loc, scale))
+        args = tuple(map(asarray, args))
+        dtyp = np.promote_types(x.dtype, np.float64)
+        x = np.asarray((x - loc)/scale, dtype=dtyp)
+        cond0 = self._argcheck(*args) & (scale > 0)
+        cond1 = self._support_mask(x, *args) & (scale > 0)
+        cond = cond0 & cond1
+        output = zeros(shape(cond), dtyp)
+        putmask(output, (1-cond0)+np.isnan(x), self.badvalue)
+        if np.any(cond):
+            goodargs = argsreduce(cond, *((x,)+args+(scale,)))
+            scale, goodargs = goodargs[-1], goodargs[:-1]
+            place(output, cond, self._pdf(*goodargs) / scale)
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def logpdf(self, x, *args, **kwds):
+        """Log of the probability density function at x of the given RV.
+
+        This uses a more numerically accurate calculation if available.
+
+        Parameters
+        ----------
+        x : array_like
+            quantiles
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            location parameter (default=0)
+        scale : array_like, optional
+            scale parameter (default=1)
+
+        Returns
+        -------
+        logpdf : array_like
+            Log of the probability density function evaluated at x
+
+        """
+        args, loc, scale = self._parse_args(*args, **kwds)
+        x, loc, scale = map(asarray, (x, loc, scale))
+        args = tuple(map(asarray, args))
+        dtyp = np.promote_types(x.dtype, np.float64)
+        x = np.asarray((x - loc)/scale, dtype=dtyp)
+        cond0 = self._argcheck(*args) & (scale > 0)
+        cond1 = self._support_mask(x, *args) & (scale > 0)
+        cond = cond0 & cond1
+        output = empty(shape(cond), dtyp)
+        output.fill(-inf)
+        putmask(output, (1-cond0)+np.isnan(x), self.badvalue)
+        if np.any(cond):
+            goodargs = argsreduce(cond, *((x,)+args+(scale,)))
+            scale, goodargs = goodargs[-1], goodargs[:-1]
+            place(output, cond, self._logpdf(*goodargs) - log(scale))
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def cdf(self, x, *args, **kwds):
+        """
+        Cumulative distribution function of the given RV.
+
+        Parameters
+        ----------
+        x : array_like
+            quantiles
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            location parameter (default=0)
+        scale : array_like, optional
+            scale parameter (default=1)
+
+        Returns
+        -------
+        cdf : ndarray
+            Cumulative distribution function evaluated at `x`
+
+        """
+        args, loc, scale = self._parse_args(*args, **kwds)
+        x, loc, scale = map(asarray, (x, loc, scale))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        dtyp = np.promote_types(x.dtype, np.float64)
+        x = np.asarray((x - loc)/scale, dtype=dtyp)
+        cond0 = self._argcheck(*args) & (scale > 0)
+        cond1 = self._open_support_mask(x, *args) & (scale > 0)
+        cond2 = (x >= np.asarray(_b)) & cond0
+        cond = cond0 & cond1
+        output = zeros(shape(cond), dtyp)
+        place(output, (1-cond0)+np.isnan(x), self.badvalue)
+        place(output, cond2, 1.0)
+        if np.any(cond):  # call only if at least 1 entry
+            goodargs = argsreduce(cond, *((x,)+args))
+            place(output, cond, self._cdf(*goodargs))
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def logcdf(self, x, *args, **kwds):
+        """Log of the cumulative distribution function at x of the given RV.
+
+        Parameters
+        ----------
+        x : array_like
+            quantiles
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            location parameter (default=0)
+        scale : array_like, optional
+            scale parameter (default=1)
+
+        Returns
+        -------
+        logcdf : array_like
+            Log of the cumulative distribution function evaluated at x
+
+        """
+        args, loc, scale = self._parse_args(*args, **kwds)
+        x, loc, scale = map(asarray, (x, loc, scale))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        dtyp = np.promote_types(x.dtype, np.float64)
+        x = np.asarray((x - loc)/scale, dtype=dtyp)
+        cond0 = self._argcheck(*args) & (scale > 0)
+        cond1 = self._open_support_mask(x, *args) & (scale > 0)
+        cond2 = (x >= _b) & cond0
+        cond = cond0 & cond1
+        output = empty(shape(cond), dtyp)
+        output.fill(-inf)
+        place(output, (1-cond0)*(cond1 == cond1)+np.isnan(x), self.badvalue)
+        place(output, cond2, 0.0)
+        if np.any(cond):  # call only if at least 1 entry
+            goodargs = argsreduce(cond, *((x,)+args))
+            place(output, cond, self._logcdf(*goodargs))
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def sf(self, x, *args, **kwds):
+        """Survival function (1 - `cdf`) at x of the given RV.
+
+        Parameters
+        ----------
+        x : array_like
+            quantiles
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            location parameter (default=0)
+        scale : array_like, optional
+            scale parameter (default=1)
+
+        Returns
+        -------
+        sf : array_like
+            Survival function evaluated at x
+
+        """
+        args, loc, scale = self._parse_args(*args, **kwds)
+        x, loc, scale = map(asarray, (x, loc, scale))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        dtyp = np.promote_types(x.dtype, np.float64)
+        x = np.asarray((x - loc)/scale, dtype=dtyp)
+        cond0 = self._argcheck(*args) & (scale > 0)
+        cond1 = self._open_support_mask(x, *args) & (scale > 0)
+        cond2 = cond0 & (x <= _a)
+        cond = cond0 & cond1
+        output = zeros(shape(cond), dtyp)
+        place(output, (1-cond0)+np.isnan(x), self.badvalue)
+        place(output, cond2, 1.0)
+        if np.any(cond):
+            goodargs = argsreduce(cond, *((x,)+args))
+            place(output, cond, self._sf(*goodargs))
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def logsf(self, x, *args, **kwds):
+        """Log of the survival function of the given RV.
+
+        Returns the log of the "survival function," defined as (1 - `cdf`),
+        evaluated at `x`.
+
+        Parameters
+        ----------
+        x : array_like
+            quantiles
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            location parameter (default=0)
+        scale : array_like, optional
+            scale parameter (default=1)
+
+        Returns
+        -------
+        logsf : ndarray
+            Log of the survival function evaluated at `x`.
+
+        """
+        args, loc, scale = self._parse_args(*args, **kwds)
+        x, loc, scale = map(asarray, (x, loc, scale))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        dtyp = np.promote_types(x.dtype, np.float64)
+        x = np.asarray((x - loc)/scale, dtype=dtyp)
+        cond0 = self._argcheck(*args) & (scale > 0)
+        cond1 = self._open_support_mask(x, *args) & (scale > 0)
+        cond2 = cond0 & (x <= _a)
+        cond = cond0 & cond1
+        output = empty(shape(cond), dtyp)
+        output.fill(-inf)
+        place(output, (1-cond0)+np.isnan(x), self.badvalue)
+        place(output, cond2, 0.0)
+        if np.any(cond):
+            goodargs = argsreduce(cond, *((x,)+args))
+            place(output, cond, self._logsf(*goodargs))
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def ppf(self, q, *args, **kwds):
+        """Percent point function (inverse of `cdf`) at q of the given RV.
+
+        Parameters
+        ----------
+        q : array_like
+            lower tail probability
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            location parameter (default=0)
+        scale : array_like, optional
+            scale parameter (default=1)
+
+        Returns
+        -------
+        x : array_like
+            quantile corresponding to the lower tail probability q.
+
+        """
+        args, loc, scale = self._parse_args(*args, **kwds)
+        q, loc, scale = map(asarray, (q, loc, scale))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        cond0 = self._argcheck(*args) & (scale > 0) & (loc == loc)
+        cond1 = (0 < q) & (q < 1)
+        cond2 = cond0 & (q == 0)
+        cond3 = cond0 & (q == 1)
+        cond = cond0 & cond1
+        output = np.full(shape(cond), fill_value=self.badvalue)
+
+        lower_bound = _a * scale + loc
+        upper_bound = _b * scale + loc
+        place(output, cond2, argsreduce(cond2, lower_bound)[0])
+        place(output, cond3, argsreduce(cond3, upper_bound)[0])
+
+        if np.any(cond):  # call only if at least 1 entry
+            goodargs = argsreduce(cond, *((q,)+args+(scale, loc)))
+            scale, loc, goodargs = goodargs[-2], goodargs[-1], goodargs[:-2]
+            place(output, cond, self._ppf(*goodargs) * scale + loc)
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def isf(self, q, *args, **kwds):
+        """Inverse survival function (inverse of `sf`) at q of the given RV.
+
+        Parameters
+        ----------
+        q : array_like
+            upper tail probability
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            location parameter (default=0)
+        scale : array_like, optional
+            scale parameter (default=1)
+
+        Returns
+        -------
+        x : ndarray or scalar
+            Quantile corresponding to the upper tail probability q.
+
+        """
+        args, loc, scale = self._parse_args(*args, **kwds)
+        q, loc, scale = map(asarray, (q, loc, scale))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        cond0 = self._argcheck(*args) & (scale > 0) & (loc == loc)
+        cond1 = (0 < q) & (q < 1)
+        cond2 = cond0 & (q == 1)
+        cond3 = cond0 & (q == 0)
+        cond = cond0 & cond1
+        output = np.full(shape(cond), fill_value=self.badvalue)
+
+        lower_bound = _a * scale + loc
+        upper_bound = _b * scale + loc
+        place(output, cond2, argsreduce(cond2, lower_bound)[0])
+        place(output, cond3, argsreduce(cond3, upper_bound)[0])
+
+        if np.any(cond):
+            goodargs = argsreduce(cond, *((q,)+args+(scale, loc)))
+            scale, loc, goodargs = goodargs[-2], goodargs[-1], goodargs[:-2]
+            place(output, cond, self._isf(*goodargs) * scale + loc)
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def _unpack_loc_scale(self, theta):
+        try:
+            loc = theta[-2]
+            scale = theta[-1]
+            args = tuple(theta[:-2])
+        except IndexError as e:
+            raise ValueError("Not enough input arguments.") from e
+        return loc, scale, args
+
+    def _nnlf_and_penalty(self, x, args):
+        """
+        Compute the penalized negative log-likelihood for the
+        "standardized" data (i.e. already shifted by loc and
+        scaled by scale) for the shape parameters in `args`.
+
+        `x` can be a 1D numpy array or a CensoredData instance.
+        """
+        if isinstance(x, CensoredData):
+            # Filter out the data that is not in the support.
+            xs = x._supported(*self._get_support(*args))
+            n_bad = len(x) - len(xs)
+            i1, i2 = xs._interval.T
+            terms = [
+                # logpdf of the noncensored data.
+                self._logpdf(xs._uncensored, *args),
+                # logcdf of the left-censored data.
+                self._logcdf(xs._left, *args),
+                # logsf of the right-censored data.
+                self._logsf(xs._right, *args),
+                # log of probability of the interval-censored data.
+                np.log(self._delta_cdf(i1, i2, *args)),
+            ]
+        else:
+            cond0 = ~self._support_mask(x, *args)
+            n_bad = np.count_nonzero(cond0)
+            if n_bad > 0:
+                x = argsreduce(~cond0, x)[0]
+            terms = [self._logpdf(x, *args)]
+
+        totals, bad_counts = zip(*[_sum_finite(term) for term in terms])
+        total = sum(totals)
+        n_bad += sum(bad_counts)
+
+        return -total + n_bad * _LOGXMAX * 100
+
+    def _penalized_nnlf(self, theta, x):
+        """Penalized negative loglikelihood function.
+
+        i.e., - sum (log pdf(x, theta), axis=0) + penalty
+        where theta are the parameters (including loc and scale)
+        """
+        loc, scale, args = self._unpack_loc_scale(theta)
+        if not self._argcheck(*args) or scale <= 0:
+            return inf
+        if isinstance(x, CensoredData):
+            x = (x - loc) / scale
+            n_log_scale = (len(x) - x.num_censored()) * log(scale)
+        else:
+            x = (x - loc) / scale
+            n_log_scale = len(x) * log(scale)
+
+        return self._nnlf_and_penalty(x, args) + n_log_scale
+
+    def _fitstart(self, data, args=None):
+        """Starting point for fit (shape arguments + loc + scale)."""
+        if args is None:
+            args = (1.0,)*self.numargs
+        loc, scale = self._fit_loc_scale_support(data, *args)
+        return args + (loc, scale)
+
+    def _reduce_func(self, args, kwds, data=None):
+        """
+        Return the (possibly reduced) function to optimize in order to find MLE
+        estimates for the .fit method.
+        """
+        # Convert fixed shape parameters to the standard numeric form: e.g. for
+        # stats.beta, shapes='a, b'. To fix `a`, the caller can give a value
+        # for `f0`, `fa` or 'fix_a'.  The following converts the latter two
+        # into the first (numeric) form.
+        shapes = []
+        if self.shapes:
+            shapes = self.shapes.replace(',', ' ').split()
+            for j, s in enumerate(shapes):
+                key = 'f' + str(j)
+                names = [key, 'f' + s, 'fix_' + s]
+                val = _get_fixed_fit_value(kwds, names)
+                if val is not None:
+                    kwds[key] = val
+
+        args = list(args)
+        Nargs = len(args)
+        fixedn = []
+        names = [f'f{n}' for n in range(Nargs - 2)] + ['floc', 'fscale']
+        x0 = []
+        for n, key in enumerate(names):
+            if key in kwds:
+                fixedn.append(n)
+                args[n] = kwds.pop(key)
+            else:
+                x0.append(args[n])
+
+        methods = {"mle", "mm"}
+        method = kwds.pop('method', "mle").lower()
+        if method == "mm":
+            n_params = len(shapes) + 2 - len(fixedn)
+            exponents = (np.arange(1, n_params+1))[:, np.newaxis]
+            data_moments = np.sum(data[None, :]**exponents/len(data), axis=1)
+
+            def objective(theta, x):
+                return self._moment_error(theta, x, data_moments)
+
+        elif method == "mle":
+            objective = self._penalized_nnlf
+        else:
+            raise ValueError(f"Method '{method}' not available; "
+                             f"must be one of {methods}")
+
+        if len(fixedn) == 0:
+            func = objective
+            restore = None
+        else:
+            if len(fixedn) == Nargs:
+                raise ValueError(
+                    "All parameters fixed. There is nothing to optimize.")
+
+            def restore(args, theta):
+                # Replace with theta for all numbers not in fixedn
+                # This allows the non-fixed values to vary, but
+                #  we still call self.nnlf with all parameters.
+                i = 0
+                for n in range(Nargs):
+                    if n not in fixedn:
+                        args[n] = theta[i]
+                        i += 1
+                return args
+
+            def func(theta, x):
+                newtheta = restore(args[:], theta)
+                return objective(newtheta, x)
+
+        return x0, func, restore, args
+
+    def _moment_error(self, theta, x, data_moments):
+        loc, scale, args = self._unpack_loc_scale(theta)
+        if not self._argcheck(*args) or scale <= 0:
+            return inf
+
+        dist_moments = np.array([self.moment(i+1, *args, loc=loc, scale=scale)
+                                 for i in range(len(data_moments))])
+        if np.any(np.isnan(dist_moments)):
+            raise ValueError("Method of moments encountered a non-finite "
+                             "distribution moment and cannot continue. "
+                             "Consider trying method='MLE'.")
+
+        return (((data_moments - dist_moments) /
+                 np.maximum(np.abs(data_moments), 1e-8))**2).sum()
+
+    def fit(self, data, *args, **kwds):
+        r"""
+        Return estimates of shape (if applicable), location, and scale
+        parameters from data. The default estimation method is Maximum
+        Likelihood Estimation (MLE), but Method of Moments (MM)
+        is also available.
+
+        Starting estimates for the fit are given by input arguments;
+        for any arguments not provided with starting estimates,
+        ``self._fitstart(data)`` is called to generate such.
+
+        One can hold some parameters fixed to specific values by passing in
+        keyword arguments ``f0``, ``f1``, ..., ``fn`` (for shape parameters)
+        and ``floc`` and ``fscale`` (for location and scale parameters,
+        respectively).
+
+        Parameters
+        ----------
+        data : array_like or `CensoredData` instance
+            Data to use in estimating the distribution parameters.
+        arg1, arg2, arg3,... : floats, optional
+            Starting value(s) for any shape-characterizing arguments (those not
+            provided will be determined by a call to ``_fitstart(data)``).
+            No default value.
+        **kwds : floats, optional
+            - `loc`: initial guess of the distribution's location parameter.
+            - `scale`: initial guess of the distribution's scale parameter.
+
+            Special keyword arguments are recognized as holding certain
+            parameters fixed:
+
+            - f0, ..., fn : hold respective shape parameters fixed.
+              Alternatively, shape parameters to fix can be specified by name.
+              For example, if ``self.shapes == "a, b"``, ``fa`` and ``fix_a``
+              are equivalent to ``f0``, and ``fb`` and ``fix_b`` are
+              equivalent to ``f1``.
+
+            - floc : hold location parameter fixed to specified value.
+
+            - fscale : hold scale parameter fixed to specified value.
+
+            - optimizer : The optimizer to use.  The optimizer must take
+              ``func`` and starting position as the first two arguments,
+              plus ``args`` (for extra arguments to pass to the
+              function to be optimized) and ``disp``.
+              The ``fit`` method calls the optimizer with ``disp=0`` to suppress output.
+              The optimizer must return the estimated parameters.
+
+            - method : The method to use. The default is "MLE" (Maximum
+              Likelihood Estimate); "MM" (Method of Moments)
+              is also available.
+
+        Raises
+        ------
+        TypeError, ValueError
+            If an input is invalid
+        `~scipy.stats.FitError`
+            If fitting fails or the fit produced would be invalid
+
+        Returns
+        -------
+        parameter_tuple : tuple of floats
+            Estimates for any shape parameters (if applicable), followed by
+            those for location and scale. For most random variables, shape
+            statistics will be returned, but there are exceptions (e.g.
+            ``norm``).
+
+        Notes
+        -----
+        With ``method="MLE"`` (default), the fit is computed by minimizing
+        the negative log-likelihood function. A large, finite penalty
+        (rather than infinite negative log-likelihood) is applied for
+        observations beyond the support of the distribution.
+
+        With ``method="MM"``, the fit is computed by minimizing the L2 norm
+        of the relative errors between the first *k* raw (about zero) data
+        moments and the corresponding distribution moments, where *k* is the
+        number of non-fixed parameters.
+        More precisely, the objective function is::
+
+            (((data_moments - dist_moments)
+              / np.maximum(np.abs(data_moments), 1e-8))**2).sum()
+
+        where the constant ``1e-8`` avoids division by zero in case of
+        vanishing data moments. Typically, this error norm can be reduced to
+        zero.
+        Note that the standard method of moments can produce parameters for
+        which some data are outside the support of the fitted distribution;
+        this implementation does nothing to prevent this.
+
+        For either method,
+        the returned answer is not guaranteed to be globally optimal; it
+        may only be locally optimal, or the optimization may fail altogether.
+        If the data contain any of ``np.nan``, ``np.inf``, or ``-np.inf``,
+        the `fit` method will raise a ``RuntimeError``.
+
+        When passing a ``CensoredData`` instance to ``data``, the log-likelihood
+        function is defined as:
+
+        .. math::
+
+            l(\pmb{\theta}; k) & = \sum
+                                    \log(f(k_u; \pmb{\theta}))
+                                + \sum
+                                    \log(F(k_l; \pmb{\theta})) \\
+                                & + \sum
+                                    \log(1 - F(k_r; \pmb{\theta})) \\
+                                & + \sum
+                                    \log(F(k_{\text{high}, i}; \pmb{\theta})
+                                    - F(k_{\text{low}, i}; \pmb{\theta}))
+
+        where :math:`f` and :math:`F` are the pdf and cdf, respectively, of the
+        function being fitted, :math:`\pmb{\theta}` is the parameter vector,
+        :math:`u` are the indices of uncensored observations,
+        :math:`l` are the indices of left-censored observations,
+        :math:`r` are the indices of right-censored observations,
+        subscripts "low"/"high" denote endpoints of interval-censored observations, and
+        :math:`i` are the indices of interval-censored observations.
+
+        Examples
+        --------
+
+        Generate some data to fit: draw random variates from the `beta`
+        distribution
+
+        >>> import numpy as np
+        >>> from scipy.stats import beta
+        >>> a, b = 1., 2.
+        >>> rng = np.random.default_rng(172786373191770012695001057628748821561)
+        >>> x = beta.rvs(a, b, size=1000, random_state=rng)
+
+        Now we can fit all four parameters (``a``, ``b``, ``loc`` and
+        ``scale``):
+
+        >>> a1, b1, loc1, scale1 = beta.fit(x)
+        >>> a1, b1, loc1, scale1
+        (1.0198945204435628, 1.9484708982737828, 4.372241314917588e-05, 0.9979078845964814)
+
+        The fit can be done also using a custom optimizer:
+
+        >>> from scipy.optimize import minimize
+        >>> def custom_optimizer(func, x0, args=(), disp=0):
+        ...     res = minimize(func, x0, args, method="slsqp", options={"disp": disp})
+        ...     if res.success:
+        ...         return res.x
+        ...     raise RuntimeError('optimization routine failed')
+        >>> a1, b1, loc1, scale1 = beta.fit(x, method="MLE", optimizer=custom_optimizer)
+        >>> a1, b1, loc1, scale1
+        (1.0198821087258905, 1.948484145914738, 4.3705304486881485e-05, 0.9979104663953395)
+
+        We can also use some prior knowledge about the dataset: let's keep
+        ``loc`` and ``scale`` fixed:
+
+        >>> a1, b1, loc1, scale1 = beta.fit(x, floc=0, fscale=1)
+        >>> loc1, scale1
+        (0, 1)
+
+        We can also keep shape parameters fixed by using ``f``-keywords. To
+        keep the zero-th shape parameter ``a`` equal 1, use ``f0=1`` or,
+        equivalently, ``fa=1``:
+
+        >>> a1, b1, loc1, scale1 = beta.fit(x, fa=1, floc=0, fscale=1)
+        >>> a1
+        1
+
+        Not all distributions return estimates for the shape parameters.
+        ``norm`` for example just returns estimates for location and scale:
+
+        >>> from scipy.stats import norm
+        >>> x = norm.rvs(a, b, size=1000, random_state=123)
+        >>> loc1, scale1 = norm.fit(x)
+        >>> loc1, scale1
+        (0.92087172783841631, 2.0015750750324668)
+        """ # noqa: E501
+        method = kwds.get('method', "mle").lower()
+
+        censored = isinstance(data, CensoredData)
+        if censored:
+            if method != 'mle':
+                raise ValueError('For censored data, the method must'
+                                 ' be "MLE".')
+            if data.num_censored() == 0:
+                # There are no censored values in data, so replace the
+                # CensoredData instance with a regular array.
+                data = data._uncensored
+                censored = False
+
+        Narg = len(args)
+        if Narg > self.numargs:
+            raise TypeError("Too many input arguments.")
+
+        # Check the finiteness of data only if data is not an instance of
+        # CensoredData.  The arrays in a CensoredData instance have already
+        # been validated.
+        if not censored:
+            # Note: `ravel()` is called for backwards compatibility.
+            data = np.asarray(data).ravel()
+            if not np.isfinite(data).all():
+                raise ValueError("The data contains non-finite values.")
+
+        start = [None]*2
+        if (Narg < self.numargs) or not ('loc' in kwds and
+                                         'scale' in kwds):
+            # get distribution specific starting locations
+            start = self._fitstart(data)
+            args += start[Narg:-2]
+        loc = kwds.pop('loc', start[-2])
+        scale = kwds.pop('scale', start[-1])
+        args += (loc, scale)
+        x0, func, restore, args = self._reduce_func(args, kwds, data=data)
+        optimizer = kwds.pop('optimizer', optimize.fmin)
+        # convert string to function in scipy.optimize
+        optimizer = _fit_determine_optimizer(optimizer)
+        # by now kwds must be empty, since everybody took what they needed
+        if kwds:
+            raise TypeError(f"Unknown arguments: {kwds}.")
+
+        # In some cases, method of moments can be done with fsolve/root
+        # instead of an optimizer, but sometimes no solution exists,
+        # especially when the user fixes parameters. Minimizing the sum
+        # of squares of the error generalizes to these cases.
+        vals = optimizer(func, x0, args=(data,), disp=0)
+        obj = func(vals, data)
+
+        if restore is not None:
+            vals = restore(args, vals)
+        vals = tuple(vals)
+
+        loc, scale, shapes = self._unpack_loc_scale(vals)
+        if not (np.all(self._argcheck(*shapes)) and scale > 0):
+            raise FitError("Optimization converged to parameters that are "
+                           "outside the range allowed by the distribution.")
+
+        if method == 'mm':
+            if not np.isfinite(obj):
+                raise FitError("Optimization failed: either a data moment "
+                               "or fitted distribution moment is "
+                               "non-finite.")
+
+        return vals
+
+    def _fit_loc_scale_support(self, data, *args):
+        """Estimate loc and scale parameters from data accounting for support.
+
+        Parameters
+        ----------
+        data : array_like
+            Data to fit.
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+
+        Returns
+        -------
+        Lhat : float
+            Estimated location parameter for the data.
+        Shat : float
+            Estimated scale parameter for the data.
+
+        """
+        if isinstance(data, CensoredData):
+            # For this estimate, "uncensor" the data by taking the
+            # given endpoints as the data for the left- or right-censored
+            # data, and the mean for the interval-censored data.
+            data = data._uncensor()
+        else:
+            data = np.asarray(data)
+
+        # Estimate location and scale according to the method of moments.
+        loc_hat, scale_hat = self.fit_loc_scale(data, *args)
+
+        # Compute the support according to the shape parameters.
+        self._argcheck(*args)
+        _a, _b = self._get_support(*args)
+        a, b = _a, _b
+        support_width = b - a
+
+        # If the support is empty then return the moment-based estimates.
+        if support_width <= 0:
+            return loc_hat, scale_hat
+
+        # Compute the proposed support according to the loc and scale
+        # estimates.
+        a_hat = loc_hat + a * scale_hat
+        b_hat = loc_hat + b * scale_hat
+
+        # Use the moment-based estimates if they are compatible with the data.
+        data_a = np.min(data)
+        data_b = np.max(data)
+        if a_hat < data_a and data_b < b_hat:
+            return loc_hat, scale_hat
+
+        # Otherwise find other estimates that are compatible with the data.
+        data_width = data_b - data_a
+        rel_margin = 0.1
+        margin = data_width * rel_margin
+
+        # For a finite interval, both the location and scale
+        # should have interesting values.
+        if support_width < np.inf:
+            loc_hat = (data_a - a) - margin
+            scale_hat = (data_width + 2 * margin) / support_width
+            return loc_hat, scale_hat
+
+        # For a one-sided interval, use only an interesting location parameter.
+        if a > -np.inf:
+            return (data_a - a) - margin, 1
+        elif b < np.inf:
+            return (data_b - b) + margin, 1
+        else:
+            raise RuntimeError
+
+    def fit_loc_scale(self, data, *args):
+        """
+        Estimate loc and scale parameters from data using 1st and 2nd moments.
+
+        Parameters
+        ----------
+        data : array_like
+            Data to fit.
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+
+        Returns
+        -------
+        Lhat : float
+            Estimated location parameter for the data.
+        Shat : float
+            Estimated scale parameter for the data.
+
+        """
+        mu, mu2 = self.stats(*args, **{'moments': 'mv'})
+        tmp = asarray(data)
+        muhat = tmp.mean()
+        mu2hat = tmp.var()
+        Shat = sqrt(mu2hat / mu2)
+        with np.errstate(invalid='ignore'):
+            Lhat = muhat - Shat*mu
+        if not np.isfinite(Lhat):
+            Lhat = 0
+        if not (np.isfinite(Shat) and (0 < Shat)):
+            Shat = 1
+        return Lhat, Shat
+
+    def _entropy(self, *args):
+        def integ(x):
+            val = self._pdf(x, *args)
+            return entr(val)
+
+        # upper limit is often inf, so suppress warnings when integrating
+        _a, _b = self._get_support(*args)
+        with np.errstate(over='ignore'):
+            h = integrate.quad(integ, _a, _b)[0]
+
+        if not np.isnan(h):
+            return h
+        else:
+            # try with different limits if integration problems
+            low, upp = self.ppf([1e-10, 1. - 1e-10], *args)
+            if np.isinf(_b):
+                upper = upp
+            else:
+                upper = _b
+            if np.isinf(_a):
+                lower = low
+            else:
+                lower = _a
+            return integrate.quad(integ, lower, upper)[0]
+
+    def expect(self, func=None, args=(), loc=0, scale=1, lb=None, ub=None,
+               conditional=False, **kwds):
+        """Calculate expected value of a function with respect to the
+        distribution by numerical integration.
+
+        The expected value of a function ``f(x)`` with respect to a
+        distribution ``dist`` is defined as::
+
+                    ub
+            E[f(x)] = Integral(f(x) * dist.pdf(x)),
+                    lb
+
+        where ``ub`` and ``lb`` are arguments and ``x`` has the ``dist.pdf(x)``
+        distribution. If the bounds ``lb`` and ``ub`` correspond to the
+        support of the distribution, e.g. ``[-inf, inf]`` in the default
+        case, then the integral is the unrestricted expectation of ``f(x)``.
+        Also, the function ``f(x)`` may be defined such that ``f(x)`` is ``0``
+        outside a finite interval in which case the expectation is
+        calculated within the finite range ``[lb, ub]``.
+
+        Parameters
+        ----------
+        func : callable, optional
+            Function for which integral is calculated. Takes only one argument.
+            The default is the identity mapping f(x) = x.
+        args : tuple, optional
+            Shape parameters of the distribution.
+        loc : float, optional
+            Location parameter (default=0).
+        scale : float, optional
+            Scale parameter (default=1).
+        lb, ub : scalar, optional
+            Lower and upper bound for integration. Default is set to the
+            support of the distribution.
+        conditional : bool, optional
+            If True, the integral is corrected by the conditional probability
+            of the integration interval.  The return value is the expectation
+            of the function, conditional on being in the given interval.
+            Default is False.
+
+        Additional keyword arguments are passed to the integration routine.
+
+        Returns
+        -------
+        expect : float
+            The calculated expected value.
+
+        Notes
+        -----
+        The integration behavior of this function is inherited from
+        `scipy.integrate.quad`. Neither this function nor
+        `scipy.integrate.quad` can verify whether the integral exists or is
+        finite. For example ``cauchy(0).mean()`` returns ``np.nan`` and
+        ``cauchy(0).expect()`` returns ``0.0``.
+
+        Likewise, the accuracy of results is not verified by the function.
+        `scipy.integrate.quad` is typically reliable for integrals that are
+        numerically favorable, but it is not guaranteed to converge
+        to a correct value for all possible intervals and integrands. This
+        function is provided for convenience; for critical applications,
+        check results against other integration methods.
+
+        The function is not vectorized.
+
+        Examples
+        --------
+
+        To understand the effect of the bounds of integration consider
+
+        >>> from scipy.stats import expon
+        >>> expon(1).expect(lambda x: 1, lb=0.0, ub=2.0)
+        0.6321205588285578
+
+        This is close to
+
+        >>> expon(1).cdf(2.0) - expon(1).cdf(0.0)
+        0.6321205588285577
+
+        If ``conditional=True``
+
+        >>> expon(1).expect(lambda x: 1, lb=0.0, ub=2.0, conditional=True)
+        1.0000000000000002
+
+        The slight deviation from 1 is due to numerical integration.
+
+        The integrand can be treated as a complex-valued function
+        by passing ``complex_func=True`` to `scipy.integrate.quad` .
+
+        >>> import numpy as np
+        >>> from scipy.stats import vonmises
+        >>> res = vonmises(loc=2, kappa=1).expect(lambda x: np.exp(1j*x),
+        ...                                       complex_func=True)
+        >>> res
+        (-0.18576377217422957+0.40590124735052263j)
+
+        >>> np.angle(res)  # location of the (circular) distribution
+        2.0
+
+        """
+        lockwds = {'loc': loc,
+                   'scale': scale}
+        self._argcheck(*args)
+        _a, _b = self._get_support(*args)
+        if func is None:
+            def fun(x, *args):
+                return x * self.pdf(x, *args, **lockwds)
+        else:
+            def fun(x, *args):
+                return func(x) * self.pdf(x, *args, **lockwds)
+        if lb is None:
+            lb = loc + _a * scale
+        if ub is None:
+            ub = loc + _b * scale
+
+        cdf_bounds = self.cdf([lb, ub], *args, **lockwds)
+        invfac = cdf_bounds[1] - cdf_bounds[0]
+
+        kwds['args'] = args
+
+        # split interval to help integrator w/ infinite support; see gh-8928
+        alpha = 0.05  # split body from tails at probability mass `alpha`
+        inner_bounds = np.array([alpha, 1-alpha])
+        cdf_inner_bounds = cdf_bounds[0] + invfac * inner_bounds
+        c, d = loc + self._ppf(cdf_inner_bounds, *args) * scale
+
+        # Do not silence warnings from integration.
+        lbc = integrate.quad(fun, lb, c, **kwds)[0]
+        cd = integrate.quad(fun, c, d, **kwds)[0]
+        dub = integrate.quad(fun, d, ub, **kwds)[0]
+        vals = (lbc + cd + dub)
+
+        if conditional:
+            vals /= invfac
+        return np.array(vals)[()]  # make it a numpy scalar like other methods
+
+    def _param_info(self):
+        shape_info = self._shape_info()
+        loc_info = _ShapeInfo("loc", False, (-np.inf, np.inf), (False, False))
+        scale_info = _ShapeInfo("scale", False, (0, np.inf), (False, False))
+        param_info = shape_info + [loc_info, scale_info]
+        return param_info
+
+    # For now, _delta_cdf is a private method.
+    def _delta_cdf(self, x1, x2, *args, loc=0, scale=1):
+        """
+        Compute CDF(x2) - CDF(x1).
+
+        Where x1 is greater than the median, compute SF(x1) - SF(x2),
+        otherwise compute CDF(x2) - CDF(x1).
+
+        This function is only useful if `dist.sf(x, ...)` has an implementation
+        that is numerically more accurate than `1 - dist.cdf(x, ...)`.
+        """
+        cdf1 = self.cdf(x1, *args, loc=loc, scale=scale)
+        # Possible optimizations (needs investigation-these might not be
+        # better):
+        # * Use xpx.apply_where instead of np.where
+        # * Instead of cdf1 > 0.5, compare x1 to the median.
+        result = np.where(cdf1 > 0.5,
+                          (self.sf(x1, *args, loc=loc, scale=scale)
+                           - self.sf(x2, *args, loc=loc, scale=scale)),
+                          self.cdf(x2, *args, loc=loc, scale=scale) - cdf1)
+        if result.ndim == 0:
+            result = result[()]
+        return result
+
+
+# Helpers for the discrete distributions
+def _drv2_moment(self, n, *args):
+    """Non-central moment of discrete distribution."""
+    def fun(x):
+        return np.power(x, n) * self._pmf(x, *args)
+
+    _a, _b = self._get_support(*args)
+    return _expect(fun, _a, _b, self._ppf(0.5, *args), self.inc)
+
+
+def _drv2_ppfsingle(self, q, *args):  # Use basic bisection algorithm
+    _a, _b = self._get_support(*args)
+    b = _b
+    a = _a
+
+    step = 10
+    if isinf(b):            # Be sure ending point is > q
+        b = float(max(100*q, 10))
+        while 1:
+            if b >= _b:
+                qb = 1.0
+                break
+            qb = self._cdf(b, *args)
+            if (qb < q):
+                b += step
+                step *= 2
+            else:
+                break
+    else:
+        qb = 1.0
+
+    step = 10
+    if isinf(a):    # be sure starting point < q
+        a = float(min(-100*q, -10))
+        while 1:
+            if a <= _a:
+                qb = 0.0
+                break
+            qa = self._cdf(a, *args)
+            if (qa > q):
+                a -= step
+                step *= 2
+            else:
+                break
+    else:
+        qa = self._cdf(a, *args)
+
+    if np.isinf(a) or np.isinf(b):
+        message = "Arguments that bracket the requested quantile could not be found."
+        raise RuntimeError(message)
+
+    # maximum number of bisections within the normal float64s
+    # maxiter = int(np.log2(finfo.max) - np.log2(finfo.smallest_normal))
+    maxiter = 2046
+    for i in range(maxiter):
+        if (qa == q):
+            return a
+        if (qb == q):
+            return b
+        if b <= a+1:
+            if qa > q:
+                return a
+            else:
+                return b
+        c = int((a+b)/2.0)
+        qc = self._cdf(c, *args)
+        if (qc < q):
+            if a != c:
+                a = c
+            else:
+                raise RuntimeError('updating stopped, endless loop')
+            qa = qc
+        elif (qc > q):
+            if b != c:
+                b = c
+            else:
+                raise RuntimeError('updating stopped, endless loop')
+            qb = qc
+        else:
+            return c
+
+
+# Must over-ride one of _pmf or _cdf or pass in
+#  x_k, p(x_k) lists in initialization
+
+
+class rv_discrete(rv_generic):
+    """A generic discrete random variable class meant for subclassing.
+
+    `rv_discrete` is a base class to construct specific distribution classes
+    and instances for discrete random variables. It can also be used
+    to construct an arbitrary distribution defined by a list of support
+    points and corresponding probabilities.
+
+    Parameters
+    ----------
+    a : float, optional
+        Lower bound of the support of the distribution, default: 0
+    b : float, optional
+        Upper bound of the support of the distribution, default: plus infinity
+    moment_tol : float, optional
+        The tolerance for the generic calculation of moments.
+    values : tuple of two array_like, optional
+        ``(xk, pk)`` where ``xk`` are integers and ``pk`` are the non-zero
+        probabilities between 0 and 1 with ``sum(pk) = 1``. ``xk``
+        and ``pk`` must have the same shape, and ``xk`` must be unique.
+    inc : int, optional
+        Increment for the support of the distribution.
+        Default is 1. (other values have not been tested)
+    badvalue : float, optional
+        The value in a result arrays that indicates a value that for which
+        some argument restriction is violated, default is np.nan.
+    name : str, optional
+        The name of the instance. This string is used to construct the default
+        example for distributions.
+    longname : str, optional
+        This string is used as part of the first line of the docstring returned
+        when a subclass has no docstring of its own. Note: `longname` exists
+        for backwards compatibility, do not use for new subclasses.
+    shapes : str, optional
+        The shape of the distribution. For example "m, n" for a distribution
+        that takes two integers as the two shape arguments for all its methods
+        If not provided, shape parameters will be inferred from
+        the signatures of the private methods, ``_pmf`` and ``_cdf`` of
+        the instance.
+    seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+        singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used,
+        seeded with `seed`.
+        If `seed` is already a ``Generator`` or ``RandomState`` instance then
+        that instance is used.
+
+    Attributes
+    ----------
+    a, b : float, optional
+        Lower/upper bound of the support of the unshifted/unscaled distribution.
+        This value is unaffected by the `loc` and `scale` parameters.
+        To calculate the support of the shifted/scaled distribution,
+        use the `support` method.
+
+    Methods
+    -------
+    rvs
+    pmf
+    logpmf
+    cdf
+    logcdf
+    sf
+    logsf
+    ppf
+    isf
+    moment
+    stats
+    entropy
+    expect
+    median
+    mean
+    std
+    var
+    interval
+    __call__
+    support
+
+    Notes
+    -----
+    This class is similar to `rv_continuous`. Whether a shape parameter is
+    valid is decided by an ``_argcheck`` method (which defaults to checking
+    that its arguments are strictly positive.)
+    The main differences are as follows.
+
+    - The support of the distribution is a set of integers.
+    - Instead of the probability density function, ``pdf`` (and the
+      corresponding private ``_pdf``), this class defines the
+      *probability mass function*, `pmf` (and the corresponding
+      private ``_pmf``.)
+    - There is no ``scale`` parameter.
+    - The default implementations of methods (e.g. ``_cdf``) are not designed
+      for distributions with support that is unbounded below (i.e.
+      ``a=-np.inf``), so they must be overridden.
+
+    To create a new discrete distribution, we would do the following:
+
+    >>> from scipy.stats import rv_discrete
+    >>> class poisson_gen(rv_discrete):
+    ...     "Poisson distribution"
+    ...     def _pmf(self, k, mu):
+    ...         return exp(-mu) * mu**k / factorial(k)
+
+    and create an instance::
+
+    >>> poisson = poisson_gen(name="poisson")
+
+    Note that above we defined the Poisson distribution in the standard form.
+    Shifting the distribution can be done by providing the ``loc`` parameter
+    to the methods of the instance. For example, ``poisson.pmf(x, mu, loc)``
+    delegates the work to ``poisson._pmf(x-loc, mu)``.
+
+    **Discrete distributions from a list of probabilities**
+
+    Alternatively, you can construct an arbitrary discrete rv defined
+    on a finite set of values ``xk`` with ``Prob{X=xk} = pk`` by using the
+    ``values`` keyword argument to the `rv_discrete` constructor.
+
+    **Deepcopying / Pickling**
+
+    If a distribution or frozen distribution is deepcopied (pickled/unpickled,
+    etc.), any underlying random number generator is deepcopied with it. An
+    implication is that if a distribution relies on the singleton RandomState
+    before copying, it will rely on a copy of that random state after copying,
+    and ``np.random.seed`` will no longer control the state.
+
+    Examples
+    --------
+    Custom made discrete distribution:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> xk = np.arange(7)
+    >>> pk = (0.1, 0.2, 0.3, 0.1, 0.1, 0.0, 0.2)
+    >>> custm = stats.rv_discrete(name='custm', values=(xk, pk))
+    >>>
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+    >>> ax.plot(xk, custm.pmf(xk), 'ro', ms=12, mec='r')
+    >>> ax.vlines(xk, 0, custm.pmf(xk), colors='r', lw=4)
+    >>> plt.show()
+
+    Random number generation:
+
+    >>> R = custm.rvs(size=100)
+
+    """
+    def __new__(cls, a=0, b=inf, name=None, badvalue=None,
+                moment_tol=1e-8, values=None, inc=1, longname=None,
+                shapes=None, seed=None):
+
+        if values is not None:
+            # dispatch to a subclass
+            return super().__new__(rv_sample)
+        else:
+            # business as usual
+            return super().__new__(cls)
+
+    def __init__(self, a=0, b=inf, name=None, badvalue=None,
+                 moment_tol=1e-8, values=None, inc=1, longname=None,
+                 shapes=None, seed=None):
+
+        super().__init__(seed)
+
+        # cf generic freeze
+        self._ctor_param = dict(
+            a=a, b=b, name=name, badvalue=badvalue,
+            moment_tol=moment_tol, values=values, inc=inc,
+            longname=longname, shapes=shapes, seed=seed)
+
+        if badvalue is None:
+            badvalue = nan
+        self.badvalue = badvalue
+        self.a = a
+        self.b = b
+        self.moment_tol = moment_tol
+        self.inc = inc
+        self.shapes = shapes
+
+        if values is not None:
+            raise ValueError("rv_discrete.__init__(..., values != None, ...)")
+
+        self._construct_argparser(meths_to_inspect=[self._pmf, self._cdf],
+                                  locscale_in='loc=0',
+                                  # scale=1 for discrete RVs
+                                  locscale_out='loc, 1')
+        self._attach_methods()
+        self._construct_docstrings(name, longname)
+
+    def __getstate__(self):
+        dct = self.__dict__.copy()
+        # these methods will be remade in __setstate__
+        attrs = ["_parse_args", "_parse_args_stats", "_parse_args_rvs",
+                 "_cdfvec", "_ppfvec", "generic_moment"]
+        [dct.pop(attr, None) for attr in attrs]
+        return dct
+
+    def _attach_methods(self):
+        """Attaches dynamically created methods to the rv_discrete instance."""
+        self._cdfvec = vectorize(self._cdf_single, otypes='d')
+        self.vecentropy = vectorize(self._entropy)
+
+        # _attach_methods is responsible for calling _attach_argparser_methods
+        self._attach_argparser_methods()
+
+        # nin correction needs to be after we know numargs
+        # correct nin for generic moment vectorization
+        _vec_generic_moment = vectorize(_drv2_moment, otypes='d')
+        _vec_generic_moment.nin = self.numargs + 2
+        self.generic_moment = types.MethodType(_vec_generic_moment, self)
+
+        # correct nin for ppf vectorization
+        _vppf = vectorize(_drv2_ppfsingle, otypes='d')
+        _vppf.nin = self.numargs + 2
+        self._ppfvec = types.MethodType(_vppf, self)
+
+        # now that self.numargs is defined, we can adjust nin
+        self._cdfvec.nin = self.numargs + 1
+
+    def _construct_docstrings(self, name, longname):
+        if name is None:
+            name = 'Distribution'
+        self.name = name
+
+        # generate docstring for subclass instances
+        if longname is None:
+            if name[0] in ['aeiouAEIOU']:
+                hstr = "An "
+            else:
+                hstr = "A "
+            longname = hstr + name
+
+        if sys.flags.optimize < 2:
+            # Skip adding docstrings if interpreter is run with -OO
+            if self.__doc__ is None:
+                self._construct_default_doc(longname=longname,
+                                            docdict=docdict_discrete,
+                                            discrete='discrete')
+            else:
+                dct = dict(distdiscrete)
+                self._construct_doc(docdict_discrete, dct.get(self.name))
+
+            # discrete RV do not have the scale parameter, remove it
+            self.__doc__ = self.__doc__.replace(
+                '\n    scale : array_like, '
+                'optional\n        scale parameter (default=1)', '')
+
+    def _updated_ctor_param(self):
+        """Return the current version of _ctor_param, possibly updated by user.
+
+        Used by freezing.
+        Keep this in sync with the signature of __init__.
+        """
+        dct = self._ctor_param.copy()
+        dct['a'] = self.a
+        dct['b'] = self.b
+        dct['badvalue'] = self.badvalue
+        dct['moment_tol'] = self.moment_tol
+        dct['inc'] = self.inc
+        dct['name'] = self.name
+        dct['shapes'] = self.shapes
+        return dct
+
+    def _nonzero(self, k, *args):
+        return floor(k) == k
+
+    def _pmf(self, k, *args):
+        return self._cdf(k, *args) - self._cdf(k-1, *args)
+
+    def _logpmf(self, k, *args):
+        with np.errstate(divide='ignore'):
+            return log(self._pmf(k, *args))
+
+    def _logpxf(self, k, *args):
+        # continuous distributions have PDF, discrete have PMF, but sometimes
+        # the distinction doesn't matter. This lets us use `_logpxf` for both
+        # discrete and continuous distributions.
+        return self._logpmf(k, *args)
+
+    def _unpack_loc_scale(self, theta):
+        try:
+            loc = theta[-1]
+            scale = 1
+            args = tuple(theta[:-1])
+        except IndexError as e:
+            raise ValueError("Not enough input arguments.") from e
+        return loc, scale, args
+
+    def _cdf_single(self, k, *args):
+        _a, _b = self._get_support(*args)
+        m = arange(int(_a), k+1)
+        return np.sum(self._pmf(m, *args), axis=0)
+
+    def _cdf(self, x, *args):
+        k = floor(x).astype(np.float64)
+        return self._cdfvec(k, *args)
+
+    # generic _logcdf, _sf, _logsf, _ppf, _isf, _rvs defined in rv_generic
+
+    def rvs(self, *args, **kwargs):
+        """Random variates of given type.
+
+        Parameters
+        ----------
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+        loc : array_like, optional
+            Location parameter (default=0).
+        size : int or tuple of ints, optional
+            Defining number of random variates (Default is 1). Note that `size`
+            has to be given as keyword, not as positional argument.
+        random_state : {None, int, `numpy.random.Generator`,
+                        `numpy.random.RandomState`}, optional
+
+            If `random_state` is None (or `np.random`), the
+            `numpy.random.RandomState` singleton is used.
+            If `random_state` is an int, a new ``RandomState`` instance is
+            used, seeded with `random_state`.
+            If `random_state` is already a ``Generator`` or ``RandomState``
+            instance, that instance is used.
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random variates of given `size`.
+
+        """
+        kwargs['discrete'] = True
+        return super().rvs(*args, **kwargs)
+
+    def pmf(self, k, *args, **kwds):
+        """Probability mass function at k of the given RV.
+
+        Parameters
+        ----------
+        k : array_like
+            Quantiles.
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information)
+        loc : array_like, optional
+            Location parameter (default=0).
+
+        Returns
+        -------
+        pmf : array_like
+            Probability mass function evaluated at k
+
+        """
+        args, loc, _ = self._parse_args(*args, **kwds)
+        k, loc = map(asarray, (k, loc))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        k = asarray(k-loc)
+        cond0 = self._argcheck(*args)
+        cond1 = (k >= _a) & (k <= _b)
+        if not isinstance(self, rv_sample):
+            cond1 = cond1 & self._nonzero(k, *args)
+        cond = cond0 & cond1
+        output = zeros(shape(cond), 'd')
+        place(output, (1-cond0) + np.isnan(k), self.badvalue)
+        if np.any(cond):
+            goodargs = argsreduce(cond, *((k,)+args))
+            place(output, cond, np.clip(self._pmf(*goodargs), 0, 1))
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def logpmf(self, k, *args, **kwds):
+        """Log of the probability mass function at k of the given RV.
+
+        Parameters
+        ----------
+        k : array_like
+            Quantiles.
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+        loc : array_like, optional
+            Location parameter. Default is 0.
+
+        Returns
+        -------
+        logpmf : array_like
+            Log of the probability mass function evaluated at k.
+
+        """
+        args, loc, _ = self._parse_args(*args, **kwds)
+        k, loc = map(asarray, (k, loc))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        k = asarray(k-loc)
+        cond0 = self._argcheck(*args)
+        cond1 = (k >= _a) & (k <= _b)
+        if not isinstance(self, rv_sample):
+            cond1 = cond1 & self._nonzero(k, *args)
+        cond = cond0 & cond1
+        output = empty(shape(cond), 'd')
+        output.fill(-inf)
+        place(output, (1-cond0) + np.isnan(k), self.badvalue)
+        if np.any(cond):
+            goodargs = argsreduce(cond, *((k,)+args))
+            place(output, cond, self._logpmf(*goodargs))
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def cdf(self, k, *args, **kwds):
+        """Cumulative distribution function of the given RV.
+
+        Parameters
+        ----------
+        k : array_like, int
+            Quantiles.
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+        loc : array_like, optional
+            Location parameter (default=0).
+
+        Returns
+        -------
+        cdf : ndarray
+            Cumulative distribution function evaluated at `k`.
+
+        """
+        args, loc, _ = self._parse_args(*args, **kwds)
+        k, loc = map(asarray, (k, loc))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        k = asarray(k-loc)
+        cond0 = self._argcheck(*args)
+        cond1 = (k >= _a) & (k < _b)
+        cond2 = (k >= _b)
+        cond3 = np.isneginf(k)
+        cond = cond0 & cond1 & np.isfinite(k)
+
+        output = zeros(shape(cond), 'd')
+        place(output, cond2*(cond0 == cond0), 1.0)
+        place(output, cond3*(cond0 == cond0), 0.0)
+        place(output, (1-cond0) + np.isnan(k), self.badvalue)
+
+        if np.any(cond):
+            goodargs = argsreduce(cond, *((k,)+args))
+            place(output, cond, np.clip(self._cdf(*goodargs), 0, 1))
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def logcdf(self, k, *args, **kwds):
+        """Log of the cumulative distribution function at k of the given RV.
+
+        Parameters
+        ----------
+        k : array_like, int
+            Quantiles.
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+        loc : array_like, optional
+            Location parameter (default=0).
+
+        Returns
+        -------
+        logcdf : array_like
+            Log of the cumulative distribution function evaluated at k.
+
+        """
+        args, loc, _ = self._parse_args(*args, **kwds)
+        k, loc = map(asarray, (k, loc))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        k = asarray(k-loc)
+        cond0 = self._argcheck(*args)
+        cond1 = (k >= _a) & (k < _b)
+        cond2 = (k >= _b)
+        cond = cond0 & cond1
+        output = empty(shape(cond), 'd')
+        output.fill(-inf)
+        place(output, (1-cond0) + np.isnan(k), self.badvalue)
+        place(output, cond2*(cond0 == cond0), 0.0)
+
+        if np.any(cond):
+            goodargs = argsreduce(cond, *((k,)+args))
+            place(output, cond, self._logcdf(*goodargs))
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def sf(self, k, *args, **kwds):
+        """Survival function (1 - `cdf`) at k of the given RV.
+
+        Parameters
+        ----------
+        k : array_like
+            Quantiles.
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+        loc : array_like, optional
+            Location parameter (default=0).
+
+        Returns
+        -------
+        sf : array_like
+            Survival function evaluated at k.
+
+        """
+        args, loc, _ = self._parse_args(*args, **kwds)
+        k, loc = map(asarray, (k, loc))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        k = asarray(k-loc)
+        cond0 = self._argcheck(*args)
+        cond1 = (k >= _a) & (k < _b)
+        cond2 = ((k < _a) | np.isneginf(k)) & cond0
+        cond = cond0 & cond1 & np.isfinite(k)
+        output = zeros(shape(cond), 'd')
+        place(output, (1-cond0) + np.isnan(k), self.badvalue)
+        place(output, cond2, 1.0)
+        if np.any(cond):
+            goodargs = argsreduce(cond, *((k,)+args))
+            place(output, cond, np.clip(self._sf(*goodargs), 0, 1))
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def logsf(self, k, *args, **kwds):
+        """Log of the survival function of the given RV.
+
+        Returns the log of the "survival function," defined as 1 - `cdf`,
+        evaluated at `k`.
+
+        Parameters
+        ----------
+        k : array_like
+            Quantiles.
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+        loc : array_like, optional
+            Location parameter (default=0).
+
+        Returns
+        -------
+        logsf : ndarray
+            Log of the survival function evaluated at `k`.
+
+        """
+        args, loc, _ = self._parse_args(*args, **kwds)
+        k, loc = map(asarray, (k, loc))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        k = asarray(k-loc)
+        cond0 = self._argcheck(*args)
+        cond1 = (k >= _a) & (k < _b)
+        cond2 = (k < _a) & cond0
+        cond = cond0 & cond1
+        output = empty(shape(cond), 'd')
+        output.fill(-inf)
+        place(output, (1-cond0) + np.isnan(k), self.badvalue)
+        place(output, cond2, 0.0)
+        if np.any(cond):
+            goodargs = argsreduce(cond, *((k,)+args))
+            place(output, cond, self._logsf(*goodargs))
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def ppf(self, q, *args, **kwds):
+        """Percent point function (inverse of `cdf`) at q of the given RV.
+
+        Parameters
+        ----------
+        q : array_like
+            Lower tail probability.
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+        loc : array_like, optional
+            Location parameter (default=0).
+
+        Returns
+        -------
+        k : array_like
+            Quantile corresponding to the lower tail probability, q.
+
+        Notes
+        -----
+        For discrete distributions, the `cdf` is not strictly invertible. By convention,
+        this method returns the minimum value `k` for which the `cdf` at `k` is at
+        least `q`. There is one exception:  the `ppf` of ``0`` is ``a-1``,
+        where ``a`` is the left endpoint of the support.
+
+        """
+        args, loc, _ = self._parse_args(*args, **kwds)
+        q, loc = map(asarray, (q, loc))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        cond0 = self._argcheck(*args) & (loc == loc)
+        cond1 = (q > 0) & (q < 1)
+        cond2 = (q == 0) & cond0
+        cond3 = (q == 1) & cond0
+        cond = cond0 & cond1
+        output = np.full(shape(cond), fill_value=self.badvalue, dtype='d')
+        # output type 'd' to handle nin and inf
+
+        place(output, cond2, argsreduce(cond2, _a-1 + loc)[0])
+        place(output, cond3, argsreduce(cond3, _b + loc)[0])
+        if np.any(cond):
+            goodargs = argsreduce(cond, *((q,)+args+(loc,)))
+            loc, goodargs = goodargs[-1], goodargs[:-1]
+            place(output, cond, self._ppf(*goodargs) + loc)
+
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def isf(self, q, *args, **kwds):
+        """Inverse survival function (inverse of `sf`) at q of the given RV.
+
+        Parameters
+        ----------
+        q : array_like
+            Upper tail probability.
+        arg1, arg2, arg3,... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+        loc : array_like, optional
+            Location parameter (default=0).
+
+        Returns
+        -------
+        k : ndarray or scalar
+            Quantile corresponding to the upper tail probability, q.
+
+        Notes
+        -----
+        For discrete distributions, the `sf` is not strictly invertible. By convention,
+        this method returns the minimum value `k` for which the `sf` at `k` is
+        no greater than `q`. There is one exception: the `isf` of ``1`` is ``a-1``,
+        where ``a`` is the left endpoint of the support.
+
+        """
+        args, loc, _ = self._parse_args(*args, **kwds)
+        q, loc = map(asarray, (q, loc))
+        args = tuple(map(asarray, args))
+        _a, _b = self._get_support(*args)
+        cond0 = self._argcheck(*args) & (loc == loc)
+        cond1 = (q > 0) & (q < 1)
+        cond2 = (q == 1) & cond0
+        cond3 = (q == 0) & cond0
+        cond = cond0 & cond1
+
+        # same problem as with ppf; copied from ppf and changed
+        output = np.full(shape(cond), fill_value=self.badvalue, dtype='d')
+        # output type 'd' to handle nin and inf
+        lower_bound = _a - 1 + loc
+        upper_bound = _b + loc
+        place(output, cond2, argsreduce(cond2, lower_bound)[0])
+        place(output, cond3, argsreduce(cond3, upper_bound)[0])
+
+        # call place only if at least 1 valid argument
+        if np.any(cond):
+            goodargs = argsreduce(cond, *((q,)+args+(loc,)))
+            loc, goodargs = goodargs[-1], goodargs[:-1]
+            # PB same as ticket 766
+            place(output, cond, self._isf(*goodargs) + loc)
+
+        if output.ndim == 0:
+            return output[()]
+        return output
+
+    def _entropy(self, *args):
+        if hasattr(self, 'pk'):
+            return stats.entropy(self.pk)
+        else:
+            _a, _b = self._get_support(*args)
+            return _expect(lambda x: entr(self._pmf(x, *args)),
+                           _a, _b, self._ppf(0.5, *args), self.inc)
+
+    def expect(self, func=None, args=(), loc=0, lb=None, ub=None,
+               conditional=False, maxcount=1000, tolerance=1e-10, chunksize=32):
+        """
+        Calculate expected value of a function with respect to the distribution
+        for discrete distribution by numerical summation.
+
+        Parameters
+        ----------
+        func : callable, optional
+            Function for which the expectation value is calculated.
+            Takes only one argument.
+            The default is the identity mapping f(k) = k.
+        args : tuple, optional
+            Shape parameters of the distribution.
+        loc : float, optional
+            Location parameter.
+            Default is 0.
+        lb, ub : int, optional
+            Lower and upper bound for the summation, default is set to the
+            support of the distribution, inclusive (``lb <= k <= ub``).
+        conditional : bool, optional
+            If true then the expectation is corrected by the conditional
+            probability of the summation interval. The return value is the
+            expectation of the function, `func`, conditional on being in
+            the given interval (k such that ``lb <= k <= ub``).
+            Default is False.
+        maxcount : int, optional
+            Maximal number of terms to evaluate (to avoid an endless loop for
+            an infinite sum). Default is 1000.
+        tolerance : float, optional
+            Absolute tolerance for the summation. Default is 1e-10.
+        chunksize : int, optional
+            Iterate over the support of a distributions in chunks of this size.
+            Default is 32.
+
+        Returns
+        -------
+        expect : float
+            Expected value.
+
+        Notes
+        -----
+        For heavy-tailed distributions, the expected value may or
+        may not exist,
+        depending on the function, `func`. If it does exist, but the
+        sum converges
+        slowly, the accuracy of the result may be rather low. For instance, for
+        ``zipf(4)``, accuracy for mean, variance in example is only 1e-5.
+        increasing `maxcount` and/or `chunksize` may improve the result,
+        but may also make zipf very slow.
+
+        The function is not vectorized.
+
+        """
+        # Although `args` is just the shape parameters, `poisson_binom` needs this
+        # to split the vector-valued shape into a tuple of separate shapes
+        args, _, _ = self._parse_args(*args)
+
+        if func is None:
+            def fun(x):
+                # loc and args from outer scope
+                return (x+loc)*self._pmf(x, *args)
+        else:
+            def fun(x):
+                # loc and args from outer scope
+                return func(x+loc)*self._pmf(x, *args)
+        # used pmf because _pmf does not check support in randint and there
+        # might be problems(?) with correct self.a, self.b at this stage maybe
+        # not anymore, seems to work now with _pmf
+
+        _a, _b = self._get_support(*args)
+        if lb is None:
+            lb = _a
+        else:
+            lb = lb - loc   # convert bound for standardized distribution
+        if ub is None:
+            ub = _b
+        else:
+            ub = ub - loc   # convert bound for standardized distribution
+        if conditional:
+            invfac = self.sf(lb-1, *args) - self.sf(ub, *args)
+        else:
+            invfac = 1.0
+
+        if isinstance(self, rv_sample):
+            res = self._expect(fun, lb, ub)
+            return res / invfac
+
+        # iterate over the support, starting from the median
+        x0 = self._ppf(0.5, *args)
+        res = _expect(fun, lb, ub, x0, self.inc, maxcount, tolerance, chunksize)
+        return res / invfac
+
+    def _param_info(self):
+        shape_info = self._shape_info()
+        loc_info = _ShapeInfo("loc", True, (-np.inf, np.inf), (False, False))
+        param_info = shape_info + [loc_info]
+        return param_info
+
+    # these methods are identical to those of `rv_generic` except that
+    # they don't accept the `scale` parameter. They need their own copy
+    # of the methods so we can adjust the documentation.
+
+    def entropy(self, *args, **kwargs):
+        return super().entropy(*args, **kwargs)
+
+    def interval(self, confidence, *args, **kwargs):
+        return super().interval(confidence, *args, **kwargs)
+
+    def mean(self, *args, **kwargs):
+        return super().mean(*args, **kwargs)
+
+    def median(self, *args, **kwargs):
+        return super().median(*args, **kwargs)
+
+    def moment(self, order, *args, **kwargs):
+        return super().moment(order, *args, **kwargs)
+
+    def stats(self, *args, **kwargs):
+        return super().stats(*args, **kwargs)
+
+    def std(self, *args, **kwargs):
+        return super().std(*args, **kwargs)
+
+    def support(self, *args, **kwargs):
+        return super().support(*args, **kwargs)
+
+    def var(self, *args, **kwargs):
+        return super().var(*args, **kwargs)
+
+
+_remove_scale_methods = ['entropy', 'interval', 'mean', 'median',
+                         'moment', 'stats', 'std', 'support', 'var']
+for method_name in _remove_scale_methods:
+    # remove `scale` parameter from method documentation
+    method = getattr(rv_discrete, method_name)
+    doc = FunctionDoc(method)
+    doc['Parameters'] = [p for p in doc['Parameters'] if p.name != 'scale']
+    doc = str(doc).split("\n", 1)[1].lstrip(" \n")  # remove signature
+    method.__doc__ = doc
+del method_name, method, doc  # pyrefly:ignore[unbound-name]
+
+
+def _expect(fun, lb, ub, x0, inc, maxcount=1000, tolerance=1e-10,
+            chunksize=32):
+    """Helper for computing the expectation value of `fun`."""
+    # short-circuit if the support size is small enough
+    if (ub - lb) <= chunksize:
+        supp = np.arange(lb, ub+1, inc)
+        vals = fun(supp)
+        return np.sum(vals)
+
+    # otherwise, iterate starting from x0
+    if x0 < lb:
+        x0 = lb
+    if x0 > ub:
+        x0 = ub
+
+    count, tot = 0, 0.
+    # iterate over [x0, ub] inclusive
+    for x in _iter_chunked(x0, ub+1, chunksize=chunksize, inc=inc):
+        count += x.size
+        delta = np.sum(fun(x))
+        tot += delta
+        if abs(delta) < tolerance * x.size:
+            break
+        if count > maxcount:
+            warnings.warn('expect(): sum did not converge',
+                          RuntimeWarning, stacklevel=3)
+            return tot
+
+    # iterate over [lb, x0)
+    for x in _iter_chunked(x0-1, lb-1, chunksize=chunksize, inc=-inc):
+        count += x.size
+        delta = np.sum(fun(x))
+        tot += delta
+        if abs(delta) < tolerance * x.size:
+            break
+        if count > maxcount:
+            warnings.warn('expect(): sum did not converge',
+                          RuntimeWarning, stacklevel=3)
+            break
+
+    return tot
+
+
+def _iter_chunked(x0, x1, chunksize=4, inc=1):
+    """Iterate from x0 to x1 in chunks of chunksize and steps inc.
+
+    x0 must be finite, x1 need not be. In the latter case, the iterator is
+    infinite.
+    Handles both x0 < x1 and x0 > x1. In the latter case, iterates downwards
+    (make sure to set inc < 0.)
+
+    >>> from scipy.stats._distn_infrastructure import _iter_chunked
+    >>> [x for x in _iter_chunked(2, 5, inc=2)]
+    [array([2, 4])]
+    >>> [x for x in _iter_chunked(2, 11, inc=2)]
+    [array([2, 4, 6, 8]), array([10])]
+    >>> [x for x in _iter_chunked(2, -5, inc=-2)]
+    [array([ 2,  0, -2, -4])]
+    >>> [x for x in _iter_chunked(2, -9, inc=-2)]
+    [array([ 2,  0, -2, -4]), array([-6, -8])]
+
+    """
+    if inc == 0:
+        raise ValueError('Cannot increment by zero.')
+    if chunksize <= 0:
+        raise ValueError(f'Chunk size must be positive; got {chunksize}.')
+
+    s = 1 if inc > 0 else -1
+    stepsize = abs(chunksize * inc)
+
+    x = np.copy(x0)
+    while (x - x1) * inc < 0:
+        delta = min(stepsize, abs(x - x1))
+        step = delta * s
+        supp = np.arange(x, x + step, inc)
+        x += step
+        yield supp
+
+
+class rv_sample(rv_discrete):
+    """A 'sample' discrete distribution defined by the support and values.
+
+    The ctor ignores most of the arguments, only needs the `values` argument.
+    """
+
+    def __init__(self, a=0, b=inf, name=None, badvalue=None,
+                 moment_tol=1e-8, values=None, inc=1, longname=None,
+                 shapes=None, seed=None):
+
+        super(rv_discrete, self).__init__(seed)
+
+        if values is None:
+            raise ValueError("rv_sample.__init__(..., values=None,...)")
+
+        # cf generic freeze
+        self._ctor_param = dict(
+            a=a, b=b, name=name, badvalue=badvalue,
+            moment_tol=moment_tol, values=values, inc=inc,
+            longname=longname, shapes=shapes, seed=seed)
+
+        if badvalue is None:
+            badvalue = nan
+        self.badvalue = badvalue
+        self.moment_tol = moment_tol
+        self.inc = inc
+        self.shapes = shapes
+        self.vecentropy = self._entropy
+
+        xk, pk = values
+
+        if np.shape(xk) != np.shape(pk):
+            raise ValueError("xk and pk must have the same shape.")
+        if np.less(pk, 0.0).any():
+            raise ValueError("All elements of pk must be non-negative.")
+        if not np.allclose(np.sum(pk), 1):
+            raise ValueError("The sum of provided pk is not 1.")
+        if not len(set(np.ravel(xk))) == np.size(xk):
+            raise ValueError("xk may not contain duplicate values.")
+
+        indx = np.argsort(np.ravel(xk))
+        self.xk = np.take(np.ravel(xk), indx, 0)
+        self.pk = np.take(np.ravel(pk), indx, 0)
+        self.a = self.xk[0]
+        self.b = self.xk[-1]
+
+        self.qvals = np.cumsum(self.pk, axis=0)
+
+        self.shapes = ' '   # bypass inspection
+
+        self._construct_argparser(meths_to_inspect=[self._pmf],
+                                  locscale_in='loc=0',
+                                  # scale=1 for discrete RVs
+                                  locscale_out='loc, 1')
+
+        self._attach_methods()
+
+        self._construct_docstrings(name, longname)
+
+    def __getstate__(self):
+        dct = self.__dict__.copy()
+
+        # these methods will be remade in rv_generic.__setstate__,
+        # which calls rv_generic._attach_methods
+        attrs = ["_parse_args", "_parse_args_stats", "_parse_args_rvs"]
+        [dct.pop(attr, None) for attr in attrs]
+
+        return dct
+
+    def _attach_methods(self):
+        """Attaches dynamically created argparser methods."""
+        self._attach_argparser_methods()
+
+    def _get_support(self, *args):
+        """Return the support of the (unscaled, unshifted) distribution.
+
+        Parameters
+        ----------
+        arg1, arg2, ... : array_like
+            The shape parameter(s) for the distribution (see docstring of the
+            instance object for more information).
+
+        Returns
+        -------
+        a, b : numeric (float, or int or +/-np.inf)
+            end-points of the distribution's support.
+        """
+        return self.a, self.b
+
+    def _pmf(self, x):
+        return np.select([x == k for k in self.xk],
+                         [np.broadcast_arrays(p, x)[0] for p in self.pk], 0)
+
+    def _cdf(self, x):
+        xx, xxk = np.broadcast_arrays(x[:, None], self.xk)
+        indx = np.argmax(xxk > xx, axis=-1) - 1
+        return self.qvals[indx]
+
+    def _ppf(self, q):
+        qq, sqq = np.broadcast_arrays(q[..., None], self.qvals)
+        indx = argmax(sqq >= qq, axis=-1)
+        return self.xk[indx]
+
+    def _rvs(self, size=None, random_state=None):
+        # Need to define it explicitly, otherwise .rvs() with size=None
+        # fails due to explicit broadcasting in _ppf
+        U = random_state.uniform(size=size)
+        if size is None:
+            U = np.array(U, ndmin=1)
+            Y = self._ppf(U)[0]
+        else:
+            Y = self._ppf(U)
+        return Y
+
+    def _entropy(self):
+        return stats.entropy(self.pk)
+
+    def generic_moment(self, n):
+        n = asarray(n)
+        return np.sum(self.xk**n[np.newaxis, ...] * self.pk, axis=0)
+
+    def _expect(self, fun, lb, ub, *args, **kwds):
+        # ignore all args, just do a brute force summation
+        supp = self.xk[(lb <= self.xk) & (self.xk <= ub)]
+        vals = fun(supp)
+        return np.sum(vals)
+
+
+def _check_shape(argshape, size):
+    """
+    This is a utility function used by `_rvs()` in the class geninvgauss_gen.
+    It compares the tuple argshape to the tuple size.
+
+    Parameters
+    ----------
+    argshape : tuple of integers
+        Shape of the arguments.
+    size : tuple of integers or integer
+        Size argument of rvs().
+
+    Returns
+    -------
+    The function returns two tuples, scalar_shape and bc.
+
+    scalar_shape : tuple
+        Shape to which the 1-d array of random variates returned by
+        _rvs_scalar() is converted when it is copied into the
+        output array of _rvs().
+
+    bc : tuple of booleans
+        bc is a tuple the same length as size. bc[j] is True if the data
+        associated with that index is generated in one call of _rvs_scalar().
+
+    """
+    scalar_shape = []
+    bc = []
+    for argdim, sizedim in zip_longest(argshape[::-1], size[::-1],
+                                       fillvalue=1):
+        if sizedim > argdim or (argdim == sizedim == 1):
+            scalar_shape.append(sizedim)
+            bc.append(True)
+        else:
+            bc.append(False)
+    return tuple(scalar_shape[::-1]), tuple(bc[::-1])
+
+
+def get_distribution_names(namespace_pairs, rv_base_class):
+    """Collect names of statistical distributions and their generators.
+
+    Parameters
+    ----------
+    namespace_pairs : sequence
+        A snapshot of (name, value) pairs in the namespace of a module.
+    rv_base_class : class
+        The base class of random variable generator classes in a module.
+
+    Returns
+    -------
+    distn_names : list of strings
+        Names of the statistical distributions.
+    distn_gen_names : list of strings
+        Names of the generators of the statistical distributions.
+        Note that these are not simply the names of the statistical
+        distributions, with a _gen suffix added.
+
+    """
+    distn_names = []
+    distn_gen_names = []
+    for name, value in namespace_pairs:
+        if name.startswith('_'):
+            continue
+        if name.endswith('_gen') and issubclass(value, rv_base_class):
+            distn_gen_names.append(name)
+        if isinstance(value, rv_base_class):
+            distn_names.append(name)
+    return distn_names, distn_gen_names
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_distr_params.py b/.venv/lib/python3.12/site-packages/scipy/stats/_distr_params.py
new file mode 100644
index 0000000000000000000000000000000000000000..3234bae358fffd18b70a008a00110c6f3e348530
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_distr_params.py
@@ -0,0 +1,300 @@
+"""
+Sane parameters for stats.distributions.
+"""
+import numpy as np
+
+distcont = [
+    ['alpha', (3.5704770516650459,)],
+    ['anglit', ()],
+    ['arcsine', ()],
+    ['argus', (1.0,)],
+    ['beta', (2.3098496451481823, 0.62687954300963677)],
+    ['betaprime', (5, 6)],
+    ['bradford', (0.29891359763170633,)],
+    ['burr', (10.5, 4.3)],
+    ['burr12', (10, 4)],
+    ['cauchy', ()],
+    ['chi', (78,)],
+    ['chi2', (55,)],
+    ['cosine', ()],
+    ['crystalball', (2.0, 3.0)],
+    ['dgamma', (1.1023326088288166,)],
+    ['dpareto_lognorm', (3, 1.2, 1.5, 2)],
+    ['dweibull', (2.0685080649914673,)],
+    ['erlang', (10,)],
+    ['expon', ()],
+    ['exponnorm', (1.5,)],
+    ['exponpow', (2.697119160358469,)],
+    ['exponweib', (2.8923945291034436, 1.9505288745913174)],
+    ['f', (29, 18)],
+    ['fatiguelife', (29,)],   # correction numargs = 1
+    ['fisk', (3.0857548622253179,)],
+    ['foldcauchy', (4.7164673455831894,)],
+    ['foldnorm', (1.9521253373555869,)],
+    ['gamma', (1.9932305483800778,)],
+    ['gausshyper', (13.763771604130699, 3.1189636648681431,
+                    2.5145980350183019, 5.1811649903971615)],  # veryslow
+    ['genexpon', (9.1325976465418908, 16.231956600590632, 3.2819552690843983)],
+    ['genextreme', (-0.1,)],
+    ['gengamma', (4.4162385429431925, 3.1193091679242761)],
+    ['gengamma', (4.4162385429431925, -3.1193091679242761)],
+    ['genhalflogistic', (0.77274727809929322,)],
+    ['genhyperbolic', (0.5, 1.5, -0.5,)],
+    ['geninvgauss', (2.3, 1.5)],
+    ['genlogistic', (0.41192440799679475,)],
+    ['gennorm', (1.2988442399460265,)],
+    ['halfgennorm', (0.6748054997000371,)],
+    ['genpareto', (0.1,)],   # use case with finite moments
+    ['gibrat', ()],
+    ['gompertz', (0.94743713075105251,)],
+    ['gumbel_l', ()],
+    ['gumbel_r', ()],
+    ['halfcauchy', ()],
+    ['halflogistic', ()],
+    ['halfnorm', ()],
+    ['hypsecant', ()],
+    ['invgamma', (4.0668996136993067,)],
+    ['invgauss', (0.14546264555347513,)],
+    ['invweibull', (10.58,)],
+    ['irwinhall', (10,)],
+    ['jf_skew_t', (8, 4)],
+    ['johnsonsb', (4.3172675099141058, 3.1837781130785063)],
+    ['johnsonsu', (2.554395574161155, 2.2482281679651965)],
+    ['kappa4', (0.0, 0.0)],
+    ['kappa4', (-0.1, 0.1)],
+    ['kappa4', (0.0, 0.1)],
+    ['kappa4', (0.1, 0.0)],
+    ['kappa3', (1.0,)],
+    ['ksone', (1000,)],  # replace 22 by 100 to avoid failing range, ticket 956
+    ['kstwo', (10,)],
+    ['kstwobign', ()],
+    ['landau', ()],
+    ['laplace', ()],
+    ['laplace_asymmetric', (2,)],
+    ['levy', ()],
+    ['levy_l', ()],
+    ['levy_stable', (1.8, -0.5)],
+    ['loggamma', (0.41411931826052117,)],
+    ['logistic', ()],
+    ['loglaplace', (3.2505926592051435,)],
+    ['lognorm', (0.95368226960575331,)],
+    ['loguniform', (0.01, 1.25)],
+    ['lomax', (1.8771398388773268,)],
+    ['maxwell', ()],
+    ['mielke', (10.4, 4.6)],
+    ['moyal', ()],
+    ['nakagami', (4.9673794866666237,)],
+    ['ncf', (27, 27, 0.41578441799226107)],
+    ['nct', (14, 0.24045031331198066)],
+    ['ncx2', (21, 1.0560465975116415)],
+    ['norm', ()],
+    ['norminvgauss', (1.25, 0.5)],
+    ['pareto', (2.621716532144454,)],
+    ['pearson3', (0.1,)],
+    ['pearson3', (-2,)],
+    ['powerlaw', (1.6591133289905851,)],
+    ['powerlaw', (0.6591133289905851,)],
+    ['powerlognorm', (2.1413923530064087, 0.44639540782048337)],
+    ['powernorm', (4.4453652254590779,)],
+    ['rayleigh', ()],
+    ['rdist', (1.6,)],
+    ['recipinvgauss', (0.63004267809369119,)],
+    ['reciprocal', (0.01, 1.25)],
+    ['rel_breitwigner', (36.545206797050334, )],
+    ['rice', (0.7749725210111873,)],
+    ['semicircular', ()],
+    ['skewcauchy', (0.5,)],
+    ['skewnorm', (4.0,)],
+    ['studentized_range', (3.0, 10.0)],
+    ['t', (2.7433514990818093,)],
+    ['trapezoid', (0.2, 0.8)],
+    ['triang', (0.15785029824528218,)],
+    ['truncexpon', (4.6907725456810478,)],
+    ['truncnorm', (-1.0978730080013919, 2.7306754109031979)],
+    ['truncnorm', (0.1, 2.)],
+    ['truncpareto', (1.8, 5.3)],
+    ['truncpareto', (2, 5)],
+    ['truncpareto', (-2, 5)],
+    ['truncweibull_min', (2.5, 0.25, 1.75)],
+    ['tukeylambda', (3.1321477856738267,)],
+    ['uniform', ()],
+    ['vonmises', (3.9939042581071398,)],
+    ['vonmises_line', (3.9939042581071398,)],
+    ['wald', ()],
+    ['weibull_max', (2.8687961709100187,)],
+    ['weibull_min', (1.7866166930421596,)],
+    ['wrapcauchy', (0.031071279018614728,)]
+]
+
+
+distdiscrete = [
+    ['bernoulli',(0.3,)],
+    ['betabinom', (5, 2.3, 0.63)],
+    ['betanbinom', (5, 9.3, 1)],
+    ['binom', (5, 0.4)],
+    ['boltzmann',(1.4, 19)],
+    ['dlaplace', (0.8,)],  # 0.5
+    ['geom', (0.5,)],
+    ['hypergeom',(30, 12, 6)],
+    ['hypergeom',(21,3,12)],  # numpy.random (3,18,12) numpy ticket:921
+    ['hypergeom',(21,18,11)],  # numpy.random (18,3,11) numpy ticket:921
+    ['nchypergeom_fisher', (140, 80, 60, 0.5)],
+    ['nchypergeom_wallenius', (140, 80, 60, 0.5)],
+    ['logser', (0.6,)],  # re-enabled, numpy ticket:921
+    ['nbinom', (0.4, 0.4)],  # from tickets: 583
+    ['nbinom', (5, 0.5)],
+    ['planck', (0.51,)],   # 4.1
+    ['poisson', (0.6,)],
+    ['poisson_binom', ([0.1, 0.6, 0.7, 0.8],)],
+    ['randint', (7, 31)],
+    ['skellam', (15, 8)],
+    ['zipf', (6.6,)],
+    ['zipfian', (0.75, 15)],
+    ['zipfian', (1.25, 10)],
+    ['yulesimon', (11.0,)],
+    ['nhypergeom', (20, 7, 1)]
+]
+
+
+invdistdiscrete = [
+    # In each of the following, at least one shape parameter is invalid
+    ['hypergeom', (3, 3, 4)],
+    ['nhypergeom', (5, 2, 8)],
+    ['nchypergeom_fisher', (3, 3, 4, 1)],
+    ['nchypergeom_wallenius', (3, 3, 4, 1)],
+    ['bernoulli', (1.5, )],
+    ['binom', (10, 1.5)],
+    ['betabinom', (10, -0.4, -0.5)],
+    ['betanbinom', (10, -0.4, -0.5)],
+    ['boltzmann', (-1, 4)],
+    ['dlaplace', (-0.5, )],
+    ['geom', (1.5, )],
+    ['logser', (1.5, )],
+    ['nbinom', (10, 1.5)],
+    ['planck', (-0.5, )],
+    ['poisson', (-0.5, )],
+    ['poisson_binom', ([-1, 2, 0.5],)],
+    ['randint', (5, 2)],
+    ['skellam', (-5, -2)],
+    ['zipf', (-2, )],
+    ['yulesimon', (-2, )],
+    ['zipfian', (-0.75, 15)]
+]
+
+
+invdistcont = [
+    # In each of the following, at least one shape parameter is invalid
+    ['alpha', (-1, )],
+    ['anglit', ()],
+    ['arcsine', ()],
+    ['argus', (-1, )],
+    ['beta', (-2, 2)],
+    ['betaprime', (-2, 2)],
+    ['bradford', (-1, )],
+    ['burr', (-1, 1)],
+    ['burr12', (-1, 1)],
+    ['cauchy', ()],
+    ['chi', (-1, )],
+    ['chi2', (-1, )],
+    ['cosine', ()],
+    ['crystalball', (-1, 2)],
+    ['dgamma', (-1, )],
+    ['dpareto_lognorm', (3, -1.2, 1.5, 2)],
+    ['dweibull', (-1, )],
+    ['erlang', (-1, )],
+    ['expon', ()],
+    ['exponnorm', (-1, )],
+    ['exponweib', (1, -1)],
+    ['exponpow', (-1, )],
+    ['f', (10, -10)],
+    ['fatiguelife', (-1, )],
+    ['fisk', (-1, )],
+    ['foldcauchy', (-1, )],
+    ['foldnorm', (-1, )],
+    ['genlogistic', (-1, )],
+    ['gennorm', (-1, )],
+    ['genpareto', (np.inf, )],
+    ['genexpon', (1, 2, -3)],
+    ['genextreme', (np.inf, )],
+    ['genhyperbolic', (0.5, -0.5, -1.5,)],
+    ['gausshyper', (1, 2, 3, -4)],
+    ['gamma', (-1, )],
+    ['gengamma', (-1, 0)],
+    ['genhalflogistic', (-1, )],
+    ['geninvgauss', (1, 0)],
+    ['gibrat', ()],
+    ['gompertz', (-1, )],
+    ['gumbel_r', ()],
+    ['gumbel_l', ()],
+    ['halfcauchy', ()],
+    ['halflogistic', ()],
+    ['halfnorm', ()],
+    ['halfgennorm', (-1, )],
+    ['hypsecant', ()],
+    ['invgamma', (-1, )],
+    ['invgauss', (-1, )],
+    ['invweibull', (-1, )],
+    ['irwinhall', (-1,)],
+    ['irwinhall', (0,)],
+    ['irwinhall', (2.5,)],
+    ['jf_skew_t', (-1, 0)],
+    ['johnsonsb', (1, -2)],
+    ['johnsonsu', (1, -2)],
+    ['kappa4', (np.nan, 0)],
+    ['kappa3', (-1, )],
+    ['ksone', (-1, )],
+    ['kstwo', (-1, )],
+    ['kstwobign', ()],
+    ['landau', ()],
+    ['laplace', ()],
+    ['laplace_asymmetric', (-1, )],
+    ['levy', ()],
+    ['levy_l', ()],
+    ['levy_stable', (-1, 1)],
+    ['logistic', ()],
+    ['loggamma', (-1, )],
+    ['loglaplace', (-1, )],
+    ['lognorm', (-1, )],
+    ['loguniform', (10, 5)],
+    ['lomax', (-1, )],
+    ['maxwell', ()],
+    ['mielke', (1, -2)],
+    ['moyal', ()],
+    ['nakagami', (-1, )],
+    ['ncx2', (-1, 2)],
+    ['ncf', (10, 20, -1)],
+    ['nct', (-1, 2)],
+    ['norm', ()],
+    ['norminvgauss', (5, -10)],
+    ['pareto', (-1, )],
+    ['pearson3', (np.nan, )],
+    ['powerlaw', (-1, )],
+    ['powerlognorm', (1, -2)],
+    ['powernorm', (-1, )],
+    ['rdist', (-1, )],
+    ['rayleigh', ()],
+    ['rice', (-1, )],
+    ['recipinvgauss', (-1, )],
+    ['semicircular', ()],
+    ['skewnorm', (np.inf, )],
+    ['studentized_range', (-1, 1)],
+    ['rel_breitwigner', (-2, )],
+    ['t', (-1, )],
+    ['trapezoid', (0, 2)],
+    ['triang', (2, )],
+    ['truncexpon', (-1, )],
+    ['truncnorm', (10, 5)],
+    ['truncpareto', (-1, -5)],
+    ['truncpareto', (1.8, .5)],
+    ['truncweibull_min', (-2.5, 0.25, 1.75)],
+    ['tukeylambda', (np.nan, )],
+    ['uniform', ()],
+    ['vonmises', (-1, )],
+    ['vonmises_line', (-1, )],
+    ['wald', ()],
+    ['weibull_min', (-1, )],
+    ['weibull_max', (-1, )],
+    ['wrapcauchy', (2, )],
+    ['reciprocal', (15, 10)],
+    ['skewcauchy', (2, )]
+]
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_distribution_infrastructure.py b/.venv/lib/python3.12/site-packages/scipy/stats/_distribution_infrastructure.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab05372648e813dc962c4ddcd3fc5171dcab07c0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_distribution_infrastructure.py
@@ -0,0 +1,5764 @@
+import functools
+from abc import ABC, abstractmethod
+from functools import cached_property
+from types import GenericAlias
+import inspect
+import math
+
+import numpy as np
+from numpy import inf
+
+from scipy._lib._array_api import xp_capabilities, xp_promote
+from scipy._lib._util import _rng_spawn
+from scipy._lib._docscrape import ClassDoc, NumpyDocString
+from scipy._external import array_api_extra as xpx
+from scipy import special, stats
+from scipy.special._ufuncs import _log1mexp
+from scipy.integrate import tanhsinh as _tanhsinh, nsum
+from scipy.optimize import elementwise
+from scipy.stats._probability_distribution import _ProbabilityDistribution
+from scipy.stats import qmc
+
+# in case we need to distinguish between None and not specified
+# Typically this is used to determine whether the tolerance has been set by the
+# user and make a decision about which method to use to evaluate a distribution
+# function. Sometimes, the logic does not consider the value of the tolerance,
+# only whether this has been defined or not. This is not intended to be the
+# best possible logic; the intent is to establish the structure, which can
+# be refined in follow-up work.
+# See https://github.com/scipy/scipy/pull/21050#discussion_r1714195433.
+_null = object()
+def _isnull(x):
+    return type(x) is object or x is None
+
+__all__ = ['make_distribution', 'Mixture', 'order_statistic',
+           'truncate', 'abs', 'exp', 'log']
+
+# Could add other policies for broadcasting and edge/out-of-bounds case handling
+# For instance, when edge case handling is known not to be needed, it's much
+# faster to turn it off, but it might still be nice to have array conversion
+# and shaping done so the user doesn't need to be so careful.
+_SKIP_ALL = "skip_all"
+# Other cache policies would be useful, too.
+_NO_CACHE = "no_cache"
+
+# TODO:
+#  Test sample dtypes
+#  Add dtype kwarg (especially for distributions with no parameters)
+#  Rewrite `test_continuous` without Hypothesis, more modular tests that are easier to
+#    run individually.
+#  Add distribution-specific tests - just enough to confirm that distribution is the
+#    one it is supposed to be; maybe check finite vs undefined vs infinite moments.
+#  `_tanhsinh` special case when there are no abscissae between the limits
+#     example: cdf of uniform betweeen 1.0 and np.nextafter(1.0, np.inf)
+#  implement symmetric distribution (take advantage of symmetry)
+#  implement composite distribution (for using different implementations depending on
+#    shape parameters - e.g. t distribution with df = np.inf delegates to normal)
+#  Add array API support
+#  Investigate use `median` information throughout, e.g. to improve integration of
+#    CDF or to provide initial bracket for ICDF. (Only if `_median_formula` is 
+#    provided.)
+#  Document user tips for faster execution:
+#  - pass NumPy arrays
+#  - pass inputs of floating point type (not integers)
+#  - prefer NumPy scalars or 0d arrays over other size 1 arrays
+#  - pass no invalid parameters and disable invalid parameter checks (validation_policy)
+#  - provide a Generator if you're going to do sampling
+#  Add options for drawing parameters: log-spacing
+#  Accuracy benchmark suite. Ideally, when all code is array API compatible and mparray
+#    provides an array-API compatible arbitrary precision array, we can just compare
+#    results when using NumPy backend against mparray backend.
+#  Should caches be attributes so we can more easily ensure that they are not
+#    modified when caching is turned off?
+#  Make ShiftedScaledDistribution more efficient - only process underlying
+#    distribution parameters as necessary.
+#  Reconsider `all_inclusive` - see comment in `contains` method of `_interval`
+# `validation_policy` needs testing
+# `tol` does not offer very fine-grained control; consider improving
+# report accuracy estimates? How?
+# reconsider distribution mutability
+
+# Originally, I planned to filter out invalid distribution parameters;
+# distribution implementation functions would always work with "compressed",
+# 1D arrays containing only valid distribution parameters. There are two
+# problems with this:
+# - This essentially requires copying all arrays, even if there is only a
+#   single invalid parameter combination. This is expensive. Then, to output
+#   the original size data to the user, we need to "decompress" the arrays
+#   and fill in the NaNs, so more copying. Unless we branch the code when
+#   there are no invalid data, these copies happen even in the normal case,
+#   where there are no invalid parameter combinations. We should not incur
+#   all this overhead in the normal case.
+# - For methods that accept arguments other than distribution parameters, the
+#   user will pass in arrays that are broadcastable with the original arrays,
+#   not the compressed arrays. This means that this same sort of invalid
+#   value detection needs to be repeated every time one of these methods is
+#   called.
+# The much simpler solution is to keep the data uncompressed but to replace
+# the invalid parameters and arguments with NaNs (and only if some are
+# invalid). With this approach, the copying happens only if/when it is
+# needed. Most functions involved in stats distribution calculations don't
+# mind NaNs; they just return NaN. The behavior "If x_i is NaN, the result
+# is NaN" is explicit in the array API. So this should be fine.
+#
+# Currently, I am still leaving the parameters and function arguments
+# in their broadcasted shapes rather than, say, raveling. The intent
+# is to avoid back and forth reshaping. If authors of distributions have
+# trouble dealing with N-D arrays, we can reconsider this.
+#
+# Another important decision is that the *private* methods must accept
+# the distribution parameters as inputs rather than relying on these
+# cached properties directly (although the public methods typically pass
+# the cached values to the private methods). This is because the elementwise
+# algorithms for quadrature, differentiation, root-finding, and minimization
+# prefer that the input functions are strictly elementwise in the sense
+# that the value output for a given input element does not depend on the
+# shape of the input or that element's location within the input array.
+# When the computation has converged for an element, it is removed from
+# the computation entirely. As a result, the shape of the arrays passed to
+# the function will almost never be broadcastable with the shape of the
+# cached parameter arrays.
+#
+# I've sprinkled in some optimizations for scalars and same-shape/type arrays
+# throughout. The biggest time sinks before were:
+# - broadcast_arrays
+# - result_dtype
+# - is_subdtype
+# It is much faster to check whether these are necessary than to do them.
+
+
+class _Domain(ABC):
+    r""" Representation of the applicable domain of a parameter or variable.
+
+    A `_Domain` object is responsible for storing information about the
+    domain of a parameter or variable, determining whether a value is within
+    the domain (`contains`), and providing a text/mathematical representation
+    of itself (`__str__`). Because the domain of a parameter/variable can have
+    a complicated relationship with other parameters and variables of a
+    distribution, `_Domain` itself does not try to represent all possibilities;
+    in fact, it has no implementation and is meant for subclassing.
+
+    Attributes
+    ----------
+    symbols : dict
+        A map from special numerical values to symbols for use in `__str__`
+
+    Methods
+    -------
+    contains(x)
+        Determine whether the argument is contained within the domain (True)
+        or not (False). Used for input validation.
+    get_numerical_endpoints()
+        Gets the numerical values of the domain endpoints, which may have been
+        defined symbolically or through a callable.
+    __str__()
+        Returns a text representation of the domain (e.g. ``[0, b)``).
+        Used for generating documentation.
+
+    """
+    symbols = {np.inf: r"\infty", -np.inf: r"-\infty", np.pi: r"\pi", -np.pi: r"-\pi"}
+
+    # generic type compatibility with scipy-stubs
+    __class_getitem__: classmethod = classmethod(GenericAlias)
+
+    @abstractmethod
+    def contains(self, x):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def draw(self, n):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def get_numerical_endpoints(self, x):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def __str__(self):
+        raise NotImplementedError()
+
+
+class _Interval(_Domain):
+    r""" Representation of an interval defined by two endpoints.
+
+    Each endpoint may be a finite scalar, positive or negative infinity, or
+    be given by a single parameter. The domain may include the endpoints or
+    not.
+
+    This class still does not provide an implementation of the __str__ method,
+    so it is meant for subclassing (e.g. a subclass for domains on the real
+    line).
+
+    Attributes
+    ----------
+    symbols : dict
+        Inherited. A map from special values to symbols for use in `__str__`.
+    endpoints : 2-tuple of float(s) and/or str(s) and/or callable(s).
+        A tuple with two values. Each may be either a float (the numerical
+        value of the endpoints of the domain), a string (the name of the
+        parameters that will define the endpoint), or a callable taking the
+        parameters used to define the endpoints of the domain as keyword only
+        arguments and returning a numerical value for the endpoint.
+    inclusive : 2-tuple of bools
+        A tuple with two boolean values; each indicates whether the
+        corresponding endpoint is included within the domain or not.
+
+    Methods
+    -------
+    define_parameters(*parameters)
+        Records any parameters used to define the endpoints of the domain
+    get_numerical_endpoints(parameter_values)
+        Gets the numerical values of the domain endpoints, which may have been
+        defined symbolically or through a callable.
+    contains(item, parameter_values)
+        Determines whether the argument is contained within the domain
+    draw(size, rng, proportions, parameter_values)
+        Draws random values based on the domain.
+
+    """
+    def __init__(self, endpoints=(-inf, inf), inclusive=(False, False)):
+        self.symbols = super().symbols.copy()
+        a, b = endpoints
+        self.endpoints = np.asarray(a)[()], np.asarray(b)[()]
+        self.inclusive = inclusive
+
+    def define_parameters(self, *parameters):
+        r""" Records any parameters used to define the endpoints of the domain.
+
+        Adds the keyword name of each parameter and its text representation
+        to the  `symbols` attribute as key:value pairs.
+        For instance, a parameter may be passed into to a distribution's
+        initializer using the keyword `log_a`, and the corresponding
+        string representation may be '\log(a)'. To form the text
+        representation of the domain for use in documentation, the
+        _Domain object needs to map from the keyword name used in the code
+        to the string representation.
+
+        Returns None, but updates the `symbols` attribute.
+
+        Parameters
+        ----------
+        *parameters : _Parameter objects
+            Parameters that may define the endpoints of the domain.
+
+        """
+        new_symbols = {param.name: param.symbol for param in parameters}
+        self.symbols.update(new_symbols)
+
+    def get_numerical_endpoints(self, parameter_values):
+        r""" Get the numerical values of the domain endpoints.
+
+        Domain endpoints may be defined symbolically or through a callable.
+        This returns numerical values of the endpoints given numerical values for
+        any variables.
+
+        Parameters
+        ----------
+        parameter_values : dict
+            A dictionary that maps between string variable names and numerical
+            values of parameters, which may define the endpoints.
+
+        Returns
+        -------
+        a, b : ndarray
+            Numerical values of the endpoints
+
+        """
+        a, b = self.endpoints
+        # If `a` (`b`) is a string - the name of the parameter that defines
+        # the endpoint of the domain - then corresponding numerical values
+        # will be found in the `parameter_values` dictionary.
+        # If a callable, it will be executed with `parameter_values` passed as
+        # keyword arguments, and it will return the numerical values.
+        # Otherwise, it is itself the array of numerical values of the endpoint.
+        try:
+            if callable(a):
+                a = a(**parameter_values)
+            else:
+                a = np.asarray(parameter_values.get(a, a))
+            if callable(b):
+                b = b(**parameter_values)
+            else:
+                b = np.asarray(parameter_values.get(b, b))
+        except TypeError as e:
+            message = ("The endpoints of the distribution are defined by "
+                       "parameters, but their values were not provided. When "
+                       f"using a private method of {self.__class__}, pass "
+                       "all required distribution parameters as keyword "
+                       "arguments.")
+            raise TypeError(message) from e
+        # Floating point types are used for even integer parameters.
+        # Convert to float here to ensure consistency throughout framework.
+        a, b = xp_promote(a, b, force_floating=True, xp=np)
+        return a, b
+
+    def contains(self, item, parameter_values=None):
+        r"""Determine whether the argument is contained within the domain.
+
+        Parameters
+        ----------
+        item : ndarray
+            The argument
+        parameter_values : dict
+            A dictionary that maps between string variable names and numerical
+            values of parameters, which may define the endpoints.
+
+        Returns
+        -------
+        out : bool
+            True if `item` is within the domain; False otherwise.
+
+        """
+        parameter_values = parameter_values or {}
+        # if self.all_inclusive:
+        #     # Returning a 0d value here makes things much faster.
+        #     # I'm not sure if it's safe, though. If it causes a bug someday,
+        #     # I guess it wasn't.
+        #     # Even if there is no bug because of the shape, it is incorrect for
+        #     # `contains` to return True when there are invalid (e.g. NaN)
+        #     # parameters.
+        #     return np.asarray(True)
+
+        a, b = self.get_numerical_endpoints(parameter_values)
+        left_inclusive, right_inclusive = self.inclusive
+
+        in_left = item >= a if left_inclusive else item > a
+        in_right = item <= b if right_inclusive else item < b
+        return in_left & in_right
+
+    def draw(self, n, type_, min, max, squeezed_base_shape, rng=None):
+        r""" Draw random values from the domain.
+
+        Parameters
+        ----------
+        n : int
+            The number of values to be drawn from the domain.
+        type_ : str
+            A string indicating whether the values are
+
+            - strictly within the domain ('in'),
+            - at one of the two endpoints ('on'),
+            - strictly outside the domain ('out'), or
+            - NaN ('nan').
+        min, max : ndarray
+            The endpoints of the domain.
+        squeezed_based_shape : tuple of ints
+            See _RealParameter.draw.
+        rng : np.Generator
+            The Generator used for drawing random values.
+
+        """
+        rng = np.random.default_rng(rng)
+
+        def ints(*args, **kwargs): return rng.integers(*args, **kwargs, endpoint=True)
+        uniform = rng.uniform if isinstance(self, _RealInterval) else ints
+
+        # get copies of min and max with no nans so that uniform doesn't fail
+        min_nn, max_nn = min.copy(), max.copy()
+        i = np.isnan(min_nn) | np.isnan(max_nn)
+        min_nn[i] = 0
+        max_nn[i] = 1
+
+        shape = (n,) + squeezed_base_shape
+
+        if type_ == 'in':
+            z = uniform(min_nn, max_nn, size=shape)
+
+        elif type_ == 'on':
+            z_on_shape = shape
+            z = np.ones(z_on_shape)
+            i = rng.random(size=n) < 0.5
+            z[i] = min
+            z[~i] = max
+
+        elif type_ == 'out':
+            z = min_nn - uniform(1, 5, size=shape)   # 1, 5 is arbitary; we just want
+            zr = max_nn + uniform(1, 5, size=shape)  # some numbers outside domain
+            i = rng.random(size=n) < 0.5
+            z[i] = zr[i]
+
+        elif type_ == 'nan':
+            z = np.full(shape, np.nan)
+
+        return z
+
+
+class _RealInterval(_Interval):
+    r""" Represents a simply-connected subset of the real line; i.e., an interval
+
+    Completes the implementation of the `_Interval` class for intervals
+    on the real line.
+
+    Methods
+    -------
+    define_parameters(*parameters)
+        (Inherited) Records any parameters used to define the endpoints of the
+        domain.
+    get_numerical_endpoints(parameter_values)
+        (Inherited) Gets the numerical values of the domain endpoints, which
+        may have been defined symbolically.
+    contains(item, parameter_values)
+        (Inherited) Determines whether the argument is contained within the
+        domain
+    __str__()
+        Returns a string representation of the domain, e.g. "[a, b)".
+    """
+
+    def __str__(self):
+        a, b = self.endpoints
+        a, b = self._get_endpoint_str(a, "f1"), self._get_endpoint_str(b, "f2")
+        left_inclusive, right_inclusive = self.inclusive
+        left = "[" if left_inclusive else "("
+        right = "]" if right_inclusive else ")"
+
+        return f"{left}{a}, {b}{right}"
+
+    def _get_endpoint_str(self, endpoint, funcname):
+        if callable(endpoint):
+            if endpoint.__doc__ is not None:
+                return endpoint.__doc__
+            params = inspect.signature(endpoint).parameters.values()
+            params = [
+                p.name for p in params if p.kind == inspect.Parameter.KEYWORD_ONLY
+            ]
+            return f"{funcname}({','.join(params)})"
+        return self.symbols.get(endpoint, f"{endpoint}")
+
+
+class _IntegerInterval(_Interval):
+    r""" Represents an interval of integers
+
+    Completes the implementation of the `_Interval` class for simple
+    domains on the integers.
+
+    Methods
+    -------
+    define_parameters(*parameters)
+        (Inherited) Records any parameters used to define the endpoints of the
+        domain.
+    get_numerical_endpoints(parameter_values)
+        (Inherited) Gets the numerical values of the domain endpoints, which
+        may have been defined symbolically.
+    contains(item, parameter_values)
+        (Overridden) Determines whether the argument is contained within the
+        domain
+    draw(n, type_, min, max, squeezed_base_shape, rng=None)
+        (Inherited) Draws random values based on the domain.
+    __str__()
+        Returns a string representation of the domain, e.g. "{a, a+1, ..., b-1, b}".
+
+    """
+    def contains(self, item, parameter_values=None):
+        super_contains = super().contains(item, parameter_values)
+        integral = (item == np.round(item))
+        return super_contains & integral
+
+    def __str__(self):
+        a, b = self.endpoints
+        a = self.symbols.get(a, a)
+        b = self.symbols.get(b, b)
+
+        a_str, b_str = isinstance(a, str), isinstance(b, str)
+        a_inf = a == r"-\infty" if a_str else np.isinf(a)
+        b_inf = b == r"\infty" if b_str else np.isinf(b)
+
+        # This doesn't work well for cases where ``a`` is floating point
+        # number large enough that ``nextafter(a, inf) > a + 1``, and
+        # similarly for ``b`` and nextafter(b, -inf). There may not be any
+        # distributions fit for SciPy where we would actually need to handle these
+        # cases though.
+        ap1 = f"{a} + 1" if a_str else f"{a + 1}"
+        bm1 = f"{b} - 1" if b_str else f"{b - 1}"
+
+        if not a_str and not b_str:
+            gap = b - a
+            if gap == 3:
+                return f"\\{{{a}, {ap1}, {bm1}, {b}\\}}"
+            if gap == 2:
+                return f"\\{{{a}, {ap1}, {b}\\}}"
+            if gap == 1:
+                return f"\\{{{a}, {b}\\}}"
+            if gap == 0:
+                return f"\\{{{a}\\}}"
+
+        if not a_inf and b_inf:
+            ap2 = f"{a} + 2" if a_str else f"{a + 2}"
+            return f"\\{{{a}, {ap1}, {ap2}, ...\\}}"
+        if a_inf and not b_inf:
+            bm2 = f"{b} - 2" if b_str else f"{b - 2}"
+            return f"\\{{{b}, {bm1}, {bm2}, ...\\}}"
+        if a_inf and b_inf:
+            return "\\{..., -2, -1, 0, 1, 2, ...\\}"
+
+        return f"\\{{{a}, {ap1}, ..., {bm1}, {b}\\}}"
+
+
+class _Parameter(ABC):
+    r""" Representation of a distribution parameter or variable.
+
+    A `_Parameter` object is responsible for storing information about a
+    parameter or variable, providing input validation/standardization of
+    values passed for that parameter, providing a text/mathematical
+    representation of the parameter for the documentation (`__str__`), and
+    drawing random values of itself for testing and benchmarking. It does
+    not provide a complete implementation of this functionality and is meant
+    for subclassing.
+
+    Attributes
+    ----------
+    name : str
+        The keyword used to pass numerical values of the parameter into the
+        initializer of the distribution
+    symbol : str
+        The text representation of the variable in the documentation. May
+        include LaTeX.
+    domain : _Domain
+        The domain of the parameter for which the distribution is valid.
+    typical : 2-tuple of floats or strings (consider making a _Domain)
+        Defines the endpoints of a typical range of values of the parameter.
+        Used for sampling.
+
+    Methods
+    -------
+    __str__():
+        Returns a string description of the variable for use in documentation,
+        including the keyword used to represent it in code, the symbol used to
+        represent it mathemtatically, and a description of the valid domain.
+    draw(size, *, rng, domain, proportions)
+        Draws random values of the parameter. Proportions of values within
+        the valid domain, on the endpoints of the domain, outside the domain,
+        and having value NaN are specified by `proportions`.
+    validate(x):
+        Validates and standardizes the argument for use as numerical values
+        of the parameter.
+
+   """
+
+    # generic type compatibility with scipy-stubs
+    __class_getitem__: classmethod = classmethod(GenericAlias)
+
+    def __init__(self, name, *, domain, symbol=None, typical=None):
+        self.name = name
+        self.symbol = symbol or name
+        self.domain = domain
+        if typical is not None and not isinstance(typical, _Domain):
+            typical = domain.__class__(typical)
+        self.typical = typical or domain
+
+    def __str__(self):
+        r""" String representation of the parameter for use in documentation."""
+        return f"`{self.name}` for :math:`{self.symbol} \\in {str(self.domain)}`"
+
+    def draw(self, size=None, *, rng=None, region='domain', proportions=None,
+             parameter_values=None):
+        r""" Draw random values of the parameter for use in testing.
+
+        Parameters
+        ----------
+        size : tuple of ints
+            The shape of the array of valid values to be drawn.
+        rng : np.Generator
+            The Generator used for drawing random values.
+        region : str
+            The region of the `_Parameter` from which to draw. Default is
+            "domain" (the *full* domain); alternative is "typical". An
+            enhancement would give a way to interpolate between the two.
+        proportions : tuple of numbers
+            A tuple of four non-negative numbers that indicate the expected
+            relative proportion of elements that:
+
+            - are strictly within the domain,
+            - are at one of the two endpoints,
+            - are strictly outside the domain, and
+            - are NaN,
+
+            respectively. Default is (1, 0, 0, 0). The number of elements in
+            each category is drawn from the multinomial distribution with
+            `np.prod(size)` as the number of trials and `proportions` as the
+            event probabilities. The values in `proportions` are automatically
+            normalized to sum to 1.
+        parameter_values : dict
+            Map between the names of parameters (that define the endpoints of
+            `typical`) and numerical values (arrays).
+
+        """
+        parameter_values = parameter_values or {}
+        domain = self.domain
+        proportions = (1, 0, 0, 0) if proportions is None else proportions
+
+        pvals = proportions / np.sum(proportions)
+
+        a, b = domain.get_numerical_endpoints(parameter_values)
+        a, b = np.broadcast_arrays(a, b)
+
+        base_shape = a.shape
+        extended_shape = np.broadcast_shapes(size, base_shape)
+        n_extended = np.prod(extended_shape)
+        n_base = np.prod(base_shape)
+        n = int(n_extended / n_base) if n_extended else 0
+
+        rng = np.random.default_rng(rng)
+        n_in, n_on, n_out, n_nan = rng.multinomial(n, pvals)
+
+        # `min` and `max` can have singleton dimensions that correspond with
+        # non-singleton dimensions in `size`. We need to be careful to avoid
+        # shuffling results (e.g. a value that was generated for the domain
+        # [min[i], max[i]] ends up at index j). To avoid this:
+        # - Squeeze the singleton dimensions out of `min`/`max`. Squeezing is
+        #   often not the right thing to do, but here is equivalent to moving
+        #   all the dimensions that are singleton in `min`/`max` (which may be
+        #   non-singleton in the result) to the left. This is what we want.
+        # - Now all the non-singleton dimensions of the result are on the left.
+        #   Ravel them to a single dimension of length `n`, which is now along
+        #   the 0th axis.
+        # - Reshape the 0th axis back to the required dimensions, and move
+        #   these axes back to their original places.
+        base_shape_padded = ((1,)*(len(extended_shape) - len(base_shape))
+                             + base_shape)
+        base_singletons = np.where(np.asarray(base_shape_padded)==1)[0]
+        new_base_singletons = tuple(range(len(base_singletons)))
+        # Base singleton dimensions are going to get expanded to these lengths
+        shape_expansion = np.asarray(extended_shape)[base_singletons]
+
+        # assert(np.prod(shape_expansion) == n)  # check understanding
+        # min = np.reshape(min, base_shape_padded)
+        # max = np.reshape(max, base_shape_padded)
+        # min = np.moveaxis(min, base_singletons, new_base_singletons)
+        # max = np.moveaxis(max, base_singletons, new_base_singletons)
+        # squeezed_base_shape = max.shape[len(base_singletons):]
+        # assert np.all(min.reshape(squeezed_base_shape) == min.squeeze())
+        # assert np.all(max.reshape(squeezed_base_shape) == max.squeeze())
+
+        # min = np.maximum(a, _fiinfo(a).min/10) if np.any(np.isinf(a)) else a
+        # max = np.minimum(b, _fiinfo(b).max/10) if np.any(np.isinf(b)) else b
+        min = np.asarray(a.squeeze())
+        max = np.asarray(b.squeeze())
+        squeezed_base_shape = max.shape
+
+        if region == 'typical':
+            typical = self.typical
+            a, b = typical.get_numerical_endpoints(parameter_values)
+            a, b = np.broadcast_arrays(a, b)
+            min_here = np.asarray(a.squeeze())
+            max_here = np.asarray(b.squeeze())
+            z_in = typical.draw(n_in, 'in', min_here, max_here, squeezed_base_shape,
+                                rng=rng)
+        else:
+            z_in = domain.draw(n_in, 'in', min, max, squeezed_base_shape, rng=rng)
+        z_on = domain.draw(n_on, 'on', min, max, squeezed_base_shape, rng=rng)
+        z_out = domain.draw(n_out, 'out', min, max, squeezed_base_shape, rng=rng)
+        z_nan= domain.draw(n_nan, 'nan', min, max, squeezed_base_shape, rng=rng)
+
+        z = np.concatenate((z_in, z_on, z_out, z_nan), axis=0)
+        z = rng.permuted(z, axis=0)
+
+        z = np.reshape(z, tuple(shape_expansion) + squeezed_base_shape)
+        z = np.moveaxis(z, new_base_singletons, base_singletons)
+        return z
+
+    @abstractmethod
+    def validate(self, arr):
+        raise NotImplementedError()
+
+
+class _RealParameter(_Parameter):
+    r""" Represents a real-valued parameter.
+
+    Implements the remaining methods of _Parameter for real parameters.
+    All attributes are inherited.
+
+    """
+    def validate(self, arr, parameter_values):
+        r""" Input validation/standardization of numerical values of a parameter.
+
+        Checks whether elements of the argument `arr` are reals, ensuring that
+        the dtype reflects this. Also produces a logical array that indicates
+        which elements meet the requirements.
+
+        Parameters
+        ----------
+        arr : ndarray
+            The argument array to be validated and standardized.
+        parameter_values : dict
+            Map of parameter names to parameter value arrays.
+
+        Returns
+        -------
+        arr : ndarray
+            The argument array that has been validated and standardized
+            (converted to an appropriate dtype, if necessary).
+        dtype : NumPy dtype
+            The appropriate floating point dtype of the parameter.
+        valid : bool ndarray
+            Logical array indicating which elements are valid (True) and
+            which are not (False). The arrays of all distribution parameters
+            will be broadcasted, and elements for which any parameter value
+            does not meet the requirements will be replaced with NaN.
+
+        """
+        arr = np.asarray(arr)
+
+        valid_dtype = None
+        # minor optimization - fast track the most common types to avoid
+        # overhead of np.issubdtype. Checking for `in {...}` doesn't work : /
+        if arr.dtype == np.float64 or arr.dtype == np.float32:
+            pass
+        elif arr.dtype == np.int32 or arr.dtype == np.int64:
+            arr = np.asarray(arr, dtype=np.float64)
+        elif np.issubdtype(arr.dtype, np.floating):
+            pass
+        elif np.issubdtype(arr.dtype, np.integer):
+            arr = np.asarray(arr, dtype=np.float64)
+        else:
+            message = f"Parameter `{self.name}` must be of real dtype."
+            raise TypeError(message)
+
+        valid = self.domain.contains(arr, parameter_values)
+        valid = valid & valid_dtype if valid_dtype is not None else valid
+
+        return arr[()], arr.dtype, valid
+
+
+class _Parameterization:
+    r""" Represents a parameterization of a distribution.
+
+    Distributions can have multiple parameterizations. A `_Parameterization`
+    object is responsible for recording the parameters used by the
+    parameterization, checking whether keyword arguments passed to the
+    distribution match the parameterization, and performing input validation
+    of the numerical values of these parameters.
+
+    Attributes
+    ----------
+    parameters : dict
+        String names (of keyword arguments) and the corresponding _Parameters.
+
+    Methods
+    -------
+    __len__()
+        Returns the number of parameters in the parameterization.
+    __str__()
+        Returns a string representation of the parameterization.
+    copy
+        Returns a copy of the parameterization. This is needed for transformed
+        distributions that add parameters to the parameterization.
+    matches(parameters)
+        Checks whether the keyword arguments match the parameterization.
+    validation(parameter_values)
+        Input validation / standardization of parameterization. Validates the
+        numerical values of all parameters.
+    draw(sizes, rng, proportions)
+        Draw random values of all parameters of the parameterization for use
+        in testing.
+    """
+    def __init__(self, *parameters):
+        self.parameters = {param.name: param for param in parameters}
+
+    def __len__(self):
+        return len(self.parameters)
+
+    def copy(self):
+        return _Parameterization(*self.parameters.values())
+
+    def matches(self, parameters):
+        r""" Checks whether the keyword arguments match the parameterization.
+
+        Parameters
+        ----------
+        parameters : set
+            Set of names of parameters passed into the distribution as keyword
+            arguments.
+
+        Returns
+        -------
+        out : bool
+            True if the keyword arguments names match the names of the
+            parameters of this parameterization.
+        """
+        return parameters == set(self.parameters.keys())
+
+    def validation(self, parameter_values):
+        r""" Input validation / standardization of parameterization.
+
+        Parameters
+        ----------
+        parameter_values : dict
+            The keyword arguments passed as parameter values to the
+            distribution.
+
+        Returns
+        -------
+        all_valid : ndarray
+            Logical array indicating the elements of the broadcasted arrays
+            for which all parameter values are valid.
+        dtype : dtype
+            The common dtype of the parameter arrays. This will determine
+            the dtype of the output of distribution methods.
+        """
+        all_valid = True
+        dtypes = set()  # avoid np.result_type if there's only one type
+        for name, arr in parameter_values.items():
+            parameter = self.parameters[name]
+            arr, dtype, valid = parameter.validate(arr, parameter_values)
+            dtypes.add(dtype)
+            all_valid = all_valid & valid
+            parameter_values[name] = arr
+        dtype = arr.dtype if len(dtypes)==1 else np.result_type(*list(dtypes))
+
+        return all_valid, dtype
+
+    def __str__(self):
+        r"""Returns a string representation of the parameterization."""
+        messages = [str(param) for name, param in self.parameters.items()]
+        return ", ".join(messages)
+
+    def draw(self, sizes=None, rng=None, proportions=None, region='domain'):
+        r"""Draw random values of all parameters for use in testing.
+
+        Parameters
+        ----------
+        sizes : iterable of shape tuples
+            The size of the array to be generated for each parameter in the
+            parameterization. Note that the order of sizes is arbitary; the
+            size of the array generated for a specific parameter is not
+            controlled individually as written.
+        rng : NumPy Generator
+            The generator used to draw random values.
+        proportions : tuple
+            A tuple of four non-negative numbers that indicate the expected
+            relative proportion of elements that are within the parameter's
+            domain, are on the boundary of the parameter's domain, are outside
+            the parameter's domain, and have value NaN. For more information,
+            see the `draw` method of the _Parameter subclasses.
+        domain : str
+            The domain of the `_Parameter` from which to draw. Default is
+            "domain" (the *full* domain); alternative is "typical".
+
+        Returns
+        -------
+        parameter_values : dict (string: array)
+            A dictionary of parameter name/value pairs.
+        """
+        # ENH: be smart about the order. The domains of some parameters
+        # depend on others. If the relationshp is simple (e.g. a < b < c),
+        # we can draw values in order a, b, c.
+        parameter_values = {}
+
+        if sizes is None or not len(sizes) or not np.iterable(sizes[0]):
+            sizes = [sizes]*len(self.parameters)
+
+        for size, param in zip(sizes, self.parameters.values()):
+            parameter_values[param.name] = param.draw(
+                size, rng=rng, proportions=proportions,
+                parameter_values=parameter_values,
+                region=region
+            )
+
+        return parameter_values
+
+
+def _set_invalid_nan(f):
+    # Wrapper for input / output validation and standardization of distribution
+    # functions that accept either the quantile or percentile as an argument:
+    # logpdf, pdf
+    # logpmf, pmf
+    # logcdf, cdf
+    # logccdf, ccdf
+    # ilogcdf, icdf
+    # ilogccdf, iccdf
+    # Arguments that are outside the required range are replaced by NaN before
+    # passing them into the underlying function. The corresponding outputs
+    # are replaced by the appropriate value before being returned to the user.
+    # For example, when the argument of `cdf` exceeds the right end of the
+    # distribution's support, the wrapper replaces the argument with NaN,
+    # ignores the output of the underlying function, and returns 1.0. It also
+    # ensures that output is of the appropriate shape and dtype.
+
+    endpoints = {'icdf': (0, 1), 'iccdf': (0, 1),
+                 'ilogcdf': (-np.inf, 0), 'ilogccdf': (-np.inf, 0)}
+    replacements = {'logpdf': (-inf, -inf), 'pdf': (0, 0),
+                    'logpmf': (-inf, -inf), 'pmf': (0, 0),
+                    '_logcdf1': (-inf, 0), '_logccdf1': (0, -inf),
+                    '_cdf1': (0, 1), '_ccdf1': (1, 0)}
+    replace_strict = {'pdf', 'logpdf', 'pmf', 'logpmf'}
+    replace_exact = {'icdf', 'iccdf', 'ilogcdf', 'ilogccdf'}
+    clip = {'_cdf1', '_ccdf1'}
+    clip_log = {'_logcdf1', '_logccdf1'}
+    # relevant to discrete distributions only
+    replace_non_integral = {'pmf', 'logpmf', 'pdf', 'logpdf'}
+
+    @functools.wraps(f)
+    def filtered(self, x, *args, **kwargs):
+        if self.validation_policy == _SKIP_ALL:
+            return f(self, x, *args, **kwargs)
+
+        method_name = f.__name__
+        x = np.asarray(x)
+        dtype = self._dtype
+        shape = self._shape
+        discrete = isinstance(self, DiscreteDistribution)
+        keep_low_endpoint = discrete and method_name in {'_cdf1', '_logcdf1',
+                                                         '_ccdf1', '_logccdf1'}
+
+        # Ensure that argument is at least as precise as distribution
+        # parameters, which are already at least floats. This will avoid issues
+        # with raising integers to negative integer powers and failure to replace
+        # invalid integers with NaNs.
+        if x.dtype != dtype:
+            dtype = np.result_type(x.dtype, dtype)
+            x = np.asarray(x, dtype=dtype)
+
+        # Broadcasting is slow. Do it only if necessary.
+        if not x.shape == shape:
+            try:
+                shape = np.broadcast_shapes(x.shape, shape)
+                x = np.broadcast_to(x, shape)
+                # Should we broadcast the distribution parameters to this shape, too?
+            except ValueError as e:
+                message = (
+                    f"The argument provided to `{self.__class__.__name__}"
+                    f".{method_name}` cannot be be broadcast to the same "
+                    "shape as the distribution parameters.")
+                raise ValueError(message) from e
+
+        low, high = endpoints.get(method_name, self.support())
+
+        # Check for arguments outside of domain. They'll be replaced with NaNs,
+        # and the result will be set to the appropriate value.
+        left_inc, right_inc = self._variable.domain.inclusive
+        mask_low = (x < low if (method_name in replace_strict and left_inc)
+                    or keep_low_endpoint else x <= low)
+        mask_high = (x > high if (method_name in replace_strict and right_inc)
+                     else x >= high)
+        mask_invalid = (mask_low | mask_high)
+        any_invalid = (mask_invalid if mask_invalid.shape == ()
+                       else np.any(mask_invalid))
+
+        # Check for arguments at domain endpoints, whether they
+        # are part of the domain or not.
+        any_endpoint = False
+        if method_name in replace_exact:
+            mask_low_endpoint = (x == low)
+            mask_high_endpoint = (x == high)
+            mask_endpoint = (mask_low_endpoint | mask_high_endpoint)
+            any_endpoint = (mask_endpoint if mask_endpoint.shape == ()
+                            else np.any(mask_endpoint))
+
+        # Check for non-integral arguments to PMF method
+        # or PDF of a discrete distribution.
+        any_non_integral = False
+        if discrete and method_name in replace_non_integral:
+            mask_non_integral = (x != np.floor(x))
+            any_non_integral = (mask_non_integral if mask_non_integral.shape == ()
+                                else np.any(mask_non_integral))
+
+        # Set out-of-domain arguments to NaN. The result will be set to the
+        # appropriate value later.
+        if any_invalid:
+            x = np.array(x, dtype=dtype, copy=True)
+            x[mask_invalid] = np.nan
+
+        res = np.asarray(f(self, x, *args, **kwargs))
+
+        # Ensure that the result is the correct dtype and shape,
+        # copying (only once) if necessary.
+        res_needs_copy = False
+        if res.dtype != dtype:
+            dtype = np.result_type(dtype, self._dtype)
+            res_needs_copy = True
+
+        if res.shape != shape:  # faster to check first
+            res = np.broadcast_to(res, self._shape)
+            res_needs_copy = (res_needs_copy or any_invalid
+                              or any_endpoint or any_non_integral)
+
+        if res_needs_copy:
+            res = np.array(res, dtype=dtype, copy=True)
+
+        # For non-integral arguments to PMF (and PDF of discrete distribution)
+        # replace with zero.
+        if any_non_integral:
+            zero = -np.inf if method_name in {'logpmf', 'logpdf'} else 0
+            res[mask_non_integral & ~np.isnan(res)] = zero
+
+        # For arguments outside the function domain, replace results
+        if any_invalid:
+            replace_low, replace_high = (
+                replacements.get(method_name, (np.nan, np.nan)))
+            res[mask_low] = replace_low
+            res[mask_high] = replace_high
+
+        # For arguments at the endpoints of the domain, replace results
+        if any_endpoint:
+            a, b = self.support()
+            if a.shape != shape:
+                a = np.array(np.broadcast_to(a, shape), copy=True)
+                b = np.array(np.broadcast_to(b, shape), copy=True)
+
+            replace_low_endpoint = (
+                b[mask_low_endpoint] if method_name.endswith('ccdf')
+                else a[mask_low_endpoint])
+            replace_high_endpoint = (
+                a[mask_high_endpoint] if method_name.endswith('ccdf')
+                else b[mask_high_endpoint])
+
+            if not keep_low_endpoint:
+                res[mask_low_endpoint] = replace_low_endpoint
+            res[mask_high_endpoint] = replace_high_endpoint
+
+        # Clip probabilities to [0, 1]
+        if method_name in clip:
+            res = np.clip(res, 0., 1.)
+        elif method_name in clip_log:
+            res = res.real  # exp(res) > 0
+            res = np.clip(res, None, 0.)  # exp(res) < 1
+
+        return res[()]
+
+    return filtered
+
+
+def _set_invalid_nan_property(f):
+    # Wrapper for input / output validation and standardization of distribution
+    # functions that represent properties of the distribution itself:
+    # logentropy, entropy
+    # median, mode
+    # moment
+    # It ensures that the output is of the correct shape and dtype and that
+    # there are NaNs wherever the distribution parameters were invalid.
+
+    @functools.wraps(f)
+    def filtered(self, *args, **kwargs):
+        if self.validation_policy == _SKIP_ALL:
+            return f(self, *args, **kwargs)
+
+        res = f(self, *args, **kwargs)
+        if res is None:
+            # message could be more appropriate
+            raise NotImplementedError(self._not_implemented)
+
+        res = np.asarray(res)
+        needs_copy = False
+        dtype = res.dtype
+
+        if dtype != self._dtype:  # this won't work for logmoments (complex)
+            dtype = np.result_type(dtype, self._dtype)
+            needs_copy = True
+
+        if res.shape != self._shape:  # faster to check first
+            res = np.broadcast_to(res, self._shape)
+            needs_copy = needs_copy or self._any_invalid
+
+        if needs_copy:
+            res = res.astype(dtype=dtype, copy=True)
+
+        if self._any_invalid:
+            # may be redundant when quadrature is used, but not necessarily
+            # when formulas are used.
+            res[self._invalid] = np.nan
+
+        return res[()]
+
+    return filtered
+
+
+def _dispatch(f):
+    # For each public method (instance function) of a distribution (e.g. ccdf),
+    # there may be several ways ("method"s) that it can be computed (e.g. a
+    # formula, as the complement of the CDF, or via numerical integration).
+    # Each "method" is implemented by a different private method (instance
+    # function).
+    # This wrapper calls the appropriate private method based on the public
+    # method and any specified `method` keyword option.
+    # - If `method` is specified as a string (by the user), the appropriate
+    #   private method is called.
+    # - If `method` is None:
+    #   - The appropriate private method for the public method is looked up
+    #     in a cache.
+    #   - If the cache does not have an entry for the public method, the
+    #     appropriate "dispatch " function is called to determine which method
+    #     is most appropriate given the available private methods and
+    #     settings (e.g. tolerance).
+
+    @functools.wraps(f)
+    def wrapped(self, *args, method=None, **kwargs):
+        func_name = f.__name__
+        method = method or self._method_cache.get(func_name, None)
+        if callable(method):
+            pass
+        elif method is not None:
+            method = 'logexp' if method == 'log/exp' else method
+            method_name = func_name.replace('dispatch', method)
+            method = getattr(self, method_name)
+        else:
+            method = f(self, *args, method=method, **kwargs)
+            if func_name != '_sample_dispatch' and self.cache_policy != _NO_CACHE:
+                self._method_cache[func_name] = method
+
+        try:
+            return method(*args, **kwargs)
+        except KeyError as e:
+            raise NotImplementedError(self._not_implemented) from e
+
+    return wrapped
+
+
+def _cdf2_input_validation(f):
+    # Wrapper that does the job of `_set_invalid_nan` when `cdf` or `logcdf`
+    # is called with two quantile arguments.
+    # Let's keep it simple; no special cases for speed right now.
+    # The strategy is a bit different than for 1-arg `cdf` (and other methods
+    # covered by `_set_invalid_nan`). For 1-arg `cdf`, elements of `x` that
+    # are outside (or at the edge of) the support get replaced by `nan`,
+    # and then the results get replaced by the appropriate value (0 or 1).
+    # We *could* do something similar, dispatching to `_cdf1` in these
+    # cases. That would be a bit more robust, but it would also be quite
+    # a bit more complex, since we'd have to do different things when
+    # `x` and `y` are both out of bounds, when just `x` is out of bounds,
+    # when just `y` is out of bounds, and when both are out of bounds.
+    # I'm not going to do that right now. Instead, simply replace values
+    # outside the support by those at the edge of the support. Here, we also
+    # omit some of the optimizations that make `_set_invalid_nan` faster for
+    # simple arguments (e.g. float64 scalars).
+
+    @functools.wraps(f)
+    def wrapped(self, x, y, *args, **kwargs):
+        func_name = f.__name__
+
+        low, high = self.support()
+        x, y, low, high = np.broadcast_arrays(x, y, low, high)
+        dtype = np.result_type(x.dtype, y.dtype, self._dtype)
+        # yes, copy to avoid modifying input arrays
+        x, y = x.astype(dtype, copy=True), y.astype(dtype, copy=True)
+
+        # Swap arguments to ensure that x < y, and replace
+        # out-of domain arguments with domain endpoints. We'll
+        # transform the result later.
+        i_swap = y < x
+        x[i_swap], y[i_swap] = y[i_swap], x[i_swap]
+        i = x < low
+        x[i] = low[i]
+        i = y < low
+        y[i] = low[i]
+        i = x > high
+        x[i] = high[i]
+        i = y > high
+        y[i] = high[i]
+
+        res = f(self, x, y, *args, **kwargs)
+
+        # Clipping probability to [0, 1]
+        if func_name in {'_cdf2', '_ccdf2'}:
+            res = np.clip(res, 0., 1.)
+        else:
+            res = np.clip(res, None, 0.)  # exp(res) < 1
+
+        # Transform the result to account for swapped argument order
+        res = np.asarray(res)
+        if func_name == '_cdf2':
+            res[i_swap] *= -1.
+        elif func_name == '_ccdf2':
+            res[i_swap] *= -1
+            res[i_swap] += 2.
+        elif func_name == '_logcdf2':
+            res = np.asarray(res + 0j) if np.any(i_swap) else res
+            res[i_swap] = res[i_swap] + np.pi*1j
+        else:
+            # res[i_swap] is always positive and less than 1, so it's
+            # safe to ensure that the result is real
+            res[i_swap] = _logexpxmexpy(np.log(2), res[i_swap]).real
+        return res[()]
+
+    return wrapped
+
+
+def _fiinfo(x):
+    if np.issubdtype(x.dtype, np.inexact):
+        return np.finfo(x.dtype)
+    else:
+        return np.iinfo(x)
+
+
+def _logexpxmexpy(x, y):
+    """ Compute the log of the difference of the exponentials of two arguments.
+
+    Avoids over/underflow, but does not prevent loss of precision otherwise.
+    """
+    return xpx.apply_where(x != y, (x, y),
+                           lambda x, y: special.logsumexp([x, y+np.pi*1j], axis=0),
+                           fill_value=-np.inf)
+
+
+def _guess_bracket(xmin, xmax):
+    a = np.full_like(xmin, -1.0)
+    b = np.ones_like(xmax)
+
+    i = np.isfinite(xmin) & np.isfinite(xmax)
+    a[i] = xmin[i]
+    b[i] = xmax[i]
+
+    i = np.isfinite(xmin) & ~np.isfinite(xmax)
+    a[i] = xmin[i]
+    b[i] = xmin[i] + 1
+
+    i = np.isfinite(xmax) & ~np.isfinite(xmin)
+    a[i] = xmax[i] - 1
+    b[i] = xmax[i]
+
+    return a, b
+
+
+def _log_real_standardize(x):
+    """Standardizes the (complex) logarithm of a real number.
+
+    The logarithm of a real number may be represented by a complex number with
+    imaginary part that is a multiple of pi*1j. Even multiples correspond with
+    a positive real and odd multiples correspond with a negative real.
+
+    Given a logarithm of a real number `x`, this function returns an equivalent
+    representation in a standard form: the log of a positive real has imaginary
+    part `0` and the log of a negative real has imaginary part `pi`.
+
+    """
+    shape = x.shape
+    x = np.atleast_1d(x)
+    real = np.real(x).astype(x.dtype)
+    complex = np.imag(x)
+    y = real
+    negative = np.exp(complex*1j) < 0.5
+    y[negative] = y[negative] + np.pi * 1j
+    return y.reshape(shape)[()]
+
+
+def _combine_docs(dist_family, *, include_examples=True):
+    fields = set(NumpyDocString.sections)
+    fields.remove('index')
+    if not include_examples:
+        fields.remove('Examples')
+
+    doc = ClassDoc(dist_family)
+    superdoc = ClassDoc(UnivariateDistribution)
+    for field in fields:
+        if field in {"Methods", "Attributes"}:
+            doc[field] = superdoc[field]
+        elif field in {"Summary"}:
+            pass
+        elif field == "Extended Summary":
+            doc[field].append(_generate_domain_support(dist_family))
+        elif field == 'Examples':
+            doc[field] = [_generate_example(dist_family)]
+        else:
+            doc[field] += superdoc[field]
+    return str(doc)
+
+
+def _generate_domain_support(dist_family):
+    n_parameterizations = len(dist_family._parameterizations)
+
+    domain = f"\nfor :math:`x \\in {dist_family._variable.domain}`.\n"
+
+    if n_parameterizations == 0:
+        support = """
+        This class accepts no distribution parameters.
+        """
+    elif n_parameterizations == 1:
+        support = f"""
+        This class accepts one parameterization:
+        {str(dist_family._parameterizations[0])}.
+        """
+    else:
+        number = {2: 'two', 3: 'three', 4: 'four', 5: 'five'}[
+            n_parameterizations]
+        parameterizations = [f"- {str(p)}" for p in
+                             dist_family._parameterizations]
+        parameterizations = "\n".join(parameterizations)
+        support = f"""
+        This class accepts {number} parameterizations:
+
+        {parameterizations}
+        """
+    support = "\n".join([line.lstrip() for line in support.split("\n")][1:])
+    return domain + support
+
+
+def _generate_example(dist_family):
+    n_parameters = dist_family._num_parameters(0)
+    shapes = [()] * n_parameters
+    rng = np.random.default_rng(615681484984984)
+    i = 0
+    dist = dist_family._draw(shapes, rng=rng, i_parameterization=i)
+
+    rng = np.random.default_rng(2354873452)
+    name = dist_family.__name__
+    if n_parameters:
+        parameter_names = list(dist._parameterizations[i].parameters)
+        parameter_values = [round(getattr(dist, name), 2) for name in
+                            parameter_names]
+        name_values = [f"{name}={value}" for name, value in
+                       zip(parameter_names, parameter_values)]
+        instantiation = f"{name}({', '.join(name_values)})"
+        attributes = ", ".join([f"X.{param}" for param in dist._parameters])
+        X = dist_family(**dict(zip(parameter_names, parameter_values)))
+    else:
+        instantiation = f"{name}()"
+        X = dist
+
+    p = 0.32
+    x = round(X.icdf(p), 2)
+    y = round(X.icdf(2 * p), 2)  # noqa: F841
+
+    example = f"""
+    To use the distribution class, it must be instantiated using keyword
+    parameters corresponding with one of the accepted parameterizations.
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy import stats
+    >>> from scipy.stats import {name}
+    >>> X = {instantiation}
+
+    For convenience, the ``plot`` method can be used to visualize the density
+    and other functions of the distribution.
+
+    >>> X.plot()
+    >>> plt.show()
+
+    The support of the underlying distribution is available using the ``support``
+    method.
+
+    >>> X.support()
+    {X.support()}
+    """
+
+    if n_parameters:
+        example += f"""
+        The numerical values of parameters associated with all parameterizations
+        are available as attributes.
+
+        >>> {attributes}
+        {tuple(X._parameters.values())}
+        """
+
+    example += f"""
+    To evaluate the probability density/mass function of the underlying distribution
+    at argument ``x={x}``:
+
+    >>> x = {x}
+    >>> X.pdf(x), X.pmf(x)
+    {X.pdf(x), X.pmf(x)}
+
+    The cumulative distribution function, its complement, and the logarithm
+    of these functions are evaluated similarly.
+
+    >>> np.allclose(np.exp(X.logccdf(x)), 1 - X.cdf(x))
+    True
+    """
+
+    # When two-arg CDF is implemented for DiscreteDistribution, consider removing
+    # the special-casing here.
+    if issubclass(dist_family, ContinuousDistribution):
+        example_continuous = f"""
+    The inverse of these functions with respect to the argument ``x`` is also
+    available.
+
+    >>> logp = np.log(1 - X.ccdf(x))
+    >>> np.allclose(X.ilogcdf(logp), x)
+    True
+
+    Note that distribution functions and their logarithms also have two-argument
+    versions for working with the probability mass between two arguments. The
+    result tends to be more accurate than the naive implementation because it avoids
+    subtractive cancellation.
+
+    >>> y = {y}
+    >>> np.allclose(X.ccdf(x, y), 1 - (X.cdf(y) - X.cdf(x)))
+    True
+        """
+        example += example_continuous
+
+    example += f"""
+    There are methods for computing measures of central tendency,
+    dispersion, higher moments, and entropy.
+
+    >>> X.mean(), X.median(), X.mode()
+    {X.mean(), X.median(), X.mode()}
+
+    >>> X.variance(), X.standard_deviation()
+    {X.variance(), X.standard_deviation()}
+
+    >>> X.skewness(), X.kurtosis()
+    {X.skewness(), X.kurtosis()}
+
+    >>> np.allclose(X.moment(order=6, kind='standardized'),
+    ...             X.moment(order=6, kind='central') / X.variance()**3)
+    True
+    """
+
+    # When logentropy is implemented for DiscreteDistribution, remove special-casing
+    if issubclass(dist_family, ContinuousDistribution):
+        example += """
+    >>> np.allclose(np.exp(X.logentropy()), X.entropy())
+    True
+        """
+    else:
+        example += f"""
+        >>> X.entropy()
+        {X.entropy()}
+        """
+
+    example += f"""
+    Pseudo-random samples can be drawn from
+    the underlying distribution using ``sample``.
+
+    >>> X.sample(shape=(4,))
+    {repr(X.sample(shape=(4,)))}  # may vary
+    """
+    # remove the indentation due to use of block quote within function;
+    # eliminate blank first line
+    example = "\n".join([line.lstrip() for line in example.split("\n")][1:])
+    return example
+
+
+class UnivariateDistribution(_ProbabilityDistribution):
+    r""" Class that represents a continuous statistical distribution.
+
+    Parameters
+    ----------
+    tol : positive float, optional
+        The desired relative tolerance of calculations. Left unspecified,
+        calculations may be faster; when provided, calculations may be
+        more likely to meet the desired accuracy.
+    validation_policy : {None, "skip_all"}
+        Specifies the level of input validation to perform. Left unspecified,
+        input validation is performed to ensure appropriate behavior in edge
+        case (e.g. parameters out of domain, argument outside of distribution
+        support, etc.) and improve consistency of output dtype, shape, etc.
+        Pass ``'skip_all'`` to avoid the computational overhead of these
+        checks when rough edges are acceptable.
+    cache_policy : {None, "no_cache"}
+        Specifies the extent to which intermediate results are cached. Left
+        unspecified, intermediate results of some calculations (e.g. distribution
+        support, moments, etc.) are cached to improve performance of future
+        calculations. Pass ``'no_cache'`` to reduce memory reserved by the class
+        instance.
+
+    Attributes
+    ----------
+    All parameters are available as attributes.
+
+    Methods
+    -------
+    support
+
+    plot
+
+    sample
+
+    moment
+    lmoment
+
+    mean
+    median
+    mode
+
+    variance
+    standard_deviation
+
+    skewness
+    kurtosis
+
+    pdf
+    logpdf
+
+    cdf
+    icdf
+    ccdf
+    iccdf
+
+    logcdf
+    ilogcdf
+    logccdf
+    ilogccdf
+
+    entropy
+    logentropy
+
+    See Also
+    --------
+    :ref:`rv_infrastructure` : Tutorial
+
+    Notes
+    -----
+    The following abbreviations are used throughout the documentation.
+
+    - PDF: probability density function
+    - CDF: cumulative distribution function
+    - CCDF: complementary CDF
+    - entropy: differential entropy
+    - log-*F*: logarithm of *F* (e.g. log-CDF)
+    - inverse *F*: inverse function of *F* (e.g. inverse CDF)
+
+    The API documentation is written to describe the API, not to serve as
+    a statistical reference. Effort is made to be correct at the level
+    required to use the functionality, not to be mathematically rigorous.
+    For example, continuity and differentiability may be implicitly assumed.
+    For precise mathematical definitions, consult your preferred mathematical
+    text.
+
+    """
+    __array_priority__ = 1
+    _parameterizations = []  # type: ignore[var-annotated]
+
+    ### Initialization
+
+    def __init__(self, *, tol=_null, validation_policy=None, cache_policy=None,
+                 **parameters):
+        self.tol = tol
+        self.validation_policy = validation_policy
+        self.cache_policy = cache_policy
+        self._not_implemented = (
+            f"`{self.__class__.__name__}` does not provide an accurate "
+            "implementation of the required method. Consider leaving "
+            "`method` and `tol` unspecified to use another implementation."
+        )
+        self._original_parameters = {}
+        # We may want to override the `__init__` method with parameters so
+        # IDEs can suggest parameter names. If there are multiple parameterizations,
+        # we'll need the default values of parameters to be None; this will
+        # filter out the parameters that were not actually specified by the user.
+        parameters = {key: val for key, val in
+                      sorted(parameters.items()) if val is not None}
+        self._update_parameters(**parameters)
+
+    def _update_parameters(self, *, validation_policy=None, **params):
+        r""" Update the numerical values of distribution parameters.
+
+        Parameters
+        ----------
+        validation_policy : {None, "skip_all"}
+            Specifies the level of input validation to perform. Left unspecified,
+            input validation is performed to ensure appropriate behavior in edge
+            case (e.g. parameters out of domain, argument outside of distribution
+            support, etc.) and improve consistency of output dtype, shape, etc.
+            Pass ``'skip_all'`` to avoid the computational overhead of these
+            checks when rough edges are acceptable.
+
+        **params : array_like
+            Desired numerical values of the distribution parameters. Any or all
+            of the parameters initially used to instantiate the distribution
+            may be modified. Parameters used in alternative parameterizations
+            are not accepted.
+        """
+
+        parameters = original_parameters = self._original_parameters.copy()
+        parameters.update(**params)
+        parameterization = None
+        self._invalid = np.asarray(False)
+        self._any_invalid = False
+        self._shape = tuple()
+        self._ndim = 0
+        self._size = 1
+        self._dtype = np.float64
+
+        if (validation_policy or self.validation_policy) == _SKIP_ALL:
+            parameters = self._process_parameters(**parameters)
+        elif not len(self._parameterizations):
+            if parameters:
+                message = (f"The `{self.__class__.__name__}` distribution "
+                           "family does not accept parameters, but parameters "
+                           f"`{set(parameters)}` were provided.")
+                raise ValueError(message)
+        else:
+            # This is default behavior, which re-runs all parameter validations
+            # even when only a single parameter is modified. For many
+            # distributions, the domain of a parameter doesn't depend on other
+            # parameters, so parameters could safely be modified without
+            # re-validating all other parameters. To handle these cases more
+            # efficiently, we could allow the developer to override this
+            # behavior.
+
+            # Currently the user can only update the original parameterization.
+            # Even though that parameterization is already known,
+            # `_identify_parameterization` is called to produce a nice error
+            # message if the user passes other values. To be a little more
+            # efficient, we could detect whether the values passed are
+            # consistent with the original parameterization rather than finding
+            # it from scratch. However, we might want other parameterizations
+            # to be accepted, which would require other changes, so I didn't
+            # optimize this.
+
+            parameterization = self._identify_parameterization(parameters)
+            parameters, shape, size, ndim = self._broadcast(parameters)
+            parameters, invalid, any_invalid, dtype = (
+                self._validate(parameterization, parameters))
+            parameters = self._process_parameters(**parameters)
+
+            self._invalid = invalid
+            self._any_invalid = any_invalid
+            self._shape = shape
+            self._size = size
+            self._ndim = ndim
+            self._dtype = dtype
+
+        self.reset_cache()
+        self._parameters = parameters
+        self._parameterization = parameterization
+        self._original_parameters = original_parameters
+        for name in self._parameters.keys():
+            # Make parameters properties of the class; return values from the instance
+            if hasattr(self.__class__, name):
+                continue
+            setattr(self.__class__, name, property(lambda self_, name_=name:
+                                                   self_._parameters[name_].copy()[()]))
+
+    def reset_cache(self):
+        r""" Clear all cached values.
+
+        To improve the speed of some calculations, the distribution's support
+        and moments are cached.
+
+        This function is called automatically whenever the distribution
+        parameters are updated.
+
+        """
+        # We could offer finer control over what is cleared.
+        # For simplicity, these will still exist even if cache_policy is
+        # NO_CACHE; they just won't be populated. This allows caching to be
+        # turned on and off easily.
+        self._moment_raw_cache = {}
+        self._moment_central_cache = {}
+        self._moment_standardized_cache = {}
+        self._lmoment_cache = {}
+        self._support_cache = None
+        self._method_cache = {}
+        self._constant_cache = None
+
+    def _identify_parameterization(self, parameters):
+        # Determine whether a `parameters` dictionary matches is consistent
+        # with one of the parameterizations of the distribution. If so,
+        # return that parameterization object; if not, raise an error.
+        #
+        # I've come back to this a few times wanting to avoid this explicit
+        # loop. I've considered several possibilities, but they've all been a
+        # little unusual. For example, we could override `_eq_` so we can
+        # use _parameterizations.index() to retrieve the parameterization,
+        # or the user could put the parameterizations in a dictionary so we
+        # could look them up with a key (e.g. frozenset of parameter names).
+        # I haven't been sure enough of these approaches to implement them.
+        parameter_names_set = set(parameters)
+
+        for parameterization in self._parameterizations:
+            if parameterization.matches(parameter_names_set):
+                break
+        else:
+            if not parameter_names_set:
+                message = (f"The `{self.__class__.__name__}` distribution "
+                           "family requires parameters, but none were "
+                           "provided.")
+            else:
+                parameter_names = self._get_parameter_str(parameters)
+                message = (f"The provided parameters `{parameter_names}` "
+                           "do not match a supported parameterization of the "
+                           f"`{self.__class__.__name__}` distribution family.")
+            raise ValueError(message)
+
+        return parameterization
+
+    def _broadcast(self, parameters):
+        # Broadcast the distribution parameters to the same shape. If the
+        # arrays are not broadcastable, raise a meaningful error.
+        #
+        # We always make sure that the parameters *are* the same shape
+        # and not just broadcastable. Users can access parameters as
+        # attributes, and I think they should see the arrays as the same shape.
+        # More importantly, arrays should be the same shape before logical
+        # indexing operations, which are needed in infrastructure code when
+        # there are invalid parameters, and may be needed in
+        # distribution-specific code. We don't want developers to need to
+        # broadcast in implementation functions.
+
+        # It's much faster to check whether broadcasting is necessary than to
+        # broadcast when it's not necessary.
+        parameter_vals = [np.asarray(parameter)
+                          for parameter in parameters.values()]
+        parameter_shapes = set(parameter.shape for parameter in parameter_vals)
+        if len(parameter_shapes) == 1:
+            return (parameters, parameter_vals[0].shape,
+                    parameter_vals[0].size, parameter_vals[0].ndim)
+
+        try:
+            parameter_vals = np.broadcast_arrays(*parameter_vals)
+        except ValueError as e:
+            parameter_names = self._get_parameter_str(parameters)
+            message = (f"The parameters `{parameter_names}` provided to the "
+                       f"`{self.__class__.__name__}` distribution family "
+                       "cannot be broadcast to the same shape.")
+            raise ValueError(message) from e
+        return (dict(zip(parameters.keys(), parameter_vals)),
+                parameter_vals[0].shape,
+                parameter_vals[0].size,
+                parameter_vals[0].ndim)
+
+    def _validate(self, parameterization, parameters):
+        # Broadcasts distribution parameter arrays and converts them to a
+        # consistent dtype. Replaces invalid parameters with `np.nan`.
+        # Returns the validated parameters, a boolean mask indicated *which*
+        # elements are invalid, a boolean scalar indicating whether *any*
+        # are invalid (to skip special treatments if none are invalid), and
+        # the common dtype.
+        valid, dtype = parameterization.validation(parameters)
+        invalid = ~valid
+        any_invalid = invalid if invalid.shape == () else np.any(invalid)
+        # If necessary, make the arrays contiguous and replace invalid with NaN
+        if any_invalid:
+            for parameter_name in parameters:
+                parameters[parameter_name] = np.copy(
+                    parameters[parameter_name])
+                parameters[parameter_name][invalid] = np.nan
+
+        return parameters, invalid, any_invalid, dtype
+
+    def _process_parameters(self, **params):
+        r""" Process and cache distribution parameters for reuse.
+
+        This is intended to be overridden by subclasses. It allows distribution
+        authors to pre-process parameters for re-use. For instance, when a user
+        parameterizes a LogUniform distribution with `a` and `b`, it makes
+        sense to calculate `log(a)` and `log(b)` because these values will be
+        used in almost all distribution methods. The dictionary returned by
+        this method is passed to all private methods that calculate functions
+        of the distribution.
+        """
+        return params
+
+    def _get_parameter_str(self, parameters):
+        # Get a string representation of the parameters like "{a, b, c}".
+        return f"{{{', '.join(parameters.keys())}}}"
+
+    def _copy_parameterization(self):
+        self._parameterizations = self._parameterizations.copy()
+        for i in range(len(self._parameterizations)):
+            self._parameterizations[i] = self._parameterizations[i].copy()
+
+    ### Attributes
+
+    @property
+    def tol(self):
+        r"""positive float:
+        The desired relative tolerance of calculations. Left unspecified,
+        calculations may be faster; when provided, calculations may be
+        more likely to meet the desired accuracy.
+        """
+        return self._tol
+
+    @tol.setter
+    def tol(self, tol):
+        if _isnull(tol):
+            self._tol = tol
+            return
+
+        tol = np.asarray(tol)
+        if (tol.shape != () or not tol > 0 or  # catches NaNs
+                not np.issubdtype(tol.dtype, np.floating)):
+            message = (f"Attribute `tol` of `{self.__class__.__name__}` must "
+                       "be a positive float, if specified.")
+            raise ValueError(message)
+        self._tol = tol[()]
+
+    @property
+    def cache_policy(self):
+        r"""{None, "no_cache"}:
+        Specifies the extent to which intermediate results are cached. Left
+        unspecified, intermediate results of some calculations (e.g. distribution
+        support, moments, etc.) are cached to improve performance of future
+        calculations. Pass ``'no_cache'`` to reduce memory reserved by the class
+        instance.
+        """
+        return self._cache_policy
+
+    @cache_policy.setter
+    def cache_policy(self, cache_policy):
+        cache_policy = str(cache_policy).lower() if cache_policy is not None else None
+        cache_policies = {None, 'no_cache'}
+        if cache_policy not in cache_policies:
+            message = (f"Attribute `cache_policy` of `{self.__class__.__name__}` "
+                       f"must be one of {cache_policies}, if specified.")
+            raise ValueError(message)
+        self._cache_policy = cache_policy
+
+    @property
+    def validation_policy(self):
+        r"""{None, "skip_all"}:
+        Specifies the level of input validation to perform. Left unspecified,
+        input validation is performed to ensure appropriate behavior in edge
+        case (e.g. parameters out of domain, argument outside of distribution
+        support, etc.) and improve consistency of output dtype, shape, etc.
+        Use ``'skip_all'`` to avoid the computational overhead of these
+        checks when rough edges are acceptable.
+        """
+        return self._validation_policy
+
+    @validation_policy.setter
+    def validation_policy(self, validation_policy):
+        validation_policy = (str(validation_policy).lower()
+                             if validation_policy is not None else None)
+        iv_policies = {None, 'skip_all'}
+        if validation_policy not in iv_policies:
+            message = (f"Attribute `validation_policy` of `{self.__class__.__name__}` "
+                       f"must be one of {iv_policies}, if specified.")
+            raise ValueError(message)
+        self._validation_policy = validation_policy
+
+    ### Other magic methods
+
+    def __repr__(self):
+        r""" Returns a string representation of the distribution.
+
+        Includes the name of the distribution family, the names of the
+        parameters and the `repr` of each of their values.
+
+
+        """
+        class_name = self.__class__.__name__
+        parameters = list(self._original_parameters.items())
+        info = []
+        with np.printoptions(threshold=10):
+            str_parameters = [f"{symbol}={repr(value)}" for symbol, value in parameters]
+        str_parameters = f"{', '.join(str_parameters)}"
+        info.append(str_parameters)
+        return f"{class_name}({', '.join(info)})"
+
+    def __str__(self):
+        class_name = self.__class__.__name__
+        parameters = list(self._original_parameters.items())
+        info = []
+        with np.printoptions(threshold=10):
+            str_parameters = [f"{symbol}={str(value)}" for symbol, value in parameters]
+        str_parameters = f"{', '.join(str_parameters)}"
+        info.append(str_parameters)
+        return f"{class_name}({', '.join(info)})"
+
+    def __add__(self, loc):
+        return ShiftedScaledDistribution(self, loc=loc)
+
+    def __sub__(self, loc):
+        return ShiftedScaledDistribution(self, loc=-loc)
+
+    def __mul__(self, scale):
+        return ShiftedScaledDistribution(self, scale=scale)
+
+    def __truediv__(self, scale):
+        return ShiftedScaledDistribution(self, scale=1/scale)
+
+    def __pow__(self, other):
+        if not np.isscalar(other) or other <= 0 or other != int(other):
+            message = ("Raising a random variable to the power of an argument is only "
+                       "implemented when the argument is a positive integer.")
+            raise NotImplementedError(message)
+
+        # Fill in repr_pattern with the repr of self before taking abs.
+        # Avoids having unnecessary abs in the repr.
+        with np.printoptions(threshold=10):
+            repr_pattern = f"({repr(self)})**{repr(other)}"
+            str_pattern = f"({str(self)})**{str(other)}"
+        X = abs(self) if other % 2 == 0 else self
+
+        funcs = dict(g=lambda u: u**other, repr_pattern=repr_pattern,
+                     str_pattern=str_pattern,
+                     h=lambda u: np.sign(u) * np.abs(u)**(1 / other),
+                     dh=lambda u: 1/other * np.abs(u)**(1/other - 1))
+
+        return MonotonicTransformedDistribution(X, **funcs, increasing=True)
+
+    def __radd__(self, other):
+        return self.__add__(other)
+
+    def __rsub__(self, other):
+        return self.__neg__().__add__(other)
+
+    def __rmul__(self, other):
+        return self.__mul__(other)
+
+    def __rtruediv__(self, other):
+        a, b = self.support()
+        with np.printoptions(threshold=10):
+            funcs = dict(g=lambda u: 1 / u,
+                         repr_pattern=f"{repr(other)}/({repr(self)})",
+                         str_pattern=f"{str(other)}/({str(self)})",
+                         h=lambda u: 1 / u, dh=lambda u: 1 / u ** 2)
+        if np.all(a >= 0) or np.all(b <= 0):
+            out = MonotonicTransformedDistribution(self, **funcs, increasing=False)
+        else:
+            message = ("Division by a random variable is only implemented "
+                       "when the support is either non-negative or non-positive.")
+            raise NotImplementedError(message)
+        if np.all(other == 1):
+            return out
+        else:
+            return out * other
+
+    def __rpow__(self, other):
+        with np.printoptions(threshold=10):
+            funcs = dict(g=lambda u: other**u,
+                         h=lambda u: np.log(u) / np.log(other),
+                         dh=lambda u: 1 / np.abs(u * np.log(other)),
+                         repr_pattern=f"{repr(other)}**({repr(self)})",
+                         str_pattern=f"{str(other)}**({str(self)})",)
+
+        if not np.isscalar(other) or other <= 0 or other == 1:
+            message = ("Raising an argument to the power of a random variable is only "
+                       "implemented when the argument is a positive scalar other than "
+                       "1.")
+            raise NotImplementedError(message)
+
+        if other > 1:
+            return MonotonicTransformedDistribution(self, **funcs, increasing=True)
+        else:
+            return MonotonicTransformedDistribution(self, **funcs, increasing=False)
+
+    def __neg__(self):
+        return self * -1
+
+    def __abs__(self):
+        return FoldedDistribution(self)
+
+    ### Utilities
+
+    ## Input validation
+
+    def _validate_order(self, order, fname='moment', min_order=0):
+        # Yet another integer validating function. Unlike others in SciPy, it
+        # Is quite flexible about what is allowed as an integer, and it
+        # raises a distribution-specific error message to facilitate
+        # identification of the source of the error.
+        if self.validation_policy == _SKIP_ALL:
+            return order
+
+        order = np.asarray(order, dtype=self._dtype)[()]
+        message = (f"Argument `order` of `{self.__class__.__name__}.{fname}` "
+                   f"must be a finite integer greater than or equal to {min_order}.")
+        try:
+            order_int = round(order.item())
+            # If this fails for any reason (e.g. it's an array, it's infinite)
+            # it's not a valid `order`.
+        except Exception as e:
+            raise ValueError(message) from e
+
+        if order_int < min_order or order_int != order:
+            raise ValueError(message)
+
+        return order
+
+    def _validate_kind(self, kind, kinds):
+        message = (f"Argument `kind` of `{self.__class__.__name__}.moment` "
+                   f"must be one of {set(kinds)}.")
+        kind = kind.lower()
+        if kind not in kinds:
+            raise ValueError(message)
+        return kind
+
+    def _preserve_type(self, x):
+        x = np.asarray(x)
+        if x.dtype != self._dtype:
+            x = x.astype(self._dtype)
+        return x[()]
+
+    ## Testing
+
+    @classmethod
+    def _draw(cls, sizes=None, rng=None, i_parameterization=None,
+              proportions=None):
+        r""" Draw a specific (fully-defined) distribution from the family.
+
+        See _Parameterization.draw for documentation details.
+        """
+        rng = np.random.default_rng(rng)
+        if len(cls._parameterizations) == 0:
+            return cls()
+        if i_parameterization is None:
+            n = cls._num_parameterizations()
+            i_parameterization = rng.integers(0, max(0, n - 1), endpoint=True)
+
+        parameterization = cls._parameterizations[i_parameterization]
+        parameters = parameterization.draw(sizes, rng, proportions=proportions,
+                                           region='typical')
+        return cls(**parameters)
+
+    @classmethod
+    def _num_parameterizations(cls):
+        # Returns the number of parameterizations accepted by the family.
+        return len(cls._parameterizations)
+
+    @classmethod
+    def _num_parameters(cls, i_parameterization=0):
+        # Returns the number of parameters used in the specified
+        # parameterization.
+        return (0 if not cls._num_parameterizations()
+                else len(cls._parameterizations[i_parameterization]))
+
+    ## Algorithms
+
+    def _quadrature(self, integrand, limits=None, args=(), params=None, log=False):
+        # Performs numerical integration/summation between limits or over support.
+        a, b = self._support(**params) if limits is None else limits
+        rtol = None if _isnull(self.tol) else self.tol
+
+        # For now, we ignore the status, but I want to return the error estimate
+        if isinstance(self, ContinuousDistribution):
+            return _tanhsinh(integrand, a, b, args=args, kwargs=params,
+                             log=log, rtol=rtol).integral
+        else:
+            res = nsum(integrand, a, b, args=args, kwargs=params,
+                       log=log, tolerances=dict(rtol=rtol)).sum
+
+            # b > a -> sum is zero... unless the parameters are nan.
+            # (Resolving gh-22321 should fix this in nsum.)
+            cond = np.isnan(params.popitem()[1]) if params else np.True_
+            return np.where(cond, np.nan, res)[()]
+
+    def _solve_bounded(self, f, p, *, bounds=None, params=None, xatol=None):
+        # Finds the argument of a function that produces the desired output.
+        xmin, xmax = self._support(**params) if bounds is None else bounds
+
+        def f2(x, _p, **kwargs):  # named `_p` to avoid conflict with shape `p`
+            return f(x, **kwargs) - _p
+
+        xl0, xr0 = _guess_bracket(xmin, xmax)
+
+        res = elementwise.bracket_root(f2, xl0=xl0, xr0=xr0, xmin=xmin, xmax=xmax,
+                                       args=(p,), kwargs=params)
+        # For now, we ignore the status, but I want to use the bracket width
+        # as an error estimate.
+
+        xrtol = None if _isnull(self.tol) else self.tol
+        xatol = None if xatol is None else xatol
+        tolerances = dict(xrtol=xrtol, xatol=xatol, fatol=0, frtol=0)
+        return elementwise.find_root(f2, res.bracket, args=(p,),
+                                     kwargs=params, tolerances=tolerances)
+
+    ## Other
+
+    def _overrides(self, method_name):
+        # Determines whether a class overrides a specified method.
+        # Returns True if the method implementation exists and is the same as
+        # that of the `ContinuousDistribution` class; otherwise returns False.
+
+        # Sometimes we use `_overrides` to check whether a certain method is overridden
+        # and if so, call it. This begs the questions of why we don't do the more
+        # obvious thing: restructure so that if the private method is overridden,
+        # Python will call it instead of the inherited version automatically. The short
+        # answer is that there are multiple ways a use might wish to evaluate a method,
+        # and simply overriding the method with a formula is not always the best option.
+        # For more complete discussion of the considerations, see:
+        # https://github.com/scipy/scipy/pull/21050#discussion_r1707798901
+        method = getattr(self.__class__, method_name, None)
+        super_method = getattr(UnivariateDistribution, method_name, None)
+        return method is not super_method
+
+    ### Distribution properties
+    # The following "distribution properties" are exposed via a public method
+    # that accepts only options (not distribution parameters or quantile/
+    # percentile argument).
+    # support
+    # logentropy, entropy,
+    # median, mode, mean,
+    # variance, standard_deviation
+    # skewness, kurtosis
+    # Common options are:
+    # method - a string that indicates which method should be used to compute
+    #          the quantity (e.g. a formula or numerical integration).
+    # Input/output validation is provided by the `_set_invalid_nan_property`
+    # decorator. These are the methods meant to be called by users.
+    #
+    # Each public method calls a private "dispatch" method that
+    # determines which "method" (strategy for calculating the desired quantity)
+    # to use by default and, via the `@_dispatch` decorator, calls the
+    # method and computes the result.
+    # Dispatch methods always accept:
+    # method - as passed from the public method
+    # params - a dictionary of distribution shape parameters passed by
+    #          the public method.
+    # Dispatch methods accept `params` rather than relying on the state of the
+    # object because iterative algorithms like `tanhsinh` and `find_root`
+    # need their callable to follow a strict elementwise protocol: each element
+    # of the output is determined solely by the values of the inputs at the
+    # corresponding location. The public methods do not satisfy this protocol
+    # because they do not accept the parameters as arguments, producing an
+    # output that generally has a different shape than that of the input. Also,
+    # by calling "dispatch" methods rather than the public methods, the
+    # iterative algorithms avoid the overhead of input validation.
+    #
+    # Each dispatch method can designate the responsibility of computing
+    # the required value to any of several "implementation" methods. These
+    # methods accept only `**params`, the parameter dictionary passed from
+    # the public method via the dispatch method. We separate the implementation
+    # methods from the dispatch methods for the sake of simplicity (via
+    # compartmentalization) and to allow subclasses to override certain
+    # implementation methods (typically only the "formula" methods). The names
+    # of implementation methods are combinations of the public method name and
+    # the name of the "method" (strategy for calculating the desired quantity)
+    # string. (In fact, the name of the implementation method is calculated
+    # from these two strings in the `_dispatch` decorator.) Common method
+    # strings are:
+    # formula - distribution-specific analytical expressions to be implemented
+    #           by subclasses.
+    # log/exp - Compute the log of a number and then exponentiate it or vice
+    #           versa.
+    # quadrature - Compute the value via numerical integration.
+    #
+    # The default method (strategy) is determined based on what implementation
+    # methods are available and the error tolerance of the user. Typically,
+    # a formula is always used if available. We fall back to "log/exp" if a
+    # formula for the logarithm or exponential of the quantity is available,
+    # and we use quadrature otherwise.
+
+    def support(self):
+        # If this were a `cached_property`, we couldn't update the value
+        # when the distribution parameters change.
+        # Caching is important, though, because calls to _support take a few
+        # microseconds even when `a` and `b` are already the same shape.
+        if self._support_cache is not None:
+            return self._support_cache
+
+        a, b = self._support(**self._parameters)
+        if a.shape != self._shape:
+            a = np.broadcast_to(a, self._shape)
+        if b.shape != self._shape:
+            b = np.broadcast_to(b, self._shape)
+
+        if self._any_invalid:
+            a, b = np.asarray(a).copy(), np.asarray(b).copy()
+            a[self._invalid], b[self._invalid] = np.nan, np.nan
+            a, b = a[()], b[()]
+
+        support = (a, b)
+
+        if self.cache_policy != _NO_CACHE:
+            self._support_cache = support
+
+        return support
+
+    def _support(self, **params):
+        # Computes the support given distribution parameters
+        a, b = self._variable.domain.get_numerical_endpoints(params)
+        if len(params):
+            # the parameters should all be of the same dtype and shape at this point
+            vals = list(params.values())
+            shape = vals[0].shape
+            a = np.broadcast_to(a, shape) if a.shape != shape else a
+            b = np.broadcast_to(b, shape) if b.shape != shape else b
+        return self._preserve_type(a), self._preserve_type(b)
+
+    @_set_invalid_nan_property
+    def logentropy(self, *, method=None):
+        return self._logentropy_dispatch(method=method, **self._parameters) + 0j
+
+    @_dispatch
+    def _logentropy_dispatch(self, method=None, **params):
+        if self._overrides('_logentropy_formula'):
+            method = self._logentropy_formula
+        elif self._overrides('_entropy_formula'):
+            method = self._logentropy_logexp_safe
+        else:
+            method = self._logentropy_quadrature
+        return method
+
+    def _logentropy_formula(self, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _logentropy_logexp(self, **params):
+        res = np.log(self._entropy_dispatch(**params)+0j)
+        return _log_real_standardize(res)
+
+    def _logentropy_logexp_safe(self, **params):
+        out = self._logentropy_logexp(**params)
+        mask = np.isinf(out.real)
+        if np.any(mask):
+            params_mask = {key:val[mask] for key, val in params.items()}
+            out = np.asarray(out)
+            out[mask] = self._logentropy_quadrature(**params_mask)
+        return out[()]
+
+    def _logentropy_quadrature(self, **params):
+        def logintegrand(x, **params):
+            logpxf = self._logpxf_dispatch(x, **params)
+            return logpxf + np.log(0j+logpxf)
+        res = self._quadrature(logintegrand, params=params, log=True)
+        return _log_real_standardize(res + np.pi*1j)
+
+    @_set_invalid_nan_property
+    def entropy(self, *, method=None):
+        return self._entropy_dispatch(method=method, **self._parameters)
+
+    @_dispatch
+    def _entropy_dispatch(self, method=None, **params):
+        if self._overrides('_entropy_formula'):
+            method = self._entropy_formula
+        elif self._overrides('_logentropy_formula'):
+            method = self._entropy_logexp
+        else:
+            method = self._entropy_quadrature
+        return method
+
+    def _entropy_formula(self, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _entropy_logexp(self, **params):
+        return np.real(np.exp(self._logentropy_dispatch(**params)))
+
+    def _entropy_quadrature(self, **params):
+        def integrand(x, **params):
+            pxf = self._pxf_dispatch(x, **params)
+            logpxf = self._logpxf_dispatch(x, **params)
+            temp = np.asarray(pxf)
+            i = (pxf != 0)  # 0 * inf -> nan; should be 0
+            temp[i] = -pxf[i]*logpxf[i]
+            return temp
+        return self._quadrature(integrand, params=params)
+
+    @_set_invalid_nan_property
+    def median(self, *, method=None):
+        return self._median_dispatch(method=method, **self._parameters)
+
+    @_dispatch
+    def _median_dispatch(self, method=None, **params):
+        if self._overrides('_median_formula'):
+            method = self._median_formula
+        else:
+            method = self._median_icdf
+        return method
+
+    def _median_formula(self, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _median_icdf(self, **params):
+        return self._icdf_dispatch(np.asarray(0.5, dtype=self._dtype), **params)
+
+    @_set_invalid_nan_property
+    def mode(self, *, method=None):
+        return self._mode_dispatch(method=method, **self._parameters)
+
+    @_dispatch
+    def _mode_dispatch(self, method=None, **params):
+        # We could add a method that looks for a critical point with
+        # differentiation and the root finder
+        if self._overrides('_mode_formula'):
+            method = self._mode_formula
+        else:
+            method = self._mode_optimization
+        return method
+
+    def _mode_formula(self, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _mode_optimization(self, xatol=None, **params):
+        a, b = self._support(**params)
+        m = self._median_dispatch(**params)
+
+        def f(x, **params):
+            return -self._pxf_dispatch(x, **params)
+
+        res_b = elementwise.bracket_minimum(f, m, xmin=a, xmax=b, kwargs=params)
+        res = elementwise.find_minimum(f, res_b.bracket, kwargs=params,
+                                       tolerances=dict(xatol=xatol))
+        mode = np.asarray(res.x)
+
+        # bracket_minimum fails with status == -1 if the mode is at a boundary.
+        # We have to consider this as a special case; there's no way to include
+        # this logic in find_minimum; it has to treat the bracket as invalid.
+        mode_at_boundary = res.status == -1
+        fl, fm, fr = res_b.f_bracket
+        mode_at_left = mode_at_boundary & (fl <= fm)
+        mode_at_right = mode_at_boundary & (fr < fm)
+        mode[mode_at_left] = a[mode_at_left]
+        mode[mode_at_right] = b[mode_at_right]
+        return mode[()]
+
+    def mean(self, *, method=None):
+        return self.moment(1, kind='raw', method=method)
+
+    def variance(self, *, method=None):
+        return self.moment(2, kind='central', method=method)
+
+    def standard_deviation(self, *, method=None):
+        return np.sqrt(self.variance(method=method))
+
+    def skewness(self, *, method=None):
+        return self.moment(3, kind='standardized', method=method)
+
+    def kurtosis(self, *, method=None, convention='non-excess'):
+        conventions = {'non-excess', 'excess'}
+        message = (f'Parameter `convention` of `{self.__class__.__name__}.kurtosis` '
+                   f"must be one of {conventions}.")
+        convention = convention.lower()
+        if convention not in conventions:
+            raise ValueError(message)
+        k = self.moment(4, kind='standardized', method=method)
+        return k - 3 if convention == 'excess' else k
+
+    ### Distribution functions
+    # The following functions related to the distribution PDF and CDF are
+    # exposed via a public method that accepts one positional argument - the
+    # quantile - and keyword options (but not distribution parameters).
+    # logpdf, pdf
+    # logcdf, cdf
+    # logccdf, ccdf
+    # The `logcdf` and `cdf` functions can also be called with two positional
+    # arguments - lower and upper quantiles - and they return the probability
+    # mass (integral of the PDF) between them. The 2-arg versions of `logccdf`
+    # and `ccdf` return the complement of this quantity.
+    # All the (1-arg) cumulative distribution functions have inverse
+    # functions, which accept one positional argument - the percentile.
+    # ilogcdf, icdf
+    # ilogccdf, iccdf
+    # Common keyword options include:
+    # method - a string that indicates which method should be used to compute
+    #          the quantity (e.g. a formula or numerical integration).
+    # Tolerance options should be added.
+    # Input/output validation is provided by the `_set_invalid_nan`
+    # decorator. These are the methods meant to be called by users.
+    #
+    # Each public method calls a private "dispatch" method that
+    # determines which "method" (strategy for calculating the desired quantity)
+    # to use by default and, via the `@_dispatch` decorator, calls the
+    # method and computes the result.
+    # Each dispatch method can designate the responsibility of computing
+    # the required value to any of several "implementation" methods. These
+    # methods accept only `**params`, the parameter dictionary passed from
+    # the public method via the dispatch method.
+    # See the note corresponding with the "Distribution Parameters" for more
+    # information.
+
+    ## Probability Density/Mass Functions
+
+    @_set_invalid_nan
+    def logpdf(self, x, /, *, method=None):
+        return self._logpdf_dispatch(x, method=method, **self._parameters)
+
+    @_dispatch
+    def _logpdf_dispatch(self, x, *, method=None, **params):
+        if self._overrides('_logpdf_formula'):
+            method = self._logpdf_formula
+        elif _isnull(self.tol):  # ensure that developers override _logpdf
+            method = self._logpdf_logexp
+        return method
+
+    def _logpdf_formula(self, x, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _logpdf_logexp(self, x, **params):
+        return np.log(self._pdf_dispatch(x, **params))
+
+    @_set_invalid_nan
+    def pdf(self, x, /, *, method=None):
+        return self._pdf_dispatch(x, method=method, **self._parameters)
+
+    @_dispatch
+    def _pdf_dispatch(self, x, *, method=None, **params):
+        if self._overrides('_pdf_formula'):
+            method = self._pdf_formula
+        else:
+            method = self._pdf_logexp
+        return method
+
+    def _pdf_formula(self, x, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _pdf_logexp(self, x, **params):
+        return np.exp(self._logpdf_dispatch(x, **params))
+
+    @_set_invalid_nan
+    def logpmf(self, x, /, *, method=None):
+        return self._logpmf_dispatch(x, method=method, **self._parameters)
+
+    @_dispatch
+    def _logpmf_dispatch(self, x, *, method=None, **params):
+        if self._overrides('_logpmf_formula'):
+            method = self._logpmf_formula
+        elif _isnull(self.tol):  # ensure that developers override _logpmf
+            method = self._logpmf_logexp
+        return method
+
+    def _logpmf_formula(self, x, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _logpmf_logexp(self, x, **params):
+        with np.errstate(divide='ignore'):
+            return np.log(self._pmf_dispatch(x, **params))
+
+    @_set_invalid_nan
+    def pmf(self, x, /, *, method=None):
+        return self._pmf_dispatch(x, method=method, **self._parameters)
+
+    @_dispatch
+    def _pmf_dispatch(self, x, *, method=None, **params):
+        if self._overrides('_pmf_formula'):
+            method = self._pmf_formula
+        else:
+            method = self._pmf_logexp
+        return method
+
+    def _pmf_formula(self, x, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _pmf_logexp(self, x, **params):
+        return np.exp(self._logpmf_dispatch(x, **params))
+
+    ## Cumulative Distribution Functions
+
+    def logcdf(self, x, y=None, /, *, method=None):
+        if y is None:
+            return self._logcdf1(x, method=method)
+        else:
+            return self._logcdf2(x, y, method=method)
+
+    @_cdf2_input_validation
+    def _logcdf2(self, x, y, *, method):
+        out = self._logcdf2_dispatch(x, y, method=method, **self._parameters)
+        return (out + 0j) if not np.issubdtype(out.dtype, np.complexfloating) else out
+
+    @_dispatch
+    def _logcdf2_dispatch(self, x, y, *, method=None, **params):
+        if self._overrides('_logcdf2_formula'):
+            method = self._logcdf2_formula
+        elif (self._overrides('_logcdf_formula')
+              or self._overrides('_logccdf_formula')):
+            method = self._logcdf2_subtraction
+        elif (self._overrides('_cdf_formula')
+              or self._overrides('_ccdf_formula')):
+            method = self._logcdf2_logexp_safe
+        else:
+            method = self._logcdf2_quadrature
+        return method
+
+    def _logcdf2_formula(self, x, y, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _logcdf2_subtraction(self, x, y, **params):
+        flip_sign = x > y  # some results will be negative
+        x, y = np.minimum(x, y), np.maximum(x, y)
+        logcdf_x = self._logcdf_dispatch(x, **params)
+        logcdf_y = self._logcdf_dispatch(y, **params)
+        logccdf_x = self._logccdf_dispatch(x, **params)
+        logccdf_y = self._logccdf_dispatch(y, **params)
+        case_left = (logcdf_x < -1) & (logcdf_y < -1)
+        case_right = (logccdf_x < -1) & (logccdf_y < -1)
+        case_central = ~(case_left | case_right)
+        log_mass = _logexpxmexpy(logcdf_y, logcdf_x)
+        log_mass[case_right] = _logexpxmexpy(logccdf_x, logccdf_y)[case_right]
+        log_tail = np.logaddexp(logcdf_x, logccdf_y)[case_central]
+        log_mass[case_central] = _log1mexp(log_tail)
+        log_mass[flip_sign] += np.pi * 1j
+        return log_mass[()] if np.any(flip_sign) else log_mass.real[()]
+
+    def _logcdf2_logexp(self, x, y, **params):
+        expres = self._cdf2_dispatch(x, y, **params)
+        expres = expres + 0j if np.any(x > y) else expres
+        return np.log(expres)
+
+    def _logcdf2_logexp_safe(self, x, y, **params):
+        out = self._logcdf2_logexp(x, y, **params)
+        mask = np.isinf(out.real)
+        if np.any(mask):
+            params_mask = {key: np.broadcast_to(val, mask.shape)[mask]
+                           for key, val in params.items()}
+            out = np.asarray(out)
+            out[mask] = self._logcdf2_quadrature(x[mask], y[mask], **params_mask)
+        return out[()]
+
+    def _logcdf2_quadrature(self, x, y, **params):
+        logres = self._quadrature(self._logpxf_dispatch, limits=(x, y),
+                                  log=True, params=params)
+        return logres
+
+    @_set_invalid_nan
+    def _logcdf1(self, x, *, method=None):
+        return self._logcdf_dispatch(x, method=method, **self._parameters)
+
+    @_dispatch
+    def _logcdf_dispatch(self, x, *, method=None, **params):
+        if self._overrides('_logcdf_formula'):
+            method = self._logcdf_formula
+        elif self._overrides('_logccdf_formula'):
+            method = self._logcdf_complement
+        elif self._overrides('_cdf_formula'):
+            method = self._logcdf_logexp_safe
+        else:
+            method = self._logcdf_quadrature
+        return method
+
+    def _logcdf_formula(self, x, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _logcdf_complement(self, x, **params):
+        return _log1mexp(self._logccdf_dispatch(x, **params))
+
+    def _logcdf_logexp(self, x, **params):
+        return np.log(self._cdf_dispatch(x, **params))
+
+    def _logcdf_logexp_safe(self, x, **params):
+        out = self._logcdf_logexp(x, **params)
+        mask = np.isinf(out)
+        if np.any(mask):
+            params_mask = {key:np.broadcast_to(val, mask.shape)[mask]
+                           for key, val in params.items()}
+            out = np.asarray(out)
+            out[mask] = self._logcdf_quadrature(x[mask], **params_mask)
+        return out[()]
+
+    def _logcdf_quadrature(self, x, **params):
+        a, _ = self._support(**params)
+        return self._quadrature(self._logpxf_dispatch, limits=(a, x),
+                                params=params, log=True)
+
+    def cdf(self, x, y=None, /, *, method=None):
+        if y is None:
+            return self._cdf1(x, method=method)
+        else:
+            return self._cdf2(x, y, method=method)
+
+    @_cdf2_input_validation
+    def _cdf2(self, x, y, *, method):
+        return self._cdf2_dispatch(x, y, method=method, **self._parameters)
+
+    @_dispatch
+    def _cdf2_dispatch(self, x, y, *, method=None, **params):
+        if self._overrides('_cdf2_formula'):
+            method = self._cdf2_formula
+        elif (self._overrides('_logcdf_formula')
+              or self._overrides('_logccdf_formula')):
+            method = self._cdf2_logexp
+        elif self._overrides('_cdf_formula') or self._overrides('_ccdf_formula'):
+            method = self._cdf2_subtraction_safe
+        else:
+            method = self._cdf2_quadrature
+        return method
+
+    def _cdf2_formula(self, x, y, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _cdf2_logexp(self, x, y, **params):
+        return np.real(np.exp(self._logcdf2_dispatch(x, y, **params)))
+
+    def _cdf2_subtraction(self, x, y, **params):
+        # Improvements:
+        # Lazy evaluation of cdf/ccdf only where needed
+        # Stack x and y to reduce function calls?
+        cdf_x = self._cdf_dispatch(x, **params)
+        cdf_y = self._cdf_dispatch(y, **params)
+        ccdf_x = self._ccdf_dispatch(x, **params)
+        ccdf_y = self._ccdf_dispatch(y, **params)
+        i = (ccdf_x < 0.5) & (ccdf_y < 0.5)
+        return np.where(i, ccdf_x-ccdf_y, cdf_y-cdf_x)
+
+    def _cdf2_subtraction_safe(self, x, y, **params):
+        cdf_x = self._cdf_dispatch(x, **params)
+        cdf_y = self._cdf_dispatch(y, **params)
+        ccdf_x = self._ccdf_dispatch(x, **params)
+        ccdf_y = self._ccdf_dispatch(y, **params)
+        i = (ccdf_x < 0.5) & (ccdf_y < 0.5)
+        out = np.where(i, ccdf_x-ccdf_y, cdf_y-cdf_x)
+
+        eps = np.finfo(self._dtype).eps
+        tol = self.tol if not _isnull(self.tol) else np.sqrt(eps)
+
+        cdf_max = np.maximum(cdf_x, cdf_y)
+        ccdf_max = np.maximum(ccdf_x, ccdf_y)
+        spacing = np.spacing(np.where(i, ccdf_max, cdf_max))
+        mask = np.abs(tol * out) < spacing
+
+        if np.any(mask):
+            params_mask = {key: np.broadcast_to(val, mask.shape)[mask]
+                           for key, val in params.items()}
+            out = np.asarray(out)
+            out[mask] = self._cdf2_quadrature(x[mask], y[mask], **params_mask)
+        return out[()]
+
+    def _cdf2_quadrature(self, x, y, **params):
+        return self._quadrature(self._pxf_dispatch, limits=(x, y), params=params)
+
+    @_set_invalid_nan
+    def _cdf1(self, x, *, method):
+        return self._cdf_dispatch(x, method=method, **self._parameters)
+
+    @_dispatch
+    def _cdf_dispatch(self, x, *, method=None, **params):
+        if self._overrides('_cdf_formula'):
+            method = self._cdf_formula
+        elif self._overrides('_logcdf_formula'):
+            method = self._cdf_logexp
+        elif self._overrides('_ccdf_formula'):
+            method = self._cdf_complement_safe
+        else:
+            method = self._cdf_quadrature
+        return method
+
+    def _cdf_formula(self, x, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _cdf_logexp(self, x, **params):
+        return np.exp(self._logcdf_dispatch(x, **params))
+
+    def _cdf_complement(self, x, **params):
+        return 1 - self._ccdf_dispatch(x, **params)
+
+    def _cdf_complement_safe(self, x, **params):
+        ccdf = self._ccdf_dispatch(x, **params)
+        out = 1 - ccdf
+        eps = np.finfo(self._dtype).eps
+        tol = self.tol if not _isnull(self.tol) else np.sqrt(eps)
+        mask = tol * out < np.spacing(ccdf)
+        if np.any(mask):
+            params_mask = {key: np.broadcast_to(val, mask.shape)[mask]
+                           for key, val in params.items()}
+            out = np.asarray(out)
+            out[mask] = self._cdf_quadrature(x[mask], **params_mask)
+        return out[()]
+
+    def _cdf_quadrature(self, x, **params):
+        a, _ = self._support(**params)
+        return self._quadrature(self._pxf_dispatch, limits=(a, x),
+                                params=params)
+
+    def logccdf(self, x, y=None, /, *, method=None):
+        if y is None:
+            return self._logccdf1(x, method=method)
+        else:
+            return self._logccdf2(x, y, method=method)
+
+    @_cdf2_input_validation
+    def _logccdf2(self, x, y, *, method):
+        return self._logccdf2_dispatch(x, y, method=method, **self._parameters)
+
+    @_dispatch
+    def _logccdf2_dispatch(self, x, y, *, method=None, **params):
+        # if _logccdf2_formula exists, we could use the complement
+        # if _ccdf2_formula exists, we could use log/exp
+        if self._overrides('_logccdf2_formula'):
+            method = self._logccdf2_formula
+        else:
+            method = self._logccdf2_addition
+        return method
+
+    def _logccdf2_formula(self, x, y, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _logccdf2_addition(self, x, y, **params):
+        logcdf_x = self._logcdf_dispatch(x, **params)
+        logccdf_y = self._logccdf_dispatch(y, **params)
+        return special.logsumexp([logcdf_x, logccdf_y], axis=0)
+
+    @_set_invalid_nan
+    def _logccdf1(self, x, *, method=None):
+        return self._logccdf_dispatch(x, method=method, **self._parameters)
+
+    @_dispatch
+    def _logccdf_dispatch(self, x, method=None, **params):
+        if self._overrides('_logccdf_formula'):
+            method = self._logccdf_formula
+        elif self._overrides('_logcdf_formula'):
+            method = self._logccdf_complement
+        elif self._overrides('_ccdf_formula'):
+            method = self._logccdf_logexp_safe
+        else:
+            method = self._logccdf_quadrature
+        return method
+
+    def _logccdf_formula(self, x, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _logccdf_complement(self, x, **params):
+        return _log1mexp(self._logcdf_dispatch(x, **params))
+
+    def _logccdf_logexp(self, x, **params):
+        return np.log(self._ccdf_dispatch(x, **params))
+
+    def _logccdf_logexp_safe(self, x, **params):
+        out = self._logccdf_logexp(x, **params)
+        mask = np.isinf(out)
+        if np.any(mask):
+            params_mask = {key: np.broadcast_to(val, mask.shape)[mask]
+                           for key, val in params.items()}
+            out = np.asarray(out)
+            out[mask] = self._logccdf_quadrature(x[mask], **params_mask)
+        return out[()]
+
+    def _logccdf_quadrature(self, x, **params):
+        _, b = self._support(**params)
+        return self._quadrature(self._logpxf_dispatch, limits=(x, b),
+                                params=params, log=True)
+
+    def ccdf(self, x, y=None, /, *, method=None):
+        if y is None:
+            return self._ccdf1(x, method=method)
+        else:
+            return self._ccdf2(x, y, method=method)
+
+    @_cdf2_input_validation
+    def _ccdf2(self, x, y, *, method):
+        return self._ccdf2_dispatch(x, y, method=method, **self._parameters)
+
+    @_dispatch
+    def _ccdf2_dispatch(self, x, y, *, method=None, **params):
+        if self._overrides('_ccdf2_formula'):
+            method = self._ccdf2_formula
+        else:
+            method = self._ccdf2_addition
+        return method
+
+    def _ccdf2_formula(self, x, y, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _ccdf2_addition(self, x, y, **params):
+        cdf_x = self._cdf_dispatch(x, **params)
+        ccdf_y = self._ccdf_dispatch(y, **params)
+        # even if x > y, cdf(x, y) + ccdf(x,y) sums to 1
+        return cdf_x + ccdf_y
+
+    @_set_invalid_nan
+    def _ccdf1(self, x, *, method):
+        return self._ccdf_dispatch(x, method=method, **self._parameters)
+
+    @_dispatch
+    def _ccdf_dispatch(self, x, method=None, **params):
+        if self._overrides('_ccdf_formula'):
+            method = self._ccdf_formula
+        elif self._overrides('_logccdf_formula'):
+            method = self._ccdf_logexp
+        elif self._overrides('_cdf_formula'):
+            method = self._ccdf_complement_safe
+        else:
+            method = self._ccdf_quadrature
+        return method
+
+    def _ccdf_formula(self, x, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _ccdf_logexp(self, x, **params):
+        return np.exp(self._logccdf_dispatch(x, **params))
+
+    def _ccdf_complement(self, x, **params):
+        return 1 - self._cdf_dispatch(x, **params)
+
+    def _ccdf_complement_safe(self, x, **params):
+        cdf = self._cdf_dispatch(x, **params)
+        out = 1 - cdf
+        eps = np.finfo(self._dtype).eps
+        tol = self.tol if not _isnull(self.tol) else np.sqrt(eps)
+        mask = tol * out < np.spacing(cdf)
+        if np.any(mask):
+            params_mask = {key: np.broadcast_to(val, mask.shape)[mask]
+                           for key, val in params.items()}
+            out = np.asarray(out)
+            out[mask] = self._ccdf_quadrature(x[mask], **params_mask)
+        return out[()]
+
+    def _ccdf_quadrature(self, x, **params):
+        _, b = self._support(**params)
+        return self._quadrature(self._pxf_dispatch, limits=(x, b),
+                                params=params)
+
+    ## Inverse cumulative distribution functions
+
+    @_set_invalid_nan
+    def ilogcdf(self, logp, /, *, method=None):
+        return self._ilogcdf_dispatch(logp, method=method, **self._parameters)
+
+    @_dispatch
+    def _ilogcdf_dispatch(self, x, method=None, **params):
+        if self._overrides('_ilogcdf_formula'):
+            method = self._ilogcdf_formula
+        elif self._overrides('_ilogccdf_formula'):
+            method = self._ilogcdf_complement
+        else:
+            method = self._ilogcdf_inversion
+        return method
+
+    def _ilogcdf_formula(self, x, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _ilogcdf_complement(self, x, **params):
+        return self._ilogccdf_dispatch(_log1mexp(x), **params)
+
+    def _ilogcdf_inversion(self, x, **params):
+        return self._solve_bounded_continuous(self._logcdf_dispatch, x, params=params)
+
+    @_set_invalid_nan
+    def icdf(self, p, /, *, method=None):
+        return self._icdf_dispatch(p, method=method, **self._parameters)
+
+    @_dispatch
+    def _icdf_dispatch(self, x, method=None, **params):
+        if self._overrides('_icdf_formula'):
+            method = self._icdf_formula
+        elif self._overrides('_iccdf_formula'):
+            method = self._icdf_complement_safe
+        else:
+            method = self._icdf_inversion
+        return method
+
+    def _icdf_formula(self, x, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _icdf_complement(self, x, **params):
+        return self._iccdf_dispatch(1 - x, **params)
+
+    def _icdf_complement_safe(self, x, **params):
+        out = self._icdf_complement(x, **params)
+        eps = np.finfo(self._dtype).eps
+        tol = self.tol if not _isnull(self.tol) else np.sqrt(eps)
+        mask = tol * x < np.spacing(1 - x)
+        if np.any(mask):
+            params_mask = {key: np.broadcast_to(val, mask.shape)[mask]
+                           for key, val in params.items()}
+            out = np.asarray(out)
+            out[mask] = self._icdf_inversion(x[mask], **params_mask)
+        return out[()]
+
+    def _icdf_inversion(self, x, **params):
+        return self._solve_bounded_continuous(self._cdf_dispatch, x, params=params)
+
+    @_set_invalid_nan
+    def ilogccdf(self, logp, /, *, method=None):
+        return self._ilogccdf_dispatch(logp, method=method, **self._parameters)
+
+    @_dispatch
+    def _ilogccdf_dispatch(self, x, method=None, **params):
+        if self._overrides('_ilogccdf_formula'):
+            method = self._ilogccdf_formula
+        elif self._overrides('_ilogcdf_formula'):
+            method = self._ilogccdf_complement
+        else:
+            method = self._ilogccdf_inversion
+        return method
+
+    def _ilogccdf_formula(self, x, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _ilogccdf_complement(self, x, **params):
+        return self._ilogcdf_dispatch(_log1mexp(x), **params)
+
+    def _ilogccdf_inversion(self, x, **params):
+        return self._solve_bounded_continuous(self._logccdf_dispatch, x, params=params)
+
+    @_set_invalid_nan
+    def iccdf(self, p, /, *, method=None):
+        return self._iccdf_dispatch(p, method=method, **self._parameters)
+
+    @_dispatch
+    def _iccdf_dispatch(self, x, method=None, **params):
+        if self._overrides('_iccdf_formula'):
+            method = self._iccdf_formula
+        elif self._overrides('_icdf_formula'):
+            method = self._iccdf_complement_safe
+        else:
+            method = self._iccdf_inversion
+        return method
+
+    def _iccdf_formula(self, x, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _iccdf_complement(self, x, **params):
+        return self._icdf_dispatch(1 - x, **params)
+
+    def _iccdf_complement_safe(self, x, **params):
+        out = self._iccdf_complement(x, **params)
+        eps = np.finfo(self._dtype).eps
+        tol = self.tol if not _isnull(self.tol) else np.sqrt(eps)
+        mask = tol * x < np.spacing(1 - x)
+        if np.any(mask):
+            params_mask = {key: np.broadcast_to(val, mask.shape)[mask]
+                           for key, val in params.items()}
+            out = np.asarray(out)
+            out[mask] = self._iccdf_inversion(x[mask], **params_mask)
+        return out[()]
+
+    def _iccdf_inversion(self, x, **params):
+        return self._solve_bounded_continuous(self._ccdf_dispatch, x, params=params)
+
+    ### Sampling Functions
+    # The following functions for drawing samples from the distribution are
+    # exposed via a public method that accepts one positional argument - the
+    # shape of the sample - and keyword options (but not distribution
+    # parameters).
+    # sample
+    # ~~qmc_sample~~ built into sample now
+    #
+    # Common keyword options include:
+    # method - a string that indicates which method should be used to compute
+    #          the quantity (e.g. a formula or numerical integration).
+    # rng - the NumPy Generator/SciPy QMCEngine object to used for drawing numbers.
+    #
+    # Input/output validation is included in each function, since there is
+    # little code to be shared.
+    # These are the methods meant to be called by users.
+    #
+    # Each public method calls a private "dispatch" method that
+    # determines which "method" (strategy for calculating the desired quantity)
+    # to use by default and, via the `@_dispatch` decorator, calls the
+    # method and computes the result.
+    # Each dispatch method can designate the responsibility of sampling to any
+    # of several "implementation" methods. These methods accept only
+    # `**params`, the parameter dictionary passed from the public method via
+    # the "dispatch" method.
+    # See the note corresponding with the "Distribution Parameters" for more
+    # information.
+
+    def sample(self, shape=(), *, method=None, rng=None):
+        # needs output validation to ensure that developer returns correct
+        # dtype and shape
+        sample_shape = (shape,) if not np.iterable(shape) else tuple(shape)
+        full_shape = sample_shape + self._shape
+        rng = np.random.default_rng(rng) if not isinstance(rng, qmc.QMCEngine) else rng
+        res = self._sample_dispatch(full_shape, method=method, rng=rng,
+                                    **self._parameters)
+
+        return res.astype(self._dtype, copy=False)
+
+    @_dispatch
+    def _sample_dispatch(self, full_shape, *, method, rng, **params):
+        # make sure that tests catch if sample is 0d array
+        if self._overrides('_sample_formula') and not isinstance(rng, qmc.QMCEngine):
+            method = self._sample_formula
+        else:
+            method = self._sample_inverse_transform
+        return method
+
+    def _sample_formula(self, full_shape, *, rng, **params):
+        raise NotImplementedError(self._not_implemented)
+
+    def _sample_inverse_transform(self, full_shape, *, rng, **params):
+        if isinstance(rng, qmc.QMCEngine):
+            uniform = self._qmc_uniform(full_shape, qrng=rng, **params)
+        else:
+            uniform = rng.random(size=full_shape, dtype=self._dtype)
+        return self._icdf_dispatch(uniform, **params)
+
+    def _qmc_uniform(self, full_shape, *, qrng, **params):
+        # Generate QMC uniform sample(s) on unit interval with specified shape;
+        # if `sample_shape != ()`, then each slice along axis 0 is independent.
+
+        sample_shape = full_shape[:len(full_shape)-len(self._shape)]
+        # Determine the number of independent sequences and the length of each.
+        n_low_discrepancy = sample_shape[0] if sample_shape else 1
+        n_independent = math.prod(full_shape[1:] if sample_shape else full_shape)
+
+        # For each independent sequence, we'll need a new QRNG of the appropriate class
+        # with its own RNG. (If scramble=False, we don't really need all the separate
+        # rngs, but I'm not going to add a special code path right now.)
+        rngs = _rng_spawn(qrng.rng, n_independent)
+        qrng_class = qrng.__class__
+        kwargs = dict(d=1, scramble=qrng.scramble, optimization=qrng._optimization)
+        if isinstance(qrng, qmc.Sobol):
+            kwargs['bits'] = qrng.bits
+
+        # Draw uniform low-discrepancy sequences scrambled with each RNG
+        uniforms = []
+        for rng in rngs:
+            qrng = qrng_class(seed=rng, **kwargs)
+            uniform = qrng.random(n_low_discrepancy)
+            uniform = uniform.reshape(n_low_discrepancy if sample_shape else ())[()]
+            uniforms.append(uniform)
+
+        # Reorder the axes and ensure that the shape is correct
+        uniform = np.moveaxis(np.stack(uniforms), -1, 0) if uniforms else np.asarray([])
+        return uniform.reshape(full_shape)
+
+    ### Moments
+    # The `moment` method accepts two positional arguments - the order and kind
+    # (raw, central, or standard) of the moment - and a keyword option:
+    # method - a string that indicates which method should be used to compute
+    #          the quantity (e.g. a formula or numerical integration).
+    # Like the distribution properties, input/output validation is provided by
+    # the `_set_invalid_nan_property` decorator.
+    #
+    # Unlike most public methods above, `moment` dispatches to one of three
+    # private methods - one for each 'kind'. Like most *public* methods above,
+    # each of these private methods calls a private "dispatch" method that
+    # determines which "method" (strategy for calculating the desired quantity)
+    # to use. Also, each dispatch method can designate the responsibility
+    # computing the moment to one of several "implementation" methods.
+    # Unlike the dispatch methods above, however, the `@_dispatch` decorator
+    # is not used, and both logic and method calls are included in the function
+    # itself.
+    # Instead of determining which method will be used based solely on the
+    # implementation methods available and calling only the corresponding
+    # implementation method, *all* the implementation methods are called
+    # in sequence until one returns the desired information. When an
+    # implementation methods cannot provide the requested information, it
+    # returns the object None (which is distinct from arrays with NaNs or infs,
+    # which are valid values of moments).
+    # The reason for this approach is that although formulae for the first
+    # few moments of a distribution may be found, general formulae that work
+    # for all orders are not always easy to find. This approach allows the
+    # developer to write "formula" implementation functions that return the
+    # desired moment when it is available and None otherwise.
+    #
+    # Note that the first implementation method called is a cache. This is
+    # important because lower-order moments are often needed to compute
+    # higher moments from formulae, so we eliminate redundant calculations
+    # when moments of several orders are needed.
+
+    @cached_property
+    def _moment_methods(self):
+        return {'cache', 'formula', 'transform',
+                'normalize', 'general', 'quadrature'}
+
+    @cached_property
+    def _lmoment_methods(self):
+        return {'cache', 'formula', 'general', 'order_statistics', 'quadrature_icdf'}
+
+    @property
+    def _zero(self):
+        return self._constants()[0]
+
+    @property
+    def _one(self):
+        return self._constants()[1]
+
+    def _constants(self):
+        if self._constant_cache is not None:
+            return self._constant_cache
+
+        constants = self._preserve_type([0, 1])
+
+        if self.cache_policy != _NO_CACHE:
+            self._constant_cache = constants
+
+        return constants
+
+    @_set_invalid_nan_property
+    def moment(self, order=1, kind='raw', *, method=None):
+        kinds = {'raw': self._moment_raw,
+                 'central': self._moment_central,
+                 'standardized': self._moment_standardized}
+        order = self._validate_order(order)
+        kind = self._validate_kind(kind, kinds)
+        moment_kind = kinds[kind]
+        return moment_kind(order, method=method)
+
+    def _moment_raw(self, order=1, *, method=None):
+        """Raw distribution moment about the origin."""
+        # Consider exposing the point about which moments are taken as an
+        # option. This is easy to support, since `_moment_transform_center`
+        # does all the work.
+        methods = self._moment_methods if method is None else {method}
+        return self._moment_raw_dispatch(order, methods=methods, **self._parameters)
+
+    def _moment_raw_dispatch(self, order, *, methods, **params):
+        moment = None
+
+        if 'cache' in methods:
+            moment = self._moment_raw_cache.get(order, None)
+
+        if moment is None and 'formula' in methods:
+            moment = self._moment_raw_formula(order, **params)
+
+        if moment is None and 'transform' in methods and order > 1:
+            moment = self._moment_raw_transform(order, **params)
+
+        if moment is None and 'general' in methods:
+            moment = self._moment_raw_general(order, **params)
+
+        if moment is None and 'quadrature' in methods:
+            moment = self._moment_from_pxf(order, center=self._zero, **params)
+
+        if moment is None and 'quadrature_icdf' in methods:
+            moment = self._moment_integrate_icdf(order, center=self._zero, **params)
+
+        if moment is not None and self.cache_policy != _NO_CACHE:
+            self._moment_raw_cache[order] = moment
+
+        return moment
+
+    def _moment_raw_formula(self, order, **params):
+        return None
+
+    def _moment_raw_transform(self, order, **params):
+        central_moments = []
+        for i in range(int(order) + 1):
+            methods = {'cache', 'formula', 'normalize', 'general'}
+            moment_i = self._moment_central_dispatch(order=i, methods=methods, **params)
+            if moment_i is None:
+                return None
+            central_moments.append(moment_i)
+
+        # Doesn't make sense to get the mean by "transform", since that's
+        # how we got here. Questionable whether 'quadrature' should be here.
+        mean_methods = {'cache', 'formula', 'quadrature'}
+        mean = self._moment_raw_dispatch(self._one, methods=mean_methods, **params)
+        if mean is None:
+            return None
+
+        moment = self._moment_transform_center(order, central_moments, mean, self._zero)
+        return moment
+
+    def _moment_raw_general(self, order, **params):
+        # This is the only general formula for a raw moment of a probability
+        # distribution
+        return self._one if order == 0 else None
+
+    def _moment_central(self, order=1, *, method=None):
+        """Distribution moment about the mean."""
+        methods = self._moment_methods if method is None else {method}
+        return self._moment_central_dispatch(order, methods=methods, **self._parameters)
+
+    def _moment_central_dispatch(self, order, *, methods, **params):
+        moment = None
+
+        if 'cache' in methods:
+            moment = self._moment_central_cache.get(order, None)
+
+        if moment is None and 'formula' in methods:
+            moment = self._moment_central_formula(order, **params)
+
+        if moment is None and 'transform' in methods:
+            moment = self._moment_central_transform(order, **params)
+
+        if moment is None and 'normalize' in methods and order > 2:
+            moment = self._moment_central_normalize(order, **params)
+
+        if moment is None and 'general' in methods:
+            moment = self._moment_central_general(order, **params)
+
+        if moment is None and 'quadrature' in methods:
+            mean = self._moment_raw_dispatch(self._one, **params,
+                                             methods=self._moment_methods)
+            moment = self._moment_from_pxf(order, center=mean, **params)
+
+        if moment is None and 'quadrature_icdf' in methods:
+            mean = self._moment_raw_dispatch(self._one, **params,
+                                             methods=self._moment_methods)
+            moment = self._moment_integrate_icdf(order, center=mean, **params)
+
+        if moment is not None and self.cache_policy != _NO_CACHE:
+            self._moment_central_cache[order] = moment
+
+        return moment
+
+    def _moment_central_formula(self, order, **params):
+        return None
+
+    def _moment_central_transform(self, order, **params):
+
+        raw_moments = []
+        for i in range(int(order) + 1):
+            methods = {'cache', 'formula', 'general'}
+            moment_i = self._moment_raw_dispatch(order=i, methods=methods, **params)
+            if moment_i is None:
+                return None
+            raw_moments.append(moment_i)
+
+        mean_methods = self._moment_methods
+        mean = self._moment_raw_dispatch(self._one, methods=mean_methods, **params)
+
+        moment = self._moment_transform_center(order, raw_moments, self._zero, mean)
+        return moment
+
+    def _moment_central_normalize(self, order, **params):
+        methods = {'cache', 'formula', 'general'}
+        standard_moment = self._moment_standardized_dispatch(order, **params,
+                                                             methods=methods)
+        if standard_moment is None:
+            return None
+        var = self._moment_central_dispatch(2, methods=self._moment_methods, **params)
+        return standard_moment*var**(order/2)
+
+    def _moment_central_general(self, order, **params):
+        general_central_moments = {0: self._one, 1: self._zero}
+        return general_central_moments.get(order, None)
+
+    def _moment_standardized(self, order=1, *, method=None):
+        """Standardized distribution moment."""
+        methods = self._moment_methods if method is None else {method}
+        return self._moment_standardized_dispatch(order, methods=methods,
+                                                  **self._parameters)
+
+    def _moment_standardized_dispatch(self, order, *, methods, **params):
+        moment = None
+
+        if 'cache' in methods:
+            moment = self._moment_standardized_cache.get(order, None)
+
+        if moment is None and 'formula' in methods:
+            moment = self._moment_standardized_formula(order, **params)
+
+        if moment is None and 'normalize' in methods:
+            moment = self._moment_standardized_normalize(order, False, **params)
+
+        if moment is None and 'general' in methods:
+            moment = self._moment_standardized_general(order, **params)
+
+        if moment is None and 'normalize' in methods:
+            moment = self._moment_standardized_normalize(order, True, **params)
+
+        if moment is not None and self.cache_policy != _NO_CACHE:
+            self._moment_standardized_cache[order] = moment
+
+        return moment
+
+    def _moment_standardized_formula(self, order, **params):
+        return None
+
+    def _moment_standardized_normalize(self, order, use_quadrature, **params):
+        methods = ({'quadrature'} if use_quadrature
+                   else {'cache', 'formula', 'transform'})
+        central_moment = self._moment_central_dispatch(order, **params,
+                                                       methods=methods)
+        if central_moment is None:
+            return None
+        var = self._moment_central_dispatch(2, methods=self._moment_methods,
+                                            **params)
+        return central_moment/var**(order/2)
+
+    def _moment_standardized_general(self, order, **params):
+        general_standard_moments = {0: self._one, 1: self._zero, 2: self._one}
+        return general_standard_moments.get(order, None)
+
+    def _moment_from_pxf(self, order, center, **params):
+        def integrand(x, order, center, **params):
+            pxf = self._pxf_dispatch(x, **params)
+            return pxf*(x-center)**order
+        return self._quadrature(integrand, args=(order, center), params=params)
+
+    def _moment_integrate_icdf(self, order, center, **params):
+        def integrand(x, order, center, **params):
+            x = self._icdf_dispatch(x, **params)
+            return (x-center)**order
+        return self._quadrature(integrand, limits=(0., 1.),
+                                args=(order, center), params=params)
+
+    def _moment_transform_center(self, order, moment_as, a, b):
+        a, b, *moment_as = np.broadcast_arrays(a, b, *moment_as)
+        n = order
+        i = np.arange(n+1).reshape([-1]+[1]*a.ndim)  # orthogonal to other axes
+        i = self._preserve_type(i)
+        n_choose_i = special.binom(n, i)
+        with np.errstate(invalid='ignore'):  # can happen with infinite moment
+            moment_b = np.sum(n_choose_i*moment_as*(a-b)**(n-i), axis=0)
+        return moment_b
+
+    ### L-Moments
+
+    @_set_invalid_nan_property
+    def lmoment(self, order=1, *, standardize=True, method=None):
+        order = self._validate_order(order, fname='lmoment', min_order=1)
+        return self._lmoment(order, standardize=standardize, method=method)
+
+    def _lmoment(self, order, *, standardize, method):
+        methods = self._lmoment_methods if method is None else {method}
+
+        lmoment = self._lmoment_dispatch(order, methods=methods, **self._parameters)
+        if lmoment is None:
+            return None
+
+        if standardize and order >= 3 and lmoment is not None:
+            lscale = self._lmoment_dispatch(2, methods=methods, **self._parameters)
+            if lscale is None:
+                return None
+            lmoment = lmoment / lscale
+
+        return lmoment
+
+    def _lmoment_dispatch(self, order, *, methods, **params):
+        lmoment = None
+
+        if 'cache' in methods:
+            lmoment = self._lmoment_cache.get(order, None)
+
+        if lmoment is None and 'formula' in methods:
+            lmoment = self._lmoment_formula(order, **params)
+
+        if lmoment is None and 'general' in methods:
+            lmoment = self._lmoment_general(order, **params)
+
+        if lmoment is None and 'quadrature_icdf' in methods and (
+                self._overrides('_icdf_formula') or self._overrides('_iccdf_formula')):
+            lmoment = self._lmoment_integrate_icdf(order, **params)
+
+        if lmoment is None and 'order_statistics' in methods:
+            lmoment = self._lmoment_from_order_statistics(order, **params)
+
+        if lmoment is None and 'quadrature_icdf' in methods:
+            lmoment = self._lmoment_integrate_icdf(order, **params)
+
+        if lmoment is not None and self.cache_policy != _NO_CACHE:
+            self._lmoment_cache[order] = lmoment
+
+        return lmoment
+
+    def _lmoment_formula(self, order, **params):
+        return None
+
+    def _lmoment_general(self, order, **params):
+        return self.mean() if order == 1 else None
+
+    def _lmoment_from_order_statistics(self, order, **params):
+        k = np.arange(order)
+        k = xpx.atleast_nd(k, ndim=self._ndim + 1).T
+        E = order_statistic(self, r=order-k, n=order).mean()
+        bc = special.binom(order-1, k)
+        return np.sum((-1)**k * bc * E, axis=0) / order
+
+    def _lmoment_integrate_icdf(self, order, **params):
+        def integrand(p, **params):
+            x = self._icdf_dispatch(p, **params)
+            P = special.eval_sh_legendre(order - 1, p)
+            return x * P
+        return self._quadrature(integrand, limits=(0., 1.), params=params)
+
+    ### Convenience
+
+    def plot(self, x='x', y=None, *, t=None, ax=None):
+        r"""Plot a function of the distribution.
+
+        Convenience function for quick visualization of the distribution
+        underlying the random variable.
+
+        Parameters
+        ----------
+        x, y : str, optional
+            String indicating the quantities to be used as the abscissa and
+            ordinate (horizontal and vertical coordinates), respectively.
+            Defaults are ``'x'`` (the domain of the random variable) and either
+            ``'pdf'`` (the probability density function) (continuous) or
+            ``'pdf'`` (the probability density function) (discrete).
+            Valid values are:
+            'x', 'pdf', 'pmf', 'cdf', 'ccdf', 'icdf', 'iccdf', 'logpdf', 'logpmf',
+            'logcdf', 'logccdf', 'ilogcdf', 'ilogccdf'.
+        t : 3-tuple of (str, float, float), optional
+            Tuple indicating the limits within which the quantities are plotted.
+            The default is ``('cdf', 0.0005, 0.9995)`` if the domain is infinite,
+            indicating that the central 99.9% of the distribution is to be shown;
+            otherwise, endpoints of the support are used where they are finite.
+            Valid values are:
+            'x', 'cdf', 'ccdf', 'icdf', 'iccdf', 'logcdf', 'logccdf',
+            'ilogcdf', 'ilogccdf'.
+        ax : `matplotlib.axes`, optional
+            Axes on which to generate the plot. If not provided, use the
+            current axes.
+
+        Returns
+        -------
+        ax : `matplotlib.axes`
+            Axes on which the plot was generated.
+            The plot can be customized by manipulating this object.
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> import numpy as np
+        >>> import matplotlib.pyplot as plt
+        >>> from scipy import stats
+        >>> X = stats.Normal(mu=1., sigma=2.)
+
+        Plot the PDF over the central 99.9% of the distribution.
+        Compare against a histogram of a random sample.
+
+        >>> ax = X.plot()
+        >>> sample = X.sample(10000)
+        >>> ax.hist(sample, density=True, bins=50, alpha=0.5)
+        >>> plt.show()
+
+        Plot ``logpdf(x)`` as a function of ``x`` in the left tail,
+        where the log of the CDF is between -10 and ``np.log(0.5)``.
+
+        >>> X.plot('x', 'logpdf', t=('logcdf', -10, np.log(0.5)))
+        >>> plt.show()
+
+        Plot the PDF of the normal distribution as a function of the
+        CDF for various values of the scale parameter.
+
+        >>> X = stats.Normal(mu=0., sigma=[0.5, 1., 2])
+        >>> X.plot('cdf', 'pdf')
+        >>> plt.show()
+
+        """
+
+        # Strategy: given t limits, get quantile limits. Form grid of
+        # quantiles, compute requested x and y at quantiles, and plot.
+        # Currently, the grid of quantiles is always linearly spaced.
+        # Instead of always computing linearly-spaced quantiles, it
+        # would be better to choose:
+        # a) quantiles or probabilities
+        # b) linearly or logarithmically spaced
+        # based on the specified `t`.
+        # TODO:
+        # - smart spacing of points
+        # - when the parameters of the distribution are an array,
+        #   use the full range of abscissae for all curves
+
+        discrete = isinstance(self, DiscreteDistribution)
+        t_is_quantile = {'x', 'icdf', 'iccdf', 'ilogcdf', 'ilogccdf'}
+        t_is_probability = {'cdf', 'ccdf', 'logcdf', 'logccdf'}
+        valid_t = t_is_quantile.union(t_is_probability)
+        valid_xy =  valid_t.union({'pdf', 'logpdf', 'pmf', 'logpmf'})
+        y_default = 'pmf' if discrete else 'pdf'
+        y = y_default if y is None else y
+
+        ndim = self._ndim
+        x_name, y_name = x, y
+        t_name = 'cdf' if t is None else t[0]
+
+        a, b = self.support()
+        tliml_default = 0 if np.all(np.isfinite(a)) else 0.0005
+        tliml = tliml_default if t is None else t[1]
+        tlimr_default = 1 if np.all(np.isfinite(b)) else 0.9995
+        tlimr = tlimr_default if t is None else t[2]
+        tlim = np.asarray([tliml, tlimr])
+        tlim = tlim[:, np.newaxis] if ndim else tlim
+
+        # pdf/logpdf are not valid for `t` because we can't easily invert them
+        message = (f'Argument `t` of `{self.__class__.__name__}.plot` "'
+                   f'must be one of {valid_t}')
+        if t_name not in valid_t:
+            raise ValueError(message)
+
+        message = (f'Argument `x` of `{self.__class__.__name__}.plot` "'
+                   f'must be one of {valid_xy}')
+        if x_name not in valid_xy:
+            raise ValueError(message)
+
+        message = (f'Argument `y` of `{self.__class__.__name__}.plot` "'
+                   f'must be one of {valid_xy}')
+        if y_name not in valid_xy:
+            raise ValueError(message)
+
+        # This could just be a warning
+        message = (f'`{self.__class__.__name__}.plot` was called on a random '
+                   'variable with at least one invalid shape parameters. When '
+                   'a parameter is invalid, no plot can be shown.')
+        if self._any_invalid:
+            raise ValueError(message)
+
+        # We could automatically ravel, but do we want to? For now, raise.
+        message = ("To use `plot`, distribution parameters must be "
+                   "scalars or arrays with one or fewer dimensions.")
+        if ndim > 1:
+            raise ValueError(message)
+
+        try:
+            import matplotlib.pyplot as plt  # noqa: F401, E402
+        except ModuleNotFoundError as exc:
+            message = ("`matplotlib` must be installed to use "
+                       f"`{self.__class__.__name__}.plot`.")
+            raise ModuleNotFoundError(message) from exc
+        ax = plt.gca() if ax is None else ax
+
+        # get quantile limits given t limits
+        qlim = tlim if t_name in t_is_quantile else getattr(self, 'i'+t_name)(tlim)
+
+        message = (f"`{self.__class__.__name__}.plot` received invalid input for `t`: "
+                   f"calling {'i'+t_name}({tlim}) produced {qlim}.")
+        if not np.all(np.isfinite(qlim)):
+            raise ValueError(message)
+
+        # form quantile grid
+        if discrete and x_name in t_is_quantile:
+            # should probably aggregate for large ranges
+            q = np.arange(np.min(qlim[0]), np.max(qlim[1]) + 1)
+            q = q[:, np.newaxis] if ndim else q
+        else:
+            grid = np.linspace(0, 1, 300)
+            grid = grid[:, np.newaxis] if ndim else grid
+            q = qlim[0] + (qlim[1] - qlim[0]) * grid
+            q = np.round(q) if discrete else q
+
+        # compute requested x and y at quantile grid
+        x = q if x_name in t_is_quantile else getattr(self, x_name)(q)
+        y = q if y_name in t_is_quantile else getattr(self, y_name)(q)
+
+        # make plot
+        x, y = np.broadcast_arrays(x.T, np.atleast_2d(y.T))
+        for xi, yi in zip(x, y):  # plot is vectorized, but bar/step don't seem to be
+            if discrete and x_name in t_is_quantile and y_name == 'pmf':
+                # should this just be a step plot, too?
+                ax.bar(xi, yi, alpha=np.sqrt(1/y.shape[0]))  # alpha heuristic
+            elif discrete and x_name in t_is_quantile:
+                values = yi
+                edges = np.concatenate((xi, [xi[-1]+1]))
+                ax.stairs(values, edges, baseline=None)
+            else:
+                ax.plot(xi, yi)
+        ax.set_xlabel(f"${x_name}$")
+        ax.set_ylabel(f"${y_name}$")
+        ax.set_title(str(self))
+
+        # only need a legend if distribution has parameters
+        if len(self._parameters):
+            label = []
+            parameters = self._parameterization.parameters
+            param_names = list(parameters)
+            param_arrays = [np.atleast_1d(self._parameters[pname])
+                            for pname in param_names]
+            for param_vals in zip(*param_arrays):
+                assignments = [f"${parameters[name].symbol}$ = {val:.4g}"
+                               for name, val in zip(param_names, param_vals)]
+                label.append(", ".join(assignments))
+            ax.legend(label)
+
+        return ax
+
+
+    ### Fitting
+    # All methods above treat the distribution parameters as fixed, and the
+    # variable argument may be a quantile or probability. The fitting functions
+    # are fundamentally different because the quantiles (often observations)
+    # are considered to be fixed, and the distribution parameters are the
+    # variables. In a sense, they are like an inverse of the sampling
+    # functions.
+    #
+    # At first glance, it would seem ideal for `fit` to be a classmethod,
+    # called like `LogUniform.fit(sample=sample)`.
+    # I tried this. I insisted on it for a while. But if `fit` is a
+    # classmethod, it cannot call instance methods. If we want to support MLE,
+    # MPS, MoM, MoLM, then we end up with most of the distribution functions
+    # above needing to be classmethods, too. All state information, such as
+    # tolerances and the underlying distribution of `ShiftedScaledDistribution`
+    # and `OrderStatisticDistribution`, would need to be passed into all
+    # methods. And I'm not really sure how we would call `fit` as a
+    # classmethod of a transformed distribution - maybe
+    # ShiftedScaledDistribution.fit would accept the class of the
+    # shifted/scaled distribution as an argument?
+    #
+    # In any case, it was a conscious decision for the infrastructure to
+    # treat the parameters as "fixed" and the quantile/percentile arguments
+    # as "variable". There are a lot of advantages to this structure, and I
+    # don't think the fact that a few methods reverse the fixed and variable
+    # quantities should make us question that choice. It can still accommodate
+    # these methods reasonably efficiently.
+
+
+class ContinuousDistribution(UnivariateDistribution):
+    def _overrides(self, method_name):
+        if method_name in {'_logpmf_formula', '_pmf_formula'}:
+            return True
+        return super()._overrides(method_name)
+
+    def _pmf_formula(self, x, **params):
+        return np.zeros_like(x)
+
+    def _logpmf_formula(self, x, **params):
+        return np.full_like(x, -np.inf)
+
+    def _pxf_dispatch(self, x, *, method=None, **params):
+        return self._pdf_dispatch(x, method=method, **params)
+
+    def _logpxf_dispatch(self, x, *, method=None, **params):
+        return self._logpdf_dispatch(x, method=method, **params)
+
+    def _solve_bounded_continuous(self, func, p, params, xatol=None):
+        return self._solve_bounded(func, p, params=params, xatol=xatol).x
+
+
+class DiscreteDistribution(UnivariateDistribution):
+    def _overrides(self, method_name):
+        if method_name in {'_logpdf_formula', '_pdf_formula'}:
+            return True
+        return super()._overrides(method_name)
+
+    def _logpdf_formula(self, x, **params):
+        if params:
+            p = next(iter(params.values()))
+            nan_result = np.isnan(x) | np.isnan(p)
+        else:
+            nan_result = np.isnan(x)
+        return np.where(nan_result, np.nan, np.inf)
+
+    def _pdf_formula(self, x, **params):
+        if params:
+            p = next(iter(params.values()))
+            nan_result = np.isnan(x) | np.isnan(p)
+        else:
+            nan_result = np.isnan(x)
+        return np.where(nan_result, np.nan, np.inf)
+
+    def _pxf_dispatch(self, x, *, method=None, **params):
+        return self._pmf_dispatch(x, method=method, **params)
+
+    def _logpxf_dispatch(self, x, *, method=None, **params):
+        return self._logpmf_dispatch(x, method=method, **params)
+
+    def _cdf_quadrature(self, x, **params):
+        return super()._cdf_quadrature(np.floor(x), **params)
+
+    def _logcdf_quadrature(self, x, **params):
+        return super()._logcdf_quadrature(np.floor(x), **params)
+
+    def _ccdf_quadrature(self, x, **params):
+        return super()._ccdf_quadrature(np.floor(x + 1), **params)
+
+    def _logccdf_quadrature(self, x, **params):
+        return super()._logccdf_quadrature(np.floor(x + 1), **params)
+
+    def _cdf2(self, x, y, *, method):
+        raise NotImplementedError(
+            "Two argument cdf functions are currently only supported for "
+            "continuous distributions.")
+
+    def _ccdf2(self, x, y, *, method):
+        raise NotImplementedError(
+            "Two argument cdf functions are currently only supported for "
+            "continuous distributions.")
+
+    def _logcdf2(self, x, y, *, method):
+        raise NotImplementedError(
+            "Two argument cdf functions are currently only supported for "
+            "continuous distributions.")
+
+    def _logccdf2(self, x, y, *, method):
+        raise NotImplementedError(
+            "Two argument cdf functions are currently only supported for "
+            "continuous distributions.")
+
+    def _lmoment(self, order, *, standardize, method):
+        raise NotImplementedError(
+            "L-moments are currently available only "
+            "for continuous distributions.")
+
+    def _solve_bounded_discrete(self, func, p, params, comp):
+        # We're trying to solve one of these two problems:
+        # a) find the smallest integer x* within the support s.t. F(x*) >= p
+        # b) find the smallest integer x* within the support s.t. G(x*) = 1 - F(x*) <= p
+        # Our approach is to solve a continuous version of the problem that narrows the
+        # solution down to an integer x s.t. either x* = x or x* = x + 1. At the end,
+        # we'll choose between them.
+
+        # First, solve func(x) == p where func is a continuous, monotone interpolant
+        # of either the monotone increasing F or monotone decreasing G.
+        res = self._solve_bounded(func, p, params=params, xatol=0.9)
+        # Here, `_solve_bounded` can terminate for one of three reasons:
+        # 1. `func(res.x) == p` (`fatol = 0` is satisfied),
+        # 2. `res.xl` and `res.xr` bracket the root and `|res.xr - res.xl| <= xatol`, or
+        # 3. There is no solution within the support.
+        # There are several possible strategies for using `res.xl`, `res.x`, and/or
+        # `res.xr` to find a solution to the original, discrete problem. Here is ours.
+
+        # Consider case 2a. Because F is an increasing function, we know
+        # that F(xr) >= p (and F(xl) <= p), so F(floor(xr) + 1) >= p.
+        # F(floor(xr)) *may* be >= p, but we can't know until we evaluate it.
+        # F(floor(xr) - 1) < p (strictly) because floor(xr) - 1 < xl and F decreases
+        # monotonically as the argument decreases. So we choose x = floor(xr), and
+        # later we'll choose between x* = x and x* = x + 1.
+        x = np.asarray(np.floor(res.bracket[-1]))
+        # This is also suitable for case 2b. Because G is a *decreasing* function, we
+        # know that G(xr) <= p (and G(xl) >= p), so G(floor(xr) + 1) <= p.
+        # G(floor(xr)) *may* be <= p, but we can't know until we evaluate it.
+        # G(floor(xr) - 1) > p (strictly) because floor(xr) - 1 < xl and G increases
+        # as the argument decreases. So we would still want to choose x = floor(xr), and
+        # later we'll choose between x* = x and x* = x + 1.
+
+        # Now we consider case 1a/b. In this case, `res.x` solved the equation
+        # *exactly*, so the algorithm may have terminated before the bracket is tight
+        # enough to rely on `res.xr`. If `res.x` happens to be integral, `res.x` is
+        # the solution to the discrete problem, and floor(res.x) == res.x, so
+        # floor(res.x) is the solution to the discrete problem. If not:
+        # a) F(floor(res.x)) < p (strictly) and F(floor(res.x) + 1) > p (strictly). So
+        #    floor(res.x) + 1 is the solution to the discrete problem.
+        # b) G(floor(res.x)) > p (strictly) and G(floor(res.x) + 1) < p (strictly). So
+        #    floor(res.x) + 1 is again the solution to the discrete problem.
+        # Either way, we can choose x = res.x, and at the end we'll choose between
+        # x* = x and x* = x + 1.
+        mask = res.f_x == 0
+        x[mask] = np.floor(res.x[mask])
+
+        # For case 3, let xmin be the left endpoint of the support, and note that in
+        # general, F(xmin) > 0 and G(xmin) < 1. Therefore it is possible that:
+        # a) F(x) > p for all x in the support (e.g. because p ~ 0)
+        # a) G(x) < p for all x in the support (e.g. because p ~ 1)
+        # In these cases, `_solve_bounded` would fail to find a root of the continuous
+        # equation above, but the solution to the original, discrete problem is the left
+        # endpoint of the support.
+        # This case is handled before we get to this function; otherwise,
+        # `_solve_bounded` may spin its wheels for a long time in vain.
+
+        # Now, we choose between x* = x and x* = x + 1: if func(x) satisfies the
+        # comparison `comp` (>= for cdf, <= for ccdf), the solution is x* = x;
+        # otherwise the solution must be x* = x + 1.
+        f = func(x, **params)
+        x = np.where(comp(f, p), x, x + 1.0)
+        x[np.isnan(f)] = np.nan  # needed? why would func(x) be NaN within support?
+
+        return x
+
+    def _base_discrete_inversion(self, p, func, comp, /, **params):
+        # For discrete distributions, icdf(p) is defined as the minimum integer x*
+        # within the support such that F(x*) >= p; iccdf(p) is the minimum integer x*
+        # within the support such that G(x*) <= p.
+
+        # Identify where the solution is xmin.
+        # (See rationale in `_solve_bounded_discrete`.)
+        xmin, xmax = self._support(**params)
+        p, xmin, _ = np.broadcast_arrays(p, xmin, xmax)
+        mask = comp(func(xmin, **params), p)
+
+        # Use `apply_where` to perform the inversion only when necessary.
+        def f1(p, *args):
+            return self._solve_bounded_discrete(
+                func, p, params=dict(zip(params.keys(), args)), comp=comp)
+
+        x = xpx.apply_where(~mask, (p, *params.values()), f1, fill_value=xmin)
+
+        # x above may be a finite value even when p is NaN, so the returned value
+        # should be NaN. We need to handle this as a special case.
+        x[np.isnan(p)] = np.nan
+        return x[()]
+
+    def _icdf_inversion(self, x, **params):
+        return self._base_discrete_inversion(x, self._cdf_dispatch,
+                                             np.greater_equal, **params)
+
+    def _ilogcdf_inversion(self, x, **params):
+        return self._base_discrete_inversion(x, self._logcdf_dispatch,
+                                             np.greater_equal, **params)
+
+    def _iccdf_inversion(self, x, **params):
+        return self._base_discrete_inversion(x, self._ccdf_dispatch,
+                                             np.less_equal, **params)
+
+    def _ilogccdf_inversion(self, x, **params):
+        return self._base_discrete_inversion(x, self._logccdf_dispatch,
+                                             np.less_equal, **params)
+
+    def _mode_optimization(self, **params):
+        # If `x` is the true mode of a unimodal continuous function, we can find
+        # the mode among integers by rounding in each direction and checking
+        # which is better. If the difference between `x` and the nearest integer
+        # is less than `xatol`, the computed value of `x` may end up on the wrong
+        # side of the nearest integer. Setting `xatol=0.5` guarantees that at most
+        # three integers need to be checked, the two nearest integers, ``floor(x)``
+        # and ``round(x)`` and the nearest integer other than these.
+        x = super()._mode_optimization(xatol=0.5, **params)
+        low, high = self.support()
+        xl, xr = np.floor(x), np.ceil(x)
+        nearest = np.round(x)
+        # Clip to stay within support. There will be redundant calculation
+        # when clipping since `xo` will be one of `xl` or `xr`, but let's
+        # keep the implementation simple for now.
+        xo = np.clip(nearest + np.copysign(1, nearest - x), low, high)
+        x = np.stack([xl, xo, xr])
+        idx = np.argmax(self._pmf_dispatch(x, **params), axis=0)
+        return np.choose(idx, [xl, xo, xr])
+
+    def _logentropy_quadrature(self, **params):
+        def logintegrand(x, **params):
+            logpmf = self._logpmf_dispatch(x, **params)
+            # Entropy summand is -pmf*log(pmf), so log-entropy summand is
+            # logpmf + log(logpmf) + pi*j. But pmf is always between 0 and 1,
+            # so logpmf is always negative, and so log(logpmf) = log(-logpmf) + pi*j.
+            # The two imaginary components "cancel" each other out (which we would
+            # expect because each term of the entropy summand is positive).
+            return np.where(np.isfinite(logpmf), logpmf + np.log(-logpmf), -np.inf)
+        return self._quadrature(logintegrand, params=params, log=True)
+
+
+# Special case the names of some new-style distributions in `make_distribution`
+_distribution_names = {
+    # Continuous
+    'argus': 'ARGUS',
+    'betaprime': 'BetaPrime',
+    'chi2': 'ChiSquared',
+    'crystalball': 'CrystalBall',
+    'dgamma': 'DoubleGamma',
+    'dweibull': 'DoubleWeibull',
+    'expon': 'Exponential',
+    'exponnorm': 'ExponentiallyModifiedNormal',
+    'exponweib': 'ExponentialWeibull',
+    'exponpow': 'ExponentialPower',
+    'fatiguelife': 'FatigueLife',
+    'foldcauchy': 'FoldedCauchy',
+    'foldnorm': 'FoldedNormal',
+    'genlogistic': 'GeneralizedLogistic',
+    'gennorm': 'GeneralizedNormal',
+    'genpareto': 'GeneralizedPareto',
+    'genexpon': 'GeneralizedExponential',
+    'genextreme': 'GeneralizedExtremeValue',
+    'gausshyper': 'GaussHypergeometric',
+    'gengamma': 'GeneralizedGamma',
+    'genhalflogistic': 'GeneralizedHalfLogistic',
+    'geninvgauss': 'GeneralizedInverseGaussian',
+    'gumbel_r': 'Gumbel',
+    'gumbel_l': 'ReflectedGumbel',
+    'halfcauchy': 'HalfCauchy',
+    'halflogistic': 'HalfLogistic',
+    'halfnorm': 'HalfNormal',
+    'halfgennorm': 'HalfGeneralizedNormal',
+    'hypsecant': 'HyperbolicSecant',
+    'invgamma': 'InverseGammma',
+    'invgauss': 'InverseGaussian',
+    'invweibull': 'InverseWeibull',
+    'irwinhall': 'IrwinHall',
+    'jf_skew_t': 'JonesFaddySkewT',
+    'johnsonsb': 'JohnsonSB',
+    'johnsonsu': 'JohnsonSU',
+    'ksone': 'KSOneSided',
+    'kstwo': 'KSTwoSided',
+    'kstwobign': 'KSTwoSidedAsymptotic',
+    'laplace_asymmetric': 'LaplaceAsymmetric',
+    'levy_l': 'LevyLeft',
+    'levy_stable': 'LevyStable',
+    'loggamma': 'ExpGamma',  # really the Exponential Gamma Distribution
+    'loglaplace': 'LogLaplace',
+    'lognorm': 'LogNormal',
+    'loguniform': 'LogUniform',
+    'ncx2': 'NoncentralChiSquared',
+    'nct': 'NoncentralT',
+    'norm': 'Normal',
+    'norminvgauss': 'NormalInverseGaussian',
+    'powerlaw': 'PowerLaw',
+    'powernorm': 'PowerNormal',
+    'rdist': 'R',
+    'rel_breitwigner': 'RelativisticBreitWigner',
+    'recipinvgauss': 'ReciprocalInverseGaussian',
+    'reciprocal': 'LogUniform',
+    'semicircular': 'SemiCircular',
+    'skewcauchy': 'SkewCauchy',
+    'skewnorm': 'SkewNormal',
+    'studentized_range': 'StudentizedRange',
+    't': 'StudentT',
+    'trapezoid': 'Trapezoidal',
+    'triang': 'Triangular',
+    'truncexpon': 'TruncatedExponential',
+    'truncnorm': 'TruncatedNormal',
+    'truncpareto': 'TruncatedPareto',
+    'truncweibull_min': 'TruncatedWeibull',
+    'tukeylambda': 'TukeyLambda',
+    'vonmises_line': 'VonMisesLine',
+    'weibull_min': 'Weibull',
+    'weibull_max': 'ReflectedWeibull',
+    'wrapcauchy': 'WrappedCauchyLine',
+    # Discrete
+    'betabinom': 'BetaBinomial',
+    'betanbinom': 'BetaNegativeBinomial',
+    'dlaplace': 'LaplaceDiscrete',
+    'geom': 'Geometric',
+    'hypergeom': 'Hypergeometric',
+    'logser': 'LogarithmicSeries',
+    'nbinom': 'NegativeBinomial',
+    'nchypergeom_fisher': 'NoncentralHypergeometricFisher',
+    'nchypergeom_wallenius': 'NoncentralHypergeometricWallenius',
+    'nhypergeom': 'NegativeHypergeometric',
+    'poisson_binom': 'PoissonBinomial',
+    'randint': 'UniformDiscrete',
+    'yulesimon': 'YuleSimon',
+    'zipf': 'Zeta',
+}
+
+
+@xp_capabilities(np_only=True)
+def make_distribution(dist):
+    """Generate a `UnivariateDistribution` class from a compatible object.
+
+    The argument may be an instance of `rv_continuous` or an instance of
+    another class that satisfies the interface described below.
+
+    The returned value is a `ContinuousDistribution` subclass if the input is an
+    instance of `rv_continuous` or a `DiscreteDistribution` subclass if the input
+    is an instance of `rv_discrete`. Like any subclass of `UnivariateDistribution`,
+    it must be instantiated (i.e. by passing all shape parameters as keyword
+    arguments) before use. Once instantiated, the resulting object will have the
+    same interface as any other instance of `UnivariateDistribution`; e.g.,
+    `scipy.stats.Normal`, `scipy.stats.Binomial`.
+
+    .. note::
+
+        `make_distribution` does not work perfectly with all instances of
+        `rv_continuous`. Known failures include `levy_stable`, `vonmises`,
+        `hypergeom`, 'nchypergeom_fisher', 'nchypergeom_wallenius', and
+        `poisson_binom`. Some methods of some distributions will not support
+        array shape parameters.
+
+    Parameters
+    ----------
+    dist : `rv_continuous`
+        Instance of `rv_continuous`, `rv_discrete`, or an instance of any class with
+        the following attributes:
+
+        __make_distribution_version__ : str
+            A string containing the version number of SciPy in which this interface
+            is defined. The preferred interface may change in future SciPy versions,
+            in which case support for an old interface version may be deprecated
+            and eventually removed.
+        parameters : dict or tuple
+            If a dictionary, each key is the name of a parameter,
+            and the corresponding value is either a dictionary or tuple.
+            If the value is a dictionary, it may have the following items, with default
+            values used for entries which aren't present.
+
+            endpoints : tuple, default: (-inf, inf)
+                A tuple defining the lower and upper endpoints of the domain of the
+                parameter; allowable values are floats, the name (string) of another
+                parameter, or a callable taking parameters as keyword only
+                arguments and returning the numerical value of an endpoint for
+                given parameter values.
+
+            inclusive : tuple of bool, default: (False, False)
+                A tuple specifying whether the endpoints are included within the domain
+                of the parameter.
+
+            typical : tuple, default: ``endpoints``
+                Defining endpoints of a typical range of values of a parameter. Can be
+                used for sampling parameter values for testing. Behaves like the
+                ``endpoints`` tuple above, and should define a subinterval of the
+                domain given by ``endpoints``.
+
+            A tuple value ``(a, b)`` associated to a key in the ``parameters``
+            dictionary is equivalent to ``{endpoints: (a, b)}``.
+
+            Custom distributions with multiple parameterizations can be defined by
+            having the ``parameters`` attribute be a tuple of dictionaries with
+            the structure described above. In this case, ``dist``\'s class must also
+            define a method ``process_parameters`` to map between the different
+            parameterizations. It must take all parameters from all parameterizations
+            as optional keyword arguments and return a dictionary mapping parameters to
+            values, filling in values from other parameterizations using values from
+            the supplied parameterization. See example.
+
+        support : dict or tuple
+            A dictionary describing the support of the distribution or a tuple
+            describing the endpoints of the support. This behaves identically to
+            the values of the parameters dict described above.
+
+        The class **must** also define a ``pdf`` method and **may** define methods
+        ``logentropy``, ``entropy``, ``median``, ``mode``, ``logpdf``,
+        ``logcdf``, ``cdf``, ``logccdf``, ``ccdf``,
+        ``ilogcdf``, ``icdf``, ``ilogccdf``, ``iccdf``,
+        ``moment``, ``lmoment``, and ``sample``.
+        If defined, these methods must accept the parameters of the distribution as
+        keyword arguments and also accept any positional-only arguments accepted by
+        the corresponding method of `ContinuousDistribution`.
+        When multiple parameterizations are defined, these methods must accept
+        all parameters from all parameterizations. The ``moment`` method
+        must accept the ``order`` and ``kind`` arguments by position or keyword, but
+        may return ``None`` if a formula is not available for the arguments; in this
+        case, the infrastructure will fall back to a default implementation.
+        The ``lmoment`` method must accept the ``order`` argument by position or
+        keyword and return the specified L-moment (not the L-moment ratio), but may
+        return ``None`` if a formula is not available for the arguments. The
+        ``sample`` method must accept ``shape`` by position or keyword, but contrary
+        to the public method of the same name, the argument it receives will be the
+        *full* shape of the output array - that is, the shape passed to the public
+        method prepended to the broadcasted shape of random variable parameters.
+
+    Returns
+    -------
+    CustomDistribution : `UnivariateDistribution`
+        A subclass of `UnivariateDistribution` corresponding with `dist`. The
+        initializer requires all shape parameters to be passed as keyword arguments
+        (using the same names as the instance of `rv_continuous`/`rv_discrete`).
+
+    Notes
+    -----
+    The documentation of `UnivariateDistribution` is not rendered. See below for
+    an example of how to instantiate the class (i.e. pass all shape parameters of
+    `dist` to the initializer as keyword arguments). Documentation of all methods
+    is identical to that of `scipy.stats.Normal`. Use ``help`` on the returned
+    class or its methods for more information.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy import stats
+    >>> from scipy import special
+
+    Create a `ContinuousDistribution` from `scipy.stats.loguniform`.
+
+    >>> LogUniform = stats.make_distribution(stats.loguniform)
+    >>> X = LogUniform(a=1.0, b=3.0)
+    >>> np.isclose((X + 0.25).median(), stats.loguniform.ppf(0.5, 1, 3, loc=0.25))
+    np.True_
+    >>> X.plot()
+    >>> sample = X.sample(10000, rng=np.random.default_rng())
+    >>> plt.hist(sample, density=True, bins=30)
+    >>> plt.legend(('pdf', 'histogram'))
+    >>> plt.show()
+
+    Create a custom distribution.
+
+    >>> class MyLogUniform:
+    ...     @property
+    ...     def __make_distribution_version__(self):
+    ...         return "1.16.0"
+    ...
+    ...     @property
+    ...     def parameters(self):
+    ...         return {'a': {'endpoints': (0, np.inf),
+    ...                       'inclusive': (False, False)},
+    ...                 'b': {'endpoints': ('a', np.inf),
+    ...                       'inclusive': (False, False)}}
+    ...
+    ...     @property
+    ...     def support(self):
+    ...         return {'endpoints': ('a', 'b'), 'inclusive': (True, True)}
+    ...
+    ...     def pdf(self, x, a, b):
+    ...         return 1 / (x * (np.log(b)- np.log(a)))
+    >>>
+    >>> MyLogUniform = stats.make_distribution(MyLogUniform())
+    >>> Y = MyLogUniform(a=1.0, b=3.0)
+    >>> np.isclose(Y.cdf(2.), X.cdf(2.))
+    np.True_
+
+    Create a custom distribution with variable support.
+
+    >>> class MyUniformCube:
+    ...     @property
+    ...     def __make_distribution_version__(self):
+    ...         return "1.16.0"
+    ...
+    ...     @property
+    ...     def parameters(self):
+    ...         return {"a": (-np.inf, np.inf),
+    ...                 "b": {'endpoints':('a', np.inf), 'inclusive':(True, False)}}
+    ...
+    ...     @property
+    ...     def support(self):
+    ...         def left(*, a, b):
+    ...             return a**3
+    ...
+    ...         def right(*, a, b):
+    ...             return b**3
+    ...         return (left, right)
+    ...
+    ...     def pdf(self, x, *, a, b):
+    ...         return 1 / (3*(b - a)*np.cbrt(x)**2)
+    ...
+    ...     def cdf(self, x, *, a, b):
+    ...         return (np.cbrt(x) - a) / (b - a)
+    >>>
+    >>> MyUniformCube = stats.make_distribution(MyUniformCube())
+    >>> X = MyUniformCube(a=-2, b=2)
+    >>> Y = stats.Uniform(a=-2, b=2)**3
+    >>> X.support()
+    (-8.0, 8.0)
+    >>> np.isclose(X.cdf(2.1), Y.cdf(2.1))
+    np.True_
+
+    Create a custom distribution with multiple parameterizations. Here we create a
+    custom version of the beta distribution that has an alternative parameterization
+    in terms of the mean ``mu`` and a dispersion parameter ``nu``.
+
+    >>> class MyBeta:
+    ...     @property
+    ...     def __make_distribution_version__(self):
+    ...         return "1.16.0"
+    ...
+    ...     @property
+    ...     def parameters(self):
+    ...         return ({"a": (0, np.inf), "b": (0, np.inf)},
+    ...                 {"mu": (0, 1), "nu": (0, np.inf)})
+    ...
+    ...     def process_parameters(self, a=None, b=None, mu=None, nu=None):
+    ...         if a is not None and b is not None:
+    ...             nu = a + b
+    ...             mu = a / nu
+    ...         else:
+    ...             a = mu * nu
+    ...             b = nu - a
+    ...         return dict(a=a, b=b, mu=mu, nu=nu)
+    ...
+    ...     @property
+    ...     def support(self):
+    ...         return {'endpoints': (0, 1)}
+    ...
+    ...     def pdf(self, x, a, b, mu, nu):
+    ...         return special._ufuncs._beta_pdf(x, a, b)
+    ...
+    ...     def cdf(self, x, a, b, mu, nu):
+    ...         return special.betainc(a, b, x)
+    >>>
+    >>> MyBeta = stats.make_distribution(MyBeta())
+    >>> X = MyBeta(a=2.0, b=2.0)
+    >>> Y = MyBeta(mu=0.5, nu=4.0)
+    >>> np.isclose(X.pdf(0.3), Y.pdf(0.3))
+    np.True_
+
+    """
+    if dist in {stats.levy_stable, stats.vonmises, stats.hypergeom,
+                stats.nchypergeom_fisher, stats.nchypergeom_wallenius,
+                stats.poisson_binom}:
+        raise NotImplementedError(f"`{dist.name}` is not supported.")
+
+    if isinstance(dist, stats.rv_continuous | stats.rv_discrete):
+        return _make_distribution_rv_generic(dist)
+    elif getattr(dist, "__make_distribution_version__", "0.0.0") >= "1.16.0":
+        return _make_distribution_custom(dist)
+    else:
+        message = ("The argument must be an instance of `rv_continuous`, "
+                   "`rv_discrete`, or an instance of a class with attribute "
+                   "`__make_distribution_version__ >= 1.16`.")
+        raise ValueError(message)
+
+def _make_distribution_rv_generic(dist):
+    parameters = []
+    names = []
+    support = getattr(dist, '_support', (dist.a, dist.b))
+    for shape_info in dist._shape_info():
+        domain = _RealInterval(endpoints=shape_info.endpoints,
+                               inclusive=shape_info.inclusive)
+        param = _RealParameter(shape_info.name, domain=domain)
+        parameters.append(param)
+        names.append(shape_info.name)
+
+    repr_str = _distribution_names.get(dist.name, dist.name.capitalize())
+    if isinstance(dist, stats.rv_continuous):
+        old_class, new_class = stats.rv_continuous, ContinuousDistribution
+    else:
+        old_class, new_class = stats.rv_discrete, DiscreteDistribution
+
+    def _overrides(method_name):
+        return (getattr(dist.__class__, method_name, None)
+                is not getattr(old_class, method_name, None))
+
+    if _overrides("_get_support"):
+        def left(**parameter_values):
+            a, _ = dist._get_support(**parameter_values)
+            return np.asarray(a)[()]
+
+        def right(**parameter_values):
+            _, b = dist._get_support(**parameter_values)
+            return np.asarray(b)[()]
+
+        endpoints = (left, right)
+    else:
+        endpoints = support
+
+    _x_support = _RealInterval(endpoints=endpoints, inclusive=(True, True))
+    _x_param = _RealParameter('x', domain=_x_support, typical=(-1, 1))
+
+    class CustomDistribution(new_class):
+        _parameterizations = ([_Parameterization(*parameters)] if parameters
+                              else [])
+        _variable = _x_param
+
+        __class_getitem__ = None
+
+        def __repr__(self):
+            s = super().__repr__()
+            return s.replace('CustomDistribution', repr_str)
+
+        def __str__(self):
+            s = super().__str__()
+            return s.replace('CustomDistribution', repr_str)
+
+    def _sample_formula(self, full_shape=(), *, rng=None, **kwargs):
+        return dist._rvs(size=full_shape, random_state=rng, **kwargs)
+
+    def _moment_raw_formula(self, order, **kwargs):
+        return dist._munp(int(order), **kwargs)
+
+    def _moment_raw_formula_1(self, order, **kwargs):
+        if order != 1:
+            return None
+        return dist._stats(**kwargs)[0]
+
+    def _moment_central_formula(self, order, **kwargs):
+        if order != 2:
+            return None
+        return dist._stats(**kwargs)[1]
+
+    def _moment_standard_formula(self, order, **kwargs):
+        if order == 3:
+            if dist._stats_has_moments:
+                kwargs['moments'] = 's'
+            return dist._stats(**kwargs)[int(order - 1)]
+        elif order == 4:
+            if dist._stats_has_moments:
+                kwargs['moments'] = 'k'
+            k = dist._stats(**kwargs)[int(order - 1)]
+            return k if k is None else k + 3
+        else:
+            return None
+
+    methods = {'_logpdf': '_logpdf_formula',
+               '_pdf': '_pdf_formula',
+               '_logpmf': '_logpmf_formula',
+               '_pmf': '_pmf_formula',
+               '_logcdf': '_logcdf_formula',
+               '_cdf': '_cdf_formula',
+               '_logsf': '_logccdf_formula',
+               '_sf': '_ccdf_formula',
+               '_ppf': '_icdf_formula',
+               '_isf': '_iccdf_formula',
+               '_entropy': '_entropy_formula',
+               '_median': '_median_formula'}
+
+    # These are not desirable overrides for the new infrastructure
+    skip_override = {'norminvgauss': {'_sf', '_isf'}}
+
+    for old_method, new_method in methods.items():
+        if dist.name in skip_override and old_method in skip_override[dist.name]:
+            continue
+        # If method of old distribution overrides generic implementation...
+        method = getattr(dist.__class__, old_method, None)
+        super_method = getattr(old_class, old_method, None)
+        if method is not super_method:
+            # Make it an attribute of the new object with the new name
+            setattr(CustomDistribution, new_method, getattr(dist, old_method))
+
+    if _overrides('_munp'):
+        CustomDistribution._moment_raw_formula = _moment_raw_formula
+
+    if _overrides('_rvs'):
+        CustomDistribution._sample_formula = _sample_formula
+
+    if _overrides('_stats'):
+        CustomDistribution._moment_standardized_formula = _moment_standard_formula
+        if not _overrides('_munp'):
+            CustomDistribution._moment_raw_formula = _moment_raw_formula_1
+            CustomDistribution._moment_central_formula = _moment_central_formula
+
+    support_etc = _combine_docs(CustomDistribution, include_examples=False).lstrip()
+    docs = [
+        f"This class represents `scipy.stats.{dist.name}` as a subclass of "
+        f"`{new_class}`.",
+        f"The `repr`/`str` of class instances is `{repr_str}`.",
+        f"The PDF of the distribution is defined {support_etc}"
+    ]
+    CustomDistribution.__doc__ = ("\n".join(docs))
+
+    return CustomDistribution
+
+
+def _get_domain_info(info):
+    domain_info = {"endpoints": info} if isinstance(info, tuple) else info
+    typical = domain_info.pop("typical", None)
+    return domain_info, typical
+
+
+def _make_distribution_custom(dist):
+    dist_parameters = (
+        dist.parameters if isinstance(dist.parameters, tuple) else (dist.parameters, )
+    )
+    parameterizations = []
+    for parameterization in dist_parameters:
+        # The attribute name ``parameters`` appears reasonable from a user facing
+        # perspective, but there is a little tension here with the internal. It's
+        # important to keep in mind that the ``parameters`` attribute in a
+        # user-created custom distribution specifies ``_parameterizations`` within
+        # the infrastructure.
+        parameters = []
+
+        for name, info in parameterization.items():
+            domain_info, typical = _get_domain_info(info)
+            domain = _RealInterval(**domain_info)
+            param = _RealParameter(name, domain=domain, typical=typical)
+            parameters.append(param)
+
+        if parameters:
+            parameterizations.append(_Parameterization(*parameters))
+
+    domain_info, typical = _get_domain_info(dist.support)
+    _x_support = _RealInterval(**domain_info)
+    _x_param = _RealParameter('x', domain=_x_support, typical=typical)
+    repr_str = dist.__class__.__name__
+
+    class CustomDistribution(ContinuousDistribution):
+        _parameterizations = parameterizations
+        _variable = _x_param
+
+        def __repr__(self):
+            s = super().__repr__()
+            return s.replace('CustomDistribution', repr_str)
+
+        def __str__(self):
+            s = super().__str__()
+            return s.replace('CustomDistribution', repr_str)
+
+    methods = {'sample', 'logentropy', 'entropy',
+               'median', 'mode', 'logpdf', 'pdf',
+               'logcdf2', 'logcdf', 'cdf2', 'cdf',
+               'logccdf2', 'logccdf', 'ccdf2', 'ccdf',
+               'ilogcdf', 'icdf', 'ilogccdf', 'iccdf',
+               'lmoment'}
+
+    for method in methods:
+        if hasattr(dist, method):
+            # Make it an attribute of the new object with the new name
+            new_method = f"_{method}_formula"
+            setattr(CustomDistribution, new_method, getattr(dist, method))
+
+    if hasattr(dist, 'moment'):
+        def _moment_raw_formula(self, order, **kwargs):
+            return dist.moment(order, kind='raw', **kwargs)
+
+        def _moment_central_formula(self, order, **kwargs):
+            return dist.moment(order, kind='central', **kwargs)
+
+        def _moment_standardized_formula(self, order, **kwargs):
+            return dist.moment(order, kind='standardized', **kwargs)
+
+        CustomDistribution._moment_raw_formula = _moment_raw_formula
+        CustomDistribution._moment_central_formula = _moment_central_formula
+        CustomDistribution._moment_standardized_formula = _moment_standardized_formula
+
+    if hasattr(dist, 'process_parameters'):
+        setattr(
+            CustomDistribution,
+            "_process_parameters",
+            getattr(dist, "process_parameters")
+        )
+
+    support_etc = _combine_docs(CustomDistribution, include_examples=False).lstrip()
+    docs = [
+        f"This class represents `{repr_str}` as a subclass of "
+        "`ContinuousDistribution`.",
+        f"The PDF of the distribution is defined {support_etc}"
+    ]
+    CustomDistribution.__doc__ = ("\n".join(docs))
+
+    return CustomDistribution
+
+
+# The purpose of making ShiftedScaledDistribution a separate class is for
+# a) simplicity of the ContinuousDistribution class and
+# b) avoiding the requirement that every distribution accept loc/scale.
+# The simplicity of ContinuousDistribution is important, because there are
+# several other distribution transformations to be supported; e.g., truncation,
+# wrapping, folding, and doubling. We wouldn't want to cram all of this
+# into the `ContinuousDistribution` class. Also, the order of the composition
+# matters (e.g. truncate then shift/scale or vice versa). It's easier to
+# accommodate different orders if the transformation is built up from
+# components rather than all built into `ContinuousDistribution`.
+
+def _shift_scale_distribution_function_2arg(func):
+    def wrapped(self, x, y, *args, loc, scale, sign, **kwargs):
+        item = func.__name__
+
+        f = getattr(self._dist, item)
+
+        # Obviously it's possible to get away with half of the work here.
+        # Let's focus on correct results first and optimize later.
+        xt = self._transform(x, loc, scale)
+        yt = self._transform(y, loc, scale)
+        fxy = f(xt, yt, *args, **kwargs)
+        fyx = f(yt, xt, *args, **kwargs)
+        return np.real_if_close(np.where(sign, fxy, fyx))[()]
+
+    return wrapped
+
+def _shift_scale_distribution_function(func):
+    # c is for complementary
+    citem = {'_logcdf_dispatch': '_logccdf_dispatch',
+             '_cdf_dispatch': '_ccdf_dispatch',
+             '_logccdf_dispatch': '_logcdf_dispatch',
+             '_ccdf_dispatch': '_cdf_dispatch'}
+    def wrapped(self, x, *args, loc, scale, sign, **kwargs):
+        item = func.__name__
+
+        f = getattr(self._dist, item)
+        cf = getattr(self._dist, citem[item])
+
+        # Obviously it's possible to get away with half of the work here.
+        # Let's focus on correct results first and optimize later.
+        xt = self._transform(x, loc, scale)
+        fx = f(xt, *args, **kwargs)
+        cfx = cf(xt, *args, **kwargs)
+        return np.where(sign, fx, cfx)[()]
+
+    return wrapped
+
+def _shift_scale_inverse_function(func):
+    citem = {'_ilogcdf_dispatch': '_ilogccdf_dispatch',
+             '_icdf_dispatch': '_iccdf_dispatch',
+             '_ilogccdf_dispatch': '_ilogcdf_dispatch',
+             '_iccdf_dispatch': '_icdf_dispatch'}
+    def wrapped(self, p, *args, loc, scale, sign, **kwargs):
+        item = func.__name__
+
+        f = getattr(self._dist, item)
+        cf = getattr(self._dist, citem[item])
+
+        # Obviously it's possible to get away with half of the work here.
+        # Let's focus on correct results first and optimize later.
+        fx =  self._itransform(f(p, *args, **kwargs), loc, scale)
+        cfx = self._itransform(cf(p, *args, **kwargs), loc, scale)
+        return np.where(sign, fx, cfx)[()]
+
+    return wrapped
+
+
+class TransformedDistribution(ContinuousDistribution):
+    def __init__(self, X, /, *args, **kwargs):
+        if not isinstance(X, ContinuousDistribution):
+            message = "Transformations are currently only supported for continuous RVs."
+            raise NotImplementedError(message)
+        self._copy_parameterization()
+        self._variable = X._variable
+        self._dist = X
+        if X._parameterization:
+            # Add standard distribution parameters to our parameterization
+            dist_parameters = X._parameterization.parameters
+            set_params = set(dist_parameters)
+            if not self._parameterizations:
+                self._parameterizations.append(_Parameterization())
+            for parameterization in self._parameterizations:
+                if set_params.intersection(parameterization.parameters):
+                    message = (f"One or more of the parameters of {X} has "
+                               "the same name as a parameter of "
+                               f"{self.__class__.__name__}. Name collisions "
+                               "create ambiguities and are not supported.")
+                    raise ValueError(message)
+                parameterization.parameters.update(dist_parameters)
+        super().__init__(*args, **kwargs)
+
+    def _overrides(self, method_name):
+        return (self._dist._overrides(method_name)
+                or super()._overrides(method_name))
+
+    def reset_cache(self):
+        super().reset_cache()
+
+    def _update_parameters(self, *, validation_policy=None, **params):
+        # maybe broadcast everything before processing?
+        parameters = {}
+        # There may be some issues with _original_parameters
+        # We only want to update with _dist._original_parameters during
+        # initialization. Afterward that, we want to start with
+        # self._original_parameters.
+        parameters.update(self._dist._original_parameters)
+        parameters.update(params)
+        super()._update_parameters(validation_policy=validation_policy, **parameters)
+
+    def _process_parameters(self, **params):
+        return self._dist._process_parameters(**params)
+
+    def __repr__(self):
+        raise NotImplementedError()
+
+    def __str__(self):
+        raise NotImplementedError()
+
+
+class TruncatedDistribution(TransformedDistribution):
+    """Truncated distribution."""
+    # TODO:
+    # - consider avoiding catastropic cancellation by using appropriate tail
+    # - if the mode of `_dist` is within the support, it's still the mode
+    # - rejection sampling might be more efficient than inverse transform
+
+    _lb_domain = _RealInterval(endpoints=(-inf, 'ub'), inclusive=(True, False))
+    _lb_param = _RealParameter('lb', symbol=r'b_l',
+                                domain=_lb_domain, typical=(0.1, 0.2))
+
+    _ub_domain = _RealInterval(endpoints=('lb', inf), inclusive=(False, True))
+    _ub_param = _RealParameter('ub', symbol=r'b_u',
+                                  domain=_ub_domain, typical=(0.8, 0.9))
+
+    _parameterizations = [_Parameterization(_lb_param, _ub_param),
+                          _Parameterization(_lb_param),
+                          _Parameterization(_ub_param)]
+
+    def __init__(self, X, /, *args, lb=-np.inf, ub=np.inf, **kwargs):
+        return super().__init__(X, *args, lb=lb, ub=ub, **kwargs)
+
+    def _process_parameters(self, lb=None, ub=None, **params):
+        lb = lb if lb is not None else np.full_like(lb, -np.inf)[()]
+        ub = ub if ub is not None else np.full_like(ub, np.inf)[()]
+        parameters = self._dist._process_parameters(**params)
+        a, b = self._support(lb=lb, ub=ub, **parameters)
+        logmass = self._dist._logcdf2_dispatch(a, b, **parameters)
+        parameters.update(dict(lb=lb, ub=ub, _a=a, _b=b, logmass=logmass))
+        return parameters
+
+    def _support(self, lb, ub, **params):
+        a, b = self._dist._support(**params)
+        return np.maximum(a, lb), np.minimum(b, ub)
+
+    def _overrides(self, method_name):
+        return False
+
+    def _logpdf_dispatch(self, x, *args, lb, ub, _a, _b, logmass, **params):
+        logpdf = self._dist._logpdf_dispatch(x, *args, **params)
+        return logpdf - logmass
+
+    def _logcdf_dispatch(self, x, *args, lb, ub, _a, _b, logmass, **params):
+        logcdf = self._dist._logcdf2_dispatch(_a, x, *args, **params)
+        # of course, if this result is small we could compute with the other tail
+        return logcdf - logmass
+
+    def _logccdf_dispatch(self, x, *args, lb, ub, _a, _b, logmass, **params):
+        logccdf = self._dist._logcdf2_dispatch(x, _b, *args, **params)
+        return logccdf - logmass
+
+    def _logcdf2_dispatch(self, x, y, *args, lb, ub, _a, _b, logmass, **params):
+        logcdf2 = self._dist._logcdf2_dispatch(x, y, *args, **params)
+        return logcdf2 - logmass
+
+    def _ilogcdf_dispatch(self, logp, *args, lb, ub, _a, _b, logmass, **params):
+        log_Fa = self._dist._logcdf_dispatch(_a, *args, **params)
+        logp_adjusted = np.logaddexp(log_Fa, logp + logmass)
+        return self._dist._ilogcdf_dispatch(logp_adjusted, *args, **params)
+
+    def _ilogccdf_dispatch(self, logp, *args, lb, ub, _a, _b, logmass, **params):
+        log_cFb = self._dist._logccdf_dispatch(_b, *args, **params)
+        logp_adjusted = np.logaddexp(log_cFb, logp + logmass)
+        return self._dist._ilogccdf_dispatch(logp_adjusted, *args, **params)
+
+    def _icdf_dispatch(self, p, *args, lb, ub, _a, _b, logmass, **params):
+        Fa = self._dist._cdf_dispatch(_a, *args, **params)
+        p_adjusted = Fa + p*np.exp(logmass)
+        return self._dist._icdf_dispatch(p_adjusted, *args, **params)
+
+    def _iccdf_dispatch(self, p, *args, lb, ub, _a, _b, logmass, **params):
+        cFb = self._dist._ccdf_dispatch(_b, *args, **params)
+        p_adjusted = cFb + p*np.exp(logmass)
+        return self._dist._iccdf_dispatch(p_adjusted, *args, **params)
+
+    def __repr__(self):
+        with np.printoptions(threshold=10):
+            return (f"truncate({repr(self._dist)}, "
+                    f"lb={repr(self.lb)}, ub={repr(self.ub)})")
+
+    def __str__(self):
+        with np.printoptions(threshold=10):
+            return (f"truncate({str(self._dist)}, "
+                    f"lb={str(self.lb)}, ub={str(self.ub)})")
+
+
+@xp_capabilities(np_only=True)
+def truncate(X, lb=-np.inf, ub=np.inf):
+    """Truncate the support of a random variable.
+
+    Given a random variable `X`, `truncate` returns a random variable with
+    support truncated to the interval between `lb` and `ub`. The underlying
+    probability density function is normalized accordingly.
+
+    Parameters
+    ----------
+    X : `ContinuousDistribution`
+        The random variable to be truncated.
+    lb, ub : float array-like
+        The lower and upper truncation points, respectively. Must be
+        broadcastable with one another and the shape of `X`.
+
+    Returns
+    -------
+    X : `ContinuousDistribution`
+        The truncated random variable.
+
+    References
+    ----------
+    .. [1] "Truncated Distribution". *Wikipedia*.
+           https://en.wikipedia.org/wiki/Truncated_distribution
+
+    Examples
+    --------
+    Compare against `scipy.stats.truncnorm`, which truncates a standard normal,
+    *then* shifts and scales it.
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy import stats
+    >>> loc, scale, lb, ub = 1, 2, -2, 2
+    >>> X = stats.truncnorm(lb, ub, loc, scale)
+    >>> Y = scale * stats.truncate(stats.Normal(), lb, ub) + loc
+    >>> x = np.linspace(-3, 5, 300)
+    >>> plt.plot(x, X.pdf(x), '-', label='X')
+    >>> plt.plot(x, Y.pdf(x), '--', label='Y')
+    >>> plt.xlabel('x')
+    >>> plt.ylabel('PDF')
+    >>> plt.title('Truncated, then Shifted/Scaled Normal')
+    >>> plt.legend()
+    >>> plt.show()
+
+    However, suppose we wish to shift and scale a normal random variable,
+    then truncate its support to given values. This is straightforward with
+    `truncate`.
+
+    >>> Z = stats.truncate(scale * stats.Normal() + loc, lb, ub)
+    >>> Z.plot()
+    >>> plt.show()
+
+    Furthermore, `truncate` can be applied to any random variable:
+
+    >>> Rayleigh = stats.make_distribution(stats.rayleigh)
+    >>> W = stats.truncate(Rayleigh(), lb=0.5, ub=3)
+    >>> W.plot()
+    >>> plt.show()
+
+    """
+    return TruncatedDistribution(X, lb=lb, ub=ub)
+
+
+class ShiftedScaledDistribution(TransformedDistribution):
+    """Distribution with a standard shift/scale transformation."""
+    # Unclear whether infinite loc/scale will work reasonably in all cases
+    _loc_domain = _RealInterval(endpoints=(-inf, inf), inclusive=(True, True))
+    _loc_param = _RealParameter('loc', symbol=r'\mu',
+                                domain=_loc_domain, typical=(1, 2))
+
+    _scale_domain = _RealInterval(endpoints=(-inf, inf), inclusive=(True, True))
+    _scale_param = _RealParameter('scale', symbol=r'\sigma',
+                                  domain=_scale_domain, typical=(0.1, 10))
+
+    _parameterizations = [_Parameterization(_loc_param, _scale_param),
+                          _Parameterization(_loc_param),
+                          _Parameterization(_scale_param)]
+
+    def _process_parameters(self, loc=None, scale=None, **params):
+        loc = loc if loc is not None else np.zeros_like(scale)[()]
+        scale = scale if scale is not None else np.ones_like(loc)[()]
+        sign = scale > 0
+        parameters = self._dist._process_parameters(**params)
+        parameters.update(dict(loc=loc, scale=scale, sign=sign))
+        return parameters
+
+    def _transform(self, x, loc, scale, **kwargs):
+        return (x - loc)/scale
+
+    def _itransform(self, x, loc, scale, **kwargs):
+        return x * scale + loc
+
+    def _support(self, loc, scale, sign, **params):
+        # Add shortcut for infinite support?
+        a, b = self._dist._support(**params)
+        a, b = self._itransform(a, loc, scale), self._itransform(b, loc, scale)
+        return np.where(sign, a, b)[()], np.where(sign, b, a)[()]
+
+    def __repr__(self):
+        with np.printoptions(threshold=10):
+            result =  f"{repr(self.scale)}*{repr(self._dist)}"
+            if not self.loc.ndim and self.loc < 0:
+                result += f" - {repr(-self.loc)}"
+            elif (np.any(self.loc != 0)
+                  or not np.can_cast(self.loc.dtype, self.scale.dtype)):
+                # We don't want to hide a zero array loc if it can cause
+                # a type promotion.
+                result += f" + {repr(self.loc)}"
+        return result
+
+    def __str__(self):
+        with np.printoptions(threshold=10):
+            result =  f"{str(self.scale)}*{str(self._dist)}"
+            if not self.loc.ndim and self.loc < 0:
+                result += f" - {str(-self.loc)}"
+            elif (np.any(self.loc != 0)
+                  or not np.can_cast(self.loc.dtype, self.scale.dtype)):
+                # We don't want to hide a zero array loc if it can cause
+                # a type promotion.
+                result += f" + {str(self.loc)}"
+        return result
+
+    # Here, we override all the `_dispatch` methods rather than the public
+    # methods or _formula methods. Why not the public methods?
+    # If we were to override the public methods, then other
+    # TransformedDistribution classes (which could transform a
+    # ShiftedScaledDistribution) would need to call the public methods of
+    # ShiftedScaledDistribution, which would run the input validation again.
+    # Why not the _formula methods? For distributions that rely on the
+    # default implementation of methods (e.g. `quadrature`, `inversion`),
+    # the implementation would "see" the location and scale like other
+    # distribution parameters, so they could affect the accuracy of the
+    # calculations. I think it is cleaner if `loc` and `scale` do not affect
+    # the underlying calculations at all.
+
+    def _entropy_dispatch(self, *args, loc, scale, sign, **params):
+        return (self._dist._entropy_dispatch(*args, **params)
+                + np.log(np.abs(scale)))
+
+    def _logentropy_dispatch(self, *args, loc, scale, sign, **params):
+        lH0 = self._dist._logentropy_dispatch(*args, **params)
+        lls = np.log(np.log(np.abs(scale))+0j)
+        return special.logsumexp(np.broadcast_arrays(lH0, lls), axis=0)
+
+    def _median_dispatch(self, *, method, loc, scale, sign, **params):
+        raw = self._dist._median_dispatch(method=method, **params)
+        return self._itransform(raw, loc, scale)
+
+    def _mode_dispatch(self, *, method, loc, scale, sign, **params):
+        raw = self._dist._mode_dispatch(method=method, **params)
+        return self._itransform(raw, loc, scale)
+
+    def _logpdf_dispatch(self, x, *args, loc, scale, sign, **params):
+        x = self._transform(x, loc, scale)
+        logpdf = self._dist._logpdf_dispatch(x, *args, **params)
+        return logpdf - np.log(np.abs(scale))
+
+    def _pdf_dispatch(self, x, *args, loc, scale, sign, **params):
+        x = self._transform(x, loc, scale)
+        pdf = self._dist._pdf_dispatch(x, *args, **params)
+        return pdf / np.abs(scale)
+
+    def _logpmf_dispatch(self, x, *args, loc, scale, sign, **params):
+        x = self._transform(x, loc, scale)
+        logpmf = self._dist._logpmf_dispatch(x, *args, **params)
+        return logpmf - np.log(np.abs(scale))
+
+    def _pmf_dispatch(self, x, *args, loc, scale, sign, **params):
+        x = self._transform(x, loc, scale)
+        pmf = self._dist._pmf_dispatch(x, *args, **params)
+        return pmf / np.abs(scale)
+
+    def _logpxf_dispatch(self, x, *args, loc, scale, sign, **params):
+        x = self._transform(x, loc, scale)
+        logpxf = self._dist._logpxf_dispatch(x, *args, **params)
+        return logpxf - np.log(np.abs(scale))
+
+    def _pxf_dispatch(self, x, *args, loc, scale, sign, **params):
+        x = self._transform(x, loc, scale)
+        pxf = self._dist._pxf_dispatch(x, *args, **params)
+        return pxf / np.abs(scale)
+
+    @_shift_scale_distribution_function
+    def _logcdf_dispatch(self, x, *, method=None, **params):
+        pass
+
+    @_shift_scale_distribution_function
+    def _cdf_dispatch(self, x, *, method=None, **params):
+        pass
+
+    @_shift_scale_distribution_function
+    def _logccdf_dispatch(self, x, *, method=None, **params):
+        pass
+
+    @_shift_scale_distribution_function
+    def _ccdf_dispatch(self, x, *, method=None, **params):
+        pass
+
+    @_shift_scale_distribution_function_2arg
+    def _logcdf2_dispatch(self, x, y, *, method=None, **params):
+        pass
+
+    @_shift_scale_distribution_function_2arg
+    def _cdf2_dispatch(self, x, y, *, method=None, **params):
+        pass
+
+    @_shift_scale_distribution_function_2arg
+    def _logccdf2_dispatch(self, x, y, *, method=None, **params):
+        pass
+
+    @_shift_scale_distribution_function_2arg
+    def _ccdf2_dispatch(self, x, y, *, method=None, **params):
+        pass
+
+    @_shift_scale_inverse_function
+    def _ilogcdf_dispatch(self, x, *, method=None, **params):
+        pass
+
+    @_shift_scale_inverse_function
+    def _icdf_dispatch(self, x, *, method=None, **params):
+        pass
+
+    @_shift_scale_inverse_function
+    def _ilogccdf_dispatch(self, x, *, method=None, **params):
+        pass
+
+    @_shift_scale_inverse_function
+    def _iccdf_dispatch(self, x, *, method=None, **params):
+        pass
+
+    def _moment_standardized_dispatch(self, order, *, loc, scale, sign, methods,
+                                      **params):
+        res = (self._dist._moment_standardized_dispatch(
+            order, methods=methods, **params))
+        return None if res is None else res * np.sign(scale)**order
+
+    def _moment_central_dispatch(self, order, *, loc, scale, sign, methods,
+                                 **params):
+        res = (self._dist._moment_central_dispatch(
+            order, methods=methods, **params))
+        return None if res is None else res * scale**order
+
+    def _moment_raw_dispatch(self, order, *, loc, scale, sign, methods,
+                             **params):
+        raw_moments = []
+        methods_highest_order = methods
+        for i in range(int(order) + 1):
+            methods = (self._moment_methods if i < order
+                       else methods_highest_order)
+            raw = self._dist._moment_raw_dispatch(i, methods=methods, **params)
+            if raw is None:
+                return None
+            moment_i = raw * scale**i
+            raw_moments.append(moment_i)
+
+        return self._moment_transform_center(
+            order, raw_moments, loc, self._zero)
+
+    def _lmoment_dispatch(self, order, *, loc, scale, sign, methods, **params):
+        res = self._dist._lmoment_dispatch(order, methods=methods, **params)
+        if res is None:  # if a specific method is requested but not available
+            return None
+        res = res * np.abs(scale) * np.sign(scale)**order
+        return res + loc if order == 1 else res
+
+    def _sample_dispatch(self, full_shape, *,
+                         rng, loc, scale, sign, method, **params):
+        rvs = self._dist._sample_dispatch(full_shape, method=method, rng=rng, **params)
+        return self._itransform(rvs, loc=loc, scale=scale, sign=sign, **params)
+
+    def __add__(self, loc):
+        return ShiftedScaledDistribution(self._dist, loc=self.loc + loc,
+                                         scale=self.scale)
+
+    def __sub__(self, loc):
+        return ShiftedScaledDistribution(self._dist, loc=self.loc - loc,
+                                         scale=self.scale)
+
+    def __mul__(self, scale):
+        return ShiftedScaledDistribution(self._dist,
+                                         loc=self.loc * scale,
+                                         scale=self.scale * scale)
+
+    def __truediv__(self, scale):
+        return ShiftedScaledDistribution(self._dist,
+                                         loc=self.loc / scale,
+                                         scale=self.scale / scale)
+
+
+class OrderStatisticDistribution(TransformedDistribution):
+    r"""Probability distribution of an order statistic.
+
+    An instance of this class represents a random variable that follows the
+    distribution of the :math:`r^{\text{th}}` order statistic of a
+    sample of :math:`n` observations of a random variable :math:`X`.
+
+    Parameters
+    ----------
+    dist : `ContinuousDistribution`
+        The random variable :math:`X`.
+    n : array_like
+        The (positive integer) sample size :math:`n`.
+    r : array_like
+        The (positive integer) rank of the order statistic :math:`r`,
+        satisfying :math:`1 \leq r \leq n`.
+
+
+    Notes
+    -----
+    If we make :math:`n` observations of a continuous random variable
+    :math:`X` and sort them in increasing order
+    :math:`X_{(1)}, \dots, X_{(r)}, \dots, X_{(n)}`, then :math:`X_{(r)}`
+    is known as the :math:`r^{\text{th}}` order statistic.
+
+    If the PDF, CDF, and CCDF of :math:`X` are denoted by :math:`f`,
+    :math:`F`, and :math:`G = 1 - F`, respectively, then the PDF of
+    :math:`X_{(r)}` is given by:
+
+    .. math::
+
+        f_r(x) = \frac{n!}{(r-1)! (n-r)!} f(x) F(x)^{r-1} G(x)^{n - r}
+
+    The CDF and other methods of the distribution of :math:`X_{(r)}`
+    are calculated using the fact that :math:`X = F^{-1}(U)`, where :math:`U` is a
+    standard uniform random variable, together with the fact that the order statistics
+    of i.i.d. uniform random variables follow a beta distribution
+    :math:`B(r, n - r + 1)`.
+
+    References
+    ----------
+    .. [1] Order statistic. *Wikipedia*. https://en.wikipedia.org/wiki/Order_statistic
+
+    Examples
+    --------
+    Suppose we are interested in order statistics of samples of size five drawn
+    from the standard normal distribution. Plot the PDF of the fourth
+    order statistic and compare with a normalized histogram from simulation.
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy import stats
+    >>> from scipy.stats._distribution_infrastructure import OrderStatisticDistribution
+    >>>
+    >>> X = stats.Normal()
+    >>> data = X.sample(shape=(10000, 5))
+    >>> sorted_data = np.sort(data, axis=1)
+    >>> Y = OrderStatisticDistribution(X, r=4, n=5)
+    >>>
+    >>> ax = plt.gca()
+    >>> Y.plot(ax=ax)
+    >>> ax.hist(sorted_data[:, 3], density=True, bins=30)
+    >>> plt.show()
+
+    """
+
+    _r_domain = _IntegerInterval(endpoints=(1, 'n'), inclusive=(True, True))
+    _r_param = _RealParameter('r', domain=_r_domain, typical=(1, 2))
+
+    _n_domain = _IntegerInterval(endpoints=(1, np.inf), inclusive=(True, True))
+    _n_param = _RealParameter('n', domain=_n_domain, typical=(1, 4))
+
+    _r_domain.define_parameters(_n_param)
+
+    _parameterizations = [_Parameterization(_r_param, _n_param)]
+
+    def __init__(self, dist, /, *args, r, n, **kwargs):
+        super().__init__(dist, *args, r=r, n=n, **kwargs)
+
+    def _support(self, *args, r, n, **kwargs):
+        return self._dist._support(*args, **kwargs)
+
+    def _process_parameters(self, r=None, n=None, **params):
+        parameters = self._dist._process_parameters(**params)
+        parameters.update(dict(r=r, n=n))
+        return parameters
+
+    def _overrides(self, method_name):
+        return method_name in {'_logpdf_formula', '_pdf_formula',
+                               '_cdf_formula', '_ccdf_formula',
+                               '_icdf_formula', '_iccdf_formula'}
+
+    def _logpdf_formula(self, x, r, n, **kwargs):
+        log_factor = special.betaln(r, n - r + 1)
+        log_fX = self._dist._logpdf_dispatch(x, **kwargs)
+        # log-methods sometimes use complex dtype with 0 imaginary component,
+        # but `_tanhsinh` doesn't accept complex limits of integration; take `real`.
+        log_FX = self._dist._logcdf_dispatch(x.real, **kwargs)
+        log_cFX = self._dist._logccdf_dispatch(x.real, **kwargs)
+        # This can be problematic when (r - 1)|(n-r) = 0 and `log_FX`|log_cFX = -inf
+        # The PDF in these cases is 0^0, so these should be replaced with log(1)=0
+        # return log_fX + (r-1)*log_FX + (n-r)*log_cFX - log_factor
+        rm1_log_FX = np.where((r - 1 == 0) & np.isneginf(log_FX), 0, (r-1)*log_FX)
+        nmr_log_cFX = np.where((n - r == 0) & np.isneginf(log_cFX), 0, (n-r)*log_cFX)
+        return log_fX + rm1_log_FX + nmr_log_cFX - log_factor
+
+    def _pdf_formula(self, x, r, n, **kwargs):
+        # 1 / factor = factorial(n) / (factorial(r-1) * factorial(n-r))
+        factor = special.beta(r, n - r + 1)
+        fX = self._dist._pdf_dispatch(x, **kwargs)
+        FX = self._dist._cdf_dispatch(x, **kwargs)
+        cFX = self._dist._ccdf_dispatch(x, **kwargs)
+        return fX * FX**(r-1) * cFX**(n-r) / factor
+
+    def _cdf_formula(self, x, r, n, **kwargs):
+        x_ = self._dist._cdf_dispatch(x, **kwargs)
+        return special.betainc(r, n-r+1, x_)
+
+    def _ccdf_formula(self, x, r, n, **kwargs):
+        x_ = self._dist._cdf_dispatch(x, **kwargs)
+        return special.betaincc(r, n-r+1, x_)
+
+    def _icdf_formula(self, p, r, n, **kwargs):
+        p_ = special.betaincinv(r, n-r+1, p)
+        return self._dist._icdf_dispatch(p_, **kwargs)
+
+    def _iccdf_formula(self, p, r, n, **kwargs):
+        p_ = special.betainccinv(r, n-r+1, p)
+        return self._dist._icdf_dispatch(p_, **kwargs)
+
+    def __repr__(self):
+        with np.printoptions(threshold=10):
+            return (f"order_statistic({repr(self._dist)}, r={repr(self.r)}, "
+                    f"n={repr(self.n)})")
+
+    def __str__(self):
+        with np.printoptions(threshold=10):
+            return (f"order_statistic({str(self._dist)}, r={str(self.r)}, "
+                    f"n={str(self.n)})")
+
+
+@xp_capabilities(np_only=True)
+def order_statistic(X, /, *, r, n):
+    r"""Probability distribution of an order statistic.
+
+    Returns a random variable that follows the distribution of the
+    :math:`r^{\text{th}}` order statistic of a sample of :math:`n`
+    observations of a random variable :math:`X`.
+
+    Parameters
+    ----------
+    X : `ContinuousDistribution`
+        The random variable :math:`X`.
+    r : array_like
+        The (positive integer) rank of the order statistic :math:`r`,
+        satisfying ``1 <= r <= n``.
+    n : array_like
+        The (positive integer) sample size :math:`n`.
+
+    Returns
+    -------
+    Y : `ContinuousDistribution`
+        A random variable that follows the distribution of the prescribed
+        order statistic.
+
+    Notes
+    -----
+    If we make :math:`n` observations of a continuous random variable
+    :math:`X` and sort them in increasing order
+    :math:`X_{(1)}, \dots, X_{(r)}, \dots, X_{(n)}`, then :math:`X_{(r)}`
+    is known as the :math:`r^{\text{th}}` order statistic.
+
+    If the PDF, CDF, and CCDF of :math:`X` are denoted by :math:`f`,
+    :math:`F`, and :math:`G = 1 - F`, respectively, then the PDF of
+    :math:`X_{(r)}` is given by:
+
+    .. math::
+
+        f_r(x) = \frac{n!}{(r-1)! (n-r)!} f(x) F(x)^{r-1} G(x)^{n - r}
+
+    The CDF and other methods of the distribution of :math:`X_{(r)}`
+    are calculated using the fact that :math:`X = F^{-1}(U)`, where :math:`U` is a
+    standard uniform random variable, together with the fact that the order statistics
+    of i.i.d. uniform random variables follow a beta distribution
+    :math:`B(r, n - r + 1)`.
+
+    References
+    ----------
+    .. [1] Order statistic. *Wikipedia*. https://en.wikipedia.org/wiki/Order_statistic
+
+    Examples
+    --------
+    Suppose we are interested in order statistics of samples of size five drawn
+    from the standard normal distribution. Plot the PDF of each
+    order statistic and compare with a normalized histogram from simulation.
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy import stats
+    >>>
+    >>> X = stats.Normal()
+    >>> data = X.sample(shape=(10000, 5))
+    >>> sorted_data = np.sort(data, axis=1)
+    >>> Y = stats.order_statistic(X, r=[1, 2, 3, 4, 5], n=5)
+    >>>
+    >>> ax = plt.gca()
+    >>> colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
+    >>> for i in range(5):
+    ...     y = sorted_data[:, i]
+    ...     ax.hist(y, density=True, bins=30, alpha=0.1, color=colors[i])
+    >>> Y.plot(ax=ax)
+    >>> plt.show()
+
+    """
+    r, n = np.asarray(r), np.asarray(n)
+    if np.any((r != np.floor(r)) | (r < 0)) or np.any((n != np.floor(n)) | (n < 0)):
+        message = "`r` and `n` must contain only positive integers."
+        raise ValueError(message)
+    return OrderStatisticDistribution(X, r=r, n=n)
+
+
+class Mixture(_ProbabilityDistribution):
+    r"""Representation of a mixture distribution.
+
+    A mixture distribution is the distribution of a random variable
+    defined in the following way: first, a random variable is selected
+    from `components` according to the probabilities given by `weights`, then
+    the selected random variable is realized.
+
+    Parameters
+    ----------
+    components : sequence of `ContinuousDistribution`
+        The underlying instances of `ContinuousDistribution`.
+        All must have scalar shape parameters (if any); e.g., the `pdf` evaluated
+        at a scalar argument must return a scalar.
+    weights : sequence of floats, optional
+        The corresponding probabilities of selecting each random variable.
+        Must be non-negative and sum to one. The default behavior is to weight
+        all components equally.
+
+    Attributes
+    ----------
+    components : sequence of `ContinuousDistribution`
+        The underlying instances of `ContinuousDistribution`.
+    weights : ndarray
+        The corresponding probabilities of selecting each random variable.
+
+    Methods
+    -------
+    support
+
+    sample
+
+    moment
+
+    mean
+    median
+    mode
+
+    variance
+    standard_deviation
+
+    skewness
+    kurtosis
+
+    pdf
+    logpdf
+
+    cdf
+    icdf
+    ccdf
+    iccdf
+
+    logcdf
+    ilogcdf
+    logccdf
+    ilogccdf
+
+    entropy
+
+    Notes
+    -----
+    The following abbreviations are used throughout the documentation.
+
+    - PDF: probability density function
+    - CDF: cumulative distribution function
+    - CCDF: complementary CDF
+    - entropy: differential entropy
+    - log-*F*: logarithm of *F* (e.g. log-CDF)
+    - inverse *F*: inverse function of *F* (e.g. inverse CDF)
+
+    References
+    ----------
+    .. [1] Mixture distribution, *Wikipedia*,
+           https://en.wikipedia.org/wiki/Mixture_distribution
+
+
+    Examples
+    --------
+    A mixture of normal distributions:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+    >>> X1 = stats.Normal(mu=-2, sigma=1)
+    >>> X2 = stats.Normal(mu=2, sigma=1)
+    >>> mixture = stats.Mixture([X1, X2], weights=[0.4, 0.6])
+    >>> print(f'mean: {mixture.mean():.2f}, '
+    ...       f'median: {mixture.median():.2f}, '
+    ...       f'mode: {mixture.mode():.2f}')
+    mean: 0.40, median: 1.04, mode: 2.00
+    >>> x = np.linspace(-10, 10, 300)
+    >>> plt.plot(x, mixture.pdf(x))
+    >>> plt.title('PDF of normal distribution mixture')
+    >>> plt.show()
+
+    """
+    # Todo:
+    # Add support for array shapes, weights
+
+    def _input_validation(self, components, weights):
+        if len(components) == 0:
+            message = ("`components` must contain at least one random variable.")
+            raise ValueError(message)
+
+        for var in components:
+            # will generalize to other kinds of distributions when there
+            # *are* other kinds of distributions
+            if not isinstance(var, ContinuousDistribution):
+                message = ("Each element of `components` must be an instance of "
+                           "`ContinuousDistribution`.")
+                raise ValueError(message)
+            if not var._shape == ():
+                message = "All elements of `components` must have scalar shapes."
+                raise ValueError(message)
+
+        if weights is None:
+            return components, weights
+
+        weights = np.asarray(weights)
+        if weights.shape != (len(components),):
+            message = "`components` and `weights` must have the same length."
+            raise ValueError(message)
+
+        if not np.issubdtype(weights.dtype, np.inexact):
+            message = "`weights` must have floating point dtype."
+            raise ValueError(message)
+
+        if not np.isclose(np.sum(weights), 1.0):
+            message = "`weights` must sum to 1.0."
+            raise ValueError(message)
+
+        if not np.all(weights >= 0):
+            message = "All `weights` must be non-negative."
+            raise ValueError(message)
+
+        return components, weights
+
+    def __init__(self, components, *, weights=None):
+        components, weights = self._input_validation(components, weights)
+        n = len(components)
+        dtype = np.result_type(*(var._dtype for var in components))
+        self._shape = np.broadcast_shapes(*(var._shape for var in components))
+        self._dtype, self._components = dtype, components
+        self._weights = np.full(n, 1/n, dtype=dtype) if weights is None else weights
+        self.validation_policy = None
+
+    @property
+    def components(self):
+        return list(self._components)
+
+    @property
+    def weights(self):
+        return self._weights.copy()
+
+    def _full(self, val, *args):
+        args = [np.asarray(arg) for arg in args]
+        dtype = np.result_type(self._dtype, *(arg.dtype for arg in args))
+        shape = np.broadcast_shapes(self._shape, *(arg.shape for arg in args))
+        return np.full(shape, val, dtype=dtype)
+
+    def _sum(self, fun, *args):
+        out = self._full(0, *args)
+        for var, weight in zip(self._components, self._weights):
+            out += getattr(var, fun)(*args) * weight
+        return out[()]
+
+    def _logsum(self, fun, *args):
+        out = self._full(-np.inf, *args)
+        for var, log_weight in zip(self._components, np.log(self._weights)):
+            np.logaddexp(out, getattr(var, fun)(*args) + log_weight, out=out)
+        return out[()]
+
+    def support(self):
+        a = self._full(np.inf)
+        b = self._full(-np.inf)
+        for var in self._components:
+            a = np.minimum(a, var.support()[0])
+            b = np.maximum(b, var.support()[1])
+        return a, b
+
+    def _raise_if_method(self, method):
+        if method is not None:
+            raise NotImplementedError("`method` not implemented for this distribution.")
+
+    def logentropy(self, *, method=None):
+        self._raise_if_method(method)
+        def log_integrand(x):
+            # `x` passed by `_tanhsinh` will be of complex dtype because
+            # `log_integrand` returns complex values, but the imaginary
+            # component is always zero. Extract the real part because
+            # `logpdf` uses `logaddexp`, which fails for complex input.
+            return self.logpdf(x.real) + np.log(self.logpdf(x.real) + 0j)
+
+        res = _tanhsinh(log_integrand, *self.support(), log=True).integral
+        return _log_real_standardize(res + np.pi*1j)
+
+    def entropy(self, *, method=None):
+        self._raise_if_method(method)
+        return _tanhsinh(lambda x: -self.pdf(x) * self.logpdf(x),
+                         *self.support()).integral
+
+    def mode(self, *, method=None):
+        self._raise_if_method(method)
+        a, b = self.support()
+        def f(x): return -self.pdf(x)
+        res = elementwise.bracket_minimum(f, 1., xmin=a, xmax=b)
+        res = elementwise.find_minimum(f, res.bracket)
+        return res.x
+
+    def median(self, *, method=None):
+        self._raise_if_method(method)
+        return self.icdf(0.5)
+
+    def mean(self, *, method=None):
+        self._raise_if_method(method)
+        return self._sum('mean')
+
+    def variance(self, *, method=None):
+        self._raise_if_method(method)
+        return self._moment_central(2)
+
+    def standard_deviation(self, *, method=None):
+        self._raise_if_method(method)
+        return self.variance()**0.5
+
+    def skewness(self, *, method=None):
+        self._raise_if_method(method)
+        return self._moment_standardized(3)
+
+    def kurtosis(self, *, method=None):
+        self._raise_if_method(method)
+        return self._moment_standardized(4)
+
+    def moment(self, order=1, kind='raw', *, method=None):
+        self._raise_if_method(method)
+        kinds = {'raw': self._moment_raw,
+                 'central': self._moment_central,
+                 'standardized': self._moment_standardized}
+        order = ContinuousDistribution._validate_order(self, order)
+        kind = ContinuousDistribution._validate_kind(self, kind, kinds)
+        moment_kind = kinds[kind]
+        return moment_kind(order)
+
+    def _moment_raw(self, order):
+        out = self._full(0)
+        for var, weight in zip(self._components, self._weights):
+            out += var.moment(order, kind='raw') * weight
+        return out[()]
+
+    def _moment_central(self, order):
+        order = int(order)
+        out = self._full(0)
+        for var, weight in zip(self._components, self._weights):
+            moment_as = [var.moment(order, kind='central')
+                         for order in range(order + 1)]
+            a, b = var.mean(), self.mean()
+            moment = var._moment_transform_center(order, moment_as, a, b)
+            out += moment * weight
+        return out[()]
+
+    def lmoment(self, order=1, *, standardize=True, method=None):
+        message = "L-moments are not currently available for mixture distributions."
+        raise NotImplementedError(message)
+
+    def _moment_standardized(self, order):
+        return self._moment_central(order) / self.standard_deviation()**order
+
+    def pdf(self, x, /, *, method=None):
+        self._raise_if_method(method)
+        return self._sum('pdf', x)
+
+    def logpdf(self, x, /, *, method=None):
+        self._raise_if_method(method)
+        return self._logsum('logpdf', x)
+
+    def pmf(self, x, /, *, method=None):
+        self._raise_if_method(method)
+        return self._sum('pmf', x)
+
+    def logpmf(self, x, /, *, method=None):
+        self._raise_if_method(method)
+        return self._logsum('logpmf', x)
+
+    def cdf(self, x, y=None, /, *, method=None):
+        self._raise_if_method(method)
+        args = (x,) if y is None else (x, y)
+        return self._sum('cdf', *args)
+
+    def logcdf(self, x, y=None, /, *, method=None):
+        self._raise_if_method(method)
+        args = (x,) if y is None else (x, y)
+        return self._logsum('logcdf', *args)
+
+    def ccdf(self, x, y=None, /, *, method=None):
+        self._raise_if_method(method)
+        args = (x,) if y is None else (x, y)
+        return self._sum('ccdf', *args)
+
+    def logccdf(self, x, y=None, /, *, method=None):
+        self._raise_if_method(method)
+        args = (x,) if y is None else (x, y)
+        return self._logsum('logccdf', *args)
+
+    def _invert(self, fun, p):
+        xmin, xmax = self.support()
+        fun = getattr(self, fun)
+        f = lambda x, p: fun(x) - p  # noqa: E731 is silly
+        xl0, xr0 = _guess_bracket(xmin, xmax)
+        res = elementwise.bracket_root(f, xl0=xl0, xr0=xr0,
+                                       xmin=xmin, xmax=xmax, args=(p,))
+        return elementwise.find_root(f, res.bracket, args=(p,)).x
+
+    def icdf(self, p, /, *, method=None):
+        self._raise_if_method(method)
+        return self._invert('cdf', p)
+
+    def iccdf(self, p, /, *, method=None):
+        self._raise_if_method(method)
+        return self._invert('ccdf', p)
+
+    def ilogcdf(self, p, /, *, method=None):
+        self._raise_if_method(method)
+        return self._invert('logcdf', p)
+
+    def ilogccdf(self, p, /, *, method=None):
+        self._raise_if_method(method)
+        return self._invert('logccdf', p)
+
+    def sample(self, shape=(), *, rng=None, method=None):
+        self._raise_if_method(method)
+        rng = np.random.default_rng(rng)
+        size = np.prod(np.atleast_1d(shape))
+        ns = rng.multinomial(size, self._weights)
+        x = [var.sample(shape=n, rng=rng) for n, var in zip(ns, self._components)]
+        x = np.reshape(rng.permuted(np.concatenate(x)), shape)
+        return x[()]
+
+    def __repr__(self):
+        result = "Mixture(\n"
+        result += "    [\n"
+        with np.printoptions(threshold=10):
+            for component in self.components:
+                result += f"        {repr(component)},\n"
+            result += "    ],\n"
+            result += f"    weights={repr(self.weights)},\n"
+        result += ")"
+        return result
+
+    def __str__(self):
+        result = "Mixture(\n"
+        result += "    [\n"
+        with np.printoptions(threshold=10):
+            for component in self.components:
+                result += f"        {str(component)},\n"
+            result += "    ],\n"
+            result += f"    weights={str(self.weights)},\n"
+        result += ")"
+        return result
+
+
+class MonotonicTransformedDistribution(TransformedDistribution):
+    r"""Distribution underlying a strictly monotonic function of a random variable
+
+    Given a random variable :math:`X`; a strictly monotonic function
+    :math:`g(u)`, its inverse :math:`h(u) = g^{-1}(u)`, and the derivative magnitude
+    :math: `|h'(u)| = \left| \frac{dh(u)}{du} \right|`, define the distribution
+    underlying the random variable :math:`Y = g(X)`.
+
+    Parameters
+    ----------
+    X : `ContinuousDistribution`
+        The random variable :math:`X`.
+    g, h, dh : callable
+        Elementwise functions representing the mathematical functions
+        :math:`g(u)`, :math:`h(u)`, and :math:`|h'(u)|`
+    logdh : callable, optional
+        Elementwise function representing :math:`\log(h'(u))`.
+        The default is ``lambda u: np.log(dh(u))``, but providing
+        a custom implementation may avoid over/underflow.
+    increasing : bool, optional
+        Whether the function is strictly increasing (True, default)
+        or strictly decreasing (False).
+    repr_pattern : str, optional
+        A string pattern for determining the __repr__. The __repr__
+        for X will be substituted into the position where `***` appears.
+        For example:
+            ``"exp(***)"`` for the repr of an exponentially transformed
+            distribution
+        The default is ``f"{g.__name__}(***)"``.
+    str_pattern : str, optional
+        A string pattern for determining `__str__`. The `__str__`
+        for X will be substituted into the position where `***` appears.
+        For example:
+            ``"exp(***)"`` for the repr of an exponentially transformed
+            distribution
+        The default is the value `repr_pattern` takes.
+    """
+
+    def __init__(self, X, /, *args, g, h, dh, logdh=None,
+                 increasing=True, repr_pattern=None,
+                 str_pattern=None, **kwargs):
+        super().__init__(X, *args, **kwargs)
+        self._g = g
+        self._h = h
+        self._dh = dh
+        self._logdh = (logdh if logdh is not None
+                       else lambda u: np.log(dh(u)))
+        if increasing:
+            self._xdf = self._dist._cdf_dispatch
+            self._cxdf = self._dist._ccdf_dispatch
+            self._ixdf = self._dist._icdf_dispatch
+            self._icxdf = self._dist._iccdf_dispatch
+            self._logxdf = self._dist._logcdf_dispatch
+            self._logcxdf = self._dist._logccdf_dispatch
+            self._ilogxdf = self._dist._ilogcdf_dispatch
+            self._ilogcxdf = self._dist._ilogccdf_dispatch
+        else:
+            self._xdf = self._dist._ccdf_dispatch
+            self._cxdf = self._dist._cdf_dispatch
+            self._ixdf = self._dist._iccdf_dispatch
+            self._icxdf = self._dist._icdf_dispatch
+            self._logxdf = self._dist._logccdf_dispatch
+            self._logcxdf = self._dist._logcdf_dispatch
+            self._ilogxdf = self._dist._ilogccdf_dispatch
+            self._ilogcxdf = self._dist._ilogcdf_dispatch
+        self._increasing = increasing
+        self._repr_pattern = repr_pattern or f"{g.__name__}(***)"
+        self._str_pattern = str_pattern or self._repr_pattern
+
+    def __repr__(self):
+        with np.printoptions(threshold=10):
+            return self._repr_pattern.replace("***", repr(self._dist))
+
+    def __str__(self):
+        with np.printoptions(threshold=10):
+            return self._str_pattern.replace("***", str(self._dist))
+
+    def _overrides(self, method_name):
+        # Do not use the generic overrides of TransformedDistribution
+        return False
+
+    def _support(self, **params):
+        a, b = self._dist._support(**params)
+        # For reciprocal transformation, we want this zero to become -inf
+        b = np.where(b==0, np.asarray("-0", dtype=b.dtype), b)
+        with np.errstate(divide='ignore'):
+            if self._increasing:
+                return self._g(a), self._g(b)
+            else:
+                return self._g(b), self._g(a)
+
+    def _logpdf_dispatch(self, x, *args, **params):
+        return self._dist._logpdf_dispatch(self._h(x), *args, **params) + self._logdh(x)
+
+    def _pdf_dispatch(self, x, *args, **params):
+        return self._dist._pdf_dispatch(self._h(x), *args, **params) * self._dh(x)
+
+    def _logcdf_dispatch(self, x, *args, **params):
+        return self._logxdf(self._h(x), *args, **params)
+
+    def _cdf_dispatch(self, x, *args, **params):
+        return self._xdf(self._h(x), *args, **params)
+
+    def _logccdf_dispatch(self, x, *args, **params):
+        return self._logcxdf(self._h(x), *args, **params)
+
+    def _ccdf_dispatch(self, x, *args, **params):
+        return self._cxdf(self._h(x), *args, **params)
+
+    def _ilogcdf_dispatch(self, p, *args, **params):
+        return self._g(self._ilogxdf(p, *args, **params))
+
+    def _icdf_dispatch(self, p, *args, **params):
+        return self._g(self._ixdf(p, *args, **params))
+
+    def _ilogccdf_dispatch(self, p, *args, **params):
+        return self._g(self._ilogcxdf(p, *args, **params))
+
+    def _iccdf_dispatch(self, p, *args, **params):
+        return self._g(self._icxdf(p, *args, **params))
+
+    def _sample_dispatch(self, full_shape, *, method, rng, **params):
+        rvs = self._dist._sample_dispatch(full_shape, method=method, rng=rng, **params)
+        return self._g(rvs)
+
+
+class FoldedDistribution(TransformedDistribution):
+    r"""Distribution underlying the absolute value of a random variable
+
+    Given a random variable :math:`X`; define the distribution
+    underlying the random variable :math:`Y = |X|`.
+
+    Parameters
+    ----------
+    X : `ContinuousDistribution`
+        The random variable :math:`X`.
+
+    Returns
+    -------
+    Y : `ContinuousDistribution`
+        The random variable :math:`Y = |X|`
+
+    """
+    # Many enhancements are possible if distribution is symmetric. Start
+    # with the general case; enhance later.
+
+    def __init__(self, X, /, *args, **kwargs):
+        super().__init__(X, *args, **kwargs)
+        # I think we need to allow `_support` to define whether the endpoints
+        # are inclusive or not. In the meantime, it's best to ensure that the lower
+        # endpoint (typically 0 for folded distribution) is inclusive so PDF evaluates
+        # correctly at that point.
+        self._variable.domain.inclusive = (True, self._variable.domain.inclusive[1])
+
+    def _overrides(self, method_name):
+        # Do not use the generic overrides of TransformedDistribution
+        return False
+
+    def _support(self, **params):
+        a, b = self._dist._support(**params)
+        a_, b_ = np.abs(a), np.abs(b)
+        a_, b_ = np.minimum(a_, b_), np.maximum(a_, b_)
+        i = (a < 0) & (b > 0)
+        a_ = np.asarray(a_)
+        a_[i] = 0
+        return a_[()], b_[()]
+
+    def _logpdf_dispatch(self, x, *args, method=None, **params):
+        x = np.abs(x)
+        right = self._dist._logpdf_dispatch(x, *args, method=method, **params)
+        left = self._dist._logpdf_dispatch(-x, *args, method=method, **params)
+        left = np.asarray(left)
+        right = np.asarray(right)
+        a, b = self._dist._support(**params)
+        left[-x < a] = -np.inf
+        right[x > b] = -np.inf
+        logpdfs = np.stack([left, right])
+        return special.logsumexp(logpdfs, axis=0)
+
+    def _pdf_dispatch(self, x, *args, method=None, **params):
+        x = np.abs(x)
+        right = self._dist._pdf_dispatch(x, *args, method=method, **params)
+        left = self._dist._pdf_dispatch(-x, *args, method=method, **params)
+        left = np.asarray(left)
+        right = np.asarray(right)
+        a, b = self._dist._support(**params)
+        left[-x < a] = 0
+        right[x > b] = 0
+        return left + right
+
+    def _logcdf_dispatch(self, x, *args, method=None, **params):
+        x = np.abs(x)
+        a, b = self._dist._support(**params)
+        xl = np.maximum(-x, a)
+        xr = np.minimum(x, b)
+        return self._dist._logcdf2_dispatch(xl, xr, *args, method=method, **params).real
+
+    def _cdf_dispatch(self, x, *args, method=None, **params):
+        x = np.abs(x)
+        a, b = self._dist._support(**params)
+        xl = np.maximum(-x, a)
+        xr = np.minimum(x, b)
+        return self._dist._cdf2_dispatch(xl, xr, *args, **params)
+
+    def _logccdf_dispatch(self, x, *args, method=None, **params):
+        x = np.abs(x)
+        a, b = self._dist._support(**params)
+        xl = np.maximum(-x, a)
+        xr = np.minimum(x, b)
+        return self._dist._logccdf2_dispatch(xl, xr, *args, method=method,
+                                             **params).real
+
+    def _ccdf_dispatch(self, x, *args, method=None, **params):
+        x = np.abs(x)
+        a, b = self._dist._support(**params)
+        xl = np.maximum(-x, a)
+        xr = np.minimum(x, b)
+        return self._dist._ccdf2_dispatch(xl, xr, *args, method=method, **params)
+
+    def _sample_dispatch(self, full_shape, *, method, rng, **params):
+        rvs = self._dist._sample_dispatch(full_shape, method=method, rng=rng, **params)
+        return np.abs(rvs)
+
+    def __repr__(self):
+        with np.printoptions(threshold=10):
+            return f"abs({repr(self._dist)})"
+
+    def __str__(self):
+        with np.printoptions(threshold=10):
+            return f"abs({str(self._dist)})"
+
+
+@xp_capabilities(np_only=True)
+def abs(X, /):
+    r"""Absolute value of a random variable.
+
+    Parameters
+    ----------
+    X : `ContinuousDistribution`
+        The random variable :math:`X`.
+
+    Returns
+    -------
+    Y : `ContinuousDistribution`
+        A random variable :math:`Y = |X|`.
+
+    Examples
+    --------
+    Suppose we have a normally distributed random variable :math:`X`:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> X = stats.Normal()
+
+    We wish to have a random variable :math:`Y` distributed according to
+    the folded normal distribution; that is, a random variable :math:`|X|`.
+
+    >>> Y = stats.abs(X)
+
+    The PDF of the distribution in the left half plane is "folded" over to
+    the right half plane. Because the normal PDF is symmetric, the resulting
+    PDF is zero for negative arguments and doubled for positive arguments.
+
+    >>> import matplotlib.pyplot as plt
+    >>> x = np.linspace(0, 5, 300)
+    >>> ax = plt.gca()
+    >>> Y.plot(x='x', y='pdf', t=('x', -1, 5), ax=ax)
+    >>> plt.plot(x, 2 * X.pdf(x), '--')
+    >>> plt.legend(('PDF of `Y`', 'Doubled PDF of `X`'))
+    >>> plt.show()
+
+    """
+    return FoldedDistribution(X)
+
+
+@xp_capabilities(np_only=True)
+def exp(X, /):
+    r"""Natural exponential of a random variable.
+
+    Parameters
+    ----------
+    X : `ContinuousDistribution`
+        The random variable :math:`X`.
+
+    Returns
+    -------
+    Y : `ContinuousDistribution`
+        A random variable :math:`Y = \exp(X)`.
+
+    Examples
+    --------
+    Suppose we have a normally distributed random variable :math:`X`:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> X = stats.Normal()
+
+    We wish to have a lognormally distributed random variable :math:`Y`,
+    a random variable whose natural logarithm is :math:`X`.
+    If :math:`X` is to be the natural logarithm of :math:`Y`, then we
+    must take :math:`Y` to be the natural exponential of :math:`X`.
+
+    >>> Y = stats.exp(X)
+
+    To demonstrate that ``X`` represents the logarithm of ``Y``,
+    we plot a normalized histogram of the logarithm of observations of
+    ``Y`` against the PDF underlying ``X``.
+
+    >>> import matplotlib.pyplot as plt
+    >>> rng = np.random.default_rng(435383595582522)
+    >>> y = Y.sample(shape=10000, rng=rng)
+    >>> ax = plt.gca()
+    >>> ax.hist(np.log(y), bins=50, density=True)
+    >>> X.plot(ax=ax)
+    >>> plt.legend(('PDF of `X`', 'histogram of `log(y)`'))
+    >>> plt.show()
+
+    """
+    return MonotonicTransformedDistribution(X, g=np.exp, h=np.log, dh=lambda u: 1 / u,
+                                            logdh=lambda u: -np.log(u))
+
+
+@xp_capabilities(np_only=True)
+def log(X, /):
+    r"""Natural logarithm of a non-negative random variable.
+
+    Parameters
+    ----------
+    X : `ContinuousDistribution`
+        The random variable :math:`X` with positive support.
+
+    Returns
+    -------
+    Y : `ContinuousDistribution`
+        A random variable :math:`Y = \log(X)`.
+
+    Examples
+    --------
+    Suppose we have a gamma distributed random variable :math:`X`:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> Gamma = stats.make_distribution(stats.gamma)
+    >>> X = Gamma(a=1.0)
+
+    We wish to have an exp-gamma distributed random variable :math:`Y`,
+    a random variable whose natural exponential is :math:`X`.
+    If :math:`X` is to be the natural exponential of :math:`Y`, then we
+    must take :math:`Y` to be the natural logarithm of :math:`X`.
+
+    >>> Y = stats.log(X)
+
+    To demonstrate that ``X`` represents the exponential of ``Y``,
+    we plot a normalized histogram of the exponential of observations of
+    ``Y`` against the PDF underlying ``X``.
+
+    >>> import matplotlib.pyplot as plt
+    >>> rng = np.random.default_rng(435383595582522)
+    >>> y = Y.sample(shape=10000, rng=rng)
+    >>> ax = plt.gca()
+    >>> ax.hist(np.exp(y), bins=50, density=True)
+    >>> X.plot(ax=ax)
+    >>> plt.legend(('PDF of `X`', 'histogram of `exp(y)`'))
+    >>> plt.show()
+
+    """
+    if np.any(X.support()[0] < 0):
+        message = ("The logarithm of a random variable is only implemented when the "
+                   "support is non-negative.")
+        raise NotImplementedError(message)
+    return MonotonicTransformedDistribution(X, g=np.log, h=np.exp, dh=np.exp,
+                                            logdh=lambda u: u)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_entropy.py b/.venv/lib/python3.12/site-packages/scipy/stats/_entropy.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab5a874cf4aeaa0bb12e63292cd3f5122658f509
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_entropy.py
@@ -0,0 +1,431 @@
+"""
+Created on Fri Apr  2 09:06:05 2021
+
+@author: matth
+"""
+
+import math
+import numpy as np
+from scipy import special
+from ._axis_nan_policy import _axis_nan_policy_factory
+from scipy._lib._array_api import (array_namespace, xp_promote, xp_device,
+                                   _masked_apply, _share_masks, xp_capabilities)
+
+__all__ = ['entropy', 'differential_entropy']
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x,
+    n_samples=lambda kwgs: (
+        2 if ("qk" in kwgs and kwgs["qk"] is not None)
+        else 1
+    ),
+    n_outputs=1, result_to_tuple=lambda x, _: (x,), paired=True,
+    too_small=-1  # entropy doesn't have too small inputs
+)
+def entropy(pk: np.typing.ArrayLike,
+            qk: np.typing.ArrayLike | None = None,
+            base: float | None = None,
+            axis: int = 0
+            ) -> np.number | np.ndarray:
+    """
+    Calculate the Shannon entropy/relative entropy of given distribution(s).
+
+    If only probabilities `pk` are given, the Shannon entropy is calculated as
+    ``H = -sum(pk * log(pk))``.
+
+    If `qk` is not None, then compute the relative entropy
+    ``D = sum(pk * log(pk / qk))``. This quantity is also known
+    as the Kullback-Leibler divergence.
+
+    This routine will normalize `pk` and `qk` if they don't sum to 1.
+
+    Parameters
+    ----------
+    pk : array_like
+        Defines the (discrete) distribution. Along each axis-slice of ``pk``,
+        element ``i`` is the  (possibly unnormalized) probability of event
+        ``i``.
+    qk : array_like, optional
+        Sequence against which the relative entropy is computed. Should be in
+        the same format as `pk`.
+    base : float, optional
+        The logarithmic base to use, defaults to ``e`` (natural logarithm).
+    axis : int, optional
+        The axis along which the entropy is calculated. Default is 0.
+
+    Returns
+    -------
+    S : {float, array_like}
+        The calculated entropy.
+
+    Notes
+    -----
+    Informally, the Shannon entropy quantifies the expected uncertainty
+    inherent in the possible outcomes of a discrete random variable.
+    For example,
+    if messages consisting of sequences of symbols from a set are to be
+    encoded and transmitted over a noiseless channel, then the Shannon entropy
+    ``H(pk)`` gives a tight lower bound for the average number of units of
+    information needed per symbol if the symbols occur with frequencies
+    governed by the discrete distribution `pk` [1]_. The choice of base
+    determines the choice of units; e.g., ``e`` for nats, ``2`` for bits, etc.
+
+    The relative entropy, ``D(pk|qk)``, quantifies the increase in the average
+    number of units of information needed per symbol if the encoding is
+    optimized for the probability distribution `qk` instead of the true
+    distribution `pk`. Informally, the relative entropy quantifies the expected
+    excess in surprise experienced if one believes the true distribution is
+    `qk` when it is actually `pk`.
+
+    A related quantity, the cross entropy ``CE(pk, qk)``, satisfies the
+    equation ``CE(pk, qk) = H(pk) + D(pk|qk)`` and can also be calculated with
+    the formula ``CE = -sum(pk * log(qk))``. It gives the average
+    number of units of information needed per symbol if an encoding is
+    optimized for the probability distribution `qk` when the true distribution
+    is `pk`. It is not computed directly by `entropy`, but it can be computed
+    using two calls to the function (see Examples).
+
+    See [2]_ for more information.
+
+    References
+    ----------
+    .. [1] Shannon, C.E. (1948), A Mathematical Theory of Communication.
+           Bell System Technical Journal, 27: 379-423.
+           :doi:`10.1002/j.1538-7305.1948.tb01338.x`.
+    .. [2] Thomas M. Cover and Joy A. Thomas. 2006. Elements of Information
+           Theory (Wiley Series in Telecommunications and Signal Processing).
+           Wiley-Interscience, USA.
+
+
+    Examples
+    --------
+    The outcome of a fair coin is the most uncertain:
+
+    >>> import numpy as np
+    >>> from scipy.stats import entropy
+    >>> base = 2  # work in units of bits
+    >>> pk = np.array([1/2, 1/2])  # fair coin
+    >>> H = entropy(pk, base=base)
+    >>> H
+    1.0
+    >>> H == -np.sum(pk * np.log(pk)) / np.log(base)
+    True
+
+    The outcome of a biased coin is less uncertain:
+
+    >>> qk = np.array([9/10, 1/10])  # biased coin
+    >>> entropy(qk, base=base)
+    0.46899559358928117
+
+    The relative entropy between the fair coin and biased coin is calculated
+    as:
+
+    >>> D = entropy(pk, qk, base=base)
+    >>> D
+    0.7369655941662062
+    >>> np.isclose(D, np.sum(pk * np.log(pk/qk)) / np.log(base), rtol=4e-16, atol=0)
+    True
+
+    The cross entropy can be calculated as the sum of the entropy and
+    relative entropy`:
+
+    >>> CE = entropy(pk, base=base) + entropy(pk, qk, base=base)
+    >>> CE
+    1.736965594166206
+    >>> CE == -np.sum(pk * np.log(qk)) / np.log(base)
+    True
+
+    """
+    if base is not None and base <= 0:
+        raise ValueError("`base` must be a positive number or `None`.")
+
+    xp = array_namespace(pk, qk)
+    pk, qk = xp_promote(pk, qk, broadcast=True, xp=xp)
+
+    with np.errstate(invalid='ignore'):
+        if qk is not None:
+            pk, qk = _share_masks(pk, qk, xp=xp)
+            qk = qk / xp.sum(qk, axis=axis, keepdims=True)
+        pk = pk / xp.sum(pk, axis=axis, keepdims=True)
+
+    if qk is None:
+        vec = special.entr(pk)
+    else:
+        vec = _masked_apply(special.rel_entr, args=(pk, qk), xp=xp)
+
+    S = xp.sum(vec, axis=axis)
+    if base is not None:
+        S /= math.log(base)
+    return S
+
+
+def _differential_entropy_is_too_small(samples, kwargs, axis=-1):
+    values = samples[0]
+    n = values.shape[axis]
+    window_length = kwargs.get("window_length")
+    if window_length is None:
+        window_length = math.floor(math.sqrt(n) + 0.5)
+    if not 2 <= 2 * window_length < n:
+        return True
+    return False
+
+
+@xp_capabilities()
+@_axis_nan_policy_factory(
+    lambda x: x, n_outputs=1, result_to_tuple=lambda x, _: (x,),
+    too_small=_differential_entropy_is_too_small
+)
+def differential_entropy(
+    values: np.typing.ArrayLike,
+    *,
+    window_length: int | None = None,
+    base: float | None = None,
+    axis: int = 0,
+    method: str = "auto",
+) -> np.number | np.ndarray:
+    r"""Given a sample of a distribution, estimate the differential entropy.
+
+    Several estimation methods are available using the `method` parameter. By
+    default, a method is selected based the size of the sample.
+
+    Parameters
+    ----------
+    values : sequence
+        Sample from a continuous distribution.
+    window_length : int, optional
+        Window length for computing Vasicek estimate. Must be an integer
+        between 1 and half of the sample size. If ``None`` (the default), it
+        uses the heuristic value
+
+        .. math::
+            \left \lfloor \sqrt{n} + 0.5 \right \rfloor
+
+        where :math:`n` is the sample size. This heuristic was originally
+        proposed in [2]_ and has become common in the literature.
+    base : float, optional
+        The logarithmic base to use, defaults to ``e`` (natural logarithm).
+    axis : int, optional
+        The axis along which the differential entropy is calculated.
+        Default is 0.
+    method : {'vasicek', 'van es', 'ebrahimi', 'correa', 'auto'}, optional
+        The method used to estimate the differential entropy from the sample.
+        Default is ``'auto'``.  See Notes for more information.
+
+    Returns
+    -------
+    entropy : float
+        The calculated differential entropy.
+
+    Notes
+    -----
+    This function will converge to the true differential entropy in the limit
+
+    .. math::
+        n \to \infty, \quad m \to \infty, \quad \frac{m}{n} \to 0
+
+    The optimal choice of ``window_length`` for a given sample size depends on
+    the (unknown) distribution. Typically, the smoother the density of the
+    distribution, the larger the optimal value of ``window_length`` [1]_.
+
+    The following options are available for the `method` parameter.
+
+    * ``'vasicek'`` uses the estimator presented in [1]_. This is
+      one of the first and most influential estimators of differential entropy.
+    * ``'van es'`` uses the bias-corrected estimator presented in [3]_, which
+      is not only consistent but, under some conditions, asymptotically normal.
+    * ``'ebrahimi'`` uses an estimator presented in [4]_, which was shown
+      in simulation to have smaller bias and mean squared error than
+      the Vasicek estimator.
+    * ``'correa'`` uses the estimator presented in [5]_ based on local linear
+      regression. In a simulation study, it had consistently smaller mean
+      square error than the Vasiceck estimator, but it is more expensive to
+      compute.
+    * ``'auto'`` selects the method automatically (default). Currently,
+      this selects ``'van es'`` for very small samples (<10), ``'ebrahimi'``
+      for moderate sample sizes (11-1000), and ``'vasicek'`` for larger
+      samples, but this behavior is subject to change in future versions.
+
+    All estimators are implemented as described in [6]_.
+
+    References
+    ----------
+    .. [1] Vasicek, O. (1976). A test for normality based on sample entropy.
+           Journal of the Royal Statistical Society:
+           Series B (Methodological), 38(1), 54-59.
+    .. [2] Crzcgorzewski, P., & Wirczorkowski, R. (1999). Entropy-based
+           goodness-of-fit test for exponentiality. Communications in
+           Statistics-Theory and Methods, 28(5), 1183-1202.
+    .. [3] Van Es, B. (1992). Estimating functionals related to a density by a
+           class of statistics based on spacings. Scandinavian Journal of
+           Statistics, 61-72.
+    .. [4] Ebrahimi, N., Pflughoeft, K., & Soofi, E. S. (1994). Two measures
+           of sample entropy. Statistics & Probability Letters, 20(3), 225-234.
+    .. [5] Correa, J. C. (1995). A new estimator of entropy. Communications
+           in Statistics-Theory and Methods, 24(10), 2439-2449.
+    .. [6] Noughabi, H. A. (2015). Entropy Estimation Using Numerical Methods.
+           Annals of Data Science, 2(2), 231-241.
+           https://link.springer.com/article/10.1007/s40745-015-0045-9
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import differential_entropy, norm
+
+    Entropy of a standard normal distribution:
+
+    >>> rng = np.random.default_rng()
+    >>> values = rng.standard_normal(100)
+    >>> differential_entropy(values)
+    1.3407817436640392
+
+    Compare with the true entropy:
+
+    >>> float(norm.entropy())
+    1.4189385332046727
+
+    For several sample sizes between 5 and 1000, compare the accuracy of
+    the ``'vasicek'``, ``'van es'``, and ``'ebrahimi'`` methods. Specifically,
+    compare the root mean squared error (over 1000 trials) between the estimate
+    and the true differential entropy of the distribution.
+
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+    >>>
+    >>>
+    >>> def rmse(res, expected):
+    ...     '''Root mean squared error'''
+    ...     return np.sqrt(np.mean((res - expected)**2))
+    >>>
+    >>>
+    >>> a, b = np.log10(5), np.log10(1000)
+    >>> ns = np.round(np.logspace(a, b, 10)).astype(int)
+    >>> reps = 1000  # number of repetitions for each sample size
+    >>> expected = stats.expon.entropy()
+    >>>
+    >>> method_errors = {'vasicek': [], 'van es': [], 'ebrahimi': []}
+    >>> for method in method_errors:
+    ...     for n in ns:
+    ...        rvs = stats.expon.rvs(size=(reps, n), random_state=rng)
+    ...        res = stats.differential_entropy(rvs, method=method, axis=-1)
+    ...        error = rmse(res, expected)
+    ...        method_errors[method].append(error)
+    >>>
+    >>> for method, errors in method_errors.items():
+    ...     plt.loglog(ns, errors, label=method)
+    >>>
+    >>> plt.legend()
+    >>> plt.xlabel('sample size')
+    >>> plt.ylabel('RMSE (1000 trials)')
+    >>> plt.title('Entropy Estimator Error (Exponential Distribution)')
+
+    """
+    xp = array_namespace(values)
+    values = xp_promote(values, force_floating=True, xp=xp)
+    values = xp.moveaxis(values, axis, -1)
+    n = values.shape[-1]  # type: ignore[union-attr]
+
+    if window_length is None:
+        window_length = math.floor(math.sqrt(n) + 0.5)
+
+    if not 2 <= 2 * window_length < n:
+        raise ValueError(
+            f"Window length ({window_length}) must be positive and less "
+            f"than half the sample size ({n}).",
+        )
+
+    if base is not None and base <= 0:
+        raise ValueError("`base` must be a positive number or `None`.")
+
+    sorted_data = xp.sort(values, axis=-1)
+
+    methods = {"vasicek": _vasicek_entropy,
+               "van es": _van_es_entropy,
+               "correa": _correa_entropy,
+               "ebrahimi": _ebrahimi_entropy,
+               "auto": _vasicek_entropy}
+    method = method.lower()
+    if method not in methods:
+        message = f"`method` must be one of {set(methods)}"
+        raise ValueError(message)
+
+    if method == "auto":
+        if n <= 10:
+            method = 'van es'
+        elif n <= 1000:
+            method = 'ebrahimi'
+        else:
+            method = 'vasicek'
+
+    res = methods[method](sorted_data, window_length, xp=xp)
+
+    if base is not None:
+        res /= math.log(base)
+
+    # avoid dtype changes due to data-apis/array-api-compat#152
+    # can be removed when data-apis/array-api-compat#152 is resolved
+    return xp.astype(res, values.dtype)  # type: ignore[union-attr]
+
+
+def _pad_along_last_axis(X, m, *, xp):
+    """Pad the data for computing the rolling window difference."""
+    # scales a  bit better than method in _vasicek_like_entropy
+    shape = X.shape[:-1] + (m,)
+    Xl = xp.broadcast_to(X[..., :1], shape)  # :1 vs 0 to maintain shape
+    Xr = xp.broadcast_to(X[..., -1:], shape)
+    return xp.concat((Xl, X, Xr), axis=-1)
+
+
+def _vasicek_entropy(X, m, *, xp):
+    """Compute the Vasicek estimator as described in [6] Eq. 1.3."""
+    n = X.shape[-1]
+    X = _pad_along_last_axis(X, m, xp=xp)
+    differences = X[..., 2 * m:] - X[..., : -2 * m:]
+    logs = xp.log(n/(2*m) * differences)
+    return xp.mean(logs, axis=-1)
+
+
+def _van_es_entropy(X, m, *, xp):
+    """Compute the van Es estimator as described in [6]."""
+    # No equation number, but referred to as HVE_mn.
+    # Typo: there should be a log within the summation.
+    n = X.shape[-1]
+    difference = X[..., m:] - X[..., :-m]
+    term1 = 1/(n-m) * xp.sum(xp.log((n+1)/m * difference), axis=-1)
+    k = xp.arange(m, n+1, dtype=term1.dtype, device=xp_device(X))
+    return term1 + xp.sum(1/k) + math.log(m) - math.log(n+1)
+
+
+def _ebrahimi_entropy(X, m, *, xp):
+    """Compute the Ebrahimi estimator as described in [6]."""
+    # No equation number, but referred to as HE_mn
+    n = X.shape[-1]
+    X = _pad_along_last_axis(X, m, xp=xp)
+
+    differences = X[..., 2 * m:] - X[..., : -2 * m:]
+
+    i = xp.arange(1, n+1, dtype=X.dtype, device=xp_device(X))
+    ci = xp.where(i <= m, 1 + (i - 1)/m, 2.)
+    ci = xp.where(i >= n - m + 1, 1 + (n - i)/m, ci)
+
+    logs = xp.log(n * differences / (ci * m))
+    return xp.mean(logs, axis=-1)
+
+
+def _correa_entropy(X, m, *, xp):
+    """Compute the Correa estimator as described in [6]."""
+    # No equation number, but referred to as HC_mn
+    n = X.shape[-1]
+    X = _pad_along_last_axis(X, m, xp=xp)
+
+    i = xp.arange(1, n+1, device=xp_device(X))
+    dj = xp.arange(-m, m+1, device=xp_device(X))[:, None]
+    j = i + dj
+    j0 = j + m - 1  # 0-indexed version of j
+
+    Xibar = xp.mean(X[..., j0], axis=-2, keepdims=True)
+    difference = X[..., j0] - Xibar
+    num = xp.sum(difference*dj, axis=-2)  # dj is d-i
+    den = n*xp.sum(difference**2, axis=-2)
+    return -xp.mean(xp.log(num/den), axis=-1)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_finite_differences.py b/.venv/lib/python3.12/site-packages/scipy/stats/_finite_differences.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ec91654c4e95f73f32edc5aa357a4e3f80fff1f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_finite_differences.py
@@ -0,0 +1,145 @@
+from numpy import arange, newaxis, hstack, prod, array
+from scipy import linalg
+
+
+def _central_diff_weights(Np, ndiv=1):
+    """
+    Return weights for an Np-point central derivative.
+
+    Assumes equally-spaced function points.
+
+    If weights are in the vector w, then
+    derivative is w[0] * f(x-ho*dx) + ... + w[-1] * f(x+h0*dx)
+
+    Parameters
+    ----------
+    Np : int
+        Number of points for the central derivative.
+    ndiv : int, optional
+        Number of divisions. Default is 1.
+
+    Returns
+    -------
+    w : ndarray
+        Weights for an Np-point central derivative. Its size is `Np`.
+
+    Notes
+    -----
+    Can be inaccurate for a large number of points.
+
+    Examples
+    --------
+    We can calculate a derivative value of a function.
+
+    >>> def f(x):
+    ...     return 2 * x**2 + 3
+    >>> x = 3.0 # derivative point
+    >>> h = 0.1 # differential step
+    >>> Np = 3 # point number for central derivative
+    >>> weights = _central_diff_weights(Np) # weights for first derivative
+    >>> vals = [f(x + (i - Np/2) * h) for i in range(Np)]
+    >>> sum(w * v for (w, v) in zip(weights, vals))/h
+    11.79999999999998
+
+    This value is close to the analytical solution:
+    f'(x) = 4x, so f'(3) = 12
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Finite_difference
+
+    """
+    if Np < ndiv + 1:
+        raise ValueError(
+            "Number of points must be at least the derivative order + 1."
+        )
+    if Np % 2 == 0:
+        raise ValueError("The number of points must be odd.")
+
+    ho = Np >> 1
+    x = arange(-ho, ho + 1.0)
+    x = x[:, newaxis]
+    X = x**0.0
+    for k in range(1, Np):
+        X = hstack([X, x**k])
+    w = prod(arange(1, ndiv + 1), axis=0) * linalg.inv(X)[ndiv]
+    return w
+
+
+def _derivative(func, x0, dx=1.0, n=1, args=(), order=3):
+    """
+    Find the nth derivative of a function at a point.
+
+    Given a function, use a central difference formula with spacing `dx` to
+    compute the nth derivative at `x0`.
+
+    Parameters
+    ----------
+    func : function
+        Input function.
+    x0 : float
+        The point at which the nth derivative is found.
+    dx : float, optional
+        Spacing.
+    n : int, optional
+        Order of the derivative. Default is 1.
+    args : tuple, optional
+        Arguments
+    order : int, optional
+        Number of points to use, must be odd.
+
+    Notes
+    -----
+    Decreasing the step size too small can result in round-off error.
+
+    Examples
+    --------
+    >>> def f(x):
+    ...     return x**3 + x**2
+    >>> _derivative(f, 1.0, dx=1e-6)
+    4.9999999999217337
+
+    """
+    if order < n + 1:
+        raise ValueError(
+            "'order' (the number of points used to compute the derivative), "
+            "must be at least the derivative order 'n' + 1."
+        )
+    if order % 2 == 0:
+        raise ValueError(
+            "'order' (the number of points used to compute the derivative) "
+            "must be odd."
+        )
+    # pre-computed for n=1 and 2 and low-order for speed.
+    if n == 1:
+        if order == 3:
+            weights = array([-1, 0, 1]) / 2.0
+        elif order == 5:
+            weights = array([1, -8, 0, 8, -1]) / 12.0
+        elif order == 7:
+            weights = array([-1, 9, -45, 0, 45, -9, 1]) / 60.0
+        elif order == 9:
+            weights = array([3, -32, 168, -672, 0, 672, -168, 32, -3]) / 840.0
+        else:
+            weights = _central_diff_weights(order, 1)
+    elif n == 2:
+        if order == 3:
+            weights = array([1, -2.0, 1])
+        elif order == 5:
+            weights = array([-1, 16, -30, 16, -1]) / 12.0
+        elif order == 7:
+            weights = array([2, -27, 270, -490, 270, -27, 2]) / 180.0
+        elif order == 9:
+            weights = (
+                array([-9, 128, -1008, 8064, -14350, 8064, -1008, 128, -9])
+                / 5040.0
+            )
+        else:
+            weights = _central_diff_weights(order, 2)
+    else:
+        weights = _central_diff_weights(order, n)
+    val = 0.0
+    ho = order >> 1
+    for k in range(order):
+        val += weights[k] * func(x0 + (k - ho) * dx, *args)
+    return val / prod((dx,) * n, axis=0)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_fit.py b/.venv/lib/python3.12/site-packages/scipy/stats/_fit.py
new file mode 100644
index 0000000000000000000000000000000000000000..a73e410753dcfc9992e31f9395bbe72e45077749
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_fit.py
@@ -0,0 +1,1349 @@
+import warnings
+from collections import namedtuple
+import numpy as np
+from scipy import optimize, stats
+from scipy._lib._array_api import xp_capabilities
+from scipy._lib._util import check_random_state, _transition_to_rng
+
+
+def _combine_bounds(name, user_bounds, shape_domain, integral):
+    """Intersection of user-defined bounds and distribution PDF/PMF domain"""
+
+    user_bounds = np.atleast_1d(user_bounds)
+
+    if user_bounds[0] > user_bounds[1]:
+        message = (f"There are no values for `{name}` on the interval "
+                   f"{list(user_bounds)}.")
+        raise ValueError(message)
+
+    bounds = (max(user_bounds[0], shape_domain[0]),
+              min(user_bounds[1], shape_domain[1]))
+
+    if integral and (np.ceil(bounds[0]) > np.floor(bounds[1])):
+        message = (f"There are no integer values for `{name}` on the interval "
+                   f"defined by the user-provided bounds and the domain "
+                   "of the distribution.")
+        raise ValueError(message)
+    elif not integral and (bounds[0] > bounds[1]):
+        message = (f"There are no values for `{name}` on the interval "
+                   f"defined by the user-provided bounds and the domain "
+                   "of the distribution.")
+        raise ValueError(message)
+
+    if not np.all(np.isfinite(bounds)):
+        message = (f"The intersection of user-provided bounds for `{name}` "
+                   f"and the domain of the distribution is not finite. Please "
+                   f"provide finite bounds for shape `{name}` in `bounds`.")
+        raise ValueError(message)
+
+    return bounds
+
+
+class FitResult:
+    r"""Result of fitting a discrete or continuous distribution to data
+
+    Attributes
+    ----------
+    params : namedtuple
+        A namedtuple containing the maximum likelihood estimates of the
+        shape parameters, location, and (if applicable) scale of the
+        distribution.
+    success : bool or None
+        Whether the optimizer considered the optimization to terminate
+        successfully or not.
+    message : str or None
+        Any status message provided by the optimizer.
+
+    """
+
+    def __init__(self, dist, data, discrete, res):
+        self._dist = dist
+        self._data = data
+        self.discrete = discrete
+        self.pxf = getattr(dist, "pmf", None) or getattr(dist, "pdf", None)
+
+        shape_names = [] if dist.shapes is None else dist.shapes.split(", ")
+        if not discrete:
+            FitParams = namedtuple('FitParams', shape_names + ['loc', 'scale'])
+        else:
+            FitParams = namedtuple('FitParams', shape_names + ['loc'])
+
+        self.params = FitParams(*res.x)
+
+        # Optimizer can report success even when nllf is infinite
+        if res.success and not np.isfinite(self.nllf()):
+            res.success = False
+            res.message = ("Optimization converged to parameter values that "
+                           "are inconsistent with the data.")
+        self.success = getattr(res, "success", None)
+        self.message = getattr(res, "message", None)
+
+    def __repr__(self):
+        keys = ["params", "success", "message"]
+        m = max(map(len, keys)) + 1
+        return '\n'.join([key.rjust(m) + ': ' + repr(getattr(self, key))
+                          for key in keys if getattr(self, key) is not None])
+
+    def nllf(self, params=None, data=None):
+        """Negative log-likelihood function
+
+        Evaluates the negative of the log-likelihood function of the provided
+        data at the provided parameters.
+
+        Parameters
+        ----------
+        params : tuple, optional
+            The shape parameters, location, and (if applicable) scale of the
+            distribution as a single tuple. Default is the maximum likelihood
+            estimates (``self.params``).
+        data : array_like, optional
+            The data for which the log-likelihood function is to be evaluated.
+            Default is the data to which the distribution was fit.
+
+        Returns
+        -------
+        nllf : float
+            The negative of the log-likelihood function.
+
+        """
+        params = params if params is not None else self.params
+        data = data if data is not None else self._data
+        return self._dist.nnlf(theta=params, x=data)
+
+    def plot(self, ax=None, *, plot_type="hist"):
+        """Visually compare the data against the fitted distribution.
+
+        Available only if `matplotlib` is installed.
+
+        Parameters
+        ----------
+        ax : `matplotlib.axes.Axes`
+            Axes object to draw the plot onto, otherwise uses the current Axes.
+        plot_type : {"hist", "qq", "pp", "cdf"}
+            Type of plot to draw. Options include:
+
+            - "hist": Superposes the PDF/PMF of the fitted distribution
+              over a normalized histogram of the data.
+            - "qq": Scatter plot of theoretical quantiles against the
+              empirical quantiles. Specifically, the x-coordinates are the
+              values of the fitted distribution PPF evaluated at the
+              percentiles ``(np.arange(1, n) - 0.5)/n``, where ``n`` is the
+              number of data points, and the y-coordinates are the sorted
+              data points.
+            - "pp": Scatter plot of theoretical percentiles against the
+              observed percentiles. Specifically, the x-coordinates are the
+              percentiles ``(np.arange(1, n) - 0.5)/n``, where ``n`` is
+              the number of data points, and the y-coordinates are the values
+              of the fitted distribution CDF evaluated at the sorted
+              data points.
+            - "cdf": Superposes the CDF of the fitted distribution over the
+              empirical CDF. Specifically, the x-coordinates of the empirical
+              CDF are the sorted data points, and the y-coordinates are the
+              percentiles ``(np.arange(1, n) - 0.5)/n``, where ``n`` is
+              the number of data points.
+
+        Returns
+        -------
+        ax : `matplotlib.axes.Axes`
+            The matplotlib Axes object on which the plot was drawn.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> import matplotlib.pyplot as plt  # matplotlib must be installed
+        >>> rng = np.random.default_rng()
+        >>> data = stats.nbinom(5, 0.5).rvs(size=1000, random_state=rng)
+        >>> bounds = [(0, 30), (0, 1)]
+        >>> res = stats.fit(stats.nbinom, data, bounds)
+        >>> ax = res.plot()  # save matplotlib Axes object
+
+        The `matplotlib.axes.Axes` object can be used to customize the plot.
+        See `matplotlib.axes.Axes` documentation for details.
+
+        >>> ax.set_xlabel('number of trials')  # customize axis label
+        >>> ax.get_children()[0].set_linewidth(5)  # customize line widths
+        >>> ax.legend()
+        >>> plt.show()
+        """
+        try:
+            import matplotlib  # noqa: F401
+        except ModuleNotFoundError as exc:
+            message = "matplotlib must be installed to use method `plot`."
+            raise ModuleNotFoundError(message) from exc
+
+        plots = {'histogram': self._hist_plot, 'qq': self._qq_plot,
+                 'pp': self._pp_plot, 'cdf': self._cdf_plot,
+                 'hist': self._hist_plot}
+        if plot_type.lower() not in plots:
+            message = f"`plot_type` must be one of {set(plots.keys())}"
+            raise ValueError(message)
+        plot = plots[plot_type.lower()]
+
+        if ax is None:
+            import matplotlib.pyplot as plt
+            ax = plt.gca()
+
+        fit_params = np.atleast_1d(self.params)
+
+        return plot(ax=ax, fit_params=fit_params)
+
+    def _hist_plot(self, ax, fit_params):
+        from matplotlib.ticker import MaxNLocator
+
+        support = self._dist.support(*fit_params)
+        lb = support[0] if np.isfinite(support[0]) else min(self._data)
+        ub = support[1] if np.isfinite(support[1]) else max(self._data)
+        pxf = "PMF" if self.discrete else "PDF"
+
+        if self.discrete:
+            x = np.arange(lb, ub + 2)
+            y = self.pxf(x, *fit_params)
+            ax.vlines(x[:-1], 0, y[:-1], label='Fitted Distribution PMF',
+                      color='C0')
+            options = dict(density=True, bins=x, align='left', color='C1')
+            ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+            ax.set_xlabel('k')
+            ax.set_ylabel('PMF')
+        else:
+            x = np.linspace(lb, ub, 200)
+            y = self.pxf(x, *fit_params)
+            ax.plot(x, y, '--', label='Fitted Distribution PDF', color='C0')
+            options = dict(density=True, bins=50, align='mid', color='C1')
+            ax.set_xlabel('x')
+            ax.set_ylabel('PDF')
+
+        if len(self._data) > 50 or self.discrete:
+            ax.hist(self._data, label="Histogram of Data", **options)
+        else:
+            ax.plot(self._data, np.zeros_like(self._data), "*",
+                    label='Data', color='C1')
+
+        ax.set_title(rf"Fitted $\tt {self._dist.name}$ {pxf} and Histogram")
+        ax.legend(*ax.get_legend_handles_labels())
+        return ax
+
+    def _qp_plot(self, ax, fit_params, qq):
+        data = np.sort(self._data)
+        ps = self._plotting_positions(len(self._data))
+
+        if qq:
+            qp = "Quantiles"
+            plot_type = 'Q-Q'
+            x = self._dist.ppf(ps, *fit_params)
+            y = data
+        else:
+            qp = "Percentiles"
+            plot_type = 'P-P'
+            x = ps
+            y = self._dist.cdf(data, *fit_params)
+
+        ax.plot(x, y, '.', label=f'Fitted Distribution {plot_type}',
+                color='C0', zorder=1)
+        xlim = ax.get_xlim()
+        ylim = ax.get_ylim()
+        lim = [min(xlim[0], ylim[0]), max(xlim[1], ylim[1])]
+        if not qq:
+            lim = max(lim[0], 0), min(lim[1], 1)
+
+        if self.discrete and qq:
+            q_min, q_max = int(lim[0]), int(lim[1]+1)
+            q_ideal = np.arange(q_min, q_max)
+            # q_ideal = np.unique(self._dist.ppf(ps, *fit_params))
+            ax.plot(q_ideal, q_ideal, 'o', label='Reference', color='k',
+                    alpha=0.25, markerfacecolor='none', clip_on=True)
+        elif self.discrete and not qq:
+            # The intent of this is to match the plot that would be produced
+            # if x were continuous on [0, 1] and y were cdf(ppf(x)).
+            # It can be approximated by letting x = np.linspace(0, 1, 1000),
+            # but this might not look great when zooming in. The vertical
+            # portions are included to indicate where the transition occurs
+            # where the data completely obscures the horizontal portions.
+            p_min, p_max = lim
+            a, b = self._dist.support(*fit_params)
+            p_min = max(p_min, 0 if np.isfinite(a) else 1e-3)
+            p_max = min(p_max, 1 if np.isfinite(b) else 1-1e-3)
+            q_min, q_max = self._dist.ppf([p_min, p_max], *fit_params)
+            qs = np.arange(q_min-1, q_max+1)
+            ps = self._dist.cdf(qs, *fit_params)
+            ax.step(ps, ps, '-', label='Reference', color='k', alpha=0.25,
+                    clip_on=True)
+        else:
+            ax.plot(lim, lim, '-', label='Reference', color='k', alpha=0.25,
+                    clip_on=True)
+
+        ax.set_xlim(lim)
+        ax.set_ylim(lim)
+        ax.set_xlabel(rf"Fitted $\tt {self._dist.name}$ Theoretical {qp}")
+        ax.set_ylabel(f"Data {qp}")
+        ax.set_title(rf"Fitted $\tt {self._dist.name}$ {plot_type} Plot")
+        ax.legend(*ax.get_legend_handles_labels())
+        ax.set_aspect('equal')
+        return ax
+
+    def _qq_plot(self, **kwargs):
+        return self._qp_plot(qq=True, **kwargs)
+
+    def _pp_plot(self, **kwargs):
+        return self._qp_plot(qq=False, **kwargs)
+
+    def _plotting_positions(self, n, a=.5):
+        # See https://en.wikipedia.org/wiki/Q%E2%80%93Q_plot#Plotting_positions
+        k = np.arange(1, n+1)
+        return (k-a) / (n + 1 - 2*a)
+
+    def _cdf_plot(self, ax, fit_params):
+        data = np.sort(self._data)
+        ecdf = self._plotting_positions(len(self._data))
+        ls = '--' if len(np.unique(data)) < 30 else '.'
+        xlabel = 'k' if self.discrete else 'x'
+        ax.step(data, ecdf, ls, label='Empirical CDF', color='C1', zorder=0)
+
+        xlim = ax.get_xlim()
+        q = np.linspace(*xlim, 300)
+        tcdf = self._dist.cdf(q, *fit_params)
+
+        ax.plot(q, tcdf, label='Fitted Distribution CDF', color='C0', zorder=1)
+        ax.set_xlim(xlim)
+        ax.set_ylim(0, 1)
+        ax.set_xlabel(xlabel)
+        ax.set_ylabel("CDF")
+        ax.set_title(rf"Fitted $\tt {self._dist.name}$ and Empirical CDF")
+        handles, labels = ax.get_legend_handles_labels()
+        ax.legend(handles[::-1], labels[::-1])
+        return ax
+
+
+@xp_capabilities(out_of_scope=True)
+def fit(dist, data, bounds=None, *, guess=None, method='mle',
+        optimizer=optimize.differential_evolution):
+    r"""Fit a discrete or continuous distribution to data.
+
+    Given a distribution, data, and bounds on the parameters of the
+    distribution, return maximum likelihood estimates of the parameters.
+
+    Parameters
+    ----------
+    dist : `scipy.stats.rv_continuous` or `scipy.stats.rv_discrete`
+        The object representing the distribution to be fit to the data.
+    data : 1D array_like
+        The data to which the distribution is to be fit. If the data contain
+        any of ``np.nan``, ``np.inf``, or -``np.inf``, the fit method will
+        raise a ``ValueError``.
+    bounds : dict or sequence of tuples, optional
+        If a dictionary, each key is the name of a parameter of the
+        distribution, and the corresponding value is a tuple containing the
+        lower and upper bound on that parameter.  If the distribution is
+        defined only for a finite range of values of that parameter, no entry
+        for that parameter is required; e.g., some distributions have
+        parameters which must be on the interval [0, 1]. Bounds for parameters
+        location (``loc``) and scale (``scale``) are optional; by default,
+        they are fixed to 0 and 1, respectively.
+
+        If a sequence, element *i* is a tuple containing the lower and upper
+        bound on the *i*\ th parameter of the distribution. In this case,
+        bounds for *all* distribution shape parameters must be provided.
+        Optionally, bounds for location and scale may follow the
+        distribution shape parameters.
+
+        If a shape is to be held fixed (e.g. if it is known), the
+        lower and upper bounds may be equal. If a user-provided lower or upper
+        bound is beyond a bound of the domain for which the distribution is
+        defined, the bound of the distribution's domain will replace the
+        user-provided value. Similarly, parameters which must be integral
+        will be constrained to integral values within the user-provided bounds.
+    guess : dict or array_like, optional
+        If a dictionary, each key is the name of a parameter of the
+        distribution, and the corresponding value is a guess for the value
+        of the parameter.
+
+        If a sequence, element *i* is a guess for the *i*\ th parameter of the
+        distribution. In this case, guesses for *all* distribution shape
+        parameters must be provided.
+
+        If `guess` is not provided, guesses for the decision variables will
+        not be passed to the optimizer. If `guess` is provided, guesses for
+        any missing parameters will be set at the mean of the lower and
+        upper bounds. Guesses for parameters which must be integral will be
+        rounded to integral values, and guesses that lie outside the
+        intersection of the user-provided bounds and the domain of the
+        distribution will be clipped.
+    method : {'mle', 'mse'}
+        With ``method="mle"`` (default), the fit is computed by minimizing
+        the negative log-likelihood function. A large, finite penalty
+        (rather than infinite negative log-likelihood) is applied for
+        observations beyond the support of the distribution.
+        With ``method="mse"``, the fit is computed by minimizing
+        the negative log-product spacing function. The same penalty is applied
+        for observations beyond the support. We follow the approach of [1]_,
+        which is generalized for samples with repeated observations.
+    optimizer : callable, optional
+        `optimizer` is a callable that accepts the following positional
+        argument.
+
+        fun : callable
+            The objective function to be optimized. `fun` accepts one argument
+            ``x``, candidate shape parameters of the distribution, and returns
+            the objective function value given ``x``, `dist`, and the provided
+            `data`.
+            The job of `optimizer` is to find values of the decision variables
+            that minimizes `fun`.
+
+        `optimizer` must also accept the following keyword argument.
+
+        bounds : sequence of tuples
+            The bounds on values of the decision variables; each element will
+            be a tuple containing the lower and upper bound on a decision
+            variable.
+
+        If `guess` is provided, `optimizer` must also accept the following
+        keyword argument.
+
+        x0 : array_like
+            The guesses for each decision variable.
+
+        If the distribution has any shape parameters that must be integral or
+        if the distribution is discrete and the location parameter is not
+        fixed, `optimizer` must also accept the following keyword argument.
+
+        integrality : array_like of bools
+            For each decision variable, True if the decision variable
+            must be constrained to integer values and False if the decision
+            variable is continuous.
+
+        `optimizer` must return an object, such as an instance of
+        `scipy.optimize.OptimizeResult`, which holds the optimal values of
+        the decision variables in an attribute ``x``. If attributes
+        ``fun``, ``status``, or ``message`` are provided, they will be
+        included in the result object returned by `fit`.
+
+    Returns
+    -------
+    result : `~scipy.stats._result_classes.FitResult`
+        An object with the following fields.
+
+        params : namedtuple
+            A namedtuple containing the maximum likelihood estimates of the
+            shape parameters, location, and (if applicable) scale of the
+            distribution.
+        success : bool or None
+            Whether the optimizer considered the optimization to terminate
+            successfully or not.
+        message : str or None
+            Any status message provided by the optimizer.
+
+        The object has the following method:
+
+        nllf(params=None, data=None)
+            By default, the negative log-likelihood function at the fitted
+            `params` for the given `data`. Accepts a tuple containing
+            alternative shapes, location, and scale of the distribution and
+            an array of alternative data.
+
+        plot(ax=None)
+            Superposes the PDF/PMF of the fitted distribution over a normalized
+            histogram of the data.
+
+    See Also
+    --------
+    rv_continuous,  rv_discrete
+
+    Notes
+    -----
+    Optimization is more likely to converge to the maximum likelihood estimate
+    when the user provides tight bounds containing the maximum likelihood
+    estimate. For example, when fitting a binomial distribution to data, the
+    number of experiments underlying each sample may be known, in which case
+    the corresponding shape parameter ``n`` can be fixed.
+
+    References
+    ----------
+    .. [1] Shao, Yongzhao, and Marjorie G. Hahn. "Maximum product of spacings
+           method: a unified formulation with illustration of strong
+           consistency." Illinois Journal of Mathematics 43.3 (1999): 489-499.
+
+    Examples
+    --------
+    Suppose we wish to fit a distribution to the following data.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+    >>> dist = stats.nbinom
+    >>> shapes = (5, 0.5)
+    >>> data = dist.rvs(*shapes, size=1000, random_state=rng)
+
+    Suppose we do not know how the data were generated, but we suspect that
+    it follows a negative binomial distribution with parameters *n* and *p*\.
+    (See `scipy.stats.nbinom`.) We believe that the parameter *n* was fewer
+    than 30, and we know that the parameter *p* must lie on the interval
+    [0, 1]. We record this information in a variable `bounds` and pass
+    this information to `fit`.
+
+    >>> bounds = [(0, 30), (0, 1)]
+    >>> res = stats.fit(dist, data, bounds)
+
+    `fit` searches within the user-specified `bounds` for the
+    values that best match the data (in the sense of maximum likelihood
+    estimation). In this case, it found shape values similar to those
+    from which the data were actually generated.
+
+    >>> res.params
+    FitParams(n=5.0, p=0.5028157644634368, loc=0.0)  # may vary
+
+    We can visualize the results by superposing the probability mass function
+    of the distribution (with the shapes fit to the data) over a normalized
+    histogram of the data.
+
+    >>> import matplotlib.pyplot as plt  # matplotlib must be installed to plot
+    >>> res.plot()
+    >>> plt.show()
+
+    Note that the estimate for *n* was exactly integral; this is because
+    the domain of the `nbinom` PMF includes only integral *n*, and the `nbinom`
+    object "knows" that. `nbinom` also knows that the shape *p* must be a
+    value between 0 and 1. In such a case - when the domain of the distribution
+    with respect to a parameter is finite - we are not required to specify
+    bounds for the parameter.
+
+    >>> bounds = {'n': (0, 30)}  # omit parameter p using a `dict`
+    >>> res2 = stats.fit(dist, data, bounds)
+    >>> res2.params
+    FitParams(n=5.0, p=0.5016492009232932, loc=0.0)  # may vary
+
+    If we wish to force the distribution to be fit with *n* fixed at 6, we can
+    set both the lower and upper bounds on *n* to 6. Note, however, that the
+    value of the objective function being optimized is typically worse (higher)
+    in this case.
+
+    >>> bounds = {'n': (6, 6)}  # fix parameter `n`
+    >>> res3 = stats.fit(dist, data, bounds)
+    >>> res3.params
+    FitParams(n=6.0, p=0.5486556076755706, loc=0.0)  # may vary
+    >>> res3.nllf() > res.nllf()
+    True  # may vary
+
+    Note that the numerical results of the previous examples are typical, but
+    they may vary because the default optimizer used by `fit`,
+    `scipy.optimize.differential_evolution`, is stochastic. However, we can
+    customize the settings used by the optimizer to ensure reproducibility -
+    or even use a different optimizer entirely - using the `optimizer`
+    parameter.
+
+    >>> from scipy.optimize import differential_evolution
+    >>> rng = np.random.default_rng(767585560716548)
+    >>> def optimizer(fun, bounds, *, integrality):
+    ...     return differential_evolution(fun, bounds, strategy='best2bin',
+    ...                                   rng=rng, integrality=integrality)
+    >>> bounds = [(0, 30), (0, 1)]
+    >>> res4 = stats.fit(dist, data, bounds, optimizer=optimizer)
+    >>> res4.params
+    FitParams(n=5.0, p=0.5015183149259951, loc=0.0)
+
+    """
+    # --- Input Validation / Standardization --- #
+    user_bounds = bounds
+    user_guess = guess
+
+    # distribution input validation and information collection
+    if hasattr(dist, "pdf"):  # can't use isinstance for types
+        default_bounds = {'loc': (0, 0), 'scale': (1, 1)}
+        discrete = False
+    elif hasattr(dist, "pmf"):
+        default_bounds = {'loc': (0, 0)}
+        discrete = True
+    else:
+        message = ("`dist` must be an instance of `rv_continuous` "
+                   "or `rv_discrete.`")
+        raise ValueError(message)
+
+    try:
+        param_info = dist._param_info()
+    except AttributeError as e:
+        message = (f"Distribution `{dist.name}` is not yet supported by "
+                   "`scipy.stats.fit` because shape information has "
+                   "not been defined.")
+        raise ValueError(message) from e
+
+    # data input validation
+    data = np.asarray(data)
+    if data.ndim != 1:
+        message = "`data` must be exactly one-dimensional."
+        raise ValueError(message)
+    if not (np.issubdtype(data.dtype, np.number)
+            and np.all(np.isfinite(data))):
+        message = "All elements of `data` must be finite numbers."
+        raise ValueError(message)
+
+    # bounds input validation and information collection
+    n_params = len(param_info)
+    n_shapes = n_params - (1 if discrete else 2)
+    param_list = [param.name for param in param_info]
+    param_names = ", ".join(param_list)
+    shape_names = ", ".join(param_list[:n_shapes])
+
+    if user_bounds is None:
+        user_bounds = {}
+
+    if isinstance(user_bounds, dict):
+        default_bounds.update(user_bounds)
+        user_bounds = default_bounds
+        user_bounds_array = np.empty((n_params, 2))
+        for i in range(n_params):
+            param_name = param_info[i].name
+            user_bound = user_bounds.pop(param_name, None)
+            if user_bound is None:
+                user_bound = param_info[i].domain
+            user_bounds_array[i] = user_bound
+        if user_bounds:
+            message = ("Bounds provided for the following unrecognized "
+                       f"parameters will be ignored: {set(user_bounds)}")
+            warnings.warn(message, RuntimeWarning, stacklevel=2)
+
+    else:
+        try:
+            user_bounds = np.asarray(user_bounds, dtype=float)
+            if user_bounds.size == 0:
+                user_bounds = np.empty((0, 2))
+        except ValueError as e:
+            message = ("Each element of a `bounds` sequence must be a tuple "
+                       "containing two elements: the lower and upper bound of "
+                       "a distribution parameter.")
+            raise ValueError(message) from e
+        if (user_bounds.ndim != 2 or user_bounds.shape[1] != 2):
+            message = ("Each element of `bounds` must be a tuple specifying "
+                       "the lower and upper bounds of a shape parameter")
+            raise ValueError(message)
+        if user_bounds.shape[0] < n_shapes:
+            message = (f"A `bounds` sequence must contain at least {n_shapes} "
+                       "elements: tuples specifying the lower and upper "
+                       f"bounds of all shape parameters {shape_names}.")
+            raise ValueError(message)
+        if user_bounds.shape[0] > n_params:
+            message = ("A `bounds` sequence may not contain more than "
+                       f"{n_params} elements: tuples specifying the lower and "
+                       "upper bounds of distribution parameters "
+                       f"{param_names}.")
+            raise ValueError(message)
+
+        user_bounds_array = np.empty((n_params, 2))
+        user_bounds_array[n_shapes:] = list(default_bounds.values())
+        user_bounds_array[:len(user_bounds)] = user_bounds
+
+    user_bounds = user_bounds_array
+    validated_bounds = []
+    for i in range(n_params):
+        name = param_info[i].name
+        user_bound = user_bounds_array[i]
+        param_domain = param_info[i].domain
+        integral = param_info[i].integrality
+        combined = _combine_bounds(name, user_bound, param_domain, integral)
+        validated_bounds.append(combined)
+
+    bounds = np.asarray(validated_bounds)
+    integrality = [param.integrality for param in param_info]
+
+    # guess input validation
+
+    if user_guess is None:
+        guess_array = None
+    elif isinstance(user_guess, dict):
+        default_guess = {param.name: np.mean(bound)
+                         for param, bound in zip(param_info, bounds)}
+        unrecognized = set(user_guess) - set(default_guess)
+        if unrecognized:
+            message = ("Guesses provided for the following unrecognized "
+                       f"parameters will be ignored: {unrecognized}")
+            warnings.warn(message, RuntimeWarning, stacklevel=2)
+        default_guess.update(user_guess)
+
+        message = ("Each element of `guess` must be a scalar "
+                   "guess for a distribution parameter.")
+        try:
+            guess_array = np.asarray([default_guess[param.name]
+                                      for param in param_info], dtype=float)
+        except ValueError as e:
+            raise ValueError(message) from e
+
+    else:
+        message = ("Each element of `guess` must be a scalar "
+                   "guess for a distribution parameter.")
+        try:
+            user_guess = np.asarray(user_guess, dtype=float)
+        except ValueError as e:
+            raise ValueError(message) from e
+        if user_guess.ndim != 1:
+            raise ValueError(message)
+        if user_guess.shape[0] < n_shapes:
+            message = (f"A `guess` sequence must contain at least {n_shapes} "
+                       "elements: scalar guesses for the distribution shape "
+                       f"parameters {shape_names}.")
+            raise ValueError(message)
+        if user_guess.shape[0] > n_params:
+            message = ("A `guess` sequence may not contain more than "
+                       f"{n_params} elements: scalar guesses for the "
+                       f"distribution parameters {param_names}.")
+            raise ValueError(message)
+
+        guess_array = np.mean(bounds, axis=1)
+        guess_array[:len(user_guess)] = user_guess
+
+    if guess_array is not None:
+        guess_rounded = guess_array.copy()
+
+        guess_rounded[integrality] = np.round(guess_rounded[integrality])
+        rounded = np.where(guess_rounded != guess_array)[0]
+        for i in rounded:
+            message = (f"Guess for parameter `{param_info[i].name}` "
+                       f"rounded from {guess_array[i]} to {guess_rounded[i]}.")
+            warnings.warn(message, RuntimeWarning, stacklevel=2)
+
+        guess_clipped = np.clip(guess_rounded, bounds[:, 0], bounds[:, 1])
+        clipped = np.where(guess_clipped != guess_rounded)[0]
+        for i in clipped:
+            message = (f"Guess for parameter `{param_info[i].name}` "
+                       f"clipped from {guess_rounded[i]} to "
+                       f"{guess_clipped[i]}.")
+            warnings.warn(message, RuntimeWarning, stacklevel=2)
+
+        guess = guess_clipped
+    else:
+        guess = None
+
+    # --- Fitting --- #
+    def nllf(free_params, data=data):  # bind data NOW
+        with np.errstate(invalid='ignore', divide='ignore'):
+            return dist._penalized_nnlf(free_params, data)
+
+    def nlpsf(free_params, data=data):  # bind data NOW
+        with np.errstate(invalid='ignore', divide='ignore'):
+            return dist._penalized_nlpsf(free_params, data)
+
+    methods = {'mle': nllf, 'mse': nlpsf}
+    objective = methods[method.lower()]
+
+    with np.errstate(invalid='ignore', divide='ignore'):
+        kwds = {}
+        if bounds is not None:
+            kwds['bounds'] = bounds
+        if np.any(integrality):
+            kwds['integrality'] = integrality
+        if guess is not None:
+            kwds['x0'] = guess
+        res = optimizer(objective, **kwds)
+
+    return FitResult(dist, data, discrete, res)
+
+
+GoodnessOfFitResult = namedtuple('GoodnessOfFitResult',
+                                 ('fit_result', 'statistic', 'pvalue',
+                                  'null_distribution'))
+
+
+@xp_capabilities(out_of_scope=True)
+@_transition_to_rng('random_state')
+def goodness_of_fit(dist, data, *, known_params=None, fit_params=None,
+                    guessed_params=None, statistic='ad', n_mc_samples=9999,
+                    rng=None):
+    r"""
+    Perform a goodness of fit test comparing data to a distribution family.
+
+    Given a distribution family and data, perform a test of the null hypothesis
+    that the data were drawn from a distribution in that family. Any known
+    parameters of the distribution may be specified. Remaining parameters of
+    the distribution will be fit to the data, and the p-value of the test
+    is computed accordingly. Several statistics for comparing the distribution
+    to data are available.
+
+    Parameters
+    ----------
+    dist : `scipy.stats.rv_continuous`
+        The object representing the distribution family under the null
+        hypothesis.
+    data : 1D array_like
+        Finite, uncensored data to be tested.
+    known_params : dict, optional
+        A dictionary containing name-value pairs of known distribution
+        parameters. Monte Carlo samples are randomly drawn from the
+        null-hypothesized distribution with these values of the parameters.
+        Before the statistic is evaluated for the observed `data` and each
+        Monte Carlo sample, only remaining unknown parameters of the
+        null-hypothesized distribution family are fit to the samples; the
+        known parameters are held fixed. If all parameters of the distribution
+        family are known, then the step of fitting the distribution family to
+        each sample is omitted.
+    fit_params : dict, optional
+        A dictionary containing name-value pairs of distribution parameters
+        that have already been fit to the data, e.g. using `scipy.stats.fit`
+        or the ``fit`` method of `dist`. Monte Carlo samples are drawn from the
+        null-hypothesized distribution with these specified values of the
+        parameter. However, these and all other unknown parameters of the
+        null-hypothesized distribution family are always fit to the sample,
+        whether that is the observed `data` or a Monte Carlo sample, before
+        the statistic is evaluated.
+    guessed_params : dict, optional
+        A dictionary containing name-value pairs of distribution parameters
+        which have been guessed. These parameters are always considered as
+        free parameters and are fit both to the provided `data` as well as
+        to the Monte Carlo samples drawn from the null-hypothesized
+        distribution. The purpose of these `guessed_params` is to be used as
+        initial values for the numerical fitting procedure.
+    statistic : {"ad", "ks", "cvm", "filliben"} or callable, optional
+        The statistic used to compare data to a distribution after fitting
+        unknown parameters of the distribution family to the data. The
+        Anderson-Darling ("ad") [1]_, Kolmogorov-Smirnov ("ks") [1]_,
+        Cramer-von Mises ("cvm") [1]_, and Filliben ("filliben") [7]_
+        statistics are available.  Alternatively, a callable with signature
+        ``(dist, data, axis)`` may be supplied to compute the statistic. Here
+        ``dist`` is a frozen distribution object (potentially with array
+        parameters), ``data`` is an array of Monte Carlo samples (of
+        compatible shape), and ``axis`` is the axis of ``data`` along which
+        the statistic must be computed.
+    n_mc_samples : int, default: 9999
+        The number of Monte Carlo samples drawn from the null hypothesized
+        distribution to form the null distribution of the statistic. The
+        sample size of each is the same as the given `data`.
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+    Returns
+    -------
+    res : GoodnessOfFitResult
+        An object with the following attributes.
+
+        fit_result : `~scipy.stats._result_classes.FitResult`
+            An object representing the fit of the provided `dist` to `data`.
+            This  object includes the values of distribution family parameters
+            that fully define the null-hypothesized distribution, that is,
+            the distribution from which Monte Carlo samples are drawn.
+        statistic : float
+            The value of the statistic comparing provided `data` to the
+            null-hypothesized distribution.
+        pvalue : float
+            The proportion of elements in the null distribution with
+            statistic values at least as extreme as the statistic value of the
+            provided `data`.
+        null_distribution : ndarray
+            The value of the statistic for each Monte Carlo sample
+            drawn from the null-hypothesized distribution.
+
+    Notes
+    -----
+    This is a generalized Monte Carlo goodness-of-fit procedure, special cases
+    of which correspond with various Anderson-Darling tests, Lilliefors' test,
+    etc. The test is described in [2]_, [3]_, and [4]_ as a parametric
+    bootstrap test. This is a Monte Carlo test in which parameters that
+    specify the distribution from which samples are drawn have been estimated
+    from the data. We describe the test using "Monte Carlo" rather than
+    "parametric bootstrap" throughout to avoid confusion with the more familiar
+    nonparametric bootstrap, and describe how the test is performed below.
+
+    *Traditional goodness of fit tests*
+
+    Traditionally, critical values corresponding with a fixed set of
+    significance levels are pre-calculated using Monte Carlo methods. Users
+    perform the test by calculating the value of the test statistic only for
+    their observed `data` and comparing this value to tabulated critical
+    values. This practice is not very flexible, as tables are not available for
+    all distributions and combinations of known and unknown parameter values.
+    Also, results can be inaccurate when critical values are interpolated from
+    limited tabulated data to correspond with the user's sample size and
+    fitted parameter values. To overcome these shortcomings, this function
+    allows the user to perform the Monte Carlo trials adapted to their
+    particular data.
+
+    *Algorithmic overview*
+
+    In brief, this routine executes the following steps:
+
+      1. Fit unknown parameters to the given `data`, thereby forming the
+         "null-hypothesized" distribution, and compute the statistic of
+         this pair of data and distribution.
+      2. Draw random samples from this null-hypothesized distribution.
+      3. Fit the unknown parameters to each random sample.
+      4. Calculate the statistic between each sample and the distribution that
+         has been fit to the sample.
+      5. Compare the value of the statistic corresponding with `data` from (1)
+         against the values of the statistic corresponding with the random
+         samples from (4). The p-value is the proportion of samples with a
+         statistic value greater than or equal to the statistic of the observed
+         data.
+
+    In more detail, the steps are as follows.
+
+    First, any unknown parameters of the distribution family specified by
+    `dist` are fit to the provided `data` using maximum likelihood estimation.
+    (One exception is the normal distribution with unknown location and scale:
+    we use the bias-corrected standard deviation ``np.std(data, ddof=1)`` for
+    the scale as recommended in [1]_.)
+    These values of the parameters specify a particular member of the
+    distribution family referred to as the "null-hypothesized distribution",
+    that is, the distribution from which the data were sampled under the null
+    hypothesis. The `statistic`, which compares data to a distribution, is
+    computed between `data` and the null-hypothesized distribution.
+
+    Next, many (specifically `n_mc_samples`) new samples, each containing the
+    same number of observations as `data`, are drawn from the
+    null-hypothesized distribution. All unknown parameters of the distribution
+    family `dist` are fit to *each resample*, and the `statistic` is computed
+    between each sample and its corresponding fitted distribution. These
+    values of the statistic form the Monte Carlo null distribution (not to be
+    confused with the "null-hypothesized distribution" above).
+
+    The p-value of the test is the proportion of statistic values in the Monte
+    Carlo null distribution that are at least as extreme as the statistic value
+    of the provided `data`. More precisely, the p-value is given by
+
+    .. math::
+
+        p = \frac{b + 1}
+                 {m + 1}
+
+    where :math:`b` is the number of statistic values in the Monte Carlo null
+    distribution that are greater than or equal to the statistic value
+    calculated for `data`, and :math:`m` is the number of elements in the
+    Monte Carlo null distribution (`n_mc_samples`). The addition of :math:`1`
+    to the numerator and denominator can be thought of as including the
+    value of the statistic corresponding with `data` in the null distribution,
+    but a more formal explanation is given in [5]_.
+
+    *Limitations*
+
+    The test can be very slow for some distribution families because unknown
+    parameters of the distribution family must be fit to each of the Monte
+    Carlo samples, and for most distributions in SciPy, distribution fitting
+    performed via numerical optimization.
+
+    *Anti-Pattern*
+
+    For this reason, it may be tempting
+    to treat parameters of the distribution pre-fit to `data` (by the user)
+    as though they were `known_params`, as specification of all parameters of
+    the distribution precludes the need to fit the distribution to each Monte
+    Carlo sample. (This is essentially how the original Kilmogorov-Smirnov
+    test is performed.) Although such a test can provide evidence against the
+    null hypothesis, the test is conservative in the sense that small p-values
+    will tend to (greatly) *overestimate* the probability of making a type I
+    error (that is, rejecting the null hypothesis although it is true), and the
+    power of the test is low (that is, it is less likely to reject the null
+    hypothesis even when the null hypothesis is false).
+    This is because the Monte Carlo samples are less likely to agree with the
+    null-hypothesized distribution as well as `data`. This tends to increase
+    the values of the statistic recorded in the null distribution, so that a
+    larger number of them exceed the value of statistic for `data`, thereby
+    inflating the p-value.
+
+    References
+    ----------
+    .. [1] M. A. Stephens (1974). "EDF Statistics for Goodness of Fit and
+           Some Comparisons." Journal of the American Statistical Association,
+           Vol. 69, pp. 730-737.
+    .. [2] W. Stute, W. G. Manteiga, and M. P. Quindimil (1993).
+           "Bootstrap based goodness-of-fit-tests." Metrika 40.1: 243-256.
+    .. [3] C. Genest, & B Rémillard. (2008). "Validity of the parametric
+           bootstrap for goodness-of-fit testing in semiparametric models."
+           Annales de l'IHP Probabilités et statistiques. Vol. 44. No. 6.
+    .. [4] I. Kojadinovic and J. Yan (2012). "Goodness-of-fit testing based on
+           a weighted bootstrap: A fast large-sample alternative to the
+           parametric bootstrap." Canadian Journal of Statistics 40.3: 480-500.
+    .. [5] B. Phipson and G. K. Smyth (2010). "Permutation P-values Should
+           Never Be Zero: Calculating Exact P-values When Permutations Are
+           Randomly Drawn." Statistical Applications in Genetics and Molecular
+           Biology 9.1.
+    .. [6] H. W. Lilliefors (1967). "On the Kolmogorov-Smirnov test for
+           normality with mean and variance unknown." Journal of the American
+           statistical Association 62.318: 399-402.
+    .. [7] Filliben, James J. "The probability plot correlation coefficient
+           test for normality." Technometrics 17.1 (1975): 111-117.
+
+    Examples
+    --------
+    A well-known test of the null hypothesis that data were drawn from a
+    given distribution is the Kolmogorov-Smirnov (KS) test, available in SciPy
+    as `scipy.stats.ks_1samp`. Suppose we wish to test whether the following
+    data:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng(1638083107694713882823079058616272161)
+    >>> x = stats.uniform.rvs(size=75, random_state=rng)
+
+    were sampled from a normal distribution. To perform a KS test, the
+    empirical distribution function of the observed data will be compared
+    against the (theoretical) cumulative distribution function of a normal
+    distribution. Of course, to do this, the normal distribution under the null
+    hypothesis must be fully specified. This is commonly done by first fitting
+    the ``loc`` and ``scale`` parameters of the distribution to the observed
+    data, then performing the test.
+
+    >>> loc, scale = np.mean(x), np.std(x, ddof=1)
+    >>> cdf = stats.norm(loc, scale).cdf
+    >>> stats.ks_1samp(x, cdf)
+    KstestResult(statistic=0.1119257570456813,
+                 pvalue=0.2827756409939257,
+                 statistic_location=0.7751845155861765,
+                 statistic_sign=-1)
+
+    An advantage of the KS-test is that the p-value - the probability of
+    obtaining a value of the test statistic under the null hypothesis as
+    extreme as the value obtained from the observed data - can be calculated
+    exactly and efficiently. `goodness_of_fit` can only approximate these
+    results.
+
+    >>> known_params = {'loc': loc, 'scale': scale}
+    >>> res = stats.goodness_of_fit(stats.norm, x, known_params=known_params,
+    ...                             statistic='ks', rng=rng)
+    >>> res.statistic, res.pvalue
+    (0.1119257570456813, 0.2788)
+
+    The statistic matches exactly, but the p-value is estimated by forming
+    a "Monte Carlo null distribution", that is, by explicitly drawing random
+    samples from `scipy.stats.norm` with the provided parameters and
+    calculating the stastic for each. The fraction of these statistic values
+    at least as extreme as ``res.statistic`` approximates the exact p-value
+    calculated by `scipy.stats.ks_1samp`.
+
+    However, in many cases, we would prefer to test only that the data were
+    sampled from one of *any* member of the normal distribution family, not
+    specifically from the normal distribution with the location and scale
+    fitted to the observed sample. In this case, Lilliefors [6]_ argued that
+    the KS test is far too conservative (that is, the p-value overstates
+    the actual probability of rejecting a true null hypothesis) and thus lacks
+    power - the ability to reject the null hypothesis when the null hypothesis
+    is actually false.
+    Indeed, our p-value above is approximately 0.28, which is far too large
+    to reject the null hypothesis at any common significance level.
+
+    Consider why this might be. Note that in the KS test above, the statistic
+    always compares data against the CDF of a normal distribution fitted to the
+    *observed data*. This tends to reduce the value of the statistic for the
+    observed data, but it is "unfair" when computing the statistic for other
+    samples, such as those we randomly draw to form the Monte Carlo null
+    distribution. It is easy to correct for this: whenever we compute the KS
+    statistic of a sample, we use the CDF of a normal distribution fitted
+    to *that sample*. The null distribution in this case has not been
+    calculated exactly and is tyically approximated using Monte Carlo methods
+    as described above. This is where `goodness_of_fit` excels.
+
+    >>> res = stats.goodness_of_fit(stats.norm, x, statistic='ks', rng=rng)
+    >>> res.statistic, res.pvalue
+    (0.1119257570456813, 0.0196)
+
+    Indeed, this p-value is much smaller, and small enough to (correctly)
+    reject the null hypothesis at common significance levels, including 5% and
+    2.5%.
+
+    However, the KS statistic is not very sensitive to all deviations from
+    normality. The original advantage of the KS statistic was the ability
+    to compute the null distribution theoretically, but a more sensitive
+    statistic - resulting in a higher test power - can be used now that we can
+    approximate the null distribution
+    computationally. The Anderson-Darling statistic [1]_ tends to be more
+    sensitive, and critical values of this statistic have been tabulated
+    for various significance levels and sample sizes using Monte Carlo methods.
+
+    >>> res = stats.anderson(x, 'norm', method='interpolate')
+    >>> print(res.statistic)
+    1.2139573337497467
+    >>> print(res.pvalue)
+    0.01
+
+    Here, the observed value of the statistic exceeds the critical value
+    corresponding with a 1% significance level. This tells us that the p-value
+    of the observed data is 1% or less, but what is it? `goodness_of_fit` can
+    estimate it directly.
+
+    >>> res = stats.goodness_of_fit(stats.norm, x, statistic='ad', rng=rng)
+    >>> res.statistic, res.pvalue
+    (1.2139573337497467, 0.0034)
+
+    A further advantage is that use of `goodness_of_fit` is not limited to
+    a particular set of distributions or conditions on which parameters
+    are known versus which must be estimated from data. Instead,
+    `goodness_of_fit` can estimate p-values relatively quickly for any
+    distribution with a sufficiently fast and reliable ``fit`` method. For
+    instance, here we perform a goodness of fit test using the Cramer-von Mises
+    statistic against the Rayleigh distribution with known location and unknown
+    scale.
+
+    >>> rng = np.random.default_rng()
+    >>> x = stats.chi(df=2.2, loc=0, scale=2).rvs(size=1000, random_state=rng)
+    >>> res = stats.goodness_of_fit(stats.rayleigh, x, statistic='cvm',
+    ...                             known_params={'loc': 0}, rng=rng)
+
+    This executes fairly quickly, but to check the reliability of the ``fit``
+    method, we should inspect the fit result.
+
+    >>> res.fit_result  # location is as specified, and scale is reasonable
+      params: FitParams(loc=0.0, scale=2.1026719844231243)
+     success: True
+     message: 'The fit was performed successfully.'
+    >>> import matplotlib.pyplot as plt  # matplotlib must be installed to plot
+    >>> res.fit_result.plot()
+    >>> plt.show()
+
+    If the distribution is not fit to the observed data as well as possible,
+    the test may not control the type I error rate, that is, the chance of
+    rejecting the null hypothesis even when it is true.
+
+    We should also look for extreme outliers in the null distribution that
+    may be caused by unreliable fitting. These do not necessarily invalidate
+    the result, but they tend to reduce the test's power.
+
+    >>> _, ax = plt.subplots()
+    >>> ax.hist(np.log10(res.null_distribution))
+    >>> ax.set_xlabel("log10 of CVM statistic under the null hypothesis")
+    >>> ax.set_ylabel("Frequency")
+    >>> ax.set_title("Histogram of the Monte Carlo null distribution")
+    >>> plt.show()
+
+    This plot seems reassuring.
+
+    If ``fit`` method is working reliably, and if the distribution of the test
+    statistic is not particularly sensitive to the values of the fitted
+    parameters, then the p-value provided by `goodness_of_fit` is expected to
+    be a good approximation.
+
+    >>> res.statistic, res.pvalue
+    (0.2231991510248692, 0.0525)
+
+    """
+    args = _gof_iv(dist, data, known_params, fit_params, guessed_params,
+                   statistic, n_mc_samples, rng)
+    (dist, data, fixed_nhd_params, fixed_rfd_params, guessed_nhd_params,
+     guessed_rfd_params, statistic, n_mc_samples_int, rng) = args
+
+    # Fit null hypothesis distribution to data
+    nhd_fit_fun = _get_fit_fun(dist, data, guessed_nhd_params,
+                               fixed_nhd_params)
+    nhd_vals = nhd_fit_fun(data)
+    nhd_dist = dist(*nhd_vals)
+
+    def rvs(size):
+        return nhd_dist.rvs(size=size, random_state=rng)
+
+    # Define statistic
+    fit_fun = _get_fit_fun(dist, data, guessed_rfd_params, fixed_rfd_params)
+    if callable(statistic):
+        compare_fun = statistic
+    else:
+        compare_fun = _compare_dict[statistic]
+    alternative = getattr(compare_fun, 'alternative', 'greater')
+
+    def statistic_fun(data, axis):
+        # Make things simple by always working along the last axis.
+        data = np.moveaxis(data, axis, -1)
+        rfd_vals = fit_fun(data)
+        rfd_dist = dist(*rfd_vals)
+        return compare_fun(rfd_dist, data, axis=-1)
+
+    res = stats.monte_carlo_test(data, rvs, statistic_fun, vectorized=True,
+                                 n_resamples=n_mc_samples, axis=-1,
+                                 alternative=alternative)
+    opt_res = optimize.OptimizeResult()
+    opt_res.success = True
+    opt_res.message = "The fit was performed successfully."
+    opt_res.x = nhd_vals
+    # Only continuous distributions for now, hence discrete=False
+    # There's no fundamental limitation; it's just that we're not using
+    # stats.fit, discrete distributions don't have `fit` method, and
+    # we haven't written any vectorized fit functions for a discrete
+    # distribution yet.
+    return GoodnessOfFitResult(FitResult(dist, data, False, opt_res),
+                               res.statistic, res.pvalue,
+                               res.null_distribution)
+
+
+def _get_fit_fun(dist, data, guessed_params, fixed_params):
+
+    shape_names = [] if dist.shapes is None else dist.shapes.split(", ")
+    param_names = shape_names + ['loc', 'scale']
+    fparam_names = ['f'+name for name in param_names]
+    all_fixed = not set(fparam_names).difference(fixed_params)
+    guessed_shapes = [guessed_params.pop(x, None)
+                      for x in shape_names if x in guessed_params]
+
+    if all_fixed:
+        def fit_fun(data):
+            return [fixed_params[name] for name in fparam_names]
+    # Define statistic, including fitting distribution to data
+    elif dist in _fit_funs:
+        def fit_fun(data):
+            params = _fit_funs[dist](data, **fixed_params)
+            params = np.asarray(np.broadcast_arrays(*params))
+            if params.ndim > 1:
+                params = params[..., np.newaxis]
+            return params
+    else:
+        def fit_fun_1d(data):
+            return dist.fit(data, *guessed_shapes, **guessed_params,
+                            **fixed_params)
+
+        def fit_fun(data):
+            params = np.apply_along_axis(fit_fun_1d, axis=-1, arr=data)
+            if params.ndim > 1:
+                params = params.T[..., np.newaxis]
+            return params
+
+    return fit_fun
+
+
+# Vectorized fitting functions. These are to accept ND `data` in which each
+# row (slice along last axis) is a sample to fit and scalar fixed parameters.
+# They return a tuple of shape parameter arrays, each of shape data.shape[:-1].
+def _fit_norm(data, floc=None, fscale=None):
+    loc = floc
+    scale = fscale
+    if loc is None and scale is None:
+        loc = np.mean(data, axis=-1)
+        scale = np.std(data, ddof=1, axis=-1)
+    elif loc is None:
+        loc = np.mean(data, axis=-1)
+    elif scale is None:
+        scale = np.sqrt(((data - loc)**2).mean(axis=-1))
+    return loc, scale
+
+
+_fit_funs = {stats.norm: _fit_norm}  # type: ignore[attr-defined]
+
+
+# Vectorized goodness of fit statistic functions. These accept a frozen
+# distribution object and `data` in which each row (slice along last axis) is
+# a sample.
+
+
+def _anderson_darling(dist, data, axis):
+    x = np.sort(data, axis=-1)
+    n = data.shape[-1]
+    i = np.arange(1, n+1)
+    Si = (2*i - 1)/n * (dist.logcdf(x) + dist.logsf(x[..., ::-1]))
+    S = np.sum(Si, axis=-1)
+    return -n - S
+
+
+def _compute_dplus(cdfvals):  # adapted from _stats_py before gh-17062
+    n = cdfvals.shape[-1]
+    return (np.arange(1.0, n + 1) / n - cdfvals).max(axis=-1)
+
+
+def _compute_dminus(cdfvals):
+    n = cdfvals.shape[-1]
+    return (cdfvals - np.arange(0.0, n)/n).max(axis=-1)
+
+
+def _kolmogorov_smirnov(dist, data, axis=-1):
+    x = np.sort(data, axis=axis)
+    cdfvals = dist.cdf(x)
+    cdfvals = np.moveaxis(cdfvals, axis, -1)
+    Dplus = _compute_dplus(cdfvals)  # always works along last axis
+    Dminus = _compute_dminus(cdfvals)
+    return np.maximum(Dplus, Dminus)
+
+
+def _corr(X, M):
+    # Correlation coefficient r, simplified and vectorized as we need it.
+    # See [7] Equation (2). Lemma 1/2 are only for distributions symmetric
+    # about 0.
+    Xm = X.mean(axis=-1, keepdims=True)
+    Mm = M.mean(axis=-1, keepdims=True)
+    num = np.sum((X - Xm) * (M - Mm), axis=-1)
+    den = np.sqrt(np.sum((X - Xm)**2, axis=-1) * np.sum((M - Mm)**2, axis=-1))
+    return num/den
+
+
+def _filliben(dist, data, axis):
+    # [7] Section 8 # 1
+    X = np.sort(data, axis=-1)
+
+    # [7] Section 8 # 2
+    n = data.shape[-1]
+    k = np.arange(1, n+1)
+    # Filliben used an approximation for the uniform distribution order
+    # statistic medians.
+    # m = (k - .3175)/(n + 0.365)
+    # m[-1] = 0.5**(1/n)
+    # m[0] = 1 - m[-1]
+    # We can just as easily use the (theoretically) exact values. See e.g.
+    # https://en.wikipedia.org/wiki/Order_statistic
+    # "Order statistics sampled from a uniform distribution"
+    m = stats.beta(k, n + 1 - k).median()
+
+    # [7] Section 8 # 3
+    M = dist.ppf(m)
+
+    # [7] Section 8 # 4
+    return _corr(X, M)
+_filliben.alternative = 'less'  # type: ignore[attr-defined]
+
+
+def _cramer_von_mises(dist, data, axis):
+    x = np.sort(data, axis=-1)
+    n = data.shape[-1]
+    cdfvals = dist.cdf(x)
+    u = (2*np.arange(1, n+1) - 1)/(2*n)
+    w = 1 / (12*n) + np.sum((u - cdfvals)**2, axis=-1)
+    return w
+
+
+_compare_dict = {"ad": _anderson_darling, "ks": _kolmogorov_smirnov,
+                 "cvm": _cramer_von_mises, "filliben": _filliben}
+
+
+def _gof_iv(dist, data, known_params, fit_params, guessed_params, statistic,
+            n_mc_samples, rng):
+
+    if not isinstance(dist, stats.rv_continuous):
+        message = ("`dist` must be a (non-frozen) instance of "
+                   "`stats.rv_continuous`.")
+        raise TypeError(message)
+
+    data = np.asarray(data, dtype=float)
+    if not data.ndim == 1:
+        message = "`data` must be a one-dimensional array of numbers."
+        raise ValueError(message)
+
+    # Leave validation of these key/value pairs to the `fit` method,
+    # but collect these into dictionaries that will be used
+    known_params = known_params or dict()
+    fit_params = fit_params or dict()
+    guessed_params = guessed_params or dict()
+
+    known_params_f = {("f"+key): val for key, val in known_params.items()}
+    fit_params_f = {("f"+key): val for key, val in fit_params.items()}
+
+    # These are the values of parameters of the null distribution family
+    # with which resamples are drawn
+    fixed_nhd_params = known_params_f.copy()
+    fixed_nhd_params.update(fit_params_f)
+
+    # These are fixed when fitting the distribution family to resamples
+    fixed_rfd_params = known_params_f.copy()
+
+    # These are used as guesses when fitting the distribution family to
+    # the original data
+    guessed_nhd_params = guessed_params.copy()
+
+    # These are used as guesses when fitting the distribution family to
+    # resamples
+    guessed_rfd_params = fit_params.copy()
+    guessed_rfd_params.update(guessed_params)
+
+    if not callable(statistic):
+        statistic = statistic.lower()
+        statistics = {'ad', 'ks', 'cvm', 'filliben'}
+        if statistic not in statistics:
+            message = f"`statistic` must be one of {statistics}."
+            raise ValueError(message)
+
+    n_mc_samples_int = int(n_mc_samples)
+    if n_mc_samples_int != n_mc_samples:
+        message = "`n_mc_samples` must be an integer."
+        raise TypeError(message)
+
+    rng = check_random_state(rng)
+
+    return (dist, data, fixed_nhd_params, fixed_rfd_params, guessed_nhd_params,
+            guessed_rfd_params, statistic, n_mc_samples_int, rng)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_hypotests.py b/.venv/lib/python3.12/site-packages/scipy/stats/_hypotests.py
new file mode 100644
index 0000000000000000000000000000000000000000..b905654a99a08b2a856923279aa3a112363cb6cf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_hypotests.py
@@ -0,0 +1,2201 @@
+from collections import namedtuple
+from dataclasses import dataclass
+import math
+import numpy as np
+import warnings
+from itertools import combinations
+import scipy.stats
+from scipy.optimize import shgo
+from . import distributions
+from ._common import ConfidenceInterval
+from ._continuous_distns import norm
+from scipy._lib._array_api import (xp_capabilities, array_namespace, xp_size,
+                                   xp_promote, xp_result_type, xp_copy, is_numpy,
+                                   is_lazy_array, _count_nonmasked, is_marray,
+                                   _masked_apply)
+import scipy._external.array_api_extra as xpx
+from scipy.special import gamma, kv, gammaln
+from scipy.fft import ifft
+from ._stats_pythran import _a_ij_Aij_Dij2
+from ._stats_pythran import (
+    _concordant_pairs as _P, _discordant_pairs as _Q
+)
+from ._axis_nan_policy import _axis_nan_policy_factory
+from scipy.stats import _stats_py
+
+__all__ = ['epps_singleton_2samp', 'cramervonmises', 'somersd',
+           'barnard_exact', 'boschloo_exact', 'cramervonmises_2samp',
+           'tukey_hsd', 'poisson_means_test']
+
+Epps_Singleton_2sampResult = namedtuple('Epps_Singleton_2sampResult',
+                                        ('statistic', 'pvalue'))
+
+
+@xp_capabilities(skip_backends=[("dask.array", "lazy -> no _axis_nan_policy")])
+@_axis_nan_policy_factory(Epps_Singleton_2sampResult, n_samples=2, too_small=4)
+def epps_singleton_2samp(x, y, t=(0.4, 0.8), *, axis=0):
+    """Compute the Epps-Singleton (ES) test statistic.
+
+    Test the null hypothesis that two samples have the same underlying
+    probability distribution.
+
+    Parameters
+    ----------
+    x, y : array-like
+        The two samples of observations to be tested. Input must not have more
+        than one dimension. Samples can have different lengths, but both
+        must have at least five observations.
+    t : array-like, optional
+        The points (t1, ..., tn) where the empirical characteristic function is
+        to be evaluated. It should be positive distinct numbers. The default
+        value (0.4, 0.8) is proposed in [1]_. Input must not have more than
+        one dimension.
+    axis : int or tuple of ints, default: 0
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    statistic : float
+        The test statistic.
+    pvalue : float
+        The associated p-value based on the asymptotic chi2-distribution.
+
+    See Also
+    --------
+    ks_2samp, anderson_ksamp
+
+    Notes
+    -----
+    Testing whether two samples are generated by the same underlying
+    distribution is a classical question in statistics. A widely used test is
+    the Kolmogorov-Smirnov (KS) test which relies on the empirical
+    distribution function. Epps and Singleton introduce a test based on the
+    empirical characteristic function in [1]_.
+
+    One advantage of the ES test compared to the KS test is that is does
+    not assume a continuous distribution. In [1]_, the authors conclude
+    that the test also has a higher power than the KS test in many
+    examples. They recommend the use of the ES test for discrete samples as
+    well as continuous samples with at least 25 observations each, whereas
+    `anderson_ksamp` is recommended for smaller sample sizes in the
+    continuous case.
+
+    The p-value is computed from the asymptotic distribution of the test
+    statistic which follows a `chi2` distribution. If the sample size of both
+    `x` and `y` is below 25, the small sample correction proposed in [1]_ is
+    applied to the test statistic.
+
+    The default values of `t` are determined in [1]_ by considering
+    various distributions and finding good values that lead to a high power
+    of the test in general. Table III in [1]_ gives the optimal values for
+    the distributions tested in that study. The values of `t` are scaled by
+    the semi-interquartile range in the implementation, see [1]_.
+
+    References
+    ----------
+    .. [1] T. W. Epps and K. J. Singleton, "An omnibus test for the two-sample
+       problem using the empirical characteristic function", Journal of
+       Statistical Computation and Simulation 26, p. 177--203, 1986.
+
+    .. [2] S. J. Goerg and J. Kaiser, "Nonparametric testing of distributions
+       - the Epps-Singleton two-sample test using the empirical characteristic
+       function", The Stata Journal 9(3), p. 454--465, 2009.
+
+    Examples
+    --------
+    Generate random samples ``x`` and ``y``.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng(66326777)
+    >>> x = rng.normal(0, 1, size=100)
+    >>> y = rng.normal(5, 3, size=100)
+
+    Test the null hypothesis that ``x`` and ``y`` are sampled from the same
+    distribution.
+
+    >>> res = stats.epps_singleton_2samp(x, y)
+    >>> res.statistic
+    np.float64(316.6441136688085)
+    >>> res.pvalue
+    np.float64(2.778946959815902e-67)
+    
+    The large value of the statistic and small p-value may be taken as evidence that
+    ``x`` and ``y`` were not sampled from the same distribution. 
+    """
+    xp = array_namespace(x, y)
+    # x and y are converted to arrays by the decorator
+    # and `axis` is guaranteed to be -1.
+    x, y = xp_promote(x, y, force_floating=True, xp=xp)
+    t = xp.asarray(t, dtype=x.dtype)
+    # check if x and y are valid inputs
+    nx, ny = x.shape[-1], y.shape[-1]
+    if (nx < 5) or (ny < 5):  # only used by test_axis_nan_policy
+        raise ValueError('x and y should have at least 5 elements, but len(x) '
+                         f'= {nx} and len(y) = {ny}.')
+    n = nx + ny
+
+    # check if t is valid
+    if t.ndim > 1:
+        raise ValueError(f't must be 1d, but t.ndim equals {t.ndim}.')
+    if is_lazy_array(t):
+        t = xpx.at(t)[t <= 0].set(xp.nan)
+    elif xp.any(t <= 0):
+        raise ValueError('t must contain positive elements only.')
+
+    # Previously, non-finite input caused an error in linalg functions.
+    # To prevent an issue in one slice from halting the calculation, replace non-finite
+    # values with a harmless one, and replace results with NaN at the end.
+    i_x = ~xp.isfinite(x)
+    i_y = ~xp.isfinite(y)
+    # Ideally we would avoid copying all data here; see
+    # discussion in data-apis/array-api-extra#506.
+    x = xp.where(i_x, 1., x)
+    y = xp.where(i_y, 1., y)
+    invalid_result = xp.any(i_x, axis=-1) | xp.any(i_y, axis=-1)
+
+    # rescale t with semi-iqr as proposed in [1]; import iqr here to avoid
+    # circular import
+    from scipy.stats import iqr
+    sigma = iqr(xp.concat((x, y), axis=-1), axis=-1, keepdims=True) / 2
+    ts = xp.reshape(t, (-1,) + (1,)*x.ndim) / sigma
+
+    # covariance estimation of ES test
+    gx = xp.concat((xp.cos(ts*x), xp.sin(ts*x)), axis=0)
+    gy = xp.concat((xp.cos(ts*y), xp.sin(ts*y)), axis=0)
+    gx, gy = xp.moveaxis(gx, 0, -2), xp.moveaxis(gy, 0, -2)
+    cov_x = xpx.cov(gx) * (nx-1)/nx  # the test uses biased cov-estimate
+    cov_y = xpx.cov(gy) * (ny-1)/ny
+    cov_x, cov_y = xp.astype(cov_x, x.dtype), xp.astype(cov_y, y.dtype)
+    est_cov = (n/nx)*cov_x + (n/ny)*cov_y
+    est_cov_inv = xp.linalg.pinv(est_cov)
+    r = xp.asarray(xp.linalg.matrix_rank(est_cov_inv), dtype=est_cov_inv.dtype)
+    if not is_lazy_array(r) and xp.any(r < 2*xp_size(t)):
+        warnings.warn('Estimated covariance matrix does not have full rank. '
+                      'This indicates a bad choice of the input t and the '
+                      'test might not be consistent.', # see p. 183 in [1]_
+                      stacklevel=2)
+
+    # compute test statistic w distributed asympt. as chisquare with df=r
+    g_diff = xp.mean(gx, axis=-1, keepdims=True) - xp.mean(gy, axis=-1, keepdims=True)
+    w = n*xp.matmul(xp.matrix_transpose(g_diff), xp.matmul(est_cov_inv, g_diff))
+    w = w[..., 0, 0]
+
+    # apply small-sample correction
+    if (max(nx, ny) < 25):
+        corr = 1.0/(1.0 + n**(-0.45) + 10.1*(nx**(-1.7) + ny**(-1.7)))
+        w *= corr
+
+    chi2 = _stats_py._SimpleChi2(r)
+    p = _stats_py._get_pvalue(w, chi2, alternative='greater', symmetric=False, xp=xp)
+
+    w = xpx.at(w)[invalid_result].set(xp.nan)
+    p = xpx.at(p)[invalid_result].set(xp.nan)
+    w = w[()] if w.ndim == 0 else w
+    p = p[()] if p.ndim == 0 else p
+    return Epps_Singleton_2sampResult(w, p)
+
+
+@xp_capabilities(np_only=True)
+def poisson_means_test(k1, n1, k2, n2, *, diff=0, alternative='two-sided'):
+    r"""
+    Performs the Poisson means test, AKA the "E-test".
+
+    This is a test of the null hypothesis that the difference between means of
+    two Poisson distributions is `diff`. The samples are provided as the
+    number of events `k1` and `k2` observed within measurement intervals
+    (e.g. of time, space, number of observations) of sizes `n1` and `n2`.
+
+    Parameters
+    ----------
+    k1 : int
+        Number of events observed from distribution 1.
+    n1 : float
+        Size of sample from distribution 1.
+    k2 : int
+        Number of events observed from distribution 2.
+    n2 : float
+        Size of sample from distribution 2.
+    diff : float, default=0
+        The hypothesized difference in means between the distributions
+        underlying the samples.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+        The following options are available (default is 'two-sided'):
+
+        * 'two-sided': the difference between distribution means is not
+          equal to `diff`
+        * 'less': the difference between distribution means is less than
+          `diff`
+        * 'greater': the difference between distribution means is greater
+          than `diff`
+
+    Returns
+    -------
+    statistic : float
+        The test statistic (see [1]_ equation 3.3).
+    pvalue : float
+        The probability of achieving such an extreme value of the test
+        statistic under the null hypothesis.
+
+    Notes
+    -----
+
+    Let:
+
+    .. math:: X_1 \sim \mbox{Poisson}(\mathtt{n1}\lambda_1)
+
+    be a random variable independent of
+
+    .. math:: X_2  \sim \mbox{Poisson}(\mathtt{n2}\lambda_2)
+
+    and let ``k1`` and ``k2`` be the observed values of :math:`X_1`
+    and :math:`X_2`, respectively. Then `poisson_means_test` uses the number
+    of observed events ``k1`` and ``k2`` from samples of size ``n1`` and
+    ``n2``, respectively, to test the null hypothesis that
+
+    .. math::
+       H_0: \lambda_1 - \lambda_2 = \mathtt{diff}
+
+    A benefit of the E-test is that it has good power for small sample sizes,
+    which can reduce sampling costs [1]_. It has been evaluated and determined
+    to be more powerful than the comparable C-test, sometimes referred to as
+    the Poisson exact test.
+
+    References
+    ----------
+    .. [1]  Krishnamoorthy, K., & Thomson, J. (2004). A more powerful test for
+       comparing two Poisson means. Journal of Statistical Planning and
+       Inference, 119(1), 23-35.
+
+    .. [2]  Przyborowski, J., & Wilenski, H. (1940). Homogeneity of results in
+       testing samples from Poisson series: With an application to testing
+       clover seed for dodder. Biometrika, 31(3/4), 313-323.
+
+    Examples
+    --------
+
+    Suppose that a gardener wishes to test the number of dodder (weed) seeds
+    in a sack of clover seeds that they buy from a seed company. It has
+    previously been established that the number of dodder seeds in clover
+    follows the Poisson distribution.
+
+    A 100 gram sample is drawn from the sack before being shipped to the
+    gardener. The sample is analyzed, and it is found to contain no dodder
+    seeds; that is, `k1` is 0. However, upon arrival, the gardener draws
+    another 100 gram sample from the sack. This time, three dodder seeds are
+    found in the sample; that is, `k2` is 3. The gardener would like to
+    know if the difference is significant and not due to chance. The
+    null hypothesis is that the difference between the two samples is merely
+    due to chance, or that :math:`\lambda_1 - \lambda_2 = \mathtt{diff}`
+    where :math:`\mathtt{diff} = 0`. The alternative hypothesis is that the
+    difference is not due to chance, or :math:`\lambda_1 - \lambda_2 \ne 0`.
+    The gardener selects a significance level of 5% to reject the null
+    hypothesis in favor of the alternative [2]_.
+
+    >>> import scipy.stats as stats
+    >>> res = stats.poisson_means_test(0, 100, 3, 100)
+    >>> res.statistic, res.pvalue
+    (-1.7320508075688772, 0.08837900929018157)
+
+    The p-value is .088, indicating a near 9% chance of observing a value of
+    the test statistic under the null hypothesis. This exceeds 5%, so the
+    gardener does not reject the null hypothesis as the difference cannot be
+    regarded as significant at this level.
+    """
+
+    _poisson_means_test_iv(k1, n1, k2, n2, diff, alternative)
+
+    # "for a given k_1 and k_2, an estimate of \lambda_2 is given by" [1] (3.4)
+    lmbd_hat2 = ((k1 + k2) / (n1 + n2) - diff * n1 / (n1 + n2))
+
+    # "\hat{\lambda_{2k}} may be less than or equal to zero ... and in this
+    # case the null hypothesis cannot be rejected ... [and] it is not necessary
+    # to compute the p-value". [1] page 26 below eq. (3.6).
+    if lmbd_hat2 <= 0:
+        return _stats_py.SignificanceResult(0, 1)
+
+    # The unbiased variance estimate [1] (3.2)
+    var = k1 / (n1 ** 2) + k2 / (n2 ** 2)
+
+    # The _observed_ pivot statistic from the input. It follows the
+    # unnumbered equation following equation (3.3) This is used later in
+    # comparison with the computed pivot statistics in an indicator function.
+    t_k1k2 = (k1 / n1 - k2 / n2 - diff) / np.sqrt(var)
+
+    # Equation (3.5) of [1] is lengthy, so it is broken into several parts,
+    # beginning here. Note that the probability mass function of poisson is
+    # exp^(-\mu)*\mu^k/k!, so and this is called with shape \mu, here noted
+    # here as nlmbd_hat*. The strategy for evaluating the double summation in
+    # (3.5) is to create two arrays of the values of the two products inside
+    # the summation and then broadcast them together into a matrix, and then
+    # sum across the entire matrix.
+
+    # Compute constants (as seen in the first and second separated products in
+    # (3.5).). (This is the shape (\mu) parameter of the poisson distribution.)
+    nlmbd_hat1 = n1 * (lmbd_hat2 + diff)
+    nlmbd_hat2 = n2 * lmbd_hat2
+
+    # Determine summation bounds for tail ends of distribution rather than
+    # summing to infinity. `x1*` is for the outer sum and `x2*` is the inner
+    # sum.
+    x1_lb, x1_ub = distributions.poisson.ppf([1e-10, 1 - 1e-16], nlmbd_hat1)
+    x2_lb, x2_ub = distributions.poisson.ppf([1e-10, 1 - 1e-16], nlmbd_hat2)
+
+    # Construct arrays to function as the x_1 and x_2 counters on the summation
+    # in (3.5). `x1` is in columns and `x2` is in rows to allow for
+    # broadcasting.
+    x1 = np.arange(x1_lb, x1_ub + 1)
+    x2 = np.arange(x2_lb, x2_ub + 1)[:, None]
+
+    # These are the two products in equation (3.5) with `prob_x1` being the
+    # first (left side) and `prob_x2` being the second (right side). (To
+    # make as clear as possible: the 1st contains a "+ d" term, the 2nd does
+    # not.)
+    prob_x1 = distributions.poisson.pmf(x1, nlmbd_hat1)
+    prob_x2 = distributions.poisson.pmf(x2, nlmbd_hat2)
+
+    # compute constants for use in the "pivot statistic" per the
+    # unnumbered equation following (3.3).
+    lmbd_x1 = x1 / n1
+    lmbd_x2 = x2 / n2
+    lmbds_diff = lmbd_x1 - lmbd_x2 - diff
+    var_x1x2 = lmbd_x1 / n1 + lmbd_x2 / n2
+
+    # This is the 'pivot statistic' for use in the indicator of the summation
+    # (left side of "I[.]").
+    with np.errstate(invalid='ignore', divide='ignore'):
+        t_x1x2 = lmbds_diff / np.sqrt(var_x1x2)
+
+    # `[indicator]` implements the "I[.] ... the indicator function" per
+    # the paragraph following equation (3.5).
+    if alternative == 'two-sided':
+        indicator = np.abs(t_x1x2) >= np.abs(t_k1k2)
+    elif alternative == 'less':
+        indicator = t_x1x2 <= t_k1k2
+    else:
+        indicator = t_x1x2 >= t_k1k2
+
+    # Multiply all combinations of the products together, exclude terms
+    # based on the `indicator` and then sum. (3.5)
+    pvalue = np.sum((prob_x1 * prob_x2)[indicator])
+    return _stats_py.SignificanceResult(t_k1k2, pvalue)
+
+
+def _poisson_means_test_iv(k1, n1, k2, n2, diff, alternative):
+    # """check for valid types and values of input to `poisson_mean_test`."""
+    if k1 != int(k1) or k2 != int(k2):
+        raise TypeError('`k1` and `k2` must be integers.')
+
+    count_err = '`k1` and `k2` must be greater than or equal to 0.'
+    if k1 < 0 or k2 < 0:
+        raise ValueError(count_err)
+
+    if n1 <= 0 or n2 <= 0:
+        raise ValueError('`n1` and `n2` must be greater than 0.')
+
+    if diff < 0:
+        raise ValueError('diff must be greater than or equal to 0.')
+
+    alternatives = {'two-sided', 'less', 'greater'}
+    if alternative.lower() not in alternatives:
+        raise ValueError(f"Alternative must be one of '{alternatives}'.")
+
+
+class CramerVonMisesResult:
+    def __init__(self, statistic, pvalue):
+        self.statistic = statistic
+        self.pvalue = pvalue
+
+    def __repr__(self):
+        return (f"{self.__class__.__name__}(statistic={self.statistic}, "
+                f"pvalue={self.pvalue})")
+
+
+def _psi1_mod(x, *, xp=None):
+    """
+    psi1 is defined in equation 1.10 in Csörgő, S. and Faraway, J. (1996).
+    This implements a modified version by excluding the term V(x) / 12
+    (here: _cdf_cvm_inf(x) / 12) to avoid evaluating _cdf_cvm_inf(x)
+    twice in _cdf_cvm.
+
+    Implementation based on MAPLE code of Julian Faraway and R code of the
+    function pCvM in the package goftest (v1.1.1), permission granted
+    by Adrian Baddeley. Main difference in the implementation: the code
+    here keeps adding terms of the series until the terms are small enough.
+    """
+    xp = array_namespace(x) if xp is None else xp
+
+    def _ed2(y):
+        z = y**2 / 4
+        z_ = np.asarray(z)
+        b = xp.asarray(kv(1/4, z_) + kv(3/4, z_))
+        return xp.exp(-z) * (y/2)**(3/2) * b / math.sqrt(np.pi)
+
+    def _ed3(y):
+        z = y**2 / 4
+        z_ = np.asarray(z)
+        c = xp.exp(-z) / math.sqrt(np.pi)
+        kv_terms = xp.asarray(2*kv(1/4, z_)
+                              + 3*kv(3/4, z_) - kv(5/4, z_))
+        return c * (y/2)**(5/2) * kv_terms
+
+    def _Ak(k, x):
+        m = 2*k + 1
+        sx = 2 * xp.sqrt(x)
+        y1 = x**(3/4)
+        y2 = x**(5/4)
+
+        gamma_kp1_2 = float(gamma(k + 1 / 2))
+        gamma_kp3_2 = float(gamma(k + 3 / 2))
+
+        e1 = m * gamma_kp1_2 * _ed2((4 * k + 3)/sx) / (9 * y1)
+        e2 = gamma_kp1_2 * _ed3((4 * k + 1) / sx) / (72 * y2)
+        e3 = 2 * (m + 2) * gamma_kp3_2 * _ed3((4 * k + 5) / sx) / (12 * y2)
+        e4 = 7 * m * gamma_kp1_2 * _ed2((4 * k + 1) / sx) / (144 * y1)
+        e5 = 7 * m * gamma_kp1_2 * _ed2((4 * k + 5) / sx) / (144 * y1)
+
+        return e1 + e2 + e3 + e4 + e5
+
+    x = xp.asarray(x)
+    tot = xp.zeros_like(x)
+    cond = xp.ones_like(x, dtype=xp.bool)
+    k = 0
+    while xp.any(cond):
+        gamma_kp1 = float(gamma(k + 1))
+        z = -_Ak(k, x[cond]) / (xp.pi * gamma_kp1)
+        tot = xpx.at(tot)[cond].set(tot[cond] + z)
+        # For float32 arithmetic, the tolerance may need to be adjusted or the
+        # algorithm may prove to be unsuitable.
+        cond = xpx.at(cond)[xp_copy(cond)].set(xp.abs(z) >= 1e-7)
+        k += 1
+
+    return tot
+
+
+def _cdf_cvm_inf(x, *, xp=None):
+    """
+    Calculate the cdf of the Cramér-von Mises statistic (infinite sample size).
+
+    See equation 1.2 in Csörgő, S. and Faraway, J. (1996).
+
+    Implementation based on MAPLE code of Julian Faraway and R code of the
+    function pCvM in the package goftest (v1.1.1), permission granted
+    by Adrian Baddeley. Main difference in the implementation: the code
+    here keeps adding terms of the series until the terms are small enough.
+
+    The function is not expected to be accurate for large values of x, say
+    x > 4, when the cdf is very close to 1.
+    """
+    xp = array_namespace(x) if xp is None else xp
+    x = xp.asarray(x)
+
+    def term(x, k):
+        # this expression can be found in [2], second line of (1.3)
+        u = math.exp(gammaln(k + 0.5) - gammaln(k+1)) / (xp.pi**1.5 * xp.sqrt(x))
+        y = 4*k + 1
+        q = y**2 / (16*x)
+        b = xp.asarray(kv(0.25, np.asarray(q)), dtype=u.dtype)  # not automatic?
+        return u * math.sqrt(y) * xp.exp(-q) * b
+
+    tot = xp.zeros_like(x, dtype=x.dtype)
+    cond = xp.ones_like(x, dtype=xp.bool)
+    k = 0
+    while xp.any(cond):
+        z = term(x[cond], k)
+        # tot[cond] = tot[cond] + z
+        tot = xpx.at(tot)[cond].add(z)
+        # cond[cond] = np.abs(z) >= 1e-7
+        cond = xpx.at(cond)[xp_copy(cond)].set(xp.abs(z) >= 1e-7)  # torch needs copy
+        k += 1
+
+    return tot
+
+
+def _cdf_cvm(x, n=None, *, xp=None):
+    """
+    Calculate the cdf of the Cramér-von Mises statistic for a finite sample
+    size n. If N is None, use the asymptotic cdf (n=inf).
+
+    See equation 1.8 in Csörgő, S. and Faraway, J. (1996) for finite samples,
+    1.2 for the asymptotic cdf.
+
+    The function is not expected to be accurate for large values of x, say
+    x > 2, when the cdf is very close to 1 and it might return values > 1
+    in that case, e.g. _cdf_cvm(2.0, 12) = 1.0000027556716846. Moreover, it
+    is not accurate for small values of n, especially close to the bounds of
+    the distribution's domain, [1/(12*n), n/3], where the value jumps to 0
+    and 1, respectively. These are limitations of the approximation by Csörgő
+    and Faraway (1996) implemented in this function.
+    """
+    xp = array_namespace(x) if xp is None else xp
+    x = xp.asarray(x)
+
+    if n is None:
+        y = _cdf_cvm_inf(x, xp=xp)
+    else:
+        # support of the test statistic is [12/n, n/3], see 1.1 in [2]
+        y = xp.zeros_like(x, dtype=x.dtype)
+        sup = (1./(12*n) < x) & (x < n/3.)
+        # note: _psi1_mod does not include the term _cdf_cvm_inf(x) / 12
+        # therefore, we need to add it here
+        y = xpx.at(y)[sup].set(_cdf_cvm_inf(x[sup], xp=xp) * (1 + 1./(12*n))
+                               + _psi1_mod(x[sup], xp=xp) / n)
+        y = xpx.at(y)[x >= n/3].set(1.)
+
+    return y[()] if y.ndim == 0 else y
+
+
+def _cvm_result_to_tuple(res, _):
+    return res.statistic, res.pvalue
+
+
+@xp_capabilities(cpu_only=True,  # needs special function `kv`
+                 skip_backends=[('dask.array', 'typical dask issues')],
+                 jax_jit=False,  # array boolean indices must be concrete
+                 marray=True)
+@_axis_nan_policy_factory(CramerVonMisesResult, n_samples=1, too_small=1,
+                          result_to_tuple=_cvm_result_to_tuple)
+def cramervonmises(rvs, cdf, args=(), *, axis=0):
+    r"""Perform the one-sample Cramér-von Mises test for goodness of fit.
+
+    This performs a test of the goodness of fit of a cumulative distribution
+    function (cdf) :math:`F` compared to the empirical distribution function
+    :math:`F_n` of observed random variates :math:`X_1, ..., X_n` that are
+    assumed to be independent and identically distributed ([1]_).
+    The null hypothesis is that the :math:`X_i` have cumulative distribution
+    :math:`F`.
+
+    The test statistic :math:`T` is defined as in [1]_, where :math:`\omega^2`
+    is the Cramér-von Mises criterion and :math:`x_i` are the observed values.
+
+    .. math::
+        T = n\omega^2 =
+        \frac{1}{12n} + \sum_{i=1}^n \left[ \frac{2i-1}{2n} - F(x_i) \right]^2
+
+    Parameters
+    ----------
+    rvs : array_like
+        A 1-D array of observed values of the random variables :math:`X_i`.
+        The sample must contain at least two observations.
+    cdf : str or callable
+        The cumulative distribution function :math:`F` to test the
+        observations against. If a string, it should be the name of a
+        distribution in `scipy.stats`. If a callable, that callable is used
+        to calculate the cdf: ``cdf(x, *args) -> float``.
+    args : tuple, optional
+        Distribution parameters. These are assumed to be known; see Notes.
+    axis : int or tuple of ints, default: 0
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    res : object with attributes
+        statistic : float
+            Cramér-von Mises statistic :math:`T`.
+        pvalue : float
+            The p-value.
+
+    See Also
+    --------
+    kstest, cramervonmises_2samp
+
+    Notes
+    -----
+    .. versionadded:: 1.6.0
+
+    The p-value relies on the approximation given by equation 1.8 in [2]_.
+    It is important to keep in mind that the p-value is only accurate if
+    one tests a simple hypothesis, i.e. the parameters of the reference
+    distribution are known. If the parameters are estimated from the data
+    (composite hypothesis), the computed p-value is not reliable.
+
+    References
+    ----------
+    .. [1] Cramér-von Mises criterion, Wikipedia,
+           https://en.wikipedia.org/wiki/Cram%C3%A9r%E2%80%93von_Mises_criterion
+    .. [2] Csörgő, S. and Faraway, J. (1996). The Exact and Asymptotic
+           Distribution of Cramér-von Mises Statistics. Journal of the
+           Royal Statistical Society, pp. 221-234.
+
+    Examples
+    --------
+
+    Suppose we wish to test whether data generated by ``scipy.stats.norm.rvs``
+    were, in fact, drawn from the standard normal distribution. We choose a
+    significance level of ``alpha=0.05``.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng(165417232101553420507139617764912913465)
+    >>> x = stats.norm.rvs(size=500, random_state=rng)
+    >>> res = stats.cramervonmises(x, 'norm')
+    >>> res.statistic, res.pvalue
+    (0.1072085112565724, 0.5508482238203407)
+
+    The p-value exceeds our chosen significance level, so we do not
+    reject the null hypothesis that the observed sample is drawn from the
+    standard normal distribution.
+
+    Now suppose we wish to check whether the same samples shifted by 2.1 is
+    consistent with being drawn from a normal distribution with a mean of 2.
+
+    >>> y = x + 2.1
+    >>> res = stats.cramervonmises(y, 'norm', args=(2,))
+    >>> res.statistic, res.pvalue
+    (0.8364446265294695, 0.00596286797008283)
+
+    Here we have used the `args` keyword to specify the mean (``loc``)
+    of the normal distribution to test the data against. This is equivalent
+    to the following, in which we create a frozen normal distribution with
+    mean 2.1, then pass its ``cdf`` method as an argument.
+
+    >>> frozen_dist = stats.norm(loc=2)
+    >>> res = stats.cramervonmises(y, frozen_dist.cdf)
+    >>> res.statistic, res.pvalue
+    (0.8364446265294695, 0.00596286797008283)
+
+    In either case, we would reject the null hypothesis that the observed
+    sample is drawn from a normal distribution with a mean of 2 (and default
+    variance of 1) because the p-value is less than our chosen
+    significance level.
+
+    """
+    # `_axis_nan_policy` decorator ensures `axis=-1`
+    xp = array_namespace(rvs)
+
+    if isinstance(cdf, str) and is_numpy(xp):
+        cdf = getattr(distributions, cdf).cdf
+    elif isinstance(cdf, str):
+        message = "`cdf` must be a callable if `rvs` is a non-NumPy array."
+        raise ValueError(message)
+
+    n_length = rvs.shape[-1]
+    if n_length <= 1:  # only needed for `test_axis_nan_policy.py`; not user-facing
+        raise ValueError('The sample must contain at least two observations.')
+
+    rvs = xp_promote(rvs, force_floating=True, xp=xp)
+    vals = xp.sort(rvs, axis=-1)
+    cdfvals = _masked_apply(cdf, args=(vals, *args), xp=xp)
+    n_count = xp.asarray(_count_nonmasked(rvs, axis=-1, xp=xp), dtype=rvs.dtype)
+
+    u = (2*xp.arange(1, n_length+1, dtype=rvs.dtype) - 1)/(2*n_count[..., xp.newaxis])
+    w = 1/(12*n_count) + xp.sum((u - cdfvals)**2, axis=-1)
+
+    # avoid small negative values that can occur due to the approximation
+    p = xp.clip(1. - _masked_apply(_cdf_cvm, args=(w, n_count), xp=xp), 0., None)
+
+    return CramerVonMisesResult(statistic=w, pvalue=p)
+
+
+def _get_wilcoxon_distr(n):
+    """
+    Distribution of probability of the Wilcoxon ranksum statistic r_plus (sum
+    of ranks of positive differences).
+    Returns an array with the probabilities of all the possible ranks
+    r = 0, ..., n*(n+1)/2
+    """
+    c = np.ones(1, dtype=np.float64)
+    for k in range(1, n + 1):
+        prev_c = c
+        c = np.zeros(k * (k + 1) // 2 + 1, dtype=np.float64)
+        m = len(prev_c)
+        c[:m] = prev_c * 0.5
+        c[-m:] += prev_c * 0.5
+    return c
+
+
+def _get_wilcoxon_distr2(n):
+    """
+    Distribution of probability of the Wilcoxon ranksum statistic r_plus (sum
+    of ranks of positive differences).
+    Returns an array with the probabilities of all the possible ranks
+    r = 0, ..., n*(n+1)/2
+    This is a slower reference function
+    References
+    ----------
+    .. [1] 1. Harris T, Hardin JW. Exact Wilcoxon Signed-Rank and Wilcoxon
+        Mann-Whitney Ranksum Tests. The Stata Journal. 2013;13(2):337-343.
+    """
+    ai = np.arange(1, n+1)[:, None]
+    t = n*(n+1)/2
+    q = 2*t
+    j = np.arange(q)
+    theta = 2*np.pi/q*j
+    phi_sp = np.prod(np.cos(theta*ai), axis=0)
+    phi_s = np.exp(1j*theta*t) * phi_sp
+    p = np.real(ifft(phi_s))
+    res = np.zeros(int(t)+1)
+    res[:-1:] = p[::2]
+    res[0] /= 2
+    res[-1] = res[0]
+    return res
+
+
+def _tau_b(A):
+    """Calculate Kendall's tau-b and p-value from contingency table."""
+    # See [2] 2.2 and 4.2
+
+    # contingency table must be truly 2D
+    if A.shape[0] == 1 or A.shape[1] == 1:
+        return np.nan, np.nan
+
+    NA = A.sum()
+    PA = _P(A)
+    QA = _Q(A)
+    Sri2 = (A.sum(axis=1)**2).sum()
+    Scj2 = (A.sum(axis=0)**2).sum()
+    denominator = (NA**2 - Sri2)*(NA**2 - Scj2)
+
+    tau = (PA-QA)/(denominator)**0.5
+
+    numerator = 4*(_a_ij_Aij_Dij2(A) - (PA - QA)**2 / NA)
+    s02_tau_b = numerator/denominator
+    if s02_tau_b == 0:  # Avoid divide by zero
+        return tau, 0
+    Z = tau/s02_tau_b**0.5
+    p = 2*norm.sf(abs(Z))  # 2-sided p-value
+
+    return tau, p
+
+
+def _somers_d(A, alternative='two-sided'):
+    """Calculate Somers' D and p-value from contingency table."""
+    # See [3] page 1740
+
+    # contingency table must be truly 2D
+    if A.shape[0] <= 1 or A.shape[1] <= 1:
+        return np.nan, np.nan
+
+    NA = A.sum()
+    NA2 = NA**2
+    PA = _P(A)
+    QA = _Q(A)
+    Sri2 = (A.sum(axis=1)**2).sum()
+
+    d = (PA - QA)/(NA2 - Sri2)
+
+    S = _a_ij_Aij_Dij2(A) - (PA-QA)**2/NA
+
+    with np.errstate(divide='ignore'):
+        Z = (PA - QA)/(4*(S))**0.5
+
+    norm = _stats_py._SimpleNormal()
+    p = _stats_py._get_pvalue(Z, norm, alternative, xp=np)
+
+    return d, p
+
+
+@dataclass
+class SomersDResult:
+    statistic: float
+    pvalue: float
+    table: np.ndarray
+
+
+@xp_capabilities(np_only=True)
+def somersd(x, y=None, alternative='two-sided'):
+    r"""Calculates Somers' D, an asymmetric measure of ordinal association.
+
+    Like Kendall's :math:`\tau`, Somers' :math:`D` is a measure of the
+    correspondence between two rankings. Both statistics consider the
+    difference between the number of concordant and discordant pairs in two
+    rankings :math:`X` and :math:`Y`, and both are normalized such that values
+    close  to 1 indicate strong agreement and values close to -1 indicate
+    strong disagreement. They differ in how they are normalized. To show the
+    relationship, Somers' :math:`D` can be defined in terms of Kendall's
+    :math:`\tau_a`:
+
+    .. math::
+        D(Y|X) = \frac{\tau_a(X, Y)}{\tau_a(X, X)}
+
+    Suppose the first ranking :math:`X` has :math:`r` distinct ranks and the
+    second ranking :math:`Y` has :math:`s` distinct ranks. These two lists of
+    :math:`n` rankings can also be viewed as an :math:`r \times s` contingency
+    table in which element :math:`i, j` is the number of rank pairs with rank
+    :math:`i` in ranking :math:`X` and rank :math:`j` in ranking :math:`Y`.
+    Accordingly, `somersd` also allows the input data to be supplied as a
+    single, 2D contingency table instead of as two separate, 1D rankings.
+
+    Note that the definition of Somers' :math:`D` is asymmetric: in general,
+    :math:`D(Y|X) \neq D(X|Y)`. ``somersd(x, y)`` calculates Somers'
+    :math:`D(Y|X)`: the "row" variable :math:`X` is treated as an independent
+    variable, and the "column" variable :math:`Y` is dependent. For Somers'
+    :math:`D(X|Y)`, swap the input lists or transpose the input table.
+
+    Parameters
+    ----------
+    x : array_like
+        1D array of rankings, treated as the (row) independent variable.
+        Alternatively, a 2D contingency table.
+    y : array_like, optional
+        If `x` is a 1D array of rankings, `y` is a 1D array of rankings of the
+        same length, treated as the (column) dependent variable.
+        If `x` is 2D, `y` is ignored.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        The following options are available:
+
+        * 'two-sided': the rank correlation is nonzero
+        * 'less': the rank correlation is negative (less than zero)
+        * 'greater':  the rank correlation is positive (greater than zero)
+
+    Returns
+    -------
+    res : SomersDResult
+        A `SomersDResult` object with the following fields:
+
+        statistic : float
+           The Somers' :math:`D` statistic.
+        pvalue : float
+           The p-value for a hypothesis test whose null
+           hypothesis is an absence of association, :math:`D=0`.
+           See notes for more information.
+        table : 2D array
+           The contingency table formed from rankings `x` and `y` (or the
+           provided contingency table, if `x` is a 2D array)
+
+    See Also
+    --------
+    kendalltau : Calculates Kendall's tau, another correlation measure.
+    weightedtau : Computes a weighted version of Kendall's tau.
+    spearmanr : Calculates a Spearman rank-order correlation coefficient.
+    pearsonr : Calculates a Pearson correlation coefficient.
+
+    Notes
+    -----
+    This function follows the contingency table approach of [2]_ and
+    [3]_. *p*-values are computed based on an asymptotic approximation of
+    the test statistic distribution under the null hypothesis :math:`D=0`.
+
+    Theoretically, hypothesis tests based on Kendall's :math:`tau` and Somers'
+    :math:`D` should be identical.
+    However, the *p*-values returned by `kendalltau` are based
+    on the null hypothesis of *independence* between :math:`X` and :math:`Y`
+    (i.e. the population from which pairs in :math:`X` and :math:`Y` are
+    sampled contains equal numbers of all possible pairs), which is more
+    specific than the null hypothesis :math:`D=0` used here. If the null
+    hypothesis of independence is desired, it is acceptable to use the
+    *p*-value returned by `kendalltau` with the statistic returned by
+    `somersd` and vice versa. For more information, see [2]_.
+
+    Contingency tables are formatted according to the convention used by
+    SAS and R: the first ranking supplied (``x``) is the "row" variable, and
+    the second ranking supplied (``y``) is the "column" variable. This is
+    opposite the convention of Somers' original paper [1]_.
+
+    References
+    ----------
+    .. [1] Robert H. Somers, "A New Asymmetric Measure of Association for
+           Ordinal Variables", *American Sociological Review*, Vol. 27, No. 6,
+           pp. 799--811, 1962.
+
+    .. [2] Morton B. Brown and Jacqueline K. Benedetti, "Sampling Behavior of
+           Tests for Correlation in Two-Way Contingency Tables", *Journal of
+           the American Statistical Association* Vol. 72, No. 358, pp.
+           309--315, 1977.
+
+    .. [3] SAS Institute, Inc., "The FREQ Procedure (Book Excerpt)",
+           *SAS/STAT 9.2 User's Guide, Second Edition*, SAS Publishing, 2009.
+
+    .. [4] Laerd Statistics, "Somers' d using SPSS Statistics", *SPSS
+           Statistics Tutorials and Statistical Guides*,
+           https://statistics.laerd.com/spss-tutorials/somers-d-using-spss-statistics.php,
+           Accessed July 31, 2020.
+
+    Examples
+    --------
+    We calculate Somers' D for the example given in [4]_, in which a hotel
+    chain owner seeks to determine the association between hotel room
+    cleanliness and customer satisfaction. The independent variable, hotel
+    room cleanliness, is ranked on an ordinal scale: "below average (1)",
+    "average (2)", or "above average (3)". The dependent variable, customer
+    satisfaction, is ranked on a second scale: "very dissatisfied (1)",
+    "moderately dissatisfied (2)", "neither dissatisfied nor satisfied (3)",
+    "moderately satisfied (4)", or "very satisfied (5)". 189 customers
+    respond to the survey, and the results are cast into a contingency table
+    with the hotel room cleanliness as the "row" variable and customer
+    satisfaction as the "column" variable.
+
+    +-----+-----+-----+-----+-----+-----+
+    |     | (1) | (2) | (3) | (4) | (5) |
+    +=====+=====+=====+=====+=====+=====+
+    | (1) | 27  | 25  | 14  | 7   | 0   |
+    +-----+-----+-----+-----+-----+-----+
+    | (2) | 7   | 14  | 18  | 35  | 12  |
+    +-----+-----+-----+-----+-----+-----+
+    | (3) | 1   | 3   | 2   | 7   | 17  |
+    +-----+-----+-----+-----+-----+-----+
+
+    For example, 27 customers assigned their room a cleanliness ranking of
+    "below average (1)" and a corresponding satisfaction of "very
+    dissatisfied (1)". We perform the analysis as follows.
+
+    >>> from scipy.stats import somersd
+    >>> table = [[27, 25, 14, 7, 0], [7, 14, 18, 35, 12], [1, 3, 2, 7, 17]]
+    >>> res = somersd(table)
+    >>> res.statistic
+    0.6032766111513396
+    >>> res.pvalue
+    1.0007091191074533e-27
+
+    The value of the Somers' D statistic is approximately 0.6, indicating
+    a positive correlation between room cleanliness and customer satisfaction
+    in the sample.
+    The *p*-value is very small, indicating a very small probability of
+    observing such an extreme value of the statistic under the null
+    hypothesis that the statistic of the entire population (from which
+    our sample of 189 customers is drawn) is zero. This supports the
+    alternative hypothesis that the true value of Somers' D for the population
+    is nonzero.
+
+    """
+    x, y = np.array(x), np.array(y)
+    if x.ndim == 1:
+        if x.size != y.size:
+            raise ValueError("Rankings must be of equal length.")
+        table = scipy.stats.contingency.crosstab(x, y)[1]
+    elif x.ndim == 2:
+        if np.any(x < 0):
+            raise ValueError("All elements of the contingency table must be "
+                             "non-negative.")
+        if np.any(x != x.astype(int)):
+            raise ValueError("All elements of the contingency table must be "
+                             "integer.")
+        if x.nonzero()[0].size < 2:
+            raise ValueError("At least two elements of the contingency table "
+                             "must be nonzero.")
+        table = x
+    else:
+        raise ValueError("x must be either a 1D or 2D array")
+    # The table type is converted to a float to avoid an integer overflow
+    d, p = _somers_d(table.astype(float), alternative)
+
+    # add alias for consistency with other correlation functions
+    res = SomersDResult(d, p, table)
+    res.correlation = d
+    return res
+
+
+# This could be combined with `_all_partitions` in `_resampling.py`
+def _all_partitions(nx, ny):
+    """
+    Partition a set of indices into two fixed-length sets in all possible ways
+
+    Partition a set of indices 0 ... nx + ny - 1 into two sets of length nx and
+    ny in all possible ways (ignoring order of elements).
+    """
+    z = np.arange(nx+ny)
+    for c in combinations(z, nx):
+        x = np.array(c)
+        mask = np.ones(nx+ny, bool)
+        mask[x] = False
+        y = z[mask]
+        yield x, y
+
+
+def _compute_log_combinations(n):
+    """Compute all log combination of C(n, k)."""
+    gammaln_arr = gammaln(np.arange(n + 1) + 1)
+    return gammaln(n + 1) - gammaln_arr - gammaln_arr[::-1]
+
+
+@dataclass
+class BarnardExactResult:
+    statistic: float
+    pvalue: float
+
+
+@xp_capabilities(np_only=True)
+def barnard_exact(table, alternative="two-sided", pooled=True, n=32):
+    r"""Perform a Barnard exact test on a 2x2 contingency table.
+
+    Parameters
+    ----------
+    table : array_like of ints
+        A 2x2 contingency table.  Elements should be non-negative integers.
+
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the null and alternative hypotheses. Default is 'two-sided'.
+        Please see explanations in the Notes section below.
+
+    pooled : bool, optional
+        Whether to compute score statistic with pooled variance (as in
+        Student's t-test, for example) or unpooled variance (as in Welch's
+        t-test). Default is ``True``.
+
+    n : int, optional
+        Number of sampling points used in the construction of the sampling
+        method. Note that this argument will automatically be converted to
+        the next higher power of 2 since `scipy.stats.qmc.Sobol` is used to
+        select sample points. Default is 32. Must be positive. In most cases,
+        32 points is enough to reach good precision. More points comes at
+        performance cost.
+
+    Returns
+    -------
+    ber : BarnardExactResult
+        A result object with the following attributes.
+
+        statistic : float
+            The Wald statistic with pooled or unpooled variance, depending
+            on the user choice of `pooled`.
+
+        pvalue : float
+            P-value, the probability of obtaining a distribution at least as
+            extreme as the one that was actually observed, assuming that the
+            null hypothesis is true.
+
+    See Also
+    --------
+    chi2_contingency : Chi-square test of independence of variables in a
+        contingency table.
+    fisher_exact : Fisher exact test on a 2x2 contingency table.
+    boschloo_exact : Boschloo's exact test on a 2x2 contingency table,
+        which is a uniformly more powerful alternative to Fisher's exact test.
+
+    Notes
+    -----
+    Barnard's test is an exact test used in the analysis of contingency
+    tables. It examines the association of two categorical variables, and
+    is a more powerful alternative than Fisher's exact test
+    for 2x2 contingency tables.
+
+    Let's define :math:`X_0` a 2x2 matrix representing the observed sample,
+    where each column stores the binomial experiment, as in the example
+    below. Let's also define :math:`p_1, p_2` the theoretical binomial
+    probabilities for  :math:`x_{11}` and :math:`x_{12}`. When using
+    Barnard exact test, we can assert three different null hypotheses :
+
+    - :math:`H_0 : p_1 \geq p_2` versus :math:`H_1 : p_1 < p_2`,
+      with `alternative` = "less"
+
+    - :math:`H_0 : p_1 \leq p_2` versus :math:`H_1 : p_1 > p_2`,
+      with `alternative` = "greater"
+
+    - :math:`H_0 : p_1 = p_2` versus :math:`H_1 : p_1 \neq p_2`,
+      with `alternative` = "two-sided" (default one)
+
+    In order to compute Barnard's exact test, we are using the Wald
+    statistic [3]_ with pooled or unpooled variance.
+    Under the default assumption that both variances are equal
+    (``pooled = True``), the statistic is computed as:
+
+    .. math::
+
+        T(X) = \frac{
+            \hat{p}_1 - \hat{p}_2
+        }{
+            \sqrt{
+                \hat{p}(1 - \hat{p})
+                (\frac{1}{c_1} +
+                \frac{1}{c_2})
+            }
+        }
+
+    with :math:`\hat{p}_1, \hat{p}_2` and :math:`\hat{p}` the estimator of
+    :math:`p_1, p_2` and :math:`p`, the latter being the combined probability,
+    given the assumption that :math:`p_1 = p_2`.
+
+    If this assumption is invalid (``pooled = False``), the statistic is:
+
+    .. math::
+
+        T(X) = \frac{
+            \hat{p}_1 - \hat{p}_2
+        }{
+            \sqrt{
+                \frac{\hat{p}_1 (1 - \hat{p}_1)}{c_1} +
+                \frac{\hat{p}_2 (1 - \hat{p}_2)}{c_2}
+            }
+        }
+
+    The p-value is then computed as:
+
+    .. math::
+
+        \sum
+            \binom{c_1}{x_{11}}
+            \binom{c_2}{x_{12}}
+            \pi^{x_{11} + x_{12}}
+            (1 - \pi)^{t - x_{11} - x_{12}}
+
+    where the sum is over all  2x2 contingency tables :math:`X` such that:
+
+    * :math:`T(X) \leq T(X_0)` when `alternative` = "less",
+    * :math:`T(X) \geq T(X_0)` when `alternative` = "greater", or
+    * :math:`T(X) \geq |T(X_0)|` when `alternative` = "two-sided".
+
+    Above, :math:`c_1, c_2` are the sum of the columns 1 and 2,
+    and :math:`t` the total (sum of the 4 sample's element).
+
+    The returned p-value is the maximum p-value taken over the nuisance
+    parameter :math:`\pi`, where :math:`0 \leq \pi \leq 1`.
+
+    This function's complexity is :math:`O(n c_1 c_2)`, where `n` is the
+    number of sample points.
+
+    References
+    ----------
+    .. [1] Barnard, G. A. "Significance Tests for 2x2 Tables". *Biometrika*.
+           34.1/2 (1947): 123-138. :doi:`10.2307/2332517`.
+
+    .. [2] Mehta, Cyrus R., and Pralay Senchaudhuri. "Conditional versus
+           unconditional exact tests for comparing two binomials."
+           *Cytel Software Corporation* 675 (2003): 1-5.
+
+    .. [3] "Wald Test". *Wikipedia*. https://en.wikipedia.org/wiki/Wald_test
+
+    Examples
+    --------
+    An example use of Barnard's test is presented in [2]_.
+
+        Consider the following example of a vaccine efficacy study
+        (Chan, 1998). In a randomized clinical trial of 30 subjects, 15 were
+        inoculated with a recombinant DNA influenza vaccine and the 15 were
+        inoculated with a placebo. Twelve of the 15 subjects in the placebo
+        group (80%) eventually became infected with influenza whereas for the
+        vaccine group, only 7 of the 15 subjects (47%) became infected. The
+        data are tabulated as a 2 x 2 table::
+
+                Vaccine  Placebo
+            Yes     7        12
+            No      8        3
+
+    When working with statistical hypothesis testing, we usually use a
+    threshold probability or significance level upon which we decide
+    to reject the null hypothesis :math:`H_0`. Suppose we choose the common
+    significance level of 5%.
+
+    Our alternative hypothesis is that the vaccine will lower the chance of
+    becoming infected with the virus; that is, the probability :math:`p_1` of
+    catching the virus with the vaccine will be *less than* the probability
+    :math:`p_2` of catching the virus without the vaccine.  Therefore, we call
+    `barnard_exact` with the ``alternative="less"`` option:
+
+    >>> import scipy.stats as stats
+    >>> res = stats.barnard_exact([[7, 12], [8, 3]], alternative="less")
+    >>> res.statistic
+    -1.894
+    >>> res.pvalue
+    0.03407
+
+    Under the null hypothesis that the vaccine will not lower the chance of
+    becoming infected, the probability of obtaining test results at least as
+    extreme as the observed data is approximately 3.4%. Since this p-value is
+    less than our chosen significance level, we have evidence to reject
+    :math:`H_0` in favor of the alternative.
+
+    Suppose we had used Fisher's exact test instead:
+
+    >>> _, pvalue = stats.fisher_exact([[7, 12], [8, 3]], alternative="less")
+    >>> pvalue
+    0.0640
+
+    With the same threshold significance of 5%, we would not have been able
+    to reject the null hypothesis in favor of the alternative. As stated in
+    [2]_, Barnard's test is uniformly more powerful than Fisher's exact test
+    because Barnard's test does not condition on any margin. Fisher's test
+    should only be used when both sets of marginals are fixed.
+
+    """
+    if n <= 0:
+        raise ValueError(
+            "Number of points `n` must be strictly positive, "
+            f"found {n!r}"
+        )
+
+    table = np.asarray(table, dtype=np.int64)
+
+    if not table.shape == (2, 2):
+        raise ValueError("The input `table` must be of shape (2, 2).")
+
+    if np.any(table < 0):
+        raise ValueError("All values in `table` must be nonnegative.")
+
+    if 0 in table.sum(axis=0):
+        # If both values in column are zero, the p-value is 1 and
+        # the score's statistic is NaN.
+        return BarnardExactResult(np.nan, 1.0)
+
+    total_col_1, total_col_2 = table.sum(axis=0)
+
+    x1 = np.arange(total_col_1 + 1, dtype=np.int64).reshape(-1, 1)
+    x2 = np.arange(total_col_2 + 1, dtype=np.int64).reshape(1, -1)
+
+    # We need to calculate the wald statistics for each combination of x1 and
+    # x2.
+    p1, p2 = x1 / total_col_1, x2 / total_col_2
+
+    if pooled:
+        p = (x1 + x2) / (total_col_1 + total_col_2)
+        variances = p * (1 - p) * (1 / total_col_1 + 1 / total_col_2)
+    else:
+        variances = p1 * (1 - p1) / total_col_1 + p2 * (1 - p2) / total_col_2
+
+    # To avoid warning when dividing by 0
+    with np.errstate(divide="ignore", invalid="ignore"):
+        wald_statistic = np.divide((p1 - p2), np.sqrt(variances))
+
+    wald_statistic[p1 == p2] = 0  # Removing NaN values
+
+    wald_stat_obs = wald_statistic[table[0, 0], table[0, 1]]
+
+    if alternative == "two-sided":
+        index_arr = np.abs(wald_statistic) >= abs(wald_stat_obs)
+    elif alternative == "less":
+        index_arr = wald_statistic <= wald_stat_obs
+    elif alternative == "greater":
+        index_arr = wald_statistic >= wald_stat_obs
+    else:
+        msg = (
+            "`alternative` should be one of {'two-sided', 'less', 'greater'},"
+            f" found {alternative!r}"
+        )
+        raise ValueError(msg)
+
+    x1_sum_x2 = x1 + x2
+
+    x1_log_comb = _compute_log_combinations(total_col_1)
+    x2_log_comb = _compute_log_combinations(total_col_2)
+    x1_sum_x2_log_comb = x1_log_comb[x1] + x2_log_comb[x2]
+
+    result = shgo(
+        _get_binomial_log_p_value_with_nuisance_param,
+        args=(x1_sum_x2, x1_sum_x2_log_comb, index_arr),
+        bounds=((0, 1),),
+        n=n,
+        sampling_method="sobol",
+    )
+
+    # result.fun is the negative log pvalue and therefore needs to be
+    # changed before return
+    p_value = np.clip(np.exp(-result.fun), a_min=0, a_max=1)
+    return BarnardExactResult(wald_stat_obs, p_value)
+
+
+@dataclass
+class BoschlooExactResult:
+    statistic: float
+    pvalue: float
+
+
+@xp_capabilities(np_only=True)
+def boschloo_exact(table, alternative="two-sided", n=32):
+    r"""Perform Boschloo's exact test on a 2x2 contingency table.
+
+    Parameters
+    ----------
+    table : array_like of ints
+        A 2x2 contingency table.  Elements should be non-negative integers.
+
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the null and alternative hypotheses. Default is 'two-sided'.
+        Please see explanations in the Notes section below.
+
+    n : int, optional
+        Number of sampling points used in the construction of the sampling
+        method. Note that this argument will automatically be converted to
+        the next higher power of 2 since `scipy.stats.qmc.Sobol` is used to
+        select sample points. Default is 32. Must be positive. In most cases,
+        32 points is enough to reach good precision. More points comes at
+        performance cost.
+
+    Returns
+    -------
+    ber : BoschlooExactResult
+        A result object with the following attributes.
+
+        statistic : float
+            The statistic used in Boschloo's test; that is, the p-value
+            from Fisher's exact test.
+
+        pvalue : float
+            P-value, the probability of obtaining a distribution at least as
+            extreme as the one that was actually observed, assuming that the
+            null hypothesis is true.
+
+    See Also
+    --------
+    chi2_contingency : Chi-square test of independence of variables in a
+        contingency table.
+    fisher_exact : Fisher exact test on a 2x2 contingency table.
+    barnard_exact : Barnard's exact test, which is a more powerful alternative
+        than Fisher's exact test for 2x2 contingency tables.
+
+    Notes
+    -----
+    Boschloo's test is an exact test used in the analysis of contingency
+    tables. It examines the association of two categorical variables, and
+    is a uniformly more powerful alternative to Fisher's exact test
+    for 2x2 contingency tables.
+
+    Boschloo's exact test uses the p-value of Fisher's exact test as a
+    statistic, and Boschloo's p-value is the probability under the null
+    hypothesis of observing such an extreme value of this statistic.
+
+    Let's define :math:`X_0` a 2x2 matrix representing the observed sample,
+    where each column stores the binomial experiment, as in the example
+    below. Let's also define :math:`p_1, p_2` the theoretical binomial
+    probabilities for  :math:`x_{11}` and :math:`x_{12}`. When using
+    Boschloo exact test, we can assert three different alternative hypotheses:
+
+    - :math:`H_0 : p_1=p_2` versus :math:`H_1 : p_1 < p_2`,
+      with `alternative` = "less"
+
+    - :math:`H_0 : p_1=p_2` versus :math:`H_1 : p_1 > p_2`,
+      with `alternative` = "greater"
+
+    - :math:`H_0 : p_1=p_2` versus :math:`H_1 : p_1 \neq p_2`,
+      with `alternative` = "two-sided" (default)
+
+    There are multiple conventions for computing a two-sided p-value when the
+    null distribution is asymmetric. Here, we apply the convention that the
+    p-value of a two-sided test is twice the minimum of the p-values of the
+    one-sided tests (clipped to 1.0). Note that `fisher_exact` follows a
+    different convention, so for a given `table`, the statistic reported by
+    `boschloo_exact` may differ from the p-value reported by `fisher_exact`
+    when ``alternative='two-sided'``.
+
+    .. versionadded:: 1.7.0
+
+    References
+    ----------
+    .. [1] R.D. Boschloo. "Raised conditional level of significance for the
+       2 x 2-table when testing the equality of two probabilities",
+       Statistica Neerlandica, 24(1), 1970
+
+    .. [2] "Boschloo's test", Wikipedia,
+       https://en.wikipedia.org/wiki/Boschloo%27s_test
+
+    .. [3] Lise M. Saari et al. "Employee attitudes and job satisfaction",
+       Human Resource Management, 43(4), 395-407, 2004,
+       :doi:`10.1002/hrm.20032`.
+
+    Examples
+    --------
+    In the following example, we consider the article "Employee
+    attitudes and job satisfaction" [3]_
+    which reports the results of a survey from 63 scientists and 117 college
+    professors. Of the 63 scientists, 31 said they were very satisfied with
+    their jobs, whereas 74 of the college professors were very satisfied
+    with their work. Is this significant evidence that college
+    professors are happier with their work than scientists?
+    The following table summarizes the data mentioned above::
+
+                         college professors   scientists
+        Very Satisfied   74                     31
+        Dissatisfied     43                     32
+
+    When working with statistical hypothesis testing, we usually use a
+    threshold probability or significance level upon which we decide
+    to reject the null hypothesis :math:`H_0`. Suppose we choose the common
+    significance level of 5%.
+
+    Our alternative hypothesis is that college professors are truly more
+    satisfied with their work than scientists. Therefore, we expect
+    :math:`p_1` the proportion of very satisfied college professors to be
+    greater than :math:`p_2`, the proportion of very satisfied scientists.
+    We thus call `boschloo_exact` with the ``alternative="greater"`` option:
+
+    >>> import scipy.stats as stats
+    >>> res = stats.boschloo_exact([[74, 31], [43, 32]], alternative="greater")
+    >>> res.statistic
+    0.0483
+    >>> res.pvalue
+    0.0355
+
+    Under the null hypothesis that scientists are happier in their work than
+    college professors, the probability of obtaining test
+    results at least as extreme as the observed data is approximately 3.55%.
+    Since this p-value is less than our chosen significance level, we have
+    evidence to reject :math:`H_0` in favor of the alternative hypothesis.
+
+    """
+    hypergeom = distributions.hypergeom
+
+    if n <= 0:
+        raise ValueError(
+            "Number of points `n` must be strictly positive,"
+            f" found {n!r}"
+        )
+
+    table = np.asarray(table, dtype=np.int64)
+
+    if not table.shape == (2, 2):
+        raise ValueError("The input `table` must be of shape (2, 2).")
+
+    if np.any(table < 0):
+        raise ValueError("All values in `table` must be nonnegative.")
+
+    if 0 in table.sum(axis=0):
+        # If both values in column are zero, the p-value is 1 and
+        # the score's statistic is NaN.
+        return BoschlooExactResult(np.nan, np.nan)
+
+    total_col_1, total_col_2 = table.sum(axis=0)
+    total = total_col_1 + total_col_2
+    x1 = np.arange(total_col_1 + 1, dtype=np.int64).reshape(1, -1)
+    x2 = np.arange(total_col_2 + 1, dtype=np.int64).reshape(-1, 1)
+    x1_sum_x2 = x1 + x2
+
+    if alternative == 'less':
+        pvalues = hypergeom.cdf(x1, total, x1_sum_x2, total_col_1).T
+    elif alternative == 'greater':
+        # Same formula as the 'less' case, but with the second column.
+        pvalues = hypergeom.cdf(x2, total, x1_sum_x2, total_col_2).T
+    elif alternative == 'two-sided':
+        boschloo_less = boschloo_exact(table, alternative="less", n=n)
+        boschloo_greater = boschloo_exact(table, alternative="greater", n=n)
+
+        res = (
+            boschloo_less if boschloo_less.pvalue < boschloo_greater.pvalue
+            else boschloo_greater
+        )
+
+        # Two-sided p-value is defined as twice the minimum of the one-sided
+        # p-values
+        pvalue = np.clip(2 * res.pvalue, a_min=0, a_max=1)
+        return BoschlooExactResult(res.statistic, pvalue)
+    else:
+        msg = (
+            f"`alternative` should be one of {'two-sided', 'less', 'greater'},"
+            f" found {alternative!r}"
+        )
+        raise ValueError(msg)
+
+    fisher_stat = pvalues[table[0, 0], table[0, 1]]
+
+    # fisher_stat * (1+1e-13) guards us from small numerical error. It is
+    # equivalent to np.isclose with relative tol of 1e-13 and absolute tol of 0
+    # For more throughout explanations, see gh-14178
+    index_arr = pvalues <= fisher_stat * (1+1e-13)
+
+    x1, x2, x1_sum_x2 = x1.T, x2.T, x1_sum_x2.T
+    x1_log_comb = _compute_log_combinations(total_col_1)
+    x2_log_comb = _compute_log_combinations(total_col_2)
+    x1_sum_x2_log_comb = x1_log_comb[x1] + x2_log_comb[x2]
+
+    result = shgo(
+        _get_binomial_log_p_value_with_nuisance_param,
+        args=(x1_sum_x2, x1_sum_x2_log_comb, index_arr),
+        bounds=((0, 1),),
+        n=n,
+        sampling_method="sobol",
+    )
+
+    # result.fun is the negative log pvalue and therefore needs to be
+    # changed before return
+    p_value = np.clip(np.exp(-result.fun), a_min=0, a_max=1)
+    return BoschlooExactResult(fisher_stat, p_value)
+
+
+def _get_binomial_log_p_value_with_nuisance_param(
+    nuisance_param, x1_sum_x2, x1_sum_x2_log_comb, index_arr
+):
+    r"""
+    Compute the log pvalue in respect of a nuisance parameter considering
+    a 2x2 sample space.
+
+    Parameters
+    ----------
+    nuisance_param : float
+        nuisance parameter used in the computation of the maximisation of
+        the p-value. Must be between 0 and 1
+
+    x1_sum_x2 : ndarray
+        Sum of x1 and x2 inside barnard_exact
+
+    x1_sum_x2_log_comb : ndarray
+        sum of the log combination of x1 and x2
+
+    index_arr : ndarray of boolean
+
+    Returns
+    -------
+    p_value : float
+        Return the maximum p-value considering every nuisance parameter
+        between 0 and 1
+
+    Notes
+    -----
+
+    Both Barnard's test and Boschloo's test iterate over a nuisance parameter
+    :math:`\pi \in [0, 1]` to find the maximum p-value. To search this
+    maxima, this function return the negative log pvalue with respect to the
+    nuisance parameter passed in params. This negative log p-value is then
+    used in `shgo` to find the minimum negative pvalue which is our maximum
+    pvalue.
+
+    Also, to compute the different combination used in the
+    p-values' computation formula, this function uses `gammaln` which is
+    more tolerant for large value than `scipy.special.comb`. `gammaln` gives
+    a log combination. For the little precision loss, performances are
+    improved a lot.
+    """
+    t1, t2 = x1_sum_x2.shape
+    n = t1 + t2 - 2
+    with np.errstate(divide="ignore", invalid="ignore"):
+        log_nuisance = np.log(
+            nuisance_param,
+            out=np.zeros_like(nuisance_param),
+            where=nuisance_param >= 0,
+        )
+        log_1_minus_nuisance = np.log(
+            1 - nuisance_param,
+            out=np.zeros_like(nuisance_param),
+            where=1 - nuisance_param >= 0,
+        )
+
+        nuisance_power_x1_x2 = log_nuisance * x1_sum_x2
+        nuisance_power_x1_x2[(x1_sum_x2 == 0)[:, :]] = 0
+
+        nuisance_power_n_minus_x1_x2 = log_1_minus_nuisance * (n - x1_sum_x2)
+        nuisance_power_n_minus_x1_x2[(x1_sum_x2 == n)[:, :]] = 0
+
+        tmp_log_values_arr = (
+            x1_sum_x2_log_comb
+            + nuisance_power_x1_x2
+            + nuisance_power_n_minus_x1_x2
+        )
+
+    tmp_values_from_index = tmp_log_values_arr[index_arr]
+
+    # To avoid dividing by zero in log function and getting inf value,
+    # values are centered according to the max
+    max_value = tmp_values_from_index.max()
+
+    # To have better result's precision, the log pvalue is taken here.
+    # Indeed, pvalue is included inside [0, 1] interval. Passing the
+    # pvalue to log makes the interval a lot bigger ([-inf, 0]), and thus
+    # help us to achieve better precision
+    with np.errstate(divide="ignore", invalid="ignore"):
+        log_probs = np.exp(tmp_values_from_index - max_value).sum()
+        log_pvalue = max_value + np.log(
+            log_probs,
+            out=np.full_like(log_probs, -np.inf),
+            where=log_probs > 0,
+        )
+
+    # Since shgo find the minima, minus log pvalue is returned
+    return -log_pvalue
+
+
+@np.vectorize(otypes=[np.float64])
+def _pval_cvm_2samp_exact(s, m, n):
+    """
+    Compute the exact p-value of the Cramer-von Mises two-sample test
+    for a given value s of the test statistic.
+    m and n are the sizes of the samples.
+
+    [1] Y. Xiao, A. Gordon, and A. Yakovlev, "A C++ Program for
+        the Cramér-Von Mises Two-Sample Test", J. Stat. Soft.,
+        vol. 17, no. 8, pp. 1-15, Dec. 2006.
+    [2] T. W. Anderson "On the Distribution of the Two-Sample Cramer-von Mises
+        Criterion," The Annals of Mathematical Statistics, Ann. Math. Statist.
+        33(3), 1148-1159, (September, 1962)
+    """
+
+    # [1, p. 3]
+    m, n = int(m), int(n)
+    lcm = np.lcm(m, n)
+    # [1, p. 4], below eq. 3
+    a = lcm // m
+    b = lcm // n
+    # Combine Eq. 9 in [2] with Eq. 2 in [1] and solve for $\zeta$
+    # Hint: `s` is $U$ in [2], and $T_2$ in [1] is $T$ in [2]
+    mn = m * n
+    zeta = lcm ** 2 * (m + n) * (6 * s - mn * (4 * mn - 1)) // (6 * mn ** 2)
+
+    # bound maximum value that may appear in `gs` (remember both rows!)
+    zeta_bound = lcm**2 * (m + n)  # bound elements in row 1
+    combinations = math.comb(m + n, m)  # sum of row 2
+    max_gs = max(zeta_bound, combinations)
+    dtype = np.min_scalar_type(max_gs)
+
+    # the frequency table of $g_{u, v}^+$ defined in [1, p. 6]
+    gs = ([np.array([[0], [1]], dtype=dtype)]
+          + [np.empty((2, 0), dtype=dtype) for _ in range(m)])
+    for u in range(n + 1):
+        next_gs = []
+        tmp = np.empty((2, 0), dtype=dtype)
+        for v, g in enumerate(gs):
+            # Calculate g recursively with eq. 11 in [1]. Even though it
+            # doesn't look like it, this also does 12/13 (all of Algorithm 1).
+            vi, i0, i1 = np.intersect1d(tmp[0], g[0], return_indices=True)
+            tmp = np.concatenate([
+                np.stack([vi, tmp[1, i0] + g[1, i1]]),
+                np.delete(tmp, i0, 1),
+                np.delete(g, i1, 1)
+            ], 1)
+            res = (a * v - b * u) ** 2
+            tmp[0] += res.astype(dtype)
+            next_gs.append(tmp)
+        gs = next_gs
+    value, freq = gs[m]
+    return np.float64(np.sum(freq[value >= zeta]) / combinations)
+
+
+def _pval_cvm_2samp_asymptotic(t, N, nx, ny, k, *, xp):
+    # compute expected value and variance of T (eq. 11 and 14 in [2])
+    nx, ny = xp.asarray(nx, dtype=t.dtype), xp.asarray(ny, dtype=t.dtype)
+    et = (1 + 1 / N) / 6
+    vt = (N + 1) * (4 * k * N - 3 * (nx ** 2 + ny ** 2) - 2 * k)
+    vt = vt / (45 * N ** 2 * 4 * k)
+
+    # computed the normalized statistic (eq. 15 in [2])
+    tn = 1 / 6 + (t - et) / xp.sqrt(45 * vt)
+
+    # approximate distribution of tn with limiting distribution
+    # of the one-sample test statistic
+    # if tn < 0.003, the _cdf_cvm_inf(tn) < 1.28*1e-18, return 1.0 directly
+    p = xpx.apply_where(tn >= 0.003,
+                        (tn,),
+                        lambda tn: xp.clip(1. - _cdf_cvm_inf(tn, xp=xp), 0.),
+                        fill_value = 1.)
+    return p
+
+
+@xp_capabilities(skip_backends=[('dask.array', 'needs rankdata')],
+                 cpu_only=True, jax_jit=False,  # due to p-value calculation
+                 marray=True,
+                 extra_note="Only `method='exact'` is compatible with MArray input.")
+@_axis_nan_policy_factory(CramerVonMisesResult, n_samples=2, too_small=1,
+                          result_to_tuple=_cvm_result_to_tuple)
+def cramervonmises_2samp(x, y, method='auto', *, axis=0):
+    r"""Perform the two-sample Cramér-von Mises test for goodness of fit.
+
+    This is the two-sample version of the Cramér-von Mises test ([1]_):
+    for two independent samples :math:`X_1, ..., X_n` and
+    :math:`Y_1, ..., Y_m`, the null hypothesis is that the samples
+    come from the same (unspecified) continuous distribution.
+
+    The test statistic :math:`T` is defined as in [1]_:
+
+    .. math::
+        T = \frac{nm}{n+m}\omega^2 =
+        \frac{U}{n m (n+m)} - \frac{4 m n - 1}{6(m+n)}
+
+    where :math:`U` is defined as below, and :math:`\omega^2` is the Cramér-von
+    Mises criterion. The function :math:`r(\cdot)` here denotes the rank of the
+    observed values :math:`x_i` and :math:`y_j` within the pooled sample of size
+    :math:`n + m`, with ties assigned mid-rank values:
+
+    .. math::
+        U = n \sum_{i=1}^n (r(x_i)-i)^2 + m \sum_{j=1}^m (r(y_j)-j)^2
+
+    Parameters
+    ----------
+    x : array_like
+        A 1-D array of observed values of the random variables :math:`X_i`.
+        Must contain at least two observations.
+    y : array_like
+        A 1-D array of observed values of the random variables :math:`Y_i`.
+        Must contain at least two observations.
+    method : {'auto', 'asymptotic', 'exact'}, optional
+        The method used to compute the p-value, see Notes for details.
+        The default is 'auto'.
+    axis : int or tuple of ints, default: 0
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    res : object with attributes
+        statistic : float
+            Cramér-von Mises statistic :math:`T`.
+        pvalue : float
+            The p-value.
+
+    See Also
+    --------
+    cramervonmises, anderson_ksamp, epps_singleton_2samp, ks_2samp
+
+    Notes
+    -----
+    .. versionadded:: 1.7.0
+
+    The statistic is computed according to equation 9 in [2]_. The
+    calculation of the p-value depends on the keyword `method`:
+
+    - ``asymptotic``: The p-value is approximated by using the limiting
+      distribution of the test statistic.
+    - ``exact``: The exact p-value is computed by enumerating all
+      possible combinations of the test statistic, see [2]_.
+
+    If ``method='auto'``, the exact approach is used
+    if both samples contain equal to or less than 20 observations,
+    otherwise the asymptotic distribution is used.
+
+    If the underlying distribution is not continuous, the p-value is likely to
+    be conservative (Section 6.2 in [3]_). When ranking the data to compute
+    the test statistic, midranks are used if there are ties.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Cramer-von_Mises_criterion
+    .. [2] Anderson, T.W. (1962). On the distribution of the two-sample
+           Cramer-von-Mises criterion. The Annals of Mathematical
+           Statistics, pp. 1148-1159.
+    .. [3] Conover, W.J., Practical Nonparametric Statistics, 1971.
+
+    Examples
+    --------
+
+    Suppose we wish to test whether two samples generated by
+    ``scipy.stats.norm.rvs`` have the same distribution. We choose a
+    significance level of alpha=0.05.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+    >>> x = stats.norm.rvs(size=100, random_state=rng)
+    >>> y = stats.norm.rvs(size=70, random_state=rng)
+    >>> res = stats.cramervonmises_2samp(x, y)
+    >>> res.statistic, res.pvalue
+    (0.29376470588235293, 0.1412873014573014)
+
+    The p-value exceeds our chosen significance level, so we do not
+    reject the null hypothesis that the observed samples are drawn from the
+    same distribution.
+
+    For small sample sizes, one can compute the exact p-values:
+
+    >>> x = stats.norm.rvs(size=7, random_state=rng)
+    >>> y = stats.t.rvs(df=2, size=6, random_state=rng)
+    >>> res = stats.cramervonmises_2samp(x, y, method='exact')
+    >>> res.statistic, res.pvalue
+    (0.197802197802198, 0.31643356643356646)
+
+    The p-value based on the asymptotic distribution is a good approximation
+    even though the sample size is small.
+
+    >>> res = stats.cramervonmises_2samp(x, y, method='asymptotic')
+    >>> res.statistic, res.pvalue
+    (0.197802197802198, 0.2966041181527128)
+
+    Independent of the method, one would not reject the null hypothesis at the
+    chosen significance level in this example.
+
+    """
+    xp = array_namespace(x, y)
+    length_x = x.shape[-1]
+    length_y = y.shape[-1]
+    count_x = _count_nonmasked(x, axis=-1, xp=xp)
+    count_y = _count_nonmasked(y, axis=-1, xp=xp)
+
+    if length_x <= 1 or length_y <= 1:  # only needed for `test_axis_nan_policy`
+        raise ValueError('x and y must contain at least two observations.')
+    if method not in ['auto', 'exact', 'asymptotic']:
+        raise ValueError('method must be either auto, exact or asymptotic.')
+
+    if is_marray(xp) and method in {'auto', 'asymptotic'}:
+        message = "Only `method='exact'` is compatible with MArray."
+        raise ValueError(message)
+
+    if method == 'auto':
+        if max(count_x, count_y) > 20:
+            method = 'asymptotic'
+        else:
+            method = 'exact'
+
+    # axis=-1 is guaranteed by _axis_nan_policy decorator
+    xa = xp.sort(x, axis=-1)
+    ya = xp.sort(y, axis=-1)
+
+    # get ranks of x and y in the pooled sample
+    z = xp.concat([xa, ya], axis=-1)
+    # in case of ties, use midrank (see [1])
+    r = scipy.stats.rankdata(z, method='average', axis=-1)
+    dtype = xp_result_type(x, y, force_floating=True, xp=xp)
+    rx = r[..., :length_x]
+    ry = r[..., length_x:]
+
+    # compute U (eq. 10 in [2])
+    u = (count_x * xp.sum((rx - xp.arange(1, length_x+1, dtype=dtype))**2, axis=-1)
+         + count_y * xp.sum((ry - xp.arange(1, length_y+1, dtype=dtype))**2, axis=-1))
+
+    # compute T (eq. 9 in [2])
+    k, N = count_x*count_y, count_x + count_y
+    t = u / (k*N) - (4*k - 1)/(6*N)
+
+    if method == 'exact':
+        if is_marray(xp):
+            u, count_x, count_y = u.data, count_x.data, count_y.data
+        p = _pval_cvm_2samp_exact(np.asarray(u), count_x, count_y)
+        p = xp.asarray(p, dtype=dtype)
+    else:
+        p = _pval_cvm_2samp_asymptotic(t, N, count_x, count_y, k, xp=xp)
+
+    t = t[()] if t.ndim == 0 else t
+    p = p[()] if p.ndim == 0 else p
+    return CramerVonMisesResult(statistic=t, pvalue=p)
+
+
+class TukeyHSDResult:
+    """Result of `scipy.stats.tukey_hsd`.
+
+    Attributes
+    ----------
+    statistic : float ndarray
+        The computed statistic of the test for each comparison. The element
+        at index ``(i, j)`` is the statistic for the comparison between groups
+        ``i`` and ``j``.
+    pvalue : float ndarray
+        The associated p-value from the studentized range distribution. The
+        element at index ``(i, j)`` is the p-value for the comparison
+        between groups ``i`` and ``j``.
+
+    Notes
+    -----
+    The string representation of this object displays the most recently
+    calculated confidence interval, and if none have been previously
+    calculated, it will evaluate ``confidence_interval()``.
+
+    References
+    ----------
+    .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, "7.4.7.1. Tukey's
+           Method."
+           https://www.itl.nist.gov/div898/handbook/prc/section4/prc471.htm,
+           28 November 2020.
+    .. [2] P. A. Games and J. F. Howell, "Pairwise Multiple Comparison Procedures
+           with Unequal N's and/or Variances: A Monte Carlo Study," Journal of
+           Educational Statistics, vol. 1, no. 2, pp. 113-125, Jun. 1976,
+           :doi:`10.3102/10769986001002113`.
+    """
+
+    def __init__(self, statistic, pvalue, _ntreatments, _df, _stand_err):
+        self.statistic = statistic
+        self.pvalue = pvalue
+        self._ntreatments = _ntreatments
+        self._df = _df
+        self._stand_err = _stand_err
+        self._ci = None
+        self._ci_cl = None
+
+    def __str__(self):
+        # Note: `__str__` prints the confidence intervals from the most
+        # recent call to `confidence_interval`. If it has not been called,
+        # it will be called with the default CL of .95.
+        if self._ci is None:
+            self.confidence_interval(confidence_level=.95)
+        s = ("Pairwise Group Comparisons"
+             f" ({self._ci_cl*100:.1f}% Confidence Interval)\n")
+        s += "Comparison  Statistic  p-value  Lower CI  Upper CI\n"
+        for i, j in np.ndindex(self.pvalue.shape):
+            if i != j:
+                s += (f" ({i} - {j}) {self.statistic[i, j]:>10.3f}"
+                      f"{self.pvalue[i, j]:>10.3f}"
+                      f"{self._ci.low[i, j]:>10.3f}"
+                      f"{self._ci.high[i, j]:>10.3f}\n")
+        return s
+
+    def confidence_interval(self, confidence_level=.95):
+        """Compute the confidence interval for the specified confidence level.
+
+        Parameters
+        ----------
+        confidence_level : float, optional
+            Confidence level for the computed confidence interval
+            of the estimated proportion. Default is .95.
+
+        Returns
+        -------
+        ci : ``ConfidenceInterval`` object
+            The object has attributes ``low`` and ``high`` that hold the
+            lower and upper bounds of the confidence intervals for each
+            comparison. The high and low values are accessible for each
+            comparison at index ``(i, j)`` between groups ``i`` and ``j``.
+
+        References
+        ----------
+        .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, "7.4.7.1.
+               Tukey's Method."
+               https://www.itl.nist.gov/div898/handbook/prc/section4/prc471.htm,
+               28 November 2020.
+        .. [2] P. A. Games and J. F. Howell, "Pairwise Multiple Comparison Procedures
+               with Unequal N's and/or Variances: A Monte Carlo Study," Journal of
+               Educational Statistics, vol. 1, no. 2, pp. 113-125, Jun. 1976,
+               :doi:`10.3102/10769986001002113`.
+
+        Examples
+        --------
+        >>> from scipy.stats import tukey_hsd
+        >>> group0 = [24.5, 23.5, 26.4, 27.1, 29.9]
+        >>> group1 = [28.4, 34.2, 29.5, 32.2, 30.1]
+        >>> group2 = [26.1, 28.3, 24.3, 26.2, 27.8]
+        >>> result = tukey_hsd(group0, group1, group2)
+        >>> ci = result.confidence_interval()
+        >>> ci.low
+        array([[-3.649159, -8.249159, -3.909159],
+               [ 0.950841, -3.649159,  0.690841],
+               [-3.389159, -7.989159, -3.649159]])
+        >>> ci.high
+        array([[ 3.649159, -0.950841,  3.389159],
+               [ 8.249159,  3.649159,  7.989159],
+               [ 3.909159, -0.690841,  3.649159]])
+        """
+        # check to see if the supplied confidence level matches that of the
+        # previously computed CI.
+        if (self._ci is not None and self._ci_cl is not None and
+                confidence_level == self._ci_cl):
+            return self._ci
+
+        if not 0 < confidence_level < 1:
+            raise ValueError("Confidence level must be between 0 and 1.")
+        # determine the critical value of the studentized range using the
+        # appropriate confidence level, number of treatments, and degrees
+        # of freedom. See [1] "Confidence limits for Tukey's method" / [2] p.117
+        # "H0 was rejected if...". Note that in the cases of unequal sample sizes,
+        # there will be a criterion for each group comparison.
+        params = (confidence_level, self._ntreatments, self._df)
+        srd = distributions.studentized_range.ppf(*params)
+        # also called maximum critical value, the confidence_radius is the
+        # studentized range critical value * the square root of mean square
+        # error over the sample size.
+        confidence_radius = srd * self._stand_err
+        # the confidence levels are determined by the
+        # `mean_differences` +- `confidence_radius`
+        upper_conf = self.statistic + confidence_radius
+        lower_conf = self.statistic - confidence_radius
+        self._ci = ConfidenceInterval(low=lower_conf, high=upper_conf)
+        self._ci_cl = confidence_level
+        return self._ci
+
+
+def _tukey_hsd_iv(args, equal_var):
+    if (len(args)) < 2:
+        raise ValueError("There must be more than 1 treatment.")
+    if not isinstance(equal_var, bool):
+        raise TypeError("Expected a boolean value for 'equal_var'")
+    args = [np.asarray(arg) for arg in args]
+    for arg in args:
+        if arg.ndim != 1:
+            raise ValueError("Input samples must be one-dimensional.")
+        if arg.size <= 1:
+            raise ValueError("Input sample size must be greater than one.")
+        if np.isinf(arg).any():
+            raise ValueError("Input samples must be finite.")
+    return args
+
+
+@xp_capabilities(np_only=True)
+def tukey_hsd(*samples, equal_var=True):
+    """Perform Tukey's HSD test for equality of means over multiple treatments.
+
+    Tukey's honestly significant difference (HSD) test performs pairwise
+    comparison of means for a set of samples. Whereas ANOVA (e.g. `f_oneway`)
+    assesses whether the true means underlying each sample are identical,
+    Tukey's HSD is a post hoc test used to compare the mean of each sample
+    to the mean of each other sample.
+
+    The null hypothesis is that the distributions underlying the samples all
+    have the same mean. The test statistic, which is computed for every
+    possible pairing of samples, is simply the difference between the sample
+    means. For each pair, the p-value is the probability under the null
+    hypothesis (and other assumptions; see notes) of observing such an extreme
+    value of the statistic, considering that many pairwise comparisons are
+    being performed. Confidence intervals for the difference between each pair
+    of means are also available.
+
+    Parameters
+    ----------
+    *samples : array_like
+        The sample measurements for each group. There must be at least
+        two arguments.
+    equal_var : bool, optional
+        If True (default) and equal sample size, perform Tukey-HSD test [6].
+        If True and unequal sample size, perform Tukey-Kramer test [4]_.
+        If False, perform Games-Howell test [7]_, which does not assume equal variances.
+
+    Returns
+    -------
+    result : `~scipy.stats._result_classes.TukeyHSDResult` instance
+        The return value is an object with the following attributes:
+
+        statistic : float ndarray
+            The computed statistic of the test for each comparison. The element
+            at index ``(i, j)`` is the statistic for the comparison between
+            groups ``i`` and ``j``.
+        pvalue : float ndarray
+            The computed p-value of the test for each comparison. The element
+            at index ``(i, j)`` is the p-value for the comparison between
+            groups ``i`` and ``j``.
+
+        The object has the following methods:
+
+        confidence_interval(confidence_level=0.95):
+            Compute the confidence interval for the specified confidence level.
+
+    See Also
+    --------
+    dunnett : performs comparison of means against a control group.
+
+    Notes
+    -----
+    The use of this test relies on several assumptions.
+
+    1. The observations are independent within and among groups.
+    2. The observations within each group are normally distributed.
+    3. The distributions from which the samples are drawn have the same finite
+       variance.
+
+    The original formulation of the test was for samples of equal size drawn from
+    populations assumed to have equal variances [6]_. In case of unequal sample sizes,
+    the test uses the Tukey-Kramer method [4]_. When equal variances are not assumed
+    (``equal_var=False``), the test uses the Games-Howell method [7]_.
+
+    References
+    ----------
+    .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, "7.4.7.1. Tukey's
+           Method."
+           https://www.itl.nist.gov/div898/handbook/prc/section4/prc471.htm,
+           28 November 2020.
+    .. [2] Abdi, Herve & Williams, Lynne. (2021). "Tukey's Honestly Significant
+           Difference (HSD) Test."
+           https://personal.utdallas.edu/~herve/abdi-HSD2010-pretty.pdf
+    .. [3] "One-Way ANOVA Using SAS PROC ANOVA & PROC GLM." SAS
+           Tutorials, 2007.
+           https://www.stattutorials.com/SAS/TUTORIAL-PROC-GLM.htm
+    .. [4] Kramer, Clyde Young. "Extension of Multiple Range Tests to Group
+           Means with Unequal Numbers of Replications." Biometrics, vol. 12,
+           no. 3, 1956, pp. 307-310. https://www.jstor.org/stable/3001469
+    .. [5] NIST/SEMATECH e-Handbook of Statistical Methods, "7.4.3.3.
+           The ANOVA table and tests of hypotheses about means"
+           https://www.itl.nist.gov/div898/handbook/prc/section4/prc433.htm,
+           2 June 2021.
+    .. [6] Tukey, John W. "Comparing Individual Means in the Analysis of
+           Variance." Biometrics, vol. 5, no. 2, 1949, pp. 99-114.
+           https://www.jstor.org/stable/3001913
+    .. [7] P. A. Games and J. F. Howell, "Pairwise Multiple Comparison Procedures
+           with Unequal N's and/or Variances: A Monte Carlo Study," Journal of
+           Educational Statistics, vol. 1, no. 2, pp. 113-125, Jun. 1976.
+           :doi:`10.3102/10769986001002113`.
+
+
+    Examples
+    --------
+    Here are some data comparing the time to relief of three brands of
+    headache medicine, reported in minutes. Data adapted from [3]_.
+
+    >>> import numpy as np
+    >>> from scipy.stats import tukey_hsd
+    >>> group0 = [24.5, 23.5, 26.4, 27.1, 29.9]
+    >>> group1 = [28.4, 34.2, 29.5, 32.2, 30.1]
+    >>> group2 = [26.1, 28.3, 24.3, 26.2, 27.8]
+
+    We would like to see if the means between any of the groups are
+    significantly different. First, visually examine a box and whisker plot.
+
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots(1, 1)
+    >>> ax.boxplot([group0, group1, group2])
+    >>> ax.set_xticklabels(["group0", "group1", "group2"]) # doctest: +SKIP
+    >>> ax.set_ylabel("mean") # doctest: +SKIP
+    >>> plt.show()
+
+    From the box and whisker plot, we can see overlap in the interquartile
+    ranges group 1 to group 2 and group 3, but we can apply the ``tukey_hsd``
+    test to determine if the difference between means is significant. We
+    set a significance level of .05 to reject the null hypothesis.
+
+    >>> res = tukey_hsd(group0, group1, group2)
+    >>> print(res)
+    Pairwise Group Comparisons (95.0% Confidence Interval)
+    Comparison  Statistic  p-value  Lower CI  Upper CI
+     (0 - 1)     -4.600     0.014    -8.249    -0.951
+     (0 - 2)     -0.260     0.980    -3.909     3.389
+     (1 - 0)      4.600     0.014     0.951     8.249
+     (1 - 2)      4.340     0.020     0.691     7.989
+     (2 - 0)      0.260     0.980    -3.389     3.909
+     (2 - 1)     -4.340     0.020    -7.989    -0.691
+
+    The null hypothesis is that each group has the same mean. The p-value for
+    comparisons between ``group0`` and ``group1`` as well as ``group1`` and
+    ``group2`` do not exceed .05, so we reject the null hypothesis that they
+    have the same means. The p-value of the comparison between ``group0``
+    and ``group2`` exceeds .05, so we accept the null hypothesis that there
+    is not a significant difference between their means.
+
+    We can also compute the confidence interval associated with our chosen
+    confidence level.
+
+    >>> group0 = [24.5, 23.5, 26.4, 27.1, 29.9]
+    >>> group1 = [28.4, 34.2, 29.5, 32.2, 30.1]
+    >>> group2 = [26.1, 28.3, 24.3, 26.2, 27.8]
+    >>> result = tukey_hsd(group0, group1, group2)
+    >>> conf = res.confidence_interval(confidence_level=.99)
+    >>> for ((i, j), l) in np.ndenumerate(conf.low):
+    ...     # filter out self comparisons
+    ...     if i != j:
+    ...         h = conf.high[i,j]
+    ...         print(f"({i} - {j}) {l:>6.3f} {h:>6.3f}")
+    (0 - 1) -9.480  0.280
+    (0 - 2) -5.140  4.620
+    (1 - 0) -0.280  9.480
+    (1 - 2) -0.540  9.220
+    (2 - 0) -4.620  5.140
+    (2 - 1) -9.220  0.540
+    """
+    args = _tukey_hsd_iv(samples, equal_var)
+    ntreatments = len(args)
+    means = np.asarray([np.mean(arg) for arg in args])
+    nsamples_treatments = np.asarray([a.size for a in args])
+    nobs = np.sum(nsamples_treatments)
+    vars_ = np.asarray([np.var(arg, ddof=1) for arg in args])
+
+    if equal_var:
+        # determine mean square error [5]. Note that this is sometimes called
+        # mean square error within.
+        mse = (np.sum(vars_ * (nsamples_treatments - 1)) / (nobs - ntreatments))
+
+        # The calculation of the standard error differs when treatments differ in
+        # size. See ("Unequal sample sizes")[1].
+        if np.unique(nsamples_treatments).size == 1:
+            # all input groups are the same length, so only one value needs to be
+            # calculated [1].
+            normalize = 2 / nsamples_treatments[0]
+        else:
+            # to compare groups of differing sizes, we must compute a variance
+            # value for each individual comparison. Use broadcasting to get the
+            # resulting matrix. [3], verified against [4] (page 308).
+            normalize = 1 / nsamples_treatments + 1 / nsamples_treatments[None].T
+
+        # the standard error is used in the computation of the tukey criterion and
+        # finding the p-values.
+        stand_err = np.sqrt(normalize * mse / 2)
+        df = nobs - ntreatments
+    else:
+        # `stand_err` is the denominator of the Behrens-Fisher statistic ($v$)
+        # with a factor of $\sqrt{2}$. Compare [7] p.116 "t-solution rejects H0 if...",
+        # [7] p. 117 "H0 was rejected", and definition of `t_stat` below.
+        sj2_nj = vars_ / nsamples_treatments
+        si2_ni = sj2_nj[:, np.newaxis]
+        stand_err = np.sqrt(si2_ni + sj2_nj) / 2**0.5
+
+        # `df` is the Welch degree of freedom $\nu$.
+        # See [7] p. 116 "and the degrees of freedom, $\nu$, are given by...".
+        njm1 = nsamples_treatments - 1
+        nim1 = njm1[:, np.newaxis]
+        df = (si2_ni + sj2_nj)**2 / (si2_ni**2 / nim1 + sj2_nj**2 / njm1)
+
+    # the mean difference is the test statistic.
+    mean_differences = means[None].T - means
+
+    # Calculate the t-statistic to use within the survival function of the
+    # studentized range to get the p-value.
+    t_stat = np.abs(mean_differences) / stand_err
+
+    params = t_stat, ntreatments, df
+    pvalues = distributions.studentized_range.sf(*params)
+
+    return TukeyHSDResult(mean_differences, pvalues, ntreatments,
+                          df, stand_err)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_kde.py b/.venv/lib/python3.12/site-packages/scipy/stats/_kde.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1be45bfa59f7816235886763761dc3a2107c9cd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_kde.py
@@ -0,0 +1,765 @@
+#-------------------------------------------------------------------------------
+#
+#  Define classes for (uni/multi)-variate kernel density estimation.
+#
+#  Currently, only Gaussian kernels are implemented.
+#
+#  Written by: Robert Kern
+#
+#  Date: 2004-08-09
+#
+#  Modified: 2005-02-10 by Robert Kern.
+#              Contributed to SciPy
+#            2005-10-07 by Robert Kern.
+#              Some fixes to match the new scipy_core
+#
+#  Copyright 2004-2005 by Enthought, Inc.
+#
+#-------------------------------------------------------------------------------
+
+from types import GenericAlias
+
+# SciPy imports.
+from scipy import linalg, special
+from scipy._lib._util import check_random_state
+
+from numpy import (asarray, atleast_2d, reshape, zeros, newaxis, exp, pi,
+                   sqrt, ravel, power, atleast_1d, squeeze, sum, transpose,
+                   ones, cov)
+import numpy as np
+
+# Local imports.
+from ._stats import gaussian_kernel_estimate, gaussian_kernel_estimate_log
+from ._multivariate import multivariate_normal
+
+__all__ = ['gaussian_kde']
+
+
+class gaussian_kde:
+    """Representation of a kernel-density estimate using Gaussian kernels.
+
+    Kernel density estimation is a way to estimate the probability density
+    function (PDF) of a random variable in a non-parametric way.
+    `gaussian_kde` works for both uni-variate and multi-variate data.   It
+    includes automatic bandwidth determination.  The estimation works best for
+    a unimodal distribution; bimodal or multi-modal distributions tend to be
+    oversmoothed.
+
+    Parameters
+    ----------
+    dataset : array_like
+        Datapoints to estimate from. In case of univariate data this is a 1-D
+        array, otherwise a 2-D array with shape (# of dims, # of data).
+    bw_method : str, scalar or callable, optional
+        The method used to calculate the bandwidth factor.  This can be
+        'scott', 'silverman', a scalar constant or a callable.  If a scalar,
+        this will be used directly as `factor`.  If a callable, it should
+        take a `gaussian_kde` instance as only parameter and return a scalar.
+        If None (default), 'scott' is used.  See Notes for more details.
+    weights : array_like, optional
+        weights of datapoints. This must be the same shape as dataset.
+        If None (default), the samples are assumed to be equally weighted
+
+    Attributes
+    ----------
+    dataset : ndarray
+        The dataset with which `gaussian_kde` was initialized.
+    d : int
+        Number of dimensions.
+    n : int
+        Number of datapoints.
+    neff : int
+        Effective number of datapoints.
+
+        .. versionadded:: 1.2.0
+    factor : float
+        The bandwidth factor obtained from `covariance_factor`.
+    covariance : ndarray
+        The kernel covariance matrix; this is the data covariance matrix
+        multiplied by the square of the bandwidth factor, e.g.
+        ``np.cov(dataset) * factor**2``.
+    inv_cov : ndarray
+        The inverse of `covariance`.
+
+    Methods
+    -------
+    evaluate
+    __call__
+    integrate_gaussian
+    integrate_box_1d
+    integrate_box
+    integrate_kde
+    pdf
+    logpdf
+    resample
+    set_bandwidth
+    covariance_factor
+    marginal
+
+    Notes
+    -----
+    Bandwidth selection strongly influences the estimate obtained from the KDE
+    (much more so than the actual shape of the kernel).  Bandwidth selection
+    can be done by a "rule of thumb", by cross-validation, by "plug-in
+    methods" or by other means; see [3]_, [4]_ for reviews.  `gaussian_kde`
+    uses a rule of thumb, the default is Scott's Rule.
+
+    Scott's Rule [1]_, implemented as `scotts_factor`, is::
+
+        n**(-1./(d+4)),
+
+    with ``n`` the number of data points and ``d`` the number of dimensions.
+    In the case of unequally weighted points, `scotts_factor` becomes::
+
+        neff**(-1./(d+4)),
+
+    with ``neff`` the effective number of datapoints.
+    Silverman's suggestion for *multivariate* data [2]_, implemented as
+    `silverman_factor`, is::
+
+        (n * (d + 2) / 4.)**(-1. / (d + 4)).
+
+    or in the case of unequally weighted points::
+
+        (neff * (d + 2) / 4.)**(-1. / (d + 4)).
+
+    Note that this is not the same as "Silverman's rule of thumb" [6]_, which
+    may be more robust in the univariate case; see documentation of the
+    ``set_bandwidth`` method for implementing a custom bandwidth rule.
+
+    Good general descriptions of kernel density estimation can be found in [1]_
+    and [2]_, the mathematics for this multi-dimensional implementation can be
+    found in [1]_.
+
+    With a set of weighted samples, the effective number of datapoints ``neff``
+    is defined by::
+
+        neff = sum(weights)^2 / sum(weights^2)
+
+    as detailed in [5]_.
+
+    `gaussian_kde` does not currently support data that lies in a
+    lower-dimensional subspace of the space in which it is expressed. For such
+    data, consider performing principal component analysis / dimensionality
+    reduction and using `gaussian_kde` with the transformed data.
+
+    References
+    ----------
+    .. [1] D.W. Scott, "Multivariate Density Estimation: Theory, Practice, and
+           Visualization", John Wiley & Sons, New York, Chicester, 1992.
+    .. [2] B.W. Silverman, "Density Estimation for Statistics and Data
+           Analysis", Vol. 26, Monographs on Statistics and Applied Probability,
+           Chapman and Hall, London, 1986.
+    .. [3] B.A. Turlach, "Bandwidth Selection in Kernel Density Estimation: A
+           Review", CORE and Institut de Statistique, Vol. 19, pp. 1-33, 1993.
+    .. [4] D.M. Bashtannyk and R.J. Hyndman, "Bandwidth selection for kernel
+           conditional density estimation", Computational Statistics & Data
+           Analysis, Vol. 36, pp. 279-298, 2001.
+    .. [5] Gray P. G., 1969, Journal of the Royal Statistical Society.
+           Series A (General), 132, 272
+    .. [6] Kernel density estimation. *Wikipedia.*
+           https://en.wikipedia.org/wiki/Kernel_density_estimation
+
+    Examples
+    --------
+    Generate some random two-dimensional data:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> def measure(n):
+    ...     "Measurement model, return two coupled measurements."
+    ...     m1 = np.random.normal(size=n)
+    ...     m2 = np.random.normal(scale=0.5, size=n)
+    ...     return m1+m2, m1-m2
+
+    >>> m1, m2 = measure(2000)
+    >>> xmin = m1.min()
+    >>> xmax = m1.max()
+    >>> ymin = m2.min()
+    >>> ymax = m2.max()
+
+    Perform a kernel density estimate on the data:
+
+    >>> X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
+    >>> positions = np.vstack([X.ravel(), Y.ravel()])
+    >>> values = np.vstack([m1, m2])
+    >>> kernel = stats.gaussian_kde(values)
+    >>> Z = np.reshape(kernel(positions).T, X.shape)
+
+    Plot the results:
+
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots()
+    >>> ax.imshow(np.rot90(Z), cmap=plt.cm.gist_earth_r,
+    ...           extent=[xmin, xmax, ymin, ymax])
+    >>> ax.plot(m1, m2, 'k.', markersize=2)
+    >>> ax.set_xlim([xmin, xmax])
+    >>> ax.set_ylim([ymin, ymax])
+    >>> plt.show()
+
+    Compare against manual KDE at a point:
+
+    >>> point = [1, 2]
+    >>> mean = values.T
+    >>> cov = kernel.factor**2 * np.cov(values)
+    >>> X = stats.multivariate_normal(cov=cov)
+    >>> res = kernel.pdf(point)
+    >>> ref = X.pdf(point - mean).sum() / len(mean)
+    >>> np.allclose(res, ref)
+    True
+    """
+
+    # generic type compatibility with scipy-stubs
+    __class_getitem__: classmethod = classmethod(GenericAlias)
+
+    def __init__(self, dataset, bw_method=None, weights=None):
+        self.dataset = atleast_2d(asarray(dataset))
+        if not self.dataset.size > 1:
+            raise ValueError("`dataset` input should have multiple elements.")
+
+        self.d, self.n = self.dataset.shape
+
+        if weights is not None:
+            self._weights = atleast_1d(weights).astype(float)
+            self._weights /= sum(self._weights)
+            if self.weights.ndim != 1:
+                raise ValueError("`weights` input should be one-dimensional.")
+            if len(self._weights) != self.n:
+                raise ValueError("`weights` input should be of length n")
+            self._neff = 1/np.vecdot(self._weights, self._weights)
+
+        # This can be converted to a warning once gh-10205 is resolved
+        if self.d > self.n:
+            msg = ("Number of dimensions is greater than number of samples. "
+                   "This results in a singular data covariance matrix, which "
+                   "cannot be treated using the algorithms implemented in "
+                   "`gaussian_kde`. Note that `gaussian_kde` interprets each "
+                   "*column* of `dataset` to be a point; consider transposing "
+                   "the input to `dataset`.")
+            raise ValueError(msg)
+
+        try:
+            self.set_bandwidth(bw_method=bw_method)
+        except linalg.LinAlgError as e:
+            msg = ("The data appears to lie in a lower-dimensional subspace "
+                   "of the space in which it is expressed. This has resulted "
+                   "in a singular data covariance matrix, which cannot be "
+                   "treated using the algorithms implemented in "
+                   "`gaussian_kde`. Consider performing principal component "
+                   "analysis / dimensionality reduction and using "
+                   "`gaussian_kde` with the transformed data.")
+            raise linalg.LinAlgError(msg) from e
+
+    def evaluate(self, points):
+        """Evaluate the estimated pdf on a set of points.
+
+        Parameters
+        ----------
+        points : (# of dimensions, # of points)-array
+            Alternatively, a (# of dimensions,) vector can be passed in and
+            treated as a single point.
+
+        Returns
+        -------
+        values : (# of points,)-array
+            The values at each point.
+
+        Raises
+        ------
+        ValueError : if the dimensionality of the input points is different than
+                     the dimensionality of the KDE.
+
+        """
+        points = atleast_2d(asarray(points))
+
+        d, m = points.shape
+        if d != self.d:
+            if d == 1 and m == self.d:
+                # points was passed in as a row vector
+                points = reshape(points, (self.d, 1))
+                m = 1
+            else:
+                msg = (f"points have dimension {d}, "
+                       f"dataset has dimension {self.d}")
+                raise ValueError(msg)
+
+        output_dtype, spec = _get_output_dtype(self.covariance, points)
+        result = gaussian_kernel_estimate[spec](
+            self.dataset.T, self.weights[:, None],
+            points.T, self.cho_cov, output_dtype)
+
+        return result[:, 0]
+
+    __call__ = evaluate
+
+    def integrate_gaussian(self, mean, cov):
+        """
+        Multiply estimated density by a multivariate Gaussian and integrate
+        over the whole space.
+
+        Parameters
+        ----------
+        mean : aray_like
+            A 1-D array, specifying the mean of the Gaussian.
+        cov : array_like
+            A 2-D array, specifying the covariance matrix of the Gaussian.
+
+        Returns
+        -------
+        result : scalar
+            The value of the integral.
+
+        Raises
+        ------
+        ValueError
+            If the mean or covariance of the input Gaussian differs from
+            the KDE's dimensionality.
+
+        """
+        mean = atleast_1d(squeeze(mean))
+        cov = atleast_2d(cov)
+
+        if mean.shape != (self.d,):
+            raise ValueError(f"mean does not have dimension {self.d}")
+        if cov.shape != (self.d, self.d):
+            raise ValueError(f"covariance does not have dimension {self.d}")
+
+        # make mean a column vector
+        mean = mean[:, newaxis]
+
+        sum_cov = self.covariance + cov
+
+        # This will raise LinAlgError if the new cov matrix is not s.p.d
+        # cho_factor returns (ndarray, bool) where bool is a flag for whether
+        # or not ndarray is upper or lower triangular
+        sum_cov_chol = linalg.cho_factor(sum_cov)
+
+        diff = self.dataset - mean
+        tdiff = linalg.cho_solve(sum_cov_chol, diff)
+
+        sqrt_det = np.prod(np.diagonal(sum_cov_chol[0]))
+        norm_const = power(2 * pi, sum_cov.shape[0] / 2.0) * sqrt_det
+
+        energies = np.vecdot(diff, tdiff, axis=0) / 2.0
+        result = np.vecdot(exp(-energies), self.weights, axis=0) / norm_const
+
+        return result
+
+    def integrate_box_1d(self, low, high):
+        """
+        Computes the integral of a 1D pdf between two bounds.
+
+        Parameters
+        ----------
+        low : scalar
+            Lower bound of integration.
+        high : scalar
+            Upper bound of integration.
+
+        Returns
+        -------
+        value : scalar
+            The result of the integral.
+
+        Raises
+        ------
+        ValueError
+            If the KDE is over more than one dimension.
+
+        """
+        if self.d != 1:
+            raise ValueError("integrate_box_1d() only handles 1D pdfs")
+
+        stdev = ravel(sqrt(self.covariance))[0]
+
+        normalized_low = ravel((low - self.dataset) / stdev)
+        normalized_high = ravel((high - self.dataset) / stdev)
+
+        delta = special.ndtr(normalized_high) - special.ndtr(normalized_low)
+        value = np.vecdot(self.weights, delta)
+        return value
+
+    def integrate_box(self, low_bounds, high_bounds, maxpts=None, *, rng=None):
+        """Computes the integral of a pdf over a rectangular interval.
+
+        Parameters
+        ----------
+        low_bounds : array_like
+            A 1-D array containing the lower bounds of integration.
+        high_bounds : array_like
+            A 1-D array containing the upper bounds of integration.
+        maxpts : int, optional
+            The maximum number of points to use for integration.
+        rng : `numpy.random.Generator`, optional
+            Pseudorandom number generator state. When `rng` is None, a new
+            generator is created using entropy from the operating system. Types
+            other than `numpy.random.Generator` are passed to
+            `numpy.random.default_rng` to instantiate a ``Generator``.
+
+        Returns
+        -------
+        value : scalar
+            The result of the integral.
+
+        """
+        low, high = low_bounds - self.dataset.T, high_bounds - self.dataset.T
+        values = multivariate_normal.cdf(
+            high, lower_limit=low, cov=self.covariance, maxpts=maxpts,
+            rng=rng
+        )
+        return np.vecdot(values, self.weights, axis=-1)
+
+    def integrate_kde(self, other):
+        """
+        Computes the integral of the product of this  kernel density estimate
+        with another.
+
+        Parameters
+        ----------
+        other : gaussian_kde instance
+            The other kde.
+
+        Returns
+        -------
+        value : scalar
+            The result of the integral.
+
+        Raises
+        ------
+        ValueError
+            If the KDEs have different dimensionality.
+
+        """
+        if other.d != self.d:
+            raise ValueError("KDEs are not the same dimensionality")
+
+        # we want to iterate over the smallest number of points
+        if other.n < self.n:
+            small = other
+            large = self
+        else:
+            small = self
+            large = other
+
+        sum_cov = small.covariance + large.covariance
+        sum_cov_chol = linalg.cho_factor(sum_cov)
+        result = 0.0
+        for i in range(small.n):
+            mean = small.dataset[:, i, newaxis]
+            diff = large.dataset - mean
+            tdiff = linalg.cho_solve(sum_cov_chol, diff)
+
+            energies = np.vecdot(diff, tdiff, axis=0) / 2.0
+            result += np.vecdot(exp(-energies), large.weights, axis=0)*small.weights[i]
+
+        sqrt_det = np.prod(np.diagonal(sum_cov_chol[0]))
+        norm_const = power(2 * pi, sum_cov.shape[0] / 2.0) * sqrt_det
+
+        result /= norm_const
+
+        return result
+
+    def resample(self, size=None, seed=None):
+        """Randomly sample a dataset from the estimated pdf.
+
+        Parameters
+        ----------
+        size : int, optional
+            The number of samples to draw.  If not provided, then the size is
+            the same as the effective number of samples in the underlying
+            dataset.
+        seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+            If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+            singleton is used.
+            If `seed` is an int, a new ``RandomState`` instance is used,
+            seeded with `seed`.
+            If `seed` is already a ``Generator`` or ``RandomState`` instance then
+            that instance is used.
+
+        Returns
+        -------
+        resample : (self.d, `size`) ndarray
+            The sampled dataset.
+
+        """ # numpy/numpydoc#87  # noqa: E501
+        if size is None:
+            size = int(self.neff)
+
+        random_state = check_random_state(seed)
+        norm = transpose(random_state.multivariate_normal(
+            zeros((self.d,), float), self.covariance, size=size
+        ))
+        indices = random_state.choice(self.n, size=size, p=self.weights)
+        means = self.dataset[:, indices]
+
+        return means + norm
+
+    def scotts_factor(self):
+        """Compute Scott's factor.
+
+        Returns
+        -------
+        s : float
+            Scott's factor.
+        """
+        return power(self.neff, -1./(self.d+4))
+
+    def silverman_factor(self):
+        """Compute the Silverman factor.
+
+        Returns
+        -------
+        s : float
+            The silverman factor.
+        """
+        return power(self.neff*(self.d+2.0)/4.0, -1./(self.d+4))
+
+    #  Default method to calculate bandwidth, can be overwritten by subclass
+    covariance_factor = scotts_factor
+    covariance_factor.__doc__ = """Computes the bandwidth factor `factor`.
+        The default is `scotts_factor`.  A subclass can overwrite this
+        method to provide a different method, or set it through a call to
+        `set_bandwidth`.
+
+        Returns
+        -------
+        factor : float
+            The bandwidth factor.
+        """
+
+    def set_bandwidth(self, bw_method=None):
+        """Compute the bandwidth factor with given method.
+
+        The new bandwidth calculated after a call to `set_bandwidth` is used
+        for subsequent evaluations of the estimated density.
+
+        Parameters
+        ----------
+        bw_method : str, scalar or callable, optional
+            The method used to calculate the bandwidth factor.  This can be
+            'scott', 'silverman', a scalar constant or a callable.  If a
+            scalar, this will be used directly as `factor`.  If a callable,
+            it should take a `gaussian_kde` instance as only parameter and
+            return a scalar.  If None (default), nothing happens; the current
+            `covariance_factor` method is kept.
+
+        Notes
+        -----
+        .. versionadded:: 0.11
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> import scipy.stats as stats
+        >>> x1 = np.array([-7, -5, 1, 4, 5.])
+        >>> kde = stats.gaussian_kde(x1)
+        >>> xs = np.linspace(-10, 10, num=50)
+        >>> y1 = kde(xs)
+        >>> kde.set_bandwidth(bw_method='silverman')
+        >>> y2 = kde(xs)
+        >>> kde.set_bandwidth(bw_method=kde.factor / 3.)
+        >>> y3 = kde(xs)
+
+        >>> import matplotlib.pyplot as plt
+        >>> fig, ax = plt.subplots()
+        >>> ax.plot(x1, np.full(x1.shape, 1 / (4. * x1.size)), 'bo',
+        ...         label='Data points (rescaled)')
+        >>> ax.plot(xs, y1, label='Scott (default)')
+        >>> ax.plot(xs, y2, label='Silverman')
+        >>> ax.plot(xs, y3, label='Const (1/3 * Silverman)')
+        >>> ax.legend()
+        >>> plt.show()
+
+        """
+        if bw_method is None:
+            pass
+        elif bw_method == 'scott':
+            self.covariance_factor = self.scotts_factor
+        elif bw_method == 'silverman':
+            self.covariance_factor = self.silverman_factor
+        elif np.isscalar(bw_method) and not isinstance(bw_method, str):
+            self._bw_method = 'use constant'
+            self.covariance_factor = lambda: bw_method
+        elif callable(bw_method):
+            self._bw_method = bw_method
+            self.covariance_factor = lambda: self._bw_method(self)
+        else:
+            msg = "`bw_method` should be 'scott', 'silverman', a scalar " \
+                  "or a callable."
+            raise ValueError(msg)
+
+        self._compute_covariance()
+
+    def _compute_covariance(self):
+        """Computes the covariance matrix for each Gaussian kernel using
+        covariance_factor().
+        """
+        self.factor = self.covariance_factor()
+        # Cache covariance and Cholesky decomp of covariance
+        if not hasattr(self, '_data_cho_cov'):
+            self._data_covariance = atleast_2d(cov(self.dataset, rowvar=1,
+                                               bias=False,
+                                               aweights=self.weights))
+            self._data_cho_cov = linalg.cholesky(self._data_covariance,
+                                                 lower=True)
+
+        self.covariance = self._data_covariance * self.factor**2
+        self.cho_cov = (self._data_cho_cov * self.factor).astype(np.float64)
+        self.log_det = 2*np.log(np.diag(self.cho_cov
+                                        * np.sqrt(2*pi))).sum()
+
+    @property
+    def inv_cov(self):
+        # Re-compute from scratch each time because I'm not sure how this is
+        # used in the wild. (Perhaps users change the `dataset`, since it's
+        # not a private attribute?) `_compute_covariance` used to recalculate
+        # all these, so we'll recalculate everything now that this is a
+        # a property.
+        self.factor = self.covariance_factor()
+        self._data_covariance = atleast_2d(cov(self.dataset, rowvar=1,
+                                           bias=False, aweights=self.weights))
+        return linalg.inv(self._data_covariance) / self.factor**2
+
+    def pdf(self, x):
+        """
+        Evaluate the estimated pdf on a provided set of points.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the pdf.
+
+        Returns
+        -------
+        pdf : ndarray
+            The pdf evaluated at `x`.
+
+        Notes
+        -----
+        This is an alias for `gaussian_kde.evaluate`.  See the ``evaluate``
+        docstring for more details.
+
+        """
+        return self.evaluate(x)
+
+    def logpdf(self, x):
+        """
+        Evaluate the log of the estimated pdf on a provided set of points.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log-pdf.
+
+        Returns
+        -------
+        logpdf : ndarray
+            The log-pdf evaluated at `x`.
+        """
+        points = atleast_2d(x)
+
+        d, m = points.shape
+        if d != self.d:
+            if d == 1 and m == self.d:
+                # points was passed in as a row vector
+                points = reshape(points, (self.d, 1))
+                m = 1
+            else:
+                msg = (f"points have dimension {d}, "
+                       f"dataset has dimension {self.d}")
+                raise ValueError(msg)
+
+        output_dtype, spec = _get_output_dtype(self.covariance, points)
+        result = gaussian_kernel_estimate_log[spec](
+            self.dataset.T, self.weights[:, None],
+            points.T, self.cho_cov, output_dtype)
+
+        return result[:, 0]
+
+    def marginal(self, dimensions):
+        """Return a marginal KDE distribution.
+
+        Parameters
+        ----------
+        dimensions : int or 1-d array_like
+            The dimensions of the multivariate distribution corresponding
+            with the marginal variables, that is, the indices of the dimensions
+            that are being retained. The other dimensions are marginalized out.
+
+        Returns
+        -------
+        marginal_kde : gaussian_kde
+            An object representing the marginal distribution.
+
+        Notes
+        -----
+        .. versionadded:: 1.10.0
+
+        """
+
+        dims = np.atleast_1d(dimensions)
+
+        if not np.issubdtype(dims.dtype, np.integer):
+            msg = ("Elements of `dimensions` must be integers - the indices "
+                   "of the marginal variables being retained.")
+            raise ValueError(msg)
+
+        n = len(self.dataset)  # number of dimensions
+        original_dims = dims.copy()
+
+        dims[dims < 0] = n + dims[dims < 0]
+
+        if len(np.unique(dims)) != len(dims):
+            msg = ("All elements of `dimensions` must be unique.")
+            raise ValueError(msg)
+
+        i_invalid = (dims < 0) | (dims >= n)
+        if np.any(i_invalid):
+            msg = (f"Dimensions {original_dims[i_invalid]} are invalid "
+                   f"for a distribution in {n} dimensions.")
+            raise ValueError(msg)
+
+        dataset = self.dataset[dims]
+        weights = self.weights
+
+        return gaussian_kde(dataset, bw_method=self.covariance_factor(),
+                            weights=weights)
+
+    @property
+    def weights(self):
+        try:
+            return self._weights
+        except AttributeError:
+            self._weights = ones(self.n)/self.n
+            return self._weights
+
+    @property
+    def neff(self):
+        try:
+            return self._neff
+        except AttributeError:
+            self._neff = 1/np.vecdot(self.weights, self.weights)
+            return self._neff
+
+
+def _get_output_dtype(covariance, points):
+    """
+    Calculates the output dtype and the "spec" (=C type name).
+
+    This was necessary in order to deal with the fused types in the Cython
+    routine `gaussian_kernel_estimate`. See gh-10824 for details.
+    """
+    output_dtype = np.common_type(covariance, points)
+    itemsize = np.dtype(output_dtype).itemsize
+    if itemsize == 4:
+        spec = 'float'
+    elif itemsize == 8:
+        spec = 'double'
+    elif itemsize in (12, 16):
+        spec = 'long double'
+    else:
+        raise ValueError(
+                f"{output_dtype} has unexpected item size: {itemsize}"
+            )
+
+    return output_dtype, spec
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_ksstats.py b/.venv/lib/python3.12/site-packages/scipy/stats/_ksstats.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6884bb834bbb060fdec5ed49869517e5b7c941b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_ksstats.py
@@ -0,0 +1,600 @@
+# Compute the two-sided one-sample Kolmogorov-Smirnov Prob(Dn <= d) where:
+#    D_n = sup_x{|F_n(x) - F(x)|},
+#    F_n(x) is the empirical CDF for a sample of size n {x_i: i=1,...,n},
+#    F(x) is the CDF of a probability distribution.
+#
+# Exact methods:
+# Prob(D_n >= d) can be computed via a matrix algorithm of Durbin[1]
+#   or a recursion algorithm due to Pomeranz[2].
+# Marsaglia, Tsang & Wang[3] gave a computation-efficient way to perform
+#   the Durbin algorithm.
+#   D_n >= d <==>  D_n+ >= d or D_n- >= d (the one-sided K-S statistics), hence
+#   Prob(D_n >= d) = 2*Prob(D_n+ >= d) - Prob(D_n+ >= d and D_n- >= d).
+#   For d > 0.5, the latter intersection probability is 0.
+#
+# Approximate methods:
+# For d close to 0.5, ignoring that intersection term may still give a
+#   reasonable approximation.
+# Li-Chien[4] and Korolyuk[5] gave an asymptotic formula extending
+# Kolmogorov's initial asymptotic, suitable for large d. (See
+#   scipy.special.kolmogorov for that asymptotic)
+# Pelz-Good[6] used the functional equation for Jacobi theta functions to
+#   transform the Li-Chien/Korolyuk formula produce a computational formula
+#   suitable for small d.
+#
+# Simard and L'Ecuyer[7] provided an algorithm to decide when to use each of
+#   the above approaches and it is that which is used here.
+#
+# Other approaches:
+# Carvalho[8] optimizes Durbin's matrix algorithm for large values of d.
+# Moscovich and Nadler[9] use FFTs to compute the convolutions.
+
+# References:
+# [1] Durbin J (1968).
+#     "The Probability that the Sample Distribution Function Lies Between Two
+#     Parallel Straight Lines."
+#     Annals of Mathematical Statistics, 39, 398-411.
+# [2] Pomeranz J (1974).
+#     "Exact Cumulative Distribution of the Kolmogorov-Smirnov Statistic for
+#     Small Samples (Algorithm 487)."
+#     Communications of the ACM, 17(12), 703-704.
+# [3] Marsaglia G, Tsang WW, Wang J (2003).
+#     "Evaluating Kolmogorov's Distribution."
+#     Journal of Statistical Software, 8(18), 1-4.
+# [4] LI-CHIEN, C. (1956).
+#     "On the exact distribution of the statistics of A. N. Kolmogorov and
+#     their asymptotic expansion."
+#     Acta Matematica Sinica, 6, 55-81.
+# [5] KOROLYUK, V. S. (1960).
+#     "Asymptotic analysis of the distribution of the maximum deviation in
+#     the Bernoulli scheme."
+#     Theor. Probability Appl., 4, 339-366.
+# [6] Pelz W, Good IJ (1976).
+#     "Approximating the Lower Tail-areas of the Kolmogorov-Smirnov One-sample
+#     Statistic."
+#     Journal of the Royal Statistical Society, Series B, 38(2), 152-156.
+#  [7] Simard, R., L'Ecuyer, P. (2011)
+# 	  "Computing the Two-Sided Kolmogorov-Smirnov Distribution",
+# 	  Journal of Statistical Software, Vol 39, 11, 1-18.
+#  [8] Carvalho, Luis (2015)
+#     "An Improved Evaluation of Kolmogorov's Distribution"
+#     Journal of Statistical Software, Code Snippets; Vol 65(3), 1-8.
+#  [9] Amit Moscovich, Boaz Nadler (2017)
+#     "Fast calculation of boundary crossing probabilities for Poisson
+#     processes",
+#     Statistics & Probability Letters, Vol 123, 177-182.
+
+
+import numpy as np
+import scipy.special
+import scipy.special._ufuncs as scu
+from scipy.stats._finite_differences import _derivative
+
+_E128 = 128
+_EP128 = np.ldexp(np.longdouble(1), _E128)
+_EM128 = np.ldexp(np.longdouble(1), -_E128)
+
+_SQRT2PI = np.sqrt(2 * np.pi)
+_LOG_2PI = np.log(2 * np.pi)
+_MIN_LOG = -708
+_SQRT3 = np.sqrt(3)
+_PI_SQUARED = np.pi ** 2
+_PI_FOUR = np.pi ** 4
+_PI_SIX = np.pi ** 6
+
+# [Lifted from _loggamma.pxd.] If B_m are the Bernoulli numbers,
+# then Stirling coeffs are B_{2j}/(2j)/(2j-1) for j=8,...1.
+_STIRLING_COEFFS = [-2.955065359477124183e-2, 6.4102564102564102564e-3,
+                    -1.9175269175269175269e-3, 8.4175084175084175084e-4,
+                    -5.952380952380952381e-4, 7.9365079365079365079e-4,
+                    -2.7777777777777777778e-3, 8.3333333333333333333e-2]
+
+
+def _log_nfactorial_div_n_pow_n(n):
+    # Computes n! / n**n
+    #    = (n-1)! / n**(n-1)
+    # Uses Stirling's approximation, but removes n*log(n) up-front to
+    # avoid subtractive cancellation.
+    #    = log(n)/2 - n + log(sqrt(2pi)) + sum B_{2j}/(2j)/(2j-1)/n**(2j-1)
+    rn = 1.0/n
+    return np.log(n)/2 - n + _LOG_2PI/2 + rn * np.polyval(_STIRLING_COEFFS, rn/n)
+
+
+def _clip_prob(p):
+    """clips a probability to range 0<=p<=1."""
+    return np.clip(p, 0.0, 1.0)
+
+
+def _select_and_clip_prob(cdfprob, sfprob, cdf=True):
+    """Selects either the CDF or SF, and then clips to range 0<=p<=1."""
+    p = np.where(cdf, cdfprob, sfprob)
+    return _clip_prob(p)
+
+
+def _kolmogn_DMTW(n, d, cdf=True):
+    r"""Computes the Kolmogorov CDF:  Pr(D_n <= d) using the MTW approach to
+    the Durbin matrix algorithm.
+
+    Durbin (1968); Marsaglia, Tsang, Wang (2003). [1], [3].
+    """
+    # Write d = (k-h)/n, where k is positive integer and 0 <= h < 1
+    # Generate initial matrix H of size m*m where m=(2k-1)
+    # Compute k-th row of (n!/n^n) * H^n, scaling intermediate results.
+    # Requires memory O(m^2) and computation O(m^2 log(n)).
+    # Most suitable for small m.
+
+    if d >= 1.0:
+        return _select_and_clip_prob(1.0, 0.0, cdf)
+    nd = n * d
+    if nd <= 0.5:
+        return _select_and_clip_prob(0.0, 1.0, cdf)
+    k = int(np.ceil(nd))
+    h = k - nd
+    m = 2 * k - 1
+
+    H = np.zeros([m, m])
+
+    # Initialize: v is first column (and last row) of H
+    #  v[j] = (1-h^(j+1)/(j+1)!  (except for v[-1])
+    #  w[j] = 1/(j)!
+    # q = k-th row of H (actually i!/n^i*H^i)
+    intm = np.arange(1, m + 1)
+    v = 1.0 - h ** intm
+    w = np.empty(m)
+    fac = 1.0
+    for j in intm:
+        w[j - 1] = fac
+        fac /= j  # This might underflow.  Isn't a problem.
+        v[j - 1] *= fac
+    tt = max(2 * h - 1.0, 0)**m - 2*h**m
+    v[-1] = (1.0 + tt) * fac
+
+    for i in range(1, m):
+        H[i - 1:, i] = w[:m - i + 1]
+    H[:, 0] = v
+    H[-1, :] = np.flip(v, axis=0)
+
+    Hpwr = np.eye(np.shape(H)[0])  # Holds intermediate powers of H
+    nn = n
+    expnt = 0  # Scaling of Hpwr
+    Hexpnt = 0  # Scaling of H
+    while nn > 0:
+        if nn % 2:
+            Hpwr = np.matmul(Hpwr, H)
+            expnt += Hexpnt
+        H = np.matmul(H, H)
+        Hexpnt *= 2
+        # Scale as needed.
+        if np.abs(H[k - 1, k - 1]) > _EP128:
+            H /= _EP128
+            Hexpnt += _E128
+        nn = nn // 2
+
+    p = Hpwr[k - 1, k - 1]
+
+    # Multiply by n!/n^n
+    for i in range(1, n + 1):
+        p = i * p / n
+        if np.abs(p) < _EM128:
+            p *= _EP128
+            expnt -= _E128
+
+    # unscale
+    if expnt != 0:
+        p = np.ldexp(p, expnt)
+
+    return _select_and_clip_prob(p, 1.0-p, cdf)
+
+
+def _pomeranz_compute_j1j2(i, n, ll, ceilf, roundf):
+    """Compute the endpoints of the interval for row i."""
+    if i == 0:
+        j1, j2 = -ll - ceilf - 1, ll + ceilf - 1
+    else:
+        # i + 1 = 2*ip1div2 + ip1mod2
+        ip1div2, ip1mod2 = divmod(i + 1, 2)
+        if ip1mod2 == 0:  # i is odd
+            if ip1div2 == n + 1:
+                j1, j2 = n - ll - ceilf - 1, n + ll + ceilf - 1
+            else:
+                j1, j2 = ip1div2 - 1 - ll - roundf - 1, ip1div2 + ll - 1 + ceilf - 1
+        else:
+            j1, j2 = ip1div2 - 1 - ll - 1, ip1div2 + ll + roundf - 1
+
+    return max(j1 + 2, 0), min(j2, n)
+
+
+def _kolmogn_Pomeranz(n, x, cdf=True):
+    r"""Computes Pr(D_n <= d) using the Pomeranz recursion algorithm.
+
+    Pomeranz (1974) [2]
+    """
+
+    # V is n*(2n+2) matrix.
+    # Each row is convolution of the previous row and probabilities from a
+    #  Poisson distribution.
+    # Desired CDF probability is n! V[n-1, 2n+1]  (final entry in final row).
+    # Only two rows are needed at any given stage:
+    #  - Call them V0 and V1.
+    #  - Swap each iteration
+    # Only a few (contiguous) entries in each row can be non-zero.
+    #  - Keep track of start and end (j1 and j2 below)
+    #  - V0s and V1s track the start in the two rows
+    # Scale intermediate results as needed.
+    # Only a few different Poisson distributions can occur
+    t = n * x
+    ll = int(np.floor(t))
+    f = 1.0 * (t - ll)  # fractional part of t
+    g = min(f, 1.0 - f)
+    ceilf = (1 if f > 0 else 0)
+    roundf = (1 if f > 0.5 else 0)
+    npwrs = 2 * (ll + 1)    # Maximum number of powers needed in convolutions
+    gpower = np.empty(npwrs)  # gpower = (g/n)^m/m!
+    twogpower = np.empty(npwrs)  # twogpower = (2g/n)^m/m!
+    onem2gpower = np.empty(npwrs)  # onem2gpower = ((1-2g)/n)^m/m!
+    # gpower etc are *almost* Poisson probs, just missing normalizing factor.
+
+    gpower[0] = 1.0
+    twogpower[0] = 1.0
+    onem2gpower[0] = 1.0
+    expnt = 0
+    g_over_n, two_g_over_n, one_minus_two_g_over_n = g/n, 2*g/n, (1 - 2*g)/n
+    for m in range(1, npwrs):
+        gpower[m] = gpower[m - 1] * g_over_n / m
+        twogpower[m] = twogpower[m - 1] * two_g_over_n / m
+        onem2gpower[m] = onem2gpower[m - 1] * one_minus_two_g_over_n / m
+
+    V0 = np.zeros([npwrs])
+    V1 = np.zeros([npwrs])
+    V1[0] = 1  # first row
+    V0s, V1s = 0, 0  # start indices of the two rows
+
+    j1, j2 = _pomeranz_compute_j1j2(0, n, ll, ceilf, roundf)
+    for i in range(1, 2 * n + 2):
+        # Preserve j1, V1, V1s, V0s from last iteration
+        k1 = j1
+        V0, V1 = V1, V0
+        V0s, V1s = V1s, V0s
+        V1.fill(0.0)
+        j1, j2 = _pomeranz_compute_j1j2(i, n, ll, ceilf, roundf)
+        if i == 1 or i == 2 * n + 1:
+            pwrs = gpower
+        else:
+            pwrs = (twogpower if i % 2 else onem2gpower)
+        ln2 = j2 - k1 + 1
+        if ln2 > 0:
+            conv = np.convolve(V0[k1 - V0s:k1 - V0s + ln2], pwrs[:ln2])
+            conv_start = j1 - k1  # First index to use from conv
+            conv_len = j2 - j1 + 1  # Number of entries to use from conv
+            V1[:conv_len] = conv[conv_start:conv_start + conv_len]
+            # Scale to avoid underflow.
+            if 0 < np.max(V1) < _EM128:
+                V1 *= _EP128
+                expnt -= _E128
+            V1s = V0s + j1 - k1
+
+    # multiply by n!
+    ans = V1[n - V1s]
+    for m in range(1, n + 1):
+        if np.abs(ans) > _EP128:
+            ans *= _EM128
+            expnt += _E128
+        ans *= m
+
+    # Undo any intermediate scaling
+    if expnt != 0:
+        ans = np.ldexp(ans, expnt)
+    ans = _select_and_clip_prob(ans, 1.0 - ans, cdf)
+    return ans
+
+
+def _kolmogn_PelzGood(n, x, cdf=True):
+    """Computes the Pelz-Good approximation to Prob(Dn <= x) with 0<=x<=1.
+
+    Start with Li-Chien, Korolyuk approximation:
+        Prob(Dn <= x) ~ K0(z) + K1(z)/sqrt(n) + K2(z)/n + K3(z)/n**1.5
+    where z = x*sqrt(n).
+    Transform each K_(z) using Jacobi theta functions into a form suitable
+    for small z.
+    Pelz-Good (1976). [6]
+    """
+    if x <= 0.0:
+        return _select_and_clip_prob(0.0, 1.0, cdf=cdf)
+    if x >= 1.0:
+        return _select_and_clip_prob(1.0, 0.0, cdf=cdf)
+
+    z = np.sqrt(n) * x
+    zsquared, zthree, zfour, zsix = z**2, z**3, z**4, z**6
+
+    qlog = -_PI_SQUARED / 8 / zsquared
+    if qlog < _MIN_LOG:  # z ~ 0.041743441416853426
+        return _select_and_clip_prob(0.0, 1.0, cdf=cdf)
+
+    q = np.exp(qlog)
+
+    # Coefficients of terms in the sums for K1, K2 and K3
+    k1a = -zsquared
+    k1b = _PI_SQUARED / 4
+
+    k2a = 6 * zsix + 2 * zfour
+    k2b = (2 * zfour - 5 * zsquared) * _PI_SQUARED / 4
+    k2c = _PI_FOUR * (1 - 2 * zsquared) / 16
+
+    k3d = _PI_SIX * (5 - 30 * zsquared) / 64
+    k3c = _PI_FOUR * (-60 * zsquared + 212 * zfour) / 16
+    k3b = _PI_SQUARED * (135 * zfour - 96 * zsix) / 4
+    k3a = -30 * zsix - 90 * z**8
+
+    K0to3 = np.zeros(4)
+    # Use a Horner scheme to evaluate sum c_i q^(i^2)
+    # Reduces to a sum over odd integers.
+    maxk = int(np.ceil(16 * z / np.pi))
+    for k in range(maxk, 0, -1):
+        m = 2 * k - 1
+        msquared, mfour, msix = m**2, m**4, m**6
+        qpower = np.power(q, 8 * k)
+        coeffs = np.array([1.0,
+                           k1a + k1b*msquared,
+                           k2a + k2b*msquared + k2c*mfour,
+                           k3a + k3b*msquared + k3c*mfour + k3d*msix])
+        K0to3 *= qpower
+        K0to3 += coeffs
+    K0to3 *= q
+    K0to3 *= _SQRT2PI
+    # z**10 > 0 as z > 0.04
+    K0to3 /= np.array([z, 6 * zfour, 72 * z**7, 6480 * z**10])
+
+    # Now do the other sum over the other terms, all integers k
+    # K_2:  (pi^2 k^2) q^(k^2),
+    # K_3:  (3pi^2 k^2 z^2 - pi^4 k^4)*q^(k^2)
+    # Don't expect much subtractive cancellation so use direct calculation
+    q = np.exp(-_PI_SQUARED / 2 / zsquared)
+    ks = np.arange(maxk, 0, -1)
+    ksquared = ks ** 2
+    sqrt3z = _SQRT3 * z
+    kspi = np.pi * ks
+    qpwers = q ** ksquared
+    k2extra = np.sum(ksquared * qpwers)
+    k2extra *= _PI_SQUARED * _SQRT2PI/(-36 * zthree)
+    K0to3[2] += k2extra
+    k3extra = np.sum((sqrt3z + kspi) * (sqrt3z - kspi) * ksquared * qpwers)
+    k3extra *= _PI_SQUARED * _SQRT2PI/(216 * zsix)
+    K0to3[3] += k3extra
+    powers_of_n = np.power(n * 1.0, np.arange(len(K0to3)) / 2.0)
+    K0to3 /= powers_of_n
+
+    if not cdf:
+        K0to3 *= -1
+        K0to3[0] += 1
+
+    Ksum = sum(K0to3)
+    return Ksum
+
+
+def _kolmogn(n, x, cdf=True):
+    """Computes the CDF(or SF) for the two-sided Kolmogorov-Smirnov statistic.
+
+    x must be of type float, n of type integer.
+
+    Simard & L'Ecuyer (2011) [7].
+    """
+    if np.isnan(n):
+        return n  # Keep the same type of nan
+    if int(n) != n or n <= 0:
+        return np.nan
+    if x >= 1.0:
+        return _select_and_clip_prob(1.0, 0.0, cdf=cdf)
+    if x <= 0.0:
+        return _select_and_clip_prob(0.0, 1.0, cdf=cdf)
+    t = n * x
+    if t <= 1.0:  # Ruben-Gambino: 1/2n <= x <= 1/n
+        if t <= 0.5:
+            return _select_and_clip_prob(0.0, 1.0, cdf=cdf)
+        if n <= 140:
+            prob = np.prod(np.arange(1, n+1) * (1.0/n) * (2*t - 1))
+        else:
+            prob = np.exp(_log_nfactorial_div_n_pow_n(n) + n * np.log(2*t-1))
+        return _select_and_clip_prob(prob, 1.0 - prob, cdf=cdf)
+    if t >= n - 1:  # Ruben-Gambino
+        prob = 2 * (1.0 - x)**n
+        return _select_and_clip_prob(1 - prob, prob, cdf=cdf)
+    if x >= 0.5:  # Exact: 2 * smirnov
+        prob = 2 * scipy.special.smirnov(n, x)
+        return _select_and_clip_prob(1.0 - prob, prob, cdf=cdf)
+
+    nxsquared = t * x
+    if n <= 140:
+        if nxsquared <= 0.754693:
+            prob = _kolmogn_DMTW(n, x, cdf=True)
+            return _select_and_clip_prob(prob, 1.0 - prob, cdf=cdf)
+        if nxsquared <= 4:
+            prob = _kolmogn_Pomeranz(n, x, cdf=True)
+            return _select_and_clip_prob(prob, 1.0 - prob, cdf=cdf)
+        # Now use Miller approximation of 2*smirnov
+        prob = 2 * scipy.special.smirnov(n, x)
+        return _select_and_clip_prob(1.0 - prob, prob, cdf=cdf)
+
+    # Split CDF and SF as they have different cutoffs on nxsquared.
+    if not cdf:
+        if nxsquared >= 370.0:
+            return 0.0
+        if nxsquared >= 2.2:
+            prob = 2 * scipy.special.smirnov(n, x)
+            return _clip_prob(prob)
+        # Fall through and compute the SF as 1.0-CDF
+    if nxsquared >= 18.0:
+        cdfprob = 1.0
+    elif n <= 100000 and n * x**1.5 <= 1.4:
+        cdfprob = _kolmogn_DMTW(n, x, cdf=True)
+    else:
+        cdfprob = _kolmogn_PelzGood(n, x, cdf=True)
+    return _select_and_clip_prob(cdfprob, 1.0 - cdfprob, cdf=cdf)
+
+
+def _kolmogn_p(n, x):
+    """Computes the PDF for the two-sided Kolmogorov-Smirnov statistic.
+
+    x must be of type float, n of type integer.
+    """
+    if np.isnan(n):
+        return n  # Keep the same type of nan
+    if int(n) != n or n <= 0:
+        return np.nan
+    if x >= 1.0 or x <= 0:
+        return 0
+    t = n * x
+    if t <= 1.0:
+        # Ruben-Gambino: n!/n^n * (2t-1)^n -> 2 n!/n^n * n^2 * (2t-1)^(n-1)
+        if t <= 0.5:
+            return 0.0
+        if n <= 140:
+            prd = np.prod(np.arange(1, n) * (1.0 / n) * (2 * t - 1))
+        else:
+            prd = np.exp(_log_nfactorial_div_n_pow_n(n) + (n-1) * np.log(2 * t - 1))
+        return prd * 2 * n**2
+    if t >= n - 1:
+        # Ruben-Gambino : 1-2(1-x)**n -> 2n*(1-x)**(n-1)
+        return 2 * (1.0 - x) ** (n-1) * n
+    if x >= 0.5:
+        return 2 * scipy.stats.ksone.pdf(x, n)
+
+    # Just take a small delta.
+    # Ideally x +/- delta would stay within [i/n, (i+1)/n] for some integer a.
+    # as the CDF is a piecewise degree n polynomial.
+    # It has knots at 1/n, 2/n, ... (n-1)/n
+    # and is not a C-infinity function at the knots
+    delta = x / 2.0**16
+    delta = min(delta, x - 1.0/n)
+    delta = min(delta, 0.5 - x)
+
+    def _kk(_x):
+        return kolmogn(n, _x)
+
+    return _derivative(_kk, x, dx=delta, order=5)
+
+
+def _kolmogni(n, p, q):
+    """Computes the PPF/ISF of kolmogn.
+
+    n of type integer, n>= 1
+    p is the CDF, q the SF, p+q=1
+    """
+    if np.isnan(n):
+        return n  # Keep the same type of nan
+    if int(n) != n or n <= 0:
+        return np.nan
+    if p <= 0:
+        return 1.0/n
+    if q <= 0:
+        return 1.0
+    delta = np.exp((np.log(p) - scipy.special.loggamma(n+1))/n)
+    if delta <= 1.0/n:
+        return (delta + 1.0 / n) / 2
+    x = -np.expm1(np.log(q/2.0)/n)
+    if x >= 1 - 1.0/n:
+        return x
+    x1 = scu._kolmogci(p)/np.sqrt(n)
+    x1 = min(x1, 1.0 - 1.0/n)
+
+    def _f(x):
+        return _kolmogn(n, x) - p
+
+    return scipy.optimize.brentq(_f, 1.0/n, x1, xtol=1e-14)
+
+
+def kolmogn(n, x, cdf=True):
+    """Computes the CDF for the two-sided Kolmogorov-Smirnov distribution.
+
+    The two-sided Kolmogorov-Smirnov distribution has as its CDF Pr(D_n <= x),
+    for a sample of size n drawn from a distribution with CDF F(t), where
+    :math:`D_n &= sup_t |F_n(t) - F(t)|`, and
+    :math:`F_n(t)` is the Empirical Cumulative Distribution Function of the sample.
+
+    Parameters
+    ----------
+    n : int, array_like
+        the number of samples
+    x : float, array_like
+        The K-S statistic, float between 0 and 1
+    cdf : bool, optional
+        whether to compute the CDF(default=true) or the SF.
+
+    Returns
+    -------
+    cdf : ndarray
+        CDF (or SF it cdf is False) at the specified locations.
+
+    The return value has shape the result of numpy broadcasting n and x.
+    """
+    it = np.nditer([n, x, cdf, None], flags=['zerosize_ok'],
+                   op_dtypes=[None, np.float64, np.bool_, np.float64])
+    for _n, _x, _cdf, z in it:
+        if np.isnan(_n):
+            z[...] = _n
+            continue
+        if int(_n) != _n:
+            raise ValueError(f'n is not integral: {_n}')
+        z[...] = _kolmogn(int(_n), _x, cdf=_cdf)
+    result = it.operands[-1]
+    return result
+
+
+def kolmognp(n, x):
+    """Computes the PDF for the two-sided Kolmogorov-Smirnov distribution.
+
+    Parameters
+    ----------
+    n : int, array_like
+        the number of samples
+    x : float, array_like
+        The K-S statistic, float between 0 and 1
+
+    Returns
+    -------
+    pdf : ndarray
+        The PDF at the specified locations
+
+    The return value has shape the result of numpy broadcasting n and x.
+    """
+    it = np.nditer([n, x, None])
+    for _n, _x, z in it:
+        if np.isnan(_n):
+            z[...] = _n
+            continue
+        if int(_n) != _n:
+            raise ValueError(f'n is not integral: {_n}')
+        z[...] = _kolmogn_p(int(_n), _x)
+    result = it.operands[-1]
+    return result
+
+
+def kolmogni(n, q, cdf=True):
+    """Computes the PPF(or ISF) for the two-sided Kolmogorov-Smirnov distribution.
+
+    Parameters
+    ----------
+    n : int, array_like
+        the number of samples
+    q : float, array_like
+        Probabilities, float between 0 and 1
+    cdf : bool, optional
+        whether to compute the PPF(default=true) or the ISF.
+
+    Returns
+    -------
+    ppf : ndarray
+        PPF (or ISF if cdf is False) at the specified locations
+
+    The return value has shape the result of numpy broadcasting n and x.
+    """
+    it = np.nditer([n, q, cdf, None])
+    for _n, _q, _cdf, z in it:
+        if np.isnan(_n):
+            z[...] = _n
+            continue
+        if int(_n) != _n:
+            raise ValueError(f'n is not integral: {_n}')
+        _pcdf, _psf = (_q, 1-_q) if _cdf else (1-_q, _q)
+        z[...] = _kolmogni(int(_n), _pcdf, _psf)
+    result = it.operands[-1]
+    return result
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_levy_stable/__init__.py b/.venv/lib/python3.12/site-packages/scipy/stats/_levy_stable/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..127c2464cebd4c168e5f01f0c88cfcdd1c9da8ad
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_levy_stable/__init__.py
@@ -0,0 +1,1247 @@
+#
+
+import warnings
+from functools import partial
+
+import numpy as np
+
+from scipy import optimize
+from scipy import integrate
+from scipy.integrate._quadrature import _builtincoeffs
+from scipy import interpolate
+from scipy.interpolate import RectBivariateSpline
+import scipy._external.array_api_extra as xpx
+import scipy.special as sc
+from .._distn_infrastructure import rv_continuous, _ShapeInfo, rv_continuous_frozen
+from .._continuous_distns import uniform, expon, _norm_pdf, _norm_cdf
+from .levyst import Nolan
+from scipy._lib.doccer import inherit_docstring_from
+
+
+__all__ = ["levy_stable", "levy_stable_gen", "pdf_from_cf_with_fft"]
+
+# Stable distributions are known for various parameterisations
+# some being advantageous for numerical considerations and others
+# useful due to their location/scale awareness.
+#
+# Here we follow [NO] convention (see the references in the docstring
+# for levy_stable_gen below).
+#
+# S0 / Z0 / x0 (aka Zoleterav's M)
+# S1 / Z1 / x1
+#
+# Where S* denotes parameterisation, Z* denotes standardized
+# version where gamma = 1, delta = 0 and x* denotes variable.
+#
+# Scipy's original Stable was a random variate generator. It
+# uses S1 and unfortunately is not a location/scale aware.
+
+
+# default numerical integration tolerance
+# used for epsrel in piecewise and both epsrel and epsabs in dni
+# (epsabs needed in dni since weighted quad requires epsabs > 0)
+_QUAD_EPS = 1.2e-14
+
+
+def _Phi_Z0(alpha, t):
+    return (
+        -np.tan(np.pi * alpha / 2) * (np.abs(t) ** (1 - alpha) - 1)
+        if alpha != 1
+        else -2.0 * np.log(np.abs(t)) / np.pi
+    )
+
+
+def _Phi_Z1(alpha, t):
+    return (
+        np.tan(np.pi * alpha / 2)
+        if alpha != 1
+        else -2.0 * np.log(np.abs(t)) / np.pi
+    )
+
+
+def _cf(Phi, t, alpha, beta):
+    """Characteristic function."""
+    return np.exp(
+        -(np.abs(t) ** alpha) * (1 - 1j * beta * np.sign(t) * Phi(alpha, t))
+    )
+
+
+_cf_Z0 = partial(_cf, _Phi_Z0)
+_cf_Z1 = partial(_cf, _Phi_Z1)
+
+
+def _pdf_single_value_cf_integrate(Phi, x, alpha, beta, **kwds):
+    """To improve DNI accuracy convert characteristic function in to real
+    valued integral using Euler's formula, then exploit cosine symmetry to
+    change limits to [0, inf). Finally use cosine addition formula to split
+    into two parts that can be handled by weighted quad pack.
+    """
+    quad_eps = kwds.get("quad_eps", _QUAD_EPS)
+
+    def integrand1(t):
+        if t == 0:
+            return 0
+        return np.exp(-(t ** alpha)) * (
+            np.cos(beta * (t ** alpha) * Phi(alpha, t))
+        )
+
+    def integrand2(t):
+        if t == 0:
+            return 0
+        return np.exp(-(t ** alpha)) * (
+            np.sin(beta * (t ** alpha) * Phi(alpha, t))
+        )
+
+    with np.errstate(invalid="ignore"):
+        int1, *ret1 = integrate.quad(
+            integrand1,
+            0,
+            np.inf,
+            weight="cos",
+            wvar=x,
+            limit=1000,
+            epsabs=quad_eps,
+            epsrel=quad_eps,
+            full_output=1,
+        )
+
+        int2, *ret2 = integrate.quad(
+            integrand2,
+            0,
+            np.inf,
+            weight="sin",
+            wvar=x,
+            limit=1000,
+            epsabs=quad_eps,
+            epsrel=quad_eps,
+            full_output=1,
+        )
+
+    return (int1 + int2) / np.pi
+
+
+_pdf_single_value_cf_integrate_Z0 = partial(
+    _pdf_single_value_cf_integrate, _Phi_Z0
+)
+_pdf_single_value_cf_integrate_Z1 = partial(
+    _pdf_single_value_cf_integrate, _Phi_Z1
+)
+
+
+def _nolan_round_x_near_zeta(x0, alpha, zeta, x_tol_near_zeta):
+    """Round x close to zeta for Nolan's method in [NO]."""
+    #   "8. When |x0-beta*tan(pi*alpha/2)| is small, the
+    #   computations of the density and cumulative have numerical problems.
+    #   The program works around this by setting
+    #   z = beta*tan(pi*alpha/2) when
+    #   |z-beta*tan(pi*alpha/2)| < tol(5)*alpha**(1/alpha).
+    #   (The bound on the right is ad hoc, to get reasonable behavior
+    #   when alpha is small)."
+    # where tol(5) = 0.5e-2 by default.
+    #
+    # We seem to have partially addressed this through re-expression of
+    # g(theta) here, but it still needs to be used in some extreme cases.
+    # Perhaps tol(5) = 0.5e-2 could be reduced for our implementation.
+    if np.abs(x0 - zeta) < x_tol_near_zeta * alpha ** (1 / alpha):
+        x0 = zeta
+    return x0
+
+
+def _nolan_round_difficult_input(
+    x0, alpha, beta, zeta, x_tol_near_zeta, alpha_tol_near_one
+):
+    """Round difficult input values for Nolan's method in [NO]."""
+
+    # following Nolan's STABLE,
+    #   "1. When 0 < |alpha-1| < 0.005, the program has numerical problems
+    #   evaluating the pdf and cdf.  The current version of the program sets
+    #   alpha=1 in these cases. This approximation is not bad in the S0
+    #   parameterization."
+    if np.abs(alpha - 1) < alpha_tol_near_one:
+        alpha = 1.0
+
+    #   "2. When alpha=1 and |beta| < 0.005, the program has numerical
+    #   problems.  The current version sets beta=0."
+    # We seem to have addressed this through re-expression of g(theta) here
+
+    x0 = _nolan_round_x_near_zeta(x0, alpha, zeta, x_tol_near_zeta)
+    return x0, alpha, beta
+
+
+def _pdf_single_value_piecewise_Z1(x, alpha, beta, **kwds):
+    # convert from Nolan's S_1 (aka S) to S_0 (aka Zolaterev M)
+    # parameterization
+
+    zeta = -beta * np.tan(np.pi * alpha / 2.0)
+    x0 = x + zeta if alpha != 1 else x
+
+    return _pdf_single_value_piecewise_Z0(x0, alpha, beta, **kwds)
+
+
+def _pdf_single_value_piecewise_Z0(x0, alpha, beta, **kwds):
+
+    quad_eps = kwds.get("quad_eps", _QUAD_EPS)
+    x_tol_near_zeta = kwds.get("piecewise_x_tol_near_zeta", 0.005)
+    alpha_tol_near_one = kwds.get("piecewise_alpha_tol_near_one", 0.005)
+
+    zeta = -beta * np.tan(np.pi * alpha / 2.0)
+    x0, alpha, beta = _nolan_round_difficult_input(
+        x0, alpha, beta, zeta, x_tol_near_zeta, alpha_tol_near_one
+    )
+
+    # some other known distribution pdfs / analytical cases
+    # TODO: add more where possible with test coverage,
+    # eg https://en.wikipedia.org/wiki/Stable_distribution#Other_analytic_cases
+    if alpha == 2.0:
+        # normal
+        return _norm_pdf(x0 / np.sqrt(2)) / np.sqrt(2)
+    elif alpha == 0.5 and beta == 1.0:
+        # levy
+        # since S(1/2, 1, gamma, delta; <x>) ==
+        # S(1/2, 1, gamma, gamma + delta; <x0>).
+        _x = x0 + 1
+        if _x <= 0:
+            return 0
+
+        return 1 / np.sqrt(2 * np.pi * _x) / _x * np.exp(-1 / (2 * _x))
+    elif alpha == 0.5 and beta == 0.0 and x0 != 0:
+        # analytical solution [HO]
+        S, C = sc.fresnel([1 / np.sqrt(2 * np.pi * np.abs(x0))])
+        arg = 1 / (4 * np.abs(x0))
+        return (
+            np.sin(arg) * (0.5 - S[0]) + np.cos(arg) * (0.5 - C[0])
+        ) / np.sqrt(2 * np.pi * np.abs(x0) ** 3)
+    elif alpha == 1.0 and beta == 0.0:
+        # cauchy
+        return 1 / (1 + x0 ** 2) / np.pi
+
+    return _pdf_single_value_piecewise_post_rounding_Z0(
+        x0, alpha, beta, quad_eps, x_tol_near_zeta
+    )
+
+
+def _pdf_single_value_piecewise_post_rounding_Z0(x0, alpha, beta, quad_eps,
+                                                 x_tol_near_zeta):
+    """Calculate pdf using Nolan's methods as detailed in [NO]."""
+
+    _nolan = Nolan(alpha, beta, x0)
+    zeta = _nolan.zeta
+    xi = _nolan.xi
+    c2 = _nolan.c2
+    g = _nolan.g
+
+    # round x0 to zeta again if needed. zeta was recomputed and may have
+    # changed due to floating point differences.
+    # See https://github.com/scipy/scipy/pull/18133
+    x0 = _nolan_round_x_near_zeta(x0, alpha, zeta, x_tol_near_zeta)
+    # handle Nolan's initial case logic
+    if x0 == zeta:
+        return (
+            sc.gamma(1 + 1 / alpha)
+            * np.cos(xi)
+            / np.pi
+            / ((1 + zeta ** 2) ** (1 / alpha / 2))
+        )
+    elif x0 < zeta:
+        return _pdf_single_value_piecewise_post_rounding_Z0(
+            -x0, alpha, -beta, quad_eps, x_tol_near_zeta
+        )
+
+    # following Nolan, we may now assume
+    #   x0 > zeta when alpha != 1
+    #   beta != 0 when alpha == 1
+
+    # spare calculating integral on null set
+    # use isclose as macos has fp differences
+    if np.isclose(-xi, np.pi / 2, rtol=1e-014, atol=1e-014):
+        return 0.0
+
+    def integrand(theta):
+        # limit any numerical issues leading to g_1 < 0 near theta limits
+        g_1 = g(theta)
+        if not np.isfinite(g_1) or g_1 < 0:
+            g_1 = 0
+        return g_1 * np.exp(-g_1)
+
+    with np.errstate(all="ignore"):
+        peak = optimize.bisect(
+            lambda t: g(t) - 1, -xi, np.pi / 2, xtol=quad_eps
+        )
+
+        # this integrand can be very peaked, so we need to force
+        # QUADPACK to evaluate the function inside its support
+        #
+
+        # lastly, we add additional samples at
+        #   ~exp(-100), ~exp(-10), ~exp(-5), ~exp(-1)
+        # to improve QUADPACK's detection of rapidly descending tail behavior
+        # (this choice is fairly ad hoc)
+        tail_points = [
+            optimize.bisect(lambda t: g(t) - exp_height, -xi, np.pi / 2)
+            for exp_height in [100, 10, 5]
+            # exp_height = 1 is handled by peak
+        ]
+        # Symmetry is used to ensure x > zeta as above. Integration 
+        # becomes numerically unstable so let `quad` handle 
+        # integration points
+        if beta != -1:
+            intg_points = [0, peak] + tail_points
+        else:
+            intg_points = None
+
+        intg, *ret = integrate.quad(
+            integrand,
+            -xi,
+            np.pi / 2,
+            points=intg_points,
+            limit=100,
+            epsrel=quad_eps,
+            epsabs=0,
+            full_output=1,
+        )
+
+    return c2 * intg
+
+
+def _cdf_single_value_piecewise_Z1(x, alpha, beta, **kwds):
+    # convert from Nolan's S_1 (aka S) to S_0 (aka Zolaterev M)
+    # parameterization
+
+    zeta = -beta * np.tan(np.pi * alpha / 2.0)
+    x0 = x + zeta if alpha != 1 else x
+
+    return _cdf_single_value_piecewise_Z0(x0, alpha, beta, **kwds)
+
+
+def _cdf_single_value_piecewise_Z0(x0, alpha, beta, **kwds):
+
+    quad_eps = kwds.get("quad_eps", _QUAD_EPS)
+    x_tol_near_zeta = kwds.get("piecewise_x_tol_near_zeta", 0.005)
+    alpha_tol_near_one = kwds.get("piecewise_alpha_tol_near_one", 0.005)
+
+    zeta = -beta * np.tan(np.pi * alpha / 2.0)
+    x0, alpha, beta = _nolan_round_difficult_input(
+        x0, alpha, beta, zeta, x_tol_near_zeta, alpha_tol_near_one
+    )
+
+    # some other known distribution cdfs / analytical cases
+    # TODO: add more where possible with test coverage,
+    # eg https://en.wikipedia.org/wiki/Stable_distribution#Other_analytic_cases
+    if alpha == 2.0:
+        # normal
+        return _norm_cdf(x0 / np.sqrt(2))
+    elif alpha == 0.5 and beta == 1.0:
+        # levy
+        # since S(1/2, 1, gamma, delta; <x>) ==
+        # S(1/2, 1, gamma, gamma + delta; <x0>).
+        _x = x0 + 1
+        if _x <= 0:
+            return 0
+
+        return sc.erfc(np.sqrt(0.5 / _x))
+    elif alpha == 1.0 and beta == 0.0:
+        # cauchy
+        return 0.5 + np.arctan(x0) / np.pi
+
+    return _cdf_single_value_piecewise_post_rounding_Z0(
+        x0, alpha, beta, quad_eps, x_tol_near_zeta
+    )
+
+
+def _cdf_single_value_piecewise_post_rounding_Z0(x0, alpha, beta, quad_eps,
+                                                 x_tol_near_zeta):
+    """Calculate cdf using Nolan's methods as detailed in [NO]."""
+    _nolan = Nolan(alpha, beta, x0)
+    zeta = _nolan.zeta
+    xi = _nolan.xi
+    c1 = _nolan.c1
+    # c2 = _nolan.c2
+    c3 = _nolan.c3
+    g = _nolan.g
+    # round x0 to zeta again if needed. zeta was recomputed and may have
+    # changed due to floating point differences.
+    # See https://github.com/scipy/scipy/pull/18133
+    x0 = _nolan_round_x_near_zeta(x0, alpha, zeta, x_tol_near_zeta)
+    # handle Nolan's initial case logic
+    if (alpha == 1 and beta < 0) or x0 < zeta:
+        # NOTE: Nolan's paper has a typo here!
+        # He states F(x) = 1 - F(x, alpha, -beta), but this is clearly
+        # incorrect since F(-infty) would be 1.0 in this case
+        # Indeed, the alpha != 1, x0 < zeta case is correct here.
+        return 1 - _cdf_single_value_piecewise_post_rounding_Z0(
+            -x0, alpha, -beta, quad_eps, x_tol_near_zeta
+        )
+    elif x0 == zeta:
+        return 0.5 - xi / np.pi
+
+    # following Nolan, we may now assume
+    #   x0 > zeta when alpha != 1
+    #   beta > 0 when alpha == 1
+
+    # spare calculating integral on null set
+    # use isclose as macos has fp differences
+    if np.isclose(-xi, np.pi / 2, rtol=1e-014, atol=1e-014):
+        return c1
+
+    def integrand(theta):
+        g_1 = g(theta)
+        return np.exp(-g_1)
+
+    with np.errstate(all="ignore"):
+        # shrink supports where required
+        left_support = -xi
+        right_support = np.pi / 2
+        if alpha > 1:
+            # integrand(t) monotonic 0 to 1
+            if integrand(-xi) != 0.0:
+                res = optimize.minimize(
+                    integrand,
+                    (-xi,),
+                    method="L-BFGS-B",
+                    bounds=[(-xi, np.pi / 2)],
+                )
+                left_support = res.x[0]
+        else:
+            # integrand(t) monotonic 1 to 0
+            if integrand(np.pi / 2) != 0.0:
+                res = optimize.minimize(
+                    integrand,
+                    (np.pi / 2,),
+                    method="L-BFGS-B",
+                    bounds=[(-xi, np.pi / 2)],
+                )
+                right_support = res.x[0]
+
+        intg, *ret = integrate.quad(
+            integrand,
+            left_support,
+            right_support,
+            points=[left_support, right_support],
+            limit=100,
+            epsrel=quad_eps,
+            epsabs=0,
+            full_output=1,
+        )
+
+    return c1 + c3 * intg
+
+
+def _rvs_Z1(alpha, beta, size=None, random_state=None):
+    """Simulate random variables using Nolan's methods as detailed in [NO].
+    """
+
+    def alpha1func(alpha, beta, TH, aTH, bTH, cosTH, tanTH, W):
+        return (
+            2
+            / np.pi
+            * (
+                (np.pi / 2 + bTH) * tanTH
+                - beta * np.log((np.pi / 2 * W * cosTH) / (np.pi / 2 + bTH))
+            )
+        )
+
+    def beta0func(alpha, beta, TH, aTH, bTH, cosTH, tanTH, W):
+        return (
+            W
+            / (cosTH / np.tan(aTH) + np.sin(TH))
+            * ((np.cos(aTH) + np.sin(aTH) * tanTH) / W) ** (1.0 / alpha)
+        )
+
+    def otherwise(alpha, beta, TH, aTH, bTH, cosTH, tanTH, W):
+        # alpha is not 1 and beta is not 0
+        val0 = beta * np.tan(np.pi * alpha / 2)
+        th0 = np.arctan(val0) / alpha
+        val3 = W / (cosTH / np.tan(alpha * (th0 + TH)) + np.sin(TH))
+        res3 = val3 * (
+            (
+                np.cos(aTH)
+                + np.sin(aTH) * tanTH
+                - val0 * (np.sin(aTH) - np.cos(aTH) * tanTH)
+            )
+            / W
+        ) ** (1.0 / alpha)
+        return res3
+
+    def alphanot1func(alpha, beta, TH, aTH, bTH, cosTH, tanTH, W):
+        return xpx.apply_where(
+            beta == 0, (alpha, beta, TH, aTH, bTH, cosTH, tanTH, W),
+            beta0func, otherwise)
+
+    alpha = np.broadcast_to(alpha, size)
+    beta = np.broadcast_to(beta, size)
+    TH = uniform.rvs(
+        loc=-np.pi / 2.0, scale=np.pi, size=size, random_state=random_state
+    )
+    W = expon.rvs(size=size, random_state=random_state)
+    aTH = alpha * TH
+    bTH = beta * TH
+    cosTH = np.cos(TH)
+    tanTH = np.tan(TH)
+    return xpx.apply_where(
+        alpha == 1, (alpha, beta, TH, aTH, bTH, cosTH, tanTH, W),
+        alpha1func, alphanot1func)
+
+
+def _fitstart_S0(data):
+    alpha, beta, delta1, gamma = _fitstart_S1(data)
+
+    # Formulas for mapping parameters in S1 parameterization to
+    # those in S0 parameterization can be found in [NO]. Note that
+    # only delta changes.
+    if alpha != 1:
+        delta0 = delta1 + beta * gamma * np.tan(np.pi * alpha / 2.0)
+    else:
+        delta0 = delta1 + 2 * beta * gamma * np.log(gamma) / np.pi
+
+    return alpha, beta, delta0, gamma
+
+
+def _fitstart_S1(data):
+    # We follow McCullock 1986 method - Simple Consistent Estimators
+    # of Stable Distribution Parameters
+
+    # fmt: off
+    # Table III and IV
+    nu_alpha_range = [2.439, 2.5, 2.6, 2.7, 2.8, 3, 3.2, 3.5, 4,
+                      5, 6, 8, 10, 15, 25]
+    nu_beta_range = [0, 0.1, 0.2, 0.3, 0.5, 0.7, 1]
+
+    # table III - alpha = psi_1(nu_alpha, nu_beta)
+    alpha_table = np.array([
+        [2.000, 2.000, 2.000, 2.000, 2.000, 2.000, 2.000],
+        [1.916, 1.924, 1.924, 1.924, 1.924, 1.924, 1.924],
+        [1.808, 1.813, 1.829, 1.829, 1.829, 1.829, 1.829],
+        [1.729, 1.730, 1.737, 1.745, 1.745, 1.745, 1.745],
+        [1.664, 1.663, 1.663, 1.668, 1.676, 1.676, 1.676],
+        [1.563, 1.560, 1.553, 1.548, 1.547, 1.547, 1.547],
+        [1.484, 1.480, 1.471, 1.460, 1.448, 1.438, 1.438],
+        [1.391, 1.386, 1.378, 1.364, 1.337, 1.318, 1.318],
+        [1.279, 1.273, 1.266, 1.250, 1.210, 1.184, 1.150],
+        [1.128, 1.121, 1.114, 1.101, 1.067, 1.027, 0.973],
+        [1.029, 1.021, 1.014, 1.004, 0.974, 0.935, 0.874],
+        [0.896, 0.892, 0.884, 0.883, 0.855, 0.823, 0.769],
+        [0.818, 0.812, 0.806, 0.801, 0.780, 0.756, 0.691],
+        [0.698, 0.695, 0.692, 0.689, 0.676, 0.656, 0.597],
+        [0.593, 0.590, 0.588, 0.586, 0.579, 0.563, 0.513]]).T
+    # transpose because interpolation with `RectBivariateSpline` is with
+    # `nu_beta` as `x` and `nu_alpha` as `y`
+
+    # table IV - beta = psi_2(nu_alpha, nu_beta)
+    beta_table = np.array([
+        [0, 2.160, 1.000, 1.000, 1.000, 1.000, 1.000],
+        [0, 1.592, 3.390, 1.000, 1.000, 1.000, 1.000],
+        [0, 0.759, 1.800, 1.000, 1.000, 1.000, 1.000],
+        [0, 0.482, 1.048, 1.694, 1.000, 1.000, 1.000],
+        [0, 0.360, 0.760, 1.232, 2.229, 1.000, 1.000],
+        [0, 0.253, 0.518, 0.823, 1.575, 1.000, 1.000],
+        [0, 0.203, 0.410, 0.632, 1.244, 1.906, 1.000],
+        [0, 0.165, 0.332, 0.499, 0.943, 1.560, 1.000],
+        [0, 0.136, 0.271, 0.404, 0.689, 1.230, 2.195],
+        [0, 0.109, 0.216, 0.323, 0.539, 0.827, 1.917],
+        [0, 0.096, 0.190, 0.284, 0.472, 0.693, 1.759],
+        [0, 0.082, 0.163, 0.243, 0.412, 0.601, 1.596],
+        [0, 0.074, 0.147, 0.220, 0.377, 0.546, 1.482],
+        [0, 0.064, 0.128, 0.191, 0.330, 0.478, 1.362],
+        [0, 0.056, 0.112, 0.167, 0.285, 0.428, 1.274]]).T
+
+    # Table V and VII
+    # These are ordered with decreasing `alpha_range`; so we will need to
+    # reverse them as required by RectBivariateSpline.
+    alpha_range = [2, 1.9, 1.8, 1.7, 1.6, 1.5, 1.4, 1.3, 1.2, 1.1,
+                   1, 0.9, 0.8, 0.7, 0.6, 0.5][::-1]
+    beta_range = [0, 0.25, 0.5, 0.75, 1]
+
+    # Table V - nu_c = psi_3(alpha, beta)
+    nu_c_table = np.array([
+        [1.908, 1.908, 1.908, 1.908, 1.908],
+        [1.914, 1.915, 1.916, 1.918, 1.921],
+        [1.921, 1.922, 1.927, 1.936, 1.947],
+        [1.927, 1.930, 1.943, 1.961, 1.987],
+        [1.933, 1.940, 1.962, 1.997, 2.043],
+        [1.939, 1.952, 1.988, 2.045, 2.116],
+        [1.946, 1.967, 2.022, 2.106, 2.211],
+        [1.955, 1.984, 2.067, 2.188, 2.333],
+        [1.965, 2.007, 2.125, 2.294, 2.491],
+        [1.980, 2.040, 2.205, 2.435, 2.696],
+        [2.000, 2.085, 2.311, 2.624, 2.973],
+        [2.040, 2.149, 2.461, 2.886, 3.356],
+        [2.098, 2.244, 2.676, 3.265, 3.912],
+        [2.189, 2.392, 3.004, 3.844, 4.775],
+        [2.337, 2.634, 3.542, 4.808, 6.247],
+        [2.588, 3.073, 4.534, 6.636, 9.144]])[::-1].T
+    # transpose because interpolation with `RectBivariateSpline` is with
+    # `beta` as `x` and `alpha` as `y`
+
+    # Table VII - nu_zeta = psi_5(alpha, beta)
+    nu_zeta_table = np.array([
+        [0, 0.000, 0.000, 0.000, 0.000],
+        [0, -0.017, -0.032, -0.049, -0.064],
+        [0, -0.030, -0.061, -0.092, -0.123],
+        [0, -0.043, -0.088, -0.132, -0.179],
+        [0, -0.056, -0.111, -0.170, -0.232],
+        [0, -0.066, -0.134, -0.206, -0.283],
+        [0, -0.075, -0.154, -0.241, -0.335],
+        [0, -0.084, -0.173, -0.276, -0.390],
+        [0, -0.090, -0.192, -0.310, -0.447],
+        [0, -0.095, -0.208, -0.346, -0.508],
+        [0, -0.098, -0.223, -0.380, -0.576],
+        [0, -0.099, -0.237, -0.424, -0.652],
+        [0, -0.096, -0.250, -0.469, -0.742],
+        [0, -0.089, -0.262, -0.520, -0.853],
+        [0, -0.078, -0.272, -0.581, -0.997],
+        [0, -0.061, -0.279, -0.659, -1.198]])[::-1].T
+    # fmt: on
+
+    psi_1 = RectBivariateSpline(nu_beta_range, nu_alpha_range,
+                                alpha_table, kx=1, ky=1, s=0)
+
+    def psi_1_1(nu_beta, nu_alpha):
+        return psi_1(nu_beta, nu_alpha) \
+            if nu_beta > 0 else psi_1(-nu_beta, nu_alpha)
+
+    psi_2 = RectBivariateSpline(nu_beta_range, nu_alpha_range,
+                                beta_table, kx=1, ky=1, s=0)
+
+    def psi_2_1(nu_beta, nu_alpha):
+        return psi_2(nu_beta, nu_alpha) \
+            if nu_beta > 0 else -psi_2(-nu_beta, nu_alpha)
+
+    phi_3 = RectBivariateSpline(beta_range, alpha_range, nu_c_table,
+                                kx=1, ky=1, s=0)
+
+    def phi_3_1(beta, alpha):
+        return phi_3(beta, alpha) if beta > 0 else phi_3(-beta, alpha)
+
+    phi_5 = RectBivariateSpline(beta_range, alpha_range, nu_zeta_table,
+                                kx=1, ky=1, s=0)
+
+    def phi_5_1(beta, alpha):
+        return phi_5(beta, alpha) if beta > 0 else -phi_5(-beta, alpha)
+
+    # quantiles
+    p05 = np.percentile(data, 5)
+    p50 = np.percentile(data, 50)
+    p95 = np.percentile(data, 95)
+    p25 = np.percentile(data, 25)
+    p75 = np.percentile(data, 75)
+
+    nu_alpha = (p95 - p05) / (p75 - p25)
+    nu_beta = (p95 + p05 - 2 * p50) / (p95 - p05)
+
+    if nu_alpha >= 2.439:
+        eps = np.finfo(float).eps
+        alpha = np.clip(psi_1_1(nu_beta, nu_alpha)[0, 0], eps, 2.)
+        beta = np.clip(psi_2_1(nu_beta, nu_alpha)[0, 0], -1.0, 1.0)
+    else:
+        alpha = 2.0
+        beta = np.sign(nu_beta)
+    c = (p75 - p25) / phi_3_1(beta, alpha)[0, 0]
+    zeta = p50 + c * phi_5_1(beta, alpha)[0, 0]
+    delta = zeta-beta*c*np.tan(np.pi*alpha/2.) if alpha != 1. else zeta
+
+    return (alpha, beta, delta, c)
+
+
+class levy_stable_gen(rv_continuous):
+    r"""A Levy-stable continuous random variable.
+
+    %(before_notes)s
+
+    See Also
+    --------
+    levy, levy_l, cauchy, norm
+
+    Notes
+    -----
+    The distribution for `levy_stable` has characteristic function:
+
+    .. math::
+
+        \varphi(t, \alpha, \beta, c, \mu) =
+        e^{it\mu -|ct|^{\alpha}(1-i\beta\operatorname{sign}(t)\Phi)}
+
+    where
+
+    - :math:`\alpha` is the stability parameter (:math:`0 < \alpha \le 2`),
+    - :math:`\beta` is the skewness parameter (:math:`-1 \le \beta \le 1`),
+    - :math:`c` is the scale parameter (:math:`c > 0`),
+    - :math:`\mu` is the location parameter (:math:`-\infty < \mu < \infty`).
+
+    Two parameterizations of :math:`\Phi` are supported.
+
+    The :math:`S_1` parameterization (default):
+
+    .. math::
+
+        \Phi = \begin{cases}
+                \tan \left({\frac {\pi \alpha }{2}}\right)&\alpha \neq 1\\
+                -{\frac {2}{\pi }}\log |t|&\alpha =1
+                \end{cases}
+
+    The :math:`S_0` parameterization:
+
+    .. math::
+
+        \Phi = \begin{cases}
+                -\tan \left({\frac {\pi \alpha }{2}}\right)(|ct|^{1-\alpha}-1)
+                &\alpha \neq 1\\
+                -{\frac {2}{\pi }}\log |ct|&\alpha =1
+                \end{cases}
+
+
+    The probability density function for `levy_stable` is:
+
+    .. math::
+
+        f(x) = \frac{1}{2\pi}\int_{-\infty}^\infty \varphi(t)e^{-ixt}\,dt
+
+    where :math:`-\infty < x < \infty`. This integral does not have a known
+    closed form.
+
+    `levy_stable` generalizes several distributions.  Where possible, they
+    should be used instead.  Specifically, when the shape parameters
+    assume the values in the table below, the corresponding equivalent
+    distribution should be used.
+
+    =========  ========  ===========
+    ``alpha``  ``beta``   Equivalent
+    =========  ========  ===========
+     1/2       -1        `levy_l`
+     1/2       1         `levy`
+     1         0         `cauchy`
+     2         any       `norm` (with ``scale=sqrt(2)``)
+    =========  ========  ===========
+
+    Evaluation of the pdf uses Nolan's piecewise integration approach with the
+    Zolotarev :math:`M` parameterization by default. There is also the option
+    to use direct numerical integration of the standard parameterization of the
+    characteristic function or to evaluate by taking the FFT of the
+    characteristic function.
+
+    The default method can changed by setting the class variable
+    ``levy_stable.pdf_default_method`` to one of 'piecewise' for Nolan's
+    approach, 'dni' for direct numerical integration, or 'fft-simpson' for the
+    FFT based approach. For the sake of backwards compatibility, the methods
+    'best' and 'zolotarev' are equivalent to 'piecewise' and the method
+    'quadrature' is equivalent to 'dni'.
+
+    The parameterization can be changed  by setting the class variable
+    ``levy_stable.parameterization`` to either 'S0' or 'S1'.
+    The default is 'S1'.
+
+    To improve performance of piecewise and direct numerical integration one
+    can specify ``levy_stable.quad_eps`` (defaults to 1.2e-14). This is used
+    as both the absolute and relative quadrature tolerance for direct numerical
+    integration and as the relative quadrature tolerance for the piecewise
+    method. One can also specify ``levy_stable.piecewise_x_tol_near_zeta``
+    (defaults to 0.005) for how close x is to zeta before it is considered the
+    same as x [NO]. The exact check is
+    ``abs(x0 - zeta) < piecewise_x_tol_near_zeta*alpha**(1/alpha)``. One can
+    also specify ``levy_stable.piecewise_alpha_tol_near_one`` (defaults to
+    0.005) for how close alpha is to 1 before being considered equal to 1.
+
+    To increase accuracy of FFT calculation one can specify
+    ``levy_stable.pdf_fft_grid_spacing`` (defaults to 0.001) and
+    ``pdf_fft_n_points_two_power`` (defaults to None which means a value is
+    calculated that sufficiently covers the input range).
+
+    Further control over FFT calculation is available by setting
+    ``pdf_fft_interpolation_degree`` (defaults to 3) for spline order and
+    ``pdf_fft_interpolation_level`` for determining the number of points to use
+    in the Newton-Cotes formula when approximating the characteristic function
+    (considered experimental).
+
+    Evaluation of the cdf uses Nolan's piecewise integration approach with the
+    Zolatarev :math:`S_0` parameterization by default. There is also the option
+    to evaluate through integration of an interpolated spline of the pdf
+    calculated by means of the FFT method. The settings affecting FFT
+    calculation are the same as for pdf calculation. The default cdf method can
+    be changed by setting ``levy_stable.cdf_default_method`` to either
+    'piecewise' or 'fft-simpson'.  For cdf calculations the Zolatarev method is
+    superior in accuracy, so FFT is disabled by default.
+
+    Fitting estimate uses quantile estimation method in [MC]. MLE estimation of
+    parameters in fit method uses this quantile estimate initially. Note that
+    MLE doesn't always converge if using FFT for pdf calculations; this will be
+    the case if alpha <= 1 where the FFT approach doesn't give good
+    approximations.
+
+    Any non-missing value for the attribute
+    ``levy_stable.pdf_fft_min_points_threshold`` will set
+    ``levy_stable.pdf_default_method`` to 'fft-simpson' if a valid
+    default method is not otherwise set.
+
+
+
+    .. warning::
+
+        For pdf calculations FFT calculation is considered experimental.
+
+        For cdf calculations FFT calculation is considered experimental. Use
+        Zolatarev's method instead (default).
+
+    The probability density above is defined in the "standardized" form. To
+    shift and/or scale the distribution use the ``loc`` and ``scale``
+    parameters.
+    Generally ``%(name)s.pdf(x, %(shapes)s, loc, scale)`` is identically
+    equivalent to ``%(name)s.pdf(y, %(shapes)s) / scale`` with
+    ``y = (x - loc) / scale``, except in the ``S1`` parameterization if
+    ``alpha == 1``.  In that case ``%(name)s.pdf(x, %(shapes)s, loc, scale)``
+    is identically equivalent to ``%(name)s.pdf(y, %(shapes)s) / scale`` with
+    ``y = (x - loc - 2 * beta * scale * np.log(scale) / np.pi) / scale``.
+    See [NO2]_ Definition 1.8 for more information.
+    Note that shifting the location of a distribution
+    does not make it a "noncentral" distribution.
+
+    References
+    ----------
+    .. [MC] McCulloch, J., 1986. Simple consistent estimators of stable
+        distribution parameters. Communications in Statistics - Simulation and
+        Computation 15, 11091136.
+    .. [WZ] Wang, Li and Zhang, Ji-Hong, 2008. Simpson's rule based FFT method
+        to compute densities of stable distribution.
+    .. [NO] Nolan, J., 1997. Numerical Calculation of Stable Densities and
+        distributions Functions.
+    .. [NO2] Nolan, J., 2018. Stable Distributions: Models for Heavy Tailed
+        Data.
+    .. [HO] Hopcraft, K. I., Jakeman, E., Tanner, R. M. J., 1999. Lévy random
+        walks with fluctuating step number and multiscale behavior.
+
+    %(example)s
+
+    """
+    # Configurable options as class variables
+    # (accessible from self by attribute lookup).
+    parameterization = "S1"
+    pdf_default_method = "piecewise"
+    cdf_default_method = "piecewise"
+    quad_eps = _QUAD_EPS
+    piecewise_x_tol_near_zeta = 0.005
+    piecewise_alpha_tol_near_one = 0.005
+    pdf_fft_min_points_threshold = None
+    pdf_fft_grid_spacing = 0.001
+    pdf_fft_n_points_two_power = None
+    pdf_fft_interpolation_level = 3
+    pdf_fft_interpolation_degree = 3
+
+    def __call__(self, *args, **params):
+        dist = levy_stable_frozen(self, *args, **params)
+        dist.parameterization = self.parameterization
+        return dist
+
+    def _argcheck(self, alpha, beta):
+        return (alpha > 0) & (alpha <= 2) & (beta <= 1) & (beta >= -1)
+
+    def _shape_info(self):
+        ialpha = _ShapeInfo("alpha", False, (0, 2), (False, True))
+        ibeta = _ShapeInfo("beta", False, (-1, 1), (True, True))
+        return [ialpha, ibeta]
+
+    def _parameterization(self):
+        allowed = ("S0", "S1")
+        pz = self.parameterization
+        if pz not in allowed:
+            raise RuntimeError(
+                f"Parameterization '{pz}' in supported list: {allowed}"
+            )
+        return pz
+
+    @inherit_docstring_from(rv_continuous)
+    def rvs(self, *args, **kwds):
+        X1 = super().rvs(*args, **kwds)
+
+        kwds.pop("discrete", None)
+        kwds.pop("random_state", None)
+        (alpha, beta), delta, gamma, size = self._parse_args_rvs(*args, **kwds)
+
+        # shift location for this parameterisation (S1)
+        X1 = np.where(
+            alpha == 1.0, X1 + 2 * beta * gamma * np.log(gamma) / np.pi, X1
+        )
+
+        if self._parameterization() == "S0":
+            return np.where(
+                alpha == 1.0,
+                X1 - (beta * 2 * gamma * np.log(gamma) / np.pi),
+                X1 - gamma * beta * np.tan(np.pi * alpha / 2.0),
+            )
+        elif self._parameterization() == "S1":
+            return X1
+
+    def _rvs(self, alpha, beta, size=None, random_state=None):
+        return _rvs_Z1(alpha, beta, size, random_state)
+
+    @inherit_docstring_from(rv_continuous)
+    def pdf(self, x, *args, **kwds):
+        # override base class version to correct
+        # location for S1 parameterization
+        if self._parameterization() == "S0":
+            return super().pdf(x, *args, **kwds)
+        elif self._parameterization() == "S1":
+            (alpha, beta), delta, gamma = self._parse_args(*args, **kwds)
+            if np.all(np.reshape(alpha, (1, -1))[0, :] != 1):
+                return super().pdf(x, *args, **kwds)
+            else:
+                # correct location for this parameterisation
+                x = np.reshape(x, (1, -1))[0, :]
+                x, alpha, beta = np.broadcast_arrays(x, alpha, beta)
+
+                data_in = np.dstack((x, alpha, beta))[0]
+                data_out = np.empty(shape=(len(data_in), 1))
+                # group data in unique arrays of alpha, beta pairs
+                uniq_param_pairs = np.unique(data_in[:, 1:], axis=0)
+                for pair in uniq_param_pairs:
+                    _alpha, _beta = pair
+                    _delta = (
+                        delta + 2 * _beta * gamma * np.log(gamma) / np.pi
+                        if _alpha == 1.0
+                        else delta
+                    )
+                    data_mask = np.all(data_in[:, 1:] == pair, axis=-1)
+                    _x = data_in[data_mask, 0]
+                    data_out[data_mask] = (
+                        super()
+                        .pdf(_x, _alpha, _beta, loc=_delta, scale=gamma)
+                        .reshape(len(_x), 1)
+                    )
+                output = data_out.T[0]
+                if output.shape == (1,):
+                    return output[0]
+                return output
+
+    def _pdf(self, x, alpha, beta):
+        if self._parameterization() == "S0":
+            _pdf_single_value_piecewise = _pdf_single_value_piecewise_Z0
+            _pdf_single_value_cf_integrate = _pdf_single_value_cf_integrate_Z0
+            _cf = _cf_Z0
+        elif self._parameterization() == "S1":
+            _pdf_single_value_piecewise = _pdf_single_value_piecewise_Z1
+            _pdf_single_value_cf_integrate = _pdf_single_value_cf_integrate_Z1
+            _cf = _cf_Z1
+
+        x = np.asarray(x).reshape(1, -1)[0, :]
+
+        x, alpha, beta = np.broadcast_arrays(x, alpha, beta)
+
+        data_in = np.dstack((x, alpha, beta))[0]
+        data_out = np.empty(shape=(len(data_in), 1))
+
+        pdf_default_method_name = self.pdf_default_method
+        if pdf_default_method_name in ("piecewise", "best", "zolotarev"):
+            pdf_single_value_method = _pdf_single_value_piecewise
+        elif pdf_default_method_name in ("dni", "quadrature"):
+            pdf_single_value_method = _pdf_single_value_cf_integrate
+        elif (
+            pdf_default_method_name == "fft-simpson"
+            or self.pdf_fft_min_points_threshold is not None
+        ):
+            pdf_single_value_method = None
+
+        pdf_single_value_kwds = {
+            "quad_eps": self.quad_eps,
+            "piecewise_x_tol_near_zeta": self.piecewise_x_tol_near_zeta,
+            "piecewise_alpha_tol_near_one": self.piecewise_alpha_tol_near_one,
+        }
+
+        fft_grid_spacing = self.pdf_fft_grid_spacing
+        fft_n_points_two_power = self.pdf_fft_n_points_two_power
+        fft_interpolation_level = self.pdf_fft_interpolation_level
+        fft_interpolation_degree = self.pdf_fft_interpolation_degree
+
+        # group data in unique arrays of alpha, beta pairs
+        uniq_param_pairs = np.unique(data_in[:, 1:], axis=0)
+        for pair in uniq_param_pairs:
+            data_mask = np.all(data_in[:, 1:] == pair, axis=-1)
+            data_subset = data_in[data_mask]
+            if pdf_single_value_method is not None:
+                data_out[data_mask] = np.array(
+                    [
+                        pdf_single_value_method(
+                            _x, _alpha, _beta, **pdf_single_value_kwds
+                        )
+                        for _x, _alpha, _beta in data_subset
+                    ]
+                ).reshape(len(data_subset), 1)
+            else:
+                warnings.warn(
+                    "Density calculations experimental for FFT method."
+                    + " Use combination of piecewise and dni methods instead.",
+                    RuntimeWarning, stacklevel=3,
+                )
+                _alpha, _beta = pair
+                _x = data_subset[:, (0,)]
+
+                if _alpha < 1.0:
+                    raise RuntimeError(
+                        "FFT method does not work well for alpha less than 1."
+                    )
+
+                # need enough points to "cover" _x for interpolation
+                if fft_grid_spacing is None and fft_n_points_two_power is None:
+                    raise ValueError(
+                        "One of fft_grid_spacing or fft_n_points_two_power "
+                        + "needs to be set."
+                    )
+                max_abs_x = np.max(np.abs(_x))
+                h = (
+                    2 ** (3 - fft_n_points_two_power) * max_abs_x
+                    if fft_grid_spacing is None
+                    else fft_grid_spacing
+                )
+                q = (
+                    np.ceil(np.log(2 * max_abs_x / h) / np.log(2)) + 2
+                    if fft_n_points_two_power is None
+                    else int(fft_n_points_two_power)
+                )
+
+                # for some parameters, the range of x can be quite
+                # large, let's choose an arbitrary cut off (8GB) to save on
+                # computer memory.
+                MAX_Q = 30
+                if q > MAX_Q:
+                    raise RuntimeError(
+                        "fft_n_points_two_power has a maximum "
+                        + f"value of {MAX_Q}"
+                    )
+
+                density_x, density = pdf_from_cf_with_fft(
+                    lambda t: _cf(t, _alpha, _beta),
+                    h=h,
+                    q=q,
+                    level=fft_interpolation_level,
+                )
+                f = interpolate.InterpolatedUnivariateSpline(
+                    density_x, np.real(density), k=fft_interpolation_degree
+                )  # patch FFT to use cubic
+                data_out[data_mask] = f(_x)
+
+        return data_out.T[0]
+
+    @inherit_docstring_from(rv_continuous)
+    def cdf(self, x, *args, **kwds):
+        # override base class version to correct
+        # location for S1 parameterization
+        # NOTE: this is near identical to pdf() above
+        if self._parameterization() == "S0":
+            return super().cdf(x, *args, **kwds)
+        elif self._parameterization() == "S1":
+            (alpha, beta), delta, gamma = self._parse_args(*args, **kwds)
+            if np.all(np.reshape(alpha, (1, -1))[0, :] != 1):
+                return super().cdf(x, *args, **kwds)
+            else:
+                # correct location for this parameterisation
+                x = np.reshape(x, (1, -1))[0, :]
+                x, alpha, beta = np.broadcast_arrays(x, alpha, beta)
+
+                data_in = np.dstack((x, alpha, beta))[0]
+                data_out = np.empty(shape=(len(data_in), 1))
+                # group data in unique arrays of alpha, beta pairs
+                uniq_param_pairs = np.unique(data_in[:, 1:], axis=0)
+                for pair in uniq_param_pairs:
+                    _alpha, _beta = pair
+                    _delta = (
+                        delta + 2 * _beta * gamma * np.log(gamma) / np.pi
+                        if _alpha == 1.0
+                        else delta
+                    )
+                    data_mask = np.all(data_in[:, 1:] == pair, axis=-1)
+                    _x = data_in[data_mask, 0]
+                    data_out[data_mask] = (
+                        super()
+                        .cdf(_x, _alpha, _beta, loc=_delta, scale=gamma)
+                        .reshape(len(_x), 1)
+                    )
+                output = data_out.T[0]
+                if output.shape == (1,):
+                    return output[0]
+                return output
+
+    def _cdf(self, x, alpha, beta):
+        if self._parameterization() == "S0":
+            _cdf_single_value_piecewise = _cdf_single_value_piecewise_Z0
+            _cf = _cf_Z0
+        elif self._parameterization() == "S1":
+            _cdf_single_value_piecewise = _cdf_single_value_piecewise_Z1
+            _cf = _cf_Z1
+
+        x = np.asarray(x).reshape(1, -1)[0, :]
+
+        x, alpha, beta = np.broadcast_arrays(x, alpha, beta)
+
+        data_in = np.dstack((x, alpha, beta))[0]
+        data_out = np.empty(shape=(len(data_in), 1))
+
+        cdf_default_method_name = self.cdf_default_method
+        if cdf_default_method_name == "piecewise":
+            cdf_single_value_method = _cdf_single_value_piecewise
+        elif cdf_default_method_name == "fft-simpson":
+            cdf_single_value_method = None
+
+        cdf_single_value_kwds = {
+            "quad_eps": self.quad_eps,
+            "piecewise_x_tol_near_zeta": self.piecewise_x_tol_near_zeta,
+            "piecewise_alpha_tol_near_one": self.piecewise_alpha_tol_near_one,
+        }
+
+        fft_grid_spacing = self.pdf_fft_grid_spacing
+        fft_n_points_two_power = self.pdf_fft_n_points_two_power
+        fft_interpolation_level = self.pdf_fft_interpolation_level
+        fft_interpolation_degree = self.pdf_fft_interpolation_degree
+
+        # group data in unique arrays of alpha, beta pairs
+        uniq_param_pairs = np.unique(data_in[:, 1:], axis=0)
+        for pair in uniq_param_pairs:
+            data_mask = np.all(data_in[:, 1:] == pair, axis=-1)
+            data_subset = data_in[data_mask]
+            if cdf_single_value_method is not None:
+                data_out[data_mask] = np.array(
+                    [
+                        cdf_single_value_method(
+                            _x, _alpha, _beta, **cdf_single_value_kwds
+                        )
+                        for _x, _alpha, _beta in data_subset
+                    ]
+                ).reshape(len(data_subset), 1)
+            else:
+                warnings.warn(
+                    "Cumulative density calculations experimental for FFT"
+                    + " method. Use piecewise method instead.",
+                    RuntimeWarning, stacklevel=3,
+                )
+                _alpha, _beta = pair
+                _x = data_subset[:, (0,)]
+
+                # need enough points to "cover" _x for interpolation
+                if fft_grid_spacing is None and fft_n_points_two_power is None:
+                    raise ValueError(
+                        "One of fft_grid_spacing or fft_n_points_two_power "
+                        + "needs to be set."
+                    )
+                max_abs_x = np.max(np.abs(_x))
+                h = (
+                    2 ** (3 - fft_n_points_two_power) * max_abs_x
+                    if fft_grid_spacing is None
+                    else fft_grid_spacing
+                )
+                q = (
+                    np.ceil(np.log(2 * max_abs_x / h) / np.log(2)) + 2
+                    if fft_n_points_two_power is None
+                    else int(fft_n_points_two_power)
+                )
+
+                density_x, density = pdf_from_cf_with_fft(
+                    lambda t: _cf(t, _alpha, _beta),
+                    h=h,
+                    q=q,
+                    level=fft_interpolation_level,
+                )
+                f = interpolate.InterpolatedUnivariateSpline(
+                    density_x, np.real(density), k=fft_interpolation_degree
+                )
+                data_out[data_mask] = np.array(
+                    [f.integral(self.a, float(x_1.squeeze())) for x_1 in _x]
+                ).reshape(data_out[data_mask].shape)
+
+        return data_out.T[0]
+
+    def _fitstart(self, data):
+        if self._parameterization() == "S0":
+            _fitstart = _fitstart_S0
+        elif self._parameterization() == "S1":
+            _fitstart = _fitstart_S1
+        return _fitstart(data)
+
+    def _stats(self, alpha, beta):
+        mu = 0 if alpha > 1 else np.nan
+        mu2 = 2 if alpha == 2 else np.inf
+        g1 = 0.0 if alpha == 2.0 else np.nan
+        g2 = 0.0 if alpha == 2.0 else np.nan
+        return mu, mu2, g1, g2
+
+
+# cotes numbers - see sequence from http://oeis.org/A100642
+Cotes_table = np.array(
+    [[], [1]] + [v[2] for v in _builtincoeffs.values()], dtype=object
+)
+Cotes = np.array(
+    [
+        np.pad(r, (0, len(Cotes_table) - 1 - len(r)), mode='constant')
+        for r in Cotes_table
+    ]
+)
+
+
+def pdf_from_cf_with_fft(cf, h=0.01, q=9, level=3):
+    """Calculates pdf from characteristic function.
+
+    Uses fast Fourier transform with Newton-Cotes integration following [WZ].
+    Defaults to using Simpson's method (3-point Newton-Cotes integration).
+
+    Parameters
+    ----------
+    cf : callable
+        Single argument function from float -> complex expressing a
+        characteristic function for some distribution.
+    h : Optional[float]
+        Step size for Newton-Cotes integration. Default: 0.01
+    q : Optional[int]
+        Use 2**q steps when performing Newton-Cotes integration.
+        The infinite integral in the inverse Fourier transform will then
+        be restricted to the interval [-2**q * h / 2, 2**q * h / 2]. Setting
+        the number of steps equal to a power of 2 allows the fft to be
+        calculated in O(n*log(n)) time rather than O(n**2).
+        Default: 9
+    level : Optional[int]
+        Calculate integral using n-point Newton-Cotes integration for
+        n = level. The 3-point Newton-Cotes formula corresponds to Simpson's
+        rule. Default: 3
+
+    Returns
+    -------
+    x_l : ndarray
+        Array of points x at which pdf is estimated. 2**q equally spaced
+        points from -pi/h up to but not including pi/h.
+    density : ndarray
+        Estimated values of pdf corresponding to cf at points in x_l.
+
+    References
+    ----------
+    .. [WZ] Wang, Li and Zhang, Ji-Hong, 2008. Simpson's rule based FFT method
+        to compute densities of stable distribution.
+    """
+    n = level
+    N = 2**q
+    steps = np.arange(0, N)
+    L = N * h / 2
+    x_l = np.pi * (steps - N / 2) / L
+    if level > 1:
+        indices = np.arange(n).reshape(n, 1)
+        s1 = np.sum(
+            (-1) ** steps * Cotes[n, indices] * np.fft.fft(
+                (-1)**steps * cf(-L + h * steps + h * indices / (n - 1))
+            ) * np.exp(
+                1j * np.pi * indices / (n - 1)
+                - 2 * 1j * np.pi * indices * steps /
+                (N * (n - 1))
+            ),
+            axis=0
+        )
+    else:
+        s1 = (-1) ** steps * Cotes[n, 0] * np.fft.fft(
+            (-1) ** steps * cf(-L + h * steps)
+        )
+    density = h * s1 / (2 * np.pi * np.sum(Cotes[n]))
+    return (x_l, density)
+
+
+levy_stable = levy_stable_gen(name="levy_stable")
+
+
+class levy_stable_frozen(rv_continuous_frozen):
+    @property
+    def parameterization(self):
+        return self.dist.parameterization
+
+    @parameterization.setter
+    def parameterization(self, value):
+        self.dist.parameterization = value
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diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_mannwhitneyu.py b/.venv/lib/python3.12/site-packages/scipy/stats/_mannwhitneyu.py
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+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_mannwhitneyu.py
@@ -0,0 +1,537 @@
+import threading
+import numpy as np
+
+from scipy._lib._array_api import (array_namespace, xp_capabilities, xp_size,
+                                   xp_promote, is_lazy_array, is_jax, is_marray,
+                                   _count_nonmasked)
+from scipy._lib._bunch import _make_tuple_bunch
+import scipy._external.array_api_extra as xpx
+from scipy import special
+from scipy import stats
+from scipy.stats._stats_py import _rankdata, _get_pvalue, _SimpleNormal
+from scipy.stats._morestats import wilcoxon_result_unpacker, wilcoxon_outputs
+from scipy.stats._wilcoxon import _correction_sign
+from ._axis_nan_policy import _axis_nan_policy_factory
+
+class _MWU:
+    '''Distribution of MWU statistic under the null hypothesis'''
+
+    def __init__(self, n1, n2):
+        self._reset(n1, n2)
+
+    def set_shapes(self, n1, n2):
+        n1, n2 = min(n1, n2), max(n1, n2)
+        if (n1, n2) == (self.n1, self.n2):
+            return
+
+        self.n1 = n1
+        self.n2 = n2
+        self.s_array = np.zeros(0, dtype=int)
+        self.configurations = np.zeros(0, dtype=np.uint64)
+
+    def reset(self):
+        self._reset(self.n1, self.n2)
+
+    def _reset(self, n1, n2):
+        self.n1 = None
+        self.n2 = None
+        self.set_shapes(n1, n2)
+
+    def pmf(self, k):
+
+        # In practice, `pmf` is never called with k > m*n/2.
+        # If it were, we'd exploit symmetry here:
+        # k = np.array(k, copy=True)
+        # k2 = m*n - k
+        # i = k2 < k
+        # k[i] = k2[i]
+
+        pmfs = self.build_u_freqs_array(np.max(k))
+        return pmfs[k]
+
+    def cdf(self, k):
+        '''Cumulative distribution function'''
+
+        # In practice, `cdf` is never called with k > m*n/2.
+        # If it were, we'd exploit symmetry here rather than in `sf`
+        pmfs = self.build_u_freqs_array(np.max(k))
+        cdfs = np.cumsum(pmfs)
+        return cdfs[k]
+
+    def sf(self, k):
+        '''Survival function'''
+        # Note that both CDF and SF include the PMF at k. The p-value is
+        # calculated from the SF and should include the mass at k, so this
+        # is desirable
+
+        # Use the fact that the distribution is symmetric and sum from the left
+        kc = np.asarray(self.n1*self.n2 - k)  # complement of k
+        i = k < kc
+        if np.any(i):
+            kc[i] = k[i]
+            cdfs = np.asarray(self.cdf(kc))
+            cdfs[i] = 1. - cdfs[i] + self.pmf(kc[i])
+        else:
+            cdfs = np.asarray(self.cdf(kc))
+        return cdfs[()]
+
+    # build_sigma_array and build_u_freqs_array adapted from code
+    # by @toobaz with permission. Thanks to @andreasloe for the suggestion.
+    # See https://github.com/scipy/scipy/pull/4933#issuecomment-1898082691
+    def build_sigma_array(self, a):
+        n1, n2 = self.n1, self.n2
+        if a + 1 <= self.s_array.size:
+            return self.s_array[1:a+1]
+
+        s_array = np.zeros(a + 1, dtype=int)
+
+        for d in np.arange(1, n1 + 1):
+            # All multiples of d, except 0:
+            indices = np.arange(d, a + 1, d)
+            # \epsilon_d = 1:
+            s_array[indices] += d
+
+        for d in np.arange(n2 + 1, n2 + n1 + 1):
+            # All multiples of d, except 0:
+            indices = np.arange(d, a + 1, d)
+            # \epsilon_d = -1:
+            s_array[indices] -= d
+
+        # We don't need 0:
+        self.s_array = s_array
+        return s_array[1:]
+
+    def build_u_freqs_array(self, maxu):
+        """
+        Build all the array of frequencies for u from 0 to maxu.
+        Assumptions:
+          n1 <= n2
+          maxu <= n1 * n2 / 2
+        """
+        n1, n2 = self.n1, self.n2
+        total = special.binom(n1 + n2, n1)
+
+        if maxu + 1 <= self.configurations.size:
+            return self.configurations[:maxu + 1] / total
+
+        s_array = self.build_sigma_array(maxu)
+
+        # Start working with ints, for maximum precision and efficiency:
+        configurations = np.zeros(maxu + 1, dtype=np.uint64)
+        configurations_is_uint = True
+        uint_max = np.iinfo(np.uint64).max
+        # How many ways to have U=0? 1
+        configurations[0] = 1
+
+        for u in np.arange(1, maxu + 1):
+            coeffs = s_array[u - 1::-1]
+            new_val = np.dot(configurations[:u], coeffs) / u
+            if new_val > uint_max and configurations_is_uint:
+                # OK, we got into numbers too big for uint64.
+                # So now we start working with floats.
+                # By doing this since the beginning, we would have lost precision.
+                # (And working on python long ints would be unbearably slow)
+                configurations = configurations.astype(float)
+                configurations_is_uint = False
+            configurations[u] = new_val
+
+        self.configurations = configurations
+        return configurations / total
+
+
+# Maintain state for faster repeat calls to `mannwhitneyu`.
+# _MWU() is calculated once per thread and stored as an attribute on
+# this thread-local variable inside mannwhitneyu().
+_mwu_state = threading.local()
+
+
+def _get_mwu_z(U, n1, n2, t, continuity=True, *, alternative, xp):
+    '''Standardized MWU statistic'''
+    # Follows mannwhitneyu [2]
+    mu = n1 * n2 / 2
+    n = n1 + n2
+
+    # Tie correction according to [2], "Normal approximation and tie correction"
+    # "A more computationally-efficient form..."
+    tie_term = xp.sum(t**3 - t, axis=-1)
+    s = xp.sqrt(n1*n2/12 * ((n + 1) - tie_term/(n*(n-1))))
+
+    numerator = U - mu
+
+    # Continuity correction.
+    # Sign is chosen to always increase the p-value to account for the rest of the
+    # probability mass _at_ q = U.
+    if continuity:
+        sign = _correction_sign(numerator, alternative, xp=xp)
+        numerator -= 0.5 * sign
+
+    # no problem evaluating the norm SF at an infinity
+    with np.errstate(divide='ignore', invalid='ignore'):
+        z = numerator / s
+    return z
+
+
+def _mwu_input_validation(x, y, use_continuity, alternative, axis, method):
+    ''' Input validation and standardization for mannwhitneyu '''
+    xp = array_namespace(x, y)
+
+    if not is_lazy_array(x) and (xp.any(xp.isnan(x)) or xp.any(xp.isnan(y))):
+        raise ValueError('`x` and `y` must not contain NaNs.')
+    if xp_size(x) == 0 or xp_size(y) == 0:
+        raise ValueError('`x` and `y` must be of nonzero size.')
+    x, y = xp_promote(x, y, force_floating=True, xp=xp)
+
+    bools = {True, False}
+    if use_continuity not in bools:
+        raise ValueError(f'`use_continuity` must be one of {bools}.')
+
+    alternatives = {"two-sided", "less", "greater"}
+    alternative = alternative.lower()
+    if alternative not in alternatives:
+        raise ValueError(f'`alternative` must be one of {alternatives}.')
+
+    axis_int = int(axis)
+    if axis != axis_int:
+        raise ValueError('`axis` must be an integer.')
+
+    if not isinstance(method, stats.PermutationMethod):
+        methods = {"asymptotic", "exact", "auto"}
+        method = method.lower()
+        if method not in methods:
+            raise ValueError(f'`method` must be one of {methods}.')
+
+    if is_marray(xp) and method != "asymptotic":
+        message = "Only `method='asymptotic'` is compatible with MArrays."
+        raise ValueError(message)
+
+    output_z = True if method == 'asymptotic' else False
+
+    return x, y, use_continuity, alternative, axis_int, method, output_z, xp
+
+
+def _mwu_choose_method(n1, n2, ties):
+    """Choose method 'asymptotic' or 'exact' depending on input size, ties"""
+
+    # if both inputs are large, asymptotic is OK
+    if n1 > 8 and n2 > 8:
+        return "asymptotic"
+
+    # if there are any ties, asymptotic is preferred
+    if ties:
+        return "asymptotic"
+
+    return "exact"
+
+
+MannwhitneyuResult = _make_tuple_bunch('MannwhitneyuResult', ['statistic', 'pvalue'])
+
+
+def mwu_result_object(statistic, pvalue, zstatistic=None):
+    res = MannwhitneyuResult(statistic, pvalue)
+    if zstatistic is not None:
+        res.zstatistic = zstatistic
+    return res
+
+
+@xp_capabilities(
+    cpu_only=True,  # exact calculation only implemented in NumPy
+    skip_backends=[('dask.array', 'needs rankdata')],
+    jax_jit=False,  # the exact null distribution is NumPy-only
+    marray=True,
+    extra_note=("Only ``method='asymptotic'`` is compatible with MArray input."
+                "``method='auto'`` is incompatible with JAX arrays."))
+@_axis_nan_policy_factory(mwu_result_object, n_samples=2,
+                          result_to_tuple=wilcoxon_result_unpacker,
+                          n_outputs=wilcoxon_outputs)
+def mannwhitneyu(x, y, use_continuity=True, alternative="two-sided",
+                 axis=0, method="auto"):
+    r'''Perform the Mann-Whitney U rank test on two independent samples.
+
+    The Mann-Whitney U test is a nonparametric test of the null hypothesis
+    that the distribution underlying sample `x` is the same as the
+    distribution underlying sample `y`. It is often used as a test of
+    difference in location between distributions.
+
+    Parameters
+    ----------
+    x, y : array-like
+        N-d arrays of samples. The arrays must be broadcastable except along
+        the dimension given by `axis`.
+    use_continuity : bool, optional
+            Whether a continuity correction (1/2) should be applied.
+            Default is True when `method` is ``'asymptotic'``; has no effect
+            otherwise.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        Let *SX(u)* and *SY(u)* be the survival functions of the
+        distributions underlying `x` and `y`, respectively. Then the following
+        alternative hypotheses are available:
+
+        * 'two-sided': the distributions are not equal, i.e. *SX(u) ≠ SY(u)* for
+          at least one *u*.
+        * 'less': the distribution underlying `x` is stochastically less
+          than the distribution underlying `y`, i.e. *SX(u) < SY(u)* for all *u*.
+        * 'greater': the distribution underlying `x` is stochastically greater
+          than the distribution underlying `y`, i.e. *SX(u) > SY(u)* for all *u*.
+
+        Under a more restrictive set of assumptions, the alternative hypotheses
+        can be expressed in terms of the locations of the distributions;
+        see [5]_ section 5.1.
+    axis : int, optional
+        Axis along which to perform the test. Default is 0.
+    method : {'auto', 'asymptotic', 'exact'} or `PermutationMethod` instance, optional
+        Selects the method used to calculate the *p*-value.
+        Default is 'auto'. The following options are available.
+
+        * ``'asymptotic'``: compares the standardized test statistic
+          against the normal distribution, correcting for ties.
+        * ``'exact'``: computes the exact *p*-value by comparing the observed
+          :math:`U` statistic against the exact distribution of the :math:`U`
+          statistic under the null hypothesis. No correction is made for ties.
+        * ``'auto'``: chooses ``'exact'`` when the size of one of the samples
+          is less than or equal to 8 and there are no ties;
+          chooses ``'asymptotic'`` otherwise.
+        * `PermutationMethod` instance. In this case, the p-value
+          is computed using `permutation_test` with the provided
+          configuration options and other appropriate settings.
+
+    Returns
+    -------
+    res : MannwhitneyuResult
+        An object containing attributes:
+
+        statistic : float
+            The Mann-Whitney U statistic corresponding with sample `x`. See
+            Notes for the test statistic corresponding with sample `y`.
+        pvalue : float
+            The associated *p*-value for the chosen `alternative`.
+        zstatistic : array_like
+            When ``method = 'approx'``, this is the normalized z-statistic::
+
+                z = (U - mn - d) / se
+
+            where ``U`` is `statistic` as defined above, ``mn`` is the mean of the
+            distribution under the null hypothesis, ``d`` is a continuity
+            correction, and ``se`` is the standard error.
+            When ``method != 'asymptotic'``, this attribute is not available.
+
+    Notes
+    -----
+    If ``U1`` is the statistic corresponding with sample `x`, then the
+    statistic corresponding with sample `y` is
+    ``U2 = x.shape[axis] * y.shape[axis] - U1``.
+
+    The p-value and standardized z-statistic produced by the Mann-Whitney U test is
+    identical to that produced by a Wilcoxon *rank sum* test, but different raw
+    statistics are associated with the tests. Specifically, the rank sum statistic
+    corresponding with the first sample is
+    ``R1 = U1 + x.shape[axis] * (x.shape[axis] + 1) / 2``.
+
+    `mannwhitneyu` is for independent samples. For related / paired samples,
+    consider `scipy.stats.wilcoxon`.
+
+    `method` ``'exact'`` is recommended when there are no ties and when either
+    sample size is less than 8 [1]_. The implementation follows the algorithm
+    reported in [3]_.
+    Note that the exact method is *not* corrected for ties, but
+    `mannwhitneyu` will not raise errors or warnings if there are ties in the
+    data. If there are ties and either samples is small (fewer than ~10
+    observations), consider passing an instance of `PermutationMethod`
+    as the `method` to perform a permutation test.
+
+    The Mann-Whitney U test is a non-parametric version of the t-test for
+    independent samples. When the means of samples from the populations
+    are normally distributed, consider `scipy.stats.ttest_ind`.
+
+    See Also
+    --------
+    scipy.stats.wilcoxon, scipy.stats.ttest_ind
+
+    References
+    ----------
+    .. [1] H.B. Mann and D.R. Whitney, "On a test of whether one of two random
+           variables is stochastically larger than the other", The Annals of
+           Mathematical Statistics, Vol. 18, pp. 50-60, 1947.
+    .. [2] Mann-Whitney U Test, Wikipedia,
+           http://en.wikipedia.org/wiki/Mann-Whitney_U_test
+    .. [3] Andreas Löffler,
+           "Über eine Partition der nat. Zahlen und ihr Anwendung beim U-Test",
+           Wiss. Z. Univ. Halle, XXXII'83 pp. 87-89.
+    .. [4] Rosie Shier, "Statistics: 2.3 The Mann-Whitney U Test", Mathematics
+           Learning Support Centre, 2004.
+    .. [5] Michael P. Fay and Michael A. Proschan. "Wilcoxon-Mann-Whitney
+           or t-test? On assumptions for hypothesis tests and multiple \
+           interpretations of decision rules." Statistics surveys, Vol. 4, pp.
+           1-39, 2010. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2857732/
+    .. [6] MathWorks. "ranksum - Wilcoxon rank sum test - MATLAB".
+           https://www.mathworks.com/help/stats/ranksum.html.
+
+    Examples
+    --------
+    We follow the example from [4]_: nine randomly sampled young adults were
+    diagnosed with type II diabetes at the ages below.
+
+    >>> males = [19, 22, 16, 29, 24]
+    >>> females = [20, 11, 17, 12]
+
+    We use the Mann-Whitney U test to assess whether there is a statistically
+    significant difference in the diagnosis age of males and females.
+    The null hypothesis is that the distribution of male diagnosis ages is
+    the same as the distribution of female diagnosis ages. We decide
+    that a confidence level of 95% is required to reject the null hypothesis
+    in favor of the alternative that the distributions are different.
+    Since the number of samples is very small and there are no ties in the
+    data, we can compare the observed test statistic against the *exact*
+    distribution of the test statistic under the null hypothesis.
+
+    >>> from scipy.stats import mannwhitneyu
+    >>> U1, p = mannwhitneyu(males, females, method="exact")
+    >>> print(U1)
+    17.0
+
+    `mannwhitneyu` always reports the statistic associated with the first
+    sample, which, in this case, is males. This agrees with :math:`U_M = 17`
+    reported in [4]_. The statistic associated with the second statistic
+    can be calculated:
+
+    >>> nx, ny = len(males), len(females)
+    >>> U2 = nx*ny - U1
+    >>> print(U2)
+    3.0
+
+    This agrees with :math:`U_F = 3` reported in [4]_. The two-sided
+    *p*-value can be calculated from either statistic, and the value produced
+    by `mannwhitneyu` agrees with :math:`p = 0.11` reported in [4]_.
+
+    >>> print(p)
+    0.1111111111111111
+
+    The exact distribution of the test statistic is asymptotically normal, so
+    the example continues by comparing the exact *p*-value against the
+    *p*-value produced using the normal approximation.
+
+    >>> _, pnorm = mannwhitneyu(males, females, method="asymptotic")
+    >>> print(pnorm)
+    0.11134688653314041
+
+    Here `mannwhitneyu`'s reported *p*-value appears to conflict with the
+    value :math:`p = 0.09` given in [4]_. The reason is that [4]_
+    does not apply the continuity correction performed by `mannwhitneyu`;
+    `mannwhitneyu` reduces the distance between the test statistic and the
+    mean :math:`\mu = n_x n_y / 2` by 0.5 to correct for the fact that the
+    discrete statistic is being compared against a continuous distribution.
+    Here, the :math:`U` statistic used is less than the mean, so we reduce
+    the distance by adding 0.5 in the numerator.
+
+    >>> import numpy as np
+    >>> from scipy.stats import norm
+    >>> U = min(U1, U2)
+    >>> N = nx + ny
+    >>> z = (U - nx*ny/2 + 0.5) / np.sqrt(nx*ny * (N + 1)/ 12)
+    >>> p = 2 * norm.cdf(z)  # use CDF to get p-value from smaller statistic
+    >>> print(p)
+    0.11134688653314041
+
+    If desired, we can disable the continuity correction to get a result
+    that agrees with that reported in [4]_.
+
+    >>> _, pnorm = mannwhitneyu(males, females, use_continuity=False,
+    ...                         method="asymptotic")
+    >>> print(pnorm)
+    0.0864107329737
+
+    Regardless of whether we perform an exact or asymptotic test, the
+    probability of the test statistic being as extreme or more extreme by
+    chance exceeds 5%, so we do not consider the results statistically
+    significant.
+
+    Suppose that, before seeing the data, we had hypothesized that females
+    would tend to be diagnosed at a younger age than males.
+    In that case, it would be natural to provide the female ages as the
+    first input, and we would have performed a one-sided test using
+    ``alternative = 'less'``: females are diagnosed at an age that is
+    stochastically less than that of males.
+
+    >>> res = mannwhitneyu(females, males, alternative="less", method="exact")
+    >>> print(res)
+    MannwhitneyuResult(statistic=3.0, pvalue=0.05555555555555555)
+
+    Again, the probability of getting a sufficiently low value of the
+    test statistic by chance under the null hypothesis is greater than 5%,
+    so we do not reject the null hypothesis in favor of our alternative.
+
+    If it is reasonable to assume that the means of samples from the
+    populations are normally distributed, we could have used a t-test to
+    perform the analysis.
+
+    >>> from scipy.stats import ttest_ind
+    >>> res = ttest_ind(females, males, alternative="less")
+    >>> print(res)
+    TtestResult(statistic=-2.239334696520584,
+                pvalue=0.030068441095757924,
+                df=7.0)
+
+    Under this assumption, the *p*-value would be low enough to reject the
+    null hypothesis in favor of the alternative.
+
+    '''
+
+    x, y, use_continuity, alternative, axis_int, method, output_z, xp = (
+        _mwu_input_validation(x, y, use_continuity, alternative, axis, method))
+
+    # axis=-1 and arrays broadcasted due to _axis_nan_policy decorator
+    xy = xp.concat((x, y), axis=-1)
+
+    n1 = _count_nonmasked(x, axis=-1, xp=xp)
+    n2 = _count_nonmasked(y, axis=-1, xp=xp)
+
+    # Follows [2]
+    ranks, _, t = _rankdata(xy, 'average', return_ties=True)  # method 2, step 1
+    ranks = xp.astype(ranks, x.dtype, copy=False)
+    t = xp.astype(t, x.dtype, copy=False)
+    R1 = xp.sum(ranks[..., :x.shape[-1]], axis=-1)         # method 2, step 2
+    U1 = R1 - n1*(n1+1)/2                                  # method 2, step 3
+    U2 = n1 * n2 - U1                                      # as U1 + U2 = n1 * n2
+
+    if alternative == "greater":
+        U, f = U1, 1  # U is the statistic to use for exact p-value, f is a factor
+    elif alternative == "less":
+        U, f = U2, 1  # Due to symmetry, it uses SF of U2 rather than CDF of U1
+    else:
+        U, f = xp.maximum(U1, U2), 2  # and will multiply SF by two for two-sided test
+
+    if method == "auto":
+        if is_jax(xp):
+            message = "`method='auto'` is incompatible with JAX arrays."
+            raise ValueError(message)
+        method = _mwu_choose_method(n1, n2, xp.any(t > 1))
+
+    if method == "exact":
+        if not hasattr(_mwu_state, 's'):
+            _mwu_state.s = _MWU(0, 0)
+        _mwu_state.s.set_shapes(n1, n2)
+        p = xpx.lazy_apply(_mwu_state.s.sf, xp.astype(U, xp.int64),
+                           as_numpy=True, dtype=xp.float64)
+        p = xp.astype(p, x.dtype, copy=False) * f
+    elif method == "asymptotic":
+        z = _get_mwu_z(U1, n1, n2, t, continuity=use_continuity,
+                       alternative=alternative, xp=xp)
+        p = _get_pvalue(z, _SimpleNormal(), alternative, xp=xp)
+    else:  # `PermutationMethod` instance (already validated)
+        def statistic(x, y, axis):
+            return mannwhitneyu(x, y, use_continuity=use_continuity,
+                                alternative=alternative, axis=axis,
+                                method="asymptotic").statistic
+
+        res = stats.permutation_test((x, y), statistic, axis=axis,
+                                     **method._asdict(), alternative=alternative)
+        p = res.pvalue
+
+    # Ensure that test statistic is not greater than 1
+    # This could happen for exact test when U = m*n/2
+    p = xp.clip(p, 0., 1.)
+
+    res = MannwhitneyuResult(U1, p)
+    if output_z:
+        res.zstatistic = z[()] if z.ndim == 0 else z
+    return res
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_mgc.py b/.venv/lib/python3.12/site-packages/scipy/stats/_mgc.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2ecbe9747acdcb4731a2755c88352896be10d0a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_mgc.py
@@ -0,0 +1,552 @@
+import warnings
+import numpy as np
+
+from scipy._lib._array_api import xp_capabilities
+from scipy._lib._util import check_random_state, MapWrapper, rng_integers, _contains_nan
+from scipy._lib._bunch import _make_tuple_bunch
+from scipy.spatial.distance import cdist
+from scipy.ndimage import _measurements
+
+from ._stats import _local_correlations  # type: ignore[import-not-found]
+from . import distributions
+
+__all__ = ['multiscale_graphcorr']
+
+# FROM MGCPY: https://github.com/neurodata/mgcpy
+
+
+class _ParallelP:
+    """Helper function to calculate parallel p-value."""
+
+    def __init__(self, x, y, random_states):
+        self.x = x
+        self.y = y
+        self.random_states = random_states
+
+    def __call__(self, index):
+        order = self.random_states[index].permutation(self.y.shape[0])
+        permy = self.y[order][:, order]
+
+        # calculate permuted stats, store in null distribution
+        perm_stat = _mgc_stat(self.x, permy)[0]
+
+        return perm_stat
+
+
+def _perm_test(x, y, stat, reps=1000, workers=-1, random_state=None):
+    r"""Helper function that calculates the p-value. See below for uses.
+
+    Parameters
+    ----------
+    x, y : ndarray
+        `x` and `y` have shapes ``(n, p)`` and ``(n, q)``.
+    stat : float
+        The sample test statistic.
+    reps : int, optional
+        The number of replications used to estimate the null when using the
+        permutation test. The default is 1000 replications.
+    workers : int or map-like callable, optional
+        If `workers` is an int the population is subdivided into `workers`
+        sections and evaluated in parallel (uses
+        `multiprocessing.Pool <multiprocessing>`). Supply `-1` to use all cores
+        available to the Process. Alternatively supply a map-like callable,
+        such as `multiprocessing.Pool.map` for evaluating the population in
+        parallel. This evaluation is carried out as `workers(func, iterable)`.
+        Requires that `func` be pickleable.
+    random_state : {None, int, `numpy.random.Generator`,
+                    `numpy.random.RandomState`}, optional
+
+        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+        singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used,
+        seeded with `seed`.
+        If `seed` is already a ``Generator`` or ``RandomState`` instance then
+        that instance is used.
+
+    Returns
+    -------
+    pvalue : float
+        The sample test p-value.
+    null_dist : list
+        The approximated null distribution.
+
+    """
+    # generate seeds for each rep (change to new parallel random number
+    # capabilities in numpy >= 1.17+)
+    random_state = check_random_state(random_state)
+    random_states = [np.random.RandomState(rng_integers(random_state, 1 << 32,
+                     size=4, dtype=np.uint32)) for _ in range(reps)]
+
+    # parallelizes with specified workers over number of reps and set seeds
+    parallelp = _ParallelP(x=x, y=y, random_states=random_states)
+    with MapWrapper(workers) as mapwrapper:
+        null_dist = np.array(list(mapwrapper(parallelp, range(reps))))
+
+    # calculate p-value and significant permutation map through list
+    pvalue = (1 + (null_dist >= stat).sum()) / (1 + reps)
+
+    return pvalue, null_dist
+
+
+def _euclidean_dist(x):
+    return cdist(x, x)
+
+
+MGCResult = _make_tuple_bunch('MGCResult',
+                              ['statistic', 'pvalue', 'mgc_dict'], [])
+
+
+@xp_capabilities(np_only=True)
+def multiscale_graphcorr(x, y, compute_distance=_euclidean_dist, reps=1000,
+                         workers=1, is_twosamp=False, random_state=None):
+    r"""Computes the Multiscale Graph Correlation (MGC) test statistic.
+
+    Specifically, for each point, MGC finds the :math:`k`-nearest neighbors for
+    one property (e.g. cloud density), and the :math:`l`-nearest neighbors for
+    the other property (e.g. grass wetness) [1]_. This pair :math:`(k, l)` is
+    called the "scale". A priori, however, it is not know which scales will be
+    most informative. So, MGC computes all distance pairs, and then efficiently
+    computes the distance correlations for all scales. The local correlations
+    illustrate which scales are relatively informative about the relationship.
+    The key, therefore, to successfully discover and decipher relationships
+    between disparate data modalities is to adaptively determine which scales
+    are the most informative, and the geometric implication for the most
+    informative scales. Doing so not only provides an estimate of whether the
+    modalities are related, but also provides insight into how the
+    determination was made. This is especially important in high-dimensional
+    data, where simple visualizations do not reveal relationships to the
+    unaided human eye. Characterizations of this implementation in particular
+    have been derived from and benchmarked within in [2]_.
+
+    Parameters
+    ----------
+    x, y : ndarray
+        If ``x`` and ``y`` have shapes ``(n, p)`` and ``(n, q)`` where `n` is
+        the number of samples and `p` and `q` are the number of dimensions,
+        then the MGC independence test will be run.  Alternatively, ``x`` and
+        ``y`` can have shapes ``(n, n)`` if they are distance or similarity
+        matrices, and ``compute_distance`` must be sent to ``None``. If ``x``
+        and ``y`` have shapes ``(n, p)`` and ``(m, p)``, an unpaired
+        two-sample MGC test will be run.
+    compute_distance : callable, optional
+        A function that computes the distance or similarity among the samples
+        within each data matrix. Set to ``None`` if ``x`` and ``y`` are
+        already distance matrices. The default uses the euclidean norm metric.
+        If you are calling a custom function, either create the distance
+        matrix before-hand or create a function of the form
+        ``compute_distance(x)`` where `x` is the data matrix for which
+        pairwise distances are calculated.
+    reps : int, optional
+        The number of replications used to estimate the null when using the
+        permutation test. The default is ``1000``.
+    workers : int or map-like callable, optional
+        If ``workers`` is an int the population is subdivided into ``workers``
+        sections and evaluated in parallel (uses ``multiprocessing.Pool
+        <multiprocessing>``). Supply ``-1`` to use all cores available to the
+        Process. Alternatively supply a map-like callable, such as
+        ``multiprocessing.Pool.map`` for evaluating the p-value in parallel.
+        This evaluation is carried out as ``workers(func, iterable)``.
+        Requires that `func` be pickleable. The default is ``1``.
+    is_twosamp : bool, optional
+        If `True`, a two sample test will be run. If ``x`` and ``y`` have
+        shapes ``(n, p)`` and ``(m, p)``, this optional will be overridden and
+        set to ``True``. Set to ``True`` if ``x`` and ``y`` both have shapes
+        ``(n, p)`` and a two sample test is desired. The default is ``False``.
+        Note that this will not run if inputs are distance matrices.
+    random_state : {None, int, `numpy.random.Generator`,
+                    `numpy.random.RandomState`}, optional
+
+        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+        singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used,
+        seeded with `seed`.
+        If `seed` is already a ``Generator`` or ``RandomState`` instance then
+        that instance is used.
+
+    Returns
+    -------
+    res : MGCResult
+        An object containing attributes:
+
+        statistic : float
+            The sample MGC test statistic within ``[-1, 1]``.
+        pvalue : float
+            The p-value obtained via permutation.
+        mgc_dict : dict
+            Contains additional useful results:
+
+            - mgc_map : ndarray
+                A 2D representation of the latent geometry of the
+                relationship.
+            - opt_scale : (int, int)
+                The estimated optimal scale as a ``(x, y)`` pair.
+            - null_dist : list
+                The null distribution derived from the permuted matrices.
+
+    See Also
+    --------
+    pearsonr : Pearson correlation coefficient and p-value for testing
+               non-correlation.
+    kendalltau : Calculates Kendall's tau.
+    spearmanr : Calculates a Spearman rank-order correlation coefficient.
+
+    Notes
+    -----
+    A description of the process of MGC and applications on neuroscience data
+    can be found in [1]_. It is performed using the following steps:
+
+    #. Two distance matrices :math:`D^X` and :math:`D^Y` are computed and
+       modified to be mean zero columnwise. This results in two
+       :math:`n \times n` distance matrices :math:`A` and :math:`B` (the
+       centering and unbiased modification) [3]_.
+
+    #. For all values :math:`k` and :math:`l` from :math:`1, ..., n`,
+
+       * The :math:`k`-nearest neighbor and :math:`l`-nearest neighbor graphs
+         are calculated for each property. Here, :math:`G_k (i, j)` indicates
+         the :math:`k`-smallest values of the :math:`i`-th row of :math:`A`
+         and :math:`H_l (i, j)` indicates the :math:`l` smallested values of
+         the :math:`i`-th row of :math:`B`
+
+       * Let :math:`\circ` denotes the entry-wise matrix product, then local
+         correlations are summed and normalized using the following statistic:
+
+    .. math::
+
+        c^{kl} = \frac{\sum_{ij} A G_k B H_l}
+                      {\sqrt{\sum_{ij} A^2 G_k \times \sum_{ij} B^2 H_l}}
+
+    #. The MGC test statistic is the smoothed optimal local correlation of
+       :math:`\{ c^{kl} \}`. Denote the smoothing operation as :math:`R(\cdot)`
+       (which essentially set all isolated large correlations) as 0 and
+       connected large correlations the same as before, see [3]_.) MGC is,
+
+    .. math::
+
+        MGC_n (x, y) = \max_{(k, l)} R \left(c^{kl} \left( x_n, y_n \right)
+                                                    \right)
+
+    The test statistic returns a value between :math:`(-1, 1)` since it is
+    normalized.
+
+    The p-value returned is calculated using a permutation test. This process
+    is completed by first randomly permuting :math:`y` to estimate the null
+    distribution and then calculating the probability of observing a test
+    statistic, under the null, at least as extreme as the observed test
+    statistic.
+
+    MGC requires at least 5 samples to run with reliable results. It can also
+    handle high-dimensional data sets.
+    In addition, by manipulating the input data matrices, the two-sample
+    testing problem can be reduced to the independence testing problem [4]_.
+    Given sample data :math:`U` and :math:`V` of sizes :math:`p \times n`
+    :math:`p \times m`, data matrix :math:`X` and :math:`Y` can be created as
+    follows:
+
+    .. math::
+
+        X = [U | V] \in \mathcal{R}^{p \times (n + m)}
+        Y = [0_{1 \times n} | 1_{1 \times m}] \in \mathcal{R}^{(n + m)}
+
+    Then, the MGC statistic can be calculated as normal. This methodology can
+    be extended to similar tests such as distance correlation [4]_.
+
+    .. versionadded:: 1.4.0
+
+    References
+    ----------
+    .. [1] Vogelstein, J. T., Bridgeford, E. W., Wang, Q., Priebe, C. E.,
+           Maggioni, M., & Shen, C. (2019). Discovering and deciphering
+           relationships across disparate data modalities. ELife.
+    .. [2] Panda, S., Palaniappan, S., Xiong, J., Swaminathan, A.,
+           Ramachandran, S., Bridgeford, E. W., ... Vogelstein, J. T. (2019).
+           mgcpy: A Comprehensive High Dimensional Independence Testing Python
+           Package. :arXiv:`1907.02088`
+    .. [3] Shen, C., Priebe, C.E., & Vogelstein, J. T. (2019). From distance
+           correlation to multiscale graph correlation. Journal of the American
+           Statistical Association.
+    .. [4] Shen, C. & Vogelstein, J. T. (2018). The Exact Equivalence of
+           Distance and Kernel Methods for Hypothesis Testing.
+           :arXiv:`1806.05514`
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import multiscale_graphcorr
+    >>> x = np.arange(100)
+    >>> y = x
+    >>> res = multiscale_graphcorr(x, y)
+    >>> res.statistic, res.pvalue
+    (1.0, 0.001)
+
+    To run an unpaired two-sample test,
+
+    >>> x = np.arange(100)
+    >>> y = np.arange(79)
+    >>> res = multiscale_graphcorr(x, y)
+    >>> res.statistic, res.pvalue  # doctest: +SKIP
+    (0.033258146255703246, 0.023)
+
+    or, if shape of the inputs are the same,
+
+    >>> x = np.arange(100)
+    >>> y = x
+    >>> res = multiscale_graphcorr(x, y, is_twosamp=True)
+    >>> res.statistic, res.pvalue  # doctest: +SKIP
+    (-0.008021809890200488, 1.0)
+
+    """
+    if not isinstance(x, np.ndarray) or not isinstance(y, np.ndarray):
+        raise ValueError("x and y must be ndarrays")
+
+    # convert arrays of type (n,) to (n, 1)
+    if x.ndim == 1:
+        x = x[:, np.newaxis]
+    elif x.ndim != 2:
+        raise ValueError(f"Expected a 2-D array `x`, found shape {x.shape}")
+    if y.ndim == 1:
+        y = y[:, np.newaxis]
+    elif y.ndim != 2:
+        raise ValueError(f"Expected a 2-D array `y`, found shape {y.shape}")
+
+    nx, px = x.shape
+    ny, py = y.shape
+
+    # check for NaNs
+    _contains_nan(x, nan_policy='raise')
+    _contains_nan(y, nan_policy='raise')
+
+    # check for positive or negative infinity and raise error
+    if np.sum(np.isinf(x)) > 0 or np.sum(np.isinf(y)) > 0:
+        raise ValueError("Inputs contain infinities")
+
+    if nx != ny:
+        if px == py:
+            # reshape x and y for two sample testing
+            is_twosamp = True
+        else:
+            raise ValueError("Shape mismatch, x and y must have shape [n, p] "
+                             "and [n, q] or have shape [n, p] and [m, p].")
+
+    if nx < 5 or ny < 5:
+        raise ValueError("MGC requires at least 5 samples to give reasonable "
+                         "results.")
+
+    # convert x and y to float
+    x = x.astype(np.float64)
+    y = y.astype(np.float64)
+
+    # check if compute_distance_matrix if a callable()
+    if not callable(compute_distance) and compute_distance is not None:
+        raise ValueError("Compute_distance must be a function.")
+
+    # check if number of reps exists, integer, or > 0 (if under 1000 raises
+    # warning)
+    if not isinstance(reps, int) or reps < 0:
+        raise ValueError("Number of reps must be an integer greater than 0.")
+    elif reps < 1000:
+        msg = ("The number of replications is low (under 1000), and p-value "
+               "calculations may be unreliable. Use the p-value result, with "
+               "caution!")
+        warnings.warn(msg, RuntimeWarning, stacklevel=2)
+
+    if is_twosamp:
+        if compute_distance is None:
+            raise ValueError("Cannot run if inputs are distance matrices")
+        x, y = _two_sample_transform(x, y)
+
+    if compute_distance is not None:
+        # compute distance matrices for x and y
+        x = compute_distance(x)
+        y = compute_distance(y)
+
+    # calculate MGC stat
+    stat, stat_dict = _mgc_stat(x, y)
+    stat_mgc_map = stat_dict["stat_mgc_map"]
+    opt_scale = stat_dict["opt_scale"]
+
+    # calculate permutation MGC p-value
+    pvalue, null_dist = _perm_test(x, y, stat, reps=reps, workers=workers,
+                                   random_state=random_state)
+
+    # save all stats (other than stat/p-value) in dictionary
+    mgc_dict = {"mgc_map": stat_mgc_map,
+                "opt_scale": opt_scale,
+                "null_dist": null_dist}
+
+    # create result object with alias for backward compatibility
+    res = MGCResult(stat, pvalue, mgc_dict)
+    res.stat = stat
+    return res
+
+
+def _mgc_stat(distx, disty):
+    r"""Helper function that calculates the MGC stat. See above for use.
+
+    Parameters
+    ----------
+    distx, disty : ndarray
+        `distx` and `disty` have shapes ``(n, p)`` and ``(n, q)`` or
+        ``(n, n)`` and ``(n, n)``
+        if distance matrices.
+
+    Returns
+    -------
+    stat : float
+        The sample MGC test statistic within ``[-1, 1]``.
+    stat_dict : dict
+        Contains additional useful additional returns containing the following
+        keys:
+
+        - stat_mgc_map : ndarray
+            MGC-map of the statistics.
+        - opt_scale : (float, float)
+            The estimated optimal scale as a ``(x, y)`` pair.
+
+    """
+    # calculate MGC map and optimal scale
+    stat_mgc_map = _local_correlations(distx, disty, global_corr='mgc')
+
+    m, n = stat_mgc_map.shape
+    if m == 1 or n == 1:
+        # the global scale at is the statistic calculated at maximal nearest
+        # neighbors. There is not enough local scale to search over, so
+        # default to global scale
+        stat = stat_mgc_map[m - 1][n - 1]
+        opt_scale = m * n
+    else:
+        samp_size = len(distx) - 1
+
+        # threshold to find connected region of significant local correlations
+        sig_connect = _threshold_mgc_map(stat_mgc_map, samp_size)
+
+        # maximum within the significant region
+        stat, opt_scale = _smooth_mgc_map(sig_connect, stat_mgc_map)
+
+    stat_dict = {"stat_mgc_map": stat_mgc_map,
+                 "opt_scale": opt_scale}
+
+    return stat, stat_dict
+
+
+def _threshold_mgc_map(stat_mgc_map, samp_size):
+    r"""
+    Finds a connected region of significance in the MGC-map by thresholding.
+
+    Parameters
+    ----------
+    stat_mgc_map : ndarray
+        All local correlations within ``[-1,1]``.
+    samp_size : int
+        The sample size of original data.
+
+    Returns
+    -------
+    sig_connect : ndarray
+        A binary matrix with 1's indicating the significant region.
+
+    """
+    m, n = stat_mgc_map.shape
+
+    # 0.02 is simply an empirical threshold, this can be set to 0.01 or 0.05
+    # with varying levels of performance. Threshold is based on a beta
+    # approximation.
+    per_sig = 1 - (0.02 / samp_size)  # Percentile to consider as significant
+    threshold = samp_size * (samp_size - 3)/4 - 1/2  # Beta approximation
+    threshold = distributions.beta.ppf(per_sig, threshold, threshold) * 2 - 1
+
+    # the global scale at is the statistic calculated at maximal nearest
+    # neighbors. Threshold is the maximum on the global and local scales
+    threshold = max(threshold, stat_mgc_map[m - 1][n - 1])
+
+    # find the largest connected component of significant correlations
+    sig_connect = stat_mgc_map > threshold
+    if np.sum(sig_connect) > 0:
+        sig_connect, _ = _measurements.label(sig_connect)
+        _, label_counts = np.unique(sig_connect, return_counts=True)
+
+        # skip the first element in label_counts, as it is count(zeros)
+        max_label = np.argmax(label_counts[1:]) + 1
+        sig_connect = sig_connect == max_label
+    else:
+        sig_connect = np.array([[False]])
+
+    return sig_connect
+
+
+def _smooth_mgc_map(sig_connect, stat_mgc_map):
+    """Finds the smoothed maximal within the significant region R.
+
+    If area of R is too small it returns the last local correlation. Otherwise,
+    returns the maximum within significant_connected_region.
+
+    Parameters
+    ----------
+    sig_connect : ndarray
+        A binary matrix with 1's indicating the significant region.
+    stat_mgc_map : ndarray
+        All local correlations within ``[-1, 1]``.
+
+    Returns
+    -------
+    stat : float
+        The sample MGC statistic within ``[-1, 1]``.
+    opt_scale: (float, float)
+        The estimated optimal scale as an ``(x, y)`` pair.
+
+    """
+    m, n = stat_mgc_map.shape
+
+    # the global scale at is the statistic calculated at maximal nearest
+    # neighbors. By default, statistic and optimal scale are global.
+    stat = stat_mgc_map[m - 1][n - 1]
+    opt_scale = [m, n]
+
+    if np.linalg.norm(sig_connect) != 0:
+        # proceed only when the connected region's area is sufficiently large
+        # 0.02 is simply an empirical threshold, this can be set to 0.01 or 0.05
+        # with varying levels of performance
+        if np.sum(sig_connect) >= np.ceil(0.02 * max(m, n)) * min(m, n):
+            max_corr = max(stat_mgc_map[sig_connect])
+
+            # find all scales within significant_connected_region that maximize
+            # the local correlation
+            max_corr_index = np.where((stat_mgc_map >= max_corr) & sig_connect)
+
+            if max_corr >= stat:
+                stat = max_corr
+
+                k, l = max_corr_index
+                one_d_indices = k * n + l  # 2D to 1D indexing
+                k = np.max(one_d_indices) // n
+                l = np.max(one_d_indices) % n
+                opt_scale = [k+1, l+1]  # adding 1s to match R indexing
+
+    return stat, opt_scale
+
+
+def _two_sample_transform(u, v):
+    """Helper function that concatenates x and y for two sample MGC stat.
+
+    See above for use.
+
+    Parameters
+    ----------
+    u, v : ndarray
+        `u` and `v` have shapes ``(n, p)`` and ``(m, p)``.
+
+    Returns
+    -------
+    x : ndarray
+        Concatenate `u` and `v` along the ``axis = 0``. `x` thus has shape
+        ``(2n, p)``.
+    y : ndarray
+        Label matrix for `x` where 0 refers to samples that comes from `u` and
+        1 refers to samples that come from `v`. `y` thus has shape ``(2n, 1)``.
+
+    """
+    nx = u.shape[0]
+    ny = v.shape[0]
+    x = np.concatenate([u, v], axis=0)
+    y = np.concatenate([np.zeros(nx), np.ones(ny)], axis=0).reshape(-1, 1)
+    return x, y
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_morestats.py b/.venv/lib/python3.12/site-packages/scipy/stats/_morestats.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ead2e29323cd51229e4e5bb018d8bf52bd87fb6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_morestats.py
@@ -0,0 +1,5086 @@
+import itertools
+import math
+import warnings
+import threading
+from collections import namedtuple
+
+import numpy as np
+from numpy import (isscalar, log, around, arange, sort, amin, amax, sqrt, array,
+                   pi, exp, ravel, count_nonzero)
+
+from scipy import optimize, special, interpolate, stats
+from scipy._lib._bunch import _make_tuple_bunch
+from scipy._lib._util import _rename_parameter, _contains_nan, _get_nan
+from scipy._lib.deprecation import _NoValue
+import scipy._external.array_api_extra as xpx
+
+from scipy._lib._array_api import (
+    array_namespace,
+    is_marray,
+    xp_capabilities,
+    is_numpy,
+    is_jax,
+    is_dask,
+    xp_size,
+    xp_vector_norm,
+    xp_promote,
+    xp_result_type,
+    xp_device,
+    xp_ravel,
+    _count_nonmasked,
+    is_lazy_array,
+)
+
+from ._ansari_swilk_statistics import gscale
+from . import _stats_py, _wilcoxon
+from ._fit import FitResult
+from ._stats_py import (_get_pvalue, SignificanceResult,  # noqa:F401
+                        _SimpleNormal, _SimpleChi2, _SimpleF, _demean)
+from .contingency import chi2_contingency
+from . import distributions
+from ._distn_infrastructure import rv_generic
+from ._axis_nan_policy import _axis_nan_policy_factory, _broadcast_arrays
+
+
+__all__ = ['mvsdist',
+           'bayes_mvs', 'kstat', 'kstatvar', 'probplot', 'ppcc_max', 'ppcc_plot',
+           'boxcox_llf', 'boxcox', 'boxcox_normmax', 'boxcox_normplot',
+           'shapiro', 'anderson', 'ansari', 'bartlett', 'levene',
+           'fligner', 'mood', 'wilcoxon', 'median_test',
+           'circmean', 'circvar', 'circstd', 'anderson_ksamp',
+           'yeojohnson_llf', 'yeojohnson', 'yeojohnson_normmax',
+           'yeojohnson_normplot', 'directional_stats',
+           'false_discovery_control'
+           ]
+
+
+Mean = namedtuple('Mean', ('statistic', 'minmax'))
+Variance = namedtuple('Variance', ('statistic', 'minmax'))
+Std_dev = namedtuple('Std_dev', ('statistic', 'minmax'))
+
+
+@xp_capabilities(np_only=True)
+def bayes_mvs(data, alpha=0.90):
+    r"""
+    Bayesian confidence intervals for the mean, var, and std.
+
+    Parameters
+    ----------
+    data : array_like
+        Input data, if multi-dimensional it is flattened to 1-D by `bayes_mvs`.
+        Requires 2 or more data points.
+    alpha : float, optional
+        Probability that the returned confidence interval contains
+        the true parameter.
+
+    Returns
+    -------
+    mean_cntr, var_cntr, std_cntr : tuple
+        The three results are for the mean, variance and standard deviation,
+        respectively.  Each result is a tuple of the form::
+
+            (center, (lower, upper))
+
+        with ``center`` the mean of the conditional pdf of the value given the
+        data, and ``(lower, upper)`` a confidence interval, centered on the
+        median, containing the estimate to a probability ``alpha``.
+
+    See Also
+    --------
+    mvsdist
+
+    Notes
+    -----
+    Each tuple of mean, variance, and standard deviation estimates represent
+    the (center, (lower, upper)) with center the mean of the conditional pdf
+    of the value given the data and (lower, upper) is a confidence interval
+    centered on the median, containing the estimate to a probability
+    ``alpha``.
+
+    Converts data to 1-D and assumes all data has the same mean and variance.
+    Uses Jeffrey's prior for variance and std.
+
+    Equivalent to ``tuple((x.mean(), x.interval(alpha)) for x in mvsdist(dat))``
+
+    References
+    ----------
+    T.E. Oliphant, "A Bayesian perspective on estimating mean, variance, and
+    standard-deviation from data", https://scholarsarchive.byu.edu/facpub/278,
+    2006.
+
+    Examples
+    --------
+    First a basic example to demonstrate the outputs:
+
+    >>> from scipy import stats
+    >>> data = [6, 9, 12, 7, 8, 8, 13]
+    >>> mean, var, std = stats.bayes_mvs(data)
+    >>> mean
+    Mean(statistic=9.0, minmax=(7.103650222612533, 10.896349777387467))
+    >>> var
+    Variance(statistic=10.0, minmax=(3.176724206, 24.45910382))
+    >>> std
+    Std_dev(statistic=2.9724954732045084,
+            minmax=(1.7823367265645143, 4.945614605014631))
+
+    Now we generate some normally distributed random data, and get estimates of
+    mean and standard deviation with 95% confidence intervals for those
+    estimates:
+
+    >>> n_samples = 100000
+    >>> data = stats.norm.rvs(size=n_samples)
+    >>> res_mean, res_var, res_std = stats.bayes_mvs(data, alpha=0.95)
+
+    >>> import matplotlib.pyplot as plt
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111)
+    >>> ax.hist(data, bins=100, density=True, label='Histogram of data')
+    >>> ax.vlines(res_mean.statistic, 0, 0.5, colors='r', label='Estimated mean')
+    >>> ax.axvspan(res_mean.minmax[0],res_mean.minmax[1], facecolor='r',
+    ...            alpha=0.2, label=r'Estimated mean (95% limits)')
+    >>> ax.vlines(res_std.statistic, 0, 0.5, colors='g', label='Estimated scale')
+    >>> ax.axvspan(res_std.minmax[0],res_std.minmax[1], facecolor='g', alpha=0.2,
+    ...            label=r'Estimated scale (95% limits)')
+
+    >>> ax.legend(fontsize=10)
+    >>> ax.set_xlim([-4, 4])
+    >>> ax.set_ylim([0, 0.5])
+    >>> plt.show()
+
+    """
+    m, v, s = mvsdist(data)
+    if alpha >= 1 or alpha <= 0:
+        raise ValueError(f"0 < alpha < 1 is required, but {alpha=} was given.")
+
+    m_res = Mean(m.mean(), m.interval(alpha))
+    v_res = Variance(v.mean(), v.interval(alpha))
+    s_res = Std_dev(s.mean(), s.interval(alpha))
+
+    return m_res, v_res, s_res
+
+
+@xp_capabilities(np_only=True)
+def mvsdist(data):
+    """
+    'Frozen' distributions for mean, variance, and standard deviation of data.
+
+    Parameters
+    ----------
+    data : array_like
+        Input array. Converted to 1-D using ravel.
+        Requires 2 or more data-points.
+
+    Returns
+    -------
+    mdist : "frozen" distribution object
+        Distribution object representing the mean of the data.
+    vdist : "frozen" distribution object
+        Distribution object representing the variance of the data.
+    sdist : "frozen" distribution object
+        Distribution object representing the standard deviation of the data.
+
+    See Also
+    --------
+    bayes_mvs
+
+    Notes
+    -----
+    The return values from ``bayes_mvs(data)`` is equivalent to
+    ``tuple((x.mean(), x.interval(0.90)) for x in mvsdist(data))``.
+
+    In other words, calling ``<dist>.mean()`` and ``<dist>.interval(0.90)``
+    on the three distribution objects returned from this function will give
+    the same results that are returned from `bayes_mvs`.
+
+    References
+    ----------
+    T.E. Oliphant, "A Bayesian perspective on estimating mean, variance, and
+    standard-deviation from data", https://scholarsarchive.byu.edu/facpub/278,
+    2006.
+
+    Examples
+    --------
+    >>> from scipy import stats
+    >>> data = [6, 9, 12, 7, 8, 8, 13]
+    >>> mean, var, std = stats.mvsdist(data)
+
+    We now have frozen distribution objects "mean", "var" and "std" that we can
+    examine:
+
+    >>> mean.mean()
+    9.0
+    >>> mean.interval(0.95)
+    (6.6120585482655692, 11.387941451734431)
+    >>> mean.std()
+    1.1952286093343936
+
+    """
+    x = ravel(data)
+    n = len(x)
+    if n < 2:
+        raise ValueError("Need at least 2 data-points.")
+    xbar = x.mean()
+    C = x.var()
+    if n > 1000:  # gaussian approximations for large n
+        mdist = distributions.norm(loc=xbar, scale=math.sqrt(C / n))
+        sdist = distributions.norm(loc=math.sqrt(C), scale=math.sqrt(C / (2. * n)))
+        vdist = distributions.norm(loc=C, scale=math.sqrt(2.0 / n) * C)
+    else:
+        nm1 = n - 1
+        fac = n * C / 2.
+        val = nm1 / 2.
+        mdist = distributions.t(nm1, loc=xbar, scale=math.sqrt(C / nm1))
+        sdist = distributions.gengamma(val, -2, scale=math.sqrt(fac))
+        vdist = distributions.invgamma(val, scale=fac)
+    return mdist, vdist, sdist
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, result_to_tuple=lambda x, _: (x,), n_outputs=1, default_axis=None
+)
+def kstat(data, n=2, *, axis=None):
+    r"""
+    Return the `n` th k-statistic ( ``1<=n<=4`` so far).
+
+    The `n` th k-statistic ``k_n`` is the unique symmetric unbiased estimator of the
+    `n` th cumulant :math:`\kappa_n` [1]_ [2]_.
+
+    Parameters
+    ----------
+    data : array_like
+        Input array.
+    n : int, {1, 2, 3, 4}, optional
+        Default is equal to 2.
+    axis : int or None, default: None
+        If an int, the axis of the input along which to compute the statistic.
+        The statistic of each axis-slice (e.g. row) of the input will appear
+        in a corresponding element of the output. If ``None``, the input will
+        be raveled before computing the statistic.
+
+    Returns
+    -------
+    kstat : float
+        The `n` th k-statistic.
+
+    See Also
+    --------
+    kstatvar : Returns an unbiased estimator of the variance of the k-statistic
+    moment : Returns the n-th central moment about the mean for a sample.
+
+    Notes
+    -----
+    For a sample size :math:`n`, the first few k-statistics are given by
+
+    .. math::
+
+        k_1 &= \frac{S_1}{n}, \\
+        k_2 &= \frac{nS_2 - S_1^2}{n(n-1)}, \\
+        k_3 &= \frac{2S_1^3 - 3nS_1S_2 + n^2S_3}{n(n-1)(n-2)}, \\
+        k_4 &= \frac{-6S_1^4 + 12nS_1^2S_2 - 3n(n-1)S_2^2 - 4n(n+1)S_1S_3
+        + n^2(n+1)S_4}{n (n-1)(n-2)(n-3)},
+
+    where
+
+    .. math::
+
+        S_r \equiv \sum_{i=1}^n X_i^r,
+
+    and :math:`X_i` is the :math:`i` th data point.
+
+    References
+    ----------
+    .. [1] http://mathworld.wolfram.com/k-Statistic.html
+
+    .. [2] http://mathworld.wolfram.com/Cumulant.html
+
+    Examples
+    --------
+    >>> from scipy import stats
+    >>> from numpy.random import default_rng
+    >>> rng = default_rng()
+
+    As sample size increases, `n`-th moment and `n`-th k-statistic converge to the
+    same number (although they aren't identical). In the case of the normal
+    distribution, they converge to zero.
+
+    >>> for i in range(2,8):
+    ...     x = rng.normal(size=10**i)
+    ...     m, k = stats.moment(x, 3), stats.kstat(x, 3)
+    ...     print(f"{i=}: {m=:.3g}, {k=:.3g}, {(m-k)=:.3g}")
+    i=2: m=-0.631, k=-0.651, (m-k)=0.0194  # random
+    i=3: m=0.0282, k=0.0283, (m-k)=-8.49e-05
+    i=4: m=-0.0454, k=-0.0454, (m-k)=1.36e-05
+    i=6: m=7.53e-05, k=7.53e-05, (m-k)=-2.26e-09
+    i=7: m=0.00166, k=0.00166, (m-k)=-4.99e-09
+    i=8: m=-2.88e-06 k=-2.88e-06, (m-k)=8.63e-13
+    """
+    xp = array_namespace(data)
+    data = xp.asarray(data)
+    if n > 4 or n < 1:
+        raise ValueError("k-statistics only supported for 1<=n<=4")
+    n = int(n)
+    if axis is None:
+        data = xp.reshape(data, (-1,))
+        axis = 0
+
+    N = _count_nonmasked(data, axis, xp=xp)
+
+    S = [None] + [xp.sum(data**k, axis=axis) for k in range(1, n + 1)]
+    if n == 1:
+        return S[1] * 1.0/N
+    elif n == 2:
+        return (N*S[2] - S[1]**2.0) / (N*(N - 1.0))
+    elif n == 3:
+        return (2*S[1]**3 - 3*N*S[1]*S[2] + N*N*S[3]) / (N*(N - 1.0)*(N - 2.0))
+    elif n == 4:
+        return ((-6*S[1]**4 + 12*N*S[1]**2 * S[2] - 3*N*(N-1.0)*S[2]**2 -
+                 4*N*(N+1)*S[1]*S[3] + N*N*(N+1)*S[4]) /
+                (N*(N-1.0)*(N-2.0)*(N-3.0)))
+    else:
+        raise ValueError("Should not be here.")
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, result_to_tuple=lambda x, _: (x,), n_outputs=1, default_axis=None
+)
+def kstatvar(data, n=2, *, axis=None):
+    r"""Return an unbiased estimator of the variance of the k-statistic.
+
+    See `kstat` and [1]_ for more details about the k-statistic.
+
+    Parameters
+    ----------
+    data : array_like
+        Input array.
+    n : int, {1, 2}, optional
+        Default is equal to 2.
+    axis : int or None, default: None
+        If an int, the axis of the input along which to compute the statistic.
+        The statistic of each axis-slice (e.g. row) of the input will appear
+        in a corresponding element of the output. If ``None``, the input will
+        be raveled before computing the statistic.
+
+    Returns
+    -------
+    kstatvar : float
+        The `n` th k-statistic variance.
+
+    See Also
+    --------
+    kstat : Returns the n-th k-statistic.
+    moment : Returns the n-th central moment about the mean for a sample.
+
+    Notes
+    -----
+    Unbiased estimators of the variances of the first two k-statistics are given by
+
+    .. math::
+
+        \mathrm{var}(k_1) &= \frac{k_2}{n}, \\
+        \mathrm{var}(k_2) &= \frac{2k_2^2n + (n-1)k_4}{n(n + 1)}.
+
+    References
+    ----------
+    .. [1] http://mathworld.wolfram.com/k-Statistic.html
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng(92366746)
+
+    As the sample size increases, the estimated variance of the k-statistic converges
+    to zero.
+
+    >>> for n in np.astype(np.logspace(1, 6, 6), int):
+    ...     x = rng.normal(size=n)
+    ...     kvar = stats.kstatvar(x, 1)
+    ...     print(f"{n=:<8}: {kvar=:.3g}")
+    n=10      : kvar=0.0954
+    n=100     : kvar=0.00974
+    n=1000    : kvar=0.000962
+    n=10000   : kvar=0.0001
+    n=100000  : kvar=9.94e-06
+    n=1000000 : kvar=9.99e-07
+    """  # noqa: E501
+    xp = array_namespace(data)
+    data = xp.asarray(data)
+    if axis is None:
+        data = xp.reshape(data, (-1,))
+        axis = 0
+    N = _count_nonmasked(data, axis, xp=xp)
+
+    if n == 1:
+        return kstat(data, n=2, axis=axis, _no_deco=True) * 1.0/N
+    elif n == 2:
+        k2 = kstat(data, n=2, axis=axis, _no_deco=True)
+        k4 = kstat(data, n=4, axis=axis, _no_deco=True)
+        return (2*N*k2**2 + (N-1)*k4) / (N*(N+1))
+    else:
+        raise ValueError("Only n=1 or n=2 supported.")
+
+
+def _calc_uniform_order_statistic_medians(n):
+    """Approximations of uniform order statistic medians.
+
+    Parameters
+    ----------
+    n : int
+        Sample size.
+
+    Returns
+    -------
+    v : 1d float array
+        Approximations of the order statistic medians.
+
+    References
+    ----------
+    .. [1] James J. Filliben, "The Probability Plot Correlation Coefficient
+           Test for Normality", Technometrics, Vol. 17, pp. 111-117, 1975.
+
+    Examples
+    --------
+    Order statistics of the uniform distribution on the unit interval
+    are marginally distributed according to beta distributions.
+    The expectations of these order statistic are evenly spaced across
+    the interval, but the distributions are skewed in a way that
+    pushes the medians slightly towards the endpoints of the unit interval:
+
+    >>> import numpy as np
+    >>> n = 4
+    >>> k = np.arange(1, n+1)
+    >>> from scipy.stats import beta
+    >>> a = k
+    >>> b = n-k+1
+    >>> beta.mean(a, b)
+    array([0.2, 0.4, 0.6, 0.8])
+    >>> beta.median(a, b)
+    array([0.15910358, 0.38572757, 0.61427243, 0.84089642])
+
+    The Filliben approximation uses the exact medians of the smallest
+    and greatest order statistics, and the remaining medians are approximated
+    by points spread evenly across a sub-interval of the unit interval:
+
+    >>> from scipy.stats._morestats import _calc_uniform_order_statistic_medians
+    >>> _calc_uniform_order_statistic_medians(n)
+    array([0.15910358, 0.38545246, 0.61454754, 0.84089642])
+
+    This plot shows the skewed distributions of the order statistics
+    of a sample of size four from a uniform distribution on the unit interval:
+
+    >>> import matplotlib.pyplot as plt
+    >>> x = np.linspace(0.0, 1.0, num=50, endpoint=True)
+    >>> pdfs = [beta.pdf(x, a[i], b[i]) for i in range(n)]
+    >>> plt.figure()
+    >>> plt.plot(x, pdfs[0], x, pdfs[1], x, pdfs[2], x, pdfs[3])
+
+    """
+    v = np.empty(n, dtype=np.float64)
+    v[-1] = 0.5**(1.0 / n)
+    v[0] = 1 - v[-1]
+    i = np.arange(2, n)
+    v[1:-1] = (i - 0.3175) / (n + 0.365)
+    return v
+
+
+def _parse_dist_kw(dist, enforce_subclass=True):
+    """Parse `dist` keyword.
+
+    Parameters
+    ----------
+    dist : str or stats.distributions instance.
+        Several functions take `dist` as a keyword, hence this utility
+        function.
+    enforce_subclass : bool, optional
+        If True (default), `dist` needs to be a
+        `_distn_infrastructure.rv_generic` instance.
+        It can sometimes be useful to set this keyword to False, if a function
+        wants to accept objects that just look somewhat like such an instance
+        (for example, they have a ``ppf`` method).
+
+    """
+    if isinstance(dist, rv_generic):
+        pass
+    elif isinstance(dist, str):
+        try:
+            dist = getattr(distributions, dist)
+        except AttributeError as e:
+            raise ValueError(f"{dist} is not a valid distribution name") from e
+    elif enforce_subclass:
+        msg = ("`dist` should be a stats.distributions instance or a string "
+               "with the name of such a distribution.")
+        raise ValueError(msg)
+
+    return dist
+
+
+def _add_axis_labels_title(plot, xlabel, ylabel, title):
+    """Helper function to add axes labels and a title to stats plots."""
+    try:
+        if hasattr(plot, 'set_title'):
+            # Matplotlib Axes instance or something that looks like it
+            plot.set_title(title)
+            plot.set_xlabel(xlabel)
+            plot.set_ylabel(ylabel)
+        else:
+            # matplotlib.pyplot module
+            plot.title(title)
+            plot.xlabel(xlabel)
+            plot.ylabel(ylabel)
+    except Exception:
+        # Not an MPL object or something that looks (enough) like it.
+        # Don't crash on adding labels or title
+        pass
+
+
+@xp_capabilities(np_only=True)
+def probplot(x, sparams=(), dist='norm', fit=True, plot=None, rvalue=False):
+    """
+    Calculate quantiles for a probability plot, and optionally show the plot.
+
+    Generates a probability plot of sample data against the quantiles of a
+    specified theoretical distribution (the normal distribution by default).
+    `probplot` optionally calculates a best-fit line for the data and plots the
+    results using Matplotlib or a given plot function.
+
+    Parameters
+    ----------
+    x : array_like
+        Sample/response data from which `probplot` creates the plot.
+    sparams : tuple, optional
+        Distribution-specific shape parameters (shape parameters plus location
+        and scale).
+    dist : str or stats.distributions instance, optional
+        Distribution or distribution function name. The default is 'norm' for a
+        normal probability plot.  Objects that look enough like a
+        stats.distributions instance (i.e. they have a ``ppf`` method) are also
+        accepted.
+    fit : bool, optional
+        Fit a least-squares regression (best-fit) line to the sample data if
+        True (default).
+    plot : object, optional
+        If given, plots the quantiles.
+        If given and `fit` is True, also plots the least squares fit.
+        `plot` is an object that has to have methods "plot" and "text".
+        The `matplotlib.pyplot` module or a Matplotlib Axes object can be used,
+        or a custom object with the same methods.
+        Default is None, which means that no plot is created.
+    rvalue : bool, optional
+        If `plot` is provided and `fit` is True, setting `rvalue` to True
+        includes the coefficient of determination on the plot.
+        Default is False.
+
+    Returns
+    -------
+    (osm, osr) : tuple of ndarrays
+        Tuple of theoretical quantiles (osm, or order statistic medians) and
+        ordered responses (osr).  `osr` is simply sorted input `x`.
+        For details on how `osm` is calculated see the Notes section.
+    (slope, intercept, r) : tuple of floats, optional
+        Tuple  containing the result of the least-squares fit, if that is
+        performed by `probplot`. `r` is the square root of the coefficient of
+        determination.  If ``fit=False`` and ``plot=None``, this tuple is not
+        returned.
+
+    Notes
+    -----
+    Even if `plot` is given, the figure is not shown or saved by `probplot`;
+    ``plt.show()`` or ``plt.savefig('figname.png')`` should be used after
+    calling `probplot`.
+
+    `probplot` generates a probability plot, which should not be confused with
+    a Q-Q or a P-P plot.  Statsmodels has more extensive functionality of this
+    type, see ``statsmodels.api.ProbPlot``.
+
+    The formula used for the theoretical quantiles (horizontal axis of the
+    probability plot) is Filliben's estimate::
+
+        quantiles = dist.ppf(val), for
+
+                0.5**(1/n),                  for i = n
+          val = (i - 0.3175) / (n + 0.365),  for i = 2, ..., n-1
+                1 - 0.5**(1/n),              for i = 1
+
+    where ``i`` indicates the i-th ordered value and ``n`` is the total number
+    of values.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+    >>> nsample = 100
+    >>> rng = np.random.default_rng()
+
+    A t distribution with small degrees of freedom:
+
+    >>> ax1 = plt.subplot(221)
+    >>> x = stats.t.rvs(3, size=nsample, random_state=rng)
+    >>> res = stats.probplot(x, plot=plt)
+
+    A t distribution with larger degrees of freedom:
+
+    >>> ax2 = plt.subplot(222)
+    >>> x = stats.t.rvs(25, size=nsample, random_state=rng)
+    >>> res = stats.probplot(x, plot=plt)
+
+    A mixture of two normal distributions with broadcasting:
+
+    >>> ax3 = plt.subplot(223)
+    >>> x = stats.norm.rvs(loc=[0,5], scale=[1,1.5],
+    ...                    size=(nsample//2,2), random_state=rng).ravel()
+    >>> res = stats.probplot(x, plot=plt)
+
+    A standard normal distribution:
+
+    >>> ax4 = plt.subplot(224)
+    >>> x = stats.norm.rvs(loc=0, scale=1, size=nsample, random_state=rng)
+    >>> res = stats.probplot(x, plot=plt)
+
+    Produce a new figure with a loggamma distribution, using the ``dist`` and
+    ``sparams`` keywords:
+
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111)
+    >>> x = stats.loggamma.rvs(c=2.5, size=500, random_state=rng)
+    >>> res = stats.probplot(x, dist=stats.loggamma, sparams=(2.5,), plot=ax)
+    >>> ax.set_title("Probplot for loggamma dist with shape parameter 2.5")
+
+    Show the results with Matplotlib:
+
+    >>> plt.show()
+
+    """
+    x = np.asarray(x)
+    if x.size == 0:
+        if fit:
+            return (x, x), (np.nan, np.nan, 0.0)
+        else:
+            return x, x
+
+    osm_uniform = _calc_uniform_order_statistic_medians(len(x))
+    dist = _parse_dist_kw(dist, enforce_subclass=False)
+    if sparams is None:
+        sparams = ()
+    if isscalar(sparams):
+        sparams = (sparams,)
+    if not isinstance(sparams, tuple):
+        sparams = tuple(sparams)
+
+    osm = dist.ppf(osm_uniform, *sparams)
+    osr = sort(x)
+    if fit:
+        # perform a linear least squares fit.
+        slope, intercept, r, prob, _ = _stats_py.linregress(osm, osr)
+
+    if plot is not None:
+        plot.plot(osm, osr, 'bo')
+        if fit:
+            plot.plot(osm, slope*osm + intercept, 'r-')
+        _add_axis_labels_title(plot, xlabel='Theoretical quantiles',
+                               ylabel='Ordered Values',
+                               title='Probability Plot')
+
+        # Add R^2 value to the plot as text
+        if fit and rvalue:
+            xmin = amin(osm)
+            xmax = amax(osm)
+            ymin = amin(x)
+            ymax = amax(x)
+            posx = xmin + 0.70 * (xmax - xmin)
+            posy = ymin + 0.01 * (ymax - ymin)
+            plot.text(posx, posy, f"$R^2={r ** 2:1.4f}$")
+
+    if fit:
+        return (osm, osr), (slope, intercept, r)
+    else:
+        return osm, osr
+
+
+@xp_capabilities(np_only=True)
+def ppcc_max(x, brack=(0.0, 1.0), dist='tukeylambda'):
+    """Calculate the shape parameter that maximizes the PPCC.
+
+    The probability plot correlation coefficient (PPCC) plot can be used
+    to determine the optimal shape parameter for a one-parameter family
+    of distributions. ``ppcc_max`` returns the shape parameter that would
+    maximize the probability plot correlation coefficient for the given
+    data to a one-parameter family of distributions.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.
+    brack : tuple, optional
+        Triple (a,b,c) where (a<b<c). If bracket consists of two numbers (a, c)
+        then they are assumed to be a starting interval for a downhill bracket
+        search (see `scipy.optimize.brent`).
+    dist : str or stats.distributions instance, optional
+        Distribution or distribution function name.  Objects that look enough
+        like a stats.distributions instance (i.e. they have a ``ppf`` method)
+        are also accepted.  The default is ``'tukeylambda'``.
+
+    Returns
+    -------
+    shape_value : float
+        The shape parameter at which the probability plot correlation
+        coefficient reaches its max value.
+
+    See Also
+    --------
+    ppcc_plot, probplot, boxcox
+
+    Notes
+    -----
+    The brack keyword serves as a starting point which is useful in corner
+    cases. One can use a plot to obtain a rough visual estimate of the location
+    for the maximum to start the search near it.
+
+    References
+    ----------
+    .. [1] J.J. Filliben, "The Probability Plot Correlation Coefficient Test
+           for Normality", Technometrics, Vol. 17, pp. 111-117, 1975.
+    .. [2] Engineering Statistics Handbook, NIST/SEMATEC,
+           https://www.itl.nist.gov/div898/handbook/eda/section3/ppccplot.htm
+
+    Examples
+    --------
+    First we generate some random data from a Weibull distribution
+    with shape parameter 2.5:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+    >>> rng = np.random.default_rng()
+    >>> c = 2.5
+    >>> x = stats.weibull_min.rvs(c, scale=4, size=2000, random_state=rng)
+
+    Generate the PPCC plot for this data with the Weibull distribution.
+
+    >>> fig, ax = plt.subplots(figsize=(8, 6))
+    >>> res = stats.ppcc_plot(x, c/2, 2*c, dist='weibull_min', plot=ax)
+
+    We calculate the value where the shape should reach its maximum and a
+    red line is drawn there. The line should coincide with the highest
+    point in the PPCC graph.
+
+    >>> cmax = stats.ppcc_max(x, brack=(c/2, 2*c), dist='weibull_min')
+    >>> ax.axvline(cmax, color='r')
+    >>> plt.show()
+
+    """
+    dist = _parse_dist_kw(dist)
+    osm_uniform = _calc_uniform_order_statistic_medians(len(x))
+    osr = sort(x)
+
+    # this function computes the x-axis values of the probability plot
+    #  and computes a linear regression (including the correlation)
+    #  and returns 1-r so that a minimization function maximizes the
+    #  correlation
+    def tempfunc(shape, mi, yvals, func):
+        xvals = func(mi, shape)
+        r, prob = _stats_py.pearsonr(xvals, yvals)
+        return 1 - r
+
+    return optimize.brent(tempfunc, brack=brack,
+                          args=(osm_uniform, osr, dist.ppf))
+
+
+@xp_capabilities(np_only=True)
+def ppcc_plot(x, a, b, dist='tukeylambda', plot=None, N=80):
+    """Calculate and optionally plot probability plot correlation coefficient.
+
+    The probability plot correlation coefficient (PPCC) plot can be used to
+    determine the optimal shape parameter for a one-parameter family of
+    distributions.  It cannot be used for distributions without shape
+    parameters
+    (like the normal distribution) or with multiple shape parameters.
+
+    By default a Tukey-Lambda distribution (`stats.tukeylambda`) is used. A
+    Tukey-Lambda PPCC plot interpolates from long-tailed to short-tailed
+    distributions via an approximately normal one, and is therefore
+    particularly useful in practice.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.
+    a, b : scalar
+        Lower and upper bounds of the shape parameter to use.
+    dist : str or stats.distributions instance, optional
+        Distribution or distribution function name.  Objects that look enough
+        like a stats.distributions instance (i.e. they have a ``ppf`` method)
+        are also accepted.  The default is ``'tukeylambda'``.
+    plot : object, optional
+        If given, plots PPCC against the shape parameter.
+        `plot` is an object that has to have methods "plot" and "text".
+        The `matplotlib.pyplot` module or a Matplotlib Axes object can be used,
+        or a custom object with the same methods.
+        Default is None, which means that no plot is created.
+    N : int, optional
+        Number of points on the horizontal axis (equally distributed from
+        `a` to `b`).
+
+    Returns
+    -------
+    svals : ndarray
+        The shape values for which `ppcc` was calculated.
+    ppcc : ndarray
+        The calculated probability plot correlation coefficient values.
+
+    See Also
+    --------
+    ppcc_max, probplot, boxcox_normplot, tukeylambda
+
+    References
+    ----------
+    J.J. Filliben, "The Probability Plot Correlation Coefficient Test for
+    Normality", Technometrics, Vol. 17, pp. 111-117, 1975.
+
+    Examples
+    --------
+    First we generate some random data from a Weibull distribution
+    with shape parameter 2.5, and plot the histogram of the data:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+    >>> rng = np.random.default_rng()
+    >>> c = 2.5
+    >>> x = stats.weibull_min.rvs(c, scale=4, size=2000, random_state=rng)
+
+    Take a look at the histogram of the data.
+
+    >>> fig1, ax = plt.subplots(figsize=(9, 4))
+    >>> ax.hist(x, bins=50)
+    >>> ax.set_title('Histogram of x')
+    >>> plt.show()
+
+    Now we explore this data with a PPCC plot as well as the related
+    probability plot and Box-Cox normplot.  A red line is drawn where we
+    expect the PPCC value to be maximal (at the shape parameter ``c``
+    used above):
+
+    >>> fig2 = plt.figure(figsize=(12, 4))
+    >>> ax1 = fig2.add_subplot(1, 3, 1)
+    >>> ax2 = fig2.add_subplot(1, 3, 2)
+    >>> ax3 = fig2.add_subplot(1, 3, 3)
+    >>> res = stats.probplot(x, plot=ax1)
+    >>> res = stats.boxcox_normplot(x, -4, 4, plot=ax2)
+    >>> res = stats.ppcc_plot(x, c/2, 2*c, dist='weibull_min', plot=ax3)
+    >>> ax3.axvline(c, color='r')
+    >>> plt.show()
+
+    """
+    if b <= a:
+        raise ValueError("`b` has to be larger than `a`.")
+
+    svals = np.linspace(a, b, num=N)
+    ppcc = np.empty_like(svals)
+    for k, sval in enumerate(svals):
+        _, r2 = probplot(x, sval, dist=dist, fit=True)
+        ppcc[k] = r2[-1]
+
+    if plot is not None:
+        plot.plot(svals, ppcc, 'x')
+        _add_axis_labels_title(plot, xlabel='Shape Values',
+                               ylabel='Prob Plot Corr. Coef.',
+                               title=f'({dist}) PPCC Plot')
+
+    return svals, ppcc
+
+
+def _log_mean(logx, axis):
+    # compute log of mean of x from log(x)
+    return (
+        special.logsumexp(logx, axis=axis, keepdims=True)
+        - math.log(logx.shape[axis])
+    )
+
+
+def _log_var(logx, xp, axis):
+    # compute log of variance of x from log(x)
+    logmean = xp.broadcast_to(_log_mean(logx, axis=axis), logx.shape)
+    ones = xp.ones_like(logx)
+    logxmu, _ = special.logsumexp(xp.stack((logx, logmean), axis=0), axis=0,
+                                  b=xp.stack((ones, -ones), axis=0), return_sign=True)
+    return special.logsumexp(2 * logxmu, axis=axis) - math.log(logx.shape[axis])
+
+
+@xp_capabilities()
+def boxcox_llf(lmb, data, *, axis=0, keepdims=False, nan_policy='propagate'):
+    r"""The boxcox log-likelihood function.
+
+    Parameters
+    ----------
+    lmb : scalar
+        Parameter for Box-Cox transformation.  See `boxcox` for details.
+    data : array_like
+        Data to calculate Box-Cox log-likelihood for.  If `data` is
+        multi-dimensional, the log-likelihood is calculated along the first
+        axis.
+    axis : int, default: 0
+        If an int, the axis of the input along which to compute the statistic.
+        The statistic of each axis-slice (e.g. row) of the input will appear in a
+        corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+    keepdims : bool, default: False
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the input array.
+    nan_policy : {'propagate', 'omit', 'raise'}
+        Defines how to handle input NaNs.
+
+        - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
+          which the  statistic is computed, the corresponding entry of the output
+          will be NaN.
+        - ``omit``: NaNs will be omitted when performing the calculation.
+          If insufficient data remains in the axis slice along which the
+          statistic is computed, the corresponding entry of the output will be
+          NaN.
+        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
+
+    Returns
+    -------
+    llf : float or ndarray
+        Box-Cox log-likelihood of `data` given `lmb`.  A float for 1-D `data`,
+        an array otherwise.
+
+    See Also
+    --------
+    boxcox, probplot, boxcox_normplot, boxcox_normmax
+
+    Notes
+    -----
+    The Box-Cox log-likelihood function :math:`l` is defined here as
+
+    .. math::
+
+        l = (\lambda - 1) \sum_i^N \log(x_i) -
+              \frac{N}{2} \log\left(\sum_i^N (y_i - \bar{y})^2 / N\right),
+
+    where :math:`N` is the number of data points ``data`` and :math:`y` is the Box-Cox
+    transformed input data.
+    This corresponds to the *profile log-likelihood* of the original data :math:`x`
+    with some constant terms dropped.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+    >>> from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+
+    Generate some random variates and calculate Box-Cox log-likelihood values
+    for them for a range of ``lmbda`` values:
+
+    >>> rng = np.random.default_rng()
+    >>> x = stats.loggamma.rvs(5, loc=10, size=1000, random_state=rng)
+    >>> lmbdas = np.linspace(-2, 10)
+    >>> llf = np.zeros(lmbdas.shape, dtype=float)
+    >>> for ii, lmbda in enumerate(lmbdas):
+    ...     llf[ii] = stats.boxcox_llf(lmbda, x)
+
+    Also find the optimal lmbda value with `boxcox`:
+
+    >>> x_most_normal, lmbda_optimal = stats.boxcox(x)
+
+    Plot the log-likelihood as function of lmbda.  Add the optimal lmbda as a
+    horizontal line to check that that's really the optimum:
+
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111)
+    >>> ax.plot(lmbdas, llf, 'b.-')
+    >>> ax.axhline(stats.boxcox_llf(lmbda_optimal, x), color='r')
+    >>> ax.set_xlabel('lmbda parameter')
+    >>> ax.set_ylabel('Box-Cox log-likelihood')
+
+    Now add some probability plots to show that where the log-likelihood is
+    maximized the data transformed with `boxcox` looks closest to normal:
+
+    >>> locs = [3, 10, 4]  # 'lower left', 'center', 'lower right'
+    >>> for lmbda, loc in zip([-1, lmbda_optimal, 9], locs):
+    ...     xt = stats.boxcox(x, lmbda=lmbda)
+    ...     (osm, osr), (slope, intercept, r_sq) = stats.probplot(xt)
+    ...     ax_inset = inset_axes(ax, width="20%", height="20%", loc=loc)
+    ...     ax_inset.plot(osm, osr, 'c.', osm, slope*osm + intercept, 'k-')
+    ...     ax_inset.set_xticklabels([])
+    ...     ax_inset.set_yticklabels([])
+    ...     ax_inset.set_title(r'$\lambda=%1.2f$' % lmbda)
+
+    >>> plt.show()
+
+    """
+    # _axis_nan_policy decorator does not currently support these for lazy arrays.
+    # We want to run tests with lazy backends, so don't pass the arguments explicitly
+    # unless necessary.
+    kwargs = {}
+    if keepdims is not False:
+        kwargs['keepdims'] = keepdims
+    if nan_policy != 'propagate':
+        kwargs['nan_policy'] = nan_policy
+    return _boxcox_llf(data, lmb=lmb, axis=axis, **kwargs)
+
+
+@_axis_nan_policy_factory(lambda x: x, n_outputs=1, default_axis=0,
+                          result_to_tuple=lambda x, _: (x,))
+def _boxcox_llf(data, axis=0, *, lmb):
+    xp = array_namespace(data)
+    dtype = xp_result_type(lmb, data, force_floating=True, xp=xp)
+    data = xp.asarray(data, dtype=dtype)
+    N = data.shape[axis]
+    if N == 0:
+        return _get_nan(data, xp=xp)
+
+    logdata = xp.log(data)
+
+    # Compute the variance of the transformed data.
+    if lmb == 0:
+        logvar = xp.log(xp.var(logdata, axis=axis))
+    else:
+        # Transform without the constant offset 1/lmb.  The offset does
+        # not affect the variance, and the subtraction of the offset can
+        # lead to loss of precision.
+        # Division by lmb can be factored out to enhance numerical stability.
+        logx = lmb * logdata
+        logvar = _log_var(logx, xp, axis) - 2 * math.log(abs(lmb))
+
+    res = (lmb - 1) * xp.sum(logdata, axis=axis) - N/2 * logvar
+    res = xp.astype(res, data.dtype, copy=False)  # compensate for NumPy <2.0
+    res = res[()] if res.ndim == 0 else res
+    return res
+
+
+def _boxcox_conf_interval(x, lmax, alpha):
+    # Need to find the lambda for which
+    #  f(x,lmbda) >= f(x,lmax) - 0.5*chi^2_alpha;1
+    fac = 0.5 * distributions.chi2.ppf(1 - alpha, 1)
+    target = boxcox_llf(lmax, x) - fac
+
+    def rootfunc(lmbda, data, target):
+        return boxcox_llf(lmbda, data) - target
+
+    # Find positive endpoint of interval in which answer is to be found
+    newlm = lmax + 0.5
+    N = 0
+    while (rootfunc(newlm, x, target) > 0.0) and (N < 500):
+        newlm += 0.1
+        N += 1
+
+    if N == 500:
+        raise RuntimeError("Could not find endpoint.")
+
+    lmplus = optimize.brentq(rootfunc, lmax, newlm, args=(x, target))
+
+    # Now find negative interval in the same way
+    newlm = lmax - 0.5
+    N = 0
+    while (rootfunc(newlm, x, target) > 0.0) and (N < 500):
+        newlm -= 0.1
+        N += 1
+
+    if N == 500:
+        raise RuntimeError("Could not find endpoint.")
+
+    lmminus = optimize.brentq(rootfunc, newlm, lmax, args=(x, target))
+    return lmminus, lmplus
+
+
+@xp_capabilities(np_only=True)
+def boxcox(x, lmbda=None, alpha=None, optimizer=None, *, nan_policy='propagate'):
+    r"""Return a dataset transformed by a Box-Cox power transformation.
+
+    Parameters
+    ----------
+    x : ndarray
+        Input array to be transformed.
+
+        If `lmbda` is not None, this is an alias of
+        `scipy.special.boxcox`.
+        Returns nan if ``x < 0``; returns -inf if ``x == 0 and lmbda < 0``.
+
+        If `lmbda` is None, array must be positive, 1-dimensional, and
+        non-constant.
+
+    lmbda : scalar, optional
+        If `lmbda` is None (default), find the value of `lmbda` that maximizes
+        the log-likelihood function and return it as the second output
+        argument.
+
+        If `lmbda` is not None, do the transformation for that value.
+
+    alpha : float, optional
+        If `lmbda` is None and `alpha` is not None (default), return the
+        ``100 * (1-alpha)%`` confidence  interval for `lmbda` as the third
+        output argument. Must be between 0.0 and 1.0.
+
+        If `lmbda` is not None, `alpha` is ignored.
+    optimizer : callable, optional
+        If `lmbda` is None, `optimizer` is the scalar optimizer used to find
+        the value of `lmbda` that minimizes the negative log-likelihood
+        function. `optimizer` is a callable that accepts one argument:
+
+        fun : callable
+            The objective function, which evaluates the negative
+            log-likelihood function at a provided value of `lmbda`
+
+        and returns an object, such as an instance of
+        `scipy.optimize.OptimizeResult`, which holds the optimal value of
+        `lmbda` in an attribute `x`.
+
+        See the example in `boxcox_normmax` or the documentation of
+        `scipy.optimize.minimize_scalar` for more information.
+
+        If `lmbda` is not None, `optimizer` is ignored.
+    nan_policy : {'propagate', 'omit', 'raise'}
+        Defines how to handle NaNs in `x`.
+
+        - ``propagate``: if a NaN is present, all outputs will contain NaNs.
+        - ``omit``: NaNs will be omitted when calculating the optimal `maxlog` and
+          confidence interval; NaNs in `x` will remain NaNs in the transformed data.
+        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
+
+    Returns
+    -------
+    boxcox : ndarray
+        Box-Cox power transformed array.
+    maxlog : float, optional
+        If the `lmbda` parameter is None, the second returned argument is
+        the `lmbda` that maximizes the log-likelihood function.
+    (min_ci, max_ci) : tuple of float, optional
+        If `lmbda` parameter is None and `alpha` is not None, this returned
+        tuple of floats represents the minimum and maximum confidence limits
+        given `alpha`.
+
+    See Also
+    --------
+    probplot, boxcox_normplot, boxcox_normmax, boxcox_llf
+
+    Notes
+    -----
+    The Box-Cox transform is given by:
+
+    .. math::
+
+        y =
+        \begin{cases}
+          \frac{x^\lambda - 1}{\lambda}, &\text{for } \lambda \neq 0 \\
+          \log(x),                       &\text{for } \lambda = 0
+        \end{cases}
+
+    `boxcox` requires the input data to be positive.  Sometimes a Box-Cox
+    transformation provides a shift parameter to achieve this; `boxcox` does
+    not.  Such a shift parameter is equivalent to adding a positive constant to
+    `x` before calling `boxcox`.
+
+    The confidence limits returned when `alpha` is provided give the interval
+    where:
+
+    .. math::
+
+        l(\hat{\lambda}) - l(\lambda) < \frac{1}{2}\chi^2(1 - \alpha, 1),
+
+    with :math:`l` the log-likelihood function and :math:`\chi^2` the chi-squared
+    function.
+
+    References
+    ----------
+    G.E.P. Box and D.R. Cox, "An Analysis of Transformations", Journal of the
+    Royal Statistical Society B, 26, 211-252 (1964).
+
+    Examples
+    --------
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+
+    We generate some random variates from a non-normal distribution and make a
+    probability plot for it, to show it is non-normal in the tails:
+
+    >>> fig = plt.figure()
+    >>> ax1 = fig.add_subplot(211)
+    >>> x = stats.loggamma.rvs(5, size=500) + 5
+    >>> prob = stats.probplot(x, dist=stats.norm, plot=ax1)
+    >>> ax1.set_xlabel('')
+    >>> ax1.set_title('Probplot against normal distribution')
+
+    We now use `boxcox` to transform the data so it's closest to normal:
+
+    >>> ax2 = fig.add_subplot(212)
+    >>> xt, _ = stats.boxcox(x)
+    >>> prob = stats.probplot(xt, dist=stats.norm, plot=ax2)
+    >>> ax2.set_title('Probplot after Box-Cox transformation')
+
+    >>> plt.show()
+
+    """
+    x = np.asarray(x)
+
+    if lmbda is not None:  # single transformation
+        _contains_nan(x, nan_policy, xp_omit_okay=True)  # handle nan_policy='raise'
+        return special.boxcox(x, lmbda)
+
+    if x.ndim != 1:
+        raise ValueError("Data must be 1-dimensional.")
+
+    if x.size == 0:
+        return x
+
+    if np.all(x == x[0]):
+        raise ValueError("Data must not be constant.")
+
+    if np.any(x <= 0):
+        raise ValueError("Data must be positive.")
+
+    # If lmbda=None, find the lmbda that maximizes the log-likelihood function.
+    lmax = boxcox_normmax(x, method='mle', optimizer=optimizer, nan_policy=nan_policy)
+    y = special.boxcox(x, lmax)
+
+    if alpha is None:
+        return y, lmax
+    else:
+        # Find confidence interval
+        interval = ((lmax, lmax) if np.isnan(lmax) else
+                    _boxcox_conf_interval(x[~np.isnan(x)], lmax, alpha))
+        return y, lmax, interval
+
+
+def _boxcox_inv_lmbda(x, y):
+    # compute lmbda given x and y for Box-Cox transformation
+    num = special.lambertw(-(x ** (-1 / y)) * np.log(x) / y, k=-1)
+    return np.real(-num / np.log(x) - 1 / y)
+
+
+class _BigFloat:
+    def __repr__(self):
+        return "BIG_FLOAT"
+
+
+_BigFloat_singleton = _BigFloat()
+
+
+@xp_capabilities(np_only=True)
+def boxcox_normmax(
+    x, brack=None, method='pearsonr', optimizer=None, *, ymax=_BigFloat_singleton,
+    nan_policy='propagate'
+):
+    """Compute optimal Box-Cox transform parameter for input data.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array. All entries must be positive, finite, real numbers.
+    brack : 2-tuple, optional, default (-2.0, 2.0)
+         The starting interval for a downhill bracket search for the default
+         `optimize.brent` solver. Note that this is in most cases not
+         critical; the final result is allowed to be outside this bracket.
+         If `optimizer` is passed, `brack` must be None.
+    method : str, optional
+        The method to determine the optimal transform parameter (`boxcox`
+        ``lmbda`` parameter). Options are:
+
+        'pearsonr'  (default)
+            Maximizes the Pearson correlation coefficient between
+            ``y = boxcox(x)`` and the expected values for ``y`` if `x` would be
+            normally-distributed.
+
+        'mle'
+            Maximizes the log-likelihood `boxcox_llf`.  This is the method used
+            in `boxcox`.
+
+        'all'
+            Use all optimization methods available, and return all results.
+            Useful to compare different methods.
+    optimizer : callable, optional
+        `optimizer` is a callable that accepts one argument:
+
+        fun : callable
+            The objective function to be minimized. `fun` accepts one argument,
+            the Box-Cox transform parameter `lmbda`, and returns the value of
+            the function (e.g., the negative log-likelihood) at the provided
+            argument. The job of `optimizer` is to find the value of `lmbda`
+            that *minimizes* `fun`.
+
+        and returns an object, such as an instance of
+        `scipy.optimize.OptimizeResult`, which holds the optimal value of
+        `lmbda` in an attribute `x`.
+
+        See the example below or the documentation of
+        `scipy.optimize.minimize_scalar` for more information.
+    ymax : float, optional
+        The unconstrained optimal transform parameter may cause Box-Cox
+        transformed data to have extreme magnitude or even overflow.
+        This parameter constrains MLE optimization such that the magnitude
+        of the transformed `x` does not exceed `ymax`. The default is
+        the maximum value of the input dtype. If set to infinity,
+        `boxcox_normmax` returns the unconstrained optimal lambda.
+        Ignored when ``method='pearsonr'``.
+    nan_policy : {'propagate', 'omit', 'raise'}
+        Defines how to handle input NaNs.
+
+        - ``propagate``: if a NaN is present in the input, the output will be NaN.
+        - ``omit``: NaNs will be omitted when performing the calculation.
+        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
+
+    Returns
+    -------
+    maxlog : float or ndarray
+        The optimal transform parameter found.  An array instead of a scalar
+        for ``method='all'``.
+
+    See Also
+    --------
+    boxcox, boxcox_llf, boxcox_normplot, scipy.optimize.minimize_scalar
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+
+    We can generate some data and determine the optimal ``lmbda`` in various
+    ways:
+
+    >>> rng = np.random.default_rng()
+    >>> x = stats.loggamma.rvs(5, size=30, random_state=rng) + 5
+    >>> y, lmax_mle = stats.boxcox(x)
+    >>> lmax_pearsonr = stats.boxcox_normmax(x)
+
+    >>> lmax_mle
+    2.217563431465757
+    >>> lmax_pearsonr
+    2.238318660200961
+    >>> stats.boxcox_normmax(x, method='all')
+    array([2.23831866, 2.21756343])
+
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111)
+    >>> prob = stats.boxcox_normplot(x, -10, 10, plot=ax)
+    >>> ax.axvline(lmax_mle, color='r')
+    >>> ax.axvline(lmax_pearsonr, color='g', ls='--')
+
+    >>> plt.show()
+
+    Alternatively, we can define our own `optimizer` function. Suppose we
+    are only interested in values of `lmbda` on the interval [6, 7], we
+    want to use `scipy.optimize.minimize_scalar` with ``method='bounded'``,
+    and we want to use tighter tolerances when optimizing the log-likelihood
+    function. To do this, we define a function that accepts positional argument
+    `fun` and uses `scipy.optimize.minimize_scalar` to minimize `fun` subject
+    to the provided bounds and tolerances:
+
+    >>> from scipy import optimize
+    >>> options = {'xatol': 1e-12}  # absolute tolerance on `x`
+    >>> def optimizer(fun):
+    ...     return optimize.minimize_scalar(fun, bounds=(6, 7),
+    ...                                     method="bounded", options=options)
+    >>> stats.boxcox_normmax(x, optimizer=optimizer)
+    6.000000000
+    """
+    x = np.asarray(x)
+
+    contains_nan = _contains_nan(x, nan_policy, xp_omit_okay=True)
+    if contains_nan:
+        if nan_policy == 'propagate':
+            NaN = _get_nan(x, xp=np)[()]
+            return np.asarray([NaN, NaN]) if method == 'all' else NaN
+        x = x[~np.isnan(x)]  # `nan_policy='omit
+
+    if not np.all(np.isfinite(x) & (x >= 0)):
+        message = ("The `x` argument of `boxcox_normmax` must contain "
+                   "only positive, finite, real numbers.")
+        raise ValueError(message)
+
+    end_msg = "exceed specified `ymax`."
+    if ymax is _BigFloat_singleton:
+        dtype = x.dtype if np.issubdtype(x.dtype, np.floating) else np.float64
+        # 10000 is a safety factor because `special.boxcox` overflows prematurely.
+        ymax = np.finfo(dtype).max / 10000
+        end_msg = f"overflow in {dtype}."
+    elif ymax <= 0:
+        raise ValueError("`ymax` must be strictly positive")
+
+    # If optimizer is not given, define default 'brent' optimizer.
+    if optimizer is None:
+
+        # Set default value for `brack`.
+        if brack is None:
+            brack = (-2.0, 2.0)
+
+        def _optimizer(func, args):
+            return optimize.brent(func, args=args, brack=brack)
+
+    # Otherwise check optimizer.
+    else:
+        if not callable(optimizer):
+            raise ValueError("`optimizer` must be a callable")
+
+        if brack is not None:
+            raise ValueError("`brack` must be None if `optimizer` is given")
+
+        # `optimizer` is expected to return a `OptimizeResult` object, we here
+        # get the solution to the optimization problem.
+        def _optimizer(func, args):
+            def func_wrapped(x):
+                return func(x, *args)
+            return getattr(optimizer(func_wrapped), 'x', None)
+
+    def _pearsonr(x):
+        osm_uniform = _calc_uniform_order_statistic_medians(len(x))
+        xvals = distributions.norm.ppf(osm_uniform)
+
+        def _eval_pearsonr(lmbda, xvals, samps):
+            # This function computes the x-axis values of the probability plot
+            # and computes a linear regression (including the correlation) and
+            # returns ``1 - r`` so that a minimization function maximizes the
+            # correlation.
+            y = boxcox(samps, lmbda)
+            yvals = np.sort(y)
+            r, prob = _stats_py.pearsonr(xvals, yvals)
+            return 1 - r
+
+        return _optimizer(_eval_pearsonr, args=(xvals, x))
+
+    def _mle(x):
+        def _eval_mle(lmb, data):
+            # function to minimize
+            return -boxcox_llf(lmb, data)
+
+        return _optimizer(_eval_mle, args=(x,))
+
+    def _all(x):
+        maxlog = np.empty(2, dtype=float)
+        maxlog[0] = _pearsonr(x)
+        maxlog[1] = _mle(x)
+        return maxlog
+
+    methods = {'pearsonr': _pearsonr,
+               'mle': _mle,
+               'all': _all}
+    if method not in methods.keys():
+        raise ValueError(f"Method {method} not recognized.")
+
+    optimfunc = methods[method]
+
+    res = optimfunc(x)
+
+    if res is None:
+        message = ("The `optimizer` argument of `boxcox_normmax` must return "
+                   "an object containing the optimal `lmbda` in attribute `x`.")
+        raise ValueError(message)
+    elif not np.isinf(ymax):  # adjust the final lambda
+        # x > 1, boxcox(x) > 0; x < 1, boxcox(x) < 0
+        xmax, xmin = np.max(x), np.min(x)
+        if xmin >= 1:
+            x_treme = xmax
+        elif xmax <= 1:
+            x_treme = xmin
+        else:  # xmin < 1 < xmax
+            indicator = special.boxcox(xmax, res) > abs(special.boxcox(xmin, res))
+            if isinstance(res, np.ndarray):
+                indicator = indicator[1]  # select corresponds with 'mle'
+            x_treme = xmax if indicator else xmin
+
+        mask = abs(special.boxcox(x_treme, res)) > ymax
+        if np.any(mask):
+            message = (
+                f"The optimal lambda is {res}, but the returned lambda is the "
+                f"constrained optimum to ensure that the maximum or the minimum "
+                f"of the transformed data does not " + end_msg
+            )
+            warnings.warn(message, stacklevel=2)
+
+            # Return the constrained lambda to ensure the transformation
+            # does not cause overflow or exceed specified `ymax`
+            constrained_res = _boxcox_inv_lmbda(x_treme, ymax * np.sign(x_treme - 1))
+
+            if isinstance(res, np.ndarray):
+                res[mask] = constrained_res
+            else:
+                res = constrained_res
+    return res
+
+
+def _normplot(method, x, la, lb, plot=None, N=80):
+    """Compute parameters for a Box-Cox or Yeo-Johnson normality plot,
+    optionally show it.
+
+    See `boxcox_normplot` or `yeojohnson_normplot` for details.
+    """
+
+    if method == 'boxcox':
+        title = 'Box-Cox Normality Plot'
+        transform_func = boxcox
+    else:
+        title = 'Yeo-Johnson Normality Plot'
+        transform_func = yeojohnson
+
+    x = np.asarray(x)
+    if x.size == 0:
+        return x
+
+    if lb <= la:
+        raise ValueError("`lb` has to be larger than `la`.")
+
+    if method == 'boxcox' and np.any(x <= 0):
+        raise ValueError("Data must be positive.")
+
+    lmbdas = np.linspace(la, lb, num=N)
+    ppcc = lmbdas * 0.0
+    for i, val in enumerate(lmbdas):
+        # Determine for each lmbda the square root of correlation coefficient
+        # of transformed x
+        z = transform_func(x, lmbda=val)
+        _, (_, _, r) = probplot(z, dist='norm', fit=True)
+        ppcc[i] = r
+
+    if plot is not None:
+        plot.plot(lmbdas, ppcc, 'x')
+        _add_axis_labels_title(plot, xlabel='$\\lambda$',
+                               ylabel='Prob Plot Corr. Coef.',
+                               title=title)
+
+    return lmbdas, ppcc
+
+
+@xp_capabilities(np_only=True)
+def boxcox_normplot(x, la, lb, plot=None, N=80):
+    """Compute parameters for a Box-Cox normality plot, optionally show it.
+
+    A Box-Cox normality plot shows graphically what the best transformation
+    parameter is to use in `boxcox` to obtain a distribution that is close
+    to normal.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.
+    la, lb : scalar
+        The lower and upper bounds for the ``lmbda`` values to pass to `boxcox`
+        for Box-Cox transformations.  These are also the limits of the
+        horizontal axis of the plot if that is generated.
+    plot : object, optional
+        If given, plots the quantiles and least squares fit.
+        `plot` is an object that has to have methods "plot" and "text".
+        The `matplotlib.pyplot` module or a Matplotlib Axes object can be used,
+        or a custom object with the same methods.
+        Default is None, which means that no plot is created.
+    N : int, optional
+        Number of points on the horizontal axis (equally distributed from
+        `la` to `lb`).
+
+    Returns
+    -------
+    lmbdas : ndarray
+        The ``lmbda`` values for which a Box-Cox transform was done.
+    ppcc : ndarray
+        Probability Plot Correlation Coefficient, as obtained from `probplot`
+        when fitting the Box-Cox transformed input `x` against a normal
+        distribution.
+
+    See Also
+    --------
+    probplot, boxcox, boxcox_normmax, boxcox_llf, ppcc_max
+
+    Notes
+    -----
+    Even if `plot` is given, the figure is not shown or saved by
+    `boxcox_normplot`; ``plt.show()`` or ``plt.savefig('figname.png')``
+    should be used after calling `probplot`.
+
+    Examples
+    --------
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+
+    Generate some non-normally distributed data, and create a Box-Cox plot:
+
+    >>> x = stats.loggamma.rvs(5, size=500) + 5
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111)
+    >>> prob = stats.boxcox_normplot(x, -20, 20, plot=ax)
+
+    Determine and plot the optimal ``lmbda`` to transform ``x`` and plot it in
+    the same plot:
+
+    >>> _, maxlog = stats.boxcox(x)
+    >>> ax.axvline(maxlog, color='r')
+
+    >>> plt.show()
+
+    """
+    return _normplot('boxcox', x, la, lb, plot, N)
+
+
+@xp_capabilities(np_only=True)
+def yeojohnson(x, lmbda=None, *, nan_policy='propagate'):
+    r"""Return a dataset transformed by a Yeo-Johnson power transformation.
+
+    Parameters
+    ----------
+    x : ndarray
+        Input array.  Should be 1-dimensional.
+    lmbda : float, optional
+        If ``lmbda`` is ``None``, find the lambda that maximizes the
+        log-likelihood function and return it as the second output argument.
+        Otherwise the transformation is done for the given value.
+    nan_policy : {'propagate', 'omit', 'raise'}
+        Defines how to handle NaNs in `x`.
+
+        - ``propagate``: if a NaN is present, all outputs will contain NaNs.
+        - ``omit``: NaNs will be omitted when calculating the optimal `maxlog`;
+           NaNs in `x` will remain NaNs in the transformed data.
+        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
+
+    Returns
+    -------
+    yeojohnson: ndarray
+        Yeo-Johnson power transformed array.
+    maxlog : float, optional
+        If the `lmbda` parameter is None, the second returned argument is
+        the lambda that maximizes the log-likelihood function.
+
+    See Also
+    --------
+    probplot, yeojohnson_normplot, yeojohnson_normmax, yeojohnson_llf, boxcox
+
+    Notes
+    -----
+    The Yeo-Johnson transform is given by:
+
+    .. math::
+
+        y =
+        \begin{cases}
+        \frac{(x + 1)^\lambda - 1}{\lambda},
+        &\text{for } x \geq 0, \lambda \neq 0
+        \\
+        \log(x + 1),
+        &\text{for } x \geq 0, \lambda = 0
+        \\
+        -\frac{(-x + 1)^{2 - \lambda} - 1}{2 - \lambda},
+        &\text{for } x < 0, \lambda \neq 2
+        \\
+        -\log(-x + 1),
+        &\text{for } x < 0, \lambda = 2
+        \end{cases}
+
+    Unlike `boxcox`, `yeojohnson` does not require the input data to be
+    positive.
+
+    .. versionadded:: 1.2.0
+
+
+    References
+    ----------
+    I. Yeo and R.A. Johnson, "A New Family of Power Transformations to
+    Improve Normality or Symmetry", Biometrika 87.4 (2000):
+
+
+    Examples
+    --------
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+
+    We generate some random variates from a non-normal distribution and make a
+    probability plot for it, to show it is non-normal in the tails:
+
+    >>> fig = plt.figure()
+    >>> ax1 = fig.add_subplot(211)
+    >>> x = stats.loggamma.rvs(5, size=500) + 5
+    >>> prob = stats.probplot(x, dist=stats.norm, plot=ax1)
+    >>> ax1.set_xlabel('')
+    >>> ax1.set_title('Probplot against normal distribution')
+
+    We now use `yeojohnson` to transform the data so it's closest to normal:
+
+    >>> ax2 = fig.add_subplot(212)
+    >>> xt, lmbda = stats.yeojohnson(x)
+    >>> prob = stats.probplot(xt, dist=stats.norm, plot=ax2)
+    >>> ax2.set_title('Probplot after Yeo-Johnson transformation')
+
+    >>> plt.show()
+
+    """
+    x = np.asarray(x)
+    if x.size == 0:
+        return x
+
+    if np.issubdtype(x.dtype, np.complexfloating):
+        raise ValueError('Yeo-Johnson transformation is not defined for '
+                         'complex numbers.')
+
+    if np.issubdtype(x.dtype, np.integer):
+        x = x.astype(np.float64, copy=False)
+
+    if lmbda is not None:
+        _contains_nan(x, nan_policy, xp_omit_okay=True)  # handle nan_policy='raise'
+        return _yeojohnson_transform(x, lmbda)
+
+    # if lmbda=None, find the lmbda that maximizes the log-likelihood function.
+    lmax = yeojohnson_normmax(x, nan_policy=nan_policy)
+    y = _yeojohnson_transform(x, lmax)
+
+    return y, lmax
+
+
+def _yeojohnson_transform(x, lmbda, xp=None):
+    """Returns `x` transformed by the Yeo-Johnson power transform with given
+    parameter `lmbda`.
+    """
+    xp = array_namespace(x) if xp is None else xp
+    dtype = xp_result_type(x, lmbda, force_floating=True, xp=xp)
+    eps = xp.finfo(dtype).eps
+    out = xp.zeros_like(x, dtype=dtype)
+    pos = x >= 0  # binary mask
+
+    if is_jax(xp):
+        return xp.select(
+            [(abs(lmbda) < eps) & pos, (abs(lmbda - 2) < eps) & ~pos, pos],
+            [xp.log1p(x), -xp.log1p(-x), xp.expm1(lmbda * xp.log1p(x)) / lmbda],
+            -xp.expm1((2 - lmbda) * xp.log1p(-x)) / (2 - lmbda),
+        )
+
+    # when x >= 0
+    if abs(lmbda) < eps:
+        out = xpx.at(out)[pos].set(xp.log1p(x[pos]))
+    else:  # lmbda != 0
+        # more stable version of: ((x + 1) ** lmbda - 1) / lmbda
+        out = xpx.at(out)[pos].set(xp.expm1(lmbda * xp.log1p(x[pos])) / lmbda)
+
+    # when x < 0
+    if abs(lmbda - 2) > eps:
+        out = xpx.at(out)[~pos].set(
+            -xp.expm1((2 - lmbda) * xp.log1p(-x[~pos])) / (2 - lmbda))
+    else:  # lmbda == 2
+        out = xpx.at(out)[~pos].set(-xp.log1p(-x[~pos]))
+
+    return out
+
+
+@xp_capabilities(skip_backends=[("dask.array", "Dask can't broadcast nan shapes")])
+def yeojohnson_llf(lmb, data, *, axis=0, nan_policy='propagate', keepdims=False):
+    r"""The Yeo-Johnson log-likelihood function.
+
+    Parameters
+    ----------
+    lmb : scalar
+        Parameter for Yeo-Johnson transformation. See `yeojohnson` for
+        details.
+    data : array_like
+        Data to calculate Yeo-Johnson log-likelihood for.
+    axis : int, default: 0
+        If an int, the axis of the input along which to compute the statistic.
+        The statistic of each axis-slice (e.g. row) of the input will appear in a
+        corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+    nan_policy : {'propagate', 'omit', 'raise'
+        Defines how to handle input NaNs.
+
+        - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
+          which the  statistic is computed, the corresponding entry of the output
+          will be NaN.
+        - ``omit``: NaNs will be omitted when performing the calculation.
+          If insufficient data remains in the axis slice along which the
+          statistic is computed, the corresponding entry of the output will be
+          NaN.
+        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
+
+    keepdims : bool, default: False
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the input array.
+
+    Returns
+    -------
+    llf : float
+        Yeo-Johnson log-likelihood of `data` given `lmb`.
+
+    See Also
+    --------
+    yeojohnson, probplot, yeojohnson_normplot, yeojohnson_normmax
+
+    Notes
+    -----
+    The Yeo-Johnson log-likelihood function :math:`l` is defined here as
+
+    .. math::
+
+        l = -\frac{N}{2} \log(\hat{\sigma}^2) + (\lambda - 1)
+              \sum_i^N \text{sign}(x_i) \log(|x_i| + 1)
+
+    where :math:`N` is the number of data points :math:`x` = ``data`` and
+    :math:`\hat{\sigma}^2` is the estimated variance of the Yeo-Johnson transformed
+    input data :math:`x`.
+    This corresponds to the *profile log-likelihood* of the original data :math:`x`
+    with some constant terms dropped.
+
+    .. versionadded:: 1.2.0
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+    >>> from mpl_toolkits.axes_grid1.inset_locator import inset_axes
+
+    Generate some random variates and calculate Yeo-Johnson log-likelihood
+    values for them for a range of ``lmbda`` values:
+
+    >>> x = stats.loggamma.rvs(5, loc=10, size=1000)
+    >>> lmbdas = np.linspace(-2, 10)
+    >>> llf = np.zeros(lmbdas.shape, dtype=float)
+    >>> for ii, lmbda in enumerate(lmbdas):
+    ...     llf[ii] = stats.yeojohnson_llf(lmbda, x)
+
+    Also find the optimal lmbda value with `yeojohnson`:
+
+    >>> x_most_normal, lmbda_optimal = stats.yeojohnson(x)
+
+    Plot the log-likelihood as function of lmbda.  Add the optimal lmbda as a
+    horizontal line to check that that's really the optimum:
+
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111)
+    >>> ax.plot(lmbdas, llf, 'b.-')
+    >>> ax.axhline(stats.yeojohnson_llf(lmbda_optimal, x), color='r')
+    >>> ax.set_xlabel('lmbda parameter')
+    >>> ax.set_ylabel('Yeo-Johnson log-likelihood')
+
+    Now add some probability plots to show that where the log-likelihood is
+    maximized the data transformed with `yeojohnson` looks closest to normal:
+
+    >>> locs = [3, 10, 4]  # 'lower left', 'center', 'lower right'
+    >>> for lmbda, loc in zip([-1, lmbda_optimal, 9], locs):
+    ...     xt = stats.yeojohnson(x, lmbda=lmbda)
+    ...     (osm, osr), (slope, intercept, r_sq) = stats.probplot(xt)
+    ...     ax_inset = inset_axes(ax, width="20%", height="20%", loc=loc)
+    ...     ax_inset.plot(osm, osr, 'c.', osm, slope*osm + intercept, 'k-')
+    ...     ax_inset.set_xticklabels([])
+    ...     ax_inset.set_yticklabels([])
+    ...     ax_inset.set_title(r'$\lambda=%1.2f$' % lmbda)
+
+    >>> plt.show()
+
+    """
+    # _axis_nan_policy decorator does not currently support these for lazy arrays.
+    # We want to run tests with lazy backends, so don't pass the arguments explicitly
+    # unless necessary.
+    kwargs = {}
+    if keepdims is not False:
+        kwargs['keepdims'] = keepdims
+    if nan_policy != 'propagate':
+        kwargs['nan_policy'] = nan_policy
+    res = _yeojohnson_llf(data, lmb=lmb, axis=axis, **kwargs)
+    return res[()] if res.ndim == 0 else res
+
+
+@_axis_nan_policy_factory(lambda x: x, n_outputs=1, default_axis=0,
+                          result_to_tuple=lambda x, _: (x,))
+def _yeojohnson_llf(data, *, lmb, axis=0):
+    xp = array_namespace(data)
+    dtype = xp_result_type(lmb, data, force_floating=True, xp=xp)
+    data = xp.asarray(data, dtype=dtype)
+
+    n = data.shape[axis]
+    if n == 0:
+        return _get_nan(data, xp=xp)
+    eps = xp.finfo(dtype).eps
+    # Special case all-positive/negative data to avoid overflow and precision loss
+    pos = data >= 0  # binary mask
+
+    # There exists numerical instability when abs(lmb) or abs(lmb - 2) is very small
+    if not is_lazy_array(pos) and xp.all(pos):
+        if abs(lmb) < eps:
+            logvar = xp.log(xp.var(xp.log1p(data), axis=axis))
+        else:
+            logvar = _log_var(lmb * xp.log1p(data), xp, axis) - 2 * math.log(abs(lmb))
+
+    elif not is_lazy_array(pos) and xp.all(~pos):
+        if abs(lmb - 2) < eps:
+            logvar = xp.log(xp.var(xp.log1p(-data), axis=axis))
+        else:
+            logvar = _log_var((2 - lmb) * xp.log1p(-data), xp, axis) - 2 * math.log(
+                abs(2 - lmb)
+            )
+
+    # overflow/precision loss not reported for mixed data; calculate `logvar` directly
+    else:  # mixed positive and negative data
+        y = _yeojohnson_transform(data, lmb, xp=xp)
+        sigma = xp.var(y, axis=axis)
+
+        # Suppress RuntimeWarning raised by np.log when the variance is too low
+        finite_variance = sigma >= xp.finfo(sigma.dtype).smallest_normal
+        logvar = xpx.apply_where(finite_variance, (sigma,), xp.log, fill_value=-xp.inf)
+
+    loglike = (-n / 2 * logvar
+               + (lmb - 1) * xp.sum(xp.sign(data) * xp.log1p(xp.abs(data)), axis=axis))
+
+    return loglike
+
+
+@xp_capabilities(np_only=True)
+def yeojohnson_normmax(x, brack=None, *, nan_policy='propagate'):
+    """Compute optimal Yeo-Johnson transform parameter.
+
+    Compute optimal Yeo-Johnson transform parameter for input data, using
+    maximum likelihood estimation.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.
+    brack : 2-tuple, optional
+        The starting interval for a downhill bracket search with
+        `optimize.brent`. Note that this is in most cases not critical; the
+        final result is allowed to be outside this bracket. If None,
+        `optimize.fminbound` is used with bounds that avoid overflow.
+    nan_policy : {'propagate', 'omit', 'raise'}
+        Defines how to handle input NaNs.
+
+        - ``propagate``: if a NaN is present in the input, the output will be NaN.
+        - ``omit``: NaNs will be omitted when performing the calculation.
+        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
+
+    Returns
+    -------
+    maxlog : float
+        The optimal transform parameter found.
+
+    See Also
+    --------
+    yeojohnson, yeojohnson_llf, yeojohnson_normplot
+
+    Notes
+    -----
+    .. versionadded:: 1.2.0
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+
+    Generate some data and determine optimal ``lmbda``
+
+    >>> rng = np.random.default_rng()
+    >>> x = stats.loggamma.rvs(5, size=30, random_state=rng) + 5
+    >>> lmax = stats.yeojohnson_normmax(x)
+
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111)
+    >>> prob = stats.yeojohnson_normplot(x, -10, 10, plot=ax)
+    >>> ax.axvline(lmax, color='r')
+
+    >>> plt.show()
+
+    """
+    def _neg_llf(lmbda, data):
+        llf = np.asarray(yeojohnson_llf(lmbda, data))
+        # reject likelihoods that are inf which are likely due to small
+        # variance in the transformed space
+        llf[np.isinf(llf)] = -np.inf
+        return -llf
+
+    with np.errstate(invalid='ignore'):
+        x = np.asarray(x)
+
+        contains_nan = _contains_nan(x, nan_policy, xp_omit_okay=True)
+        if contains_nan:
+            if nan_policy == 'propagate':
+                return _get_nan(x, xp=np)[()]
+            x = x[~np.isnan(x)]  # `nan_policy='omit
+
+        if not np.all(np.isfinite(x)):
+            raise ValueError('Yeo-Johnson input must be finite.')
+        if np.all(x == 0):
+            return 1.0
+        if brack is not None:
+            return optimize.brent(_neg_llf, brack=brack, args=(x,))
+
+        dtype = x.dtype if np.issubdtype(x.dtype, np.floating) else np.float64
+        # Allow values up to 20 times the maximum observed value to be safely
+        # transformed without over- or underflow.
+        log1p_max_x = np.log1p(20 * np.max(np.abs(x)))
+        # Use half of floating point's exponent range to allow safe computation
+        # of the variance of the transformed data.
+        log_eps = np.log(np.finfo(dtype).eps)
+        log_tiny_float = (np.log(np.finfo(dtype).tiny) - log_eps) / 2
+        log_max_float = (np.log(np.finfo(dtype).max) + log_eps) / 2
+        # Compute the bounds by approximating the inverse of the Yeo-Johnson
+        # transform on the smallest and largest floating point exponents, given
+        # the largest data we expect to observe. See [1] for further details.
+        # [1] https://github.com/scipy/scipy/pull/18852#issuecomment-1630286174
+        lb = log_tiny_float / log1p_max_x
+        ub = log_max_float / log1p_max_x
+        # Convert the bounds if all or some of the data is negative.
+        if np.all(x < 0):
+            lb, ub = 2 - ub, 2 - lb
+        elif np.any(x < 0):
+            lb, ub = max(2 - ub, lb), min(2 - lb, ub)
+        # Match `optimize.brent`'s tolerance.
+        tol_brent = 1.48e-08
+        return optimize.fminbound(_neg_llf, lb, ub, args=(x,), xtol=tol_brent)
+
+
+@xp_capabilities(np_only=True)
+def yeojohnson_normplot(x, la, lb, plot=None, N=80):
+    """Compute parameters for a Yeo-Johnson normality plot, optionally show it.
+
+    A Yeo-Johnson normality plot shows graphically what the best
+    transformation parameter is to use in `yeojohnson` to obtain a
+    distribution that is close to normal.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.
+    la, lb : scalar
+        The lower and upper bounds for the ``lmbda`` values to pass to
+        `yeojohnson` for Yeo-Johnson transformations. These are also the
+        limits of the horizontal axis of the plot if that is generated.
+    plot : object, optional
+        If given, plots the quantiles and least squares fit.
+        `plot` is an object that has to have methods "plot" and "text".
+        The `matplotlib.pyplot` module or a Matplotlib Axes object can be used,
+        or a custom object with the same methods.
+        Default is None, which means that no plot is created.
+    N : int, optional
+        Number of points on the horizontal axis (equally distributed from
+        `la` to `lb`).
+
+    Returns
+    -------
+    lmbdas : ndarray
+        The ``lmbda`` values for which a Yeo-Johnson transform was done.
+    ppcc : ndarray
+        Probability Plot Correlation Coefficient, as obtained from `probplot`
+        when fitting the Box-Cox transformed input `x` against a normal
+        distribution.
+
+    See Also
+    --------
+    probplot, yeojohnson, yeojohnson_normmax, yeojohnson_llf, ppcc_max
+
+    Notes
+    -----
+    Even if `plot` is given, the figure is not shown or saved by
+    `boxcox_normplot`; ``plt.show()`` or ``plt.savefig('figname.png')``
+    should be used after calling `probplot`.
+
+    .. versionadded:: 1.2.0
+
+    Examples
+    --------
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+
+    Generate some non-normally distributed data, and create a Yeo-Johnson plot:
+
+    >>> x = stats.loggamma.rvs(5, size=500) + 5
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111)
+    >>> prob = stats.yeojohnson_normplot(x, -20, 20, plot=ax)
+
+    Determine and plot the optimal ``lmbda`` to transform ``x`` and plot it in
+    the same plot:
+
+    >>> _, maxlog = stats.yeojohnson(x)
+    >>> ax.axvline(maxlog, color='r')
+
+    >>> plt.show()
+
+    """
+    return _normplot('yeojohnson', x, la, lb, plot, N)
+
+
+ShapiroResult = namedtuple('ShapiroResult', ('statistic', 'pvalue'))
+
+
+@xp_capabilities()
+@_axis_nan_policy_factory(ShapiroResult, n_samples=1, too_small=2, default_axis=None)
+def shapiro(x, *, axis=None):
+    r"""Perform the Shapiro-Wilk test for normality.
+
+    The Shapiro-Wilk test tests the null hypothesis that the
+    data was drawn from a normal distribution.
+
+    Parameters
+    ----------
+    x : array_like
+        Array of sample data. Must contain at least three observations.
+    axis : int or tuple of ints, default: 0
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    statistic : float
+        The test statistic.
+    p-value : float
+        The p-value for the hypothesis test.
+
+    See Also
+    --------
+    anderson : The Anderson-Darling test for normality
+    kstest : The Kolmogorov-Smirnov test for goodness of fit.
+    :ref:`hypothesis_shapiro` : Extended example
+
+    Notes
+    -----
+    The algorithm used is described in [4]_, but censoring parameters as
+    described are not implemented. For N > 5000 the W test statistic is
+    accurate, but the p-value may not be.
+
+    References
+    ----------
+    .. [1] https://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm
+           :doi:`10.18434/M32189`
+    .. [2] Shapiro, S. S. & Wilk, M.B, "An analysis of variance test for
+           normality (complete samples)", Biometrika, 1965, Vol. 52,
+           pp. 591-611, :doi:`10.2307/2333709`
+    .. [3] Razali, N. M. & Wah, Y. B., "Power comparisons of Shapiro-Wilk,
+           Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests", Journal
+           of Statistical Modeling and Analytics, 2011, Vol. 2, pp. 21-33.
+    .. [4] Royston, P. "A toolkit for testing for non-normality in complete and
+           censored samples." Journal of the Royal Statistical Society: Series D
+           (The Statistician) 42.1 (1993): 37-43.
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+    >>> x = stats.norm.rvs(loc=5, scale=3, size=100, random_state=rng)
+    >>> shapiro_test = stats.shapiro(x)
+    >>> shapiro_test
+    ShapiroResult(statistic=0.9813305735588074, pvalue=0.16855233907699585)
+    >>> shapiro_test.statistic
+    0.9813305735588074
+    >>> shapiro_test.pvalue
+    0.16855233907699585
+
+    For a more detailed example, see :ref:`hypothesis_shapiro`.
+    """
+    # `x` is an array and axis=-1 due to _axis_nan_policy decorator
+    xp = array_namespace(x)
+
+    N = x.shape[-1]
+    if N < 3:
+        raise ValueError("Data must be at least length 3.")
+
+    y = xp.sort(x, axis=-1)
+    y -= x[..., N//2:N//2+1]  # subtract the median (or a nearby value); see gh-15777
+
+    y = xp_promote(y, force_floating=True, xp=xp)
+    w, pw = _swilk(y, xp=xp)
+    if N > 5000:
+        warnings.warn("scipy.stats.shapiro: For N > 5000, computed p-value "
+                      f"may not be accurate. Current N is {N}.",
+                      stacklevel=2)
+
+    # `w` and `pw` are always Python floats, which are double precision.
+    # We want to ensure that they are NumPy floats, so until dtypes are
+    # respected, we can explicitly convert each to float64 (faster than
+    # `np.array([w, pw])`).
+    return ShapiroResult(w[()], pw[()])
+
+
+def _swilk_w(y, a, *, xp):
+    # calculate Shapiro-Wilk statistic given sorted sample and weights
+    # Follows [4] Section 2.1
+    num = xp.vecdot(a, y, axis=-1) ** 2
+    y_ = _demean(y, mean=xp.mean(y, axis=-1, keepdims=True), axis=-1, xp=xp)
+    den = xp.vecdot(y_, y_, axis=-1)
+    return num / den
+
+
+def _swilk(y, *, xp):
+    # calculate Shapiro-Wilk statistic and p-value from sorted sample
+    n = y.shape[-1]
+
+    if n == 3:
+        # [2] Table 5 gives the first four digits
+        c = math.sqrt(2) / 2
+        a = xp.asarray([-c, 0, c])
+        # [2] Corollary 4; discussed in https://github.com/scipy/scipy/issues/18322
+        W = xp.clip(_swilk_w(y, a, xp=xp), 0.75, 1.)
+        pvalue = xp.clip(1. - 6/np.pi * xp.acos(xp.sqrt(W)), 0., 1.)
+        return W, pvalue
+
+    # Follows [4] section 2.2
+    # could calculate half the coefficients and get the rest by antisymmetry
+    i = xp.arange(1, n + 1, dtype=y.dtype)
+    m = special.ndtri((i - 3 / 8) / (n + 1 / 4))
+    u = n**(-0.5)
+    mTm = xp.vecdot(m, m)
+    c = mTm**(-0.5) * m
+    mnm1, mn = m[..., -2], m[..., -1]
+    cnm1, cn = c[..., -2], c[..., -1]
+    an = (cn + 0.221157*u - 0.147981*u**2
+          - 2.071190*u**3 + 4.434685*u**4 - 2.706056*u**5)
+    anm1 = (cnm1 + 0.042981*u - 0.293762*u**2
+            - 1.752461*u**3 + 5.682633*u**4 - 3.582633*u**5)
+    phi = ((mTm - 2*mn**2) / (1 - 2*an**2) if n <= 5
+           else (mTm - 2*mn**2 - 2*mnm1**2) / (1 - 2*an**2 - 2*anm1**2))
+    a = phi**(-0.5) * m
+    if n > 5:
+        a = xpx.at(a)[..., -2].set(anm1)
+        a = xpx.at(a)[..., 1].set(-anm1)
+    a = xpx.at(a)[..., -1].set(an)
+    a = xpx.at(a)[..., -0].set(-an)
+    W = _swilk_w(y, a, xp=xp)
+
+    # Follows [4] Table 1
+    if n <= 11:
+        u = n
+        gamma = -2.273 + 0.459*u
+        mu = 0.5440 - 0.39978*u + 0.025054*u**2 - 0.0006714*u**3
+        sigma = math.exp(1.3822 - 0.77857*u + 0.062767*u**2 - 0.0020322*u**3)
+        gW = -xp.log(gamma - xp.log(1 - W))
+    else:
+        u = math.log(n)
+        mu = -1.5861 - 0.31082*u - 0.083751*u**2 + 0.0038915*u**3
+        sigma = math.exp(-0.4803 - 0.082676*u + 0.0030302*u**2)
+        gW = xp.log(1 - W)
+
+    z = (gW - mu) / sigma
+    pvalue = special.ndtr(-z)
+    return W, pvalue
+
+
+# Values from [8]
+_Avals_norm = array([0.561, 0.631, 0.752, 0.873, 1.035])
+_Avals_expon = array([0.916, 1.062, 1.321, 1.591, 1.959])
+# From Stephens, M A, "Goodness of Fit for the Extreme Value Distribution",
+#             Biometrika, Vol. 64, Issue 3, Dec. 1977, pp 583-588.
+_Avals_gumbel = array([0.474, 0.637, 0.757, 0.877, 1.038])
+# From Stephens, M A, "Tests of Fit for the Logistic Distribution Based
+#             on the Empirical Distribution Function.", Biometrika,
+#             Vol. 66, Issue 3, Dec. 1979, pp 591-595.
+_Avals_logistic = array([0.426, 0.563, 0.660, 0.769, 0.906, 1.010])
+# From Richard A. Lockhart and Michael A. Stephens "Estimation and Tests of
+#             Fit for the Three-Parameter Weibull Distribution"
+#             Journal of the Royal Statistical Society.Series B(Methodological)
+#             Vol. 56, No. 3 (1994), pp. 491-500, table 1. Keys are c*100
+_Avals_weibull = [[0.292, 0.395, 0.467, 0.522, 0.617, 0.711, 0.836, 0.931],
+                  [0.295, 0.399, 0.471, 0.527, 0.623, 0.719, 0.845, 0.941],
+                  [0.298, 0.403, 0.476, 0.534, 0.631, 0.728, 0.856, 0.954],
+                  [0.301, 0.408, 0.483, 0.541, 0.640, 0.738, 0.869, 0.969],
+                  [0.305, 0.414, 0.490, 0.549, 0.650, 0.751, 0.885, 0.986],
+                  [0.309, 0.421, 0.498, 0.559, 0.662, 0.765, 0.902, 1.007],
+                  [0.314, 0.429, 0.508, 0.570, 0.676, 0.782, 0.923, 1.030],
+                  [0.320, 0.438, 0.519, 0.583, 0.692, 0.802, 0.947, 1.057],
+                  [0.327, 0.448, 0.532, 0.598, 0.711, 0.824, 0.974, 1.089],
+                  [0.334, 0.469, 0.547, 0.615, 0.732, 0.850, 1.006, 1.125],
+                  [0.342, 0.472, 0.563, 0.636, 0.757, 0.879, 1.043, 1.167]]
+_Avals_weibull = np.array(_Avals_weibull)
+_cvals_weibull = np.linspace(0, 0.5, 11)
+_get_As_weibull = interpolate.interp1d(_cvals_weibull, _Avals_weibull.T,
+                                       kind='linear', bounds_error=False,
+                                       fill_value=_Avals_weibull[-1])
+
+
+def _weibull_fit_check(params, x):
+    # Refine the fit returned by `weibull_min.fit` to ensure that the first
+    # order necessary conditions are satisfied. If not, raise an error.
+    # Here, use `m` for the shape parameter to be consistent with [7]
+    # and avoid confusion with `c` as defined in [7].
+    n = len(x)
+    m, u, s = params
+
+    def dnllf_dm(m, u):
+        # Partial w.r.t. shape w/ optimal scale. See [7] Equation 5.
+        xu = x-u
+        return (1/m - (xu**m*np.log(xu)).sum()/(xu**m).sum()
+                + np.log(xu).sum()/n)
+
+    def dnllf_du(m, u):
+        # Partial w.r.t. loc w/ optimal scale. See [7] Equation 6.
+        xu = x-u
+        return (m-1)/m*(xu**-1).sum() - n*(xu**(m-1)).sum()/(xu**m).sum()
+
+    def get_scale(m, u):
+        # Partial w.r.t. scale solved in terms of shape and location.
+        # See [7] Equation 7.
+        return ((x-u)**m/n).sum()**(1/m)
+
+    def dnllf(params):
+        # Partial derivatives of the NLLF w.r.t. parameters, i.e.
+        # first order necessary conditions for MLE fit.
+        return [dnllf_dm(*params), dnllf_du(*params)]
+
+    suggestion = ("Maximum likelihood estimation is known to be challenging "
+                  "for the three-parameter Weibull distribution. Consider "
+                  "performing a custom goodness-of-fit test using "
+                  "`scipy.stats.monte_carlo_test`.")
+
+    if np.allclose(u, np.min(x)) or m < 1:
+        # The critical values provided by [7] don't seem to control the
+        # Type I error rate in this case. Error out.
+        message = ("Maximum likelihood estimation has converged to "
+                   "a solution in which the location is equal to the minimum "
+                   "of the data, the shape parameter is less than 2, or both. "
+                   "The table of critical values in [7] does not "
+                   "include this case. " + suggestion)
+        raise ValueError(message)
+
+    try:
+        # Refine the MLE / verify that first-order necessary conditions are
+        # satisfied. If so, the critical values provided in [7] seem reliable.
+        with np.errstate(over='raise', invalid='raise'):
+            res = optimize.root(dnllf, params[:-1])
+
+        message = ("Solution of MLE first-order conditions failed: "
+                   f"{res.message}. `anderson` cannot continue. " + suggestion)
+        if not res.success:
+            raise ValueError(message)
+
+    except (FloatingPointError, ValueError) as e:
+        message = ("An error occurred while fitting the Weibull distribution "
+                   "to the data, so `anderson` cannot continue. " + suggestion)
+        raise ValueError(message) from e
+
+    m, u = res.x
+    s = get_scale(m, u)
+    return m, u, s
+
+
+AndersonResult = _make_tuple_bunch('AndersonResult',
+                                   ['statistic', 'critical_values',
+                                    'significance_level'], ['fit_result'])
+
+
+_anderson_warning_message = (
+"""As of SciPy 1.17, users must choose a p-value calculation method by providing the
+`method` parameter. `method='interpolate'` interpolates the p-value from pre-calculated
+tables; `method` may also be an instance of `MonteCarloMethod` to approximate the
+p-value via Monte Carlo simulation. When `method` is specified, the result object will
+include a `pvalue` attribute and not attributes `critical_value`, `significance_level`,
+or `fit_result`. Beginning in 1.19.0, these other attributes will no longer be
+available, and a p-value will always be computed according to one of the available
+`method` options.""".replace('\n', ' '))
+
+
+@xp_capabilities(np_only=True)
+def anderson(x, dist='norm', *, method=None):
+    """Anderson-Darling test for data coming from a particular distribution.
+
+    The Anderson-Darling test tests the null hypothesis that a sample is
+    drawn from a population that follows a particular distribution.
+    For the Anderson-Darling test, the critical values depend on
+    which distribution is being tested against.  This function works
+    for normal, exponential, logistic, weibull_min, or Gumbel (Extreme Value
+    Type I) distributions.
+
+    Parameters
+    ----------
+    x : array_like
+        Array of sample data.
+    dist : {'norm', 'expon', 'logistic', 'gumbel', 'gumbel_l', 'gumbel_r', 'extreme1', 'weibull_min'}, optional
+        The type of distribution to test against.  The default is 'norm'.
+        The names 'extreme1', 'gumbel_l' and 'gumbel' are synonyms for the
+        same distribution.
+    method : str or instance of `MonteCarloMethod`
+        Defines the method used to compute the p-value.
+        If `method` is ``"interpolated"``, the p-value is interpolated from
+        pre-calculated tables.
+        If `method` is an instance of `MonteCarloMethod`, the p-value is computed using
+        `scipy.stats.monte_carlo_test` with the provided configuration options and other
+        appropriate settings.
+
+        .. versionadded:: 1.17.0
+            If `method` is not specified, `anderson` will emit a ``FutureWarning``
+            specifying that the user must opt into a p-value calculation method.
+            When `method` is specified, the object returned will include a ``pvalue``
+            attribute, but no ``critical_value``, ``significance_level``, or
+            ``fit_result`` attributes. Beginning in 1.19.0, these other attributes will
+            no longer be available, and a p-value will always be computed according to
+            one of the available `method` options.
+
+    Returns
+    -------
+    result : AndersonResult
+        If `method` is provided, this is an object with the following attributes:
+
+        statistic : float
+            The Anderson-Darling test statistic.
+        pvalue: float
+            The p-value corresponding with the test statistic, calculated according to
+            the specified `method`.
+
+        If `method` is unspecified, this is an object with the following attributes:
+
+        statistic : float
+            The Anderson-Darling test statistic.
+        critical_values : list
+            The critical values for this distribution.
+        significance_level : list
+            The significance levels for the corresponding critical values
+            in percents.  The function returns critical values for a
+            differing set of significance levels depending on the
+            distribution that is being tested against.
+        fit_result : `~scipy.stats._result_classes.FitResult`
+            An object containing the results of fitting the distribution to
+            the data.
+
+        .. deprecated:: 1.17.0
+            The tuple-unpacking behavior of the return object and attributes
+            ``critical_values``, ``significance_level``, and ``fit_result`` are
+            deprecated. Beginning in SciPy 1.19.0, these features will no longer be
+            available, and the object returned will have attributes ``statistic`` and
+            ``pvalue``.
+
+    See Also
+    --------
+    kstest : The Kolmogorov-Smirnov test for goodness-of-fit.
+
+    Notes
+    -----
+    Critical values provided when `method` is unspecified are for the following
+    significance levels:
+
+    normal/exponential
+        15%, 10%, 5%, 2.5%, 1%
+    logistic
+        25%, 10%, 5%, 2.5%, 1%, 0.5%
+    gumbel_l / gumbel_r
+        25%, 10%, 5%, 2.5%, 1%
+    weibull_min
+        50%, 25%, 15%, 10%, 5%, 2.5%, 1%, 0.5%
+
+    If the returned statistic is larger than these critical values then
+    for the corresponding significance level, the null hypothesis that
+    the data come from the chosen distribution can be rejected.
+    The returned statistic is referred to as 'A2' in the references.
+
+    For `weibull_min`, maximum likelihood estimation is known to be
+    challenging. If the test returns successfully, then the first order
+    conditions for a maximum likelihood estimate have been verified and
+    the critical values correspond relatively well to the significance levels,
+    provided that the sample is sufficiently large (>10 observations [7]).
+    However, for some data - especially data with no left tail - `anderson`
+    is likely to result in an error message. In this case, consider
+    performing a custom goodness of fit test using
+    `scipy.stats.monte_carlo_test`.
+
+    References
+    ----------
+    .. [1] https://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm
+    .. [2] Stephens, M. A. (1974). EDF Statistics for Goodness of Fit and
+           Some Comparisons, Journal of the American Statistical Association,
+           Vol. 69, pp. 730-737.
+    .. [3] Stephens, M. A. (1976). Asymptotic Results for Goodness-of-Fit
+           Statistics with Unknown Parameters, Annals of Statistics, Vol. 4,
+           pp. 357-369.
+    .. [4] Stephens, M. A. (1977). Goodness of Fit for the Extreme Value
+           Distribution, Biometrika, Vol. 64, pp. 583-588.
+    .. [5] Stephens, M. A. (1977). Goodness of Fit with Special Reference
+           to Tests for Exponentiality , Technical Report No. 262,
+           Department of Statistics, Stanford University, Stanford, CA.
+    .. [6] Stephens, M. A. (1979). Tests of Fit for the Logistic Distribution
+           Based on the Empirical Distribution Function, Biometrika, Vol. 66,
+           pp. 591-595.
+    .. [7] Richard A. Lockhart and Michael A. Stephens "Estimation and Tests of
+           Fit for the Three-Parameter Weibull Distribution"
+           Journal of the Royal Statistical Society.Series B(Methodological)
+           Vol. 56, No. 3 (1994), pp. 491-500, Table 0.
+    .. [8] D'Agostino, Ralph B. (1986). "Tests for the Normal Distribution".
+           In: Goodness-of-Fit Techniques. Ed. by Ralph B. D'Agostino and
+           Michael A. Stephens. New York: Marcel Dekker, pp. 122-141. ISBN:
+           0-8247-7487-6.
+
+    Examples
+    --------
+    Test the null hypothesis that a random sample was drawn from a normal
+    distribution (with unspecified mean and standard deviation).
+
+    >>> import numpy as np
+    >>> from scipy.stats import anderson
+    >>> rng = np.random.default_rng(9781234521)
+    >>> data = rng.random(size=35)
+    >>> res = anderson(data, dist='norm', method='interpolate')
+    >>> res.statistic
+    np.float64(0.9887620209957291)
+    >>> res.pvalue
+    np.float64(0.012111200538380142)
+
+    The p-value is approximately 0.012,, so the null hypothesis may be rejected
+    at a significance level of 2.5%, but not at a significance level of 1%.
+
+    """ # numpy/numpydoc#87  # noqa: E501
+    dist = dist.lower()
+    if dist in {'extreme1', 'gumbel'}:
+        dist = 'gumbel_l'
+    dists = {'norm', 'expon', 'gumbel_l',
+             'gumbel_r', 'logistic', 'weibull_min'}
+
+    if dist not in dists:
+        raise ValueError(f"Invalid distribution; dist must be in {dists}.")
+    y = sort(x)
+    xbar = np.mean(x, axis=0)
+    N = len(y)
+    if dist == 'norm':
+        s = np.std(x, ddof=1, axis=0)
+        w = (y - xbar) / s
+        fit_params = xbar, s
+        logcdf = distributions.norm.logcdf(w)
+        logsf = distributions.norm.logsf(w)
+        sig = array([15, 10, 5, 2.5, 1])
+        critical = around(_Avals_norm / (1.0 + 0.75/N + 2.25/N/N), 3)
+    elif dist == 'expon':
+        w = y / xbar
+        fit_params = 0, xbar
+        logcdf = distributions.expon.logcdf(w)
+        logsf = distributions.expon.logsf(w)
+        sig = array([15, 10, 5, 2.5, 1])
+        critical = around(_Avals_expon / (1.0 + 0.6/N), 3)
+    elif dist == 'logistic':
+        def rootfunc(ab, xj, N):
+            a, b = ab
+            tmp = (xj - a) / b
+            tmp2 = exp(tmp)
+            val = [np.sum(1.0/(1+tmp2), axis=0) - 0.5*N,
+                   np.sum(tmp*(1.0-tmp2)/(1+tmp2), axis=0) + N]
+            return array(val)
+
+        sol0 = array([xbar, np.std(x, ddof=1, axis=0)])
+        sol = optimize.fsolve(rootfunc, sol0, args=(x, N), xtol=1e-5)
+        w = (y - sol[0]) / sol[1]
+        fit_params = sol
+        logcdf = distributions.logistic.logcdf(w)
+        logsf = distributions.logistic.logsf(w)
+        sig = array([25, 10, 5, 2.5, 1, 0.5])
+        critical = around(_Avals_logistic / (1.0 + 0.25/N), 3)
+    elif dist == 'gumbel_r':
+        xbar, s = distributions.gumbel_r.fit(x)
+        w = (y - xbar) / s
+        fit_params = xbar, s
+        logcdf = distributions.gumbel_r.logcdf(w)
+        logsf = distributions.gumbel_r.logsf(w)
+        sig = array([25, 10, 5, 2.5, 1])
+        critical = around(_Avals_gumbel / (1.0 + 0.2/sqrt(N)), 3)
+    elif dist == 'gumbel_l':
+        xbar, s = distributions.gumbel_l.fit(x)
+        w = (y - xbar) / s
+        fit_params = xbar, s
+        logcdf = distributions.gumbel_l.logcdf(w)
+        logsf = distributions.gumbel_l.logsf(w)
+        sig = array([25, 10, 5, 2.5, 1])
+        critical = around(_Avals_gumbel / (1.0 + 0.2/sqrt(N)), 3)
+    elif dist == 'weibull_min':
+        message = ("Critical values of the test statistic are given for the "
+                   "asymptotic distribution. These may not be accurate for "
+                   "samples with fewer than 10 observations. Consider using "
+                   "`scipy.stats.monte_carlo_test`.")
+        if N < 10:
+            warnings.warn(message, stacklevel=2)
+        # [7] writes our 'c' as 'm', and they write `c = 1/m`. Use their names.
+        m, loc, scale = distributions.weibull_min.fit(y)
+        m, loc, scale = _weibull_fit_check((m, loc, scale), y)
+        fit_params = m, loc, scale
+        logcdf = stats.weibull_min(*fit_params).logcdf(y)
+        logsf = stats.weibull_min(*fit_params).logsf(y)
+        c = 1 / m  # m and c are as used in [7]
+        sig = array([0.5, 0.75, 0.85, 0.9, 0.95, 0.975, 0.99, 0.995])
+        critical = _get_As_weibull(c)
+        # Goodness-of-fit tests should only be used to provide evidence
+        # _against_ the null hypothesis. Be conservative and round up.
+        critical = np.round(critical + 0.0005, decimals=3)
+
+    i = arange(1, N + 1)
+    A2 = -N - np.sum((2*i - 1.0) / N * (logcdf + logsf[::-1]), axis=0)
+
+    # FitResult initializer expects an optimize result, so let's work with it
+    message = '`anderson` successfully fit the distribution to the data.'
+    res = optimize.OptimizeResult(success=True, message=message)
+    res.x = np.array(fit_params)
+    fit_result = FitResult(getattr(distributions, dist), y,
+                           discrete=False, res=res)
+
+    if method is None:
+        warnings.warn(_anderson_warning_message, FutureWarning, stacklevel=2)
+        return AndersonResult(A2, critical, sig, fit_result=fit_result)
+
+    if method == 'interpolate':
+        sig = 1 - sig if dist == 'weibull_min' else sig / 100
+        pvalue = np.interp(A2, critical, sig)
+    elif isinstance(method, stats.MonteCarloMethod):
+        pvalue = _anderson_simulate_pvalue(x, dist, method)
+    else:
+        message = ("`method` must be either 'interpolate' or "
+                   "an instance of `MonteCarloMethod`.")
+        raise ValueError(message)
+    return SignificanceResult(statistic=A2, pvalue=pvalue)
+
+
+def _anderson_simulate_pvalue(x, dist, method):
+    message = ("The `___` attribute of a `MonteCarloMethod` object passed as the "
+               "`method` parameter of `scipy.stats.anderson` is ignored.")
+
+    method = method._asdict()
+    if method.pop('rvs', False):
+        warnings.warn(message.replace('___', 'rvs'), UserWarning, stacklevel=3)
+    if method.pop('batch', False):
+        warnings.warn(message.replace('___', 'batch'), UserWarning, stacklevel=3)
+    method['n_mc_samples'] = method.pop('n_resamples')
+
+    kwargs= {'known_params': {'loc': 0}} if dist == 'expon' else {}
+    dist = getattr(stats, dist)
+    res = stats.goodness_of_fit(dist, x, statistic='ad', **kwargs, **method)
+    return res.pvalue
+
+
+def _anderson_ksamp_continuous(samples, Z, Zstar, k, n, N):
+    """Compute A2akN equation 3 of Scholz & Stephens.
+
+    Parameters
+    ----------
+    samples : sequence of 1-D array_like
+        Array of sample arrays.
+    Z : array_like
+        Sorted array of all observations.
+    Zstar : array_like
+        Sorted array of unique observations. Unused.
+    k : int
+        Number of samples.
+    n : array_like
+        Number of observations in each sample.
+    N : int
+        Total number of observations.
+
+    Returns
+    -------
+    A2KN : float
+        The A2KN statistics of Scholz and Stephens 1987.
+
+    """
+    A2kN = 0.
+
+    j = np.arange(1, N)
+    for i in arange(0, k):
+        s = np.sort(samples[i])
+        Mij = s.searchsorted(Z[:-1], side='right')
+        inner = (N*Mij - j*n[i])**2 / (j * (N - j))
+        A2kN += inner.sum() / n[i]
+    return A2kN / N
+
+
+def _anderson_ksamp_midrank(samples, Z, Zstar, k, n, N):
+    """Compute A2akN equation 7 of Scholz and Stephens.
+
+    Parameters
+    ----------
+    samples : sequence of 1-D array_like
+        Array of sample arrays.
+    Z : array_like
+        Sorted array of all observations.
+    Zstar : array_like
+        Sorted array of unique observations.
+    k : int
+        Number of samples.
+    n : array_like
+        Number of observations in each sample.
+    N : int
+        Total number of observations.
+
+    Returns
+    -------
+    A2aKN : float
+        The A2aKN statistics of Scholz and Stephens 1987.
+
+    """
+    A2akN = 0.
+    Z_ssorted_left = Z.searchsorted(Zstar, 'left')
+    if N == Zstar.size:
+        lj = 1.
+    else:
+        lj = Z.searchsorted(Zstar, 'right') - Z_ssorted_left
+    Bj = Z_ssorted_left + lj / 2.
+    for i in arange(0, k):
+        s = np.sort(samples[i])
+        s_ssorted_right = s.searchsorted(Zstar, side='right')
+        Mij = s_ssorted_right.astype(float)
+        fij = s_ssorted_right - s.searchsorted(Zstar, 'left')
+        Mij -= fij / 2.
+        inner = lj / float(N) * (N*Mij - Bj*n[i])**2 / (Bj*(N - Bj) - N*lj/4.)
+        A2akN += inner.sum() / n[i]
+    A2akN *= (N - 1.) / N
+    return A2akN
+
+
+def _anderson_ksamp_right(samples, Z, Zstar, k, n, N):
+    """Compute A2akN equation 6 of Scholz & Stephens.
+
+    Parameters
+    ----------
+    samples : sequence of 1-D array_like
+        Array of sample arrays.
+    Z : array_like
+        Sorted array of all observations.
+    Zstar : array_like
+        Sorted array of unique observations.
+    k : int
+        Number of samples.
+    n : array_like
+        Number of observations in each sample.
+    N : int
+        Total number of observations.
+
+    Returns
+    -------
+    A2KN : float
+        The A2KN statistics of Scholz and Stephens 1987.
+
+    """
+    A2kN = 0.
+    lj = Z.searchsorted(Zstar[:-1], 'right') - Z.searchsorted(Zstar[:-1],
+                                                              'left')
+    Bj = lj.cumsum()
+    for i in arange(0, k):
+        s = np.sort(samples[i])
+        Mij = s.searchsorted(Zstar[:-1], side='right')
+        inner = lj / float(N) * (N * Mij - Bj * n[i])**2 / (Bj * (N - Bj))
+        A2kN += inner.sum() / n[i]
+    return A2kN
+
+
+Anderson_ksampResult = _make_tuple_bunch(
+    'Anderson_ksampResult',
+    ['statistic', 'critical_values', 'pvalue'], []
+)
+
+
+@xp_capabilities(np_only=True)
+def anderson_ksamp(samples, midrank=_NoValue, *, variant=_NoValue, method=None):
+    """The Anderson-Darling test for k-samples.
+
+    The k-sample Anderson-Darling test is a modification of the
+    one-sample Anderson-Darling test. It tests the null hypothesis
+    that k-samples are drawn from the same population without having
+    to specify the distribution function of that population. The
+    critical values depend on the number of samples.
+
+    Parameters
+    ----------
+    samples : sequence of 1-D array_like
+        Array of sample data in arrays.
+    midrank : bool, optional
+        Variant of Anderson-Darling test which is computed. Default
+        (True) is the midrank test applicable to continuous and
+        discrete populations. If False, the right side empirical
+        distribution is used.
+
+        .. deprecated:: 1.17.0
+            Use parameter `variant` instead.
+    variant : {'midrank', 'right', 'continuous'}
+        Variant of Anderson-Darling test to be computed. ``'midrank'`` is applicable
+        to both continuous and discrete populations. ``'discrete'`` and ``'continuous'``
+        perform alternative versions of the test for discrete  and continuous
+        populations, respectively.
+        When `variant` is specified, the return object will not be unpackable as a
+        tuple, and only attributes ``statistic`` and ``pvalue`` will be present.
+    method : PermutationMethod, optional
+        Defines the method used to compute the p-value. If `method` is an
+        instance of `PermutationMethod`, the p-value is computed using
+        `scipy.stats.permutation_test` with the provided configuration options
+        and other appropriate settings. Otherwise, the p-value is interpolated
+        from tabulated values.
+
+    Returns
+    -------
+    res : Anderson_ksampResult
+        An object containing attributes:
+
+        statistic : float
+            Normalized k-sample Anderson-Darling test statistic.
+        critical_values : array
+            The critical values for significance levels 25%, 10%, 5%, 2.5%, 1%,
+            0.5%, 0.1%.
+
+            .. deprecated:: 1.17.0
+                 Present only when `variant` is unspecified.
+
+        pvalue : float
+            The approximate p-value of the test. If `method` is not
+            provided, the value is floored / capped at 0.1% / 25%.
+
+    Raises
+    ------
+    ValueError
+        If fewer than 2 samples are provided, a sample is empty, or no
+        distinct observations are in the samples.
+
+    See Also
+    --------
+    ks_2samp : 2 sample Kolmogorov-Smirnov test
+    anderson : 1 sample Anderson-Darling test
+
+    Notes
+    -----
+    [1]_ defines three versions of the k-sample Anderson-Darling test:
+    one for continuous distributions and two for discrete
+    distributions, in which ties between samples may occur. The
+    default of this routine is to compute the version based on the
+    midrank empirical distribution function. This test is applicable
+    to continuous and discrete data. If `variant` is set to ``'discrete'``, the
+    right side empirical distribution is used for a test for discrete
+    data; if `variant` is ``'continuous'``, the same test statistic and p-value are
+    computed for data with no ties, but with less computation. According to [1]_,
+    the two discrete test statistics differ only slightly if a few collisions due
+    to round-off errors occur in the test not adjusted for ties between samples.
+
+    The critical values corresponding to the significance levels from 0.01
+    to 0.25 are taken from [1]_. p-values are floored / capped
+    at 0.1% / 25%. Since the range of critical values might be extended in
+    future releases, it is recommended not to test ``p == 0.25``, but rather
+    ``p >= 0.25`` (analogously for the lower bound).
+
+    .. versionadded:: 0.14.0
+
+    References
+    ----------
+    .. [1] Scholz, F. W and Stephens, M. A. (1987), K-Sample
+           Anderson-Darling Tests, Journal of the American Statistical
+           Association, Vol. 82, pp. 918-924.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng(44925884305279435)
+    >>> res = stats.anderson_ksamp([rng.normal(size=50), rng.normal(loc=0.5, size=30)],
+    ...                            variant='midrank')
+    >>> res.statistic, res.pvalue
+    (3.4444310693448936, 0.013106682406720973)
+
+    The null hypothesis that the two random samples come from the same
+    distribution can be rejected at the 5% level because the returned
+    p-value is less than 0.05, but not at the 1% level.
+
+    >>> samples = [rng.normal(size=50), rng.normal(size=30),
+    ...            rng.normal(size=20)]
+    >>> res = stats.anderson_ksamp(samples, variant='continuous')
+    >>> res.statistic, res.pvalue
+    (-0.6309662273193832, 0.25)
+
+    As we might expect, the null hypothesis cannot be rejected here for three samples
+    from an identical distribution. The reported p-value (25%) has been capped at the
+    maximum value for which pre-computed p-values are available.
+
+    In such cases where the p-value is capped or when sample sizes are
+    small, a permutation test may be more accurate.
+
+    >>> method = stats.PermutationMethod(n_resamples=9999, random_state=rng)
+    >>> res = stats.anderson_ksamp(samples, variant='continuous', method=method)
+    >>> res.pvalue
+    0.699
+
+    """
+    k = len(samples)
+    if (k < 2):
+        raise ValueError("anderson_ksamp needs at least two samples")
+
+    samples = list(map(np.asarray, samples))
+    Z = np.sort(np.hstack(samples))
+    N = Z.size
+    Zstar = np.unique(Z)
+    if Zstar.size < 2:
+        raise ValueError("anderson_ksamp needs more than one distinct "
+                         "observation")
+
+    n = np.array([sample.size for sample in samples])
+    if np.any(n == 0):
+        raise ValueError("anderson_ksamp encountered sample without "
+                         "observations")
+
+    if variant == _NoValue or midrank != _NoValue:
+        message = ("Parameter `variant` has been introduced to replace `midrank`; "
+                   "`midrank` will be removed in SciPy 1.19.0. Specify `variant` to "
+                   "silence this warning. Note that the returned object will no longer "
+                   "be unpackable as a tuple, and `critical_values` will be omitted.")
+        warnings.warn(message, category=UserWarning, stacklevel=2)
+
+    return_critical_values = False
+    if variant == _NoValue:
+        return_critical_values = True
+        variant = 'midrank' if midrank else 'right'
+
+    if variant == 'midrank':
+        A2kN_fun = _anderson_ksamp_midrank
+    elif variant == 'right':
+        A2kN_fun = _anderson_ksamp_right
+    elif variant == 'continuous':
+        A2kN_fun = _anderson_ksamp_continuous
+    else:
+        message = "`variant` must be one of 'midrank', 'right', or 'continuous'."
+        raise ValueError(message)
+
+    A2kN = A2kN_fun(samples, Z, Zstar, k, n, N)
+
+    def statistic(*samples):
+        return A2kN_fun(samples, Z, Zstar, k, n, N)
+
+    if method is not None:
+        res = stats.permutation_test(samples, statistic, **method._asdict(),
+                                     alternative='greater')
+
+    H = (1. / n).sum()
+    hs_cs = (1. / arange(N - 1, 1, -1)).cumsum()
+    h = hs_cs[-1] + 1
+    g = (hs_cs / arange(2, N)).sum()
+
+    a = (4*g - 6) * (k - 1) + (10 - 6*g)*H
+    b = (2*g - 4)*k**2 + 8*h*k + (2*g - 14*h - 4)*H - 8*h + 4*g - 6
+    c = (6*h + 2*g - 2)*k**2 + (4*h - 4*g + 6)*k + (2*h - 6)*H + 4*h
+    d = (2*h + 6)*k**2 - 4*h*k
+    sigmasq = (a*N**3 + b*N**2 + c*N + d) / ((N - 1.) * (N - 2.) * (N - 3.))
+    m = k - 1
+    A2 = (A2kN - m) / math.sqrt(sigmasq)
+
+    # The b_i values are the interpolation coefficients from Table 2
+    # of Scholz and Stephens 1987
+    b0 = np.array([0.675, 1.281, 1.645, 1.96, 2.326, 2.573, 3.085])
+    b1 = np.array([-0.245, 0.25, 0.678, 1.149, 1.822, 2.364, 3.615])
+    b2 = np.array([-0.105, -0.305, -0.362, -0.391, -0.396, -0.345, -0.154])
+    critical = b0 + b1 / math.sqrt(m) + b2 / m
+
+    sig = np.array([0.25, 0.1, 0.05, 0.025, 0.01, 0.005, 0.001])
+
+    if A2 < critical.min() and method is None:
+        p = sig.max()
+        msg = (f"p-value capped: true value larger than {p}. Consider "
+               "specifying `method` "
+               "(e.g. `method=stats.PermutationMethod()`.)")
+        warnings.warn(msg, stacklevel=2)
+    elif A2 > critical.max() and method is None:
+        p = sig.min()
+        msg = (f"p-value floored: true value smaller than {p}. Consider "
+               "specifying `method` "
+               "(e.g. `method=stats.PermutationMethod()`.)")
+        warnings.warn(msg, stacklevel=2)
+    elif method is None:
+        # interpolation of probit of significance level
+        pf = np.polyfit(critical, log(sig), 2)
+        p = math.exp(np.polyval(pf, A2))
+    else:
+        p = res.pvalue if method is not None else p
+
+    if return_critical_values:
+        # create result object with alias for backward compatibility
+        res = Anderson_ksampResult(A2, critical, p)
+        res.significance_level = p
+    else:
+        res = SignificanceResult(statistic=A2, pvalue=p)
+
+    return res
+
+
+
+AnsariResult = namedtuple('AnsariResult', ('statistic', 'pvalue'))
+
+
+class _ABW:
+    """Distribution of Ansari-Bradley W-statistic under the null hypothesis."""
+    # TODO: calculate exact distribution considering ties
+    # We could avoid summing over more than half the frequencies,
+    # but initially it doesn't seem worth the extra complexity
+
+    def __init__(self):
+        """Minimal initializer."""
+        self.m = None
+        self.n = None
+        self.astart = None
+        self.total = None
+        self.freqs = None
+
+    def _recalc(self, n, m):
+        """When necessary, recalculate exact distribution."""
+        if n != self.n or m != self.m:
+            self.n, self.m = n, m
+            # distribution is NOT symmetric when m + n is odd
+            # n is len(x), m is len(y), and ratio of scales is defined x/y
+            astart, a1, _ = gscale(n, m)
+            self.astart = astart  # minimum value of statistic
+            # Exact distribution of test statistic under null hypothesis
+            # expressed as frequencies/counts/integers to maintain precision.
+            # Stored as floats to avoid overflow of sums.
+            self.freqs = a1.astype(np.float64)
+            self.total = self.freqs.sum()  # could calculate from m and n
+            # probability mass is self.freqs / self.total;
+
+    def pmf(self, k, n, m):
+        """Probability mass function."""
+        self._recalc(n, m)
+        # The convention here is that PMF at k = 12.5 is the same as at k = 12,
+        # -> use `floor` in case of ties.
+        ind = np.floor(k - self.astart).astype(int)
+        return self.freqs[ind] / self.total
+
+    def cdf(self, k, n, m):
+        """Cumulative distribution function."""
+        self._recalc(n, m)
+        # Null distribution derived without considering ties is
+        # approximate. Round down to avoid Type I error.
+        ind = np.ceil(k - self.astart).astype(int)
+        return self.freqs[:ind+1].sum() / self.total
+
+    def sf(self, k, n, m):
+        """Survival function."""
+        self._recalc(n, m)
+        # Null distribution derived without considering ties is
+        # approximate. Round down to avoid Type I error.
+        ind = np.floor(k - self.astart).astype(int)
+        return self.freqs[ind:].sum() / self.total
+
+
+# Maintain state for faster repeat calls to ansari w/ method='exact'
+# _ABW() is calculated once per thread and stored as an attribute on
+# this thread-local variable inside ansari().
+_abw_state = threading.local()
+
+
+@xp_capabilities(cpu_only=True, skip_backends=[('dask.array', 'no rankdata')])
+@_axis_nan_policy_factory(AnsariResult, n_samples=2)
+def ansari(x, y, alternative='two-sided', *, axis=0, method='auto'):
+    """Perform the Ansari-Bradley test for equal scale parameters.
+
+    The Ansari-Bradley test ([1]_, [2]_) is a non-parametric test
+    for the equality of the scale parameter of the distributions
+    from which two samples were drawn. The null hypothesis states that
+    the ratio of the scale of the distribution underlying `x` to the scale
+    of the distribution underlying `y` is 1.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Arrays of sample data.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        The following options are available:
+
+        * 'two-sided': the ratio of scales is not equal to 1.
+        * 'less': the ratio of scales is less than 1.
+        * 'greater': the ratio of scales is greater than 1.
+
+        .. versionadded:: 1.7.0
+    axis : int or tuple of ints, default: 0
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+    method : {'auto', 'asymptotic', 'exact'} or `PermutationMethod` instance, optional
+        Selects the method used to calculate the *p*-value.
+        Default is 'auto'. The following options are available.
+
+        * ``'asymptotic'``: compares the standardized test statistic
+          against the normal distribution, correcting for ties.
+        * ``'exact'``: computes the exact *p*-value by comparing the observed
+          statistic against the exact distribution of the statistic under the
+          null hypothesis. No correction is made for ties.
+        * ``'auto'``: chooses ``'exact'`` when the size of both
+          samples is less than or equal to 55 and there are no ties;
+          chooses ``'asymptotic'`` otherwise.
+        * `PermutationMethod` instance. In this case, the p-value
+          is computed using `permutation_test` with the provided
+          configuration options and other appropriate settings.
+
+    Returns
+    -------
+    statistic : float
+        The Ansari-Bradley test statistic.
+    pvalue : float
+        The p-value of the hypothesis test.
+
+    See Also
+    --------
+    fligner : A non-parametric test for the equality of k variances
+    mood : A non-parametric test for the equality of two scale parameters
+
+    Notes
+    -----
+    The p-value given is exact when the sample sizes are both less than
+    55 and there are no ties, otherwise a normal approximation for the
+    p-value is used.
+
+    References
+    ----------
+    .. [1] Ansari, A. R. and Bradley, R. A. (1960) Rank-sum tests for
+           dispersions, Annals of Mathematical Statistics, 31, 1174-1189.
+    .. [2] Sprent, Peter and N.C. Smeeton.  Applied nonparametric
+           statistical methods.  3rd ed. Chapman and Hall/CRC. 2001.
+           Section 5.8.2.
+    .. [3] Nathaniel E. Helwig "Nonparametric Dispersion and Equality
+           Tests" at http://users.stat.umn.edu/~helwig/notes/npde-Notes.pdf
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import ansari
+    >>> rng = np.random.default_rng()
+
+    For these examples, we'll create three random data sets.  The first
+    two, with sizes 35 and 25, are drawn from a normal distribution with
+    mean 0 and standard deviation 2.  The third data set has size 25 and
+    is drawn from a normal distribution with standard deviation 1.25.
+
+    >>> x1 = rng.normal(loc=0, scale=2, size=35)
+    >>> x2 = rng.normal(loc=0, scale=2, size=25)
+    >>> x3 = rng.normal(loc=0, scale=1.25, size=25)
+
+    First we apply `ansari` to `x1` and `x2`.  These samples are drawn
+    from the same distribution, so we expect the Ansari-Bradley test
+    should not lead us to conclude that the scales of the distributions
+    are different.
+
+    >>> ansari(x1, x2)
+    AnsariResult(statistic=541.0, pvalue=0.9762532927399098)
+
+    With a p-value close to 1, we cannot conclude that there is a
+    significant difference in the scales (as expected).
+
+    Now apply the test to `x1` and `x3`:
+
+    >>> ansari(x1, x3)
+    AnsariResult(statistic=425.0, pvalue=0.0003087020407974518)
+
+    The probability of observing such an extreme value of the statistic
+    under the null hypothesis of equal scales is only 0.03087%. We take this
+    as evidence against the null hypothesis in favor of the alternative:
+    the scales of the distributions from which the samples were drawn
+    are not equal.
+
+    We can use the `alternative` parameter to perform a one-tailed test.
+    In the above example, the scale of `x1` is greater than `x3` and so
+    the ratio of scales of `x1` and `x3` is greater than 1. This means
+    that the p-value when ``alternative='greater'`` should be near 0 and
+    hence we should be able to reject the null hypothesis:
+
+    >>> ansari(x1, x3, alternative='greater')
+    AnsariResult(statistic=425.0, pvalue=0.0001543510203987259)
+
+    As we can see, the p-value is indeed quite low. Use of
+    ``alternative='less'`` should thus yield a large p-value:
+
+    >>> ansari(x1, x3, alternative='less')
+    AnsariResult(statistic=425.0, pvalue=0.9998643258449039)
+
+    """
+    xp = array_namespace(x, y)
+    dtype = xp_result_type(x, y, force_floating=True, xp=xp)
+
+    if alternative not in {'two-sided', 'greater', 'less'}:
+        raise ValueError("'alternative' must be 'two-sided',"
+                         " 'greater', or 'less'.")
+    methods = {'auto', 'asymptotic', 'exact'}
+    if str(method) not in methods and not isinstance(method, stats.PermutationMethod):
+        message = (f"`method` must be one of {methods} or "
+                   "an instance of `PermutationMethod`.")
+        raise ValueError(message)
+    if is_lazy_array(x) and method == 'auto':
+        message = ("`method` must be 'exact', 'asymptotic', or an instance of "
+                   "`PermutationMethod` when used with lazy arrays.")
+        raise ValueError(message)
+
+    if not hasattr(_abw_state, 'a'):
+        _abw_state.a = _ABW()
+
+    # _axis_nan_policy decorator guarantees that axis=-1
+    n = x.shape[-1]
+    m = y.shape[-1]
+    if m < 1:  # needed by test_axis_nan_policy; not user-facing
+        raise ValueError("Not enough other observations.")
+    if n < 1:
+        raise ValueError("Not enough test observations.")
+
+    N = m + n
+    xy = xp.concat([x, y], axis=-1)  # combine
+    rank, _, t = _stats_py._rankdata(xy, method='average', return_ties=True)
+    symrank = xp.minimum(rank, N - rank + 1)
+    AB = xp.sum(symrank[..., :n], axis=-1)
+    repeats = xp.any(t > 1)  # in theory we could branch for each slice separately
+
+    if method == "auto":
+        method = 'exact' if ((m < 55) and (n < 55) and not repeats) else 'asymptotic'
+
+    if method == 'exact':
+        # np.vectorize converts to NumPy here, and we convert back to the result
+        # type before returning
+        cdf = np.vectorize(_abw_state.a.cdf, otypes=[np.float64])
+        sf = np.vectorize(_abw_state.a.sf, otypes=[np.float64])
+        def get_ansari_pvalue(AB):
+            if alternative == 'two-sided':
+                pval = 2.0 * np.minimum(cdf(AB, n, m), sf(AB, n, m))
+            elif alternative == 'greater':
+                # AB statistic is _smaller_ when ratio of scales is larger,
+                # so this is the opposite of the usual calculation
+                pval = cdf(AB, n, m)
+            else:
+                pval = sf(AB, n, m)
+            return pval
+
+        pval = xpx.lazy_apply(get_ansari_pvalue, AB, shape=AB.shape)
+        pval = xp.clip(xp.asarray(pval, dtype=dtype), max=1.0)
+        AB = AB[()] if AB.ndim == 0 else AB
+        pval = pval[()] if pval.ndim == 0 else pval
+        return AnsariResult(AB, pval)
+
+    elif isinstance(method, stats.PermutationMethod):
+        def statistic(x, y, axis):
+            return ansari(x, y, axis=axis, method="asymptotic").statistic
+        alternative = dict(less='greater', greater='less').get(alternative, 'two-sided')
+        res = stats.permutation_test((x, y), statistic, axis=axis,
+                                     **method._asdict(), alternative=alternative)
+        return AnsariResult(AB, res.pvalue)
+
+    mnAB = (n * (N + 1.0) ** 2 / 4.0 / N) if N % 2 else (n * (N + 2.0) / 4.0)
+
+    if is_lazy_array(repeats) or repeats:   # adjust variance estimates
+        # compute np.sum(tj * rj**2,axis=0)
+        fac = xp.sum(symrank**2, axis=-1)
+        if N % 2:  # N odd
+            varAB = m * n * (16*N*fac - (N+1)**4) / (16.0 * N**2 * (N-1))
+        else:  # N even
+            varAB = m * n * (16*fac - N*(N+2)**2) / (16.0 * N * (N-1))
+    else:
+        # otherwise compute normal approximation
+        if N % 2:  # N odd
+            varAB = n * m * (N + 1.0) * (3 + N ** 2) / (48.0 * N ** 2)
+        else:
+            varAB = m * n * (N + 2) * (N - 2.0) / 48 / (N - 1.0)
+        varAB = xp.asarray(varAB, dtype=dtype)
+
+    # Small values of AB indicate larger dispersion for the x sample.
+    # Large values of AB indicate larger dispersion for the y sample.
+    # This is opposite to the way we define the ratio of scales. see [1]_.
+    z = (mnAB - AB) / xp.sqrt(varAB)
+    pvalue = _get_pvalue(z, _SimpleNormal(), alternative, xp=xp)
+    AB = AB[()] if AB.ndim == 0 else AB
+    pvalue = pvalue[()] if pvalue.ndim == 0 else pvalue
+    return AnsariResult(AB, pvalue)
+
+
+BartlettResult = namedtuple('BartlettResult', ('statistic', 'pvalue'))
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(BartlettResult, n_samples=None)
+def bartlett(*samples, axis=0):
+    r"""Perform Bartlett's test for equal variances.
+
+    Bartlett's test tests the null hypothesis that all input samples
+    are from populations with equal variances.  For samples
+    from significantly non-normal populations, Levene's test
+    `levene` is more robust.
+
+    Parameters
+    ----------
+    *samples : array_like
+        arrays of sample data.  Only 1d arrays are accepted, they may have
+        different lengths.
+
+    Returns
+    -------
+    statistic : float
+        The test statistic.
+    pvalue : float
+        The p-value of the test.
+
+    See Also
+    --------
+    fligner : A non-parametric test for the equality of k variances
+    levene : A robust parametric test for equality of k variances
+    :ref:`hypothesis_bartlett` : Extended example
+
+    Notes
+    -----
+    Conover et al. (1981) examine many of the existing parametric and
+    nonparametric tests by extensive simulations and they conclude that the
+    tests proposed by Fligner and Killeen (1976) and Levene (1960) appear to be
+    superior in terms of robustness of departures from normality and power
+    ([3]_).
+
+    References
+    ----------
+    .. [1]  https://www.itl.nist.gov/div898/handbook/eda/section3/eda357.htm
+    .. [2]  Snedecor, George W. and Cochran, William G. (1989), Statistical
+              Methods, Eighth Edition, Iowa State University Press.
+    .. [3] Park, C. and Lindsay, B. G. (1999). Robust Scale Estimation and
+           Hypothesis Testing based on Quadratic Inference Function. Technical
+           Report #99-03, Center for Likelihood Studies, Pennsylvania State
+           University.
+    .. [4] Bartlett, M. S. (1937). Properties of Sufficiency and Statistical
+           Tests. Proceedings of the Royal Society of London. Series A,
+           Mathematical and Physical Sciences, Vol. 160, No.901, pp. 268-282.
+
+    Examples
+    --------
+
+    Test whether the lists `a`, `b` and `c` come from populations
+    with equal variances.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> a = [8.88, 9.12, 9.04, 8.98, 9.00, 9.08, 9.01, 8.85, 9.06, 8.99]
+    >>> b = [8.88, 8.95, 9.29, 9.44, 9.15, 9.58, 8.36, 9.18, 8.67, 9.05]
+    >>> c = [8.95, 9.12, 8.95, 8.85, 9.03, 8.84, 9.07, 8.98, 8.86, 8.98]
+    >>> stat, p = stats.bartlett(a, b, c)
+    >>> p
+    1.1254782518834628e-05
+
+    The very small p-value suggests that the populations do not have equal
+    variances.
+
+    This is not surprising, given that the sample variance of `b` is much
+    larger than that of `a` and `c`:
+
+    >>> [np.var(x, ddof=1) for x in [a, b, c]]
+    [0.007054444444444413, 0.13073888888888888, 0.008890000000000002]
+
+    For a more detailed example, see :ref:`hypothesis_bartlett`.
+    """
+    xp = array_namespace(*samples)
+
+    k = len(samples)
+    if k < 2:
+        raise ValueError("Must enter at least two input sample vectors.")
+
+    if axis is None:
+        samples = [xp_ravel(sample) for sample in samples]
+    else:
+        samples = _broadcast_arrays(samples, axis=axis, xp=xp)
+        samples = [xp.moveaxis(sample, axis, -1) for sample in samples]
+
+    Ni = [xp.asarray(_count_nonmasked(sample, axis=-1, xp=xp),
+                     dtype=sample.dtype, device=xp_device(sample))
+          for sample in samples]
+    Ni = [xp.broadcast_to(N, samples[0].shape[:-1]) for N in Ni]
+    ssq = [xp.var(sample, correction=1, axis=-1) for sample in samples]
+    Ni = [arr[xp.newaxis, ...] for arr in Ni]
+    ssq = [arr[xp.newaxis, ...] for arr in ssq]
+    Ni = xp.concat(Ni, axis=0)
+    Ni = xpx.at(Ni)[Ni == 0].set(xp.nan)
+    ssq = xp.concat(ssq, axis=0)
+    dtype = Ni.dtype
+    Ntot = xp.sum(Ni, axis=0)
+    spsq = xp.sum((Ni - 1)*ssq, axis=0, dtype=dtype) / (Ntot - k)
+    numer = ((Ntot - k) * xp.log(spsq)
+             - xp.sum((Ni - 1)*xp.log(ssq), axis=0, dtype=dtype))
+    denom = (1 + 1/(3*(k - 1))
+             * ((xp.sum(1/(Ni - 1), axis=0)) - 1/(Ntot - k)))
+    T = numer / denom
+
+    chi2 = _SimpleChi2(xp.asarray(k-1, dtype=dtype, device=xp_device(T)))
+    pvalue = _get_pvalue(T, chi2, alternative='greater', symmetric=False, xp=xp)
+
+    T = xp.clip(T, min=0., max=xp.inf)
+    T = T[()] if T.ndim == 0 else T
+    pvalue = pvalue[()] if pvalue.ndim == 0 else pvalue
+
+    return BartlettResult(T, pvalue)
+
+
+LeveneResult = namedtuple('LeveneResult', ('statistic', 'pvalue'))
+
+
+@xp_capabilities(cpu_only=True, exceptions=['cupy'], marray=True)
+@_axis_nan_policy_factory(LeveneResult, n_samples=None)
+def levene(*samples, center='median', proportiontocut=0.05, axis=0):
+    r"""Perform Levene test for equal variances.
+
+    The Levene test tests the null hypothesis that all input samples
+    are from populations with equal variances.  Levene's test is an
+    alternative to Bartlett's test `bartlett` in the case where
+    there are significant deviations from normality.
+
+    Parameters
+    ----------
+    *samples : array_like
+        The sample data, possibly with different lengths.
+    center : {'mean', 'median', 'trimmed'}, optional
+        Which statistics to use to center data points within each sample.  Default
+        is 'median'.
+    proportiontocut : float, optional
+        When `center` is 'trimmed', this gives the proportion of data points
+        to cut from each end. (See `scipy.stats.trim_mean`.)
+        Default is 0.05.
+    axis : int or tuple of ints, default: 0
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    statistic : float
+        The test statistic.
+    pvalue : float
+        The p-value for the test.
+
+    See Also
+    --------
+    fligner : A non-parametric test for the equality of k variances
+    bartlett : A parametric test for equality of k variances in normal samples
+    :ref:`hypothesis_levene` : Extended example
+
+    Notes
+    -----
+    Three variations of Levene's test are possible.  The possibilities
+    and their recommended usages are:
+
+    * 'median' : Recommended for skewed (non-normal) distributions>
+    * 'mean' : Recommended for symmetric, moderate-tailed distributions.
+    * 'trimmed' : Recommended for heavy-tailed distributions.
+
+    The test version using the mean was proposed in the original article
+    of Levene ([2]_) while the median and trimmed mean have been studied by
+    Brown and Forsythe ([3]_), sometimes also referred to as Brown-Forsythe
+    test.
+
+    References
+    ----------
+    .. [1] https://www.itl.nist.gov/div898/handbook/eda/section3/eda35a.htm
+    .. [2] Levene, H. (1960). In Contributions to Probability and Statistics:
+           Essays in Honor of Harold Hotelling, I. Olkin et al. eds.,
+           Stanford University Press, pp. 278-292.
+    .. [3] Brown, M. B. and Forsythe, A. B. (1974), Journal of the American
+           Statistical Association, 69, 364-367
+
+    Examples
+    --------
+
+    Test whether the lists `a`, `b` and `c` come from populations
+    with equal variances.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> a = [8.88, 9.12, 9.04, 8.98, 9.00, 9.08, 9.01, 8.85, 9.06, 8.99]
+    >>> b = [8.88, 8.95, 9.29, 9.44, 9.15, 9.58, 8.36, 9.18, 8.67, 9.05]
+    >>> c = [8.95, 9.12, 8.95, 8.85, 9.03, 8.84, 9.07, 8.98, 8.86, 8.98]
+    >>> stat, p = stats.levene(a, b, c)
+    >>> p
+    0.002431505967249681
+
+    The small p-value suggests that the populations do not have equal
+    variances.
+
+    This is not surprising, given that the sample variance of `b` is much
+    larger than that of `a` and `c`:
+
+    >>> [np.var(x, ddof=1) for x in [a, b, c]]
+    [0.007054444444444413, 0.13073888888888888, 0.008890000000000002]
+
+    For a more detailed example, see :ref:`hypothesis_levene`.
+    """
+    xp = array_namespace(*samples)
+
+    if center not in ['mean', 'median', 'trimmed']:
+        raise ValueError("center must be 'mean', 'median' or 'trimmed'.")
+
+    k = len(samples)
+    if k < 2:
+        raise ValueError("Must provide at least two samples.")
+
+    if center == 'median':
+
+        def func(x):
+            return (xp.median(x, axis=-1, keepdims=True)
+                    if (is_numpy(xp) or is_dask(xp))
+                    else stats.quantile(x, 0.5, axis=-1, keepdims=True))
+
+    elif center == 'mean':
+
+        def func(x):
+            return xp.mean(x, axis=-1, keepdims=True)
+
+    else:  # center == 'trimmed'
+
+        def func(x):
+            # keepdims=True doesn't currently work for Dask
+            return _stats_py.trim_mean(x, proportiontocut, axis=-1)[..., xp.newaxis]
+
+    Nis = [_count_nonmasked(sample, axis=-1, keepdims=True, xp=xp)
+           for sample in samples]
+    Ycis = [func(sample) for sample in samples]
+    Ntot = sum(Nis)
+
+    # compute Zij's
+    Zijs = [xp.abs(sample - Yc) for sample, Yc in zip(samples, Ycis)]
+
+    # compute Zbari
+    Zbaris = [xp.mean(Zij, axis=-1, keepdims=True) for Zij in Zijs]
+    Zbar = sum(Ni*Zbari for Ni, Zbari in zip(Nis, Zbaris)) / Ntot
+
+    # compute numerator and denominator
+    dfd = (Ntot - k)
+    numer = dfd * sum(Ni * (Zbari - Zbar)**2
+                      for Ni, Zbari in zip(Nis, Zbaris))
+    dfn = (k - 1.0)
+    denom = dfn * sum(xp.sum((Zij - Zbari)**2, axis=-1, keepdims=True)
+                      for Zij, Zbari in zip(Zijs, Zbaris))
+
+    W = numer / denom
+    W = xp.squeeze(W, axis=-1)
+    dfd = xp.squeeze(dfd, axis=-1) if is_marray(xp) else dfd
+    dfn, dfd = xp.asarray(dfn, dtype=W.dtype), xp.asarray(dfd, dtype=W.dtype)
+    pval = _get_pvalue(W, _SimpleF(dfn, dfd), 'greater', xp=xp)
+    W = W[()] if W.ndim == 0 else W
+    pval = pval[()] if pval.ndim == 0 else pval
+    return LeveneResult(W, pval)
+
+
+FlignerResult = namedtuple('FlignerResult', ('statistic', 'pvalue'))
+
+
+@xp_capabilities(skip_backends=[('dask.array', 'no rankdata')],
+                 marray=True)
+@_axis_nan_policy_factory(FlignerResult, n_samples=None)
+def fligner(*samples, center='median', proportiontocut=0.05, axis=0):
+    r"""Perform Fligner-Killeen test for equality of variance.
+
+    Fligner's test tests the null hypothesis that all input samples
+    are from populations with equal variances.  Fligner-Killeen's test is
+    distribution free when populations are identical [2]_.
+
+    Parameters
+    ----------
+    *samples : array_like
+        Arrays of sample data.  Need not be the same length.
+    center : {'mean', 'median', 'trimmed'}, optional
+        Which statistics to use to center data points within each sample. Default
+        is 'median'.
+    proportiontocut : float, optional
+        When `center` is 'trimmed', this gives the proportion of data points
+        to cut from each end. (See `scipy.stats.trim_mean`.)
+        Default is 0.05.
+    axis : int or tuple of ints, default: 0
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    statistic : float
+        The test statistic.
+    pvalue : float
+        The p-value for the hypothesis test.
+
+    See Also
+    --------
+    bartlett : A parametric test for equality of k variances in normal samples
+    levene : A robust parametric test for equality of k variances
+    :ref:`hypothesis_fligner` : Extended example
+
+    Notes
+    -----
+    As with Levene's test there are three variants of Fligner's test that
+    differ by the measure of central tendency used in the test.  See `levene`
+    for more information.
+
+    Conover et al. (1981) examine many of the existing parametric and
+    nonparametric tests by extensive simulations and they conclude that the
+    tests proposed by Fligner and Killeen (1976) and Levene (1960) appear to be
+    superior in terms of robustness of departures from normality and power
+    [3]_.
+
+    References
+    ----------
+    .. [1] Qu, A., Lindsay, B. G., and Li, B. (2000). Improving generalized
+           estimating equations using quadratic inference functions.
+           Biometrika, 87(4), 823-836.
+           :doi:`10.1093/biomet/87.4.823`
+    .. [2] Fligner, M.A. and Killeen, T.J. (1976). Distribution-free two-sample
+           tests for scale. Journal of the American Statistical Association.
+           71(353), 210-213.
+    .. [3] Conover, W. J., Johnson, M. E. and Johnson M. M. (1981). A
+           comparative study of tests for homogeneity of variances, with
+           applications to the outer continental shelf bidding data.
+           Technometrics, 23(4), 351-361.
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from scipy import stats
+
+    Test whether the lists `a`, `b` and `c` come from populations
+    with equal variances.
+
+    >>> a = [8.88, 9.12, 9.04, 8.98, 9.00, 9.08, 9.01, 8.85, 9.06, 8.99]
+    >>> b = [8.88, 8.95, 9.29, 9.44, 9.15, 9.58, 8.36, 9.18, 8.67, 9.05]
+    >>> c = [8.95, 9.12, 8.95, 8.85, 9.03, 8.84, 9.07, 8.98, 8.86, 8.98]
+    >>> stat, p = stats.fligner(a, b, c)
+    >>> p
+    0.00450826080004775
+
+    The small p-value suggests that the populations do not have equal
+    variances.
+
+    This is not surprising, given that the sample variance of `b` is much
+    larger than that of `a` and `c`:
+
+    >>> [np.var(x, ddof=1) for x in [a, b, c]]
+    [0.007054444444444413, 0.13073888888888888, 0.008890000000000002]
+
+    For a more detailed example, see :ref:`hypothesis_fligner`.
+    """
+    xp = array_namespace(*samples)
+
+    if center not in ['mean', 'median', 'trimmed']:
+        raise ValueError("center must be 'mean', 'median' or 'trimmed'.")
+
+    k = len(samples)
+    if k < 2:
+        raise ValueError("Must provide at least two samples.")
+
+    samples = xp_promote(*samples, force_floating=True, xp=xp)
+    dtype = samples[0].dtype
+
+    # Handle empty input
+    for sample in samples:
+        if sample.size == 0:
+            NaN = _get_nan(*samples, xp=xp)
+            return FlignerResult(NaN, NaN)
+
+    if center == 'median':
+
+        def func(x):
+            return (xp.median(x, axis=-1, keepdims=True)
+                    if (is_numpy(xp) or is_dask(xp))
+                    else stats.quantile(x, 0.5, axis=-1, keepdims=True))
+
+    elif center == 'mean':
+
+        def func(x):
+            return xp.mean(x, axis=-1, keepdims=True)
+
+    else:  # center == 'trimmed'
+
+        def func(x):
+            # keepdims=True doesn't currently work for lazy arrays
+            return _stats_py.trim_mean(x, proportiontocut, axis=-1)[..., xp.newaxis]
+
+    lengths = [sample.shape[-1] for sample in samples]
+    ni = [_count_nonmasked(sample, axis=-1, keepdims=True, xp=xp)
+          for sample in samples]
+    N = sum(ni)
+    ni = [xp.squeeze(ni_, axis=-1) for ni_ in ni] if is_marray(xp) else ni
+
+    # Implementation follows [3] pg 355 F-K.
+    Xibar = [func(sample) for sample in samples]
+    Xij_Xibar = [xp.abs(sample - Xibar_) for sample, Xibar_ in zip(samples, Xibar)]
+    Xij_Xibar = xp.concat(Xij_Xibar, axis=-1)
+    ranks = stats.rankdata(Xij_Xibar, method='average', axis=-1)
+    a_Ni = special.ndtri(ranks / (2*(N + 1.0)) + 0.5)
+
+    # [3] Equation 2.1
+    splits = list(itertools.accumulate(lengths, initial=0))
+    Ai = [a_Ni[..., i:j] for i, j in zip(splits[:-1], splits[1:])]
+    Aibar = [xp.mean(Ai_, axis=-1) for Ai_ in Ai]
+    abar = xp.mean(a_Ni, axis=-1)
+    V2 = xp.var(a_Ni, axis=-1, correction=1)
+    statistic = sum(ni_ * (Aibar_ - abar)**2 for ni_, Aibar_ in zip(ni, Aibar)) / V2
+
+    chi2 = _SimpleChi2(xp.asarray(k-1, dtype=dtype))
+    pval = _get_pvalue(statistic, chi2, alternative='greater', symmetric=False, xp=xp)
+    return FlignerResult(statistic, pval)
+
+
+def _mood_statistic_with_ties(x, y, t, m, n, N, xp):
+    # First equation of "Mood's Squared Rank Test", Mielke pg 313
+    E_0_T = m * (N * N - 1) / 12
+
+    # m, n, N, t, and S are defined in the second paragraph of Mielke pg 312
+    # The only difference is that our `t` has zeros interspersed with the relevant
+    # numbers to keep the array rectangular, but these terms add nothing to the sum.
+    S = xp.cumulative_sum(t, include_initial=True, axis=-1)
+    S_i, S_i_m1 = S[..., 1:], S[..., :-1]
+    # Second equation of "Mood's Squared Rank Test", Mielke pg 313
+    varM = (m * n * (N + 1.0) * (N**2 - 4) / 180
+            - m * n / (180 * N * (N - 1))
+            * xp.sum(t * (t ** 2 - 1) * (t ** 2 - 4 + (15 * (N - S_i - S_i_m1) ** 2)),
+                     axis=-1))
+
+    # There is a formula for Phi (`phi` in code) in terms of t, S, and Psi(I) at the
+    # bottom of Mielke pg 312. Psi(I) = [I - (N+1)/2]^2 is defined (with a mistake in
+    # the location of the ^2) at the beginning of "Mood's Squared Rank Test" (pg 313).
+    # To vectorize this calculation, let c = (N + 1) / 2, so Psi(I) = I^2 - 2*c*I + c^2.
+    # We sum each of these three parts of Psi separately using formulas for sums from a
+    # to b (inclusive) of terms I^2, I, and 1 where I takes on successive integers.
+    def sum_I2(a, b=None):
+        return (a * (a + 1) * (2 * a + 1) / 6 if b is None
+                else sum_I2(b) - sum_I2(a) + a**2)
+
+    def sum_I(a, b=None):
+        return (a * (a + 1) / 2 if b is None
+                else sum_I(b) - sum_I(a) + a)
+
+    def sum_1(a, b):
+        return (b - a) + 1
+
+    with np.errstate(invalid='ignore', divide='ignore'):
+        sum_I2 = sum_I2(S_i_m1 + 1, S_i)
+        sum_I = sum_I(S_i_m1 + 1, S_i)
+        sum_1 = sum_1(S_i_m1 + 1, S_i)
+        c = (N + 1) / 2
+        phi = (sum_I2 - 2*c*sum_I + sum_1*c**2) / t
+
+    phi = xpx.at(phi)[t == 0].set(0.)  # where t = 0 we get NaNs; eliminate them
+
+    # Mielke pg 312 defines `a` as the count of elements in sample `x` for each of the
+    # unique values in the combined sample. The tricky thing is getting these to line
+    # up with the locations of nonzero elements in `t`/`phi`.
+    x = xp.sort(x, axis=-1)
+    xy = xp.concat((x, y), axis=-1)
+    i = xp.argsort(xy, stable=True, axis=-1)
+    _, _, a = _stats_py._rankdata(x, method='average', return_ties=True)
+
+    zeros = xp.zeros(a.shape[:-1] + (n,), dtype=a.dtype)
+    a = xp.concat((a, zeros), axis=-1)
+    a = xp.take_along_axis(a, i, axis=-1)
+
+    # Mielke pg 312 defines test statistic `T` as the inner product `a` and `phi`
+    T = xp.vecdot(a, phi, axis=-1)
+
+    return (T - E_0_T) / xp.sqrt(varM)
+
+
+def _mood_statistic_no_ties(r, m, n, N, xp):
+    rx = r[..., :m]
+    M = xp.sum((rx - (N + 1.0) / 2) ** 2, axis=-1)
+    E_0_T = m * (N * N - 1.0) / 12
+    varM = m * n * (N + 1.0) * (N + 2) * (N - 2) / 180
+    return (M - E_0_T) / math.sqrt(varM)
+
+
+def _mood_too_small(samples, kwargs, axis=-1):
+    x, y = samples
+    m = x.shape[axis]
+    n = y.shape[axis]
+    N = m + n
+    return N < 3
+
+
+@xp_capabilities(skip_backends=[('dask.array', 'no rankdata')])
+@_axis_nan_policy_factory(SignificanceResult, n_samples=2, too_small=_mood_too_small)
+def mood(x, y, axis=0, alternative="two-sided"):
+    """Perform Mood's test for equal scale parameters.
+
+    Mood's two-sample test for scale parameters is a non-parametric
+    test for the null hypothesis that two samples are drawn from the
+    same distribution with the same scale parameter.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Arrays of sample data. There must be at least three observations
+        total.
+    axis : int, optional
+        The axis along which the samples are tested.  `x` and `y` can be of
+        different length along `axis`.
+        If `axis` is None, `x` and `y` are flattened and the test is done on
+        all values in the flattened arrays.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        The following options are available:
+
+        * 'two-sided': the scales of the distributions underlying `x` and `y`
+          are different.
+        * 'less': the scale of the distribution underlying `x` is less than
+          the scale of the distribution underlying `y`.
+        * 'greater': the scale of the distribution underlying `x` is greater
+          than the scale of the distribution underlying `y`.
+
+        .. versionadded:: 1.7.0
+
+    Returns
+    -------
+    res : SignificanceResult
+        An object containing attributes:
+
+        statistic : scalar or ndarray
+            The z-score for the hypothesis test.  For 1-D inputs a scalar is
+            returned.
+        pvalue : scalar ndarray
+            The p-value for the hypothesis test.
+
+    See Also
+    --------
+    fligner : A non-parametric test for the equality of k variances
+    ansari : A non-parametric test for the equality of 2 variances
+    bartlett : A parametric test for equality of k variances in normal samples
+    levene : A parametric test for equality of k variances
+
+    Notes
+    -----
+    The data are assumed to be drawn from probability distributions ``f(x)``
+    and ``f(x/s) / s`` respectively, for some probability density function f.
+    The null hypothesis is that ``s == 1``.
+
+    For multi-dimensional arrays, if the inputs are of shapes
+    ``(n0, n1, n2, n3)``  and ``(n0, m1, n2, n3)``, then if ``axis=1``, the
+    resulting z and p values will have shape ``(n0, n2, n3)``.  Note that
+    ``n1`` and ``m1`` don't have to be equal, but the other dimensions do.
+
+    References
+    ----------
+    [1] Mielke, Paul W. "Note on Some Squared Rank Tests with Existing Ties."
+        Technometrics, vol. 9, no. 2, 1967, pp. 312-14. JSTOR,
+        :doi:`10.2307/1266427`. Accessed 18 May 2022.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+    >>> x2 = rng.standard_normal((2, 45, 6, 7))
+    >>> x1 = rng.standard_normal((2, 30, 6, 7))
+    >>> res = stats.mood(x1, x2, axis=1)
+    >>> res.pvalue.shape
+    (2, 6, 7)
+
+    Find the number of points where the difference in scale is not significant:
+
+    >>> (res.pvalue > 0.1).sum()
+    78
+
+    Perform the test with different scales:
+
+    >>> x1 = rng.standard_normal((2, 30))
+    >>> x2 = rng.standard_normal((2, 35)) * 10.0
+    >>> stats.mood(x1, x2, axis=1)
+    SignificanceResult(statistic=array([-5.76174136, -6.12650783]),
+                       pvalue=array([8.32505043e-09, 8.98287869e-10]))
+
+    """
+    xp = array_namespace(x, y)
+    x, y = xp_promote(x, y, force_floating=True, xp=xp)
+
+    # _axis_nan_policy decorator ensures axis=-1
+    xy = xp.concat((x, y), axis=-1)
+
+    m = x.shape[-1]
+    n = y.shape[-1]
+    N = m + n
+
+    if m == 0 or n == 0 or N < 3:  # only needed for test_axis_nan_policy
+        NaN = _get_nan(x, y, xp=xp)
+        return SignificanceResult(NaN, NaN)
+
+    # determine if any of the samples contain ties
+    # `a` represents ties within `x`; `t` represents ties within `xy`
+    r, _, t = _stats_py._rankdata(xy, method='average', return_ties=True)
+
+    if is_lazy_array(t) or xp.any(t > 1):
+        z = _mood_statistic_with_ties(x, y, t, m, n, N, xp=xp)
+    else:
+        z = _mood_statistic_no_ties(r, m, n, N, xp=xp)
+
+    pval = _get_pvalue(z, _SimpleNormal(), alternative, xp=xp)
+
+    z = z[()] if z.ndim == 0 else z
+    pval = pval[()] if pval.ndim == 0 else pval
+    return SignificanceResult(z, pval)
+
+
+WilcoxonResult = _make_tuple_bunch('WilcoxonResult', ['statistic', 'pvalue'])
+
+
+def wilcoxon_result_unpacker(res, _):
+    if hasattr(res, 'zstatistic'):
+        return res.statistic, res.pvalue, res.zstatistic
+    else:
+        return res.statistic, res.pvalue
+
+
+def wilcoxon_result_object(statistic, pvalue, zstatistic=None):
+    res = WilcoxonResult(statistic, pvalue)
+    if zstatistic is not None:
+        res.zstatistic = zstatistic
+    return res
+
+
+def wilcoxon_outputs(kwds):
+    method = kwds.get('method', 'auto')
+    if method == 'asymptotic':
+        return 3
+    return 2
+
+
+@xp_capabilities(skip_backends=[("dask.array", "no rankdata")],
+                 cpu_only=True,  # null distribution is CPU only
+                 marray=True,
+                 extra_note=("Only ``method='asymptotic'``/``zero_method='zsplit'`` is "
+                             "compatible with MArray input. "
+                             "``method='auto'`` is incompatible with JAX arrays."))
+@_rename_parameter("mode", "method")
+@_axis_nan_policy_factory(
+    wilcoxon_result_object, paired=True,
+    n_samples=lambda kwds: 2 if kwds.get('y', None) is not None else 1,
+    result_to_tuple=wilcoxon_result_unpacker, n_outputs=wilcoxon_outputs,
+)
+def wilcoxon(x, y=None, zero_method="wilcox", correction=False,
+             alternative="two-sided", method='auto', *, axis=0):
+    """Calculate the Wilcoxon signed-rank test.
+
+    The Wilcoxon signed-rank test tests the null hypothesis that two
+    related paired samples come from the same distribution. In particular,
+    it tests whether the distribution of the differences ``x - y`` is symmetric
+    about zero. It is a non-parametric version of the paired T-test.
+
+    Parameters
+    ----------
+    x : array_like
+        Either the first set of measurements (in which case ``y`` is the second
+        set of measurements), or the differences between two sets of
+        measurements (in which case ``y`` is not to be specified.)  Must be
+        one-dimensional.
+    y : array_like, optional
+        Either the second set of measurements (if ``x`` is the first set of
+        measurements), or not specified (if ``x`` is the differences between
+        two sets of measurements.)  Must be one-dimensional.
+
+        .. warning::
+            When `y` is provided, `wilcoxon` calculates the test statistic
+            based on the ranks of the absolute values of ``d = x - y``.
+            Roundoff error in the subtraction can result in elements of ``d``
+            being assigned different ranks even when they would be tied with
+            exact arithmetic. Rather than passing `x` and `y` separately,
+            consider computing the difference ``x - y``, rounding as needed to
+            ensure that only truly unique elements are numerically distinct,
+            and passing the result as `x`, leaving `y` at the default (None).
+
+    zero_method : {"wilcox", "pratt", "zsplit"}, optional
+        There are different conventions for handling pairs of observations
+        with equal values ("zero-differences", or "zeros").
+
+        * "wilcox": Discards all zero-differences (default); see [4]_.
+        * "pratt": Includes zero-differences in the ranking process,
+          but drops the ranks of the zeros (more conservative); see [3]_.
+          In this case, the normal approximation is adjusted as in [5]_.
+        * "zsplit": Includes zero-differences in the ranking process and
+          splits the zero rank between positive and negative ones.
+
+    correction : bool, optional
+        If True, apply continuity correction by adjusting the Wilcoxon rank
+        statistic by 0.5 towards the mean value when computing the
+        z-statistic if a normal approximation is used.  Default is False.
+    alternative : {"two-sided", "greater", "less"}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        In the following, let ``d`` represent the difference between the paired
+        samples: ``d = x - y`` if both ``x`` and ``y`` are provided, or
+        ``d = x`` otherwise.
+
+        * 'two-sided': the distribution underlying ``d`` is not symmetric
+          about zero.
+        * 'less': the distribution underlying ``d`` is stochastically less
+          than a distribution symmetric about zero.
+        * 'greater': the distribution underlying ``d`` is stochastically
+          greater than a distribution symmetric about zero.
+
+    method : {"auto", "exact", "asymptotic"} or `PermutationMethod` instance, optional
+        Method to calculate the p-value, see Notes. Default is "auto".
+
+    axis : int or None, default: 0
+        If an int, the axis of the input along which to compute the statistic.
+        The statistic of each axis-slice (e.g. row) of the input will appear
+        in a corresponding element of the output. If ``None``, the input will
+        be raveled before computing the statistic.
+
+    Returns
+    -------
+    result : WilcoxonResult
+        An object with the following attributes.
+
+        statistic : array_like
+            If `alternative` is "two-sided", the sum of the ranks of the
+            differences above or below zero, whichever is smaller.
+            Otherwise the sum of the ranks of the differences above zero.
+        pvalue : array_like
+            The p-value for the test depending on `alternative` and `method`.
+        zstatistic : array_like
+            When ``method = 'asymptotic'``, this is the normalized z-statistic::
+
+                z = (T - mn - d) / se
+
+            where ``T`` is `statistic` as defined above, ``mn`` is the mean of the
+            distribution under the null hypothesis, ``d`` is a continuity
+            correction, and ``se`` is the standard error.
+            When ``method != 'asymptotic'``, this attribute is not available.
+
+    See Also
+    --------
+    kruskal, mannwhitneyu
+
+    Notes
+    -----
+    In the following, let ``d`` represent the difference between the paired
+    samples: ``d = x - y`` if both ``x`` and ``y`` are provided, or ``d = x``
+    otherwise. Assume that all elements of ``d`` are independent and
+    identically distributed observations, and all are distinct and nonzero.
+
+    - When ``len(d)`` is sufficiently large, the null distribution of the
+      normalized test statistic (`zstatistic` above) is approximately normal,
+      and ``method = 'asymptotic'`` can be used to compute the p-value.
+    - When ``len(d)`` is small, the normal approximation may not be accurate,
+      and ``method='exact'`` is preferred (at the cost of additional
+      execution time).
+    - The default, ``method='auto'``, selects between the two:
+      ``method='exact'`` is used when ``len(d) <= 50``, and
+      ``method='asymptotic'`` is used otherwise.
+
+    The presence of "ties" (i.e. not all elements of ``d`` are unique) or
+    "zeros" (i.e. elements of ``d`` are zero) changes the null distribution
+    of the test statistic, and ``method='exact'`` no longer calculates
+    the exact p-value. If ``method='asymptotic'``, the z-statistic is adjusted
+    for more accurate comparison against the standard normal, but still,
+    for finite sample sizes, the standard normal is only an approximation of
+    the true null distribution of the z-statistic. For such situations, the
+    `method` parameter also accepts instances of `PermutationMethod`. In this
+    case, the p-value is computed using `permutation_test` with the provided
+    configuration options and other appropriate settings.
+
+    The presence of ties and zeros affects the resolution of ``method='auto'``
+    accordingly: exhasutive permutations are performed when ``len(d) <= 13``,
+    and the asymptotic method is used otherwise. Note that they asymptotic
+    method may not be very accurate even for ``len(d) > 14``; the threshold
+    was chosen as a compromise between execution time and accuracy under the
+    constraint that the results must be deterministic. Consider providing an
+    instance of `PermutationMethod` method manually, choosing the
+    ``n_resamples`` parameter to balance time constraints and accuracy
+    requirements.
+
+    Please also note that in the edge case that all elements of ``d`` are zero,
+    the p-value relying on the normal approximaton cannot be computed (NaN)
+    if ``zero_method='wilcox'`` or ``zero_method='pratt'``.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test
+    .. [2] Conover, W.J., Practical Nonparametric Statistics, 1971.
+    .. [3] Pratt, J.W., Remarks on Zeros and Ties in the Wilcoxon Signed
+       Rank Procedures, Journal of the American Statistical Association,
+       Vol. 54, 1959, pp. 655-667. :doi:`10.1080/01621459.1959.10501526`
+    .. [4] Wilcoxon, F., Individual Comparisons by Ranking Methods,
+       Biometrics Bulletin, Vol. 1, 1945, pp. 80-83. :doi:`10.2307/3001968`
+    .. [5] Cureton, E.E., The Normal Approximation to the Signed-Rank
+       Sampling Distribution When Zero Differences are Present,
+       Journal of the American Statistical Association, Vol. 62, 1967,
+       pp. 1068-1069. :doi:`10.1080/01621459.1967.10500917`
+
+    Examples
+    --------
+    In [4]_, the differences in height between cross- and self-fertilized
+    corn plants is given as follows:
+
+    >>> d = [6, 8, 14, 16, 23, 24, 28, 29, 41, -48, 49, 56, 60, -67, 75]
+
+    Cross-fertilized plants appear to be higher. To test the null
+    hypothesis that there is no height difference, we can apply the
+    two-sided test:
+
+    >>> from scipy.stats import wilcoxon
+    >>> res = wilcoxon(d)
+    >>> res.statistic, res.pvalue
+    (24.0, 0.041259765625)
+
+    Hence, we would reject the null hypothesis at a confidence level of 5%,
+    concluding that there is a difference in height between the groups.
+    To confirm that the median of the differences can be assumed to be
+    positive, we use:
+
+    >>> res = wilcoxon(d, alternative='greater')
+    >>> res.statistic, res.pvalue
+    (96.0, 0.0206298828125)
+
+    This shows that the null hypothesis that the median is negative can be
+    rejected at a confidence level of 5% in favor of the alternative that
+    the median is greater than zero. The p-values above are exact. Using the
+    normal approximation gives very similar values:
+
+    >>> res = wilcoxon(d, method='asymptotic')
+    >>> res.statistic, res.pvalue
+    (24.0, 0.04088813291185591)
+
+    Note that the statistic changed to 96 in the one-sided case (the sum
+    of ranks of positive differences) whereas it is 24 in the two-sided
+    case (the minimum of sum of ranks above and below zero).
+
+    In the example above, the differences in height between paired plants are
+    provided to `wilcoxon` directly. Alternatively, `wilcoxon` accepts two
+    samples of equal length, calculates the differences between paired
+    elements, then performs the test. Consider the samples ``x`` and ``y``:
+
+    >>> import numpy as np
+    >>> x = np.array([0.5, 0.825, 0.375, 0.5])
+    >>> y = np.array([0.525, 0.775, 0.325, 0.55])
+    >>> res = wilcoxon(x, y, alternative='greater')
+    >>> res
+    WilcoxonResult(statistic=5.0, pvalue=0.5625)
+
+    Note that had we calculated the differences by hand, the test would have
+    produced different results:
+
+    >>> d = [-0.025, 0.05, 0.05, -0.05]
+    >>> ref = wilcoxon(d, alternative='greater')
+    >>> ref
+    WilcoxonResult(statistic=6.0, pvalue=0.5)
+
+    The substantial difference is due to roundoff error in the results of
+    ``x-y``:
+
+    >>> d - (x-y)
+    array([2.08166817e-17, 6.93889390e-17, 1.38777878e-17, 4.16333634e-17])
+
+    Even though we expected all the elements of ``(x-y)[1:]`` to have the same
+    magnitude ``0.05``, they have slightly different magnitudes in practice,
+    and therefore are assigned different ranks in the test. Before performing
+    the test, consider calculating ``d`` and adjusting it as necessary to
+    ensure that theoretically identically values are not numerically distinct.
+    For example:
+
+    >>> d2 = np.around(x - y, decimals=3)
+    >>> wilcoxon(d2, alternative='greater')
+    WilcoxonResult(statistic=6.0, pvalue=0.5)
+
+    """
+    # replace approx by asymptotic to ensure backwards compatability
+    if method == "approx":
+        method = "asymptotic"
+    return _wilcoxon._wilcoxon_nd(x, y, zero_method, correction, alternative,
+                                  method, axis)
+
+
+MedianTestResult = _make_tuple_bunch(
+    'MedianTestResult',
+    ['statistic', 'pvalue', 'median', 'table'], []
+)
+
+
+@xp_capabilities(np_only=True)
+def median_test(*samples, ties='below', correction=True, lambda_=1,
+                nan_policy='propagate'):
+    """Perform a Mood's median test.
+
+    Test that two or more samples come from populations with the same median.
+
+    Let ``n = len(samples)`` be the number of samples.  The "grand median" of
+    all the data is computed, and a contingency table is formed by
+    classifying the values in each sample as being above or below the grand
+    median.  The contingency table, along with `correction` and `lambda_`,
+    are passed to `scipy.stats.chi2_contingency` to compute the test statistic
+    and p-value.
+
+    Parameters
+    ----------
+    *samples : array_like
+        The set of samples.  There must be at least two samples.
+        Each sample must be a one-dimensional sequence containing at least
+        one value.  The samples are not required to have the same length.
+    ties : str, optional
+        Determines how values equal to the grand median are classified in
+        the contingency table.  The string must be one of::
+
+            "below":
+                Values equal to the grand median are counted as "below".
+            "above":
+                Values equal to the grand median are counted as "above".
+            "ignore":
+                Values equal to the grand median are not counted.
+
+        The default is "below".
+    correction : bool, optional
+        If True, *and* there are just two samples, apply Yates' correction
+        for continuity when computing the test statistic associated with
+        the contingency table.  Default is True.
+    lambda_ : float or str, optional
+        By default, the statistic computed in this test is Pearson's
+        chi-squared statistic.  `lambda_` allows a statistic from the
+        Cressie-Read power divergence family to be used instead.  See
+        `power_divergence` for details.
+        Default is 1 (Pearson's chi-squared statistic).
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan. 'propagate' returns nan,
+        'raise' throws an error, 'omit' performs the calculations ignoring nan
+        values. Default is 'propagate'.
+
+    Returns
+    -------
+    res : MedianTestResult
+        An object containing attributes:
+
+        statistic : float
+            The test statistic.  The statistic that is returned is determined
+            by `lambda_`.  The default is Pearson's chi-squared statistic.
+        pvalue : float
+            The p-value of the test.
+        median : float
+            The grand median.
+        table : ndarray
+            The contingency table.  The shape of the table is (2, n), where
+            n is the number of samples.  The first row holds the counts of the
+            values above the grand median, and the second row holds the counts
+            of the values below the grand median.  The table allows further
+            analysis with, for example, `scipy.stats.chi2_contingency`, or with
+            `scipy.stats.fisher_exact` if there are two samples, without having
+            to recompute the table.  If ``nan_policy`` is "propagate" and there
+            are nans in the input, the return value for ``table`` is ``None``.
+
+    See Also
+    --------
+    kruskal : Compute the Kruskal-Wallis H-test for independent samples.
+    mannwhitneyu : Computes the Mann-Whitney rank test on samples x and y.
+
+    Notes
+    -----
+    .. versionadded:: 0.15.0
+
+    References
+    ----------
+    .. [1] Mood, A. M., Introduction to the Theory of Statistics. McGraw-Hill
+        (1950), pp. 394-399.
+    .. [2] Zar, J. H., Biostatistical Analysis, 5th ed. Prentice Hall (2010).
+        See Sections 8.12 and 10.15.
+
+    Examples
+    --------
+    A biologist runs an experiment in which there are three groups of plants.
+    Group 1 has 16 plants, group 2 has 15 plants, and group 3 has 17 plants.
+    Each plant produces a number of seeds.  The seed counts for each group
+    are::
+
+        Group 1: 10 14 14 18 20 22 24 25 31 31 32 39 43 43 48 49
+        Group 2: 28 30 31 33 34 35 36 40 44 55 57 61 91 92 99
+        Group 3:  0  3  9 22 23 25 25 33 34 34 40 45 46 48 62 67 84
+
+    The following code applies Mood's median test to these samples.
+
+    >>> g1 = [10, 14, 14, 18, 20, 22, 24, 25, 31, 31, 32, 39, 43, 43, 48, 49]
+    >>> g2 = [28, 30, 31, 33, 34, 35, 36, 40, 44, 55, 57, 61, 91, 92, 99]
+    >>> g3 = [0, 3, 9, 22, 23, 25, 25, 33, 34, 34, 40, 45, 46, 48, 62, 67, 84]
+    >>> from scipy.stats import median_test
+    >>> res = median_test(g1, g2, g3)
+
+    The median is
+
+    >>> res.median
+    34.0
+
+    and the contingency table is
+
+    >>> res.table
+    array([[ 5, 10,  7],
+           [11,  5, 10]])
+
+    `p` is too large to conclude that the medians are not the same:
+
+    >>> res.pvalue
+    0.12609082774093244
+
+    The "G-test" can be performed by passing ``lambda_="log-likelihood"`` to
+    `median_test`.
+
+    >>> res = median_test(g1, g2, g3, lambda_="log-likelihood")
+    >>> res.pvalue
+    0.12224779737117837
+
+    The median occurs several times in the data, so we'll get a different
+    result if, for example, ``ties="above"`` is used:
+
+    >>> res = median_test(g1, g2, g3, ties="above")
+    >>> res.pvalue
+    0.063873276069553273
+
+    >>> res.table
+    array([[ 5, 11,  9],
+           [11,  4,  8]])
+
+    This example demonstrates that if the data set is not large and there
+    are values equal to the median, the p-value can be sensitive to the
+    choice of `ties`.
+
+    """
+    if len(samples) < 2:
+        raise ValueError('median_test requires two or more samples.')
+
+    ties_options = ['below', 'above', 'ignore']
+    if ties not in ties_options:
+        raise ValueError(f"invalid 'ties' option '{ties}'; 'ties' must be one "
+                         f"of: {str(ties_options)[1:-1]}")
+
+    data = [np.asarray(sample) for sample in samples]
+
+    # Validate the sizes and shapes of the arguments.
+    for k, d in enumerate(data):
+        if d.size == 0:
+            raise ValueError(f"Sample {k + 1} is empty. All samples must "
+                             f"contain at least one value.")
+        if d.ndim != 1:
+            raise ValueError(f"Sample {k + 1} has {d.ndim} dimensions. "
+                             f"All samples must be one-dimensional sequences.")
+
+    cdata = np.concatenate(data)
+    contains_nan = _contains_nan(cdata, nan_policy)
+    if nan_policy == 'propagate' and contains_nan:
+        return MedianTestResult(np.nan, np.nan, np.nan, None)
+
+    if contains_nan:
+        grand_median = np.median(cdata[~np.isnan(cdata)])
+    else:
+        grand_median = np.median(cdata)
+    # When the minimum version of numpy supported by scipy is 1.9.0,
+    # the above if/else statement can be replaced by the single line:
+    #     grand_median = np.nanmedian(cdata)
+
+    # Create the contingency table.
+    table = np.zeros((2, len(data)), dtype=np.int64)
+    for k, sample in enumerate(data):
+        sample = sample[~np.isnan(sample)]
+
+        nabove = count_nonzero(sample > grand_median)
+        nbelow = count_nonzero(sample < grand_median)
+        nequal = sample.size - (nabove + nbelow)
+        table[0, k] += nabove
+        table[1, k] += nbelow
+        if ties == "below":
+            table[1, k] += nequal
+        elif ties == "above":
+            table[0, k] += nequal
+
+    # Check that no row or column of the table is all zero.
+    # Such a table can not be given to chi2_contingency, because it would have
+    # a zero in the table of expected frequencies.
+    rowsums = table.sum(axis=1)
+    if rowsums[0] == 0:
+        raise ValueError(f"All values are below the grand median ({grand_median}).")
+    if rowsums[1] == 0:
+        raise ValueError(f"All values are above the grand median ({grand_median}).")
+    if ties == "ignore":
+        # We already checked that each sample has at least one value, but it
+        # is possible that all those values equal the grand median.  If `ties`
+        # is "ignore", that would result in a column of zeros in `table`.  We
+        # check for that case here.
+        zero_cols = np.nonzero((table == 0).all(axis=0))[0]
+        if len(zero_cols) > 0:
+            raise ValueError(
+                f"All values in sample {zero_cols[0] + 1} are equal to the grand "
+                f"median ({grand_median!r}), so they are ignored, resulting in an "
+                f"empty sample."
+            )
+
+    stat, p, dof, expected = chi2_contingency(table, lambda_=lambda_,
+                                              correction=correction)
+    return MedianTestResult(stat, p, grand_median, table)
+
+
+def _circfuncs_common(samples, period, xp=None):
+    xp = array_namespace(samples) if xp is None else xp
+
+    samples = xp_promote(samples, force_floating=True, xp=xp)
+
+    # Recast samples as radians that range between 0 and 2 pi and calculate
+    # the sine and cosine
+    scaled_samples = samples * ((2.0 * pi) / period)
+    sin_samp = xp.sin(scaled_samples)
+    cos_samp = xp.cos(scaled_samples)
+
+    return samples, sin_samp, cos_samp
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, n_outputs=1, default_axis=None,
+    result_to_tuple=lambda x, _: (x,)
+)
+def circmean(samples, high=2*pi, low=0, axis=None, nan_policy='propagate'):
+    r"""Compute the circular mean of a sample of angle observations.
+
+    Given :math:`n` angle observations :math:`x_1, \cdots, x_n` measured in
+    radians, their *circular mean* is defined by ([1]_, Eq. 2.2.4)
+
+    .. math::
+
+       \mathrm{Arg} \left( \frac{1}{n} \sum_{k=1}^n e^{i x_k} \right)
+
+    where :math:`i` is the imaginary unit and :math:`\mathop{\mathrm{Arg}} z`
+    gives the principal value of the argument of complex number :math:`z`,
+    restricted to the range :math:`[0,2\pi]` by default.  :math:`z` in the
+    above expression is known as the `mean resultant vector`.
+
+    Parameters
+    ----------
+    samples : array_like
+        Input array of angle observations.  The value of a full angle is
+        equal to ``(high - low)``.
+    high : float, optional
+        Upper boundary of the principal value of an angle.  Default is ``2*pi``.
+    low : float, optional
+        Lower boundary of the principal value of an angle.  Default is ``0``.
+
+    Returns
+    -------
+    circmean : float
+        Circular mean, restricted to the range ``[low, high]``.
+
+        If the mean resultant vector is zero, an input-dependent,
+        implementation-defined number between ``[low, high]`` is returned.
+        If the input array is empty, ``np.nan`` is returned.
+
+    See Also
+    --------
+    circstd : Circular standard deviation.
+    circvar : Circular variance.
+
+    References
+    ----------
+    .. [1] Mardia, K. V. and Jupp, P. E. *Directional Statistics*.
+           John Wiley & Sons, 1999.
+
+    Examples
+    --------
+    For readability, all angles are printed out in degrees.
+
+    >>> import numpy as np
+    >>> from scipy.stats import circmean
+    >>> import matplotlib.pyplot as plt
+    >>> angles = np.deg2rad(np.array([20, 30, 330]))
+    >>> circmean = circmean(angles)
+    >>> np.rad2deg(circmean)
+    7.294976657784009
+
+    >>> mean = angles.mean()
+    >>> np.rad2deg(mean)
+    126.66666666666666
+
+    Plot and compare the circular mean against the arithmetic mean.
+
+    >>> plt.plot(np.cos(np.linspace(0, 2*np.pi, 500)),
+    ...          np.sin(np.linspace(0, 2*np.pi, 500)),
+    ...          c='k')
+    >>> plt.scatter(np.cos(angles), np.sin(angles), c='k')
+    >>> plt.scatter(np.cos(circmean), np.sin(circmean), c='b',
+    ...             label='circmean')
+    >>> plt.scatter(np.cos(mean), np.sin(mean), c='r', label='mean')
+    >>> plt.legend()
+    >>> plt.axis('equal')
+    >>> plt.show()
+
+    """
+    xp = array_namespace(samples)
+    # Needed for non-NumPy arrays to get appropriate NaN result
+    # Apparently atan2(0, 0) is 0, even though it is mathematically undefined
+    if xp_size(samples) == 0:
+        return xp.mean(samples, axis=axis)
+    period = high - low
+    samples, sin_samp, cos_samp = _circfuncs_common(samples, period, xp=xp)
+    sin_sum = xp.sum(sin_samp, axis=axis)
+    cos_sum = xp.sum(cos_samp, axis=axis)
+    res = xp.atan2(sin_sum, cos_sum)
+
+    res = res[()] if res.ndim == 0 else res
+    return (res * (period / (2.0 * pi)) - low) % period + low
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, n_outputs=1, default_axis=None,
+    result_to_tuple=lambda x, _: (x,)
+)
+def circvar(samples, high=2*pi, low=0, axis=None, nan_policy='propagate'):
+    r"""Compute the circular variance of a sample of angle observations.
+
+    Given :math:`n` angle observations :math:`x_1, \cdots, x_n` measured in
+    radians, their *circular variance* is defined by ([2]_, Eq. 2.3.3)
+
+    .. math::
+
+       1 - \left| \frac{1}{n} \sum_{k=1}^n e^{i x_k} \right|
+
+    where :math:`i` is the imaginary unit and :math:`|z|` gives the length
+    of the complex number :math:`z`.  :math:`|z|` in the above expression
+    is known as the `mean resultant length`.
+
+    Parameters
+    ----------
+    samples : array_like
+        Input array of angle observations.  The value of a full angle is
+        equal to ``(high - low)``.
+    high : float, optional
+        Upper boundary of the principal value of an angle.  Default is ``2*pi``.
+    low : float, optional
+        Lower boundary of the principal value of an angle.  Default is ``0``.
+
+    Returns
+    -------
+    circvar : float
+        Circular variance.  The returned value is in the range ``[0, 1]``,
+        where ``0`` indicates no variance and ``1`` indicates large variance.
+
+        If the input array is empty, ``np.nan`` is returned.
+
+    See Also
+    --------
+    circmean : Circular mean.
+    circstd : Circular standard deviation.
+
+    Notes
+    -----
+    In the limit of small angles, the circular variance is close to
+    half the 'linear' variance if measured in radians.
+
+    References
+    ----------
+    .. [1] Fisher, N.I. *Statistical analysis of circular data*. Cambridge
+           University Press, 1993.
+    .. [2] Mardia, K. V. and Jupp, P. E. *Directional Statistics*.
+           John Wiley & Sons, 1999.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import circvar
+    >>> import matplotlib.pyplot as plt
+    >>> samples_1 = np.array([0.072, -0.158, 0.077, 0.108, 0.286,
+    ...                       0.133, -0.473, -0.001, -0.348, 0.131])
+    >>> samples_2 = np.array([0.111, -0.879, 0.078, 0.733, 0.421,
+    ...                       0.104, -0.136, -0.867,  0.012,  0.105])
+    >>> circvar_1 = circvar(samples_1)
+    >>> circvar_2 = circvar(samples_2)
+
+    Plot the samples.
+
+    >>> fig, (left, right) = plt.subplots(ncols=2)
+    >>> for image in (left, right):
+    ...     image.plot(np.cos(np.linspace(0, 2*np.pi, 500)),
+    ...                np.sin(np.linspace(0, 2*np.pi, 500)),
+    ...                c='k')
+    ...     image.axis('equal')
+    ...     image.axis('off')
+    >>> left.scatter(np.cos(samples_1), np.sin(samples_1), c='k', s=15)
+    >>> left.set_title(f"circular variance: {np.round(circvar_1, 2)!r}")
+    >>> right.scatter(np.cos(samples_2), np.sin(samples_2), c='k', s=15)
+    >>> right.set_title(f"circular variance: {np.round(circvar_2, 2)!r}")
+    >>> plt.show()
+
+    """
+    xp = array_namespace(samples)
+    period = high - low
+    samples, sin_samp, cos_samp = _circfuncs_common(samples, period, xp=xp)
+    sin_mean = xp.mean(sin_samp, axis=axis)
+    cos_mean = xp.mean(cos_samp, axis=axis)
+    hypotenuse = (sin_mean**2. + cos_mean**2.)**0.5
+    # hypotenuse can go slightly above 1 due to rounding errors
+    R = xp.clip(hypotenuse, max=1.)
+
+    res = 1. - R
+    return res
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, n_outputs=1, default_axis=None,
+    result_to_tuple=lambda x, _: (x,)
+)
+def circstd(samples, high=2*pi, low=0, axis=None, nan_policy='propagate', *,
+            normalize=False):
+    r"""
+    Compute the circular standard deviation of a sample of angle observations.
+
+    Given :math:`n` angle observations :math:`x_1, \cdots, x_n` measured in
+    radians, their `circular standard deviation` is defined by
+    ([2]_, Eq. 2.3.11)
+
+    .. math::
+
+       \sqrt{ -2 \log \left| \frac{1}{n} \sum_{k=1}^n e^{i x_k} \right| }
+
+    where :math:`i` is the imaginary unit and :math:`|z|` gives the length
+    of the complex number :math:`z`.  :math:`|z|` in the above expression
+    is known as the `mean resultant length`.
+
+    Parameters
+    ----------
+    samples : array_like
+        Input array of angle observations.  The value of a full angle is
+        equal to ``(high - low)``.
+    high : float, optional
+        Upper boundary of the principal value of an angle.  Default is ``2*pi``.
+    low : float, optional
+        Lower boundary of the principal value of an angle.  Default is ``0``.
+    axis : int or None, default: None
+        If an int, the axis of the input along which to compute the statistic.
+        The statistic of each axis-slice (e.g. row) of the input will appear in a
+        corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+    nan_policy : {'propagate', 'omit', 'raise'}
+        Defines how to handle input NaNs.
+
+        - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
+        which the  statistic is computed, the corresponding entry of the output
+        will be NaN.
+        - ``omit``: NaNs will be omitted when performing the calculation.
+        If insufficient data remains in the axis slice along which the
+        statistic is computed, the corresponding entry of the output will be
+        NaN.
+        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
+
+    normalize : bool, optional
+        If ``False`` (the default), the return value is computed from the
+        above formula with the input scaled by ``(2*pi)/(high-low)`` and
+        the output scaled (back) by ``(high-low)/(2*pi)``.  If ``True``,
+        the output is not scaled and is returned directly.
+
+    Returns
+    -------
+    circstd : float
+        Circular standard deviation, optionally normalized.
+
+        If the input array is empty, ``np.nan`` is returned.
+
+    See Also
+    --------
+    circmean : Circular mean.
+    circvar : Circular variance.
+
+    Notes
+    -----
+    In the limit of small angles, the circular standard deviation is close
+    to the 'linear' standard deviation if ``normalize`` is ``False``.
+
+    References
+    ----------
+    .. [1] Mardia, K. V. (1972). 2. In *Statistics of Directional Data*
+       (pp. 18-24). Academic Press. :doi:`10.1016/C2013-0-07425-7`.
+    .. [2] Mardia, K. V. and Jupp, P. E. *Directional Statistics*.
+           John Wiley & Sons, 1999.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import circstd
+    >>> import matplotlib.pyplot as plt
+    >>> samples_1 = np.array([0.072, -0.158, 0.077, 0.108, 0.286,
+    ...                       0.133, -0.473, -0.001, -0.348, 0.131])
+    >>> samples_2 = np.array([0.111, -0.879, 0.078, 0.733, 0.421,
+    ...                       0.104, -0.136, -0.867,  0.012,  0.105])
+    >>> circstd_1 = circstd(samples_1)
+    >>> circstd_2 = circstd(samples_2)
+
+    Plot the samples.
+
+    >>> fig, (left, right) = plt.subplots(ncols=2)
+    >>> for image in (left, right):
+    ...     image.plot(np.cos(np.linspace(0, 2*np.pi, 500)),
+    ...                np.sin(np.linspace(0, 2*np.pi, 500)),
+    ...                c='k')
+    ...     image.axis('equal')
+    ...     image.axis('off')
+    >>> left.scatter(np.cos(samples_1), np.sin(samples_1), c='k', s=15)
+    >>> left.set_title(f"circular std: {np.round(circstd_1, 2)!r}")
+    >>> right.plot(np.cos(np.linspace(0, 2*np.pi, 500)),
+    ...            np.sin(np.linspace(0, 2*np.pi, 500)),
+    ...            c='k')
+    >>> right.scatter(np.cos(samples_2), np.sin(samples_2), c='k', s=15)
+    >>> right.set_title(f"circular std: {np.round(circstd_2, 2)!r}")
+    >>> plt.show()
+
+    """
+    xp = array_namespace(samples)
+    period = high - low
+    samples, sin_samp, cos_samp = _circfuncs_common(samples, period, xp=xp)
+    sin_mean = xp.mean(sin_samp, axis=axis)  # [1] (2.2.3)
+    cos_mean = xp.mean(cos_samp, axis=axis)  # [1] (2.2.3)
+    hypotenuse = (sin_mean**2. + cos_mean**2.)**0.5
+    # hypotenuse can go slightly above 1 due to rounding errors
+    R = xp.clip(hypotenuse, max=1.)  # [1] (2.2.4)
+
+    res = (-2*xp.log(R))**0.5+0.0  # torch.pow returns -0.0 if R==1
+    if not normalize:
+        res *= (high-low)/(2.*pi)  # [1] (2.3.14) w/ (2.3.7)
+    return res
+
+
+class DirectionalStats:
+    def __init__(self, mean_direction, mean_resultant_length):
+        self.mean_direction = mean_direction
+        self.mean_resultant_length = mean_resultant_length
+
+    def __repr__(self):
+        return (f"DirectionalStats(mean_direction={self.mean_direction},"
+                f" mean_resultant_length={self.mean_resultant_length})")
+
+
+@xp_capabilities(marray=True)
+def directional_stats(samples, *, axis=0, normalize=True):
+    """
+    Computes sample statistics for directional data.
+
+    Computes the directional mean (also called the mean direction vector) and
+    mean resultant length of a sample of vectors.
+
+    The directional mean is a measure of "preferred direction" of vector data.
+    It is analogous to the sample mean, but it is for use when the length of
+    the data is irrelevant (e.g. unit vectors).
+
+    The mean resultant length is a value between 0 and 1 used to quantify the
+    dispersion of directional data: the smaller the mean resultant length, the
+    greater the dispersion. Several definitions of directional variance
+    involving the mean resultant length are given in [1]_ and [2]_.
+
+    Parameters
+    ----------
+    samples : array_like
+        Input array. Must be at least two-dimensional, and the last axis of the
+        input must correspond with the dimensionality of the vector space.
+        When the input is exactly two dimensional, this means that each row
+        of the data is a vector observation.
+    axis : int, default: 0
+        Axis along which the directional mean is computed.
+    normalize : bool, default: True
+        If True, normalize the input to ensure that each observation is a
+        unit vector. It the observations are already unit vectors, consider
+        setting this to False to avoid unnecessary computation.
+
+    Returns
+    -------
+    res : DirectionalStats
+        An object containing attributes:
+
+        mean_direction : ndarray
+            Directional mean.
+        mean_resultant_length : ndarray
+            The mean resultant length [1]_.
+
+    See Also
+    --------
+    circmean: circular mean; i.e. directional mean for 2D *angles*
+    circvar: circular variance; i.e. directional variance for 2D *angles*
+
+    Notes
+    -----
+    This uses a definition of directional mean from [1]_.
+    Assuming the observations are unit vectors, the calculation is as follows.
+
+    .. code-block:: python
+
+        mean = samples.mean(axis=0)
+        mean_resultant_length = np.linalg.norm(mean)
+        mean_direction = mean / mean_resultant_length
+
+    This definition is appropriate for *directional* data (i.e. vector data
+    for which the magnitude of each observation is irrelevant) but not
+    for *axial* data (i.e. vector data for which the magnitude and *sign* of
+    each observation is irrelevant).
+
+    Several definitions of directional variance involving the mean resultant
+    length ``R`` have been proposed, including ``1 - R`` [1]_, ``1 - R**2``
+    [2]_, and ``2 * (1 - R)`` [2]_. Rather than choosing one, this function
+    returns ``R`` as attribute `mean_resultant_length` so the user can compute
+    their preferred measure of dispersion.
+
+    References
+    ----------
+    .. [1] Mardia, Jupp. (2000). *Directional Statistics*
+       (p. 163). Wiley.
+
+    .. [2] https://en.wikipedia.org/wiki/Directional_statistics
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import directional_stats
+    >>> data = np.array([[3, 4],    # first observation, 2D vector space
+    ...                  [6, -8]])  # second observation
+    >>> dirstats = directional_stats(data)
+    >>> dirstats.mean_direction
+    array([1., 0.])
+
+    In contrast, the regular sample mean of the vectors would be influenced
+    by the magnitude of each observation. Furthermore, the result would not be
+    a unit vector.
+
+    >>> data.mean(axis=0)
+    array([4.5, -2.])
+
+    An exemplary use case for `directional_stats` is to find a *meaningful*
+    center for a set of observations on a sphere, e.g. geographical locations.
+
+    >>> data = np.array([[0.8660254, 0.5, 0.],
+    ...                  [0.8660254, -0.5, 0.]])
+    >>> dirstats = directional_stats(data)
+    >>> dirstats.mean_direction
+    array([1., 0., 0.])
+
+    The regular sample mean on the other hand yields a result which does not
+    lie on the surface of the sphere.
+
+    >>> data.mean(axis=0)
+    array([0.8660254, 0., 0.])
+
+    The function also returns the mean resultant length, which
+    can be used to calculate a directional variance. For example, using the
+    definition ``Var(z) = 1 - R`` from [2]_ where ``R`` is the
+    mean resultant length, we can calculate the directional variance of the
+    vectors in the above example as:
+
+    >>> 1 - dirstats.mean_resultant_length
+    0.13397459716167093
+    """
+    xp = array_namespace(samples)
+    samples = xp.asarray(samples)
+
+    if samples.ndim < 2:
+        raise ValueError("samples must at least be two-dimensional. "
+                         f"Instead samples has shape: {tuple(samples.shape)}")
+    samples = xp.moveaxis(samples, axis, 0)
+
+    if is_marray(xp):
+        _xp = array_namespace(samples.mask)
+        mask = _xp.any(samples.mask, axis=-1, keepdims=True)
+        samples = xp.asarray(samples.data, mask=mask)
+
+    if normalize:
+        vectornorms = xp_vector_norm(samples, axis=-1, keepdims=True, xp=xp)
+        samples = samples/vectornorms
+    mean = xp.mean(samples, axis=0)
+    mean_resultant_length = xp_vector_norm(mean, axis=-1, keepdims=True, xp=xp)
+    mean_direction = mean / mean_resultant_length
+    mrl = xp.squeeze(mean_resultant_length, axis=-1)
+    mean_resultant_length = mrl[()] if mrl.ndim == 0 else mrl
+    return DirectionalStats(mean_direction, mean_resultant_length)
+
+
+@xp_capabilities(skip_backends=[('dask.array', "no take_along_axis")])
+def false_discovery_control(ps, *, axis=0, method='bh'):
+    """Adjust p-values to control the false discovery rate.
+
+    The false discovery rate (FDR) is the expected proportion of rejected null
+    hypotheses that are actually true.
+    If the null hypothesis is rejected when the *adjusted* p-value falls below
+    a specified level, the false discovery rate is controlled at that level.
+
+    Parameters
+    ----------
+    ps : 1D array_like
+        The p-values to adjust. Elements must be real numbers between 0 and 1.
+    axis : int
+        The axis along which to perform the adjustment. The adjustment is
+        performed independently along each axis-slice. If `axis` is None, `ps`
+        is raveled before performing the adjustment.
+    method : {'bh', 'by'}
+        The false discovery rate control procedure to apply: ``'bh'`` is for
+        Benjamini-Hochberg [1]_ (Eq. 1), ``'by'`` is for Benjaminini-Yekutieli
+        [2]_ (Theorem 1.3). The latter is more conservative, but it is
+        guaranteed to control the FDR even when the p-values are not from
+        independent tests.
+
+    Returns
+    -------
+    ps_adusted : array_like
+        The adjusted p-values. If the null hypothesis is rejected where these
+        fall below a specified level, the false discovery rate is controlled
+        at that level.
+
+    See Also
+    --------
+    combine_pvalues
+    statsmodels.stats.multitest.multipletests
+
+    Notes
+    -----
+    In multiple hypothesis testing, false discovery control procedures tend to
+    offer higher power than familywise error rate control procedures (e.g.
+    Bonferroni correction [1]_).
+
+    If the p-values correspond with independent tests (or tests with
+    "positive regression dependencies" [2]_), rejecting null hypotheses
+    corresponding with Benjamini-Hochberg-adjusted p-values below :math:`q`
+    controls the false discovery rate at a level less than or equal to
+    :math:`q m_0 / m`, where :math:`m_0` is the number of true null hypotheses
+    and :math:`m` is the total number of null hypotheses tested. The same is
+    true even for dependent tests when the p-values are adjusted accorded to
+    the more conservative Benjaminini-Yekutieli procedure.
+
+    The adjusted p-values produced by this function are comparable to those
+    produced by the R function ``p.adjust`` and the statsmodels function
+    `statsmodels.stats.multitest.multipletests`. Please consider the latter
+    for more advanced methods of multiple comparison correction.
+
+    References
+    ----------
+    .. [1] Benjamini, Yoav, and Yosef Hochberg. "Controlling the false
+           discovery rate: a practical and powerful approach to multiple
+           testing." Journal of the Royal statistical society: series B
+           (Methodological) 57.1 (1995): 289-300.
+
+    .. [2] Benjamini, Yoav, and Daniel Yekutieli. "The control of the false
+           discovery rate in multiple testing under dependency." Annals of
+           statistics (2001): 1165-1188.
+
+    .. [3] TileStats. FDR - Benjamini-Hochberg explained - Youtube.
+           https://www.youtube.com/watch?v=rZKa4tW2NKs.
+
+    .. [4] Neuhaus, Karl-Ludwig, et al. "Improved thrombolysis in acute
+           myocardial infarction with front-loaded administration of alteplase:
+           results of the rt-PA-APSAC patency study (TAPS)." Journal of the
+           American College of Cardiology 19.5 (1992): 885-891.
+
+    Examples
+    --------
+    We follow the example from [1]_.
+
+        Thrombolysis with recombinant tissue-type plasminogen activator (rt-PA)
+        and anisoylated plasminogen streptokinase activator (APSAC) in
+        myocardial infarction has been proved to reduce mortality. [4]_
+        investigated the effects of a new front-loaded administration of rt-PA
+        versus those obtained with a standard regimen of APSAC, in a randomized
+        multicentre trial in 421 patients with acute myocardial infarction.
+
+    There were four families of hypotheses tested in the study, the last of
+    which was "cardiac and other events after the start of thrombolitic
+    treatment". FDR control may be desired in this family of hypotheses
+    because it would not be appropriate to conclude that the front-loaded
+    treatment is better if it is merely equivalent to the previous treatment.
+
+    The p-values corresponding with the 15 hypotheses in this family were
+
+    >>> ps = [0.0001, 0.0004, 0.0019, 0.0095, 0.0201, 0.0278, 0.0298, 0.0344,
+    ...       0.0459, 0.3240, 0.4262, 0.5719, 0.6528, 0.7590, 1.000]
+
+    If the chosen significance level is 0.05, we may be tempted to reject the
+    null hypotheses for the tests corresponding with the first nine p-values,
+    as the first nine p-values fall below the chosen significance level.
+    However, this would ignore the problem of "multiplicity": if we fail to
+    correct for the fact that multiple comparisons are being performed, we
+    are more likely to incorrectly reject true null hypotheses.
+
+    One approach to the multiplicity problem is to control the family-wise
+    error rate (FWER), that is, the rate at which the null hypothesis is
+    rejected when it is actually true. A common procedure of this kind is the
+    Bonferroni correction [1]_.  We begin by multiplying the p-values by the
+    number of hypotheses tested.
+
+    >>> import numpy as np
+    >>> np.array(ps) * len(ps)
+    array([1.5000e-03, 6.0000e-03, 2.8500e-02, 1.4250e-01, 3.0150e-01,
+           4.1700e-01, 4.4700e-01, 5.1600e-01, 6.8850e-01, 4.8600e+00,
+           6.3930e+00, 8.5785e+00, 9.7920e+00, 1.1385e+01, 1.5000e+01])
+
+    To control the FWER at 5%, we reject only the hypotheses corresponding
+    with adjusted p-values less than 0.05. In this case, only the hypotheses
+    corresponding with the first three p-values can be rejected. According to
+    [1]_, these three hypotheses concerned "allergic reaction" and "two
+    different aspects of bleeding."
+
+    An alternative approach is to control the false discovery rate: the
+    expected fraction of rejected null hypotheses that are actually true. The
+    advantage of this approach is that it typically affords greater power: an
+    increased rate of rejecting the null hypothesis when it is indeed false. To
+    control the false discovery rate at 5%, we apply the Benjamini-Hochberg
+    p-value adjustment.
+
+    >>> from scipy import stats
+    >>> stats.false_discovery_control(ps)
+    array([0.0015    , 0.003     , 0.0095    , 0.035625  , 0.0603    ,
+           0.06385714, 0.06385714, 0.0645    , 0.0765    , 0.486     ,
+           0.58118182, 0.714875  , 0.75323077, 0.81321429, 1.        ])
+
+    Now, the first *four* adjusted p-values fall below 0.05, so we would reject
+    the null hypotheses corresponding with these *four* p-values. Rejection
+    of the fourth null hypothesis was particularly important to the original
+    study as it led to the conclusion that the new treatment had a
+    "substantially lower in-hospital mortality rate."
+
+    For simplicity of exposition, the p-values in the example above were given in
+    sorted order, but this is not required; `false_discovery_control` returns
+    adjusted p-values in order corresponding with the input `ps`.
+
+    >>> stats.false_discovery_control([0.5, 0.6, 0.1, 0.001])
+    array([0.6  , 0.6  , 0.2  , 0.004])
+
+    """
+    xp = array_namespace(ps)
+
+    # Input Validation and Special Cases
+    ps = xp.asarray(ps)
+
+    if not xp.isdtype(ps.dtype, ("integral", "real floating")):
+        raise ValueError("`ps` must contain only real numbers.")
+
+    if is_lazy_array(ps):
+        ps = xp.where((ps < 0.) | (ps > 1.), xp.nan, ps)
+    else:
+        if not xp.all(ps == xp.clip(ps, 0., 1.)):
+            raise ValueError("All values in `ps` must lie between 0. and 1.")
+
+    methods = {'bh', 'by'}
+    if method.lower() not in methods:
+        raise ValueError(f"Unrecognized `method` '{method}'."
+                         f"Method must be one of {methods}.")
+    method = method.lower()
+
+    if axis is None:
+        axis = 0
+        ps = xp_ravel(ps)
+
+    axis = np.asarray(axis)[()]  # use of NumPy for input validation is OK
+    if not np.issubdtype(axis.dtype, np.integer) or axis.size != 1:
+        raise ValueError("`axis` must be an integer or `None`")
+    axis = int(axis)
+
+    if xp_size(ps) <= 1 or ps.shape[axis] <= 1:
+        return ps[()] if ps.ndim == 0 else ps
+
+    ps = xp.moveaxis(ps, axis, -1)
+    m = ps.shape[-1]
+
+    # Main Algorithm
+    # Equivalent to the ideas of [1] and [2], except that this adjusts the
+    # p-values as described in [3]. The results are similar to those produced
+    # by R's p.adjust.
+
+    # "Let [ps] be the ordered observed p-values..."
+    order = xp.argsort(ps, axis=-1)
+    ps = xp.take_along_axis(ps, order, axis=-1)  # this copies ps
+
+    # Equation 1 of [1] rearranged to reject when p is less than specified q
+    i = xp.arange(1, m+1, dtype=ps.dtype, device=xp_device(ps))
+    # ps *= m / i
+    ps = xpx.at(ps)[...].multiply(m / i)
+
+    # Theorem 1.3 of [2]
+    if method == 'by':
+        # ps *= np.sum(1 / i)
+        ps = xpx.at(ps)[...].multiply(xp.sum(1 / i))
+
+    # accounts for rejecting all null hypotheses i for i < k, where k is
+    # defined in Eq. 1 of either [1] or [2]. See [3]. Starting with the index j
+    # of the second to last element, we replace element j with element j+1 if
+    # the latter is smaller.
+    if is_numpy(xp):
+        np.minimum.accumulate(ps[..., ::-1], out=ps[..., ::-1], axis=-1)
+    else:
+        n = ps.shape[-1]
+        for j in range(n-2, -1, -1):
+            # ps[..., j] = xp.minimum(ps[..., j], ps[..., j+1])
+            ps = xpx.at(ps)[..., j].set(xp.minimum(ps[..., j], ps[..., j+1]))
+
+    # Restore original order of axes and data
+    ps = _reorder_along_axis(ps, order, axis=-1, xp=xp)
+    ps = xp.moveaxis(ps, -1, axis)
+
+    return xp.clip(ps, 0., 1.)
+
+
+def _reorder_along_axis(x, i, *, axis, xp):
+    if is_jax(xp):
+        return xp.put_along_axis(x, i, values=x, axis=axis, inplace=False)
+    if hasattr(xp, 'put_along_axis'):
+        xp.put_along_axis(x, i, values=x.copy(), axis=axis)
+        return x
+    else:
+        return xp.take_along_axis(x, xp.argsort(i, axis=-1), axis=-1)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_mstats_basic.py b/.venv/lib/python3.12/site-packages/scipy/stats/_mstats_basic.py
new file mode 100644
index 0000000000000000000000000000000000000000..96517b32ae2a8865403d2a02ac9a64fbd3ea5805
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_mstats_basic.py
@@ -0,0 +1,3833 @@
+"""
+An extension of scipy.stats._stats_py to support masked arrays
+
+"""
+# Original author (2007): Pierre GF Gerard-Marchant
+
+
+__all__ = ['argstoarray',
+           'count_tied_groups',
+           'describe',
+           'f_oneway', 'find_repeats','friedmanchisquare',
+           'kendalltau','kendalltau_seasonal','kruskal','kruskalwallis',
+           'ks_twosamp', 'ks_2samp', 'kurtosis', 'kurtosistest',
+           'ks_1samp', 'kstest',
+           'linregress',
+           'mannwhitneyu', 'meppf','mode','moment','mquantiles','msign',
+           'normaltest',
+           'obrientransform',
+           'pearsonr','plotting_positions','pointbiserialr',
+           'rankdata',
+           'scoreatpercentile','sem',
+           'sen_seasonal_slopes','skew','skewtest','spearmanr',
+           'siegelslopes', 'theilslopes',
+           'tmax','tmean','tmin','trim','trimboth',
+           'trimtail','trima','trimr','trimmed_mean','trimmed_std',
+           'trimmed_stde','trimmed_var','tsem','ttest_1samp','ttest_onesamp',
+           'ttest_ind','ttest_rel','tvar',
+           'variation',
+           'winsorize',
+           'brunnermunzel',
+           ]
+
+import numpy as np
+from numpy import ndarray
+import numpy.ma as ma
+from numpy.ma import masked, nomask
+import math
+
+import itertools
+import warnings
+from collections import namedtuple
+
+from . import distributions
+from scipy._lib._util import _rename_parameter, _contains_nan, _dedent_for_py313
+from scipy._lib._bunch import _make_tuple_bunch
+import scipy.special as special
+import scipy.stats._stats_py
+import scipy.stats._stats_py as _stats_py
+from scipy.stats._stats_pythran import siegelslopes as siegelslopes_pythran
+from ._stats_mstats_common import _find_repeats, TheilslopesResult, SiegelslopesResult
+
+
+def _chk_asarray(a, axis):
+    # Always returns a masked array, raveled for axis=None
+    a = ma.asanyarray(a)
+    if axis is None:
+        a = ma.ravel(a)
+        outaxis = 0
+    else:
+        outaxis = axis
+    return a, outaxis
+
+
+def _chk2_asarray(a, b, axis):
+    a = ma.asanyarray(a)
+    b = ma.asanyarray(b)
+    if axis is None:
+        a = ma.ravel(a)
+        b = ma.ravel(b)
+        outaxis = 0
+    else:
+        outaxis = axis
+    return a, b, outaxis
+
+
+def _chk_size(a, b):
+    a = ma.asanyarray(a)
+    b = ma.asanyarray(b)
+    (na, nb) = (a.size, b.size)
+    if na != nb:
+        raise ValueError("The size of the input array should match!"
+                         f" ({na} <> {nb})")
+    return (a, b, na)
+
+
+def _ttest_finish(df, t, alternative):
+    """Common code between all 3 t-test functions."""
+    # We use ``stdtr`` directly here to preserve masked arrays
+
+    if alternative == 'less':
+        pval = special._ufuncs.stdtr(df, t)
+    elif alternative == 'greater':
+        pval = special._ufuncs.stdtr(df, -t)
+    elif alternative == 'two-sided':
+        pval = special._ufuncs.stdtr(df, -np.abs(t))*2
+    else:
+        raise ValueError("alternative must be "
+                         "'less', 'greater' or 'two-sided'")
+
+    if t.ndim == 0:
+        t = t[()]
+    if pval.ndim == 0:
+        pval = pval[()]
+
+    return t, pval
+
+
+def argstoarray(*args):
+    """
+    Constructs a 2D array from a group of sequences.
+
+    Sequences are filled with missing values to match the length of the longest
+    sequence.
+
+    Parameters
+    ----------
+    *args : sequences
+        Group of sequences.
+
+    Returns
+    -------
+    argstoarray : MaskedArray
+        A ( `m` x `n` ) masked array, where `m` is the number of arguments and
+        `n` the length of the longest argument.
+
+    Notes
+    -----
+    `numpy.ma.vstack` has identical behavior, but is called with a sequence
+    of sequences.
+
+    Examples
+    --------
+    A 2D masked array constructed from a group of sequences is returned.
+
+    >>> from scipy.stats.mstats import argstoarray
+    >>> argstoarray([1, 2, 3], [4, 5, 6])
+    masked_array(
+     data=[[1.0, 2.0, 3.0],
+           [4.0, 5.0, 6.0]],
+     mask=[[False, False, False],
+           [False, False, False]],
+     fill_value=1e+20)
+
+    The returned masked array filled with missing values when the lengths of
+    sequences are different.
+
+    >>> argstoarray([1, 3], [4, 5, 6])
+    masked_array(
+     data=[[1.0, 3.0, --],
+           [4.0, 5.0, 6.0]],
+     mask=[[False, False,  True],
+           [False, False, False]],
+     fill_value=1e+20)
+
+    """
+    if len(args) == 1 and not isinstance(args[0], ndarray):
+        output = ma.asarray(args[0])
+        if output.ndim != 2:
+            raise ValueError("The input should be 2D")
+    else:
+        n = len(args)
+        m = max([len(k) for k in args])
+        output = ma.array(np.empty((n,m), dtype=float), mask=True)
+        for (k,v) in enumerate(args):
+            output[k,:len(v)] = v
+
+    output[np.logical_not(np.isfinite(output._data))] = masked
+    return output
+
+
+def find_repeats(arr):
+    """Find repeats in arr and return a tuple (repeats, repeat_count).
+
+    The input is cast to float64. Masked values are discarded.
+
+    Parameters
+    ----------
+    arr : sequence
+        Input array. The array is flattened if it is not 1D.
+
+    Returns
+    -------
+    repeats : ndarray
+        Array of repeated values.
+    counts : ndarray
+        Array of counts.
+
+    Examples
+    --------
+    >>> from scipy.stats import mstats
+    >>> mstats.find_repeats([2, 1, 2, 3, 2, 2, 5])
+    (array([2.]), array([4]))
+
+    In the above example, 2 repeats 4 times.
+
+    >>> mstats.find_repeats([[10, 20, 1, 2], [5, 5, 4, 4]])
+    (array([4., 5.]), array([2, 2]))
+
+    In the above example, both 4 and 5 repeat 2 times.
+
+    """
+    # Make sure we get a copy. ma.compressed promises a "new array", but can
+    # actually return a reference.
+    compr = np.asarray(ma.compressed(arr), dtype=np.float64)
+    need_copy = np.may_share_memory(compr, arr)
+    if need_copy:
+        compr = compr.copy()
+    return _find_repeats(compr)
+
+
+def count_tied_groups(x, use_missing=False):
+    """
+    Counts the number of tied values.
+
+    Parameters
+    ----------
+    x : sequence
+        Sequence of data on which to counts the ties
+    use_missing : bool, optional
+        Whether to consider missing values as tied.
+
+    Returns
+    -------
+    count_tied_groups : dict
+        Returns a dictionary (nb of ties: nb of groups).
+
+    Examples
+    --------
+    >>> from scipy.stats import mstats
+    >>> import numpy as np
+    >>> z = [0, 0, 0, 2, 2, 2, 3, 3, 4, 5, 6]
+    >>> mstats.count_tied_groups(z)
+    {2: 1, 3: 2}
+
+    In the above example, the ties were 0 (3x), 2 (3x) and 3 (2x).
+
+    >>> z = np.ma.array([0, 0, 1, 2, 2, 2, 3, 3, 4, 5, 6])
+    >>> mstats.count_tied_groups(z)
+    {2: 2, 3: 1}
+    >>> z[[1,-1]] = np.ma.masked
+    >>> mstats.count_tied_groups(z, use_missing=True)
+    {2: 2, 3: 1}
+
+    """
+    nmasked = ma.getmask(x).sum()
+    # We need the copy as find_repeats will overwrite the initial data
+    data = ma.compressed(x).copy()
+    (ties, counts) = find_repeats(data)
+    nties = {}
+    if len(ties):
+        nties = dict(zip(np.unique(counts), itertools.repeat(1)))
+        nties.update(dict(zip(*find_repeats(counts))))
+
+    if nmasked and use_missing:
+        try:
+            nties[nmasked] += 1
+        except KeyError:
+            nties[nmasked] = 1
+
+    return nties
+
+
+def rankdata(data, axis=None, use_missing=False):
+    """Returns the rank (also known as order statistics) of each data point
+    along the given axis.
+
+    If some values are tied, their rank is averaged.
+    If some values are masked, their rank is set to 0 if use_missing is False,
+    or set to the average rank of the unmasked values if use_missing is True.
+
+    Parameters
+    ----------
+    data : sequence
+        Input data. The data is transformed to a masked array
+    axis : {None,int}, optional
+        Axis along which to perform the ranking.
+        If None, the array is first flattened. An exception is raised if
+        the axis is specified for arrays with a dimension larger than 2
+    use_missing : bool, optional
+        Whether the masked values have a rank of 0 (False) or equal to the
+        average rank of the unmasked values (True).
+
+    """  # numpydoc ignore=RT01
+    def _rank1d(data, use_missing=False):
+        n = data.count()
+        rk = np.empty(data.size, dtype=float)
+        idx = data.argsort()
+        rk[idx[:n]] = np.arange(1,n+1)
+
+        if use_missing:
+            rk[idx[n:]] = (n+1)/2.
+        else:
+            rk[idx[n:]] = 0
+
+        repeats = find_repeats(data.copy())
+        for r in repeats[0]:
+            condition = (data == r).filled(False)
+            rk[condition] = rk[condition].mean()
+        return rk
+
+    data = ma.array(data, copy=False)
+    if axis is None:
+        if data.ndim > 1:
+            return _rank1d(data.ravel(), use_missing).reshape(data.shape)
+        else:
+            return _rank1d(data, use_missing)
+    else:
+        return ma.apply_along_axis(_rank1d,axis,data,use_missing).view(ndarray)
+
+
+ModeResult = namedtuple('ModeResult', ('mode', 'count'))
+
+
+def mode(a, axis=0):
+    """
+    Returns an array of the modal (most common) value in the passed array.
+
+    Parameters
+    ----------
+    a : array_like
+        n-dimensional array of which to find mode(s).
+    axis : int or None, optional
+        Axis along which to operate. Default is 0. If None, compute over
+        the whole array `a`.
+
+    Returns
+    -------
+    mode : ndarray
+        Array of modal values.
+    count : ndarray
+        Array of counts for each mode.
+
+    Notes
+    -----
+    For more details, see `scipy.stats.mode`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> from scipy.stats import mstats
+    >>> m_arr = np.ma.array([1, 1, 0, 0, 0, 0], mask=[0, 0, 1, 1, 1, 0])
+    >>> mstats.mode(m_arr)  # note that most zeros are masked
+    ModeResult(mode=array([1.]), count=array([2.]))
+
+    """
+    return _mode(a, axis=axis, keepdims=True)
+
+
+def _mode(a, axis=0, keepdims=True):
+    # Don't want to expose `keepdims` from the public `mstats.mode`
+    a, axis = _chk_asarray(a, axis)
+
+    def _mode1D(a):
+        (rep,cnt) = find_repeats(a)
+        if not cnt.ndim:
+            return (0, 0)
+        elif cnt.size:
+            return (rep[cnt.argmax()], cnt.max())
+        else:
+            return (a.min(), 1)
+
+    if axis is None:
+        output = _mode1D(ma.ravel(a))
+        output = (ma.array(output[0]), ma.array(output[1]))
+    else:
+        output = ma.apply_along_axis(_mode1D, axis, a)
+        if keepdims is None or keepdims:
+            newshape = list(a.shape)
+            newshape[axis] = 1
+            slices = [slice(None)] * output.ndim
+            slices[axis] = 0
+            modes = output[tuple(slices)].reshape(newshape)
+            slices[axis] = 1
+            counts = output[tuple(slices)].reshape(newshape)
+            output = (modes, counts)
+        else:
+            output = np.moveaxis(output, axis, 0)
+
+    return ModeResult(*output)
+
+
+def _betai(a, b, x):
+    x = np.asanyarray(x)
+    x = ma.where(x < 1.0, x, 1.0)  # if x > 1 then return 1.0
+    return special.betainc(a, b, x)
+
+
+def msign(x):
+    """Returns the sign of x, or 0 if x is masked."""  # numpydoc ignore=RT01
+    return ma.filled(np.sign(x), 0)
+
+
+def pearsonr(x, y):
+    r"""
+    Pearson correlation coefficient and p-value for testing non-correlation.
+
+    The Pearson correlation coefficient [1]_ measures the linear relationship
+    between two datasets.  The calculation of the p-value relies on the
+    assumption that each dataset is normally distributed.  (See Kowalski [3]_
+    for a discussion of the effects of non-normality of the input on the
+    distribution of the correlation coefficient.)  Like other correlation
+    coefficients, this one varies between -1 and +1 with 0 implying no
+    correlation. Correlations of -1 or +1 imply an exact linear relationship.
+
+    Parameters
+    ----------
+    x : (N,) array_like
+        Input array.
+    y : (N,) array_like
+        Input array.
+
+    Returns
+    -------
+    r : float
+        Pearson's correlation coefficient.
+    p-value : float
+        Two-tailed p-value.
+
+    Warns
+    -----
+    `~scipy.stats.ConstantInputWarning`
+        Raised if an input is a constant array.  The correlation coefficient
+        is not defined in this case, so ``np.nan`` is returned.
+
+    `~scipy.stats.NearConstantInputWarning`
+        Raised if an input is "nearly" constant.  The array ``x`` is considered
+        nearly constant if ``norm(x - mean(x)) < 1e-13 * abs(mean(x))``.
+        Numerical errors in the calculation ``x - mean(x)`` in this case might
+        result in an inaccurate calculation of r.
+
+    See Also
+    --------
+    spearmanr : Spearman rank-order correlation coefficient.
+    kendalltau : Kendall's tau, a correlation measure for ordinal data.
+
+    Notes
+    -----
+    The correlation coefficient is calculated as follows:
+
+    .. math::
+
+        r = \frac{\sum (x - m_x) (y - m_y)}
+                 {\sqrt{\sum (x - m_x)^2 \sum (y - m_y)^2}}
+
+    where :math:`m_x` is the mean of the vector x and :math:`m_y` is
+    the mean of the vector y.
+
+    Under the assumption that x and y are drawn from
+    independent normal distributions (so the population correlation coefficient
+    is 0), the probability density function of the sample correlation
+    coefficient r is ([1]_, [2]_):
+
+    .. math::
+
+        f(r) = \frac{{(1-r^2)}^{n/2-2}}{\mathrm{B}(\frac{1}{2},\frac{n}{2}-1)}
+
+    where n is the number of samples, and B is the beta function.  This
+    is sometimes referred to as the exact distribution of r.  This is
+    the distribution that is used in `pearsonr` to compute the p-value.
+    The distribution is a beta distribution on the interval [-1, 1],
+    with equal shape parameters a = b = n/2 - 1.  In terms of SciPy's
+    implementation of the beta distribution, the distribution of r is::
+
+        dist = scipy.stats.beta(n/2 - 1, n/2 - 1, loc=-1, scale=2)
+
+    The p-value returned by `pearsonr` is a two-sided p-value. The p-value
+    roughly indicates the probability of an uncorrelated system
+    producing datasets that have a Pearson correlation at least as extreme
+    as the one computed from these datasets. More precisely, for a
+    given sample with correlation coefficient r, the p-value is
+    the probability that abs(r') of a random sample x' and y' drawn from
+    the population with zero correlation would be greater than or equal
+    to abs(r). In terms of the object ``dist`` shown above, the p-value
+    for a given r and length n can be computed as::
+
+        p = 2*dist.cdf(-abs(r))
+
+    When n is 2, the above continuous distribution is not well-defined.
+    One can interpret the limit of the beta distribution as the shape
+    parameters a and b approach a = b = 0 as a discrete distribution with
+    equal probability masses at r = 1 and r = -1.  More directly, one
+    can observe that, given the data x = [x1, x2] and y = [y1, y2], and
+    assuming x1 != x2 and y1 != y2, the only possible values for r are 1
+    and -1.  Because abs(r') for any sample x' and y' with length 2 will
+    be 1, the two-sided p-value for a sample of length 2 is always 1.
+
+    References
+    ----------
+    .. [1] "Pearson correlation coefficient", Wikipedia,
+           https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
+    .. [2] Student, "Probable error of a correlation coefficient",
+           Biometrika, Volume 6, Issue 2-3, 1 September 1908, pp. 302-310.
+    .. [3] C. J. Kowalski, "On the Effects of Non-Normality on the Distribution
+           of the Sample Product-Moment Correlation Coefficient"
+           Journal of the Royal Statistical Society. Series C (Applied
+           Statistics), Vol. 21, No. 1 (1972), pp. 1-12.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> from scipy.stats import mstats
+    >>> mstats.pearsonr([1, 2, 3, 4, 5], [10, 9, 2.5, 6, 4])
+    (-0.7426106572325057, 0.1505558088534455)
+
+    There is a linear dependence between x and y if y = a + b*x + e, where
+    a,b are constants and e is a random error term, assumed to be independent
+    of x. For simplicity, assume that x is standard normal, a=0, b=1 and let
+    e follow a normal distribution with mean zero and standard deviation s>0.
+
+    >>> s = 0.5
+    >>> x = stats.norm.rvs(size=500)
+    >>> e = stats.norm.rvs(scale=s, size=500)
+    >>> y = x + e
+    >>> mstats.pearsonr(x, y)
+    (0.9029601878969703, 8.428978827629898e-185) # may vary
+
+    This should be close to the exact value given by
+
+    >>> 1/np.sqrt(1 + s**2)
+    0.8944271909999159
+
+    For s=0.5, we observe a high level of correlation. In general, a large
+    variance of the noise reduces the correlation, while the correlation
+    approaches one as the variance of the error goes to zero.
+
+    It is important to keep in mind that no correlation does not imply
+    independence unless (x, y) is jointly normal. Correlation can even be zero
+    when there is a very simple dependence structure: if X follows a
+    standard normal distribution, let y = abs(x). Note that the correlation
+    between x and y is zero. Indeed, since the expectation of x is zero,
+    cov(x, y) = E[x*y]. By definition, this equals E[x*abs(x)] which is zero
+    by symmetry. The following lines of code illustrate this observation:
+
+    >>> y = np.abs(x)
+    >>> mstats.pearsonr(x, y)
+    (-0.016172891856853524, 0.7182823678751942) # may vary
+
+    A non-zero correlation coefficient can be misleading. For example, if X has
+    a standard normal distribution, define y = x if x < 0 and y = 0 otherwise.
+    A simple calculation shows that corr(x, y) = sqrt(2/Pi) = 0.797...,
+    implying a high level of correlation:
+
+    >>> y = np.where(x < 0, x, 0)
+    >>> mstats.pearsonr(x, y)
+    (0.8537091583771509, 3.183461621422181e-143) # may vary
+
+    This is unintuitive since there is no dependence of x and y if x is larger
+    than zero which happens in about half of the cases if we sample x and y.
+    """
+    (x, y, n) = _chk_size(x, y)
+    (x, y) = (x.ravel(), y.ravel())
+    # Get the common mask and the total nb of unmasked elements
+    m = ma.mask_or(ma.getmask(x), ma.getmask(y))
+    n -= m.sum()
+    df = n-2
+    if df < 0:
+        return (masked, masked)
+
+    return scipy.stats._stats_py.pearsonr(
+                ma.masked_array(x, mask=m).compressed(),
+                ma.masked_array(y, mask=m).compressed())
+
+
+def spearmanr(x, y=None, use_ties=True, axis=None, nan_policy='propagate',
+              alternative='two-sided'):
+    """
+    Calculates a Spearman rank-order correlation coefficient and the p-value
+    to test for non-correlation.
+
+    The Spearman correlation is a nonparametric measure of the linear
+    relationship between two datasets. Unlike the Pearson correlation, the
+    Spearman correlation does not assume that both datasets are normally
+    distributed. Like other correlation coefficients, this one varies
+    between -1 and +1 with 0 implying no correlation. Correlations of -1 or
+    +1 imply a monotonic relationship. Positive correlations imply that
+    as `x` increases, so does `y`. Negative correlations imply that as `x`
+    increases, `y` decreases.
+
+    Missing values are discarded pair-wise: if a value is missing in `x`, the
+    corresponding value in `y` is masked.
+
+    The p-value roughly indicates the probability of an uncorrelated system
+    producing datasets that have a Spearman correlation at least as extreme
+    as the one computed from these datasets. The p-values are not entirely
+    reliable but are probably reasonable for datasets larger than 500 or so.
+
+    Parameters
+    ----------
+    x, y : 1D or 2D array_like, y is optional
+        One or two 1-D or 2-D arrays containing multiple variables and
+        observations. When these are 1-D, each represents a vector of
+        observations of a single variable. For the behavior in the 2-D case,
+        see under ``axis``, below.
+    use_ties : bool, optional
+        DO NOT USE.  Does not do anything, keyword is only left in place for
+        backwards compatibility reasons.
+    axis : int or None, optional
+        If axis=0 (default), then each column represents a variable, with
+        observations in the rows. If axis=1, the relationship is transposed:
+        each row represents a variable, while the columns contain observations.
+        If axis=None, then both arrays will be raveled.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan. 'propagate' returns nan,
+        'raise' throws an error, 'omit' performs the calculations ignoring nan
+        values. Default is 'propagate'.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        The following options are available:
+
+        * 'two-sided': the correlation is nonzero
+        * 'less': the correlation is negative (less than zero)
+        * 'greater':  the correlation is positive (greater than zero)
+
+        .. versionadded:: 1.7.0
+
+    Returns
+    -------
+    res : SignificanceResult
+        An object containing attributes:
+
+        statistic : float or ndarray (2-D square)
+            Spearman correlation matrix or correlation coefficient (if only 2
+            variables are given as parameters). Correlation matrix is square
+            with length equal to total number of variables (columns or rows) in
+            ``a`` and ``b`` combined.
+        pvalue : float
+            The p-value for a hypothesis test whose null hypothesis
+            is that two sets of data are linearly uncorrelated. See
+            `alternative` above for alternative hypotheses. `pvalue` has the
+            same shape as `statistic`.
+
+    References
+    ----------
+    [CRCProbStat2000] section 14.7
+
+    """
+    if not use_ties:
+        raise ValueError("`use_ties=False` is not supported in SciPy >= 1.2.0")
+
+    # Always returns a masked array, raveled if axis=None
+    x, axisout = _chk_asarray(x, axis)
+    if y is not None:
+        # Deal only with 2-D `x` case.
+        y, _ = _chk_asarray(y, axis)
+        if axisout == 0:
+            x = ma.column_stack((x, y))
+        else:
+            x = ma.vstack((x, y))
+
+    if axisout == 1:
+        # To simplify the code that follow (always use `n_obs, n_vars` shape)
+        x = x.T
+
+    if nan_policy == 'omit':
+        x = ma.masked_invalid(x)
+
+    def _spearmanr_2cols(x):
+        # Mask the same observations for all variables, and then drop those
+        # observations (can't leave them masked, rankdata is weird).
+        x = ma.mask_rowcols(x, axis=0)
+        x = x[~x.mask.any(axis=1), :]
+
+        # If either column is entirely NaN or Inf
+        if not np.any(x.data):
+            res = scipy.stats._stats_py.SignificanceResult(np.nan, np.nan)
+            res.correlation = np.nan
+            return res
+
+        m = ma.getmask(x)
+        n_obs = x.shape[0]
+        dof = n_obs - 2 - int(m.sum(axis=0)[0])
+        if dof < 0:
+            raise ValueError("The input must have at least 3 entries!")
+
+        # Gets the ranks and rank differences
+        x_ranked = rankdata(x, axis=0)
+        rs = ma.corrcoef(x_ranked, rowvar=False).data
+
+        # rs can have elements equal to 1, so avoid zero division warnings
+        with np.errstate(divide='ignore'):
+            # clip the small negative values possibly caused by rounding
+            # errors before taking the square root
+            t = rs * np.sqrt((dof / ((rs+1.0) * (1.0-rs))).clip(0))
+
+        t, prob = _ttest_finish(dof, t, alternative)
+
+        # For backwards compatibility, return scalars when comparing 2 columns
+        if rs.shape == (2, 2):
+            res = scipy.stats._stats_py.SignificanceResult(rs[1, 0],
+                                                           prob[1, 0])
+            res.correlation = rs[1, 0]
+            return res
+        else:
+            res = scipy.stats._stats_py.SignificanceResult(rs, prob)
+            res.correlation = rs
+            return res
+
+    # Need to do this per pair of variables, otherwise the dropped observations
+    # in a third column mess up the result for a pair.
+    n_vars = x.shape[1]
+    if n_vars == 2:
+        return _spearmanr_2cols(x)
+    else:
+        rs = np.ones((n_vars, n_vars), dtype=float)
+        prob = np.zeros((n_vars, n_vars), dtype=float)
+        for var1 in range(n_vars - 1):
+            for var2 in range(var1+1, n_vars):
+                result = _spearmanr_2cols(x[:, [var1, var2]])
+                rs[var1, var2] = result.correlation
+                rs[var2, var1] = result.correlation
+                prob[var1, var2] = result.pvalue
+                prob[var2, var1] = result.pvalue
+
+        res = scipy.stats._stats_py.SignificanceResult(rs, prob)
+        res.correlation = rs
+        return res
+
+
+def _kendall_p_exact(n, c, alternative='two-sided'):
+
+    # Use the fact that distribution is symmetric: always calculate a CDF in
+    # the left tail.
+    # This will be the one-sided p-value if `c` is on the side of
+    # the null distribution predicted by the alternative hypothesis.
+    # The two-sided p-value will be twice this value.
+    # If `c` is on the other side of the null distribution, we'll need to
+    # take the complement and add back the probability mass at `c`.
+    in_right_tail = (c >= (n*(n-1))//2 - c)
+    alternative_greater = (alternative == 'greater')
+    c = int(min(c, (n*(n-1))//2 - c))
+
+    # Exact p-value, see Maurice G. Kendall, "Rank Correlation Methods"
+    # (4th Edition), Charles Griffin & Co., 1970.
+    if n <= 0:
+        raise ValueError(f'n ({n}) must be positive')
+    elif c < 0 or 4*c > n*(n-1):
+        raise ValueError(f'c ({c}) must satisfy 0 <= 4c <= n(n-1) = {n*(n-1)}.')
+    elif n == 1:
+        prob = 1.0
+        p_mass_at_c = 1
+    elif n == 2:
+        prob = 1.0
+        p_mass_at_c = 0.5
+    elif c == 0:
+        prob = 2.0/math.factorial(n) if n < 171 else 0.0
+        p_mass_at_c = prob/2
+    elif c == 1:
+        prob = 2.0/math.factorial(n-1) if n < 172 else 0.0
+        p_mass_at_c = (n-1)/math.factorial(n)
+    elif 4*c == n*(n-1) and alternative == 'two-sided':
+        # I'm sure there's a simple formula for p_mass_at_c in this
+        # case, but I don't know it. Use generic formula for one-sided p-value.
+        prob = 1.0
+    elif n < 171:
+        new = np.zeros(c+1)
+        new[0:2] = 1.0
+        for j in range(3,n+1):
+            new = np.cumsum(new)
+            if j <= c:
+                new[j:] -= new[:c+1-j]
+        prob = 2.0*np.sum(new)/math.factorial(n)
+        p_mass_at_c = new[-1]/math.factorial(n)
+    else:
+        new = np.zeros(c+1)
+        new[0:2] = 1.0
+        for j in range(3, n+1):
+            new = np.cumsum(new)/j
+            if j <= c:
+                new[j:] -= new[:c+1-j]
+        prob = np.sum(new)
+        p_mass_at_c = new[-1]/2
+
+    if alternative != 'two-sided':
+        # if the alternative hypothesis and alternative agree,
+        # one-sided p-value is half the two-sided p-value
+        if in_right_tail == alternative_greater:
+            prob /= 2
+        else:
+            prob = 1 - prob/2 + p_mass_at_c
+
+    prob = np.clip(prob, 0, 1)
+
+    return prob
+
+
+def kendalltau(x, y, use_ties=True, use_missing=False, method='auto',
+               alternative='two-sided'):
+    """
+    Computes Kendall's rank correlation tau on two variables *x* and *y*.
+
+    Parameters
+    ----------
+    x : sequence
+        First data list (for example, time).
+    y : sequence
+        Second data list.
+    use_ties : {True, False}, optional
+        Whether ties correction should be performed.
+    use_missing : {False, True}, optional
+        Whether missing data should be allocated a rank of 0 (False) or the
+        average rank (True)
+    method : {'auto', 'asymptotic', 'exact'}, optional
+        Defines which method is used to calculate the p-value [1]_.
+        'asymptotic' uses a normal approximation valid for large samples.
+        'exact' computes the exact p-value, but can only be used if no ties
+        are present. As the sample size increases, the 'exact' computation
+        time may grow and the result may lose some precision.
+        'auto' is the default and selects the appropriate
+        method based on a trade-off between speed and accuracy.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        The following options are available:
+
+        * 'two-sided': the rank correlation is nonzero
+        * 'less': the rank correlation is negative (less than zero)
+        * 'greater':  the rank correlation is positive (greater than zero)
+
+    Returns
+    -------
+    res : SignificanceResult
+        An object containing attributes:
+
+        statistic : float
+           The tau statistic.
+        pvalue : float
+           The p-value for a hypothesis test whose null hypothesis is
+           an absence of association, tau = 0.
+
+    References
+    ----------
+    .. [1] Maurice G. Kendall, "Rank Correlation Methods" (4th Edition),
+           Charles Griffin & Co., 1970.
+
+    """
+    (x, y, n) = _chk_size(x, y)
+    (x, y) = (x.flatten(), y.flatten())
+    m = ma.mask_or(ma.getmask(x), ma.getmask(y))
+    if m is not nomask:
+        x = ma.array(x, mask=m, copy=True)
+        y = ma.array(y, mask=m, copy=True)
+        # need int() here, otherwise numpy defaults to 32 bit
+        # integer on all Windows architectures, causing overflow.
+        # int() will keep it infinite precision.
+        n -= int(m.sum())
+
+    if n < 2:
+        res = scipy.stats._stats_py.SignificanceResult(np.nan, np.nan)
+        res.correlation = np.nan
+        return res
+
+    rx = ma.masked_equal(rankdata(x, use_missing=use_missing), 0)
+    ry = ma.masked_equal(rankdata(y, use_missing=use_missing), 0)
+    idx = rx.argsort()
+    (rx, ry) = (rx[idx], ry[idx])
+    C = np.sum([((ry[i+1:] > ry[i]) * (rx[i+1:] > rx[i])).filled(0).sum()
+                for i in range(len(ry)-1)], dtype=float)
+    D = np.sum([((ry[i+1:] < ry[i])*(rx[i+1:] > rx[i])).filled(0).sum()
+                for i in range(len(ry)-1)], dtype=float)
+    xties = count_tied_groups(x)
+    yties = count_tied_groups(y)
+    if use_ties:
+        corr_x = np.sum([v*k*(k-1) for (k,v) in xties.items()], dtype=float)
+        corr_y = np.sum([v*k*(k-1) for (k,v) in yties.items()], dtype=float)
+        denom = ma.sqrt((n*(n-1)-corr_x)/2. * (n*(n-1)-corr_y)/2.)
+    else:
+        denom = n*(n-1)/2.
+    tau = (C-D) / denom
+
+    if method == 'exact' and (xties or yties):
+        raise ValueError("Ties found, exact method cannot be used.")
+
+    if method == 'auto':
+        if (not xties and not yties) and (n <= 33 or min(C, n*(n-1)/2.0-C) <= 1):
+            method = 'exact'
+        else:
+            method = 'asymptotic'
+
+    if not xties and not yties and method == 'exact':
+        prob = _kendall_p_exact(n, C, alternative)
+
+    elif method == 'asymptotic':
+        var_s = n*(n-1)*(2*n+5)
+        if use_ties:
+            var_s -= np.sum([v*k*(k-1)*(2*k+5)*1. for (k,v) in xties.items()])
+            var_s -= np.sum([v*k*(k-1)*(2*k+5)*1. for (k,v) in yties.items()])
+            v1 = (np.sum([v*k*(k-1) for (k, v) in xties.items()], dtype=float) *
+                  np.sum([v*k*(k-1) for (k, v) in yties.items()], dtype=float))
+            v1 /= 2.*n*(n-1)
+            if n > 2:
+                v2 = np.sum([v*k*(k-1)*(k-2) for (k,v) in xties.items()],
+                            dtype=float) * \
+                     np.sum([v*k*(k-1)*(k-2) for (k,v) in yties.items()],
+                            dtype=float)
+                v2 /= 9.*n*(n-1)*(n-2)
+            else:
+                v2 = 0
+        else:
+            v1 = v2 = 0
+
+        var_s /= 18.
+        var_s += (v1 + v2)
+        z = (C-D)/np.sqrt(var_s)
+        prob = scipy.stats._stats_py._get_pvalue(z, distributions.norm, alternative)
+    else:
+        raise ValueError("Unknown method "+str(method)+" specified, please "
+                         "use auto, exact or asymptotic.")
+
+    res = scipy.stats._stats_py.SignificanceResult(tau[()], prob[()])
+    res.correlation = tau
+    return res
+
+
+def kendalltau_seasonal(x):
+    """
+    Computes a multivariate Kendall's rank correlation tau, for seasonal data.
+
+    Parameters
+    ----------
+    x : 2-D ndarray
+        Array of seasonal data, with seasons in columns.
+
+    """  # numpydoc ignore=RT01
+    x = ma.array(x, subok=True, copy=False, ndmin=2)
+    (n,m) = x.shape
+    n_p = x.count(0)
+
+    S_szn = sum(msign(x[i:]-x[i]).sum(0) for i in range(n))
+    S_tot = S_szn.sum()
+
+    n_tot = x.count()
+    ties = count_tied_groups(x.compressed())
+    corr_ties = sum(v*k*(k-1) for (k,v) in ties.items())
+    denom_tot = ma.sqrt(1.*n_tot*(n_tot-1)*(n_tot*(n_tot-1)-corr_ties))/2.
+
+    R = rankdata(x, axis=0, use_missing=True)
+    K = ma.empty((m,m), dtype=int)
+    covmat = ma.empty((m,m), dtype=float)
+    denom_szn = ma.empty(m, dtype=float)
+    for j in range(m):
+        ties_j = count_tied_groups(x[:,j].compressed())
+        corr_j = sum(v*k*(k-1) for (k,v) in ties_j.items())
+        cmb = n_p[j]*(n_p[j]-1)
+        for k in range(j,m,1):
+            K[j,k] = sum(msign((x[i:,j]-x[i,j])*(x[i:,k]-x[i,k])).sum()
+                         for i in range(n))
+            covmat[j,k] = (K[j,k] + 4*(R[:,j]*R[:,k]).sum() -
+                           n*(n_p[j]+1)*(n_p[k]+1))/3.
+            K[k,j] = K[j,k]
+            covmat[k,j] = covmat[j,k]
+
+        denom_szn[j] = ma.sqrt(cmb*(cmb-corr_j)) / 2.
+
+    var_szn = covmat.diagonal()
+
+    z_szn = msign(S_szn) * (abs(S_szn)-1) / ma.sqrt(var_szn)
+    z_tot_ind = msign(S_tot) * (abs(S_tot)-1) / ma.sqrt(var_szn.sum())
+    z_tot_dep = msign(S_tot) * (abs(S_tot)-1) / ma.sqrt(covmat.sum())
+
+    prob_szn = special.erfc(abs(z_szn.data)/np.sqrt(2))
+    prob_tot_ind = special.erfc(abs(z_tot_ind)/np.sqrt(2))
+    prob_tot_dep = special.erfc(abs(z_tot_dep)/np.sqrt(2))
+
+    chi2_tot = (z_szn*z_szn).sum()
+    chi2_trd = m * z_szn.mean()**2
+    output = {'seasonal tau': S_szn/denom_szn,
+              'global tau': S_tot/denom_tot,
+              'global tau (alt)': S_tot/denom_szn.sum(),
+              'seasonal p-value': prob_szn,
+              'global p-value (indep)': prob_tot_ind,
+              'global p-value (dep)': prob_tot_dep,
+              'chi2 total': chi2_tot,
+              'chi2 trend': chi2_trd,
+              }
+    return output
+
+
+PointbiserialrResult = namedtuple('PointbiserialrResult', ('correlation',
+                                                           'pvalue'))
+
+
+def pointbiserialr(x, y):
+    """Calculates a point biserial correlation coefficient and its p-value.
+
+    Parameters
+    ----------
+    x : array_like of bools
+        Input array.
+    y : array_like
+        Input array.
+
+    Returns
+    -------
+    correlation : float
+        R value
+    pvalue : float
+        2-tailed p-value
+
+    Notes
+    -----
+    Missing values are considered pair-wise: if a value is missing in x,
+    the corresponding value in y is masked.
+
+    For more details on `pointbiserialr`, see `scipy.stats.pointbiserialr`.
+
+    """
+    x = ma.fix_invalid(x, copy=True).astype(bool)
+    y = ma.fix_invalid(y, copy=True).astype(float)
+    # Get rid of the missing data
+    m = ma.mask_or(ma.getmask(x), ma.getmask(y))
+    if m is not nomask:
+        unmask = np.logical_not(m)
+        x = x[unmask]
+        y = y[unmask]
+
+    n = len(x)
+    # phat is the fraction of x values that are True
+    phat = x.sum() / float(n)
+    y0 = y[~x]  # y-values where x is False
+    y1 = y[x]  # y-values where x is True
+    y0m = y0.mean()
+    y1m = y1.mean()
+
+    rpb = (y1m - y0m)*np.sqrt(phat * (1-phat)) / y.std()
+
+    df = n-2
+    t = rpb*ma.sqrt(df/(1.0-rpb**2))
+    prob = _betai(0.5*df, 0.5, df/(df+t*t))
+
+    return PointbiserialrResult(rpb, prob)
+
+
+def linregress(x, y=None):
+    r"""
+    Calculate a linear least-squares regression for two sets of measurements.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Two sets of measurements.  Both arrays should have the same length N.  If
+        only `x` is given (and ``y=None``), then it must be a two-dimensional
+        array where one dimension has length 2.  The two sets of measurements
+        are then found by splitting the array along the length-2 dimension. In
+        the case where ``y=None`` and `x` is a 2xN array, ``linregress(x)`` is
+        equivalent to ``linregress(x[0], x[1])``.
+
+    Returns
+    -------
+    result : ``LinregressResult`` instance
+        The return value is an object with the following attributes:
+
+        slope : float
+            Slope of the regression line.
+        intercept : float
+            Intercept of the regression line.
+        rvalue : float
+            The Pearson correlation coefficient. The square of ``rvalue``
+            is equal to the coefficient of determination.
+        pvalue : float
+            The p-value for a hypothesis test whose null hypothesis is
+            that the slope is zero, using Wald Test with t-distribution of
+            the test statistic. See `alternative` above for alternative
+            hypotheses.
+        stderr : float
+            Standard error of the estimated slope (gradient), under the
+            assumption of residual normality.
+        intercept_stderr : float
+            Standard error of the estimated intercept, under the assumption
+            of residual normality.
+
+    See Also
+    --------
+    scipy.optimize.curve_fit :
+        Use non-linear least squares to fit a function to data.
+    scipy.optimize.leastsq :
+        Minimize the sum of squares of a set of equations.
+
+    Notes
+    -----
+    Missing values are considered pair-wise: if a value is missing in `x`,
+    the corresponding value in `y` is masked.
+
+    For compatibility with older versions of SciPy, the return value acts
+    like a ``namedtuple`` of length 5, with fields ``slope``, ``intercept``,
+    ``rvalue``, ``pvalue`` and ``stderr``, so one can continue to write::
+
+        slope, intercept, r, p, se = linregress(x, y)
+
+    With that style, however, the standard error of the intercept is not
+    available.  To have access to all the computed values, including the
+    standard error of the intercept, use the return value as an object
+    with attributes, e.g.::
+
+        result = linregress(x, y)
+        print(result.intercept, result.intercept_stderr)
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+
+    Generate some data:
+
+    >>> x = rng.random(10)
+    >>> y = 1.6*x + rng.random(10)
+
+    Perform the linear regression:
+
+    >>> res = stats.mstats.linregress(x, y)
+
+    Coefficient of determination (R-squared):
+
+    >>> print(f"R-squared: {res.rvalue**2:.6f}")
+    R-squared: 0.717533
+
+    Plot the data along with the fitted line:
+
+    >>> plt.plot(x, y, 'o', label='original data')
+    >>> plt.plot(x, res.intercept + res.slope*x, 'r', label='fitted line')
+    >>> plt.legend()
+    >>> plt.show()
+
+    Calculate 95% confidence interval on slope and intercept:
+
+    >>> # Two-sided inverse Students t-distribution
+    >>> # p - probability, df - degrees of freedom
+    >>> from scipy.stats import t
+    >>> tinv = lambda p, df: abs(t.ppf(p/2, df))
+
+    >>> ts = tinv(0.05, len(x)-2)
+    >>> print(f"slope (95%): {res.slope:.6f} +/- {ts*res.stderr:.6f}")
+    slope (95%): 1.453392 +/- 0.743465
+    >>> print(f"intercept (95%): {res.intercept:.6f}"
+    ...       f" +/- {ts*res.intercept_stderr:.6f}")
+    intercept (95%): 0.616950 +/- 0.544475
+
+    """
+    if y is None:
+        x = ma.array(x)
+        if x.shape[0] == 2:
+            x, y = x
+        elif x.shape[1] == 2:
+            x, y = x.T
+        else:
+            raise ValueError("If only `x` is given as input, "
+                             "it has to be of shape (2, N) or (N, 2), "
+                             f"provided shape was {x.shape}")
+    else:
+        x = ma.array(x)
+        y = ma.array(y)
+
+    x = x.flatten()
+    y = y.flatten()
+
+    if np.amax(x) == np.amin(x) and len(x) > 1:
+        raise ValueError("Cannot calculate a linear regression "
+                         "if all x values are identical")
+
+    m = ma.mask_or(ma.getmask(x), ma.getmask(y), shrink=False)
+    if m is not nomask:
+        x = ma.array(x, mask=m)
+        y = ma.array(y, mask=m)
+        if np.any(~m):
+            result = _stats_py.linregress(x.data[~m], y.data[~m])
+        else:
+            # All data is masked
+            result = _stats_py.LinregressResult(slope=None, intercept=None,
+                                                rvalue=None, pvalue=None,
+                                                stderr=None,
+                                                intercept_stderr=None)
+    else:
+        result = _stats_py.linregress(x.data, y.data)
+
+    return result
+
+
+def _theilslopes(y, x=None, alpha=0.95, method='separate'):
+    r"""
+    Computes the Theil-Sen estimator for a set of points (x, y).
+
+    `theilslopes` implements a method for robust linear regression.  It
+    computes the slope as the median of all slopes between paired values.
+
+    Parameters
+    ----------
+    y : array_like
+        Dependent variable.
+    x : array_like or None, optional
+        Independent variable. If None, use ``arange(len(y))`` instead.
+    alpha : float, optional
+        Confidence degree between 0 and 1. Default is 95% confidence.
+        Note that `alpha` is symmetric around 0.5, i.e. both 0.1 and 0.9 are
+        interpreted as "find the 90% confidence interval".
+    method : {'joint', 'separate'}, optional
+        Method to be used for computing estimate for intercept.
+        Following methods are supported,
+
+        * 'joint': Uses np.median(y - slope * x) as intercept.
+        * 'separate': Uses np.median(y) - slope * np.median(x)
+                      as intercept.
+
+        The default is 'separate'.
+
+        .. versionadded:: 1.8.0
+
+    Returns
+    -------
+    result : ``TheilslopesResult`` instance
+        The return value is an object with the following attributes:
+
+        slope : float
+            Theil slope.
+        intercept : float
+            Intercept of the Theil line.
+        low_slope : float
+            Lower bound of the confidence interval on `slope`.
+        high_slope : float
+            Upper bound of the confidence interval on `slope`.
+
+    See Also
+    --------
+    siegelslopes : a similar technique using repeated medians
+
+    Notes
+    -----
+    The implementation of `theilslopes` follows [1]_. The intercept is
+    not defined in [1]_, and here it is defined as ``median(y) -
+    slope*median(x)``, which is given in [3]_. Other definitions of
+    the intercept exist in the literature such as  ``median(y - slope*x)``
+    in [4]_. The approach to compute the intercept can be determined by the
+    parameter ``method``. A confidence interval for the intercept is not
+    given as this question is not addressed in [1]_.
+
+    For compatibility with older versions of SciPy, the return value acts
+    like a ``namedtuple`` of length 4, with fields ``slope``, ``intercept``,
+    ``low_slope``, and ``high_slope``, so one can continue to write::
+
+        slope, intercept, low_slope, high_slope = theilslopes(y, x)
+
+    References
+    ----------
+    .. [1] P.K. Sen, "Estimates of the regression coefficient based on
+           Kendall's tau", J. Am. Stat. Assoc., Vol. 63, pp. 1379-1389, 1968.
+    .. [2] H. Theil, "A rank-invariant method of linear and polynomial
+           regression analysis I, II and III",  Nederl. Akad. Wetensch., Proc.
+           53:, pp. 386-392, pp. 521-525, pp. 1397-1412, 1950.
+    .. [3] W.L. Conover, "Practical nonparametric statistics", 2nd ed.,
+           John Wiley and Sons, New York, pp. 493.
+    .. [4] https://en.wikipedia.org/wiki/Theil%E2%80%93Sen_estimator
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+
+    >>> x = np.linspace(-5, 5, num=150)
+    >>> y = x + np.random.normal(size=x.size)
+    >>> y[11:15] += 10  # add outliers
+    >>> y[-5:] -= 7
+
+    Compute the slope, intercept and 90% confidence interval.  For comparison,
+    also compute the least-squares fit with `linregress`:
+
+    >>> res = stats.theilslopes(y, x, 0.90, method='separate')
+    >>> lsq_res = stats.linregress(x, y)
+
+    Plot the results. The Theil-Sen regression line is shown in red, with the
+    dashed red lines illustrating the confidence interval of the slope (note
+    that the dashed red lines are not the confidence interval of the regression
+    as the confidence interval of the intercept is not included). The green
+    line shows the least-squares fit for comparison.
+
+    >>> fig = plt.figure()
+    >>> ax = fig.add_subplot(111)
+    >>> ax.plot(x, y, 'b.')
+    >>> ax.plot(x, res[1] + res[0] * x, 'r-')
+    >>> ax.plot(x, res[1] + res[2] * x, 'r--')
+    >>> ax.plot(x, res[1] + res[3] * x, 'r--')
+    >>> ax.plot(x, lsq_res[1] + lsq_res[0] * x, 'g-')
+    >>> plt.show()
+
+    """
+    if method not in ['joint', 'separate']:
+        raise ValueError("method must be either 'joint' or 'separate'."
+                         f"'{method}' is invalid.")
+    # We copy both x and y so we can use _find_repeats.
+    y = np.array(y, dtype=float, copy=True).ravel()
+    if x is None:
+        x = np.arange(len(y), dtype=float)
+    else:
+        x = np.array(x, dtype=float, copy=True).ravel()
+        if len(x) != len(y):
+            raise ValueError("Array shapes are incompatible for broadcasting.")
+    if len(x) < 2:
+        raise ValueError("`x` and `y` must have length at least 2.")
+
+    # Compute sorted slopes only when deltax > 0
+    deltax = x[:, np.newaxis] - x
+    deltay = y[:, np.newaxis] - y
+    slopes = deltay[deltax > 0] / deltax[deltax > 0]
+    if not slopes.size:
+        msg = "All `x` coordinates are identical."
+        warnings.warn(msg, RuntimeWarning, stacklevel=2)
+    slopes.sort()
+    medslope = np.median(slopes)
+    if method == 'joint':
+        medinter = np.median(y - medslope * x)
+    else:
+        medinter = np.median(y) - medslope * np.median(x)
+    # Now compute confidence intervals
+    if alpha > 0.5:
+        alpha = 1. - alpha
+
+    z = distributions.norm.ppf(alpha / 2.)
+    # This implements (2.6) from Sen (1968)
+    _, nxreps = _find_repeats(x)
+    _, nyreps = _find_repeats(y)
+    nt = len(slopes)       # N in Sen (1968)
+    ny = len(y)            # n in Sen (1968)
+    # Equation 2.6 in Sen (1968):
+    sigsq = 1/18. * (ny * (ny-1) * (2*ny+5) -
+                     sum(k * (k-1) * (2*k + 5) for k in nxreps) -
+                     sum(k * (k-1) * (2*k + 5) for k in nyreps))
+    # Find the confidence interval indices in `slopes`
+    try:
+        sigma = np.sqrt(sigsq)
+        Ru = min(int(np.round((nt - z*sigma)/2.)), len(slopes)-1)
+        Rl = max(int(np.round((nt + z*sigma)/2.)) - 1, 0)
+        delta = slopes[[Rl, Ru]]
+    except (ValueError, IndexError):
+        delta = (np.nan, np.nan)
+
+    return TheilslopesResult(slope=medslope, intercept=medinter,
+                             low_slope=delta[0], high_slope=delta[1])
+
+
+def theilslopes(y, x=None, alpha=0.95, method='separate'):
+    r"""
+    Computes the Theil-Sen estimator for a set of points (x, y).
+
+    `theilslopes` implements a method for robust linear regression.  It
+    computes the slope as the median of all slopes between paired values.
+
+    Parameters
+    ----------
+    y : array_like
+        Dependent variable.
+    x : array_like or None, optional
+        Independent variable. If None, use ``arange(len(y))`` instead.
+    alpha : float, optional
+        Confidence degree between 0 and 1. Default is 95% confidence.
+        Note that `alpha` is symmetric around 0.5, i.e. both 0.1 and 0.9 are
+        interpreted as "find the 90% confidence interval".
+    method : {'joint', 'separate'}, optional
+        Method to be used for computing estimate for intercept.
+        Following methods are supported,
+
+        * 'joint': Uses np.median(y - slope * x) as intercept.
+        * 'separate': Uses np.median(y) - slope * np.median(x)
+                      as intercept.
+
+        The default is 'separate'.
+
+        .. versionadded:: 1.8.0
+
+    Returns
+    -------
+    result : ``TheilslopesResult`` instance
+        The return value is an object with the following attributes:
+
+        slope : float
+            Theil slope.
+        intercept : float
+            Intercept of the Theil line.
+        low_slope : float
+            Lower bound of the confidence interval on `slope`.
+        high_slope : float
+            Upper bound of the confidence interval on `slope`.
+
+    See Also
+    --------
+    siegelslopes : a similar technique using repeated medians
+
+
+    Notes
+    -----
+    For more details on `theilslopes`, see `scipy.stats.theilslopes`.
+
+    """
+    y = ma.asarray(y).flatten()
+    if x is None:
+        x = ma.arange(len(y), dtype=float)
+    else:
+        x = ma.asarray(x).flatten()
+        if len(x) != len(y):
+            raise ValueError(f"Incompatible lengths ! ({len(y)}<>{len(x)})")
+
+    m = ma.mask_or(ma.getmask(x), ma.getmask(y))
+    y._mask = x._mask = m
+    # Disregard any masked elements of x or y
+    y = y.compressed()
+    x = x.compressed().astype(float)
+    # We now have unmasked arrays so can use `scipy.stats.theilslopes`
+    return _theilslopes(y, x, alpha=alpha, method=method)
+
+
+def _siegelslopes(y, x=None, method="hierarchical"):
+    if method not in ['hierarchical', 'separate']:
+        raise ValueError("method can only be 'hierarchical' or 'separate'")
+    y = np.asarray(y).ravel()
+    if x is None:
+        x = np.arange(len(y), dtype=float)
+    else:
+        x = np.asarray(x, dtype=float).ravel()
+        if len(x) != len(y):
+            raise ValueError("Array shapes are incompatible for broadcasting.")
+    if len(x) < 2:
+        raise ValueError("`x` and `y` must have length at least 2.")
+
+    dtype = np.result_type(x, y, np.float32)  # use at least float32
+    y, x = y.astype(dtype), x.astype(dtype)
+    medslope, medinter = siegelslopes_pythran(y, x, method)
+    medslope, medinter = np.asarray(medslope)[()], np.asarray(medinter)[()]
+    return SiegelslopesResult(slope=medslope, intercept=medinter)
+
+
+def siegelslopes(y, x=None, method="hierarchical"):
+    r"""
+    Computes the Siegel estimator for a set of points (x, y).
+
+    `siegelslopes` implements a method for robust linear regression
+    using repeated medians to fit a line to the points (x, y).
+    The method is robust to outliers with an asymptotic breakdown point
+    of 50%.
+
+    Parameters
+    ----------
+    y : array_like
+        Dependent variable.
+    x : array_like or None, optional
+        Independent variable. If None, use ``arange(len(y))`` instead.
+    method : {'hierarchical', 'separate'}
+        If 'hierarchical', estimate the intercept using the estimated
+        slope ``slope`` (default option).
+        If 'separate', estimate the intercept independent of the estimated
+        slope. See Notes for details.
+
+    Returns
+    -------
+    result : ``SiegelslopesResult`` instance
+        The return value is an object with the following attributes:
+
+        slope : float
+            Estimate of the slope of the regression line.
+        intercept : float
+            Estimate of the intercept of the regression line.
+
+    See Also
+    --------
+    theilslopes : a similar technique without repeated medians
+
+    Notes
+    -----
+    For more details on `siegelslopes`, see `scipy.stats.siegelslopes`.
+
+    """
+    y = ma.asarray(y).ravel()
+    if x is None:
+        x = ma.arange(len(y), dtype=float)
+    else:
+        x = ma.asarray(x).ravel()
+        if len(x) != len(y):
+            raise ValueError(f"Incompatible lengths ! ({len(y)}<>{len(x)})")
+
+    m = ma.mask_or(ma.getmask(x), ma.getmask(y))
+    y._mask = x._mask = m
+    # Disregard any masked elements of x or y
+    y = y.compressed()
+    x = x.compressed().astype(float)
+    # We now have unmasked arrays so can use `scipy.stats.siegelslopes`
+    return _siegelslopes(y, x, method=method)
+
+
+SenSeasonalSlopesResult = _make_tuple_bunch('SenSeasonalSlopesResult',
+                                            ['intra_slope', 'inter_slope'])
+
+
+def sen_seasonal_slopes(x):
+    r"""
+    Computes seasonal Theil-Sen and Kendall slope estimators.
+
+    The seasonal generalization of Sen's slope computes the slopes between all
+    pairs of values within a "season" (column) of a 2D array. It returns an
+    array containing the median of these "within-season" slopes for each
+    season (the Theil-Sen slope estimator of each season), and it returns the
+    median of the within-season slopes across all seasons (the seasonal Kendall
+    slope estimator).
+
+    Parameters
+    ----------
+    x : 2D array_like
+        Each column of `x` contains measurements of the dependent variable
+        within a season. The independent variable (usually time) of each season
+        is assumed to be ``np.arange(x.shape[0])``.
+
+    Returns
+    -------
+    result : ``SenSeasonalSlopesResult`` instance
+        The return value is an object with the following attributes:
+
+        intra_slope : ndarray
+            For each season, the Theil-Sen slope estimator: the median of
+            within-season slopes.
+        inter_slope : float
+            The seasonal Kendall slope estimator: the median of within-season
+            slopes *across all* seasons.
+
+    See Also
+    --------
+    theilslopes : the analogous function for non-seasonal data
+    scipy.stats.theilslopes : non-seasonal slopes for non-masked arrays
+
+    Notes
+    -----
+    The slopes :math:`d_{ijk}` within season :math:`i` are:
+
+    .. math::
+
+        d_{ijk} = \frac{x_{ij} - x_{ik}}
+                            {j - k}
+
+    for pairs of distinct integer indices :math:`j, k` of :math:`x`.
+
+    Element :math:`i` of the returned `intra_slope` array is the median of the
+    :math:`d_{ijk}` over all :math:`j < k`; this is the Theil-Sen slope
+    estimator of season :math:`i`. The returned `inter_slope` value, better
+    known as the seasonal Kendall slope estimator, is the median of the
+    :math:`d_{ijk}` over all :math:`i, j, k`.
+
+    References
+    ----------
+    .. [1] Hirsch, Robert M., James R. Slack, and Richard A. Smith.
+           "Techniques of trend analysis for monthly water quality data."
+           *Water Resources Research* 18.1 (1982): 107-121.
+
+    Examples
+    --------
+    Suppose we have 100 observations of a dependent variable for each of four
+    seasons:
+
+    >>> import numpy as np
+    >>> rng = np.random.default_rng()
+    >>> x = rng.random(size=(100, 4))
+
+    We compute the seasonal slopes as:
+
+    >>> from scipy import stats
+    >>> intra_slope, inter_slope = stats.mstats.sen_seasonal_slopes(x)
+
+    If we define a function to compute all slopes between observations within
+    a season:
+
+    >>> def dijk(yi):
+    ...     n = len(yi)
+    ...     x = np.arange(n)
+    ...     dy = yi - yi[:, np.newaxis]
+    ...     dx = x - x[:, np.newaxis]
+    ...     # we only want unique pairs of distinct indices
+    ...     mask = np.triu(np.ones((n, n), dtype=bool), k=1)
+    ...     return dy[mask]/dx[mask]
+
+    then element ``i`` of ``intra_slope`` is the median of ``dijk[x[:, i]]``:
+
+    >>> i = 2
+    >>> np.allclose(np.median(dijk(x[:, i])), intra_slope[i])
+    True
+
+    and ``inter_slope`` is the median of the values returned by ``dijk`` for
+    all seasons:
+
+    >>> all_slopes = np.concatenate([dijk(x[:, i]) for i in range(x.shape[1])])
+    >>> np.allclose(np.median(all_slopes), inter_slope)
+    True
+
+    Because the data are randomly generated, we would expect the median slopes
+    to be nearly zero both within and across all seasons, and indeed they are:
+
+    >>> intra_slope.data
+    array([ 0.00124504, -0.00277761, -0.00221245, -0.00036338])
+    >>> inter_slope
+    -0.0010511779872922058
+
+    """
+    x = ma.array(x, subok=True, copy=False, ndmin=2)
+    (n,_) = x.shape
+    # Get list of slopes per season
+    szn_slopes = ma.vstack([(x[i+1:]-x[i])/np.arange(1,n-i)[:,None]
+                            for i in range(n)])
+    szn_medslopes = ma.median(szn_slopes, axis=0)
+    medslope = ma.median(szn_slopes, axis=None)
+    return SenSeasonalSlopesResult(szn_medslopes, medslope)
+
+
+Ttest_1sampResult = namedtuple('Ttest_1sampResult', ('statistic', 'pvalue'))
+
+
+def ttest_1samp(a, popmean, axis=0, alternative='two-sided'):
+    """
+    Calculates the T-test for the mean of ONE group of scores.
+
+    Parameters
+    ----------
+    a : array_like
+        sample observation
+    popmean : float or array_like
+        expected value in null hypothesis, if array_like than it must have the
+        same shape as `a` excluding the axis dimension
+    axis : int or None, optional
+        Axis along which to compute test. If None, compute over the whole
+        array `a`.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+        The following options are available (default is 'two-sided'):
+
+        * 'two-sided': the mean of the underlying distribution of the sample
+          is different than the given population mean (`popmean`)
+        * 'less': the mean of the underlying distribution of the sample is
+          less than the given population mean (`popmean`)
+        * 'greater': the mean of the underlying distribution of the sample is
+          greater than the given population mean (`popmean`)
+
+        .. versionadded:: 1.7.0
+
+    Returns
+    -------
+    statistic : float or array
+        t-statistic
+    pvalue : float or array
+        The p-value
+
+    Notes
+    -----
+    For more details on `ttest_1samp`, see `scipy.stats.ttest_1samp`.
+
+    """
+    a, axis = _chk_asarray(a, axis)
+    if a.size == 0:
+        return (np.nan, np.nan)
+
+    x = a.mean(axis=axis)
+    v = a.var(axis=axis, ddof=1)
+    n = a.count(axis=axis)
+    # force df to be an array for masked division not to throw a warning
+    df = ma.asanyarray(n - 1.0)
+    svar = ((n - 1.0) * v) / df
+    with np.errstate(divide='ignore', invalid='ignore'):
+        t = (x - popmean) / ma.sqrt(svar / n)
+
+    t, prob = _ttest_finish(df, t, alternative)
+    return Ttest_1sampResult(t, prob)
+
+
+ttest_onesamp = ttest_1samp
+
+
+Ttest_indResult = namedtuple('Ttest_indResult', ('statistic', 'pvalue'))
+
+
+def ttest_ind(a, b, axis=0, equal_var=True, alternative='two-sided'):
+    """
+    Calculates the T-test for the means of TWO INDEPENDENT samples of scores.
+
+    Parameters
+    ----------
+    a, b : array_like
+        The arrays must have the same shape, except in the dimension
+        corresponding to `axis` (the first, by default).
+    axis : int or None, optional
+        Axis along which to compute test. If None, compute over the whole
+        arrays, `a`, and `b`.
+    equal_var : bool, optional
+        If True, perform a standard independent 2 sample test that assumes equal
+        population variances.
+        If False, perform Welch's t-test, which does not assume equal population
+        variance.
+
+        .. versionadded:: 0.17.0
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+        The following options are available (default is 'two-sided'):
+
+        * 'two-sided': the means of the distributions underlying the samples
+          are unequal.
+        * 'less': the mean of the distribution underlying the first sample
+          is less than the mean of the distribution underlying the second
+          sample.
+        * 'greater': the mean of the distribution underlying the first
+          sample is greater than the mean of the distribution underlying
+          the second sample.
+
+        .. versionadded:: 1.7.0
+
+    Returns
+    -------
+    statistic : float or array
+        The calculated t-statistic.
+    pvalue : float or array
+        The p-value.
+
+    Notes
+    -----
+    For more details on `ttest_ind`, see `scipy.stats.ttest_ind`.
+
+    """
+    a, b, axis = _chk2_asarray(a, b, axis)
+
+    if a.size == 0 or b.size == 0:
+        return Ttest_indResult(np.nan, np.nan)
+
+    (x1, x2) = (a.mean(axis), b.mean(axis))
+    (v1, v2) = (a.var(axis=axis, ddof=1), b.var(axis=axis, ddof=1))
+    (n1, n2) = (a.count(axis), b.count(axis))
+
+    if equal_var:
+        # force df to be an array for masked division not to throw a warning
+        df = ma.asanyarray(n1 + n2 - 2.0)
+        svar = ((n1-1)*v1+(n2-1)*v2) / df
+        denom = ma.sqrt(svar*(1.0/n1 + 1.0/n2))  # n-D computation here!
+    else:
+        vn1 = v1/n1
+        vn2 = v2/n2
+        with np.errstate(divide='ignore', invalid='ignore'):
+            df = (vn1 + vn2)**2 / (vn1**2 / (n1 - 1) + vn2**2 / (n2 - 1))
+
+        # If df is undefined, variances are zero.
+        # It doesn't matter what df is as long as it is not NaN.
+        df = np.where(np.isnan(df), 1, df)
+        denom = ma.sqrt(vn1 + vn2)
+
+    with np.errstate(divide='ignore', invalid='ignore'):
+        t = (x1-x2) / denom
+
+    t, prob = _ttest_finish(df, t, alternative)
+    return Ttest_indResult(t, prob)
+
+
+Ttest_relResult = namedtuple('Ttest_relResult', ('statistic', 'pvalue'))
+
+
+def ttest_rel(a, b, axis=0, alternative='two-sided'):
+    """
+    Calculates the T-test on TWO RELATED samples of scores, a and b.
+
+    Parameters
+    ----------
+    a, b : array_like
+        The arrays must have the same shape.
+    axis : int or None, optional
+        Axis along which to compute test. If None, compute over the whole
+        arrays, `a`, and `b`.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+        The following options are available (default is 'two-sided'):
+
+        * 'two-sided': the means of the distributions underlying the samples
+          are unequal.
+        * 'less': the mean of the distribution underlying the first sample
+          is less than the mean of the distribution underlying the second
+          sample.
+        * 'greater': the mean of the distribution underlying the first
+          sample is greater than the mean of the distribution underlying
+          the second sample.
+
+        .. versionadded:: 1.7.0
+
+    Returns
+    -------
+    statistic : float or array
+        t-statistic
+    pvalue : float or array
+        two-tailed p-value
+
+    Notes
+    -----
+    For more details on `ttest_rel`, see `scipy.stats.ttest_rel`.
+
+    """
+    a, b, axis = _chk2_asarray(a, b, axis)
+    if len(a) != len(b):
+        raise ValueError('unequal length arrays')
+
+    if a.size == 0 or b.size == 0:
+        return Ttest_relResult(np.nan, np.nan)
+
+    n = a.count(axis)
+    df = ma.asanyarray(n-1.0)
+    d = (a-b).astype('d')
+    dm = d.mean(axis)
+    v = d.var(axis=axis, ddof=1)
+    denom = ma.sqrt(v / n)
+    with np.errstate(divide='ignore', invalid='ignore'):
+        t = dm / denom
+
+    t, prob = _ttest_finish(df, t, alternative)
+    return Ttest_relResult(t, prob)
+
+
+MannwhitneyuResult = namedtuple('MannwhitneyuResult', ('statistic',
+                                                       'pvalue'))
+
+
+def mannwhitneyu(x,y, use_continuity=True):
+    """
+    Computes the Mann-Whitney statistic
+
+    Missing values in `x` and/or `y` are discarded.
+
+    Parameters
+    ----------
+    x : sequence
+        Input
+    y : sequence
+        Input
+    use_continuity : {True, False}, optional
+        Whether a continuity correction (1/2.) should be taken into account.
+
+    Returns
+    -------
+    statistic : float
+        The minimum of the Mann-Whitney statistics
+    pvalue : float
+        Approximate two-sided p-value assuming a normal distribution.
+
+    """
+    x = ma.asarray(x).compressed().view(ndarray)
+    y = ma.asarray(y).compressed().view(ndarray)
+    ranks = rankdata(np.concatenate([x,y]))
+    (nx, ny) = (len(x), len(y))
+    nt = nx + ny
+    U = ranks[:nx].sum() - nx*(nx+1)/2.
+    U = max(U, nx*ny - U)
+    u = nx*ny - U
+
+    mu = (nx*ny)/2.
+    sigsq = (nt**3 - nt)/12.
+    ties = count_tied_groups(ranks)
+    sigsq -= sum(v*(k**3-k) for (k,v) in ties.items())/12.
+    sigsq *= nx*ny/float(nt*(nt-1))
+
+    if use_continuity:
+        z = (U - 1/2. - mu) / ma.sqrt(sigsq)
+    else:
+        z = (U - mu) / ma.sqrt(sigsq)
+
+    prob = special.erfc(abs(z)/np.sqrt(2))
+    return MannwhitneyuResult(u, prob)
+
+
+KruskalResult = namedtuple('KruskalResult', ('statistic', 'pvalue'))
+
+
+def kruskal(*samples):
+    """
+    Compute the Kruskal-Wallis H-test for independent samples
+
+    Parameters
+    ----------
+    *samples : array_like
+       Two or more arrays with the sample measurements can be given as
+       arguments.
+
+    Returns
+    -------
+    statistic : float
+       The Kruskal-Wallis H statistic, corrected for ties
+    pvalue : float
+       The p-value for the test using the assumption that H has a chi
+       square distribution
+
+    Notes
+    -----
+    For more details on `kruskal`, see `scipy.stats.kruskal`.
+
+    Examples
+    --------
+    >>> from scipy.stats.mstats import kruskal
+
+    Random samples from three different brands of batteries were tested
+    to see how long the charge lasted. Results were as follows:
+
+    >>> a = [6.3, 5.4, 5.7, 5.2, 5.0]
+    >>> b = [6.9, 7.0, 6.1, 7.9]
+    >>> c = [7.2, 6.9, 6.1, 6.5]
+
+    Test the hypothesis that the distribution functions for all of the brands'
+    durations are identical. Use 5% level of significance.
+
+    >>> kruskal(a, b, c)
+    KruskalResult(statistic=7.113812154696133, pvalue=0.028526948491942164)
+
+    The null hypothesis is rejected at the 5% level of significance
+    because the returned p-value is less than the critical value of 5%.
+
+    """
+    output = argstoarray(*samples)
+    ranks = ma.masked_equal(rankdata(output, use_missing=False), 0)
+    sumrk = ranks.sum(-1)
+    ngrp = ranks.count(-1)
+    ntot = ranks.count()
+    H = 12./(ntot*(ntot+1)) * (sumrk**2/ngrp).sum() - 3*(ntot+1)
+    # Tie correction
+    ties = count_tied_groups(ranks)
+    T = 1. - sum(v*(k**3-k) for (k,v) in ties.items())/float(ntot**3-ntot)
+    if T == 0:
+        raise ValueError('All numbers are identical in kruskal')
+
+    H /= T
+    df = len(output) - 1
+    prob = distributions.chi2.sf(H, df)
+    return KruskalResult(H, prob)
+
+
+kruskalwallis = kruskal
+
+
+@_rename_parameter("mode", "method")
+def ks_1samp(x, cdf, args=(), alternative="two-sided", method='auto'):
+    """
+    Computes the Kolmogorov-Smirnov test on one sample of masked values.
+
+    Missing values in `x` are discarded.
+
+    Parameters
+    ----------
+    x : array_like
+        a 1-D array of observations of random variables.
+    cdf : str or callable
+        If a string, it should be the name of a distribution in `scipy.stats`.
+        If a callable, that callable is used to calculate the cdf.
+    args : tuple, sequence, optional
+        Distribution parameters, used if `cdf` is a string.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Indicates the alternative hypothesis.  Default is 'two-sided'.
+    method : {'auto', 'exact', 'asymp'}, optional
+        Defines the method used for calculating the p-value.
+        The following options are available (default is 'auto'):
+
+        * 'auto' : use 'exact' for small size arrays, 'asymp' for large
+        * 'exact' : use approximation to exact distribution of test statistic
+        * 'asymp' : use asymptotic distribution of test statistic
+
+    Returns
+    -------
+    d : float
+        Value of the Kolmogorov Smirnov test
+    p : float
+        Corresponding p-value.
+
+    """
+    alternative = {'t': 'two-sided', 'g': 'greater', 'l': 'less'}.get(
+       alternative.lower()[0], alternative)
+    return scipy.stats._stats_py.ks_1samp(
+        x, cdf, args=args, alternative=alternative, method=method)
+
+
+@_rename_parameter("mode", "method")
+def ks_2samp(data1, data2, alternative="two-sided", method='auto'):
+    """
+    Computes the Kolmogorov-Smirnov test on two samples.
+
+    Missing values in `x` and/or `y` are discarded.
+
+    Parameters
+    ----------
+    data1 : array_like
+        First data set
+    data2 : array_like
+        Second data set
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Indicates the alternative hypothesis.  Default is 'two-sided'.
+    method : {'auto', 'exact', 'asymp'}, optional
+        Defines the method used for calculating the p-value.
+        The following options are available (default is 'auto'):
+
+        * 'auto' : use 'exact' for small size arrays, 'asymp' for large
+        * 'exact' : use approximation to exact distribution of test statistic
+        * 'asymp' : use asymptotic distribution of test statistic
+
+    Returns
+    -------
+    d : float
+        Value of the Kolmogorov Smirnov test
+    p : float
+        Corresponding p-value.
+
+    """
+    # Ideally this would be accomplished by
+    # ks_2samp = scipy.stats._stats_py.ks_2samp
+    # but the circular dependencies between _mstats_basic and stats prevent that.
+    alternative = {'t': 'two-sided', 'g': 'greater', 'l': 'less'}.get(
+       alternative.lower()[0], alternative)
+    return scipy.stats._stats_py.ks_2samp(data1, data2,
+                                          alternative=alternative,
+                                          method=method)
+
+
+ks_twosamp = ks_2samp
+
+
+@_rename_parameter("mode", "method")
+def kstest(data1, data2, args=(), alternative='two-sided', method='auto'):
+    """
+    Performs the Kolmogorov-Smirnov test for goodness of fit.
+
+    Parameters
+    ----------
+    data1 : array_like
+    data2 : str, callable or array_like
+    args : tuple, sequence, optional
+        Distribution parameters, used if `data1` or `data2` are strings.
+    alternative : str, as documented in stats.kstest
+    method : str, as documented in stats.kstest
+
+    Returns
+    -------
+    tuple of (K-S statistic, probability)
+
+    """  # numpydoc ignore=RT03
+    return scipy.stats._stats_py.kstest(data1, data2, args,
+                                        alternative=alternative, method=method)
+
+
+def trima(a, limits=None, inclusive=(True,True)):
+    """
+    Trims an array by masking the data outside some given limits.
+
+    Returns a masked version of the input array.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    limits : {None, tuple}, optional
+        Tuple of (lower limit, upper limit) in absolute values.
+        Values of the input array lower (greater) than the lower (upper) limit
+        will be masked.  A limit is None indicates an open interval.
+    inclusive : (bool, bool) tuple, optional
+        Tuple of (lower flag, upper flag), indicating whether values exactly
+        equal to the lower (upper) limit are allowed.
+
+    Examples
+    --------
+    >>> from scipy.stats.mstats import trima
+    >>> import numpy as np
+
+    >>> a = np.arange(10)
+
+    The interval is left-closed and right-open, i.e., `[2, 8)`.
+    Trim the array by keeping only values in the interval.
+
+    >>> trima(a, limits=(2, 8), inclusive=(True, False))
+    masked_array(data=[--, --, 2, 3, 4, 5, 6, 7, --, --],
+                 mask=[ True,  True, False, False, False, False, False, False,
+                        True,  True],
+           fill_value=999999)
+
+    """  # numpydoc ignore=RT01
+    a = ma.asarray(a)
+    a.unshare_mask()
+    if (limits is None) or (limits == (None, None)):
+        return a
+
+    (lower_lim, upper_lim) = limits
+    (lower_in, upper_in) = inclusive
+    condition = False
+    if lower_lim is not None:
+        if lower_in:
+            condition |= (a < lower_lim)
+        else:
+            condition |= (a <= lower_lim)
+
+    if upper_lim is not None:
+        if upper_in:
+            condition |= (a > upper_lim)
+        else:
+            condition |= (a >= upper_lim)
+
+    a[condition.filled(True)] = masked
+    return a
+
+
+def trimr(a, limits=None, inclusive=(True, True), axis=None):
+    """
+    Trims an array by masking some proportion of the data on each end.
+    Returns a masked version of the input array.
+
+    Parameters
+    ----------
+    a : sequence
+        Input array.
+    limits : {None, tuple}, optional
+        Tuple of the percentages to cut on each side of the array, with respect
+        to the number of unmasked data, as floats between 0. and 1.
+        Noting n the number of unmasked data before trimming, the
+        (n*limits[0])th smallest data and the (n*limits[1])th largest data are
+        masked, and the total number of unmasked data after trimming is
+        n*(1.-sum(limits)).  The value of one limit can be set to None to
+        indicate an open interval.
+    inclusive : {(True,True) tuple}, optional
+        Tuple of flags indicating whether the number of data being masked on
+        the left (right) end should be truncated (True) or rounded (False) to
+        integers.
+    axis : {None,int}, optional
+        Axis along which to trim. If None, the whole array is trimmed, but its
+        shape is maintained.
+
+    """  # numpydoc ignore=RT01
+    def _trimr1D(a, low_limit, up_limit, low_inclusive, up_inclusive):
+        n = a.count()
+        idx = a.argsort()
+        if low_limit:
+            if low_inclusive:
+                lowidx = int(low_limit*n)
+            else:
+                lowidx = int(np.round(low_limit*n))
+            a[idx[:lowidx]] = masked
+        if up_limit is not None:
+            if up_inclusive:
+                upidx = n - int(n*up_limit)
+            else:
+                upidx = n - int(np.round(n*up_limit))
+            a[idx[upidx:]] = masked
+        return a
+
+    a = ma.asarray(a)
+    a.unshare_mask()
+    if limits is None:
+        return a
+
+    # Check the limits
+    (lolim, uplim) = limits
+    errmsg = "The proportion to cut from the %s should be between 0. and 1."
+    if lolim is not None:
+        if lolim > 1. or lolim < 0:
+            raise ValueError(errmsg % 'beginning' + f"(got {lolim})")
+    if uplim is not None:
+        if uplim > 1. or uplim < 0:
+            raise ValueError(errmsg % 'end' + f"(got {uplim})")
+
+    (loinc, upinc) = inclusive
+
+    if axis is None:
+        shp = a.shape
+        return _trimr1D(a.ravel(),lolim,uplim,loinc,upinc).reshape(shp)
+    else:
+        return ma.apply_along_axis(_trimr1D, axis, a, lolim,uplim,loinc,upinc)
+
+
+trimdoc = _dedent_for_py313("""
+    Parameters
+    ----------
+    a : sequence
+        Input array
+    limits : {None, tuple}, optional
+        If `relative` is False, tuple (lower limit, upper limit) in absolute values.
+        Values of the input array lower (greater) than the lower (upper) limit are
+        masked.
+
+        If `relative` is True, tuple (lower percentage, upper percentage) to cut
+        on each side of the  array, with respect to the number of unmasked data.
+
+        Noting n the number of unmasked data before trimming, the (n*limits[0])th
+        smallest data and the (n*limits[1])th largest data are masked, and the
+        total number of unmasked data after trimming is n*(1.-sum(limits))
+        In each case, the value of one limit can be set to None to indicate an
+        open interval.
+
+        If limits is None, no trimming is performed
+    inclusive : {(bool, bool) tuple}, optional
+        If `relative` is False, tuple indicating whether values exactly equal
+        to the absolute limits are allowed.
+        If `relative` is True, tuple indicating whether the number of data
+        being masked on each side should be rounded (True) or truncated
+        (False).
+    relative : bool, optional
+        Whether to consider the limits as absolute values (False) or proportions
+        to cut (True).
+    axis : int, optional
+        Axis along which to trim.""")
+
+
+def trim(a, limits=None, inclusive=(True,True), relative=False, axis=None):
+    """
+    Trims an array by masking the data outside some given limits.
+
+    Returns a masked version of the input array.
+
+    %s
+
+    Examples
+    --------
+    >>> from scipy.stats.mstats import trim
+    >>> z = [ 1, 2, 3, 4, 5, 6, 7, 8, 9,10]
+    >>> print(trim(z,(3,8)))
+    [-- -- 3 4 5 6 7 8 -- --]
+    >>> print(trim(z,(0.1,0.2),relative=True))
+    [-- 2 3 4 5 6 7 8 -- --]
+
+    """  # numpydoc ignore=RT01
+    if relative:
+        return trimr(a, limits=limits, inclusive=inclusive, axis=axis)
+    else:
+        return trima(a, limits=limits, inclusive=inclusive)
+
+
+if trim.__doc__:
+    trim.__doc__ = trim.__doc__ % trimdoc
+
+
+def trimboth(data, proportiontocut=0.2, inclusive=(True,True), axis=None):
+    """
+    Trims the smallest and largest data values.
+
+    Trims the `data` by masking the ``int(proportiontocut * n)`` smallest and
+    ``int(proportiontocut * n)`` largest values of data along the given axis,
+    where n is the number of unmasked values before trimming.
+
+    Parameters
+    ----------
+    data : ndarray
+        Data to trim.
+    proportiontocut : float, optional
+        Percentage of trimming (as a float between 0 and 1).
+        If n is the number of unmasked values before trimming, the number of
+        values after trimming is ``(1 - 2*proportiontocut) * n``.
+        Default is 0.2.
+    inclusive : {(bool, bool) tuple}, optional
+        Tuple indicating whether the number of data being masked on each side
+        should be rounded (True) or truncated (False).
+    axis : int, optional
+        Axis along which to perform the trimming.
+        If None, the input array is first flattened.
+
+    """  # numpydoc ignore=RT01
+    return trimr(data, limits=(proportiontocut,proportiontocut),
+                 inclusive=inclusive, axis=axis)
+
+
+def trimtail(data, proportiontocut=0.2, tail='left', inclusive=(True,True),
+             axis=None):
+    """
+    Trims the data by masking values from one tail.
+
+    Parameters
+    ----------
+    data : array_like
+        Data to trim.
+    proportiontocut : float, optional
+        Percentage of trimming. If n is the number of unmasked values
+        before trimming, the number of values after trimming is
+        ``(1 - proportiontocut) * n``.  Default is 0.2.
+    tail : {'left','right'}, optional
+        If 'left' the `proportiontocut` lowest values will be masked.
+        If 'right' the `proportiontocut` highest values will be masked.
+        Default is 'left'.
+    inclusive : {(bool, bool) tuple}, optional
+        Tuple indicating whether the number of data being masked on each side
+        should be rounded (True) or truncated (False).  Default is
+        (True, True).
+    axis : int, optional
+        Axis along which to perform the trimming.
+        If None, the input array is first flattened.  Default is None.
+
+    Returns
+    -------
+    trimtail : ndarray
+        Returned array of same shape as `data` with masked tail values.
+
+    """
+    tail = str(tail).lower()[0]
+    if tail == 'l':
+        limits = (proportiontocut,None)
+    elif tail == 'r':
+        limits = (None, proportiontocut)
+    else:
+        raise TypeError("The tail argument should be in ('left','right')")
+
+    return trimr(data, limits=limits, axis=axis, inclusive=inclusive)
+
+
+trim1 = trimtail
+
+
+def trimmed_mean(a, limits=(0.1,0.1), inclusive=(1,1), relative=True,
+                 axis=None):
+    """Returns the trimmed mean of the data along the given axis.
+
+    %s
+
+    """  # numpydoc ignore=RT01
+    if (not isinstance(limits,tuple)) and isinstance(limits,float):
+        limits = (limits, limits)
+    if relative:
+        return trimr(a,limits=limits,inclusive=inclusive,axis=axis).mean(axis=axis)
+    else:
+        return trima(a,limits=limits,inclusive=inclusive).mean(axis=axis)
+
+
+if trimmed_mean.__doc__:
+    trimmed_mean.__doc__ = trimmed_mean.__doc__ % trimdoc
+
+
+def trimmed_var(a, limits=(0.1,0.1), inclusive=(1,1), relative=True,
+                axis=None, ddof=0):
+    """Returns the trimmed variance of the data along the given axis.
+
+    %s
+    ddof : {0,integer}, optional
+        Means Delta Degrees of Freedom. The denominator used during computations
+        is (n-ddof). DDOF=0 corresponds to a biased estimate, DDOF=1 to an un-
+        biased estimate of the variance.
+
+    """  # numpydoc ignore=RT01
+    if (not isinstance(limits,tuple)) and isinstance(limits,float):
+        limits = (limits, limits)
+    if relative:
+        out = trimr(a,limits=limits, inclusive=inclusive,axis=axis)
+    else:
+        out = trima(a,limits=limits,inclusive=inclusive)
+
+    return out.var(axis=axis, ddof=ddof)
+
+
+if trimmed_var.__doc__:
+    trimmed_var.__doc__ = trimmed_var.__doc__ % trimdoc
+
+
+def trimmed_std(a, limits=(0.1,0.1), inclusive=(1,1), relative=True,
+                axis=None, ddof=0):
+    """Returns the trimmed standard deviation of the data along the given axis.
+
+    %s
+    ddof : {0,integer}, optional
+        Means Delta Degrees of Freedom. The denominator used during computations
+        is (n-ddof). DDOF=0 corresponds to a biased estimate, DDOF=1 to an un-
+        biased estimate of the variance.
+
+    """  # numpydoc ignore=RT01
+    if (not isinstance(limits,tuple)) and isinstance(limits,float):
+        limits = (limits, limits)
+    if relative:
+        out = trimr(a,limits=limits,inclusive=inclusive,axis=axis)
+    else:
+        out = trima(a,limits=limits,inclusive=inclusive)
+    return out.std(axis=axis,ddof=ddof)
+
+
+if trimmed_std.__doc__:
+    trimmed_std.__doc__ = trimmed_std.__doc__ % trimdoc
+
+
+def trimmed_stde(a, limits=(0.1,0.1), inclusive=(1,1), axis=None):
+    """
+    Returns the standard error of the trimmed mean along the given axis.
+
+    Parameters
+    ----------
+    a : sequence
+        Input array
+    limits : {(0.1,0.1), tuple of float}, optional
+        tuple (lower percentage, upper percentage) to cut  on each side of the
+        array, with respect to the number of unmasked data.
+
+        If n is the number of unmasked data before trimming, the values
+        smaller than ``n * limits[0]`` and the values larger than
+        ``n * `limits[1]`` are masked, and the total number of unmasked
+        data after trimming is ``n * (1.-sum(limits))``.  In each case,
+        the value of one limit can be set to None to indicate an open interval.
+        If `limits` is None, no trimming is performed.
+    inclusive : {(bool, bool) tuple} optional
+        Tuple indicating whether the number of data being masked on each side
+        should be rounded (True) or truncated (False).
+    axis : int, optional
+        Axis along which to trim.
+
+    Returns
+    -------
+    trimmed_stde : scalar or ndarray
+
+    """  # numpydoc ignore=RT03
+    def _trimmed_stde_1D(a, low_limit, up_limit, low_inclusive, up_inclusive):
+        "Returns the standard error of the trimmed mean for a 1D input data."
+        n = a.count()
+        idx = a.argsort()
+        if low_limit:
+            if low_inclusive:
+                lowidx = int(low_limit*n)
+            else:
+                lowidx = np.round(low_limit*n)
+            a[idx[:lowidx]] = masked
+        if up_limit is not None:
+            if up_inclusive:
+                upidx = n - int(n*up_limit)
+            else:
+                upidx = n - np.round(n*up_limit)
+            a[idx[upidx:]] = masked
+        a[idx[:lowidx]] = a[idx[lowidx]]
+        a[idx[upidx:]] = a[idx[upidx-1]]
+        winstd = a.std(ddof=1)
+        return winstd / ((1-low_limit-up_limit)*np.sqrt(len(a)))
+
+    a = ma.array(a, copy=True, subok=True)
+    a.unshare_mask()
+    if limits is None:
+        return a.std(axis=axis,ddof=1)/ma.sqrt(a.count(axis))
+    if (not isinstance(limits,tuple)) and isinstance(limits,float):
+        limits = (limits, limits)
+
+    # Check the limits
+    (lolim, uplim) = limits
+    errmsg = "The proportion to cut from the %s should be between 0. and 1."
+    if lolim is not None:
+        if lolim > 1. or lolim < 0:
+            raise ValueError(errmsg % 'beginning' + f"(got {lolim})")
+    if uplim is not None:
+        if uplim > 1. or uplim < 0:
+            raise ValueError(errmsg % 'end' + f"(got {uplim})")
+
+    (loinc, upinc) = inclusive
+    if (axis is None):
+        return _trimmed_stde_1D(a.ravel(),lolim,uplim,loinc,upinc)
+    else:
+        if a.ndim > 2:
+            raise ValueError(f"Array 'a' must be at most two dimensional, "
+                             f"but got a.ndim = {a.ndim}")
+        return ma.apply_along_axis(_trimmed_stde_1D, axis, a,
+                                   lolim,uplim,loinc,upinc)
+
+
+def _mask_to_limits(a, limits, inclusive):
+    """Mask an array for values outside of given limits.
+
+    This is primarily a utility function.
+
+    Parameters
+    ----------
+    a : array
+    limits : (float or None, float or None)
+    A tuple consisting of the (lower limit, upper limit).  Values in the
+    input array less than the lower limit or greater than the upper limit
+    will be masked out. None implies no limit.
+    inclusive : (bool, bool)
+    A tuple consisting of the (lower flag, upper flag).  These flags
+    determine whether values exactly equal to lower or upper are allowed.
+
+    Returns
+    -------
+    A MaskedArray.
+
+    Raises
+    ------
+    A ValueError if there are no values within the given limits.
+    """
+    lower_limit, upper_limit = limits
+    lower_include, upper_include = inclusive
+    am = ma.MaskedArray(a)
+    if lower_limit is not None:
+        if lower_include:
+            am = ma.masked_less(am, lower_limit)
+        else:
+            am = ma.masked_less_equal(am, lower_limit)
+
+    if upper_limit is not None:
+        if upper_include:
+            am = ma.masked_greater(am, upper_limit)
+        else:
+            am = ma.masked_greater_equal(am, upper_limit)
+
+    if am.count() == 0:
+        raise ValueError("No array values within given limits")
+
+    return am
+
+
+def tmean(a, limits=None, inclusive=(True, True), axis=None):
+    """
+    Compute the trimmed mean.
+
+    Parameters
+    ----------
+    a : array_like
+        Array of values.
+    limits : None or (lower limit, upper limit), optional
+        Values in the input array less than the lower limit or greater than the
+        upper limit will be ignored.  When limits is None (default), then all
+        values are used.  Either of the limit values in the tuple can also be
+        None representing a half-open interval.
+    inclusive : (bool, bool), optional
+        A tuple consisting of the (lower flag, upper flag).  These flags
+        determine whether values exactly equal to the lower or upper limits
+        are included.  The default value is (True, True).
+    axis : int or None, optional
+        Axis along which to operate. If None, compute over the
+        whole array. Default is None.
+
+    Returns
+    -------
+    tmean : float
+
+    Notes
+    -----
+    For more details on `tmean`, see `scipy.stats.tmean`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import mstats
+    >>> a = np.array([[6, 8, 3, 0],
+    ...               [3, 9, 1, 2],
+    ...               [8, 7, 8, 2],
+    ...               [5, 6, 0, 2],
+    ...               [4, 5, 5, 2]])
+    ...
+    ...
+    >>> mstats.tmean(a, (2,5))
+    3.3
+    >>> mstats.tmean(a, (2,5), axis=0)
+    masked_array(data=[4.0, 5.0, 4.0, 2.0],
+                 mask=[False, False, False, False],
+           fill_value=1e+20)
+
+    """  # numpydoc ignore=RT03
+    return trima(a, limits=limits, inclusive=inclusive).mean(axis=axis)
+
+
+def tvar(a, limits=None, inclusive=(True, True), axis=0, ddof=1):
+    """
+    Compute the trimmed variance
+
+    This function computes the sample variance of an array of values,
+    while ignoring values which are outside of given `limits`.
+
+    Parameters
+    ----------
+    a : array_like
+        Array of values.
+    limits : None or (lower limit, upper limit), optional
+        Values in the input array less than the lower limit or greater than the
+        upper limit will be ignored. When limits is None, then all values are
+        used. Either of the limit values in the tuple can also be None
+        representing a half-open interval.  The default value is None.
+    inclusive : (bool, bool), optional
+        A tuple consisting of the (lower flag, upper flag).  These flags
+        determine whether values exactly equal to the lower or upper limits
+        are included.  The default value is (True, True).
+    axis : int or None, optional
+        Axis along which to operate. If None, compute over the
+        whole array. Default is zero.
+    ddof : int, optional
+        Delta degrees of freedom. Default is 1.
+
+    Returns
+    -------
+    tvar : float
+        Trimmed variance.
+
+    Notes
+    -----
+    For more details on `tvar`, see `scipy.stats.tvar`.
+
+    """
+    a = a.astype(float).ravel()
+    if limits is None:
+        n = (~a.mask).sum()  # todo: better way to do that?
+        return np.ma.var(a) * n/(n-1.)
+    am = _mask_to_limits(a, limits=limits, inclusive=inclusive)
+
+    return np.ma.var(am, axis=axis, ddof=ddof)
+
+
+def tmin(a, lowerlimit=None, axis=0, inclusive=True):
+    """
+    Compute the trimmed minimum
+
+    Parameters
+    ----------
+    a : array_like
+        array of values
+    lowerlimit : None or float, optional
+        Values in the input array less than the given limit will be ignored.
+        When lowerlimit is None, then all values are used. The default value
+        is None.
+    axis : int or None, optional
+        Axis along which to operate. Default is 0. If None, compute over the
+        whole array `a`.
+    inclusive : {True, False}, optional
+        This flag determines whether values exactly equal to the lower limit
+        are included.  The default value is True.
+
+    Returns
+    -------
+    tmin : float, int or ndarray
+
+    Notes
+    -----
+    For more details on `tmin`, see `scipy.stats.tmin`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import mstats
+    >>> a = np.array([[6, 8, 3, 0],
+    ...               [3, 2, 1, 2],
+    ...               [8, 1, 8, 2],
+    ...               [5, 3, 0, 2],
+    ...               [4, 7, 5, 2]])
+    ...
+    >>> mstats.tmin(a, 5)
+    masked_array(data=[5, 7, 5, --],
+                 mask=[False, False, False,  True],
+           fill_value=999999)
+
+    """  # numpydoc ignore=RT03
+    a, axis = _chk_asarray(a, axis)
+    am = trima(a, (lowerlimit, None), (inclusive, False))
+    return ma.minimum.reduce(am, axis)
+
+
+def tmax(a, upperlimit=None, axis=0, inclusive=True):
+    """
+    Compute the trimmed maximum
+
+    This function computes the maximum value of an array along a given axis,
+    while ignoring values larger than a specified upper limit.
+
+    Parameters
+    ----------
+    a : array_like
+        array of values
+    upperlimit : None or float, optional
+        Values in the input array greater than the given limit will be ignored.
+        When upperlimit is None, then all values are used. The default value
+        is None.
+    axis : int or None, optional
+        Axis along which to operate. Default is 0. If None, compute over the
+        whole array `a`.
+    inclusive : {True, False}, optional
+        This flag determines whether values exactly equal to the upper limit
+        are included.  The default value is True.
+
+    Returns
+    -------
+    tmax : float, int or ndarray
+
+    Notes
+    -----
+    For more details on `tmax`, see `scipy.stats.tmax`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import mstats
+    >>> a = np.array([[6, 8, 3, 0],
+    ...               [3, 9, 1, 2],
+    ...               [8, 7, 8, 2],
+    ...               [5, 6, 0, 2],
+    ...               [4, 5, 5, 2]])
+    ...
+    ...
+    >>> mstats.tmax(a, 4)
+    masked_array(data=[4, --, 3, 2],
+                 mask=[False,  True, False, False],
+           fill_value=999999)
+
+    """  # numpydoc ignore=RT03
+    a, axis = _chk_asarray(a, axis)
+    am = trima(a, (None, upperlimit), (False, inclusive))
+    return ma.maximum.reduce(am, axis)
+
+
+def tsem(a, limits=None, inclusive=(True, True), axis=0, ddof=1):
+    """
+    Compute the trimmed standard error of the mean.
+
+    This function finds the standard error of the mean for given
+    values, ignoring values outside the given `limits`.
+
+    Parameters
+    ----------
+    a : array_like
+        array of values
+    limits : None or (lower limit, upper limit), optional
+        Values in the input array less than the lower limit or greater than the
+        upper limit will be ignored. When limits is None, then all values are
+        used. Either of the limit values in the tuple can also be None
+        representing a half-open interval.  The default value is None.
+    inclusive : (bool, bool), optional
+        A tuple consisting of the (lower flag, upper flag).  These flags
+        determine whether values exactly equal to the lower or upper limits
+        are included.  The default value is (True, True).
+    axis : int or None, optional
+        Axis along which to operate. If None, compute over the
+        whole array. Default is zero.
+    ddof : int, optional
+        Delta degrees of freedom. Default is 1.
+
+    Returns
+    -------
+    tsem : float
+
+    Notes
+    -----
+    For more details on `tsem`, see `scipy.stats.tsem`.
+
+    """  # numpydoc ignore=RT03
+    a = ma.asarray(a).ravel()
+    if limits is None:
+        n = float(a.count())
+        return a.std(axis=axis, ddof=ddof)/ma.sqrt(n)
+
+    am = trima(a.ravel(), limits, inclusive)
+    sd = np.sqrt(am.var(axis=axis, ddof=ddof))
+    return sd / np.sqrt(am.count())
+
+
+def winsorize(a, limits=None, inclusive=(True, True), inplace=False,
+              axis=None, nan_policy='propagate'):
+    """Returns a Winsorized version of the input array.
+
+    The (limits[0])th lowest values are set to the (limits[0])th percentile,
+    and the (limits[1])th highest values are set to the (1 - limits[1])th
+    percentile.
+    Masked values are skipped.
+
+
+    Parameters
+    ----------
+    a : sequence
+        Input array.
+    limits : {None, tuple of float}, optional
+        Tuple of the percentages to cut on each side of the array, with respect
+        to the number of unmasked data, as floats between 0. and 1.
+        Noting n the number of unmasked data before trimming, the
+        (n*limits[0])th smallest data and the (n*limits[1])th largest data are
+        masked, and the total number of unmasked data after trimming
+        is n*(1.-sum(limits)) The value of one limit can be set to None to
+        indicate an open interval.
+    inclusive : {(True, True) tuple}, optional
+        Tuple indicating whether the number of data being masked on each side
+        should be truncated (True) or rounded (False).
+    inplace : {False, True}, optional
+        Whether to winsorize in place (True) or to use a copy (False)
+    axis : {None, int}, optional
+        Axis along which to trim. If None, the whole array is trimmed, but its
+        shape is maintained.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': allows nan values and may overwrite or propagate them
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    Notes
+    -----
+    This function is applied to reduce the effect of possibly spurious outliers
+    by limiting the extreme values.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats.mstats import winsorize
+
+    A shuffled array contains integers from 1 to 10.
+
+    >>> a = np.array([10, 4, 9, 8, 5, 3, 7, 2, 1, 6])
+
+    The 10% of the lowest value (i.e., ``1``) and the 20% of the highest
+    values (i.e., ``9`` and ``10``) are replaced.
+
+    >>> winsorize(a, limits=[0.1, 0.2])
+    masked_array(data=[8, 4, 8, 8, 5, 3, 7, 2, 2, 6],
+                 mask=False,
+           fill_value=999999)
+
+    """  # numpydoc ignore=RT01
+    def _winsorize1D(a, low_limit, up_limit, low_include, up_include,
+                     contains_nan, nan_policy):
+        n = a.count()
+        idx = a.argsort()
+        if contains_nan:
+            nan_count = np.count_nonzero(np.isnan(a))
+        if low_limit:
+            if low_include:
+                lowidx = int(low_limit * n)
+            else:
+                lowidx = np.round(low_limit * n).astype(int)
+            if contains_nan and nan_policy == 'omit':
+                lowidx = min(lowidx, n-nan_count-1)
+            a[idx[:lowidx]] = a[idx[lowidx]]
+        if up_limit is not None:
+            if up_include:
+                upidx = n - int(n * up_limit)
+            else:
+                upidx = n - np.round(n * up_limit).astype(int)
+            if contains_nan and nan_policy == 'omit':
+                a[idx[upidx:-nan_count]] = a[idx[upidx - 1]]
+            else:
+                a[idx[upidx:]] = a[idx[upidx - 1]]
+        return a
+
+    contains_nan = _contains_nan(a, nan_policy)
+    # We are going to modify a: better make a copy
+    a = ma.array(a, copy=np.logical_not(inplace))
+
+    if limits is None:
+        return a
+    if (not isinstance(limits, tuple)) and isinstance(limits, float):
+        limits = (limits, limits)
+
+    # Check the limits
+    (lolim, uplim) = limits
+    errmsg = "The proportion to cut from the %s should be between 0. and 1."
+    if lolim is not None:
+        if lolim > 1. or lolim < 0:
+            raise ValueError(errmsg % 'beginning' + f"(got {lolim})")
+    if uplim is not None:
+        if uplim > 1. or uplim < 0:
+            raise ValueError(errmsg % 'end' + f"(got {uplim})")
+
+    (loinc, upinc) = inclusive
+
+    if axis is None:
+        shp = a.shape
+        return _winsorize1D(a.ravel(), lolim, uplim, loinc, upinc,
+                            contains_nan, nan_policy).reshape(shp)
+    else:
+        return ma.apply_along_axis(_winsorize1D, axis, a, lolim, uplim, loinc,
+                                   upinc, contains_nan, nan_policy)
+
+
+def moment(a, moment=1, axis=0):
+    """
+    Calculates the nth moment about the mean for a sample.
+
+    Parameters
+    ----------
+    a : array_like
+       data
+    moment : int, optional
+       order of central moment that is returned
+    axis : int or None, optional
+       Axis along which the central moment is computed. Default is 0.
+       If None, compute over the whole array `a`.
+
+    Returns
+    -------
+    n-th central moment : ndarray or float
+       The appropriate moment along the given axis or over all values if axis
+       is None. The denominator for the moment calculation is the number of
+       observations, no degrees of freedom correction is done.
+
+    Notes
+    -----
+    For more details about `moment`, see `scipy.stats.moment`.
+
+    """
+    a, axis = _chk_asarray(a, axis)
+    if a.size == 0:
+        moment_shape = list(a.shape)
+        del moment_shape[axis]
+        dtype = a.dtype.type if a.dtype.kind in 'fc' else np.float64
+        # empty array, return nan(s) with shape matching `moment`
+        out_shape = (moment_shape if np.isscalar(moment)
+                     else [len(moment)] + moment_shape)
+        if len(out_shape) == 0:
+            return dtype(np.nan)
+        else:
+            return ma.array(np.full(out_shape, np.nan, dtype=dtype))
+
+    # for array_like moment input, return a value for each.
+    if not np.isscalar(moment):
+        mean = a.mean(axis, keepdims=True)
+        mmnt = [_moment(a, i, axis, mean=mean) for i in moment]
+        return ma.array(mmnt)
+    else:
+        return _moment(a, moment, axis)
+
+
+# Moment with optional pre-computed mean, equal to a.mean(axis, keepdims=True)
+def _moment(a, moment, axis, *, mean=None):
+    if np.abs(moment - np.round(moment)) > 0:
+        raise ValueError("All moment parameters must be integers")
+
+    if moment == 0 or moment == 1:
+        # By definition the zeroth moment about the mean is 1, and the first
+        # moment is 0.
+        shape = list(a.shape)
+        del shape[axis]
+        dtype = a.dtype.type if a.dtype.kind in 'fc' else np.float64
+
+        if len(shape) == 0:
+            return dtype(1.0 if moment == 0 else 0.0)
+        else:
+            return (ma.ones(shape, dtype=dtype) if moment == 0
+                    else ma.zeros(shape, dtype=dtype))
+    else:
+        # Exponentiation by squares: form exponent sequence
+        n_list = [moment]
+        current_n = moment
+        while current_n > 2:
+            if current_n % 2:
+                current_n = (current_n-1)/2
+            else:
+                current_n /= 2
+            n_list.append(current_n)
+
+        # Starting point for exponentiation by squares
+        mean = a.mean(axis, keepdims=True) if mean is None else mean
+        a_zero_mean = a - mean
+        if n_list[-1] == 1:
+            s = a_zero_mean.copy()
+        else:
+            s = a_zero_mean**2
+
+        # Perform multiplications
+        for n in n_list[-2::-1]:
+            s = s**2
+            if n % 2:
+                s *= a_zero_mean
+        return s.mean(axis)
+
+
+def variation(a, axis=0, ddof=0):
+    """
+    Compute the coefficient of variation.
+
+    The coefficient of variation is the standard deviation divided by the
+    mean.  This function is equivalent to::
+
+        np.std(x, axis=axis, ddof=ddof) / np.mean(x)
+
+    The default for ``ddof`` is 0, but many definitions of the coefficient
+    of variation use the square root of the unbiased sample variance
+    for the sample standard deviation, which corresponds to ``ddof=1``.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    axis : int or None, optional
+        Axis along which to calculate the coefficient of variation. Default
+        is 0. If None, compute over the whole array `a`.
+    ddof : int, optional
+        Delta degrees of freedom.  Default is 0.
+
+    Returns
+    -------
+    variation : ndarray
+        The calculated variation along the requested axis.
+
+    Notes
+    -----
+    For more details about `variation`, see `scipy.stats.variation`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats.mstats import variation
+    >>> a = np.array([2,8,4])
+    >>> variation(a)
+    0.5345224838248487
+    >>> b = np.array([2,8,3,4])
+    >>> c = np.ma.masked_array(b, mask=[0,0,1,0])
+    >>> variation(c)
+    0.5345224838248487
+
+    In the example above, it can be seen that this works the same as
+    `scipy.stats.variation` except 'stats.mstats.variation' ignores masked
+    array elements.
+
+    """
+    a, axis = _chk_asarray(a, axis)
+    return a.std(axis, ddof=ddof)/a.mean(axis)
+
+
+def skew(a, axis=0, bias=True):
+    """
+    Computes the skewness of a data set.
+
+    Parameters
+    ----------
+    a : ndarray
+        data
+    axis : int or None, optional
+        Axis along which skewness is calculated. Default is 0.
+        If None, compute over the whole array `a`.
+    bias : bool, optional
+        If False, then the calculations are corrected for statistical bias.
+
+    Returns
+    -------
+    skewness : ndarray
+        The skewness of values along an axis, returning 0 where all values are
+        equal.
+
+    Notes
+    -----
+    For more details about `skew`, see `scipy.stats.skew`.
+
+    """
+    a, axis = _chk_asarray(a,axis)
+    mean = a.mean(axis, keepdims=True)
+    m2 = _moment(a, 2, axis, mean=mean)
+    m3 = _moment(a, 3, axis, mean=mean)
+    zero = (m2 <= (np.finfo(m2.dtype).resolution * mean.squeeze(axis))**2)
+    with np.errstate(all='ignore'):
+        vals = ma.where(zero, 0, m3 / m2**1.5)
+
+    if not bias and zero is not ma.masked and m2 is not ma.masked:
+        n = a.count(axis)
+        can_correct = ~zero & (n > 2)
+        if can_correct.any():
+            n = np.extract(can_correct, n)
+            m2 = np.extract(can_correct, m2)
+            m3 = np.extract(can_correct, m3)
+            nval = ma.sqrt((n-1.0)*n)/(n-2.0)*m3/m2**1.5
+            np.place(vals, can_correct, nval)
+    return vals
+
+
+def kurtosis(a, axis=0, fisher=True, bias=True):
+    """
+    Computes the kurtosis (Fisher or Pearson) of a dataset.
+
+    Kurtosis is the fourth central moment divided by the square of the
+    variance. If Fisher's definition is used, then 3.0 is subtracted from
+    the result to give 0.0 for a normal distribution.
+
+    If bias is False then the kurtosis is calculated using k statistics to
+    eliminate bias coming from biased moment estimators
+
+    Use `kurtosistest` to see if result is close enough to normal.
+
+    Parameters
+    ----------
+    a : array
+        data for which the kurtosis is calculated
+    axis : int or None, optional
+        Axis along which the kurtosis is calculated. Default is 0.
+        If None, compute over the whole array `a`.
+    fisher : bool, optional
+        If True, Fisher's definition is used (normal ==> 0.0). If False,
+        Pearson's definition is used (normal ==> 3.0).
+    bias : bool, optional
+        If False, then the calculations are corrected for statistical bias.
+
+    Returns
+    -------
+    kurtosis : array
+        The kurtosis of values along an axis. If all values are equal,
+        return -3 for Fisher's definition and 0 for Pearson's definition.
+
+    Notes
+    -----
+    For more details about `kurtosis`, see `scipy.stats.kurtosis`.
+
+    """
+    a, axis = _chk_asarray(a, axis)
+    mean = a.mean(axis, keepdims=True)
+    m2 = _moment(a, 2, axis, mean=mean)
+    m4 = _moment(a, 4, axis, mean=mean)
+    zero = (m2 <= (np.finfo(m2.dtype).resolution * mean.squeeze(axis))**2)
+    with np.errstate(all='ignore'):
+        vals = ma.where(zero, 0, m4 / m2**2.0)
+
+    if not bias and zero is not ma.masked and m2 is not ma.masked:
+        n = a.count(axis)
+        can_correct = ~zero & (n > 3)
+        if can_correct.any():
+            n = np.extract(can_correct, n)
+            m2 = np.extract(can_correct, m2)
+            m4 = np.extract(can_correct, m4)
+            nval = 1.0/(n-2)/(n-3)*((n*n-1.0)*m4/m2**2.0-3*(n-1)**2.0)
+            np.place(vals, can_correct, nval+3.0)
+    if fisher:
+        return vals - 3
+    else:
+        return vals
+
+
+DescribeResult = namedtuple('DescribeResult', ('nobs', 'minmax', 'mean',
+                                               'variance', 'skewness',
+                                               'kurtosis'))
+
+
+def describe(a, axis=0, ddof=0, bias=True):
+    """
+    Computes several descriptive statistics of the passed array.
+
+    Parameters
+    ----------
+    a : array_like
+        Data array
+    axis : int or None, optional
+        Axis along which to calculate statistics. Default 0. If None,
+        compute over the whole array `a`.
+    ddof : int, optional
+        degree of freedom (default 0); note that default ddof is different
+        from the same routine in stats.describe
+    bias : bool, optional
+        If False, then the skewness and kurtosis calculations are corrected for
+        statistical bias.
+
+    Returns
+    -------
+    nobs : int
+        (size of the data (discarding missing values)
+
+    minmax : (int, int)
+        min, max
+
+    mean : float
+        arithmetic mean
+
+    variance : float
+        unbiased variance
+
+    skewness : float
+        biased skewness
+
+    kurtosis : float
+        biased kurtosis
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats.mstats import describe
+    >>> ma = np.ma.array(range(6), mask=[0, 0, 0, 1, 1, 1])
+    >>> describe(ma)
+    DescribeResult(nobs=np.int64(3), minmax=(masked_array(data=0,
+                 mask=False,
+           fill_value=999999), masked_array(data=2,
+                 mask=False,
+           fill_value=999999)), mean=np.float64(1.0),
+           variance=np.float64(0.6666666666666666),
+           skewness=masked_array(data=0., mask=False, fill_value=1e+20),
+            kurtosis=np.float64(-1.5))
+
+    """
+    a, axis = _chk_asarray(a, axis)
+    n = a.count(axis)
+    mm = (ma.minimum.reduce(a, axis=axis), ma.maximum.reduce(a, axis=axis))
+    m = a.mean(axis)
+    v = a.var(axis, ddof=ddof)
+    sk = skew(a, axis, bias=bias)
+    kurt = kurtosis(a, axis, bias=bias)
+
+    return DescribeResult(n, mm, m, v, sk, kurt)
+
+
+def stde_median(data, axis=None):
+    """Returns the McKean-Schrader estimate of the standard error of the sample
+    median along the given axis. masked values are discarded.
+
+    Parameters
+    ----------
+    data : ndarray
+        Data to trim.
+    axis : {None,int}, optional
+        Axis along which to perform the trimming.
+        If None, the input array is first flattened.
+
+    """
+    def _stdemed_1D(data):
+        data = np.sort(data.compressed())
+        n = len(data)
+        z = 2.5758293035489004
+        k = int(np.round((n+1)/2. - z * np.sqrt(n/4.),0))
+        return ((data[n-k] - data[k-1])/(2.*z))
+
+    data = ma.array(data, copy=False, subok=True)
+    if (axis is None):
+        return _stdemed_1D(data)
+    else:
+        if data.ndim > 2:
+            raise ValueError(f"Array 'data' must be at most two dimensional, "
+                             f"but got data.ndim = {data.ndim}")
+        return ma.apply_along_axis(_stdemed_1D, axis, data)
+
+
+SkewtestResult = namedtuple('SkewtestResult', ('statistic', 'pvalue'))
+
+
+def skewtest(a, axis=0, alternative='two-sided'):
+    """
+    Tests whether the skew is different from the normal distribution.
+
+    Parameters
+    ----------
+    a : array_like
+        The data to be tested
+    axis : int or None, optional
+       Axis along which statistics are calculated. Default is 0.
+       If None, compute over the whole array `a`.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        The following options are available:
+
+        * 'two-sided': the skewness of the distribution underlying the sample
+          is different from that of the normal distribution (i.e. 0)
+        * 'less': the skewness of the distribution underlying the sample
+          is less than that of the normal distribution
+        * 'greater': the skewness of the distribution underlying the sample
+          is greater than that of the normal distribution
+
+        .. versionadded:: 1.7.0
+
+    Returns
+    -------
+    statistic : array_like
+        The computed z-score for this test.
+    pvalue : array_like
+        A p-value for the hypothesis test
+
+    Notes
+    -----
+    For more details about `skewtest`, see `scipy.stats.skewtest`.
+
+    """
+    a, axis = _chk_asarray(a, axis)
+    if axis is None:
+        a = a.ravel()
+        axis = 0
+    b2 = skew(a,axis)
+    n = a.count(axis)
+    if np.min(n) < 8:
+        raise ValueError(f"skewtest is not valid with less than 8 samples; "
+                         f"{np.min(n)} samples were given.")
+
+    y = b2 * ma.sqrt(((n+1)*(n+3)) / (6.0*(n-2)))
+    beta2 = (3.0*(n*n+27*n-70)*(n+1)*(n+3)) / ((n-2.0)*(n+5)*(n+7)*(n+9))
+    W2 = -1 + ma.sqrt(2*(beta2-1))
+    delta = 1/ma.sqrt(0.5*ma.log(W2))
+    alpha = ma.sqrt(2.0/(W2-1))
+    y = ma.where(y == 0, 1, y)
+    Z = delta*ma.log(y/alpha + ma.sqrt((y/alpha)**2+1))
+    pvalue = scipy.stats._stats_py._get_pvalue(Z, distributions.norm, alternative)
+
+    return SkewtestResult(Z[()], pvalue[()])
+
+
+KurtosistestResult = namedtuple('KurtosistestResult', ('statistic', 'pvalue'))
+
+
+def kurtosistest(a, axis=0, alternative='two-sided'):
+    """
+    Tests whether a dataset has normal kurtosis
+
+    Parameters
+    ----------
+    a : array_like
+        array of the sample data
+    axis : int or None, optional
+       Axis along which to compute test. Default is 0. If None,
+       compute over the whole array `a`.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+        The following options are available (default is 'two-sided'):
+
+        * 'two-sided': the kurtosis of the distribution underlying the sample
+          is different from that of the normal distribution
+        * 'less': the kurtosis of the distribution underlying the sample
+          is less than that of the normal distribution
+        * 'greater': the kurtosis of the distribution underlying the sample
+          is greater than that of the normal distribution
+
+        .. versionadded:: 1.7.0
+
+    Returns
+    -------
+    statistic : array_like
+        The computed z-score for this test.
+    pvalue : array_like
+        The p-value for the hypothesis test
+
+    Notes
+    -----
+    For more details about `kurtosistest`, see `scipy.stats.kurtosistest`.
+
+    """
+    a, axis = _chk_asarray(a, axis)
+    n = a.count(axis=axis)
+    if np.min(n) < 5:
+        raise ValueError(f"kurtosistest requires at least 5 observations; "
+                         f"{np.min(n)} observations were given.")
+    if np.min(n) < 20:
+        warnings.warn(f"kurtosistest only valid for n>=20 ... continuing "
+                      f"anyway, n={np.min(n)}", stacklevel=2)
+
+    b2 = kurtosis(a, axis, fisher=False)
+    E = 3.0*(n-1) / (n+1)
+    varb2 = 24.0*n*(n-2.)*(n-3) / ((n+1)*(n+1.)*(n+3)*(n+5))
+    x = (b2-E)/ma.sqrt(varb2)
+    sqrtbeta1 = 6.0*(n*n-5*n+2)/((n+7)*(n+9)) * np.sqrt((6.0*(n+3)*(n+5)) /
+                                                        (n*(n-2)*(n-3)))
+    A = 6.0 + 8.0/sqrtbeta1 * (2.0/sqrtbeta1 + np.sqrt(1+4.0/(sqrtbeta1**2)))
+    term1 = 1 - 2./(9.0*A)
+    denom = 1 + x*ma.sqrt(2/(A-4.0))
+    if np.ma.isMaskedArray(denom):
+        # For multi-dimensional array input
+        denom[denom == 0.0] = masked
+    elif denom == 0.0:
+        denom = masked
+
+    term2 = np.ma.where(denom > 0, ma.power((1-2.0/A)/denom, 1/3.0),
+                        -ma.power(-(1-2.0/A)/denom, 1/3.0))
+    Z = (term1 - term2) / np.sqrt(2/(9.0*A))
+    pvalue = scipy.stats._stats_py._get_pvalue(Z, distributions.norm, alternative)
+
+    return KurtosistestResult(Z[()], pvalue[()])
+
+
+NormaltestResult = namedtuple('NormaltestResult', ('statistic', 'pvalue'))
+
+
+def normaltest(a, axis=0):
+    """
+    Tests whether a sample differs from a normal distribution.
+
+    Parameters
+    ----------
+    a : array_like
+        The array containing the data to be tested.
+    axis : int or None, optional
+        Axis along which to compute test. Default is 0. If None,
+        compute over the whole array `a`.
+
+    Returns
+    -------
+    statistic : float or array
+        ``s^2 + k^2``, where ``s`` is the z-score returned by `skewtest` and
+        ``k`` is the z-score returned by `kurtosistest`.
+    pvalue : float or array
+         A 2-sided chi squared probability for the hypothesis test.
+
+    Notes
+    -----
+    For more details about `normaltest`, see `scipy.stats.normaltest`.
+
+    """
+    a, axis = _chk_asarray(a, axis)
+    s, _ = skewtest(a, axis)
+    k, _ = kurtosistest(a, axis)
+    k2 = s*s + k*k
+
+    return NormaltestResult(k2, distributions.chi2.sf(k2, 2))
+
+
+def mquantiles(a, prob=(.25, .5, .75), alphap=.4, betap=.4, axis=None,
+               limit=()):
+    """
+    Computes empirical quantiles for a data array.
+
+    Samples quantile are defined by ``Q(p) = (1-gamma)*x[j] + gamma*x[j+1]``,
+    where ``x[j]`` is the j-th order statistic, and gamma is a function of
+    ``j = floor(n*p + m)``, ``m = alphap + p*(1 - alphap - betap)`` and
+    ``g = n*p + m - j``.
+
+    Reinterpreting the above equations to compare to **R** lead to the
+    equation: ``p(k) = (k - alphap)/(n + 1 - alphap - betap)``
+
+    Typical values of (alphap,betap) are:
+
+    - (0,1)    : ``p(k) = k/n`` : linear interpolation of cdf
+      (**R** type 4)
+    - (.5,.5)  : ``p(k) = (k - 1/2.)/n`` : piecewise linear function
+      (**R** type 5)
+    - (0,0)    : ``p(k) = k/(n+1)`` :
+      (**R** type 6)
+    - (1,1)    : ``p(k) = (k-1)/(n-1)``: p(k) = mode[F(x[k])].
+      (**R** type 7, **R** default)
+    - (1/3,1/3): ``p(k) = (k-1/3)/(n+1/3)``: Then p(k) ~ median[F(x[k])].
+      The resulting quantile estimates are approximately median-unbiased
+      regardless of the distribution of x.
+      (**R** type 8)
+    - (3/8,3/8): ``p(k) = (k-3/8)/(n+1/4)``: Blom.
+      The resulting quantile estimates are approximately unbiased
+      if x is normally distributed
+      (**R** type 9)
+    - (.4,.4)  : approximately quantile unbiased (Cunnane)
+    - (.35,.35): APL, used with PWM
+
+    Parameters
+    ----------
+    a : array_like
+        Input data, as a sequence or array of dimension at most 2.
+    prob : array_like, optional
+        List of quantiles to compute.
+    alphap : float, optional
+        Plotting positions parameter, default is 0.4.
+    betap : float, optional
+        Plotting positions parameter, default is 0.4.
+    axis : int, optional
+        Axis along which to perform the trimming.
+        If None (default), the input array is first flattened.
+    limit : tuple, optional
+        Tuple of (lower, upper) values.
+        Values of `a` outside this open interval are ignored.
+
+    Returns
+    -------
+    mquantiles : MaskedArray
+        An array containing the calculated quantiles.
+
+    Notes
+    -----
+    This formulation is very similar to **R** except the calculation of
+    ``m`` from ``alphap`` and ``betap``, where in **R** ``m`` is defined
+    with each type.
+
+    References
+    ----------
+    .. [1] *R* statistical software: https://www.r-project.org/
+    .. [2] *R* ``quantile`` function:
+            http://stat.ethz.ch/R-manual/R-devel/library/stats/html/quantile.html
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats.mstats import mquantiles
+    >>> a = np.array([6., 47., 49., 15., 42., 41., 7., 39., 43., 40., 36.])
+    >>> mquantiles(a)
+    array([ 19.2,  40. ,  42.8])
+
+    Using a 2D array, specifying axis and limit.
+
+    >>> data = np.array([[   6.,    7.,    1.],
+    ...                  [  47.,   15.,    2.],
+    ...                  [  49.,   36.,    3.],
+    ...                  [  15.,   39.,    4.],
+    ...                  [  42.,   40., -999.],
+    ...                  [  41.,   41., -999.],
+    ...                  [   7., -999., -999.],
+    ...                  [  39., -999., -999.],
+    ...                  [  43., -999., -999.],
+    ...                  [  40., -999., -999.],
+    ...                  [  36., -999., -999.]])
+    >>> print(mquantiles(data, axis=0, limit=(0, 50)))
+    [[19.2  14.6   1.45]
+     [40.   37.5   2.5 ]
+     [42.8  40.05  3.55]]
+
+    >>> data[:, 2] = -999.
+    >>> print(mquantiles(data, axis=0, limit=(0, 50)))
+    [[19.200000000000003 14.6 --]
+     [40.0 37.5 --]
+     [42.800000000000004 40.05 --]]
+
+    """
+    def _quantiles1D(data,m,p):
+        x = np.sort(data.compressed())
+        n = len(x)
+        if n == 0:
+            return ma.array(np.empty(len(p), dtype=float), mask=True)
+        elif n == 1:
+            return ma.array(np.resize(x, p.shape), mask=nomask)
+        aleph = (n*p + m)
+        k = np.floor(aleph.clip(1, n-1)).astype(int)
+        gamma = (aleph-k).clip(0,1)
+        return (1.-gamma)*x[(k-1).tolist()] + gamma*x[k.tolist()]
+
+    data = ma.array(a, copy=False)
+    if data.ndim > 2:
+        raise TypeError("Array should be 2D at most !")
+
+    if limit:
+        condition = (limit[0] < data) & (data < limit[1])
+        data[~condition.filled(True)] = masked
+
+    p = np.atleast_1d(np.asarray(prob))
+    m = alphap + p*(1.-alphap-betap)
+    # Computes quantiles along axis (or globally)
+    if (axis is None):
+        return _quantiles1D(data, m, p)
+
+    return ma.apply_along_axis(_quantiles1D, axis, data, m, p)
+
+
+def scoreatpercentile(data, per, limit=(), alphap=.4, betap=.4):
+    """Calculate the score at the given 'per' percentile of the
+    sequence a.  For example, the score at per=50 is the median.
+
+    This function is a shortcut to mquantile
+
+    """  # numpydoc ignore=RT01
+    if (per < 0) or (per > 100.):
+        raise ValueError(f"The percentile should be between 0. and 100. ! (got {per})")
+
+    return mquantiles(data, prob=[per/100.], alphap=alphap, betap=betap,
+                      limit=limit, axis=0).squeeze()
+
+
+def plotting_positions(data, alpha=0.4, beta=0.4):
+    """
+    Returns plotting positions (or empirical percentile points) for the data.
+
+    Plotting positions are defined as ``(i-alpha)/(n+1-alpha-beta)``, where:
+
+    - i is the rank order statistics
+    - n is the number of unmasked values along the given axis
+    - `alpha` and `beta` are two parameters.
+
+    Typical values for `alpha` and `beta` are:
+
+    - (0,1)    : ``p(k) = k/n``, linear interpolation of cdf (R, type 4)
+    - (.5,.5)  : ``p(k) = (k-1/2.)/n``, piecewise linear function
+      (R, type 5)
+    - (0,0)    : ``p(k) = k/(n+1)``, Weibull (R type 6)
+    - (1,1)    : ``p(k) = (k-1)/(n-1)``, in this case,
+      ``p(k) = mode[F(x[k])]``. That's R default (R type 7)
+    - (1/3,1/3): ``p(k) = (k-1/3)/(n+1/3)``, then
+      ``p(k) ~ median[F(x[k])]``.
+      The resulting quantile estimates are approximately median-unbiased
+      regardless of the distribution of x. (R type 8)
+    - (3/8,3/8): ``p(k) = (k-3/8)/(n+1/4)``, Blom.
+      The resulting quantile estimates are approximately unbiased
+      if x is normally distributed (R type 9)
+    - (.4,.4)  : approximately quantile unbiased (Cunnane)
+    - (.35,.35): APL, used with PWM
+    - (.3175, .3175): used in scipy.stats.probplot
+
+    Parameters
+    ----------
+    data : array_like
+        Input data, as a sequence or array of dimension at most 2.
+    alpha : float, optional
+        Plotting positions parameter. Default is 0.4.
+    beta : float, optional
+        Plotting positions parameter. Default is 0.4.
+
+    Returns
+    -------
+    positions : MaskedArray
+        The calculated plotting positions.
+
+    """
+    data = ma.array(data, copy=False).reshape(1,-1)
+    n = data.count()
+    plpos = np.empty(data.size, dtype=float)
+    plpos[n:] = 0
+    plpos[data.argsort(axis=None)[:n]] = ((np.arange(1, n+1) - alpha) /
+                                          (n + 1.0 - alpha - beta))
+    return ma.array(plpos, mask=data._mask)
+
+
+meppf = plotting_positions
+
+
+def obrientransform(*args):
+    """
+    Computes a transform on input data (any number of columns).  Used to
+    test for homogeneity of variance prior to running one-way stats.  Each
+    array in ``*args`` is one level of a factor.  If an `f_oneway()` run on
+    the transformed data and found significant, variances are unequal.   From
+    Maxwell and Delaney, p.112.
+
+    Returns: transformed data for use in an ANOVA
+    """  # numpydoc ignore=RT01
+    data = argstoarray(*args).T
+    v = data.var(axis=0,ddof=1)
+    m = data.mean(0)
+    n = data.count(0).astype(float)
+    # result = ((N-1.5)*N*(a-m)**2 - 0.5*v*(n-1))/((n-1)*(n-2))
+    data -= m
+    data **= 2
+    data *= (n-1.5)*n
+    data -= 0.5*v*(n-1)
+    data /= (n-1.)*(n-2.)
+    if not ma.allclose(v,data.mean(0)):
+        raise ValueError("Lack of convergence in obrientransform.")
+
+    return data
+
+
+def sem(a, axis=0, ddof=1):
+    """
+    Calculates the standard error of the mean of the input array.
+
+    Also sometimes called standard error of measurement.
+
+    Parameters
+    ----------
+    a : array_like
+        An array containing the values for which the standard error is
+        returned.
+    axis : int or None, optional
+        If axis is None, ravel `a` first. If axis is an integer, this will be
+        the axis over which to operate. Defaults to 0.
+    ddof : int, optional
+        Delta degrees-of-freedom. How many degrees of freedom to adjust
+        for bias in limited samples relative to the population estimate
+        of variance. Defaults to 1.
+
+    Returns
+    -------
+    s : ndarray or float
+        The standard error of the mean in the sample(s), along the input axis.
+
+    Notes
+    -----
+    The default value for `ddof` changed in scipy 0.15.0 to be consistent with
+    `scipy.stats.sem` as well as with the most common definition used (like in
+    the R documentation).
+
+    Examples
+    --------
+    Find standard error along the first axis:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> a = np.arange(20).reshape(5,4)
+    >>> print(stats.mstats.sem(a))
+    [2.8284271247461903 2.8284271247461903 2.8284271247461903
+     2.8284271247461903]
+
+    Find standard error across the whole array, using n degrees of freedom:
+
+    >>> print(stats.mstats.sem(a, axis=None, ddof=0))
+    1.2893796958227628
+
+    """
+    a, axis = _chk_asarray(a, axis)
+    n = a.count(axis=axis)
+    s = a.std(axis=axis, ddof=ddof) / ma.sqrt(n)
+    return s
+
+
+F_onewayResult = namedtuple('F_onewayResult', ('statistic', 'pvalue'))
+
+
+def f_oneway(*args):
+    """
+    Performs a 1-way ANOVA, returning an F-value and probability given
+    any number of groups.  From Heiman, pp.394-7.
+
+    Usage: ``f_oneway(*args)``, where ``*args`` is 2 or more arrays,
+    one per treatment group.
+
+    Returns
+    -------
+    statistic : float
+        The computed F-value of the test.
+    pvalue : float
+        The associated p-value from the F-distribution.
+
+    """
+    # Construct a single array of arguments: each row is a group
+    data = argstoarray(*args)
+    ngroups = len(data)
+    ntot = data.count()
+    sstot = (data**2).sum() - (data.sum())**2/float(ntot)
+    ssbg = (data.count(-1) * (data.mean(-1)-data.mean())**2).sum()
+    sswg = sstot-ssbg
+    dfbg = ngroups-1
+    dfwg = ntot - ngroups
+    msb = ssbg/float(dfbg)
+    msw = sswg/float(dfwg)
+    f = msb/msw
+    prob = special.fdtrc(dfbg, dfwg, f)  # equivalent to stats.f.sf
+
+    return F_onewayResult(f, prob)
+
+
+FriedmanchisquareResult = namedtuple('FriedmanchisquareResult',
+                                     ('statistic', 'pvalue'))
+
+
+def friedmanchisquare(*args):
+    """Friedman Chi-Square is a non-parametric, one-way within-subjects ANOVA.
+    This function calculates the Friedman Chi-square test for repeated measures
+    and returns the result, along with the associated probability value.
+
+    Each input is considered a given group. Ideally, the number of treatments
+    among each group should be equal. If this is not the case, only the first
+    n treatments are taken into account, where n is the number of treatments
+    of the smallest group.
+    If a group has some missing values, the corresponding treatments are masked
+    in the other groups.
+    The test statistic is corrected for ties.
+
+    Masked values in one group are propagated to the other groups.
+
+    Returns
+    -------
+    statistic : float
+        the test statistic.
+    pvalue : float
+        the associated p-value.
+
+    """
+    data = argstoarray(*args).astype(float)
+    k = len(data)
+    if k < 3:
+        raise ValueError(f"Less than 3 groups ({k}): the Friedman test "
+                         f"is NOT appropriate.")
+
+    ranked = ma.masked_values(rankdata(data, axis=0), 0)
+    if ranked._mask is not nomask:
+        ranked = ma.mask_cols(ranked)
+        ranked = ranked.compressed().reshape(k,-1).view(ndarray)
+    else:
+        ranked = ranked._data
+    (k,n) = ranked.shape
+    # Ties correction
+    repeats = [find_repeats(row) for row in ranked.T]
+    ties = np.array([y for x, y in repeats if x.size > 0])
+    tie_correction = 1 - (ties**3-ties).sum()/float(n*(k**3-k))
+
+    ssbg = np.sum((ranked.sum(-1) - n*(k+1)/2.)**2)
+    chisq = ssbg * 12./(n*k*(k+1)) * 1./tie_correction
+
+    return FriedmanchisquareResult(chisq,
+                                   distributions.chi2.sf(chisq, k-1))
+
+
+BrunnerMunzelResult = namedtuple('BrunnerMunzelResult', ('statistic', 'pvalue'))
+
+
+def brunnermunzel(x, y, alternative="two-sided", distribution="t"):
+    """
+    Compute the Brunner-Munzel test on samples x and y.
+
+    Any missing values in `x` and/or `y` are discarded.
+
+    The Brunner-Munzel test is a nonparametric test of the null hypothesis that
+    when values are taken one by one from each group, the probabilities of
+    getting large values in both groups are equal.
+    Unlike the Wilcoxon-Mann-Whitney's U test, this does not require the
+    assumption of equivariance of two groups. Note that this does not assume
+    the distributions are same. This test works on two independent samples,
+    which may have different sizes.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Array of samples, should be one-dimensional.
+    alternative : 'less', 'two-sided', or 'greater', optional
+        Whether to get the p-value for the one-sided hypothesis ('less'
+        or 'greater') or for the two-sided hypothesis ('two-sided').
+        Defaults value is 'two-sided' .
+    distribution : 't' or 'normal', optional
+        Whether to get the p-value by t-distribution or by standard normal
+        distribution.
+        Defaults value is 't' .
+
+    Returns
+    -------
+    statistic : float
+        The Brunner-Munzer W statistic.
+    pvalue : float
+        p-value assuming a t distribution. One-sided or
+        two-sided, depending on the choice of `alternative` and `distribution`.
+
+    See Also
+    --------
+    mannwhitneyu : Mann-Whitney rank test on two samples.
+
+    Notes
+    -----
+    For more details on `brunnermunzel`, see `scipy.stats.brunnermunzel`.
+
+    Examples
+    --------
+    >>> from scipy.stats.mstats import brunnermunzel
+    >>> import numpy as np
+    >>> x1 = [1, 2, np.nan, np.nan, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1]
+    >>> x2 = [3, 3, 4, 3, 1, 2, 3, 1, 1, 5, 4]
+    >>> brunnermunzel(x1, x2)
+    BrunnerMunzelResult(statistic=1.4723186918922935, pvalue=0.15479415300426624)  # may vary
+
+    """  # noqa: E501
+    x = ma.asarray(x).compressed().view(ndarray)
+    y = ma.asarray(y).compressed().view(ndarray)
+    nx = len(x)
+    ny = len(y)
+    if nx == 0 or ny == 0:
+        return BrunnerMunzelResult(np.nan, np.nan)
+    rankc = rankdata(np.concatenate((x,y)))
+    rankcx = rankc[0:nx]
+    rankcy = rankc[nx:nx+ny]
+    rankcx_mean = np.mean(rankcx)
+    rankcy_mean = np.mean(rankcy)
+    rankx = rankdata(x)
+    ranky = rankdata(y)
+    rankx_mean = np.mean(rankx)
+    ranky_mean = np.mean(ranky)
+
+    Sx = np.sum(np.power(rankcx - rankx - rankcx_mean + rankx_mean, 2.0))
+    Sx /= nx - 1
+    Sy = np.sum(np.power(rankcy - ranky - rankcy_mean + ranky_mean, 2.0))
+    Sy /= ny - 1
+
+    wbfn = nx * ny * (rankcy_mean - rankcx_mean)
+    wbfn /= (nx + ny) * np.sqrt(nx * Sx + ny * Sy)
+
+    if distribution == "t":
+        df_numer = np.power(nx * Sx + ny * Sy, 2.0)
+        df_denom = np.power(nx * Sx, 2.0) / (nx - 1)
+        df_denom += np.power(ny * Sy, 2.0) / (ny - 1)
+        df = df_numer / df_denom
+        p = distributions.t.cdf(wbfn, df)
+    elif distribution == "normal":
+        p = distributions.norm.cdf(wbfn)
+    else:
+        raise ValueError(
+            "distribution should be 't' or 'normal'")
+
+    if alternative == "greater":
+        pass
+    elif alternative == "less":
+        p = 1 - p
+    elif alternative == "two-sided":
+        p = 2 * np.min([p, 1-p])
+    else:
+        raise ValueError(
+            "alternative should be 'less', 'greater' or 'two-sided'")
+
+    return BrunnerMunzelResult(wbfn, p)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_mstats_extras.py b/.venv/lib/python3.12/site-packages/scipy/stats/_mstats_extras.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7b5484fb930edadb0538576e0cb736c82957e7a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_mstats_extras.py
@@ -0,0 +1,520 @@
+"""
+Additional statistics functions with support for masked arrays.
+
+"""
+
+# Original author (2007): Pierre GF Gerard-Marchant
+
+
+__all__ = ['compare_medians_ms',
+           'hdquantiles', 'hdmedian', 'hdquantiles_sd',
+           'idealfourths',
+           'median_cihs','mjci','mquantiles_cimj',
+           'rsh',
+           'trimmed_mean_ci',]
+
+
+import numpy as np
+from numpy import float64, ndarray
+
+import numpy.ma as ma
+from numpy.ma import MaskedArray
+
+from . import _mstats_basic as mstats
+
+from scipy.stats.distributions import norm, beta, t, binom
+
+
+def hdquantiles(data, prob=(.25, .5, .75), axis=None, var=False,):
+    """
+    Computes quantile estimates with the Harrell-Davis method.
+
+    The quantile estimates are calculated as a weighted linear combination
+    of order statistics.
+
+    Parameters
+    ----------
+    data : array_like
+        Data array.
+    prob : sequence, optional
+        Sequence of probabilities at which to compute the quantiles.
+    axis : int or None, optional
+        Axis along which to compute the quantiles. If None, use a flattened
+        array.
+    var : bool, optional
+        Whether to return the variance of the estimate.
+
+    Returns
+    -------
+    hdquantiles : MaskedArray
+        A (p,) array of quantiles (if `var` is False), or a (2,p) array of
+        quantiles and variances (if `var` is True), where ``p`` is the
+        number of quantiles.
+
+    See Also
+    --------
+    hdquantiles_sd
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats.mstats import hdquantiles
+    >>>
+    >>> # Sample data
+    >>> data = np.array([1.2, 2.5, 3.7, 4.0, 5.1, 6.3, 7.0, 8.2, 9.4])
+    >>>
+    >>> # Probabilities at which to compute quantiles
+    >>> probabilities = [0.25, 0.5, 0.75]
+    >>>
+    >>> # Compute Harrell-Davis quantile estimates
+    >>> quantile_estimates = hdquantiles(data, prob=probabilities)
+    >>>
+    >>> # Display the quantile estimates
+    >>> for i, quantile in enumerate(probabilities):
+    ...     print(f"{int(quantile * 100)}th percentile: {quantile_estimates[i]}")
+    25th percentile: 3.1505820231763066 # may vary
+    50th percentile: 5.194344084883956
+    75th percentile: 7.430626414674935
+
+    """
+    def _hd_1D(data,prob,var):
+        "Computes the HD quantiles for a 1D array. Returns nan for invalid data."
+        xsorted = np.squeeze(np.sort(data.compressed().view(ndarray)))
+        # Don't use length here, in case we have a numpy scalar
+        n = xsorted.size
+
+        hd = np.empty((2,len(prob)), float64)
+        if n < 2:
+            hd.flat = np.nan
+            if var:
+                return hd
+            return hd[0]
+
+        v = np.arange(n+1) / float(n)
+        betacdf = beta.cdf
+        for (i,p) in enumerate(prob):
+            _w = betacdf(v, (n+1)*p, (n+1)*(1-p))
+            w = _w[1:] - _w[:-1]
+            hd_mean = np.dot(w, xsorted)
+            hd[0,i] = hd_mean
+            #
+            hd[1,i] = np.dot(w, (xsorted-hd_mean)**2)
+            #
+        hd[0, prob == 0] = xsorted[0]
+        hd[0, prob == 1] = xsorted[-1]
+        if var:
+            hd[1, prob == 0] = hd[1, prob == 1] = np.nan
+            return hd
+        return hd[0]
+    # Initialization & checks
+    data = ma.array(data, copy=False, dtype=float64)
+    p = np.atleast_1d(np.asarray(prob))
+    # Computes quantiles along axis (or globally)
+    if (axis is None) or (data.ndim == 1):
+        result = _hd_1D(data, p, var)
+    else:
+        if data.ndim > 2:
+            raise ValueError(f"Array 'data' must be at most two dimensional, "
+                             f"but got data.ndim = {data.ndim}")
+        result = ma.apply_along_axis(_hd_1D, axis, data, p, var)
+
+    return ma.fix_invalid(result, copy=False)
+
+
+def hdmedian(data, axis=-1, var=False):
+    """
+    Returns the Harrell-Davis estimate of the median along the given axis.
+
+    Parameters
+    ----------
+    data : ndarray
+        Data array.
+    axis : int, optional
+        Axis along which to compute the quantiles. If None, use a flattened
+        array.
+    var : bool, optional
+        Whether to return the variance of the estimate.
+
+    Returns
+    -------
+    hdmedian : MaskedArray
+        The median values.  If ``var=True``, the variance is returned inside
+        the masked array.  E.g. for a 1-D array the shape change from (1,) to
+        (2,).
+
+    """
+    result = hdquantiles(data,[0.5], axis=axis, var=var)
+    return result.squeeze()
+
+
+def hdquantiles_sd(data, prob=(.25, .5, .75), axis=None):
+    """
+    The standard error of the Harrell-Davis quantile estimates by jackknife.
+
+    Parameters
+    ----------
+    data : array_like
+        Data array.
+    prob : sequence, optional
+        Sequence of quantiles to compute.
+    axis : int, optional
+        Axis along which to compute the quantiles. If None, use a flattened
+        array.
+
+    Returns
+    -------
+    hdquantiles_sd : MaskedArray
+        Standard error of the Harrell-Davis quantile estimates.
+
+    See Also
+    --------
+    hdquantiles
+
+    """
+    def _hdsd_1D(data, prob):
+        "Computes the std error for 1D arrays."
+        xsorted = np.sort(data.compressed())
+        n = len(xsorted)
+
+        hdsd = np.empty(len(prob), float64)
+        if n < 2:
+            hdsd.flat = np.nan
+
+        vv = np.arange(n) / float(n-1)
+        betacdf = beta.cdf
+
+        for (i,p) in enumerate(prob):
+            _w = betacdf(vv, n*p, n*(1-p))
+            w = _w[1:] - _w[:-1]
+            # cumulative sum of weights and data points if
+            # ith point is left out for jackknife
+            mx_ = np.zeros_like(xsorted)
+            mx_[1:] = np.cumsum(w * xsorted[:-1])
+            # similar but from the right
+            mx_[:-1] += np.cumsum(w[::-1] * xsorted[:0:-1])[::-1]
+            hdsd[i] = np.sqrt(mx_.var() * (n - 1))
+        return hdsd
+
+    # Initialization & checks
+    data = ma.array(data, copy=False, dtype=float64)
+    p = np.atleast_1d(np.asarray(prob))
+    # Computes quantiles along axis (or globally)
+    if (axis is None):
+        result = _hdsd_1D(data, p)
+    else:
+        if data.ndim > 2:
+            raise ValueError(f"Array 'data' must be at most two dimensional, "
+                             f"but got data.ndim = {data.ndim}")
+        result = ma.apply_along_axis(_hdsd_1D, axis, data, p)
+
+    return ma.fix_invalid(result, copy=False).ravel()
+
+
+def trimmed_mean_ci(data, limits=(0.2,0.2), inclusive=(True,True),
+                    alpha=0.05, axis=None):
+    """
+    Selected confidence interval of the trimmed mean along the given axis.
+
+    Parameters
+    ----------
+    data : array_like
+        Input data.
+    limits : {None, tuple}, optional
+        None or a two item tuple.
+        Tuple of the percentages to cut on each side of the array, with respect
+        to the number of unmasked data, as floats between 0. and 1. If ``n``
+        is the number of unmasked data before trimming, then
+        (``n * limits[0]``)th smallest data and (``n * limits[1]``)th
+        largest data are masked.  The total number of unmasked data after
+        trimming is ``n * (1. - sum(limits))``.
+        The value of one limit can be set to None to indicate an open interval.
+
+        Defaults to (0.2, 0.2).
+    inclusive : (2,) tuple of boolean, optional
+        If relative==False, tuple indicating whether values exactly equal to
+        the absolute limits are allowed.
+        If relative==True, tuple indicating whether the number of data being
+        masked on each side should be rounded (True) or truncated (False).
+
+        Defaults to (True, True).
+    alpha : float, optional
+        Confidence level of the intervals.
+
+        Defaults to 0.05.
+    axis : int, optional
+        Axis along which to cut. If None, uses a flattened version of `data`.
+
+        Defaults to None.
+
+    Returns
+    -------
+    trimmed_mean_ci : (2,) ndarray
+        The lower and upper confidence intervals of the trimmed data.
+
+    """
+    data = ma.array(data, copy=False)
+    trimmed = mstats.trimr(data, limits=limits, inclusive=inclusive, axis=axis)
+    tmean = trimmed.mean(axis)
+    tstde = mstats.trimmed_stde(data,limits=limits,inclusive=inclusive,axis=axis)
+    df = trimmed.count(axis) - 1
+    tppf = t.ppf(1-alpha/2.,df)
+    return np.array((tmean - tppf*tstde, tmean+tppf*tstde))
+
+
+def mjci(data, prob=(0.25, 0.5, 0.75), axis=None):
+    """
+    Returns the Maritz-Jarrett estimators of the standard error of selected
+    experimental quantiles of the data.
+
+    Parameters
+    ----------
+    data : ndarray
+        Data array.
+    prob : sequence, optional
+        Sequence of quantiles to compute.
+    axis : int or None, optional
+        Axis along which to compute the quantiles. If None, use a flattened
+        array.
+
+    """  # numpydoc ignore=RT01
+    def _mjci_1D(data, p):
+        data = np.sort(data.compressed())
+        n = data.size
+        prob = (np.array(p) * n + 0.5).astype(int)
+        betacdf = beta.cdf
+
+        mj = np.empty(len(prob), float64)
+        x = np.arange(1,n+1, dtype=float64) / n
+        y = x - 1./n
+        for (i,m) in enumerate(prob):
+            W = betacdf(x,m-1,n-m) - betacdf(y,m-1,n-m)
+            C1 = np.dot(W,data)
+            C2 = np.dot(W,data**2)
+            mj[i] = np.sqrt(C2 - C1**2)
+        return mj
+
+    data = ma.array(data, copy=False)
+    if data.ndim > 2:
+        raise ValueError(f"Array 'data' must be at most two dimensional, "
+                         f"but got data.ndim = {data.ndim}")
+
+    p = np.atleast_1d(np.asarray(prob))
+    # Computes quantiles along axis (or globally)
+    if (axis is None):
+        return _mjci_1D(data, p)
+    else:
+        return ma.apply_along_axis(_mjci_1D, axis, data, p)
+
+
+def mquantiles_cimj(data, prob=(0.25, 0.50, 0.75), alpha=0.05, axis=None):
+    """
+    Computes the alpha confidence interval for the selected quantiles of the
+    data, with Maritz-Jarrett estimators.
+
+    Parameters
+    ----------
+    data : ndarray
+        Data array.
+    prob : sequence, optional
+        Sequence of quantiles to compute.
+    alpha : float, optional
+        Confidence level of the intervals.
+    axis : int or None, optional
+        Axis along which to compute the quantiles.
+        If None, use a flattened array.
+
+    Returns
+    -------
+    ci_lower : ndarray
+        The lower boundaries of the confidence interval.  Of the same length as
+        `prob`.
+    ci_upper : ndarray
+        The upper boundaries of the confidence interval.  Of the same length as
+        `prob`.
+
+    """
+    alpha = min(alpha, 1 - alpha)
+    z = norm.ppf(1 - alpha/2.)
+    xq = mstats.mquantiles(data, prob, alphap=0, betap=0, axis=axis)
+    smj = mjci(data, prob, axis=axis)
+    return (xq - z * smj, xq + z * smj)
+
+
+def median_cihs(data, alpha=0.05, axis=None):
+    """
+    Computes the alpha-level confidence interval for the median of the data.
+
+    Uses the Hettmasperger-Sheather method.
+
+    Parameters
+    ----------
+    data : array_like
+        Input data. Masked values are discarded. The input should be 1D only,
+        or `axis` should be set to None.
+    alpha : float, optional
+        Confidence level of the intervals.
+    axis : int or None, optional
+        Axis along which to compute the quantiles. If None, use a flattened
+        array.
+
+    Returns
+    -------
+    median_cihs
+        Alpha level confidence interval.
+
+    """
+    def _cihs_1D(data, alpha):
+        data = np.sort(data.compressed())
+        n = len(data)
+        alpha = min(alpha, 1-alpha)
+        k = int(binom._ppf(alpha/2., n, 0.5))
+        gk = binom.cdf(n-k,n,0.5) - binom.cdf(k-1,n,0.5)
+        if gk < 1-alpha:
+            k -= 1
+            gk = binom.cdf(n-k,n,0.5) - binom.cdf(k-1,n,0.5)
+        gkk = binom.cdf(n-k-1,n,0.5) - binom.cdf(k,n,0.5)
+        I = (gk - 1 + alpha)/(gk - gkk)
+        lambd = (n-k) * I / float(k + (n-2*k)*I)
+        lims = (lambd*data[k] + (1-lambd)*data[k-1],
+                lambd*data[n-k-1] + (1-lambd)*data[n-k])
+        return lims
+    data = ma.array(data, copy=False)
+    # Computes quantiles along axis (or globally)
+    if (axis is None):
+        result = _cihs_1D(data, alpha)
+    else:
+        if data.ndim > 2:
+            raise ValueError(f"Array 'data' must be at most two dimensional, "
+                             f"but got data.ndim = {data.ndim}")
+        result = ma.apply_along_axis(_cihs_1D, axis, data, alpha)
+
+    return result
+
+
+def compare_medians_ms(group_1, group_2, axis=None):
+    """
+    Compares the medians from two independent groups along the given axis.
+
+    The comparison is performed using the McKean-Schrader estimate of the
+    standard error of the medians.
+
+    Parameters
+    ----------
+    group_1 : array_like
+        First dataset.  Has to be of size >=7.
+    group_2 : array_like
+        Second dataset.  Has to be of size >=7.
+    axis : int, optional
+        Axis along which the medians are estimated. If None, the arrays are
+        flattened.  If `axis` is not None, then `group_1` and `group_2`
+        should have the same shape.
+
+    Returns
+    -------
+    compare_medians_ms : {float, ndarray}
+        If `axis` is None, then returns a float, otherwise returns a 1-D
+        ndarray of floats with a length equal to the length of `group_1`
+        along `axis`.
+
+    References
+    ----------
+    .. [1] McKean, Joseph W., and Ronald M. Schrader. "A comparison of methods
+       for studentizing the sample median." Communications in
+       Statistics-Simulation and Computation 13.6 (1984): 751-773.
+
+    Examples
+    --------
+
+    >>> from scipy import stats
+    >>> a = [1, 2, 3, 4, 5, 6, 7]
+    >>> b = [8, 9, 10, 11, 12, 13, 14]
+    >>> stats.mstats.compare_medians_ms(a, b, axis=None)
+    1.0693225866553746e-05
+
+    The function is vectorized to compute along a given axis.
+
+    >>> import numpy as np
+    >>> rng = np.random.default_rng()
+    >>> x = rng.random(size=(3, 7))
+    >>> y = rng.random(size=(3, 8))
+    >>> stats.mstats.compare_medians_ms(x, y, axis=1)
+    array([0.36908985, 0.36092538, 0.2765313 ])
+    """
+    (med_1, med_2) = (ma.median(group_1,axis=axis), ma.median(group_2,axis=axis))
+    (std_1, std_2) = (mstats.stde_median(group_1, axis=axis),
+                      mstats.stde_median(group_2, axis=axis))
+    W = np.abs(med_1 - med_2) / ma.sqrt(std_1**2 + std_2**2)
+    return 1 - norm.cdf(W)
+
+
+def idealfourths(data, axis=None):
+    """
+    Returns an estimate of the lower and upper quartiles.
+
+    Uses the ideal fourths algorithm.
+
+    Parameters
+    ----------
+    data : array_like
+        Input array.
+    axis : int, optional
+        Axis along which the quartiles are estimated. If None, the arrays are
+        flattened.
+
+    Returns
+    -------
+    idealfourths : {list of floats, masked array}
+        Returns the two internal values that divide `data` into four parts
+        using the ideal fourths algorithm either along the flattened array
+        (if `axis` is None) or along `axis` of `data`.
+
+    """
+    def _idf(data):
+        x = data.compressed()
+        n = len(x)
+        if n < 3:
+            return [np.nan,np.nan]
+        (j,h) = divmod(n/4. + 5/12.,1)
+        j = int(j)
+        qlo = (1-h)*x[j-1] + h*x[j]
+        k = n - j
+        qup = (1-h)*x[k] + h*x[k-1]
+        return [qlo, qup]
+    data = ma.sort(data, axis=axis).view(MaskedArray)
+    if (axis is None):
+        return _idf(data)
+    else:
+        return ma.apply_along_axis(_idf, axis, data)
+
+
+def rsh(data, points=None):
+    """
+    Evaluates Rosenblatt's shifted histogram estimators for each data point.
+
+    Rosenblatt's estimator is a centered finite-difference approximation to the
+    derivative of the empirical cumulative distribution function.
+
+    Parameters
+    ----------
+    data : sequence
+        Input data, should be 1-D. Masked values are ignored.
+    points : sequence or None, optional
+        Sequence of points where to evaluate Rosenblatt shifted histogram.
+        If None, use the data.
+
+    """  # numpydoc ignore=RT01
+    data = ma.array(data, copy=False)
+    if points is None:
+        points = data
+    else:
+        points = np.atleast_1d(np.asarray(points))
+
+    if data.ndim != 1:
+        raise AttributeError("The input array should be 1D only !")
+
+    n = data.count()
+    r = idealfourths(data, axis=None)
+    h = 1.2 * (r[-1]-r[0]) / n**(1./5)
+    nhi = (data[:,None] <= points[None,:] + h).sum(0)
+    nlo = (data[:,None] < points[None,:] - h).sum(0)
+    return (nhi-nlo) / (2.*n*h)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_multicomp.py b/.venv/lib/python3.12/site-packages/scipy/stats/_multicomp.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7251be0912025e81afee9169e781e9b16ff5ce7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_multicomp.py
@@ -0,0 +1,457 @@
+import warnings
+from collections.abc import Sequence
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Literal
+
+import numpy as np
+
+from scipy import stats
+from scipy.optimize import minimize_scalar
+from scipy.stats._common import ConfidenceInterval
+from scipy.stats._qmc import check_random_state
+from scipy.stats._stats_py import _var
+from scipy._lib._array_api import xp_capabilities
+from scipy._lib._util import _transition_to_rng, DecimalNumber, SeedType
+
+
+if TYPE_CHECKING:
+    import numpy.typing as npt
+
+
+__all__ = [
+    'dunnett'
+]
+
+
+@dataclass
+class DunnettResult:
+    """Result object returned by `scipy.stats.dunnett`.
+
+    Attributes
+    ----------
+    statistic : float ndarray
+        The computed statistic of the test for each comparison. The element
+        at index ``i`` is the statistic for the comparison between
+        groups ``i`` and the control.
+    pvalue : float ndarray
+        The computed p-value of the test for each comparison. The element
+        at index ``i`` is the p-value for the comparison between
+        group ``i`` and the control.
+    """
+    statistic: np.ndarray
+    pvalue: np.ndarray
+    _alternative: Literal['two-sided', 'less', 'greater'] = field(repr=False)
+    _rho: np.ndarray = field(repr=False)
+    _df: int = field(repr=False)
+    _std: float = field(repr=False)
+    _mean_samples: np.ndarray = field(repr=False)
+    _mean_control: np.ndarray = field(repr=False)
+    _n_samples: np.ndarray = field(repr=False)
+    _n_control: int = field(repr=False)
+    _rng: SeedType = field(repr=False)
+    _ci: ConfidenceInterval | None = field(default=None, repr=False)
+    _ci_cl: DecimalNumber | None = field(default=None, repr=False)
+
+    @classmethod
+    def __class_getitem__(cls, arg, /):
+        # generic type compatibility with scipy-stubs
+        from types import GenericAlias
+        return GenericAlias(cls, arg)
+
+    def __str__(self):
+        # Note: `__str__` prints the confidence intervals from the most
+        # recent call to `confidence_interval`. If it has not been called,
+        # it will be called with the default CL of .95.
+        if self._ci is None:
+            self.confidence_interval(confidence_level=.95)
+        s = (
+            "Dunnett's test"
+            f" ({self._ci_cl*100:.1f}% Confidence Interval)\n"
+            "Comparison               Statistic  p-value  Lower CI  Upper CI\n"
+        )
+        for i in range(self.pvalue.size):
+            s += (f" (Sample {i} - Control) {self.statistic[i]:>10.3f}"
+                  f"{self.pvalue[i]:>10.3f}"
+                  f"{self._ci.low[i]:>10.3f}"
+                  f"{self._ci.high[i]:>10.3f}\n")
+
+        return s
+
+    def _allowance(
+        self, confidence_level: DecimalNumber = 0.95, tol: DecimalNumber = 1e-3
+    ) -> float:
+        """Allowance.
+
+        It is the quantity to add/subtract from the observed difference
+        between the means of observed groups and the mean of the control
+        group. The result gives confidence limits.
+
+        Parameters
+        ----------
+        confidence_level : float, optional
+            Confidence level for the computed confidence interval.
+            Default is .95.
+        tol : float, optional
+            A tolerance for numerical optimization: the allowance will produce
+            a confidence within ``10*tol*(1 - confidence_level)`` of the
+            specified level, or a warning will be emitted. Tight tolerances
+            may be impractical due to noisy evaluation of the objective.
+            Default is 1e-3.
+
+        Returns
+        -------
+        allowance : float
+            Allowance around the mean.
+        """
+        alpha = 1 - confidence_level
+
+        def pvalue_from_stat(statistic):
+            statistic = np.array(statistic)
+            sf = _pvalue_dunnett(
+                rho=self._rho, df=self._df,
+                statistic=statistic, alternative=self._alternative,
+                rng=self._rng
+            )
+            return abs(sf - alpha)/alpha
+
+        # Evaluation of `pvalue_from_stat` is noisy due to the use of RQMC to
+        # evaluate `multivariate_t.cdf`. `minimize_scalar` is not designed
+        # to tolerate a noisy objective function and may fail to find the
+        # minimum accurately. We mitigate this possibility with the validation
+        # step below, but implementation of a noise-tolerant root finder or
+        # minimizer would be a welcome enhancement. See gh-18150.
+        res = minimize_scalar(pvalue_from_stat, method='brent', tol=tol)
+        critical_value = res.x
+
+        # validation
+        # tol*10 because tol=1e-3 means we tolerate a 1% change at most
+        if res.success is False or res.fun >= tol*10:
+            warnings.warn(
+                "Computation of the confidence interval did not converge to "
+                "the desired level. The confidence level corresponding with "
+                f"the returned interval is approximately {alpha*(1+res.fun)}.",
+                stacklevel=3
+            )
+
+        # From [1] p. 1101 between (1) and (3)
+        allowance = critical_value*self._std*np.sqrt(
+            1/self._n_samples + 1/self._n_control
+        )
+        return abs(allowance)
+
+    def confidence_interval(
+        self, confidence_level: DecimalNumber = 0.95
+    ) -> ConfidenceInterval:
+        """Compute the confidence interval for the specified confidence level.
+
+        Parameters
+        ----------
+        confidence_level : float, optional
+            Confidence level for the computed confidence interval.
+            Default is .95.
+
+        Returns
+        -------
+        ci : ``ConfidenceInterval`` object
+            The object has attributes ``low`` and ``high`` that hold the
+            lower and upper bounds of the confidence intervals for each
+            comparison. The high and low values are accessible for each
+            comparison at index ``i`` for each group ``i``.
+
+        """
+        # check to see if the supplied confidence level matches that of the
+        # previously computed CI.
+        if (self._ci is not None) and (confidence_level == self._ci_cl):
+            return self._ci
+
+        if not (0 < confidence_level < 1):
+            raise ValueError("Confidence level must be between 0 and 1.")
+
+        allowance = self._allowance(confidence_level=confidence_level)
+        diff_means = self._mean_samples - self._mean_control
+
+        low = diff_means-allowance
+        high = diff_means+allowance
+
+        if self._alternative == 'greater':
+            high = [np.inf] * len(diff_means)
+        elif self._alternative == 'less':
+            low = [-np.inf] * len(diff_means)
+
+        self._ci_cl = confidence_level
+        self._ci = ConfidenceInterval(
+            low=low,
+            high=high
+        )
+        return self._ci
+
+
+@xp_capabilities(np_only=True)
+@_transition_to_rng('random_state', replace_doc=False)
+def dunnett(
+    *samples: "npt.ArrayLike",  # noqa: D417
+    control: "npt.ArrayLike",
+    alternative: Literal['two-sided', 'less', 'greater'] = "two-sided",
+    rng: SeedType = None
+) -> DunnettResult:
+    """Dunnett's test: multiple comparisons of means against a control group.
+
+    This is an implementation of Dunnett's original, single-step test as
+    described in [1]_.
+
+    Parameters
+    ----------
+    *samples : 1D array_like
+        The sample measurements for each experimental group.
+    control : 1D array_like
+        The sample measurements for the control group.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+
+        The null hypothesis is that the means of the distributions underlying
+        the samples and control are equal. The following alternative
+        hypotheses are available (default is 'two-sided'):
+
+        * 'two-sided': the means of the distributions underlying the samples
+          and control are unequal.
+        * 'less': the means of the distributions underlying the samples
+          are less than the mean of the distribution underlying the control.
+        * 'greater': the means of the distributions underlying the
+          samples are greater than the mean of the distribution underlying
+          the control.
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+        .. versionchanged:: 1.15.0
+
+            As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
+            transition from use of `numpy.random.RandomState` to
+            `numpy.random.Generator`, this keyword was changed from `random_state` to
+            `rng`. For an interim period, both keywords will continue to work, although
+            only one may be specified at a time. After the interim period, function
+            calls using the `random_state` keyword will emit warnings. Following a
+            deprecation period, the `random_state` keyword will be removed.
+
+    Returns
+    -------
+    res : `~scipy.stats._result_classes.DunnettResult`
+        An object containing attributes:
+
+        statistic : float ndarray
+            The computed statistic of the test for each comparison. The element
+            at index ``i`` is the statistic for the comparison between
+            groups ``i`` and the control.
+        pvalue : float ndarray
+            The computed p-value of the test for each comparison. The element
+            at index ``i`` is the p-value for the comparison between
+            group ``i`` and the control.
+
+        And the following method:
+
+        confidence_interval(confidence_level=0.95) :
+            Compute the difference in means of the groups
+            with the control +- the allowance.
+
+    See Also
+    --------
+    tukey_hsd : performs pairwise comparison of means.
+    :ref:`hypothesis_dunnett` : Extended example
+
+    Notes
+    -----
+    Like the independent-sample t-test, Dunnett's test [1]_ is used to make
+    inferences about the means of distributions from which samples were drawn.
+    However, when multiple t-tests are performed at a fixed significance level,
+    the "family-wise error rate" - the probability of incorrectly rejecting the
+    null hypothesis in at least one test - will exceed the significance level.
+    Dunnett's test is designed to perform multiple comparisons while
+    controlling the family-wise error rate.
+
+    Dunnett's test compares the means of multiple experimental groups
+    against a single control group. Tukey's Honestly Significant Difference Test
+    is another multiple-comparison test that controls the family-wise error
+    rate, but `tukey_hsd` performs *all* pairwise comparisons between groups.
+    When pairwise comparisons between experimental groups are not needed,
+    Dunnett's test is preferable due to its higher power.
+
+    The use of this test relies on several assumptions.
+
+    1. The observations are independent within and among groups.
+    2. The observations within each group are normally distributed.
+    3. The distributions from which the samples are drawn have the same finite
+       variance.
+
+    References
+    ----------
+    .. [1] Dunnett, Charles W. (1955) "A Multiple Comparison Procedure for
+           Comparing Several Treatments with a Control." Journal of the American
+           Statistical Association, 50:272, 1096-1121,
+           :doi:`10.1080/01621459.1955.10501294`
+    .. [2] Thomson, M. L., & Short, M. D. (1969). Mucociliary function in
+           health, chronic obstructive airway disease, and asbestosis. Journal
+           of applied physiology, 26(5), 535-539.
+           :doi:`10.1152/jappl.1969.26.5.535`
+
+    Examples
+    --------
+    We'll use data from [2]_, Table 1. The null hypothesis is that the means of
+    the distributions underlying the samples and control are equal.
+
+    First, we test that the means of the distributions underlying the samples
+    and control are unequal (``alternative='two-sided'``, the default).
+
+    >>> import numpy as np
+    >>> from scipy.stats import dunnett
+    >>> samples = [[3.8, 2.7, 4.0, 2.4], [2.8, 3.4, 3.7, 2.2, 2.0]]
+    >>> control = [2.9, 3.0, 2.5, 2.6, 3.2]
+    >>> res = dunnett(*samples, control=control)
+    >>> res.statistic
+    array([ 0.90874545, -0.05007117])
+    >>> res.pvalue
+    array([0.58325114, 0.99819341])
+
+    Now, we test that the means of the distributions underlying the samples are
+    greater than the mean of the distribution underlying the control.
+
+    >>> res = dunnett(*samples, control=control, alternative='greater')
+    >>> res.statistic
+    array([ 0.90874545, -0.05007117])
+    >>> res.pvalue
+    array([0.30230596, 0.69115597])
+
+    For a more detailed example, see :ref:`hypothesis_dunnett`.
+    """
+    samples_, control_, rng = _iv_dunnett(
+        samples=samples, control=control,
+        alternative=alternative, rng=rng
+    )
+
+    rho, df, n_group, n_samples, n_control = _params_dunnett(
+        samples=samples_, control=control_
+    )
+
+    statistic, std, mean_control, mean_samples = _statistic_dunnett(
+        samples_, control_, df, n_samples, n_control
+    )
+
+    pvalue = _pvalue_dunnett(
+        rho=rho, df=df, statistic=statistic, alternative=alternative, rng=rng
+    )
+
+    return DunnettResult(
+        statistic=statistic, pvalue=pvalue,
+        _alternative=alternative,
+        _rho=rho, _df=df, _std=std,
+        _mean_samples=mean_samples,
+        _mean_control=mean_control,
+        _n_samples=n_samples,
+        _n_control=n_control,
+        _rng=rng
+    )
+
+
+def _iv_dunnett(
+    samples: Sequence["npt.ArrayLike"],
+    control: "npt.ArrayLike",
+    alternative: Literal['two-sided', 'less', 'greater'],
+    rng: SeedType
+) -> tuple[list[np.ndarray], np.ndarray, SeedType]:
+    """Input validation for Dunnett's test."""
+    rng = check_random_state(rng)
+
+    if alternative not in {'two-sided', 'less', 'greater'}:
+        raise ValueError(
+            "alternative must be 'less', 'greater' or 'two-sided'"
+        )
+
+    ndim_msg = "Control and samples groups must be 1D arrays"
+    n_obs_msg = "Control and samples groups must have at least 1 observation"
+
+    control = np.asarray(control)
+    samples_ = [np.asarray(sample) for sample in samples]
+
+    # samples checks
+    samples_control: list[np.ndarray] = samples_ + [control]
+    for sample in samples_control:
+        if sample.ndim > 1:
+            raise ValueError(ndim_msg)
+
+        if sample.size < 1:
+            raise ValueError(n_obs_msg)
+
+    return samples_, control, rng
+
+
+def _params_dunnett(
+    samples: list[np.ndarray], control: np.ndarray
+) -> tuple[np.ndarray, int, int, np.ndarray, int]:
+    """Specific parameters for Dunnett's test.
+
+    Degree of freedom is the number of observations minus the number of groups
+    including the control.
+    """
+    n_samples = np.array([sample.size for sample in samples])
+
+    # From [1] p. 1100 d.f. = (sum N)-(p+1)
+    n_sample = n_samples.sum()
+    n_control = control.size
+    n = n_sample + n_control
+    n_groups = len(samples)
+    df = n - n_groups - 1
+
+    # From [1] p. 1103 rho_ij = 1/sqrt((N0/Ni+1)(N0/Nj+1))
+    rho = n_control/n_samples + 1
+    rho = 1/np.sqrt(rho[:, None] * rho[None, :])
+    np.fill_diagonal(rho, 1)
+
+    return rho, df, n_groups, n_samples, n_control
+
+
+def _statistic_dunnett(
+    samples: list[np.ndarray], control: np.ndarray, df: int,
+    n_samples: np.ndarray, n_control: int
+) -> tuple[np.ndarray, float, np.ndarray, np.ndarray]:
+    """Statistic of Dunnett's test.
+
+    Computation based on the original single-step test from [1].
+    """
+    mean_control = np.mean(control)
+    mean_samples = np.array([np.mean(sample) for sample in samples])
+    all_samples = [control] + samples
+    all_means = np.concatenate([[mean_control], mean_samples])
+
+    # Variance estimate s^2 from [1] Eq. 1
+    s2 = np.sum([_var(sample, mean=mean)*sample.size
+                 for sample, mean in zip(all_samples, all_means)]) / df
+    std = np.sqrt(s2)
+
+    # z score inferred from [1] unlabeled equation after Eq. 1
+    z = (mean_samples - mean_control) / np.sqrt(1/n_samples + 1/n_control)
+
+    return z / std, std, mean_control, mean_samples
+
+
+def _pvalue_dunnett(
+    rho: np.ndarray, df: int, statistic: np.ndarray,
+    alternative: Literal['two-sided', 'less', 'greater'],
+    rng: SeedType = None
+) -> np.ndarray:
+    """pvalue from the multivariate t-distribution.
+
+    Critical values come from the multivariate student-t distribution.
+    """
+    statistic = statistic.reshape(-1, 1)
+
+    mvt = stats.multivariate_t(shape=rho, df=df, seed=rng)
+    if alternative == "two-sided":
+        statistic = abs(statistic)
+        pvalue = 1 - mvt.cdf(statistic, lower_limit=-statistic)
+    elif alternative == "greater":
+        pvalue = 1 - mvt.cdf(statistic, lower_limit=-np.inf)
+    else:
+        pvalue = 1 - mvt.cdf(np.inf, lower_limit=statistic)
+
+    return np.atleast_1d(pvalue)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_multivariate.py b/.venv/lib/python3.12/site-packages/scipy/stats/_multivariate.py
new file mode 100644
index 0000000000000000000000000000000000000000..51e5c8dc8f5272381d17e8edc33662a635628bba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_multivariate.py
@@ -0,0 +1,8054 @@
+#
+# Author: Joris Vankerschaver 2013
+#
+import math
+import threading
+import types
+import numpy as np
+import scipy.linalg
+from scipy._lib import doccer
+from scipy.special import (gammaln, psi, multigammaln, xlogy, entr, betaln,
+                           ive, loggamma)
+from scipy import special
+import scipy._external.array_api_extra as xpx
+from scipy._lib._util import check_random_state
+from scipy.linalg.blas import get_blas_funcs
+from ._continuous_distns import norm, invgamma
+from ._discrete_distns import binom
+from . import _covariance, _rcont
+from ._qmvnt import _qmvt, _qmvn, _qauto
+from ._morestats import directional_stats
+from scipy.optimize import root_scalar
+
+__all__ = ['multivariate_normal',
+           'matrix_normal',
+           'dirichlet',
+           'dirichlet_multinomial',
+           'wishart',
+           'invwishart',
+           'multinomial',
+           'special_ortho_group',
+           'ortho_group',
+           'random_correlation',
+           'unitary_group',
+           'multivariate_t',
+           'multivariate_hypergeom',
+           'random_table',
+           'uniform_direction',
+           'vonmises_fisher',
+           'normal_inverse_gamma',
+           'matrix_t']
+
+_LOG_2PI = np.log(2 * np.pi)
+_LOG_2 = np.log(2)
+_LOG_PI = np.log(np.pi)
+MVN_LOCK = threading.Lock()
+
+
+_doc_random_state = """\
+seed : {None, int, np.random.RandomState, np.random.Generator}, optional
+    Used for drawing random variates.
+    If `seed` is `None`, the `~np.random.RandomState` singleton is used.
+    If `seed` is an int, a new ``RandomState`` instance is used, seeded
+    with seed.
+    If `seed` is already a ``RandomState`` or ``Generator`` instance,
+    then that object is used.
+    Default is `None`.
+"""
+
+
+def _squeeze_output(out):
+    """
+    Remove single-dimensional entries from array and convert to scalar,
+    if necessary.
+    """
+    out = out.squeeze()
+    if out.ndim == 0:
+        out = out[()]
+    return out
+
+
+def _eigvalsh_to_eps(spectrum, cond=None, rcond=None):
+    """Determine which eigenvalues are "small" given the spectrum.
+
+    This is for compatibility across various linear algebra functions
+    that should agree about whether or not a Hermitian matrix is numerically
+    singular and what is its numerical matrix rank.
+    This is designed to be compatible with scipy.linalg.pinvh.
+
+    Parameters
+    ----------
+    spectrum : 1d ndarray
+        Array of eigenvalues of a Hermitian matrix.
+    cond, rcond : float, optional
+        Cutoff for small eigenvalues.
+        Singular values smaller than rcond * largest_eigenvalue are
+        considered zero.
+        If None or -1, suitable machine precision is used.
+
+    Returns
+    -------
+    eps : float
+        Magnitude cutoff for numerical negligibility.
+
+    """
+    if rcond is not None:
+        cond = rcond
+    if cond in [None, -1]:
+        t = spectrum.dtype.char.lower()
+        factor = {'f': 1E3, 'd': 1E6}
+        cond = factor[t] * np.finfo(t).eps
+    eps = cond * np.max(abs(spectrum))
+    return eps
+
+
+def _pinv_1d(v, eps=1e-5):
+    """A helper function for computing the pseudoinverse.
+
+    Parameters
+    ----------
+    v : iterable of numbers
+        This may be thought of as a vector of eigenvalues or singular values.
+    eps : float
+        Values with magnitude no greater than eps are considered negligible.
+
+    Returns
+    -------
+    v_pinv : 1d float ndarray
+        A vector of pseudo-inverted numbers.
+
+    """
+    return np.array([0 if abs(x) <= eps else 1/x for x in v], dtype=float)
+
+
+def _validate_marginal_input(dimensions, multivariate_dims):
+    """Determine if input dimensions can be marginalized.
+
+    Parameters
+    ----------
+    dimensions : float, ndarray
+        Input dimensions to be marginalized
+    multivariate_dims : int
+        Number of dimensions of multivariate distribution.
+
+    Returns
+    -------
+    dims : ndarray
+        Array of indices to marginalize
+    """
+    dims = np.copy(dimensions)
+    dims = np.atleast_1d(dims)
+
+    if len(dims) == 0:
+        msg = "Cannot marginalize all dimensions."
+        raise ValueError(msg)
+
+    if not np.issubdtype(dims.dtype, np.integer):
+        msg = ("Elements of `dimensions` must be integers - the indices "
+               "of the marginal variables being retained.")
+        raise ValueError(msg)
+
+    original_dims = np.copy(dims)
+
+    dims[dims < 0] += multivariate_dims
+
+    if len(np.unique(dims)) != len(dims):
+        msg = "All elements of `dimensions` must be unique."
+        raise ValueError(msg)
+
+    i_invalid = (dims < 0) | (dims >= multivariate_dims)
+    if np.any(i_invalid):
+        msg = (f"Dimensions {original_dims[i_invalid]} are invalid "
+               f"for a distribution in {multivariate_dims} dimensions.")
+        raise ValueError(msg)
+
+    return dims
+
+
+class _PSD:
+    """
+    Compute coordinated functions of a symmetric positive semidefinite matrix.
+
+    This class addresses two issues.  Firstly it allows the pseudoinverse,
+    the logarithm of the pseudo-determinant, and the rank of the matrix
+    to be computed using one call to eigh instead of three.
+    Secondly it allows these functions to be computed in a way
+    that gives mutually compatible results.
+    All of the functions are computed with a common understanding as to
+    which of the eigenvalues are to be considered negligibly small.
+    The functions are designed to coordinate with scipy.linalg.pinvh()
+    but not necessarily with np.linalg.det() or with np.linalg.matrix_rank().
+
+    Parameters
+    ----------
+    M : array_like
+        Symmetric positive semidefinite matrix (2-D).
+    cond, rcond : float, optional
+        Cutoff for small eigenvalues.
+        Singular values smaller than rcond * largest_eigenvalue are
+        considered zero.
+        If None or -1, suitable machine precision is used.
+    lower : bool, optional
+        Whether the pertinent array data is taken from the lower
+        or upper triangle of M. (Default: lower)
+    check_finite : bool, optional
+        Whether to check that the input matrices contain only finite
+        numbers. Disabling may give a performance gain, but may result
+        in problems (crashes, non-termination) if the inputs do contain
+        infinities or NaNs.
+    allow_singular : bool, optional
+        Whether to allow a singular matrix.  (Default: True)
+
+    Notes
+    -----
+    The arguments are similar to those of scipy.linalg.pinvh().
+
+    """
+
+    def __init__(self, M, cond=None, rcond=None, lower=True,
+                 check_finite=True, allow_singular=True):
+        self._M = np.asarray(M)
+
+        # Compute the symmetric eigendecomposition.
+        # Note that eigh takes care of array conversion, chkfinite,
+        # and assertion that the matrix is square.
+        s, u = scipy.linalg.eigh(M, lower=lower, check_finite=check_finite)
+
+        eps = _eigvalsh_to_eps(s, cond, rcond)
+        if np.min(s) < -eps:
+            msg = "The input matrix must be symmetric positive semidefinite."
+            raise ValueError(msg)
+        d = s[s > eps]
+        if len(d) < len(s) and not allow_singular:
+            msg = ("When `allow_singular is False`, the input matrix must be "
+                   "symmetric positive definite.")
+            raise np.linalg.LinAlgError(msg)
+        s_pinv = _pinv_1d(s, eps)
+        U = np.multiply(u, np.sqrt(s_pinv))
+
+        # Save the eigenvector basis, and tolerance for testing support
+        self.eps = 1e3*eps
+        self.V = u[:, s <= eps]
+
+        # Initialize the eagerly precomputed attributes.
+        self.rank = len(d)
+        self.U = U
+        self.log_pdet = np.sum(np.log(d))
+
+        # Initialize attributes to be lazily computed.
+        self._pinv = None
+
+    def _support_mask(self, x):
+        """
+        Check whether x lies in the support of the distribution.
+        """
+        residual = np.linalg.norm(x @ self.V, axis=-1)
+        in_support = residual < self.eps
+        return in_support
+
+    @property
+    def pinv(self):
+        if self._pinv is None:
+            self._pinv = np.dot(self.U, self.U.T)
+        return self._pinv
+
+
+class multi_rv_generic:
+    """
+    Class which encapsulates common functionality between all multivariate
+    distributions.
+    """
+
+    def __init__(self, seed=None):
+        super().__init__()
+        self._random_state = check_random_state(seed)
+
+    @property
+    def random_state(self):
+        """ Get or set the Generator object for generating random variates.
+
+        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+        singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used,
+        seeded with `seed`.
+        If `seed` is already a ``Generator`` or ``RandomState`` instance then
+        that instance is used.
+
+        """
+        return self._random_state
+
+    @random_state.setter
+    def random_state(self, seed):
+        self._random_state = check_random_state(seed)
+
+    def _get_random_state(self, random_state):
+        if random_state is not None:
+            return check_random_state(random_state)
+        else:
+            return self._random_state
+
+
+class multi_rv_frozen:
+    """
+    Class which encapsulates common functionality between all frozen
+    multivariate distributions.
+    """
+
+    # generic type compatibility with scipy-stubs
+    __class_getitem__: classmethod = classmethod(types.GenericAlias)
+
+    @property
+    def random_state(self):
+        return self._dist._random_state
+
+    @random_state.setter
+    def random_state(self, seed):
+        self._dist._random_state = check_random_state(seed)
+
+
+_mvn_doc_default_callparams = """\
+mean : array_like, default: ``[0]``
+    Mean of the distribution.
+cov : array_like or `Covariance`, default: ``[1]``
+    Symmetric positive (semi)definite covariance matrix of the distribution.
+allow_singular : bool, default: ``False``
+    Whether to allow a singular covariance matrix. This is ignored if `cov` is
+    a `Covariance` object.
+"""
+
+_mvn_doc_callparams_note = """\
+Setting the parameter `mean` to `None` is equivalent to having `mean`
+be the zero-vector. The parameter `cov` can be a scalar, in which case
+the covariance matrix is the identity times that value, a vector of
+diagonal entries for the covariance matrix, a two-dimensional array_like,
+or a `Covariance` object.
+"""
+
+_mvn_doc_frozen_callparams = ""
+
+_mvn_doc_frozen_callparams_note = """\
+See class definition for a detailed description of parameters."""
+
+mvn_docdict_params = {
+    '_mvn_doc_default_callparams': _mvn_doc_default_callparams,
+    '_mvn_doc_callparams_note': _mvn_doc_callparams_note,
+    '_doc_random_state': _doc_random_state
+}
+
+mvn_docdict_noparams = {
+    '_mvn_doc_default_callparams': _mvn_doc_frozen_callparams,
+    '_mvn_doc_callparams_note': _mvn_doc_frozen_callparams_note,
+    '_doc_random_state': _doc_random_state
+}
+
+
+class multivariate_normal_gen(multi_rv_generic):
+    r"""
+    A multivariate normal random variable.
+
+    The `mean` keyword specifies the mean. The `cov` keyword specifies the
+    covariance matrix.
+
+    Parameters
+    ----------
+    %(_mvn_doc_default_callparams)s
+    %(_doc_random_state)s
+
+    Methods
+    -------
+    pdf(x, mean=None, cov=1, allow_singular=False)
+        Probability density function.
+    logpdf(x, mean=None, cov=1, allow_singular=False)
+        Log of the probability density function.
+    cdf(x, mean=None, cov=1, allow_singular=False, maxpts=1000000*dim, abseps=1e-5, releps=1e-5, lower_limit=None)
+        Cumulative distribution function.
+    logcdf(x, mean=None, cov=1, allow_singular=False, maxpts=1000000*dim, abseps=1e-5, releps=1e-5)
+        Log of the cumulative distribution function.
+    rvs(mean=None, cov=1, size=1, random_state=None)
+        Draw random samples from a multivariate normal distribution.
+    entropy(mean=None, cov=1)
+        Compute the differential entropy of the multivariate normal.
+    marginal(dimensions, mean=None, cov=1, allow_singular=False)
+        Return a marginal multivariate normal distribution.
+    fit(x, fix_mean=None, fix_cov=None)
+        Fit a multivariate normal distribution to data.
+
+    Notes
+    -----
+    %(_mvn_doc_callparams_note)s
+
+    The covariance matrix `cov` may be an instance of a subclass of
+    `Covariance`, e.g. `scipy.stats.CovViaPrecision`. If so, `allow_singular`
+    is ignored.
+
+    Otherwise, `cov` must be a symmetric positive semidefinite
+    matrix when `allow_singular` is True; it must be (strictly) positive
+    definite when `allow_singular` is False.
+    Symmetry is not checked; only the lower triangular portion is used.
+    The determinant and inverse of `cov` are computed
+    as the pseudo-determinant and pseudo-inverse, respectively, so
+    that `cov` does not need to have full rank.
+
+    The probability density function for `multivariate_normal` is
+
+    .. math::
+
+        f(x) = \frac{1}{\sqrt{(2 \pi)^k \det \Sigma}}
+               \exp\left( -\frac{1}{2} (x - \mu)^T \Sigma^{-1} (x - \mu) \right),
+
+    where :math:`\mu` is the mean, :math:`\Sigma` the covariance matrix,
+    :math:`k` the rank of :math:`\Sigma`. In case of singular :math:`\Sigma`,
+    SciPy extends this definition according to [1]_.
+
+    .. versionadded:: 0.14.0
+
+    References
+    ----------
+    .. [1] Multivariate Normal Distribution - Degenerate Case, Wikipedia,
+           https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Degenerate_case
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy.stats import multivariate_normal
+
+    >>> x = np.linspace(0, 5, 10, endpoint=False)
+    >>> y = multivariate_normal.pdf(x, mean=2.5, cov=0.5); y
+    array([ 0.00108914,  0.01033349,  0.05946514,  0.20755375,  0.43939129,
+            0.56418958,  0.43939129,  0.20755375,  0.05946514,  0.01033349])
+    >>> fig1 = plt.figure()
+    >>> ax = fig1.add_subplot(111)
+    >>> ax.plot(x, y)
+    >>> plt.show()
+
+    Alternatively, the object may be called (as a function) to fix the mean
+    and covariance parameters, returning a "frozen" multivariate normal
+    random variable:
+
+    >>> rv = multivariate_normal(mean=None, cov=1, allow_singular=False)
+    >>> # Frozen object with the same methods but holding the given
+    >>> # mean and covariance fixed.
+
+    The input quantiles can be any shape of array, as long as the last
+    axis labels the components.  This allows us for instance to
+    display the frozen pdf for a non-isotropic random variable in 2D as
+    follows:
+
+    >>> x, y = np.mgrid[-1:1:.01, -1:1:.01]
+    >>> pos = np.dstack((x, y))
+    >>> rv = multivariate_normal([0.5, -0.2], [[2.0, 0.3], [0.3, 0.5]])
+    >>> fig2 = plt.figure()
+    >>> ax2 = fig2.add_subplot(111)
+    >>> ax2.contourf(x, y, rv.pdf(pos))
+    """  # noqa: E501
+
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = doccer.docformat(self.__doc__, mvn_docdict_params)
+
+    def __call__(self, mean=None, cov=1, allow_singular=False, seed=None, **kwds):
+        """Create a frozen multivariate normal distribution.
+
+        See `multivariate_normal_frozen` for more information.
+        """
+        return multivariate_normal_frozen(mean, cov,
+                                          allow_singular=allow_singular,
+                                          seed=seed, **kwds)
+
+    def _process_parameters(self, mean, cov, allow_singular=True):
+        """
+        Infer dimensionality from mean or covariance matrix, ensure that
+        mean and covariance are full vector resp. matrix.
+        """
+        if isinstance(cov, _covariance.Covariance):
+            return self._process_parameters_Covariance(mean, cov)
+        else:
+            # Before `Covariance` classes were introduced,
+            # `multivariate_normal` accepted plain arrays as `cov` and used the
+            # following input validation. To avoid disturbing the behavior of
+            # `multivariate_normal` when plain arrays are used, we use the
+            # original input validation here.
+            dim, mean, cov = self._process_parameters_psd(None, mean, cov)
+            # After input validation, some methods then processed the arrays
+            # with a `_PSD` object and used that to perform computation.
+            # To avoid branching statements in each method depending on whether
+            # `cov` is an array or `Covariance` object, we always process the
+            # array with `_PSD`, and then use wrapper that satisfies the
+            # `Covariance` interface, `CovViaPSD`.
+            psd = _PSD(cov, allow_singular=allow_singular)
+            cov_object = _covariance.CovViaPSD(psd)
+            return dim, mean, cov_object
+
+    def _process_parameters_Covariance(self, mean, cov):
+        dim = cov.shape[-1]
+        mean = np.array([0.]) if mean is None else mean
+        message = (f"`cov` represents a covariance matrix in {dim} dimensions,"
+                   f"and so `mean` must be broadcastable to shape {(dim,)}")
+        try:
+            mean = np.broadcast_to(mean, dim)
+        except ValueError as e:
+            raise ValueError(message) from e
+        return dim, mean, cov
+
+    def _process_parameters_psd(self, dim, mean, cov):
+        # Try to infer dimensionality
+        if dim is None:
+            if mean is None:
+                if cov is None:
+                    dim = 1
+                else:
+                    cov = np.asarray(cov, dtype=float)
+                    if cov.ndim < 2:
+                        dim = 1
+                    else:
+                        dim = cov.shape[0]
+            else:
+                mean = np.asarray(mean, dtype=float)
+                dim = mean.size
+        else:
+            if not np.isscalar(dim):
+                raise ValueError("Dimension of random variable must be "
+                                 "a scalar.")
+
+        # Check input sizes and return full arrays for mean and cov if
+        # necessary
+        if mean is None:
+            mean = np.zeros(dim)
+        mean = np.asarray(mean, dtype=float)
+
+        if cov is None:
+            cov = 1.0
+        cov = np.asarray(cov, dtype=float)
+
+        if dim == 1:
+            mean = mean.reshape(1)
+            cov = cov.reshape(1, 1)
+
+        if mean.ndim != 1 or mean.shape[0] != dim:
+            raise ValueError(f"Array 'mean' must be a vector of length {dim}.")
+        if cov.ndim == 0:
+            cov = cov * np.eye(dim)
+        elif cov.ndim == 1:
+            cov = np.diag(cov)
+        elif cov.ndim == 2 and cov.shape != (dim, dim):
+            rows, cols = cov.shape
+            if rows != cols:
+                msg = ("Array 'cov' must be square if it is two dimensional,"
+                       f" but cov.shape = {str(cov.shape)}.")
+            else:
+                msg = (f"Dimension mismatch: array 'cov' is of shape {cov.shape}, "
+                       f"but 'mean' is a vector of length {len(mean)}.")
+            raise ValueError(msg)
+        elif cov.ndim > 2:
+            raise ValueError(f"Array 'cov' must be at most two-dimensional, "
+                             f"but cov.ndim = {cov.ndim}")
+
+        return dim, mean, cov
+
+    def _process_quantiles(self, x, dim):
+        """
+        Adjust quantiles array so that last axis labels the components of
+        each data point.
+        """
+        x = np.asarray(x, dtype=float)
+
+        if x.ndim == 0:
+            x = x[np.newaxis]
+        elif x.ndim == 1:
+            if dim == 1:
+                x = x[:, np.newaxis]
+            else:
+                x = x[np.newaxis, :]
+
+        return x
+
+    def _logpdf(self, x, mean, cov_object):
+        """Log of the multivariate normal probability density function.
+
+        Parameters
+        ----------
+        x : ndarray
+            Points at which to evaluate the log of the probability
+            density function
+        mean : ndarray
+            Mean of the distribution
+        cov_object : Covariance
+            An object representing the Covariance matrix
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'logpdf' instead.
+
+        """
+        log_det_cov, rank = cov_object.log_pdet, cov_object.rank
+        dev = x - mean
+        if dev.ndim > 1:
+            log_det_cov = log_det_cov[..., np.newaxis]
+            rank = rank[..., np.newaxis]
+        maha = np.sum(np.square(cov_object.whiten(dev)), axis=-1)
+        return -0.5 * (rank * _LOG_2PI + log_det_cov + maha)
+
+    def logpdf(self, x, mean=None, cov=1, allow_singular=False):
+        """Log of the multivariate normal probability density function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+        %(_mvn_doc_default_callparams)s
+
+        Returns
+        -------
+        pdf : ndarray or scalar
+            Log of the probability density function evaluated at `x`
+
+        Notes
+        -----
+        %(_mvn_doc_callparams_note)s
+
+        """
+        params = self._process_parameters(mean, cov, allow_singular)
+        dim, mean, cov_object = params
+        x = self._process_quantiles(x, dim)
+        out = self._logpdf(x, mean, cov_object)
+        if np.any(cov_object.rank < dim):
+            out_of_bounds = ~cov_object._support_mask(x-mean)
+            out[out_of_bounds] = -np.inf
+        return _squeeze_output(out)
+
+    def pdf(self, x, mean=None, cov=1, allow_singular=False):
+        """Multivariate normal probability density function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+        %(_mvn_doc_default_callparams)s
+
+        Returns
+        -------
+        pdf : ndarray or scalar
+            Probability density function evaluated at `x`
+
+        Notes
+        -----
+        %(_mvn_doc_callparams_note)s
+
+        """
+        params = self._process_parameters(mean, cov, allow_singular)
+        dim, mean, cov_object = params
+        x = self._process_quantiles(x, dim)
+        out = np.exp(self._logpdf(x, mean, cov_object))
+        if np.any(cov_object.rank < dim):
+            out_of_bounds = ~cov_object._support_mask(x-mean)
+            out[out_of_bounds] = 0.0
+        return _squeeze_output(out)
+
+    def _cdf(self, x, mean, cov, maxpts, abseps, releps, lower_limit, rng):
+        """Multivariate normal cumulative distribution function.
+
+        Parameters
+        ----------
+        x : ndarray
+            Points at which to evaluate the cumulative distribution function.
+        mean : ndarray
+            Mean of the distribution
+        cov : array_like
+            Covariance matrix of the distribution
+        maxpts : int
+            The maximum number of points to use for integration
+        abseps : float
+            Absolute error tolerance
+        releps : float
+            Relative error tolerance
+        lower_limit : array_like, optional
+            Lower limit of integration of the cumulative distribution function.
+            Default is negative infinity. Must be broadcastable with `x`.
+        rng : Generator
+            an instance of ``np.random.Generator``, which is used internally
+            for QMC integration.
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'cdf' instead.
+
+
+        .. versionadded:: 1.0.0
+
+        """
+        lower = (np.full(mean.shape, -np.inf)
+                 if lower_limit is None else lower_limit)
+        # In 2d, _mvn.mvnun accepts input in which `lower` bound elements
+        # are greater than `x`. Not so in other dimensions. Fix this by
+        # ensuring that lower bounds are indeed lower when passed, then
+        # set signs of resulting CDF manually.
+        b, a = np.broadcast_arrays(x, lower)
+        b, a = b - mean, a - mean  # _qmvn only accepts zero mean
+        i_swap = b < a
+        signs = (-1)**(i_swap.sum(axis=-1))  # odd # of swaps -> negative
+        a, b = a.copy(), b.copy()
+        a[i_swap], b[i_swap] = b[i_swap], a[i_swap]
+        n = x.shape[-1]
+        limits = np.concatenate((a, b), axis=-1)
+
+        # qmvn expects 1-d arguments, so process points sequentially
+        # XXX: if cov.ndim == 2 and limits.ndim == 1, can avoid apply_along_axis
+        def func1d(limits):
+            # res0 = _qmvn(maxpts, cov, limits[:n], limits[n:], rng)[0]
+            res = _qauto(_qmvn, cov, limits[:n], limits[n:],
+                         rng, error=abseps, limit=maxpts, n_batches=10)
+            return np.squeeze(res[0])
+
+        out = np.apply_along_axis(func1d, -1, limits) * signs
+        return _squeeze_output(out)
+
+    def logcdf(self, x, mean=None, cov=1, allow_singular=False, maxpts=None,
+               abseps=1e-5, releps=1e-5, *, lower_limit=None, rng=None):
+        """Log of the multivariate normal cumulative distribution function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+        %(_mvn_doc_default_callparams)s
+        maxpts : int, optional
+            The maximum number of points to use for integration
+            (default ``1000000*dim``)
+        abseps : float, optional
+            Absolute error tolerance (default 1e-5)
+        releps : float, optional
+            Relative error tolerance (default 1e-5)
+        lower_limit : array_like, optional
+            Lower limit of integration of the cumulative distribution function.
+            Default is negative infinity. Must be broadcastable with `x`.
+        rng : Generator, optional
+            an instance of ``np.random.Generator``, which is used internally
+            for QMC integration.
+
+        Returns
+        -------
+        cdf : ndarray or scalar
+            Log of the cumulative distribution function evaluated at `x`
+
+        Notes
+        -----
+        %(_mvn_doc_callparams_note)s
+
+        .. versionadded:: 1.0.0
+
+        """
+        params = self._process_parameters(mean, cov, allow_singular)
+        dim, mean, cov_object = params
+        cov = cov_object.covariance
+        x = self._process_quantiles(x, dim)
+        if not maxpts:
+            maxpts = 1000000 * dim
+
+        rng = self._get_random_state(rng)
+        cdf = self._cdf(x, mean, cov, maxpts, abseps, releps, lower_limit, rng)
+        # the log of a negative real is complex, and cdf can be negative
+        # if lower limit is greater than upper limit
+        cdf = cdf + 0j if np.any(cdf < 0) else cdf
+        out = np.log(cdf)
+        return out
+
+    def cdf(self, x, mean=None, cov=1, allow_singular=False, maxpts=None,
+            abseps=1e-5, releps=1e-5, *, lower_limit=None, rng=None):
+        """Multivariate normal cumulative distribution function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+        %(_mvn_doc_default_callparams)s
+        maxpts : int, optional
+            The maximum number of points to use for integration
+            (default ``1000000*dim``)
+        abseps : float, optional
+            Absolute error tolerance (default 1e-5)
+        releps : float, optional
+            Relative error tolerance (default 1e-5)
+        lower_limit : array_like, optional
+            Lower limit of integration of the cumulative distribution function.
+            Default is negative infinity. Must be broadcastable with `x`.
+        rng : Generator, optional
+            an instance of ``np.random.Generator``, which is used internally
+            for QMC integration.
+
+        Returns
+        -------
+        cdf : ndarray or scalar
+            Cumulative distribution function evaluated at `x`
+
+        Notes
+        -----
+        %(_mvn_doc_callparams_note)s
+
+        .. versionadded:: 1.0.0
+
+        """
+        params = self._process_parameters(mean, cov, allow_singular)
+        dim, mean, cov_object = params
+        cov = cov_object.covariance
+        x = self._process_quantiles(x, dim)
+        if not maxpts:
+            maxpts = 1000000 * dim
+        rng = self._get_random_state(rng)
+        out = self._cdf(x, mean, cov, maxpts, abseps, releps, lower_limit, rng)
+        return out
+
+    def rvs(self, mean=None, cov=1, size=1, random_state=None):
+        """Draw random samples from a multivariate normal distribution.
+
+        Parameters
+        ----------
+        %(_mvn_doc_default_callparams)s
+        size : int, optional
+            Number of samples to draw (default 1).
+        %(_doc_random_state)s
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random variates of size (`size`, `N`), where `N` is the
+            dimension of the random variable.
+
+        Notes
+        -----
+        %(_mvn_doc_callparams_note)s
+
+        """
+        dim, mean, cov_object = self._process_parameters(mean, cov)
+        random_state = self._get_random_state(random_state)
+
+        if isinstance(cov_object, _covariance.CovViaPSD):
+            cov = cov_object.covariance
+            out = random_state.multivariate_normal(mean, cov, size)
+            out = _squeeze_output(out)
+        else:
+            size = size or tuple()
+            if not np.iterable(size):
+                size = (size,)
+            shape = tuple(size) + (cov_object.shape[-1],)
+            x = random_state.normal(size=shape)
+            out = mean + cov_object.colorize(x)
+        return out
+
+    def entropy(self, mean=None, cov=1):
+        """Compute the differential entropy of the multivariate normal.
+
+        Parameters
+        ----------
+        %(_mvn_doc_default_callparams)s
+
+        Returns
+        -------
+        h : scalar
+            Entropy of the multivariate normal distribution
+
+        Notes
+        -----
+        %(_mvn_doc_callparams_note)s
+
+        """
+        dim, mean, cov_object = self._process_parameters(mean, cov)
+        return 0.5 * (cov_object.rank * (_LOG_2PI + 1) + cov_object.log_pdet)
+
+    def fit(self, x, fix_mean=None, fix_cov=None):
+        """Fit a multivariate normal distribution to data.
+
+        Parameters
+        ----------
+        x : ndarray (m, n)
+            Data the distribution is fitted to. Must have two axes.
+            The first axis of length `m` represents the number of vectors
+            the distribution is fitted to. The second axis of length `n`
+            determines the dimensionality of the fitted distribution.
+        fix_mean : ndarray(n, )
+            Fixed mean vector. Must have length `n`.
+        fix_cov: ndarray (n, n)
+            Fixed covariance matrix. Must have shape ``(n, n)``.
+
+        Returns
+        -------
+        mean : ndarray (n, )
+            Maximum likelihood estimate of the mean vector
+        cov : ndarray (n, n)
+            Maximum likelihood estimate of the covariance matrix
+
+        """
+        # input validation for data to be fitted
+        x = np.asarray(x)
+        if x.ndim != 2:
+            raise ValueError("`x` must be two-dimensional.")
+
+        n_vectors, dim = x.shape
+
+        # parameter estimation
+        # reference: https://home.ttic.edu/~shubhendu/Slides/Estimation.pdf
+        if fix_mean is not None:
+            # input validation for `fix_mean`
+            fix_mean = np.atleast_1d(fix_mean)
+            if fix_mean.shape != (dim, ):
+                msg = ("`fix_mean` must be a one-dimensional array the same "
+                       "length as the dimensionality of the vectors `x`.")
+                raise ValueError(msg)
+            mean = fix_mean
+        else:
+            mean = x.mean(axis=0)
+
+        if fix_cov is not None:
+            # input validation for `fix_cov`
+            fix_cov = np.atleast_2d(fix_cov)
+            # validate shape
+            if fix_cov.shape != (dim, dim):
+                msg = ("`fix_cov` must be a two-dimensional square array "
+                       "of same side length as the dimensionality of the "
+                       "vectors `x`.")
+                raise ValueError(msg)
+            # validate positive semidefiniteness
+            # a trimmed down copy from _PSD
+            s, u = scipy.linalg.eigh(fix_cov, lower=True, check_finite=True)
+            eps = _eigvalsh_to_eps(s)
+            if np.min(s) < -eps:
+                msg = "`fix_cov` must be symmetric positive semidefinite."
+                raise ValueError(msg)
+            cov = fix_cov
+        else:
+            centered_data = x - mean
+            cov = centered_data.T @ centered_data / n_vectors
+        return mean, cov
+
+    def marginal(self, dimensions, mean=None, cov=1, allow_singular=False):
+        """Return a marginal multivariate normal distribution.
+
+        Parameters
+        ----------
+        dimensions : int or 1-d array_like
+            The dimensions of the multivariate distribution corresponding
+            with the marginal variables, that is, the indices of the dimensions
+            that are being retained. The other dimensions are marginalized out.
+        %(_mvn_doc_default_callparams)s
+
+        Returns
+        -------
+        marginal_multivariate_normal : multivariate_normal_frozen
+            An object representing the marginal distribution.
+
+        Notes
+        -----
+        %(_mvn_doc_callparams_note)s
+        """
+        params = self._process_parameters(mean, cov, allow_singular)
+        n, mean, cov_object = params
+        dims = _validate_marginal_input(dimensions, n)
+
+        mean = mean[dims]
+        cov = cov_object.covariance[np.ix_(dims, dims)]
+
+        return multivariate_normal_frozen(mean, cov, allow_singular)
+
+multivariate_normal = multivariate_normal_gen()
+
+
+class multivariate_normal_frozen(multi_rv_frozen):
+    __class_getitem__ = None  # pyrefly:ignore[bad-assignment]
+
+    def __init__(self, mean=None, cov=1, allow_singular=False, seed=None,
+                 maxpts=None, abseps=1e-5, releps=1e-5):
+        """Create a frozen multivariate normal distribution.
+
+        Parameters
+        ----------
+        mean : array_like, default: ``[0]``
+            Mean of the distribution.
+        cov : array_like, default: ``[1]``
+            Symmetric positive (semi)definite covariance matrix of the
+            distribution.
+        allow_singular : bool, default: ``False``
+            Whether to allow a singular covariance matrix.
+        seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+            If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+            singleton is used.
+            If `seed` is an int, a new ``RandomState`` instance is used,
+            seeded with `seed`.
+            If `seed` is already a ``Generator`` or ``RandomState`` instance
+            then that instance is used.
+        maxpts : int, optional
+            The maximum number of points to use for integration of the
+            cumulative distribution function (default ``1000000*dim``)
+        abseps : float, optional
+            Absolute error tolerance for the cumulative distribution function
+            (default 1e-5)
+        releps : float, optional
+            Relative error tolerance for the cumulative distribution function
+            (default 1e-5)
+
+        Examples
+        --------
+        When called with the default parameters, this will create a 1D random
+        variable with mean 0 and covariance 1:
+
+        >>> from scipy.stats import multivariate_normal
+        >>> r = multivariate_normal()
+        >>> r.mean
+        array([ 0.])
+        >>> r.cov
+        array([[1.]])
+
+        """ # numpy/numpydoc#87  # noqa: E501
+        self._dist = multivariate_normal_gen(seed)
+        self.dim, self.mean, self.cov_object = (
+            self._dist._process_parameters(mean, cov, allow_singular))
+        self.allow_singular = allow_singular or self.cov_object._allow_singular
+        if not maxpts:
+            maxpts = 1000000 * self.dim
+        self.maxpts = maxpts
+        self.abseps = abseps
+        self.releps = releps
+
+    @property
+    def cov(self):
+        return self.cov_object.covariance
+
+    def logpdf(self, x):
+        x = self._dist._process_quantiles(x, self.dim)
+        out = self._dist._logpdf(x, self.mean, self.cov_object)
+        if np.any(self.cov_object.rank < self.dim):
+            out_of_bounds = ~self.cov_object._support_mask(x-self.mean)
+            out[out_of_bounds] = -np.inf
+        return _squeeze_output(out)
+
+    def pdf(self, x):
+        return np.exp(self.logpdf(x))
+
+    def logcdf(self, x, *, lower_limit=None, rng=None):
+        cdf = self.cdf(x, lower_limit=lower_limit, rng=rng)
+        # the log of a negative real is complex, and cdf can be negative
+        # if lower limit is greater than upper limit
+        cdf = cdf + 0j if np.any(cdf < 0) else cdf
+        out = np.log(cdf)
+        return out
+
+    def cdf(self, x, *, lower_limit=None, rng=None):
+        x = self._dist._process_quantiles(x, self.dim)
+        rng = self._dist._get_random_state(rng)
+        out = self._dist._cdf(x, self.mean, self.cov_object.covariance,
+                              self.maxpts, self.abseps, self.releps,
+                              lower_limit, rng)
+        return _squeeze_output(out)
+
+    def rvs(self, size=1, random_state=None):
+        return self._dist.rvs(self.mean, self.cov_object, size, random_state)
+
+    def entropy(self):
+        """Computes the differential entropy of the multivariate normal.
+
+        Returns
+        -------
+        h : scalar
+            Entropy of the multivariate normal distribution
+
+        """
+        log_pdet = self.cov_object.log_pdet
+        rank = self.cov_object.rank
+        return 0.5 * (rank * (_LOG_2PI + 1) + log_pdet)
+
+    def marginal(self, dimensions):
+        return self._dist.marginal(dimensions, self.mean,
+                                   self.cov_object, self.allow_singular)
+
+# Set frozen generator docstrings from corresponding docstrings in
+# multivariate_normal_gen and fill in default strings in class docstrings
+for name in ['logpdf', 'pdf', 'logcdf', 'cdf', 'rvs']:
+    method = multivariate_normal_gen.__dict__[name]
+    method_frozen = multivariate_normal_frozen.__dict__[name]
+    method_frozen.__doc__ = doccer.docformat(method.__doc__,
+                                             mvn_docdict_noparams)
+    method.__doc__ = doccer.docformat(method.__doc__, mvn_docdict_params)
+
+_matnorm_doc_default_callparams = """\
+mean : array_like, optional
+    Mean of the distribution (default: `None`)
+rowcov : array_like, optional
+    Among-row covariance matrix of the distribution (default: ``1``)
+colcov : array_like, optional
+    Among-column covariance matrix of the distribution (default: ``1``)
+"""
+
+_matnorm_doc_callparams_note = """\
+If `mean` is set to `None` then a matrix of zeros is used for the mean.
+The dimensions of this matrix are inferred from the shape of `rowcov` and
+`colcov`, if these are provided, or set to ``1`` if ambiguous.
+
+`rowcov` and `colcov` can be two-dimensional array_likes specifying the
+covariance matrices directly. Alternatively, a one-dimensional array will
+be be interpreted as the entries of a diagonal matrix, and a scalar or
+zero-dimensional array will be interpreted as this value times the
+identity matrix.
+"""
+
+_matnorm_doc_frozen_callparams = ""
+
+_matnorm_doc_frozen_callparams_note = """\
+See class definition for a detailed description of parameters."""
+
+matnorm_docdict_params = {
+    '_matnorm_doc_default_callparams': _matnorm_doc_default_callparams,
+    '_matnorm_doc_callparams_note': _matnorm_doc_callparams_note,
+    '_doc_random_state': _doc_random_state
+}
+
+matnorm_docdict_noparams = {
+    '_matnorm_doc_default_callparams': _matnorm_doc_frozen_callparams,
+    '_matnorm_doc_callparams_note': _matnorm_doc_frozen_callparams_note,
+    '_doc_random_state': _doc_random_state
+}
+
+
+class matrix_normal_gen(multi_rv_generic):
+    r"""
+    A matrix normal random variable.
+
+    The `mean` keyword specifies the mean. The `rowcov` keyword specifies the
+    among-row covariance matrix. The 'colcov' keyword specifies the
+    among-column covariance matrix.
+
+    Parameters
+    ----------
+    %(_matnorm_doc_default_callparams)s
+    %(_doc_random_state)s
+
+    Methods
+    -------
+    pdf(X, mean=None, rowcov=1, colcov=1)
+        Probability density function.
+    logpdf(X, mean=None, rowcov=1, colcov=1)
+        Log of the probability density function.
+    rvs(mean=None, rowcov=1, colcov=1, size=1, random_state=None)
+        Draw random samples.
+    entropy(rowcol=1, colcov=1)
+        Differential entropy.
+
+    Notes
+    -----
+    %(_matnorm_doc_callparams_note)s
+
+    The covariance matrices specified by `rowcov` and `colcov` must be
+    (symmetric) positive definite. If the samples in `X` are
+    :math:`m \times n`, then `rowcov` must be :math:`m \times m` and
+    `colcov` must be :math:`n \times n`. `mean` must be the same shape as `X`.
+
+    The probability density function for `matrix_normal` is
+
+    .. math::
+
+        f(X) = (2 \pi)^{-\frac{mn}{2}}|U|^{-\frac{n}{2}} |V|^{-\frac{m}{2}}
+               \exp\left( -\frac{1}{2} \mathrm{Tr}\left[ U^{-1} (X-M) V^{-1}
+               (X-M)^T \right] \right),
+
+    where :math:`M` is the mean, :math:`U` the among-row covariance matrix,
+    :math:`V` the among-column covariance matrix.
+
+    The `allow_singular` behaviour of the `multivariate_normal`
+    distribution is not currently supported. Covariance matrices must be
+    full rank.
+
+    The `matrix_normal` distribution is closely related to the
+    `multivariate_normal` distribution. Specifically, :math:`\mathrm{Vec}(X)`
+    (the vector formed by concatenating the columns  of :math:`X`) has a
+    multivariate normal distribution with mean :math:`\mathrm{Vec}(M)`
+    and covariance :math:`V \otimes U` (where :math:`\otimes` is the Kronecker
+    product). Sampling and pdf evaluation are
+    :math:`\mathcal{O}(m^3 + n^3 + m^2 n + m n^2)` for the matrix normal, but
+    :math:`\mathcal{O}(m^3 n^3)` for the equivalent multivariate normal,
+    making this equivalent form algorithmically inefficient.
+
+    .. versionadded:: 0.17.0
+
+    References
+    ----------
+    .. [1] "Matrix normal distribution", Wikipedia,
+           https://en.wikipedia.org/wiki/Matrix_normal_distribution
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from scipy.stats import matrix_normal
+
+    >>> M = np.arange(6).reshape(3,2); M
+    array([[0, 1],
+           [2, 3],
+           [4, 5]])
+    >>> U = np.diag([1,2,3]); U
+    array([[1, 0, 0],
+           [0, 2, 0],
+           [0, 0, 3]])
+    >>> V = 0.3*np.identity(2); V
+    array([[ 0.3,  0. ],
+           [ 0. ,  0.3]])
+    >>> X = M + 0.1; X
+    array([[ 0.1,  1.1],
+           [ 2.1,  3.1],
+           [ 4.1,  5.1]])
+    >>> matrix_normal.pdf(X, mean=M, rowcov=U, colcov=V)
+    0.023410202050005054
+
+    >>> # Equivalent multivariate normal
+    >>> from scipy.stats import multivariate_normal
+    >>> vectorised_X = X.T.flatten()
+    >>> equiv_mean = M.T.flatten()
+    >>> equiv_cov = np.kron(V,U)
+    >>> multivariate_normal.pdf(vectorised_X, mean=equiv_mean, cov=equiv_cov)
+    0.023410202050005054
+
+    Alternatively, the object may be called (as a function) to fix the mean
+    and covariance parameters, returning a "frozen" matrix normal
+    random variable:
+
+    >>> rv = matrix_normal(mean=None, rowcov=1, colcov=1)
+    >>> # Frozen object with the same methods but holding the given
+    >>> # mean and covariance fixed.
+    """
+
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = doccer.docformat(self.__doc__, matnorm_docdict_params)
+
+    def __call__(self, mean=None, rowcov=1, colcov=1, seed=None):
+        """Create a frozen matrix normal distribution.
+
+        See `matrix_normal_frozen` for more information.
+
+        """
+        return matrix_normal_frozen(mean, rowcov, colcov, seed=seed)
+
+    def _process_parameters(self, mean, rowcov, colcov):
+        """
+        Infer dimensionality from mean or covariance matrices. Handle
+        defaults. Ensure compatible dimensions.
+        """
+
+        # Process mean
+        if mean is not None:
+            mean = np.asarray(mean, dtype=float)
+            meanshape = mean.shape
+            if len(meanshape) != 2:
+                raise ValueError("Array `mean` must be two dimensional.")
+            if np.any(meanshape == 0):
+                raise ValueError("Array `mean` has invalid shape.")
+
+        # Process among-row covariance
+        rowcov = np.asarray(rowcov, dtype=float)
+        if rowcov.ndim == 0:
+            if mean is not None:
+                rowcov = rowcov * np.identity(meanshape[0])
+            else:
+                rowcov = rowcov * np.identity(1)
+        elif rowcov.ndim == 1:
+            rowcov = np.diag(rowcov)
+        rowshape = rowcov.shape
+        if len(rowshape) != 2:
+            raise ValueError("`rowcov` must be a scalar or a 2D array.")
+        if rowshape[0] != rowshape[1]:
+            raise ValueError("Array `rowcov` must be square.")
+        if rowshape[0] == 0:
+            raise ValueError("Array `rowcov` has invalid shape.")
+        numrows = rowshape[0]
+
+        # Process among-column covariance
+        colcov = np.asarray(colcov, dtype=float)
+        if colcov.ndim == 0:
+            if mean is not None:
+                colcov = colcov * np.identity(meanshape[1])
+            else:
+                colcov = colcov * np.identity(1)
+        elif colcov.ndim == 1:
+            colcov = np.diag(colcov)
+        colshape = colcov.shape
+        if len(colshape) != 2:
+            raise ValueError("`colcov` must be a scalar or a 2D array.")
+        if colshape[0] != colshape[1]:
+            raise ValueError("Array `colcov` must be square.")
+        if colshape[0] == 0:
+            raise ValueError("Array `colcov` has invalid shape.")
+        numcols = colshape[0]
+
+        # Ensure mean and covariances compatible
+        if mean is not None:
+            if meanshape[0] != numrows:
+                raise ValueError("Arrays `mean` and `rowcov` must have the "
+                                 "same number of rows.")
+            if meanshape[1] != numcols:
+                raise ValueError("Arrays `mean` and `colcov` must have the "
+                                 "same number of columns.")
+        else:
+            mean = np.zeros((numrows, numcols))
+
+        dims = (numrows, numcols)
+
+        return dims, mean, rowcov, colcov
+
+    def _process_quantiles(self, X, dims):
+        """
+        Adjust quantiles array so that last two axes labels the components of
+        each data point.
+        """
+        X = np.asarray(X, dtype=float)
+        if X.ndim == 2:
+            X = X[np.newaxis, :]
+        if X.shape[-2:] != dims:
+            raise ValueError("The shape of array `X` is not compatible "
+                             "with the distribution parameters.")
+        return X
+
+    def _logpdf(self, dims, X, mean, row_prec_rt, log_det_rowcov,
+                col_prec_rt, log_det_colcov):
+        """Log of the matrix normal probability density function.
+
+        Parameters
+        ----------
+        dims : tuple
+            Dimensions of the matrix variates
+        X : ndarray
+            Points at which to evaluate the log of the probability
+            density function
+        mean : ndarray
+            Mean of the distribution
+        row_prec_rt : ndarray
+            A decomposition such that np.dot(row_prec_rt, row_prec_rt.T)
+            is the inverse of the among-row covariance matrix
+        log_det_rowcov : float
+            Logarithm of the determinant of the among-row covariance matrix
+        col_prec_rt : ndarray
+            A decomposition such that np.dot(col_prec_rt, col_prec_rt.T)
+            is the inverse of the among-column covariance matrix
+        log_det_colcov : float
+            Logarithm of the determinant of the among-column covariance matrix
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'logpdf' instead.
+
+        """
+        numrows, numcols = dims
+        roll_dev = np.moveaxis(X-mean, -1, 0)
+        scale_dev = np.tensordot(col_prec_rt.T,
+                                 np.dot(roll_dev, row_prec_rt), 1)
+        maha = np.sum(np.sum(np.square(scale_dev), axis=-1), axis=0)
+        return -0.5 * (numrows*numcols*_LOG_2PI + numcols*log_det_rowcov
+                       + numrows*log_det_colcov + maha)
+
+    def logpdf(self, X, mean=None, rowcov=1, colcov=1):
+        """Log of the matrix normal probability density function.
+
+        Parameters
+        ----------
+        X : array_like
+            Quantiles, with the last two axes of `X` denoting the components.
+        %(_matnorm_doc_default_callparams)s
+
+        Returns
+        -------
+        logpdf : ndarray
+            Log of the probability density function evaluated at `X`
+
+        Notes
+        -----
+        %(_matnorm_doc_callparams_note)s
+
+        """
+        dims, mean, rowcov, colcov = self._process_parameters(mean, rowcov,
+                                                              colcov)
+        X = self._process_quantiles(X, dims)
+        rowpsd = _PSD(rowcov, allow_singular=False)
+        colpsd = _PSD(colcov, allow_singular=False)
+        out = self._logpdf(dims, X, mean, rowpsd.U, rowpsd.log_pdet, colpsd.U,
+                           colpsd.log_pdet)
+        return _squeeze_output(out)
+
+    def pdf(self, X, mean=None, rowcov=1, colcov=1):
+        """Matrix normal probability density function.
+
+        Parameters
+        ----------
+        X : array_like
+            Quantiles, with the last two axes of `X` denoting the components.
+        %(_matnorm_doc_default_callparams)s
+
+        Returns
+        -------
+        pdf : ndarray
+            Probability density function evaluated at `X`
+
+        Notes
+        -----
+        %(_matnorm_doc_callparams_note)s
+
+        """
+        return np.exp(self.logpdf(X, mean, rowcov, colcov))
+
+    def rvs(self, mean=None, rowcov=1, colcov=1, size=1, random_state=None):
+        """Draw random samples from a matrix normal distribution.
+
+        Parameters
+        ----------
+        %(_matnorm_doc_default_callparams)s
+        size : int, optional
+            Number of samples to draw (default 1).
+        %(_doc_random_state)s
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random variates of size (`size`, `dims`), where `dims` is the
+            dimension of the random matrices.
+
+        Notes
+        -----
+        %(_matnorm_doc_callparams_note)s
+
+        """
+        size = int(size)
+        dims, mean, rowcov, colcov = self._process_parameters(mean, rowcov,
+                                                              colcov)
+        rowchol = scipy.linalg.cholesky(rowcov, lower=True)
+        colchol = scipy.linalg.cholesky(colcov, lower=True)
+        random_state = self._get_random_state(random_state)
+        # We aren't generating standard normal variates with size=(size,
+        # dims[0], dims[1]) directly to ensure random variates remain backwards
+        # compatible. See https://github.com/scipy/scipy/pull/12312 for more
+        # details.
+        std_norm = random_state.standard_normal(
+            size=(dims[1], size, dims[0])
+        ).transpose(1, 2, 0)
+        out = mean + np.einsum('jp,ipq,kq->ijk',
+                               rowchol, std_norm, colchol,
+                               optimize=True)
+        if size == 1:
+            out = out.reshape(mean.shape)
+        return out
+
+    def entropy(self, rowcov=1, colcov=1):
+        """Log of the matrix normal probability density function.
+
+        Parameters
+        ----------
+        rowcov : array_like, optional
+            Among-row covariance matrix of the distribution (default: ``1``)
+        colcov : array_like, optional
+            Among-column covariance matrix of the distribution (default: ``1``)
+
+        Returns
+        -------
+        entropy : float
+            Entropy of the distribution
+
+        Notes
+        -----
+        %(_matnorm_doc_callparams_note)s
+
+        """
+        dummy_mean = np.zeros((rowcov.shape[0], colcov.shape[0]))
+        dims, _, rowcov, colcov = self._process_parameters(dummy_mean,
+                                                           rowcov,
+                                                           colcov)
+        rowpsd = _PSD(rowcov, allow_singular=False)
+        colpsd = _PSD(colcov, allow_singular=False)
+
+        return self._entropy(dims, rowpsd.log_pdet, colpsd.log_pdet)
+
+    def _entropy(self, dims, row_cov_logdet, col_cov_logdet):
+        n, p = dims
+        return (0.5 * n * p * (1 + _LOG_2PI) + 0.5 * p * row_cov_logdet +
+                0.5 * n * col_cov_logdet)
+
+
+matrix_normal = matrix_normal_gen()
+
+
+class matrix_normal_frozen(multi_rv_frozen):
+    """
+    Create a frozen matrix normal distribution.
+
+    Parameters
+    ----------
+    %(_matnorm_doc_default_callparams)s
+    seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+        If `seed` is `None` the `~np.random.RandomState` singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used, seeded
+        with seed.
+        If `seed` is already a ``RandomState`` or ``Generator`` instance,
+        then that object is used.
+        Default is `None`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import matrix_normal
+
+    >>> distn = matrix_normal(mean=np.zeros((3,3)))
+    >>> X = distn.rvs(); X
+    array([[-0.02976962,  0.93339138, -0.09663178],
+           [ 0.67405524,  0.28250467, -0.93308929],
+           [-0.31144782,  0.74535536,  1.30412916]])
+    >>> distn.pdf(X)
+    2.5160642368346784e-05
+    >>> distn.logpdf(X)
+    -10.590229595124615
+    """
+    __class_getitem__ = None  # pyrefly:ignore[bad-assignment]
+
+    def __init__(self, mean=None, rowcov=1, colcov=1, seed=None):
+        self._dist = matrix_normal_gen(seed)
+        self.dims, self.mean, self.rowcov, self.colcov = \
+            self._dist._process_parameters(mean, rowcov, colcov)
+        self.rowpsd = _PSD(self.rowcov, allow_singular=False)
+        self.colpsd = _PSD(self.colcov, allow_singular=False)
+
+    def logpdf(self, X):
+        X = self._dist._process_quantiles(X, self.dims)
+        out = self._dist._logpdf(self.dims, X, self.mean, self.rowpsd.U,
+                                 self.rowpsd.log_pdet, self.colpsd.U,
+                                 self.colpsd.log_pdet)
+        return _squeeze_output(out)
+
+    def pdf(self, X):
+        return np.exp(self.logpdf(X))
+
+    def rvs(self, size=1, random_state=None):
+        return self._dist.rvs(self.mean, self.rowcov, self.colcov, size,
+                              random_state)
+
+    def entropy(self):
+        return self._dist._entropy(self.dims, self.rowpsd.log_pdet,
+                                   self.colpsd.log_pdet)
+
+# Set frozen generator docstrings from corresponding docstrings in
+# matrix_normal_gen and fill in default strings in class docstrings
+for name in ['logpdf', 'pdf', 'rvs', 'entropy']:
+    method = matrix_normal_gen.__dict__[name]
+    method_frozen = matrix_normal_frozen.__dict__[name]
+    method_frozen.__doc__ = doccer.docformat(method.__doc__,
+                                             matnorm_docdict_noparams)
+    method.__doc__ = doccer.docformat(method.__doc__, matnorm_docdict_params)
+
+
+_matt_doc_default_callparams = """\
+mean : array_like, optional
+    Mean of the distribution (default: `None`)
+row_spread : array_like, optional
+    Row-wise 2nd order raw central moment matrix (default: ``1``)
+col_spread : array_like, optional
+    Column-wise 2nd order raw central moment matrix (default: ``1``)
+df : scalar, optional
+    Degrees of freedom (default: ``1``)
+"""
+
+_matt_doc_callparams_note = """\
+If `mean` is set to `None` then a matrix of zeros is used for the mean.
+The dimensions of this matrix are inferred from the shape of `row_spread` and
+`col_spread`, if these are provided, or set to ``1`` if ambiguous.
+
+`row_spread` and `col_spread` can be two-dimensional array_likes specifying the
+spread matrices directly. Alternatively, a one-dimensional array will
+be be interpreted as the entries of a diagonal matrix, and a scalar or
+zero-dimensional array will be interpreted as this value times the
+identity matrix.
+"""
+
+_matt_doc_frozen_callparams = ""
+
+_matt_doc_frozen_callparams_note = """\
+See class definition for a detailed description of parameters."""
+
+matrix_t_docdict_params = {
+    "_matt_doc_default_callparams": _matt_doc_default_callparams,
+    "_matt_doc_callparams_note": _matt_doc_callparams_note,
+    "_doc_random_state": _doc_random_state,
+}
+
+matrix_t_docdict_noparams = {
+    "_matt_doc_default_callparams": _matt_doc_frozen_callparams,
+    "_matt_doc_callparams_note": _matt_doc_frozen_callparams_note,
+    "_doc_random_state": _doc_random_state,
+}
+
+
+class matrix_t_gen(multi_rv_generic):
+    r"""
+    A matrix t-random variable.
+
+    The `mean` keyword specifies the mean.
+    The `row_spread` keyword specifies the row-wise spread matrix.
+    The `col_spread` keyword specifies the column-wise spread matrix.
+
+    Parameters
+    ----------
+    %(_matt_doc_default_callparams)s
+    %(_doc_random_state)s
+
+    Methods
+    -------
+    pdf(x, mean=None, row_spread=None, col_spread=None)
+        Probability density function.
+    logpdf(x, mean=None, row_spread=None, col_spread=None)
+        Log of the probability density function.
+    rvs(mean=None, row_spread=1, col_spread=1, df=1, size=1, random_state=None)
+        Draw random samples.
+
+    Notes
+    -----
+    %(_matt_doc_callparams_note)s
+
+    The spread matrices specified by `row_spread` and `col_spread` must be
+    (symmetric) positive definite. If the samples in `X` have shape `(m,n)`
+    then `row_spread` must have shape `(m,m)` and `col_spread` must have shape `(n,n)`.
+    Spread matrices must be full rank.
+
+    The probability density function for `matrix_t` is
+
+    .. math::
+
+        f(X \vert \mathrm{M}, \Sigma, \Omega, \mathrm{df}) =
+        \frac{
+            \Gamma_n \left(
+                \frac{\mathrm{df} + m + n - 1}{2}
+            \right)
+            \left(
+            \det \left(
+                I_n + (X - \mathrm{M})^T \Sigma^{-1} (X - \mathrm{M}) \Omega^{-1}
+            \right)
+        \right)^{ -\frac{\mathrm{df} + m + n - 1}{2} }
+        }{
+            \Gamma_n \left(
+                \frac{\mathrm{df} + n - 1}{2}
+            \right)
+            \pi^{mn / 2}
+            \left( \det \Sigma \right)^{n/2}
+            \left( \det \Omega \right)^{m/2}
+        }
+
+    or, alternatively,
+
+    .. math::
+
+        f(X \vert \mathrm{M}, \Sigma, \Omega, \mathrm{df}) =
+        \frac{
+            \Gamma_m \left(
+                \frac{\mathrm{df} + m + n - 1}{2}
+            \right)
+            \left(
+                \det \left(
+                    I_m + \Sigma^{-1} (X - \mathrm{M}) \Omega^{-1} (X - \mathrm{M})^T
+                    \right)
+            \right)^{ -\frac{\mathrm{df} + m + n - 1}{2} }
+        }{
+            \Gamma_m \left(
+                \frac{\mathrm{df} + n - 1}{2}
+            \right)
+            \pi^{mn / 2}
+            \left( \det \Sigma \right)^{n/2}
+            \left( \det \Omega \right)^{m/2}
+        }
+
+    where :math:`\mathrm{M}` is the mean,
+    :math:`\Sigma` is the row-wise spread matrix,
+    :math:`\Omega` is the column-wise matrix,
+    :math:`\mathrm{df}` is the degrees of freedom,
+    and :math:`\Gamma_n` is the multivariate gamma function.
+
+    These equivalent formulations come from the identity
+
+    .. math::
+
+        \det\left( I_m + A B \right) = \det\left( I_n + B A \right)
+
+    for :math:`m \times n` arrays :math:`A` and :math:`B^T`
+    and the fact that
+    :math:`\gamma_n(\mathrm{df} + m) / \gamma_n(\mathrm{df})`
+    is equal to
+    :math:`\gamma_m(\mathrm{df} + n) / \gamma_m(\mathrm{df})`,
+    where
+
+    .. math::
+
+        \gamma_m(\mathrm{df}) = 2^{m(m-1)/2}
+        \Gamma_m\left( (\mathrm{df} + m - 1) / 2 \right)
+
+    denotes a normalized multivariate gamma function.
+
+    When :math:`\mathrm{df} = 1` this distribution is known as the matrix
+    variate Cauchy.
+
+    .. versionadded:: 1.17.0
+
+    References
+    ----------
+    .. [1] Gupta, A.K., & Nagar, D.K. (2000). Matrix Variate Distributions (1st ed.).
+           Chapman and Hall/CRC.
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from scipy.stats import matrix_t
+    >>> M = np.arange(6).reshape(3,2)
+    >>> M
+    array([[0, 1],
+           [2, 3],
+           [4, 5]])
+    >>> Sigma = np.diag([1,2,3])
+    >>> Sigma
+    array([[1, 0, 0],
+           [0, 2, 0],
+           [0, 0, 3]])
+    >>> Omega = 0.3*np.identity(2)
+    >>> Omega
+    array([[ 0.3,  0. ],
+           [ 0. ,  0.3]])
+    >>> X = M + 0.1
+    >>> X
+    array([[ 0.1,  1.1],
+           [ 2.1,  3.1],
+           [ 4.1,  5.1]])
+    >>> df = 3
+    >>> matrix_t.pdf(X, mean=M, row_spread=Sigma, col_spread=Omega, df=df)
+    0.9972880280135796
+
+    Alternatively, the object may be called (as a function) to fix the mean
+    and spread parameters, returning a "frozen" matrix t
+    random variable:
+
+    >>> rv = matrix_t(mean=None, row_spread=1, col_spread=1, df=1)
+    >>> # Frozen object with the same methods but holding the given
+    >>> # mean and spreads and degrees of freedom fixed.
+    """
+
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = scipy._lib.doccer.docformat(
+            self.__doc__, matrix_t_docdict_params
+        )
+
+    def __call__(self, mean=None, row_spread=1, col_spread=1, df=None, seed=None):
+        """Create a frozen matrix t distribution.
+
+        See `matrix_t_frozen` for more information.
+        """
+        return matrix_t_frozen(mean, row_spread, col_spread, df, seed)
+
+    def _process_parameters(self, mean, row_spread, col_spread, df):
+        """
+        Infer dimensionality from mean or covariance matrices.
+        Handle defaults. Ensure conformality.
+
+        Parameters
+        ----------
+        mean : ndarray, shape (m,n)
+            Mean of the distribution
+        row_spread : ndarray, shape (m,m)
+            Row-wise spread matrix
+        col_spread : ndarray, shape (n,n)
+            Column-wise spread matrix
+        df : float
+            Degrees of freedom
+        """
+
+        # Process mean
+        if mean is not None:
+            mean = np.asarray(mean, dtype=float)
+            meanshape = mean.shape
+            if 0 in meanshape:
+                raise ValueError("Array `mean` has invalid shape.")
+            if len(meanshape) != 2:
+                raise ValueError("Array `mean` must be 2D.")
+
+        # Process row-wise spread
+        row_spread = np.asarray(row_spread, dtype=float)
+        if row_spread.ndim == 0:
+            if mean is not None:
+                row_spread = row_spread * np.identity(meanshape[0])
+            else:
+                row_spread = row_spread * np.identity(1)
+        elif row_spread.ndim == 1:
+            row_spread = np.diag(row_spread)
+        rowshape = row_spread.shape
+        if 0 in rowshape:
+            raise ValueError("Array `row_spread` has invalid shape.")
+        if len(rowshape) != 2:
+            raise ValueError("Array `row_spread` must be a scalar or a 2D array.")
+        if rowshape[0] != rowshape[1]:
+            raise ValueError("Array `row_spread` must be square.")
+        numrows = rowshape[0]
+
+        # Process column-wise spread
+        col_spread = np.asarray(col_spread, dtype=float)
+        if col_spread.ndim == 0:
+            if mean is not None:
+                col_spread = col_spread * np.identity(meanshape[1])
+            else:
+                col_spread = col_spread * np.identity(1)
+        elif col_spread.ndim == 1:
+            col_spread = np.diag(col_spread)
+        colshape = col_spread.shape
+        if 0 in colshape:
+            raise ValueError("Array `col_spread` has invalid shape.")
+        if len(colshape) != 2:
+            raise ValueError("Array `col_spread` must be a scalar or a 2D array.")
+        if colshape[0] != colshape[1]:
+            raise ValueError("Array `col_spread` must be square.")
+        numcols = colshape[0]
+
+        # Ensure mean and spreads are conformal
+        if mean is not None:
+            if meanshape[0] != numrows:
+                raise ValueError(
+                    "Arrays `mean` and `row_spread` must have the same number of rows."
+                )
+            if meanshape[1] != numcols:
+                raise ValueError(
+                    "Arrays `mean` and `col_spread` must have the same number "
+                    "of columns."
+                )
+        else:
+            mean = np.zeros((numrows, numcols))
+
+        dims = (numrows, numcols)
+
+        if df is None:
+            df = 1  # default to matrix variate Cauchy
+        elif not np.isscalar(df):
+            raise ValueError("Degrees of freedom must be a scalar.")
+        elif df <= 0:
+            raise ValueError("Degrees of freedom must be positive.")
+
+        return dims, mean, row_spread, col_spread, df
+
+    def _process_quantiles(self, X, dims):
+        """
+        Adjust quantiles array so that last two axes labels the component of
+        each data point.
+        """
+        X = np.asarray(X, dtype=float)
+        if X.ndim == 2:
+            X = X[np.newaxis, :]
+        if X.shape[-2:] != dims:
+            raise ValueError(
+                "The shape of array `X` is not conformal with "
+                "the distribution parameters."
+            )
+        return X
+
+    def _logpdf(
+        self,
+        dims,
+        X,
+        mean,
+        df,
+        invrow_spread,
+        invcol_spread,
+        logdetrow_spread,
+        logdetcol_spread,
+    ):
+        """
+        Log of the matrix t probability density function.
+
+        Parameters
+        ----------
+        dims : tuple
+            Dimensions of the matrix variates
+        X : ndarray, shape (m,n) (equal to `dims`)
+            Points at which to evaluate the log of the probability density function
+        mean : ndarray, shape (m,n)
+            Mean of the distribution
+        df : float
+            Degrees-of-freedom parameter
+        invrow_spread : ndarray, shape (m,m)
+            Inverse of the row-wise spread matrix
+        invcol_spread : ndarray, shape (n,n)
+            Inverse of the column-wise spread matrix
+        logdetrow_spread : float
+            Log-determinant of the row-wise spread matrix
+        detcol_spread : float
+            Log-determinant of the column-wise spread matrix
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use `logpdf` instead.
+        """
+        m, n = dims
+        X_shape = X.shape
+        if X.ndim > 3:
+            X = X.reshape(-1, m, n)
+        X_centered = X - mean[np.newaxis, ...]
+        det_arg = np.identity(n) + np.einsum(
+            "nij,njk,nkl,nlp->nip",
+            X_centered.transpose(0, 2, 1),
+            invrow_spread[np.newaxis, ...],
+            X_centered,
+            invcol_spread[np.newaxis, ...],
+            optimize=True,
+        )
+        _, logdet = np.linalg.slogdet(det_arg)
+        log_d_mn = -((df + m + n - 1) / 2) * logdet
+        log_f_mn = (
+            scipy.special.multigammaln((df + m + n - 1) / 2, n)
+            - scipy.special.multigammaln((df + n - 1) / 2, n)
+            - (m * n / 2) * _LOG_PI
+            - (n / 2) * logdetrow_spread
+            - (m / 2) * logdetcol_spread
+        )
+        retval = log_d_mn + log_f_mn
+        if len(X_shape) > 3:
+            retval = retval.reshape(X_shape[:-2])
+        return retval
+
+    def logpdf(self, X, mean=None, row_spread=1, col_spread=1, df=1):
+        """Log of the matrix normal probability density function.
+
+        Parameters
+        ----------
+        X : array_like
+            Quantiles, with the last two axes of `X` denoting the components.
+        %(_matt_doc_default_callparams)s
+
+        Returns
+        -------
+        logpdf : ndarray
+            Log of the probability density function evaluated at `X`
+
+        Notes
+        -----
+        %(_matt_doc_callparams_note)s
+
+        Examples
+        -------
+
+        >>> import numpy as np
+        >>> from scipy.stats import matrix_t
+        >>> M = np.arange(6).reshape(3,2); M
+        array([[0, 1],
+            [2, 3],
+            [4, 5]])
+        >>> Sigma = np.diag([1,2,3]); Sigma
+        array([[1, 0, 0],
+            [0, 2, 0],
+            [0, 0, 3]])
+        >>> Omega = 0.3*np.identity(2); Omega
+        array([[ 0.3,  0. ],
+            [ 0. ,  0.3]])
+        >>> X = M + 0.1; X
+        array([[ 0.1,  1.1],
+            [ 2.1,  3.1],
+            [ 4.1,  5.1]])
+        >>> df = 3; df
+        3
+        >>> matrix_t.logpdf(X, mean=M, row_spread=Sigma, col_spread=Omega, df=df)
+        -0.002715656044664061
+        """
+        dims, mean, row_spread, col_spread, df = self._process_parameters(
+            mean, row_spread, col_spread, df
+        )
+        X = self._process_quantiles(X, dims)
+        rowpsd = _PSD(row_spread, allow_singular=False)
+        colpsd = _PSD(col_spread, allow_singular=False)
+        invrow_spread = rowpsd.pinv
+        invcol_spread = colpsd.pinv
+        logdetrow_spread = rowpsd.log_pdet
+        logdetcol_spread = colpsd.log_pdet
+        out = self._logpdf(
+            dims,
+            X,
+            mean,
+            df,
+            invrow_spread,
+            invcol_spread,
+            logdetrow_spread,
+            logdetcol_spread,
+        )
+        return _squeeze_output(out)
+
+    def pdf(self, X, mean=None, row_spread=1, col_spread=1, df=1):
+        """Matrix t probability density function.
+
+        Parameters
+        ----------
+        X : array_like
+            Quantiles, with the last two axes of `X` denoting the components.
+        %(_matt_doc_default_callparams)s
+
+        Returns
+        -------
+        pdf : ndarray
+            Probability density function evaluated at `X`
+
+        Notes
+        -----
+        %(_matt_doc_callparams_note)s
+
+        Examples
+        --------
+
+        >>> import numpy as np
+        >>> from scipy.stats import matrix_t
+        >>> M = np.arange(6).reshape(3,2); M
+        array([[0, 1],
+            [2, 3],
+            [4, 5]])
+        >>> Sigma = np.diag([1,2,3]); Sigma
+        array([[1, 0, 0],
+            [0, 2, 0],
+            [0, 0, 3]])
+        >>> Omega = 0.3*np.identity(2); Omega
+        array([[ 0.3,  0. ],
+            [ 0. ,  0.3]])
+        >>> X = M + 0.1; X
+        array([[ 0.1,  1.1],
+            [ 2.1,  3.1],
+            [ 4.1,  5.1]])
+        >>> df = 3; df
+        3
+        >>> matrix_t.logpdf(X, mean=M, row_spread=Sigma, col_spread=Omega, df=df)
+        0.9972880280135796
+        """
+        return np.exp(self.logpdf(X, mean, row_spread, col_spread, df))
+
+    def rvs(
+        self, mean=None, row_spread=1, col_spread=1, df=1, size=1, random_state=None
+    ) -> np.ndarray:
+        """Draw random samples from a matrix t distribution.
+
+        Parameters
+        ----------
+        %(_matt_doc_default_callparams)s
+        size : int, optional
+            Number of samples to draw (default 1).
+        %(_doc_random_state)s
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random variates of size (`size`, `dims`), where `dims` is the
+            dimension of the random matrices.
+
+        Notes
+        -----
+        %(_matt_doc_callparams_note)s
+
+        This method takes advantage of the two equivalent expressions of the
+        probability density function. It samples a Cholesky factor of a
+        random variate of the appropriate inverse Wishart distribution using
+        the smaller of the row/column dimensions.
+        """
+        size = int(size)
+        dims, mean, row_spread, col_spread, df = self._process_parameters(
+            mean, row_spread, col_spread, df
+        )
+        random_state = self._get_random_state(random_state)
+        # see scipy.stats.matrix_normal.rvs
+        std_norm = random_state.standard_normal(
+            size=(dims[1], size, dims[0])
+        ).transpose(1, 2, 0)
+        if dims[0] <= dims[1]:
+            rowchol = _cholesky_invwishart_rvs(df, row_spread, size, random_state)
+            colchol = scipy.linalg.cholesky(col_spread, lower=True)[np.newaxis, ...]
+        else:
+            rowchol = scipy.linalg.cholesky(row_spread, lower=True)[np.newaxis, ...]
+            colchol = _cholesky_invwishart_rvs(df, col_spread, size, random_state)
+        t_raw = np.einsum("ijp,ipq,ikq->ijk", rowchol, std_norm, colchol, optimize=True)
+        t_centered = mean[np.newaxis, ...] + t_raw
+        if size == 1:
+            t_centered = t_centered.reshape(mean.shape)
+        return t_centered
+
+
+matrix_t = matrix_t_gen()
+
+
+class matrix_t_frozen:
+    def __init__(self, mean, row_spread, col_spread, df, seed=None):
+        self._dist = matrix_t_gen(seed)
+        self.dims, self.mean, self.row_spread, self.col_spread, self.df = (
+            self._dist._process_parameters(mean, row_spread, col_spread, df)
+        )
+        self._random_state = np.random.RandomState(seed)
+        self.rowpsd = _PSD(self.row_spread, allow_singular=False)
+        self.colpsd = _PSD(self.col_spread, allow_singular=False)
+
+    def logpdf(self, X):
+        X = self._dist._process_quantiles(X, self.dims)
+        rowpsd = _PSD(self.row_spread, allow_singular=False)
+        colpsd = _PSD(self.col_spread, allow_singular=False)
+        invrow_spread = rowpsd.pinv
+        invcol_spread = colpsd.pinv
+        logdetrow_spread = rowpsd.log_pdet
+        logdetcol_spread = colpsd.log_pdet
+        out = self._dist._logpdf(
+            self.dims,
+            X,
+            self.mean,
+            self.df,
+            invrow_spread,
+            invcol_spread,
+            logdetrow_spread,
+            logdetcol_spread,
+        )
+        return _squeeze_output(out)
+
+    def pdf(self, X):
+        return np.exp(self.logpdf(X))
+
+    def rvs(self, size=1, random_state=None):
+        return self._dist.rvs(
+            self.mean, self.row_spread, self.col_spread, self.df, size, random_state
+        )
+
+
+# Set frozen generator docstrings from corresponding docstrings in
+# matrix_t_gen and fill in default strings in class docstrings
+for name in ["logpdf", "pdf", "rvs"]:
+    method = matrix_t_gen.__dict__[name]
+    method_frozen = matrix_t_frozen.__dict__[name]
+    method_frozen.__doc__ = scipy._lib.doccer.docformat(
+        method.__doc__, matrix_t_docdict_noparams
+    )
+    method.__doc__ = scipy._lib.doccer.docformat(
+        method.__doc__, matrix_t_docdict_params
+    )
+
+
+
+def _cholesky_invwishart_rvs(
+    df: float, scale: np.ndarray, size: int, random_state: np.random.Generator
+) -> np.ndarray:
+    r"""Samples the lower Cholesky factor of a matrix following an inverse
+    Wishart distribution.
+
+    Notes
+    -----
+    Intended to be used *as a step in the process* for computing random variates
+    of a matrix t distribution :math:`\mathcal{T}_{m,n}` by appealing to its
+    alternative form as a matrix mixture
+    .. math::
+        \mathcal{T}_{m,n}( \mathrm{df}, \mathrm{M}, \Sigma, \Omega )
+        = \mathcal{N}_{m,n}(
+            \mathrm{M},
+            \mathcal{W}^{-1}_m(\mathrm{df} + m - 1, \Sigma),
+            \Omega
+            )
+        = \mathcal{N}_{m,n}(
+            \mathrm{M},
+            \Sigma,
+            \mathcal{W}^{-1}_n(\mathrm{df} - n + 1, \Omega)
+            )
+    where :math:`\mathcal{N}_{m,n}` is a matrix normal distribution
+    and :math:`\mathcal{W}^{-1}_d` is an inverse Wishart distribution.
+    Accordingly, the degrees of freedom adjustment
+    :math:`\mathrm{df} \to \mathrm{df} + d - 1`
+    occurrs in the scope of this function.
+    """
+    df_iw = df + scale.shape[0] - 1
+    iw_samples = scipy.stats.invwishart.rvs(df_iw, scale, size, random_state)
+    if size == 1:
+        iw_samples = iw_samples[np.newaxis, ...]
+    chol_samples = np.empty_like(iw_samples)
+    for idx in range(size):
+        chol_samples[idx] = scipy.linalg.cholesky(
+            iw_samples[idx], lower=True, check_finite=False
+        ).reshape(iw_samples.shape[1:])
+    return chol_samples.reshape((size, *scale.shape))
+
+
+_dirichlet_doc_default_callparams = """\
+alpha : array_like
+    The concentration parameters. The number of entries determines the
+    dimensionality of the distribution.
+"""
+_dirichlet_doc_frozen_callparams = ""
+
+_dirichlet_doc_frozen_callparams_note = """\
+See class definition for a detailed description of parameters."""
+
+dirichlet_docdict_params = {
+    '_dirichlet_doc_default_callparams': _dirichlet_doc_default_callparams,
+    '_doc_random_state': _doc_random_state
+}
+
+dirichlet_docdict_noparams = {
+    '_dirichlet_doc_default_callparams': _dirichlet_doc_frozen_callparams,
+    '_doc_random_state': _doc_random_state
+}
+
+
+def _dirichlet_check_parameters(alpha):
+    alpha = np.asarray(alpha)
+    if np.min(alpha) <= 0:
+        raise ValueError("All parameters must be greater than 0")
+    elif alpha.ndim != 1:
+        raise ValueError("Parameter vector 'a' must be one dimensional, "
+                         f"but a.shape = {alpha.shape}.")
+    return alpha
+
+
+def _dirichlet_check_input(alpha, x):
+    x = np.asarray(x)
+
+    if x.shape[0] + 1 != alpha.shape[0] and x.shape[0] != alpha.shape[0]:
+        raise ValueError("Vector 'x' must have either the same number "
+                         "of entries as, or one entry fewer than, "
+                         f"parameter vector 'a', but alpha.shape = {alpha.shape} "
+                         f"and x.shape = {x.shape}.")
+
+    if x.shape[0] != alpha.shape[0]:
+        xk = np.array([1 - np.sum(x, 0)])
+        if xk.ndim == 1:
+            x = np.append(x, xk)
+        elif xk.ndim == 2:
+            x = np.vstack((x, xk))
+        else:
+            raise ValueError("The input must be one dimensional or a two "
+                             "dimensional matrix containing the entries.")
+
+    if np.min(x) < 0:
+        raise ValueError("Each entry in 'x' must be greater than or equal "
+                         "to zero.")
+
+    if np.max(x) > 1:
+        raise ValueError("Each entry in 'x' must be smaller or equal one.")
+
+    # Check x_i > 0 or alpha_i > 1
+    xeq0 = (x == 0)
+    alphalt1 = (alpha < 1)
+    if x.shape != alpha.shape:
+        alphalt1 = np.repeat(alphalt1, x.shape[-1], axis=-1).reshape(x.shape)
+    chk = np.logical_and(xeq0, alphalt1)
+
+    if np.sum(chk):
+        raise ValueError("Each entry in 'x' must be greater than zero if its "
+                         "alpha is less than one.")
+
+    if (np.abs(np.sum(x, 0) - 1.0) > 10e-10).any():
+        raise ValueError("The input vector 'x' must lie within the normal "
+                         f"simplex. but np.sum(x, 0) = {np.sum(x, 0)}.")
+
+    return x
+
+
+def _lnB(alpha):
+    r"""Internal helper function to compute the log of the useful quotient.
+
+    .. math::
+
+        B(\alpha) = \frac{\prod_{i=1}{K}\Gamma(\alpha_i)}
+                         {\Gamma\left(\sum_{i=1}^{K} \alpha_i \right)}
+
+    Parameters
+    ----------
+    %(_dirichlet_doc_default_callparams)s
+
+    Returns
+    -------
+    B : scalar
+        Helper quotient, internal use only
+
+    """
+    return np.sum(gammaln(alpha)) - gammaln(np.sum(alpha))
+
+
+class dirichlet_gen(multi_rv_generic):
+    r"""
+    A Dirichlet random variable.
+
+    The ``alpha`` keyword specifies the concentration parameters of the
+    distribution.
+
+    .. versionadded:: 0.15.0
+
+    Parameters
+    ----------
+    %(_dirichlet_doc_default_callparams)s
+    %(_doc_random_state)s
+
+    Methods
+    -------
+    pdf(x, alpha)
+        Probability density function.
+    logpdf(x, alpha)
+        Log of the probability density function.
+    rvs(alpha, size=1, random_state=None)
+        Draw random samples from a Dirichlet distribution.
+    mean(alpha)
+        The mean of the Dirichlet distribution
+    var(alpha)
+        The variance of the Dirichlet distribution
+    cov(alpha)
+        The covariance of the Dirichlet distribution
+    entropy(alpha)
+        Compute the differential entropy of the Dirichlet distribution.
+
+    Notes
+    -----
+    Each :math:`\alpha` entry must be positive. The distribution has only
+    support on the simplex defined by
+
+    .. math::
+        \sum_{i=1}^{K} x_i = 1
+
+    where :math:`0 < x_i < 1`.
+
+    If the quantiles don't lie within the simplex, a ValueError is raised.
+
+    The probability density function for `dirichlet` is
+
+    .. math::
+
+        f(x) = \frac{1}{\mathrm{B}(\boldsymbol\alpha)} \prod_{i=1}^K x_i^{\alpha_i - 1}
+
+    where
+
+    .. math::
+
+        \mathrm{B}(\boldsymbol\alpha) = \frac{\prod_{i=1}^K \Gamma(\alpha_i)}
+                                     {\Gamma\bigl(\sum_{i=1}^K \alpha_i\bigr)}
+
+    and :math:`\boldsymbol\alpha=(\alpha_1,\ldots,\alpha_K)`, the
+    concentration parameters and :math:`K` is the dimension of the space
+    where :math:`x` takes values.
+
+    Note that the `dirichlet` interface is somewhat inconsistent.
+    The array returned by the rvs function is transposed
+    with respect to the format expected by the pdf and logpdf.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import dirichlet
+
+    Generate a dirichlet random variable
+
+    >>> quantiles = np.array([0.2, 0.2, 0.6])  # specify quantiles
+    >>> alpha = np.array([0.4, 5, 15])  # specify concentration parameters
+    >>> dirichlet.pdf(quantiles, alpha)
+    0.2843831684937255
+
+    The same PDF but following a log scale
+
+    >>> dirichlet.logpdf(quantiles, alpha)
+    -1.2574327653159187
+
+    Once we specify the dirichlet distribution
+    we can then calculate quantities of interest
+
+    >>> dirichlet.mean(alpha)  # get the mean of the distribution
+    array([0.01960784, 0.24509804, 0.73529412])
+    >>> dirichlet.var(alpha) # get variance
+    array([0.00089829, 0.00864603, 0.00909517])
+    >>> dirichlet.entropy(alpha)  # calculate the differential entropy
+    -4.3280162474082715
+
+    We can also return random samples from the distribution
+
+    >>> dirichlet.rvs(alpha, size=1, random_state=1)
+    array([[0.00766178, 0.24670518, 0.74563305]])
+    >>> dirichlet.rvs(alpha, size=2, random_state=2)
+    array([[0.01639427, 0.1292273 , 0.85437844],
+           [0.00156917, 0.19033695, 0.80809388]])
+
+    Alternatively, the object may be called (as a function) to fix
+    concentration parameters, returning a "frozen" Dirichlet
+    random variable:
+
+    >>> rv = dirichlet(alpha)
+    >>> # Frozen object with the same methods but holding the given
+    >>> # concentration parameters fixed.
+    """
+
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = doccer.docformat(self.__doc__, dirichlet_docdict_params)
+
+    def __call__(self, alpha, seed=None):
+        return dirichlet_frozen(alpha, seed=seed)
+
+    def _logpdf(self, x, alpha):
+        """Log of the Dirichlet probability density function.
+
+        Parameters
+        ----------
+        x : ndarray
+            Points at which to evaluate the log of the probability
+            density function
+        %(_dirichlet_doc_default_callparams)s
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'logpdf' instead.
+
+        """
+        lnB = _lnB(alpha)
+        return - lnB + np.sum((xlogy(alpha - 1, x.T)).T, 0)
+
+    def logpdf(self, x, alpha):
+        """Log of the Dirichlet probability density function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+        %(_dirichlet_doc_default_callparams)s
+
+        Returns
+        -------
+        pdf : ndarray or scalar
+            Log of the probability density function evaluated at `x`.
+
+        """
+        alpha = _dirichlet_check_parameters(alpha)
+        x = _dirichlet_check_input(alpha, x)
+
+        out = self._logpdf(x, alpha)
+        return _squeeze_output(out)
+
+    def pdf(self, x, alpha):
+        """The Dirichlet probability density function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+        %(_dirichlet_doc_default_callparams)s
+
+        Returns
+        -------
+        pdf : ndarray or scalar
+            The probability density function evaluated at `x`.
+
+        """
+        alpha = _dirichlet_check_parameters(alpha)
+        x = _dirichlet_check_input(alpha, x)
+
+        out = np.exp(self._logpdf(x, alpha))
+        return _squeeze_output(out)
+
+    def mean(self, alpha):
+        """Mean of the Dirichlet distribution.
+
+        Parameters
+        ----------
+        %(_dirichlet_doc_default_callparams)s
+
+        Returns
+        -------
+        mu : ndarray or scalar
+            Mean of the Dirichlet distribution.
+
+        """
+        alpha = _dirichlet_check_parameters(alpha)
+
+        out = alpha / (np.sum(alpha))
+        return _squeeze_output(out)
+
+    def var(self, alpha):
+        """Variance of the Dirichlet distribution.
+
+        Parameters
+        ----------
+        %(_dirichlet_doc_default_callparams)s
+
+        Returns
+        -------
+        v : ndarray or scalar
+            Variance of the Dirichlet distribution.
+
+        """
+
+        alpha = _dirichlet_check_parameters(alpha)
+
+        alpha0 = np.sum(alpha)
+        out = (alpha * (alpha0 - alpha)) / ((alpha0 * alpha0) * (alpha0 + 1))
+        return _squeeze_output(out)
+
+    def cov(self, alpha):
+        """Covariance matrix of the Dirichlet distribution.
+
+        Parameters
+        ----------
+        %(_dirichlet_doc_default_callparams)s
+
+        Returns
+        -------
+        cov : ndarray
+            The covariance matrix of the distribution.
+        """
+
+        alpha = _dirichlet_check_parameters(alpha)
+        alpha0 = np.sum(alpha)
+        a = alpha / alpha0
+
+        cov = (np.diag(a) - np.outer(a, a)) / (alpha0 + 1)
+        return _squeeze_output(cov)
+
+    def entropy(self, alpha):
+        """
+        Differential entropy of the Dirichlet distribution.
+
+        Parameters
+        ----------
+        %(_dirichlet_doc_default_callparams)s
+
+        Returns
+        -------
+        h : scalar
+            Entropy of the Dirichlet distribution
+
+        """
+
+        alpha = _dirichlet_check_parameters(alpha)
+
+        alpha0 = np.sum(alpha)
+        lnB = _lnB(alpha)
+        K = alpha.shape[0]
+
+        out = lnB + (alpha0 - K) * scipy.special.psi(alpha0) - np.sum(
+            (alpha - 1) * scipy.special.psi(alpha))
+        return _squeeze_output(out)
+
+    def rvs(self, alpha, size=1, random_state=None):
+        """
+        Draw random samples from a Dirichlet distribution.
+
+        Parameters
+        ----------
+        %(_dirichlet_doc_default_callparams)s
+        size : int, optional
+            Number of samples to draw (default 1).
+        %(_doc_random_state)s
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random variates of size (`size`, `N`), where `N` is the
+            dimension of the random variable.
+
+        """
+        alpha = _dirichlet_check_parameters(alpha)
+        random_state = self._get_random_state(random_state)
+        return random_state.dirichlet(alpha, size=size)
+
+
+dirichlet = dirichlet_gen()
+
+
+class dirichlet_frozen(multi_rv_frozen):
+    __class_getitem__ = None  # pyrefly:ignore[bad-assignment]
+
+    def __init__(self, alpha, seed=None):
+        self.alpha = _dirichlet_check_parameters(alpha)
+        self._dist = dirichlet_gen(seed)
+
+    def logpdf(self, x):
+        return self._dist.logpdf(x, self.alpha)
+
+    def pdf(self, x):
+        return self._dist.pdf(x, self.alpha)
+
+    def mean(self):
+        return self._dist.mean(self.alpha)
+
+    def var(self):
+        return self._dist.var(self.alpha)
+
+    def cov(self):
+        return self._dist.cov(self.alpha)
+
+    def entropy(self):
+        return self._dist.entropy(self.alpha)
+
+    def rvs(self, size=1, random_state=None):
+        return self._dist.rvs(self.alpha, size, random_state)
+
+
+# Set frozen generator docstrings from corresponding docstrings in
+# multivariate_normal_gen and fill in default strings in class docstrings
+for name in ['logpdf', 'pdf', 'rvs', 'mean', 'var', 'cov', 'entropy']:
+    method = dirichlet_gen.__dict__[name]
+    method_frozen = dirichlet_frozen.__dict__[name]
+    method_frozen.__doc__ = doccer.docformat(
+        method.__doc__, dirichlet_docdict_noparams)
+    method.__doc__ = doccer.docformat(method.__doc__, dirichlet_docdict_params)
+
+
+_wishart_doc_default_callparams = """\
+df : int
+    Degrees of freedom, must be greater than or equal to dimension of the
+    scale matrix
+scale : array_like
+    Symmetric positive definite scale matrix of the distribution
+"""
+
+_wishart_doc_callparams_note = ""
+
+_wishart_doc_frozen_callparams = ""
+
+_wishart_doc_frozen_callparams_note = """\
+See class definition for a detailed description of parameters."""
+
+wishart_docdict_params = {
+    '_doc_default_callparams': _wishart_doc_default_callparams,
+    '_doc_callparams_note': _wishart_doc_callparams_note,
+    '_doc_random_state': _doc_random_state
+}
+
+wishart_docdict_noparams = {
+    '_doc_default_callparams': _wishart_doc_frozen_callparams,
+    '_doc_callparams_note': _wishart_doc_frozen_callparams_note,
+    '_doc_random_state': _doc_random_state
+}
+
+
+class wishart_gen(multi_rv_generic):
+    r"""
+    A Wishart random variable.
+
+    The `df` keyword specifies the degrees of freedom. The `scale` keyword
+    specifies the scale matrix, which must be symmetric and positive definite.
+    In this context, the scale matrix is often interpreted in terms of a
+    multivariate normal precision matrix (the inverse of the covariance
+    matrix). These arguments must satisfy the relationship
+    ``df > scale.ndim - 1``, but see notes on using the `rvs` method with
+    ``df < scale.ndim``.
+
+    Parameters
+    ----------
+    %(_doc_default_callparams)s
+    %(_doc_random_state)s
+
+    Methods
+    -------
+    pdf(x, df, scale)
+        Probability density function.
+    logpdf(x, df, scale)
+        Log of the probability density function.
+    rvs(df, scale, size=1, random_state=None)
+        Draw random samples from a Wishart distribution.
+    entropy()
+        Compute the differential entropy of the Wishart distribution.
+
+    Raises
+    ------
+    scipy.linalg.LinAlgError
+        If the scale matrix `scale` is not positive definite.
+
+    See Also
+    --------
+    invwishart, chi2
+
+    Notes
+    -----
+    %(_doc_callparams_note)s
+
+    The scale matrix `scale` must be a symmetric positive definite
+    matrix. Singular matrices, including the symmetric positive semi-definite
+    case, are not supported. Symmetry is not checked; only the lower triangular
+    portion is used.
+
+    The Wishart distribution is often denoted
+
+    .. math::
+
+        W_p(\nu, \Sigma)
+
+    where :math:`\nu` is the degrees of freedom and :math:`\Sigma` is the
+    :math:`p \times p` scale matrix.
+
+    The probability density function for `wishart` has support over positive
+    definite matrices :math:`S`; if :math:`S \sim W_p(\nu, \Sigma)`, then
+    its PDF is given by:
+
+    .. math::
+
+        f(S) = \frac{|S|^{\frac{\nu - p - 1}{2}}}{2^{ \frac{\nu p}{2} }
+               |\Sigma|^\frac{\nu}{2} \Gamma_p \left ( \frac{\nu}{2} \right )}
+               \exp\left( -tr(\Sigma^{-1} S) / 2 \right)
+
+    If :math:`S \sim W_p(\nu, \Sigma)` (Wishart) then
+    :math:`S^{-1} \sim W_p^{-1}(\nu, \Sigma^{-1})` (inverse Wishart).
+
+    If the scale matrix is 1-dimensional and equal to one, then the Wishart
+    distribution :math:`W_1(\nu, 1)` collapses to the :math:`\chi^2(\nu)`
+    distribution.
+
+    The algorithm [2]_ implemented by the `rvs` method may
+    produce numerically singular matrices with :math:`p - 1 < \nu < p`; the
+    user may wish to check for this condition and generate replacement samples
+    as necessary.
+
+
+    .. versionadded:: 0.16.0
+
+    References
+    ----------
+    .. [1] M.L. Eaton, "Multivariate Statistics: A Vector Space Approach",
+           Wiley, 1983.
+    .. [2] W.B. Smith and R.R. Hocking, "Algorithm AS 53: Wishart Variate
+           Generator", Applied Statistics, vol. 21, pp. 341-345, 1972.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy.stats import wishart, chi2
+    >>> x = np.linspace(1e-5, 8, 100)
+    >>> w = wishart.pdf(x, df=3, scale=1); w[:5]
+    array([ 0.00126156,  0.10892176,  0.14793434,  0.17400548,  0.1929669 ])
+    >>> c = chi2.pdf(x, 3); c[:5]
+    array([ 0.00126156,  0.10892176,  0.14793434,  0.17400548,  0.1929669 ])
+    >>> plt.plot(x, w)
+    >>> plt.show()
+
+    The input quantiles can be any shape of array, as long as the last
+    axis labels the components.
+
+    Alternatively, the object may be called (as a function) to fix the degrees
+    of freedom and scale parameters, returning a "frozen" Wishart random
+    variable:
+
+    >>> rv = wishart(df=1, scale=1)
+    >>> # Frozen object with the same methods but holding the given
+    >>> # degrees of freedom and scale fixed.
+    """
+
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = doccer.docformat(self.__doc__, wishart_docdict_params)
+
+    def __call__(self, df=None, scale=None, seed=None):
+        """Create a frozen Wishart distribution.
+
+        See `wishart_frozen` for more information.
+        """
+        return wishart_frozen(df, scale, seed)
+
+    def _process_parameters(self, df, scale):
+        if scale is None:
+            scale = 1.0
+        scale = np.asarray(scale, dtype=float)
+
+        if scale.ndim == 0:
+            scale = scale[np.newaxis, np.newaxis]
+        elif scale.ndim == 1:
+            scale = np.diag(scale)
+        elif scale.ndim == 2 and not scale.shape[0] == scale.shape[1]:
+            raise ValueError("Array 'scale' must be square if it is two dimensional,"
+                             f" but scale.scale = {str(scale.shape)}.")
+        elif scale.ndim > 2:
+            raise ValueError(f"Array 'scale' must be at most two-dimensional, "
+                             f"but scale.ndim = {scale.ndim}")
+
+        dim = scale.shape[0]
+
+        if df is None:
+            df = dim
+        elif not np.isscalar(df):
+            raise ValueError("Degrees of freedom must be a scalar.")
+        elif df <= dim - 1:
+            raise ValueError("Degrees of freedom must be greater than the "
+                             "dimension of scale matrix minus 1.")
+
+        return dim, df, scale
+
+    def _process_quantiles(self, x, dim):
+        """
+        Adjust quantiles array so that last axis labels the components of
+        each data point.
+        """
+        x = np.asarray(x, dtype=float)
+
+        if x.ndim == 0:
+            x = x * np.eye(dim)[:, :, np.newaxis]
+        if x.ndim == 1:
+            if dim == 1:
+                x = x[np.newaxis, np.newaxis, :]
+            else:
+                x = np.diag(x)[:, :, np.newaxis]
+        elif x.ndim == 2:
+            if not x.shape[0] == x.shape[1]:
+                raise ValueError(
+                    "Quantiles must be square if they are two dimensional,"
+                    f" but x.shape = {str(x.shape)}.")
+            x = x[:, :, np.newaxis]
+        elif x.ndim == 3:
+            if not x.shape[0] == x.shape[1]:
+                raise ValueError(
+                    "Quantiles must be square in the first two dimensions "
+                    f"if they are three dimensional, but x.shape = {str(x.shape)}.")
+        elif x.ndim > 3:
+            raise ValueError(f"Quantiles must be at most two-dimensional with an "
+                             f"additional dimension for multiple components, "
+                             f"but x.ndim = {x.ndim}")
+
+        # Now we have 3-dim array; should have shape [dim, dim, *]
+        if not x.shape[0:2] == (dim, dim):
+            raise ValueError('Quantiles have incompatible dimensions: should'
+                             f' be {(dim, dim)}, got {x.shape[0:2]}.')
+
+        return x
+
+    def _process_size(self, size):
+        size = np.asarray(size)
+
+        if size.ndim == 0:
+            size = size[np.newaxis]
+        elif size.ndim > 1:
+            raise ValueError('Size must be an integer or tuple of integers;'
+                 ' thus must have dimension <= 1.'
+                 f' Got size.ndim = {str(tuple(size))}')
+        n = size.prod()
+        shape = tuple(size)
+
+        return n, shape
+
+    def _logpdf(self, x, dim, df, scale, log_det_scale, C):
+        """Log of the Wishart probability density function.
+
+        Parameters
+        ----------
+        x : ndarray
+            Points at which to evaluate the log of the probability
+            density function
+        dim : int
+            Dimension of the scale matrix
+        df : int
+            Degrees of freedom
+        scale : ndarray
+            Scale matrix
+        log_det_scale : float
+            Logarithm of the determinant of the scale matrix
+        C : ndarray
+            Cholesky factorization of the scale matrix, lower triangular.
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'logpdf' instead.
+
+        """
+        # log determinant of x
+        # Note: x has components along the last axis, so that x.T has
+        # components alone the 0-th axis. Then since det(A) = det(A'), this
+        # gives us a 1-dim vector of determinants
+
+        # Retrieve tr(scale^{-1} x)
+        log_det_x = np.empty(x.shape[-1])
+        scale_inv_x = np.empty(x.shape)
+        tr_scale_inv_x = np.empty(x.shape[-1])
+        for i in range(x.shape[-1]):
+            _, log_det_x[i] = self._cholesky_logdet(x[:, :, i])
+            scale_inv_x[:, :, i] = scipy.linalg.cho_solve((C, True), x[:, :, i])
+            tr_scale_inv_x[i] = scale_inv_x[:, :, i].trace()
+
+        # Log PDF
+        out = ((0.5 * (df - dim - 1) * log_det_x - 0.5 * tr_scale_inv_x) -
+               (0.5 * df * dim * _LOG_2 + 0.5 * df * log_det_scale +
+                multigammaln(0.5*df, dim)))
+
+        return out
+
+    def logpdf(self, x, df, scale):
+        """Log of the Wishart probability density function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+            Each quantile must be a symmetric positive definite matrix.
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        pdf : ndarray
+            Log of the probability density function evaluated at `x`
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+
+        """
+        dim, df, scale = self._process_parameters(df, scale)
+        x = self._process_quantiles(x, dim)
+
+        # Cholesky decomposition of scale, get log(det(scale))
+        C, log_det_scale = self._cholesky_logdet(scale)
+
+        out = self._logpdf(x, dim, df, scale, log_det_scale, C)
+        return _squeeze_output(out)
+
+    def pdf(self, x, df, scale):
+        """Wishart probability density function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+            Each quantile must be a symmetric positive definite matrix.
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        pdf : ndarray
+            Probability density function evaluated at `x`
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+
+        """
+        return np.exp(self.logpdf(x, df, scale))
+
+    def _mean(self, dim, df, scale):
+        """Mean of the Wishart distribution.
+
+        Parameters
+        ----------
+        dim : int
+            Dimension of the scale matrix
+        %(_doc_default_callparams)s
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'mean' instead.
+
+        """
+        return df * scale
+
+    def mean(self, df, scale):
+        """Mean of the Wishart distribution.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        mean : float
+            The mean of the distribution
+        """
+        dim, df, scale = self._process_parameters(df, scale)
+        out = self._mean(dim, df, scale)
+        return _squeeze_output(out)
+
+    def _mode(self, dim, df, scale):
+        """Mode of the Wishart distribution.
+
+        Parameters
+        ----------
+        dim : int
+            Dimension of the scale matrix
+        %(_doc_default_callparams)s
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'mode' instead.
+
+        """
+        if df >= dim + 1:
+            out = (df-dim-1) * scale
+        else:
+            out = None
+        return out
+
+    def mode(self, df, scale):
+        """Mode of the Wishart distribution
+
+        Only valid if the degrees of freedom are greater than the dimension of
+        the scale matrix.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        mode : float or None
+            The Mode of the distribution
+        """
+        dim, df, scale = self._process_parameters(df, scale)
+        out = self._mode(dim, df, scale)
+        return _squeeze_output(out) if out is not None else out
+
+    def _var(self, dim, df, scale):
+        """Variance of the Wishart distribution.
+
+        Parameters
+        ----------
+        dim : int
+            Dimension of the scale matrix
+        %(_doc_default_callparams)s
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'var' instead.
+
+        """
+        var = scale**2
+        diag = scale.diagonal()  # 1 x dim array
+        var += np.outer(diag, diag)
+        var *= df
+        return var
+
+    def var(self, df, scale):
+        """Variance of the Wishart distribution.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        var : float
+            The variance of the distribution
+        """
+        dim, df, scale = self._process_parameters(df, scale)
+        out = self._var(dim, df, scale)
+        return _squeeze_output(out)
+
+    def _standard_rvs(self, n, shape, dim, df, random_state):
+        """
+        Parameters
+        ----------
+        n : int
+            Number of variates to generate
+        shape : iterable
+            Shape of the variates to generate
+        dim : int
+            Dimension of the scale matrix
+        df : int
+            Degrees of freedom
+        random_state : {None, int, `numpy.random.Generator`,
+                        `numpy.random.RandomState`}, optional
+
+            If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+            singleton is used.
+            If `seed` is an int, a new ``RandomState`` instance is used,
+            seeded with `seed`.
+            If `seed` is already a ``Generator`` or ``RandomState`` instance
+            then that instance is used.
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'rvs' instead.
+
+        """
+        # Random normal variates for off-diagonal elements
+        n_tril = dim * (dim-1) // 2
+        covariances = random_state.normal(
+            size=n*n_tril).reshape(shape+(n_tril,))
+
+        # Random chi-square variates for diagonal elements
+        variances = (np.r_[[random_state.chisquare(df-(i+1)+1, size=n)**0.5
+                            for i in range(dim)]].reshape((dim,) +
+                                                          shape[::-1]).T)
+
+        # Create the A matri(ces) - lower triangular
+        A = np.zeros(shape + (dim, dim))
+
+        # Input the covariances
+        size_idx = tuple([slice(None, None, None)]*len(shape))
+        tril_idx = np.tril_indices(dim, k=-1)
+        A[size_idx + tril_idx] = covariances
+
+        # Input the variances
+        diag_idx = np.diag_indices(dim)
+        A[size_idx + diag_idx] = variances
+
+        return A
+
+    def _rvs(self, n, shape, dim, df, C, random_state):
+        """Draw random samples from a Wishart distribution.
+
+        Parameters
+        ----------
+        n : int
+            Number of variates to generate
+        shape : iterable
+            Shape of the variates to generate
+        dim : int
+            Dimension of the scale matrix
+        df : int
+            Degrees of freedom
+        C : ndarray
+            Cholesky factorization of the scale matrix, lower triangular.
+        %(_doc_random_state)s
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'rvs' instead.
+
+        """
+        random_state = self._get_random_state(random_state)
+        # Calculate the matrices A, which are actually lower triangular
+        # Cholesky factorizations of a matrix B such that B ~ W(df, I)
+        A = self._standard_rvs(n, shape, dim, df, random_state)
+
+        # Calculate SA = C A A' C', where SA ~ W(df, scale)
+        # Note: this is the product of a (lower) (lower) (lower)' (lower)'
+        #       or, denoting B = AA', it is C B C' where C is the lower
+        #       triangular Cholesky factorization of the scale matrix.
+        #       this appears to conflict with the instructions in [1]_, which
+        #       suggest that it should be D' B D where D is the lower
+        #       triangular factorization of the scale matrix. However, it is
+        #       meant to refer to the Bartlett (1933) representation of a
+        #       Wishart random variate as L A A' L' where L is lower triangular
+        #       so it appears that understanding D' to be upper triangular
+        #       is either a typo in or misreading of [1]_.
+        for index in np.ndindex(shape):
+            CA = np.dot(C, A[index])
+            A[index] = np.dot(CA, CA.T)
+
+        return A
+
+    def rvs(self, df, scale, size=1, random_state=None):
+        """Draw random samples from a Wishart distribution.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+        size : int or iterable of integers, optional
+            Number of samples to draw (default 1).
+        %(_doc_random_state)s
+
+        Returns
+        -------
+        rvs : ndarray
+            Random variates of shape (`size`) + (``dim``, ``dim``), where
+            ``dim`` is the dimension of the scale matrix.
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+
+        """
+        n, shape = self._process_size(size)
+        dim, df, scale = self._process_parameters(df, scale)
+
+        # Cholesky decomposition of scale
+        C = scipy.linalg.cholesky(scale, lower=True)
+
+        out = self._rvs(n, shape, dim, df, C, random_state)
+
+        return _squeeze_output(out)
+
+    def _entropy(self, dim, df, log_det_scale):
+        """Compute the differential entropy of the Wishart.
+
+        Parameters
+        ----------
+        dim : int
+            Dimension of the scale matrix
+        df : int
+            Degrees of freedom
+        log_det_scale : float
+            Logarithm of the determinant of the scale matrix
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'entropy' instead.
+
+        """
+        return (
+            0.5 * (dim+1) * log_det_scale +
+            0.5 * dim * (dim+1) * _LOG_2 +
+            multigammaln(0.5*df, dim) -
+            0.5 * (df - dim - 1) * np.sum(
+                [psi(0.5*(df + 1 - (i+1))) for i in range(dim)]
+            ) +
+            0.5 * df * dim
+        )
+
+    def entropy(self, df, scale):
+        """Compute the differential entropy of the Wishart.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        h : scalar
+            Entropy of the Wishart distribution
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+
+        """
+        dim, df, scale = self._process_parameters(df, scale)
+        _, log_det_scale = self._cholesky_logdet(scale)
+        return self._entropy(dim, df, log_det_scale)
+
+    def _cholesky_logdet(self, scale):
+        """Compute Cholesky decomposition and determine (log(det(scale)).
+
+        Parameters
+        ----------
+        scale : ndarray
+            Scale matrix.
+
+        Returns
+        -------
+        c_decomp : ndarray
+            The Cholesky decomposition of `scale`.
+        logdet : scalar
+            The log of the determinant of `scale`.
+
+        Notes
+        -----
+        This computation of ``logdet`` is equivalent to
+        ``np.linalg.slogdet(scale)``.  It is ~2x faster though.
+
+        """
+        c_decomp = scipy.linalg.cholesky(scale, lower=True)
+        logdet = 2 * np.sum(np.log(c_decomp.diagonal()))
+        return c_decomp, logdet
+
+
+wishart = wishart_gen()
+
+
+class wishart_frozen(multi_rv_frozen):
+    """Create a frozen Wishart distribution.
+
+    Parameters
+    ----------
+    df : array_like
+        Degrees of freedom of the distribution
+    scale : array_like
+        Scale matrix of the distribution
+    seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+        singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used,
+        seeded with `seed`.
+        If `seed` is already a ``Generator`` or ``RandomState`` instance then
+        that instance is used.
+
+    """
+    __class_getitem__ = None  # pyrefly:ignore[bad-assignment]
+
+    def __init__(self, df, scale, seed=None):
+        self._dist = wishart_gen(seed)
+        self.dim, self.df, self.scale = self._dist._process_parameters(
+            df, scale)
+        self.C, self.log_det_scale = self._dist._cholesky_logdet(self.scale)
+
+    def logpdf(self, x):
+        x = self._dist._process_quantiles(x, self.dim)
+
+        out = self._dist._logpdf(x, self.dim, self.df, self.scale,
+                                 self.log_det_scale, self.C)
+        return _squeeze_output(out)
+
+    def pdf(self, x):
+        return np.exp(self.logpdf(x))
+
+    def mean(self):
+        out = self._dist._mean(self.dim, self.df, self.scale)
+        return _squeeze_output(out)
+
+    def mode(self):
+        out = self._dist._mode(self.dim, self.df, self.scale)
+        return _squeeze_output(out) if out is not None else out
+
+    def var(self):
+        out = self._dist._var(self.dim, self.df, self.scale)
+        return _squeeze_output(out)
+
+    def rvs(self, size=1, random_state=None):
+        n, shape = self._dist._process_size(size)
+        out = self._dist._rvs(n, shape, self.dim, self.df,
+                              self.C, random_state)
+        return _squeeze_output(out)
+
+    def entropy(self):
+        return self._dist._entropy(self.dim, self.df, self.log_det_scale)
+
+
+# Set frozen generator docstrings from corresponding docstrings in
+# Wishart and fill in default strings in class docstrings
+for name in ['logpdf', 'pdf', 'mean', 'mode', 'var', 'rvs', 'entropy']:
+    method = wishart_gen.__dict__[name]
+    method_frozen = wishart_frozen.__dict__[name]
+    method_frozen.__doc__ = doccer.docformat(
+        method.__doc__, wishart_docdict_noparams)
+    method.__doc__ = doccer.docformat(method.__doc__, wishart_docdict_params)
+
+
+class invwishart_gen(wishart_gen):
+    r"""
+    An inverse Wishart random variable.
+
+    The `df` keyword specifies the degrees of freedom. The `scale` keyword
+    specifies the scale matrix, which must be symmetric and positive definite.
+    In this context, the scale matrix is often interpreted in terms of a
+    multivariate normal covariance matrix.
+
+    Parameters
+    ----------
+    %(_doc_default_callparams)s
+    %(_doc_random_state)s
+
+    Methods
+    -------
+    pdf(x, df, scale)
+        Probability density function.
+    logpdf(x, df, scale)
+        Log of the probability density function.
+    rvs(df, scale, size=1, random_state=None)
+        Draw random samples from an inverse Wishart distribution.
+    entropy(df, scale)
+        Differential entropy of the distribution.
+
+    Raises
+    ------
+    scipy.linalg.LinAlgError
+        If the scale matrix `scale` is not positive definite.
+
+    See Also
+    --------
+    wishart
+
+    Notes
+    -----
+    %(_doc_callparams_note)s
+
+    The scale matrix `scale` must be a symmetric positive definite
+    matrix. Singular matrices, including the symmetric positive semi-definite
+    case, are not supported. Symmetry is not checked; only the lower triangular
+    portion is used.
+
+    The inverse Wishart distribution is often denoted
+
+    .. math::
+
+        W_p^{-1}(\nu, \Psi)
+
+    where :math:`\nu` is the degrees of freedom and :math:`\Psi` is the
+    :math:`p \times p` scale matrix.
+
+    The probability density function for `invwishart` has support over positive
+    definite matrices :math:`S`; if :math:`S \sim W^{-1}_p(\nu, \Sigma)`,
+    then its PDF is given by:
+
+    .. math::
+
+        f(S) = \frac{|\Sigma|^\frac{\nu}{2}}{2^{ \frac{\nu p}{2} }
+               |S|^{\frac{\nu + p + 1}{2}} \Gamma_p \left(\frac{\nu}{2} \right)}
+               \exp\left( -tr(\Sigma S^{-1}) / 2 \right)
+
+    If :math:`S \sim W_p^{-1}(\nu, \Psi)` (inverse Wishart) then
+    :math:`S^{-1} \sim W_p(\nu, \Psi^{-1})` (Wishart).
+
+    If the scale matrix is 1-dimensional and equal to one, then the inverse
+    Wishart distribution :math:`W_1(\nu, 1)` collapses to the
+    inverse Gamma distribution with parameters shape = :math:`\frac{\nu}{2}`
+    and scale = :math:`\frac{1}{2}`.
+
+    Instead of inverting a randomly generated Wishart matrix as described in [2],
+    here the algorithm in [4] is used to directly generate a random inverse-Wishart
+    matrix without inversion.
+
+    .. versionadded:: 0.16.0
+
+    References
+    ----------
+    .. [1] M.L. Eaton, "Multivariate Statistics: A Vector Space Approach",
+           Wiley, 1983.
+    .. [2] M.C. Jones, "Generating Inverse Wishart Matrices", Communications
+           in Statistics - Simulation and Computation, vol. 14.2, pp.511-514,
+           1985.
+    .. [3] Gupta, M. and Srivastava, S. "Parametric Bayesian Estimation of
+           Differential Entropy and Relative Entropy". Entropy 12, 818 - 843.
+           2010.
+    .. [4] S.D. Axen, "Efficiently generating inverse-Wishart matrices and
+           their Cholesky factors", :arXiv:`2310.15884v1`. 2023.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy.stats import invwishart, invgamma
+    >>> x = np.linspace(0.01, 1, 100)
+    >>> iw = invwishart.pdf(x, df=6, scale=1)
+    >>> iw[:3]
+    array([  1.20546865e-15,   5.42497807e-06,   4.45813929e-03])
+    >>> ig = invgamma.pdf(x, 6/2., scale=1./2)
+    >>> ig[:3]
+    array([  1.20546865e-15,   5.42497807e-06,   4.45813929e-03])
+    >>> plt.plot(x, iw)
+    >>> plt.show()
+
+    The input quantiles can be any shape of array, as long as the last
+    axis labels the components.
+
+    Alternatively, the object may be called (as a function) to fix the degrees
+    of freedom and scale parameters, returning a "frozen" inverse Wishart
+    random variable:
+
+    >>> rv = invwishart(df=1, scale=1)
+    >>> # Frozen object with the same methods but holding the given
+    >>> # degrees of freedom and scale fixed.
+    """
+
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = doccer.docformat(self.__doc__, wishart_docdict_params)
+
+    def __call__(self, df=None, scale=None, seed=None):
+        """Create a frozen inverse Wishart distribution.
+
+        See `invwishart_frozen` for more information.
+
+        """
+        return invwishart_frozen(df, scale, seed)
+
+    def _logpdf(self, x, dim, df, log_det_scale, C):
+        """Log of the inverse Wishart probability density function.
+
+        Parameters
+        ----------
+        x : ndarray
+            Points at which to evaluate the log of the probability
+            density function.
+        dim : int
+            Dimension of the scale matrix
+        df : int
+            Degrees of freedom
+        log_det_scale : float
+            Logarithm of the determinant of the scale matrix
+        C : ndarray
+            Cholesky factorization of the scale matrix, lower triangular.
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'logpdf' instead.
+
+        """
+        # Retrieve tr(scale x^{-1})
+        log_det_x = np.empty(x.shape[-1])
+        tr_scale_x_inv = np.empty(x.shape[-1])
+        trsm = get_blas_funcs(('trsm'), (x,))
+        if dim > 1:
+            for i in range(x.shape[-1]):
+                Cx, log_det_x[i] = self._cholesky_logdet(x[:, :, i])
+                A = trsm(1., Cx, C, side=0, lower=True)
+                tr_scale_x_inv[i] = np.linalg.norm(A)**2
+        else:
+            log_det_x[:] = np.log(x[0, 0])
+            tr_scale_x_inv[:] = C[0, 0]**2 / x[0, 0]
+
+        # Log PDF
+        out = ((0.5 * df * log_det_scale - 0.5 * tr_scale_x_inv) -
+               (0.5 * df * dim * _LOG_2 + 0.5 * (df + dim + 1) * log_det_x) -
+               multigammaln(0.5*df, dim))
+
+        return out
+
+    def logpdf(self, x, df, scale):
+        """Log of the inverse Wishart probability density function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+            Each quantile must be a symmetric positive definite matrix.
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        pdf : ndarray
+            Log of the probability density function evaluated at `x`
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+
+        """
+        dim, df, scale = self._process_parameters(df, scale)
+        x = self._process_quantiles(x, dim)
+        C, log_det_scale = self._cholesky_logdet(scale)
+        out = self._logpdf(x, dim, df, log_det_scale, C)
+        return _squeeze_output(out)
+
+    def pdf(self, x, df, scale):
+        """Inverse Wishart probability density function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+            Each quantile must be a symmetric positive definite matrix.
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        pdf : ndarray
+            Probability density function evaluated at `x`
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+
+        """
+        return np.exp(self.logpdf(x, df, scale))
+
+    def _mean(self, dim, df, scale):
+        """Mean of the inverse Wishart distribution.
+
+        Parameters
+        ----------
+        dim : int
+            Dimension of the scale matrix
+        %(_doc_default_callparams)s
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'mean' instead.
+
+        """
+        if df > dim + 1:
+            out = scale / (df - dim - 1)
+        else:
+            out = None
+        return out
+
+    def mean(self, df, scale):
+        """Mean of the inverse Wishart distribution.
+
+        Only valid if the degrees of freedom are greater than the dimension of
+        the scale matrix plus one.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        mean : float or None
+            The mean of the distribution
+
+        """
+        dim, df, scale = self._process_parameters(df, scale)
+        out = self._mean(dim, df, scale)
+        return _squeeze_output(out) if out is not None else out
+
+    def _mode(self, dim, df, scale):
+        """Mode of the inverse Wishart distribution.
+
+        Parameters
+        ----------
+        dim : int
+            Dimension of the scale matrix
+        %(_doc_default_callparams)s
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'mode' instead.
+
+        """
+        return scale / (df + dim + 1)
+
+    def mode(self, df, scale):
+        """Mode of the inverse Wishart distribution.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        mode : float
+            The Mode of the distribution
+
+        """
+        dim, df, scale = self._process_parameters(df, scale)
+        out = self._mode(dim, df, scale)
+        return _squeeze_output(out)
+
+    def _var(self, dim, df, scale):
+        """Variance of the inverse Wishart distribution.
+
+        Parameters
+        ----------
+        dim : int
+            Dimension of the scale matrix
+        %(_doc_default_callparams)s
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'var' instead.
+
+        """
+        if df > dim + 3:
+            var = (df - dim + 1) * scale**2
+            diag = scale.diagonal()  # 1 x dim array
+            var += (df - dim - 1) * np.outer(diag, diag)
+            var /= (df - dim) * (df - dim - 1)**2 * (df - dim - 3)
+        else:
+            var = None
+        return var
+
+    def var(self, df, scale):
+        """Variance of the inverse Wishart distribution.
+
+        Only valid if the degrees of freedom are greater than the dimension of
+        the scale matrix plus three.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        var : float
+            The variance of the distribution
+        """
+        dim, df, scale = self._process_parameters(df, scale)
+        out = self._var(dim, df, scale)
+        return _squeeze_output(out) if out is not None else out
+
+    def _inv_standard_rvs(self, n, shape, dim, df, random_state):
+        """
+        Parameters
+        ----------
+        n : int
+            Number of variates to generate
+        shape : iterable
+            Shape of the variates to generate
+        dim : int
+            Dimension of the scale matrix
+        df : int
+            Degrees of freedom
+        random_state : {None, int, `numpy.random.Generator`,
+                        `numpy.random.RandomState`}, optional
+
+            If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+            singleton is used.
+            If `seed` is an int, a new ``RandomState`` instance is used,
+            seeded with `seed`.
+            If `seed` is already a ``Generator`` or ``RandomState`` instance
+            then that instance is used.
+
+        Returns
+        -------
+        A : ndarray
+            Random variates of shape (`shape`) + (``dim``, ``dim``).
+            Each slice `A[..., :, :]` is lower-triangular, and its
+            inverse is the lower Cholesky factor of a draw from
+            `invwishart(df, np.eye(dim))`.
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'rvs' instead.
+
+        """
+        A = np.zeros(shape + (dim, dim))
+
+        # Random normal variates for off-diagonal elements
+        tri_rows, tri_cols = np.tril_indices(dim, k=-1)
+        n_tril = dim * (dim-1) // 2
+        A[..., tri_rows, tri_cols] = random_state.normal(
+            size=(*shape, n_tril),
+        )
+
+        # Random chi variates for diagonal elements
+        rows = np.arange(dim)
+        chi_dfs = (df - dim + 1) + rows
+        A[..., rows, rows] = random_state.chisquare(
+            df=chi_dfs, size=(*shape, dim),
+        )**0.5
+
+        return A
+
+    def _rvs(self, n, shape, dim, df, C, random_state):
+        """Draw random samples from an inverse Wishart distribution.
+
+        Parameters
+        ----------
+        n : int
+            Number of variates to generate
+        shape : iterable
+            Shape of the variates to generate
+        dim : int
+            Dimension of the scale matrix
+        df : int
+            Degrees of freedom
+        C : ndarray
+            Cholesky factorization of the scale matrix, lower triangular.
+        %(_doc_random_state)s
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be
+        called directly; use 'rvs' instead.
+
+        """
+        random_state = self._get_random_state(random_state)
+        # Get random draws A such that inv(A) ~ iW(df, I)
+        A = self._inv_standard_rvs(n, shape, dim, df, random_state)
+
+        # Calculate SA = (CA)'^{-1} (CA)^{-1} ~ iW(df, scale)
+        trsm = get_blas_funcs(('trsm'), (A,))
+        trmm = get_blas_funcs(('trmm'), (A,))
+
+        for index in np.ndindex(A.shape[:-2]):
+            if dim > 1:
+                # Calculate CA
+                # Get CA = C A^{-1} via triangular solver
+                CA = trsm(1., A[index], C, side=1, lower=True)
+                # get SA
+                A[index] = trmm(1., CA, CA, side=1, lower=True, trans_a=True)
+            else:
+                A[index][0, 0] = (C[0, 0] / A[index][0, 0])**2
+
+        return A
+
+    def rvs(self, df, scale, size=1, random_state=None):
+        """Draw random samples from an inverse Wishart distribution.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+        size : int or iterable of integers, optional
+            Number of samples to draw (default 1).
+        %(_doc_random_state)s
+
+        Returns
+        -------
+        rvs : ndarray
+            Random variates of shape (`size`) + (``dim``, ``dim``), where
+            ``dim`` is the dimension of the scale matrix.
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+
+        """
+        n, shape = self._process_size(size)
+        dim, df, scale = self._process_parameters(df, scale)
+
+        # Cholesky decomposition of scale
+        C = scipy.linalg.cholesky(scale, lower=True)
+
+        out = self._rvs(n, shape, dim, df, C, random_state)
+
+        return _squeeze_output(out)
+
+    def _entropy(self, dim, df, log_det_scale):
+        # reference: eq. (17) from ref. 3
+        psi_eval_points = [0.5 * (df - dim + i) for i in range(1, dim + 1)]
+        psi_eval_points = np.asarray(psi_eval_points)
+        return multigammaln(0.5 * df, dim) + 0.5 * dim * df + \
+            0.5 * (dim + 1) * (log_det_scale - _LOG_2) - \
+            0.5 * (df + dim + 1) * \
+            psi(psi_eval_points, out=psi_eval_points).sum()
+
+    def entropy(self, df, scale):
+        dim, df, scale = self._process_parameters(df, scale)
+        _, log_det_scale = self._cholesky_logdet(scale)
+        return self._entropy(dim, df, log_det_scale)
+
+
+invwishart = invwishart_gen()
+
+
+class invwishart_frozen(multi_rv_frozen):
+    __class_getitem__ = None  # pyrefly:ignore[bad-assignment]
+
+    def __init__(self, df, scale, seed=None):
+        """Create a frozen inverse Wishart distribution.
+
+        Parameters
+        ----------
+        df : array_like
+            Degrees of freedom of the distribution
+        scale : array_like
+            Scale matrix of the distribution
+        seed : {None, int, `numpy.random.Generator`}, optional
+            If `seed` is None the `numpy.random.Generator` singleton is used.
+            If `seed` is an int, a new ``Generator`` instance is used,
+            seeded with `seed`.
+            If `seed` is already a ``Generator`` instance then that instance is
+            used.
+
+        """
+        self._dist = invwishart_gen(seed)
+        self.dim, self.df, self.scale = self._dist._process_parameters(
+            df, scale
+        )
+
+        # Get the determinant via Cholesky factorization
+        self.C = scipy.linalg.cholesky(self.scale, lower=True)
+        self.log_det_scale = 2 * np.sum(np.log(self.C.diagonal()))
+
+    def logpdf(self, x):
+        x = self._dist._process_quantiles(x, self.dim)
+        out = self._dist._logpdf(x, self.dim, self.df,
+                                 self.log_det_scale, self.C)
+        return _squeeze_output(out)
+
+    def pdf(self, x):
+        return np.exp(self.logpdf(x))
+
+    def mean(self):
+        out = self._dist._mean(self.dim, self.df, self.scale)
+        return _squeeze_output(out) if out is not None else out
+
+    def mode(self):
+        out = self._dist._mode(self.dim, self.df, self.scale)
+        return _squeeze_output(out)
+
+    def var(self):
+        out = self._dist._var(self.dim, self.df, self.scale)
+        return _squeeze_output(out) if out is not None else out
+
+    def rvs(self, size=1, random_state=None):
+        n, shape = self._dist._process_size(size)
+
+        out = self._dist._rvs(n, shape, self.dim, self.df,
+                              self.C, random_state)
+
+        return _squeeze_output(out)
+
+    def entropy(self):
+        return self._dist._entropy(self.dim, self.df, self.log_det_scale)
+
+
+# Set frozen generator docstrings from corresponding docstrings in
+# inverse Wishart and fill in default strings in class docstrings
+for name in ['logpdf', 'pdf', 'mean', 'mode', 'var', 'rvs']:
+    method = invwishart_gen.__dict__[name]
+    method_frozen = wishart_frozen.__dict__[name]
+    method_frozen.__doc__ = doccer.docformat(
+        method.__doc__, wishart_docdict_noparams)
+    method.__doc__ = doccer.docformat(method.__doc__, wishart_docdict_params)
+
+_multinomial_doc_default_callparams = """\
+n : int
+    Number of trials
+p : array_like
+    Probability of a trial falling into each category; should sum to 1
+"""
+
+_multinomial_doc_callparams_note = """\
+`n` should be a nonnegative integer. Each element of `p` should be in the
+interval :math:`[0,1]` and the elements should sum to 1. If they do not sum to
+1, the last element of the `p` array is not used and is replaced with the
+remaining probability left over from the earlier elements.
+"""
+
+_multinomial_doc_frozen_callparams = ""
+
+_multinomial_doc_frozen_callparams_note = """\
+See class definition for a detailed description of parameters."""
+
+multinomial_docdict_params = {
+    '_doc_default_callparams': _multinomial_doc_default_callparams,
+    '_doc_callparams_note': _multinomial_doc_callparams_note,
+    '_doc_random_state': _doc_random_state
+}
+
+multinomial_docdict_noparams = {
+    '_doc_default_callparams': _multinomial_doc_frozen_callparams,
+    '_doc_callparams_note': _multinomial_doc_frozen_callparams_note,
+    '_doc_random_state': _doc_random_state
+}
+
+
+class multinomial_gen(multi_rv_generic):
+    r"""
+    A multinomial random variable.
+
+    Parameters
+    ----------
+    %(_doc_default_callparams)s
+    %(_doc_random_state)s
+
+    Methods
+    -------
+    pmf(x, n, p)
+        Probability mass function.
+    logpmf(x, n, p)
+        Log of the probability mass function.
+    rvs(n, p, size=1, random_state=None)
+        Draw random samples from a multinomial distribution.
+    entropy(n, p)
+        Compute the entropy of the multinomial distribution.
+    cov(n, p)
+        Compute the covariance matrix of the multinomial distribution.
+
+    See Also
+    --------
+    scipy.stats.binom : The binomial distribution.
+    numpy.random.Generator.multinomial : Sampling from the multinomial distribution.
+    scipy.stats.multivariate_hypergeom :
+        The multivariate hypergeometric distribution.
+
+    Notes
+    -----
+    %(_doc_callparams_note)s
+
+    The probability mass function for `multinomial` is
+
+    .. math::
+
+        f(x) = \frac{n!}{x_1! \cdots x_k!} p_1^{x_1} \cdots p_k^{x_k},
+
+    supported on :math:`x=(x_1, \ldots, x_k)` where each :math:`x_i` is a
+    nonnegative integer and their sum is :math:`n`.
+
+    .. versionadded:: 0.19.0
+
+    Examples
+    --------
+
+    >>> from scipy.stats import multinomial
+    >>> rv = multinomial(8, [0.3, 0.2, 0.5])
+    >>> rv.pmf([1, 3, 4])
+    0.042000000000000072
+
+    The multinomial distribution for :math:`k=2` is identical to the
+    corresponding binomial distribution (tiny numerical differences
+    notwithstanding):
+
+    >>> from scipy.stats import binom
+    >>> multinomial.pmf([3, 4], n=7, p=[0.4, 0.6])
+    0.29030399999999973
+    >>> binom.pmf(3, 7, 0.4)
+    0.29030400000000012
+
+    The functions ``pmf``, ``logpmf``, ``entropy``, and ``cov`` support
+    broadcasting, under the convention that the vector parameters (``x`` and
+    ``p``) are interpreted as if each row along the last axis is a single
+    object. For instance:
+
+    >>> multinomial.pmf([[3, 4], [3, 5]], n=[7, 8], p=[.3, .7])
+    array([0.2268945,  0.25412184])
+
+    Here, ``x.shape == (2, 2)``, ``n.shape == (2,)``, and ``p.shape == (2,)``,
+    but following the rules mentioned above they behave as if the rows
+    ``[3, 4]`` and ``[3, 5]`` in ``x`` and ``[.3, .7]`` in ``p`` were a single
+    object, and as if we had ``x.shape = (2,)``, ``n.shape = (2,)``, and
+    ``p.shape = ()``. To obtain the individual elements without broadcasting,
+    we would do this:
+
+    >>> multinomial.pmf([3, 4], n=7, p=[.3, .7])
+    0.2268945
+    >>> multinomial.pmf([3, 5], 8, p=[.3, .7])
+    0.25412184
+
+    This broadcasting also works for ``cov``, where the output objects are
+    square matrices of size ``p.shape[-1]``. For example:
+
+    >>> multinomial.cov([4, 5], [[.3, .7], [.4, .6]])
+    array([[[ 0.84, -0.84],
+            [-0.84,  0.84]],
+           [[ 1.2 , -1.2 ],
+            [-1.2 ,  1.2 ]]])
+
+    In this example, ``n.shape == (2,)`` and ``p.shape == (2, 2)``, and
+    following the rules above, these broadcast as if ``p.shape == (2,)``.
+    Thus the result should also be of shape ``(2,)``, but since each output is
+    a :math:`2 \times 2` matrix, the result in fact has shape ``(2, 2, 2)``,
+    where ``result[0]`` is equal to ``multinomial.cov(n=4, p=[.3, .7])`` and
+    ``result[1]`` is equal to ``multinomial.cov(n=5, p=[.4, .6])``.
+
+    Alternatively, the object may be called (as a function) to fix the `n` and
+    `p` parameters, returning a "frozen" multinomial random variable:
+
+    >>> rv = multinomial(n=7, p=[.3, .7])
+    >>> # Frozen object with the same methods but holding the given
+    >>> # degrees of freedom and scale fixed.
+    """
+
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = \
+            doccer.docformat(self.__doc__, multinomial_docdict_params)
+
+    def __call__(self, n, p, seed=None):
+        """Create a frozen multinomial distribution.
+
+        See `multinomial_frozen` for more information.
+        """
+        return multinomial_frozen(n, p, seed)
+
+    def _process_parameters(self, n, p):
+        """Returns: n_, p_, npcond.
+
+        n_ and p_ are arrays of the correct shape; npcond is a boolean array
+        flagging values out of the domain.
+        """
+        eps = np.finfo(np.result_type(np.asarray(p), np.float32)).eps * 10
+        p = np.array(p, dtype=np.float64, copy=True)
+
+        # true for bad p
+        pcond = np.abs(1 - p.sum(axis=-1)) > eps
+        pcond |= np.any(p < 0, axis=-1)
+        pcond |= np.any(p > 1, axis=-1)
+
+        n = np.array(n, dtype=int, copy=True)
+
+        # true for bad n
+        ncond = n < 0
+
+        return n, p, ncond | pcond
+
+    def _process_quantiles(self, x, n, p):
+        """Returns: x_, xcond.
+
+        x_ is an int array; xcond is a boolean array flagging values out of the
+        domain.
+        """
+        xx = np.asarray(x, dtype=int)
+
+        if xx.ndim == 0:
+            raise ValueError("x must be an array.")
+
+        if xx.size != 0 and not xx.shape[-1] == p.shape[-1]:
+            raise ValueError(f"Size of each quantile should be size of p: "
+                             f"received {xx.shape[-1]}, but expected "
+                             f"{p.shape[-1]}.")
+
+        # true for x out of the domain
+        cond = np.any(xx != x, axis=-1)
+        cond |= np.any(xx < 0, axis=-1)
+        cond = cond | (np.sum(xx, axis=-1) != n)
+
+        return xx, cond
+
+    def _checkresult(self, result, cond, bad_value):
+        result = np.asarray(result)
+
+        if cond.ndim != 0:
+            result[cond] = bad_value
+        elif cond:
+            if result.ndim == 0:
+                return bad_value
+            result[...] = bad_value
+        return result
+
+    def _logpmf(self, x, n, p):
+        return gammaln(n+1) + np.sum(xlogy(x, p) - gammaln(x+1), axis=-1)
+
+    def logpmf(self, x, n, p):
+        """Log of the Multinomial probability mass function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        logpmf : ndarray or scalar
+            Log of the probability mass function evaluated at `x`
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+        """
+        n, p, npcond = self._process_parameters(n, p)
+        x, xcond = self._process_quantiles(x, n, p)
+
+        result = self._logpmf(x, n, p)
+
+        # replace values for which x was out of the domain; broadcast
+        # xcond to the right shape
+        xcond_ = xcond | np.zeros(npcond.shape, dtype=np.bool_)
+        result = self._checkresult(result, xcond_, -np.inf)
+
+        # replace values bad for n or p; broadcast npcond to the right shape
+        npcond_ = npcond | np.zeros(xcond.shape, dtype=np.bool_)
+        return self._checkresult(result, npcond_, np.nan)
+
+    def pmf(self, x, n, p):
+        """Multinomial probability mass function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        pmf : ndarray or scalar
+            Probability density function evaluated at `x`
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+        """
+        return np.exp(self.logpmf(x, n, p))
+
+    def mean(self, n, p):
+        """Mean of the Multinomial distribution.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        mean : float
+            The mean of the distribution
+        """
+        n, p, npcond = self._process_parameters(n, p)
+        result = n[..., np.newaxis]*p
+        return self._checkresult(result, npcond, np.nan)
+
+    def cov(self, n, p):
+        """Covariance matrix of the multinomial distribution.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        cov : ndarray
+            The covariance matrix of the distribution
+        """
+        n, p, npcond = self._process_parameters(n, p)
+
+        nn = n[..., np.newaxis, np.newaxis]
+        result = nn * np.einsum('...j,...k->...jk', -p, p)
+
+        # change the diagonal
+        for i in range(p.shape[-1]):
+            result[..., i, i] += n*p[..., i]
+
+        return self._checkresult(result, npcond, np.nan)
+
+    def entropy(self, n, p):
+        r"""Compute the entropy of the multinomial distribution.
+
+        The entropy is computed using this expression:
+
+        .. math::
+
+            f(x) = - \log n! - n\sum_{i=1}^k p_i \log p_i +
+            \sum_{i=1}^k \sum_{x=0}^n \binom n x p_i^x(1-p_i)^{n-x} \log x!
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        h : scalar
+            Entropy of the Multinomial distribution
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+        """
+        n, p, npcond = self._process_parameters(n, p)
+
+        x = np.r_[1:np.max(n)+1]
+
+        term1 = n*np.sum(entr(p), axis=-1)
+        term1 -= gammaln(n+1)
+
+        n = n[..., np.newaxis]
+        new_axes_needed = max(p.ndim, n.ndim) - x.ndim + 1
+        new_shape = x.shape + (1,)*new_axes_needed
+        x = x.reshape(new_shape)
+
+        term2 = np.sum(binom.pmf(x, n, p)*gammaln(x+1),
+                       axis=(-1, -1-new_axes_needed))
+
+        return self._checkresult(term1 + term2, npcond, np.nan)
+
+    def rvs(self, n, p, size=None, random_state=None):
+        """Draw random samples from a Multinomial distribution.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+        size : int or iterable of integers, optional
+            Number of samples to draw (default 1).
+        %(_doc_random_state)s
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random variates of shape (`size`, `len(p)`)
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+        """
+        n, p, npcond = self._process_parameters(n, p)
+        if np.any(npcond):
+            message = ("`multinomial.rvs` requires `n > 0`, `(p > 0).all()`, and"
+                       "`p.sum() == 1`.")
+            raise ValueError(message)
+        random_state = self._get_random_state(random_state)
+        return random_state.multinomial(n, p, size)
+
+
+multinomial = multinomial_gen()
+
+
+class multinomial_frozen(multi_rv_frozen):
+    r"""Create a frozen Multinomial distribution.
+
+    Parameters
+    ----------
+    n : int
+        number of trials
+    p: array_like
+        probability of a trial falling into each category; should sum to 1
+    seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+        singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used,
+        seeded with `seed`.
+        If `seed` is already a ``Generator`` or ``RandomState`` instance then
+        that instance is used.
+    """
+    def __init__(self, n, p, seed=None):
+        self._dist = multinomial_gen(seed)
+        self.n, self.p, self.npcond = self._dist._process_parameters(n, p)
+
+        # monkey patch self._dist
+        def _process_parameters(n, p):
+            return self.n, self.p, self.npcond
+
+        self._dist._process_parameters = _process_parameters
+
+    def logpmf(self, x):
+        return self._dist.logpmf(x, self.n, self.p)
+
+    def pmf(self, x):
+        return self._dist.pmf(x, self.n, self.p)
+
+    def mean(self):
+        return self._dist.mean(self.n, self.p)
+
+    def cov(self):
+        return self._dist.cov(self.n, self.p)
+
+    def entropy(self):
+        return self._dist.entropy(self.n, self.p)
+
+    def rvs(self, size=1, random_state=None):
+        return self._dist.rvs(self.n, self.p, size, random_state)
+
+
+# Set frozen generator docstrings from corresponding docstrings in
+# multinomial and fill in default strings in class docstrings
+for name in ['logpmf', 'pmf', 'mean', 'cov', 'rvs']:
+    method = multinomial_gen.__dict__[name]
+    method_frozen = multinomial_frozen.__dict__[name]
+    method_frozen.__doc__ = doccer.docformat(
+        method.__doc__, multinomial_docdict_noparams)
+    method.__doc__ = doccer.docformat(method.__doc__,
+                                      multinomial_docdict_params)
+
+
+class special_ortho_group_gen(multi_rv_generic):
+    r"""
+    A Special Orthogonal matrix (SO(N)) random variable.
+
+    Return a random rotation matrix, drawn from the Haar distribution
+    (the only uniform distribution on SO(N)) with a determinant of +1.
+
+    The `dim` keyword specifies the dimension N.
+
+    Parameters
+    ----------
+    dim : scalar
+        Dimension of matrices
+    seed : {None, int, np.random.RandomState, np.random.Generator}, optional
+        Used for drawing random variates.
+        If `seed` is `None`, the `~np.random.RandomState` singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used, seeded
+        with seed.
+        If `seed` is already a ``RandomState`` or ``Generator`` instance,
+        then that object is used.
+        Default is `None`.
+
+    Methods
+    -------
+    rvs(dim=None, size=1, random_state=None)
+        Draw random samples from SO(N).
+
+    See Also
+    --------
+    ortho_group, scipy.spatial.transform.Rotation.random
+
+    Notes
+    -----
+    The ``rvs`` method returns a random rotation matrix drawn from the Haar
+    distribution, the only uniform distribution on SO(N). The algorithm generates
+    a Haar-distributed orthogonal matrix in O(N) using the ``rvs`` method of
+    `ortho_group`, then adjusts the matrix to ensure that the determinant is +1.
+
+    For a random rotation in three dimensions, see
+    `scipy.spatial.transform.Rotation.random`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import special_ortho_group
+    >>> x = special_ortho_group.rvs(3)
+
+    >>> np.dot(x, x.T)
+    array([[  1.00000000e+00,   1.13231364e-17,  -2.86852790e-16],
+           [  1.13231364e-17,   1.00000000e+00,  -1.46845020e-16],
+           [ -2.86852790e-16,  -1.46845020e-16,   1.00000000e+00]])
+
+    >>> import scipy.linalg
+    >>> scipy.linalg.det(x)
+    1.0
+
+    This generates one random matrix from SO(3). It is orthogonal and
+    has a determinant of 1.
+
+    Alternatively, the object may be called (as a function) to fix the `dim`
+    parameter, returning a "frozen" special_ortho_group random variable:
+
+    >>> rv = special_ortho_group(5)
+    >>> # Frozen object with the same methods but holding the
+    >>> # dimension parameter fixed.
+    """
+
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = doccer.docformat(self.__doc__)
+
+    def __call__(self, dim=None, seed=None):
+        """Create a frozen SO(N) distribution.
+
+        See `special_ortho_group_frozen` for more information.
+        """
+        return special_ortho_group_frozen(dim, seed=seed)
+
+    def _process_parameters(self, dim):
+        """Dimension N must be specified; it cannot be inferred."""
+        if dim is None or not np.isscalar(dim) or dim < 0 or dim != int(dim):
+            raise ValueError("""Dimension of rotation must be specified,
+                                and must be a scalar nonnegative integer.""")
+
+        return dim
+
+    def rvs(self, dim, size=1, random_state=None):
+        """Draw random samples from SO(N).
+
+        Parameters
+        ----------
+        dim : int
+            Dimension of rotation space (N).
+        size : int, optional
+            Number of samples to draw (default 1).
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random size N-dimensional matrices, dimension (size, dim, dim)
+
+        """
+        random_state = self._get_random_state(random_state)
+
+        q = ortho_group.rvs(dim, size, random_state)
+        dets = np.linalg.det(q)
+        if dim:
+            q[..., 0, :] /= dets[..., np.newaxis]
+        return q
+
+special_ortho_group = special_ortho_group_gen()
+
+
+class special_ortho_group_frozen(multi_rv_frozen):
+    __class_getitem__ = None  # pyrefly:ignore[bad-assignment]
+
+    def __init__(self, dim=None, seed=None):
+        """Create a frozen SO(N) distribution.
+
+        Parameters
+        ----------
+        dim : scalar
+            Dimension of matrices
+        seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+            If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+            singleton is used.
+            If `seed` is an int, a new ``RandomState`` instance is used,
+            seeded with `seed`.
+            If `seed` is already a ``Generator`` or ``RandomState`` instance
+            then that instance is used.
+
+        Examples
+        --------
+        >>> from scipy.stats import special_ortho_group
+        >>> g = special_ortho_group(5)
+        >>> x = g.rvs()
+
+        """ # numpy/numpydoc#87  # noqa: E501
+        self._dist = special_ortho_group_gen(seed)
+        self.dim = self._dist._process_parameters(dim)
+
+    def rvs(self, size=1, random_state=None):
+        return self._dist.rvs(self.dim, size, random_state)
+
+
+class ortho_group_gen(multi_rv_generic):
+    r"""
+    An Orthogonal matrix (O(N)) random variable.
+
+    Return a random orthogonal matrix, drawn from the O(N) Haar
+    distribution (the only uniform distribution on O(N)).
+
+    The `dim` keyword specifies the dimension N.
+
+    Parameters
+    ----------
+    dim : scalar
+        Dimension of matrices
+    seed : {None, int, np.random.RandomState, np.random.Generator}, optional
+        Used for drawing random variates.
+        If `seed` is `None`, the `~np.random.RandomState` singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used, seeded
+        with seed.
+        If `seed` is already a ``RandomState`` or ``Generator`` instance,
+        then that object is used.
+        Default is `None`.
+
+    Methods
+    -------
+    rvs(dim=None, size=1, random_state=None)
+        Draw random samples from O(N).
+
+    See Also
+    --------
+    special_ortho_group
+
+    Notes
+    -----
+    This class is closely related to `special_ortho_group`.
+
+    Some care is taken to avoid numerical error, as per the paper by Mezzadri.
+
+    References
+    ----------
+    .. [1] F. Mezzadri, "How to generate random matrices from the classical
+           compact groups", :arXiv:`math-ph/0609050v2`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import ortho_group
+    >>> x = ortho_group.rvs(3)
+
+    >>> np.dot(x, x.T)
+    array([[  1.00000000e+00,   1.13231364e-17,  -2.86852790e-16],
+           [  1.13231364e-17,   1.00000000e+00,  -1.46845020e-16],
+           [ -2.86852790e-16,  -1.46845020e-16,   1.00000000e+00]])
+
+    >>> import scipy.linalg
+    >>> np.fabs(scipy.linalg.det(x))
+    1.0
+
+    This generates one random matrix from O(3). It is orthogonal and
+    has a determinant of +1 or -1.
+
+    Alternatively, the object may be called (as a function) to fix the `dim`
+    parameter, returning a "frozen" ortho_group random variable:
+
+    >>> rv = ortho_group(5)
+    >>> # Frozen object with the same methods but holding the
+    >>> # dimension parameter fixed.
+    """
+
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = doccer.docformat(self.__doc__)
+
+    def __call__(self, dim=None, seed=None):
+        """Create a frozen O(N) distribution.
+
+        See `ortho_group_frozen` for more information.
+        """
+        return ortho_group_frozen(dim, seed=seed)
+
+    def _process_parameters(self, dim):
+        """Dimension N must be specified; it cannot be inferred."""
+        if dim is None or not np.isscalar(dim) or dim < 0 or dim != int(dim):
+            raise ValueError("Dimension of rotation must be specified,"
+                             "and must be a scalar nonnegative integer.")
+
+        return dim
+
+    def rvs(self, dim, size=1, random_state=None):
+        """Draw random samples from O(N).
+
+        Parameters
+        ----------
+        dim : int
+            Dimension of rotation space (N).
+        size : int, optional
+            Number of samples to draw (default 1).
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random size N-dimensional matrices, dimension (size, dim, dim)
+
+        """
+        random_state = self._get_random_state(random_state)
+
+        size = int(size)
+
+        dim = self._process_parameters(dim)
+
+        size = (size,) if size > 1 else ()
+        z = random_state.normal(size=size + (dim, dim))
+        q, r = np.linalg.qr(z)
+        # The last two dimensions are the rows and columns of R matrices.
+        # Extract the diagonals. Note that this eliminates a dimension.
+        d = r.diagonal(offset=0, axis1=-2, axis2=-1)
+        # Add back a dimension for proper broadcasting: we're dividing
+        # each row of each R matrix by the diagonal of the R matrix.
+        q *= (d/abs(d))[..., np.newaxis, :]  # to broadcast properly
+        return q
+
+
+ortho_group = ortho_group_gen()
+
+
+class ortho_group_frozen(multi_rv_frozen):
+    __class_getitem__ = None  # pyrefly:ignore[bad-assignment]
+
+    def __init__(self, dim=None, seed=None):
+        """Create a frozen O(N) distribution.
+
+        Parameters
+        ----------
+        dim : scalar
+            Dimension of matrices
+        seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+            If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+            singleton is used.
+            If `seed` is an int, a new ``RandomState`` instance is used,
+            seeded with `seed`.
+            If `seed` is already a ``Generator`` or ``RandomState`` instance
+            then that instance is used.
+
+        Examples
+        --------
+        >>> from scipy.stats import ortho_group
+        >>> g = ortho_group(5)
+        >>> x = g.rvs()
+
+        """ # numpy/numpydoc#87  # noqa: E501
+        self._dist = ortho_group_gen(seed)
+        self.dim = self._dist._process_parameters(dim)
+
+    def rvs(self, size=1, random_state=None):
+        return self._dist.rvs(self.dim, size, random_state)
+
+
+class random_correlation_gen(multi_rv_generic):
+    r"""
+    A random correlation matrix.
+
+    Return a random correlation matrix, given a vector of eigenvalues.
+    The returned matrix is symmetric positive semidefinite with unit diagonal.
+
+    The `eigs` keyword specifies the eigenvalues of the correlation matrix,
+    and implies the dimension.
+
+    Parameters
+    ----------
+    eigs : 1d ndarray
+        Eigenvalues of correlation matrix. All eigenvalues need to be non-negative and
+        need to sum to the number of eigenvalues.
+    seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+        singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used,
+        seeded with `seed`.
+        If `seed` is already a ``Generator`` or ``RandomState`` instance
+        then that instance is used.
+    tol : float, optional
+        Tolerance for input parameter checks
+    diag_tol : float, optional
+        Tolerance for deviation of the diagonal of the resulting
+        matrix. Default: 1e-7
+
+    Methods
+    -------
+    rvs(eigs=None, random_state=None)
+        Draw random correlation matrices, all with eigenvalues eigs.
+
+    Returns
+    -------
+    rvs : ndarray or scalar
+        Random size N-dimensional matrices, dimension (size, dim, dim),
+        each having eigenvalues eigs.
+
+    Raises
+    ------
+    RuntimeError
+        Floating point error prevented generating a valid correlation
+        matrix.
+
+    Notes
+    -----
+
+    Generates a random correlation matrix following a numerically stable
+    algorithm spelled out by Davies & Higham. This algorithm uses a single O(N)
+    similarity transformation to construct a symmetric positive semi-definite
+    matrix, and applies a series of Givens rotations to scale it to have ones
+    on the diagonal.
+
+    References
+    ----------
+
+    .. [1] Davies, Philip I; Higham, Nicholas J; "Numerically stable generation
+           of correlation matrices and their factors", BIT 2000, Vol. 40,
+           No. 4, pp. 640 651
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import random_correlation
+    >>> rng = np.random.default_rng()
+    >>> x = random_correlation.rvs((.5, .8, 1.2, 1.5), random_state=rng)
+    >>> x
+    array([[ 1.        , -0.02423399,  0.03130519,  0.4946965 ],
+           [-0.02423399,  1.        ,  0.20334736,  0.04039817],
+           [ 0.03130519,  0.20334736,  1.        ,  0.02694275],
+           [ 0.4946965 ,  0.04039817,  0.02694275,  1.        ]])
+    >>> import scipy.linalg
+    >>> e, v = scipy.linalg.eigh(x)
+    >>> e
+    array([ 0.5,  0.8,  1.2,  1.5])
+    """
+
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = doccer.docformat(self.__doc__)
+
+    def __call__(self, eigs, seed=None, tol=1e-13, diag_tol=1e-7):
+        """Create a frozen random correlation matrix.
+
+        See `random_correlation_frozen` for more information.
+        """
+        return random_correlation_frozen(eigs, seed=seed, tol=tol,
+                                         diag_tol=diag_tol)
+
+    def _process_parameters(self, eigs, tol):
+        eigs = np.asarray(eigs, dtype=float)
+        dim = eigs.size
+
+        if eigs.ndim != 1 or eigs.shape[0] != dim or dim <= 1:
+            raise ValueError("Array 'eigs' must be a vector of length "
+                             "greater than 1.")
+
+        if np.fabs(np.sum(eigs) - dim) > tol:
+            raise ValueError("Sum of eigenvalues must equal dimensionality.")
+
+        for x in eigs:
+            if x < -tol:
+                raise ValueError("All eigenvalues must be non-negative.")
+
+        return dim, eigs
+
+    def _givens_to_1(self, aii, ajj, aij):
+        """Computes a 2x2 Givens matrix to put 1's on the diagonal.
+
+        The input matrix is a 2x2 symmetric matrix M = [ aii aij ; aij ajj ].
+
+        The output matrix g is a 2x2 anti-symmetric matrix of the form
+        [ c s ; -s c ];  the elements c and s are returned.
+
+        Applying the output matrix to the input matrix (as b=g.T M g)
+        results in a matrix with bii=1, provided tr(M) - det(M) >= 1
+        and floating point issues do not occur. Otherwise, some other
+        valid rotation is returned. When tr(M)==2, also bjj=1.
+
+        """
+        aiid = aii - 1.
+        ajjd = ajj - 1.
+
+        if ajjd == 0:
+            # ajj==1, so swap aii and ajj to avoid division by zero
+            return 0., 1.
+
+        dd = math.sqrt(max(aij**2 - aiid*ajjd, 0))
+
+        # The choice of t should be chosen to avoid cancellation [1]
+        t = (aij + math.copysign(dd, aij)) / ajjd
+        c = 1. / math.sqrt(1. + t*t)
+        if c == 0:
+            # Underflow
+            s = 1.0
+        else:
+            s = c*t
+        return c, s
+
+    def _to_corr(self, m):
+        """
+        Given a psd matrix m, rotate to put one's on the diagonal, turning it
+        into a correlation matrix.  This also requires the trace equal the
+        dimensionality. Note: modifies input matrix
+        """
+        # Check requirements for in-place Givens
+        if not (m.flags.c_contiguous and m.dtype == np.float64 and
+                m.shape[0] == m.shape[1]):
+            raise ValueError()
+
+        drot = get_blas_funcs('rot', dtype=np.float64, ilp64='preferred')
+
+        d = m.shape[0]
+        for i in range(d-1):
+            if m[i, i] == 1:
+                continue
+            elif m[i, i] > 1:
+                for j in range(i+1, d):
+                    if m[j, j] < 1:
+                        break
+            else:
+                for j in range(i+1, d):
+                    if m[j, j] > 1:
+                        break
+
+            c, s = self._givens_to_1(m[i, i], m[j, j], m[i, j])
+
+            # Use BLAS to apply Givens rotations in-place. Equivalent to:
+            # g = np.eye(d)
+            # g[i, i] = g[j,j] = c
+            # g[j, i] = -s; g[i, j] = s
+            # m = np.dot(g.T, np.dot(m, g))
+            mv = m.ravel()
+            drot(mv, mv, c, -s, n=d,
+                 offx=i*d, incx=1, offy=j*d, incy=1,
+                 overwrite_x=True, overwrite_y=True)
+            drot(mv, mv, c, -s, n=d,
+                 offx=i, incx=d, offy=j, incy=d,
+                 overwrite_x=True, overwrite_y=True)
+
+        return m
+
+    def rvs(self, eigs, random_state=None, tol=1e-13, diag_tol=1e-7):
+        """Draw random correlation matrices.
+
+        Parameters
+        ----------
+        eigs : 1d ndarray
+            Eigenvalues of correlation matrix
+        tol : float, optional
+            Tolerance for input parameter checks
+        diag_tol : float, optional
+            Tolerance for deviation of the diagonal of the resulting
+            matrix. Default: 1e-7
+
+        Raises
+        ------
+        RuntimeError
+            Floating point error prevented generating a valid correlation
+            matrix.
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random size N-dimensional matrices, dimension (size, dim, dim),
+            each having eigenvalues eigs.
+
+        """
+        dim, eigs = self._process_parameters(eigs, tol=tol)
+
+        random_state = self._get_random_state(random_state)
+
+        m = ortho_group.rvs(dim, random_state=random_state)
+        m = np.dot(np.dot(m, np.diag(eigs)), m.T)  # Set the trace of m
+        m = self._to_corr(m)  # Carefully rotate to unit diagonal
+
+        # Check diagonal
+        if abs(m.diagonal() - 1).max() > diag_tol:
+            raise RuntimeError("Failed to generate a valid correlation matrix")
+
+        return m
+
+
+random_correlation = random_correlation_gen()
+
+
+class random_correlation_frozen(multi_rv_frozen):
+    __class_getitem__ = None  # pyrefly:ignore[bad-assignment]
+
+    def __init__(self, eigs, seed=None, tol=1e-13, diag_tol=1e-7):
+        """Create a frozen random correlation matrix distribution.
+
+        Parameters
+        ----------
+        eigs : 1d ndarray
+            Eigenvalues of correlation matrix
+        seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+            If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+            singleton is used.
+            If `seed` is an int, a new ``RandomState`` instance is used,
+            seeded with `seed`.
+            If `seed` is already a ``Generator`` or ``RandomState`` instance
+            then that instance is used.
+        tol : float, optional
+            Tolerance for input parameter checks
+        diag_tol : float, optional
+            Tolerance for deviation of the diagonal of the resulting
+            matrix. Default: 1e-7
+
+        Raises
+        ------
+        RuntimeError
+            Floating point error prevented generating a valid correlation
+            matrix.
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random size N-dimensional matrices, dimension (size, dim, dim),
+            each having eigenvalues eigs.
+        """ # numpy/numpydoc#87  # noqa: E501
+
+        self._dist = random_correlation_gen(seed)
+        self.tol = tol
+        self.diag_tol = diag_tol
+        _, self.eigs = self._dist._process_parameters(eigs, tol=self.tol)
+
+    def rvs(self, random_state=None):
+        return self._dist.rvs(self.eigs, random_state=random_state,
+                              tol=self.tol, diag_tol=self.diag_tol)
+
+
+class unitary_group_gen(multi_rv_generic):
+    r"""
+    A matrix-valued U(N) random variable.
+
+    Return a random unitary matrix.
+
+    The `dim` keyword specifies the dimension N.
+
+    Parameters
+    ----------
+    dim : scalar
+        Dimension of matrices.
+    seed : {None, int, np.random.RandomState, np.random.Generator}, optional
+        Used for drawing random variates.
+        If `seed` is `None`, the `~np.random.RandomState` singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used, seeded
+        with seed.
+        If `seed` is already a ``RandomState`` or ``Generator`` instance,
+        then that object is used.
+        Default is `None`.
+
+    Methods
+    -------
+    rvs(dim=None, size=1, random_state=None)
+        Draw random samples from U(N).
+
+    See Also
+    --------
+    ortho_group
+
+    Notes
+    -----
+    This class is similar to `ortho_group`.
+
+    References
+    ----------
+    .. [1] F. Mezzadri, "How to generate random matrices from the classical
+           compact groups", :arXiv:`math-ph/0609050v2`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import unitary_group
+    >>> x = unitary_group.rvs(3)
+
+    >>> np.dot(x, x.conj().T)
+    array([[  1.00000000e+00,   1.13231364e-17,  -2.86852790e-16],
+           [  1.13231364e-17,   1.00000000e+00,  -1.46845020e-16],
+           [ -2.86852790e-16,  -1.46845020e-16,   1.00000000e+00]])  # may vary
+
+    This generates one random matrix from U(3). The dot product confirms that
+    it is unitary up to machine precision.
+
+    Alternatively, the object may be called (as a function) to fix the `dim`
+    parameter, return a "frozen" unitary_group random variable:
+
+    >>> rv = unitary_group(5)
+    """
+
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = doccer.docformat(self.__doc__)
+
+    def __call__(self, dim=None, seed=None):
+        """Create a frozen (U(N)) n-dimensional unitary matrix distribution.
+
+        See `unitary_group_frozen` for more information.
+        """
+        return unitary_group_frozen(dim, seed=seed)
+
+    def _process_parameters(self, dim):
+        """Dimension N must be specified; it cannot be inferred."""
+        if dim is None or not np.isscalar(dim) or dim < 0 or dim != int(dim):
+            raise ValueError("Dimension of rotation must be specified,"
+                             "and must be a scalar nonnegative integer.")
+
+        return dim
+
+    def rvs(self, dim, size=1, random_state=None):
+        """Draw random samples from U(N).
+
+        Parameters
+        ----------
+        dim : int
+            Dimension of space (N).
+        size : int, optional
+            Number of samples to draw (default 1).
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random size N-dimensional matrices, dimension (size, dim, dim)
+
+        """
+        random_state = self._get_random_state(random_state)
+
+        size = int(size)
+
+        dim = self._process_parameters(dim)
+
+        size = (size,) if size > 1 else ()
+        z = 1/math.sqrt(2)*(random_state.normal(size=size + (dim, dim)) +
+                            1j*random_state.normal(size=size + (dim, dim)))
+        q, r = np.linalg.qr(z)
+        # The last two dimensions are the rows and columns of R matrices.
+        # Extract the diagonals. Note that this eliminates a dimension.
+        d = r.diagonal(offset=0, axis1=-2, axis2=-1)
+        # Add back a dimension for proper broadcasting: we're dividing
+        # each row of each R matrix by the diagonal of the R matrix.
+        q *= (d/abs(d))[..., np.newaxis, :]  # to broadcast properly
+        return q
+
+
+unitary_group = unitary_group_gen()
+
+
+class unitary_group_frozen(multi_rv_frozen):
+    __class_getitem__ = None  # pyrefly:ignore[bad-assignment]
+
+    def __init__(self, dim=None, seed=None):
+        """Create a frozen (U(N)) n-dimensional unitary matrix distribution.
+
+        Parameters
+        ----------
+        dim : scalar
+            Dimension of matrices
+        seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+            If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+            singleton is used.
+            If `seed` is an int, a new ``RandomState`` instance is used,
+            seeded with `seed`.
+            If `seed` is already a ``Generator`` or ``RandomState`` instance
+            then that instance is used.
+
+        Examples
+        --------
+        >>> from scipy.stats import unitary_group
+        >>> x = unitary_group(3)
+        >>> x.rvs()
+
+        """ # numpy/numpydoc#87  # noqa: E501
+        self._dist = unitary_group_gen(seed)
+        self.dim = self._dist._process_parameters(dim)
+
+    def rvs(self, size=1, random_state=None):
+        return self._dist.rvs(self.dim, size, random_state)
+
+
+_mvt_doc_default_callparams = """\
+loc : array_like, optional
+    Location of the distribution. (default ``0``)
+shape : array_like, optional
+    Positive semidefinite matrix of the distribution. (default ``1``)
+df : float, optional
+    Degrees of freedom of the distribution; must be greater than zero.
+    If ``np.inf`` then results are multivariate normal. The default is ``1``.
+allow_singular : bool, optional
+    Whether to allow a singular matrix. (default ``False``)
+"""
+
+_mvt_doc_callparams_note = """\
+Setting the parameter `loc` to ``None`` is equivalent to having `loc`
+be the zero-vector. The parameter `shape` can be a scalar, in which case
+the shape matrix is the identity times that value, a vector of
+diagonal entries for the shape matrix, or a two-dimensional array_like.
+"""
+
+_mvt_doc_frozen_callparams_note = """\
+See class definition for a detailed description of parameters."""
+
+mvt_docdict_params = {
+    '_mvt_doc_default_callparams': _mvt_doc_default_callparams,
+    '_mvt_doc_callparams_note': _mvt_doc_callparams_note,
+    '_doc_random_state': _doc_random_state
+}
+
+mvt_docdict_noparams = {
+    '_mvt_doc_default_callparams': "",
+    '_mvt_doc_callparams_note': _mvt_doc_frozen_callparams_note,
+    '_doc_random_state': _doc_random_state
+}
+
+
+class multivariate_t_gen(multi_rv_generic):
+    r"""
+    A multivariate t-distributed random variable.
+
+    The `loc` parameter specifies the location. The `shape` parameter specifies
+    the positive semidefinite shape matrix. The `df` parameter specifies the
+    degrees of freedom.
+
+    In addition to calling the methods below, the object itself may be called
+    as a function to fix the location, shape matrix, and degrees of freedom
+    parameters, returning a "frozen" multivariate t-distribution random.
+
+    Parameters
+    ----------
+    %(_mvt_doc_default_callparams)s
+    %(_doc_random_state)s
+
+    Methods
+    -------
+    pdf(x, loc=None, shape=1, df=1, allow_singular=False)
+        Probability density function.
+    logpdf(x, loc=None, shape=1, df=1, allow_singular=False)
+        Log of the probability density function.
+    cdf(x, loc=None, shape=1, df=1, allow_singular=False, *,
+        maxpts=None, lower_limit=None, random_state=None)
+        Cumulative distribution function.
+    rvs(loc=None, shape=1, df=1, size=1, random_state=None)
+        Draw random samples from a multivariate t-distribution.
+    entropy(loc=None, shape=1, df=1)
+        Differential entropy of a multivariate t-distribution.
+    marginal(dimensions, loc=None, shape=1, df=1, allow_singular=False)
+        Return a marginal multivariate t-distribution.
+
+    Notes
+    -----
+    %(_mvt_doc_callparams_note)s
+    The matrix `shape` must be a (symmetric) positive semidefinite matrix. The
+    determinant and inverse of `shape` are computed as the pseudo-determinant
+    and pseudo-inverse, respectively, so that `shape` does not need to have
+    full rank.
+
+    The probability density function for `multivariate_t` is
+
+    .. math::
+
+        f(x) = \frac{\Gamma((\nu + p)/2)}{\Gamma(\nu/2)\nu^{p/2}\pi^{p/2}|\Sigma|^{1/2}}
+               \left[1 + \frac{1}{\nu} (\mathbf{x} - \boldsymbol{\mu})^{\top}
+               \boldsymbol{\Sigma}^{-1}
+               (\mathbf{x} - \boldsymbol{\mu}) \right]^{-(\nu + p)/2},
+
+    where :math:`p` is the dimension of :math:`\mathbf{x}`,
+    :math:`\boldsymbol{\mu}` is the :math:`p`-dimensional location,
+    :math:`\boldsymbol{\Sigma}` the :math:`p \times p`-dimensional shape
+    matrix, and :math:`\nu` is the degrees of freedom.
+
+    .. versionadded:: 1.6.0
+
+    References
+    ----------
+    .. [1] Arellano-Valle et al. "Shannon Entropy and Mutual Information for
+           Multivariate Skew-Elliptical Distributions". Scandinavian Journal
+           of Statistics. Vol. 40, issue 1.
+
+    Examples
+    --------
+    The object may be called (as a function) to fix the `loc`, `shape`,
+    `df`, and `allow_singular` parameters, returning a "frozen"
+    multivariate_t random variable:
+
+    >>> import numpy as np
+    >>> from scipy.stats import multivariate_t
+    >>> rv = multivariate_t([1.0, -0.5], [[2.1, 0.3], [0.3, 1.5]], df=2)
+    >>> # Frozen object with the same methods but holding the given location,
+    >>> # scale, and degrees of freedom fixed.
+
+    Create a contour plot of the PDF.
+
+    >>> import matplotlib.pyplot as plt
+    >>> x, y = np.mgrid[-1:3:.01, -2:1.5:.01]
+    >>> pos = np.dstack((x, y))
+    >>> fig, ax = plt.subplots(1, 1)
+    >>> ax.set_aspect('equal')
+    >>> plt.contourf(x, y, rv.pdf(pos))
+    """
+
+    def __init__(self, seed=None):
+        """Initialize a multivariate t-distributed random variable.
+
+        Parameters
+        ----------
+        seed : Random state.
+
+        """
+        super().__init__(seed)
+        self.__doc__ = doccer.docformat(self.__doc__, mvt_docdict_params)
+        self._random_state = check_random_state(seed)
+
+    def __call__(self, loc=None, shape=1, df=1, allow_singular=False,
+                 seed=None):
+        """Create a frozen multivariate t-distribution.
+
+        See `multivariate_t_frozen` for parameters.
+        """
+        if df == np.inf:
+            return multivariate_normal_frozen(mean=loc, cov=shape,
+                                              allow_singular=allow_singular,
+                                              seed=seed)
+        return multivariate_t_frozen(loc=loc, shape=shape, df=df,
+                                     allow_singular=allow_singular, seed=seed)
+
+    def pdf(self, x, loc=None, shape=1, df=1, allow_singular=False):
+        """Multivariate t-distribution probability density function.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the probability density function.
+        %(_mvt_doc_default_callparams)s
+
+        Returns
+        -------
+        pdf : Probability density function evaluated at `x`.
+
+        Examples
+        --------
+        >>> from scipy.stats import multivariate_t
+        >>> x = [0.4, 5]
+        >>> loc = [0, 1]
+        >>> shape = [[1, 0.1], [0.1, 1]]
+        >>> df = 7
+        >>> multivariate_t.pdf(x, loc, shape, df)
+        0.00075713
+
+        """
+        dim, loc, shape, df = self._process_parameters(loc, shape, df)
+        x = self._process_quantiles(x, dim)
+        shape_info = _PSD(shape, allow_singular=allow_singular)
+        logpdf = self._logpdf(x, loc, shape_info.U, shape_info.log_pdet, df,
+                              dim, shape_info.rank)
+        return np.exp(logpdf)
+
+    def logpdf(self, x, loc=None, shape=1, df=1):
+        """Log of the multivariate t-distribution probability density function.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log of the probability density
+            function.
+        %(_mvt_doc_default_callparams)s
+
+        Returns
+        -------
+        logpdf : Log of the probability density function evaluated at `x`.
+
+        Examples
+        --------
+        >>> from scipy.stats import multivariate_t
+        >>> x = [0.4, 5]
+        >>> loc = [0, 1]
+        >>> shape = [[1, 0.1], [0.1, 1]]
+        >>> df = 7
+        >>> multivariate_t.logpdf(x, loc, shape, df)
+        -7.1859802
+
+        See Also
+        --------
+        pdf : Probability density function.
+
+        """
+        dim, loc, shape, df = self._process_parameters(loc, shape, df)
+        x = self._process_quantiles(x, dim)
+        shape_info = _PSD(shape)
+        cov_object = _covariance.CovViaPSD(shape_info)
+
+        return self._logpdf(x, loc, shape_info.U, shape_info.log_pdet, df, dim,
+                            shape_info.rank, cov_object)
+
+    def _logpdf(self, x, loc, prec_U, log_pdet, df, dim, rank, cov_object=None):
+        """Utility method `pdf`, `logpdf` for parameters.
+
+        Parameters
+        ----------
+        x : ndarray
+            Points at which to evaluate the log of the probability density
+            function.
+        loc : ndarray
+            Location of the distribution.
+        prec_U : ndarray
+            A decomposition such that `np.dot(prec_U, prec_U.T)` is the inverse
+            of the shape matrix.
+        log_pdet : float
+            Logarithm of the determinant of the shape matrix.
+        df : float
+            Degrees of freedom of the distribution.
+        dim : int
+            Dimension of the quantiles x.
+        rank : int
+            Rank of the shape matrix.
+
+        Notes
+        -----
+        As this function does no argument checking, it should not be called
+        directly; use 'logpdf' instead.
+
+        """
+        if df == np.inf:
+            return multivariate_normal._logpdf(x, loc, cov_object)
+
+        dev = x - loc
+        maha = np.square(np.dot(dev, prec_U)).sum(axis=-1)
+
+        t = 0.5 * (df + dim)
+        A = gammaln(t)
+        B = gammaln(0.5 * df)
+        C = dim/2. * np.log(df * np.pi)
+        D = 0.5 * log_pdet
+        E = -t * np.log(1 + (1./df) * maha)
+
+        return _squeeze_output(A - B - C - D + E)
+
+    def _cdf(self, x, loc, shape, df, dim, maxpts=None, lower_limit=None,
+             random_state=None):
+
+        # All of this -  random state validation, maxpts, apply_along_axis,
+        # etc. needs to go in this private method unless we want
+        # frozen distribution's `cdf` method to duplicate it or call `cdf`,
+        # which would require re-processing parameters
+        if random_state is not None:
+            rng = check_random_state(random_state)
+        else:
+            rng = self._random_state
+
+        if not maxpts:
+            maxpts = 1000 * dim
+
+        x = self._process_quantiles(x, dim)
+        lower_limit = (np.full(loc.shape, -np.inf)
+                       if lower_limit is None else lower_limit)
+
+        # remove the mean
+        x, lower_limit = x - loc, lower_limit - loc
+
+        b, a = np.broadcast_arrays(x, lower_limit)
+        i_swap = b < a
+        signs = (-1)**(i_swap.sum(axis=-1))  # odd # of swaps -> negative
+        a, b = a.copy(), b.copy()
+        a[i_swap], b[i_swap] = b[i_swap], a[i_swap]
+        n = x.shape[-1]
+        limits = np.concatenate((a, b), axis=-1)
+
+        def func1d(limits):
+            a, b = limits[:n], limits[n:]
+            return _qmvt(maxpts, df, shape, a, b, rng)[0]
+
+        res = np.apply_along_axis(func1d, -1, limits) * signs
+        # Fixing the output shape for existing distributions is a separate
+        # issue. For now, let's keep this consistent with pdf.
+        return _squeeze_output(res)
+
+    def cdf(self, x, loc=None, shape=1, df=1, allow_singular=False, *,
+            maxpts=None, lower_limit=None, random_state=None):
+        """Multivariate t-distribution cumulative distribution function.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the cumulative distribution function.
+        %(_mvt_doc_default_callparams)s
+        maxpts : int, optional
+            Maximum number of points to use for integration. The default is
+            1000 times the number of dimensions.
+        lower_limit : array_like, optional
+            Lower limit of integration of the cumulative distribution function.
+            Default is negative infinity. Must be broadcastable with `x`.
+        %(_doc_random_state)s
+
+        Returns
+        -------
+        cdf : ndarray or scalar
+            Cumulative distribution function evaluated at `x`.
+
+        Examples
+        --------
+        >>> from scipy.stats import multivariate_t
+        >>> x = [0.4, 5]
+        >>> loc = [0, 1]
+        >>> shape = [[1, 0.1], [0.1, 1]]
+        >>> df = 7
+        >>> multivariate_t.cdf(x, loc, shape, df)
+        0.64798491
+
+        """
+        dim, loc, shape, df = self._process_parameters(loc, shape, df)
+        shape = _PSD(shape, allow_singular=allow_singular)._M
+
+        return self._cdf(x, loc, shape, df, dim, maxpts,
+                         lower_limit, random_state)
+
+    def _entropy(self, dim, df=1, shape=1):
+        if df == np.inf:
+            return multivariate_normal(None, cov=shape).entropy()
+
+        shape_info = _PSD(shape)
+        shape_term = 0.5 * shape_info.log_pdet
+
+        def regular(dim, df):
+            halfsum = 0.5 * (dim + df)
+            half_df = 0.5 * df
+            return (
+                -gammaln(halfsum) + gammaln(half_df)
+                + 0.5 * dim * np.log(df * np.pi) + halfsum
+                * (psi(halfsum) - psi(half_df))
+                + shape_term
+            )
+
+        def asymptotic(dim, df):
+            # Formula from Wolfram Alpha:
+            # "asymptotic expansion -gammaln((m+d)/2) + gammaln(d/2) + (m*log(d*pi))/2
+            #  + ((m+d)/2) * (digamma((m+d)/2) - digamma(d/2))"
+            return (
+                dim * norm._entropy() + dim / df
+                - dim * (dim - 2) * df**-2.0 / 4
+                + dim**2 * (dim - 2) * df**-3.0 / 6
+                + dim * (-3 * dim**3 + 8 * dim**2 - 8) * df**-4.0 / 24
+                + dim**2 * (3 * dim**3 - 10 * dim**2 + 16) * df**-5.0 / 30
+                + shape_term
+            )[()]
+
+        # preserves ~12 digits accuracy up to at least `dim=1e5`. See gh-18465.
+        threshold = dim * 100 * 4 / (np.log(dim) + 1)
+        return xpx.apply_where(df >= threshold, (dim, df), asymptotic, regular)
+
+    def entropy(self, loc=None, shape=1, df=1):
+        """Calculate the differential entropy of a multivariate
+        t-distribution.
+
+        Parameters
+        ----------
+        %(_mvt_doc_default_callparams)s
+
+        Returns
+        -------
+        h : float
+            Differential entropy
+
+        """
+        dim, loc, shape, df = self._process_parameters(None, shape, df)
+        return self._entropy(dim, df, shape)
+
+    def rvs(self, loc=None, shape=1, df=1, size=1, random_state=None):
+        """Draw random samples from a multivariate t-distribution.
+
+        Parameters
+        ----------
+        %(_mvt_doc_default_callparams)s
+        size : int, optional
+            Number of samples to draw (default 1).
+        %(_doc_random_state)s
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random variates of size (`size`, `P`), where `P` is the
+            dimension of the random variable.
+
+        Examples
+        --------
+        >>> from scipy.stats import multivariate_t
+        >>> x = [0.4, 5]
+        >>> loc = [0, 1]
+        >>> shape = [[1, 0.1], [0.1, 1]]
+        >>> df = 7
+        >>> multivariate_t.rvs(loc, shape, df)
+        array([[0.93477495, 3.00408716]])
+
+        """
+        # For implementation details, see equation (3):
+        #
+        #    Hofert, "On Sampling from the Multivariatet Distribution", 2013
+        #     http://rjournal.github.io/archive/2013-2/hofert.pdf
+        #
+        dim, loc, shape, df = self._process_parameters(loc, shape, df)
+        if random_state is not None:
+            rng = check_random_state(random_state)
+        else:
+            rng = self._random_state
+
+        if np.isinf(df):
+            x = np.ones(size)
+        else:
+            x = rng.chisquare(df, size=size) / df
+
+        z = rng.multivariate_normal(np.zeros(dim), shape, size=size)
+        samples = loc + z / np.sqrt(x)[..., None]
+        return _squeeze_output(samples)
+
+    def _process_quantiles(self, x, dim):
+        """
+        Adjust quantiles array so that last axis labels the components of
+        each data point.
+        """
+        x = np.asarray(x, dtype=float)
+        if x.ndim == 0:
+            x = x[np.newaxis]
+        elif x.ndim == 1:
+            if dim == 1:
+                x = x[:, np.newaxis]
+            else:
+                x = x[np.newaxis, :]
+        return x
+
+    def _process_parameters(self, loc, shape, df):
+        """
+        Infer dimensionality from location array and shape matrix, handle
+        defaults, and ensure compatible dimensions.
+        """
+        if loc is None and shape is None:
+            loc = np.asarray(0, dtype=float)
+            shape = np.asarray(1, dtype=float)
+            dim = 1
+        elif loc is None:
+            shape = np.asarray(shape, dtype=float)
+            if shape.ndim < 2:
+                dim = 1
+            else:
+                dim = shape.shape[0]
+            loc = np.zeros(dim)
+        elif shape is None:
+            loc = np.asarray(loc, dtype=float)
+            dim = loc.size
+            shape = np.eye(dim)
+        else:
+            shape = np.asarray(shape, dtype=float)
+            loc = np.asarray(loc, dtype=float)
+            dim = loc.size
+
+        if dim == 1:
+            loc = loc.reshape(1)
+            shape = shape.reshape(1, 1)
+
+        if loc.ndim != 1 or loc.shape[0] != dim:
+            raise ValueError(f"Array 'loc' must be a vector of length {dim}.")
+        if shape.ndim == 0:
+            shape = shape * np.eye(dim)
+        elif shape.ndim == 1:
+            shape = np.diag(shape)
+        elif shape.ndim == 2 and shape.shape != (dim, dim):
+            rows, cols = shape.shape
+            if rows != cols:
+                msg = ("Array 'cov' must be square if it is two dimensional,"
+                       f" but cov.shape = {str(shape.shape)}.")
+            else:
+                msg = ("Dimension mismatch: array 'cov' is of shape %s,"
+                       " but 'loc' is a vector of length %d.")
+                msg = msg % (str(shape.shape), len(loc))
+            raise ValueError(msg)
+        elif shape.ndim > 2:
+            raise ValueError(f"Array 'cov' must be at most two-dimensional, "
+                             f"but cov.ndim = {shape.ndim}")
+
+        # Process degrees of freedom.
+        if df is None:
+            df = 1
+        elif df <= 0:
+            raise ValueError("'df' must be greater than zero.")
+        elif np.isnan(df):
+            raise ValueError("'df' is 'nan' but must be greater than zero or 'np.inf'.")
+
+        return dim, loc, shape, df
+
+    def marginal(self, dimensions, loc=None, shape=1, df=1, allow_singular=False):
+        """Return a marginal multivariate t-distribution.
+
+        Parameters
+        ----------
+        dimensions : int or 1-d array_like
+            The dimensions of the multivariate t corresponding
+            with the marginal variables, that is, the indices of the dimensions
+            that are being retained. The other dimensions are marginalized out.
+        %(_mvt_doc_default_callparams)s
+
+        Returns
+        -------
+        marginal_multivariate_t : multivariate_t_frozen
+            An object representing the marginal t-distribution.
+
+        Notes
+        -----
+        %(_mvt_doc_frozen_callparams_note)s
+        """
+        params = self._process_parameters(loc, shape, df)
+        n, loc, shape, df = params
+        dims = _validate_marginal_input(dimensions, n)
+
+        loc = loc[dims]
+        shape = shape[np.ix_(dims, dims)]
+
+        return multivariate_t_frozen(loc, shape, df, allow_singular)
+
+
+class multivariate_t_frozen(multi_rv_frozen):
+    __class_getitem__ = None  # pyrefly:ignore[bad-assignment]
+
+    def __init__(self, loc=None, shape=1, df=1, allow_singular=False,
+                 seed=None):
+        """Create a frozen multivariate t distribution.
+
+        Parameters
+        ----------
+        %(_mvt_doc_default_callparams)s
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> from scipy.stats import multivariate_t
+        >>> loc = np.zeros(3)
+        >>> shape = np.eye(3)
+        >>> df = 10
+        >>> dist = multivariate_t(loc, shape, df)
+        >>> dist.rvs()
+        array([[ 0.81412036, -1.53612361,  0.42199647]])
+        >>> dist.pdf([1, 1, 1])
+        array([0.01237803])
+
+        """
+        self._dist = multivariate_t_gen(seed)
+        dim, loc, shape, df = self._dist._process_parameters(loc, shape, df)
+        self.dim, self.loc, self.shape, self.df = dim, loc, shape, df
+        self.shape_info = _PSD(shape, allow_singular=allow_singular)
+        self.allow_singular = allow_singular
+
+    def logpdf(self, x):
+        x = self._dist._process_quantiles(x, self.dim)
+        U = self.shape_info.U
+        log_pdet = self.shape_info.log_pdet
+        return self._dist._logpdf(x, self.loc, U, log_pdet, self.df, self.dim,
+                                  self.shape_info.rank)
+
+    def cdf(self, x, *, maxpts=None, lower_limit=None, random_state=None):
+        x = self._dist._process_quantiles(x, self.dim)
+        return self._dist._cdf(x, self.loc, self.shape, self.df, self.dim,
+                               maxpts, lower_limit, random_state)
+
+    def pdf(self, x):
+        return np.exp(self.logpdf(x))
+
+    def rvs(self, size=1, random_state=None):
+        return self._dist.rvs(loc=self.loc,
+                              shape=self.shape,
+                              df=self.df,
+                              size=size,
+                              random_state=random_state)
+
+    def entropy(self):
+        return self._dist._entropy(self.dim, self.df, self.shape)
+
+    def marginal(self, dimensions):
+        return self._dist.marginal(dimensions, self.loc,
+                                   self.shape, self.df, self.allow_singular)
+
+multivariate_t = multivariate_t_gen()
+
+
+# Set frozen generator docstrings from corresponding docstrings in
+# multivariate_t_gen and fill in default strings in class docstrings
+for name in ['logpdf', 'pdf', 'rvs', 'cdf', 'entropy']:
+    method = multivariate_t_gen.__dict__[name]
+    method_frozen = multivariate_t_frozen.__dict__[name]
+    method_frozen.__doc__ = doccer.docformat(method.__doc__,
+                                             mvt_docdict_noparams)
+    method.__doc__ = doccer.docformat(method.__doc__, mvt_docdict_params)
+
+
+_mhg_doc_default_callparams = """\
+m : array_like
+    The number of each type of object in the population.
+    That is, :math:`m[i]` is the number of objects of
+    type :math:`i`.
+n : array_like
+    The number of samples taken from the population.
+"""
+
+_mhg_doc_callparams_note = """\
+`m` must be an array of positive integers. If the quantile
+:math:`i` contains values out of the range :math:`[0, m_i]`
+where :math:`m_i` is the number of objects of type :math:`i`
+in the population or if the parameters are inconsistent with one
+another (e.g. ``x.sum() != n``), methods return the appropriate
+value (e.g. ``0`` for ``pmf``). If `m` or `n` contain negative
+values, the result will contain ``nan`` there.
+"""
+
+_mhg_doc_frozen_callparams = ""
+
+_mhg_doc_frozen_callparams_note = """\
+See class definition for a detailed description of parameters."""
+
+mhg_docdict_params = {
+    '_doc_default_callparams': _mhg_doc_default_callparams,
+    '_doc_callparams_note': _mhg_doc_callparams_note,
+    '_doc_random_state': _doc_random_state
+}
+
+mhg_docdict_noparams = {
+    '_doc_default_callparams': _mhg_doc_frozen_callparams,
+    '_doc_callparams_note': _mhg_doc_frozen_callparams_note,
+    '_doc_random_state': _doc_random_state
+}
+
+
+class multivariate_hypergeom_gen(multi_rv_generic):
+    r"""
+    A multivariate hypergeometric random variable.
+
+    Parameters
+    ----------
+    %(_doc_default_callparams)s
+    %(_doc_random_state)s
+
+    Methods
+    -------
+    pmf(x, m, n)
+        Probability mass function.
+    logpmf(x, m, n)
+        Log of the probability mass function.
+    rvs(m, n, size=1, random_state=None)
+        Draw random samples from a multivariate hypergeometric
+        distribution.
+    mean(m, n)
+        Mean of the multivariate hypergeometric distribution.
+    var(m, n)
+        Variance of the multivariate hypergeometric distribution.
+    cov(m, n)
+        Compute the covariance matrix of the multivariate
+        hypergeometric distribution.
+
+    See Also
+    --------
+    scipy.stats.hypergeom : The hypergeometric distribution.
+    scipy.stats.multinomial : The multinomial distribution.
+
+    Notes
+    -----
+    %(_doc_callparams_note)s
+
+    The probability mass function for `multivariate_hypergeom` is
+
+    .. math::
+
+        P(X_1 = x_1, X_2 = x_2, \ldots, X_k = x_k) = \frac{\binom{m_1}{x_1}
+        \binom{m_2}{x_2} \cdots \binom{m_k}{x_k}}{\binom{M}{n}}, \\ \quad
+        (x_1, x_2, \ldots, x_k) \in \mathbb{N}^k \text{ with }
+        \sum_{i=1}^k x_i = n
+
+    where :math:`m_i` are the number of objects of type :math:`i`, :math:`M`
+    is the total number of objects in the population (sum of all the
+    :math:`m_i`), and :math:`n` is the size of the sample to be taken
+    from the population.
+
+    .. versionadded:: 1.6.0
+
+    References
+    ----------
+    .. [1] The Multivariate Hypergeometric Distribution,
+           http://www.randomservices.org/random/urn/MultiHypergeometric.html
+    .. [2] Thomas J. Sargent and John Stachurski, 2020,
+           Multivariate Hypergeometric Distribution
+           https://python.quantecon.org/multi_hyper.html
+
+    Examples
+    --------
+    To evaluate the probability mass function of the multivariate
+    hypergeometric distribution, with a dichotomous population of size
+    :math:`10` and :math:`20`, at a sample of size :math:`12` with
+    :math:`8` objects of the first type and :math:`4` objects of the
+    second type, use:
+
+    >>> from scipy.stats import multivariate_hypergeom
+    >>> multivariate_hypergeom.pmf(x=[8, 4], m=[10, 20], n=12)
+    0.0025207176631464523
+
+    The `multivariate_hypergeom` distribution is identical to the
+    corresponding `hypergeom` distribution (tiny numerical differences
+    notwithstanding) when only two types (good and bad) of objects
+    are present in the population as in the example above. Consider
+    another example for a comparison with the hypergeometric distribution:
+
+    >>> from scipy.stats import hypergeom
+    >>> multivariate_hypergeom.pmf(x=[3, 1], m=[10, 5], n=4)
+    0.4395604395604395
+    >>> hypergeom.pmf(k=3, M=15, n=4, N=10)
+    0.43956043956044005
+
+    The functions ``pmf``, ``logpmf``, ``mean``, ``var``, ``cov``, and ``rvs``
+    support broadcasting, under the convention that the vector parameters
+    (``x``, ``m``, and ``n``) are interpreted as if each row along the last
+    axis is a single object. For instance, we can combine the previous two
+    calls to `multivariate_hypergeom` as
+
+    >>> multivariate_hypergeom.pmf(x=[[8, 4], [3, 1]], m=[[10, 20], [10, 5]],
+    ...                            n=[12, 4])
+    array([0.00252072, 0.43956044])
+
+    This broadcasting also works for ``cov``, where the output objects are
+    square matrices of size ``m.shape[-1]``. For example:
+
+    >>> multivariate_hypergeom.cov(m=[[7, 9], [10, 15]], n=[8, 12])
+    array([[[ 1.05, -1.05],
+            [-1.05,  1.05]],
+           [[ 1.56, -1.56],
+            [-1.56,  1.56]]])
+
+    That is, ``result[0]`` is equal to
+    ``multivariate_hypergeom.cov(m=[7, 9], n=8)`` and ``result[1]`` is equal
+    to ``multivariate_hypergeom.cov(m=[10, 15], n=12)``.
+
+    Alternatively, the object may be called (as a function) to fix the `m`
+    and `n` parameters, returning a "frozen" multivariate hypergeometric
+    random variable.
+
+    >>> rv = multivariate_hypergeom(m=[10, 20], n=12)
+    >>> rv.pmf(x=[8, 4])
+    0.0025207176631464523
+    """
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = doccer.docformat(self.__doc__, mhg_docdict_params)
+
+    def __call__(self, m, n, seed=None):
+        """Create a frozen multivariate_hypergeom distribution.
+
+        See `multivariate_hypergeom_frozen` for more information.
+        """
+        return multivariate_hypergeom_frozen(m, n, seed=seed)
+
+    def _process_parameters(self, m, n):
+        m = np.asarray(m)
+        n = np.asarray(n)
+        if m.size == 0:
+            m = m.astype(int)
+        if n.size == 0:
+            n = n.astype(int)
+        if not np.issubdtype(m.dtype, np.integer):
+            raise TypeError("'m' must an array of integers.")
+        if not np.issubdtype(n.dtype, np.integer):
+            raise TypeError("'n' must an array of integers.")
+        if m.ndim == 0:
+            raise ValueError("'m' must be an array with"
+                             " at least one dimension.")
+
+        # check for empty arrays
+        if m.size != 0:
+            n = n[..., np.newaxis]
+
+        m, n = np.broadcast_arrays(m, n)
+
+        # check for empty arrays
+        if m.size != 0:
+            n = n[..., 0]
+
+        mcond = m < 0
+
+        M = m.sum(axis=-1)
+
+        ncond = (n < 0) | (n > M)
+        return M, m, n, mcond, ncond, np.any(mcond, axis=-1) | ncond
+
+    def _process_quantiles(self, x, M, m, n):
+        x = np.asarray(x)
+        if not np.issubdtype(x.dtype, np.integer):
+            raise TypeError("'x' must an array of integers.")
+        if x.ndim == 0:
+            raise ValueError("'x' must be an array with"
+                             " at least one dimension.")
+        if not x.shape[-1] == m.shape[-1]:
+            raise ValueError(f"Size of each quantile must be size of 'm': "
+                             f"received {x.shape[-1]}, "
+                             f"but expected {m.shape[-1]}.")
+
+        # check for empty arrays
+        if m.size != 0:
+            n = n[..., np.newaxis]
+            M = M[..., np.newaxis]
+
+        x, m, n, M = np.broadcast_arrays(x, m, n, M)
+
+        # check for empty arrays
+        if m.size != 0:
+            n, M = n[..., 0], M[..., 0]
+
+        xcond = (x < 0) | (x > m)
+        return (x, M, m, n, xcond,
+                np.any(xcond, axis=-1) | (x.sum(axis=-1) != n))
+
+    def _checkresult(self, result, cond, bad_value):
+        result = np.asarray(result)
+        if cond.ndim != 0:
+            result[cond] = bad_value
+        elif cond:
+            return bad_value
+        if result.ndim == 0:
+            return result[()]
+        return result
+
+    def _logpmf(self, x, M, m, n, mxcond, ncond):
+        # This equation of the pmf comes from the relation,
+        # n combine r = beta(n+1, 1) / beta(r+1, n-r+1)
+        num = np.zeros_like(m, dtype=np.float64)
+        den = np.zeros_like(n, dtype=np.float64)
+        m, x = m[~mxcond], x[~mxcond]
+        M, n = M[~ncond], n[~ncond]
+        num[~mxcond] = (betaln(m+1, 1) - betaln(x+1, m-x+1))
+        den[~ncond] = (betaln(M+1, 1) - betaln(n+1, M-n+1))
+        num[mxcond] = np.nan
+        den[ncond] = np.nan
+        num = num.sum(axis=-1)
+        return num - den
+
+    def logpmf(self, x, m, n):
+        """Log of the multivariate hypergeometric probability mass function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        logpmf : ndarray or scalar
+            Log of the probability mass function evaluated at `x`
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+        """
+        M, m, n, mcond, ncond, mncond = self._process_parameters(m, n)
+        (x, M, m, n, xcond,
+         xcond_reduced) = self._process_quantiles(x, M, m, n)
+        mxcond = mcond | xcond
+        ncond = ncond | np.zeros(n.shape, dtype=np.bool_)
+
+        result = self._logpmf(x, M, m, n, mxcond, ncond)
+
+        # replace values for which x was out of the domain; broadcast
+        # xcond to the right shape
+        xcond_ = xcond_reduced | np.zeros(mncond.shape, dtype=np.bool_)
+        result = self._checkresult(result, xcond_, -np.inf)
+
+        # replace values bad for n or m; broadcast
+        # mncond to the right shape
+        mncond_ = mncond | np.zeros(xcond_reduced.shape, dtype=np.bool_)
+        return self._checkresult(result, mncond_, np.nan)
+
+    def pmf(self, x, m, n):
+        """Multivariate hypergeometric probability mass function.
+
+        Parameters
+        ----------
+        x : array_like
+            Quantiles, with the last axis of `x` denoting the components.
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        pmf : ndarray or scalar
+            Probability density function evaluated at `x`
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+        """
+        out = np.exp(self.logpmf(x, m, n))
+        return out
+
+    def mean(self, m, n):
+        """Mean of the multivariate hypergeometric distribution.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        mean : array_like or scalar
+            The mean of the distribution
+        """
+        M, m, n, _, _, mncond = self._process_parameters(m, n)
+        # check for empty arrays
+        if m.size != 0:
+            M, n = M[..., np.newaxis], n[..., np.newaxis]
+        cond = (M == 0)
+        M = np.ma.masked_array(M, mask=cond)
+        mu = n*(m/M)
+        if m.size != 0:
+            mncond = (mncond[..., np.newaxis] |
+                      np.zeros(mu.shape, dtype=np.bool_))
+        return self._checkresult(mu, mncond, np.nan)
+
+    def var(self, m, n):
+        """Variance of the multivariate hypergeometric distribution.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        array_like
+            The variances of the components of the distribution.  This is
+            the diagonal of the covariance matrix of the distribution
+        """
+        M, m, n, _, _, mncond = self._process_parameters(m, n)
+        # check for empty arrays
+        if m.size != 0:
+            M, n = M[..., np.newaxis], n[..., np.newaxis]
+        cond = (M == 0) & (M-1 == 0)
+        M = np.ma.masked_array(M, mask=cond)
+        output = n * m/M * (M-m)/M * (M-n)/(M-1)
+        if m.size != 0:
+            mncond = (mncond[..., np.newaxis] |
+                      np.zeros(output.shape, dtype=np.bool_))
+        return self._checkresult(output, mncond, np.nan)
+
+    def cov(self, m, n):
+        """Covariance matrix of the multivariate hypergeometric distribution.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+
+        Returns
+        -------
+        cov : array_like
+            The covariance matrix of the distribution
+        """
+        # see [1]_ for the formula and [2]_ for implementation
+        # cov( x_i,x_j ) = -n * (M-n)/(M-1) * (K_i*K_j) / (M**2)
+        M, m, n, _, _, mncond = self._process_parameters(m, n)
+        # check for empty arrays
+        if m.size != 0:
+            M = M[..., np.newaxis, np.newaxis]
+            n = n[..., np.newaxis, np.newaxis]
+        cond = (M == 0) & (M-1 == 0)
+        M = np.ma.masked_array(M, mask=cond)
+        output = (-n * (M-n)/(M-1) *
+                  np.einsum("...i,...j->...ij", m, m) / (M**2))
+        # check for empty arrays
+        if m.size != 0:
+            M, n = M[..., 0, 0], n[..., 0, 0]
+            cond = cond[..., 0, 0]
+        dim = m.shape[-1]
+        # diagonal entries need to be computed differently
+        for i in range(dim):
+            output[..., i, i] = (n * (M-n) * m[..., i]*(M-m[..., i]))
+            output[..., i, i] = output[..., i, i] / (M-1)
+            output[..., i, i] = output[..., i, i] / (M**2)
+        if m.size != 0:
+            mncond = (mncond[..., np.newaxis, np.newaxis] |
+                      np.zeros(output.shape, dtype=np.bool_))
+        return self._checkresult(output, mncond, np.nan)
+
+    def rvs(self, m, n, size=None, random_state=None):
+        """Draw random samples from a multivariate hypergeometric distribution.
+
+        Parameters
+        ----------
+        %(_doc_default_callparams)s
+        size : int or iterable of integers, optional
+            Number of samples to draw. Default is ``None``, in which case a
+            single variate is returned as an array with shape ``m.shape``.
+        %(_doc_random_state)s
+
+        Returns
+        -------
+        rvs : array_like
+            Random variates of shape ``size`` or ``m.shape``
+            (if ``size=None``).
+
+        Notes
+        -----
+        %(_doc_callparams_note)s
+
+        Also note that NumPy's `multivariate_hypergeometric` sampler is not
+        used as it doesn't support broadcasting.
+        """
+        M, m, n, _, _, _ = self._process_parameters(m, n)
+
+        random_state = self._get_random_state(random_state)
+
+        if size is not None and isinstance(size, int):
+            size = (size, )
+
+        if size is None:
+            rvs = np.empty(m.shape, dtype=m.dtype)
+        else:
+            rvs = np.empty(size + (m.shape[-1], ), dtype=m.dtype)
+        rem = M
+
+        # This sampler has been taken from numpy gh-13794
+        # https://github.com/numpy/numpy/pull/13794
+        for c in range(m.shape[-1] - 1):
+            rem = rem - m[..., c]
+            n0mask = n == 0
+            rvs[..., c] = (~n0mask *
+                           random_state.hypergeometric(m[..., c],
+                                                       rem + n0mask,
+                                                       n + n0mask,
+                                                       size=size))
+            n = n - rvs[..., c]
+        rvs[..., m.shape[-1] - 1] = n
+
+        return rvs
+
+
+multivariate_hypergeom = multivariate_hypergeom_gen()
+
+
+class multivariate_hypergeom_frozen(multi_rv_frozen):
+    def __init__(self, m, n, seed=None):
+        self._dist = multivariate_hypergeom_gen(seed)
+        (self.M, self.m, self.n,
+         self.mcond, self.ncond,
+         self.mncond) = self._dist._process_parameters(m, n)
+
+        # monkey patch self._dist
+        def _process_parameters(m, n):
+            return (self.M, self.m, self.n,
+                    self.mcond, self.ncond,
+                    self.mncond)
+        self._dist._process_parameters = _process_parameters
+
+    def logpmf(self, x):
+        return self._dist.logpmf(x, self.m, self.n)
+
+    def pmf(self, x):
+        return self._dist.pmf(x, self.m, self.n)
+
+    def mean(self):
+        return self._dist.mean(self.m, self.n)
+
+    def var(self):
+        return self._dist.var(self.m, self.n)
+
+    def cov(self):
+        return self._dist.cov(self.m, self.n)
+
+    def rvs(self, size=1, random_state=None):
+        return self._dist.rvs(self.m, self.n,
+                              size=size,
+                              random_state=random_state)
+
+
+# Set frozen generator docstrings from corresponding docstrings in
+# multivariate_hypergeom and fill in default strings in class docstrings
+for name in ['logpmf', 'pmf', 'mean', 'var', 'cov', 'rvs']:
+    method = multivariate_hypergeom_gen.__dict__[name]
+    method_frozen = multivariate_hypergeom_frozen.__dict__[name]
+    method_frozen.__doc__ = doccer.docformat(
+        method.__doc__, mhg_docdict_noparams)
+    method.__doc__ = doccer.docformat(method.__doc__,
+                                      mhg_docdict_params)
+
+
+class random_table_gen(multi_rv_generic):
+    r"""
+    Contingency tables from independent samples with fixed marginal sums.
+
+    This is the distribution of random tables with given row and column vector
+    sums. This distribution represents the set of random tables under the null
+    hypothesis that rows and columns are independent. It is used in hypothesis
+    tests of independence.
+
+    Because of assumed independence, the expected frequency of each table
+    element can be computed from the row and column sums, so that the
+    distribution is completely determined by these two vectors.
+
+    Parameters
+    ----------
+    %(_doc_row_col)s
+    %(_doc_random_state)s
+
+    Methods
+    -------
+    logpmf(x)
+        Log-probability of table `x` to occur in the distribution.
+    pmf(x)
+        Probability of table `x` to occur in the distribution.
+    mean(row, col)
+        Mean table.
+    rvs(row, col, size=None, method=None, random_state=None)
+        Draw random tables with given row and column vector sums.
+
+    Notes
+    -----
+    %(_doc_row_col_note)s
+
+    Random elements from the distribution are generated either with Boyett's
+    [1]_ or Patefield's algorithm [2]_. Boyett's algorithm has
+    O(N) time and space complexity, where N is the total sum of entries in the
+    table. Patefield's algorithm has O(K x log(N)) time complexity, where K is
+    the number of cells in the table and requires only a small constant work
+    space. By default, the `rvs` method selects the fastest algorithm based on
+    the input, but you can specify the algorithm with the keyword `method`.
+    Allowed values are "boyett" and "patefield".
+
+    .. versionadded:: 1.10.0
+
+    References
+    ----------
+    .. [1] J. Boyett, AS 144 Appl. Statist. 28 (1979) 329-332
+    .. [2] W.M. Patefield, AS 159 Appl. Statist. 30 (1981) 91-97
+
+    Examples
+    --------
+    >>> from scipy.stats import random_table
+
+    >>> row = [1, 5]
+    >>> col = [2, 3, 1]
+    >>> random_table.mean(row, col)
+    array([[0.33333333, 0.5       , 0.16666667],
+           [1.66666667, 2.5       , 0.83333333]])
+
+    Alternatively, the object may be called (as a function) to fix the row
+    and column vector sums, returning a "frozen" distribution.
+
+    >>> dist = random_table(row, col)
+    >>> dist.rvs(random_state=123)
+    array([[1, 0, 0],
+           [1, 3, 1]])
+    """
+
+    def __init__(self, seed=None):
+        super().__init__(seed)
+
+    def __call__(self, row, col, *, seed=None):
+        """Create a frozen distribution of tables with given marginals.
+
+        See `random_table_frozen` for more information.
+        """
+        return random_table_frozen(row, col, seed=seed)
+
+    def logpmf(self, x, row, col):
+        """Log-probability of table to occur in the distribution.
+
+        Parameters
+        ----------
+        %(_doc_x)s
+        %(_doc_row_col)s
+
+        Returns
+        -------
+        logpmf : ndarray or scalar
+            Log of the probability mass function evaluated at `x`.
+
+        Notes
+        -----
+        %(_doc_row_col_note)s
+
+        If row and column marginals of `x` do not match `row` and `col`,
+        negative infinity is returned.
+
+        Examples
+        --------
+        >>> from scipy.stats import random_table
+        >>> import numpy as np
+
+        >>> x = [[1, 5, 1], [2, 3, 1]]
+        >>> row = np.sum(x, axis=1)
+        >>> col = np.sum(x, axis=0)
+        >>> random_table.logpmf(x, row, col)
+        -1.6306401200847027
+
+        Alternatively, the object may be called (as a function) to fix the row
+        and column vector sums, returning a "frozen" distribution.
+
+        >>> d = random_table(row, col)
+        >>> d.logpmf(x)
+        -1.6306401200847027
+        """
+        r, c, n = self._process_parameters(row, col)
+        x = np.asarray(x)
+
+        if x.ndim < 2:
+            raise ValueError("`x` must be at least two-dimensional")
+
+        dtype_is_int = np.issubdtype(x.dtype, np.integer)
+        with np.errstate(invalid='ignore'):
+            if not dtype_is_int and not np.all(x.astype(int) == x):
+                raise ValueError("`x` must contain only integral values")
+
+        # x does not contain NaN if we arrive here
+        if np.any(x < 0):
+            raise ValueError("`x` must contain only non-negative values")
+
+        r2 = np.sum(x, axis=-1)
+        c2 = np.sum(x, axis=-2)
+
+        if r2.shape[-1] != len(r):
+            raise ValueError("shape of `x` must agree with `row`")
+
+        if c2.shape[-1] != len(c):
+            raise ValueError("shape of `x` must agree with `col`")
+
+        res = np.empty(x.shape[:-2])
+
+        mask = np.all(r2 == r, axis=-1) & np.all(c2 == c, axis=-1)
+
+        def lnfac(x):
+            return gammaln(x + 1)
+
+        res[mask] = (np.sum(lnfac(r), axis=-1) + np.sum(lnfac(c), axis=-1)
+                     - lnfac(n) - np.sum(lnfac(x[mask]), axis=(-1, -2)))
+        res[~mask] = -np.inf
+
+        return res[()]
+
+    def pmf(self, x, row, col):
+        """Probability of table to occur in the distribution.
+
+        Parameters
+        ----------
+        %(_doc_x)s
+        %(_doc_row_col)s
+
+        Returns
+        -------
+        pmf : ndarray or scalar
+            Probability mass function evaluated at `x`.
+
+        Notes
+        -----
+        %(_doc_row_col_note)s
+
+        If row and column marginals of `x` do not match `row` and `col`,
+        zero is returned.
+
+        Examples
+        --------
+        >>> from scipy.stats import random_table
+        >>> import numpy as np
+
+        >>> x = [[1, 5, 1], [2, 3, 1]]
+        >>> row = np.sum(x, axis=1)
+        >>> col = np.sum(x, axis=0)
+        >>> random_table.pmf(x, row, col)
+        0.19580419580419592
+
+        Alternatively, the object may be called (as a function) to fix the row
+        and column vector sums, returning a "frozen" distribution.
+
+        >>> d = random_table(row, col)
+        >>> d.pmf(x)
+        0.19580419580419592
+        """
+        return np.exp(self.logpmf(x, row, col))
+
+    def mean(self, row, col):
+        """Mean of distribution of conditional tables.
+        %(_doc_mean_params)s
+
+        Returns
+        -------
+        mean: ndarray
+            Mean of the distribution.
+
+        Notes
+        -----
+        %(_doc_row_col_note)s
+
+        Examples
+        --------
+        >>> from scipy.stats import random_table
+
+        >>> row = [1, 5]
+        >>> col = [2, 3, 1]
+        >>> random_table.mean(row, col)
+        array([[0.33333333, 0.5       , 0.16666667],
+               [1.66666667, 2.5       , 0.83333333]])
+
+        Alternatively, the object may be called (as a function) to fix the row
+        and column vector sums, returning a "frozen" distribution.
+
+        >>> d = random_table(row, col)
+        >>> d.mean()
+        array([[0.33333333, 0.5       , 0.16666667],
+               [1.66666667, 2.5       , 0.83333333]])
+        """
+        r, c, n = self._process_parameters(row, col)
+        return np.outer(r, c) / n
+
+    def rvs(self, row, col, *, size=None, method=None, random_state=None):
+        """Draw random tables with fixed column and row marginals.
+
+        Parameters
+        ----------
+        %(_doc_row_col)s
+        size : int, optional
+            Number of samples to draw (default 1).
+        method : str, optional
+            Which method to use, "boyett" or "patefield". If None (default),
+            selects the fastest method for this input.
+        %(_doc_random_state)s
+
+        Returns
+        -------
+        rvs : ndarray
+            Random 2D tables of shape (`size`, `len(row)`, `len(col)`).
+
+        Notes
+        -----
+        %(_doc_row_col_note)s
+
+        Examples
+        --------
+        >>> from scipy.stats import random_table
+
+        >>> row = [1, 5]
+        >>> col = [2, 3, 1]
+        >>> random_table.rvs(row, col, random_state=123)
+        array([[1., 0., 0.],
+               [1., 3., 1.]])
+
+        Alternatively, the object may be called (as a function) to fix the row
+        and column vector sums, returning a "frozen" distribution.
+
+        >>> d = random_table(row, col)
+        >>> d.rvs(random_state=123)
+        array([[1., 0., 0.],
+               [1., 3., 1.]])
+        """
+        r, c, n = self._process_parameters(row, col)
+        size, shape = self._process_size_shape(size, r, c)
+
+        random_state = self._get_random_state(random_state)
+        meth = self._process_rvs_method(method, r, c, n)
+
+        return meth(r, c, n, size, random_state).reshape(shape)
+
+    @staticmethod
+    def _process_parameters(row, col):
+        """
+        Check that row and column vectors are one-dimensional, that they do
+        not contain negative or non-integer entries, and that the sums over
+        both vectors are equal.
+        """
+        r = np.array(row, dtype=np.int64, copy=True)
+        c = np.array(col, dtype=np.int64, copy=True)
+
+        if np.ndim(r) != 1:
+            raise ValueError("`row` must be one-dimensional")
+        if np.ndim(c) != 1:
+            raise ValueError("`col` must be one-dimensional")
+
+        if np.any(r < 0):
+            raise ValueError("each element of `row` must be non-negative")
+        if np.any(c < 0):
+            raise ValueError("each element of `col` must be non-negative")
+
+        n = np.sum(r)
+        if n != np.sum(c):
+            raise ValueError("sums over `row` and `col` must be equal")
+
+        if not np.all(r == np.asarray(row)):
+            raise ValueError("each element of `row` must be an integer")
+        if not np.all(c == np.asarray(col)):
+            raise ValueError("each element of `col` must be an integer")
+
+        return r, c, n
+
+    @staticmethod
+    def _process_size_shape(size, r, c):
+        """
+        Compute the number of samples to be drawn and the shape of the output
+        """
+        shape = (len(r), len(c))
+
+        if size is None:
+            return 1, shape
+
+        size = np.atleast_1d(size)
+        if not np.issubdtype(size.dtype, np.integer) or np.any(size < 0):
+            raise ValueError("`size` must be a non-negative integer or `None`")
+
+        return np.prod(size), tuple(size) + shape
+
+    @classmethod
+    def _process_rvs_method(cls, method, r, c, n):
+        known_methods = {
+            None: cls._rvs_select(r, c, n),
+            "boyett": cls._rvs_boyett,
+            "patefield": cls._rvs_patefield,
+        }
+        try:
+            return known_methods[method]
+        except KeyError:
+            raise ValueError(f"'{method}' not recognized, "
+                             f"must be one of {set(known_methods)}")
+
+    @classmethod
+    def _rvs_select(cls, r, c, n):
+        fac = 1.0  # benchmarks show that this value is about 1
+        k = len(r) * len(c)  # number of cells
+        # n + 1 guards against failure if n == 0
+        if n > fac * np.log(n + 1) * k:
+            return cls._rvs_patefield
+        return cls._rvs_boyett
+
+    @staticmethod
+    def _rvs_boyett(row, col, ntot, size, random_state):
+        return _rcont.rvs_rcont1(row, col, ntot, size, random_state)
+
+    @staticmethod
+    def _rvs_patefield(row, col, ntot, size, random_state):
+        return _rcont.rvs_rcont2(row, col, ntot, size, random_state)
+
+
+random_table = random_table_gen()
+
+
+class random_table_frozen(multi_rv_frozen):
+    __class_getitem__ = None  # pyrefly:ignore[bad-assignment]
+
+    def __init__(self, row, col, *, seed=None):
+        self._dist = random_table_gen(seed)
+        self._params = self._dist._process_parameters(row, col)
+
+        # monkey patch self._dist
+        def _process_parameters(r, c):
+            return self._params
+        self._dist._process_parameters = _process_parameters
+
+    def logpmf(self, x):
+        return self._dist.logpmf(x, None, None)
+
+    def pmf(self, x):
+        return self._dist.pmf(x, None, None)
+
+    def mean(self):
+        return self._dist.mean(None, None)
+
+    def rvs(self, size=None, method=None, random_state=None):
+        # optimisations are possible here
+        return self._dist.rvs(None, None, size=size, method=method,
+                              random_state=random_state)
+
+
+_ctab_doc_row_col = """\
+row : array_like
+    Sum of table entries in each row.
+col : array_like
+    Sum of table entries in each column."""
+
+_ctab_doc_x = """\
+x : array-like
+   Two-dimensional table of non-negative integers, or a
+   multi-dimensional array with the last two dimensions
+   corresponding with the tables."""
+
+_ctab_doc_row_col_note = """\
+The row and column vectors must be one-dimensional, not empty,
+and each sum up to the same value. They cannot contain negative
+or noninteger entries."""
+
+_ctab_doc_mean_params = f"""
+Parameters
+----------
+{_ctab_doc_row_col}"""
+
+_ctab_doc_row_col_note_frozen = """\
+See class definition for a detailed description of parameters."""
+
+_ctab_docdict = {
+    "_doc_random_state": _doc_random_state,
+    "_doc_row_col": _ctab_doc_row_col,
+    "_doc_x": _ctab_doc_x,
+    "_doc_mean_params": _ctab_doc_mean_params,
+    "_doc_row_col_note": _ctab_doc_row_col_note,
+}
+
+_ctab_docdict_frozen = _ctab_docdict.copy()
+_ctab_docdict_frozen.update({
+    "_doc_row_col": "",
+    "_doc_mean_params": "",
+    "_doc_row_col_note": _ctab_doc_row_col_note_frozen,
+})
+
+
+def _docfill(obj, docdict, template=None):
+    obj.__doc__ = doccer.docformat(template or obj.__doc__, docdict)
+
+
+# Set frozen generator docstrings from corresponding docstrings in
+# random_table and fill in default strings in class docstrings
+_docfill(random_table_gen, _ctab_docdict)
+for name in ['logpmf', 'pmf', 'mean', 'rvs']:
+    method = random_table_gen.__dict__[name]
+    method_frozen = random_table_frozen.__dict__[name]
+    _docfill(method_frozen, _ctab_docdict_frozen, method.__doc__)
+    _docfill(method, _ctab_docdict)
+
+
+class uniform_direction_gen(multi_rv_generic):
+    r"""
+    A vector-valued uniform direction.
+
+    Return a random direction (unit vector). The `dim` keyword specifies
+    the dimensionality of the space.
+
+    Parameters
+    ----------
+    dim : scalar
+        Dimension of directions.
+    seed : {None, int, `numpy.random.Generator`,
+            `numpy.random.RandomState`}, optional
+
+        Used for drawing random variates.
+        If `seed` is `None`, the `~np.random.RandomState` singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used, seeded
+        with seed.
+        If `seed` is already a ``RandomState`` or ``Generator`` instance,
+        then that object is used.
+        Default is `None`.
+
+    Methods
+    -------
+    rvs(dim=None, size=1, random_state=None)
+        Draw random directions.
+
+    Notes
+    -----
+    This distribution generates unit vectors uniformly distributed on
+    the surface of a hypersphere. These can be interpreted as random
+    directions.
+    For example, if `dim` is 3, 3D vectors from the surface of :math:`S^2`
+    will be sampled.
+
+    References
+    ----------
+    .. [1] Marsaglia, G. (1972). "Choosing a Point from the Surface of a
+           Sphere". Annals of Mathematical Statistics. 43 (2): 645-646.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import uniform_direction
+    >>> x = uniform_direction.rvs(3)
+    >>> np.linalg.norm(x)
+    1.
+
+    This generates one random direction, a vector on the surface of
+    :math:`S^2`.
+
+    Alternatively, the object may be called (as a function) to return a frozen
+    distribution with fixed `dim` parameter. Here,
+    we create a `uniform_direction` with ``dim=3`` and draw 5 observations.
+    The samples are then arranged in an array of shape 5x3.
+
+    >>> rng = np.random.default_rng()
+    >>> uniform_sphere_dist = uniform_direction(3)
+    >>> unit_vectors = uniform_sphere_dist.rvs(5, random_state=rng)
+    >>> unit_vectors
+    array([[ 0.56688642, -0.1332634 , -0.81294566],
+           [-0.427126  , -0.74779278,  0.50830044],
+           [ 0.3793989 ,  0.92346629,  0.05715323],
+           [ 0.36428383, -0.92449076, -0.11231259],
+           [-0.27733285,  0.94410968, -0.17816678]])
+    """
+
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = doccer.docformat(self.__doc__)
+
+    def __call__(self, dim=None, seed=None):
+        """Create a frozen n-dimensional uniform direction distribution.
+
+        See `uniform_direction` for more information.
+        """
+        return uniform_direction_frozen(dim, seed=seed)
+
+    def _process_parameters(self, dim):
+        """Dimension N must be specified; it cannot be inferred."""
+        if dim is None or not np.isscalar(dim) or dim < 1 or dim != int(dim):
+            raise ValueError("Dimension of vector must be specified, "
+                             "and must be an integer greater than 0.")
+
+        return int(dim)
+
+    def rvs(self, dim, size=None, random_state=None):
+        """Draw random samples from S(N-1).
+
+        Parameters
+        ----------
+        dim : int
+            Dimension of space (N).
+        size : int or tuple of ints, optional
+            Given a shape of, for example, (m,n,k), m*n*k samples are
+            generated, and packed in an m-by-n-by-k arrangement.
+            Because each sample is N-dimensional, the output shape
+            is (m,n,k,N). If no shape is specified, a single (N-D)
+            sample is returned.
+        random_state : {None, int, `numpy.random.Generator`,
+                        `numpy.random.RandomState`}, optional
+
+            Pseudorandom number generator state used to generate resamples.
+
+            If `random_state` is ``None`` (or `np.random`), the
+            `numpy.random.RandomState` singleton is used.
+            If `random_state` is an int, a new ``RandomState`` instance is
+            used, seeded with `random_state`.
+            If `random_state` is already a ``Generator`` or ``RandomState``
+            instance then that instance is used.
+
+        Returns
+        -------
+        rvs : ndarray
+            Random direction vectors
+
+        """
+        random_state = self._get_random_state(random_state)
+        if size is None:
+            size = np.array([], dtype=int)
+        size = np.atleast_1d(size)
+
+        dim = self._process_parameters(dim)
+
+        samples = _sample_uniform_direction(dim, size, random_state)
+        return samples
+
+
+uniform_direction = uniform_direction_gen()
+
+
+class uniform_direction_frozen(multi_rv_frozen):
+    def __init__(self, dim=None, seed=None):
+        """Create a frozen n-dimensional uniform direction distribution.
+
+        Parameters
+        ----------
+        dim : int
+            Dimension of matrices
+        seed : {None, int, `numpy.random.Generator`,
+                `numpy.random.RandomState`}, optional
+
+            If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+            singleton is used.
+            If `seed` is an int, a new ``RandomState`` instance is used,
+            seeded with `seed`.
+            If `seed` is already a ``Generator`` or ``RandomState`` instance
+            then that instance is used.
+
+        Examples
+        --------
+        >>> from scipy.stats import uniform_direction
+        >>> x = uniform_direction(3)
+        >>> x.rvs()
+
+        """
+        self._dist = uniform_direction_gen(seed)
+        self.dim = self._dist._process_parameters(dim)
+
+    def rvs(self, size=None, random_state=None):
+        return self._dist.rvs(self.dim, size, random_state)
+
+
+def _sample_uniform_direction(dim, size, random_state):
+    """
+    Private method to generate uniform directions
+    Reference: Marsaglia, G. (1972). "Choosing a Point from the Surface of a
+               Sphere". Annals of Mathematical Statistics. 43 (2): 645-646.
+    """
+    samples_shape = np.append(size, dim)
+    samples = random_state.standard_normal(samples_shape)
+    samples /= np.linalg.norm(samples, axis=-1, keepdims=True)
+    return samples
+
+
+_dirichlet_mn_doc_default_callparams = """\
+alpha : array_like
+    The concentration parameters. The number of entries along the last axis
+    determines the dimensionality of the distribution. Each entry must be
+    strictly positive.
+n : int or array_like
+    The number of trials. Each element must be a non-negative integer.
+"""
+
+_dirichlet_mn_doc_frozen_callparams = ""
+
+_dirichlet_mn_doc_frozen_callparams_note = """\
+See class definition for a detailed description of parameters."""
+
+dirichlet_mn_docdict_params = {
+    '_dirichlet_mn_doc_default_callparams': _dirichlet_mn_doc_default_callparams,
+    '_doc_random_state': _doc_random_state
+}
+
+dirichlet_mn_docdict_noparams = {
+    '_dirichlet_mn_doc_default_callparams': _dirichlet_mn_doc_frozen_callparams,
+    '_doc_random_state': _doc_random_state
+}
+
+
+def _dirichlet_multinomial_check_parameters(alpha, n, x=None):
+
+    alpha = np.asarray(alpha)
+    n = np.asarray(n)
+
+    if x is not None:
+        # Ensure that `x` and `alpha` are arrays. If the shapes are
+        # incompatible, NumPy will raise an appropriate error.
+        try:
+            x, alpha = np.broadcast_arrays(x, alpha)
+        except ValueError as e:
+            msg = "`x` and `alpha` must be broadcastable."
+            raise ValueError(msg) from e
+
+        x_int = np.floor(x)
+        if np.any(x < 0) or np.any(x != x_int):
+            raise ValueError("`x` must contain only non-negative integers.")
+        x = x_int
+
+    if np.any(alpha <= 0):
+        raise ValueError("`alpha` must contain only positive values.")
+
+    n_int = np.floor(n)
+    if np.any(n < 0) or np.any(n != n_int):
+        raise ValueError("`n` must be a non-negative integer.")
+    n = n_int
+
+    sum_alpha = np.sum(alpha, axis=-1)
+    sum_alpha, n = np.broadcast_arrays(sum_alpha, n)
+
+    return (alpha, sum_alpha, n) if x is None else (alpha, sum_alpha, n, x)
+
+
+class dirichlet_multinomial_gen(multi_rv_generic):
+    r"""
+    A Dirichlet multinomial random variable.
+
+    The Dirichlet multinomial distribution is a compound probability
+    distribution: it is the multinomial distribution with number of trials
+    `n` and class probabilities ``p`` randomly sampled from a Dirichlet
+    distribution with concentration parameters ``alpha``.
+
+    Parameters
+    ----------
+    %(_dirichlet_mn_doc_default_callparams)s
+    %(_doc_random_state)s
+
+    Methods
+    -------
+    logpmf(x, alpha, n):
+        Log of the probability mass function.
+    pmf(x, alpha, n):
+        Probability mass function.
+    mean(alpha, n):
+        Mean of the Dirichlet multinomial distribution.
+    var(alpha, n):
+        Variance of the Dirichlet multinomial distribution.
+    cov(alpha, n):
+        The covariance of the Dirichlet multinomial distribution.
+
+    See Also
+    --------
+    scipy.stats.dirichlet : The dirichlet distribution.
+    scipy.stats.multinomial : The multinomial distribution.
+
+    References
+    ----------
+    .. [1] Dirichlet-multinomial distribution, Wikipedia,
+           https://www.wikipedia.org/wiki/Dirichlet-multinomial_distribution
+
+    Examples
+    --------
+    >>> from scipy.stats import dirichlet_multinomial
+
+    Get the PMF
+
+    >>> n = 6  # number of trials
+    >>> alpha = [3, 4, 5]  # concentration parameters
+    >>> x = [1, 2, 3]  # counts
+    >>> dirichlet_multinomial.pmf(x, alpha, n)
+    0.08484162895927604
+
+    If the sum of category counts does not equal the number of trials,
+    the probability mass is zero.
+
+    >>> dirichlet_multinomial.pmf(x, alpha, n=7)
+    0.0
+
+    Get the log of the PMF
+
+    >>> dirichlet_multinomial.logpmf(x, alpha, n)
+    -2.4669689491013327
+
+    Get the mean
+
+    >>> dirichlet_multinomial.mean(alpha, n)
+    array([1.5, 2. , 2.5])
+
+    Get the variance
+
+    >>> dirichlet_multinomial.var(alpha, n)
+    array([1.55769231, 1.84615385, 2.01923077])
+
+    Get the covariance
+
+    >>> dirichlet_multinomial.cov(alpha, n)
+    array([[ 1.55769231, -0.69230769, -0.86538462],
+           [-0.69230769,  1.84615385, -1.15384615],
+           [-0.86538462, -1.15384615,  2.01923077]])
+
+    Alternatively, the object may be called (as a function) to fix the
+    `alpha` and `n` parameters, returning a "frozen" Dirichlet multinomial
+    random variable.
+
+    >>> dm = dirichlet_multinomial(alpha, n)
+    >>> dm.pmf(x)
+    0.08484162895927579
+
+    All methods are fully vectorized. Each element of `x` and `alpha` is
+    a vector (along the last axis), each element of `n` is an
+    integer (scalar), and the result is computed element-wise.
+
+    >>> x = [[1, 2, 3], [4, 5, 6]]
+    >>> alpha = [[1, 2, 3], [4, 5, 6]]
+    >>> n = [6, 15]
+    >>> dirichlet_multinomial.pmf(x, alpha, n)
+    array([0.06493506, 0.02626937])
+
+    >>> dirichlet_multinomial.cov(alpha, n).shape  # both covariance matrices
+    (2, 3, 3)
+
+    Broadcasting according to standard NumPy conventions is supported. Here,
+    we have four sets of concentration parameters (each a two element vector)
+    for each of three numbers of trials (each a scalar).
+
+    >>> alpha = [[3, 4], [4, 5], [5, 6], [6, 7]]
+    >>> n = [[6], [7], [8]]
+    >>> dirichlet_multinomial.mean(alpha, n).shape
+    (3, 4, 2)
+    """
+    def __init__(self, seed=None):
+        super().__init__(seed)
+        self.__doc__ = doccer.docformat(self.__doc__,
+                                        dirichlet_mn_docdict_params)
+
+    def __call__(self, alpha, n, seed=None):
+        return dirichlet_multinomial_frozen(alpha, n, seed=seed)
+
+    def logpmf(self, x, alpha, n):
+        """The log of the probability mass function.
+
+        Parameters
+        ----------
+        x: ndarray
+            Category counts (non-negative integers). Must be broadcastable
+            with shape parameter ``alpha``. If multidimensional, the last axis
+            must correspond with the categories.
+        %(_dirichlet_mn_doc_default_callparams)s
+
+        Returns
+        -------
+        out: ndarray or scalar
+            Log of the probability mass function.
+
+        """
+
+        a, Sa, n, x = _dirichlet_multinomial_check_parameters(alpha, n, x)
+
+        out = np.asarray(loggamma(Sa) + loggamma(n + 1) - loggamma(n + Sa))
+        out += (loggamma(x + a) - (loggamma(a) + loggamma(x + 1))).sum(axis=-1)
+        np.place(out, n != x.sum(axis=-1), -np.inf)
+        return out[()]
+
+    def pmf(self, x, alpha, n):
+        """Probability mass function for a Dirichlet multinomial distribution.
+
+        Parameters
+        ----------
+        x: ndarray
+            Category counts (non-negative integers). Must be broadcastable
+            with shape parameter ``alpha``. If multidimensional, the last axis
+            must correspond with the categories.
+        %(_dirichlet_mn_doc_default_callparams)s
+
+        Returns
+        -------
+        out: ndarray or scalar
+            Probability mass function.
+
+        """
+        return np.exp(self.logpmf(x, alpha, n))
+
+    def mean(self, alpha, n):
+        """Mean of a Dirichlet multinomial distribution.
+
+        Parameters
+        ----------
+        %(_dirichlet_mn_doc_default_callparams)s
+
+        Returns
+        -------
+        out: ndarray
+            Mean of a Dirichlet multinomial distribution.
+
+        """
+        a, Sa, n = _dirichlet_multinomial_check_parameters(alpha, n)
+        n, Sa = n[..., np.newaxis], Sa[..., np.newaxis]
+        return n * a / Sa
+
+    def var(self, alpha, n):
+        """The variance of the Dirichlet multinomial distribution.
+
+        Parameters
+        ----------
+        %(_dirichlet_mn_doc_default_callparams)s
+
+        Returns
+        -------
+        out: array_like
+            The variances of the components of the distribution. This is
+            the diagonal of the covariance matrix of the distribution.
+
+        """
+        a, Sa, n = _dirichlet_multinomial_check_parameters(alpha, n)
+        n, Sa = n[..., np.newaxis], Sa[..., np.newaxis]
+        return n * a / Sa * (1 - a/Sa) * (n + Sa) / (1 + Sa)
+
+    def cov(self, alpha, n):
+        """Covariance matrix of a Dirichlet multinomial distribution.
+
+        Parameters
+        ----------
+        %(_dirichlet_mn_doc_default_callparams)s
+
+        Returns
+        -------
+        out : array_like
+            The covariance matrix of the distribution.
+
+        """
+        a, Sa, n = _dirichlet_multinomial_check_parameters(alpha, n)
+        var = dirichlet_multinomial.var(a, n)
+
+        n, Sa = n[..., np.newaxis, np.newaxis], Sa[..., np.newaxis, np.newaxis]
+        aiaj = a[..., :, np.newaxis] * a[..., np.newaxis, :]
+        cov = -n * aiaj / Sa ** 2 * (n + Sa) / (1 + Sa)
+
+        ii = np.arange(cov.shape[-1])
+        cov[..., ii, ii] = var
+        return cov
+
+
+dirichlet_multinomial = dirichlet_multinomial_gen()
+
+
+class dirichlet_multinomial_frozen(multi_rv_frozen):
+    def __init__(self, alpha, n, seed=None):
+        alpha, Sa, n = _dirichlet_multinomial_check_parameters(alpha, n)
+        self.alpha = alpha
+        self.n = n
+        self._dist = dirichlet_multinomial_gen(seed)
+
+    def logpmf(self, x):
+        return self._dist.logpmf(x, self.alpha, self.n)
+
+    def pmf(self, x):
+        return self._dist.pmf(x, self.alpha, self.n)
+
+    def mean(self):
+        return self._dist.mean(self.alpha, self.n)
+
+    def var(self):
+        return self._dist.var(self.alpha, self.n)
+
+    def cov(self):
+        return self._dist.cov(self.alpha, self.n)
+
+
+# Set frozen generator docstrings from corresponding docstrings in
+# dirichlet_multinomial and fill in default strings in class docstrings.
+for name in ['logpmf', 'pmf', 'mean', 'var', 'cov']:
+    method = dirichlet_multinomial_gen.__dict__[name]
+    method_frozen = dirichlet_multinomial_frozen.__dict__[name]
+    method_frozen.__doc__ = doccer.docformat(
+        method.__doc__, dirichlet_mn_docdict_noparams)
+    method.__doc__ = doccer.docformat(method.__doc__,
+                                      dirichlet_mn_docdict_params)
+
+
+class vonmises_fisher_gen(multi_rv_generic):
+    r"""
+    A von Mises-Fisher variable.
+
+    The `mu` keyword specifies the mean direction vector. The `kappa` keyword
+    specifies the concentration parameter.
+
+    Parameters
+    ----------
+    mu : array_like
+        Mean direction of the distribution. Must be a one-dimensional unit
+        vector of norm 1.
+    kappa : float
+        Concentration parameter. Must be positive.
+    seed : {None, int, np.random.RandomState, np.random.Generator}, optional
+        Used for drawing random variates.
+        If `seed` is `None`, the `~np.random.RandomState` singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used, seeded
+        with seed.
+        If `seed` is already a ``RandomState`` or ``Generator`` instance,
+        then that object is used.
+        Default is `None`.
+
+    Methods
+    -------
+    pdf(x, mu=None, kappa=1)
+        Probability density function.
+    logpdf(x, mu=None, kappa=1)
+        Log of the probability density function.
+    rvs(mu=None, kappa=1, size=1, random_state=None)
+        Draw random samples from a von Mises-Fisher distribution.
+    entropy(mu=None, kappa=1)
+        Compute the differential entropy of the von Mises-Fisher distribution.
+    fit(data)
+        Fit a von Mises-Fisher distribution to data.
+
+    See Also
+    --------
+    scipy.stats.vonmises : Von-Mises Fisher distribution in 2D on a circle
+    uniform_direction : uniform distribution on the surface of a hypersphere
+
+    Notes
+    -----
+    The von Mises-Fisher distribution is a directional distribution on the
+    surface of the unit hypersphere. The probability density
+    function of a unit vector :math:`\mathbf{x}` is
+
+    .. math::
+
+        f(\mathbf{x}) = \frac{\kappa^{d/2-1}}{(2\pi)^{d/2}I_{d/2-1}(\kappa)}
+               \exp\left(\kappa \mathbf{\mu}^T\mathbf{x}\right),
+
+    where :math:`\mathbf{\mu}` is the mean direction, :math:`\kappa` the
+    concentration parameter, :math:`d` the dimension and :math:`I` the
+    modified Bessel function of the first kind. As :math:`\mu` represents
+    a direction, it must be a unit vector or in other words, a point
+    on the hypersphere: :math:`\mathbf{\mu}\in S^{d-1}`. :math:`\kappa` is a
+    concentration parameter, which means that it must be positive
+    (:math:`\kappa>0`) and that the distribution becomes more narrow with
+    increasing :math:`\kappa`. In that sense, the reciprocal value
+    :math:`1/\kappa` resembles the variance parameter of the normal
+    distribution.
+
+    The von Mises-Fisher distribution often serves as an analogue of the
+    normal distribution on the sphere. Intuitively, for unit vectors, a
+    useful distance measure is given by the angle :math:`\alpha` between
+    them. This is exactly what the scalar product
+    :math:`\mathbf{\mu}^T\mathbf{x}=\cos(\alpha)` in the
+    von Mises-Fisher probability density function describes: the angle
+    between the mean direction :math:`\mathbf{\mu}` and the vector
+    :math:`\mathbf{x}`. The larger the angle between them, the smaller the
+    probability to observe :math:`\mathbf{x}` for this particular mean
+    direction :math:`\mathbf{\mu}`.
+
+    In dimensions 2 and 3, specialized algorithms are used for fast sampling
+    [2]_, [3]_. For dimensions of 4 or higher the rejection sampling algorithm
+    described in [4]_ is utilized. This implementation is partially based on
+    the geomstats package [5]_, [6]_.
+
+    .. versionadded:: 1.11
+
+    References
+    ----------
+    .. [1] Von Mises-Fisher distribution, Wikipedia,
+           https://en.wikipedia.org/wiki/Von_Mises%E2%80%93Fisher_distribution
+    .. [2] Mardia, K., and Jupp, P. Directional statistics. Wiley, 2000.
+    .. [3] J. Wenzel. Numerically stable sampling of the von Mises Fisher
+           distribution on S2.
+           https://www.mitsuba-renderer.org/~wenzel/files/vmf.pdf
+    .. [4] Wood, A. Simulation of the von mises fisher distribution.
+           Communications in statistics-simulation and computation 23,
+           1 (1994), 157-164. :doi:`10.1080/03610919408813161`.
+    .. [5] geomstats, Github. MIT License. Accessed: 06.01.2023.
+           https://github.com/geomstats/geomstats
+    .. [6] Miolane, N. et al. Geomstats:  A Python Package for Riemannian
+           Geometry in Machine Learning. Journal of Machine Learning Research
+           21 (2020). http://jmlr.org/papers/v21/19-027.html
+
+    Examples
+    --------
+    **Visualization of the probability density**
+
+    Plot the probability density in three dimensions for increasing
+    concentration parameter. The density is calculated by the ``pdf``
+    method.
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy.stats import vonmises_fisher
+    >>> from matplotlib.colors import Normalize
+    >>> n_grid = 100
+    >>> u = np.linspace(0, np.pi, n_grid)
+    >>> v = np.linspace(0, 2 * np.pi, n_grid)
+    >>> u_grid, v_grid = np.meshgrid(u, v)
+    >>> vertices = np.stack([np.cos(v_grid) * np.sin(u_grid),
+    ...                      np.sin(v_grid) * np.sin(u_grid),
+    ...                      np.cos(u_grid)],
+    ...                     axis=2)
+    >>> x = np.outer(np.cos(v), np.sin(u))
+    >>> y = np.outer(np.sin(v), np.sin(u))
+    >>> z = np.outer(np.ones_like(u), np.cos(u))
+    >>> def plot_vmf_density(ax, x, y, z, vertices, mu, kappa):
+    ...     vmf = vonmises_fisher(mu, kappa)
+    ...     pdf_values = vmf.pdf(vertices)
+    ...     pdfnorm = Normalize(vmin=pdf_values.min(), vmax=pdf_values.max())
+    ...     ax.plot_surface(x, y, z, rstride=1, cstride=1,
+    ...                     facecolors=plt.cm.viridis(pdfnorm(pdf_values)),
+    ...                     linewidth=0)
+    ...     ax.set_aspect('equal')
+    ...     ax.view_init(azim=-130, elev=0)
+    ...     ax.axis('off')
+    ...     ax.set_title(rf"$\kappa={kappa}$")
+    >>> fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(9, 4),
+    ...                          subplot_kw={"projection": "3d"})
+    >>> left, middle, right = axes
+    >>> mu = np.array([-np.sqrt(0.5), -np.sqrt(0.5), 0])
+    >>> plot_vmf_density(left, x, y, z, vertices, mu, 5)
+    >>> plot_vmf_density(middle, x, y, z, vertices, mu, 20)
+    >>> plot_vmf_density(right, x, y, z, vertices, mu, 100)
+    >>> plt.subplots_adjust(top=1, bottom=0.0, left=0.0, right=1.0, wspace=0.)
+    >>> plt.show()
+
+    As we increase the concentration parameter, the points are getting more
+    clustered together around the mean direction.
+
+    **Sampling**
+
+    Draw 5 samples from the distribution using the ``rvs`` method resulting
+    in a 5x3 array.
+
+    >>> rng = np.random.default_rng()
+    >>> mu = np.array([0, 0, 1])
+    >>> samples = vonmises_fisher(mu, 20).rvs(5, random_state=rng)
+    >>> samples
+    array([[ 0.3884594 , -0.32482588,  0.86231516],
+           [ 0.00611366, -0.09878289,  0.99509023],
+           [-0.04154772, -0.01637135,  0.99900239],
+           [-0.14613735,  0.12553507,  0.98126695],
+           [-0.04429884, -0.23474054,  0.97104814]])
+
+    These samples are unit vectors on the sphere :math:`S^2`. To verify,
+    let us calculate their euclidean norms:
+
+    >>> np.linalg.norm(samples, axis=1)
+    array([1., 1., 1., 1., 1.])
+
+    Plot 20 observations drawn from the von Mises-Fisher distribution for
+    increasing concentration parameter :math:`\kappa`. The red dot highlights
+    the mean direction :math:`\mu`.
+
+    >>> def plot_vmf_samples(ax, x, y, z, mu, kappa):
+    ...     vmf = vonmises_fisher(mu, kappa)
+    ...     samples = vmf.rvs(20)
+    ...     ax.plot_surface(x, y, z, rstride=1, cstride=1, linewidth=0,
+    ...                     alpha=0.2)
+    ...     ax.scatter(samples[:, 0], samples[:, 1], samples[:, 2], c='k', s=5)
+    ...     ax.scatter(mu[0], mu[1], mu[2], c='r', s=30)
+    ...     ax.set_aspect('equal')
+    ...     ax.view_init(azim=-130, elev=0)
+    ...     ax.axis('off')
+    ...     ax.set_title(rf"$\kappa={kappa}$")
+    >>> mu = np.array([-np.sqrt(0.5), -np.sqrt(0.5), 0])
+    >>> fig, axes = plt.subplots(nrows=1, ncols=3,
+    ...                          subplot_kw={"projection": "3d"},
+    ...                          figsize=(9, 4))
+    >>> left, middle, right = axes
+    >>> plot_vmf_samples(left, x, y, z, mu, 5)
+    >>> plot_vmf_samples(middle, x, y, z, mu, 20)
+    >>> plot_vmf_samples(right, x, y, z, mu, 100)
+    >>> plt.subplots_adjust(top=1, bottom=0.0, left=0.0,
+    ...                     right=1.0, wspace=0.)
+    >>> plt.show()
+
+    The plots show that with increasing concentration :math:`\kappa` the
+    resulting samples are centered more closely around the mean direction.
+
+    **Fitting the distribution parameters**
+
+    The distribution can be fitted to data using the ``fit`` method returning
+    the estimated parameters. As a toy example let's fit the distribution to
+    samples drawn from a known von Mises-Fisher distribution.
+
+    >>> mu, kappa = np.array([0, 0, 1]), 20
+    >>> samples = vonmises_fisher(mu, kappa).rvs(1000, random_state=rng)
+    >>> mu_fit, kappa_fit = vonmises_fisher.fit(samples)
+    >>> mu_fit, kappa_fit
+    (array([0.01126519, 0.01044501, 0.99988199]), 19.306398751730995)
+
+    We see that the estimated parameters `mu_fit` and `kappa_fit` are
+    very close to the ground truth parameters.
+    """
+    def __init__(self, seed=None):
+        super().__init__(seed)
+
+    def __call__(self, mu=None, kappa=1, seed=None):
+        """Create a frozen von Mises-Fisher distribution.
+
+        See `vonmises_fisher_frozen` for more information.
+        """
+        return vonmises_fisher_frozen(mu, kappa, seed=seed)
+
+    def _process_parameters(self, mu, kappa):
+        """
+        Infer dimensionality from mu and ensure that mu is a one-dimensional
+        unit vector and kappa positive.
+        """
+        mu = np.asarray(mu)
+        if mu.ndim > 1:
+            raise ValueError("'mu' must have one-dimensional shape.")
+        if not np.allclose(np.linalg.norm(mu), 1.):
+            raise ValueError("'mu' must be a unit vector of norm 1.")
+        if not mu.size > 1:
+            raise ValueError("'mu' must have at least two entries.")
+        kappa_error_msg = "'kappa' must be a positive scalar."
+        if not np.isscalar(kappa) or kappa < 0:
+            raise ValueError(kappa_error_msg)
+        if float(kappa) == 0.:
+            raise ValueError("For 'kappa=0' the von Mises-Fisher distribution "
+                             "becomes the uniform distribution on the sphere "
+                             "surface. Consider using "
+                             "'scipy.stats.uniform_direction' instead.")
+        dim = mu.size
+
+        return dim, mu, kappa
+
+    def _check_data_vs_dist(self, x, dim):
+        if x.shape[-1] != dim:
+            raise ValueError("The dimensionality of the last axis of 'x' must "
+                             "match the dimensionality of the "
+                             "von Mises Fisher distribution.")
+        if not np.allclose(np.linalg.norm(x, axis=-1), 1.):
+            msg = "'x' must be unit vectors of norm 1 along last dimension."
+            raise ValueError(msg)
+
+    def _log_norm_factor(self, dim, kappa):
+        # normalization factor is given by
+        # c = kappa**(dim/2-1)/((2*pi)**(dim/2)*I[dim/2-1](kappa))
+        #   = kappa**(dim/2-1)*exp(-kappa) /
+        #     ((2*pi)**(dim/2)*I[dim/2-1](kappa)*exp(-kappa)
+        #   = kappa**(dim/2-1)*exp(-kappa) /
+        #     ((2*pi)**(dim/2)*ive[dim/2-1](kappa)
+        # Then the log is given by
+        # log c = 1/2*(dim -1)*log(kappa) - kappa - -1/2*dim*ln(2*pi) -
+        #         ive[dim/2-1](kappa)
+        halfdim = 0.5 * dim
+        return (0.5 * (dim - 2)*np.log(kappa) - halfdim * _LOG_2PI -
+                np.log(ive(halfdim - 1, kappa)) - kappa)
+
+    def _logpdf(self, x, dim, mu, kappa):
+        """Log of the von Mises-Fisher probability density function.
+
+        As this function does no argument checking, it should not be
+        called directly; use 'logpdf' instead.
+
+        """
+        x = np.asarray(x)
+        self._check_data_vs_dist(x, dim)
+        dotproducts = np.einsum('i,...i->...', mu, x)
+        return self._log_norm_factor(dim, kappa) + kappa * dotproducts
+
+    def logpdf(self, x, mu=None, kappa=1):
+        """Log of the von Mises-Fisher probability density function.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log of the probability
+            density function. The last axis of `x` must correspond
+            to unit vectors of the same dimensionality as the distribution.
+        mu : array_like, default: None
+            Mean direction of the distribution. Must be a one-dimensional unit
+            vector of norm 1.
+        kappa : float, default: 1
+            Concentration parameter. Must be positive.
+
+        Returns
+        -------
+        logpdf : ndarray or scalar
+            Log of the probability density function evaluated at `x`.
+
+        """
+        dim, mu, kappa = self._process_parameters(mu, kappa)
+        return self._logpdf(x, dim, mu, kappa)
+
+    def pdf(self, x, mu=None, kappa=1):
+        """Von Mises-Fisher probability density function.
+
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the probability
+            density function. The last axis of `x` must correspond
+            to unit vectors of the same dimensionality as the distribution.
+        mu : array_like
+            Mean direction of the distribution. Must be a one-dimensional unit
+            vector of norm 1.
+        kappa : float
+            Concentration parameter. Must be positive.
+
+        Returns
+        -------
+        pdf : ndarray or scalar
+            Probability density function evaluated at `x`.
+
+        """
+        dim, mu, kappa = self._process_parameters(mu, kappa)
+        return np.exp(self._logpdf(x, dim, mu, kappa))
+
+    def _rvs_2d(self, mu, kappa, size, random_state):
+        """
+        In 2D, the von Mises-Fisher distribution reduces to the
+        von Mises distribution which can be efficiently sampled by numpy.
+        This method is much faster than the general rejection
+        sampling based algorithm.
+
+        """
+        mean_angle = np.arctan2(mu[1], mu[0])
+        angle_samples = random_state.vonmises(mean_angle, kappa, size=size)
+        samples = np.stack([np.cos(angle_samples), np.sin(angle_samples)],
+                           axis=-1)
+        return samples
+
+    def _rvs_3d(self, kappa, size, random_state):
+        """
+        Generate samples from a von Mises-Fisher distribution
+        with mu = [1, 0, 0] and kappa. Samples then have to be
+        rotated towards the desired mean direction mu.
+        This method is much faster than the general rejection
+        sampling based algorithm.
+        Reference: https://www.mitsuba-renderer.org/~wenzel/files/vmf.pdf
+
+        """
+        if size is None:
+            sample_size = 1
+        else:
+            sample_size = size
+
+        # compute x coordinate acc. to equation from section 3.1
+        x = random_state.random(sample_size)
+        x = 1. + np.log(x + (1. - x) * np.exp(-2 * kappa))/kappa
+
+        # (y, z) are random 2D vectors that only have to be
+        # normalized accordingly. Then (x, y z) follow a VMF distribution
+        temp = np.sqrt(1. - np.square(x))
+        uniformcircle = _sample_uniform_direction(2, sample_size, random_state)
+        samples = np.stack([x, temp * uniformcircle[..., 0],
+                            temp * uniformcircle[..., 1]],
+                           axis=-1)
+        if size is None:
+            samples = np.squeeze(samples)
+        return samples
+
+    def _rejection_sampling(self, dim, kappa, size, random_state):
+        """
+        Generate samples from an n-dimensional von Mises-Fisher distribution
+        with mu = [1, 0, ..., 0] and kappa via rejection sampling.
+        Samples then have to be rotated towards the desired mean direction mu.
+        Reference: :doi:`10.1080/03610919408813161`.
+        """
+        dim_minus_one = dim - 1
+        # calculate number of requested samples
+        if size is not None:
+            if not np.iterable(size):
+                size = (size, )
+            n_samples = math.prod(size)
+        else:
+            n_samples = 1
+        # calculate envelope for rejection sampler (eq. 4)
+        sqrt = np.sqrt(4 * kappa ** 2. + dim_minus_one ** 2)
+        envelop_param = (-2 * kappa + sqrt) / dim_minus_one
+        if envelop_param == 0:
+            # the regular formula suffers from loss of precision for high
+            # kappa. This can only be detected by checking for 0 here.
+            # Workaround: expansion for sqrt variable
+            # https://www.wolframalpha.com/input?i=sqrt%284*x%5E2%2Bd%5E2%29
+            # e = (-2 * k + sqrt(k**2 + d**2)) / d
+            #   ~ (-2 * k + 2 * k + d**2/(4 * k) - d**4/(64 * k**3)) / d
+            #   = d/(4 * k) - d**3/(64 * k**3)
+            envelop_param = (dim_minus_one/4 * kappa**-1.
+                             - dim_minus_one**3/64 * kappa**-3.)
+        # reference step 0
+        node = (1. - envelop_param) / (1. + envelop_param)
+        # t = ln(1 - ((1-x)/(1+x))**2)
+        #   = ln(4 * x / (1+x)**2)
+        #   = ln(4) + ln(x) - 2*log1p(x)
+        correction = (kappa * node + dim_minus_one
+                      * (np.log(4) + np.log(envelop_param)
+                      - 2 * np.log1p(envelop_param)))
+        n_accepted = 0
+        x = np.zeros((n_samples, ))
+        halfdim = 0.5 * dim_minus_one
+        # main loop
+        while n_accepted < n_samples:
+            # generate candidates acc. to reference step 1
+            sym_beta = random_state.beta(halfdim, halfdim,
+                                         size=n_samples - n_accepted)
+            coord_x = (1 - (1 + envelop_param) * sym_beta) / (
+                1 - (1 - envelop_param) * sym_beta)
+            # accept or reject: reference step 2
+            # reformulation for numerical stability:
+            # t = ln(1 - (1-x)/(1+x) * y)
+            #   = ln((1 + x - y +x*y)/(1 +x))
+            accept_tol = random_state.random(n_samples - n_accepted)
+            criterion = (
+                kappa * coord_x
+                + dim_minus_one * (np.log((1 + envelop_param - coord_x
+                + coord_x * envelop_param) / (1 + envelop_param)))
+                - correction) > np.log(accept_tol)
+            accepted_iter = np.sum(criterion)
+            x[n_accepted:n_accepted + accepted_iter] = coord_x[criterion]
+            n_accepted += accepted_iter
+        # concatenate x and remaining coordinates: step 3
+        coord_rest = _sample_uniform_direction(dim_minus_one, n_accepted,
+                                               random_state)
+        coord_rest = np.einsum(
+            '...,...i->...i', np.sqrt(1 - x ** 2), coord_rest)
+        samples = np.concatenate([x[..., None], coord_rest], axis=1)
+        # reshape output to (size, dim)
+        if size is not None:
+            samples = samples.reshape(size + (dim, ))
+        else:
+            samples = np.squeeze(samples)
+        return samples
+
+    def _rotate_samples(self, samples, mu, dim):
+        """A QR decomposition is used to find the rotation that maps the
+        north pole (1, 0,...,0) to the vector mu. This rotation is then
+        applied to all samples.
+
+        Parameters
+        ----------
+        samples: array_like, shape = [..., n]
+        mu : array-like, shape=[n, ]
+            Point to parametrise the rotation.
+
+        Returns
+        -------
+        samples : rotated samples
+
+        """
+        base_point = np.zeros((dim, ))
+        base_point[0] = 1.
+        embedded = np.concatenate([mu[None, :], np.zeros((dim - 1, dim))])
+        rotmatrix, _ = np.linalg.qr(np.transpose(embedded))
+        if np.allclose(np.matmul(rotmatrix, base_point[:, None])[:, 0], mu):
+            rotsign = 1
+        else:
+            rotsign = -1
+
+        # apply rotation
+        samples = np.einsum('ij,...j->...i', rotmatrix, samples) * rotsign
+        return samples
+
+    def _rvs(self, dim, mu, kappa, size, random_state):
+        if dim == 2:
+            samples = self._rvs_2d(mu, kappa, size, random_state)
+        elif dim == 3:
+            samples = self._rvs_3d(kappa, size, random_state)
+        else:
+            samples = self._rejection_sampling(dim, kappa, size,
+                                               random_state)
+
+        if dim != 2:
+            samples = self._rotate_samples(samples, mu, dim)
+        return samples
+
+    def rvs(self, mu=None, kappa=1, size=1, random_state=None):
+        """Draw random samples from a von Mises-Fisher distribution.
+
+        Parameters
+        ----------
+        mu : array_like
+            Mean direction of the distribution. Must be a one-dimensional unit
+            vector of norm 1.
+        kappa : float
+            Concentration parameter. Must be positive.
+        size : int or tuple of ints, optional
+            Given a shape of, for example, (m,n,k), m*n*k samples are
+            generated, and packed in an m-by-n-by-k arrangement.
+            Because each sample is N-dimensional, the output shape
+            is (m,n,k,N). If no shape is specified, a single (N-D)
+            sample is returned.
+        random_state : {None, int, np.random.RandomState, np.random.Generator},
+                        optional
+            Used for drawing random variates.
+            If `seed` is `None`, the `~np.random.RandomState` singleton is used.
+            If `seed` is an int, a new ``RandomState`` instance is used, seeded
+            with seed.
+            If `seed` is already a ``RandomState`` or ``Generator`` instance,
+            then that object is used.
+            Default is `None`.
+
+        Returns
+        -------
+        rvs : ndarray
+            Random variates of shape (`size`, `N`), where `N` is the
+            dimension of the distribution.
+
+        """
+        dim, mu, kappa = self._process_parameters(mu, kappa)
+        random_state = self._get_random_state(random_state)
+        samples = self._rvs(dim, mu, kappa, size, random_state)
+        return samples
+
+    def _entropy(self, dim, kappa):
+        halfdim = 0.5 * dim
+        return (-self._log_norm_factor(dim, kappa) - kappa *
+                ive(halfdim, kappa) / ive(halfdim - 1, kappa))
+
+    def entropy(self, mu=None, kappa=1):
+        """Compute the differential entropy of the von Mises-Fisher
+        distribution.
+
+        Parameters
+        ----------
+        mu : array_like, default: None
+            Mean direction of the distribution. Must be a one-dimensional unit
+            vector of norm 1.
+        kappa : float, default: 1
+            Concentration parameter. Must be positive.
+
+        Returns
+        -------
+        h : scalar
+            Entropy of the von Mises-Fisher distribution.
+
+        """
+        dim, _, kappa = self._process_parameters(mu, kappa)
+        return self._entropy(dim, kappa)
+
+    def fit(self, x):
+        """Fit the von Mises-Fisher distribution to data.
+
+        Parameters
+        ----------
+        x : array-like
+            Data the distribution is fitted to. Must be two dimensional.
+            The second axis of `x` must be unit vectors of norm 1 and
+            determine the dimensionality of the fitted
+            von Mises-Fisher distribution.
+
+        Returns
+        -------
+        mu : ndarray
+            Estimated mean direction.
+        kappa : float
+            Estimated concentration parameter.
+
+        """
+        # validate input data
+        x = np.asarray(x)
+        if x.ndim != 2:
+            raise ValueError("'x' must be two dimensional.")
+        if not np.allclose(np.linalg.norm(x, axis=-1), 1.):
+            msg = "'x' must be unit vectors of norm 1 along last dimension."
+            raise ValueError(msg)
+        dim = x.shape[-1]
+
+        # mu is simply the directional mean
+        dirstats = directional_stats(x)
+        mu = dirstats.mean_direction
+        r = dirstats.mean_resultant_length
+
+        # kappa is the solution to the equation:
+        # r = I[dim/2](kappa) / I[dim/2 -1](kappa)
+        #   = I[dim/2](kappa) * exp(-kappa) / I[dim/2 -1](kappa) * exp(-kappa)
+        #   = ive(dim/2, kappa) / ive(dim/2 -1, kappa)
+
+        halfdim = 0.5 * dim
+
+        def solve_for_kappa(kappa):
+            bessel_vals = ive([halfdim, halfdim - 1], kappa)
+            return bessel_vals[0]/bessel_vals[1] - r
+
+        root_res = root_scalar(solve_for_kappa, method="brentq",
+                               bracket=(1e-8, 1e9))
+        kappa = root_res.root
+        return mu, kappa
+
+
+vonmises_fisher = vonmises_fisher_gen()
+
+
+class vonmises_fisher_frozen(multi_rv_frozen):
+    def __init__(self, mu=None, kappa=1, seed=None):
+        """Create a frozen von Mises-Fisher distribution.
+
+        Parameters
+        ----------
+        mu : array_like, default: None
+            Mean direction of the distribution.
+        kappa : float, default: 1
+            Concentration parameter. Must be positive.
+        seed : {None, int, `numpy.random.Generator`,
+                `numpy.random.RandomState`}, optional
+            If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+            singleton is used.
+            If `seed` is an int, a new ``RandomState`` instance is used,
+            seeded with `seed`.
+            If `seed` is already a ``Generator`` or ``RandomState`` instance
+            then that instance is used.
+
+        """
+        self._dist = vonmises_fisher_gen(seed)
+        self.dim, self.mu, self.kappa = (
+            self._dist._process_parameters(mu, kappa)
+        )
+
+    def logpdf(self, x):
+        """
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log of the probability
+            density function. The last axis of `x` must correspond
+            to unit vectors of the same dimensionality as the distribution.
+
+        Returns
+        -------
+        logpdf : ndarray or scalar
+            Log of probability density function evaluated at `x`.
+
+        """
+        return self._dist._logpdf(x, self.dim, self.mu, self.kappa)
+
+    def pdf(self, x):
+        """
+        Parameters
+        ----------
+        x : array_like
+            Points at which to evaluate the log of the probability
+            density function. The last axis of `x` must correspond
+            to unit vectors of the same dimensionality as the distribution.
+
+        Returns
+        -------
+        pdf : ndarray or scalar
+            Probability density function evaluated at `x`.
+
+        """
+        return np.exp(self.logpdf(x))
+
+    def rvs(self, size=1, random_state=None):
+        """Draw random variates from the Von Mises-Fisher distribution.
+
+        Parameters
+        ----------
+        size : int or tuple of ints, optional
+            Given a shape of, for example, (m,n,k), m*n*k samples are
+            generated, and packed in an m-by-n-by-k arrangement.
+            Because each sample is N-dimensional, the output shape
+            is (m,n,k,N). If no shape is specified, a single (N-D)
+            sample is returned.
+        random_state : {None, int, `numpy.random.Generator`,
+                        `numpy.random.RandomState`}, optional
+            If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+            singleton is used.
+            If `seed` is an int, a new ``RandomState`` instance is used,
+            seeded with `seed`.
+            If `seed` is already a ``Generator`` or ``RandomState`` instance
+            then that instance is used.
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Random variates of size (`size`, `N`), where `N` is the
+            dimension of the distribution.
+
+        """
+        random_state = self._dist._get_random_state(random_state)
+        return self._dist._rvs(self.dim, self.mu, self.kappa, size,
+                               random_state)
+
+    def entropy(self):
+        """
+        Calculate the differential entropy of the von Mises-Fisher
+        distribution.
+
+        Returns
+        -------
+        h: float
+            Entropy of the Von Mises-Fisher distribution.
+
+        """
+        return self._dist._entropy(self.dim, self.kappa)
+
+
+class normal_inverse_gamma_gen(multi_rv_generic):
+    r"""
+    Normal-inverse-gamma distribution.
+
+    The normal-inverse-gamma distribution is the conjugate prior of a normal
+    distribution with unknown mean and variance.
+
+    Parameters
+    ----------
+    mu, lmbda, a, b : array_like
+        Shape parameters of the distribution. See notes.
+    seed : {None, int, np.random.RandomState, np.random.Generator}, optional
+        Used for drawing random variates.
+        If `seed` is `None`, the `~np.random.RandomState` singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used, seeded
+        with seed.
+        If `seed` is already a ``RandomState`` or ``Generator`` instance,
+        then that object is used.
+        Default is `None`.
+
+    Methods
+    -------
+    pdf(x, s2, mu=0, lmbda=1, a=1, b=1)
+        Probability density function.
+    logpdf(x, s2, mu=0, lmbda=1, a=1, b=1)
+        Log of the probability density function.
+    mean(mu=0, lmbda=1, a=1, b=1)
+        Distribution mean.
+    var(mu=0, lmbda=1, a=1, b=1)
+        Distribution variance.
+    rvs(mu=0, lmbda=1, a=1, b=1, size=None, random_state=None)
+        Draw random samples.
+
+    See Also
+    --------
+    norm
+    invgamma
+
+    Notes
+    -----
+
+    The probability density function of `normal_inverse_gamma` is:
+
+    .. math::
+
+        f(x, \sigma^2; \mu, \lambda, \alpha, \beta) =
+            \frac{\sqrt{\lambda}}{\sqrt{2 \pi \sigma^2}}
+            \frac{\beta^\alpha}{\Gamma(\alpha)}
+            \left( \frac{1}{\sigma^2} \right)^{\alpha + 1}
+            \exp \left(- \frac{2 \beta + \lambda (x - \mu)^2} {2 \sigma^2} \right)
+
+    where all parameters are real and finite, and :math:`\sigma^2 > 0`,
+    :math:`\lambda > 0`, :math:`\alpha > 0`, and :math:`\beta > 0`.
+
+    Methods ``normal_inverse_gamma.pdf`` and ``normal_inverse_gamma.logpdf``
+    accept `x` and `s2` for arguments :math:`x` and :math:`\sigma^2`.
+    All methods accept `mu`, `lmbda`, `a`, and `b` for shape parameters
+    :math:`\mu`, :math:`\lambda`, :math:`\alpha`, and :math:`\beta`,
+    respectively.
+
+    .. versionadded:: 1.15
+
+    References
+    ----------
+    .. [1] Normal-inverse-gamma distribution, Wikipedia,
+           https://en.wikipedia.org/wiki/Normal-inverse-gamma_distribution
+
+    Examples
+    --------
+    Suppose we wish to investigate the relationship between the
+    normal-inverse-gamma distribution and the inverse gamma distribution.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> import matplotlib.pyplot as plt
+    >>> rng = np.random.default_rng(527484872345)
+    >>> mu, lmbda, a, b = 0, 1, 20, 20
+    >>> norm_inv_gamma = stats.normal_inverse_gamma(mu, lmbda, a, b)
+    >>> inv_gamma = stats.invgamma(a, scale=b)
+
+    One approach is to compare the distribution of the `s2` elements of
+    random variates against the PDF of an inverse gamma distribution.
+
+    >>> _, s2 = norm_inv_gamma.rvs(size=10000, random_state=rng)
+    >>> bins = np.linspace(s2.min(), s2.max(), 50)
+    >>> plt.hist(s2, bins=bins, density=True, label='Frequency density')
+    >>> s2 = np.linspace(s2.min(), s2.max(), 300)
+    >>> plt.plot(s2, inv_gamma.pdf(s2), label='PDF')
+    >>> plt.xlabel(r'$\sigma^2$')
+    >>> plt.ylabel('Frequency density / PMF')
+    >>> plt.show()
+
+    Similarly, we can compare the marginal distribution of `s2` against
+    an inverse gamma distribution.
+
+    >>> from scipy.integrate import quad_vec
+    >>> from scipy import integrate
+    >>> s2 = np.linspace(0.5, 3, 6)
+    >>> res = quad_vec(lambda x: norm_inv_gamma.pdf(x, s2), -np.inf, np.inf)[0]
+    >>> np.allclose(res, inv_gamma.pdf(s2))
+    True
+
+    The sample mean is comparable to the mean of the distribution.
+
+    >>> x, s2 = norm_inv_gamma.rvs(size=10000, random_state=rng)
+    >>> x.mean(), s2.mean()
+    (np.float64(-0.005254750127304425), np.float64(1.050438111436508))
+    >>> norm_inv_gamma.mean()
+    (np.float64(0.0), np.float64(1.0526315789473684))
+
+    Similarly, for the variance:
+
+    >>> x.var(ddof=1), s2.var(ddof=1)
+    (np.float64(1.0546150578185023), np.float64(0.061829865266330754))
+    >>> norm_inv_gamma.var()
+    (np.float64(1.0526315789473684), np.float64(0.061557402277623886))
+    """
+    def rvs(self, mu=0, lmbda=1, a=1, b=1, size=None, random_state=None):
+        """Draw random samples from the distribution.
+
+        Parameters
+        ----------
+        mu, lmbda, a, b : array_like, optional
+            Shape parameters. `lmbda`, `a`, and `b` must be greater
+            than zero.
+        size : int or tuple of ints, optional
+            Shape of samples to draw.
+        random_state : {None, int, np.random.RandomState, np.random.Generator}, optional
+            Used for drawing random variates.
+            If `random_state` is `None`, the `~np.random.RandomState` singleton is used.
+            If `random_state` is an int, a new ``RandomState`` instance is used, seeded
+            with `random_state`.
+            If `random_state` is already a ``RandomState`` or ``Generator`` instance,
+            then that object is used.
+            Default is `None`.
+
+        Returns
+        -------
+        x, s2 : ndarray
+            Random variates.
+
+        """
+        random_state = self._get_random_state(random_state)
+        s2 = invgamma(a, scale=b).rvs(size=size, random_state=random_state)
+        scale = (s2 / lmbda)**0.5
+        x = norm(loc=mu, scale=scale).rvs(size=size, random_state=random_state)
+        dtype = np.result_type(1.0, mu, lmbda, a, b)
+        return x.astype(dtype), s2.astype(dtype)
+
+    def _logpdf(self, x, s2, mu, lmbda, a, b):
+        t1 = 0.5 * (np.log(lmbda) - np.log(2 * np.pi * s2))
+        t2 = a*np.log(b) - special.gammaln(a).astype(a.dtype)
+        t3 = -(a + 1) * np.log(s2)
+        t4 = -(2*b + lmbda*(x - mu)**2) / (2*s2)
+        return t1 + t2 + t3 + t4
+
+    def logpdf(self, x, s2, mu=0, lmbda=1, a=1, b=1):
+        """Log of the probability density function.
+
+        Parameters
+        ----------
+        x, s2 : array_like
+            Arguments. `s2` must be greater than zero.
+        mu, lmbda, a, b : array_like, optional
+            Shape parameters. `lmbda`, `a`, and `b` must be greater
+            than zero.
+
+        Returns
+        -------
+        logpdf : ndarray or scalar
+            Log of the probability density function.
+
+        """
+        invalid, args = self._process_parameters_pdf(x, s2, mu, lmbda, a, b)
+        s2 = args[1]
+        # Keep it simple for now; lazyselect later, perhaps.
+        with np.errstate(all='ignore'):
+            logpdf = np.asarray(self._logpdf(*args))
+        logpdf[s2 <= 0] = -np.inf
+        logpdf[invalid] = np.nan
+        return logpdf[()]
+
+    def _pdf(self, x, s2, mu, lmbda, a, b):
+        t1 = np.sqrt(lmbda / (2 * np.pi * s2))
+        t2 = b**a / special.gamma(a).astype(a.dtype)
+        t3 = (1 / s2)**(a + 1)
+        t4 = np.exp(-(2*b + lmbda*(x - mu)**2) / (2*s2))
+        return t1 * t2 * t3 * t4
+
+    def pdf(self, x, s2, mu=0, lmbda=1, a=1, b=1):
+        """The probability density function.
+
+        Parameters
+        ----------
+        x, s2 : array_like
+            Arguments. `s2` must be greater than zero.
+        mu, lmbda, a, b : array_like, optional
+            Shape parameters. `lmbda`, `a`, and `b` must be greater
+            than zero.
+
+        Returns
+        -------
+        logpdf : ndarray or scalar
+            The probability density function.
+
+        """
+        invalid, args = self._process_parameters_pdf(x, s2, mu, lmbda, a, b)
+        s2 = args[1]
+        # Keep it simple for now; lazyselect later, perhaps.
+        with np.errstate(all='ignore'):
+            pdf = np.asarray(self._pdf(*args))
+        pdf[s2 <= 0] = 0
+        pdf[invalid] = np.nan
+        return pdf[()]
+
+    def mean(self, mu=0, lmbda=1, a=1, b=1):
+        """The mean of the distribution.
+
+        Parameters
+        ----------
+        mu, lmbda, a, b : array_like, optional
+            Shape parameters. `lmbda` and `b` must be greater
+            than zero, and `a` must be greater than one.
+
+        Returns
+        -------
+        x, s2 : ndarray
+            The mean of the distribution.
+
+        """
+        invalid, args = self._process_shapes(mu, lmbda, a, b)
+        mu, lmbda, a, b = args
+        invalid |= ~(a > 1)
+        mean_x = np.asarray(mu).copy()
+        mean_s2 = np.asarray(b / (a - 1))
+        mean_x[invalid] = np.nan
+        mean_s2[invalid] = np.nan
+        return mean_x[()], mean_s2[()]
+
+    def var(self, mu=0, lmbda=1, a=1, b=1):
+        """The variance of the distribution.
+
+        Parameters
+        ----------
+        mu, lmbda, a, b : array_like, optional
+            Shape parameters. `lmbda` and `b` must be greater
+            than zero, and `a` must be greater than two.
+
+        Returns
+        -------
+        x, s2 : ndarray
+            The variance of the distribution.
+
+        """
+        invalid, args = self._process_shapes(mu, lmbda, a, b)
+        mu, lmbda, a, b = args
+        invalid_x = invalid | ~(a > 1)
+        invalid_s2 = invalid | ~(a > 2)
+        var_x = b / ((a - 1) * lmbda)
+        var_s2 = b**2 / ((a - 1)**2 * (a - 2))
+        var_x, var_s2 = np.asarray(var_x), np.asarray(var_s2)
+        var_x[invalid_x] = np.nan
+        var_s2[invalid_s2] = np.nan
+        return var_x[()], var_s2[()]
+
+    def _process_parameters_pdf(self, x, s2, mu, lmbda, a, b):
+        args = np.broadcast_arrays(x, s2, mu, lmbda, a, b)
+        dtype = np.result_type(1.0, *(arg.dtype for arg in args))
+        args = [arg.astype(dtype, copy=False) for arg in args]
+        x, s2, mu, lmbda, a, b = args
+        invalid = ~((lmbda > 0) & (a > 0) & (b > 0))
+        return invalid, args
+
+    def _process_shapes(self, mu, lmbda, a, b):
+        args = np.broadcast_arrays(mu, lmbda, a, b)
+        dtype = np.result_type(1.0, *(arg.dtype for arg in args))
+        args = [arg.astype(dtype, copy=False) for arg in args]
+        mu, lmbda, a, b = args
+        invalid = ~((lmbda > 0) & (a > 0) & (b > 0))
+        return invalid, args
+
+    def __call__(self, mu=0, lmbda=1, a=1, b=1, seed=None):
+        return normal_inverse_gamma_frozen(mu, lmbda, a, b, seed=seed)
+
+
+normal_inverse_gamma = normal_inverse_gamma_gen()
+
+
+class normal_inverse_gamma_frozen(multi_rv_frozen):
+
+    def __init__(self, mu=0, lmbda=1, a=1, b=1, seed=None):
+        self._dist = normal_inverse_gamma_gen(seed)
+        self._shapes = mu, lmbda, a, b
+
+    def logpdf(self, x, s2):
+        return self._dist.logpdf(x, s2, *self._shapes)
+
+    def pdf(self, x, s2):
+        return self._dist.pdf(x, s2, *self._shapes)
+
+    def mean(self):
+        return self._dist.mean(*self._shapes)
+
+    def var(self):
+        return self._dist.var(*self._shapes)
+
+    def rvs(self, size=None, random_state=None):
+        return self._dist.rvs(*self._shapes, size=size, random_state=random_state)
+
+
+# Set frozen generator docstrings from corresponding docstrings in
+# normal_inverse_gamma_gen and fill in default strings in class docstrings
+for name in ['logpdf', 'pdf', 'mean', 'var', 'rvs']:
+    method = normal_inverse_gamma_gen.__dict__[name]
+    method_frozen = normal_inverse_gamma_frozen.__dict__[name]
+    method_frozen.__doc__ = doccer.docformat(method.__doc__,
+                                             mvn_docdict_noparams)
+    method.__doc__ = doccer.docformat(method.__doc__, mvn_docdict_params)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_new_distributions.py b/.venv/lib/python3.12/site-packages/scipy/stats/_new_distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..8393e7718b7300cc4aed4f620f31c8d44c0848ce
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_new_distributions.py
@@ -0,0 +1,551 @@
+import sys
+
+import numpy as np
+from numpy import inf
+
+from scipy._external import array_api_extra as xpx
+from scipy import special
+from scipy.special import _ufuncs as scu
+from scipy.stats._distribution_infrastructure import (
+    ContinuousDistribution, DiscreteDistribution, _RealInterval, _IntegerInterval,
+    _RealParameter, _Parameterization, _combine_docs)
+
+__all__ = ['Normal', 'Logistic', 'Uniform', 'Binomial']
+
+
+class Normal(ContinuousDistribution):
+    r"""Normal distribution with prescribed mean and standard deviation.
+
+    The probability density function of the normal distribution is:
+
+    .. math::
+
+        f(x) = \frac{1}{\sigma \sqrt{2 \pi}} \exp {
+            \left( -\frac{1}{2}\left( \frac{x - \mu}{\sigma} \right)^2 \right)}
+
+    """
+    # `ShiftedScaledDistribution` allows this to be generated automatically from
+    # an instance of `StandardNormal`, but the normal distribution is so frequently
+    # used that it's worth a bit of code duplication to get better performance.
+    _mu_domain = _RealInterval(endpoints=(-inf, inf))
+    _sigma_domain = _RealInterval(endpoints=(0, inf))
+    _x_support = _RealInterval(endpoints=(-inf, inf))
+
+    _mu_param = _RealParameter('mu',  symbol=r'\mu', domain=_mu_domain,
+                               typical=(-1, 1))
+    _sigma_param = _RealParameter('sigma', symbol=r'\sigma', domain=_sigma_domain,
+                                  typical=(0.5, 1.5))
+    _x_param = _RealParameter('x', domain=_x_support, typical=(-1, 1))
+
+    _parameterizations = [_Parameterization(_mu_param, _sigma_param)]
+
+    _variable = _x_param
+    _normalization = 1/np.sqrt(2*np.pi)
+    _log_normalization = np.log(2*np.pi)/2
+
+    def __new__(cls, mu=None, sigma=None, **kwargs):
+        if mu is None and sigma is None:
+            return super().__new__(StandardNormal)
+        return super().__new__(cls)
+
+    def __init__(self, *, mu=0., sigma=1., **kwargs):
+        super().__init__(mu=mu, sigma=sigma, **kwargs)
+
+    def _logpdf_formula(self, x, *, mu, sigma, **kwargs):
+        return StandardNormal._logpdf_formula(self, (x - mu)/sigma) - np.log(sigma)
+
+    def _pdf_formula(self, x, *, mu, sigma, **kwargs):
+        return StandardNormal._pdf_formula(self, (x - mu)/sigma) / sigma
+
+    def _logcdf_formula(self, x, *, mu, sigma, **kwargs):
+        return StandardNormal._logcdf_formula(self, (x - mu)/sigma)
+
+    def _cdf_formula(self, x, *, mu, sigma, **kwargs):
+        return StandardNormal._cdf_formula(self, (x - mu)/sigma)
+
+    def _logccdf_formula(self, x, *, mu, sigma, **kwargs):
+        return StandardNormal._logccdf_formula(self, (x - mu)/sigma)
+
+    def _ccdf_formula(self, x, *, mu, sigma, **kwargs):
+        return StandardNormal._ccdf_formula(self, (x - mu)/sigma)
+
+    def _icdf_formula(self, x, *, mu, sigma, **kwargs):
+        return StandardNormal._icdf_formula(self, x) * sigma + mu
+
+    def _ilogcdf_formula(self, x, *, mu, sigma, **kwargs):
+        return StandardNormal._ilogcdf_formula(self, x) * sigma + mu
+
+    def _iccdf_formula(self, x, *, mu, sigma, **kwargs):
+        return StandardNormal._iccdf_formula(self, x) * sigma + mu
+
+    def _ilogccdf_formula(self, x, *, mu, sigma, **kwargs):
+        return StandardNormal._ilogccdf_formula(self, x) * sigma + mu
+
+    def _entropy_formula(self, *, mu, sigma, **kwargs):
+        return StandardNormal._entropy_formula(self) + np.log(abs(sigma))
+
+    def _logentropy_formula(self, *, mu, sigma, **kwargs):
+        lH0 = StandardNormal._logentropy_formula(self)
+        with np.errstate(divide='ignore'):
+            # sigma = 1 -> log(sigma) = 0 -> log(log(sigma)) = -inf
+            # Silence the unnecessary runtime warning
+            lls = np.log(np.log(abs(sigma))+0j)
+        return special.logsumexp(np.broadcast_arrays(lH0, lls), axis=0)
+
+    def _median_formula(self, *, mu, sigma, **kwargs):
+        return mu
+
+    def _mode_formula(self, *, mu, sigma, **kwargs):
+        return mu
+
+    def _moment_raw_formula(self, order, *, mu, sigma, **kwargs):
+        if order == 0:
+            return np.ones_like(mu)
+        elif order == 1:
+            return mu
+        else:
+            return None
+    _moment_raw_formula.orders = [0, 1]  # type: ignore[attr-defined]
+
+    def _moment_central_formula(self, order, *, mu, sigma, **kwargs):
+        if order == 0:
+            return np.ones_like(mu)
+        elif order % 2:
+            return np.zeros_like(mu)
+        else:
+            # exact is faster (and obviously more accurate) for reasonable orders
+            return sigma**order * special.factorial2(int(order) - 1, exact=True)
+
+    def _lmoment_formula(self, order, *, mu, sigma, **kwargs):
+        lscale = sigma / np.sqrt(np.pi)
+        lkurtosis = 30*np.arctan(np.sqrt(2))/np.pi - 9
+        lmoments = {1: mu, 2: lscale, 3: 0, 4: lkurtosis * lscale}
+        return lmoments.get(order, None)
+
+    def _sample_formula(self, full_shape, rng, *, mu, sigma, **kwargs):
+        return rng.normal(loc=mu, scale=sigma, size=full_shape)[()]
+
+
+def _log_diff(log_p, log_q):
+    return special.logsumexp([log_p, log_q+np.pi*1j], axis=0)
+
+
+class StandardNormal(Normal):
+    r"""Standard normal distribution.
+
+    The probability density function of the standard normal distribution is:
+
+    .. math::
+
+        f(x) = \frac{1}{\sqrt{2 \pi}} \exp \left( -\frac{1}{2} x^2 \right)
+
+    """
+    _x_support = _RealInterval(endpoints=(-inf, inf))
+    _x_param = _RealParameter('x', domain=_x_support, typical=(-5, 5))
+    _variable = _x_param
+    _parameterizations = []
+    _normalization = 1/np.sqrt(2*np.pi)
+    _log_normalization = np.log(2*np.pi)/2
+    mu = np.float64(0.)
+    sigma = np.float64(1.)
+
+    def __init__(self, **kwargs):
+        ContinuousDistribution.__init__(self, **kwargs)
+
+    def _logpdf_formula(self, x, **kwargs):
+        return -(self._log_normalization + x**2/2)
+
+    def _pdf_formula(self, x, **kwargs):
+        return self._normalization * np.exp(-x**2/2)
+
+    def _logcdf_formula(self, x, **kwargs):
+        return special.log_ndtr(x)
+
+    def _cdf_formula(self, x, **kwargs):
+        return special.ndtr(x)
+
+    def _logccdf_formula(self, x, **kwargs):
+        return special.log_ndtr(-x)
+
+    def _ccdf_formula(self, x, **kwargs):
+        return special.ndtr(-x)
+
+    def _icdf_formula(self, x, **kwargs):
+        return special.ndtri(x)
+
+    def _ilogcdf_formula(self, x, **kwargs):
+        return special.ndtri_exp(x)
+
+    def _iccdf_formula(self, x, **kwargs):
+        return -special.ndtri(x)
+
+    def _ilogccdf_formula(self, x, **kwargs):
+        return -special.ndtri_exp(x)
+
+    def _entropy_formula(self, **kwargs):
+        return (1 + np.log(2*np.pi))/2
+
+    def _logentropy_formula(self, **kwargs):
+        return np.log1p(np.log(2*np.pi)) - np.log(2)
+
+    def _median_formula(self, **kwargs):
+        return 0
+
+    def _mode_formula(self, **kwargs):
+        return 0
+
+    def _moment_raw_formula(self, order, **kwargs):
+        raw_moments = {0: 1, 1: 0, 2: 1, 3: 0, 4: 3, 5: 0}
+        return raw_moments.get(order, None)
+
+    def _moment_central_formula(self, order, **kwargs):
+        return self._moment_raw_formula(order, **kwargs)
+
+    def _moment_standardized_formula(self, order, **kwargs):
+        return self._moment_raw_formula(order, **kwargs)
+
+    def _lmoment_formula(self, order, **kwargs):
+        return super()._lmoment_formula(order, mu=0., sigma=1., **kwargs)
+
+    def _sample_formula(self, full_shape, rng, **kwargs):
+        return rng.normal(size=full_shape)[()]
+
+
+class Logistic(ContinuousDistribution):
+    r"""Standard logistic distribution.
+
+    The probability density function of the standard logistic distribution is:
+
+    .. math::
+
+        f(x) = \frac{1}{\left( e^{x / 2} + e^{-x / 2} \right)^2}
+
+    """
+    _x_support = _RealInterval(endpoints=(-inf, inf))
+    _variable = _x_param = _RealParameter('x', domain=_x_support, typical=(-9, 9))
+    _parameterizations = ()   # type:ignore[assignment]
+
+    _scale = np.pi / np.sqrt(3)
+
+    def _logpdf_formula(self, x, **kwargs):
+        y = -np.abs(x)
+        return y - 2 * special.log1p(np.exp(y))
+
+    def _pdf_formula(self, x, **kwargs):
+        # f(x) = sech(x / 2)**2 / 4
+        return (.5 / np.cosh(x / 2))**2
+
+    def _logcdf_formula(self, x, **kwargs):
+        return special.log_expit(x)
+
+    def _cdf_formula(self, x, **kwargs):
+        return special.expit(x)
+
+    def _logccdf_formula(self, x, **kwargs):
+        return special.log_expit(-x)
+
+    def _ccdf_formula(self, x, **kwargs):
+        return special.expit(-x)
+
+    def _icdf_formula(self, x, **kwargs):
+        return special.logit(x)
+
+    def _iccdf_formula(self, x, **kwargs):
+        return -special.logit(x)
+
+    def _entropy_formula(self, **kwargs):
+        return 2.0
+
+    def _logentropy_formula(self, **kwargs):
+        return np.log(2)
+
+    def _median_formula(self, **kwargs):
+        return 0
+
+    def _mode_formula(self, **kwargs):
+        return 0
+
+    def _moment_raw_formula(self, order, **kwargs):
+        n = int(order)
+        if n % 2:
+            return 0.0
+        return np.pi**n * abs((2**n - 2) * float(special.bernoulli(n)[-1]))
+
+    def _moment_central_formula(self, order, **kwargs):
+        return self._moment_raw_formula(order, **kwargs)
+
+    def _moment_standardized_formula(self, order, **kwargs):
+        return self._moment_raw_formula(order, **kwargs) / self._scale**order
+
+    def _lmoment_formula(self, order, **kwargs):
+        lmoments = {1: 0, 2: 1, 3: 0, 4: 1/6}
+        return lmoments.get(order, None)
+
+    def _sample_formula(self, full_shape, rng, **kwargs):
+        return rng.logistic(size=full_shape)[()]
+
+
+# currently for testing only
+class _LogUniform(ContinuousDistribution):
+    r"""Log-uniform distribution.
+
+    The probability density function of the log-uniform distribution is:
+
+    .. math::
+
+        f(x; a, b) = \frac{1}
+                          {x (\log(b) - \log(a))}
+
+    If :math:`\log(X)` is a random variable that follows a uniform distribution
+    between :math:`\log(a)` and :math:`\log(b)`, then :math:`X` is log-uniformly
+    distributed with shape parameters :math:`a` and :math:`b`.
+
+    """
+
+    _a_domain = _RealInterval(endpoints=(0, inf))
+    _b_domain = _RealInterval(endpoints=('a', inf))
+    _log_a_domain = _RealInterval(endpoints=(-inf, inf))
+    _log_b_domain = _RealInterval(endpoints=('log_a', inf))
+    _x_support = _RealInterval(endpoints=('a', 'b'), inclusive=(True, True))
+
+    _a_param = _RealParameter('a', domain=_a_domain, typical=(1e-3, 0.9))
+    _b_param = _RealParameter('b', domain=_b_domain, typical=(1.1, 1e3))
+    _log_a_param = _RealParameter('log_a', symbol=r'\log(a)',
+                                  domain=_log_a_domain, typical=(-3, -0.1))
+    _log_b_param = _RealParameter('log_b', symbol=r'\log(b)',
+                                  domain=_log_b_domain, typical=(0.1, 3))
+    _x_param = _RealParameter('x', domain=_x_support, typical=('a', 'b'))
+
+    _b_domain.define_parameters(_a_param)
+    _log_b_domain.define_parameters(_log_a_param)
+    _x_support.define_parameters(_a_param, _b_param)
+
+    _parameterizations = [_Parameterization(_log_a_param, _log_b_param),
+                          _Parameterization(_a_param, _b_param)]
+    _variable = _x_param
+
+    def __init__(self, *, a=None, b=None, log_a=None, log_b=None, **kwargs):
+        super().__init__(a=a, b=b, log_a=log_a, log_b=log_b, **kwargs)
+
+    def _process_parameters(self, a=None, b=None, log_a=None, log_b=None, **kwargs):
+        a = np.exp(log_a) if a is None else a
+        b = np.exp(log_b) if b is None else b
+        log_a = np.log(a) if log_a is None else log_a
+        log_b = np.log(b) if log_b is None else log_b
+        kwargs.update(dict(a=a, b=b, log_a=log_a, log_b=log_b))
+        return kwargs
+
+    def _pdf_formula(self, x, *, log_a, log_b, **kwargs):
+        return ((log_b - log_a)*x)**-1
+
+    def _moment_raw_formula(self, order, log_a, log_b, **kwargs):
+        if order == 0:
+            return self._one
+        t1 = self._one / (log_b - log_a) / order
+        t2 = np.real(np.exp(_log_diff(order * log_b, order * log_a)))
+        return t1 * t2
+
+
+class Uniform(ContinuousDistribution):
+    r"""Uniform distribution.
+
+    The probability density function of the uniform distribution is:
+
+    .. math::
+
+        f(x; a, b) = \frac{1}
+                          {b - a}
+
+    """
+
+    _a_domain = _RealInterval(endpoints=(-inf, inf))
+    _b_domain = _RealInterval(endpoints=('a', inf))
+    _x_support = _RealInterval(endpoints=('a', 'b'), inclusive=(True, True))
+
+    _a_param = _RealParameter('a', domain=_a_domain, typical=(1e-3, 0.9))
+    _b_param = _RealParameter('b', domain=_b_domain, typical=(1.1, 1e3))
+    _x_param = _RealParameter('x', domain=_x_support, typical=('a', 'b'))
+
+    _b_domain.define_parameters(_a_param)
+    _x_support.define_parameters(_a_param, _b_param)
+
+    _parameterizations = [_Parameterization(_a_param, _b_param)]
+    _variable = _x_param
+
+    def __init__(self, *, a=None, b=None, **kwargs):
+        super().__init__(a=a, b=b, **kwargs)
+
+    def _process_parameters(self, a=None, b=None, ab=None, **kwargs):
+        ab = b - a
+        kwargs.update(dict(a=a, b=b, ab=ab))
+        return kwargs
+
+    def _logpdf_formula(self, x, *, ab, **kwargs):
+        return np.where(np.isnan(x), np.nan, -np.log(ab))
+
+    def _pdf_formula(self, x, *, ab, **kwargs):
+        return np.where(np.isnan(x), np.nan, 1/ab)
+
+    def _logcdf_formula(self, x, *, a, ab, **kwargs):
+        with np.errstate(divide='ignore'):
+            return np.log(x - a) - np.log(ab)
+
+    def _cdf_formula(self, x, *, a, ab, **kwargs):
+        return (x - a) / ab
+
+    def _logccdf_formula(self, x, *, b, ab, **kwargs):
+        with np.errstate(divide='ignore'):
+            return np.log(b - x) - np.log(ab)
+
+    def _ccdf_formula(self, x, *, b, ab, **kwargs):
+        return (b - x) / ab
+
+    def _icdf_formula(self, p, *, a, ab, **kwargs):
+        return a + ab*p
+
+    def _iccdf_formula(self, p, *, b, ab, **kwargs):
+        return b - ab*p
+
+    def _entropy_formula(self, *, ab, **kwargs):
+        return np.log(ab)
+
+    def _mode_formula(self, *, a, b, ab, **kwargs):
+        return a + 0.5*ab
+
+    def _median_formula(self, *, a, b, ab, **kwargs):
+        return a + 0.5*ab
+
+    def _moment_raw_formula(self, order, a, b, ab, **kwargs):
+        np1 = order + 1
+        return (b**np1 - a**np1) / (np1 * ab)
+
+    def _moment_central_formula(self, order, ab, **kwargs):
+        return ab**2/12 if order == 2 else None
+
+    _moment_central_formula.orders = [2]  # type: ignore[attr-defined]
+
+    def _lmoment_formula(self, order, *, a, b, ab, **kwargs):
+        lmoments = {1: 0.5*(a + b), 2: ab / 6}
+        return lmoments.get(order, np.zeros_like(ab))
+
+    def _sample_formula(self, full_shape, rng, a, b, ab, **kwargs):
+        try:
+            return rng.uniform(a, b, size=full_shape)[()]
+        except OverflowError:  # happens when there are NaNs
+            return rng.uniform(0, 1, size=full_shape)*ab + a
+
+
+class _Gamma(ContinuousDistribution):
+    # Gamma distribution for testing only
+    _a_domain = _RealInterval(endpoints=(0, inf))
+    _x_support = _RealInterval(endpoints=(0, inf), inclusive=(False, False))
+
+    _a_param = _RealParameter('a', domain=_a_domain, typical=(0.1, 10))
+    _x_param = _RealParameter('x', domain=_x_support, typical=(0.1, 10))
+
+    _parameterizations = [_Parameterization(_a_param)]
+    _variable = _x_param
+
+    def _pdf_formula(self, x, *, a, **kwargs):
+        return x ** (a - 1) * np.exp(-x) / special.gamma(a)
+
+
+class Binomial(DiscreteDistribution):
+    r"""Binomial distribution with prescribed success probability and number of trials
+
+    The probability density function of the binomial distribution is:
+
+    .. math::
+
+        f(x) = {n \choose x} p^x (1 - p)^{n-x}
+
+    """
+    _n_domain = _IntegerInterval(endpoints=(0, inf), inclusive=(False, False))
+    _p_domain = _RealInterval(endpoints=(0, 1), inclusive=(False, False))
+    _x_support = _IntegerInterval(endpoints=(0, 'n'), inclusive=(True, True))
+
+    _n_param = _RealParameter('n', domain=_n_domain, typical=(10, 20))
+    _p_param = _RealParameter('p', domain=_p_domain, typical=(0.25, 0.75))
+    _x_param = _RealParameter('x', domain=_x_support, typical=(0, 10))
+
+    _parameterizations = [_Parameterization(_n_param, _p_param)]
+    _variable = _x_param
+
+    def __init__(self, *, n, p, **kwargs):
+        super().__init__(n=n, p=p, **kwargs)
+
+    def _pmf_formula(self, x, *, n, p, **kwargs):
+        return scu._binom_pmf(x, n, p)
+
+    def _logpmf_formula(self, x, *, n, p, **kwargs):
+        # This implementation is from the ``scipy.stats.binom`` and could be improved
+        # by using a more numerically sound implementation of the absolute value of
+        # the binomial coefficient.
+        combiln = (
+            special.gammaln(n+1) - (special.gammaln(x+1) + special.gammaln(n-x+1))
+        )
+        return combiln + special.xlogy(x, p) + special.xlog1py(n-x, -p)
+
+    def _cdf_formula(self, x, *, n, p, **kwargs):
+        return scu._binom_cdf(x, n, p)
+
+    def _logcdf_formula(self, x, *, n, p, **kwargs):
+        # todo: add this strategy to infrastructure more generally, but allow dist
+        #   author to specify threshold other than median in case median is expensive
+        median = self._icdf_formula(0.5, n=n, p=p)
+        return xpx.apply_where(x < median, (x, n, p),
+            lambda *args: np.log(scu._binom_cdf(*args)),
+            lambda *args: np.log1p(-scu._binom_sf(*args))
+        )
+
+    def _ccdf_formula(self, x, *, n, p, **kwargs):
+        return scu._binom_sf(x, n, p)
+
+    def _logccdf_formula(self, x, *, n, p, **kwargs):
+        median = self._icdf_formula(0.5, n=n, p=p)
+        return xpx.apply_where(x < median, (x, n, p),
+            lambda *args: np.log1p(-scu._binom_cdf(*args)),
+            lambda *args: np.log(scu._binom_sf(*args))
+        )
+
+    def _icdf_formula(self, x, *, n, p, **kwargs):
+        return scu._binom_ppf(x, n, p)
+
+    def _iccdf_formula(self, x, *, n, p, **kwargs):
+        return scu._binom_isf(x, n, p)
+
+    def _mode_formula(self, *, n, p, **kwargs):
+        # https://en.wikipedia.org/wiki/Binomial_distribution#Mode
+        mode = np.floor((n+1)*p)
+        mode = np.where(p == 1, mode - 1, mode)
+        return mode[()]
+
+    def _moment_raw_formula(self, order, *, n, p, **kwargs):
+        # https://en.wikipedia.org/wiki/Binomial_distribution#Higher_moments
+        if order == 1:
+            return n*p
+        if order == 2:
+            return n*p*(1 - p + n*p)
+        return None
+    _moment_raw_formula.orders = [1, 2]  # type: ignore[attr-defined]
+
+    def _moment_central_formula(self, order, *, n, p, **kwargs):
+        # https://en.wikipedia.org/wiki/Binomial_distribution#Higher_moments
+        if order == 1:
+            return np.zeros_like(n)
+        if order == 2:
+            return n*p*(1 - p)
+        if order == 3:
+            return n*p*(1 - p)*(1 - 2*p)
+        if order == 4:
+            return n*p*(1 - p)*(1 + (3*n - 6)*p*(1 - p))
+        return None
+    _moment_central_formula.orders = [1, 2, 3, 4]  # type: ignore[attr-defined]
+
+
+# Distribution classes need only define the summary and beginning of the extended
+# summary portion of the class documentation. All other documentation, including
+# examples, is generated automatically.
+_module = sys.modules[__name__].__dict__
+for dist_name in __all__:
+    _module[dist_name].__doc__ = _combine_docs(_module[dist_name])
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_odds_ratio.py b/.venv/lib/python3.12/site-packages/scipy/stats/_odds_ratio.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc593f5adc9a700c618c721b6c37c801809d868b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_odds_ratio.py
@@ -0,0 +1,466 @@
+import numpy as np
+
+from scipy.special import ndtri
+from scipy.optimize import brentq
+from ._discrete_distns import nchypergeom_fisher
+from ._common import ConfidenceInterval
+
+
+def _sample_odds_ratio(table):
+    """
+    Given a table [[a, b], [c, d]], compute a*d/(b*c).
+
+    Return nan if the numerator and denominator are 0.
+    Return inf if just the denominator is 0.
+    """
+    # table must be a 2x2 numpy array.
+    if table[1, 0] > 0 and table[0, 1] > 0:
+        oddsratio = table[0, 0] * table[1, 1] / (table[1, 0] * table[0, 1])
+    elif table[0, 0] == 0 or table[1, 1] == 0:
+        oddsratio = np.nan
+    else:
+        oddsratio = np.inf
+    return oddsratio
+
+
+def _solve(func):
+    """
+    Solve func(nc) = 0.  func must be an increasing function.
+    """
+    # We could just as well call the variable `x` instead of `nc`, but we
+    # always call this function with functions for which nc (the noncentrality
+    # parameter) is the variable for which we are solving.
+    nc = 1.0
+    value = func(nc)
+    if value == 0:
+        return nc
+
+    # Multiplicative factor by which to increase or decrease nc when
+    # searching for a bracketing interval.
+    factor = 2.0
+    # Find a bracketing interval.
+    if value > 0:
+        nc /= factor
+        while func(nc) > 0:
+            nc /= factor
+        lo = nc
+        hi = factor*nc
+    else:
+        nc *= factor
+        while func(nc) < 0:
+            nc *= factor
+        lo = nc/factor
+        hi = nc
+
+    # lo and hi bracket the solution for nc.
+    nc = brentq(func, lo, hi, xtol=1e-13)
+    return nc
+
+
+def _nc_hypergeom_mean_inverse(x, M, n, N):
+    """
+    For the given noncentral hypergeometric parameters x, M, n,and N
+    (table[0,0], total, row 0 sum and column 0 sum, resp., of a 2x2
+    contingency table), find the noncentrality parameter of Fisher's
+    noncentral hypergeometric distribution whose mean is x.
+    """
+    nc = _solve(lambda nc: nchypergeom_fisher.mean(M, n, N, nc) - x)
+    return nc
+
+
+def _hypergeom_params_from_table(table):
+    # The notation M, n and N is consistent with stats.hypergeom and
+    # stats.nchypergeom_fisher.
+    x = table[0, 0]
+    M = table.sum()
+    n = table[0].sum()
+    N = table[:, 0].sum()
+    return x, M, n, N
+
+
+def _ci_upper(table, alpha):
+    """
+    Compute the upper end of the confidence interval.
+    """
+    if _sample_odds_ratio(table) == np.inf:
+        return np.inf
+
+    x, M, n, N = _hypergeom_params_from_table(table)
+
+    # nchypergeom_fisher.cdf is a decreasing function of nc, so we negate
+    # it in the lambda expression.
+    nc = _solve(lambda nc: -nchypergeom_fisher.cdf(x, M, n, N, nc) + alpha)
+    return nc
+
+
+def _ci_lower(table, alpha):
+    """
+    Compute the lower end of the confidence interval.
+    """
+    if _sample_odds_ratio(table) == 0:
+        return 0
+
+    x, M, n, N = _hypergeom_params_from_table(table)
+
+    nc = _solve(lambda nc: nchypergeom_fisher.sf(x - 1, M, n, N, nc) - alpha)
+    return nc
+
+
+def _conditional_oddsratio(table):
+    """
+    Conditional MLE of the odds ratio for the 2x2 contingency table.
+    """
+    x, M, n, N = _hypergeom_params_from_table(table)
+    # Get the bounds of the support.  The support of the noncentral
+    # hypergeometric distribution with parameters M, n, and N is the same
+    # for all values of the noncentrality parameter, so we can use 1 here.
+    lo, hi = nchypergeom_fisher.support(M, n, N, 1)
+
+    # Check if x is at one of the extremes of the support.  If so, we know
+    # the odds ratio is either 0 or inf.
+    if x == lo:
+        # x is at the low end of the support.
+        return 0
+    if x == hi:
+        # x is at the high end of the support.
+        return np.inf
+
+    nc = _nc_hypergeom_mean_inverse(x, M, n, N)
+    return nc
+
+
+def _conditional_oddsratio_ci(table, confidence_level=0.95,
+                              alternative='two-sided'):
+    """
+    Conditional exact confidence interval for the odds ratio.
+    """
+    if alternative == 'two-sided':
+        alpha = 0.5*(1 - confidence_level)
+        lower = _ci_lower(table, alpha)
+        upper = _ci_upper(table, alpha)
+    elif alternative == 'less':
+        lower = 0.0
+        upper = _ci_upper(table, 1 - confidence_level)
+    else:
+        # alternative == 'greater'
+        lower = _ci_lower(table, 1 - confidence_level)
+        upper = np.inf
+
+    return lower, upper
+
+
+def _sample_odds_ratio_ci(table, confidence_level=0.95,
+                          alternative='two-sided'):
+    oddsratio = _sample_odds_ratio(table)
+    log_or = np.log(oddsratio)
+    se = np.sqrt((1/table).sum())
+    if alternative == 'less':
+        z = ndtri(confidence_level)
+        loglow = -np.inf
+        loghigh = log_or + z*se
+    elif alternative == 'greater':
+        z = ndtri(confidence_level)
+        loglow = log_or - z*se
+        loghigh = np.inf
+    else:
+        # alternative is 'two-sided'
+        z = ndtri(0.5*confidence_level + 0.5)
+        loglow = log_or - z*se
+        loghigh = log_or + z*se
+
+    return np.exp(loglow), np.exp(loghigh)
+
+
+class OddsRatioResult:
+    """
+    Result of `scipy.stats.contingency.odds_ratio`.  See the
+    docstring for `odds_ratio` for more details.
+
+    Attributes
+    ----------
+    statistic : float
+        The computed odds ratio.
+
+        * If `kind` is ``'sample'``, this is sample (or unconditional)
+          estimate, given by
+          ``table[0, 0]*table[1, 1]/(table[0, 1]*table[1, 0])``.
+        * If `kind` is ``'conditional'``, this is the conditional
+          maximum likelihood estimate for the odds ratio. It is
+          the noncentrality parameter of Fisher's noncentral
+          hypergeometric distribution with the same hypergeometric
+          parameters as `table` and whose mean is ``table[0, 0]``.
+
+    Methods
+    -------
+    confidence_interval :
+        Confidence interval for the odds ratio.
+    """
+
+    def __init__(self, _table, _kind, statistic):
+        # for now, no need to make _table and _kind public, since this sort of
+        # information is returned in very few `scipy.stats` results
+        self._table = _table
+        self._kind = _kind
+        self.statistic = statistic
+
+    def __repr__(self):
+        return f"OddsRatioResult(statistic={self.statistic})"
+
+    def confidence_interval(self, confidence_level=0.95,
+                            alternative='two-sided'):
+        """
+        Confidence interval for the odds ratio.
+
+        Parameters
+        ----------
+        confidence_level: float
+            Desired confidence level for the confidence interval.
+            The value must be given as a fraction between 0 and 1.
+            Default is 0.95 (meaning 95%).
+
+        alternative : {'two-sided', 'less', 'greater'}, optional
+            The alternative hypothesis of the hypothesis test to which the
+            confidence interval corresponds. That is, suppose the null
+            hypothesis is that the true odds ratio equals ``OR`` and the
+            confidence interval is ``(low, high)``. Then the following options
+            for `alternative` are available (default is 'two-sided'):
+
+            * 'two-sided': the true odds ratio is not equal to ``OR``. There
+              is evidence against the null hypothesis at the chosen
+              `confidence_level` if ``high < OR`` or ``low > OR``.
+            * 'less': the true odds ratio is less than ``OR``. The ``low`` end
+              of the confidence interval is 0, and there is evidence against
+              the null hypothesis at  the chosen `confidence_level` if
+              ``high < OR``.
+            * 'greater': the true odds ratio is greater than ``OR``.  The
+              ``high`` end of the confidence interval is ``np.inf``, and there
+              is evidence against the null hypothesis at the chosen
+              `confidence_level` if ``low > OR``.
+
+        Returns
+        -------
+        ci : ``ConfidenceInterval`` instance
+            The confidence interval, represented as an object with
+            attributes ``low`` and ``high``.
+
+        Notes
+        -----
+        When `kind` is ``'conditional'``, the limits of the confidence
+        interval are the conditional "exact confidence limits" as described
+        by Fisher [1]_. The conditional odds ratio and confidence interval are
+        also discussed in Section 4.1.2 of the text by Sahai and Khurshid [2]_.
+
+        When `kind` is ``'sample'``, the confidence interval is computed
+        under the assumption that the logarithm of the odds ratio is normally
+        distributed with standard error given by::
+
+            se = sqrt(1/a + 1/b + 1/c + 1/d)
+
+        where ``a``, ``b``, ``c`` and ``d`` are the elements of the
+        contingency table.  (See, for example, [2]_, section 3.1.3.2,
+        or [3]_, section 2.3.3).
+
+        References
+        ----------
+        .. [1] R. A. Fisher (1935), The logic of inductive inference,
+               Journal of the Royal Statistical Society, Vol. 98, No. 1,
+               pp. 39-82.
+        .. [2] H. Sahai and A. Khurshid (1996), Statistics in Epidemiology:
+               Methods, Techniques, and Applications, CRC Press LLC, Boca
+               Raton, Florida.
+        .. [3] Alan Agresti, An Introduction to Categorical Data Analysis
+               (second edition), Wiley, Hoboken, NJ, USA (2007).
+        """
+        if alternative not in ['two-sided', 'less', 'greater']:
+            raise ValueError("`alternative` must be 'two-sided', 'less' or "
+                             "'greater'.")
+
+        if confidence_level < 0 or confidence_level > 1:
+            raise ValueError('confidence_level must be between 0 and 1')
+
+        if self._kind == 'conditional':
+            ci = self._conditional_odds_ratio_ci(confidence_level, alternative)
+        else:
+            ci = self._sample_odds_ratio_ci(confidence_level, alternative)
+        return ci
+
+    def _conditional_odds_ratio_ci(self, confidence_level=0.95,
+                                   alternative='two-sided'):
+        """
+        Confidence interval for the conditional odds ratio.
+        """
+
+        table = self._table
+        if 0 in table.sum(axis=0) or 0 in table.sum(axis=1):
+            # If both values in a row or column are zero, the p-value is 1,
+            # the odds ratio is NaN and the confidence interval is (0, inf).
+            ci = (0, np.inf)
+        else:
+            ci = _conditional_oddsratio_ci(table,
+                                           confidence_level=confidence_level,
+                                           alternative=alternative)
+        return ConfidenceInterval(low=ci[0], high=ci[1])
+
+    def _sample_odds_ratio_ci(self, confidence_level=0.95,
+                              alternative='two-sided'):
+        """
+        Confidence interval for the sample odds ratio.
+        """
+        if confidence_level < 0 or confidence_level > 1:
+            raise ValueError('confidence_level must be between 0 and 1')
+
+        table = self._table
+        if 0 in table.sum(axis=0) or 0 in table.sum(axis=1):
+            # If both values in a row or column are zero, the p-value is 1,
+            # the odds ratio is NaN and the confidence interval is (0, inf).
+            ci = (0, np.inf)
+        else:
+            ci = _sample_odds_ratio_ci(table,
+                                       confidence_level=confidence_level,
+                                       alternative=alternative)
+        return ConfidenceInterval(low=ci[0], high=ci[1])
+
+
+def odds_ratio(table, *, kind='conditional'):
+    r"""
+    Compute the odds ratio for a 2x2 contingency table.
+
+    Parameters
+    ----------
+    table : array_like of ints
+        A 2x2 contingency table.  Elements must be non-negative integers.
+    kind : str, optional
+        Which kind of odds ratio to compute, either the sample
+        odds ratio (``kind='sample'``) or the conditional odds ratio
+        (``kind='conditional'``).  Default is ``'conditional'``.
+
+    Returns
+    -------
+    result : `~scipy.stats._result_classes.OddsRatioResult` instance
+        The returned object has two computed attributes:
+
+        statistic : float
+            * If `kind` is ``'sample'``, this is sample (or unconditional)
+              estimate, given by
+              ``table[0, 0]*table[1, 1]/(table[0, 1]*table[1, 0])``.
+            * If `kind` is ``'conditional'``, this is the conditional
+              maximum likelihood estimate for the odds ratio. It is
+              the noncentrality parameter of Fisher's noncentral
+              hypergeometric distribution with the same hypergeometric
+              parameters as `table` and whose mean is ``table[0, 0]``.
+
+        The object has the method `confidence_interval` that computes
+        the confidence interval of the odds ratio.
+
+    See Also
+    --------
+    scipy.stats.fisher_exact
+    relative_risk
+    :ref:`hypothesis_odds_ratio` : Extended example
+
+    Notes
+    -----
+    The conditional odds ratio was discussed by Fisher (see "Example 1"
+    of [1]_).  Texts that cover the odds ratio include [2]_ and [3]_.
+
+    .. versionadded:: 1.10.0
+
+    References
+    ----------
+    .. [1] R. A. Fisher (1935), The logic of inductive inference,
+           Journal of the Royal Statistical Society, Vol. 98, No. 1,
+           pp. 39-82.
+    .. [2] Breslow NE, Day NE (1980). Statistical methods in cancer research.
+           Volume I - The analysis of case-control studies. IARC Sci Publ.
+           (32):5-338. PMID: 7216345. (See section 4.2.)
+    .. [3] H. Sahai and A. Khurshid (1996), Statistics in Epidemiology:
+           Methods, Techniques, and Applications, CRC Press LLC, Boca
+           Raton, Florida.
+
+    Examples
+    --------
+    In epidemiology, individuals are classified as "exposed" or
+    "unexposed" to some factor or treatment. If the occurrence of some
+    illness is under study, those who have the illness are often
+    classified as "cases", and those without it are "noncases".  The
+    counts of the occurrences of these classes gives a contingency
+    table::
+
+                    exposed    unexposed
+        cases          a           b
+        noncases       c           d
+
+    The sample odds ratio may be written ``(a/c) / (b/d)``.  ``a/c`` can
+    be interpreted as the odds of a case occurring in the exposed group,
+    and ``b/d`` as the odds of a case occurring in the unexposed group.
+    The sample odds ratio is the ratio of these odds.  If the odds ratio
+    is greater than 1, it suggests that there is a positive association
+    between being exposed and being a case.
+
+    Interchanging the rows or columns of the contingency table inverts
+    the odds ratio, so it is important to understand the meaning of labels
+    given to the rows and columns of the table when interpreting the
+    odds ratio.
+
+    Consider a hypothetical example where it is hypothesized that exposure to a
+    certain chemical is associated with increased occurrence of a certain
+    disease. Suppose we have the following table for a collection of 410 people::
+
+                exposed unexposed
+        cases        7       15
+        noncases    58      472
+
+    The question we ask is "Is exposure to the chemical associated with
+    increased risk of the disease?"
+
+    Compute the odds ratio:
+
+    >>> from scipy.stats.contingency import odds_ratio
+    >>> res = odds_ratio([[7, 15], [58, 472]])
+    >>> res.statistic
+    3.7836687705553493
+
+    For this sample, the odds of getting the disease for those who have been
+    exposed to the chemical are almost 3.8 times that of those who have not been
+    exposed.
+
+    We can compute the 95% confidence interval for the odds ratio:
+
+    >>> res.confidence_interval(confidence_level=0.95)
+    ConfidenceInterval(low=1.2514829132266785, high=10.363493716701269)
+
+    The 95% confidence interval for the conditional odds ratio is approximately
+    (1.25, 10.4).
+
+    For a more detailed example, see :ref:`hypothesis_odds_ratio`.
+    """
+    if kind not in ['conditional', 'sample']:
+        raise ValueError("`kind` must be 'conditional' or 'sample'.")
+
+    c = np.asarray(table)
+
+    if c.shape != (2, 2):
+        raise ValueError(f"Invalid shape {c.shape}. The input `table` must be "
+                         "of shape (2, 2).")
+
+    if not np.issubdtype(c.dtype, np.integer):
+        raise ValueError("`table` must be an array of integers, but got "
+                         f"type {c.dtype}")
+    c = c.astype(np.int64)
+
+    if np.any(c < 0):
+        raise ValueError("All values in `table` must be nonnegative.")
+
+    if 0 in c.sum(axis=0) or 0 in c.sum(axis=1):
+        # If both values in a row or column are zero, the p-value is NaN and
+        # the odds ratio is NaN.
+        result = OddsRatioResult(_table=c, _kind=kind, statistic=np.nan)
+        return result
+
+    if kind == 'sample':
+        oddsratio = _sample_odds_ratio(c)
+    else:  # kind is 'conditional'
+        oddsratio = _conditional_oddsratio(c)
+
+    result = OddsRatioResult(_table=c, _kind=kind, statistic=oddsratio)
+    return result
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_page_trend_test.py b/.venv/lib/python3.12/site-packages/scipy/stats/_page_trend_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..27f8dda8c043eafb0c0380f0cd3269afd30046e9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_page_trend_test.py
@@ -0,0 +1,488 @@
+from dataclasses import dataclass
+from itertools import permutations
+import math
+import threading
+
+import numpy as np
+
+from ._continuous_distns import norm
+from scipy._lib._array_api import xp_capabilities
+import scipy.stats
+
+
+@dataclass
+class PageTrendTestResult:
+    statistic: float
+    pvalue: float
+    method: str
+
+
+@xp_capabilities(np_only=True)
+def page_trend_test(data, ranked=False, predicted_ranks=None, method='auto'):
+    r"""
+    Perform Page's Test, a measure of trend in observations between treatments.
+
+    Page's Test (also known as Page's :math:`L` test) is useful when:
+
+    * there are :math:`n \geq 3` treatments,
+    * :math:`m \geq 2` subjects are observed for each treatment, and
+    * the observations are hypothesized to have a particular order.
+
+    Specifically, the test considers the null hypothesis that
+
+    .. math::
+
+        m_1 = m_2 = m_3 \cdots = m_n,
+
+    where :math:`m_j` is the mean of the observed quantity under treatment
+    :math:`j`, against the alternative hypothesis that
+
+    .. math::
+
+        m_1 \leq m_2 \leq m_3 \leq \cdots \leq m_n,
+
+    where at least one inequality is strict.
+
+    As noted by [4]_, Page's :math:`L` test has greater statistical power than
+    the Friedman test against the alternative that there is a difference in
+    trend, as Friedman's test only considers a difference in the means of the
+    observations without considering their order. Whereas Spearman :math:`\rho`
+    considers the correlation between the ranked observations of two variables
+    (e.g. the airspeed velocity of a swallow vs. the weight of the coconut it
+    carries), Page's :math:`L` is concerned with a trend in an observation
+    (e.g. the airspeed velocity of a swallow) across several distinct
+    treatments (e.g. carrying each of five coconuts of different weight) even
+    as the observation is repeated with multiple subjects (e.g. one European
+    swallow and one African swallow).
+
+    Parameters
+    ----------
+    data : array-like
+        A :math:`m \times n` array; the element in row :math:`i` and
+        column :math:`j` is the observation corresponding with subject
+        :math:`i` and treatment :math:`j`. By default, the columns are
+        assumed to be arranged in order of increasing predicted mean.
+
+    ranked : bool, optional
+        By default, `data` is assumed to be observations rather than ranks;
+        it will be ranked with `scipy.stats.rankdata` along ``axis=1``. If
+        `data` is provided in the form of ranks, pass argument ``True``.
+
+    predicted_ranks : array-like, optional
+        The predicted ranks of the column means. If not specified,
+        the columns are assumed to be arranged in order of increasing
+        predicted mean, so the default `predicted_ranks` are
+        :math:`[1, 2, \dots, n-1, n]`.
+
+    method : {'auto', 'asymptotic', 'exact'}, optional
+        Selects the method used to calculate the *p*-value. The following
+        options are available.
+
+        * 'auto': selects between 'exact' and 'asymptotic' to
+          achieve reasonably accurate results in reasonable time (default)
+        * 'asymptotic': compares the standardized test statistic against
+          the normal distribution
+        * 'exact': computes the exact *p*-value by comparing the observed
+          :math:`L` statistic against those realized by all possible
+          permutations of ranks (under the null hypothesis that each
+          permutation is equally likely)
+
+    Returns
+    -------
+    res : PageTrendTestResult
+        An object containing attributes:
+
+        statistic : float
+            Page's :math:`L` test statistic.
+        pvalue : float
+            The associated *p*-value
+        method : {'asymptotic', 'exact'}
+            The method used to compute the *p*-value
+
+    See Also
+    --------
+    rankdata, friedmanchisquare, spearmanr
+
+    Notes
+    -----
+    As noted in [1]_, "the :math:`n` 'treatments' could just as well represent
+    :math:`n` objects or events or performances or persons or trials ranked."
+    Similarly, the :math:`m` 'subjects' could equally stand for :math:`m`
+    "groupings by ability or some other control variable, or judges doing
+    the ranking, or random replications of some other sort."
+
+    The procedure for calculating the :math:`L` statistic, adapted from
+    [1]_, is:
+
+    1. "Predetermine with careful logic the appropriate hypotheses
+       concerning the predicted ordering of the experimental results.
+       If no reasonable basis for ordering any treatments is known, the
+       :math:`L` test is not appropriate."
+    2. "As in other experiments, determine at what level of confidence
+       you will reject the null hypothesis that there is no agreement of
+       experimental results with the monotonic hypothesis."
+    3. "Cast the experimental material into a two-way table of :math:`n`
+       columns (treatments, objects ranked, conditions) and :math:`m`
+       rows (subjects, replication groups, levels of control variables)."
+    4. "When experimental observations are recorded, rank them across each
+       row", e.g. ``ranks = scipy.stats.rankdata(data, axis=1)``.
+    5. "Add the ranks in each column", e.g.
+       ``colsums = np.sum(ranks, axis=0)``.
+    6. "Multiply each sum of ranks by the predicted rank for that same
+       column", e.g. ``products = predicted_ranks * colsums``.
+    7. "Sum all such products", e.g. ``L = products.sum()``.
+
+    [1]_ continues by suggesting use of the standardized statistic
+
+    .. math::
+
+        \chi_L^2 = \frac{\left[12L-3mn(n+1)^2\right]^2}{mn^2(n^2-1)(n+1)}
+
+    "which is distributed approximately as chi-square with 1 degree of
+    freedom. The ordinary use of :math:`\chi^2` tables would be
+    equivalent to a two-sided test of agreement. If a one-sided test
+    is desired, *as will almost always be the case*, the probability
+    discovered in the chi-square table should be *halved*."
+
+    However, this standardized statistic does not distinguish between the
+    observed values being well correlated with the predicted ranks and being
+    _anti_-correlated with the predicted ranks. Instead, we follow [2]_
+    and calculate the standardized statistic
+
+    .. math::
+
+        \Lambda = \frac{L - E_0}{\sqrt{V_0}},
+
+    where :math:`E_0 = \frac{1}{4} mn(n+1)^2` and
+    :math:`V_0 = \frac{1}{144} mn^2(n+1)(n^2-1)`, "which is asymptotically
+    normal under the null hypothesis".
+
+    The *p*-value for ``method='exact'`` is generated by comparing the observed
+    value of :math:`L` against the :math:`L` values generated for all
+    :math:`(n!)^m` possible permutations of ranks. The calculation is performed
+    using the recursive method of [5].
+
+    The *p*-values are not adjusted for the possibility of ties. When
+    ties are present, the reported  ``'exact'`` *p*-values may be somewhat
+    larger (i.e. more conservative) than the true *p*-value [2]_. The
+    ``'asymptotic'`` *p*-values, however, tend to be smaller (i.e. less
+    conservative) than the ``'exact'`` *p*-values.
+
+    References
+    ----------
+    .. [1] Ellis Batten Page, "Ordered hypotheses for multiple treatments:
+       a significant test for linear ranks", *Journal of the American
+       Statistical Association* 58(301), p. 216--230, 1963.
+
+    .. [2] Markus Neuhauser, *Nonparametric Statistical Test: A computational
+       approach*, CRC Press, p. 150--152, 2012.
+
+    .. [3] Statext LLC, "Page's L Trend Test - Easy Statistics", *Statext -
+       Statistics Study*, https://www.statext.com/practice/PageTrendTest03.php,
+       Accessed July 12, 2020.
+
+    .. [4] "Page's Trend Test", *Wikipedia*, WikimediaFoundation,
+       https://en.wikipedia.org/wiki/Page%27s_trend_test,
+       Accessed July 12, 2020.
+
+    .. [5] Robert E. Odeh, "The exact distribution of Page's L-statistic in
+       the two-way layout", *Communications in Statistics - Simulation and
+       Computation*,  6(1), p. 49--61, 1977.
+
+    Examples
+    --------
+    We use the example from [3]_: 10 students are asked to rate three
+    teaching methods - tutorial, lecture, and seminar - on a scale of 1-5,
+    with 1 being the lowest and 5 being the highest. We have decided that
+    a confidence level of 99% is required to reject the null hypothesis in
+    favor of our alternative: that the seminar will have the highest ratings
+    and the tutorial will have the lowest. Initially, the data have been
+    tabulated with each row representing an individual student's ratings of
+    the three methods in the following order: tutorial, lecture, seminar.
+
+    >>> table = [[3, 4, 3],
+    ...          [2, 2, 4],
+    ...          [3, 3, 5],
+    ...          [1, 3, 2],
+    ...          [2, 3, 2],
+    ...          [2, 4, 5],
+    ...          [1, 2, 4],
+    ...          [3, 4, 4],
+    ...          [2, 4, 5],
+    ...          [1, 3, 4]]
+
+    Because the tutorial is hypothesized to have the lowest ratings, the
+    column corresponding with tutorial rankings should be first; the seminar
+    is hypothesized to have the highest ratings, so its column should be last.
+    Since the columns are already arranged in this order of increasing
+    predicted mean, we can pass the table directly into `page_trend_test`.
+
+    >>> from scipy.stats import page_trend_test
+    >>> res = page_trend_test(table)
+    >>> res
+    PageTrendTestResult(statistic=133.5, pvalue=0.0018191161948127822,
+                        method='exact')
+
+    This *p*-value indicates that there is a 0.1819% chance that
+    the :math:`L` statistic would reach such an extreme value under the null
+    hypothesis. Because 0.1819% is less than 1%, we have evidence to reject
+    the null hypothesis in favor of our alternative at a 99% confidence level.
+
+    The value of the :math:`L` statistic is 133.5. To check this manually,
+    we rank the data such that high scores correspond with high ranks, settling
+    ties with an average rank:
+
+    >>> from scipy.stats import rankdata
+    >>> ranks = rankdata(table, axis=1)
+    >>> ranks
+    array([[1.5, 3. , 1.5],
+           [1.5, 1.5, 3. ],
+           [1.5, 1.5, 3. ],
+           [1. , 3. , 2. ],
+           [1.5, 3. , 1.5],
+           [1. , 2. , 3. ],
+           [1. , 2. , 3. ],
+           [1. , 2.5, 2.5],
+           [1. , 2. , 3. ],
+           [1. , 2. , 3. ]])
+
+    We add the ranks within each column, multiply the sums by the
+    predicted ranks, and sum the products.
+
+    >>> import numpy as np
+    >>> m, n = ranks.shape
+    >>> predicted_ranks = np.arange(1, n+1)
+    >>> L = (predicted_ranks * np.sum(ranks, axis=0)).sum()
+    >>> res.statistic == L
+    True
+
+    As presented in [3]_, the asymptotic approximation of the *p*-value is the
+    survival function of the normal distribution evaluated at the standardized
+    test statistic:
+
+    >>> from scipy.stats import norm
+    >>> E0 = (m*n*(n+1)**2)/4
+    >>> V0 = (m*n**2*(n+1)*(n**2-1))/144
+    >>> Lambda = (L-E0)/np.sqrt(V0)
+    >>> p = norm.sf(Lambda)
+    >>> p
+    0.0012693433690751756
+
+    This does not precisely match the *p*-value reported by `page_trend_test`
+    above. The asymptotic distribution is not very accurate, nor conservative,
+    for :math:`m \leq 12` and :math:`n \leq 8`, so `page_trend_test` chose to
+    use ``method='exact'`` based on the dimensions of the table and the
+    recommendations in Page's original paper [1]_. To override
+    `page_trend_test`'s choice, provide the `method` argument.
+
+    >>> res = page_trend_test(table, method="asymptotic")
+    >>> res
+    PageTrendTestResult(statistic=133.5, pvalue=0.0012693433690751756,
+                        method='asymptotic')
+
+    If the data are already ranked, we can pass in the ``ranks`` instead of
+    the ``table`` to save computation time.
+
+    >>> res = page_trend_test(ranks,             # ranks of data
+    ...                       ranked=True,       # data is already ranked
+    ...                       )
+    >>> res
+    PageTrendTestResult(statistic=133.5, pvalue=0.0018191161948127822,
+                        method='exact')
+
+    Suppose the raw data had been tabulated in an order different from the
+    order of predicted means, say lecture, seminar, tutorial.
+
+    >>> table = np.asarray(table)[:, [1, 2, 0]]
+
+    Since the arrangement of this table is not consistent with the assumed
+    ordering, we can either rearrange the table or provide the
+    `predicted_ranks`. Remembering that the lecture is predicted
+    to have the middle rank, the seminar the highest, and tutorial the lowest,
+    we pass:
+
+    >>> res = page_trend_test(table,             # data as originally tabulated
+    ...                       predicted_ranks=[2, 3, 1],  # our predicted order
+    ...                       )
+    >>> res
+    PageTrendTestResult(statistic=133.5, pvalue=0.0018191161948127822,
+                        method='exact')
+
+    """
+    if not hasattr(_pagel_state, 'state'):
+        _pagel_state.state = _PageL()
+
+    # Possible values of the method parameter and the corresponding function
+    # used to evaluate the p value
+    methods = {"asymptotic": _l_p_asymptotic,
+               "exact": _l_p_exact,
+               "auto": None}
+    if method not in methods:
+        raise ValueError(f"`method` must be in {set(methods)}")
+
+    ranks = np.asarray(data)
+    if ranks.ndim != 2:  # TODO: relax this to accept 3d arrays?
+        raise ValueError("`data` must be a 2d array.")
+
+    m, n = ranks.shape
+    if m < 2 or n < 3:
+        raise ValueError("Page's L is only appropriate for data with two "
+                         "or more rows and three or more columns.")
+
+    if np.any(np.isnan(data)):
+        raise ValueError("`data` contains NaNs, which cannot be ranked "
+                         "meaningfully")
+
+    # ensure NumPy array and rank the data if it's not already ranked
+    if ranked:
+        # Only a basic check on whether data is ranked. Checking that the data
+        # is properly ranked could take as much time as ranking it.
+        if not (ranks.min() >= 1 and ranks.max() <= ranks.shape[1]):
+            raise ValueError("`data` is not properly ranked. Rank the data or "
+                             "pass `ranked=False`.")
+    else:
+        ranks = scipy.stats.rankdata(data, axis=-1)
+
+    # generate predicted ranks if not provided, ensure valid NumPy array
+    if predicted_ranks is None:
+        predicted_ranks = np.arange(1, n+1)
+    else:
+        predicted_ranks = np.asarray(predicted_ranks)
+        if (predicted_ranks.ndim < 1 or
+                (set(predicted_ranks) != set(range(1, n+1)) or
+                 len(predicted_ranks) != n)):
+            raise ValueError(f"`predicted_ranks` must include each integer "
+                             f"from 1 to {n} (the number of columns in "
+                             f"`data`) exactly once.")
+
+    if not isinstance(ranked, bool):
+        raise TypeError("`ranked` must be boolean.")
+
+    # Calculate the L statistic
+    L = _l_vectorized(ranks, predicted_ranks)
+
+    # Calculate the p-value
+    if method == "auto":
+        method = _choose_method(ranks)
+    p_fun = methods[method]  # get the function corresponding with the method
+    p = p_fun(L, m, n)
+
+    page_result = PageTrendTestResult(statistic=L, pvalue=p, method=method)
+    return page_result
+
+
+def _choose_method(ranks):
+    '''Choose method for computing p-value automatically'''
+    m, n = ranks.shape
+    if n > 8 or (m > 12 and n > 3) or m > 20:  # as in [1], [4]
+        method = "asymptotic"
+    else:
+        method = "exact"
+    return method
+
+
+def _l_vectorized(ranks, predicted_ranks):
+    '''Calculate's Page's L statistic for each page of a 3d array'''
+    colsums = ranks.sum(axis=-2, keepdims=True)
+    products = predicted_ranks * colsums
+    Ls = products.sum(axis=-1)
+    Ls = Ls[0] if Ls.size == 1 else Ls.ravel()
+    return Ls
+
+
+def _l_p_asymptotic(L, m, n):
+    '''Calculate the p-value of Page's L from the asymptotic distribution'''
+    # Using [1] as a reference, the asymptotic p-value would be calculated as:
+    # chi_L = (12*L - 3*m*n*(n+1)**2)**2/(m*n**2*(n**2-1)*(n+1))
+    # p = chi2.sf(chi_L, df=1, loc=0, scale=1)/2
+    # but this is insensitive to the direction of the hypothesized ranking
+
+    # See [2] page 151
+    E0 = (m*n*(n+1)**2)/4
+    V0 = (m*n**2*(n+1)*(n**2-1))/144
+    Lambda = (L-E0)/np.sqrt(V0)
+    # This is a one-sided "greater" test - calculate the probability that the
+    # L statistic under H0 would be greater than the observed L statistic
+    p = norm.sf(Lambda)
+    return p
+
+
+def _l_p_exact(L, m, n):
+    '''Calculate the p-value of Page's L exactly'''
+    # [1] uses m, n; [5] uses n, k.
+    # Switch convention here because exact calculation code references [5].
+    L, n, k = int(L), int(m), int(n)
+    _pagel_state.state.set_k(k)
+    return _pagel_state.state.sf(L, n)
+
+
+class _PageL:
+    '''Maintains state between `page_trend_test` executions'''
+
+    def __init__(self):
+        '''Lightweight initialization'''
+        self.all_pmfs = {}
+
+    def set_k(self, k):
+        '''Calculate lower and upper limits of L for single row'''
+        self.k = k
+        # See [5] top of page 52
+        self.a, self.b = (k*(k+1)*(k+2))//6, (k*(k+1)*(2*k+1))//6
+
+    def sf(self, l, n):
+        '''Survival function of Page's L statistic'''
+        ps = [self.pmf(l, n) for l in range(l, n*self.b + 1)]
+        return np.sum(ps)
+
+    def p_l_k_1(self):
+        '''Relative frequency of each L value over all possible single rows'''
+
+        # See [5] Equation (6)
+        ranks = range(1, self.k+1)
+        # generate all possible rows of length k
+        rank_perms = np.array(list(permutations(ranks)))
+        # compute Page's L for all possible rows
+        Ls = (ranks*rank_perms).sum(axis=1)
+        # count occurrences of each L value
+        counts = np.histogram(Ls, np.arange(self.a-0.5, self.b+1.5))[0]
+        # factorial(k) is number of possible permutations
+        return counts/math.factorial(self.k)
+
+    def pmf(self, l, n):
+        '''Recursive function to evaluate p(l, k, n); see [5] Equation 1'''
+
+        if n not in self.all_pmfs:
+            self.all_pmfs[n] = {}
+        if self.k not in self.all_pmfs[n]:
+            self.all_pmfs[n][self.k] = {}
+
+        # Cache results to avoid repeating calculation. Initially this was
+        # written with lru_cache, but this seems faster? Also, we could add
+        # an option to save this for future lookup.
+        if l in self.all_pmfs[n][self.k]:
+            return self.all_pmfs[n][self.k][l]
+
+        if n == 1:
+            ps = self.p_l_k_1()  # [5] Equation 6
+            ls = range(self.a, self.b+1)
+            # not fast, but we'll only be here once
+            self.all_pmfs[n][self.k] = {l: p for l, p in zip(ls, ps)}
+            return self.all_pmfs[n][self.k][l]
+
+        p = 0
+        low = max(l-(n-1)*self.b, self.a)  # [5] Equation 2
+        high = min(l-(n-1)*self.a, self.b)
+
+        # [5] Equation 1
+        for t in range(low, high+1):
+            p1 = self.pmf(l-t, n-1)
+            p2 = self.pmf(t, 1)
+            p += p1*p2
+        self.all_pmfs[n][self.k][l] = p
+        return p
+
+
+# Maintain state for faster repeat calls to page_trend_test w/ method='exact'
+# _PageL() is calculated once per thread and stored as an attribute on
+# this thread-local variable inside page_trend_test().
+_pagel_state = threading.local()
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_probability_distribution.py b/.venv/lib/python3.12/site-packages/scipy/stats/_probability_distribution.py
new file mode 100644
index 0000000000000000000000000000000000000000..71ed8f48920f3031a88cf1285fae25e4bb28eefa
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_probability_distribution.py
@@ -0,0 +1,2091 @@
+# Temporary file separated from _distribution_infrastructure.py
+# to simplify the diff during PR review.
+from abc import ABC, abstractmethod
+from types import GenericAlias
+
+class _ProbabilityDistribution(ABC):
+
+    # generic type compatibility with scipy-stubs
+    __class_getitem__: classmethod = classmethod(GenericAlias)
+
+    @abstractmethod
+    def support(self):
+        r"""Support of the random variable
+
+        The support of a random variable is set of all possible outcomes;
+        i.e., the subset of the domain of argument :math:`x` for which
+        the probability density function :math:`f(x)` is nonzero.
+
+        This function returns lower and upper bounds of the support.
+
+        Returns
+        -------
+        out : tuple of Array
+            The lower and upper bounds of the support.
+
+        See Also
+        --------
+        pdf
+
+        References
+        ----------
+        .. [1] Support (mathematics), *Wikipedia*,
+               https://en.wikipedia.org/wiki/Support_(mathematics)
+
+        Notes
+        -----
+        Suppose a continuous probability distribution has support ``(l, r)``.
+        The following table summarizes the value returned by several
+        methods when the argument is outside the support.
+
+        +----------------+---------------------+---------------------+
+        | Method         | Value for ``x < l`` | Value for ``x > r`` |
+        +================+=====================+=====================+
+        | ``pdf(x)``     | 0                   | 0                   |
+        +----------------+---------------------+---------------------+
+        | ``logpdf(x)``  | -inf                | -inf                |
+        +----------------+---------------------+---------------------+
+        | ``cdf(x)``     | 0                   | 1                   |
+        +----------------+---------------------+---------------------+
+        | ``logcdf(x)``  | -inf                | 0                   |
+        +----------------+---------------------+---------------------+
+        | ``ccdf(x)``    | 1                   | 0                   |
+        +----------------+---------------------+---------------------+
+        | ``logccdf(x)`` | 0                   | -inf                |
+        +----------------+---------------------+---------------------+
+
+        For discrete distributions, the same table is applicable with
+        ``pmf`` and ``logpmf`` substituted for ``pdf`` and ``logpdf``.
+
+        For the ``cdf`` and related methods of continuous distributions, the
+        inequality need not be strict; i.e. the tabulated value is returned
+        when the method is evaluated *at* the corresponding boundary.
+
+        The following table summarizes the value returned by the inverse
+        methods for arguments ``0`` and ``1``, whether the distribution
+        is continuous or discrete.
+
+        +-------------+-----------+-----------+
+        | Method      | ``x = 0`` | ``x = 1`` |
+        +=============+===========+===========+
+        | ``icdf(x)`` | ``l``     | ``r``     |
+        +-------------+-----------+-----------+
+        | ``icdf(x)`` | ``r``     | ``l``     |
+        +-------------+-----------+-----------+
+
+        For the inverse log-functions, the same values are returned
+        for ``x = log(0)`` and ``x = log(1)``. All inverse functions return
+        ``nan`` when evaluated at an argument outside the domain ``0`` to ``1``.
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=-0.5, b=0.5)
+
+        Retrieve the support of the distribution:
+
+        >>> X.support()
+        (-0.5, 0.5)
+
+        For a distribution with infinite support,
+
+        >>> X = stats.Normal()
+        >>> X.support()
+        (-inf, inf)
+
+        Due to underflow, the numerical value returned by the PDF may be zero
+        even for arguments within the support, even if the true value is
+        nonzero. In such cases, the log-PDF may be useful.
+
+        >>> X.pdf([-100., 100.])
+        array([0., 0.])
+        >>> X.logpdf([-100., 100.])
+        array([-5000.91893853, -5000.91893853])
+
+        Use cases for the log-CDF and related methods are analogous.
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def sample(self, shape, *, method, rng):
+        r"""Random sample from the distribution.
+
+        Parameters
+        ----------
+        shape : tuple of ints, default: ()
+            The shape of the sample to draw. If the parameters of the distribution
+            underlying the random variable are arrays of shape ``param_shape``,
+            the output array will be of shape ``shape + param_shape``.
+        method : {None, 'formula', 'inverse_transform'}
+            The strategy used to produce the sample. By default (``None``),
+            the infrastructure chooses between the following options,
+            listed in order of precedence.
+
+            - ``'formula'``: an implementation specific to the distribution
+            - ``'inverse_transform'``: generate a uniformly distributed sample and
+              return the inverse CDF at these arguments.
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a `NotImplementedError``
+            will be raised.
+        rng : `numpy.random.Generator` or `scipy.stats.qmc.QMCEngine`, optional
+            Pseudo- or quasi-random number generator state. When `rng` is None,
+            a new `numpy.random.Generator` is created using entropy from the
+            operating system. Types other than `numpy.random.Generator` and
+            `scipy.stats.qmc.QMCEngine` are passed to `numpy.random.default_rng`
+            to instantiate a ``Generator``.
+
+            If `rng` is an instance of `scipy.stats.qmc.QMCEngine` configured to use
+            scrambling and `shape` is not empty, then each slice along the zeroth
+            axis of the result is a "quasi-independent", low-discrepancy sequence;
+            that is, they are distinct sequences that can be treated as statistically
+            independent for most practical purposes. Separate calls to `sample`
+            produce new quasi-independent, low-discrepancy sequences. The dimensionality
+            (``d``) of the provided ``QMCEngine`` is ignored; new instances of the same
+            class and configuration options (``scramble``, ``optimization``, and
+            ``bits``, where applicable) will be created with ``d=1``.
+
+        References
+        ----------
+        .. [1] Sampling (statistics), *Wikipedia*,
+               https://en.wikipedia.org/wiki/Sampling_(statistics)
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=0., b=1.)
+
+        Generate a pseudorandom sample:
+
+        >>> x = X.sample((1000, 1))
+        >>> octiles = (np.arange(8) + 1) / 8
+        >>> np.count_nonzero(x <= octiles, axis=0)
+        array([ 148,  263,  387,  516,  636,  751,  865, 1000])  # may vary
+
+        >>> X = stats.Uniform(a=np.zeros((3, 1)), b=np.ones(2))
+        >>> X.a.shape,
+        (3, 2)
+        >>> x = X.sample(shape=(5, 4))
+        >>> x.shape
+        (5, 4, 3, 2)
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def moment(self, order, kind, *, method):
+        r"""Raw, central, or standard moment of positive integer order.
+
+        In terms of probability density function :math:`f(x)` and support
+        :math:`\chi`, the "raw" moment (about the origin) of order :math:`n` of
+        a continuous random variable :math:`X` is:
+
+        .. math::
+
+            \mu'_n(X) = \int_{\chi} x^n f(x) dx
+
+        The "central" moment is the raw moment taken about the mean,
+        :math:`\mu = \mu'_1`:
+
+        .. math::
+
+            \mu_n(X) = \int_{\chi} (x - \mu) ^n f(x) dx
+
+        The "standardized" moment is the central moment normalized by the
+        :math:`n^\text{th}` power of the standard deviation
+        :math:`\sigma = \sqrt{\mu_2}` to produce a scale invariant quantity:
+
+        .. math::
+
+            \tilde{\mu}_n(X) = \frac{\mu_n(X)}
+                                    {\sigma^n}
+
+        The definitions for discrete random variables are analogous, with
+        sums over the support replacing the integrals.
+
+        Parameters
+        ----------
+        order : int
+            The integer order of the moment; i.e. :math:`n` in the formulae above.
+        kind : {'raw', 'central', 'standardized'}
+            Whether to return the raw (default), central, or standardized moment
+            defined above.
+        method : {None, 'formula', 'general', 'transform', 'normalize', 'quadrature', 'cache'}
+            The strategy used to evaluate the moment. By default (``None``),
+            the infrastructure chooses between the following options,
+            listed in order of precedence.
+
+            - ``'cache'``: use the value of the moment most recently calculated
+              via another method
+            - ``'formula'``: use a formula for the moment itself
+            - ``'general'``: use a general result that is true for all distributions
+              with finite moments; for instance, the zeroth raw moment is
+              identically 1
+            - ``'transform'``: transform a raw moment to a central moment or
+              vice versa (see Notes)
+            - ``'normalize'``: normalize a central moment to get a standardized
+              or vice versa
+            - ``'quadrature'``: numerically integrate (or, in the discrete case, sum)
+              according to the definition
+
+            Not all `method` options are available for all orders, kinds, and
+            distributions. If the selected `method` is not available, a
+            ``NotImplementedError`` will be raised.
+
+        Returns
+        -------
+        out : array
+            The moment of the random variable of the specified order and kind.
+
+        See Also
+        --------
+        pdf
+        mean
+        variance
+        standard_deviation
+        skewness
+        kurtosis
+        lmoment
+
+        Notes
+        -----
+        Not all distributions have finite moments of all orders; moments of some
+        orders may be undefined or infinite. If a formula for the moment is not
+        specifically implemented for the chosen distribution, SciPy will attempt
+        to compute the moment via a generic method, which may yield a finite
+        result where none exists. This is not a critical bug, but an opportunity
+        for an enhancement.
+
+        The definition of a raw moment in the summary is specific to the raw moment
+        about the origin. The raw moment about any point :math:`a` is:
+
+        .. math::
+
+            E[(X-a)^n] = \int_{\chi} (x-a)^n f(x) dx
+
+        In this notation, a raw moment about the origin is :math:`\mu'_n = E[x^n]`,
+        and a central moment is :math:`\mu_n = E[(x-\mu)^n]`, where :math:`\mu`
+        is the first raw moment; i.e. the mean.
+
+        The ``'transform'`` method takes advantage of the following relationships
+        between moments taken about different points :math:`a` and :math:`b`.
+
+        .. math::
+
+            E[(X-b)^n] =  \sum_{i=0}^n E[(X-a)^i] {n \choose i} (a - b)^{n-i}
+
+        For instance, to transform the raw moment to the central moment, we let
+        :math:`b = \mu` and :math:`a = 0`.
+
+        The distribution infrastructure provides flexibility for distribution
+        authors to implement separate formulas for raw moments, central moments,
+        and standardized moments of any order. By default, the moment of the
+        desired order and kind is evaluated from the formula if such a formula
+        is available; if not, the infrastructure uses any formulas that are
+        available rather than resorting directly to numerical integration.
+        For instance, if formulas for the first three raw moments are
+        available and the third standardized moments is desired, the
+        infrastructure will evaluate the raw moments and perform the transforms
+        and standardization required. The decision tree is somewhat complex,
+        but the strategy for obtaining a moment of a given order and kind
+        (possibly as an intermediate step due to the recursive nature of the
+        transform formula above) roughly follows this order of priority:
+
+        #. Use cache (if order of same moment and kind has been calculated)
+        #. Use formula (if available)
+        #. Transform between raw and central moment and/or normalize to convert
+           between central and standardized moments (if efficient)
+        #. Use a generic result true for most distributions (if available)
+        #. Use quadrature
+
+        References
+        ----------
+        .. [1] Moment, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Moment_(mathematics)
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> from scipy import stats
+        >>> X = stats.Normal(mu=1., sigma=2.)
+
+        Evaluate the first raw moment:
+
+        >>> X.moment(order=1, kind='raw')
+        1.0
+        >>> X.moment(order=1, kind='raw') == X.mean() == X.mu
+        True
+
+        Evaluate the second central moment:
+
+        >>> X.moment(order=2, kind='central')
+        4.0
+        >>> X.moment(order=2, kind='central') == X.variance() == X.sigma**2
+        True
+
+        Evaluate the fourth standardized moment:
+
+        >>> X.moment(order=4, kind='standardized')
+        3.0
+        >>> X.moment(order=4, kind='standardized') == X.kurtosis(convention='non-excess')
+        True
+
+        """  # noqa:E501
+        raise NotImplementedError()
+
+    @abstractmethod
+    def lmoment(self, order, *, standardize, method):
+        r"""L-moment or L-moment ratio of positive integer order.
+
+        The L-moment of order :math:`n` of a continuous random variable :math:`X` is:
+
+        .. math::
+
+            \lambda_n(X) = \frac{1}{n} \sum_{k=0}^{n-1} (-1)^{k} {{n-1}\choose k} E[X_{(n-k)}]
+
+        where :math:`X_{(1)}, \dots, X_{(r)}, \dots, X_{(n)}` are the order statistics
+        of an independent sample of size :math:`n`.
+
+        The L-moment can also be expressed in terms of the random variable's inverse
+        cumulative distribution function :math:`F^{-1}` and the shifted Legendre
+        polynomial :math:`\widetilde{P}_{n-1}`:
+
+        .. math::
+
+            \lambda_n(X) = \int_0^1 F^{-1}(p) \widetilde{P}_{n-1}(p) dp
+
+        For order :math:`n \geq 3`, the "standardized" L-moment, known as the L-moment
+        ratio, is the L-moment normalized by the L-moment of order 2, resulting in a
+        scale invariant quantity:
+
+        .. math::
+
+            \tau_n(X) = \frac{\lambda_n(X)}
+                             {\lambda_2(X)}
+
+        Parameters
+        ----------
+        order : int
+            The positive integer order of the L-moment; i.e. :math:`n` in the formulae
+            above.
+        standardize : bool, default: True
+            Whether to return L-moment ratios for orders 3 and higher. L-moment ratios
+            are analogous to standardized conventional moments: they are the
+            non-standardized L-moments divided by the L-moment of order 2.
+        method : {None, 'formula', 'general', 'order_statistics', 'quadrature_icdf', 'cache'}
+            The strategy used to evaluate the L-moment. By default (``None``),
+            the infrastructure chooses between the following options,
+            listed in order of precedence.
+
+            - ``'cache'``: use the value of the L-moment most recently calculated
+              via another method
+            - ``'formula'``: use a formula specific to the distribution.
+            - ``'general'``: use a general result that is true for all distributions
+              with finite L-moments; for instance, the first L-moment is identically
+              equal to the mean.
+            - ``'quadrature_icdf'``: numerically integrate according to the definition
+              in terms of the inverse cumulative distribution function.
+            - ``'order_statistics'``: compute according to the definition in terms of
+              order statistics.
+
+            Not all `method` options are available for all orders and distributions.
+            If the selected `method` is not available, a ``NotImplementedError`` will
+            be raised.
+
+        Returns
+        -------
+        out : array
+            The L-moment of the random variable of the specified order.
+
+        See Also
+        --------
+        moment
+        order_statistic
+
+        Notes
+        -----
+        L-moments are only defined for distributions with finite mean. If a formula for
+        the L-moment is not specifically implemented for the chosen distribution, SciPy
+        will attempt to compute the moment via a generic method, which may yield a
+        finite result where none exists. This is not a critical bug, but an opportunity
+        for an enhancement.
+
+        SciPy offers only basic capabilities for working with L-moments. For more advanced
+        features, consider the ``lmo`` package [2]_.
+
+        References
+        ----------
+        .. [1] L-moment, *Wikipedia*,
+               https://en.wikipedia.org/wiki/L-moment
+        .. [2] @jorenham, *Lmo*, https://github.com/jorenham/Lmo/
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> X = stats.Normal(mu=1., sigma=2.)
+
+        Evaluate the first L-moment:
+
+        >>> X.lmoment(order=1)
+        1.0
+        >>> X.lmoment(order=1) == X.mean() == X.mu
+        True
+
+        Evaluate the second L-moment:
+
+        >>> X.lmoment(order=2)
+        np.float64(1.1283791670955123)
+        >>> np.allclose(X.lmoment(order=2), X.sigma / np.sqrt(np.pi))
+        True
+
+        Evaluate the fourth L-moment ratio, that is, the L-kurtosis:
+
+        >>> X.lmoment(order=4)
+        np.float64(0.12260171954089069)
+        >>> X.lmoment(order=4) == X.lmoment(order=4, standardize=False) / X.lmoment(order=2)
+        True
+
+        """  # noqa:E501
+        raise NotImplementedError()
+
+    @abstractmethod
+    def mean(self, *, method):
+        r"""Mean (raw first moment about the origin)
+
+        Parameters
+        ----------
+        method : {None, 'formula', 'transform', 'quadrature', 'cache'}
+            Method used to calculate the raw first moment. Not
+            all methods are available for all distributions. See
+            `moment` for details.
+
+        See Also
+        --------
+        moment
+        median
+        mode
+
+        References
+        ----------
+        .. [1] Mean, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Mean#Mean_of_a_probability_distribution
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> from scipy import stats
+        >>> X = stats.Normal(mu=1., sigma=2.)
+
+        Evaluate the variance:
+
+        >>> X.mean()
+        1.0
+        >>> X.mean() == X.moment(order=1, kind='raw') == X.mu
+        True
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def median(self, *, method):
+        r"""Median (50th percentile)
+
+        If a continuous random variable :math:`X` has probability :math:`0.5` of
+        taking on a value less than :math:`m`, then :math:`m` is the median.
+
+        More generally, a median is a value :math:`m` for which:
+
+        .. math::
+
+            P(X ≤ m) ≤ 0.5 ≥ P(X ≥ m)
+
+        For discrete random variables, the median may not be unique, in which
+        case the smallest value satisfying the definition is reported.
+
+        Parameters
+        ----------
+        method : {None, 'formula', 'icdf'}
+            The strategy used to evaluate the median.
+            By default (``None``), the infrastructure chooses between the
+            following options, listed in order of precedence.
+
+            - ``'formula'``: use a formula for the median
+            - ``'icdf'``: evaluate the inverse CDF of 0.5
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The median
+
+        See Also
+        --------
+        mean
+        mode
+        icdf
+
+        References
+        ----------
+        .. [1] Median, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Median#Probability_distributions
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=0., b=10.)
+
+        Compute the median:
+
+        >>> X.median()
+        np.float64(5.0)
+        >>> X.median() == X.icdf(0.5) == X.iccdf(0.5)
+        True
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def mode(self, *, method):
+        r"""Mode (most likely value)
+
+        Informally, the mode is a value that a random variable has the highest
+        probability (density) of assuming. That is, the mode is the element of
+        the support :math:`\chi` that maximizes the probability density (or mass,
+        for discrete random variables) function :math:`f(x)`:
+
+        .. math::
+
+            \text{mode} = \arg\max_{x \in \chi} f(x)
+
+        Parameters
+        ----------
+        method : {None, 'formula', 'optimization'}
+            The strategy used to evaluate the mode.
+            By default (``None``), the infrastructure chooses between the
+            following options, listed in order of precedence.
+
+            - ``'formula'``: use a formula for the median
+            - ``'optimization'``: numerically maximize the PDF/PMF
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The mode
+
+        See Also
+        --------
+        mean
+        median
+        pdf
+
+        Notes
+        -----
+        For some distributions
+
+        #. the mode is not unique (e.g. the uniform distribution);
+        #. the PDF has one or more singularities, and it is debatable whether
+           a singularity is considered to be in the domain and called the mode
+           (e.g. the gamma distribution with shape parameter less than 1); and/or
+        #. the probability density function may have one or more local maxima
+           that are not a global maximum (e.g. mixture distributions).
+
+        In such cases, `mode` will
+
+        #. return a single value,
+        #. consider the mode to occur at a singularity, and/or
+        #. return a local maximum which may or may not be a global maximum.
+
+        If a formula for the mode is not specifically implemented for the
+        chosen distribution, SciPy will attempt to compute the mode
+        numerically, which may not meet the user's preferred definition of a
+        mode. In such cases, the user is encouraged to subclass the
+        distribution and override ``mode``.
+
+        References
+        ----------
+        .. [1] Mode (statistics), *Wikipedia*,
+               https://en.wikipedia.org/wiki/Mode_(statistics)
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> from scipy import stats
+        >>> X = stats.Normal(mu=1., sigma=2.)
+
+        Evaluate the mode:
+
+        >>> X.mode()
+        1.0
+
+        If the mode is not uniquely defined, ``mode`` nonetheless returns a
+        single value.
+
+        >>> X = stats.Uniform(a=0., b=1.)
+        >>> X.mode()
+        0.5
+
+        If this choice does not satisfy your requirements, subclass the
+        distribution and override ``mode``:
+
+        >>> class BetterUniform(stats.Uniform):
+        ...     def mode(self):
+        ...         return self.b
+        >>> X = BetterUniform(a=0., b=1.)
+        >>> X.mode()
+        1.0
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def variance(self, *, method):
+        r"""Variance (central second moment)
+
+        Parameters
+        ----------
+        method : {None, 'formula', 'transform', 'normalize', 'quadrature', 'cache'}
+            Method used to calculate the central second moment. Not
+            all methods are available for all distributions. See
+            `moment` for details.
+
+        See Also
+        --------
+        moment
+        standard_deviation
+        mean
+
+        References
+        ----------
+        .. [1] Variance, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Variance#Absolutely_continuous_random_variable
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> from scipy import stats
+        >>> X = stats.Normal(mu=1., sigma=2.)
+
+        Evaluate the variance:
+
+        >>> X.variance()
+        4.0
+        >>> X.variance() == X.moment(order=2, kind='central') == X.sigma**2
+        True
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def standard_deviation(self, *, method):
+        r"""Standard deviation (square root of the second central moment)
+
+        Parameters
+        ----------
+        method : {None, 'formula', 'transform', 'normalize', 'quadrature', 'cache'}
+            Method used to calculate the central second moment. Not
+            all methods are available for all distributions. See
+            `moment` for details.
+
+        See Also
+        --------
+        variance
+        mean
+        moment
+
+        References
+        ----------
+        .. [1] Standard deviation, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Standard_deviation#Definition_of_population_values
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> from scipy import stats
+        >>> X = stats.Normal(mu=1., sigma=2.)
+
+        Evaluate the standard deviation:
+
+        >>> X.standard_deviation()
+        2.0
+        >>> X.standard_deviation() == X.moment(order=2, kind='central')**0.5 == X.sigma
+        True
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def skewness(self, *, method):
+        r"""Skewness (standardized third moment)
+
+        Parameters
+        ----------
+        method : {None, 'formula', 'general', 'transform', 'normalize', 'cache'}
+            Method used to calculate the standardized third moment. Not
+            all methods are available for all distributions. See
+            `moment` for details.
+
+        See Also
+        --------
+        moment
+        mean
+        variance
+
+        References
+        ----------
+        .. [1] Skewness, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Skewness
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> from scipy import stats
+        >>> X = stats.Normal(mu=1., sigma=2.)
+
+        Evaluate the skewness:
+
+        >>> X.skewness()
+        0.0
+        >>> X.skewness() == X.moment(order=3, kind='standardized')
+        True
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def kurtosis(self, *, method):
+        r"""Kurtosis (standardized fourth moment)
+
+        By default, this is the standardized fourth moment, also known as the
+        "non-excess" or "Pearson" kurtosis (e.g. the kurtosis of the normal
+        distribution is 3). The "excess" or "Fisher" kurtosis (the standardized
+        fourth moment minus 3) is available via the `convention` parameter.
+
+        Parameters
+        ----------
+        method : {None, 'formula', 'general', 'transform', 'normalize', 'cache'}
+            Method used to calculate the standardized fourth moment. Not
+            all methods are available for all distributions. See
+            `moment` for details.
+        convention : {'non-excess', 'excess'}
+            Two distinct conventions are available:
+
+            - ``'non-excess'``: the standardized fourth moment (Pearson's kurtosis)
+            - ``'excess'``: the standardized fourth moment minus 3 (Fisher's kurtosis)
+
+            The default is ``'non-excess'``.
+
+        See Also
+        --------
+        moment
+        mean
+        variance
+
+        References
+        ----------
+        .. [1] Kurtosis, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Kurtosis
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> from scipy import stats
+        >>> X = stats.Normal(mu=1., sigma=2.)
+
+        Evaluate the kurtosis:
+
+        >>> X.kurtosis()
+        3.0
+        >>> (X.kurtosis()
+        ...  == X.kurtosis(convention='excess') + 3.
+        ...  == X.moment(order=4, kind='standardized'))
+        True
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def pdf(self, x, /, *, method):
+        r"""Probability density function
+
+        The probability density function ("PDF"), denoted :math:`f(x)`, is the
+        probability *per unit length* that the random variable will assume the
+        value :math:`x`. Mathematically, it can be defined as the derivative
+        of the cumulative distribution function :math:`F(x)`:
+
+        .. math::
+
+            f(x) = \frac{d}{dx} F(x)
+
+        `pdf` accepts `x` for :math:`x`.
+
+        Parameters
+        ----------
+        x : array_like
+            The argument of the PDF.
+        method : {None, 'formula', 'logexp'}
+            The strategy used to evaluate the PDF. By default (``None``), the
+            infrastructure chooses between the following options, listed in
+            order of precedence.
+
+            - ``'formula'``: use a formula for the PDF itself
+            - ``'logexp'``: evaluate the log-PDF and exponentiate
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The PDF evaluated at the argument `x`.
+
+        See Also
+        --------
+        cdf
+        logpdf
+
+        Notes
+        -----
+        Suppose a continuous probability distribution has support :math:`[l, r]`.
+        By definition of the support, the PDF evaluates to its minimum value
+        of :math:`0` outside the support; i.e. for :math:`x < l` or
+        :math:`x > r`. The maximum of the PDF may be less than or greater than
+        :math:`1`; since the value is a probability *density*, only its integral
+        over the support must equal :math:`1`.
+
+        For discrete distributions, `pdf` returns ``inf`` at supported points
+        and ``0`` elsewhere.
+
+        References
+        ----------
+        .. [1] Probability density function, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Probability_density_function
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=-1., b=1.)
+
+        Evaluate the PDF at the desired argument:
+
+        >>> X.pdf(0.25)
+        0.5
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def logpdf(self, x, /, *, method):
+        r"""Log of the probability density function
+
+        The probability density function ("PDF"), denoted :math:`f(x)`, is the
+        probability *per unit length* that the random variable will assume the
+        value :math:`x`. Mathematically, it can be defined as the derivative
+        of the cumulative distribution function :math:`F(x)`:
+
+        .. math::
+
+            f(x) = \frac{d}{dx} F(x)
+
+        `logpdf` computes the logarithm of the probability density function
+        ("log-PDF"), :math:`\log(f(x))`, but it may be numerically favorable
+        compared to the naive implementation (computing :math:`f(x)` and
+        taking the logarithm).
+
+        `logpdf` accepts `x` for :math:`x`.
+
+        Parameters
+        ----------
+        x : array_like
+            The argument of the log-PDF.
+        method : {None, 'formula', 'logexp'}
+            The strategy used to evaluate the log-PDF. By default (``None``), the
+            infrastructure chooses between the following options, listed in order
+            of precedence.
+
+            - ``'formula'``: use a formula for the log-PDF itself
+            - ``'logexp'``: evaluate the PDF and takes its logarithm
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The log-PDF evaluated at the argument `x`.
+
+        See Also
+        --------
+        pdf
+        logcdf
+
+        Notes
+        -----
+        Suppose a continuous probability distribution has support :math:`[l, r]`.
+        By definition of the support, the log-PDF evaluates to its minimum value
+        of :math:`-\infty` (i.e. :math:`\log(0)`) outside the support; i.e. for
+        :math:`x < l` or :math:`x > r`. The maximum of the log-PDF may be less
+        than or greater than :math:`\log(1) = 0` because the maximum of the PDF
+        can be any positive real.
+
+        For distributions with infinite support, it is common for `pdf` to return
+        a value of ``0`` when the argument is theoretically within the support;
+        this can occur because the true value of the PDF is too small to be
+        represented by the chosen dtype. The log-PDF, however, will often be finite
+        (not ``-inf``) over a much larger domain. Consequently, it may be preferred
+        to work with the logarithms of probabilities and probability densities to
+        avoid underflow.
+
+        For discrete distributions, `logpdf` returns ``inf`` at supported points and
+        ``-inf`` (``log(0)``) elsewhere.
+
+        References
+        ----------
+        .. [1] Probability density function, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Probability_density_function
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=-1.0, b=1.0)
+
+        Evaluate the log-PDF at the desired argument:
+
+        >>> X.logpdf(0.5)
+        -0.6931471805599453
+        >>> np.allclose(X.logpdf(0.5), np.log(X.pdf(0.5)))
+        True
+
+        """
+        raise NotImplementedError()
+
+    def pmf(self, x, /, *, method=None):
+        r"""Probability mass function
+
+        The probability mass function ("PMF"), denoted :math:`f(x)`, is the
+        probability that the random variable :math:`X` will assume the value :math:`x`.
+
+        .. math::
+
+            f(x) = P(X = x)
+
+        `pmf` accepts `x` for :math:`x`.
+
+        Parameters
+        ----------
+        x : array_like
+            The argument of the PMF.
+        method : {None, 'formula', 'logexp'}
+            The strategy used to evaluate the PMF. By default (``None``), the
+            infrastructure chooses between the following options, listed in
+            order of precedence.
+
+            - ``'formula'``: use a formula for the PMF itself
+            - ``'logexp'``: evaluate the log-PMF and exponentiate
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The PMF evaluated at the argument `x`.
+
+        See Also
+        --------
+        cdf
+        logpmf
+
+        Notes
+        -----
+        Suppose a discrete probability distribution has support over the integers
+        :math:`{l, l+1, ..., r-1, r}`.
+        By definition of the support, the PMF evaluates to its minimum value
+        of :math:`0` for non-integral :math:`x` and for :math:`x` outside the support;
+        i.e. for :math:`x < l` or :math:`x > r`.
+
+        For continuous distributions, `pmf` returns ``0`` at all real arguments.
+
+        References
+        ----------
+        .. [1] Probability mass function, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Probability_mass_function
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> from scipy import stats
+        >>> X = stats.Binomial(n=10, p=0.5)
+
+        Evaluate the PMF at the desired argument:
+
+        >>> X.pmf(5)
+        np.float64(0.24609375)
+
+        """
+        raise NotImplementedError()
+
+    def logpmf(self, x, /, *, method=None):
+        r"""Log of the probability mass function
+
+        The probability mass function ("PMF"), denoted :math:`f(x)`, is the
+        probability that the random variable :math:`X` will assume the value :math:`x`.
+
+        .. math::
+
+            f(x) = \frac{d}{dx} F(x)
+
+        `logpmf` computes the logarithm of the probability mass function
+        ("log-PMF"), :math:`\log(f(x))`, but it may be numerically favorable
+        compared to the naive implementation (computing :math:`f(x)` and
+        taking the logarithm).
+
+        `logpmf` accepts `x` for :math:`x`.
+
+        Parameters
+        ----------
+        x : array_like
+            The argument of the log-PMF.
+        method : {None, 'formula', 'logexp'}
+            The strategy used to evaluate the log-PMF. By default (``None``), the
+            infrastructure chooses between the following options, listed in order
+            of precedence.
+
+            - ``'formula'``: use a formula for the log-PMF itself
+            - ``'logexp'``: evaluate the PMF and takes its logarithm
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The log-PMF evaluated at the argument `x`.
+
+        See Also
+        --------
+        pmf
+        logcdf
+
+        Notes
+        -----
+        Suppose a discrete probability distribution has support over the integers
+        :math:`{l, l+1, ..., r-1, r}`.
+        By definition of the support, the log-PMF evaluates to its minimum value
+        of :math:`-\infty` (i.e. :math:`\log(0)`) for non-integral :math:`x` and
+        for :math:`x` outside the support; i.e. for :math:`x < l` or :math:`x > r`.
+
+        For distributions with infinite support, it is common for `pmf` to return
+        a value of ``0`` when the argument is theoretically within the support;
+        this can occur because the true value of the PMF is too small to be
+        represented by the chosen dtype. The log-PMF, however, will often be finite
+        (not ``-inf``) over a much larger domain. Consequently, it may be preferred
+        to work with the logarithms of probabilities and probability densities to
+        avoid underflow.
+
+        References
+        ----------
+        .. [1] Probability density function, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Probability_density_function
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> X = stats.Binomial(n=10, p=0.5)
+
+        Evaluate the log-PMF at the desired argument:
+
+        >>> X.logpmf(5)
+        np.float64(-1.4020427180880297)
+        >>> np.allclose(X.logpmf(5), np.log(X.pmf(5)))
+        True
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def cdf(self, x, y, /, *, method):
+        r"""Cumulative distribution function
+
+        The cumulative distribution function ("CDF"), denoted :math:`F(x)`, is
+        the probability the random variable :math:`X` will assume a value
+        less than or equal to :math:`x`:
+
+        .. math::
+
+            F(x) = P(X ≤ x)
+
+        A two-argument variant of this function is also defined as the
+        probability the random variable :math:`X` will assume a value between
+        :math:`x` and :math:`y`.
+
+        .. math::
+
+            F(x, y) = P(x ≤ X ≤ y)
+
+        `cdf` accepts `x` for :math:`x` and `y` for :math:`y`.
+
+        Parameters
+        ----------
+        x, y : array_like
+            The arguments of the CDF. `x` is required; `y` is optional.
+        method : {None, 'formula', 'logexp', 'complement', 'quadrature', 'subtraction'}
+            The strategy used to evaluate the CDF.
+            By default (``None``), the one-argument form of the function
+            chooses between the following options, listed in order of precedence.
+
+            - ``'formula'``: use a formula for the CDF itself
+            - ``'logexp'``: evaluate the log-CDF and exponentiate
+            - ``'complement'``: evaluate the CCDF and take the complement
+            - ``'quadrature'``: numerically integrate the PDF (or, in the discrete
+              case, sum the PMF)
+
+            In place of ``'complement'``, the two-argument form accepts:
+
+            - ``'subtraction'``: compute the CDF at each argument and take
+              the difference.
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The CDF evaluated at the provided argument(s).
+
+        See Also
+        --------
+        logcdf
+        ccdf
+
+        Notes
+        -----
+        Suppose a continuous probability distribution has support :math:`[l, r]`.
+        The CDF :math:`F(x)` is related to the probability density function
+        :math:`f(x)` by:
+
+        .. math::
+
+            F(x) = \int_l^x f(u) du
+
+        The two argument version is:
+
+        .. math::
+
+            F(x, y) = \int_x^y f(u) du = F(y) - F(x)
+
+        The CDF evaluates to its minimum value of :math:`0` for :math:`x ≤ l`
+        and its maximum value of :math:`1` for :math:`x ≥ r`.
+
+        Suppose a discrete probability distribution has support :math:`[l, r]`.
+        The CDF :math:`F(x)` is related to the probability mass function
+        :math:`f(x)` by:
+
+        .. math::
+
+            F(x) = \sum_{u=l}^{\lfloor x \rfloor} f(u)
+
+        The CDF evaluates to its minimum value of :math:`0` for :math:`x < l`
+        and its maximum value of :math:`1` for :math:`x ≥ r`.
+
+        The CDF is also known simply as the "distribution function".
+
+        References
+        ----------
+        .. [1] Cumulative distribution function, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Cumulative_distribution_function
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=-0.5, b=0.5)
+
+        Evaluate the CDF at the desired argument:
+
+        >>> X.cdf(0.25)
+        0.75
+
+        Evaluate the cumulative probability between two arguments:
+
+        >>> X.cdf(-0.25, 0.25) == X.cdf(0.25) - X.cdf(-0.25)
+        True
+
+        """  # noqa: E501
+        raise NotImplementedError()
+
+    @abstractmethod
+    def icdf(self, p, /, *, method):
+        r"""Inverse of the cumulative distribution function.
+
+        For monotonic continuous distributions, the inverse of the cumulative
+        distribution function ("inverse CDF"), denoted :math:`F^{-1}(p)`, is the
+        argument :math:`x` for which the cumulative distribution function
+        :math:`F(x)` evaluates to :math:`p`.
+
+        .. math::
+
+            F^{-1}(p) = x \quad \text{s.t.} \quad F(x) = p
+
+        When a strict "inverse" of the cumulative distribution function does not
+        exist (e.g. discrete random variables), the "inverse CDF" is defined by
+        convention as the smallest value within the support :math:`\chi` for which
+        :math:`F(x)` is at least :math:`p`.
+
+        .. math::
+
+            F^{-1}(p) = \min_\chi \quad \text{s.t.} \quad F(x) ≥ p
+
+        `icdf` accepts `p` for :math:`p \in [0, 1]`.
+
+        Parameters
+        ----------
+        p : array_like
+            The argument of the inverse CDF.
+        method : {None, 'formula', 'complement', 'inversion'}
+            The strategy used to evaluate the inverse CDF.
+            By default (``None``), the infrastructure chooses between the
+            following options, listed in order of precedence.
+
+            - ``'formula'``: use a formula for the inverse CDF itself
+            - ``'complement'``: evaluate the inverse CCDF at the
+              complement of `p`
+            - ``'inversion'``: solve numerically for the argument at which the
+              CDF is equal to `p`
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The inverse CDF evaluated at the provided argument.
+
+        See Also
+        --------
+        cdf
+        ilogcdf
+
+        Notes
+        -----
+        Suppose a probability distribution has support :math:`[l, r]`. The
+        inverse CDF returns its minimum value of :math:`l` at :math:`p = 0`
+        and its maximum value of :math:`r` at :math:`p = 1`. Because the CDF
+        has range :math:`[0, 1]`, the inverse CDF is only defined on the
+        domain :math:`[0, 1]`; for :math:`p < 0` and :math:`p > 1`, `icdf`
+        returns ``nan``.
+
+        The inverse CDF is also known as the quantile function, percentile function,
+        and percent-point function.
+
+        References
+        ----------
+        .. [1] Quantile function, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Quantile_function
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=-0.5, b=0.5)
+
+        Evaluate the inverse CDF at the desired argument:
+
+        >>> X.icdf(0.25)
+        -0.25
+        >>> np.allclose(X.cdf(X.icdf(0.25)), 0.25)
+        True
+
+        This function returns NaN when the argument is outside the domain.
+
+        >>> X.icdf([-0.1, 0, 1, 1.1])
+        array([ nan, -0.5,  0.5,  nan])
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def ccdf(self, x, y, /, *, method):
+        r"""Complementary cumulative distribution function
+
+        The complementary cumulative distribution function ("CCDF"), denoted
+        :math:`G(x)`, is the complement of the cumulative distribution function
+        :math:`F(x)`; i.e., probability the random variable :math:`X` will
+        assume a value greater than :math:`x`:
+
+        .. math::
+
+            G(x) = 1 - F(x) = P(X > x)
+
+        A two-argument variant of this function is:
+
+        .. math::
+
+            G(x, y) = 1 - F(x, y) = P(X < x \text{ or } X > y)
+
+        `ccdf` accepts `x` for :math:`x` and `y` for :math:`y`.
+
+        Parameters
+        ----------
+        x, y : array_like
+            The arguments of the CCDF. `x` is required; `y` is optional.
+        method : {None, 'formula', 'logexp', 'complement', 'quadrature', 'addition'}
+            The strategy used to evaluate the CCDF.
+            By default (``None``), the infrastructure chooses between the
+            following options, listed in order of precedence.
+
+            - ``'formula'``: use a formula for the CCDF itself
+            - ``'logexp'``: evaluate the log-CCDF and exponentiate
+            - ``'complement'``: evaluate the CDF and take the complement
+            - ``'quadrature'``: numerically integrate the PDF (or, in the discrete
+              case, sum the PMF)
+
+            The two-argument form chooses between:
+
+            - ``'formula'``: use a formula for the CCDF itself
+            - ``'addition'``: compute the CDF at `x` and the CCDF at `y`, then add
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The CCDF evaluated at the provided argument(s).
+
+        See Also
+        --------
+        cdf
+        logccdf
+
+        Notes
+        -----
+        Suppose a continuous probability distribution has support :math:`[l, r]`.
+        The CCDF :math:`G(x)` is related to the probability density function
+        :math:`f(x)` by:
+
+        .. math::
+
+            G(x) = \int_x^r f(u) du
+
+        The two argument version is:
+
+        .. math::
+
+            G(x, y) = \int_l^x f(u) du + \int_y^r f(u) du
+
+        The CCDF returns its minimum value of :math:`0` for :math:`x ≥ r`
+        and its maximum value of :math:`1` for :math:`x ≤ l`.
+
+        Suppose a discrete probability distribution has support :math:`[l, r]`.
+        The CCDF :math:`G(x)` is related to the probability mass function
+        :math:`f(x)` by:
+
+        .. math::
+
+            G(x) = \sum_{u=\lfloor x + 1 \rfloor}^{r} f(u)
+
+        The CCDF evaluates to its minimum value of :math:`0` for :math:`x ≥ r`
+        and its maximum value of :math:`1` for :math:`x < l`.
+
+        The CCDF is also known as the "survival function".
+
+        References
+        ----------
+        .. [1] Cumulative distribution function, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Cumulative_distribution_function#Derived_functions
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=-0.5, b=0.5)
+
+        Evaluate the CCDF at the desired argument:
+
+        >>> X.ccdf(0.25)
+        0.25
+        >>> np.allclose(X.ccdf(0.25), 1-X.cdf(0.25))
+        True
+
+        Evaluate the complement of the cumulative probability between two arguments:
+
+        >>> X.ccdf(-0.25, 0.25) == X.cdf(-0.25) + X.ccdf(0.25)
+        True
+
+        """  # noqa: E501
+        raise NotImplementedError()
+
+    @abstractmethod
+    def iccdf(self, p, /, *, method):
+        r"""Inverse complementary cumulative distribution function.
+
+        The inverse complementary cumulative distribution function ("inverse CCDF"),
+        denoted :math:`G^{-1}(p)`, is the argument :math:`x` for which the
+        complementary cumulative distribution function :math:`G(x)` evaluates to
+        :math:`p`.
+
+        .. math::
+
+            G^{-1}(p) = x \quad \text{s.t.} \quad G(x) = p
+
+        When a strict "inverse" of the complementary cumulative distribution function
+        does not exist (e.g. discrete random variables), the "inverse CCDF" is defined
+        by convention as the smallest value within the support :math:`\chi` for which
+        :math:`G(x)` is no greater than :math:`p`.
+
+        .. math::
+
+            G^{-1}(p) = \min_\chi \quad \text{s.t.} \quad G(x) ≤ p
+
+        `iccdf` accepts `p` for :math:`p \in [0, 1]`.
+
+        Parameters
+        ----------
+        p : array_like
+            The argument of the inverse CCDF.
+        method : {None, 'formula', 'complement', 'inversion'}
+            The strategy used to evaluate the inverse CCDF.
+            By default (``None``), the infrastructure chooses between the
+            following options, listed in order of precedence.
+
+            - ``'formula'``: use a formula for the inverse CCDF itself
+            - ``'complement'``: evaluate the inverse CDF at the
+              complement of `p`
+            - ``'inversion'``: solve numerically for the argument at which the
+              CCDF is equal to `p`
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The inverse CCDF evaluated at the provided argument.
+
+        Notes
+        -----
+        Suppose a probability distribution has support :math:`[l, r]`. The
+        inverse CCDF returns its minimum value of :math:`l` at :math:`p = 1`
+        and its maximum value of :math:`r` at :math:`p = 0`. Because the CCDF
+        has range :math:`[0, 1]`, the inverse CCDF is only defined on the
+        domain :math:`[0, 1]`; for :math:`p < 0` and :math:`p > 1`, ``iccdf``
+        returns ``nan``.
+
+        See Also
+        --------
+        icdf
+        ilogccdf
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=-0.5, b=0.5)
+
+        Evaluate the inverse CCDF at the desired argument:
+
+        >>> X.iccdf(0.25)
+        0.25
+        >>> np.allclose(X.iccdf(0.25), X.icdf(1-0.25))
+        True
+
+        This function returns NaN when the argument is outside the domain.
+
+        >>> X.iccdf([-0.1, 0, 1, 1.1])
+        array([ nan,  0.5, -0.5,  nan])
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def logcdf(self, x, y, /, *, method):
+        r"""Log of the cumulative distribution function
+
+        The cumulative distribution function ("CDF"), denoted :math:`F(x)`, is
+        the probability the random variable :math:`X` will assume a value
+        less than or equal to :math:`x`:
+
+        .. math::
+
+            F(x) = P(X ≤ x)
+
+        A two-argument variant of this function is also defined as the
+        probability the random variable :math:`X` will assume a value between
+        :math:`x` and :math:`y`.
+
+        .. math::
+
+            F(x, y) = P(x ≤ X ≤ y)
+
+        `logcdf` computes the logarithm of the cumulative distribution function
+        ("log-CDF"), :math:`\log(F(x))`/:math:`\log(F(x, y))`, but it may be
+        numerically favorable compared to the naive implementation (computing
+        the CDF and taking the logarithm).
+
+        `logcdf` accepts `x` for :math:`x` and `y` for :math:`y`.
+
+        Parameters
+        ----------
+        x, y : array_like
+            The arguments of the log-CDF. `x` is required; `y` is optional.
+        method : {None, 'formula', 'logexp', 'complement', 'quadrature', 'subtraction'}
+            The strategy used to evaluate the log-CDF.
+            By default (``None``), the one-argument form of the function
+            chooses between the following options, listed in order of precedence.
+
+            - ``'formula'``: use a formula for the log-CDF itself
+            - ``'logexp'``: evaluate the CDF and take the logarithm
+            - ``'complement'``: evaluate the log-CCDF and take the
+              logarithmic complement (see Notes)
+            - ``'quadrature'``: numerically log-integrate the log-PDF (or, in the
+              discrete case, log-sum the log-PMF)
+
+            In place of ``'complement'``, the two-argument form accepts:
+
+            - ``'subtraction'``: compute the log-CDF at each argument and take
+              the logarithmic difference (see Notes)
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The log-CDF evaluated at the provided argument(s).
+
+        See Also
+        --------
+        cdf
+        logccdf
+
+        Notes
+        -----
+        Suppose a continuous probability distribution has support :math:`[l, r]`.
+        The log-CDF evaluates to its minimum value of :math:`\log(0) = -\infty`
+        for :math:`x ≤ l` and its maximum value of :math:`\log(1) = 0` for
+        :math:`x ≥ r`. An analogous statement can be made for discrete distributions,
+        but the inequality governing the minimum value is strict.
+
+        For distributions with infinite support, it is common for
+        `cdf` to return a value of ``0`` when the argument
+        is theoretically within the support; this can occur because the true value
+        of the CDF is too small to be represented by the chosen dtype. `logcdf`,
+        however, will often return a finite (not ``-inf``) result over a much larger
+        domain. Similarly, `logcdf` may provided a strictly negative result with
+        arguments for which `cdf` would return ``1.0``. Consequently, it may be
+        preferred to work with the logarithms of probabilities to avoid underflow
+        and related limitations of floating point numbers.
+
+        The "logarithmic complement" of a number :math:`z` is mathematically
+        equivalent to :math:`\log(1-\exp(z))`, but it is computed to avoid loss
+        of precision when :math:`\exp(z)` is nearly :math:`0` or :math:`1`.
+        Similarly, the term "logarithmic difference" of :math:`w` and :math:`z`
+        is used here to mean :math:`\log(\exp(w)-\exp(z))`.
+
+        If ``y < x``, the CDF is negative, and therefore the log-CCDF
+        is complex with imaginary part :math:`\pi`. For
+        consistency, the result of this function always has complex dtype
+        when `y` is provided, regardless of the value of the imaginary part.
+
+        References
+        ----------
+        .. [1] Cumulative distribution function, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Cumulative_distribution_function
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=-0.5, b=0.5)
+
+        Evaluate the log-CDF at the desired argument:
+
+        >>> X.logcdf(0.25)
+        -0.287682072451781
+        >>> np.allclose(X.logcdf(0.), np.log(X.cdf(0.)))
+        True
+
+        """  # noqa: E501
+        raise NotImplementedError()
+
+    @abstractmethod
+    def ilogcdf(self, logp, /, *, method):
+        r"""Inverse of the logarithm of the cumulative distribution function.
+
+        The inverse of the logarithm of the cumulative distribution function
+        ("inverse log-CDF") is the argument :math:`x` for which the logarithm
+        of the cumulative distribution function :math:`\log(F(x))` evaluates
+        to :math:`\log(p)`.
+
+        Mathematically, it is equivalent to :math:`F^{-1}(\exp(y))`, where
+        :math:`y = \log(p)`, but it may be numerically favorable compared to
+        the naive implementation (computing :math:`p = \exp(y)`, then
+        :math:`F^{-1}(p)`).
+
+        `ilogcdf` accepts `logp` for :math:`\log(p) ≤ 0`.
+
+        Parameters
+        ----------
+        logp : array_like
+            The argument of the inverse log-CDF.
+        method : {None, 'formula', 'complement', 'inversion'}
+            The strategy used to evaluate the inverse log-CDF.
+            By default (``None``), the infrastructure chooses between the
+            following options, listed in order of precedence.
+
+            - ``'formula'``: use a formula for the inverse log-CDF itself
+            - ``'complement'``: evaluate the inverse log-CCDF at the
+              logarithmic complement of `logp` (see Notes)
+            - ``'inversion'``: solve numerically for the argument at which the
+              log-CDF is equal to `logp`
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The inverse log-CDF evaluated at the provided argument.
+
+        See Also
+        --------
+        icdf
+        logcdf
+
+        Notes
+        -----
+        Suppose a probability distribution has support :math:`[l, r]`.
+        The inverse log-CDF returns its minimum value of :math:`l` at
+        :math:`\log(p) = \log(0) = -\infty` and its maximum value of :math:`r` at
+        :math:`\log(p) = \log(1) = 0`. Because the log-CDF has range
+        :math:`[-\infty, 0]`, the inverse log-CDF is only defined on the
+        negative reals; for :math:`\log(p) > 0`, `ilogcdf` returns ``nan``.
+
+        Occasionally, it is needed to find the argument of the CDF for which
+        the resulting probability is very close to ``0`` or ``1`` - too close to
+        represent accurately with floating point arithmetic. In many cases,
+        however, the *logarithm* of this resulting probability may be
+        represented in floating point arithmetic, in which case this function
+        may be used to find the argument of the CDF for which the *logarithm*
+        of the resulting probability is :math:`y = \log(p)`.
+
+        The "logarithmic complement" of a number :math:`z` is mathematically
+        equivalent to :math:`\log(1-\exp(z))`, but it is computed to avoid loss
+        of precision when :math:`\exp(z)` is nearly :math:`0` or :math:`1`.
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=-0.5, b=0.5)
+
+        Evaluate the inverse log-CDF at the desired argument:
+
+        >>> X.ilogcdf(-0.25)
+        0.2788007830714034
+        >>> np.allclose(X.ilogcdf(-0.25), X.icdf(np.exp(-0.25)))
+        True
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def logccdf(self, x, y, /, *, method):
+        r"""Log of the complementary cumulative distribution function
+
+        The complementary cumulative distribution function ("CCDF"), denoted
+        :math:`G(x)` is the complement of the cumulative distribution function
+        :math:`F(x)`; i.e., probability the random variable :math:`X` will
+        assume a value greater than :math:`x`:
+
+        .. math::
+
+            G(x) = 1 - F(x) = P(X > x)
+
+         A two-argument variant of this function is:
+
+        .. math::
+
+            G(x, y) = 1 - F(x, y) = P(X < x \quad \text{or} \quad X > y)
+
+        `logccdf` computes the logarithm of the complementary cumulative
+        distribution function ("log-CCDF"), :math:`\log(G(x))`/:math:`\log(G(x, y))`,
+        but it may be numerically favorable compared to the naive implementation
+        (computing the CDF and taking the logarithm).
+
+        `logccdf` accepts `x` for :math:`x` and `y` for :math:`y`.
+
+        Parameters
+        ----------
+        x, y : array_like
+            The arguments of the log-CCDF. `x` is required; `y` is optional.
+        method : {None, 'formula', 'logexp', 'complement', 'quadrature', 'addition'}
+            The strategy used to evaluate the log-CCDF.
+            By default (``None``), the one-argument form of the function
+            chooses between the following options, listed in order of precedence.
+
+            - ``'formula'``: use a formula for the log CCDF itself
+            - ``'logexp'``: evaluate the CCDF and take the logarithm
+            - ``'complement'``: evaluate the log-CDF and take the
+              logarithmic complement (see Notes)
+            - ``'quadrature'``: numerically log-integrate the log-PDF (or, in the
+              discrete case, log-sum the log-PMF)
+
+            The two-argument form chooses between:
+
+            - ``'formula'``: use a formula for the log CCDF itself
+            - ``'addition'``: compute the log-CDF at `x` and the log-CCDF at `y`,
+              then take the logarithmic sum (see Notes)
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The log-CCDF evaluated at the provided argument(s).
+
+        See Also
+        --------
+        ccdf
+        logcdf
+
+        Notes
+        -----
+        Suppose a continuous probability distribution has support :math:`[l, r]`.
+        The log-CCDF returns its minimum value of :math:`\log(0)=-\infty` for
+        :math:`x ≥ r` and its maximum value of :math:`\log(1) = 0` for
+        :math:`x ≤ l`. An analogous statement can be made for discrete distributions,
+        but the inequality governing the maximum value is strict.
+
+        For distributions with infinite support, it is common for
+        `ccdf` to return a value of ``0`` when the argument
+        is theoretically within the support; this can occur because the true value
+        of the CCDF is too small to be represented by the chosen dtype. The log
+        of the CCDF, however, will often be finite (not ``-inf``) over a much larger
+        domain. Similarly, `logccdf` may provided a strictly negative result with
+        arguments for which `ccdf` would return ``1.0``. Consequently, it may be
+        preferred to work with the logarithms of probabilities to avoid underflow
+        and related limitations of floating point numbers.
+
+        The "logarithmic complement" of a number :math:`z` is mathematically
+        equivalent to :math:`\log(1-\exp(z))`, but it is computed to avoid loss
+        of precision when :math:`\exp(z)` is nearly :math:`0` or :math:`1`.
+        Similarly, the term "logarithmic sum" of :math:`w` and :math:`z`
+        is used here to mean the :math:`\log(\exp(w)+\exp(z))`, AKA
+        :math:`\text{LogSumExp}(w, z)`.
+
+        References
+        ----------
+        .. [1] Cumulative distribution function, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Cumulative_distribution_function#Derived_functions
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=-0.5, b=0.5)
+
+        Evaluate the log-CCDF at the desired argument:
+
+        >>> X.logccdf(0.25)
+        -1.3862943611198906
+        >>> np.allclose(X.logccdf(0.), np.log(X.ccdf(0.)))
+        True
+
+        """  # noqa: E501
+        raise NotImplementedError()
+
+    @abstractmethod
+    def ilogccdf(self, logp, /, *, method):
+        r"""Inverse of the log of the complementary cumulative distribution function.
+
+        The inverse of the logarithm of the complementary cumulative distribution
+        function ("inverse log-CCDF") is the argument :math:`x` for which the logarithm
+        of the complementary cumulative distribution function :math:`\log(G(x))`
+        evaluates to :math:`\log(p)`.
+
+        Mathematically, it is equivalent to :math:`G^{-1}(\exp(y))`, where
+        :math:`y = \log(p)`, but it may be numerically favorable compared to the naive
+        implementation (computing :math:`p = \exp(y)`, then :math:`G^{-1}(p)`).
+
+        `ilogccdf` accepts `logp` for :math:`\log(p) ≤ 0`.
+
+        Parameters
+        ----------
+        x : array_like
+            The argument of the inverse log-CCDF.
+        method : {None, 'formula', 'complement', 'inversion'}
+            The strategy used to evaluate the inverse log-CCDF.
+            By default (``None``), the infrastructure chooses between the
+            following options, listed in order of precedence.
+
+            - ``'formula'``: use a formula for the inverse log-CCDF itself
+            - ``'complement'``: evaluate the inverse log-CDF at the
+              logarithmic complement of `x` (see Notes)
+            - ``'inversion'``: solve numerically for the argument at which the
+              log-CCDF is equal to `x`
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The inverse log-CCDF evaluated at the provided argument.
+
+        Notes
+        -----
+        Suppose a probability distribution has support :math:`[l, r]`. The
+        inverse log-CCDF returns its minimum value of :math:`l` at
+        :math:`\log(p) = \log(1) = 0` and its maximum value of :math:`r` at
+        :math:`\log(p) = \log(0) = -\infty`. Because the log-CCDF has range
+        :math:`[-\infty, 0]`, the inverse log-CDF is only defined on the
+        negative reals; for :math:`\log(p) > 0`, `ilogccdf` returns ``nan``.
+
+        Occasionally, it is needed to find the argument of the CCDF for which
+        the resulting probability is very close to ``0`` or ``1`` - too close to
+        represent accurately with floating point arithmetic. In many cases,
+        however, the *logarithm* of this resulting probability may be
+        represented in floating point arithmetic, in which case this function
+        may be used to find the argument of the CCDF for which the *logarithm*
+        of the resulting probability is :math:`y = \log(p)`.
+
+        The "logarithmic complement" of a number :math:`z` is mathematically
+        equivalent to :math:`\log(1-\exp(z))`, but it is computed to avoid loss
+        of precision when :math:`\exp(z)` is nearly :math:`0` or :math:`1`.
+
+        See Also
+        --------
+        iccdf
+        ilogccdf
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=-0.5, b=0.5)
+
+        Evaluate the inverse log-CCDF at the desired argument:
+
+        >>> X.ilogccdf(-0.25)
+        -0.2788007830714034
+        >>> np.allclose(X.ilogccdf(-0.25), X.iccdf(np.exp(-0.25)))
+        True
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def logentropy(self, *, method):
+        r"""Logarithm of the differential entropy
+
+        In terms of probability density function :math:`f(x)` and support
+        :math:`\chi`, the differential entropy (or simply "entropy") of a
+        continuous random variable :math:`X` is:
+
+        .. math::
+
+            h(X) = - \int_{\chi} f(x) \log f(x) dx
+
+        The definition for a discrete random variable is analogous, with the PMF
+        replacing the PDF and a sum over the support replacing the integral.
+
+        `logentropy` computes the logarithm of the differential entropy
+        ("log-entropy"), :math:`\log(h(X))`, but it may be numerically favorable
+        compared to the naive implementation (computing :math:`h(X)` then
+        taking the logarithm).
+
+        Parameters
+        ----------
+        method : {None, 'formula', 'logexp', 'quadrature}
+            The strategy used to evaluate the log-entropy. By default
+            (``None``), the infrastructure chooses between the following options,
+            listed in order of precedence.
+
+            - ``'formula'``: use a formula for the log-entropy itself
+            - ``'logexp'``: evaluate the entropy and take the logarithm
+            - ``'quadrature'``: numerically log-integrate (or, in the discrete
+              case, log-sum) the logarithm of the entropy integrand (summand)
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The log-entropy.
+
+        See Also
+        --------
+        entropy
+        logpdf
+
+        Notes
+        -----
+        The differential entropy of a continuous distribution can be negative.
+        In this case, the log-entropy is complex with imaginary part :math:`\pi`.
+        For consistency, the result of this function always has complex dtype,
+        regardless of the value of the imaginary part.
+
+        References
+        ----------
+        .. [1] Differential entropy, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Differential_entropy
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=-1., b=1.)
+
+        Evaluate the log-entropy:
+
+        >>> X.logentropy()
+        (-0.3665129205816642+0j)
+        >>> np.allclose(np.exp(X.logentropy()), X.entropy())
+        True
+
+        For a random variable with negative entropy, the log-entropy has an
+        imaginary part equal to `np.pi`.
+
+        >>> X = stats.Uniform(a=-.1, b=.1)
+        >>> X.entropy(), X.logentropy()
+        (-1.6094379124341007, (0.4758849953271105+3.141592653589793j))
+
+        """
+        raise NotImplementedError()
+
+    @abstractmethod
+    def entropy(self, *, method):
+        r"""Differential entropy
+
+        In terms of probability density function :math:`f(x)` and support
+        :math:`\chi`, the differential entropy (or simply "entropy") of a
+        continuous random variable :math:`X` is:
+
+        .. math::
+
+            h(X) = - \int_{\chi} f(x) \log f(x) dx
+
+        The definition for a discrete random variable is analogous, with the
+        PMF replacing the PDF and a sum over the support replacing the integral.
+
+        Parameters
+        ----------
+        method : {None, 'formula', 'logexp', 'quadrature'}
+            The strategy used to evaluate the entropy. By default (``None``),
+            the infrastructure chooses between the following options, listed
+            in order of precedence.
+
+            - ``'formula'``: use a formula for the entropy itself
+            - ``'logexp'``: evaluate the log-entropy and exponentiate
+            - ``'quadrature'``:  numerically integrate (or, in the discrete
+              case, sum) the entropy integrand (summand)
+
+
+            Not all `method` options are available for all distributions.
+            If the selected `method` is not available, a ``NotImplementedError``
+            will be raised.
+
+        Returns
+        -------
+        out : array
+            The entropy of the random variable.
+
+        See Also
+        --------
+        logentropy
+        pdf
+
+        Notes
+        -----
+        This function calculates the entropy using the natural logarithm; i.e.
+        the logarithm with base :math:`e`. Consequently, the value is expressed
+        in (dimensionless) "units" of nats. To convert the entropy to different
+        units (i.e. corresponding with a different base), divide the result by
+        the natural logarithm of the desired base.
+
+        References
+        ----------
+        .. [1] Differential entropy, *Wikipedia*,
+               https://en.wikipedia.org/wiki/Differential_entropy
+
+        Examples
+        --------
+        Instantiate a distribution with the desired parameters:
+
+        >>> from scipy import stats
+        >>> X = stats.Uniform(a=-1., b=1.)
+
+        Evaluate the entropy:
+
+        >>> X.entropy()
+        0.6931471805599454
+
+        """
+        raise NotImplementedError()
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_qmc.py b/.venv/lib/python3.12/site-packages/scipy/stats/_qmc.py
new file mode 100644
index 0000000000000000000000000000000000000000..3358bc68222035ddc02a5f7977041bd313f3d0a9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_qmc.py
@@ -0,0 +1,2951 @@
+"""Quasi-Monte Carlo engines and helpers."""
+import copy
+import math
+import numbers
+import os
+import warnings
+from abc import ABC, abstractmethod
+from functools import partial
+from typing import (
+    ClassVar,
+    Literal,
+    overload,
+    TYPE_CHECKING,
+)
+from collections.abc import Callable
+
+import numpy as np
+
+from scipy._lib._util import DecimalNumber, GeneratorType, IntNumber, SeedType
+
+if TYPE_CHECKING:
+    import numpy.typing as npt
+
+import scipy.stats as stats
+from scipy._lib._util import rng_integers, _rng_spawn, _transition_to_rng
+from scipy.sparse.csgraph import minimum_spanning_tree
+from scipy.spatial import distance, Voronoi
+from scipy.special import gammainc
+from ._sobol import (
+    _initialize_v, _cscramble, _fill_p_cumulative, _draw, _fast_forward,
+    _categorize, _MAXDIM
+)
+from ._qmc_cy import (
+    _cy_wrapper_centered_discrepancy,
+    _cy_wrapper_wrap_around_discrepancy,
+    _cy_wrapper_mixture_discrepancy,
+    _cy_wrapper_l2_star_discrepancy,
+    _cy_wrapper_update_discrepancy,
+    _cy_van_der_corput_scrambled,
+    _cy_van_der_corput,
+)
+
+
+__all__ = ['scale', 'discrepancy', 'geometric_discrepancy', 'update_discrepancy',
+           'QMCEngine', 'Sobol', 'Halton', 'LatinHypercube', 'PoissonDisk',
+           'MultinomialQMC', 'MultivariateNormalQMC']
+
+
+@overload
+def check_random_state(seed: IntNumber | None = ...) -> np.random.Generator:
+    ...
+
+
+@overload
+def check_random_state(seed: GeneratorType) -> GeneratorType:
+    ...
+
+
+# Based on scipy._lib._util.check_random_state
+# This is going to be removed at the end of the SPEC 7 transition,
+# so I'll just leave the argument name `seed` alone
+def check_random_state(seed=None):
+    """Turn `seed` into a `numpy.random.Generator` instance.
+
+    Parameters
+    ----------
+    seed : {None, int, `numpy.random.Generator`, `numpy.random.RandomState`}, optional
+        If `seed` is an int or None, a new `numpy.random.Generator` is
+        created using ``np.random.default_rng(seed)``.
+        If `seed` is already a ``Generator`` or ``RandomState`` instance, then
+        the provided instance is used.
+
+    Returns
+    -------
+    seed : {`numpy.random.Generator`, `numpy.random.RandomState`}
+        Random number generator.
+
+    """
+    if seed is None or isinstance(seed, numbers.Integral | np.integer):
+        return np.random.default_rng(seed)
+    elif isinstance(seed, np.random.RandomState | np.random.Generator):
+        return seed
+    else:
+        raise ValueError(f'{seed!r} cannot be used to seed a'
+                         ' numpy.random.Generator instance')
+
+
+def scale(
+    sample: "npt.ArrayLike",
+    l_bounds: "npt.ArrayLike",
+    u_bounds: "npt.ArrayLike",
+    *,
+    reverse: bool = False
+) -> np.ndarray:
+    r"""Sample scaling from unit hypercube to different bounds.
+
+    To convert a sample from :math:`[0, 1)` to :math:`[a, b), b>a`,
+    with :math:`a` the lower bounds and :math:`b` the upper bounds.
+    The following transformation is used:
+
+    .. math::
+
+        (b - a) \cdot \text{sample} + a
+
+    Parameters
+    ----------
+    sample : array_like (n, d)
+        Sample to scale.
+    l_bounds, u_bounds : array_like (d,)
+        Lower and upper bounds (resp. :math:`a`, :math:`b`) of transformed
+        data. If `reverse` is True, range of the original data to transform
+        to the unit hypercube.
+    reverse : bool, optional
+        Reverse the transformation from different bounds to the unit hypercube.
+        Default is False.
+
+    Returns
+    -------
+    sample : array_like (n, d)
+        Scaled sample.
+
+    Examples
+    --------
+    Transform 3 samples in the unit hypercube to bounds:
+
+    >>> from scipy.stats import qmc
+    >>> l_bounds = [-2, 0]
+    >>> u_bounds = [6, 5]
+    >>> sample = [[0.5 , 0.75],
+    ...           [0.5 , 0.5],
+    ...           [0.75, 0.25]]
+    >>> sample_scaled = qmc.scale(sample, l_bounds, u_bounds)
+    >>> sample_scaled
+    array([[2.  , 3.75],
+           [2.  , 2.5 ],
+           [4.  , 1.25]])
+
+    And convert back to the unit hypercube:
+
+    >>> sample_ = qmc.scale(sample_scaled, l_bounds, u_bounds, reverse=True)
+    >>> sample_
+    array([[0.5 , 0.75],
+           [0.5 , 0.5 ],
+           [0.75, 0.25]])
+
+    """
+    sample = np.asarray(sample)
+
+    # Checking bounds and sample
+    if not sample.ndim == 2:
+        raise ValueError('Sample is not a 2D array')
+
+    lower, upper = _validate_bounds(
+        l_bounds=l_bounds, u_bounds=u_bounds, d=sample.shape[1]
+    )
+
+    if not reverse:
+        # Checking that sample is within the hypercube
+        if (sample.max() > 1.) or (sample.min() < 0.):
+            raise ValueError('Sample is not in unit hypercube')
+
+        return sample * (upper - lower) + lower
+    else:
+        # Checking that sample is within the bounds
+        if not (np.all(sample >= lower) and np.all(sample <= upper)):
+            raise ValueError('Sample is out of bounds')
+
+        return (sample - lower) / (upper - lower)
+
+
+def _ensure_in_unit_hypercube(sample: "npt.ArrayLike") -> np.ndarray:
+    """Ensure that sample is a 2D array and is within a unit hypercube
+
+    Parameters
+    ----------
+    sample : array_like (n, d)
+        A 2D array of points.
+
+    Returns
+    -------
+    np.ndarray
+        The array interpretation of the input sample
+
+    Raises
+    ------
+    ValueError
+        If the input is not a 2D array or contains points outside of
+        a unit hypercube.
+    """
+    sample = np.asarray(sample, dtype=np.float64, order="C")
+
+    if not sample.ndim == 2:
+        raise ValueError("Sample is not a 2D array")
+
+    if (sample.max() > 1.) or (sample.min() < 0.):
+        raise ValueError("Sample is not in unit hypercube")
+
+    return sample
+
+
+def discrepancy(
+        sample: "npt.ArrayLike",
+        *,
+        iterative: bool = False,
+        method: Literal["CD", "WD", "MD", "L2-star"] = "CD",
+        workers: IntNumber = 1) -> float:
+    """Discrepancy of a given sample.
+
+    Parameters
+    ----------
+    sample : array_like (n, d)
+        The sample to compute the discrepancy from.
+    iterative : bool, optional
+        Must be False if not using it for updating the discrepancy.
+        Default is False. Refer to the notes for more details.
+    method : str, optional
+        Type of discrepancy, can be ``CD``, ``WD``, ``MD`` or ``L2-star``.
+        Refer to the notes for more details. Default is ``CD``.
+    workers : int, optional
+        Number of workers to use for parallel processing. If -1 is given all
+        CPU threads are used. Default is 1.
+
+    Returns
+    -------
+    discrepancy : float
+        Discrepancy.
+
+    See Also
+    --------
+    geometric_discrepancy
+
+    Notes
+    -----
+    The discrepancy is a uniformity criterion used to assess the space filling
+    of a number of samples in a hypercube. A discrepancy quantifies the
+    distance between the continuous uniform distribution on a hypercube and the
+    discrete uniform distribution on :math:`n` distinct sample points.
+
+    The lower the value is, the better the coverage of the parameter space is.
+
+    For a collection of subsets of the hypercube, the discrepancy is the
+    difference between the fraction of sample points in one of those
+    subsets and the volume of that subset. There are different definitions of
+    discrepancy corresponding to different collections of subsets. Some
+    versions take a root mean square difference over subsets instead of
+    a maximum.
+
+    A measure of uniformity is reasonable if it satisfies the following
+    criteria [1]_:
+
+    1. It is invariant under permuting factors and/or runs.
+    2. It is invariant under rotation of the coordinates.
+    3. It can measure not only uniformity of the sample over the hypercube,
+       but also the projection uniformity of the sample over non-empty
+       subset of lower dimension hypercubes.
+    4. There is some reasonable geometric meaning.
+    5. It is easy to compute.
+    6. It satisfies the Koksma-Hlawka-like inequality.
+    7. It is consistent with other criteria in experimental design.
+
+    Four methods are available:
+
+    * ``CD``: Centered Discrepancy - subspace involves a corner of the
+      hypercube
+    * ``WD``: Wrap-around Discrepancy - subspace can wrap around bounds
+    * ``MD``: Mixture Discrepancy - mix between CD/WD covering more criteria
+    * ``L2-star``: L2-star discrepancy - like CD BUT variant to rotation
+
+    Methods ``CD``, ``WD``, and ``MD`` implement the right hand side of equations
+    9, 10, and 18 of [2]_, respectively; the square root is not taken. On the
+    other hand, ``L2-star`` computes the quantity given by equation 10 of
+    [3]_ as implemented by subsequent equations; the square root is taken.
+
+    Lastly, using ``iterative=True``, it is possible to compute the
+    discrepancy as if we had :math:`n+1` samples. This is useful if we want
+    to add a point to a sampling and check the candidate which would give the
+    lowest discrepancy. Then you could just update the discrepancy with
+    each candidate using `update_discrepancy`. This method is faster than
+    computing the discrepancy for a large number of candidates.
+
+    References
+    ----------
+    .. [1] Fang et al. "Design and modeling for computer experiments".
+       Computer Science and Data Analysis Series, 2006.
+    .. [2] Zhou Y.-D. et al. "Mixture discrepancy for quasi-random point sets."
+       Journal of Complexity, 29 (3-4) , pp. 283-301, 2013.
+    .. [3] T. T. Warnock. "Computational investigations of low discrepancy
+       point sets." Applications of Number Theory to Numerical
+       Analysis, Academic Press, pp. 319-343, 1972.
+
+    Examples
+    --------
+    Calculate the quality of the sample using the discrepancy:
+
+    >>> import numpy as np
+    >>> from scipy.stats import qmc
+    >>> space = np.array([[1, 3], [2, 6], [3, 2], [4, 5], [5, 1], [6, 4]])
+    >>> l_bounds = [0.5, 0.5]
+    >>> u_bounds = [6.5, 6.5]
+    >>> space = qmc.scale(space, l_bounds, u_bounds, reverse=True)
+    >>> space
+    array([[0.08333333, 0.41666667],
+           [0.25      , 0.91666667],
+           [0.41666667, 0.25      ],
+           [0.58333333, 0.75      ],
+           [0.75      , 0.08333333],
+           [0.91666667, 0.58333333]])
+    >>> qmc.discrepancy(space)
+    0.008142039609053464
+
+    We can also compute iteratively the ``CD`` discrepancy by using
+    ``iterative=True``.
+
+    >>> disc_init = qmc.discrepancy(space[:-1], iterative=True)
+    >>> disc_init
+    0.04769081147119336
+    >>> qmc.update_discrepancy(space[-1], space[:-1], disc_init)
+    0.008142039609053513
+
+    """
+    sample = _ensure_in_unit_hypercube(sample)
+
+    workers = _validate_workers(workers)
+
+    methods = {
+        "CD": _cy_wrapper_centered_discrepancy,
+        "WD": _cy_wrapper_wrap_around_discrepancy,
+        "MD": _cy_wrapper_mixture_discrepancy,
+        "L2-star": _cy_wrapper_l2_star_discrepancy,
+    }
+
+    if method in methods:
+        return methods[method](sample, iterative, workers=workers)
+    else:
+        raise ValueError(f"{method!r} is not a valid method. It must be one of"
+                         f" {set(methods)!r}")
+
+
+def geometric_discrepancy(
+        sample: "npt.ArrayLike",
+        method: Literal["mindist", "mst"] = "mindist",
+        metric: str = "euclidean") -> float:
+    """Discrepancy of a given sample based on its geometric properties.
+
+    Parameters
+    ----------
+    sample : array_like (n, d)
+        The sample to compute the discrepancy from.
+    method : {"mindist", "mst"}, optional
+        The method to use. One of ``mindist`` for minimum distance (default)
+        or ``mst`` for minimum spanning tree.
+    metric : str or callable, optional
+        The distance metric to use. See the documentation
+        for `scipy.spatial.distance.pdist` for the available metrics and
+        the default.
+
+    Returns
+    -------
+    discrepancy : float
+        Discrepancy (higher values correspond to greater sample uniformity).
+
+    See Also
+    --------
+    discrepancy
+
+    Notes
+    -----
+    The discrepancy can serve as a simple measure of quality of a random sample.
+    This measure is based on the geometric properties of the distribution of points
+    in the sample, such as the minimum distance between any pair of points, or
+    the mean edge length in a minimum spanning tree.
+
+    The higher the value is, the better the coverage of the parameter space is.
+    Note that this is different from `scipy.stats.qmc.discrepancy`, where lower
+    values correspond to higher quality of the sample.
+
+    Also note that when comparing different sampling strategies using this function,
+    the sample size must be kept constant.
+
+    It is possible to calculate two metrics from the minimum spanning tree:
+    the mean edge length and the standard deviation of edges lengths. Using
+    both metrics offers a better picture of uniformity than either metric alone,
+    with higher mean and lower standard deviation being preferable (see [1]_
+    for a brief discussion). This function currently only calculates the mean
+    edge length.
+
+    References
+    ----------
+    .. [1] Franco J. et al. "Minimum Spanning Tree: A new approach to assess the quality
+       of the design of computer experiments." Chemometrics and Intelligent Laboratory
+       Systems, 97 (2), pp. 164-169, 2009.
+
+    Examples
+    --------
+    Calculate the quality of the sample using the minimum euclidean distance
+    (the defaults):
+
+    >>> import numpy as np
+    >>> from scipy.stats import qmc
+    >>> rng = np.random.default_rng(191468432622931918890291693003068437394)
+    >>> sample = qmc.LatinHypercube(d=2, rng=rng).random(50)
+    >>> qmc.geometric_discrepancy(sample)
+    0.03708161435687876
+
+    Calculate the quality using the mean edge length in the minimum
+    spanning tree:
+
+    >>> qmc.geometric_discrepancy(sample, method='mst')
+    0.1105149978798376
+
+    Display the minimum spanning tree and the points with
+    the smallest distance:
+
+    >>> import matplotlib.pyplot as plt
+    >>> from matplotlib.lines import Line2D
+    >>> from scipy.sparse.csgraph import minimum_spanning_tree
+    >>> from scipy.spatial.distance import pdist, squareform
+    >>> dist = pdist(sample)
+    >>> mst = minimum_spanning_tree(squareform(dist))
+    >>> edges = np.where(mst.toarray() > 0)
+    >>> edges = np.asarray(edges).T
+    >>> min_dist = np.min(dist)
+    >>> min_idx = np.argwhere(squareform(dist) == min_dist)[0]
+    >>> fig, ax = plt.subplots(figsize=(10, 5))
+    >>> _ = ax.set(aspect='equal', xlabel=r'$x_1$', ylabel=r'$x_2$',
+    ...            xlim=[0, 1], ylim=[0, 1])
+    >>> for edge in edges:
+    ...     ax.plot(sample[edge, 0], sample[edge, 1], c='k')
+    >>> ax.scatter(sample[:, 0], sample[:, 1])
+    >>> ax.add_patch(plt.Circle(sample[min_idx[0]], min_dist, color='red', fill=False))
+    >>> markers = [
+    ...     Line2D([0], [0], marker='o', lw=0, label='Sample points'),
+    ...     Line2D([0], [0], color='k', label='Minimum spanning tree'),
+    ...     Line2D([0], [0], marker='o', lw=0, markerfacecolor='w', markeredgecolor='r',
+    ...            label='Minimum point-to-point distance'),
+    ... ]
+    >>> ax.legend(handles=markers, loc='center left', bbox_to_anchor=(1, 0.5));
+    >>> plt.show()
+
+    """
+    sample = _ensure_in_unit_hypercube(sample)
+    if sample.shape[0] < 2:
+        raise ValueError("Sample must contain at least two points")
+
+    distances = distance.pdist(sample, metric=metric)  # type: ignore[call-overload]
+
+    if np.any(distances == 0.0):
+        warnings.warn("Sample contains duplicate points.", stacklevel=2)
+
+    if method == "mindist":
+        return np.min(distances[distances.nonzero()])
+    elif method == "mst":
+        fully_connected_graph = distance.squareform(distances)
+        mst = minimum_spanning_tree(fully_connected_graph)
+        distances = mst[mst.nonzero()]
+        # TODO consider returning both the mean and the standard deviation
+        # see [1] for a discussion
+        return np.mean(distances)
+    else:
+        raise ValueError(f"{method!r} is not a valid method. "
+                         f"It must be one of {{'mindist', 'mst'}}")
+
+
+def update_discrepancy(
+        x_new: "npt.ArrayLike",
+        sample: "npt.ArrayLike",
+        initial_disc: DecimalNumber) -> float:
+    """Update the centered discrepancy with a new sample.
+
+    Parameters
+    ----------
+    x_new : array_like (1, d)
+        The new sample to add in `sample`.
+    sample : array_like (n, d)
+        The initial sample.
+    initial_disc : float
+        Centered discrepancy of the `sample`.
+
+    Returns
+    -------
+    discrepancy : float
+        Centered discrepancy of the sample composed of `x_new` and `sample`.
+
+    Examples
+    --------
+    We can also compute iteratively the discrepancy by using
+    ``iterative=True``.
+
+    >>> import numpy as np
+    >>> from scipy.stats import qmc
+    >>> space = np.array([[1, 3], [2, 6], [3, 2], [4, 5], [5, 1], [6, 4]])
+    >>> l_bounds = [0.5, 0.5]
+    >>> u_bounds = [6.5, 6.5]
+    >>> space = qmc.scale(space, l_bounds, u_bounds, reverse=True)
+    >>> disc_init = qmc.discrepancy(space[:-1], iterative=True)
+    >>> disc_init
+    0.04769081147119336
+    >>> qmc.update_discrepancy(space[-1], space[:-1], disc_init)
+    0.008142039609053513
+
+    """
+    sample = np.asarray(sample, dtype=np.float64, order="C")
+    x_new = np.asarray(x_new, dtype=np.float64, order="C")
+
+    # Checking that sample is within the hypercube and 2D
+    if not sample.ndim == 2:
+        raise ValueError('Sample is not a 2D array')
+
+    if (sample.max() > 1.) or (sample.min() < 0.):
+        raise ValueError('Sample is not in unit hypercube')
+
+    # Checking that x_new is within the hypercube and 1D
+    if not x_new.ndim == 1:
+        raise ValueError('x_new is not a 1D array')
+
+    if not (np.all(x_new >= 0) and np.all(x_new <= 1)):
+        raise ValueError('x_new is not in unit hypercube')
+
+    if x_new.shape[0] != sample.shape[1]:
+        raise ValueError("x_new and sample must be broadcastable")
+
+    return _cy_wrapper_update_discrepancy(x_new, sample, initial_disc)
+
+
+def _perturb_discrepancy(sample: np.ndarray, i1: int, i2: int, k: int,
+                         disc: float):
+    """Centered discrepancy after an elementary perturbation of a LHS.
+
+    An elementary perturbation consists of an exchange of coordinates between
+    two points: ``sample[i1, k] <-> sample[i2, k]``. By construction,
+    this operation conserves the LHS properties.
+
+    Parameters
+    ----------
+    sample : array_like (n, d)
+        The sample (before permutation) to compute the discrepancy from.
+    i1 : int
+        The first line of the elementary permutation.
+    i2 : int
+        The second line of the elementary permutation.
+    k : int
+        The column of the elementary permutation.
+    disc : float
+        Centered discrepancy of the design before permutation.
+
+    Returns
+    -------
+    discrepancy : float
+        Centered discrepancy of the design after permutation.
+
+    References
+    ----------
+    .. [1] Jin et al. "An efficient algorithm for constructing optimal design
+       of computer experiments", Journal of Statistical Planning and
+       Inference, 2005.
+
+    """
+    n = sample.shape[0]
+
+    z_ij = sample - 0.5
+
+    # Eq (19)
+    c_i1j = (1. / n ** 2.
+             * np.prod(0.5 * (2. + abs(z_ij[i1, :])
+                              + abs(z_ij) - abs(z_ij[i1, :] - z_ij)), axis=1))
+    c_i2j = (1. / n ** 2.
+             * np.prod(0.5 * (2. + abs(z_ij[i2, :])
+                              + abs(z_ij) - abs(z_ij[i2, :] - z_ij)), axis=1))
+
+    # Eq (20)
+    c_i1i1 = (1. / n ** 2 * np.prod(1 + abs(z_ij[i1, :]))
+              - 2. / n * np.prod(1. + 0.5 * abs(z_ij[i1, :])
+                                 - 0.5 * z_ij[i1, :] ** 2))
+    c_i2i2 = (1. / n ** 2 * np.prod(1 + abs(z_ij[i2, :]))
+              - 2. / n * np.prod(1. + 0.5 * abs(z_ij[i2, :])
+                                 - 0.5 * z_ij[i2, :] ** 2))
+
+    # Eq (22), typo in the article in the denominator i2 -> i1
+    num = (2 + abs(z_ij[i2, k]) + abs(z_ij[:, k])
+           - abs(z_ij[i2, k] - z_ij[:, k]))
+    denum = (2 + abs(z_ij[i1, k]) + abs(z_ij[:, k])
+             - abs(z_ij[i1, k] - z_ij[:, k]))
+    gamma = num / denum
+
+    # Eq (23)
+    c_p_i1j = gamma * c_i1j
+    # Eq (24)
+    c_p_i2j = c_i2j / gamma
+
+    alpha = (1 + abs(z_ij[i2, k])) / (1 + abs(z_ij[i1, k]))
+    beta = (2 - abs(z_ij[i2, k])) / (2 - abs(z_ij[i1, k]))
+
+    g_i1 = np.prod(1. + abs(z_ij[i1, :]))
+    g_i2 = np.prod(1. + abs(z_ij[i2, :]))
+    h_i1 = np.prod(1. + 0.5 * abs(z_ij[i1, :]) - 0.5 * (z_ij[i1, :] ** 2))
+    h_i2 = np.prod(1. + 0.5 * abs(z_ij[i2, :]) - 0.5 * (z_ij[i2, :] ** 2))
+
+    # Eq (25), typo in the article g is missing
+    c_p_i1i1 = ((g_i1 * alpha) / (n ** 2) - 2. * alpha * beta * h_i1 / n)
+    # Eq (26), typo in the article n ** 2
+    c_p_i2i2 = ((g_i2 / ((n ** 2) * alpha)) - (2. * h_i2 / (n * alpha * beta)))
+
+    # Eq (26)
+    sum_ = c_p_i1j - c_i1j + c_p_i2j - c_i2j
+
+    mask = np.ones(n, dtype=bool)
+    mask[[i1, i2]] = False
+    sum_ = sum(sum_[mask])
+
+    disc_ep = (disc + c_p_i1i1 - c_i1i1 + c_p_i2i2 - c_i2i2 + 2 * sum_)
+
+    return disc_ep
+
+
+def primes_from_2_to(n: int) -> np.ndarray:
+    """Prime numbers from 2 to *n*.
+
+    Parameters
+    ----------
+    n : int
+        Sup bound with ``n >= 6``.
+
+    Returns
+    -------
+    primes : list(int)
+        Primes in ``2 <= p < n``.
+
+    Notes
+    -----
+    Taken from [1]_ by P.T. Roy, written consent given on 23.04.2021
+    by the original author, Bruno Astrolino, for free use in SciPy under
+    the 3-clause BSD.
+
+    References
+    ----------
+    .. [1] `StackOverflow <https://stackoverflow.com/questions/2068372>`_.
+
+    """
+    sieve = np.ones(n // 3 + (n % 6 == 2), dtype=bool)
+    for i in range(1, int(n ** 0.5) // 3 + 1):
+        k = 3 * i + 1 | 1
+        sieve[k * k // 3::2 * k] = False
+        sieve[k * (k - 2 * (i & 1) + 4) // 3::2 * k] = False
+    return np.r_[2, 3, ((3 * np.nonzero(sieve)[0][1:] + 1) | 1)]
+
+
+def n_primes(n: IntNumber) -> list[int] | np.ndarray:
+    """List of the n-first prime numbers.
+
+    Parameters
+    ----------
+    n : int
+        Number of prime numbers wanted.
+
+    Returns
+    -------
+    primes : list(int)
+        List of primes.
+
+    """
+    primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59,
+              61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127,
+              131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193,
+              197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269,
+              271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349,
+              353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431,
+              433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503,
+              509, 521, 523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599,
+              601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661, 673,
+              677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761,
+              769, 773, 787, 797, 809, 811, 821, 823, 827, 829, 839, 853, 857,
+              859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941, 947,
+              953, 967, 971, 977, 983, 991, 997][:n]
+
+    if len(primes) < n:
+        big_number = 2000
+        while 'Not enough primes':
+            primes = primes_from_2_to(big_number)[:n]  # type: ignore
+            if len(primes) == n:
+                break
+            big_number += 1000
+
+    return primes
+
+
+def _van_der_corput_permutations(
+    base: IntNumber, *, rng: SeedType = None
+) -> np.ndarray:
+    """Permutations for scrambling a Van der Corput sequence.
+
+    Parameters
+    ----------
+    base : int
+        Base of the sequence.
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+        .. versionchanged:: 1.15.0
+
+            As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
+            transition from use of `numpy.random.RandomState` to
+            `numpy.random.Generator`, this keyword was changed from `seed` to
+            `rng`. During the transition, the behavior documented above is not
+            accurate; see `check_random_state` for actual behavior. After the
+            transition, this admonition can be removed.
+
+    Returns
+    -------
+    permutations : array_like
+        Permutation indices.
+
+    Notes
+    -----
+    In Algorithm 1 of Owen 2017, a permutation of `np.arange(base)` is
+    created for each positive integer `k` such that ``1 - base**-k < 1``
+    using floating-point arithmetic. For double precision floats, the
+    condition ``1 - base**-k < 1`` can also be written as ``base**-k >
+    2**-54``, which makes it more apparent how many permutations we need
+    to create.
+    """
+    rng = check_random_state(rng)
+    count = math.ceil(54 / math.log2(base)) - 1
+    permutations = np.repeat(np.arange(base)[None], count, axis=0)
+    for perm in permutations:
+        rng.shuffle(perm)
+
+    return permutations
+
+
+def van_der_corput(
+        n: IntNumber,
+        base: IntNumber = 2,
+        *,
+        start_index: IntNumber = 0,
+        scramble: bool = False,
+        permutations: "npt.ArrayLike | None" = None,
+        rng: SeedType = None,
+        workers: IntNumber = 1) -> np.ndarray:
+    """Van der Corput sequence.
+
+    Pseudo-random number generator based on a b-adic expansion.
+
+    Scrambling uses permutations of the remainders (see [1]_). Multiple
+    permutations are applied to construct a point. The sequence of
+    permutations has to be the same for all points of the sequence.
+
+    Parameters
+    ----------
+    n : int
+        Number of element of the sequence.
+    base : int, optional
+        Base of the sequence. Default is 2.
+    start_index : int, optional
+        Index to start the sequence from. Default is 0.
+    scramble : bool, optional
+        If True, use Owen scrambling. Otherwise no scrambling is done.
+        Default is True.
+    permutations : array_like, optional
+        Permutations used for scrambling.
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+    workers : int, optional
+        Number of workers to use for parallel processing. If -1 is
+        given all CPU threads are used. Default is 1.
+
+    Returns
+    -------
+    sequence : list (n,)
+        Sequence of Van der Corput.
+
+    References
+    ----------
+    .. [1] A. B. Owen. "A randomized Halton algorithm in R",
+       :arxiv:`1706.02808`, 2017.
+
+    """
+    if base < 2:
+        raise ValueError("'base' must be at least 2")
+
+    if scramble:
+        if permutations is None:
+            permutations = _van_der_corput_permutations(
+                base=base, rng=rng
+            )
+        else:
+            permutations = np.asarray(permutations)
+
+        permutations = permutations.astype(np.int64)
+        return _cy_van_der_corput_scrambled(n, base, start_index,
+                                            permutations, workers)
+
+    else:
+        return _cy_van_der_corput(n, base, start_index, workers)
+
+
+class QMCEngine(ABC):
+    """A generic Quasi-Monte Carlo sampler class meant for subclassing.
+
+    QMCEngine is a base class to construct a specific Quasi-Monte Carlo
+    sampler. It cannot be used directly as a sampler.
+
+    Parameters
+    ----------
+    d : int
+        Dimension of the parameter space.
+    optimization : {None, "random-cd", "lloyd"}, optional
+        Whether to use an optimization scheme to improve the quality after
+        sampling. Note that this is a post-processing step that does not
+        guarantee that all properties of the sample will be conserved.
+        Default is None.
+
+        * ``random-cd``: random permutations of coordinates to lower the
+          centered discrepancy. The best sample based on the centered
+          discrepancy is constantly updated. Centered discrepancy-based
+          sampling shows better space-filling robustness toward 2D and 3D
+          subprojections compared to using other discrepancy measures.
+        * ``lloyd``: Perturb samples using a modified Lloyd-Max algorithm.
+          The process converges to equally spaced samples.
+
+        .. versionadded:: 1.10.0
+
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+        .. versionchanged:: 1.15.0
+
+            As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
+            transition from use of `numpy.random.RandomState` to
+            `numpy.random.Generator`, this keyword was changed from `seed` to
+            `rng`. For an interim period, both keywords will continue to work, although
+            only one may be specified at a time. After the interim period, function
+            calls using the `seed` keyword will emit warnings. Following a
+            deprecation period, the `seed` keyword will be removed.
+
+    Notes
+    -----
+    By convention samples are distributed over the half-open interval
+    ``[0, 1)``. Instances of the class can access the attributes: ``d`` for
+    the dimension; and ``rng`` for the random number generator.
+
+    **Subclassing**
+
+    When subclassing `QMCEngine` to create a new sampler,  ``__init__`` and
+    ``random`` must be redefined.
+
+    * ``__init__(d, rng=None)``: at least fix the dimension. If the sampler
+      does not take advantage of a ``rng`` (deterministic methods like
+      Halton), this parameter can be omitted.
+    * ``_random(n, *, workers=1)``: draw ``n`` from the engine. ``workers``
+      is used for parallelism. See `Halton` for example.
+
+    Optionally, two other methods can be overwritten by subclasses:
+
+    * ``reset``: Reset the engine to its original state.
+    * ``fast_forward``: If the sequence is deterministic (like Halton
+      sequence), then ``fast_forward(n)`` is skipping the ``n`` first draw.
+
+    Examples
+    --------
+    To create a random sampler based on ``np.random.random``, we would do the
+    following:
+
+    >>> from scipy.stats import qmc
+    >>> class RandomEngine(qmc.QMCEngine):
+    ...     def __init__(self, d, rng=None):
+    ...         super().__init__(d=d, rng=rng)
+    ...
+    ...
+    ...     def _random(self, n=1, *, workers=1):
+    ...         return self.rng.random((n, self.d))
+    ...
+    ...
+    ...     def reset(self):
+    ...         super().__init__(d=self.d, rng=self.rng_seed)
+    ...         return self
+    ...
+    ...
+    ...     def fast_forward(self, n):
+    ...         self.random(n)
+    ...         return self
+
+    After subclassing `QMCEngine` to define the sampling strategy we want to
+    use, we can create an instance to sample from.
+
+    >>> engine = RandomEngine(2)
+    >>> engine.random(5)
+    array([[0.22733602, 0.31675834],  # random
+           [0.79736546, 0.67625467],
+           [0.39110955, 0.33281393],
+           [0.59830875, 0.18673419],
+           [0.67275604, 0.94180287]])
+
+    We can also reset the state of the generator and resample again.
+
+    >>> _ = engine.reset()
+    >>> engine.random(5)
+    array([[0.22733602, 0.31675834],  # random
+           [0.79736546, 0.67625467],
+           [0.39110955, 0.33281393],
+           [0.59830875, 0.18673419],
+           [0.67275604, 0.94180287]])
+
+    """
+
+    @abstractmethod
+    @_transition_to_rng('seed', replace_doc=False)
+    def __init__(
+        self,
+        d: IntNumber,
+        *,
+        optimization: Literal["random-cd", "lloyd"] | None = None,
+        rng: SeedType = None
+    ) -> None:
+        self._initialize(d, optimization=optimization, rng=rng)
+
+    # During SPEC 7 transition:
+    # `__init__` has to be wrapped with @_transition_to_rng decorator
+    # because it is public. Subclasses previously called `__init__`
+    # directly, but this was problematic because arguments passed to
+    # subclass `__init__` as `seed` would get passed to superclass
+    # `__init__` as `rng`, rejecting `RandomState` arguments.
+    def _initialize(
+        self,
+        d: IntNumber,
+        *,
+        optimization: Literal["random-cd", "lloyd"] | None = None,
+        rng: SeedType = None
+    ) -> None:
+        if not np.issubdtype(type(d), np.integer) or d < 0:
+            raise ValueError('d must be a non-negative integer value')
+
+        self.d = d
+
+        if isinstance(rng, np.random.Generator):
+            # Spawn a Generator that we can own and reset.
+            self.rng = _rng_spawn(rng, 1)[0]
+        else:
+            # Create our instance of Generator, does not need spawning
+            # Also catch RandomState which cannot be spawned
+            self.rng = check_random_state(rng)
+        self.rng_seed = copy.deepcopy(self.rng)
+
+        self.num_generated = 0
+
+        config = {
+            # random-cd
+            "n_nochange": 100,
+            "n_iters": 10_000,
+            "rng": self.rng,
+
+            # lloyd
+            "tol": 1e-5,
+            "maxiter": 10,
+            "qhull_options": None,
+        }
+        self._optimization = optimization
+        self.optimization_method = _select_optimizer(optimization, config)
+
+    @abstractmethod
+    def _random(
+        self, n: IntNumber = 1, *, workers: IntNumber = 1
+    ) -> np.ndarray:
+        ...
+
+    def random(
+        self, n: IntNumber = 1, *, workers: IntNumber = 1
+    ) -> np.ndarray:
+        """Draw `n` in the half-open interval ``[0, 1)``.
+
+        Parameters
+        ----------
+        n : int, optional
+            Number of samples to generate in the parameter space.
+            Default is 1.
+        workers : int, optional
+            Only supported with `Halton`.
+            Number of workers to use for parallel processing. If -1 is
+            given all CPU threads are used. Default is 1. It becomes faster
+            than one worker for `n` greater than :math:`10^3`.
+
+        Returns
+        -------
+        sample : array_like (n, d)
+            QMC sample.
+
+        """
+        sample = self._random(n, workers=workers)
+        if self.optimization_method is not None:
+            sample = self.optimization_method(sample)
+
+        self.num_generated += n
+        return sample
+
+    def integers(
+        self,
+        l_bounds: "npt.ArrayLike",
+        *,
+        u_bounds: "npt.ArrayLike | None" = None,
+        n: IntNumber = 1,
+        endpoint: bool = False,
+        workers: IntNumber = 1
+    ) -> np.ndarray:
+        r"""
+        Draw `n` integers from `l_bounds` (inclusive) to `u_bounds`
+        (exclusive), or if endpoint=True, `l_bounds` (inclusive) to
+        `u_bounds` (inclusive).
+
+        Parameters
+        ----------
+        l_bounds : int or array-like of ints
+            Lowest (signed) integers to be drawn (unless ``u_bounds=None``,
+            in which case this parameter is 0 and this value is used for
+            `u_bounds`).
+        u_bounds : int or array-like of ints, optional
+            If provided, one above the largest (signed) integer to be drawn
+            (see above for behavior if ``u_bounds=None``).
+            If array-like, must contain integer values.
+        n : int, optional
+            Number of samples to generate in the parameter space.
+            Default is 1.
+        endpoint : bool, optional
+            If true, sample from the interval ``[l_bounds, u_bounds]`` instead
+            of the default ``[l_bounds, u_bounds)``. Defaults is False.
+        workers : int, optional
+            Number of workers to use for parallel processing. If -1 is
+            given all CPU threads are used. Only supported when using `Halton`
+            Default is 1.
+
+        Returns
+        -------
+        sample : array_like (n, d)
+            QMC sample.
+
+        Notes
+        -----
+        It is safe to just use the same ``[0, 1)`` to integer mapping
+        with QMC that you would use with MC. You still get unbiasedness,
+        a strong law of large numbers, an asymptotically infinite variance
+        reduction and a finite sample variance bound.
+
+        To convert a sample from :math:`[0, 1)` to :math:`[a, b), b>a`,
+        with :math:`a` the lower bounds and :math:`b` the upper bounds,
+        the following transformation is used:
+
+        .. math::
+
+            \text{floor}((b - a) \cdot \text{sample} + a)
+
+        """
+        if u_bounds is None:
+            u_bounds = l_bounds
+            l_bounds = 0
+
+        u_bounds = np.atleast_1d(u_bounds)
+        l_bounds = np.atleast_1d(l_bounds)
+
+        if endpoint:
+            u_bounds = u_bounds + 1
+
+        if (not np.issubdtype(l_bounds.dtype, np.integer) or
+                not np.issubdtype(u_bounds.dtype, np.integer)):
+            message = ("'u_bounds' and 'l_bounds' must be integers or"
+                       " array-like of integers")
+            raise ValueError(message)
+
+        if isinstance(self, Halton):
+            sample = self.random(n=n, workers=workers)
+        else:
+            sample = self.random(n=n)
+
+        sample = scale(sample, l_bounds=l_bounds, u_bounds=u_bounds)
+        sample = np.floor(sample).astype(np.int64)
+
+        return sample
+
+    def reset(self) -> "QMCEngine":
+        """Reset the engine to base state.
+
+        Returns
+        -------
+        engine : QMCEngine
+            Engine reset to its base state.
+
+        """
+        rng = copy.deepcopy(self.rng_seed)
+        self.rng = check_random_state(rng)
+        self.num_generated = 0
+        return self
+
+    def fast_forward(self, n: IntNumber) -> "QMCEngine":
+        """Fast-forward the sequence by `n` positions.
+
+        Parameters
+        ----------
+        n : int
+            Number of points to skip in the sequence.
+
+        Returns
+        -------
+        engine : QMCEngine
+            Engine reset to its base state.
+
+        """
+        self.random(n=n)
+        return self
+
+
+class Halton(QMCEngine):
+    """Halton sequence.
+
+    Pseudo-random number generator that generalize the Van der Corput sequence
+    for multiple dimensions. The Halton sequence uses the base-two Van der
+    Corput sequence for the first dimension, base-three for its second and
+    base-:math:`p` for its :math:`n`-dimension, with :math:`p` the
+    :math:`n`'th prime.
+
+    Parameters
+    ----------
+    d : int
+        Dimension of the parameter space.
+    scramble : bool, optional
+        If True, use random scrambling from [2]_. Otherwise no scrambling
+        is done.
+        Default is True.
+    optimization : {None, "random-cd", "lloyd"}, optional
+        Whether to use an optimization scheme to improve the quality after
+        sampling. Note that this is a post-processing step that does not
+        guarantee that all properties of the sample will be conserved.
+        Default is None.
+
+        * ``random-cd``: random permutations of coordinates to lower the
+          centered discrepancy. The best sample based on the centered
+          discrepancy is constantly updated. Centered discrepancy-based
+          sampling shows better space-filling robustness toward 2D and 3D
+          subprojections compared to using other discrepancy measures.
+        * ``lloyd``: Perturb samples using a modified Lloyd-Max algorithm.
+          The process converges to equally spaced samples.
+
+        .. versionadded:: 1.10.0
+
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+        .. versionchanged:: 1.15.0
+
+            As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
+            transition from use of `numpy.random.RandomState` to
+            `numpy.random.Generator`, this keyword was changed from `seed` to
+            `rng`. For an interim period, both keywords will continue to work, although
+            only one may be specified at a time. After the interim period, function
+            calls using the `seed` keyword will emit warnings. Following a
+            deprecation period, the `seed` keyword will be removed.
+
+    Notes
+    -----
+    The Halton sequence has severe striping artifacts for even modestly
+    large dimensions. These can be ameliorated by scrambling. Scrambling
+    also supports replication-based error estimates and extends
+    applicability to unbounded integrands.
+
+    References
+    ----------
+    .. [1] Halton, "On the efficiency of certain quasi-random sequences of
+       points in evaluating multi-dimensional integrals", Numerische
+       Mathematik, 1960.
+    .. [2] A. B. Owen. "A randomized Halton algorithm in R",
+       :arxiv:`1706.02808`, 2017.
+
+    Examples
+    --------
+    Generate samples from a low discrepancy sequence of Halton.
+
+    >>> from scipy.stats import qmc
+    >>> sampler = qmc.Halton(d=2, scramble=False)
+    >>> sample = sampler.random(n=5)
+    >>> sample
+    array([[0.        , 0.        ],
+           [0.5       , 0.33333333],
+           [0.25      , 0.66666667],
+           [0.75      , 0.11111111],
+           [0.125     , 0.44444444]])
+
+    Compute the quality of the sample using the discrepancy criterion.
+
+    >>> qmc.discrepancy(sample)
+    0.088893711419753
+
+    If some wants to continue an existing design, extra points can be obtained
+    by calling again `random`. Alternatively, you can skip some points like:
+
+    >>> _ = sampler.fast_forward(5)
+    >>> sample_continued = sampler.random(n=5)
+    >>> sample_continued
+    array([[0.3125    , 0.37037037],
+           [0.8125    , 0.7037037 ],
+           [0.1875    , 0.14814815],
+           [0.6875    , 0.48148148],
+           [0.4375    , 0.81481481]])
+
+    Finally, samples can be scaled to bounds.
+
+    >>> l_bounds = [0, 2]
+    >>> u_bounds = [10, 5]
+    >>> qmc.scale(sample_continued, l_bounds, u_bounds)
+    array([[3.125     , 3.11111111],
+           [8.125     , 4.11111111],
+           [1.875     , 2.44444444],
+           [6.875     , 3.44444444],
+           [4.375     , 4.44444444]])
+
+    """
+    @_transition_to_rng('seed', replace_doc=False)
+    def __init__(
+        self, d: IntNumber, *, scramble: bool = True,
+        optimization: Literal["random-cd", "lloyd"] | None = None,
+        rng: SeedType = None
+    ) -> None:
+        # Used in `scipy.integrate.qmc_quad`
+        self._init_quad = {'d': d, 'scramble': True,
+                           'optimization': optimization}
+        super()._initialize(d=d, optimization=optimization, rng=rng)
+
+        # important to have ``type(bdim) == int`` for performance reason
+        self.base = [int(bdim) for bdim in n_primes(d)]
+        self.scramble = scramble
+
+        self._initialize_permutations()
+
+    def _initialize_permutations(self) -> None:
+        """Initialize permutations for all Van der Corput sequences.
+
+        Permutations are only needed for scrambling.
+        """
+        self._permutations: list = [None] * len(self.base)
+        if self.scramble:
+            for i, bdim in enumerate(self.base):
+                permutations = _van_der_corput_permutations(
+                    base=bdim, rng=self.rng
+                )
+
+                self._permutations[i] = permutations
+
+    def _random(
+        self, n: IntNumber = 1, *, workers: IntNumber = 1
+    ) -> np.ndarray:
+        """Draw `n` in the half-open interval ``[0, 1)``.
+
+        Parameters
+        ----------
+        n : int, optional
+            Number of samples to generate in the parameter space. Default is 1.
+        workers : int, optional
+            Number of workers to use for parallel processing. If -1 is
+            given all CPU threads are used. Default is 1. It becomes faster
+            than one worker for `n` greater than :math:`10^3`.
+
+        Returns
+        -------
+        sample : array_like (n, d)
+            QMC sample.
+
+        """
+        workers = _validate_workers(workers)
+        # Generate a sample using a Van der Corput sequence per dimension.
+        sample = [van_der_corput(n, bdim, start_index=self.num_generated,
+                                 scramble=self.scramble,
+                                 permutations=self._permutations[i],
+                                 workers=workers)
+                  for i, bdim in enumerate(self.base)]
+
+        return np.array(sample).T.reshape(n, self.d)
+
+
+class LatinHypercube(QMCEngine):
+    r"""Latin hypercube sampling (LHS).
+
+    A Latin hypercube sample [1]_ generates :math:`n` points in
+    :math:`[0,1)^{d}`. Each univariate marginal distribution is stratified,
+    placing exactly one point in :math:`[j/n, (j+1)/n)` for
+    :math:`j=0,1,...,n-1`. They are still applicable when :math:`n << d`.
+
+    Parameters
+    ----------
+    d : int
+        Dimension of the parameter space.
+    scramble : bool, optional
+        When False, center samples within cells of a multi-dimensional grid.
+        Otherwise, samples are randomly placed within cells of the grid.
+
+        .. note::
+            Setting ``scramble=False`` does not ensure deterministic output.
+            For that, use the `rng` parameter.
+
+        Default is True.
+
+        .. versionadded:: 1.10.0
+
+    optimization : {None, "random-cd", "lloyd"}, optional
+        Whether to use an optimization scheme to improve the quality after
+        sampling. Note that this is a post-processing step that does not
+        guarantee that all properties of the sample will be conserved.
+        Default is None.
+
+        * ``random-cd``: random permutations of coordinates to lower the
+          centered discrepancy. The best sample based on the centered
+          discrepancy is constantly updated. Centered discrepancy-based
+          sampling shows better space-filling robustness toward 2D and 3D
+          subprojections compared to using other discrepancy measures.
+        * ``lloyd``: Perturb samples using a modified Lloyd-Max algorithm.
+          The process converges to equally spaced samples.
+
+        .. versionadded:: 1.8.0
+        .. versionchanged:: 1.10.0
+            Add ``lloyd``.
+
+    strength : {1, 2}, optional
+        Strength of the LHS. ``strength=1`` produces a plain LHS while
+        ``strength=2`` produces an orthogonal array based LHS of strength 2
+        [7]_, [8]_. In that case, only ``n=p**2`` points can be sampled,
+        with ``p`` a prime number. It also constrains ``d <= p + 1``.
+        Default is 1.
+
+        .. versionadded:: 1.8.0
+
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+        .. versionchanged:: 1.15.0
+
+            As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
+            transition from use of `numpy.random.RandomState` to
+            `numpy.random.Generator`, this keyword was changed from `seed` to
+            `rng`. For an interim period, both keywords will continue to work, although
+            only one may be specified at a time. After the interim period, function
+            calls using the `seed` keyword will emit warnings. Following a
+            deprecation period, the `seed` keyword will be removed.
+
+    See Also
+    --------
+    :ref:`quasi-monte-carlo`
+
+    Notes
+    -----
+
+    When LHS is used for integrating a function :math:`f` over :math:`n`,
+    LHS is extremely effective on integrands that are nearly additive [2]_.
+    With a LHS of :math:`n` points, the variance of the integral is always
+    lower than plain MC on :math:`n-1` points [3]_. There is a central limit
+    theorem for LHS on the mean and variance of the integral [4]_, but not
+    necessarily for optimized LHS due to the randomization.
+
+    :math:`A` is called an orthogonal array of strength :math:`t` if in each
+    n-row-by-t-column submatrix of :math:`A`: all :math:`p^t` possible
+    distinct rows occur the same number of times. The elements of :math:`A`
+    are in the set :math:`\{0, 1, ..., p-1\}`, also called symbols.
+    The constraint that :math:`p` must be a prime number is to allow modular
+    arithmetic. Increasing strength adds some symmetry to the sub-projections
+    of a sample. With strength 2, samples are symmetric along the diagonals of
+    2D sub-projections. This may be undesirable, but on the other hand, the
+    sample dispersion is improved.
+
+    Strength 1 (plain LHS) brings an advantage over strength 0 (MC) and
+    strength 2 is a useful increment over strength 1. Going to strength 3 is
+    a smaller increment and scrambled QMC like Sobol', Halton are more
+    performant [7]_.
+
+    To create a LHS of strength 2, the orthogonal array :math:`A` is
+    randomized by applying a random, bijective map of the set of symbols onto
+    itself. For example, in column 0, all 0s might become 2; in column 1,
+    all 0s might become 1, etc.
+    Then, for each column :math:`i` and symbol :math:`j`, we add a plain,
+    one-dimensional LHS of size :math:`p` to the subarray where
+    :math:`A^i = j`. The resulting matrix is finally divided by :math:`p`.
+
+    References
+    ----------
+    .. [1] Mckay et al., "A Comparison of Three Methods for Selecting Values
+       of Input Variables in the Analysis of Output from a Computer Code."
+       Technometrics, 1979.
+    .. [2] M. Stein, "Large sample properties of simulations using Latin
+       hypercube sampling." Technometrics 29, no. 2: 143-151, 1987.
+    .. [3] A. B. Owen, "Monte Carlo variance of scrambled net quadrature."
+       SIAM Journal on Numerical Analysis 34, no. 5: 1884-1910, 1997
+    .. [4]  Loh, W.-L. "On Latin hypercube sampling." The annals of statistics
+       24, no. 5: 2058-2080, 1996.
+    .. [5] Fang et al. "Design and modeling for computer experiments".
+       Computer Science and Data Analysis Series, 2006.
+    .. [6] Damblin et al., "Numerical studies of space filling designs:
+       optimization of Latin Hypercube Samples and subprojection properties."
+       Journal of Simulation, 2013.
+    .. [7] A. B. Owen , "Orthogonal arrays for computer experiments,
+       integration and visualization." Statistica Sinica, 1992.
+    .. [8] B. Tang, "Orthogonal Array-Based Latin Hypercubes."
+       Journal of the American Statistical Association, 1993.
+    .. [9] Seaholm, Susan K. et al. (1988). Latin hypercube sampling and the
+       sensitivity analysis of a Monte Carlo epidemic model. Int J Biomed
+       Comput, 23(1-2), 97-112. :doi:`10.1016/0020-7101(88)90067-0`
+
+    Examples
+    --------
+    Generate samples from a Latin hypercube generator.
+
+    >>> from scipy.stats import qmc
+    >>> sampler = qmc.LatinHypercube(d=2)
+    >>> sample = sampler.random(n=5)
+    >>> sample
+    array([[0.1545328 , 0.53664833], # random
+            [0.84052691, 0.06474907],
+            [0.52177809, 0.93343721],
+            [0.68033825, 0.36265316],
+            [0.26544879, 0.61163943]])
+
+    Compute the quality of the sample using the discrepancy criterion.
+
+    >>> qmc.discrepancy(sample)
+    0.0196... # random
+
+    Samples can be scaled to bounds.
+
+    >>> l_bounds = [0, 2]
+    >>> u_bounds = [10, 5]
+    >>> qmc.scale(sample, l_bounds, u_bounds)
+    array([[1.54532796, 3.609945 ], # random
+            [8.40526909, 2.1942472 ],
+            [5.2177809 , 4.80031164],
+            [6.80338249, 3.08795949],
+            [2.65448791, 3.83491828]])
+
+    Below are other examples showing alternative ways to construct LHS with
+    even better coverage of the space.
+
+    Using a base LHS as a baseline.
+
+    >>> sampler = qmc.LatinHypercube(d=2)
+    >>> sample = sampler.random(n=5)
+    >>> qmc.discrepancy(sample)
+    0.0196...  # random
+
+    Use the `optimization` keyword argument to produce a LHS with
+    lower discrepancy at higher computational cost.
+
+    >>> sampler = qmc.LatinHypercube(d=2, optimization="random-cd")
+    >>> sample = sampler.random(n=5)
+    >>> qmc.discrepancy(sample)
+    0.0176...  # random
+
+    Use the `strength` keyword argument to produce an orthogonal array based
+    LHS of strength 2. In this case, the number of sample points must be the
+    square of a prime number.
+
+    >>> sampler = qmc.LatinHypercube(d=2, strength=2)
+    >>> sample = sampler.random(n=9)
+    >>> qmc.discrepancy(sample)
+    0.00526...  # random
+
+    Options could be combined to produce an optimized centered
+    orthogonal array based LHS. After optimization, the result would not
+    be guaranteed to be of strength 2.
+
+    **Real-world example**
+
+    In [9]_, a Latin Hypercube sampling (LHS) strategy was used to sample a
+    parameter space to study the importance of each parameter of an epidemic
+    model. Such analysis is also called a sensitivity analysis.
+
+    Since the dimensionality of the problem is high (6), it is computationally
+    expensive to cover the space. When numerical experiments are costly, QMC
+    enables analysis that may not be possible if using a grid.
+
+    The six parameters of the model represented the probability of illness,
+    the probability of withdrawal, and four contact probabilities. The
+    authors assumed uniform distributions for all parameters and generated
+    50 samples.
+
+    Using `scipy.stats.qmc.LatinHypercube` to replicate the protocol,
+    the first step is to create a sample in the unit hypercube:
+
+    >>> from scipy.stats import qmc
+    >>> sampler = qmc.LatinHypercube(d=6)
+    >>> sample = sampler.random(n=50)
+
+    Then the sample can be scaled to the appropriate bounds:
+
+    >>> l_bounds = [0.000125, 0.01, 0.0025, 0.05, 0.47, 0.7]
+    >>> u_bounds = [0.000375, 0.03, 0.0075, 0.15, 0.87, 0.9]
+    >>> sample_scaled = qmc.scale(sample, l_bounds, u_bounds)
+
+    Such a sample was used to run the model 50 times, and a polynomial
+    response surface was constructed. This allowed the authors to study the
+    relative importance of each parameter across the range of possibilities
+    of every other parameter.
+
+    In this computer experiment, they showed a 14-fold reduction in the
+    number of samples required to maintain an error below 2% on their
+    response surface when compared to a grid sampling.
+
+    """
+
+    @_transition_to_rng('seed', replace_doc=False)
+    def __init__(
+        self, d: IntNumber, *,
+        scramble: bool = True,
+        strength: int = 1,
+        optimization: Literal["random-cd", "lloyd"] | None = None,
+        rng: SeedType = None
+    ) -> None:
+        # Used in `scipy.integrate.qmc_quad`
+        self._init_quad = {'d': d, 'scramble': True, 'strength': strength,
+                           'optimization': optimization}
+        super()._initialize(d=d, rng=rng, optimization=optimization)
+        self.scramble = scramble
+
+        lhs_method_strength = {
+            1: self._random_lhs,
+            2: self._random_oa_lhs
+        }
+
+        try:
+            self.lhs_method: Callable = lhs_method_strength[strength]
+        except KeyError as exc:
+            message = (f"{strength!r} is not a valid strength. It must be one"
+                       f" of {set(lhs_method_strength)!r}")
+            raise ValueError(message) from exc
+
+    def _random(
+        self, n: IntNumber = 1, *, workers: IntNumber = 1
+    ) -> np.ndarray:
+        lhs = self.lhs_method(n)
+        return lhs
+
+    def _random_lhs(self, n: IntNumber = 1) -> np.ndarray:
+        """Base LHS algorithm."""
+        if not self.scramble:
+            samples: np.ndarray | float = 0.5
+        else:
+            samples = self.rng.uniform(size=(n, self.d))
+
+        perms = np.tile(np.arange(1, n + 1), (int(self.d), 1))
+        for i in range(self.d):
+            self.rng.shuffle(perms[i, :])
+        perms = perms.T
+
+        samples = (perms - samples) / n
+        return samples
+
+    def _random_oa_lhs(self, n: IntNumber = 4) -> np.ndarray:
+        """Orthogonal array based LHS of strength 2."""
+        p = np.sqrt(n).astype(int)
+        n_row = p**2
+        n_col = p + 1
+
+        primes = primes_from_2_to(p + 1)
+        if p not in primes or n != n_row:
+            raise ValueError(
+                "n is not the square of a prime number. Close"
+                f" values are {primes[-2:]**2}"
+            )
+        if self.d > p + 1:
+            raise ValueError("n is too small for d. Must be n > (d-1)**2")
+
+        oa_sample = np.zeros(shape=(n_row, n_col), dtype=int)
+
+        # OA of strength 2
+        arrays = np.tile(np.arange(p), (2, 1))
+        oa_sample[:, :2] = np.stack(np.meshgrid(*arrays),
+                                    axis=-1).reshape(-1, 2)
+        for p_ in range(1, p):
+            oa_sample[:, 2+p_-1] = np.mod(oa_sample[:, 0]
+                                          + p_*oa_sample[:, 1], p)
+
+        # scramble the OA
+        oa_sample_ = np.empty(shape=(n_row, n_col), dtype=int)
+        for j in range(n_col):
+            perms = self.rng.permutation(p)
+            oa_sample_[:, j] = perms[oa_sample[:, j]]
+
+        oa_sample = oa_sample_
+        # following is making a scrambled OA into an OA-LHS
+        oa_lhs_sample = np.zeros(shape=(n_row, n_col))
+        lhs_engine = LatinHypercube(d=1, scramble=self.scramble, strength=1,
+                                    rng=self.rng)  # type: QMCEngine
+        for j in range(n_col):
+            for k in range(p):
+                idx = oa_sample[:, j] == k
+                lhs = lhs_engine.random(p).flatten()
+                oa_lhs_sample[:, j][idx] = lhs + oa_sample[:, j][idx]
+
+        oa_lhs_sample /= p
+
+        return oa_lhs_sample[:, :self.d]
+
+
+class Sobol(QMCEngine):
+    """Engine for generating (scrambled) Sobol' sequences.
+
+    Sobol' sequences are low-discrepancy, quasi-random numbers. Points
+    can be drawn using two methods:
+
+    * `random_base2`: safely draw :math:`n=2^m` points. This method
+      guarantees the balance properties of the sequence.
+    * `random`: draw an arbitrary number of points from the
+      sequence. See warning below.
+
+    Parameters
+    ----------
+    d : int
+        Dimensionality of the sequence. Max dimensionality is 21201.
+    scramble : bool, optional
+        If True, use LMS+shift scrambling. Otherwise, no scrambling is done.
+        Default is True.
+    bits : int, optional
+        Number of bits of the generator. Control the maximum number of points
+        that can be generated, which is ``2**bits``. Maximal value is 64.
+        It does not correspond to the return type, which is always
+        ``np.float64`` to prevent points from repeating themselves.
+        Default is None, which for backward compatibility, corresponds to 30.
+
+        .. versionadded:: 1.9.0
+    optimization : {None, "random-cd", "lloyd"}, optional
+        Whether to use an optimization scheme to improve the quality after
+        sampling. Note that this is a post-processing step that does not
+        guarantee that all properties of the sample will be conserved.
+        Default is None.
+
+        * ``random-cd``: random permutations of coordinates to lower the
+          centered discrepancy. The best sample based on the centered
+          discrepancy is constantly updated. Centered discrepancy-based
+          sampling shows better space-filling robustness toward 2D and 3D
+          subprojections compared to using other discrepancy measures.
+        * ``lloyd``: Perturb samples using a modified Lloyd-Max algorithm.
+          The process converges to equally spaced samples.
+
+        .. versionadded:: 1.10.0
+
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+        .. versionchanged:: 1.15.0
+
+            As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
+            transition from use of `numpy.random.RandomState` to
+            `numpy.random.Generator`, this keyword was changed from `seed` to
+            `rng`. For an interim period, both keywords will continue to work, although
+            only one may be specified at a time. After the interim period, function
+            calls using the `seed` keyword will emit warnings. Following a
+            deprecation period, the `seed` keyword will be removed.
+
+    Notes
+    -----
+    Sobol' sequences [1]_ provide :math:`n=2^m` low discrepancy points in
+    :math:`[0,1)^{d}`. Scrambling them [3]_ makes them suitable for singular
+    integrands, provides a means of error estimation, and can improve their
+    rate of convergence. The scrambling strategy which is implemented is a
+    (left) linear matrix scramble (LMS) followed by a digital random shift
+    (LMS+shift) [2]_.
+
+    There are many versions of Sobol' sequences depending on their
+    'direction numbers'. This code uses direction numbers from [4]_. Hence,
+    the maximum number of dimension is 21201. The direction numbers have been
+    precomputed with search criterion 6 and can be retrieved at
+    https://web.maths.unsw.edu.au/~fkuo/sobol/.
+
+    .. warning::
+
+       Sobol' sequences are a quadrature rule and they lose their balance
+       properties if one uses a sample size that is not a power of 2, or skips
+       the first point, or thins the sequence [5]_.
+
+       If :math:`n=2^m` points are not enough then one should take :math:`2^M`
+       points for :math:`M>m`. When scrambling, the number R of independent
+       replicates does not have to be a power of 2.
+
+       Sobol' sequences are generated to some number :math:`B` of bits.
+       After :math:`2^B` points have been generated, the sequence would
+       repeat. Hence, an error is raised.
+       The number of bits can be controlled with the parameter `bits`.
+
+    References
+    ----------
+    .. [1] I. M. Sobol', "The distribution of points in a cube and the accurate
+       evaluation of integrals." Zh. Vychisl. Mat. i Mat. Phys., 7:784-802,
+       1967.
+    .. [2] J. Matousek, "On the L2-discrepancy for anchored boxes."
+       J. of Complexity 14, 527-556, 1998.
+    .. [3] Art B. Owen, "Scrambling Sobol and Niederreiter-Xing points."
+       Journal of Complexity, 14(4):466-489, December 1998.
+    .. [4] S. Joe and F. Y. Kuo, "Constructing sobol sequences with better
+       two-dimensional projections." SIAM Journal on Scientific Computing,
+       30(5):2635-2654, 2008.
+    .. [5] Art B. Owen, "On dropping the first Sobol' point."
+       :arxiv:`2008.08051`, 2020.
+
+    Examples
+    --------
+    Generate samples from a low discrepancy sequence of Sobol'.
+
+    >>> from scipy.stats import qmc
+    >>> sampler = qmc.Sobol(d=2, scramble=False)
+    >>> sample = sampler.random_base2(m=3)
+    >>> sample
+    array([[0.   , 0.   ],
+           [0.5  , 0.5  ],
+           [0.75 , 0.25 ],
+           [0.25 , 0.75 ],
+           [0.375, 0.375],
+           [0.875, 0.875],
+           [0.625, 0.125],
+           [0.125, 0.625]])
+
+    Compute the quality of the sample using the discrepancy criterion.
+
+    >>> qmc.discrepancy(sample)
+    0.013882107204860938
+
+    To continue an existing design, extra points can be obtained
+    by calling again `random_base2`. Alternatively, you can skip some
+    points like:
+
+    >>> _ = sampler.reset()
+    >>> _ = sampler.fast_forward(4)
+    >>> sample_continued = sampler.random_base2(m=2)
+    >>> sample_continued
+    array([[0.375, 0.375],
+           [0.875, 0.875],
+           [0.625, 0.125],
+           [0.125, 0.625]])
+
+    Finally, samples can be scaled to bounds.
+
+    >>> l_bounds = [0, 2]
+    >>> u_bounds = [10, 5]
+    >>> qmc.scale(sample_continued, l_bounds, u_bounds)
+    array([[3.75 , 3.125],
+           [8.75 , 4.625],
+           [6.25 , 2.375],
+           [1.25 , 3.875]])
+
+    """
+
+    MAXDIM: ClassVar[int] = _MAXDIM
+
+    @_transition_to_rng('seed', replace_doc=False)
+    def __init__(
+        self, d: IntNumber, *, scramble: bool = True,
+        bits: IntNumber | None = None, rng: SeedType = None,
+        optimization: Literal["random-cd", "lloyd"] | None = None
+    ) -> None:
+        # Used in `scipy.integrate.qmc_quad`
+        self._init_quad = {'d': d, 'scramble': True, 'bits': bits,
+                           'optimization': optimization}
+
+        super()._initialize(d=d, optimization=optimization, rng=rng)
+        if d > self.MAXDIM:
+            raise ValueError(
+                f"Maximum supported dimensionality is {self.MAXDIM}."
+            )
+
+        self.bits = bits
+        self.dtype_i: type
+        self.scramble = scramble
+
+        if self.bits is None:
+            self.bits = 30
+
+        if self.bits <= 32:
+            self.dtype_i = np.uint32
+        elif 32 < self.bits <= 64:
+            self.dtype_i = np.uint64
+        else:
+            raise ValueError("Maximum supported 'bits' is 64")
+
+        self.maxn = 2**self.bits
+
+        # v is d x maxbit matrix
+        self._sv: np.ndarray = np.zeros((d, self.bits), dtype=self.dtype_i)
+        _initialize_v(self._sv, dim=d, bits=self.bits)
+
+        if not scramble:
+            self._shift: np.ndarray = np.zeros(d, dtype=self.dtype_i)
+        else:
+            # scramble self._shift and self._sv
+            self._scramble()
+
+        self._quasi = self._shift.copy()
+
+        # normalization constant with the largest possible number
+        # calculate in Python to not overflow int with 2**64
+        self._scale = 1.0 / 2 ** self.bits
+
+        self._first_point = (self._quasi * self._scale).reshape(1, -1)
+        # explicit casting to float64
+        self._first_point = self._first_point.astype(np.float64)
+
+    def _scramble(self) -> None:
+        """Scramble the sequence using LMS+shift."""
+        # Generate shift vector
+        self._shift = np.dot(
+            rng_integers(self.rng, 2, size=(self.d, self.bits),
+                         dtype=self.dtype_i),
+            # pyrefly:ignore[no-matching-overload]
+            2 ** np.arange(self.bits, dtype=self.dtype_i),
+        )
+        # Generate lower triangular matrices (stacked across dimensions)
+        ltm = np.tril(rng_integers(self.rng, 2,
+                                   size=(self.d, self.bits, self.bits),
+                                   dtype=self.dtype_i))
+        _cscramble(
+            dim=self.d, bits=self.bits,  # type: ignore[arg-type]
+            ltm=ltm, sv=self._sv
+        )
+
+    def _random(
+        self, n: IntNumber = 1, *, workers: IntNumber = 1
+    ) -> np.ndarray:
+        """Draw next point(s) in the Sobol' sequence.
+
+        Parameters
+        ----------
+        n : int, optional
+            Number of samples to generate in the parameter space. Default is 1.
+
+        Returns
+        -------
+        sample : array_like (n, d)
+            Sobol' sample.
+
+        """
+        sample: np.ndarray = np.empty((n, self.d), dtype=np.float64)
+
+        if n == 0:
+            return sample
+
+        total_n = self.num_generated + n
+        if total_n > self.maxn:
+            msg = (
+                f"At most 2**{self.bits}={self.maxn} distinct points can be "
+                f"generated. {self.num_generated} points have been previously "
+                f"generated, then: n={self.num_generated}+{n}={total_n}. "
+            )
+            if self.bits != 64:
+                msg += "Consider increasing `bits`."
+            raise ValueError(msg)
+
+        if self.num_generated == 0:
+            # verify n is 2**n
+            if not (n & (n - 1) == 0):
+                warnings.warn("The balance properties of Sobol' points require"
+                              " n to be a power of 2.", stacklevel=3)
+
+            if n == 1:
+                sample = self._first_point
+            else:
+                _draw(
+                    n=n - 1, num_gen=self.num_generated, dim=self.d,
+                    scale=self._scale, sv=self._sv, quasi=self._quasi,
+                    sample=sample
+                )
+                sample = np.concatenate(
+                    [self._first_point, sample]
+                )[:n]
+        else:
+            _draw(
+                n=n, num_gen=self.num_generated - 1, dim=self.d,
+                scale=self._scale, sv=self._sv, quasi=self._quasi,
+                sample=sample
+            )
+
+        return sample
+
+    def random_base2(self, m: IntNumber) -> np.ndarray:
+        """Draw point(s) from the Sobol' sequence.
+
+        This function draws :math:`n=2^m` points in the parameter space
+        ensuring the balance properties of the sequence.
+
+        Parameters
+        ----------
+        m : int
+            Logarithm in base 2 of the number of samples; i.e., n = 2^m.
+
+        Returns
+        -------
+        sample : array_like (n, d)
+            Sobol' sample.
+
+        """
+        n = 2 ** m
+
+        total_n = self.num_generated + n
+        if not (total_n & (total_n - 1) == 0):
+            raise ValueError('The balance properties of Sobol\' points require '
+                             f'n to be a power of 2. {self.num_generated} points '
+                             'have been previously generated, then: '
+                             f'n={self.num_generated}+2**{m}={total_n}. '
+                             'If you still want to do this, the function '
+                             '\'Sobol.random()\' can be used.'
+                             )
+
+        return self.random(n)
+
+    def reset(self) -> "Sobol":
+        """Reset the engine to base state.
+
+        Returns
+        -------
+        engine : Sobol
+            Engine reset to its base state.
+
+        """
+        super().reset()
+        self._quasi = self._shift.copy()
+        return self
+
+    def fast_forward(self, n: IntNumber) -> "Sobol":
+        """Fast-forward the sequence by `n` positions.
+
+        Parameters
+        ----------
+        n : int
+            Number of points to skip in the sequence.
+
+        Returns
+        -------
+        engine : Sobol
+            The fast-forwarded engine.
+
+        """
+        if self.num_generated == 0:
+            _fast_forward(
+                n=n - 1, num_gen=self.num_generated, dim=self.d,
+                sv=self._sv, quasi=self._quasi
+            )
+        else:
+            _fast_forward(
+                n=n, num_gen=self.num_generated - 1, dim=self.d,
+                sv=self._sv, quasi=self._quasi
+            )
+        self.num_generated += n
+        return self
+
+
+class PoissonDisk(QMCEngine):
+    """Poisson disk sampling.
+
+    Parameters
+    ----------
+    d : int
+        Dimension of the parameter space.
+    radius : float
+        Minimal distance to keep between points when sampling new candidates.
+    hypersphere : {"volume", "surface"}, optional
+        Sampling strategy to generate potential candidates to be added in the
+        final sample. Default is "volume".
+
+        * ``volume``: original Bridson algorithm as described in [1]_.
+          New candidates are sampled *within* the hypersphere.
+        * ``surface``: only sample the surface of the hypersphere.
+    ncandidates : int
+        Number of candidates to sample per iteration. More candidates result
+        in a denser sampling as more candidates can be accepted per iteration.
+    optimization : {None, "random-cd", "lloyd"}, optional
+        Whether to use an optimization scheme to improve the quality after
+        sampling. Note that this is a post-processing step that does not
+        guarantee that all properties of the sample will be conserved.
+        Default is None.
+
+        * ``random-cd``: random permutations of coordinates to lower the
+          centered discrepancy. The best sample based on the centered
+          discrepancy is constantly updated. Centered discrepancy-based
+          sampling shows better space-filling robustness toward 2D and 3D
+          subprojections compared to using other discrepancy measures.
+        * ``lloyd``: Perturb samples using a modified Lloyd-Max algorithm.
+          The process converges to equally spaced samples.
+
+        .. versionadded:: 1.10.0
+
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+        .. versionchanged:: 1.15.0
+
+            As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
+            transition from use of `numpy.random.RandomState` to
+            `numpy.random.Generator`, this keyword was changed from `seed` to
+            `rng`. For an interim period, both keywords will continue to work, although
+            only one may be specified at a time. After the interim period, function
+            calls using the `seed` keyword will emit warnings. Following a
+            deprecation period, the `seed` keyword will be removed.
+
+    l_bounds, u_bounds : array_like (d,)
+        Lower and upper bounds of target sample data.
+
+    Notes
+    -----
+    Poisson disk sampling is an iterative sampling strategy. Starting from
+    a seed sample, `ncandidates` are sampled in the hypersphere
+    surrounding the seed. Candidates below a certain `radius` or outside the
+    domain are rejected. New samples are added in a pool of sample seed. The
+    process stops when the pool is empty or when the number of required
+    samples is reached.
+
+    The maximum number of point that a sample can contain is directly linked
+    to the `radius`. As the dimension of the space increases, a higher radius
+    spreads the points further and help overcome the curse of dimensionality.
+    See the :ref:`quasi monte carlo tutorial <quasi-monte-carlo>` for more
+    details.
+
+    .. warning::
+
+       The algorithm is more suitable for low dimensions and sampling size
+       due to its iterative nature and memory requirements.
+       Selecting a small radius with a high dimension would
+       mean that the space could contain more samples than using lower
+       dimension or a bigger radius.
+
+    Some code taken from [2]_, written consent given on 31.03.2021
+    by the original author, Shamis, for free use in SciPy under
+    the 3-clause BSD.
+
+    References
+    ----------
+    .. [1] Robert Bridson, "Fast Poisson Disk Sampling in Arbitrary
+       Dimensions." SIGGRAPH, 2007.
+    .. [2] `StackOverflow <https://stackoverflow.com/questions/66047540>`__.
+
+    Examples
+    --------
+    Generate a 2D sample using a `radius` of 0.2.
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from matplotlib.collections import PatchCollection
+    >>> from scipy.stats import qmc
+    >>>
+    >>> rng = np.random.default_rng()
+    >>> radius = 0.2
+    >>> engine = qmc.PoissonDisk(d=2, radius=radius, rng=rng)
+    >>> sample = engine.random(20)
+
+    Visualizing the 2D sample and showing that no points are closer than
+    `radius`. ``radius/2`` is used to visualize non-intersecting circles.
+    If two samples are exactly at `radius` from each other, then their circle
+    of radius ``radius/2`` will touch.
+
+    >>> fig, ax = plt.subplots()
+    >>> _ = ax.scatter(sample[:, 0], sample[:, 1])
+    >>> circles = [plt.Circle((xi, yi), radius=radius/2, fill=False)
+    ...            for xi, yi in sample]
+    >>> collection = PatchCollection(circles, match_original=True)
+    >>> ax.add_collection(collection)
+    >>> _ = ax.set(aspect='equal', xlabel=r'$x_1$', ylabel=r'$x_2$',
+    ...            xlim=[0, 1], ylim=[0, 1])
+    >>> plt.show()
+
+    Such visualization can be seen as circle packing: how many circle can
+    we put in the space. It is an np-hard problem. The method `fill_space`
+    can be used to add samples until no more samples can be added. This is
+    a hard problem and parameters may need to be adjusted manually. Beware of
+    the dimension: as the dimensionality increases, the number of samples
+    required to fill the space increases exponentially
+    (curse-of-dimensionality).
+
+    """
+
+    @_transition_to_rng('seed', replace_doc=False)
+    def __init__(
+        self,
+        d: IntNumber,
+        *,
+        radius: DecimalNumber = 0.05,
+        hypersphere: Literal["volume", "surface"] = "volume",
+        ncandidates: IntNumber = 30,
+        optimization: Literal["random-cd", "lloyd"] | None = None,
+        rng: SeedType = None,
+        l_bounds: "npt.ArrayLike | None" = None,
+        u_bounds: "npt.ArrayLike | None" = None,
+    ) -> None:
+        # Used in `scipy.integrate.qmc_quad`
+        self._init_quad = {'d': d, 'radius': radius,
+                           'hypersphere': hypersphere,
+                           'ncandidates': ncandidates,
+                           'optimization': optimization}
+        super()._initialize(d=d, optimization=optimization, rng=rng)
+
+        hypersphere_sample = {
+            "volume": self._hypersphere_volume_sample,
+            "surface": self._hypersphere_surface_sample
+        }
+
+        try:
+            self.hypersphere_method = hypersphere_sample[hypersphere]
+        except KeyError as exc:
+            message = (
+                f"{hypersphere!r} is not a valid hypersphere sampling"
+                f" method. It must be one of {set(hypersphere_sample)!r}")
+            raise ValueError(message) from exc
+
+        # size of the sphere from which the samples are drawn relative to the
+        # size of a disk (radius)
+        # for the surface sampler, all new points are almost exactly 1 radius
+        # away from at least one existing sample +eps to avoid rejection
+        self.radius_factor = 2 if hypersphere == "volume" else 1.001
+        self.radius = radius
+        self.radius_squared = self.radius**2
+
+        # sample to generate per iteration in the hypersphere around center
+        self.ncandidates = ncandidates
+
+        if u_bounds is None:
+            u_bounds = np.ones(d)
+        if l_bounds is None:
+            l_bounds = np.zeros(d)
+        self.l_bounds, self.u_bounds = _validate_bounds(
+            l_bounds=l_bounds, u_bounds=u_bounds, d=int(d)
+        )
+
+        with np.errstate(divide='ignore'):
+            self.cell_size = self.radius / np.sqrt(self.d)
+            self.grid_size = (
+                np.ceil((self.u_bounds - self.l_bounds) / self.cell_size)
+            ).astype(int)
+
+        self._initialize_grid_pool()
+
+    def _initialize_grid_pool(self):
+        """Sampling pool and sample grid."""
+        self.sample_pool = []
+        # Positions of cells
+        # n-dim value for each grid cell
+        self.sample_grid = np.empty(
+            np.append(self.grid_size, self.d),
+            dtype=np.float32
+        )
+        # Initialise empty cells with NaNs
+        self.sample_grid.fill(np.nan)
+
+    def _random(
+        self, n: IntNumber = 1, *, workers: IntNumber = 1
+    ) -> np.ndarray:
+        """Draw `n` in the interval ``[l_bounds, u_bounds]``.
+
+        Note that it can return fewer samples if the space is full.
+        See the note section of the class.
+
+        Parameters
+        ----------
+        n : int, optional
+            Number of samples to generate in the parameter space. Default is 1.
+
+        Returns
+        -------
+        sample : array_like (n, d)
+            QMC sample.
+
+        """
+        if n == 0 or self.d == 0:
+            return np.empty((n, self.d))
+
+        def in_limits(sample: np.ndarray) -> bool:
+            for i in range(self.d):
+                if (sample[i] > self.u_bounds[i] or sample[i] < self.l_bounds[i]):
+                    return False
+            return True
+
+        def in_neighborhood(candidate: np.ndarray, n: int = 2) -> bool:
+            """
+            Check if there are samples closer than ``radius_squared`` to the
+            `candidate` sample.
+            """
+            indices = ((candidate - self.l_bounds) / self.cell_size).astype(int)
+            ind_min = np.maximum(indices - n, 0)
+            ind_max = np.minimum(indices + n + 1, self.grid_size)
+
+            # Check if the center cell is empty
+            if not np.isnan(self.sample_grid[tuple(indices)][0]):
+                return True
+
+            a = [slice(ind_min[i], ind_max[i]) for i in range(self.d)]
+
+            # guards against: invalid value encountered in less as we are
+            # comparing with nan and returns False. Which is wanted.
+            with np.errstate(invalid='ignore'):
+                if np.any(
+                    np.sum(
+                        np.square(candidate - self.sample_grid[tuple(a)]),
+                        axis=self.d
+                    ) < self.radius_squared
+                ):
+                    return True
+
+            return False
+
+        def add_sample(candidate: np.ndarray) -> None:
+            self.sample_pool.append(candidate)
+            indices = ((candidate - self.l_bounds) / self.cell_size).astype(int)
+            self.sample_grid[tuple(indices)] = candidate
+            curr_sample.append(candidate)
+
+        curr_sample: list[np.ndarray] = []
+
+        if len(self.sample_pool) == 0:
+            # the pool is being initialized with a single random sample
+            add_sample(self.rng.uniform(self.l_bounds, self.u_bounds))
+            num_drawn = 1
+        else:
+            num_drawn = 0
+
+        # exhaust sample pool to have up to n sample
+        while len(self.sample_pool) and num_drawn < n:
+            # select a sample from the available pool
+            idx_center = rng_integers(self.rng, len(self.sample_pool))
+            center = self.sample_pool[idx_center]
+            del self.sample_pool[idx_center]
+
+            # generate candidates around the center sample
+            candidates = self.hypersphere_method(
+                center, self.radius * self.radius_factor, self.ncandidates
+            )
+
+            # keep candidates that satisfy some conditions
+            for candidate in candidates:
+                if in_limits(candidate) and not in_neighborhood(candidate):
+                    add_sample(candidate)
+
+                    num_drawn += 1
+                    if num_drawn >= n:
+                        break
+
+        self.num_generated += num_drawn
+        return np.array(curr_sample)
+
+    def fill_space(self) -> np.ndarray:
+        """Draw ``n`` samples in the interval ``[l_bounds, u_bounds]``.
+
+        Unlike `random`, this method will try to add points until
+        the space is full. Depending on ``candidates`` (and to a lesser extent
+        other parameters), some empty areas can still be present in the sample.
+
+        .. warning::
+
+           This can be extremely slow in high dimensions or if the
+           ``radius`` is very small-with respect to the dimensionality.
+
+        Returns
+        -------
+        sample : array_like (n, d)
+            QMC sample.
+
+        """
+        return self.random(np.inf)  # type: ignore[arg-type]
+
+    def reset(self) -> "PoissonDisk":
+        """Reset the engine to base state.
+
+        Returns
+        -------
+        engine : PoissonDisk
+            Engine reset to its base state.
+
+        """
+        super().reset()
+        self._initialize_grid_pool()
+        return self
+
+    def _hypersphere_volume_sample(
+        self, center: np.ndarray, radius: DecimalNumber,
+        candidates: IntNumber = 1
+    ) -> np.ndarray:
+        """Uniform sampling within hypersphere."""
+        # should remove samples within r/2
+        x = self.rng.standard_normal(size=(candidates, self.d))
+        ssq = np.sum(x**2, axis=1)
+        fr = radius * gammainc(self.d/2, ssq/2)**(1/self.d) / np.sqrt(ssq)
+        fr_tiled = np.tile(fr.reshape(-1, 1), (1, int(self.d)))
+        p = center + np.multiply(x, fr_tiled)
+        return p
+
+    def _hypersphere_surface_sample(
+        self, center: np.ndarray, radius: DecimalNumber,
+        candidates: IntNumber = 1
+    ) -> np.ndarray:
+        """Uniform sampling on the hypersphere's surface."""
+        vec = self.rng.standard_normal(size=(candidates, self.d))
+        vec /= np.linalg.norm(vec, axis=1)[:, None]
+        p = center + np.multiply(vec, radius)
+        return p
+
+
+class MultivariateNormalQMC:
+    r"""QMC sampling from a multivariate Normal :math:`N(\mu, \Sigma)`.
+
+    Parameters
+    ----------
+    mean : array_like (d,)
+        The mean vector. Where ``d`` is the dimension.
+    cov : array_like (d, d), optional
+        The covariance matrix. If omitted, use `cov_root` instead.
+        If both `cov` and `cov_root` are omitted, use the identity matrix.
+    cov_root : array_like (d, d'), optional
+        A root decomposition of the covariance matrix, where ``d'`` may be less
+        than ``d`` if the covariance is not full rank. If omitted, use `cov`.
+    inv_transform : bool, optional
+        If True, use inverse transform instead of Box-Muller. Default is True.
+    engine : QMCEngine, optional
+        Quasi-Monte Carlo engine sampler. If None, `Sobol` is used.
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+        .. versionchanged:: 1.15.0
+
+            As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
+            transition from use of `numpy.random.RandomState` to
+            `numpy.random.Generator`, this keyword was changed from `seed` to
+            `rng`. For an interim period, both keywords will continue to work, although
+            only one may be specified at a time. After the interim period, function
+            calls using the `seed` keyword will emit warnings. Following a
+            deprecation period, the `seed` keyword will be removed.
+
+    Examples
+    --------
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy.stats import qmc
+    >>> dist = qmc.MultivariateNormalQMC(mean=[0, 5], cov=[[1, 0], [0, 1]])
+    >>> sample = dist.random(512)
+    >>> _ = plt.scatter(sample[:, 0], sample[:, 1])
+    >>> plt.show()
+
+    """
+
+    @_transition_to_rng('seed', replace_doc=False)
+    def __init__(
+            self,
+            mean: "npt.ArrayLike",
+            cov: "npt.ArrayLike | None" = None,
+            *,
+            cov_root: "npt.ArrayLike | None" = None,
+            inv_transform: bool = True,
+            engine: QMCEngine | None = None,
+            rng: SeedType = None,
+    ) -> None:
+        mean = np.asarray(np.atleast_1d(mean))
+        d = mean.shape[0]
+        if cov is not None:
+            # covariance matrix provided
+            cov = np.asarray(np.atleast_2d(cov))
+            # check for square/symmetric cov matrix and mean vector has the
+            # same d
+            if not mean.shape[0] == cov.shape[0]:
+                raise ValueError("Dimension mismatch between mean and "
+                                 "covariance.")
+            if not np.allclose(cov, cov.transpose()):
+                raise ValueError("Covariance matrix is not symmetric.")
+            # compute Cholesky decomp; if it fails, do the eigen decomposition
+            try:
+                cov_root = np.linalg.cholesky(cov).transpose()
+            except np.linalg.LinAlgError:
+                eigval, eigvec = np.linalg.eigh(cov)
+                if not np.all(eigval >= -1.0e-8):
+                    raise ValueError("Covariance matrix not PSD.")
+                eigval = np.clip(eigval, 0.0, None)
+                cov_root = (eigvec * np.sqrt(eigval)).transpose()
+        elif cov_root is not None:
+            # root decomposition provided
+            cov_root = np.atleast_2d(cov_root)
+            if not mean.shape[0] == cov_root.shape[0]:
+                raise ValueError("Dimension mismatch between mean and "
+                                 "covariance.")
+        else:
+            # corresponds to identity covariance matrix
+            cov_root = None
+
+        self._inv_transform = inv_transform
+
+        if not inv_transform:
+            # to apply Box-Muller, we need an even number of dimensions
+            engine_dim = 2 * math.ceil(d / 2)
+        else:
+            engine_dim = d
+        if engine is None:
+            # Need this during SPEC 7 transition to prevent `RandomState`
+            # from being passed via `rng`.
+            kwarg = "seed" if isinstance(rng, np.random.RandomState) else "rng"
+            kwargs = {kwarg: rng}
+            self.engine: QMCEngine = Sobol(
+                d=engine_dim, scramble=True, bits=30, **kwargs
+            )
+        elif isinstance(engine, QMCEngine):
+            if engine.d != engine_dim:
+                raise ValueError("Dimension of `engine` must be consistent"
+                                 " with dimensions of mean and covariance."
+                                 " If `inv_transform` is False, it must be"
+                                 " an even number.")
+            self.engine = engine
+        else:
+            raise ValueError("`engine` must be an instance of "
+                             "`scipy.stats.qmc.QMCEngine` or `None`.")
+
+        self._mean = mean
+        self._corr_matrix = cov_root
+
+        self._d = d
+
+    def random(self, n: IntNumber = 1) -> np.ndarray:
+        """Draw `n` QMC samples from the multivariate Normal.
+
+        Parameters
+        ----------
+        n : int, optional
+            Number of samples to generate in the parameter space. Default is 1.
+
+        Returns
+        -------
+        sample : array_like (n, d)
+            Sample.
+
+        """
+        base_samples = self._standard_normal_samples(n)
+        return self._correlate(base_samples)
+
+    def _correlate(self, base_samples: np.ndarray) -> np.ndarray:
+        if self._corr_matrix is not None:
+            return base_samples @ self._corr_matrix + self._mean
+        else:
+            # avoid multiplying with identity here
+            return base_samples + self._mean
+
+    def _standard_normal_samples(self, n: IntNumber = 1) -> np.ndarray:
+        """Draw `n` QMC samples from the standard Normal :math:`N(0, I_d)`.
+
+        Parameters
+        ----------
+        n : int, optional
+            Number of samples to generate in the parameter space. Default is 1.
+
+        Returns
+        -------
+        sample : array_like (n, d)
+            Sample.
+
+        """
+        # get base samples
+        samples = self.engine.random(n)
+        if self._inv_transform:
+            # apply inverse transform
+            # (values to close to 0/1 result in inf values)
+            return stats.norm.ppf(0.5 + (1 - 1e-10) * (samples - 0.5))  # type: ignore[attr-defined]  # noqa: E501
+        else:
+            # apply Box-Muller transform (note: indexes starting from 1)
+            even = np.arange(0, samples.shape[-1], 2)
+            Rs = np.sqrt(-2 * np.log(samples[:, even]))
+            thetas = 2 * math.pi * samples[:, 1 + even]
+            cos = np.cos(thetas)
+            sin = np.sin(thetas)
+            transf_samples = np.stack([Rs * cos, Rs * sin],
+                                      -1).reshape(n, -1)
+            # make sure we only return the number of dimension requested
+            return transf_samples[:, : self._d]
+
+
+class MultinomialQMC:
+    r"""QMC sampling from a multinomial distribution.
+
+    Parameters
+    ----------
+    pvals : array_like (k,)
+        Vector of probabilities of size ``k``, where ``k`` is the number
+        of categories. Elements must be non-negative and sum to 1.
+    n_trials : int
+        Number of trials.
+    engine : QMCEngine, optional
+        Quasi-Monte Carlo engine sampler. If None, `Sobol` is used.
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+        .. versionchanged:: 1.15.0
+
+            As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
+            transition from use of `numpy.random.RandomState` to
+            `numpy.random.Generator`, this keyword was changed from `seed` to
+            `rng`. For an interim period, both keywords will continue to work, although
+            only one may be specified at a time. After the interim period, function
+            calls using the `seed` keyword will emit warnings. Following a
+            deprecation period, the `seed` keyword will be removed.
+
+    Examples
+    --------
+    Let's define 3 categories and for a given sample, the sum of the trials
+    of each category is 8. The number of trials per category is determined
+    by the `pvals` associated to each category.
+    Then, we sample this distribution 64 times.
+
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy.stats import qmc
+    >>> dist = qmc.MultinomialQMC(
+    ...     pvals=[0.2, 0.4, 0.4], n_trials=10, engine=qmc.Halton(d=1)
+    ... )
+    >>> sample = dist.random(64)
+
+    We can plot the sample and verify that the median of number of trials
+    for each category is following the `pvals`. That would be
+    ``pvals * n_trials = [2, 4, 4]``.
+
+    >>> fig, ax = plt.subplots()
+    >>> ax.yaxis.get_major_locator().set_params(integer=True)
+    >>> _ = ax.boxplot(sample)
+    >>> ax.set(xlabel="Categories", ylabel="Trials")
+    >>> plt.show()
+
+    """
+
+    @_transition_to_rng('seed', replace_doc=False)
+    def __init__(
+        self,
+        pvals: "npt.ArrayLike",
+        n_trials: IntNumber,
+        *,
+        engine: QMCEngine | None = None,
+        rng: SeedType = None,
+    ) -> None:
+        self.pvals = np.atleast_1d(np.asarray(pvals))
+        if np.min(pvals) < 0:
+            raise ValueError('Elements of pvals must be non-negative.')
+        if not np.isclose(np.sum(pvals), 1):
+            raise ValueError('Elements of pvals must sum to 1.')
+        self.n_trials = n_trials
+        if engine is None:
+            # Need this during SPEC 7 transition to prevent `RandomState`
+            # from being passed via `rng`.
+            kwarg = "seed" if isinstance(rng, np.random.RandomState) else "rng"
+            kwargs = {kwarg: rng}
+            self.engine: QMCEngine = Sobol(d=1, scramble=True, bits=30, **kwargs)
+        elif isinstance(engine, QMCEngine):
+            if engine.d != 1:
+                raise ValueError("Dimension of `engine` must be 1.")
+            self.engine = engine
+        else:
+            raise ValueError("`engine` must be an instance of "
+                             "`scipy.stats.qmc.QMCEngine` or `None`.")
+
+    def random(self, n: IntNumber = 1) -> np.ndarray:
+        """Draw `n` QMC samples from the multinomial distribution.
+
+        Parameters
+        ----------
+        n : int, optional
+            Number of samples to generate in the parameter space. Default is 1.
+
+        Returns
+        -------
+        samples : array_like (n, pvals)
+            Sample.
+
+        """
+        sample = np.empty((n, len(self.pvals)))
+        for i in range(n):
+            base_draws = self.engine.random(self.n_trials).ravel()
+            p_cumulative = np.empty_like(self.pvals, dtype=float)
+            _fill_p_cumulative(np.array(self.pvals, dtype=float), p_cumulative)
+            sample_ = np.zeros_like(self.pvals, dtype=np.intp)
+            _categorize(base_draws, p_cumulative, sample_)
+            sample[i] = sample_
+        return sample
+
+
+def _select_optimizer(
+    optimization: Literal["random-cd", "lloyd"] | None, config: dict
+) -> Callable | None:
+    """A factory for optimization methods."""
+    optimization_method: dict[str, Callable] = {
+        "random-cd": _random_cd,
+        "lloyd": _lloyd_centroidal_voronoi_tessellation
+    }
+
+    optimizer: partial | None
+    if optimization is not None:
+        try:
+            optimizer_ = optimization_method[optimization.lower()]
+        except KeyError as exc:
+            message = (f"{optimization!r} is not a valid optimization"
+                       f" method. It must be one of"
+                       f" {set(optimization_method)!r}")
+            raise ValueError(message) from exc
+
+        # config
+        optimizer = partial(optimizer_, **config)
+    else:
+        optimizer = None
+
+    return optimizer
+
+
+def _random_cd(
+    best_sample: np.ndarray, n_iters: int, n_nochange: int, rng: GeneratorType,
+    **kwargs: dict
+) -> np.ndarray:
+    """Optimal LHS on CD.
+
+    Create a base LHS and do random permutations of coordinates to
+    lower the centered discrepancy.
+    Because it starts with a normal LHS, it also works with the
+    `scramble` keyword argument.
+
+    Two stopping criterion are used to stop the algorithm: at most,
+    `n_iters` iterations are performed; or if there is no improvement
+    for `n_nochange` consecutive iterations.
+    """
+    del kwargs  # only use keywords which are defined, needed by factory
+
+    n, d = best_sample.shape
+
+    if d == 0 or n == 0:
+        return np.empty((n, d))
+
+    if d == 1 or n == 1:
+        # discrepancy measures are invariant under permuting factors and runs
+        return best_sample
+
+    best_disc = discrepancy(best_sample)
+
+    bounds = ([0, d - 1],
+              [0, n - 1],
+              [0, n - 1])
+
+    n_nochange_ = 0
+    n_iters_ = 0
+    while n_nochange_ < n_nochange and n_iters_ < n_iters:
+        n_iters_ += 1
+
+        col = rng_integers(rng, *bounds[0], endpoint=True)  # type: ignore[misc]
+        row_1 = rng_integers(rng, *bounds[1], endpoint=True)  # type: ignore[misc]
+        row_2 = rng_integers(rng, *bounds[2], endpoint=True)  # type: ignore[misc]
+        disc = _perturb_discrepancy(best_sample,
+                                    row_1, row_2, col,
+                                    best_disc)
+        if disc < best_disc:
+            best_sample[row_1, col], best_sample[row_2, col] = (
+                best_sample[row_2, col], best_sample[row_1, col])
+
+            best_disc = disc
+            n_nochange_ = 0
+        else:
+            n_nochange_ += 1
+
+    return best_sample
+
+
+def _l1_norm(sample: np.ndarray) -> float:
+    return distance.pdist(sample, 'cityblock').min()
+
+
+def _lloyd_iteration(
+    sample: np.ndarray,
+    decay: float,
+    qhull_options: str
+) -> np.ndarray:
+    """Lloyd-Max algorithm iteration.
+
+    Based on the implementation of Stéfan van der Walt:
+
+    https://github.com/stefanv/lloyd
+
+    which is:
+
+        Copyright (c) 2021-04-21 Stéfan van der Walt
+        https://github.com/stefanv/lloyd
+        MIT License
+
+    Parameters
+    ----------
+    sample : array_like (n, d)
+        The sample to iterate on.
+    decay : float
+        Relaxation decay. A positive value would move the samples toward
+        their centroid, and negative value would move them away.
+        1 would move the samples to their centroid.
+    qhull_options : str
+        Additional options to pass to Qhull. See Qhull manual
+        for details. (Default: "Qbb Qc Qz Qj Qx" for ndim > 4 and
+        "Qbb Qc Qz Qj" otherwise.)
+
+    Returns
+    -------
+    sample : array_like (n, d)
+        The sample after an iteration of Lloyd's algorithm.
+
+    """
+    new_sample = np.empty_like(sample)
+
+    voronoi = Voronoi(sample, qhull_options=qhull_options)
+
+    for ii, idx in enumerate(voronoi.point_region):
+        # the region is a series of indices into self.voronoi.vertices
+        # remove samples at infinity, designated by index -1
+        region = [i for i in voronoi.regions[idx] if i != -1]
+
+        # get the vertices for this region
+        verts = voronoi.vertices[region]
+
+        # clipping would be wrong, we need to intersect
+        # verts = np.clip(verts, 0, 1)
+
+        # move samples towards centroids:
+        # Centroid in n-D is the mean for uniformly distributed nodes
+        # of a geometry.
+        centroid = np.mean(verts, axis=0)
+        new_sample[ii] = sample[ii] + (centroid - sample[ii]) * decay
+
+    # only update sample to centroid within the region
+    is_valid = np.all(np.logical_and(new_sample >= 0, new_sample <= 1), axis=1)
+    sample[is_valid] = new_sample[is_valid]
+
+    return sample
+
+
+def _lloyd_centroidal_voronoi_tessellation(
+    sample: "npt.ArrayLike",
+    *,
+    tol: DecimalNumber = 1e-5,
+    maxiter: IntNumber = 10,
+    qhull_options: str | None = None,
+    **kwargs: dict
+) -> np.ndarray:
+    """Approximate Centroidal Voronoi Tessellation.
+
+    Perturb samples in N-dimensions using Lloyd-Max algorithm.
+
+    Parameters
+    ----------
+    sample : array_like (n, d)
+        The sample to iterate on. With ``n`` the number of samples and ``d``
+        the dimension. Samples must be in :math:`[0, 1]^d`, with ``d>=2``.
+    tol : float, optional
+        Tolerance for termination. If the min of the L1-norm over the samples
+        changes less than `tol`, it stops the algorithm. Default is 1e-5.
+    maxiter : int, optional
+        Maximum number of iterations. It will stop the algorithm even if
+        `tol` is above the threshold.
+        Too many iterations tend to cluster the samples as a hypersphere.
+        Default is 10.
+    qhull_options : str, optional
+        Additional options to pass to Qhull. See Qhull manual
+        for details. (Default: "Qbb Qc Qz Qj Qx" for ndim > 4 and
+        "Qbb Qc Qz Qj" otherwise.)
+
+    Returns
+    -------
+    sample : array_like (n, d)
+        The sample after being processed by Lloyd-Max algorithm.
+
+    Notes
+    -----
+    Lloyd-Max algorithm is an iterative process with the purpose of improving
+    the dispersion of samples. For given sample: (i) compute a Voronoi
+    Tessellation; (ii) find the centroid of each Voronoi cell; (iii) move the
+    samples toward the centroid of their respective cell. See [1]_, [2]_.
+
+    A relaxation factor is used to control how fast samples can move at each
+    iteration. This factor is starting at 2 and ending at 1 after `maxiter`
+    following an exponential decay.
+
+    The process converges to equally spaced samples. It implies that measures
+    like the discrepancy could suffer from too many iterations. On the other
+    hand, L1 and L2 distances should improve. This is especially true with
+    QMC methods which tend to favor the discrepancy over other criteria.
+
+    .. note::
+
+        The current implementation does not intersect the Voronoi Tessellation
+        with the boundaries. This implies that for a low number of samples,
+        empirically below 20, no Voronoi cell is touching the boundaries.
+        Hence, samples cannot be moved close to the boundaries.
+
+        Further improvements could consider the samples at infinity so that
+        all boundaries are segments of some Voronoi cells. This would fix
+        the computation of the centroid position.
+
+    .. warning::
+
+       The Voronoi Tessellation step is expensive and quickly becomes
+       intractable with dimensions as low as 10 even for a sample
+       of size as low as 1000.
+
+    .. versionadded:: 1.9.0
+
+    References
+    ----------
+    .. [1] Lloyd. "Least Squares Quantization in PCM".
+       IEEE Transactions on Information Theory, 1982.
+    .. [2] Max J. "Quantizing for minimum distortion".
+       IEEE Transactions on Information Theory, 1960.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.spatial import distance
+    >>> from scipy.stats._qmc import _lloyd_centroidal_voronoi_tessellation
+    >>> rng = np.random.default_rng()
+    >>> sample = rng.random((128, 2))
+
+    .. note::
+
+        The samples need to be in :math:`[0, 1]^d`. `scipy.stats.qmc.scale`
+        can be used to scale the samples from their
+        original bounds to :math:`[0, 1]^d`. And back to their original bounds.
+
+    Compute the quality of the sample using the L1 criterion.
+
+    >>> def l1_norm(sample):
+    ...    return distance.pdist(sample, 'cityblock').min()
+
+    >>> l1_norm(sample)
+    0.00161...  # random
+
+    Now process the sample using Lloyd's algorithm and check the improvement
+    on the L1. The value should increase.
+
+    >>> sample = _lloyd_centroidal_voronoi_tessellation(sample)
+    >>> l1_norm(sample)
+    0.0278...  # random
+
+    """
+    del kwargs  # only use keywords which are defined, needed by factory
+
+    sample = np.asarray(sample).copy()
+
+    if not sample.ndim == 2:
+        raise ValueError('`sample` is not a 2D array')
+
+    if not sample.shape[1] >= 2:
+        raise ValueError('`sample` dimension is not >= 2')
+
+    # Checking that sample is within the hypercube
+    if (sample.max() > 1.) or (sample.min() < 0.):
+        raise ValueError('`sample` is not in unit hypercube')
+
+    if qhull_options is None:
+        qhull_options = 'Qbb Qc Qz QJ'
+
+        if sample.shape[1] >= 5:
+            qhull_options += ' Qx'
+
+    # Fit an exponential to be 2 at 0 and 1 at `maxiter`.
+    # The decay is used for relaxation.
+    # analytical solution for y=exp(-maxiter/x) - 0.1
+    root = -maxiter / np.log(0.1)
+    decay = [np.exp(-x / root)+0.9 for x in range(maxiter)]
+
+    l1_old = _l1_norm(sample=sample)
+    for i in range(maxiter):
+        sample = _lloyd_iteration(
+                sample=sample, decay=decay[i],
+                qhull_options=qhull_options,
+        )
+
+        l1_new = _l1_norm(sample=sample)
+
+        if abs(l1_new - l1_old) < tol:
+            break
+        else:
+            l1_old = l1_new
+
+    return sample
+
+
+def _validate_workers(workers: IntNumber = 1) -> IntNumber:
+    """Validate `workers` based on platform and value.
+
+    Parameters
+    ----------
+    workers : int, optional
+        Number of workers to use for parallel processing. If -1 is
+        given all CPU threads are used. Default is 1.
+
+    Returns
+    -------
+    Workers : int
+        Number of CPU used by the algorithm
+
+    """
+    workers = int(workers)
+    if workers == -1:
+        workers = os.cpu_count()  # type: ignore[assignment]
+        if workers is None:
+            raise NotImplementedError(
+                "Cannot determine the number of cpus using os.cpu_count(), "
+                "cannot use -1 for the number of workers"
+            )
+    elif workers <= 0:
+        raise ValueError(f"Invalid number of workers: {workers}, must be -1 "
+                         "or > 0")
+
+    return workers
+
+
+def _validate_bounds(
+    l_bounds: "npt.ArrayLike", u_bounds: "npt.ArrayLike", d: int
+) -> tuple[np.ndarray, np.ndarray]:
+    """Bounds input validation.
+
+    Parameters
+    ----------
+    l_bounds, u_bounds : array_like (d,)
+        Lower and upper bounds.
+    d : int
+        Dimension to use for broadcasting.
+
+    Returns
+    -------
+    l_bounds, u_bounds : array_like (d,)
+        Lower and upper bounds.
+
+    """
+    try:
+        lower = np.broadcast_to(l_bounds, d)
+        upper = np.broadcast_to(u_bounds, d)
+    except ValueError as exc:
+        msg = ("'l_bounds' and 'u_bounds' must be broadcastable and respect"
+               " the sample dimension")
+        raise ValueError(msg) from exc
+
+    if not np.all(lower < upper):
+        raise ValueError("Bounds are not consistent 'l_bounds' < 'u_bounds'")
+
+    return lower, upper
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_qmc_cy.cpython-312-x86_64-linux-gnu.so b/.venv/lib/python3.12/site-packages/scipy/stats/_qmc_cy.cpython-312-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..969de55140fc1255072d311f25b11a23212d5c27
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_qmc_cy.cpython-312-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:380316a38d74d214f834b41b674b511f51cb3ba7064646ec8a9d914d52672ea7
+size 134800
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_qmc_cy.pyi b/.venv/lib/python3.12/site-packages/scipy/stats/_qmc_cy.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1006385a43179478a9a4a32ae5f825aa5b8b35c4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_qmc_cy.pyi
@@ -0,0 +1,54 @@
+import numpy as np
+from scipy._lib._util import DecimalNumber, IntNumber
+
+
+def _cy_wrapper_centered_discrepancy(
+        sample: np.ndarray, 
+        iterative: bool, 
+        workers: IntNumber,
+) -> float: ...
+
+
+def _cy_wrapper_wrap_around_discrepancy(
+        sample: np.ndarray,
+        iterative: bool, 
+        workers: IntNumber,
+) -> float: ...
+
+
+def _cy_wrapper_mixture_discrepancy(
+        sample: np.ndarray,
+        iterative: bool, 
+        workers: IntNumber,
+) -> float: ...
+
+
+def _cy_wrapper_l2_star_discrepancy(
+        sample: np.ndarray,
+        iterative: bool,
+        workers: IntNumber,
+) -> float: ...
+
+
+def _cy_wrapper_update_discrepancy(
+        x_new_view: np.ndarray,
+        sample_view: np.ndarray,
+        initial_disc: DecimalNumber,
+) -> float: ...
+
+
+def _cy_van_der_corput(
+        n: IntNumber,
+        base: IntNumber,
+        start_index: IntNumber,
+        workers: IntNumber,
+) -> np.ndarray: ...
+
+
+def _cy_van_der_corput_scrambled(
+        n: IntNumber,
+        base: IntNumber,
+        start_index: IntNumber,
+        permutations: np.ndarray,
+        workers: IntNumber,
+) -> np.ndarray: ...
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_qmvnt.py b/.venv/lib/python3.12/site-packages/scipy/stats/_qmvnt.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f942da89e0a03c456b4ee0011b711ba217b4024
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_qmvnt.py
@@ -0,0 +1,475 @@
+# Integration of multivariate normal and t distributions.
+
+# Adapted from the MATLAB original implementations by Dr. Alan Genz.
+
+#     http://www.math.wsu.edu/faculty/genz/software/software.html
+
+# Copyright (C) 2013, Alan Genz,  All rights reserved.
+# Python implementation is copyright (C) 2022, Robert Kern,  All rights
+# reserved.
+
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided the following conditions are met:
+#   1. Redistributions of source code must retain the above copyright
+#      notice, this list of conditions and the following disclaimer.
+#   2. Redistributions in binary form must reproduce the above copyright
+#      notice, this list of conditions and the following disclaimer in
+#      the documentation and/or other materials provided with the
+#      distribution.
+#   3. The contributor name(s) may not be used to endorse or promote
+#      products derived from this software without specific prior
+#      written permission.
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
+# OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
+# TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+import math
+import numpy as np
+
+from scipy.fft import fft, ifft
+from scipy.special import ndtr as phi, ndtri as phinv
+from scipy.special._ufuncs import _bivariate_normal_cdf as _bvnu
+from scipy.stats._qmc import primes_from_2_to
+
+from ._qmvnt_cy import _qmvn_inner, _qmvt_inner
+
+
+def _factorize_int(n):
+    """Return a sorted list of the unique prime factors of a positive integer.
+    """
+    # NOTE: There are lots faster ways to do this, but this isn't terrible.
+    factors = set()
+    for p in primes_from_2_to(int(np.sqrt(n)) + 1):
+        while not (n % p):
+            factors.add(p)
+            n //= p
+        if n == 1:
+            break
+    if n != 1:
+        factors.add(n)
+    return sorted(factors)
+
+
+def _primitive_root(p):
+    """Compute a primitive root of the prime number `p`.
+
+    Used in the CBC lattice construction.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Primitive_root_modulo_n
+    """
+    # p is prime
+    pm = p - 1
+    factors = _factorize_int(pm)
+    n = len(factors)
+    r = 2
+    k = 0
+    while k < n:
+        d = pm // factors[k]
+        # pow() doesn't like numpy scalar types.
+        rd = pow(int(r), int(d), int(p))
+        if rd == 1:
+            r += 1
+            k = 0
+        else:
+            k += 1
+    return r
+
+
+def _cbc_lattice(n_dim, n_qmc_samples):
+    """Compute a QMC lattice generator using a Fast CBC construction.
+
+    Parameters
+    ----------
+    n_dim : int > 0
+        The number of dimensions for the lattice.
+    n_qmc_samples : int > 0
+        The desired number of QMC samples. This will be rounded down to the
+        nearest prime to enable the CBC construction.
+
+    Returns
+    -------
+    q : float array : shape=(n_dim,)
+        The lattice generator vector. All values are in the open interval
+        ``(0, 1)``.
+    actual_n_qmc_samples : int
+        The prime number of QMC samples that must be used with this lattice,
+        no more, no less.
+
+    References
+    ----------
+    .. [1] Nuyens, D. and Cools, R. "Fast Component-by-Component Construction,
+           a Reprise for Different Kernels", In H. Niederreiter and D. Talay,
+           editors, Monte-Carlo and Quasi-Monte Carlo Methods 2004,
+           Springer-Verlag, 2006, 371-385.
+    """
+    # Round down to the nearest prime number.
+    primes = primes_from_2_to(n_qmc_samples + 1)
+    n_qmc_samples = primes[-1]
+
+    bt = np.ones(n_dim)
+    gm = np.hstack([1.0, 0.8 ** np.arange(n_dim - 1)])
+    q = 1
+    w = 0
+    z = np.arange(1, n_dim + 1)
+    m = (n_qmc_samples - 1) // 2
+    g = _primitive_root(n_qmc_samples)
+    # Slightly faster way to compute perm[j] = pow(g, j, n_qmc_samples)
+    # Shame that we don't have modulo pow() implemented as a ufunc.
+    perm = np.ones(m, dtype=int)
+    for j in range(m - 1):
+        perm[j + 1] = (g * perm[j]) % n_qmc_samples
+    perm = np.minimum(n_qmc_samples - perm, perm)
+    pn = perm / n_qmc_samples
+    c = pn * pn - pn + 1.0 / 6
+    fc = fft(c)
+    for s in range(1, n_dim):
+        reordered = np.hstack([
+            c[:w+1][::-1],
+            c[w+1:m][::-1],
+        ])
+        q = q * (bt[s-1] + gm[s-1] * reordered)
+        w = ifft(fc * fft(q)).real.argmin()
+        z[s] = perm[w]
+    q = z / n_qmc_samples
+    return q, n_qmc_samples
+
+
+def _qauto(func, covar, low, high, rng, error=1e-3, limit=10_000, **kwds):
+    """Automatically rerun the integration to get the required error bound.
+
+    Parameters
+    ----------
+    func : callable
+        Either :func:`_qmvn` or :func:`_qmvt`.
+    covar, low, high : array
+        As specified in :func:`_qmvn` and :func:`_qmvt`.
+    rng : Generator, optional
+        default_rng(), yada, yada
+    error : float > 0
+        The desired error bound.
+    limit : int > 0:
+        The rough limit of the number of integration points to consider. The
+        integration will stop looping once this limit has been *exceeded*.
+    **kwds :
+        Other keyword arguments to pass to `func`. When using :func:`_qmvt`, be
+        sure to include ``nu=`` as one of these.
+
+    Returns
+    -------
+    prob : float
+        The estimated probability mass within the bounds.
+    est_error : float
+        3 times the standard error of the batch estimates.
+    n_samples : int
+        The number of integration points actually used.
+    """
+    n = len(covar)
+    n_samples = 0
+    if n == 1:
+        prob = phi(high / covar**0.5) - phi(low / covar**0.5)
+        # More or less
+        est_error = 1e-15
+    elif n == 2:
+        prob = _bvn(low, high, covar)
+        est_error = 1e-15
+    else:
+        mi = min(limit, n * 1000)
+        prob = 0.0
+        est_error = 1.0
+        ei = 0.0
+        while est_error > error and n_samples < limit:
+            mi = round(np.sqrt(2) * mi)
+            pi, ei, ni = func(mi, covar, low, high, rng=rng, **kwds)
+            n_samples += ni
+            wt = 1.0 / (1 + (ei / est_error)**2)
+            prob += wt * (pi - prob)
+            est_error = np.sqrt(wt) * ei
+    return prob, est_error, n_samples
+
+
+def _qmvn(m, covar, low, high, rng, lattice='cbc', n_batches=10):
+    """Multivariate normal integration over box bounds.
+
+    Parameters
+    ----------
+    m : int > n_batches
+        The number of points to sample. This number will be divided into
+        `n_batches` batches that apply random offsets of the sampling lattice
+        for each batch in order to estimate the error.
+    covar : (n, n) float array
+        Possibly singular, positive semidefinite symmetric covariance matrix.
+    low, high : (n,) float array
+        The low and high integration bounds.
+    rng : Generator, optional
+        default_rng(), yada, yada
+    lattice : 'cbc' or callable
+        The type of lattice rule to use to construct the integration points.
+    n_batches : int > 0, optional
+        The number of QMC batches to apply.
+
+    Returns
+    -------
+    prob : float
+        The estimated probability mass within the bounds.
+    est_error : float
+        3 times the standard error of the batch estimates.
+    """
+    cho, lo, hi = _permuted_cholesky(covar, low, high)
+    if not cho.flags.c_contiguous:
+        # qmvn_inner expects contiguous buffers
+        cho = cho.copy()
+
+    n = cho.shape[0]
+    q, n_qmc_samples = _cbc_lattice(n - 1, max(m // n_batches, 1))
+    rndm = rng.random(size=(n_batches, n))
+
+    prob, est_error, n_samples = _qmvn_inner(
+        q, rndm, int(n_qmc_samples), int(n_batches), cho, lo, hi
+    )
+    return prob, est_error, n_samples
+
+
+# Note: this function is not currently used or tested by any SciPy code. It is
+# included in this file to facilitate the resolution of gh-8367, gh-16142, and
+# possibly gh-14286, but must be reviewed and tested before use.
+def _mvn_qmc_integrand(covar, low, high, use_tent=False):
+    """Transform the multivariate normal integration into a QMC integrand over
+    a unit hypercube.
+
+    The dimensionality of the resulting hypercube integration domain is one
+    less than the dimensionality of the original integrand. Note that this
+    transformation subsumes the integration bounds in order to account for
+    infinite bounds. The QMC integration one does with the returned integrand
+    should be on the unit hypercube.
+
+    Parameters
+    ----------
+    covar : (n, n) float array
+        Possibly singular, positive semidefinite symmetric covariance matrix.
+    low, high : (n,) float array
+        The low and high integration bounds.
+    use_tent : bool, optional
+        If True, then use tent periodization. Only helpful for lattice rules.
+
+    Returns
+    -------
+    integrand : Callable[[NDArray], NDArray]
+        The QMC-integrable integrand. It takes an
+        ``(n_qmc_samples, ndim_integrand)`` array of QMC samples in the unit
+        hypercube and returns the ``(n_qmc_samples,)`` evaluations of at these
+        QMC points.
+    ndim_integrand : int
+        The dimensionality of the integrand. Equal to ``n-1``.
+    """
+    cho, lo, hi = _permuted_cholesky(covar, low, high)
+    n = cho.shape[0]
+    ndim_integrand = n - 1
+    ct = cho[0, 0]
+    c = phi(lo[0] / ct)
+    d = phi(hi[0] / ct)
+    ci = c
+    dci = d - ci
+
+    def integrand(*zs):
+        ndim_qmc = len(zs)
+        n_qmc_samples = len(np.atleast_1d(zs[0]))
+        assert ndim_qmc == ndim_integrand
+        y = np.zeros((ndim_qmc, n_qmc_samples))
+        c = np.full(n_qmc_samples, ci)
+        dc = np.full(n_qmc_samples, dci)
+        pv = dc.copy()
+        for i in range(1, n):
+            if use_tent:
+                # Tent periodization transform.
+                x = abs(2 * zs[i-1] - 1)
+            else:
+                x = zs[i-1]
+            y[i - 1, :] = phinv(c + x * dc)
+            s = cho[i, :i] @ y[:i, :]
+            ct = cho[i, i]
+            c = phi((lo[i] - s) / ct)
+            d = phi((hi[i] - s) / ct)
+            dc = d - c
+            pv = pv * dc
+        return pv
+
+    return integrand, ndim_integrand
+
+
+def _qmvt(m, nu, covar, low, high, rng, lattice='cbc', n_batches=10):
+    """Multivariate t integration over box bounds.
+
+    Parameters
+    ----------
+    m : int > n_batches
+        The number of points to sample. This number will be divided into
+        `n_batches` batches that apply random offsets of the sampling lattice
+        for each batch in order to estimate the error.
+    nu : float >= 0
+        The shape parameter of the multivariate t distribution.
+    covar : (n, n) float array
+        Possibly singular, positive semidefinite symmetric covariance matrix.
+    low, high : (n,) float array
+        The low and high integration bounds.
+    rng : Generator, optional
+        default_rng(), yada, yada
+    lattice : 'cbc' or callable
+        The type of lattice rule to use to construct the integration points.
+    n_batches : int > 0, optional
+        The number of QMC batches to apply.
+
+    Returns
+    -------
+    prob : float
+        The estimated probability mass within the bounds.
+    est_error : float
+        3 times the standard error of the batch estimates.
+    n_samples : int
+        The number of samples actually used.
+    """
+    sn = max(1.0, np.sqrt(nu))
+    low = np.asarray(low, dtype=np.float64)
+    high = np.asarray(high, dtype=np.float64)
+    cho, lo, hi = _permuted_cholesky(covar, low / sn, high / sn)
+    n = cho.shape[0]
+    q, n_qmc_samples = _cbc_lattice(n, max(m // n_batches, 1))
+    rndm = rng.random(size=(n_batches, n))
+    prob, est_error, n_samples = _qmvt_inner(
+        q, rndm, int(n_qmc_samples), int(n_batches), cho, lo, hi, float(nu)
+    )
+    return prob, est_error, n_samples
+
+
+def _permuted_cholesky(covar, low, high, tol=1e-10):
+    """Compute a scaled, permuted Cholesky factor, with integration bounds.
+
+    The scaling and permuting of the dimensions accomplishes part of the
+    transformation of the original integration problem into a more numerically
+    tractable form. The lower-triangular Cholesky factor will then be used in
+    the subsequent integration. The integration bounds will be scaled and
+    permuted as well.
+
+    Parameters
+    ----------
+    covar : (n, n) float array
+        Possibly singular, positive semidefinite symmetric covariance matrix.
+    low, high : (n,) float array
+        The low and high integration bounds.
+    tol : float, optional
+        The singularity tolerance.
+
+    Returns
+    -------
+    cho : (n, n) float array
+        Lower Cholesky factor, scaled and permuted.
+    new_low, new_high : (n,) float array
+        The scaled and permuted low and high integration bounds.
+    """
+    # Make copies for outputting.
+    cho = np.array(covar, dtype=np.float64)
+    new_lo = np.array(low, dtype=np.float64)
+    new_hi = np.array(high, dtype=np.float64)
+    n = cho.shape[0]
+    if cho.shape != (n, n):
+        raise ValueError("expected a square symmetric array")
+    if new_lo.shape != (n,) or new_hi.shape != (n,):
+        raise ValueError(
+            "expected integration boundaries the same dimensions "
+            "as the covariance matrix"
+        )
+    # Scale by the sqrt of the diagonal.
+    dc = np.sqrt(np.maximum(np.diag(cho), 0.0))
+    # But don't divide by 0.
+    dc[dc == 0.0] = 1.0
+    new_lo /= dc
+    new_hi /= dc
+    cho /= dc
+    cho /= dc[:, np.newaxis]
+
+    y = np.zeros(n)
+    sqtp = np.sqrt(2 * np.pi)
+    for k in range(n):
+        epk = (k + 1) * tol
+        im = k
+        ck = 0.0
+        dem = 1.0
+        s = 0.0
+        lo_m = 0.0
+        hi_m = 0.0
+        for i in range(k, n):
+            if cho[i, i] > tol:
+                ci = np.sqrt(cho[i, i])
+                if i > 0:
+                    s = cho[i, :k] @ y[:k]
+                lo_i = (new_lo[i] - s) / ci
+                hi_i = (new_hi[i] - s) / ci
+                de = phi(hi_i) - phi(lo_i)
+                if de <= dem:
+                    ck = ci
+                    dem = de
+                    lo_m = lo_i
+                    hi_m = hi_i
+                    im = i
+        if im > k:
+            # Swap im and k
+            cho[im, im] = cho[k, k]
+            _swap_slices(cho, np.s_[im, :k], np.s_[k, :k])
+            _swap_slices(cho, np.s_[im + 1:, im], np.s_[im + 1:, k])
+            _swap_slices(cho, np.s_[k + 1:im, k], np.s_[im, k + 1:im])
+            _swap_slices(new_lo, k, im)
+            _swap_slices(new_hi, k, im)
+        if ck > epk:
+            cho[k, k] = ck
+            cho[k, k + 1:] = 0.0
+            for i in range(k + 1, n):
+                cho[i, k] /= ck
+                cho[i, k + 1:i + 1] -= cho[i, k] * cho[k + 1:i + 1, k]
+            if abs(dem) > tol:
+                y[k] = ((np.exp(-lo_m * lo_m / 2) - np.exp(-hi_m * hi_m / 2)) /
+                        (sqtp * dem))
+            else:
+                y[k] = (lo_m + hi_m) / 2
+                if lo_m < -10:
+                    y[k] = hi_m
+                elif hi_m > 10:
+                    y[k] = lo_m
+            cho[k, :k + 1] /= ck
+            new_lo[k] /= ck
+            new_hi[k] /= ck
+        else:
+            cho[k:, k] = 0.0
+            y[k] = (new_lo[k] + new_hi[k]) / 2
+    return cho, new_lo, new_hi
+
+
+def _swap_slices(x, slc1, slc2):
+    t = x[slc1].copy()
+    x[slc1] = x[slc2].copy()
+    x[slc2] = t
+
+
+def _bvn(a, b, A):
+    # covariance matrix is written [[s1**2, rho*s1*s2], [rho*s1*s2, s2**2]]
+    # e.g. https://en.wikipedia.org/wiki/Multivariate_normal_distribution
+    # therefore, s12 = rho*s1*s2 -> rho = s12/(s1*s2)
+    s1 = math.sqrt(A[0, 0])
+    s2 = math.sqrt(A[1, 1])
+    s12 = A[0, 1]
+    r = s12 / (s1 * s2)
+    # the x and y coordinates seem to be normalized by the standard devs
+    xl, xu = a[0] / s1, b[0] / s1
+    yl, yu = a[1] / s2, b[1] / s2
+    p = _bvnu(xl, yl, r) - _bvnu(xu, yl, r) - _bvnu(xl, yu, r) + _bvnu(xu, yu, r)
+    p = max( 0., min( p, 1. ) )
+    return p
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_qmvnt_cy.cpython-312-x86_64-linux-gnu.so b/.venv/lib/python3.12/site-packages/scipy/stats/_qmvnt_cy.cpython-312-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..47e54dee19e8a394faa0fad527a38e3ab7496bda
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_qmvnt_cy.cpython-312-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1410f5f0735b0d90b8738fb9eddaf56b5599e2aa5620a923428d5a9c99cee542
+size 135192
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_quantile.py b/.venv/lib/python3.12/site-packages/scipy/stats/_quantile.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd3756f23138ae04cfabb7b47dfaa24a47c4c10a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_quantile.py
@@ -0,0 +1,816 @@
+import math
+import numpy as np
+from scipy.special import betainc
+from scipy._lib._array_api import (
+    xp_capabilities,
+    xp_ravel,
+    array_namespace,
+    xp_promote,
+    xp_device,
+    xp_size,
+    _count_nonmasked,
+    is_torch,
+    is_lazy_array,
+)
+import scipy._external.array_api_extra as xpx
+from scipy.stats._axis_nan_policy import _broadcast_arrays, _contains_nan
+
+
+def _quantile_iv(x, p, method, axis, nan_policy, keepdims, weights, fun='quantile'):
+    xp = array_namespace(x, p, weights)
+
+    if fun == "quantile":
+        methods = {'inverted_cdf', 'averaged_inverted_cdf', 'closest_observation',
+                   'hazen', 'interpolated_inverted_cdf', 'linear',
+                   'median_unbiased', 'normal_unbiased', 'weibull',
+                   'harrell-davis', '_lower', '_midpoint', '_higher', '_nearest',
+                   'round_outward', 'round_inward', 'round_nearest'}
+        allowed_types = 'real floating'
+        def mask_fun(p): return (p > 1) | (p < 0) | xp.isnan(p)
+        var2_name = 'p'
+        var2_type_msg = '`p` must have real floating dtype.'
+    else:
+        methods = set(_estimated_cdf_methods)
+        allowed_types = ('integral', 'real floating')
+        mask_fun = xp.isnan
+        var2_name = 'y'
+        var2_type_msg = '`y` must have real dtype.'
+
+    if not xp.isdtype(xp.asarray(x).dtype, ('integral', 'real floating')):
+        raise ValueError("`x` must have real dtype.")
+
+    if not xp.isdtype(xp.asarray(p).dtype, allowed_types):
+        raise ValueError(var2_type_msg)
+
+    if not (weights is None
+            or xp.isdtype(xp.asarray(weights).dtype, ('integral', 'real floating'))):
+        raise ValueError("`weights` must have real dtype.")
+
+    x, p, weights = xp_promote(x, p, weights, force_floating=True, xp=xp)
+    p = xp.asarray(p, device=xp_device(x))
+    dtype = x.dtype
+
+    axis_none = axis is None
+    ndim = max(x.ndim, p.ndim)
+    if axis_none:
+        x = xp_ravel(x)
+        p = xp_ravel(p)
+        axis = 0
+    elif np.iterable(axis) or int(axis) != axis:
+        message = "`axis` must be an integer or None."
+        raise ValueError(message)
+    elif (axis >= ndim) or (axis < -ndim):
+        message = "`axis` is not compatible with the shapes of the inputs."
+        raise ValueError(message)
+    axis = int(axis)
+
+    if method not in methods:
+        message = f"`method` must be one of {methods}"
+        raise ValueError(message)
+
+    no_weights = {'_lower', '_midpoint', '_higher', '_nearest', 'harrell-davis',
+                  'round_nearest', 'round_inward', 'round_outward'}
+    if weights is not None and method in no_weights:
+        message = f"`method='{method}'` does not support `weights`."
+        raise ValueError(message)
+
+    contains_nans = _contains_nan(x, nan_policy, xp_omit_okay=True, xp=xp)
+
+    if keepdims not in {None, True, False}:
+        message = "If specified, `keepdims` must be True or False."
+        raise ValueError(message)
+
+    # If data has length zero along `axis`, the result will be an array of NaNs just
+    # as if the data had length 1 along axis and were filled with NaNs. This is treated
+    # naturally below whether `nan_policy` is `'propagate'` or `'omit'`.
+    if x.shape[axis] == 0 and fun == 'quantile':
+        shape = list(x.shape)
+        shape[axis] = 1
+        x = xp.full(shape, xp.nan, dtype=dtype, device=xp_device(x))
+
+    if weights is None:
+        y = xp.sort(x, axis=axis, stable=False)
+        y, p = _broadcast_arrays((y, p), axis=axis)
+        n_zero_weight = None
+    else:
+        x, weights = xp.broadcast_arrays(x, weights)
+        i_zero_weight = (weights == 0)
+        n_zero_weight = xp.count_nonzero(i_zero_weight, axis=axis, keepdims=True)
+        x = xpx.at(x)[i_zero_weight].set(xp.inf, copy=True)
+        i_y = xp.argsort(x, axis=axis, stable=False)
+        y = xp.take_along_axis(x, i_y, axis=axis)
+        weights = xp.take_along_axis(weights, i_y, axis=axis)
+        y, p, weights, i_y, n_zero_weight = _broadcast_arrays(
+            (y, p, weights, i_y, n_zero_weight), axis=axis)
+
+    if (keepdims is False) and (p.shape[axis] != 1):
+        message = ("`keepdims` may be False only if the length of "
+                   f"`{var2_name}` along `axis` is 1.")
+        raise ValueError(message)
+    keepdims = (p.shape[axis] != 1) if keepdims is None else keepdims
+
+    y = xp.moveaxis(y, axis, -1)
+    p = xp.moveaxis(p, axis, -1)
+    weights = weights if weights is None else xp.moveaxis(weights, axis, -1)
+    n_zero_weight = (n_zero_weight if n_zero_weight is None
+                     else xp.moveaxis(n_zero_weight, axis, -1))
+
+    n = _count_nonmasked(y, -1, xp=xp, keepdims=True)
+    n = n if n_zero_weight is None else n - n_zero_weight
+
+    nan_out = None
+    if is_lazy_array(y) or contains_nans:
+        nans = xp.isnan(y)
+
+        # Note that if length along `axis` were 0 to begin with,
+        # it is now length 1 and filled with NaNs.
+        if nan_policy == 'propagate':
+            nan_out = xp.any(nans, axis=-1, keepdims=True)
+        else:  # 'omit'
+            n_int = n - xp.count_nonzero(nans, axis=-1, keepdims=True)
+            n = xp.astype(n_int, dtype)
+            # NaNs are produced only if slice is empty after removing NaNs
+            nan_out = n == 0
+            n = xpx.at(n, nan_out).set(y.shape[-1])  # avoids pytorch/pytorch#146211
+
+        if (is_lazy_array(nans) or xp.any(nans)) and (method == 'harrell-davis'):
+            y = xp.asarray(y, copy=True)  # ensure writable
+            y = xpx.at(y, nans).set(0)  # any non-nan will prevent NaN from propagating
+        if is_lazy_array(nan_out) or xp.any(nan_out):
+            y = xp.asarray(y, copy=True)  # ensure writable
+            y = xpx.at(y, xp.broadcast_to(nan_out, y.shape)).set(xp.nan)
+
+    n = xp.asarray(n, dtype=dtype, device=xp_device(y))
+
+    # apparently xpx.at is accepting nan_out as a mask even though it doesn't have the
+    # same number of dimensions as `y`, yet it still appears to work correctly?
+    # should refactor for clarity, and p_mask that gets returned here should probably
+    # get a different name - `nan_out` would be more appropriate!
+    p_mask = mask_fun(p) if nan_out is None else mask_fun(p) | nan_out
+    if is_lazy_array(p_mask) or xp.any(p_mask):
+        p = xp.where(p_mask, 0.5, p)  # these get NaN-ed out at the end
+
+    return (y, p, method, axis, nan_policy, keepdims,
+            n, axis_none, ndim, p_mask, weights, xp)
+
+
+@xp_capabilities(skip_backends=[("dask.array", "No take_along_axis yet.")],
+                 marray=True,
+                 extra_note=("Use of `weights` is incompatible with MArray. "
+                             "``method='harrell-davis'`` is CPU-only."))
+def quantile(x, p, *, method='linear', axis=0, nan_policy='propagate', keepdims=None,
+             weights=None):
+    """
+    Compute the p-th quantile of the data along the specified axis.
+
+    Parameters
+    ----------
+    x : array_like of real numbers
+        Data array.
+    p : array_like of float
+        Probability or sequence of probabilities of the quantiles to compute.
+        Values must be between 0 and 1 (inclusive).
+        While `numpy.quantile` can only compute quantiles according to the Cartesian
+        product of the first two arguments, this function enables calculation of
+        quantiles at different probabilities for each axis slice by following
+        broadcasting rules like those of `scipy.stats` reducing functions.
+        See `axis`, `keepdims`, and the examples.
+    method : str, default: 'linear'
+        The method to use for estimating the quantile.
+        The available options, numbered as they appear in [1]_, are:
+
+        1. 'inverted_cdf'
+        2. 'averaged_inverted_cdf'
+        3. 'closest_observation'
+        4. 'interpolated_inverted_cdf'
+        5. 'hazen'
+        6. 'weibull'
+        7. 'linear'  (default)
+        8. 'median_unbiased'
+        9. 'normal_unbiased'
+
+        'harrell-davis' is also available to compute the quantile estimate
+        according to [2]_.
+
+        'round_outward', 'round_inward', and 'round_nearest' are available for use
+        in trimming and winsorizing data.
+
+        See Notes for details.
+    axis : int or None, default: 0
+        Axis along which the quantiles are computed.
+        ``None`` ravels both `x` and `p` before performing the calculation,
+        without checking whether the original shapes were compatible.
+        As in other `scipy.stats` functions, a positive integer `axis` is resolved
+        after prepending 1s to the shape of `x` or `p` as needed until the two arrays
+        have the same dimensionality. When providing `x` and `p` with different
+        dimensionality, consider using negative `axis` integers for clarity.
+    nan_policy : str, default: 'propagate'
+        Defines how to handle NaNs in the input data `x`.
+
+        - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
+          which the  statistic is computed, the corresponding slice of the output
+          will contain NaN(s).
+        - ``omit``: NaNs will be omitted when performing the calculation.
+          If insufficient data remains in the axis slice along which the
+          statistic is computed, the corresponding slice of the output will
+          contain NaN(s).
+        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
+
+        If NaNs are present in `p`, a ``ValueError`` will be raised.
+    keepdims : bool, optional
+        Consider the case in which `x` is 1-D and `p` is a scalar: the quantile
+        is a reducing statistic, and the default behavior is to return a scalar.
+        If `keepdims` is set to True, the axis will not be reduced away, and the
+        result will be a 1-D array with one element.
+
+        The general case is more subtle, since multiple quantiles may be
+        requested for each axis-slice of `x`. For instance, if both `x` and `p`
+        are 1-D and ``p.size > 1``, no axis can be reduced away; there must be an
+        axis to contain the number of quantiles given by ``p.size``. Therefore:
+
+        - By default, the axis will be reduced away if possible (i.e. if there is
+          exactly one element of `p` per axis-slice of `x`).
+        - If `keepdims` is set to True, the axis will not be reduced away.
+        - If `keepdims` is set to False, the axis will be reduced away
+          if possible, and an error will be raised otherwise.
+
+    weights : array_like of finite, non-negative real numbers
+        Frequency weights; e.g., for counting number weights,
+        ``quantile(x, p, weights=weights)`` is equivalent to
+        ``quantile(np.repeat(x, weights), p)``. Values other than finite counting
+        numbers are accepted, but may not have valid statistical interpretations.
+        Not compatible with ``method='harrell-davis'`` or those that begin with
+        ``'round_'``.
+
+    Returns
+    -------
+    quantile : scalar or ndarray
+        The resulting quantile(s). The dtype is the result dtype of `x` and `p`.
+
+    See Also
+    --------
+    numpy.quantile
+    estimated_cdf
+    :ref:`outliers`
+
+    Notes
+    -----
+    Given a sample `x` from an underlying distribution, `quantile` provides a
+    nonparametric estimate of the inverse cumulative distribution function.
+
+    By default, this is done by interpolating between adjacent elements in
+    ``y``, a sorted copy of `x`::
+
+        (1-g)*y[j] + g*y[j+1]
+
+    where the index ``j`` and coefficient ``g`` are the integral and
+    fractional components of ``p * (n-1)``, and ``n`` is the number of
+    elements in the sample.
+
+    This is a special case of Equation 1 of H&F [1]_. More generally,
+
+    - ``j = (p*n + m - 1) // 1``, and
+    - ``g = (p*n + m - 1) % 1``,
+
+    where ``m`` may be defined according to several different conventions.
+    The preferred convention may be selected using the ``method`` parameter:
+
+    =============================== =============== ===============
+    ``method``                      number in H&F   ``m``
+    =============================== =============== ===============
+    ``interpolated_inverted_cdf``   4               ``0``
+    ``hazen``                       5               ``1/2``
+    ``weibull``                     6               ``p``
+    ``linear`` (default)            7               ``1 - p``
+    ``median_unbiased``             8               ``p/3 + 1/3``
+    ``normal_unbiased``             9               ``p/4 + 3/8``
+    =============================== =============== ===============
+
+    Note that indices ``j`` and ``j + 1`` are clipped to the range ``0`` to
+    ``n - 1`` when the results of the formula would be outside the allowed
+    range of non-negative indices. When ``j`` is clipped to zero, ``g`` is
+    set to zero as well. The ``-1`` in the formulas for ``j`` and ``g``
+    accounts for Python's 0-based indexing.
+
+    The table above includes only the estimators from [1]_ that are continuous
+    functions of probability `p` (estimators 4-9). SciPy also provides the
+    three discontinuous estimators from [1]_ (estimators 1-3), where ``j`` is
+    defined as above, ``m`` is defined as follows, and ``g`` is ``0`` when
+    ``index = p*n + m - 1`` is less than ``0`` and otherwise is defined below.
+
+    1. ``inverted_cdf``: ``m = 0`` and ``g = int(index - j > 0)``
+    2. ``averaged_inverted_cdf``: ``m = 0`` and
+       ``g = (1 + int(index - j > 0)) / 2``
+    3. ``closest_observation``: ``m = -1/2`` and
+       ``g = 1 - int((index == j) & (j%2 == 1))``
+
+    Note that for methods ``inverted_cdf`` and ``averaged_inverted_cdf``, only the
+    relative proportions of tied observations (and relative weights) affect the
+    results; for all other methods, the total number of observations (and absolute
+    weights) matter.
+
+    A different strategy for computing quantiles from [2]_, ``method='harrell-davis'``,
+    uses a weighted combination of all elements. The weights are computed as:
+
+    .. math::
+
+        w_{n, i} = I_{i/n}(a, b) - I_{(i - 1)/n}(a, b)
+
+    where :math:`n` is the number of elements in the sample,
+    :math:`i` are the indices :math:`1, 2, ..., n-1, n` of the sorted elements,
+    :math:`a = p (n + 1)`, :math:`b = (1 - p)(n + 1)`,
+    :math:`p` is the probability of the quantile, and
+    :math:`I` is the regularized, lower incomplete beta function
+    (`scipy.special.betainc`).
+
+    ``method='round_nearest'`` is equivalent to indexing ``y[j]``, where::
+
+        j = int(np.round(p*n) if p < 0.5 else np.round(n*p - 1))
+
+    This is useful when winsorizing data: replacing ``p*n`` of the most extreme
+    observations with the next most extreme observation. ``method='round_outward'``
+    adjusts the direction of rounding to winsorize fewer elements::
+
+        j = int(np.floor(p*n) if p < 0.5 else np.ceil(n*p - 1))
+
+    and ``method='round_inward'`` rounds to winsorize more elements::
+
+        j = int(np.ceil(p*n) if p < 0.5 else np.floor(n*p - 1))
+
+    These methods are also useful for trimming data: removing ``p*n`` of the most
+    extreme observations. See :ref:`outliers` for example applications.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> x = np.asarray([[10, 8, 7, 5, 4],
+    ...                 [0, 1, 2, 3, 5]])
+
+    Take the median of each row.
+
+    >>> stats.quantile(x, 0.5, axis=-1)
+    array([7.,  2.])
+
+    Take a different quantile for each row.
+
+    >>> stats.quantile(x, [[0.25], [0.75]], axis=-1, keepdims=True)
+    array([[5.],
+           [3.]])
+
+    Take multiple quantiles for each row.
+
+    >>> stats.quantile(x, [0.25, 0.75], axis=-1)
+    array([[5., 8.],
+           [1., 3.]])
+
+    Take different quantiles for each row.
+
+    >>> p = np.asarray([[0.25, 0.75],
+    ...                 [0.5, 1.0]])
+    >>> stats.quantile(x, p, axis=-1)
+    array([[5., 8.],
+           [2., 5.]])
+
+    Take different quantiles for each column.
+
+    >>> stats.quantile(x.T, p.T, axis=0)
+    array([[5., 2.],
+           [8., 5.]])
+
+    References
+    ----------
+    .. [1] R. J. Hyndman and Y. Fan,
+       "Sample quantiles in statistical packages,"
+       The American Statistician, 50(4), pp. 361-365, 1996
+    .. [2] Harrell, Frank E., and C. E. Davis.
+       "A new distribution-free quantile estimator."
+       Biometrika 69.3 (1982): 635-640.
+
+    """
+    # Input validation / standardization
+
+    temp = _quantile_iv(x, p, method, axis, nan_policy, keepdims, weights)
+    (y, p, method, axis, nan_policy, keepdims,
+     n, axis_none, ndim, p_mask, weights, xp) = temp
+
+    if method in {'inverted_cdf', 'averaged_inverted_cdf', 'closest_observation',
+                  'hazen', 'interpolated_inverted_cdf', 'linear',
+                  'median_unbiased', 'normal_unbiased', 'weibull'}:
+        res = _quantile_hf(y, p, n, method, weights, xp)
+    elif method in {'harrell-davis'}:
+        res = _quantile_hd(y, p, n, xp)
+    elif method in {'_lower', '_midpoint', '_higher', '_nearest'}:
+        res = _quantile_bc(y, p, n, method, xp)
+    else:  # method.startswith('round'):
+        res = _quantile_winsor(y, p, n, method, xp)
+
+    return _post_quantile(res, p_mask, axis, axis_none, ndim, keepdims, xp)
+
+
+def _post_quantile(res, p_mask, axis, axis_none, ndim, keepdims, xp):
+    res = xpx.at(res, p_mask).set(xp.nan)
+
+    # Reshape per axis/keepdims
+    if axis_none and keepdims:
+        shape = (1,)*(ndim - 1) + res.shape
+        res = xp.reshape(res, shape)
+        axis = -1
+
+    res = xp.moveaxis(res, -1, axis)
+
+    if not keepdims:
+        res = xp.squeeze(res, axis=axis)
+
+    return res[()] if res.ndim == 0 else res
+
+
+def _quantile_hf(y, p, n, method, weights, xp):
+    ms = dict(inverted_cdf=0, averaged_inverted_cdf=0, closest_observation=-0.5,
+              interpolated_inverted_cdf=0, hazen=0.5, weibull=p, linear=1 - p,
+              median_unbiased=p/3 + 1/3, normal_unbiased=p/4 + 3/8)
+    m = ms[method]
+
+    if weights is None:
+        jg = p * n + m
+        jp1 = jg // 1
+        j = jp1 - 1
+    else:
+        cumulative_weights = xp.cumulative_sum(weights, axis=-1)
+        n_int = xp.asarray(n, dtype=xp.int64)
+        n_int = xp.broadcast_to(n_int, cumulative_weights.shape[:-1] + (1,))
+        total_weight = xp.take_along_axis(cumulative_weights, n_int-1, axis=-1)
+        jg = p * total_weight + m
+        jp1 = _xp_searchsorted(cumulative_weights, jg, side='right')
+        j = _xp_searchsorted(cumulative_weights, jg-1, side='right')
+        j, jp1 = xp.astype(j, y.dtype), xp.astype(jp1, y.dtype)
+
+    g = jg % 1
+    if method == 'inverted_cdf':
+        g = xp.astype((g > 0), jg.dtype)
+    elif method == 'averaged_inverted_cdf':
+        g = (1 + xp.astype((g > 0), jg.dtype)) / 2
+    elif method == 'closest_observation':
+        g = (1 - xp.astype((g == 0) & (j % 2 == 1), jg.dtype))
+    if method in {'inverted_cdf', 'averaged_inverted_cdf', 'closest_observation'}:
+        g = xp.asarray(g)
+        g = xpx.at(g, jg < 0).set(0)
+
+    g = xpx.at(g)[j < 0].set(0)
+    j = xp.clip(j, 0., n - 1)
+    jp1 = xp.clip(jp1, 0., n - 1)
+
+    return ((1 - g) * xp.take_along_axis(y, xp.astype(j, xp.int64), axis=-1)
+            + g * xp.take_along_axis(y, xp.astype(jp1, xp.int64), axis=-1))
+
+
+def _quantile_hd(y, p, n, xp):
+    # RE axis handling: We need to perform a reducing operation over rows of `y` for
+    # each element in the corresponding row of `p` (a la Cartesian product). Strategy:
+    # move rows of `p` to an axis at the front that is orthogonal to all the rest,
+    # perform the reducing operating over the last axis, then move the front axis back
+    # to the end.
+    p = xp.moveaxis(p, -1, 0)[..., xp.newaxis]
+    a = p * (n + 1)
+    b = (1 - p) * (n + 1)
+    i = xp.arange(y.shape[-1] + 1, dtype=y.dtype, device=xp_device(y))
+    w = betainc(a, b, i / n)
+    w = w[..., 1:] - w[..., :-1]
+    w = xpx.at(w, xp.isnan(w)).set(0)
+    res = xp.vecdot(w, y, axis=-1)
+    return xp.moveaxis(res, 0, -1)
+
+
+def _quantile_winsor(y, p, n, method, xp):
+    ops = dict(round_outward=(xp.floor, xp.ceil),
+               round_inward=(xp.ceil, xp.floor),
+               round_nearest=(xp.round, xp.round))
+    op_left, op_right = ops[method]
+    j = xp.where(p < 0.5, op_left(p*n), op_right(n*p - 1))
+    return xp.take_along_axis(y, xp.astype(j, xp.int64), axis=-1)
+
+
+def _quantile_bc(y, p, n, method, xp):
+    # Methods retained for backward compatibility. NumPy documentation is not
+    # quite right about what these methods do: if `p * (n - 1)` is integral,
+    # that is used as the index. See numpy/numpy#28910.
+    ij = p * (n - 1)
+    if method == '_midpoint':
+        return (xp.take_along_axis(y, xp.astype(xp.floor(ij), xp.int64), axis=-1)
+                + xp.take_along_axis(y, xp.astype(xp.ceil(ij), xp.int64), axis=-1)) / 2
+    elif method == '_lower':
+        k = xp.floor(ij)
+    elif method == '_higher':
+        k = xp.ceil(ij)
+    elif method == '_nearest':
+        k = xp.round(ij)
+    return xp.take_along_axis(y, xp.astype(k, xp.int64), axis=-1)
+
+
+@xp_capabilities(skip_backends=[("dask.array", "No take_along_axis yet.")])
+def _xp_searchsorted(x, y, *, side='left', xp=None):
+    # Vectorized xp.searchsorted. Search is performed along last axis. The shape of the
+    # output is that of `y`, broadcasting the batch dimensions with those of `x` if
+    # necessary.
+    xp = array_namespace(x, y) if xp is None else xp
+    xp_default_int = xp.asarray(1).dtype
+    y_0d = xp.asarray(y).ndim == 0
+    x, y = _broadcast_arrays((x, y), axis=-1, xp=xp)
+    x_1d = x.ndim <= 1
+
+    if x_1d or is_torch(xp):
+        y = xp.reshape(y, ()) if (y_0d and x_1d) else y
+        out = xp.searchsorted(x, y, side=side)
+        out = xp.astype(out, xp_default_int, copy=False)
+        return out
+
+    a = xp.full(y.shape, 0, device=xp_device(x))
+
+    if x.shape[-1] == 0:
+        return a
+
+    n = xp.count_nonzero(~xp.isnan(x), axis=-1, keepdims=True)
+    b = xp.broadcast_to(n, y.shape)
+
+    compare = xp.less_equal if side == 'left' else xp.less
+
+    # while xp.any(b - a > 1):
+    # refactored to for loop with ~log2(n) iterations for JAX JIT
+    for i in range(int(math.log2(x.shape[-1])) + 1):
+        c = (a + b) // 2
+        x0 = xp.take_along_axis(x, c, axis=-1)
+        j = compare(y, x0)
+        b = xp.where(j, c, b)
+        a = xp.where(j, a, c)
+
+    out = xp.where(compare(y, xp.min(x, axis=-1, keepdims=True)), 0, b)
+    out = xp.where(xp.isnan(y), x.shape[-1], out) if side == 'right' else out
+    out = xp.astype(out, xp_default_int, copy=False)
+    return out
+
+
+@xp_capabilities(skip_backends=[("dask.array", "No take_along_axis yet.")],
+                 marray=True)
+def estimated_cdf(x, y, *, method='linear',
+                  axis=0, nan_policy='propagate', keepdims=None):
+    """
+    Estimate the CDF of the distribution underlying a sample.
+
+    Parameters
+    ----------
+    x : array_like of real numbers
+        Data array.
+    y : array_like of real numbers
+        Points at which to evaluate the estimated cdf.
+        See `axis`, `keepdims`, and the examples for broadcasting behavior.
+    method : str, default: 'linear'
+        The method to use for estimating the cumulative distribution function.
+        The available options, numbered as they appear in [1]_, are:
+
+        1. 'inverted_cdf' (AKA *the* empirical cumulative distribution function)
+        2. 'averaged_inverted_cdf'
+        3. 'closest_observation'
+        4. 'interpolated_inverted_cdf'
+        5. 'hazen'
+        6. 'weibull'
+        7. 'linear'  (default)
+        8. 'median_unbiased'
+        9. 'normal_unbiased'
+
+        See Notes for details.
+    axis : int or None, default: 0
+        Axis along which samples in `x` are given in ND case.
+        ``None`` ravels both `x` and `y` before performing the calculation,
+        without checking whether the original shapes were compatible.
+        As in other `scipy.stats` functions, a positive integer `axis` is resolved
+        after prepending 1s to the shape of `x` or `y` as needed until the two arrays
+        have the same dimensionality. When providing `x` and `y` with different
+        dimensionality, consider using negative `axis` integers for clarity.
+    nan_policy : str, default: 'propagate'
+        Defines how to handle NaNs in the input data `x`.
+
+        - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
+          which the  statistic is computed, the corresponding slice of the output
+          will contain NaN(s).
+        - ``omit``: NaNs will be omitted when performing the calculation.
+          If insufficient data remains in the axis slice along which the
+          statistic is computed, the corresponding slice of the output will
+          contain NaN(s).
+        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
+
+        If NaNs are present in `y`, the corresponding entries in the output will be NaN.
+    keepdims : bool, optional
+        Consider the case in which `x` is 1-D and `y` is a scalar: the estimated
+        cumulative distribution function at a point is a reducing statistic, and the
+        default behavior is to return a scalar.
+        If `keepdims` is set to True, the axis will not be reduced away, and the
+        result will be a 1-D array with one element.
+
+        The general case is more subtle, since multiple values of `y` may be
+        specified for each axis-slice of `x`. For instance, if both `x` and `y`
+        are 1-D and ``y.size > 1``, no axis can be reduced away; there must be an
+        axis to contain the number of values given by ``y.size``. Therefore:
+
+        - By default, the axis will be reduced away if possible (i.e. if there is
+          exactly one element of `y` per axis-slice of `x`).
+        - If `keepdims` is set to True, the axis will not be reduced away.
+        - If `keepdims` is set to False, the axis will be reduced away
+          if possible, and an error will be raised otherwise.
+
+    Returns
+    -------
+    probability : scalar or ndarray
+        The resulting probabilities(s). The dtype is the result dtype of `x`, `y`, and
+        the Python ``float`` type.
+
+    See Also
+    --------
+    quantile
+
+    Notes
+    -----
+    Given a sample `x` from an underlying distribution, `estimated_cdf` provides a
+    nonparametric estimate of the cumulative distribution function.
+
+    By default, this is done by computing the "fractional index" ``p`` at which ``y``
+    would appear within ``z``, a sorted copy of `x`::
+
+        p = 1 / (n - 1) * (j +  (     y - z[j])
+                              / (z[j+1] - z[j]))
+
+    where the index ``j`` is that of the largest element of ``z`` that does not exceed
+    ``y``, and ``n`` is the number of elements in the sample. Note that if ``y`` is an
+    element of ``z``, then ``j`` is the index such that ``y = z[j]``, and the formula
+    simplifies to the intuitive ``j / (n - 1)``. The full formula linearly interpolates
+    between ``j / (n - 1)`` and ``(j + 1) / (n - 1)``.
+
+    This is a special case of the more general::
+
+        p = (j + (y - z[j]) / (z[j+1] - z[j] + 1 - m) / n
+
+    where ``m`` may be defined according to several different conventions.
+    The preferred convention may be selected using the ``method`` parameter:
+
+    =============================== =============== ===============
+    ``method``                      number in H&F   ``m``
+    =============================== =============== ===============
+    ``interpolated_inverted_cdf``   4               ``0``
+    ``hazen``                       5               ``1/2``
+    ``weibull``                     6               ``p``
+    ``linear`` (default)            7               ``1 - p``
+    ``median_unbiased``             8               ``p/3 + 1/3``
+    ``normal_unbiased``             9               ``p/4 + 3/8``
+    =============================== =============== ===============
+
+    Note that indices ``j`` and ``j + 1`` are clipped to the range ``0`` to
+    ``n - 1`` when the results of the formula would be outside the allowed
+    range of non-negative indices, and resulting ``p`` is clipped to the range
+    ``0`` to ``1``.
+
+    The table above includes only the estimators from [1]_ that are continuous
+    functions of probability `p` (estimators 4-9). SciPy also provides the
+    three discontinuous estimators from [1]_ (estimators 1-3), where ``j`` is
+    defined as above, ``m`` is defined as follows, and ``g`` is ``0`` when
+    ``index = p*n + m - 1`` is less than ``0`` and otherwise is defined below.
+
+    1. ``inverted_cdf``: ``m = 0`` and ``g = int(index - j > 0)``
+    2. ``averaged_inverted_cdf``: ``m = 0`` and
+       ``g = (1 + int(index - j > 0)) / 2``
+    3. ``closest_observation``: ``m = -1/2`` and
+       ``g = 1 - int((index == j) & (j%2 == 1))``
+
+    When all the data in ``x`` are unique, `estimated_cdf` and `quantile` are are
+    inverses of one another within a certain domain.
+    Although `quantile` with ``method='linear'`` is invertible over the whole domain
+    of ``p`` from ``0`` to ``1``, this is not true of other methods.
+
+    References
+    ----------
+    .. [1] R. J. Hyndman and Y. Fan,
+           "Sample quantiles in statistical packages,"
+           The American Statistician, 50(4), pp. 361-365, 1996
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+
+    Estimate the cumulative distribution function of one sample at a single point.
+
+    >>> stats.estimated_cdf(x, 5, axis=-1)
+    np.float64(0.5)
+
+    Estimate the cumulative distribution function of one sample at two points.
+
+    >>> stats.estimated_cdf(x, [2.5, 7.5], axis=-1)
+    array([0.25, 0.75])
+
+    Estimate the cumulative distribution function of two samples at different points.
+
+    >>> x = np.stack((np.arange(0, 11), np.arange(10, 21)))
+    >>> stats.estimated_cdf(x, [[2.5], [17.5]], axis=-1, keepdims=True)
+    array([[0.25],
+           [0.75]])
+
+    Estimate the cumulative distribution function at many points for each of two
+    samples.
+
+    >>> import matplotlib.pyplot as plt
+    >>> rng = np.random.default_rng(6110515095)
+    >>> x = stats.Normal(mu=[-1, 1]).sample(10000)
+    >>> y = np.linspace(-4, 4, 5000)[:, np.newaxis]
+    >>> p = stats.estimated_cdf(x, y, axis=0)
+    >>> plt.plot(y, p)
+    >>> plt.show()
+
+    Note that the `quantile` and `estimated_cdf` functions are inverses of one another
+    within a certain domain.
+
+    >>> p = np.linspace(0, 1, 300)
+    >>> x = rng.standard_normal(300)
+    >>> y = stats.quantile(x, p)
+    >>> p2 = stats.estimated_cdf(x, y)
+    >>> np.testing.assert_allclose(p2, p)
+    >>> y2 = stats.quantile(x, p2)
+    >>> np.testing.assert_allclose(y2, y)
+
+    However, the domain over which `quantile` can be inverted by `estimated_cdf` depends
+    on the `method` used. This is most noticeable when there are few observations in the
+    sample.
+
+    >>> x = np.asarray([0, 1])
+    >>> y_linear = stats.quantile(x, p, method='linear')
+    >>> y_weibull = stats.quantile(x, p, method='weibull')
+    >>> y_iicdf = stats.quantile(x, p, method='interpolated_inverted_cdf')
+    >>> plt.plot(p, y_linear, p, y_weibull, p, y_iicdf)
+    >>> plt.legend(['linear', 'weibull', 'iicdf'])
+    >>> plt.xlabel('p')
+    >>> plt.ylabel('y = quantile(x, p)')
+    >>> plt.show()
+
+    For example, in the case above, `quantile` is only invertible from
+    ``p = 0.5`` to ``p = 1.0`` with ``method = 'interpolated_inverted_cdf'``. This is a
+    fundamental characteristic of the methods, not a shortcoming of `estimated_cdf`.
+
+    """
+    temp = _quantile_iv(x, y, method, axis, nan_policy, keepdims, weights=None,
+                        fun='estimated_cdf')
+    x, y, method, axis, nan_policy, keepdims, n, axis_none, ndim, y_mask, _, xp = temp
+
+    if xp_size(x) == 0:
+        res = xp.full_like(y, xp.nan)
+    else:
+        res = _estimated_cdf_hf(x, y, n, method, xp)
+
+    return _post_quantile(res, y_mask, axis, axis_none, ndim, keepdims, xp)
+
+
+_estimated_cdf_discontinuous_methods = dict(
+    inverted_cdf=0.0,
+    averaged_inverted_cdf=0.0,
+    closest_observation=0.5,
+)
+
+
+_estimated_cdf_continuous_methods = dict(
+    interpolated_inverted_cdf=(0, 1),
+    hazen=(0.5, 0.5),
+    weibull=(0, 0),
+    linear=(1, 1),
+    median_unbiased=(1 / 3, 1 / 3),
+    normal_unbiased=(3 / 8, 3 / 8),
+)
+
+
+_estimated_cdf_methods = (set(_estimated_cdf_continuous_methods).union(
+                          set(_estimated_cdf_discontinuous_methods)))
+
+
+def _estimated_cdf_hf(x, y, n, method, xp):
+    n_int = xp.astype(n, xp.int64)
+    j_max = n_int - 1
+    j_min = xp.minimum(j_max, xp.asarray(1, dtype=j_max.dtype))
+    jp1 = _xp_searchsorted(x, y, side='right')
+
+    if method in _estimated_cdf_discontinuous_methods:
+        dp = _estimated_cdf_discontinuous_methods[method]
+        p = (xp.astype(jp1, x.dtype)+dp)/n
+
+    else:
+        jp1 = xp.clip(jp1, j_min, j_max)
+        j = xp.clip(jp1-1, 0)
+        xj = xp.take_along_axis(x, j, axis=-1)
+        xjp1 = xp.take_along_axis(x, jp1, axis=-1)
+        with np.errstate(divide='ignore', invalid='ignore'):  # refactor to apply_where?
+            delta = xp.where((xjp1 > xj) & xp.isfinite(xj), (y - xj) / (xjp1 - xj), 1.)
+
+        a, b = _estimated_cdf_continuous_methods[method]
+        p = (xp.astype(jp1, x.dtype) + delta - a) / (n + 1 - a - b)
+
+    xmin = x[..., :1]
+    xmax = (x[..., -1:] if n.shape == () else xp.take_along_axis(x, j_max))
+    p = xpx.at(p)[y < xmin].set(0.)
+    p = xpx.at(p)[y > xmax].set(1.)
+
+    return xp.clip(p, 0., 1.)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_rcont/__init__.py b/.venv/lib/python3.12/site-packages/scipy/stats/_rcont/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..25728ead5f7ea277e6e94c359d5a5603b99eeb38
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_rcont/__init__.py
@@ -0,0 +1,4 @@
+#
+from .rcont import rvs_rcont1, rvs_rcont2
+
+__all__ = ["rvs_rcont1", "rvs_rcont2"]
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_rcont/rcont.cpython-312-x86_64-linux-gnu.so b/.venv/lib/python3.12/site-packages/scipy/stats/_rcont/rcont.cpython-312-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..a70ed43fae63ddae01c77b066ed22d7e8517ff12
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_rcont/rcont.cpython-312-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:386c4510ac183eb266ce540cc35415a980b62d2f169c30e9691a849a9475b99d
+size 108424
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_relative_risk.py b/.venv/lib/python3.12/site-packages/scipy/stats/_relative_risk.py
new file mode 100644
index 0000000000000000000000000000000000000000..51525fd28adb37c72b12106450e4178c786091b2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_relative_risk.py
@@ -0,0 +1,263 @@
+import operator
+from dataclasses import dataclass
+import numpy as np
+from scipy.special import ndtri
+from ._common import ConfidenceInterval
+
+
+def _validate_int(n, bound, name):
+    msg = f'{name} must be an integer not less than {bound}, but got {n!r}'
+    try:
+        n = operator.index(n)
+    except TypeError:
+        raise TypeError(msg) from None
+    if n < bound:
+        raise ValueError(msg)
+    return n
+
+
+@dataclass
+class RelativeRiskResult:
+    """
+    Result of `scipy.stats.contingency.relative_risk`.
+
+    Attributes
+    ----------
+    relative_risk : float
+        This is::
+
+            (exposed_cases/exposed_total) / (control_cases/control_total)
+
+    exposed_cases : int
+        The number of "cases" (i.e. occurrence of disease or other event
+        of interest) among the sample of "exposed" individuals.
+    exposed_total : int
+        The total number of "exposed" individuals in the sample.
+    control_cases : int
+        The number of "cases" among the sample of "control" or non-exposed
+        individuals.
+    control_total : int
+        The total number of "control" individuals in the sample.
+
+    Methods
+    -------
+    confidence_interval :
+        Compute the confidence interval for the relative risk estimate.
+    """
+
+    relative_risk: float
+    exposed_cases: int
+    exposed_total: int
+    control_cases: int
+    control_total: int
+
+    def confidence_interval(self, confidence_level=0.95):
+        """
+        Compute the confidence interval for the relative risk.
+
+        The confidence interval is computed using the Katz method
+        (i.e. "Method C" of [1]_; see also [2]_, section 3.1.2).
+
+        Parameters
+        ----------
+        confidence_level : float, optional
+            The confidence level to use for the confidence interval.
+            Default is 0.95.
+
+        Returns
+        -------
+        ci : ConfidenceInterval instance
+            The return value is an object with attributes ``low`` and
+            ``high`` that hold the confidence interval.
+
+        References
+        ----------
+        .. [1] D. Katz, J. Baptista, S. P. Azen and M. C. Pike, "Obtaining
+               confidence intervals for the risk ratio in cohort studies",
+               Biometrics, 34, 469-474 (1978).
+        .. [2] Hardeo Sahai and Anwer Khurshid, Statistics in Epidemiology,
+               CRC Press LLC, Boca Raton, FL, USA (1996).
+
+
+        Examples
+        --------
+        >>> from scipy.stats.contingency import relative_risk
+        >>> result = relative_risk(exposed_cases=10, exposed_total=75,
+        ...                        control_cases=12, control_total=225)
+        >>> result.relative_risk
+        2.5
+        >>> result.confidence_interval()
+        ConfidenceInterval(low=1.1261564003469628, high=5.549850800541033)
+        """
+        if not 0 <= confidence_level <= 1:
+            raise ValueError('confidence_level must be in the interval '
+                             '[0, 1].')
+
+        # Handle edge cases where either exposed_cases or control_cases
+        # is zero.  We follow the convention of the R function riskratio
+        # from the epitools library.
+        if self.exposed_cases == 0 and self.control_cases == 0:
+            # relative risk is nan.
+            return ConfidenceInterval(low=np.nan, high=np.nan)
+        elif self.exposed_cases == 0:
+            # relative risk is 0.
+            return ConfidenceInterval(low=0.0, high=np.nan)
+        elif self.control_cases == 0:
+            # relative risk is inf
+            return ConfidenceInterval(low=np.nan, high=np.inf)
+
+        alpha = 1 - confidence_level
+        z = ndtri(1 - alpha/2)
+        rr = self.relative_risk
+
+        # Estimate of the variance of log(rr) is
+        # var(log(rr)) = 1/exposed_cases - 1/exposed_total +
+        #                1/control_cases - 1/control_total
+        # and the standard error is the square root of that.
+        se = np.sqrt(1/self.exposed_cases - 1/self.exposed_total +
+                     1/self.control_cases - 1/self.control_total)
+        delta = z*se
+        katz_lo = rr*np.exp(-delta)
+        katz_hi = rr*np.exp(delta)
+        return ConfidenceInterval(low=katz_lo, high=katz_hi)
+
+
+def relative_risk(exposed_cases, exposed_total, control_cases, control_total):
+    """
+    Compute the relative risk (also known as the risk ratio).
+
+    This function computes the relative risk associated with a 2x2
+    contingency table ([1]_, section 2.2.3; [2]_, section 3.1.2). Instead
+    of accepting a table as an argument, the individual numbers that are
+    used to compute the relative risk are given as separate parameters.
+    This is to avoid the ambiguity of which row or column of the contingency
+    table corresponds to the "exposed" cases and which corresponds to the
+    "control" cases.  Unlike, say, the odds ratio, the relative risk is not
+    invariant under an interchange of the rows or columns.
+
+    Parameters
+    ----------
+    exposed_cases : nonnegative int
+        The number of "cases" (i.e. occurrence of disease or other event
+        of interest) among the sample of "exposed" individuals.
+    exposed_total : positive int
+        The total number of "exposed" individuals in the sample.
+    control_cases : nonnegative int
+        The number of "cases" among the sample of "control" or non-exposed
+        individuals.
+    control_total : positive int
+        The total number of "control" individuals in the sample.
+
+    Returns
+    -------
+    result : instance of `~scipy.stats._result_classes.RelativeRiskResult`
+        The object has the float attribute ``relative_risk``, which is::
+
+            rr = (exposed_cases/exposed_total) / (control_cases/control_total)
+
+        The object also has the method ``confidence_interval`` to compute
+        the confidence interval of the relative risk for a given confidence
+        level.
+
+    See Also
+    --------
+    odds_ratio
+
+    Notes
+    -----
+    The R package epitools has the function `riskratio`, which accepts
+    a table with the following layout::
+
+                        disease=0   disease=1
+        exposed=0 (ref)    n00         n01
+        exposed=1          n10         n11
+
+    With a 2x2 table in the above format, the estimate of the CI is
+    computed by `riskratio` when the argument method="wald" is given,
+    or with the function `riskratio.wald`.
+
+    For example, in a test of the incidence of lung cancer among a
+    sample of smokers and nonsmokers, the "exposed" category would
+    correspond to "is a smoker" and the "disease" category would
+    correspond to "has or had lung cancer".
+
+    To pass the same data to ``relative_risk``, use::
+
+        relative_risk(n11, n10 + n11, n01, n00 + n01)
+
+    .. versionadded:: 1.7.0
+
+    References
+    ----------
+    .. [1] Alan Agresti, An Introduction to Categorical Data Analysis
+           (second edition), Wiley, Hoboken, NJ, USA (2007).
+    .. [2] Hardeo Sahai and Anwer Khurshid, Statistics in Epidemiology,
+           CRC Press LLC, Boca Raton, FL, USA (1996).
+
+    Examples
+    --------
+    >>> from scipy.stats.contingency import relative_risk
+
+    This example is from Example 3.1 of [2]_.  The results of a heart
+    disease study are summarized in the following table::
+
+                 High CAT   Low CAT    Total
+                 --------   -------    -----
+        CHD         27         44        71
+        No CHD      95        443       538
+
+        Total      122        487       609
+
+    CHD is coronary heart disease, and CAT refers to the level of
+    circulating catecholamine.  CAT is the "exposure" variable, and
+    high CAT is the "exposed" category. So the data from the table
+    to be passed to ``relative_risk`` is::
+
+        exposed_cases = 27
+        exposed_total = 122
+        control_cases = 44
+        control_total = 487
+
+    >>> result = relative_risk(27, 122, 44, 487)
+    >>> result.relative_risk
+    2.4495156482861398
+
+    Find the confidence interval for the relative risk.
+
+    >>> result.confidence_interval(confidence_level=0.95)
+    ConfidenceInterval(low=1.5836990926700116, high=3.7886786315466354)
+
+    The interval does not contain 1, so the data supports the statement
+    that high CAT is associated with greater risk of CHD.
+    """
+    # Relative risk is a trivial calculation.  The nontrivial part is in the
+    # `confidence_interval` method of the RelativeRiskResult class.
+
+    exposed_cases = _validate_int(exposed_cases, 0, "exposed_cases")
+    exposed_total = _validate_int(exposed_total, 1, "exposed_total")
+    control_cases = _validate_int(control_cases, 0, "control_cases")
+    control_total = _validate_int(control_total, 1, "control_total")
+
+    if exposed_cases > exposed_total:
+        raise ValueError('exposed_cases must not exceed exposed_total.')
+    if control_cases > control_total:
+        raise ValueError('control_cases must not exceed control_total.')
+
+    if exposed_cases == 0 and control_cases == 0:
+        # relative risk is 0/0.
+        rr = np.nan
+    elif exposed_cases == 0:
+        # relative risk is 0/nonzero
+        rr = 0.0
+    elif control_cases == 0:
+        # relative risk is nonzero/0.
+        rr = np.inf
+    else:
+        p1 = exposed_cases / exposed_total
+        p2 = control_cases / control_total
+        rr = p1 / p2
+    return RelativeRiskResult(relative_risk=rr,
+                              exposed_cases=exposed_cases,
+                              exposed_total=exposed_total,
+                              control_cases=control_cases,
+                              control_total=control_total)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_resampling.py b/.venv/lib/python3.12/site-packages/scipy/stats/_resampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..3977e3718809ff6772dbee247c506f1257c9a624
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_resampling.py
@@ -0,0 +1,2425 @@
+import warnings
+import numpy as np
+from itertools import combinations, permutations, product, accumulate
+from collections.abc import Sequence
+from dataclasses import dataclass, field
+import inspect
+import math
+
+from scipy._lib._util import (check_random_state, _rename_parameter, rng_integers,
+                              _transition_to_rng)
+from scipy._lib._array_api import (
+    array_namespace,
+    is_numpy,
+    is_array_api_strict,
+    xp_capabilities,
+    xp_result_type,
+    xp_size,
+    xp_device,
+    xp_swapaxes,
+    is_lazy_array,
+)
+from scipy._external import array_api_extra as xpx
+from scipy.special import ndtr, ndtri
+from scipy import stats
+
+from ._common import ConfidenceInterval
+from ._axis_nan_policy import _broadcast_concatenate, _broadcast_arrays
+from ._warnings_errors import DegenerateDataWarning
+
+__all__ = ['bootstrap', 'monte_carlo_test', 'permutation_test']
+
+
+def _vectorize_statistic(statistic):
+    """Vectorize an n-sample statistic"""
+    # This is a little cleaner than np.nditer at the expense of some data
+    # copying: concatenate samples together, then use np.apply_along_axis
+    def stat_nd(*data, axis=0):
+        lengths = [sample.shape[axis] for sample in data]
+        split_indices = np.cumsum(lengths)[:-1]
+        z = _broadcast_concatenate(data, axis)
+
+        # move working axis to position 0 so that new dimensions in the output
+        # of `statistic` are _prepended_. ("This axis is removed, and replaced
+        # with new dimensions...")
+        z = np.moveaxis(z, axis, 0)
+
+        def stat_1d(z):
+            data = np.split(z, split_indices)
+            return statistic(*data)
+
+        return np.apply_along_axis(stat_1d, 0, z)[()]
+    return stat_nd
+
+
+def _jackknife_resample(sample, batch=None, *, xp):
+    """Jackknife resample the sample. Only one-sample stats for now."""
+    n = sample.shape[-1]
+    batch_nominal = batch or n
+
+    for k in range(0, n, batch_nominal):
+        # col_start:col_end are the observations to remove
+        batch_actual = min(batch_nominal, n-k)
+
+        # jackknife - each row leaves out one observation
+        j = np.ones((batch_actual, n), dtype=bool)
+        np.fill_diagonal(j[:, k:k+batch_actual], False)
+        i = np.arange(n)
+        i = np.broadcast_to(i, (batch_actual, n))
+        i = i[j].reshape((batch_actual, n-1))
+
+        i = xp.asarray(i, device=xp_device(sample))
+        yield _get_from_last_axis(sample, i, xp=xp)
+
+
+def _bootstrap_resample(sample, n_resamples=None, rng=None, *, xp):
+    """Bootstrap resample the sample."""
+    n = sample.shape[-1]
+
+    # bootstrap - each row is a random resample of original observations
+    i = rng_integers(rng, 0, n, (n_resamples, n))
+
+    i = xp.asarray(i, device=xp_device(sample))
+    return _get_from_last_axis(sample, i, xp=xp)
+
+
+def _get_from_last_axis(sample, i, xp):
+    if not is_array_api_strict(xp):
+        return sample[..., i]
+
+    # Equivalent to `sample[..., i]` as used in `bootstrap`. Assumes i.ndim <=2.
+    if i.ndim == 2:
+        sample = xp.expand_dims(sample, axis=-2)
+    sample, i = _broadcast_arrays((sample, i), axis=-1, xp=xp)
+    return xp.take_along_axis(sample, i, axis=-1)
+
+
+def _percentile_of_score(a, score, axis, xp):
+    """Vectorized, simplified `scipy.stats.percentileofscore`.
+    Uses logic of the 'mean' value of percentileofscore's kind parameter.
+
+    Unlike `stats.percentileofscore`, the percentile returned is a fraction
+    in [0, 1].
+    """
+    B = a.shape[axis]
+    nonzeros = (xp.count_nonzero(a < score, axis=axis)
+                + xp.count_nonzero(a <= score, axis=axis))
+    return xp.astype(nonzeros, score.dtype) / (2 * B)
+
+
+def _bca_interval(data, statistic, axis, alpha, theta_hat_b, batch, xp):
+    """Bias-corrected and accelerated interval."""
+    # closely follows [1] 14.3 and 15.4 (Eq. 15.36)
+
+    # calculate z0_hat
+    theta_hat = xp.expand_dims(statistic(*data, axis=axis), axis=-1)
+    percentile = _percentile_of_score(theta_hat_b, theta_hat, axis=-1, xp=xp)
+    percentile = xp.asarray(percentile, dtype=theta_hat.dtype,
+                            device=xp_device(theta_hat))
+    z0_hat = ndtri(percentile)
+
+    # calculate a_hat
+    theta_hat_ji = []  # j is for sample of data, i is for jackknife resample
+    for j, sample in enumerate(data):
+        # _jackknife_resample will add an axis prior to the last axis that
+        # corresponds with the different jackknife resamples. Do the same for
+        # each sample of the data to ensure broadcastability. We need to
+        # create a copy of the list containing the samples anyway, so do this
+        # in the loop to simplify the code. This is not the bottleneck...
+        samples = [xp.expand_dims(sample, axis=-2) for sample in data]
+        theta_hat_i = []
+        for jackknife_sample in _jackknife_resample(sample, batch, xp=xp):
+            samples[j] = jackknife_sample
+            broadcasted = _broadcast_arrays(samples, axis=-1, xp=xp)
+            theta_hat_i.append(statistic(*broadcasted, axis=-1))
+        theta_hat_ji.append(theta_hat_i)
+
+    theta_hat_ji = [xp.concat(theta_hat_i, axis=-1)
+                    for theta_hat_i in theta_hat_ji]
+
+    # CuPy array dtype is unstable in 1.13.6 when divided by large Python int:
+    # (cp.ones(1, dtype=cp.float32)/10000).dtype   # float32
+    # (cp.ones(1, dtype=cp.float32)/100000).dtype  # float64
+    # Solution for now is to make the divisor a Python float
+    # Looks like this will be fixed in 1.14.0
+    n_j = [float(theta_hat_i.shape[-1]) for theta_hat_i in theta_hat_ji]
+
+    theta_hat_j_dot = [xp.mean(theta_hat_i, axis=-1, keepdims=True)
+                       for theta_hat_i in theta_hat_ji]
+
+    U_ji = [(n - 1) * (theta_hat_dot - theta_hat_i)
+            for theta_hat_dot, theta_hat_i, n
+            in zip(theta_hat_j_dot, theta_hat_ji, n_j)]
+
+    nums = [xp.sum(U_i**3, axis=-1)/n**3 for U_i, n in zip(U_ji, n_j)]
+    dens = [xp.sum(U_i**2, axis=-1)/n**2 for U_i, n in zip(U_ji, n_j)]
+    a_hat = 1/6 * sum(nums) / sum(dens)**(3/2)
+
+    # calculate alpha_1, alpha_2
+    # `float` because dtype of ndtri output (float64) should not promote other vals
+    z_alpha = float(ndtri(alpha))
+    z_1alpha = -z_alpha
+    num1 = z0_hat + z_alpha
+    alpha_1 = ndtr(z0_hat + num1/(1 - a_hat*num1))
+    num2 = z0_hat + z_1alpha
+    alpha_2 = ndtr(z0_hat + num2/(1 - a_hat*num2))
+    return alpha_1, alpha_2, a_hat  # return a_hat for testing
+
+
+def _bootstrap_iv(data, statistic, vectorized, paired, axis, confidence_level,
+                  alternative, n_resamples, batch, method, bootstrap_result,
+                  rng):
+    """Input validation and standardization for `bootstrap`."""
+    xp = array_namespace(*data)
+
+    if vectorized not in {True, False, None}:
+        raise ValueError("`vectorized` must be `True`, `False`, or `None`.")
+
+    if vectorized is None:
+        vectorized = 'axis' in inspect.signature(statistic).parameters
+
+    if not vectorized:
+        if not is_numpy(xp):
+            message = (f"When using array library {xp.__name__}, `func` must be "
+                       "vectorized and accept argument `axis`.")
+            raise TypeError(message)
+
+        statistic = _vectorize_statistic(statistic)
+
+    axis_int = int(axis)
+    if axis != axis_int:
+        raise ValueError("`axis` must be an integer.")
+
+    n_samples = 0
+    try:
+        n_samples = len(data)
+    except TypeError:
+        raise ValueError("`data` must be a sequence of samples.")
+
+    if n_samples == 0:
+        raise ValueError("`data` must contain at least one sample.")
+
+    data = _broadcast_arrays(data, axis_int, xp=xp)
+
+    data_iv = []
+    for sample in data:
+        if sample.shape[axis_int] <= 1:
+            raise ValueError("each sample in `data` must contain two or more "
+                             "observations along `axis`.")
+        sample = xp.moveaxis(sample, axis_int, -1)
+        data_iv.append(sample)
+
+    if paired not in {True, False}:
+        raise ValueError("`paired` must be `True` or `False`.")
+
+    if paired:
+        n = data_iv[0].shape[-1]
+        for sample in data_iv[1:]:
+            if sample.shape[-1] != n:
+                message = ("When `paired is True`, all samples must have the "
+                           "same length along `axis`")
+                raise ValueError(message)
+
+        # to generate the bootstrap distribution for paired-sample statistics,
+        # resample the indices of the observations
+        def statistic(i, axis=-1, data=data_iv, unpaired_statistic=statistic):
+            # data = [sample[..., i] for sample in data]
+            data = [_get_from_last_axis(sample, i, xp=xp) for sample in data]
+            return unpaired_statistic(*data, axis=axis)
+
+        data_iv = [xp.arange(n)]
+
+    confidence_level_float = float(confidence_level)
+
+    alternative = alternative.lower()
+    alternatives = {'two-sided', 'less', 'greater'}
+    if alternative not in alternatives:
+        raise ValueError(f"`alternative` must be one of {alternatives}")
+
+    n_resamples_int = int(n_resamples)
+    if n_resamples != n_resamples_int or n_resamples_int < 0:
+        raise ValueError("`n_resamples` must be a non-negative integer.")
+
+    if batch is None:
+        batch_iv = batch
+    else:
+        batch_iv = int(batch)
+        if batch != batch_iv or batch_iv <= 0:
+            raise ValueError("`batch` must be a positive integer or None.")
+
+    methods = {'percentile', 'basic', 'bca'}
+    method = method.lower()
+    if method not in methods:
+        raise ValueError(f"`method` must be in {methods}")
+
+    message = "`bootstrap_result` must have attribute `bootstrap_distribution'"
+    if (bootstrap_result is not None
+            and not hasattr(bootstrap_result, "bootstrap_distribution")):
+        raise ValueError(message)
+
+    message = ("Either `bootstrap_result.bootstrap_distribution.size` or "
+               "`n_resamples` must be positive.")
+    if ((not bootstrap_result or
+         not xp_size(bootstrap_result.bootstrap_distribution))
+            and n_resamples_int == 0):
+        raise ValueError(message)
+
+    rng = check_random_state(rng)
+
+    return (data_iv, statistic, vectorized, paired, axis_int,
+            confidence_level_float, alternative, n_resamples_int, batch_iv,
+            method, bootstrap_result, rng, xp)
+
+
+@dataclass
+class BootstrapResult:
+    """Result object returned by `scipy.stats.bootstrap`.
+
+    Attributes
+    ----------
+    confidence_interval : ConfidenceInterval
+        The bootstrap confidence interval as an instance of
+        `collections.namedtuple` with attributes `low` and `high`.
+    bootstrap_distribution : ndarray
+        The bootstrap distribution, that is, the value of `statistic` for
+        each resample. The last dimension corresponds with the resamples
+        (e.g. ``res.bootstrap_distribution.shape[-1] == n_resamples``).
+    standard_error : float or ndarray
+        The bootstrap standard error, that is, the sample standard
+        deviation of the bootstrap distribution.
+
+    """
+    confidence_interval: ConfidenceInterval
+    bootstrap_distribution: np.ndarray
+    standard_error: float | np.ndarray
+
+
+@xp_capabilities(skip_backends=[("dask.array", "Dask doesn't have take_along_axis.")],
+                 jax_jit=False)  # a few failed assertions - not sure what's going on
+@_transition_to_rng('random_state')
+def bootstrap(data, statistic, *, n_resamples=9999, batch=None,
+              vectorized=None, paired=False, axis=0, confidence_level=0.95,
+              alternative='two-sided', method='BCa', bootstrap_result=None,
+              rng=None):
+    r"""
+    Compute a two-sided bootstrap confidence interval of a statistic.
+
+    When `method` is ``'percentile'`` and `alternative` is ``'two-sided'``,
+    a bootstrap confidence interval is computed according to the following
+    procedure.
+
+    1. Resample the data: for each sample in `data` and for each of
+       `n_resamples`, take a random sample of the original sample
+       (with replacement) of the same size as the original sample.
+
+    2. Compute the bootstrap distribution of the statistic: for each set of
+       resamples, compute the test statistic.
+
+    3. Determine the confidence interval: find the interval of the bootstrap
+       distribution that is
+
+       - symmetric about the median and
+       - contains `confidence_level` of the resampled statistic values.
+
+    While the ``'percentile'`` method is the most intuitive, it is rarely
+    used in practice. Two more common methods are available, ``'basic'``
+    ('reverse percentile') and ``'BCa'`` ('bias-corrected and accelerated');
+    they differ in how step 3 is performed.
+
+    If the samples in `data` are  taken at random from their respective
+    distributions :math:`n` times, the confidence interval returned by
+    `bootstrap` will contain the true value of the statistic for those
+    distributions approximately `confidence_level`:math:`\, \times \, n` times.
+
+    Parameters
+    ----------
+    data : sequence of array-like
+         Each element of `data` is a sample containing scalar observations from an
+         underlying distribution. Elements of `data` must be broadcastable to the
+         same shape (with the possible exception of the dimension specified by `axis`).
+    statistic : callable
+        Statistic for which the confidence interval is to be calculated.
+        `statistic` must be a callable that accepts ``len(data)`` samples
+        as separate arguments and returns the resulting statistic.
+        If `vectorized` is set ``True``,
+        `statistic` must also accept a keyword argument `axis` and be
+        vectorized to compute the statistic along the provided `axis`.
+    n_resamples : int, default: ``9999``
+        The number of resamples performed to form the bootstrap distribution
+        of the statistic.
+    batch : int, optional
+        The number of resamples to process in each vectorized call to
+        `statistic`. Memory usage is O( `batch` * ``n`` ), where ``n`` is the
+        sample size. Default is ``None``, in which case ``batch = n_resamples``
+        (or ``batch = max(n_resamples, n)`` for ``method='BCa'``).
+    vectorized : bool, optional
+        If `vectorized` is set ``False``, `statistic` will not be passed
+        keyword argument `axis` and is expected to calculate the statistic
+        only for 1D samples. If ``True``, `statistic` will be passed keyword
+        argument `axis` and is expected to calculate the statistic along `axis`
+        when passed an ND sample array. If ``None`` (default), `vectorized`
+        will be set ``True`` if ``axis`` is a parameter of `statistic`. Use of
+        a vectorized statistic typically reduces computation time.
+    paired : bool, default: ``False``
+        Whether the statistic treats corresponding elements of the samples
+        in `data` as paired. If True, `bootstrap` resamples an array of
+        *indices* and uses the same indices for all arrays in `data`; otherwise,
+        `bootstrap` independently resamples the elements of each array.
+    axis : int, default: ``0``
+        The axis of the samples in `data` along which the `statistic` is
+        calculated.
+    confidence_level : float, default: ``0.95``
+        The confidence level of the confidence interval.
+    alternative : {'two-sided', 'less', 'greater'}, default: ``'two-sided'``
+        Choose ``'two-sided'`` (default) for a two-sided confidence interval,
+        ``'less'`` for a one-sided confidence interval with the lower bound
+        at ``-np.inf``, and ``'greater'`` for a one-sided confidence interval
+        with the upper bound at ``np.inf``. The other bound of the one-sided
+        confidence intervals is the same as that of a two-sided confidence
+        interval with `confidence_level` twice as far from 1.0; e.g. the upper
+        bound of a 95% ``'less'``  confidence interval is the same as the upper
+        bound of a 90% ``'two-sided'`` confidence interval.
+    method : {'percentile', 'basic', 'bca'}, default: ``'BCa'``
+        Whether to return the 'percentile' bootstrap confidence interval
+        (``'percentile'``), the 'basic' (AKA 'reverse') bootstrap confidence
+        interval (``'basic'``), or the bias-corrected and accelerated bootstrap
+        confidence interval (``'BCa'``).
+    bootstrap_result : BootstrapResult, optional
+        Provide the result object returned by a previous call to `bootstrap`
+        to include the previous bootstrap distribution in the new bootstrap
+        distribution. This can be used, for example, to change
+        `confidence_level`, change `method`, or see the effect of performing
+        additional resampling without repeating computations.
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+    Returns
+    -------
+    res : BootstrapResult
+        An object with attributes:
+
+        confidence_interval : ConfidenceInterval
+            The bootstrap confidence interval as an instance of
+            `collections.namedtuple` with attributes `low` and `high`.
+        bootstrap_distribution : ndarray
+            The bootstrap distribution, that is, the value of `statistic` for
+            each resample. The last dimension corresponds with the resamples
+            (e.g. ``res.bootstrap_distribution.shape[-1] == n_resamples``).
+        standard_error : float or ndarray
+            The bootstrap standard error, that is, the sample standard
+            deviation of the bootstrap distribution.
+
+    Warns
+    -----
+    `~scipy.stats.DegenerateDataWarning`
+        Generated when ``method='BCa'`` and the bootstrap distribution is
+        degenerate (e.g. all elements are identical).
+
+    Notes
+    -----
+    Elements of the confidence interval may be NaN for ``method='BCa'`` if
+    the bootstrap distribution is degenerate (e.g. all elements are identical).
+    In this case, consider using another `method` or inspecting `data` for
+    indications that other analysis may be more appropriate (e.g. all
+    observations are identical).
+
+    References
+    ----------
+    .. [1] B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap,
+       Chapman & Hall/CRC, Boca Raton, FL, USA (1993)
+    .. [2] Nathaniel E. Helwig, "Bootstrap Confidence Intervals",
+       http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf
+    .. [3] Bootstrapping (statistics), Wikipedia,
+       https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29
+
+    Examples
+    --------
+    Suppose we have sampled data from an unknown distribution.
+
+    >>> import numpy as np
+    >>> rng = np.random.default_rng()
+    >>> from scipy.stats import norm
+    >>> dist = norm(loc=2, scale=4)  # our "unknown" distribution
+    >>> data = dist.rvs(size=100, random_state=rng)
+
+    We are interested in the standard deviation of the distribution.
+
+    >>> std_true = dist.std()      # the true value of the statistic
+    >>> print(std_true)
+    4.0
+    >>> std_sample = np.std(data)  # the sample statistic
+    >>> print(std_sample)
+    3.9460644295563863
+
+    The bootstrap is used to approximate the variability we would expect if we
+    were to repeatedly sample from the unknown distribution and calculate the
+    statistic of the sample each time. It does this by repeatedly resampling
+    values *from the original sample* with replacement and calculating the
+    statistic of each resample. This results in a "bootstrap distribution" of
+    the statistic.
+
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy.stats import bootstrap
+    >>> data = (data,)  # samples must be in a sequence
+    >>> res = bootstrap(data, np.std, confidence_level=0.9, rng=rng)
+    >>> fig, ax = plt.subplots()
+    >>> ax.hist(res.bootstrap_distribution, bins=25)
+    >>> ax.set_title('Bootstrap Distribution')
+    >>> ax.set_xlabel('statistic value')
+    >>> ax.set_ylabel('frequency')
+    >>> plt.show()
+
+    The standard error quantifies this variability. It is calculated as the
+    standard deviation of the bootstrap distribution.
+
+    >>> res.standard_error
+    0.24427002125829136
+    >>> res.standard_error == np.std(res.bootstrap_distribution, ddof=1)
+    True
+
+    The bootstrap distribution of the statistic is often approximately normal
+    with scale equal to the standard error.
+
+    >>> x = np.linspace(3, 5)
+    >>> pdf = norm.pdf(x, loc=std_sample, scale=res.standard_error)
+    >>> fig, ax = plt.subplots()
+    >>> ax.hist(res.bootstrap_distribution, bins=25, density=True)
+    >>> ax.plot(x, pdf)
+    >>> ax.set_title('Normal Approximation of the Bootstrap Distribution')
+    >>> ax.set_xlabel('statistic value')
+    >>> ax.set_ylabel('pdf')
+    >>> plt.show()
+
+    This suggests that we could construct a 90% confidence interval on the
+    statistic based on quantiles of this normal distribution.
+
+    >>> norm.interval(0.9, loc=std_sample, scale=res.standard_error)
+    (3.5442759991341726, 4.3478528599786)
+
+    Due to central limit theorem, this normal approximation is accurate for a
+    variety of statistics and distributions underlying the samples; however,
+    the approximation is not reliable in all cases. Because `bootstrap` is
+    designed to work with arbitrary underlying distributions and statistics,
+    it uses more advanced techniques to generate an accurate confidence
+    interval.
+
+    >>> print(res.confidence_interval)
+    ConfidenceInterval(low=3.57655333533867, high=4.382043696342881)
+
+    If we sample from the original distribution 100 times and form a bootstrap
+    confidence interval for each sample, the confidence interval
+    contains the true value of the statistic approximately 90% of the time.
+
+    >>> n_trials = 100
+    >>> ci_contains_true_std = 0
+    >>> for i in range(n_trials):
+    ...    data = (dist.rvs(size=100, random_state=rng),)
+    ...    res = bootstrap(data, np.std, confidence_level=0.9,
+    ...                    n_resamples=999, rng=rng)
+    ...    ci = res.confidence_interval
+    ...    if ci[0] < std_true < ci[1]:
+    ...        ci_contains_true_std += 1
+    >>> print(ci_contains_true_std)
+    88
+
+    Rather than writing a loop, we can also determine the confidence intervals
+    for all 100 samples at once.
+
+    >>> data = (dist.rvs(size=(n_trials, 100), random_state=rng),)
+    >>> res = bootstrap(data, np.std, axis=-1, confidence_level=0.9,
+    ...                 n_resamples=999, rng=rng)
+    >>> ci_l, ci_u = res.confidence_interval
+
+    Here, `ci_l` and `ci_u` contain the confidence interval for each of the
+    ``n_trials = 100`` samples.
+
+    >>> print(ci_l[:5])
+    [3.86401283 3.33304394 3.52474647 3.54160981 3.80569252]
+    >>> print(ci_u[:5])
+    [4.80217409 4.18143252 4.39734707 4.37549713 4.72843584]
+
+    And again, approximately 90% contain the true value, ``std_true = 4``.
+
+    >>> print(np.sum((ci_l < std_true) & (std_true < ci_u)))
+    93
+
+    `bootstrap` can also be used to estimate confidence intervals of
+    multi-sample statistics. For example, to get a confidence interval
+    for the difference between means, we write a function that accepts
+    two sample arguments and returns only the statistic. The use of the
+    ``axis`` argument ensures that all mean calculations are perform in
+    a single vectorized call, which is faster than looping over pairs
+    of resamples in Python.
+
+    >>> def my_statistic(sample1, sample2, axis=-1):
+    ...     mean1 = np.mean(sample1, axis=axis)
+    ...     mean2 = np.mean(sample2, axis=axis)
+    ...     return mean1 - mean2
+
+    Here, we use the 'percentile' method with the default 95% confidence level.
+
+    >>> sample1 = norm.rvs(scale=1, size=100, random_state=rng)
+    >>> sample2 = norm.rvs(scale=2, size=100, random_state=rng)
+    >>> data = (sample1, sample2)
+    >>> res = bootstrap(data, my_statistic, method='basic', rng=rng)
+    >>> print(my_statistic(sample1, sample2))
+    0.16661030792089523
+    >>> print(res.confidence_interval)
+    ConfidenceInterval(low=-0.29087973240818693, high=0.6371338699912273)
+
+    The bootstrap estimate of the standard error is also available.
+
+    >>> print(res.standard_error)
+    0.238323948262459
+
+    Paired-sample statistics work, too. For example, consider the Pearson
+    correlation coefficient.
+
+    >>> from scipy.stats import pearsonr
+    >>> n = 100
+    >>> x = np.linspace(0, 10, n)
+    >>> y = x + rng.uniform(size=n)
+    >>> print(pearsonr(x, y)[0])  # element 0 is the statistic
+    0.9954306665125647
+
+    We wrap `pearsonr` so that it returns only the statistic, ensuring
+    that we use the `axis` argument because it is available.
+
+    >>> def my_statistic(x, y, axis=-1):
+    ...     return pearsonr(x, y, axis=axis)[0]
+
+    We call `bootstrap` using ``paired=True``.
+
+    >>> res = bootstrap((x, y), my_statistic, paired=True, rng=rng)
+    >>> print(res.confidence_interval)
+    ConfidenceInterval(low=0.9941504301315878, high=0.996377412215445)
+
+    The result object can be passed back into `bootstrap` to perform additional
+    resampling:
+
+    >>> len(res.bootstrap_distribution)
+    9999
+    >>> res = bootstrap((x, y), my_statistic, paired=True,
+    ...                 n_resamples=1000, rng=rng,
+    ...                 bootstrap_result=res)
+    >>> len(res.bootstrap_distribution)
+    10999
+
+    or to change the confidence interval options:
+
+    >>> res2 = bootstrap((x, y), my_statistic, paired=True,
+    ...                  n_resamples=0, rng=rng, bootstrap_result=res,
+    ...                  method='percentile', confidence_level=0.9)
+    >>> np.testing.assert_equal(res2.bootstrap_distribution,
+    ...                         res.bootstrap_distribution)
+    >>> res.confidence_interval
+    ConfidenceInterval(low=0.9941574828235082, high=0.9963781698210212)
+
+    without repeating computation of the original bootstrap distribution.
+
+    """
+    # Input validation
+    args = _bootstrap_iv(data, statistic, vectorized, paired, axis,
+                         confidence_level, alternative, n_resamples, batch,
+                         method, bootstrap_result, rng)
+    (data, statistic, vectorized, paired, axis, confidence_level,
+     alternative, n_resamples, batch, method, bootstrap_result,
+     rng, xp) = args
+
+    theta_hat_b = ([] if bootstrap_result is None
+                   else [bootstrap_result.bootstrap_distribution])
+
+    batch_nominal = batch or n_resamples or 1
+
+    for k in range(0, n_resamples, batch_nominal):
+        batch_actual = min(batch_nominal, n_resamples-k)
+        # Generate resamples
+        resampled_data = []
+        for sample in data:
+            resample = _bootstrap_resample(sample, n_resamples=batch_actual,
+                                           rng=rng, xp=xp)
+            resampled_data.append(resample)
+
+        # Compute bootstrap distribution of statistic
+        theta_hat_b.append(statistic(*resampled_data, axis=-1))
+    theta_hat_b = xp.concat(theta_hat_b, axis=-1)
+
+    # Calculate percentile interval
+    alpha = ((1 - confidence_level)/2 if alternative == 'two-sided'
+             else (1 - confidence_level))
+    if method == 'bca':
+        interval = _bca_interval(data, statistic, axis=-1, alpha=alpha,
+                                 theta_hat_b=theta_hat_b, batch=batch, xp=xp)[:2]
+    else:
+        alpha = xp.asarray(alpha, dtype=theta_hat_b.dtype,
+                           device=xp_device(theta_hat_b))
+        interval = alpha, 1 - alpha
+
+    # Calculate confidence interval of statistic
+    interval = xp.stack(interval, axis=-1)
+    ci = stats.quantile(theta_hat_b, interval, axis=-1)
+    if not is_lazy_array(ci) and xp.any(xp.isnan(ci)):
+        msg = (
+            "The BCa confidence interval cannot be calculated. "
+            "This problem is known to occur when the distribution "
+            "is degenerate or the statistic is np.min."
+        )
+        warnings.warn(DegenerateDataWarning(msg), stacklevel=2)
+
+    ci_l = ci[..., 0]
+    ci_u = ci[..., 1]
+
+    if method == 'basic':  # see [3]
+        theta_hat = statistic(*data, axis=-1)
+        ci_l, ci_u = 2*theta_hat - ci_u, 2*theta_hat - ci_l
+
+    if alternative == 'less':
+        ci_l = xp.full_like(ci_l, -xp.inf)
+    elif alternative == 'greater':
+        ci_u = xp.full_like(ci_u, xp.inf)
+
+    standard_error = xp.std(theta_hat_b, correction=1, axis=-1)
+
+    ci_l = ci_l[()] if ci_l.ndim == 0 else ci_l
+    ci_u = ci_u[()] if ci_u.ndim == 0 else ci_u
+    standard_error = standard_error[()] if standard_error.ndim == 0 else standard_error
+
+    return BootstrapResult(confidence_interval=ConfidenceInterval(ci_l, ci_u),
+                           bootstrap_distribution=theta_hat_b,
+                           standard_error=standard_error)
+
+
+def _monte_carlo_test_iv(data, rvs, statistic, vectorized, n_resamples,
+                         batch, alternative, axis):
+    """Input validation for `monte_carlo_test`."""
+    axis_int = int(axis)
+    if axis != axis_int:
+        raise ValueError("`axis` must be an integer.")
+
+    if vectorized not in {True, False, None}:
+        raise ValueError("`vectorized` must be `True`, `False`, or `None`.")
+
+    if not isinstance(rvs, Sequence):
+        rvs = (rvs,)
+        data = (data,)
+    for rvs_i in rvs:
+        if not callable(rvs_i):
+            raise TypeError("`rvs` must be callable or sequence of callables.")
+
+    # At this point, `data` should be a sequence
+    # If it isn't, the user passed a sequence for `rvs` but not `data`
+    message = "If `rvs` is a sequence, `len(rvs)` must equal `len(data)`."
+    try:
+        len(data)
+    except TypeError as e:
+        raise ValueError(message) from e
+    if not len(rvs) == len(data):
+        raise ValueError(message)
+
+    if not callable(statistic):
+        raise TypeError("`statistic` must be callable.")
+
+    if vectorized is None:
+        try:
+            signature = inspect.signature(statistic).parameters
+        except ValueError as e:
+            message = (f"Signature inspection of {statistic=} failed; "
+                       "pass `vectorize` explicitly.")
+            raise ValueError(message) from e
+        vectorized = 'axis' in signature
+
+    xp = array_namespace(*data)
+    dtype = xp_result_type(*data, force_floating=True, xp=xp)
+
+    if not vectorized:
+        if is_numpy(xp):
+            statistic_vectorized = _vectorize_statistic(statistic)
+        else:
+            message = ("`statistic` must be vectorized (i.e. support an `axis` "
+                       f"argument) when `data` contains {xp.__name__} arrays.")
+            raise ValueError(message)
+    else:
+        statistic_vectorized = statistic
+
+    data = _broadcast_arrays(data, axis, xp=xp)
+    data_iv = []
+    for sample in data:
+        sample = xp.broadcast_to(sample, (1,)) if sample.ndim == 0 else sample
+        sample = xp.moveaxis(sample, axis_int, -1)
+        data_iv.append(sample)
+
+    n_resamples_int = int(n_resamples)
+    if n_resamples != n_resamples_int or n_resamples_int <= 0:
+        raise ValueError("`n_resamples` must be a positive integer.")
+
+    if batch is None:
+        batch_iv = batch
+    else:
+        batch_iv = int(batch)
+        if batch != batch_iv or batch_iv <= 0:
+            raise ValueError("`batch` must be a positive integer or None.")
+
+    alternatives = {'two-sided', 'greater', 'less'}
+    alternative = alternative.lower()
+    if alternative not in alternatives:
+        raise ValueError(f"`alternative` must be in {alternatives}")
+
+    return (data_iv, rvs, statistic_vectorized, vectorized, n_resamples_int,
+            batch_iv, alternative, axis_int, dtype, xp)
+
+
+@dataclass
+class MonteCarloTestResult:
+    """Result object returned by `scipy.stats.monte_carlo_test`.
+
+    Attributes
+    ----------
+    statistic : float or ndarray
+        The observed test statistic of the sample.
+    pvalue : float or ndarray
+        The p-value for the given alternative.
+    null_distribution : ndarray
+        The values of the test statistic generated under the null
+        hypothesis.
+    """
+    statistic: float | np.ndarray
+    pvalue: float | np.ndarray
+    null_distribution: np.ndarray
+
+
+@xp_capabilities()
+@_rename_parameter('sample', 'data')
+def monte_carlo_test(data, rvs, statistic, *, vectorized=None,
+                     n_resamples=9999, batch=None, alternative="two-sided",
+                     axis=0):
+    r"""Perform a Monte Carlo hypothesis test.
+
+    `data` contains a sample or a sequence of one or more samples. `rvs`
+    specifies the distribution(s) of the sample(s) in `data` under the null
+    hypothesis. The value of `statistic` for the given `data` is compared
+    against a Monte Carlo null distribution: the value of the statistic for
+    each of `n_resamples` sets of samples generated using `rvs`. This gives
+    the p-value, the probability of observing such an extreme value of the
+    test statistic under the null hypothesis.
+
+    Parameters
+    ----------
+    data : array-like or sequence of array-like
+        An array or sequence of arrays of observations.
+    rvs : callable or tuple of callables
+        A callable or sequence of callables that generates random variates
+        under the null hypothesis. Each element of `rvs` must be a callable
+        that accepts keyword argument ``size`` (e.g. ``rvs(size=(m, n))``) and
+        returns an N-d array sample of that shape. If `rvs` is a sequence, the
+        number of callables in `rvs` must match the number of samples in
+        `data`, i.e. ``len(rvs) == len(data)``. If `rvs` is a single callable,
+        `data` is treated as a single sample.
+    statistic : callable
+        Statistic for which the p-value of the hypothesis test is to be
+        calculated. `statistic` must be a callable that accepts a sample
+        (e.g. ``statistic(sample)``) or ``len(rvs)`` separate samples (e.g.
+        ``statistic(samples1, sample2)`` if `rvs` contains two callables and
+        `data` contains two samples) and returns the resulting statistic.
+        If `vectorized` is set ``True``, `statistic` must also accept a keyword
+        argument `axis` and be vectorized to compute the statistic along the
+        provided `axis` of the samples in `data`.
+    vectorized : bool, optional
+        If `vectorized` is set ``False``, `statistic` will not be passed
+        keyword argument `axis` and is expected to calculate the statistic
+        only for 1D samples. If ``True``, `statistic` will be passed keyword
+        argument `axis` and is expected to calculate the statistic along `axis`
+        when passed ND sample arrays. If ``None`` (default), `vectorized`
+        will be set ``True`` if ``axis`` is a parameter of `statistic`. Use of
+        a vectorized statistic typically reduces computation time.
+    n_resamples : int, default: 9999
+        Number of samples drawn from each of the callables of `rvs`.
+        Equivalently, the number statistic values under the null hypothesis
+        used as the Monte Carlo null distribution.
+    batch : int, optional
+        The number of Monte Carlo samples to process in each call to
+        `statistic`. Memory usage is O( `batch` * ``sample.size[axis]`` ). Default
+        is ``None``, in which case `batch` equals `n_resamples`.
+    alternative : {'two-sided', 'less', 'greater'}
+        The alternative hypothesis for which the p-value is calculated.
+        For each alternative, the p-value is defined as follows.
+
+        - ``'greater'`` : the percentage of the null distribution that is
+          greater than or equal to the observed value of the test statistic.
+        - ``'less'`` : the percentage of the null distribution that is
+          less than or equal to the observed value of the test statistic.
+        - ``'two-sided'`` : twice the smaller of the p-values above.
+
+    axis : int, default: 0
+        The axis of `data` (or each sample within `data`) over which to
+        calculate the statistic.
+
+    Returns
+    -------
+    res : MonteCarloTestResult
+        An object with attributes:
+
+        statistic : float or ndarray
+            The test statistic of the observed `data`.
+        pvalue : float or ndarray
+            The p-value for the given alternative.
+        null_distribution : ndarray
+            The values of the test statistic generated under the null
+            hypothesis.
+
+    .. warning::
+        The p-value is calculated by counting the elements of the null
+        distribution that are as extreme or more extreme than the observed
+        value of the statistic. Due to the use of finite precision arithmetic,
+        some statistic functions return numerically distinct values when the
+        theoretical values would be exactly equal. In some cases, this could
+        lead to a large error in the calculated p-value. `monte_carlo_test`
+        guards against this by considering elements in the null distribution
+        that are "close" (within a relative tolerance of 100 times the
+        floating point epsilon of inexact dtypes) to the observed
+        value of the test statistic as equal to the observed value of the
+        test statistic. However, the user is advised to inspect the null
+        distribution to assess whether this method of comparison is
+        appropriate, and if not, calculate the p-value manually.
+
+    References
+    ----------
+
+    .. [1] B. Phipson and G. K. Smyth. "Permutation P-values Should Never Be
+       Zero: Calculating Exact P-values When Permutations Are Randomly Drawn."
+       Statistical Applications in Genetics and Molecular Biology 9.1 (2010).
+
+    Examples
+    --------
+
+    Suppose we wish to test whether a small sample has been drawn from a normal
+    distribution. We decide that we will use the skew of the sample as a
+    test statistic, and we will consider a p-value of 0.05 to be statistically
+    significant.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> def statistic(x, axis):
+    ...     return stats.skew(x, axis)
+
+    After collecting our data, we calculate the observed value of the test
+    statistic.
+
+    >>> rng = np.random.default_rng()
+    >>> x = stats.skewnorm.rvs(a=1, size=50, random_state=rng)
+    >>> statistic(x, axis=0)
+    0.12457412450240658
+
+    To determine the probability of observing such an extreme value of the
+    skewness by chance if the sample were drawn from the normal distribution,
+    we can perform a Monte Carlo hypothesis test. The test will draw many
+    samples at random from their normal distribution, calculate the skewness
+    of each sample, and compare our original skewness against this
+    distribution to determine an approximate p-value.
+
+    >>> from scipy.stats import monte_carlo_test
+    >>> # because our statistic is vectorized, we pass `vectorized=True`
+    >>> rvs = lambda size: stats.norm.rvs(size=size, random_state=rng)
+    >>> res = monte_carlo_test(x, rvs, statistic, vectorized=True)
+    >>> print(res.statistic)
+    0.12457412450240658
+    >>> print(res.pvalue)
+    0.7012
+
+    The probability of obtaining a test statistic less than or equal to the
+    observed value under the null hypothesis is ~70%. This is greater than
+    our chosen threshold of 5%, so we cannot consider this to be significant
+    evidence against the null hypothesis.
+
+    Note that this p-value essentially matches that of
+    `scipy.stats.skewtest`, which relies on an asymptotic distribution of a
+    test statistic based on the sample skewness.
+
+    >>> stats.skewtest(x).pvalue
+    0.6892046027110614
+
+    This asymptotic approximation is not valid for small sample sizes, but
+    `monte_carlo_test` can be used with samples of any size.
+
+    >>> x = stats.skewnorm.rvs(a=1, size=7, random_state=rng)
+    >>> # stats.skewtest(x) would produce an error due to small sample
+    >>> res = monte_carlo_test(x, rvs, statistic, vectorized=True)
+
+    The Monte Carlo distribution of the test statistic is provided for
+    further investigation.
+
+    >>> import matplotlib.pyplot as plt
+    >>> fig, ax = plt.subplots()
+    >>> ax.hist(res.null_distribution, bins=50)
+    >>> ax.set_title("Monte Carlo distribution of test statistic")
+    >>> ax.set_xlabel("Value of Statistic")
+    >>> ax.set_ylabel("Frequency")
+    >>> plt.show()
+
+    """
+    args = _monte_carlo_test_iv(data, rvs, statistic, vectorized,
+                                n_resamples, batch, alternative, axis)
+    (data, rvs, statistic, vectorized, n_resamples,
+     batch, alternative, axis, dtype, xp) = args
+
+    # Some statistics return plain floats; ensure they're at least a NumPy float
+    observed = xp.asarray(statistic(*data, axis=-1))
+    observed = observed[()] if observed.ndim == 0 else observed
+
+    n_observations = [sample.shape[-1] for sample in data]
+    batch_nominal = batch or n_resamples
+    null_distribution = []
+    for k in range(0, n_resamples, batch_nominal):
+        batch_actual = min(batch_nominal, n_resamples - k)
+        resamples = [rvs_i(size=(batch_actual, n_observations_i))
+                     for rvs_i, n_observations_i in zip(rvs, n_observations)]
+        null_distribution.append(statistic(*resamples, axis=-1))
+    null_distribution = xp.concat(null_distribution)
+    null_distribution = xp.reshape(null_distribution, (-1,) + (1,)*observed.ndim)
+
+    # relative tolerance for detecting numerically distinct but
+    # theoretically equal values in the null distribution
+    eps =  (0 if not xp.isdtype(observed.dtype, ('real floating'))
+            else xp.finfo(observed.dtype).eps*100)
+    gamma = xp.abs(eps * observed)
+
+    def less(null_distribution, observed):
+        cmps = null_distribution <= observed + gamma
+        cmps = xp.asarray(cmps, dtype=dtype)
+        pvalues = (xp.sum(cmps, axis=0, dtype=dtype) + 1.) / (n_resamples + 1.)
+        return pvalues
+
+    def greater(null_distribution, observed):
+        cmps = null_distribution >= observed - gamma
+        cmps = xp.asarray(cmps, dtype=dtype)
+        pvalues = (xp.sum(cmps, axis=0, dtype=dtype) + 1.) / (n_resamples + 1.)
+        return pvalues
+
+    def two_sided(null_distribution, observed):
+        pvalues_less = less(null_distribution, observed)
+        pvalues_greater = greater(null_distribution, observed)
+        pvalues = xp.minimum(pvalues_less, pvalues_greater) * 2
+        return pvalues
+
+    compare = {"less": less,
+               "greater": greater,
+               "two-sided": two_sided}
+
+    pvalues = compare[alternative](null_distribution, observed)
+    pvalues = xp.clip(pvalues, 0., 1.)
+
+    return MonteCarloTestResult(observed, pvalues, null_distribution)
+
+
+@dataclass
+class PowerResult:
+    """Result object returned by `scipy.stats.power`.
+
+    Attributes
+    ----------
+    power : float or ndarray
+        The estimated power.
+    pvalues : float or ndarray
+        The simulated p-values.
+    """
+    power: float | np.ndarray
+    pvalues: float | np.ndarray
+
+
+def _wrap_kwargs(fun):
+    """Wrap callable to accept arbitrary kwargs and ignore unused ones"""
+
+    try:
+        keys = set(inspect.signature(fun).parameters.keys())
+    except ValueError:
+        # NumPy Generator methods can't be inspected
+        keys = {'size'}
+
+    # Set keys=keys/fun=fun to avoid late binding gotcha
+    def wrapped_rvs_i(*args, keys=keys, fun=fun, **all_kwargs):
+        kwargs = {key: val for key, val in all_kwargs.items()
+                  if key in keys}
+        return fun(*args, **kwargs)
+    return wrapped_rvs_i
+
+
+def _power_iv(rvs, test, n_observations, significance, vectorized,
+              n_resamples, batch, kwargs):
+    """Input validation for `monte_carlo_test`."""
+    if vectorized not in {True, False, None}:
+        raise ValueError("`vectorized` must be `True`, `False`, or `None`.")
+
+    if not isinstance(rvs, Sequence):
+        rvs = (rvs,)
+        n_observations = (n_observations,)
+    for rvs_i in rvs:
+        if not callable(rvs_i):
+            raise TypeError("`rvs` must be callable or sequence of callables.")
+
+    if not len(rvs) == len(n_observations):
+        message = ("If `rvs` is a sequence, `len(rvs)` "
+                   "must equal `len(n_observations)`.")
+        raise ValueError(message)
+
+    kwargs = dict() if kwargs is None else kwargs
+    if not isinstance(kwargs, dict):
+        raise TypeError("`kwargs` must be a dictionary that maps keywords to arrays.")
+
+    vals = kwargs.values()
+    keys = kwargs.keys()
+
+    xp = array_namespace(*n_observations, significance, *vals)
+
+    significance = xp.asarray(significance)
+    if not xp.isdtype(significance.dtype, "real floating"):
+        raise ValueError("`significance` must be of floating point dtype.")
+
+    if is_lazy_array(significance):
+        significance = xp.where((significance < 0.) | (significance > 1.),
+                                xp.nan, significance)
+    elif xp.min(significance) < 0. or xp.max(significance) > 1.:
+        raise ValueError("All elements of `significance` must be between 0. and 1.")
+
+    # Wrap callables to ignore unused keyword arguments
+    wrapped_rvs = [_wrap_kwargs(rvs_i) for rvs_i in rvs]
+
+    # Broadcast, then ravel nobs/kwarg combinations. In the end,
+    # `nobs` and `vals` have shape (# of combinations, number of variables)
+    # todo: find a better way to do this without combining arrays
+    tmp = xp.stack(xp.broadcast_arrays(*n_observations, *vals))
+    shape = tmp.shape
+    if tmp.ndim == 1:
+        tmp = xp.expand_dims(tmp, axis=0)
+    else:
+        tmp = xp.reshape(tmp, (shape[0], -1)).T
+    nobs, vals = tmp[:, :len(rvs)], tmp[:, len(rvs):]
+    integer_dtype = xp_result_type(*n_observations, xp=xp)
+    nobs = xp.astype(nobs, integer_dtype)
+
+    if not callable(test):
+        raise TypeError("`test` must be callable.")
+
+    if vectorized is None:
+        vectorized = 'axis' in inspect.signature(test).parameters
+
+    test_vectorized = test
+    if not vectorized:
+        if not is_numpy(xp):
+            message = (f"When using array library {xp.__name__}, `test` must be "
+                       "be vectorized and accept argument `axis`.")
+            raise TypeError(message)
+
+        test_vectorized = _vectorize_statistic(test)
+
+    # Wrap `test` function to ignore unused kwargs
+    test_vectorized = _wrap_kwargs(test_vectorized)
+
+    n_resamples_int = int(n_resamples)
+    if n_resamples != n_resamples_int or n_resamples_int <= 0:
+        raise ValueError("`n_resamples` must be a positive integer.")
+
+    if batch is None:
+        batch_iv = batch
+    else:
+        batch_iv = int(batch)
+        if batch != batch_iv or batch_iv <= 0:
+            raise ValueError("`batch` must be a positive integer or None.")
+
+    return (wrapped_rvs, test_vectorized, nobs, significance, vectorized,
+            n_resamples_int, batch_iv, vals, keys, shape[1:], xp)
+
+
+@xp_capabilities(skip_backends=[('dask.array', 'just because')],
+                 jax_jit=False)  # some problem with batch looping
+def power(test, rvs, n_observations, *, significance=0.01, vectorized=None,
+          n_resamples=10000, batch=None, kwargs=None):
+    r"""Simulate the power of a hypothesis test under an alternative hypothesis.
+
+    Parameters
+    ----------
+    test : callable
+        Hypothesis test for which the power is to be simulated.
+        `test` must be a callable that accepts a sample (e.g. ``test(sample)``)
+        or ``len(rvs)`` separate samples (e.g. ``test(samples1, sample2)`` if
+        `rvs` contains two callables and `n_observations` contains two values)
+        and returns the p-value of the test.
+        If `vectorized` is set to ``True``, `test` must also accept a keyword
+        argument `axis` and be vectorized to perform the test along the
+        provided `axis` of the samples.
+        Any callable from `scipy.stats` with an `axis` argument that returns an
+        object with a `pvalue` attribute is also acceptable.
+    rvs : callable or tuple of callables
+        A callable or sequence of callables that generate(s) random variates
+        under the alternative hypothesis. Each element of `rvs` must accept
+        keyword argument ``size`` (e.g. ``rvs(size=(m, n))``) and return an
+        N-d array of that shape. If `rvs` is a sequence, the number of callables
+        in `rvs` must match the number of elements of `n_observations`, i.e.
+        ``len(rvs) == len(n_observations)``. If `rvs` is a single callable,
+        `n_observations` is treated as a single element.
+    n_observations : tuple of ints or tuple of int arrays
+        If a sequence of ints, each is the sizes of a sample to be passed to `test`.
+        If a sequence of integer arrays, the power is simulated for each
+        set of corresponding sample sizes. See Examples.
+    significance : float or array_like of floats, default: 0.01
+        The threshold for significance; i.e., the p-value below which the
+        hypothesis test results will be considered as evidence against the null
+        hypothesis. Equivalently, the acceptable rate of Type I error under
+        the null hypothesis. If an array, the power is simulated for each
+        significance threshold.
+    vectorized : bool, optional
+        If `vectorized` is set to ``False``, `test` will not be passed keyword
+        argument `axis` and is expected to perform the test only for 1D samples.
+        If ``True``, `test` will be passed keyword argument `axis` and is
+        expected to perform the test along `axis` when passed N-D sample arrays.
+        If ``None`` (default), `vectorized` will be set ``True`` if ``axis`` is
+        a parameter of `test`. Use of a vectorized test typically reduces
+        computation time.
+    n_resamples : int, default: 10000
+        Number of samples drawn from each of the callables of `rvs`.
+        Equivalently, the number tests performed under the alternative
+        hypothesis to approximate the power.
+    batch : int, optional
+        The number of samples to process in each call to `test`. Memory usage is
+        proportional to the product of `batch` and the largest sample size. Default
+        is ``None``, in which case `batch` equals `n_resamples`.
+    kwargs : dict, optional
+        Keyword arguments to be passed to `rvs` and/or `test` callables.
+        Introspection is used to determine which keyword arguments may be
+        passed to each callable.
+        The value corresponding with each keyword must be an array.
+        Arrays must be broadcastable with one another and with each array in
+        `n_observations`. The power is simulated for each set of corresponding
+        sample sizes and arguments. See Examples.
+
+    Returns
+    -------
+    res : PowerResult
+        An object with attributes:
+
+        power : float or ndarray
+            The estimated power against the alternative.
+        pvalues : ndarray
+            The p-values observed under the alternative hypothesis.
+
+    Notes
+    -----
+    The power is simulated as follows:
+
+    - Draw many random samples (or sets of samples), each of the size(s)
+      specified by `n_observations`, under the alternative specified by
+      `rvs`.
+    - For each sample (or set of samples), compute the p-value according to
+      `test`. These p-values are recorded in the ``pvalues`` attribute of
+      the result object.
+    - Compute the proportion of p-values that are less than the `significance`
+      level. This is the power recorded in the ``power`` attribute of the
+      result object.
+
+    Suppose that `significance` is an array with shape ``shape1``, the elements
+    of `kwargs` and `n_observations` are mutually broadcastable to shape ``shape2``,
+    and `test` returns an array of p-values of shape ``shape3``. Then the result
+    object ``power`` attribute will be of shape ``shape1 + shape2 + shape3``, and
+    the ``pvalues`` attribute will be of shape ``shape2 + shape3 + (n_resamples,)``.
+
+    Examples
+    --------
+    Suppose we wish to simulate the power of the independent sample t-test
+    under the following conditions:
+
+    - The first sample has 10 observations drawn from a normal distribution
+      with mean 0.
+    - The second sample has 12 observations drawn from a normal distribution
+      with mean 1.0.
+    - The threshold on p-values for significance is 0.05.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng(2549598345528)
+    >>>
+    >>> test = stats.ttest_ind
+    >>> n_observations = (10, 12)
+    >>> rvs1 = rng.normal
+    >>> rvs2 = lambda size: rng.normal(loc=1, size=size)
+    >>> rvs = (rvs1, rvs2)
+    >>> res = stats.power(test, rvs, n_observations, significance=0.05)
+    >>> res.power
+    0.6116
+
+    With samples of size 10 and 12, respectively, the power of the t-test
+    with a significance threshold of 0.05 is approximately 60% under the chosen
+    alternative. We can investigate the effect of sample size on the power
+    by passing sample size arrays.
+
+    >>> import matplotlib.pyplot as plt
+    >>> nobs_x = np.arange(5, 21)
+    >>> nobs_y = nobs_x
+    >>> n_observations = (nobs_x, nobs_y)
+    >>> res = stats.power(test, rvs, n_observations, significance=0.05)
+    >>> ax = plt.subplot()
+    >>> ax.plot(nobs_x, res.power)
+    >>> ax.set_xlabel('Sample Size')
+    >>> ax.set_ylabel('Simulated Power')
+    >>> ax.set_title('Simulated Power of `ttest_ind` with Equal Sample Sizes')
+    >>> plt.show()
+
+    Alternatively, we can investigate the impact that effect size has on the power.
+    In this case, the effect size is the location of the distribution underlying
+    the second sample.
+
+    >>> n_observations = (10, 12)
+    >>> loc = np.linspace(0, 1, 20)
+    >>> rvs2 = lambda size, loc: rng.normal(loc=loc, size=size)
+    >>> rvs = (rvs1, rvs2)
+    >>> res = stats.power(test, rvs, n_observations, significance=0.05,
+    ...                   kwargs={'loc': loc})
+    >>> ax = plt.subplot()
+    >>> ax.plot(loc, res.power)
+    >>> ax.set_xlabel('Effect Size')
+    >>> ax.set_ylabel('Simulated Power')
+    >>> ax.set_title('Simulated Power of `ttest_ind`, Varying Effect Size')
+    >>> plt.show()
+
+    We can also use `power` to estimate the Type I error rate (also referred to by the
+    ambiguous term "size") of a test and assess whether it matches the nominal level.
+    For example, the null hypothesis of `jarque_bera` is that the sample was drawn from
+    a distribution with the same skewness and kurtosis as the normal distribution. To
+    estimate the Type I error rate, we can consider the null hypothesis to be a true
+    *alternative* hypothesis and calculate the power.
+
+    >>> test = stats.jarque_bera
+    >>> n_observations = 10
+    >>> rvs = rng.normal
+    >>> significance = np.linspace(0.0001, 0.1, 1000)
+    >>> res = stats.power(test, rvs, n_observations, significance=significance)
+    >>> size = res.power
+
+    As shown below, the Type I error rate of the test is far below the nominal level
+    for such a small sample, as mentioned in its documentation.
+
+    >>> ax = plt.subplot()
+    >>> ax.plot(significance, size)
+    >>> ax.plot([0, 0.1], [0, 0.1], '--')
+    >>> ax.set_xlabel('nominal significance level')
+    >>> ax.set_ylabel('estimated test size (Type I error rate)')
+    >>> ax.set_title('Estimated test size vs nominal significance level')
+    >>> ax.set_aspect('equal', 'box')
+    >>> ax.legend(('`ttest_1samp`', 'ideal test'))
+    >>> plt.show()
+
+    As one might expect from such a conservative test, the power is quite low with
+    respect to some alternatives. For example, the power of the test under the
+    alternative that the sample was drawn from the Laplace distribution may not
+    be much greater than the Type I error rate.
+
+    >>> rvs = rng.laplace
+    >>> significance = np.linspace(0.0001, 0.1, 1000)
+    >>> res = stats.power(test, rvs, n_observations, significance=0.05)
+    >>> print(res.power)
+    0.0587
+
+    This is not a mistake in SciPy's implementation; it is simply due to the fact
+    that the null distribution of the test statistic is derived under the assumption
+    that the sample size is large (i.e. approaches infinity), and this asymptotic
+    approximation is not accurate for small samples. In such cases, resampling
+    and Monte Carlo methods (e.g. `permutation_test`, `goodness_of_fit`,
+    `monte_carlo_test`) may be more appropriate.
+
+    """
+    tmp = _power_iv(rvs, test, n_observations, significance,
+                    vectorized, n_resamples, batch, kwargs)
+    (rvs, test, nobs, significance,
+     vectorized, n_resamples, batch, args, kwds, shape, xp) = tmp
+
+    batch_nominal = batch or n_resamples
+    pvalues = []  # results of various nobs/kwargs combinations
+    for i in range(nobs.shape[0]):
+        nobs_i, args_i = nobs[i, ...], args[i, ...]
+        kwargs_i = dict(zip(kwds, args_i))
+        pvalues_i = []  # results of batches; fixed nobs/kwargs combination
+        for k in range(0, n_resamples, batch_nominal):
+            batch_actual = min(batch_nominal, n_resamples - k)
+            resamples = [rvs_j(size=(batch_actual, int(nobs_ij)), **kwargs_i)
+                         for rvs_j, nobs_ij in zip(rvs, nobs_i)]
+            res = test(*resamples, **kwargs_i, axis=-1)
+            p = getattr(res, 'pvalue', res)
+            pvalues_i.append(p)
+        # Concatenate results from batches
+        pvalues_i = xp.concat(pvalues_i, axis=-1)
+        pvalues.append(pvalues_i)
+    # `test` can return result with array of p-values
+    shape += pvalues_i.shape[:-1]
+    # Concatenate results from various nobs/kwargs combinations
+    pvalues = xp.concat(pvalues, axis=0)
+    # nobs/kwargs arrays were raveled to single axis; unravel
+    pvalues = xp.reshape(pvalues, shape + (-1,))
+    if significance.ndim > 0:
+        newdims = tuple(range(significance.ndim, pvalues.ndim + significance.ndim))
+        significance = xpx.expand_dims(significance, axis=newdims)
+
+    float_dtype = xp_result_type(significance, pvalues, xp=xp)
+    powers = xp.mean(xp.astype(pvalues < significance, float_dtype), axis=-1)
+
+    return PowerResult(power=powers, pvalues=pvalues)
+
+
+@dataclass
+class PermutationTestResult:
+    """Result object returned by `scipy.stats.permutation_test`.
+
+    Attributes
+    ----------
+    statistic : float or ndarray
+        The observed test statistic of the data.
+    pvalue : float or ndarray
+        The p-value for the given alternative.
+    null_distribution : ndarray
+        The values of the test statistic generated under the null
+        hypothesis.
+    """
+    statistic: float | np.ndarray
+    pvalue: float | np.ndarray
+    null_distribution: np.ndarray
+
+
+def _all_partitions_concatenated(ns):
+    """
+    Generate all partitions of indices of groups of given sizes, concatenated
+
+    `ns` is an iterable of ints.
+    """
+    def all_partitions(z, n):
+        for c in combinations(z, n):
+            x0 = set(c)
+            x1 = z - x0
+            yield [x0, x1]
+
+    def all_partitions_n(z, ns):
+        if len(ns) == 0:
+            yield [z]
+            return
+        for c in all_partitions(z, ns[0]):
+            for d in all_partitions_n(c[1], ns[1:]):
+                yield c[0:1] + d
+
+    z = set(range(np.sum(ns)))
+    for partitioning in all_partitions_n(z, ns[:]):
+        x = np.concatenate([list(partition)
+                            for partition in partitioning]).astype(int)
+        yield x
+
+
+def _batch_generator(iterable, batch):
+    """A generator that yields batches of elements from an iterable"""
+    iterator = iter(iterable)
+    if batch <= 0:
+        raise ValueError("`batch` must be positive.")
+    z = [item for i, item in zip(range(batch), iterator)]
+    while z:  # we don't want StopIteration without yielding an empty list
+        yield z
+        z = [item for i, item in zip(range(batch), iterator)]
+
+
+def _pairings_permutations_gen(n_permutations, n_samples, n_obs_sample, batch,
+                               rng):
+    # Returns a generator that yields arrays of size
+    # `(batch, n_samples, n_obs_sample)`.
+    # Each row is an independent permutation of indices 0 to `n_obs_sample`.
+    batch = min(batch, n_permutations)
+
+    if hasattr(rng, 'permuted'):
+        def batched_perm_generator():
+            indices = np.arange(n_obs_sample)
+            indices = np.tile(indices, (batch, n_samples, 1))
+            for k in range(0, n_permutations, batch):
+                batch_actual = min(batch, n_permutations-k)
+                # Don't permute in place, otherwise results depend on `batch`
+                permuted_indices = rng.permuted(indices, axis=-1)
+                yield permuted_indices[:batch_actual]
+    else:  # RandomState and early Generators don't have `permuted`
+        def batched_perm_generator():
+            for k in range(0, n_permutations, batch):
+                batch_actual = min(batch, n_permutations-k)
+                size = (batch_actual, n_samples, n_obs_sample)
+                x = rng.random(size=size)
+                yield np.argsort(x, axis=-1)[:batch_actual]
+
+    return batched_perm_generator()
+
+
+def _calculate_null_both(data, statistic, n_permutations, batch,
+                         rng=None, *, xp):
+    """
+    Calculate null distribution for independent sample tests.
+    """
+    # compute number of permutations
+    # (distinct partitions of data into samples of these sizes)
+    n_obs_i = [sample.shape[-1] for sample in data]  # observations per sample
+    n_obs_ic = list(accumulate(n_obs_i, initial=0))
+    n_obs = n_obs_ic[-1]  # total number of observations
+    n_max = math.prod([math.comb(n, k) for n, k in zip(n_obs_ic[1:], n_obs_ic[:-1])])
+
+    # perm_generator is an iterator that produces permutations of indices
+    # from 0 to n_obs. We'll concatenate the samples, use these indices to
+    # permute the data, then split the samples apart again.
+    if n_permutations >= n_max:
+        exact_test = True
+        n_permutations = n_max
+        perm_generator = _all_partitions_concatenated(n_obs_i)
+    else:
+        exact_test = False
+        # Neither RandomState.permutation nor Generator.permutation
+        # can permute axis-slices independently. If this feature is
+        # added in the future, batches of the desired size should be
+        # generated in a single call.
+        perm_generator = (rng.permutation(n_obs)
+                          for i in range(n_permutations))
+
+    batch = batch or int(n_permutations)
+    null_distribution = []
+
+    # First, concatenate all the samples. In batches, permute samples with
+    # indices produced by the `perm_generator`, split them into new samples of
+    # the original sizes, compute the statistic for each batch, and add these
+    # statistic values to the null distribution.
+    data = xp.concat(data, axis=-1)
+    for indices in _batch_generator(perm_generator, batch=batch):
+        # Creating a tensor from a list of numpy.ndarrays is extremely slow...
+        indices = np.asarray(indices)
+        indices = xp.asarray(indices)
+
+        # `indices` is 2D: each row is a permutation of the indices.
+        # We use it to index `data` along its last axis, which corresponds
+        # with observations.
+        # After indexing, the second to last axis of `data_batch` corresponds
+        # with permutations, and the last axis corresponds with observations.
+        # data_batch = data[..., indices]
+        data_batch = _get_from_last_axis(data, indices, xp=xp)
+
+        # Move the permutation axis to the front: we'll concatenate a list
+        # of batched statistic values along this zeroth axis to form the
+        # null distribution.
+        data_batch = xp.moveaxis(data_batch, -2, 0)
+        # data_batch = np.split(data_batch, n_obs_ic[:-1], axis=-1)
+        data_batch = [data_batch[..., i:j] for i, j in zip(n_obs_ic[:-1], n_obs_ic[1:])]
+        null_distribution.append(statistic(*data_batch, axis=-1))
+    null_distribution = xp.concat(null_distribution, axis=0)
+
+    return null_distribution, n_permutations, exact_test
+
+
+def _calculate_null_pairings(data, statistic, n_permutations, batch,
+                             rng=None, *, xp):
+    """
+    Calculate null distribution for association tests.
+    """
+    n_samples = len(data)
+
+    # compute number of permutations (factorial(n) permutations of each sample)
+    n_obs_sample = data[0].shape[-1]  # observations per sample; same for each
+    n_max = math.factorial(n_obs_sample)**n_samples
+
+    # `perm_generator` is an iterator that produces a list of permutations of
+    # indices from 0 to n_obs_sample, one for each sample.
+    if n_permutations >= n_max:
+        exact_test = True
+        n_permutations = n_max
+        batch = batch or int(n_permutations)
+        # Cartesian product of the sets of all permutations of indices
+        perm_generator = product(*(permutations(range(n_obs_sample))
+                                   for i in range(n_samples)))
+        batched_perm_generator = _batch_generator(perm_generator, batch=batch)
+    else:
+        exact_test = False
+        batch = batch or int(n_permutations)
+        # Separate random permutations of indices for each sample.
+        # Again, it would be nice if RandomState/Generator.permutation
+        # could permute each axis-slice separately.
+        args = n_permutations, n_samples, n_obs_sample, batch, rng
+        batched_perm_generator = _pairings_permutations_gen(*args)
+
+    null_distribution = []
+
+    for indices in batched_perm_generator:
+        indices = xp.asarray(indices)
+
+        # `indices` is 3D: the zeroth axis is for permutations, the next is
+        # for samples, and the last is for observations. Swap the first two
+        # to make the zeroth axis correspond with samples, as it does for
+        # `data`.
+        indices = xp_swapaxes(indices, 0, 1, xp=xp)
+
+        # When we're done, `data_batch` will be a list of length `n_samples`.
+        # Each element will be a batch of random permutations of one sample.
+        # The zeroth axis of each batch will correspond with permutations,
+        # and the last will correspond with observations. (This makes it
+        # easy to pass into `statistic`.)
+        data_batch = [None]*n_samples
+        for i in range(n_samples):
+            # data_batch[i] = data[i][..., indices[i]]
+            data_batch[i] = _get_from_last_axis(data[i], indices[i, ...], xp=xp)
+            data_batch[i] = xp.moveaxis(data_batch[i], -2, 0)
+
+        null_distribution.append(statistic(*data_batch, axis=-1))
+    null_distribution = xp.concat(null_distribution, axis=0)
+
+    return null_distribution, n_permutations, exact_test
+
+
+def _calculate_null_samples(data, statistic, n_permutations, batch,
+                            rng=None, *, xp):
+    """
+    Calculate null distribution for paired-sample tests.
+    """
+    n_samples = len(data)
+
+    # By convention, the meaning of the "samples" permutations type for
+    # data with only one sample is to flip the sign of the observations.
+    # Achieve this by adding a second sample - the negative of the original.
+    if n_samples == 1:
+        data = [data[0], -data[0]]
+
+    # The "samples" permutation strategy is the same as the "pairings"
+    # strategy except the roles of samples and observations are flipped.
+    # So swap these axes, then we'll use the function for the "pairings"
+    # strategy to do all the work!
+    data = xp.stack(data, axis=0)
+    data = xp_swapaxes(data, 0, -1, xp=xp)
+
+    # (Of course, the user's statistic doesn't know what we've done here,
+    # so we need to pass it what it's expecting.)
+    def statistic_wrapped(*data, axis):
+        # can we do this without converting back and forth between
+        # array and list?
+        data = xp.stack(data, axis=0)
+        data = xp_swapaxes(data, 0, -1, xp=xp)
+        if n_samples == 1:
+            data = data[0:1, ...]
+        data = [data[i, ...] for i in range(data.shape[0])]
+        return statistic(*data, axis=axis)
+
+    data = [data[i, ...] for i in range(data.shape[0])]
+    return _calculate_null_pairings(data, statistic_wrapped, n_permutations,
+                                    batch, rng, xp=xp)
+
+
+def _permutation_test_iv(data, statistic, permutation_type, vectorized,
+                         n_resamples, batch, alternative, axis, rng):
+    """Input validation for `permutation_test`."""
+    axis_int = int(axis)
+    if axis != axis_int:
+        raise ValueError("`axis` must be an integer.")
+
+    permutation_types = {'samples', 'pairings', 'independent'}
+    permutation_type = permutation_type.lower()
+    if permutation_type not in permutation_types:
+        raise ValueError(f"`permutation_type` must be in {permutation_types}.")
+
+    if vectorized not in {True, False, None}:
+        raise ValueError("`vectorized` must be `True`, `False`, or `None`.")
+
+    if vectorized is None:
+        vectorized = 'axis' in inspect.signature(statistic).parameters
+
+    message = "`data` must be a tuple containing at least two samples"
+    try:
+        if len(data) < 2 and permutation_type == 'independent':
+            raise ValueError(message)
+    except TypeError:
+        raise TypeError(message)
+
+    xp = array_namespace(*data)
+
+    if not vectorized:
+        if not is_numpy(xp):
+            message = (f"When using array library {xp.__name__}, `func` must be "
+                       "vectorized and accept argument `axis`.")
+            raise TypeError(message)
+
+        statistic = _vectorize_statistic(statistic)
+
+    data = _broadcast_arrays(data, axis, xp=xp)
+    data_iv = []
+    for sample in data:
+        sample = xpx.atleast_nd(sample, ndim=1)
+        if sample.shape[axis] <= 1:
+            raise ValueError("each sample in `data` must contain two or more "
+                             "observations along `axis`.")
+        sample = xp.moveaxis(sample, axis_int, -1)
+        data_iv.append(sample)
+
+    n_resamples_int = (int(n_resamples) if not math.isinf(n_resamples)
+                       else xp.inf)
+    if n_resamples != n_resamples_int or n_resamples_int <= 0:
+        raise ValueError("`n_resamples` must be a positive integer.")
+
+    if batch is None:
+        batch_iv = batch
+    else:
+        batch_iv = int(batch)
+        if batch != batch_iv or batch_iv <= 0:
+            raise ValueError("`batch` must be a positive integer or None.")
+
+    alternatives = {'two-sided', 'greater', 'less'}
+    alternative = alternative.lower()
+    if alternative not in alternatives:
+        raise ValueError(f"`alternative` must be in {alternatives}")
+
+    rng = check_random_state(rng)
+
+    float_dtype = xp_result_type(*data_iv, force_floating=True, xp=xp)
+
+    return (data_iv, statistic, permutation_type, vectorized, n_resamples_int,
+            batch_iv, alternative, axis_int, rng, float_dtype, xp)
+
+
+@xp_capabilities(skip_backends=[('dask.array', "lacks required indexing capabilities")])
+@_transition_to_rng('random_state')
+def permutation_test(data, statistic, *, permutation_type='independent',
+                     vectorized=None, n_resamples=9999, batch=None,
+                     alternative="two-sided", axis=0, rng=None):
+    r"""
+    Performs a permutation test of a given statistic on provided data.
+
+    For independent sample statistics, the null hypothesis is that the data are
+    randomly sampled from the same distribution.
+    For paired sample statistics, two null hypothesis can be tested:
+    that the data are paired at random or that the data are assigned to samples
+    at random.
+
+    Parameters
+    ----------
+    data : iterable of array-like
+        Contains the samples, each of which is an array of observations.
+        Dimensions of sample arrays must be compatible for broadcasting except
+        along `axis`.
+    statistic : callable
+        Statistic for which the p-value of the hypothesis test is to be
+        calculated. `statistic` must be a callable that accepts samples
+        as separate arguments (e.g. ``statistic(*data)``) and returns the
+        resulting statistic.
+        If `vectorized` is set ``True``, `statistic` must also accept a keyword
+        argument `axis` and be vectorized to compute the statistic along the
+        provided `axis` of the sample arrays.
+    permutation_type : {'independent', 'samples', 'pairings'}, optional
+        The type of permutations to be performed, in accordance with the
+        null hypothesis. The first two permutation types are for paired sample
+        statistics, in which all samples contain the same number of
+        observations and observations with corresponding indices along `axis`
+        are considered to be paired; the third is for independent sample
+        statistics.
+
+        - ``'samples'`` : observations are assigned to different samples
+          but remain paired with the same observations from other samples.
+          This permutation type is appropriate for paired sample hypothesis
+          tests such as the Wilcoxon signed-rank test and the paired t-test.
+        - ``'pairings'`` : observations are paired with different observations,
+          but they remain within the same sample. This permutation type is
+          appropriate for association/correlation tests with statistics such
+          as Spearman's :math:`\rho`, Kendall's :math:`\tau`, and Pearson's
+          :math:`r`.
+        - ``'independent'`` (default) : observations are assigned to different
+          samples. Samples may contain different numbers of observations. This
+          permutation type is appropriate for independent sample hypothesis
+          tests such as the Mann-Whitney :math:`U` test and the independent
+          sample t-test.
+
+          Please see the Notes section below for more detailed descriptions
+          of the permutation types.
+
+    vectorized : bool, optional
+        If `vectorized` is set ``False``, `statistic` will not be passed
+        keyword argument `axis` and is expected to calculate the statistic
+        only for 1D samples. If ``True``, `statistic` will be passed keyword
+        argument `axis` and is expected to calculate the statistic along `axis`
+        when passed an ND sample array. If ``None`` (default), `vectorized`
+        will be set ``True`` if ``axis`` is a parameter of `statistic`. Use
+        of a vectorized statistic typically reduces computation time.
+    n_resamples : int or np.inf, default: 9999
+        Number of random permutations (resamples) used to approximate the null
+        distribution. If greater than or equal to the number of distinct
+        permutations, the exact null distribution will be computed.
+        Note that the number of distinct permutations grows very rapidly with
+        the sizes of samples, so exact tests are feasible only for very small
+        data sets.
+    batch : int, optional
+        The number of permutations to process in each call to `statistic`.
+        Memory usage is O( `batch` * ``n`` ), where ``n`` is the total size
+        of all samples, regardless of the value of `vectorized`. Default is
+        ``None``, in which case ``batch`` is the number of permutations.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        The alternative hypothesis for which the p-value is calculated.
+        For each alternative, the p-value is defined for exact tests as
+        follows.
+
+        - ``'greater'`` : the percentage of the null distribution that is
+          greater than or equal to the observed value of the test statistic.
+        - ``'less'`` : the percentage of the null distribution that is
+          less than or equal to the observed value of the test statistic.
+        - ``'two-sided'`` (default) : twice the smaller of the p-values above.
+
+        Note that p-values for randomized tests are calculated according to the
+        conservative (over-estimated) approximation suggested in [2]_ and [3]_
+        rather than the unbiased estimator suggested in [4]_. That is, when
+        calculating the proportion of the randomized null distribution that is
+        as extreme as the observed value of the test statistic, the values in
+        the numerator and denominator are both increased by one. An
+        interpretation of this adjustment is that the observed value of the
+        test statistic is always included as an element of the randomized
+        null distribution.
+        The convention used for two-sided p-values is not universal;
+        the observed test statistic and null distribution are returned in
+        case a different definition is preferred.
+
+    axis : int, default: 0
+        The axis of the (broadcasted) samples over which to calculate the
+        statistic. If samples have a different number of dimensions,
+        singleton dimensions are prepended to samples with fewer dimensions
+        before `axis` is considered.
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+    Returns
+    -------
+    res : PermutationTestResult
+        An object with attributes:
+
+        statistic : float or ndarray
+            The observed test statistic of the data.
+        pvalue : float or ndarray
+            The p-value for the given alternative.
+        null_distribution : ndarray
+            The values of the test statistic generated under the null
+            hypothesis.
+
+    Notes
+    -----
+
+    The three types of permutation tests supported by this function are
+    described below.
+
+    **Unpaired statistics** (``permutation_type='independent'``):
+
+    The null hypothesis associated with this permutation type is that all
+    observations are sampled from the same underlying distribution and that
+    they have been assigned to one of the samples at random.
+
+    Suppose ``data`` contains two samples; e.g. ``a, b = data``.
+    When ``1 < n_resamples < binom(n, k)``, where
+
+    * ``k`` is the number of observations in ``a``,
+    * ``n`` is the total number of observations in ``a`` and ``b``, and
+    * ``binom(n, k)`` is the binomial coefficient (``n`` choose ``k``),
+
+    the data are pooled (concatenated), randomly assigned to either the first
+    or second sample, and the statistic is calculated. This process is
+    performed repeatedly, `permutation` times, generating a distribution of the
+    statistic under the null hypothesis. The statistic of the original
+    data is compared to this distribution to determine the p-value.
+
+    When ``n_resamples >= binom(n, k)``, an exact test is performed: the data
+    are *partitioned* between the samples in each distinct way exactly once,
+    and the exact null distribution is formed.
+    Note that for a given partitioning of the data between the samples,
+    only one ordering/permutation of the data *within* each sample is
+    considered. For statistics that do not depend on the order of the data
+    within samples, this dramatically reduces computational cost without
+    affecting the shape of the null distribution (because the frequency/count
+    of each value is affected by the same factor).
+
+    For ``a = [a1, a2, a3, a4]`` and ``b = [b1, b2, b3]``, an example of this
+    permutation type is ``x = [b3, a1, a2, b2]`` and ``y = [a4, b1, a3]``.
+    Because only one ordering/permutation of the data *within* each sample
+    is considered in an exact test, a resampling like ``x = [b3, a1, b2, a2]``
+    and ``y = [a4, a3, b1]`` would *not* be considered distinct from the
+    example above.
+
+    ``permutation_type='independent'`` does not support one-sample statistics,
+    but it can be applied to statistics with more than two samples. In this
+    case, if ``n`` is an array of the number of observations within each
+    sample, the number of distinct partitions is::
+
+        np.prod([binom(sum(n[i:]), sum(n[i+1:])) for i in range(len(n)-1)])
+
+    **Paired statistics, permute pairings** (``permutation_type='pairings'``):
+
+    The null hypothesis associated with this permutation type is that
+    observations within each sample are drawn from the same underlying
+    distribution and that pairings with elements of other samples are
+    assigned at random.
+
+    Suppose ``data`` contains only one sample; e.g. ``a, = data``, and we
+    wish to consider all possible pairings of elements of ``a`` with elements
+    of a second sample, ``b``. Let ``n`` be the number of observations in
+    ``a``, which must also equal the number of observations in ``b``.
+
+    When ``1 < n_resamples < factorial(n)``, the elements of ``a`` are
+    randomly permuted. The user-supplied statistic accepts one data argument,
+    say ``a_perm``, and calculates the statistic considering ``a_perm`` and
+    ``b``. This process is performed repeatedly, `permutation` times,
+    generating a distribution of the statistic under the null hypothesis.
+    The statistic of the original data is compared to this distribution to
+    determine the p-value.
+
+    When ``n_resamples >= factorial(n)``, an exact test is performed:
+    ``a`` is permuted in each distinct way exactly once. Therefore, the
+    `statistic` is computed for each unique pairing of samples between ``a``
+    and ``b`` exactly once.
+
+    For ``a = [a1, a2, a3]`` and ``b = [b1, b2, b3]``, an example of this
+    permutation type is ``a_perm = [a3, a1, a2]`` while ``b`` is left
+    in its original order.
+
+    ``permutation_type='pairings'`` supports ``data`` containing any number
+    of samples, each of which must contain the same number of observations.
+    All samples provided in ``data`` are permuted *independently*. Therefore,
+    if ``m`` is the number of samples and ``n`` is the number of observations
+    within each sample, then the number of permutations in an exact test is::
+
+        factorial(n)**m
+
+    Note that if a two-sample statistic, for example, does not inherently
+    depend on the order in which observations are provided - only on the
+    *pairings* of observations - then only one of the two samples should be
+    provided in ``data``. This dramatically reduces computational cost without
+    affecting the shape of the null distribution (because the frequency/count
+    of each value is affected by the same factor).
+
+    **Paired statistics, permute samples** (``permutation_type='samples'``):
+
+    The null hypothesis associated with this permutation type is that
+    observations within each pair are drawn from the same underlying
+    distribution and that the sample to which they are assigned is random.
+
+    Suppose ``data`` contains two samples; e.g. ``a, b = data``.
+    Let ``n`` be the number of observations in ``a``, which must also equal
+    the number of observations in ``b``.
+
+    When ``1 < n_resamples < 2**n``, the elements of ``a`` are ``b`` are
+    randomly swapped between samples (maintaining their pairings) and the
+    statistic is calculated. This process is performed repeatedly,
+    `permutation` times,  generating a distribution of the statistic under the
+    null hypothesis. The statistic of the original data is compared to this
+    distribution to determine the p-value.
+
+    When ``n_resamples >= 2**n``, an exact test is performed: the observations
+    are assigned to the two samples in each distinct way (while maintaining
+    pairings) exactly once.
+
+    For ``a = [a1, a2, a3]`` and ``b = [b1, b2, b3]``, an example of this
+    permutation type is ``x = [b1, a2, b3]`` and ``y = [a1, b2, a3]``.
+
+    ``permutation_type='samples'`` supports ``data`` containing any number
+    of samples, each of which must contain the same number of observations.
+    If ``data`` contains more than one sample, paired observations within
+    ``data`` are exchanged between samples *independently*. Therefore, if ``m``
+    is the number of samples and ``n`` is the number of observations within
+    each sample, then the number of permutations in an exact test is::
+
+        factorial(m)**n
+
+    Several paired-sample statistical tests, such as the Wilcoxon signed rank
+    test and paired-sample t-test, can be performed considering only the
+    *difference* between two paired elements. Accordingly, if ``data`` contains
+    only one sample, then the null distribution is formed by independently
+    changing the *sign* of each observation.
+
+    .. warning::
+        The p-value is calculated by counting the elements of the null
+        distribution that are as extreme or more extreme than the observed
+        value of the statistic. Due to the use of finite precision arithmetic,
+        some statistic functions return numerically distinct values when the
+        theoretical values would be exactly equal. In some cases, this could
+        lead to a large error in the calculated p-value. `permutation_test`
+        guards against this by considering elements in the null distribution
+        that are "close" (within a relative tolerance of 100 times the
+        floating point epsilon of inexact dtypes) to the observed
+        value of the test statistic as equal to the observed value of the
+        test statistic. However, the user is advised to inspect the null
+        distribution to assess whether this method of comparison is
+        appropriate, and if not, calculate the p-value manually. See example
+        below.
+
+    References
+    ----------
+
+    .. [1] R. A. Fisher. The Design of Experiments, 6th Ed (1951).
+    .. [2] B. Phipson and G. K. Smyth. "Permutation P-values Should Never Be
+       Zero: Calculating Exact P-values When Permutations Are Randomly Drawn."
+       Statistical Applications in Genetics and Molecular Biology 9.1 (2010).
+    .. [3] M. D. Ernst. "Permutation Methods: A Basis for Exact Inference".
+       Statistical Science (2004).
+    .. [4] B. Efron and R. J. Tibshirani. An Introduction to the Bootstrap
+       (1993).
+
+    Examples
+    --------
+
+    Suppose we wish to test whether two samples are drawn from the same
+    distribution. Assume that the underlying distributions are unknown to us,
+    and that before observing the data, we hypothesized that the mean of the
+    first sample would be less than that of the second sample. We decide that
+    we will use the difference between the sample means as a test statistic,
+    and we will consider a p-value of 0.05 to be statistically significant.
+
+    For efficiency, we write the function defining the test statistic in a
+    vectorized fashion: the samples ``x`` and ``y`` can be ND arrays, and the
+    statistic will be calculated for each axis-slice along `axis`.
+
+    >>> import numpy as np
+    >>> def statistic(x, y, axis):
+    ...     return np.mean(x, axis=axis) - np.mean(y, axis=axis)
+
+    After collecting our data, we calculate the observed value of the test
+    statistic.
+
+    >>> from scipy.stats import norm
+    >>> rng = np.random.default_rng()
+    >>> x = norm.rvs(size=5, random_state=rng)
+    >>> y = norm.rvs(size=6, loc = 3, random_state=rng)
+    >>> statistic(x, y, 0)
+    -3.5411688580987266
+
+    Indeed, the test statistic is negative, suggesting that the true mean of
+    the distribution underlying ``x`` is less than that of the distribution
+    underlying ``y``. To determine the probability of this occurring by chance
+    if the two samples were drawn from the same distribution, we perform
+    a permutation test.
+
+    >>> from scipy.stats import permutation_test
+    >>> # because our statistic is vectorized, we pass `vectorized=True`
+    >>> # `n_resamples=np.inf` indicates that an exact test is to be performed
+    >>> res = permutation_test((x, y), statistic, vectorized=True,
+    ...                        n_resamples=np.inf, alternative='less')
+    >>> print(res.statistic)
+    -3.5411688580987266
+    >>> print(res.pvalue)
+    0.004329004329004329
+
+    The probability of obtaining a test statistic less than or equal to the
+    observed value under the null hypothesis is 0.4329%. This is less than our
+    chosen threshold of 5%, so we consider this to be significant evidence
+    against the null hypothesis in favor of the alternative.
+
+    Because the size of the samples above was small, `permutation_test` could
+    perform an exact test. For larger samples, we resort to a randomized
+    permutation test.
+
+    >>> x = norm.rvs(size=100, random_state=rng)
+    >>> y = norm.rvs(size=120, loc=0.2, random_state=rng)
+    >>> res = permutation_test((x, y), statistic, n_resamples=9999,
+    ...                        vectorized=True, alternative='less',
+    ...                        rng=rng)
+    >>> print(res.statistic)
+    -0.4230459671240913
+    >>> print(res.pvalue)
+    0.0015
+
+    The approximate probability of obtaining a test statistic less than or
+    equal to the observed value under the null hypothesis is 0.0225%. This is
+    again less than our chosen threshold of 5%, so again we have significant
+    evidence to reject the null hypothesis in favor of the alternative.
+
+    For large samples and number of permutations, the result is comparable to
+    that of the corresponding asymptotic test, the independent sample t-test.
+
+    >>> from scipy.stats import ttest_ind
+    >>> res_asymptotic = ttest_ind(x, y, alternative='less')
+    >>> print(res_asymptotic.pvalue)
+    0.0014669545224902675
+
+    The permutation distribution of the test statistic is provided for
+    further investigation.
+
+    >>> import matplotlib.pyplot as plt
+    >>> plt.hist(res.null_distribution, bins=50)
+    >>> plt.title("Permutation distribution of test statistic")
+    >>> plt.xlabel("Value of Statistic")
+    >>> plt.ylabel("Frequency")
+    >>> plt.show()
+
+    Inspection of the null distribution is essential if the statistic suffers
+    from inaccuracy due to limited machine precision. Consider the following
+    case:
+
+    >>> from scipy.stats import pearsonr
+    >>> x = [1, 2, 4, 3]
+    >>> y = [2, 4, 6, 8]
+    >>> def statistic(x, y, axis=-1):
+    ...     return pearsonr(x, y, axis=axis).statistic
+    >>> res = permutation_test((x, y), statistic, vectorized=True,
+    ...                        permutation_type='pairings',
+    ...                        alternative='greater')
+    >>> r, pvalue, null = res.statistic, res.pvalue, res.null_distribution
+
+    In this case, some elements of the null distribution differ from the
+    observed value of the correlation coefficient ``r`` due to numerical noise.
+    We manually inspect the elements of the null distribution that are nearly
+    the same as the observed value of the test statistic.
+
+    >>> r
+    0.7999999999999999
+    >>> unique = np.unique(null)
+    >>> unique
+    array([-1. , -1. , -0.8, -0.8, -0.8, -0.6, -0.4, -0.4, -0.2, -0.2, -0.2,
+        0. ,  0.2,  0.2,  0.2,  0.4,  0.4,  0.6,  0.8,  0.8,  0.8,  1. ,
+        1. ])  # may vary
+    >>> unique[np.isclose(r, unique)].tolist()
+    [0.7999999999999998, 0.7999999999999999, 0.8]  # may vary
+
+    If `permutation_test` were to perform the comparison naively, the
+    elements of the null distribution with value ``0.7999999999999998`` would
+    not be considered as extreme or more extreme as the observed value of the
+    statistic, so the calculated p-value would be too small.
+
+    >>> incorrect_pvalue = np.count_nonzero(null >= r) / len(null)
+    >>> incorrect_pvalue
+    0.14583333333333334  # may vary
+
+    Instead, `permutation_test` treats elements of the null distribution that
+    are within ``max(1e-14, abs(r)*1e-14)`` of the observed value of the
+    statistic ``r`` to be equal to ``r``.
+
+    >>> correct_pvalue = np.count_nonzero(null >= r - 1e-14) / len(null)
+    >>> correct_pvalue
+    0.16666666666666666
+    >>> res.pvalue == correct_pvalue
+    True
+
+    This method of comparison is expected to be accurate in most practical
+    situations, but the user is advised to assess this by inspecting the
+    elements of the null distribution that are close to the observed value
+    of the statistic. Also, consider the use of statistics that can be
+    calculated using exact arithmetic (e.g. integer statistics).
+
+    """
+    args = _permutation_test_iv(data, statistic, permutation_type, vectorized,
+                                n_resamples, batch, alternative, axis,
+                                rng)
+    (data, statistic, permutation_type, vectorized, n_resamples, batch,
+     alternative, axis, rng, float_dtype, xp) = args
+
+    observed = statistic(*data, axis=-1)
+
+    null_calculators = {"pairings": _calculate_null_pairings,
+                        "samples": _calculate_null_samples,
+                        "independent": _calculate_null_both}
+    null_calculator_args = (data, statistic, n_resamples,
+                            batch, rng)
+    calculate_null = null_calculators[permutation_type]
+    null_distribution, n_resamples, exact_test = (
+        calculate_null(*null_calculator_args, xp=xp))
+
+    # See References [2] and [3]
+    adjustment = 0 if exact_test else 1
+
+    # relative tolerance for detecting numerically distinct but
+    # theoretically equal values in the null distribution
+    eps =  (0 if not xp.isdtype(observed.dtype, 'real floating')
+            else xp.finfo(observed.dtype).eps*100)
+    gamma = xp.abs(eps * observed)
+
+    def less(null_distribution, observed):
+        cmps = null_distribution <= observed + gamma
+        count = xp.count_nonzero(cmps, axis=0) + adjustment
+        pvalues = xp.astype(count, float_dtype) / (n_resamples + adjustment)
+        return pvalues
+
+    def greater(null_distribution, observed):
+        cmps = null_distribution >= observed - gamma
+        count = xp.count_nonzero(cmps, axis=0) + adjustment
+        pvalues = xp.astype(count, float_dtype) / (n_resamples + adjustment)
+        return pvalues
+
+    def two_sided(null_distribution, observed):
+        pvalues_less = less(null_distribution, observed)
+        pvalues_greater = greater(null_distribution, observed)
+        pvalues = xp.minimum(pvalues_less, pvalues_greater) * 2
+        return pvalues
+
+    compare = {"less": less,
+               "greater": greater,
+               "two-sided": two_sided}
+
+    pvalues = compare[alternative](null_distribution, observed)
+    pvalues = xpx.at(pvalues)[xp.isnan(observed)].set(xp.nan)
+    pvalues = xp.clip(pvalues, 0., 1.)
+
+    return PermutationTestResult(observed, pvalues, null_distribution)
+
+
+@dataclass
+class ResamplingMethod:
+    """Configuration information for a statistical resampling method.
+
+    Instances of this class can be passed into the `method` parameter of some
+    hypothesis test functions to perform a resampling or Monte Carlo version
+    of the hypothesis test.
+
+    Attributes
+    ----------
+    n_resamples : int
+        The number of resamples to perform or Monte Carlo samples to draw.
+    batch : int, optional
+        The number of resamples to process in each vectorized call to
+        the statistic. Batch sizes >>1 tend to be faster when the statistic
+        is vectorized, but memory usage scales linearly with the batch size.
+        Default is ``None``, which processes all resamples in a single batch.
+
+    """
+    n_resamples: int = 9999
+    batch: int = None  # type: ignore[assignment]
+
+
+@dataclass
+class MonteCarloMethod(ResamplingMethod):
+    """Configuration information for a Monte Carlo hypothesis test.
+
+    Instances of this class can be passed into the `method` parameter of some
+    hypothesis test functions to perform a Monte Carlo version of the
+    hypothesis tests.
+
+    Parameters
+    ----------
+    n_resamples : int, optional
+        The number of Monte Carlo samples to draw. Default is 9999.
+    batch : int, optional
+        The number of Monte Carlo samples to process in each vectorized call to
+        the statistic. Batch sizes >>1 tend to be faster when the statistic
+        is vectorized, but memory usage scales linearly with the batch size.
+        Default is ``None``, which processes all samples in a single batch.
+    rvs : callable or tuple of callables, optional
+        A callable or sequence of callables that generates random variates
+        under the null hypothesis. Each element of `rvs` must be a callable
+        that accepts keyword argument ``size`` (e.g. ``rvs(size=(m, n))``) and
+        returns an N-d array sample of that shape. If `rvs` is a sequence, the
+        number of callables in `rvs` must match the number of samples passed
+        to the hypothesis test in which the `MonteCarloMethod` is used. Default
+        is ``None``, in which case the hypothesis test function chooses values
+        to match the standard version of the hypothesis test. For example,
+        the null hypothesis of `scipy.stats.pearsonr` is typically that the
+        samples are drawn from the standard normal distribution, so
+        ``rvs = (rng.normal, rng.normal)`` where
+        ``rng = np.random.default_rng()``.
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+    """
+    rvs: object = None
+    rng: object = None
+
+    def __init__(self, n_resamples=9999, batch=None, rvs=None, rng=None):
+        if (rvs is not None) and (rng is not None):
+            message = 'Use of `rvs` and `rng` are mutually exclusive.'
+            raise ValueError(message)
+
+        self.n_resamples = n_resamples
+        self.batch = batch
+        self.rvs = rvs
+        self.rng = rng
+
+    def _asdict(self):
+        # `dataclasses.asdict` deepcopies; we don't want that.
+        return dict(n_resamples=self.n_resamples, batch=self.batch,
+                    rvs=self.rvs, rng=self.rng)
+
+
+_rs_deprecation = ("Use of attribute `random_state` is deprecated and replaced by "
+                   "`rng`. Support for `random_state` will be removed in SciPy 1.19.0. "
+                   "To silence this warning and ensure consistent behavior in SciPy "
+                   "1.19.0, control the RNG using attribute `rng`. Values set using "
+                   "attribute `rng` will be validated by `np.random.default_rng`, so "
+                   "the behavior corresponding with a given value may change compared "
+                   "to use of `random_state`. For example, 1) `None` will result in "
+                   "unpredictable random numbers, 2) an integer will result in a "
+                   "different stream of random numbers, (with the same distribution), "
+                   "and 3) `np.random` or `RandomState` instances will result in an "
+                   "error. See the documentation of `default_rng` for more "
+                   "information.")
+
+
+@dataclass
+class PermutationMethod(ResamplingMethod):
+    """Configuration information for a permutation hypothesis test.
+
+    Instances of this class can be passed into the `method` parameter of some
+    hypothesis test functions to perform a permutation version of the
+    hypothesis tests.
+
+    Parameters
+    ----------
+    n_resamples : int, optional
+        The number of resamples to perform. Default is 9999.
+    batch : int, optional
+        The number of resamples to process in each vectorized call to
+        the statistic. Batch sizes >>1 tend to be faster when the statistic
+        is vectorized, but memory usage scales linearly with the batch size.
+        Default is ``None``, which processes all resamples in a single batch.
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator used to perform resampling.
+
+        If `rng` is passed by keyword to the initializer or the `rng` attribute is used
+        directly, types other than `numpy.random.Generator` are passed to
+        `numpy.random.default_rng` to instantiate a ``Generator`` before use.
+        If `rng` is already a ``Generator`` instance, then the provided instance is
+        used. Specify `rng` for repeatable behavior.
+
+        If this argument is passed by position, if `random_state` is passed by keyword
+        into the initializer, or if the `random_state` attribute is used directly,
+        legacy behavior for `random_state` applies:
+
+        - If `random_state` is None (or `numpy.random`), the `numpy.random.RandomState`
+          singleton is used.
+        - If `random_state` is an int, a new ``RandomState`` instance is used,
+          seeded with `random_state`.
+        - If `random_state` is already a ``Generator`` or ``RandomState`` instance then
+          that instance is used.
+
+        .. versionchanged:: 1.15.0
+
+            As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
+            transition from use of `numpy.random.RandomState` to
+            `numpy.random.Generator`, this attribute name was changed from
+            `random_state` to `rng`. For an interim period, both names will continue to
+            work, although only one may be specified at a time. After the interim
+            period, uses of `random_state` will emit warnings. The behavior of both
+            `random_state` and `rng` are outlined above, but only `rng` should be used
+            in new code.
+
+    """
+    rng: object  # type: ignore[misc]
+    _rng: object = field(init=False, repr=False, default=None)  # type: ignore[assignment]
+
+    @property
+    def random_state(self):
+        # Uncomment in SciPy 1.17.0
+        # warnings.warn(_rs_deprecation, DeprecationWarning, stacklevel=2)
+        return self._random_state
+
+    @random_state.setter
+    def random_state(self, val):
+        # Uncomment in SciPy 1.17.0
+        # warnings.warn(_rs_deprecation, DeprecationWarning, stacklevel=2)
+        self._random_state = val
+
+    @property  # type: ignore[no-redef]
+    def rng(self):  # noqa: F811
+        return self._rng
+
+    def __init__(self, n_resamples=9999, batch=None, random_state=None, *, rng=None):
+        # Uncomment in SciPy 1.17.0
+        # warnings.warn(_rs_deprecation.replace('attribute', 'argument'),
+        #               DeprecationWarning, stacklevel=2)
+        self._rng = rng
+        self._random_state = random_state
+        super().__init__(n_resamples=n_resamples, batch=batch)
+
+    def _asdict(self):
+        # `dataclasses.asdict` deepcopies; we don't want that.
+        d = dict(n_resamples=self.n_resamples, batch=self.batch)
+        if self.rng is not None:
+            d['rng'] = self.rng
+        if self.random_state is not None:
+            d['random_state'] = self.random_state
+        return d
+
+
+@dataclass
+class BootstrapMethod(ResamplingMethod):
+    """Configuration information for a bootstrap confidence interval.
+
+    Instances of this class can be passed into the `method` parameter of some
+    confidence interval methods to generate a bootstrap confidence interval.
+
+    Parameters
+    ----------
+    n_resamples : int, optional
+        The number of resamples to perform. Default is 9999.
+    batch : int, optional
+        The number of resamples to process in each vectorized call to
+        the statistic. Batch sizes >>1 tend to be faster when the statistic
+        is vectorized, but memory usage scales linearly with the batch size.
+        Default is ``None``, which processes all resamples in a single batch.
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator used to perform resampling.
+
+        If `rng` is passed by keyword to the initializer or the `rng` attribute is used
+        directly, types other than `numpy.random.Generator` are passed to
+        `numpy.random.default_rng` to instantiate a ``Generator``  before use.
+        If `rng` is already a ``Generator`` instance, then the provided instance is
+        used. Specify `rng` for repeatable behavior.
+
+        If this argument is passed by position, if `random_state` is passed by keyword
+        into the initializer, or if the `random_state` attribute is used directly,
+        legacy behavior for `random_state` applies:
+
+        - If `random_state` is None (or `numpy.random`), the `numpy.random.RandomState`
+          singleton is used.
+        - If `random_state` is an int, a new ``RandomState`` instance is used,
+          seeded with `random_state`.
+        - If `random_state` is already a ``Generator`` or ``RandomState`` instance then
+          that instance is used.
+
+        .. versionchanged:: 1.15.0
+
+            As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
+            transition from use of `numpy.random.RandomState` to
+            `numpy.random.Generator`, this attribute name was changed from
+            `random_state` to `rng`. For an interim period, both names will continue to
+            work, although only one may be specified at a time. After the interim
+            period, uses of `random_state` will emit warnings. The behavior of both
+            `random_state` and `rng` are outlined above, but only `rng` should be used
+            in new code.
+
+    method : {'BCa', 'percentile', 'basic'}
+        Whether to use the 'percentile' bootstrap ('percentile'), the 'basic'
+        (AKA 'reverse') bootstrap ('basic'), or the bias-corrected and
+        accelerated bootstrap ('BCa', default).
+
+    """
+    rng: object  # type: ignore[misc]
+    _rng: object = field(init=False, repr=False, default=None)  # type: ignore[assignment]
+    method: str = 'BCa'
+
+    @property
+    def random_state(self):
+        # Uncomment in SciPy 1.17.0
+        # warnings.warn(_rs_deprecation, DeprecationWarning, stacklevel=2)
+        return self._random_state
+
+    @random_state.setter
+    def random_state(self, val):
+        # Uncomment in SciPy 1.17.0
+        # warnings.warn(_rs_deprecation, DeprecationWarning, stacklevel=2)
+        self._random_state = val
+
+    @property  # type: ignore[no-redef]
+    def rng(self):  # noqa: F811
+        return self._rng
+
+    def __init__(self, n_resamples=9999, batch=None, random_state=None,
+                 method='BCa', *, rng=None):
+        # Uncomment in SciPy 1.17.0
+        # warnings.warn(_rs_deprecation.replace('attribute', 'argument'),
+        #               DeprecationWarning, stacklevel=2)
+        self._rng = rng  # don't validate with `default_rng`
+        self._random_state = random_state
+        self.method = method
+        super().__init__(n_resamples=n_resamples, batch=batch)
+
+    def _asdict(self):
+        # `dataclasses.asdict` deepcopies; we don't want that.
+        d = dict(n_resamples=self.n_resamples, batch=self.batch,
+                 method=self.method)
+        if self.rng is not None:
+            d['rng'] = self.rng
+        if self.random_state is not None:
+            d['random_state'] = self.random_state
+        return d
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_result_classes.py b/.venv/lib/python3.12/site-packages/scipy/stats/_result_classes.py
new file mode 100644
index 0000000000000000000000000000000000000000..975af9310efb0c9a414439fd8d531fb95c988951
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_result_classes.py
@@ -0,0 +1,40 @@
+# This module exists only to allow Sphinx to generate docs
+# for the result objects returned by some functions in stats
+# _without_ adding them to the main stats documentation page.
+
+"""
+Result classes
+--------------
+
+.. currentmodule:: scipy.stats._result_classes
+
+.. autosummary::
+   :toctree: generated/
+
+   RelativeRiskResult
+   BinomTestResult
+   TukeyHSDResult
+   DunnettResult
+   PearsonRResult
+   FitResult
+   OddsRatioResult
+   TtestResult
+   ECDFResult
+   EmpiricalDistributionFunction
+
+"""
+
+__all__ = ['BinomTestResult', 'RelativeRiskResult', 'TukeyHSDResult',
+           'PearsonRResult', 'FitResult', 'OddsRatioResult',
+           'TtestResult', 'DunnettResult', 'ECDFResult',
+           'EmpiricalDistributionFunction']
+
+
+from ._binomtest import BinomTestResult
+from ._odds_ratio import OddsRatioResult
+from ._relative_risk import RelativeRiskResult
+from ._hypotests import TukeyHSDResult
+from ._multicomp import DunnettResult
+from ._stats_py import PearsonRResult, TtestResult
+from ._fit import FitResult
+from ._survival import ECDFResult, EmpiricalDistributionFunction
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_sampling.py b/.venv/lib/python3.12/site-packages/scipy/stats/_sampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c0abd14bb9e7f14bb3ae98cb13bcb2b6d8144c7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_sampling.py
@@ -0,0 +1,1319 @@
+import math
+import numbers
+import numpy as np
+from scipy import stats
+from scipy import special as sc
+from ._qmc import (check_random_state as check_random_state_qmc,
+                   Halton, QMCEngine)
+from ._unuran.unuran_wrapper import NumericalInversePolynomial
+from scipy._lib._util import check_random_state
+
+
+__all__ = ['FastGeneratorInversion', 'RatioUniforms']
+
+
+# define pdfs and other helper functions to create the generators
+
+def argus_pdf(x, chi):
+    # approach follows Baumgarten/Hoermann: Generating ARGUS random variates
+    # for chi > 5, use relationship of the ARGUS distribution to Gamma(1.5)
+    if chi <= 5:
+        y = 1 - x * x
+        return x * math.sqrt(y) * math.exp(-0.5 * chi**2 * y)
+    return math.sqrt(x) * math.exp(-x)
+
+
+def argus_gamma_trf(x, chi):
+    if chi <= 5:
+        return x
+    return np.sqrt(1.0 - 2 * x / chi**2)
+
+
+def argus_gamma_inv_trf(x, chi):
+    if chi <= 5:
+        return x
+    return 0.5 * chi**2 * (1 - x**2)
+
+
+def betaprime_pdf(x, a, b):
+    if x > 0:
+        logf = (a - 1) * math.log(x) - (a + b) * math.log1p(x) - sc.betaln(a, b)
+        return math.exp(logf)
+    else:
+        # return pdf at x == 0 separately to avoid runtime warnings
+        if a > 1:
+            return 0
+        elif a < 1:
+            return np.inf
+        else:
+            return 1 / sc.beta(a, b)
+
+
+def beta_valid_params(a, b):
+    return (min(a, b) >= 0.1) and (max(a, b) <= 700)
+
+
+def gamma_pdf(x, a):
+    if x > 0:
+        return math.exp(-math.lgamma(a) + (a - 1.0) * math.log(x) - x)
+    else:
+        return 0 if a >= 1 else np.inf
+
+
+def invgamma_pdf(x, a):
+    if x > 0:
+        return math.exp(-(a + 1.0) * math.log(x) - math.lgamma(a) - 1 / x)
+    else:
+        return 0 if a >= 1 else np.inf
+
+
+def burr_pdf(x, cc, dd):
+    # note: we use np.exp instead of math.exp, otherwise an overflow
+    # error can occur in the setup, e.g., for parameters
+    # 1.89128135, 0.30195177, see test test_burr_overflow
+    if x > 0:
+        lx = math.log(x)
+        return np.exp(-(cc + 1) * lx - (dd + 1) * math.log1p(np.exp(-cc * lx)))
+    else:
+        return 0
+
+
+def burr12_pdf(x, cc, dd):
+    if x > 0:
+        lx = math.log(x)
+        logterm = math.log1p(math.exp(cc * lx))
+        return math.exp((cc - 1) * lx - (dd + 1) * logterm + math.log(cc * dd))
+    else:
+        return 0
+
+
+def chi_pdf(x, a):
+    if x > 0:
+        return math.exp(
+            (a - 1) * math.log(x)
+            - 0.5 * (x * x)
+            - (a / 2 - 1) * math.log(2)
+            - math.lgamma(0.5 * a)
+        )
+    else:
+        return 0 if a >= 1 else np.inf
+
+
+def chi2_pdf(x, df):
+    if x > 0:
+        return math.exp(
+            (df / 2 - 1) * math.log(x)
+            - 0.5 * x
+            - (df / 2) * math.log(2)
+            - math.lgamma(0.5 * df)
+        )
+    else:
+        return 0 if df >= 1 else np.inf
+
+
+def alpha_pdf(x, a):
+    if x > 0:
+        return math.exp(-2.0 * math.log(x) - 0.5 * (a - 1.0 / x) ** 2)
+    return 0.0
+
+
+def bradford_pdf(x, c):
+    if 0 <= x <= 1:
+        return 1.0 / (1.0 + c * x)
+    return 0.0
+
+
+def crystalball_pdf(x, b, m):
+    if x > -b:
+        return math.exp(-0.5 * x * x)
+    return math.exp(m * math.log(m / b) - 0.5 * b * b - m * math.log(m / b - b - x))
+
+
+def weibull_min_pdf(x, c):
+    if x > 0:
+        return c * math.exp((c - 1) * math.log(x) - x**c)
+    return 0.0
+
+
+def weibull_max_pdf(x, c):
+    if x < 0:
+        return c * math.exp((c - 1) * math.log(-x) - ((-x) ** c))
+    return 0.0
+
+
+def invweibull_pdf(x, c):
+    if x > 0:
+        return c * math.exp(-(c + 1) * math.log(x) - x ** (-c))
+    return 0.0
+
+
+def wald_pdf(x):
+    if x > 0:
+        return math.exp(-((x - 1) ** 2) / (2 * x)) / math.sqrt(x**3)
+    return 0.0
+
+
+def geninvgauss_mode(p, b):
+    if p > 1:  # equivalent mode formulas numerical more stable versions
+        return (math.sqrt((1 - p) ** 2 + b**2) - (1 - p)) / b
+    return b / (math.sqrt((1 - p) ** 2 + b**2) + (1 - p))
+
+
+def geninvgauss_pdf(x, p, b):
+    m = geninvgauss_mode(p, b)
+    lfm = (p - 1) * math.log(m) - 0.5 * b * (m + 1 / m)
+    if x > 0:
+        return math.exp((p - 1) * math.log(x) - 0.5 * b * (x + 1 / x) - lfm)
+    return 0.0
+
+
+def invgauss_mode(mu):
+    return 1.0 / (math.sqrt(1.5 * 1.5 + 1 / (mu * mu)) + 1.5)
+
+
+def invgauss_pdf(x, mu):
+    m = invgauss_mode(mu)
+    lfm = -1.5 * math.log(m) - (m - mu) ** 2 / (2 * m * mu**2)
+    if x > 0:
+        return math.exp(-1.5 * math.log(x) - (x - mu) ** 2 / (2 * x * mu**2) - lfm)
+    return 0.0
+
+
+def powerlaw_pdf(x, a):
+    if x > 0:
+        return x ** (a - 1)
+    return 0.0
+
+
+# Define a dictionary: for a given distribution (keys), another dictionary
+# (values) specifies the parameters for NumericalInversePolynomial (PINV).
+# The keys of the latter dictionary are:
+# - pdf: the pdf of the distribution (callable). The signature of the pdf
+#   is float -> float (i.e., the function does not have to be vectorized).
+#   If possible, functions like log or exp from the module math should be
+#   preferred over functions from numpy since the PINV setup will be faster
+#   in that case.
+# - check_pinv_params: callable f that returns true if the shape parameters
+#   (args) are recommended parameters for PINV (i.e., the u-error does
+#   not exceed the default tolerance)
+# - center: scalar if the center does not depend on args, otherwise
+#   callable that returns the center as a function of the shape parameters
+# - rvs_transform: a callable that can be used to transform the rvs that
+#   are distributed according to the pdf to the target distribution
+#   (as an example, see the entry for the beta distribution)
+# - rvs_transform_inv: the inverse of rvs_transform (it is required
+#   for the transformed ppf)
+# - mirror_uniform: bool or a callable that returns true or false
+#   depending on the shape parameters. If True, the ppf is applied
+#   to 1-u instead of u to generate rvs, where u is a uniform rv.
+#   While both u and 1-u are uniform, it can be required to use 1-u
+#   to compute the u-error correctly. This is only relevant for the argus
+#   distribution.
+# The only required keys are "pdf" and "check_pinv_params".
+# All other keys are optional.
+
+PINV_CONFIG = {
+    "alpha": {
+        "pdf": alpha_pdf,
+        "check_pinv_params": lambda a: 1.0e-11 <= a < 2.1e5,
+        "center": lambda a: 0.25 * (math.sqrt(a * a + 8.0) - a),
+    },
+    "anglit": {
+        "pdf": lambda x: math.cos(2 * x) + 1.0e-13,
+        # +1.e-13 is necessary, otherwise PINV has strange problems as
+        # f(upper border) is very close to 0
+        "center": 0,
+    },
+    "argus": {
+        "pdf": argus_pdf,
+        "center": lambda chi: 0.7 if chi <= 5 else 0.5,
+        "check_pinv_params": lambda chi: 1e-20 < chi < 901,
+        "rvs_transform": argus_gamma_trf,
+        "rvs_transform_inv": argus_gamma_inv_trf,
+        "mirror_uniform": lambda chi: chi > 5,
+    },
+    "beta": {
+        "pdf": betaprime_pdf,
+        "center": lambda a, b: max(0.1, (a - 1) / (b + 1)),
+        "check_pinv_params": beta_valid_params,
+        "rvs_transform": lambda x, *args: x / (1 + x),
+        "rvs_transform_inv": lambda x, *args: x / (1 - x) if x < 1 else np.inf,
+    },
+    "betaprime": {
+        "pdf": betaprime_pdf,
+        "center": lambda a, b: max(0.1, (a - 1) / (b + 1)),
+        "check_pinv_params": beta_valid_params,
+    },
+    "bradford": {
+        "pdf": bradford_pdf,
+        "check_pinv_params": lambda a: 1.0e-6 <= a <= 1e9,
+        "center": 0.5,
+    },
+    "burr": {
+        "pdf": burr_pdf,
+        "center": lambda a, b: (2 ** (1 / b) - 1) ** (-1 / a),
+        "check_pinv_params": lambda a, b: (min(a, b) >= 0.3) and (max(a, b) <= 50),
+    },
+    "burr12": {
+        "pdf": burr12_pdf,
+        "center": lambda a, b: (2 ** (1 / b) - 1) ** (1 / a),
+        "check_pinv_params": lambda a, b: (min(a, b) >= 0.2) and (max(a, b) <= 50),
+    },
+    "cauchy": {
+        "pdf": lambda x: 1 / (1 + (x * x)),
+        "center": 0,
+    },
+    "chi": {
+        "pdf": chi_pdf,
+        "check_pinv_params": lambda df: 0.05 <= df <= 1.0e6,
+        "center": lambda a: math.sqrt(a),
+    },
+    "chi2": {
+        "pdf": chi2_pdf,
+        "check_pinv_params": lambda df: 0.07 <= df <= 1e6,
+        "center": lambda a: a,
+    },
+    "cosine": {
+        "pdf": lambda x: 1 + math.cos(x),
+        "center": 0,
+    },
+    "crystalball": {
+        "pdf": crystalball_pdf,
+        "check_pinv_params": lambda b, m: (0.01 <= b <= 5.5)
+        and (1.1 <= m <= 75.1),
+        "center": 0.0,
+    },
+    "expon": {
+        "pdf": lambda x: math.exp(-x),
+        "center": 1.0,
+    },
+    "gamma": {
+        "pdf": gamma_pdf,
+        "check_pinv_params": lambda a: 0.04 <= a <= 1e6,
+        "center": lambda a: a,
+    },
+    "gennorm": {
+        "pdf": lambda x, b: math.exp(-abs(x) ** b),
+        "check_pinv_params": lambda b: 0.081 <= b <= 45.0,
+        "center": 0.0,
+    },
+    "geninvgauss": {
+        "pdf": geninvgauss_pdf,
+        "check_pinv_params": lambda p, b: (abs(p) <= 1200.0)
+        and (1.0e-10 <= b <= 1200.0),
+        "center": geninvgauss_mode,
+    },
+    "gumbel_l": {
+        "pdf": lambda x: math.exp(x - math.exp(x)),
+        "center": -0.6,
+    },
+    "gumbel_r": {
+        "pdf": lambda x: math.exp(-x - math.exp(-x)),
+        "center": 0.6,
+    },
+    "hypsecant": {
+        "pdf": lambda x: 1.0 / (math.exp(x) + math.exp(-x)),
+        "center": 0.0,
+    },
+    "invgamma": {
+        "pdf": invgamma_pdf,
+        "check_pinv_params": lambda a: 0.04 <= a <= 1e6,
+        "center": lambda a: 1 / a,
+    },
+    "invgauss": {
+        "pdf": invgauss_pdf,
+        "check_pinv_params": lambda mu: 1.0e-10 <= mu <= 1.0e9,
+        "center": invgauss_mode,
+    },
+    "invweibull": {
+        "pdf": invweibull_pdf,
+        "check_pinv_params": lambda a: 0.12 <= a <= 512,
+        "center": 1.0,
+    },
+    "laplace": {
+        "pdf": lambda x: math.exp(-abs(x)),
+        "center": 0.0,
+    },
+    "logistic": {
+        "pdf": lambda x: math.exp(-x) / (1 + math.exp(-x)) ** 2,
+        "center": 0.0,
+    },
+    "maxwell": {
+        "pdf": lambda x: x * x * math.exp(-0.5 * x * x),
+        "center": 1.41421,
+    },
+    "moyal": {
+        "pdf": lambda x: math.exp(-(x + math.exp(-x)) / 2),
+        "center": 1.2,
+    },
+    "norm": {
+        "pdf": lambda x: math.exp(-x * x / 2),
+        "center": 0.0,
+    },
+    "pareto": {
+        "pdf": lambda x, b: x ** -(b + 1),
+        "center": lambda b: b / (b - 1) if b > 2 else 1.5,
+        "check_pinv_params": lambda b: 0.08 <= b <= 400000,
+    },
+    "powerlaw": {
+        "pdf": powerlaw_pdf,
+        "center": 1.0,
+        "check_pinv_params": lambda a: 0.06 <= a <= 1.0e5,
+    },
+    "t": {
+        "pdf": lambda x, df: (1 + x * x / df) ** (-0.5 * (df + 1)),
+        "check_pinv_params": lambda a: 0.07 <= a <= 1e6,
+        "center": 0.0,
+    },
+    "rayleigh": {
+        "pdf": lambda x: x * math.exp(-0.5 * (x * x)),
+        "center": 1.0,
+    },
+    "semicircular": {
+        "pdf": lambda x: math.sqrt(1.0 - (x * x)),
+        "center": 0,
+    },
+    "wald": {
+        "pdf": wald_pdf,
+        "center": 1.0,
+    },
+    "weibull_max": {
+        "pdf": weibull_max_pdf,
+        "check_pinv_params": lambda a: 0.25 <= a <= 512,
+        "center": -1.0,
+    },
+    "weibull_min": {
+        "pdf": weibull_min_pdf,
+        "check_pinv_params": lambda a: 0.25 <= a <= 512,
+        "center": 1.0,
+    },
+}
+
+
+def _validate_qmc_input(qmc_engine, d, seed):
+    # Input validation for `qmc_engine` and `d`
+    # Error messages for invalid `d` are raised by QMCEngine
+    # we could probably use a stats.qmc.check_qrandom_state
+    if isinstance(qmc_engine, QMCEngine):
+        if d is not None and qmc_engine.d != d:
+            message = "`d` must be consistent with dimension of `qmc_engine`."
+            raise ValueError(message)
+        d = qmc_engine.d if d is None else d
+    elif qmc_engine is None:
+        d = 1 if d is None else d
+        qmc_engine = Halton(d, seed=seed)
+    else:
+        message = (
+            "`qmc_engine` must be an instance of "
+            "`scipy.stats.qmc.QMCEngine` or `None`."
+        )
+        raise ValueError(message)
+
+    return qmc_engine, d
+
+
+class CustomDistPINV:
+    def __init__(self, pdf, args):
+        self._pdf = lambda x: pdf(x, *args)
+
+    def pdf(self, x):
+        return self._pdf(x)
+
+
+class FastGeneratorInversion:
+    """
+    Fast sampling by numerical inversion of the CDF for a large class of
+    continuous distributions in `scipy.stats`.
+
+    Parameters
+    ----------
+    dist : rv_frozen object
+        Frozen distribution object from `scipy.stats`. The list of supported
+        distributions can be found in the Notes section. The shape parameters,
+        `loc` and `scale` used to create the distributions must be scalars.
+        For example, for the Gamma distribution with shape parameter `p`,
+        `p` has to be a float, and for the beta distribution with shape
+        parameters (a, b), both a and b have to be floats.
+    domain : tuple of floats, optional
+        If one wishes to sample from a truncated/conditional distribution,
+        the domain has to be specified.
+        The default is None. In that case, the random variates are not
+        truncated, and the domain is inferred from the support of the
+        distribution.
+    ignore_shape_range : bool, optional
+        If False, shape parameters that are outside of the valid range
+        of values to ensure that the numerical accuracy (see Notes) is
+        high, raise a ValueError. If True, any shape parameters that are valid
+        for the distribution are accepted. This can be useful for testing.
+        The default is False.
+    random_state : {None, int, `numpy.random.Generator`,
+                        `numpy.random.RandomState`}, optional
+
+            A NumPy random number generator or seed for the underlying NumPy
+            random number generator used to generate the stream of uniform
+            random numbers.
+            If `random_state` is None, it uses ``self.random_state``.
+            If `random_state` is an int,
+            ``np.random.default_rng(random_state)`` is used.
+            If `random_state` is already a ``Generator`` or ``RandomState``
+            instance then that instance is used.
+
+    Attributes
+    ----------
+    loc : float
+        The location parameter.
+    random_state : {`numpy.random.Generator`, `numpy.random.RandomState`}
+        The random state used in relevant methods like `rvs` (unless
+        another `random_state` is passed as an argument to these methods).
+    scale : float
+        The scale parameter.
+
+    Methods
+    -------
+    cdf
+    evaluate_error
+    ppf
+    qrvs
+    rvs
+    support
+
+    Notes
+    -----
+    The class creates an object for continuous distributions specified
+    by `dist`. The method `rvs` uses a generator from
+    `scipy.stats.sampling` that is created when the object is instantiated.
+    In addition, the methods `qrvs` and `ppf` are added.
+    `qrvs` generate samples based on quasi-random numbers from
+    `scipy.stats.qmc`. `ppf` is the PPF based on the
+    numerical inversion method in [1]_ (`NumericalInversePolynomial`) that is
+    used to generate random variates.
+
+    Supported distributions (`distname`) are:
+    ``alpha``, ``anglit``, ``argus``, ``beta``, ``betaprime``, ``bradford``,
+    ``burr``, ``burr12``, ``cauchy``, ``chi``, ``chi2``, ``cosine``,
+    ``crystalball``, ``expon``, ``gamma``, ``gennorm``, ``geninvgauss``,
+    ``gumbel_l``, ``gumbel_r``, ``hypsecant``, ``invgamma``, ``invgauss``,
+    ``invweibull``, ``laplace``, ``logistic``, ``maxwell``, ``moyal``,
+    ``norm``, ``pareto``, ``powerlaw``, ``t``, ``rayleigh``, ``semicircular``,
+    ``wald``, ``weibull_max``, ``weibull_min``.
+
+    `rvs` relies on the accuracy of the numerical inversion. If very extreme
+    shape parameters are used, the numerical inversion might not work. However,
+    for all implemented distributions, the admissible shape parameters have
+    been tested, and an error will be raised if the user supplies values
+    outside of the allowed range. The u-error should not exceed 1e-10 for all
+    valid parameters. Note that warnings might be raised even if parameters
+    are within the valid range when the object is instantiated.
+    To check numerical accuracy, the method `evaluate_error` can be used.
+
+    Note that all implemented distributions are also part of `scipy.stats`, and
+    the object created by `FastGeneratorInversion` relies on methods like
+    `ppf`, `cdf` and `pdf` from `rv_frozen`. The main benefit of using this
+    class can be summarized as follows: Once the generator to sample random
+    variates is created in the setup step, sampling and evaluation of
+    the PPF using `ppf` are very fast,
+    and performance is essentially independent of the distribution. Therefore,
+    a substantial speed-up can be achieved for many distributions if large
+    numbers of random variates are required. It is important to know that this
+    fast sampling is achieved by inversion of the CDF. Thus, one uniform
+    random variate is transformed into a non-uniform variate, which is an
+    advantage for several simulation methods, e.g., when
+    the variance reduction methods of common random variates or
+    antithetic variates are be used ([2]_).
+
+    In addition, inversion makes it possible to
+
+    - to use a QMC generator from `scipy.stats.qmc` (method `qrvs`),
+    - to generate random variates truncated to an interval. For example, if
+
+    one aims to sample standard normal random variates from
+    the interval (2, 4), this can be easily achieved by using the parameter
+    `domain`.
+
+    The location and scale that are initially defined by `dist`
+    can be reset without having to rerun the setup
+    step to create the generator that is used for sampling. The relation
+    of the distribution `Y` with `loc` and `scale` to the standard
+    distribution `X` (i.e., ``loc=0`` and ``scale=1``) is given by
+    ``Y = loc + scale * X``.
+
+    References
+    ----------
+    .. [1] Derflinger, Gerhard, Wolfgang Hörmann, and Josef Leydold.
+           "Random variate  generation by numerical inversion when only the
+           density is known." ACM Transactions on Modeling and Computer
+           Simulation (TOMACS) 20.4 (2010): 1-25.
+    .. [2] Hörmann, Wolfgang, Josef Leydold and Gerhard Derflinger.
+           "Automatic nonuniform random number generation."
+           Springer, 2004.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> from scipy.stats.sampling import FastGeneratorInversion
+
+    Let's start with a simple example to illustrate the main features:
+
+    >>> gamma_frozen = stats.gamma(1.5)
+    >>> gamma_dist = FastGeneratorInversion(gamma_frozen)
+    >>> r = gamma_dist.rvs(size=1000)
+
+    The mean should be approximately equal to the shape parameter 1.5:
+
+    >>> r.mean()
+    1.52423591130436  # may vary
+
+    Similarly, we can draw a sample based on quasi-random numbers:
+
+    >>> r = gamma_dist.qrvs(size=1000)
+    >>> r.mean()
+    1.4996639255942914  # may vary
+
+    Compare the PPF against approximation `ppf`.
+
+    >>> q = [0.001, 0.2, 0.5, 0.8, 0.999]
+    >>> np.max(np.abs(gamma_frozen.ppf(q) - gamma_dist.ppf(q)))
+    4.313394796895409e-08
+
+    To confirm that the numerical inversion is accurate, we evaluate the
+    approximation error (u-error), which should be below 1e-10 (for more
+    details, refer to the documentation of `evaluate_error`):
+
+    >>> gamma_dist.evaluate_error()
+    (7.446320551265581e-11, nan)  # may vary
+
+    Note that the location and scale can be changed without instantiating a
+    new generator:
+
+    >>> gamma_dist.loc = 2
+    >>> gamma_dist.scale = 3
+    >>> r = gamma_dist.rvs(size=1000)
+
+    The mean should be approximately 2 + 3*1.5 = 6.5.
+
+    >>> r.mean()
+    6.399549295242894  # may vary
+
+    Let us also illustrate how truncation can be applied:
+
+    >>> trunc_norm = FastGeneratorInversion(stats.norm(), domain=(3, 4))
+    >>> r = trunc_norm.rvs(size=1000)
+    >>> 3 < r.min() < r.max() < 4
+    True
+
+    Check the mean:
+
+    >>> r.mean()
+    3.250433367078603  # may vary
+
+    >>> stats.norm.expect(lb=3, ub=4, conditional=True)
+    3.260454285589997
+
+    In this particular, case, `scipy.stats.truncnorm` could also be used to
+    generate truncated normal random variates.
+
+    """
+
+    def __init__(
+        self,
+        dist,
+        *,
+        domain=None,
+        ignore_shape_range=False,
+        random_state=None,
+    ):
+
+        if isinstance(dist, stats.distributions.rv_frozen):
+            distname = dist.dist.name
+            if distname not in PINV_CONFIG.keys():
+                raise ValueError(
+                    f"Distribution '{distname}' is not supported."
+                    f"It must be one of {list(PINV_CONFIG.keys())}"
+                    )
+        else:
+            raise ValueError("`dist` must be a frozen distribution object")
+
+        loc = dist.kwds.get("loc", 0)
+        scale = dist.kwds.get("scale", 1)
+        args = dist.args
+        if not np.isscalar(loc):
+            raise ValueError("loc must be scalar.")
+        if not np.isscalar(scale):
+            raise ValueError("scale must be scalar.")
+
+        self._frozendist = getattr(stats, distname)(
+            *args,
+            loc=loc,
+            scale=scale,
+        )
+        self._distname = distname
+
+        nargs = np.broadcast_arrays(args)[0].size
+        nargs_expected = self._frozendist.dist.numargs
+        if nargs != nargs_expected:
+            raise ValueError(
+                f"Each of the {nargs_expected} shape parameters must be a "
+                f"scalar, but {nargs} values are provided."
+            )
+
+        self.random_state = random_state
+
+        if domain is None:
+            self._domain = self._frozendist.support()
+            self._p_lower = 0.0
+            self._p_domain = 1.0
+        else:
+            self._domain = domain
+            self._p_lower = self._frozendist.cdf(self._domain[0])
+            _p_domain = self._frozendist.cdf(self._domain[1]) - self._p_lower
+            self._p_domain = _p_domain
+        self._set_domain_adj()
+        self._ignore_shape_range = ignore_shape_range
+
+        # the domain to be passed to NumericalInversePolynomial
+        # define a separate variable since in case of a transformation,
+        # domain_pinv will not be the same as self._domain
+        self._domain_pinv = self._domain
+
+        # get information about the distribution from the config to set up
+        # the generator
+        dist = self._process_config(distname, args)
+
+        if self._rvs_transform_inv is not None:
+            d0 = self._rvs_transform_inv(self._domain[0], *args)
+            d1 = self._rvs_transform_inv(self._domain[1], *args)
+            if d0 > d1:
+                # swap values if transformation if decreasing
+                d0, d1 = d1, d0
+            # only update _domain_pinv and not _domain
+            # _domain refers to the original distribution, _domain_pinv
+            # to the transformed distribution
+            self._domain_pinv = d0, d1
+
+        # self._center has been set by the call self._process_config
+        # check if self._center is inside the transformed domain
+        # _domain_pinv, otherwise move it to the endpoint that is closer
+        if self._center is not None:
+            if self._center < self._domain_pinv[0]:
+                self._center = self._domain_pinv[0]
+            elif self._center > self._domain_pinv[1]:
+                self._center = self._domain_pinv[1]
+
+        self._rng = NumericalInversePolynomial(
+            dist,
+            random_state=self.random_state,
+            domain=self._domain_pinv,
+            center=self._center,
+            )
+
+    @property
+    def random_state(self):
+        return self._random_state
+
+    @random_state.setter
+    def random_state(self, random_state):
+        self._random_state = check_random_state_qmc(random_state)
+
+    @property
+    def loc(self):
+        return self._frozendist.kwds.get("loc", 0)
+
+    @loc.setter
+    def loc(self, loc):
+        if not np.isscalar(loc):
+            raise ValueError("loc must be scalar.")
+        self._frozendist.kwds["loc"] = loc
+        # update the adjusted domain that depends on loc and scale
+        self._set_domain_adj()
+
+    @property
+    def scale(self):
+        return self._frozendist.kwds.get("scale", 0)
+
+    @scale.setter
+    def scale(self, scale):
+        if not np.isscalar(scale):
+            raise ValueError("scale must be scalar.")
+        self._frozendist.kwds["scale"] = scale
+        # update the adjusted domain that depends on loc and scale
+        self._set_domain_adj()
+
+    def _set_domain_adj(self):
+        """ Adjust the domain based on loc and scale. """
+        loc = self.loc
+        scale = self.scale
+        lb = self._domain[0] * scale + loc
+        ub = self._domain[1] * scale + loc
+        self._domain_adj = (lb, ub)
+
+    def _process_config(self, distname, args):
+        cfg = PINV_CONFIG[distname]
+        if "check_pinv_params" in cfg:
+            if not self._ignore_shape_range:
+                if not cfg["check_pinv_params"](*args):
+                    msg = ("No generator is defined for the shape parameters "
+                           f"{args}. Use ignore_shape_range to proceed "
+                           "with the selected values.")
+                    raise ValueError(msg)
+
+        if "center" in cfg.keys():
+            if not np.isscalar(cfg["center"]):
+                self._center = cfg["center"](*args)
+            else:
+                self._center = cfg["center"]
+        else:
+            self._center = None
+        self._rvs_transform = cfg.get("rvs_transform", None)
+        self._rvs_transform_inv = cfg.get("rvs_transform_inv", None)
+        _mirror_uniform = cfg.get("mirror_uniform", None)
+        if _mirror_uniform is None:
+            self._mirror_uniform = False
+        else:
+            self._mirror_uniform = _mirror_uniform(*args)
+
+        return CustomDistPINV(cfg["pdf"], args)
+
+    def rvs(self, size=None):
+        """
+        Sample from the distribution by inversion.
+
+        Parameters
+        ----------
+        size : int or tuple, optional
+            The shape of samples. Default is ``None`` in which case a scalar
+            sample is returned.
+
+        Returns
+        -------
+        rvs : array_like
+            A NumPy array of random variates.
+
+        Notes
+        -----
+        Random variates are generated by numerical inversion of the CDF, i.e.,
+        `ppf` computed by `NumericalInversePolynomial` when the class
+        is instantiated. Note that the
+        default ``rvs`` method of the rv_continuous class is
+        overwritten. Hence, a different stream of random numbers is generated
+        even if the same seed is used.
+        """
+        # note: we cannot use self._rng.rvs directly in case
+        # self._mirror_uniform is true
+        u = self.random_state.uniform(size=size)
+        if self._mirror_uniform:
+            u = 1 - u
+        r = self._rng.ppf(u)
+        if self._rvs_transform is not None:
+            r = self._rvs_transform(r, *self._frozendist.args)
+        return self.loc + self.scale * r
+
+    def ppf(self, q):
+        """
+        Very fast PPF (inverse CDF) of the distribution which
+        is a very close approximation of the exact PPF values.
+
+        Parameters
+        ----------
+        q : array_like
+            Array with probabilities.
+
+        Returns
+        -------
+        ppf : array_like
+            Quantiles corresponding to the values in `q`.
+
+        Notes
+        -----
+        The evaluation of the PPF is very fast but it may have a large
+        relative error in the far tails. The numerical precision of the PPF
+        is controlled by the q-error, that is,
+        ``max |q - CDF(PPF(q))|`` where the max is taken over points in
+        the interval [0,1], see `evaluate_error`.
+
+        Note that this PPF is designed to generate random samples.
+        """
+        q = np.asarray(q)
+        if self._mirror_uniform:
+            x = self._rng.ppf(1 - q)
+        else:
+            x = self._rng.ppf(q)
+        if self._rvs_transform is not None:
+            x = self._rvs_transform(x, *self._frozendist.args)
+        return self.scale * x + self.loc
+
+    def qrvs(self, size=None, d=None, qmc_engine=None):
+        """
+        Quasi-random variates of the given distribution.
+
+        The `qmc_engine` is used to draw uniform quasi-random variates, and
+        these are converted to quasi-random variates of the given distribution
+        using inverse transform sampling.
+
+        Parameters
+        ----------
+        size : int, tuple of ints, or None; optional
+            Defines shape of random variates array. Default is ``None``.
+        d : int or None, optional
+            Defines dimension of uniform quasi-random variates to be
+            transformed. Default is ``None``.
+        qmc_engine : scipy.stats.qmc.QMCEngine(d=1), optional
+            Defines the object to use for drawing
+            quasi-random variates. Default is ``None``, which uses
+            `scipy.stats.qmc.Halton(1)`.
+
+        Returns
+        -------
+        rvs : ndarray or scalar
+            Quasi-random variates. See Notes for shape information.
+
+        Notes
+        -----
+        The shape of the output array depends on `size`, `d`, and `qmc_engine`.
+        The intent is for the interface to be natural, but the detailed rules
+        to achieve this are complicated.
+
+        - If `qmc_engine` is ``None``, a `scipy.stats.qmc.Halton` instance is
+          created with dimension `d`. If `d` is not provided, ``d=1``.
+        - If `qmc_engine` is not ``None`` and `d` is ``None``, `d` is
+          determined from the dimension of the `qmc_engine`.
+        - If `qmc_engine` is not ``None`` and `d` is not ``None`` but the
+          dimensions are inconsistent, a ``ValueError`` is raised.
+        - After `d` is determined according to the rules above, the output
+          shape is ``tuple_shape + d_shape``, where:
+
+              - ``tuple_shape = tuple()`` if `size` is ``None``,
+              - ``tuple_shape = (size,)`` if `size` is an ``int``,
+              - ``tuple_shape = size`` if `size` is a sequence,
+              - ``d_shape = tuple()`` if `d` is ``None`` or `d` is 1, and
+              - ``d_shape = (d,)`` if `d` is greater than 1.
+
+        The elements of the returned array are part of a low-discrepancy
+        sequence. If `d` is 1, this means that none of the samples are truly
+        independent. If `d` > 1, each slice ``rvs[..., i]`` will be of a
+        quasi-independent sequence; see `scipy.stats.qmc.QMCEngine` for
+        details. Note that when `d` > 1, the samples returned are still those
+        of the provided univariate distribution, not a multivariate
+        generalization of that distribution.
+
+        """
+        qmc_engine, d = _validate_qmc_input(qmc_engine, d, self.random_state)
+        # mainly copied from unuran_wrapper.pyx.templ
+        # `rvs` is flexible about whether `size` is an int or tuple, so this
+        # should be, too.
+        try:
+            if size is None:
+                tuple_size = (1,)
+            else:
+                tuple_size = tuple(size)
+        except TypeError:
+            tuple_size = (size,)
+        # we do not use rng.qrvs directly since we need to be
+        # able to apply the ppf to 1 - u
+        N = 1 if size is None else np.prod(size)
+        u = qmc_engine.random(N)
+        if self._mirror_uniform:
+            u = 1 - u
+        qrvs = self._ppf(u)
+        if self._rvs_transform is not None:
+            qrvs = self._rvs_transform(qrvs, *self._frozendist.args)
+        if size is None:
+            qrvs = qrvs.squeeze()[()]
+        else:
+            if d == 1:
+                qrvs = qrvs.reshape(tuple_size)
+            else:
+                qrvs = qrvs.reshape(tuple_size + (d,))
+        return self.loc + self.scale * qrvs
+
+    def evaluate_error(self, size=100000, random_state=None, x_error=False):
+        """
+        Evaluate the numerical accuracy of the inversion (u- and x-error).
+
+        Parameters
+        ----------
+        size : int, optional
+            The number of random points over which the error is estimated.
+            Default is ``100000``.
+        random_state : {None, int, `numpy.random.Generator`,
+                        `numpy.random.RandomState`}, optional
+
+            A NumPy random number generator or seed for the underlying NumPy
+            random number generator used to generate the stream of uniform
+            random numbers.
+            If `random_state` is None, use ``self.random_state``.
+            If `random_state` is an int,
+            ``np.random.default_rng(random_state)`` is used.
+            If `random_state` is already a ``Generator`` or ``RandomState``
+            instance then that instance is used.
+        x_error : bool
+            If ``True``, return the x-error, as per the notes section. If ``False``,
+            returns ``NaN``.
+
+        Returns
+        -------
+        u_error, x_error : tuple of floats
+            A NumPy array of random variates.
+
+        Notes
+        -----
+        The numerical precision of the inverse CDF `ppf` is controlled by
+        the u-error. It is computed as follows:
+        ``max |u - CDF(PPF(u))|`` where the max is taken `size` random
+        points in the interval [0,1]. `random_state` determines the random
+        sample. Note that if `ppf` was exact, the u-error would be zero.
+
+        The x-error measures the direct distance between the exact PPF
+        and `ppf`. If ``x_error`` is set to ``True`, it is
+        computed as the maximum of the minimum of the relative and absolute
+        x-error:
+        ``max(min(x_error_abs[i], x_error_rel[i]))`` where
+        ``x_error_abs[i] = |PPF(u[i]) - PPF_fast(u[i])|``,
+        ``x_error_rel[i] = max |(PPF(u[i]) - PPF_fast(u[i])) / PPF(u[i])|``.
+        Note that it is important to consider the relative x-error in the case
+        that ``PPF(u)`` is close to zero or very large.
+
+        By default, only the u-error is evaluated and the x-error is set to
+        ``np.nan``. Note that the evaluation of the x-error will be very slow
+        if the implementation of the PPF is slow.
+
+        Further information about these error measures can be found in [1]_.
+
+        References
+        ----------
+        .. [1] Derflinger, Gerhard, Wolfgang Hörmann, and Josef Leydold.
+               "Random variate  generation by numerical inversion when only the
+               density is known." ACM Transactions on Modeling and Computer
+               Simulation (TOMACS) 20.4 (2010): 1-25.
+
+        Examples
+        --------
+
+        >>> import numpy as np
+        >>> from scipy import stats
+        >>> from scipy.stats.sampling import FastGeneratorInversion
+
+        Create an object for the normal distribution:
+
+        >>> d_norm_frozen = stats.norm()
+        >>> d_norm = FastGeneratorInversion(d_norm_frozen)
+
+        To confirm that the numerical inversion is accurate, we evaluate the
+        approximation error (u-error and x-error).
+
+        >>> u_error, x_error = d_norm.evaluate_error(x_error=True)
+
+        The u-error should be below 1e-10:
+
+        >>> u_error
+        8.785783212061915e-11  # may vary
+
+        Compare the PPF against approximation `ppf`:
+
+        >>> q = [0.001, 0.2, 0.4, 0.6, 0.8, 0.999]
+        >>> diff = np.abs(d_norm_frozen.ppf(q) - d_norm.ppf(q))
+        >>> x_error_abs = np.max(diff)
+        >>> x_error_abs
+        1.2937954707581412e-08
+
+        This is the absolute x-error evaluated at the points q. The relative
+        error is given by
+
+        >>> x_error_rel = np.max(diff / np.abs(d_norm_frozen.ppf(q)))
+        >>> x_error_rel
+        4.186725600453555e-09
+
+        The x_error computed above is derived in a very similar way over a
+        much larger set of random values q. At each value q[i], the minimum
+        of the relative and absolute error is taken. The final value is then
+        derived as the maximum of these values. In our example, we get the
+        following value:
+
+        >>> x_error
+        4.507068014335139e-07  # may vary
+
+        """
+        if not isinstance(size, numbers.Integral | np.integer):
+            raise ValueError("size must be an integer.")
+        # urng will be used to draw the samples for testing the error
+        # it must not interfere with self.random_state. therefore, do not
+        # call self.rvs, but draw uniform random numbers and apply
+        # self.ppf (note: like in rvs, consider self._mirror_uniform)
+        urng = check_random_state_qmc(random_state)
+        u = urng.uniform(size=size)
+        if self._mirror_uniform:
+            u = 1 - u
+        x = self.ppf(u)
+        uerr = np.max(np.abs(self._cdf(x) - u))
+        if not x_error:
+            return uerr, np.nan
+        ppf_u = self._ppf(u)
+        x_error_abs = np.abs(self.ppf(u)-ppf_u)
+        x_error_rel = x_error_abs / np.abs(ppf_u)
+        x_error_combined = np.array([x_error_abs, x_error_rel]).min(axis=0)
+        return uerr, np.max(x_error_combined)
+
+    def support(self):
+        """Support of the distribution.
+
+        Returns
+        -------
+        a, b : float
+            end-points of the distribution's support.
+
+        Notes
+        -----
+
+        Note that the support of the distribution depends on `loc`,
+        `scale` and `domain`.
+
+        Examples
+        --------
+
+        >>> from scipy import stats
+        >>> from scipy.stats.sampling import FastGeneratorInversion
+
+        Define a truncated normal distribution:
+
+        >>> d_norm = FastGeneratorInversion(stats.norm(), domain=(0, 1))
+        >>> d_norm.support()
+        (0, 1)
+
+        Shift the distribution:
+
+        >>> d_norm.loc = 2.5
+        >>> d_norm.support()
+        (2.5, 3.5)
+
+        """
+        return self._domain_adj
+
+    def _cdf(self, x):
+        """Cumulative distribution function (CDF)
+
+        Parameters
+        ----------
+        x : array_like
+            The values where the CDF is evaluated
+
+        Returns
+        -------
+        y : ndarray
+            CDF evaluated at x
+
+        """
+        y = self._frozendist.cdf(x)
+        if self._p_domain == 1.0:
+            return y
+        return np.clip((y - self._p_lower) / self._p_domain, 0, 1)
+
+    def _ppf(self, q):
+        """Percent point function (inverse of `cdf`)
+
+        Parameters
+        ----------
+        q : array_like
+            lower tail probability
+
+        Returns
+        -------
+        x : array_like
+            quantile corresponding to the lower tail probability q.
+
+        """
+        if self._p_domain == 1.0:
+            return self._frozendist.ppf(q)
+        x = self._frozendist.ppf(self._p_domain * np.array(q) + self._p_lower)
+        return np.clip(x, self._domain_adj[0], self._domain_adj[1])
+
+
+class RatioUniforms:
+    """
+    Generate random samples from a probability density function using the
+    ratio-of-uniforms method.
+
+    Parameters
+    ----------
+    pdf : callable
+        A function with signature `pdf(x)` that is proportional to the
+        probability density function of the distribution.
+    umax : float
+        The upper bound of the bounding rectangle in the u-direction.
+    vmin : float
+        The lower bound of the bounding rectangle in the v-direction.
+    vmax : float
+        The upper bound of the bounding rectangle in the v-direction.
+    c : float, optional
+        Shift parameter of ratio-of-uniforms method, see Notes. Default is 0.
+    random_state : {None, int, `numpy.random.Generator`,
+                    `numpy.random.RandomState`}, optional
+
+        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
+        singleton is used.
+        If `seed` is an int, a new ``RandomState`` instance is used,
+        seeded with `seed`.
+        If `seed` is already a ``Generator`` or ``RandomState`` instance then
+        that instance is used.
+
+    Methods
+    -------
+    rvs
+
+    Notes
+    -----
+    Given a univariate probability density function `pdf` and a constant `c`,
+    define the set ``A = {(u, v) : 0 < u <= sqrt(pdf(v/u + c))}``.
+    If ``(U, V)`` is a random vector uniformly distributed over ``A``,
+    then ``V/U + c`` follows a distribution according to `pdf`.
+
+    The above result (see [1]_, [2]_) can be used to sample random variables
+    using only the PDF, i.e. no inversion of the CDF is required. Typical
+    choices of `c` are zero or the mode of `pdf`. The set ``A`` is a subset of
+    the rectangle ``R = [0, umax] x [vmin, vmax]`` where
+
+    - ``umax = sup sqrt(pdf(x))``
+    - ``vmin = inf (x - c) sqrt(pdf(x))``
+    - ``vmax = sup (x - c) sqrt(pdf(x))``
+
+    In particular, these values are finite if `pdf` is bounded and
+    ``x**2 * pdf(x)`` is bounded (i.e. subquadratic tails).
+    One can generate ``(U, V)`` uniformly on ``R`` and return
+    ``V/U + c`` if ``(U, V)`` are also in ``A`` which can be directly
+    verified.
+
+    The algorithm is not changed if one replaces `pdf` by k * `pdf` for any
+    constant k > 0. Thus, it is often convenient to work with a function
+    that is proportional to the probability density function by dropping
+    unnecessary normalization factors.
+
+    Intuitively, the method works well if ``A`` fills up most of the
+    enclosing rectangle such that the probability is high that ``(U, V)``
+    lies in ``A`` whenever it lies in ``R`` as the number of required
+    iterations becomes too large otherwise. To be more precise, note that
+    the expected number of iterations to draw ``(U, V)`` uniformly
+    distributed on ``R`` such that ``(U, V)`` is also in ``A`` is given by
+    the ratio ``area(R) / area(A) = 2 * umax * (vmax - vmin) / area(pdf)``,
+    where `area(pdf)` is the integral of `pdf` (which is equal to one if the
+    probability density function is used but can take on other values if a
+    function proportional to the density is used). The equality holds since
+    the area of ``A`` is equal to ``0.5 * area(pdf)`` (Theorem 7.1 in [1]_).
+    If the sampling fails to generate a single random variate after 50000
+    iterations (i.e. not a single draw is in ``A``), an exception is raised.
+
+    If the bounding rectangle is not correctly specified (i.e. if it does not
+    contain ``A``), the algorithm samples from a distribution different from
+    the one given by `pdf`. It is therefore recommended to perform a
+    test such as `~scipy.stats.kstest` as a check.
+
+    References
+    ----------
+    .. [1] L. Devroye, "Non-Uniform Random Variate Generation",
+       Springer-Verlag, 1986.
+
+    .. [2] W. Hoermann and J. Leydold, "Generating generalized inverse Gaussian
+       random variates", Statistics and Computing, 24(4), p. 547--557, 2014.
+
+    .. [3] A.J. Kinderman and J.F. Monahan, "Computer Generation of Random
+       Variables Using the Ratio of Uniform Deviates",
+       ACM Transactions on Mathematical Software, 3(3), p. 257--260, 1977.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+
+    >>> from scipy.stats.sampling import RatioUniforms
+    >>> rng = np.random.default_rng()
+
+    Simulate normally distributed random variables. It is easy to compute the
+    bounding rectangle explicitly in that case. For simplicity, we drop the
+    normalization factor of the density.
+
+    >>> f = lambda x: np.exp(-x**2 / 2)
+    >>> v = np.sqrt(f(np.sqrt(2))) * np.sqrt(2)
+    >>> umax = np.sqrt(f(0))
+    >>> gen = RatioUniforms(f, umax=umax, vmin=-v, vmax=v, random_state=rng)
+    >>> r = gen.rvs(size=2500)
+
+    The K-S test confirms that the random variates are indeed normally
+    distributed (normality is not rejected at 5% significance level):
+
+    >>> stats.kstest(r, 'norm')[1]
+    0.250634764150542
+
+    The exponential distribution provides another example where the bounding
+    rectangle can be determined explicitly.
+
+    >>> gen = RatioUniforms(lambda x: np.exp(-x), umax=1, vmin=0,
+    ...                     vmax=2*np.exp(-1), random_state=rng)
+    >>> r = gen.rvs(1000)
+    >>> stats.kstest(r, 'expon')[1]
+    0.21121052054580314
+
+    """
+
+    def __init__(self, pdf, *, umax, vmin, vmax, c=0, random_state=None):
+        if vmin >= vmax:
+            raise ValueError("vmin must be smaller than vmax.")
+
+        if umax <= 0:
+            raise ValueError("umax must be positive.")
+
+        self._pdf = pdf
+        self._umax = umax
+        self._vmin = vmin
+        self._vmax = vmax
+        self._c = c
+        self._rng = check_random_state(random_state)
+
+    def rvs(self, size=1):
+        """Sampling of random variates.
+
+        Parameters
+        ----------
+        size : int or tuple of ints, optional
+            Number of random variates to be generated (default is 1).
+
+        Returns
+        -------
+        rvs : ndarray
+            The random variates distributed according to the probability
+            distribution defined by the pdf.
+
+        """
+        size1d = tuple(np.atleast_1d(size))
+        N = np.prod(size1d)  # number of rvs needed, reshape upon return
+
+        # start sampling using ratio of uniforms method
+        x = np.zeros(N)
+        simulated, i = 0, 1
+
+        # loop until N rvs have been generated: expected runtime is finite.
+        # to avoid infinite loop, raise exception if not a single rv has been
+        # generated after 50000 tries. even if the expected number of iterations
+        # is 1000, the probability of this event is (1-1/1000)**50000
+        # which is of order 10e-22
+        while simulated < N:
+            k = N - simulated
+            # simulate uniform rvs on [0, umax] and [vmin, vmax]
+            u1 = self._umax * self._rng.uniform(size=k)
+            v1 = self._rng.uniform(self._vmin, self._vmax, size=k)
+            # apply rejection method
+            rvs = v1 / u1 + self._c
+            accept = (u1**2 <= self._pdf(rvs))
+            num_accept = np.sum(accept)
+            if num_accept > 0:
+                x[simulated:(simulated + num_accept)] = rvs[accept]
+                simulated += num_accept
+
+            if (simulated == 0) and (i*N >= 50000):
+                msg = (
+                    f"Not a single random variate could be generated in {i*N} "
+                    "attempts. The ratio of uniforms method does not appear "
+                    "to work for the provided parameters. Please check the "
+                    "pdf and the bounds."
+                )
+                raise RuntimeError(msg)
+            i += 1
+
+        return np.reshape(x, size1d)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_sensitivity_analysis.py b/.venv/lib/python3.12/site-packages/scipy/stats/_sensitivity_analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..529c1e6120a63ff65313928ee87b4ee1e881a73c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_sensitivity_analysis.py
@@ -0,0 +1,718 @@
+import inspect
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+from collections.abc import Callable
+
+import numpy as np
+
+from scipy.stats._common import ConfidenceInterval
+from scipy.stats._qmc import check_random_state
+from scipy.stats._resampling import BootstrapResult
+from scipy.stats import qmc, bootstrap
+from scipy._lib._array_api import xp_capabilities
+from scipy._lib._util import _transition_to_rng
+
+
+if TYPE_CHECKING:
+    import numpy.typing as npt
+    from scipy._lib._util import DecimalNumber, IntNumber
+
+
+__all__ = [
+    'sobol_indices'
+]
+
+
+def f_ishigami(x: "npt.ArrayLike") -> "npt.NDArray[np.inexact[Any]]":
+    r"""Ishigami function.
+
+    .. math::
+
+        Y(\mathbf{x}) = \sin x_1 + 7 \sin^2 x_2 + 0.1 x_3^4 \sin x_1
+
+    with :math:`\mathbf{x} \in [-\pi, \pi]^3`.
+
+    Parameters
+    ----------
+    x : array_like ([x1, x2, x3], n)
+
+    Returns
+    -------
+    f : array_like (n,)
+        Function evaluation.
+
+    References
+    ----------
+    .. [1] Ishigami, T. and T. Homma. "An importance quantification technique
+       in uncertainty analysis for computer models." IEEE,
+       :doi:`10.1109/ISUMA.1990.151285`, 1990.
+    """
+    x = np.atleast_2d(x)
+    f_eval = (
+        np.sin(x[0])
+        + 7 * np.sin(x[1])**2
+        + 0.1 * (x[2]**4) * np.sin(x[0])
+    )
+    return f_eval
+
+
+def sample_A_B(
+    n,
+    dists,
+    rng=None
+):
+    """Sample two matrices A and B.
+
+    Uses a Sobol' sequence with 2`d` columns to have 2 uncorrelated matrices.
+    This is more efficient than using 2 random draw of Sobol'.
+    See sec. 5 from [1]_.
+
+    Output shape is (d, n).
+
+    References
+    ----------
+    .. [1] Saltelli, A., P. Annoni, I. Azzini, F. Campolongo, M. Ratto, and
+       S. Tarantola. "Variance based sensitivity analysis of model
+       output. Design and estimator for the total sensitivity index."
+       Computer Physics Communications, 181(2):259-270,
+       :doi:`10.1016/j.cpc.2009.09.018`, 2010.
+    """
+    d = len(dists)
+    A_B = qmc.Sobol(d=2*d, seed=rng, bits=64).random(n).T
+    A_B = A_B.reshape(2, d, -1)
+    try:
+        for d_, dist in enumerate(dists):
+            A_B[:, d_] = dist.ppf(A_B[:, d_])
+    except AttributeError as exc:
+        message = "Each distribution in `dists` must have method `ppf`."
+        raise ValueError(message) from exc
+    return A_B
+
+
+def sample_AB(A: np.ndarray, B: np.ndarray) -> np.ndarray:
+    """AB matrix.
+
+    AB: rows of B into A. Shape (d, d, n).
+
+    - Copy A into d "pages"
+    - In the first page, replace 1st rows of A with 1st row of B.
+    ...
+    - In the dth page, replace dth row of A with dth row of B.
+    - return the stack of pages
+
+    """
+    d, n = A.shape
+    AB = np.tile(A, (d, 1, 1))
+    i = np.arange(d)
+    AB[i, i] = B[i]
+    return AB
+
+
+def saltelli_2010(
+    f_A: np.ndarray, f_B: np.ndarray, f_AB: np.ndarray
+) -> tuple[np.ndarray, np.ndarray]:
+    r"""Saltelli2010 formulation.
+
+    .. math::
+
+        S_i = \frac{1}{N} \sum_{j=1}^N
+        f(\mathbf{B})_j (f(\mathbf{AB}^{(i)})_j - f(\mathbf{A})_j)
+
+    .. math::
+
+        S_{T_i} = \frac{1}{N} \sum_{j=1}^N
+        (f(\mathbf{A})_j - f(\mathbf{AB}^{(i)})_j)^2
+
+    Parameters
+    ----------
+    f_A, f_B : array_like (s, n)
+        Function values at A and B, respectively
+    f_AB : array_like (d, s, n)
+        Function values at each of the AB pages
+
+    Returns
+    -------
+    s, st : array_like (s, d)
+        First order and total order Sobol' indices.
+
+    References
+    ----------
+    .. [1] Saltelli, A., P. Annoni, I. Azzini, F. Campolongo, M. Ratto, and
+       S. Tarantola. "Variance based sensitivity analysis of model
+       output. Design and estimator for the total sensitivity index."
+       Computer Physics Communications, 181(2):259-270,
+       :doi:`10.1016/j.cpc.2009.09.018`, 2010.
+    """
+    # Empirical variance calculated using output from A and B which are
+    # independent. Output of AB is not independent and cannot be used
+    var = np.var([f_A, f_B], axis=(0, -1))
+
+    # We divide by the variance to have a ratio of variance
+    # this leads to eq. 2
+    s = np.mean(f_B * (f_AB - f_A), axis=-1) / var  # Table 2 (b)
+    st = 0.5 * np.mean((f_A - f_AB) ** 2, axis=-1) / var  # Table 2 (f)
+
+    return s.T, st.T
+
+
+@dataclass
+class BootstrapSobolResult:
+    first_order: BootstrapResult
+    total_order: BootstrapResult
+
+
+@dataclass
+class SobolResult:
+    first_order: np.ndarray
+    total_order: np.ndarray
+    _indices_method: Callable
+    _f_A: np.ndarray
+    _f_B: np.ndarray
+    _f_AB: np.ndarray
+    _A: np.ndarray | None = None
+    _B: np.ndarray | None = None
+    _AB: np.ndarray | None = None
+    _bootstrap_result: BootstrapResult | None = None
+
+    def bootstrap(
+        self,
+        confidence_level: "DecimalNumber" = 0.95,
+        n_resamples: "IntNumber" = 999
+    ) -> BootstrapSobolResult:
+        """Bootstrap Sobol' indices to provide confidence intervals.
+
+        Parameters
+        ----------
+        confidence_level : float, default: ``0.95``
+            The confidence level of the confidence intervals.
+        n_resamples : int, default: ``999``
+            The number of resamples performed to form the bootstrap
+            distribution of the indices.
+
+        Returns
+        -------
+        res : BootstrapSobolResult
+            Bootstrap result containing the confidence intervals and the
+            bootstrap distribution of the indices.
+
+            An object with attributes:
+
+            first_order : BootstrapResult
+                Bootstrap result of the first order indices.
+            total_order : BootstrapResult
+                Bootstrap result of the total order indices.
+            See `BootstrapResult` for more details.
+
+        """
+        def statistic(idx):
+            f_A_ = self._f_A[:, idx]
+            f_B_ = self._f_B[:, idx]
+            f_AB_ = self._f_AB[..., idx]
+            return self._indices_method(f_A_, f_B_, f_AB_)
+
+        n = self._f_A.shape[1]
+
+        res = bootstrap(
+            [np.arange(n)], statistic=statistic, method="BCa",
+            n_resamples=n_resamples,
+            confidence_level=confidence_level,
+            bootstrap_result=self._bootstrap_result
+        )
+        self._bootstrap_result = res
+
+        first_order = BootstrapResult(
+            confidence_interval=ConfidenceInterval(
+                res.confidence_interval.low[0], res.confidence_interval.high[0]
+            ),
+            bootstrap_distribution=res.bootstrap_distribution[0],
+            standard_error=res.standard_error[0],
+        )
+        total_order = BootstrapResult(
+            confidence_interval=ConfidenceInterval(
+                res.confidence_interval.low[1], res.confidence_interval.high[1]
+            ),
+            bootstrap_distribution=res.bootstrap_distribution[1],
+            standard_error=res.standard_error[1],
+        )
+
+        return BootstrapSobolResult(
+            first_order=first_order, total_order=total_order
+        )
+
+
+@xp_capabilities(np_only=True)
+@_transition_to_rng('random_state', replace_doc=False)
+def sobol_indices(
+    *,
+    func,
+    n,
+    dists=None,
+    method='saltelli_2010',
+    rng=None
+):
+    r"""Global sensitivity indices of Sobol'.
+
+    Parameters
+    ----------
+    func : callable or dict(str, array_like)
+        If `func` is a callable, function to compute the Sobol' indices from.
+        Its signature must be::
+
+            func(x: ArrayLike) -> ArrayLike
+
+        with ``x`` of shape ``(d, n)`` and output of shape ``(s, n)`` where:
+
+        - ``d`` is the input dimensionality of `func`
+          (number of input variables),
+        - ``s`` is the output dimensionality of `func`
+          (number of output variables), and
+        - ``n`` is the number of samples (see `n` below).
+
+        Function evaluation values must be finite.
+
+        If `func` is a dictionary, contains the function evaluations from three
+        different arrays. Keys must be: ``f_A``, ``f_B`` and ``f_AB``.
+        ``f_A`` and ``f_B`` should have a shape ``(s, n)`` and ``f_AB``
+        should have a shape ``(d, s, n)``.
+        This is an advanced feature and misuse can lead to wrong analysis.
+    n : int
+        Number of samples used to generate the matrices ``A`` and ``B``.
+        Must be a power of 2. The total number of points at which `func` is
+        evaluated will be ``n*(d+2)``.
+    dists : list(distributions), optional
+        List of each parameter's distribution. The distribution of parameters
+        depends on the application and should be carefully chosen.
+        Parameters are assumed to be independently distributed, meaning there
+        is no constraint nor relationship between their values.
+
+        Distributions must be an instance of a class with a ``ppf``
+        method.
+
+        Must be specified if `func` is a callable, and ignored otherwise.
+    method : Callable or str, default: 'saltelli_2010'
+        Method used to compute the first and total Sobol' indices.
+
+        If a callable, its signature must be::
+
+            func(f_A: np.ndarray, f_B: np.ndarray, f_AB: np.ndarray)
+            -> tuple[np.ndarray, np.ndarray]
+
+        with ``f_A, f_B`` of shape ``(s, n)`` and ``f_AB`` of shape
+        ``(d, s, n)``.
+        These arrays contain the function evaluations from three different sets
+        of samples.
+        The output is a tuple of the first and total indices with
+        shape ``(s, d)``.
+        This is an advanced feature and misuse can lead to wrong analysis.
+    rng : `numpy.random.Generator`, optional
+        Pseudorandom number generator state. When `rng` is None, a new
+        `numpy.random.Generator` is created using entropy from the
+        operating system. Types other than `numpy.random.Generator` are
+        passed to `numpy.random.default_rng` to instantiate a ``Generator``.
+
+        .. versionchanged:: 1.15.0
+
+            As part of the `SPEC-007 <https://scientific-python.org/specs/spec-0007/>`_
+            transition from use of `numpy.random.RandomState` to
+            `numpy.random.Generator`, this keyword was changed from `random_state` to
+            `rng`. For an interim period, both keywords will continue to work, although
+            only one may be specified at a time. After the interim period, function
+            calls using the `random_state` keyword will emit warnings. Following a
+            deprecation period, the `random_state` keyword will be removed.
+
+    Returns
+    -------
+    res : SobolResult
+        An object with attributes:
+
+        first_order : ndarray of shape (s, d)
+            First order Sobol' indices.
+        total_order : ndarray of shape (s, d)
+            Total order Sobol' indices.
+
+        And method:
+
+        bootstrap(confidence_level: float, n_resamples: int)
+        -> BootstrapSobolResult
+
+            A method providing confidence intervals on the indices.
+            See `scipy.stats.bootstrap` for more details.
+
+            The bootstrapping is done on both first and total order indices,
+            and they are available in `BootstrapSobolResult` as attributes
+            ``first_order`` and ``total_order``.
+
+    Notes
+    -----
+    The Sobol' method [1]_, [2]_ is a variance-based Sensitivity Analysis which
+    obtains the contribution of each parameter to the variance of the
+    quantities of interest (QoIs; i.e., the outputs of `func`).
+    Respective contributions can be used to rank the parameters and
+    also gauge the complexity of the model by computing the
+    model's effective (or mean) dimension.
+
+    .. note::
+
+        Parameters are assumed to be independently distributed. Each
+        parameter can still follow any distribution. In fact, the distribution
+        is very important and should match the real distribution of the
+        parameters.
+
+    It uses a functional decomposition of the variance of the function to
+    explore
+
+    .. math::
+
+        \mathbb{V}(Y) = \sum_{i}^{d} \mathbb{V}_i (Y) + \sum_{i<j}^{d}
+        \mathbb{V}_{ij}(Y) + ... + \mathbb{V}_{1,2,...,d}(Y),
+
+    introducing conditional variances:
+
+    .. math::
+
+        \mathbb{V}_i(Y) = \mathbb{\mathbb{V}}[\mathbb{E}(Y|x_i)]
+        \qquad
+        \mathbb{V}_{ij}(Y) = \mathbb{\mathbb{V}}[\mathbb{E}(Y|x_i x_j)]
+        - \mathbb{V}_i(Y) - \mathbb{V}_j(Y),
+
+    Sobol' indices are expressed as
+
+    .. math::
+
+        S_i = \frac{\mathbb{V}_i(Y)}{\mathbb{V}[Y]}
+        \qquad
+        S_{ij} =\frac{\mathbb{V}_{ij}(Y)}{\mathbb{V}[Y]}.
+
+    :math:`S_{i}` corresponds to the first-order term which apprises the
+    contribution of the i-th parameter, while :math:`S_{ij}` corresponds to the
+    second-order term which informs about the contribution of interactions
+    between the i-th and the j-th parameters. These equations can be
+    generalized to compute higher order terms; however, they are expensive to
+    compute and their interpretation is complex.
+    This is why only first order indices are provided.
+
+    Total order indices represent the global contribution of the parameters
+    to the variance of the QoI and are defined as:
+
+    .. math::
+
+        S_{T_i} = S_i + \sum_j S_{ij} + \sum_{j,k} S_{ijk} + ...
+        = 1 - \frac{\mathbb{V}[\mathbb{E}(Y|x_{\sim i})]}{\mathbb{V}[Y]}.
+
+    First order indices sum to at most 1, while total order indices sum to at
+    least 1. If there are no interactions, then first and total order indices
+    are equal, and both first and total order indices sum to 1.
+
+    .. warning::
+
+        Negative Sobol' values are due to numerical errors. Increasing the
+        number of points `n` should help.
+
+        The number of sample required to have a good analysis increases with
+        the dimensionality of the problem. e.g. for a 3 dimension problem,
+        consider at minima ``n >= 2**12``. The more complex the model is,
+        the more samples will be needed.
+
+        Even for a purely additive model, the indices may not sum to 1 due
+        to numerical noise.
+
+    References
+    ----------
+    .. [1] Sobol, I. M.. "Sensitivity analysis for nonlinear mathematical
+       models." Mathematical Modeling and Computational Experiment, 1:407-414,
+       1993.
+    .. [2] Sobol, I. M. (2001). "Global sensitivity indices for nonlinear
+       mathematical models and their Monte Carlo estimates." Mathematics
+       and Computers in Simulation, 55(1-3):271-280,
+       :doi:`10.1016/S0378-4754(00)00270-6`, 2001.
+    .. [3] Saltelli, A. "Making best use of model evaluations to
+       compute sensitivity indices."  Computer Physics Communications,
+       145(2):280-297, :doi:`10.1016/S0010-4655(02)00280-1`, 2002.
+    .. [4] Saltelli, A., M. Ratto, T. Andres, F. Campolongo, J. Cariboni,
+       D. Gatelli, M. Saisana, and S. Tarantola. "Global Sensitivity Analysis.
+       The Primer." 2007.
+    .. [5] Saltelli, A., P. Annoni, I. Azzini, F. Campolongo, M. Ratto, and
+       S. Tarantola. "Variance based sensitivity analysis of model
+       output. Design and estimator for the total sensitivity index."
+       Computer Physics Communications, 181(2):259-270,
+       :doi:`10.1016/j.cpc.2009.09.018`, 2010.
+    .. [6] Ishigami, T. and T. Homma. "An importance quantification technique
+       in uncertainty analysis for computer models." IEEE,
+       :doi:`10.1109/ISUMA.1990.151285`, 1990.
+
+    Examples
+    --------
+    The following is an example with the Ishigami function [6]_
+
+    .. math::
+
+        Y(\mathbf{x}) = \sin x_1 + 7 \sin^2 x_2 + 0.1 x_3^4 \sin x_1,
+
+    with :math:`\mathbf{x} \in [-\pi, \pi]^3`. This function exhibits strong
+    non-linearity and non-monotonicity.
+
+    Remember, Sobol' indices assumes that samples are independently
+    distributed. In this case we use a uniform distribution on each marginals.
+
+    >>> import numpy as np
+    >>> from scipy.stats import sobol_indices, uniform
+    >>> rng = np.random.default_rng()
+    >>> def f_ishigami(x):
+    ...     f_eval = (
+    ...         np.sin(x[0])
+    ...         + 7 * np.sin(x[1])**2
+    ...         + 0.1 * (x[2]**4) * np.sin(x[0])
+    ...     )
+    ...     return f_eval
+    >>> indices = sobol_indices(
+    ...     func=f_ishigami, n=1024,
+    ...     dists=[
+    ...         uniform(loc=-np.pi, scale=2*np.pi),
+    ...         uniform(loc=-np.pi, scale=2*np.pi),
+    ...         uniform(loc=-np.pi, scale=2*np.pi)
+    ...     ],
+    ...     rng=rng
+    ... )
+    >>> indices.first_order
+    array([0.31637954, 0.43781162, 0.00318825])
+    >>> indices.total_order
+    array([0.56122127, 0.44287857, 0.24229595])
+
+    Confidence interval can be obtained using bootstrapping.
+
+    >>> boot = indices.bootstrap()
+
+    Then, this information can be easily visualized.
+
+    >>> import matplotlib.pyplot as plt
+    >>> fig, axs = plt.subplots(1, 2, figsize=(9, 4))
+    >>> _ = axs[0].errorbar(
+    ...     [1, 2, 3], indices.first_order, fmt='o',
+    ...     yerr=[
+    ...         indices.first_order - boot.first_order.confidence_interval.low,
+    ...         boot.first_order.confidence_interval.high - indices.first_order
+    ...     ],
+    ... )
+    >>> axs[0].set_ylabel("First order Sobol' indices")
+    >>> axs[0].set_xlabel('Input parameters')
+    >>> axs[0].set_xticks([1, 2, 3])
+    >>> _ = axs[1].errorbar(
+    ...     [1, 2, 3], indices.total_order, fmt='o',
+    ...     yerr=[
+    ...         indices.total_order - boot.total_order.confidence_interval.low,
+    ...         boot.total_order.confidence_interval.high - indices.total_order
+    ...     ],
+    ... )
+    >>> axs[1].set_ylabel("Total order Sobol' indices")
+    >>> axs[1].set_xlabel('Input parameters')
+    >>> axs[1].set_xticks([1, 2, 3])
+    >>> plt.tight_layout()
+    >>> plt.show()
+
+    .. note::
+
+        By default, `scipy.stats.uniform` has support ``[0, 1]``.
+        Using the parameters ``loc`` and ``scale``, one obtains the uniform
+        distribution on ``[loc, loc + scale]``.
+
+    This result is particularly interesting because the first order index
+    :math:`S_{x_3} = 0` whereas its total order is :math:`S_{T_{x_3}} = 0.244`.
+    This means that higher order interactions with :math:`x_3` are responsible
+    for the difference. Almost 25% of the observed variance
+    on the QoI is due to the correlations between :math:`x_3` and :math:`x_1`,
+    although :math:`x_3` by itself has no impact on the QoI.
+
+    The following gives a visual explanation of Sobol' indices on this
+    function. Let's generate 1024 samples in :math:`[-\pi, \pi]^3` and
+    calculate the value of the output.
+
+    >>> from scipy.stats import qmc
+    >>> n_dim = 3
+    >>> p_labels = ['$x_1$', '$x_2$', '$x_3$']
+    >>> sample = qmc.Sobol(d=n_dim, seed=rng).random(1024)
+    >>> sample = qmc.scale(
+    ...     sample=sample,
+    ...     l_bounds=[-np.pi, -np.pi, -np.pi],
+    ...     u_bounds=[np.pi, np.pi, np.pi]
+    ... )
+    >>> output = f_ishigami(sample.T)
+
+    Now we can do scatter plots of the output with respect to each parameter.
+    This gives a visual way to understand how each parameter impacts the
+    output of the function.
+
+    >>> fig, ax = plt.subplots(1, n_dim, figsize=(12, 4))
+    >>> for i in range(n_dim):
+    ...     xi = sample[:, i]
+    ...     ax[i].scatter(xi, output, marker='+')
+    ...     ax[i].set_xlabel(p_labels[i])
+    >>> ax[0].set_ylabel('Y')
+    >>> plt.tight_layout()
+    >>> plt.show()
+
+    Now Sobol' goes a step further:
+    by conditioning the output value by given values of the parameter
+    (black lines), the conditional output mean is computed. It corresponds to
+    the term :math:`\mathbb{E}(Y|x_i)`. Taking the variance of this term gives
+    the numerator of the Sobol' indices.
+
+    >>> mini = np.min(output)
+    >>> maxi = np.max(output)
+    >>> n_bins = 10
+    >>> bins = np.linspace(-np.pi, np.pi, num=n_bins, endpoint=False)
+    >>> dx = bins[1] - bins[0]
+    >>> fig, ax = plt.subplots(1, n_dim, figsize=(12, 4))
+    >>> for i in range(n_dim):
+    ...     xi = sample[:, i]
+    ...     ax[i].scatter(xi, output, marker='+')
+    ...     ax[i].set_xlabel(p_labels[i])
+    ...     for bin_ in bins:
+    ...         idx = np.where((bin_ <= xi) & (xi <= bin_ + dx))
+    ...         xi_ = xi[idx]
+    ...         y_ = output[idx]
+    ...         ave_y_ = np.mean(y_)
+    ...         ax[i].plot([bin_ + dx/2] * 2, [mini, maxi], c='k')
+    ...         ax[i].scatter(bin_ + dx/2, ave_y_, c='r')
+    >>> ax[0].set_ylabel('Y')
+    >>> plt.tight_layout()
+    >>> plt.show()
+
+    Looking at :math:`x_3`, the variance
+    of the mean is zero leading to :math:`S_{x_3} = 0`. But we can further
+    observe that the variance of the output is not constant along the parameter
+    values of :math:`x_3`. This heteroscedasticity is explained by higher order
+    interactions. Moreover, a heteroscedasticity is also noticeable on
+    :math:`x_1` leading to an interaction between :math:`x_3` and :math:`x_1`.
+    On :math:`x_2`, the variance seems to be constant and thus null interaction
+    with this parameter can be supposed.
+
+    This case is fairly simple to analyse visually---although it is only a
+    qualitative analysis. Nevertheless, when the number of input parameters
+    increases such analysis becomes unrealistic as it would be difficult to
+    conclude on high-order terms. Hence the benefit of using Sobol' indices.
+
+    """
+    rng = check_random_state(rng)
+
+    n_ = int(n)
+    if not (n_ & (n_ - 1) == 0) or n != n_:
+        raise ValueError(
+            "The balance properties of Sobol' points require 'n' "
+            "to be a power of 2."
+        )
+    n = n_
+
+    if not callable(method):
+        indices_methods = {
+            "saltelli_2010": saltelli_2010,
+        }
+        try:
+            method = method.lower()  # type: ignore[assignment]
+            indices_method_ = indices_methods[method]
+        except KeyError as exc:
+            message = (
+                f"{method!r} is not a valid 'method'. It must be one of"
+                f" {set(indices_methods)!r} or a callable."
+            )
+            raise ValueError(message) from exc
+    else:
+        indices_method_ = method
+        sig = inspect.signature(indices_method_)
+
+        if set(sig.parameters) != {'f_A', 'f_B', 'f_AB'}:
+            message = (
+                "If 'method' is a callable, it must have the following"
+                f" signature: {inspect.signature(saltelli_2010)}"
+            )
+            raise ValueError(message)
+
+    def indices_method(f_A, f_B, f_AB):
+        """Wrap indices method to ensure proper output dimension.
+
+        1D when single output, 2D otherwise.
+        """
+        return np.squeeze(indices_method_(f_A=f_A, f_B=f_B, f_AB=f_AB))
+
+    if callable(func):
+        if dists is None:
+            raise ValueError(
+                "'dists' must be defined when 'func' is a callable."
+            )
+
+        def wrapped_func(x):
+            return np.atleast_2d(func(x))
+
+        A, B = sample_A_B(n=n, dists=dists, rng=rng)
+        AB = sample_AB(A=A, B=B)
+
+        f_A = wrapped_func(A)
+
+        if f_A.shape[1] != n:
+            raise ValueError(
+                "'func' output should have a shape ``(s, -1)`` with ``s`` "
+                "the number of output."
+            )
+
+        def funcAB(AB):
+            d, d, n = AB.shape
+            AB = np.moveaxis(AB, 0, -1).reshape(d, n*d)
+            f_AB = wrapped_func(AB)
+            return np.moveaxis(f_AB.reshape((-1, n, d)), -1, 0)
+
+        f_B = wrapped_func(B)
+        f_AB = funcAB(AB)
+    else:
+        message = (
+            "When 'func' is a dictionary, it must contain the following "
+            "keys: 'f_A', 'f_B' and 'f_AB'."
+            "'f_A' and 'f_B' should have a shape ``(s, n)`` and 'f_AB' "
+            "should have a shape ``(d, s, n)``."
+        )
+        try:
+            f_A, f_B, f_AB = map(lambda arr: arr.copy(), np.atleast_2d(
+                func['f_A'], func['f_B'], func['f_AB']
+            ))
+        except KeyError as exc:
+            raise ValueError(message) from exc
+
+        if f_A.shape[1] != n or f_A.shape != f_B.shape or \
+                f_AB.shape == f_A.shape or f_AB.shape[-1] % n != 0:
+            raise ValueError(message)
+
+    # Normalization by mean
+    # Sobol', I. and Levitan, Y. L. (1999). On the use of variance reducing
+    # multipliers in monte carlo computations of a global sensitivity index.
+    # Computer Physics Communications, 117(1) :52-61.
+    mean = np.mean([f_A, f_B], axis=(0, -1)).reshape(-1, 1)
+    f_A -= mean
+    f_B -= mean
+    f_AB -= mean
+
+    # Compute indices
+    # Filter warnings for constant output as var = 0
+    with np.errstate(divide='ignore', invalid='ignore'):
+        first_order, total_order = indices_method(f_A=f_A, f_B=f_B, f_AB=f_AB)
+
+    # null variance means null indices
+    first_order[~np.isfinite(first_order)] = 0
+    total_order[~np.isfinite(total_order)] = 0
+
+    res = dict(
+        first_order=first_order,
+        total_order=total_order,
+        _indices_method=indices_method,
+        _f_A=f_A,
+        _f_B=f_B,
+        _f_AB=f_AB
+    )
+
+    if callable(func):
+        res.update(
+            dict(
+                _A=A,
+                _B=B,
+                _AB=AB,
+            )
+        )
+
+    return SobolResult(**res)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_sobol.cpython-312-x86_64-linux-gnu.so b/.venv/lib/python3.12/site-packages/scipy/stats/_sobol.cpython-312-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..f6641ab19bff3619dfa94f383c0e9789d9f352bb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_sobol.cpython-312-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76fa218da7c4fdc5daf8ca3d0900af5a2249a57072a354f9906ca2ce00853675
+size 185416
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_sobol.pyi b/.venv/lib/python3.12/site-packages/scipy/stats/_sobol.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7ca5e3a9c1a142b25ac26401e9ab1cb6726c877f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_sobol.pyi
@@ -0,0 +1,54 @@
+import numpy as np
+from scipy._lib._util import IntNumber
+from typing import Literal
+
+def _initialize_v(
+    v : np.ndarray, 
+    dim : IntNumber,
+    bits: IntNumber
+) -> None: ...
+
+def _cscramble (
+    dim : IntNumber,
+    bits: IntNumber,
+    ltm : np.ndarray,
+    sv: np.ndarray
+) -> None: ...
+
+def _fill_p_cumulative(
+    p: np.ndarray,
+    p_cumulative: np.ndarray
+) -> None: ...
+
+def _draw(
+    n : IntNumber,
+    num_gen: IntNumber,
+    dim: IntNumber,
+    scale: float,
+    sv: np.ndarray,
+    quasi: np.ndarray,
+    sample: np.ndarray
+    ) -> None: ...
+
+def _fast_forward(
+    n: IntNumber,
+    num_gen: IntNumber,
+    dim: IntNumber,
+    sv: np.ndarray,
+    quasi: np.ndarray
+    ) -> None: ...
+
+def _categorize(
+    draws: np.ndarray,
+    p_cumulative: np.ndarray,
+    result: np.ndarray
+    ) -> None: ...
+
+_MAXDIM: Literal[21201]
+_MAXDEG: Literal[18]
+
+def _test_find_index(
+    p_cumulative: np.ndarray, 
+    size: int, 
+    value: float
+    ) -> int: ...
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_sobol_direction_numbers.npz b/.venv/lib/python3.12/site-packages/scipy/stats/_sobol_direction_numbers.npz
new file mode 100644
index 0000000000000000000000000000000000000000..44f1f1e9ebd1eb188289ca9adb8027855c1a23b6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_sobol_direction_numbers.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4859931147d42ce465b8605cb277f957d98b839d03194fdf06579357906d193b
+size 589334
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_stats.cpython-312-x86_64-linux-gnu.so b/.venv/lib/python3.12/site-packages/scipy/stats/_stats.cpython-312-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..056caacf02a1b38f1193690fb0a3a30fd6119181
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_stats.cpython-312-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6ccbb930f99601864c93f92622fc9bd5ec65b86f4387a284875f191350d17f29
+size 502096
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_stats.pxd b/.venv/lib/python3.12/site-packages/scipy/stats/_stats.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..e01565f75fe232446e4b8b0b50fdf645c8506108
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_stats.pxd
@@ -0,0 +1,10 @@
+# destined to be used in a LowLevelCallable
+
+cdef double _geninvgauss_pdf(double x, void *user_data) noexcept nogil
+cdef double _studentized_range_cdf(int n, double[2] x, void *user_data) noexcept nogil
+cdef double _studentized_range_cdf_asymptotic(double z, void *user_data) noexcept nogil
+cdef double _studentized_range_pdf(int n, double[2] x, void *user_data) noexcept nogil
+cdef double _studentized_range_pdf_asymptotic(double z, void *user_data) noexcept nogil
+cdef double _studentized_range_moment(int n, double[3] x_arg, void *user_data) noexcept nogil
+cdef double _genhyperbolic_pdf(double x, void *user_data) noexcept nogil
+cdef double _genhyperbolic_logpdf(double x, void *user_data) noexcept nogil
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_stats_mstats_common.py b/.venv/lib/python3.12/site-packages/scipy/stats/_stats_mstats_common.py
new file mode 100644
index 0000000000000000000000000000000000000000..c272fcc1a31a6f296cb54c28859ea51b3ca03362
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_stats_mstats_common.py
@@ -0,0 +1,34 @@
+import numpy as np
+from .._lib._bunch import _make_tuple_bunch
+
+__all__ = ['_find_repeats']
+
+# This is not a namedtuple for backwards compatibility. See PR #12983
+TheilslopesResult = _make_tuple_bunch('TheilslopesResult',
+                                      ['slope', 'intercept',
+                                       'low_slope', 'high_slope'])
+SiegelslopesResult = _make_tuple_bunch('SiegelslopesResult',
+                                       ['slope', 'intercept'])
+
+
+def _n_samples_optional_x(kwargs):
+    return 2 if kwargs.get('x', None) is not None else 1
+
+
+def _find_repeats(arr):
+    # This function assumes it may clobber its input.
+    if len(arr) == 0:
+        return np.array(0, np.float64), np.array(0, np.intp)
+
+    # XXX This cast was previously needed for the Fortran implementation,
+    # should we ditch it?
+    arr = np.asarray(arr, np.float64).ravel()
+    arr.sort()
+
+    # Taken from NumPy 1.9's np.unique.
+    change = np.concatenate(([True], arr[1:] != arr[:-1]))
+    unique = arr[change]
+    change_idx = np.concatenate(np.nonzero(change) + ([arr.size],))
+    freq = np.diff(change_idx)
+    atleast2 = freq > 1
+    return unique[atleast2], freq[atleast2]
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_stats_py.py b/.venv/lib/python3.12/site-packages/scipy/stats/_stats_py.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf10af5738d8c2fa4ec7ea33cf0deca9e9bc2a19
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_stats_py.py
@@ -0,0 +1,11250 @@
+# Copyright 2002 Gary Strangman.  All rights reserved
+# Copyright 2002-2016 The SciPy Developers
+#
+# The original code from Gary Strangman was heavily adapted for
+# use in SciPy by Travis Oliphant.  The original code came with the
+# following disclaimer:
+#
+# This software is provided "as-is".  There are no expressed or implied
+# warranties of any kind, including, but not limited to, the warranties
+# of merchantability and fitness for a given application.  In no event
+# shall Gary Strangman be liable for any direct, indirect, incidental,
+# special, exemplary or consequential damages (including, but not limited
+# to, loss of use, data or profits, or business interruption) however
+# caused and on any theory of liability, whether in contract, strict
+# liability or tort (including negligence or otherwise) arising in any way
+# out of the use of this software, even if advised of the possibility of
+# such damage.
+
+"""
+A collection of basic statistical functions for Python.
+
+References
+----------
+.. [CRCProbStat2000] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
+   Probability and Statistics Tables and Formulae. Chapman & Hall: New
+   York. 2000.
+
+"""
+import math
+import itertools
+import operator
+import warnings
+from collections import namedtuple
+from collections.abc import Sequence
+
+import numpy as np
+from numpy import array, asarray, ma
+
+from scipy import sparse
+from scipy.spatial.distance import cdist
+
+from scipy.optimize import milp, LinearConstraint
+from scipy.optimize.elementwise import find_root
+from scipy._lib._util import _get_nan, _rename_parameter, _contains_nan
+
+import scipy.special as special
+# Import unused here but needs to stay until end of deprecation periode
+# See https://github.com/scipy/scipy/issues/15765#issuecomment-1875564522
+from scipy import linalg  # noqa: F401
+from . import distributions
+from . import _mstats_basic as mstats_basic
+
+from ._stats import _kendall_dis, _toint64, _weightedrankedtau
+
+from dataclasses import dataclass, field
+from ._stats_pythran import _compute_outer_prob_inside_method
+from ._resampling import (MonteCarloMethod, PermutationMethod, BootstrapMethod,
+                          monte_carlo_test, permutation_test, bootstrap,)
+from ._axis_nan_policy import (_axis_nan_policy_factory, _broadcast_shapes,
+                               _broadcast_arrays,
+                               _broadcast_array_shapes_remove_axis, SmallSampleWarning,
+                               too_small_1d_not_omit, too_small_1d_omit,
+                               too_small_nd_not_omit, too_small_nd_omit)
+from ._binomtest import _binary_search_for_binom_tst as _binary_search, _SimpleBinomial
+from scipy._lib._bunch import _make_tuple_bunch
+from scipy import stats
+from scipy._lib._array_api import (
+    _asarray,
+    array_namespace,
+    is_lazy_array,
+    is_dask,
+    is_numpy,
+    is_cupy,
+    is_jax,
+    is_marray,
+    xp_size,
+    xp_vector_norm,
+    xp_promote,
+    xp_result_type,
+    xp_capabilities,
+    xp_ravel,
+    _count_nonmasked,
+    _share_masks,
+    _masked_apply,
+    xp_swapaxes,
+    xp_device,
+)
+import scipy._external.array_api_extra as xpx
+from scipy.stats._quantile import _xp_searchsorted
+
+
+# Functions/classes in other files should be added in `__init__.py`, not here
+__all__ = ['gmean', 'hmean', 'pmean', 'mode', 'tmean', 'tvar',
+           'tmin', 'tmax', 'tstd', 'tsem', 'moment',
+           'skew', 'kurtosis', 'describe', 'skewtest', 'kurtosistest',
+           'normaltest', 'jarque_bera',
+           'scoreatpercentile', 'percentileofscore',
+           'cumfreq', 'relfreq', 'obrientransform',
+           'sem', 'zmap', 'zscore', 'gzscore', 'iqr', 'gstd',
+           'median_abs_deviation',
+           'sigmaclip', 'trimboth', 'trim1', 'trim_mean',
+           'f_oneway', 'pearsonr', 'fisher_exact',
+           'spearmanr', 'pointbiserialr',
+           'kendalltau', 'weightedtau',
+           'linregress', 'ttest_1samp',
+           'ttest_ind', 'ttest_ind_from_stats', 'ttest_rel',
+           'kstest', 'ks_1samp', 'ks_2samp',
+           'chisquare', 'power_divergence',
+           'tiecorrect', 'ranksums', 'kruskal', 'friedmanchisquare',
+           'rankdata', 'combine_pvalues', 'quantile_test',
+           'wasserstein_distance', 'wasserstein_distance_nd', 'energy_distance',
+           'brunnermunzel', 'alexandergovern',
+           'expectile', 'lmoment']
+
+
+def _chk_asarray(a, axis, *, xp=None):
+    if xp is None:
+        xp = array_namespace(a)
+
+    if axis is None:
+        a = xp.reshape(a, (-1,))
+        outaxis = 0
+    else:
+        a = xp.asarray(a)
+        outaxis = axis
+
+    if a.ndim == 0:
+        a = xp.reshape(a, (-1,))
+
+    return a, outaxis
+
+
+SignificanceResult = _make_tuple_bunch('SignificanceResult',
+                                       ['statistic', 'pvalue'], [])
+# Let's call a SignificanceResult with legacy :correlation" attribute a
+# "CorrelationResult". Don't add to `extra_field_names`- shouldn't be in repr.
+
+
+def _pack_CorrelationResult(statistic, pvalue, correlation):
+    res = SignificanceResult(statistic, pvalue)
+    res.correlation = correlation
+    return res
+
+
+def _unpack_CorrelationResult(res, _):
+    return res.statistic, res.pvalue, res.correlation
+
+
+# note that `weights` are paired with `x`
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+        lambda x: x, n_samples=1, n_outputs=1, too_small=0, paired=True,
+        result_to_tuple=lambda x, _: (x,), kwd_samples=['weights'])
+def gmean(a, axis=0, dtype=None, weights=None):
+    r"""Compute the weighted geometric mean along the specified axis.
+
+    The weighted geometric mean of the array :math:`a_i` associated to weights
+    :math:`w_i` is:
+
+    .. math::
+
+        \exp \left( \frac{ \sum_{i=1}^n w_i \ln a_i }{ \sum_{i=1}^n w_i }
+                   \right) \, ,
+
+    and, with equal weights, it gives:
+
+    .. math::
+
+        \sqrt[n]{ \prod_{i=1}^n a_i } \, .
+
+    Parameters
+    ----------
+    a : array_like
+        Input array or object that can be converted to an array.
+    axis : int or None, optional
+        Axis along which the geometric mean is computed. Default is 0.
+        If None, compute over the whole array `a`.
+    dtype : dtype, optional
+        (Floating point) dtype to which numerical arguments are cast before performing
+        the calculation. If `dtype` is not specified, it defaults to the floating point
+        result dtype of the numerical arguments.
+    weights : array_like, optional
+        The `weights` array must be broadcastable to the same shape as `a`.
+        Default is None, which gives each value a weight of 1.0.
+
+    Returns
+    -------
+    gmean : ndarray
+        See `dtype` parameter above.
+
+    See Also
+    --------
+    numpy.mean : Arithmetic average
+    numpy.average : Weighted average
+    hmean : Harmonic mean
+
+    Notes
+    -----
+    The sample geometric mean is the exponential of the mean of the natural
+    logarithms of the observations.
+    Negative observations will produce NaNs in the output because the *natural*
+    logarithm (as opposed to the *complex* logarithm) is defined only for
+    non-negative reals.
+
+    References
+    ----------
+    .. [1] "Weighted Geometric Mean", *Wikipedia*,
+           https://en.wikipedia.org/wiki/Weighted_geometric_mean.
+    .. [2] Grossman, J., Grossman, M., Katz, R., "Averages: A New Approach",
+           Archimedes Foundation, 1983
+
+    Examples
+    --------
+    >>> from scipy.stats import gmean
+    >>> gmean([1, 4])
+    2.0
+    >>> gmean([1, 2, 3, 4, 5, 6, 7])
+    3.3800151591412964
+    >>> gmean([1, 4, 7], weights=[3, 1, 3])
+    2.80668351922014
+
+    """
+    xp = array_namespace(a, weights)
+    dtype = (xp_result_type(a, weights, force_floating=True, xp=xp)
+             if dtype is None else dtype)
+    a = xp.asarray(a, dtype=dtype)
+
+    if weights is not None:
+        weights = xp.asarray(weights, dtype=dtype)
+
+    with np.errstate(divide='ignore'):
+        log_a = xp.log(a)
+
+    return xp.exp(_xp_mean(log_a, axis=axis, weights=weights))
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+        lambda x: x, n_samples=1, n_outputs=1, too_small=0, paired=True,
+        result_to_tuple=lambda x, _: (x,), kwd_samples=['weights'])
+def hmean(a, axis=0, dtype=None, *, weights=None):
+    r"""Calculate the weighted harmonic mean along the specified axis.
+
+    The weighted harmonic mean of the array :math:`a_i` associated to weights
+    :math:`w_i` is:
+
+    .. math::
+
+        \frac{ \sum_{i=1}^n w_i }{ \sum_{i=1}^n \frac{w_i}{a_i} } \, ,
+
+    and, with equal weights, it gives:
+
+    .. math::
+
+        \frac{ n }{ \sum_{i=1}^n \frac{1}{a_i} } \, .
+
+    Parameters
+    ----------
+    a : array_like
+        Input array, masked array or object that can be converted to an array.
+    axis : int or None, optional
+        Axis along which the harmonic mean is computed. Default is 0.
+        If None, compute over the whole array `a`.
+    dtype : dtype, optional
+        (Floating point) dtype to which numerical arguments are cast before performing
+        the calculation. If `dtype` is not specified, it defaults to the floating point
+        result dtype of the numerical arguments.
+    weights : array_like, optional
+        The weights array can either be 1-D (in which case its length must be
+        the size of `a` along the given `axis`) or of the same shape as `a`.
+        Default is None, which gives each value a weight of 1.0.
+
+        .. versionadded:: 1.9
+
+    Returns
+    -------
+    hmean : ndarray
+        See `dtype` parameter above.
+
+    See Also
+    --------
+    numpy.mean : Arithmetic average
+    numpy.average : Weighted average
+    gmean : Geometric mean
+
+    Notes
+    -----
+    The sample harmonic mean is the reciprocal of the mean of the reciprocals
+    of the observations.
+
+    The harmonic mean is computed over a single dimension of the input
+    array, axis=0 by default, or all values in the array if axis=None.
+    float64 intermediate and return values are used for integer inputs.
+
+    The harmonic mean is only defined if all observations are non-negative;
+    otherwise, the result is NaN.
+
+    References
+    ----------
+    .. [1] "Weighted Harmonic Mean", *Wikipedia*,
+           https://en.wikipedia.org/wiki/Harmonic_mean#Weighted_harmonic_mean
+    .. [2] Ferger, F., "The nature and use of the harmonic mean", Journal of
+           the American Statistical Association, vol. 26, pp. 36-40, 1931
+
+    Examples
+    --------
+    >>> from scipy.stats import hmean
+    >>> hmean([1, 4])
+    1.6000000000000001
+    >>> hmean([1, 2, 3, 4, 5, 6, 7])
+    2.6997245179063363
+    >>> hmean([1, 4, 7], weights=[3, 1, 3])
+    1.9029126213592233
+
+    """
+    xp = array_namespace(a, weights)
+    dtype = (xp_result_type(a, weights, force_floating=True, xp=xp)
+             if dtype is None else dtype)
+    a = xp.asarray(a, dtype=dtype)
+
+    if weights is not None:
+        weights = xp.asarray(weights, dtype=dtype)
+
+    negative_mask = a < 0
+    a = xp.where(negative_mask, xp.nan, a)
+
+    if not is_lazy_array(negative_mask) and xp.any(negative_mask):
+        message = ("The harmonic mean is only defined if all elements are "
+                   "non-negative; otherwise, the result is NaN.")
+        warnings.warn(message, RuntimeWarning, stacklevel=2)
+
+    with np.errstate(divide='ignore'):
+        return 1.0 / _xp_mean(1.0 / a, axis=axis, weights=weights)
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+        lambda x: x, n_samples=1, n_outputs=1, too_small=0, paired=True,
+        result_to_tuple=lambda x, _: (x,), kwd_samples=['weights'])
+def pmean(a, p, *, axis=0, dtype=None, weights=None):
+    r"""Calculate the weighted power mean along the specified axis.
+
+    The weighted power mean of the array :math:`a_i` associated to weights
+    :math:`w_i` is:
+
+    .. math::
+
+        \left( \frac{ \sum_{i=1}^n w_i a_i^p }{ \sum_{i=1}^n w_i }
+              \right)^{ 1 / p } \, ,
+
+    and, with equal weights, it gives:
+
+    .. math::
+
+        \left( \frac{ 1 }{ n } \sum_{i=1}^n a_i^p \right)^{ 1 / p }  \, .
+
+    When ``p=0``, it returns the geometric mean.
+
+    This mean is also called generalized mean or Hölder mean, and must not be
+    confused with the Kolmogorov generalized mean, also called
+    quasi-arithmetic mean or generalized f-mean [3]_.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array, masked array or object that can be converted to an array.
+    p : int or float
+        Exponent. Must be finite.
+    axis : int or None, optional
+        Axis along which the power mean is computed. Default is 0.
+        If None, compute over the whole array `a`.
+    dtype : dtype, optional
+        (Floating point) dtype to which numerical arguments are cast before performing
+        the calculation. If `dtype` is not specified, it defaults to the floating point
+        result dtype of the numerical arguments.
+    weights : array_like, optional
+        The weights array can either be 1-D (in which case its length must be
+        the size of `a` along the given `axis`) or of the same shape as `a`.
+        Default is None, which gives each value a weight of 1.0.
+
+    Returns
+    -------
+    pmean : ndarray, see `dtype` parameter above.
+        Output array containing the power mean values.
+
+    See Also
+    --------
+    numpy.average : Weighted average
+    gmean : Geometric mean
+    hmean : Harmonic mean
+
+    Notes
+    -----
+    The power mean is computed over a single dimension of the input
+    array, ``axis=0`` by default, or all values in the array if ``axis=None``.
+    float64 intermediate and return values are used for integer inputs.
+
+    The power mean is only defined if all observations are non-negative;
+    otherwise, the result is NaN.
+
+    .. versionadded:: 1.9
+
+    References
+    ----------
+    .. [1] "Generalized Mean", *Wikipedia*,
+           https://en.wikipedia.org/wiki/Generalized_mean
+    .. [2] Norris, N., "Convexity properties of generalized mean value
+           functions", The Annals of Mathematical Statistics, vol. 8,
+           pp. 118-120, 1937
+    .. [3] Bullen, P.S., Handbook of Means and Their Inequalities, 2003
+
+    Examples
+    --------
+    >>> from scipy.stats import pmean, hmean, gmean
+    >>> pmean([1, 4], 1.3)
+    2.639372938300652
+    >>> pmean([1, 2, 3, 4, 5, 6, 7], 1.3)
+    4.157111214492084
+    >>> pmean([1, 4, 7], -2, weights=[3, 1, 3])
+    1.4969684896631954
+
+    For p=-1, power mean is equal to harmonic mean:
+
+    >>> pmean([1, 4, 7], -1, weights=[3, 1, 3])
+    1.9029126213592233
+    >>> hmean([1, 4, 7], weights=[3, 1, 3])
+    1.9029126213592233
+
+    For p=0, power mean is defined as the geometric mean:
+
+    >>> pmean([1, 4, 7], 0, weights=[3, 1, 3])
+    2.80668351922014
+    >>> gmean([1, 4, 7], weights=[3, 1, 3])
+    2.80668351922014
+
+    """
+    if not isinstance(p, int | float):
+        raise ValueError("Power mean only defined for exponent of type int or "
+                         "float.")
+    if p == 0:
+        return gmean(a, axis=axis, dtype=dtype, weights=weights)
+    elif math.isinf(p):
+        message = "Power mean only implemented for finite `p`"
+        raise NotImplementedError(message)
+
+    xp = array_namespace(a, weights)
+    dtype = (xp_result_type(a, p, weights, force_floating=True, xp=xp)
+             if dtype is None else dtype)
+    a = xp.asarray(a, dtype=dtype)
+
+    if weights is not None:
+        weights = xp.asarray(weights, dtype=dtype)
+
+    negative_mask = a < 0
+    a = xp.where(negative_mask, np.nan, a)
+
+    if not is_lazy_array(negative_mask) and xp.any(negative_mask):
+        message = ("The power mean is only defined if all elements are "
+                   "non-negative; otherwise, the result is NaN.")
+        warnings.warn(message, RuntimeWarning, stacklevel=2)
+
+    # Linearized approximation for small p to avoid numerical issues; see gh-23407
+    p_threshold = 2e-6
+    if abs(p) <= p_threshold:
+        return _linearized_pmean(a, p, axis=axis, weights=weights, xp=xp)
+
+    with np.errstate(divide='ignore', invalid='ignore'):
+        return _xp_mean(a**float(p), axis=axis, weights=weights)**(1/p)
+
+
+ModeResult = namedtuple('ModeResult', ('mode', 'count'))
+
+
+def _mode_result(mode, count):
+    # When a slice is empty, `_axis_nan_policy` automatically produces
+    # NaN for `mode` and `count`. This is a reasonable convention for `mode`,
+    # but `count` should not be NaN; it should be zero.
+    xp = array_namespace(mode, count)
+    i = xp.isnan(count)
+    if i.shape == ():
+        count = xp.asarray(0, dtype=count.dtype)[()] if i else count
+    else:
+        count = xpx.at(count)[i].set(0)
+    if is_numpy(xp) and mode.ndim > 0:
+        # When a) xp is NumPy, b) array is multidimensional, c) NaNs are present,
+        # and d) nan_policy='omit', `_axis_nan_policy` decorator uses
+        # `np.apply_along_axis` to compute mode/count along each axis-slice separately.
+        # The result is a single array with the result dtype of `mode` and `count`.
+        # We need to change `count` back to an integer.
+        count = xp.astype(count, xp.asarray(0).dtype)
+    return ModeResult(mode, count)
+
+
+@xp_capabilities(skip_backends=[('dask.array', "can't compute chunk size"),
+                                ('cupy', "data-apis/array-api-compat#312")],
+                 jax_jit=False,  # would delegate, but jax-ml/jax#34486
+                 marray=True,
+                 extra_note=("Values in MArray input must be real numbers "
+                             "that cast safely to floating point."))
+@_axis_nan_policy_factory(_mode_result, override={'nan_propagation': False})
+def mode(a, axis=0, nan_policy='propagate', keepdims=False):
+    r"""Return an array of the modal (most common) value in the passed array.
+
+    If there is more than one such value, only one is returned.
+    The bin-count for the modal bins is also returned.
+
+    Parameters
+    ----------
+    a : array_like
+        Numeric, n-dimensional array of which to find mode(s).
+    axis : int or None, optional
+        Axis along which to operate. Default is 0. If None, compute over
+        the whole array `a`.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': treats nan as it would treat any other value
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    keepdims : bool, optional
+        If set to ``False``, the `axis` over which the statistic is taken
+        is consumed (eliminated from the output array). If set to ``True``,
+        the `axis` is retained with size one, and the result will broadcast
+        correctly against the input array.
+
+    Returns
+    -------
+    mode : ndarray
+        Array of modal values.
+    count : ndarray
+        Array of counts for each mode.
+
+    Notes
+    -----
+    The mode  is calculated using `numpy.unique`.
+    In NumPy versions 1.21 and after, all NaNs - even those with different
+    binary representations - are treated as equivalent and counted as separate
+    instances of the same value.
+
+    By convention, the mode of an empty array is NaN, and the associated count
+    is zero.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([[3, 0, 3, 7],
+    ...               [3, 2, 6, 2],
+    ...               [1, 7, 2, 8],
+    ...               [3, 0, 6, 1],
+    ...               [3, 2, 5, 5]])
+    >>> from scipy import stats
+    >>> stats.mode(a, keepdims=True)
+    ModeResult(mode=array([[3, 0, 6, 1]]), count=array([[4, 2, 2, 1]]))
+
+    To get mode of whole array, specify ``axis=None``:
+
+    >>> stats.mode(a, axis=None, keepdims=True)
+    ModeResult(mode=[[3]], count=[[5]])
+    >>> stats.mode(a, axis=None, keepdims=False)
+    ModeResult(mode=3, count=5)
+
+    """
+    xp = array_namespace(a)
+
+    # `axis`, `nan_policy`, and `keepdims` are handled by `_axis_nan_policy`
+    if not xp.isdtype(a.dtype, 'numeric'):
+        message = ("Argument `a` is not recognized as numeric. "
+                   "Support for input that cannot be coerced to a numeric "
+                   "array was deprecated in SciPy 1.9.0 and removed in SciPy "
+                   "1.11.0. Please consider `np.unique`.")
+        raise TypeError(message)
+
+    if xp_size(a) == 0:
+        NaN = _get_nan(a, xp=xp)
+        return ModeResult(*xp.asarray([NaN, 0], dtype=NaN.dtype))
+
+    if a.ndim == 1 and not is_marray(xp):
+        vals, cnts = xp.unique_counts(a)
+        # in contrast with np.unique, `unique_counts` treats all NaNs as distinct,
+        # but we have always grouped them. Replace `cnts` corresponding with NaNs
+        # with the number of NaNs.
+        mask = xp.isnan(vals)
+        cnts = xpx.at(cnts)[mask].set(xp.count_nonzero(mask))
+        modes, counts = vals[xp.argmax(cnts)], xp.max(cnts)
+        default_int = xp.asarray(1).dtype  # fail slow CI job failed - incorrect dtype
+        counts = xp.astype(counts, default_int, copy=False)
+        modes = modes[()] if modes.ndim == 0 else modes
+        counts = counts[()] if counts.ndim == 0 else counts
+        return ModeResult(modes, counts)
+
+    # `axis` is always -1 after the `_axis_nan_policy` decorator
+    y = xp.sort(a, axis=-1)
+
+    # Get boolean array of elements that are different from the previous element
+    if is_marray(xp):
+        # Treat masked elements as NaNs and *don't* count NaNs as equal. Two downsides:
+        # - integer arrays are converted to floating point, so some very large distinct
+        #   integers will no longer be distinct.
+        # - NaNs in the original data *will* be treated as distinct.
+        y_data = xp_promote(y.data, force_floating=True, xp=y._xp)
+        y_data = y._xp.where(y.mask, y._xp.nan, y_data)
+        i = xp.concat([xp.ones(y.shape[:-1] + (1,), dtype=xp.bool),
+                      (y_data[..., :-1] != y_data[..., 1:])], axis=-1)
+    else:
+        i = xp.concat([xp.ones(y.shape[:-1] + (1,), dtype=xp.bool),
+                      (y[..., :-1] != y[..., 1:]) & ~xp.isnan(y[..., :-1])], axis=-1)
+    # Get linear integer indices of these elements in a raveled array
+    indices = xp.arange(xp_size(y), device=xp_device(y))[xp_ravel(i)]
+    # The difference between integer indices is the number of repeats
+    append = xp.full(indices.shape[:-1] + (1,), xp_size(y), dtype=indices.dtype)
+    counts = xp.diff(indices, append=append)
+    # Now we form an array of `counts` corresponding with each element of `y`...
+    counts = xp.reshape(xp.repeat(counts, counts), y.shape)
+    # ... so we can get the argmax of *each slice* separately.
+    k = xp.argmax(counts, axis=-1, keepdims=True)
+    # Extract the corresponding element/count, and eliminate the reduced dimension
+    modes = xp.take_along_axis(y, k, axis=-1)[..., 0]
+    counts = xp.take_along_axis(counts, k, axis=-1)[..., 0]
+    modes = modes[()] if modes.ndim == 0 else modes
+    counts = counts[()] if counts.ndim == 0 else counts
+    return ModeResult(modes, counts)
+
+
+def _put_val_to_limits(a, limits, inclusive, val=np.nan, xp=None):
+    """Replace elements outside limits with a value.
+
+    This is primarily a utility function.
+
+    Parameters
+    ----------
+    a : array
+    limits : (float or None, float or None)
+        A tuple consisting of the (lower limit, upper limit).  Elements in the
+        input array less than the lower limit or greater than the upper limit
+        will be replaced with `val`. None implies no limit.
+    inclusive : (bool, bool)
+        A tuple consisting of the (lower flag, upper flag).  These flags
+        determine whether values exactly equal to lower or upper are allowed.
+    val : float, default: NaN
+        The value with which extreme elements of the array are replaced.
+
+    """
+    xp = array_namespace(a) if xp is None else xp
+    mask = xp.zeros_like(a, dtype=xp.bool)
+    if limits is None:
+        return a, mask
+    lower_limit, upper_limit = limits
+    lower_include, upper_include = inclusive
+    if lower_limit is not None:
+        mask = mask | ((a < lower_limit) if lower_include else a <= lower_limit)
+    if upper_limit is not None:
+        mask = mask | ((a > upper_limit) if upper_include else a >= upper_limit)
+    lazy = is_lazy_array(mask)
+    if not lazy and xp.all(mask):
+        raise ValueError("No array values within given limits")
+    if lazy or xp.any(mask):
+        a = xp.where(mask, val, a)
+    return a, mask
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, n_outputs=1, default_axis=None,
+    result_to_tuple=lambda x, _: (x,)
+)
+def tmean(a, limits=None, inclusive=(True, True), axis=None):
+    """Compute the trimmed mean.
+
+    This function finds the arithmetic mean of given values, ignoring values
+    outside the given `limits`.
+
+    Parameters
+    ----------
+    a : array_like
+        Array of values.
+    limits : None or (lower limit, upper limit), optional
+        Values in the input array less than the lower limit or greater than the
+        upper limit will be ignored.  When limits is None (default), then all
+        values are used.  Either of the limit values in the tuple can also be
+        None representing a half-open interval.
+    inclusive : (bool, bool), optional
+        A tuple consisting of the (lower flag, upper flag).  These flags
+        determine whether values exactly equal to the lower or upper limits
+        are included.  The default value is (True, True).
+    axis : int or None, optional
+        Axis along which to compute test. Default is None.
+
+    Returns
+    -------
+    tmean : ndarray
+        Trimmed mean.
+
+    See Also
+    --------
+    trim_mean : Returns mean after trimming a proportion from both tails.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> x = np.arange(20)
+    >>> stats.tmean(x)
+    9.5
+    >>> stats.tmean(x, (3,17))
+    10.0
+
+    """
+    xp = array_namespace(a)
+    a, mask = _put_val_to_limits(a, limits, inclusive, val=0., xp=xp)
+    # explicit dtype specification required due to data-apis/array-api-compat#152
+    sum = xp.sum(a, axis=axis, dtype=a.dtype)
+    n = xp.sum(xp.asarray(~mask, dtype=a.dtype, device=xp_device(a)), axis=axis,
+               dtype=a.dtype)
+    mean = xpx.apply_where(n != 0, (sum, n), operator.truediv, fill_value=xp.nan)
+    return mean[()] if mean.ndim == 0 else mean
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, n_outputs=1, result_to_tuple=lambda x, _: (x,)
+)
+def tvar(a, limits=None, inclusive=(True, True), axis=0, ddof=1):
+    """Compute the trimmed variance.
+
+    This function computes the sample variance of an array of values,
+    while ignoring values which are outside of given `limits`.
+
+    Parameters
+    ----------
+    a : array_like
+        Array of values.
+    limits : None or (lower limit, upper limit), optional
+        Values in the input array less than the lower limit or greater than the
+        upper limit will be ignored. When limits is None, then all values are
+        used. Either of the limit values in the tuple can also be None
+        representing a half-open interval.  The default value is None.
+    inclusive : (bool, bool), optional
+        A tuple consisting of the (lower flag, upper flag).  These flags
+        determine whether values exactly equal to the lower or upper limits
+        are included.  The default value is (True, True).
+    axis : int or None, optional
+        Axis along which to operate. Default is 0. If None, compute over the
+        whole array `a`.
+    ddof : int, optional
+        Delta degrees of freedom.  Default is 1.
+
+    Returns
+    -------
+    tvar : float
+        Trimmed variance.
+
+    Notes
+    -----
+    `tvar` computes the unbiased sample variance, i.e. it uses a correction
+    factor ``n / (n - 1)``.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> x = np.arange(20)
+    >>> stats.tvar(x)
+    35.0
+    >>> stats.tvar(x, (3,17))
+    20.0
+
+    """
+    xp = array_namespace(a)
+    a, _ = _put_val_to_limits(a, limits, inclusive, xp=xp)
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", SmallSampleWarning)
+        # Currently, this behaves like nan_policy='omit' for alternative array
+        # backends, but nan_policy='propagate' will be handled for other backends
+        # by the axis_nan_policy decorator shortly.
+        return _xp_var(a, correction=ddof, axis=axis, nan_policy='omit', xp=xp)
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, n_outputs=1, result_to_tuple=lambda x, _: (x,)
+)
+def tmin(a, lowerlimit=None, axis=0, inclusive=True, nan_policy='propagate'):
+    """Compute the trimmed minimum.
+
+    This function finds the minimum value of an array `a` along the
+    specified axis, but only considering values greater than a specified
+    lower limit.
+
+    Parameters
+    ----------
+    a : array_like
+        Array of values.
+    lowerlimit : None or float, optional
+        Values in the input array less than the given limit will be ignored.
+        When lowerlimit is None, then all values are used. The default value
+        is None.
+    axis : int or None, optional
+        Axis along which to operate. Default is 0. If None, compute over the
+        whole array `a`.
+    inclusive : {True, False}, optional
+        This flag determines whether values exactly equal to the lower limit
+        are included.  The default value is True.
+
+    Returns
+    -------
+    tmin : float, int or ndarray
+        Trimmed minimum.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> x = np.arange(20)
+    >>> stats.tmin(x)
+    0
+
+    >>> stats.tmin(x, 13)
+    13
+
+    >>> stats.tmin(x, 13, inclusive=False)
+    14
+
+    """
+    xp = array_namespace(a)
+
+    max_ = xp.iinfo(a.dtype).max if xp.isdtype(a.dtype, 'integral') else xp.inf
+    a, mask = _put_val_to_limits(a, (lowerlimit, None), (inclusive, None),
+                                 val=max_, xp=xp)
+
+    res = xp.min(a, axis=axis)
+    invalid = xp.all(mask, axis=axis)  # All elements are below lowerlimit
+
+    # For eager backends, output dtype is data-dependent
+    if is_lazy_array(invalid) or xp.any(invalid):
+        # Possible loss of precision for int types
+        res = xp_promote(res, force_floating=True, xp=xp)
+        res = xp.where(invalid, xp.nan, res)
+
+    return res[()] if res.ndim == 0 else res
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, n_outputs=1, result_to_tuple=lambda x, _: (x,)
+)
+def tmax(a, upperlimit=None, axis=0, inclusive=True, nan_policy='propagate'):
+    """Compute the trimmed maximum.
+
+    This function computes the maximum value of an array along a given axis,
+    while ignoring values larger than a specified upper limit.
+
+    Parameters
+    ----------
+    a : array_like
+        Array of values.
+    upperlimit : None or float, optional
+        Values in the input array greater than the given limit will be ignored.
+        When upperlimit is None, then all values are used. The default value
+        is None.
+    axis : int or None, optional
+        Axis along which to operate. Default is 0. If None, compute over the
+        whole array `a`.
+    inclusive : {True, False}, optional
+        This flag determines whether values exactly equal to the upper limit
+        are included.  The default value is True.
+
+    Returns
+    -------
+    tmax : float, int or ndarray
+        Trimmed maximum.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> x = np.arange(20)
+    >>> stats.tmax(x)
+    19
+
+    >>> stats.tmax(x, 13)
+    13
+
+    >>> stats.tmax(x, 13, inclusive=False)
+    12
+
+    """
+    xp = array_namespace(a)
+
+    min_ = xp.iinfo(a.dtype).min if xp.isdtype(a.dtype, 'integral') else -xp.inf
+    a, mask = _put_val_to_limits(a, (None, upperlimit), (None, inclusive),
+                                 val=min_, xp=xp)
+
+    res = xp.max(a, axis=axis)
+    invalid = xp.all(mask, axis=axis)  # All elements are above upperlimit
+
+    # For eager backends, output dtype is data-dependent
+    if is_lazy_array(invalid) or xp.any(invalid):
+        # Possible loss of precision for int types
+        res = xp_promote(res, force_floating=True, xp=xp)
+        res = xp.where(invalid, xp.nan, res)
+
+    return res[()] if res.ndim == 0 else res
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, n_outputs=1, result_to_tuple=lambda x, _: (x,)
+)
+def tstd(a, limits=None, inclusive=(True, True), axis=0, ddof=1):
+    """Compute the trimmed sample standard deviation.
+
+    This function finds the sample standard deviation of given values,
+    ignoring values outside the given `limits`.
+
+    Parameters
+    ----------
+    a : array_like
+        Array of values.
+    limits : None or (lower limit, upper limit), optional
+        Values in the input array less than the lower limit or greater than the
+        upper limit will be ignored. When limits is None, then all values are
+        used. Either of the limit values in the tuple can also be None
+        representing a half-open interval.  The default value is None.
+    inclusive : (bool, bool), optional
+        A tuple consisting of the (lower flag, upper flag).  These flags
+        determine whether values exactly equal to the lower or upper limits
+        are included.  The default value is (True, True).
+    axis : int or None, optional
+        Axis along which to operate. Default is 0. If None, compute over the
+        whole array `a`.
+    ddof : int, optional
+        Delta degrees of freedom.  Default is 1.
+
+    Returns
+    -------
+    tstd : float
+        Trimmed sample standard deviation.
+
+    Notes
+    -----
+    `tstd` computes the unbiased sample standard deviation, i.e. it uses a
+    correction factor ``n / (n - 1)``.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> x = np.arange(20)
+    >>> stats.tstd(x)
+    5.9160797830996161
+    >>> stats.tstd(x, (3,17))
+    4.4721359549995796
+
+    """
+    return tvar(a, limits, inclusive, axis, ddof, _no_deco=True)**0.5
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, n_outputs=1, result_to_tuple=lambda x, _: (x,)
+)
+def tsem(a, limits=None, inclusive=(True, True), axis=0, ddof=1):
+    """Compute the trimmed standard error of the mean.
+
+    This function finds the standard error of the mean for given
+    values, ignoring values outside the given `limits`.
+
+    Parameters
+    ----------
+    a : array_like
+        Array of values.
+    limits : None or (lower limit, upper limit), optional
+        Values in the input array less than the lower limit or greater than the
+        upper limit will be ignored. When limits is None, then all values are
+        used. Either of the limit values in the tuple can also be None
+        representing a half-open interval.  The default value is None.
+    inclusive : (bool, bool), optional
+        A tuple consisting of the (lower flag, upper flag).  These flags
+        determine whether values exactly equal to the lower or upper limits
+        are included.  The default value is (True, True).
+    axis : int or None, optional
+        Axis along which to operate. Default is 0. If None, compute over the
+        whole array `a`.
+    ddof : int, optional
+        Delta degrees of freedom.  Default is 1.
+
+    Returns
+    -------
+    tsem : float
+        Trimmed standard error of the mean.
+
+    Notes
+    -----
+    `tsem` uses unbiased sample standard deviation, i.e. it uses a
+    correction factor ``n / (n - 1)``.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> x = np.arange(20)
+    >>> stats.tsem(x)
+    1.3228756555322954
+    >>> stats.tsem(x, (3,17))
+    1.1547005383792515
+
+    """
+    xp = array_namespace(a)
+    a, _ = _put_val_to_limits(a, limits, inclusive, xp=xp)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", SmallSampleWarning)
+        # Currently, this behaves like nan_policy='omit' for alternative array
+        # backends, but nan_policy='propagate' will be handled for other backends
+        # by the axis_nan_policy decorator shortly.
+        sd = _xp_var(a, correction=ddof, axis=axis, nan_policy='omit', xp=xp)**0.5
+
+    not_nan = xp.astype(~xp.isnan(a), a.dtype)
+    n_obs = xp.sum(not_nan, axis=axis, dtype=sd.dtype)
+    return sd / n_obs**0.5
+
+
+#####################################
+#              MOMENTS              #
+#####################################
+
+
+def _moment_outputs(kwds, default_order=1):
+    order = np.atleast_1d(kwds.get('order', default_order))
+    message = "`order` must be a scalar or a non-empty 1D array."
+    if order.size == 0 or order.ndim > 1:
+        raise ValueError(message)
+    return len(order)
+
+
+def _moment_result_object(*args):
+    if len(args) == 1:
+        return args[0]
+    xp = array_namespace(*args)
+    return xp.stack(args)
+
+
+# When `order` is array-like with size > 1, moment produces an *array*
+# rather than a tuple, but the zeroth dimension is to be treated like
+# separate outputs. It is important to make the distinction between
+# separate outputs when adding the reduced axes back (`keepdims=True`).
+def _moment_tuple(x, n_out):
+    return tuple(x[i, ...] for i in range(x.shape[0])) if n_out > 1 else (x,)
+
+
+# `moment` fits into the `_axis_nan_policy` pattern, but it is a bit unusual
+# because the number of outputs is variable. Specifically,
+# `result_to_tuple=lambda x: (x,)` may be surprising for a function that
+# can produce more than one output, but it is intended here.
+# When `moment is called to produce the output:
+# - `result_to_tuple` packs the returned array into a single-element tuple,
+# - `_moment_result_object` extracts and returns that single element.
+# However, when the input array is empty, `moment` is never called. Instead,
+# - `_check_empty_inputs` is used to produce an empty array with the
+#   appropriate dimensions.
+# - A list comprehension creates the appropriate number of copies of this
+#   array, depending on `n_outputs`.
+# - This list - which may have multiple elements - is passed into
+#   `_moment_result_object`.
+# - If there is a single output, `_moment_result_object` extracts and returns
+#   the single output from the list.
+# - If there are multiple outputs, and therefore multiple elements in the list,
+#   `_moment_result_object` converts the list of arrays to a single array and
+#   returns it.
+# Currently, this leads to a slight inconsistency: when the input array is
+# empty, there is no distinction between the `moment` function being called
+# with parameter `order=1` and `order=[1]`; the latter *should* produce
+# the same as the former but with a singleton zeroth dimension.
+@xp_capabilities(skip_backends=[('dask.array', 'needs axis_nan_policy decorator')],
+                 marray=True)
+@_rename_parameter('moment', 'order')
+@_axis_nan_policy_factory(  # noqa: E302
+    _moment_result_object, n_samples=1, result_to_tuple=_moment_tuple,
+    n_outputs=_moment_outputs
+)
+def moment(a, order=1, axis=0, nan_policy='propagate', *, center=None):
+    r"""Calculate the nth moment about the mean for a sample.
+
+    A moment is a specific quantitative measure of the shape of a set of
+    points. It is often used to calculate coefficients of skewness and kurtosis
+    due to its close relationship with them.
+
+    Parameters
+    ----------
+    a : array_like
+       Input array.
+    order : int or 1-D array_like of ints, optional
+       Order of central moment that is returned. Default is 1.
+    axis : int or None, optional
+       Axis along which the central moment is computed. Default is 0.
+       If None, compute over the whole array `a`.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    center : float or None, optional
+       The point about which moments are taken. This can be the sample mean,
+       the origin, or any other be point. If `None` (default) compute the
+       center as the sample mean.
+
+    Returns
+    -------
+    n-th moment about the `center` : ndarray or float
+       The appropriate moment along the given axis or over all values if axis
+       is None. The denominator for the moment calculation is the number of
+       observations, no degrees of freedom correction is done.
+
+    See Also
+    --------
+    kurtosis, skew, describe
+
+    Notes
+    -----
+    The k-th moment of a data sample is:
+
+    .. math::
+
+        m_k = \frac{1}{n} \sum_{i = 1}^n (x_i - c)^k
+
+    Where `n` is the number of samples, and `c` is the center around which the
+    moment is calculated. This function uses exponentiation by squares [1]_ for
+    efficiency.
+
+    Note that, if `a` is an empty array (``a.size == 0``), array `moment` with
+    one element (`moment.size == 1`) is treated the same as scalar `moment`
+    (``np.isscalar(moment)``). This might produce arrays of unexpected shape.
+
+    References
+    ----------
+    .. [1] https://eli.thegreenplace.net/2009/03/21/efficient-integer-exponentiation-algorithms
+
+    Examples
+    --------
+    >>> from scipy.stats import moment
+    >>> moment([1, 2, 3, 4, 5], order=1)
+    0.0
+    >>> moment([1, 2, 3, 4, 5], order=2)
+    2.0
+
+    """
+    xp = array_namespace(a)
+    a, center, order = xp_promote(a, center, order, force_floating=True, xp=xp)
+
+    if not is_lazy_array(order) and xp.any(order != xp.round(order)):
+        raise ValueError("All elements of `order` must be integral.")
+
+    # _axis_nan_policy decorator ensures that axis=-1
+    if order.ndim > 0:
+        order = xp.reshape(order, (-1,) + (1,)*a.ndim)
+        return _moment(a, order, axis=-1, center=center)
+    else:
+        res = _moment(a, order, axis=-1, center=center)
+        return res[()] if res.ndim == 0 else res
+
+
+def _demean(a, mean, axis, *, xp, precision_warning=True):
+    # subtracts `mean` from `a` and returns the result,
+    # warning if there is catastrophic cancellation. `mean`
+    # must be the mean of `a` along axis with `keepdims=True`.
+    # Used in e.g. `_moment`, `_zscore`, `_xp_var`. See gh-15905.
+    a_zero_mean = a - mean
+
+    if (xp_size(a_zero_mean) == 0 or not precision_warning
+        or is_lazy_array(a_zero_mean)):
+        return a_zero_mean
+
+    eps = xp.finfo(mean.dtype).eps * 10
+
+    with np.errstate(divide='ignore', invalid='ignore'):
+        rel_diff = xp.max(xp.abs(a_zero_mean), axis=axis,
+                          keepdims=True) / xp.abs(mean)
+
+    n = _count_nonmasked(a, axis, xp=xp)
+    with np.errstate(invalid='ignore'):
+        device = {'device': xp_device(a)}
+        precision_loss = xp.any(xp.asarray(rel_diff < eps, **device)
+                                & xp.asarray(n > 1, **device))
+
+    if precision_loss:
+        message = ("Precision loss occurred in moment calculation due to "
+                   "catastrophic cancellation. This occurs when the data "
+                   "are nearly identical. Results may be unreliable.")
+        warnings.warn(message, RuntimeWarning, stacklevel=5)
+    return a_zero_mean
+
+
+def _moment(a, order, axis, *, center=None, xp=None):
+    """Vectorized calculation of raw moment about specified center
+
+    When `center` is None, the mean is computed and used as the center;
+    otherwise, the provided value is used as the center.
+    """
+    xp = array_namespace(a) if xp is None else xp
+
+    order = xp.asarray(order, dtype=a.dtype, device=xp_device(a))
+    order_0 = order == 0
+    order_1 = (order == 1) & (center is None)
+    center = xp.mean(a, axis=axis, keepdims=True) if center is None else center
+    a_zero_mean = _demean(a, center, axis, xp=xp)
+    res = xp.mean(a_zero_mean**order, axis=axis, keepdims=True)
+    if a.shape[-1] > 0 and (is_lazy_array(res)
+                                or xp.any(order_0) or xp.any(order_1)):
+        res = xp.where(order_0, xp.ones_like(res), res)
+        res = xp.where(order_1, xp.zeros_like(res), res)
+
+    return xp.squeeze(res, axis=axis)
+
+
+def _var(x, axis=0, ddof=0, mean=None, xp=None):
+    # Calculate variance of sample, warning if precision is lost
+    xp = array_namespace(x) if xp is None else xp
+    var = _moment(x, 2, axis, center=mean, xp=xp)
+    if ddof != 0:
+        n = _count_nonmasked(x, axis, xp=xp)
+        n = xp.asarray(n, dtype=x.dtype, device=xp_device(x))
+        var *= (n / (n-ddof))  # to avoid error on division by zero
+    return var
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, result_to_tuple=lambda x, _: (x,), n_outputs=1
+)
+# nan_policy handled by `_axis_nan_policy`, but needs to be left
+# in signature to preserve use as a positional argument
+def skew(a, axis=0, bias=True, nan_policy='propagate'):
+    r"""Compute the sample skewness of a data set.
+
+    For normally distributed data, the skewness should be about zero. For
+    unimodal continuous distributions, a skewness value greater than zero means
+    that there is more weight in the right tail of the distribution. The
+    function `skewtest` can be used to determine if the skewness value
+    is close enough to zero, statistically speaking.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array.
+    axis : int or None, optional
+        Axis along which skewness is calculated. Default is 0.
+        If None, compute over the whole array `a`.
+    bias : bool, optional
+        If False, then the calculations are corrected for statistical bias.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    Returns
+    -------
+    skewness : ndarray
+        The skewness of values along an axis, returning NaN where all values
+        are equal.
+
+    Notes
+    -----
+    The sample skewness is computed as the Fisher-Pearson coefficient
+    of skewness, i.e.
+
+    .. math::
+
+        g_1=\frac{m_3}{m_2^{3/2}}
+
+    where
+
+    .. math::
+
+        m_i=\frac{1}{N}\sum_{n=1}^N(x[n]-\bar{x})^i
+
+    is the biased sample :math:`i\texttt{th}` central moment, and
+    :math:`\bar{x}` is
+    the sample mean.  If ``bias`` is False, the calculations are
+    corrected for bias and the value computed is the adjusted
+    Fisher-Pearson standardized moment coefficient, i.e.
+
+    .. math::
+
+        G_1=\frac{k_3}{k_2^{3/2}}=
+            \frac{\sqrt{N(N-1)}}{N-2}\frac{m_3}{m_2^{3/2}}.
+
+    References
+    ----------
+    .. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
+       Probability and Statistics Tables and Formulae. Chapman & Hall: New
+       York. 2000.
+       Section 2.2.24.1
+
+    Examples
+    --------
+    >>> from scipy.stats import skew
+    >>> skew([1, 2, 3, 4, 5])
+    0.0
+    >>> skew([2, 8, 0, 4, 1, 9, 9, 0])
+    0.2650554122698573
+
+    """
+    xp = array_namespace(a)
+    a, axis = _chk_asarray(a, axis, xp=xp)
+    n = _count_nonmasked(a, axis, xp=xp)
+
+    mean = xp.mean(a, axis=axis, keepdims=True)
+    mean_reduced = xp.squeeze(mean, axis=axis)  # needed later
+    m2 = _moment(a, 2, axis, center=mean, xp=xp)
+    m3 = _moment(a, 3, axis, center=mean, xp=xp)
+    with np.errstate(all='ignore'):
+        eps = xp.finfo(m2.dtype).eps
+        zero = m2 <= (eps * mean_reduced)**2
+        vals = xp.where(zero, xp.nan, m3 / m2**1.5)
+    if not bias:
+        can_correct = ~zero & (n > 2)
+        if is_lazy_array(can_correct) or xp.any(can_correct):
+            nval = ((n - 1.0) * n)**0.5 / (n - 2.0) * m3 / m2**1.5
+            vals = xp.where(can_correct, nval, vals)
+
+    return vals[()] if vals.ndim == 0 else vals
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, result_to_tuple=lambda x, _: (x,), n_outputs=1
+)
+# nan_policy handled by `_axis_nan_policy`, but needs to be left
+# in signature to preserve use as a positional argument
+def kurtosis(a, axis=0, fisher=True, bias=True, nan_policy='propagate'):
+    """Compute the kurtosis (Fisher or Pearson) of a dataset.
+
+    Kurtosis is the fourth central moment divided by the square of the
+    variance. If Fisher's definition is used, then 3.0 is subtracted from
+    the result to give 0.0 for a normal distribution.
+
+    If bias is False then the kurtosis is calculated using k statistics to
+    eliminate bias coming from biased moment estimators
+
+    Use `kurtosistest` to see if result is close enough to normal.
+
+    Parameters
+    ----------
+    a : array
+        Data for which the kurtosis is calculated.
+    axis : int or None, optional
+        Axis along which the kurtosis is calculated. Default is 0.
+        If None, compute over the whole array `a`.
+    fisher : bool, optional
+        If True, Fisher's definition is used (normal ==> 0.0). If False,
+        Pearson's definition is used (normal ==> 3.0).
+    bias : bool, optional
+        If False, then the calculations are corrected for statistical bias.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan. 'propagate' returns nan,
+        'raise' throws an error, 'omit' performs the calculations ignoring nan
+        values. Default is 'propagate'.
+
+    Returns
+    -------
+    kurtosis : array
+        The kurtosis of values along an axis, returning NaN where all values
+        are equal.
+
+    References
+    ----------
+    .. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
+       Probability and Statistics Tables and Formulae. Chapman & Hall: New
+       York. 2000.
+
+    Examples
+    --------
+    In Fisher's definition, the kurtosis of the normal distribution is zero.
+    In the following example, the kurtosis is close to zero, because it was
+    calculated from the dataset, not from the continuous distribution.
+
+    >>> import numpy as np
+    >>> from scipy.stats import norm, kurtosis
+    >>> data = norm.rvs(size=1000, random_state=3)
+    >>> kurtosis(data)
+    -0.06928694200380558
+
+    The distribution with a higher kurtosis has a heavier tail.
+    The zero valued kurtosis of the normal distribution in Fisher's definition
+    can serve as a reference point.
+
+    >>> import matplotlib.pyplot as plt
+    >>> import scipy.stats as stats
+    >>> from scipy.stats import kurtosis
+
+    >>> x = np.linspace(-5, 5, 100)
+    >>> ax = plt.subplot()
+    >>> distnames = ['laplace', 'norm', 'uniform']
+
+    >>> for distname in distnames:
+    ...     if distname == 'uniform':
+    ...         dist = getattr(stats, distname)(loc=-2, scale=4)
+    ...     else:
+    ...         dist = getattr(stats, distname)
+    ...     data = dist.rvs(size=1000)
+    ...     kur = kurtosis(data, fisher=True)
+    ...     y = dist.pdf(x)
+    ...     ax.plot(x, y, label="{}, {}".format(distname, round(kur, 3)))
+    ...     ax.legend()
+
+    The Laplace distribution has a heavier tail than the normal distribution.
+    The uniform distribution (which has negative kurtosis) has the thinnest
+    tail.
+
+    """
+    xp = array_namespace(a)
+    a, axis = _chk_asarray(a, axis, xp=xp)
+
+    n = _count_nonmasked(a, axis, xp=xp)
+    mean = xp.mean(a, axis=axis, keepdims=True)
+    mean_reduced = xp.squeeze(mean, axis=axis)  # needed later
+    m2 = _moment(a, 2, axis, center=mean, xp=xp)
+    m4 = _moment(a, 4, axis, center=mean, xp=xp)
+    with np.errstate(all='ignore'):
+        zero = m2 <= (xp.finfo(m2.dtype).eps * mean_reduced)**2
+        vals = xp.where(zero, xp.nan, m4 / m2**2.0)
+
+    if not bias:
+        can_correct = ~zero & (n > 3)
+        if is_lazy_array(can_correct) or xp.any(can_correct):
+            nval = 1.0/(n-2)/(n-3) * ((n**2-1.0)*m4/m2**2.0 - 3*(n-1)**2.0)
+            vals = xp.where(can_correct, nval + 3.0, vals)
+
+    vals = vals - 3 if fisher else vals
+    return vals[()] if vals.ndim == 0 else vals
+
+
+DescribeResult = namedtuple('DescribeResult',
+                            ('nobs', 'minmax', 'mean', 'variance', 'skewness',
+                             'kurtosis'))
+
+
+@xp_capabilities(marray=True)
+def describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate'):
+    """Compute several descriptive statistics of the passed array.
+
+    Parameters
+    ----------
+    a : array_like
+        Input data.
+    axis : int or None, optional
+        Axis along which statistics are calculated. Default is 0.
+        If None, compute over the whole array `a`.
+    ddof : int, optional
+        Delta degrees of freedom (only for variance).  Default is 1.
+    bias : bool, optional
+        If False, then the skewness and kurtosis calculations are corrected
+        for statistical bias.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    Returns
+    -------
+    nobs : int or ndarray of ints
+        Number of observations (length of data along `axis`).
+        When 'omit' is chosen as nan_policy, the length along each axis
+        slice is counted separately.
+    minmax: tuple of ndarrays or floats
+        Minimum and maximum value of `a` along the given axis.
+    mean : ndarray or float
+        Arithmetic mean of `a` along the given axis.
+    variance : ndarray or float
+        Unbiased variance of `a` along the given axis; denominator is number
+        of observations minus one.
+    skewness : ndarray or float
+        Skewness of `a` along the given axis, based on moment calculations
+        with denominator equal to the number of observations, i.e. no degrees
+        of freedom correction.
+    kurtosis : ndarray or float
+        Kurtosis (Fisher) of `a` along the given axis.  The kurtosis is
+        normalized so that it is zero for the normal distribution.  No
+        degrees of freedom are used.
+
+    Raises
+    ------
+    ValueError
+        If size of `a` is 0.
+
+    See Also
+    --------
+    skew, kurtosis
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> a = np.arange(10)
+    >>> stats.describe(a)
+    DescribeResult(nobs=10, minmax=(0, 9), mean=4.5,
+                   variance=9.166666666666666, skewness=0.0,
+                   kurtosis=-1.2242424242424244)
+    >>> b = [[1, 2], [3, 4]]
+    >>> stats.describe(b)
+    DescribeResult(nobs=2, minmax=(array([1, 2]), array([3, 4])),
+                   mean=array([2., 3.]), variance=array([2., 2.]),
+                   skewness=array([0., 0.]), kurtosis=array([-2., -2.]))
+
+    """
+    xp = array_namespace(a)
+    a, axis = _chk_asarray(a, axis, xp=xp)
+
+    contains_nan = _contains_nan(a, nan_policy)
+
+    # Test nan_policy before the implicit call to bool(contains_nan)
+    # to avoid raising on lazy xps on the default nan_policy='propagate'
+    if nan_policy == 'omit' and contains_nan:
+        # only NumPy gets here; `_contains_nan` raises error for the rest
+        a = ma.masked_invalid(a)
+        return mstats_basic.describe(a, axis, ddof, bias)
+
+    if xp_size(a) == 0:
+        raise ValueError("The input must not be empty.")
+
+    # use xp.astype when data-apis/array-api-compat#226 is resolved
+    n = xp.asarray(_count_nonmasked(a, axis, xp=xp), dtype=xp.int64,
+                   device=xp_device(a))
+    n = n[()] if n.ndim == 0 else n
+    mm = (xp.min(a, axis=axis), xp.max(a, axis=axis))
+    a = xp_promote(a, force_floating=True, xp=xp)
+    m = xp.mean(a, axis=axis)
+    v = _var(a, axis=axis, ddof=ddof, xp=xp)
+    v = v[()] if v.ndim == 0 else v
+    sk = skew(a, axis, bias=bias)
+    kurt = kurtosis(a, axis, bias=bias)
+
+    return DescribeResult(n, mm, m, v, sk, kurt)
+
+#####################################
+#         NORMALITY TESTS           #
+#####################################
+
+
+def _get_pvalue(statistic, distribution, alternative, symmetric=True, xp=None):
+    """Get p-value given the statistic, (continuous) distribution, and alternative"""
+    xp = array_namespace(statistic) if xp is None else xp
+
+    if alternative == 'less':
+        pvalue = distribution.cdf(statistic)
+    elif alternative == 'greater':
+        pvalue = distribution.sf(statistic)
+    elif alternative == 'two-sided':
+        pvalue = 2 * (distribution.sf(xp.abs(statistic)) if symmetric
+                      else xp.minimum(distribution.cdf(statistic),
+                                      distribution.sf(statistic)))
+    else:
+        message = "`alternative` must be 'less', 'greater', or 'two-sided'."
+        raise ValueError(message)
+
+    return pvalue
+
+
+SkewtestResult = namedtuple('SkewtestResult', ('statistic', 'pvalue'))
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(SkewtestResult, n_samples=1, too_small=7)
+# nan_policy handled by `_axis_nan_policy`, but needs to be left
+# in signature to preserve use as a positional argument
+def skewtest(a, axis=0, nan_policy='propagate', alternative='two-sided'):
+    r"""Test whether the skew is different from the normal distribution.
+
+    This function tests the null hypothesis that the skewness of
+    the population that the sample was drawn from is the same
+    as that of a corresponding normal distribution.
+
+    Parameters
+    ----------
+    a : array
+        The data to be tested. Must contain at least eight observations.
+    axis : int or None, optional
+       Axis along which statistics are calculated. Default is 0.
+       If None, compute over the whole array `a`.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        The following options are available:
+
+        * 'two-sided': the skewness of the distribution underlying the sample
+          is different from that of the normal distribution (i.e. 0)
+        * 'less': the skewness of the distribution underlying the sample
+          is less than that of the normal distribution
+        * 'greater': the skewness of the distribution underlying the sample
+          is greater than that of the normal distribution
+
+        .. versionadded:: 1.7.0
+
+    Returns
+    -------
+    statistic : float
+        The computed z-score for this test.
+    pvalue : float
+        The p-value for the hypothesis test.
+
+    See Also
+    --------
+    :ref:`hypothesis_skewtest` : Extended example
+
+    Notes
+    -----
+    The sample size must be at least 8.
+
+    References
+    ----------
+    .. [1] R. B. D'Agostino, A. J. Belanger and R. B. D'Agostino Jr.,
+            "A suggestion for using powerful and informative tests of
+            normality", American Statistician 44, pp. 316-321, 1990.
+
+    Examples
+    --------
+
+    >>> from scipy.stats import skewtest
+    >>> skewtest([1, 2, 3, 4, 5, 6, 7, 8])
+    SkewtestResult(statistic=1.0108048609177787, pvalue=0.3121098361421897)
+    >>> skewtest([2, 8, 0, 4, 1, 9, 9, 0])
+    SkewtestResult(statistic=0.44626385374196975, pvalue=0.6554066631275459)
+    >>> skewtest([1, 2, 3, 4, 5, 6, 7, 8000])
+    SkewtestResult(statistic=3.571773510360407, pvalue=0.0003545719905823133)
+    >>> skewtest([100, 100, 100, 100, 100, 100, 100, 101])
+    SkewtestResult(statistic=3.5717766638478072, pvalue=0.000354567720281634)
+    >>> skewtest([1, 2, 3, 4, 5, 6, 7, 8], alternative='less')
+    SkewtestResult(statistic=1.0108048609177787, pvalue=0.8439450819289052)
+    >>> skewtest([1, 2, 3, 4, 5, 6, 7, 8], alternative='greater')
+    SkewtestResult(statistic=1.0108048609177787, pvalue=0.15605491807109484)
+
+    For a more detailed example, see :ref:`hypothesis_skewtest`.
+    """
+    xp = array_namespace(a)
+    a, axis = _chk_asarray(a, axis, xp=xp)
+
+    b2 = skew(a, axis, _no_deco=True)
+
+    n = xp.asarray(_count_nonmasked(a, axis), dtype=b2.dtype, device=xp_device(a))
+    n = xpx.at(n, n < 8).set(xp.nan)
+
+    with np.errstate(divide='ignore', invalid='ignore'):
+        y = b2 * xp.sqrt(((n + 1) * (n + 3)) / (6.0 * (n - 2)))
+        beta2 = (3.0 * (n**2 + 27*n - 70) * (n+1) * (n+3) /
+                 ((n-2.0) * (n+5) * (n+7) * (n+9)))
+        W2 = -1 + xp.sqrt(2 * (beta2 - 1))
+        delta = 1 / xp.sqrt(0.5 * xp.log(W2))
+        alpha = xp.sqrt(2.0 / (W2 - 1))
+        y = xp.where(y == 0, 1., y)
+        Z = delta * xp.log(y / alpha + xp.sqrt((y / alpha)**2 + 1))
+        pvalue = _get_pvalue(Z, _SimpleNormal(), alternative, xp=xp)
+
+    Z = Z[()] if Z.ndim == 0 else Z
+    pvalue = pvalue[()] if pvalue.ndim == 0 else pvalue
+    return SkewtestResult(Z, pvalue)
+
+
+KurtosistestResult = namedtuple('KurtosistestResult', ('statistic', 'pvalue'))
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(KurtosistestResult, n_samples=1, too_small=4)
+def kurtosistest(a, axis=0, nan_policy='propagate', alternative='two-sided'):
+    r"""Test whether a dataset has normal kurtosis.
+
+    This function tests the null hypothesis that the kurtosis
+    of the population from which the sample was drawn is that
+    of the normal distribution.
+
+    Parameters
+    ----------
+    a : array
+        Array of the sample data. Must contain at least five observations.
+    axis : int or None, optional
+       Axis along which to compute test. Default is 0. If None,
+       compute over the whole array `a`.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+        The following options are available (default is 'two-sided'):
+
+        * 'two-sided': the kurtosis of the distribution underlying the sample
+          is different from that of the normal distribution
+        * 'less': the kurtosis of the distribution underlying the sample
+          is less than that of the normal distribution
+        * 'greater': the kurtosis of the distribution underlying the sample
+          is greater than that of the normal distribution
+
+        .. versionadded:: 1.7.0
+
+    Returns
+    -------
+    statistic : float
+        The computed z-score for this test.
+    pvalue : float
+        The p-value for the hypothesis test.
+
+    See Also
+    --------
+    :ref:`hypothesis_kurtosistest` : Extended example
+
+    Notes
+    -----
+    Valid only for n>20. This function uses the method described in [1]_.
+
+    References
+    ----------
+    .. [1] F. J. Anscombe, W. J. Glynn, "Distribution of the kurtosis
+       statistic b2 for normal samples", Biometrika, vol. 70, pp. 227-234, 1983.
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from scipy.stats import kurtosistest
+    >>> kurtosistest(list(range(20)))
+    KurtosistestResult(statistic=-1.7058104152122062, pvalue=0.08804338332528348)
+    >>> kurtosistest(list(range(20)), alternative='less')
+    KurtosistestResult(statistic=-1.7058104152122062, pvalue=0.04402169166264174)
+    >>> kurtosistest(list(range(20)), alternative='greater')
+    KurtosistestResult(statistic=-1.7058104152122062, pvalue=0.9559783083373583)
+    >>> rng = np.random.default_rng()
+    >>> s = rng.normal(0, 1, 1000)
+    >>> kurtosistest(s)
+    KurtosistestResult(statistic=-1.475047944490622, pvalue=0.14019965402996987)
+
+    For a more detailed example, see :ref:`hypothesis_kurtosistest`.
+    """
+    xp = array_namespace(a)
+    a, axis = _chk_asarray(a, axis, xp=xp)
+
+    b2 = kurtosis(a, axis, fisher=False, _no_deco=True)
+
+    n = xp.asarray(_count_nonmasked(a, axis), dtype=b2.dtype, device=xp_device(a))
+    n = xpx.at(n, n < 5).set(xp.nan)
+
+    E = 3.0*(n-1) / (n+1)
+    varb2 = 24.0*n*(n-2)*(n-3) / ((n+1)*(n+1.)*(n+3)*(n+5))  # [1]_ Eq. 1
+    x = (b2-E) / varb2**0.5  # [1]_ Eq. 4
+    # [1]_ Eq. 2:
+    sqrtbeta1 = 6.0*(n*n-5*n+2)/((n+7)*(n+9)) * ((6.0*(n+3)*(n+5))
+                                                 / (n*(n-2)*(n-3)))**0.5
+    # [1]_ Eq. 3:
+    A = 6.0 + 8.0/sqrtbeta1 * (2.0/sqrtbeta1 + (1+4.0/(sqrtbeta1**2))**0.5)
+    term1 = 1 - 2/(9.0*A)
+    denom = 1 + x * (2/(A-4.0))**0.5
+    term2 = xp.sign(denom) * xp.where(denom == 0.0, xp.nan,
+                                      ((1-2.0/A)/xp.abs(denom))**(1/3))
+    if not is_lazy_array(denom) and xp.any(denom == 0):
+        msg = ("Test statistic not defined in some cases due to division by "
+               "zero. Return nan in that case...")
+        warnings.warn(msg, RuntimeWarning, stacklevel=2)
+
+    Z = (term1 - term2) / (2/(9.0*A))**0.5  # [1]_ Eq. 5
+    pvalue = _get_pvalue(Z, _SimpleNormal(), alternative, xp=xp)
+
+    Z = Z[()] if Z.ndim == 0 else Z
+    pvalue = pvalue[()] if pvalue.ndim == 0 else pvalue
+    return KurtosistestResult(Z, pvalue)
+
+
+NormaltestResult = namedtuple('NormaltestResult', ('statistic', 'pvalue'))
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(NormaltestResult, n_samples=1, too_small=7)
+def normaltest(a, axis=0, nan_policy='propagate'):
+    r"""Test whether a sample differs from a normal distribution.
+
+    This function tests the null hypothesis that a sample comes
+    from a normal distribution.  It is based on D'Agostino and
+    Pearson's [1]_, [2]_ test that combines skew and kurtosis to
+    produce an omnibus test of normality.
+
+    Parameters
+    ----------
+    a : array_like
+        The array containing the sample to be tested. Must contain
+        at least eight observations.
+    axis : int or None, optional
+        Axis along which to compute test. Default is 0. If None,
+        compute over the whole array `a`.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    Returns
+    -------
+    statistic : float or array
+        ``s^2 + k^2``, where ``s`` is the z-score returned by `skewtest` and
+        ``k`` is the z-score returned by `kurtosistest`.
+    pvalue : float or array
+        A 2-sided chi squared probability for the hypothesis test.
+
+    See Also
+    --------
+    :ref:`hypothesis_normaltest` : Extended example
+
+    References
+    ----------
+    .. [1] D'Agostino, R. B. (1971), "An omnibus test of normality for
+            moderate and large sample size", Biometrika, 58, 341-348
+    .. [2] D'Agostino, R. and Pearson, E. S. (1973), "Tests for departure from
+            normality", Biometrika, 60, 613-622
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+    >>> pts = 1000
+    >>> a = rng.normal(0, 1, size=pts)
+    >>> b = rng.normal(2, 1, size=pts)
+    >>> x = np.concatenate((a, b))
+    >>> res = stats.normaltest(x)
+    >>> res.statistic
+    53.619...  # random
+    >>> res.pvalue
+    2.273917413209226e-12  # random
+
+    For a more detailed example, see :ref:`hypothesis_normaltest`.
+    """
+    xp = array_namespace(a)
+
+    s, _ = skewtest(a, axis, _no_deco=True)
+    k, _ = kurtosistest(a, axis, _no_deco=True)
+    statistic = s*s + k*k
+
+    chi2 = _SimpleChi2(xp.asarray(2., dtype=statistic.dtype, device=xp_device(a)))
+    pvalue = _get_pvalue(statistic, chi2, alternative='greater', symmetric=False, xp=xp)
+
+    statistic = statistic[()] if statistic.ndim == 0 else statistic
+    pvalue = pvalue[()] if pvalue.ndim == 0 else pvalue
+
+    return NormaltestResult(statistic, pvalue)
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(SignificanceResult, default_axis=None)
+def jarque_bera(x, *, axis=None):
+    r"""Perform the Jarque-Bera goodness of fit test on sample data.
+
+    The Jarque-Bera test tests whether the sample data has the skewness and
+    kurtosis matching a normal distribution.
+
+    Note that this test only works for a large enough number of data samples
+    (>2000) as the test statistic asymptotically has a Chi-squared distribution
+    with 2 degrees of freedom.
+
+    Parameters
+    ----------
+    x : array_like
+        Observations of a random variable.
+    axis : int or None, default: 0
+        If an int, the axis of the input along which to compute the statistic.
+        The statistic of each axis-slice (e.g. row) of the input will appear in
+        a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    result : SignificanceResult
+        An object with the following attributes:
+
+        statistic : float
+            The test statistic.
+        pvalue : float
+            The p-value for the hypothesis test.
+
+    See Also
+    --------
+    :ref:`hypothesis_jarque_bera` : Extended example
+
+    References
+    ----------
+    .. [1] Jarque, C. and Bera, A. (1980) "Efficient tests for normality,
+           homoscedasticity and serial independence of regression residuals",
+           6 Econometric Letters 255-259.
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+    >>> x = rng.normal(0, 1, 100000)
+    >>> jarque_bera_test = stats.jarque_bera(x)
+    >>> jarque_bera_test
+    Jarque_beraResult(statistic=3.3415184718131554, pvalue=0.18810419594996775)
+    >>> jarque_bera_test.statistic
+    3.3415184718131554
+    >>> jarque_bera_test.pvalue
+    0.18810419594996775
+
+    For a more detailed example, see :ref:`hypothesis_jarque_bera`.
+    """
+    xp = array_namespace(x)
+    x, axis = _chk_asarray(x, axis, xp=xp)
+
+    mu = _xp_mean(x, axis=axis, keepdims=True)
+    diffx = x - mu
+    s = skew(diffx, axis=axis, _no_deco=True)
+    k = kurtosis(diffx, axis=axis, _no_deco=True)
+
+    n = xp.asarray(_count_nonmasked(x, axis), dtype=mu.dtype, device=xp_device(x))
+    statistic = n / 6 * (s**2 + k**2 / 4)
+
+    chi2 = _SimpleChi2(xp.asarray(2., dtype=mu.dtype, device=xp_device(x)))
+    pvalue = _get_pvalue(statistic, chi2, alternative='greater', symmetric=False, xp=xp)
+
+    statistic = statistic[()] if statistic.ndim == 0 else statistic
+    pvalue = pvalue[()] if pvalue.ndim == 0 else pvalue
+
+    return SignificanceResult(statistic, pvalue)
+
+
+#####################################
+#        FREQUENCY FUNCTIONS        #
+#####################################
+
+
+@xp_capabilities(np_only=True)
+def scoreatpercentile(a, per, limit=(), interpolation_method='fraction',
+                      axis=None):
+    """Calculate the score at a given percentile of the input sequence.
+
+    For example, the score at ``per=50`` is the median. If the desired quantile
+    lies between two data points, we interpolate between them, according to
+    the value of `interpolation`. If the parameter `limit` is provided, it
+    should be a tuple (lower, upper) of two values.
+
+    Parameters
+    ----------
+    a : array_like
+        A 1-D array of values from which to extract score.
+    per : array_like
+        Percentile(s) at which to extract score.  Values should be in range
+        [0,100].
+    limit : tuple, optional
+        Tuple of two scalars, the lower and upper limits within which to
+        compute the percentile. Values of `a` outside
+        this (closed) interval will be ignored.
+    interpolation_method : {'fraction', 'lower', 'higher'}, optional
+        Specifies the interpolation method to use,
+        when the desired quantile lies between two data points `i` and `j`
+        The following options are available (default is 'fraction'):
+
+        * 'fraction': ``i + (j - i) * fraction`` where ``fraction`` is the
+            fractional part of the index surrounded by ``i`` and ``j``
+        * 'lower': ``i``
+        * 'higher': ``j``
+
+    axis : int, optional
+        Axis along which the percentiles are computed. Default is None. If
+        None, compute over the whole array `a`.
+
+    Returns
+    -------
+    score : float or ndarray
+        Score at percentile(s).
+
+    See Also
+    --------
+    percentileofscore, numpy.percentile
+
+    Notes
+    -----
+    This function will become obsolete in the future.
+    For NumPy 1.9 and higher, `numpy.percentile` provides all the functionality
+    that `scoreatpercentile` provides.  And it's significantly faster.
+    Therefore it's recommended to use `numpy.percentile` for users that have
+    numpy >= 1.9.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> a = np.arange(100)
+    >>> stats.scoreatpercentile(a, 50)
+    49.5
+
+    """
+    # adapted from NumPy's percentile function.  When we require numpy >= 1.8,
+    # the implementation of this function can be replaced by np.percentile.
+    a = np.asarray(a)
+    if a.size == 0:
+        # empty array, return nan(s) with shape matching `per`
+        if np.isscalar(per):
+            return np.nan
+        else:
+            return np.full(np.asarray(per).shape, np.nan, dtype=np.float64)
+
+    if limit:
+        a = a[(limit[0] <= a) & (a <= limit[1])]
+
+    sorted_ = np.sort(a, axis=axis)
+    if axis is None:
+        axis = 0
+
+    return _compute_qth_percentile(sorted_, per, interpolation_method, axis)
+
+
+# handle sequence of per's without calling sort multiple times
+def _compute_qth_percentile(sorted_, per, interpolation_method, axis):
+    if not np.isscalar(per):
+        score = [_compute_qth_percentile(sorted_, i,
+                                         interpolation_method, axis)
+                 for i in per]
+        return np.array(score)
+
+    if not (0 <= per <= 100):
+        raise ValueError("percentile must be in the range [0, 100]")
+
+    indexer = [slice(None)] * sorted_.ndim
+    idx = per / 100. * (sorted_.shape[axis] - 1)
+
+    if int(idx) != idx:
+        # round fractional indices according to interpolation method
+        if interpolation_method == 'lower':
+            idx = int(np.floor(idx))
+        elif interpolation_method == 'higher':
+            idx = int(np.ceil(idx))
+        elif interpolation_method == 'fraction':
+            pass  # keep idx as fraction and interpolate
+        else:
+            raise ValueError("interpolation_method can only be 'fraction', "
+                             "'lower' or 'higher'")
+
+    i = int(idx)
+    if i == idx:
+        indexer[axis] = slice(i, i + 1)
+        weights = array(1)
+        sumval = 1.0
+    else:
+        indexer[axis] = slice(i, i + 2)
+        j = i + 1
+        weights = array([(j - idx), (idx - i)], float)
+        wshape = [1] * sorted_.ndim
+        wshape[axis] = 2
+        weights = weights.reshape(wshape)
+        sumval = weights.sum()
+
+    # Use np.add.reduce (== np.sum but a little faster) to coerce data type
+    return np.add.reduce(sorted_[tuple(indexer)] * weights, axis=axis) / sumval
+
+
+@xp_capabilities(np_only=True)
+def percentileofscore(a, score, kind='rank', nan_policy='propagate'):
+    """Compute the percentile rank of a score relative to a list of scores.
+
+    A `percentileofscore` of, for example, 80% means that 80% of the
+    scores in `a` are below the given score. In the case of gaps or
+    ties, the exact definition depends on the optional keyword, `kind`.
+
+    Parameters
+    ----------
+    a : array_like
+        A 1-D array to which `score` is compared.
+    score : float or array_like
+        A float score or array of scores for which to compute the percentile(s).
+    kind : {'rank', 'weak', 'strict', 'mean'}, optional
+        Specifies the interpretation of the resulting score.
+        The following options are available (default is 'rank'):
+
+        * 'rank': Average percentage ranking of score.  In case of multiple
+          matches, average the percentage rankings of all matching scores.
+        * 'weak': This kind corresponds to the definition of a cumulative
+          distribution function.  A percentileofscore of 80% means that 80%
+          of values are less than or equal to the provided score.
+        * 'strict': Similar to "weak", except that only values that are
+          strictly less than the given score are counted.
+        * 'mean': The average of the "weak" and "strict" scores, often used
+          in testing.  See https://en.wikipedia.org/wiki/Percentile_rank
+
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Specifies how to treat `nan` values in `a`.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan (for each value in `score`).
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    Returns
+    -------
+    pcos : float or array-like
+        Percentile-position(s) of `score` (0-100) relative to `a`.
+
+    See Also
+    --------
+    numpy.percentile
+    scipy.stats.scoreatpercentile, scipy.stats.rankdata
+
+    Examples
+    --------
+    Three-quarters of the given values lie below a given score:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> stats.percentileofscore([1, 2, 3, 4], 3)
+    75.0
+
+    With multiple matches, note how the scores of the two matches, 0.6
+    and 0.8 respectively, are averaged:
+
+    >>> stats.percentileofscore([1, 2, 3, 3, 4], 3)
+    70.0
+
+    Only 2/5 values are strictly less than 3:
+
+    >>> stats.percentileofscore([1, 2, 3, 3, 4], 3, kind='strict')
+    40.0
+
+    But 4/5 values are less than or equal to 3:
+
+    >>> stats.percentileofscore([1, 2, 3, 3, 4], 3, kind='weak')
+    80.0
+
+    The average between the weak and the strict scores is:
+
+    >>> stats.percentileofscore([1, 2, 3, 3, 4], 3, kind='mean')
+    60.0
+
+    Score arrays (of any dimensionality) are supported:
+
+    >>> stats.percentileofscore([1, 2, 3, 3, 4], [2, 3])
+    array([40., 70.])
+
+    The inputs can be infinite:
+
+    >>> stats.percentileofscore([-np.inf, 0, 1, np.inf], [1, 2, np.inf])
+    array([75., 75., 100.])
+
+    If `a` is empty, then the resulting percentiles are all `nan`:
+
+    >>> stats.percentileofscore([], [1, 2])
+    array([nan, nan])
+    """
+
+    a = np.asarray(a)
+    score = np.asarray(score)
+
+    if a.ndim != 1:
+        raise ValueError("`a` must be 1-dimensional.")
+
+    n = len(a)
+
+    # Nan treatment
+    cna = _contains_nan(a, nan_policy)
+    cns = _contains_nan(score, nan_policy)
+
+    if cns:
+        # If a score is nan, then the output should be nan
+        # (also if nan_policy is "omit", because it only applies to `a`)
+        score = ma.masked_where(np.isnan(score), score)
+
+    if cna:
+        if nan_policy == "omit":
+            # Don't count nans
+            a = ma.masked_where(np.isnan(a), a)
+            n = a.count()
+
+        if nan_policy == "propagate":
+            # All outputs should be nans
+            n = 0
+
+    # Cannot compare to empty list ==> nan
+    if n == 0:
+        perct = np.full_like(score, np.nan, dtype=np.float64)
+
+    else:
+        # Prepare broadcasting
+        score = score[..., None]
+
+        def count(x):
+            return np.count_nonzero(x, -1)
+
+        # Main computations/logic
+        if kind == 'rank':
+            left = count(a < score)
+            right = count(a <= score)
+            plus1 = left < right
+            perct = (left + right + plus1) * (50.0 / n)
+        elif kind == 'strict':
+            perct = count(a < score) * (100.0 / n)
+        elif kind == 'weak':
+            perct = count(a <= score) * (100.0 / n)
+        elif kind == 'mean':
+            left = count(a < score)
+            right = count(a <= score)
+            perct = (left + right) * (50.0 / n)
+        else:
+            raise ValueError(
+                "kind can only be 'rank', 'strict', 'weak' or 'mean'")
+
+    # Re-insert nan values
+    perct = ma.filled(perct, np.nan)
+
+    if perct.ndim == 0:
+        return perct[()]
+    return perct
+
+
+HistogramResult = namedtuple('HistogramResult',
+                             ('count', 'lowerlimit', 'binsize', 'extrapoints'))
+
+
+def _histogram(a, numbins=10, defaultlimits=None, weights=None, *,
+               density=False, cumulative=False):
+    """Create a histogram.
+
+    Separate the range into several bins and return the number of instances
+    in each bin.
+
+    Parameters
+    ----------
+    a : array_like
+        Array of scores which will be put into bins.
+    numbins : int, optional
+        The number of bins to use for the histogram. Default is 10.
+    defaultlimits : tuple (lower, upper), optional
+        The lower and upper values for the range of the histogram.
+        If no value is given, a range slightly larger than the range of the
+        values in a is used. Specifically ``(a.min() - s, a.max() + s)``,
+        where ``s = (1/2)(a.max() - a.min()) / (numbins - 1)``.
+    weights : array_like, optional
+        The weights for each value in `a`. Default is None, which gives each
+        value a weight of 1.0
+
+    Returns
+    -------
+    count : ndarray
+        Number of points (or sum of weights) in each bin.
+    lowerlimit : float
+        Lowest value of histogram, the lower limit of the first bin.
+    binsize : float
+        The size of the bins (all bins have the same size).
+    extrapoints : int
+        The number of points outside the range of the histogram.
+
+    See Also
+    --------
+    numpy.histogram
+
+    Notes
+    -----
+    This histogram is based on numpy's histogram but has a larger range by
+    default if default limits is not set.
+
+    """
+    xp = array_namespace(a)
+    a = xp_ravel(a)
+    a, weights = xp_promote(a, weights, force_floating=True, xp=xp)
+
+    if defaultlimits is None:
+        if xp_size(a) == 0:
+            # handle empty arrays. Undetermined range, so use 0-1.
+            defaultlimits = xp.asarray(0., dtype=a.dtype), xp.asarray(1., dtype=a.dtype)
+        else:
+            # no range given, so use values in `a`
+            data_min = xp.min(a)
+            data_max = xp.max(a)
+            # Have bins extend past min and max values slightly
+            s = (data_max - data_min) / (2. * (numbins - 1.))
+            defaultlimits = (data_min - s, data_max + s)
+    else:
+        if not (np.iterable(defaultlimits) and len(defaultlimits)==2
+                and defaultlimits[0] < defaultlimits[1]):
+            message = ('If specified, `defaultreallimits` must be given as an iterable '
+                       'in the order (lower limit, upper limit).')
+            raise ValueError(message)
+        if not (xp.isdtype(a.dtype, 'real floating')
+                and (weights is None or xp.isdtype(weights.dtype, 'real floating'))):
+            message = '`a` and (if specified) `weights` must have real dtype.'
+            raise ValueError(message)
+        if weights is not None and not is_lazy_array(weights) and xp.any(weights < 0):
+            message = 'All `weights` must be non-negative.'
+            raise ValueError(message)
+
+    bin_edges = xp.linspace(*defaultlimits, numbins+1, dtype=a.dtype)
+    if weights is None:
+        indices = xp.searchsorted(xp.sort(a), bin_edges, side='left')
+        hist = xp.diff(indices)
+        n_right_limit = xp.count_nonzero(a == bin_edges[-1])
+        hist = xpx.at(hist)[-1].add(xp.astype(n_right_limit, hist.dtype))
+    else:
+        i = xp.argsort(a)
+        weights, a = weights[i], a[i]
+        cumulative_weights = xp.cumulative_sum(weights, include_initial=True)
+        indices = xp.searchsorted(a, bin_edges, side='left')
+        hist = xp.diff(xp.take_along_axis(cumulative_weights, indices, axis=-1))
+        hist = xpx.at(hist)[-1].add(xp.sum(xp.where(a == bin_edges[-1], weights, 0.)))
+
+    # fixed width for bins is assumed, as numpy's histogram gives
+    # fixed width bins for int values for 'bins'
+    binsize = bin_edges[1] - bin_edges[0]
+    # calculate number of extra points
+    binnedpoints = (xp.sum(hist) if weights is None
+                    else xp.count_nonzero((bin_edges[0] <= a) & (a <= bin_edges[-1])))
+    extrapoints = a.shape[0] - binnedpoints
+
+    lowerlimit = xp.asarray(defaultlimits[0], dtype=a.dtype)[()]
+
+    hist = xp.asarray(hist, dtype=a.dtype)
+    if density:
+        hist = hist / (a.shape[0] if weights is None else cumulative_weights[-1])
+    if cumulative:
+        hist = xp.cumulative_sum(hist, axis=0)
+
+    return HistogramResult(hist, lowerlimit, binsize, extrapoints)
+
+
+CumfreqResult = namedtuple('CumfreqResult',
+                           ('cumcount', 'lowerlimit', 'binsize',
+                            'extrapoints'))
+
+
+@xp_capabilities(skip_backends=[('dask.array', 'fails tests')])
+def cumfreq(a, numbins=10, defaultreallimits=None, weights=None):
+    """Return a cumulative frequency histogram, using the histogram function.
+
+    A cumulative histogram is a mapping that counts the cumulative number of
+    observations in all of the bins up to the specified bin.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    numbins : int, optional
+        The number of bins to use for the histogram. Default is 10.
+    defaultreallimits : tuple (lower, upper), optional
+        The lower and upper values for the range of the histogram.
+        If no value is given, a range slightly larger than the range of the
+        values in `a` is used. Specifically ``(a.min() - s, a.max() + s)``,
+        where ``s = (1/2)(a.max() - a.min()) / (numbins - 1)``.
+    weights : array_like, optional
+        The weights for each value in `a`. Default is None, which gives each
+        value a weight of 1.0
+
+    Returns
+    -------
+    cumcount : ndarray
+        Binned values of cumulative frequency.
+    lowerlimit : float
+        Lower real limit
+    binsize : float
+        Width of each bin.
+    extrapoints : int
+        Extra points.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+    >>> x = [1, 4, 2, 1, 3, 1]
+    >>> res = stats.cumfreq(x, numbins=4, defaultreallimits=(1.5, 5))
+    >>> res.cumcount
+    array([ 1.,  2.,  3.,  3.])
+    >>> res.extrapoints
+    3
+
+    Create a normal distribution with 1000 random values
+
+    >>> samples = stats.norm.rvs(size=1000, random_state=rng)
+
+    Calculate cumulative frequencies
+
+    >>> res = stats.cumfreq(samples, numbins=25)
+
+    Calculate space of values for x
+
+    >>> x = res.lowerlimit + np.linspace(0, res.binsize*res.cumcount.size,
+    ...                                  res.cumcount.size + 1)
+
+    Plot histogram and cumulative histogram
+
+    >>> fig = plt.figure(figsize=(10, 4))
+    >>> ax1 = fig.add_subplot(1, 2, 1)
+    >>> ax2 = fig.add_subplot(1, 2, 2)
+    >>> ax1.hist(samples, bins=25)
+    >>> ax1.set_title('Histogram')
+    >>> ax2.bar(x[:-1], res.cumcount, width=res.binsize, align='edge')
+    >>> ax2.set_title('Cumulative histogram')
+    >>> ax2.set_xlim([x.min(), x.max()])
+
+    >>> plt.show()
+
+    """
+    h, l, b, e = _histogram(a, numbins, defaultreallimits, weights=weights,
+                            cumulative=True)
+    return CumfreqResult(h, l, b, e)
+
+
+RelfreqResult = namedtuple('RelfreqResult',
+                           ('frequency', 'lowerlimit', 'binsize',
+                            'extrapoints'))
+
+
+@xp_capabilities(skip_backends=[('dask.array', 'fails tests')])
+def relfreq(a, numbins=10, defaultreallimits=None, weights=None):
+    """Return a relative frequency histogram, using the histogram function.
+
+    A relative frequency  histogram is a mapping of the number of
+    observations in each of the bins relative to the total of observations.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    numbins : int, optional
+        The number of bins to use for the histogram. Default is 10.
+    defaultreallimits : tuple (lower, upper), optional
+        The lower and upper values for the range of the histogram.
+        If no value is given, a range slightly larger than the range of the
+        values in a is used. Specifically ``(a.min() - s, a.max() + s)``,
+        where ``s = (1/2)(a.max() - a.min()) / (numbins - 1)``.
+    weights : array_like, optional
+        The weights for each value in `a`. Default is None, which gives each
+        value a weight of 1.0
+
+    Returns
+    -------
+    frequency : ndarray
+        Binned values of relative frequency.
+    lowerlimit : float
+        Lower real limit.
+    binsize : float
+        Width of each bin.
+    extrapoints : int
+        Extra points.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+    >>> a = np.array([2, 4, 1, 2, 3, 2])
+    >>> res = stats.relfreq(a, numbins=4)
+    >>> res.frequency
+    array([ 0.16666667, 0.5       , 0.16666667,  0.16666667])
+    >>> np.sum(res.frequency)  # relative frequencies should add up to 1
+    1.0
+
+    Create a normal distribution with 1000 random values
+
+    >>> samples = stats.norm.rvs(size=1000, random_state=rng)
+
+    Calculate relative frequencies
+
+    >>> res = stats.relfreq(samples, numbins=25)
+
+    Calculate space of values for x
+
+    >>> x = res.lowerlimit + np.linspace(0, res.binsize*res.frequency.size,
+    ...                                  res.frequency.size)
+
+    Plot relative frequency histogram
+
+    >>> fig = plt.figure(figsize=(5, 4))
+    >>> ax = fig.add_subplot(1, 1, 1)
+    >>> ax.bar(x, res.frequency, width=res.binsize)
+    >>> ax.set_title('Relative frequency histogram')
+    >>> ax.set_xlim([x.min(), x.max()])
+
+    >>> plt.show()
+
+    """
+    h, l, b, e = _histogram(a, numbins, defaultreallimits, weights=weights,
+                            density=True)
+    return RelfreqResult(h, l, b, e)
+
+
+#####################################
+#        VARIABILITY FUNCTIONS      #
+#####################################
+
+@xp_capabilities()
+def obrientransform(*samples, nan_policy='propagate'):
+    """Compute the O'Brien transform on input data (any number of arrays).
+
+    Used to test for homogeneity of variance prior to running one-way stats.
+    Each array in ``*samples`` is one level of a factor.
+    Significant results of `f_oneway` on the transformed data suggest that the
+    variances of the underlying distributions are unequal.
+    See Maxwell and Delaney [1]_, p.112.
+
+    Parameters
+    ----------
+    *samples : array_like
+        Any number of arrays.
+    nan_policy : {'propagate', 'omit', 'raise'}
+        Defines how to handle input NaNs.
+
+        - ``propagate``: if a NaN is present in a sample, all elements of the
+          transformed sample will be NaN.
+        - ``omit``: NaNs will be omitted when computing reducing statistics for the
+          transform, but NaNs in the sample will remain NaNs in the transformed sample.
+        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
+
+    Returns
+    -------
+    obrientransform : tuple of arrays
+        Transformed arrays for use in ANOVA.
+
+    Raises
+    ------
+    ValueError
+        If the mean of the transformed data is not equal to the original
+        variance, indicating a lack of convergence in the O'Brien transform.
+
+    References
+    ----------
+    .. [1] S. E. Maxwell and H. D. Delaney, "Designing Experiments and
+           Analyzing Data: A Model Comparison Perspective", Wadsworth, 1990.
+
+    Examples
+    --------
+    We'll test the following data sets for differences in their variance.
+
+    >>> x = [10, 11, 13, 9, 7, 12, 12, 9, 10]
+    >>> y = [13, 21, 5, 10, 8, 14, 10, 12, 7, 15]
+
+    Apply the O'Brien transform to the data.
+
+    >>> from scipy.stats import obrientransform
+    >>> tx, ty = obrientransform(x, y)
+
+    Use `scipy.stats.f_oneway` to apply a one-way ANOVA test to the
+    transformed data.
+
+    >>> from scipy.stats import f_oneway
+    >>> F, p = f_oneway(tx, ty)
+    >>> p
+    0.1314139477040335
+
+    If we require that ``p < 0.05`` for significance, we cannot conclude
+    that the variances are different.
+
+    """
+    xp = array_namespace(*samples)
+    n_samples = len(samples)
+    samples = xp_promote(*samples, force_floating=True, xp=xp)
+    samples = (samples,) if n_samples == 1 else samples
+    return tuple(_xp_obrientransform_one_sample(sample, xp=xp, nan_policy=nan_policy)
+                 for sample in samples)
+
+
+def _xp_obrientransform_one_sample(a, *, xp, nan_policy):
+    _contains_nan(a, nan_policy, xp_omit_okay=True)  # handle `nan_policy='raise'`
+    n = xp.asarray(xp.count_nonzero(~xp.isnan(a)), dtype=a.dtype)
+    mu = _xp_mean(a, nan_policy=nan_policy)
+    sq = (a - mu)**2
+    sumsq = _xp_mean(sq, nan_policy=nan_policy) * n
+    return ((n - 1.5) * n * sq - 0.5 * sumsq) / ((n - 1) * (n - 2))
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, result_to_tuple=lambda x, _: (x,), n_outputs=1, too_small=1
+)
+def sem(a, axis=0, ddof=1, nan_policy='propagate'):
+    """Compute standard error of the mean.
+
+    Calculate the standard error of the mean (or standard error of
+    measurement) of the values in the input array.
+
+    Parameters
+    ----------
+    a : array_like
+        An array containing the values for which the standard error is
+        returned. Must contain at least two observations.
+    axis : int or None, optional
+        Axis along which to operate. Default is 0. If None, compute over
+        the whole array `a`.
+    ddof : int, optional
+        Delta degrees-of-freedom. How many degrees of freedom to adjust
+        for bias in limited samples relative to the population estimate
+        of variance. Defaults to 1.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    Returns
+    -------
+    s : ndarray or float
+        The standard error of the mean in the sample(s), along the input axis.
+
+    Notes
+    -----
+    The default value for `ddof` is different to the default (0) used by other
+    ddof containing routines, such as np.std and np.nanstd.
+
+    Examples
+    --------
+    Find standard error along the first axis:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> a = np.arange(20).reshape(5,4)
+    >>> stats.sem(a)
+    array([ 2.8284,  2.8284,  2.8284,  2.8284])
+
+    Find standard error across the whole array, using n degrees of freedom:
+
+    >>> stats.sem(a, axis=None, ddof=0)
+    1.2893796958227628
+
+    """
+    xp = array_namespace(a)
+    if axis is None:
+        a = xp.reshape(a, (-1,))
+        axis = 0
+    a = xpx.atleast_nd(xp.asarray(a), ndim=1, xp=xp)
+    n = _count_nonmasked(a, axis, xp=xp)
+    s = xp.std(a, axis=axis, correction=ddof) / n**0.5
+    return s
+
+
+def _isconst(x):
+    """
+    Check if all values in x are the same.  nans are ignored.
+
+    x must be a 1d array.
+
+    The return value is a 1d array with length 1, so it can be used
+    in np.apply_along_axis.
+    """
+    y = x[~np.isnan(x)]
+    if y.size == 0:
+        return np.array([True])
+    else:
+        return (y[0] == y).all(keepdims=True)
+
+
+@xp_capabilities(marray=True)
+def zscore(a, axis=0, ddof=0, nan_policy='propagate'):
+    """
+    Compute the z score.
+
+    Compute the z score of each value in the sample, relative to the
+    sample mean and standard deviation.
+
+    Parameters
+    ----------
+    a : array_like
+        An array like object containing the sample data.
+    axis : int or None, optional
+        Axis along which to operate. Default is 0. If None, compute over
+        the whole array `a`.
+    ddof : int, optional
+        Degrees of freedom correction in the calculation of the
+        standard deviation. Default is 0.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan. 'propagate' returns nan,
+        'raise' throws an error, 'omit' performs the calculations ignoring nan
+        values. Default is 'propagate'.  Note that when the value is 'omit',
+        nans in the input also propagate to the output, but they do not affect
+        the z-scores computed for the non-nan values.
+
+    Returns
+    -------
+    zscore : array_like
+        The z-scores, standardized by mean and standard deviation of
+        input array `a`.
+
+    See Also
+    --------
+    numpy.mean : Arithmetic average
+    numpy.std : Arithmetic standard deviation
+    scipy.stats.gzscore : Geometric standard score
+
+    Notes
+    -----
+    This function preserves ndarray subclasses, and works also with
+    matrices and masked arrays (it uses `asanyarray` instead of
+    `asarray` for parameters).
+
+    References
+    ----------
+    .. [1] "Standard score", *Wikipedia*,
+           https://en.wikipedia.org/wiki/Standard_score.
+    .. [2] Huck, S. W., Cross, T. L., Clark, S. B, "Overcoming misconceptions
+           about Z-scores", Teaching Statistics, vol. 8, pp. 38-40, 1986
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> a = np.array([ 0.7972,  0.0767,  0.4383,  0.7866,  0.8091,
+    ...                0.1954,  0.6307,  0.6599,  0.1065,  0.0508])
+    >>> from scipy import stats
+    >>> stats.zscore(a)
+    array([ 1.1273, -1.247 , -0.0552,  1.0923,  1.1664, -0.8559,  0.5786,
+            0.6748, -1.1488, -1.3324])
+
+    Computing along a specified axis, using n-1 degrees of freedom
+    (``ddof=1``) to calculate the standard deviation:
+
+    >>> b = np.array([[ 0.3148,  0.0478,  0.6243,  0.4608],
+    ...               [ 0.7149,  0.0775,  0.6072,  0.9656],
+    ...               [ 0.6341,  0.1403,  0.9759,  0.4064],
+    ...               [ 0.5918,  0.6948,  0.904 ,  0.3721],
+    ...               [ 0.0921,  0.2481,  0.1188,  0.1366]])
+    >>> stats.zscore(b, axis=1, ddof=1)
+    array([[-0.19264823, -1.28415119,  1.07259584,  0.40420358],
+           [ 0.33048416, -1.37380874,  0.04251374,  1.00081084],
+           [ 0.26796377, -1.12598418,  1.23283094, -0.37481053],
+           [-0.22095197,  0.24468594,  1.19042819, -1.21416216],
+           [-0.82780366,  1.4457416 , -0.43867764, -0.1792603 ]])
+
+    An example with ``nan_policy='omit'``:
+
+    >>> x = np.array([[25.11, 30.10, np.nan, 32.02, 43.15],
+    ...               [14.95, 16.06, 121.25, 94.35, 29.81]])
+    >>> stats.zscore(x, axis=1, nan_policy='omit')
+    array([[-1.13490897, -0.37830299,         nan, -0.08718406,  1.60039602],
+           [-0.91611681, -0.89090508,  1.4983032 ,  0.88731639, -0.5785977 ]])
+    """
+    return zmap(a, a, axis=axis, ddof=ddof, nan_policy=nan_policy)
+
+
+@xp_capabilities(marray=True)
+def gzscore(a, *, axis=0, ddof=0, nan_policy='propagate'):
+    """
+    Compute the geometric standard score.
+
+    Compute the geometric z score of each strictly positive value in the
+    sample, relative to the geometric mean and standard deviation.
+    Mathematically the geometric z score can be evaluated as::
+
+        gzscore = log(a/gmu) / log(gsigma)
+
+    where ``gmu`` (resp. ``gsigma``) is the geometric mean (resp. standard
+    deviation).
+
+    Parameters
+    ----------
+    a : array_like
+        Sample data.
+    axis : int or None, optional
+        Axis along which to operate. Default is 0. If None, compute over
+        the whole array `a`.
+    ddof : int, optional
+        Degrees of freedom correction in the calculation of the
+        standard deviation. Default is 0.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan. 'propagate' returns nan,
+        'raise' throws an error, 'omit' performs the calculations ignoring nan
+        values. Default is 'propagate'.  Note that when the value is 'omit',
+        nans in the input also propagate to the output, but they do not affect
+        the geometric z scores computed for the non-nan values.
+
+    Returns
+    -------
+    gzscore : array_like
+        The geometric z scores, standardized by geometric mean and geometric
+        standard deviation of input array `a`.
+
+    See Also
+    --------
+    gmean : Geometric mean
+    gstd : Geometric standard deviation
+    zscore : Standard score
+
+    Notes
+    -----
+    This function preserves ndarray subclasses, and works also with
+    matrices and masked arrays (it uses ``asanyarray`` instead of
+    ``asarray`` for parameters).
+
+    .. versionadded:: 1.8
+
+    References
+    ----------
+    .. [1] "Geometric standard score", *Wikipedia*,
+           https://en.wikipedia.org/wiki/Geometric_standard_deviation#Geometric_standard_score.
+
+    Examples
+    --------
+    Draw samples from a log-normal distribution:
+
+    >>> import numpy as np
+    >>> from scipy.stats import zscore, gzscore
+    >>> import matplotlib.pyplot as plt
+
+    >>> rng = np.random.default_rng()
+    >>> mu, sigma = 3., 1.  # mean and standard deviation
+    >>> x = rng.lognormal(mu, sigma, size=500)
+
+    Display the histogram of the samples:
+
+    >>> fig, ax = plt.subplots()
+    >>> ax.hist(x, 50)
+    >>> plt.show()
+
+    Display the histogram of the samples standardized by the classical zscore.
+    Distribution is rescaled but its shape is unchanged.
+
+    >>> fig, ax = plt.subplots()
+    >>> ax.hist(zscore(x), 50)
+    >>> plt.show()
+
+    Demonstrate that the distribution of geometric zscores is rescaled and
+    quasinormal:
+
+    >>> fig, ax = plt.subplots()
+    >>> ax.hist(gzscore(x), 50)
+    >>> plt.show()
+
+    """
+    xp = array_namespace(a)
+    a = xp_promote(a, force_floating=True, xp=xp)
+    log = ma.log if isinstance(a, ma.MaskedArray) else xp.log
+    return zscore(log(a), axis=axis, ddof=ddof, nan_policy=nan_policy)
+
+
+@xp_capabilities(marray=True)
+def zmap(scores, compare, axis=0, ddof=0, nan_policy='propagate'):
+    """
+    Calculate the relative z-scores.
+
+    Return an array of z-scores, i.e., scores that are standardized to
+    zero mean and unit variance, where mean and variance are calculated
+    from the comparison array.
+
+    Parameters
+    ----------
+    scores : array_like
+        The input for which z-scores are calculated.
+    compare : array_like
+        The input from which the mean and standard deviation of the
+        normalization are taken; assumed to have the same dimension as
+        `scores`.
+    axis : int or None, optional
+        Axis over which mean and variance of `compare` are calculated.
+        Default is 0. If None, compute over the whole array `scores`.
+    ddof : int, optional
+        Degrees of freedom correction in the calculation of the
+        standard deviation. Default is 0.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle the occurrence of nans in `compare`.
+        'propagate' returns nan, 'raise' raises an exception, 'omit'
+        performs the calculations ignoring nan values. Default is
+        'propagate'. Note that when the value is 'omit', nans in `scores`
+        also propagate to the output, but they do not affect the z-scores
+        computed for the non-nan values.
+
+    Returns
+    -------
+    zscore : array_like
+        Z-scores, in the same shape as `scores`.
+
+    Notes
+    -----
+    This function preserves ndarray subclasses, and works also with
+    matrices and masked arrays (it uses `asanyarray` instead of
+    `asarray` for parameters).
+
+    Examples
+    --------
+    >>> from scipy.stats import zmap
+    >>> a = [0.5, 2.0, 2.5, 3]
+    >>> b = [0, 1, 2, 3, 4]
+    >>> zmap(a, b)
+    array([-1.06066017,  0.        ,  0.35355339,  0.70710678])
+
+    """
+    # The docstring explicitly states that it preserves subclasses.
+    # Let's table deprecating that and just get the array API version
+    # working.
+
+    like_zscore = (scores is compare)
+    xp = array_namespace(scores, compare)
+    scores, compare = xp_promote(scores, compare, force_floating=True, xp=xp)
+
+    with warnings.catch_warnings():
+        if like_zscore:  # zscore should not emit SmallSampleWarning
+            warnings.simplefilter('ignore', SmallSampleWarning)
+
+        mn = _xp_mean(compare, axis=axis, keepdims=True, nan_policy=nan_policy)
+        std = _xp_var(compare, axis=axis, correction=ddof,
+                      keepdims=True, nan_policy=nan_policy)**0.5
+
+    with np.errstate(invalid='ignore', divide='ignore'):
+        z = _demean(scores, mn, axis, xp=xp, precision_warning=False) / std
+
+    # If we know that scores and compare are identical, we can infer that
+    # some slices should have NaNs.
+    if like_zscore:
+        eps = xp.finfo(z.dtype).eps
+        zero = std <= xp.abs(eps * mn)
+        zero = xp.broadcast_to(zero, z.shape)
+        z = xpx.at(z, zero).set(xp.nan)
+
+    return z
+
+
+@xp_capabilities(marray=True)
+def gstd(a, axis=0, ddof=1, *, keepdims=False, nan_policy='propagate'):
+    r"""
+    Calculate the geometric standard deviation of an array.
+
+    The geometric standard deviation describes the spread of a set of numbers
+    where the geometric mean is preferred. It is a multiplicative factor, and
+    so a dimensionless quantity.
+
+    It is defined as the exponential of the standard deviation of the
+    natural logarithms of the observations.
+
+    Parameters
+    ----------
+    a : array_like
+        An array containing finite, strictly positive, real numbers.
+    axis : int, tuple or None, optional
+        Axis along which to operate. Default is 0. If None, compute over
+        the whole array `a`.
+    ddof : int, optional
+        Degree of freedom correction in the calculation of the
+        geometric standard deviation. Default is 1.
+    keepdims : bool, optional
+        If this is set to ``True``, the axes which are reduced are left
+        in the result as dimensions with length one. With this option,
+        the result will broadcast correctly against the input array.
+    nan_policy : {'propagate', 'omit', 'raise'}, default: 'propagate'
+        Defines how to handle input NaNs.
+
+        - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
+          which the statistic is computed, the corresponding entry of the output
+          will be NaN.
+        - ``omit``: NaNs will be omitted when performing the calculation.
+          If insufficient data remains in the axis slice along which the
+          statistic is computed, the corresponding entry of the output will be
+          NaN.
+        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
+
+    Returns
+    -------
+    gstd : ndarray or float
+        An array of the geometric standard deviation. If `axis` is None or `a`
+        is a 1d array a float is returned.
+
+    See Also
+    --------
+    gmean : Geometric mean
+    numpy.std : Standard deviation
+    gzscore : Geometric standard score
+
+    Notes
+    -----
+    Mathematically, the sample geometric standard deviation :math:`s_G` can be
+    defined in terms of the natural logarithms of the observations
+    :math:`y_i = \log(x_i)`:
+
+    .. math::
+
+        s_G = \exp(s), \quad s = \sqrt{\frac{1}{n - d} \sum_{i=1}^n (y_i - \bar y)^2}
+
+    where :math:`n` is the number of observations, :math:`d` is the adjustment `ddof`
+    to the degrees of freedom, and :math:`\bar y` denotes the mean of the natural
+    logarithms of the observations. Note that the default ``ddof=1`` is different from
+    the default value used by similar functions, such as `numpy.std` and `numpy.var`.
+
+    When an observation is infinite, the geometric standard deviation is
+    NaN (undefined). Non-positive observations will also produce NaNs in the
+    output because the *natural* logarithm (as opposed to the *complex*
+    logarithm) is defined and finite only for positive reals.
+    The geometric standard deviation is sometimes confused with the exponential
+    of the standard deviation, ``exp(std(a))``. Instead, the geometric standard
+    deviation is ``exp(std(log(a)))``.
+
+    References
+    ----------
+    .. [1] "Geometric standard deviation", *Wikipedia*,
+           https://en.wikipedia.org/wiki/Geometric_standard_deviation.
+    .. [2] Kirkwood, T. B., "Geometric means and measures of dispersion",
+           Biometrics, vol. 35, pp. 908-909, 1979
+
+    Examples
+    --------
+    Find the geometric standard deviation of a log-normally distributed sample.
+    Note that the standard deviation of the distribution is one; on a
+    log scale this evaluates to approximately ``exp(1)``.
+
+    >>> import numpy as np
+    >>> from scipy.stats import gstd
+    >>> rng = np.random.default_rng()
+    >>> sample = rng.lognormal(mean=0, sigma=1, size=1000)
+    >>> gstd(sample)
+    2.810010162475324
+
+    Compute the geometric standard deviation of a multidimensional array and
+    of a given axis.
+
+    >>> a = np.arange(1, 25).reshape(2, 3, 4)
+    >>> gstd(a, axis=None)
+    2.2944076136018947
+    >>> gstd(a, axis=2)
+    array([[1.82424757, 1.22436866, 1.13183117],
+           [1.09348306, 1.07244798, 1.05914985]])
+    >>> gstd(a, axis=(1,2))
+    array([2.12939215, 1.22120169])
+
+    """
+    xp = array_namespace(a)
+    a = xp_promote(a, force_floating=True, xp=xp)
+
+    kwargs = dict(axis=axis, correction=ddof, keepdims=keepdims, nan_policy=nan_policy)
+    with np.errstate(invalid='ignore', divide='ignore'):
+        res = xp.exp(_xp_var(xp.log(a), **kwargs)**0.5)
+
+    if not is_lazy_array(a) and xp.any(a <= 0):
+        message = ("The geometric standard deviation is only defined if all elements "
+                   "are greater than or equal to zero; otherwise, the result is NaN.")
+        warnings.warn(message, RuntimeWarning, stacklevel=2)
+
+    return res
+
+# Private dictionary initialized only once at module level
+# See https://en.wikipedia.org/wiki/Robust_measures_of_scale
+_scale_conversions = {'normal': float(special.erfinv(0.5) * 2.0 * math.sqrt(2.0))}
+
+
+@xp_capabilities(skip_backends=[('dask.array', 'no quantile (take_along_axis)')],
+                 marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, result_to_tuple=lambda x, _: (x,), n_outputs=1,
+    default_axis=None, override={'nan_propagation': False}
+)
+def iqr(x, axis=None, rng=(25, 75), scale=1.0, nan_policy='propagate',
+        interpolation='linear', keepdims=False):
+    r"""
+    Compute the interquartile range of the data along the specified axis.
+
+    The interquartile range (IQR) is the difference between the 75th and
+    25th percentile of the data. It is a measure of the dispersion
+    similar to standard deviation or variance, but is much more robust
+    against outliers [2]_.
+
+    The ``rng`` parameter allows this function to compute other
+    percentile ranges than the actual IQR. For example, setting
+    ``rng=(0, 100)`` is equivalent to `numpy.ptp`.
+
+    The IQR of an empty array is `np.nan`.
+
+    .. versionadded:: 0.18.0
+
+    Parameters
+    ----------
+    x : array_like
+        Input array or object that can be converted to an array.
+    axis : int or sequence of int, optional
+        Axis along which the range is computed. The default is to
+        compute the IQR for the entire array.
+    rng : Two-element sequence containing floats in range of [0,100] optional
+        Percentiles over which to compute the range. Each must be
+        between 0 and 100, inclusive. The default is the true IQR:
+        ``(25, 75)``. The order of the elements is not important.
+    scale : scalar or str or array_like of reals, optional
+        The numerical value of scale will be divided out of the final
+        result. The following string value is also recognized:
+
+        * 'normal' : Scale by
+          :math:`2 \sqrt{2} erf^{-1}(\frac{1}{2}) \approx 1.349`.
+
+        The default is 1.0.
+        Array-like `scale` of real dtype is also allowed, as long
+        as it broadcasts correctly to the output such that
+        ``out / scale`` is a valid operation. The output dimensions
+        depend on the input array, `x`, the `axis` argument, and the
+        `keepdims` flag.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    interpolation : str, optional
+
+        Specifies the interpolation method to use when the percentile
+        boundaries lie between two data points ``i`` and ``j``.
+        The following options are available (default is 'linear'):
+
+        * 'linear': ``i + (j - i)*fraction``, where ``fraction`` is the
+          fractional part of the index surrounded by ``i`` and ``j``.
+        * 'lower': ``i``.
+        * 'higher': ``j``.
+        * 'nearest': ``i`` or ``j`` whichever is nearest.
+        * 'midpoint': ``(i + j)/2``.
+
+        For NumPy >= 1.22.0, the additional options provided by the ``method``
+        keyword of `numpy.percentile` are also valid.
+
+    keepdims : bool, optional
+        If this is set to True, the reduced axes are left in the
+        result as dimensions with size one. With this option, the result
+        will broadcast correctly against the original array `x`.
+
+    Returns
+    -------
+    iqr : scalar or ndarray
+        If ``axis=None``, a scalar is returned. If the input contains
+        integers or floats of smaller precision than ``np.float64``, then the
+        output data-type is ``np.float64``. Otherwise, the output data-type is
+        the same as that of the input.
+
+    See Also
+    --------
+    numpy.std, numpy.var
+
+    References
+    ----------
+    .. [1] "Interquartile range" https://en.wikipedia.org/wiki/Interquartile_range
+    .. [2] "Robust measures of scale" https://en.wikipedia.org/wiki/Robust_measures_of_scale
+    .. [3] "Quantile" https://en.wikipedia.org/wiki/Quantile
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import iqr
+    >>> x = np.array([[10, 7, 4], [3, 2, 1]])
+    >>> x
+    array([[10,  7,  4],
+           [ 3,  2,  1]])
+    >>> iqr(x)
+    4.0
+    >>> iqr(x, axis=0)
+    array([ 3.5,  2.5,  1.5])
+    >>> iqr(x, axis=1)
+    array([ 3.,  1.])
+    >>> iqr(x, axis=1, keepdims=True)
+    array([[ 3.],
+           [ 1.]])
+
+    """
+    xp = array_namespace(x)
+
+    # An error may be raised here, so fail-fast, before doing lengthy
+    # computations, even though `scale` is not used until later
+    if isinstance(scale, str):
+        scale_key = scale.lower()
+        if scale_key not in _scale_conversions:
+            raise ValueError(f"{scale} not a valid scale for `iqr`")
+        scale = _scale_conversions[scale_key]
+
+    if len(rng) != 2:
+        raise TypeError("`rng` must be a two element sequence.")
+
+    if np.isnan(rng).any():  # OK to use NumPy; this shouldn't be an array
+        raise ValueError("`rng` must not contain NaNs.")
+
+    rng = (rng[0]/100, rng[1]/100) if rng[0] < rng[1] else (rng[1]/100, rng[0]/100)
+
+    if rng[0] < 0 or rng[1] > 1:
+        raise ValueError("Elements of `rng` must be in the range [0, 100].")
+
+    if interpolation in {'lower', 'midpoint', 'higher', 'nearest'}:
+        interpolation = '_' + interpolation
+
+    rng = xp.asarray(rng, dtype=xp_result_type(x, force_floating=True, xp=xp))
+    pct = stats.quantile(x, rng, axis=-1, method=interpolation, keepdims=True)
+    out = pct[..., 1:2] - pct[..., 0:1]
+
+    if scale != 1.0:
+        out /= scale
+
+    out = out if keepdims else xp.squeeze(out, axis=-1)
+    return out[()] if out.ndim == 0 else out
+
+
+def _mad_1d(x, center, nan_policy):
+    # Median absolute deviation for 1-d array x.
+    # This is a helper function for `median_abs_deviation`; it assumes its
+    # arguments have been validated already.  In particular,  x must be a
+    # 1-d numpy array, center must be callable, and if nan_policy is not
+    # 'propagate', it is assumed to be 'omit', because 'raise' is handled
+    # in `median_abs_deviation`.
+    # No warning is generated if x is empty or all nan.
+    isnan = np.isnan(x)
+    if isnan.any():
+        if nan_policy == 'propagate':
+            return np.nan
+        x = x[~isnan]
+    if x.size == 0:
+        # MAD of an empty array is nan.
+        return np.nan
+    # Edge cases have been handled, so do the basic MAD calculation.
+    med = center(x)
+    mad = np.median(np.abs(x - med))
+    return mad
+
+
+@xp_capabilities(skip_backends=[('dask.array', 'not supported by `quantile`')],
+                 marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, result_to_tuple=lambda x, _: (x,), n_outputs=1, default_axis=0
+)
+def median_abs_deviation(x, axis=0, center=None, scale=1.0,
+                         nan_policy='propagate'):
+    r"""
+    Compute the median absolute deviation of the data along the given axis.
+
+    The median absolute deviation (MAD, [1]_) computes the median over the
+    absolute deviations from the median. It is a measure of dispersion
+    similar to the standard deviation but more robust to outliers [2]_.
+
+    The MAD of an empty array is ``np.nan``.
+
+    .. versionadded:: 1.5.0
+
+    Parameters
+    ----------
+    x : array_like
+        Input array or object that can be converted to an array.
+    axis : int or None, optional
+        Axis along which the range is computed. Default is 0. If None, compute
+        the MAD over the entire array.
+    center : callable, optional
+        A function that will return the central value. The default is to use
+        np.median. Any user defined function used will need to have the
+        function signature ``func(arr, axis)``.
+    scale : scalar or str, optional
+        The numerical value of scale will be divided out of the final
+        result. The default is 1.0. The string "normal" is also accepted,
+        and results in `scale` being the inverse of the standard normal
+        quantile function at 0.75, which is approximately 0.67449.
+        Array-like scale is also allowed, as long as it broadcasts correctly
+        to the output such that ``out / scale`` is a valid operation. The
+        output dimensions depend on the input array, `x`, and the `axis`
+        argument.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    Returns
+    -------
+    mad : scalar or ndarray
+        If ``axis=None``, a scalar is returned. If the input contains
+        integers or floats of smaller precision than ``np.float64``, then the
+        output data-type is ``np.float64``. Otherwise, the output data-type is
+        the same as that of the input.
+
+    See Also
+    --------
+    numpy.std, numpy.var, numpy.median, scipy.stats.iqr, scipy.stats.tmean,
+    scipy.stats.tstd, scipy.stats.tvar
+
+    Notes
+    -----
+    The `center` argument only affects the calculation of the central value
+    around which the MAD is calculated. That is, passing in ``center=np.mean``
+    will calculate the MAD around the mean - it will not calculate the *mean*
+    absolute deviation.
+
+    The input array may contain `inf`, but if `center` returns `inf`, the
+    corresponding MAD for that data will be `nan`.
+
+    References
+    ----------
+    .. [1] "Median absolute deviation",
+           https://en.wikipedia.org/wiki/Median_absolute_deviation
+    .. [2] "Robust measures of scale",
+           https://en.wikipedia.org/wiki/Robust_measures_of_scale
+
+    Examples
+    --------
+    When comparing the behavior of `median_abs_deviation` with ``np.std``,
+    the latter is affected when we change a single value of an array to have an
+    outlier value while the MAD hardly changes:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> x = stats.norm.rvs(size=100, scale=1, random_state=123456)
+    >>> x.std()
+    0.9973906394005013
+    >>> stats.median_abs_deviation(x)
+    0.82832610097857
+    >>> x[0] = 345.6
+    >>> x.std()
+    34.42304872314415
+    >>> stats.median_abs_deviation(x)
+    0.8323442311590675
+
+    Axis handling example:
+
+    >>> x = np.array([[10, 7, 4], [3, 2, 1]])
+    >>> x
+    array([[10,  7,  4],
+           [ 3,  2,  1]])
+    >>> stats.median_abs_deviation(x)
+    array([3.5, 2.5, 1.5])
+    >>> stats.median_abs_deviation(x, axis=None)
+    2.0
+
+    Scale normal example:
+
+    >>> x = stats.norm.rvs(size=1000000, scale=2, random_state=123456)
+    >>> stats.median_abs_deviation(x)
+    1.3487398527041636
+    >>> stats.median_abs_deviation(x, scale='normal')
+    1.9996446978061115
+
+    """
+    xp = array_namespace(x)
+    xp_median = (xp.median if is_numpy(xp)
+                 else lambda x, axis: stats.quantile(x, 0.5, axis=axis))
+    center = (xp_median if center is None else center)
+
+    if not callable(center):
+        raise TypeError("The argument 'center' must be callable. The given "
+                        f"value {repr(center)} is not callable.")
+
+    # An error may be raised here, so fail-fast, before doing lengthy
+    # computations, even though `scale` is not used until later
+    if isinstance(scale, str):
+        if scale.lower() == 'normal':
+            scale = 0.6744897501960817  # special.ndtri(0.75)
+        else:
+            raise ValueError(f"{scale} is not a valid scale value.")
+
+    # Wrap the call to center() in expand_dims() so it acts like
+    # keepdims=True was used.
+    med = xp.expand_dims(center(x, axis=axis), axis=axis)
+    mad = xp_median(xp.abs(x - med), axis=axis)
+
+    return mad / scale
+
+
+#####################################
+#         TRIMMING FUNCTIONS        #
+#####################################
+
+
+SigmaclipResult = namedtuple('SigmaclipResult', ('clipped', 'lower', 'upper'))
+
+
+@xp_capabilities(skip_backends=[('dask.array', "doesn't know array size")],
+                 jax_jit=False)
+def sigmaclip(a, low=4., high=4., *, nan_policy='propagate'):
+    """Perform iterative sigma-clipping of array elements.
+
+    Starting from the full sample, all elements outside the critical range are
+    removed, i.e. all elements of the input array `c` that satisfy either of
+    the following conditions::
+
+        c < mean(c) - std(c)*low
+        c > mean(c) + std(c)*high
+
+    The iteration continues with the updated sample until no
+    elements are outside the (updated) range.
+
+    Parameters
+    ----------
+    a : array_like
+        Data array, will be raveled if not 1-D.
+    low : float, optional
+        Lower bound factor of sigma clipping. Default is 4.
+    high : float, optional
+        Upper bound factor of sigma clipping. Default is 4.
+    nan_policy : {'propagate', 'omit', 'raise'}
+        Defines how to handle input NaNs.
+
+        - ``propagate``: if a NaN is present in the input, the clipped array will be
+          empty, and the upper and lower thresholds will be NaN.
+        - ``omit``: NaNs will be omitted when performing the calculation.
+        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
+
+    Returns
+    -------
+    clipped : ndarray
+        Input array with clipped elements removed.
+    lower : float
+        Lower threshold value use for clipping.
+    upper : float
+        Upper threshold value use for clipping.
+
+    Notes
+    -----
+    This function iteratively *removes* observations. Once observations are
+    removed, they are not re-added in subsequent iterations. Consequently,
+    although it is often the case that ``clipped`` is identical to
+    ``a[(a >= lower) & (a <= upper)]``, this property is not guaranteed to be
+    satisfied; ``clipped`` may have fewer elements.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import sigmaclip
+    >>> a = np.concatenate((np.linspace(9.5, 10.5, 31),
+    ...                     np.linspace(0, 20, 5)))
+    >>> fact = 1.5
+    >>> c, low, upp = sigmaclip(a, fact, fact)
+    >>> c
+    array([  9.96666667,  10.        ,  10.03333333,  10.        ])
+    >>> c.var(), c.std()
+    (0.00055555555555555165, 0.023570226039551501)
+    >>> low, c.mean() - fact*c.std(), c.min()
+    (9.9646446609406727, 9.9646446609406727, 9.9666666666666668)
+    >>> upp, c.mean() + fact*c.std(), c.max()
+    (10.035355339059327, 10.035355339059327, 10.033333333333333)
+
+    >>> a = np.concatenate((np.linspace(9.5, 10.5, 11),
+    ...                     np.linspace(-100, -50, 3)))
+    >>> c, low, upp = sigmaclip(a, 1.8, 1.8)
+    >>> (c == np.linspace(9.5, 10.5, 11)).all()
+    True
+
+    """
+    xp = array_namespace(a)
+    c = xp_ravel(xp.asarray(a))
+    contains_nan = _contains_nan(c, nan_policy, xp_omit_okay=True)
+    if contains_nan:
+        if nan_policy == 'propagate':
+            NaN = _get_nan(c, xp=xp)
+            clipped = xp.empty_like(c[0:0])
+            return SigmaclipResult(clipped, NaN, NaN)
+        elif nan_policy == 'omit':
+            c = c[~xp.isnan(c)]
+
+    delta = 1
+    while delta:
+        c_std = xp.std(c)
+        c_mean = xp.mean(c)
+        size = xp_size(c)
+        critlower = c_mean - c_std * low
+        critupper = c_mean + c_std * high
+        c = c[(c >= critlower) & (c <= critupper)]
+        delta = size - xp_size(c)
+
+    return SigmaclipResult(c, critlower, critupper)
+
+
+@xp_capabilities(np_only=True)
+def trimboth(a, proportiontocut, axis=0):
+    """Slice off a proportion of items from both ends of an array.
+
+    Slice off the passed proportion of items from both ends of the passed
+    array (i.e., with `proportiontocut` = 0.1, slices leftmost 10% **and**
+    rightmost 10% of scores). The trimmed values are the lowest and
+    highest ones.
+    Slice off less if proportion results in a non-integer slice index (i.e.
+    conservatively slices off `proportiontocut`).
+
+    Parameters
+    ----------
+    a : array_like
+        Data to trim.
+    proportiontocut : float
+        Proportion (in range 0-1) of total data set to trim of each end.
+    axis : int or None, optional
+        Axis along which to trim data. Default is 0. If None, compute over
+        the whole array `a`.
+
+    Returns
+    -------
+    out : ndarray
+        Trimmed version of array `a`. The order of the trimmed content
+        is undefined.
+
+    See Also
+    --------
+    trim_mean
+
+    Examples
+    --------
+    Create an array of 10 values and trim 10% of those values from each end:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+    >>> stats.trimboth(a, 0.1)
+    array([1, 3, 2, 4, 5, 6, 7, 8])
+
+    Note that the elements of the input array are trimmed by value, but the
+    output array is not necessarily sorted.
+
+    The proportion to trim is rounded down to the nearest integer. For
+    instance, trimming 25% of the values from each end of an array of 10
+    values will return an array of 6 values:
+
+    >>> b = np.arange(10)
+    >>> stats.trimboth(b, 1/4).shape
+    (6,)
+
+    Multidimensional arrays can be trimmed along any axis or across the entire
+    array:
+
+    >>> c = [2, 4, 6, 8, 0, 1, 3, 5, 7, 9]
+    >>> d = np.array([a, b, c])
+    >>> stats.trimboth(d, 0.4, axis=0).shape
+    (1, 10)
+    >>> stats.trimboth(d, 0.4, axis=1).shape
+    (3, 2)
+    >>> stats.trimboth(d, 0.4, axis=None).shape
+    (6,)
+
+    """
+    a = np.asarray(a)
+
+    if a.size == 0:
+        return a
+
+    if axis is None:
+        a = a.ravel()
+        axis = 0
+
+    nobs = a.shape[axis]
+    lowercut = int(proportiontocut * nobs)
+    uppercut = nobs - lowercut
+    if (lowercut >= uppercut):
+        raise ValueError("Proportion too big.")
+
+    atmp = np.partition(a, (lowercut, uppercut - 1), axis)
+
+    sl = [slice(None)] * atmp.ndim
+    sl[axis] = slice(lowercut, uppercut)
+    return atmp[tuple(sl)]
+
+
+@xp_capabilities(np_only=True)
+def trim1(a, proportiontocut, tail='right', axis=0):
+    """Slice off a proportion from ONE end of the passed array distribution.
+
+    If `proportiontocut` = 0.1, slices off 'leftmost' or 'rightmost'
+    10% of scores. The lowest or highest values are trimmed (depending on
+    the tail).
+    Slice off less if proportion results in a non-integer slice index
+    (i.e. conservatively slices off `proportiontocut` ).
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    proportiontocut : float
+        Fraction to cut off of 'left' or 'right' of distribution.
+    tail : {'left', 'right'}, optional
+        Defaults to 'right'.
+    axis : int or None, optional
+        Axis along which to trim data. Default is 0. If None, compute over
+        the whole array `a`.
+
+    Returns
+    -------
+    trim1 : ndarray
+        Trimmed version of array `a`. The order of the trimmed content is
+        undefined.
+
+    Examples
+    --------
+    Create an array of 10 values and trim 20% of its lowest values:
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+    >>> stats.trim1(a, 0.2, 'left')
+    array([2, 4, 3, 5, 6, 7, 8, 9])
+
+    Note that the elements of the input array are trimmed by value, but the
+    output array is not necessarily sorted.
+
+    The proportion to trim is rounded down to the nearest integer. For
+    instance, trimming 25% of the values from an array of 10 values will
+    return an array of 8 values:
+
+    >>> b = np.arange(10)
+    >>> stats.trim1(b, 1/4).shape
+    (8,)
+
+    Multidimensional arrays can be trimmed along any axis or across the entire
+    array:
+
+    >>> c = [2, 4, 6, 8, 0, 1, 3, 5, 7, 9]
+    >>> d = np.array([a, b, c])
+    >>> stats.trim1(d, 0.8, axis=0).shape
+    (1, 10)
+    >>> stats.trim1(d, 0.8, axis=1).shape
+    (3, 2)
+    >>> stats.trim1(d, 0.8, axis=None).shape
+    (6,)
+
+    """
+    a = np.asarray(a)
+    if axis is None:
+        a = a.ravel()
+        axis = 0
+
+    nobs = a.shape[axis]
+
+    # avoid possible corner case
+    if proportiontocut >= 1:
+        return []
+
+    if tail.lower() == 'right':
+        lowercut = 0
+        uppercut = nobs - int(proportiontocut * nobs)
+
+    elif tail.lower() == 'left':
+        lowercut = int(proportiontocut * nobs)
+        uppercut = nobs
+
+    atmp = np.partition(a, (lowercut, uppercut - 1), axis)
+
+    sl = [slice(None)] * atmp.ndim
+    sl[axis] = slice(lowercut, uppercut)
+    return atmp[tuple(sl)]
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(lambda x: x, result_to_tuple=lambda x, _: (x,), n_outputs=1)
+def trim_mean(a, proportiontocut, axis=0):
+    """Return mean of array after trimming a specified fraction of extreme values.
+
+    Removes the specified proportion of elements from *each* end of the
+    sorted array, then computes the mean of the remaining elements.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    proportiontocut : float
+        Fraction of the most positive and most negative elements to remove.
+        When the specified proportion does not result in an integer number of
+        elements, the number of elements to trim is rounded down.
+    axis : int or None, default: 0
+        Axis along which the trimmed means are computed.
+        If None, compute over the raveled array.
+
+    Returns
+    -------
+    trim_mean : ndarray
+        Mean of trimmed array.
+
+    See Also
+    --------
+    trimboth : Remove a proportion of elements from each end of an array.
+    tmean : Compute the mean after trimming values outside specified limits.
+
+    Notes
+    -----
+    For 1-D array `a`, `trim_mean` is approximately equivalent to the following
+    calculation::
+
+        import numpy as np
+        a = np.sort(a)
+        m = int(proportiontocut * len(a))
+        np.mean(a[m: len(a) - m])
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> x = [1, 2, 3, 5]
+    >>> stats.trim_mean(x, 0.25)
+    2.5
+
+    When the specified proportion does not result in an integer number of
+    elements, the number of elements to trim is rounded down.
+
+    >>> stats.trim_mean(x, 0.24999) == np.mean(x)
+    True
+
+    Use `axis` to specify the axis along which the calculation is performed.
+
+    >>> x2 = [[1, 2, 3, 5],
+    ...       [10, 20, 30, 50]]
+    >>> stats.trim_mean(x2, 0.25)
+    array([ 5.5, 11. , 16.5, 27.5])
+    >>> stats.trim_mean(x2, 0.25, axis=1)
+    array([ 2.5, 25. ])
+
+    """
+    xp = array_namespace(a)
+
+    a = xp.asarray(a)
+
+    if xp_size(a) == 0:
+        return _get_nan(a, xp=xp)
+
+    if axis is None:
+        a = xp_ravel(a)
+        axis = 0
+
+    nobs = _count_nonmasked(a, axis=axis, keepdims=True, xp=xp)
+    lowercut = proportiontocut * nobs
+    nobs, lowercut = ((nobs, int(lowercut)) if not is_marray(xp)
+                      else (xp.astype(nobs, xp.int64), xp.astype(lowercut, xp.int64)))
+    uppercut = nobs - lowercut
+    if not is_marray(xp) and (lowercut > uppercut):
+        raise ValueError("Proportion too big.")
+
+    atmp = (np.partition(a, (lowercut, uppercut - 1), axis) if is_numpy(xp)
+            else xp.sort(a, axis=axis))
+
+    if is_marray(xp):
+        indices = xp.arange(a.shape[-1])  # axis_nan_policy decorator -> axis=-1
+        mask = (indices < lowercut) | (indices >= (nobs - lowercut)) | atmp.mask
+        trimmed = xp.asarray(atmp.data, mask=mask.data)
+    else:
+        sl = [slice(None)] * atmp.ndim
+        sl[axis] = slice(lowercut, uppercut)
+        trimmed = atmp[tuple(sl)]
+
+    trimmed = xp_promote(trimmed, force_floating=True, xp=xp)
+    return xp.mean(trimmed, axis=axis)
+
+
+F_onewayResult = namedtuple('F_onewayResult', ('statistic', 'pvalue'))
+
+
+def _f_oneway_is_too_small(samples, kwargs=None, axis=-1):
+    message = f"At least two samples are required; got {len(samples)}."
+    if len(samples) < 2:
+        raise TypeError(message)
+
+    # Check this after forming alldata, so shape errors are detected
+    # and reported before checking for 0 length inputs.
+    if any(sample.shape[axis] == 0 for sample in samples):
+        return True
+
+    # Must have at least one group with length greater than 1.
+    if all(sample.shape[axis] == 1 for sample in samples):
+        msg = ('all input arrays have length 1.  f_oneway requires that at '
+               'least one input has length greater than 1.')
+        warnings.warn(SmallSampleWarning(msg), stacklevel=2)
+        return True
+
+    return False
+
+
+# Torch GPU need fdtrc
+@xp_capabilities(cpu_only=True, exceptions=['cupy'], marray=True)
+@_axis_nan_policy_factory(
+    F_onewayResult, n_samples=None, too_small=_f_oneway_is_too_small)
+def f_oneway(*samples, axis=0, equal_var=True):
+    """Perform one-way ANOVA.
+
+    The one-way ANOVA tests the null hypothesis that two or more groups have
+    the same population mean.  The test is applied to samples from two or
+    more groups, possibly with differing sizes.
+
+    Parameters
+    ----------
+    *samples : array_like
+        The sample measurements for each group.  There must be at least
+        two arguments.  If the arrays are multidimensional, then all the
+        dimensions of the array must be the same except for `axis`.
+    axis : int, optional
+        Axis of the input arrays along which the test is applied.
+        Default is 0.
+    equal_var : bool, optional
+        If True (default), perform a standard one-way ANOVA test that
+        assumes equal population variances [2]_.
+        If False, perform Welch's ANOVA test, which does not assume
+        equal population variances [4]_.
+
+        .. versionadded:: 1.16.0
+
+    Returns
+    -------
+    statistic : float
+        The computed F statistic of the test.
+    pvalue : float
+        The associated p-value from the F distribution.
+
+    Warns
+    -----
+    `~scipy.stats.ConstantInputWarning`
+        Emitted if all values within each of the input arrays are identical.
+        In this case the F statistic is either infinite or isn't defined,
+        so ``np.inf`` or ``np.nan`` is returned.
+
+    RuntimeWarning
+        Emitted if the length of any input array is 0, or if all the input
+        arrays have length 1.  ``np.nan`` is returned for the F statistic
+        and the p-value in these cases.
+
+    Notes
+    -----
+    The ANOVA test has important assumptions that must be satisfied in order
+    for the associated p-value to be valid.
+
+    1. The samples are independent.
+    2. Each sample is from a normally distributed population.
+    3. The population standard deviations of the groups are all equal.  This
+       property is known as homoscedasticity.
+
+    If these assumptions are not true for a given set of data, it may still
+    be possible to use the Kruskal-Wallis H-test (`scipy.stats.kruskal`) or
+    the Alexander-Govern test (`scipy.stats.alexandergovern`) although with
+    some loss of power.
+
+    The length of each group must be at least one, and there must be at
+    least one group with length greater than one.  If these conditions
+    are not satisfied, a warning is generated and (``np.nan``, ``np.nan``)
+    is returned.
+
+    If all values in each group are identical, and there exist at least two
+    groups with different values, the function generates a warning and
+    returns (``np.inf``, 0).
+
+    If all values in all groups are the same, function generates a warning
+    and returns (``np.nan``, ``np.nan``).
+
+    The algorithm is from Heiman [2]_, pp.394-7.
+
+    References
+    ----------
+    .. [1] R. Lowry, "Concepts and Applications of Inferential Statistics",
+           Chapter 14, 2014, http://vassarstats.net/textbook/
+
+    .. [2] G.W. Heiman, "Understanding research methods and statistics: An
+           integrated introduction for psychology", Houghton, Mifflin and
+           Company, 2001.
+
+    .. [3] J.H. McDonald, "Handbook of Biological Statistics",
+           One-way ANOVA, 2014.
+           http://www.biostathandbook.com/onewayanova.html
+
+    .. [4] B. L. Welch, "On the Comparison of Several Mean Values:
+           An Alternative Approach", Biometrika, vol. 38, no. 3/4,
+           pp. 330-336, 1951. :doi:`10.2307/2332579`.
+
+    .. [5] J.H. McDonald, R. Seed and R.K. Koehn, "Allozymes and
+           morphometric characters of three species of Mytilus in
+           the Northern and Southern Hemispheres",
+           Marine Biology, vol. 111, pp. 323-333, 1991.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import f_oneway
+
+    Here are some data [3]_ on a shell measurement (the length of the anterior
+    adductor muscle scar, standardized by dividing by length) in the mussel
+    Mytilus trossulus from five locations: Tillamook, Oregon; Newport, Oregon;
+    Petersburg, Alaska; Magadan, Russia; and Tvarminne, Finland, taken from a
+    much larger data set used in [5]_.
+
+    >>> tillamook = [0.0571, 0.0813, 0.0831, 0.0976, 0.0817, 0.0859, 0.0735,
+    ...              0.0659, 0.0923, 0.0836]
+    >>> newport = [0.0873, 0.0662, 0.0672, 0.0819, 0.0749, 0.0649, 0.0835,
+    ...            0.0725]
+    >>> petersburg = [0.0974, 0.1352, 0.0817, 0.1016, 0.0968, 0.1064, 0.105]
+    >>> magadan = [0.1033, 0.0915, 0.0781, 0.0685, 0.0677, 0.0697, 0.0764,
+    ...            0.0689]
+    >>> tvarminne = [0.0703, 0.1026, 0.0956, 0.0973, 0.1039, 0.1045]
+    >>> f_oneway(tillamook, newport, petersburg, magadan, tvarminne)
+    F_onewayResult(statistic=7.121019471642447, pvalue=0.0002812242314534544)
+
+    `f_oneway` accepts multidimensional input arrays.  When the inputs
+    are multidimensional and `axis` is not given, the test is performed
+    along the first axis of the input arrays.  For the following data, the
+    test is performed three times, once for each column.
+
+    >>> a = np.array([[9.87, 9.03, 6.81],
+    ...               [7.18, 8.35, 7.00],
+    ...               [8.39, 7.58, 7.68],
+    ...               [7.45, 6.33, 9.35],
+    ...               [6.41, 7.10, 9.33],
+    ...               [8.00, 8.24, 8.44]])
+    >>> b = np.array([[6.35, 7.30, 7.16],
+    ...               [6.65, 6.68, 7.63],
+    ...               [5.72, 7.73, 6.72],
+    ...               [7.01, 9.19, 7.41],
+    ...               [7.75, 7.87, 8.30],
+    ...               [6.90, 7.97, 6.97]])
+    >>> c = np.array([[3.31, 8.77, 1.01],
+    ...               [8.25, 3.24, 3.62],
+    ...               [6.32, 8.81, 5.19],
+    ...               [7.48, 8.83, 8.91],
+    ...               [8.59, 6.01, 6.07],
+    ...               [3.07, 9.72, 7.48]])
+    >>> F = f_oneway(a, b, c)
+    >>> F.statistic
+    array([1.75676344, 0.03701228, 3.76439349])
+    >>> F.pvalue
+    array([0.20630784, 0.96375203, 0.04733157])
+
+    Welch ANOVA will be performed if `equal_var` is False.
+
+    """
+    xp = array_namespace(*samples)
+    samples = xp_promote(*samples, force_floating=True, xp=xp)
+
+    if len(samples) < 2:
+        raise TypeError('at least two inputs are required;'
+                        f' got {len(samples)}.')
+
+    # ANOVA on N groups, each in its own array
+    num_groups = len(samples)
+
+    # axis is guaranteed to be -1 by the _axis_nan_policy decorator
+    alldata = xp.concat(samples, axis=-1)
+    bign = _count_nonmasked(alldata, axis=-1, xp=xp)
+
+    # Check if the inputs are too small (for testing _axis_nan_policy decorator)
+    if _f_oneway_is_too_small(samples):
+        NaN = _get_nan(*samples, xp=xp)
+        return F_onewayResult(NaN, NaN)
+
+    # Check if all values within each group are identical, and if the common
+    # value in at least one group is different from that in another group.
+    # Based on https://github.com/scipy/scipy/issues/11669
+
+    # If axis=0, say, and the groups have shape (n0, ...), (n1, ...), ...,
+    # then is_const is a boolean array with shape (num_groups, ...).
+    # It is True if the values within the groups along the axis slice are
+    # identical. In the typical case where each input array is 1-d, is_const is
+    # a 1-d array with length num_groups.
+    is_const = xp.concat([xp.all(xp.diff(sample, axis=-1) == 0, axis=-1, keepdims=True)
+                          for sample in samples], axis=-1)
+
+    # all_const is a boolean array with shape (...) (see previous comment).
+    # It is True if the values within each group along the axis slice are
+    # the same (e.g. [[3, 3, 3], [5, 5, 5, 5], [4, 4, 4]]).
+    all_const = xp.all(is_const, axis=-1)
+
+    # all_same_const is True if all the values in the groups along the axis=0
+    # slice are the same (e.g. [[3, 3, 3], [3, 3, 3, 3], [3, 3, 3]]).
+    all_same_const = xp.all(xp.diff(alldata, axis=-1) == 0, axis=-1)
+
+    if not isinstance(equal_var, bool):
+        raise TypeError("Expected a boolean value for 'equal_var'")
+
+    if equal_var:
+        # Determine the mean of the data, and subtract that from all inputs to a
+        # variance (via sum_of_sq / sq_of_sum) calculation.  Variance is invariant
+        # to a shift in location, and centering all data around zero vastly
+        # improves numerical stability.
+        offset = xp.mean(alldata, axis=-1, keepdims=True)
+        alldata = alldata - offset
+
+        normalized_ss = xp.sum(alldata, axis=-1)**2. / bign
+
+        sstot = xp.vecdot(alldata, alldata, axis=-1) - normalized_ss
+
+        ssbn = 0
+        for sample in samples:
+            smo_ss = xp.sum(sample - offset, axis=-1)**2.
+            ssbn = ssbn + smo_ss / _count_nonmasked(sample, axis=-1, xp=xp)
+
+        # Naming: variables ending in bn/b are for "between treatments", wn/w are
+        # for "within treatments"
+        ssbn = ssbn - normalized_ss
+        sswn = sstot - ssbn
+        dfbn = num_groups - 1
+        dfwn = bign - num_groups
+        msb = ssbn / dfbn
+        msw = sswn / dfwn
+        with np.errstate(divide='ignore', invalid='ignore'):
+            f = msb / msw
+        dfn, dfd = dfbn, dfwn
+
+    else:
+        # calculate basic statistics for each sample
+        # Beginning of second paragraph [4] page 1:
+        # "As a particular case $y_t$ may be the means ... of samples
+        y_t = xp.stack([xp.mean(sample, axis=-1) for sample in samples])
+        # "... of $n_t$ observations..."
+        if is_marray(xp):
+            n_t = xp.stack([_count_nonmasked(sample, axis=-1, xp=xp)
+                            for sample in samples])
+            n_t = xp.asarray(n_t, dtype=n_t.dtype)
+        else:
+            n_t = xp.asarray([sample.shape[-1] for sample in samples], dtype=y_t.dtype)
+            n_t = xp.reshape(n_t, (-1,) + (1,) * (y_t.ndim - 1))
+        # "... from $k$ different normal populations..."
+        k = len(samples)
+        # "The separate samples provide estimates $s_t^2$ of the $\sigma_t^2$."
+        s_t2 = xp.stack([xp.var(sample, axis=-1, correction=1) for sample in samples])
+
+        # calculate weight by number of data and variance
+        # "we have $\lambda_t = 1 / n_t$ ... where w_t = 1 / {\lambda_t s_t^2}$"
+        w_t = n_t / s_t2
+        # sum of w_t
+        s_w_t = xp.sum(w_t, axis=0)
+
+        # calculate adjusted grand mean
+        # "... and $\hat{y} = \sum w_t y_t / \sum w_t$. When all..."
+        axis_zero = -w_t.ndim
+        y_hat = xp.vecdot(w_t, y_t, axis=axis_zero) / xp.sum(w_t, axis=0)
+
+        # adjust f statistic
+        # ref.[4] p.334 eq.29
+        numerator =  xp.vecdot(w_t, (y_t - y_hat)**2, axis=axis_zero) / (k - 1)
+        denominator = (
+                1 + 2 * (k - 2) / (k**2 - 1) *
+                xp.vecdot(1 / (n_t - 1), (1 - w_t / s_w_t)**2, axis=axis_zero)
+        )
+        f = numerator / denominator
+
+        # degree of freedom 1
+        # ref.[4] p.334 eq.30
+        hat_f1 = k - 1
+
+        # adjusted degree of freedom 2
+        # ref.[4] p.334 eq.30
+        hat_f2 = (
+                (k**2 - 1) /
+                (3 * xp.vecdot(1 / (n_t - 1), (1 - w_t / s_w_t)**2, axis=axis_zero))
+        )
+
+        dfn, dfd = hat_f1, hat_f2
+
+    # Fix any f values that should be inf or nan because the corresponding
+    # inputs were constant.
+    f = xpx.at(f)[all_const].set(xp.inf)
+    f = xpx.at(f)[all_same_const].set(xp.nan)
+
+    # calculate p value
+    # ref.[4] p.334 eq.28
+    prob = special.fdtrc(dfn, dfd, f)
+
+    f, prob = (f[()], prob[()]) if f.ndim == 0 else (f, prob)
+    return F_onewayResult(f, prob)
+
+
+@dataclass
+class AlexanderGovernResult:
+    statistic: float
+    pvalue: float
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    AlexanderGovernResult, n_samples=None,
+    result_to_tuple=lambda x, _: (x.statistic, x.pvalue),
+    too_small=1
+)
+def alexandergovern(*samples, nan_policy='propagate', axis=0):
+    """Performs the Alexander Govern test.
+
+    The Alexander-Govern approximation tests the equality of k independent
+    means in the face of heterogeneity of variance. The test is applied to
+    samples from two or more groups, possibly with differing sizes.
+
+    Parameters
+    ----------
+    *samples : array_like
+        The sample measurements for each group.  There must be at least
+        two samples, and each sample must contain at least two observations.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    Returns
+    -------
+    res : AlexanderGovernResult
+        An object with attributes:
+
+        statistic : float
+            The computed A statistic of the test.
+        pvalue : float
+            The associated p-value from the chi-squared distribution.
+
+    Warns
+    -----
+    `~scipy.stats.ConstantInputWarning`
+        Raised if an input is a constant array.  The statistic is not defined
+        in this case, so ``np.nan`` is returned.
+
+    See Also
+    --------
+    f_oneway : one-way ANOVA
+
+    Notes
+    -----
+    The use of this test relies on several assumptions.
+
+    1. The samples are independent.
+    2. Each sample is from a normally distributed population.
+    3. Unlike `f_oneway`, this test does not assume on homoscedasticity,
+       instead relaxing the assumption of equal variances.
+
+    Input samples must be finite, one dimensional, and with size greater than
+    one.
+
+    References
+    ----------
+    .. [1] Alexander, Ralph A., and Diane M. Govern. "A New and Simpler
+           Approximation for ANOVA under Variance Heterogeneity." Journal
+           of Educational Statistics, vol. 19, no. 2, 1994, pp. 91-101.
+           https://www.jstor.org/stable/1165140
+
+    Examples
+    --------
+    >>> from scipy.stats import alexandergovern
+
+    Here are some data on annual percentage rate of interest charged on
+    new car loans at nine of the largest banks in four American cities
+    taken from the National Institute of Standards and Technology's
+    ANOVA dataset.
+
+    We use `alexandergovern` to test the null hypothesis that all cities
+    have the same mean APR against the alternative that the cities do not
+    all have the same mean APR. We decide that a significance level of 5%
+    is required to reject the null hypothesis in favor of the alternative.
+
+    >>> atlanta = [13.75, 13.75, 13.5, 13.5, 13.0, 13.0, 13.0, 12.75, 12.5]
+    >>> chicago = [14.25, 13.0, 12.75, 12.5, 12.5, 12.4, 12.3, 11.9, 11.9]
+    >>> houston = [14.0, 14.0, 13.51, 13.5, 13.5, 13.25, 13.0, 12.5, 12.5]
+    >>> memphis = [15.0, 14.0, 13.75, 13.59, 13.25, 12.97, 12.5, 12.25,
+    ...           11.89]
+    >>> alexandergovern(atlanta, chicago, houston, memphis)
+    AlexanderGovernResult(statistic=4.65087071883494,
+                          pvalue=0.19922132490385214)
+
+    The p-value is 0.1992, indicating a nearly 20% chance of observing
+    such an extreme value of the test statistic under the null hypothesis.
+    This exceeds 5%, so we do not reject the null hypothesis in favor of
+    the alternative.
+
+    """
+    xp = array_namespace(*samples)
+    samples = _alexandergovern_input_validation(samples, nan_policy, axis, xp=xp)
+
+    # The following formula numbers reference the equation described on
+    # page 92 by Alexander, Govern. Formulas 5, 6, and 7 describe other
+    # tests that serve as the basis for equation (8) but are not needed
+    # to perform the test.
+
+    # precalculate mean and length of each sample
+    lengths = [_count_nonmasked(sample, axis=-1, xp=xp) for sample in samples]
+    means = xp.stack([_xp_mean(sample, axis=-1) for sample in samples])
+
+    # (1) determine standard error of the mean for each sample
+    se2 = [(_xp_var(sample, correction=1, axis=-1) / length)
+           for sample, length in zip(samples, lengths)]
+    standard_errors_squared = xp.stack(se2)
+    standard_errors = standard_errors_squared**0.5
+
+    # Special case: statistic is NaN when variance is zero
+    eps = xp.finfo(standard_errors.dtype).eps
+    zero = standard_errors <= xp.abs(eps * means)
+    NaN = xp.asarray(xp.nan, dtype=standard_errors.dtype)
+    standard_errors = xp.where(zero, NaN, standard_errors)
+
+    # (2) define a weight for each sample
+    inv_sq_se = 1 / standard_errors_squared
+    weights = inv_sq_se / xp.sum(inv_sq_se, axis=0, keepdims=True)
+
+    # (3) determine variance-weighted estimate of the common mean
+    # Consider replacing with vecdot when data-apis/array-api#910 is resolved
+    var_w = xp.sum(weights * means, axis=0, keepdims=True)
+
+    # (4) determine one-sample t statistic for each group
+    t_stats = _demean(means, var_w, axis=0, xp=xp) / standard_errors
+
+    # calculate parameters to be used in transformation
+    if is_marray(xp):
+        v = xp.stack(lengths) - 1
+    else:
+        v = xp.asarray(lengths, dtype=t_stats.dtype) - 1
+        # align along 0th axis, which corresponds with separate samples
+        v = xp.reshape(v, (-1,) + (1,)*(t_stats.ndim-1))
+    a = v - .5
+    b = 48 * a**2
+    c = (a * xp.log(1 + (t_stats ** 2)/v))**.5
+
+    # (8) perform a normalizing transformation on t statistic
+    z = (c + ((c**3 + 3*c)/b) -
+         ((4*c**7 + 33*c**5 + 240*c**3 + 855*c) /
+          (b**2*10 + 8*b*c**4 + 1000*b)))
+
+    # (9) calculate statistic
+    A = xp.vecdot(z, z, axis=-z.ndim)
+    A = A[()] if A.ndim == 0 else A  # data-apis/array-api-compat#355
+
+    # "[the p value is determined from] central chi-square random deviates
+    # with k - 1 degrees of freedom". Alexander, Govern (94)
+    df = xp.asarray(len(samples) - 1, dtype=A.dtype)
+    chi2 = _SimpleChi2(df)
+    p = _get_pvalue(A, chi2, alternative='greater', symmetric=False, xp=xp)
+    return AlexanderGovernResult(A, p)
+
+
+def _alexandergovern_input_validation(samples, nan_policy, axis, xp):
+    if len(samples) < 2:
+        raise TypeError(f"2 or more inputs required, got {len(samples)}")
+
+    for sample in samples:
+        if sample.shape[axis] <= 1:
+            raise ValueError("Input sample size must be greater than one.")
+
+    samples = [xp.moveaxis(sample, axis, -1) for sample in samples]
+
+    return samples
+
+
+def _pearsonr_fisher_ci(r, n, confidence_level, alternative):
+    """
+    Compute the confidence interval for Pearson's R.
+
+    Fisher's transformation is used to compute the confidence interval
+    (https://en.wikipedia.org/wiki/Fisher_transformation).
+    """
+    xp = array_namespace(r)
+
+    ones = xp.ones_like(r)
+    n = xp.asarray(n, dtype=r.dtype, device=xp_device(r))
+    confidence_level = xp.asarray(confidence_level, dtype=r.dtype, device=xp_device(r))
+
+    with np.errstate(divide='ignore', invalid='ignore'):
+        zr = xp.atanh(r)
+        se = xp.sqrt(1 / (n - 3))
+
+    if alternative == "two-sided":
+        h = special.ndtri(0.5 + confidence_level/2)
+        zlo = zr - h*se
+        zhi = zr + h*se
+        rlo = xp.tanh(zlo)
+        rhi = xp.tanh(zhi)
+    elif alternative == "less":
+        h = special.ndtri(confidence_level)
+        zhi = zr + h*se
+        rhi = xp.tanh(zhi)
+        rlo = -ones
+    else:
+        # alternative == "greater":
+        h = special.ndtri(confidence_level)
+        zlo = zr - h*se
+        rlo = xp.tanh(zlo)
+        rhi = ones
+
+    mask = (n <= 3)
+    if mask.ndim == 0:
+        # This is Array API legal, but Dask doesn't like it.
+        mask = xp.broadcast_to(mask, rlo.shape)
+
+    rlo = xpx.at(rlo)[mask].set(-1)
+    rhi = xpx.at(rhi)[mask].set(1)
+
+    rlo = rlo[()] if rlo.ndim == 0 else rlo
+    rhi = rhi[()] if rhi.ndim == 0 else rhi
+    return ConfidenceInterval(low=rlo, high=rhi)
+
+
+def _pearsonr_bootstrap_ci(confidence_level, method, x, y, alternative, axis):
+    """
+    Compute the confidence interval for Pearson's R using the bootstrap.
+    """
+    def statistic(x, y, axis):
+        statistic, _ = pearsonr(x, y, axis=axis)
+        return statistic
+
+    res = bootstrap((x, y), statistic, confidence_level=confidence_level, axis=axis,
+                    paired=True, alternative=alternative, **method._asdict())
+    # for one-sided confidence intervals, bootstrap gives +/- inf on one side
+    res.confidence_interval = np.clip(res.confidence_interval, -1, 1)
+
+    return ConfidenceInterval(*res.confidence_interval)
+
+
+ConfidenceInterval = namedtuple('ConfidenceInterval', ['low', 'high'])
+
+PearsonRResultBase = _make_tuple_bunch('PearsonRResultBase',
+                                       ['statistic', 'pvalue'], [])
+
+
+class PearsonRResult(PearsonRResultBase):
+    """
+    Result of `scipy.stats.pearsonr`
+
+    Attributes
+    ----------
+    statistic : float
+        Pearson product-moment correlation coefficient.
+    pvalue : float
+        The p-value associated with the chosen alternative.
+
+    Methods
+    -------
+    confidence_interval
+        Computes the confidence interval of the correlation
+        coefficient `statistic` for the given confidence level.
+
+    """
+    def __init__(self, statistic, pvalue, alternative, n, x, y, axis):
+        super().__init__(statistic, pvalue)
+        self._alternative = alternative
+        self._n = n
+        self._x = x
+        self._y = y
+        self._axis = axis
+
+        # add alias for consistency with other correlation functions
+        self.correlation = statistic
+
+    def confidence_interval(self, confidence_level=0.95, method=None):
+        """
+        The confidence interval for the correlation coefficient.
+
+        Compute the confidence interval for the correlation coefficient
+        ``statistic`` with the given confidence level.
+
+        If `method` is not provided,
+        The confidence interval is computed using the Fisher transformation
+        F(r) = arctanh(r) [1]_.  When the sample pairs are drawn from a
+        bivariate normal distribution, F(r) approximately follows a normal
+        distribution with standard error ``1/sqrt(n - 3)``, where ``n`` is the
+        length of the original samples along the calculation axis. When
+        ``n <= 3``, this approximation does not yield a finite, real standard
+        error, so we define the confidence interval to be -1 to 1.
+
+        If `method` is an instance of `BootstrapMethod`, the confidence
+        interval is computed using `scipy.stats.bootstrap` with the provided
+        configuration options and other appropriate settings. In some cases,
+        confidence limits may be NaN due to a degenerate resample, and this is
+        typical for very small samples (~6 observations).
+
+        Parameters
+        ----------
+        confidence_level : float
+            The confidence level for the calculation of the correlation
+            coefficient confidence interval. Default is 0.95.
+
+        method : BootstrapMethod, optional
+            Defines the method used to compute the confidence interval. See
+            method description for details.
+
+            .. versionadded:: 1.11.0
+
+        Returns
+        -------
+        ci : namedtuple
+            The confidence interval is returned in a ``namedtuple`` with
+            fields `low` and `high`.
+
+        References
+        ----------
+        .. [1] "Pearson correlation coefficient", Wikipedia,
+               https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
+        """
+        if isinstance(method, BootstrapMethod):
+            xp = array_namespace(self._x)
+            message = ('`method` must be `None` if `pearsonr` '
+                       'arguments were not NumPy arrays.')
+            if not is_numpy(xp):
+                raise ValueError(message)
+
+            ci = _pearsonr_bootstrap_ci(confidence_level, method, self._x, self._y,
+                                        self._alternative, self._axis)
+        elif method is None:
+            ci = _pearsonr_fisher_ci(self.statistic, self._n, confidence_level,
+                                     self._alternative)
+        else:
+            message = ('`method` must be an instance of `BootstrapMethod` '
+                       'or None.')
+            raise ValueError(message)
+        return ci
+
+
+# Missing special.betainc on torch
+@xp_capabilities(cpu_only=True, exceptions=['cupy', 'jax.numpy'], marray=True,
+    extra_note='Only the default `method` is compatible with MArray input.')
+def pearsonr(x, y, *, alternative='two-sided', method=None, axis=0):
+    r"""
+    Pearson correlation coefficient and p-value for testing non-correlation.
+
+    The Pearson correlation coefficient [1]_ measures the linear relationship
+    between two datasets. Like other correlation
+    coefficients, this one varies between -1 and +1 with 0 implying no
+    correlation. Correlations of -1 or +1 imply an exact linear relationship.
+    Positive correlations imply that as x increases, so does y. Negative
+    correlations imply that as x increases, y decreases.
+
+    This function also performs a test of the null hypothesis that the
+    distributions underlying the samples are uncorrelated and normally
+    distributed. (See Kowalski [3]_
+    for a discussion of the effects of non-normality of the input on the
+    distribution of the correlation coefficient.)
+    The p-value roughly indicates the probability of an uncorrelated system
+    producing datasets that have a Pearson correlation at least as extreme
+    as the one computed from these datasets.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array.
+    y : array_like
+        Input array.
+    alternative : {'two-sided', 'greater', 'less'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        The following options are available:
+
+        * 'two-sided': the correlation is nonzero
+        * 'less': the correlation is negative (less than zero)
+        * 'greater':  the correlation is positive (greater than zero)
+
+        .. versionadded:: 1.9.0
+    method : ResamplingMethod, optional
+        Defines the method used to compute the p-value. If `method` is an
+        instance of `PermutationMethod`/`MonteCarloMethod`, the p-value is
+        computed using
+        `scipy.stats.permutation_test`/`scipy.stats.monte_carlo_test` with the
+        provided configuration options and other appropriate settings.
+        Otherwise, the p-value is computed as documented in the notes.
+
+        .. versionadded:: 1.11.0
+    axis : int or None, default
+        Axis along which to perform the calculation. Default is 0.
+        If None, ravel both arrays before performing the calculation.
+
+        .. versionadded:: 1.14.0
+
+    Returns
+    -------
+    result : `~scipy.stats._result_classes.PearsonRResult`
+        An object with the following attributes:
+
+        statistic : float
+            Pearson product-moment correlation coefficient.
+        pvalue : float
+            The p-value associated with the chosen alternative.
+
+        The object has the following method:
+
+        confidence_interval(confidence_level, method)
+            This computes the confidence interval of the correlation
+            coefficient `statistic` for the given confidence level.
+            The confidence interval is returned in a ``namedtuple`` with
+            fields `low` and `high`. If `method` is not provided, the
+            confidence interval is computed using the Fisher transformation
+            [1]_. If `method` is an instance of `BootstrapMethod`, the
+            confidence interval is computed using `scipy.stats.bootstrap` with
+            the provided configuration options and other appropriate settings.
+            In some cases, confidence limits may be NaN due to a degenerate
+            resample, and this is typical for very small samples (~6
+            observations).
+
+    Raises
+    ------
+    ValueError
+        If `x` and `y` do not have length at least 2.
+
+    Warns
+    -----
+    `~scipy.stats.ConstantInputWarning`
+        Raised if an input is a constant array.  The correlation coefficient
+        is not defined in this case, so ``np.nan`` is returned.
+
+    `~scipy.stats.NearConstantInputWarning`
+        Raised if an input is "nearly" constant.  The array ``x`` is considered
+        nearly constant if ``norm(x - mean(x)) < 1e-13 * abs(mean(x))``.
+        Numerical errors in the calculation ``x - mean(x)`` in this case might
+        result in an inaccurate calculation of r.
+
+    See Also
+    --------
+    spearmanr : Spearman rank-order correlation coefficient.
+    kendalltau : Kendall's tau, a correlation measure for ordinal data.
+    :ref:`hypothesis_pearsonr` : Extended example
+
+    Notes
+    -----
+    The correlation coefficient is calculated as follows:
+
+    .. math::
+
+        r = \frac{\sum (x - m_x) (y - m_y)}
+                 {\sqrt{\sum (x - m_x)^2 \sum (y - m_y)^2}}
+
+    where :math:`m_x` is the mean of the vector x and :math:`m_y` is
+    the mean of the vector y.
+
+    Under the assumption that x and y are drawn from
+    independent normal distributions (so the population correlation coefficient
+    is 0), the probability density function of the sample correlation
+    coefficient r is ([1]_, [2]_):
+
+    .. math::
+        f(r) = \frac{{(1-r^2)}^{n/2-2}}{\mathrm{B}(\frac{1}{2},\frac{n}{2}-1)}
+
+    where n is the number of samples, and B is the beta function.  This
+    is sometimes referred to as the exact distribution of r.  This is
+    the distribution that is used in `pearsonr` to compute the p-value when
+    the `method` parameter is left at its default value (None).
+    The distribution is a beta distribution on the interval [-1, 1],
+    with equal shape parameters a = b = n/2 - 1.  In terms of SciPy's
+    implementation of the beta distribution, the distribution of r is::
+
+        dist = scipy.stats.beta(n/2 - 1, n/2 - 1, loc=-1, scale=2)
+
+    The default p-value returned by `pearsonr` is a two-sided p-value. For a
+    given sample with correlation coefficient r, the p-value is
+    the probability that abs(r') of a random sample x' and y' drawn from
+    the population with zero correlation would be greater than or equal
+    to abs(r). In terms of the object ``dist`` shown above, the p-value
+    for a given r and length n can be computed as::
+
+        p = 2*dist.cdf(-abs(r))
+
+    When n is 2, the above continuous distribution is not well-defined.
+    One can interpret the limit of the beta distribution as the shape
+    parameters a and b approach a = b = 0 as a discrete distribution with
+    equal probability masses at r = 1 and r = -1.  More directly, one
+    can observe that, given the data x = [x1, x2] and y = [y1, y2], and
+    assuming x1 != x2 and y1 != y2, the only possible values for r are 1
+    and -1.  Because abs(r') for any sample x' and y' with length 2 will
+    be 1, the two-sided p-value for a sample of length 2 is always 1.
+
+    For backwards compatibility, the object that is returned also behaves
+    like a tuple of length two that holds the statistic and the p-value.
+
+    References
+    ----------
+    .. [1] "Pearson correlation coefficient", Wikipedia,
+           https://en.wikipedia.org/wiki/Pearson_correlation_coefficient
+    .. [2] Student, "Probable error of a correlation coefficient",
+           Biometrika, Volume 6, Issue 2-3, 1 September 1908, pp. 302-310.
+    .. [3] C. J. Kowalski, "On the Effects of Non-Normality on the Distribution
+           of the Sample Product-Moment Correlation Coefficient"
+           Journal of the Royal Statistical Society. Series C (Applied
+           Statistics), Vol. 21, No. 1 (1972), pp. 1-12.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> x, y = [1, 2, 3, 4, 5, 6, 7], [10, 9, 2.5, 6, 4, 3, 2]
+    >>> res = stats.pearsonr(x, y)
+    >>> res
+    PearsonRResult(statistic=-0.828503883588428, pvalue=0.021280260007523286)
+
+    To perform an exact permutation version of the test:
+
+    >>> rng = np.random.default_rng(7796654889291491997)
+    >>> method = stats.PermutationMethod(n_resamples=np.inf, random_state=rng)
+    >>> stats.pearsonr(x, y, method=method)
+    PearsonRResult(statistic=-0.828503883588428, pvalue=0.028174603174603175)
+
+    To perform the test under the null hypothesis that the data were drawn from
+    *uniform* distributions:
+
+    >>> method = stats.MonteCarloMethod(rvs=(rng.uniform, rng.uniform))
+    >>> stats.pearsonr(x, y, method=method)
+    PearsonRResult(statistic=-0.828503883588428, pvalue=0.0188)
+
+    To produce an asymptotic 90% confidence interval:
+
+    >>> res.confidence_interval(confidence_level=0.9)
+    ConfidenceInterval(low=-0.9644331982722841, high=-0.3460237473272273)
+
+    And for a bootstrap confidence interval:
+
+    >>> method = stats.BootstrapMethod(method='BCa', rng=rng)
+    >>> res.confidence_interval(confidence_level=0.9, method=method)
+    ConfidenceInterval(low=-0.9983163756488651, high=-0.22771001702132443)  # may vary
+
+    If N-dimensional arrays are provided, multiple tests are performed in a
+    single call according to the same conventions as most `scipy.stats` functions:
+
+    >>> rng = np.random.default_rng(2348246935601934321)
+    >>> x = rng.standard_normal((8, 15))
+    >>> y = rng.standard_normal((8, 15))
+    >>> stats.pearsonr(x, y, axis=0).statistic.shape  # between corresponding columns
+    (15,)
+    >>> stats.pearsonr(x, y, axis=1).statistic.shape  # between corresponding rows
+    (8,)
+
+    To perform all pairwise comparisons between slices of the arrays,
+    use standard NumPy broadcasting techniques. For instance, to compute the
+    correlation between all pairs of rows:
+
+    >>> stats.pearsonr(x[:, np.newaxis, :], y, axis=-1).statistic.shape
+    (8, 8)
+
+    There is a linear dependence between x and y if y = a + b*x + e, where
+    a,b are constants and e is a random error term, assumed to be independent
+    of x. For simplicity, assume that x is standard normal, a=0, b=1 and let
+    e follow a normal distribution with mean zero and standard deviation s>0.
+
+    >>> rng = np.random.default_rng()
+    >>> s = 0.5
+    >>> x = stats.norm.rvs(size=500, random_state=rng)
+    >>> e = stats.norm.rvs(scale=s, size=500, random_state=rng)
+    >>> y = x + e
+    >>> stats.pearsonr(x, y).statistic
+    0.9001942438244763
+
+    This should be close to the exact value given by
+
+    >>> 1/np.sqrt(1 + s**2)
+    0.8944271909999159
+
+    For s=0.5, we observe a high level of correlation. In general, a large
+    variance of the noise reduces the correlation, while the correlation
+    approaches one as the variance of the error goes to zero.
+
+    It is important to keep in mind that no correlation does not imply
+    independence unless (x, y) is jointly normal. Correlation can even be zero
+    when there is a very simple dependence structure: if X follows a
+    standard normal distribution, let y = abs(x). Note that the correlation
+    between x and y is zero. Indeed, since the expectation of x is zero,
+    cov(x, y) = E[x*y]. By definition, this equals E[x*abs(x)] which is zero
+    by symmetry. The following lines of code illustrate this observation:
+
+    >>> y = np.abs(x)
+    >>> stats.pearsonr(x, y)
+    PearsonRResult(statistic=-0.05444919272687482, pvalue=0.22422294836207743)
+
+    A non-zero correlation coefficient can be misleading. For example, if X has
+    a standard normal distribution, define y = x if x < 0 and y = 0 otherwise.
+    A simple calculation shows that corr(x, y) = sqrt(2/Pi) = 0.797...,
+    implying a high level of correlation:
+
+    >>> y = np.where(x < 0, x, 0)
+    >>> stats.pearsonr(x, y)
+    PearsonRResult(statistic=0.861985781588, pvalue=4.813432002751103e-149)
+
+    This is unintuitive since there is no dependence of x and y if x is larger
+    than zero which happens in about half of the cases if we sample x and y.
+
+    For a more detailed example, see :ref:`hypothesis_pearsonr`.
+
+    """
+    xp = array_namespace(x, y)
+    x, y = xp_promote(x, y, force_floating=True, xp=xp)
+    dtype = x.dtype
+
+    if not is_numpy(xp) and method is not None:
+        method = 'invalid'
+
+    if axis is None:
+        x = xp.reshape(x, (-1,))
+        y = xp.reshape(y, (-1,))
+        axis = -1
+
+    axis_int = int(axis)
+    if axis_int != axis:
+        raise ValueError('`axis` must be an integer.')
+    axis = axis_int
+
+    try:
+        np.broadcast_shapes(x.shape, y.shape)
+        # For consistency with other `stats` functions, we need to
+        # match the dimensionalities before looking at `axis`.
+        # (Note: this is not the NEP 5 / gufunc order of operations;
+        #  see TestPearsonr::test_different_dimensionality for more information.)
+        ndim = max(x.ndim, y.ndim)
+        x = xp.reshape(x, (1,) * (ndim - x.ndim) + x.shape)
+        y = xp.reshape(y, (1,) * (ndim - y.ndim) + y.shape)
+
+    except (ValueError, RuntimeError) as e:
+        message = '`x` and `y` must be broadcastable.'
+        raise ValueError(message) from e
+
+    if x.shape[axis] != y.shape[axis]:
+        raise ValueError('`x` and `y` must have the same length along `axis`.')
+
+    if x.shape[axis] < 2:
+        raise ValueError('`x` and `y` must have length at least 2.')
+
+    x, y = _share_masks(x, y, xp=xp)
+    n = xp.asarray(_count_nonmasked(x, axis=axis), dtype=x.dtype)
+
+    x = xp.moveaxis(x, axis, -1)
+    y = xp.moveaxis(y, axis, -1)
+    axis = -1
+
+    if xp.isdtype(dtype, "complex floating"):
+        raise ValueError('This function does not support complex data')
+
+    x = xp.astype(x, dtype, copy=False)
+    y = xp.astype(y, dtype, copy=False)
+    threshold = xp.finfo(dtype).eps ** 0.75
+
+    # If an input is constant, the correlation coefficient is not defined.
+    if is_marray(xp):
+        # sort to ensure that we are comparing to a non-masked element
+        const_x = xp.all(x == xp.sort(x, axis=-1)[..., 0:1], axis=-1)
+        const_y = xp.all(y == xp.sort(y, axis=-1)[..., 0:1], axis=-1)
+    else:
+        const_x = xp.all(x == x[..., 0:1], axis=-1)
+        const_y = xp.all(y == y[..., 0:1], axis=-1)
+    const_xy = const_x | const_y
+
+    any_const_xy = xp.any(const_xy)
+    lazy = is_lazy_array(const_xy)
+    if not lazy and any_const_xy:
+        msg = ("An input array is constant; the correlation coefficient "
+               "is not defined.")
+        warnings.warn(stats.ConstantInputWarning(msg), stacklevel=2)
+    if lazy or any_const_xy:
+        x = xp.where(const_x[..., xp.newaxis], xp.nan, x)
+        y = xp.where(const_y[..., xp.newaxis], xp.nan, y)
+
+    if isinstance(method, PermutationMethod):
+        def statistic(y, axis):
+            statistic, _ = pearsonr(x, y, axis=axis, alternative=alternative)
+            return statistic
+
+        res = permutation_test((y,), statistic, permutation_type='pairings',
+                               axis=axis, alternative=alternative, **method._asdict())
+
+        return PearsonRResult(statistic=res.statistic, pvalue=res.pvalue, n=n,
+                              alternative=alternative, x=x, y=y, axis=axis)
+    elif isinstance(method, MonteCarloMethod):
+        def statistic(x, y, axis):
+            statistic, _ = pearsonr(x, y, axis=axis, alternative=alternative)
+            return statistic
+
+        # `monte_carlo_test` accepts an `rvs` tuple of callables, not an `rng`
+        # If the user specified an `rng`, replace it with the appropriate callables
+        method = method._asdict()
+        if (rng := method.pop('rng', None)) is not None:  # goo-goo g'joob
+            rng = np.random.default_rng(rng)
+            method['rvs'] = rng.normal, rng.normal
+
+        res = monte_carlo_test((x, y,), statistic=statistic, axis=axis,
+                               alternative=alternative, **method)
+
+        return PearsonRResult(statistic=res.statistic, pvalue=res.pvalue, n=n,
+                              alternative=alternative, x=x, y=y, axis=axis)
+    elif method == 'invalid':
+        message = '`method` must be `None` if arguments are not NumPy arrays.'
+        raise ValueError(message)
+    elif method is not None:
+        message = ('`method` must be an instance of `PermutationMethod`, '
+                   '`MonteCarloMethod`, or None.')
+        raise ValueError(message)
+
+    xmean = xp.mean(x, axis=axis, keepdims=True)
+    ymean = xp.mean(y, axis=axis, keepdims=True)
+    xm = x - xmean
+    ym = y - ymean
+
+    # scipy.linalg.norm(xm) avoids premature overflow when xm is e.g.
+    # [-5e210, 5e210, 3e200, -3e200]
+    # but not when `axis` is provided, so scale manually. scipy.linalg.norm
+    # also raises an error with NaN input rather than returning NaN, so
+    # use np.linalg.norm.
+    xmax = xp.max(xp.abs(xm), axis=axis, keepdims=True)
+    ymax = xp.max(xp.abs(ym), axis=axis, keepdims=True)
+    with np.errstate(invalid='ignore', divide='ignore'):
+        normxm = xmax * xp_vector_norm(xm/xmax, axis=axis, keepdims=True)
+        normym = ymax * xp_vector_norm(ym/ymax, axis=axis, keepdims=True)
+
+    if not lazy:
+        nconst_x = xp.any(normxm < threshold*xp.abs(xmean), axis=axis)
+        nconst_y = xp.any(normym < threshold*xp.abs(ymean), axis=axis)
+        nconst_xy = nconst_x | nconst_y
+        if xp.any(nconst_xy & (~const_xy)):
+            # If all the values in x (likewise y) are very close to the mean,
+            # the loss of precision that occurs in the subtraction xm = x - xmean
+            # might result in large errors in r.
+            msg = ("An input array is nearly constant; the computed "
+                "correlation coefficient may be inaccurate.")
+            warnings.warn(stats.NearConstantInputWarning(msg), stacklevel=2)
+
+    with np.errstate(invalid='ignore', divide='ignore'):
+        r = xp.vecdot(xm / normxm, ym / normym, axis=axis)
+
+    # Presumably, if abs(r) > 1, then it is only some small artifact of
+    # floating point arithmetic.
+    r = xp.clip(r, -1., 1.)
+    r = xpx.at(r, const_xy).set(xp.nan)
+
+    # As explained in the docstring, the distribution of `r` under the null
+    # hypothesis is the beta distribution on (-1, 1) with a = b = n/2 - 1.
+    ab = xp.asarray(n/2 - 1, dtype=dtype, device=xp_device(x))
+    dist = _SimpleBeta(ab, ab, loc=-1, scale=2)
+    pvalue = _get_pvalue(r, dist, alternative, xp=xp)
+
+    mask = (n == 2)   #  return exactly 1.0 or -1.0 values for n == 2 case as promised
+    # data-apis/array-api-extra#196
+    mxp = array_namespace(r._meta) if is_dask(xp) else xp
+    def special_case(r):
+        return mxp.where(mxp.isnan(r), mxp.nan, mxp.ones_like(r))
+    r = xpx.apply_where(mask, r, mxp.round, fill_value=r)
+    pvalue = xpx.apply_where(mask, (r,), special_case, fill_value=pvalue)
+
+    r = r[()] if r.ndim == 0 else r
+    pvalue = pvalue[()] if pvalue.ndim == 0 else pvalue
+    return PearsonRResult(statistic=r, pvalue=pvalue, n=n,
+                          alternative=alternative, x=x, y=y, axis=axis)
+
+
+@xp_capabilities(np_only=True)
+def fisher_exact(table, alternative=None, *, method=None):
+    """Perform a Fisher exact test on a contingency table.
+
+    For a 2x2 table,
+    the null hypothesis is that the true odds ratio of the populations
+    underlying the observations is one, and the observations were sampled
+    from these populations under a condition: the marginals of the
+    resulting table must equal those of the observed table.
+    The statistic is the unconditional maximum likelihood estimate of the odds
+    ratio, and the p-value is the probability under the null hypothesis of
+    obtaining a table at least as extreme as the one that was actually
+    observed.
+
+    For other table sizes, or if `method` is provided, the null hypothesis
+    is that the rows and columns of the tables have fixed sums and are
+    independent; i.e., the table was sampled from a `scipy.stats.random_table`
+    distribution with the observed marginals. The statistic is the
+    probability mass of this distribution evaluated at `table`, and the
+    p-value is the percentage of the population of tables with statistic at
+    least as extreme (small) as that of `table`. There is only one alternative
+    hypothesis available: the rows and columns are not independent.
+
+    There are other possible choices of statistic and two-sided
+    p-value definition associated with Fisher's exact test; please see the
+    Notes for more information.
+
+    Parameters
+    ----------
+    table : array_like of ints
+        A contingency table.  Elements must be non-negative integers.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis for 2x2 tables; unused for other
+        table sizes.
+        The following options are available (default is 'two-sided'):
+
+        * 'two-sided': the odds ratio of the underlying population is not one
+        * 'less': the odds ratio of the underlying population is less than one
+        * 'greater': the odds ratio of the underlying population is greater
+          than one
+
+        See the Notes for more details.
+
+    method : ResamplingMethod, optional
+        Defines the method used to compute the p-value.
+        If `method` is an instance of `PermutationMethod`/`MonteCarloMethod`,
+        the p-value is computed using
+        `scipy.stats.permutation_test`/`scipy.stats.monte_carlo_test` with the
+        provided configuration options and other appropriate settings.
+        Note that if `method` is an instance of `MonteCarloMethod`, the ``rvs``
+        attribute must be left unspecified; Monte Carlo samples are always drawn
+        using the ``rvs`` method of `scipy.stats.random_table`.
+        Otherwise, the p-value is computed as documented in the notes.
+
+        .. versionadded:: 1.15.0
+
+    Returns
+    -------
+    res : SignificanceResult
+        An object containing attributes:
+
+        statistic : float
+            For a 2x2 table with default `method`, this is the odds ratio - the
+            prior odds ratio not a posterior estimate. In all other cases, this
+            is the probability density of obtaining the observed table under the
+            null hypothesis of independence with marginals fixed.
+        pvalue : float
+            The probability under the null hypothesis of obtaining a
+            table at least as extreme as the one that was actually observed.
+
+    Raises
+    ------
+    ValueError
+        If `table` is not two-dimensional or has negative entries.
+
+    See Also
+    --------
+    chi2_contingency : Chi-square test of independence of variables in a
+        contingency table.  This can be used as an alternative to
+        `fisher_exact` when the numbers in the table are large.
+    contingency.odds_ratio : Compute the odds ratio (sample or conditional
+        MLE) for a 2x2 contingency table.
+    barnard_exact : Barnard's exact test, which is a more powerful alternative
+        than Fisher's exact test for 2x2 contingency tables.
+    boschloo_exact : Boschloo's exact test, which is a more powerful
+        alternative than Fisher's exact test for 2x2 contingency tables.
+    :ref:`hypothesis_fisher_exact` : Extended example
+
+    Notes
+    -----
+    *Null hypothesis and p-values*
+
+    The null hypothesis is that the true odds ratio of the populations
+    underlying the observations is one, and the observations were sampled at
+    random from these populations under a condition: the marginals of the
+    resulting table must equal those of the observed table. Equivalently,
+    the null hypothesis is that the input table is from the hypergeometric
+    distribution with parameters (as used in `hypergeom`)
+    ``M = a + b + c + d``, ``n = a + b`` and ``N = a + c``, where the
+    input table is ``[[a, b], [c, d]]``.  This distribution has support
+    ``max(0, N + n - M) <= x <= min(N, n)``, or, in terms of the values
+    in the input table, ``min(0, a - d) <= x <= a + min(b, c)``.  ``x``
+    can be interpreted as the upper-left element of a 2x2 table, so the
+    tables in the distribution have form::
+
+        [  x           n - x     ]
+        [N - x    M - (n + N) + x]
+
+    For example, if::
+
+        table = [6  2]
+                [1  4]
+
+    then the support is ``2 <= x <= 7``, and the tables in the distribution
+    are::
+
+        [2 6]   [3 5]   [4 4]   [5 3]   [6 2]  [7 1]
+        [5 0]   [4 1]   [3 2]   [2 3]   [1 4]  [0 5]
+
+    The probability of each table is given by the hypergeometric distribution
+    ``hypergeom.pmf(x, M, n, N)``.  For this example, these are (rounded to
+    three significant digits)::
+
+        x       2      3      4      5       6        7
+        p  0.0163  0.163  0.408  0.326  0.0816  0.00466
+
+    These can be computed with::
+
+        >>> import numpy as np
+        >>> from scipy.stats import hypergeom
+        >>> table = np.array([[6, 2], [1, 4]])
+        >>> M = table.sum()
+        >>> n = table[0].sum()
+        >>> N = table[:, 0].sum()
+        >>> start, end = hypergeom.support(M, n, N)
+        >>> hypergeom.pmf(np.arange(start, end+1), M, n, N)
+        array([0.01631702, 0.16317016, 0.40792541, 0.32634033, 0.08158508,
+               0.004662  ])
+
+    The two-sided p-value is the probability that, under the null hypothesis,
+    a random table would have a probability equal to or less than the
+    probability of the input table.  For our example, the probability of
+    the input table (where ``x = 6``) is 0.0816.  The x values where the
+    probability does not exceed this are 2, 6 and 7, so the two-sided p-value
+    is ``0.0163 + 0.0816 + 0.00466 ~= 0.10256``::
+
+        >>> from scipy.stats import fisher_exact
+        >>> res = fisher_exact(table, alternative='two-sided')
+        >>> res.pvalue
+        0.10256410256410257
+
+    The one-sided p-value for ``alternative='greater'`` is the probability
+    that a random table has ``x >= a``, which in our example is ``x >= 6``,
+    or ``0.0816 + 0.00466 ~= 0.08626``::
+
+        >>> res = fisher_exact(table, alternative='greater')
+        >>> res.pvalue
+        0.08624708624708627
+
+    This is equivalent to computing the survival function of the
+    distribution at ``x = 5`` (one less than ``x`` from the input table,
+    because we want to include the probability of ``x = 6`` in the sum)::
+
+        >>> hypergeom.sf(5, M, n, N)
+        0.08624708624708627
+
+    For ``alternative='less'``, the one-sided p-value is the probability
+    that a random table has ``x <= a``, (i.e. ``x <= 6`` in our example),
+    or ``0.0163 + 0.163 + 0.408 + 0.326 + 0.0816 ~= 0.9949``::
+
+        >>> res = fisher_exact(table, alternative='less')
+        >>> res.pvalue
+        0.9953379953379957
+
+    This is equivalent to computing the cumulative distribution function
+    of the distribution at ``x = 6``:
+
+        >>> hypergeom.cdf(6, M, n, N)
+        0.9953379953379957
+
+    *Odds ratio*
+
+    The calculated odds ratio is different from the value computed by the
+    R function ``fisher.test``.  This implementation returns the "sample"
+    or "unconditional" maximum likelihood estimate, while ``fisher.test``
+    in R uses the conditional maximum likelihood estimate.  To compute the
+    conditional maximum likelihood estimate of the odds ratio, use
+    `scipy.stats.contingency.odds_ratio`.
+
+    References
+    ----------
+    .. [1] Fisher, Sir Ronald A, "The Design of Experiments:
+           Mathematics of a Lady Tasting Tea." ISBN 978-0-486-41151-4, 1935.
+    .. [2] "Fisher's exact test",
+           https://en.wikipedia.org/wiki/Fisher's_exact_test
+
+    Examples
+    --------
+
+    >>> from scipy.stats import fisher_exact
+    >>> res = fisher_exact([[8, 2], [1, 5]])
+    >>> res.statistic
+    20.0
+    >>> res.pvalue
+    0.034965034965034975
+
+    For tables with shape other than ``(2, 2)``, provide an instance of
+    `scipy.stats.MonteCarloMethod` or `scipy.stats.PermutationMethod` for the
+    `method` parameter:
+
+    >>> import numpy as np
+    >>> from scipy.stats import MonteCarloMethod
+    >>> rng = np.random.default_rng(4507195762371367)
+    >>> method = MonteCarloMethod(rng=rng)
+    >>> fisher_exact([[8, 2, 3], [1, 5, 4]], method=method)
+    SignificanceResult(statistic=np.float64(0.005782), pvalue=np.float64(0.0603))
+
+    For a more detailed example, see :ref:`hypothesis_fisher_exact`.
+    """
+    hypergeom = distributions.hypergeom
+    # int32 is not enough for the algorithm
+    c = np.asarray(table, dtype=np.int64)
+    if not c.ndim == 2:
+        raise ValueError("The input `table` must have two dimensions.")
+
+    if np.any(c < 0):
+        raise ValueError("All values in `table` must be nonnegative.")
+
+    if not c.shape == (2, 2) or method is not None:
+        return _fisher_exact_rxc(c, alternative, method)
+    alternative = 'two-sided' if alternative is None else alternative
+
+    if 0 in c.sum(axis=0) or 0 in c.sum(axis=1):
+        # If both values in a row or column are zero, the p-value is 1 and
+        # the odds ratio is NaN.
+        return SignificanceResult(np.nan, 1.0)
+
+    if c[1, 0] > 0 and c[0, 1] > 0:
+        oddsratio = c[0, 0] * c[1, 1] / (c[1, 0] * c[0, 1])
+    else:
+        oddsratio = np.inf
+
+    n1 = c[0, 0] + c[0, 1]
+    n2 = c[1, 0] + c[1, 1]
+    n = c[0, 0] + c[1, 0]
+
+    def pmf(x):
+        return hypergeom.pmf(x, n1 + n2, n1, n)
+
+    if alternative == 'less':
+        pvalue = hypergeom.cdf(c[0, 0], n1 + n2, n1, n)
+    elif alternative == 'greater':
+        # Same formula as the 'less' case, but with the second column.
+        pvalue = hypergeom.cdf(c[0, 1], n1 + n2, n1, c[0, 1] + c[1, 1])
+    elif alternative == 'two-sided':
+        mode = int((n + 1) * (n1 + 1) / (n1 + n2 + 2))
+        pexact = hypergeom.pmf(c[0, 0], n1 + n2, n1, n)
+        pmode = hypergeom.pmf(mode, n1 + n2, n1, n)
+
+        epsilon = 1e-14
+        gamma = 1 + epsilon
+
+        if np.abs(pexact - pmode) / np.maximum(pexact, pmode) <= epsilon:
+            return SignificanceResult(oddsratio, 1.)
+
+        elif c[0, 0] < mode:
+            plower = hypergeom.cdf(c[0, 0], n1 + n2, n1, n)
+            if hypergeom.pmf(n, n1 + n2, n1, n) > pexact * gamma:
+                return SignificanceResult(oddsratio, plower)
+
+            guess = _binary_search(lambda x: -pmf(x), -pexact * gamma, mode, n, xp=np)
+            pvalue = plower + hypergeom.sf(guess, n1 + n2, n1, n)
+        else:
+            pupper = hypergeom.sf(c[0, 0] - 1, n1 + n2, n1, n)
+            if hypergeom.pmf(0, n1 + n2, n1, n) > pexact * gamma:
+                return SignificanceResult(oddsratio, pupper)
+
+            guess = _binary_search(pmf, pexact * gamma, 0, mode, xp=np)
+            pvalue = pupper + hypergeom.cdf(guess, n1 + n2, n1, n)
+    else:
+        msg = "`alternative` should be one of {'two-sided', 'less', 'greater'}"
+        raise ValueError(msg)
+
+    pvalue = min(pvalue, 1.0)
+
+    return SignificanceResult(oddsratio, pvalue)
+
+
+def _fisher_exact_rxc(table, alternative, method):
+    if alternative is not None:
+        message = ('`alternative` must be the default (None) unless '
+                  '`table` has shape `(2, 2)` and `method is None`.')
+        raise ValueError(message)
+
+    if table.size == 0:
+        raise ValueError("`table` must have at least one row and one column.")
+
+    if table.shape[0] == 1 or table.shape[1] == 1 or np.all(table == 0):
+        # Only one such table with those marginals
+        return SignificanceResult(1.0, 1.0)
+
+    if method is None:
+        method = stats.MonteCarloMethod()
+
+    if isinstance(method, stats.PermutationMethod):
+        res = _fisher_exact_permutation_method(table, method)
+    elif isinstance(method, stats.MonteCarloMethod):
+        res = _fisher_exact_monte_carlo_method(table, method)
+    else:
+        message = (f'`{method=}` not recognized; if provided, `method` must be an '
+                   'instance of `PermutationMethod` or `MonteCarloMethod`.')
+        raise ValueError(message)
+
+    return SignificanceResult(np.clip(res.statistic, None, 1.0), res.pvalue)
+
+
+def _fisher_exact_permutation_method(table, method):
+    x, y = _untabulate(table)
+    colsums = np.sum(table, axis=0)
+    rowsums = np.sum(table, axis=1)
+    X = stats.random_table(rowsums, colsums)
+
+    # `permutation_test` with `permutation_type='pairings' permutes the order of `x`,
+    # which pairs observations in `x` with different observations in `y`.
+    def statistic(x):
+        # crosstab the resample and compute the statistic
+        table = stats.contingency.crosstab(x, y)[1]
+        return X.pmf(table)
+
+    # tables with *smaller* probability mass are considered to be more extreme
+    return stats.permutation_test((x,), statistic, permutation_type='pairings',
+                                  alternative='less', **method._asdict())
+
+
+def _fisher_exact_monte_carlo_method(table, method):
+    method = method._asdict()
+
+    if method.pop('rvs', None) is not None:
+        message = ('If the `method` argument of `fisher_exact` is an '
+                   'instance of `MonteCarloMethod`, its `rvs` attribute '
+                   'must be unspecified. Use the `MonteCarloMethod` `rng` argument '
+                   'to control the random state.')
+        raise ValueError(message)
+    rng = np.random.default_rng(method.pop('rng', None))
+
+    # `random_table.rvs` produces random contingency tables with the given marginals
+    # under the null hypothesis of independence
+    shape = table.shape
+    colsums = np.sum(table, axis=0)
+    rowsums = np.sum(table, axis=1)
+    totsum = np.sum(table)
+    X = stats.random_table(rowsums, colsums, seed=rng)
+
+    def rvs(size):
+        n_resamples = size[0]
+        return X.rvs(size=n_resamples).reshape(size)
+
+    # axis signals to `monte_carlo_test` that statistic is vectorized, but we know
+    # how it will pass the table(s), so we don't need to use `axis` explicitly.
+    def statistic(table, axis):
+        shape_ = (-1,) + shape if table.size > totsum else shape
+        return X.pmf(table.reshape(shape_))
+
+    # tables with *smaller* probability mass are considered to be more extreme
+    return stats.monte_carlo_test(table.ravel(), rvs, statistic,
+                                  alternative='less', **method)
+
+
+def _untabulate(table):
+    # converts a contingency table to paired samples indicating the
+    # correspondence between row and column indices
+    r, c = table.shape
+    x, y = [], []
+    for i in range(r):
+        for j in range(c):
+            x.append([i] * table[i, j])
+            y.append([j] * table[i, j])
+    return np.concatenate(x), np.concatenate(y)
+
+
+@xp_capabilities(np_only=True)
+def spearmanr(a, b=None, axis=0, nan_policy='propagate',
+              alternative='two-sided'):
+    r"""Calculate a Spearman correlation coefficient with associated p-value.
+
+    The Spearman rank-order correlation coefficient is a nonparametric measure
+    of the monotonicity of the relationship between two datasets.
+    Like other correlation coefficients,
+    this one varies between -1 and +1 with 0 implying no correlation.
+    Correlations of -1 or +1 imply an exact monotonic relationship. Positive
+    correlations imply that as x increases, so does y. Negative correlations
+    imply that as x increases, y decreases.
+
+    The p-value roughly indicates the probability of an uncorrelated system
+    producing datasets that have a Spearman correlation at least as extreme
+    as the one computed from these datasets. Although calculation of the
+    p-value does not make strong assumptions about the distributions underlying
+    the samples, it is only accurate for very large samples (>500
+    observations). For smaller sample sizes, consider a permutation test (see
+    Examples section below).
+
+    Parameters
+    ----------
+    a, b : 1D or 2D array_like, b is optional
+        One or two 1-D or 2-D arrays containing multiple variables and
+        observations. When these are 1-D, each represents a vector of
+        observations of a single variable. For the behavior in the 2-D case,
+        see under ``axis``, below.
+        Both arrays need to have the same length in the ``axis`` dimension.
+    axis : int or None, optional
+        If axis=0 (default), then each column represents a variable, with
+        observations in the rows. If axis=1, the relationship is transposed:
+        each row represents a variable, while the columns contain observations.
+        If axis=None, then both arrays will be raveled.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        The following options are available:
+
+        * 'two-sided': the correlation is nonzero
+        * 'less': the correlation is negative (less than zero)
+        * 'greater':  the correlation is positive (greater than zero)
+
+        .. versionadded:: 1.7.0
+
+    Returns
+    -------
+    res : SignificanceResult
+        An object containing attributes:
+
+        statistic : float or ndarray (2-D square)
+            Spearman correlation matrix or correlation coefficient (if only 2
+            variables are given as parameters). Correlation matrix is square
+            with length equal to total number of variables (columns or rows) in
+            ``a`` and ``b`` combined.
+        pvalue : float
+            The p-value for a hypothesis test whose null hypothesis
+            is that two samples have no ordinal correlation. See
+            `alternative` above for alternative hypotheses. `pvalue` has the
+            same shape as `statistic`.
+
+    Raises
+    ------
+    ValueError
+        If `axis` is not 0, 1 or None, or if the number of dimensions of `a`
+        is greater than 2, or if `b` is None and the number of dimensions of
+        `a` is less than 2.
+
+    Warns
+    -----
+    `~scipy.stats.ConstantInputWarning`
+        Raised if an input is a constant array.  The correlation coefficient
+        is not defined in this case, so ``np.nan`` is returned.
+
+    See Also
+    --------
+    :ref:`hypothesis_spearmanr` : Extended example
+
+    References
+    ----------
+    .. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
+       Probability and Statistics Tables and Formulae. Chapman & Hall: New
+       York. 2000.
+       Section  14.7
+    .. [2] Kendall, M. G. and Stuart, A. (1973).
+       The Advanced Theory of Statistics, Volume 2: Inference and Relationship.
+       Griffin. 1973.
+       Section 31.18
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> res = stats.spearmanr([1, 2, 3, 4, 5], [5, 6, 7, 8, 7])
+    >>> res.statistic
+    0.8207826816681233
+    >>> res.pvalue
+    0.08858700531354381
+
+    >>> rng = np.random.default_rng()
+    >>> x2n = rng.standard_normal((100, 2))
+    >>> y2n = rng.standard_normal((100, 2))
+    >>> res = stats.spearmanr(x2n)
+    >>> res.statistic, res.pvalue
+    (-0.07960396039603959, 0.4311168705769747)
+
+    >>> res = stats.spearmanr(x2n[:, 0], x2n[:, 1])
+    >>> res.statistic, res.pvalue
+    (-0.07960396039603959, 0.4311168705769747)
+
+    >>> res = stats.spearmanr(x2n, y2n)
+    >>> res.statistic
+    array([[ 1. , -0.07960396, -0.08314431, 0.09662166],
+           [-0.07960396, 1. , -0.14448245, 0.16738074],
+           [-0.08314431, -0.14448245, 1. , 0.03234323],
+           [ 0.09662166, 0.16738074, 0.03234323, 1. ]])
+    >>> res.pvalue
+    array([[0. , 0.43111687, 0.41084066, 0.33891628],
+           [0.43111687, 0. , 0.15151618, 0.09600687],
+           [0.41084066, 0.15151618, 0. , 0.74938561],
+           [0.33891628, 0.09600687, 0.74938561, 0. ]])
+
+    >>> res = stats.spearmanr(x2n.T, y2n.T, axis=1)
+    >>> res.statistic
+    array([[ 1. , -0.07960396, -0.08314431, 0.09662166],
+           [-0.07960396, 1. , -0.14448245, 0.16738074],
+           [-0.08314431, -0.14448245, 1. , 0.03234323],
+           [ 0.09662166, 0.16738074, 0.03234323, 1. ]])
+
+    >>> res = stats.spearmanr(x2n, y2n, axis=None)
+    >>> res.statistic, res.pvalue
+    (0.044981624540613524, 0.5270803651336189)
+
+    >>> res = stats.spearmanr(x2n.ravel(), y2n.ravel())
+    >>> res.statistic, res.pvalue
+    (0.044981624540613524, 0.5270803651336189)
+
+    >>> rng = np.random.default_rng()
+    >>> xint = rng.integers(10, size=(100, 2))
+    >>> res = stats.spearmanr(xint)
+    >>> res.statistic, res.pvalue
+    (0.09800224850707953, 0.3320271757932076)
+
+    For small samples, consider performing a permutation test instead of
+    relying on the asymptotic p-value. Note that to calculate the null
+    distribution of the statistic (for all possibly pairings between
+    observations in sample ``x`` and ``y``), only one of the two inputs needs
+    to be permuted.
+
+    >>> x = [1.76405235, 0.40015721, 0.97873798,
+    ... 2.2408932, 1.86755799, -0.97727788]
+    >>> y = [2.71414076, 0.2488, 0.87551913,
+    ... 2.6514917, 2.01160156, 0.47699563]
+
+    >>> def statistic(x): # permute only `x`
+    ...     return stats.spearmanr(x, y).statistic
+    >>> res_exact = stats.permutation_test((x,), statistic,
+    ...     permutation_type='pairings')
+    >>> res_asymptotic = stats.spearmanr(x, y)
+    >>> res_exact.pvalue, res_asymptotic.pvalue # asymptotic pvalue is too low
+    (0.10277777777777777, 0.07239650145772594)
+
+    For a more detailed example, see :ref:`hypothesis_spearmanr`.
+    """
+    if axis is not None and axis > 1:
+        raise ValueError("spearmanr only handles 1-D or 2-D arrays, "
+                         f"supplied axis argument {axis}, please use only "
+                         "values 0, 1 or None for axis")
+
+    a, axisout = _chk_asarray(a, axis)
+    if a.ndim > 2:
+        raise ValueError("spearmanr only handles 1-D or 2-D arrays")
+
+    if b is None:
+        if a.ndim < 2:
+            raise ValueError("`spearmanr` needs at least 2 "
+                             "variables to compare")
+    else:
+        # Concatenate a and b, so that we now only have to handle the case
+        # of a 2-D `a`.
+        b, _ = _chk_asarray(b, axis)
+        if axisout == 0:
+            a = np.column_stack((a, b))
+        else:
+            a = np.vstack((a, b))
+
+    n_vars = a.shape[1 - axisout]
+    n_obs = a.shape[axisout]
+    if n_obs <= 1:
+        # Handle empty arrays or single observations.
+        res = SignificanceResult(np.nan, np.nan)
+        res.correlation = np.nan
+        return res
+
+    warn_msg = ("An input array is constant; the correlation coefficient "
+                "is not defined.")
+
+    constant_axis = False
+    if axisout == 0:
+        constant_columns = np.all(a == a[0, :], axis=0)
+        constant_axis = np.any(constant_columns)
+        if constant_axis:
+            # If an input is constant, the correlation coefficient
+            # is not defined.
+            warnings.warn(stats.ConstantInputWarning(warn_msg), stacklevel=2)
+    else:  # case when axisout == 1 b/c a is 2 dim only
+        constant_rows = np.all(a.T == a.T[0, :], axis=0)
+        constant_axis = np.any(constant_rows)
+        if constant_axis:
+            # If an input is constant, the correlation coefficient
+            # is not defined.
+            warnings.warn(stats.ConstantInputWarning(warn_msg), stacklevel=2)
+
+    a_contains_nan = _contains_nan(a, nan_policy)
+    variable_has_nan = np.zeros(n_vars, dtype=bool)
+    if a_contains_nan:
+        if nan_policy == 'omit':
+            return mstats_basic.spearmanr(a, axis=axis, nan_policy=nan_policy,
+                                          alternative=alternative)
+        elif nan_policy == 'propagate':
+            if a.ndim == 1 or n_vars <= 2:
+                res = SignificanceResult(np.nan, np.nan)
+                res.correlation = np.nan
+                return res
+            else:
+                # Keep track of variables with NaNs, set the outputs to NaN
+                # only for those variables
+                variable_has_nan = np.isnan(a).any(axis=axisout)
+
+    a_ranked = np.apply_along_axis(rankdata, axisout, a)
+
+    if constant_axis:
+        with np.errstate(invalid='ignore'):
+            rs = np.corrcoef(a_ranked, rowvar=axisout)
+    else:
+        rs = np.corrcoef(a_ranked, rowvar=axisout)
+
+    dof = n_obs - 2  # degrees of freedom
+
+    # rs can have elements equal to 1, so avoid zero division warnings
+    with np.errstate(divide='ignore'):
+        # clip the small negative values possibly caused by rounding
+        # errors before taking the square root
+        t = rs * np.sqrt((dof/((rs+1.0)*(1.0-rs))).clip(0))
+
+    dist = _SimpleStudentT(dof)
+    prob = _get_pvalue(t, dist, alternative, xp=np)
+
+    # For backwards compatibility, return scalars when comparing 2 columns
+    if rs.shape == (2, 2):
+        res = SignificanceResult(rs[1, 0], prob[1, 0])
+        res.correlation = rs[1, 0]
+        return res
+    else:
+        rs[variable_has_nan, :] = np.nan
+        rs[:, variable_has_nan] = np.nan
+        res = SignificanceResult(rs[()], prob[()])
+        res.correlation = rs
+        return res
+
+
+@xp_capabilities(cpu_only=True, exceptions=['cupy', 'jax.numpy'], marray=True)
+@_axis_nan_policy_factory(_pack_CorrelationResult, n_samples=2,
+                          result_to_tuple=_unpack_CorrelationResult, paired=True,
+                          too_small=1, n_outputs=3)
+def pointbiserialr(x, y, *, axis=0):
+    r"""Calculate a point biserial correlation coefficient and its p-value.
+
+    The point biserial correlation is used to measure the relationship
+    between a binary variable, x, and a continuous variable, y. Like other
+    correlation coefficients, this one varies between -1 and +1 with 0
+    implying no correlation. Correlations of -1 or +1 imply a determinative
+    relationship.
+
+    This function may be computed using a shortcut formula but produces the
+    same result as `pearsonr`.
+
+    Parameters
+    ----------
+    x : array_like of bools
+        Input array.
+    y : array_like
+        Input array.
+    axis : int or None, default: 0
+        Axis along which to perform the calculation. Default is 0.
+        If None, ravel both arrays before performing the calculation.
+
+    Returns
+    -------
+    res: SignificanceResult
+        An object containing attributes:
+
+        statistic : float
+            The R value.
+        pvalue : float
+            The two-sided p-value.
+
+    Notes
+    -----
+    `pointbiserialr` uses a t-test with ``n-1`` degrees of freedom.
+    It is equivalent to `pearsonr`.
+
+    The value of the point-biserial correlation can be calculated from:
+
+    .. math::
+
+        r_{pb} = \frac{\overline{Y_1} - \overline{Y_0}}
+                      {s_y}
+                 \sqrt{\frac{N_0 N_1}
+                            {N (N - 1)}}
+
+    Where :math:`\overline{Y_{0}}` and :math:`\overline{Y_{1}}` are means
+    of the metric observations coded 0 and 1 respectively; :math:`N_{0}` and
+    :math:`N_{1}` are number of observations coded 0 and 1 respectively;
+    :math:`N` is the total number of observations and :math:`s_{y}` is the
+    standard deviation of all the metric observations.
+
+    A value of :math:`r_{pb}` that is significantly different from zero is
+    completely equivalent to a significant difference in means between the two
+    groups. Thus, an independent groups t Test with :math:`N-2` degrees of
+    freedom may be used to test whether :math:`r_{pb}` is nonzero. The
+    relation between the t-statistic for comparing two independent groups and
+    :math:`r_{pb}` is given by:
+
+    .. math::
+
+        t = \sqrt{N - 2}\frac{r_{pb}}{\sqrt{1 - r^{2}_{pb}}}
+
+    References
+    ----------
+    .. [1] J. Lev, "The Point Biserial Coefficient of Correlation", Ann. Math.
+           Statist., Vol. 20, no.1, pp. 125-126, 1949.
+
+    .. [2] R.F. Tate, "Correlation Between a Discrete and a Continuous
+           Variable. Point-Biserial Correlation.", Ann. Math. Statist., Vol. 25,
+           np. 3, pp. 603-607, 1954.
+
+    .. [3] D. Kornbrot "Point Biserial Correlation", In Wiley StatsRef:
+           Statistics Reference Online (eds N. Balakrishnan, et al.), 2014.
+           :doi:`10.1002/9781118445112.stat06227`
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> a = np.array([0, 0, 0, 1, 1, 1, 1])
+    >>> b = np.arange(7)
+    >>> stats.pointbiserialr(a, b)
+    (0.8660254037844386, 0.011724811003954652)
+    >>> stats.pearsonr(a, b)
+    (0.86602540378443871, 0.011724811003954626)
+    >>> np.corrcoef(a, b)
+    array([[ 1.       ,  0.8660254],
+           [ 0.8660254,  1.       ]])
+
+    """
+    rpb, prob = pearsonr(x, y, axis=axis)
+    # create result object with alias for backward compatibility
+    res = SignificanceResult(rpb, prob)
+    res.correlation = rpb
+    return res
+
+
+@xp_capabilities(cpu_only=True, jax_jit=False, allow_dask_compute=2,
+                 marray=True, extra_note=("`nan_policy='propagate'` is "
+                                          "incompatible with MArray input."))
+def kendalltau(x, y, *, nan_policy='propagate', method='auto', variant='b',
+               alternative='two-sided', axis=None, keepdims=False):
+    r"""Calculate Kendall's tau, a correlation measure for ordinal data.
+
+    Kendall's tau is a measure of the correspondence between two rankings.
+    Values close to 1 indicate strong agreement, and values close to -1
+    indicate strong disagreement. This implements two variants of Kendall's
+    tau: tau-b (the default) and tau-c (also known as Stuart's tau-c). These
+    differ only in how they are normalized to lie within the range -1 to 1;
+    the hypothesis tests (their p-values) are identical. Kendall's original
+    tau-a is not implemented separately because both tau-b and tau-c reduce
+    to tau-a in the absence of ties.
+
+    Although a naive implementation has O(n^2) complexity, this implementation
+    uses a Fenwick tree to do the computation in O(n log(n)) complexity.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Arrays of rankings, of the same shape. If arrays are not 1-D, they
+        will be flattened to 1-D.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    method : {'auto', 'asymptotic', 'exact'}, optional
+        Defines which method is used to calculate the p-value [5]_.
+        The following options are available (default is 'auto'):
+
+        * 'auto': selects the appropriate method based on a trade-off
+          between speed and accuracy
+        * 'asymptotic': uses a normal approximation valid for large samples
+        * 'exact': computes the exact p-value, but can only be used if no ties
+          are present. As the sample size increases, the 'exact' computation
+          time may grow and the result may lose some precision.
+
+    variant : {'b', 'c'}, optional
+        Defines which variant of Kendall's tau is returned. Default is 'b'.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        The following options are available:
+
+        * 'two-sided': the rank correlation is nonzero
+        * 'less': the rank correlation is negative (less than zero)
+        * 'greater': the rank correlation is positive (greater than zero)
+    axis : int or None, default: None
+        If an int, the axis of the input along which to compute the statistic.
+        The statistic of each axis-slice (e.g. row) of the input will appear in a
+        corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+    keepdims : bool, optional
+        If this is set to ``True``, the axes which are reduced are left
+        in the result as dimensions with length one. With this option,
+        the result will broadcast correctly against the input array.
+
+    Returns
+    -------
+    res : SignificanceResult
+        An object containing attributes:
+
+        statistic : float
+           The tau statistic.
+        pvalue : float
+           The p-value for a hypothesis test whose null hypothesis is
+           an absence of association, tau = 0.
+
+    Raises
+    ------
+    ValueError
+        If `nan_policy` is 'omit' and `variant` is not 'b' or
+        if `method` is 'exact' and there are ties between `x` and `y`.
+
+    See Also
+    --------
+    spearmanr : Calculates a Spearman rank-order correlation coefficient.
+    theilslopes : Computes the Theil-Sen estimator for a set of points (x, y).
+    weightedtau : Computes a weighted version of Kendall's tau.
+    :ref:`hypothesis_kendalltau` : Extended example
+
+    Notes
+    -----
+    The definition of Kendall's tau that is used is [2]_::
+
+      tau_b = (P - Q) / sqrt((P + Q + T) * (P + Q + U))
+
+      tau_c = 2 (P - Q) / (n**2 * (m - 1) / m)
+
+    where P is the number of concordant pairs, Q the number of discordant
+    pairs, T the number of tied pairs only in `x`, and U the number of tied pairs only
+    in `y`.  If a tie occurs for the same pair in both `x` and `y`, it is not
+    added to either T or U. n is the total number of samples, and m is the
+    number of unique values in either `x` or `y`, whichever is smaller.
+
+    References
+    ----------
+    .. [1] Maurice G. Kendall, "A New Measure of Rank Correlation", Biometrika
+           Vol. 30, No. 1/2, pp. 81-93, 1938.
+    .. [2] Maurice G. Kendall, "The treatment of ties in ranking problems",
+           Biometrika Vol. 33, No. 3, pp. 239-251. 1945.
+    .. [3] Gottfried E. Noether, "Elements of Nonparametric Statistics", John
+           Wiley & Sons, 1967.
+    .. [4] Peter M. Fenwick, "A new data structure for cumulative frequency
+           tables", Software: Practice and Experience, Vol. 24, No. 3,
+           pp. 327-336, 1994.
+    .. [5] Maurice G. Kendall, "Rank Correlation Methods" (4th Edition),
+           Charles Griffin & Co., 1970.
+
+    Examples
+    --------
+
+    >>> from scipy import stats
+    >>> x1 = [12, 2, 1, 12, 2]
+    >>> x2 = [1, 4, 7, 1, 0]
+    >>> res = stats.kendalltau(x1, x2)
+    >>> res.statistic
+    -0.47140452079103173
+    >>> res.pvalue
+    0.2827454599327748
+
+    For a more detailed example, see :ref:`hypothesis_kendalltau`.
+    """
+    xp = array_namespace(x, y)
+    x, y = xp_promote(x, y, force_floating=True, xp=xp)
+    dtype = x.dtype
+    if is_marray(xp):
+        mask_x, mask_y = np.asarray(x.mask), np.asarray(y.mask)
+        x, y = np.asarray(x.data).copy(), np.asarray(y.data).copy()
+        unmasked_nans = np.any(np.isnan(x[~mask_x])) | np.any(np.isnan(y[~mask_y]))
+        if unmasked_nans:
+            if nan_policy == 'propagate':
+                message = "`nan_policy='propagate'` is incompatible with MArray input."
+            elif nan_policy == 'raise':
+                message = "The input contains nan values."
+            raise ValueError(message)
+        nan_policy = 'omit'
+        x[mask_x], y[mask_y] = np.nan, np.nan
+    else:
+        x, y = _asarray(x, subok=True, xp=np), _asarray(y, subok=True, xp=np)
+    res = _kendalltau(x, y, nan_policy=nan_policy, method=method, variant=variant,
+                      alternative=alternative, axis=axis, keepdims=keepdims)
+    vals = res.statistic, res.pvalue, res.statistic
+    vals = (xp.asarray(val, dtype=dtype)[()] if val.ndim == 0
+            else xp.asarray(val, dtype=dtype) for val in vals)
+    return _pack_CorrelationResult(*vals)
+
+
+@_axis_nan_policy_factory(_pack_CorrelationResult, default_axis=None, n_samples=2,
+                          result_to_tuple=_unpack_CorrelationResult, paired=True,
+                          too_small=1, n_outputs=3)
+def _kendalltau(x, y, *, method='auto', variant='b', alternative='two-sided'):
+    x = np.asarray(x).ravel()
+    y = np.asarray(y).ravel()
+
+    if x.size != y.size:
+        raise ValueError("Array shapes are incompatible for broadcasting.")
+    elif not x.size or not y.size:
+        # Return NaN if arrays are empty
+        NaN = _get_nan(x, y)
+        res = SignificanceResult(NaN, NaN)
+        res.correlation = NaN
+        return res
+
+    def count_rank_tie(ranks):
+        cnt = np.bincount(ranks).astype('int64', copy=False)
+        cnt = cnt[cnt > 1]
+        # Python ints to avoid overflow down the line
+        return (int((cnt * (cnt - 1) // 2).sum()),
+                int((cnt * (cnt - 1.) * (cnt - 2)).sum()),
+                int((cnt * (cnt - 1.) * (2*cnt + 5)).sum()))
+
+    size = x.size
+    perm = np.argsort(y)  # sort on y and convert y to dense ranks
+    x, y = x[perm], y[perm]
+    y = np.r_[True, y[1:] != y[:-1]].cumsum(dtype=np.intp)
+
+    # stable sort on x and convert x to dense ranks
+    perm = np.argsort(x, kind='mergesort')
+    x, y = x[perm], y[perm]
+    x = np.r_[True, x[1:] != x[:-1]].cumsum(dtype=np.intp)
+
+    dis = _kendall_dis(x, y)  # discordant pairs
+
+    obs = np.r_[True, (x[1:] != x[:-1]) | (y[1:] != y[:-1]), True]
+    cnt = np.diff(np.nonzero(obs)[0]).astype('int64', copy=False)
+
+    ntie = int((cnt * (cnt - 1) // 2).sum())  # joint ties
+    xtie, x0, x1 = count_rank_tie(x)     # ties in x, stats
+    ytie, y0, y1 = count_rank_tie(y)     # ties in y, stats
+
+    tot = (size * (size - 1)) // 2
+
+    if xtie == tot or ytie == tot:
+        NaN = _get_nan(x, y)
+        res = SignificanceResult(NaN, NaN)
+        res.correlation = NaN
+        return res
+
+    # Note that tot = con + dis + (xtie - ntie) + (ytie - ntie) + ntie
+    #               = con + dis + xtie + ytie - ntie
+    con_minus_dis = tot - xtie - ytie + ntie - 2 * dis
+    if variant == 'b':
+        tau = con_minus_dis / np.sqrt(tot - xtie) / np.sqrt(tot - ytie)
+    elif variant == 'c':
+        minclasses = min(len(set(x)), len(set(y)))
+        tau = 2*con_minus_dis / (size**2 * (minclasses-1)/minclasses)
+    else:
+        raise ValueError(f"Unknown `variant` '{variant}' specified. "
+                         "`variant` must be 'b' or 'c'.")
+
+    # Limit range to fix computational errors
+    tau = np.minimum(1., max(-1., tau))
+
+    # The p-value calculation is the same for all variants since the p-value
+    # depends only on con_minus_dis.
+    if method == 'exact' and (xtie != 0 or ytie != 0):
+        raise ValueError("Ties found; exact method cannot be used.")
+
+    if method == 'auto':
+        if (xtie == 0 and ytie == 0) and (size <= 33 or
+                                          min(dis, tot-dis) <= 1):
+            method = 'exact'
+        else:
+            method = 'asymptotic'
+
+    if xtie == 0 and ytie == 0 and method == 'exact':
+        pvalue = mstats_basic._kendall_p_exact(size, tot-dis, alternative)
+    elif method == 'asymptotic':
+        # con_minus_dis is approx normally distributed with this variance [3]_
+        m = size * (size - 1.)
+        var = ((m * (2*size + 5) - x1 - y1) / 18 +
+               (2 * xtie * ytie) / m + x0 * y0 / (9 * m * (size - 2)))
+        z = con_minus_dis / np.sqrt(var)
+        pvalue = _get_pvalue(z, _SimpleNormal(), alternative, xp=np)
+    else:
+        raise ValueError(f"Unknown `method` '{method}' specified.  `method` must be"
+                         "'auto', 'exact' or 'asymptotic'.")
+
+    # create result object with alias for backward compatibility
+    res = SignificanceResult(tau[()], pvalue[()])
+    res.correlation = tau[()]
+    return res
+
+
+def _weightedtau_n_samples(kwargs):
+    rank = kwargs.get('rank', False)
+    return 2 if (isinstance(rank, bool) or rank is None) else 3
+
+
+@xp_capabilities(np_only=True)
+@_axis_nan_policy_factory(_pack_CorrelationResult, default_axis=None,
+                          n_samples=_weightedtau_n_samples,
+                          result_to_tuple=_unpack_CorrelationResult, paired=True,
+                          too_small=1, n_outputs=3, override={'nan_propagation': False})
+def weightedtau(x, y, rank=True, weigher=None, additive=True):
+    r"""Compute a weighted version of Kendall's :math:`\tau`.
+
+    The weighted :math:`\tau` is a weighted version of Kendall's
+    :math:`\tau` in which exchanges of high weight are more influential than
+    exchanges of low weight. The default parameters compute the additive
+    hyperbolic version of the index, :math:`\tau_\mathrm h`, which has
+    been shown to provide the best balance between important and
+    unimportant elements [1]_.
+
+    The weighting is defined by means of a rank array, which assigns a
+    nonnegative rank to each element (higher importance ranks being
+    associated with smaller values, e.g., 0 is the highest possible rank),
+    and a weigher function, which assigns a weight based on the rank to
+    each element. The weight of an exchange is then the sum or the product
+    of the weights of the ranks of the exchanged elements. The default
+    parameters compute :math:`\tau_\mathrm h`: an exchange between
+    elements with rank :math:`r` and :math:`s` (starting from zero) has
+    weight :math:`1/(r+1) + 1/(s+1)`.
+
+    Specifying a rank array is meaningful only if you have in mind an
+    external criterion of importance. If, as it usually happens, you do
+    not have in mind a specific rank, the weighted :math:`\tau` is
+    defined by averaging the values obtained using the decreasing
+    lexicographical rank by (`x`, `y`) and by (`y`, `x`). This is the
+    behavior with default parameters. Note that the convention used
+    here for ranking (lower values imply higher importance) is opposite
+    to that used by other SciPy statistical functions.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Arrays of scores, of the same shape. If arrays are not 1-D, they will
+        be flattened to 1-D.
+    rank : array_like of ints or bool, optional
+        A nonnegative rank assigned to each element. If it is None, the
+        decreasing lexicographical rank by (`x`, `y`) will be used: elements of
+        higher rank will be those with larger `x`-values, using `y`-values to
+        break ties (in particular, swapping `x` and `y` will give a different
+        result). If it is False, the element indices will be used
+        directly as ranks. The default is True, in which case this
+        function returns the average of the values obtained using the
+        decreasing lexicographical rank by (`x`, `y`) and by (`y`, `x`).
+    weigher : callable, optional
+        The weigher function. Must map nonnegative integers (zero
+        representing the most important element) to a nonnegative weight.
+        The default, None, provides hyperbolic weighing, that is,
+        rank :math:`r` is mapped to weight :math:`1/(r+1)`.
+    additive : bool, optional
+        If True, the weight of an exchange is computed by adding the
+        weights of the ranks of the exchanged elements; otherwise, the weights
+        are multiplied. The default is True.
+
+    Returns
+    -------
+    res: SignificanceResult
+        An object containing attributes:
+
+        statistic : float
+           The weighted :math:`\tau` correlation index.
+        pvalue : float
+           Presently ``np.nan``, as the null distribution of the statistic is
+           unknown (even in the additive hyperbolic case).
+
+    See Also
+    --------
+    kendalltau : Calculates Kendall's tau.
+    spearmanr : Calculates a Spearman rank-order correlation coefficient.
+    theilslopes : Computes the Theil-Sen estimator for a set of points (x, y).
+
+    Notes
+    -----
+    This function uses an :math:`O(n \log n)`, mergesort-based algorithm
+    [1]_ that is a weighted extension of Knight's algorithm for Kendall's
+    :math:`\tau` [2]_. It can compute Shieh's weighted :math:`\tau` [3]_
+    between rankings without ties (i.e., permutations) by setting
+    `additive` and `rank` to False, as the definition given in [1]_ is a
+    generalization of Shieh's.
+
+    NaNs are considered the smallest possible score.
+
+    .. versionadded:: 0.19.0
+
+    References
+    ----------
+    .. [1] Sebastiano Vigna, "A weighted correlation index for rankings with
+           ties", Proceedings of the 24th international conference on World
+           Wide Web, pp. 1166-1176, ACM, 2015.
+    .. [2] W.R. Knight, "A Computer Method for Calculating Kendall's Tau with
+           Ungrouped Data", Journal of the American Statistical Association,
+           Vol. 61, No. 314, Part 1, pp. 436-439, 1966.
+    .. [3] Grace S. Shieh. "A weighted Kendall's tau statistic", Statistics &
+           Probability Letters, Vol. 39, No. 1, pp. 17-24, 1998.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> x = [12, 2, 1, 12, 2]
+    >>> y = [1, 4, 7, 1, 0]
+    >>> res = stats.weightedtau(x, y)
+    >>> res.statistic
+    -0.56694968153682723
+    >>> res.pvalue
+    nan
+    >>> res = stats.weightedtau(x, y, additive=False)
+    >>> res.statistic
+    -0.62205716951801038
+
+    NaNs are considered the smallest possible score:
+
+    >>> x = [12, 2, 1, 12, 2]
+    >>> y = [1, 4, 7, 1, np.nan]
+    >>> res = stats.weightedtau(x, y)
+    >>> res.statistic
+    -0.56694968153682723
+
+    This is exactly Kendall's tau:
+
+    >>> x = [12, 2, 1, 12, 2]
+    >>> y = [1, 4, 7, 1, 0]
+    >>> res = stats.weightedtau(x, y, weigher=lambda x: 1)
+    >>> res.statistic
+    -0.47140452079103173
+
+    >>> x = [12, 2, 1, 12, 2]
+    >>> y = [1, 4, 7, 1, 0]
+    >>> stats.weightedtau(x, y, rank=None)
+    SignificanceResult(statistic=-0.4157652301037516, pvalue=nan)
+    >>> stats.weightedtau(y, x, rank=None)
+    SignificanceResult(statistic=-0.7181341329699028, pvalue=nan)
+
+    """
+    x = np.asarray(x).ravel()
+    y = np.asarray(y).ravel()
+    NaN = _get_nan(x, y)
+
+    if x.size != y.size:
+        raise ValueError("Array shapes are incompatible for broadcasting.")
+    if not x.size:
+        # Return NaN if arrays are empty
+        res = SignificanceResult(NaN, NaN)
+        res.correlation = NaN
+        return res
+
+    # If there are NaNs we apply _toint64()
+    if np.isnan(np.sum(x)):
+        x = _toint64(x)
+    if np.isnan(np.sum(y)):
+        y = _toint64(y)
+
+    # Reduce to ranks unsupported types
+    if x.dtype != y.dtype:
+        if x.dtype != np.int64:
+            x = _toint64(x)
+        if y.dtype != np.int64:
+            y = _toint64(y)
+    else:
+        if x.dtype not in (np.int32, np.int64, np.float32, np.float64):
+            x = _toint64(x)
+            y = _toint64(y)
+
+    if rank is True:
+        tau = np.asarray(
+            _weightedrankedtau(x, y, None, weigher, additive) +
+            _weightedrankedtau(y, x, None, weigher, additive)
+        )[()] / 2
+        res = SignificanceResult(tau, NaN)
+        res.correlation = tau
+        return res
+
+    if rank is False:
+        rank = np.arange(x.size, dtype=np.intp)
+    elif rank is not None:
+        rank = np.asarray(rank).ravel()
+        rank = _toint64(rank).astype(np.intp)
+        if rank.size != x.size:
+            raise ValueError(
+                "All inputs to `weightedtau` must be of the same size, "
+                f"found x-size {x.size} and rank-size {rank.size}"
+            )
+
+    tau = np.asarray(_weightedrankedtau(x, y, rank, weigher, additive))[()]
+    res = SignificanceResult(tau, NaN)
+    res.correlation = tau
+    return res
+
+
+#####################################
+#       INFERENTIAL STATISTICS      #
+#####################################
+
+TtestResultBase = _make_tuple_bunch('TtestResultBase',
+                                    ['statistic', 'pvalue'], ['df'])
+
+
+class TtestResult(TtestResultBase):
+    """
+    Result of a t-test.
+
+    See the documentation of the particular t-test function for more
+    information about the definition of the statistic and meaning of
+    the confidence interval.
+
+    Attributes
+    ----------
+    statistic : float or array
+        The t-statistic of the sample.
+    pvalue : float or array
+        The p-value associated with the given alternative.
+    df : float or array
+        The number of degrees of freedom used in calculation of the
+        t-statistic; this is one less than the size of the sample
+        (``a.shape[axis]-1`` if there are no masked elements or omitted NaNs).
+
+    Methods
+    -------
+    confidence_interval
+        Computes a confidence interval around the population statistic
+        for the given confidence level.
+        The confidence interval is returned in a ``namedtuple`` with
+        fields `low` and `high`.
+
+    """
+
+    def __init__(self, statistic, pvalue, df,  # public
+                 alternative, standard_error, estimate,  # private
+                 statistic_np=None, xp=None):  # private
+        super().__init__(statistic, pvalue, df=df)
+        self._alternative = alternative
+        self._standard_error = standard_error  # denominator of t-statistic
+        self._estimate = estimate  # point estimate of sample mean
+        self._statistic_np = statistic if statistic_np is None else statistic_np
+        self._dtype = statistic.dtype
+        self._xp = array_namespace(statistic, pvalue) if xp is None else xp
+
+
+    def confidence_interval(self, confidence_level=0.95):
+        """
+        Parameters
+        ----------
+        confidence_level : float
+            The confidence level for the calculation of the population mean
+            confidence interval. Default is 0.95.
+
+        Returns
+        -------
+        ci : namedtuple
+            The confidence interval is returned in a ``namedtuple`` with
+            fields `low` and `high`.
+
+        """
+        low, high = _t_confidence_interval(self.df, self._statistic_np,
+                                           confidence_level, self._alternative,
+                                           self._dtype, self._xp)
+        low = low * self._standard_error + self._estimate
+        high = high * self._standard_error + self._estimate
+        return ConfidenceInterval(low=low, high=high)
+
+
+def pack_TtestResult(statistic, pvalue, df, alternative, standard_error,
+                     estimate):
+    xp = array_namespace(statistic, pvalue)
+    # Due to behavior of `_axis_nan_policy` decorator, `alternative` can be any number
+    # of dimensions, but there is at most one unique non-NaN value.
+    # `_xp_mean` with `nan_policy='omit'` is a JIT-compatible way to extract it.
+    alternative = xp.asarray(alternative)
+    alternative = (_xp_mean(alternative, axis=None, nan_policy='omit', warn=False)
+                   if xp_size(alternative) != 0 else xp.nan)
+    return TtestResult(statistic, pvalue, df=df, alternative=alternative,
+                       standard_error=standard_error, estimate=estimate)
+
+
+def unpack_TtestResult(res, _):
+    return (res.statistic, res.pvalue, res.df, res._alternative,
+            res._standard_error, res._estimate)
+
+
+@xp_capabilities(cpu_only=True, exceptions=["cupy", "jax.numpy"], marray=True,
+                 extra_note=("The ``confidence_interval`` method of the output object "
+                             "is incompatible with JAX JIT."))
+@_axis_nan_policy_factory(pack_TtestResult, default_axis=0, n_samples=2,
+                          result_to_tuple=unpack_TtestResult, n_outputs=6)
+# nan_policy handled by `_axis_nan_policy`, but needs to be left
+# in signature to preserve use as a positional argument
+def ttest_1samp(a, popmean, axis=0, nan_policy="propagate", alternative="two-sided"):
+    """Calculate the T-test for the mean of ONE group of scores.
+
+    This is a test for the null hypothesis that the expected value
+    (mean) of a sample of independent observations `a` is equal to the given
+    population mean, `popmean`.
+
+    Parameters
+    ----------
+    a : array_like
+        Sample observations.
+    popmean : float or array_like
+        Expected value in null hypothesis. If array_like, then its length along
+        `axis` must equal 1, and it must otherwise be broadcastable with `a`.
+    axis : int or None, optional
+        Axis along which to compute test; default is 0. If None, compute over
+        the whole array `a`.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+        The following options are available (default is 'two-sided'):
+
+        * 'two-sided': the mean of the underlying distribution of the sample
+          is different than the given population mean (`popmean`)
+        * 'less': the mean of the underlying distribution of the sample is
+          less than the given population mean (`popmean`)
+        * 'greater': the mean of the underlying distribution of the sample is
+          greater than the given population mean (`popmean`)
+
+    Returns
+    -------
+    result : `~scipy.stats._result_classes.TtestResult`
+        An object with the following attributes:
+
+        statistic : float or array
+            The t-statistic.
+        pvalue : float or array
+            The p-value associated with the given alternative.
+        df : float or array
+            The number of degrees of freedom used in calculation of the
+            t-statistic; this is one less than the size of the sample
+            (``a.shape[axis]``).
+
+            .. versionadded:: 1.10.0
+
+        The object also has the following method:
+
+        confidence_interval(confidence_level=0.95)
+            Computes a confidence interval around the population
+            mean for the given confidence level.
+            The confidence interval is returned in a ``namedtuple`` with
+            fields `low` and `high`.
+
+            .. versionadded:: 1.10.0
+
+    Notes
+    -----
+    The statistic is calculated as ``(np.mean(a) - popmean)/se``, where
+    ``se`` is the standard error. Therefore, the statistic will be positive
+    when the sample mean is greater than the population mean and negative when
+    the sample mean is less than the population mean.
+
+    Examples
+    --------
+    Suppose we wish to test the null hypothesis that the mean of a population
+    is equal to 0.5. We choose a confidence level of 99%; that is, we will
+    reject the null hypothesis in favor of the alternative if the p-value is
+    less than 0.01.
+
+    When testing random variates from the standard uniform distribution, which
+    has a mean of 0.5, we expect the data to be consistent with the null
+    hypothesis most of the time.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+    >>> rvs = stats.uniform.rvs(size=50, random_state=rng)
+    >>> stats.ttest_1samp(rvs, popmean=0.5)
+    TtestResult(statistic=2.456308468440, pvalue=0.017628209047638, df=49)
+
+    As expected, the p-value of 0.017 is not below our threshold of 0.01, so
+    we cannot reject the null hypothesis.
+
+    When testing data from the standard *normal* distribution, which has a mean
+    of 0, we would expect the null hypothesis to be rejected.
+
+    >>> rvs = stats.norm.rvs(size=50, random_state=rng)
+    >>> stats.ttest_1samp(rvs, popmean=0.5)
+    TtestResult(statistic=-7.433605518875, pvalue=1.416760157221e-09, df=49)
+
+    Indeed, the p-value is lower than our threshold of 0.01, so we reject the
+    null hypothesis in favor of the default "two-sided" alternative: the mean
+    of the population is *not* equal to 0.5.
+
+    However, suppose we were to test the null hypothesis against the
+    one-sided alternative that the mean of the population is *greater* than
+    0.5. Since the mean of the standard normal is less than 0.5, we would not
+    expect the null hypothesis to be rejected.
+
+    >>> stats.ttest_1samp(rvs, popmean=0.5, alternative='greater')
+    TtestResult(statistic=-7.433605518875, pvalue=0.99999999929, df=49)
+
+    Unsurprisingly, with a p-value greater than our threshold, we would not
+    reject the null hypothesis.
+
+    Note that when working with a confidence level of 99%, a true null
+    hypothesis will be rejected approximately 1% of the time.
+
+    >>> rvs = stats.uniform.rvs(size=(100, 50), random_state=rng)
+    >>> res = stats.ttest_1samp(rvs, popmean=0.5, axis=1)
+    >>> np.sum(res.pvalue < 0.01)
+    1
+
+    Indeed, even though all 100 samples above were drawn from the standard
+    uniform distribution, which *does* have a population mean of 0.5, we would
+    mistakenly reject the null hypothesis for one of them.
+
+    `ttest_1samp` can also compute a confidence interval around the population
+    mean.
+
+    >>> rvs = stats.norm.rvs(size=50, random_state=rng)
+    >>> res = stats.ttest_1samp(rvs, popmean=0)
+    >>> ci = res.confidence_interval(confidence_level=0.95)
+    >>> ci
+    ConfidenceInterval(low=-0.3193887540880017, high=0.2898583388980972)
+
+    The bounds of the 95% confidence interval are the
+    minimum and maximum values of the parameter `popmean` for which the
+    p-value of the test would be 0.05.
+
+    >>> res = stats.ttest_1samp(rvs, popmean=ci.low)
+    >>> np.testing.assert_allclose(res.pvalue, 0.05)
+    >>> res = stats.ttest_1samp(rvs, popmean=ci.high)
+    >>> np.testing.assert_allclose(res.pvalue, 0.05)
+
+    Under certain assumptions about the population from which a sample
+    is drawn, the confidence interval with confidence level 95% is expected
+    to contain the true population mean in 95% of sample replications.
+
+    >>> rvs = stats.norm.rvs(size=(50, 1000), loc=1, random_state=rng)
+    >>> res = stats.ttest_1samp(rvs, popmean=0)
+    >>> ci = res.confidence_interval()
+    >>> contains_pop_mean = (ci.low < 1) & (ci.high > 1)
+    >>> contains_pop_mean.sum()
+    953
+
+    """
+    xp = array_namespace(a)
+    a, popmean = xp_promote(a, popmean, force_floating=True, xp=xp)
+    a, axis = _chk_asarray(a, axis, xp=xp)
+
+    n = _count_nonmasked(a, axis)
+    df = n - 1
+
+    if a.shape[axis] == 0:
+        # This is really only needed for *testing* _axis_nan_policy decorator
+        # It won't happen when the decorator is used.
+        NaN = _get_nan(a)
+        return TtestResult(NaN, NaN, df=NaN, alternative=NaN,
+                           standard_error=NaN, estimate=NaN)
+
+    mean = xp.mean(a, axis=axis)
+    try:
+        popmean = xp.asarray(popmean)
+        popmean = xp.squeeze(popmean, axis=axis) if popmean.ndim > 0 else popmean
+    except ValueError as e:
+        raise ValueError("`popmean.shape[axis]` must equal 1.") from e
+    d = mean - popmean
+    v = _var(a, axis=axis, ddof=1)
+    denom = xp.sqrt(v / n)
+
+    with np.errstate(divide='ignore', invalid='ignore'):
+        t = xp.divide(d, denom)
+        t = t[()] if t.ndim == 0 else t
+
+    dist = _SimpleStudentT(xp.asarray(df, dtype=t.dtype, device=xp_device(a)))
+    prob = _get_pvalue(t, dist, alternative, xp=xp)
+    prob = prob[()] if prob.ndim == 0 else prob
+
+    # when nan_policy='omit', `df` can be different for different axis-slices
+    df = xp.broadcast_to(xp.asarray(df, device=xp_device(a)), t.shape)
+    df = df[()] if df.ndim == 0 else df
+    # _axis_nan_policy decorator doesn't play well with strings
+    alternative_num = {"less": -1, "two-sided": 0, "greater": 1}[alternative]
+    return TtestResult(t, prob, df=df, alternative=alternative_num,
+                       standard_error=denom, estimate=mean,
+                       statistic_np=xp.asarray(t), xp=xp)
+
+
+def _t_confidence_interval(df, t, confidence_level, alternative, dtype=None, xp=None):
+    # Input validation on `alternative` is already done
+    # We just need IV on confidence_level
+    dtype = t.dtype if dtype is None else dtype
+    xp = array_namespace(t) if xp is None else xp
+
+    if confidence_level < 0 or confidence_level > 1:
+        message = "`confidence_level` must be a number between 0 and 1."
+        raise ValueError(message)
+
+    confidence_level = xp.asarray(confidence_level, dtype=dtype, device=xp_device(t))
+    inf = xp.asarray(xp.inf, dtype=dtype)
+
+    if alternative < 0:  # 'less'
+        p = confidence_level
+        low, high = xp.broadcast_arrays(-inf, special.stdtrit(df, p))
+    elif alternative > 0:  # 'greater'
+        p = 1 - confidence_level
+        low, high = xp.broadcast_arrays(special.stdtrit(df, p), inf)
+    elif alternative == 0:  # 'two-sided'
+        tail_probability = (1 - confidence_level)/2
+        p = xp.stack((tail_probability, 1-tail_probability))
+        # axis of p must be the zeroth and orthogonal to all the rest
+        p = xp.reshape(p, tuple([2] + [1]*xp.asarray(df, device=xp_device(t)).ndim))
+        ci = special.stdtrit(df, p)
+        low, high = ci[0, ...], ci[1, ...]
+    else:  # alternative is NaN when input is empty (see _axis_nan_policy)
+        nan = xp.asarray(xp.nan, device=xp_device(t))
+        p, nans = xp.broadcast_arrays(t, nan)
+        low, high = nans, nans
+
+    low = xp.asarray(low, dtype=dtype)
+    low = low[()] if low.ndim == 0 else low
+    high = xp.asarray(high, dtype=dtype)
+    high = high[()] if high.ndim == 0 else high
+    return low, high
+
+
+def _ttest_ind_from_stats(mean1, mean2, denom, df, alternative, xp=None):
+    xp = array_namespace(mean1, mean2, denom) if xp is None else xp
+
+    d = mean1 - mean2
+    with np.errstate(divide='ignore', invalid='ignore'):
+        t = xp.divide(d, denom)
+
+    dist = _SimpleStudentT(xp.asarray(df, dtype=t.dtype, device=xp_device(t)))
+    prob = _get_pvalue(t, dist, alternative, xp=xp)
+    prob = prob[()] if prob.ndim == 0 else prob
+
+    t = t[()] if t.ndim == 0 else t
+    prob = prob[()] if prob.ndim == 0 else prob
+    return t, prob
+
+
+def _unequal_var_ttest_denom(v1, n1, v2, n2, xp=None):
+    xp = array_namespace(v1, v2) if xp is None else xp
+    vn1 = v1 / n1
+    vn2 = v2 / n2
+    with np.errstate(divide='ignore', invalid='ignore'):
+        df = (vn1 + vn2)**2 / (vn1**2 / (n1 - 1) + vn2**2 / (n2 - 1))
+
+    # If df is undefined, variances are zero (assumes n1 > 0 & n2 > 0).
+    # Hence it doesn't matter what df is as long as it's not NaN.
+    df = xp.where(xp.isnan(df), 1., df)
+    denom = xp.sqrt(vn1 + vn2)
+    return df, denom
+
+
+def _equal_var_ttest_denom(v1, n1, v2, n2, xp=None):
+    xp = array_namespace(v1, v2) if xp is None else xp
+
+    # If there is a single observation in one sample, this formula for pooled
+    # variance breaks down because the variance of that sample is undefined.
+    # The pooled variance is still defined, though, because the (n-1) in the
+    # numerator should cancel with the (n-1) in the denominator, leaving only
+    # the sum of squared differences from the mean: zero.
+    v1 = xp.where(xp.asarray(n1 == 1), 0., v1)
+    v2 = xp.where(xp.asarray(n2 == 1), 0., v2)
+
+    df = n1 + n2 - 2.0
+    svar = ((n1 - 1) * v1 + (n2 - 1) * v2) / df
+    denom = xp.sqrt(svar * (1.0 / n1 + 1.0 / n2))
+    df = xp.asarray(df, dtype=denom.dtype)
+    return df, denom
+
+
+Ttest_indResult = namedtuple('Ttest_indResult', ('statistic', 'pvalue'))
+
+
+@xp_capabilities(cpu_only=True, exceptions=["cupy", "jax.numpy"], marray=True)
+def ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2,
+                         equal_var=True, alternative="two-sided"):
+    r"""
+    T-test for means of two independent samples from descriptive statistics.
+
+    This is a test for the null hypothesis that two independent
+    samples have identical average (expected) values.
+
+    Parameters
+    ----------
+    mean1 : array_like
+        The mean(s) of sample 1.
+    std1 : array_like
+        The corrected sample standard deviation of sample 1 (i.e. ``ddof=1``).
+    nobs1 : array_like
+        The number(s) of observations of sample 1.
+    mean2 : array_like
+        The mean(s) of sample 2.
+    std2 : array_like
+        The corrected sample standard deviation of sample 2 (i.e. ``ddof=1``).
+    nobs2 : array_like
+        The number(s) of observations of sample 2.
+    equal_var : bool, optional
+        If True (default), perform a standard independent 2 sample test
+        that assumes equal population variances [1]_.
+        If False, perform Welch's t-test, which does not assume equal
+        population variance [2]_.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+        The following options are available (default is 'two-sided'):
+
+        * 'two-sided': the means of the distributions are unequal.
+        * 'less': the mean of the first distribution is less than the
+          mean of the second distribution.
+        * 'greater': the mean of the first distribution is greater than the
+          mean of the second distribution.
+
+        .. versionadded:: 1.6.0
+
+    Returns
+    -------
+    statistic : float or array
+        The calculated t-statistics.
+    pvalue : float or array
+        The two-tailed p-value.
+
+    See Also
+    --------
+    scipy.stats.ttest_ind
+
+    Notes
+    -----
+    The statistic is calculated as ``(mean1 - mean2)/se``, where ``se`` is the
+    standard error. Therefore, the statistic will be positive when `mean1` is
+    greater than `mean2` and negative when `mean1` is less than `mean2`.
+
+    This method does not check whether any of the elements of `std1` or `std2`
+    are negative. If any elements of the `std1` or `std2` parameters are
+    negative in a call to this method, this method will return the same result
+    as if it were passed ``numpy.abs(std1)`` and ``numpy.abs(std2)``,
+    respectively, instead; no exceptions or warnings will be emitted.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/T-test#Independent_two-sample_t-test
+
+    .. [2] https://en.wikipedia.org/wiki/Welch%27s_t-test
+
+    Examples
+    --------
+    Suppose we have the summary data for two samples, as follows (with the
+    Sample Variance being the corrected sample variance)::
+
+                         Sample   Sample
+                   Size   Mean   Variance
+        Sample 1    13    15.0     87.5
+        Sample 2    11    12.0     39.0
+
+    Apply the t-test to this data (with the assumption that the population
+    variances are equal):
+
+    >>> import numpy as np
+    >>> from scipy.stats import ttest_ind_from_stats
+    >>> ttest_ind_from_stats(mean1=15.0, std1=np.sqrt(87.5), nobs1=13,
+    ...                      mean2=12.0, std2=np.sqrt(39.0), nobs2=11)
+    Ttest_indResult(statistic=0.9051358093310269, pvalue=0.3751996797581487)
+
+    For comparison, here is the data from which those summary statistics
+    were taken.  With this data, we can compute the same result using
+    `scipy.stats.ttest_ind`:
+
+    >>> a = np.array([1, 3, 4, 6, 11, 13, 15, 19, 22, 24, 25, 26, 26])
+    >>> b = np.array([2, 4, 6, 9, 11, 13, 14, 15, 18, 19, 21])
+    >>> from scipy.stats import ttest_ind
+    >>> ttest_ind(a, b)
+    TtestResult(statistic=0.905135809331027,
+                pvalue=0.3751996797581486,
+                df=22.0)
+
+    Suppose we instead have binary data and would like to apply a t-test to
+    compare the proportion of 1s in two independent groups::
+
+                          Number of    Sample     Sample
+                    Size    ones        Mean     Variance
+        Sample 1    150      30         0.2        0.161073
+        Sample 2    200      45         0.225      0.175251
+
+    The sample mean :math:`\hat{p}` is the proportion of ones in the sample
+    and the variance for a binary observation is estimated by
+    :math:`\hat{p}(1-\hat{p})`.
+
+    >>> ttest_ind_from_stats(mean1=0.2, std1=np.sqrt(0.161073), nobs1=150,
+    ...                      mean2=0.225, std2=np.sqrt(0.175251), nobs2=200)
+    Ttest_indResult(statistic=-0.5627187905196761, pvalue=0.5739887114209541)
+
+    For comparison, we could compute the t statistic and p-value using
+    arrays of 0s and 1s and `scipy.stat.ttest_ind`, as above.
+
+    >>> group1 = np.array([1]*30 + [0]*(150-30))
+    >>> group2 = np.array([1]*45 + [0]*(200-45))
+    >>> ttest_ind(group1, group2)
+    TtestResult(statistic=-0.5627179589855622,
+                pvalue=0.573989277115258,
+                df=348.0)
+
+    """
+    xp = array_namespace(mean1, std1, mean2, std2)
+
+    mean1, std1, nobs1, mean2, std2, nobs2 = xp_promote(
+        mean1, std1, nobs1, mean2, std2, nobs2, force_floating=True, xp=xp)
+
+    if equal_var:
+        df, denom = _equal_var_ttest_denom(std1**2, nobs1, std2**2, nobs2, xp=xp)
+    else:
+        df, denom = _unequal_var_ttest_denom(std1**2, nobs1, std2**2, nobs2, xp=xp)
+
+    res = _ttest_ind_from_stats(mean1, mean2, denom, df, alternative)
+    return Ttest_indResult(*res)
+
+
+@xp_capabilities(cpu_only=True, exceptions=["cupy", "jax.numpy"], marray=True,
+                 extra_note=("`trim` and `method` are incompatible with MArray input."
+                             "The ``confidence_interval`` method of the output object "
+                             "is incompatible with JAX JIT."))
+@_axis_nan_policy_factory(pack_TtestResult, default_axis=0, n_samples=2,
+                          result_to_tuple=unpack_TtestResult, n_outputs=6)
+def ttest_ind(a, b, *, axis=0, equal_var=True, nan_policy='propagate',
+              alternative="two-sided", trim=0, method=None):
+    """
+    Calculate the T-test for the means of *two independent* samples of scores.
+
+    This is a test for the null hypothesis that 2 independent samples
+    have identical average (expected) values. This test assumes that the
+    populations have identical variances by default.
+
+    Parameters
+    ----------
+    a, b : array_like
+        The arrays must have the same shape, except in the dimension
+        corresponding to `axis` (the first, by default).
+    axis : int or None, optional
+        Axis along which to compute test. If None, compute over the whole
+        arrays, `a`, and `b`.
+    equal_var : bool, optional
+        If True (default), perform a standard independent 2 sample test
+        that assumes equal population variances [1]_.
+        If False, perform Welch's t-test, which does not assume equal
+        population variance [2]_.
+
+        .. versionadded:: 0.11.0
+
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+        The 'omit' option is not currently available for one-sided asymptotic tests.
+
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+        The following options are available (default is 'two-sided'):
+
+        * 'two-sided': the means of the distributions underlying the samples
+          are unequal.
+        * 'less': the mean of the distribution underlying the first sample
+          is less than the mean of the distribution underlying the second
+          sample.
+        * 'greater': the mean of the distribution underlying the first
+          sample is greater than the mean of the distribution underlying
+          the second sample.
+
+    trim : float, optional
+        If nonzero, performs a trimmed (Yuen's) t-test.
+        Defines the fraction of elements to be trimmed from each end of the
+        input samples. If 0 (default), no elements will be trimmed from either
+        side. The number of trimmed elements from each tail is the floor of the
+        trim times the number of elements. Valid range is [0, .5).
+    method : ResamplingMethod, optional
+        Defines the method used to compute the p-value. If `method` is an
+        instance of `PermutationMethod`/`MonteCarloMethod`, the p-value is
+        computed using
+        `scipy.stats.permutation_test`/`scipy.stats.monte_carlo_test` with the
+        provided configuration options and other appropriate settings.
+        Otherwise, the p-value is computed by comparing the test statistic
+        against a theoretical t-distribution.
+
+        .. versionadded:: 1.15.0
+
+    Returns
+    -------
+    result : `~scipy.stats._result_classes.TtestResult`
+        An object with the following attributes:
+
+        statistic : float or ndarray
+            The t-statistic.
+        pvalue : float or ndarray
+            The p-value associated with the given alternative.
+        df : float or ndarray
+            The number of degrees of freedom used in calculation of the
+            t-statistic.
+
+            .. versionadded:: 1.11.0
+
+        The object also has the following method:
+
+        confidence_interval(confidence_level=0.95)
+            Computes a confidence interval around the difference in
+            population means for the given confidence level.
+            The confidence interval is returned in a ``namedtuple`` with
+            fields ``low`` and ``high``.
+
+            .. versionadded:: 1.11.0
+
+    Notes
+    -----
+    Suppose we observe two independent samples, e.g. flower petal lengths, and
+    we are considering whether the two samples were drawn from the same
+    population (e.g. the same species of flower or two species with similar
+    petal characteristics) or two different populations.
+
+    The t-test quantifies the difference between the arithmetic means
+    of the two samples. The p-value quantifies the probability of observing
+    as or more extreme values assuming the null hypothesis, that the
+    samples are drawn from populations with the same population means, is true.
+    A p-value larger than a chosen threshold (e.g. 5% or 1%) indicates that
+    our observation is not so unlikely to have occurred by chance. Therefore,
+    we do not reject the null hypothesis of equal population means.
+    If the p-value is smaller than our threshold, then we have evidence
+    against the null hypothesis of equal population means.
+
+    By default, the p-value is determined by comparing the t-statistic of the
+    observed data against a theoretical t-distribution.
+
+    It is also possible to compute the test statistic using a permutation test by
+    passing ``method=scipy.stats.PermutationMethod(n_resamples=permutations)``,
+    where ``permutations`` is the desired number of "permutations" to use in
+    forming the null distribution. When ``1 < permutations < binom(n, k)``, where
+
+    * ``k`` is the number of observations in `a`,
+    * ``n`` is the total number of observations in `a` and `b`, and
+    * ``binom(n, k)`` is the binomial coefficient (``n`` choose ``k``),
+
+    the data are pooled (concatenated), randomly assigned to either group `a`
+    or `b`, and the t-statistic is calculated. This process is performed
+    repeatedly (``permutations`` times), generating a distribution of the
+    t-statistic under the null hypothesis, and the t-statistic of the observed
+    data is compared to this distribution to determine the p-value.
+    Specifically, the p-value reported is the "achieved significance level"
+    (ASL) as defined in 4.4 of [3]_. Note that there are other ways of
+    estimating p-values using randomized permutation tests; for other
+    options, see the more general `permutation_test`.
+
+    When ``permutations >= binom(n, k)``, an exact test is performed: the data
+    are partitioned between the groups in each distinct way exactly once.
+
+    The permutation test can be computationally expensive and not necessarily
+    more accurate than the analytical test, but it does not make strong
+    assumptions about the shape of the underlying distribution.
+
+    Use of trimming is commonly referred to as the trimmed t-test. At times
+    called Yuen's t-test, this is an extension of Welch's t-test, with the
+    difference being the use of winsorized means in calculation of the variance
+    and the trimmed sample size in calculation of the statistic. Trimming is
+    recommended if the underlying distribution is long-tailed or contaminated
+    with outliers [4]_.
+
+    The statistic is calculated as ``(np.mean(a) - np.mean(b))/se``, where
+    ``se`` is the standard error. Therefore, the statistic will be positive
+    when the sample mean of `a` is greater than the sample mean of `b` and
+    negative when the sample mean of `a` is less than the sample mean of
+    `b`.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/T-test#Independent_two-sample_t-test
+
+    .. [2] https://en.wikipedia.org/wiki/Welch%27s_t-test
+
+    .. [3] B. Efron and T. Hastie.
+           Computer Age Statistical Inference. (2016).
+
+    .. [4] Yuen, Karen K. "The Two-Sample Trimmed t for Unequal Population
+           Variances." Biometrika, vol. 61, no. 1, 1974, pp. 165-170.
+           https://www.jstor.org/stable/2334299
+
+    .. [5] Yuen, Karen K., and W. J. Dixon. "The Approximate Behaviour and
+           Performance of the Two-Sample Trimmed t." Biometrika, vol. 60,
+           no. 2, 1973, pp. 369-374.
+           https://www.jstor.org/stable/2334550
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+
+    Test with sample with identical means:
+
+    >>> rvs1 = stats.norm.rvs(loc=5, scale=10, size=500, random_state=rng)
+    >>> rvs2 = stats.norm.rvs(loc=5, scale=10, size=500, random_state=rng)
+    >>> stats.ttest_ind(rvs1, rvs2)
+    TtestResult(statistic=-0.4390847099199348,
+                pvalue=0.6606952038870015,
+                df=998.0)
+    >>> stats.ttest_ind(rvs1, rvs2, equal_var=False)
+    TtestResult(statistic=-0.4390847099199348,
+                pvalue=0.6606952553131064,
+                df=997.4602304121448)
+
+    `ttest_ind` underestimates p for unequal variances:
+
+    >>> rvs3 = stats.norm.rvs(loc=5, scale=20, size=500, random_state=rng)
+    >>> stats.ttest_ind(rvs1, rvs3)
+    TtestResult(statistic=-1.6370984482905417,
+                pvalue=0.1019251574705033,
+                df=998.0)
+    >>> stats.ttest_ind(rvs1, rvs3, equal_var=False)
+    TtestResult(statistic=-1.637098448290542,
+                pvalue=0.10202110497954867,
+                df=765.1098655246868)
+
+    When ``n1 != n2``, the equal variance t-statistic is no longer equal to the
+    unequal variance t-statistic:
+
+    >>> rvs4 = stats.norm.rvs(loc=5, scale=20, size=100, random_state=rng)
+    >>> stats.ttest_ind(rvs1, rvs4)
+    TtestResult(statistic=-1.9481646859513422,
+                pvalue=0.05186270935842703,
+                df=598.0)
+    >>> stats.ttest_ind(rvs1, rvs4, equal_var=False)
+    TtestResult(statistic=-1.3146566100751664,
+                pvalue=0.1913495266513811,
+                df=110.41349083985212)
+
+    T-test with different means, variance, and n:
+
+    >>> rvs5 = stats.norm.rvs(loc=8, scale=20, size=100, random_state=rng)
+    >>> stats.ttest_ind(rvs1, rvs5)
+    TtestResult(statistic=-2.8415950600298774,
+                pvalue=0.0046418707568707885,
+                df=598.0)
+    >>> stats.ttest_ind(rvs1, rvs5, equal_var=False)
+    TtestResult(statistic=-1.8686598649188084,
+                pvalue=0.06434714193919686,
+                df=109.32167496550137)
+
+    Take these two samples, one of which has an extreme tail.
+
+    >>> a = (56, 128.6, 12, 123.8, 64.34, 78, 763.3)
+    >>> b = (1.1, 2.9, 4.2)
+
+    Use the `trim` keyword to perform a trimmed (Yuen) t-test. For example,
+    using 20% trimming, ``trim=.2``, the test will reduce the impact of one
+    (``np.floor(trim*len(a))``) element from each tail of sample `a`. It will
+    have no effect on sample `b` because ``np.floor(trim*len(b))`` is 0.
+
+    >>> stats.ttest_ind(a, b, trim=.2)
+    TtestResult(statistic=3.4463884028073513,
+                pvalue=0.01369338726499547,
+                df=6.0)
+    """
+    xp = array_namespace(a, b)
+
+    a, b = xp_promote(a, b, force_floating=True, xp=xp)
+
+    if axis is None:
+        a, b, axis = xp_ravel(a), xp_ravel(b), 0
+
+    if not (0 <= trim < .5):
+        raise ValueError("Trimming percentage should be 0 <= `trim` < .5.")
+
+    if not isinstance(method, PermutationMethod | MonteCarloMethod | None):
+        message = ("`method` must be an instance of `PermutationMethod`, an instance "
+                   "of `MonteCarloMethod`, or None (default).")
+        raise ValueError(message)
+
+    result_shape = _broadcast_array_shapes_remove_axis((a, b), axis=axis)
+    NaN = _get_nan(a, b, shape=result_shape, xp=xp)
+    if xp_size(a) == 0 or xp_size(b) == 0:
+        return TtestResult(NaN, NaN, df=NaN, alternative=NaN,
+                           standard_error=NaN, estimate=NaN)
+
+    alternative_nums = {"less": -1, "two-sided": 0, "greater": 1}
+
+    n1 = _count_nonmasked(a, axis)
+    n2 = _count_nonmasked(b, axis)
+
+    if trim == 0:
+        with np.errstate(divide='ignore', invalid='ignore'):
+            v1 = _var(a, axis, ddof=1, xp=xp)
+            v2 = _var(b, axis, ddof=1, xp=xp)
+
+        m1 = xp.mean(a, axis=axis)
+        m2 = xp.mean(b, axis=axis)
+    else:
+        v1, m1, n1 = _ttest_trim_var_mean_len(a, trim, axis, xp=xp)
+        v2, m2, n2 = _ttest_trim_var_mean_len(b, trim, axis, xp=xp)
+
+    if equal_var:
+        df, denom = _equal_var_ttest_denom(v1, n1, v2, n2, xp=xp)
+    else:
+        df, denom = _unequal_var_ttest_denom(v1, n1, v2, n2, xp=xp)
+
+    if method is None:
+        t, prob = _ttest_ind_from_stats(m1, m2, denom, df, alternative)
+    else:
+        # nan_policy is taken care of by axis_nan_policy decorator
+        ttest_kwargs = dict(equal_var=equal_var, trim=trim)
+        t, prob = _ttest_resampling(a, b, axis, alternative,
+                                    ttest_kwargs, method, xp=xp)
+
+    # when nan_policy='omit', `df` can be different for different axis-slices
+    df = xp.broadcast_to(df, t.shape)
+    df = df[()] if df.ndim ==0 else df
+    estimate = m1 - m2
+
+    return TtestResult(t, prob, df=df, alternative=alternative_nums[alternative],
+                       standard_error=denom, estimate=estimate)
+
+
+def _ttest_resampling(x, y, axis, alternative, ttest_kwargs, method, *, xp):
+    def statistic(x, y, axis):
+        x, y = xp.asarray(x), xp.asarray(y)
+        return ttest_ind(x, y, axis=axis, **ttest_kwargs).statistic
+
+    test = (permutation_test if isinstance(method, PermutationMethod)
+            else monte_carlo_test)
+    method = method._asdict()
+
+    if test is monte_carlo_test:
+        # `monte_carlo_test` accepts an `rvs` tuple of callables, not an `rng`
+        # If the user specified an `rng`, replace it with the default callables
+        if (rng := method.pop('rng', None)) is not None:
+            rng = np.random.default_rng(rng)
+            method['rvs'] = rng.normal, rng.normal
+
+    res = test((x, y,), statistic=statistic, axis=axis,
+               alternative=alternative, **method)
+
+    return res.statistic, res.pvalue
+
+
+def _ttest_trim_var_mean_len(a, trim, axis, *, xp):
+    """Variance, mean, and length of winsorized input along specified axis"""
+    # for use with `ttest_ind` when trimming.
+    # further calculations in this test assume that the inputs are sorted.
+    # From [4] Section 1 "Let x_1, ..., x_n be n ordered observations..."
+    a = xp.sort(a, axis=axis)
+
+    # `g` is the number of elements to be replaced on each tail, converted
+    # from a percentage amount of trimming
+    n = a.shape[axis]
+    g = int(n * trim)
+
+    # Calculate the Winsorized variance of the input samples according to
+    # specified `g`
+    v = _calculate_winsorized_variance(a, g, axis, xp=xp)
+
+    # the total number of elements in the trimmed samples
+    n -= 2 * g
+
+    # calculate the g-times trimmed mean, as defined in [4] (1-1)
+    m = trim_mean(a, trim, axis=axis)
+    return v, m, n
+
+
+def _calculate_winsorized_variance(a, g, axis, *, xp):
+    """Calculates g-times winsorized variance along specified axis"""
+    # it is expected that the input `a` is sorted along the correct axis
+    if g == 0:
+        return _var(a, ddof=1, axis=axis, xp=xp)
+    # move the intended axis to the end that way it is easier to manipulate
+    a_win = xp.moveaxis(a, axis, -1)
+
+    # save where NaNs are for later use.
+    nans_indices = xp.any(xp.isnan(a_win), axis=-1)
+
+    # Winsorization and variance calculation are done in one step in [4]
+    # (1-3), but here winsorization is done first; replace the left and
+    # right sides with the repeating value. This can be see in effect in (
+    # 1-3) in [4], where the leftmost and rightmost tails are replaced with
+    # `(g + 1) * x_{g + 1}` on the left and `(g + 1) * x_{n - g}` on the
+    # right. Zero-indexing turns `g + 1` to `g`, and `n - g` to `- g - 1` in
+    # array indexing.
+    a_win = xpx.at(a_win)[..., :g].set(a_win[..., g:g+1])
+    a_win = xpx.at(a_win)[..., -g:].set(a_win[..., -g - 1:-g])
+
+    # Determine the variance. In [4], the degrees of freedom is expressed as
+    # `h - 1`, where `h = n - 2g` (unnumbered equations in Section 1, end of
+    # page 369, beginning of page 370). This is converted to NumPy's format,
+    # `n - ddof` for use with `np.var`. The result is converted to an
+    # array to accommodate indexing later.
+    var_win = xp.asarray(_var(a_win, ddof=(2 * g + 1), axis=-1, xp=xp))
+
+    # with `nan_policy='propagate'`, NaNs may be completely trimmed out
+    # because they were sorted into the tail of the array. In these cases,
+    # replace computed variances with `np.nan`.
+    var_win = xpx.at(var_win)[nans_indices].set(xp.nan)
+    return var_win
+
+
+@xp_capabilities(cpu_only=True, exceptions=["cupy", "jax.numpy"], marray=True,
+                 extra_note=("The ``confidence_interval`` method of the output object"
+                             "is incompatible with JAX JIT."))
+@_axis_nan_policy_factory(pack_TtestResult, default_axis=0, n_samples=2,
+                          result_to_tuple=unpack_TtestResult, n_outputs=6,
+                          paired=True)
+def ttest_rel(a, b, axis=0, nan_policy='propagate', alternative="two-sided"):
+    """Calculate the t-test on TWO RELATED samples of scores, a and b.
+
+    This is a test for the null hypothesis that two related or
+    repeated samples have identical average (expected) values.
+
+    Parameters
+    ----------
+    a, b : array_like
+        The arrays must have the same shape.
+    axis : int or None, optional
+        Axis along which to compute test. If None, compute over the whole
+        arrays, `a`, and `b`.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+        The following options are available (default is 'two-sided'):
+
+        * 'two-sided': the means of the distributions underlying the samples
+          are unequal.
+        * 'less': the mean of the distribution underlying the first sample
+          is less than the mean of the distribution underlying the second
+          sample.
+        * 'greater': the mean of the distribution underlying the first
+          sample is greater than the mean of the distribution underlying
+          the second sample.
+
+        .. versionadded:: 1.6.0
+
+    Returns
+    -------
+    result : `~scipy.stats._result_classes.TtestResult`
+        An object with the following attributes:
+
+        statistic : float or array
+            The t-statistic.
+        pvalue : float or array
+            The p-value associated with the given alternative.
+        df : float or array
+            The number of degrees of freedom used in calculation of the
+            t-statistic; this is one less than the size of the sample
+            (``a.shape[axis]``).
+
+            .. versionadded:: 1.10.0
+
+        The object also has the following method:
+
+        confidence_interval(confidence_level=0.95)
+            Computes a confidence interval around the difference in
+            population means for the given confidence level.
+            The confidence interval is returned in a ``namedtuple`` with
+            fields `low` and `high`.
+
+            .. versionadded:: 1.10.0
+
+    Notes
+    -----
+    Examples for use are scores of the same set of student in
+    different exams, or repeated sampling from the same units. The
+    test measures whether the average score differs significantly
+    across samples (e.g. exams). If we observe a large p-value, for
+    example greater than 0.05 or 0.1 then we cannot reject the null
+    hypothesis of identical average scores. If the p-value is smaller
+    than the threshold, e.g. 1%, 5% or 10%, then we reject the null
+    hypothesis of equal averages. Small p-values are associated with
+    large t-statistics.
+
+    The t-statistic is calculated as ``np.mean(a - b)/se``, where ``se`` is the
+    standard error. Therefore, the t-statistic will be positive when the sample
+    mean of ``a - b`` is greater than zero and negative when the sample mean of
+    ``a - b`` is less than zero.
+
+    References
+    ----------
+    https://en.wikipedia.org/wiki/T-test#Dependent_t-test_for_paired_samples
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+
+    >>> rvs1 = stats.norm.rvs(loc=5, scale=10, size=500, random_state=rng)
+    >>> rvs2 = (stats.norm.rvs(loc=5, scale=10, size=500, random_state=rng)
+    ...         + stats.norm.rvs(scale=0.2, size=500, random_state=rng))
+    >>> stats.ttest_rel(rvs1, rvs2)
+    TtestResult(statistic=-0.4549717054410304, pvalue=0.6493274702088672, df=499)
+    >>> rvs3 = (stats.norm.rvs(loc=8, scale=10, size=500, random_state=rng)
+    ...         + stats.norm.rvs(scale=0.2, size=500, random_state=rng))
+    >>> stats.ttest_rel(rvs1, rvs3)
+    TtestResult(statistic=-5.879467544540889, pvalue=7.540777129099917e-09, df=499)
+
+    """
+    return ttest_1samp(a - b, popmean=0., axis=axis, alternative=alternative,
+                       _no_deco=True)
+
+
+# Map from names to lambda_ values used in power_divergence().
+_power_div_lambda_names = {
+    "pearson": 1,
+    "log-likelihood": 0,
+    "freeman-tukey": -0.5,
+    "mod-log-likelihood": -1,
+    "neyman": -2,
+    "cressie-read": 2/3,
+}
+
+
+Power_divergenceResult = namedtuple('Power_divergenceResult',
+                                    ('statistic', 'pvalue'))
+
+
+def _pd_nsamples(kwargs):
+    return 2 if kwargs.get('f_exp', None) is not None else 1
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(Power_divergenceResult, paired=True, n_samples=_pd_nsamples,
+                          too_small=-1)
+def power_divergence(f_obs, f_exp=None, ddof=0, axis=0, lambda_=None):
+    """Cressie-Read power divergence statistic and goodness of fit test.
+
+    This function tests the null hypothesis that the categorical data
+    has the given frequencies, using the Cressie-Read power divergence
+    statistic.
+
+    Parameters
+    ----------
+    f_obs : array_like
+        Observed frequencies in each category.
+    f_exp : array_like, optional
+        Expected frequencies in each category.  By default the categories are
+        assumed to be equally likely.
+    ddof : int, optional
+        "Delta degrees of freedom": adjustment to the degrees of freedom
+        for the p-value.  The p-value is computed using a chi-squared
+        distribution with ``k - 1 - ddof`` degrees of freedom, where `k`
+        is the number of observed frequencies.  The default value of `ddof`
+        is 0.
+    axis : int or None, optional
+        The axis of the broadcast result of `f_obs` and `f_exp` along which to
+        apply the test.  If axis is None, all values in `f_obs` are treated
+        as a single data set.  Default is 0.
+    lambda_ : float or str, optional
+        The power in the Cressie-Read power divergence statistic.  The default
+        is 1.  For convenience, `lambda_` may be assigned one of the following
+        strings, in which case the corresponding numerical value is used:
+
+        * ``"pearson"`` (value 1)
+            Pearson's chi-squared statistic. In this case, the function is
+            equivalent to `chisquare`.
+        * ``"log-likelihood"`` (value 0)
+            Log-likelihood ratio. Also known as the G-test [3]_.
+        * ``"freeman-tukey"`` (value -1/2)
+            Freeman-Tukey statistic.
+        * ``"mod-log-likelihood"`` (value -1)
+            Modified log-likelihood ratio.
+        * ``"neyman"`` (value -2)
+            Neyman's statistic.
+        * ``"cressie-read"`` (value 2/3)
+            The power recommended in [5]_.
+
+    Returns
+    -------
+    res: Power_divergenceResult
+        An object containing attributes:
+
+        statistic : float or ndarray
+            The Cressie-Read power divergence test statistic.  The value is
+            a float if `axis` is None or if` `f_obs` and `f_exp` are 1-D.
+        pvalue : float or ndarray
+            The p-value of the test.  The value is a float if `ddof` and the
+            return value `stat` are scalars.
+
+    See Also
+    --------
+    chisquare
+
+    Notes
+    -----
+    This test is invalid when the observed or expected frequencies in each
+    category are too small.  A typical rule is that all of the observed
+    and expected frequencies should be at least 5.
+
+    Also, the sum of the observed and expected frequencies must be the same
+    for the test to be valid; `power_divergence` raises an error if the sums
+    do not agree within a relative tolerance of ``eps**0.5``, where ``eps``
+    is the precision of the input dtype.
+
+    When `lambda_` is less than zero, the formula for the statistic involves
+    dividing by `f_obs`, so a warning or error may be generated if any value
+    in `f_obs` is 0.
+
+    Similarly, a warning or error may be generated if any value in `f_exp` is
+    zero when `lambda_` >= 0.
+
+    The default degrees of freedom, k-1, are for the case when no parameters
+    of the distribution are estimated. If p parameters are estimated by
+    efficient maximum likelihood then the correct degrees of freedom are
+    k-1-p. If the parameters are estimated in a different way, then the
+    dof can be between k-1-p and k-1. However, it is also possible that
+    the asymptotic distribution is not a chisquare, in which case this
+    test is not appropriate.
+
+    References
+    ----------
+    .. [1] Lowry, Richard.  "Concepts and Applications of Inferential
+           Statistics". Chapter 8.
+           https://web.archive.org/web/20171015035606/http://faculty.vassar.edu/lowry/ch8pt1.html
+    .. [2] "Chi-squared test", https://en.wikipedia.org/wiki/Chi-squared_test
+    .. [3] "G-test", https://en.wikipedia.org/wiki/G-test
+    .. [4] Sokal, R. R. and Rohlf, F. J. "Biometry: the principles and
+           practice of statistics in biological research", New York: Freeman
+           (1981)
+    .. [5] Cressie, N. and Read, T. R. C., "Multinomial Goodness-of-Fit
+           Tests", J. Royal Stat. Soc. Series B, Vol. 46, No. 3 (1984),
+           pp. 440-464.
+
+    Examples
+    --------
+    (See `chisquare` for more examples.)
+
+    When just `f_obs` is given, it is assumed that the expected frequencies
+    are uniform and given by the mean of the observed frequencies.  Here we
+    perform a G-test (i.e. use the log-likelihood ratio statistic):
+
+    >>> import numpy as np
+    >>> from scipy.stats import power_divergence
+    >>> power_divergence([16, 18, 16, 14, 12, 12], lambda_='log-likelihood')
+    (2.006573162632538, 0.84823476779463769)
+
+    The expected frequencies can be given with the `f_exp` argument:
+
+    >>> power_divergence([16, 18, 16, 14, 12, 12],
+    ...                  f_exp=[16, 16, 16, 16, 16, 8],
+    ...                  lambda_='log-likelihood')
+    (3.3281031458963746, 0.6495419288047497)
+
+    When `f_obs` is 2-D, by default the test is applied to each column.
+
+    >>> obs = np.array([[16, 18, 16, 14, 12, 12], [32, 24, 16, 28, 20, 24]]).T
+    >>> obs.shape
+    (6, 2)
+    >>> power_divergence(obs, lambda_="log-likelihood")
+    (array([ 2.00657316,  6.77634498]), array([ 0.84823477,  0.23781225]))
+
+    By setting ``axis=None``, the test is applied to all data in the array,
+    which is equivalent to applying the test to the flattened array.
+
+    >>> power_divergence(obs, axis=None)
+    (23.31034482758621, 0.015975692534127565)
+    >>> power_divergence(obs.ravel())
+    (23.31034482758621, 0.015975692534127565)
+
+    `ddof` is the change to make to the default degrees of freedom.
+
+    >>> power_divergence([16, 18, 16, 14, 12, 12], ddof=1)
+    (2.0, 0.73575888234288467)
+
+    The calculation of the p-values is done by broadcasting the
+    test statistic with `ddof`.
+
+    >>> power_divergence([16, 18, 16, 14, 12, 12], ddof=[0,1,2])
+    (2.0, array([ 0.84914504,  0.73575888,  0.5724067 ]))
+
+    `f_obs` and `f_exp` are also broadcast.  In the following, `f_obs` has
+    shape (6,) and `f_exp` has shape (2, 6), so the result of broadcasting
+    `f_obs` and `f_exp` has shape (2, 6).  To compute the desired chi-squared
+    statistics, we must use ``axis=1``:
+
+    >>> power_divergence([16, 18, 16, 14, 12, 12],
+    ...                  f_exp=[[16, 16, 16, 16, 16, 8],
+    ...                         [8, 20, 20, 16, 12, 12]],
+    ...                  axis=1)
+    (array([ 3.5 ,  9.25]), array([ 0.62338763,  0.09949846]))
+
+    """
+    return _power_divergence(f_obs, f_exp=f_exp, ddof=ddof, axis=axis, lambda_=lambda_)
+
+
+def _power_divergence(f_obs, f_exp, ddof, axis, lambda_, sum_check=True):
+    xp = array_namespace(f_obs, f_exp, ddof)
+    f_obs, f_exp, ddof = xp_promote(f_obs, f_exp, ddof,
+                                    force_floating=True, xp=xp)
+
+    # Convert the input argument `lambda_` to a numerical value.
+    if isinstance(lambda_, str):
+        if lambda_ not in _power_div_lambda_names:
+            names = repr(list(_power_div_lambda_names.keys()))[1:-1]
+            raise ValueError(f"invalid string for lambda_: {lambda_!r}. "
+                             f"Valid strings are {names}")
+        lambda_ = _power_div_lambda_names[lambda_]
+    elif lambda_ is None:
+        lambda_ = 1
+
+    if f_exp is not None:
+        # not sure why we force to float64, but not going to touch it
+        f_obs_float = xp.asarray(f_obs, dtype=xp.float64)
+        bshape = _broadcast_shapes((f_obs_float.shape, f_exp.shape))
+        f_obs_float = xp.broadcast_to(f_obs_float, bshape)
+        f_exp = xp.broadcast_to(f_exp, bshape)
+        f_obs_float, f_exp = _share_masks(f_obs_float, f_exp, xp=xp)
+
+        if sum_check:
+            dtype_res = xp.result_type(f_obs.dtype, f_exp.dtype)
+            rtol = xp.finfo(dtype_res).eps**0.5  # to pass existing tests
+            with np.errstate(invalid='ignore'):
+                f_obs_sum = xp.sum(f_obs_float, axis=axis, keepdims=True)
+                f_exp_sum = xp.sum(f_exp, axis=axis, keepdims=True)
+                relative_diff = (xp.abs(f_obs_sum - f_exp_sum) /
+                                 xp.minimum(f_obs_sum, f_exp_sum))
+                diff_gt_tol = xp.any(relative_diff > rtol, axis=axis, keepdims=True)
+
+            if not is_lazy_array(diff_gt_tol) and xp.any(diff_gt_tol):
+                msg = (f"For each axis slice, the sum of the observed "
+                       f"frequencies must agree with the sum of the "
+                       f"expected frequencies to a relative tolerance "
+                       f"of {rtol}, but the percent differences are:\n"
+                       f"{relative_diff}")
+                raise ValueError(msg)
+            elif is_lazy_array(diff_gt_tol):
+                diff_gt_tol = xp.broadcast_to(diff_gt_tol, f_obs.shape)
+                f_obs = xpx.at(f_obs)[diff_gt_tol].set(xp.nan)
+
+    else:
+        # Avoid warnings with the edge case of a data set with length 0
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            f_exp = xp.mean(f_obs, axis=axis, keepdims=True)
+
+    # `terms` is the array of terms that are summed along `axis` to create
+    # the test statistic.  We use some specialized code for a few special
+    # cases of lambda_.
+    if lambda_ == 1:
+        # Pearson's chi-squared statistic
+        terms = (f_obs - f_exp)**2 / f_exp
+    elif lambda_ == 0:
+        # Log-likelihood ratio (i.e. G-test)
+        terms = 2.0 * special.xlogy(f_obs, f_obs / f_exp)
+    elif lambda_ == -1:
+        # Modified log-likelihood ratio
+        terms = 2.0 * special.xlogy(f_exp, f_exp / f_obs)
+    else:
+        # General Cressie-Read power divergence.
+        terms = f_obs * ((f_obs / f_exp)**lambda_ - 1)
+        terms /= 0.5 * lambda_ * (lambda_ + 1)
+
+    stat = xp.sum(terms, axis=axis)
+
+    num_obs = xp.asarray(_count_nonmasked(terms, axis), device=xp_device(terms),
+                         dtype=f_obs.dtype)
+
+    df = num_obs - 1 - ddof
+    chi2 = _SimpleChi2(df)
+    pvalue = _get_pvalue(stat, chi2 , alternative='greater', symmetric=False, xp=xp)
+
+    stat = stat[()] if stat.ndim == 0 else stat
+    pvalue = pvalue[()] if pvalue.ndim == 0 else pvalue
+
+    return Power_divergenceResult(stat, pvalue)
+
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(Power_divergenceResult, paired=True, n_samples=_pd_nsamples,
+                          too_small=-1)
+def chisquare(f_obs, f_exp=None, ddof=0, axis=0, *, sum_check=True):
+    """Perform Pearson's chi-squared test.
+
+    Pearson's chi-squared test [1]_ is a goodness-of-fit test for a multinomial
+    distribution with given probabilities; that is, it assesses the null hypothesis
+    that the observed frequencies (counts) are obtained by independent
+    sampling of *N* observations from a categorical distribution with given
+    expected frequencies.
+
+    Parameters
+    ----------
+    f_obs : array_like
+        Observed frequencies in each category.
+    f_exp : array_like, optional
+        Expected frequencies in each category. By default, the categories are
+        assumed to be equally likely.
+    ddof : int, optional
+        "Delta degrees of freedom": adjustment to the degrees of freedom
+        for the p-value.  The p-value is computed using a chi-squared
+        distribution with ``k - 1 - ddof`` degrees of freedom, where ``k``
+        is the number of categories.  The default value of `ddof` is 0.
+    axis : int or None, optional
+        The axis of the broadcast result of `f_obs` and `f_exp` along which to
+        apply the test.  If axis is None, all values in `f_obs` are treated
+        as a single data set.  Default is 0.
+    sum_check : bool, optional
+        Whether to perform a check that ``sum(f_obs) - sum(f_exp) == 0``. If True,
+        (default) raise an error (or, for lazy backends, return NaN) when the relative
+        difference exceeds the square root of the precision of the data type.
+        See Notes for rationale and possible exceptions.
+
+    Returns
+    -------
+    res: Power_divergenceResult
+        An object containing attributes:
+
+        statistic : float or ndarray
+            The chi-squared test statistic.  The value is a float if `axis` is
+            None or `f_obs` and `f_exp` are 1-D.
+        pvalue : float or ndarray
+            The p-value of the test.  The value is a float if `ddof` and the
+            result attribute `statistic` are scalars.
+
+    See Also
+    --------
+    scipy.stats.power_divergence
+    scipy.stats.fisher_exact : Fisher exact test on a 2x2 contingency table.
+    scipy.stats.barnard_exact : An unconditional exact test. An alternative
+        to chi-squared test for small sample sizes.
+    :ref:`hypothesis_chisquare` : Extended example
+
+    Notes
+    -----
+    This test is invalid when the observed or expected frequencies in each
+    category are too small.  A typical rule is that all of the observed
+    and expected frequencies should be at least 5. According to [2]_, the
+    total number of observations is recommended to be greater than 13,
+    otherwise exact tests (such as Barnard's Exact test) should be used
+    because they do not overreject.
+
+    The default degrees of freedom, k-1, are for the case when no parameters
+    of the distribution are estimated. If p parameters are estimated by
+    efficient maximum likelihood then the correct degrees of freedom are
+    k-1-p. If the parameters are estimated in a different way, then the
+    dof can be between k-1-p and k-1. However, it is also possible that
+    the asymptotic distribution is not chi-square, in which case this test
+    is not appropriate.
+
+    For Pearson's chi-squared test, the total observed and expected counts must match
+    for the p-value to accurately reflect the probability of observing such an extreme
+    value of the statistic under the null hypothesis.
+    This function may be used to perform other statistical tests that do not require
+    the total counts to be equal. For instance, to test the null hypothesis that
+    ``f_obs[i]`` is Poisson-distributed with expectation ``f_exp[i]``, set ``ddof=-1``
+    and ``sum_check=False``. This test follows from the fact that a Poisson random
+    variable with mean and variance ``f_exp[i]`` is approximately normal with the
+    same mean and variance; the chi-squared statistic standardizes, squares, and sums
+    the observations; and the sum of ``n`` squared standard normal variables follows
+    the chi-squared distribution with ``n`` degrees of freedom.
+
+    References
+    ----------
+    .. [1] "Pearson's chi-squared test".
+           *Wikipedia*. https://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test
+    .. [2] Pearson, Karl. "On the criterion that a given system of deviations from the probable
+           in the case of a correlated system of variables is such that it can be reasonably
+           supposed to have arisen from random sampling", Philosophical Magazine. Series 5. 50
+           (1900), pp. 157-175.
+
+    Examples
+    --------
+    When only the mandatory `f_obs` argument is given, it is assumed that the
+    expected frequencies are uniform and given by the mean of the observed
+    frequencies:
+
+    >>> import numpy as np
+    >>> from scipy.stats import chisquare
+    >>> chisquare([16, 18, 16, 14, 12, 12])
+    Power_divergenceResult(statistic=2.0, pvalue=0.84914503608460956)
+
+    The optional `f_exp` argument gives the expected frequencies.
+
+    >>> chisquare([16, 18, 16, 14, 12, 12], f_exp=[16, 16, 16, 16, 16, 8])
+    Power_divergenceResult(statistic=3.5, pvalue=0.62338762774958223)
+
+    When `f_obs` is 2-D, by default the test is applied to each column.
+
+    >>> obs = np.array([[16, 18, 16, 14, 12, 12], [32, 24, 16, 28, 20, 24]]).T
+    >>> obs.shape
+    (6, 2)
+    >>> chisquare(obs)
+    Power_divergenceResult(statistic=array([2.        , 6.66666667]), pvalue=array([0.84914504, 0.24663415]))
+
+    By setting ``axis=None``, the test is applied to all data in the array,
+    which is equivalent to applying the test to the flattened array.
+
+    >>> chisquare(obs, axis=None)
+    Power_divergenceResult(statistic=23.31034482758621, pvalue=0.015975692534127565)
+    >>> chisquare(obs.ravel())
+    Power_divergenceResult(statistic=23.310344827586206, pvalue=0.01597569253412758)
+
+    `ddof` is the change to make to the default degrees of freedom.
+
+    >>> chisquare([16, 18, 16, 14, 12, 12], ddof=1)
+    Power_divergenceResult(statistic=2.0, pvalue=0.7357588823428847)
+
+    The calculation of the p-values is done by broadcasting the
+    chi-squared statistic with `ddof`.
+
+    >>> chisquare([16, 18, 16, 14, 12, 12], ddof=[0, 1, 2])
+    Power_divergenceResult(statistic=2.0, pvalue=array([0.84914504, 0.73575888, 0.5724067 ]))
+
+    `f_obs` and `f_exp` are also broadcast.  In the following, `f_obs` has
+    shape (6,) and `f_exp` has shape (2, 6), so the result of broadcasting
+    `f_obs` and `f_exp` has shape (2, 6).  To compute the desired chi-squared
+    statistics, we use ``axis=1``:
+
+    >>> chisquare([16, 18, 16, 14, 12, 12],
+    ...           f_exp=[[16, 16, 16, 16, 16, 8], [8, 20, 20, 16, 12, 12]],
+    ...           axis=1)
+    Power_divergenceResult(statistic=array([3.5 , 9.25]), pvalue=array([0.62338763, 0.09949846]))
+
+    For a more detailed example, see :ref:`hypothesis_chisquare`.
+    """  # noqa: E501
+    return _power_divergence(f_obs, f_exp=f_exp, ddof=ddof, axis=axis,
+                             lambda_="pearson", sum_check=sum_check)
+
+
+KstestResult = _make_tuple_bunch('KstestResult', ['statistic', 'pvalue'],
+                                 ['statistic_location', 'statistic_sign'])
+
+
+def _compute_d(cdfvals, x, sign, xp=None):
+    """Computes D+/D- as used in the Kolmogorov-Smirnov test.
+
+    Vectorized along the last axis.
+
+    Parameters
+    ----------
+    cdfvals : array_like
+        Sorted array of CDF values between 0 and 1
+    x: array_like
+        Sorted array of the stochastic variable itself
+    sign: int
+        Indicates whether to compute D+ (+1) or D- (-1).
+
+    Returns
+    -------
+    D : float or array
+        The maximum distance of the CDF values below/above (D+/D-) Uniform(0, 1).
+    loc_max : float or array
+        The location at which the maximum is reached.
+    """
+    xp = array_namespace(cdfvals, x) if xp is None else xp
+    length = cdfvals.shape[-1]
+    n = _count_nonmasked(cdfvals, axis=-1, keepdims=True)
+    D = (xp.arange(1.0, length + 1, dtype=x.dtype) / n - cdfvals if sign == +1
+         else (cdfvals - xp.arange(0.0, length, dtype=x.dtype) / n))
+    amax = xp.argmax(D, axis=-1, keepdims=True)
+    loc_max = xp.squeeze(xp.take_along_axis(x, amax, axis=-1), axis=-1)
+    D = xp.squeeze(xp.take_along_axis(D, amax, axis=-1), axis=-1)
+    return D[()] if D.ndim == 0 else D, loc_max[()] if loc_max.ndim == 0 else loc_max
+
+
+def _tuple_to_KstestResult(statistic, pvalue,
+                           statistic_location, statistic_sign):
+    return KstestResult(statistic, pvalue,
+                        statistic_location=statistic_location,
+                        statistic_sign=statistic_sign)
+
+
+def _KstestResult_to_tuple(res, _):
+    return *res, res.statistic_location, res.statistic_sign
+
+
+# JAX JIT / Torch GPU / CuPy need null distribution
+@xp_capabilities(cpu_only=True, jax_jit=False, marray=True,
+                 skip_backends=[('dask.array', 'needs take_along_axis')])
+@_axis_nan_policy_factory(_tuple_to_KstestResult, n_samples=1, n_outputs=4,
+                          result_to_tuple=_KstestResult_to_tuple)
+@_rename_parameter("mode", "method")
+def ks_1samp(x, cdf, args=(), alternative='two-sided', method='auto', *, axis=0):
+    """
+    Performs the one-sample Kolmogorov-Smirnov test for goodness of fit.
+
+    This test compares the underlying distribution F(x) of a sample
+    against a given continuous distribution G(x). See Notes for a description
+    of the available null and alternative hypotheses.
+
+    Parameters
+    ----------
+    x : array_like
+        a 1-D array of observations of iid random variables.
+    cdf : callable
+        callable used to calculate the cdf.
+    args : tuple, sequence, optional
+        Distribution parameters, used with `cdf`.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the null and alternative hypotheses. Default is 'two-sided'.
+        Please see explanations in the Notes below.
+    method : {'auto', 'exact', 'approx', 'asymp'}, optional
+        Defines the distribution used for calculating the p-value.
+        The following options are available (default is 'auto'):
+
+        * 'auto' : selects one of the other options.
+        * 'exact' : uses the exact distribution of test statistic.
+        * 'approx' : approximates the two-sided probability with twice
+          the one-sided probability
+        * 'asymp': uses asymptotic distribution of test statistic
+
+    axis : int or tuple of ints, default: 0
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    res: KstestResult
+        An object containing attributes:
+
+        statistic : float
+            KS test statistic, either D+, D-, or D (the maximum of the two)
+        pvalue : float
+            One-tailed or two-tailed p-value.
+        statistic_location : float
+            Value of `x` corresponding with the KS statistic; i.e., the
+            distance between the empirical distribution function and the
+            hypothesized cumulative distribution function is measured at this
+            observation.
+        statistic_sign : int
+            +1 if the KS statistic is the maximum positive difference between
+            the empirical distribution function and the hypothesized cumulative
+            distribution function (D+); -1 if the KS statistic is the maximum
+            negative difference (D-).
+
+
+    See Also
+    --------
+    ks_2samp, kstest
+
+    Notes
+    -----
+    There are three options for the null and corresponding alternative
+    hypothesis that can be selected using the `alternative` parameter.
+
+    - `two-sided`: The null hypothesis is that the two distributions are
+      identical, F(x)=G(x) for all x; the alternative is that they are not
+      identical.
+
+    - `less`: The null hypothesis is that F(x) >= G(x) for all x; the
+      alternative is that F(x) < G(x) for at least one x.
+
+    - `greater`: The null hypothesis is that F(x) <= G(x) for all x; the
+      alternative is that F(x) > G(x) for at least one x.
+
+    Note that the alternative hypotheses describe the *CDFs* of the
+    underlying distributions, not the observed values. For example,
+    suppose x1 ~ F and x2 ~ G. If F(x) > G(x) for all x, the values in
+    x1 tend to be less than those in x2.
+
+    Examples
+    --------
+    Suppose we wish to test the null hypothesis that a sample is distributed
+    according to the standard normal.
+    We choose a confidence level of 95%; that is, we will reject the null
+    hypothesis in favor of the alternative if the p-value is less than 0.05.
+
+    When testing uniformly distributed data, we would expect the
+    null hypothesis to be rejected.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+    >>> stats.ks_1samp(stats.uniform.rvs(size=100, random_state=rng),
+    ...                stats.norm.cdf)
+    KstestResult(statistic=0.5001899973268688,
+                 pvalue=1.1616392184763533e-23,
+                 statistic_location=0.00047625268963724654,
+                 statistic_sign=-1)
+
+    Indeed, the p-value is lower than our threshold of 0.05, so we reject the
+    null hypothesis in favor of the default "two-sided" alternative: the data
+    are *not* distributed according to the standard normal.
+
+    When testing random variates from the standard normal distribution, we
+    expect the data to be consistent with the null hypothesis most of the time.
+
+    >>> x = stats.norm.rvs(size=100, random_state=rng)
+    >>> stats.ks_1samp(x, stats.norm.cdf)
+    KstestResult(statistic=0.05345882212970396,
+                 pvalue=0.9227159037744717,
+                 statistic_location=-1.2451343873745018,
+                 statistic_sign=1)
+
+    As expected, the p-value of 0.92 is not below our threshold of 0.05, so
+    we cannot reject the null hypothesis.
+
+    Suppose, however, that the random variates are distributed according to
+    a normal distribution that is shifted toward greater values. In this case,
+    the cumulative density function (CDF) of the underlying distribution tends
+    to be *less* than the CDF of the standard normal. Therefore, we would
+    expect the null hypothesis to be rejected with ``alternative='less'``:
+
+    >>> x = stats.norm.rvs(size=100, loc=0.5, random_state=rng)
+    >>> stats.ks_1samp(x, stats.norm.cdf, alternative='less')
+    KstestResult(statistic=0.17482387821055168,
+                 pvalue=0.001913921057766743,
+                 statistic_location=0.3713830565352756,
+                 statistic_sign=-1)
+
+    and indeed, with p-value smaller than our threshold, we reject the null
+    hypothesis in favor of the alternative.
+
+    """
+    # `_axis_nan_policy` decorator ensures `axis=-1`
+    xp = array_namespace(x)
+    mode = method
+    if mode not in ['auto', 'exact', 'approx', 'asymp']:
+        raise ValueError(
+            f"Invalid value for method: {mode!r}. "
+            f"Must be one of 'auto', 'exact', 'approx', 'asymp'."
+        )
+
+    alternative = {'t': 'two-sided', 'g': 'greater', 'l': 'less'}.get(
+        alternative.lower()[0], alternative)
+    if alternative not in ['two-sided', 'greater', 'less']:
+        raise ValueError(f"Unexpected value {alternative=}")
+
+    x = xp.sort(x, axis=-1)
+    x = xp_promote(x, force_floating=True, xp=xp)
+    N = _count_nonmasked(x, axis=-1, xp=xp)
+    cdfvals = _masked_apply(cdf, args=(x, *args), xp=xp)
+
+    ones = xp.ones(x.shape[:-1], dtype=xp.int8)
+    ones = ones[()] if ones.ndim == 0 else ones
+
+    if alternative == 'greater':
+        Dplus, d_location = _compute_d(cdfvals, x, +1)
+        pvalue = _masked_apply(distributions.ksone.sf, args=(Dplus, N), xp=xp)
+        pvalue = xp.asarray(pvalue, dtype=x.dtype)
+        pvalue = pvalue[()] if pvalue.ndim == 0 else pvalue
+        Dplus = xp.asarray(Dplus) if is_marray(xp) else Dplus
+        return KstestResult(Dplus, pvalue,
+                            statistic_location=d_location,
+                            statistic_sign=ones)
+
+    if alternative == 'less':
+        Dminus, d_location = _compute_d(cdfvals, x, -1)
+        pvalue = _masked_apply(distributions.ksone.sf, args=(Dminus, N), xp=xp)
+        pvalue = xp.asarray(pvalue, dtype=x.dtype)
+        pvalue = pvalue[()] if pvalue.ndim == 0 else pvalue
+        Dminus = xp.asarray(Dminus) if is_marray(xp) else Dminus
+        return KstestResult(Dminus, pvalue,
+                            statistic_location=d_location,
+                            statistic_sign=-ones)
+
+    # alternative == 'two-sided':
+    Dplus, dplus_location = _compute_d(cdfvals, x, +1)
+    Dminus, dminus_location = _compute_d(cdfvals, x, -1)
+    i_plus = Dplus > Dminus
+    D = xp.where(i_plus, Dplus, Dminus)
+    d_location = xp.where(i_plus, dplus_location, dminus_location)
+    d_sign = xp.where(i_plus, ones, -ones)
+    if D.ndim == 0:
+        D, d_location, d_sign = D[()], d_location[()], d_sign[()]
+
+    if mode == 'auto':  # Always select exact
+        mode = 'exact'
+    if mode == 'exact':
+        prob = _masked_apply(distributions.kstwo.sf, args=(D, N), xp=xp)
+    elif mode == 'asymp':
+        prob = _masked_apply(distributions.kstwobign.sf, args=(D * N**0.5,), xp=xp)
+    else:
+        # mode == 'approx'
+        prob = 2 * _masked_apply(distributions.ksone.sf, args=(D, N), xp=xp)
+    prob = xp.clip(xp.asarray(prob, dtype=x.dtype), 0., 1.)
+    return KstestResult(D, prob,
+                        statistic_location=d_location,
+                        statistic_sign=d_sign)
+
+
+Ks_2sampResult = KstestResult
+
+
+def _compute_prob_outside_square(n, h):
+    """
+    Compute the proportion of paths that pass outside the two diagonal lines.
+
+    Parameters
+    ----------
+    n : int
+        n > 0
+    h : int
+        0 <= h <= n
+
+    Returns
+    -------
+    p : float
+        The proportion of paths that pass outside the lines x-y = +/-h.
+
+    """
+    # Compute Pr(D_{n,n} >= h/n)
+    # Prob = 2 * ( binom(2n, n-h) - binom(2n, n-2a) + binom(2n, n-3a) - ... )
+    # / binom(2n, n)
+    # This formulation exhibits subtractive cancellation.
+    # Instead divide each term by binom(2n, n), then factor common terms
+    # and use a Horner-like algorithm
+    # P = 2 * A0 * (1 - A1*(1 - A2*(1 - A3*(1 - A4*(...)))))
+
+    P = 0.0
+    k = int(np.floor(n / h))
+    while k >= 0:
+        p1 = 1.0
+        # Each of the Ai terms has numerator and denominator with
+        # h simple terms.
+        for j in range(h):
+            p1 = (n - k * h - j) * p1 / (n + k * h + j + 1)
+        P = p1 * (1.0 - P)
+        k -= 1
+    return 2 * P
+
+
+def _count_paths_outside_method(m, n, g, h):
+    """Count the number of paths that pass outside the specified diagonal.
+
+    Parameters
+    ----------
+    m : int
+        m > 0
+    n : int
+        n > 0
+    g : int
+        g is greatest common divisor of m and n
+    h : int
+        0 <= h <= lcm(m,n)
+
+    Returns
+    -------
+    p : float
+        The number of paths that go low.
+        The calculation may overflow - check for a finite answer.
+
+    Notes
+    -----
+    Count the integer lattice paths from (0, 0) to (m, n), which at some
+    point (x, y) along the path, satisfy:
+      m*y <= n*x - h*g
+    The paths make steps of size +1 in either positive x or positive y
+    directions.
+
+    We generally follow Hodges' treatment of Drion/Gnedenko/Korolyuk.
+    Hodges, J.L. Jr.,
+    "The Significance Probability of the Smirnov Two-Sample Test,"
+    Arkiv fiur Matematik, 3, No. 43 (1958), 469-86.
+
+    """
+    # Compute #paths which stay lower than x/m-y/n = h/lcm(m,n)
+    # B(x, y) = #{paths from (0,0) to (x,y) without
+    #             previously crossing the boundary}
+    #         = binom(x, y) - #{paths which already reached the boundary}
+    # Multiply by the number of path extensions going from (x, y) to (m, n)
+    # Sum.
+
+    # Probability is symmetrical in m, n.  Computation below assumes m >= n.
+    if m < n:
+        m, n = n, m
+    mg = m // g
+    ng = n // g
+
+    # Not every x needs to be considered.
+    # xj holds the list of x values to be checked.
+    # Wherever n*x/m + ng*h crosses an integer
+    lxj = n + (mg-h)//mg
+    xj = [(h + mg * j + ng-1)//ng for j in range(lxj)]
+    # B is an array just holding a few values of B(x,y), the ones needed.
+    # B[j] == B(x_j, j)
+    if lxj == 0:
+        return special.binom(m + n, n)
+    B = np.zeros(lxj)
+    B[0] = 1
+    # Compute the B(x, y) terms
+    for j in range(1, lxj):
+        Bj = special.binom(xj[j] + j, j)
+        for i in range(j):
+            bin = special.binom(xj[j] - xj[i] + j - i, j-i)
+            Bj -= bin * B[i]
+        B[j] = Bj
+    # Compute the number of path extensions...
+    num_paths = 0
+    for j in range(lxj):
+        bin = special.binom((m-xj[j]) + (n - j), n-j)
+        term = B[j] * bin
+        num_paths += term
+    return num_paths
+
+
+def _attempt_exact_2kssamp(n1, n2, g, d, alternative):
+    """Attempts to compute the exact 2sample probability.
+
+    n1, n2 are the sample sizes
+    g is the gcd(n1, n2)
+    d is the computed max difference in ECDFs
+
+    Returns (success, d, probability)
+    """
+    lcm = (n1 // g) * n2
+    h = int(np.round(d * lcm))
+    d = h * 1.0 / lcm
+    if h == 0:
+        return True, d, 1.0
+    saw_fp_error, prob = False, np.nan
+    try:
+        with np.errstate(invalid="raise", over="raise"):
+            if alternative == 'two-sided':
+                if n1 == n2:
+                    prob = _compute_prob_outside_square(n1, h)
+                else:
+                    prob = _compute_outer_prob_inside_method(n1, n2, g, h)
+            else:
+                if n1 == n2:
+                    # prob = binom(2n, n-h) / binom(2n, n)
+                    # Evaluating in that form incurs roundoff errors
+                    # from special.binom. Instead calculate directly
+                    jrange = np.arange(h)
+                    prob = np.prod((n1 - jrange) / (n1 + jrange + 1.0))
+                else:
+                    with np.errstate(over='raise'):
+                        num_paths = _count_paths_outside_method(n1, n2, g, h)
+                    bin = special.binom(n1 + n2, n1)
+                    if num_paths > bin or np.isinf(bin):
+                        saw_fp_error = True
+                    else:
+                        prob = num_paths / bin
+
+    except (FloatingPointError, OverflowError):
+        saw_fp_error = True
+
+    if saw_fp_error:
+        return False, d, np.nan
+    if not (0 <= prob <= 1):
+        return False, d, prob
+    return True, d, prob
+
+
+@xp_capabilities(skip_backends=[('dask.array', 'no rankdata')],
+                 jax_jit=False, cpu_only=True,  # null distribution is NumPy-only
+                 marray=True)
+@_axis_nan_policy_factory(_tuple_to_KstestResult, n_samples=2, n_outputs=4,
+                          result_to_tuple=_KstestResult_to_tuple)
+@_rename_parameter("mode", "method")
+def ks_2samp(data1, data2, alternative='two-sided', method='auto', *, axis=0):
+    """
+    Performs the two-sample Kolmogorov-Smirnov test for goodness of fit.
+
+    This test compares the underlying continuous distributions F(x) and G(x)
+    of two independent samples.  See Notes for a description of the available
+    null and alternative hypotheses.
+
+    Parameters
+    ----------
+    data1, data2 : array_like, 1-Dimensional
+        Two arrays of sample observations assumed to be drawn from a continuous
+        distribution, sample sizes can be different.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the null and alternative hypotheses. Default is 'two-sided'.
+        Please see explanations in the Notes below.
+    method : {'auto', 'exact', 'asymp'}, optional
+        Defines the method used for calculating the p-value.
+        The following options are available (default is 'auto'):
+
+        * 'auto' : use 'exact' for small size arrays, 'asymp' for large
+        * 'exact' : use exact distribution of test statistic
+        * 'asymp' : use asymptotic distribution of test statistic
+
+    axis : int or tuple of ints, default: 0
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    res: KstestResult
+        An object containing attributes:
+
+        statistic : float
+            KS test statistic.
+        pvalue : float
+            One-tailed or two-tailed p-value.
+        statistic_location : float
+            Value from `data1` or `data2` corresponding with the KS statistic;
+            i.e., the distance between the empirical distribution functions is
+            measured at this observation.
+        statistic_sign : int
+            +1 if the empirical distribution function of `data1` exceeds
+            the empirical distribution function of `data2` at
+            `statistic_location`, otherwise -1.
+
+    See Also
+    --------
+    kstest, ks_1samp, epps_singleton_2samp, anderson_ksamp
+
+    Notes
+    -----
+    There are three options for the null and corresponding alternative
+    hypothesis that can be selected using the `alternative` parameter.
+
+    - `less`: The null hypothesis is that F(x) >= G(x) for all x; the
+      alternative is that F(x) < G(x) for at least one x. The statistic
+      is the magnitude of the minimum (most negative) difference between the
+      empirical distribution functions of the samples.
+
+    - `greater`: The null hypothesis is that F(x) <= G(x) for all x; the
+      alternative is that F(x) > G(x) for at least one x. The statistic
+      is the maximum (most positive) difference between the empirical
+      distribution functions of the samples.
+
+    - `two-sided`: The null hypothesis is that the two distributions are
+      identical, F(x)=G(x) for all x; the alternative is that they are not
+      identical. The statistic is the maximum absolute difference between the
+      empirical distribution functions of the samples.
+
+    Note that the alternative hypotheses describe the *CDFs* of the
+    underlying distributions, not the observed values of the data. For example,
+    suppose x1 ~ F and x2 ~ G. If F(x) > G(x) for all x, the values in
+    x1 tend to be less than those in x2.
+
+    If the KS statistic is large, then the p-value will be small, and this may
+    be taken as evidence against the null hypothesis in favor of the
+    alternative.
+
+    If ``method='exact'``, `ks_2samp` attempts to compute an exact p-value,
+    that is, the probability under the null hypothesis of obtaining a test
+    statistic value as extreme as the value computed from the data.
+    If ``method='asymp'``, the asymptotic Kolmogorov-Smirnov distribution is
+    used to compute an approximate p-value.
+    If ``method='auto'``, an exact p-value computation is attempted if both
+    sample sizes are less than 10000; otherwise, the asymptotic method is used.
+    In any case, if an exact p-value calculation is attempted and fails, a
+    warning will be emitted, and the asymptotic p-value will be returned.
+
+    The 'two-sided' 'exact' computation computes the complementary probability
+    and then subtracts from 1.  As such, the minimum probability it can return
+    is about 1e-16.  While the algorithm itself is exact, numerical
+    errors may accumulate for large sample sizes.   It is most suited to
+    situations in which one of the sample sizes is only a few thousand.
+
+    We generally follow Hodges' treatment of Drion/Gnedenko/Korolyuk [1]_.
+
+    References
+    ----------
+    .. [1] Hodges, J.L. Jr.,  "The Significance Probability of the Smirnov
+           Two-Sample Test," Arkiv fiur Matematik, 3, No. 43 (1958), 469-486.
+
+    Examples
+    --------
+    Suppose we wish to test the null hypothesis that two samples were drawn
+    from the same distribution.
+    We choose a confidence level of 95%; that is, we will reject the null
+    hypothesis in favor of the alternative if the p-value is less than 0.05.
+
+    If the first sample were drawn from a uniform distribution and the second
+    were drawn from the standard normal, we would expect the null hypothesis
+    to be rejected.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+    >>> sample1 = stats.uniform.rvs(size=100, random_state=rng)
+    >>> sample2 = stats.norm.rvs(size=110, random_state=rng)
+    >>> stats.ks_2samp(sample1, sample2)
+    KstestResult(statistic=0.5454545454545454,
+                 pvalue=7.37417839555191e-15,
+                 statistic_location=-0.014071496412861274,
+                 statistic_sign=-1)
+
+
+    Indeed, the p-value is lower than our threshold of 0.05, so we reject the
+    null hypothesis in favor of the default "two-sided" alternative: the data
+    were *not* drawn from the same distribution.
+
+    When both samples are drawn from the same distribution, we expect the data
+    to be consistent with the null hypothesis most of the time.
+
+    >>> sample1 = stats.norm.rvs(size=105, random_state=rng)
+    >>> sample2 = stats.norm.rvs(size=95, random_state=rng)
+    >>> stats.ks_2samp(sample1, sample2)
+    KstestResult(statistic=0.10927318295739348,
+                 pvalue=0.5438289009927495,
+                 statistic_location=-0.1670157701848795,
+                 statistic_sign=-1)
+
+    As expected, the p-value of 0.54 is not below our threshold of 0.05, so
+    we cannot reject the null hypothesis.
+
+    Suppose, however, that the first sample were drawn from
+    a normal distribution shifted toward greater values. In this case,
+    the cumulative density function (CDF) of the underlying distribution tends
+    to be *less* than the CDF underlying the second sample. Therefore, we would
+    expect the null hypothesis to be rejected with ``alternative='less'``:
+
+    >>> sample1 = stats.norm.rvs(size=105, loc=0.5, random_state=rng)
+    >>> stats.ks_2samp(sample1, sample2, alternative='less')
+    KstestResult(statistic=0.4055137844611529,
+                 pvalue=3.5474563068855554e-08,
+                 statistic_location=-0.13249370614972575,
+                 statistic_sign=-1)
+
+    and indeed, with p-value smaller than our threshold, we reject the null
+    hypothesis in favor of the alternative.
+
+    """
+    # because of the _axis_nan_policy decorator, we can assume the arrays
+    # are broadcasted and `axis=-1`
+    mode = method
+
+    if mode not in ['auto', 'exact', 'asymp']:
+        raise ValueError(f'Invalid value for mode: {mode}')
+    alternative = {'t': 'two-sided', 'g': 'greater', 'l': 'less'}.get(
+        alternative.lower()[0], alternative)
+    if alternative not in ['two-sided', 'less', 'greater']:
+        raise ValueError(f'Invalid value for alternative: {alternative}')
+    MAX_AUTO_N = 10000  # 'auto' will attempt to be exact if n1,n2 <= MAX_AUTO_N
+
+    xp = array_namespace(data1, data2)
+    data1 = xp.sort(data1, axis=-1)
+    data2 = xp.sort(data2, axis=-1)
+    n1 = data1.shape[-1]
+    n2 = data2.shape[-1]
+    if min(n1, n2) == 0:
+        raise ValueError('Data passed to ks_2samp must not be empty')
+
+    data_all = xp.concat((data1, data2), axis=-1)
+    batch_shape = data_all.shape[:-1]
+    dtype = xp_result_type(data1, data2, force_floating=True, xp=xp)
+
+    if is_marray(xp):
+        # Previously, we used this algorithm for all backends:
+        n1 = xp.astype(_count_nonmasked(data1, axis=-1), dtype)
+        n2 = xp.astype(_count_nonmasked(data2, axis=-1), dtype)
+        cdf1 = xp.astype(_xp_searchsorted(data1, data_all, side='right'), dtype)
+        cdf2 = xp.astype(_xp_searchsorted(data2, data_all, side='right'), dtype)
+        cddiffs = cdf1/n1[..., xp.newaxis] - cdf2/n2[..., xp.newaxis]
+        # but the switch from `xp.searchsorted` to `_xp_searchsorted` would come with a
+        # pretty steep performance hit, so the algorithm below is faster.
+        # When `xp.searchsorted` is vectorized, we can revert.
+    else:
+        n = n1 + n2
+
+        # We want the ECDF of each sample evaluated at *all* the points in the pooled
+        # sample. The values each ECDF can assume are given by:
+        cdf1_vals = xp.broadcast_to(xp.linspace(0, 1, n1 + 1, dtype=dtype),
+                                    batch_shape + (n1 + 1,))
+        cdf2_vals = xp.broadcast_to(xp.linspace(0, 1, n2 + 1, dtype=dtype),
+                                    batch_shape + (n2 + 1,))
+        # Now we "just" need to know how many times each of these values *will* be
+        # assumed when we evaluate the ECDFs at all points in the pooled sample.
+        # These counts are given by the differences between consecutive ("min" or "max")
+        # ranks corresponding with the observations in the (sorted) samples.
+        ranks, data_all, _ = _rankdata(data_all, method='min', return_sorted=True)
+        ranks = xp.astype(ranks, xp.asarray(1).dtype)  # default int type
+        one = xp.ones((*ranks.shape[:-1], 1), dtype=ranks.dtype,
+                      device=xp_device(ranks))
+        cdf1_counts = xp.diff(ranks[..., :n1], prepend=one, append=n + one, axis=-1)
+        cdf2_counts = xp.diff(ranks[..., -n2:], prepend=one, append=n + one, axis=-1)
+        # Repeat isn't vectorized - in general, this would produce a ragged array.
+        # However, in our case, the sum of repeats for each slice is the same, so we can
+        # do a vectorized repeat by raveling, repeating, then restoring the shape.
+        cdf1 = xp.repeat(xp_ravel(cdf1_vals), xp_ravel(cdf1_counts), axis=-1)
+        cdf2 = xp.repeat(xp_ravel(cdf2_vals), xp_ravel(cdf2_counts), axis=-1)
+        cddiffs = xp.reshape(cdf1 - cdf2, ranks.shape[:-1] + (-1,))
+
+    # Identify the location of the statistic
+    argminS = xp.argmin(cddiffs, axis=-1, keepdims=True)
+    argmaxS = xp.argmax(cddiffs, axis=-1, keepdims=True)
+    loc_minS = xp.squeeze(xp.take_along_axis(data_all, argminS, axis=-1), axis=-1)
+    loc_maxS = xp.squeeze(xp.take_along_axis(data_all, argmaxS, axis=-1), axis=-1)
+
+    # Ensure sign of minS is not negative.
+    minS = -xp.squeeze(xp.take_along_axis(cddiffs, argminS, axis=-1), axis=-1)
+    maxS = xp.squeeze(xp.take_along_axis(cddiffs, argmaxS, axis=-1), axis=-1)
+    minS = xp.clip(minS, 0., 1.)
+
+    if alternative == 'less':
+        selector = xp.ones(minS.shape, dtype=xp.bool)
+    elif alternative == 'two-sided':
+        selector = minS > maxS
+    else:
+        selector = xp.zeros(minS.shape, dtype=xp.bool)
+
+    d = xp.where(selector, minS, maxS)
+    d_location = xp.where(selector, loc_minS, loc_maxS)
+    one = xp.asarray(1, dtype=xp.int8)
+    d_sign = xp.where(selector, -one, one)
+
+    if is_marray(xp):
+        d = d.data  # converted to NumPy below
+        n1, n2 = np.asarray(n1.data, dtype=int), np.asarray(n2.data, dtype=int)
+    prob = _ks_2samp_prob(np.asarray(d), n1, n2, mode, MAX_AUTO_N, alternative)
+    dtype = xp_result_type(data1, data2, force_floating=True, xp=xp)
+    prob = xp.asarray(prob, dtype=dtype)
+    d = xp.asarray(d, dtype=dtype)
+    if d.ndim == 0:
+        d, prob, d_location, d_sign = d[()], prob[()], d_location[()], d_sign[()]
+    return KstestResult(d, prob, statistic_location=d_location, statistic_sign=d_sign)
+
+
+@np.vectorize(excluded={3, 4, 5}, otypes=[np.float64])
+def _ks_2samp_prob(d, n1, n2, mode, MAX_AUTO_N, alternative):
+    # math.gcd used to return an `int`, but now that these return arrays, NumPy can
+    # return an int32. Unless we want the pythranized functions to be compiled for
+    # int32, this needs to produce an int64.
+    g = np.gcd(n1, n2).astype(np.int64)
+    n1g = n1 // g
+    n2g = n2 // g
+    prob = -np.inf
+    if mode == 'auto':
+        mode = 'exact' if max(n1, n2) <= MAX_AUTO_N else 'asymp'
+    elif mode == 'exact':
+        # If lcm(n1, n2) is too big, switch from exact to asymp
+        if n1g >= np.iinfo(np.int32).max / n2g:
+            mode = 'asymp'
+            warnings.warn(
+                f"Exact ks_2samp calculation not possible with samples sizes "
+                f"{n1} and {n2}. Switching to 'asymp'.", RuntimeWarning,
+                stacklevel=3)
+
+    if mode == 'exact':
+        success, d, prob = _attempt_exact_2kssamp(n1, n2, g, d, alternative)
+        if not success:
+            mode = 'asymp'
+            warnings.warn(f"ks_2samp: Exact calculation unsuccessful. "
+                          f"Switching to method={mode}.", RuntimeWarning,
+                          stacklevel=3)
+
+    if mode == 'asymp':
+        # The product n1*n2 is large.  Use Smirnov's asymptotic formula.
+        # Ensure float to avoid overflow in multiplication
+        # sorted because the one-sided formula is not symmetric in n1, n2
+        m, n = sorted([float(n1), float(n2)], reverse=True)
+        en = m * n / (m + n)
+        if alternative == 'two-sided':
+            prob = distributions.kstwo.sf(d, np.round(en))
+        else:
+            z = np.sqrt(en) * d
+            # Use Hodges' suggested approximation Eqn 5.3
+            # Requires m to be the larger of (n1, n2)
+            expt = -2 * z**2 - 2 * z * (m + 2*n)/np.sqrt(m*n*(m+n))/3.0
+            prob = np.exp(expt)
+
+    prob = np.clip(prob, 0, 1)
+    return prob
+
+
+def _parse_kstest_args(data1, data2, args, N):
+    # kstest allows many different variations of arguments.
+    # Pull out the parsing into a separate function
+    # (xvals, yvals, )  # 2sample
+    # (xvals, cdf function,..)
+    # (xvals, name of distribution, ...)
+    # (name of distribution, name of distribution, ...)
+
+    # Returns xvals, yvals, cdf
+    # where cdf is a cdf function, or None
+    # and yvals is either an array_like of values, or None
+    # and xvals is array_like.
+    rvsfunc, cdf = None, None
+    if isinstance(data1, str):
+        rvsfunc = getattr(distributions, data1).rvs
+    elif callable(data1):
+        rvsfunc = data1
+
+    if isinstance(data2, str):
+        special_distributions = {'norm': special.ndtr}
+        cdf = special_distributions.get(data2, getattr(distributions, data2).cdf)
+        data2 = None
+    elif callable(data2):
+        cdf = data2
+        data2 = None
+
+    xp = array_namespace(data1, data2, *args)
+    data1 = xp.sort(rvsfunc(*args, size=N) if rvsfunc else data1)
+    return data1, data2, cdf
+
+
+def _kstest_n_samples(kwargs):
+    cdf = kwargs['cdf']
+    return 1 if (isinstance(cdf, str) or callable(cdf)) else 2
+
+
+@xp_capabilities(skip_backends=[('dask.array', 'no rankdata')],
+                 jax_jit=False, cpu_only=True,  # see ks_1samp/ks_2samp
+                 marray=True)
+@_axis_nan_policy_factory(_tuple_to_KstestResult, n_samples=_kstest_n_samples,
+                          n_outputs=4, result_to_tuple=_KstestResult_to_tuple)
+@_rename_parameter("mode", "method")
+def kstest(rvs, cdf, args=(), N=20, alternative='two-sided', method='auto', *,
+           axis=0):
+    """
+    Performs the (one-sample or two-sample) Kolmogorov-Smirnov test for
+    goodness of fit.
+
+    The one-sample test compares the underlying distribution F(x) of a sample
+    against a given distribution G(x). The two-sample test compares the
+    underlying distributions of two independent samples. Both tests are valid
+    only for continuous distributions.
+
+    Parameters
+    ----------
+    rvs : str, array_like, or callable
+        If an array, it should be a 1-D array of observations of random
+        variables.
+        If a callable, it should be a function to generate random variables;
+        it is required to have a keyword argument `size`.
+        If a string, it should be the name of a distribution in `scipy.stats`,
+        which will be used to generate random variables.
+    cdf : str, array_like or callable
+        If array_like, it should be a 1-D array of observations of random
+        variables, and the two-sample test is performed
+        (and rvs must be array_like).
+        If a callable, that callable is used to calculate the cdf.
+        If a string, it should be the name of a distribution in `scipy.stats`,
+        which will be used as the cdf function.
+    args : tuple, sequence, optional
+        Distribution parameters, used if `rvs` or `cdf` are strings or
+        callables.
+    N : int, optional
+        Sample size if `rvs` is string or callable.  Default is 20.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the null and alternative hypotheses. Default is 'two-sided'.
+        Please see explanations in the Notes below.
+    method : {'auto', 'exact', 'approx', 'asymp'}, optional
+        Defines the distribution used for calculating the p-value.
+        The following options are available (default is 'auto'):
+
+        * 'auto' : selects one of the other options.
+        * 'exact' : uses the exact distribution of test statistic.
+        * 'approx' : approximates the two-sided probability with twice the
+          one-sided probability
+        * 'asymp': uses asymptotic distribution of test statistic
+
+    Returns
+    -------
+    res: KstestResult
+        An object containing attributes:
+
+        statistic : float
+            KS test statistic, either D+, D-, or D (the maximum of the two)
+        pvalue : float
+            One-tailed or two-tailed p-value.
+        statistic_location : float
+            In a one-sample test, this is the value of `rvs`
+            corresponding with the KS statistic; i.e., the distance between
+            the empirical distribution function and the hypothesized cumulative
+            distribution function is measured at this observation.
+
+            In a two-sample test, this is the value from `rvs` or `cdf`
+            corresponding with the KS statistic; i.e., the distance between
+            the empirical distribution functions is measured at this
+            observation.
+        statistic_sign : int
+            In a one-sample test, this is +1 if the KS statistic is the
+            maximum positive difference between the empirical distribution
+            function and the hypothesized cumulative distribution function
+            (D+); it is -1 if the KS statistic is the maximum negative
+            difference (D-).
+
+            In a two-sample test, this is +1 if the empirical distribution
+            function of `rvs` exceeds the empirical distribution
+            function of `cdf` at `statistic_location`, otherwise -1.
+
+    See Also
+    --------
+    ks_1samp, ks_2samp
+
+    Notes
+    -----
+    There are three options for the null and corresponding alternative
+    hypothesis that can be selected using the `alternative` parameter.
+
+    - `two-sided`: The null hypothesis is that the two distributions are
+      identical, F(x)=G(x) for all x; the alternative is that they are not
+      identical.
+
+    - `less`: The null hypothesis is that F(x) >= G(x) for all x; the
+      alternative is that F(x) < G(x) for at least one x.
+
+    - `greater`: The null hypothesis is that F(x) <= G(x) for all x; the
+      alternative is that F(x) > G(x) for at least one x.
+
+    Note that the alternative hypotheses describe the *CDFs* of the
+    underlying distributions, not the observed values. For example,
+    suppose x1 ~ F and x2 ~ G. If F(x) > G(x) for all x, the values in
+    x1 tend to be less than those in x2.
+
+
+    Examples
+    --------
+    Suppose we wish to test the null hypothesis that a sample is distributed
+    according to the standard normal.
+    We choose a confidence level of 95%; that is, we will reject the null
+    hypothesis in favor of the alternative if the p-value is less than 0.05.
+
+    When testing uniformly distributed data, we would expect the
+    null hypothesis to be rejected.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+    >>> stats.kstest(stats.uniform.rvs(size=100, random_state=rng),
+    ...              stats.norm.cdf)
+    KstestResult(statistic=0.5001899973268688,
+                 pvalue=1.1616392184763533e-23,
+                 statistic_location=0.00047625268963724654,
+                 statistic_sign=-1)
+
+    Indeed, the p-value is lower than our threshold of 0.05, so we reject the
+    null hypothesis in favor of the default "two-sided" alternative: the data
+    are *not* distributed according to the standard normal.
+
+    When testing random variates from the standard normal distribution, we
+    expect the data to be consistent with the null hypothesis most of the time.
+
+    >>> x = stats.norm.rvs(size=100, random_state=rng)
+    >>> stats.kstest(x, stats.norm.cdf)
+    KstestResult(statistic=0.05345882212970396,
+                 pvalue=0.9227159037744717,
+                 statistic_location=-1.2451343873745018,
+                 statistic_sign=1)
+
+
+    As expected, the p-value of 0.92 is not below our threshold of 0.05, so
+    we cannot reject the null hypothesis.
+
+    Suppose, however, that the random variates are distributed according to
+    a normal distribution that is shifted toward greater values. In this case,
+    the cumulative density function (CDF) of the underlying distribution tends
+    to be *less* than the CDF of the standard normal. Therefore, we would
+    expect the null hypothesis to be rejected with ``alternative='less'``:
+
+    >>> x = stats.norm.rvs(size=100, loc=0.5, random_state=rng)
+    >>> stats.kstest(x, stats.norm.cdf, alternative='less')
+    KstestResult(statistic=0.17482387821055168,
+                 pvalue=0.001913921057766743,
+                 statistic_location=0.3713830565352756,
+                 statistic_sign=-1)
+
+    and indeed, with p-value smaller than our threshold, we reject the null
+    hypothesis in favor of the alternative.
+
+    For convenience, the previous test can be performed using the name of the
+    distribution as the second argument.
+
+    >>> stats.kstest(x, "norm", alternative='less')
+    KstestResult(statistic=0.17482387821055168,
+                 pvalue=0.001913921057766743,
+                 statistic_location=0.3713830565352756,
+                 statistic_sign=-1)
+
+    The examples above have all been one-sample tests identical to those
+    performed by `ks_1samp`. Note that `kstest` can also perform two-sample
+    tests identical to those performed by `ks_2samp`. For example, when two
+    samples are drawn from the same distribution, we expect the data to be
+    consistent with the null hypothesis most of the time.
+
+    >>> sample1 = stats.laplace.rvs(size=105, random_state=rng)
+    >>> sample2 = stats.laplace.rvs(size=95, random_state=rng)
+    >>> stats.kstest(sample1, sample2)
+    KstestResult(statistic=0.11779448621553884,
+                 pvalue=0.4494256912629795,
+                 statistic_location=0.6138814275424155,
+                 statistic_sign=1)
+
+    As expected, the p-value of 0.45 is not below our threshold of 0.05, so
+    we cannot reject the null hypothesis.
+
+    """
+    # to not break compatibility with existing code
+    if alternative == 'two_sided':
+        alternative = 'two-sided'
+    if alternative not in ['two-sided', 'greater', 'less']:
+        raise ValueError(f"Unexpected alternative: {alternative}")
+    xvals, yvals, cdf = _parse_kstest_args(rvs, cdf, args, N)
+    if cdf:
+        return ks_1samp(xvals, cdf, args=args, alternative=alternative,
+                        method=method, axis=axis, _no_deco=True)
+    return ks_2samp(xvals, yvals, alternative=alternative, method=method,
+                    axis=axis, _no_deco=True)
+
+
+@xp_capabilities(np_only=True)
+def tiecorrect(rankvals):
+    """Tie correction factor for Mann-Whitney U and Kruskal-Wallis H tests.
+
+    Parameters
+    ----------
+    rankvals : array_like
+        A 1-D sequence of ranks.  Typically this will be the array
+        returned by `~scipy.stats.rankdata`.
+
+    Returns
+    -------
+    factor : float
+        Correction factor for U or H.
+
+    See Also
+    --------
+    rankdata : Assign ranks to the data
+    mannwhitneyu : Mann-Whitney rank test
+    kruskal : Kruskal-Wallis H test
+
+    References
+    ----------
+    .. [1] Siegel, S. (1956) Nonparametric Statistics for the Behavioral
+           Sciences.  New York: McGraw-Hill.
+
+    Examples
+    --------
+    >>> from scipy.stats import tiecorrect, rankdata
+    >>> tiecorrect([1, 2.5, 2.5, 4])
+    0.9
+    >>> ranks = rankdata([1, 3, 2, 4, 5, 7, 2, 8, 4])
+    >>> ranks
+    array([ 1. ,  4. ,  2.5,  5.5,  7. ,  8. ,  2.5,  9. ,  5.5])
+    >>> tiecorrect(ranks)
+    0.9833333333333333
+
+    """
+    arr = np.sort(rankvals)
+    idx = np.nonzero(np.r_[True, arr[1:] != arr[:-1], True])[0]
+    cnt = np.diff(idx).astype(np.float64)
+
+    size = np.float64(arr.size)
+    return 1.0 if size < 2 else 1.0 - (cnt**3 - cnt).sum() / (size**3 - size)
+
+
+RanksumsResult = namedtuple('RanksumsResult', ('statistic', 'pvalue'))
+
+
+@xp_capabilities(out_of_scope=True)  # legacy
+@_axis_nan_policy_factory(RanksumsResult, n_samples=2)
+def ranksums(x, y, alternative='two-sided'):
+    """Compute the Wilcoxon rank-sum statistic for two samples.
+
+    .. legacy:: function
+
+        This function includes a subset of the features of `mannwhitneyu`.
+        Its statistic and p-value can be reproduced using `mannwhitneyu` with
+        options ``use_continuity=False`` and ``method='asymptotic'``.
+        Prefer `mannwhitneyu` for use in new code.
+
+    The Wilcoxon rank-sum test tests the null hypothesis that two sets
+    of measurements are drawn from the same distribution.  The alternative
+    hypothesis is that values in one sample are more likely to be
+    larger than the values in the other sample.
+
+    This test should be used to compare two samples from continuous
+    distributions.  It does not handle ties between measurements
+    in x and y.  For tie-handling and an optional continuity correction
+    see `scipy.stats.mannwhitneyu`.
+
+    Parameters
+    ----------
+    x, y : array_like
+        The data from the two samples.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        The following options are available:
+
+        * 'two-sided': one of the distributions (underlying `x` or `y`) is
+          stochastically greater than the other.
+        * 'less': the distribution underlying `x` is stochastically less
+          than the distribution underlying `y`.
+        * 'greater': the distribution underlying `x` is stochastically greater
+          than the distribution underlying `y`.
+
+        .. versionadded:: 1.7.0
+
+    Returns
+    -------
+    statistic : float
+        The test statistic under the large-sample approximation that the
+        rank sum statistic is normally distributed.
+    pvalue : float
+        The p-value of the test.
+
+    See Also
+    --------
+    scipy.stats.mannwhitneyu
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Wilcoxon_rank-sum_test
+
+    Examples
+    --------
+    We can test the hypothesis that two independent unequal-sized samples are
+    drawn from the same distribution with computing the Wilcoxon rank-sum
+    statistic.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng(6004253334)
+    >>> sample1 = rng.uniform(-1, 1, 200)
+    >>> sample2 = rng.uniform(-0.5, 1.5, 300) # a shifted distribution
+    >>> stats.ranksums(sample1, sample2)
+    RanksumsResult(statistic=np.float64(-7.576201867),
+                   pvalue=np.float64(3.5581802537469756e-14))
+    >>> stats.ranksums(sample1, sample2, alternative='less')
+    RanksumsResult(statistic=np.float64(-7.576201867066302),
+                   pvalue=np.float64(1.7790901268734878e-14))
+    >>> stats.ranksums(sample1, sample2, alternative='greater')
+    RanksumsResult(statistic=np.float64(-7.576201867066302),
+                   pvalue=np.float64(0.9999999999999822))
+
+    The p-value of less than ``0.05`` indicates that this test rejects the
+    hypothesis at the 5% significance level.
+
+    Note that identical hypothesis tests (and more accurate ones) can be performed
+    using `mannwhitneyu`. Prefer `mannwhitneyu` for use in new code.
+
+    >>> res = stats.mannwhitneyu(sample1, sample2, alternative='greater',
+    ...                          method='asymptotic', use_continuity = False)
+    >>> res.zstatistic
+    np.float64(-7.576201867066302)
+    >>> res.pvalue
+    np.float64(0.9999999999999822)
+    """
+    x, y = map(np.asarray, (x, y))
+    n1 = len(x)
+    n2 = len(y)
+    alldata = np.concatenate((x, y))
+    ranked = rankdata(alldata)
+    x = ranked[:n1]
+    s = np.sum(x, axis=0)
+    expected = n1 * (n1+n2+1) / 2.0
+    z = (s - expected) / np.sqrt(n1*n2*(n1+n2+1)/12.0)
+    pvalue = _get_pvalue(z, _SimpleNormal(), alternative, xp=np)
+
+    return RanksumsResult(z[()], pvalue[()])
+
+
+KruskalResult = namedtuple('KruskalResult', ('statistic', 'pvalue'))
+
+
+@xp_capabilities(skip_backends=[('dask.array', 'no rankdata')],
+                 marray=True)
+@_axis_nan_policy_factory(KruskalResult, n_samples=None)
+def kruskal(*samples, nan_policy='propagate', axis=0):
+    """Compute the Kruskal-Wallis H-test for independent samples.
+
+    The Kruskal-Wallis H-test tests the null hypothesis that the population
+    median of all of the groups are equal.  It is a non-parametric version of
+    ANOVA.  The test works on 2 or more independent samples, which may have
+    different sizes.  Note that rejecting the null hypothesis does not
+    indicate which of the groups differs.  Post hoc comparisons between
+    groups are required to determine which groups are different.
+
+    Parameters
+    ----------
+    *samples : array_like
+       Two or more arrays with the sample measurements can be given as
+       arguments. Samples must be one-dimensional.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    axis : int or tuple of ints, default: 0
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    statistic : float
+       The Kruskal-Wallis H statistic, corrected for ties.
+    pvalue : float
+       The p-value for the test using the assumption that H has a chi
+       square distribution. The p-value returned is the survival function of
+       the chi square distribution evaluated at H.
+
+    See Also
+    --------
+    f_oneway : 1-way ANOVA.
+    mannwhitneyu : Mann-Whitney rank test on two samples.
+    friedmanchisquare : Friedman test for repeated measurements.
+
+    Notes
+    -----
+    Due to the assumption that H has a chi square distribution, the number
+    of samples in each group must not be too small.  A typical rule is
+    that each sample must have at least 5 measurements.
+
+    References
+    ----------
+    .. [1] W. H. Kruskal & W. W. Wallis, "Use of Ranks in
+       One-Criterion Variance Analysis", Journal of the American Statistical
+       Association, Vol. 47, Issue 260, pp. 583-621, 1952.
+    .. [2] https://en.wikipedia.org/wiki/Kruskal-Wallis_one-way_analysis_of_variance
+
+    Examples
+    --------
+    >>> from scipy import stats
+    >>> x = [1, 3, 5, 7, 9]
+    >>> y = [2, 4, 6, 8, 10]
+    >>> stats.kruskal(x, y)
+    KruskalResult(statistic=0.2727272727272734, pvalue=0.6015081344405895)
+
+    >>> x = [1, 1, 1]
+    >>> y = [2, 2, 2]
+    >>> z = [2, 2]
+    >>> stats.kruskal(x, y, z)
+    KruskalResult(statistic=7.0, pvalue=0.0301973834223185)
+
+    """
+    xp = array_namespace(*samples)
+    samples = xp_promote(*samples, force_floating=True, xp=xp)
+
+    num_groups = len(samples)
+    if num_groups < 2:
+        raise ValueError("Need at least two groups in stats.kruskal()")
+
+    lengths = [sample.shape[-1] for sample in samples]
+    if any(lengths) < 1:  # Only needed for `test_axis_nan_policy`
+        raise ValueError("Inputs must not be empty.")
+
+    alldata = xp.concat(samples, axis=-1)
+    ranked, _, t = _rankdata(alldata, method='average', return_ties=True)
+    counts = [xp.asarray(_count_nonmasked(sample, -1), dtype=t.dtype)
+              for sample in samples]
+    totaln = sum(counts)
+    ties = 1 - xp.sum(t**3 - t, axis=-1) / (totaln**3 - totaln)  # tiecorrect(ranked)
+
+    # Compute sum^2/count for each group and sum
+    j = list(itertools.accumulate(lengths, initial=0))
+    ssbn = sum(xp.sum(ranked[..., j[i]:j[i + 1]], axis=-1)**2 / counts[i]
+               for i in range(num_groups))
+
+    h = 12.0 / (totaln * (totaln + 1)) * ssbn - 3 * (totaln + 1)
+    df = xp.asarray(num_groups - 1, dtype=h.dtype)
+    h /= ties
+
+    chi2 = _SimpleChi2(df)
+    pvalue = _get_pvalue(h, chi2, alternative='greater', symmetric=False, xp=np)
+    return KruskalResult(h, pvalue)
+
+
+FriedmanchisquareResult = namedtuple('FriedmanchisquareResult',
+                                     ('statistic', 'pvalue'))
+
+
+@xp_capabilities(skip_backends=[("dask.array", "no rankdata")],
+                 marray=True)
+@_axis_nan_policy_factory(FriedmanchisquareResult, n_samples=None, paired=True)
+def friedmanchisquare(*samples, axis=0):
+    """Compute the Friedman test for repeated samples.
+
+    The Friedman test tests the null hypothesis that repeated samples of
+    the same individuals have the same distribution.  It is often used
+    to test for consistency among samples obtained in different ways.
+    For example, if two sampling techniques are used on the same set of
+    individuals, the Friedman test can be used to determine if the two
+    sampling techniques are consistent.
+
+    Parameters
+    ----------
+    *samples : array_like
+        Arrays of observations.  All of the arrays must have the same number
+        of elements.  At least three samples must be given.
+    axis : int or tuple of ints, default: 0
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    statistic : float
+        The test statistic, correcting for ties.
+    pvalue : float
+        The associated p-value assuming that the test statistic has a chi
+        squared distribution.
+
+    See Also
+    --------
+    :ref:`hypothesis_friedmanchisquare` : Extended example
+
+    Notes
+    -----
+    Due to the assumption that the test statistic has a chi squared
+    distribution, the p-value is only reliable for n > 10 and more than
+    6 repeated samples.
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Friedman_test
+    .. [2] Demsar, J. (2006). Statistical comparisons of classifiers over
+           multiple data sets. Journal of Machine Learning Research, 7, 1-30.
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> rng = np.random.default_rng(seed=18)
+    >>> x = rng.random((6, 10))
+    >>> from scipy.stats import friedmanchisquare
+    >>> res = friedmanchisquare(x[0], x[1], x[2], x[3], x[4], x[5])
+    >>> res.statistic, res.pvalue
+    (11.428571428571416, 0.043514520866727614)
+
+    The p-value is less than 0.05; however, as noted above, the results may not
+    be reliable since we have a small number of repeated samples.
+
+    For a more detailed example, see :ref:`hypothesis_friedmanchisquare`.
+    """
+    k = len(samples)
+    if k < 3:
+        raise ValueError('At least 3 samples must be given '
+                         f'for Friedman test, got {k}.')
+
+    xp = array_namespace(*samples)
+    samples = xp_promote(*samples, force_floating=True, xp=xp)
+    dtype = samples[0].dtype
+    samples = _share_masks(*samples, xp=xp)  # paired-sample test
+
+    n = samples[0].shape[-1]
+    if n == 0:  # only for `test_axis_nan_policy`; user doesn't see this
+        raise ValueError("One or more sample arguments is too small.")
+
+    # Rank data
+    # axis-slices are aligned with axis -1 by decorator; stack puts samples along axis 0
+    # The transpose flips this so we can work with axis-slices along -1. This is a
+    # reducing statistic, so both axes 0 and -1 are consumed.
+    data = xp_swapaxes(xp.stack(samples), 0, -1)
+    data, _, t = _rankdata(data, method='average', return_ties=True)
+
+    # Handle ties
+    ties = xp.sum(t * (t*t - 1), axis=(0, -1))
+    count = xp.asarray(_count_nonmasked(samples[0], axis=-1), dtype=ties.dtype)
+    c = 1 - ties / (k*(k*k - 1)*count)
+
+    ssbn = xp.sum(xp.sum(data, axis=0)**2, axis=-1)
+    statistic = (12.0 / (k*count*(k+1)) * ssbn - 3*count*(k+1)) / c
+
+    chi2 = _SimpleChi2(xp.asarray(k - 1, dtype=dtype))
+    pvalue = _get_pvalue(statistic, chi2, alternative='greater', symmetric=False, xp=xp)
+    return FriedmanchisquareResult(statistic, pvalue)
+
+
+BrunnerMunzelResult = namedtuple('BrunnerMunzelResult',
+                                 ('statistic', 'pvalue'))
+
+
+@xp_capabilities(cpu_only=True, # torch GPU can't use `stdtr`
+                 marray=True,
+                 skip_backends=[('dask.array', 'needs rankdata')])
+@_axis_nan_policy_factory(BrunnerMunzelResult, n_samples=2)
+def brunnermunzel(x, y, alternative="two-sided", distribution="t",
+                  nan_policy='propagate', *, axis=0):
+    """Compute the Brunner-Munzel test on samples x and y.
+
+    The Brunner-Munzel test is a nonparametric test of the null hypothesis that
+    when values are taken one by one from each group, the probabilities of
+    getting large values in both groups are equal.
+    Unlike the Wilcoxon-Mann-Whitney's U test, this does not require the
+    assumption of equivariance of two groups. Note that this does not assume
+    the distributions are same. This test works on two independent samples,
+    which may have different sizes.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Array of samples, should be one-dimensional.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+        The following options are available (default is 'two-sided'):
+
+        * 'two-sided'
+        * 'less': one-sided
+        * 'greater': one-sided
+
+    distribution : {'t', 'normal'}, optional
+        Defines how to get the p-value.
+        The following options are available (default is 't'):
+
+        * 't': get the p-value by t-distribution
+        * 'normal': get the p-value by standard normal distribution.
+
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': returns nan
+        * 'raise': throws an error
+        * 'omit': performs the calculations ignoring nan values
+
+    axis : int or None, default=0
+        If an int, the axis of the input along which to compute the statistic.
+        The statistic of each axis-slice (e.g. row) of the input will appear
+        in a corresponding element of the output. If None, the input will be
+        raveled before computing the statistic.
+
+    Returns
+    -------
+    statistic : float
+        The Brunner-Munzer W statistic.
+    pvalue : float
+        p-value assuming an t distribution. One-sided or
+        two-sided, depending on the choice of `alternative` and `distribution`.
+
+    See Also
+    --------
+    mannwhitneyu : Mann-Whitney rank test on two samples.
+
+    Notes
+    -----
+    Brunner and Munzel recommended to estimate the p-value by t-distribution
+    when the size of data is 50 or less. If the size is lower than 10, it would
+    be better to use permuted Brunner Munzel test (see [2]_).
+
+    References
+    ----------
+    .. [1] Brunner, E. and Munzel, U. "The nonparametric Benhrens-Fisher
+           problem: Asymptotic theory and a small-sample approximation".
+           Biometrical Journal. Vol. 42(2000): 17-25.
+    .. [2] Neubert, K. and Brunner, E. "A studentized permutation test for the
+           non-parametric Behrens-Fisher problem". Computational Statistics and
+           Data Analysis. Vol. 51(2007): 5192-5204.
+
+    Examples
+    --------
+    >>> from scipy import stats
+    >>> x1 = [1,2,1,1,1,1,1,1,1,1,2,4,1,1]
+    >>> x2 = [3,3,4,3,1,2,3,1,1,5,4]
+    >>> w, p_value = stats.brunnermunzel(x1, x2)
+    >>> w
+    3.1374674823029505
+    >>> p_value
+    0.0057862086661515377
+
+    """
+    # _axis_nan_policy decorator ensures we can work along the last axis
+    xp = array_namespace(x, y)
+    length_x = x.shape[-1]
+    nx = _count_nonmasked(x, axis=-1)
+    ny = _count_nonmasked(y, axis=-1)
+
+    rankc = rankdata(xp.concat((x, y), axis=axis), axis=-1)
+    rankcx = rankc[..., :length_x]
+    rankcy = rankc[..., length_x:]
+    rankcx_mean = xp.mean(rankcx, axis=-1, keepdims=True)
+    rankcy_mean = xp.mean(rankcy, axis=-1, keepdims=True)
+    rankx = rankdata(x, axis=-1)
+    ranky = rankdata(y, axis=-1)
+    rankx_mean = xp.mean(rankx, axis=-1, keepdims=True)
+    ranky_mean = xp.mean(ranky, axis=-1, keepdims=True)
+
+    temp_x = rankcx - rankx - rankcx_mean + rankx_mean
+    Sx = xp.vecdot(temp_x, temp_x, axis=-1)
+    Sx /= nx - 1
+    temp_y = rankcy - ranky - rankcy_mean + ranky_mean
+    Sy = xp.vecdot(temp_y, temp_y, axis=-1)
+    Sy /= ny - 1
+
+    rankcx_mean = xp.squeeze(rankcx_mean, axis=-1)
+    rankcy_mean = xp.squeeze(rankcy_mean, axis=-1)
+    wbfn = nx * ny * (rankcy_mean - rankcx_mean)
+    wbfn /= (nx + ny) * xp.sqrt(nx * Sx + ny * Sy)
+
+    if distribution == "t":
+        df_numer = xp.pow(nx * Sx + ny * Sy, 2.0)
+        df_denom = xp.pow(nx * Sx, 2.0) / (nx - 1)
+        df_denom += xp.pow(ny * Sy, 2.0) / (ny - 1)
+        df = df_numer / df_denom
+
+        if not is_lazy_array(df_numer) and not is_lazy_array(df_denom) and (
+                xp.any(df_numer == 0) and xp.any(df_denom == 0)):
+            message = ("p-value cannot be estimated with `distribution='t' "
+                       "because degrees of freedom parameter is undefined "
+                       "(0/0). Try using `distribution='normal'")
+            warnings.warn(message, RuntimeWarning, stacklevel=2)
+
+        distribution = _SimpleStudentT(df)
+    elif distribution == "normal":
+        distribution = _SimpleNormal()
+    else:
+        raise ValueError(
+            "distribution should be 't' or 'normal'")
+
+    p = _get_pvalue(-wbfn, distribution, alternative, xp=xp)
+
+    return BrunnerMunzelResult(wbfn, p)
+
+
+@xp_capabilities(cpu_only=True, exceptions=['cupy', 'jax.numpy'],
+    reason='Delegation for `special.stdtr` only implemented for CuPy and JAX.',
+    marray=True)
+@_axis_nan_policy_factory(SignificanceResult, kwd_samples=['weights'], paired=True)
+def combine_pvalues(pvalues, method='fisher', weights=None, *, axis=0):
+    """
+    Combine p-values from independent tests that bear upon the same hypothesis.
+
+    These methods are intended only for combining p-values from hypothesis
+    tests based upon continuous distributions.
+
+    Each method assumes that under the null hypothesis, the p-values are
+    sampled independently and uniformly from the interval [0, 1]. A test
+    statistic (different for each method) is computed and a combined
+    p-value is calculated based upon the distribution of this test statistic
+    under the null hypothesis.
+
+    Parameters
+    ----------
+    pvalues : array_like
+        Array of p-values assumed to come from independent tests based on
+        continuous distributions.
+    method : {'fisher', 'pearson', 'tippett', 'stouffer', 'mudholkar_george'}
+
+        Name of method to use to combine p-values.
+
+        The available methods are (see Notes for details):
+
+        * 'fisher': Fisher's method (Fisher's combined probability test)
+        * 'pearson': Pearson's method
+        * 'mudholkar_george': Mudholkar's and George's method
+        * 'tippett': Tippett's method
+        * 'stouffer': Stouffer's Z-score method
+
+    weights : array_like, optional
+        Optional array of weights used only for Stouffer's Z-score method.
+        Ignored by other methods.
+
+    Returns
+    -------
+    res : SignificanceResult
+        An object containing attributes:
+
+        statistic : float
+            The statistic calculated by the specified method.
+        pvalue : float
+            The combined p-value.
+
+    Examples
+    --------
+    Suppose we wish to combine p-values from four independent tests
+    of the same null hypothesis using Fisher's method (default).
+
+    >>> from scipy.stats import combine_pvalues
+    >>> pvalues = [0.1, 0.05, 0.02, 0.3]
+    >>> combine_pvalues(pvalues)
+    SignificanceResult(statistic=20.828626352604235, pvalue=0.007616871850449092)
+
+    When the individual p-values carry different weights, consider Stouffer's
+    method.
+
+    >>> weights = [1, 2, 3, 4]
+    >>> res = combine_pvalues(pvalues, method='stouffer', weights=weights)
+    >>> res.pvalue
+    0.009578891494533616
+
+    Notes
+    -----
+    If this function is applied to tests with a discrete statistics such as
+    any rank test or contingency-table test, it will yield systematically
+    wrong results, e.g. Fisher's method will systematically overestimate the
+    p-value [1]_. This problem becomes less severe for large sample sizes
+    when the discrete distributions become approximately continuous.
+
+    The differences between the methods can be best illustrated by their
+    statistics and what aspects of a combination of p-values they emphasise
+    when considering significance [2]_. For example, methods emphasising large
+    p-values are more sensitive to strong false and true negatives; conversely
+    methods focussing on small p-values are sensitive to positives.
+
+    * The statistics of Fisher's method (also known as Fisher's combined
+      probability test) [3]_ is :math:`-2\\sum_i \\log(p_i)`, which is
+      equivalent (as a test statistics) to the product of individual p-values:
+      :math:`\\prod_i p_i`. Under the null hypothesis, this statistics follows
+      a :math:`\\chi^2` distribution. This method emphasises small p-values.
+    * Pearson's method uses :math:`-2\\sum_i\\log(1-p_i)`, which is equivalent
+      to :math:`\\prod_i \\frac{1}{1-p_i}` [2]_.
+      It thus emphasises large p-values.
+    * Mudholkar and George compromise between Fisher's and Pearson's method by
+      averaging their statistics [4]_. Their method emphasises extreme
+      p-values, both close to 1 and 0.
+    * Stouffer's method [5]_ uses Z-scores and the statistic:
+      :math:`\\sum_i \\Phi^{-1} (p_i)`, where :math:`\\Phi` is the CDF of the
+      standard normal distribution. The advantage of this method is that it is
+      straightforward to introduce weights, which can make Stouffer's method
+      more powerful than Fisher's method when the p-values are from studies
+      of different size [6]_ [7]_.
+    * Tippett's method uses the smallest p-value as a statistic.
+      (Mind that this minimum is not the combined p-value.)
+
+    Fisher's method may be extended to combine p-values from dependent tests
+    [8]_. Extensions such as Brown's method and Kost's method are not currently
+    implemented.
+
+    .. versionadded:: 0.15.0
+
+    References
+    ----------
+    .. [1] Kincaid, W. M., "The Combination of Tests Based on Discrete
+           Distributions." Journal of the American Statistical Association 57,
+           no. 297 (1962), 10-19.
+    .. [2] Heard, N. and Rubin-Delanchey, P. "Choosing between methods of
+           combining p-values."  Biometrika 105.1 (2018): 239-246.
+    .. [3] https://en.wikipedia.org/wiki/Fisher%27s_method
+    .. [4] George, E. O., and G. S. Mudholkar. "On the convolution of logistic
+           random variables." Metrika 30.1 (1983): 1-13.
+    .. [5] https://en.wikipedia.org/wiki/Fisher%27s_method#Relation_to_Stouffer.27s_Z-score_method
+    .. [6] Whitlock, M. C. "Combining probability from independent tests: the
+           weighted Z-method is superior to Fisher's approach." Journal of
+           Evolutionary Biology 18, no. 5 (2005): 1368-1373.
+    .. [7] Zaykin, Dmitri V. "Optimally weighted Z-test is a powerful method
+           for combining probabilities in meta-analysis." Journal of
+           Evolutionary Biology 24, no. 8 (2011): 1836-1841.
+    .. [8] https://en.wikipedia.org/wiki/Extensions_of_Fisher%27s_method
+
+    """
+    xp = array_namespace(pvalues, weights)
+    pvalues, weights = xp_promote(pvalues, weights, broadcast=True,
+                                  force_floating=True, xp=xp)
+
+    if xp_size(pvalues) == 0:
+        # This is really only needed for *testing* _axis_nan_policy decorator
+        # It won't happen when the decorator is used.
+        NaN = _get_nan(pvalues)
+        return SignificanceResult(NaN, NaN)
+
+    n = _count_nonmasked(pvalues, axis)
+    n = xp.asarray(n, dtype=pvalues.dtype, device=xp_device(pvalues))
+
+    if method == 'fisher':
+        statistic = -2 * xp.sum(xp.log(pvalues), axis=axis)
+        chi2 = _SimpleChi2(2*n)
+        pval = _get_pvalue(statistic, chi2, alternative='greater',
+                           symmetric=False, xp=xp)
+    elif method == 'pearson':
+        statistic = 2 * xp.sum(xp.log1p(-pvalues), axis=axis)
+        chi2 = _SimpleChi2(2*n)
+        pval = _get_pvalue(-statistic, chi2, alternative='less', symmetric=False, xp=xp)
+    elif method == 'mudholkar_george':
+        normalizing_factor = xp.sqrt(3/n)/xp.pi
+        statistic = (-xp.sum(xp.log(pvalues), axis=axis)
+                     + xp.sum(xp.log1p(-pvalues), axis=axis))
+        nu = 5*n + 4
+        approx_factor = xp.sqrt(nu / (nu - 2))
+        t = _SimpleStudentT(nu)
+        pval = _get_pvalue(statistic * normalizing_factor * approx_factor, t,
+                           alternative="greater", xp=xp)
+    elif method == 'tippett':
+        statistic = xp.min(pvalues, axis=axis)
+        beta = _SimpleBeta(xp.ones_like(n), n)
+        pval = _get_pvalue(statistic, beta, alternative='less', symmetric=False, xp=xp)
+    elif method == 'stouffer':
+        if weights is None:
+            weights = xp.ones_like(pvalues, dtype=pvalues.dtype)
+        pvalues, weights = _share_masks(pvalues, weights, xp=xp)
+
+        norm = _SimpleNormal()
+        Zi = norm.isf(pvalues)
+        # Consider `vecdot` when data-apis/array-api#910 is resolved
+        statistic = (xp.sum(weights * Zi, axis=axis)
+                     / xp_vector_norm(weights, axis=axis))
+        pval = _get_pvalue(statistic, norm, alternative="greater", xp=xp)
+
+    else:
+        raise ValueError(
+            f"Invalid method {method!r}. Valid methods are 'fisher', "
+            "'pearson', 'mudholkar_george', 'tippett', and 'stouffer'"
+        )
+
+    return SignificanceResult(statistic, pval)
+
+
+@dataclass
+class QuantileTestResult:
+    r"""
+    Result of `scipy.stats.quantile_test`.
+
+    Attributes
+    ----------
+    statistic: float
+        The statistic used to calculate the p-value; either ``T1``, the
+        number of observations less than or equal to the hypothesized quantile,
+        or ``T2``, the number of observations strictly less than the
+        hypothesized quantile. Two test statistics are required to handle the
+        possibility the data was generated from a discrete or mixed
+        distribution.
+
+    statistic_type : int
+        ``1`` or ``2`` depending on which of ``T1`` or ``T2`` was used to
+        calculate the p-value respectively. ``T1`` corresponds to the
+        ``"greater"`` alternative hypothesis and ``T2`` to the ``"less"``.  For
+        the ``"two-sided"`` case, the statistic type that leads to smallest
+        p-value is used.  For significant tests, ``statistic_type = 1`` means
+        there is evidence that the population quantile is significantly greater
+        than the hypothesized value and ``statistic_type = 2`` means there is
+        evidence that it is significantly less than the hypothesized value.
+
+    pvalue : float
+        The p-value of the hypothesis test.
+    """
+    statistic: float
+    statistic_type: int
+    pvalue: float
+    _alternative: list[str] = field(repr=False)
+    _x : np.ndarray = field(repr=False)
+    _p : float = field(repr=False)
+    _statistic : float = field(repr=False)
+    _statistic_type : int = field(repr=False)
+    _pvalue : float = field(repr=False)
+    _axis : int = field(repr=False)
+    _axis_none : bool = field(repr=False)
+    _keepdims : bool = field(repr=False)
+    _ndim : int = field(repr=False)
+    _nan_out : bool = field(repr=False)
+    _xp : bool = field(repr=False)
+
+
+    def confidence_interval(self, confidence_level=0.95):
+        """
+        Compute the confidence interval of the quantile.
+
+        Parameters
+        ----------
+        confidence_level : float, default: 0.95
+            Confidence level for the computed confidence interval
+            of the quantile. Default is 0.95.
+
+        Returns
+        -------
+        ci : ``ConfidenceInterval`` object
+            The object has attributes ``low`` and ``high`` that hold the
+            lower and upper bounds of the confidence interval.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> import scipy.stats as stats
+        >>> p = 0.75  # quantile of interest
+        >>> q = 0  # hypothesized value of the quantile
+        >>> x = np.exp(np.arange(0, 1.01, 0.01))
+        >>> res = stats.quantile_test(x, q=q, p=p, alternative='less')
+        >>> lb, ub = res.confidence_interval()
+        >>> lb, ub
+        (-inf, 2.293318740264183)
+        >>> res = stats.quantile_test(x, q=q, p=p, alternative='two-sided')
+        >>> lb, ub = res.confidence_interval(0.9)
+        >>> lb, ub
+        (1.9542373206359396, 2.293318740264183)
+        """
+
+        xp = self._xp
+        alternative = self._alternative
+        p = self._p
+        x = xp.sort(self._x, axis=-1)
+        n = x.shape[-1]
+        bd = _SimpleBinomial(n=n, p=p)
+        shape = p.shape
+
+        if confidence_level <= 0 or confidence_level >= 1:
+            message = "`confidence_level` must be a number between 0 and 1."
+            raise ValueError(message)
+
+        if n == 0:
+            zeros = xp.zeros(p.shape, dtype=x.dtype)
+            low, high = zeros, zeros
+        elif alternative == 'less':
+            p = 1 - confidence_level
+            low = xp.full(shape, -xp.inf)
+            high_index = xp.astype(bd.isf(p), xp.int64)
+            valid_index = high_index < n
+            high_index = xpx.at(high_index)[~valid_index].set(0)
+            x_high = xp.take_along_axis(x, high_index, axis=-1)
+            high = xp.where(valid_index, x_high, xp.nan)
+        elif alternative == 'greater':
+            p = 1 - confidence_level
+            low_index = xp.astype(bd.ppf(p), xp.int64) - 1
+            valid_index = low_index >= 0
+            low_index = xpx.at(low_index)[~valid_index].set(0)
+            x_low = xp.take_along_axis(x, low_index, axis=-1)
+            low = xp.where(valid_index, x_low, xp.nan)
+            high = xp.full(shape, xp.inf)
+        elif alternative == 'two-sided':
+            p = (1 - confidence_level) / 2
+            low_index = xp.astype(bd.ppf(p), xp.int64) - 1
+            valid_index = low_index >= 0
+            low_index = xpx.at(low_index)[~valid_index].set(0)
+            x_low = xp.take_along_axis(x, low_index, axis=-1)
+            low = xp.where(valid_index, x_low, xp.nan)
+            high_index = xp.astype(bd.isf(p), xp.int64)
+            valid_index = high_index < n
+            high_index = xpx.at(high_index)[~valid_index].set(0)
+            x_high = xp.take_along_axis(x, high_index, axis=-1)
+            high = xp.where(valid_index, x_high, xp.nan)
+
+        args = (self._axis, self._axis_none, self._keepdims,
+                self._ndim, self._nan_out, self._xp)
+        low = _quantile_test_postprocess(low, *args)
+        high = _quantile_test_postprocess(high, *args)
+        return ConfidenceInterval(low, high)
+
+
+def quantile_test_iv(x, q, p, alternative, axis, keepdims):
+    xp = array_namespace(x, q, p)
+    dtype = xp_result_type(x, q, p, force_floating=True, xp=xp)
+
+    x = xpx.atleast_nd(x, ndim=1, xp=xp)
+    message = '`x` must be an array of numbers.'
+    if not xp.isdtype(x.dtype, 'numeric'):
+        raise ValueError(message)
+
+    q = xp.asarray(q)
+    message = "`q` must be a scalar or array of numbers."
+    if not xp.isdtype(q.dtype, 'numeric'):
+        raise ValueError(message)
+
+    p = xp.asarray(p)
+    message = "`p` must be a scalar or array of floats."
+    if not xp.isdtype(p.dtype, 'real floating'):
+        raise ValueError(message)
+
+    axis_none = axis is None
+    ndim = max(x.ndim, p.ndim)
+    if axis_none:
+        x = xp_ravel(x)
+        q = xp_ravel(q)
+        p = xp_ravel(p)
+        axis = 0
+    elif np.iterable(axis) or int(axis) != axis:
+        message = "`axis` must be an integer or None."
+        raise ValueError(message)
+    elif (axis >= ndim) or (axis < -ndim):
+        message = "`axis` is not compatible with the shapes of the inputs."
+        raise ValueError(message)
+    axis = int(axis)
+
+    alternatives = {'two-sided', 'less', 'greater'}
+    message = f"`alternative` must be one of {alternatives}"
+    if alternative not in alternatives:
+        raise ValueError(message)
+
+    if keepdims not in {None, True, False}:
+        message = "If specified, `keepdims` must be True or False."
+        raise ValueError(message)
+
+    x = xp.sort(x, axis=axis)
+    q, p = xp.broadcast_arrays(q, p)  # length along axis matters for q and p
+    x, q, p = _broadcast_arrays((x, q, p), axis=axis, xp=xp)
+
+    if (keepdims is False) and (p.shape[axis] != 1):
+        message = ("`keepdims` may be False only if `p` and `q` are scalars or the "
+                   "length of `p` and `q` along `axis` is 1.")
+        raise ValueError(message)
+    keepdims = (p.shape[axis] != 1) if keepdims is None else keepdims
+
+    x = xp.moveaxis(x, axis, -1)
+    q = xp.moveaxis(q, axis, -1)
+    p = xp.moveaxis(p, axis, -1)
+
+    nan_out = ((p >= 1) | (p <= 0) | xp.isnan(p) | xp.isnan(q)
+               | xp.any(xp.isnan(x), axis=-1, keepdims=True))
+    if is_lazy_array(nan_out) or xp.any(nan_out):
+        # These get NaN-ed out at the end. In the meantime, we want them
+        # to pass through calculations without warnings or errors
+        q = xpx.at(q, nan_out).set(0.0, copy=True)
+        p = xpx.at(p, nan_out).set(0.5, copy=True)
+
+    return x, q, p, alternative, axis, keepdims, axis_none, ndim, nan_out, dtype, xp
+
+
+def _quantile_test_postprocess(res, axis, axis_none, keepdims, ndim, nan_out, xp):
+    # Reshape per axis/keepdims
+
+    res = xpx.at(res)[nan_out].set(xp.nan)
+
+    if axis_none and keepdims:
+        shape = (1,)*(ndim - 1) + res.shape
+        res = xp.reshape(res, shape)
+        axis = -1
+
+    res = xp.moveaxis(res, -1, axis)
+
+    if not keepdims:
+        res = xp.squeeze(res, axis=axis)
+    return res[()] if res.ndim == 0 else res
+
+
+@xp_capabilities(skip_backends=[("dask.array", "no `take_along_axis`")], cpu_only=True,
+                 reason="binomial distribution ufuncs only available for NumPy")
+def quantile_test(x, *, q=0.0, p=0.5, alternative='two-sided', axis=0, keepdims=None):
+    r"""
+    Perform a quantile test and compute a confidence interval of the quantile.
+
+    This function tests the null hypothesis that `q` is the value of the
+    quantile associated with probability `p` of the population underlying
+    sample `x`. For example, with default parameters, it tests that the
+    median of the population underlying `x` is zero. The function returns an
+    object including the test statistic, a p-value, and a method for computing
+    the confidence interval around the quantile.
+
+    Parameters
+    ----------
+    x : array_like
+        A one-dimensional sample.
+    q : float, default: 0
+        The hypothesized value of the quantile.
+    p : float, default: 0.5
+        The probability associated with the quantile; i.e. the proportion of
+        the population less than `q` is `p`. Must be strictly between 0 and 1.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+        The following options are available (default is 'two-sided'):
+
+        * 'two-sided': the quantile associated with the probability `p`
+          is not `q`.
+        * 'less': the quantile associated with the probability `p` is less
+          than `q`.
+        * 'greater': the quantile associated with the probability `p` is
+          greater than `q`.
+    axis : int or None, default: 0
+        Axis of `x` along which the test is performed.
+        ``None`` ravels `x`, `q`, and `p` before performing the calculation,
+        without checking whether the original shapes were compatible.
+        As in other `scipy.stats` functions, a positive integer `axis` is resolved
+        after prepending 1s to the shape of `x`, `q`, and `p` as needed until the arrays
+        have the same dimensionality. When providing `x`, `q`, and `p` with different
+        dimensionality, consider using negative `axis` integers for clarity.
+    keepdims : bool, optional
+        Consider the case in which `x` is 1-D and `p` and `q` are scalars: the test
+        computes a reducing statistic, and the default behavior is to return a scalar.
+        If `keepdims` is set to True, the axis will not be reduced away, and the
+        result will be a 1-D array with one element.
+
+        The general case is more subtle, since multiple tests may be
+        requested for each axis-slice of `x`. For instance, if `x`, `q`, and `p`
+        are 1-D and ``p.size == q.size > 1``, no axis can be reduced away; there must be
+        an axis to contain the results. Therefore:
+
+        - By default, the axis will be reduced away if possible (i.e. if there is
+          exactly one element of `p` and `q` per axis-slice of `x`).
+        - If `keepdims` is set to True, the axis will not be reduced away.
+        - If `keepdims` is set to False, the axis will be reduced away
+          if possible, and an error will be raised otherwise.
+
+    Returns
+    -------
+    result : QuantileTestResult
+        An object with the following attributes:
+
+        statistic : float
+            One of two test statistics that may be used in the quantile test.
+            The first test statistic, ``T1``, is the number of observations in
+            `x` that are less than or equal to the hypothesized quantile
+            `q`. The second test statistic, ``T2``, is the number of
+            observations in `x` that are strictly less than the hypothesized
+            quantile `q`.
+
+            When ``alternative = 'greater'``, ``T1`` is used to calculate the
+            p-value and ``statistic`` is set to ``T1``.
+
+            When ``alternative = 'less'``, ``T2`` is used to calculate the
+            p-value and ``statistic`` is set to ``T2``.
+
+            When ``alternative = 'two-sided'``, both ``T1`` and ``T2`` are
+            considered, and the one that leads to the smallest p-value is used.
+
+        statistic_type : float
+            Either ``1.`` or ``2.`` depending on which of ``T1`` or ``T2`` was
+            used to calculate the p-value.
+
+        pvalue : float
+            The p-value associated with the given alternative.
+
+        The object also has the following method:
+
+        confidence_interval(confidence_level=0.95)
+            Computes a confidence interval around the the
+            population quantile associated with the probability `p`. The
+            confidence interval is returned in a ``namedtuple`` with
+            fields `low` and `high`.  Values are `nan` when there are
+            not enough observations to compute the confidence interval at
+            the desired confidence.
+
+    Notes
+    -----
+    This test and its method for computing confidence intervals are
+    non-parametric. They are valid if and only if the observations are i.i.d.
+
+    The implementation of the test follows Conover [1]_. Two test statistics
+    are considered.
+
+    ``T1``: The number of observations in `x` less than or equal to `q`.
+
+        ``T1 = (x <= q).sum()``
+
+    ``T2``: The number of observations in `x` strictly less than `q`.
+
+        ``T2 = (x < q).sum()``
+
+    The use of two test statistics is necessary to handle the possibility that
+    `x` was generated from a discrete or mixed distribution.
+
+    The null hypothesis for the test is:
+
+        H0: The :math:`p^{\mathrm{th}}` population quantile is `q`.
+
+    and the null distribution for each test statistic is
+    :math:`\mathrm{binom}\left(n, p\right)`. When ``alternative='less'``,
+    the alternative hypothesis is:
+
+        H1: The :math:`p^{\mathrm{th}}` population quantile is less than `q`.
+
+    and the p-value is the probability that the binomial random variable
+
+    .. math::
+        Y \sim \mathrm{binom}\left(n, p\right)
+
+    is greater than or equal to the observed value ``T2``.
+
+    When ``alternative='greater'``, the alternative hypothesis is:
+
+        H1: The :math:`p^{\mathrm{th}}` population quantile is greater than `q`
+
+    and the p-value is the probability that the binomial random variable Y
+    is less than or equal to the observed value ``T1``.
+
+    When ``alternative='two-sided'``, the alternative hypothesis is
+
+        H1: `q` is not the :math:`p^{\mathrm{th}}` population quantile.
+
+    and the p-value is twice the smaller of the p-values for the ``'less'``
+    and ``'greater'`` cases. Both of these p-values can exceed 0.5 for the same
+    data, so the value is clipped into the interval :math:`[0, 1]`.
+
+    The approach for confidence intervals is attributed to Thompson [2]_ and
+    later proven to be applicable to any i.i.d. sample [3]_. The
+    computation is based on the observation that the probability of a quantile
+    :math:`q` to be larger than any observations :math:`x_m (1\leq m \leq N)`
+    can be computed as
+
+    .. math::
+
+        \mathbb{P}(x_m \leq q) = 1 - \sum_{k=0}^{m-1} \binom{N}{k}
+        q^k(1-q)^{N-k}
+
+    By default, confidence intervals are computed for a 95% confidence level.
+    A common interpretation of a 95% confidence intervals is that if i.i.d.
+    samples are drawn repeatedly from the same population and confidence
+    intervals are formed each time, the confidence interval will contain the
+    true value of the specified quantile in approximately 95% of trials.
+
+    A similar function is available in the QuantileNPCI R package [4]_. The
+    foundation is the same, but it computes the confidence interval bounds by
+    doing interpolations between the sample values, whereas this function uses
+    only sample values as bounds. Thus, ``quantile_test.confidence_interval``
+    returns more conservative intervals (i.e., larger).
+
+    The same computation of confidence intervals for quantiles is included in
+    the confintr package [5]_.
+
+    Two-sided confidence intervals are not guaranteed to be optimal; i.e.,
+    there may exist a tighter interval that may contain the quantile of
+    interest with probability larger than the confidence level.
+    Without further assumption on the nature of the
+    underlying distribution, the one-sided intervals are optimally tight.
+
+    References
+    ----------
+    .. [1] W. J. Conover. Practical Nonparametric Statistics, 3rd Ed. 1999.
+    .. [2] W. R. Thompson, "On Confidence Ranges for the Median and Other
+           Expectation Distributions for Populations of Unknown Distribution
+           Form," The Annals of Mathematical Statistics, vol. 7, no. 3,
+           pp. 122-128, 1936, Accessed: Sep. 18, 2019. [Online]. Available:
+           https://www.jstor.org/stable/2957563.
+    .. [3] H. A. David and H. N. Nagaraja, "Order Statistics in Nonparametric
+           Inference" in Order Statistics, John Wiley & Sons, Ltd, 2005, pp.
+           159-170. :doi:`10.1002/0471722162.ch7`.
+    .. [4] N. Hutson, A. Hutson, L. Yan, "QuantileNPCI: Nonparametric
+           Confidence Intervals for Quantiles," R package,
+           https://cran.r-project.org/package=QuantileNPCI
+    .. [5] M. Mayer, "confintr: Confidence Intervals," R package,
+           https://cran.r-project.org/package=confintr
+
+
+    Examples
+    --------
+
+    Suppose we wish to test the null hypothesis that the median of a population
+    is equal to 0.5. We choose a confidence level of 99%; that is, we will
+    reject the null hypothesis in favor of the alternative if the p-value is
+    less than 0.01.
+
+    When testing random variates from the standard uniform distribution, which
+    has a median of 0.5, we expect the data to be consistent with the null
+    hypothesis most of the time.
+
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng(6981396440634228121)
+    >>> rvs = stats.uniform.rvs(size=100, random_state=rng)
+    >>> stats.quantile_test(rvs, q=0.5, p=0.5)
+    QuantileTestResult(statistic=45, statistic_type=1, pvalue=0.36820161732669576)
+
+    As expected, the p-value is not below our threshold of 0.01, so
+    we cannot reject the null hypothesis.
+
+    When testing data from the standard *normal* distribution, which has a
+    median of 0, we would expect the null hypothesis to be rejected.
+
+    >>> rvs = stats.norm.rvs(size=100, random_state=rng)
+    >>> stats.quantile_test(rvs, q=0.5, p=0.5)
+    QuantileTestResult(statistic=67, statistic_type=2, pvalue=0.0008737198369123724)
+
+    Indeed, the p-value is lower than our threshold of 0.01, so we reject the
+    null hypothesis in favor of the default "two-sided" alternative: the median
+    of the population is *not* equal to 0.5.
+
+    However, suppose we were to test the null hypothesis against the
+    one-sided alternative that the median of the population is *greater* than
+    0.5. Since the median of the standard normal is less than 0.5, we would not
+    expect the null hypothesis to be rejected.
+
+    >>> stats.quantile_test(rvs, q=0.5, p=0.5, alternative='greater')
+    QuantileTestResult(statistic=67, statistic_type=1, pvalue=0.9997956114162866)
+
+    Unsurprisingly, with a p-value greater than our threshold, we would not
+    reject the null hypothesis in favor of the chosen alternative.
+
+    The quantile test can be used for any quantile, not only the median. For
+    example, we can test whether the third quartile of the distribution
+    underlying the sample is greater than 0.6.
+
+    >>> rvs = stats.uniform.rvs(size=100, random_state=rng)
+    >>> stats.quantile_test(rvs, q=0.6, p=0.75, alternative='greater')
+    QuantileTestResult(statistic=64, statistic_type=1, pvalue=0.00940696592998271)
+
+    The p-value is lower than the threshold. We reject the null hypothesis in
+    favor of the alternative: the third quartile of the distribution underlying
+    our sample is greater than 0.6.
+
+    `quantile_test` can also compute confidence intervals for any quantile.
+
+    >>> rvs = stats.norm.rvs(size=100, random_state=rng)
+    >>> res = stats.quantile_test(rvs, q=0.6, p=0.75)
+    >>> ci = res.confidence_interval(confidence_level=0.95)
+    >>> ci
+    ConfidenceInterval(low=0.284491604437432, high=0.8912531024914844)
+
+    When testing a one-sided alternative, the confidence interval contains
+    all observations such that if passed as `q`, the p-value of the
+    test would be greater than 0.05, and therefore the null hypothesis
+    would not be rejected. For example:
+
+    >>> rvs.sort()
+    >>> q, p, alpha = 0.6, 0.75, 0.95
+    >>> res = stats.quantile_test(rvs, q=q, p=p, alternative='less')
+    >>> ci = res.confidence_interval(confidence_level=alpha)
+    >>> for x in rvs[rvs <= ci.high]:
+    ...     res = stats.quantile_test(rvs, q=x, p=p, alternative='less')
+    ...     assert res.pvalue > 1-alpha
+    >>> for x in rvs[rvs > ci.high]:
+    ...     res = stats.quantile_test(rvs, q=x, p=p, alternative='less')
+    ...     assert res.pvalue < 1-alpha
+
+    Also, if a 95% confidence interval is repeatedly generated for random
+    samples, the confidence interval will contain the true quantile value in
+    approximately 95% of replications.
+
+    >>> dist = stats.rayleigh() # our "unknown" distribution
+    >>> p = 0.2
+    >>> true_stat = dist.ppf(p) # the true value of the statistic
+    >>> n_trials = 1000
+    >>> quantile_ci_contains_true_stat = 0
+    >>> for i in range(n_trials):
+    ...     data = dist.rvs(size=100, random_state=rng)
+    ...     res = stats.quantile_test(data, p=p)
+    ...     ci = res.confidence_interval(0.95)
+    ...     if ci[0] < true_stat < ci[1]:
+    ...         quantile_ci_contains_true_stat += 1
+    >>> quantile_ci_contains_true_stat >= 950
+    True
+
+    This works with any distribution and any quantile, as long as the observations
+    are i.i.d.
+    """
+    # Implementation carefully follows [1] 3.2
+    # "H0: the p*th quantile of X is x*"
+    # To facilitate comparison with [1], we'll use variable names that
+    # best match Conover's notation
+    X, x_star, p_star, H1, axis, keepdims, axis_none, ndim, nan_out, dtype, xp = (
+        quantile_test_iv(x, q, p, alternative, axis, keepdims))
+    # `axis` is the original `axis`; the working axis is -1.
+
+    # "We will use two test statistics in this test. Let T1 equal "
+    # "the number of observations less than or equal to x*, and "
+    # "let T2 equal the number of observations less than x*."
+    if X.shape[-1] > 0:
+        T1 = _xp_searchsorted(X, x_star, side='right', xp=xp)
+        T2 = _xp_searchsorted(X, x_star, side='left', xp=xp)
+        T1, T2 = xp.astype(T1, dtype), xp.astype(T2, dtype)
+    else:
+        nan_out = xp.ones_like(nan_out)
+        T = xp.zeros(x_star.shape, dtype=dtype)
+        T1, T2 = T, T
+
+    # "The null distribution of the test statistics T1 and T2 is "
+    # "the binomial distribution, with parameters n = sample size, and "
+    # "p = p* as given in the null hypothesis.... Y has the binomial "
+    # "distribution with parameters n and p*."
+    n = X.shape[-1]
+    Y = _SimpleBinomial(n=n, p=p_star)
+
+    # "H1: the p* population quantile is less than x*"
+    if H1 == 'less':
+        # "The p-value is the probability that a binomial random variable Y "
+        # "is greater than *or equal to* the observed value of T2...using p=p*"
+        pvalue = Y.sf(T2-1)  # Y.pmf(T2) + Y.sf(T2)
+        statistic = xp.broadcast_to(T2, pvalue.shape)
+        statistic_type = xp.full_like(statistic, 2.)
+    # "H1: the p* population quantile is greater than x*"
+    elif H1 == 'greater':
+        # "The p-value is the probability that a binomial random variable Y "
+        # "is less than or equal to the observed value of T1... using p = p*"
+        pvalue = Y.cdf(T1)
+        statistic = xp.broadcast_to(T1, pvalue.shape)
+        statistic_type = xp.full_like(statistic, 1.)
+    # "H1: x* is not the p*th population quantile"
+    elif H1 == 'two-sided':
+        # "The p-value is twice the smaller of the probabilities that a
+        # binomial random variable Y is less than or equal to the observed
+        # value of T1 or greater than or equal to the observed value of T2
+        # using p=p*."
+        # Note: both one-sided p-values can exceed 0.5 for the same data, so
+        # `clip`
+        p_min = xp.minimum(Y.cdf(T1), Y.sf(T2 - 1))
+        pvalue = xp.clip(2*p_min, 0., 1.)
+        i2 = Y.cdf(T1) > Y.sf(T2 - 1)
+        statistic = xp.where(i2, T2, T1)
+        statistic_type = xp.where(i2, xp.full_like(T2, 2.), xp.full_like(T1, 1.))
+
+    statistic = xp.astype(statistic, dtype)
+    statistic_type = xp.astype(statistic_type, dtype)
+    pvalue = xp.astype(pvalue, dtype)
+    X = xp.astype(X, dtype)
+
+    _statistic, _statistic_type, _pvalue = statistic, statistic_type, pvalue
+    args = axis, axis_none, keepdims, ndim, nan_out, xp
+    statistic = _quantile_test_postprocess(statistic, *args)
+    statistic_type = _quantile_test_postprocess(statistic_type, *args)
+    pvalue = _quantile_test_postprocess(pvalue, *args)
+
+    return QuantileTestResult(
+        statistic=statistic,
+        statistic_type=statistic_type,
+        pvalue=pvalue,
+        _alternative=H1,
+        _x=X,
+        _p=p_star,
+        _statistic=statistic,
+        _statistic_type=_statistic_type,
+        _pvalue=_pvalue,
+        _axis=axis,
+        _axis_none=axis_none,
+        _keepdims=keepdims,
+        _ndim=ndim,
+        _nan_out=nan_out,
+        _xp=xp,
+    )
+
+
+#####################################
+#       STATISTICAL DISTANCES       #
+#####################################
+
+
+@xp_capabilities(np_only=True)
+def wasserstein_distance_nd(u_values, v_values, u_weights=None, v_weights=None):
+    r"""
+    Compute the Wasserstein-1 distance between two N-D discrete distributions.
+
+    The Wasserstein distance, also called the Earth mover's distance or the
+    optimal transport distance, is a similarity metric between two probability
+    distributions [1]_. In the discrete case, the Wasserstein distance can be
+    understood as the cost of an optimal transport plan to convert one
+    distribution into the other. The cost is calculated as the product of the
+    amount of probability mass being moved and the distance it is being moved.
+    A brief and intuitive introduction can be found at [2]_.
+
+    .. versionadded:: 1.13.0
+
+    Parameters
+    ----------
+    u_values : 2d array_like
+        A sample from a probability distribution or the support (set of all
+        possible values) of a probability distribution. Each element along
+        axis 0 is an observation or possible value, and axis 1 represents the
+        dimensionality of the distribution; i.e., each row is a vector
+        observation or possible value.
+
+    v_values : 2d array_like
+        A sample from or the support of a second distribution.
+
+    u_weights, v_weights : 1d array_like, optional
+        Weights or counts corresponding with the sample or probability masses
+        corresponding with the support values. Sum of elements must be positive
+        and finite. If unspecified, each value is assigned the same weight.
+
+    Returns
+    -------
+    distance : float
+        The computed distance between the distributions.
+
+    Notes
+    -----
+    Given two probability mass functions, :math:`u`
+    and :math:`v`, the first Wasserstein distance between the distributions
+    using the Euclidean norm is:
+
+    .. math::
+
+        l_1 (u, v) = \inf_{\pi \in \Gamma (u, v)} \int \| x-y \|_2 \mathrm{d} \pi (x, y)
+
+    where :math:`\Gamma (u, v)` is the set of (probability) distributions on
+    :math:`\mathbb{R}^n \times \mathbb{R}^n` whose marginals are :math:`u` and
+    :math:`v` on the first and second factors respectively. For a given value
+    :math:`x`, :math:`u(x)` gives the probability of :math:`u` at position
+    :math:`x`, and the same for :math:`v(x)`.
+
+    This is also called the optimal transport problem or the Monge problem.
+    Let the finite point sets :math:`\{x_i\}` and :math:`\{y_j\}` denote
+    the support set of probability mass function :math:`u` and :math:`v`
+    respectively. The Monge problem can be expressed as follows,
+
+    Let :math:`\Gamma` denote the transport plan, :math:`D` denote the
+    distance matrix and,
+
+    .. math::
+
+        x = \text{vec}(\Gamma)          \\
+        c = \text{vec}(D)               \\
+        b = \begin{bmatrix}
+                u\\
+                v\\
+            \end{bmatrix}
+
+    The :math:`\text{vec}()` function denotes the Vectorization function
+    that transforms a matrix into a column vector by vertically stacking
+    the columns of the matrix.
+    The transport plan :math:`\Gamma` is a matrix :math:`[\gamma_{ij}]` in
+    which :math:`\gamma_{ij}` is a positive value representing the amount of
+    probability mass transported from :math:`u(x_i)` to :math:`v(y_i)`.
+    Summing over the rows of :math:`\Gamma` should give the source distribution
+    :math:`u` : :math:`\sum_j \gamma_{ij} = u(x_i)` holds for all :math:`i`
+    and summing over the columns of :math:`\Gamma` should give the target
+    distribution :math:`v`: :math:`\sum_i \gamma_{ij} = v(y_j)` holds for all
+    :math:`j`.
+    The distance matrix :math:`D` is a matrix :math:`[d_{ij}]`, in which
+    :math:`d_{ij} = d(x_i, y_j)`.
+
+    Given :math:`\Gamma`, :math:`D`, :math:`b`, the Monge problem can be
+    transformed into a linear programming problem by
+    taking :math:`A x = b` as constraints and :math:`z = c^T x` as minimization
+    target (sum of costs) , where matrix :math:`A` has the form
+
+    .. math::
+
+        \begin{array} {rrrr|rrrr|r|rrrr}
+            1 & 1 & \dots & 1 & 0 & 0 & \dots & 0 & \dots & 0 & 0 & \dots &
+                0 \cr
+            0 & 0 & \dots & 0 & 1 & 1 & \dots & 1 & \dots & 0 & 0 &\dots &
+                0 \cr
+            \vdots & \vdots & \ddots & \vdots & \vdots & \vdots & \ddots
+                & \vdots & \vdots & \vdots & \vdots & \ddots & \vdots  \cr
+            0 & 0 & \dots & 0 & 0 & 0 & \dots & 0 & \dots & 1 & 1 & \dots &
+                1 \cr \hline
+
+            1 & 0 & \dots & 0 & 1 & 0 & \dots & \dots & \dots & 1 & 0 & \dots &
+                0 \cr
+            0 & 1 & \dots & 0 & 0 & 1 & \dots & \dots & \dots & 0 & 1 & \dots &
+                0 \cr
+            \vdots & \vdots & \ddots & \vdots & \vdots & \vdots & \ddots &
+                \vdots & \vdots & \vdots & \vdots & \ddots & \vdots \cr
+            0 & 0 & \dots & 1 & 0 & 0 & \dots & 1 & \dots & 0 & 0 & \dots & 1
+        \end{array}
+
+    By solving the dual form of the above linear programming problem (with
+    solution :math:`y^*`), the Wasserstein distance :math:`l_1 (u, v)` can
+    be computed as :math:`b^T y^*`.
+
+    The above solution is inspired by Vincent Herrmann's blog [3]_ . For a
+    more thorough explanation, see [4]_ .
+
+    The input distributions can be empirical, therefore coming from samples
+    whose values are effectively inputs of the function, or they can be seen as
+    generalized functions, in which case they are weighted sums of Dirac delta
+    functions located at the specified values.
+
+    References
+    ----------
+    .. [1] "Wasserstein metric",
+           https://en.wikipedia.org/wiki/Wasserstein_metric
+    .. [2] Lili Weng, "What is Wasserstein distance?", Lil'log,
+           https://lilianweng.github.io/posts/2017-08-20-gan/#what-is-wasserstein-distance.
+    .. [3] Hermann, Vincent. "Wasserstein GAN and the Kantorovich-Rubinstein
+           Duality". https://vincentherrmann.github.io/blog/wasserstein/.
+    .. [4] Peyré, Gabriel, and Marco Cuturi. "Computational optimal
+           transport." Center for Research in Economics and Statistics
+           Working Papers 2017-86 (2017).
+
+    See Also
+    --------
+    wasserstein_distance: Compute the Wasserstein-1 distance between two
+        1D discrete distributions.
+
+    Examples
+    --------
+    Compute the Wasserstein distance between two three-dimensional samples,
+    each with two observations.
+
+    >>> from scipy.stats import wasserstein_distance_nd
+    >>> wasserstein_distance_nd([[0, 2, 3], [1, 2, 5]], [[3, 2, 3], [4, 2, 5]])
+    3.0
+
+    Compute the Wasserstein distance between two two-dimensional distributions
+    with three and two weighted observations, respectively.
+
+    >>> wasserstein_distance_nd([[0, 2.75], [2, 209.3], [0, 0]],
+    ...                      [[0.2, 0.322], [4.5, 25.1808]],
+    ...                      [0.4, 5.2, 0.114], [0.8, 1.5])
+    174.15840245217169
+    """
+    m, n = len(u_values), len(v_values)
+    u_values = asarray(u_values)
+    v_values = asarray(v_values)
+
+    if u_values.ndim > 2 or v_values.ndim > 2:
+        raise ValueError('Invalid input values. The inputs must have either '
+                         'one or two dimensions.')
+    # if dimensions are not equal throw error
+    if u_values.ndim != v_values.ndim:
+        raise ValueError('Invalid input values. Dimensions of inputs must be '
+                         'equal.')
+    # if data is 1D then call the cdf_distance function
+    if u_values.ndim == 1 and v_values.ndim == 1:
+        return _cdf_distance(1, u_values, v_values, u_weights, v_weights)
+
+    u_values, u_weights = _validate_distribution(u_values, u_weights)
+    v_values, v_weights = _validate_distribution(v_values, v_weights)
+    # if number of columns is not equal throw error
+    if u_values.shape[1] != v_values.shape[1]:
+        raise ValueError('Invalid input values. If two-dimensional, '
+                         '`u_values` and `v_values` must have the same '
+                         'number of columns.')
+
+    # if data contains np.inf then return inf or nan
+    if np.any(np.isinf(u_values)) ^ np.any(np.isinf(v_values)):
+        return np.inf
+    elif np.any(np.isinf(u_values)) and np.any(np.isinf(v_values)):
+        return np.nan
+
+    # create constraints
+    A_upper_part = sparse.block_diag((sparse.coo_array(np.ones((1, n))), ) * m)
+    A_lower_part = sparse.hstack((sparse.eye_array(n), ) * m)
+    # sparse constraint matrix of size (m + n)*(m * n)
+    A = sparse.vstack((A_upper_part, A_lower_part))
+
+    # get cost matrix
+    D = cdist(u_values, v_values)
+    cost = D.ravel()
+
+    # create the minimization target
+    p_u = np.full(m, 1/m) if u_weights is None else u_weights/np.sum(u_weights)
+    p_v = np.full(n, 1/n) if v_weights is None else v_weights/np.sum(v_weights)
+    b = np.concatenate((p_u, p_v), axis=0)
+
+    # solving LP
+    constraints = LinearConstraint(A=A.T, ub=cost)
+    opt_res = milp(c=-b, constraints=constraints, bounds=(-np.inf, np.inf))
+    return -opt_res.fun
+
+
+@xp_capabilities(np_only=True)
+def wasserstein_distance(u_values, v_values, u_weights=None, v_weights=None):
+    r"""
+    Compute the Wasserstein-1 distance between two 1D discrete distributions.
+
+    The Wasserstein distance, also called the Earth mover's distance or the
+    optimal transport distance, is a similarity metric between two probability
+    distributions [1]_. In the discrete case, the Wasserstein distance can be
+    understood as the cost of an optimal transport plan to convert one
+    distribution into the other. The cost is calculated as the product of the
+    amount of probability mass being moved and the distance it is being moved.
+    A brief and intuitive introduction can be found at [2]_.
+
+    .. versionadded:: 1.0.0
+
+    Parameters
+    ----------
+    u_values : 1d array_like
+        A sample from a probability distribution or the support (set of all
+        possible values) of a probability distribution. Each element is an
+        observation or possible value.
+
+    v_values : 1d array_like
+        A sample from or the support of a second distribution.
+
+    u_weights, v_weights : 1d array_like, optional
+        Weights or counts corresponding with the sample or probability masses
+        corresponding with the support values. Sum of elements must be positive
+        and finite. If unspecified, each value is assigned the same weight.
+
+    Returns
+    -------
+    distance : float
+        The computed distance between the distributions.
+
+    Notes
+    -----
+    Given two 1D probability mass functions, :math:`u` and :math:`v`, the first
+    Wasserstein distance between the distributions is:
+
+    .. math::
+
+        l_1 (u, v) = \inf_{\pi \in \Gamma (u, v)} \int_{\mathbb{R} \times
+        \mathbb{R}} |x-y| \mathrm{d} \pi (x, y)
+
+    where :math:`\Gamma (u, v)` is the set of (probability) distributions on
+    :math:`\mathbb{R} \times \mathbb{R}` whose marginals are :math:`u` and
+    :math:`v` on the first and second factors respectively. For a given value
+    :math:`x`, :math:`u(x)` gives the probability of :math:`u` at position
+    :math:`x`, and the same for :math:`v(x)`.
+
+    If :math:`U` and :math:`V` are the respective CDFs of :math:`u` and
+    :math:`v`, this distance also equals to:
+
+    .. math::
+
+        l_1(u, v) = \int_{-\infty}^{+\infty} |U-V|
+
+    See [3]_ for a proof of the equivalence of both definitions.
+
+    The input distributions can be empirical, therefore coming from samples
+    whose values are effectively inputs of the function, or they can be seen as
+    generalized functions, in which case they are weighted sums of Dirac delta
+    functions located at the specified values.
+
+    References
+    ----------
+    .. [1] "Wasserstein metric", https://en.wikipedia.org/wiki/Wasserstein_metric
+    .. [2] Lili Weng, "What is Wasserstein distance?", Lil'log,
+           https://lilianweng.github.io/posts/2017-08-20-gan/#what-is-wasserstein-distance.
+    .. [3] Ramdas, Garcia, Cuturi "On Wasserstein Two Sample Testing and Related
+           Families of Nonparametric Tests" (2015). :arXiv:`1509.02237`.
+
+    See Also
+    --------
+    wasserstein_distance_nd: Compute the Wasserstein-1 distance between two N-D
+        discrete distributions.
+
+    Examples
+    --------
+    >>> from scipy.stats import wasserstein_distance
+    >>> wasserstein_distance([0, 1, 3], [5, 6, 8])
+    5.0
+    >>> wasserstein_distance([0, 1], [0, 1], [3, 1], [2, 2])
+    0.25
+    >>> wasserstein_distance([3.4, 3.9, 7.5, 7.8], [4.5, 1.4],
+    ...                      [1.4, 0.9, 3.1, 7.2], [3.2, 3.5])
+    4.0781331438047861
+
+    """
+    return _cdf_distance(1, u_values, v_values, u_weights, v_weights)
+
+
+@xp_capabilities(np_only=True)
+def energy_distance(u_values, v_values, u_weights=None, v_weights=None):
+    r"""Compute the energy distance between two 1D distributions.
+
+    .. versionadded:: 1.0.0
+
+    Parameters
+    ----------
+    u_values, v_values : array_like
+        Values observed in the (empirical) distribution.
+    u_weights, v_weights : array_like, optional
+        Weight for each value. If unspecified, each value is assigned the same
+        weight.
+        `u_weights` (resp. `v_weights`) must have the same length as
+        `u_values` (resp. `v_values`). If the weight sum differs from 1, it
+        must still be positive and finite so that the weights can be normalized
+        to sum to 1.
+
+    Returns
+    -------
+    distance : float
+        The computed distance between the distributions.
+
+    Notes
+    -----
+    The energy distance between two distributions :math:`u` and :math:`v`, whose
+    respective CDFs are :math:`U` and :math:`V`, equals to:
+
+    .. math::
+
+        D(u, v) = \left( 2\mathbb E|X - Y| - \mathbb E|X - X'| -
+        \mathbb E|Y - Y'| \right)^{1/2}
+
+    where :math:`X` and :math:`X'` (resp. :math:`Y` and :math:`Y'`) are
+    independent random variables whose probability distribution is :math:`u`
+    (resp. :math:`v`).
+
+    Sometimes the square of this quantity is referred to as the "energy
+    distance" (e.g. in [2]_, [4]_), but as noted in [1]_ and [3]_, only the
+    definition above satisfies the axioms of a distance function (metric).
+
+    As shown in [2]_, for one-dimensional real-valued variables, the energy
+    distance is linked to the non-distribution-free version of the Cramér-von
+    Mises distance:
+
+    .. math::
+
+        D(u, v) = \sqrt{2} l_2(u, v) = \left( 2 \int_{-\infty}^{+\infty} (U-V)^2
+        \right)^{1/2}
+
+    Note that the common Cramér-von Mises criterion uses the distribution-free
+    version of the distance. See [2]_ (section 2), for more details about both
+    versions of the distance.
+
+    The input distributions can be empirical, therefore coming from samples
+    whose values are effectively inputs of the function, or they can be seen as
+    generalized functions, in which case they are weighted sums of Dirac delta
+    functions located at the specified values.
+
+    References
+    ----------
+    .. [1] Rizzo, Szekely "Energy distance." Wiley Interdisciplinary Reviews:
+           Computational Statistics, 8(1):27-38 (2015).
+    .. [2] Szekely "E-statistics: The energy of statistical samples." Bowling
+           Green State University, Department of Mathematics and Statistics,
+           Technical Report 02-16 (2002).
+    .. [3] "Energy distance", https://en.wikipedia.org/wiki/Energy_distance
+    .. [4] Bellemare, Danihelka, Dabney, Mohamed, Lakshminarayanan, Hoyer,
+           Munos "The Cramer Distance as a Solution to Biased Wasserstein
+           Gradients" (2017). :arXiv:`1705.10743`.
+
+    Examples
+    --------
+    >>> from scipy.stats import energy_distance
+    >>> energy_distance([0], [2])
+    2.0000000000000004
+    >>> energy_distance([0, 8], [0, 8], [3, 1], [2, 2])
+    1.0000000000000002
+    >>> energy_distance([0.7, 7.4, 2.4, 6.8], [1.4, 8. ],
+    ...                 [2.1, 4.2, 7.4, 8. ], [7.6, 8.8])
+    0.88003340976158217
+
+    """
+    return np.sqrt(2) * _cdf_distance(2, u_values, v_values,
+                                      u_weights, v_weights)
+
+
+def _cdf_distance(p, u_values, v_values, u_weights=None, v_weights=None):
+    r"""
+    Compute, between two one-dimensional distributions :math:`u` and
+    :math:`v`, whose respective CDFs are :math:`U` and :math:`V`, the
+    statistical distance that is defined as:
+
+    .. math::
+
+        l_p(u, v) = \left( \int_{-\infty}^{+\infty} |U-V|^p \right)^{1/p}
+
+    p is a positive parameter; p = 1 gives the Wasserstein distance, p = 2
+    gives the energy distance.
+
+    Parameters
+    ----------
+    u_values, v_values : array_like
+        Values observed in the (empirical) distribution.
+    u_weights, v_weights : array_like, optional
+        Weight for each value. If unspecified, each value is assigned the same
+        weight.
+        `u_weights` (resp. `v_weights`) must have the same length as
+        `u_values` (resp. `v_values`). If the weight sum differs from 1, it
+        must still be positive and finite so that the weights can be normalized
+        to sum to 1.
+
+    Returns
+    -------
+    distance : float
+        The computed distance between the distributions.
+
+    Notes
+    -----
+    The input distributions can be empirical, therefore coming from samples
+    whose values are effectively inputs of the function, or they can be seen as
+    generalized functions, in which case they are weighted sums of Dirac delta
+    functions located at the specified values.
+
+    References
+    ----------
+    .. [1] Bellemare, Danihelka, Dabney, Mohamed, Lakshminarayanan, Hoyer,
+           Munos "The Cramer Distance as a Solution to Biased Wasserstein
+           Gradients" (2017). :arXiv:`1705.10743`.
+
+    """
+    u_values, u_weights = _validate_distribution(u_values, u_weights)
+    v_values, v_weights = _validate_distribution(v_values, v_weights)
+
+    u_sorter = np.argsort(u_values)
+    v_sorter = np.argsort(v_values)
+
+    all_values = np.concatenate((u_values, v_values))
+    all_values.sort(kind='mergesort')
+
+    # Compute the differences between pairs of successive values of u and v.
+    deltas = np.diff(all_values)
+
+    # Get the respective positions of the values of u and v among the values of
+    # both distributions.
+    u_cdf_indices = u_values[u_sorter].searchsorted(all_values[:-1], 'right')
+    v_cdf_indices = v_values[v_sorter].searchsorted(all_values[:-1], 'right')
+
+    # Calculate the CDFs of u and v using their weights, if specified.
+    if u_weights is None:
+        u_cdf = u_cdf_indices / u_values.size
+    else:
+        u_sorted_cumweights = np.concatenate(([0],
+                                              np.cumsum(u_weights[u_sorter])))
+        u_cdf = u_sorted_cumweights[u_cdf_indices] / u_sorted_cumweights[-1]
+
+    if v_weights is None:
+        v_cdf = v_cdf_indices / v_values.size
+    else:
+        v_sorted_cumweights = np.concatenate(([0],
+                                              np.cumsum(v_weights[v_sorter])))
+        v_cdf = v_sorted_cumweights[v_cdf_indices] / v_sorted_cumweights[-1]
+
+    # Compute the value of the integral based on the CDFs.
+    # If p = 1 or p = 2, we avoid using np.power, which introduces an overhead
+    # of about 15%.
+    if p == 1:
+        return np.vecdot(np.abs(u_cdf - v_cdf), deltas)
+    if p == 2:
+        return np.sqrt(np.vecdot(np.square(u_cdf - v_cdf), deltas))
+    return np.power(np.vecdot(np.power(np.abs(u_cdf - v_cdf), p), deltas), 1/p)
+
+
+def _validate_distribution(values, weights):
+    """
+    Validate the values and weights from a distribution input of `cdf_distance`
+    and return them as ndarray objects.
+
+    Parameters
+    ----------
+    values : array_like
+        Values observed in the (empirical) distribution.
+    weights : array_like
+        Weight for each value.
+
+    Returns
+    -------
+    values : ndarray
+        Values as ndarray.
+    weights : ndarray
+        Weights as ndarray.
+
+    """
+    # Validate the value array.
+    values = np.asarray(values, dtype=float)
+    if len(values) == 0:
+        raise ValueError("Distribution can't be empty.")
+
+    # Validate the weight array, if specified.
+    if weights is not None:
+        weights = np.asarray(weights, dtype=float)
+        if len(weights) != len(values):
+            raise ValueError('Value and weight array-likes for the same '
+                             'empirical distribution must be of the same size.')
+        if np.any(weights < 0):
+            raise ValueError('All weights must be non-negative.')
+        if not 0 < np.sum(weights) < np.inf:
+            raise ValueError('Weight array-like sum must be positive and '
+                             'finite. Set as None for an equal distribution of '
+                             'weight.')
+
+        return values, weights
+
+    return values, None
+
+
+@xp_capabilities(skip_backends=[("dask.array", "no `take_along_axis`")],
+                 marray=True)
+def rankdata(a, method='average', *, axis=None, nan_policy='propagate'):
+    """Assign ranks to data, dealing with ties appropriately.
+
+    By default (``axis=None``), the data array is first flattened, and a flat
+    array of ranks is returned. Separately reshape the rank array to the
+    shape of the data array if desired (see Examples).
+
+    Ranks begin at 1.  The `method` argument controls how ranks are assigned
+    to equal values.  See [1]_ for further discussion of ranking methods.
+
+    Parameters
+    ----------
+    a : array_like
+        The array of values to be ranked.
+    method : {'average', 'min', 'max', 'dense', 'ordinal'}, optional
+        The method used to assign ranks to tied elements.
+        The following methods are available (default is 'average'):
+
+        * 'average': The average of the ranks that would have been assigned to
+          all the tied values is assigned to each value.
+        * 'min': The minimum of the ranks that would have been assigned to all
+          the tied values is assigned to each value.  (This is also
+          referred to as "competition" ranking.)
+        * 'max': The maximum of the ranks that would have been assigned to all
+          the tied values is assigned to each value.
+        * 'dense': Like 'min', but the rank of the next highest element is
+          assigned the rank immediately after those assigned to the tied
+          elements.
+        * 'ordinal': All values are given a distinct rank, corresponding to
+          the order that the values occur in `a`.
+
+    axis : {None, int}, optional
+        Axis along which to perform the ranking. If ``None``, the data array
+        is first flattened.
+    nan_policy : {'propagate', 'omit', 'raise'}, optional
+        Defines how to handle when input contains nan.
+        The following options are available (default is 'propagate'):
+
+        * 'propagate': propagates nans through the rank calculation
+        * 'omit': performs the calculations ignoring nan values
+        * 'raise': raises an error
+
+        .. note::
+
+            When `nan_policy` is 'propagate', the output is an array of *all*
+            nans because ranks relative to nans in the input are undefined.
+            When `nan_policy` is 'omit', nans in `a` are ignored when ranking
+            the other values, and the corresponding locations of the output
+            are nan.
+
+        .. versionadded:: 1.10
+
+    Returns
+    -------
+    ranks : ndarray
+         An array of size equal to the size of `a`, containing rank
+         scores. The dtype is the result dtype of `a` and a Python float.
+
+    References
+    ----------
+    .. [1] "Ranking", https://en.wikipedia.org/wiki/Ranking
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import rankdata
+    >>> rankdata([0, 2, 3, 2])
+    array([1. , 2.5, 4. , 2.5])
+    >>> rankdata([0, 2, 3, 2], method='min')
+    array([1., 2., 4., 2.])
+    >>> rankdata([0, 2, 3, 2], method='max')
+    array([1., 3., 4., 3.])
+    >>> rankdata([0, 2, 3, 2], method='dense')
+    array([1., 2., 3., 2.])
+    >>> rankdata([0, 2, 3, 2], method='ordinal')
+    array([1., 2., 4., 3.])
+    >>> rankdata([[0, 2], [3, 2]]).reshape(2, 2)
+    array([[1. , 2.5],
+           [4. , 2.5]])
+    >>> rankdata([[0, 2, 2], [3, 2, 5]], axis=1)
+    array([[1. , 2.5, 2.5],
+           [2. , 1. , 3. ]])
+    >>> rankdata([0, 2, 3, np.nan, -2, np.nan], nan_policy="propagate")
+    array([nan, nan, nan, nan, nan, nan])
+    >>> rankdata([0, 2, 3, np.nan, -2, np.nan], nan_policy="omit")
+    array([ 2.,  3.,  4., nan,  1., nan])
+
+    """
+    methods = ('average', 'min', 'max', 'dense', 'ordinal')
+    if method not in methods:
+        raise ValueError(f'unknown method "{method}"')
+
+    xp = array_namespace(a)
+    x = xp.asarray(a)
+
+    if axis is None:
+        x = xp_ravel(x)
+        axis = -1
+
+    if xp_size(x) == 0:
+        dtype = xp_result_type(x, force_floating=True, xp=xp)
+        return xp.empty_like(x, dtype=dtype)
+
+    contains_nan = _contains_nan(x, nan_policy)
+
+    x = xp_swapaxes(x, axis, -1, xp=xp)
+    ranks, _, _ = _rankdata(x, method, xp=xp)
+
+    # JIT won't allow use of `contains_nan` for control flow here, so we always have to
+    # run this with JIT.
+    if is_lazy_array(x) or contains_nan:
+        i_nan = (xp.isnan(x) if nan_policy == 'omit'
+                 else xp.any(xp.isnan(x), axis=-1, keepdims=True))
+        i_nan = xp.broadcast_to(i_nan, ranks.shape)
+        ranks = xpx.at(ranks)[i_nan].set(xp.nan)
+
+    ranks = xp_swapaxes(ranks, axis, -1, xp=xp)
+    return ranks
+
+
+def _order_ranks(ranks, j, *, xp):
+    # Reorder ascending order `ranks` according to `j`
+    xp = array_namespace(ranks) if xp is None else xp
+    if is_numpy(xp) or is_cupy(xp):
+        ordered_ranks = xp.empty(j.shape, dtype=ranks.dtype)
+        xp.put_along_axis(ordered_ranks, j, ranks, axis=-1)
+    else:
+        # `put_along_axis` not in array API (data-apis/array-api#177)
+        #  so argsort the argsort and take_along_axis...
+        j_inv = xp.argsort(j, axis=-1, stable=True)
+        ordered_ranks = xp.take_along_axis(ranks, j_inv, axis=-1)
+    return ordered_ranks
+
+
+def _rankdata(x, method, return_sorted=False, return_ties=False, xp=None):
+    # Rank data `x` by desired `method`. For methods other than 'ordinal':
+    # - `return_sorted=True` ensures that the second output is sorted `x`
+    # - `return_ties=True` ensures that the third output is tie data
+    # Otherwise, the second/third output will be None.
+    xp = array_namespace(x) if xp is None else xp
+    dtype = xp_result_type(x, force_floating=True, xp=xp)
+
+    if is_jax(xp):
+        import jax.scipy.stats as jax_stats
+        ranks = jax_stats.rankdata(x, method=method, axis=-1)
+        ranks = xp.astype(ranks, dtype, copy=False)
+        y = xp.sort(x, axis=-1) if (return_ties or return_sorted) else None
+        t = None
+        if return_ties:
+            max_ranks = jax_stats.rankdata(y, method='max', axis=-1)
+            t = xp.diff(max_ranks, axis=-1, prepend=0)
+            t = xp.astype(t, dtype, copy=False)
+        return ranks, y, t
+
+    if is_marray(xp):
+        data, mask = x.data, x.mask
+        uxp = array_namespace(data)
+        data = uxp.where(mask, uxp.nan, data)
+        ranks, sorted, ties = _rankdata(data, method, return_sorted, return_ties, uxp)
+        ranks = xp.asarray(ranks, mask=mask)
+        mask_sorted = uxp.isnan(sorted) if (return_sorted or return_ties) else None
+        sorted = xp.asarray(sorted, mask=mask_sorted) if return_sorted else None
+        ties = xp.asarray(ties, mask=mask_sorted) if return_ties else None
+        return ranks, sorted, ties
+
+    shape = x.shape
+
+    # Get sort order
+    j = xp.argsort(x, axis=-1, stable=True)
+    ordinal_ranks = xp.broadcast_to(xp.arange(1, shape[-1]+1, dtype=dtype), shape)
+
+    # Ordinal ranks is very easy because ties don't matter. We're done.
+    if method == 'ordinal':
+        ranks = _order_ranks(ordinal_ranks, j, xp=xp)
+        return (ranks, None, None)
+
+    # Sort array
+    y = xp.take_along_axis(x, j, axis=-1)
+    # Logical indices of unique elements
+    i = xp.concat([xp.ones(shape[:-1] + (1,), dtype=xp.bool),
+                   y[..., :-1] != y[..., 1:]], axis=-1)
+
+    # Integer indices of unique elements
+    indices = xp.arange(xp_size(y))[xp.reshape(i, (-1,))]  # i gets raveled
+    # Counts of unique elements
+    counts = xp.diff(indices, append=xp.asarray([xp_size(y)], dtype=indices.dtype))
+
+    # Compute `'min'`, `'max'`, and `'mid'` ranks of unique elements
+    if method == 'min':
+        ranks = ordinal_ranks[i]
+    elif method == 'max':
+        ranks = ordinal_ranks[i] + xp.astype(counts, dtype) - 1
+    elif method == 'average':
+        # array API doesn't promote integers to floats
+        ranks = ordinal_ranks[i] + (xp.astype(counts, dtype) - 1)/2
+    elif method == 'dense':
+        ranks = xp.cumulative_sum(xp.astype(i, dtype, copy=False), axis=-1)[i]
+
+    if not is_cupy(xp):
+        ranks = xp.reshape(xp.repeat(ranks, counts), shape)
+    else:
+        # workaround for cupy.repeat not accepting arrays for repeats
+        # (ref https://github.com/cupy/cupy/issues/3849).
+        # The cumulative sum over i will
+        # increment every time a new unique rank appears, giving the correct
+        # indices to replicate xp.repeats.
+        ranks = xp.reshape(
+            ranks[xp.cumulative_sum(xp.astype(xp.reshape(i, (-1,)), xp.int64)) - 1],
+            shape,
+        )
+    ranks = _order_ranks(ranks, j, xp=xp)
+
+    t = None
+    if return_ties:
+        # Tie information is returned in a format that is useful to functions that
+        # rely on this (private) function. Example:
+        # >>> x = np.asarray([3, 2, 1, 2, 2, 2, 1])
+        # >>> _, t = _rankdata(x, 'average', return_ties=True)
+        # >>> t  # array([2., 0., 4., 0., 0., 0., 1.])  # two 1s, four 2s, and one 3
+        # Unlike ranks, tie counts are *not* reordered to correspond with the order of
+        # the input; e.g. the number of appearances of the lowest rank element comes
+        # first. This is a useful format because:
+        # - The shape of the result is the shape of the input. Different slices can
+        #   have different numbers of tied elements but not result in a ragged array.
+        # - Functions that use `t` usually don't need to which each element of the
+        #   original array is associated with each tie count; they perform a reduction
+        #   over the tie counts onnly. The tie counts are naturally computed in a
+        #   sorted order, so this does not unnecessarily reorder them.
+        # - One exception is `wilcoxon`, which needs the number of zeros. Zeros always
+        #   have the lowest rank, so it is easy to find them at the zeroth index.
+        t = xp.zeros(shape, dtype=dtype)
+        t = xpx.at(t)[i].set(xp.astype(counts, dtype, copy=False))
+
+    return ranks, y, t
+
+
+@xp_capabilities(marray=True,
+    skip_backends=[('dask.array', 'uses `optimize.elementwise.find_root`'),
+                   ('array_api_strict', 'uses `optimize.elementwise.find_root`'),
+                   ('jax.numpy', 'uses `optimize.elementwise.find_root`')])
+@_axis_nan_policy_factory(
+    lambda x: x, n_outputs=1, result_to_tuple=lambda x, _: (x,),
+    kwd_samples=['weights'], paired=True
+)
+def expectile(a, alpha=0.5, *, weights=None, axis=None):
+    r"""Compute the expectile at the specified level.
+
+    Expectiles are a generalization of the expectation in the same way as
+    quantiles are a generalization of the median. The expectile at level
+    `alpha = 0.5` is the mean (average). See Notes for more details.
+
+    Parameters
+    ----------
+    a : array_like
+        Array containing numbers whose expectile is desired.
+    alpha : float, default: 0.5
+        The level of the expectile; e.g., ``alpha=0.5`` gives the mean.
+    weights : array_like, optional
+        An array of weights associated with the values in `a`.
+        The `weights` must be broadcastable to the same shape as `a`.
+        Default is None, which gives each value a weight of 1.0.
+        An integer valued weight element acts like repeating the corresponding
+        observation in `a` that many times. See Notes for more details.
+    axis : int or tuple of ints, default: None
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    expectile : ndarray
+        The empirical expectile at level `alpha`.
+
+    See Also
+    --------
+    numpy.mean : Arithmetic average
+    numpy.quantile : Quantile
+
+    Notes
+    -----
+    In general, the expectile at level :math:`\alpha` of a random variable
+    :math:`X` with cumulative distribution function (CDF) :math:`F` is given
+    by the unique solution :math:`t` of:
+
+    .. math::
+
+        \alpha E((X - t)_+) = (1 - \alpha) E((t - X)_+) \,.
+
+    Here, :math:`(x)_+ = \max(0, x)` is the positive part of :math:`x`.
+    This equation can be equivalently written as:
+
+    .. math::
+
+        \alpha \int_t^\infty (x - t)\mathrm{d}F(x)
+        = (1 - \alpha) \int_{-\infty}^t (t - x)\mathrm{d}F(x) \,.
+
+    The empirical expectile at level :math:`\alpha` (`alpha`) of a sample
+    :math:`a_i` (the array `a`) is defined by plugging in the empirical CDF of
+    `a`. Given sample or case weights :math:`w` (the array `weights`), it
+    reads :math:`F_a(x) = \frac{1}{\sum_i w_i} \sum_i w_i 1_{a_i \leq x}`
+    with indicator function :math:`1_{A}`. This leads to the definition of the
+    empirical expectile at level `alpha` as the unique solution :math:`t` of:
+
+    .. math::
+
+        \alpha \sum_{i=1}^n w_i (a_i - t)_+ =
+            (1 - \alpha) \sum_{i=1}^n w_i (t - a_i)_+ \,.
+
+    For :math:`\alpha=0.5`, this simplifies to the weighted average.
+    Furthermore, the larger :math:`\alpha`, the larger the value of the
+    expectile.
+
+    As a final remark, the expectile at level :math:`\alpha` can also be
+    written as a minimization problem. One often used choice is
+
+    .. math::
+
+        \operatorname{argmin}_t
+        E(\lvert 1_{t\geq X} - \alpha\rvert(t - X)^2) \,.
+
+    References
+    ----------
+    .. [1] W. K. Newey and J. L. Powell (1987), "Asymmetric Least Squares
+           Estimation and Testing," Econometrica, 55, 819-847.
+    .. [2] T. Gneiting (2009). "Making and Evaluating Point Forecasts,"
+           Journal of the American Statistical Association, 106, 746 - 762.
+           :doi:`10.48550/arXiv.0912.0902`
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import expectile
+    >>> a = [1, 4, 2, -1]
+    >>> expectile(a, alpha=0.5) == np.mean(a)
+    True
+    >>> expectile(a, alpha=0.2)
+    0.42857142857142855
+    >>> expectile(a, alpha=0.8)
+    2.5714285714285716
+    >>> weights = [1, 3, 1, 1]
+    >>> expectile(a, alpha=0.8, weights=weights)
+    3.3333333333333335
+    """
+    xp = array_namespace(a, weights)
+    a, weights = xp_promote(a, weights, force_floating=True, xp=xp)
+
+    if alpha < 0 or alpha > 1:
+        raise ValueError(
+            "The expectile level alpha must be in the range [0, 1]."
+        )
+
+    if a.shape[-1] == 0:  #  Only for *testing* _axis_nan_policy decorator
+        return _get_nan(a, xp=xp)
+
+    # for simplicity, ensure that shape is (batch size, sample size) and at least 2d
+    shape = a.shape  # remember original shape, though, to ensure correct output shape
+    # `reshape` can't infer shape element -1 when array is size 0, so take `prod`
+    a = xp.reshape(a, (math.prod(shape[:-1]), shape[-1]))
+    weights = weights if weights is None else xp.reshape(weights, (-1, shape[-1]))
+
+    # This is the empirical equivalent of Eq. (13) with identification
+    # function from Table 9 (omitting a factor of 2) in [2] (their y is our
+    # data a, their x is our t)
+    def first_order(t, i):
+        ai = a[i, ...].T
+        wi = weights if weights is None else weights[i, ...].T
+        return _xp_mean(xp.abs(xp.astype(ai <= t, ai.dtype) - alpha) * (t - ai),
+                        weights=wi, axis=0)
+
+    # axis is -1 per the decorator. Use mean to produce NaNs of the correct shape/type.
+    x0 = xp.min(a, axis=-1)
+    x1 = xp.max(a, axis=-1)
+
+    # Note that the expectile is the unique solution, so no worries about
+    # finding a wrong root.
+    i = xp.arange(a.shape[0])
+    res = find_root(first_order, (x0, x1), args=(i,))
+    res = xp.reshape(res.x, shape[:-1])
+    return res[()] if res.ndim == 0 else res
+
+
+def _lmoment_iv(sample, order, axis, sorted, standardize, xp):
+    # input validation/standardization for `lmoment`
+    sample = xp_promote(sample, force_floating=True, xp=xp)
+
+    message = "`sample` must be an array of real numbers."
+    if not xp.isdtype(sample.dtype, "real floating"):
+        raise ValueError(message)
+
+    message = "`order` must be a scalar or a non-empty array of positive integers."
+    order = xp.arange(1, 5) if order is None else xp.asarray(order)
+    if (not xp.isdtype(order.dtype, "integral") or order.size == 0 or order.ndim > 1
+            or (not is_lazy_array(order) and xp.any(order <= 0))):
+        raise ValueError(message)
+
+    # input validation of non-array types can still be performed with NumPy
+    axis = np.asarray(axis)[()]
+    message = "`axis` must be an integer."
+    if not np.issubdtype(axis.dtype, np.integer) or axis.ndim != 0:
+        raise ValueError(message)
+    axis = int(axis)
+
+    sorted = np.asarray(sorted)[()]
+    message = "`sorted` must be True or False."
+    if not np.issubdtype(sorted.dtype, np.bool_) or sorted.ndim != 0:
+        raise ValueError(message)
+    sorted = bool(sorted)
+
+    standardize = np.asarray(standardize)[()]
+    message = "`standardize` must be True or False."
+    if not np.issubdtype(standardize.dtype, np.bool_) or standardize.ndim != 0:
+        raise ValueError(message)
+    standardize = bool(standardize)
+
+    sample = xp.moveaxis(sample, axis, -1)
+    sample = xp.sort(sample, axis=-1) if not sorted else sample
+
+    return sample, order, axis, sorted, standardize
+
+
+def _br(x, *, r=0, xp):
+    n = x.shape[-1]
+    x = xp.expand_dims(x, axis=-2)
+    x = xp.broadcast_to(x, x.shape[:-2] + (r.shape[0], n))
+    x = xp.triu(x)
+    j = xp.arange(n, dtype=x.dtype)
+    n = xp.asarray(n, dtype=x.dtype)[()]
+    binom_j_r = xpx.lazy_apply(special.binom, j, r[:, xp.newaxis])
+    binom_nm1_r = xpx.lazy_apply(special.binom, n-1, r)
+    return xp.vecdot(binom_j_r, x, axis=-1) / binom_nm1_r / n
+
+
+def _prk(r, k):
+    # Writen to match [1] Equation 27 closely to facilitate review.
+    # This does not protect against overflow, so improvements to
+    # robustness would be a welcome follow-up.
+    binom_r_k = xpx.lazy_apply(special.binom, r, k)
+    binom_rpk_k = xpx.lazy_apply(special.binom, r+k, k)
+    return (-1)**(r-k)*binom_r_k*binom_rpk_k
+
+
+@xp_capabilities(skip_backends=[('dask.array', "too many issues")],
+                 cpu_only=True,  # torch doesn't have `binom`
+                 exceptions=('cupy', 'jax.numpy'),
+                 extra_note="Lazy backends do not support `order` greater than 4.")
+@_axis_nan_policy_factory(  # noqa: E302
+    _moment_result_object, n_samples=1, result_to_tuple=_moment_tuple,
+    n_outputs=lambda kwds: _moment_outputs(kwds, [1, 2, 3, 4])
+)
+def lmoment(sample, order=None, *, axis=0, sorted=False, standardize=True):
+    r"""Compute L-moments of a sample from a continuous distribution.
+
+    The L-moments of a probability distribution are summary statistics with
+    uses similar to those of conventional moments, but they are defined in
+    terms of the expected values of order statistics.
+    Sample L-moments are defined analogously to population L-moments, and
+    they can serve as estimators of population L-moments. They tend to be less
+    sensitive to extreme observations than conventional moments.
+
+    Parameters
+    ----------
+    sample : array_like
+        The real-valued sample whose L-moments are desired.
+    order : array_like, optional
+        The (positive integer) orders of the desired L-moments.
+        Must be a scalar or non-empty 1D array. Default is [1, 2, 3, 4].
+    axis : int or None, default=0
+        If an int, the axis of the input along which to compute the statistic.
+        The statistic of each axis-slice (e.g. row) of the input will appear
+        in a corresponding element of the output. If None, the input will be
+        raveled before computing the statistic.
+    sorted : bool, default=False
+        Whether `sample` is already sorted in increasing order along `axis`.
+        If False (default), `sample` will be sorted.
+    standardize : bool, default=True
+        Whether to return L-moment ratios for orders 3 and higher.
+        L-moment ratios are analogous to standardized conventional
+        moments: they are the non-standardized L-moments divided
+        by the L-moment of order 2.
+
+    Returns
+    -------
+    lmoments : ndarray
+        The sample L-moments of order `order`.
+
+    See Also
+    --------
+    moment
+
+    Notes
+    -----
+    SciPy offers only basic capabilities for working with L-moments. For more advanced
+    features, consider the ``lmo`` package [4]_.
+
+    References
+    ----------
+    .. [1] D. Bilkova. "L-Moments and TL-Moments as an Alternative Tool of
+           Statistical Data Analysis". Journal of Applied Mathematics and
+           Physics. 2014. :doi:`10.4236/jamp.2014.210104`
+    .. [2] J. R. M. Hosking. "L-Moments: Analysis and Estimation of Distributions
+           Using Linear Combinations of Order Statistics". Journal of the Royal
+           Statistical Society. 1990. :doi:`10.1111/j.2517-6161.1990.tb01775.x`
+    .. [3] "L-moment". *Wikipedia*. https://en.wikipedia.org/wiki/L-moment.
+    .. [4] @jorenham, *Lmo*, https://github.com/jorenham/Lmo/
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng(328458568356392)
+    >>> sample = rng.exponential(size=100000)
+    >>> stats.lmoment(sample)
+    array([1.00124272, 0.50111437, 0.3340092 , 0.16755338])
+
+    Note that the first four standardized population L-moments of the standard
+    exponential distribution are 1, 1/2, 1/3, and 1/6; the sample L-moments
+    provide reasonable estimates.
+
+    """
+    xp = array_namespace(sample)
+    args = _lmoment_iv(sample, order, axis, sorted, standardize, xp=xp)
+    sample, order, axis, sorted, standardize = args
+
+    n_moments = 4 if is_lazy_array(order) else int(xp.max(order))
+    k = xp.arange(n_moments, dtype=sample.dtype)
+    prk = _prk(xpx.expand_dims(k, axis=tuple(range(1, sample.ndim+1))), k)
+    bk = _br(sample, r=k, xp=xp)
+
+    n = sample.shape[-1]
+    if n < bk.shape[-1]:
+        bk = xpx.at(bk)[..., n:].set(0)  # remove NaNs due to n_moments > n
+
+    lmoms = xp.vecdot(prk, bk, axis=-1)
+    if standardize and n_moments > 2:
+        lmoms = xpx.at(lmoms)[2:, ...].divide(lmoms[1, ...])
+
+    if n < lmoms.shape[0]:
+        lmoms = xpx.at(lmoms)[n:, ...].set(xp.nan)  # add NaNs where appropriate
+    # return lmoms[order-1]  # strict can't handle fancy indexing plus ellipses
+    return xp.take(lmoms, order - 1, axis=0) if order.ndim == 1 else lmoms[order - 1]
+
+
+LinregressResult = _make_tuple_bunch('LinregressResult',
+                                     ['slope', 'intercept', 'rvalue',
+                                      'pvalue', 'stderr'],
+                                     extra_field_names=['intercept_stderr'])
+
+
+def _pack_LinregressResult(slope, intercept, rvalue, pvalue, stderr, intercept_stderr):
+    return LinregressResult(slope, intercept, rvalue, pvalue, stderr,
+                            intercept_stderr=intercept_stderr)
+
+
+def _unpack_LinregressResult(res, _):
+    return tuple(res) + (res.intercept_stderr,)
+
+
+@xp_capabilities(skip_backends=[('dask.array', '`dtype` inference failed....')],
+                 cpu_only=True, exceptions=['cupy', 'jax.numpy'],
+                 marray=True)
+@_axis_nan_policy_factory(_pack_LinregressResult, n_samples=2,
+                          result_to_tuple=_unpack_LinregressResult, paired=True,
+                          too_small=1, n_outputs=6)
+def linregress(x, y, alternative='two-sided', *, axis=0):
+    """
+    Calculate a linear least-squares regression for two sets of measurements.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Two sets of measurements.  Both arrays should have the same length N.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis. Default is 'two-sided'.
+        The following options are available:
+
+        * 'two-sided': the slope of the regression line is nonzero
+        * 'less': the slope of the regression line is less than zero
+        * 'greater':  the slope of the regression line is greater than zero
+
+        .. versionadded:: 1.7.0
+    axis : int or None, default: 0
+        If an int, the axis of the input along which to compute the statistic.
+        The statistic of each axis-slice (e.g. row) of the input will appear in a
+        corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+
+    Returns
+    -------
+    result : ``LinregressResult`` instance
+        The return value is an object with the following attributes:
+
+        slope : float
+            Slope of the regression line.
+        intercept : float
+            Intercept of the regression line.
+        rvalue : float
+            The Pearson correlation coefficient. The square of ``rvalue``
+            is equal to the coefficient of determination.
+        pvalue : float
+            The p-value for a hypothesis test whose null hypothesis is
+            that the slope is zero, using Wald Test with t-distribution of
+            the test statistic. See `alternative` above for alternative
+            hypotheses.
+        stderr : float
+            Standard error of the estimated slope (gradient), under the
+            assumption of residual normality.
+        intercept_stderr : float
+            Standard error of the estimated intercept, under the assumption
+            of residual normality.
+
+    See Also
+    --------
+    scipy.optimize.curve_fit :
+        Use non-linear least squares to fit a function to data.
+    scipy.optimize.leastsq :
+        Minimize the sum of squares of a set of equations.
+
+    Notes
+    -----
+    For compatibility with older versions of SciPy, the return value acts
+    like a ``namedtuple`` of length 5, with fields ``slope``, ``intercept``,
+    ``rvalue``, ``pvalue`` and ``stderr``, so one can continue to write::
+
+        slope, intercept, r, p, se = linregress(x, y)
+
+    With that style, however, the standard error of the intercept is not
+    available.  To have access to all the computed values, including the
+    standard error of the intercept, use the return value as an object
+    with attributes, e.g.::
+
+        result = linregress(x, y)
+        print(result.intercept, result.intercept_stderr)
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> from scipy import stats
+    >>> rng = np.random.default_rng()
+
+    Generate some data:
+
+    >>> x = rng.random(10)
+    >>> y = 1.6*x + rng.random(10)
+
+    Perform the linear regression:
+
+    >>> res = stats.linregress(x, y)
+
+    Coefficient of determination (R-squared):
+
+    >>> print(f"R-squared: {res.rvalue**2:.6f}")
+    R-squared: 0.717533
+
+    Plot the data along with the fitted line:
+
+    >>> plt.plot(x, y, 'o', label='original data')
+    >>> plt.plot(x, res.intercept + res.slope*x, 'r', label='fitted line')
+    >>> plt.legend()
+    >>> plt.show()
+
+    Calculate 95% confidence interval on slope and intercept:
+
+    >>> # Two-sided inverse Students t-distribution
+    >>> # p - probability, df - degrees of freedom
+    >>> from scipy.stats import t
+    >>> tinv = lambda p, df: abs(t.ppf(p/2, df))
+
+    >>> ts = tinv(0.05, len(x)-2)
+    >>> print(f"slope (95%): {res.slope:.6f} +/- {ts*res.stderr:.6f}")
+    slope (95%): 1.453392 +/- 0.743465
+    >>> print(f"intercept (95%): {res.intercept:.6f}"
+    ...       f" +/- {ts*res.intercept_stderr:.6f}")
+    intercept (95%): 0.616950 +/- 0.544475
+
+    """
+    xp = array_namespace(x, y)
+    x, y = xp_promote(x, y, force_floating=True, xp=xp)
+
+    TINY = 1.0e-20
+
+    # _axis_nan_policy decorator ensures that `axis=-1`
+    x, y = _share_masks(x, y, xp=xp)
+    n = _count_nonmasked(x, axis=-1, keepdims=False, xp=xp)
+    xmean = xp.mean(x, axis=-1, keepdims=True)
+    ymean = xp.mean(y, axis=-1, keepdims=True)
+
+    # Average sums of square differences from the mean
+    #   ssxm = mean( (x-mean(x))^2 )
+    #   ssxym = mean( (x-mean(x)) * (y-mean(y)) )
+    x_ = _demean(x, xmean, axis=-1, xp=xp)
+    y_ = _demean(y, ymean, axis=-1, xp=xp, precision_warning=False)
+    xmean = xp.squeeze(xmean, axis=-1)
+    ymean = xp.squeeze(ymean, axis=-1)
+
+    ssxm = xp.vecdot(x_, x_, axis=-1) / n
+    ssym = xp.vecdot(y_, y_, axis=-1) / n
+    ssxym = xp.vecdot(x_, y_, axis=-1) / n
+
+    # R-value
+    #   r = ssxym / sqrt( ssxm * ssym )
+    degenerate = (ssxm == 0.0) | (ssym == 0.0)
+    NaN = xp.asarray(xp.nan, dtype=ssxym.dtype)
+    r = xpx.apply_where(
+        ~degenerate,
+        (ssxym, ssxm, ssym),
+        lambda ssxym, ssxm, ssym: xp.clip(ssxym / xp.sqrt(ssxm * ssym), -1.0, 1.0),
+        lambda ssxym, ssxm, ssym: xp.where(ssxym==0, NaN, 0.0)
+    )
+
+    slope = ssxym / ssxm
+    intercept = ymean - slope*xmean
+    with np.errstate(invalid='ignore', divide='ignore'):
+        df = n - 2  # Number of degrees of freedom
+        # n-2 degrees of freedom because 2 has been used up
+        # to estimate the mean and standard deviation
+        t = r * xp.sqrt(df / ((1.0 - r + TINY)*(1.0 + r + TINY)))
+
+        dist = _SimpleStudentT(xp.asarray(df, dtype=t.dtype))
+        prob = _get_pvalue(t, dist, alternative, xp=xp)
+        prob = prob[()] if prob.ndim == 0 else prob
+
+        slope_stderr = xp.sqrt((1 - r**2) * ssym / ssxm / df)
+
+        # Also calculate the standard error of the intercept
+        # The following relationship is used:
+        #   ssxm = mean( (x-mean(x))^2 )
+        #        = ssx - sx*sx
+        #        = mean( x^2 ) - mean(x)^2
+        intercept_stderr = slope_stderr * xp.sqrt(ssxm + xmean**2)
+
+    outputs = slope, intercept, r, prob, slope_stderr, intercept_stderr
+    outputs = (output[()] if output.ndim == 0 else output for output in outputs)
+    slope, intercept, r, prob, slope_stderr, intercept_stderr = outputs
+
+    return LinregressResult(slope=slope, intercept=intercept, rvalue=r,
+                            pvalue=prob, stderr=slope_stderr,
+                            intercept_stderr=intercept_stderr)
+
+def _linearized_pmean(a, p, *, axis=None, weights=None, xp=None):
+    # pmean linearized as a function of p about p = 0; see gh-23407
+    M0 = gmean(a, axis=axis, weights=weights)
+
+    loga = xp.log(a)
+
+    ln_avg = _xp_mean(loga, axis=axis, weights=weights)
+    ln2_avg = _xp_mean(loga * loga, axis=axis, weights=weights)
+
+    return M0 * (1 + 0.5 * p * (ln2_avg - ln_avg * ln_avg))
+
+
+@xp_capabilities()
+def _xp_mean(x, /, *, axis=None, weights=None, keepdims=False, nan_policy='propagate',
+             dtype=None, warn=True, xp=None):
+    r"""Compute the arithmetic mean along the specified axis.
+
+    Parameters
+    ----------
+    x : real array
+        Array containing real numbers whose mean is desired.
+    axis : int or tuple of ints, default: None
+        If an int or tuple of ints, the axis or axes of the input along which
+        to compute the statistic. The statistic of each axis-slice (e.g. row)
+        of the input will appear in a corresponding element of the output.
+        If ``None``, the input will be raveled before computing the statistic.
+    weights : real array, optional
+        If specified, an array of weights associated with the values in `x`;
+        otherwise ``1``. If `weights` and `x` do not have the same shape, the
+        arrays will be broadcasted before performing the calculation. See
+        Notes for details.
+    keepdims : bool, optional
+        If this is set to ``True``, the axes which are reduced are left
+        in the result as dimensions with length one. With this option,
+        the result will broadcast correctly against the input array.
+    nan_policy : {'propagate', 'omit', 'raise'}, default: 'propagate'
+        Defines how to handle input NaNs.
+
+        - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
+          which the statistic is computed, the corresponding entry of the output
+          will be NaN.
+        - ``omit``: NaNs will be omitted when performing the calculation.
+          If insufficient data remains in the axis slice along which the
+          statistic is computed, the corresponding entry of the output will be
+          NaN.
+        - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
+
+    dtype : dtype, optional
+        Type to use in computing the mean. For integer inputs, the default is
+        the default float type of the array library; for floating point inputs,
+        the dtype is that of the input.
+
+    Returns
+    -------
+    out : array
+        The mean of each slice
+
+    Notes
+    -----
+    Let :math:`x_i` represent element :math:`i` of data `x` and let :math:`w_i`
+    represent the corresponding element of `weights` after broadcasting. Then the
+    (weighted) mean :math:`\bar{x}_w` is given by:
+
+    .. math::
+
+        \bar{x}_w = \frac{ \sum_{i=0}^{n-1} w_i x_i }
+                         { \sum_{i=0}^{n-1} w_i }
+
+    where :math:`n` is the number of elements along a slice. Note that this simplifies
+    to the familiar :math:`(\sum_i x_i) / n` when the weights are all ``1`` (default).
+
+    The behavior of this function with respect to weights is somewhat different
+    from that of `np.average`. For instance,
+    `np.average` raises an error when `axis` is not specified and the shapes of `x`
+    and the `weights` array are not the same; `xp_mean` simply broadcasts the two.
+    Also, `np.average` raises an error when weights sum to zero along a slice;
+    `xp_mean` computes the appropriate result. The intent is for this function's
+    interface to be consistent with the rest of `scipy.stats`.
+
+    Note that according to the formula, including NaNs with zero weights is not
+    the same as *omitting* NaNs with ``nan_policy='omit'``; in the former case,
+    the NaNs will continue to propagate through the calculation whereas in the
+    latter case, the NaNs are excluded entirely.
+
+    """
+    # ensure that `x` and `weights` are array-API compatible arrays of identical shape
+    xp = array_namespace(x) if xp is None else xp
+    x = _asarray(x, dtype=dtype, subok=True)
+    weights = xp.asarray(weights, dtype=dtype) if weights is not None else weights
+
+    # to ensure that this matches the behavior of decorated functions when one of the
+    # arguments has size zero, it's easiest to call a similar decorated function.
+    if is_numpy(xp) and (xp_size(x) == 0
+                         or (weights is not None and xp_size(weights) == 0)):
+        return gmean(x, weights=weights, axis=axis, keepdims=keepdims)
+
+    x, weights = xp_promote(x, weights, broadcast=True, force_floating=True, xp=xp)
+    if weights is not None:
+        x, weights = _share_masks(x, weights, xp=xp)
+
+    # handle the special case of zero-sized arrays
+    message = (too_small_1d_not_omit if (x.ndim == 1 or axis is None)
+               else too_small_nd_not_omit)
+    if xp_size(x) == 0:
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            res = xp.mean(x, axis=axis, keepdims=keepdims)
+        if warn and xp_size(res) != 0:
+            warnings.warn(message, SmallSampleWarning, stacklevel=2)
+        return res
+
+    contains_nan = _contains_nan(x, nan_policy, xp_omit_okay=True, xp=xp)
+    if weights is not None:
+        contains_nan_w = _contains_nan(weights, nan_policy, xp_omit_okay=True, xp=xp)
+        contains_nan = contains_nan | contains_nan_w
+
+    # Handle `nan_policy='omit'` by giving zero weight to NaNs, whether they
+    # appear in `x` or `weights`. Emit warning if there is an all-NaN slice.
+    # Test nan_policy before the implicit call to bool(contains_nan)
+    # to avoid raising on lazy xps on the default nan_policy='propagate'
+    lazy = is_lazy_array(x)
+    if nan_policy == 'omit' and (lazy or contains_nan):
+        nan_mask = xp.isnan(x)
+        if weights is not None:
+            nan_mask |= xp.isnan(weights)
+        if warn and not lazy and xp.any(xp.all(nan_mask, axis=axis)):
+            message = (too_small_1d_omit if (x.ndim == 1 or axis is None)
+                       else too_small_nd_omit)
+            warnings.warn(message, SmallSampleWarning, stacklevel=2)
+        weights = xp.ones_like(x) if weights is None else weights
+        x = xp.where(nan_mask, 0., x)
+        weights = xp.where(nan_mask, 0., weights)
+
+    # Perform the mean calculation itself
+    if weights is None:
+        return xp.mean(x, axis=axis, keepdims=keepdims)
+
+    # consider using `vecdot` if `axis` tuple support is added (data-apis/array-api#910)
+    norm = xp.sum(weights, axis=axis)
+    wsum = xp.sum(x * weights, axis=axis)
+    with np.errstate(divide='ignore', invalid='ignore'):
+        res = wsum/norm
+
+    # Respect `keepdims` and convert NumPy 0-D arrays to scalars
+    if keepdims:
+
+        if axis is None:
+            final_shape = (1,) * len(x.shape)
+        else:
+            # axis can be a scalar or sequence
+            axes = (axis,) if not isinstance(axis, Sequence) else axis
+            final_shape = list(x.shape)
+            for i in axes:
+                final_shape[i] = 1
+
+        res = xp.reshape(res, tuple(final_shape))
+
+    return res[()] if res.ndim == 0 else res
+
+
+@xp_capabilities()
+def _xp_var(x, /, *, axis=None, correction=0, keepdims=False, nan_policy='propagate',
+            dtype=None, xp=None):
+    # an array-api compatible function for variance with scipy.stats interface
+    # and features (e.g. `nan_policy`).
+    xp = array_namespace(x) if xp is None else xp
+    x = _asarray(x, subok=True)
+
+    # use `_xp_mean` instead of `xp.var` for desired warning behavior
+    # it would be nice to combine this with `_var`, which uses `_moment`
+    # and therefore warns when precision is lost, but that does not support
+    # `axis` tuples or keepdims. Eventually, `_axis_nan_policy` will simplify
+    # `axis` tuples and implement `keepdims` for non-NumPy arrays; then it will
+    # be easy.
+    kwargs = dict(axis=axis, nan_policy=nan_policy, dtype=dtype, xp=xp)
+    mean = _xp_mean(x, keepdims=True, **kwargs)
+    x = _asarray(x, dtype=mean.dtype, subok=True)
+    x_mean = _demean(x, mean, axis, xp=xp)
+    x_mean_conj = (xp.conj(x_mean) if xp.isdtype(x_mean.dtype, 'complex floating')
+                   else x_mean)  # crossref data-apis/array-api#824
+    var = _xp_mean(x_mean * x_mean_conj, keepdims=keepdims, **kwargs)
+
+    if correction != 0:
+        n = _count_nonmasked(x, axis, xp=xp, keepdims=keepdims)
+        # Or two lines with ternaries : )
+        # axis = range(x.ndim) if axis is None else axis
+        # n = math.prod(x.shape[i] for i in axis) if iterable(axis) else x.shape[axis]
+
+        n = xp.asarray(n, dtype=var.dtype, device=xp_device(x))
+
+        if nan_policy == 'omit':
+            nan_mask = xp.astype(xp.isnan(x), var.dtype)
+            n = n - xp.sum(nan_mask, axis=axis, keepdims=keepdims)
+
+        # Produce NaNs silently when n - correction <= 0
+        nc = n - correction
+        factor = xpx.apply_where(nc > 0, (n, nc), operator.truediv, fill_value=xp.nan)
+        var *= factor
+
+    return var[()] if var.ndim == 0 else var
+
+
+class _SimpleNormal:
+    # A very simple, array-API compatible normal distribution for use in
+    # hypothesis tests. May be replaced by new infrastructure Normal
+    # distribution in due time.
+
+    def cdf(self, x):
+        return special.ndtr(x)
+
+    def sf(self, x):
+        return special.ndtr(-x)
+
+    def isf(self, x):
+        return -special.ndtri(x)
+
+
+class _SimpleChi2:
+    # A very simple, array-API compatible chi-squared distribution for use in
+    # hypothesis tests. May be replaced by new infrastructure chi-squared
+    # distribution in due time.
+    def __init__(self, df):
+        self.df = df
+
+    def cdf(self, x):
+        return special.chdtr(self.df, x)
+
+    def sf(self, x):
+        return special.chdtrc(self.df, x)
+
+
+class _SimpleBeta:
+    # A very simple, array-API compatible beta distribution for use in
+    # hypothesis tests. May be replaced by new infrastructure beta
+    # distribution in due time.
+    def __init__(self, a, b, *, loc=None, scale=None):
+        self.a = a
+        self.b = b
+        self.loc = loc
+        self.scale = scale
+
+    def cdf(self, x):
+        if self.loc is not None or self.scale is not None:
+            loc = 0 if self.loc is None else self.loc
+            scale = 1 if self.scale is None else self.scale
+            return special.betainc(self.a, self.b, (x - loc)/scale)
+        return special.betainc(self.a, self.b, x)
+
+    def sf(self, x):
+        if self.loc is not None or self.scale is not None:
+            loc = 0 if self.loc is None else self.loc
+            scale = 1 if self.scale is None else self.scale
+            return special.betaincc(self.a, self.b, (x - loc)/scale)
+        return special.betaincc(self.a, self.b, x)
+
+
+class _SimpleStudentT:
+    # A very simple, array-API compatible t distribution for use in
+    # hypothesis tests. May be replaced by new infrastructure t
+    # distribution in due time.
+    def __init__(self, df):
+        self.df = df
+
+    def cdf(self, t):
+        return special.stdtr(self.df, t)
+
+    def sf(self, t):
+        return special.stdtr(self.df, -t)
+
+
+class _SimpleF:
+    # A very simple, array-API compatible F distribution for use in
+    # hypothesis tests.
+    def __init__(self, dfn, dfd):
+        self.dfn = dfn
+        self.dfd = dfd
+
+    def cdf(self, x):
+        return special.fdtr(self.dfn, self.dfd, x)
+
+    def sf(self, x):
+        return special.fdtrc(self.dfn, self.dfd, x)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_stats_pythran.cpython-312-x86_64-linux-gnu.so b/.venv/lib/python3.12/site-packages/scipy/stats/_stats_pythran.cpython-312-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..dc09b17366b3db7ab8806a30ab4fc22a60f922ac
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_stats_pythran.cpython-312-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eea2b8c755b166f51249605642d9dc7946bb5590a0b1d65e46a31052491dbd00
+size 136848
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_survival.py b/.venv/lib/python3.12/site-packages/scipy/stats/_survival.py
new file mode 100644
index 0000000000000000000000000000000000000000..70a84806a95ae82b2968bb9cf5ee37f48808c59c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_survival.py
@@ -0,0 +1,686 @@
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Literal
+import warnings
+
+import numpy as np
+from scipy import special, interpolate, stats
+from scipy._lib._array_api import xp_capabilities
+from scipy.stats._censored_data import CensoredData
+from scipy.stats._common import ConfidenceInterval
+
+if TYPE_CHECKING:
+    import numpy.typing as npt
+
+
+__all__ = ['ecdf', 'logrank']
+
+
+@dataclass
+class EmpiricalDistributionFunction:
+    """An empirical distribution function produced by `scipy.stats.ecdf`
+
+    Attributes
+    ----------
+    quantiles : ndarray
+        The unique values of the sample from which the
+        `EmpiricalDistributionFunction` was estimated.
+    probabilities : ndarray
+        The point estimates of the cumulative distribution function (CDF) or
+        its complement, the survival function (SF), corresponding with
+        `quantiles`.
+    """
+    quantiles: np.ndarray
+    probabilities: np.ndarray
+    # Exclude these from __str__
+    _n: np.ndarray = field(repr=False)  # number "at risk"
+    _d: np.ndarray = field(repr=False)  # number of "deaths"
+    _sf: np.ndarray = field(repr=False)  # survival function for var estimate
+    _kind: str = field(repr=False)  # type of function: "cdf" or "sf"
+
+    def __init__(self, q, p, n, d, kind):
+        self.probabilities = p
+        self.quantiles = q
+        self._n = n
+        self._d = d
+        self._sf = p if kind == 'sf' else 1 - p
+        self._kind = kind
+
+        f0 = 1 if kind == 'sf' else 0  # leftmost function value
+        f1 = 1 - f0
+        # fill_value can't handle edge cases at infinity
+        x = np.insert(q, [0, len(q)], [-np.inf, np.inf])
+        y = np.insert(p, [0, len(p)], [f0, f1])
+        # `or` conditions handle the case of empty x, points
+        self._f = interpolate.interp1d(x, y, kind='previous',
+                                       assume_sorted=True)
+
+    def evaluate(self, x):
+        """Evaluate the empirical CDF/SF function at the input.
+
+        Parameters
+        ----------
+        x : ndarray
+            Argument to the CDF/SF
+
+        Returns
+        -------
+        y : ndarray
+            The CDF/SF evaluated at the input
+        """
+        return self._f(x)
+
+    def plot(self, ax=None, **matplotlib_kwargs):
+        """Plot the empirical distribution function
+
+        Available only if ``matplotlib`` is installed.
+
+        Parameters
+        ----------
+        ax : matplotlib.axes.Axes
+            Axes object to draw the plot onto, otherwise uses the current Axes.
+
+        **matplotlib_kwargs : dict, optional
+            Keyword arguments passed directly to `matplotlib.axes.Axes.step`.
+            Unless overridden, ``where='post'``.
+
+        Returns
+        -------
+        lines : list of `matplotlib.lines.Line2D`
+            Objects representing the plotted data
+        """
+        try:
+            import matplotlib  # noqa: F401
+        except ModuleNotFoundError as exc:
+            message = "matplotlib must be installed to use method `plot`."
+            raise ModuleNotFoundError(message) from exc
+
+        if ax is None:
+            import matplotlib.pyplot as plt
+            ax = plt.gca()
+
+        kwargs = {'where': 'post'}
+        kwargs.update(matplotlib_kwargs)
+
+        delta = np.ptp(self.quantiles)*0.05  # how far past sample edge to plot
+        q = self.quantiles
+        q = [q[0] - delta] + list(q) + [q[-1] + delta]
+
+        return ax.step(q, self.evaluate(q), **kwargs)
+
+    def confidence_interval(self, confidence_level=0.95, *, method='linear'):
+        """Compute a confidence interval around the CDF/SF point estimate
+
+        Parameters
+        ----------
+        confidence_level : float, default: 0.95
+            Confidence level for the computed confidence interval
+
+        method : str, {"linear", "log-log"}
+            Method used to compute the confidence interval. Options are
+            "linear" for the conventional Greenwood confidence interval
+            (default)  and "log-log" for the "exponential Greenwood",
+            log-negative-log-transformed confidence interval.
+
+        Returns
+        -------
+        ci : ``ConfidenceInterval``
+            An object with attributes ``low`` and ``high``, instances of
+            `~scipy.stats._result_classes.EmpiricalDistributionFunction` that
+            represent the lower and upper bounds (respectively) of the
+            confidence interval.
+
+        Notes
+        -----
+        Confidence intervals are computed according to the Greenwood formula
+        (``method='linear'``) or the more recent "exponential Greenwood"
+        formula (``method='log-log'``) as described in [1]_. The conventional
+        Greenwood formula can result in lower confidence limits less than 0
+        and upper confidence limits greater than 1; these are clipped to the
+        unit interval. NaNs may be produced by either method; these are
+        features of the formulas.
+
+        References
+        ----------
+        .. [1] Sawyer, Stanley. "The Greenwood and Exponential Greenwood
+               Confidence Intervals in Survival Analysis."
+               https://www.math.wustl.edu/~sawyer/handouts/greenwood.pdf
+
+        """
+        message = ("Confidence interval bounds do not implement a "
+                   "`confidence_interval` method.")
+        if self._n is None:
+            raise NotImplementedError(message)
+
+        methods = {'linear': self._linear_ci,
+                   'log-log': self._loglog_ci}
+
+        message = f"`method` must be one of {set(methods)}."
+        if method.lower() not in methods:
+            raise ValueError(message)
+
+        message = "`confidence_level` must be a scalar between 0 and 1."
+        confidence_level = np.asarray(confidence_level)[()]
+        if confidence_level.shape or not (0 <= confidence_level <= 1):
+            raise ValueError(message)
+
+        method_fun = methods[method.lower()]
+        low, high = method_fun(confidence_level)
+
+        message = ("The confidence interval is undefined at some observations."
+                   " This is a feature of the mathematical formula used, not"
+                   " an error in its implementation.")
+        if np.any(np.isnan(low) | np.isnan(high)):
+            warnings.warn(message, RuntimeWarning, stacklevel=2)
+
+        low, high = np.clip(low, 0, 1), np.clip(high, 0, 1)
+        low = EmpiricalDistributionFunction(self.quantiles, low, None, None,
+                                            self._kind)
+        high = EmpiricalDistributionFunction(self.quantiles, high, None, None,
+                                             self._kind)
+        return ConfidenceInterval(low, high)
+
+    def _linear_ci(self, confidence_level):
+        sf, d, n = self._sf, self._d, self._n
+        # When n == d, Greenwood's formula divides by zero.
+        # When s != 0, this can be ignored: var == inf, and CI is [0, 1]
+        # When s == 0, this results in NaNs. Produce an informative warning.
+        with np.errstate(divide='ignore', invalid='ignore'):
+            var = sf ** 2 * np.cumsum(d / (n * (n - d)))
+
+        se = np.sqrt(var)
+        z = special.ndtri(1 / 2 + confidence_level / 2)
+
+        z_se = z * se
+        low = self.probabilities - z_se
+        high = self.probabilities + z_se
+
+        return low, high
+
+    def _loglog_ci(self, confidence_level):
+        sf, d, n = self._sf, self._d, self._n
+
+        with np.errstate(divide='ignore', invalid='ignore'):
+            var = 1 / np.log(sf) ** 2 * np.cumsum(d / (n * (n - d)))
+
+        se = np.sqrt(var)
+        z = special.ndtri(1 / 2 + confidence_level / 2)
+
+        with np.errstate(divide='ignore'):
+            lnl_points = np.log(-np.log(sf))
+
+        z_se = z * se
+        low = np.exp(-np.exp(lnl_points + z_se))
+        high = np.exp(-np.exp(lnl_points - z_se))
+        if self._kind == "cdf":
+            low, high = 1-high, 1-low
+
+        return low, high
+
+
+@dataclass
+class ECDFResult:
+    """ Result object returned by `scipy.stats.ecdf`
+
+    Attributes
+    ----------
+    cdf : `~scipy.stats._result_classes.EmpiricalDistributionFunction`
+        An object representing the empirical cumulative distribution function.
+    sf : `~scipy.stats._result_classes.EmpiricalDistributionFunction`
+        An object representing the complement of the empirical cumulative
+        distribution function.
+    """
+    cdf: EmpiricalDistributionFunction
+    sf: EmpiricalDistributionFunction
+
+    def __init__(self, q, cdf, sf, n, d):
+        self.cdf = EmpiricalDistributionFunction(q, cdf, n, d, "cdf")
+        self.sf = EmpiricalDistributionFunction(q, sf, n, d, "sf")
+
+
+def _iv_CensoredData(
+    sample: "npt.ArrayLike | CensoredData", param_name: str = "sample"
+) -> CensoredData:
+    """Attempt to convert `sample` to `CensoredData`."""
+    if not isinstance(sample, CensoredData):
+        try:  # takes care of input standardization/validation
+            sample = CensoredData(uncensored=sample)
+        except ValueError as e:
+            message = str(e).replace('uncensored', param_name)
+            raise type(e)(message) from e
+    return sample
+
+
+@xp_capabilities(np_only=True)
+def ecdf(sample: "npt.ArrayLike | CensoredData") -> ECDFResult:
+    """Empirical cumulative distribution function of a sample.
+
+    The empirical cumulative distribution function (ECDF) is a step function
+    estimate of the CDF of the distribution underlying a sample. This function
+    returns objects representing both the empirical distribution function and
+    its complement, the empirical survival function.
+
+    Parameters
+    ----------
+    sample : 1D array_like or `scipy.stats.CensoredData`
+        Besides array_like, instances of `scipy.stats.CensoredData` containing
+        uncensored and right-censored observations are supported. Currently,
+        other instances of `scipy.stats.CensoredData` will result in a
+        ``NotImplementedError``.
+
+    Returns
+    -------
+    res : `~scipy.stats._result_classes.ECDFResult`
+        An object with the following attributes.
+
+        cdf : `~scipy.stats._result_classes.EmpiricalDistributionFunction`
+            An object representing the empirical cumulative distribution
+            function.
+        sf : `~scipy.stats._result_classes.EmpiricalDistributionFunction`
+            An object representing the empirical survival function.
+
+        The `cdf` and `sf` attributes themselves have the following attributes.
+
+        quantiles : ndarray
+            The unique values in the sample that defines the empirical CDF/SF.
+        probabilities : ndarray
+            The point estimates of the probabilities corresponding with
+            `quantiles`.
+
+        And the following methods:
+
+        evaluate(x) :
+            Evaluate the CDF/SF at the argument.
+
+        plot(ax) :
+            Plot the CDF/SF on the provided axes.
+
+        confidence_interval(confidence_level=0.95) :
+            Compute the confidence interval around the CDF/SF at the values in
+            `quantiles`.
+
+    Notes
+    -----
+    When each observation of the sample is a precise measurement, the ECDF
+    steps up by ``1/len(sample)`` at each of the observations [1]_.
+
+    When observations are lower bounds, upper bounds, or both upper and lower
+    bounds, the data is said to be "censored", and `sample` may be provided as
+    an instance of `scipy.stats.CensoredData`.
+
+    For right-censored data, the ECDF is given by the Kaplan-Meier estimator
+    [2]_; other forms of censoring are not supported at this time.
+
+    Confidence intervals are computed according to the Greenwood formula or the
+    more recent "Exponential Greenwood" formula as described in [4]_.
+
+    References
+    ----------
+    .. [1] Conover, William Jay. Practical nonparametric statistics. Vol. 350.
+           John Wiley & Sons, 1999.
+
+    .. [2] Kaplan, Edward L., and Paul Meier. "Nonparametric estimation from
+           incomplete observations." Journal of the American statistical
+           association 53.282 (1958): 457-481.
+
+    .. [3] Goel, Manish Kumar, Pardeep Khanna, and Jugal Kishore.
+           "Understanding survival analysis: Kaplan-Meier estimate."
+           International journal of Ayurveda research 1.4 (2010): 274.
+
+    .. [4] Sawyer, Stanley. "The Greenwood and Exponential Greenwood Confidence
+           Intervals in Survival Analysis."
+           https://www.math.wustl.edu/~sawyer/handouts/greenwood.pdf
+
+    Examples
+    --------
+    **Uncensored Data**
+
+    As in the example from [1]_ page 79, five boys were selected at random from
+    those in a single high school. Their one-mile run times were recorded as
+    follows.
+
+    >>> sample = [6.23, 5.58, 7.06, 6.42, 5.20]  # one-mile run times (minutes)
+
+    The empirical distribution function, which approximates the distribution
+    function of one-mile run times of the population from which the boys were
+    sampled, is calculated as follows.
+
+    >>> from scipy import stats
+    >>> res = stats.ecdf(sample)
+    >>> res.cdf.quantiles
+    array([5.2 , 5.58, 6.23, 6.42, 7.06])
+    >>> res.cdf.probabilities
+    array([0.2, 0.4, 0.6, 0.8, 1. ])
+
+    To plot the result as a step function:
+
+    >>> import matplotlib.pyplot as plt
+    >>> ax = plt.subplot()
+    >>> res.cdf.plot(ax)
+    >>> ax.set_xlabel('One-Mile Run Time (minutes)')
+    >>> ax.set_ylabel('Empirical CDF')
+    >>> plt.show()
+
+    **Right-censored Data**
+
+    As in the example from [1]_ page 91, the lives of ten car fanbelts were
+    tested. Five tests concluded because the fanbelt being tested broke, but
+    the remaining tests concluded for other reasons (e.g. the study ran out of
+    funding, but the fanbelt was still functional). The mileage driven
+    with the fanbelts were recorded as follows.
+
+    >>> broken = [77, 47, 81, 56, 80]  # in thousands of miles driven
+    >>> unbroken = [62, 60, 43, 71, 37]
+
+    Precise survival times of the fanbelts that were still functional at the
+    end of the tests are unknown, but they are known to exceed the values
+    recorded in ``unbroken``. Therefore, these observations are said to be
+    "right-censored", and the data is represented using
+    `scipy.stats.CensoredData`.
+
+    >>> sample = stats.CensoredData(uncensored=broken, right=unbroken)
+
+    The empirical survival function is calculated as follows.
+
+    >>> res = stats.ecdf(sample)
+    >>> res.sf.quantiles
+    array([37., 43., 47., 56., 60., 62., 71., 77., 80., 81.])
+    >>> res.sf.probabilities
+    array([1.   , 1.   , 0.875, 0.75 , 0.75 , 0.75 , 0.75 , 0.5  , 0.25 , 0.   ])
+
+    To plot the result as a step function:
+
+    >>> ax = plt.subplot()
+    >>> res.sf.plot(ax)
+    >>> ax.set_xlabel('Fanbelt Survival Time (thousands of miles)')
+    >>> ax.set_ylabel('Empirical SF')
+    >>> plt.show()
+
+    """
+    sample = _iv_CensoredData(sample)
+
+    if sample.num_censored() == 0:
+        res = _ecdf_uncensored(sample._uncensor())
+    elif sample.num_censored() == sample._right.size:
+        res = _ecdf_right_censored(sample)
+    else:
+        # Support additional censoring options in follow-up PRs
+        message = ("Currently, only uncensored and right-censored data is "
+                   "supported.")
+        raise NotImplementedError(message)
+
+    t, cdf, sf, n, d = res
+    return ECDFResult(t, cdf, sf, n, d)
+
+
+def _ecdf_uncensored(sample):
+    sample = np.sort(sample)
+    x, counts = np.unique(sample, return_counts=True)
+
+    # [1].81 "the fraction of [observations] that are less than or equal to x
+    events = np.cumsum(counts)
+    n = sample.size
+    cdf = events / n
+
+    # [1].89 "the relative frequency of the sample that exceeds x in value"
+    sf = 1 - cdf
+
+    at_risk = np.concatenate(([n], n - events[:-1]))
+    return x, cdf, sf, at_risk, counts
+
+
+def _ecdf_right_censored(sample):
+    # It is conventional to discuss right-censored data in terms of
+    # "survival time", "death", and "loss" (e.g. [2]). We'll use that
+    # terminology here.
+    # This implementation was influenced by the references cited and also
+    # https://www.youtube.com/watch?v=lxoWsVco_iM
+    # https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator
+    # In retrospect it is probably most easily compared against [3].
+    # Ultimately, the data needs to be sorted, so this implementation is
+    # written to avoid a separate call to `unique` after sorting. In hope of
+    # better performance on large datasets, it also computes survival
+    # probabilities at unique times only rather than at each observation.
+    tod = sample._uncensored  # time of "death"
+    tol = sample._right  # time of "loss"
+    times = np.concatenate((tod, tol))
+    died = np.asarray([1]*tod.size + [0]*tol.size)
+
+    # sort by times
+    i = np.argsort(times)
+    times = times[i]
+    died = died[i]
+    at_risk = np.arange(times.size, 0, -1)
+
+    # logical indices of unique times
+    j = np.diff(times, prepend=-np.inf, append=np.inf) > 0
+    j_l = j[:-1]  # first instances of unique times
+    j_r = j[1:]  # last instances of unique times
+
+    # get number at risk and deaths at each unique time
+    t = times[j_l]  # unique times
+    n = at_risk[j_l]  # number at risk at each unique time
+    cd = np.cumsum(died)[j_r]  # cumulative deaths up to/including unique times
+    d = np.diff(cd, prepend=0)  # deaths at each unique time
+
+    # compute survival function
+    sf = np.cumprod((n - d) / n)
+    cdf = 1 - sf
+    return t, cdf, sf, n, d
+
+
+@dataclass
+class LogRankResult:
+    """Result object returned by `scipy.stats.logrank`.
+
+    Attributes
+    ----------
+    statistic : float ndarray
+        The computed statistic (defined below). Its magnitude is the
+        square root of the magnitude returned by most other logrank test
+        implementations.
+    pvalue : float ndarray
+        The computed p-value of the test.
+    """
+    statistic: np.ndarray
+    pvalue: np.ndarray
+
+
+@xp_capabilities(np_only=True)
+def logrank(
+    x: "npt.ArrayLike | CensoredData",
+    y: "npt.ArrayLike | CensoredData",
+    alternative: Literal['two-sided', 'less', 'greater'] = "two-sided"
+) -> LogRankResult:
+    r"""Compare the survival distributions of two samples via the logrank test.
+
+    Parameters
+    ----------
+    x, y : array_like or CensoredData
+        Samples to compare based on their empirical survival functions.
+    alternative : {'two-sided', 'less', 'greater'}, optional
+        Defines the alternative hypothesis.
+
+        The null hypothesis is that the survival distributions of the two
+        groups, say *X* and *Y*, are identical.
+
+        The following alternative hypotheses [4]_ are available (default is
+        'two-sided'):
+
+        * 'two-sided': the survival distributions of the two groups are not
+          identical.
+        * 'less': survival of group *X* is favored: the group *X* failure rate
+          function is less than the group *Y* failure rate function at some
+          times.
+        * 'greater': survival of group *Y* is favored: the group *X* failure
+          rate function is greater than the group *Y* failure rate function at
+          some times.
+
+    Returns
+    -------
+    res : `~scipy.stats._result_classes.LogRankResult`
+        An object containing attributes:
+
+        statistic : float ndarray
+            The computed statistic (defined below). Its magnitude is the
+            square root of the magnitude returned by most other logrank test
+            implementations.
+        pvalue : float ndarray
+            The computed p-value of the test.
+
+    See Also
+    --------
+    scipy.stats.ecdf
+
+    Notes
+    -----
+    The logrank test [1]_ compares the observed number of events to
+    the expected number of events under the null hypothesis that the two
+    samples were drawn from the same distribution. The statistic is
+
+    .. math::
+
+        Z_i = \frac{\sum_{j=1}^J(O_{i,j}-E_{i,j})}{\sqrt{\sum_{j=1}^J V_{i,j}}}
+        \rightarrow \mathcal{N}(0,1)
+
+    where
+
+    .. math::
+
+        E_{i,j} = O_j \frac{N_{i,j}}{N_j},
+        \qquad
+        V_{i,j} = E_{i,j} \left(\frac{N_j-O_j}{N_j}\right)
+        \left(\frac{N_j-N_{i,j}}{N_j-1}\right),
+
+    :math:`i` denotes the group (i.e. it may assume values :math:`x` or
+    :math:`y`, or it may be omitted to refer to the combined sample)
+    :math:`j` denotes the time (at which an event occurred),
+    :math:`N` is the number of subjects at risk just before an event occurred,
+    and :math:`O` is the observed number of events at that time.
+
+    The ``statistic`` :math:`Z_x` returned by `logrank` is the (signed) square
+    root of the statistic returned by many other implementations. Under the
+    null hypothesis, :math:`Z_x**2` is asymptotically distributed according to
+    the chi-squared distribution with one degree of freedom. Consequently,
+    :math:`Z_x` is asymptotically distributed according to the standard normal
+    distribution. The advantage of using :math:`Z_x` is that the sign
+    information (i.e. whether the observed number of events tends to be less
+    than or greater than the number expected under the null hypothesis) is
+    preserved, allowing `scipy.stats.logrank` to offer one-sided alternative
+    hypotheses.
+
+    References
+    ----------
+    .. [1] Mantel N. "Evaluation of survival data and two new rank order
+           statistics arising in its consideration."
+           Cancer Chemotherapy Reports, 50(3):163-170, PMID: 5910392, 1966
+    .. [2] Bland, Altman, "The logrank test", BMJ, 328:1073,
+           :doi:`10.1136/bmj.328.7447.1073`, 2004
+    .. [3] "Logrank test", Wikipedia,
+           https://en.wikipedia.org/wiki/Logrank_test
+    .. [4] Brown, Mark. "On the choice of variance for the log rank test."
+           Biometrika 71.1 (1984): 65-74.
+    .. [5] Klein, John P., and Melvin L. Moeschberger. Survival analysis:
+           techniques for censored and truncated data. Vol. 1230. New York:
+           Springer, 2003.
+
+    Examples
+    --------
+    Reference [2]_ compared the survival times of patients with two different
+    types of recurrent malignant gliomas. The samples below record the time
+    (number of weeks) for which each patient participated in the study. The
+    `scipy.stats.CensoredData` class is used because the data is
+    right-censored: the uncensored observations correspond with observed deaths
+    whereas the censored observations correspond with the patient leaving the
+    study for another reason.
+
+    >>> from scipy import stats
+    >>> x = stats.CensoredData(
+    ...     uncensored=[6, 13, 21, 30, 37, 38, 49, 50,
+    ...                 63, 79, 86, 98, 202, 219],
+    ...     right=[31, 47, 80, 82, 82, 149]
+    ... )
+    >>> y = stats.CensoredData(
+    ...     uncensored=[10, 10, 12, 13, 14, 15, 16, 17, 18, 20, 24, 24,
+    ...                 25, 28,30, 33, 35, 37, 40, 40, 46, 48, 76, 81,
+    ...                 82, 91, 112, 181],
+    ...     right=[34, 40, 70]
+    ... )
+
+    We can calculate and visualize the empirical survival functions
+    of both groups as follows.
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> ax = plt.subplot()
+    >>> ecdf_x = stats.ecdf(x)
+    >>> ecdf_x.sf.plot(ax, label='Astrocytoma')
+    >>> ecdf_y = stats.ecdf(y)
+    >>> ecdf_y.sf.plot(ax, label='Glioblastoma')
+    >>> ax.set_xlabel('Time to death (weeks)')
+    >>> ax.set_ylabel('Empirical SF')
+    >>> plt.legend()
+    >>> plt.show()
+
+    Visual inspection of the empirical survival functions suggests that the
+    survival times tend to be different between the two groups. To formally
+    assess whether the difference is significant at the 1% level, we use the
+    logrank test.
+
+    >>> res = stats.logrank(x=x, y=y)
+    >>> res.statistic
+    -2.73799
+    >>> res.pvalue
+    0.00618
+
+    The p-value is less than 1%, so we can consider the data to be evidence
+    against the null hypothesis in favor of the alternative that there is a
+    difference between the two survival functions.
+
+    """
+    # Input validation. `alternative` IV handled in `_get_pvalue` below.
+    x = _iv_CensoredData(sample=x, param_name='x')
+    y = _iv_CensoredData(sample=y, param_name='y')
+
+    # Combined sample. (Under H0, the two groups are identical.)
+    xy = CensoredData(
+        uncensored=np.concatenate((x._uncensored, y._uncensored)),
+        right=np.concatenate((x._right, y._right))
+    )
+
+    # Extract data from the combined sample
+    res = ecdf(xy)
+    idx = res.sf._d.astype(bool)  # indices of observed events
+    times_xy = res.sf.quantiles[idx]  # unique times of observed events
+    at_risk_xy = res.sf._n[idx]  # combined number of subjects at risk
+    deaths_xy = res.sf._d[idx]  # combined number of events
+
+    # Get the number at risk within each sample.
+    # First compute the number at risk in group X at each of the `times_xy`.
+    # Could use `interpolate_1d`, but this is more compact.
+    res_x = ecdf(x)
+    i = np.searchsorted(res_x.sf.quantiles, times_xy)
+    at_risk_x = np.append(res_x.sf._n, 0)[i]  # 0 at risk after last time
+    # Subtract from the combined number at risk to get number at risk in Y
+    at_risk_y = at_risk_xy - at_risk_x
+
+    # Compute the variance.
+    num = at_risk_x * at_risk_y * deaths_xy * (at_risk_xy - deaths_xy)
+    den = at_risk_xy**2 * (at_risk_xy - 1)
+    # Note: when `at_risk_xy == 1`, we would have `at_risk_xy - 1 == 0` in the
+    # numerator and denominator. Simplifying the fraction symbolically, we
+    # would always find the overall quotient to be zero, so don't compute it.
+    i = at_risk_xy > 1
+    sum_var = np.sum(num[i]/den[i])
+
+    # Get the observed and expected number of deaths in group X
+    n_died_x = x._uncensored.size
+    sum_exp_deaths_x = np.sum(at_risk_x * (deaths_xy/at_risk_xy))
+
+    # Compute the statistic. This is the square root of that in references.
+    statistic = (n_died_x - sum_exp_deaths_x)/np.sqrt(sum_var)
+
+    # Equivalent to chi2(df=1).sf(statistic**2) when alternative='two-sided'
+    norm = stats._stats_py._SimpleNormal()
+    pvalue = stats._stats_py._get_pvalue(statistic, norm, alternative, xp=np)
+
+    return LogRankResult(statistic=statistic[()], pvalue=pvalue[()])
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_tukeylambda_stats.py b/.venv/lib/python3.12/site-packages/scipy/stats/_tukeylambda_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3cda54fa2801f7659419b3c9c68d15ed80fc13e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_tukeylambda_stats.py
@@ -0,0 +1,199 @@
+import numpy as np
+from numpy import poly1d
+from scipy.special import beta
+
+
+# The following code was used to generate the Pade coefficients for the
+# Tukey Lambda variance function.  Version 0.17 of mpmath was used.
+#---------------------------------------------------------------------------
+# import mpmath as mp
+#
+# mp.mp.dps = 60
+#
+# one   = mp.mpf(1)
+# two   = mp.mpf(2)
+#
+# def mpvar(lam):
+#     if lam == 0:
+#         v = mp.pi**2 / three
+#     else:
+#         v = (two / lam**2) * (one / (one + two*lam) -
+#                               mp.beta(lam + one, lam + one))
+#     return v
+#
+# t = mp.taylor(mpvar, 0, 8)
+# p, q = mp.pade(t, 4, 4)
+# print("p =", [mp.fp.mpf(c) for c in p])
+# print("q =", [mp.fp.mpf(c) for c in q])
+#---------------------------------------------------------------------------
+
+# Pade coefficients for the Tukey Lambda variance function.
+_tukeylambda_var_pc = [3.289868133696453, 0.7306125098871127,
+                       -0.5370742306855439, 0.17292046290190008,
+                       -0.02371146284628187]
+_tukeylambda_var_qc = [1.0, 3.683605511659861, 4.184152498888124,
+                       1.7660926747377275, 0.2643989311168465]
+
+# numpy.poly1d instances for the numerator and denominator of the
+# Pade approximation to the Tukey Lambda variance.
+_tukeylambda_var_p = poly1d(_tukeylambda_var_pc[::-1])
+_tukeylambda_var_q = poly1d(_tukeylambda_var_qc[::-1])
+
+
+def tukeylambda_variance(lam):
+    """Variance of the Tukey Lambda distribution.
+
+    Parameters
+    ----------
+    lam : array_like
+        The lambda values at which to compute the variance.
+
+    Returns
+    -------
+    v : ndarray
+        The variance.  For lam < -0.5, the variance is not defined, so
+        np.nan is returned.  For lam = 0.5, np.inf is returned.
+
+    Notes
+    -----
+    In an interval around lambda=0, this function uses the [4,4] Pade
+    approximation to compute the variance.  Otherwise it uses the standard
+    formula (https://en.wikipedia.org/wiki/Tukey_lambda_distribution).  The
+    Pade approximation is used because the standard formula has a removable
+    discontinuity at lambda = 0, and does not produce accurate numerical
+    results near lambda = 0.
+    """
+    lam = np.asarray(lam)
+    shp = lam.shape
+    lam = np.atleast_1d(lam).astype(np.float64)
+
+    # For absolute values of lam less than threshold, use the Pade
+    # approximation.
+    threshold = 0.075
+
+    # Play games with masks to implement the conditional evaluation of
+    # the distribution.
+    # lambda < -0.5:  var = nan
+    low_mask = lam < -0.5
+    # lambda == -0.5: var = inf
+    neghalf_mask = lam == -0.5
+    # abs(lambda) < threshold:  use Pade approximation
+    small_mask = np.abs(lam) < threshold
+    # else the "regular" case:  use the explicit formula.
+    reg_mask = ~(low_mask | neghalf_mask | small_mask)
+
+    # Get the 'lam' values for the cases where they are needed.
+    small = lam[small_mask]
+    reg = lam[reg_mask]
+
+    # Compute the function for each case.
+    v = np.empty_like(lam)
+    v[low_mask] = np.nan
+    v[neghalf_mask] = np.inf
+    if small.size > 0:
+        # Use the Pade approximation near lambda = 0.
+        v[small_mask] = _tukeylambda_var_p(small) / _tukeylambda_var_q(small)
+    if reg.size > 0:
+        v[reg_mask] = (2.0 / reg**2) * (1.0 / (1.0 + 2 * reg) -
+                                        beta(reg + 1, reg + 1))
+    v = v.reshape(shp)
+    return v
+
+
+# The following code was used to generate the Pade coefficients for the
+# Tukey Lambda kurtosis function.  Version 0.17 of mpmath was used.
+#---------------------------------------------------------------------------
+# import mpmath as mp
+#
+# mp.mp.dps = 60
+#
+# one   = mp.mpf(1)
+# two   = mp.mpf(2)
+# three = mp.mpf(3)
+# four  = mp.mpf(4)
+#
+# def mpkurt(lam):
+#     if lam == 0:
+#         k = mp.mpf(6)/5
+#     else:
+#         numer = (one/(four*lam+one) - four*mp.beta(three*lam+one, lam+one) +
+#                  three*mp.beta(two*lam+one, two*lam+one))
+#         denom = two*(one/(two*lam+one) - mp.beta(lam+one,lam+one))**2
+#         k = numer / denom - three
+#     return k
+#
+# # There is a bug in mpmath 0.17: when we use the 'method' keyword of the
+# # taylor function and we request a degree 9 Taylor polynomial, we actually
+# # get degree 8.
+# t = mp.taylor(mpkurt, 0, 9, method='quad', radius=0.01)
+# t = [mp.chop(c, tol=1e-15) for c in t]
+# p, q = mp.pade(t, 4, 4)
+# print("p =", [mp.fp.mpf(c) for c in p])
+# print("q =", [mp.fp.mpf(c) for c in q])
+#---------------------------------------------------------------------------
+
+# Pade coefficients for the Tukey Lambda kurtosis function.
+_tukeylambda_kurt_pc = [1.2, -5.853465139719495, -22.653447381131077,
+                        0.20601184383406815, 4.59796302262789]
+_tukeylambda_kurt_qc = [1.0, 7.171149192233599, 12.96663094361842,
+                        0.43075235247853005, -2.789746758009912]
+
+# numpy.poly1d instances for the numerator and denominator of the
+# Pade approximation to the Tukey Lambda kurtosis.
+_tukeylambda_kurt_p = poly1d(_tukeylambda_kurt_pc[::-1])
+_tukeylambda_kurt_q = poly1d(_tukeylambda_kurt_qc[::-1])
+
+
+def tukeylambda_kurtosis(lam):
+    """Kurtosis of the Tukey Lambda distribution.
+
+    Parameters
+    ----------
+    lam : array_like
+        The lambda values at which to compute the variance.
+
+    Returns
+    -------
+    v : ndarray
+        The variance.  For lam < -0.25, the variance is not defined, so
+        np.nan is returned.  For lam = 0.25, np.inf is returned.
+
+    """
+    lam = np.asarray(lam)
+    shp = lam.shape
+    lam = np.atleast_1d(lam).astype(np.float64)
+
+    # For absolute values of lam less than threshold, use the Pade
+    # approximation.
+    threshold = 0.055
+
+    # Use masks to implement the conditional evaluation of the kurtosis.
+    # lambda < -0.25:  kurtosis = nan
+    low_mask = lam < -0.25
+    # lambda == -0.25: kurtosis = inf
+    negqrtr_mask = lam == -0.25
+    # lambda near 0:  use Pade approximation
+    small_mask = np.abs(lam) < threshold
+    # else the "regular" case:  use the explicit formula.
+    reg_mask = ~(low_mask | negqrtr_mask | small_mask)
+
+    # Get the 'lam' values for the cases where they are needed.
+    small = lam[small_mask]
+    reg = lam[reg_mask]
+
+    # Compute the function for each case.
+    k = np.empty_like(lam)
+    k[low_mask] = np.nan
+    k[negqrtr_mask] = np.inf
+    if small.size > 0:
+        k[small_mask] = _tukeylambda_kurt_p(small) / _tukeylambda_kurt_q(small)
+    if reg.size > 0:
+        numer = (1.0 / (4 * reg + 1) - 4 * beta(3 * reg + 1, reg + 1) +
+                 3 * beta(2 * reg + 1, 2 * reg + 1))
+        denom = 2 * (1.0/(2 * reg + 1) - beta(reg + 1, reg + 1))**2
+        k[reg_mask] = numer / denom - 3
+
+    # The return value will be a numpy array; resetting the shape ensures that
+    # if `lam` was a scalar, the return value is a 0-d array.
+    k = k.reshape(shp)
+    return k
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_unuran/__init__.py b/.venv/lib/python3.12/site-packages/scipy/stats/_unuran/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_unuran/unuran_wrapper.cpython-312-x86_64-linux-gnu.so b/.venv/lib/python3.12/site-packages/scipy/stats/_unuran/unuran_wrapper.cpython-312-x86_64-linux-gnu.so
new file mode 100644
index 0000000000000000000000000000000000000000..a8fa9ee25868560b90178c1dad63662c925032c0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_unuran/unuran_wrapper.cpython-312-x86_64-linux-gnu.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:85894ace6fcd620c2b5d255df2e91116dc39d0a8de9e24ee1df89e785d690abb
+size 1335480
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_unuran/unuran_wrapper.pyi b/.venv/lib/python3.12/site-packages/scipy/stats/_unuran/unuran_wrapper.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1ef1bd443b7dbf6a1d673fdc976c04536606c8d3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_unuran/unuran_wrapper.pyi
@@ -0,0 +1,179 @@
+import numpy as np
+from typing import (overload, NamedTuple, Protocol)
+from collections.abc import Callable
+import numpy.typing as npt
+from scipy._lib._util import SeedType
+import scipy.stats as stats
+
+
+ArrayLike0D = bool | int | float | complex | str | bytes | np.generic
+
+
+__all__: list[str]
+
+
+class UNURANError(RuntimeError):
+    ...
+
+
+class Method:
+    @overload
+    def rvs(self, size: None = ...) -> float | int: ...  # type: ignore[overload-overlap]
+    @overload
+    def rvs(self, size: int | tuple[int, ...] = ...) -> np.ndarray: ...
+    def set_random_state(self, random_state: SeedType) -> None: ...
+
+
+class TDRDist(Protocol):
+    @property
+    def pdf(self) -> Callable[..., float]: ...
+    @property
+    def dpdf(self) -> Callable[..., float]: ...
+    @property
+    def support(self) -> tuple[float, float]: ...
+
+
+class TransformedDensityRejection(Method):
+    def __init__(self,
+                 dist: TDRDist,
+                 *,
+                 mode: None | float = ...,
+                 center: None | float = ...,
+                 domain: None | tuple[float, float] = ...,
+                 c: float = ...,
+                 construction_points: int | npt.ArrayLike = ...,
+                 use_dars: bool = ...,
+                 max_squeeze_hat_ratio: float = ...,
+                 random_state: SeedType = ...) -> None: ...
+    @property
+    def squeeze_hat_ratio(self) -> float: ...
+    @property
+    def squeeze_area(self) -> float: ...
+    @overload
+    def ppf_hat(self, u: ArrayLike0D) -> float: ...  # type: ignore[overload-overlap]
+    @overload
+    def ppf_hat(self, u: npt.ArrayLike) -> np.ndarray: ...
+
+
+class SROUDist(Protocol):
+    @property
+    def pdf(self) -> Callable[..., float]: ...
+    @property
+    def support(self) -> tuple[float, float]: ...
+
+
+class SimpleRatioUniforms(Method):
+    def __init__(self,
+                 dist: SROUDist,
+                 *,
+                 mode: None | float = ...,
+                 pdf_area: float = ...,
+                 domain: None | tuple[float, float] = ...,
+                 cdf_at_mode: float = ...,
+                 random_state: SeedType = ...) -> None: ...
+
+
+class UError(NamedTuple):
+    max_error: float
+    mean_absolute_error: float
+
+class PINVDist(Protocol):
+    @property
+    def pdf(self) -> Callable[..., float]: ...
+    @property
+    def cdf(self) -> Callable[..., float]: ...
+    @property
+    def logpdf(self) -> Callable[..., float]: ...
+
+
+class NumericalInversePolynomial(Method):
+    def __init__(self,
+                 dist: PINVDist,
+                 *,
+                 mode: None | float = ...,
+                 center: None | float = ...,
+                 domain: None | tuple[float, float] = ...,
+                 order: int = ...,
+                 u_resolution: float = ...,
+                 random_state: SeedType = ...) -> None: ...
+    @property
+    def intervals(self) -> int: ...
+    @overload
+    def ppf(self, u: ArrayLike0D) -> float: ...  # type: ignore[overload-overlap]
+    @overload
+    def ppf(self, u: npt.ArrayLike) -> np.ndarray: ...
+    @overload
+    def cdf(self, x: ArrayLike0D) -> float: ...  # type: ignore[overload-overlap]
+    @overload
+    def cdf(self, x: npt.ArrayLike) -> np.ndarray: ...
+    def u_error(self, sample_size: int = ...) -> UError: ...
+    def qrvs(self,
+             size: None | int | tuple[int, ...] = ...,
+             d: None | int = ...,
+             qmc_engine: None | stats.qmc.QMCEngine = ...) -> npt.ArrayLike: ...
+
+
+class HINVDist(Protocol):
+    @property
+    def pdf(self) -> Callable[..., float]: ...
+    @property
+    def cdf(self) -> Callable[..., float]: ...
+    @property
+    def support(self) -> tuple[float, float]: ...
+
+
+class NumericalInverseHermite(Method):
+    def __init__(self,
+                 dist: HINVDist,
+                 *,
+                 domain: None | tuple[float, float] = ...,
+                 order: int= ...,
+                 u_resolution: float = ...,
+                 construction_points: None | npt.ArrayLike = ...,
+                 max_intervals: int = ...,
+                 random_state: SeedType = ...) -> None: ...
+    @property
+    def intervals(self) -> int: ...
+    @overload
+    def ppf(self, u: ArrayLike0D) -> float: ...  # type: ignore[overload-overlap]
+    @overload
+    def ppf(self, u: npt.ArrayLike) -> np.ndarray: ...
+    def qrvs(self,
+             size: None | int | tuple[int, ...] = ...,
+             d: None | int = ...,
+             qmc_engine: None | stats.qmc.QMCEngine = ...) -> npt.ArrayLike: ...
+    def u_error(self, sample_size: int = ...) -> UError: ...
+
+
+class DAUDist(Protocol):
+    @property
+    def pmf(self) -> Callable[..., float]: ...
+    @property
+    def support(self) -> tuple[float, float]: ...
+
+class DiscreteAliasUrn(Method):
+    def __init__(self,
+                 dist: npt.ArrayLike | DAUDist,
+                 *,
+                 domain: None | tuple[float, float] = ...,
+                 urn_factor: float = ...,
+                 random_state: SeedType = ...) -> None: ...
+
+
+class DGTDist(Protocol):
+    @property
+    def pmf(self) -> Callable[..., float]: ...
+    @property
+    def support(self) -> tuple[float, float]: ...
+
+class DiscreteGuideTable(Method):
+    def __init__(self,
+                 dist: npt.ArrayLike | DGTDist,
+                 *,
+                 domain: None | tuple[float, float] = ...,
+                 guide_factor: float = ...,
+                 random_state: SeedType = ...) -> None: ...
+    @overload
+    def ppf(self, u: ArrayLike0D) -> float: ...  # type: ignore[overload-overlap]
+    @overload
+    def ppf(self, u: npt.ArrayLike) -> np.ndarray: ...
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_variation.py b/.venv/lib/python3.12/site-packages/scipy/stats/_variation.py
new file mode 100644
index 0000000000000000000000000000000000000000..c991a70158a226a1b0fb6a1a0fce6f1ec4a67400
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_variation.py
@@ -0,0 +1,133 @@
+import warnings
+import numpy as np
+
+from scipy._lib._array_api import (
+    array_namespace,
+    xp_capabilities,
+    xp_device,
+    _count_nonmasked,
+    xp_promote,
+)
+import scipy._external.array_api_extra as xpx
+
+from ._axis_nan_policy import _axis_nan_policy_factory
+
+
+@xp_capabilities(marray=True)
+@_axis_nan_policy_factory(
+    lambda x: x, n_outputs=1, result_to_tuple=lambda x, _: (x,)
+)
+def variation(a, axis=0, nan_policy='propagate', ddof=0, *, keepdims=False):
+    """
+    Compute the coefficient of variation.
+
+    The coefficient of variation is the standard deviation divided by the
+    mean.  This function is equivalent to::
+
+        np.std(x, axis=axis, ddof=ddof) / np.mean(x)
+
+    The default for ``ddof`` is 0, but many definitions of the coefficient
+    of variation use the square root of the unbiased sample variance
+    for the sample standard deviation, which corresponds to ``ddof=1``.
+
+    The function does not take the absolute value of the mean of the data,
+    so the return value is negative if the mean is negative.
+
+    Parameters
+    ----------
+    a : array_like
+        Input array.
+    axis : int or None, optional
+        Axis along which to calculate the coefficient of variation.
+        Default is 0. If None, compute over the whole array `a`.
+    nan_policy : {'propagate', 'raise', 'omit'}, optional
+        Defines how to handle when input contains ``nan``.
+        The following options are available:
+
+        * 'propagate': return ``nan``
+        * 'raise': raise an exception
+        * 'omit': perform the calculation with ``nan`` values omitted
+
+        The default is 'propagate'.
+    ddof : int, optional
+        Gives the "Delta Degrees Of Freedom" used when computing the
+        standard deviation.  The divisor used in the calculation of the
+        standard deviation is ``N - ddof``, where ``N`` is the number of
+        elements.  `ddof` must be less than ``N``; if it isn't, the result
+        will be ``nan`` or ``inf``, depending on ``N`` and the values in
+        the array.  By default `ddof` is zero for backwards compatibility,
+        but it is recommended to use ``ddof=1`` to ensure that the sample
+        standard deviation is computed as the square root of the unbiased
+        sample variance.
+
+    Returns
+    -------
+    variation : ndarray
+        The calculated variation along the requested axis.
+
+    Notes
+    -----
+    There are several edge cases that are handled without generating a
+    warning:
+
+    * If both the mean and the standard deviation are zero, ``nan``
+      is returned.
+    * If the mean is zero and the standard deviation is nonzero, ``inf``
+      is returned.
+    * If the input has length zero (either because the array has zero
+      length, or all the input values are ``nan`` and ``nan_policy`` is
+      ``'omit'``), ``nan`` is returned.
+    * If the input contains ``inf``, ``nan`` is returned.
+
+    References
+    ----------
+    .. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
+       Probability and Statistics Tables and Formulae. Chapman & Hall: New
+       York. 2000.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats import variation
+    >>> variation([1, 2, 3, 4, 5], ddof=1)
+    0.5270462766947299
+
+    Compute the variation along a given dimension of an array that contains
+    a few ``nan`` values:
+
+    >>> x = np.array([[  10.0, np.nan, 11.0, 19.0, 23.0, 29.0, 98.0],
+    ...               [  29.0,   30.0, 32.0, 33.0, 35.0, 56.0, 57.0],
+    ...               [np.nan, np.nan, 12.0, 13.0, 16.0, 16.0, 17.0]])
+    >>> variation(x, axis=1, ddof=1, nan_policy='omit')
+    array([1.05109361, 0.31428986, 0.146483  ])
+
+    """
+    xp = array_namespace(a)
+    a = xp_promote(a, force_floating=True, xp=xp)
+
+    # `nan_policy` and `keepdims` are handled by `_axis_nan_policy`
+    if axis is None:
+        a = xp.reshape(a, (-1,))
+        axis = 0
+
+    n = xp.asarray(_count_nonmasked(a, axis=axis), dtype=a.dtype, device=xp_device(a))
+
+    with (np.errstate(divide='ignore', invalid='ignore'), warnings.catch_warnings()):
+        warnings.simplefilter("ignore")
+        mean_a = xp.mean(a, axis=axis)
+        std_a = xp.std(a, axis=axis)
+        correction = (n / (n - ddof))**0.5  # we may need uncorrected std below
+        result = std_a * correction / mean_a
+
+    def special_case(std_a, mean_a):
+        # xref data-apis/array-api-extra#196
+        mxp = array_namespace(std_a, mean_a)
+        # `_xp_inf` is a workaround for torch.copysign not accepting a scalar yet,
+        # xref data-apis/array-api-compat#271
+        _xp_inf = mxp.asarray(mxp.inf, dtype=mean_a.dtype, device=xp_device(mean_a))
+        return mxp.where(std_a > 0, mxp.copysign(_xp_inf, mean_a), mxp.nan)
+
+    result = xpx.apply_where((ddof == n), (std_a, mean_a),
+                             special_case, fill_value=result)
+
+    return result[()] if result.ndim == 0 else result
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_warnings_errors.py b/.venv/lib/python3.12/site-packages/scipy/stats/_warnings_errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..57bc8fcabd7a0f382960046cd253bbc74d4b76e4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_warnings_errors.py
@@ -0,0 +1,62 @@
+# Warnings
+
+
+class DegenerateDataWarning(RuntimeWarning):
+    """Warns when data is degenerate and results may not be reliable.
+
+    Parameters
+    ----------
+    msg : str, optional
+        Warning message.
+    """
+    def __init__(self, msg=None):
+        if msg is None:
+            msg = ("Degenerate data encountered; results may not be reliable.")
+        self.args = (msg,)
+
+
+class ConstantInputWarning(DegenerateDataWarning):
+    """Warns when all values in data are exactly equal.
+
+    Parameters
+    ----------
+    msg : str, optional
+        Warning message.
+    """
+    def __init__(self, msg=None):
+        if msg is None:
+            msg = ("All values in data are exactly equal; "
+                   "results may not be reliable.")
+        self.args = (msg,)
+
+
+class NearConstantInputWarning(DegenerateDataWarning):
+    """Warns when all values in data are nearly equal.
+
+    Parameters
+    ----------
+    msg : str, optional
+        Warning message.
+    """
+    def __init__(self, msg=None):
+        if msg is None:
+            msg = ("All values in data are nearly equal; "
+                   "results may not be reliable.")
+        self.args = (msg,)
+
+
+# Errors
+
+
+class FitError(RuntimeError):
+    """Represents an error condition when fitting a distribution to data.
+
+    Parameters
+    ----------
+    msg : str, optional
+        Error message.
+    """
+    def __init__(self, msg=None):
+        if msg is None:
+            msg = ("An error occurred when fitting a distribution to data.")
+        self.args = (msg,)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/_wilcoxon.py b/.venv/lib/python3.12/site-packages/scipy/stats/_wilcoxon.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4437ba01a80deb0a09d36b4f3fca54ae5da960e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/_wilcoxon.py
@@ -0,0 +1,284 @@
+import numpy as np
+
+from scipy import stats
+from ._stats_py import _get_pvalue, _rankdata, _SimpleNormal
+from . import _morestats
+from ._axis_nan_policy import _broadcast_arrays
+from ._hypotests import _get_wilcoxon_distr
+from scipy._lib._util import _get_nan
+from scipy._lib._array_api import (array_namespace, xp_promote, xp_size, is_jax,
+                                   is_marray, _count_nonmasked)
+import scipy._external.array_api_extra as xpx
+
+
+class WilcoxonDistribution:
+
+    def __init__(self, n):
+        n = np.asarray(n).astype(int, copy=False)
+        self.n = n
+        self._dists = {ni: _get_wilcoxon_distr(ni) for ni in np.unique(n)}
+
+    def _cdf1(self, k, n):
+        pmfs = self._dists[n]
+        return pmfs[:k + 1].sum()
+
+    def _cdf(self, k, n):
+        return np.vectorize(self._cdf1, otypes=[float])(k, n)
+
+    def _sf1(self, k, n):
+        pmfs = self._dists[n]
+        return pmfs[k:].sum()
+
+    def _sf(self, k, n):
+        return np.vectorize(self._sf1, otypes=[float])(k, n)
+
+    def mean(self):
+        return self.n * (self.n + 1) / 4
+
+    def _prep(self, k):
+        k = np.asarray(k).astype(int, copy=False)
+        mn = self.mean()
+        out = np.empty(k.shape, dtype=np.float64)
+        return k, mn, out
+
+    def cdf(self, k):
+        k, mn, out = self._prep(k)
+        return xpx.apply_where(
+            k <= mn, (k, self.n),
+            self._cdf,
+            lambda k, n: 1 - self._sf(k+1, n))[()]
+
+    def sf(self, k):
+        k, mn, out = self._prep(k)
+        return xpx.apply_where(
+            k <= mn, (k, self.n),
+            self._sf,
+            lambda k, n: 1 - self._cdf(k-1, n))[()]
+
+
+def _wilcoxon_iv(x, y, zero_method, correction, alternative, method, axis):
+    xp = array_namespace(x, y)
+    x, y = xp_promote(x, y, force_floating=True, xp=xp)
+
+    axis = np.asarray(axis)[()]  # OK to use NumPy for input validation
+    message = "`axis` must be an integer."
+    if not np.issubdtype(axis.dtype, np.integer) or axis.ndim != 0:
+        raise ValueError(message)
+    axis = int(axis)
+
+    message = '`axis` must be compatible with the shape(s) of `x` (and `y`)'
+    AxisError = getattr(np, 'AxisError', None) or np.exceptions.AxisError
+    try:
+        if y is None:
+            d = x
+        else:
+            x, y = _broadcast_arrays((x, y), axis=axis, xp=xp)
+            d = x - y
+        d = xp.moveaxis(d, axis, -1)
+    except AxisError as e:
+        raise AxisError(message) from e
+
+    message = "`x` and `y` must have the same length along `axis`."
+    if y is not None and x.shape[axis] != y.shape[axis]:
+        raise ValueError(message)
+
+    message = "`x` (and `y`, if provided) must be an array of real numbers."
+    if not xp.isdtype(d.dtype, "real floating"):
+        raise ValueError(message)
+
+    zero_method = str(zero_method).lower()
+    zero_methods = {"wilcox", "pratt", "zsplit"}
+    message = f"`zero_method` must be one of {zero_methods}."
+    if zero_method not in zero_methods:
+        raise ValueError(message)
+
+    corrections = {True, False}
+    message = f"`correction` must be one of {corrections}."
+    if correction not in corrections:
+        raise ValueError(message)
+
+    alternative = str(alternative).lower()
+    alternatives = {"two-sided", "less", "greater"}
+    message = f"`alternative` must be one of {alternatives}."
+    if alternative not in alternatives:
+        raise ValueError(message)
+
+    if not isinstance(method, stats.PermutationMethod):
+        methods = {"auto", "asymptotic", "exact"}
+        message = (f"`method` must be one of {methods} or "
+                   "an instance of `stats.PermutationMethod`.")
+        if method not in methods:
+            raise ValueError(message)
+    output_z = True if method == 'asymptotic' else False
+
+    if is_jax(xp) and str(method) in {"auto", "exact"}:
+        message = ("When using `wilcoxon` with `jax.numpy` arrays, `method` must be "
+                   "either 'asymptotic' or an instance of `stats.PermutationMethod`.")
+        raise ValueError(message)
+
+    if is_marray(xp) and (method != "asymptotic" or zero_method != 'zsplit'):
+        message = ("Only `method='asymptotic'`/`zero_method='zsplit'` is compatible "
+                   "with MArrays.")
+        raise ValueError(message)
+
+    # For small samples, we decide later whether to perform an exact test or a
+    # permutation test. The reason is that the presence of ties is not
+    # known at the input validation stage.
+    n_zero = xp.count_nonzero(d == 0, axis=None)
+    if method == "auto" and d.shape[-1] > 50:
+        method = "asymptotic"
+
+    return d, zero_method, correction, alternative, method, axis, output_z, n_zero, xp
+
+
+def _wilcoxon_statistic(d, method, zero_method='wilcox', *, xp):
+    dtype = d.dtype
+    i_zeros = (d == 0)
+
+    if zero_method == 'wilcox':
+        # Wilcoxon's method for treating zeros was to remove them from
+        # the calculation. We do this by replacing 0s with NaNs, which
+        # are ignored anyway.
+        # Copy required for array-api-strict. See data-apis/array-api-extra#506.
+        d = xpx.at(d)[i_zeros].set(xp.nan, copy=True)
+
+    i_nan = xp.isnan(d)
+    n_nan = xp.astype(xp.count_nonzero(i_nan, axis=-1), dtype)
+    count = _count_nonmasked(d, axis=-1) - n_nan
+
+    r, _, t = _rankdata(xp.abs(d), 'average', return_ties=True, xp=xp)
+    r, t = xp.astype(r, dtype, copy=False), xp.astype(t, dtype, copy=False)
+
+    r_plus = xp.sum(xp.astype(d > 0, dtype) * r, axis=-1)
+    r_minus = xp.sum(xp.astype(d < 0, dtype) * r, axis=-1)
+
+    has_ties = xp.any(t == 0)
+
+    if zero_method == "zsplit":
+        # The "zero-split" method for treating zeros is to add half their contribution
+        # to r_plus and half to r_minus.
+        # See gh-2263 for the origin of this method.
+        r_zero_2 = xp.sum(xp.astype(i_zeros, dtype) * r, axis=-1) / 2
+        r_plus = xpx.at(r_plus)[...].add(r_zero_2)
+        r_minus = xpx.at(r_minus)[...].add(r_zero_2)
+
+    mn = count * (count + 1.) * 0.25
+    se = count * (count + 1.) * (2. * count + 1.)
+
+    if zero_method == "pratt":
+        # Pratt's method for treating zeros was just to modify the z-statistic.
+
+        # normal approximation needs to be adjusted, see Cureton (1967)
+        n_zero = xp.astype(xp.count_nonzero(i_zeros, axis=-1), dtype)
+        mn = xpx.at(mn)[...].subtract(n_zero * (n_zero + 1.) * 0.25)
+        se = xpx.at(se)[...].subtract(n_zero * (n_zero + 1.) * (2. * n_zero + 1.))
+
+        # zeros are not to be included in tie-correction.
+        # any tie counts corresponding with zeros are in the 0th column
+        # t[xp.any(i_zeros, axis=-1), 0] = 0
+        t_i_zeros = xp.zeros_like(i_zeros)
+        t_i_zeros = xpx.at(t_i_zeros)[..., 0].set(xp.any(i_zeros, axis=-1))
+        t = xpx.at(t)[t_i_zeros].set(0.)
+
+    tie_correct = xp.sum(t**3 - t, axis=-1)
+    se = xp.sqrt((se - tie_correct/2) / 24)
+
+    # se = 0 means that no non-zero values are left in d. we only need z
+    # if method is asymptotic. however, if method="auto", the switch to
+    # asymptotic might only happen after the statistic is calculated, so z
+    # needs to be computed. in all other cases, avoid division by zero warning
+    # (z is not needed anyways)
+    if method in ["asymptotic", "auto"]:
+        z = (r_plus - mn) / se
+    else:
+        z = xp.nan
+
+    return r_plus, r_minus, se, z, count, has_ties
+
+
+def _correction_sign(z, alternative, xp):
+    if alternative == 'greater':
+        return 1
+    elif alternative == 'less':
+        return -1
+    else:
+        return xp.sign(z)
+
+
+def _wilcoxon_nd(x, y=None, zero_method='wilcox', correction=True,
+                 alternative='two-sided', method='auto', axis=0):
+
+    temp = _wilcoxon_iv(x, y, zero_method, correction, alternative, method, axis)
+    d, zero_method, correction, alternative, method, axis, output_z, n_zero, xp = temp
+
+    if xp_size(d) == 0:
+        NaN = _get_nan(d, xp=xp)
+        res = _morestats.WilcoxonResult(statistic=NaN, pvalue=NaN)
+        if method == 'asymptotic':
+            res.zstatistic = NaN
+        return res
+
+    r_plus, r_minus, se, z, count, has_ties = _wilcoxon_statistic(
+        d, method, zero_method, xp=xp
+    )
+
+    # we only know if there are ties after computing the statistic and not
+    # at the input validation stage. if the original method was auto and
+    # the decision was to use an exact test, we override this to
+    # a permutation test now (since method='exact' is not exact in the
+    # presence of ties)
+    if method == "auto":
+        if not (has_ties or n_zero > 0):
+            method = "exact"
+        elif d.shape[-1] <= 13:
+            # the possible outcomes to be simulated by the permutation test
+            # are 2**n, where n is the sample size.
+            # if n <= 13, the p-value is deterministic since 2**13 is less
+            # than 9999, the default number of n_resamples
+            method = stats.PermutationMethod()
+        else:
+            # if there are ties and the sample size is too large to
+            # run a deterministic permutation test, fall back to asymptotic
+            method = "asymptotic"
+
+    if method == 'asymptotic':
+        if correction:
+            sign = _correction_sign(z, alternative, xp=xp)
+            z = xpx.at(z)[...].subtract(sign * 0.5 / se)
+        p = _get_pvalue(z, _SimpleNormal(), alternative, xp=xp)
+    elif method == 'exact':
+        dist = WilcoxonDistribution(count)
+        # The null distribution in `dist` is exact only if there are no ties
+        # or zeros. If there are ties or zeros, the statistic can be non-
+        # integral, but the null distribution is only defined for integral
+        # values of the statistic. Therefore, we're conservative: round
+        # non-integral statistic up before computing CDF and down before
+        # computing SF. This preserves symmetry w.r.t. alternatives and
+        # order of the input arguments. See gh-19872.
+        r_plus_np = np.asarray(r_plus)
+        if alternative == 'less':
+            p = dist.cdf(np.ceil(r_plus_np))
+        elif alternative == 'greater':
+            p = dist.sf(np.floor(r_plus_np))
+        else:
+            p = 2 * np.minimum(dist.sf(np.floor(r_plus_np)),
+                               dist.cdf(np.ceil(r_plus_np)))
+            p = np.clip(p, 0, 1)
+        p = xp.asarray(p, dtype=d.dtype)
+    else:  # `PermutationMethod` instance (already validated)
+        p = stats.permutation_test(
+            # permutation_test always uses `axis=-1` as `_wilcoxon_statistic` assumes
+            (d,), lambda d, axis: _wilcoxon_statistic(d, method, zero_method, xp=xp)[0],
+            permutation_type='samples', **method._asdict(),
+            alternative=alternative, axis=-1).pvalue
+
+    # for backward compatibility...
+    statistic = xp.minimum(r_plus, r_minus) if alternative=='two-sided' else r_plus
+    z = -xp.abs(z) if (alternative == 'two-sided' and method == 'asymptotic') else z
+
+    statistic = statistic[()] if statistic.ndim == 0 else statistic
+    p = p[()] if p.ndim == 0 else p
+    res = _morestats.WilcoxonResult(statistic=statistic, pvalue=p)
+    if output_z:
+        res.zstatistic = z[()] if z.ndim == 0 else z
+    return res
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/biasedurn.py b/.venv/lib/python3.12/site-packages/scipy/stats/biasedurn.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b1c9f1cf2b3b39acdcaeda97a90e8c11c589d89
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/biasedurn.py
@@ -0,0 +1,16 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+
+__all__: list[str] = []
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package="stats", module="biasedurn",
+                                   private_modules=["_biasedurn"], all=__all__,
+                                   attribute=name)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/contingency.py b/.venv/lib/python3.12/site-packages/scipy/stats/contingency.py
new file mode 100644
index 0000000000000000000000000000000000000000..c45112f3f1d7ae346681732d75943c75e6ef3fad
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/contingency.py
@@ -0,0 +1,526 @@
+"""
+Contingency table functions (:mod:`scipy.stats.contingency`)
+============================================================
+
+Functions for creating and analyzing contingency tables.
+
+.. currentmodule:: scipy.stats.contingency
+
+.. autosummary::
+   :toctree: generated/
+
+   chi2_contingency
+   relative_risk
+   odds_ratio
+   crosstab
+   association
+
+   expected_freq
+   margins
+
+"""
+
+
+from functools import reduce
+import math
+import numpy as np
+from ._stats_py import power_divergence, _untabulate
+from ._relative_risk import relative_risk
+from ._crosstab import crosstab
+from ._odds_ratio import odds_ratio
+from scipy._lib._array_api import xp_capabilities
+from scipy._lib._bunch import _make_tuple_bunch
+from scipy import stats
+
+
+__all__ = ['margins', 'expected_freq', 'chi2_contingency', 'crosstab',
+           'association', 'relative_risk', 'odds_ratio']
+
+
+@xp_capabilities(np_only=True)
+def margins(a):
+    """Return a list of the marginal sums of the array `a`.
+
+    Parameters
+    ----------
+    a : ndarray
+        The array for which to compute the marginal sums.
+
+    Returns
+    -------
+    margsums : list of ndarrays
+        A list of length `a.ndim`.  `margsums[k]` is the result
+        of summing `a` over all axes except `k`; it has the same
+        number of dimensions as `a`, but the length of each axis
+        except axis `k` will be 1.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats.contingency import margins
+
+    >>> a = np.arange(12).reshape(2, 6)
+    >>> a
+    array([[ 0,  1,  2,  3,  4,  5],
+           [ 6,  7,  8,  9, 10, 11]])
+    >>> m0, m1 = margins(a)
+    >>> m0
+    array([[15],
+           [51]])
+    >>> m1
+    array([[ 6,  8, 10, 12, 14, 16]])
+
+    >>> b = np.arange(24).reshape(2,3,4)
+    >>> m0, m1, m2 = margins(b)
+    >>> m0
+    array([[[ 66]],
+           [[210]]])
+    >>> m1
+    array([[[ 60],
+            [ 92],
+            [124]]])
+    >>> m2
+    array([[[60, 66, 72, 78]]])
+    """
+    margsums = []
+    ranged = list(range(a.ndim))
+    for k in ranged:
+        marg = np.apply_over_axes(np.sum, a, [j for j in ranged if j != k])
+        margsums.append(marg)
+    return margsums
+
+
+@xp_capabilities(np_only=True)
+def expected_freq(observed):
+    """
+    Compute the expected frequencies from a contingency table.
+
+    Given an n-dimensional contingency table of observed frequencies,
+    compute the expected frequencies for the table based on the marginal
+    sums under the assumption that the groups associated with each
+    dimension are independent.
+
+    Parameters
+    ----------
+    observed : array_like
+        The table of observed frequencies.  (While this function can handle
+        a 1-D array, that case is trivial.  Generally `observed` is at
+        least 2-D.)
+
+    Returns
+    -------
+    expected : ndarray of float64
+        The expected frequencies, based on the marginal sums of the table.
+        Same shape as `observed`.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.stats.contingency import expected_freq
+    >>> observed = np.array([[10, 10, 20],[20, 20, 20]])
+    >>> expected_freq(observed)
+    array([[ 12.,  12.,  16.],
+           [ 18.,  18.,  24.]])
+
+    """
+    # Typically `observed` is an integer array. If `observed` has a large
+    # number of dimensions or holds large values, some of the following
+    # computations may overflow, so we first switch to floating point.
+    observed = np.asarray(observed, dtype=np.float64)
+
+    # Create a list of the marginal sums.
+    margsums = margins(observed)
+
+    # Create the array of expected frequencies.  The shapes of the
+    # marginal sums returned by apply_over_axes() are just what we
+    # need for broadcasting in the following product.
+    d = observed.ndim
+    expected = reduce(np.multiply, margsums) / observed.sum() ** (d - 1)
+    return expected
+
+
+Chi2ContingencyResult = _make_tuple_bunch(
+    'Chi2ContingencyResult',
+    ['statistic', 'pvalue', 'dof', 'expected_freq'], []
+)
+
+
+@xp_capabilities(np_only=True)
+def chi2_contingency(observed, correction=True, lambda_=None, *, method=None):
+    """Chi-square test of independence of variables in a contingency table.
+
+    This function computes the chi-square statistic and p-value for the
+    hypothesis test of independence of the observed frequencies in the
+    contingency table [1]_ `observed`.  The expected frequencies are computed
+    based on the marginal sums under the assumption of independence; see
+    `scipy.stats.contingency.expected_freq`.  The number of degrees of
+    freedom is (expressed using numpy functions and attributes)::
+
+        dof = observed.size - sum(observed.shape) + observed.ndim - 1
+
+
+    Parameters
+    ----------
+    observed : array_like
+        The contingency table. The table contains the observed frequencies
+        (i.e. number of occurrences) in each category.  In the two-dimensional
+        case, the table is often described as an "R x C table".
+    correction : bool, optional
+        If True, *and* the degrees of freedom is 1, apply Yates' correction
+        for continuity.  The effect of the correction is to adjust each
+        observed value by 0.5 towards the corresponding expected value.
+    lambda_ : float or str, optional
+        By default, the statistic computed in this test is Pearson's
+        chi-squared statistic [2]_.  `lambda_` allows a statistic from the
+        Cressie-Read power divergence family [3]_ to be used instead.  See
+        `scipy.stats.power_divergence` for details.
+    method : ResamplingMethod, optional
+        Defines the method used to compute the p-value. Compatible only with
+        ``correction=False``,  default `lambda_`, and two-way tables.
+        If `method` is an instance of `scipy.stats.PermutationMethod` or
+        `scipy.stats.MonteCarloMethod`, the p-value is computed using
+        `scipy.stats.permutation_test`/`scipy.stats.monte_carlo_test` with the
+        provided configuration options and other appropriate settings.
+        Otherwise, the p-value is computed as documented in the notes.
+        Note that if `method` is an instance of `scipy.stats.MonteCarloMethod`, the
+        ``rvs`` attribute must be left unspecified; Monte Carlo samples are always drawn
+        using the ``rvs`` method of `scipy.stats.random_table`.
+
+        .. versionadded:: 1.15.0
+
+
+    Returns
+    -------
+    res : Chi2ContingencyResult
+        An object containing attributes:
+
+        statistic : float
+            The test statistic.
+        pvalue : float
+            The p-value of the test.
+        dof : int
+            The degrees of freedom. NaN if `method` is not ``None``.
+        expected_freq : ndarray, same shape as `observed`
+            The expected frequencies, based on the marginal sums of the table.
+
+    See Also
+    --------
+    scipy.stats.contingency.expected_freq
+    scipy.stats.fisher_exact
+    scipy.stats.chisquare
+    scipy.stats.power_divergence
+    scipy.stats.barnard_exact
+    scipy.stats.boschloo_exact
+    :ref:`hypothesis_chi2_contingency` : Extended example
+
+    Notes
+    -----
+    An often quoted guideline for the validity of this calculation is that
+    the test should be used only if the observed and expected frequencies
+    in each cell are at least 5.
+
+    This is a test for the independence of different categories of a
+    population. The test is only meaningful when the dimension of
+    `observed` is two or more.  Applying the test to a one-dimensional
+    table will always result in `expected` equal to `observed` and a
+    chi-square statistic equal to 0.
+
+    This function does not handle masked arrays, because the calculation
+    does not make sense with missing values.
+
+    Like `scipy.stats.chisquare`, this function computes a chi-square
+    statistic; the convenience this function provides is to figure out the
+    expected frequencies and degrees of freedom from the given contingency
+    table. If these were already known, and if the Yates' correction was not
+    required, one could use `scipy.stats.chisquare`.  That is, if one calls::
+
+        res = chi2_contingency(obs, correction=False)
+
+    then the following is true::
+
+        (res.statistic, res.pvalue) == stats.chisquare(obs.ravel(),
+                                                       f_exp=ex.ravel(),
+                                                       ddof=obs.size - 1 - dof)
+
+    The `lambda_` argument was added in version 0.13.0 of scipy.
+
+    References
+    ----------
+    .. [1] "Contingency table",
+           https://en.wikipedia.org/wiki/Contingency_table
+    .. [2] "Pearson's chi-squared test",
+           https://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test
+    .. [3] Cressie, N. and Read, T. R. C., "Multinomial Goodness-of-Fit
+           Tests", J. Royal Stat. Soc. Series B, Vol. 46, No. 3 (1984),
+           pp. 440-464.
+
+    Examples
+    --------
+    A two-way example (2 x 3):
+
+    >>> import numpy as np
+    >>> from scipy.stats import chi2_contingency
+    >>> obs = np.array([[10, 10, 20], [20, 20, 20]])
+    >>> res = chi2_contingency(obs)
+    >>> res.statistic
+    2.7777777777777777
+    >>> res.pvalue
+    0.24935220877729619
+    >>> res.dof
+    2
+    >>> res.expected_freq
+    array([[ 12.,  12.,  16.],
+           [ 18.,  18.,  24.]])
+
+    Perform the test using the log-likelihood ratio (i.e. the "G-test")
+    instead of Pearson's chi-squared statistic.
+
+    >>> res = chi2_contingency(obs, lambda_="log-likelihood")
+    >>> res.statistic
+    2.7688587616781319
+    >>> res.pvalue
+    0.25046668010954165
+
+    A four-way example (2 x 2 x 2 x 2):
+
+    >>> obs = np.array(
+    ...     [[[[12, 17],
+    ...        [11, 16]],
+    ...       [[11, 12],
+    ...        [15, 16]]],
+    ...      [[[23, 15],
+    ...        [30, 22]],
+    ...       [[14, 17],
+    ...        [15, 16]]]])
+    >>> res = chi2_contingency(obs)
+    >>> res.statistic
+    8.7584514426741897
+    >>> res.pvalue
+    0.64417725029295503
+
+    When the sum of the elements in a two-way table is small, the p-value
+    produced by the default asymptotic approximation may be inaccurate.
+    Consider passing a `PermutationMethod` or `MonteCarloMethod` as the
+    `method` parameter with `correction=False`.
+
+    >>> from scipy.stats import PermutationMethod
+    >>> obs = np.asarray([[12, 3],
+    ...                   [17, 16]])
+    >>> res = chi2_contingency(obs, correction=False)
+    >>> ref = chi2_contingency(obs, correction=False, method=PermutationMethod())
+    >>> res.pvalue, ref.pvalue
+    (0.0614122539870913, 0.1074)  # may vary
+
+    For a more detailed example, see :ref:`hypothesis_chi2_contingency`.
+
+    """
+    observed = np.asarray(observed)
+    if np.any(observed < 0):
+        raise ValueError("All values in `observed` must be nonnegative.")
+    if observed.size == 0:
+        raise ValueError("No data; `observed` has size 0.")
+
+    expected = expected_freq(observed)
+    if np.any(expected == 0):
+        # Include one of the positions where expected is zero in
+        # the exception message.
+        zeropos = list(zip(*np.nonzero(expected == 0)))[0]
+        raise ValueError("The internally computed table of expected "
+                         f"frequencies has a zero element at {zeropos}.")
+
+    if method is not None:
+        return _chi2_resampling_methods(observed, expected, correction, lambda_, method)
+
+    # The degrees of freedom
+    dof = expected.size - sum(expected.shape) + expected.ndim - 1
+
+    if dof == 0:
+        # Degenerate case; this occurs when `observed` is 1D (or, more
+        # generally, when it has only one nontrivial dimension).  In this
+        # case, we also have observed == expected, so chi2 is 0.
+        chi2 = 0.0
+        p = 1.0
+    else:
+        if dof == 1 and correction:
+            # Adjust `observed` according to Yates' correction for continuity.
+            # Magnitude of correction no bigger than difference; see gh-13875
+            diff = expected - observed
+            direction = np.sign(diff)
+            magnitude = np.minimum(0.5, np.abs(diff))
+            observed = observed + magnitude * direction
+
+        chi2, p = power_divergence(observed, expected,
+                                   ddof=observed.size - 1 - dof, axis=None,
+                                   lambda_=lambda_)
+
+    return Chi2ContingencyResult(chi2, p, dof, expected)
+
+
+def _chi2_resampling_methods(observed, expected, correction, lambda_, method):
+
+    if observed.ndim != 2:
+        message = 'Use of `method` is only compatible with two-way tables.'
+        raise ValueError(message)
+
+    if correction:
+        message = f'`{correction=}` is not compatible with `{method=}.`'
+        raise ValueError(message)
+
+    if lambda_ is not None:
+        message = f'`{lambda_=}` is not compatible with `{method=}.`'
+        raise ValueError(message)
+
+    if isinstance(method, stats.PermutationMethod):
+        res = _chi2_permutation_method(observed, expected, method)
+    elif isinstance(method, stats.MonteCarloMethod):
+        res = _chi2_monte_carlo_method(observed, expected, method)
+    else:
+        message = (f'`{method=}` not recognized; if provided, `method` must be an '
+                   'instance of `PermutationMethod` or `MonteCarloMethod`.')
+        raise ValueError(message)
+
+    return Chi2ContingencyResult(res.statistic, res.pvalue, np.nan, expected)
+
+
+def _chi2_permutation_method(observed, expected, method):
+    x, y = _untabulate(observed)
+    # `permutation_test` with `permutation_type='pairings' permutes the order of `x`,
+    # which pairs observations in `x` with different observations in `y`.
+    def statistic(x):
+        # crosstab the resample and compute the statistic
+        table = crosstab(x, y)[1]
+        return np.sum((table - expected)**2/expected)
+
+    return stats.permutation_test((x,), statistic, permutation_type='pairings',
+                                  alternative='greater', **method._asdict())
+
+
+def _chi2_monte_carlo_method(observed, expected, method):
+    method = method._asdict()
+
+    if method.pop('rvs', None) is not None:
+        message = ('If the `method` argument of `chi2_contingency` is an '
+                   'instance of `MonteCarloMethod`, its `rvs` attribute '
+                   'must be unspecified. Use the `MonteCarloMethod` `rng` argument '
+                   'to control the random state.')
+        raise ValueError(message)
+    rng = np.random.default_rng(method.pop('rng', None))
+
+    # `random_table.rvs` produces random contingency tables with the given marginals
+    # under the null hypothesis of independence
+    rowsums, colsums = stats.contingency.margins(observed)
+    X = stats.random_table(rowsums.ravel(), colsums.ravel(), seed=rng)
+    def rvs(size):
+        n_resamples = size[0]
+        return X.rvs(size=n_resamples).reshape(size)
+
+    expected = expected.ravel()
+    def statistic(table, axis):
+        return np.sum((table - expected)**2/expected, axis=axis)
+
+    return stats.monte_carlo_test(observed.ravel(), rvs, statistic,
+                                  alternative='greater', **method)
+
+
+@xp_capabilities(np_only=True)
+def association(observed, method="cramer", correction=False, lambda_=None):
+    """Calculates degree of association between two nominal variables.
+
+    The function provides the option for computing one of three measures of
+    association between two nominal variables from the data given in a 2d
+    contingency table: Tschuprow's T, Pearson's Contingency Coefficient
+    and Cramer's V.
+
+    Parameters
+    ----------
+    observed : array-like
+        The array of observed values
+    method : {"cramer", "tschuprow", "pearson"} (default = "cramer")
+        The association test statistic.
+    correction : bool, optional
+        Inherited from `scipy.stats.contingency.chi2_contingency()`
+    lambda_ : float or str, optional
+        Inherited from `scipy.stats.contingency.chi2_contingency()`
+
+    Returns
+    -------
+    statistic : float
+        Value of the test statistic
+
+    Notes
+    -----
+    Cramer's V, Tschuprow's T and Pearson's Contingency Coefficient, all
+    measure the degree to which two nominal or ordinal variables are related,
+    or the level of their association. This differs from correlation, although
+    many often mistakenly consider them equivalent. Correlation measures in
+    what way two variables are related, whereas, association measures how
+    related the variables are. As such, association does not subsume
+    independent variables, and is rather a test of independence. A value of
+    1.0 indicates perfect association, and 0.0 means the variables have no
+    association.
+
+    Both the Cramer's V and Tschuprow's T are extensions of the phi
+    coefficient.  Moreover, due to the close relationship between the
+    Cramer's V and Tschuprow's T the returned values can often be similar
+    or even equivalent.  They are likely to diverge more as the array shape
+    diverges from a 2x2.
+
+    References
+    ----------
+    .. [1] "Tschuprow's T",
+           https://en.wikipedia.org/wiki/Tschuprow's_T
+    .. [2] Tschuprow, A. A. (1939)
+           Principles of the Mathematical Theory of Correlation;
+           translated by M. Kantorowitsch. W. Hodge & Co.
+    .. [3] "Cramer's V", https://en.wikipedia.org/wiki/Cramer's_V
+    .. [4] "Nominal Association: Phi and Cramer's V",
+           http://www.people.vcu.edu/~pdattalo/702SuppRead/MeasAssoc/NominalAssoc.html
+    .. [5] Gingrich, Paul, "Association Between Variables",
+           http://uregina.ca/~gingrich/ch11a.pdf
+
+    Examples
+    --------
+    An example with a 4x2 contingency table:
+
+    >>> import numpy as np
+    >>> from scipy.stats.contingency import association
+    >>> obs4x2 = np.array([[100, 150], [203, 322], [420, 700], [320, 210]])
+
+    Pearson's contingency coefficient
+
+    >>> association(obs4x2, method="pearson")
+    0.18303298140595667
+
+    Cramer's V
+
+    >>> association(obs4x2, method="cramer")
+    0.18617813077483678
+
+    Tschuprow's T
+
+    >>> association(obs4x2, method="tschuprow")
+    0.14146478765062995
+    """
+    arr = np.asarray(observed)
+    if not np.issubdtype(arr.dtype, np.integer):
+        raise ValueError("`observed` must be an integer array.")
+
+    if len(arr.shape) != 2:
+        raise ValueError("method only accepts 2d arrays")
+
+    chi2_stat = chi2_contingency(arr, correction=correction,
+                                 lambda_=lambda_)
+
+    phi2 = chi2_stat.statistic / arr.sum()
+    n_rows, n_cols = arr.shape
+    if method == "cramer":
+        value = phi2 / min(n_cols - 1, n_rows - 1)
+    elif method == "tschuprow":
+        value = phi2 / math.sqrt((n_rows - 1) * (n_cols - 1))
+    elif method == 'pearson':
+        value = phi2 / (1 + phi2)
+    else:
+        raise ValueError("Invalid argument value: 'method' argument must "
+                         "be 'cramer', 'tschuprow', or 'pearson'")
+
+    return math.sqrt(value)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/distributions.py b/.venv/lib/python3.12/site-packages/scipy/stats/distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac9c37aa98c9545b2616c8d32e8f676d8d49289e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/distributions.py
@@ -0,0 +1,24 @@
+#
+# Author:  Travis Oliphant  2002-2011 with contributions from
+#          SciPy Developers 2004-2011
+#
+# NOTE: To look at history using `git blame`, use `git blame -M -C -C`
+#       instead of `git blame -Lxxx,+x`.
+#
+from ._distn_infrastructure import (rv_discrete, rv_continuous, rv_frozen)  # noqa: F401
+
+from . import _continuous_distns
+from . import _discrete_distns
+
+from ._continuous_distns import *  # noqa: F403
+from ._levy_stable import levy_stable
+from ._discrete_distns import *  # noqa: F403
+from ._entropy import entropy
+
+# For backwards compatibility e.g. pymc expects distributions.__all__.
+__all__ = ['rv_discrete', 'rv_continuous', 'rv_histogram', 'entropy']  # noqa: F405
+
+# Add only the distribution names, not the *_gen names.
+__all__ += _continuous_distns._distn_names
+__all__ += ['levy_stable']
+__all__ += _discrete_distns._distn_names
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/kde.py b/.venv/lib/python3.12/site-packages/scipy/stats/kde.py
new file mode 100644
index 0000000000000000000000000000000000000000..4401da5a30f4452ab394232d3928493d0e3b77ec
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/kde.py
@@ -0,0 +1,18 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.stats` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+
+__all__ = ["gaussian_kde"]  # noqa: F822
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package="stats", module="kde",
+                                   private_modules=["_kde"], all=__all__,
+                                   attribute=name)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/morestats.py b/.venv/lib/python3.12/site-packages/scipy/stats/morestats.py
new file mode 100644
index 0000000000000000000000000000000000000000..76040ea0ca5251cabbdda7fb7936af4b281b3212
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/morestats.py
@@ -0,0 +1,27 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.stats` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+
+__all__ = [  # noqa: F822
+    'mvsdist',
+    'bayes_mvs', 'kstat', 'kstatvar', 'probplot', 'ppcc_max', 'ppcc_plot',
+    'boxcox_llf', 'boxcox', 'boxcox_normmax', 'boxcox_normplot',
+    'shapiro', 'anderson', 'ansari', 'bartlett', 'levene',
+    'fligner', 'mood', 'wilcoxon', 'median_test',
+    'circmean', 'circvar', 'circstd', 'anderson_ksamp',
+    'yeojohnson_llf', 'yeojohnson', 'yeojohnson_normmax',
+    'yeojohnson_normplot', 'chi2_contingency', 'distributions',
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package="stats", module="morestats",
+                                   private_modules=["_morestats"], all=__all__,
+                                   attribute=name)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/mstats.py b/.venv/lib/python3.12/site-packages/scipy/stats/mstats.py
new file mode 100644
index 0000000000000000000000000000000000000000..88016af71803dc5c4ebadba168f22cdcd8273dbb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/mstats.py
@@ -0,0 +1,140 @@
+"""
+===================================================================
+Statistical functions for masked arrays (:mod:`scipy.stats.mstats`)
+===================================================================
+
+.. currentmodule:: scipy.stats.mstats
+
+This module contains a large number of statistical functions that can
+be used with masked arrays.
+
+Most of these functions are similar to those in `scipy.stats` but might
+have small differences in the API or in the algorithm used. Since this
+is a relatively new package, some API changes are still possible.
+
+Summary statistics
+==================
+
+.. autosummary::
+   :toctree: generated/
+
+   describe
+   gmean
+   hmean
+   kurtosis
+   mode
+   mquantiles
+   hdmedian
+   hdquantiles
+   hdquantiles_sd
+   idealfourths
+   plotting_positions
+   meppf
+   moment
+   skew
+   tmean
+   tvar
+   tmin
+   tmax
+   tsem
+   variation
+   find_repeats
+   sem
+   trimmed_mean
+   trimmed_mean_ci
+   trimmed_std
+   trimmed_var
+
+Frequency statistics
+====================
+
+.. autosummary::
+   :toctree: generated/
+
+   scoreatpercentile
+
+Correlation functions
+=====================
+
+.. autosummary::
+   :toctree: generated/
+
+   f_oneway
+   pearsonr
+   spearmanr
+   pointbiserialr
+   kendalltau
+   kendalltau_seasonal
+   linregress
+   siegelslopes
+   theilslopes
+   sen_seasonal_slopes
+
+Statistical tests
+=================
+
+.. autosummary::
+   :toctree: generated/
+
+   ttest_1samp
+   ttest_onesamp
+   ttest_ind
+   ttest_rel
+   chisquare
+   kstest
+   ks_2samp
+   ks_1samp
+   ks_twosamp
+   mannwhitneyu
+   rankdata
+   kruskal
+   kruskalwallis
+   friedmanchisquare
+   brunnermunzel
+   skewtest
+   kurtosistest
+   normaltest
+
+Transformations
+===============
+
+.. autosummary::
+   :toctree: generated/
+
+   obrientransform
+   trim
+   trima
+   trimmed_stde
+   trimr
+   trimtail
+   trimboth
+   winsorize
+   zmap
+   zscore
+
+Other
+=====
+
+.. autosummary::
+   :toctree: generated/
+
+   argstoarray
+   count_tied_groups
+   msign
+   compare_medians_ms
+   median_cihs
+   mjci
+   mquantiles_cimj
+   rsh
+
+"""
+from . import _mstats_basic
+from . import _mstats_extras
+from ._mstats_basic import *  # noqa: F403
+from ._mstats_extras import *  # noqa: F403
+# Functions that support masked array input in stats but need to be kept in the
+# mstats namespace for backwards compatibility:
+from scipy.stats import gmean, hmean, zmap, zscore, chisquare
+
+__all__ = _mstats_basic.__all__ + _mstats_extras.__all__
+__all__ += ['gmean', 'hmean', 'zmap', 'zscore', 'chisquare']
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/mstats_basic.py b/.venv/lib/python3.12/site-packages/scipy/stats/mstats_basic.py
new file mode 100644
index 0000000000000000000000000000000000000000..19cc67a6acdfa054ffa2b29b6e774dd7aafda263
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/mstats_basic.py
@@ -0,0 +1,42 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.stats` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+
+__all__ = [  # noqa: F822
+    'argstoarray',
+    'count_tied_groups',
+    'describe',
+    'f_oneway', 'find_repeats','friedmanchisquare',
+    'kendalltau','kendalltau_seasonal','kruskal','kruskalwallis',
+    'ks_twosamp', 'ks_2samp', 'kurtosis', 'kurtosistest',
+    'ks_1samp', 'kstest',
+    'linregress',
+    'mannwhitneyu', 'meppf','mode','moment','mquantiles','msign',
+    'normaltest',
+    'obrientransform',
+    'pearsonr','plotting_positions','pointbiserialr',
+    'rankdata',
+    'scoreatpercentile','sem',
+    'sen_seasonal_slopes','skew','skewtest','spearmanr',
+    'siegelslopes', 'theilslopes',
+    'tmax','tmean','tmin','trim','trimboth',
+    'trimtail','trima','trimr','trimmed_mean','trimmed_std',
+    'trimmed_stde','trimmed_var','tsem','ttest_1samp','ttest_onesamp',
+    'ttest_ind','ttest_rel','tvar',
+    'variation',
+    'winsorize',
+    'brunnermunzel',
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package="stats", module="mstats_basic",
+                                   private_modules=["_mstats_basic"], all=__all__,
+                                   attribute=name, correct_module="mstats")
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/mstats_extras.py b/.venv/lib/python3.12/site-packages/scipy/stats/mstats_extras.py
new file mode 100644
index 0000000000000000000000000000000000000000..fec695329cf2c2d58a4918cc99e209c0650c3ea6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/mstats_extras.py
@@ -0,0 +1,25 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.stats` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+
+__all__ = [  # noqa: F822
+    'compare_medians_ms',
+    'hdquantiles', 'hdmedian', 'hdquantiles_sd',
+    'idealfourths',
+    'median_cihs','mjci','mquantiles_cimj',
+    'rsh',
+    'trimmed_mean_ci',
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package="stats", module="mstats_extras",
+                                   private_modules=["_mstats_extras"], all=__all__,
+                                   attribute=name, correct_module="mstats")
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/mvn.py b/.venv/lib/python3.12/site-packages/scipy/stats/mvn.py
new file mode 100644
index 0000000000000000000000000000000000000000..65da9e20f6a4e6d24c1cb206c59821730fb6ab83
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/mvn.py
@@ -0,0 +1,17 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.stats` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+__all__: list[str] = []
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package="stats", module="mvn",
+                                   private_modules=["_mvn"], all=__all__,
+                                   attribute=name)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/qmc.py b/.venv/lib/python3.12/site-packages/scipy/stats/qmc.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8a08343cf4c759938b31c29e32aaa644bf6e0fd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/qmc.py
@@ -0,0 +1,236 @@
+r"""
+====================================================
+Quasi-Monte Carlo submodule (:mod:`scipy.stats.qmc`)
+====================================================
+
+.. currentmodule:: scipy.stats.qmc
+
+This module provides Quasi-Monte Carlo generators and associated helper
+functions.
+
+
+Quasi-Monte Carlo
+=================
+
+Engines
+-------
+
+.. autosummary::
+   :toctree: generated/
+
+   QMCEngine
+   Sobol
+   Halton
+   LatinHypercube
+   PoissonDisk
+   MultinomialQMC
+   MultivariateNormalQMC
+
+Helpers
+-------
+
+.. autosummary::
+   :toctree: generated/
+
+   discrepancy
+   geometric_discrepancy
+   update_discrepancy
+   scale
+
+
+Introduction to Quasi-Monte Carlo
+=================================
+
+Quasi-Monte Carlo (QMC) methods [1]_, [2]_, [3]_ provide an
+:math:`n \times d` array of numbers in :math:`[0,1]`. They can be used in
+place of :math:`n` points from the :math:`U[0,1]^{d}` distribution. Compared to
+random points, QMC points are designed to have fewer gaps and clumps. This is
+quantified by discrepancy measures [4]_. From the Koksma-Hlawka
+inequality [5]_ we know that low discrepancy reduces a bound on
+integration error. Averaging a function :math:`f` over :math:`n` QMC points
+can achieve an integration error close to :math:`O(n^{-1})` for well
+behaved functions [2]_.
+
+Most QMC constructions are designed for special values of :math:`n`
+such as powers of 2 or large primes. Changing the sample
+size by even one can degrade their performance, even their
+rate of convergence [6]_. For instance :math:`n=100` points may give less
+accuracy than :math:`n=64` if the method was designed for :math:`n=2^m`.
+
+Some QMC constructions are extensible in :math:`n`: we can find
+another special sample size :math:`n' > n` and often an infinite
+sequence of increasing special sample sizes. Some QMC
+constructions are extensible in :math:`d`: we can increase the dimension,
+possibly to some upper bound, and typically without requiring
+special values of :math:`d`. Some QMC methods are extensible in
+both :math:`n` and :math:`d`.
+
+QMC points are deterministic. That makes it hard to estimate the accuracy of
+integrals estimated by averages over QMC points. Randomized QMC (RQMC) [7]_
+points are constructed so that each point is individually :math:`U[0,1]^{d}`
+while collectively the :math:`n` points retain their low discrepancy.
+One can make :math:`R` independent replications of RQMC points to
+see how stable a computation is. From :math:`R` independent values,
+a t-test (or bootstrap t-test [8]_) then gives approximate confidence
+intervals on the mean value. Some RQMC methods produce a
+root mean squared error that is actually :math:`o(1/n)` and smaller than
+the rate seen in unrandomized QMC. An intuitive explanation is
+that the error is a sum of many small ones and random errors
+cancel in a way that deterministic ones do not. RQMC also
+has advantages on integrands that are singular or, for other
+reasons, fail to be Riemann integrable.
+
+(R)QMC cannot beat Bahkvalov's curse of dimension (see [9]_). For
+any random or deterministic method, there are worst case functions
+that will give it poor performance in high dimensions. A worst
+case function for QMC might be 0 at all n points but very
+large elsewhere. Worst case analyses get very pessimistic
+in high dimensions. (R)QMC can bring a great improvement over
+MC when the functions on which it is used are not worst case.
+For instance (R)QMC can be especially effective on integrands
+that are well approximated by sums of functions of
+some small number of their input variables at a time [10]_, [11]_.
+That property is often a surprising finding about those functions.
+
+Also, to see an improvement over IID MC, (R)QMC requires a bit of smoothness of
+the integrand, roughly the mixed first order derivative in each direction,
+:math:`\partial^d f/\partial x_1 \cdots \partial x_d`, must be integral.
+For instance, a function that is 1 inside the hypersphere and 0 outside of it
+has infinite variation in the sense of Hardy and Krause for any dimension
+:math:`d = 2`.
+
+Scrambled nets are a kind of RQMC that have some valuable robustness
+properties [12]_. If the integrand is square integrable, they give variance
+:math:`var_{SNET} = o(1/n)`. There is a finite upper bound on
+:math:`var_{SNET} / var_{MC}` that holds simultaneously for every square
+integrable integrand. Scrambled nets satisfy a strong law of large numbers
+for :math:`f` in :math:`L^p` when :math:`p>1`. In some
+special cases there is a central limit theorem [13]_. For smooth enough
+integrands they can achieve RMSE nearly :math:`O(n^{-3})`. See [12]_
+for references about these properties.
+
+The main kinds of QMC methods are lattice rules [14]_ and digital
+nets and sequences [2]_, [15]_. The theories meet up in polynomial
+lattice rules [16]_ which can produce digital nets. Lattice rules
+require some form of search for good constructions. For digital
+nets there are widely used default constructions.
+
+The most widely used QMC methods are Sobol' sequences [17]_.
+These are digital nets. They are extensible in both :math:`n` and :math:`d`.
+They can be scrambled. The special sample sizes are powers
+of 2. Another popular method are Halton sequences [18]_.
+The constructions resemble those of digital nets. The earlier
+dimensions have much better equidistribution properties than
+later ones. There are essentially no special sample sizes.
+They are not thought to be as accurate as Sobol' sequences.
+They can be scrambled. The nets of Faure [19]_ are also widely
+used. All dimensions are equally good, but the special sample
+sizes grow rapidly with dimension :math:`d`. They can be scrambled.
+The nets of Niederreiter and Xing [20]_ have the best asymptotic
+properties but have not shown good empirical performance [21]_.
+
+Higher order digital nets are formed by a digit interleaving process
+in the digits of the constructed points. They can achieve higher
+levels of asymptotic accuracy given higher smoothness conditions on :math:`f`
+and they can be scrambled [22]_. There is little or no empirical work
+showing the improved rate to be attained.
+
+Using QMC is like using the entire period of a small random
+number generator. The constructions are similar and so
+therefore are the computational costs [23]_.
+
+(R)QMC is sometimes improved by passing the points through
+a baker's transformation (tent function) prior to using them.
+That function has the form :math:`1-2|x-1/2|`. As :math:`x` goes from 0 to
+1, this function goes from 0 to 1 and then back. It is very
+useful to produce a periodic function for lattice rules [14]_,
+and sometimes it improves the convergence rate [24]_.
+
+It is not straightforward to apply QMC methods to Markov
+chain Monte Carlo (MCMC).  We can think of MCMC as using
+:math:`n=1` point in :math:`[0,1]^{d}` for very large :math:`d`, with
+ergodic results corresponding to :math:`d \to \infty`. One proposal is
+in [25]_ and under strong conditions an improved rate of convergence
+has been shown [26]_.
+
+Returning to Sobol' points: there are many versions depending
+on what are called direction numbers. Those are the result of
+searches and are tabulated. A very widely used set of direction
+numbers come from [27]_. It is extensible in dimension up to
+:math:`d=21201`.
+
+References
+----------
+.. [1] Owen, Art B. "Monte Carlo Book: the Quasi-Monte Carlo parts." 2019.
+.. [2] Niederreiter, Harald. "Random number generation and quasi-Monte Carlo
+   methods." Society for Industrial and Applied Mathematics, 1992.
+.. [3] Dick, Josef, Frances Y. Kuo, and Ian H. Sloan. "High-dimensional
+   integration: the quasi-Monte Carlo way." Acta Numerica no. 22: 133, 2013.
+.. [4] Aho, A. V., C. Aistleitner, T. Anderson, K. Appel, V. Arnol'd, N.
+   Aronszajn, D. Asotsky et al. "W. Chen et al.(eds.), "A Panorama of
+   Discrepancy Theory", Sringer International Publishing,
+   Switzerland: 679, 2014.
+.. [5] Hickernell, Fred J. "Koksma-Hlawka Inequality." Wiley StatsRef:
+   Statistics Reference Online, 2014.
+.. [6] Owen, Art B. "On dropping the first Sobol' point." :arxiv:`2008.08051`,
+   2020.
+.. [7] L'Ecuyer, Pierre, and Christiane Lemieux. "Recent advances in randomized
+   quasi-Monte Carlo methods." In Modeling uncertainty, pp. 419-474. Springer,
+   New York, NY, 2002.
+.. [8] DiCiccio, Thomas J., and Bradley Efron. "Bootstrap confidence
+   intervals." Statistical science: 189-212, 1996.
+.. [9] Dimov, Ivan T. "Monte Carlo methods for applied scientists." World
+   Scientific, 2008.
+.. [10] Caflisch, Russel E., William J. Morokoff, and Art B. Owen. "Valuation
+   of mortgage backed securities using Brownian bridges to reduce effective
+   dimension." Journal of Computational Finance: no. 1 27-46, 1997.
+.. [11] Sloan, Ian H., and Henryk Wozniakowski. "When are quasi-Monte Carlo
+   algorithms efficient for high dimensional integrals?." Journal of Complexity
+   14, no. 1 (1998): 1-33.
+.. [12] Owen, Art B., and Daniel Rudolf, "A strong law of large numbers for
+   scrambled net integration." SIAM Review, to appear.
+.. [13] Loh, Wei-Liem. "On the asymptotic distribution of scrambled net
+   quadrature." The Annals of Statistics 31, no. 4: 1282-1324, 2003.
+.. [14] Sloan, Ian H. and S. Joe. "Lattice methods for multiple integration."
+   Oxford University Press, 1994.
+.. [15] Dick, Josef, and Friedrich Pillichshammer. "Digital nets and sequences:
+   discrepancy theory and quasi-Monte Carlo integration." Cambridge University
+   Press, 2010.
+.. [16] Dick, Josef, F. Kuo, Friedrich Pillichshammer, and I. Sloan.
+   "Construction algorithms for polynomial lattice rules for multivariate
+   integration." Mathematics of computation 74, no. 252: 1895-1921, 2005.
+.. [17] Sobol', Il'ya Meerovich. "On the distribution of points in a cube and
+   the approximate evaluation of integrals." Zhurnal Vychislitel'noi Matematiki
+   i Matematicheskoi Fiziki 7, no. 4: 784-802, 1967.
+.. [18] Halton, John H. "On the efficiency of certain quasi-random sequences of
+   points in evaluating multi-dimensional integrals." Numerische Mathematik 2,
+   no. 1: 84-90, 1960.
+.. [19] Faure, Henri. "Discrepance de suites associees a un systeme de
+   numeration (en dimension s)." Acta arithmetica 41, no. 4: 337-351, 1982.
+.. [20] Niederreiter, Harold, and Chaoping Xing. "Low-discrepancy sequences and
+   global function fields with many rational places." Finite Fields and their
+   applications 2, no. 3: 241-273, 1996.
+.. [21] Hong, Hee Sun, and Fred J. Hickernell. "Algorithm 823: Implementing
+   scrambled digital sequences." ACM Transactions on Mathematical Software
+   (TOMS) 29, no. 2: 95-109, 2003.
+.. [22] Dick, Josef. "Higher order scrambled digital nets achieve the optimal
+   rate of the root mean square error for smooth integrands." The Annals of
+   Statistics 39, no. 3: 1372-1398, 2011.
+.. [23] Niederreiter, Harald. "Multidimensional numerical integration using
+   pseudorandom numbers." In Stochastic Programming 84 Part I, pp. 17-38.
+   Springer, Berlin, Heidelberg, 1986.
+.. [24] Hickernell, Fred J. "Obtaining O (N-2+e) Convergence for Lattice
+   Quadrature Rules." In Monte Carlo and Quasi-Monte Carlo Methods 2000,
+   pp. 274-289. Springer, Berlin, Heidelberg, 2002.
+.. [25] Owen, Art B., and Seth D. Tribble. "A quasi-Monte Carlo Metropolis
+   algorithm." Proceedings of the National Academy of Sciences 102,
+   no. 25: 8844-8849, 2005.
+.. [26] Chen, Su. "Consistency and convergence rate of Markov chain quasi Monte
+   Carlo with examples." PhD diss., Stanford University, 2011.
+.. [27] Joe, Stephen, and Frances Y. Kuo. "Constructing Sobol sequences with
+   better two-dimensional projections." SIAM Journal on Scientific Computing
+   30, no. 5: 2635-2654, 2008.
+
+"""
+from ._qmc import *  # noqa: F403
+from ._qmc import __all__  # noqa: F401
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/sampling.py b/.venv/lib/python3.12/site-packages/scipy/stats/sampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..12174d9dfb3cb93fa33811ed4b5d233817512e36
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/sampling.py
@@ -0,0 +1,73 @@
+"""
+======================================================
+Random Number Generators (:mod:`scipy.stats.sampling`)
+======================================================
+
+.. currentmodule:: scipy.stats.sampling
+
+This module contains a collection of random number generators to sample
+from univariate continuous and discrete distributions. It uses the
+implementation of a C library called "UNU.RAN". The only exception is
+RatioUniforms, which is a pure Python implementation of the
+Ratio-of-Uniforms method.
+
+Generators Wrapped
+==================
+
+For continuous distributions
+----------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   NumericalInverseHermite
+   NumericalInversePolynomial
+   TransformedDensityRejection
+   SimpleRatioUniforms
+   RatioUniforms
+
+For discrete distributions
+--------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   DiscreteAliasUrn
+   DiscreteGuideTable
+
+Warnings / Errors used in :mod:`scipy.stats.sampling`
+-----------------------------------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   UNURANError
+
+
+Generators for pre-defined distributions
+========================================
+
+To easily apply the above methods for some of the continuous distributions
+in :mod:`scipy.stats`, the following functionality can be used:
+
+.. autosummary::
+   :toctree: generated/
+
+   FastGeneratorInversion
+
+"""
+from ._sampling import FastGeneratorInversion, RatioUniforms  # noqa: F401
+from ._unuran.unuran_wrapper import (  # noqa: F401
+   TransformedDensityRejection,
+   DiscreteAliasUrn,
+   DiscreteGuideTable,
+   NumericalInversePolynomial,
+   NumericalInverseHermite,
+   SimpleRatioUniforms,
+   UNURANError
+)
+
+__all__ = ["NumericalInverseHermite", "NumericalInversePolynomial",
+           "TransformedDensityRejection", "SimpleRatioUniforms",
+           "RatioUniforms", "DiscreteAliasUrn", "DiscreteGuideTable",
+           "UNURANError", "FastGeneratorInversion"]
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/stats.py b/.venv/lib/python3.12/site-packages/scipy/stats/stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0589a9907d3cd181739135a3e6fd1d14d18dc50
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/stats.py
@@ -0,0 +1,43 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.stats` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+
+__all__ = [  # noqa: F822
+    'gmean', 'hmean', 'pmean', 'mode', 'tmean', 'tvar',
+    'tmin', 'tmax', 'tstd', 'tsem', 'moment',
+    'skew', 'kurtosis', 'describe', 'skewtest', 'kurtosistest',
+    'normaltest', 'jarque_bera',
+    'scoreatpercentile', 'percentileofscore',
+    'cumfreq', 'relfreq', 'obrientransform',
+    'sem', 'zmap', 'zscore', 'gzscore', 'iqr', 'gstd',
+    'median_abs_deviation',
+    'sigmaclip', 'trimboth', 'trim1', 'trim_mean',
+    'f_oneway',
+    'pearsonr', 'fisher_exact',
+    'spearmanr', 'pointbiserialr',
+    'kendalltau', 'weightedtau', 'multiscale_graphcorr',
+    'linregress', 'siegelslopes', 'theilslopes', 'ttest_1samp',
+    'ttest_ind', 'ttest_ind_from_stats', 'ttest_rel',
+    'kstest', 'ks_1samp', 'ks_2samp',
+    'chisquare', 'power_divergence',
+    'tiecorrect', 'ranksums', 'kruskal', 'friedmanchisquare',
+    'rankdata',
+    'combine_pvalues', 'wasserstein_distance', 'energy_distance',
+    'brunnermunzel', 'alexandergovern', 'distributions',
+    'mstats_basic',
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(
+        sub_package="stats", module="stats",
+        private_modules=["_stats_py", "_mgc", "_correlation"],
+        all=__all__,
+        attribute=name)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/__init__.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/common_tests.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/common_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..981bfc3061e3158116a9a2c9f448eb1eb9ff2c61
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/common_tests.py
@@ -0,0 +1,347 @@
+import pickle
+
+import numpy as np
+import numpy.testing as npt
+from numpy.testing import assert_allclose, assert_equal
+from pytest import raises as assert_raises
+
+import numpy.ma.testutils as ma_npt
+
+from scipy._lib._util import getfullargspec_no_self as _getfullargspec
+from scipy._lib._array_api_no_0d import xp_assert_equal
+from scipy import stats
+
+
+def check_named_results(res, attributes, ma=False, xp=None):
+    for i, attr in enumerate(attributes):
+        if ma:
+            ma_npt.assert_equal(res[i], getattr(res, attr))
+        elif xp is not None:
+            xp_assert_equal(res[i], getattr(res, attr))
+        else:
+            npt.assert_equal(res[i], getattr(res, attr))
+
+
+def check_normalization(distfn, args, distname):
+    norm_moment = distfn.moment(0, *args)
+    npt.assert_allclose(norm_moment, 1.0)
+
+    if distname == "rv_histogram_instance":
+        atol, rtol = 1e-5, 0
+    else:
+        atol, rtol = 1e-7, 1e-7
+
+    normalization_expect = distfn.expect(lambda x: 1, args=args)
+    npt.assert_allclose(normalization_expect, 1.0, atol=atol, rtol=rtol,
+                        err_msg=distname, verbose=True)
+
+    _a, _b = distfn.support(*args)
+    normalization_cdf = distfn.cdf(_b, *args)
+    npt.assert_allclose(normalization_cdf, 1.0)
+
+
+def check_moment(distfn, arg, m, v, msg):
+    m1 = distfn.moment(1, *arg)
+    m2 = distfn.moment(2, *arg)
+    if not np.isinf(m):
+        npt.assert_almost_equal(m1, m, decimal=10,
+                                err_msg=msg + ' - 1st moment')
+    else:                     # or np.isnan(m1),
+        npt.assert_(np.isinf(m1),
+                    msg + f' - 1st moment -infinite, m1={str(m1)}')
+
+    if not np.isinf(v):
+        npt.assert_almost_equal(m2 - m1 * m1, v, decimal=10,
+                                err_msg=msg + ' - 2ndt moment')
+    else:                     # or np.isnan(m2),
+        npt.assert_(np.isinf(m2), msg + f' - 2nd moment -infinite, {m2=}')
+
+
+def check_mean_expect(distfn, arg, m, msg):
+    if np.isfinite(m):
+        m1 = distfn.expect(lambda x: x, arg)
+        npt.assert_almost_equal(m1, m, decimal=5,
+                                err_msg=msg + ' - 1st moment (expect)')
+
+
+def check_var_expect(distfn, arg, m, v, msg):
+    dist_looser_tolerances = {"rv_histogram_instance" , "ksone"}
+    kwargs = {'rtol': 5e-6} if msg in dist_looser_tolerances else {}
+    if np.isfinite(v):
+        m2 = distfn.expect(lambda x: x*x, arg)
+        npt.assert_allclose(m2, v + m*m, **kwargs)
+
+
+def check_skew_expect(distfn, arg, m, v, s, msg):
+    if np.isfinite(s):
+        m3e = distfn.expect(lambda x: np.power(x-m, 3), arg)
+        npt.assert_almost_equal(m3e, s * np.power(v, 1.5),
+                                decimal=5, err_msg=msg + ' - skew')
+    else:
+        npt.assert_(np.isnan(s))
+
+
+def check_kurt_expect(distfn, arg, m, v, k, msg):
+    if np.isfinite(k):
+        m4e = distfn.expect(lambda x: np.power(x-m, 4), arg)
+        npt.assert_allclose(m4e, (k + 3.) * np.power(v, 2),
+                            atol=1e-5, rtol=1e-5,
+                            err_msg=msg + ' - kurtosis')
+    elif not np.isposinf(k):
+        npt.assert_(np.isnan(k))
+
+
+def check_munp_expect(dist, args, msg):
+    # If _munp is overridden, test a higher moment. (Before gh-18634, some
+    # distributions had issues with moments 5 and higher.)
+    if dist._munp.__func__ != stats.rv_continuous._munp:
+        res = dist.moment(5, *args)  # shouldn't raise an error
+        ref = dist.expect(lambda x: x ** 5, args, lb=-np.inf, ub=np.inf)
+        if not np.isfinite(res):  # could be valid; automated test can't know
+            return
+        # loose tolerance, mostly to see whether _munp returns *something*
+        assert_allclose(res, ref, atol=1e-10, rtol=1e-4,
+                        err_msg=msg + ' - higher moment / _munp')
+
+
+def check_entropy(distfn, arg, msg):
+    ent = distfn.entropy(*arg)
+    npt.assert_(not np.isnan(ent), msg + 'test Entropy is nan')
+
+
+def check_private_entropy(distfn, args, superclass):
+    # compare a generic _entropy with the distribution-specific implementation
+    npt.assert_allclose(distfn._entropy(*args),
+                        superclass._entropy(distfn, *args))
+
+
+def check_entropy_vect_scale(distfn, arg):
+    # check 2-d
+    sc = np.asarray([[1, 2], [3, 4]])
+    v_ent = distfn.entropy(*arg, scale=sc)
+    s_ent = [distfn.entropy(*arg, scale=s) for s in sc.ravel()]
+    s_ent = np.asarray(s_ent).reshape(v_ent.shape)
+    assert_allclose(v_ent, s_ent, atol=1e-14)
+
+    # check invalid value, check cast
+    sc = [1, 2, -3]
+    v_ent = distfn.entropy(*arg, scale=sc)
+    s_ent = [distfn.entropy(*arg, scale=s) for s in sc]
+    s_ent = np.asarray(s_ent).reshape(v_ent.shape)
+    assert_allclose(v_ent, s_ent, atol=1e-14)
+
+
+def check_edge_support(distfn, args):
+    # Make sure that x=self.a and self.b are handled correctly.
+    x = distfn.support(*args)
+    if isinstance(distfn, stats.rv_discrete):
+        x = x[0]-1, x[1]
+
+    npt.assert_equal(distfn.cdf(x, *args), [0.0, 1.0])
+    npt.assert_equal(distfn.sf(x, *args), [1.0, 0.0])
+
+    if distfn.name not in ('skellam', 'dlaplace'):
+        # with a = -inf, log(0) generates warnings
+        npt.assert_equal(distfn.logcdf(x, *args), [-np.inf, 0.0])
+        npt.assert_equal(distfn.logsf(x, *args), [0.0, -np.inf])
+
+    npt.assert_equal(distfn.ppf([0.0, 1.0], *args), x)
+    npt.assert_equal(distfn.isf([0.0, 1.0], *args), x[::-1])
+
+    # out-of-bounds for isf & ppf
+    npt.assert_(np.isnan(distfn.isf([-1, 2], *args)).all())
+    npt.assert_(np.isnan(distfn.ppf([-1, 2], *args)).all())
+
+
+def check_named_args(distfn, x, shape_args, defaults, meths):
+    ## Check calling w/ named arguments.
+
+    # check consistency of shapes, numargs and _parse signature
+    signature = _getfullargspec(distfn._parse_args)
+    npt.assert_(signature.varargs is None)
+    npt.assert_(signature.varkw is None)
+    npt.assert_(not signature.kwonlyargs)
+    npt.assert_(list(signature.defaults) == list(defaults))
+
+    shape_argnames = signature.args[:-len(defaults)]  # a, b, loc=0, scale=1
+    if distfn.shapes:
+        shapes_ = distfn.shapes.replace(',', ' ').split()
+    else:
+        shapes_ = ''
+    npt.assert_(len(shapes_) == distfn.numargs)
+    npt.assert_(len(shapes_) == len(shape_argnames))
+
+    # check calling w/ named arguments
+    shape_args = list(shape_args)
+
+    vals = [meth(x, *shape_args) for meth in meths]
+    npt.assert_(np.all(np.isfinite(vals)))
+
+    names, a, k = shape_argnames[:], shape_args[:], {}
+    while names:
+        k.update({names.pop(): a.pop()})
+        v = [meth(x, *a, **k) for meth in meths]
+        npt.assert_array_equal(vals, v)
+        if 'n' not in k.keys():
+            # `n` is first parameter of moment(), so can't be used as named arg
+            npt.assert_equal(distfn.moment(1, *a, **k),
+                             distfn.moment(1, *shape_args))
+
+    # unknown arguments should not go through:
+    k.update({'kaboom': 42})
+    assert_raises(TypeError, distfn.cdf, x, **k)
+
+
+def check_random_state_property(distfn, args):
+    # check the random_state attribute of a distribution *instance*
+
+    # baseline: this relies on the global state
+    np.random.seed(1234)  # valid use of np.random.seed
+    distfn.random_state = None
+    r0 = distfn.rvs(*args, size=8)
+
+    # use an explicit instance-level random_state
+    distfn.random_state = 1234
+    r1 = distfn.rvs(*args, size=8)
+    npt.assert_equal(r0, r1)
+
+    distfn.random_state = np.random.RandomState(1234)
+    r2 = distfn.rvs(*args, size=8)
+    npt.assert_equal(r0, r2)
+
+    # check that np.random.Generator can be used (numpy >= 1.17)
+    if hasattr(np.random, 'default_rng'):
+        # obtain a np.random.Generator object
+        rng = np.random.default_rng(1234)
+        distfn.rvs(*args, size=1, random_state=rng)
+
+    # can override the instance-level random_state for an individual .rvs call
+    distfn.random_state = 2
+    orig_state = distfn.random_state.get_state()
+
+    r3 = distfn.rvs(*args, size=8, random_state=np.random.RandomState(1234))
+    npt.assert_equal(r0, r3)
+
+    # ... and that does not alter the instance-level random_state!
+    npt.assert_equal(distfn.random_state.get_state(), orig_state)
+
+
+def check_meth_dtype(distfn, arg, meths):
+    q0 = [0.25, 0.5, 0.75]
+    x0 = distfn.ppf(q0, *arg)
+    x_cast = [x0.astype(tp) for tp in (np.long, np.float16, np.float32,
+                                       np.float64)]
+
+    for x in x_cast:
+        # casting may have clipped the values, exclude those
+        distfn._argcheck(*arg)
+        x = x[(distfn.a < x) & (x < distfn.b)]
+        for meth in meths:
+            val = meth(x, *arg)
+            npt.assert_(val.dtype == np.float64)
+
+
+def check_ppf_dtype(distfn, arg):
+    q0 = np.asarray([0.25, 0.5, 0.75])
+    q_cast = [q0.astype(tp) for tp in (np.float16, np.float32, np.float64)]
+    for q in q_cast:
+        for meth in [distfn.ppf, distfn.isf]:
+            val = meth(q, *arg)
+            npt.assert_(val.dtype == np.float64)
+
+
+def check_cmplx_deriv(distfn, arg):
+    # Distributions allow complex arguments.
+    def deriv(f, x, *arg):
+        x = np.asarray(x)
+        h = 1e-10
+        return (f(x + h*1j, *arg)/h).imag
+
+    x0 = distfn.ppf([0.25, 0.51, 0.75], *arg)
+    x_cast = [x0.astype(tp) for tp in (np.long, np.float16, np.float32,
+                                       np.float64)]
+
+    for x in x_cast:
+        # casting may have clipped the values, exclude those
+        distfn._argcheck(*arg)
+        x = x[(distfn.a < x) & (x < distfn.b)]
+
+        pdf, cdf, sf = distfn.pdf(x, *arg), distfn.cdf(x, *arg), distfn.sf(x, *arg)
+        assert_allclose(deriv(distfn.cdf, x, *arg), pdf, rtol=1e-5)
+        assert_allclose(deriv(distfn.logcdf, x, *arg), pdf/cdf, rtol=1e-5)
+
+        assert_allclose(deriv(distfn.sf, x, *arg), -pdf, rtol=1e-5)
+        assert_allclose(deriv(distfn.logsf, x, *arg), -pdf/sf, rtol=1e-5)
+
+        assert_allclose(deriv(distfn.logpdf, x, *arg),
+                        deriv(distfn.pdf, x, *arg) / distfn.pdf(x, *arg),
+                        rtol=1e-5)
+
+
+def check_pickling(distfn, args):
+    # check that a distribution instance pickles and unpickles
+    # pay special attention to the random_state property
+
+    # save the random_state (restore later)
+    rndm = distfn.random_state
+
+    # check unfrozen
+    distfn.random_state = 1234
+    distfn.rvs(*args, size=8)
+    s = pickle.dumps(distfn)
+    r0 = distfn.rvs(*args, size=8)
+
+    unpickled = pickle.loads(s)
+    r1 = unpickled.rvs(*args, size=8)
+    npt.assert_equal(r0, r1)
+
+    # also smoke test some methods
+    medians = [distfn.ppf(0.5, *args), unpickled.ppf(0.5, *args)]
+    npt.assert_equal(medians[0], medians[1])
+    npt.assert_equal(distfn.cdf(medians[0], *args),
+                     unpickled.cdf(medians[1], *args))
+
+    # check frozen pickling/unpickling with rvs
+    frozen_dist = distfn(*args)
+    pkl = pickle.dumps(frozen_dist)
+    unpickled = pickle.loads(pkl)
+
+    r0 = frozen_dist.rvs(size=8)
+    r1 = unpickled.rvs(size=8)
+    npt.assert_equal(r0, r1)
+
+    # check pickling/unpickling of .fit method
+    if hasattr(distfn, "fit"):
+        fit_function = distfn.fit
+        pickled_fit_function = pickle.dumps(fit_function)
+        unpickled_fit_function = pickle.loads(pickled_fit_function)
+        assert fit_function.__name__ == unpickled_fit_function.__name__ == "fit"
+
+    # restore the random_state
+    distfn.random_state = rndm
+
+
+def check_freezing(distfn, args):
+    # regression test for gh-11089: freezing a distribution fails
+    # if loc and/or scale are specified
+    if isinstance(distfn, stats.rv_continuous):
+        locscale = {'loc': 1, 'scale': 2}
+    else:
+        locscale = {'loc': 1}
+
+    rv = distfn(*args, **locscale)
+    assert rv.a == distfn(*args).a
+    assert rv.b == distfn(*args).b
+
+
+def check_rvs_broadcast(distfunc, distname, allargs, shape, shape_only, otype):
+    rng = np.random.RandomState(123)
+    sample = distfunc.rvs(*allargs, random_state=rng)
+    assert_equal(sample.shape, shape, f"{distname}: rvs failed to broadcast")
+    if not shape_only:
+        rvs = np.vectorize(
+            lambda *allargs: distfunc.rvs(*allargs, random_state=rng),
+            otypes=otype)
+        rng = np.random.RandomState(123)
+        expected = rvs(*allargs)
+        assert_allclose(sample, expected, rtol=1e-13)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/_mvt.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/_mvt.py
new file mode 100644
index 0000000000000000000000000000000000000000..c346d0daded4b6e734718742cc8950b84ed333f7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/_mvt.py
@@ -0,0 +1,171 @@
+import math
+import numpy as np
+from scipy import special
+from scipy.stats._qmc import primes_from_2_to
+
+
+def _primes(n):
+    # Defined to facilitate comparison between translation and source
+    # In Matlab, primes(10.5) -> first four primes, primes(11.5) -> first five
+    return primes_from_2_to(math.ceil(n))
+
+
+def _gaminv(a, b):
+    # Defined to facilitate comparison between translation and source
+    # Matlab's `gaminv` is like `special.gammaincinv` but args are reversed
+    return special.gammaincinv(b, a)
+
+
+def _qsimvtv(m, nu, sigma, a, b, rng):
+    """Estimates the multivariate t CDF using randomized QMC
+
+    Parameters
+    ----------
+    m : int
+        The number of points
+    nu : float
+        Degrees of freedom
+    sigma : ndarray
+        A 2D positive semidefinite covariance matrix
+    a : ndarray
+        Lower integration limits
+    b : ndarray
+        Upper integration limits.
+    rng : Generator
+        Pseudorandom number generator
+
+    Returns
+    -------
+    p : float
+        The estimated CDF.
+    e : float
+        An absolute error estimate.
+
+    """
+    # _qsimvtv is a Python translation of the Matlab function qsimvtv,
+    # semicolons and all.
+    #
+    #   This function uses an algorithm given in the paper
+    #      "Comparison of Methods for the Numerical Computation of
+    #       Multivariate t Probabilities", in
+    #      J. of Computational and Graphical Stat., 11(2002), pp. 950-971, by
+    #          Alan Genz and Frank Bretz
+    #
+    #   The primary references for the numerical integration are
+    #    "On a Number-Theoretical Integration Method"
+    #    H. Niederreiter, Aequationes Mathematicae, 8(1972), pp. 304-11.
+    #    and
+    #    "Randomization of Number Theoretic Methods for Multiple Integration"
+    #     R. Cranley & T.N.L. Patterson, SIAM J Numer Anal, 13(1976), pp. 904-14.
+    #
+    #   Alan Genz is the author of this function and following Matlab functions.
+    #          Alan Genz, WSU Math, PO Box 643113, Pullman, WA 99164-3113
+    #          Email : alangenz@wsu.edu
+    #
+    # Copyright (C) 2013, Alan Genz,  All rights reserved.
+    #
+    # Redistribution and use in source and binary forms, with or without
+    # modification, are permitted provided the following conditions are met:
+    #   1. Redistributions of source code must retain the above copyright
+    #      notice, this list of conditions and the following disclaimer.
+    #   2. Redistributions in binary form must reproduce the above copyright
+    #      notice, this list of conditions and the following disclaimer in
+    #      the documentation and/or other materials provided with the
+    #      distribution.
+    #   3. The contributor name(s) may not be used to endorse or promote
+    #      products derived from this software without specific prior
+    #      written permission.
+    # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+    # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+    # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+    # FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+    # COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+    # INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+    # BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
+    # OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
+    # ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR
+    # TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF USE
+    # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+    # Initialization
+    sn = max(1, math.sqrt(nu)); ch, az, bz = _chlrps(sigma, a/sn, b/sn)
+    n = len(sigma); N = 10; P = math.ceil(m/N); on = np.ones(P); p = 0; e = 0
+    ps = np.sqrt(_primes(5*n*math.log(n+4)/4)); q = ps[:, np.newaxis]  # Richtmyer gens.
+
+    # Randomization loop for ns samples
+    c = None; dc = None
+    for S in range(N):
+        vp = on.copy(); s = np.zeros((n, P))
+        for i in range(n):
+            x = np.abs(2*np.mod(q[i]*np.arange(1, P+1) + rng.random(), 1)-1)  # periodizing transform
+            if i == 0:
+                r = on
+                if nu > 0:
+                    r = np.sqrt(2*_gaminv(x, nu/2))
+            else:
+                y = _Phinv(c + x*dc)
+                s[i:] += ch[i:, i-1:i] * y
+            si = s[i, :]; c = on.copy(); ai = az[i]*r - si; d = on.copy(); bi = bz[i]*r - si
+            c[ai <= -9] = 0; tl = abs(ai) < 9; c[tl] = _Phi(ai[tl])
+            d[bi <= -9] = 0; tl = abs(bi) < 9; d[tl] = _Phi(bi[tl])
+            dc = d - c; vp = vp * dc
+        d = (np.mean(vp) - p)/(S + 1); p = p + d; e = (S - 1)*e/(S + 1) + d**2
+    e = math.sqrt(e)  # error estimate is 3 times std error with N samples.
+    return p, e
+
+
+#  Standard statistical normal distribution functions
+def _Phi(z):
+    return special.ndtr(z)
+
+
+def _Phinv(p):
+    return special.ndtri(p)
+
+
+def _chlrps(R, a, b):
+    """
+    Computes permuted and scaled lower Cholesky factor c for R which may be
+    singular, also permuting and scaling integration limit vectors a and b.
+    """
+    ep = 1e-10  # singularity tolerance
+    eps = np.finfo(R.dtype).eps
+
+    n = len(R); c = R.copy(); ap = a.copy(); bp = b.copy(); d = np.sqrt(np.maximum(np.diag(c), 0))
+    for i in range(n):
+        if d[i] > 0:
+            c[:, i] /= d[i]; c[i, :] /= d[i]
+            ap[i] /= d[i]; bp[i] /= d[i]
+    y = np.zeros((n, 1)); sqtp = math.sqrt(2*math.pi)
+
+    for k in range(n):
+        im = k; ckk = 0; dem = 1; s = 0
+        for i in range(k, n):
+            if c[i, i] > eps:
+                cii = math.sqrt(max(c[i, i], 0))
+                if i > 0: s = c[i, :k] @ y[:k]
+                ai = (ap[i]-s)/cii; bi = (bp[i]-s)/cii; de = _Phi(bi)-_Phi(ai)
+                if de <= dem:
+                    ckk = cii; dem = de; am = ai; bm = bi; im = i
+        if im > k:
+            ap[[im, k]] = ap[[k, im]]; bp[[im, k]] = bp[[k, im]]; c[im, im] = c[k, k]
+            t = c[im, :k].copy(); c[im, :k] = c[k, :k]; c[k, :k] = t
+            t = c[im+1:, im].copy(); c[im+1:, im] = c[im+1:, k]; c[im+1:, k] = t
+            t = c[k+1:im, k].copy(); c[k+1:im, k] = c[im, k+1:im].T; c[im, k+1:im] = t.T
+        if ckk > ep*(k+1):
+            c[k, k] = ckk; c[k, k+1:] = 0
+            for i in range(k+1, n):
+                c[i, k] = c[i, k]/ckk; c[i, k+1:i+1] = c[i, k+1:i+1] - c[i, k]*c[k+1:i+1, k].T
+            if abs(dem) > ep:
+                y[k] = (np.exp(-am**2/2) - np.exp(-bm**2/2)) / (sqtp*dem)
+            else:
+                y[k] = (am + bm) / 2
+                if am < -10:
+                    y[k] = bm
+                elif bm > 10:
+                    y[k] = am
+            c[k, :k+1] /= ckk; ap[k] /= ckk; bp[k] /= ckk
+        else:
+            c[k:, k] = 0; y[k] = (ap[k] + bp[k])/2
+        pass
+    return c, ap, bp
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/fisher_exact_results_from_r.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/fisher_exact_results_from_r.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7dd8936018eae2f74cc6f5966235a86fa821793
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/fisher_exact_results_from_r.py
@@ -0,0 +1,607 @@
+# DO NOT EDIT THIS FILE!
+# This file was generated by the R script
+#     generate_fisher_exact_results_from_r.R
+# The script was run with R version 3.6.2 (2019-12-12) at 2020-11-09 06:16:09
+
+
+from collections import namedtuple
+import numpy as np
+
+
+Inf = np.inf
+
+Parameters = namedtuple('Parameters',
+                        ['table', 'confidence_level', 'alternative'])
+RResults = namedtuple('RResults',
+                      ['pvalue', 'conditional_odds_ratio',
+                       'conditional_odds_ratio_ci'])
+data = [
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.1300759363430016,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0.04035202926536294,
+                                         2.662846672960251))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.02301413756522116,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0.004668988338943325,
+                                         0.895792956493601))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.1973244147157191,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0.4153910882532168,
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+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=2.005657880388915e-122,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(297.9619252357688,
+                                         Inf))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=5.728437460831983e-44,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(130.3213490295859,
+                                         Inf))),
+    (Parameters(table=[[190, 800], [200, 900]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.2959825901308897,
+              conditional_odds_ratio=1.068697577856801,
+              conditional_odds_ratio_ci=(0.8176272148267533,
+                                         Inf))),
+]
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/jf_skew_t_gamlss_pdf_data.npy b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/jf_skew_t_gamlss_pdf_data.npy
new file mode 100644
index 0000000000000000000000000000000000000000..721749bcd853fa5c5efe5a1f5ba6e105658395dc
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/jf_skew_t_gamlss_pdf_data.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:254d2dee4a4d547b9331c60243c6fcfcaffd26c8b104d08d4f6045a7645b3bba
+size 4064
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/levy_stable/stable-Z1-cdf-sample-data.npy b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/levy_stable/stable-Z1-cdf-sample-data.npy
new file mode 100644
index 0000000000000000000000000000000000000000..adda664a7b5442fc0977ddbaa572c864ddd31f08
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/levy_stable/stable-Z1-cdf-sample-data.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cf18c1f2d65a232bf2c7121282df31bf2a8be827afafc4ed810ed37457ee898a
+size 183728
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/levy_stable/stable-Z1-pdf-sample-data.npy b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/levy_stable/stable-Z1-pdf-sample-data.npy
new file mode 100644
index 0000000000000000000000000000000000000000..6c41166721b891a801cdc6828804c6da7233d625
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/levy_stable/stable-Z1-pdf-sample-data.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fee99512bab4ccc6569b47b924e4b034e1cdbab5624fafc7e120648bd5f7a128
+size 183688
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/levy_stable/stable-loc-scale-sample-data.npy b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/levy_stable/stable-loc-scale-sample-data.npy
new file mode 100644
index 0000000000000000000000000000000000000000..0a1460e407521836a9b73a081609af4ccdb6deae
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/levy_stable/stable-loc-scale-sample-data.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3c719edd5431fb9e7b9ecb6d19e3ca7a9095298bd19f226685b0fca40f0c073
+size 9328
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/AtmWtAg.dat b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/AtmWtAg.dat
new file mode 100644
index 0000000000000000000000000000000000000000..30537565fe8c47f74da0e63a39f4b46600f7768f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/AtmWtAg.dat
@@ -0,0 +1,108 @@
+NIST/ITL StRD 
+Dataset Name:   AtmWtAg   (AtmWtAg.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 108) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Powell, L.J., Murphy, T.J. and Gramlich, J.W. (1982).
+                "The Absolute Isotopic Abundance & Atomic Weight
+                of a Reference Sample of Silver".
+                NBS Journal of Research, 87, pp. 9-19.
+
+
+Data:           1 Factor
+                2 Treatments
+                24 Replicates/Cell
+                48 Observations
+                7 Constant Leading Digits
+                Average Level of Difficulty
+                Observed Data
+
+
+Model:          3 Parameters (mu, tau_1, tau_2)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares             F Statistic
+
+
+Between Instrument  1 3.63834187500000E-09 3.63834187500000E-09 1.59467335677930E+01
+Within Instrument  46 1.04951729166667E-08 2.28155932971014E-10
+
+                   Certified R-Squared 2.57426544538321E-01
+
+                   Certified Residual
+                   Standard Deviation  1.51048314446410E-05
+
+
+
+
+
+
+
+
+
+
+
+Data:  Instrument           AgWt
+           1            107.8681568
+           1            107.8681465
+           1            107.8681572
+           1            107.8681785
+           1            107.8681446
+           1            107.8681903
+           1            107.8681526
+           1            107.8681494
+           1            107.8681616
+           1            107.8681587
+           1            107.8681519
+           1            107.8681486
+           1            107.8681419
+           1            107.8681569
+           1            107.8681508
+           1            107.8681672
+           1            107.8681385
+           1            107.8681518
+           1            107.8681662
+           1            107.8681424
+           1            107.8681360
+           1            107.8681333
+           1            107.8681610
+           1            107.8681477
+           2            107.8681079
+           2            107.8681344
+           2            107.8681513
+           2            107.8681197
+           2            107.8681604
+           2            107.8681385
+           2            107.8681642
+           2            107.8681365
+           2            107.8681151
+           2            107.8681082
+           2            107.8681517
+           2            107.8681448
+           2            107.8681198
+           2            107.8681482
+           2            107.8681334
+           2            107.8681609
+           2            107.8681101
+           2            107.8681512
+           2            107.8681469
+           2            107.8681360
+           2            107.8681254
+           2            107.8681261
+           2            107.8681450
+           2            107.8681368
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SiRstv.dat b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SiRstv.dat
new file mode 100644
index 0000000000000000000000000000000000000000..18ea8971fd7a4d67800dafe98ac5ea5acef53025
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SiRstv.dat
@@ -0,0 +1,85 @@
+NIST/ITL StRD 
+Dataset Name:   SiRstv     (SiRstv.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 85) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Ehrstein, James and Croarkin, M. Carroll.
+                Unpublished NIST dataset.
+
+
+Data:           1 Factor
+                5 Treatments
+                5  Replicates/Cell
+                25 Observations
+                3 Constant Leading Digits
+                Lower Level of Difficulty
+                Observed Data
+
+
+Model:          6 Parameters (mu,tau_1, ... , tau_5)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares             F Statistic
+
+Between Instrument  4 5.11462616000000E-02 1.27865654000000E-02 1.18046237440255E+00
+Within Instrument  20 2.16636560000000E-01 1.08318280000000E-02
+
+                   Certified R-Squared 1.90999039051129E-01
+
+                   Certified Residual
+                   Standard Deviation  1.04076068334656E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Instrument   Resistance
+           1         196.3052
+           1         196.1240
+           1         196.1890
+           1         196.2569
+           1         196.3403
+           2         196.3042
+           2         196.3825
+           2         196.1669
+           2         196.3257
+           2         196.0422
+           3         196.1303
+           3         196.2005
+           3         196.2889
+           3         196.0343
+           3         196.1811
+           4         196.2795
+           4         196.1748
+           4         196.1494
+           4         196.1485
+           4         195.9885
+           5         196.2119
+           5         196.1051
+           5         196.1850
+           5         196.0052
+           5         196.2090
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs01.dat b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs01.dat
new file mode 100644
index 0000000000000000000000000000000000000000..945b24bf35422152a5faba73ed054ab78fda1bdf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs01.dat
@@ -0,0 +1,249 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs01   (SmLs01.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 249) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                21 Replicates/Cell
+                189 Observations
+                1 Constant Leading Digit
+                Lower Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares            F Statistic
+
+Between Treatment   8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
+Within Treatment  180 1.80000000000000E+00 1.00000000000000E-02
+
+                  Certified R-Squared 4.82758620689655E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1         1.4
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           2         1.3
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           2         1.2
+           2         1.4
+           3         1.5
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           3         1.4
+           3         1.6
+           4         1.3
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           4         1.2
+           4         1.4
+           5         1.5
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           5         1.4
+           5         1.6
+           6         1.3
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           7         1.5
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           8         1.3
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           9         1.5
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs02.dat b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs02.dat
new file mode 100644
index 0000000000000000000000000000000000000000..ee76633a660a48225064bbb86a25f6a2f36c6d9a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs02.dat
@@ -0,0 +1,1869 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs02   (SmLs02.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 1869) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                201 Replicates/Cell
+                1809 Observations
+                1 Constant Leading Digit
+                Lower Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares             F Statistic
+
+Between Treatment    8 1.60800000000000E+01 2.01000000000000E+00 2.01000000000000E+02
+Within Treatment  1800 1.80000000000000E+01 1.00000000000000E-02
+
+                  Certified R-Squared 4.71830985915493E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1         1.4
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
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+           6         1.4
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+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           6         1.2
+           6         1.4
+           7         1.5
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
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+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           7         1.4
+           7         1.6
+           8         1.3
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
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+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
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+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           8         1.2
+           8         1.4
+           9         1.5
+           9         1.4
+           9         1.6
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+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
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+           9         1.4
+           9         1.6
+           9         1.4
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+           9         1.4
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+           9         1.6
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+           9         1.6
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+           9         1.6
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+           9         1.4
+           9         1.6
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+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
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+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
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+           9         1.4
+           9         1.6
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+           9         1.4
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+           9         1.6
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+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
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+           9         1.4
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+           9         1.4
+           9         1.6
+           9         1.4
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+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
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+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
+           9         1.4
+           9         1.6
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs03.dat b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs03.dat
new file mode 100644
index 0000000000000000000000000000000000000000..55dfa2313ffb152709c58b47c0058567b710d903
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs03.dat
@@ -0,0 +1,18069 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs03   (SmLs03.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 18069) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                2001 Replicates/Cell
+                18009 Observations
+                1 Constant Leading Digit
+                Lower Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares             F Statistic
+
+Between Treatment     8 1.60080000000000E+02 2.00100000000000E+01 2.00100000000000E+03
+Within Treatment  18000 1.80000000000000E+02 1.00000000000000E-02
+
+                  Certified R-Squared 4.70712773465067E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1         1.4
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
+           1         1.3
+           1         1.5
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diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs04.dat b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs04.dat
new file mode 100644
index 0000000000000000000000000000000000000000..6a2a9fc935a56989b166de9b23f3df3bc4f64879
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs04.dat
@@ -0,0 +1,249 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs04   (SmLs04.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 249) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                21 Replicates/Cell
+                189 Observations
+                7 Constant Leading Digits
+                Average Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares             F Statistic
+
+Between Treatment   8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
+Within Treatment  180 1.80000000000000E+00 1.00000000000000E-02
+
+                  Certified R-Squared 4.82758620689655E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1       1000000.4
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           2       1000000.3
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           2       1000000.2
+           2       1000000.4
+           3       1000000.5
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           3       1000000.4
+           3       1000000.6
+           4       1000000.3
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           4       1000000.2
+           4       1000000.4
+           5       1000000.5
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           5       1000000.4
+           5       1000000.6
+           6       1000000.3
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           6       1000000.2
+           6       1000000.4
+           7       1000000.5
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           7       1000000.4
+           7       1000000.6
+           8       1000000.3
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
+           8       1000000.4
+           8       1000000.2
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diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs05.dat b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs05.dat
new file mode 100644
index 0000000000000000000000000000000000000000..fe11c40b5f51aefc81d4d1501a74e627f2b2d992
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs05.dat
@@ -0,0 +1,1869 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs05   (SmLs05.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 1869) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                201 Replicates/Cell
+                1809 Observations
+                7 Constant Leading Digits
+                Average Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares            F Statistic
+
+Between Treatment    8 1.60800000000000E+01 2.01000000000000E+00 2.01000000000000E+02
+Within Treatment  1800 1.80000000000000E+01 1.00000000000000E-02
+
+                  Certified R-Squared 4.71830985915493E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1       1000000.4
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
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diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs06.dat b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs06.dat
new file mode 100644
index 0000000000000000000000000000000000000000..602e4fbdaa26bbb8d95ce78d1f48dbbfa883e7e9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs06.dat
@@ -0,0 +1,18069 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs06   (SmLs06.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 18069) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                2001 Replicates/Cell
+                18009 Observations
+                7 Constant Leading Digits
+                Average Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares             F Statistic
+
+Between Treatment     8 1.60080000000000E+02 2.00100000000000E+01 2.00100000000000E+03
+Within Treatment  18000 1.80000000000000E+02 1.00000000000000E-02
+
+                  Certified R-Squared 4.70712773465067E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1       1000000.4
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
+           1       1000000.5
+           1       1000000.3
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diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs07.dat b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs07.dat
new file mode 100644
index 0000000000000000000000000000000000000000..deeac955e65ffaf55838568baa54951efaf2662b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs07.dat
@@ -0,0 +1,249 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs07   (SmLs07.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 249) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                21 Replicates/Cell
+                189 Observations
+                13 Constant Leading Digits
+                Higher Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares            F Statistic
+
+Between Treatment   8 1.68000000000000E+00 2.10000000000000E-01 2.10000000000000E+01
+Within Treatment  180 1.80000000000000E+00 1.00000000000000E-02
+
+                  Certified R-Squared 4.82758620689655E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1    1000000000000.4
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           2    1000000000000.3
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           2    1000000000000.2
+           2    1000000000000.4
+           3    1000000000000.5
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           3    1000000000000.4
+           3    1000000000000.6
+           4    1000000000000.3
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           4    1000000000000.2
+           4    1000000000000.4
+           5    1000000000000.5
+           5    1000000000000.4
+           5    1000000000000.6
+           5    1000000000000.4
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+           5    1000000000000.4
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+           5    1000000000000.4
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+           5    1000000000000.4
+           5    1000000000000.6
+           6    1000000000000.3
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           6    1000000000000.2
+           6    1000000000000.4
+           7    1000000000000.5
+           7    1000000000000.4
+           7    1000000000000.6
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+           7    1000000000000.4
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+           7    1000000000000.4
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+           8    1000000000000.3
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+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
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+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           8    1000000000000.2
+           8    1000000000000.4
+           9    1000000000000.5
+           9    1000000000000.4
+           9    1000000000000.6
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+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
+           9    1000000000000.4
+           9    1000000000000.6
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs08.dat b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs08.dat
new file mode 100644
index 0000000000000000000000000000000000000000..c5ee643fb8c6ef849ab8e34352bc60f15c715a45
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs08.dat
@@ -0,0 +1,1869 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs08   (SmLs08.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 1869) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                201 Replicates/Cell
+                1809 Observations
+                13 Constant Leading Digits
+                Higher Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares              F Statistic
+
+Between Treatment    8 1.60800000000000E+01 2.01000000000000E+00 2.01000000000000E+02
+Within Treatment  1800 1.80000000000000E+01 1.00000000000000E-02
+
+                  Certified R-Squared 4.71830985915493E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1    1000000000000.4
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
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+           1    1000000000000.3
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+           1    1000000000000.3
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+           1    1000000000000.3
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+           1    1000000000000.3
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+           1    1000000000000.3
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+           1    1000000000000.3
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+           1    1000000000000.3
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+           1    1000000000000.5
+           1    1000000000000.3
+           1    1000000000000.5
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diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs09.dat b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs09.dat
new file mode 100644
index 0000000000000000000000000000000000000000..887905e355a2a13801f1b004187631f2301f7eef
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_anova/SmLs09.dat
@@ -0,0 +1,18069 @@
+NIST/ITL StRD 
+Dataset Name:   SmLs09   (SmLs09.dat)
+
+
+File Format:    ASCII
+                Certified Values   (lines 41 to 47)
+                Data               (lines 61 to 18069) 
+
+
+Procedure:      Analysis of Variance
+
+
+Reference:      Simon, Stephen D. and Lesage, James P. (1989).
+                "Assessing the Accuracy of ANOVA Calculations in
+                Statistical Software".
+                Computational Statistics & Data Analysis, 8, pp. 325-332.
+
+
+Data:           1 Factor
+                9 Treatments
+                2001 Replicates/Cell
+                18009 Observations
+                13 Constant Leading Digits
+                Higher Level of Difficulty
+                Generated Data
+
+
+Model:          10 Parameters (mu,tau_1, ... , tau_9)
+                y_{ij} = mu + tau_i + epsilon_{ij}
+
+
+
+
+
+
+Certified Values:
+
+Source of                  Sums of               Mean               
+Variation          df      Squares              Squares              F Statistic
+
+Between Treatment     8 1.60080000000000E+02 2.00100000000000E+01 2.00100000000000E+03
+Within Treatment  18000 1.80000000000000E+02 1.00000000000000E-02
+
+                  Certified R-Squared 4.70712773465067E-01
+
+                  Certified Residual
+                  Standard Deviation  1.00000000000000E-01
+
+
+
+
+
+
+
+
+
+
+
+
+Data:  Treatment   Response
+           1    1000000000000.4
+           1    1000000000000.3
+           1    1000000000000.5
+           1    1000000000000.3
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diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_linregress/Norris.dat b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_linregress/Norris.dat
new file mode 100644
index 0000000000000000000000000000000000000000..4bf8ed911cae75824b27e5f5d5e444e17fa8eae8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/nist_linregress/Norris.dat
@@ -0,0 +1,97 @@
+NIST/ITL StRD
+Dataset Name:  Norris (Norris.dat)
+
+File Format:   ASCII
+               Certified Values  (lines 31 to 46)
+               Data              (lines 61 to 96)
+
+Procedure:     Linear Least Squares Regression
+
+Reference:     Norris, J., NIST.  
+               Calibration of Ozone Monitors.
+
+Data:          1 Response Variable (y)
+               1 Predictor Variable (x)
+               36 Observations
+               Lower Level of Difficulty
+               Observed Data
+
+Model:         Linear Class
+               2 Parameters (B0,B1)
+
+               y = B0 + B1*x + e
+
+
+
+               Certified Regression Statistics
+
+                                          Standard Deviation
+     Parameter          Estimate             of Estimate
+
+        B0        -0.262323073774029     0.232818234301152
+        B1         1.00211681802045      0.429796848199937E-03
+
+     Residual
+     Standard Deviation   0.884796396144373
+
+     R-Squared            0.999993745883712
+
+
+               Certified Analysis of Variance Table
+
+Source of Degrees of    Sums of             Mean  
+Variation  Freedom      Squares            Squares           F Statistic
+              
+Regression    1     4255954.13232369   4255954.13232369   5436385.54079785
+Residual     34     26.6173985294224   0.782864662630069
+
+                 
+                                          
+                                          
+                                                           
+
+                            
+                                   
+                                                       
+
+
+
+
+Data:       y          x
+           0.1        0.2
+         338.8      337.4
+         118.1      118.2
+         888.0      884.6
+           9.2       10.1
+         228.1      226.5
+         668.5      666.3
+         998.5      996.3
+         449.1      448.6
+         778.9      777.0
+         559.2      558.2
+           0.3        0.4
+           0.1        0.6
+         778.1      775.5
+         668.8      666.9
+         339.3      338.0
+         448.9      447.5
+          10.8       11.6
+         557.7      556.0
+         228.3      228.1
+         998.0      995.8
+         888.8      887.6
+         119.6      120.2
+           0.3        0.3
+           0.6        0.3
+         557.6      556.8
+         339.3      339.1
+         888.0      887.2
+         998.5      999.0
+         778.9      779.0
+          10.2       11.1
+         117.6      118.3
+         228.9      229.2
+         668.4      669.1
+         449.2      448.9
+           0.2        0.5
+                                   
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/rel_breitwigner_pdf_sample_data_ROOT.npy b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/rel_breitwigner_pdf_sample_data_ROOT.npy
new file mode 100644
index 0000000000000000000000000000000000000000..80dde74dcda9a23dcdbf9a2f677eb9c98337b0a7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/rel_breitwigner_pdf_sample_data_ROOT.npy
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eef4dc702dd8c6e31c18c74e1f81284c3e9ca2ab50282de39c9ad30b7bb8e76d
+size 38624
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/studentized_range_mpmath_ref.json b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/studentized_range_mpmath_ref.json
new file mode 100644
index 0000000000000000000000000000000000000000..bb971286cf85b28738a80bacececfb90c2566782
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/data/studentized_range_mpmath_ref.json
@@ -0,0 +1,1499 @@
+{
+  "COMMENT": "!!!!!! THIS FILE WAS AUTOGENERATED BY RUNNING `python studentized_range_mpmath_ref.py` !!!!!!",
+  "moment_data": [
+    {
+      "src_case": {
+        "m": 0,
+        "k": 3,
+        "v": 10,
+        "expected_atol": 1e-09,
+        "expected_rtol": 1e-09
+      },
+      "mp_result": 1.0
+    },
+    {
+      "src_case": {
+        "m": 1,
+        "k": 3,
+        "v": 10,
+        "expected_atol": 1e-09,
+        "expected_rtol": 1e-09
+      },
+      "mp_result": 1.8342745127927962
+    },
+    {
+      "src_case": {
+        "m": 2,
+        "k": 3,
+        "v": 10,
+        "expected_atol": 1e-09,
+        "expected_rtol": 1e-09
+      },
+      "mp_result": 4.567483357831711
+    },
+    {
+      "src_case": {
+        "m": 3,
+        "k": 3,
+        "v": 10,
+        "expected_atol": 1e-09,
+        "expected_rtol": 1e-09
+      },
+      "mp_result": 14.412156886227011
+    },
+    {
+      "src_case": {
+        "m": 4,
+        "k": 3,
+        "v": 10,
+        "expected_atol": 1e-09,
+        "expected_rtol": 1e-09
+      },
+      "mp_result": 56.012250366720444
+    }
+  ],
+  "cdf_data": [
+    {
+      "src_case": {
+        "q": 0.1,
+        "k": 3,
+        "v": 3,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.0027502772229359594
+    },
+    {
+      "src_case": {
+        "q": 0.1,
+        "k": 10,
+        "v": 10,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 2.8544145010066327e-12
+    },
+    {
+      "src_case": {
+        "q": 0.1,
+        "k": 3,
+        "v": 10,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.0027520560662338336
+    },
+    {
+      "src_case": {
+        "q": 0.1,
+        "k": 10,
+        "v": 100,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 9.39089126131273e-13
+    },
+    {
+      "src_case": {
+        "q": 0.1,
+        "k": 3,
+        "v": 20,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.002752437649536182
+    },
+    {
+      "src_case": {
+        "q": 0.1,
+        "k": 10,
+        "v": 50,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 1.0862189999210748e-12
+    },
+    {
+      "src_case": {
+        "q": 0.1,
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+      "mp_result": 5.266341131767418e-23
+    },
+    {
+      "src_case": {
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+        "k": 10,
+        "v": 120,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+      "mp_result": 8.234556126446594e-11
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+    {
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+        "expected_rtol": 1e-11
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+      "mp_result": 9.32929780487562e-26
+    },
+    {
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+        "v": 3,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+      "mp_result": 0.36083736990527154
+    },
+    {
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+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+      "mp_result": 0.4137959132282269
+    },
+    {
+      "src_case": {
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+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+      "mp_result": 0.4080239698771056
+    },
+    {
+      "src_case": {
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+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+      "mp_result": 0.398772020275752
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+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+    {
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+        "v": 100,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+      "mp_result": 0.4157583991350054
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+    {
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+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+    },
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+        "expected_rtol": 1e-11
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+    {
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+        "expected_rtol": 1e-11
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+      "mp_result": 2.630674470916427e-06
+    },
+    {
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+        "expected_rtol": 1e-11
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+      "mp_result": 4.121713278199428e-05
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+    {
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+        "expected_rtol": 1e-11
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+    {
+      "src_case": {
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+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+      "mp_result": 1.5585754418789747e-06
+    },
+    {
+      "src_case": {
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+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+      "mp_result": 1.4190335899441991e-06
+    },
+    {
+      "src_case": {
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+        "v": 3,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.07185383302009114
+    },
+    {
+      "src_case": {
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+        "expected_atol": 1e-11,
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+    {
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+        "expected_rtol": 1e-11
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+    {
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+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+      "mp_result": 0.04044172384981084
+    },
+    {
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+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+      "mp_result": 0.030571365659999617
+    },
+    {
+      "src_case": {
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+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+      "mp_result": 0.030120779149073032
+    },
+    {
+      "src_case": {
+        "q": 4,
+        "k": 10,
+        "v": 3,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.17501664247670937
+    },
+    {
+      "src_case": {
+        "q": 4,
+        "k": 10,
+        "v": 10,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.22374394725370736
+    },
+    {
+      "src_case": {
+        "q": 4,
+        "k": 10,
+        "v": 50,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.23246597521020534
+    },
+    {
+      "src_case": {
+        "q": 4,
+        "k": 10,
+        "v": 20,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.23239043677504484
+    },
+    {
+      "src_case": {
+        "q": 4,
+        "k": 10,
+        "v": 100,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.23057775622748988
+    },
+    {
+      "src_case": {
+        "q": 4,
+        "k": 10,
+        "v": 120,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.23012666145240815
+    },
+    {
+      "src_case": {
+        "q": 4,
+        "k": 20,
+        "v": 3,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
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+      "mp_result": 0.2073676639537027
+    },
+    {
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+        "expected_atol": 1e-11,
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+      "mp_result": 0.3245990542431859
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+    {
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+        "v": 3,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.0033733228559870584
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 3,
+        "v": 10,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 7.728665739003835e-05
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+    {
+      "src_case": {
+        "q": 4,
+        "k": 20,
+        "v": 20,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.38244500549096866
+    },
+    {
+      "src_case": {
+        "q": 4,
+        "k": 20,
+        "v": 100,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.45434978340834464
+    },
+    {
+      "src_case": {
+        "q": 4,
+        "k": 20,
+        "v": 50,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.43334135870667473
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 3,
+        "v": 100,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 2.159522630228393e-09
+    },
+    {
+      "src_case": {
+        "q": 4,
+        "k": 20,
+        "v": 120,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.45807877248528855
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 3,
+        "v": 50,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 3.5303467191175695e-08
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 3,
+        "v": 20,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 3.121281850105421e-06
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 3,
+        "v": 120,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 1.1901591191700855e-09
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 10,
+        "v": 10,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.0006784051704217357
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 10,
+        "v": 3,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.011845582636101885
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 10,
+        "v": 20,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 3.844183552674918e-05
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 10,
+        "v": 100,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 3.215093171597309e-08
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 10,
+        "v": 50,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 5.125792577534542e-07
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 10,
+        "v": 120,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 1.7759015355532446e-08
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 20,
+        "v": 10,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.0017957646258393628
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 20,
+        "v": 3,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.018534407764819284
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 20,
+        "v": 20,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 0.00013316083413164858
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 20,
+        "v": 50,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 2.082489228991225e-06
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 20,
+        "v": 100,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 1.3444226792257012e-07
+    },
+    {
+      "src_case": {
+        "q": 10,
+        "k": 20,
+        "v": 120,
+        "expected_atol": 1e-11,
+        "expected_rtol": 1e-11
+      },
+      "mp_result": 7.446912854228521e-08
+    }
+  ]
+}
\ No newline at end of file
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_axis_nan_policy.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_axis_nan_policy.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b7f1bccccbe41d1cd40dd7479159f31282be151
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_axis_nan_policy.py
@@ -0,0 +1,1495 @@
+# Many scipy.stats functions support `axis` and `nan_policy` parameters.
+# When the two are combined, it can be tricky to get all the behavior just
+# right. This file contains a suite of common tests for scipy.stats functions
+# that support `axis` and `nan_policy` and additional tests for some associated
+# functions in stats._util.
+
+from itertools import product, combinations_with_replacement, permutations
+import os
+import re
+import pickle
+import pytest
+import warnings
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+from scipy import stats, special
+from scipy.stats._axis_nan_policy import (_masked_arrays_2_sentinel_arrays,
+                                          SmallSampleWarning,
+                                          too_small_nd_omit, too_small_nd_not_omit,
+                                          too_small_1d_omit, too_small_1d_not_omit)
+from scipy._lib._util import AxisError
+from scipy._lib._array_api import make_xp_test_case
+from scipy.conftest import skip_xp_invalid_arg
+
+
+SCIPY_XSLOW = int(os.environ.get('SCIPY_XSLOW', '0'))
+
+
+def _using_accelerate():
+    config = np.show_config('dicts')
+    return config['Build Dependencies']['blas']['name'].lower() == 'accelerate'
+
+
+RTOL = 1e-6 if _using_accelerate() else 1e-15
+
+
+tolerance_overrides = {stats.epps_singleton_2samp: 1e-10}
+
+
+def unpack_ttest_result(res):
+    low, high = res.confidence_interval()
+    return (res.statistic, res.pvalue, res.df, res._standard_error,
+            res._estimate, low, high)
+
+
+def _get_ttest_ci(ttest):
+    # get a function that returns the CI bounds of provided `ttest`
+    def ttest_ci(*args, **kwargs):
+        res = ttest(*args, **kwargs)
+        return res.confidence_interval()
+    return ttest_ci
+
+
+def xp_mean_1samp(*args, **kwargs):
+    kwargs.pop('_no_deco', None)
+    return stats._stats_py._xp_mean(*args, **kwargs)
+
+
+def xp_mean_2samp(*args, **kwargs):
+    kwargs.pop('_no_deco', None)
+    weights = args[1]
+    return stats._stats_py._xp_mean(args[0], *args[2:], weights=weights, **kwargs)
+
+
+def xp_var(*args, **kwargs):
+    kwargs.pop('_no_deco', None)
+    return stats._stats_py._xp_var(*args, **kwargs)
+
+
+def gstd(*args, **kwargs):
+    kwargs.pop('_no_deco', None)
+    return stats.gstd(*args, **kwargs)
+
+
+def combine_pvalues_weighted(*args, **kwargs):
+    return stats.combine_pvalues(args[0], *args[2:], weights=args[1],
+                                 method='stouffer', **kwargs)
+
+
+def weightedtau_weighted(x, y, rank, **kwargs):
+    axis = kwargs.get('axis', 0)
+    nan_policy = kwargs.get('nan_policy', 'propagate')
+    rank = stats.rankdata(rank, axis=axis, nan_policy=nan_policy)
+    return stats.weightedtau(x, y, rank, **kwargs)
+
+
+def boxcox_llf(data, lmb, axis=0, _no_deco=False, **kwargs):
+    if _no_deco:
+        return stats._morestats._boxcox_llf(data, lmb=lmb, axis=axis)
+    return stats.boxcox_llf(lmb, data, axis=axis, **kwargs)
+
+
+def yeojohnson_llf(data, lmb, axis=0, _no_deco=False, **kwargs):
+    if _no_deco:
+        return stats._morestats._yeojohnson_llf(data, lmb=lmb, axis=axis)
+    return stats.yeojohnson_llf(lmb, data, axis=axis, **kwargs)
+
+
+def kendalltau(*args, _no_deco=False, **kwargs):
+    if _no_deco:
+        return stats._stats_py._kendalltau(*args, _no_deco=_no_deco, **kwargs)
+    return stats.kendalltau(*args, **kwargs)
+
+
+axis_nan_policy_cases = [
+    # function, args, kwds, number of samples, number of outputs,
+    # ... paired, unpacker function
+    # args, kwds typically aren't needed; just showing that they work
+    (stats.kruskal, tuple(), dict(), 3, 2, False, None),  # 4 samples is slow
+    (stats.ranksums, ('less',), dict(), 2, 2, False, None),
+    (stats.mannwhitneyu, tuple(), {'method': 'asymptotic'}, 2, 3, False,
+     lambda res: (res.statistic, res.pvalue, res.zstatistic)),
+    (stats.mannwhitneyu, tuple(), {'method': 'auto'}, 2, 2, False, None),
+    (stats.wilcoxon, ('pratt',), {'mode': 'auto'}, 2, 2, True,
+     lambda res: (res.statistic, res.pvalue)),
+    (stats.wilcoxon, tuple(), dict(), 1, 2, True,
+     lambda res: (res.statistic, res.pvalue)),
+    (stats.wilcoxon, tuple(), {'method': 'asymptotic'}, 1, 3, True,
+     lambda res: (res.statistic, res.pvalue, res.zstatistic)),
+    (stats.gmean, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.hmean, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.pmean, (1.42,), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.trim_mean, (0.05,), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.sem, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.iqr, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.kurtosis, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.skew, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.kstat, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.kstatvar, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.moment, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.moment, tuple(), dict(order=[1, 2]), 1, 2, False, None),
+    (stats.jarque_bera, tuple(), dict(), 1, 2, False, None),
+    (stats.ttest_1samp, (np.array([0]),), dict(), 1, 7, False,
+     unpack_ttest_result),
+    (stats.ttest_rel, tuple(), dict(), 2, 7, True, unpack_ttest_result),
+    (stats.ttest_ind, tuple(), dict(), 2, 7, False, unpack_ttest_result),
+    (_get_ttest_ci(stats.ttest_1samp), (0,), dict(), 1, 2, False, None),
+    (_get_ttest_ci(stats.ttest_rel), tuple(), dict(), 2, 2, True, None),
+    (_get_ttest_ci(stats.ttest_ind), tuple(), dict(), 2, 2, False, None),
+    (stats.mode, tuple(), dict(), 1, 2, True, lambda x: (x.mode, x.count)),
+    (stats.differential_entropy, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.variation, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.friedmanchisquare, tuple(), dict(), 3, 2, True, None),
+    (stats.brunnermunzel, tuple(), dict(distribution='normal'), 2, 2, False, None),
+    (stats.mood, tuple(), {}, 2, 2, False, None),
+    (stats.shapiro, tuple(), {}, 1, 2, False, None),
+    (stats.ks_1samp, (special.ndtr,), dict(), 1, 4, False,
+     lambda res: (*res, res.statistic_location, res.statistic_sign)),
+    (stats.ks_1samp, (special.ndtr,), dict(alternative='greater'), 1, 4, False,
+     lambda res: (*res, res.statistic_location, res.statistic_sign)),
+    (stats.ks_1samp, (special.ndtr,), dict(alternative='less'), 1, 4, False,
+     lambda res: (*res, res.statistic_location, res.statistic_sign)),
+    (stats.ks_2samp, tuple(), dict(), 2, 4, False,
+     lambda res: (*res, res.statistic_location, res.statistic_sign)),
+    (stats.kstest, (special.ndtr,), dict(), 1, 4, False,
+     lambda res: (*res, res.statistic_location, res.statistic_sign)),
+    (stats.kstest, tuple(), dict(), 2, 4, False,
+     lambda res: (*res, res.statistic_location, res.statistic_sign)),
+    (stats.levene, tuple(), {}, 2, 2, False, None),
+    (stats.fligner, tuple(), {'center': 'trimmed', 'proportiontocut': 0.01},
+     2, 2, False, None),
+    (stats.ansari, tuple(), {}, 2, 2, False, None),
+    (stats.entropy, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.entropy, tuple(), dict(), 2, 1, True, lambda x: (x,)),
+    (stats.skewtest, tuple(), dict(), 1, 2, False, None),
+    (stats.kurtosistest, tuple(), dict(), 1, 2, False, None),
+    (stats.normaltest, tuple(), dict(), 1, 2, False, None),
+    (stats.cramervonmises, (special.ndtr,), dict(), 1, 2, False,
+     lambda res: (res.statistic, res.pvalue)),
+    (stats.cramervonmises_2samp, tuple(), dict(method='asymptotic'), 2, 2, False,
+     lambda res: (res.statistic, res.pvalue)),
+    (stats.epps_singleton_2samp, tuple(), dict(), 2, 2, False, None),
+    (stats.bartlett, tuple(), {}, 2, 2, False, None),
+    (stats.tmean, tuple(), {}, 1, 1, False, lambda x: (x,)),
+    (stats.tvar, tuple(), {}, 1, 1, False, lambda x: (x,)),
+    (stats.tmin, tuple(), {}, 1, 1, False, lambda x: (x,)),
+    (stats.tmax, tuple(), {}, 1, 1, False, lambda x: (x,)),
+    (stats.tstd, tuple(), {}, 1, 1, False, lambda x: (x,)),
+    (stats.tsem, tuple(), {}, 1, 1, False, lambda x: (x,)),
+    (stats.circmean, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.circvar, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.circstd, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.f_oneway, tuple(), {}, 2, 2, False, None),
+    (stats.f_oneway, tuple(), {'equal_var': False}, 2, 2, False, None),
+    (stats.alexandergovern, tuple(), {}, 2, 2, False,
+     lambda res: (res.statistic, res.pvalue)),
+    (stats.combine_pvalues, tuple(), {}, 1, 2, False, None),
+    (stats.lmoment, tuple(), dict(), 1, 4, False, lambda x: tuple(x)),
+    (combine_pvalues_weighted, tuple(), {}, 2, 2, True, None),
+    (xp_mean_1samp, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (xp_mean_2samp, tuple(), dict(), 2, 1, True, lambda x: (x,)),
+    (xp_var, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.chatterjeexi, tuple(), dict(), 2, 2, True,
+     lambda res: (res.statistic, res.pvalue)),
+    (stats.spearmanrho, tuple(), dict(), 2, 2, True,
+     lambda res: (res.statistic, res.pvalue)),
+    (stats.pointbiserialr, tuple(), dict(), 2, 3, True,
+     lambda res: (res.statistic, res.pvalue, res.correlation)),
+    (kendalltau, tuple(), dict(), 2, 3, True,
+     lambda res: (res.statistic, res.pvalue, res.correlation)),
+    (stats.weightedtau, tuple(), dict(), 2, 3, True,
+     lambda res: (res.statistic, res.pvalue, res.correlation)),
+    (weightedtau_weighted, tuple(), dict(), 3, 3, True,
+     lambda res: (res.statistic, res.pvalue, res.correlation)),
+    (stats.linregress, tuple(), dict(), 2, 6, True,
+     lambda res: tuple(res) + (res.intercept_stderr,)),
+    (stats.theilslopes, tuple(), dict(), 2, 4, True, tuple),
+    (stats.theilslopes, tuple(), dict(), 1, 4, True, tuple),
+    (stats.theilslopes, tuple(), dict(method='joint'), 2, 4, True, tuple),
+    (stats.siegelslopes, tuple(), dict(), 2, 2, True, tuple),
+    (stats.siegelslopes, tuple(), dict(), 1, 2, True, tuple),
+    (stats.siegelslopes, tuple(), dict(method='hierarchical'), 2, 2, True, tuple),
+    (gstd, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (stats.power_divergence, tuple(), dict(), 1, 2, False, None),
+    (stats.chisquare, tuple(), dict(), 1, 2, False, None),
+    (stats.median_abs_deviation, tuple(), dict(), 1, 1, False, lambda x: (x,)),
+    (boxcox_llf, tuple(), dict(lmb=1.5), 1, 1, False, lambda x: (x,)),
+    (yeojohnson_llf, tuple(), dict(lmb=1.5), 1, 1, False, lambda x: (x,)),
+    (stats.expectile, (0.4,), dict(), 1, 1, False, lambda x: (x,)),
+]
+
+# If the message is one of those expected, put nans in
+# appropriate places of `statistics` and `pvalues`
+too_small_messages = {"Degrees of freedom <= 0 for slice",
+                      "x and y should have at least 5 elements",
+                      "Data must be at least length 3",
+                      "The sample must contain at least two",
+                      "x and y must contain at least two",
+                      "division by zero",
+                      "Mean of empty slice",
+                      "Data passed to ks_2samp must not be empty",
+                      "Not enough test observations",
+                      "Not enough other observations",
+                      "Not enough observations.",
+                      "At least one observation is required",
+                      "zero-size array to reduction operation maximum",
+                      "`x` and `y` must be of nonzero size.",
+                      "The exact distribution of the Wilcoxon test",
+                      "Data input must not be empty",
+                      "Window length (0) must be positive and less",
+                      "Window length (1) must be positive and less",
+                      "Window length (2) must be positive and less",
+                      "`skewtest` requires at least",
+                      "`kurtosistest` requires at least",
+                      "attempt to get argmax of an empty sequence",
+                      "No array values within given limits",
+                      "Input sample size must be greater than one.",
+                      "At least one slice along `axis` has zero length",
+                      "One or more sample arguments is too small",
+                      "invalid value encountered",
+                      "divide by zero encountered",
+                      "`x` and `y` must have length at least 2.",
+                      "Inputs must not be empty.",
+                      "All `x` coordinates are identical.",
+}
+
+# If the message is one of these, results of the function may be inaccurate,
+# but NaNs are not to be placed
+inaccuracy_messages = {"Precision loss occurred in moment calculation",
+                       "Sample size too small for normal approximation."}
+
+# For some functions, nan_policy='propagate' should not just return NaNs
+override_propagate_funcs = {stats.mode, weightedtau_weighted, stats.weightedtau}
+
+# For some functions, empty arrays produce non-NaN results
+empty_special_case_funcs = {stats.entropy, stats.chisquare, stats.power_divergence}
+
+# Some functions don't follow the usual "too small" warning rules
+too_small_special_case_funcs = {stats.entropy, stats.chisquare, stats.power_divergence}
+
+def _mixed_data_generator(n_samples, n_repetitions, axis, rng,
+                          paired=False):
+    # generate random samples to check the response of hypothesis tests to
+    # samples with different (but broadcastable) shapes and various
+    # nan patterns (e.g. all nans, some nans, no nans) along axis-slices
+
+    data = []
+    for i in range(n_samples):
+        n_patterns = 6  # number of distinct nan patterns
+        n_obs = 20 if paired else 20 + i  # observations per axis-slice
+        x = np.ones((n_repetitions, n_patterns, n_obs)) * np.nan
+
+        for j in range(n_repetitions):
+            samples = x[j, :, :]
+
+            # case 0: axis-slice with all nans (0 reals)
+            # cases 1-3: axis-slice with 1-3 reals (the rest nans)
+            # case 4: axis-slice with mostly (all but two) reals
+            # case 5: axis slice with all reals
+            for k, n_reals in enumerate([0, 1, 2, 3, n_obs-2, n_obs]):
+                # for cases 1-3, need paired nansw  to be in the same place
+                indices = rng.permutation(n_obs)[:n_reals]
+                samples[k, indices] = rng.random(size=n_reals)
+
+            # permute the axis-slices just to show that order doesn't matter
+            samples[:] = rng.permutation(samples, axis=0)
+
+        # For multi-sample tests, we want to test broadcasting and check
+        # that nan policy works correctly for each nan pattern for each input.
+        # This takes care of both simultaneously.
+        new_shape = [n_repetitions] + [1]*n_samples + [n_obs]
+        new_shape[1 + i] = 6
+        x = x.reshape(new_shape)
+
+        x = np.moveaxis(x, -1, axis)
+        data.append(x)
+    return data
+
+
+def _homogeneous_data_generator(n_samples, n_repetitions, axis, rng,
+                                paired=False, all_nans=True):
+    # generate random samples to check the response of hypothesis tests to
+    # samples with different (but broadcastable) shapes and homogeneous
+    # data (all nans or all finite)
+    data = []
+    for i in range(n_samples):
+        n_obs = 20 if paired else 20 + i  # observations per axis-slice
+        shape = [n_repetitions] + [1]*n_samples + [n_obs]
+        shape[1 + i] = 2
+        x = np.ones(shape) * np.nan if all_nans else rng.random(shape)
+        x = np.moveaxis(x, -1, axis)
+        data.append(x)
+    return data
+
+
+def nan_policy_1d(hypotest, data1d, unpacker, *args, n_outputs=2,
+                  nan_policy='raise', paired=False, _no_deco=True, **kwds):
+    # Reference implementation for how `nan_policy` should work for 1d samples
+
+    if nan_policy == 'raise':
+        for sample in data1d:
+            if np.any(np.isnan(sample)):
+                raise ValueError("The input contains nan values")
+
+    elif (nan_policy == 'propagate'
+          and hypotest not in override_propagate_funcs):
+        # For all hypothesis tests tested, returning nans is the right thing.
+        # But many hypothesis tests don't propagate correctly (e.g. they treat
+        # np.nan the same as np.inf, which doesn't make sense when ranks are
+        # involved) so override that behavior here.
+        for sample in data1d:
+            if np.any(np.isnan(sample)):
+                return np.full(n_outputs, np.nan)
+
+    elif nan_policy == 'omit':
+        # manually omit nans (or pairs in which at least one element is nan)
+        if not paired:
+            data1d = [sample[~np.isnan(sample)] for sample in data1d]
+        else:
+            nan_mask = np.isnan(data1d[0])
+            for sample in data1d[1:]:
+                nan_mask = np.logical_or(nan_mask, np.isnan(sample))
+            data1d = [sample[~nan_mask] for sample in data1d]
+
+    return unpacker(hypotest(*data1d, *args, _no_deco=_no_deco, **kwds))
+
+
+# These three warnings are intentional
+# For `wilcoxon` when the sample size < 50
+@pytest.mark.filterwarnings('ignore:Sample size too small for normal:UserWarning')
+# `kurtosistest` and `normaltest` when sample size < 20
+@pytest.mark.filterwarnings('ignore:`kurtosistest` p-value may be:UserWarning')
+# `foneway`
+@pytest.mark.filterwarnings('ignore:all input arrays have length 1.:RuntimeWarning')
+
+# The rest of these may or may not be desirable. They need further investigation
+# to determine whether the function's decorator should define `too_small.
+# `bartlett`, `tvar`, `tstd`, `tsem`
+@pytest.mark.filterwarnings('ignore:Degrees of freedom <= 0 for slice:RuntimeWarning')
+# kstat, kstatvar, ttest_1samp, ttest_rel, ttest_ind, ttest_ci, brunnermunzel
+# mood, levene, fligner, bartlett
+@pytest.mark.filterwarnings('ignore:Invalid value encountered in:RuntimeWarning')
+# kstatvar, ttest_1samp, ttest_rel, ttest_ci, brunnermunzel, levene, bartlett
+@pytest.mark.filterwarnings('ignore:divide by zero encountered:RuntimeWarning')
+
+@pytest.mark.parametrize(("hypotest", "args", "kwds", "n_samples", "n_outputs",
+                          "paired", "unpacker"), axis_nan_policy_cases)
+@pytest.mark.parametrize(("nan_policy"), ("propagate", "omit", "raise"))
+@pytest.mark.parametrize(("axis"), (1,))
+@pytest.mark.parametrize(("data_generator"), ("mixed",))
+def test_axis_nan_policy_fast(hypotest, args, kwds, n_samples, n_outputs,
+                              paired, unpacker, nan_policy, axis,
+                              data_generator):
+    if hypotest in {stats.kruskal} and not SCIPY_XSLOW:
+        pytest.skip("Too slow.")
+    _axis_nan_policy_test(hypotest, args, kwds, n_samples, n_outputs, paired,
+                          unpacker, nan_policy, axis, data_generator)
+
+
+if SCIPY_XSLOW:
+    # Takes O(1 min) to run, and even skipping with the `xslow` decorator takes
+    # about 3 sec because this is >3,000 tests. So ensure pytest doesn't see
+    # them at all unless `SCIPY_XSLOW` is defined.
+
+    # These three warnings are intentional
+    # For `wilcoxon` when the sample size < 50
+    @pytest.mark.filterwarnings('ignore:Sample size too small for normal:UserWarning')
+    # `kurtosistest` and `normaltest` when sample size < 20
+    @pytest.mark.filterwarnings('ignore:`kurtosistest` p-value may be:UserWarning')
+    # `foneway`
+    @pytest.mark.filterwarnings('ignore:all input arrays have length 1.:RuntimeWarning')
+
+    # The rest of these may or may not be desirable. They need further investigation
+    # to determine whether the function's decorator should define `too_small.
+    # `bartlett`, `tvar`, `tstd`, `tsem`
+    @pytest.mark.filterwarnings('ignore:Degrees of freedom <= 0 for:RuntimeWarning')
+    # kstat, kstatvar, ttest_1samp, ttest_rel, ttest_ind, ttest_ci, brunnermunzel
+    # mood, levene, fligner, bartlett
+    @pytest.mark.filterwarnings('ignore:Invalid value encountered in:RuntimeWarning')
+    # kstatvar, ttest_1samp, ttest_rel, ttest_ci, brunnermunzel, levene, bartlett
+    @pytest.mark.filterwarnings('ignore:divide by zero encountered:RuntimeWarning')
+
+    @pytest.mark.parametrize(("hypotest", "args", "kwds", "n_samples", "n_outputs",
+                              "paired", "unpacker"), axis_nan_policy_cases)
+    @pytest.mark.parametrize(("nan_policy"), ("propagate", "omit", "raise"))
+    @pytest.mark.parametrize(("axis"), list(range(-3, 3)))
+    @pytest.mark.parametrize(("data_generator"),
+                             ("all_nans", "all_finite", "mixed"))
+    def test_axis_nan_policy_full(hypotest, args, kwds, n_samples, n_outputs,
+                                  paired, unpacker, nan_policy, axis,
+                                  data_generator):
+        _axis_nan_policy_test(hypotest, args, kwds, n_samples, n_outputs, paired,
+                              unpacker, nan_policy, axis, data_generator)
+
+
+def _axis_nan_policy_test(hypotest, args, kwds, n_samples, n_outputs, paired,
+                          unpacker, nan_policy, axis, data_generator):
+    # Tests the 1D and vectorized behavior of hypothesis tests against a
+    # reference implementation (nan_policy_1d with np.ndenumerate)
+
+    # Some hypothesis tests return a non-iterable that needs an `unpacker` to
+    # extract the statistic and p-value. For those that don't:
+    if not unpacker:
+        def unpacker(res):
+            return res
+
+    rng = np.random.default_rng(0)
+
+    # Generate multi-dimensional test data with all important combinations
+    # of patterns of nans along `axis`
+    n_repetitions = 3  # number of repetitions of each pattern
+    data_gen_kwds = {'n_samples': n_samples, 'n_repetitions': n_repetitions,
+                     'axis': axis, 'rng': rng, 'paired': paired}
+    if data_generator == 'mixed':
+        inherent_size = 6  # number of distinct types of patterns
+        data = _mixed_data_generator(**data_gen_kwds)
+    elif data_generator == 'all_nans':
+        inherent_size = 2  # hard-coded in _homogeneous_data_generator
+        data_gen_kwds['all_nans'] = True
+        data = _homogeneous_data_generator(**data_gen_kwds)
+    elif data_generator == 'all_finite':
+        inherent_size = 2  # hard-coded in _homogeneous_data_generator
+        data_gen_kwds['all_nans'] = False
+        data = _homogeneous_data_generator(**data_gen_kwds)
+
+    output_shape = [n_repetitions] + [inherent_size]*n_samples
+
+    # To generate reference behavior to compare against, loop over the axis-
+    # slices in data. Make indexing easier by moving `axis` to the end and
+    # broadcasting all samples to the same shape.
+    data_b = [np.moveaxis(sample, axis, -1) for sample in data]
+    data_b = [np.broadcast_to(sample, output_shape + [sample.shape[-1]])
+              for sample in data_b]
+    res_1d = np.zeros(output_shape + [n_outputs])
+
+    for i, _ in np.ndenumerate(np.zeros(output_shape)):
+        data1d = [sample[i] for sample in data_b]
+        contains_nan = any([np.isnan(sample).any() for sample in data1d])
+
+        # Take care of `nan_policy='raise'`.
+        # Afterward, the 1D part of the test is over
+        message = "The input contains nan values"
+        if nan_policy == 'raise' and contains_nan:
+            with pytest.raises(ValueError, match=message):
+                nan_policy_1d(hypotest, data1d, unpacker, *args,
+                              n_outputs=n_outputs,
+                              nan_policy=nan_policy,
+                              paired=paired, _no_deco=True, **kwds)
+
+            with pytest.raises(ValueError, match=message):
+                hypotest(*data1d, *args, nan_policy=nan_policy, **kwds)
+
+            continue
+
+        # Take care of `nan_policy='propagate'` and `nan_policy='omit'`
+
+        # Get results of simple reference implementation
+        try:
+            res_1da = nan_policy_1d(hypotest, data1d, unpacker, *args,
+                                    n_outputs=n_outputs,
+                                    nan_policy=nan_policy,
+                                    paired=paired, _no_deco=True, **kwds)
+        except (ValueError, RuntimeWarning, ZeroDivisionError, UserWarning) as ea:
+            ea_str = str(ea)
+            if any([str(ea_str).startswith(msg) for msg in too_small_messages]):
+                res_1da = np.full(n_outputs, np.nan)
+            else:
+                raise
+
+        # Get results of public function with 1D slices
+        # Should warn for all slices
+        if (nan_policy == 'omit' and data_generator == "all_nans"
+              and hypotest not in too_small_special_case_funcs):
+            with pytest.warns(SmallSampleWarning, match=too_small_1d_omit):
+                res = hypotest(*data1d, *args, nan_policy=nan_policy, **kwds)
+        # warning depends on slice
+        elif (nan_policy == 'omit' and data_generator == "mixed"
+              and hypotest not in too_small_special_case_funcs):
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", too_small_1d_omit, SmallSampleWarning)
+                res = hypotest(*data1d, *args, nan_policy=nan_policy, **kwds)
+        # shouldn't complain if there are no NaNs
+        else:
+            res = hypotest(*data1d, *args, nan_policy=nan_policy, **kwds)
+        res_1db = unpacker(res)
+
+        assert_allclose(res_1db, res_1da, rtol=RTOL)
+        res_1d[i] = res_1db
+
+    res_1d = np.moveaxis(res_1d, -1, 0)
+
+    # Perform a vectorized call to the hypothesis test.
+
+    # If `nan_policy == 'raise'`, check that it raises the appropriate error.
+    # Test is done, so return
+    if nan_policy == 'raise' and not data_generator == "all_finite":
+        message = 'The input contains nan values'
+        with pytest.raises(ValueError, match=message):
+            hypotest(*data, axis=axis, nan_policy=nan_policy, *args, **kwds)
+        return
+
+    # If `nan_policy == 'omit', we might be left with a small sample.
+    # Check for the appropriate warning.
+    if (nan_policy == 'omit' and data_generator in {"all_nans", "mixed"}
+          and hypotest not in too_small_special_case_funcs):
+        with pytest.warns(SmallSampleWarning, match=too_small_nd_omit):
+            res = hypotest(*data, axis=axis, nan_policy=nan_policy, *args, **kwds)
+    else:  # otherwise, there should be no warning
+        res = hypotest(*data, axis=axis, nan_policy=nan_policy, *args, **kwds)
+
+    # Compare against the output against looping over 1D slices
+    res_nd = unpacker(res)
+
+    rtol = max(tolerance_overrides.get(hypotest, RTOL), RTOL)
+    assert_allclose(res_nd, res_1d, rtol=rtol)
+
+# nan should not raise an exception in np.mean()
+# but does on some mips64el systems, triggering failure in some test cases
+# see https://github.com/scipy/scipy/issues/22360
+# and https://github.com/numpy/numpy/issues/23158
+def skip_nan_unexpected_exception():
+    try:
+        # should not raise an exception
+        with np.errstate(all='raise'):
+            x = np.asarray([1, 2, np.nan])
+            np.mean(x)
+    except Exception as e:
+        pytest.skip(f"nan raises unexpected {e.__class__.__name__} in numpy")
+
+@pytest.mark.parametrize(("hypotest", "args", "kwds", "n_samples", "n_outputs",
+                          "paired", "unpacker"), axis_nan_policy_cases)
+@pytest.mark.parametrize(("nan_policy"), ("propagate", "omit", "raise"))
+@pytest.mark.parametrize(("data_generator"),
+                         ("all_nans", "all_finite", "mixed", "empty"))
+def test_axis_nan_policy_axis_is_None(hypotest, args, kwds, n_samples,
+                                      n_outputs, paired, unpacker, nan_policy,
+                                      data_generator):
+    # check for correct behavior when `axis=None`
+    if not unpacker:
+        def unpacker(res):
+            return res
+
+    # skip if nan emits unexpected RuntimeWarning in np.mean()
+    # seen on mips64el, https://github.com/scipy/scipy/issues/22360
+    # Only affects nan_policy "mixed-propagate" for selected hypotests
+    if data_generator=="mixed" and nan_policy=="propagate":
+        # only skip affected hypotests
+        if hypotest.__name__ in ["iqr", "ttest_ci",
+                                 "xp_mean_1samp", "xp_mean_2samp", "xp_var",
+                                 "weightedtau", "weightedtau_weighted"]:
+            skip_nan_unexpected_exception()
+    # all_nans-propagate-ttest_ci is also affected, via scalar multiply
+    if (data_generator=="all_nans" and nan_policy=="propagate"
+        and hypotest.__name__=="ttest_ci"):
+        skip_nan_unexpected_exception()
+    # mixed-omit-xp_var is also affected, via subtract
+    if (data_generator=="mixed" and nan_policy=="omit"
+        and hypotest.__name__=="xp_var"):
+        skip_nan_unexpected_exception()
+
+    rng = np.random.default_rng(0)
+
+    if data_generator == "empty":
+        data = [rng.random((2, 0)) for i in range(n_samples)]
+    else:
+        data = [rng.random((2, 20)) for i in range(n_samples)]
+
+    if data_generator == "mixed":
+        masks = [rng.random((2, 20)) > 0.9 for i in range(n_samples)]
+        for sample, mask in zip(data, masks):
+            sample[mask] = np.nan
+    elif data_generator == "all_nans":
+        data = [sample * np.nan for sample in data]
+
+    data_raveled = [sample.ravel() for sample in data]
+
+    if nan_policy == 'raise' and data_generator not in {"all_finite", "empty"}:
+        message = 'The input contains nan values'
+
+        # check for correct behavior whether or not data is 1d to begin with
+        with pytest.raises(ValueError, match=message):
+            hypotest(*data, axis=None, nan_policy=nan_policy,
+                     *args, **kwds)
+        with pytest.raises(ValueError, match=message):
+            hypotest(*data_raveled, axis=None, nan_policy=nan_policy,
+                     *args, **kwds)
+
+        return
+
+    # behavior of reference implementation with 1d input, public function with 1d
+    # input, and public function with Nd input and `axis=None` should be consistent.
+    # This means:
+    # - If the reference version raises an error or emits a warning, it's because
+    #   the sample is too small, so check that the public function emits an
+    #   appropriate "too small" warning
+    # - Any results returned by the three versions should be the same.
+    with warnings.catch_warnings():  # treat warnings as errors
+        warnings.simplefilter("error")
+
+        ea_str, eb_str, ec_str = None, None, None
+        try:
+            res1da = nan_policy_1d(hypotest, data_raveled, unpacker, *args,
+                                   n_outputs=n_outputs, nan_policy=nan_policy,
+                                   paired=paired, _no_deco=True, **kwds)
+        except (RuntimeWarning, ValueError, ZeroDivisionError, UserWarning) as ea:
+            res1da = None
+            ea_str = str(ea)
+
+        try:
+            res1db = hypotest(*data_raveled, *args, nan_policy=nan_policy, **kwds)
+        except SmallSampleWarning as eb:
+            eb_str = str(eb)
+
+        try:
+            res1dc = hypotest(*data, *args, axis=None, nan_policy=nan_policy, **kwds)
+        except SmallSampleWarning as ec:
+            ec_str = str(ec)
+
+    if ea_str or eb_str or ec_str:  # *if* there is some sort of error or warning
+        # If the reference implemented generated an error or warning, make sure the
+        # message was one of the expected "too small" messages. Note that some
+        # functions don't complain at all without the decorator; that's OK, too.
+        ok_msg = any([str(ea_str).startswith(msg) for msg in too_small_messages])
+        assert (ea_str is None) or ok_msg
+
+        # make sure the wrapped function emits the *intended* warning
+        desired_warnings = {too_small_1d_omit, too_small_1d_not_omit}
+        assert str(eb_str) in desired_warnings
+        assert str(ec_str) in desired_warnings
+
+        with warnings.catch_warnings():  # ignore warnings to get return value
+            warnings.simplefilter("ignore")
+            res1db = hypotest(*data_raveled, *args, nan_policy=nan_policy, **kwds)
+            res1dc = hypotest(*data, *args, axis=None, nan_policy=nan_policy, **kwds)
+
+    # Make sure any results returned by reference/public function are identical
+    # and all attributes are *NumPy* scalars
+    res1db, res1dc = unpacker(res1db), unpacker(res1dc)
+    # changed from 1e-15 solely to appease macosx-x86_64+Accelerate
+    assert_allclose(res1dc, res1db, rtol=2e-14)
+    all_results = list(res1db) + list(res1dc)
+
+    if res1da is not None:
+        # changed from 1e-15 solely to appease macosx-x86_64+Accelerate
+        assert_allclose(res1db, res1da, rtol=2e-14)
+        all_results += list(res1da)
+
+    for item in all_results:
+        assert np.issubdtype(item.dtype, np.number)
+        assert np.isscalar(item)
+
+
+# Test keepdims for:
+#     - Axis negative, positive, None, and tuple
+#     - 1D with no NaNs
+#     - 1D with NaN propagation
+#     - Zero-sized output
+# We're working on making `stats` quieter, but that's not what this test
+# is about. For now, we expect all sorts of warnings here due to small samples.
+@pytest.mark.filterwarnings('ignore::UserWarning')
+@pytest.mark.filterwarnings('ignore::RuntimeWarning')
+@pytest.mark.parametrize("nan_policy", ("omit", "propagate"))
+@pytest.mark.parametrize(("hypotest", "args", "kwds", "n_samples", "n_outputs",
+                          "paired", "unpacker"), axis_nan_policy_cases)
+@pytest.mark.parametrize(
+    ("sample_shape", "axis_cases"),
+    (((2, 3, 3, 4), (None, 0, -1, (0, 2), (1, -1), (3, 1, 2, 0))),
+     ((10, ), (0, -1)),
+     ((20, 0), (0, 1)))
+)
+def test_keepdims(hypotest, args, kwds, n_samples, n_outputs, paired, unpacker,
+                  sample_shape, axis_cases, nan_policy):
+    small_sample_raises = {stats.skewtest, stats.kurtosistest, stats.normaltest,
+                           stats.differential_entropy, stats.epps_singleton_2samp,
+                           stats.shapiro}
+    if sample_shape == (2, 3, 3, 4) and hypotest in small_sample_raises:
+        pytest.skip("Sample too small; test raises error.")
+    if hypotest in {weightedtau_weighted}:
+        pytest.skip("`rankdata` used in testing doesn't support axis tuple.")
+    # test if keepdims parameter works correctly
+    if not unpacker:
+        def unpacker(res):
+            return res
+    rng = np.random.default_rng(0)
+    data = [rng.random(sample_shape) for _ in range(n_samples)]
+    nan_data = [sample.copy() for sample in data]
+    nan_mask = [rng.random(sample_shape) < 0.2 for _ in range(n_samples)]
+    for sample, mask in zip(nan_data, nan_mask):
+        sample[mask] = np.nan
+    for axis in axis_cases:
+        expected_shape = list(sample_shape)
+        if axis is None:
+            expected_shape = np.ones(len(sample_shape))
+        else:
+            if isinstance(axis, int):
+                expected_shape[axis] = 1
+            else:
+                for ax in axis:
+                    expected_shape[ax] = 1
+        expected_shape = tuple(expected_shape)
+        res = unpacker(hypotest(*data, *args, axis=axis, keepdims=True,
+                                **kwds))
+        res_base = unpacker(hypotest(*data, *args, axis=axis, keepdims=False,
+                                     **kwds))
+        nan_res = unpacker(hypotest(*nan_data, *args, axis=axis,
+                                    keepdims=True, nan_policy=nan_policy,
+                                    **kwds))
+        nan_res_base = unpacker(hypotest(*nan_data, *args, axis=axis,
+                                         keepdims=False,
+                                         nan_policy=nan_policy, **kwds))
+        for r, r_base, rn, rn_base in zip(res, res_base, nan_res,
+                                          nan_res_base):
+            assert r.shape == expected_shape
+            r = np.squeeze(r, axis=axis)
+            assert_allclose(r, r_base, atol=1e-16)
+            assert rn.shape == expected_shape
+            rn = np.squeeze(rn, axis=axis)
+            # ideally assert_equal, but `combine_pvalues` failed on 32-bit build
+            assert_allclose(rn, rn_base, atol=1e-16)
+
+
+@pytest.mark.parametrize(("fun", "nsamp"),
+                         [(stats.kstat, 1),
+                          (stats.kstatvar, 1)])
+def test_hypotest_back_compat_no_axis(fun, nsamp):
+    m, n = 8, 9
+
+    rng = np.random.default_rng(0)
+    x = rng.random((nsamp, m, n))
+    res = fun(*x)
+    res2 = fun(*x, _no_deco=True)
+    res3 = fun([xi.ravel() for xi in x])
+    assert_equal(res, res2)
+    assert_equal(res, res3)
+
+
+@pytest.mark.parametrize(("axis"), (0, 1, 2))
+def test_axis_nan_policy_decorated_positional_axis(axis):
+    # Test for correct behavior of function decorated with
+    # _axis_nan_policy_decorator whether `axis` is provided as positional or
+    # keyword argument
+
+    shape = (8, 9, 10)
+    rng = np.random.default_rng(0)
+    x = rng.random(shape)
+    y = rng.random(shape)
+    res1 = stats.mannwhitneyu(x, y, True, 'two-sided', axis)
+    res2 = stats.mannwhitneyu(x, y, True, 'two-sided', axis=axis)
+    assert_equal(res1, res2)
+
+    message = "mannwhitneyu() got multiple values for argument 'axis'"
+    with pytest.raises(TypeError, match=re.escape(message)):
+        stats.mannwhitneyu(x, y, True, 'two-sided', axis, axis=axis)
+
+
+def test_axis_nan_policy_decorated_positional_args():
+    # Test for correct behavior of function decorated with
+    # _axis_nan_policy_decorator when function accepts *args
+
+    shape = (3, 8, 9, 10)
+    rng = np.random.default_rng(0)
+    x = rng.random(shape)
+    x[0, 0, 0, 0] = np.nan
+    stats.kruskal(*x)
+
+    message = "kruskal() got an unexpected keyword argument 'samples'"
+    with pytest.raises(TypeError, match=re.escape(message)):
+        stats.kruskal(samples=x)
+
+    with pytest.raises(TypeError, match=re.escape(message)):
+        stats.kruskal(*x, samples=x)
+
+
+def test_axis_nan_policy_decorated_keyword_samples():
+    # Test for correct behavior of function decorated with
+    # _axis_nan_policy_decorator whether samples are provided as positional or
+    # keyword arguments
+
+    shape = (2, 8, 9, 10)
+    rng = np.random.default_rng(0)
+    x = rng.random(shape)
+    x[0, 0, 0, 0] = np.nan
+    res1 = stats.mannwhitneyu(*x)
+    res2 = stats.mannwhitneyu(x=x[0], y=x[1])
+    assert_equal(res1, res2)
+
+    message = "mannwhitneyu() got multiple values for argument"
+    with pytest.raises(TypeError, match=re.escape(message)):
+        stats.mannwhitneyu(*x, x=x[0], y=x[1])
+
+
+@pytest.mark.parametrize(("hypotest", "args", "kwds", "n_samples", "n_outputs",
+                          "paired", "unpacker"), axis_nan_policy_cases)
+def test_axis_nan_policy_decorated_pickled(hypotest, args, kwds, n_samples,
+                                           n_outputs, paired, unpacker):
+    if "ttest_ci" in hypotest.__name__:
+        pytest.skip("Can't pickle functions defined within functions.")
+
+    rng = np.random.default_rng(0)
+
+    # Some hypothesis tests return a non-iterable that needs an `unpacker` to
+    # extract the statistic and p-value. For those that don't:
+    if not unpacker:
+        def unpacker(res):
+            return res
+
+    data = rng.uniform(size=(n_samples, 2, 30))
+    pickled_hypotest = pickle.dumps(hypotest)
+    unpickled_hypotest = pickle.loads(pickled_hypotest)
+    res1 = unpacker(hypotest(*data, *args, axis=-1, **kwds))
+    res2 = unpacker(unpickled_hypotest(*data, *args, axis=-1, **kwds))
+    assert_allclose(res1, res2, rtol=1e-12)
+
+
+def test_check_empty_inputs():
+    # Test that _check_empty_inputs is doing its job, at least for single-
+    # sample inputs. (Multi-sample functionality is tested below.)
+    # If the input sample is not empty, it should return None.
+    # If the input sample is empty, it should return an array of NaNs or an
+    # empty array of appropriate shape. np.mean is used as a reference for the
+    # output because, like the statistics calculated by these functions,
+    # it works along and "consumes" `axis` but preserves the other axes.
+    for i in range(5):
+        for combo in combinations_with_replacement([0, 1, 2], i):
+            for axis in range(len(combo)):
+                samples = (np.zeros(combo),)
+                output = stats._axis_nan_policy._check_empty_inputs(samples,
+                                                                    axis)
+                if output is not None:
+                    with warnings.catch_warnings():
+                        warnings.filterwarnings(
+                            "ignore", "Mean of empty slice", RuntimeWarning)
+                        warnings.filterwarnings(
+                            "ignore", "invalid value encountered", RuntimeWarning)
+                        reference = samples[0].mean(axis=axis)
+                    np.testing.assert_equal(output, reference)
+
+
+def _check_arrays_broadcastable(arrays, axis):
+    # https://numpy.org/doc/stable/user/basics.broadcasting.html
+    # "When operating on two arrays, NumPy compares their shapes element-wise.
+    # It starts with the trailing (i.e. rightmost) dimensions and works its
+    # way left.
+    # Two dimensions are compatible when
+    # 1. they are equal, or
+    # 2. one of them is 1
+    # ...
+    # Arrays do not need to have the same number of dimensions."
+    # (Clarification: if the arrays are compatible according to the criteria
+    #  above and an array runs out of dimensions, it is still compatible.)
+    # Below, we follow the rules above except ignoring `axis`
+
+    n_dims = max([arr.ndim for arr in arrays])
+    if axis is not None:
+        # convert to negative axis
+        axis = (-n_dims + axis) if axis >= 0 else axis
+
+    for dim in range(1, n_dims+1):  # we'll index from -1 to -n_dims, inclusive
+        if -dim == axis:
+            continue  # ignore lengths along `axis`
+
+        dim_lengths = set()
+        for arr in arrays:
+            if dim <= arr.ndim and arr.shape[-dim] != 1:
+                dim_lengths.add(arr.shape[-dim])
+
+        if len(dim_lengths) > 1:
+            return False
+    return True
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize(("hypotest", "args", "kwds", "n_samples", "n_outputs",
+                          "paired", "unpacker"), axis_nan_policy_cases)
+def test_empty(hypotest, args, kwds, n_samples, n_outputs, paired, unpacker):
+    # test for correct output shape when at least one input is empty
+    if hypotest in {stats.kruskal, stats.friedmanchisquare} and not SCIPY_XSLOW:
+        pytest.skip("Too slow.")
+
+    if hypotest in override_propagate_funcs:
+        reason = "Doesn't follow the usual pattern. Tested separately."
+        pytest.skip(reason=reason)
+
+    if unpacker is None:
+        unpacker = lambda res: (res[0], res[1])  # noqa: E731
+
+    def small_data_generator(n_samples, n_dims):
+
+        def small_sample_generator(n_dims):
+            # return all possible "small" arrays in up to n_dim dimensions
+            for i in n_dims:
+                # "small" means with size along dimension either 0 or 1
+                for combo in combinations_with_replacement([0, 1, 2], i):
+                    yield np.zeros(combo)
+
+        # yield all possible combinations of small samples
+        gens = [small_sample_generator(n_dims) for i in range(n_samples)]
+        yield from product(*gens)
+
+    n_dims = [1, 2, 3]
+    for samples in small_data_generator(n_samples, n_dims):
+
+        # this test is only for arrays of zero size
+        if not any(sample.size == 0 for sample in samples):
+            continue
+
+        max_axis = max(sample.ndim for sample in samples)
+
+        # need to test for all valid values of `axis` parameter, too
+        for axis in range(-max_axis, max_axis):
+
+            try:
+                # After broadcasting, all arrays are the same shape, so
+                # the shape of the output should be the same as a single-
+                # sample statistic. Use np.mean as a reference.
+                concat = stats._axis_nan_policy._broadcast_concatenate(samples, axis,
+                                                                       paired=paired)
+                with warnings.catch_warnings():
+                    warnings.filterwarnings(
+                        "ignore", "Mean of empty slice", RuntimeWarning)
+                    warnings.filterwarnings(
+                        "ignore", "invalid value encountered", RuntimeWarning)
+                    expected = np.mean(concat, axis=axis) * np.nan
+                    mask = np.isnan(expected)
+                    expected = [np.asarray(expected.copy()) for i in range(n_outputs)]
+
+                if hypotest in empty_special_case_funcs:
+                    empty_val = hypotest(*([[]]*len(samples)), *args, **kwds)
+                    empty_val = list(unpacker(empty_val))
+                    for i in range(n_outputs):
+                        expected[i][mask] = empty_val[i]
+
+                if expected[0].size and hypotest not in too_small_special_case_funcs:
+                    message = (too_small_1d_not_omit if max_axis == 1
+                               else too_small_nd_not_omit)
+                    with pytest.warns(SmallSampleWarning, match=message):
+                        res = hypotest(*samples, *args, axis=axis, **kwds)
+                else:
+                    with warnings.catch_warnings():
+                        # f_oneway special case
+                        msg = "all input arrays have length 1"
+                        warnings.filterwarnings("ignore", msg, SmallSampleWarning)
+                        res = hypotest(*samples, *args, axis=axis, **kwds)
+                res = unpacker(res)
+
+                for i in range(n_outputs):
+                    assert_equal(res[i], expected[i])
+
+            except ValueError:
+                # confirm that the arrays truly are not broadcastable
+                assert not _check_arrays_broadcastable(samples,
+                                                       None if paired else axis)
+
+                # confirm that _both_ `_broadcast_concatenate` and `hypotest`
+                # produce this information.
+                message = "Array shapes are incompatible for broadcasting."
+                with pytest.raises(ValueError, match=message):
+                    stats._axis_nan_policy._broadcast_concatenate(samples, axis, paired)
+                with pytest.raises(ValueError, match=message):
+                    hypotest(*samples, *args, axis=axis, **kwds)
+
+
+def paired_non_broadcastable_cases():
+    for case in axis_nan_policy_cases:
+        hypotest, args, kwds, n_samples, n_outputs, paired, unpacker = case
+        if n_samples == 1:  # broadcasting only needed with >1 sample
+            continue
+        yield case
+
+
+@pytest.mark.parametrize("axis", [0, 1])
+@pytest.mark.parametrize(("hypotest", "args", "kwds", "n_samples", "n_outputs",
+                          "paired", "unpacker"),
+                         list(paired_non_broadcastable_cases()))
+def test_non_broadcastable(hypotest, args, kwds, n_samples, n_outputs, paired,
+                           unpacker, axis):
+    # test for correct error message when shapes are not broadcastable
+    rng = np.random.default_rng(91359824598245)
+    get_samples = True
+    while get_samples:
+        samples = [rng.random(size=rng.integers(2, 100, size=2))
+                   for i in range(n_samples)]
+        # if samples are broadcastable, try again
+        get_samples = _check_arrays_broadcastable(samples, axis=axis)
+
+    message = "Array shapes are incompatible for broadcasting."
+    with pytest.raises(ValueError, match=message):
+        hypotest(*samples, *args, axis=axis, **kwds)
+
+    if not paired:  # there's another test for paired-sample statistics
+        return
+
+    # Previously, paired sample statistics did not raise an error
+    # message when the shapes were broadcastable except along `axis`
+    # https://github.com/scipy/scipy/pull/19578#pullrequestreview-1766857165
+    shape = rng.integers(2, 10, size=2)
+    most_samples = [rng.random(size=shape) for i in range(n_samples-1)]
+    shape = list(shape)
+    shape[axis] += 1
+    other_sample = rng.random(size=shape)
+    with pytest.raises(ValueError, match=message):
+        hypotest(other_sample, *most_samples, *args, axis=axis, **kwds)
+
+def test_masked_array_2_sentinel_array():
+    # prepare arrays
+    rng = np.random.default_rng(805715284)
+    A = rng.random((10, 11, 12))
+    B = rng.random(12)
+    mask = A < 0.5
+    A = np.ma.masked_array(A, mask)
+
+    # set arbitrary elements to special values
+    # (these values might have been considered for use as sentinel values)
+    max_float = np.finfo(np.float64).max
+    max_float2 = np.nextafter(max_float, -np.inf)
+    max_float3 = np.nextafter(max_float2, -np.inf)
+    A[3, 4, 1] = np.nan
+    A[4, 5, 2] = np.inf
+    A[5, 6, 3] = max_float
+    B[8] = np.nan
+    B[7] = np.inf
+    B[6] = max_float2
+
+    # convert masked A to array with sentinel value, don't modify B
+    out_arrays, sentinel = _masked_arrays_2_sentinel_arrays([A, B])
+    A_out, B_out = out_arrays
+
+    # check that good sentinel value was chosen (according to intended logic)
+    assert (sentinel != max_float) and (sentinel != max_float2)
+    assert sentinel == max_float3
+
+    # check that output arrays are as intended
+    A_reference = A.data
+    A_reference[A.mask] = sentinel
+    np.testing.assert_array_equal(A_out, A_reference)
+    assert B_out is B
+
+
+@skip_xp_invalid_arg
+def test_masked_dtype():
+    # When _masked_arrays_2_sentinel_arrays was first added, it always
+    # upcast the arrays to np.float64. After gh16662, check expected promotion
+    # and that the expected sentinel is found.
+
+    # these are important because the max of the promoted dtype is the first
+    # candidate to be the sentinel value
+    max16 = np.iinfo(np.int16).max
+    max128c = np.finfo(np.complex128).max
+
+    # a is a regular array, b has masked elements, and c has no masked elements
+    a = np.array([1, 2, max16], dtype=np.int16)
+    b = np.ma.array([1, 2, 1], dtype=np.int8, mask=[0, 1, 0])
+    c = np.ma.array([1, 2, 1], dtype=np.complex128, mask=[0, 0, 0])
+
+    # check integer masked -> sentinel conversion
+    out_arrays, sentinel = _masked_arrays_2_sentinel_arrays([a, b])
+    a_out, b_out = out_arrays
+    assert sentinel == max16-1  # not max16 because max16 was in the data
+    assert b_out.dtype == np.int16  # check expected promotion
+    assert_allclose(b_out, [b[0], sentinel, b[-1]])  # check sentinel placement
+    assert a_out is a  # not a masked array, so left untouched
+    assert not isinstance(b_out, np.ma.MaskedArray)  # b became regular array
+
+    # similarly with complex
+    out_arrays, sentinel = _masked_arrays_2_sentinel_arrays([b, c])
+    b_out, c_out = out_arrays
+    assert sentinel == max128c  # max128c was not in the data
+    assert b_out.dtype == np.complex128  # b got promoted
+    assert_allclose(b_out, [b[0], sentinel, b[-1]])  # check sentinel placement
+    assert not isinstance(b_out, np.ma.MaskedArray)  # b became regular array
+    assert not isinstance(c_out, np.ma.MaskedArray)  # c became regular array
+
+    # Also, check edge case when a sentinel value cannot be found in the data
+    min8, max8 = np.iinfo(np.int8).min, np.iinfo(np.int8).max
+    a = np.arange(min8, max8+1, dtype=np.int8)  # use all possible values
+    mask1 = np.zeros_like(a, dtype=bool)
+    mask0 = np.zeros_like(a, dtype=bool)
+
+    # a masked value can be used as the sentinel
+    mask1[1] = True
+    a1 = np.ma.array(a, mask=mask1)
+    out_arrays, sentinel = _masked_arrays_2_sentinel_arrays([a1])
+    assert sentinel == min8+1
+
+    # unless it's the smallest possible; skipped for simiplicity (see code)
+    mask0[0] = True
+    a0 = np.ma.array(a, mask=mask0)
+    message = "This function replaces masked elements with sentinel..."
+    with pytest.raises(ValueError, match=message):
+        _masked_arrays_2_sentinel_arrays([a0])
+
+    # test that dtype is preserved in functions
+    a = np.ma.array([1, 2, 3], mask=[0, 1, 0], dtype=np.float32)
+    assert stats.gmean(a).dtype == np.float32
+
+
+@skip_xp_invalid_arg
+def test_masked_stat_1d():
+    # basic test of _axis_nan_policy_factory with 1D masked sample
+    males = [19, 22, 16, 29, 24]
+    females = [20, 11, 17, 12]
+    res = stats.mannwhitneyu(males, females)
+
+    # same result when extra nan is omitted
+    females2 = [20, 11, 17, np.nan, 12]
+    res2 = stats.mannwhitneyu(males, females2, nan_policy='omit')
+    np.testing.assert_array_equal(res2, res)
+
+    # same result when extra element is masked
+    females3 = [20, 11, 17, 1000, 12]
+    mask3 = [False, False, False, True, False]
+    females3 = np.ma.masked_array(females3, mask=mask3)
+    res3 = stats.mannwhitneyu(males, females3)
+    np.testing.assert_array_equal(res3, res)
+
+    # same result when extra nan is omitted and additional element is masked
+    females4 = [20, 11, 17, np.nan, 1000, 12]
+    mask4 = [False, False, False, False, True, False]
+    females4 = np.ma.masked_array(females4, mask=mask4)
+    res4 = stats.mannwhitneyu(males, females4, nan_policy='omit')
+    np.testing.assert_array_equal(res4, res)
+
+    # same result when extra elements, including nan, are masked
+    females5 = [20, 11, 17, np.nan, 1000, 12]
+    mask5 = [False, False, False, True, True, False]
+    females5 = np.ma.masked_array(females5, mask=mask5)
+    res5 = stats.mannwhitneyu(males, females5, nan_policy='propagate')
+    res6 = stats.mannwhitneyu(males, females5, nan_policy='raise')
+    np.testing.assert_array_equal(res5, res)
+    np.testing.assert_array_equal(res6, res)
+
+
+@pytest.mark.filterwarnings('ignore:After omitting NaNs...')
+@pytest.mark.filterwarnings('ignore:One or more axis-slices of one...')
+@skip_xp_invalid_arg
+@pytest.mark.parametrize(("axis"), list(range(-3, 3)))
+def test_masked_stat_3d(axis):
+    # basic test of _axis_nan_policy_factory with 3D masked sample
+    rng = np.random.default_rng(3679428403)
+    a = rng.random((3, 4, 5))
+    b = rng.random((4, 5))
+    c = rng.random((4, 1))
+
+    mask_a = a < 0.1
+    mask_c = [False, False, False, True]
+    a_masked = np.ma.masked_array(a, mask=mask_a)
+    c_masked = np.ma.masked_array(c, mask=mask_c)
+
+    a_nans = a.copy()
+    a_nans[mask_a] = np.nan
+    c_nans = c.copy()
+    c_nans[mask_c] = np.nan
+
+    res = stats.kruskal(a_nans, b, c_nans, nan_policy='omit', axis=axis)
+    res2 = stats.kruskal(a_masked, b, c_masked, axis=axis)
+    np.testing.assert_array_equal(res, res2)
+
+
+@pytest.mark.filterwarnings('ignore:After omitting NaNs...')
+@pytest.mark.filterwarnings('ignore:One or more axis-slices of one...')
+@skip_xp_invalid_arg
+def test_mixed_mask_nan_1():
+    # targeted test of _axis_nan_policy_factory with 2D masked sample:
+    # omitting samples with masks and nan_policy='omit' are equivalent
+    # also checks paired-sample sentinel value removal
+    m, n = 3, 20
+    axis = -1
+
+    rng = np.random.RandomState(0)
+    a = rng.rand(m, n)
+    b = rng.rand(m, n)
+    mask_a1 = rng.rand(m, n) < 0.2
+    mask_a2 = rng.rand(m, n) < 0.1
+    mask_b1 = rng.rand(m, n) < 0.15
+    mask_b2 = rng.rand(m, n) < 0.15
+    mask_a1[2, :] = True
+
+    a_nans = a.copy()
+    b_nans = b.copy()
+    a_nans[mask_a1 | mask_a2] = np.nan
+    b_nans[mask_b1 | mask_b2] = np.nan
+
+    a_masked1 = np.ma.masked_array(a, mask=mask_a1)
+    b_masked1 = np.ma.masked_array(b, mask=mask_b1)
+    a_masked1[mask_a2] = np.nan
+    b_masked1[mask_b2] = np.nan
+
+    a_masked2 = np.ma.masked_array(a, mask=mask_a2)
+    b_masked2 = np.ma.masked_array(b, mask=mask_b2)
+    a_masked2[mask_a1] = np.nan
+    b_masked2[mask_b1] = np.nan
+
+    a_masked3 = np.ma.masked_array(a, mask=(mask_a1 | mask_a2))
+    b_masked3 = np.ma.masked_array(b, mask=(mask_b1 | mask_b2))
+
+    res = stats.wilcoxon(a_nans, b_nans, nan_policy='omit', axis=axis)
+    res1 = stats.wilcoxon(a_masked1, b_masked1, nan_policy='omit', axis=axis)
+    res2 = stats.wilcoxon(a_masked2, b_masked2, nan_policy='omit', axis=axis)
+    res3 = stats.wilcoxon(a_masked3, b_masked3, nan_policy='raise', axis=axis)
+    res4 = stats.wilcoxon(a_masked3, b_masked3,
+                          nan_policy='propagate', axis=axis)
+
+    np.testing.assert_array_equal(res1, res)
+    np.testing.assert_array_equal(res2, res)
+    np.testing.assert_array_equal(res3, res)
+    np.testing.assert_array_equal(res4, res)
+
+
+@pytest.mark.filterwarnings('ignore:After omitting NaNs...')
+@pytest.mark.filterwarnings('ignore:One or more axis-slices of one...')
+@skip_xp_invalid_arg
+def test_mixed_mask_nan_2():
+    # targeted test of _axis_nan_policy_factory with 2D masked sample:
+    # check for expected interaction between masks and nans
+
+    # Cases here are
+    # [mixed nan/mask, all nans, all masked,
+    # unmasked nan, masked nan, unmasked non-nan]
+    a = [[1, np.nan, 2], [np.nan, np.nan, np.nan], [1, 2, 3],
+         [1, np.nan, 3], [1, np.nan, 3], [1, 2, 3]]
+    mask = [[1, 0, 1], [0, 0, 0], [1, 1, 1],
+            [0, 0, 0], [0, 1, 0], [0, 0, 0]]
+    a_masked = np.ma.masked_array(a, mask=mask)
+    b = [[4, 5, 6]]
+    ref1 = stats.ranksums([1, 3], [4, 5, 6])
+    ref2 = stats.ranksums([1, 2, 3], [4, 5, 6])
+
+    # nan_policy = 'omit'
+    # all elements are removed from first three rows
+    # middle element is removed from fourth and fifth rows
+    # no elements removed from last row
+    res = stats.ranksums(a_masked, b, nan_policy='omit', axis=-1)
+    stat_ref = [np.nan, np.nan, np.nan,
+                ref1.statistic, ref1.statistic, ref2.statistic]
+    p_ref = [np.nan, np.nan, np.nan,
+             ref1.pvalue, ref1.pvalue, ref2.pvalue]
+    np.testing.assert_array_equal(res.statistic, stat_ref)
+    np.testing.assert_array_equal(res.pvalue, p_ref)
+
+    # nan_policy = 'propagate'
+    # nans propagate in first, second, and fourth row
+    # all elements are removed by mask from third row
+    # middle element is removed from fifth row
+    # no elements removed from last row
+    res = stats.ranksums(a_masked, b, nan_policy='propagate', axis=-1)
+    stat_ref = [np.nan, np.nan, np.nan,
+                np.nan, ref1.statistic, ref2.statistic]
+    p_ref = [np.nan, np.nan, np.nan,
+             np.nan, ref1.pvalue, ref2.pvalue]
+    np.testing.assert_array_equal(res.statistic, stat_ref)
+    np.testing.assert_array_equal(res.pvalue, p_ref)
+
+
+def test_axis_None_vs_tuple():
+    # `axis` `None` should be equivalent to tuple with all axes
+    shape = (3, 8, 9, 10)
+    rng = np.random.default_rng(0)
+    x = rng.random(shape)
+    res = stats.kruskal(*x, axis=None)
+    res2 = stats.kruskal(*x, axis=(0, 1, 2))
+    np.testing.assert_array_equal(res, res2)
+
+
+def test_axis_None_vs_tuple_with_broadcasting():
+    # `axis` `None` should be equivalent to tuple with all axes,
+    # which should be equivalent to raveling the arrays before passing them
+    rng = np.random.default_rng(0)
+    x = rng.random((5, 1))
+    y = rng.random((1, 5))
+    x2, y2 = np.broadcast_arrays(x, y)
+
+    res0 = stats.mannwhitneyu(x.ravel(), y.ravel())
+    res1 = stats.mannwhitneyu(x, y, axis=None)
+    res2 = stats.mannwhitneyu(x, y, axis=(0, 1))
+    res3 = stats.mannwhitneyu(x2.ravel(), y2.ravel())
+
+    assert res1 == res0
+    assert res2 == res0
+    assert res3 != res0
+
+
+@pytest.mark.parametrize(("axis"),
+                         list(permutations(range(-3, 3), 2)) + [(-4, 1)])
+def test_other_axis_tuples(axis):
+    # Check that _axis_nan_policy_factory treats all `axis` tuples as expected
+    rng = np.random.default_rng(0)
+    shape_x = (4, 5, 6)
+    shape_y = (1, 6)
+    x = rng.random(shape_x)
+    y = rng.random(shape_y)
+    axis_original = axis
+
+    # convert axis elements to positive
+    axis = tuple([(i if i >= 0 else 3 + i) for i in axis])
+    axis = sorted(axis)
+
+    if len(set(axis)) != len(axis):
+        message = "`axis` must contain only distinct elements"
+        with pytest.raises(AxisError, match=re.escape(message)):
+            stats.mannwhitneyu(x, y, axis=axis_original)
+        return
+
+    if axis[0] < 0 or axis[-1] > 2:
+        message = "`axis` is out of bounds for array of dimension 3"
+        with pytest.raises(AxisError, match=re.escape(message)):
+            stats.mannwhitneyu(x, y, axis=axis_original)
+        return
+
+    res = stats.mannwhitneyu(x, y, axis=axis_original)
+
+    # reference behavior
+    not_axis = {0, 1, 2} - set(axis)  # which axis is not part of `axis`
+    not_axis = next(iter(not_axis))  # take it out of the set
+
+    x2 = x
+    shape_y_broadcasted = [1, 1, 6]
+    shape_y_broadcasted[not_axis] = shape_x[not_axis]
+    y2 = np.broadcast_to(y, shape_y_broadcasted)
+
+    m = x2.shape[not_axis]
+    x2 = np.moveaxis(x2, axis, (1, 2))
+    y2 = np.moveaxis(y2, axis, (1, 2))
+    x2 = np.reshape(x2, (m, -1))
+    y2 = np.reshape(y2, (m, -1))
+    res2 = stats.mannwhitneyu(x2, y2, axis=1)
+
+    np.testing.assert_array_equal(res, res2)
+
+
+@pytest.mark.filterwarnings('ignore:After omitting NaNs...')
+@pytest.mark.filterwarnings('ignore:One or more axis-slices of one...')
+@skip_xp_invalid_arg
+@pytest.mark.parametrize(
+    ("weighted_fun_name, unpacker"),
+    [
+        ("gmean", lambda x: x),
+        ("hmean", lambda x: x),
+        ("pmean", lambda x: x),
+        ("combine_pvalues", lambda x: (x.pvalue, x.statistic)),
+    ],
+)
+def test_mean_mixed_mask_nan_weights(weighted_fun_name, unpacker):
+    # targeted test of _axis_nan_policy_factory with 2D masked sample:
+    # omitting samples with masks and nan_policy='omit' are equivalent
+    # also checks paired-sample sentinel value removal
+
+    if weighted_fun_name == 'pmean':
+        def weighted_fun(a, **kwargs):
+            return stats.pmean(a, p=0.42, **kwargs)
+    else:
+        weighted_fun = getattr(stats, weighted_fun_name)
+
+    def func(*args, **kwargs):
+        return unpacker(weighted_fun(*args, **kwargs))
+
+    m, n = 3, 20
+    axis = -1
+
+    rng = np.random.default_rng(6541968121)
+    a = rng.uniform(size=(m, n))
+    b = rng.uniform(size=(m, n))
+    mask_a1 = rng.uniform(size=(m, n)) < 0.2
+    mask_a2 = rng.uniform(size=(m, n)) < 0.1
+    mask_b1 = rng.uniform(size=(m, n)) < 0.15
+    mask_b2 = rng.uniform(size=(m, n)) < 0.15
+    mask_a1[2, :] = True
+
+    a_nans = a.copy()
+    b_nans = b.copy()
+    a_nans[mask_a1 | mask_a2] = np.nan
+    b_nans[mask_b1 | mask_b2] = np.nan
+
+    a_masked1 = np.ma.masked_array(a, mask=mask_a1)
+    b_masked1 = np.ma.masked_array(b, mask=mask_b1)
+    a_masked1[mask_a2] = np.nan
+    b_masked1[mask_b2] = np.nan
+
+    a_masked2 = np.ma.masked_array(a, mask=mask_a2)
+    b_masked2 = np.ma.masked_array(b, mask=mask_b2)
+    a_masked2[mask_a1] = np.nan
+    b_masked2[mask_b1] = np.nan
+
+    a_masked3 = np.ma.masked_array(a, mask=(mask_a1 | mask_a2))
+    b_masked3 = np.ma.masked_array(b, mask=(mask_b1 | mask_b2))
+
+    with warnings.catch_warnings():
+        message = 'invalid value encountered'
+        warnings.filterwarnings("ignore", message, RuntimeWarning)
+        res = func(a_nans, weights=b_nans, nan_policy="omit", axis=axis)
+        res1 = func(a_masked1, weights=b_masked1, nan_policy="omit", axis=axis)
+        res2 = func(a_masked2, weights=b_masked2, nan_policy="omit", axis=axis)
+        res3 = func(a_masked3, weights=b_masked3, nan_policy="raise", axis=axis)
+        res4 = func(a_masked3, weights=b_masked3, nan_policy="propagate", axis=axis)
+
+    np.testing.assert_array_equal(res1, res)
+    np.testing.assert_array_equal(res2, res)
+    np.testing.assert_array_equal(res3, res)
+    np.testing.assert_array_equal(res4, res)
+
+
+def test_raise_invalid_args_g17713():
+    # other cases are handled in:
+    # test_axis_nan_policy_decorated_positional_axis - multiple values for arg
+    # test_axis_nan_policy_decorated_positional_args - unexpected kwd arg
+    message = "got an unexpected keyword argument"
+    with pytest.raises(TypeError, match=message):
+        stats.gmean([1, 2, 3], invalid_arg=True)
+
+    message = " got multiple values for argument"
+    with pytest.raises(TypeError, match=message):
+        stats.gmean([1, 2, 3], a=True)
+
+    message = "missing 1 required positional argument"
+    with pytest.raises(TypeError, match=message):
+        stats.gmean()
+
+    message = "takes from 1 to 4 positional arguments but 5 were given"
+    with pytest.raises(TypeError, match=message):
+        stats.gmean([1, 2, 3], 0, float, [1, 1, 1], 10)
+
+
+@pytest.mark.parametrize('dtype', [np.int16, np.float32, np.complex128])
+def test_array_like_input(dtype):
+    # Check that `_axis_nan_policy`-decorated functions work with custom
+    # containers that are coercible to numeric arrays
+
+    class ArrLike:
+        def __init__(self, x, dtype):
+            self._x = x
+            self._dtype = dtype
+
+        def __array__(self, dtype=None, copy=None):
+            return np.asarray(x, dtype=self._dtype)
+
+        def __iter__(self):
+            # I don't know of a canonical way to determine whether an object should
+            # be coerced to a NumPy array or not. Currently, `xp_promote` checks
+            # whether they are iterable, and if so uses `_asarray` with whatever
+            # `xp` is. So for this to get coerced, it needs to be iterable.
+            return iter(self._x)
+
+    x = [1]*2 + [3, 4, 5]
+    res = stats.mode(ArrLike(x, dtype=dtype))
+    assert res.mode == 1
+    assert res.count == 2
+
+
+@make_xp_test_case(stats._axis_nan_policy._broadcast_concatenate)
+def test__broadcast_concatenate(xp):
+    # test that _broadcast_concatenate properly broadcasts arrays along all
+    # axes except `axis`, then concatenates along axis
+    rng = np.random.default_rng(7544340069)
+    a = rng.random((5, 4, 4, 3, 1, 6))
+    b = rng.random((4, 1, 8, 2, 6))
+    arrays = (xp.asarray(a), xp.asarray(b))
+    c = stats._axis_nan_policy._broadcast_concatenate(arrays, axis=-3, xp=xp)
+    # broadcast manually as an independent check
+    a = np.tile(a, (1, 1, 1, 1, 2, 1))
+    b = np.tile(b[None, ...], (5, 1, 4, 1, 1, 1))
+    for index in product(*(range(i) for i in c.shape)):
+        i, j, k, l, m, n = index
+        if l < a.shape[-3]:
+            assert a[i, j, k, l, m, n] == c[i, j, k, l, m, n]
+        else:
+            assert b[i, j, k, l - a.shape[-3], m, n] == c[i, j, k, l, m, n]
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_binned_statistic.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_binned_statistic.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1fc499123f8f0eed07a767f2d9e361a84452bcb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_binned_statistic.py
@@ -0,0 +1,602 @@
+import numpy as np
+from numpy.testing import assert_allclose
+import pytest
+from pytest import raises as assert_raises
+from scipy.stats import (binned_statistic, binned_statistic_2d,
+                         binned_statistic_dd)
+from scipy._lib._util import check_random_state
+
+from .common_tests import check_named_results
+
+
+class TestBinnedStatistic:
+
+    @classmethod
+    def setup_class(cls):
+        rng = check_random_state(9865)
+        cls.x = rng.uniform(size=100)
+        cls.y = rng.uniform(size=100)
+        cls.v = rng.uniform(size=100)
+        cls.X = rng.uniform(size=(100, 3))
+        cls.w = rng.uniform(size=100)
+        cls.u = rng.uniform(size=100) + 1e6
+
+    def test_1d_count(self):
+        x = self.x
+        v = self.v
+
+        count1, edges1, bc = binned_statistic(x, v, 'count', bins=10)
+        count2, edges2 = np.histogram(x, bins=10)
+
+        assert_allclose(count1, count2)
+        assert_allclose(edges1, edges2)
+
+    def test_gh5927(self):
+        # smoke test for gh5927 - binned_statistic was using `is` for string
+        # comparison
+        x = self.x
+        v = self.v
+        statistics = ['mean', 'median', 'count', 'sum']
+        for statistic in statistics:
+            binned_statistic(x, v, statistic, bins=10)
+
+    def test_big_number_std(self):
+        # tests for numerical stability of std calculation
+        # see issue gh-10126 for more
+        x = self.x
+        u = self.u
+        stat1, edges1, bc = binned_statistic(x, u, 'std', bins=10)
+        stat2, edges2, bc = binned_statistic(x, u, np.std, bins=10)
+
+        assert_allclose(stat1, stat2)
+
+    def test_empty_bins_std(self):
+        # tests that std returns gives nan for empty bins
+        x = self.x
+        u = self.u
+        print(binned_statistic(x, u, 'count', bins=1000))
+        stat1, edges1, bc = binned_statistic(x, u, 'std', bins=1000)
+        stat2, edges2, bc = binned_statistic(x, u, np.std, bins=1000)
+
+        assert_allclose(stat1, stat2)
+
+    def test_1d_result_attributes(self):
+        x = self.x
+        v = self.v
+
+        res = binned_statistic(x, v, 'count', bins=10)
+        attributes = ('statistic', 'bin_edges', 'binnumber')
+        check_named_results(res, attributes)
+
+    def test_1d_sum(self):
+        x = self.x
+        v = self.v
+
+        sum1, edges1, bc = binned_statistic(x, v, 'sum', bins=10)
+        sum2, edges2 = np.histogram(x, bins=10, weights=v)
+
+        assert_allclose(sum1, sum2)
+        assert_allclose(edges1, edges2)
+
+    def test_1d_mean(self):
+        x = self.x
+        v = self.v
+
+        stat1, edges1, bc = binned_statistic(x, v, 'mean', bins=10)
+        stat2, edges2, bc = binned_statistic(x, v, np.mean, bins=10)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(edges1, edges2)
+
+    def test_1d_std(self):
+        x = self.x
+        v = self.v
+
+        stat1, edges1, bc = binned_statistic(x, v, 'std', bins=10)
+        stat2, edges2, bc = binned_statistic(x, v, np.std, bins=10)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(edges1, edges2)
+
+    def test_1d_min(self):
+        x = self.x
+        v = self.v
+
+        stat1, edges1, bc = binned_statistic(x, v, 'min', bins=10)
+        stat2, edges2, bc = binned_statistic(x, v, np.min, bins=10)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(edges1, edges2)
+
+    def test_1d_max(self):
+        x = self.x
+        v = self.v
+
+        stat1, edges1, bc = binned_statistic(x, v, 'max', bins=10)
+        stat2, edges2, bc = binned_statistic(x, v, np.max, bins=10)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(edges1, edges2)
+
+    def test_1d_median(self):
+        x = self.x
+        v = self.v
+
+        stat1, edges1, bc = binned_statistic(x, v, 'median', bins=10)
+        stat2, edges2, bc = binned_statistic(x, v, np.median, bins=10)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(edges1, edges2)
+
+    def test_1d_bincode(self):
+        x = self.x[:20]
+        v = self.v[:20]
+
+        count1, edges1, bc = binned_statistic(x, v, 'count', bins=3)
+        bc2 = np.array([3, 2, 1, 3, 2, 3, 3, 3, 3, 1, 1, 3, 3, 1, 2, 3, 1,
+                        1, 2, 1])
+
+        bcount = [(bc == i).sum() for i in np.unique(bc)]
+
+        assert_allclose(bc, bc2)
+        assert_allclose(bcount, count1)
+
+    def test_1d_range_keyword(self):
+        # Regression test for gh-3063, range can be (min, max) or [(min, max)]
+        rng = np.random.default_rng(6823616729)
+        x = np.arange(30)
+        data = rng.random(30)
+
+        mean, bins, _ = binned_statistic(x[:15], data[:15])
+        mean_range, bins_range, _ = binned_statistic(x, data, range=[(0, 14)])
+        mean_range2, bins_range2, _ = binned_statistic(x, data, range=(0, 14))
+
+        assert_allclose(mean, mean_range)
+        assert_allclose(bins, bins_range)
+        assert_allclose(mean, mean_range2)
+        assert_allclose(bins, bins_range2)
+
+    def test_1d_multi_values(self):
+        x = self.x
+        v = self.v
+        w = self.w
+
+        stat1v, edges1v, bc1v = binned_statistic(x, v, 'mean', bins=10)
+        stat1w, edges1w, bc1w = binned_statistic(x, w, 'mean', bins=10)
+        stat2, edges2, bc2 = binned_statistic(x, [v, w], 'mean', bins=10)
+
+        assert_allclose(stat2[0], stat1v)
+        assert_allclose(stat2[1], stat1w)
+        assert_allclose(edges1v, edges2)
+        assert_allclose(bc1v, bc2)
+
+    def test_2d_count(self):
+        x = self.x
+        y = self.y
+        v = self.v
+
+        count1, binx1, biny1, bc = binned_statistic_2d(
+            x, y, v, 'count', bins=5)
+        count2, binx2, biny2 = np.histogram2d(x, y, bins=5)
+
+        assert_allclose(count1, count2)
+        assert_allclose(binx1, binx2)
+        assert_allclose(biny1, biny2)
+
+    def test_2d_result_attributes(self):
+        x = self.x
+        y = self.y
+        v = self.v
+
+        res = binned_statistic_2d(x, y, v, 'count', bins=5)
+        attributes = ('statistic', 'x_edge', 'y_edge', 'binnumber')
+        check_named_results(res, attributes)
+
+    def test_2d_sum(self):
+        x = self.x
+        y = self.y
+        v = self.v
+
+        sum1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'sum', bins=5)
+        sum2, binx2, biny2 = np.histogram2d(x, y, bins=5, weights=v)
+
+        assert_allclose(sum1, sum2)
+        assert_allclose(binx1, binx2)
+        assert_allclose(biny1, biny2)
+
+    def test_2d_mean(self):
+        x = self.x
+        y = self.y
+        v = self.v
+
+        stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'mean', bins=5)
+        stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.mean, bins=5)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(binx1, binx2)
+        assert_allclose(biny1, biny2)
+
+    def test_2d_mean_unicode(self):
+        x = self.x
+        y = self.y
+        v = self.v
+        stat1, binx1, biny1, bc = binned_statistic_2d(
+            x, y, v, 'mean', bins=5)
+        stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.mean, bins=5)
+        assert_allclose(stat1, stat2)
+        assert_allclose(binx1, binx2)
+        assert_allclose(biny1, biny2)
+
+    def test_2d_std(self):
+        x = self.x
+        y = self.y
+        v = self.v
+
+        stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'std', bins=5)
+        stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.std, bins=5)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(binx1, binx2)
+        assert_allclose(biny1, biny2)
+
+    def test_2d_min(self):
+        x = self.x
+        y = self.y
+        v = self.v
+
+        stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'min', bins=5)
+        stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.min, bins=5)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(binx1, binx2)
+        assert_allclose(biny1, biny2)
+
+    def test_2d_max(self):
+        x = self.x
+        y = self.y
+        v = self.v
+
+        stat1, binx1, biny1, bc = binned_statistic_2d(x, y, v, 'max', bins=5)
+        stat2, binx2, biny2, bc = binned_statistic_2d(x, y, v, np.max, bins=5)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(binx1, binx2)
+        assert_allclose(biny1, biny2)
+
+    def test_2d_median(self):
+        x = self.x
+        y = self.y
+        v = self.v
+
+        stat1, binx1, biny1, bc = binned_statistic_2d(
+            x, y, v, 'median', bins=5)
+        stat2, binx2, biny2, bc = binned_statistic_2d(
+            x, y, v, np.median, bins=5)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(binx1, binx2)
+        assert_allclose(biny1, biny2)
+
+    def test_2d_bincode(self):
+        x = self.x[:20]
+        y = self.y[:20]
+        v = self.v[:20]
+
+        count1, binx1, biny1, bc = binned_statistic_2d(
+            x, y, v, 'count', bins=3)
+        bc2 = np.array([17, 11, 6, 16, 11, 17, 18, 17, 17, 7, 6, 18, 16,
+                        6, 11, 16, 6, 6, 11, 8])
+
+        bcount = [(bc == i).sum() for i in np.unique(bc)]
+
+        assert_allclose(bc, bc2)
+        count1adj = count1[count1.nonzero()]
+        assert_allclose(bcount, count1adj)
+
+    def test_2d_multi_values(self):
+        x = self.x
+        y = self.y
+        v = self.v
+        w = self.w
+
+        stat1v, binx1v, biny1v, bc1v = binned_statistic_2d(
+            x, y, v, 'mean', bins=8)
+        stat1w, binx1w, biny1w, bc1w = binned_statistic_2d(
+            x, y, w, 'mean', bins=8)
+        stat2, binx2, biny2, bc2 = binned_statistic_2d(
+            x, y, [v, w], 'mean', bins=8)
+
+        assert_allclose(stat2[0], stat1v)
+        assert_allclose(stat2[1], stat1w)
+        assert_allclose(binx1v, binx2)
+        assert_allclose(biny1w, biny2)
+        assert_allclose(bc1v, bc2)
+
+    def test_2d_binnumbers_unraveled(self):
+        x = self.x
+        y = self.y
+        v = self.v
+
+        stat, edgesx, bcx = binned_statistic(x, v, 'mean', bins=20)
+        stat, edgesy, bcy = binned_statistic(y, v, 'mean', bins=10)
+
+        stat2, edgesx2, edgesy2, bc2 = binned_statistic_2d(
+            x, y, v, 'mean', bins=(20, 10), expand_binnumbers=True)
+
+        bcx3 = np.searchsorted(edgesx, x, side='right')
+        bcy3 = np.searchsorted(edgesy, y, side='right')
+
+        # `numpy.searchsorted` is non-inclusive on right-edge, compensate
+        bcx3[x == x.max()] -= 1
+        bcy3[y == y.max()] -= 1
+
+        assert_allclose(bcx, bc2[0])
+        assert_allclose(bcy, bc2[1])
+        assert_allclose(bcx3, bc2[0])
+        assert_allclose(bcy3, bc2[1])
+
+    def test_dd_count(self):
+        X = self.X
+        v = self.v
+
+        count1, edges1, bc = binned_statistic_dd(X, v, 'count', bins=3)
+        count2, edges2 = np.histogramdd(X, bins=3)
+
+        assert_allclose(count1, count2)
+        assert_allclose(edges1, edges2)
+
+    def test_dd_result_attributes(self):
+        X = self.X
+        v = self.v
+
+        res = binned_statistic_dd(X, v, 'count', bins=3)
+        attributes = ('statistic', 'bin_edges', 'binnumber')
+        check_named_results(res, attributes)
+
+    def test_dd_sum(self):
+        X = self.X
+        v = self.v
+
+        sum1, edges1, bc = binned_statistic_dd(X, v, 'sum', bins=3)
+        sum2, edges2 = np.histogramdd(X, bins=3, weights=v)
+        sum3, edges3, bc = binned_statistic_dd(X, v, np.sum, bins=3)
+
+        assert_allclose(sum1, sum2)
+        assert_allclose(edges1, edges2)
+        assert_allclose(sum1, sum3)
+        assert_allclose(edges1, edges3)
+
+    def test_dd_mean(self):
+        X = self.X
+        v = self.v
+
+        stat1, edges1, bc = binned_statistic_dd(X, v, 'mean', bins=3)
+        stat2, edges2, bc = binned_statistic_dd(X, v, np.mean, bins=3)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(edges1, edges2)
+
+    def test_dd_std(self):
+        X = self.X
+        v = self.v
+
+        stat1, edges1, bc = binned_statistic_dd(X, v, 'std', bins=3)
+        stat2, edges2, bc = binned_statistic_dd(X, v, np.std, bins=3)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(edges1, edges2)
+
+    def test_dd_min(self):
+        X = self.X
+        v = self.v
+
+        stat1, edges1, bc = binned_statistic_dd(X, v, 'min', bins=3)
+        stat2, edges2, bc = binned_statistic_dd(X, v, np.min, bins=3)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(edges1, edges2)
+
+    def test_dd_max(self):
+        X = self.X
+        v = self.v
+
+        stat1, edges1, bc = binned_statistic_dd(X, v, 'max', bins=3)
+        stat2, edges2, bc = binned_statistic_dd(X, v, np.max, bins=3)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(edges1, edges2)
+
+    def test_dd_median(self):
+        X = self.X
+        v = self.v
+
+        stat1, edges1, bc = binned_statistic_dd(X, v, 'median', bins=3)
+        stat2, edges2, bc = binned_statistic_dd(X, v, np.median, bins=3)
+
+        assert_allclose(stat1, stat2)
+        assert_allclose(edges1, edges2)
+
+    def test_dd_bincode(self):
+        X = self.X[:20]
+        v = self.v[:20]
+
+        count1, edges1, bc = binned_statistic_dd(X, v, 'count', bins=3)
+        bc2 = np.array([63, 33, 86, 83, 88, 67, 57, 33, 42, 41, 82, 83, 92,
+                        32, 36, 91, 43, 87, 81, 81])
+
+        bcount = [(bc == i).sum() for i in np.unique(bc)]
+
+        assert_allclose(bc, bc2)
+        count1adj = count1[count1.nonzero()]
+        assert_allclose(bcount, count1adj)
+
+    def test_dd_multi_values(self):
+        X = self.X
+        v = self.v
+        w = self.w
+
+        for stat in ["count", "sum", "mean", "std", "min", "max", "median",
+                     np.std]:
+            stat1v, edges1v, bc1v = binned_statistic_dd(X, v, stat, bins=8)
+            stat1w, edges1w, bc1w = binned_statistic_dd(X, w, stat, bins=8)
+            stat2, edges2, bc2 = binned_statistic_dd(X, [v, w], stat, bins=8)
+            assert_allclose(stat2[0], stat1v)
+            assert_allclose(stat2[1], stat1w)
+            assert_allclose(edges1v, edges2)
+            assert_allclose(edges1w, edges2)
+            assert_allclose(bc1v, bc2)
+
+    def test_dd_binnumbers_unraveled(self):
+        X = self.X
+        v = self.v
+
+        stat, edgesx, bcx = binned_statistic(X[:, 0], v, 'mean', bins=15)
+        stat, edgesy, bcy = binned_statistic(X[:, 1], v, 'mean', bins=20)
+        stat, edgesz, bcz = binned_statistic(X[:, 2], v, 'mean', bins=10)
+
+        stat2, edges2, bc2 = binned_statistic_dd(
+            X, v, 'mean', bins=(15, 20, 10), expand_binnumbers=True)
+
+        assert_allclose(bcx, bc2[0])
+        assert_allclose(bcy, bc2[1])
+        assert_allclose(bcz, bc2[2])
+
+    def test_dd_binned_statistic_result(self):
+        # NOTE: tests the reuse of bin_edges from previous call
+        rng = np.random.default_rng(8111360615)
+        x = rng.random((10000, 3))
+        v = rng.random(10000)
+        bins = np.linspace(0, 1, 10)
+        bins = (bins, bins, bins)
+
+        result = binned_statistic_dd(x, v, 'mean', bins=bins)
+        stat = result.statistic
+
+        result = binned_statistic_dd(x, v, 'mean',
+                                     binned_statistic_result=result)
+        stat2 = result.statistic
+
+        assert_allclose(stat, stat2)
+
+    def test_dd_zero_dedges(self):
+        rng = np.random.default_rng(1132724173)
+        x = rng.random((10000, 3))
+        v = rng.random(10000)
+        bins = np.linspace(0, 1, 10)
+        bins = np.append(bins, 1)
+        bins = (bins, bins, bins)
+        with assert_raises(ValueError, match='difference is numerically 0'):
+            binned_statistic_dd(x, v, 'mean', bins=bins)
+
+    def test_dd_range_errors(self):
+        # Test that descriptive exceptions are raised as appropriate for bad
+        # values of the `range` argument. (See gh-12996)
+        with assert_raises(ValueError,
+                           match='In range, start must be <= stop'):
+            binned_statistic_dd([self.y], self.v,
+                                range=[[1, 0]])
+        with assert_raises(
+                ValueError,
+                match='In dimension 1 of range, start must be <= stop'):
+            binned_statistic_dd([self.x, self.y], self.v,
+                                range=[[1, 0], [0, 1]])
+        with assert_raises(
+                ValueError,
+                match='In dimension 2 of range, start must be <= stop'):
+            binned_statistic_dd([self.x, self.y], self.v,
+                                range=[[0, 1], [1, 0]])
+        with assert_raises(
+                ValueError,
+                match='range given for 1 dimensions; 2 required'):
+            binned_statistic_dd([self.x, self.y], self.v,
+                                range=[[0, 1]])
+
+    def test_binned_statistic_float32(self):
+        X = np.array([0, 0.42358226], dtype=np.float32)
+        stat, _, _ = binned_statistic(X, None, 'count', bins=5)
+        assert_allclose(stat, np.array([1, 0, 0, 0, 1], dtype=np.float64))
+
+    def test_gh14332(self):
+        # Test the wrong output when the `sample` is close to bin edge
+        x = []
+        size = 20
+        for i in range(size):
+            x += [1-0.1**i]
+
+        bins = np.linspace(0,1,11)
+        sum1, edges1, bc = binned_statistic_dd(x, np.ones(len(x)),
+                                               bins=[bins], statistic='sum')
+        sum2, edges2 = np.histogram(x, bins=bins)
+
+        assert_allclose(sum1, sum2)
+        assert_allclose(edges1[0], edges2)
+
+    @pytest.mark.parametrize("dtype", [np.float64, np.complex128])
+    @pytest.mark.parametrize("statistic", [np.mean, np.median, np.sum, np.std,
+                                           np.min, np.max, 'count',
+                                           lambda x: (x**2).sum(),
+                                           lambda x: (x**2).sum() * 1j])
+    def test_dd_all(self, dtype, statistic):
+        def ref_statistic(x):
+            return len(x) if statistic == 'count' else statistic(x)
+
+        rng = np.random.default_rng(3704743126639371)
+        n = 10
+        x = rng.random(size=n)
+        i = x >= 0.5
+        v = rng.random(size=n)
+        if dtype is np.complex128:
+            v = v + rng.random(size=n)*1j
+
+        stat, _, _ = binned_statistic_dd(x, v, statistic, bins=2)
+        ref = np.array([ref_statistic(v[~i]), ref_statistic(v[i])])
+        assert_allclose(stat, ref)
+        assert stat.dtype == np.result_type(ref.dtype, np.float64)
+
+    @pytest.mark.parametrize('bins', [10, (10, 10, 10), [np.arange(10)]*3])
+    def test_nan_gh17154(self, bins):
+        # gh-17154 reported that `binned_statistic_dd` raised an error when `samples`
+        # contains NaNs and argued that this was an inappropriate default. gh-23751
+        # decided that it was indeed an appropriate choice, and went further, ensuring
+        # that the same error would be raised regardless of how `bins` is specified.
+
+        # Generate a samples (with NaNs) and values
+        rng = np.random.default_rng(529847529874529829)
+        samples = rng.standard_normal(size=(100, 3))
+        values = rng.standard_normal(100)
+        i = rng.random(samples.shape) < 0.05
+        samples[i] = np.nan
+
+        message = "The sample on which `values` is to be binned..."
+        with pytest.raises(ValueError, match=message):
+            binned_statistic_dd(samples, values)
+
+    def test_inf_gh17154(self):
+        # While addressing gh-17154, it seemed that `inf`s should not be ignored
+        # entirely; rather, values corresponding with `inf`s can always be in outlier
+        # bins.
+
+        # Generate a samples (with infs) and values
+        rng = np.random.default_rng(529847529874529829)
+        samples = rng.standard_normal(size=(100, 3))
+        values = rng.standard_normal(100)
+        i_pinf = rng.random(samples.shape) < 0.05
+        samples[i_pinf] = np.inf
+        i_ninf = rng.random(samples.shape) < 0.05
+        samples[i_ninf] = -np.inf
+
+        # Compute results
+        res = binned_statistic_dd(samples, values)
+
+        # Compute reference values (with bins computed ignoring infs)
+        i_inf = i_pinf | i_ninf
+        temp = samples.copy()
+        temp[i_inf] = 0
+        low = np.min(temp, axis=0)
+        high = np.max(temp, axis=0)
+        ref = binned_statistic_dd(samples, values, range=list(zip(low, high)))
+
+        # Compare results against reference values
+        np.testing.assert_allclose(res[0], ref[0])
+        np.testing.assert_allclose(res[1], ref[1])
+        np.testing.assert_allclose(res[2], ref[2])
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_censored_data.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_censored_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae71dcfaccf899645051287f8944131ec48a1eee
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_censored_data.py
@@ -0,0 +1,152 @@
+# Tests for the CensoredData class.
+
+import pytest
+import numpy as np
+from numpy.testing import assert_equal, assert_array_equal
+from scipy.stats import CensoredData
+
+
+class TestCensoredData:
+
+    def test_basic(self):
+        uncensored = [1]
+        left = [0]
+        right = [2, 5]
+        interval = [[2, 3]]
+        data = CensoredData(uncensored, left=left, right=right,
+                            interval=interval)
+        assert_equal(data._uncensored, uncensored)
+        assert_equal(data._left, left)
+        assert_equal(data._right, right)
+        assert_equal(data._interval, interval)
+
+        udata = data._uncensor()
+        assert_equal(udata, np.concatenate((uncensored, left, right,
+                                            np.mean(interval, axis=1))))
+
+    def test_right_censored(self):
+        x = np.array([0, 3, 2.5])
+        is_censored = np.array([0, 1, 0], dtype=bool)
+        data = CensoredData.right_censored(x, is_censored)
+        assert_equal(data._uncensored, x[~is_censored])
+        assert_equal(data._right, x[is_censored])
+        assert_equal(data._left, [])
+        assert_equal(data._interval, np.empty((0, 2)))
+
+    def test_left_censored(self):
+        x = np.array([0, 3, 2.5])
+        is_censored = np.array([0, 1, 0], dtype=bool)
+        data = CensoredData.left_censored(x, is_censored)
+        assert_equal(data._uncensored, x[~is_censored])
+        assert_equal(data._left, x[is_censored])
+        assert_equal(data._right, [])
+        assert_equal(data._interval, np.empty((0, 2)))
+
+    def test_interval_censored_basic(self):
+        a = [0.5, 2.0, 3.0, 5.5]
+        b = [1.0, 2.5, 3.5, 7.0]
+        data = CensoredData.interval_censored(low=a, high=b)
+        assert_array_equal(data._interval, np.array(list(zip(a, b))))
+        assert data._uncensored.shape == (0,)
+        assert data._left.shape == (0,)
+        assert data._right.shape == (0,)
+
+    def test_interval_censored_mixed(self):
+        # This is actually a mix of uncensored, left-censored, right-censored
+        # and interval-censored data.  Check that when the `interval_censored`
+        # class method is used, the data is correctly separated into the
+        # appropriate arrays.
+        a = [0.5, -np.inf, -13.0, 2.0, 1.0, 10.0, -1.0]
+        b = [0.5, 2500.0, np.inf, 3.0, 1.0, 11.0, np.inf]
+        data = CensoredData.interval_censored(low=a, high=b)
+        assert_array_equal(data._interval, [[2.0, 3.0], [10.0, 11.0]])
+        assert_array_equal(data._uncensored, [0.5, 1.0])
+        assert_array_equal(data._left, [2500.0])
+        assert_array_equal(data._right, [-13.0, -1.0])
+
+    def test_interval_to_other_types(self):
+        # The interval parameter can represent uncensored and
+        # left- or right-censored data.  Test the conversion of such
+        # an example to the canonical form in which the different
+        # types have been split into the separate arrays.
+        interval = np.array([[0, 1],        # interval-censored
+                             [2, 2],        # not censored
+                             [3, 3],        # not censored
+                             [9, np.inf],   # right-censored
+                             [8, np.inf],   # right-censored
+                             [-np.inf, 0],  # left-censored
+                             [1, 2]])       # interval-censored
+        data = CensoredData(interval=interval)
+        assert_equal(data._uncensored, [2, 3])
+        assert_equal(data._left, [0])
+        assert_equal(data._right, [9, 8])
+        assert_equal(data._interval, [[0, 1], [1, 2]])
+
+    def test_empty_arrays(self):
+        data = CensoredData(uncensored=[], left=[], right=[], interval=[])
+        assert data._uncensored.shape == (0,)
+        assert data._left.shape == (0,)
+        assert data._right.shape == (0,)
+        assert data._interval.shape == (0, 2)
+        assert len(data) == 0
+
+    def test_invalid_constructor_args(self):
+        with pytest.raises(ValueError, match='must be a one-dimensional'):
+            CensoredData(uncensored=[[1, 2, 3]])
+        with pytest.raises(ValueError, match='must be a one-dimensional'):
+            CensoredData(left=[[1, 2, 3]])
+        with pytest.raises(ValueError, match='must be a one-dimensional'):
+            CensoredData(right=[[1, 2, 3]])
+        with pytest.raises(ValueError, match='must be a two-dimensional'):
+            CensoredData(interval=[[1, 2, 3]])
+
+        with pytest.raises(ValueError, match='must not contain nan'):
+            CensoredData(uncensored=[1, np.nan, 2])
+        with pytest.raises(ValueError, match='must not contain nan'):
+            CensoredData(left=[1, np.nan, 2])
+        with pytest.raises(ValueError, match='must not contain nan'):
+            CensoredData(right=[1, np.nan, 2])
+        with pytest.raises(ValueError, match='must not contain nan'):
+            CensoredData(interval=[[1, np.nan], [2, 3]])
+
+        with pytest.raises(ValueError,
+                           match='both values must not be infinite'):
+            CensoredData(interval=[[1, 3], [2, 9], [np.inf, np.inf]])
+
+        with pytest.raises(ValueError,
+                           match='left value must not exceed the right'):
+            CensoredData(interval=[[1, 0], [2, 2]])
+
+    @pytest.mark.parametrize('func', [CensoredData.left_censored,
+                                      CensoredData.right_censored])
+    def test_invalid_left_right_censored_args(self, func):
+        with pytest.raises(ValueError,
+                           match='`x` must be one-dimensional'):
+            func([[1, 2, 3]], [0, 1, 1])
+        with pytest.raises(ValueError,
+                           match='`censored` must be one-dimensional'):
+            func([1, 2, 3], [[0, 1, 1]])
+        with pytest.raises(ValueError, match='`x` must not contain'):
+            func([1, 2, np.nan], [0, 1, 1])
+        with pytest.raises(ValueError, match='must have the same length'):
+            func([1, 2, 3], [0, 0, 1, 1])
+
+    def test_invalid_censored_args(self):
+        with pytest.raises(ValueError,
+                           match='`low` must be a one-dimensional'):
+            CensoredData.interval_censored(low=[[3]], high=[4, 5])
+        with pytest.raises(ValueError,
+                           match='`high` must be a one-dimensional'):
+            CensoredData.interval_censored(low=[3], high=[[4, 5]])
+        with pytest.raises(ValueError, match='`low` must not contain'):
+            CensoredData.interval_censored([1, 2, np.nan], [0, 1, 1])
+        with pytest.raises(ValueError, match='must have the same length'):
+            CensoredData.interval_censored([1, 2, 3], [0, 0, 1, 1])
+
+    def test_count_censored(self):
+        x = [1, 2, 3]
+        # data1 has no censored data.
+        data1 = CensoredData(x)
+        assert data1.num_censored() == 0
+        data2 = CensoredData(uncensored=[2.5], left=[10], interval=[[0, 1]])
+        assert data2.num_censored() == 2
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_contingency.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_contingency.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa83e05d7da6b3d21d5515a332344c2347f0d7eb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_contingency.py
@@ -0,0 +1,294 @@
+import numpy as np
+from numpy.testing import (assert_equal, assert_array_equal,
+                           assert_array_almost_equal, assert_approx_equal,
+                           assert_allclose)
+import pytest
+from pytest import raises as assert_raises
+from scipy import stats
+from scipy.special import xlogy
+from scipy.stats.contingency import (margins, expected_freq,
+                                     chi2_contingency, association)
+
+
+def test_margins():
+    a = np.array([1])
+    m = margins(a)
+    assert_equal(len(m), 1)
+    m0 = m[0]
+    assert_array_equal(m0, np.array([1]))
+
+    a = np.array([[1]])
+    m0, m1 = margins(a)
+    expected0 = np.array([[1]])
+    expected1 = np.array([[1]])
+    assert_array_equal(m0, expected0)
+    assert_array_equal(m1, expected1)
+
+    a = np.arange(12).reshape(2, 6)
+    m0, m1 = margins(a)
+    expected0 = np.array([[15], [51]])
+    expected1 = np.array([[6, 8, 10, 12, 14, 16]])
+    assert_array_equal(m0, expected0)
+    assert_array_equal(m1, expected1)
+
+    a = np.arange(24).reshape(2, 3, 4)
+    m0, m1, m2 = margins(a)
+    expected0 = np.array([[[66]], [[210]]])
+    expected1 = np.array([[[60], [92], [124]]])
+    expected2 = np.array([[[60, 66, 72, 78]]])
+    assert_array_equal(m0, expected0)
+    assert_array_equal(m1, expected1)
+    assert_array_equal(m2, expected2)
+
+
+def test_expected_freq():
+    assert_array_equal(expected_freq([1]), np.array([1.0]))
+
+    observed = np.array([[[2, 0], [0, 2]], [[0, 2], [2, 0]], [[1, 1], [1, 1]]])
+    e = expected_freq(observed)
+    assert_array_equal(e, np.ones_like(observed))
+
+    observed = np.array([[10, 10, 20], [20, 20, 20]])
+    e = expected_freq(observed)
+    correct = np.array([[12., 12., 16.], [18., 18., 24.]])
+    assert_array_almost_equal(e, correct)
+
+
+class TestChi2Contingency:
+    def test_chi2_contingency_trivial(self):
+        # Some very simple tests for chi2_contingency.
+
+        # A trivial case
+        obs = np.array([[1, 2], [1, 2]])
+        chi2, p, dof, expected = chi2_contingency(obs, correction=False)
+        assert_equal(chi2, 0.0)
+        assert_equal(p, 1.0)
+        assert_equal(dof, 1)
+        assert_array_equal(obs, expected)
+
+        # A *really* trivial case: 1-D data.
+        obs = np.array([1, 2, 3])
+        chi2, p, dof, expected = chi2_contingency(obs, correction=False)
+        assert_equal(chi2, 0.0)
+        assert_equal(p, 1.0)
+        assert_equal(dof, 0)
+        assert_array_equal(obs, expected)
+
+    def test_chi2_contingency_R(self):
+        # Some test cases that were computed independently, using R.
+
+        # Rcode = \
+        # """
+        # # Data vector.
+        # data <- c(
+        #   12, 34, 23,     4,  47,  11,
+        #   35, 31, 11,    34,  10,  18,
+        #   12, 32,  9,    18,  13,  19,
+        #   12, 12, 14,     9,  33,  25
+        #   )
+        #
+        # # Create factor tags:r=rows, c=columns, t=tiers
+        # r <- factor(gl(4, 2*3, 2*3*4, labels=c("r1", "r2", "r3", "r4")))
+        # c <- factor(gl(3, 1,   2*3*4, labels=c("c1", "c2", "c3")))
+        # t <- factor(gl(2, 3,   2*3*4, labels=c("t1", "t2")))
+        #
+        # # 3-way Chi squared test of independence
+        # s = summary(xtabs(data~r+c+t))
+        # print(s)
+        # """
+        # Routput = \
+        # """
+        # Call: xtabs(formula = data ~ r + c + t)
+        # Number of cases in table: 478
+        # Number of factors: 3
+        # Test for independence of all factors:
+        #         Chisq = 102.17, df = 17, p-value = 3.514e-14
+        # """
+        obs = np.array(
+            [[[12, 34, 23],
+              [35, 31, 11],
+              [12, 32, 9],
+              [12, 12, 14]],
+             [[4, 47, 11],
+              [34, 10, 18],
+              [18, 13, 19],
+              [9, 33, 25]]])
+        chi2, p, dof, expected = chi2_contingency(obs)
+        assert_approx_equal(chi2, 102.17, significant=5)
+        assert_approx_equal(p, 3.514e-14, significant=4)
+        assert_equal(dof, 17)
+
+        # Rcode = \
+        # """
+        # # Data vector.
+        # data <- c(
+        #     #
+        #     12, 17,
+        #     11, 16,
+        #     #
+        #     11, 12,
+        #     15, 16,
+        #     #
+        #     23, 15,
+        #     30, 22,
+        #     #
+        #     14, 17,
+        #     15, 16
+        #     )
+        #
+        # # Create factor tags:r=rows, c=columns, d=depths(?), t=tiers
+        # r <- factor(gl(2, 2,  2*2*2*2, labels=c("r1", "r2")))
+        # c <- factor(gl(2, 1,  2*2*2*2, labels=c("c1", "c2")))
+        # d <- factor(gl(2, 4,  2*2*2*2, labels=c("d1", "d2")))
+        # t <- factor(gl(2, 8,  2*2*2*2, labels=c("t1", "t2")))
+        #
+        # # 4-way Chi squared test of independence
+        # s = summary(xtabs(data~r+c+d+t))
+        # print(s)
+        # """
+        # Routput = \
+        # """
+        # Call: xtabs(formula = data ~ r + c + d + t)
+        # Number of cases in table: 262
+        # Number of factors: 4
+        # Test for independence of all factors:
+        #         Chisq = 8.758, df = 11, p-value = 0.6442
+        # """
+        obs = np.array(
+            [[[[12, 17],
+               [11, 16]],
+              [[11, 12],
+               [15, 16]]],
+             [[[23, 15],
+               [30, 22]],
+              [[14, 17],
+               [15, 16]]]])
+        chi2, p, dof, expected = chi2_contingency(obs)
+        assert_approx_equal(chi2, 8.758, significant=4)
+        assert_approx_equal(p, 0.6442, significant=4)
+        assert_equal(dof, 11)
+
+    def test_chi2_contingency_g(self):
+        c = np.array([[15, 60], [15, 90]])
+        g, p, dof, e = chi2_contingency(c, lambda_='log-likelihood',
+                                        correction=False)
+        assert_allclose(g, 2*xlogy(c, c/e).sum())
+
+        g, p, dof, e = chi2_contingency(c, lambda_='log-likelihood',
+                                        correction=True)
+        c_corr = c + np.array([[-0.5, 0.5], [0.5, -0.5]])
+        assert_allclose(g, 2*xlogy(c_corr, c_corr/e).sum())
+
+        c = np.array([[10, 12, 10], [12, 10, 10]])
+        g, p, dof, e = chi2_contingency(c, lambda_='log-likelihood')
+        assert_allclose(g, 2*xlogy(c, c/e).sum())
+
+    def test_chi2_contingency_bad_args(self):
+        # Test that "bad" inputs raise a ValueError.
+
+        # Negative value in the array of observed frequencies.
+        obs = np.array([[-1, 10], [1, 2]])
+        assert_raises(ValueError, chi2_contingency, obs)
+
+        # The zeros in this will result in zeros in the array
+        # of expected frequencies.
+        obs = np.array([[0, 1], [0, 1]])
+        assert_raises(ValueError, chi2_contingency, obs)
+
+        # A degenerate case: `observed` has size 0.
+        obs = np.empty((0, 8))
+        assert_raises(ValueError, chi2_contingency, obs)
+
+    def test_chi2_contingency_yates_gh13875(self):
+        # Magnitude of Yates' continuity correction should not exceed difference
+        # between expected and observed value of the statistic; see gh-13875
+        observed = np.array([[1573, 3], [4, 0]])
+        p = chi2_contingency(observed)[1]
+        assert_allclose(p, 1, rtol=1e-12)
+
+    @pytest.mark.parametrize("correction", [False, True])
+    def test_result(self, correction):
+        obs = np.array([[1, 2], [1, 2]])
+        res = chi2_contingency(obs, correction=correction)
+        assert_equal((res.statistic, res.pvalue, res.dof, res.expected_freq), res)
+
+    @pytest.mark.slow
+    def test_exact_permutation(self):
+        table = np.arange(4).reshape(2, 2)
+        ref_statistic = chi2_contingency(table, correction=False).statistic
+        ref_pvalue = stats.fisher_exact(table).pvalue
+        method = stats.PermutationMethod(n_resamples=50000)
+        res = chi2_contingency(table, correction=False, method=method)
+        assert_equal(res.statistic, ref_statistic)
+        assert_allclose(res.pvalue, ref_pvalue, rtol=1e-15)
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize('method', (stats.PermutationMethod,
+                                        stats.MonteCarloMethod))
+    def test_resampling_randomized(self, method):
+        rng = np.random.default_rng(2592340925)
+        # need to have big sum for asymptotic approximation to be good
+        rows = [300, 1000, 800]
+        cols = [200, 400, 800, 700]
+        table = stats.random_table(rows, cols, seed=rng).rvs()
+        res = chi2_contingency(table, correction=False, method=method(rng=rng))
+        ref = chi2_contingency(table, correction=False)
+        assert_equal(res.statistic, ref.statistic)
+        assert_allclose(res.pvalue, ref.pvalue, atol=5e-3)
+        assert_equal(res.dof, np.nan)
+        assert_equal(res.expected_freq, ref.expected_freq)
+
+    def test_resampling_invalid_args(self):
+        table = np.arange(8).reshape(2, 2, 2)
+
+        method = stats.PermutationMethod()
+        message = "Use of `method` is only compatible with two-way tables."
+        with pytest.raises(ValueError, match=message):
+            chi2_contingency(table, correction=False, method=method)
+
+        table = np.arange(4).reshape(2, 2)
+
+        method = stats.PermutationMethod()
+        message = "`correction=True` is not compatible with..."
+        with pytest.raises(ValueError, match=message):
+            chi2_contingency(table, method=method)
+
+        method = stats.MonteCarloMethod()
+        message = "`lambda_=2` is not compatible with..."
+        with pytest.raises(ValueError, match=message):
+            chi2_contingency(table, correction=False, lambda_=2, method=method)
+
+        method = 'herring'
+        message = "`method='herring'` not recognized; if provided, `method`..."
+        with pytest.raises(ValueError, match=message):
+            chi2_contingency(table, correction=False, method=method)
+
+        method = stats.MonteCarloMethod(rvs=stats.norm.rvs)
+        message = "If the `method` argument of `chi2_contingency` is..."
+        with pytest.raises(ValueError, match=message):
+            chi2_contingency(table, correction=False, method=method)
+
+
+def test_bad_association_args():
+    # Invalid Test Statistic
+    assert_raises(ValueError, association, [[1, 2], [3, 4]], "X")
+    # Invalid array shape
+    assert_raises(ValueError, association, [[[1, 2]], [[3, 4]]], "cramer")
+    # chi2_contingency exception
+    assert_raises(ValueError, association, [[-1, 10], [1, 2]], 'cramer')
+    # Invalid Array Item Data Type
+    assert_raises(ValueError, association,
+                  np.array([[1, 2], ["dd", 4]], dtype=object), 'cramer')
+
+
+@pytest.mark.parametrize('stat, expected',
+                         [('cramer', 0.09222412010290792),
+                          ('tschuprow', 0.0775509319944633),
+                          ('pearson', 0.12932925727138758)])
+def test_assoc(stat, expected):
+    # 2d Array
+    obs1 = np.array([[12, 13, 14, 15, 16],
+                     [17, 16, 18, 19, 11],
+                     [9, 15, 14, 12, 11]])
+    a = association(observed=obs1, method=stat)
+    assert_allclose(a, expected)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_continued_fraction.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_continued_fraction.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6911aa4956cc66a95eeb1e87aedb04782e2440b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_continued_fraction.py
@@ -0,0 +1,168 @@
+import math
+
+import pytest
+import numpy as np
+
+from scipy._lib._array_api import array_namespace
+from scipy._lib._array_api_no_0d import xp_assert_close, xp_assert_less, xp_assert_equal
+from scipy.stats._continued_fraction import _continued_fraction
+
+
+@pytest.mark.skip_xp_backends('array_api_strict', reason='No fancy indexing assignment')
+@pytest.mark.skip_xp_backends('jax.numpy', reason="Don't support mutation")
+# dask doesn't like lines like this
+# n = int(xp.real(xp_ravel(n))[0])
+# (at some point in here the shape becomes nan)
+@pytest.mark.skip_xp_backends('dask.array', reason="dask has issues with the shapes")
+@pytest.mark.uses_xp_capabilities(False, reason="private")
+class TestContinuedFraction:
+    rng = np.random.default_rng(5895448232066142650)
+    p = rng.uniform(1, 10, size=10)
+
+    def a1(self, n, x=1.5):
+        if n == 0:
+            y = 0*x
+        elif n == 1:
+            y = x
+        else:
+            y = -x**2
+        return y
+
+    def b1(self, n, x=1.5):
+        if n == 0:
+            y = 0*x
+        else:
+            one = x/x  # gets array of correct type, dtype, and shape
+            y = one * (2*n - 1)
+        return y
+
+    def log_a1(self, n, x):
+        xp = array_namespace(x)
+        if n == 0:
+            y = xp.full_like(x, -xp.asarray(math.inf, dtype=x.dtype))
+        elif n == 1:
+            y = xp.log(x)
+        else:
+            y = 2 * xp.log(x) + math.pi * 1j
+        return y
+
+    def log_b1(self, n, x):
+        xp = array_namespace(x)
+        if n == 0:
+            y = xp.full_like(x, -xp.asarray(math.inf, dtype=x.dtype))
+        else:
+            one = x - x  # gets array of correct type, dtype, and shape
+            y = one + math.log(2 * n - 1)
+        return y
+
+    def test_input_validation(self, xp):
+        a1 = self.a1
+        b1 = self.b1
+
+        message = '`a` and `b` must be callable.'
+        with pytest.raises(ValueError, match=message):
+            _continued_fraction(1, b1)
+        with pytest.raises(ValueError, match=message):
+            _continued_fraction(a1, 1)
+
+        message = r'`eps` and `tiny` must be \(or represent the logarithm of\)...'
+        with pytest.raises(ValueError, match=message):
+            _continued_fraction(a1, b1, tolerances={'eps': -10})
+        with pytest.raises(ValueError, match=message):
+            _continued_fraction(a1, b1, tolerances={'eps': np.nan})
+        with pytest.raises(ValueError, match=message):
+            _continued_fraction(a1, b1, tolerances={'eps': 1+1j}, log=True)
+        with pytest.raises(ValueError, match=message):
+            _continued_fraction(a1, b1, tolerances={'tiny': 0})
+        with pytest.raises(ValueError, match=message):
+            _continued_fraction(a1, b1, tolerances={'tiny': np.inf})
+        with pytest.raises(ValueError, match=message):
+            _continued_fraction(a1, b1, tolerances={'tiny': np.inf}, log=True)
+        # this should not raise
+        kwargs = dict(args=xp.asarray(1.5+0j), log=True, maxiter=0)
+        _continued_fraction(a1, b1, tolerances={'eps': -10}, **kwargs)
+        _continued_fraction(a1, b1, tolerances={'tiny': -10}, **kwargs)
+
+        message = '`maxiter` must be a non-negative integer.'
+        with pytest.raises(ValueError, match=message):
+            _continued_fraction(a1, b1, maxiter=-1)
+
+        message = '`log` must be boolean.'
+        with pytest.raises(ValueError, match=message):
+            _continued_fraction(a1, b1, log=2)
+
+    @pytest.mark.parametrize('dtype', ['float32', 'float64', 'complex64', 'complex128'])
+    @pytest.mark.parametrize('shape', [(), (1,), (3,), (3, 2)])
+    def test_basic(self, shape, dtype, xp):
+        np_dtype = getattr(np, dtype)
+        xp_dtype = getattr(xp, dtype)
+        rng = np.random.default_rng(2435908729190400)
+
+        x = rng.random(shape).astype(np_dtype)
+        x = x + rng.random(shape).astype(np_dtype)*1j if dtype.startswith('c') else x
+        x = xp.asarray(x, dtype=xp_dtype)
+
+        res = _continued_fraction(self.a1, self.b1, args=(x,))
+        ref = xp.tan(x)
+        xp_assert_close(res.f, ref)
+
+    @pytest.mark.skip_xp_backends('torch', reason='pytorch/pytorch#136063')
+    @pytest.mark.parametrize('dtype', ['float32', 'float64'])
+    @pytest.mark.parametrize('shape', [(), (1,), (3,), (3, 2)])
+    def test_log(self, shape, dtype, xp):
+        np_dtype = getattr(np, dtype)
+        rng = np.random.default_rng(2435908729190400)
+        x = rng.random(shape).astype(np_dtype)
+        x = xp.asarray(x)
+
+        res = _continued_fraction(self.log_a1, self.log_b1, args=(x + 0j,), log=True)
+        ref = xp.tan(x)
+        xp_assert_close(xp.exp(xp.real(res.f)), ref)
+
+    def test_maxiter(self, xp):
+        rng = np.random.default_rng(2435908729190400)
+        x = xp.asarray(rng.random(), dtype=xp.float64)
+        ref = xp.tan(x)
+
+        res1 = _continued_fraction(self.a1, self.b1, args=(x,), maxiter=3)
+        assert res1.nit == 3
+
+        res2 = _continued_fraction(self.a1, self.b1, args=(x,), maxiter=6)
+        assert res2.nit == 6
+
+        xp_assert_less(xp.abs(res2.f - ref), xp.abs(res1.f - ref))
+
+    def test_eps(self, xp):
+        x = xp.asarray(1.5, dtype=xp.float64)  # x = 1.5 is the default defined above
+        ref = xp.tan(x)
+        res1 = _continued_fraction(self.a1, self.b1, args=(x,),
+                                   tolerances={'eps': 1e-6})
+        res2 = _continued_fraction(self.a1, self.b1, args=(x,))
+        xp_assert_less(res1.nit, res2.nit)
+        xp_assert_less(xp.abs(res2.f - ref), xp.abs(res1.f - ref))
+
+    def test_feval(self, xp):
+        def a(n, x):
+            a.nfev += 1
+            return n * x
+
+        def b(n, x):
+            b.nfev += 1
+            return n * x
+
+        a.nfev, b.nfev = 0, 0
+
+        res = _continued_fraction(a, b, args=(xp.asarray(1.),))
+        assert res.nfev == a.nfev == b.nfev == res.nit + 1
+
+    def test_status(self, xp):
+        x = xp.asarray([1, 10, np.nan], dtype=xp.float64)
+        res = _continued_fraction(self.a1, self.b1, args=(x,), maxiter=15)
+        xp_assert_equal(res.success, xp.asarray([True, False, False]))
+        xp_assert_equal(res.status, xp.asarray([0, -2, -3], dtype=xp.int32))
+
+    def test_special_cases(self, xp):
+        one = xp.asarray(1)
+        res = _continued_fraction(lambda x: one, lambda x: one, maxiter=0)
+        xp_assert_close(res.f, xp.asarray(1.))
+        assert res.nit == res.nfev - 1 == 0
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_continuous.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_continuous.py
new file mode 100644
index 0000000000000000000000000000000000000000..898b7bcdf6b5ae4d72404fe98a3fd32ea8a90564
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_continuous.py
@@ -0,0 +1,2328 @@
+import itertools as it
+import os
+import pickle
+from copy import deepcopy
+
+import numpy as np
+from numpy import inf
+import pytest
+from numpy.testing import assert_allclose, assert_equal
+from hypothesis import strategies, given, reproduce_failure, settings  # noqa: F401
+import hypothesis.extra.numpy as npst
+
+from scipy import special
+from scipy import stats
+from scipy.stats._fit import _kolmogorov_smirnov
+from scipy.stats._ksstats import kolmogn
+from scipy.stats import qmc
+from scipy.stats._distr_params import distcont, distdiscrete
+from scipy.stats._distribution_infrastructure import (
+    _Domain, _RealInterval, _Parameter, _Parameterization, _RealParameter,
+    ContinuousDistribution, ShiftedScaledDistribution, _fiinfo,
+    _generate_domain_support, Mixture, _logexpxmexpy)
+from scipy.stats._new_distributions import StandardNormal, _LogUniform, _Gamma
+from scipy.stats._new_distributions import DiscreteDistribution
+from scipy.stats import Normal, Logistic, Uniform, Binomial
+
+
+class Test_RealInterval:
+    rng = np.random.default_rng(349849812549824)
+
+    def test_iv(self):
+        domain = _RealInterval(endpoints=('a', 'b'))
+        message = "The endpoints of the distribution are defined..."
+        with pytest.raises(TypeError, match=message):
+            domain.get_numerical_endpoints(dict)
+
+    @pytest.mark.parametrize('x', [rng.uniform(10, 10, size=(2, 3, 4)),
+                                   -np.inf, np.pi])
+    def test_contains_simple(self, x):
+        # Test `contains` when endpoints are defined by constants
+        a, b = -np.inf, np.pi
+        domain = _RealInterval(endpoints=(a, b), inclusive=(False, True))
+        assert_equal(domain.contains(x), (a < x) & (x <= b))
+
+    @pytest.mark.slow
+    @given(shapes=npst.mutually_broadcastable_shapes(num_shapes=3, min_side=0),
+           inclusive_a=strategies.booleans(),
+           inclusive_b=strategies.booleans(),
+           data=strategies.data())
+    def test_contains(self, shapes, inclusive_a, inclusive_b, data):
+        # Test `contains` when endpoints are defined by parameters
+        input_shapes, result_shape = shapes
+        shape_a, shape_b, shape_x = input_shapes
+
+        # Without defining min and max values, I spent forever trying to set
+        # up a valid test without overflows or similar just drawing arrays.
+        a_elements = dict(allow_nan=False, allow_infinity=False,
+                          min_value=-1e3, max_value=1)
+        b_elements = dict(allow_nan=False, allow_infinity=False,
+                          min_value=2, max_value=1e3)
+        a = data.draw(npst.arrays(npst.floating_dtypes(),
+                                  shape_a, elements=a_elements))
+        b = data.draw(npst.arrays(npst.floating_dtypes(),
+                                  shape_b, elements=b_elements))
+        # ensure some points are to the left, some to the right, and some
+        # are exactly on the boundary
+        d = b - a
+        x = np.concatenate([np.linspace(a-d, a, 10),
+                            np.linspace(a, b, 10),
+                            np.linspace(b, b+d, 10)])
+        # Domain is defined by two parameters, 'a' and 'b'
+        domain = _RealInterval(endpoints=('a', 'b'),
+                             inclusive=(inclusive_a, inclusive_b))
+        domain.define_parameters(_RealParameter('a', domain=_RealInterval()),
+                                 _RealParameter('b', domain=_RealInterval()))
+        # Check that domain and string evaluation give the same result
+        res = domain.contains(x, dict(a=a, b=b))
+
+        # Apparently, `np.float16([2]) < np.float32(2.0009766)` is False
+        # but `np.float16([2]) < np.float32([2.0009766])` is True
+        # dtype = np.result_type(a.dtype, b.dtype, x.dtype)
+        # a, b, x = a.astype(dtype), b.astype(dtype), x.astype(dtype)
+        # unclear whether we should be careful about this, since it will be
+        # fixed with NEP50. Just do what makes the test pass.
+        left_comparison = '<=' if inclusive_a else '<'
+        right_comparison = '<=' if inclusive_b else '<'
+        ref = eval(f'(a {left_comparison} x) & (x {right_comparison} b)')
+        assert_equal(res, ref)
+
+    @pytest.mark.parametrize("inclusive", list(it.product([True, False], repeat=2)))
+    @pytest.mark.parametrize("a,b", [(0, 1), (3, 1)])
+    def test_contains_function_endpoints(self, inclusive, a, b):
+        # Test `contains` when endpoints are defined by functions.
+        endpoints = (lambda a, b: (a - b) / 2, lambda a, b: (a + b) / 2)
+        domain = _RealInterval(endpoints=endpoints, inclusive=inclusive)
+        x = np.asarray([(a - 2*b)/2, (a - b)/2, a/2, (a + b)/2, (a + 2*b)/2])
+        res = domain.contains(x, dict(a=a, b=b))
+
+        numerical_endpoints = ((a - b) / 2, (a + b) / 2)
+        assert numerical_endpoints == domain.get_numerical_endpoints(dict(a=a, b=b))
+        alpha, beta = numerical_endpoints
+
+        above_left = alpha <= x if inclusive[0] else alpha < x
+        below_right = x <= beta if inclusive[1] else x < beta
+        ref = above_left & below_right
+        assert_equal(res, ref)
+
+
+    @pytest.mark.parametrize('case', [
+        (-np.inf, np.pi, False, True, r"(-\infty, \pi]"),
+        ('a', 5, True, False, "[a, 5)")
+    ])
+    def test_str(self, case):
+        domain = _RealInterval(endpoints=case[:2], inclusive=case[2:4])
+        assert str(domain) == case[4]
+
+    @pytest.mark.slow
+    @given(a=strategies.one_of(
+        strategies.decimals(allow_nan=False),
+        strategies.characters(whitelist_categories="L"),  # type: ignore[arg-type]
+        strategies.sampled_from(list(_Domain.symbols))),
+           b=strategies.one_of(
+        strategies.decimals(allow_nan=False),
+        strategies.characters(whitelist_categories="L"),  # type: ignore[arg-type]
+        strategies.sampled_from(list(_Domain.symbols))),
+           inclusive_a=strategies.booleans(),
+           inclusive_b=strategies.booleans(),
+           )
+    def test_str2(self, a, b, inclusive_a, inclusive_b):
+        # I wrote this independently from the implementation of __str__, but
+        # I imagine it looks pretty similar to __str__.
+        a = _Domain.symbols.get(a, a)
+        b = _Domain.symbols.get(b, b)
+        left_bracket = '[' if inclusive_a else '('
+        right_bracket = ']' if inclusive_b else ')'
+        domain = _RealInterval(endpoints=(a, b),
+                             inclusive=(inclusive_a, inclusive_b))
+        ref = f"{left_bracket}{a}, {b}{right_bracket}"
+        assert str(domain) == ref
+
+    def test_symbols_gh22137(self):
+        # `symbols` was accidentally shared between instances originally
+        # Check that this is no longer the case
+        domain1 = _RealInterval(endpoints=(0, 1))
+        domain2 = _RealInterval(endpoints=(0, 1))
+        assert domain1.symbols is not domain2.symbols
+
+
+def draw_distribution_from_family(family, data, rng, proportions, min_side=0):
+    # If the distribution has parameters, choose a parameterization and
+    # draw broadcastable shapes for the parameter arrays.
+    n_parameterizations = family._num_parameterizations()
+    if n_parameterizations > 0:
+        i = data.draw(strategies.integers(0, max_value=n_parameterizations-1))
+        n_parameters = family._num_parameters(i)
+        shapes, result_shape = data.draw(
+            npst.mutually_broadcastable_shapes(num_shapes=n_parameters,
+                                               min_side=min_side))
+        dist = family._draw(shapes, rng=rng, proportions=proportions,
+                            i_parameterization=i)
+    else:
+        dist = family._draw(rng=rng)
+        result_shape = tuple()
+
+    # Draw a broadcastable shape for the arguments, and draw values for the
+    # arguments.
+    x_shape = data.draw(npst.broadcastable_shapes(result_shape,
+                                                  min_side=min_side))
+    x = dist._variable.draw(x_shape, parameter_values=dist._parameters,
+                            proportions=proportions, rng=rng, region='typical')
+    x_result_shape = np.broadcast_shapes(x_shape, result_shape)
+    y_shape = data.draw(npst.broadcastable_shapes(x_result_shape,
+                                                  min_side=min_side))
+    y = dist._variable.draw(y_shape, parameter_values=dist._parameters,
+                            proportions=proportions, rng=rng, region='typical')
+    xy_result_shape = np.broadcast_shapes(y_shape, x_result_shape)
+    p_domain = _RealInterval((0, 1), (True, True))
+    p_var = _RealParameter('p', domain=p_domain)
+    p = p_var.draw(x_shape, proportions=proportions, rng=rng)
+    with np.errstate(divide='ignore', invalid='ignore'):
+        logp = np.log(p)
+
+    return dist, x, y, p, logp, result_shape, x_result_shape, xy_result_shape
+
+
+continuous_families = [
+    StandardNormal,
+    Normal,
+    Logistic,
+    Uniform,
+    _LogUniform
+]
+
+discrete_families = [
+    Binomial,
+]
+
+families = continuous_families + discrete_families
+
+
+class TestDistributions:
+    @pytest.mark.fail_slow(60)  # need to break up check_moment_funcs
+    @settings(max_examples=20)
+    @pytest.mark.parametrize('family', families)
+    @given(data=strategies.data(), seed=strategies.integers(min_value=0))
+    def test_support_moments_sample(self, family, data, seed):
+        rng = np.random.default_rng(seed)
+
+        # relative proportions of valid, endpoint, out of bounds, and NaN params
+        proportions = (0.7, 0.1, 0.1, 0.1)
+        tmp = draw_distribution_from_family(family, data, rng, proportions)
+        dist, x, y, p, logp, result_shape, x_result_shape, xy_result_shape = tmp
+        sample_shape = data.draw(npst.array_shapes(min_dims=0, min_side=0,
+                                                   max_side=20))
+
+        with np.errstate(invalid='ignore', divide='ignore'):
+            check_support(dist)
+            check_moment_funcs(dist, result_shape)  # this needs to get split up
+            check_lmoment_funcs(dist, result_shape)
+            check_sample_shape_NaNs(dist, 'sample', sample_shape, result_shape, rng)
+            qrng = qmc.Halton(d=1, seed=rng)
+            check_sample_shape_NaNs(dist, 'sample', sample_shape, result_shape, qrng)
+
+    @pytest.mark.fail_slow(10)
+    @pytest.mark.parametrize('family', families)
+    @pytest.mark.parametrize('func, methods, arg',
+                             [('entropy', {'log/exp', 'quadrature'}, None),
+                              ('logentropy', {'log/exp', 'quadrature'}, None),
+                              ('median', {'icdf'}, None),
+                              ('mode', {'optimization'}, None),
+                              ('mean', {'cache'}, None),
+                              ('variance', {'cache'}, None),
+                              ('skewness', {'cache'}, None),
+                              ('kurtosis', {'cache'}, None),
+                              ('pdf', {'log/exp'}, 'x'),
+                              ('logpdf', {'log/exp'}, 'x'),
+                              ('logcdf', {'log/exp', 'complement', 'quadrature'}, 'x'),
+                              ('cdf', {'log/exp', 'complement', 'quadrature'}, 'x'),
+                              ('logccdf', {'log/exp', 'complement', 'quadrature'}, 'x'),
+                              ('ccdf', {'log/exp', 'complement', 'quadrature'}, 'x'),
+                              ('ilogccdf', {'complement', 'inversion'}, 'logp'),
+                              ('iccdf', {'complement', 'inversion'}, 'p'),
+                              ])
+    @settings(max_examples=20)
+    @given(data=strategies.data(), seed=strategies.integers(min_value=0))
+    def test_funcs(self, family, data, seed, func, methods, arg):
+        if family == Uniform and func == 'mode':
+            pytest.skip("Mode is not unique; `method`s disagree.")
+
+        rng = np.random.default_rng(seed)
+
+        # relative proportions of valid, endpoint, out of bounds, and NaN params
+        proportions = (0.7, 0.1, 0.1, 0.1)
+        tmp = draw_distribution_from_family(family, data, rng, proportions)
+        dist, x, y, p, logp, result_shape, x_result_shape, xy_result_shape = tmp
+
+        args = {'x': x, 'p': p, 'logp': p}
+        with np.errstate(invalid='ignore', divide='ignore', over='ignore'):
+            if arg is None:
+                check_dist_func(dist, func, None, result_shape, methods)
+            elif arg in args:
+                check_dist_func(dist, func, args[arg], x_result_shape, methods)
+
+        if func == 'variance':
+            assert_allclose(dist.standard_deviation()**2, dist.variance())
+
+        # invalid and divide are to be expected; maybe look into over
+        with np.errstate(invalid='ignore', divide='ignore', over='ignore'):
+            if not isinstance(dist, ShiftedScaledDistribution):
+                if func == 'cdf':
+                    methods = {'quadrature'}
+                    check_cdf2(dist, False, x, y, xy_result_shape, methods)
+                    check_cdf2(dist, True, x, y, xy_result_shape, methods)
+                elif func == 'ccdf':
+                    methods = {'addition'}
+                    check_ccdf2(dist, False, x, y, xy_result_shape, methods)
+                    check_ccdf2(dist, True, x, y, xy_result_shape, methods)
+
+    def test_plot(self):
+        try:
+            import matplotlib.pyplot as plt
+        except ImportError:
+            return
+
+        X = Uniform(a=0., b=1.)
+        ax = X.plot()
+        assert ax == plt.gca()
+
+    @pytest.mark.parametrize('method_name', ['cdf', 'ccdf'])
+    def test_complement_safe(self, method_name):
+        X = stats.Normal(mu=1, sigma=2)
+        X.tol = 1e-12
+        p = np.asarray([1e-4, 1e-3])
+        func = getattr(X, method_name)
+        ifunc = getattr(X, 'i'+method_name)
+        x = ifunc(p, method='formula')
+        p1 = func(x, method='complement_safe')
+        p2 = func(x, method='complement')
+        assert_equal(p1[1], p2[1])
+        assert p1[0] != p2[0]
+        assert_allclose(p1[0], p[0], rtol=X.tol)
+
+    @pytest.mark.parametrize('method_name', ['cdf', 'ccdf'])
+    def test_icomplement_safe(self, method_name):
+        X = stats.Normal(mu=1, sigma=2)
+        X.tol = 1e-12
+        p = np.asarray([1e-4, 1e-3])
+        func = getattr(X, method_name)
+        ifunc = getattr(X, 'i'+method_name)
+        x1 = ifunc(p, method='complement_safe')
+        x2 = ifunc(p, method='complement')
+        assert_equal(x1[1], x2[1])
+        assert x1[0] != x2[0]
+        assert_allclose(func(x1[0]), p[0], rtol=X.tol)
+
+    def test_subtraction_safe(self):
+        X = stats.Normal()
+        X.tol = 1e-12
+
+        # Regular subtraction is fine in either tail (and of course, across tails)
+        x = [-11, -10, 10, 11]
+        y = [-10, -11, 11, 10]
+        p0 = X.cdf(x, y, method='quadrature')
+        p1 = X.cdf(x, y, method='subtraction_safe')
+        p2 = X.cdf(x, y, method='subtraction')
+        assert_equal(p2, p1)
+        assert_allclose(p1, p0, rtol=X.tol)
+
+        # Safe subtraction is needed in special cases
+        x = np.asarray([-1e-20, -1e-21, 1e-20, 1e-21, -1e-20])
+        y = np.asarray([-1e-21, -1e-20, 1e-21, 1e-20, 1e-20])
+
+
+        p0 = X.pdf(0)*(y-x)
+        p1 = X.cdf(x, y, method='subtraction_safe')
+        p2 = X.cdf(x, y, method='subtraction')
+        assert_equal(p2, 0)
+        assert_allclose(p1, p0, rtol=X.tol)
+
+    def test_logentropy_safe(self):
+        # simulate an `entropy` calculation over/underflowing with extreme parameters
+        class _Normal(stats.Normal):
+            def _entropy_formula(self, **params):
+                out = np.asarray(super()._entropy_formula(**params))
+                out[0] = 0
+                out[-1] = np.inf
+                return out
+
+        X = _Normal(sigma=[1, 2, 3])
+        with np.errstate(divide='ignore'):
+            res1 = X.logentropy(method='logexp_safe')
+            res2 = X.logentropy(method='logexp')
+        ref = X.logentropy(method='quadrature')
+        i_fl = [0, -1]  # first and last
+        assert np.isinf(res2[i_fl]).all()
+        assert res1[1] == res2[1]
+        # quadrature happens to be perfectly accurate on some platforms
+        # assert res1[1] != ref[1]
+        assert_equal(res1[i_fl], ref[i_fl])
+
+    def test_logcdf2_safe(self):
+        # test what happens when 2-arg `cdf` underflows
+        X = stats.Normal(sigma=[1, 2, 3])
+        x = [-301, 1, 300]
+        y = [-300, 2, 301]
+        with np.errstate(divide='ignore'):
+            res1 = X.logcdf(x, y, method='logexp_safe')
+            res2 = X.logcdf(x, y, method='logexp')
+        ref = X.logcdf(x, y, method='quadrature')
+        i_fl = [0, -1]  # first and last
+        assert np.isinf(res2[i_fl]).all()
+        assert res1[1] == res2[1]
+        # quadrature happens to be perfectly accurate on some platforms
+        # assert res1[1] != ref[1]
+        assert_equal(res1[i_fl], ref[i_fl])
+
+    @pytest.mark.parametrize('method_name', ['logcdf', 'logccdf'])
+    def test_logexp_safe(self, method_name):
+        # test what happens when `cdf`/`ccdf` underflows
+        X = stats.Normal(sigma=2)
+        x = [-301, 1] if method_name == 'logcdf' else [301, 1]
+        func = getattr(X, method_name)
+        with np.errstate(divide='ignore'):
+            res1 = func(x, method='logexp_safe')
+            res2 = func(x, method='logexp')
+        ref = func(x, method='quadrature')
+        assert res1[0] == ref[0]
+        assert res1[0] != res2[0]
+        assert res1[1] == res2[1]
+        assert res1[1] != ref[1]
+
+def check_sample_shape_NaNs(dist, fname, sample_shape, result_shape, rng):
+    full_shape = sample_shape + result_shape
+    if fname == 'sample':
+        sample_method = dist.sample
+
+    methods = {'inverse_transform'}
+    if dist._overrides(f'_{fname}_formula') and not isinstance(rng, qmc.QMCEngine):
+        methods.add('formula')
+
+    for method in methods:
+        res = sample_method(sample_shape, method=method, rng=rng)
+        valid_parameters = np.broadcast_to(get_valid_parameters(dist),
+                                           res.shape)
+        assert_equal(res.shape, full_shape)
+        np.testing.assert_equal(res.dtype, dist._dtype)
+
+        if full_shape == ():
+            # NumPy random makes a distinction between a 0d array and a scalar.
+            # In stats, we consistently turn 0d arrays into scalars, so
+            # maintain that behavior here. (With Array API arrays, this will
+            # change.)
+            assert np.isscalar(res)
+        assert np.all(np.isfinite(res[valid_parameters]))
+        assert_equal(res[~valid_parameters], np.nan)
+
+        sample1 = sample_method(sample_shape, method=method, rng=42)
+        sample2 = sample_method(sample_shape, method=method, rng=42)
+        if not isinstance(dist, DiscreteDistribution):
+            # The idea is that it's very unlikely that the random sample
+            # for a randomly chosen seed will match that for seed 42,
+            # but it is not so unlikely if `dist` is a discrete distribution.
+            assert not np.any(np.equal(res, sample1))
+        assert_equal(sample1, sample2)
+
+
+def check_support(dist):
+    a, b = dist.support()
+    check_nans_and_edges(dist, 'support', None, a)
+    check_nans_and_edges(dist, 'support', None, b)
+    assert a.shape == dist._shape
+    assert b.shape == dist._shape
+    assert a.dtype == dist._dtype
+    assert b.dtype == dist._dtype
+
+
+def check_dist_func(dist, fname, arg, result_shape, methods):
+    # Check that all computation methods of all distribution functions agree
+    # with one another, effectively testing the correctness of the generic
+    # computation methods and confirming the consistency of specific
+    # distributions with their pdf/logpdf.
+
+    args = tuple() if arg is None else (arg,)
+    methods = methods.copy()
+
+    if "cache" in methods:
+        # If "cache" is specified before the value has been evaluated, it
+        # raises an error. After the value is evaluated, it will succeed.
+        with pytest.raises(NotImplementedError):
+            getattr(dist, fname)(*args, method="cache")
+
+    ref = getattr(dist, fname)(*args)
+    check_nans_and_edges(dist, fname, arg, ref)
+
+    # Remove this after fixing `draw`
+    tol_override = {'atol': 1e-15}
+    # Mean can be 0, which makes logmean -inf.
+    if fname in {'logmean', 'mean', 'logskewness', 'skewness'}:
+        tol_override = {'atol': 1e-15}
+    elif fname in {'mode'}:
+        # can only expect about half of machine precision for optimization
+        # because math
+        tol_override = {'atol': 1e-6}
+    elif fname in {'logcdf'}:  # gh-22276
+        tol_override = {'rtol': 2e-7}
+
+    if dist._overrides(f'_{fname}_formula'):
+        methods.add('formula')
+
+    np.testing.assert_equal(ref.shape, result_shape)
+    # Until we convert to array API, let's do the familiar thing:
+    # 0d things are scalars, not arrays
+    if result_shape == tuple():
+        assert np.isscalar(ref)
+
+    for method in methods:
+        res = getattr(dist, fname)(*args, method=method)
+        if 'log' in fname:
+            np.testing.assert_allclose(np.exp(res), np.exp(ref),
+                                       **tol_override)
+        else:
+            np.testing.assert_allclose(res, ref, **tol_override)
+
+        # for now, make sure dtypes are consistent; later, we can check whether
+        # they are correct.
+        np.testing.assert_equal(res.dtype, ref.dtype)
+        np.testing.assert_equal(res.shape, result_shape)
+        if result_shape == tuple():
+            assert np.isscalar(res)
+
+def check_cdf2(dist, log, x, y, result_shape, methods):
+    # Specialized test for 2-arg cdf since the interface is a bit different
+    # from the other methods. Here, we'll use 1-arg cdf as a reference, and
+    # since we have already checked 1-arg cdf in `check_nans_and_edges`, this
+    # checks the equivalent of both `check_dist_func` and
+    # `check_nans_and_edges`.
+    methods = methods.copy()
+
+    if log:
+        if dist._overrides('_logcdf2_formula'):
+            methods.add('formula')
+        if dist._overrides('_logcdf_formula') or dist._overrides('_logccdf_formula'):
+            methods.add('subtraction')
+        if (dist._overrides('_cdf_formula')
+                or dist._overrides('_ccdf_formula')):
+            methods.add('log/exp')
+    else:
+        if dist._overrides('_cdf2_formula'):
+            methods.add('formula')
+        if dist._overrides('_cdf_formula') or dist._overrides('_ccdf_formula'):
+            methods.add('subtraction')
+        if (dist._overrides('_logcdf_formula')
+                or dist._overrides('_logccdf_formula')):
+            methods.add('log/exp')
+
+    ref = dist.cdf(y) - dist.cdf(x)
+    np.testing.assert_equal(ref.shape, result_shape)
+
+    if result_shape == tuple():
+        assert np.isscalar(ref)
+
+    for method in methods:
+        if isinstance(dist, DiscreteDistribution):
+            message = ("Two argument cdf functions are currently only supported for "
+                       "continuous distributions.")
+            with pytest.raises(NotImplementedError, match=message):
+                res = (np.exp(dist.logcdf(x, y, method=method)) if log
+                       else dist.cdf(x, y, method=method))
+            continue
+        res = (np.exp(dist.logcdf(x, y, method=method)) if log
+               else dist.cdf(x, y, method=method))
+        np.testing.assert_allclose(res, ref, atol=1e-14)
+        if log:
+            np.testing.assert_equal(res.dtype, (ref + 0j).dtype)
+        else:
+            np.testing.assert_equal(res.dtype, ref.dtype)
+        np.testing.assert_equal(res.shape, result_shape)
+        if result_shape == tuple():
+            assert np.isscalar(res)
+
+
+def check_ccdf2(dist, log, x, y, result_shape, methods):
+    # Specialized test for 2-arg ccdf since the interface is a bit different
+    # from the other methods. Could be combined with check_cdf2 above, but
+    # writing it separately is simpler.
+    methods = methods.copy()
+
+    if dist._overrides(f'_{"log" if log else ""}ccdf2_formula'):
+        methods.add('formula')
+
+    ref = dist.cdf(x) + dist.ccdf(y)
+    np.testing.assert_equal(ref.shape, result_shape)
+
+    if result_shape == tuple():
+        assert np.isscalar(ref)
+
+    for method in methods:
+        message = ("Two argument cdf functions are currently only supported for "
+                   "continuous distributions.")
+        if isinstance(dist, DiscreteDistribution):
+            with pytest.raises(NotImplementedError, match=message):
+                res = (np.exp(dist.logccdf(x, y, method=method)) if log
+                       else dist.ccdf(x, y, method=method))
+            continue
+        res = (np.exp(dist.logccdf(x, y, method=method)) if log
+               else dist.ccdf(x, y, method=method))
+        np.testing.assert_allclose(res, ref, atol=1e-14)
+        np.testing.assert_equal(res.dtype, ref.dtype)
+        np.testing.assert_equal(res.shape, result_shape)
+        if result_shape == tuple():
+            assert np.isscalar(res)
+
+
+def check_nans_and_edges(dist, fname, arg, res):
+
+    valid_parameters = get_valid_parameters(dist)
+    if fname in {'icdf', 'iccdf'}:
+        arg_domain = _RealInterval(endpoints=(0, 1), inclusive=(True, True))
+    elif fname in {'ilogcdf', 'ilogccdf'}:
+        arg_domain = _RealInterval(endpoints=(-inf, 0), inclusive=(True, True))
+    else:
+        arg_domain = dist._variable.domain
+
+    classified_args = classify_arg(dist, arg, arg_domain)
+    valid_parameters, *classified_args = np.broadcast_arrays(valid_parameters,
+                                                             *classified_args)
+    valid_arg, endpoint_arg, outside_arg, nan_arg = classified_args
+    all_valid = valid_arg & valid_parameters
+
+    # Check NaN pattern and edge cases
+    assert_equal(res[~valid_parameters], np.nan)
+    assert_equal(res[nan_arg], np.nan)
+
+    a, b = dist.support()
+    a = np.broadcast_to(a, res.shape)
+    b = np.broadcast_to(b, res.shape)
+
+    outside_arg_minus = (outside_arg == -1) & valid_parameters
+    outside_arg_plus = (outside_arg == 1) & valid_parameters
+    endpoint_arg_minus = (endpoint_arg == -1) & valid_parameters
+    endpoint_arg_plus = (endpoint_arg == 1) & valid_parameters
+
+    is_discrete = isinstance(dist, DiscreteDistribution)
+    # Writing this independently of how the are set in the distribution
+    # infrastructure. That is very compact; this is very verbose.
+    if fname in {'logpdf'}:
+        assert_equal(res[outside_arg_minus], -np.inf)
+        assert_equal(res[outside_arg_plus], -np.inf)
+        ref = -np.inf if not is_discrete else np.inf
+        assert_equal(res[endpoint_arg_minus & ~valid_arg], ref)
+        assert_equal(res[endpoint_arg_plus & ~valid_arg], ref)
+    elif fname in {'pdf'}:
+        assert_equal(res[outside_arg_minus], 0)
+        assert_equal(res[outside_arg_plus], 0)
+        ref = 0 if not is_discrete else np.inf
+        assert_equal(res[endpoint_arg_minus & ~valid_arg], ref)
+        assert_equal(res[endpoint_arg_plus & ~valid_arg], ref)
+    elif fname in {'logcdf'} and not is_discrete:
+        assert_equal(res[outside_arg_minus], -inf)
+        assert_equal(res[outside_arg_plus], 0)
+        assert_equal(res[endpoint_arg_minus], -inf)
+        assert_equal(res[endpoint_arg_plus], 0)
+    elif fname in {'cdf'} and not is_discrete:
+        assert_equal(res[outside_arg_minus], 0)
+        assert_equal(res[outside_arg_plus], 1)
+        assert_equal(res[endpoint_arg_minus], 0)
+        assert_equal(res[endpoint_arg_plus], 1)
+    elif fname in {'logccdf'} and not is_discrete:
+        assert_equal(res[outside_arg_minus], 0)
+        assert_equal(res[outside_arg_plus], -inf)
+        assert_equal(res[endpoint_arg_minus], 0)
+        assert_equal(res[endpoint_arg_plus], -inf)
+    elif fname in {'ccdf'} and not is_discrete:
+        assert_equal(res[outside_arg_minus], 1)
+        assert_equal(res[outside_arg_plus], 0)
+        assert_equal(res[endpoint_arg_minus], 1)
+        assert_equal(res[endpoint_arg_plus], 0)
+    elif fname in {'ilogcdf', 'icdf'} and not is_discrete:
+        assert_equal(res[outside_arg == -1], np.nan)
+        assert_equal(res[outside_arg == 1], np.nan)
+        assert_equal(res[endpoint_arg == -1], a[endpoint_arg == -1])
+        assert_equal(res[endpoint_arg == 1], b[endpoint_arg == 1])
+    elif fname in {'ilogccdf', 'iccdf'} and not is_discrete:
+        assert_equal(res[outside_arg == -1], np.nan)
+        assert_equal(res[outside_arg == 1], np.nan)
+        assert_equal(res[endpoint_arg == -1], b[endpoint_arg == -1])
+        assert_equal(res[endpoint_arg == 1], a[endpoint_arg == 1])
+
+    exclude = {'logmean', 'mean', 'logskewness', 'skewness', 'support'}
+    if isinstance(dist, DiscreteDistribution):
+        exclude.update({'pdf', 'logpdf'})
+
+    if fname not in exclude:
+        assert np.isfinite(res[all_valid & (endpoint_arg == 0)]).all()
+
+
+def check_moment_funcs(dist, result_shape):
+    # Check that all computation methods of all distribution functions agree
+    # with one another, effectively testing the correctness of the generic
+    # computation methods and confirming the consistency of specific
+    # distributions with their pdf/logpdf.
+
+    atol = 1e-9  # make this tighter (e.g. 1e-13) after fixing `draw`
+
+    def check(order, kind, method=None, ref=None, success=True):
+        if success:
+            res = dist.moment(order, kind, method=method)
+            assert_allclose(res, ref, atol=atol*10**order)
+            assert res.shape == ref.shape
+        else:
+            with pytest.raises(NotImplementedError):
+                dist.moment(order, kind, method=method)
+
+    def has_formula(order, kind):
+        formula_name = f'_moment_{kind}_formula'
+        overrides = dist._overrides(formula_name)
+        if not overrides:
+            return False
+        formula = getattr(dist, formula_name)
+        orders = getattr(formula, 'orders', set(range(6)))
+        return order in orders
+
+    dist.reset_cache()
+
+    ### Check Raw Moments ###
+    for i in range(6):
+        check(i, 'raw', 'cache', success=False)  # not cached yet
+        ref = dist.moment(i, 'raw', method='quadrature')
+        check_nans_and_edges(dist, 'moment', None, ref)
+        assert ref.shape == result_shape
+        check(i, 'raw','cache', ref, success=True)  # cached now
+        check(i, 'raw', 'formula', ref, success=has_formula(i, 'raw'))
+        check(i, 'raw', 'general', ref, success=(i == 0))
+        if dist.__class__ == stats.Normal:
+            check(i, 'raw', 'quadrature_icdf', ref, success=True)
+
+
+    # Clearing caches to better check their behavior
+    dist.reset_cache()
+
+    # If we have central or standard moment formulas, or if there are
+    # values in their cache, we can use method='transform'
+    dist.moment(0, 'central')  # build up the cache
+    dist.moment(1, 'central')
+    for i in range(2, 6):
+        ref = dist.moment(i, 'raw', method='quadrature')
+        check(i, 'raw', 'transform', ref,
+              success=has_formula(i, 'central') or has_formula(i, 'standardized'))
+        dist.moment(i, 'central')  # build up the cache
+        check(i, 'raw', 'transform', ref)
+
+    dist.reset_cache()
+
+    ### Check Central Moments ###
+
+    for i in range(6):
+        check(i, 'central', 'cache', success=False)
+        ref = dist.moment(i, 'central', method='quadrature')
+        assert ref.shape == result_shape
+        check(i, 'central', 'cache', ref, success=True)
+        check(i, 'central', 'formula', ref, success=has_formula(i, 'central'))
+        check(i, 'central', 'general', ref, success=i <= 1)
+        if dist.__class__ == stats.Normal:
+            check(i, 'central', 'quadrature_icdf', ref, success=True)
+        if not (dist.__class__ == stats.Uniform and i == 5):
+            # Quadrature is not super accurate for 5th central moment when the
+            # support is really big. Skip this one failing test. We need to come
+            # up with a better system of skipping individual failures w/ hypothesis.
+            check(i, 'central', 'transform', ref,
+                  success=has_formula(i, 'raw') or (i <= 1))
+        if not has_formula(i, 'raw'):
+            dist.moment(i, 'raw')
+            check(i, 'central', 'transform', ref)
+
+    variance = dist.variance()
+    dist.reset_cache()
+
+    # If we have standard moment formulas, or if there are
+    # values in their cache, we can use method='normalize'
+    dist.moment(0, 'standardized')  # build up the cache
+    dist.moment(1, 'standardized')
+    dist.moment(2, 'standardized')
+    for i in range(3, 6):
+        ref = dist.moment(i, 'central', method='quadrature')
+        check(i, 'central', 'normalize', ref,
+              success=has_formula(i, 'standardized') and not np.any(variance == 0))
+        dist.moment(i, 'standardized')  # build up the cache
+        check(i, 'central', 'normalize', ref, success=not np.any(variance == 0))
+
+    ### Check Standardized Moments ###
+
+    var = dist.moment(2, 'central', method='quadrature')
+    dist.reset_cache()
+
+    for i in range(6):
+        check(i, 'standardized', 'cache', success=False)
+        ref = dist.moment(i, 'central', method='quadrature') / var ** (i / 2)
+        assert ref.shape == result_shape
+        check(i, 'standardized', 'formula', ref,
+              success=has_formula(i, 'standardized'))
+        check(i, 'standardized', 'general', ref, success=i <= 2)
+        check(i, 'standardized', 'normalize', ref)
+
+    dist.reset_cache()
+
+
+def check_lmoment_funcs(dist, result_shape):
+    # Perform consistency check for L-moments similar to check_moment_funcs above
+
+    if not isinstance(dist, ContinuousDistribution):
+        message = "L-moments are currently available only for continuous..."
+        with pytest.raises(NotImplementedError, match=message):
+            dist.lmoment(1)
+        return
+
+    atol = 2e-9
+
+    def check(order, standardize=False, method=None, ref=None, success=True):
+        if success:
+            res = dist.lmoment(order, standardize=standardize, method=method)
+            assert_allclose(res, ref, atol=atol)
+            assert res.shape == ref.shape
+        else:
+            with pytest.raises(NotImplementedError):
+                dist.lmoment(order, standardize=standardize, method=method)
+
+    ### Check L-Moments ###
+
+    standardize = False
+    for i in range(1, 6):
+        check(i, standardize, 'cache', success=standardize)  # not cached yet
+        ref = dist.lmoment(i, standardize=standardize, method='order_statistics')
+        check_nans_and_edges(dist, 'lmoment', None, ref)
+        assert ref.shape == result_shape
+        check(i, standardize, 'cache', ref, success=True)  # cached now
+        check(i, standardize, 'formula', ref,
+              success=dist._overrides('_lmoment_formula')
+                      and (i < 5 or dist.__class__.__name__ == "Uniform"))
+        check(i, standardize, 'general', ref, success=(i == 1))
+        if dist._overrides('_icdf_formula'):
+            check(i, standardize, 'quadrature_icdf', ref, success=True)
+
+    standardize=True
+    for i in range(3, 6):
+        ref = dist.lmoment(i, standardize=standardize, method='order_statistics')
+        assert ref.shape == result_shape
+        check(i, standardize, 'formula', ref,
+              success=dist._overrides('_lmoment_formula')
+                      and (i < 5 or dist.__class__.__name__ == "Uniform"))
+        check(i, standardize, 'general', ref, success=False)
+        if dist._overrides('_icdf_formula'):
+            check(i, standardize, 'quadrature_icdf', ref, success=True)
+
+
+@pytest.mark.parametrize('family', (Normal,))
+@pytest.mark.parametrize('x_shape', [tuple(), (2, 3)])
+@pytest.mark.parametrize('dist_shape', [tuple(), (4, 1)])
+@pytest.mark.parametrize('fname', ['sample'])
+@pytest.mark.parametrize('rng_type', [np.random.Generator, qmc.Halton, qmc.Sobol])
+def test_sample_against_cdf(family, dist_shape, x_shape, fname, rng_type):
+    rng = np.random.default_rng(842582438235635)
+    num_parameters = family._num_parameters()
+
+    if dist_shape and num_parameters == 0:
+        pytest.skip("Distribution can't have a shape without parameters.")
+
+    dist = family._draw(dist_shape, rng)
+
+    n = 1024
+    sample_size = (n,) + x_shape
+    sample_array_shape = sample_size + dist_shape
+
+    if fname == 'sample':
+        sample_method = dist.sample
+
+    if rng_type != np.random.Generator:
+        rng = rng_type(d=1, seed=rng)
+    x = sample_method(sample_size, rng=rng)
+    assert x.shape == sample_array_shape
+
+    # probably should give `axis` argument to ks_1samp, review that separately
+    statistic = _kolmogorov_smirnov(dist, x, axis=0)
+    pvalue = kolmogn(x.shape[0], statistic, cdf=False)
+    p_threshold = 0.01
+    num_pvalues = pvalue.size
+    num_small_pvalues = np.sum(pvalue < p_threshold)
+    assert num_small_pvalues < p_threshold * num_pvalues
+
+
+def get_valid_parameters(dist):
+    # Given a distribution, return a logical array that is true where all
+    # distribution parameters are within their respective domains. The code
+    # here is probably quite similar to that used to form the `_invalid`
+    # attribute of the distribution, but this was written about a week later
+    # without referring to that code, so it is a somewhat independent check.
+
+    # Get all parameter values and `_Parameter` objects
+    parameter_values = dist._parameters
+    parameters = {}
+    for parameterization in dist._parameterizations:
+        parameters.update(parameterization.parameters)
+
+    all_valid = np.ones(dist._shape, dtype=bool)
+    for name, value in parameter_values.items():
+        if name not in parameters:  # cached value not part of parameterization
+            continue
+        parameter = parameters[name]
+
+        # Check that the numerical endpoints and inclusivity attribute
+        # agree with the `contains` method about which parameter values are
+        # within the domain.
+        a, b = parameter.domain.get_numerical_endpoints(
+            parameter_values=parameter_values)
+        a_included, b_included = parameter.domain.inclusive
+        valid = (a <= value) if a_included else a < value
+        valid &= (value <= b) if b_included else value < b
+        assert_equal(valid, parameter.domain.contains(
+            value, parameter_values=parameter_values))
+
+        # Form `all_valid` mask that is True where *all* parameters are valid
+        all_valid &= valid
+
+    # Check that the `all_valid` mask formed here is the complement of the
+    # `dist._invalid` mask stored by the infrastructure
+    assert_equal(~all_valid, dist._invalid)
+
+    return all_valid
+
+def classify_arg(dist, arg, arg_domain):
+    if arg is None:
+        valid_args = np.ones(dist._shape, dtype=bool)
+        endpoint_args = np.zeros(dist._shape, dtype=bool)
+        outside_args = np.zeros(dist._shape, dtype=bool)
+        nan_args = np.zeros(dist._shape, dtype=bool)
+        return valid_args, endpoint_args, outside_args, nan_args
+
+    a, b = arg_domain.get_numerical_endpoints(
+        parameter_values=dist._parameters)
+
+    a, b, arg = np.broadcast_arrays(a, b, arg)
+    a_included, b_included = arg_domain.inclusive
+
+    inside = (a <= arg) if a_included else a < arg
+    inside &= (arg <= b) if b_included else arg < b
+    # TODO: add `supported` method and check here
+    on = np.zeros(a.shape, dtype=int)
+    on[a == arg] = -1
+    on[b == arg] = 1
+    outside = np.zeros(a.shape, dtype=int)
+    outside[(arg < a) if a_included else arg <= a] = -1
+    outside[(b < arg) if b_included else b <= arg] = 1
+    nan = np.isnan(arg)
+
+    return inside, on, outside, nan
+
+
+def test_input_validation():
+    class Test(ContinuousDistribution):
+        _variable = _RealParameter('x', domain=_RealInterval())
+
+    message = ("The `Test` distribution family does not accept parameters, "
+               "but parameters `{'a'}` were provided.")
+    with pytest.raises(ValueError, match=message):
+        Test(a=1, )
+
+    message = "Attribute `tol` of `Test` must be a positive float, if specified."
+    with pytest.raises(ValueError, match=message):
+        Test(tol=np.asarray([]))
+    with pytest.raises(ValueError, match=message):
+        Test(tol=[1, 2, 3])
+    with pytest.raises(ValueError, match=message):
+        Test(tol=np.nan)
+    with pytest.raises(ValueError, match=message):
+        Test(tol=-1)
+
+    message = ("Argument `order` of `Test.moment` must be a "
+               "finite integer greater than or equal to 0.")
+    with pytest.raises(ValueError, match=message):
+        Test().moment(-1)
+    with pytest.raises(ValueError, match=message):
+        Test().moment(np.inf)
+
+    message = ("Argument `order` of `Test.lmoment` must be a "
+               "finite integer greater than or equal to 1.")
+    with pytest.raises(ValueError, match=message):
+        Test().lmoment(0)
+
+    message = "Argument `kind` of `Test.moment` must be one of..."
+    with pytest.raises(ValueError, match=message):
+        Test().moment(2, kind='coconut')
+
+    class Test2(ContinuousDistribution):
+        _p1 = _RealParameter('c', domain=_RealInterval())
+        _p2 = _RealParameter('d', domain=_RealInterval())
+        _parameterizations = [_Parameterization(_p1, _p2)]
+        _variable = _RealParameter('x', domain=_RealInterval())
+
+    message = ("The provided parameters `{a}` do not match a supported "
+               "parameterization of the `Test2` distribution family.")
+    with pytest.raises(ValueError, match=message):
+        Test2(a=1)
+
+    message = ("The `Test2` distribution family requires parameters, but none "
+               "were provided.")
+    with pytest.raises(ValueError, match=message):
+        Test2()
+
+    message = ("The parameters `{c, d}` provided to the `Test2` "
+               "distribution family cannot be broadcast to the same shape.")
+    with pytest.raises(ValueError, match=message):
+        Test2(c=[1, 2], d=[1, 2, 3])
+
+    message = ("The argument provided to `Test2.pdf` cannot be be broadcast to "
+              "the same shape as the distribution parameters.")
+    with pytest.raises(ValueError, match=message):
+        dist = Test2(c=[1, 2, 3], d=[1, 2, 3])
+        dist.pdf([1, 2])
+
+    message = "Parameter `c` must be of real dtype."
+    with pytest.raises(TypeError, match=message):
+        Test2(c=[1, object()], d=[1, 2])
+
+    message = "Parameter `convention` of `Test2.kurtosis` must be one of..."
+    with pytest.raises(ValueError, match=message):
+        dist = Test2(c=[1, 2, 3], d=[1, 2, 3])
+        dist.kurtosis(convention='coconut')
+
+
+def test_rng_deepcopy_pickle():
+    # test behavior of `rng` attribute and copy behavior
+    kwargs = dict(a=[-1, 2], b=10)
+    dist1 = Uniform(**kwargs)
+    dist2 = deepcopy(dist1)
+    dist3 = pickle.loads(pickle.dumps(dist1))
+
+    res1, res2, res3 = dist1.sample(), dist2.sample(), dist3.sample()
+    assert np.all(res2 != res1)
+    assert np.all(res3 != res1)
+
+    res1, res2, res3 = dist1.sample(rng=42), dist2.sample(rng=42), dist3.sample(rng=42)
+    assert np.all(res2 == res1)
+    assert np.all(res3 == res1)
+
+
+class TestAttributes:
+    def test_cache_policy(self):
+        dist = StandardNormal(cache_policy="no_cache")
+        # make error message more appropriate
+        message = "`StandardNormal` does not provide an accurate implementation of the "
+        with pytest.raises(NotImplementedError, match=message):
+            dist.mean(method='cache')
+        mean = dist.mean()
+        with pytest.raises(NotImplementedError, match=message):
+            dist.mean(method='cache')
+
+        # add to enum
+        dist.cache_policy = None
+        with pytest.raises(NotImplementedError, match=message):
+            dist.mean(method='cache')
+        mean = dist.mean()  # method is 'formula' by default
+        cached_mean = dist.mean(method='cache')
+        assert_equal(cached_mean, mean)
+
+        # cache is not cleared when used in a TransformedDistribution
+        dist2 = stats.exp(dist)
+        assert_equal(dist.mean(method='cache'), cached_mean)
+        dist2.reset_cache()
+        assert_equal(dist.mean(method='cache'), cached_mean)
+
+        # cache is overridden by latest evaluation
+        quadrature_mean = dist.mean(method='quadrature')
+        cached_mean = dist.mean(method='cache')
+        assert_equal(cached_mean, quadrature_mean)
+        assert not np.all(mean == quadrature_mean)
+
+        # We can turn the cache off, and it won't change, but the old cache is
+        # still available
+        dist.cache_policy = "no_cache"
+        mean = dist.mean(method='formula')
+        cached_mean = dist.mean(method='cache')
+        assert_equal(cached_mean, quadrature_mean)
+        assert not np.all(mean == quadrature_mean)
+
+        dist.reset_cache()
+        with pytest.raises(NotImplementedError, match=message):
+            dist.mean(method='cache')
+
+        message = "Attribute `cache_policy` of `StandardNormal`..."
+        with pytest.raises(ValueError, match=message):
+            dist.cache_policy = "invalid"
+
+    def test_tol(self):
+        x = 3.
+        X = stats.Normal()
+
+        message = "Attribute `tol` of `StandardNormal` must..."
+        with pytest.raises(ValueError, match=message):
+            X.tol = -1.
+        with pytest.raises(ValueError, match=message):
+            X.tol = (0.1,)
+        with pytest.raises(ValueError, match=message):
+            X.tol = np.nan
+
+        X1 = stats.Normal(tol=1e-1)
+        X2 = stats.Normal(tol=1e-12)
+        ref = X.cdf(x)
+        res1 = X1.cdf(x, method='quadrature')
+        res2 = X2.cdf(x, method='quadrature')
+        assert_allclose(res1, ref, rtol=X1.tol)
+        assert_allclose(res2, ref, rtol=X2.tol)
+        assert abs(res1 - ref) > abs(res2 - ref)
+
+        p = 0.99
+        X1.tol, X2.tol = X2.tol, X1.tol
+        ref = X.icdf(p)
+        res1 = X1.icdf(p, method='inversion')
+        res2 = X2.icdf(p, method='inversion')
+        assert_allclose(res1, ref, rtol=X1.tol)
+        assert_allclose(res2, ref, rtol=X2.tol)
+        assert abs(res2 - ref) > abs(res1 - ref)
+
+    def test_iv_policy(self):
+        X = Uniform(a=0, b=1)
+        assert X.pdf(2) == 0
+
+        X.validation_policy = 'skip_all'
+        assert X.pdf(np.asarray(2.)) == 1
+
+        # Tests _set_invalid_nan
+        a, b = np.asarray(1.), np.asarray(0.)  # invalid parameters
+        X = Uniform(a=a, b=b, validation_policy='skip_all')
+        assert X.pdf(np.asarray(2.)) == -1
+
+        # Tests _set_invalid_nan_property
+        class MyUniform(Uniform):
+            def _entropy_formula(self, *args, **kwargs):
+                return 'incorrect'
+
+            def _moment_raw_formula(self, order, **params):
+                return 'incorrect'
+
+        X = MyUniform(a=a, b=b, validation_policy='skip_all')
+        assert X.entropy() == 'incorrect'
+
+        # Tests _validate_order_kind
+        assert X.moment(kind='raw', order=-1) == 'incorrect'
+
+        # Test input validation
+        message = "Attribute `validation_policy` of `MyUniform`..."
+        with pytest.raises(ValueError, match=message):
+            X.validation_policy = "invalid"
+
+    def test_shapes(self):
+        X = stats.Normal(mu=1, sigma=2)
+        Y = stats.Normal(mu=[2], sigma=3)
+
+        # Check that attributes are available as expected
+        assert X.mu == 1
+        assert X.sigma == 2
+        assert Y.mu[0] == 2
+        assert Y.sigma[0] == 3
+
+        # Trying to set an attribute raises
+        # message depends on Python version
+        with pytest.raises(AttributeError):
+            X.mu = 2
+
+        # Trying to mutate an attribute really mutates a copy
+        Y.mu[0] = 10
+        assert Y.mu[0] == 2
+
+
+class TestMakeDistribution:
+    @pytest.mark.parametrize('i, distdata', list(enumerate(distcont + distdiscrete)))
+    def test_rv_generic(self, i, distdata):
+        distname = distdata[0]
+
+        slow = {'argus', 'exponpow', 'exponweib', 'genexpon', 'gompertz',
+                'johnsonsb', 'kappa4', 'ksone', 'kstwo', 'kstwobign', 'norminvgauss',
+                'powerlognorm', 'powernorm', 'recipinvgauss', 'studentized_range',
+                'vonmises_line'}  # continuous
+        if not int(os.environ.get('SCIPY_XSLOW', '0')) and distname in slow:
+            pytest.skip('Skipping as XSLOW')
+
+        if distname in {              # skip these distributions
+            'levy_stable',            # private methods seem to require >= 1d args
+            'vonmises',               # circular distribution; shouldn't work
+            'poisson_binom',          # vector shape parameter
+            'hypergeom',              # distribution functions need interpolation
+            'nchypergeom_fisher',     # distribution functions need interpolation
+            'nchypergeom_wallenius',  # distribution functions need interpolation
+        }:
+            return
+
+        # skip single test, mostly due to slight disagreement
+        custom_tolerances = {'ksone': 1e-5, 'kstwo': 1e-5}  # discontinuous PDF
+        skip_entropy = {'kstwobign', 'pearson3'}  # tolerance issue
+        skip_skewness = {'exponpow', 'ksone', 'nchypergeom_wallenius'}  # tolerance
+        skip_kurtosis = {'chi', 'exponpow', 'invgamma',  # tolerance
+                         'johnsonsb', 'ksone', 'kstwo',  # tolerance
+                         'nchypergeom_wallenius'}  # tolerance
+        skip_logccdf = {'arcsine', 'skewcauchy', 'trapezoid', 'triang',  # tolerance
+                        'dlaplace'}  # slight tolerance issue, but only for array shape
+        skip_raw = {2: {'alpha', 'foldcauchy', 'halfcauchy', 'levy', 'levy_l'},
+                    3: {'pareto'},  # stats.pareto is just wrong
+                    4: {'invgamma'}}  # tolerance issue
+        skip_standardized = {'exponpow', 'ksone'}  # tolerances
+
+        dist = getattr(stats, distname)
+        params = dict(zip(dist.shapes.split(', '), distdata[1])) if dist.shapes else {}
+        rng = np.random.default_rng(7548723590230982)
+        CustomDistribution = stats.make_distribution(dist)
+        params = {key: np.asarray([val, val]) for key, val in params.items()}
+        X = CustomDistribution(**params)
+        Y = dist(**params)
+        x = X.sample(shape=10, rng=rng)
+        p = X.cdf(x)
+        rtol = custom_tolerances.get(distname, 1e-7)
+        atol = 1e-12
+
+        with np.errstate(divide='ignore', invalid='ignore'):
+            m, v, s, k = Y.stats('mvsk')
+            assert_allclose(X.support(), Y.support())
+            if distname not in skip_entropy:
+                assert_allclose(X.entropy(), Y.entropy(), rtol=rtol)
+            if isinstance(Y, stats.rv_discrete):
+                # some continuous distributions have trouble with `logentropy` because
+                # it uses complex numbers
+                assert_allclose(np.exp(X.logentropy()), Y.entropy(), rtol=rtol)
+            assert_allclose(X.median(), Y.median(), rtol=rtol)
+            assert_allclose(X.mean(), m, rtol=rtol, atol=atol)
+            assert_allclose(X.variance(), v, rtol=rtol, atol=atol)
+            if distname not in skip_skewness:
+                assert_allclose(X.skewness(), s, rtol=rtol, atol=atol)
+            if distname not in skip_kurtosis:
+                assert_allclose(X.kurtosis(convention='excess'), k,
+                                rtol=rtol, atol=atol)
+            if isinstance(dist, stats.rv_continuous):
+                assert_allclose(X.logpdf(x), Y.logpdf(x), rtol=rtol)
+                assert_allclose(X.pdf(x), Y.pdf(x), rtol=rtol)
+            else:
+                assert_allclose(X.logpmf(x), Y.logpmf(x), rtol=rtol)
+                assert_allclose(X.pmf(x), Y.pmf(x), rtol=rtol)
+            assert_allclose(X.logcdf(x), Y.logcdf(x), rtol=rtol)
+            assert_allclose(X.cdf(x), Y.cdf(x), rtol=rtol)
+            if distname not in skip_logccdf:
+                assert_allclose(X.logccdf(x), Y.logsf(x), rtol=rtol)
+            assert_allclose(X.ccdf(x), Y.sf(x), rtol=rtol)
+
+            # old infrastructure convention for ppf(p=0) and isf(p=1) is different than
+            # new infrastructure. Adjust reference values accordingly.
+            a, _ = Y.support()
+            a, p = np.broadcast_arrays(a, p)
+            ref_ppf = Y.ppf(p)
+            ref_ppf[p == 0] = a[p == 0]
+            ref_isf = Y.isf(p)
+            ref_isf[p == 1] = a[p == 1]
+
+            assert_allclose(X.icdf(p), ref_ppf, rtol=rtol)
+            assert_allclose(X.iccdf(p), ref_isf, rtol=rtol)
+
+            for order in range(5):
+                if distname not in skip_raw.get(order, {}):
+                    assert_allclose(X.moment(order, kind='raw'),
+                                    Y.moment(order), rtol=rtol, atol=atol)
+            for order in range(3, 4):
+                if distname not in skip_standardized:
+                    assert_allclose(X.moment(order, kind='standardized'),
+                                    Y.stats('mvsk'[order-1]), rtol=rtol, atol=atol)
+            if isinstance(dist, stats.rv_continuous):
+                # For discrete distributions, these won't agree at the far left end
+                # of the support, and the new infrastructure is slow there (for now).
+                seed = 845298245687345
+                assert_allclose(X.sample(shape=10, rng=seed),
+                                Y.rvs(size=p.shape,
+                                      random_state=np.random.default_rng(seed)),
+                                rtol=rtol)
+
+    def test_custom(self):
+        rng = np.random.default_rng(7548723590230982)
+
+        class MyLogUniform:
+            @property
+            def __make_distribution_version__(self):
+                return "1.16.0"
+
+            @property
+            def parameters(self):
+                return {'a': {'endpoints': (0, np.inf), 'inclusive': (False, False)},
+                        'b': {'endpoints': ('a', np.inf), 'inclusive': (False, False)}}
+
+            @property
+            def support(self):
+                return {'endpoints': ('a', 'b')}
+
+            def pdf(self, x, a, b):
+                return 1 / (x * (np.log(b) - np.log(a)))
+
+            def sample(self, shape, *, a, b, rng=None):
+                p = rng.uniform(size=shape)
+                return np.exp(np.log(a) + p * (np.log(b) - np.log(a)))
+
+            def moment(self, order, kind='raw', *, a, b):
+                if order == 1 and kind == 'raw':
+                    # quadrature is perfectly accurate here; add 1e-10 error so we
+                    # can tell the difference between the two
+                    return (b - a) / np.log(b/a) + 1e-10
+
+            def lmoment(self, order, *, a, b):
+                s = np.log(b/a)
+                l1 = (b - a) / s
+                l2 = ((s - 2) * b + (s + 2) * a) / s**2
+                l3 = ((s**2 - 6*s + 12) * b - (s**2 + 6*s + 12) * a) / s ** 3
+                l4 = ((s**3 - 12*s**2 + 60*s - 120) * b
+                      + (s**3 + 12*s**2 + 60*s + 120) * a) / s ** 4
+                ls = {1: l1, 2: l2, 3: l3, 4: l4}
+                return ls.get(int(order), None)
+
+        LogUniform = stats.make_distribution(MyLogUniform())
+
+        X = LogUniform(a=1., b=np.e)
+        Y = stats.exp(Uniform(a=0., b=1.))
+
+        assert str(X) == f"MyLogUniform(a=1.0, b={np.e})"
+        assert repr(X) == f"MyLogUniform(a=np.float64(1.0), b=np.float64({np.e}))"
+
+        x = X.sample(shape=10, rng=rng)
+        p = X.cdf(x)
+
+        assert_allclose(X.support(), Y.support())
+        assert_allclose(X.entropy(), Y.entropy())
+        assert_allclose(X.median(), Y.median())
+        assert_allclose(X.logpdf(x), Y.logpdf(x))
+        assert_allclose(X.pdf(x), Y.pdf(x))
+        assert_allclose(X.logcdf(x), Y.logcdf(x))
+        assert_allclose(X.cdf(x), Y.cdf(x))
+        assert_allclose(X.logccdf(x), Y.logccdf(x))
+        assert_allclose(X.ccdf(x), Y.ccdf(x))
+        assert_allclose(X.icdf(p), Y.icdf(p))
+        assert_allclose(X.iccdf(p), Y.iccdf(p))
+        for kind in ['raw', 'central', 'standardized']:
+            for order in range(5):
+                assert_allclose(X.moment(order, kind=kind),
+                                Y.moment(order, kind=kind))
+        for standardize in [False, True]:
+            for order in range(1, 5):
+                if standardize and order < 3:
+                    continue
+                assert_allclose(X.lmoment(order, standardize=standardize),
+                                Y.lmoment(order, standardize=standardize))
+
+        # Confirm that the `sample` and `moment` methods are overriden as expected
+        sample_formula = X.sample(shape=10, rng=0, method='formula')
+        sample_inverse = X.sample(shape=10, rng=0, method='inverse_transform')
+        assert_allclose(sample_formula, sample_inverse)
+        assert not np.all(sample_formula == sample_inverse)
+
+        assert_allclose(X.mean(method='formula'), X.mean(method='quadrature'))
+        assert not X.mean(method='formula') == X.mean(method='quadrature')
+
+        assert_allclose(X.lmoment(method='formula'),
+                        X.lmoment(method='quadrature_icdf'))
+        assert not X.lmoment(method='formula') == X.lmoment(method='quadrature_icdf')
+
+    # pdf and cdf formulas below can warn on boundary of support in some cases.
+    # See https://github.com/scipy/scipy/pull/22560#discussion_r1962763840.
+    @pytest.mark.slow
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")
+    @pytest.mark.parametrize("c", [-1, 0, 1, np.asarray([-2.1, -1., 0., 1., 2.1])])
+    def test_custom_variable_support(self, c):
+        rng = np.random.default_rng(7548723590230982)
+
+        class MyGenExtreme:
+            @property
+            def __make_distribution_version__(self):
+                return "1.16.0"
+
+            @property
+            def parameters(self):
+                return {
+                    'c': {'endpoints': (-np.inf, np.inf), 'inclusive': (False, False)},
+                    'mu': {'endpoints': (-np.inf, np.inf), 'inclusive': (False, False)},
+                    'sigma': {'endpoints': (0, np.inf), 'inclusive': (False, False)}
+                }
+
+            @property
+            def support(self):
+                def left(*, c, mu, sigma):
+                    c, mu, sigma = np.broadcast_arrays(c, mu, sigma)
+                    result = np.empty_like(c)
+                    result[c >= 0] = -np.inf
+                    result[c < 0] = mu[c < 0] + sigma[c < 0] / c[c < 0]
+                    return result[()]
+
+                def right(*, c, mu, sigma):
+                    c, mu, sigma = np.broadcast_arrays(c, mu, sigma)
+                    result = np.empty_like(c)
+                    result[c <= 0] = np.inf
+                    result[c > 0] = mu[c > 0] + sigma[c > 0] / c[c > 0]
+                    return result[()]
+
+                return {"endpoints": (left, right), "inclusive": (False, False)}
+
+            def pdf(self, x, *, c, mu, sigma):
+                x, c, mu, sigma = np.broadcast_arrays(x, c, mu, sigma)
+                t = np.empty_like(x)
+                mask = (c == 0)
+                t[mask] = np.exp(-(x[mask] - mu[mask])/sigma[mask])
+                t[~mask] = (
+                    1  - c[~mask]*(x[~mask] - mu[~mask])/sigma[~mask]
+                )**(1/c[~mask])
+                result = 1/sigma * t**(1 - c)*np.exp(-t)
+                return result[()]
+
+            def cdf(self, x, *, c, mu, sigma):
+                x, c, mu, sigma = np.broadcast_arrays(x, c, mu, sigma)
+                t = np.empty_like(x)
+                mask = (c == 0)
+                t[mask] = np.exp(-(x[mask] - mu[mask])/sigma[mask])
+                t[~mask] = (
+                    1  - c[~mask]*(x[~mask] - mu[~mask])/sigma[~mask]
+                )**(1/c[~mask])
+                return np.exp(-t)[()]
+
+        GenExtreme1 = stats.make_distribution(MyGenExtreme())
+        GenExtreme2 = stats.make_distribution(stats.genextreme)
+
+        X1 = GenExtreme1(c=c, mu=0, sigma=1)
+        X2 = GenExtreme2(c=c)
+
+        x = X1.sample(shape=10, rng=rng)
+        p = X1.cdf(x)
+
+        assert_allclose(X1.support(), X2.support())
+        assert_allclose(X1.entropy(), X2.entropy(), rtol=5e-6)
+        assert_allclose(X1.median(), X2.median())
+        assert_allclose(X1.logpdf(x), X2.logpdf(x))
+        assert_allclose(X1.pdf(x), X2.pdf(x))
+        assert_allclose(X1.logcdf(x), X2.logcdf(x))
+        assert_allclose(X1.cdf(x), X2.cdf(x))
+        assert_allclose(X1.logccdf(x), X2.logccdf(x))
+        assert_allclose(X1.ccdf(x), X2.ccdf(x))
+        assert_allclose(X1.icdf(p), X2.icdf(p))
+        assert_allclose(X1.iccdf(p), X2.iccdf(p))
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize("a", [0.5, np.asarray([0.5, 1.0, 2.0, 4.0, 8.0])])
+    @pytest.mark.parametrize("b", [0.5, np.asarray([0.5, 1.0, 2.0, 4.0, 8.0])])
+    def test_custom_multiple_parameterizations(self, a, b):
+        rng = np.random.default_rng(7548723590230982)
+        class MyBeta:
+            @property
+            def __make_distribution_version__(self):
+                return "1.16.0"
+
+            @property
+            def parameters(self):
+                return (
+                    {"a": (0, np.inf), "b": (0, np.inf)},
+                    {"mu": (0, 1), "nu": (0, np.inf)},
+                )
+
+            def process_parameters(self, a=None, b=None, mu=None, nu=None):
+                if a is not None and b is not None and mu is None and nu is None:
+                    nu = a + b
+                    mu = a / nu
+                else:
+                    a = mu * nu
+                    b = nu - a
+                return {"a": a, "b": b, "mu": mu, "nu": nu}
+
+            @property
+            def support(self):
+                return {'endpoints': (0, 1)}
+
+            def pdf(self, x, a, b, mu, nu):
+                return special._ufuncs._beta_pdf(x, a, b)
+
+            def cdf(self, x, a, b, mu, nu):
+                return special.betainc(a, b, x)
+
+        Beta = stats.make_distribution(stats.beta)
+        MyBeta = stats.make_distribution(MyBeta())
+
+        mu = a / (a + b)
+        nu = a + b
+
+        X = MyBeta(a=a, b=b)
+        Y = MyBeta(mu=mu, nu=nu)
+        Z = Beta(a=a, b=b)
+
+        x = Z.sample(shape=10, rng=rng)
+        p = Z.cdf(x)
+
+        assert_allclose(X.support(), Z.support())
+        assert_allclose(X.median(), Z.median())
+        assert_allclose(X.pdf(x), Z.pdf(x))
+        assert_allclose(X.cdf(x), Z.cdf(x))
+        assert_allclose(X.ccdf(x), Z.ccdf(x))
+        assert_allclose(X.icdf(p), Z.icdf(p))
+        assert_allclose(X.iccdf(p), Z.iccdf(p))
+
+        assert_allclose(Y.support(), Z.support())
+        assert_allclose(Y.median(), Z.median())
+        assert_allclose(Y.pdf(x), Z.pdf(x))
+        assert_allclose(Y.cdf(x), Z.cdf(x))
+        assert_allclose(Y.ccdf(x), Z.ccdf(x))
+        assert_allclose(Y.icdf(p), Z.icdf(p))
+        assert_allclose(Y.iccdf(p), Z.iccdf(p))
+
+    def test_input_validation(self):
+        message = '`levy_stable` is not supported.'
+        with pytest.raises(NotImplementedError, match=message):
+            stats.make_distribution(stats.levy_stable)
+
+        message = '`vonmises` is not supported.'
+        with pytest.raises(NotImplementedError, match=message):
+            stats.make_distribution(stats.vonmises)
+
+        message = "The argument must be an instance of..."
+        with pytest.raises(ValueError, match=message):
+            stats.make_distribution(object())
+
+    def test_repr_str_docs(self):
+        from scipy.stats._distribution_infrastructure import _distribution_names
+        for dist in _distribution_names.keys():
+            assert hasattr(stats, dist)
+
+        dist = stats.make_distribution(stats.gamma)
+        assert str(dist(a=2)) == "Gamma(a=2.0)"
+        assert repr(dist(a=2)) == "Gamma(a=np.float64(2.0))"
+        assert 'Gamma' in dist.__doc__
+
+        dist = stats.make_distribution(stats.halfgennorm)
+        assert str(dist(beta=2)) == "HalfGeneralizedNormal(beta=2.0)"
+        assert repr(dist(beta=2)) == "HalfGeneralizedNormal(beta=np.float64(2.0))"
+        assert 'HalfGeneralizedNormal' in dist.__doc__
+
+    @pytest.mark.slow  # just in case
+    @settings(max_examples=20)  # no need for more
+    @given(data=strategies.data())
+    def test_draw_distribution(self, data):
+        # `draw_distribution_from_family` is a private function right now, but we will
+        # want that functionality to be public someday. It was broken for custom
+        # distributions because the `typical` parameter of the support was ignored.
+        # Check that this is resolved.
+        rng = np.random.default_rng(8465652168548465121)
+        u_typical = tuple(np.sort(rng.standard_normal(2)))
+        s_typical = tuple(np.sort(rng.random(2)*2))
+        x_typical = tuple(np.sort(rng.standard_normal(2)))
+
+        class MyNormal:
+            __make_distribution_version__ = "1.16.0"
+            parameters = {'u': {'endpoints': (-np.inf, np.inf), 'typical': u_typical},
+                          's': {'endpoints': (0, np.inf), 'typical': s_typical}}
+            support = {'endpoints': (-np.inf, np.inf), 'typical': x_typical}
+
+            def pdf(self, x, u, s):
+                return 1 / np.sqrt(2*np.pi) / s * np.exp(-((x-u)/s)**2/2)
+
+        family = stats.make_distribution(MyNormal())
+        proportions = (1.0, 0., 0., 0.)
+        tmp = draw_distribution_from_family(family, data, rng, proportions, min_side=1)
+        dist, x, y, p, logp, result_shape, x_result_shape, xy_result_shape = tmp
+        assert u_typical[0] < np.min(dist.u) and np.max(dist.u) < u_typical[1]
+        assert s_typical[0] < np.min(dist.s) and np.max(dist.s) < s_typical[1]
+        assert x_typical[0] < np.min(x) and np.max(x) < x_typical[1]
+
+    @pytest.mark.parametrize('p', [None, ()])
+    def test_no_parameters(self, p):
+        # To create a distribution without parameters, it is natural to try an empty
+        # dictionary (since a dictionary with entries is valid if there is only
+        # one parameterization), but that didn't work; only an empty tuple (no
+        # parameterizations) worked originally. Check that this is resolved.
+        class MyStandardNormal:
+            __make_distribution_version__ = "1.16.0"
+            parameters = {} if p is None else p
+            support = {'endpoints': (-np.inf, np.inf)}
+
+            def pdf(self, x):
+                return 1 / np.sqrt(2*np.pi) * np.exp(-x**2/2)
+
+        StandardNormal = stats.make_distribution(MyStandardNormal())
+        X = StandardNormal()
+        assert X.support() == (-np.inf, np.inf)
+
+
+class TestTransforms:
+
+    def test_ContinuousDistribution_only(self):
+        X = stats.Binomial(n=10, p=0.5)
+        # This is applied at the top level TransformedDistribution,
+        # so testing one subclass is enough
+        message = "Transformations are currently only supported for continuous RVs."
+        with pytest.raises(NotImplementedError, match=message):
+            stats.exp(X)
+
+    def test_truncate(self):
+        rng = np.random.default_rng(81345982345826)
+        lb = rng.random((3, 1))
+        ub = rng.random((3, 1))
+        lb, ub = np.minimum(lb, ub), np.maximum(lb, ub)
+
+        Y = stats.truncate(Normal(), lb=lb, ub=ub)
+        Y0 = stats.truncnorm(lb, ub)
+
+        y = Y0.rvs((3, 10), random_state=rng)
+        p = Y0.cdf(y)
+
+        assert_allclose(Y.logentropy(), np.log(Y0.entropy() + 0j))
+        assert_allclose(Y.entropy(), Y0.entropy())
+        assert_allclose(Y.median(), Y0.ppf(0.5))
+        assert_allclose(Y.mean(), Y0.mean())
+        assert_allclose(Y.variance(), Y0.var())
+        assert_allclose(Y.standard_deviation(), np.sqrt(Y0.var()))
+        assert_allclose(Y.skewness(), Y0.stats('s'))
+        assert_allclose(Y.kurtosis(), Y0.stats('k') + 3)
+        assert_allclose(Y.support(), Y0.support())
+        assert_allclose(Y.pdf(y), Y0.pdf(y))
+        assert_allclose(Y.cdf(y), Y0.cdf(y))
+        assert_allclose(Y.ccdf(y), Y0.sf(y))
+        assert_allclose(Y.icdf(p), Y0.ppf(p))
+        assert_allclose(Y.iccdf(p), Y0.isf(p))
+        assert_allclose(Y.logpdf(y), Y0.logpdf(y))
+        assert_allclose(Y.logcdf(y), Y0.logcdf(y))
+        assert_allclose(Y.logccdf(y), Y0.logsf(y))
+        assert_allclose(Y.ilogcdf(np.log(p)), Y0.ppf(p))
+        assert_allclose(Y.ilogccdf(np.log(p)), Y0.isf(p))
+        sample = Y.sample(10)
+        assert np.all((sample > lb) & (sample < ub))
+
+    @pytest.mark.fail_slow(10)
+    @given(data=strategies.data(), seed=strategies.integers(min_value=0))
+    def test_loc_scale(self, data, seed):
+        # Need tests with negative scale
+        rng = np.random.default_rng(seed)
+
+        class TransformedNormal(ShiftedScaledDistribution):
+            def __init__(self, *args, **kwargs):
+                super().__init__(StandardNormal(), *args, **kwargs)
+
+        tmp = draw_distribution_from_family(
+            TransformedNormal, data, rng, proportions=(1, 0, 0, 0), min_side=1)
+        dist, x, y, p, logp, result_shape, x_result_shape, xy_result_shape = tmp
+
+        loc = dist.loc
+        scale = dist.scale
+        dist0 = StandardNormal()
+        dist_ref = stats.norm(loc=loc, scale=scale)
+        dist_ref_lmoment = Normal(mu=loc, sigma=scale)
+
+        x0 = (x - loc) / scale
+        y0 = (y - loc) / scale
+
+        a, b = dist.support()
+        a0, b0 = dist0.support()
+        assert_allclose(a, a0 + loc)
+        assert_allclose(b, b0 + loc)
+
+        with np.errstate(invalid='ignore', divide='ignore'):
+            assert_allclose(np.exp(dist.logentropy()), dist.entropy())
+            assert_allclose(dist.entropy(), dist_ref.entropy())
+            assert_allclose(dist.median(), dist0.median() + loc)
+            assert_allclose(dist.mode(), dist0.mode() + loc)
+            assert_allclose(dist.mean(), dist0.mean() + loc)
+            assert_allclose(dist.variance(), dist0.variance() * scale**2)
+            assert_allclose(dist.standard_deviation(), dist.variance()**0.5)
+            assert_allclose(dist.skewness(), dist0.skewness() * np.sign(scale))
+            assert_allclose(dist.kurtosis(), dist0.kurtosis())
+            assert_allclose(dist.logpdf(x), dist0.logpdf(x0) - np.log(scale))
+            assert_allclose(dist.pdf(x), dist0.pdf(x0) / scale)
+            assert_allclose(dist.logcdf(x), dist0.logcdf(x0))
+            assert_allclose(dist.cdf(x), dist0.cdf(x0))
+            assert_allclose(dist.logccdf(x), dist0.logccdf(x0))
+            assert_allclose(dist.ccdf(x), dist0.ccdf(x0))
+            assert_allclose(dist.logcdf(x, y), dist0.logcdf(x0, y0))
+            assert_allclose(dist.cdf(x, y), dist0.cdf(x0, y0))
+            assert_allclose(dist.logccdf(x, y), dist0.logccdf(x0, y0))
+            assert_allclose(dist.ccdf(x, y), dist0.ccdf(x0, y0))
+            assert_allclose(dist.ilogcdf(logp), dist0.ilogcdf(logp)*scale + loc)
+            assert_allclose(dist.icdf(p), dist0.icdf(p)*scale + loc)
+            assert_allclose(dist.ilogccdf(logp), dist0.ilogccdf(logp)*scale + loc)
+            assert_allclose(dist.iccdf(p), dist0.iccdf(p)*scale + loc)
+            for i in range(1, 5):
+                assert_allclose(dist.moment(i, 'raw'), dist_ref.moment(i))
+                assert_allclose(dist.moment(i, 'central'),
+                                dist0.moment(i, 'central') * scale**i)
+                assert_allclose(dist.moment(i, 'standardized'),
+                                dist0.moment(i, 'standardized') * np.sign(scale)**i)
+            for i in range(1, 5):
+                assert_allclose(dist.lmoment(i), dist_ref_lmoment.lmoment(i), atol=1e-8)
+                if i >= 3:
+                    assert_allclose(dist.lmoment(i, standardize=True),
+                                    dist_ref_lmoment.lmoment(i, standardize=True),
+                                    atol=1e-8)
+
+        # Transform back to the original distribution using all arithmetic
+        # operations; check that it behaves as expected.
+        dist = (dist - 2*loc) + loc
+        dist = dist/scale**2 * scale
+        z = np.zeros(dist._shape)  # compact broadcasting
+
+        a, b = dist.support()
+        a0, b0 = dist0.support()
+        assert_allclose(a, a0 + z)
+        assert_allclose(b, b0 + z)
+
+        with np.errstate(invalid='ignore', divide='ignore'):
+            assert_allclose(dist.logentropy(), dist0.logentropy() + z)
+            assert_allclose(dist.entropy(), dist0.entropy() + z)
+            assert_allclose(dist.median(), dist0.median() + z)
+            assert_allclose(dist.mode(), dist0.mode() + z)
+            assert_allclose(dist.mean(), dist0.mean() + z)
+            assert_allclose(dist.variance(), dist0.variance() + z)
+            assert_allclose(dist.standard_deviation(), dist0.standard_deviation() + z)
+            assert_allclose(dist.skewness(), dist0.skewness() + z)
+            assert_allclose(dist.kurtosis(), dist0.kurtosis() + z)
+            assert_allclose(dist.logpdf(x), dist0.logpdf(x)+z)
+            assert_allclose(dist.pdf(x), dist0.pdf(x) + z)
+            assert_allclose(dist.logcdf(x), dist0.logcdf(x) + z)
+            assert_allclose(dist.cdf(x), dist0.cdf(x) + z)
+            assert_allclose(dist.logccdf(x), dist0.logccdf(x) + z)
+            assert_allclose(dist.ccdf(x), dist0.ccdf(x) + z)
+            assert_allclose(dist.ilogcdf(logp), dist0.ilogcdf(logp) + z)
+            assert_allclose(dist.icdf(p), dist0.icdf(p) + z)
+            assert_allclose(dist.ilogccdf(logp), dist0.ilogccdf(logp) + z)
+            assert_allclose(dist.iccdf(p), dist0.iccdf(p) + z)
+            for i in range(1, 5):
+                assert_allclose(dist.moment(i, 'raw'), dist0.moment(i, 'raw'))
+                assert_allclose(dist.moment(i, 'central'), dist0.moment(i, 'central'))
+                assert_allclose(dist.moment(i, 'standardized'),
+                                dist0.moment(i, 'standardized'))
+            for i in range(1, 5):
+                assert_allclose(dist.lmoment(i), dist0.lmoment(i))
+                if i >= 3:
+                    assert_allclose(dist.lmoment(i, standardize=True),
+                                    dist0.lmoment(i, standardize=True))
+
+            # These are tough to compare because of the way the shape works
+            # rng = np.random.default_rng(seed)
+            # rng0 = np.random.default_rng(seed)
+            # assert_allclose(dist.sample(x_result_shape, rng=rng),
+            #                 dist0.sample(x_result_shape, rng=rng0) * scale + loc)
+            # Should also try to test fit, plot?
+
+    @pytest.mark.fail_slow(5)
+    @pytest.mark.parametrize('exp_pow', ['exp', 'pow'])
+    def test_exp_pow(self, exp_pow):
+        rng = np.random.default_rng(81345982345826)
+        mu = rng.random((3, 1))
+        sigma = rng.random((3, 1))
+
+        X = Normal()*sigma + mu
+        if exp_pow == 'exp':
+            Y = stats.exp(X)
+        else:
+            Y = np.e ** X
+        Y0 = stats.lognorm(sigma, scale=np.exp(mu))
+
+        y = Y0.rvs((3, 10), random_state=rng)
+        p = Y0.cdf(y)
+
+        assert_allclose(Y.logentropy(), np.log(Y0.entropy()))
+        assert_allclose(Y.entropy(), Y0.entropy())
+        assert_allclose(Y.median(), Y0.ppf(0.5))
+        assert_allclose(Y.mean(), Y0.mean())
+        assert_allclose(Y.variance(), Y0.var())
+        assert_allclose(Y.standard_deviation(), np.sqrt(Y0.var()))
+        assert_allclose(Y.skewness(), Y0.stats('s'))
+        assert_allclose(Y.kurtosis(), Y0.stats('k') + 3)
+        assert_allclose(Y.support(), Y0.support())
+        assert_allclose(Y.pdf(y), Y0.pdf(y))
+        assert_allclose(Y.cdf(y), Y0.cdf(y))
+        assert_allclose(Y.ccdf(y), Y0.sf(y))
+        assert_allclose(Y.icdf(p), Y0.ppf(p))
+        assert_allclose(Y.iccdf(p), Y0.isf(p))
+        assert_allclose(Y.logpdf(y), Y0.logpdf(y))
+        assert_allclose(Y.logcdf(y), Y0.logcdf(y))
+        assert_allclose(Y.logccdf(y), Y0.logsf(y))
+        assert_allclose(Y.ilogcdf(np.log(p)), Y0.ppf(p))
+        assert_allclose(Y.ilogccdf(np.log(p)), Y0.isf(p))
+        seed = 3984593485
+        assert_allclose(Y.sample(rng=seed), np.exp(X.sample(rng=seed)))
+
+
+    @pytest.mark.fail_slow(10)
+    @pytest.mark.parametrize('scale', [1, 2, -1])
+    @pytest.mark.xfail_on_32bit("`scale=-1` fails on 32-bit; needs investigation")
+    def test_reciprocal(self, scale):
+        rng = np.random.default_rng(81345982345826)
+        a = rng.random((3, 1))
+
+        # Separate sign from scale. It's easy to scale the resulting
+        # RV with negative scale; we want to test the ability to divide
+        # by a RV with negative support
+        sign, scale = np.sign(scale), abs(scale)
+
+        # Reference distribution
+        InvGamma = stats.make_distribution(stats.invgamma)
+        Y0 = sign * scale * InvGamma(a=a)
+
+        # Test distribution
+        X = _Gamma(a=a) if sign > 0 else -_Gamma(a=a)
+        Y = scale / X
+
+        y = Y0.sample(shape=(3, 10), rng=rng)
+        p = Y0.cdf(y)
+        logp = np.log(p)
+
+        assert_allclose(Y.logentropy(), np.log(Y0.entropy()))
+        assert_allclose(Y.entropy(), Y0.entropy())
+        assert_allclose(Y.median(), Y0.median())
+        # moments are not finite
+        assert_allclose(Y.support(), Y0.support())
+        assert_allclose(Y.pdf(y), Y0.pdf(y))
+        assert_allclose(Y.cdf(y), Y0.cdf(y))
+        assert_allclose(Y.ccdf(y), Y0.ccdf(y))
+        assert_allclose(Y.icdf(p), Y0.icdf(p))
+        assert_allclose(Y.iccdf(p), Y0.iccdf(p))
+        assert_allclose(Y.logpdf(y), Y0.logpdf(y))
+        assert_allclose(Y.logcdf(y), Y0.logcdf(y))
+        assert_allclose(Y.logccdf(y), Y0.logccdf(y))
+        with np.errstate(divide='ignore', invalid='ignore'):
+            assert_allclose(Y.ilogcdf(logp), Y0.ilogcdf(logp))
+            assert_allclose(Y.ilogccdf(logp), Y0.ilogccdf(logp))
+        seed = 3984593485
+        assert_allclose(Y.sample(rng=seed), scale/(X.sample(rng=seed)))
+
+    @pytest.mark.fail_slow(5)
+    def test_log(self):
+        rng = np.random.default_rng(81345982345826)
+        a = rng.random((3, 1))
+
+        X = _Gamma(a=a)
+        Y0 = stats.loggamma(a)
+        Y = stats.log(X)
+        y = Y0.rvs((3, 10), random_state=rng)
+        p = Y0.cdf(y)
+
+        assert_allclose(Y.logentropy(), np.log(Y0.entropy()))
+        assert_allclose(Y.entropy(), Y0.entropy())
+        assert_allclose(Y.median(), Y0.ppf(0.5))
+        assert_allclose(Y.mean(), Y0.mean())
+        assert_allclose(Y.variance(), Y0.var())
+        assert_allclose(Y.standard_deviation(), np.sqrt(Y0.var()))
+        assert_allclose(Y.skewness(), Y0.stats('s'))
+        assert_allclose(Y.kurtosis(), Y0.stats('k') + 3)
+        assert_allclose(Y.support(), Y0.support())
+        assert_allclose(Y.pdf(y), Y0.pdf(y))
+        assert_allclose(Y.cdf(y), Y0.cdf(y))
+        assert_allclose(Y.ccdf(y), Y0.sf(y))
+        assert_allclose(Y.icdf(p), Y0.ppf(p))
+        assert_allclose(Y.iccdf(p), Y0.isf(p))
+        assert_allclose(Y.logpdf(y), Y0.logpdf(y))
+        assert_allclose(Y.logcdf(y), Y0.logcdf(y))
+        assert_allclose(Y.logccdf(y), Y0.logsf(y))
+        with np.errstate(invalid='ignore'):
+            assert_allclose(Y.ilogcdf(np.log(p)), Y0.ppf(p))
+            assert_allclose(Y.ilogccdf(np.log(p)), Y0.isf(p))
+        seed = 3984593485
+        assert_allclose(Y.sample(rng=seed), np.log(X.sample(rng=seed)))
+
+    def test_monotonic_transforms(self):
+        # Some tests of monotonic transforms that are better to be grouped or
+        # don't fit well above
+
+        X = Uniform(a=1, b=2)
+        X_str = "Uniform(a=1.0, b=2.0)"
+
+        assert str(stats.log(X)) == f"log({X_str})"
+        assert str(1 / X) == f"1/({X_str})"
+        assert str(stats.exp(X)) == f"exp({X_str})"
+
+        X = Uniform(a=-1, b=2)
+        message = "Division by a random variable is only implemented when the..."
+        with pytest.raises(NotImplementedError, match=message):
+            1 / X
+        message = "The logarithm of a random variable is only implemented when the..."
+        with pytest.raises(NotImplementedError, match=message):
+            stats.log(X)
+        message = "Raising an argument to the power of a random variable is only..."
+        with pytest.raises(NotImplementedError, match=message):
+            (-2) ** X
+        with pytest.raises(NotImplementedError, match=message):
+            1 ** X
+        with pytest.raises(NotImplementedError, match=message):
+            [0.5, 1.5] ** X
+
+        message = "Raising a random variable to the power of an argument is only"
+        with pytest.raises(NotImplementedError, match=message):
+            X ** (-2)
+        with pytest.raises(NotImplementedError, match=message):
+            X ** 0
+        with pytest.raises(NotImplementedError, match=message):
+            X ** [0.5, 1.5]
+
+    def test_arithmetic_operators(self):
+        rng = np.random.default_rng(2348923495832349834)
+
+        a, b, loc, scale = 0.294, 1.34, 0.57, 1.16
+
+        x = rng.uniform(-3, 3, 100)
+        Y = _LogUniform(a=a, b=b)
+
+        X = scale*Y + loc
+        assert_allclose(X.cdf(x), Y.cdf((x - loc) / scale))
+        X = loc + Y*scale
+        assert_allclose(X.cdf(x), Y.cdf((x - loc) / scale))
+
+        X = Y/scale - loc
+        assert_allclose(X.cdf(x), Y.cdf((x + loc) * scale))
+        X = loc -_LogUniform(a=a, b=b)/scale
+        assert_allclose(X.cdf(x), Y.ccdf((-x + loc)*scale))
+
+    def test_abs(self):
+        rng = np.random.default_rng(81345982345826)
+        loc = rng.random((3, 1))
+
+        Y = stats.abs(Normal() + loc)
+        Y0 = stats.foldnorm(loc)
+
+        y = Y0.rvs((3, 10), random_state=rng)
+        p = Y0.cdf(y)
+
+        assert_allclose(Y.logentropy(), np.log(Y0.entropy() + 0j))
+        assert_allclose(Y.entropy(), Y0.entropy())
+        assert_allclose(Y.median(), Y0.ppf(0.5))
+        assert_allclose(Y.mean(), Y0.mean())
+        assert_allclose(Y.variance(), Y0.var())
+        assert_allclose(Y.standard_deviation(), np.sqrt(Y0.var()))
+        assert_allclose(Y.skewness(), Y0.stats('s'))
+        assert_allclose(Y.kurtosis(), Y0.stats('k') + 3)
+        assert_allclose(Y.support(), Y0.support())
+        assert_allclose(Y.pdf(y), Y0.pdf(y))
+        assert_allclose(Y.cdf(y), Y0.cdf(y))
+        assert_allclose(Y.ccdf(y), Y0.sf(y))
+        assert_allclose(Y.icdf(p), Y0.ppf(p))
+        assert_allclose(Y.iccdf(p), Y0.isf(p))
+        assert_allclose(Y.logpdf(y), Y0.logpdf(y))
+        assert_allclose(Y.logcdf(y), Y0.logcdf(y))
+        assert_allclose(Y.logccdf(y), Y0.logsf(y))
+        assert_allclose(Y.ilogcdf(np.log(p)), Y0.ppf(p))
+        assert_allclose(Y.ilogccdf(np.log(p)), Y0.isf(p))
+        sample = Y.sample(10)
+        assert np.all(sample > 0)
+
+    def test_abs_finite_support(self):
+        # The original implementation of `FoldedDistribution` might evaluate
+        # the private distribution methods outside the support. Check that this
+        # is resolved.
+        Weibull = stats.make_distribution(stats.weibull_min)
+        X = Weibull(c=2)
+        Y = abs(-X)
+        assert_equal(X.logpdf(1), Y.logpdf(1))
+        assert_equal(X.pdf(1), Y.pdf(1))
+        assert_equal(X.logcdf(1), Y.logcdf(1))
+        assert_equal(X.cdf(1), Y.cdf(1))
+        assert_equal(X.logccdf(1), Y.logccdf(1))
+        assert_equal(X.ccdf(1), Y.ccdf(1))
+
+    def test_pow(self):
+        rng = np.random.default_rng(81345982345826)
+
+        Y = Normal()**2
+        Y0 = stats.chi2(df=1)
+
+        y = Y0.rvs(10, random_state=rng)
+        p = Y0.cdf(y)
+
+        assert_allclose(Y.logentropy(), np.log(Y0.entropy() + 0j), rtol=1e-6)
+        assert_allclose(Y.entropy(), Y0.entropy(), rtol=1e-6)
+        assert_allclose(Y.median(), Y0.median())
+        assert_allclose(Y.mean(), Y0.mean())
+        assert_allclose(Y.variance(), Y0.var())
+        assert_allclose(Y.standard_deviation(), np.sqrt(Y0.var()))
+        assert_allclose(Y.skewness(), Y0.stats('s'))
+        assert_allclose(Y.kurtosis(), Y0.stats('k') + 3)
+        assert_allclose(Y.support(), Y0.support())
+        assert_allclose(Y.pdf(y), Y0.pdf(y))
+        assert_allclose(Y.cdf(y), Y0.cdf(y))
+        assert_allclose(Y.ccdf(y), Y0.sf(y))
+        assert_allclose(Y.icdf(p), Y0.ppf(p))
+        assert_allclose(Y.iccdf(p), Y0.isf(p))
+        assert_allclose(Y.logpdf(y), Y0.logpdf(y))
+        assert_allclose(Y.logcdf(y), Y0.logcdf(y))
+        assert_allclose(Y.logccdf(y), Y0.logsf(y))
+        assert_allclose(Y.ilogcdf(np.log(p)), Y0.ppf(p))
+        assert_allclose(Y.ilogccdf(np.log(p)), Y0.isf(p))
+        sample = Y.sample(10)
+        assert np.all(sample > 0)
+
+class TestOrderStatistic:
+    @pytest.mark.fail_slow(20)  # Moments require integration
+    def test_order_statistic(self):
+        rng = np.random.default_rng(7546349802439582)
+        X = Uniform(a=0, b=1)
+        n = 5
+        r = np.asarray([[1], [3], [5]])
+        Y = stats.order_statistic(X, n=n, r=r)
+        Y0 = stats.beta(r, n + 1 - r)
+
+        y = Y0.rvs((3, 10), random_state=rng)
+        p = Y0.cdf(y)
+
+        # log methods need some attention before merge
+        assert_allclose(np.exp(Y.logentropy()), Y0.entropy())
+        assert_allclose(Y.entropy(), Y0.entropy())
+        assert_allclose(Y.mean(), Y0.mean())
+        assert_allclose(Y.variance(), Y0.var())
+        assert_allclose(Y.skewness(), Y0.stats('s'), atol=1e-15)
+        assert_allclose(Y.kurtosis(), Y0.stats('k') + 3, atol=1e-15)
+        assert_allclose(Y.median(), Y0.ppf(0.5))
+        assert_allclose(Y.support(), Y0.support())
+        assert_allclose(Y.pdf(y), Y0.pdf(y))
+        assert_allclose(Y.cdf(y, method='formula'), Y.cdf(y, method='quadrature'))
+        assert_allclose(Y.ccdf(y, method='formula'), Y.ccdf(y, method='quadrature'))
+        assert_allclose(Y.icdf(p, method='formula'), Y.icdf(p, method='inversion'))
+        assert_allclose(Y.iccdf(p, method='formula'), Y.iccdf(p, method='inversion'))
+        assert_allclose(Y.logpdf(y), Y0.logpdf(y))
+        assert_allclose(Y.logcdf(y), Y0.logcdf(y))
+        assert_allclose(Y.logccdf(y), Y0.logsf(y))
+        with np.errstate(invalid='ignore', divide='ignore'):
+            assert_allclose(Y.ilogcdf(np.log(p),), Y0.ppf(p))
+            assert_allclose(Y.ilogccdf(np.log(p)), Y0.isf(p))
+
+        message = "`r` and `n` must contain only positive integers."
+        with pytest.raises(ValueError, match=message):
+            stats.order_statistic(X, n=n, r=-1)
+        with pytest.raises(ValueError, match=message):
+            stats.order_statistic(X, n=-1, r=r)
+        with pytest.raises(ValueError, match=message):
+            stats.order_statistic(X, n=n, r=1.5)
+        with pytest.raises(ValueError, match=message):
+            stats.order_statistic(X, n=1.5, r=r)
+
+    def test_support_gh22037(self):
+        # During review of gh-22037, it was noted that the `support` of
+        # an `OrderStatisticDistribution` returned incorrect results;
+        # this was resolved by overriding `_support`.
+        Uniform = stats.make_distribution(stats.uniform)
+        X = Uniform()
+        Y = X*5 + 2
+        Z = stats.order_statistic(Y, r=3, n=5)
+        assert_allclose(Z.support(), Y.support())
+
+    def test_composition_gh22037(self):
+        # During review of gh-22037, it was noted that an error was
+        # raised when creating an `OrderStatisticDistribution` from
+        # a `TruncatedDistribution`. This was resolved by overriding
+        # `_update_parameters`.
+        Normal = stats.make_distribution(stats.norm)
+        TruncatedNormal = stats.make_distribution(stats.truncnorm)
+        a, b = [-2, -1], 1
+        r, n = 3, [[4], [5]]
+        x = [[[-0.3]], [[0.1]]]
+        X1 = Normal()
+        Y1 = stats.truncate(X1, a, b)
+        Z1 = stats.order_statistic(Y1, r=r, n=n)
+        X2 = TruncatedNormal(a=a, b=b)
+        Z2 = stats.order_statistic(X2, r=r, n=n)
+        np.testing.assert_allclose(Z1.cdf(x), Z2.cdf(x))
+
+
+class TestFullCoverage:
+    # Adds tests just to get to 100% test coverage; this way it's more obvious
+    # if new lines are untested.
+    def test_Domain(self):
+        with pytest.raises(NotImplementedError):
+            _Domain.contains(None, 1.)
+        with pytest.raises(NotImplementedError):
+            _Domain.get_numerical_endpoints(None, 1.)
+        with pytest.raises(NotImplementedError):
+            _Domain.__str__(None)
+
+    def test_Parameter(self):
+        with pytest.raises(NotImplementedError):
+            _Parameter.validate(None, 1.)
+
+    @pytest.mark.parametrize(("dtype_in", "dtype_out"),
+                              [(np.float16, np.float16),
+                               (np.int16, np.float64)])
+    def test_RealParameter_uncommon_dtypes(self, dtype_in, dtype_out):
+        domain = _RealInterval((-1, 1))
+        parameter = _RealParameter('x', domain=domain)
+
+        x = np.asarray([0.5, 2.5], dtype=dtype_in)
+        arr, dtype, valid = parameter.validate(x, parameter_values={})
+        assert_equal(arr, x)
+        assert dtype == dtype_out
+        assert_equal(valid, [True, False])
+
+    def test_ContinuousDistribution_set_invalid_nan(self):
+        # Exercise code paths when formula returns wrong shape and dtype
+        # We could consider making this raise an error to force authors
+        # to return the right shape and dytpe, but this would need to be
+        # configurable.
+        class TestDist(ContinuousDistribution):
+            _variable = _RealParameter('x', domain=_RealInterval(endpoints=(0., 1.)))
+            def _logpdf_formula(self, x, *args, **kwargs):
+                return 0
+
+        X = TestDist()
+        dtype = np.float32
+        X._dtype = dtype
+        x = np.asarray([0.5], dtype=dtype)
+        assert X.logpdf(x).dtype == dtype
+
+    def test_fiinfo(self):
+        assert _fiinfo(np.float64(1.)).max == np.finfo(np.float64).max
+        assert _fiinfo(np.int64(1)).max == np.iinfo(np.int64).max
+
+    def test_generate_domain_support(self):
+        msg = _generate_domain_support(StandardNormal)
+        assert "accepts no distribution parameters" in msg
+
+        msg = _generate_domain_support(Normal)
+        assert "accepts one parameterization" in msg
+
+        msg = _generate_domain_support(_LogUniform)
+        assert "accepts two parameterizations" in msg
+
+    def test_ContinuousDistribution__repr__(self):
+        X = Uniform(a=0, b=1)
+        assert repr(X) == "Uniform(a=np.float64(0.0), b=np.float64(1.0))"
+        assert repr(X*3 + 2) == (
+            "np.float64(3.0)*Uniform(a=np.float64(0.0), b=np.float64(1.0))"
+            " + np.float64(2.0)"
+        )
+
+        X = Uniform(a=np.zeros(4), b=1)
+        assert repr(X) == "Uniform(a=array([0., 0., 0., 0.]), b=1)"
+
+        X = Uniform(a=np.zeros(4, dtype=np.float32), b=np.ones(4, dtype=np.float32))
+        assert repr(X) == (
+            "Uniform(a=array([0., 0., 0., 0.], dtype=float32),"
+            " b=array([1., 1., 1., 1.], dtype=float32))"
+        )
+
+
+class TestReprs:
+    U = Uniform(a=0, b=1)
+    V = Uniform(a=np.float32(0.0), b=np.float32(1.0))
+    X = Normal(mu=-1, sigma=1)
+    Y = Normal(mu=1, sigma=1)
+    Z = Normal(mu=np.zeros(1000), sigma=1)
+
+    @pytest.mark.parametrize(
+        "dist",
+        [
+            U,
+            U - np.array([1.0, 2.0]),
+            V,
+            np.ones(2, dtype=np.float32)*V + np.zeros(2, dtype=np.float64),
+            3*U + 2,
+            U**4,
+            (3*U + 2)**4,
+            (3*U + 2)**3,
+            2**U,
+            2**(3*U + 1),
+            1 / (1 + U),
+            stats.order_statistic(U, r=3, n=5),
+            stats.truncate(U, 0.2, 0.8),
+            stats.Mixture([X, Y], weights=[0.3, 0.7]),
+            abs(U),
+            stats.exp(U),
+            stats.log(1 + U),
+            np.array([1.0, 2.0])*U + np.array([2.0, 3.0]),
+        ]
+    )
+    def test_executable(self, dist):
+        # Test that reprs actually evaluate to proper distribution
+        # provided relevant imports are made.
+        from numpy import array  # noqa: F401
+        from numpy import float32  # noqa: F401
+        from scipy.stats import abs, exp, log, order_statistic, truncate # noqa: F401
+        from scipy.stats import Mixture, Normal # noqa: F401
+        from scipy.stats._new_distributions import Uniform # noqa: F401
+        new_dist = eval(repr(dist))
+        # A basic check that the distributions are the same
+        sample1 = dist.sample(shape=10, rng=1234)
+        sample2 = new_dist.sample(shape=10, rng=1234)
+        assert_equal(sample1, sample2)
+        assert sample1.dtype is sample2.dtype
+
+    @pytest.mark.parametrize(
+        "dist",
+        [
+            Z,
+            np.full(1000, 2.0) * X + 1.0,
+            2.0 * X + np.full(1000, 1.0),
+            np.full(1000, 2.0) * X + 1.0,
+            stats.truncate(Z, -1, 1),
+            stats.truncate(Z, -np.ones(1000), np.ones(1000)),
+            stats.order_statistic(X, r=np.arange(1, 1000), n=1000),
+            Z**2,
+            1.0 / (1 + stats.exp(Z)),
+            2**Z,
+        ]
+    )
+    def test_not_too_long(self, dist):
+        # Tests that array summarization is working to ensure reprs aren't too long.
+        # None of the reprs above will be executable.
+        assert len(repr(dist)) < 250
+
+
+class MixedDist(ContinuousDistribution):
+    _variable = _RealParameter('x', domain=_RealInterval(endpoints=(-np.inf, np.inf)))
+    def _pdf_formula(self, x, *args, **kwargs):
+        return (0.4 * 1/(1.1 * np.sqrt(2*np.pi)) * np.exp(-0.5*((x+0.25)/1.1)**2)
+                + 0.6 * 1/(0.9 * np.sqrt(2*np.pi)) * np.exp(-0.5*((x-0.5)/0.9)**2))
+
+
+class TestMixture:
+    def test_input_validation(self):
+        message = "`components` must contain at least one random variable."
+        with pytest.raises(ValueError, match=message):
+            Mixture([])
+
+        message = "Each element of `components` must be an instance..."
+        with pytest.raises(ValueError, match=message):
+            Mixture((1, 2, 3))
+
+        message = "All elements of `components` must have scalar shapes."
+        with pytest.raises(ValueError, match=message):
+            Mixture([Normal(mu=[1, 2]), Normal()])
+
+        message = "`components` and `weights` must have the same length."
+        with pytest.raises(ValueError, match=message):
+            Mixture([Normal()], weights=[0.5, 0.5])
+
+        message = "`weights` must have floating point dtype."
+        with pytest.raises(ValueError, match=message):
+            Mixture([Normal()], weights=[1])
+
+        message = "`weights` must have floating point dtype."
+        with pytest.raises(ValueError, match=message):
+            Mixture([Normal()], weights=[1])
+
+        message = "`weights` must sum to 1.0."
+        with pytest.raises(ValueError, match=message):
+            Mixture([Normal(), Normal()], weights=[0.5, 1.0])
+
+        message = "All `weights` must be non-negative."
+        with pytest.raises(ValueError, match=message):
+            Mixture([Normal(), Normal()], weights=[1.5, -0.5])
+
+    @pytest.mark.parametrize('shape', [(), (10,)])
+    def test_basic(self, shape):
+        rng = np.random.default_rng(582348972387243524)
+        X = Mixture((Normal(mu=-0.25, sigma=1.1), Normal(mu=0.5, sigma=0.9)),
+                    weights=(0.4, 0.6))
+        Y = MixedDist()
+        x = rng.random(shape)
+
+        def assert_allclose(res, ref, **kwargs):
+            if shape == ():
+                assert np.isscalar(res)
+            np.testing.assert_allclose(res, ref, **kwargs)
+
+        assert_allclose(X.logentropy(), Y.logentropy())
+        assert_allclose(X.entropy(), Y.entropy())
+        assert_allclose(X.mode(), Y.mode())
+        assert_allclose(X.median(), Y.median())
+        assert_allclose(X.mean(), Y.mean())
+        assert_allclose(X.variance(), Y.variance())
+        assert_allclose(X.standard_deviation(), Y.standard_deviation())
+        assert_allclose(X.skewness(), Y.skewness())
+        assert_allclose(X.kurtosis(), Y.kurtosis())
+        assert_allclose(X.logpdf(x), Y.logpdf(x))
+        assert_allclose(X.pdf(x), Y.pdf(x))
+        assert_allclose(X.logcdf(x), Y.logcdf(x))
+        assert_allclose(X.cdf(x), Y.cdf(x))
+        assert_allclose(X.logccdf(x), Y.logccdf(x))
+        assert_allclose(X.ccdf(x), Y.ccdf(x))
+        assert_allclose(X.ilogcdf(x), Y.ilogcdf(x))
+        assert_allclose(X.icdf(x), Y.icdf(x))
+        assert_allclose(X.ilogccdf(x), Y.ilogccdf(x))
+        assert_allclose(X.iccdf(x), Y.iccdf(x))
+        for kind in ['raw', 'central', 'standardized']:
+            for order in range(5):
+                assert_allclose(X.moment(order, kind=kind),
+                                Y.moment(order, kind=kind),
+                                atol=1e-15)
+        message = "L-moments are not currently available..."
+        with pytest.raises(NotImplementedError, match=message):
+            X.lmoment(1)
+
+        # weak test of `sample`
+        shape = (10, 20, 5)
+        y = X.sample(shape, rng=rng)
+        assert y.shape == shape
+        assert stats.ks_1samp(y.ravel(), X.cdf).pvalue > 0.05
+
+    def test_default_weights(self):
+        a = 1.1
+        Gamma = stats.make_distribution(stats.gamma)
+        X = Gamma(a=a)
+        Y = stats.Mixture((X, -X))
+        x = np.linspace(-4, 4, 300)
+        assert_allclose(Y.pdf(x), stats.dgamma(a=a).pdf(x))
+
+    def test_properties(self):
+        components = [Normal(mu=-0.25, sigma=1.1), Normal(mu=0.5, sigma=0.9)]
+        weights = (0.4, 0.6)
+        X = Mixture(components, weights=weights)
+
+        # Replacing properties doesn't work
+        # Different version of Python have different messages
+        with pytest.raises(AttributeError):
+            X.components = 10
+        with pytest.raises(AttributeError):
+            X.weights = 10
+
+        # Mutation doesn't work
+        X.components[0] = components[1]
+        assert X.components[0] == components[0]
+        X.weights[0] = weights[1]
+        assert X.weights[0] == weights[0]
+
+    def test_inverse(self):
+        # Originally, inverse relied on the mean to start the bracket search.
+        # This didn't work for distributions with non-finite mean. Check that
+        # this is resolved.
+        rng = np.random.default_rng(24358934657854237863456)
+        Cauchy = stats.make_distribution(stats.cauchy)
+        X0 = Cauchy()
+        X = stats.Mixture([X0, X0])
+        p = rng.random(size=10)
+        np.testing.assert_allclose(X.icdf(p), X0.icdf(p))
+        np.testing.assert_allclose(X.iccdf(p), X0.iccdf(p))
+        np.testing.assert_allclose(X.ilogcdf(p), X0.ilogcdf(p))
+        np.testing.assert_allclose(X.ilogccdf(p), X0.ilogccdf(p))
+
+
+def test_zipfian_distribution_wrapper():
+    # Regression test for gh-23678: calling the cdf method at the end
+    # point of the Zipfian distribution would generate a warning.
+    Zipfian = stats.make_distribution(stats.zipfian)
+    zdist = Zipfian(a=0.75, n=15)
+    # This should not generate any warnings.
+    assert_equal(zdist.cdf(15), 1.0)
+
+
+class Test_logexpxmexpy:
+    # Regression tests for some cases that the simplest version of `_logexpmexpy`
+    # did not originally handle correctly.
+
+    def test_x_equals_y(self):
+        # Test x - x == 0 in log-space
+        x = np.asarray(2.)
+        assert_equal(_logexpxmexpy(x, x), -np.inf)
+
+    def test_y_neg_inf(self):
+        # Test x - 0 == x in log-space
+        x, y = np.asarray(2.), np.asarray(-inf)
+        assert_equal(_logexpxmexpy(x, y), x)
+
+    def test_nan(self):
+        # operations involving NaNs should not produce warnings
+        x = np.asarray(np.nan)
+        assert_equal(_logexpxmexpy(x, x), x)
+
+
+def test_gh_25180():
+    lu = _LogUniform(log_a=np.float64(-0.28915434544814245),
+                     log_b=np.float64(0.3085224670197856))
+    actual = lu.mode(method='optimization')
+    assert np.isfinite(actual)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_continuous_basic.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_continuous_basic.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2a8ee50c83d8149b8a551cfcd068683c2babe3f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_continuous_basic.py
@@ -0,0 +1,1065 @@
+import sys
+import warnings
+import platform
+
+import numpy as np
+import numpy.testing as npt
+import pytest
+from pytest import raises as assert_raises
+from scipy.integrate import IntegrationWarning
+import itertools
+
+from scipy import stats
+from .common_tests import (check_normalization, check_moment,
+                           check_mean_expect,
+                           check_var_expect, check_skew_expect,
+                           check_kurt_expect, check_entropy,
+                           check_private_entropy, check_entropy_vect_scale,
+                           check_edge_support, check_named_args,
+                           check_random_state_property,
+                           check_meth_dtype, check_ppf_dtype,
+                           check_cmplx_deriv,
+                           check_pickling, check_rvs_broadcast,
+                           check_freezing, check_munp_expect,)
+from scipy.stats._distr_params import distcont
+from scipy.stats._distn_infrastructure import rv_continuous_frozen
+
+"""
+Test all continuous distributions.
+
+Parameters were chosen for those distributions that pass the
+Kolmogorov-Smirnov test.  This provides safe parameters for each
+distributions so that we can perform further testing of class methods.
+
+These tests currently check only/mostly for serious errors and exceptions,
+not for numerically exact results.
+"""
+
+# Note that you need to add new distributions you want tested
+# to _distr_params
+
+DECIMAL = 5  # specify the precision of the tests  # increased from 0 to 5
+_IS_32BIT = (sys.maxsize < 2**32)
+
+# Sets of tests to skip.
+# Entries sorted by speed (very slow to slow).
+# xslow took > 1s; slow took > 0.5s
+
+xslow_test_cont_basic = {'studentized_range', 'kstwo', 'ksone', 'vonmises', 'kappa4',
+                         'recipinvgauss', 'vonmises_line', 'gausshyper',
+                         'rel_breitwigner', 'norminvgauss'}
+slow_test_cont_basic = {'crystalball', 'powerlognorm', 'pearson3'}
+
+# test_moments is already marked slow
+xslow_test_moments = {'studentized_range', 'ksone', 'vonmises', 'vonmises_line',
+                      'recipinvgauss', 'kstwo', 'kappa4'}
+
+slow_fit_mle = {'exponweib', 'genexpon', 'genhyperbolic', 'johnsonsb',
+                'kappa4', 'powerlognorm', 'tukeylambda'}
+xslow_fit_mle = {'gausshyper', 'ncf', 'ncx2', 'recipinvgauss', 'vonmises_line'}
+xfail_fit_mle = {'ksone', 'kstwo', 'truncpareto', 'irwinhall'}
+skip_fit_mle = {'levy_stable', 'studentized_range'}  # far too slow (>10min)
+slow_fit_mm = {'chi2', 'expon', 'lognorm', 'loguniform', 'powerlaw', 'reciprocal'}
+xslow_fit_mm = {'argus', 'beta', 'exponpow', 'gausshyper', 'gengamma',
+                'genhalflogistic', 'geninvgauss', 'gompertz',
+                'johnsonsb', 'kstwobign', 'ncx2', 'norminvgauss', 'trapezoid',
+                'truncnorm', 'truncweibull_min', 'wrapcauchy'}
+xfail_fit_mm = {'alpha', 'betaprime', 'bradford', 'burr', 'burr12', 'cauchy',
+                'crystalball', 'dpareto_lognorm', 'exponweib', 'f', 'fisk',
+                'foldcauchy', 'genextreme', 'genpareto', 'halfcauchy', 'invgamma',
+                'irwinhall', 'jf_skew_t', 'johnsonsu', 'kappa3', 'kappa4', 'landau',
+                'levy', 'levy_l', 'loglaplace', 'lomax', 'mielke', 'ncf', 'nct',
+                'pareto', 'powerlognorm', 'powernorm', 'rel_breitwigner',
+                'skewcauchy', 't', 'truncexpon', 'truncpareto',
+                'tukeylambda', 'vonmises', 'vonmises_line'}
+skip_fit_mm = {'genexpon', 'genhyperbolic', 'ksone', 'kstwo', 'levy_stable',
+               'recipinvgauss', 'studentized_range'}  # far too slow (>10min)
+
+# These distributions fail the complex derivative test below.
+# Here 'fail' mean produce wrong results and/or raise exceptions, depending
+# on the implementation details of corresponding special functions.
+# cf https://github.com/scipy/scipy/pull/4979 for a discussion.
+fails_cmplx = {'argus', 'beta', 'betaprime', 'cauchy', 'chi', 'chi2', 'cosine',
+               'dgamma', 'dpareto_lognorm', 'dweibull', 'erlang', 'f', 'foldcauchy',
+               'gamma', 'gausshyper', 'gengamma', 'genhyperbolic',
+               'geninvgauss', 'gennorm', 'genpareto',
+               'halfcauchy', 'halfgennorm', 'invgamma', 'irwinhall', 'jf_skew_t',
+               'ksone', 'kstwo', 'kstwobign', 'landau', 'levy_l', 'loggamma',
+               'logistic', 'loguniform', 'maxwell', 'nakagami',
+               'ncf', 'nct', 'ncx2', 'norminvgauss', 'pearson3',
+               'powerlaw', 'rdist', 'reciprocal', 'rice',
+               'skewnorm', 't', 'truncpareto', 'truncweibull_min',
+               'tukeylambda', 'vonmises', 'vonmises_line',
+               'rv_histogram_instance', 'truncnorm', 'studentized_range',
+               'johnsonsb', 'halflogistic', 'rel_breitwigner'}
+
+# Slow test_method_with_lists
+slow_with_lists = {'studentized_range'}
+
+
+# rv_histogram instances, with uniform and non-uniform bins;
+# stored as (dist, arg) tuples for cases_test_cont_basic
+# and cases_test_moments.
+histogram_test_instances = []
+case1 = {'a': [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6,
+               6, 6, 6, 7, 7, 7, 8, 8, 9], 'bins': 8}  # equal width bins
+case2 = {'a': [1, 1], 'bins': [0, 1, 10]}  # unequal width bins
+for case, density in itertools.product([case1, case2], [True, False]):
+    _hist = np.histogram(**case, density=density)
+    _rv_hist = stats.rv_histogram(_hist, density=density)
+    histogram_test_instances.append((_rv_hist, tuple()))
+
+
+def cases_test_cont_basic():
+    for distname, arg in distcont[:] + histogram_test_instances:
+        if distname == 'levy_stable':  # fails; tested separately
+            continue
+        if distname in slow_test_cont_basic:
+            yield pytest.param(distname, arg, marks=pytest.mark.slow)
+        elif distname in xslow_test_cont_basic:
+            yield pytest.param(distname, arg, marks=pytest.mark.xslow)
+        else:
+            yield distname, arg
+
+
+@pytest.mark.parametrize('distname,arg', list(cases_test_cont_basic()))
+@pytest.mark.parametrize('sn', [500])
+def test_cont_basic(distname, arg, sn, num_parallel_threads):
+    try:
+        distfn = getattr(stats, distname)
+    except TypeError:
+        distfn = distname
+        distname = 'rv_histogram_instance'
+
+    rng = np.random.default_rng(7654565)
+    rvs = distfn.rvs(size=sn, *arg, random_state=rng)
+    m, v = distfn.stats(*arg)
+
+    if distname not in {'laplace_asymmetric'}:
+        # TODO: multiple checks in this function are not robust, tweaking the
+        # seed above will make different distributions fail.
+        check_sample_meanvar_(m, v, rvs, rng)
+    check_cdf_ppf(distfn, arg, distname)
+    check_sf_isf(distfn, arg, distname)
+    check_cdf_sf(distfn, arg, distname)
+    check_ppf_isf(distfn, arg, distname)
+    check_pdf(distfn, arg, distname)
+    check_pdf_logpdf(distfn, arg, distname)
+    check_pdf_logpdf_at_endpoints(distfn, arg, distname)
+    check_cdf_logcdf(distfn, arg, distname)
+    check_sf_logsf(distfn, arg, distname)
+    check_ppf_broadcast(distfn, arg, distname)
+
+    alpha = 0.01
+    if distname == 'rv_histogram_instance':
+        check_distribution_rvs(distfn.cdf, arg, alpha, rvs)
+    elif distname != 'geninvgauss':
+        # skip kstest for geninvgauss since cdf is too slow; see test for
+        # rv generation in TestGenInvGauss in test_distributions.py
+        check_distribution_rvs(distname, arg, alpha, rvs)
+
+    locscale_defaults = (0, 1)
+    meths = [distfn.pdf, distfn.logpdf, distfn.cdf, distfn.logcdf,
+             distfn.logsf]
+    # make sure arguments are within support
+    spec_x = {'weibull_max': -0.5, 'levy_l': -0.5,
+              'pareto': 1.5, 'truncpareto': 3.2, 'tukeylambda': 0.3,
+              'rv_histogram_instance': 5.0}
+    x = spec_x.get(distname, 0.5)
+    if distname == 'invweibull':
+        arg = (1,)
+    elif distname == 'ksone':
+        arg = (3,)
+
+    check_named_args(distfn, x, arg, locscale_defaults, meths)
+    if num_parallel_threads == 1:
+        check_random_state_property(distfn, arg)
+
+        if distname in ['rel_breitwigner'] and _IS_32BIT:
+            # gh18414
+            pytest.skip("fails on Linux 32-bit")
+        else:
+            check_pickling(distfn, arg)
+    check_freezing(distfn, arg)
+
+    # Entropy
+    if distname not in ['kstwobign', 'kstwo', 'ncf']:
+        check_entropy(distfn, arg, distname)
+
+    if distfn.numargs == 0:
+        check_vecentropy(distfn, arg)
+
+    if (distfn.__class__._entropy != stats.rv_continuous._entropy
+            and distname != 'vonmises'):
+        check_private_entropy(distfn, arg, stats.rv_continuous)
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore", "The occurrence of roundoff error", IntegrationWarning)
+        warnings.filterwarnings("ignore", "Extremely bad integrand", IntegrationWarning)
+        warnings.filterwarnings("ignore", "invalid value", RuntimeWarning)
+        check_entropy_vect_scale(distfn, arg)
+
+    check_retrieving_support(distfn, arg)
+    check_edge_support(distfn, arg)
+
+    check_meth_dtype(distfn, arg, meths)
+    check_ppf_dtype(distfn, arg)
+
+    # complex special functions known to fail on sparc64 (gh-22577)
+    if distname not in fails_cmplx and platform.machine() != 'sparc64':
+        check_cmplx_deriv(distfn, arg)
+
+    if distname != 'truncnorm':
+        check_ppf_private(distfn, arg, distname)
+
+
+def cases_test_cont_basic_fit():
+    slow = pytest.mark.slow
+    xslow = pytest.mark.xslow
+    fail = pytest.mark.skip(reason="Test fails and may be slow.")
+    skip = pytest.mark.skip(reason="Test too slow to run to completion (>10m).")
+
+    for distname, arg in distcont[:] + histogram_test_instances:
+        for method in ["MLE", "MM"]:
+            for fix_args in [True, False]:
+                if method == 'MLE' and distname in slow_fit_mle:
+                    yield pytest.param(distname, arg, method, fix_args, marks=slow)
+                    continue
+                if method == 'MLE' and distname in xslow_fit_mle:
+                    yield pytest.param(distname, arg, method, fix_args, marks=xslow)
+                    continue
+                if method == 'MLE' and distname in xfail_fit_mle:
+                    yield pytest.param(distname, arg, method, fix_args, marks=fail)
+                    continue
+                if method == 'MLE' and distname in skip_fit_mle:
+                    yield pytest.param(distname, arg, method, fix_args, marks=skip)
+                    continue
+                if method == 'MM' and distname in slow_fit_mm:
+                    yield pytest.param(distname, arg, method, fix_args, marks=slow)
+                    continue
+                if method == 'MM' and distname in xslow_fit_mm:
+                    yield pytest.param(distname, arg, method, fix_args, marks=xslow)
+                    continue
+                if method == 'MM' and distname in xfail_fit_mm:
+                    yield pytest.param(distname, arg, method, fix_args, marks=fail)
+                    continue
+                if method == 'MM' and distname in skip_fit_mm:
+                    yield pytest.param(distname, arg, method, fix_args, marks=skip)
+                    continue
+
+                yield distname, arg, method, fix_args
+
+
+def test_cont_basic_fit_cases():
+    # Distribution names should not be in multiple MLE or MM sets
+    assert (len(xslow_fit_mle.union(xfail_fit_mle).union(skip_fit_mle)) ==
+            len(xslow_fit_mle) + len(xfail_fit_mle) + len(skip_fit_mle))
+    assert (len(xslow_fit_mm.union(xfail_fit_mm).union(skip_fit_mm)) ==
+            len(xslow_fit_mm) + len(xfail_fit_mm) + len(skip_fit_mm))
+
+
+@pytest.mark.parametrize('distname, arg, method, fix_args',
+                         list(cases_test_cont_basic_fit()))
+@pytest.mark.parametrize('n_fit_samples', [200])
+def test_cont_basic_fit(distname, arg, n_fit_samples, method, fix_args):
+    try:
+        distfn = getattr(stats, distname)
+    except TypeError:
+        distfn = distname
+
+    rng = np.random.RandomState(765456)
+    rvs = distfn.rvs(size=n_fit_samples, *arg, random_state=rng)
+    if fix_args:
+        check_fit_args_fix(distfn, arg, rvs, method)
+    else:
+        check_fit_args(distfn, arg, rvs, method)
+
+@pytest.mark.parametrize('distname,arg', list(cases_test_cont_basic()))
+def test_rvs_scalar(distname, arg):
+    # rvs should return a scalar when given scalar arguments (gh-12428)
+    try:
+        distfn = getattr(stats, distname)
+    except TypeError:
+        distfn = distname
+        distname = 'rv_histogram_instance'
+
+    assert np.isscalar(distfn.rvs(*arg))
+    assert np.isscalar(distfn.rvs(*arg, size=()))
+    assert np.isscalar(distfn.rvs(*arg, size=None))
+
+
+@pytest.mark.thread_unsafe(reason="global rng")
+def test_levy_stable_random_state_property():
+    # levy_stable only implements rvs(), so it is skipped in the
+    # main loop in test_cont_basic(). Here we apply just the test
+    # check_random_state_property to levy_stable.
+    check_random_state_property(stats.levy_stable, (0.5, 0.1))
+
+
+def cases_test_moments():
+    fail_normalization = set()
+    fail_higher = {'ncf'}
+    fail_moment = {'johnsonsu'}  # generic `munp` is inaccurate for johnsonsu
+
+    for distname, arg in distcont[:] + histogram_test_instances:
+        if distname == 'levy_stable':
+            continue
+
+        if distname in xslow_test_moments:
+            yield pytest.param(distname, arg, True, True, True, True,
+                               marks=pytest.mark.xslow(reason="too slow"))
+            continue
+
+        cond1 = distname not in fail_normalization
+        cond2 = distname not in fail_higher
+        cond3 = distname not in fail_moment
+
+        marks = list()
+        # Currently unused, `marks` can be used to add a timeout to a test of
+        # a specific distribution.  For example, this shows how a timeout could
+        # be added for the 'skewnorm' distribution:
+        #
+        #     marks = list()
+        #     if distname == 'skewnorm':
+        #         marks.append(pytest.mark.timeout(300))
+
+        yield pytest.param(distname, arg, cond1, cond2, cond3,
+                           False, marks=marks)
+
+        if not cond1 or not cond2 or not cond3:
+            # Run the distributions that have issues twice, once skipping the
+            # not_ok parts, once with the not_ok parts but marked as knownfail
+            yield pytest.param(distname, arg, True, True, True, True,
+                               marks=[pytest.mark.xfail] + marks)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize('distname,arg,normalization_ok,higher_ok,moment_ok,'
+                         'is_xfailing',
+                         list(cases_test_moments()))
+def test_moments(distname, arg, normalization_ok, higher_ok, moment_ok,
+                 is_xfailing):
+    try:
+        distfn = getattr(stats, distname)
+    except TypeError:
+        distfn = distname
+        distname = 'rv_histogram_instance'
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore",
+            "The integral is probably divergent, or slowly convergent.",
+            IntegrationWarning,
+        )
+        warnings.filterwarnings(
+            "ignore",
+            "The maximum number of subdivisions.",
+            IntegrationWarning
+        )
+        warnings.filterwarnings(
+            "ignore",
+            "The algorithm does not converge.",
+            IntegrationWarning
+        )
+
+        if is_xfailing:
+            warnings.simplefilter("ignore", IntegrationWarning)
+
+        m, v, s, k = distfn.stats(*arg, moments='mvsk')
+
+        with np.errstate(all="ignore"):
+            if normalization_ok:
+                check_normalization(distfn, arg, distname)
+
+            if higher_ok:
+                check_mean_expect(distfn, arg, m, distname)
+                check_skew_expect(distfn, arg, m, v, s, distname)
+                check_var_expect(distfn, arg, m, v, distname)
+                check_kurt_expect(distfn, arg, m, v, k, distname)
+                check_munp_expect(distfn, arg, distname)
+
+        check_loc_scale(distfn, arg, m, v, distname)
+
+        if moment_ok:
+            check_moment(distfn, arg, m, v, distname)
+
+
+@pytest.mark.parametrize('dist,shape_args', distcont)
+def test_rvs_broadcast(dist, shape_args):
+    if dist in ['gausshyper', 'studentized_range']:
+        pytest.skip("too slow")
+
+    if dist in ['rel_breitwigner'] and _IS_32BIT:
+        # gh18414
+        pytest.skip("fails on Linux 32-bit")
+
+    # If shape_only is True, it means the _rvs method of the
+    # distribution uses more than one random number to generate a random
+    # variate.  That means the result of using rvs with broadcasting or
+    # with a nontrivial size will not necessarily be the same as using the
+    # numpy.vectorize'd version of rvs(), so we can only compare the shapes
+    # of the results, not the values.
+    # Whether or not a distribution is in the following list is an
+    # implementation detail of the distribution, not a requirement.  If
+    # the implementation the rvs() method of a distribution changes, this
+    # test might also have to be changed.
+    shape_only = dist in ['argus', 'betaprime', 'dgamma', 'dpareto_lognorm', 'dweibull',
+                          'exponnorm', 'genhyperbolic', 'geninvgauss', 'landau',
+                          'levy_stable', 'nct', 'norminvgauss', 'rice',
+                          'skewnorm', 'semicircular', 'gennorm', 'loggamma']
+
+    distfunc = getattr(stats, dist)
+    loc = np.zeros(2)
+    scale = np.ones((3, 1))
+    nargs = distfunc.numargs
+    allargs = []
+    bshape = [3, 2]
+    # Generate shape parameter arguments...
+    for k in range(nargs):
+        shp = (k + 4,) + (1,)*(k + 2)
+        allargs.append(shape_args[k]*np.ones(shp))
+        bshape.insert(0, k + 4)
+    allargs.extend([loc, scale])
+    # bshape holds the expected shape when loc, scale, and the shape
+    # parameters are all broadcast together.
+
+    check_rvs_broadcast(distfunc, dist, allargs, bshape, shape_only, 'd')
+
+
+# Expected values of the SF, CDF, PDF were computed using
+# mpmath with mpmath.mp.dps = 50 and output at 20:
+#
+# def ks(x, n):
+#     x = mpmath.mpf(x)
+#     logp = -mpmath.power(6.0*n*x+1.0, 2)/18.0/n
+#     sf, cdf = mpmath.exp(logp), -mpmath.expm1(logp)
+#     pdf = (6.0*n*x+1.0) * 2 * sf/3
+#     print(mpmath.nstr(sf, 20), mpmath.nstr(cdf, 20), mpmath.nstr(pdf, 20))
+#
+# Tests use 1/n < x < 1-1/n and n > 1e6 to use the asymptotic computation.
+# Larger x has a smaller sf.
+@pytest.mark.parametrize('x,n,sf,cdf,pdf,rtol',
+                         [(2.0e-5, 1000000000,
+                           0.44932297307934442379, 0.55067702692065557621,
+                           35946.137394996276407, 5e-15),
+                          (2.0e-9, 1000000000,
+                           0.99999999061111115519, 9.3888888448132728224e-9,
+                           8.6666665852962971765, 5e-14),
+                          (5.0e-4, 1000000000,
+                           7.1222019433090374624e-218, 1.0,
+                           1.4244408634752704094e-211, 5e-14)])
+def test_gh17775_regression(x, n, sf, cdf, pdf, rtol):
+    # Regression test for gh-17775. In scipy 1.9.3 and earlier,
+    # these test would fail.
+    #
+    # KS one asymptotic sf ~ e^(-(6nx+1)^2 / 18n)
+    # Given a large 32-bit integer n, 6n will overflow in the c implementation.
+    # Example of broken behaviour:
+    # ksone.sf(2.0e-5, 1000000000) == 0.9374359693473666
+    ks = stats.ksone
+    vals = np.array([ks.sf(x, n), ks.cdf(x, n), ks.pdf(x, n)])
+    expected = np.array([sf, cdf, pdf])
+    npt.assert_allclose(vals, expected, rtol=rtol)
+    # The sf+cdf must sum to 1.0.
+    npt.assert_equal(vals[0] + vals[1], 1.0)
+    # Check inverting the (potentially very small) sf (uses a lower tolerance)
+    npt.assert_allclose([ks.isf(sf, n)], [x], rtol=1e-8)
+
+
+def test_rvs_gh2069_regression():
+    # Regression tests for gh-2069.  In scipy 0.17 and earlier,
+    # these tests would fail.
+    #
+    # A typical example of the broken behavior:
+    # >>> norm.rvs(loc=np.zeros(5), scale=np.ones(5))
+    # array([-2.49613705, -2.49613705, -2.49613705, -2.49613705, -2.49613705])
+    rng = np.random.RandomState(123)
+    vals = stats.norm.rvs(loc=np.zeros(5), scale=1, random_state=rng)
+    d = np.diff(vals)
+    npt.assert_(np.all(d != 0), "All the values are equal, but they shouldn't be!")
+    vals = stats.norm.rvs(loc=0, scale=np.ones(5), random_state=rng)
+    d = np.diff(vals)
+    npt.assert_(np.all(d != 0), "All the values are equal, but they shouldn't be!")
+    vals = stats.norm.rvs(loc=np.zeros(5), scale=np.ones(5), random_state=rng)
+    d = np.diff(vals)
+    npt.assert_(np.all(d != 0), "All the values are equal, but they shouldn't be!")
+    vals = stats.norm.rvs(loc=np.array([[0], [0]]), scale=np.ones(5),
+                          random_state=rng)
+    d = np.diff(vals.ravel())
+    npt.assert_(np.all(d != 0), "All the values are equal, but they shouldn't be!")
+
+    assert_raises(ValueError, stats.norm.rvs, [[0, 0], [0, 0]],
+                  [[1, 1], [1, 1]], 1)
+    assert_raises(ValueError, stats.gamma.rvs, [2, 3, 4, 5], 0, 1, (2, 2))
+    assert_raises(ValueError, stats.gamma.rvs, [1, 1, 1, 1], [0, 0, 0, 0],
+                  [[1], [2]], (4,))
+
+
+def test_nomodify_gh9900_regression():
+    # Regression test for gh-9990
+    # Prior to gh-9990, calls to stats.truncnorm._cdf() use what ever was
+    # set inside the stats.truncnorm instance during stats.truncnorm.cdf().
+    # This could cause issues with multi-threaded code.
+    # Since then, the calls to cdf() are not permitted to modify the global
+    # stats.truncnorm instance.
+    tn = stats.truncnorm
+    # Use the right-half truncated normal
+    # Check that the cdf and _cdf return the same result.
+    npt.assert_almost_equal(tn.cdf(1, 0, np.inf),
+                            0.6826894921370859)
+    npt.assert_almost_equal(tn._cdf([1], [0], [np.inf]),
+                            0.6826894921370859)
+
+    # Now use the left-half truncated normal
+    npt.assert_almost_equal(tn.cdf(-1, -np.inf, 0),
+                            0.31731050786291415)
+    npt.assert_almost_equal(tn._cdf([-1], [-np.inf], [0]),
+                            0.31731050786291415)
+
+    # Check that the right-half truncated normal _cdf hasn't changed
+    npt.assert_almost_equal(tn._cdf([1], [0], [np.inf]),
+                            0.6826894921370859)  # Not 1.6826894921370859
+    npt.assert_almost_equal(tn.cdf(1, 0, np.inf),
+                            0.6826894921370859)
+
+    # Check that the left-half truncated normal _cdf hasn't changed
+    npt.assert_almost_equal(tn._cdf([-1], [-np.inf], [0]),
+                            0.31731050786291415)  # Not -0.6826894921370859
+    npt.assert_almost_equal(tn.cdf(1, -np.inf, 0),
+                            1)  # Not 1.6826894921370859
+    npt.assert_almost_equal(tn.cdf(-1, -np.inf, 0),
+                            0.31731050786291415)  # Not -0.6826894921370859
+
+
+def test_broadcast_gh9990_regression():
+    # Regression test for gh-9990
+    # The x-value 7 only lies within the support of 4 of the supplied
+    # distributions.  Prior to 9990, one array passed to
+    # stats.reciprocal._cdf would have 4 elements, but an array
+    # previously stored by stats.reciprocal_argcheck() would have 6, leading
+    # to a broadcast error.
+    a = np.array([1, 2, 3, 4, 5, 6])
+    b = np.array([8, 16, 1, 32, 1, 48])
+    ans = [stats.reciprocal.cdf(7, _a, _b) for _a, _b in zip(a,b)]
+    npt.assert_array_almost_equal(stats.reciprocal.cdf(7, a, b), ans)
+
+    ans = [stats.reciprocal.cdf(1, _a, _b) for _a, _b in zip(a,b)]
+    npt.assert_array_almost_equal(stats.reciprocal.cdf(1, a, b), ans)
+
+    ans = [stats.reciprocal.cdf(_a, _a, _b) for _a, _b in zip(a,b)]
+    npt.assert_array_almost_equal(stats.reciprocal.cdf(a, a, b), ans)
+
+    ans = [stats.reciprocal.cdf(_b, _a, _b) for _a, _b in zip(a,b)]
+    npt.assert_array_almost_equal(stats.reciprocal.cdf(b, a, b), ans)
+
+
+def test_broadcast_gh7933_regression():
+    # Check broadcast works
+    stats.truncnorm.logpdf(
+        np.array([3.0, 2.0, 1.0]),
+        a=(1.5 - np.array([6.0, 5.0, 4.0])) / 3.0,
+        b=np.inf,
+        loc=np.array([6.0, 5.0, 4.0]),
+        scale=3.0
+    )
+
+
+def test_gh2002_regression():
+    # Add a check that broadcast works in situations where only some
+    # x-values are compatible with some of the shape arguments.
+    x = np.r_[-2:2:101j]
+    a = np.r_[-np.ones(50), np.ones(51)]
+    expected = [stats.truncnorm.pdf(_x, _a, np.inf) for _x, _a in zip(x, a)]
+    ans = stats.truncnorm.pdf(x, a, np.inf)
+    npt.assert_array_almost_equal(ans, expected)
+
+
+def test_gh1320_regression():
+    # Check that the first example from gh-1320 now works.
+    c = 2.62
+    stats.genextreme.ppf(0.5, np.array([[c], [c + 0.5]]))
+    # The other examples in gh-1320 appear to have stopped working
+    # some time ago.
+    # ans = stats.genextreme.moment(2, np.array([c, c + 0.5]))
+    # expected = np.array([25.50105963, 115.11191437])
+    # stats.genextreme.moment(5, np.array([[c], [c + 0.5]]))
+    # stats.genextreme.moment(5, np.array([c, c + 0.5]))
+
+
+def test_method_of_moments():
+    # example from https://en.wikipedia.org/wiki/Method_of_moments_(statistics)
+    x = [0, 0, 0, 0, 1]
+    a = 1/5 - 2*np.sqrt(3)/5
+    b = 1/5 + 2*np.sqrt(3)/5
+    # force use of method of moments (uniform.fit is overridden)
+    loc, scale = super(type(stats.uniform), stats.uniform).fit(x, method="MM")
+    npt.assert_almost_equal(loc, a, decimal=4)
+    npt.assert_almost_equal(loc+scale, b, decimal=4)
+
+
+def check_sample_meanvar_(popmean, popvar, sample, rng):
+    if np.isfinite(popmean):
+        check_sample_mean(sample, popmean)
+    if np.isfinite(popvar):
+        check_sample_var(sample, popvar, rng)
+
+
+def check_sample_mean(sample, popmean):
+    # Checks for unlikely difference between sample mean and population mean
+    prob = stats.ttest_1samp(sample, popmean).pvalue
+    assert prob > 0.01
+
+
+def check_sample_var(sample, popvar, rng):
+    # check that population mean lies within the CI bootstrapped from the
+    # sample. This used to be a chi-squared test for variance, but there were
+    # too many false positives
+    res = stats.bootstrap(
+        (sample,),
+        lambda x, axis: x.var(ddof=1, axis=axis),
+        confidence_level=0.995,
+        rng=rng,
+    )
+    conf = res.confidence_interval
+    low, high = conf.low, conf.high
+    assert low <= popvar <= high
+
+
+def check_cdf_ppf(distfn, arg, msg):
+    values = [0.001, 0.5, 0.999]
+    npt.assert_almost_equal(distfn.cdf(distfn.ppf(values, *arg), *arg),
+                            values, decimal=DECIMAL, err_msg=msg +
+                            ' - cdf-ppf roundtrip')
+
+
+def check_sf_isf(distfn, arg, msg):
+    npt.assert_almost_equal(distfn.sf(distfn.isf([0.1, 0.5, 0.9], *arg), *arg),
+                            [0.1, 0.5, 0.9], decimal=DECIMAL, err_msg=msg +
+                            ' - sf-isf roundtrip')
+
+
+def check_cdf_sf(distfn, arg, msg):
+    npt.assert_almost_equal(distfn.cdf([0.1, 0.9], *arg),
+                            1.0 - distfn.sf([0.1, 0.9], *arg),
+                            decimal=DECIMAL, err_msg=msg +
+                            ' - cdf-sf relationship')
+
+
+def check_ppf_isf(distfn, arg, msg):
+    p = np.array([0.1, 0.9])
+    npt.assert_almost_equal(distfn.isf(p, *arg), distfn.ppf(1-p, *arg),
+                            decimal=DECIMAL, err_msg=msg +
+                            ' - ppf-isf relationship')
+
+
+def check_pdf(distfn, arg, msg):
+    # compares pdf at median with numerical derivative of cdf
+    median = distfn.ppf(0.5, *arg)
+    eps = 1e-6
+    pdfv = distfn.pdf(median, *arg)
+    if (pdfv < 1e-4) or (pdfv > 1e4):
+        # avoid checking a case where pdf is close to zero or
+        # huge (singularity)
+        median = median + 0.1
+        pdfv = distfn.pdf(median, *arg)
+    cdfdiff = (distfn.cdf(median + eps, *arg) -
+               distfn.cdf(median - eps, *arg))/eps/2.0
+    # replace with better diff and better test (more points),
+    # actually, this works pretty well
+    msg += ' - cdf-pdf relationship'
+    npt.assert_almost_equal(pdfv, cdfdiff, decimal=DECIMAL, err_msg=msg)
+
+
+def check_pdf_logpdf(distfn, args, msg):
+    # compares pdf at several points with the log of the pdf
+    points = np.array([0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8])
+    vals = distfn.ppf(points, *args)
+    vals = vals[np.isfinite(vals)]
+    pdf = distfn.pdf(vals, *args)
+    logpdf = distfn.logpdf(vals, *args)
+    pdf = pdf[(pdf != 0) & np.isfinite(pdf)]
+    logpdf = logpdf[np.isfinite(logpdf)]
+    msg += " - logpdf-log(pdf) relationship"
+    npt.assert_almost_equal(np.log(pdf), logpdf, decimal=7, err_msg=msg)
+
+
+def check_pdf_logpdf_at_endpoints(distfn, args, msg):
+    # compares pdf with the log of the pdf at the (finite) end points
+    points = np.array([0, 1])
+    vals = distfn.ppf(points, *args)
+    vals = vals[np.isfinite(vals)]
+    pdf = distfn.pdf(vals, *args)
+    logpdf = distfn.logpdf(vals, *args)
+    pdf = pdf[(pdf != 0) & np.isfinite(pdf)]
+    logpdf = logpdf[np.isfinite(logpdf)]
+    msg += " - logpdf-log(pdf) relationship"
+    npt.assert_almost_equal(np.log(pdf), logpdf, decimal=7, err_msg=msg)
+
+
+def check_sf_logsf(distfn, args, msg):
+    # compares sf at several points with the log of the sf
+    points = np.array([0.0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1.0])
+    vals = distfn.ppf(points, *args)
+    vals = vals[np.isfinite(vals)]
+    sf = distfn.sf(vals, *args)
+    logsf = distfn.logsf(vals, *args)
+    sf = sf[sf != 0]
+    logsf = logsf[np.isfinite(logsf)]
+    msg += " - logsf-log(sf) relationship"
+    npt.assert_almost_equal(np.log(sf), logsf, decimal=7, err_msg=msg)
+
+
+def check_cdf_logcdf(distfn, args, msg):
+    # compares cdf at several points with the log of the cdf
+    points = np.array([0, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1.0])
+    vals = distfn.ppf(points, *args)
+    vals = vals[np.isfinite(vals)]
+    cdf = distfn.cdf(vals, *args)
+    logcdf = distfn.logcdf(vals, *args)
+    cdf = cdf[cdf != 0]
+    logcdf = logcdf[np.isfinite(logcdf)]
+    msg += " - logcdf-log(cdf) relationship"
+    npt.assert_almost_equal(np.log(cdf), logcdf, decimal=7, err_msg=msg)
+
+
+def check_ppf_broadcast(distfn, arg, msg):
+    # compares ppf for multiple argsets.
+    num_repeats = 5
+    args = [] * num_repeats
+    if arg:
+        args = [np.array([_] * num_repeats) for _ in arg]
+
+    median = distfn.ppf(0.5, *arg)
+    medians = distfn.ppf(0.5, *args)
+    msg += " - ppf multiple"
+    npt.assert_almost_equal(medians, [median] * num_repeats, decimal=7, err_msg=msg)
+
+
+def check_distribution_rvs(dist, args, alpha, rvs):
+    # dist is either a cdf function or name of a distribution in scipy.stats.
+    # args are the args for scipy.stats.dist(*args)
+    # alpha is a significance level, ~0.01
+    # rvs is array_like of random variables
+    # test from scipy.stats.tests
+    # this version reuses existing random variables
+    D, pval = stats.kstest(rvs, dist, args=args, N=1000)
+    if (pval < alpha):
+        # The rvs passed in failed the K-S test, which _could_ happen
+        # but is unlikely if alpha is small enough.
+        # Repeat the test with a new sample of rvs.
+        # Generate 1000 rvs, perform a K-S test that the new sample of rvs
+        # are distributed according to the distribution.
+        D, pval = stats.kstest(dist, dist, args=args, N=1000)
+        npt.assert_(pval > alpha, "D = " + str(D) + "; pval = " + str(pval) +
+                    "; alpha = " + str(alpha) + "\nargs = " + str(args))
+
+
+def check_vecentropy(distfn, args):
+    npt.assert_equal(distfn.vecentropy(*args), distfn._entropy(*args))
+
+
+def check_loc_scale(distfn, arg, m, v, msg):
+    # Make `loc` and `scale` arrays to catch bugs like gh-13580 where
+    # `loc` and `scale` arrays improperly broadcast with shapes.
+    loc, scale = np.array([10.0, 20.0]), np.array([10.0, 20.0])
+    mt, vt = distfn.stats(*arg, loc=loc, scale=scale)
+    npt.assert_allclose(m*scale + loc, mt)
+    npt.assert_allclose(v*scale*scale, vt)
+
+
+def check_ppf_private(distfn, arg, msg):
+    # fails by design for truncnorm self.nb not defined
+    ppfs = distfn._ppf(np.array([0.1, 0.5, 0.9]), *arg)
+    npt.assert_(not np.any(np.isnan(ppfs)), msg + 'ppf private is nan')
+
+
+def check_retrieving_support(distfn, args):
+    loc, scale = 1, 2
+    supp = distfn.support(*args)
+    supp_loc_scale = distfn.support(*args, loc=loc, scale=scale)
+    npt.assert_almost_equal(np.array(supp)*scale + loc,
+                            np.array(supp_loc_scale))
+
+
+def check_fit_args(distfn, arg, rvs, method):
+    with np.errstate(all='ignore'), warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=RuntimeWarning,
+                   message="The shape parameter of the erlang")
+        warnings.filterwarnings("ignore", category=RuntimeWarning,
+                   message="floating point number truncated")
+        vals = distfn.fit(rvs, method=method)
+        vals2 = distfn.fit(rvs, optimizer='powell', method=method)
+    # Only check the length of the return; accuracy tested in test_fit.py
+    npt.assert_(len(vals) == 2+len(arg))
+    npt.assert_(len(vals2) == 2+len(arg))
+
+
+def check_fit_args_fix(distfn, arg, rvs, method):
+    with np.errstate(all='ignore'), warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=RuntimeWarning,
+                   message="The shape parameter of the erlang")
+
+        vals = distfn.fit(rvs, floc=0, method=method)
+        vals2 = distfn.fit(rvs, fscale=1, method=method)
+        npt.assert_(len(vals) == 2+len(arg))
+        npt.assert_(vals[-2] == 0)
+        npt.assert_(vals2[-1] == 1)
+        npt.assert_(len(vals2) == 2+len(arg))
+        if len(arg) > 0:
+            vals3 = distfn.fit(rvs, f0=arg[0], method=method)
+            npt.assert_(len(vals3) == 2+len(arg))
+            npt.assert_(vals3[0] == arg[0])
+        if len(arg) > 1:
+            vals4 = distfn.fit(rvs, f1=arg[1], method=method)
+            npt.assert_(len(vals4) == 2+len(arg))
+            npt.assert_(vals4[1] == arg[1])
+        if len(arg) > 2:
+            vals5 = distfn.fit(rvs, f2=arg[2], method=method)
+            npt.assert_(len(vals5) == 2+len(arg))
+            npt.assert_(vals5[2] == arg[2])
+
+
+def cases_test_methods_with_lists():
+    for distname, arg in distcont:
+        if distname in slow_with_lists:
+            yield pytest.param(distname, arg, marks=pytest.mark.slow)
+        else:
+            yield distname, arg
+
+
+@pytest.mark.parametrize('method', ['pdf', 'logpdf', 'cdf', 'logcdf',
+                                    'sf', 'logsf', 'ppf', 'isf'])
+@pytest.mark.parametrize('distname, args', list(cases_test_methods_with_lists()))
+def test_methods_with_lists(method, distname, args):
+    # Test that the continuous distributions can accept Python lists
+    # as arguments.
+    dist = getattr(stats, distname)
+    f = getattr(dist, method)
+    if distname == 'invweibull' and method.startswith('log'):
+        x = [1.5, 2]
+    else:
+        x = [0.1, 0.2]
+
+    shape2 = [[a]*2 for a in args]
+    loc = [0, 0.1]
+    scale = [1, 1.01]
+    result = f(x, *shape2, loc=loc, scale=scale)
+    npt.assert_allclose(result,
+                        [f(*v) for v in zip(x, *shape2, loc, scale)],
+                        rtol=1e-14, atol=5e-14)
+
+
+def test_burr_fisk_moment_gh13234_regression():
+    vals0 = stats.burr.moment(1, 5, 4)
+    assert isinstance(vals0, float)
+
+    vals1 = stats.fisk.moment(1, 8)
+    assert isinstance(vals1, float)
+
+
+def test_moments_with_array_gh12192_regression():
+    # array loc and scalar scale
+    vals0 = stats.norm.moment(order=1, loc=np.array([1, 2, 3]), scale=1)
+    expected0 = np.array([1., 2., 3.])
+    npt.assert_equal(vals0, expected0)
+
+    # array loc and invalid scalar scale
+    vals1 = stats.norm.moment(order=1, loc=np.array([1, 2, 3]), scale=-1)
+    expected1 = np.array([np.nan, np.nan, np.nan])
+    npt.assert_equal(vals1, expected1)
+
+    # array loc and array scale with invalid entries
+    vals2 = stats.norm.moment(order=1, loc=np.array([1, 2, 3]),
+                              scale=[-3, 1, 0])
+    expected2 = np.array([np.nan, 2., np.nan])
+    npt.assert_equal(vals2, expected2)
+
+    # (loc == 0) & (scale < 0)
+    vals3 = stats.norm.moment(order=2, loc=0, scale=-4)
+    expected3 = np.nan
+    npt.assert_equal(vals3, expected3)
+    assert isinstance(vals3, expected3.__class__)
+
+    # array loc with 0 entries and scale with invalid entries
+    vals4 = stats.norm.moment(order=2, loc=[1, 0, 2], scale=[3, -4, -5])
+    expected4 = np.array([10., np.nan, np.nan])
+    npt.assert_equal(vals4, expected4)
+
+    # all(loc == 0) & (array scale with invalid entries)
+    vals5 = stats.norm.moment(order=2, loc=[0, 0, 0], scale=[5., -2, 100.])
+    expected5 = np.array([25., np.nan, 10000.])
+    npt.assert_equal(vals5, expected5)
+
+    # all( (loc == 0) & (scale < 0) )
+    vals6 = stats.norm.moment(order=2, loc=[0, 0, 0], scale=[-5., -2, -100.])
+    expected6 = np.array([np.nan, np.nan, np.nan])
+    npt.assert_equal(vals6, expected6)
+
+    # scalar args, loc, and scale
+    vals7 = stats.chi.moment(order=2, df=1, loc=0, scale=0)
+    expected7 = np.nan
+    npt.assert_equal(vals7, expected7)
+    assert isinstance(vals7, expected7.__class__)
+
+    # array args, scalar loc, and scalar scale
+    vals8 = stats.chi.moment(order=2, df=[1, 2, 3], loc=0, scale=0)
+    expected8 = np.array([np.nan, np.nan, np.nan])
+    npt.assert_equal(vals8, expected8)
+
+    # array args, array loc, and array scale
+    vals9 = stats.chi.moment(order=2, df=[1, 2, 3], loc=[1., 0., 2.],
+                             scale=[1., -3., 0.])
+    expected9 = np.array([3.59576912, np.nan, np.nan])
+    npt.assert_allclose(vals9, expected9, rtol=1e-8)
+
+    # (n > 4), all(loc != 0), and all(scale != 0)
+    vals10 = stats.norm.moment(5, [1., 2.], [1., 2.])
+    expected10 = np.array([26., 832.])
+    npt.assert_allclose(vals10, expected10, rtol=1e-13)
+
+    # test broadcasting and more
+    a = [-1.1, 0, 1, 2.2, np.pi]
+    b = [-1.1, 0, 1, 2.2, np.pi]
+    loc = [-1.1, 0, np.sqrt(2)]
+    scale = [-2.1, 0, 1, 2.2, np.pi]
+
+    a = np.array(a).reshape((-1, 1, 1, 1))
+    b = np.array(b).reshape((-1, 1, 1))
+    loc = np.array(loc).reshape((-1, 1))
+    scale = np.array(scale)
+
+    vals11 = stats.beta.moment(order=2, a=a, b=b, loc=loc, scale=scale)
+
+    a, b, loc, scale = np.broadcast_arrays(a, b, loc, scale)
+
+    for i in np.ndenumerate(a):
+        with np.errstate(invalid='ignore', divide='ignore'):
+            i = i[0]  # just get the index
+            # check against same function with scalar input
+            expected = stats.beta.moment(order=2, a=a[i], b=b[i],
+                                         loc=loc[i], scale=scale[i])
+            np.testing.assert_equal(vals11[i], expected)
+
+
+def test_broadcasting_in_moments_gh12192_regression():
+    vals0 = stats.norm.moment(order=1, loc=np.array([1, 2, 3]), scale=[[1]])
+    expected0 = np.array([[1., 2., 3.]])
+    npt.assert_equal(vals0, expected0)
+    assert vals0.shape == expected0.shape
+
+    vals1 = stats.norm.moment(order=1, loc=np.array([[1], [2], [3]]),
+                              scale=[1, 2, 3])
+    expected1 = np.array([[1., 1., 1.], [2., 2., 2.], [3., 3., 3.]])
+    npt.assert_equal(vals1, expected1)
+    assert vals1.shape == expected1.shape
+
+    vals2 = stats.chi.moment(order=1, df=[1., 2., 3.], loc=0., scale=1.)
+    expected2 = np.array([0.79788456, 1.25331414, 1.59576912])
+    npt.assert_allclose(vals2, expected2, rtol=1e-8)
+    assert vals2.shape == expected2.shape
+
+    vals3 = stats.chi.moment(order=1, df=[[1.], [2.], [3.]], loc=[0., 1., 2.],
+                             scale=[-1., 0., 3.])
+    expected3 = np.array([[np.nan, np.nan, 4.39365368],
+                          [np.nan, np.nan, 5.75994241],
+                          [np.nan, np.nan, 6.78730736]])
+    npt.assert_allclose(vals3, expected3, rtol=1e-8)
+    assert vals3.shape == expected3.shape
+
+
+@pytest.mark.slow
+def test_kappa3_array_gh13582():
+    # https://github.com/scipy/scipy/pull/15140#issuecomment-994958241
+    shapes = [0.5, 1.5, 2.5, 3.5, 4.5]
+    moments = 'mvsk'
+    res = np.array([[stats.kappa3.stats(shape, moments=moment)
+                   for shape in shapes] for moment in moments])
+    res2 = np.array(stats.kappa3.stats(shapes, moments=moments))
+    npt.assert_allclose(res, res2)
+
+
+@pytest.mark.xslow
+def test_kappa4_array_gh13582():
+    h = np.array([-0.5, 2.5, 3.5, 4.5, -3])
+    k = np.array([-0.5, 1, -1.5, 0, 3.5])
+    moments = 'mvsk'
+    res = np.array([[stats.kappa4.stats(h[i], k[i], moments=moment)
+                   for i in range(5)] for moment in moments])
+    res2 = np.array(stats.kappa4.stats(h, k, moments=moments))
+    npt.assert_allclose(res, res2)
+
+    # https://github.com/scipy/scipy/pull/15250#discussion_r775112913
+    h = np.array([-1, -1/4, -1/4, 1, -1, 0])
+    k = np.array([1, 1, 1/2, -1/3, -1, 0])
+    res = np.array([[stats.kappa4.stats(h[i], k[i], moments=moment)
+                   for i in range(6)] for moment in moments])
+    res2 = np.array(stats.kappa4.stats(h, k, moments=moments))
+    npt.assert_allclose(res, res2)
+
+    # https://github.com/scipy/scipy/pull/15250#discussion_r775115021
+    h = np.array([-1, -0.5, 1])
+    k = np.array([-1, -0.5, 0, 1])[:, None]
+    res2 = np.array(stats.kappa4.stats(h, k, moments=moments))
+    assert res2.shape == (4, 4, 3)
+
+
+def test_frozen_attributes(monkeypatch):
+    # gh-14827 reported that all frozen distributions had both pmf and pdf
+    # attributes; continuous should have pdf and discrete should have pmf.
+    message = "'rv_continuous_frozen' object has no attribute"
+    with pytest.raises(AttributeError, match=message):
+        stats.norm().pmf
+    with pytest.raises(AttributeError, match=message):
+        stats.norm().logpmf
+    monkeypatch.setattr(stats.norm, "pmf", "herring", raising=False)
+    frozen_norm = stats.norm()
+    assert isinstance(frozen_norm, rv_continuous_frozen)
+    assert not hasattr(frozen_norm, "pmf")
+
+
+def test_skewnorm_pdf_gh16038():
+    rng = np.random.default_rng(0)
+    x, a = -np.inf, 0
+    npt.assert_equal(stats.skewnorm.pdf(x, a), stats.norm.pdf(x))
+    x, a = rng.random(size=(3, 3)), rng.random(size=(3, 3))
+    mask = rng.random(size=(3, 3)) < 0.5
+    a[mask] = 0
+    x_norm = x[mask]
+    res = stats.skewnorm.pdf(x, a)
+    npt.assert_equal(res[mask], stats.norm.pdf(x_norm))
+    npt.assert_equal(res[~mask], stats.skewnorm.pdf(x[~mask], a[~mask]))
+
+
+# for scalar input, these functions should return scalar output
+scalar_out = [['rvs', []], ['pdf', [0]], ['logpdf', [0]], ['cdf', [0]],
+              ['logcdf', [0]], ['sf', [0]], ['logsf', [0]], ['ppf', [0]],
+              ['isf', [0]], ['moment', [1]], ['entropy', []], ['expect', []],
+              ['median', []], ['mean', []], ['std', []], ['var', []]]
+scalars_out = [['interval', [0.95]], ['support', []], ['stats', ['mv']]]
+
+
+@pytest.mark.parametrize('case', scalar_out + scalars_out)
+def test_scalar_for_scalar(case):
+    # Some rv_continuous functions returned 0d array instead of NumPy scalar
+    # Guard against regression
+    method_name, args = case
+    method = getattr(stats.norm(), method_name)
+    res = method(*args)
+    if case in scalar_out:
+        assert isinstance(res, np.number)
+    else:
+        assert isinstance(res[0], np.number)
+        assert isinstance(res[1], np.number)
+
+
+def test_scalar_for_scalar2():
+    # test methods that are not attributes of frozen distributions
+    res = stats.norm.fit([1, 2, 3])
+    assert isinstance(res[0], np.number)
+    assert isinstance(res[1], np.number)
+    res = stats.norm.fit_loc_scale([1, 2, 3])
+    assert isinstance(res[0], np.number)
+    assert isinstance(res[1], np.number)
+    res = stats.norm.nnlf((0, 1), [1, 2, 3])
+    assert isinstance(res, np.number)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_continuous_fit_censored.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_continuous_fit_censored.py
new file mode 100644
index 0000000000000000000000000000000000000000..4508b49712e5bc8975bf2f9b2681ccc6504b0ae0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_continuous_fit_censored.py
@@ -0,0 +1,683 @@
+# Tests for fitting specific distributions to censored data.
+
+import numpy as np
+from numpy.testing import assert_allclose
+
+from scipy.optimize import fmin
+from scipy.stats import (CensoredData, beta, cauchy, chi2, expon, gamma,
+                         gumbel_l, gumbel_r, invgauss, invweibull, laplace,
+                         logistic, lognorm, nct, ncx2, norm, weibull_max,
+                         weibull_min)
+
+
+# In some tests, we'll use this optimizer for improved accuracy.
+def optimizer(func, x0, args=(), disp=0):
+    return fmin(func, x0, args=args, disp=disp, xtol=1e-12, ftol=1e-12)
+
+
+def test_beta():
+    """
+    Test fitting beta shape parameters to interval-censored data.
+
+    Calculation in R:
+
+    > library(fitdistrplus)
+    > data <- data.frame(left=c(0.10, 0.50, 0.75, 0.80),
+    +                    right=c(0.20, 0.55, 0.90, 0.95))
+    > result = fitdistcens(data, 'beta', control=list(reltol=1e-14))
+
+    > result
+    Fitting of the distribution ' beta ' on censored data by maximum likelihood
+    Parameters:
+           estimate
+    shape1 1.419941
+    shape2 1.027066
+    > result$sd
+       shape1    shape2
+    0.9914177 0.6866565
+    """
+    data = CensoredData(interval=[[0.10, 0.20],
+                                  [0.50, 0.55],
+                                  [0.75, 0.90],
+                                  [0.80, 0.95]])
+
+    # For this test, fit only the shape parameters; loc and scale are fixed.
+    a, b, loc, scale = beta.fit(data, floc=0, fscale=1, optimizer=optimizer)
+
+    assert_allclose(a, 1.419941, rtol=5e-6)
+    assert_allclose(b, 1.027066, rtol=5e-6)
+    assert loc == 0
+    assert scale == 1
+
+
+def test_cauchy_right_censored():
+    """
+    Test fitting the Cauchy distribution to right-censored data.
+
+    Calculation in R, with two values not censored [1, 10] and
+    one right-censored value [30].
+
+    > library(fitdistrplus)
+    > data <- data.frame(left=c(1, 10, 30), right=c(1, 10, NA))
+    > result = fitdistcens(data, 'cauchy', control=list(reltol=1e-14))
+    > result
+    Fitting of the distribution ' cauchy ' on censored data by maximum
+    likelihood
+    Parameters:
+             estimate
+    location 7.100001
+    scale    7.455866
+    """
+    data = CensoredData(uncensored=[1, 10], right=[30])
+    loc, scale = cauchy.fit(data, optimizer=optimizer)
+    assert_allclose(loc, 7.10001, rtol=5e-6)
+    assert_allclose(scale, 7.455866, rtol=5e-6)
+
+
+def test_cauchy_mixed():
+    """
+    Test fitting the Cauchy distribution to data with mixed censoring.
+
+    Calculation in R, with:
+    * two values not censored [1, 10],
+    * one left-censored [1],
+    * one right-censored [30], and
+    * one interval-censored [[4, 8]].
+
+    > library(fitdistrplus)
+    > data <- data.frame(left=c(NA, 1, 4, 10, 30), right=c(1, 1, 8, 10, NA))
+    > result = fitdistcens(data, 'cauchy', control=list(reltol=1e-14))
+    > result
+    Fitting of the distribution ' cauchy ' on censored data by maximum
+    likelihood
+    Parameters:
+             estimate
+    location 4.605150
+    scale    5.900852
+    """
+    data = CensoredData(uncensored=[1, 10], left=[1], right=[30],
+                        interval=[[4, 8]])
+    loc, scale = cauchy.fit(data, optimizer=optimizer)
+    assert_allclose(loc, 4.605150, rtol=5e-6)
+    assert_allclose(scale, 5.900852, rtol=5e-6)
+
+
+def test_chi2_mixed():
+    """
+    Test fitting just the shape parameter (df) of chi2 to mixed data.
+
+    Calculation in R, with:
+    * two values not censored [1, 10],
+    * one left-censored [1],
+    * one right-censored [30], and
+    * one interval-censored [[4, 8]].
+
+    > library(fitdistrplus)
+    > data <- data.frame(left=c(NA, 1, 4, 10, 30), right=c(1, 1, 8, 10, NA))
+    > result = fitdistcens(data, 'chisq', control=list(reltol=1e-14))
+    > result
+    Fitting of the distribution ' chisq ' on censored data by maximum
+    likelihood
+    Parameters:
+             estimate
+    df 5.060329
+    """
+    data = CensoredData(uncensored=[1, 10], left=[1], right=[30],
+                        interval=[[4, 8]])
+    df, loc, scale = chi2.fit(data, floc=0, fscale=1, optimizer=optimizer)
+    assert_allclose(df, 5.060329, rtol=5e-6)
+    assert loc == 0
+    assert scale == 1
+
+
+def test_expon_right_censored():
+    """
+    For the exponential distribution with loc=0, the exact solution for
+    fitting n uncensored points x[0]...x[n-1] and m right-censored points
+    x[n]..x[n+m-1] is
+
+        scale = sum(x)/n
+
+    That is, divide the sum of all the values (not censored and
+    right-censored) by the number of uncensored values.  (See, for example,
+    https://en.wikipedia.org/wiki/Censoring_(statistics)#Likelihood.)
+
+    The second derivative of the log-likelihood function is
+
+        n/scale**2 - 2*sum(x)/scale**3
+
+    from which the estimate of the standard error can be computed.
+
+    -----
+
+    Calculation in R, for reference only. The R results are not
+    used in the test.
+
+    > library(fitdistrplus)
+    > dexps <- function(x, scale) {
+    +     return(dexp(x, 1/scale))
+    + }
+    > pexps <- function(q, scale) {
+    +     return(pexp(q, 1/scale))
+    + }
+    > left <- c(1, 2.5, 3, 6, 7.5, 10, 12, 12, 14.5, 15,
+    +                                     16, 16, 20, 20, 21, 22)
+    > right <- c(1, 2.5, 3, 6, 7.5, 10, 12, 12, 14.5, 15,
+    +                                     NA, NA, NA, NA, NA, NA)
+    > result = fitdistcens(data, 'exps', start=list(scale=mean(data$left)),
+    +                      control=list(reltol=1e-14))
+    > result
+    Fitting of the distribution ' exps ' on censored data by maximum likelihood
+    Parameters:
+          estimate
+    scale    19.85
+    > result$sd
+       scale
+    6.277119
+    """
+    # This data has 10 uncensored values and 6 right-censored values.
+    obs = [1, 2.5, 3, 6, 7.5, 10, 12, 12, 14.5, 15, 16, 16, 20, 20, 21, 22]
+    cens = [False]*10 + [True]*6
+    data = CensoredData.right_censored(obs, cens)
+
+    loc, scale = expon.fit(data, floc=0, optimizer=optimizer)
+
+    assert loc == 0
+    # Use the analytical solution to compute the expected value.  This
+    # is the sum of the observed values divided by the number of uncensored
+    # values.
+    n = len(data) - data.num_censored()
+    total = data._uncensored.sum() + data._right.sum()
+    expected = total / n
+    assert_allclose(scale, expected, 1e-8)
+
+
+def test_gamma_right_censored():
+    """
+    Fit gamma shape and scale to data with one right-censored value.
+
+    Calculation in R:
+
+    > library(fitdistrplus)
+    > data <- data.frame(left=c(2.5, 2.9, 3.8, 9.1, 9.3, 12.0, 23.0, 25.0),
+    +                    right=c(2.5, 2.9, 3.8, 9.1, 9.3, 12.0, 23.0, NA))
+    > result = fitdistcens(data, 'gamma', start=list(shape=1, scale=10),
+    +                      control=list(reltol=1e-13))
+    > result
+    Fitting of the distribution ' gamma ' on censored data by maximum
+      likelihood
+    Parameters:
+          estimate
+    shape 1.447623
+    scale 8.360197
+    > result$sd
+        shape     scale
+    0.7053086 5.1016531
+    """
+    # The last value is right-censored.
+    x = CensoredData.right_censored([2.5, 2.9, 3.8, 9.1, 9.3, 12.0, 23.0,
+                                     25.0],
+                                    [0]*7 + [1])
+
+    a, loc, scale = gamma.fit(x, floc=0, optimizer=optimizer)
+
+    assert_allclose(a, 1.447623, rtol=5e-6)
+    assert loc == 0
+    assert_allclose(scale, 8.360197, rtol=5e-6)
+
+
+def test_gumbel():
+    """
+    Fit gumbel_l and gumbel_r to censored data.
+
+    This R calculation should match gumbel_r.
+
+    > library(evd)
+    > library(fitdistrplus)
+    > data = data.frame(left=c(0, 2, 3, 9, 10, 10),
+    +                   right=c(1, 2, 3, 9, NA, NA))
+    > result = fitdistcens(data, 'gumbel',
+    +                      control=list(reltol=1e-14),
+    +                      start=list(loc=4, scale=5))
+    > result
+    Fitting of the distribution ' gumbel ' on censored data by maximum
+    likelihood
+    Parameters:
+          estimate
+    loc   4.487853
+    scale 4.843640
+    """
+    # First value is interval-censored. Last two are right-censored.
+    uncensored = np.array([2, 3, 9])
+    right = np.array([10, 10])
+    interval = np.array([[0, 1]])
+    data = CensoredData(uncensored, right=right, interval=interval)
+    loc, scale = gumbel_r.fit(data, optimizer=optimizer)
+    assert_allclose(loc, 4.487853, rtol=5e-6)
+    assert_allclose(scale, 4.843640, rtol=5e-6)
+
+    # Negate the data and reverse the intervals, and test with gumbel_l.
+    data2 = CensoredData(-uncensored, left=-right,
+                         interval=-interval[:, ::-1])
+    # Fitting gumbel_l to data2 should give the same result as above, but
+    # with loc negated.
+    loc2, scale2 = gumbel_l.fit(data2, optimizer=optimizer)
+    assert_allclose(loc2, -4.487853, rtol=5e-6)
+    assert_allclose(scale2, 4.843640, rtol=5e-6)
+
+
+def test_invgauss():
+    """
+    Fit just the shape parameter of invgauss to data with one value
+    left-censored and one value right-censored.
+
+    Calculation in R; using a fixed dispersion parameter amounts to fixing
+    the scale to be 1.
+
+    > library(statmod)
+    > library(fitdistrplus)
+    > left <- c(NA, 0.4813096, 0.5571880, 0.5132463, 0.3801414, 0.5904386,
+    +           0.4822340, 0.3478597, 3, 0.7191797, 1.5810902, 0.4442299)
+    > right <- c(0.15, 0.4813096, 0.5571880, 0.5132463, 0.3801414, 0.5904386,
+    +            0.4822340, 0.3478597, NA, 0.7191797, 1.5810902, 0.4442299)
+    > data <- data.frame(left=left, right=right)
+    > result = fitdistcens(data, 'invgauss', control=list(reltol=1e-12),
+    +                      fix.arg=list(dispersion=1), start=list(mean=3))
+    > result
+    Fitting of the distribution ' invgauss ' on censored data by maximum
+      likelihood
+    Parameters:
+         estimate
+    mean 0.853469
+    Fixed parameters:
+               value
+    dispersion     1
+    > result$sd
+        mean
+    0.247636
+
+    Here's the R calculation with the dispersion as a free parameter to
+    be fit.
+
+    > result = fitdistcens(data, 'invgauss', control=list(reltol=1e-12),
+    +                      start=list(mean=3, dispersion=1))
+    > result
+    Fitting of the distribution ' invgauss ' on censored data by maximum
+    likelihood
+    Parameters:
+                estimate
+    mean       0.8699819
+    dispersion 1.2261362
+
+    The parametrization of the inverse Gaussian distribution in the
+    `statmod` package is not the same as in SciPy (see
+        https://arxiv.org/abs/1603.06687
+    for details).  The translation from R to SciPy is
+
+        scale = 1/dispersion
+        mu    = mean * dispersion
+
+    > 1/result$estimate['dispersion']  # 1/dispersion
+    dispersion
+     0.8155701
+    > result$estimate['mean'] * result$estimate['dispersion']
+        mean
+    1.066716
+
+    Those last two values are the SciPy scale and shape parameters.
+    """
+    # One point is left-censored, and one is right-censored.
+    x = [0.4813096, 0.5571880, 0.5132463, 0.3801414,
+         0.5904386, 0.4822340, 0.3478597, 0.7191797,
+         1.5810902, 0.4442299]
+    data = CensoredData(uncensored=x, left=[0.15], right=[3])
+
+    # Fit only the shape parameter.
+    mu, loc, scale = invgauss.fit(data, floc=0, fscale=1, optimizer=optimizer)
+
+    assert_allclose(mu, 0.853469, rtol=5e-5)
+    assert loc == 0
+    assert scale == 1
+
+    # Fit the shape and scale.
+    mu, loc, scale = invgauss.fit(data, floc=0, optimizer=optimizer)
+
+    assert_allclose(mu, 1.066716, rtol=5e-5)
+    assert loc == 0
+    assert_allclose(scale, 0.8155701, rtol=5e-5)
+
+
+def test_invweibull():
+    """
+    Fit invweibull to censored data.
+
+    Here is the calculation in R.  The 'frechet' distribution from the evd
+    package matches SciPy's invweibull distribution.  The `loc` parameter
+    is fixed at 0.
+
+    > library(evd)
+    > library(fitdistrplus)
+    > data = data.frame(left=c(0, 2, 3, 9, 10, 10),
+    +                   right=c(1, 2, 3, 9, NA, NA))
+    > result = fitdistcens(data, 'frechet',
+    +                      control=list(reltol=1e-14),
+    +                      start=list(loc=4, scale=5))
+    > result
+    Fitting of the distribution ' frechet ' on censored data by maximum
+    likelihood
+    Parameters:
+           estimate
+    scale 2.7902200
+    shape 0.6379845
+    Fixed parameters:
+        value
+    loc     0
+    """
+    # In the R data, the first value is interval-censored, and the last
+    # two are right-censored.  The rest are not censored.
+    data = CensoredData(uncensored=[2, 3, 9], right=[10, 10],
+                        interval=[[0, 1]])
+    c, loc, scale = invweibull.fit(data, floc=0, optimizer=optimizer)
+    assert_allclose(c, 0.6379845, rtol=5e-6)
+    assert loc == 0
+    assert_allclose(scale, 2.7902200, rtol=5e-6)
+
+
+def test_laplace():
+    """
+    Fir the Laplace distribution to left- and right-censored data.
+
+    Calculation in R:
+
+    > library(fitdistrplus)
+    > dlaplace <- function(x, location=0, scale=1) {
+    +     return(0.5*exp(-abs((x - location)/scale))/scale)
+    + }
+    > plaplace <- function(q, location=0, scale=1) {
+    +     z <- (q - location)/scale
+    +     s <- sign(z)
+    +     f <- -s*0.5*exp(-abs(z)) + (s+1)/2
+    +     return(f)
+    + }
+    > left <- c(NA, -41.564, 50.0, 15.7384, 50.0, 10.0452, -2.0684,
+    +           -19.5399, 50.0,   9.0005, 27.1227, 4.3113, -3.7372,
+    +           25.3111, 14.7987,  34.0887,  50.0, 42.8496, 18.5862,
+    +           32.8921, 9.0448, -27.4591, NA, 19.5083, -9.7199)
+    > right <- c(-50.0, -41.564,  NA, 15.7384, NA, 10.0452, -2.0684,
+    +            -19.5399, NA, 9.0005, 27.1227, 4.3113, -3.7372,
+    +            25.3111, 14.7987, 34.0887, NA,  42.8496, 18.5862,
+    +            32.8921, 9.0448, -27.4591, -50.0, 19.5083, -9.7199)
+    > data <- data.frame(left=left, right=right)
+    > result <- fitdistcens(data, 'laplace', start=list(location=10, scale=10),
+    +                       control=list(reltol=1e-13))
+    > result
+    Fitting of the distribution ' laplace ' on censored data by maximum
+      likelihood
+    Parameters:
+             estimate
+    location 14.79870
+    scale    30.93601
+    > result$sd
+         location     scale
+    0.1758864 7.0972125
+    """
+    # The value -50 is left-censored, and the value 50 is right-censored.
+    obs = np.array([-50.0, -41.564, 50.0, 15.7384, 50.0, 10.0452, -2.0684,
+                    -19.5399, 50.0, 9.0005, 27.1227, 4.3113, -3.7372,
+                    25.3111, 14.7987, 34.0887, 50.0, 42.8496, 18.5862,
+                    32.8921, 9.0448, -27.4591, -50.0, 19.5083, -9.7199])
+    x = obs[(obs != -50.0) & (obs != 50)]
+    left = obs[obs == -50.0]
+    right = obs[obs == 50.0]
+    data = CensoredData(uncensored=x, left=left, right=right)
+    loc, scale = laplace.fit(data, loc=10, scale=10, optimizer=optimizer)
+    assert_allclose(loc, 14.79870, rtol=5e-6)
+    assert_allclose(scale, 30.93601, rtol=5e-6)
+
+
+def test_logistic():
+    """
+    Fit the logistic distribution to left-censored data.
+
+    Calculation in R:
+    > library(fitdistrplus)
+    > left = c(13.5401, 37.4235, 11.906 , 13.998 ,  NA    ,  0.4023,  NA    ,
+    +          10.9044, 21.0629,  9.6985,  NA    , 12.9016, 39.164 , 34.6396,
+    +          NA    , 20.3665, 16.5889, 18.0952, 45.3818, 35.3306,  8.4949,
+    +          3.4041,  NA    ,  7.2828, 37.1265,  6.5969, 17.6868, 17.4977,
+    +          16.3391, 36.0541)
+    > right = c(13.5401, 37.4235, 11.906 , 13.998 ,  0.    ,  0.4023,  0.    ,
+    +           10.9044, 21.0629,  9.6985,  0.    , 12.9016, 39.164 , 34.6396,
+    +           0.    , 20.3665, 16.5889, 18.0952, 45.3818, 35.3306,  8.4949,
+    +           3.4041,  0.    ,  7.2828, 37.1265,  6.5969, 17.6868, 17.4977,
+    +           16.3391, 36.0541)
+    > data = data.frame(left=left, right=right)
+    > result = fitdistcens(data, 'logis', control=list(reltol=1e-14))
+    > result
+    Fitting of the distribution ' logis ' on censored data by maximum
+      likelihood
+    Parameters:
+              estimate
+    location 14.633459
+    scale     9.232736
+    > result$sd
+    location    scale
+    2.931505 1.546879
+    """
+    # Values that are zero are left-censored; the true values are less than 0.
+    x = np.array([13.5401, 37.4235, 11.906, 13.998, 0.0, 0.4023, 0.0, 10.9044,
+                  21.0629, 9.6985, 0.0, 12.9016, 39.164, 34.6396, 0.0, 20.3665,
+                  16.5889, 18.0952, 45.3818, 35.3306, 8.4949, 3.4041, 0.0,
+                  7.2828, 37.1265, 6.5969, 17.6868, 17.4977, 16.3391,
+                  36.0541])
+    data = CensoredData.left_censored(x, censored=(x == 0))
+    loc, scale = logistic.fit(data, optimizer=optimizer)
+    assert_allclose(loc, 14.633459, rtol=5e-7)
+    assert_allclose(scale, 9.232736, rtol=5e-6)
+
+
+def test_lognorm():
+    """
+    Ref: https://math.montana.edu/jobo/st528/documents/relc.pdf
+
+    The data is the locomotive control time to failure example that starts
+    on page 8.  That's the 8th page in the PDF; the page number shown in
+    the text is 270).
+    The document includes SAS output for the data.
+    """
+    # These are the uncensored measurements.  There are also 59 right-censored
+    # measurements where the lower bound is 135.
+    miles_to_fail = [22.5, 37.5, 46.0, 48.5, 51.5, 53.0, 54.5, 57.5, 66.5,
+                     68.0, 69.5, 76.5, 77.0, 78.5, 80.0, 81.5, 82.0, 83.0,
+                     84.0, 91.5, 93.5, 102.5, 107.0, 108.5, 112.5, 113.5,
+                     116.0, 117.0, 118.5, 119.0, 120.0, 122.5, 123.0, 127.5,
+                     131.0, 132.5, 134.0]
+
+    data = CensoredData.right_censored(miles_to_fail + [135]*59,
+                                       [0]*len(miles_to_fail) + [1]*59)
+    sigma, loc, scale = lognorm.fit(data, floc=0)
+
+    assert loc == 0
+    # Convert the lognorm parameters to the mu and sigma of the underlying
+    # normal distribution.
+    mu = np.log(scale)
+    # The expected results are from the 17th page of the PDF document
+    # (labeled page 279), in the SAS output on the right side of the page.
+    assert_allclose(mu, 5.1169, rtol=5e-4)
+    assert_allclose(sigma, 0.7055, rtol=5e-3)
+
+
+def test_nct():
+    """
+    Test fitting the noncentral t distribution to censored data.
+
+    Calculation in R:
+
+    > library(fitdistrplus)
+    > data <- data.frame(left=c(1, 2, 3, 5, 8, 10, 25, 25),
+    +                    right=c(1, 2, 3, 5, 8, 10, NA, NA))
+    > result = fitdistcens(data, 't', control=list(reltol=1e-14),
+    +                      start=list(df=1, ncp=2))
+    > result
+    Fitting of the distribution ' t ' on censored data by maximum likelihood
+    Parameters:
+         estimate
+    df  0.5432336
+    ncp 2.8893565
+
+    """
+    data = CensoredData.right_censored([1, 2, 3, 5, 8, 10, 25, 25],
+                                       [0, 0, 0, 0, 0, 0, 1, 1])
+    # Fit just the shape parameter df and nc; loc and scale are fixed.
+    with np.errstate(over='ignore'):  # remove context when gh-14901 is closed
+        df, nc, loc, scale = nct.fit(data, floc=0, fscale=1,
+                                     optimizer=optimizer)
+    assert_allclose(df, 0.5432336, rtol=5e-6)
+    assert_allclose(nc, 2.8893565, rtol=5e-6)
+    assert loc == 0
+    assert scale == 1
+
+
+def test_ncx2():
+    """
+    Test fitting the shape parameters (df, ncp) of ncx2 to mixed data.
+
+    Calculation in R, with
+    * 5 not censored values [2.7, 0.2, 6.5, 0.4, 0.1],
+    * 1 interval-censored value [[0.6, 1.0]], and
+    * 2 right-censored values [8, 8].
+
+    > library(fitdistrplus)
+    > data <- data.frame(left=c(2.7, 0.2, 6.5, 0.4, 0.1, 0.6, 8, 8),
+    +                    right=c(2.7, 0.2, 6.5, 0.4, 0.1, 1.0, NA, NA))
+    > result = fitdistcens(data, 'chisq', control=list(reltol=1e-14),
+    +                      start=list(df=1, ncp=2))
+    > result
+    Fitting of the distribution ' chisq ' on censored data by maximum
+    likelihood
+    Parameters:
+        estimate
+    df  1.052871
+    ncp 2.362934
+    """
+    data = CensoredData(uncensored=[2.7, 0.2, 6.5, 0.4, 0.1], right=[8, 8],
+                        interval=[[0.6, 1.0]])
+    with np.errstate(over='ignore'):  # remove context when gh-14901 is closed
+        df, ncp, loc, scale = ncx2.fit(data, floc=0, fscale=1,
+                                       optimizer=optimizer)
+    assert_allclose(df, 1.052871, rtol=5e-6)
+    assert_allclose(ncp, 2.362934, rtol=5e-6)
+    assert loc == 0
+    assert scale == 1
+
+
+def test_norm():
+    """
+    Test fitting the normal distribution to interval-censored data.
+
+    Calculation in R:
+
+    > library(fitdistrplus)
+    > data <- data.frame(left=c(0.10, 0.50, 0.75, 0.80),
+    +                    right=c(0.20, 0.55, 0.90, 0.95))
+    > result = fitdistcens(data, 'norm', control=list(reltol=1e-14))
+
+    > result
+    Fitting of the distribution ' norm ' on censored data by maximum likelihood
+    Parameters:
+          estimate
+    mean 0.5919990
+    sd   0.2868042
+    > result$sd
+         mean        sd
+    0.1444432 0.1029451
+    """
+    data = CensoredData(interval=[[0.10, 0.20],
+                                  [0.50, 0.55],
+                                  [0.75, 0.90],
+                                  [0.80, 0.95]])
+
+    loc, scale = norm.fit(data, optimizer=optimizer)
+
+    assert_allclose(loc, 0.5919990, rtol=5e-6)
+    assert_allclose(scale, 0.2868042, rtol=5e-6)
+
+
+def test_weibull_censored1():
+    # Ref: http://www.ams.sunysb.edu/~zhu/ams588/Lecture_3_likelihood.pdf
+
+    # Survival times; '*' indicates right-censored.
+    s = "3,5,6*,8,10*,11*,15,20*,22,23,27*,29,32,35,40,26,28,33*,21,24*"
+
+    times, cens = zip(*[(float(t[0]), len(t) == 2)
+                        for t in [w.split('*') for w in s.split(',')]])
+    data = CensoredData.right_censored(times, cens)
+
+    c, loc, scale = weibull_min.fit(data, floc=0)
+
+    # Expected values are from the reference.
+    assert_allclose(c, 2.149, rtol=1e-3)
+    assert loc == 0
+    assert_allclose(scale, 28.99, rtol=1e-3)
+
+    # Flip the sign of the data, and make the censored values
+    # left-censored. We should get the same parameters when we fit
+    # weibull_max to the flipped data.
+    data2 = CensoredData.left_censored(-np.array(times), cens)
+
+    c2, loc2, scale2 = weibull_max.fit(data2, floc=0)
+
+    assert_allclose(c2, 2.149, rtol=1e-3)
+    assert loc2 == 0
+    assert_allclose(scale2, 28.99, rtol=1e-3)
+
+
+def test_weibull_min_sas1():
+    # Data and SAS results from
+    #   https://support.sas.com/documentation/cdl/en/qcug/63922/HTML/default/
+    #         viewer.htm#qcug_reliability_sect004.htm
+
+    text = """
+           450 0    460 1   1150 0   1150 0   1560 1
+          1600 0   1660 1   1850 1   1850 1   1850 1
+          1850 1   1850 1   2030 1   2030 1   2030 1
+          2070 0   2070 0   2080 0   2200 1   3000 1
+          3000 1   3000 1   3000 1   3100 0   3200 1
+          3450 0   3750 1   3750 1   4150 1   4150 1
+          4150 1   4150 1   4300 1   4300 1   4300 1
+          4300 1   4600 0   4850 1   4850 1   4850 1
+          4850 1   5000 1   5000 1   5000 1   6100 1
+          6100 0   6100 1   6100 1   6300 1   6450 1
+          6450 1   6700 1   7450 1   7800 1   7800 1
+          8100 1   8100 1   8200 1   8500 1   8500 1
+          8500 1   8750 1   8750 0   8750 1   9400 1
+          9900 1  10100 1  10100 1  10100 1  11500 1
+    """
+
+    life, cens = np.array([int(w) for w in text.split()]).reshape(-1, 2).T
+    life = life/1000.0
+
+    data = CensoredData.right_censored(life, cens)
+
+    c, loc, scale = weibull_min.fit(data, floc=0, optimizer=optimizer)
+    assert_allclose(c, 1.0584, rtol=1e-4)
+    assert_allclose(scale, 26.2968, rtol=1e-5)
+    assert loc == 0
+
+
+def test_weibull_min_sas2():
+    # http://support.sas.com/documentation/cdl/en/ormpug/67517/HTML/default/
+    #      viewer.htm#ormpug_nlpsolver_examples06.htm
+
+    # The last two values are right-censored.
+    days = np.array([143, 164, 188, 188, 190, 192, 206, 209, 213, 216, 220,
+                     227, 230, 234, 246, 265, 304, 216, 244])
+
+    data = CensoredData.right_censored(days, [0]*(len(days) - 2) + [1]*2)
+
+    c, loc, scale = weibull_min.fit(data, 1, loc=100, scale=100,
+                                    optimizer=optimizer)
+
+    assert_allclose(c, 2.7112, rtol=5e-4)
+    assert_allclose(loc, 122.03, rtol=5e-4)
+    assert_allclose(scale, 108.37, rtol=5e-4)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_correlation.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_correlation.py
new file mode 100644
index 0000000000000000000000000000000000000000..fdc8abb068dd92b71aeb3081a895045c24faebe0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_correlation.py
@@ -0,0 +1,423 @@
+import pytest
+import numpy as np
+from numpy.testing import assert_allclose
+
+from scipy.conftest import skip_xp_invalid_arg
+import scipy._external.array_api_extra as xpx
+from scipy._lib._array_api import (make_xp_test_case, xp_default_dtype, is_jax, is_cupy,
+                                   eager_warns, xp_result_type, is_array_api_strict)
+from scipy._lib._array_api_no_0d import xp_assert_close, xp_assert_equal
+from scipy import stats
+from scipy.stats._axis_nan_policy import SmallSampleWarning
+
+
+lazy_xp_modules = [stats]
+
+
+@make_xp_test_case(stats.chatterjeexi)
+class TestChatterjeeXi:
+    @pytest.mark.parametrize('case', [
+        dict(y_cont=True, statistic=-0.303030303030303, pvalue=0.9351329808526656),
+        dict(y_cont=False, statistic=0.07407407407407396, pvalue=0.3709859367123997)])
+    @pytest.mark.parametrize('dtype', ['float32', 'float64', None])
+    def test_against_R_XICOR(self, case, dtype, xp):
+        # Test against R package XICOR, e.g.
+        # library(XICOR)
+        # options(digits=16)
+        # x = c(0.11027287231363914, 0.8154770102474279, 0.7073943466920335,
+        #       0.6651317324378386, 0.6905752850115503, 0.06115250587536558,
+        #       0.5209906494474178, 0.3155763519785274, 0.18405731803625924,
+        #       0.8613557911541495)
+        # y = c(0.8402081904493103, 0.5946972833914318, 0.23481606164114155,
+        #       0.49754786197715384, 0.9146460831206026, 0.5848057749217579,
+        #       0.7620801065573549, 0.31410063302647495, 0.7935620302236199,
+        #       0.5423085761365468)
+        # xicor(x, y, ties=FALSE, pvalue=TRUE)
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        rng = np.random.default_rng(25982435982346983)
+        x = rng.random(size=10)
+        y = (rng.random(size=10) if case['y_cont']
+             else rng.integers(0, 5, size=10))
+
+        x, y = xp.asarray(x, dtype=dtype), xp.asarray(y, dtype=dtype)
+        res = stats.chatterjeexi(x, y, y_continuous=case['y_cont'])
+
+        xp_assert_close(res.statistic, xp.asarray(case['statistic'], dtype=dtype))
+        xp_assert_close(res.pvalue, xp.asarray(case['pvalue'], dtype=dtype))
+
+    @pytest.mark.parametrize('y_continuous', (False, True))
+    def test_permutation_asymptotic(self, y_continuous):
+        # XICOR doesn't seem to perform the permutation test as advertised, so
+        # compare the result of a permutation test against an asymptotic test.
+        rng = np.random.default_rng(2524579827426)
+        n = np.floor(rng.uniform(100, 150)).astype(int)
+        shape = (2, n)
+        x = rng.random(size=shape)
+        y = (rng.random(size=shape) if y_continuous
+             else rng.integers(0, 10, size=shape))
+        method = stats.PermutationMethod(rng=rng)
+        res = stats.chatterjeexi(x, y, method=method,
+                                 y_continuous=y_continuous, axis=-1)
+        ref = stats.chatterjeexi(x, y, y_continuous=y_continuous, axis=-1)
+        np.testing.assert_allclose(res.statistic, ref.statistic, rtol=1e-15)
+        np.testing.assert_allclose(res.pvalue, ref.pvalue, rtol=2e-2)
+
+    def test_input_validation(self, xp):
+        rng = np.random.default_rng(25932435798274926)
+        x, y = rng.random(size=(2, 10))
+        x, y = xp.asarray(x), xp.asarray(y)
+
+        message = 'Array shapes are incompatible for broadcasting.|Incompatible shapes'
+        with pytest.raises((ValueError, TypeError), match=message):
+            stats.chatterjeexi(x, y[:-1])
+
+        if not (is_jax(xp) or is_cupy(xp)):
+            # jax misses out on some input validation from _axis_nan_policy decorator
+            # This also fails with cupy for reasons that need to be investigated.
+            message = '...axis 10 is out of bounds for array...|out of range'
+            with pytest.raises((ValueError, IndexError), match=message):
+                stats.chatterjeexi(x, y, axis=10)
+
+        message = '`y_continuous` must be boolean.'
+        with pytest.raises(ValueError, match=message):
+            stats.chatterjeexi(x, y, y_continuous='a herring')
+
+        message = "`method` must be 'asymptotic' or"
+        with pytest.raises(ValueError, match=message):
+            stats.chatterjeexi(x, y, method='ekki ekii')
+
+    def test_special_cases(self, xp):
+        message = 'One or more sample arguments is too small...'
+        with pytest.warns(SmallSampleWarning, match=message):
+            res = stats.chatterjeexi(xp.asarray([1]), xp.asarray([2]))
+
+        assert xp.isnan(res.statistic)
+        assert xp.isnan(res.pvalue)
+
+
+@make_xp_test_case(stats.spearmanrho)
+class TestSpearmanRho:
+    @pytest.mark.parametrize('alternative, statistic, pvalue', [
+        ('two-sided', -0.2727272727272727, 0.4458383415428),
+        ('greater', -0.2727272727272727, 0.7770808292286),
+        ('less', -0.2727272727272727, 0.2229191707714),
+    ])
+    @pytest.mark.parametrize('dtype', ['float32', 'float64', None])
+    def test_against_R_cor_test(self, alternative, statistic, pvalue, dtype, xp):
+        # Test against R cor.test, e.g.
+        # options(digits=16)
+        # x = c(0.11027287231363914, 0.8154770102474279, 0.7073943466920335,
+        #       0.6651317324378386, 0.6905752850115503, 0.06115250587536558,
+        #       0.5209906494474178, 0.3155763519785274, 0.18405731803625924,
+        #       0.8613557911541495)
+        # y = c(0.8402081904493103, 0.5946972833914318, 0.23481606164114155,
+        #       0.49754786197715384, 0.9146460831206026, 0.5848057749217579,
+        #       0.7620801065573549, 0.31410063302647495, 0.7935620302236199,
+        #       0.5423085761365468)
+        # cor.test(x, y, method='spearman', alternative='t', exact=FALSE)
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        rng = np.random.default_rng(25982435982346983)
+        x = rng.random(size=10)
+        y = rng.random(size=10)
+
+        x, y = xp.asarray(x, dtype=dtype), xp.asarray(y, dtype=dtype)
+        res = stats.spearmanrho(x, y, alternative=alternative)
+
+        xp_assert_close(res.statistic, xp.asarray(statistic, dtype=dtype))
+        xp_assert_close(res.pvalue, xp.asarray(pvalue, dtype=dtype))
+
+
+    @pytest.mark.parametrize('alternative, statistic, pvalue', [
+        ('two-sided', -0.4857142857142857, 0.3555555555556),
+        ('greater', -0.4857142857142857, 0.8513888888889),
+        ('less', -0.4857142857142857, 0.1777777777778),
+    ])
+    def test_against_R_cor_test_exact(self, alternative, statistic, pvalue):
+        xp = np  # will test for multiple backends when gh-23772 merges
+        # Test against R cor.test exact=TRUE, e.g.
+        # options(digits=16)
+        # x = c(0.11027287231363914, 0.8154770102474279, 0.7073943466920335,
+        #       0.6651317324378386, 0.6905752850115503, 0.06115250587536558)
+        # y = c(0.5209906494474178, 0.3155763519785274, 0.18405731803625924,
+        #       0.8613557911541495, 0.8402081904493103, 0.5946972833914318)
+        # cor.test(x, y, method='spearman', alternative='t', exact=TRUE)
+        rng = np.random.default_rng(25982435982346983)
+        x = rng.random(size=6)
+        y = rng.random(size=6)
+        x, y = xp.asarray(x), xp.asarray(y)
+
+        method = stats.PermutationMethod()
+        res = stats.spearmanrho(x, y, method=method, alternative=alternative)
+
+        xp_assert_close(res.statistic, xp.asarray(statistic))
+        xp_assert_close(res.pvalue, xp.asarray(pvalue))
+
+
+    @pytest.mark.parametrize('alternative', ('two-sided', 'greater', 'less'))
+    @pytest.mark.parametrize('n', [9, 99, 999])
+    def test_against_scipy_spearmanr(self, alternative, n, xp):
+        rng = np.random.default_rng(5982435982346983)
+        x = rng.integers(n//2, size=n)
+        y = rng.integers(n//2, size=n)
+        dtype = xp_default_dtype(xp)
+
+        ref = stats.spearmanr(x, y, alternative=alternative)
+        ref_statistic = xp.asarray(ref.statistic, dtype=dtype)
+        ref_pvalue = xp.asarray(ref.pvalue, dtype=dtype)
+
+        x = xp.asarray(x, dtype=dtype)
+        y = xp.asarray(y, dtype=dtype)
+        res = stats.spearmanrho(x, y, alternative=alternative)
+
+        xp_assert_close(res.statistic, ref_statistic)
+        xp_assert_close(res.pvalue, ref_pvalue)
+
+
+    @pytest.mark.parametrize('axis', [-1, 0, 1])
+    def test_other_backends_nd(self, axis, xp):
+        # NumPy n-d behavior is tested by `test_axis_nan_policy`;
+        # check other backends against it.
+        rng = np.random.default_rng(8243598346983259)
+        shape = (8, 9, 10)
+        x = rng.standard_normal(size=shape)
+        y = rng.standard_normal(size=shape)
+        res = stats.spearmanrho(xp.asarray(x), xp.asarray(y), axis=axis)
+        ref = stats.spearmanrho(x, y, axis=axis)
+        xp_assert_close(res.statistic, xp.asarray(ref.statistic), atol=1e-16)
+        xp_assert_close(res.pvalue, xp.asarray(ref.pvalue), atol=1e-16)
+
+
+    def test_input_validation(self, xp):
+        rng = np.random.default_rng(25932435798274926)
+        x, y = rng.random(size=(2, 10))
+        x, y = xp.asarray(x), xp.asarray(y)
+
+        msg = 'incompatible for broadcasting|Incompatible shapes|must be broadcastable'
+        with pytest.raises((ValueError, TypeError), match=msg):
+            stats.spearmanrho(x, y[:-1])
+
+        if not is_jax(xp):
+            # jax misses out on some input validation from _axis_nan_policy decorator
+            message = '...axis 10 is out of bounds for array...|out of range'
+            with pytest.raises((ValueError, IndexError), match=message):
+                stats.spearmanrho(x, y, axis=10)
+
+        message = "`alternative` must be 'less', 'greater', or 'two-sided'."
+        with pytest.raises(ValueError, match=message):
+            stats.spearmanrho(x, y, alternative='alternative')
+
+        message = ("`method` must be...")
+        with pytest.raises(ValueError, match=message):
+            stats.spearmanrho(x, y, method='method')
+
+    def test_special_cases(self, xp):
+        def check_nan(res):
+            assert xp.isnan(res.statistic)
+            assert xp.isnan(res.pvalue)
+
+        message = 'One or more sample arguments is too small...'
+        with eager_warns(SmallSampleWarning, match=message, xp=xp):
+            res = stats.spearmanrho(xp.asarray([1]), xp.asarray([2]))
+            check_nan(res)
+
+        x = xp.asarray([1, 1, 1, 1, 1])
+        y = xp.asarray([1, 2, 3, 4, 5])
+        message = 'An input array is constant; the correlation coefficient...'
+        with eager_warns(stats.ConstantInputWarning, match=message, xp=xp):
+            res = stats.spearmanrho(x, y)
+            check_nan(res)
+        with eager_warns(stats.ConstantInputWarning, match=message, xp=xp):
+            res = stats.spearmanrho(y, x)
+            check_nan(res)
+
+
+class RobustSlopesTest:
+    #  'iv, mask, warnings, consistency, gh19678, mstats'
+    def test_input_validation(self, xp):
+        pfun = getattr(stats, self.pfun)
+        other_method = 'joint' if pfun == stats.theilslopes else 'hierarchical'
+        msg = (f"method must be either '{other_method}' or 'separate'. "
+               "'joint_separate' is invalid.")
+        with pytest.raises(ValueError, match=msg):
+            pfun(xp.asarray([0, 1, 1]), method='joint_separate')
+
+    @skip_xp_invalid_arg
+    @pytest.mark.parametrize("method", ['separate', 'other'])
+    def test_mask(self, method):
+        pfun = getattr(stats, self.pfun)
+        if method == 'other':
+            method = 'joint' if self.pfun =='theilslopes' else 'hierarchical'
+
+        # Test for correct masking.
+        mask = np.asarray([False, False, True, False])
+        y = np.ma.array([0, 1, 100, 1], mask=mask)
+        res = pfun(y, method=method)
+        ref = pfun(y[~mask], method=method)
+
+        assert_allclose(res.slope, ref.slope)
+        assert_allclose(res.intercept, ref.intercept)
+
+    def test_degenerate(self, xp):
+        # Test with degenerate input; see gh-15943
+        pfun = getattr(stats, self.pfun)
+        res = pfun(xp.asarray([0, 1]), xp.asarray([0, 0]))
+        assert xp.all(xp.isnan(xp.stack(res)))
+        res = pfun(xp.asarray([0, 0, 0]), xp.asarray([0, 1, 0]))
+        xp_assert_equal(res[0], xp.asarray(0.))
+        xp_assert_equal(res[1], xp.asarray(0.))
+        if pfun == stats.theilslopes:
+            xp_assert_equal(res[2], xp.asarray(xp.nan))
+            xp_assert_equal(res[3], xp.asarray(xp.nan))
+
+    def test_namedtuple_consistency(self, xp):
+        """
+        Simple test to ensure tuple backwards-compatibility of the returned object.
+        """
+        pfun = getattr(stats, self.pfun)
+
+        y = xp.asarray([1, 2, 4])
+        x = xp.asarray([4, 6, 8])
+
+        result = pfun(y, x)
+
+        # note both returned values are distinct here
+        xp_assert_equal(result[0], result.slope)
+        xp_assert_equal(result[1], result.intercept)
+        if pfun == stats.theilslopes:
+            xp_assert_equal(result[2], result.low_slope)
+            xp_assert_equal(result[3], result.high_slope)
+
+    def test_gh19678_uint8(self, xp):
+        # `theilslopes` returned unexpected results when `y` was an unsigned type.
+        # Check that this is resolved.
+        pfun = getattr(stats, self.pfun)
+        rng = np.random.default_rng(2549824598234528)
+        y = xp.asarray(rng.integers(0, 255, size=10, dtype=np.uint8))
+        res = pfun(y, y)
+        xp_assert_close(res.slope, xp.asarray(1.))
+
+    @pytest.mark.parametrize("method", ['separate', 'other'])
+    @pytest.mark.parametrize("case_number", [0, 1, 2, 3])
+    def test_against_mstats(self, method, case_number, xp):
+        pfun = getattr(stats, self.pfun)
+        if method == 'other':
+            method = 'joint' if self.pfun =='theilslopes' else 'hierarchical'
+
+        rng = np.random.default_rng(349824598234528554)
+        match case_number:
+            case 0:  # no x
+                x = None
+                y = rng.random(100)
+            case 1:  # no ties
+                x = rng.random(100)
+                y = rng.random(100)
+            case 2:  # no x ties
+                x = rng.random(100)
+                y = rng.integers(50, size=100)
+            case 3:  # ties in x and y
+                x = rng.integers(25, size=100)
+                y = rng.integers(50, size=100)
+
+        ref = pfun(y, x, method=method)
+
+        y, x = xp.asarray(y), x if x is None else xp.asarray(x)
+        if (is_array_api_strict(xp) and xp.isdtype(y.dtype, 'integral')
+                and xp.isdtype(x.dtype, 'real floating')):
+            # array API strict doesn't allow mixed type promotion, so promote for it
+            y = xp.astype(y, xp.float64)
+
+        dtype = xp_result_type(y, x, force_floating=True, xp=xp)
+
+        res = pfun(y, x, method=method, axis=-1)
+
+        assert res.slope != 0  # ensure randomly generated test case isn't trivial
+        xp_assert_close(res.slope, xp.asarray(ref.slope, dtype=dtype))
+        xp_assert_close(res.intercept, xp.asarray(ref.intercept, dtype=dtype))
+        if pfun == stats.theilslopes:
+            xp_assert_close(res.low_slope, xp.asarray(ref.low_slope, dtype=dtype))
+            xp_assert_close(res.high_slope, xp.asarray(ref.high_slope, dtype=dtype))
+
+
+@make_xp_test_case(stats.theilslopes)
+class TestTheilslopes(RobustSlopesTest):
+    pfun = 'theilslopes'
+    def test_theilslopes(self, xp):
+        # Test for basic slope and intercept.
+        y = xp.asarray([0, 1, 1])
+        slope, intercept, lower, upper = stats.theilslopes(y)
+        xp_assert_close(slope, xp.asarray(0.5))
+        xp_assert_close(intercept, xp.asarray(0.5))
+
+        slope, intercept, lower, upper = stats.theilslopes(y, method='joint')
+        xp_assert_close(slope, xp.asarray(0.5))
+        xp_assert_close(intercept, xp.asarray(0.0))
+
+        # Test of confidence intervals from example in Sen (1968).
+        x = xp.asarray([1, 2, 3, 4, 10, 12, 18])
+        y = xp.asarray([9, 15, 19, 20, 45, 55, 78])
+        slope, intercept, lower, upper = stats.theilslopes(y, x, 0.07)
+        xp_assert_close(slope, xp.asarray(4.0))
+        xp_assert_close(intercept, xp.asarray(4.0))
+        xp_assert_close(upper, xp.asarray(4.38), rtol=5e-3)
+        xp_assert_close(lower, xp.asarray(3.71), rtol=5e-3)
+
+        slope, intercept, lower, upper = stats.theilslopes(y, x, 0.07,
+                                                           method='joint')
+        xp_assert_close(slope, xp.asarray(4.0))
+        xp_assert_close(intercept, xp.asarray(6.0))
+        xp_assert_close(upper, xp.asarray(4.38), rtol=5e-3)
+        xp_assert_close(lower, xp.asarray(3.71), rtol=5e-3)
+
+
+@make_xp_test_case(stats.siegelslopes)
+class TestSiegelslopes(RobustSlopesTest):
+    pfun = 'siegelslopes'
+    def test_siegelslopes(self, xp):
+        # method should be exact for straight line
+        y = 2 * xp.arange(10.) + 0.5
+        slope, intercept = stats.siegelslopes(y)
+        xp_assert_close(slope, xp.asarray(2.0))
+        xp_assert_close(intercept, xp.asarray(0.5))
+        slope, intercept = stats.siegelslopes(y, method='separate')
+        xp_assert_close(slope, xp.asarray(2.0))
+        xp_assert_close(intercept, xp.asarray(0.5))
+
+        x = 2 * xp.arange(10.)
+        y = 5 * x - 3.0
+        slope, intercept = stats.siegelslopes(y, x)
+        xp_assert_close(slope, xp.asarray(5.0))
+        xp_assert_close(intercept, xp.asarray(-3.0))
+        slope, intercept = stats.siegelslopes(y, x, method='separate')
+        xp_assert_close(slope, xp.asarray(5.0))
+        xp_assert_close(intercept, xp.asarray(-3.0))
+
+        # method is robust to outliers: breakdown point of 50%
+        y = xpx.at(y)[:4].set(1000.)
+        xp_assert_close(slope, xp.asarray(5.0))
+        xp_assert_close(intercept, xp.asarray(-3.0))
+
+        # if there are no outliers, results should be comparable to linregress
+        x = np.arange(10.)
+        y = -2.3 + 0.3 * x + stats.norm.rvs(size=10, random_state=231)
+        slope_ols, intercept_ols, _, _, _ = stats.linregress(x, y)
+
+        y, x = xp.asarray(y), xp.asarray(x)
+        slope, intercept = stats.siegelslopes(y, x)
+        xp_assert_close(slope, xp.asarray(slope_ols), rtol=0.1)
+        xp_assert_close(intercept, xp.asarray(intercept_ols), rtol=0.1)
+
+        slope, intercept = stats.siegelslopes(y, x, method='separate')
+        xp_assert_close(slope, xp.asarray(slope_ols), rtol=0.1)
+        xp_assert_close(intercept, xp.asarray(intercept_ols), rtol=0.1)
+
+    def test_method(self, xp):
+        # Test that distinguishes between the methods. Reference values generated with
+        # SciPy 1.17 (results unchanged since at least 1.12, so assumed "correct") to
+        # detect *changes*.
+        y = xp.asarray([2, 1, 3, 0, 4])
+        slope, intercept = stats.siegelslopes(y, method='separate')
+        xp_assert_close(slope, xp.asarray(0.25))
+        xp_assert_close(intercept, xp.asarray(1.75))
+        slope, intercept = stats.siegelslopes(y, method='hierarchical')
+        xp_assert_close(slope, xp.asarray(0.25))  # always the same between the methods
+        xp_assert_close(intercept, xp.asarray(2.0))
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_crosstab.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_crosstab.py
new file mode 100644
index 0000000000000000000000000000000000000000..42dc525045b6e455c0bf498adcac5faabe2106ff
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_crosstab.py
@@ -0,0 +1,118 @@
+import pytest
+import numpy as np
+from numpy.testing import assert_array_equal, assert_equal
+from scipy.stats.contingency import crosstab
+
+
+@pytest.mark.parametrize('sparse', [False, True])
+def test_crosstab_basic(sparse):
+    a = [0, 0, 9, 9, 0, 0, 9]
+    b = [2, 1, 3, 1, 2, 3, 3]
+    expected_avals = [0, 9]
+    expected_bvals = [1, 2, 3]
+    expected_count = np.array([[1, 2, 1],
+                               [1, 0, 2]])
+    (avals, bvals), count = crosstab(a, b, sparse=sparse)
+    assert_array_equal(avals, expected_avals)
+    assert_array_equal(bvals, expected_bvals)
+    if sparse:
+        assert_array_equal(count.toarray(), expected_count)
+    else:
+        assert_array_equal(count, expected_count)
+
+
+@pytest.mark.parametrize('sparse', [False, True])
+def test_crosstab_basic_1d(sparse):
+    # Verify that a single input sequence works as expected.
+    x = [1, 2, 3, 1, 2, 3, 3]
+    expected_xvals = [1, 2, 3]
+    expected_count = np.array([2, 2, 3])
+    (xvals,), count = crosstab(x, sparse=sparse)
+    assert_array_equal(xvals, expected_xvals)
+    if sparse:
+        assert_array_equal(count.toarray(), expected_count)
+    else:
+        assert_array_equal(count, expected_count)
+
+
+@pytest.mark.parametrize('sparse', [False, True])
+def test_crosstab_basic_3d(sparse):
+    # Verify the function for three input sequences.
+    a = 'a'
+    b = 'b'
+    x = [0, 0, 9, 9, 0, 0, 9, 9]
+    y = [a, a, a, a, b, b, b, a]
+    z = [1, 2, 3, 1, 2, 3, 3, 1]
+    expected_xvals = [0, 9]
+    expected_yvals = [a, b]
+    expected_zvals = [1, 2, 3]
+    expected_count = np.array([[[1, 1, 0],
+                                [0, 1, 1]],
+                               [[2, 0, 1],
+                                [0, 0, 1]]])
+    (xvals, yvals, zvals), count = crosstab(x, y, z, sparse=sparse)
+    assert_array_equal(xvals, expected_xvals)
+    assert_array_equal(yvals, expected_yvals)
+    assert_array_equal(zvals, expected_zvals)
+    if sparse:
+        assert_array_equal(count.toarray(), expected_count)
+    else:
+        assert_array_equal(count, expected_count)
+
+
+@pytest.mark.parametrize('sparse', [False, True])
+def test_crosstab_levels(sparse):
+    a = [0, 0, 9, 9, 0, 0, 9]
+    b = [1, 2, 3, 1, 2, 3, 3]
+    expected_avals = [0, 9]
+    expected_bvals = [0, 1, 2, 3]
+    expected_count = np.array([[0, 1, 2, 1],
+                               [0, 1, 0, 2]])
+    (avals, bvals), count = crosstab(a, b, levels=[None, [0, 1, 2, 3]],
+                                     sparse=sparse)
+    assert_array_equal(avals, expected_avals)
+    assert_array_equal(bvals, expected_bvals)
+    if sparse:
+        assert_array_equal(count.toarray(), expected_count)
+    else:
+        assert_array_equal(count, expected_count)
+
+
+@pytest.mark.parametrize('sparse', [False, True])
+def test_crosstab_extra_levels(sparse):
+    # The pair of values (-1, 3) will be ignored, because we explicitly
+    # request the counted `a` values to be [0, 9].
+    a = [0, 0, 9, 9, 0, 0, 9, -1]
+    b = [1, 2, 3, 1, 2, 3, 3, 3]
+    expected_avals = [0, 9]
+    expected_bvals = [0, 1, 2, 3]
+    expected_count = np.array([[0, 1, 2, 1],
+                               [0, 1, 0, 2]])
+    (avals, bvals), count = crosstab(a, b, levels=[[0, 9], [0, 1, 2, 3]],
+                                     sparse=sparse)
+    assert_array_equal(avals, expected_avals)
+    assert_array_equal(bvals, expected_bvals)
+    if sparse:
+        assert_array_equal(count.toarray(), expected_count)
+    else:
+        assert_array_equal(count, expected_count)
+
+
+def test_validation_at_least_one():
+    with pytest.raises(TypeError, match='At least one'):
+        crosstab()
+
+
+def test_validation_same_lengths():
+    with pytest.raises(ValueError, match='must have the same length'):
+        crosstab([1, 2], [1, 2, 3, 4])
+
+
+def test_validation_len_levels_matches_args():
+    with pytest.raises(ValueError, match='number of input sequences'):
+        crosstab([0, 1, 1], [8, 8, 9], levels=([0, 1, 2, 3],))
+
+
+def test_result():
+    res = crosstab([0, 1], [1, 2])
+    assert_equal((res.elements, res.count), res)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_device_dtype.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_device_dtype.py
new file mode 100644
index 0000000000000000000000000000000000000000..c20bfc1104a7c3b0dcd2ee6484aed9169380eeef
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_device_dtype.py
@@ -0,0 +1,248 @@
+import pytest
+import numpy as np
+from scipy import stats
+
+from scipy._lib._array_api import xp_device, is_array_api_strict, is_torch
+from scipy.stats._stats_py import _xp_mean, _xp_var
+
+skip_xp_backends = pytest.mark.skip_xp_backends
+
+dtypes = ['float32', 'float64']
+
+
+def get_arrays(n_arrays, *, dtype=np.float64, xp=np, shape=(30,), device=None,
+               seed=84912165484321):
+    rng = np.random.default_rng(seed)
+
+    datas = []
+    for i in range(n_arrays):
+        data = 10*rng.random(size=shape)
+        if xp.isdtype(dtype, 'complex floating'):
+            data = data * 10j*rng.standard_normal(size=shape)
+        data = xp.asarray(data, dtype=dtype, device=device)
+        datas.append(data)
+
+    return datas
+
+
+@pytest.mark.parametrize('fun, kwargs', [(stats.gmean, {}),
+                                         (stats.hmean, {}),
+                                         (stats.pmean, {'p': 2}),
+                                         (_xp_mean, {}),
+                                         ])
+@pytest.mark.parametrize('dtype', dtypes)
+@pytest.mark.uses_xp_capabilities(False, reason="not used in this test yet")
+def test_xmean(fun, kwargs, dtype, xp, devices):
+    dtype = getattr(xp, dtype)
+    for device in devices:
+        x, weights = get_arrays(2, device=device, dtype=dtype, xp=xp)
+        res = fun(x, weights=weights, **kwargs)
+        assert xp_device(res) == xp_device(x)
+        assert x.dtype == dtype
+
+
+@skip_xp_backends('array_api_strict',
+                  reason="special functions don't work with 'device1'")
+@pytest.mark.parametrize('nargs', [1, 2])
+@pytest.mark.parametrize('dtype', dtypes)
+@pytest.mark.uses_xp_capabilities(False, reason="not used in this test yet")
+def test_entropy(nargs, dtype, xp, devices):
+    dtype = getattr(xp, dtype)
+    for device in devices:
+        args = get_arrays(nargs, device=device, dtype=dtype, xp=xp)
+        res = stats.entropy(*args)
+        assert xp_device(res) == xp_device(args[0])
+        assert res.dtype == dtype
+
+
+@pytest.mark.parametrize('dtype', dtypes)
+@pytest.mark.uses_xp_capabilities(False, reason="not used in this test yet")
+def test_directional_stats(dtype, xp, devices):
+    dtype = getattr(xp, dtype)
+    for device in devices:
+        x = get_arrays(1, shape=(30, 3), device=device, dtype=dtype, xp=xp)[0]
+        res = stats.directional_stats(x)
+        assert xp_device(res.mean_direction) == xp_device(x)
+        assert res.mean_direction.dtype == dtype
+        assert xp_device(res.mean_resultant_length) == xp_device(x)
+        assert res.mean_resultant_length.dtype == dtype
+
+
+@pytest.mark.parametrize('method', [
+    "vasicek",
+    "van es",
+    pytest.param(
+        "correa",
+        marks=[
+            skip_xp_backends("array_api_strict", reason="Invalid fancy indexing"),
+            skip_xp_backends("dask.array", reason="Invalid fancy indexing"),
+        ],
+    ),
+    "ebrahimi",
+    "auto",
+])
+@pytest.mark.parametrize('dtype', dtypes)
+@pytest.mark.uses_xp_capabilities(False, reason="not used in this test yet")
+def test_differential_entropy(method, dtype, xp, devices):
+    dtype = getattr(xp, dtype)
+    for device in devices:
+        values = get_arrays(1, device=device, dtype=dtype, xp=xp)[0]
+        res = stats.differential_entropy(values, method=method)
+        assert xp_device(res) == xp_device(values)
+        assert values.dtype == dtype
+
+
+@skip_xp_backends('dask.array', reason='no take_along_axis')
+@pytest.mark.parametrize('method', ['inverted_cdf', 'averaged_inverted_cdf',
+                                    'closest_observation', 'interpolated_inverted_cdf',
+                                    'hazen', 'weibull', 'linear', 'median_unbiased',
+                                    'normal_unbiased', 'harrell-davis'])
+@pytest.mark.parametrize('dtype', dtypes)
+@pytest.mark.uses_xp_capabilities(False, reason="not used in this test yet")
+def test_quantile(method, dtype, xp, devices):
+    if (is_array_api_strict(xp) or is_torch(xp)) and method == 'harrell-davis':
+        pytest.skip("'harrell-davis' not currently supported on GPU.")
+
+    dtype = getattr(xp, dtype)
+    for device in devices:
+        values = get_arrays(1, device=device, dtype=dtype, xp=xp)[0]
+        res = stats.quantile(values, 0.5, method=method)
+        assert xp_device(res) == xp_device(values)
+        assert values.dtype == dtype
+
+
+@pytest.mark.parametrize('dtype', dtypes)
+@pytest.mark.uses_xp_capabilities(False, reason="not used in this test yet")
+def test_boxcox_llf(dtype, xp, devices):
+    dtype = getattr(xp, dtype)
+    for device in devices:
+        data = get_arrays(1, device=device, dtype=dtype, xp=xp)[0]
+        res = stats.boxcox_llf(1, data)
+        assert xp_device(res) == xp_device(data)
+        assert data.dtype == dtype
+
+
+@pytest.mark.parametrize('fun, kwargs',
+    [(stats.moment, {'order': 2}),
+     (stats.skew, {}),
+     (stats.skew, {'bias': False}),
+     (stats.kurtosis, {}),
+     (stats.kurtosis, {'bias': False}),
+     (stats.sem, {}),
+     (stats.kstat, {'n': 1}),
+     (stats.kstat, {'n': 2}),
+     (stats.kstat, {'n': 3}),
+     (stats.kstat, {'n': 4}),
+     (stats.kstatvar, {'n': 1}),
+     (stats.kstatvar, {'n': 2}),
+     (stats.circmean, {}),
+     (stats.circvar, {}),
+     (stats.circstd, {}),
+     (_xp_var, {}),
+     (stats.gstd, {}),
+     (stats.variation, {}),
+     (stats.tmean, {'limits': (0.1, 0.9)}),
+     (stats.tvar, {'limits': (0.1, 0.9)}),
+     (stats.tmin, {'lowerlimit': 0.1}),
+     (stats.tmax, {'upperlimit': 0.9}),
+     (stats.tstd, {'limits': (0.1, 0.9)}),
+     (stats.tsem, {'limits': (0.1, 0.9)}),
+     ])
+@pytest.mark.parametrize('dtype', dtypes)
+@pytest.mark.uses_xp_capabilities(False, reason="not used in this test yet")
+def test_one_in_one_out(fun, kwargs, dtype, xp, devices):
+    dtype = getattr(xp, dtype)
+    for device in devices:
+        array = get_arrays(1, device=device, dtype=dtype, xp=xp)[0]
+        res = fun(array, **kwargs)
+        assert xp_device(res) == xp_device(array)
+        assert res.dtype == dtype
+
+
+@pytest.mark.parametrize('dtype', dtypes)
+@pytest.mark.uses_xp_capabilities(False, reason="not used in this test yet")
+def test_describe(dtype, xp, devices):
+    dtype = getattr(xp, dtype)
+    for device in devices:
+        array = get_arrays(1, shape=10, dtype=dtype, device=device, xp=xp)[0]
+        res = stats.describe(array, axis=-1)
+
+        assert xp_device(res.nobs) == xp_device(array)
+        assert xp_device(res.minmax[0]) == xp_device(array)
+        assert xp_device(res.minmax[1]) == xp_device(array)
+        assert xp_device(res.variance) == xp_device(array)
+        assert xp_device(res.skewness) == xp_device(array)
+        assert xp_device(res.kurtosis) == xp_device(array)
+
+        assert res.minmax[0].dtype == dtype
+        assert res.minmax[1].dtype == dtype
+        assert res.variance.dtype == dtype
+        assert res.skewness.dtype == dtype
+        assert res.kurtosis.dtype == dtype
+
+
+@pytest.mark.parametrize('fun', [stats.zscore, stats.gzscore, stats.zmap])
+@pytest.mark.parametrize('dtype', dtypes)
+@pytest.mark.uses_xp_capabilities(False, reason="not used in this test yet")
+def test_zscore(fun, dtype, xp, devices):
+    dtype = getattr(xp, dtype)
+    for device in devices:
+        n = 2 if (fun == stats.zmap) else 1
+        arrays = get_arrays(n, device=device, dtype=dtype, xp=xp)
+        res = fun(*arrays)
+        assert xp_device(res) == xp_device(arrays[0])
+        assert res.dtype == dtype
+
+
+@skip_xp_backends('array_api_strict', reason="special/_support_alternative_backends")
+@skip_xp_backends(cpu_only=True, exceptions=['cupy', 'jax.numpy'])
+@pytest.mark.parametrize('f_name', ['ttest_1samp', 'ttest_rel', 'ttest_ind', 'skewtest',
+                                    'kurtosistest', 'normaltest', 'jarque_bera',
+                                    'bartlett', 'pearsonr', 'chisquare',
+                                    'power_divergence'])
+@pytest.mark.parametrize('dtype', dtypes)
+@pytest.mark.uses_xp_capabilities(False, reason="not used in this test yet")
+def test_hypothesis_tests(f_name, dtype, xp, devices):
+    dtype = getattr(xp, dtype)
+    for device in devices:
+        f = getattr(stats, f_name)
+
+        n = 2 if f_name in {'ttest_1samp', 'ttest_rel', 'ttest_ind', 'bartlett',
+                            'pearsonr', 'chisquare'} else 1
+
+        arrays = get_arrays(n, xp=xp, dtype=dtype, device=device)
+        if f_name == 'ttest_1samp':
+            arrays[1] = xp.mean(arrays[1])
+        if f_name == 'chisquare':
+            arrays[1] = xp.sum(arrays[0]) * arrays[1] / xp.sum(arrays[1])
+
+        res = f(*arrays)
+        assert xp_device(res.statistic) == xp_device(arrays[0])
+        assert xp_device(res.pvalue) == xp_device(arrays[0])
+        assert res.statistic.dtype == dtype
+        assert res.pvalue.dtype == dtype
+
+        if f_name in {'ttest_1samp', 'ttest_rel', 'ttest_ind', 'pearsonr'}:
+            res_ci = res.confidence_interval()
+            assert xp_device(res_ci.low) == xp_device(arrays[0])
+            assert xp_device(res_ci.high) == xp_device(arrays[0])
+            assert res_ci.low.dtype == dtype
+            assert res_ci.high.dtype == dtype
+
+
+@skip_xp_backends('array_api_strict',
+                  reason="special functions don't work with 'device1'")
+@skip_xp_backends(cpu_only=True, exceptions=['cupy', 'jax.numpy'])
+@pytest.mark.parametrize('method', ['fisher', 'pearson', 'tippett', 'stouffer',
+                                    'mudholkar_george'])
+@pytest.mark.parametrize('dtype', dtypes)
+@pytest.mark.uses_xp_capabilities(False, reason="not used in this test yet")
+def test_combine_pvalues(method, dtype, xp, devices):
+    dtype = getattr(xp, dtype)
+    for device in devices:
+        pvalues = get_arrays(1, xp=xp, dtype=dtype, device=device)[0] / 10
+        res = stats.combine_pvalues(pvalues, method=method)
+        assert xp_device(res.statistic) == xp_device(pvalues)
+        assert xp_device(res.pvalue) == xp_device(pvalues)
+        assert res.statistic.dtype == dtype
+        assert res.pvalue.dtype == dtype
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_discrete_basic.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_discrete_basic.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e67c60a21d193ee3f304b80f6660110fb95b177
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_discrete_basic.py
@@ -0,0 +1,625 @@
+import warnings
+
+import numpy.testing as npt
+from numpy.testing import assert_allclose
+
+import numpy as np
+import pytest
+
+from scipy import stats
+from scipy.special import _ufuncs
+from .common_tests import (check_normalization, check_moment,
+                           check_mean_expect,
+                           check_var_expect, check_skew_expect,
+                           check_kurt_expect, check_entropy,
+                           check_private_entropy, check_edge_support,
+                           check_named_args, check_random_state_property,
+                           check_pickling, check_rvs_broadcast,
+                           check_freezing,)
+from scipy.stats._distr_params import distdiscrete, invdistdiscrete
+from scipy.stats._distn_infrastructure import rv_discrete_frozen
+
+vals = ([1, 2, 3, 4], [0.1, 0.2, 0.3, 0.4])
+distdiscrete += [[stats.rv_discrete(values=vals), ()]]
+
+# For these distributions, test_discrete_basic only runs with test mode full
+distslow = {'nhypergeom'}
+
+# Override number of ULPs adjustment for `check_cdf_ppf`
+roundtrip_cdf_ppf_exceptions = {'nbinom': 30}
+
+def cases_test_discrete_basic():
+    seen = set()
+    for distname, arg in distdiscrete:
+        if distname in distslow:
+            yield pytest.param(distname, arg, distname, marks=pytest.mark.slow)
+        else:
+            yield distname, arg, distname not in seen
+        seen.add(distname)
+
+
+@pytest.mark.parametrize('distname,arg,first_case', list(cases_test_discrete_basic()))
+def test_discrete_basic(distname, arg, first_case, num_parallel_threads):
+    if (isinstance(distname, str) and distname.startswith('nchypergeom')
+            and num_parallel_threads > 1):
+        pytest.skip(reason='nchypergeom has a global random generator')
+
+    try:
+        distfn = getattr(stats, distname)
+    except TypeError:
+        distfn = distname
+        distname = 'sample distribution'
+    rng = np.random.RandomState(9765456)
+    rvs = distfn.rvs(*arg, size=2000, random_state=rng)
+    supp = np.unique(rvs)
+    m, v = distfn.stats(*arg)
+    check_cdf_ppf(distfn, arg, supp, distname + ' cdf_ppf')
+
+    check_pmf_cdf(distfn, arg, distname)
+    check_oth(distfn, arg, supp, distname + ' oth')
+    check_edge_support(distfn, arg)
+
+    alpha = 0.01
+    check_discrete_chisquare(distfn, arg, rvs, alpha,
+                             distname + ' chisquare')
+
+    if first_case:
+        locscale_defaults = (0,)
+        meths = [distfn.pmf, distfn.logpmf, distfn.cdf, distfn.logcdf,
+                 distfn.logsf]
+        # make sure arguments are within support
+        # for some distributions, this needs to be overridden
+        spec_k = {'randint': 11, 'hypergeom': 4, 'bernoulli': 0,
+                  'nchypergeom_wallenius': 6}
+        k = spec_k.get(distname, 1)
+        check_named_args(distfn, k, arg, locscale_defaults, meths)
+        if distname != 'sample distribution':
+            check_scale_docstring(distfn)
+        if num_parallel_threads == 1:
+            check_random_state_property(distfn, arg)
+            if distname not in {'poisson_binom'}:  # can't be pickled
+                check_pickling(distfn, arg)
+        check_freezing(distfn, arg)
+
+        # Entropy
+        check_entropy(distfn, arg, distname)
+        if distfn.__class__._entropy != stats.rv_discrete._entropy:
+            check_private_entropy(distfn, arg, stats.rv_discrete)
+
+
+@pytest.mark.parametrize('distname,arg', distdiscrete)
+def test_moments(distname, arg):
+    try:
+        distfn = getattr(stats, distname)
+    except TypeError:
+        distfn = distname
+        distname = 'sample distribution'
+    m, v, s, k = distfn.stats(*arg, moments='mvsk')
+    check_normalization(distfn, arg, distname)
+
+    # compare `stats` and `moment` methods
+    check_moment(distfn, arg, m, v, distname)
+    check_mean_expect(distfn, arg, m, distname)
+    check_var_expect(distfn, arg, m, v, distname)
+    check_skew_expect(distfn, arg, m, v, s, distname)
+    with warnings.catch_warnings():
+        if distname in ['zipf', 'betanbinom']:
+            warnings.simplefilter("ignore", RuntimeWarning)
+        check_kurt_expect(distfn, arg, m, v, k, distname)
+
+    # frozen distr moments
+    check_moment_frozen(distfn, arg, m, 1)
+    check_moment_frozen(distfn, arg, v+m*m, 2)
+
+
+@pytest.mark.parametrize('dist,shape_args', distdiscrete)
+def test_rvs_broadcast(dist, shape_args):
+    # If shape_only is True, it means the _rvs method of the
+    # distribution uses more than one random number to generate a random
+    # variate.  That means the result of using rvs with broadcasting or
+    # with a nontrivial size will not necessarily be the same as using the
+    # numpy.vectorize'd version of rvs(), so we can only compare the shapes
+    # of the results, not the values.
+    # Whether or not a distribution is in the following list is an
+    # implementation detail of the distribution, not a requirement.  If
+    # the implementation the rvs() method of a distribution changes, this
+    # test might also have to be changed.
+    shape_only = dist in ['betabinom', 'betanbinom', 'skellam', 'yulesimon',
+                          'dlaplace', 'nchypergeom_fisher',
+                          'nchypergeom_wallenius', 'poisson_binom']
+    try:
+        distfunc = getattr(stats, dist)
+    except TypeError:
+        distfunc = dist
+        dist = f'rv_discrete(values=({dist.xk!r}, {dist.pk!r}))'
+    loc = np.zeros(2)
+    nargs = distfunc.numargs
+    allargs = []
+    bshape = []
+
+    if dist == 'poisson_binom':
+        # normal rules apply except the last axis of `p` is ignored
+        p = np.full((3, 1, 10), 0.5)
+        allargs = (p, loc)
+        bshape = (3, 2)
+        check_rvs_broadcast(distfunc, dist, allargs,
+                            bshape, shape_only, [np.dtype(int)])
+        return
+
+    # Generate shape parameter arguments...
+    for k in range(nargs):
+        shp = (k + 3,) + (1,)*(k + 1)
+        param_val = shape_args[k]
+        allargs.append(np.full(shp, param_val))
+        bshape.insert(0, shp[0])
+    allargs.append(loc)
+    bshape.append(loc.size)
+    # bshape holds the expected shape when loc, scale, and the shape
+    # parameters are all broadcast together.
+    check_rvs_broadcast(
+        distfunc, dist, allargs, bshape, shape_only, [np.dtype(int)]
+    )
+
+
+@pytest.mark.parametrize('dist,args', distdiscrete)
+def test_ppf_with_loc(dist, args):
+    try:
+        distfn = getattr(stats, dist)
+    except TypeError:
+        distfn = dist
+    #check with a negative, no and positive relocation.
+    rng = np.random.default_rng(5108587887)
+
+    re_locs = [rng.integers(-10, -1), 0, rng.integers(1, 10)]
+    _a, _b = distfn.support(*args)
+    for loc in re_locs:
+        npt.assert_array_equal(
+            [_a-1+loc, _b+loc],
+            [distfn.ppf(0.0, *args, loc=loc), distfn.ppf(1.0, *args, loc=loc)]
+            )
+
+
+@pytest.mark.parametrize('dist, args', distdiscrete)
+def test_isf_with_loc(dist, args):
+    try:
+        distfn = getattr(stats, dist)
+    except TypeError:
+        distfn = dist
+    # check with a negative, no and positive relocation.
+    rng = np.random.default_rng(4030503535)
+    re_locs = [rng.integers(-10, -1), 0, rng.integers(1, 10)]
+    _a, _b = distfn.support(*args)
+    for loc in re_locs:
+        expected = _b + loc, _a - 1 + loc
+        res = distfn.isf(0., *args, loc=loc), distfn.isf(1., *args, loc=loc)
+        npt.assert_array_equal(expected, res)
+    # test broadcasting behaviour
+    re_locs = [rng.integers(-10, -1, size=(5, 3)),
+               np.zeros((5, 3)),
+               rng.integers(1, 10, size=(5, 3))]
+    _a, _b = distfn.support(*args)
+    for loc in re_locs:
+        expected = _b + loc, _a - 1 + loc
+        res = distfn.isf(0., *args, loc=loc), distfn.isf(1., *args, loc=loc)
+        npt.assert_array_equal(expected, res)
+
+
+def check_cdf_ppf(distfn, arg, supp, msg):
+    # supp is assumed to be an array of integers in the support of distfn
+    # (but not necessarily all the integers in the support).
+    # This test assumes that the PMF of any value in the support of the
+    # distribution is greater than 1e-8.
+
+    # cdf is a step function, and ppf(q) = min{k : cdf(k) >= q, k integer}
+    cdf_supp = distfn.cdf(supp, *arg)
+    # In very rare cases, the finite precision calculation of ppf(cdf(supp))
+    # can produce an array in which an element is off by one.  We nudge the
+    # CDF values down by a few ULPs help to avoid this.
+    n_ulps = roundtrip_cdf_ppf_exceptions.get(distfn.name, 15)
+    cdf_supp0 = cdf_supp - n_ulps*np.spacing(cdf_supp)
+    npt.assert_array_equal(distfn.ppf(cdf_supp0, *arg),
+                           supp, msg + '-roundtrip')
+    # Repeat the same calculation, but with the CDF values decreased by 1e-8.
+    npt.assert_array_equal(distfn.ppf(distfn.cdf(supp, *arg) - 1e-8, *arg),
+                           supp, msg + '-roundtrip')
+
+    if not hasattr(distfn, 'xk'):
+        _a, _b = distfn.support(*arg)
+        supp1 = supp[supp < _b]
+        npt.assert_array_equal(distfn.ppf(distfn.cdf(supp1, *arg) + 1e-8, *arg),
+                               supp1 + distfn.inc, msg + ' ppf-cdf-next')
+
+
+def check_pmf_cdf(distfn, arg, distname):
+    if hasattr(distfn, 'xk'):
+        index = distfn.xk
+    else:
+        startind = int(distfn.ppf(0.01, *arg) - 1)
+        index = list(range(startind, startind + 10))
+    cdfs = distfn.cdf(index, *arg)
+    pmfs_cum = distfn.pmf(index, *arg).cumsum()
+
+    atol, rtol = 1e-10, 1e-10
+    if distname == 'skellam':    # ncx2 accuracy
+        atol, rtol = 1e-5, 1e-5
+    npt.assert_allclose(cdfs - cdfs[0], pmfs_cum - pmfs_cum[0],
+                        atol=atol, rtol=rtol)
+
+    # also check that pmf at non-integral k is zero
+    k = np.asarray(index)
+    k_shifted = k[:-1] + np.diff(k)/2
+    npt.assert_equal(distfn.pmf(k_shifted, *arg), 0)
+
+    # better check frozen distributions, and also when loc != 0
+    loc = 0.5
+    dist = distfn(loc=loc, *arg)
+    npt.assert_allclose(dist.pmf(k[1:] + loc), np.diff(dist.cdf(k + loc)))
+    npt.assert_equal(dist.pmf(k_shifted + loc), 0)
+
+
+def check_moment_frozen(distfn, arg, m, k):
+    npt.assert_allclose(distfn(*arg).moment(k), m,
+                        atol=1e-10, rtol=1e-10)
+
+
+def check_oth(distfn, arg, supp, msg):
+    # checking other methods of distfn
+    npt.assert_allclose(distfn.sf(supp, *arg), 1. - distfn.cdf(supp, *arg),
+                        atol=1e-10, rtol=1e-10)
+
+    q = np.linspace(0.01, 0.99, 20)
+    npt.assert_allclose(distfn.isf(q, *arg), distfn.ppf(1. - q, *arg),
+                        atol=1e-10, rtol=1e-10)
+
+    median_sf = distfn.isf(0.5, *arg)
+    npt.assert_(distfn.sf(median_sf - 1, *arg) > 0.5)
+    npt.assert_(distfn.cdf(median_sf + 1, *arg) > 0.5)
+
+
+def check_discrete_chisquare(distfn, arg, rvs, alpha, msg):
+    """Perform chisquare test for random sample of a discrete distribution
+
+    Parameters
+    ----------
+    distname : str
+        name of distribution function
+    arg : sequence
+        parameters of distribution
+    alpha : float
+        significance level, threshold for p-value
+
+    Returns
+    -------
+    result : bool
+        0 if test passes, 1 if test fails
+
+    """
+    wsupp = 0.05
+
+    # construct intervals with minimum mass `wsupp`.
+    # intervals are left-half-open as in a cdf difference
+    _a, _b = distfn.support(*arg)
+    lo = int(max(_a, -1000))
+    high = int(min(_b, 1000)) + 1
+    distsupport = range(lo, high)
+    last = 0
+    distsupp = [lo]
+    distmass = []
+    for ii in distsupport:
+        current = distfn.cdf(ii, *arg)
+        if current - last >= wsupp - 1e-14:
+            distsupp.append(ii)
+            distmass.append(current - last)
+            last = current
+            if current > (1 - wsupp):
+                break
+    if distsupp[-1] < _b:
+        distsupp.append(_b)
+        distmass.append(1 - last)
+    distsupp = np.array(distsupp)
+    distmass = np.array(distmass)
+
+    # convert intervals to right-half-open as required by histogram
+    histsupp = distsupp + 1e-8
+    histsupp[0] = _a
+
+    # find sample frequencies and perform chisquare test
+    freq, hsupp = np.histogram(rvs, histsupp)
+    chis, pval = stats.chisquare(np.array(freq), len(rvs)*distmass)
+
+    npt.assert_(
+        pval > alpha,
+        f'chisquare - test for {msg} at arg = {str(arg)} with pval = {str(pval)}'
+    )
+
+
+def check_scale_docstring(distfn):
+    if distfn.__doc__ is not None:
+        # Docstrings can be stripped if interpreter is run with -OO
+        npt.assert_('scale' not in distfn.__doc__)
+
+
+@pytest.mark.parametrize('method', ['pmf', 'logpmf', 'cdf', 'logcdf',
+                                    'sf', 'logsf', 'ppf', 'isf'])
+@pytest.mark.parametrize('distname, args', distdiscrete)
+def test_methods_with_lists(method, distname, args):
+    # Test that the discrete distributions can accept Python lists
+    # as arguments.
+    try:
+        dist = getattr(stats, distname)
+    except TypeError:
+        return
+    dist_method = getattr(dist, method)
+    if method in ['ppf', 'isf']:
+        z = [0.1, 0.2]
+    else:
+        z = [0, 1]
+    p2 = [[p]*2 for p in args]
+    loc = [0, 1]
+    result = dist_method(z, *p2, loc=loc)
+    npt.assert_allclose(result,
+                        [dist_method(*v) for v in zip(z, *p2, loc)],
+                        rtol=1e-15, atol=1e-15)
+
+
+@pytest.mark.parametrize('distname, args', invdistdiscrete)
+def test_cdf_gh13280_regression(distname, args):
+    # Test for nan output when shape parameters are invalid
+    dist = getattr(stats, distname)
+    x = np.arange(-2, 15)
+    vals = dist.cdf(x, *args)
+    expected = np.nan
+    npt.assert_equal(vals, expected)
+
+
+def cases_test_discrete_integer_shapes():
+    # distributions parameters that are only allowed to be integral when
+    # fitting, but are allowed to be real as input to PDF, etc.
+    integrality_exceptions = {'nbinom': {'n'}, 'betanbinom': {'n'}}
+
+    seen = set()
+    for distname, shapes in distdiscrete:
+        if distname in seen:
+            continue
+        seen.add(distname)
+
+        try:
+            dist = getattr(stats, distname)
+        except TypeError:
+            continue
+
+        shape_info = dist._shape_info()
+
+        for i, shape in enumerate(shape_info):
+            if (shape.name in integrality_exceptions.get(distname, set()) or
+                    not shape.integrality):
+                continue
+
+            yield distname, shape.name, shapes
+
+
+@pytest.mark.parametrize('distname, shapename, shapes',
+                         list(cases_test_discrete_integer_shapes()))
+def test_integer_shapes(distname, shapename, shapes):
+    dist = getattr(stats, distname)
+    shape_info = dist._shape_info()
+    shape_names = [shape.name for shape in shape_info]
+    i = shape_names.index(shapename)  # this element of params must be integral
+
+    shapes_copy = list(shapes)
+
+    valid_shape = shapes[i]
+    invalid_shape = valid_shape - 0.5  # arbitrary non-integral value
+    new_valid_shape = valid_shape - 1
+    shapes_copy[i] = [[valid_shape], [invalid_shape], [new_valid_shape]]
+
+    a, b = dist.support(*shapes)
+    x = np.round(np.linspace(a, b, 5))
+
+    pmf = dist.pmf(x, *shapes_copy)
+    assert not np.any(np.isnan(pmf[0, :]))
+    assert np.all(np.isnan(pmf[1, :]))
+    assert not np.any(np.isnan(pmf[2, :]))
+
+
+def test_frozen_attributes(monkeypatch):
+    # gh-14827 reported that all frozen distributions had both pmf and pdf
+    # attributes; continuous should have pdf and discrete should have pmf.
+    message = "'rv_discrete_frozen' object has no attribute"
+    with pytest.raises(AttributeError, match=message):
+        stats.binom(10, 0.5).pdf
+    with pytest.raises(AttributeError, match=message):
+        stats.binom(10, 0.5).logpdf
+    monkeypatch.setattr(stats.binom, "pdf", "herring", raising=False)
+    frozen_binom = stats.binom(10, 0.5)
+    assert isinstance(frozen_binom, rv_discrete_frozen)
+    assert not hasattr(frozen_binom, "pdf")
+
+
+@pytest.mark.parametrize('distname, shapes', distdiscrete)
+def test_interval(distname, shapes):
+    # gh-11026 reported that `interval` returns incorrect values when
+    # `confidence=1`. The values were not incorrect, but it was not intuitive
+    # that the left end of the interval should extend beyond the support of the
+    # distribution. Confirm that this is the behavior for all distributions.
+    if isinstance(distname, str):
+        dist = getattr(stats, distname)
+    else:
+        dist = distname
+    a, b = dist.support(*shapes)
+    npt.assert_equal(dist.ppf([0, 1], *shapes), (a-1, b))
+    npt.assert_equal(dist.isf([1, 0], *shapes), (a-1, b))
+    npt.assert_equal(dist.interval(1, *shapes), (a-1, b))
+
+
+@pytest.mark.xfail_on_32bit("Sensible to machine precision")
+def test_rv_sample():
+    # Thoroughly test rv_sample and check that gh-3758 is resolved
+
+    # Generate a random discrete distribution
+    rng = np.random.default_rng(98430143469)
+    xk = np.sort(rng.random(10) * 10)
+    pk = rng.random(10)
+    pk /= np.sum(pk)
+    dist = stats.rv_discrete(values=(xk, pk))
+
+    # Generate points to the left and right of xk
+    xk_left = (np.array([0] + xk[:-1].tolist()) + xk)/2
+    xk_right = (np.array(xk[1:].tolist() + [xk[-1]+1]) + xk)/2
+
+    # Generate points to the left and right of cdf
+    cdf2 = np.cumsum(pk)
+    cdf2_left = (np.array([0] + cdf2[:-1].tolist()) + cdf2)/2
+    cdf2_right = (np.array(cdf2[1:].tolist() + [1]) + cdf2)/2
+
+    # support - leftmost and rightmost xk
+    a, b = dist.support()
+    assert_allclose(a, xk[0])
+    assert_allclose(b, xk[-1])
+
+    # pmf - supported only on the xk
+    assert_allclose(dist.pmf(xk), pk)
+    assert_allclose(dist.pmf(xk_right), 0)
+    assert_allclose(dist.pmf(xk_left), 0)
+
+    # logpmf is log of the pmf; log(0) = -np.inf
+    with np.errstate(divide='ignore'):
+        assert_allclose(dist.logpmf(xk), np.log(pk))
+        assert_allclose(dist.logpmf(xk_right), -np.inf)
+        assert_allclose(dist.logpmf(xk_left), -np.inf)
+
+    # cdf - the cumulative sum of the pmf
+    assert_allclose(dist.cdf(xk), cdf2)
+    assert_allclose(dist.cdf(xk_right), cdf2)
+    assert_allclose(dist.cdf(xk_left), [0]+cdf2[:-1].tolist())
+
+    with np.errstate(divide='ignore'):
+        assert_allclose(dist.logcdf(xk), np.log(dist.cdf(xk)),
+                        atol=1e-15)
+        assert_allclose(dist.logcdf(xk_right), np.log(dist.cdf(xk_right)),
+                        atol=1e-15)
+        assert_allclose(dist.logcdf(xk_left), np.log(dist.cdf(xk_left)),
+                        atol=1e-15)
+
+    # sf is 1-cdf
+    assert_allclose(dist.sf(xk), 1-dist.cdf(xk))
+    assert_allclose(dist.sf(xk_right), 1-dist.cdf(xk_right))
+    assert_allclose(dist.sf(xk_left), 1-dist.cdf(xk_left))
+
+    with np.errstate(divide='ignore'):
+        assert_allclose(dist.logsf(xk), np.log(dist.sf(xk)),
+                        atol=1e-15)
+        assert_allclose(dist.logsf(xk_right), np.log(dist.sf(xk_right)),
+                        atol=1e-15)
+        assert_allclose(dist.logsf(xk_left), np.log(dist.sf(xk_left)),
+                        atol=1e-15)
+
+    # ppf
+    assert_allclose(dist.ppf(cdf2), xk)
+    assert_allclose(dist.ppf(cdf2_left), xk)
+    assert_allclose(dist.ppf(cdf2_right)[:-1], xk[1:])
+    assert_allclose(dist.ppf(0), a - 1)
+    assert_allclose(dist.ppf(1), b)
+
+    # isf
+    sf2 = dist.sf(xk)
+    assert_allclose(dist.isf(sf2), xk)
+    assert_allclose(dist.isf(1-cdf2_left), dist.ppf(cdf2_left))
+    assert_allclose(dist.isf(1-cdf2_right), dist.ppf(cdf2_right))
+    assert_allclose(dist.isf(0), b)
+    assert_allclose(dist.isf(1), a - 1)
+
+    # interval is (ppf(alpha/2), isf(alpha/2))
+    ps = np.linspace(0.01, 0.99, 10)
+    int2 = dist.ppf(ps/2), dist.isf(ps/2)
+    assert_allclose(dist.interval(1-ps), int2)
+    assert_allclose(dist.interval(0), dist.median())
+    assert_allclose(dist.interval(1), (a-1, b))
+
+    # median is simply ppf(0.5)
+    med2 = dist.ppf(0.5)
+    assert_allclose(dist.median(), med2)
+
+    # all four stats (mean, var, skew, and kurtosis) from the definitions
+    mean2 = np.sum(xk*pk)
+    var2 = np.sum((xk - mean2)**2 * pk)
+    skew2 = np.sum((xk - mean2)**3 * pk) / var2**(3/2)
+    kurt2 = np.sum((xk - mean2)**4 * pk) / var2**2 - 3
+    assert_allclose(dist.mean(), mean2)
+    assert_allclose(dist.std(), np.sqrt(var2))
+    assert_allclose(dist.var(), var2)
+    assert_allclose(dist.stats(moments='mvsk'), (mean2, var2, skew2, kurt2))
+
+    # noncentral moment against definition
+    mom3 = np.sum((xk**3) * pk)
+    assert_allclose(dist.moment(3), mom3)
+
+    # expect - check against moments
+    assert_allclose(dist.expect(lambda x: 1), 1)
+    assert_allclose(dist.expect(), mean2)
+    assert_allclose(dist.expect(lambda x: x**3), mom3)
+
+    # entropy is the negative of the expected value of log(p)
+    with np.errstate(divide='ignore'):
+        assert_allclose(-dist.expect(lambda x: dist.logpmf(x)), dist.entropy())
+
+    # RVS is just ppf of uniform random variates
+    rng = np.random.default_rng(98430143469)
+    rvs = dist.rvs(size=100, random_state=rng)
+    rng = np.random.default_rng(98430143469)
+    rvs0 = dist.ppf(rng.random(size=100))
+    assert_allclose(rvs, rvs0)
+
+def test__pmf_float_input():
+    # gh-21272
+    # test that `rvs()` can be computed when `_pmf` requires float input
+
+    class rv_exponential(stats.rv_discrete):
+        def _pmf(self, i):
+            return (2/3)*3**(1 - i)
+
+    rv = rv_exponential(a=0.0, b=float('inf'))
+    rvs = rv.rvs(random_state=42)  # should not crash due to integer input to `_pmf`
+    assert_allclose(rvs, 0)
+
+
+def test_gh18919_ppf_array_args():
+    # gh-18919 reported incorrect results for ppf and isf of discrete distributions when
+    # arguments were arrays and first argument (`q`) had elements at the boundaries of
+    # the support.
+    q = [[0.5, 1.0, 0.5],
+         [1.0, 0.5, 1.0],
+         [0.5, 1.0, 0.5]]
+
+    n = [[45, 46, 47],
+         [48, 49, 50],
+         [51, 52, 53]]
+
+    p = 0.5
+
+    ref = _ufuncs._binom_ppf(q, n, p)
+    res = stats.binom.ppf(q, n, p)
+    np.testing.assert_allclose(res, ref)
+
+@pytest.mark.parametrize("dist", [stats.binom, stats.boltzmann])
+def test_gh18919_ppf_isf_array_args2(dist):
+    # a more general version of the test above. Requires that arguments are broadcasted
+    # by the infrastructure.
+    rng = np.random.default_rng(34873457824358729823)
+    q = rng.random(size=(30, 1, 1, 1))
+    n = rng.integers(10, 30, size=(10, 1, 1))
+    p = rng.random(size=(4, 1))
+    loc = rng.integers(5, size=(3,))
+
+    q[rng.random(size=30) > 0.7] = 0
+    q[rng.random(size=30) > 0.7] = 1
+
+    args = (q, n, p) if dist == stats.binom else (q, p, n)
+
+    res = dist.ppf(*args, loc=loc)
+    ref = np.vectorize(dist.ppf)(*args) + loc
+    np.testing.assert_allclose(res, ref)
+
+    res = dist.isf(*args, loc=loc)
+    ref = np.vectorize(dist.isf)(*args) + loc
+    np.testing.assert_allclose(res, ref)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_discrete_distns.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_discrete_distns.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ba8da84e2dca928426a3b5283d85f5cf29be07d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_discrete_distns.py
@@ -0,0 +1,747 @@
+import pytest
+import itertools
+import warnings
+
+from scipy import stats
+from scipy.stats import (betabinom, betanbinom, hypergeom, nhypergeom,
+                         bernoulli, boltzmann, skellam, zipf, zipfian, binom,
+                         nbinom, nchypergeom_fisher, nchypergeom_wallenius,
+                         randint, poisson_binom)
+
+import numpy as np
+from numpy.testing import (
+    assert_almost_equal, assert_equal, assert_allclose
+)
+from scipy.special import binom as special_binom
+from scipy.optimize import root_scalar
+from scipy.integrate import quad
+
+
+# The expected values were computed with Wolfram Alpha, using
+# the expression CDF[HypergeometricDistribution[N, n, M], k].
+@pytest.mark.parametrize('k, M, n, N, expected, rtol',
+                         [(3, 10, 4, 5,
+                           0.9761904761904762, 1e-15),
+                          (107, 10000, 3000, 215,
+                           0.9999999997226765, 1e-15),
+                          (10, 10000, 3000, 215,
+                           2.681682217692179e-21, 5e-11)])
+def test_hypergeom_cdf(k, M, n, N, expected, rtol):
+    p = hypergeom.cdf(k, M, n, N)
+    assert_allclose(p, expected, rtol=rtol)
+
+
+# The expected values were computed with Wolfram Alpha, using
+# the expression SurvivalFunction[HypergeometricDistribution[N, n, M], k].
+@pytest.mark.parametrize('k, M, n, N, expected, rtol',
+                         [(25, 10000, 3000, 215,
+                           0.9999999999052958, 1e-15),
+                          (125, 10000, 3000, 215,
+                           1.4416781705752128e-18, 5e-11)])
+def test_hypergeom_sf(k, M, n, N, expected, rtol):
+    p = hypergeom.sf(k, M, n, N)
+    assert_allclose(p, expected, rtol=rtol)
+
+
+def test_hypergeom_logpmf():
+    # symmetries test
+    # f(k,N,K,n) = f(n-k,N,N-K,n) = f(K-k,N,K,N-n) = f(k,N,n,K)
+    k = 5
+    N = 50
+    K = 10
+    n = 5
+    logpmf1 = hypergeom.logpmf(k, N, K, n)
+    logpmf2 = hypergeom.logpmf(n - k, N, N - K, n)
+    logpmf3 = hypergeom.logpmf(K - k, N, K, N - n)
+    logpmf4 = hypergeom.logpmf(k, N, n, K)
+    assert_almost_equal(logpmf1, logpmf2, decimal=12)
+    assert_almost_equal(logpmf1, logpmf3, decimal=12)
+    assert_almost_equal(logpmf1, logpmf4, decimal=12)
+
+    # test related distribution
+    # Bernoulli distribution if n = 1
+    k = 1
+    N = 10
+    K = 7
+    n = 1
+    hypergeom_logpmf = hypergeom.logpmf(k, N, K, n)
+    bernoulli_logpmf = bernoulli.logpmf(k, K/N)
+    assert_almost_equal(hypergeom_logpmf, bernoulli_logpmf, decimal=12)
+
+
+def test_nhypergeom_pmf():
+    # test with hypergeom
+    M, n, r = 45, 13, 8
+    k = 6
+    NHG = nhypergeom.pmf(k, M, n, r)
+    HG = hypergeom.pmf(k, M, n, k+r-1) * (M - n - (r-1)) / (M - (k+r-1))
+    assert_allclose(HG, NHG, rtol=1e-10)
+
+
+def test_nhypergeom_pmfcdf():
+    # test pmf and cdf with arbitrary values.
+    M = 8
+    n = 3
+    r = 4
+    support = np.arange(n+1)
+    pmf = nhypergeom.pmf(support, M, n, r)
+    cdf = nhypergeom.cdf(support, M, n, r)
+    assert_allclose(pmf, [1/14, 3/14, 5/14, 5/14], rtol=1e-13)
+    assert_allclose(cdf, [1/14, 4/14, 9/14, 1.0], rtol=1e-13)
+
+
+def test_nhypergeom_r0():
+    # test with `r = 0`.
+    M = 10
+    n = 3
+    r = 0
+    pmf = nhypergeom.pmf([[0, 1, 2, 0], [1, 2, 0, 3]], M, n, r)
+    assert_allclose(pmf, [[1, 0, 0, 1], [0, 0, 1, 0]], rtol=1e-13)
+
+
+def test_nhypergeom_rvs_shape():
+    # Check that when given a size with more dimensions than the
+    # dimensions of the broadcast parameters, rvs returns an array
+    # with the correct shape.
+    x = nhypergeom.rvs(22, [7, 8, 9], [[12], [13]], size=(5, 1, 2, 3))
+    assert x.shape == (5, 1, 2, 3)
+
+
+def test_nhypergeom_accuracy():
+    # Check that nhypergeom.rvs post-gh-13431 gives the same values as
+    # inverse transform sampling
+    rng = np.random.RandomState(0)
+    x = nhypergeom.rvs(22, 7, 11, size=100, random_state=rng)
+    rng = np.random.RandomState(0)
+    p = rng.uniform(size=100)
+    y = nhypergeom.ppf(p, 22, 7, 11)
+    assert_equal(x, y)
+
+
+def test_boltzmann_upper_bound():
+    k = np.arange(-3, 5)
+
+    N = 1
+    p = boltzmann.pmf(k, 0.123, N)
+    expected = k == 0
+    assert_equal(p, expected)
+
+    lam = np.log(2)
+    N = 3
+    p = boltzmann.pmf(k, lam, N)
+    expected = [0, 0, 0, 4/7, 2/7, 1/7, 0, 0]
+    assert_allclose(p, expected, rtol=1e-13)
+
+    c = boltzmann.cdf(k, lam, N)
+    expected = [0, 0, 0, 4/7, 6/7, 1, 1, 1]
+    assert_allclose(c, expected, rtol=1e-13)
+
+
+def test_betabinom_a_and_b_unity():
+    # test limiting case that betabinom(n, 1, 1) is a discrete uniform
+    # distribution from 0 to n
+    n = 20
+    k = np.arange(n + 1)
+    p = betabinom(n, 1, 1).pmf(k)
+    expected = np.repeat(1 / (n + 1), n + 1)
+    assert_almost_equal(p, expected)
+
+
+@pytest.mark.parametrize('dtypes', list(itertools.product(*[(int, float)]*3)))
+def test_betabinom_stats_a_and_b_integers_gh18026(dtypes):
+    # gh-18026 reported that `betabinom` kurtosis calculation fails when some
+    # parameters are integers. Check that this is resolved.
+    n_type, a_type, b_type = dtypes
+    n, a, b = n_type(10), a_type(2), b_type(3)
+    assert_allclose(betabinom.stats(n, a, b, moments='k'), -0.6904761904761907)
+
+
+def test_betabinom_bernoulli():
+    # test limiting case that betabinom(1, a, b) = bernoulli(a / (a + b))
+    a = 2.3
+    b = 0.63
+    k = np.arange(2)
+    p = betabinom(1, a, b).pmf(k)
+    expected = bernoulli(a / (a + b)).pmf(k)
+    assert_almost_equal(p, expected)
+
+
+def test_issue_10317():
+    alpha, n, p = 0.9, 10, 1
+    assert_equal(nbinom.interval(confidence=alpha, n=n, p=p), (0, 0))
+
+
+def test_issue_11134():
+    alpha, n, p = 0.95, 10, 0
+    assert_equal(binom.interval(confidence=alpha, n=n, p=p), (0, 0))
+
+
+def test_issue_7406():
+    rng = np.random.default_rng(4763112764)
+    assert_equal(binom.ppf(rng.random(10), 0, 0.5), 0)
+
+    # Also check that endpoints (q=0, q=1) are correct
+    assert_equal(binom.ppf(0, 0, 0.5), -1)
+    assert_equal(binom.ppf(1, 0, 0.5), 0)
+
+
+def test_issue_5122():
+    rng = np.random.default_rng(8312492117)
+    p = 0
+    n = rng.integers(100, size=10)
+
+    x = 0
+    ppf = binom.ppf(x, n, p)
+    assert_equal(ppf, -1)
+
+    x = np.linspace(0.01, 0.99, 10)
+    ppf = binom.ppf(x, n, p)
+    assert_equal(ppf, 0)
+
+    x = 1
+    ppf = binom.ppf(x, n, p)
+    assert_equal(ppf, n)
+
+
+def test_issue_1603():
+    assert_equal(binom(1000, np.logspace(-3, -100)).ppf(0.01), 0)
+
+
+def test_issue_5503():
+    p = 0.5
+    x = np.logspace(3, 14, 12)
+    assert_allclose(binom.cdf(x, 2*x, p), 0.5, atol=1e-2)
+
+
+@pytest.mark.parametrize('x, n, p, cdf_desired', [
+    (300, 1000, 3/10, 0.51559351981411995636),
+    (3000, 10000, 3/10, 0.50493298381929698016),
+    (30000, 100000, 3/10, 0.50156000591726422864),
+    (300000, 1000000, 3/10, 0.50049331906666960038),
+    (3000000, 10000000, 3/10, 0.50015600124585261196),
+    (30000000, 100000000, 3/10, 0.50004933192735230102),
+    (30010000, 100000000, 3/10, 0.98545384016570790717),
+    (29990000, 100000000, 3/10, 0.01455017177985268670),
+    (29950000, 100000000, 3/10, 5.02250963487432024943e-28),
+    (300_000_000, 1_000_000_000, 3/10, 0.50001560012523938),
+    (3_000_000_000, 10_000_000_000, 3/10, 0.50000493319275)
+])
+def test_issue_5503pt2(x, n, p, cdf_desired):
+    assert_allclose(binom.cdf(x, n, p), cdf_desired)
+
+
+def test_issue_5503pt3():
+    # From Wolfram Alpha: CDF[BinomialDistribution[1e12, 1e-12], 2]
+    assert_allclose(binom.cdf(2, 10**12, 10**-12), 0.91969860292869777384)
+
+
+def test_issue_6682():
+    # Reference value from R:
+    # options(digits=16)
+    # print(pnbinom(250, 50, 32/63, lower.tail=FALSE))
+    assert_allclose(nbinom.sf(250, 50, 32./63.), 1.460458510976452e-35)
+
+
+def test_issue_19747():
+    # test that negative k does not raise an error in nbinom.logcdf
+    result = nbinom.logcdf([5, -1, 1], 5, 0.5)
+    reference = [-0.47313352, -np.inf, -2.21297293]
+    assert_allclose(result, reference)
+
+
+def test_boost_divide_by_zero_issue_15101():
+    n = 1000
+    p = 0.01
+    k = 996
+    assert_allclose(binom.pmf(k, n, p), 0.0)
+
+
+def test_skellam_gh11474():
+    # test issue reported in gh-11474 caused by `cdfchn`
+    mu = [1, 10, 100, 1000, 5000, 5050, 5100, 5250, 6000]
+    cdf = skellam.cdf(0, mu, mu)
+    # generated in R
+    # library(skellam)
+    # options(digits = 16)
+    # mu = c(1, 10, 100, 1000, 5000, 5050, 5100, 5250, 6000)
+    # pskellam(0, mu, mu, TRUE)
+    cdf_expected = [0.6542541612768356, 0.5448901559424127, 0.5141135799745580,
+                    0.5044605891382528, 0.5019947363350450, 0.5019848365953181,
+                    0.5019750827993392, 0.5019466621805060, 0.5018209330219539]
+    assert_allclose(cdf, cdf_expected)
+
+
+class TestZipfian:
+    def test_zipfian_asymptotic(self):
+        # test limiting case that zipfian(a, n) -> zipf(a) as n-> oo
+        a = 6.5
+        N = 10000000
+        k = np.arange(1, 21)
+        assert_allclose(zipfian.pmf(k, a, N), zipf.pmf(k, a))
+        assert_allclose(zipfian.cdf(k, a, N), zipf.cdf(k, a))
+        assert_allclose(zipfian.sf(k, a, N), zipf.sf(k, a))
+        assert_allclose(zipfian.stats(a, N, moments='msvk'),
+                        zipf.stats(a, moments='msvk'))
+
+    def test_zipfian_continuity(self):
+        # test that zipfian(0.999999, n) ~ zipfian(1.000001, n)
+        # (a = 1 switches between methods of calculating harmonic sum)
+        alt1, agt1 = 0.99999999, 1.00000001
+        N = 30
+        k = np.arange(1, N + 1)
+        assert_allclose(zipfian.pmf(k, alt1, N), zipfian.pmf(k, agt1, N),
+                        rtol=5e-7)
+        assert_allclose(zipfian.cdf(k, alt1, N), zipfian.cdf(k, agt1, N),
+                        rtol=5e-7)
+        assert_allclose(zipfian.sf(k, alt1, N), zipfian.sf(k, agt1, N),
+                        rtol=5e-7)
+        assert_allclose(zipfian.stats(alt1, N, moments='msvk'),
+                        zipfian.stats(agt1, N, moments='msvk'), rtol=5e-7)
+
+    def test_zipfian_R(self):
+        # test against R VGAM package
+        # library(VGAM)
+        # k <- c(13, 16,  1,  4,  4,  8, 10, 19,  5,  7)
+        # a <- c(1.56712977, 3.72656295, 5.77665117, 9.12168729, 5.79977172,
+        #        4.92784796, 9.36078764, 4.3739616 , 7.48171872, 4.6824154)
+        # n <- c(70, 80, 48, 65, 83, 89, 50, 30, 20, 20)
+        # pmf <- dzipf(k, N = n, shape = a)
+        # cdf <- pzipf(k, N = n, shape = a)
+        # print(pmf)
+        # print(cdf)
+        rng = np.random.RandomState(0)
+        k = rng.randint(1, 20, size=10)
+        a = rng.rand(10)*10 + 1
+        n = rng.randint(1, 100, size=10)
+        pmf = [8.076972e-03, 2.950214e-05, 9.799333e-01, 3.216601e-06,
+               3.158895e-04, 3.412497e-05, 4.350472e-10, 2.405773e-06,
+               5.860662e-06, 1.053948e-04]
+        cdf = [0.8964133, 0.9998666, 0.9799333, 0.9999995, 0.9998584,
+               0.9999458, 1.0000000, 0.9999920, 0.9999977, 0.9998498]
+        # skip the first point; zipUC is not accurate for low a, n
+        assert_allclose(zipfian.pmf(k, a, n)[1:], pmf[1:], rtol=1e-6)
+        assert_allclose(zipfian.cdf(k, a, n)[1:], cdf[1:], rtol=5e-5)
+
+    rng = np.random.RandomState(0)
+    naive_tests = np.vstack((np.logspace(-2, 1, 10),
+                             rng.randint(2, 40, 10))).T
+
+    @pytest.mark.parametrize("a, n", list(naive_tests))
+    def test_zipfian_naive(self, a, n):
+        # test against bare-bones implementation
+
+        @np.vectorize
+        def Hns(n, s):
+            """Naive implementation of harmonic sum"""
+            return (1/np.arange(1, n+1)**s).sum()
+
+        @np.vectorize
+        def pzip(k, a, n):
+            """Naive implementation of zipfian pmf"""
+            if k < 1 or k > n:
+                return 0.
+            else:
+                return 1 / k**a / Hns(n, a)
+
+        k = np.arange(n+1)
+        pmf = pzip(k, a, n)
+        cdf = np.cumsum(pmf)
+        mean = np.average(k, weights=pmf)
+        var = np.average((k - mean)**2, weights=pmf)
+        std = var**0.5
+        skew = np.average(((k-mean)/std)**3, weights=pmf)
+        kurtosis = np.average(((k-mean)/std)**4, weights=pmf) - 3
+        assert_allclose(zipfian.pmf(k, a, n), pmf)
+        assert_allclose(zipfian.cdf(k, a, n), cdf)
+        assert_allclose(zipfian.stats(a, n, moments="mvsk"),
+                        [mean, var, skew, kurtosis])
+
+    def test_pmf_integer_k(self):
+        k = np.arange(0, 1000)
+        k_int32 = k.astype(np.int32)
+        dist = zipfian(111, 22)
+        pmf = dist.pmf(k)
+        pmf_k_int32 = dist.pmf(k_int32)
+        assert_equal(pmf, pmf_k_int32)
+
+    # Reference values were computed with mpmath.
+    @pytest.mark.parametrize(
+        'k, a, n, ref',
+        [(3, 1 + 1e-12, 10, 0.11380571738244807),
+         (995, 1 + 1e-9, 1000, 0.0001342634472310051)]
+    )
+    def test_pmf_against_mpmath(self, k, a, n, ref):
+        p = zipfian.pmf(k, a, n)
+        assert_allclose(p, ref, 5e-16)
+
+    # Reference values were computed with mpmath.
+    @pytest.mark.parametrize(
+        'k, a, n, ref',
+        [(4990, 1.25, 5000, 5.780138225335147e-05),
+         (9998, 3.5, 10000, 1.775352757966365e-14),
+         (50000, 3.5, 100000, 5.227803621367486e-13),
+         (10, 6.0, 100, 1.523108153557902e-06),
+         (95, 6.0, 100, 5.572438078308601e-12)]
+    )
+    def test_sf_against_mpmath(self, k, a, n, ref):
+        sf = zipfian.sf(k, a, n)
+        assert_allclose(sf, ref, rtol=8e-15)
+
+    # Reference values were computed with mpmath.
+    @pytest.mark.parametrize(
+        'a, n, ref',
+        [(1 + 1e-9, 100, 19.27756356649707),
+         (1 + 1e-7, 100000, 8271.194454731552),
+         (1.001, 3, 1.6360225521183804)]
+    )
+    def test_mean_against_mpmath(self, a, n, ref):
+        m = zipfian.mean(a, n)
+        assert_allclose(m, ref, rtol=8e-15)
+
+    def test_ridiculously_large_n(self):
+        # This should return nan with no errors or warnings.
+        a = 2.5
+        n = 1e100
+        p = zipfian.pmf(10, a, n)
+        assert_equal(p, np.nan)
+
+
+class TestNCH:
+    def setup_method(self):
+        rng = np.random.default_rng(7162434334)
+        shape = (2, 4, 3)
+        max_m = 100
+        m1 = rng.integers(1, max_m, size=shape)  # red balls
+        m2 = rng.integers(1, max_m, size=shape)  # white balls
+        N = m1 + m2  # total balls
+        n = randint.rvs(0, N, size=N.shape, random_state=rng)  # number of draws
+        xl = np.maximum(0, n-m2)  # lower bound of support
+        xu = np.minimum(n, m1)  # upper bound of support
+        x = randint.rvs(xl, xu, size=xl.shape, random_state=rng)
+        odds = rng.random(x.shape)*2
+        self.x, self.N, self.m1, self.n, self.odds = x, N, m1, n, odds
+
+    # test output is more readable when function names (strings) are passed
+    @pytest.mark.parametrize('dist_name',
+                             ['nchypergeom_fisher', 'nchypergeom_wallenius'])
+    def test_nch_hypergeom(self, dist_name):
+        # Both noncentral hypergeometric distributions reduce to the
+        # hypergeometric distribution when odds = 1
+        dists = {'nchypergeom_fisher': nchypergeom_fisher,
+                 'nchypergeom_wallenius': nchypergeom_wallenius}
+        dist = dists[dist_name]
+        x, N, m1, n = self.x, self.N, self.m1, self.n
+        assert_allclose(dist.pmf(x, N, m1, n, odds=1),
+                        hypergeom.pmf(x, N, m1, n))
+
+    def test_nchypergeom_fisher_naive(self):
+        # test against a very simple implementation
+        x, N, m1, n, odds = self.x, self.N, self.m1, self.n, self.odds
+
+        @np.vectorize
+        def pmf_mean_var(x, N, m1, n, w):
+            # simple implementation of nchypergeom_fisher pmf
+            m2 = N - m1
+            xl = np.maximum(0, n-m2)
+            xu = np.minimum(n, m1)
+
+            def f(x):
+                t1 = special_binom(m1, x)
+                t2 = special_binom(m2, n - x)
+                return t1 * t2 * w**x
+
+            def P(k):
+                return sum(f(y)*y**k for y in range(xl, xu + 1))
+
+            P0 = P(0)
+            P1 = P(1)
+            P2 = P(2)
+            pmf = f(x) / P0
+            mean = P1 / P0
+            var = P2 / P0 - (P1 / P0)**2
+            return pmf, mean, var
+
+        pmf, mean, var = pmf_mean_var(x, N, m1, n, odds)
+        assert_allclose(nchypergeom_fisher.pmf(x, N, m1, n, odds), pmf)
+        assert_allclose(nchypergeom_fisher.stats(N, m1, n, odds, moments='m'),
+                        mean)
+        assert_allclose(nchypergeom_fisher.stats(N, m1, n, odds, moments='v'),
+                        var)
+
+    def test_nchypergeom_wallenius_naive(self):
+        # test against a very simple implementation
+
+        rng = np.random.RandomState(2)
+        shape = (2, 4, 3)
+        max_m = 100
+        m1 = rng.randint(1, max_m, size=shape)
+        m2 = rng.randint(1, max_m, size=shape)
+        N = m1 + m2
+        n = randint.rvs(0, N, size=N.shape, random_state=rng)
+        xl = np.maximum(0, n-m2)
+        xu = np.minimum(n, m1)
+        x = randint.rvs(xl, xu, size=xl.shape, random_state=rng)
+        w = rng.rand(*x.shape)*2
+
+        def support(N, m1, n, w):
+            m2 = N - m1
+            xl = np.maximum(0, n-m2)
+            xu = np.minimum(n, m1)
+            return xl, xu
+
+        @np.vectorize
+        def mean(N, m1, n, w):
+            m2 = N - m1
+            xl, xu = support(N, m1, n, w)
+
+            def fun(u):
+                return u/m1 + (1 - (n-u)/m2)**w - 1
+
+            return root_scalar(fun, bracket=(xl, xu)).root
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=RuntimeWarning,
+                       message="invalid value encountered in mean")
+            assert_allclose(nchypergeom_wallenius.mean(N, m1, n, w),
+                            mean(N, m1, n, w), rtol=2e-2)
+
+        @np.vectorize
+        def variance(N, m1, n, w):
+            m2 = N - m1
+            u = mean(N, m1, n, w)
+            a = u * (m1 - u)
+            b = (n-u)*(u + m2 - n)
+            return N*a*b / ((N-1) * (m1*b + m2*a))
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=RuntimeWarning,
+                       message="invalid value encountered in mean")
+            assert_allclose(
+                nchypergeom_wallenius.stats(N, m1, n, w, moments='v'),
+                variance(N, m1, n, w),
+                rtol=5e-2
+            )
+
+        @np.vectorize
+        def pmf(x, N, m1, n, w):
+            m2 = N - m1
+            xl, xu = support(N, m1, n, w)
+
+            def integrand(t):
+                D = w*(m1 - x) + (m2 - (n-x))
+                res = (1-t**(w/D))**x * (1-t**(1/D))**(n-x)
+                return res
+
+            def f(x):
+                t1 = special_binom(m1, x)
+                t2 = special_binom(m2, n - x)
+                the_integral = quad(integrand, 0, 1,
+                                    epsrel=1e-16, epsabs=1e-16)
+                return t1 * t2 * the_integral[0]
+
+            return f(x)
+
+        pmf0 = pmf(x, N, m1, n, w)
+        pmf1 = nchypergeom_wallenius.pmf(x, N, m1, n, w)
+
+        atol, rtol = 1e-6, 1e-6
+        i = np.abs(pmf1 - pmf0) < atol + rtol*np.abs(pmf0)
+        assert i.sum() > np.prod(shape) / 2  # works at least half the time
+
+        # for those that fail, discredit the naive implementation
+        for N, m1, n, w in zip(N[~i], m1[~i], n[~i], w[~i]):
+            # get the support
+            m2 = N - m1
+            xl, xu = support(N, m1, n, w)
+            x = np.arange(xl, xu + 1)
+
+            # calculate sum of pmf over the support
+            # the naive implementation is very wrong in these cases
+            assert pmf(x, N, m1, n, w).sum() < .5
+            assert_allclose(nchypergeom_wallenius.pmf(x, N, m1, n, w).sum(), 1)
+
+    def test_wallenius_against_mpmath(self):
+        # precompute data with mpmath since naive implementation above
+        # is not reliable. See source code in gh-13330.
+        M = 50
+        n = 30
+        N = 20
+        odds = 2.25
+        # Expected results, computed with mpmath.
+        sup = np.arange(21)
+        pmf = np.array([3.699003068656875e-20,
+                        5.89398584245431e-17,
+                        2.1594437742911123e-14,
+                        3.221458044649955e-12,
+                        2.4658279241205077e-10,
+                        1.0965862603981212e-08,
+                        3.057890479665704e-07,
+                        5.622818831643761e-06,
+                        7.056482841531681e-05,
+                        0.000618899425358671,
+                        0.003854172932571669,
+                        0.01720592676256026,
+                        0.05528844897093792,
+                        0.12772363313574242,
+                        0.21065898367825722,
+                        0.24465958845359234,
+                        0.1955114898110033,
+                        0.10355390084949237,
+                        0.03414490375225675,
+                        0.006231989845775931,
+                        0.0004715577304677075])
+        mean = 14.808018384813426
+        var = 2.6085975877923717
+
+        # nchypergeom_wallenius.pmf returns 0 for pmf(0) and pmf(1), and pmf(2)
+        # has only three digits of accuracy (~ 2.1511e-14).
+        assert_allclose(nchypergeom_wallenius.pmf(sup, M, n, N, odds), pmf,
+                        rtol=1e-13, atol=1e-13)
+        assert_allclose(nchypergeom_wallenius.mean(M, n, N, odds),
+                        mean, rtol=1e-13)
+        assert_allclose(nchypergeom_wallenius.var(M, n, N, odds),
+                        var, rtol=1e-11)
+
+    @pytest.mark.parametrize('dist_name',
+                             ['nchypergeom_fisher', 'nchypergeom_wallenius'])
+    def test_rvs_shape(self, dist_name):
+        # Check that when given a size with more dimensions than the
+        # dimensions of the broadcast parameters, rvs returns an array
+        # with the correct shape.
+        dists = {'nchypergeom_fisher': nchypergeom_fisher,
+                 'nchypergeom_wallenius': nchypergeom_wallenius}
+        dist = dists[dist_name]
+        x = dist.rvs(50, 30, [[10], [20]], [0.5, 1.0, 2.0], size=(5, 1, 2, 3))
+        assert x.shape == (5, 1, 2, 3)
+
+
+@pytest.mark.parametrize("mu, q, expected",
+                         [[10, 120, -1.240089881791596e-38],
+                          [1500, 0, -86.61466680572661]])
+def test_nbinom_11465(mu, q, expected):
+    # test nbinom.logcdf at extreme tails
+    size = 20
+    n, p = size, size/(size+mu)
+    # In R:
+    # options(digits=16)
+    # pnbinom(mu=10, size=20, q=120, log.p=TRUE)
+    assert_allclose(nbinom.logcdf(q, n, p), expected)
+
+
+def test_gh_17146():
+    # Check that discrete distributions return PMF of zero at non-integral x.
+    # See gh-17146.
+    x = np.linspace(0, 1, 11)
+    p = 0.8
+    pmf = bernoulli(p).pmf(x)
+    i = (x % 1 == 0)
+    assert_allclose(pmf[-1], p)
+    assert_allclose(pmf[0], 1-p)
+    assert_equal(pmf[~i], 0)
+
+
+class TestBetaNBinom:
+    @pytest.mark.parametrize('x, n, a, b, ref',
+                            [[5, 5e6, 5, 20, 1.1520944824139114e-107],
+                            [100, 50, 5, 20, 0.002855762954310226],
+                            [10000, 1000, 5, 20, 1.9648515726019154e-05]])
+    def test_betanbinom_pmf(self, x, n, a, b, ref):
+        # test that PMF stays accurate in the distribution tails
+        # reference values computed with mpmath
+        # from mpmath import mp
+        # mp.dps = 500
+        # def betanbinom_pmf(k, n, a, b):
+        #     k = mp.mpf(k)
+        #     a = mp.mpf(a)
+        #     b = mp.mpf(b)
+        #     n = mp.mpf(n)
+        #     return float(mp.binomial(n + k - mp.one, k)
+        #                  * mp.beta(a + n, b + k) / mp.beta(a, b))
+        assert_allclose(betanbinom.pmf(x, n, a, b), ref, rtol=1e-10)
+
+
+    @pytest.mark.parametrize('n, a, b, ref',
+                            [[10000, 5000, 50, 0.12841520515722202],
+                            [10, 9, 9, 7.9224400871459695],
+                            [100, 1000, 10, 1.5849602176622748]])
+    def test_betanbinom_kurtosis(self, n, a, b, ref):
+        # reference values were computed via mpmath
+        # from mpmath import mp
+        # def kurtosis_betanegbinom(n, a, b):
+        #     n = mp.mpf(n)
+        #     a = mp.mpf(a)
+        #     b = mp.mpf(b)
+        #     four = mp.mpf(4.)
+        #     mean = n * b / (a - mp.one)
+        #     var = (n * b * (n + a - 1.) * (a + b - 1.)
+        #            / ((a - 2.) * (a - 1.)**2.))
+        #     def f(k):
+        #         return (mp.binomial(n + k - mp.one, k)
+        #                 * mp.beta(a + n, b + k) / mp.beta(a, b)
+        #                 * (k - mean)**four)
+        #      fourth_moment = mp.nsum(f, [0, mp.inf])
+        #      return float(fourth_moment/var**2 - 3.)
+        assert_allclose(betanbinom.stats(n, a, b, moments="k"),
+                        ref, rtol=3e-15)
+
+
+class TestZipf:
+    def test_gh20692(self):
+        # test that int32 data for k generates same output as double
+        k = np.arange(0, 1000)
+        k_int32 = k.astype(np.int32)
+        dist = zipf(9)
+        pmf = dist.pmf(k)
+        pmf_k_int32 = dist.pmf(k_int32)
+        assert_equal(pmf, pmf_k_int32)
+
+
+def test_gh20048():
+    # gh-20048 reported an infinite loop in _drv2_ppfsingle
+    # check that the one identified is resolved
+    class test_dist_gen(stats.rv_discrete):
+        def _cdf(self, k):
+            return min(k / 100, 0.99)
+
+    test_dist = test_dist_gen(b=np.inf)
+
+    message = "Arguments that bracket..."
+    with pytest.raises(RuntimeError, match=message):
+        test_dist.ppf(0.999)
+
+
+class TestPoissonBinomial:
+    def test_pmf(self):
+        # Test pmf against R `poisbinom` to confirm that this is indeed the Poisson
+        # binomial distribution. Consistency of other methods and all other behavior
+        # should be covered by generic tests. (If not, please add a generic test.)
+        # Like many other distributions, no special attempt is made to be more
+        # accurate than the usual formulas provide, so we use default tolerances.
+        #
+        # library(poisbinom)
+        # options(digits=16)
+        # k = c(0, 1, 2, 3, 4)
+        # p = c(0.9480654803913988, 0.052428488100509374,
+        #       0.25863527358887417, 0.057764076043633206)
+        # dpoisbinom(k, p)
+        rng = np.random.default_rng(259823598254)
+        n = rng.integers(10)  # 4
+        k = np.arange(n + 1)
+        p = rng.random(n)  #  [0.9480654803913988, 0.052428488100509374,
+                           #   0.25863527358887417, 0.057764076043633206]
+        res = poisson_binom.pmf(k, p)
+        ref = [0.0343763443678060318, 0.6435428452689714307, 0.2936345519235536994,
+               0.0277036647503902354, 0.0007425936892786034]
+        assert_allclose(res, ref)
+
+
+class TestRandInt:
+    def test_gh19759(self):
+        # test zero PMF values within the support reported by gh-19759
+        a = -354
+        max_range = abs(a)
+        all_b_1 = [a + 2 ** 31 + i for i in range(max_range)]
+        res = randint.pmf(325, a, all_b_1)
+        assert (res > 0).all()
+        ref = 1 / (np.asarray(all_b_1, dtype=np.float64) - a)
+        assert_allclose(res, ref)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_distributions.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..a355a702807438358bd88c00de026008055dbc51
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_distributions.py
@@ -0,0 +1,10606 @@
+"""
+Test functions for stats module
+"""
+import warnings
+import re
+import sys
+import pickle
+import threading
+from pathlib import Path
+import os
+import json
+import platform
+
+from numpy.testing import (assert_equal, assert_array_equal,
+                           assert_almost_equal, assert_array_almost_equal,
+                           assert_allclose, assert_,
+                           assert_array_less, assert_array_max_ulp)
+import pytest
+from pytest import raises as assert_raises
+
+import numpy as np
+from numpy import typecodes, array
+from numpy.lib.recfunctions import rec_append_fields
+from scipy import special
+from scipy._lib._util import check_random_state
+from scipy.integrate import (IntegrationWarning, quad, trapezoid,
+                             cumulative_trapezoid)
+import scipy.stats as stats
+from scipy.stats._distn_infrastructure import argsreduce
+from scipy.stats._constants import _XMAX
+import scipy.stats.distributions
+
+from scipy.special import xlogy, polygamma, entr
+from scipy.stats._distr_params import distcont, invdistcont
+from .test_discrete_basic import distdiscrete, invdistdiscrete
+from scipy.stats._continuous_distns import FitDataError, _argus_phi
+from scipy.optimize import root, fmin, differential_evolution
+from itertools import product
+
+# python -OO strips docstrings
+DOCSTRINGS_STRIPPED = sys.flags.optimize > 1
+
+# Failing on macOS 11, Intel CPUs. See gh-14901
+MACOS_INTEL = (sys.platform == 'darwin') and (platform.machine() == 'x86_64')
+
+
+# distributions to skip while testing the fix for the support method
+# introduced in gh-13294. These distributions are skipped as they
+# always return a non-nan support for every parametrization.
+skip_test_support_gh13294_regression = ['tukeylambda', 'pearson3']
+
+
+def _assert_hasattr(a, b, msg=None):
+    if msg is None:
+        msg = f'{a} does not have attribute {b}'
+    assert_(hasattr(a, b), msg=msg)
+
+
+def test_api_regression():
+    # https://github.com/scipy/scipy/issues/3802
+    _assert_hasattr(scipy.stats.distributions, 'f_gen')
+
+
+def test_distributions_submodule():
+    actual = set(scipy.stats.distributions.__all__)
+    continuous = [dist[0] for dist in distcont]    # continuous dist names
+    discrete = [dist[0] for dist in distdiscrete]  # discrete dist names
+    other = ['rv_discrete', 'rv_continuous', 'rv_histogram',
+             'entropy']
+    expected = continuous + discrete + other
+
+    # need to remove, e.g.,
+    # <scipy.stats._continuous_distns.trapezoid_gen at 0x1df83bbc688>
+    expected = set(filter(lambda s: not str(s).startswith('<'), expected))
+
+    assert actual == expected
+
+
+class TestVonMises:
+    def setup_method(self):
+        self.rng = np.random.default_rng(6320571663)
+
+    @pytest.mark.parametrize('k', [0.1, 1, 101])
+    @pytest.mark.parametrize('x', [0, 1, np.pi, 10, 100])
+    def test_vonmises_periodic(self, k, x):
+        def check_vonmises_pdf_periodic(k, L, s, x):
+            vm = stats.vonmises(k, loc=L, scale=s)
+            assert_almost_equal(vm.pdf(x), vm.pdf(x % (2 * np.pi * s)))
+
+        def check_vonmises_cdf_periodic(k, L, s, x):
+            vm = stats.vonmises(k, loc=L, scale=s)
+            assert_almost_equal(vm.cdf(x) % 1,
+                                vm.cdf(x % (2 * np.pi * s)) % 1)
+
+        check_vonmises_pdf_periodic(k, 0, 1, x)
+        check_vonmises_pdf_periodic(k, 1, 1, x)
+        check_vonmises_pdf_periodic(k, 0, 10, x)
+
+        check_vonmises_cdf_periodic(k, 0, 1, x)
+        check_vonmises_cdf_periodic(k, 1, 1, x)
+        check_vonmises_cdf_periodic(k, 0, 10, x)
+
+    def test_vonmises_line_support(self):
+        assert_equal(stats.vonmises_line.a, -np.pi)
+        assert_equal(stats.vonmises_line.b, np.pi)
+
+    def test_vonmises_numerical(self):
+        vm = stats.vonmises(800)
+        assert_almost_equal(vm.cdf(0), 0.5)
+
+    # Expected values of the vonmises PDF were computed using
+    # mpmath with 50 digits of precision:
+    #
+    # def vmpdf_mp(x, kappa):
+    #     x = mpmath.mpf(x)
+    #     kappa = mpmath.mpf(kappa)
+    #     num = mpmath.exp(kappa*mpmath.cos(x))
+    #     den = 2 * mpmath.pi * mpmath.besseli(0, kappa)
+    #     return num/den
+
+    @pytest.mark.parametrize('x, kappa, expected_pdf',
+                             [(0.1, 0.01, 0.16074242744907072),
+                              (0.1, 25.0, 1.7515464099118245),
+                              (0.1, 800, 0.2073272544458798),
+                              (2.0, 0.01, 0.15849003875385817),
+                              (2.0, 25.0, 8.356882934278192e-16),
+                              (2.0, 800, 0.0)])
+    def test_vonmises_pdf(self, x, kappa, expected_pdf):
+        pdf = stats.vonmises.pdf(x, kappa)
+        assert_allclose(pdf, expected_pdf, rtol=1e-15)
+
+    # Expected values of the vonmises entropy were computed using
+    # mpmath with 50 digits of precision:
+    #
+    # def vonmises_entropy(kappa):
+    #     kappa = mpmath.mpf(kappa)
+    #     return (-kappa * mpmath.besseli(1, kappa) /
+    #             mpmath.besseli(0, kappa) + mpmath.log(2 * mpmath.pi *
+    #             mpmath.besseli(0, kappa)))
+    # >>> float(vonmises_entropy(kappa))
+
+    @pytest.mark.parametrize('kappa, expected_entropy',
+                             [(1, 1.6274014590199897),
+                              (5, 0.6756431570114528),
+                              (100, -0.8811275441649473),
+                              (1000, -2.03468891852547),
+                              (2000, -2.3813876496587847)])
+    def test_vonmises_entropy(self, kappa, expected_entropy):
+        entropy = stats.vonmises.entropy(kappa)
+        assert_allclose(entropy, expected_entropy, rtol=1e-13)
+
+    def test_vonmises_rvs_gh4598(self):
+        # check that random variates wrap around as discussed in gh-4598
+        seed = 30899520
+        rng1 = np.random.default_rng(seed)
+        rng2 = np.random.default_rng(seed)
+        rng3 = np.random.default_rng(seed)
+        rvs1 = stats.vonmises(1, loc=0, scale=1).rvs(random_state=rng1)
+        rvs2 = stats.vonmises(1, loc=2*np.pi, scale=1).rvs(random_state=rng2)
+        rvs3 = stats.vonmises(1, loc=0,
+                              scale=(2*np.pi/abs(rvs1)+1)).rvs(random_state=rng3)
+        assert_allclose(rvs1, rvs2, atol=1e-15)
+        assert_allclose(rvs1, rvs3, atol=1e-15)
+
+    # Expected values of the vonmises LOGPDF were computed
+    # using wolfram alpha:
+    # kappa * cos(x) - log(2*pi*I0(kappa))
+    @pytest.mark.parametrize('x, kappa, expected_logpdf',
+                             [(0.1, 0.01, -1.8279520246003170),
+                              (0.1, 25.0, 0.5604990605420549),
+                              (0.1, 800, -1.5734567947337514),
+                              (2.0, 0.01, -1.8420635346185686),
+                              (2.0, 25.0, -34.7182759850871489),
+                              (2.0, 800, -1130.4942582548682739)])
+    def test_vonmises_logpdf(self, x, kappa, expected_logpdf):
+        logpdf = stats.vonmises.logpdf(x, kappa)
+        assert_allclose(logpdf, expected_logpdf, rtol=1e-15)
+
+    def test_vonmises_expect(self):
+        """
+        Test that the vonmises expectation values are
+        computed correctly.  This test checks that the
+        numeric integration estimates the correct normalization
+        (1) and mean angle (loc).  These expectations are
+        independent of the chosen 2pi interval.
+        """
+        rng = np.random.default_rng(6762668991392531563)
+
+        loc, kappa, lb = rng.random(3) * 10
+        res = stats.vonmises(loc=loc, kappa=kappa).expect(lambda x: 1)
+        assert_allclose(res, 1)
+        assert np.issubdtype(res.dtype, np.floating)
+
+        bounds = lb, lb + 2 * np.pi
+        res = stats.vonmises(loc=loc, kappa=kappa).expect(lambda x: 1, *bounds)
+        assert_allclose(res, 1)
+        assert np.issubdtype(res.dtype, np.floating)
+
+        bounds = lb, lb + 2 * np.pi
+        res = stats.vonmises(loc=loc, kappa=kappa).expect(lambda x: np.exp(1j*x),
+                                                          *bounds, complex_func=1)
+        assert_allclose(np.angle(res), loc % (2*np.pi))
+        assert np.issubdtype(res.dtype, np.complexfloating)
+
+    @pytest.mark.xslow
+    @pytest.mark.parametrize("rvs_loc", [0, 2])
+    @pytest.mark.parametrize("rvs_shape", [1, 100, 1e8])
+    @pytest.mark.parametrize('fix_loc', [True, False])
+    @pytest.mark.parametrize('fix_shape', [True, False])
+    def test_fit_MLE_comp_optimizer(self, rvs_loc, rvs_shape,
+                                    fix_loc, fix_shape):
+        if fix_shape and fix_loc:
+            pytest.skip("Nothing to fit.")
+
+        rng = np.random.default_rng(6762668991392531563)
+        data = stats.vonmises.rvs(rvs_shape, size=1000, loc=rvs_loc,
+                                  random_state=rng)
+
+        kwds = {'fscale': 1}
+        if fix_loc:
+            kwds['floc'] = rvs_loc
+        if fix_shape:
+            kwds['f0'] = rvs_shape
+
+        _assert_less_or_close_loglike(stats.vonmises, data,
+                                      stats.vonmises.nnlf, **kwds)
+
+    @pytest.mark.slow
+    def test_vonmises_fit_bad_floc(self):
+        data = [-0.92923506, -0.32498224, 0.13054989, -0.97252014, 2.79658071,
+                -0.89110948, 1.22520295, 1.44398065, 2.49163859, 1.50315096,
+                3.05437696, -2.73126329, -3.06272048, 1.64647173, 1.94509247,
+                -1.14328023, 0.8499056, 2.36714682, -1.6823179, -0.88359996]
+        data = np.asarray(data)
+        loc = -0.5 * np.pi
+        kappa_fit, loc_fit, scale_fit = stats.vonmises.fit(data, floc=loc)
+        assert kappa_fit == np.finfo(float).tiny
+        _assert_less_or_close_loglike(stats.vonmises, data,
+                                      stats.vonmises.nnlf, fscale=1, floc=loc)
+
+    @pytest.mark.parametrize('sign', [-1, 1])
+    def test_vonmises_fit_unwrapped_data(self, sign):
+        rng = np.random.default_rng(6762668991392531563)
+        data = stats.vonmises(loc=sign*0.5*np.pi, kappa=10).rvs(100000,
+                                                                random_state=rng)
+        shifted_data = data + 4*np.pi
+        kappa_fit, loc_fit, scale_fit = stats.vonmises.fit(data)
+        kappa_fit_shifted, loc_fit_shifted, _ = stats.vonmises.fit(shifted_data)
+        assert_allclose(loc_fit, loc_fit_shifted)
+        assert_allclose(kappa_fit, kappa_fit_shifted)
+        assert scale_fit == 1
+        assert -np.pi < loc_fit < np.pi
+
+    def test_vonmises_kappa_0_gh18166(self):
+        # Check that kappa = 0 is supported.
+        dist = stats.vonmises(0)
+        assert_allclose(dist.pdf(0), 1 / (2 * np.pi), rtol=1e-15)
+        assert_allclose(dist.cdf(np.pi/2), 0.75, rtol=1e-15)
+        assert_allclose(dist.sf(-np.pi/2), 0.75, rtol=1e-15)
+        assert_allclose(dist.ppf(0.9), np.pi*0.8, rtol=1e-15)
+        assert_allclose(dist.mean(), 0, atol=1e-15)
+        assert_allclose(dist.expect(), 0, atol=1e-15)
+        assert np.all(np.abs(dist.rvs(size=10, random_state=self.rng)) <= np.pi)
+
+    def test_vonmises_fit_equal_data(self):
+        # When all data are equal, expect kappa = 1e16.
+        kappa, loc, scale = stats.vonmises.fit([0])
+        assert kappa == 1e16 and loc == 0 and scale == 1
+
+    def test_vonmises_fit_bounds(self):
+        # For certain input data, the root bracket is violated numerically.
+        # Test that this situation is handled.  The input data below are
+        # crafted to trigger the bound violation for the current choice of
+        # bounds and the specific way the bounds and the objective function
+        # are computed.
+
+        # Test that no exception is raised when the lower bound is violated.
+        scipy.stats.vonmises.fit([0, 3.7e-08], floc=0)
+
+        # Test that no exception is raised when the upper bound is violated.
+        scipy.stats.vonmises.fit([np.pi/2*(1-4.86e-9)], floc=0)
+
+
+def _assert_less_or_close_loglike(dist, data, func=None, maybe_identical=False,
+                                  **kwds):
+    """
+    This utility function checks that the negative log-likelihood function
+    (or `func`) of the result computed using dist.fit() is less than or equal
+    to the result computed using the generic fit method.  Because of
+    normal numerical imprecision, the "equality" check is made using
+    `np.allclose` with a relative tolerance of 1e-15.
+    """
+    if func is None:
+        func = dist.nnlf
+
+    mle_analytical = dist.fit(data, **kwds)
+    numerical_opt = super(type(dist), dist).fit(data, **kwds)
+
+    # Sanity check that the analytical MLE is actually executed.
+    # Due to floating point arithmetic, the generic MLE is unlikely
+    # to produce the exact same result as the analytical MLE.
+    if not maybe_identical:
+        assert np.any(mle_analytical != numerical_opt)
+
+    ll_mle_analytical = func(mle_analytical, data)
+    ll_numerical_opt = func(numerical_opt, data)
+    assert (ll_mle_analytical <= ll_numerical_opt or
+            np.allclose(ll_mle_analytical, ll_numerical_opt, rtol=1e-15))
+
+    # Ideally we'd check that shapes are correctly fixed, too, but that is
+    # complicated by the many ways of fixing them (e.g. f0, fix_a, fa).
+    if 'floc' in kwds:
+        assert mle_analytical[-2] == kwds['floc']
+    if 'fscale' in kwds:
+        assert mle_analytical[-1] == kwds['fscale']
+
+
+def assert_fit_warnings(dist):
+    param = ['floc', 'fscale']
+    if dist.shapes:
+        nshapes = len(dist.shapes.split(","))
+        param += ['f0', 'f1', 'f2'][:nshapes]
+    all_fixed = dict(zip(param, np.arange(len(param))))
+    data = [1, 2, 3]
+    with pytest.raises(RuntimeError,
+                       match="All parameters fixed. There is nothing "
+                       "to optimize."):
+        dist.fit(data, **all_fixed)
+    with pytest.raises(ValueError,
+                       match="The data contains non-finite values"):
+        dist.fit([np.nan])
+    with pytest.raises(ValueError,
+                       match="The data contains non-finite values"):
+        dist.fit([np.inf])
+    with pytest.raises(TypeError, match="Unknown keyword arguments:"):
+        dist.fit(data, extra_keyword=2)
+    with pytest.raises(TypeError, match="Too many positional arguments."):
+        dist.fit(data, *[1]*(len(param) - 1))
+
+
+@pytest.mark.parametrize('dist',
+                         ['alpha', 'betaprime',
+                          'fatiguelife', 'invgamma', 'invgauss', 'invweibull',
+                          'johnsonsb', 'levy', 'levy_l', 'lognorm', 'gibrat',
+                          'powerlognorm', 'rayleigh', 'wald'])
+def test_support(dist):
+    """gh-6235"""
+    dct = dict(distcont)
+    args = dct[dist]
+
+    dist = getattr(stats, dist)
+
+    assert_almost_equal(dist.pdf(dist.a, *args), 0)
+    assert_equal(dist.logpdf(dist.a, *args), -np.inf)
+    assert_almost_equal(dist.pdf(dist.b, *args), 0)
+    assert_equal(dist.logpdf(dist.b, *args), -np.inf)
+
+
+class TestRandInt:
+    def setup_method(self):
+        self.rng = np.random.default_rng(8826485737)
+
+    def test_rvs(self):
+        vals = stats.randint.rvs(5, 30, size=100, random_state=self.rng)
+        assert_(np.all(vals < 30) & np.all(vals >= 5))
+        assert_(len(vals) == 100)
+        vals = stats.randint.rvs(5, 30, size=(2, 50), random_state=self.rng)
+        assert_(np.shape(vals) == (2, 50))
+        assert_(vals.dtype.char in typecodes['AllInteger'])
+        val = stats.randint.rvs(15, 46, random_state=self.rng)
+        assert_((val >= 15) & (val < 46))
+        assert_(isinstance(val, np.ScalarType), msg=repr(type(val)))
+        val = stats.randint(15, 46).rvs(3, random_state=self.rng)
+        assert_(val.dtype.char in typecodes['AllInteger'])
+
+    def test_pdf(self):
+        k = np.r_[0:36]
+        out = np.where((k >= 5) & (k < 30), 1.0/(30-5), 0)
+        vals = stats.randint.pmf(k, 5, 30)
+        assert_array_almost_equal(vals, out)
+
+    def test_cdf(self):
+        x = np.linspace(0, 36, 100)
+        k = np.floor(x)
+        out = np.select([k >= 30, k >= 5], [1.0, (k-5.0+1)/(30-5.0)], 0)
+        vals = stats.randint.cdf(x, 5, 30)
+        assert_array_almost_equal(vals, out, decimal=12)
+
+
+class TestBinom:
+    def setup_method(self):
+        self.rng = np.random.default_rng(1778595878)
+
+    def test_rvs(self):
+        vals = stats.binom.rvs(10, 0.75, size=(2, 50), random_state=self.rng)
+        assert_(np.all(vals >= 0) & np.all(vals <= 10))
+        assert_(np.shape(vals) == (2, 50))
+        assert_(vals.dtype.char in typecodes['AllInteger'])
+        val = stats.binom.rvs(10, 0.75, random_state=self.rng)
+        assert_(isinstance(val, int))
+        val = stats.binom(10, 0.75).rvs(3, random_state=self.rng)
+        assert_(isinstance(val, np.ndarray))
+        assert_(val.dtype.char in typecodes['AllInteger'])
+
+    def test_pmf(self):
+        # regression test for Ticket #1842
+        vals1 = stats.binom.pmf(100, 100, 1)
+        vals2 = stats.binom.pmf(0, 100, 0)
+        assert_allclose(vals1, 1.0, rtol=1e-15, atol=0)
+        assert_allclose(vals2, 1.0, rtol=1e-15, atol=0)
+
+    def test_entropy(self):
+        # Basic entropy tests.
+        b = stats.binom(2, 0.5)
+        expected_p = np.array([0.25, 0.5, 0.25])
+        expected_h = -sum(xlogy(expected_p, expected_p))
+        h = b.entropy()
+        assert_allclose(h, expected_h)
+
+        b = stats.binom(2, 0.0)
+        h = b.entropy()
+        assert_equal(h, 0.0)
+
+        b = stats.binom(2, 1.0)
+        h = b.entropy()
+        assert_equal(h, 0.0)
+
+    def test_warns_p0(self):
+        # no spurious warnings are generated for p=0; gh-3817
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", RuntimeWarning)
+            assert_equal(stats.binom(n=2, p=0).mean(), 0)
+            assert_equal(stats.binom(n=2, p=0).std(), 0)
+
+    def test_ppf_p1(self):
+        # Check that gh-17388 is resolved: PPF == n when p = 1
+        n = 4
+        assert stats.binom.ppf(q=0.3, n=n, p=1.0) == n
+
+    def test_pmf_poisson(self):
+        # Check that gh-17146 is resolved: binom -> poisson
+        n = 1541096362225563.0
+        p = 1.0477878413173978e-18
+        x = np.arange(3)
+        res = stats.binom.pmf(x, n=n, p=p)
+        ref = stats.poisson.pmf(x, n * p)
+        assert_allclose(res, ref, atol=1e-16)
+
+    def test_pmf_cdf(self):
+        # Check that gh-17809 is resolved: binom.pmf(0) ~ binom.cdf(0)
+        n = 25.0 * 10 ** 21
+        p = 1.0 * 10 ** -21
+        r = 0
+        res = stats.binom.pmf(r, n, p)
+        ref = stats.binom.cdf(r, n, p)
+        assert_allclose(res, ref, atol=1e-16)
+
+    def test_pmf_gh15101(self):
+        # Check that gh-15101 is resolved (no divide warnings when p~1, n~oo)
+        res = stats.binom.pmf(3, 2000, 0.999)
+        assert_allclose(res, 0, atol=1e-16)
+
+
+class TestArcsine:
+
+    def test_endpoints(self):
+        # Regression test for gh-13697.  The following calculation
+        # should not generate a warning.
+        p = stats.arcsine.pdf([0, 1])
+        assert_equal(p, [np.inf, np.inf])
+
+
+class TestBernoulli:
+    def setup_method(self):
+        self.rng = np.random.default_rng(7836792223)
+
+    def test_rvs(self):
+        vals = stats.bernoulli.rvs(0.75, size=(2, 50), random_state=self.rng)
+        assert_(np.all(vals >= 0) & np.all(vals <= 1))
+        assert_(np.shape(vals) == (2, 50))
+        assert_(vals.dtype.char in typecodes['AllInteger'])
+        val = stats.bernoulli.rvs(0.75, random_state=self.rng)
+        assert_(isinstance(val, int))
+        val = stats.bernoulli(0.75).rvs(3, random_state=self.rng)
+        assert_(isinstance(val, np.ndarray))
+        assert_(val.dtype.char in typecodes['AllInteger'])
+
+    def test_entropy(self):
+        # Simple tests of entropy.
+        b = stats.bernoulli(0.25)
+        expected_h = -0.25*np.log(0.25) - 0.75*np.log(0.75)
+        h = b.entropy()
+        assert_allclose(h, expected_h)
+
+        b = stats.bernoulli(0.0)
+        h = b.entropy()
+        assert_equal(h, 0.0)
+
+        b = stats.bernoulli(1.0)
+        h = b.entropy()
+        assert_equal(h, 0.0)
+
+
+class TestBradford:
+    # gh-6216
+    def test_cdf_ppf(self):
+        c = 0.1
+        x = np.logspace(-20, -4)
+        q = stats.bradford.cdf(x, c)
+        xx = stats.bradford.ppf(q, c)
+        assert_allclose(x, xx)
+
+
+class TestCauchy:
+
+    def test_pdf_no_overflow_warning(self):
+        # The argument is large enough that x**2 will overflow to
+        # infinity and 1/(1 + x**2) will be 0.  This should not
+        # trigger a warning.
+        p = stats.cauchy.pdf(1e200)
+        assert p == 0.0
+
+    # Reference values were computed with mpmath.
+    @pytest.mark.parametrize(
+        'x, ref',
+        [(0.0, -1.1447298858494002),
+         (5e-324, -1.1447298858494002),
+         (1e-34, -1.1447298858494002),
+         (2.2e-16, -1.1447298858494002),
+         (2e-8, -1.1447298858494006),
+         (5e-4, -1.144730135849369),
+         (0.1, -1.1546802167025683),
+         (1.5, -2.3233848821910463),
+         (2e18, -85.42408759475494),
+         (1e200, -922.1787670834676),
+         (_XMAX, -1420.7101556726175)])
+    def test_logpdf(self, x, ref):
+        logp = stats.cauchy.logpdf([x, -x])
+        assert_allclose(logp, [ref, ref], rtol=1e-15)
+
+    # Reference values were computed with mpmath.
+    @pytest.mark.parametrize(
+        'x, ref',
+        [(-5e15, 6.366197723675814e-17),
+         (-5, 0.06283295818900118),
+         (-1, 0.25),
+         (0, 0.5),
+         (1, 0.75),
+         (5, 0.9371670418109989),
+         (5e15, 0.9999999999999999)]
+    )
+    @pytest.mark.parametrize(
+        'method, sgn',
+        [(stats.cauchy.cdf, 1),
+         (stats.cauchy.sf, -1)]
+    )
+    def test_cdf_sf(self, x, ref, method, sgn):
+        p = method(sgn*x)
+        assert_allclose(p, ref, rtol=1e-15)
+
+    # Reference values were computed with mpmath.
+    @pytest.mark.parametrize('x, ref',
+                            [(4e250, -7.957747154594767e-252),
+                             (1e25, -3.1830988618379063e-26),
+                             (10.0, -0.03223967552667532),
+                             (0.0, -0.6931471805599453),
+                             (-10.0, -3.4506339556469654),
+                             (-7e45, -106.70696921963678),
+                             (-3e225, -520.3249880981778)])
+    def test_logcdf_logsf(self, x, ref):
+        logcdf = stats.cauchy.logcdf(x)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+        logsf = stats.cauchy.logsf(-x)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+    # Reference values were computed with mpmath.
+    @pytest.mark.parametrize(
+        'p, ref',
+        [(1e-20, -3.1830988618379067e+19),
+         (1e-9, -318309886.1837906),
+         (0.25, -1.0),
+         (0.50, 0.0),
+         (0.75, 1.0),
+         (0.999999, 318309.88617359026),
+         (0.999999999999, 318316927901.77966)]
+    )
+    @pytest.mark.parametrize(
+        'method, sgn',
+        [(stats.cauchy.ppf, 1),
+         (stats.cauchy.isf, -1)])
+    def test_ppf_isf(self, p, ref, method, sgn):
+        x = sgn*method(p)
+        assert_allclose(x, ref, rtol=1e-15)
+
+
+class TestChi:
+
+    # "Exact" value of chi.sf(10, 4), as computed by Wolfram Alpha with
+    #     1 - CDF[ChiDistribution[4], 10]
+    CHI_SF_10_4 = 9.83662422461598e-21
+    # "Exact" value of chi.mean(df=1000) as computed by Wolfram Alpha with
+    #       Mean[ChiDistribution[1000]]
+    CHI_MEAN_1000 = 31.614871896980
+
+    def test_sf(self):
+        s = stats.chi.sf(10, 4)
+        assert_allclose(s, self.CHI_SF_10_4, rtol=1e-15)
+
+    def test_isf(self):
+        x = stats.chi.isf(self.CHI_SF_10_4, 4)
+        assert_allclose(x, 10, rtol=1e-15)
+
+    def test_logcdf(self):
+        x = 10.0
+        df = 15
+        logcdf = stats.chi.logcdf(x, df)
+        # Reference value computed with mpath.
+        assert_allclose(logcdf, -1.304704343625153e-14, rtol=5e-15)
+
+    def test_logsf(self):
+        x = 0.01
+        df = 15
+        logsf = stats.chi.logsf(x, df)
+        # Reference value computed with mpath.
+        assert_allclose(logsf, -3.936060782678026e-37, rtol=5e-15)
+
+    # reference value for 1e14 was computed via mpmath
+    # from mpmath import mp
+    # mp.dps = 500
+    # df = mp.mpf(1e14)
+    # float(mp.rf(mp.mpf(0.5) * df, mp.mpf(0.5)) * mp.sqrt(2.))
+
+    @pytest.mark.parametrize('df, ref',
+                             [(1e3, CHI_MEAN_1000),
+                              (1e14, 9999999.999999976)])
+    def test_mean(self, df, ref):
+        assert_allclose(stats.chi.mean(df), ref, rtol=1e-12)
+
+    # Entropy references values were computed with the following mpmath code
+    # from mpmath import mp
+    # mp.dps = 50
+    # def chi_entropy_mpmath(df):
+    #     df = mp.mpf(df)
+    #     half_df = 0.5 * df
+    #     entropy = mp.log(mp.gamma(half_df)) + 0.5 * \
+    #               (df - mp.log(2) - (df - mp.one) * mp.digamma(half_df))
+    #     return float(entropy)
+
+    @pytest.mark.parametrize('df, ref',
+                             [(1e-4, -9989.7316027504),
+                              (1, 0.7257913526447274),
+                              (1e3, 1.0721981095025448),
+                              (1e10, 1.0723649429080335),
+                              (1e100, 1.0723649429247002)])
+    def test_entropy(self, df, ref):
+        assert_allclose(stats.chi(df).entropy(), ref, rtol=1e-15)
+
+
+class TestCrystalBall:
+
+    def test_pdf(self):
+        """
+        All values are calculated using the independent implementation of the
+        ROOT framework (see https://root.cern.ch/).
+        Corresponding ROOT code is given in the comments.
+        """
+        X = np.linspace(-5.0, 5.0, 21)[:-1]
+
+        # for (double x = -5.0; x < 5.0; x += 0.5) {
+        #     cout << setprecision(16)
+        #          << ROOT::Math::crystalball_pdf(x, 1.0, 2.0, 1.0)
+        #          << ", ";
+        # }
+        calculated = stats.crystalball.pdf(X, beta=1.0, m=2.0)
+        expected = np.array([0.02028666423671257, 0.02414280702550917,
+                             0.02921279650086611, 0.03606518086526679,
+                             0.04564499453260328, 0.05961795204258388,
+                             0.08114665694685029, 0.1168511860034644,
+                             0.1825799781304131, 0.2656523006609301,
+                             0.3010234935475763, 0.2656523006609301,
+                             0.1825799781304131, 0.09772801991305094,
+                             0.0407390997601359, 0.01322604925508607,
+                             0.003344068947749631, 0.0006584862184997063,
+                             0.0001009821322058648, 1.206059579124873e-05])
+        assert_allclose(expected, calculated, rtol=1e-14)
+
+        # for (double x = -5.0; x < 5.0; x += 0.5) {
+        #     cout << setprecision(16)
+        #          << ROOT::Math::crystalball_pdf(x, 2.0, 3.0, 1.0)
+        #          << ", ";
+        # }
+        calculated = stats.crystalball.pdf(X, beta=2.0, m=3.0)
+        expected = np.array([0.00196480373120913, 0.0027975428126005,
+                             0.004175923965164595, 0.006631212592830816,
+                             0.01145873536041165, 0.022380342500804,
+                             0.05304970074264653, 0.1272596164638828,
+                             0.237752264003024, 0.3459275029304401,
+                             0.3919872148188981, 0.3459275029304401,
+                             0.237752264003024, 0.1272596164638828,
+                             0.05304970074264653, 0.01722271623872227,
+                             0.004354584612458383, 0.0008574685508575863,
+                             0.000131497061187334, 1.570508433595375e-05])
+        assert_allclose(expected, calculated, rtol=1e-14)
+
+        # for (double x = -5.0; x < 5.0; x += 0.5) {
+        #   cout << setprecision(16)
+        #        << ROOT::Math::crystalball_pdf(x, 2.0, 3.0, 2.0, 0.5)
+        #        << ", ";
+        # }
+        calculated = stats.crystalball.pdf(X, beta=2.0, m=3.0, loc=0.5, scale=2.0)
+        expected = np.array([0.007859214924836521, 0.011190171250402,
+                             0.01670369586065838, 0.02652485037132326,
+                             0.04238659020399594, 0.06362980823194138,
+                             0.08973241216601403, 0.118876132001512,
+                             0.1479437366093383, 0.17296375146522,
+                             0.1899635180461471, 0.1959936074094491,
+                             0.1899635180461471, 0.17296375146522,
+                             0.1479437366093383, 0.118876132001512,
+                             0.08973241216601403, 0.06362980823194138,
+                             0.04238659020399594, 0.02652485037132326])
+        assert_allclose(expected, calculated, rtol=1e-14)
+
+    def test_cdf(self):
+        """
+        All values are calculated using the independent implementation of the
+        ROOT framework (see https://root.cern.ch/).
+        Corresponding ROOT code is given in the comments.
+        """
+        X = np.linspace(-5.0, 5.0, 21)[:-1]
+
+        # for (double x = -5.0; x < 5.0; x += 0.5) {
+        #     cout << setprecision(16)
+        #          << ROOT::Math::crystalball_cdf(x, 1.0, 2.0, 1.0)
+        #          << ", ";
+        # }
+        calculated = stats.crystalball.cdf(X, beta=1.0, m=2.0)
+        expected = np.array([0.1217199854202754, 0.1327854386403005,
+                             0.1460639825043305, 0.1622933138937006,
+                             0.1825799781304132, 0.2086628321490436,
+                             0.2434399708405509, 0.292127965008661,
+                             0.3651599562608263, 0.4782542338198316,
+                             0.6227229998727213, 0.7671917659256111,
+                             0.8802860434846165, 0.9495903590367718,
+                             0.9828337969321823, 0.9953144721881936,
+                             0.9989814290402977, 0.9998244687978383,
+                             0.9999761023377818, 0.9999974362721522])
+        assert_allclose(expected, calculated, rtol=1e-13)
+
+        # for (double x = -5.0; x < 5.0; x += 0.5) {
+        #     cout << setprecision(16)
+        #          << ROOT::Math::crystalball_cdf(x, 2.0, 3.0, 1.0)
+        #          << ", ";
+        # }
+        calculated = stats.crystalball.cdf(X, beta=2.0, m=3.0)
+        expected = np.array([0.004420808395220632, 0.005595085625200946,
+                             0.007307866939038177, 0.009946818889246312,
+                             0.01432341920051472, 0.02238034250080412,
+                             0.03978727555698502, 0.08307626432678494,
+                             0.1733230597116304, 0.3205923321191123,
+                             0.508716882020547, 0.6968414319219818,
+                             0.8441107043294638, 0.934357499714309,
+                             0.9776464884841091, 0.9938985925142876,
+                             0.9986736357721329, 0.9997714265214375,
+                             0.9999688809071239, 0.9999966615611068])
+        assert_allclose(expected, calculated, rtol=1e-13)
+
+        # for (double x = -5.0; x < 5.0; x += 0.5) {
+        #     cout << setprecision(16)
+        #          << ROOT::Math::crystalball_cdf(x, 2.0, 3.0, 2.0, 0.5);
+        #          << ", ";
+        # }
+        calculated = stats.crystalball.cdf(X, beta=2.0, m=3.0, loc=0.5, scale=2.0)
+        expected = np.array([0.0176832335808822, 0.02238034250080412,
+                             0.02923146775615237, 0.03978727555698502,
+                             0.05679453901646225, 0.08307626432678494,
+                             0.1212416644828466, 0.1733230597116304,
+                             0.2401101486313661, 0.3205923321191123,
+                             0.4117313791289429, 0.508716882020547,
+                             0.6057023849121512, 0.6968414319219818,
+                             0.7773236154097279, 0.8441107043294638,
+                             0.8961920995582476, 0.934357499714309,
+                             0.9606392250246318, 0.9776464884841091])
+        assert_allclose(expected, calculated, rtol=1e-13)
+
+    # Reference value computed with ROOT, e.g.
+    #     cout << setprecision(16)
+    #          << ROOT::Math::crystalball_cdf_c(12.0, 1.0, 2.0, 1.0)
+    #          << endl;
+    @pytest.mark.parametrize(
+        'x, beta, m, rootref',
+        [(12.0, 1.0, 2.0, 1.340451684048897e-33),
+         (9.0, 4.0, 1.25, 1.12843537145273e-19),
+         (20, 0.1, 1.001, 6.929038716892384e-93),
+         (-4.5, 2.0, 3.0, 0.9944049143747991),
+         (-30.0, 0.5, 5.0, 0.9976994814571858),
+         (-1e50, 1.5, 1.1, 0.9999951099570382)]
+    )
+    def test_sf(self, x, beta, m, rootref):
+        sf = stats.crystalball.sf(x, beta=beta, m=m)
+        assert_allclose(sf, rootref, rtol=1e-13)
+
+    def test_moments(self):
+        """
+        All values are calculated using the pdf formula and the integrate function
+        of Mathematica
+        """
+        # The Last two (alpha, n) pairs test the special case n == alpha**2
+        beta = np.array([2.0, 1.0, 3.0, 2.0, 3.0])
+        m = np.array([3.0, 3.0, 2.0, 4.0, 9.0])
+
+        # The distribution should be correctly normalised
+        expected_0th_moment = np.array([1.0, 1.0, 1.0, 1.0, 1.0])
+        calculated_0th_moment = stats.crystalball._munp(0, beta, m)
+        assert_allclose(expected_0th_moment, calculated_0th_moment, rtol=0.001)
+
+        # calculated using wolframalpha.com
+        # e.g. for beta = 2 and m = 3 we calculate the norm like this:
+        #   integrate exp(-x^2/2) from -2 to infinity +
+        #   integrate (3/2)^3*exp(-2^2/2)*(3/2-2-x)^(-3) from -infinity to -2
+        norm = np.array([2.5511, 3.01873, 2.51065, 2.53983, 2.507410455])
+
+        a = np.array([-0.21992, -3.03265, np.inf, -0.135335, -0.003174])
+        expected_1th_moment = a / norm
+        calculated_1th_moment = stats.crystalball._munp(1, beta, m)
+        assert_allclose(expected_1th_moment, calculated_1th_moment, rtol=0.001)
+
+        a = np.array([np.inf, np.inf, np.inf, 3.2616, 2.519908])
+        expected_2th_moment = a / norm
+        calculated_2th_moment = stats.crystalball._munp(2, beta, m)
+        assert_allclose(expected_2th_moment, calculated_2th_moment, rtol=0.001)
+
+        a = np.array([np.inf, np.inf, np.inf, np.inf, -0.0577668])
+        expected_3th_moment = a / norm
+        calculated_3th_moment = stats.crystalball._munp(3, beta, m)
+        assert_allclose(expected_3th_moment, calculated_3th_moment, rtol=0.001)
+
+        a = np.array([np.inf, np.inf, np.inf, np.inf, 7.78468])
+        expected_4th_moment = a / norm
+        calculated_4th_moment = stats.crystalball._munp(4, beta, m)
+        assert_allclose(expected_4th_moment, calculated_4th_moment, rtol=0.001)
+
+        a = np.array([np.inf, np.inf, np.inf, np.inf, -1.31086])
+        expected_5th_moment = a / norm
+        calculated_5th_moment = stats.crystalball._munp(5, beta, m)
+        assert_allclose(expected_5th_moment, calculated_5th_moment, rtol=0.001)
+
+    def test_entropy(self):
+        # regression test for gh-13602
+        cb = stats.crystalball(2, 3)
+        res1 = cb.entropy()
+        # -20000 and 30 are negative and positive infinity, respectively
+        lo, hi, N = -20000, 30, 200000
+        x = np.linspace(lo, hi, N)
+        res2 = trapezoid(entr(cb.pdf(x)), x)
+        assert_allclose(res1, res2, rtol=1e-7)
+
+
+class TestNBinom:
+    def setup_method(self):
+        self.rng = np.random.default_rng(5861367021)
+
+    def test_rvs(self):
+        vals = stats.nbinom.rvs(10, 0.75, size=(2, 50), random_state=self.rng)
+        assert_(np.all(vals >= 0))
+        assert_(np.shape(vals) == (2, 50))
+        assert_(vals.dtype.char in typecodes['AllInteger'])
+        val = stats.nbinom.rvs(10, 0.75, random_state=self.rng)
+        assert_(isinstance(val, int))
+        val = stats.nbinom(10, 0.75).rvs(3, random_state=self.rng)
+        assert_(isinstance(val, np.ndarray))
+        assert_(val.dtype.char in typecodes['AllInteger'])
+
+    def test_pmf(self):
+        # regression test for ticket 1779
+        assert_allclose(np.exp(stats.nbinom.logpmf(700, 721, 0.52)),
+                        stats.nbinom.pmf(700, 721, 0.52))
+        # logpmf(0,1,1) shouldn't return nan (regression test for gh-4029)
+        val = scipy.stats.nbinom.logpmf(0, 1, 1)
+        assert_equal(val, 0)
+
+    def test_logcdf_gh16159(self):
+        # check that gh16159 is resolved.
+        vals = stats.nbinom.logcdf([0, 5, 0, 5], n=4.8, p=0.45)
+        ref = np.log(stats.nbinom.cdf([0, 5, 0, 5], n=4.8, p=0.45))
+        assert_allclose(vals, ref)
+
+
+class TestGenInvGauss:
+    def setup_method(self):
+        self.rng = np.random.default_rng(6473281180)
+
+    @pytest.mark.slow
+    def test_rvs_with_mode_shift(self):
+        # ratio_unif w/ mode shift
+        gig = stats.geninvgauss(2.3, 1.5)
+        _, p = stats.kstest(gig.rvs(size=1500, random_state=self.rng), gig.cdf)
+        assert_equal(p > 0.05, True)
+
+    @pytest.mark.slow
+    def test_rvs_without_mode_shift(self):
+        # ratio_unif w/o mode shift
+        gig = stats.geninvgauss(0.9, 0.75)
+        _, p = stats.kstest(gig.rvs(size=1500, random_state=self.rng), gig.cdf)
+        assert_equal(p > 0.05, True)
+
+    @pytest.mark.slow
+    def test_rvs_new_method(self):
+        # new algorithm of Hoermann / Leydold
+        gig = stats.geninvgauss(0.1, 0.2)
+        _, p = stats.kstest(gig.rvs(size=1500, random_state=self.rng), gig.cdf)
+        assert_equal(p > 0.05, True)
+
+    @pytest.mark.slow
+    def test_rvs_p_zero(self):
+        def my_ks_check(p, b):
+            gig = stats.geninvgauss(p, b)
+            rvs = gig.rvs(size=1500, random_state=self.rng)
+            return stats.kstest(rvs, gig.cdf)[1] > 0.05
+        # boundary cases when p = 0
+        assert_equal(my_ks_check(0, 0.2), True)  # new algo
+        assert_equal(my_ks_check(0, 0.9), True)  # ratio_unif w/o shift
+        assert_equal(my_ks_check(0, 1.5), True)  # ratio_unif with shift
+
+    def test_rvs_negative_p(self):
+        # if p negative, return inverse
+        assert_equal(
+                stats.geninvgauss(-1.5, 2).rvs(size=10, random_state=1234),
+                1 / stats.geninvgauss(1.5, 2).rvs(size=10, random_state=1234))
+
+    def test_invgauss(self):
+        # test that invgauss is special case
+        ig = stats.geninvgauss.rvs(size=1500, p=-0.5, b=1, random_state=1464878613)
+        assert_equal(stats.kstest(ig, 'invgauss', args=[1])[1] > 0.15, True)
+        # test pdf and cdf
+        mu, x = 100, np.linspace(0.01, 1, 10)
+        pdf_ig = stats.geninvgauss.pdf(x, p=-0.5, b=1 / mu, scale=mu)
+        assert_allclose(pdf_ig, stats.invgauss(mu).pdf(x))
+        cdf_ig = stats.geninvgauss.cdf(x, p=-0.5, b=1 / mu, scale=mu)
+        assert_allclose(cdf_ig, stats.invgauss(mu).cdf(x))
+
+    def test_pdf_R(self):
+        # test against R package GIGrvg
+        # x <- seq(0.01, 5, length.out = 10)
+        # GIGrvg::dgig(x, 0.5, 1, 1)
+        vals_R = np.array([2.081176820e-21, 4.488660034e-01, 3.747774338e-01,
+                           2.693297528e-01, 1.905637275e-01, 1.351476913e-01,
+                           9.636538981e-02, 6.909040154e-02, 4.978006801e-02,
+                           3.602084467e-02])
+        x = np.linspace(0.01, 5, 10)
+        assert_allclose(vals_R, stats.geninvgauss.pdf(x, 0.5, 1))
+
+    def test_pdf_zero(self):
+        # pdf at 0 is 0, needs special treatment to avoid 1/x in pdf
+        assert_equal(stats.geninvgauss.pdf(0, 0.5, 0.5), 0)
+        # if x is large and p is moderate, make sure that pdf does not
+        # overflow because of x**(p-1); exp(-b*x) forces pdf to zero
+        assert_equal(stats.geninvgauss.pdf(2e6, 50, 2), 0)
+
+
+class TestGenHyperbolic:
+
+    def test_pdf_r(self):
+        # test against R package GeneralizedHyperbolic
+        # x <- seq(-10, 10, length.out = 10)
+        # GeneralizedHyperbolic::dghyp(
+        #    x = x, lambda = 2, alpha = 2, beta = 1, delta = 1.5, mu = 0.5
+        # )
+        vals_R = np.array([
+            2.94895678275316e-13, 1.75746848647696e-10, 9.48149804073045e-08,
+            4.17862521692026e-05, 0.0103947630463822, 0.240864958986839,
+            0.162833527161649, 0.0374609592899472, 0.00634894847327781,
+            0.000941920705790324
+            ])
+
+        lmbda, alpha, beta = 2, 2, 1
+        mu, delta = 0.5, 1.5
+        args = (lmbda, alpha*delta, beta*delta)
+
+        gh = stats.genhyperbolic(*args, loc=mu, scale=delta)
+        x = np.linspace(-10, 10, 10)
+
+        assert_allclose(gh.pdf(x), vals_R, atol=0, rtol=1e-13)
+
+    def test_cdf_r(self):
+        # test against R package GeneralizedHyperbolic
+        # q <- seq(-10, 10, length.out = 10)
+        # GeneralizedHyperbolic::pghyp(
+        #   q = q, lambda = 2, alpha = 2, beta = 1, delta = 1.5, mu = 0.5
+        # )
+        vals_R = np.array([
+            1.01881590921421e-13, 6.13697274983578e-11, 3.37504977637992e-08,
+            1.55258698166181e-05, 0.00447005453832497, 0.228935323956347,
+            0.755759458895243, 0.953061062884484, 0.992598013917513,
+            0.998942646586662
+            ])
+
+        lmbda, alpha, beta = 2, 2, 1
+        mu, delta = 0.5, 1.5
+        args = (lmbda, alpha*delta, beta*delta)
+
+        gh = stats.genhyperbolic(*args, loc=mu, scale=delta)
+        x = np.linspace(-10, 10, 10)
+
+        assert_allclose(gh.cdf(x), vals_R, atol=0, rtol=1e-6)
+
+    # The reference values were computed by implementing the PDF with mpmath
+    # and integrating it with mp.quad.  The values were computed with
+    # mp.dps=250, and then again with mp.dps=400 to ensure the full 64 bit
+    # precision was computed.
+    @pytest.mark.parametrize(
+        'x, p, a, b, loc, scale, ref',
+        [(-15, 2, 3, 1.5, 0.5, 1.5, 4.770036428808252e-20),
+         (-15, 10, 1.5, 0.25, 1, 5, 0.03282964575089294),
+         (-15, 10, 1.5, 1.375, 0, 1, 3.3711159600215594e-23),
+         (-15, 0.125, 1.5, 1.49995, 0, 1, 4.729401428898605e-23),
+         (-1, 0.125, 1.5, 1.49995, 0, 1, 0.0003565725914786859),
+         (5, -0.125, 1.5, 1.49995, 0, 1, 0.2600651974023352),
+         (5, -0.125, 1000, 999, 0, 1, 5.923270556517253e-28),
+         (20, -0.125, 1000, 999, 0, 1, 0.23452293711665634),
+         (40, -0.125, 1000, 999, 0, 1, 0.9999648749561968),
+         (60, -0.125, 1000, 999, 0, 1, 0.9999999999975475)]
+    )
+    def test_cdf_mpmath(self, x, p, a, b, loc, scale, ref):
+        cdf = stats.genhyperbolic.cdf(x, p, a, b, loc=loc, scale=scale)
+        assert_allclose(cdf, ref, rtol=5e-12)
+
+    # The reference values were computed by implementing the PDF with mpmath
+    # and integrating it with mp.quad.  The values were computed with
+    # mp.dps=250, and then again with mp.dps=400 to ensure the full 64 bit
+    # precision was computed.
+    @pytest.mark.parametrize(
+        'x, p, a, b, loc, scale, ref',
+        [(0, 1e-6, 12, -1, 0, 1, 0.38520358671350524),
+         (-1, 3, 2.5, 2.375, 1, 3, 0.9999901774267577),
+         (-20, 3, 2.5, 2.375, 1, 3, 1.0),
+         (25, 2, 3, 1.5, 0.5, 1.5, 8.593419916523976e-10),
+         (300, 10, 1.5, 0.25, 1, 5, 6.137415609872158e-24),
+         (60, -0.125, 1000, 999, 0, 1, 2.4524915075944173e-12),
+         (75, -0.125, 1000, 999, 0, 1, 2.9435194886214633e-18)]
+    )
+    def test_sf_mpmath(self, x, p, a, b, loc, scale, ref):
+        sf = stats.genhyperbolic.sf(x, p, a, b, loc=loc, scale=scale)
+        assert_allclose(sf, ref, rtol=5e-12)
+
+    def test_moments_r(self):
+        # test against R package GeneralizedHyperbolic
+        # sapply(1:4,
+        #    function(x) GeneralizedHyperbolic::ghypMom(
+        #        order = x, lambda = 2, alpha = 2,
+        #        beta = 1, delta = 1.5, mu = 0.5,
+        #        momType = 'raw')
+        # )
+
+        vals_R = [2.36848366948115, 8.4739346779246,
+                  37.8870502710066, 205.76608511485]
+
+        lmbda, alpha, beta = 2, 2, 1
+        mu, delta = 0.5, 1.5
+        args = (lmbda, alpha*delta, beta*delta)
+
+        vals_us = [
+            stats.genhyperbolic(*args, loc=mu, scale=delta).moment(i)
+            for i in range(1, 5)
+            ]
+
+        assert_allclose(vals_us, vals_R, atol=0, rtol=1e-13)
+
+    def test_rvs(self):
+        # Kolmogorov-Smirnov test to ensure alignment
+        # of analytical and empirical cdfs
+
+        lmbda, alpha, beta = 2, 2, 1
+        mu, delta = 0.5, 1.5
+        args = (lmbda, alpha*delta, beta*delta)
+
+        gh = stats.genhyperbolic(*args, loc=mu, scale=delta)
+        _, p = stats.kstest(gh.rvs(size=1500, random_state=1234), gh.cdf)
+
+        assert_equal(p > 0.05, True)
+
+    def test_pdf_t(self):
+        # Test Against T-Student with 1 - 30 df
+        df = np.linspace(1, 30, 10)
+
+        # in principle alpha should be zero in practice for big lmbdas
+        # alpha cannot be too small else pdf does not integrate
+        alpha, beta = np.float_power(df, 2)*np.finfo(np.float32).eps, 0
+        mu, delta = 0, np.sqrt(df)
+        args = (-df/2, alpha, beta)
+
+        gh = stats.genhyperbolic(*args, loc=mu, scale=delta)
+        x = np.linspace(gh.ppf(0.01), gh.ppf(0.99), 50)[:, np.newaxis]
+
+        assert_allclose(
+            gh.pdf(x), stats.t.pdf(x, df),
+            atol=0, rtol=1e-6
+            )
+
+    def test_pdf_cauchy(self):
+        # Test Against Cauchy distribution
+
+        # in principle alpha should be zero in practice for big lmbdas
+        # alpha cannot be too small else pdf does not integrate
+        lmbda, alpha, beta = -0.5, np.finfo(np.float32).eps, 0
+        mu, delta = 0, 1
+        args = (lmbda, alpha, beta)
+
+        gh = stats.genhyperbolic(*args, loc=mu, scale=delta)
+        x = np.linspace(gh.ppf(0.01), gh.ppf(0.99), 50)[:, np.newaxis]
+
+        assert_allclose(
+            gh.pdf(x), stats.cauchy.pdf(x),
+            atol=0, rtol=1e-6
+            )
+
+    def test_pdf_laplace(self):
+        # Test Against Laplace with location param [-10, 10]
+        loc = np.linspace(-10, 10, 10)
+
+        # in principle delta should be zero in practice for big loc delta
+        # cannot be too small else pdf does not integrate
+        delta = np.finfo(np.float32).eps
+
+        lmbda, alpha, beta = 1, 1, 0
+        args = (lmbda, alpha*delta, beta*delta)
+
+        # ppf does not integrate for scale < 5e-4
+        # therefore using simple linspace to define the support
+        gh = stats.genhyperbolic(*args, loc=loc, scale=delta)
+        x = np.linspace(-20, 20, 50)[:, np.newaxis]
+
+        assert_allclose(
+            gh.pdf(x), stats.laplace.pdf(x, loc=loc, scale=1),
+            atol=0, rtol=1e-11
+            )
+
+    def test_pdf_norminvgauss(self):
+        # Test Against NIG with varying alpha/beta/delta/mu
+
+        alpha, beta, delta, mu = (
+                np.linspace(1, 20, 10),
+                np.linspace(0, 19, 10)*np.float_power(-1, range(10)),
+                np.linspace(1, 1, 10),
+                np.linspace(-100, 100, 10)
+                )
+
+        lmbda = - 0.5
+        args = (lmbda, alpha * delta, beta * delta)
+
+        gh = stats.genhyperbolic(*args, loc=mu, scale=delta)
+        x = np.linspace(gh.ppf(0.01), gh.ppf(0.99), 50)[:, np.newaxis]
+
+        assert_allclose(
+            gh.pdf(x), stats.norminvgauss.pdf(
+                x, a=alpha, b=beta, loc=mu, scale=delta),
+            atol=0, rtol=1e-13
+            )
+
+
+class TestHypSecant:
+
+    # Reference values were computed with the mpmath expression
+    #     float((2/mp.pi)*mp.atan(mp.exp(-x)))
+    # and mp.dps = 50.
+    @pytest.mark.parametrize('x, reference',
+                             [(30, 5.957247804324683e-14),
+                              (50, 1.2278802891647964e-22)])
+    def test_sf(self, x, reference):
+        sf = stats.hypsecant.sf(x)
+        assert_allclose(sf, reference, rtol=5e-15)
+
+    # Reference values were computed with the mpmath expression
+    #     float(-mp.log(mp.tan((mp.pi/2)*p)))
+    # and mp.dps = 50.
+    @pytest.mark.parametrize('p, reference',
+                             [(1e-6, 13.363927852673998),
+                              (1e-12, 27.179438410639094)])
+    def test_isf(self, p, reference):
+        x = stats.hypsecant.isf(p)
+        assert_allclose(x, reference, rtol=5e-15)
+
+    def test_logcdf_logsf(self):
+        x = 50.0
+        # Reference value was computed with mpmath.
+        ref = -1.2278802891647964e-22
+        logcdf = stats.hypsecant.logcdf(x)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+        logsf = stats.hypsecant.logsf(-x)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+
+class TestNormInvGauss:
+    def test_cdf_R(self):
+        # test pdf and cdf vals against R
+        # require("GeneralizedHyperbolic")
+        # x_test <- c(-7, -5, 0, 8, 15)
+        # r_cdf <- GeneralizedHyperbolic::pnig(x_test, mu = 0, a = 1, b = 0.5)
+        # r_pdf <- GeneralizedHyperbolic::dnig(x_test, mu = 0, a = 1, b = 0.5)
+        r_cdf = np.array([8.034920282e-07, 2.512671945e-05, 3.186661051e-01,
+                          9.988650664e-01, 9.999848769e-01])
+        x_test = np.array([-7, -5, 0, 8, 15])
+        vals_cdf = stats.norminvgauss.cdf(x_test, a=1, b=0.5)
+        assert_allclose(vals_cdf, r_cdf, atol=1e-9)
+
+    def test_pdf_R(self):
+        # values from R as defined in test_cdf_R
+        r_pdf = np.array([1.359600783e-06, 4.413878805e-05, 4.555014266e-01,
+                          7.450485342e-04, 8.917889931e-06])
+        x_test = np.array([-7, -5, 0, 8, 15])
+        vals_pdf = stats.norminvgauss.pdf(x_test, a=1, b=0.5)
+        assert_allclose(vals_pdf, r_pdf, atol=1e-9)
+
+    @pytest.mark.parametrize('x, a, b, sf, rtol',
+                             [(-1, 1, 0, 0.8759652211005315, 1e-13),
+                              (25, 1, 0, 1.1318690184042579e-13, 1e-4),
+                              (1, 5, -1.5, 0.002066711134653577, 1e-12),
+                              (10, 5, -1.5, 2.308435233930669e-29, 1e-9)])
+    def test_sf_isf_mpmath(self, x, a, b, sf, rtol):
+        # Reference data generated with `reference_distributions.NormInvGauss`,
+        # e.g. `NormInvGauss(alpha=1, beta=0).sf(-1)` with mp.dps = 50
+        s = stats.norminvgauss.sf(x, a, b)
+        assert_allclose(s, sf, rtol=rtol)
+        i = stats.norminvgauss.isf(sf, a, b)
+        assert_allclose(i, x, rtol=rtol)
+
+    def test_sf_isf_mpmath_vectorized(self):
+        x = [-1, 25]
+        a = [1, 1]
+        b = 0
+        sf = [0.8759652211005315, 1.1318690184042579e-13]  # see previous test
+        s = stats.norminvgauss.sf(x, a, b)
+        assert_allclose(s, sf, rtol=1e-13, atol=1e-16)
+        i = stats.norminvgauss.isf(sf, a, b)
+        # Not perfect, but better than it was. See gh-13338.
+        assert_allclose(i, x, rtol=1e-6)
+
+    def test_gh8718(self):
+        # Add test that gh-13338 resolved gh-8718
+        dst = stats.norminvgauss(1, 0)
+        x = np.arange(0, 20, 2)
+        sf = dst.sf(x)
+        isf = dst.isf(sf)
+        assert_allclose(isf, x)
+
+    def test_stats(self):
+        a, b = 1, 0.5
+        gamma = np.sqrt(a**2 - b**2)
+        v_stats = (b / gamma, a**2 / gamma**3, 3.0 * b / (a * np.sqrt(gamma)),
+                   3.0 * (1 + 4 * b**2 / a**2) / gamma)
+        assert_equal(v_stats, stats.norminvgauss.stats(a, b, moments='mvsk'))
+
+    def test_ppf(self):
+        a, b = 1, 0.5
+        x_test = np.array([0.001, 0.5, 0.999])
+        vals = stats.norminvgauss.ppf(x_test, a, b)
+        assert_allclose(x_test, stats.norminvgauss.cdf(vals, a, b))
+
+    @pytest.mark.parametrize('a', [0.1, 1.0, 10.0])
+    @pytest.mark.parametrize('b_a', [-0.9, -0.5, -0.1, 0.1, 0.5, 0.9])
+    def test_gh23196_tails_cdf_sf(self, a, b_a):
+        # gh-23196 reported that the norminvgauss CDF would drop to zero in the far
+        # right tail. The SF had a similar problem, dropping to zero at the far left.
+        # Check that this is resolved by checking that values in the deep tails are
+        # close to 1.0.
+        b = b_a * a
+        res = stats.norminvgauss(a=a, b=b).cdf(300)
+        np.testing.assert_allclose(res, 1, atol=1e-2)
+        res = stats.norminvgauss(a=a, b=b).sf(-300)
+        np.testing.assert_allclose(res, 1, atol=1e-2)
+
+    @pytest.mark.parametrize("x, a, b, ref", [
+        (100, 1.0, 0.0, 1.0),  # originally reported in gh-23196
+        (1500, 0.1, 0.09, 0.9999999999418577),  # suggested in gh-24567
+    ])
+    def test_gh23196_tails_cdf_sf_accuracy(self, x, a, b, ref):
+        # Spot check the accuracy of the CDF/SF in the deep tails.
+        # library(GeneralizedHyperbolic)
+        # options(digits=16)
+        # pnig(1500, alpha=0.1, beta=0.09)
+        rtol = 1e-14
+        np.testing.assert_allclose(stats.norminvgauss(a=a, b=b).cdf(x), ref, rtol=rtol)
+        np.testing.assert_allclose(stats.norminvgauss(a=a, b=-b).sf(-x), ref, rtol=rtol)
+
+
+class TestGeom:
+    def setup_method(self):
+        self.rng = np.random.default_rng(7672986002)
+
+    def test_rvs(self):
+        vals = stats.geom.rvs(0.75, size=(2, 50), random_state=self.rng)
+        assert_(np.all(vals >= 0))
+        assert_(np.shape(vals) == (2, 50))
+        assert_(vals.dtype.char in typecodes['AllInteger'])
+        val = stats.geom.rvs(0.75, random_state=self.rng)
+        assert_(isinstance(val, int))
+        val = stats.geom(0.75).rvs(3, random_state=self.rng)
+        assert_(isinstance(val, np.ndarray))
+        assert_(val.dtype.char in typecodes['AllInteger'])
+
+    def test_rvs_9313(self):
+        # previously, RVS were converted to `np.int32` on some platforms,
+        # causing overflow for moderately large integer output (gh-9313).
+        # Check that this is resolved to the extent possible w/ `np.int64`.
+        rvs = stats.geom.rvs(np.exp(-35), size=5, random_state=self.rng)
+        assert rvs.dtype == np.int64
+        assert np.all(rvs > np.iinfo(np.int32).max)
+
+    def test_pmf(self):
+        vals = stats.geom.pmf([1, 2, 3], 0.5)
+        assert_array_almost_equal(vals, [0.5, 0.25, 0.125])
+
+    def test_logpmf(self):
+        # regression test for ticket 1793
+        vals1 = np.log(stats.geom.pmf([1, 2, 3], 0.5))
+        vals2 = stats.geom.logpmf([1, 2, 3], 0.5)
+        assert_allclose(vals1, vals2, rtol=1e-15, atol=0)
+
+        # regression test for gh-4028
+        val = stats.geom.logpmf(1, 1)
+        assert_equal(val, 0.0)
+
+    def test_cdf_sf(self):
+        vals = stats.geom.cdf([1, 2, 3], 0.5)
+        vals_sf = stats.geom.sf([1, 2, 3], 0.5)
+        expected = array([0.5, 0.75, 0.875])
+        assert_array_almost_equal(vals, expected)
+        assert_array_almost_equal(vals_sf, 1-expected)
+
+    def test_logcdf_logsf(self):
+        vals = stats.geom.logcdf([1, 2, 3], 0.5)
+        vals_sf = stats.geom.logsf([1, 2, 3], 0.5)
+        expected = array([0.5, 0.75, 0.875])
+        assert_array_almost_equal(vals, np.log(expected))
+        assert_array_almost_equal(vals_sf, np.log1p(-expected))
+
+    def test_ppf(self):
+        vals = stats.geom.ppf([0.5, 0.75, 0.875], 0.5)
+        expected = array([1.0, 2.0, 3.0])
+        assert_array_almost_equal(vals, expected)
+
+    def test_ppf_underflow(self):
+        # this should not underflow
+        assert_allclose(stats.geom.ppf(1e-20, 1e-20), 1.0, atol=1e-14)
+
+    def test_entropy_gh18226(self):
+        # gh-18226 reported that `geom.entropy` produced a warning and
+        # inaccurate output for small p. Check that this is resolved.
+        h = stats.geom(0.0146).entropy()
+        assert_allclose(h, 5.219397961962308, rtol=1e-15)
+
+    def test_rvs_gh18372(self):
+        # gh-18372 reported that `geom.rvs` could produce negative numbers,
+        # with `RandomState` PRNG, but the support is positive integers.
+        # Check that this is resolved.
+        random_state = np.random.RandomState(294582935)
+        assert (stats.geom.rvs(1e-30, size=10, random_state=random_state) > 0).all()
+
+
+class TestPlanck:
+
+    def test_sf(self):
+        vals = stats.planck.sf([1, 2, 3], 5.)
+        expected = array([4.5399929762484854e-05,
+                          3.0590232050182579e-07,
+                          2.0611536224385579e-09])
+        assert_array_almost_equal(vals, expected)
+
+    def test_logsf(self):
+        vals = stats.planck.logsf([1000., 2000., 3000.], 1000.)
+        expected = array([-1001000., -2001000., -3001000.])
+        assert_array_almost_equal(vals, expected)
+
+
+class TestGennorm:
+    def test_laplace(self):
+        # test against Laplace (special case for beta=1)
+        points = [1, 2, 3]
+        pdf1 = stats.gennorm.pdf(points, 1)
+        pdf2 = stats.laplace.pdf(points)
+        assert_almost_equal(pdf1, pdf2)
+
+    def test_norm(self):
+        # test against normal (special case for beta=2)
+        points = [1, 2, 3]
+        pdf1 = stats.gennorm.pdf(points, 2)
+        pdf2 = stats.norm.pdf(points, scale=2**-.5)
+        assert_almost_equal(pdf1, pdf2)
+
+    def test_rvs(self):
+        # 0 < beta < 1
+        dist = stats.gennorm(0.5)
+        rng = np.random.default_rng(2204049394)
+        rvs = dist.rvs(size=1000, random_state=rng)
+        assert stats.kstest(rvs, dist.cdf).pvalue > 0.1
+        # beta = 1
+        dist = stats.gennorm(1)
+        rvs = dist.rvs(size=1000, random_state=rng)
+        rvs_laplace = stats.laplace.rvs(size=1000, random_state=rng)
+        assert stats.ks_2samp(rvs, rvs_laplace).pvalue > 0.1
+        # beta = 2
+        dist = stats.gennorm(2)
+        dist.random_state = rng
+        rvs = dist.rvs(size=1000, random_state=rng)
+        rvs_norm = stats.norm.rvs(scale=1/2**0.5, size=1000, random_state=rng)
+        assert stats.ks_2samp(rvs, rvs_norm).pvalue > 0.1
+
+    def test_rvs_broadcasting(self):
+        dist = stats.gennorm([[0.5, 1.], [2., 5.]])
+        rng = np.random.default_rng(2204049394)
+        rvs = dist.rvs(size=[1000, 2, 2], random_state=rng)
+        assert stats.kstest(rvs[:, 0, 0], stats.gennorm(0.5).cdf)[1] > 0.1
+        assert stats.kstest(rvs[:, 0, 1], stats.gennorm(1.0).cdf)[1] > 0.1
+        assert stats.kstest(rvs[:, 1, 0], stats.gennorm(2.0).cdf)[1] > 0.1
+        assert stats.kstest(rvs[:, 1, 1], stats.gennorm(5.0).cdf)[1] > 0.1
+
+
+class TestGibrat:
+
+    # sfx is sf(x).  The values were computed with mpmath:
+    #
+    #   from mpmath import mp
+    #   mp.dps = 100
+    #   def gibrat_sf(x):
+    #       return 1 - mp.ncdf(mp.log(x))
+    #
+    # E.g.
+    #
+    #   >>> float(gibrat_sf(1.5))
+    #   0.3425678305148459
+    #
+    @pytest.mark.parametrize('x, sfx', [(1.5, 0.3425678305148459),
+                                        (5000, 8.173334352522493e-18)])
+    def test_sf_isf(self, x, sfx):
+        assert_allclose(stats.gibrat.sf(x), sfx, rtol=2e-14)
+        assert_allclose(stats.gibrat.isf(sfx), x, rtol=2e-14)
+
+
+class TestGompertz:
+
+    def test_gompertz_accuracy(self):
+        # Regression test for gh-4031
+        p = stats.gompertz.ppf(stats.gompertz.cdf(1e-100, 1), 1)
+        assert_allclose(p, 1e-100)
+
+    # sfx is sf(x).  The values were computed with mpmath:
+    #
+    #   from mpmath import mp
+    #   mp.dps = 100
+    #   def gompertz_sf(x, c):
+    #       return mp.exp(-c*mp.expm1(x))
+    #
+    # E.g.
+    #
+    #   >>> float(gompertz_sf(1, 2.5))
+    #   0.013626967146253437
+    #
+    @pytest.mark.parametrize('x, c, sfx', [(1, 2.5, 0.013626967146253437),
+                                           (3, 2.5, 1.8973243273704087e-21),
+                                           (0.05, 5, 0.7738668242570479),
+                                           (2.25, 5, 3.707795833465481e-19)])
+    def test_sf_isf(self, x, c, sfx):
+        assert_allclose(stats.gompertz.sf(x, c), sfx, rtol=1e-14)
+        assert_allclose(stats.gompertz.isf(sfx, c), x, rtol=1e-14)
+
+    def test_logcdf(self):
+        x = 8.0
+        c = 0.1
+        # Reference value computed with mpmath.
+        ref = -3.820049516821143e-130
+        logcdf = stats.gompertz.logcdf(x, c)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+
+    def test_logsf(self):
+        x = 3e-80
+        c = 12
+        # Reference value computed with mpmath.
+        ref = -3.6e-79
+        logsf = stats.gompertz.logsf(x, c)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+    # reference values were computed with mpmath
+    # from mpmath import mp
+    # mp.dps = 100
+    # def gompertz_entropy(c):
+    #     c = mp.mpf(c)
+    #     return float(mp.one - mp.log(c) - mp.exp(c)*mp.e1(c))
+
+    @pytest.mark.parametrize('c, ref', [(1e-4, 1.5762523017634573),
+                                        (1, 0.4036526376768059),
+                                        (1000, -5.908754280976161),
+                                        (1e10, -22.025850930040455)])
+    def test_entropy(self, c, ref):
+        assert_allclose(stats.gompertz.entropy(c), ref, rtol=1e-14)
+
+
+class TestFoldNorm:
+
+    # reference values were computed with mpmath with 50 digits of precision
+    # from mpmath import mp
+    # mp.dps = 50
+    # mp.mpf(0.5) * (mp.erf((x - c)/mp.sqrt(2)) + mp.erf((x + c)/mp.sqrt(2)))
+
+    @pytest.mark.parametrize('x, c, ref', [(1e-4, 1e-8, 7.978845594730578e-05),
+                                           (1e-4, 1e-4, 7.97884555483635e-05)])
+    def test_cdf(self, x, c, ref):
+        assert_allclose(stats.foldnorm.cdf(x, c), ref, rtol=1e-15)
+
+
+class TestHalfNorm:
+
+    # sfx is sf(x).  The values were computed with mpmath:
+    #
+    #   from mpmath import mp
+    #   mp.dps = 100
+    #   def halfnorm_sf(x):
+    #       return 2*(1 - mp.ncdf(x))
+    #
+    # E.g.
+    #
+    #   >>> float(halfnorm_sf(1))
+    #   0.3173105078629141
+    #
+    @pytest.mark.parametrize('x, sfx', [(1, 0.3173105078629141),
+                                        (10, 1.523970604832105e-23)])
+    def test_sf_isf(self, x, sfx):
+        assert_allclose(stats.halfnorm.sf(x), sfx, rtol=1e-14)
+        assert_allclose(stats.halfnorm.isf(sfx), x, rtol=1e-14)
+
+    #   reference values were computed via mpmath
+    #   from mpmath import mp
+    #   mp.dps = 100
+    #   def halfnorm_cdf_mpmath(x):
+    #       x = mp.mpf(x)
+    #       return float(mp.erf(x/mp.sqrt(2.)))
+
+    @pytest.mark.parametrize('x, ref', [(1e-40, 7.978845608028653e-41),
+                                        (1e-18, 7.978845608028654e-19),
+                                        (8, 0.9999999999999988)])
+    def test_cdf(self, x, ref):
+        assert_allclose(stats.halfnorm.cdf(x), ref, rtol=1e-15)
+
+    @pytest.mark.parametrize("rvs_loc", [1e-5, 1e10])
+    @pytest.mark.parametrize("rvs_scale", [1e-2, 100, 1e8])
+    @pytest.mark.parametrize('fix_loc', [True, False])
+    @pytest.mark.parametrize('fix_scale', [True, False])
+    def test_fit_MLE_comp_optimizer(self, rvs_loc, rvs_scale,
+                                    fix_loc, fix_scale):
+
+        rng = np.random.default_rng(6762668991392531563)
+        data = stats.halfnorm.rvs(loc=rvs_loc, scale=rvs_scale, size=1000,
+                                  random_state=rng)
+
+        if fix_loc and fix_scale:
+            error_msg = ("All parameters fixed. There is nothing to "
+                         "optimize.")
+            with pytest.raises(RuntimeError, match=error_msg):
+                stats.halflogistic.fit(data, floc=rvs_loc, fscale=rvs_scale)
+            return
+
+        kwds = {}
+        if fix_loc:
+            kwds['floc'] = rvs_loc
+        if fix_scale:
+            kwds['fscale'] = rvs_scale
+
+        # Numerical result may equal analytical result if the initial guess
+        # computed from moment condition is already optimal.
+        _assert_less_or_close_loglike(stats.halfnorm, data, **kwds,
+                                      maybe_identical=True)
+
+    def test_fit_error(self):
+        # `floc` bigger than the minimal data point
+        with pytest.raises(FitDataError):
+            stats.halfnorm.fit([1, 2, 3], floc=2)
+
+
+class TestHalfCauchy:
+
+    @pytest.mark.parametrize("rvs_loc", [1e-5, 1e10])
+    @pytest.mark.parametrize("rvs_scale", [1e-2, 1e8])
+    @pytest.mark.parametrize('fix_loc', [True, False])
+    @pytest.mark.parametrize('fix_scale', [True, False])
+    def test_fit_MLE_comp_optimizer(self, rvs_loc, rvs_scale,
+                                    fix_loc, fix_scale):
+
+        rng = np.random.default_rng(6762668991392531563)
+        data = stats.halfnorm.rvs(loc=rvs_loc, scale=rvs_scale, size=1000,
+                                  random_state=rng)
+
+        if fix_loc and fix_scale:
+            error_msg = ("All parameters fixed. There is nothing to "
+                         "optimize.")
+            with pytest.raises(RuntimeError, match=error_msg):
+                stats.halfcauchy.fit(data, floc=rvs_loc, fscale=rvs_scale)
+            return
+
+        kwds = {}
+        if fix_loc:
+            kwds['floc'] = rvs_loc
+        if fix_scale:
+            kwds['fscale'] = rvs_scale
+
+        _assert_less_or_close_loglike(stats.halfcauchy, data, **kwds)
+
+    def test_fit_error(self):
+        # `floc` bigger than the minimal data point
+        with pytest.raises(FitDataError):
+            stats.halfcauchy.fit([1, 2, 3], floc=2)
+
+
+class TestHalfLogistic:
+    # survival function reference values were computed with mpmath
+    # from mpmath import mp
+    # mp.dps = 50
+    # def sf_mpmath(x):
+    #     x = mp.mpf(x)
+    #     return float(mp.mpf(2.)/(mp.exp(x) + mp.one))
+
+    @pytest.mark.parametrize('x, ref', [(100, 7.440151952041672e-44),
+                                        (200, 2.767793053473475e-87)])
+    def test_sf(self, x, ref):
+        assert_allclose(stats.halflogistic.sf(x), ref, rtol=1e-15)
+
+    # inverse survival function reference values were computed with mpmath
+    # from mpmath import mp
+    # mp.dps = 200
+    # def isf_mpmath(x):
+    #     halfx = mp.mpf(x)/2
+    #     return float(-mp.log(halfx/(mp.one - halfx)))
+
+    @pytest.mark.parametrize('q, ref', [(7.440151952041672e-44, 100),
+                                        (2.767793053473475e-87, 200),
+                                        (1-1e-9, 1.999999943436137e-09),
+                                        (1-1e-15, 1.9984014443252818e-15)])
+    def test_isf(self, q, ref):
+        assert_allclose(stats.halflogistic.isf(q), ref, rtol=1e-15)
+
+    def test_logcdf(self):
+        x = 30.0
+        # Reference value computed with mpmath.
+        ref = -1.871524593768035e-13
+        logcdf = stats.halflogistic.logcdf(x)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+
+    def test_logsf(self):
+        x = 2e-14
+        # Reference value computed with mpmath.
+        ref = -1.000000000000005e-14
+        logsf = stats.halflogistic.logsf(x)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+    @pytest.mark.parametrize("rvs_loc", [1e-5, 1e10])
+    @pytest.mark.parametrize("rvs_scale", [1e-2, 100, 1e8])
+    @pytest.mark.parametrize('fix_loc', [True, False])
+    @pytest.mark.parametrize('fix_scale', [True, False])
+    def test_fit_MLE_comp_optimizer(self, rvs_loc, rvs_scale,
+                                    fix_loc, fix_scale):
+
+        rng = np.random.default_rng(6762668991392531563)
+        data = stats.halflogistic.rvs(loc=rvs_loc, scale=rvs_scale, size=1000,
+                                      random_state=rng)
+
+        kwds = {}
+        if fix_loc and fix_scale:
+            error_msg = ("All parameters fixed. There is nothing to "
+                         "optimize.")
+            with pytest.raises(RuntimeError, match=error_msg):
+                stats.halflogistic.fit(data, floc=rvs_loc, fscale=rvs_scale)
+            return
+
+        if fix_loc:
+            kwds['floc'] = rvs_loc
+        if fix_scale:
+            kwds['fscale'] = rvs_scale
+
+        # Numerical result may equal analytical result if the initial guess
+        # computed from moment condition is already optimal.
+        _assert_less_or_close_loglike(stats.halflogistic, data, **kwds,
+                                      maybe_identical=True)
+
+    def test_fit_bad_floc(self):
+        msg = r" Maximum likelihood estimation with 'halflogistic' requires"
+        with assert_raises(FitDataError, match=msg):
+            stats.halflogistic.fit([0, 2, 4], floc=1)
+
+
+class TestHalfgennorm:
+    def test_expon(self):
+        # test against exponential (special case for beta=1)
+        points = [1, 2, 3]
+        pdf1 = stats.halfgennorm.pdf(points, 1)
+        pdf2 = stats.expon.pdf(points)
+        assert_almost_equal(pdf1, pdf2)
+
+    def test_halfnorm(self):
+        # test against half normal (special case for beta=2)
+        points = [1, 2, 3]
+        pdf1 = stats.halfgennorm.pdf(points, 2)
+        pdf2 = stats.halfnorm.pdf(points, scale=2**-.5)
+        assert_almost_equal(pdf1, pdf2)
+
+    def test_gennorm(self):
+        # test against generalized normal
+        points = [1, 2, 3]
+        pdf1 = stats.halfgennorm.pdf(points, .497324)
+        pdf2 = stats.gennorm.pdf(points, .497324)
+        assert_almost_equal(pdf1, 2*pdf2)
+
+    # Reference values (e.g. for n=3, beta=1.25)
+    #    from mpmath import mp
+    #    mp.dps = 100
+    #    n = 3
+    #    beta = mp.mpf(1.25)
+    #    ref = float(mp.rf(1/beta, n/beta))
+    @pytest.mark.parametrize('n, beta, ref',
+                             [(0, 2.25, 1),
+                              (1, 0.5, 6.0),
+                              (2, 12.5, 0.3155489903298712),
+                              (3, 0.125, 1.631515605987683e+30)])
+    def test_munp(self, n, beta, ref):
+        munp = stats.halfgennorm._munp(n, beta)
+        assert_allclose(munp, ref, rtol=5e-15)
+
+    # Reference values (e.g. for beta=0.02)
+    #    from mpmath import mp
+    #    mp.dps = 100
+    #    beta = mp.mpf(0.02)
+    #    ref = float(mp.rf(1/beta, 1/beta))
+    @pytest.mark.parametrize('beta, ref',
+                             [(0.02, 1.5342593781274747e+93),
+                              (0.125, 259459200.0),
+                              (0.5, 6.0),
+                              (10.0, 0.48256057149575027),
+                              (125.0, 0.49777435261046765),
+                              (15000.0, 0.49998076533051666)])
+    def test_mean(self, beta, ref):
+        m = stats.halfgennorm.mean(beta)
+        assert_allclose(m, ref, rtol=1e-14)
+
+    # Reference values (e.g. for beta=0.02)
+    #    from mpmath import mp
+    #    mp.dps = 100
+    #    beta = mp.mpf(0.02)
+    #    mu = mp.rf(1/beta, 1/beta)
+    #    # var = E(X**2) - E(X)**2:
+    #    ref = float(mp.rf(1/beta, 2/beta) - mu**2)
+    @pytest.mark.parametrize('beta, ref',
+                             [(0.02, 6.261772482175519e+197),
+                              (0.125, 5.062049324107776e+18),
+                              (0.5, 84.0),
+                              (10.0, 0.08159018176717249),
+                              (125.0, 0.0826270885971827),
+                              (15000.0, 0.08332692433644018)])
+    def test_var(self, beta, ref):
+        v = stats.halfgennorm.var(beta)
+        assert_allclose(v, ref, rtol=1.5e-14)
+
+
+class TestLaplaceasymmetric:
+    def test_laplace(self):
+        # test against Laplace (special case for kappa=1)
+        points = np.array([1, 2, 3])
+        pdf1 = stats.laplace_asymmetric.pdf(points, 1)
+        pdf2 = stats.laplace.pdf(points)
+        assert_allclose(pdf1, pdf2)
+
+    def test_asymmetric_laplace_pdf(self):
+        # test asymmetric Laplace
+        points = np.array([1, 2, 3])
+        kappa = 2
+        kapinv = 1/kappa
+        pdf1 = stats.laplace_asymmetric.pdf(points, kappa)
+        pdf2 = stats.laplace_asymmetric.pdf(points*(kappa**2), kapinv)
+        assert_allclose(pdf1, pdf2)
+
+    def test_asymmetric_laplace_log_10_16(self):
+        # test asymmetric Laplace
+        points = np.array([-np.log(16), np.log(10)])
+        kappa = 2
+        pdf1 = stats.laplace_asymmetric.pdf(points, kappa)
+        cdf1 = stats.laplace_asymmetric.cdf(points, kappa)
+        sf1 = stats.laplace_asymmetric.sf(points, kappa)
+        pdf2 = np.array([1/10, 1/250])
+        cdf2 = np.array([1/5, 1 - 1/500])
+        sf2 = np.array([4/5, 1/500])
+        ppf1 = stats.laplace_asymmetric.ppf(cdf2, kappa)
+        ppf2 = points
+        isf1 = stats.laplace_asymmetric.isf(sf2, kappa)
+        isf2 = points
+        assert_allclose(np.concatenate((pdf1, cdf1, sf1, ppf1, isf1)),
+                        np.concatenate((pdf2, cdf2, sf2, ppf2, isf2)))
+
+
+class TestTruncnorm:
+    def setup_method(self):
+        self.rng = np.random.default_rng(3255963201)
+
+    @pytest.mark.parametrize("a, b, ref",
+                             [(0, 100, 0.7257913526447274),
+                             (0.6, 0.7, -2.3027610681852573),
+                             (1e-06, 2e-06, -13.815510557964274)])
+    def test_entropy(self, a, b, ref):
+        # All reference values were calculated with mpmath:
+        # import numpy as np
+        # from mpmath import mp
+        # mp.dps = 50
+        # def entropy_trun(a, b):
+        #     a, b = mp.mpf(a), mp.mpf(b)
+        #     Z = mp.ncdf(b) - mp.ncdf(a)
+        #
+        #     def pdf(x):
+        #         return mp.npdf(x) / Z
+        #
+        #     res = -mp.quad(lambda t: pdf(t) * mp.log(pdf(t)), [a, b])
+        #     return np.float64(res)
+        assert_allclose(stats.truncnorm.entropy(a, b), ref, rtol=1e-10)
+
+    @pytest.mark.parametrize("a, b, ref",
+                             [(1e-11, 10000000000.0, 0.725791352640738),
+                             (1e-100, 1e+100, 0.7257913526447274),
+                             (-1e-100, 1e+100, 0.7257913526447274),
+                             (-1e+100, 1e+100, 1.4189385332046727)])
+    def test_extreme_entropy(self, a, b, ref):
+        # The reference values were calculated with mpmath
+        # import numpy as np
+        # from mpmath import mp
+        # mp.dps = 50
+        # def trunc_norm_entropy(a, b):
+        #     a, b = mp.mpf(a), mp.mpf(b)
+        #     Z = mp.ncdf(b) - mp.ncdf(a)
+        #     A = mp.log(mp.sqrt(2 * mp.pi * mp.e) * Z)
+        #     B = (a * mp.npdf(a) - b * mp.npdf(b)) / (2 * Z)
+        #     return np.float64(A + B)
+        assert_allclose(stats.truncnorm.entropy(a, b), ref, rtol=1e-14)
+
+    def test_ppf_ticket1131(self):
+        vals = stats.truncnorm.ppf([-0.5, 0, 1e-4, 0.5, 1-1e-4, 1, 2], -1., 1.,
+                                   loc=[3]*7, scale=2)
+        expected = np.array([np.nan, 1, 1.00056419, 3, 4.99943581, 5, np.nan])
+        assert_array_almost_equal(vals, expected)
+
+    def test_isf_ticket1131(self):
+        vals = stats.truncnorm.isf([-0.5, 0, 1e-4, 0.5, 1-1e-4, 1, 2], -1., 1.,
+                                   loc=[3]*7, scale=2)
+        expected = np.array([np.nan, 5, 4.99943581, 3, 1.00056419, 1, np.nan])
+        assert_array_almost_equal(vals, expected)
+
+    def test_gh_2477_small_values(self):
+        # Check a case that worked in the original issue.
+        low, high = -11, -10
+        x = stats.truncnorm.rvs(low, high, 0, 1, size=10, random_state=self.rng)
+        assert_(low < x.min() < x.max() < high)
+        # Check a case that failed in the original issue.
+        low, high = 10, 11
+        x = stats.truncnorm.rvs(low, high, 0, 1, size=10, random_state=self.rng)
+        assert_(low < x.min() < x.max() < high)
+
+    def test_gh_2477_large_values(self):
+        # Check a case that used to fail because of extreme tailness.
+        low, high = 100, 101
+        x = stats.truncnorm.rvs(low, high, 0, 1, size=10, random_state=self.rng)
+        assert_(low <= x.min() <= x.max() <= high), str([low, high, x])
+
+        # Check some additional extreme tails
+        low, high = 1000, 1001
+        x = stats.truncnorm.rvs(low, high, 0, 1, size=10, random_state=self.rng)
+        assert_(low < x.min() < x.max() < high)
+
+        low, high = 10000, 10001
+        x = stats.truncnorm.rvs(low, high, 0, 1, size=10, random_state=self.rng)
+        assert_(low < x.min() < x.max() < high)
+
+        low, high = -10001, -10000
+        x = stats.truncnorm.rvs(low, high, 0, 1, size=10, random_state=self.rng)
+        assert_(low < x.min() < x.max() < high)
+
+    def test_gh_9403_nontail_values(self):
+        for low, high in [[3, 4], [-4, -3]]:
+            xvals = np.array([-np.inf, low, high, np.inf])
+            xmid = (high+low)/2.0
+            cdfs = stats.truncnorm.cdf(xvals, low, high)
+            sfs = stats.truncnorm.sf(xvals, low, high)
+            pdfs = stats.truncnorm.pdf(xvals, low, high)
+            expected_cdfs = np.array([0, 0, 1, 1])
+            expected_sfs = np.array([1.0, 1.0, 0.0, 0.0])
+            expected_pdfs = np.array([0, 3.3619772, 0.1015229, 0])
+            if low < 0:
+                expected_pdfs = np.array([0, 0.1015229, 3.3619772, 0])
+            assert_almost_equal(cdfs, expected_cdfs)
+            assert_almost_equal(sfs, expected_sfs)
+            assert_almost_equal(pdfs, expected_pdfs)
+            assert_almost_equal(np.log(expected_pdfs[1]/expected_pdfs[2]),
+                                low + 0.5)
+            pvals = np.array([0, 0.5, 1.0])
+            ppfs = stats.truncnorm.ppf(pvals, low, high)
+            expected_ppfs = np.array([low, np.sign(low)*3.1984741, high])
+            assert_almost_equal(ppfs, expected_ppfs)
+
+            if low < 0:
+                assert_almost_equal(stats.truncnorm.sf(xmid, low, high),
+                                    0.8475544278436675)
+                assert_almost_equal(stats.truncnorm.cdf(xmid, low, high),
+                                    0.1524455721563326)
+            else:
+                assert_almost_equal(stats.truncnorm.cdf(xmid, low, high),
+                                    0.8475544278436675)
+                assert_almost_equal(stats.truncnorm.sf(xmid, low, high),
+                                    0.1524455721563326)
+            pdf = stats.truncnorm.pdf(xmid, low, high)
+            assert_almost_equal(np.log(pdf/expected_pdfs[2]), (xmid+0.25)/2)
+
+    def test_gh_9403_medium_tail_values(self):
+        for low, high in [[39, 40], [-40, -39]]:
+            xvals = np.array([-np.inf, low, high, np.inf])
+            xmid = (high+low)/2.0
+            cdfs = stats.truncnorm.cdf(xvals, low, high)
+            sfs = stats.truncnorm.sf(xvals, low, high)
+            pdfs = stats.truncnorm.pdf(xvals, low, high)
+            expected_cdfs = np.array([0, 0, 1, 1])
+            expected_sfs = np.array([1.0, 1.0, 0.0, 0.0])
+            expected_pdfs = np.array([0, 3.90256074e+01, 2.73349092e-16, 0])
+            if low < 0:
+                expected_pdfs = np.array([0, 2.73349092e-16,
+                                          3.90256074e+01, 0])
+            assert_almost_equal(cdfs, expected_cdfs)
+            assert_almost_equal(sfs, expected_sfs)
+            assert_almost_equal(pdfs, expected_pdfs)
+            assert_almost_equal(np.log(expected_pdfs[1]/expected_pdfs[2]),
+                                low + 0.5)
+            pvals = np.array([0, 0.5, 1.0])
+            ppfs = stats.truncnorm.ppf(pvals, low, high)
+            expected_ppfs = np.array([low, np.sign(low)*39.01775731, high])
+            assert_almost_equal(ppfs, expected_ppfs)
+            cdfs = stats.truncnorm.cdf(ppfs, low, high)
+            assert_almost_equal(cdfs, pvals)
+
+            if low < 0:
+                assert_almost_equal(stats.truncnorm.sf(xmid, low, high),
+                                    0.9999999970389126)
+                assert_almost_equal(stats.truncnorm.cdf(xmid, low, high),
+                                    2.961048103554866e-09)
+            else:
+                assert_almost_equal(stats.truncnorm.cdf(xmid, low, high),
+                                    0.9999999970389126)
+                assert_almost_equal(stats.truncnorm.sf(xmid, low, high),
+                                    2.961048103554866e-09)
+            pdf = stats.truncnorm.pdf(xmid, low, high)
+            assert_almost_equal(np.log(pdf/expected_pdfs[2]), (xmid+0.25)/2)
+
+            xvals = np.linspace(low, high, 11)
+            xvals2 = -xvals[::-1]
+            assert_almost_equal(stats.truncnorm.cdf(xvals, low, high),
+                                stats.truncnorm.sf(xvals2, -high, -low)[::-1])
+            assert_almost_equal(stats.truncnorm.sf(xvals, low, high),
+                                stats.truncnorm.cdf(xvals2, -high, -low)[::-1])
+            assert_almost_equal(stats.truncnorm.pdf(xvals, low, high),
+                                stats.truncnorm.pdf(xvals2, -high, -low)[::-1])
+
+    def test_cdf_tail_15110_14753(self):
+        # Check accuracy issues reported in gh-14753 and gh-155110
+        # Ground truth values calculated using Wolfram Alpha, e.g.
+        # (CDF[NormalDistribution[0,1],83/10]-CDF[NormalDistribution[0,1],8])/
+        #     (1 - CDF[NormalDistribution[0,1],8])
+        assert_allclose(stats.truncnorm(13., 15.).cdf(14.),
+                        0.9999987259565643)
+        assert_allclose(stats.truncnorm(8, np.inf).cdf(8.3),
+                        0.9163220907327540)
+
+    # Test data for the truncnorm stats() method.
+    # The data in each row is:
+    #   a, b, mean, variance, skewness, excess kurtosis. Generated using
+    # https://gist.github.com/WarrenWeckesser/636b537ee889679227d53543d333a720
+    _truncnorm_stats_data = [
+        [-30, 30,
+         0.0, 1.0, 0.0, 0.0],
+        [-10, 10,
+         0.0, 1.0, 0.0, -1.4927521335810455e-19],
+        [-3, 3,
+         0.0, 0.9733369246625415, 0.0, -0.17111443639774404],
+        [-2, 2,
+         0.0, 0.7737413035499232, 0.0, -0.6344632828703505],
+        [0, np.inf,
+         0.7978845608028654,
+         0.3633802276324187,
+         0.995271746431156,
+         0.8691773036059741],
+        [-np.inf, 0,
+         -0.7978845608028654,
+         0.3633802276324187,
+         -0.995271746431156,
+         0.8691773036059741],
+        [-1, 3,
+         0.282786110727154,
+         0.6161417353578293,
+         0.5393018494027877,
+         -0.20582065135274694],
+        [-3, 1,
+         -0.282786110727154,
+         0.6161417353578293,
+         -0.5393018494027877,
+         -0.20582065135274694],
+        [-10, -9,
+         -9.108456288012409,
+         0.011448805821636248,
+         -1.8985607290949496,
+         5.0733461105025075],
+    ]
+    _truncnorm_stats_data = np.array(_truncnorm_stats_data)
+
+    @pytest.mark.parametrize("case", _truncnorm_stats_data)
+    def test_moments(self, case):
+        a, b, m0, v0, s0, k0 = case
+        m, v, s, k = stats.truncnorm.stats(a, b, moments='mvsk')
+        assert_allclose([m, v, s, k], [m0, v0, s0, k0], atol=1e-17)
+
+    def test_9902_moments(self):
+        m, v = stats.truncnorm.stats(0, np.inf, moments='mv')
+        assert_almost_equal(m, 0.79788456)
+        assert_almost_equal(v, 0.36338023)
+
+    def test_gh_1489_trac_962_rvs(self):
+        # Check the original example.
+        low, high = 10, 15
+        x = stats.truncnorm.rvs(low, high, 0, 1, size=10, random_state=self.rng)
+        assert_(low < x.min() < x.max() < high)
+
+    def test_gh_11299_rvs(self):
+        # Arose from investigating gh-11299
+        # Test multiple shape parameters simultaneously.
+        low = [-10, 10, -np.inf, -5, -np.inf, -np.inf, -45, -45, 40, -10, 40]
+        high = [-5, 11, 5, np.inf, 40, -40, 40, -40, 45, np.inf, np.inf]
+        x = stats.truncnorm.rvs(low, high, size=(5, len(low)), random_state=self.rng)
+        assert np.shape(x) == (5, len(low))
+        assert_(np.all(low <= x.min(axis=0)))
+        assert_(np.all(x.max(axis=0) <= high))
+
+    def test_rvs_Generator(self):
+        # check that rvs can use a Generator
+        if hasattr(np.random, "default_rng"):
+            stats.truncnorm.rvs(-10, -5, size=5, random_state=self.rng)
+
+    def test_logcdf_gh17064(self):
+        # regression test for gh-17064 - avoid roundoff error for logcdfs ~0
+        a = np.array([-np.inf, -np.inf, -8, -np.inf, 10])
+        b = np.array([np.inf, np.inf, 8, 10, np.inf])
+        x = np.array([10, 7.5, 7.5, 9, 20])
+        expected = [-7.619853024160525e-24, -3.190891672910947e-14,
+                    -3.128682067168231e-14, -1.1285122074235991e-19,
+                    -3.61374964828753e-66]
+        assert_allclose(stats.truncnorm(a, b).logcdf(x), expected)
+        assert_allclose(stats.truncnorm(-b, -a).logsf(-x), expected)
+
+    def test_moments_gh18634(self):
+        # gh-18634 reported that moments 5 and higher didn't work; check that
+        # this is resolved
+        res = stats.truncnorm(-2, 3).moment(5)
+        # From Mathematica:
+        # Moment[TruncatedDistribution[{-2, 3}, NormalDistribution[]], 5]
+        ref = 1.645309620208361
+        assert_allclose(res, ref)
+
+
+class TestGenLogistic:
+
+    # Expected values computed with mpmath with 50 digits of precision.
+    @pytest.mark.parametrize('x, expected', [(-1000, -1499.5945348918917),
+                                             (-125, -187.09453489189184),
+                                             (0, -1.3274028432916989),
+                                             (100, -99.59453489189184),
+                                             (1000, -999.5945348918918)])
+    def test_logpdf(self, x, expected):
+        c = 1.5
+        logp = stats.genlogistic.logpdf(x, c)
+        assert_allclose(logp, expected, rtol=1e-13)
+
+    # Expected values computed with mpmath with 50 digits of precision
+    # from mpmath import mp
+    # mp.dps = 50
+    # def entropy_mp(c):
+    #     c = mp.mpf(c)
+    #     return float(-mp.log(c)+mp.one+mp.digamma(c + mp.one) + mp.euler)
+
+    @pytest.mark.parametrize('c, ref', [(1e-100, 231.25850929940458),
+                                        (1e-4, 10.21050485336338),
+                                        (1e8, 1.577215669901533),
+                                        (1e100, 1.5772156649015328)])
+    def test_entropy(self, c, ref):
+        assert_allclose(stats.genlogistic.entropy(c), ref, rtol=5e-15)
+
+    # Expected values computed with mpmath with 50 digits of precision
+    # from mpmath import mp
+    # mp.dps = 1000
+    #
+    # def genlogistic_cdf_mp(x, c):
+    #     x = mp.mpf(x)
+    #     c = mp.mpf(c)
+    #     return (mp.one + mp.exp(-x)) ** (-c)
+    #
+    # def genlogistic_sf_mp(x, c):
+    #     return mp.one - genlogistic_cdf_mp(x, c)
+    #
+    # x, c, ref = 100, 0.02, -7.440151952041672e-466
+    # print(float(mp.log(genlogistic_cdf_mp(x, c))))
+    # ppf/isf reference values generated by passing in `ref` (`q` is produced)
+
+    @pytest.mark.parametrize('x, c, ref', [(200, 10, 1.3838965267367375e-86),
+                                           (500, 20, 1.424915281348257e-216)])
+    def test_sf(self, x, c, ref):
+        assert_allclose(stats.genlogistic.sf(x, c), ref, rtol=1e-14)
+
+    @pytest.mark.parametrize('q, c, ref', [(0.01, 200, 9.898441467379765),
+                                           (0.001, 2, 7.600152115573173)])
+    def test_isf(self, q, c, ref):
+        assert_allclose(stats.genlogistic.isf(q, c), ref, rtol=5e-16)
+
+    @pytest.mark.parametrize('q, c, ref', [(0.5, 200, 5.6630969187064615),
+                                           (0.99, 20, 7.595630231412436)])
+    def test_ppf(self, q, c, ref):
+        assert_allclose(stats.genlogistic.ppf(q, c), ref, rtol=5e-16)
+
+    @pytest.mark.parametrize('x, c, ref', [(100, 0.02, -7.440151952041672e-46),
+                                           (50, 20, -3.857499695927835e-21)])
+    def test_logcdf(self, x, c, ref):
+        assert_allclose(stats.genlogistic.logcdf(x, c), ref, rtol=1e-15)
+
+
+class TestHypergeom:
+    def setup_method(self):
+        self.rng = np.random.default_rng(1765545342)
+
+    def test_rvs(self):
+        vals = stats.hypergeom.rvs(20, 10, 3, size=(2, 50), random_state=self.rng)
+        assert np.all(vals >= 0) & np.all(vals <= 3)
+        assert np.shape(vals) == (2, 50)
+        assert vals.dtype.char in typecodes['AllInteger']
+        val = stats.hypergeom.rvs(20, 3, 10, random_state=self.rng)
+        assert isinstance(val, int)
+        val = stats.hypergeom(20, 3, 10).rvs(3, random_state=self.rng)
+        assert isinstance(val, np.ndarray)
+        assert val.dtype.char in typecodes['AllInteger']
+
+    def test_precision(self):
+        # comparison number from mpmath
+        M = 2500
+        n = 50
+        N = 500
+        tot = M
+        good = n
+        hgpmf = stats.hypergeom.pmf(2, tot, good, N)
+        assert_almost_equal(hgpmf, 0.0010114963068932233, 11)
+
+    def test_args(self):
+        # test correct output for corner cases of arguments
+        # see gh-2325
+        assert_almost_equal(stats.hypergeom.pmf(0, 2, 1, 0), 1.0, 11)
+        assert_almost_equal(stats.hypergeom.pmf(1, 2, 1, 0), 0.0, 11)
+
+        assert_almost_equal(stats.hypergeom.pmf(0, 2, 0, 2), 1.0, 11)
+        assert_almost_equal(stats.hypergeom.pmf(1, 2, 1, 0), 0.0, 11)
+
+    def test_cdf_above_one(self):
+        # for some values of parameters, hypergeom cdf was >1, see gh-2238
+        assert_(0 <= stats.hypergeom.cdf(30, 13397950, 4363, 12390) <= 1.0)
+
+    def test_precision2(self):
+        # Test hypergeom precision for large numbers.  See #1218.
+        # Results compared with those from R.
+        oranges = 9.9e4
+        pears = 1.1e5
+        fruits_eaten = np.array([3, 3.8, 3.9, 4, 4.1, 4.2, 5]) * 1e4
+        quantile = 2e4
+        res = [stats.hypergeom.sf(quantile, oranges + pears, oranges, eaten)
+               for eaten in fruits_eaten]
+        expected = np.array([0, 1.904153e-114, 2.752693e-66, 4.931217e-32,
+                             8.265601e-11, 0.1237904, 1])
+        assert_allclose(res, expected, atol=0, rtol=5e-7)
+
+        # Test with array_like first argument
+        quantiles = [1.9e4, 2e4, 2.1e4, 2.15e4]
+        res2 = stats.hypergeom.sf(quantiles, oranges + pears, oranges, 4.2e4)
+        expected2 = [1, 0.1237904, 6.511452e-34, 3.277667e-69]
+        assert_allclose(res2, expected2, atol=0, rtol=5e-7)
+
+    def test_entropy(self):
+        # Simple tests of entropy.
+        hg = stats.hypergeom(4, 1, 1)
+        h = hg.entropy()
+        expected_p = np.array([0.75, 0.25])
+        expected_h = -np.sum(xlogy(expected_p, expected_p))
+        assert_allclose(h, expected_h)
+
+        hg = stats.hypergeom(1, 1, 1)
+        h = hg.entropy()
+        assert_equal(h, 0.0)
+
+    def test_logsf(self):
+        # Test logsf for very large numbers. See issue #4982
+        # Results compare with those from R (v3.2.0):
+        # phyper(k, n, M-n, N, lower.tail=FALSE, log.p=TRUE)
+        # -2239.771
+
+        k = 1e4
+        M = 1e7
+        n = 1e6
+        N = 5e4
+
+        result = stats.hypergeom.logsf(k, M, n, N)
+        expected = -2239.771   # From R
+        assert_almost_equal(result, expected, decimal=3)
+
+        k = 1
+        M = 1600
+        n = 600
+        N = 300
+
+        result = stats.hypergeom.logsf(k, M, n, N)
+        expected = -2.566567e-68   # From R
+        assert_almost_equal(result, expected, decimal=15)
+
+    def test_logcdf(self):
+        # Test logcdf for very large numbers. See issue #8692
+        # Results compare with those from R (v3.3.2):
+        # phyper(k, n, M-n, N, lower.tail=TRUE, log.p=TRUE)
+        # -5273.335
+
+        k = 1
+        M = 1e7
+        n = 1e6
+        N = 5e4
+
+        result = stats.hypergeom.logcdf(k, M, n, N)
+        expected = -5273.335   # From R
+        assert_almost_equal(result, expected, decimal=3)
+
+        # Same example as in issue #8692
+        k = 40
+        M = 1600
+        n = 50
+        N = 300
+
+        result = stats.hypergeom.logcdf(k, M, n, N)
+        expected = -7.565148879229e-23    # From R
+        assert_almost_equal(result, expected, decimal=15)
+
+        k = 125
+        M = 1600
+        n = 250
+        N = 500
+
+        result = stats.hypergeom.logcdf(k, M, n, N)
+        expected = -4.242688e-12    # From R
+        assert_almost_equal(result, expected, decimal=15)
+
+        # test broadcasting robustness based on reviewer
+        # concerns in PR 9603; using an array version of
+        # the example from issue #8692
+        k = np.array([40, 40, 40])
+        M = 1600
+        n = 50
+        N = 300
+
+        result = stats.hypergeom.logcdf(k, M, n, N)
+        expected = np.full(3, -7.565148879229e-23)  # filled from R result
+        assert_almost_equal(result, expected, decimal=15)
+
+    def test_mean_gh18511(self):
+        # gh-18511 reported that the `mean` was incorrect for large arguments;
+        # check that this is resolved
+        M = 390_000
+        n = 370_000
+        N = 12_000
+
+        hm = stats.hypergeom.mean(M, n, N)
+        rm = n / M * N
+        assert_allclose(hm, rm)
+
+    @pytest.mark.xslow
+    def test_sf_gh18506(self):
+        # gh-18506 reported that `sf` was incorrect for large population;
+        # check that this is resolved
+        n = 10
+        N = 10**5
+        i = np.arange(5, 15)
+        population_size = 10.**i
+        p = stats.hypergeom.sf(n - 1, population_size, N, n)
+        assert np.all(p > 0)
+        assert np.all(np.diff(p) < 0)
+
+
+class TestLoggamma:
+    def setup_method(self):
+        self.rng = np.random.default_rng(8638464332)
+
+    # Expected cdf values were computed with mpmath. For given x and c,
+    #     x = mpmath.mpf(x)
+    #     c = mpmath.mpf(c)
+    #     cdf = mpmath.gammainc(c, 0, mpmath.exp(x),
+    #                           regularized=True)
+    @pytest.mark.parametrize('x, c, cdf',
+                             [(1, 2, 0.7546378854206702),
+                              (-1, 14, 6.768116452566383e-18),
+                              (-745.1, 0.001, 0.4749605142005238),
+                              (-800, 0.001, 0.44958802911019136),
+                              (-725, 0.1, 3.4301205868273265e-32),
+                              (-740, 0.75, 1.0074360436599631e-241)])
+    def test_cdf_ppf(self, x, c, cdf):
+        p = stats.loggamma.cdf(x, c)
+        assert_allclose(p, cdf, rtol=1e-13)
+        y = stats.loggamma.ppf(cdf, c)
+        assert_allclose(y, x, rtol=1e-13)
+
+    # Expected sf values were computed with mpmath. For given x and c,
+    #     x = mpmath.mpf(x)
+    #     c = mpmath.mpf(c)
+    #     sf = mpmath.gammainc(c, mpmath.exp(x), mpmath.inf,
+    #                          regularized=True)
+    @pytest.mark.parametrize('x, c, sf',
+                             [(4, 1.5, 1.6341528919488565e-23),
+                              (6, 100, 8.23836829202024e-74),
+                              (-800, 0.001, 0.5504119708898086),
+                              (-743, 0.0025, 0.8437131370024089)])
+    def test_sf_isf(self, x, c, sf):
+        s = stats.loggamma.sf(x, c)
+        assert_allclose(s, sf, rtol=1e-13)
+        y = stats.loggamma.isf(sf, c)
+        assert_allclose(y, x, rtol=1e-13)
+
+    def test_logpdf(self):
+        # Test logpdf with x=-500, c=2.  ln(gamma(2)) = 0, and
+        # exp(-500) ~= 7e-218, which is far smaller than the ULP
+        # of c*x=-1000, so logpdf(-500, 2) = c*x - exp(x) - ln(gamma(2))
+        # should give -1000.0.
+        lp = stats.loggamma.logpdf(-500, 2)
+        assert_allclose(lp, -1000.0, rtol=1e-14)
+
+    def test_logcdf(self):
+        x = 4.0
+        c = 4.5
+        logcdf = stats.loggamma.logcdf(x, c)
+        # Reference value computed with mpmath.
+        ref = -2.1429747073164531e-19
+        assert_allclose(logcdf, ref, rtol=5e-15)
+
+    def test_logsf(self):
+        x = -25.0
+        c = 3.5
+        logsf = stats.loggamma.logsf(x, c)
+        # Reference value computed with mpmath.
+        ref = -8.58200139319556e-40
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+    def test_stats(self):
+        # The following precomputed values are from the table in section 2.2
+        # of "A Statistical Study of Log-Gamma Distribution", by Ping Shing
+        # Chan (thesis, McMaster University, 1993).
+        table = np.array([
+                # c,    mean,   var,    skew,    exc. kurt.
+                0.5, -1.9635, 4.9348, -1.5351, 4.0000,
+                1.0, -0.5772, 1.6449, -1.1395, 2.4000,
+                12.0, 2.4427, 0.0869, -0.2946, 0.1735,
+            ]).reshape(-1, 5)
+        for c, mean, var, skew, kurt in table:
+            computed = stats.loggamma.stats(c, moments='msvk')
+            assert_array_almost_equal(computed, [mean, var, skew, kurt],
+                                      decimal=4)
+
+    @pytest.mark.parametrize('c', [0.1, 0.001])
+    def test_rvs(self, c):
+        # Regression test for gh-11094.
+        x = stats.loggamma.rvs(c, size=100000, random_state=self.rng)
+        # Before gh-11094 was fixed, the case with c=0.001 would
+        # generate many -inf values.
+        assert np.isfinite(x).all()
+        # Crude statistical test.  About half the values should be
+        # less than the median and half greater than the median.
+        med = stats.loggamma.median(c)
+        btest = stats.binomtest(np.count_nonzero(x < med), len(x))
+        ci = btest.proportion_ci(confidence_level=0.999)
+        assert ci.low < 0.5 < ci.high
+
+    @pytest.mark.parametrize("c, ref",
+                             [(1e-8, 19.420680753952364),
+                              (1, 1.5772156649015328),
+                              (1e4, -3.186214986116763),
+                              (1e10, -10.093986931748889),
+                              (1e100, -113.71031611649761)])
+    def test_entropy(self, c, ref):
+
+        # Reference values were calculated with mpmath
+        # from mpmath import mp
+        # mp.dps = 500
+        # def loggamma_entropy_mpmath(c):
+        #     c = mp.mpf(c)
+        #     return float(mp.log(mp.gamma(c)) + c * (mp.one - mp.digamma(c)))
+
+        assert_allclose(stats.loggamma.entropy(c), ref, rtol=1e-14)
+
+
+class TestJohnsonsu:
+    # reference values were computed via mpmath
+    # from mpmath import mp
+    # mp.dps = 50
+    # def johnsonsu_sf(x, a, b):
+    #     x = mp.mpf(x)
+    #     a = mp.mpf(a)
+    #     b = mp.mpf(b)
+    #     return float(mp.ncdf(-(a + b * mp.log(x + mp.sqrt(x*x + 1)))))
+    # Order is x, a, b, sf, isf tol
+    # (Can't expect full precision when the ISF input is very nearly 1)
+    cases = [(-500, 1, 1, 0.9999999982660072, 1e-8),
+             (2000, 1, 1, 7.426351000595343e-21, 5e-14),
+             (100000, 1, 1, 4.046923979269977e-40, 5e-14)]
+
+    @pytest.mark.parametrize("case", cases)
+    def test_sf_isf(self, case):
+        x, a, b, sf, tol = case
+        assert_allclose(stats.johnsonsu.sf(x, a, b), sf, rtol=5e-14)
+        assert_allclose(stats.johnsonsu.isf(sf, a, b), x, rtol=tol)
+
+
+class TestJohnsonb:
+    # reference values were computed via mpmath
+    # from mpmath import mp
+    # mp.dps = 50
+    # def johnsonb_sf(x, a, b):
+    #     x = mp.mpf(x)
+    #     a = mp.mpf(a)
+    #     b = mp.mpf(b)
+    #     return float(mp.ncdf(-(a + b * mp.log(x/(mp.one - x)))))
+    # Order is x, a, b, sf, isf atol
+    # (Can't expect full precision when the ISF input is very nearly 1)
+    cases = [(1e-4, 1, 1, 0.9999999999999999, 1e-7),
+             (0.9999, 1, 1, 8.921114313932308e-25, 5e-14),
+             (0.999999, 1, 1, 5.815197487181902e-50, 5e-14)]
+
+    @pytest.mark.parametrize("case", cases)
+    def test_sf_isf(self, case):
+        x, a, b, sf, tol = case
+        assert_allclose(stats.johnsonsb.sf(x, a, b), sf, rtol=5e-14)
+        assert_allclose(stats.johnsonsb.isf(sf, a, b), x, atol=tol)
+
+
+class TestLogistic:
+    def setup_method(self):
+        self.rng = np.random.default_rng(2807014525)
+
+    # gh-6226
+    def test_cdf_ppf(self):
+        x = np.linspace(-20, 20)
+        y = stats.logistic.cdf(x)
+        xx = stats.logistic.ppf(y)
+        assert_allclose(x, xx)
+
+    def test_sf_isf(self):
+        x = np.linspace(-20, 20)
+        y = stats.logistic.sf(x)
+        xx = stats.logistic.isf(y)
+        assert_allclose(x, xx)
+
+    def test_extreme_values(self):
+        # p is chosen so that 1 - (1 - p) == p in double precision
+        p = 9.992007221626409e-16
+        desired = 34.53957599234088
+        assert_allclose(stats.logistic.ppf(1 - p), desired)
+        assert_allclose(stats.logistic.isf(p), desired)
+
+    def test_logpdf_basic(self):
+        logp = stats.logistic.logpdf([-15, 0, 10])
+        # Expected values computed with mpmath with 50 digits of precision.
+        expected = [-15.000000611804547,
+                    -1.3862943611198906,
+                    -10.000090797798434]
+        assert_allclose(logp, expected, rtol=1e-13)
+
+    def test_logpdf_extreme_values(self):
+        logp = stats.logistic.logpdf([800, -800])
+        # For such large arguments, logpdf(x) = -abs(x) when computed
+        # with 64 bit floating point.
+        assert_equal(logp, [-800, -800])
+
+    @pytest.mark.parametrize("loc_rvs,scale_rvs", [(0.4484955, 0.10216821),
+                                                   (0.62918191, 0.74367064)])
+    def test_fit(self, loc_rvs, scale_rvs):
+        data = stats.logistic.rvs(size=100, loc=loc_rvs, scale=scale_rvs,
+                                  random_state=self.rng)
+
+        # test that result of fit method is the same as optimization
+        def func(input, data):
+            a, b = input
+            n = len(data)
+            x1 = np.sum(np.exp((data - a) / b) /
+                        (1 + np.exp((data - a) / b))) - n / 2
+            x2 = np.sum(((data - a) / b) *
+                        ((np.exp((data - a) / b) - 1) /
+                         (np.exp((data - a) / b) + 1))) - n
+            return x1, x2
+
+        expected_solution = root(func, stats.logistic._fitstart(data), args=(
+            data,)).x
+        fit_method = stats.logistic.fit(data)
+
+        # other than computational variances, the fit method and the solution
+        # to this system of equations are equal
+        assert_allclose(fit_method, expected_solution, atol=1e-30)
+
+    def test_fit_comp_optimizer(self):
+        data = stats.logistic.rvs(size=100, loc=0.5, scale=2, random_state=self.rng)
+        _assert_less_or_close_loglike(stats.logistic, data)
+        _assert_less_or_close_loglike(stats.logistic, data, floc=1)
+        _assert_less_or_close_loglike(stats.logistic, data, fscale=1)
+
+    @pytest.mark.parametrize('testlogcdf', [True, False])
+    def test_logcdfsf_tails(self, testlogcdf):
+        # Test either logcdf or logsf.  By symmetry, we can use the same
+        # expected values for both by switching the sign of x for logsf.
+        x = np.array([-10000, -800, 17, 50, 500])
+        if testlogcdf:
+            y = stats.logistic.logcdf(x)
+        else:
+            y = stats.logistic.logsf(-x)
+        # The expected values were computed with mpmath.
+        expected = [-10000.0, -800.0, -4.139937633089748e-08,
+                    -1.9287498479639178e-22, -7.124576406741286e-218]
+        assert_allclose(y, expected, rtol=2e-15)
+
+    def test_fit_gh_18176(self):
+        # logistic.fit returned `scale < 0` for this data. Check that this has
+        # been fixed.
+        data = np.array([-459, 37, 43, 45, 45, 48, 54, 55, 58]
+                        + [59] * 3 + [61] * 9)
+        # If scale were negative, NLLF would be infinite, so this would fail
+        _assert_less_or_close_loglike(stats.logistic, data)
+
+
+class TestLogser:
+    def setup_method(self):
+        self.rng = np.random.default_rng(187505461)
+
+    def test_rvs(self):
+        vals = stats.logser.rvs(0.75, size=(2, 50), random_state=self.rng)
+        assert np.all(vals >= 1)
+        assert np.shape(vals) == (2, 50)
+        assert vals.dtype.char in typecodes['AllInteger']
+        val = stats.logser.rvs(0.75, random_state=self.rng)
+        assert isinstance(val, int)
+        val = stats.logser(0.75).rvs(3, random_state=self.rng)
+        assert isinstance(val, np.ndarray)
+        assert val.dtype.char in typecodes['AllInteger']
+
+    def test_pmf_small_p(self):
+        m = stats.logser.pmf(4, 1e-20)
+        # The expected value was computed using mpmath:
+        #   >>> import mpmath
+        #   >>> mpmath.mp.dps = 64
+        #   >>> k = 4
+        #   >>> p = mpmath.mpf('1e-20')
+        #   >>> float(-(p**k)/k/mpmath.log(1-p))
+        #   2.5e-61
+        # It is also clear from noticing that for very small p,
+        # log(1-p) is approximately -p, and the formula becomes
+        #    p**(k-1) / k
+        assert_allclose(m, 2.5e-61)
+
+    def test_mean_small_p(self):
+        m = stats.logser.mean(1e-8)
+        # The expected mean was computed using mpmath:
+        #   >>> import mpmath
+        #   >>> mpmath.dps = 60
+        #   >>> p = mpmath.mpf('1e-8')
+        #   >>> float(-p / ((1 - p)*mpmath.log(1 - p)))
+        #   1.000000005
+        assert_allclose(m, 1.000000005)
+
+    def test_sf(self):
+        p = [[0.5], [1e-5], [1 - 1e-5]]
+        k = [1, 10, 100, 1000]
+        # Reference values from Wolfram Alpha, e.g.
+        # SurvivalFunction[LogSeriesDistribution[99999/100000], 1000]
+        # 0.35068668662799737584735036958139157462633608106500173019897861351038286634
+        ref = [[0.2786524795555183, 0.00011876901682721189,
+                1.1159788564768581e-32, 1.3437300083506688e-304],
+               [5.000008333375e-06, 9.090946969973778e-52, 0, 0],
+               [0.9131419722083134, 0.745601735620566,
+                0.5495169511115096, 0.3506866866279974]]
+        res = stats.logser.sf(k, p)
+        np.testing.assert_allclose(res, ref, atol=1e-300)
+
+
+class TestGumbel_r_l:
+    @pytest.fixture(scope='function')
+    def rng(self):
+        return np.random.default_rng(1234)
+
+    @pytest.mark.parametrize("dist", [stats.gumbel_r, stats.gumbel_l])
+    @pytest.mark.parametrize("loc_rvs", [-1, 0, 1])
+    @pytest.mark.parametrize("scale_rvs", [.1, 1, 5])
+    @pytest.mark.parametrize('fix_loc, fix_scale',
+                             ([True, False], [False, True]))
+    def test_fit_comp_optimizer(self, dist, loc_rvs, scale_rvs,
+                                fix_loc, fix_scale, rng):
+        data = dist.rvs(size=100, loc=loc_rvs, scale=scale_rvs,
+                        random_state=rng)
+
+        kwds = dict()
+        # the fixed location and scales are arbitrarily modified to not be
+        # close to the true value.
+        if fix_loc:
+            kwds['floc'] = loc_rvs * 2
+        if fix_scale:
+            kwds['fscale'] = scale_rvs * 2
+
+        # test that the gumbel_* fit method is better than super method
+        _assert_less_or_close_loglike(dist, data, **kwds)
+
+    @pytest.mark.parametrize("dist, sgn", [(stats.gumbel_r, 1),
+                                           (stats.gumbel_l, -1)])
+    def test_fit(self, dist, sgn):
+        z = sgn*np.array([3, 3, 3, 3, 3, 3, 3, 3.00000001])
+        loc, scale = dist.fit(z)
+        # The expected values were computed with mpmath with 60 digits
+        # of precision.
+        assert_allclose(loc, sgn*3.0000000001667906)
+        assert_allclose(scale, 1.2495222465145514e-09, rtol=1e-6)
+
+
+class TestPareto:
+    def test_stats(self):
+        # Check the stats() method with some simple values. Also check
+        # that the calculations do not trigger RuntimeWarnings.
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", RuntimeWarning)
+
+            m, v, s, k = stats.pareto.stats(0.5, moments='mvsk')
+            assert_equal(m, np.inf)
+            assert_equal(v, np.inf)
+            assert_equal(s, np.nan)
+            assert_equal(k, np.nan)
+
+            m, v, s, k = stats.pareto.stats(1.0, moments='mvsk')
+            assert_equal(m, np.inf)
+            assert_equal(v, np.inf)
+            assert_equal(s, np.nan)
+            assert_equal(k, np.nan)
+
+            m, v, s, k = stats.pareto.stats(1.5, moments='mvsk')
+            assert_equal(m, 3.0)
+            assert_equal(v, np.inf)
+            assert_equal(s, np.nan)
+            assert_equal(k, np.nan)
+
+            m, v, s, k = stats.pareto.stats(2.0, moments='mvsk')
+            assert_equal(m, 2.0)
+            assert_equal(v, np.inf)
+            assert_equal(s, np.nan)
+            assert_equal(k, np.nan)
+
+            m, v, s, k = stats.pareto.stats(2.5, moments='mvsk')
+            assert_allclose(m, 2.5 / 1.5)
+            assert_allclose(v, 2.5 / (1.5*1.5*0.5))
+            assert_equal(s, np.nan)
+            assert_equal(k, np.nan)
+
+            m, v, s, k = stats.pareto.stats(3.0, moments='mvsk')
+            assert_allclose(m, 1.5)
+            assert_allclose(v, 0.75)
+            assert_equal(s, np.nan)
+            assert_equal(k, np.nan)
+
+            m, v, s, k = stats.pareto.stats(3.5, moments='mvsk')
+            assert_allclose(m, 3.5 / 2.5)
+            assert_allclose(v, 3.5 / (2.5*2.5*1.5))
+            assert_allclose(s, (2*4.5/0.5)*np.sqrt(1.5/3.5))
+            assert_equal(k, np.nan)
+
+            m, v, s, k = stats.pareto.stats(4.0, moments='mvsk')
+            assert_allclose(m, 4.0 / 3.0)
+            assert_allclose(v, 4.0 / 18.0)
+            assert_allclose(s, 2*(1+4.0)/(4.0-3) * np.sqrt((4.0-2)/4.0))
+            assert_equal(k, np.nan)
+
+            m, v, s, k = stats.pareto.stats(4.5, moments='mvsk')
+            assert_allclose(m, 4.5 / 3.5)
+            assert_allclose(v, 4.5 / (3.5*3.5*2.5))
+            assert_allclose(s, (2*5.5/1.5) * np.sqrt(2.5/4.5))
+            assert_allclose(k, 6*(4.5**3 + 4.5**2 - 6*4.5 - 2)/(4.5*1.5*0.5))
+
+    def test_sf(self):
+        x = 1e9
+        b = 2
+        scale = 1.5
+        p = stats.pareto.sf(x, b, loc=0, scale=scale)
+        expected = (scale/x)**b   # 2.25e-18
+        assert_allclose(p, expected)
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered in "
+                                "double_scalars")
+    @pytest.mark.parametrize("rvs_shape", [1, 2])
+    @pytest.mark.parametrize("rvs_loc", [0, 2])
+    @pytest.mark.parametrize("rvs_scale", [1, 5])
+    def test_fit(self, rvs_shape, rvs_loc, rvs_scale):
+        rng = np.random.default_rng(1234)
+        data = stats.pareto.rvs(size=100, b=rvs_shape, scale=rvs_scale,
+                                loc=rvs_loc, random_state=rng)
+
+        # shape can still be fixed with multiple names
+        shape_mle_analytical1 = stats.pareto.fit(data, floc=0, f0=1.04)[0]
+        shape_mle_analytical2 = stats.pareto.fit(data, floc=0, fix_b=1.04)[0]
+        shape_mle_analytical3 = stats.pareto.fit(data, floc=0, fb=1.04)[0]
+        assert (shape_mle_analytical1 == shape_mle_analytical2 ==
+                shape_mle_analytical3 == 1.04)
+
+        # data can be shifted with changes to `loc`
+        data = stats.pareto.rvs(size=100, b=rvs_shape, scale=rvs_scale,
+                                loc=(rvs_loc + 2), random_state=rng)
+        shape_mle_a, loc_mle_a, scale_mle_a = stats.pareto.fit(data, floc=2)
+        assert_equal(scale_mle_a + 2, data.min())
+
+        data_shift = data - 2
+        ndata = data_shift.shape[0]
+        assert_equal(shape_mle_a,
+                     ndata / np.sum(np.log(data_shift/data_shift.min())))
+        assert_equal(loc_mle_a, 2)
+
+    @pytest.mark.parametrize("rvs_shape", [.1, 2])
+    @pytest.mark.parametrize("rvs_loc", [0, 2])
+    @pytest.mark.parametrize("rvs_scale", [1, 5])
+    @pytest.mark.parametrize('fix_shape, fix_loc, fix_scale',
+                             [p for p in product([True, False], repeat=3)
+                              if False in p])
+    @np.errstate(invalid="ignore")
+    def test_fit_MLE_comp_optimizer(self, rvs_shape, rvs_loc, rvs_scale,
+                                    fix_shape, fix_loc, fix_scale):
+        rng = np.random.default_rng(1234)
+        data = stats.pareto.rvs(size=100, b=rvs_shape, scale=rvs_scale,
+                                loc=rvs_loc, random_state=rng)
+
+        kwds = {}
+        if fix_shape:
+            kwds['f0'] = rvs_shape
+        if fix_loc:
+            kwds['floc'] = rvs_loc
+        if fix_scale:
+            kwds['fscale'] = rvs_scale
+
+        _assert_less_or_close_loglike(stats.pareto, data, **kwds)
+
+    @np.errstate(invalid="ignore")
+    def test_fit_known_bad_seed(self):
+        # Tests a known seed and set of parameters that would produce a result
+        # would violate the support of Pareto if the fit method did not check
+        # the constraint `fscale + floc < min(data)`.
+        shape, location, scale = 1, 0, 1
+        data = stats.pareto.rvs(shape, location, scale, size=100,
+                                random_state=np.random.default_rng(2535619))
+        _assert_less_or_close_loglike(stats.pareto, data)
+
+    def test_fit_warnings(self):
+        assert_fit_warnings(stats.pareto)
+        # `floc` that causes invalid negative data
+        assert_raises(FitDataError, stats.pareto.fit, [1, 2, 3], floc=2)
+        # `floc` and `fscale` combination causes invalid data
+        assert_raises(FitDataError, stats.pareto.fit, [5, 2, 3], floc=1,
+                      fscale=3)
+
+    def test_negative_data(self):
+        rng = np.random.default_rng(1234)
+        data = stats.pareto.rvs(loc=-130, b=1, size=100, random_state=rng)
+        assert_array_less(data, 0)
+        # The purpose of this test is to make sure that no runtime warnings are
+        # raised for all negative data, not the output of the fit method. Other
+        # methods test the output but have to silence warnings from the super
+        # method.
+        _ = stats.pareto.fit(data)
+
+
+class TestGenpareto:
+    def test_ab(self):
+        # c >= 0: a, b = [0, inf]
+        for c in [1., 0.]:
+            c = np.asarray(c)
+            a, b = stats.genpareto._get_support(c)
+            assert_equal(a, 0.)
+            assert_(np.isposinf(b))
+
+        # c < 0: a=0, b=1/|c|
+        c = np.asarray(-2.)
+        a, b = stats.genpareto._get_support(c)
+        assert_allclose([a, b], [0., 0.5])
+
+    def test_c0(self):
+        # with c=0, genpareto reduces to the exponential distribution
+        # rv = stats.genpareto(c=0.)
+        rv = stats.genpareto(c=0.)
+        x = np.linspace(0, 10., 30)
+        assert_allclose(rv.pdf(x), stats.expon.pdf(x))
+        assert_allclose(rv.cdf(x), stats.expon.cdf(x))
+        assert_allclose(rv.sf(x), stats.expon.sf(x))
+
+        q = np.linspace(0., 1., 10)
+        assert_allclose(rv.ppf(q), stats.expon.ppf(q))
+
+    def test_cm1(self):
+        # with c=-1, genpareto reduces to the uniform distr on [0, 1]
+        rv = stats.genpareto(c=-1.)
+        x = np.linspace(0, 10., 30)
+        assert_allclose(rv.pdf(x), stats.uniform.pdf(x))
+        assert_allclose(rv.cdf(x), stats.uniform.cdf(x))
+        assert_allclose(rv.sf(x), stats.uniform.sf(x))
+
+        q = np.linspace(0., 1., 10)
+        assert_allclose(rv.ppf(q), stats.uniform.ppf(q))
+
+        # logpdf(1., c=-1) should be zero
+        assert_allclose(rv.logpdf(1), 0)
+
+    def test_x_inf(self):
+        # make sure x=inf is handled gracefully
+        rv = stats.genpareto(c=0.1)
+        assert_allclose([rv.pdf(np.inf), rv.cdf(np.inf)], [0., 1.])
+        assert_(np.isneginf(rv.logpdf(np.inf)))
+
+        rv = stats.genpareto(c=0.)
+        assert_allclose([rv.pdf(np.inf), rv.cdf(np.inf)], [0., 1.])
+        assert_(np.isneginf(rv.logpdf(np.inf)))
+
+        rv = stats.genpareto(c=-1.)
+        assert_allclose([rv.pdf(np.inf), rv.cdf(np.inf)], [0., 1.])
+        assert_(np.isneginf(rv.logpdf(np.inf)))
+
+    def test_c_continuity(self):
+        # pdf is continuous at c=0, -1
+        x = np.linspace(0, 10, 30)
+        for c in [0, -1]:
+            pdf0 = stats.genpareto.pdf(x, c)
+            for dc in [1e-14, -1e-14]:
+                pdfc = stats.genpareto.pdf(x, c + dc)
+                assert_allclose(pdf0, pdfc, atol=1e-12)
+
+            cdf0 = stats.genpareto.cdf(x, c)
+            for dc in [1e-14, 1e-14]:
+                cdfc = stats.genpareto.cdf(x, c + dc)
+                assert_allclose(cdf0, cdfc, atol=1e-12)
+
+    def test_c_continuity_ppf(self):
+        q = np.r_[np.logspace(1e-12, 0.01, base=0.1),
+                  np.linspace(0.01, 1, 30, endpoint=False),
+                  1. - np.logspace(1e-12, 0.01, base=0.1)]
+        for c in [0., -1.]:
+            ppf0 = stats.genpareto.ppf(q, c)
+            for dc in [1e-14, -1e-14]:
+                ppfc = stats.genpareto.ppf(q, c + dc)
+                assert_allclose(ppf0, ppfc, atol=1e-12)
+
+    def test_c_continuity_isf(self):
+        q = np.r_[np.logspace(1e-12, 0.01, base=0.1),
+                  np.linspace(0.01, 1, 30, endpoint=False),
+                  1. - np.logspace(1e-12, 0.01, base=0.1)]
+        for c in [0., -1.]:
+            isf0 = stats.genpareto.isf(q, c)
+            for dc in [1e-14, -1e-14]:
+                isfc = stats.genpareto.isf(q, c + dc)
+                assert_allclose(isf0, isfc, atol=1e-12)
+
+    def test_cdf_ppf_roundtrip(self):
+        # this should pass with machine precision. hat tip @pbrod
+        q = np.r_[np.logspace(1e-12, 0.01, base=0.1),
+                  np.linspace(0.01, 1, 30, endpoint=False),
+                  1. - np.logspace(1e-12, 0.01, base=0.1)]
+        for c in [1e-8, -1e-18, 1e-15, -1e-15]:
+            assert_allclose(stats.genpareto.cdf(stats.genpareto.ppf(q, c), c),
+                            q, atol=1e-15)
+
+    def test_logsf(self):
+        logp = stats.genpareto.logsf(1e10, .01, 0, 1)
+        assert_allclose(logp, -1842.0680753952365)
+
+    # Values in 'expected_stats' are
+    # [mean, variance, skewness, excess kurtosis].
+    @pytest.mark.parametrize(
+        'c, expected_stats',
+        [(0, [1, 1, 2, 6]),
+         (1/4, [4/3, 32/9, 10/np.sqrt(2), np.nan]),
+         (1/9, [9/8, (81/64)*(9/7), (10/9)*np.sqrt(7), 754/45]),
+         (-1, [1/2, 1/12, 0, -6/5])])
+    def test_stats(self, c, expected_stats):
+        result = stats.genpareto.stats(c, moments='mvsk')
+        assert_allclose(result, expected_stats, rtol=1e-13, atol=1e-15)
+
+    def test_var(self):
+        # Regression test for gh-11168.
+        v = stats.genpareto.var(1e-8)
+        assert_allclose(v, 1.000000040000001, rtol=1e-13)
+
+
+class TestPearson3:
+    def setup_method(self):
+        self.rng = np.random.default_rng(2775995570)
+
+    def test_rvs(self):
+        vals = stats.pearson3.rvs(0.1, size=(2, 50), random_state=self.rng)
+        assert np.shape(vals) == (2, 50)
+        assert vals.dtype.char in typecodes['AllFloat']
+        val = stats.pearson3.rvs(0.5, random_state=self.rng)
+        assert isinstance(val, float)
+        val = stats.pearson3(0.5).rvs(3, random_state=self.rng)
+        assert isinstance(val, np.ndarray)
+        assert val.dtype.char in typecodes['AllFloat']
+        assert len(val) == 3
+
+    def test_pdf(self):
+        vals = stats.pearson3.pdf(2, [0.0, 0.1, 0.2])
+        assert_allclose(vals, np.array([0.05399097, 0.05555481, 0.05670246]),
+                        atol=1e-6)
+        vals = stats.pearson3.pdf(-3, 0.1)
+        assert_allclose(vals, np.array([0.00313791]), atol=1e-6)
+        vals = stats.pearson3.pdf([-3, -2, -1, 0, 1], 0.1)
+        assert_allclose(vals, np.array([0.00313791, 0.05192304, 0.25028092,
+                                        0.39885918, 0.23413173]), atol=1e-6)
+
+    def test_cdf(self):
+        vals = stats.pearson3.cdf(2, [0.0, 0.1, 0.2])
+        assert_allclose(vals, np.array([0.97724987, 0.97462004, 0.97213626]),
+                        atol=1e-6)
+        vals = stats.pearson3.cdf(-3, 0.1)
+        assert_allclose(vals, [0.00082256], atol=1e-6)
+        vals = stats.pearson3.cdf([-3, -2, -1, 0, 1], 0.1)
+        assert_allclose(vals, [8.22563821e-04, 1.99860448e-02, 1.58550710e-01,
+                               5.06649130e-01, 8.41442111e-01], atol=1e-6)
+
+    def test_negative_cdf_bug_11186(self):
+        # incorrect CDFs for negative skews in gh-11186; fixed in gh-12640
+        # Also check vectorization w/ negative, zero, and positive skews
+        skews = [-3, -1, 0, 0.5]
+        x_eval = 0.5
+        neg_inf = -30  # avoid RuntimeWarning caused by np.log(0)
+        cdfs = stats.pearson3.cdf(x_eval, skews)
+        int_pdfs = [quad(stats.pearson3(skew).pdf, neg_inf, x_eval)[0]
+                    for skew in skews]
+        assert_allclose(cdfs, int_pdfs)
+
+    def test_return_array_bug_11746(self):
+        # pearson3.moment was returning size 0 or 1 array instead of float
+        # The first moment is equal to the loc, which defaults to zero
+        moment = stats.pearson3.moment(1, 2)
+        assert_equal(moment, 0)
+        assert isinstance(moment, np.number)
+
+        moment = stats.pearson3.moment(1, 0.000001)
+        assert_equal(moment, 0)
+        assert isinstance(moment, np.number)
+
+    def test_ppf_bug_17050(self):
+        # incorrect PPF for negative skews were reported in gh-17050
+        # Check that this is fixed (even in the array case)
+        skews = [-3, -1, 0, 0.5]
+        x_eval = 0.5
+        res = stats.pearson3.ppf(stats.pearson3.cdf(x_eval, skews), skews)
+        assert_allclose(res, x_eval)
+
+        # Negation of the skew flips the distribution about the origin, so
+        # the following should hold
+        skew = np.array([[-0.5], [1.5]])
+        x = np.linspace(-2, 2)
+        assert_allclose(stats.pearson3.pdf(x, skew),
+                        stats.pearson3.pdf(-x, -skew))
+        assert_allclose(stats.pearson3.cdf(x, skew),
+                        stats.pearson3.sf(-x, -skew))
+        assert_allclose(stats.pearson3.ppf(x, skew),
+                        -stats.pearson3.isf(x, -skew))
+
+    def test_sf(self):
+        # reference values were computed via the reference distribution, e.g.
+        # mp.dps = 50; Pearson3(skew=skew).sf(x). Check positive, negative,
+        # and zero skew due to branching.
+        skew = [0.1, 0.5, 1.0, -0.1]
+        x = [5.0, 10.0, 50.0, 8.0]
+        ref = [1.64721926440872e-06, 8.271911573556123e-11,
+               1.3149506021756343e-40, 2.763057937820296e-21]
+        assert_allclose(stats.pearson3.sf(x, skew), ref, rtol=2e-14)
+        assert_allclose(stats.pearson3.sf(x, 0), stats.norm.sf(x), rtol=2e-14)
+
+
+class TestKappa4:
+    def test_cdf_genpareto(self):
+        # h = 1 and k != 0 is generalized Pareto
+        x = [0.0, 0.1, 0.2, 0.5]
+        h = 1.0
+        for k in [-1.9, -1.0, -0.5, -0.2, -0.1, 0.1, 0.2, 0.5, 1.0,
+                  1.9]:
+            vals = stats.kappa4.cdf(x, h, k)
+            # shape parameter is opposite what is expected
+            vals_comp = stats.genpareto.cdf(x, -k)
+            assert_allclose(vals, vals_comp)
+
+    def test_cdf_genextreme(self):
+        # h = 0 and k != 0 is generalized extreme value
+        x = np.linspace(-5, 5, 10)
+        h = 0.0
+        k = np.linspace(-3, 3, 10)
+        vals = stats.kappa4.cdf(x, h, k)
+        vals_comp = stats.genextreme.cdf(x, k)
+        assert_allclose(vals, vals_comp)
+
+    def test_cdf_expon(self):
+        # h = 1 and k = 0 is exponential
+        x = np.linspace(0, 10, 10)
+        h = 1.0
+        k = 0.0
+        vals = stats.kappa4.cdf(x, h, k)
+        vals_comp = stats.expon.cdf(x)
+        assert_allclose(vals, vals_comp)
+
+    def test_cdf_gumbel_r(self):
+        # h = 0 and k = 0 is gumbel_r
+        x = np.linspace(-5, 5, 10)
+        h = 0.0
+        k = 0.0
+        vals = stats.kappa4.cdf(x, h, k)
+        vals_comp = stats.gumbel_r.cdf(x)
+        assert_allclose(vals, vals_comp)
+
+    def test_cdf_logistic(self):
+        # h = -1 and k = 0 is logistic
+        x = np.linspace(-5, 5, 10)
+        h = -1.0
+        k = 0.0
+        vals = stats.kappa4.cdf(x, h, k)
+        vals_comp = stats.logistic.cdf(x)
+        assert_allclose(vals, vals_comp)
+
+    def test_cdf_uniform(self):
+        # h = 1 and k = 1 is uniform
+        x = np.linspace(-5, 5, 10)
+        h = 1.0
+        k = 1.0
+        vals = stats.kappa4.cdf(x, h, k)
+        vals_comp = stats.uniform.cdf(x)
+        assert_allclose(vals, vals_comp)
+
+    def test_integers_ctor(self):
+        # regression test for gh-7416: _argcheck fails for integer h and k
+        # in numpy 1.12
+        stats.kappa4(1, 2)
+
+
+class TestPoisson:
+    def setup_method(self):
+        self.rng = np.random.default_rng(9799340796)
+
+    def test_pmf_basic(self):
+        # Basic case
+        ln2 = np.log(2)
+        vals = stats.poisson.pmf([0, 1, 2], ln2)
+        expected = [0.5, ln2/2, ln2**2/4]
+        assert_allclose(vals, expected)
+
+    def test_mu0(self):
+        # Edge case: mu=0
+        vals = stats.poisson.pmf([0, 1, 2], 0)
+        expected = [1, 0, 0]
+        assert_array_equal(vals, expected)
+
+        interval = stats.poisson.interval(0.95, 0)
+        assert_equal(interval, (0, 0))
+
+    def test_rvs(self):
+        vals = stats.poisson.rvs(0.5, size=(2, 50), random_state=self.rng)
+        assert np.all(vals >= 0)
+        assert np.shape(vals) == (2, 50)
+        assert vals.dtype.char in typecodes['AllInteger']
+        val = stats.poisson.rvs(0.5, random_state=self.rng)
+        assert isinstance(val, int)
+        val = stats.poisson(0.5).rvs(3, random_state=self.rng)
+        assert isinstance(val, np.ndarray)
+        assert val.dtype.char in typecodes['AllInteger']
+
+    def test_stats(self):
+        mu = 16.0
+        result = stats.poisson.stats(mu, moments='mvsk')
+        assert_allclose(result, [mu, mu, np.sqrt(1.0/mu), 1.0/mu])
+
+        mu = np.array([0.0, 1.0, 2.0])
+        result = stats.poisson.stats(mu, moments='mvsk')
+        expected = (mu, mu, [np.inf, 1, 1/np.sqrt(2)], [np.inf, 1, 0.5])
+        assert_allclose(result, expected)
+
+
+class TestKSTwo:
+
+    def test_cdf(self):
+        for n in [1, 2, 3, 10, 100, 1000]:
+            # Test x-values:
+            #  0, 1/2n, where the cdf should be 0
+            #  1/n, where the cdf should be n!/n^n
+            #  0.5, where the cdf should match ksone.cdf
+            # 1-1/n, where cdf = 1-2/n^n
+            # 1, where cdf == 1
+            # (E.g. Exact values given by Eqn 1 in Simard / L'Ecuyer)
+            x = np.array([0, 0.5/n, 1/n, 0.5, 1-1.0/n, 1])
+            v1 = (1.0/n)**n
+            lg = scipy.special.gammaln(n+1)
+            elg = (np.exp(lg) if v1 != 0 else 0)
+            expected = np.array([0, 0, v1 * elg,
+                                 1 - 2*stats.ksone.sf(0.5, n),
+                                 max(1 - 2*v1, 0.0),
+                                 1.0])
+            vals_cdf = stats.kstwo.cdf(x, n)
+            assert_allclose(vals_cdf, expected)
+
+    def test_sf(self):
+        x = np.linspace(0, 1, 11)
+        for n in [1, 2, 3, 10, 100, 1000]:
+            # Same x values as in test_cdf, and use sf = 1 - cdf
+            x = np.array([0, 0.5/n, 1/n, 0.5, 1-1.0/n, 1])
+            v1 = (1.0/n)**n
+            lg = scipy.special.gammaln(n+1)
+            elg = (np.exp(lg) if v1 != 0 else 0)
+            expected = np.array([1.0, 1.0,
+                                 1 - v1 * elg,
+                                 2*stats.ksone.sf(0.5, n),
+                                 min(2*v1, 1.0), 0])
+            vals_sf = stats.kstwo.sf(x, n)
+            assert_allclose(vals_sf, expected)
+
+    def test_cdf_sqrtn(self):
+        # For fixed a, cdf(a/sqrt(n), n) -> kstwobign(a) as n->infinity
+        # cdf(a/sqrt(n), n) is an increasing function of n (and a)
+        # Check that the function is indeed increasing (allowing for some
+        # small floating point and algorithm differences.)
+        x = np.linspace(0, 2, 11)[1:]
+        ns = [50, 100, 200, 400, 1000, 2000]
+        for _x in x:
+            xn = _x / np.sqrt(ns)
+            probs = stats.kstwo.cdf(xn, ns)
+            diffs = np.diff(probs)
+            assert_array_less(diffs, 1e-8)
+
+    def test_cdf_sf(self):
+        x = np.linspace(0, 1, 11)
+        for n in [1, 2, 3, 10, 100, 1000]:
+            vals_cdf = stats.kstwo.cdf(x, n)
+            vals_sf = stats.kstwo.sf(x, n)
+            assert_array_almost_equal(vals_cdf, 1 - vals_sf)
+
+    def test_cdf_sf_sqrtn(self):
+        x = np.linspace(0, 1, 11)
+        for n in [1, 2, 3, 10, 100, 1000]:
+            xn = x / np.sqrt(n)
+            vals_cdf = stats.kstwo.cdf(xn, n)
+            vals_sf = stats.kstwo.sf(xn, n)
+            assert_array_almost_equal(vals_cdf, 1 - vals_sf)
+
+    def test_ppf_of_cdf(self):
+        x = np.linspace(0, 1, 11)
+        for n in [1, 2, 3, 10, 100, 1000]:
+            xn = x[x > 0.5/n]
+            vals_cdf = stats.kstwo.cdf(xn, n)
+            # CDFs close to 1 are better dealt with using the SF
+            cond = (0 < vals_cdf) & (vals_cdf < 0.99)
+            vals = stats.kstwo.ppf(vals_cdf, n)
+            assert_allclose(vals[cond], xn[cond], rtol=1e-4)
+
+    def test_isf_of_sf(self):
+        x = np.linspace(0, 1, 11)
+        for n in [1, 2, 3, 10, 100, 1000]:
+            xn = x[x > 0.5/n]
+            vals_isf = stats.kstwo.isf(xn, n)
+            cond = (0 < vals_isf) & (vals_isf < 1.0)
+            vals = stats.kstwo.sf(vals_isf, n)
+            assert_allclose(vals[cond], xn[cond], rtol=1e-4)
+
+    def test_ppf_of_cdf_sqrtn(self):
+        x = np.linspace(0, 1, 11)
+        for n in [1, 2, 3, 10, 100, 1000]:
+            xn = (x / np.sqrt(n))[x > 0.5/n]
+            vals_cdf = stats.kstwo.cdf(xn, n)
+            cond = (0 < vals_cdf) & (vals_cdf < 1.0)
+            vals = stats.kstwo.ppf(vals_cdf, n)
+            assert_allclose(vals[cond], xn[cond])
+
+    def test_isf_of_sf_sqrtn(self):
+        x = np.linspace(0, 1, 11)
+        for n in [1, 2, 3, 10, 100, 1000]:
+            xn = (x / np.sqrt(n))[x > 0.5/n]
+            vals_sf = stats.kstwo.sf(xn, n)
+            # SFs close to 1 are better dealt with using the CDF
+            cond = (0 < vals_sf) & (vals_sf < 0.95)
+            vals = stats.kstwo.isf(vals_sf, n)
+            assert_allclose(vals[cond], xn[cond])
+
+    def test_ppf(self):
+        probs = np.linspace(0, 1, 11)[1:]
+        for n in [1, 2, 3, 10, 100, 1000]:
+            xn = stats.kstwo.ppf(probs, n)
+            vals_cdf = stats.kstwo.cdf(xn, n)
+            assert_allclose(vals_cdf, probs)
+
+    def test_simard_lecuyer_table1(self):
+        # Compute the cdf for values near the mean of the distribution.
+        # The mean u ~ log(2)*sqrt(pi/(2n))
+        # Compute for x in [u/4, u/3, u/2, u, 2u, 3u]
+        # This is the computation of Table 1 of Simard, R., L'Ecuyer, P. (2011)
+        #  "Computing the Two-Sided Kolmogorov-Smirnov Distribution".
+        # Except that the values below are not from the published table, but
+        # were generated using an independent SageMath implementation of
+        # Durbin's algorithm (with the exponentiation and scaling of
+        # Marsaglia/Tsang/Wang's version) using 500 bit arithmetic.
+        # Some of the values in the published table have relative
+        # errors greater than 1e-4.
+        ns = [10, 50, 100, 200, 500, 1000]
+        ratios = np.array([1.0/4, 1.0/3, 1.0/2, 1, 2, 3])
+        expected = np.array([
+            [1.92155292e-08, 5.72933228e-05, 2.15233226e-02, 6.31566589e-01,
+             9.97685592e-01, 9.99999942e-01],
+            [2.28096224e-09, 1.99142563e-05, 1.42617934e-02, 5.95345542e-01,
+             9.96177701e-01, 9.99998662e-01],
+            [1.00201886e-09, 1.32673079e-05, 1.24608594e-02, 5.86163220e-01,
+             9.95866877e-01, 9.99998240e-01],
+            [4.93313022e-10, 9.52658029e-06, 1.12123138e-02, 5.79486872e-01,
+             9.95661824e-01, 9.99997964e-01],
+            [2.37049293e-10, 6.85002458e-06, 1.01309221e-02, 5.73427224e-01,
+             9.95491207e-01, 9.99997750e-01],
+            [1.56990874e-10, 5.71738276e-06, 9.59725430e-03, 5.70322692e-01,
+             9.95409545e-01, 9.99997657e-01]
+        ])
+        for idx, n in enumerate(ns):
+            x = ratios * np.log(2) * np.sqrt(np.pi/2/n)
+            vals_cdf = stats.kstwo.cdf(x, n)
+            assert_allclose(vals_cdf, expected[idx], rtol=1e-5)
+
+
+class TestZipf:
+    def setup_method(self):
+        self.rng = np.random.default_rng(2444103536)
+
+    def test_rvs(self):
+        vals = stats.zipf.rvs(1.5, size=(2, 50), random_state=self.rng)
+        assert np.all(vals >= 1)
+        assert np.shape(vals) == (2, 50)
+        assert vals.dtype.char in typecodes['AllInteger']
+        val = stats.zipf.rvs(1.5, random_state=self.rng)
+        assert isinstance(val, int)
+        val = stats.zipf(1.5).rvs(3, random_state=self.rng)
+        assert isinstance(val, np.ndarray)
+        assert val.dtype.char in typecodes['AllInteger']
+
+    def test_moments(self):
+        # n-th moment is finite iff a > n + 1
+        m, v = stats.zipf.stats(a=2.8)
+        assert_(np.isfinite(m))
+        assert_equal(v, np.inf)
+
+        s, k = stats.zipf.stats(a=4.8, moments='sk')
+        assert_(not np.isfinite([s, k]).all())
+
+
+class TestDLaplace:
+    def setup_method(self):
+        self.rng = np.random.default_rng(460403075)
+
+    def test_rvs(self):
+        vals = stats.dlaplace.rvs(1.5, size=(2, 50), random_state=self.rng)
+        assert np.shape(vals) == (2, 50)
+        assert vals.dtype.char in typecodes['AllInteger']
+        val = stats.dlaplace.rvs(1.5, random_state=self.rng)
+        assert isinstance(val, int)
+        val = stats.dlaplace(1.5).rvs(3, random_state=self.rng)
+        assert isinstance(val, np.ndarray)
+        assert val.dtype.char in typecodes['AllInteger']
+        assert stats.dlaplace.rvs(0.8, random_state=self.rng) is not None
+
+    def test_stats(self):
+        # compare the explicit formulas w/ direct summation using pmf
+        a = 1.
+        dl = stats.dlaplace(a)
+        m, v, s, k = dl.stats('mvsk')
+
+        N = 37
+        xx = np.arange(-N, N+1)
+        pp = dl.pmf(xx)
+        m2, m4 = np.sum(pp*xx**2), np.sum(pp*xx**4)
+        assert_equal((m, s), (0, 0))
+        assert_allclose((v, k), (m2, m4/m2**2 - 3.), atol=1e-14, rtol=1e-8)
+
+    def test_stats2(self):
+        a = np.log(2.)
+        dl = stats.dlaplace(a)
+        m, v, s, k = dl.stats('mvsk')
+        assert_equal((m, s), (0., 0.))
+        assert_allclose((v, k), (4., 3.25))
+
+
+class TestInvgauss:
+    def setup_method(self):
+        self.rng = np.random.default_rng(5422839947)
+
+    @pytest.mark.parametrize("rvs_mu,rvs_loc,rvs_scale",
+                             [(2, 0, 1), (4.635, 4.362, 6.303)])
+    def test_fit(self, rvs_mu, rvs_loc, rvs_scale):
+        data = stats.invgauss.rvs(size=100, mu=rvs_mu,
+                                  loc=rvs_loc, scale=rvs_scale, random_state=self.rng)
+        # Analytical MLEs are calculated with formula when `floc` is fixed
+        mu, loc, scale = stats.invgauss.fit(data, floc=rvs_loc)
+
+        data = data - rvs_loc
+        mu_temp = np.mean(data)
+        scale_mle = len(data) / (np.sum(data**(-1) - mu_temp**(-1)))
+        mu_mle = mu_temp/scale_mle
+
+        # `mu` and `scale` match analytical formula
+        assert_allclose(mu_mle, mu, atol=1e-15, rtol=1e-15)
+        assert_allclose(scale_mle, scale, atol=1e-15, rtol=1e-15)
+        assert_equal(loc, rvs_loc)
+        data = stats.invgauss.rvs(size=100, mu=rvs_mu,
+                                  loc=rvs_loc, scale=rvs_scale, random_state=self.rng)
+        # fixed parameters are returned
+        mu, loc, scale = stats.invgauss.fit(data, floc=rvs_loc - 1,
+                                            fscale=rvs_scale + 1)
+        assert_equal(rvs_scale + 1, scale)
+        assert_equal(rvs_loc - 1, loc)
+
+        # shape can still be fixed with multiple names
+        shape_mle1 = stats.invgauss.fit(data, fmu=1.04)[0]
+        shape_mle2 = stats.invgauss.fit(data, fix_mu=1.04)[0]
+        shape_mle3 = stats.invgauss.fit(data, f0=1.04)[0]
+        assert shape_mle1 == shape_mle2 == shape_mle3 == 1.04
+
+    @pytest.mark.parametrize("rvs_mu,rvs_loc,rvs_scale",
+                             [(2, 0, 1), (6.311, 3.225, 4.520)])
+    def test_fit_MLE_comp_optimizer(self, rvs_mu, rvs_loc, rvs_scale):
+        rng = np.random.RandomState(1234)
+        data = stats.invgauss.rvs(size=100, mu=rvs_mu,
+                                  loc=rvs_loc, scale=rvs_scale, random_state=rng)
+
+        super_fit = super(type(stats.invgauss), stats.invgauss).fit
+        # fitting without `floc` uses superclass fit method
+        super_fitted = super_fit(data)
+        invgauss_fit = stats.invgauss.fit(data)
+        assert_equal(super_fitted, invgauss_fit)
+
+        # fitting with `fmu` is uses superclass fit method
+        super_fitted = super_fit(data, floc=0, fmu=2)
+        invgauss_fit = stats.invgauss.fit(data, floc=0, fmu=2)
+        assert_equal(super_fitted, invgauss_fit)
+
+        # fixed `floc` uses analytical formula and provides better fit than
+        # super method
+        _assert_less_or_close_loglike(stats.invgauss, data, floc=rvs_loc)
+
+        # fixed `floc` not resulting in invalid data < 0 uses analytical
+        # formulas and provides a better fit than the super method
+        assert np.all((data - (rvs_loc - 1)) > 0)
+        _assert_less_or_close_loglike(stats.invgauss, data, floc=rvs_loc - 1)
+
+        # fixed `floc` to an arbitrary number, 0, still provides a better fit
+        # than the super method
+        _assert_less_or_close_loglike(stats.invgauss, data, floc=0)
+
+        # fixed `fscale` to an arbitrary number still provides a better fit
+        # than the super method
+        _assert_less_or_close_loglike(stats.invgauss, data, floc=rvs_loc,
+                                      fscale=self.rng.random(1)[0])
+
+    def test_fit_raise_errors(self):
+        assert_fit_warnings(stats.invgauss)
+        # FitDataError is raised when negative invalid data
+        with pytest.raises(FitDataError):
+            stats.invgauss.fit([1, 2, 3], floc=2)
+
+    def test_cdf_sf(self):
+        # Regression tests for gh-13614.
+        # Ground truth from R's statmod library (pinvgauss), e.g.
+        # library(statmod)
+        # options(digits=15)
+        # mu = c(4.17022005e-04, 7.20324493e-03, 1.14374817e-06,
+        #        3.02332573e-03, 1.46755891e-03)
+        # print(pinvgauss(5, mu, 1))
+
+        # make sure a finite value is returned when mu is very small. see
+        # GH-13614
+        mu = [4.17022005e-04, 7.20324493e-03, 1.14374817e-06,
+              3.02332573e-03, 1.46755891e-03]
+        expected = [1, 1, 1, 1, 1]
+        actual = stats.invgauss.cdf(0.4, mu=mu)
+        assert_equal(expected, actual)
+
+        # test if the function can distinguish small left/right tail
+        # probabilities from zero.
+        cdf_actual = stats.invgauss.cdf(0.001, mu=1.05)
+        assert_allclose(cdf_actual, 4.65246506892667e-219)
+        sf_actual = stats.invgauss.sf(110, mu=1.05)
+        assert_allclose(sf_actual, 4.12851625944048e-25)
+
+        # test if x does not cause numerical issues when mu is very small
+        # and x is close to mu in value.
+
+        # slightly smaller than mu
+        actual = stats.invgauss.cdf(0.00009, 0.0001)
+        assert_allclose(actual, 2.9458022894924e-26)
+
+        # slightly bigger than mu
+        actual = stats.invgauss.cdf(0.000102, 0.0001)
+        assert_allclose(actual, 0.976445540507925)
+
+    def test_logcdf_logsf(self):
+        # Regression tests for improvements made in gh-13616.
+        # Ground truth from R's statmod library (pinvgauss), e.g.
+        # library(statmod)
+        # options(digits=15)
+        # print(pinvgauss(0.001, 1.05, 1, log.p=TRUE, lower.tail=FALSE))
+
+        # test if logcdf and logsf can compute values too small to
+        # be represented on the unlogged scale. See: gh-13616
+        logcdf = stats.invgauss.logcdf(0.0001, mu=1.05)
+        assert_allclose(logcdf, -5003.87872590367)
+        logcdf = stats.invgauss.logcdf(110, 1.05)
+        assert_allclose(logcdf, -4.12851625944087e-25)
+        logsf = stats.invgauss.logsf(0.001, mu=1.05)
+        assert_allclose(logsf, -4.65246506892676e-219)
+        logsf = stats.invgauss.logsf(110, 1.05)
+        assert_allclose(logsf, -56.1467092416426)
+
+    # from mpmath import mp
+    # mp.dps = 100
+    # mu = mp.mpf(1e-2)
+    # ref = (1/2 * mp.log(2 * mp.pi * mp.e * mu**3)
+    #        - 3/2* mp.exp(2/mu) * mp.e1(2/mu))
+    @pytest.mark.parametrize("mu, ref", [(2e-8, -25.172361826883957),
+                                         (1e-3, -8.943444010642972),
+                                         (1e-2, -5.4962796152622335),
+                                         (1e8, 3.3244822568873476),
+                                         (1e100, 3.32448280139689)])
+    def test_entropy(self, mu, ref):
+        assert_allclose(stats.invgauss.entropy(mu), ref, rtol=5e-14)
+
+    def test_mu_inf_gh13666(self):
+        # invgauss methods should return correct result when mu=inf
+        # invgauss as mu -> oo is invgamma with shape and scale 0.5;
+        # see gh-13666 and gh-22496
+        dist = stats.invgauss(mu=np.inf)
+        dist0 = stats.invgamma(0.5, scale=0.5)
+        x, p = 1., 0.5
+        assert_allclose(dist.logpdf(x), dist0.logpdf(x))
+        assert_allclose(dist.pdf(x), dist0.pdf(x))
+        assert_allclose(dist.logcdf(x), dist0.logcdf(x))
+        assert_allclose(dist.cdf(x), dist0.cdf(x))
+        assert_allclose(dist.logsf(x), dist0.logsf(x))
+        assert_allclose(dist.sf(x), dist0.sf(x))
+        assert_allclose(dist.ppf(p), dist0.ppf(p))
+        assert_allclose(dist.isf(p), dist0.isf(p))
+
+
+class TestLandau:
+    @pytest.mark.parametrize('name', ['pdf', 'cdf', 'sf', 'ppf', 'isf'])
+    def test_landau_levy_agreement(self, name):
+        # Test PDF to confirm that this is the Landau distribution
+        # Test other methods with tighter tolerance than generic tests
+        # Levy entropy is slow and inaccurate, and RVS is tested generically
+        if name in {'ppf', 'isf'}:
+            x = np.linspace(0.1, 0.9, 5),
+        else:
+            x = np.linspace(-2, 5, 10),
+
+        landau_method = getattr(stats.landau, name)
+        levy_method = getattr(stats.levy_stable, name)
+        res = landau_method(*x)
+        ref = levy_method(*x, 1, 1)
+        assert_allclose(res, ref, rtol=1e-14)
+
+    def test_moments(self):
+        # I would test these against Levy above, but Levy says variance is infinite.
+        assert_equal(stats.landau.stats(moments='mvsk'), (np.nan,)*4)
+        assert_equal(stats.landau.moment(5), np.nan)
+
+
+class TestLaplace:
+    @pytest.mark.parametrize("rvs_loc", [-5, 0, 1, 2])
+    @pytest.mark.parametrize("rvs_scale", [1, 2, 3, 10])
+    def test_fit(self, rvs_loc, rvs_scale):
+        # tests that various inputs follow expected behavior
+        # for a variety of `loc` and `scale`.
+        rng = np.random.RandomState(1234)
+        data = stats.laplace.rvs(size=100, loc=rvs_loc, scale=rvs_scale,
+                                 random_state=rng)
+
+        # MLE estimates are given by
+        loc_mle = np.median(data)
+        scale_mle = np.sum(np.abs(data - loc_mle)) / len(data)
+
+        # standard outputs should match analytical MLE formulas
+        loc, scale = stats.laplace.fit(data)
+        assert_allclose(loc, loc_mle, atol=1e-15, rtol=1e-15)
+        assert_allclose(scale, scale_mle, atol=1e-15, rtol=1e-15)
+
+        # fixed parameter should use analytical formula for other
+        loc, scale = stats.laplace.fit(data, floc=loc_mle)
+        assert_allclose(scale, scale_mle, atol=1e-15, rtol=1e-15)
+        loc, scale = stats.laplace.fit(data, fscale=scale_mle)
+        assert_allclose(loc, loc_mle)
+
+        # test with non-mle fixed parameter
+        # create scale with non-median loc
+        loc = rvs_loc * 2
+        scale_mle = np.sum(np.abs(data - loc)) / len(data)
+
+        # fixed loc to non median, scale should match
+        # scale calculation with modified loc
+        loc, scale = stats.laplace.fit(data, floc=loc)
+        assert_equal(scale_mle, scale)
+
+        # fixed scale created with non median loc,
+        # loc output should still be the data median.
+        loc, scale = stats.laplace.fit(data, fscale=scale_mle)
+        assert_equal(loc_mle, loc)
+
+        # error raised when both `floc` and `fscale` are fixed
+        assert_raises(RuntimeError, stats.laplace.fit, data, floc=loc_mle,
+                      fscale=scale_mle)
+
+        # error is raised with non-finite values
+        assert_raises(ValueError, stats.laplace.fit, [np.nan])
+        assert_raises(ValueError, stats.laplace.fit, [np.inf])
+
+    @pytest.mark.parametrize("rvs_loc,rvs_scale", [(-5, 10),
+                                                   (10, 5),
+                                                   (0.5, 0.2)])
+    def test_fit_MLE_comp_optimizer(self, rvs_loc, rvs_scale):
+        rng = np.random.RandomState(1234)
+        data = stats.laplace.rvs(size=1000, loc=rvs_loc, scale=rvs_scale,
+                                 random_state=rng)
+
+        # the log-likelihood function for laplace is given by
+        def ll(loc, scale, data):
+            return -1 * (- (len(data)) * np.log(2*scale) -
+                         (1/scale)*np.sum(np.abs(data - loc)))
+
+        # test that the objective function result of the analytical MLEs is
+        # less than or equal to that of the numerically optimized estimate
+        loc, scale = stats.laplace.fit(data)
+        loc_opt, scale_opt = super(type(stats.laplace),
+                                   stats.laplace).fit(data)
+        ll_mle = ll(loc, scale, data)
+        ll_opt = ll(loc_opt, scale_opt, data)
+        assert ll_mle < ll_opt or np.allclose(ll_mle, ll_opt,
+                                              atol=1e-15, rtol=1e-15)
+
+    def test_fit_simple_non_random_data(self):
+        data = np.array([1.0, 1.0, 3.0, 5.0, 8.0, 14.0])
+        # with `floc` fixed to 6, scale should be 4.
+        loc, scale = stats.laplace.fit(data, floc=6)
+        assert_allclose(scale, 4, atol=1e-15, rtol=1e-15)
+        # with `fscale` fixed to 6, loc should be 4.
+        loc, scale = stats.laplace.fit(data, fscale=6)
+        assert_allclose(loc, 4, atol=1e-15, rtol=1e-15)
+
+    def test_sf_cdf_extremes(self):
+        # These calculations should not generate warnings.
+        x = 1000
+        p0 = stats.laplace.cdf(-x)
+        # The exact value is smaller than can be represented with
+        # 64 bit floating point, so the expected result is 0.
+        assert p0 == 0.0
+        # The closest 64 bit floating point representation of the
+        # exact value is 1.0.
+        p1 = stats.laplace.cdf(x)
+        assert p1 == 1.0
+
+        p0 = stats.laplace.sf(x)
+        # The exact value is smaller than can be represented with
+        # 64 bit floating point, so the expected result is 0.
+        assert p0 == 0.0
+        # The closest 64 bit floating point representation of the
+        # exact value is 1.0.
+        p1 = stats.laplace.sf(-x)
+        assert p1 == 1.0
+
+    def test_sf(self):
+        x = 200
+        p = stats.laplace.sf(x)
+        assert_allclose(p, np.exp(-x)/2, rtol=1e-13)
+
+    def test_isf(self):
+        p = 1e-25
+        x = stats.laplace.isf(p)
+        assert_allclose(x, -np.log(2*p), rtol=1e-13)
+
+    def test_logcdf_logsf(self):
+        x = 40
+        # Reference value computed with mpmath.
+        ref = -2.1241771276457944e-18
+        logcdf = stats.laplace.logcdf(x)
+        assert_allclose(logcdf, ref)
+        logsf = stats.laplace.logsf(-x)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+
+class TestLogLaplace:
+
+    def test_sf(self):
+        # reference values were computed via the reference distribution, e.g.
+        # mp.dps = 100; LogLaplace(c=c).sf(x).
+        c = np.array([2.0, 3.0, 5.0])
+        x = np.array([1e-5, 1e10, 1e15])
+        ref = [0.99999999995, 5e-31, 5e-76]
+        assert_allclose(stats.loglaplace.sf(x, c), ref, rtol=1e-15)
+
+    def test_isf(self):
+        # reference values were computed via the reference distribution, e.g.
+        # mp.dps = 100; LogLaplace(c=c).isf(q).
+        c = 3.25
+        q = [0.8, 0.1, 1e-10, 1e-20, 1e-40]
+        ref = [0.7543222539245642, 1.6408455124660906, 964.4916294395846,
+               1151387.578354072, 1640845512466.0906]
+        assert_allclose(stats.loglaplace.isf(q, c), ref, rtol=1e-14)
+
+    @pytest.mark.parametrize('r', [1, 2, 3, 4])
+    def test_moments_stats(self, r):
+        mom = 'mvsk'[r - 1]
+        c = np.arange(0.5, r + 0.5, 0.5)
+
+        # r-th non-central moment is infinite if |r| >= c.
+        assert_allclose(stats.loglaplace.moment(r, c), np.inf)
+
+        # r-th non-central moment is non-finite (inf or nan) if r >= c.
+        assert not np.any(np.isfinite(stats.loglaplace.stats(c, moments=mom)))
+
+    @pytest.mark.parametrize("c", [0.5, 1.0, 2.0])
+    @pytest.mark.parametrize("loc, scale", [(-1.2, 3.45)])
+    @pytest.mark.parametrize("fix_c", [True, False])
+    @pytest.mark.parametrize("fix_scale", [True, False])
+    def test_fit_analytic_mle(self, c, loc, scale, fix_c, fix_scale):
+        # Test that the analytical MLE produces no worse result than the
+        # generic (numerical) MLE.
+
+        rng = np.random.default_rng(6762668991392531563)
+        data = stats.loglaplace.rvs(c, loc=loc, scale=scale, size=100,
+                                    random_state=rng)
+
+        kwds = {'floc': loc}
+        if fix_c:
+            kwds['fc'] = c
+        if fix_scale:
+            kwds['fscale'] = scale
+        nfree = 3 - len(kwds)
+
+        if nfree == 0:
+            error_msg = "All parameters fixed. There is nothing to optimize."
+            with pytest.raises((RuntimeError, ValueError), match=error_msg):
+                stats.loglaplace.fit(data, **kwds)
+            return
+
+        _assert_less_or_close_loglike(stats.loglaplace, data, **kwds)
+
+
+class TestPowerlaw:
+
+    # In the following data, `sf` was computed with mpmath.
+    @pytest.mark.parametrize('x, a, sf',
+                             [(0.25, 2.0, 0.9375),
+                              (0.99609375, 1/256, 1.528855235208108e-05)])
+    def test_sf(self, x, a, sf):
+        assert_allclose(stats.powerlaw.sf(x, a), sf, rtol=1e-15)
+
+    @pytest.fixture(scope='function')
+    def rng(self):
+        return np.random.default_rng(1234)
+
+    @pytest.mark.parametrize("rvs_shape", [.1, .5, .75, 1, 2])
+    @pytest.mark.parametrize("rvs_loc", [-1, 0, 1])
+    @pytest.mark.parametrize("rvs_scale", [.1, 1, 5])
+    @pytest.mark.parametrize('fix_shape, fix_loc, fix_scale',
+                             [p for p in product([True, False], repeat=3)
+                              if False in p])
+    def test_fit_MLE_comp_optimizer(self, rvs_shape, rvs_loc, rvs_scale,
+                                    fix_shape, fix_loc, fix_scale, rng):
+        data = stats.powerlaw.rvs(size=250, a=rvs_shape, loc=rvs_loc,
+                                  scale=rvs_scale, random_state=rng)
+
+        kwds = dict()
+        if fix_shape:
+            kwds['f0'] = rvs_shape
+        if fix_loc:
+            kwds['floc'] = np.nextafter(data.min(), -np.inf)
+        if fix_scale:
+            kwds['fscale'] = rvs_scale
+
+        # Numerical result may equal analytical result if some code path
+        # of the analytical routine makes use of numerical optimization.
+        _assert_less_or_close_loglike(stats.powerlaw, data, **kwds,
+                                      maybe_identical=True)
+
+    def test_problem_case(self):
+        # An observed problem with the test method indicated that some fixed
+        # scale values could cause bad results, this is now corrected.
+        a = 2.50002862645130604506
+        location = 0.0
+        scale = 35.249023299873095
+
+        data = stats.powerlaw.rvs(a=a, loc=location, scale=scale, size=100,
+                                  random_state=np.random.default_rng(5))
+
+        kwds = {'fscale': np.ptp(data) * 2}
+
+        _assert_less_or_close_loglike(stats.powerlaw, data, **kwds)
+
+    def test_fit_warnings(self):
+        assert_fit_warnings(stats.powerlaw)
+        # test for error when `fscale + floc <= np.max(data)` is not satisfied
+        msg = r" Maximum likelihood estimation with 'powerlaw' requires"
+        with assert_raises(FitDataError, match=msg):
+            stats.powerlaw.fit([1, 2, 4], floc=0, fscale=3)
+
+        # test for error when `data - floc >= 0`  is not satisfied
+        msg = r" Maximum likelihood estimation with 'powerlaw' requires"
+        with assert_raises(FitDataError, match=msg):
+            stats.powerlaw.fit([1, 2, 4], floc=2)
+
+        # test for fixed location not less than `min(data)`.
+        msg = r" Maximum likelihood estimation with 'powerlaw' requires"
+        with assert_raises(FitDataError, match=msg):
+            stats.powerlaw.fit([1, 2, 4], floc=1)
+
+        # test for when fixed scale is less than or equal to range of data
+        msg = r"Negative or zero `fscale` is outside"
+        with assert_raises(ValueError, match=msg):
+            stats.powerlaw.fit([1, 2, 4], fscale=-3)
+
+        # test for when fixed scale is less than or equal to range of data
+        msg = r"`fscale` must be greater than the range of data."
+        with assert_raises(ValueError, match=msg):
+            stats.powerlaw.fit([1, 2, 4], fscale=3)
+
+    def test_minimum_data_zero_gh17801(self):
+        # gh-17801 reported an overflow error when the minimum value of the
+        # data is zero. Check that this problem is resolved.
+        data = [0, 1, 2, 2, 3, 3, 3, 3, 4, 4, 5, 6]
+        dist = stats.powerlaw
+        with np.errstate(over='ignore'):
+            _assert_less_or_close_loglike(dist, data)
+
+
+class TestPowerLogNorm:
+
+    # reference values were computed via mpmath
+    # from mpmath import mp
+    # mp.dps = 80
+    # def powerlognorm_sf_mp(x, c, s):
+    #     x = mp.mpf(x)
+    #     c = mp.mpf(c)
+    #     s = mp.mpf(s)
+    #     return mp.ncdf(-mp.log(x) / s)**c
+    #
+    # def powerlognormal_cdf_mp(x, c, s):
+    #     return mp.one - powerlognorm_sf_mp(x, c, s)
+    #
+    # x, c, s = 100, 20, 1
+    # print(float(powerlognorm_sf_mp(x, c, s)))
+
+    @pytest.mark.parametrize("x, c, s, ref",
+                             [(100, 20, 1, 1.9057100820561928e-114),
+                              (1e-3, 20, 1, 0.9999999999507617),
+                              (1e-3, 0.02, 1, 0.9999999999999508),
+                              (1e22, 0.02, 1, 6.50744044621611e-12)])
+    def test_sf(self, x, c, s, ref):
+        assert_allclose(stats.powerlognorm.sf(x, c, s), ref, rtol=1e-13)
+
+    # reference values were computed via mpmath using the survival
+    # function above (passing in `ref` and getting `q`).
+    @pytest.mark.parametrize("q, c, s, ref",
+                             [(0.9999999587870905, 0.02, 1, 0.01),
+                              (6.690376686108851e-233, 20, 1, 1000)])
+    def test_isf(self, q, c, s, ref):
+        assert_allclose(stats.powerlognorm.isf(q, c, s), ref, rtol=5e-11)
+
+    @pytest.mark.parametrize("x, c, s, ref",
+                             [(1e25, 0.02, 1, 0.9999999999999963),
+                              (1e-6, 0.02, 1, 2.054921078040843e-45),
+                              (1e-6, 200, 1, 2.0549210780408428e-41),
+                              (0.3, 200, 1, 0.9999999999713368)])
+    def test_cdf(self, x, c, s, ref):
+        assert_allclose(stats.powerlognorm.cdf(x, c, s), ref, rtol=3e-14)
+
+    # reference values were computed via mpmath
+    # from mpmath import mp
+    # mp.dps = 50
+    # def powerlognorm_pdf_mpmath(x, c, s):
+    #     x = mp.mpf(x)
+    #     c = mp.mpf(c)
+    #     s = mp.mpf(s)
+    #     res = (c/(x * s) * mp.npdf(mp.log(x)/s) *
+    #            mp.ncdf(-mp.log(x)/s)**(c - mp.one))
+    #     return float(res)
+
+    @pytest.mark.parametrize("x, c, s, ref",
+                             [(1e22, 0.02, 1, 6.5954987852335016e-34),
+                              (1e20, 1e-3, 1, 1.588073750563988e-22),
+                              (1e40, 1e-3, 1, 1.3179391812506349e-43)])
+    def test_pdf(self, x, c, s, ref):
+        assert_allclose(stats.powerlognorm.pdf(x, c, s), ref, rtol=3e-12)
+
+
+class TestPowerNorm:
+
+    # survival function references were computed with mpmath via
+    # from mpmath import mp
+    # x = mp.mpf(x)
+    # c = mp.mpf(x)
+    # float(mp.ncdf(-x)**c)
+
+    @pytest.mark.parametrize("x, c, ref",
+                             [(9, 1, 1.1285884059538405e-19),
+                              (20, 2, 7.582445786569958e-178),
+                              (100, 0.02, 3.330957891903866e-44),
+                              (200, 0.01, 1.3004759092324774e-87)])
+    def test_sf(self, x, c, ref):
+        assert_allclose(stats.powernorm.sf(x, c), ref, rtol=1e-13)
+
+    # inverse survival function references were computed with mpmath via
+    # from mpmath import mp
+    # def isf_mp(q, c):
+    #     q = mp.mpf(q)
+    #     c = mp.mpf(c)
+    #     arg = q**(mp.one / c)
+    #     return float(-mp.sqrt(2) * mp.erfinv(mp.mpf(2.) * arg - mp.one))
+
+    @pytest.mark.parametrize("q, c, ref",
+                             [(1e-5, 20, -0.15690800666514138),
+                              (0.99999, 100, -5.19933666203545),
+                              (0.9999, 0.02, -2.576676052143387),
+                              (5e-2, 0.02, 17.089518110222244),
+                              (1e-18, 2, 5.9978070150076865),
+                              (1e-50, 5, 6.361340902404057)])
+    def test_isf(self, q, c, ref):
+        assert_allclose(stats.powernorm.isf(q, c), ref, rtol=5e-12)
+
+    # CDF reference values were computed with mpmath via
+    # from mpmath import mp
+    # def cdf_mp(x, c):
+    #     x = mp.mpf(x)
+    #     c = mp.mpf(c)
+    #     return float(mp.one - mp.ncdf(-x)**c)
+
+    @pytest.mark.parametrize("x, c, ref",
+                             [(-12, 9, 1.598833900869911e-32),
+                              (2, 9, 0.9999999999999983),
+                              (-20, 9, 2.4782617067456103e-88),
+                              (-5, 0.02, 5.733032242841443e-09),
+                              (-20, 0.02, 5.507248237212467e-91)])
+    def test_cdf(self, x, c, ref):
+        assert_allclose(stats.powernorm.cdf(x, c), ref, rtol=5e-14)
+
+
+class TestInvGamma:
+    def test_invgamma_inf_gh_1866(self):
+        # invgamma's moments are only finite for a>n
+        # specific numbers checked w/ boost 1.54
+        with warnings.catch_warnings():
+            warnings.simplefilter('error', RuntimeWarning)
+            mvsk = stats.invgamma.stats(a=19.31, moments='mvsk')
+            expected = [0.05461496450, 0.0001723162534, 1.020362676,
+                        2.055616582]
+            assert_allclose(mvsk, expected)
+
+            a = [1.1, 3.1, 5.6]
+            mvsk = stats.invgamma.stats(a=a, moments='mvsk')
+            expected = ([10., 0.476190476, 0.2173913043],       # mmm
+                        [np.inf, 0.2061430632, 0.01312749422],  # vvv
+                        [np.nan, 41.95235392, 2.919025532],     # sss
+                        [np.nan, np.nan, 24.51923076])          # kkk
+            for x, y in zip(mvsk, expected):
+                assert_almost_equal(x, y)
+
+    def test_cdf_ppf(self):
+        # gh-6245
+        x = np.logspace(-2.6, 0)
+        y = stats.invgamma.cdf(x, 1)
+        xx = stats.invgamma.ppf(y, 1)
+        assert_allclose(x, xx)
+
+    def test_sf_isf(self):
+        # gh-6245
+        if sys.maxsize > 2**32:
+            x = np.logspace(2, 100)
+        else:
+            # Invgamme roundtrip on 32-bit systems has relative accuracy
+            # ~1e-15 until x=1e+15, and becomes inf above x=1e+18
+            x = np.logspace(2, 18)
+
+        y = stats.invgamma.sf(x, 1)
+        xx = stats.invgamma.isf(y, 1)
+        assert_allclose(x, xx, rtol=1.0)
+
+    def test_logcdf(self):
+        x = 1e7
+        a = 2.25
+        # Reference value computed with mpmath.
+        ref = -6.97567687425534e-17
+        logcdf = stats.invgamma.logcdf(x, a)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+
+    def test_logsf(self):
+        x = 0.01
+        a = 3.5
+        # Reference value computed with mpmath.
+        ref = -1.147781224014262e-39
+        logsf = stats.invgamma.logsf(x, a)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+    @pytest.mark.parametrize("a, ref",
+                             [(100000000.0, -26.21208257605721),
+                              (1e+100, -343.9688254159022)])
+    def test_large_entropy(self, a, ref):
+        # The reference values were calculated with mpmath:
+        # from mpmath import mp
+        # mp.dps = 500
+
+        # def invgamma_entropy(a):
+        #     a = mp.mpf(a)
+        #     h = a + mp.loggamma(a) - (mp.one + a) * mp.digamma(a)
+        #     return float(h)
+        assert_allclose(stats.invgamma.entropy(a), ref, rtol=1e-15)
+
+
+class TestF:
+    def test_endpoints(self):
+        # Compute the pdf at the left endpoint dst.a.
+        data = [[stats.f, (2, 1), 1.0]]
+        for _f, _args, _correct in data:
+            ans = _f.pdf(_f.a, *_args)
+
+        ans = [_f.pdf(_f.a, *_args) for _f, _args, _ in data]
+        correct = [_correct_ for _f, _args, _correct_ in data]
+        assert_array_almost_equal(ans, correct)
+
+    def test_f_moments(self):
+        # n-th moment of F distributions is only finite for n < dfd / 2
+        m, v, s, k = stats.f.stats(11, 6.5, moments='mvsk')
+        assert_(np.isfinite(m))
+        assert_(np.isfinite(v))
+        assert_(np.isfinite(s))
+        assert_(not np.isfinite(k))
+
+    def test_moments_warnings(self):
+        # no warnings should be generated for dfd = 2, 4, 6, 8 (div by zero)
+        with warnings.catch_warnings():
+            warnings.simplefilter('error', RuntimeWarning)
+            stats.f.stats(dfn=[11]*4, dfd=[2, 4, 6, 8], moments='mvsk')
+
+    def test_stats_broadcast(self):
+        dfn = np.array([[3], [11]])
+        dfd = np.array([11, 12])
+        m, v, s, k = stats.f.stats(dfn=dfn, dfd=dfd, moments='mvsk')
+        m2 = [dfd / (dfd - 2)]*2
+        assert_allclose(m, m2)
+        v2 = 2 * dfd**2 * (dfn + dfd - 2) / dfn / (dfd - 2)**2 / (dfd - 4)
+        assert_allclose(v, v2)
+        s2 = ((2*dfn + dfd - 2) * np.sqrt(8*(dfd - 4)) /
+              ((dfd - 6) * np.sqrt(dfn*(dfn + dfd - 2))))
+        assert_allclose(s, s2)
+        k2num = 12 * (dfn * (5*dfd - 22) * (dfn + dfd - 2) +
+                      (dfd - 4) * (dfd - 2)**2)
+        k2den = dfn * (dfd - 6) * (dfd - 8) * (dfn + dfd - 2)
+        k2 = k2num / k2den
+        assert_allclose(k, k2)
+
+
+class TestStudentT:
+    def test_rvgeneric_std(self):
+        # Regression test for #1191
+        assert_array_almost_equal(stats.t.std([5, 6]), [1.29099445, 1.22474487])
+
+    def test_moments_t(self):
+        # regression test for #8786
+        assert_equal(stats.t.stats(df=1, moments='mvsk'),
+                    (np.inf, np.nan, np.nan, np.nan))
+        assert_equal(stats.t.stats(df=1.01, moments='mvsk'),
+                    (0.0, np.inf, np.nan, np.nan))
+        assert_equal(stats.t.stats(df=2, moments='mvsk'),
+                    (0.0, np.inf, np.nan, np.nan))
+        assert_equal(stats.t.stats(df=2.01, moments='mvsk'),
+                    (0.0, 2.01/(2.01-2.0), np.nan, np.inf))
+        assert_equal(stats.t.stats(df=3, moments='sk'), (np.nan, np.inf))
+        assert_equal(stats.t.stats(df=3.01, moments='sk'), (0.0, np.inf))
+        assert_equal(stats.t.stats(df=4, moments='sk'), (0.0, np.inf))
+        assert_allclose(stats.t.stats(df=4.01, moments='sk'), (0.0, 6.0/(4.01 - 4.0)),
+                        rtol=1e-14)
+
+    def test_t_entropy(self):
+        df = [1, 2, 25, 100]
+        # Expected values were computed with mpmath.
+        expected = [2.5310242469692907, 1.9602792291600821,
+                    1.459327578078393, 1.4289633653182439]
+        assert_allclose(stats.t.entropy(df), expected, rtol=1e-13)
+
+    @pytest.mark.parametrize("v, ref",
+                            [(100, 1.4289633653182439),
+                            (1e+100, 1.4189385332046727)])
+    def test_t_extreme_entropy(self, v, ref):
+        # Reference values were calculated with mpmath:
+        # from mpmath import mp
+        # mp.dps = 500
+        #
+        # def t_entropy(v):
+        #   v = mp.mpf(v)
+        #   C = (v + mp.one) / 2
+        #   A = C * (mp.digamma(C) - mp.digamma(v / 2))
+        #   B = 0.5 * mp.log(v) + mp.log(mp.beta(v / 2, mp.one / 2))
+        #   h = A + B
+        #   return float(h)
+        assert_allclose(stats.t.entropy(v), ref, rtol=1e-14)
+
+    @pytest.mark.parametrize("methname", ["pdf", "logpdf", "cdf",
+                                        "ppf", "sf", "isf"])
+    @pytest.mark.parametrize("df_infmask", [[0, 0], [1, 1], [0, 1],
+                                            [[0, 1, 0], [1, 1, 1]],
+                                            [[1, 0], [0, 1]],
+                                            [[0], [1]]])
+    def test_t_inf_df(self, methname, df_infmask):
+        df_infmask = np.asarray(df_infmask, dtype=bool)
+        rng = np.random.default_rng(5442451539)
+        df = rng.uniform(0, 10, size=df_infmask.shape)
+        x = rng.standard_normal(df_infmask.shape)
+        df[df_infmask] = np.inf
+        t_dist = stats.t(df=df, loc=3, scale=1)
+        t_dist_ref = stats.t(df=df[~df_infmask], loc=3, scale=1)
+        norm_dist = stats.norm(loc=3, scale=1)
+        t_meth = getattr(t_dist, methname)
+        t_meth_ref = getattr(t_dist_ref, methname)
+        norm_meth = getattr(norm_dist, methname)
+        res = t_meth(x)
+        assert_allclose(res[df_infmask], norm_meth(x[df_infmask]), rtol=5e-15)
+        assert_equal(res[~df_infmask], t_meth_ref(x[~df_infmask]))
+
+    @pytest.mark.parametrize("df_infmask", [[0, 0], [1, 1], [0, 1],
+                                            [[0, 1, 0], [1, 1, 1]],
+                                            [[1, 0], [0, 1]],
+                                            [[0], [1]]])
+    def test_t_inf_df_stats_entropy(self, df_infmask):
+        df_infmask = np.asarray(df_infmask, dtype=bool)
+        rng = np.random.default_rng(5442451539)
+        df = rng.uniform(0, 10, size=df_infmask.shape)
+        df[df_infmask] = np.inf
+        res = stats.t.stats(df=df, loc=3, scale=1, moments='mvsk')
+        res_ex_inf = stats.norm.stats(loc=3, scale=1, moments='mvsk')
+        res_ex_noinf = stats.t.stats(df=df[~df_infmask], loc=3, scale=1,
+                                    moments='mvsk')
+        for i in range(4):
+            assert_equal(res[i][df_infmask], res_ex_inf[i])
+            assert_equal(res[i][~df_infmask], res_ex_noinf[i])
+
+        res = stats.t.entropy(df=df, loc=3, scale=1)
+        res_ex_inf = stats.norm.entropy(loc=3, scale=1)
+        res_ex_noinf = stats.t.entropy(df=df[~df_infmask], loc=3, scale=1)
+        assert_equal(res[df_infmask], res_ex_inf)
+        assert_equal(res[~df_infmask], res_ex_noinf)
+
+    def test_logpdf_pdf(self):
+        # reference values were computed via the reference distribution, e.g.
+        # mp.dps = 500; StudentT(df=df).logpdf(x), StudentT(df=df).pdf(x)
+        x = [1, 1e3, 10, 1]
+        df = [1e100, 1e50, 1e20, 1]
+        logpdf_ref = [-1.4189385332046727, -500000.9189385332,
+                      -50.918938533204674, -1.8378770664093456]
+        pdf_ref = [0.24197072451914334, 0,
+                   7.69459862670642e-23, 0.15915494309189535]
+        assert_allclose(stats.t.logpdf(x, df), logpdf_ref, rtol=1e-14)
+        assert_allclose(stats.t.pdf(x, df), pdf_ref, rtol=1e-14)
+
+    # Reference values were computed with mpmath, and double-checked with
+    # Wolfram Alpha.
+    @pytest.mark.parametrize('x, df, ref',
+                             [(-75.0, 15, -46.76036184546812),
+                              (0, 15, -0.6931471805599453),
+                              (75.0, 15, -4.9230344937641665e-21)])
+    def test_logcdf_logsf(self, x, df, ref):
+        logcdf = stats.t.logcdf(x, df)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+        # The reference value is logcdf(x, df) == logsf(-x, df).
+        logsf = stats.t.logsf(-x, df)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+
+class TestRvDiscrete:
+    def setup_method(self):
+        self.rng = np.random.default_rng(333348228)
+
+    def test_rvs(self):
+        states = [-1, 0, 1, 2, 3, 4]
+        probability = [0.0, 0.3, 0.4, 0.0, 0.3, 0.0]
+        samples = 1000
+        r = stats.rv_discrete(name='sample', values=(states, probability))
+        x = r.rvs(size=samples, random_state=self.rng)
+        assert isinstance(x, np.ndarray)
+
+        for s, p in zip(states, probability):
+            assert abs(sum(x == s)/float(samples) - p) < 0.05
+
+        x = r.rvs(random_state=self.rng)
+        assert np.issubdtype(type(x), np.integer)
+
+    def test_entropy(self):
+        # Basic tests of entropy.
+        pvals = np.array([0.25, 0.45, 0.3])
+        p = stats.rv_discrete(values=([0, 1, 2], pvals))
+        expected_h = -sum(xlogy(pvals, pvals))
+        h = p.entropy()
+        assert_allclose(h, expected_h)
+
+        p = stats.rv_discrete(values=([0, 1, 2], [1.0, 0, 0]))
+        h = p.entropy()
+        assert_equal(h, 0.0)
+
+    def test_pmf(self):
+        xk = [1, 2, 4]
+        pk = [0.5, 0.3, 0.2]
+        rv = stats.rv_discrete(values=(xk, pk))
+
+        x = [[1., 4.],
+             [3., 2]]
+        assert_allclose(rv.pmf(x),
+                        [[0.5, 0.2],
+                         [0., 0.3]], atol=1e-14)
+
+    def test_cdf(self):
+        xk = [1, 2, 4]
+        pk = [0.5, 0.3, 0.2]
+        rv = stats.rv_discrete(values=(xk, pk))
+
+        x_values = [-2, 1., 1.1, 1.5, 2.0, 3.0, 4, 5]
+        expected = [0, 0.5, 0.5, 0.5, 0.8, 0.8, 1, 1]
+        assert_allclose(rv.cdf(x_values), expected, atol=1e-14)
+
+        # also check scalar arguments
+        assert_allclose([rv.cdf(xx) for xx in x_values],
+                        expected, atol=1e-14)
+
+    def test_ppf(self):
+        xk = [1, 2, 4]
+        pk = [0.5, 0.3, 0.2]
+        rv = stats.rv_discrete(values=(xk, pk))
+
+        q_values = [0.1, 0.5, 0.6, 0.8, 0.9, 1.]
+        expected = [1, 1, 2, 2, 4, 4]
+        assert_allclose(rv.ppf(q_values), expected, atol=1e-14)
+
+        # also check scalar arguments
+        assert_allclose([rv.ppf(q) for q in q_values],
+                        expected, atol=1e-14)
+
+    def test_cdf_ppf_next(self):
+        # copied and special cased from test_discrete_basic
+        vals = ([1, 2, 4, 7, 8], [0.1, 0.2, 0.3, 0.3, 0.1])
+        rv = stats.rv_discrete(values=vals)
+
+        assert_array_equal(rv.ppf(rv.cdf(rv.xk[:-1]) + 1e-8),
+                           rv.xk[1:])
+
+    def test_multidimension(self):
+        xk = np.arange(12).reshape((3, 4))
+        pk = np.array([[0.1, 0.1, 0.15, 0.05],
+                       [0.1, 0.1, 0.05, 0.05],
+                       [0.1, 0.1, 0.05, 0.05]])
+        rv = stats.rv_discrete(values=(xk, pk))
+
+        assert_allclose(rv.expect(), np.sum(rv.xk * rv.pk), atol=1e-14)
+
+    def test_bad_input(self):
+        xk = [1, 2, 3]
+        pk = [0.5, 0.5]
+        assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))
+
+        pk = [1, 2, 3]
+        assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))
+
+        xk = [1, 2, 3]
+        pk = [0.5, 1.2, -0.7]
+        assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))
+
+        xk = [1, 2, 3, 4, 5]
+        pk = [0.3, 0.3, 0.3, 0.3, -0.2]
+        assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))
+
+        xk = [1, 1]
+        pk = [0.5, 0.5]
+        assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))
+
+    def test_shape_rv_sample(self):
+        # tests added for gh-9565
+
+        # mismatch of 2d inputs
+        xk, pk = np.arange(4).reshape((2, 2)), np.full((2, 3), 1/6)
+        assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))
+
+        # same number of elements, but shapes not compatible
+        xk, pk = np.arange(6).reshape((3, 2)), np.full((2, 3), 1/6)
+        assert_raises(ValueError, stats.rv_discrete, **dict(values=(xk, pk)))
+
+        # same shapes => no error
+        xk, pk = np.arange(6).reshape((3, 2)), np.full((3, 2), 1/6)
+        assert_equal(stats.rv_discrete(values=(xk, pk)).pmf(0), 1/6)
+
+    def test_expect1(self):
+        xk = [1, 2, 4, 6, 7, 11]
+        pk = [0.1, 0.2, 0.2, 0.2, 0.2, 0.1]
+        rv = stats.rv_discrete(values=(xk, pk))
+
+        assert_allclose(rv.expect(), np.sum(rv.xk * rv.pk), atol=1e-14)
+
+    def test_expect2(self):
+        # rv_sample should override _expect. Bug report from
+        # https://stackoverflow.com/questions/63199792
+        y = [200.0, 300.0, 400.0, 500.0, 600.0, 700.0, 800.0, 900.0, 1000.0,
+             1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1600.0, 1700.0, 1800.0,
+             1900.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0,
+             2700.0, 2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0,
+             3500.0, 3600.0, 3700.0, 3800.0, 3900.0, 4000.0, 4100.0, 4200.0,
+             4300.0, 4400.0, 4500.0, 4600.0, 4700.0, 4800.0]
+
+        py = [0.0004, 0.0, 0.0033, 0.006500000000000001, 0.0, 0.0,
+              0.004399999999999999, 0.6862, 0.0, 0.0, 0.0,
+              0.00019999999999997797, 0.0006000000000000449,
+              0.024499999999999966, 0.006400000000000072,
+              0.0043999999999999595, 0.019499999999999962,
+              0.03770000000000007, 0.01759999999999995, 0.015199999999999991,
+              0.018100000000000005, 0.04500000000000004, 0.0025999999999999357,
+              0.0, 0.0041000000000001036, 0.005999999999999894,
+              0.0042000000000000925, 0.0050000000000000044,
+              0.0041999999999999815, 0.0004999999999999449,
+              0.009199999999999986, 0.008200000000000096,
+              0.0, 0.0, 0.0046999999999999265, 0.0019000000000000128,
+              0.0006000000000000449, 0.02510000000000001, 0.0,
+              0.007199999999999984, 0.0, 0.012699999999999934, 0.0, 0.0,
+              0.008199999999999985, 0.005600000000000049, 0.0]
+
+        rv = stats.rv_discrete(values=(y, py))
+
+        # check the mean
+        assert_allclose(rv.expect(), rv.mean(), atol=1e-14)
+        assert_allclose(rv.expect(),
+                        sum(v * w for v, w in zip(y, py)), atol=1e-14)
+
+        # also check the second moment
+        assert_allclose(rv.expect(lambda x: x**2),
+                        sum(v**2 * w for v, w in zip(y, py)), atol=1e-14)
+
+
+class TestSkewCauchy:
+    def test_cauchy(self):
+        x = np.linspace(-5, 5, 100)
+        assert_array_almost_equal(stats.skewcauchy.pdf(x, a=0),
+                                  stats.cauchy.pdf(x))
+        assert_array_almost_equal(stats.skewcauchy.cdf(x, a=0),
+                                  stats.cauchy.cdf(x))
+        assert_array_almost_equal(stats.skewcauchy.ppf(x, a=0),
+                                  stats.cauchy.ppf(x))
+
+    def test_skewcauchy_R(self):
+        # options(digits=16)
+        # library(sgt)
+        # # lmbda, x contain the values generated for a, x below
+        # lmbda <- c(0.0976270078546495, 0.430378732744839, 0.2055267521432877,
+        #            0.0897663659937937, -0.15269040132219, 0.2917882261333122,
+        #            -0.12482557747462, 0.7835460015641595, 0.9273255210020589,
+        #            -0.2331169623484446)
+        # x <- c(2.917250380826646, 0.2889491975290444, 0.6804456109393229,
+        #        4.25596638292661, -4.289639418021131, -4.1287070029845925,
+        #        -4.797816025596743, 3.32619845547938, 2.7815675094985046,
+        #        3.700121482468191)
+        # pdf = dsgt(x, mu=0, lambda=lambda, sigma=1, q=1/2, mean.cent=FALSE,
+        #            var.adj = sqrt(2))
+        # cdf = psgt(x, mu=0, lambda=lambda, sigma=1, q=1/2, mean.cent=FALSE,
+        #            var.adj = sqrt(2))
+        # qsgt(cdf, mu=0, lambda=lambda, sigma=1, q=1/2, mean.cent=FALSE,
+        #      var.adj = sqrt(2))
+
+        rng = np.random.RandomState(0)
+        a = rng.rand(10) * 2 - 1
+        x = rng.rand(10) * 10 - 5
+        pdf = [0.039473975217333909, 0.305829714049903223, 0.24140158118994162,
+               0.019585772402693054, 0.021436553695989482, 0.00909817103867518,
+               0.01658423410016873, 0.071083288030394126, 0.103250045941454524,
+               0.013110230778426242]
+        cdf = [0.87426677718213752, 0.37556468910780882, 0.59442096496538066,
+               0.91304659850890202, 0.09631964100300605, 0.03829624330921733,
+               0.08245240578402535, 0.72057062945510386, 0.62826415852515449,
+               0.95011308463898292]
+        assert_allclose(stats.skewcauchy.pdf(x, a), pdf)
+        assert_allclose(stats.skewcauchy.cdf(x, a), cdf)
+        assert_allclose(stats.skewcauchy.ppf(cdf, a), x)
+
+
+class TestJFSkewT:
+    def test_compare_t(self):
+        # Verify that jf_skew_t with a=b recovers the t distribution with 2a
+        # degrees of freedom
+        a = b = 5
+        df = a * 2
+        x = [-1.0, 0.0, 1.0, 2.0]
+        q = [0.0, 0.1, 0.25, 0.75, 0.90, 1.0]
+
+        jf = stats.jf_skew_t(a, b)
+        t = stats.t(df)
+
+        assert_allclose(jf.pdf(x), t.pdf(x))
+        assert_allclose(jf.cdf(x), t.cdf(x))
+        assert_allclose(jf.ppf(q), t.ppf(q))
+        assert_allclose(jf.stats('mvsk'), t.stats('mvsk'))
+
+    @pytest.fixture
+    def gamlss_pdf_data(self):
+        """Sample data points computed using the `ST5` distribution from the
+        GAMLSS package in R. The pdf has been calculated for (a,b)=(2,3),
+        (a,b)=(8,4), and (a,b)=(12,13) for x in `np.linspace(-10, 10, 41)`.
+
+        N.B. the `ST5` distribution in R uses an alternative parameterization
+        in terms of nu and tau, where:
+            - nu = (a - b) / (a * b * (a + b)) ** 0.5
+            - tau = 2 / (a + b)
+        """
+        data = np.load(
+            Path(__file__).parent / "data/jf_skew_t_gamlss_pdf_data.npy"
+        )
+        return np.rec.fromarrays(data, names="x,pdf,a,b")
+
+    @pytest.mark.parametrize("a,b", [(2, 3), (8, 4), (12, 13)])
+    def test_compare_with_gamlss_r(self, gamlss_pdf_data, a, b):
+        """Compare the pdf with a table of reference values. The table of
+        reference values was produced using R, where the Jones and Faddy skew
+        t distribution is available in the GAMLSS package as `ST5`.
+        """
+        data = gamlss_pdf_data[
+            (gamlss_pdf_data["a"] == a) & (gamlss_pdf_data["b"] == b)
+        ]
+        x, pdf = data["x"], data["pdf"]
+        assert_allclose(pdf, stats.jf_skew_t(a, b).pdf(x), rtol=1e-12)
+
+
+# Test data for TestSkewNorm.test_noncentral_moments()
+# The expected noncentral moments were computed by Wolfram Alpha.
+# In Wolfram Alpha, enter
+#    SkewNormalDistribution[0, 1, a] moment
+# with `a` replaced by the desired shape parameter.  In the results, there
+# should be a table of the first four moments. Click on "More" to get more
+# moments.  The expected moments start with the first moment (order = 1).
+_skewnorm_noncentral_moments = [
+    (2, [2*np.sqrt(2/(5*np.pi)),
+         1,
+         22/5*np.sqrt(2/(5*np.pi)),
+         3,
+         446/25*np.sqrt(2/(5*np.pi)),
+         15,
+         2682/25*np.sqrt(2/(5*np.pi)),
+         105,
+         107322/125*np.sqrt(2/(5*np.pi))]),
+    (0.1, [np.sqrt(2/(101*np.pi)),
+           1,
+           302/101*np.sqrt(2/(101*np.pi)),
+           3,
+           (152008*np.sqrt(2/(101*np.pi)))/10201,
+           15,
+           (107116848*np.sqrt(2/(101*np.pi)))/1030301,
+           105,
+           (97050413184*np.sqrt(2/(101*np.pi)))/104060401]),
+    (-3, [-3/np.sqrt(5*np.pi),
+          1,
+          -63/(10*np.sqrt(5*np.pi)),
+          3,
+          -2529/(100*np.sqrt(5*np.pi)),
+          15,
+          -30357/(200*np.sqrt(5*np.pi)),
+          105,
+          -2428623/(2000*np.sqrt(5*np.pi)),
+          945,
+          -242862867/(20000*np.sqrt(5*np.pi)),
+          10395,
+          -29143550277/(200000*np.sqrt(5*np.pi)),
+          135135]),
+]
+
+
+class TestSkewNorm:
+    def setup_method(self):
+        self.rng = check_random_state(1234)
+
+    def test_normal(self):
+        # When the skewness is 0 the distribution is normal
+        x = np.linspace(-5, 5, 100)
+        assert_array_almost_equal(stats.skewnorm.pdf(x, a=0),
+                                  stats.norm.pdf(x))
+
+    def test_rvs(self):
+        rng = check_random_state(1234)
+        shape = (3, 4, 5)
+        x = stats.skewnorm.rvs(a=0.75, size=shape, random_state=rng)
+        assert_equal(shape, x.shape)
+
+        x = stats.skewnorm.rvs(a=-3, size=shape, random_state=rng)
+        assert_equal(shape, x.shape)
+
+    def test_moments(self):
+        rng = check_random_state(1234)
+        X = stats.skewnorm.rvs(a=4, size=int(1e6), loc=5, scale=2,
+                               random_state=rng)
+        expected = [np.mean(X), np.var(X), stats.skew(X), stats.kurtosis(X)]
+        computed = stats.skewnorm.stats(a=4, loc=5, scale=2, moments='mvsk')
+        assert_array_almost_equal(computed, expected, decimal=2)
+
+        X = stats.skewnorm.rvs(a=-4, size=int(1e6), loc=5, scale=2,
+                               random_state=rng)
+        expected = [np.mean(X), np.var(X), stats.skew(X), stats.kurtosis(X)]
+        computed = stats.skewnorm.stats(a=-4, loc=5, scale=2, moments='mvsk')
+        assert_array_almost_equal(computed, expected, decimal=2)
+
+    def test_pdf_large_x(self):
+        # Triples are [x, a, logpdf(x, a)].  These values were computed
+        # using Log[PDF[SkewNormalDistribution[0, 1, a], x]] in Wolfram Alpha.
+        logpdfvals = [
+            [40, -1, -1604.834233366398515598970],
+            [40, -1/2, -1004.142946723741991369168],
+            [40, 0, -800.9189385332046727417803],
+            [40, 1/2, -800.2257913526447274323631],
+            [-40, -1/2, -800.2257913526447274323631],
+            [-2, 1e7, -2.000000000000199559727173e14],
+            [2, -1e7, -2.000000000000199559727173e14],
+        ]
+        for x, a, logpdfval in logpdfvals:
+            logp = stats.skewnorm.logpdf(x, a)
+            assert_allclose(logp, logpdfval, rtol=1e-8)
+
+    def test_cdf_large_x(self):
+        # Regression test for gh-7746.
+        # The x values are large enough that the closest 64 bit floating
+        # point representation of the exact CDF is 1.0.
+        p = stats.skewnorm.cdf([10, 20, 30], -1)
+        assert_allclose(p, np.ones(3), rtol=1e-14)
+        p = stats.skewnorm.cdf(25, 2.5)
+        assert_allclose(p, 1.0, rtol=1e-14)
+
+    def test_cdf_sf_small_values(self):
+        # Triples are [x, a, cdf(x, a)].  These values were computed
+        # using CDF[SkewNormalDistribution[0, 1, a], x] in Wolfram Alpha.
+        cdfvals = [
+            [-8, 1, 3.870035046664392611e-31],
+            [-4, 2, 8.1298399188811398e-21],
+            [-2, 5, 1.55326826787106273e-26],
+            [-9, -1, 2.257176811907681295e-19],
+            [-10, -4, 1.523970604832105213e-23],
+        ]
+        for x, a, cdfval in cdfvals:
+            p = stats.skewnorm.cdf(x, a)
+            assert_allclose(p, cdfval, rtol=1e-8)
+            # For the skew normal distribution, sf(-x, -a) = cdf(x, a).
+            p = stats.skewnorm.sf(-x, -a)
+            assert_allclose(p, cdfval, rtol=1e-8)
+
+    @pytest.mark.parametrize('a, moments', _skewnorm_noncentral_moments)
+    def test_noncentral_moments(self, a, moments):
+        for order, expected in enumerate(moments, start=1):
+            mom = stats.skewnorm.moment(order, a)
+            assert_allclose(mom, expected, rtol=1e-14)
+
+    def test_fit(self):
+        rng = np.random.default_rng(4609813989115202851)
+
+        a, loc, scale = -2, 3.5, 0.5  # arbitrary, valid parameters
+        dist = stats.skewnorm(a, loc, scale)
+        rvs = dist.rvs(size=100, random_state=rng)
+
+        # test that MLE still honors guesses and fixed parameters
+        a2, loc2, scale2 = stats.skewnorm.fit(rvs, -1.5, floc=3)
+        a3, loc3, scale3 = stats.skewnorm.fit(rvs, -1.6, floc=3)
+        assert loc2 == loc3 == 3  # fixed parameter is respected
+        assert a2 != a3  # different guess -> (slightly) different outcome
+        # quality of fit is tested elsewhere
+
+        # test that MoM honors fixed parameters, accepts (but ignores) guesses
+        a4, loc4, scale4 = stats.skewnorm.fit(rvs, 3, fscale=3, method='mm')
+        assert scale4 == 3
+        # because scale was fixed, only the mean and skewness will be matched
+        dist4 = stats.skewnorm(a4, loc4, scale4)
+        res = dist4.stats(moments='ms')
+        ref = np.mean(rvs), stats.skew(rvs)
+        assert_allclose(res, ref)
+
+        # Test behavior when skew of data is beyond maximum of skewnorm
+        rvs2 = stats.pareto.rvs(1, size=100, random_state=rng)
+
+        # MLE still works
+        res = stats.skewnorm.fit(rvs2)
+        assert np.all(np.isfinite(res))
+
+        # MoM fits variance and skewness
+        a5, loc5, scale5 = stats.skewnorm.fit(rvs2, method='mm')
+        assert np.isinf(a5)
+        # distribution infrastructure doesn't allow infinite shape parameters
+        # into _stats; it just bypasses it and produces NaNs. Calculate
+        # moments manually.
+        m, v = np.mean(rvs2), np.var(rvs2)
+        assert_allclose(m, loc5 + scale5 * np.sqrt(2/np.pi))
+        assert_allclose(v, scale5**2 * (1 - 2 / np.pi))
+
+        # test that MLE and MoM behave as expected under sign changes
+        a6p, loc6p, scale6p = stats.skewnorm.fit(rvs, method='mle')
+        a6m, loc6m, scale6m = stats.skewnorm.fit(-rvs, method='mle')
+        assert_allclose([a6m, loc6m, scale6m], [-a6p, -loc6p, scale6p])
+        a7p, loc7p, scale7p = stats.skewnorm.fit(rvs, method='mm')
+        a7m, loc7m, scale7m = stats.skewnorm.fit(-rvs, method='mm')
+        assert_allclose([a7m, loc7m, scale7m], [-a7p, -loc7p, scale7p])
+
+    def test_fit_gh19332(self):
+        # When the skewness of the data was high, `skewnorm.fit` fell back on
+        # generic `fit` behavior with a bad guess of the skewness parameter.
+        # Test that this is improved; `skewnorm.fit` is now better at finding
+        # the global optimum when the sample is highly skewed. See gh-19332.
+        x = np.array([-5, -1, 1 / 100_000] + 12 * [1] + [5])
+
+        params = stats.skewnorm.fit(x)
+        res = stats.skewnorm.nnlf(params, x)
+
+        # Compare overridden fit against generic fit.
+        # res should be about 32.01, and generic fit is worse at 32.64.
+        # In case the generic fit improves, remove this assertion (see gh-19333).
+        params_super = stats.skewnorm.fit(x, superfit=True)
+        ref = stats.skewnorm.nnlf(params_super, x)
+        assert res < ref - 0.5
+
+        # Compare overridden fit against stats.fit
+        rng = np.random.default_rng(9842356982345693637)
+        bounds = {'a': (-5, 5), 'loc': (-10, 10), 'scale': (1e-16, 10)}
+
+        def optimizer(fun, bounds):
+            return differential_evolution(fun, bounds, rng=rng)
+
+        fit_result = stats.fit(stats.skewnorm, x, bounds, optimizer=optimizer)
+        np.testing.assert_allclose(params, fit_result.params, rtol=1e-4)
+
+    def test_ppf(self):
+        # gh-20124 reported that Boost's ppf was wrong for high skewness
+        # Reference value was calculated using
+        # N[InverseCDF[SkewNormalDistribution[0, 1, 500], 1/100], 14] in Wolfram Alpha.
+        assert_allclose(stats.skewnorm.ppf(0.01, 500), 0.012533469508013, rtol=1e-13)
+
+
+class TestExpon:
+    def test_zero(self):
+        assert_equal(stats.expon.pdf(0), 1)
+
+    def test_tail(self):  # Regression test for ticket 807
+        assert_equal(stats.expon.cdf(1e-18), 1e-18)
+        assert_equal(stats.expon.isf(stats.expon.sf(40)), 40)
+
+    def test_nan_raises_error(self):
+        # see gh-issue 10300
+        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
+        assert_raises(ValueError, stats.expon.fit, x)
+
+    def test_inf_raises_error(self):
+        # see gh-issue 10300
+        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
+        assert_raises(ValueError, stats.expon.fit, x)
+
+
+class TestNorm:
+    def test_nan_raises_error(self):
+        # see gh-issue 10300
+        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
+        assert_raises(ValueError, stats.norm.fit, x)
+
+    def test_inf_raises_error(self):
+        # see gh-issue 10300
+        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
+        assert_raises(ValueError, stats.norm.fit, x)
+
+    def test_bad_keyword_arg(self):
+        x = [1, 2, 3]
+        assert_raises(TypeError, stats.norm.fit, x, plate="shrimp")
+
+    @pytest.mark.parametrize('loc', [0, 1])
+    def test_delta_cdf(self, loc):
+        # The expected value is computed with mpmath:
+        # >>> import mpmath
+        # >>> mpmath.mp.dps = 60
+        # >>> float(mpmath.ncdf(12) - mpmath.ncdf(11))
+        # 1.910641809677555e-28
+        expected = 1.910641809677555e-28
+        delta = stats.norm._delta_cdf(11+loc, 12+loc, loc=loc)
+        assert_allclose(delta, expected, rtol=1e-13)
+        delta = stats.norm._delta_cdf(-(12+loc), -(11+loc), loc=-loc)
+        assert_allclose(delta, expected, rtol=1e-13)
+
+
+class TestUniform:
+    """gh-10300"""
+    def test_nan_raises_error(self):
+        # see gh-issue 10300
+        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
+        assert_raises(ValueError, stats.uniform.fit, x)
+
+    def test_inf_raises_error(self):
+        # see gh-issue 10300
+        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
+        assert_raises(ValueError, stats.uniform.fit, x)
+
+
+class TestExponNorm:
+    def test_moments(self):
+        # Some moment test cases based on non-loc/scaled formula
+        def get_moms(lam, sig, mu):
+            # See wikipedia for these formulae
+            #  where it is listed as an exponentially modified gaussian
+            opK2 = 1.0 + 1 / (lam*sig)**2
+            exp_skew = 2 / (lam * sig)**3 * opK2**(-1.5)
+            exp_kurt = 6.0 * (1 + (lam * sig)**2)**(-2)
+            return [mu + 1/lam, sig*sig + 1.0/(lam*lam), exp_skew, exp_kurt]
+
+        mu, sig, lam = 0, 1, 1
+        K = 1.0 / (lam * sig)
+        sts = stats.exponnorm.stats(K, loc=mu, scale=sig, moments='mvsk')
+        assert_almost_equal(sts, get_moms(lam, sig, mu))
+        mu, sig, lam = -3, 2, 0.1
+        K = 1.0 / (lam * sig)
+        sts = stats.exponnorm.stats(K, loc=mu, scale=sig, moments='mvsk')
+        assert_almost_equal(sts, get_moms(lam, sig, mu))
+        mu, sig, lam = 0, 3, 1
+        K = 1.0 / (lam * sig)
+        sts = stats.exponnorm.stats(K, loc=mu, scale=sig, moments='mvsk')
+        assert_almost_equal(sts, get_moms(lam, sig, mu))
+        mu, sig, lam = -5, 11, 3.5
+        K = 1.0 / (lam * sig)
+        sts = stats.exponnorm.stats(K, loc=mu, scale=sig, moments='mvsk')
+        assert_almost_equal(sts, get_moms(lam, sig, mu))
+
+    def test_nan_raises_error(self):
+        # see gh-issue 10300
+        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
+        assert_raises(ValueError, stats.exponnorm.fit, x, floc=0, fscale=1)
+
+    def test_inf_raises_error(self):
+        # see gh-issue 10300
+        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
+        assert_raises(ValueError, stats.exponnorm.fit, x, floc=0, fscale=1)
+
+    def test_extremes_x(self):
+        # Test for extreme values against overflows
+        assert_almost_equal(stats.exponnorm.pdf(-900, 1), 0.0)
+        assert_almost_equal(stats.exponnorm.pdf(+900, 1), 0.0)
+        assert_almost_equal(stats.exponnorm.pdf(-900, 0.01), 0.0)
+        assert_almost_equal(stats.exponnorm.pdf(+900, 0.01), 0.0)
+
+    # Expected values for the PDF were computed with mpmath, with
+    # the following function, and with mpmath.mp.dps = 50.
+    #
+    #   def exponnorm_stdpdf(x, K):
+    #       x = mpmath.mpf(x)
+    #       K = mpmath.mpf(K)
+    #       t1 = mpmath.exp(1/(2*K**2) - x/K)
+    #       erfcarg = -(x - 1/K)/mpmath.sqrt(2)
+    #       t2 = mpmath.erfc(erfcarg)
+    #       return t1 * t2 / (2*K)
+    #
+    @pytest.mark.parametrize('x, K, expected',
+                             [(20, 0.01, 6.90010764753618e-88),
+                              (1, 0.01, 0.24438994313247364),
+                              (-1, 0.01, 0.23955149623472075),
+                              (-20, 0.01, 4.6004708690125477e-88),
+                              (10, 1, 7.48518298877006e-05),
+                              (10, 10000, 9.990005048283775e-05)])
+    def test_std_pdf(self, x, K, expected):
+        assert_allclose(stats.exponnorm.pdf(x, K), expected, rtol=5e-12)
+
+    # Expected values for the CDF were computed with mpmath using
+    # the following function and with mpmath.mp.dps = 60:
+    #
+    #   def mp_exponnorm_cdf(x, K, loc=0, scale=1):
+    #       x = mpmath.mpf(x)
+    #       K = mpmath.mpf(K)
+    #       loc = mpmath.mpf(loc)
+    #       scale = mpmath.mpf(scale)
+    #       z = (x - loc)/scale
+    #       return (mpmath.ncdf(z)
+    #               - mpmath.exp((1/(2*K) - z)/K)*mpmath.ncdf(z - 1/K))
+    #
+    @pytest.mark.parametrize('x, K, scale, expected',
+                             [[0, 0.01, 1, 0.4960109760186432],
+                              [-5, 0.005, 1, 2.7939945412195734e-07],
+                              [-1e4, 0.01, 100, 0.0],
+                              [-1e4, 0.01, 1000, 6.920401854427357e-24],
+                              [5, 0.001, 1, 0.9999997118542392]])
+    def test_cdf_small_K(self, x, K, scale, expected):
+        p = stats.exponnorm.cdf(x, K, scale=scale)
+        if expected == 0.0:
+            assert p == 0.0
+        else:
+            assert_allclose(p, expected, rtol=1e-13)
+
+    # Expected values for the SF were computed with mpmath using
+    # the following function and with mpmath.mp.dps = 60:
+    #
+    #   def mp_exponnorm_sf(x, K, loc=0, scale=1):
+    #       x = mpmath.mpf(x)
+    #       K = mpmath.mpf(K)
+    #       loc = mpmath.mpf(loc)
+    #       scale = mpmath.mpf(scale)
+    #       z = (x - loc)/scale
+    #       return (mpmath.ncdf(-z)
+    #               + mpmath.exp((1/(2*K) - z)/K)*mpmath.ncdf(z - 1/K))
+    #
+    @pytest.mark.parametrize('x, K, scale, expected',
+                             [[10, 0.01, 1, 8.474702916146657e-24],
+                              [2, 0.005, 1, 0.02302280664231312],
+                              [5, 0.005, 0.5, 8.024820681931086e-24],
+                              [10, 0.005, 0.5, 3.0603340062892486e-89],
+                              [20, 0.005, 0.5, 0.0],
+                              [-3, 0.001, 1, 0.9986545205566117]])
+    def test_sf_small_K(self, x, K, scale, expected):
+        p = stats.exponnorm.sf(x, K, scale=scale)
+        if expected == 0.0:
+            assert p == 0.0
+        else:
+            assert_allclose(p, expected, rtol=5e-13)
+
+    @pytest.mark.parametrize('K', [1e-9, 1e-12, 1e-15])
+    def test_extremely_small_K(self, K):
+        # see gh-issue 24551
+        # test whether distribution approaches standard normal for K -> 0
+        x = np.array([-10.5, -3.0, -1.0, 1.0, 3.0, 10.5])
+        dist = stats.exponnorm(K)
+        dist_norm = stats.norm()
+        assert_allclose(dist.logpdf(x), dist_norm.logpdf(x))
+        assert_allclose(dist.cdf(x), dist_norm.cdf(x))
+        assert_allclose(dist.sf(x), dist_norm.sf(x)) 
+
+class TestGenExpon:
+    def test_pdf_unity_area(self):
+        from scipy.integrate import simpson
+        # PDF should integrate to one
+        p = stats.genexpon.pdf(np.arange(0, 10, 0.01), 0.5, 0.5, 2.0)
+        assert_almost_equal(simpson(p, dx=0.01), 1, 1)
+
+    def test_cdf_bounds(self):
+        # CDF should always be positive
+        cdf = stats.genexpon.cdf(np.arange(0, 10, 0.01), 0.5, 0.5, 2.0)
+        assert np.all((0 <= cdf) & (cdf <= 1))
+
+    # The values of p in the following data were computed with mpmath.
+    # E.g. the script
+    #     from mpmath import mp
+    #     mp.dps = 80
+    #     x = mp.mpf('15.0')
+    #     a = mp.mpf('1.0')
+    #     b = mp.mpf('2.0')
+    #     c = mp.mpf('1.5')
+    #     print(float(mp.exp((-a-b)*x + (b/c)*-mp.expm1(-c*x))))
+    # prints
+    #     1.0859444834514553e-19
+    @pytest.mark.parametrize('x, p, a, b, c',
+                             [(15, 1.0859444834514553e-19, 1, 2, 1.5),
+                              (0.25, 0.7609068232534623, 0.5, 2, 3),
+                              (0.25, 0.09026661397565876, 9.5, 2, 0.5),
+                              (0.01, 0.9753038265071597, 2.5, 0.25, 0.5),
+                              (3.25, 0.0001962824553094492, 2.5, 0.25, 0.5),
+                              (0.125, 0.9508674287164001, 0.25, 5, 0.5)])
+    def test_sf_isf(self, x, p, a, b, c):
+        sf = stats.genexpon.sf(x, a, b, c)
+        assert_allclose(sf, p, rtol=2e-14)
+        isf = stats.genexpon.isf(p, a, b, c)
+        assert_allclose(isf, x, rtol=2e-14)
+
+    # The values of p in the following data were computed with mpmath.
+    @pytest.mark.parametrize('x, p, a, b, c',
+                             [(0.25, 0.2390931767465377, 0.5, 2, 3),
+                              (0.25, 0.9097333860243412, 9.5, 2, 0.5),
+                              (0.01, 0.0246961734928403, 2.5, 0.25, 0.5),
+                              (3.25, 0.9998037175446906, 2.5, 0.25, 0.5),
+                              (0.125, 0.04913257128359998, 0.25, 5, 0.5)])
+    def test_cdf_ppf(self, x, p, a, b, c):
+        cdf = stats.genexpon.cdf(x, a, b, c)
+        assert_allclose(cdf, p, rtol=2e-14)
+        ppf = stats.genexpon.ppf(p, a, b, c)
+        assert_allclose(ppf, x, rtol=2e-14)
+
+
+class TestTruncexpon:
+
+    def test_sf_isf(self):
+        # reference values were computed via the reference distribution, e.g.
+        # mp.dps = 50; TruncExpon(b=b).sf(x)
+        b = [20, 100]
+        x = [19.999999, 99.999999]
+        ref = [2.0611546593828472e-15, 3.7200778266671455e-50]
+        assert_allclose(stats.truncexpon.sf(x, b), ref, rtol=1.5e-10)
+        assert_allclose(stats.truncexpon.isf(ref, b), x, rtol=1e-12)
+
+
+class TestExponpow:
+    def test_tail(self):
+        assert_almost_equal(stats.exponpow.cdf(1e-10, 2.), 1e-20)
+        assert_almost_equal(stats.exponpow.isf(stats.exponpow.sf(5, .8), .8),
+                            5)
+
+
+class TestSkellam:
+    def test_pmf(self):
+        # comparison to R
+        k = np.arange(-10, 15)
+        mu1, mu2 = 10, 5
+        skpmfR = np.array(
+                   [4.2254582961926893e-005, 1.1404838449648488e-004,
+                    2.8979625801752660e-004, 6.9177078182101231e-004,
+                    1.5480716105844708e-003, 3.2412274963433889e-003,
+                    6.3373707175123292e-003, 1.1552351566696643e-002,
+                    1.9606152375042644e-002, 3.0947164083410337e-002,
+                    4.5401737566767360e-002, 6.1894328166820688e-002,
+                    7.8424609500170578e-002, 9.2418812533573133e-002,
+                    1.0139793148019728e-001, 1.0371927988298846e-001,
+                    9.9076583077406091e-002, 8.8546660073089561e-002,
+                    7.4187842052486810e-002, 5.8392772862200251e-002,
+                    4.3268692953013159e-002, 3.0248159818374226e-002,
+                    1.9991434305603021e-002, 1.2516877303301180e-002,
+                    7.4389876226229707e-003])
+
+        assert_almost_equal(stats.skellam.pmf(k, mu1, mu2), skpmfR, decimal=15)
+
+    def test_cdf(self):
+        # comparison to R, only 5 decimals
+        k = np.arange(-10, 15)
+        mu1, mu2 = 10, 5
+        skcdfR = np.array(
+                   [6.4061475386192104e-005, 1.7810985988267694e-004,
+                    4.6790611790020336e-004, 1.1596768997212152e-003,
+                    2.7077485103056847e-003, 5.9489760066490718e-003,
+                    1.2286346724161398e-002, 2.3838698290858034e-002,
+                    4.3444850665900668e-002, 7.4392014749310995e-002,
+                    1.1979375231607835e-001, 1.8168808048289900e-001,
+                    2.6011268998306952e-001, 3.5253150251664261e-001,
+                    4.5392943399683988e-001, 5.5764871387982828e-001,
+                    6.5672529695723436e-001, 7.4527195703032389e-001,
+                    8.1945979908281064e-001, 8.7785257194501087e-001,
+                    9.2112126489802404e-001, 9.5136942471639818e-001,
+                    9.7136085902200120e-001, 9.8387773632530240e-001,
+                    9.9131672394792536e-001])
+
+        assert_almost_equal(stats.skellam.cdf(k, mu1, mu2), skcdfR, decimal=5)
+
+    def test_extreme_mu2(self):
+        # check that crash reported by gh-17916 large mu2 is resolved
+        x, mu1, mu2 = 0, 1, 4820232647677555.0
+        assert_allclose(stats.skellam.pmf(x, mu1, mu2), 0, atol=1e-16)
+        assert_allclose(stats.skellam.cdf(x, mu1, mu2), 1, atol=1e-16)
+
+
+class TestLognorm:
+    def test_pdf(self):
+        # Regression test for Ticket #1471: avoid nan with 0/0 situation
+        # Also make sure there are no warnings at x=0, cf gh-5202
+        with warnings.catch_warnings():
+            warnings.simplefilter('error', RuntimeWarning)
+            pdf = stats.lognorm.pdf([0, 0.5, 1], 1)
+            assert_array_almost_equal(pdf, [0.0, 0.62749608, 0.39894228])
+
+    def test_logcdf(self):
+        # Regression test for gh-5940: sf et al would underflow too early
+        x2, mu, sigma = 201.68, 195, 0.149
+        assert_allclose(stats.lognorm.sf(x2-mu, s=sigma),
+                        stats.norm.sf(np.log(x2-mu)/sigma))
+        assert_allclose(stats.lognorm.logsf(x2-mu, s=sigma),
+                        stats.norm.logsf(np.log(x2-mu)/sigma))
+
+    @pytest.fixture(scope='function')
+    def rng(self):
+        return np.random.default_rng(1234)
+
+    @pytest.mark.parametrize("rvs_shape", [.1, 2])
+    @pytest.mark.parametrize("rvs_loc", [-2, 0, 2])
+    @pytest.mark.parametrize("rvs_scale", [.2, 1, 5])
+    @pytest.mark.parametrize('fix_shape, fix_loc, fix_scale',
+                             [e for e in product((False, True), repeat=3)
+                              if False in e])
+    @np.errstate(invalid="ignore")
+    def test_fit_MLE_comp_optimizer(self, rvs_shape, rvs_loc, rvs_scale,
+                                    fix_shape, fix_loc, fix_scale, rng):
+        data = stats.lognorm.rvs(size=100, s=rvs_shape, scale=rvs_scale,
+                                 loc=rvs_loc, random_state=rng)
+
+        kwds = {}
+        if fix_shape:
+            kwds['f0'] = rvs_shape
+        if fix_loc:
+            kwds['floc'] = rvs_loc
+        if fix_scale:
+            kwds['fscale'] = rvs_scale
+
+        # Numerical result may equal analytical result if some code path
+        # of the analytical routine makes use of numerical optimization.
+        _assert_less_or_close_loglike(stats.lognorm, data, **kwds,
+                                      maybe_identical=True)
+
+    def test_isf(self):
+        # reference values were computed via the reference distribution, e.g.
+        # mp.dps = 100;
+        # LogNormal(s=s).isf(q=0.1, guess=0)
+        # LogNormal(s=s).isf(q=2e-10, guess=100)
+        s = 0.954
+        q = [0.1, 2e-10, 5e-20, 6e-40]
+        ref = [3.3960065375794937, 390.07632793595974, 5830.5020828128445,
+               287872.84087457904]
+        assert_allclose(stats.lognorm.isf(q, s), ref, rtol=1e-14)
+
+
+class TestBeta:
+    def test_logpdf(self):
+        # Regression test for Ticket #1326: avoid nan with 0*log(0) situation
+        logpdf = stats.beta.logpdf(0, 1, 0.5)
+        assert_almost_equal(logpdf, -0.69314718056)
+        logpdf = stats.beta.logpdf(0, 0.5, 1)
+        assert_almost_equal(logpdf, np.inf)
+
+    def test_logpdf_ticket_1866(self):
+        alpha, beta = 267, 1472
+        x = np.array([0.2, 0.5, 0.6])
+        b = stats.beta(alpha, beta)
+        assert_allclose(b.logpdf(x).sum(), -1201.699061824062)
+        assert_allclose(b.pdf(x), np.exp(b.logpdf(x)))
+
+    def test_fit_bad_keyword_args(self):
+        x = [0.1, 0.5, 0.6]
+        assert_raises(TypeError, stats.beta.fit, x, floc=0, fscale=1,
+                      plate="shrimp")
+
+    def test_fit_duplicated_fixed_parameter(self):
+        # At most one of 'f0', 'fa' or 'fix_a' can be given to the fit method.
+        # More than one raises a ValueError.
+        x = [0.1, 0.5, 0.6]
+        assert_raises(ValueError, stats.beta.fit, x, fa=0.5, fix_a=0.5)
+
+    @pytest.mark.skipif(MACOS_INTEL, reason="Overflow, see gh-14901")
+    def test_issue_12635(self):
+        # Confirm that Boost's beta distribution resolves gh-12635.
+        # Check against R:
+        # options(digits=16)
+        # p = 0.9999999999997369
+        # a = 75.0
+        # b = 66334470.0
+        # print(qbeta(p, a, b))
+        p, a, b = 0.9999999999997369, 75.0, 66334470.0
+        assert_allclose(stats.beta.ppf(p, a, b), 2.343620802982393e-06)
+
+    @pytest.mark.skipif(MACOS_INTEL, reason="Overflow, see gh-14901")
+    def test_issue_12794(self):
+        # Confirm that Boost's beta distribution resolves gh-12794.
+        # Check against R.
+        # options(digits=16)
+        # p = 1e-11
+        # count_list = c(10,100,1000)
+        # print(qbeta(1-p, count_list + 1, 100000 - count_list))
+        inv_R = np.array([0.0004944464889611935,
+                          0.0018360586912635726,
+                          0.0122663919942518351])
+        count_list = np.array([10, 100, 1000])
+        p = 1e-11
+        inv = stats.beta.isf(p, count_list + 1, 100000 - count_list)
+        assert_allclose(inv, inv_R)
+        res = stats.beta.sf(inv, count_list + 1, 100000 - count_list)
+        assert_allclose(res, p)
+
+    @pytest.mark.skipif(MACOS_INTEL, reason="Overflow, see gh-14901")
+    def test_issue_12796(self):
+        # Confirm that Boost's beta distribution succeeds in the case
+        # of gh-12796
+        alpha_2 = 5e-6
+        count_ = np.arange(1, 20)
+        nobs = 100000
+        q, a, b = 1 - alpha_2, count_ + 1, nobs - count_
+        inv = stats.beta.ppf(q, a, b)
+        res = stats.beta.cdf(inv, a, b)
+        assert_allclose(res, 1 - alpha_2)
+
+    def test_endpoints(self):
+        # Confirm that boost's beta distribution returns inf at x=1
+        # when b<1
+        a, b = 1, 0.5
+        assert_equal(stats.beta.pdf(1, a, b), np.inf)
+
+        # Confirm that boost's beta distribution returns inf at x=0
+        # when a<1
+        a, b = 0.2, 3
+        assert_equal(stats.beta.pdf(0, a, b), np.inf)
+
+        # Confirm that boost's beta distribution returns 5 at x=0
+        # when a=1, b=5
+        a, b = 1, 5
+        assert_equal(stats.beta.pdf(0, a, b), 5)
+        assert_equal(stats.beta.pdf(1e-310, a, b), 5)
+
+        # Confirm that boost's beta distribution returns 5 at x=1
+        # when a=5, b=1
+        a, b = 5, 1
+        assert_equal(stats.beta.pdf(1, a, b), 5)
+        assert_equal(stats.beta.pdf(1-1e-310, a, b), 5)
+
+    def test_boost_eval_issue_14606(self):
+        q, a, b = 0.995, 1.0e11, 1.0e13
+        stats.beta.ppf(q, a, b)
+
+    @pytest.mark.parametrize('method', [stats.beta.ppf, stats.beta.isf])
+    @pytest.mark.parametrize('a, b', [(1e-310, 12.5), (12.5, 1e-310)])
+    def test_beta_ppf_with_subnormal_a_b(self, method, a, b):
+        # Regression test for gh-17444: beta.ppf(p, a, b) and beta.isf(p, a, b)
+        # would result in a segmentation fault if either a or b was subnormal.
+        p = 0.9
+        # Depending on the version of Boost that we have vendored and
+        # our setting of the Boost double promotion policy, the call
+        # `stats.beta.ppf(p, a, b)` might raise an OverflowError or
+        # return a value.  We'll accept either behavior (and not care about
+        # the value), because our goal here is to verify that the call does
+        # not trigger a segmentation fault.
+        try:
+            method(p, a, b)
+        except OverflowError:
+            # The OverflowError exception occurs with Boost 1.80 or earlier
+            # when Boost's double promotion policy is false; see
+            #   https://github.com/boostorg/math/issues/882
+            # and
+            #   https://github.com/boostorg/math/pull/883
+            # Once we have vendored the fixed version of Boost, we can drop
+            # this try-except wrapper and just call the function.
+            pass
+
+    # Reference values computed with mpmath.
+    @pytest.mark.parametrize('x, a, b, ref',
+                             [(0.999, 1.5, 2.5, -6.439838145196121e-08),
+                              (2e-9, 3.25, 2.5, -63.13030939685114)])
+    def test_logcdf(self, x, a, b, ref):
+        logcdf = stats.beta.logcdf(x, a, b)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+
+    # Reference values computed with mpmath.
+    @pytest.mark.parametrize('x, a, b, ref',
+                             [(2e-9, 1.5, 2.5, -3.0368535131140806e-13),
+                              (0.998, 3.25, 2.5, -13.309796070871489)])
+    def test_logsf(self, x, a, b, ref):
+        logsf = stats.beta.logsf(x, a, b)
+        assert_allclose(logsf, ref, 5e-15)
+
+    # entropy accuracy was confirmed using the following mpmath function
+    # from mpmath import mp
+    # mp.dps = 50
+    # def beta_entropy_mpmath(a, b):
+    #     a = mp.mpf(a)
+    #     b = mp.mpf(b)
+    #     entropy = mp.log(mp.beta(a, b)) - (a - 1) * mp.digamma(a) -\
+    #              (b - 1) * mp.digamma(b) + (a + b -2) * mp.digamma(a + b)
+    #     return float(entropy)
+
+    @pytest.mark.parametrize('a, b, ref',
+                             [(0.5, 0.5, -0.24156447527049044),
+                              (0.001, 1, -992.0922447210179),
+                              (1, 10000, -8.210440371976183),
+                              (100000, 100000, -5.377247470132859)])
+    def test_entropy(self, a, b, ref):
+        assert_allclose(stats.beta(a, b).entropy(), ref)
+
+    @pytest.mark.parametrize(
+        "a, b, ref, tol",
+        [
+            (1, 10, -1.4025850929940458, 1e-14),
+            (10, 20, -1.0567887388936708, 1e-13),
+            (4e6, 4e6+20, -7.221686009678741, 1e-9),
+            (5e6, 5e6+10, -7.333257022834638, 1e-8),
+            (1e10, 1e10+20, -11.133707703130474, 1e-11),
+            (1e50, 1e50+20, -57.185409562486385, 1e-15),
+            (2, 1e10, -21.448635265288925, 1e-11),
+            (2, 1e20, -44.47448619497938, 1e-14),
+            (2, 1e50, -113.55203898480075, 1e-14),
+            (5, 1e10, -20.87226777401971, 1e-10),
+            (5, 1e20, -43.89811870326017, 1e-14),
+            (5, 1e50, -112.97567149308153, 1e-14),
+            (10, 1e10, -20.489796752909477, 1e-9),
+            (10, 1e20, -43.51564768139993, 1e-14),
+            (10, 1e50, -112.59320047122131, 1e-14),
+            (1e20, 2, -44.47448619497938, 1e-14),
+            (1e20, 5, -43.89811870326017, 1e-14),
+            (1e50, 10, -112.59320047122131, 1e-14),
+        ]
+    )
+    def test_extreme_entropy(self, a, b, ref, tol):
+        # Reference values were calculated with mpmath:
+        # from mpmath import mp
+        # mp.dps = 500
+        #
+        # def beta_entropy_mpmath(a, b):
+        #     a = mp.mpf(a)
+        #     b = mp.mpf(b)
+        #     entropy = (
+        #       mp.log(mp.beta(a, b)) - (a - 1) * mp.digamma(a)
+        #       - (b - 1) * mp.digamma(b) + (a + b - 2) * mp.digamma(a + b)
+        #     )
+        #     return float(entropy)
+        assert_allclose(stats.beta(a, b).entropy(), ref, rtol=tol)
+
+    def test_entropy_broadcasting(self):
+        # gh-23127 reported that the entropy method of the beta
+        # distribution did not broadcast correctly.
+        Beta = stats.make_distribution(stats.beta)
+        a = np.asarray([5e6, 100, 1e9, 10])
+        b = np.asarray([5e6, 1e9, 100, 20])
+        res = Beta(a=a, b=b).entropy()
+        ref = np.asarray([Beta(a=a[0], b=b[0]).entropy(),
+                          Beta(a=a[1], b=b[1]).entropy(),
+                          Beta(a=a[2], b=b[2]).entropy(),
+                          Beta(a=a[3], b=b[3]).entropy()])
+        assert_allclose(res, ref)
+
+
+class TestBetaPrime:
+    # the test values are used in test_cdf_gh_17631 / test_ppf_gh_17631
+    # They are computed with mpmath. Example:
+    # from mpmath import mp
+    # mp.dps = 50
+    # a, b = mp.mpf(0.05), mp.mpf(0.1)
+    # x = mp.mpf(1e22)
+    # float(mp.betainc(a, b, 0.0, x/(1+x), regularized=True))
+    # note: we use the values computed by the cdf to test whether
+    # ppf(cdf(x)) == x (up to a small tolerance)
+    # since the ppf can be very sensitive to small variations of the input,
+    # it can be required to generate the test case for the ppf separately,
+    # see self.test_ppf
+    cdf_vals = [
+        (1e22, 100.0, 0.05, 0.8973027435427167),
+        (1e10, 100.0, 0.05, 0.5911548582766262),
+        (1e8, 0.05, 0.1, 0.9467768090820048),
+        (1e8, 100.0, 0.05, 0.4852944858726726),
+        (1e-10, 0.05, 0.1, 0.21238845427095),
+        (1e-10, 1.5, 1.5, 1.697652726007973e-15),
+        (1e-10, 0.05, 100.0, 0.40884514172337383),
+        (1e-22, 0.05, 0.1, 0.053349567649287326),
+        (1e-22, 1.5, 1.5, 1.6976527263135503e-33),
+        (1e-22, 0.05, 100.0, 0.10269725645728331),
+        (1e-100, 0.05, 0.1, 6.7163126421919795e-06),
+        (1e-100, 1.5, 1.5, 1.6976527263135503e-150),
+        (1e-100, 0.05, 100.0, 1.2928818587561651e-05),
+    ]
+
+    def test_logpdf(self):
+        alpha, beta = 267, 1472
+        x = np.array([0.2, 0.5, 0.6])
+        b = stats.betaprime(alpha, beta)
+        assert_(np.isfinite(b.logpdf(x)).all())
+        assert_allclose(b.pdf(x), np.exp(b.logpdf(x)))
+
+    def test_cdf(self):
+        # regression test for gh-4030: Implementation of
+        # scipy.stats.betaprime.cdf()
+        x = stats.betaprime.cdf(0, 0.2, 0.3)
+        assert_equal(x, 0.0)
+
+        alpha, beta = 267, 1472
+        x = np.array([0.2, 0.5, 0.6])
+        cdfs = stats.betaprime.cdf(x, alpha, beta)
+        assert_(np.isfinite(cdfs).all())
+
+        # check the new cdf implementation vs generic one:
+        gen_cdf = stats.rv_continuous._cdf_single
+        cdfs_g = [gen_cdf(stats.betaprime, val, alpha, beta) for val in x]
+        assert_allclose(cdfs, cdfs_g, atol=0, rtol=2e-12)
+
+    # The expected values for test_ppf() were computed with mpmath, e.g.
+    #
+    #   from mpmath import mp
+    #   mp.dps = 125
+    #   p = 0.01
+    #   a, b = 1.25, 2.5
+    #   x = mp.findroot(lambda t: mp.betainc(a, b, x1=0, x2=t/(1+t),
+    #                                        regularized=True) - p,
+    #                   x0=(0.01, 0.011), method='secant')
+    #   print(float(x))
+    #
+    # prints
+    #
+    #   0.01080162700956614
+    #
+    @pytest.mark.parametrize(
+        'p, a, b, expected',
+        [(0.010, 1.25, 2.5, 0.01080162700956614),
+         (1e-12, 1.25, 2.5, 1.0610141996279122e-10),
+         (1e-18, 1.25, 2.5, 1.6815941817974941e-15),
+         (1e-17, 0.25, 7.0, 1.0179194531881782e-69),
+         (0.375, 0.25, 7.0, 0.002036820346115211),
+         (0.9978811466052919, 0.05, 0.1, 1.0000000000001218e22),]
+    )
+    def test_ppf(self, p, a, b, expected):
+        x = stats.betaprime.ppf(p, a, b)
+        assert_allclose(x, expected, rtol=1e-14)
+
+    @pytest.mark.parametrize('x, a, b, p', cdf_vals)
+    def test_ppf_gh_17631(self, x, a, b, p):
+        assert_allclose(stats.betaprime.ppf(p, a, b), x, rtol=2e-14)
+
+    def test__ppf(self):
+        # Verify that _ppf supports scalar arrays.
+        a = np.array(1.0)
+        b = np.array(1.0)
+        p = np.array(0.5)
+        assert_allclose(stats.betaprime._ppf(p, a, b), 1.0, rtol=5e-16)
+
+    @pytest.mark.parametrize(
+        'x, a, b, expected',
+        cdf_vals + [
+            (1e10, 1.5, 1.5, 0.9999999999999983),
+            (1e10, 0.05, 0.1, 0.9664184367890859),
+            (1e22, 0.05, 0.1, 0.9978811466052919),
+        ])
+    def test_cdf_gh_17631(self, x, a, b, expected):
+        assert_allclose(stats.betaprime.cdf(x, a, b), expected, rtol=1e-14)
+
+    @pytest.mark.parametrize(
+        'x, a, b, expected',
+        [(1e50, 0.05, 0.1, 0.9999966641709545),
+         (1e50, 100.0, 0.05, 0.995925162631006)])
+    def test_cdf_extreme_tails(self, x, a, b, expected):
+        # for even more extreme values, we only get a few correct digits
+        # results are still < 1
+        y = stats.betaprime.cdf(x, a, b)
+        assert y < 1.0
+        assert_allclose(y, expected, rtol=2e-5)
+
+    def test_sf(self):
+        # reference values were computed via the reference distribution,
+        # e.g.
+        # mp.dps = 50
+        # a, b = 5, 3
+        # x = 1e10
+        # BetaPrime(a=a, b=b).sf(x); returns 3.4999999979e-29
+        a = [5, 4, 2, 0.05, 0.05, 0.05, 0.05, 100.0, 100.0, 0.05, 0.05,
+             0.05, 1.5, 1.5]
+        b = [3, 2, 1, 0.1, 0.1, 0.1, 0.1, 0.05, 0.05, 100.0, 100.0,
+             100.0, 1.5, 1.5]
+        x = [1e10, 1e20, 1e30, 1e22, 1e-10, 1e-22, 1e-100, 1e22, 1e10,
+             1e-10, 1e-22, 1e-100, 1e10, 1e-10]
+        ref = [3.4999999979e-29, 9.999999999994357e-40, 1.9999999999999998e-30,
+               0.0021188533947081017, 0.78761154572905, 0.9466504323507127,
+               0.9999932836873578, 0.10269725645728331, 0.40884514172337383,
+               0.5911548582766262, 0.8973027435427167, 0.9999870711814124,
+               1.6976527260079727e-15, 0.9999999999999983]
+        sf_values = stats.betaprime.sf(x, a, b)
+        assert_allclose(sf_values, ref, rtol=1e-12)
+
+    def test_logcdf(self):
+        x = 800
+        a = 0.5
+        b = 5.0
+        ref = -7.467307556554531e-16
+        logcdf = stats.betaprime.logcdf(x, a, b)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+
+    def test_logsf(self):
+        x = 1e-8
+        a = 4.5
+        b = 0.5
+        ref = -2.5868992866500915e-37
+        logsf = stats.betaprime.logsf(x, a, b)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+
+    def test_fit_stats_gh18274(self):
+        # gh-18274 reported spurious warning emitted when fitting `betaprime`
+        # to data. Some of these were emitted by stats, too. Check that the
+        # warnings are no longer emitted.
+        stats.betaprime.fit([0.1, 0.25, 0.3, 1.2, 1.6], floc=0, fscale=1)
+        stats.betaprime(a=1, b=1).stats('mvsk')
+
+    def test_moment_gh18634(self):
+        # Testing for gh-18634 revealed that `betaprime` raised a
+        # NotImplementedError for higher moments. Check that this is
+        # resolved. Parameters are arbitrary but lie on either side of the
+        # moment order (5) to test both branches of `xpx.apply_where`.
+        # Reference values produced with Mathematica, e.g.
+        # `Moment[BetaPrimeDistribution[2,7],5]`
+        ref = [np.inf, 0.867096912929055]
+        res = stats.betaprime(2, [4.2, 7.1]).moment(5)
+        assert_allclose(res, ref)
+
+
+class TestGamma:
+    def test_pdf(self):
+        # a few test cases to compare with R
+        pdf = stats.gamma.pdf(90, 394, scale=1./5)
+        assert_almost_equal(pdf, 0.002312341)
+
+        pdf = stats.gamma.pdf(3, 10, scale=1./5)
+        assert_almost_equal(pdf, 0.1620358)
+
+    def test_logpdf(self):
+        # Regression test for Ticket #1326: cornercase avoid nan with 0*log(0)
+        # situation
+        logpdf = stats.gamma.logpdf(0, 1)
+        assert_almost_equal(logpdf, 0)
+
+    def test_fit_bad_keyword_args(self):
+        x = [0.1, 0.5, 0.6]
+        assert_raises(TypeError, stats.gamma.fit, x, floc=0, plate="shrimp")
+
+    def test_isf(self):
+        # Test cases for when the probability is very small. See gh-13664.
+        # The expected values can be checked with mpmath.  With mpmath,
+        # the survival function sf(x, k) can be computed as
+        #
+        #     mpmath.gammainc(k, x, mpmath.inf, regularized=True)
+        #
+        # Here we have:
+        #
+        # >>> mpmath.mp.dps = 60
+        # >>> float(mpmath.gammainc(1, 39.14394658089878, mpmath.inf,
+        # ...                       regularized=True))
+        # 9.99999999999999e-18
+        # >>> float(mpmath.gammainc(100, 330.6557590436547, mpmath.inf,
+        #                           regularized=True))
+        # 1.000000000000028e-50
+        #
+        assert np.isclose(stats.gamma.isf(1e-17, 1),
+                          39.14394658089878, atol=1e-14)
+        assert np.isclose(stats.gamma.isf(1e-50, 100),
+                          330.6557590436547, atol=1e-13)
+
+    def test_logcdf(self):
+        x = 80
+        a = 7
+        ref = -7.096510270453943e-27
+        logcdf = stats.gamma.logcdf(x, a)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+
+    def test_logsf(self):
+        x = 0.001
+        a = 3.0
+        ref = -1.6654171666664883e-10
+        logsf = stats.gamma.logsf(x, a)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+    @pytest.mark.parametrize('scale', [1.0, 5.0])
+    def test_delta_cdf(self, scale):
+        # Expected value computed with mpmath:
+        #
+        # >>> import mpmath
+        # >>> mpmath.mp.dps = 150
+        # >>> cdf1 = mpmath.gammainc(3, 0, 245, regularized=True)
+        # >>> cdf2 = mpmath.gammainc(3, 0, 250, regularized=True)
+        # >>> float(cdf2 - cdf1)
+        # 1.1902609356171962e-102
+        #
+        delta = stats.gamma._delta_cdf(scale*245, scale*250, 3, scale=scale)
+        assert_allclose(delta, 1.1902609356171962e-102, rtol=1e-13)
+
+    @pytest.mark.parametrize('a, ref, rtol',
+                             [(1e-4, -9990.366610819761, 1e-15),
+                              (2, 1.5772156649015328, 1e-15),
+                              (100, 3.7181819485047463, 1e-13),
+                              (1e4, 6.024075385026086, 1e-15),
+                              (1e18, 22.142204370151084, 1e-15),
+                              (1e100, 116.54819318290696, 1e-15)])
+    def test_entropy(self, a, ref, rtol):
+        # expected value computed with mpmath:
+        # from mpmath import mp
+        # mp.dps = 500
+        # def gamma_entropy_reference(x):
+        #     x = mp.mpf(x)
+        #     return float(mp.digamma(x) * (mp.one - x) + x + mp.loggamma(x))
+
+        assert_allclose(stats.gamma.entropy(a), ref, rtol=rtol)
+
+    @pytest.mark.parametrize("a", [1e-2, 1, 1e2])
+    @pytest.mark.parametrize("loc", [1e-2, 0, 1e2])
+    @pytest.mark.parametrize('scale', [1e-2, 1, 1e2])
+    @pytest.mark.parametrize('fix_a', [True, False])
+    @pytest.mark.parametrize('fix_loc', [True, False])
+    @pytest.mark.parametrize('fix_scale', [True, False])
+    def test_fit_mm(self, a, loc, scale, fix_a, fix_loc, fix_scale):
+        rng = np.random.default_rng(6762668991392531563)
+        data = stats.gamma.rvs(a, loc=loc, scale=scale, size=100,
+                               random_state=rng)
+
+        kwds = {}
+        if fix_a:
+            kwds['fa'] = a
+        if fix_loc:
+            kwds['floc'] = loc
+        if fix_scale:
+            kwds['fscale'] = scale
+        nfree = 3 - len(kwds)
+
+        if nfree == 0:
+            error_msg = "All parameters fixed. There is nothing to optimize."
+            with pytest.raises(ValueError, match=error_msg):
+                stats.gamma.fit(data, method='mm', **kwds)
+            return
+
+        theta = stats.gamma.fit(data, method='mm', **kwds)
+        dist = stats.gamma(*theta)
+        if nfree >= 1:
+            assert_allclose(dist.mean(), np.mean(data))
+        if nfree >= 2:
+            assert_allclose(dist.moment(2), np.mean(data**2))
+        if nfree >= 3:
+            assert_allclose(dist.moment(3), np.mean(data**3))
+
+
+def test_pdf_overflow_gh19616():
+    # Confirm that gh19616 (intermediate over/underflows in PDF) is resolved
+    # Reference value from R GeneralizedHyperbolic library
+    # library(GeneralizedHyperbolic)
+    # options(digits=16)
+    # jitter = 1e-3
+    # dnig(1, a=2**0.5 / jitter**2, b=1 / jitter**2)
+    jitter = 1e-3
+    Z = stats.norminvgauss(2**0.5 / jitter**2, 1 / jitter**2, loc=0, scale=1)
+    assert_allclose(Z.pdf(1.0), 282.0948446666433)
+
+
+class TestDgamma:
+    def test_pdf(self):
+        rng = np.random.default_rng(3791303244302340058)
+        size = 10  # number of points to check
+        x = rng.normal(scale=10, size=size)
+        a = rng.uniform(high=10, size=size)
+        res = stats.dgamma.pdf(x, a)
+        ref = stats.gamma.pdf(np.abs(x), a) / 2
+        assert_allclose(res, ref)
+
+        dist = stats.dgamma(a)
+        # There was an intermittent failure with assert_equal on Linux - 32 bit
+        assert_allclose(dist.pdf(x), res, rtol=5e-16)
+
+    # mpmath was used to compute the expected values.
+    # For x < 0, cdf(x, a) is mp.gammainc(a, -x, mp.inf, regularized=True)/2
+    # For x > 0, cdf(x, a) is (1 + mp.gammainc(a, 0, x, regularized=True))/2
+    # E.g.
+    #    from mpmath import mp
+    #    mp.dps = 50
+    #    print(float(mp.gammainc(1, 20, mp.inf, regularized=True)/2))
+    # prints
+    #    1.030576811219279e-09
+    @pytest.mark.parametrize('x, a, expected',
+                             [(-20, 1, 1.030576811219279e-09),
+                              (-40, 1, 2.1241771276457944e-18),
+                              (-50, 5, 2.7248509914602648e-17),
+                              (-25, 0.125, 5.333071920958156e-14),
+                              (5, 1, 0.9966310265004573)])
+    def test_cdf_ppf_sf_isf_tail(self, x, a, expected):
+        cdf = stats.dgamma.cdf(x, a)
+        assert_allclose(cdf, expected, rtol=5e-15)
+        ppf = stats.dgamma.ppf(expected, a)
+        assert_allclose(ppf, x, rtol=5e-15)
+        sf = stats.dgamma.sf(-x, a)
+        assert_allclose(sf, expected, rtol=5e-15)
+        isf = stats.dgamma.isf(expected, a)
+        assert_allclose(isf, -x, rtol=5e-15)
+
+    @pytest.mark.parametrize("a, ref",
+                             [(1.5, 2.0541199559354117),
+                             (1.3, 1.9357296377121247),
+                             (1.1, 1.7856502333412134)])
+    def test_entropy(self, a, ref):
+        # The reference values were calculated with mpmath:
+        # def entropy_dgamma(a):
+        #    def pdf(x):
+        #        A = mp.one / (mp.mpf(2.) * mp.gamma(a))
+        #        B = mp.fabs(x) ** (a - mp.one)
+        #        C = mp.exp(-mp.fabs(x))
+        #        h = A * B * C
+        #        return h
+        #
+        #    return -mp.quad(lambda t: pdf(t) * mp.log(pdf(t)),
+        #                    [-mp.inf, mp.inf])
+        assert_allclose(stats.dgamma.entropy(a), ref, rtol=1e-14)
+
+    @pytest.mark.parametrize("a, ref",
+                             [(1e-100, -1e+100),
+                             (1e-10, -9999999975.858217),
+                             (1e-5, -99987.37111657023),
+                             (1e4, 6.717222565586032),
+                             (1000000000000000.0, 19.38147391121996),
+                             (1e+100, 117.2413403634669)])
+    def test_entropy_entreme_values(self, a, ref):
+        # The reference values were calculated with mpmath:
+        # from mpmath import mp
+        # mp.dps = 500
+        # def second_dgamma(a):
+        #     a = mp.mpf(a)
+        #     x_1 = a + mp.log(2) + mp.loggamma(a)
+        #     x_2 = (mp.one - a) * mp.digamma(a)
+        #     h = x_1 + x_2
+        #     return h
+        assert_allclose(stats.dgamma.entropy(a), ref, rtol=1e-10)
+
+    def test_entropy_array_input(self):
+        x = np.array([1, 5, 1e20, 1e-5])
+        y = stats.dgamma.entropy(x)
+        for i in range(len(y)):
+            assert y[i] == stats.dgamma.entropy(x[i])
+
+
+class TestChi2:
+
+    # regression tests after precision improvements, ticket:1041, not verified
+    def test_precision(self):
+        assert_almost_equal(stats.chi2.pdf(1000, 1000), 8.919133934753128e-003,
+                            decimal=14)
+        assert_almost_equal(stats.chi2.pdf(100, 100), 0.028162503162596778,
+                            decimal=14)
+
+    # Reference values computed with mpmath.
+    @pytest.mark.parametrize(
+        'x, df, ref',
+        [(750.0, 3, -3.0172957781136564e-162),
+         (120.0, 15, -1.8924849646375648e-18),
+         (15.0, 13, -0.36723446372517876)]
+    )
+    def test_logcdf(self, x, df, ref):
+        logcdf = stats.chi2.logcdf(x, df)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+
+    # Reference values computed with mpmath.
+    @pytest.mark.parametrize(
+        'x, df, ref',
+        [(1e-4, 15, -3.936060782678026e-37),
+         (1.5, 40, -6.384797888313517e-22),
+         (3.0, 10, -0.018750635779926784)]
+    )
+    def test_logsf(self, x, df, ref):
+        logsf = stats.chi2.logsf(x, df)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+    def test_ppf(self):
+        # Expected values computed with mpmath.
+        df = 4.8
+        x = stats.chi2.ppf(2e-47, df)
+        assert_allclose(x, 1.098472479575179840604902808e-19, rtol=1e-10)
+        x = stats.chi2.ppf(0.5, df)
+        assert_allclose(x, 4.15231407598589358660093156, rtol=1e-10)
+
+        df = 13
+        x = stats.chi2.ppf(2e-77, df)
+        assert_allclose(x, 1.0106330688195199050507943e-11, rtol=1e-10)
+        x = stats.chi2.ppf(0.1, df)
+        assert_allclose(x, 7.041504580095461859307179763, rtol=1e-10)
+
+    # Entropy references values were computed with the following mpmath code
+    # from mpmath import mp
+    # mp.dps = 50
+    # def chisq_entropy_mpmath(df):
+    #     df = mp.mpf(df)
+    #     half_df = 0.5 * df
+    #     entropy = (half_df + mp.log(2) + mp.log(mp.gamma(half_df)) +
+    #                (mp.one - half_df) * mp.digamma(half_df))
+    #     return float(entropy)
+
+    @pytest.mark.parametrize('df, ref',
+                             [(1e-4, -19988.980448690163),
+                              (1, 0.7837571104739337),
+                              (100, 4.061397128938114),
+                              (251, 4.525577254045129),
+                              (1e15, 19.034900320939986)])
+    def test_entropy(self, df, ref):
+        assert_allclose(stats.chi2(df).entropy(), ref, rtol=1e-13)
+
+    def test_regression_ticket_1326(self):
+        # adjust to avoid nan with 0*log(0)
+        assert_almost_equal(stats.chi2.pdf(0.0, 2), 0.5, 14)
+
+
+class TestGumbelL:
+    def setup_method(self):
+        self.rng = np.random.default_rng(3922444007)
+
+    # gh-6228
+    def test_cdf_ppf(self):
+        x = np.linspace(-100, -4)
+        y = stats.gumbel_l.cdf(x)
+        xx = stats.gumbel_l.ppf(y)
+        assert_allclose(x, xx)
+
+    def test_logcdf_logsf(self):
+        x = np.linspace(-100, -4)
+        y = stats.gumbel_l.logcdf(x)
+        z = stats.gumbel_l.logsf(x)
+        u = np.exp(y)
+        v = -special.expm1(z)
+        assert_allclose(u, v)
+
+    def test_sf_isf(self):
+        x = np.linspace(-20, 5)
+        y = stats.gumbel_l.sf(x)
+        xx = stats.gumbel_l.isf(y)
+        assert_allclose(x, xx)
+
+    @pytest.mark.parametrize('loc', [-1, 1])
+    def test_fit_fixed_param(self, loc):
+        # ensure fixed location is correctly reflected from `gumbel_r.fit`
+        # See comments at end of gh-12737.
+        data = stats.gumbel_l.rvs(size=100, loc=loc, random_state=self.rng)
+        fitted_loc, _ = stats.gumbel_l.fit(data, floc=loc)
+        assert_equal(fitted_loc, loc)
+
+
+class TestGumbelR:
+
+    def test_sf(self):
+        # Expected value computed with mpmath:
+        #   >>> import mpmath
+        #   >>> mpmath.mp.dps = 40
+        #   >>> float(mpmath.mp.one - mpmath.exp(-mpmath.exp(-50)))
+        #   1.9287498479639178e-22
+        assert_allclose(stats.gumbel_r.sf(50), 1.9287498479639178e-22,
+                        rtol=1e-14)
+
+    def test_isf(self):
+        # Expected value computed with mpmath:
+        #   >>> import mpmath
+        #   >>> mpmath.mp.dps = 40
+        #   >>> float(-mpmath.log(-mpmath.log(mpmath.mp.one - 1e-17)))
+        #   39.14394658089878
+        assert_allclose(stats.gumbel_r.isf(1e-17), 39.14394658089878,
+                        rtol=1e-14)
+
+
+class TestLevyStable:
+    def setup_method(self):
+        self.rng = np.random.default_rng(7195199371)
+
+    @pytest.fixture(autouse=True)
+    def reset_levy_stable_params(self):
+        """Setup default parameters for levy_stable generator"""
+        stats.levy_stable.parameterization = "S1"
+        stats.levy_stable.cdf_default_method = "piecewise"
+        stats.levy_stable.pdf_default_method = "piecewise"
+        stats.levy_stable.quad_eps = stats._levy_stable._QUAD_EPS
+
+    @pytest.fixture
+    def nolan_pdf_sample_data(self):
+        """Sample data points for pdf computed with Nolan's stablec
+
+        See - http://fs2.american.edu/jpnolan/www/stable/stable.html
+
+        There's a known limitation of Nolan's executable for alpha < 0.2.
+
+        The data table loaded below is generated from Nolan's stablec
+        with the following parameter space:
+
+            alpha = 0.1, 0.2, ..., 2.0
+            beta = -1.0, -0.9, ..., 1.0
+            p = 0.01, 0.05, 0.1, 0.25, 0.35, 0.5,
+        and the equivalent for the right tail
+
+        Typically inputs for stablec:
+
+            stablec.exe <<
+            1 # pdf
+            1 # Nolan S equivalent to S0 in scipy
+            .25,2,.25 # alpha
+            -1,-1,0 # beta
+            -10,10,1 # x
+            1,0 # gamma, delta
+            2 # output file
+        """
+        data = np.load(
+            Path(__file__).parent /
+            'data/levy_stable/stable-Z1-pdf-sample-data.npy'
+        )
+        data = np.rec.fromarrays(data.T, names='x,p,alpha,beta,pct')
+        return data
+
+    @pytest.fixture
+    def nolan_cdf_sample_data(self):
+        """Sample data points for cdf computed with Nolan's stablec
+
+        See - http://fs2.american.edu/jpnolan/www/stable/stable.html
+
+        There's a known limitation of Nolan's executable for alpha < 0.2.
+
+        The data table loaded below is generated from Nolan's stablec
+        with the following parameter space:
+
+            alpha = 0.1, 0.2, ..., 2.0
+            beta = -1.0, -0.9, ..., 1.0
+            p = 0.01, 0.05, 0.1, 0.25, 0.35, 0.5,
+
+        and the equivalent for the right tail
+
+        Ideally, Nolan's output for CDF values should match the percentile
+        from where they have been sampled from. Even more so as we extract
+        percentile x positions from stablec too. However, we note at places
+        Nolan's stablec will produce absolute errors in order of 1e-5. We
+        compare against his calculations here. In future, once we less
+        reliant on Nolan's paper we might switch to comparing directly at
+        percentiles (those x values being produced from some alternative
+        means).
+
+        Typically inputs for stablec:
+
+            stablec.exe <<
+            2 # cdf
+            1 # Nolan S equivalent to S0 in scipy
+            .25,2,.25 # alpha
+            -1,-1,0 # beta
+            -10,10,1 # x
+            1,0 # gamma, delta
+            2 # output file
+        """
+        data = np.load(
+            Path(__file__).parent /
+            'data/levy_stable/stable-Z1-cdf-sample-data.npy'
+        )
+        data = np.rec.fromarrays(data.T, names='x,p,alpha,beta,pct')
+        return data
+
+    @pytest.fixture
+    def nolan_loc_scale_sample_data(self):
+        """Sample data where loc, scale are different from 0, 1
+
+        Data extracted in similar way to pdf/cdf above using
+        Nolan's stablec but set to an arbitrary location scale of
+        (2, 3) for various important parameters alpha, beta and for
+        parameterisations S0 and S1.
+        """
+        data = np.load(
+            Path(__file__).parent /
+            'data/levy_stable/stable-loc-scale-sample-data.npy'
+        )
+        return data
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize(
+        "sample_size", [
+            pytest.param(50), pytest.param(1500, marks=pytest.mark.slow)
+        ]
+    )
+    @pytest.mark.parametrize("parameterization", ["S0", "S1"])
+    @pytest.mark.parametrize(
+        "alpha,beta", [(1.0, 0), (1.0, -0.5), (1.5, 0), (1.9, 0.5)]
+    )
+    @pytest.mark.parametrize("gamma,delta", [(1, 0), (3, 2)])
+    def test_rvs(
+            self,
+            parameterization,
+            alpha,
+            beta,
+            gamma,
+            delta,
+            sample_size,
+    ):
+        stats.levy_stable.parameterization = parameterization
+        ls = stats.levy_stable(
+            alpha=alpha, beta=beta, scale=gamma, loc=delta
+        )
+        _, p = stats.kstest(
+            ls.rvs(size=sample_size, random_state=self.rng), ls.cdf
+        )
+        assert p > 0.05
+
+    @pytest.mark.xslow
+    @pytest.mark.parametrize('beta', [0.5, 1])
+    def test_rvs_alpha1(self, beta):
+        """Additional test cases for rvs for alpha equal to 1."""
+        alpha = 1.0
+        loc = 0.5
+        scale = 1.5
+        x = stats.levy_stable.rvs(alpha, beta, loc=loc, scale=scale,
+                                  size=5000, random_state=self.rng)
+        stat, p = stats.kstest(x, 'levy_stable',
+                               args=(alpha, beta, loc, scale))
+        assert p > 0.01
+
+    def test_fit(self):
+        # construct data to have percentiles that match
+        # example in McCulloch 1986.
+        x = [
+            -.05413, -.05413, 0., 0., 0., 0., .00533, .00533, .00533, .00533,
+            .00533, .03354, .03354, .03354, .03354, .03354, .05309, .05309,
+            .05309, .05309, .05309
+        ]
+        alpha1, beta1, loc1, scale1 = stats.levy_stable._fitstart(x)
+        assert_allclose(alpha1, 1.48, rtol=0, atol=0.01)
+        assert_almost_equal(beta1, -.22, 2)
+        assert_almost_equal(scale1, 0.01717, 4)
+        assert_almost_equal(
+            loc1, 0.00233, 2
+        )  # to 2 dps due to rounding error in McCulloch86
+
+        # cover alpha=2 scenario
+        x2 = x + [.05309, .05309, .05309, .05309, .05309]
+        alpha2, beta2, loc2, scale2 = stats.levy_stable._fitstart(x2)
+        assert_equal(alpha2, 2)
+        assert_equal(beta2, -1)
+        assert_almost_equal(scale2, .02503, 4)
+        assert_almost_equal(loc2, .03354, 4)
+
+    @pytest.mark.xfail(reason="Unknown problem with fitstart.")
+    @pytest.mark.parametrize(
+        "alpha,beta,delta,gamma",
+        [
+            (1.5, 0.4, 2, 3),
+            (1.0, 0.4, 2, 3),
+        ]
+    )
+    @pytest.mark.parametrize(
+        "parametrization", ["S0", "S1"]
+    )
+    def test_fit_rvs(self, alpha, beta, delta, gamma, parametrization):
+        """Test that fit agrees with rvs for each parametrization."""
+        stats.levy_stable.parametrization = parametrization
+        data = stats.levy_stable.rvs(
+            alpha, beta, loc=delta, scale=gamma, size=10000, random_state=self.rng
+        )
+        fit = stats.levy_stable._fitstart(data)
+        alpha_obs, beta_obs, delta_obs, gamma_obs = fit
+        assert_allclose(
+            [alpha, beta, delta, gamma],
+            [alpha_obs, beta_obs, delta_obs, gamma_obs],
+            rtol=0.01,
+        )
+
+    def test_fit_beta_flip(self):
+        # Confirm that sign of beta affects loc, not alpha or scale.
+        x = np.array([1, 1, 3, 3, 10, 10, 10, 30, 30, 100, 100])
+        alpha1, beta1, loc1, scale1 = stats.levy_stable._fitstart(x)
+        alpha2, beta2, loc2, scale2 = stats.levy_stable._fitstart(-x)
+        assert_equal(beta1, 1)
+        assert loc1 != 0
+        assert_almost_equal(alpha2, alpha1)
+        assert_almost_equal(beta2, -beta1)
+        assert_almost_equal(loc2, -loc1)
+        assert_almost_equal(scale2, scale1)
+
+    def test_fit_delta_shift(self):
+        # Confirm that loc slides up and down if data shifts.
+        SHIFT = 1
+        x = np.array([1, 1, 3, 3, 10, 10, 10, 30, 30, 100, 100])
+        alpha1, beta1, loc1, scale1 = stats.levy_stable._fitstart(-x)
+        alpha2, beta2, loc2, scale2 = stats.levy_stable._fitstart(-x + SHIFT)
+        assert_almost_equal(alpha2, alpha1)
+        assert_almost_equal(beta2, beta1)
+        assert_almost_equal(loc2, loc1 + SHIFT)
+        assert_almost_equal(scale2, scale1)
+
+    def test_fit_loc_extrap(self):
+        # Confirm that loc goes out of sample for alpha close to 1.
+        x = [1, 1, 3, 3, 10, 10, 10, 30, 30, 140, 140]
+        alpha1, beta1, loc1, scale1 = stats.levy_stable._fitstart(x)
+        assert alpha1 < 1, f"Expected alpha < 1, got {alpha1}"
+        assert loc1 < min(x), f"Expected loc < {min(x)}, got {loc1}"
+
+        x2 = [1, 1, 3, 3, 10, 10, 10, 30, 30, 130, 130]
+        alpha2, beta2, loc2, scale2 = stats.levy_stable._fitstart(x2)
+        assert alpha2 > 1, f"Expected alpha > 1, got {alpha2}"
+        assert loc2 > max(x2), f"Expected loc > {max(x2)}, got {loc2}"
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize(
+        "pct_range,alpha_range,beta_range", [
+            pytest.param(
+                [.01, .5, .99],
+                [.1, 1, 2],
+                [-1, 0, .8],
+            ),
+            pytest.param(
+                [.01, .05, .5, .95, .99],
+                [.1, .5, 1, 1.5, 2],
+                [-.9, -.5, 0, .3, .6, 1],
+                marks=pytest.mark.slow
+            ),
+            pytest.param(
+                [.01, .05, .1, .25, .35, .5, .65, .75, .9, .95, .99],
+                np.linspace(0.1, 2, 20),
+                np.linspace(-1, 1, 21),
+                marks=pytest.mark.xslow,
+            ),
+        ]
+    )
+    def test_pdf_nolan_samples(
+            self, nolan_pdf_sample_data, pct_range, alpha_range, beta_range
+    ):
+        """Test pdf values against Nolan's stablec.exe output"""
+        data = nolan_pdf_sample_data
+
+        # some tests break on linux 32 bit
+        uname = platform.uname()
+        is_linux_32 = uname.system == 'Linux' and uname.machine == 'i686'
+        platform_desc = "/".join(
+            [uname.system, uname.machine, uname.processor])
+
+        # fmt: off
+        # There are a number of cases which fail on some but not all platforms.
+        # These are excluded by the filters below. TODO: Rewrite tests so that
+        # the now filtered out test cases are still run but marked in pytest as
+        # expected to fail.
+        tests = [
+            [
+                'dni', 1e-7, lambda r: (
+                    np.isin(r['pct'], pct_range) &
+                    np.isin(r['alpha'], alpha_range) &
+                    np.isin(r['beta'], beta_range) &
+                    ~(
+                        (
+                            (r['beta'] == 0) &
+                            (r['pct'] == 0.5)
+                        ) |
+                        (
+                            (r['beta'] >= 0.9) &
+                            (r['alpha'] >= 1.6) &
+                            (r['pct'] == 0.5)
+                        ) |
+                        (
+                            (r['alpha'] <= 0.4) &
+                            np.isin(r['pct'], [.01, .99])
+                        ) |
+                        (
+                            (r['alpha'] <= 0.3) &
+                            np.isin(r['pct'], [.05, .95])
+                        ) |
+                        (
+                            (r['alpha'] <= 0.2) &
+                            np.isin(r['pct'], [.1, .9])
+                        ) |
+                        (
+                            (r['alpha'] == 0.1) &
+                            np.isin(r['pct'], [.25, .75]) &
+                            np.isin(np.abs(r['beta']), [.5, .6, .7])
+                        ) |
+                        (
+                            (r['alpha'] == 0.1) &
+                            np.isin(r['pct'], [.5]) &
+                            np.isin(np.abs(r['beta']), [.1])
+                        ) |
+                        (
+                            (r['alpha'] == 0.1) &
+                            np.isin(r['pct'], [.35, .65]) &
+                            np.isin(np.abs(r['beta']), [-.4, -.3, .3, .4, .5])
+                        ) |
+                        (
+                            (r['alpha'] == 0.2) &
+                            (r['beta'] == 0.5) &
+                            (r['pct'] == 0.25)
+                        ) |
+                        (
+                            (r['alpha'] == 0.2) &
+                            (r['beta'] == -0.3) &
+                            (r['pct'] == 0.65)
+                        ) |
+                        (
+                            (r['alpha'] == 0.2) &
+                            (r['beta'] == 0.3) &
+                            (r['pct'] == 0.35)
+                        ) |
+                        (
+                            (r['alpha'] == 1.) &
+                            np.isin(r['pct'], [.5]) &
+                            np.isin(np.abs(r['beta']), [.1, .2, .3, .4])
+                        ) |
+                        (
+                            (r['alpha'] == 1.) &
+                            np.isin(r['pct'], [.35, .65]) &
+                            np.isin(np.abs(r['beta']), [.8, .9, 1.])
+                        ) |
+                        (
+                            (r['alpha'] == 1.) &
+                            np.isin(r['pct'], [.01, .99]) &
+                            np.isin(np.abs(r['beta']), [-.1, .1])
+                        ) |
+                        # various points ok but too sparse to list
+                        (r['alpha'] >= 1.1)
+                    )
+                )
+            ],
+            # piecewise generally good accuracy
+            [
+                'piecewise', 1e-11, lambda r: (
+                    np.isin(r['pct'], pct_range) &
+                    np.isin(r['alpha'], alpha_range) &
+                    np.isin(r['beta'], beta_range) &
+                    (r['alpha'] > 0.2) &
+                    (r['alpha'] != 1.)
+                )
+            ],
+            # for alpha = 1. for linux 32 bit optimize.bisect
+            # has some issues for .01 and .99 percentile
+            [
+                'piecewise', 1e-11, lambda r: (
+                    (r['alpha'] == 1.) &
+                    (not is_linux_32) &
+                    np.isin(r['pct'], pct_range) &
+                    (1. in alpha_range) &
+                    np.isin(r['beta'], beta_range)
+                )
+            ],
+            # for small alpha very slightly reduced accuracy
+            [
+                'piecewise', 2.5e-10, lambda r: (
+                    np.isin(r['pct'], pct_range) &
+                    np.isin(r['alpha'], alpha_range) &
+                    np.isin(r['beta'], beta_range) &
+                    (r['alpha'] <= 0.2)
+                )
+            ],
+            # fft accuracy reduces as alpha decreases
+            [
+                'fft-simpson', 1e-5, lambda r: (
+                    (r['alpha'] >= 1.9) &
+                    np.isin(r['pct'], pct_range) &
+                    np.isin(r['alpha'], alpha_range) &
+                    np.isin(r['beta'], beta_range)
+                ),
+            ],
+            [
+                'fft-simpson', 1e-6, lambda r: (
+                    np.isin(r['pct'], pct_range) &
+                    np.isin(r['alpha'], alpha_range) &
+                    np.isin(r['beta'], beta_range) &
+                    (r['alpha'] > 1) &
+                    (r['alpha'] < 1.9)
+                )
+            ],
+            # fft relative errors for alpha < 1, will raise if enabled
+            # ['fft-simpson', 1e-4, lambda r: r['alpha'] == 0.9],
+            # ['fft-simpson', 1e-3, lambda r: r['alpha'] == 0.8],
+            # ['fft-simpson', 1e-2, lambda r: r['alpha'] == 0.7],
+            # ['fft-simpson', 1e-1, lambda r: r['alpha'] == 0.6],
+        ]
+        # fmt: on
+        for ix, (default_method, rtol,
+                 filter_func) in enumerate(tests):
+            stats.levy_stable.pdf_default_method = default_method
+            subdata = data[filter_func(data)
+                           ] if filter_func is not None else data
+            msg = "Density calculations experimental for FFT method"
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", msg, RuntimeWarning)
+                # occurs in FFT methods only
+                p = stats.levy_stable.pdf(
+                    subdata['x'],
+                    subdata['alpha'],
+                    subdata['beta'],
+                    scale=1,
+                    loc=0
+                )
+                with np.errstate(over="ignore"):
+                    subdata2 = rec_append_fields(
+                        subdata,
+                        ['calc', 'abserr', 'relerr'],
+                        [
+                            p,
+                            np.abs(p - subdata['p']),
+                            np.abs(p - subdata['p']) / np.abs(subdata['p'])
+                        ]
+                    )
+                failures = subdata2[
+                  (subdata2['relerr'] >= rtol) |
+                  np.isnan(p)
+                ]
+                message = (
+                    f"pdf test {ix} failed with method '{default_method}' "
+                    f"[platform: {platform_desc}]\n{failures.dtype.names}\n{failures}"
+                )
+                assert_allclose(
+                    p,
+                    subdata['p'],
+                    rtol,
+                    err_msg=message,
+                    verbose=False
+                )
+
+    @pytest.mark.parametrize(
+        "pct_range,alpha_range,beta_range", [
+            pytest.param(
+                [.01, .5, .99],
+                [.1, 1, 2],
+                [-1, 0, .8],
+            ),
+            pytest.param(
+                [.01, .05, .5, .95, .99],
+                [.1, .5, 1, 1.5, 2],
+                [-.9, -.5, 0, .3, .6, 1],
+                marks=pytest.mark.slow
+            ),
+            pytest.param(
+                [.01, .05, .1, .25, .35, .5, .65, .75, .9, .95, .99],
+                np.linspace(0.1, 2, 20),
+                np.linspace(-1, 1, 21),
+                marks=pytest.mark.xslow,
+            ),
+        ]
+    )
+    def test_cdf_nolan_samples(
+            self, nolan_cdf_sample_data, pct_range, alpha_range, beta_range
+    ):
+        """ Test cdf values against Nolan's stablec.exe output."""
+        data = nolan_cdf_sample_data
+        tests = [
+            # piecewise generally good accuracy
+            [
+                'piecewise', 2e-12, lambda r: (
+                    np.isin(r['pct'], pct_range) &
+                    np.isin(r['alpha'], alpha_range) &
+                    np.isin(r['beta'], beta_range) &
+                    ~(
+                        (
+                            (r['alpha'] == 1.) &
+                            np.isin(r['beta'], [-0.3, -0.2, -0.1]) &
+                            (r['pct'] == 0.01)
+                        ) |
+                        (
+                            (r['alpha'] == 1.) &
+                            np.isin(r['beta'], [0.1, 0.2, 0.3]) &
+                            (r['pct'] == 0.99)
+                        )
+                    )
+                )
+            ],
+            # for some points with alpha=1, Nolan's STABLE clearly
+            # loses accuracy
+            [
+                'piecewise', 5e-2, lambda r: (
+                    np.isin(r['pct'], pct_range) &
+                    np.isin(r['alpha'], alpha_range) &
+                    np.isin(r['beta'], beta_range) &
+                    (
+                        (r['alpha'] == 1.) &
+                        np.isin(r['beta'], [-0.3, -0.2, -0.1]) &
+                        (r['pct'] == 0.01)
+                    ) |
+                    (
+                        (r['alpha'] == 1.) &
+                        np.isin(r['beta'], [0.1, 0.2, 0.3]) &
+                        (r['pct'] == 0.99)
+                    )
+                )
+            ],
+            # fft accuracy poor, very poor alpha < 1
+            [
+                'fft-simpson', 1e-5, lambda r: (
+                    np.isin(r['pct'], pct_range) &
+                    np.isin(r['alpha'], alpha_range) &
+                    np.isin(r['beta'], beta_range) &
+                    (r['alpha'] > 1.7)
+                )
+            ],
+            [
+                'fft-simpson', 1e-4, lambda r: (
+                    np.isin(r['pct'], pct_range) &
+                    np.isin(r['alpha'], alpha_range) &
+                    np.isin(r['beta'], beta_range) &
+                    (r['alpha'] > 1.5) &
+                    (r['alpha'] <= 1.7)
+                )
+            ],
+            [
+                'fft-simpson', 1e-3, lambda r: (
+                    np.isin(r['pct'], pct_range) &
+                    np.isin(r['alpha'], alpha_range) &
+                    np.isin(r['beta'], beta_range) &
+                    (r['alpha'] > 1.3) &
+                    (r['alpha'] <= 1.5)
+                )
+            ],
+            [
+                'fft-simpson', 1e-2, lambda r: (
+                    np.isin(r['pct'], pct_range) &
+                    np.isin(r['alpha'], alpha_range) &
+                    np.isin(r['beta'], beta_range) &
+                    (r['alpha'] > 1.0) &
+                    (r['alpha'] <= 1.3)
+                )
+            ],
+        ]
+        for ix, (default_method, rtol,
+                 filter_func) in enumerate(tests):
+            stats.levy_stable.cdf_default_method = default_method
+            subdata = data[filter_func(data)
+                           ] if filter_func is not None else data
+            with warnings.catch_warnings():
+                warnings.filterwarnings(
+                    'ignore',
+                    ('Cumulative density calculations experimental for FFT'
+                     ' method. Use piecewise method instead.'),
+                    RuntimeWarning)
+                p = stats.levy_stable.cdf(
+                    subdata['x'],
+                    subdata['alpha'],
+                    subdata['beta'],
+                    scale=1,
+                    loc=0
+                )
+                with np.errstate(over="ignore"):
+                    subdata2 = rec_append_fields(
+                        subdata,
+                        ['calc', 'abserr', 'relerr'],
+                        [
+                            p,
+                            np.abs(p - subdata['p']),
+                            np.abs(p - subdata['p']) / np.abs(subdata['p'])
+                        ]
+                    )
+                failures = subdata2[
+                  (subdata2['relerr'] >= rtol) |
+                  np.isnan(p)
+                ]
+                message = (f"cdf test {ix} failed with method '{default_method}'\n"
+                           f"{failures.dtype.names}\n{failures}")
+                assert_allclose(
+                    p,
+                    subdata['p'],
+                    rtol,
+                    err_msg=message,
+                    verbose=False
+                )
+
+    @pytest.mark.parametrize("param", [0, 1])
+    @pytest.mark.parametrize("case", ["pdf", "cdf"])
+    def test_location_scale(
+            self, nolan_loc_scale_sample_data, param, case
+    ):
+        """Tests for pdf and cdf where loc, scale are different from 0, 1
+        """
+
+        uname = platform.uname()
+        is_linux_32 = uname.system == 'Linux' and "32bit" in platform.architecture()[0]
+        # Test seems to be unstable (see gh-17839 for a bug report on Debian
+        # i386), so skip it.
+        if is_linux_32 and case == 'pdf':
+            pytest.skip("Test unstable on some platforms; see gh-17839, 17859")
+
+        data = nolan_loc_scale_sample_data
+        # We only test against piecewise as location/scale transforms
+        # are same for other methods.
+        stats.levy_stable.cdf_default_method = "piecewise"
+        stats.levy_stable.pdf_default_method = "piecewise"
+
+        subdata = data[data["param"] == param]
+        stats.levy_stable.parameterization = f"S{param}"
+
+        assert case in ["pdf", "cdf"]
+        function = (
+            stats.levy_stable.pdf if case == "pdf" else stats.levy_stable.cdf
+        )
+
+        v1 = function(
+            subdata['x'], subdata['alpha'], subdata['beta'], scale=2, loc=3
+        )
+        assert_allclose(v1, subdata[case], 1e-5)
+
+    @pytest.mark.parametrize(
+        "method,decimal_places",
+        [
+            ['dni', 4],
+            ['piecewise', 4],
+        ]
+    )
+    def test_pdf_alpha_equals_one_beta_non_zero(self, method, decimal_places):
+        """ sample points extracted from Tables and Graphs of Stable
+        Probability Density Functions - Donald R Holt - 1973 - p 187.
+        """
+        xs = np.array(
+            [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4]
+        )
+        density = np.array(
+            [
+                .3183, .3096, .2925, .2622, .1591, .1587, .1599, .1635, .0637,
+                .0729, .0812, .0955, .0318, .0390, .0458, .0586, .0187, .0236,
+                .0285, .0384
+            ]
+        )
+        betas = np.array(
+            [
+                0, .25, .5, 1, 0, .25, .5, 1, 0, .25, .5, 1, 0, .25, .5, 1, 0,
+                .25, .5, 1
+            ]
+        )
+        with np.errstate(all='ignore'), warnings.catch_warnings():
+            warnings.filterwarnings("ignore",
+                category=RuntimeWarning,
+                message="Density calculation unstable.*"
+            )
+            stats.levy_stable.pdf_default_method = method
+            # stats.levy_stable.fft_grid_spacing = 0.0001
+            pdf = stats.levy_stable.pdf(xs, 1, betas, scale=1, loc=0)
+            assert_almost_equal(
+                pdf, density, decimal_places, method
+            )
+
+    @pytest.mark.parametrize(
+        "params,expected",
+        [
+            [(1.48, -.22, 0, 1), (0, np.inf, np.nan, np.nan)],
+            [(2, .9, 10, 1.5), (10, 4.5, 0, 0)]
+        ]
+    )
+    def test_stats(self, params, expected):
+        observed = stats.levy_stable.stats(
+            params[0], params[1], loc=params[2], scale=params[3],
+            moments='mvsk'
+        )
+        assert_almost_equal(observed, expected)
+
+    @pytest.mark.parametrize('alpha', [0.25, 0.5, 0.75])
+    @pytest.mark.parametrize(
+        'function,beta,points,expected',
+        [
+            (
+                stats.levy_stable.cdf,
+                1.0,
+                np.linspace(-25, 0, 10),
+                0.0,
+            ),
+            (
+                stats.levy_stable.pdf,
+                1.0,
+                np.linspace(-25, 0, 10),
+                0.0,
+            ),
+            (
+                stats.levy_stable.cdf,
+                -1.0,
+                np.linspace(0, 25, 10),
+                1.0,
+            ),
+            (
+                stats.levy_stable.pdf,
+                -1.0,
+                np.linspace(0, 25, 10),
+                0.0,
+            )
+        ]
+    )
+    def test_distribution_outside_support(
+            self, alpha, function, beta, points, expected
+    ):
+        """Ensure the pdf/cdf routines do not return nan outside support.
+
+        This distribution's support becomes truncated in a few special cases:
+            support is [mu, infty) if alpha < 1 and beta = 1
+            support is (-infty, mu] if alpha < 1 and beta = -1
+        Otherwise, the support is all reals. Here, mu is zero by default.
+        """
+        assert 0 < alpha < 1
+        assert_almost_equal(
+            function(points, alpha=alpha, beta=beta),
+            np.full(len(points), expected)
+        )
+
+    @pytest.mark.parametrize(
+        'x,alpha,beta,expected',
+        # Reference values from Matlab
+        # format long
+        # alphas = [1.7720732804618808, 1.9217001522410235, 1.5654806051633634,
+        #           1.7420803447784388, 1.5748002527689913];
+        # betas = [0.5059373136902996, -0.8779442746685926, -0.4016220341911392,
+        #          -0.38180029468259247, -0.25200194914153684];
+        # x0s = [0, 1e-4, -1e-4];
+        # for x0 = x0s
+        #     disp("x0 = " + x0)
+        #     for ii = 1:5
+        #         alpha = alphas(ii);
+        #         beta = betas(ii);
+        #         pd = makedist('Stable','alpha',alpha,'beta',beta,'gam',1,'delta',0);
+        #         % we need to adjust x. It is the same as x = 0 In scipy.
+        #         x = x0 - beta * tan(pi * alpha / 2);
+        #         disp(pd.pdf(x))
+        #     end
+        # end
+        [
+            (0, 1.7720732804618808, 0.5059373136902996, 0.278932636798268),
+            (0, 1.9217001522410235, -0.8779442746685926, 0.281054757202316),
+            (0, 1.5654806051633634, -0.4016220341911392, 0.271282133194204),
+            (0, 1.7420803447784388, -0.38180029468259247, 0.280202199244247),
+            (0, 1.5748002527689913, -0.25200194914153684, 0.280136576218665),
+        ]
+    )
+    def test_x_equal_zeta(
+            self, x, alpha, beta, expected
+    ):
+        """Test pdf for x equal to zeta.
+
+        With S1 parametrization: x0 = x + zeta if alpha != 1 So, for x = 0, x0
+        will be close to zeta.
+
+        When case "x equal zeta" is not handled properly and quad_eps is not
+        low enough: - pdf may be less than 0 - logpdf is nan
+
+        The points from the parametrize block are found randomly so that PDF is
+        less than 0.
+
+        Reference values taken from MATLAB
+        https://www.mathworks.com/help/stats/stable-distribution.html
+        """
+        stats.levy_stable.quad_eps = 1.2e-11
+
+        assert_almost_equal(
+            stats.levy_stable.pdf(x, alpha=alpha, beta=beta),
+            expected,
+        )
+
+    @pytest.mark.xfail
+    @pytest.mark.parametrize(
+        # See comment for test_x_equal_zeta for script for reference values
+        'x,alpha,beta,expected',
+        [
+            (1e-4, 1.7720732804618808, 0.5059373136902996, 0.278929165340670),
+            (1e-4, 1.9217001522410235, -0.8779442746685926, 0.281056564327953),
+            (1e-4, 1.5654806051633634, -0.4016220341911392, 0.271252432161167),
+            (1e-4, 1.7420803447784388, -0.38180029468259247, 0.280205311264134),
+            (1e-4, 1.5748002527689913, -0.25200194914153684, 0.280140965235426),
+            (-1e-4, 1.7720732804618808, 0.5059373136902996, 0.278936106741754),
+            (-1e-4, 1.9217001522410235, -0.8779442746685926, 0.281052948629429),
+            (-1e-4, 1.5654806051633634, -0.4016220341911392, 0.271275394392385),
+            (-1e-4, 1.7420803447784388, -0.38180029468259247, 0.280199085645099),
+            (-1e-4, 1.5748002527689913, -0.25200194914153684, 0.280132185432842),
+        ]
+    )
+    def test_x_near_zeta(
+            self, x, alpha, beta, expected
+    ):
+        """Test pdf for x near zeta.
+
+        With S1 parametrization: x0 = x + zeta if alpha != 1 So, for x = 0, x0
+        will be close to zeta.
+
+        When case "x near zeta" is not handled properly and quad_eps is not
+        low enough: - pdf may be less than 0 - logpdf is nan
+
+        The points from the parametrize block are found randomly so that PDF is
+        less than 0.
+
+        Reference values taken from MATLAB
+        https://www.mathworks.com/help/stats/stable-distribution.html
+        """
+        stats.levy_stable.quad_eps = 1.2e-11
+
+        assert_almost_equal(
+            stats.levy_stable.pdf(x, alpha=alpha, beta=beta),
+            expected,
+        )
+
+    @pytest.fixture
+    def levy_stable_lock(self):
+        return threading.Lock()
+
+    def test_frozen_parameterization_gh20821(self, levy_stable_lock):
+        # gh-20821 reported that frozen distributions ignore the parameterization.
+        # Check that this is resolved and that the frozen distribution's
+        # parameterization can be changed independently of stats.levy_stable
+        rng = np.random.default_rng
+        shapes = dict(alpha=1.9, beta=0.1, loc=0.0, scale=1.0)
+        unfrozen = stats.levy_stable
+        frozen = stats.levy_stable(**shapes)
+
+        with levy_stable_lock:
+            unfrozen.parameterization = "S0"
+            frozen.parameterization = "S1"
+            unfrozen_a = unfrozen.rvs(**shapes, size=10, random_state=rng(329823498))
+            frozen_a = frozen.rvs(size=10, random_state=rng(329823498))
+            assert not np.any(frozen_a == unfrozen_a)
+
+            unfrozen.parameterization = "S1"
+            frozen.parameterization = "S0"
+            unfrozen_b = unfrozen.rvs(**shapes, size=10, random_state=rng(329823498))
+            frozen_b = frozen.rvs(size=10, random_state=rng(329823498))
+            assert_equal(frozen_b, unfrozen_a)
+            assert_equal(unfrozen_b, frozen_a)
+
+    def test_frozen_parameterization_gh20821b(self, levy_stable_lock):
+        # Check that the parameterization of the frozen distribution is that of
+        # the unfrozen distribution at the time of freezing
+        rng = np.random.default_rng
+        shapes = dict(alpha=1.9, beta=0.1, loc=0.0, scale=1.0)
+        unfrozen = stats.levy_stable
+
+        with levy_stable_lock:
+            unfrozen.parameterization = "S0"
+            frozen = stats.levy_stable(**shapes)
+            unfrozen_a = unfrozen.rvs(**shapes, size=10, random_state=rng(329823498))
+            frozen_a = frozen.rvs(size=10, random_state=rng(329823498))
+            assert_equal(frozen_a, unfrozen_a)
+
+            unfrozen.parameterization = "S1"
+            frozen = stats.levy_stable(**shapes)
+            unfrozen_b = unfrozen.rvs(**shapes, size=10, random_state=rng(329823498))
+            frozen_b = frozen.rvs(size=10, random_state=rng(329823498))
+            assert_equal(frozen_b, unfrozen_b)
+
+    @pytest.mark.parametrize(
+        "param, alpha, beta, xs, expected",
+        [
+            (0, 1.6, 1, [-15, -14, -13], [-187.1444263, -157.1411663, -130.3526688]),
+            (1, 1.6, 1, [-15, -14, -13], [-165.0203129, -137.3671717, -112.8223545]),
+            (0, 1.4, -1, [13, 14, 15], [-367.1943293, -464.7049565, -579.4399286]),
+            (1, 1.4, -1, [13, 14, 15], [-258.1216497, -334.6139857, -426.0724094]),
+        ],
+    )
+    @pytest.mark.xfail_on_32bit("`bisect` fails to recognize valid bracket")
+    def test_logpdf_tail(self, param, alpha, beta, xs, expected):
+        # gh-20636
+        # Expected values computed from Mathematica. Mathematica command is:
+        # Log[PDF[StableDistribution[0, 1.6, 1, 0, 1], -15.0]] // InputForm
+        # Test levy_stable.logpdf accuracy in the tails when beta=1 or beta=-1
+        # for both S0/S1 parameterizations.
+        # Testing logpdf instead of pdf as more compact.
+        stats.levy_stable.parameterization = f"S{param}"
+        logpdfs = stats.levy_stable(alpha=alpha, beta=beta).logpdf(xs)
+        assert_allclose(logpdfs, expected, rtol=1e-5)
+
+class TestArrayArgument:  # test for ticket:992
+    def setup_method(self):
+        self.rng = np.random.default_rng(7556981556)
+
+    def test_noexception(self):
+        rvs = stats.norm.rvs(loc=(np.arange(5)), scale=np.ones(5),
+                             size=(10, 5), random_state=self.rng)
+        assert_equal(rvs.shape, (10, 5))
+
+
+class TestDocstring:
+    def test_docstrings(self):
+        # See ticket #761
+        if stats.rayleigh.__doc__ is not None:
+            assert_("rayleigh" in stats.rayleigh.__doc__.lower())
+        if stats.bernoulli.__doc__ is not None:
+            assert_("bernoulli" in stats.bernoulli.__doc__.lower())
+
+    def test_no_name_arg(self):
+        # If name is not given, construction shouldn't fail.  See #1508.
+        stats.rv_continuous()
+        stats.rv_discrete()
+
+
+def test_args_reduce():
+    a = array([1, 3, 2, 1, 2, 3, 3])
+    b, c = argsreduce(a > 1, a, 2)
+
+    assert_array_equal(b, [3, 2, 2, 3, 3])
+    assert_array_equal(c, [2])
+
+    b, c = argsreduce(2 > 1, a, 2)
+    assert_array_equal(b, a)
+    assert_array_equal(c, [2] * np.size(a))
+
+    b, c = argsreduce(a > 0, a, 2)
+    assert_array_equal(b, a)
+    assert_array_equal(c, [2] * np.size(a))
+
+
+class TestFitMethod:
+    # fitting assumes continuous parameters
+    skip = ['ncf', 'ksone', 'kstwo', 'irwinhall']
+
+    def setup_method(self):
+        self.rng = np.random.default_rng(4522425749)
+
+    # skip these b/c deprecated, or only loc and scale arguments
+    fitSkipNonFinite = ['expon', 'norm', 'uniform', 'irwinhall']
+
+    @pytest.mark.parametrize('dist,args', distcont)
+    def test_fit_w_non_finite_data_values(self, dist, args):
+        """gh-10300"""
+        if dist in self.fitSkipNonFinite:
+            pytest.skip(f"{dist} fit known to fail or deprecated")
+        x = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.nan])
+        y = np.array([1.6483, 2.7169, 2.4667, 1.1791, 3.5433, np.inf])
+        distfunc = getattr(stats, dist)
+        assert_raises(ValueError, distfunc.fit, x, fscale=1)
+        assert_raises(ValueError, distfunc.fit, y, fscale=1)
+
+    def test_fix_fit_2args_lognorm(self):
+        # Regression test for #1551.
+        with np.errstate(all='ignore'):
+            x = stats.lognorm.rvs(0.25, 0., 20.0, size=20, random_state=self.rng)
+            expected_shape = np.sqrt(((np.log(x) - np.log(20))**2).mean())
+            assert_allclose(np.array(stats.lognorm.fit(x, floc=0, fscale=20)),
+                            [expected_shape, 0, 20], atol=1e-8)
+
+    def test_fix_fit_norm(self):
+        x = np.arange(1, 6)
+
+        loc, scale = stats.norm.fit(x)
+        assert_almost_equal(loc, 3)
+        assert_almost_equal(scale, np.sqrt(2))
+
+        loc, scale = stats.norm.fit(x, floc=2)
+        assert_equal(loc, 2)
+        assert_equal(scale, np.sqrt(3))
+
+        loc, scale = stats.norm.fit(x, fscale=2)
+        assert_almost_equal(loc, 3)
+        assert_equal(scale, 2)
+
+    def test_fix_fit_gamma(self):
+        x = np.arange(1, 6)
+        meanlog = np.log(x).mean()
+
+        # A basic test of gamma.fit with floc=0.
+        floc = 0
+        a, loc, scale = stats.gamma.fit(x, floc=floc)
+        s = np.log(x.mean()) - meanlog
+        assert_almost_equal(np.log(a) - special.digamma(a), s, decimal=5)
+        assert_equal(loc, floc)
+        assert_almost_equal(scale, x.mean()/a, decimal=8)
+
+        # Regression tests for gh-2514.
+        # The problem was that if `floc=0` was given, any other fixed
+        # parameters were ignored.
+        f0 = 1
+        floc = 0
+        a, loc, scale = stats.gamma.fit(x, f0=f0, floc=floc)
+        assert_equal(a, f0)
+        assert_equal(loc, floc)
+        assert_almost_equal(scale, x.mean()/a, decimal=8)
+
+        f0 = 2
+        floc = 0
+        a, loc, scale = stats.gamma.fit(x, f0=f0, floc=floc)
+        assert_equal(a, f0)
+        assert_equal(loc, floc)
+        assert_almost_equal(scale, x.mean()/a, decimal=8)
+
+        # loc and scale fixed.
+        floc = 0
+        fscale = 2
+        a, loc, scale = stats.gamma.fit(x, floc=floc, fscale=fscale)
+        assert_equal(loc, floc)
+        assert_equal(scale, fscale)
+        c = meanlog - np.log(fscale)
+        assert_almost_equal(special.digamma(a), c)
+
+    def test_fix_fit_beta(self):
+        # Test beta.fit when both floc and fscale are given.
+
+        def mlefunc(a, b, x):
+            # Zeros of this function are critical points of
+            # the maximum likelihood function.
+            n = len(x)
+            s1 = np.log(x).sum()
+            s2 = np.log(1-x).sum()
+            psiab = special.psi(a + b)
+            func = [s1 - n * (-psiab + special.psi(a)),
+                    s2 - n * (-psiab + special.psi(b))]
+            return func
+
+        # Basic test with floc and fscale given.
+        x = np.array([0.125, 0.25, 0.5])
+        a, b, loc, scale = stats.beta.fit(x, floc=0, fscale=1)
+        assert_equal(loc, 0)
+        assert_equal(scale, 1)
+        assert_allclose(mlefunc(a, b, x), [0, 0], atol=1e-6)
+
+        # Basic test with f0, floc and fscale given.
+        # This is also a regression test for gh-2514.
+        x = np.array([0.125, 0.25, 0.5])
+        a, b, loc, scale = stats.beta.fit(x, f0=2, floc=0, fscale=1)
+        assert_equal(a, 2)
+        assert_equal(loc, 0)
+        assert_equal(scale, 1)
+        da, db = mlefunc(a, b, x)
+        assert_allclose(db, 0, atol=1e-5)
+
+        # Same floc and fscale values as above, but reverse the data
+        # and fix b (f1).
+        x2 = 1 - x
+        a2, b2, loc2, scale2 = stats.beta.fit(x2, f1=2, floc=0, fscale=1)
+        assert_equal(b2, 2)
+        assert_equal(loc2, 0)
+        assert_equal(scale2, 1)
+        da, db = mlefunc(a2, b2, x2)
+        assert_allclose(da, 0, atol=1e-5)
+        # a2 of this test should equal b from above.
+        assert_almost_equal(a2, b)
+
+        # Check for detection of data out of bounds when floc and fscale
+        # are given.
+        assert_raises(ValueError, stats.beta.fit, x, floc=0.5, fscale=1)
+        y = np.array([0, .5, 1])
+        assert_raises(ValueError, stats.beta.fit, y, floc=0, fscale=1)
+        assert_raises(ValueError, stats.beta.fit, y, floc=0, fscale=1, f0=2)
+        assert_raises(ValueError, stats.beta.fit, y, floc=0, fscale=1, f1=2)
+
+        # Check that attempting to fix all the parameters raises a ValueError.
+        assert_raises(ValueError, stats.beta.fit, y, f0=0, f1=1,
+                      floc=2, fscale=3)
+
+    def test_expon_fit(self):
+        x = np.array([2, 2, 4, 4, 4, 4, 4, 8])
+
+        loc, scale = stats.expon.fit(x)
+        assert_equal(loc, 2)    # x.min()
+        assert_equal(scale, 2)  # x.mean() - x.min()
+
+        loc, scale = stats.expon.fit(x, fscale=3)
+        assert_equal(loc, 2)    # x.min()
+        assert_equal(scale, 3)  # fscale
+
+        loc, scale = stats.expon.fit(x, floc=0)
+        assert_equal(loc, 0)    # floc
+        assert_equal(scale, 4)  # x.mean() - loc
+
+    def test_lognorm_fit(self):
+        x = np.array([1.5, 3, 10, 15, 23, 59])
+        lnxm1 = np.log(x - 1)
+
+        shape, loc, scale = stats.lognorm.fit(x, floc=1)
+        assert_allclose(shape, lnxm1.std(), rtol=1e-12)
+        assert_equal(loc, 1)
+        assert_allclose(scale, np.exp(lnxm1.mean()), rtol=1e-12)
+
+        shape, loc, scale = stats.lognorm.fit(x, floc=1, fscale=6)
+        assert_allclose(shape, np.sqrt(((lnxm1 - np.log(6))**2).mean()),
+                        rtol=1e-12)
+        assert_equal(loc, 1)
+        assert_equal(scale, 6)
+
+        shape, loc, scale = stats.lognorm.fit(x, floc=1, fix_s=0.75)
+        assert_equal(shape, 0.75)
+        assert_equal(loc, 1)
+        assert_allclose(scale, np.exp(lnxm1.mean()), rtol=1e-12)
+
+    def test_uniform_fit(self):
+        x = np.array([1.0, 1.1, 1.2, 9.0])
+
+        loc, scale = stats.uniform.fit(x)
+        assert_equal(loc, x.min())
+        assert_equal(scale, np.ptp(x))
+
+        loc, scale = stats.uniform.fit(x, floc=0)
+        assert_equal(loc, 0)
+        assert_equal(scale, x.max())
+
+        loc, scale = stats.uniform.fit(x, fscale=10)
+        assert_equal(loc, 0)
+        assert_equal(scale, 10)
+
+        assert_raises(ValueError, stats.uniform.fit, x, floc=2.0)
+        assert_raises(ValueError, stats.uniform.fit, x, fscale=5.0)
+
+    @pytest.mark.xslow
+    @pytest.mark.parametrize("method", ["MLE", "MM"])
+    def test_fshapes(self, method):
+        # take a beta distribution, with shapes='a, b', and make sure that
+        # fa is equivalent to f0, and fb is equivalent to f1
+        a, b = 3., 4.
+        x = stats.beta.rvs(a, b, size=100, random_state=self.rng)
+        res_1 = stats.beta.fit(x, f0=3., method=method)
+        res_2 = stats.beta.fit(x, fa=3., method=method)
+        assert_allclose(res_1, res_2, atol=1e-12, rtol=1e-12)
+
+        res_2 = stats.beta.fit(x, fix_a=3., method=method)
+        assert_allclose(res_1, res_2, atol=1e-12, rtol=1e-12)
+
+        res_3 = stats.beta.fit(x, f1=4., method=method)
+        res_4 = stats.beta.fit(x, fb=4., method=method)
+        assert_allclose(res_3, res_4, atol=1e-12, rtol=1e-12)
+
+        res_4 = stats.beta.fit(x, fix_b=4., method=method)
+        assert_allclose(res_3, res_4, atol=1e-12, rtol=1e-12)
+
+        # cannot specify both positional and named args at the same time
+        assert_raises(ValueError, stats.beta.fit, x, fa=1, f0=2, method=method)
+
+        # check that attempting to fix all parameters raises a ValueError
+        assert_raises(ValueError, stats.beta.fit, x, fa=0, f1=1,
+                      floc=2, fscale=3, method=method)
+
+        # check that specifying floc, fscale and fshapes works for
+        # beta and gamma which override the generic fit method
+        res_5 = stats.beta.fit(x, fa=3., floc=0, fscale=1, method=method)
+        aa, bb, ll, ss = res_5
+        assert_equal([aa, ll, ss], [3., 0, 1])
+
+        # gamma distribution
+        a = 3.
+        data = stats.gamma.rvs(a, size=100, random_state=self.rng)
+        aa, ll, ss = stats.gamma.fit(data, fa=a, method=method)
+        assert_equal(aa, a)
+
+    @pytest.mark.parametrize("method", ["MLE", "MM"])
+    def test_extra_params(self, method):
+        # unknown parameters should raise rather than be silently ignored
+        dist = stats.exponnorm
+        data = dist.rvs(K=2, size=100, random_state=self.rng)
+        dct = dict(enikibeniki=-101)
+        assert_raises(TypeError, dist.fit, data, **dct, method=method)
+
+
+class TestFrozen:
+    # Test that a frozen distribution gives the same results as the original
+    # object.
+    #
+    # Only tested for the normal distribution (with loc and scale specified)
+    # and for the gamma distribution (with a shape parameter specified).
+    def test_norm(self):
+        dist = stats.norm
+        frozen = stats.norm(loc=10.0, scale=3.0)
+
+        result_f = frozen.pdf(20.0)
+        result = dist.pdf(20.0, loc=10.0, scale=3.0)
+        assert_equal(result_f, result)
+
+        result_f = frozen.cdf(20.0)
+        result = dist.cdf(20.0, loc=10.0, scale=3.0)
+        assert_equal(result_f, result)
+
+        result_f = frozen.ppf(0.25)
+        result = dist.ppf(0.25, loc=10.0, scale=3.0)
+        assert_equal(result_f, result)
+
+        result_f = frozen.isf(0.25)
+        result = dist.isf(0.25, loc=10.0, scale=3.0)
+        assert_equal(result_f, result)
+
+        result_f = frozen.sf(10.0)
+        result = dist.sf(10.0, loc=10.0, scale=3.0)
+        assert_equal(result_f, result)
+
+        result_f = frozen.median()
+        result = dist.median(loc=10.0, scale=3.0)
+        assert_equal(result_f, result)
+
+        result_f = frozen.mean()
+        result = dist.mean(loc=10.0, scale=3.0)
+        assert_equal(result_f, result)
+
+        result_f = frozen.var()
+        result = dist.var(loc=10.0, scale=3.0)
+        assert_equal(result_f, result)
+
+        result_f = frozen.std()
+        result = dist.std(loc=10.0, scale=3.0)
+        assert_equal(result_f, result)
+
+        result_f = frozen.entropy()
+        result = dist.entropy(loc=10.0, scale=3.0)
+        assert_equal(result_f, result)
+
+        result_f = frozen.moment(2)
+        result = dist.moment(2, loc=10.0, scale=3.0)
+        assert_equal(result_f, result)
+
+        assert_equal(frozen.a, dist.a)
+        assert_equal(frozen.b, dist.b)
+
+    def test_gamma(self):
+        a = 2.0
+        dist = stats.gamma
+        frozen = stats.gamma(a)
+
+        result_f = frozen.pdf(20.0)
+        result = dist.pdf(20.0, a)
+        assert_equal(result_f, result)
+
+        result_f = frozen.cdf(20.0)
+        result = dist.cdf(20.0, a)
+        assert_equal(result_f, result)
+
+        result_f = frozen.ppf(0.25)
+        result = dist.ppf(0.25, a)
+        assert_equal(result_f, result)
+
+        result_f = frozen.isf(0.25)
+        result = dist.isf(0.25, a)
+        assert_equal(result_f, result)
+
+        result_f = frozen.sf(10.0)
+        result = dist.sf(10.0, a)
+        assert_equal(result_f, result)
+
+        result_f = frozen.median()
+        result = dist.median(a)
+        assert_equal(result_f, result)
+
+        result_f = frozen.mean()
+        result = dist.mean(a)
+        assert_equal(result_f, result)
+
+        result_f = frozen.var()
+        result = dist.var(a)
+        assert_equal(result_f, result)
+
+        result_f = frozen.std()
+        result = dist.std(a)
+        assert_equal(result_f, result)
+
+        result_f = frozen.entropy()
+        result = dist.entropy(a)
+        assert_equal(result_f, result)
+
+        result_f = frozen.moment(2)
+        result = dist.moment(2, a)
+        assert_equal(result_f, result)
+
+        assert_equal(frozen.a, frozen.dist.a)
+        assert_equal(frozen.b, frozen.dist.b)
+
+    def test_regression_ticket_1293(self):
+        # Create a frozen distribution.
+        frozen = stats.lognorm(1)
+        # Call one of its methods that does not take any keyword arguments.
+        m1 = frozen.moment(2)
+        # Now call a method that takes a keyword argument.
+        frozen.stats(moments='mvsk')
+        # Call moment(2) again.
+        # After calling stats(), the following was raising an exception.
+        # So this test passes if the following does not raise an exception.
+        m2 = frozen.moment(2)
+        # The following should also be true, of course.  But it is not
+        # the focus of this test.
+        assert_equal(m1, m2)
+
+    def test_ab(self):
+        # test that the support of a frozen distribution
+        # (i) remains frozen even if it changes for the original one
+        # (ii) is actually correct if the shape parameters are such that
+        #      the values of [a, b] are not the default [0, inf]
+        # take a genpareto as an example where the support
+        # depends on the value of the shape parameter:
+        # for c > 0: a, b = 0, inf
+        # for c < 0: a, b = 0, -1/c
+
+        c = -0.1
+        rv = stats.genpareto(c=c)
+        a, b = rv.dist._get_support(c)
+        assert_equal([a, b], [0., 10.])
+
+        c = 0.1
+        stats.genpareto.pdf(0, c=c)
+        assert_equal(rv.dist._get_support(c), [0, np.inf])
+
+        c = -0.1
+        rv = stats.genpareto(c=c)
+        a, b = rv.dist._get_support(c)
+        assert_equal([a, b], [0., 10.])
+
+        c = 0.1
+        stats.genpareto.pdf(0, c)  # this should NOT change genpareto.b
+        assert_equal((rv.dist.a, rv.dist.b), stats.genpareto._get_support(c))
+
+        rv1 = stats.genpareto(c=0.1)
+        assert_(rv1.dist is not rv.dist)
+
+        # c >= 0: a, b = [0, inf]
+        for c in [1., 0.]:
+            c = np.asarray(c)
+            rv = stats.genpareto(c=c)
+            a, b = rv.a, rv.b
+            assert_equal(a, 0.)
+            assert_(np.isposinf(b))
+
+            # c < 0: a=0, b=1/|c|
+            c = np.asarray(-2.)
+            a, b = stats.genpareto._get_support(c)
+            assert_allclose([a, b], [0., 0.5])
+
+    def test_rv_frozen_in_namespace(self):
+        # Regression test for gh-3522
+        assert_(hasattr(stats.distributions, 'rv_frozen'))
+
+    def test_random_state(self):
+        # only check that the random_state attribute exists,
+        frozen = stats.norm()
+        assert_(hasattr(frozen, 'random_state'))
+
+        # ... that it can be set,
+        frozen.random_state = 42
+        assert_equal(frozen.random_state.get_state(),
+                     np.random.RandomState(42).get_state())
+
+        # ... and that .rvs method accepts it as an argument
+        rndm = np.random.RandomState(1234)
+        frozen.rvs(size=8, random_state=rndm)
+
+    def test_pickling(self):
+        # test that a frozen instance pickles and unpickles
+        # (this method is a clone of common_tests.check_pickling)
+        beta = stats.beta(2.3098496451481823, 0.62687954300963677)
+        poiss = stats.poisson(3.)
+        sample = stats.rv_discrete(values=([0, 1, 2, 3],
+                                           [0.1, 0.2, 0.3, 0.4]))
+
+        for distfn in [beta, poiss, sample]:
+            distfn.random_state = 1234
+            distfn.rvs(size=8)
+            s = pickle.dumps(distfn)
+            r0 = distfn.rvs(size=8)
+
+            unpickled = pickle.loads(s)
+            r1 = unpickled.rvs(size=8)
+            assert_equal(r0, r1)
+
+            # also smoke test some methods
+            medians = [distfn.ppf(0.5), unpickled.ppf(0.5)]
+            assert_equal(medians[0], medians[1])
+            assert_equal(distfn.cdf(medians[0]),
+                         unpickled.cdf(medians[1]))
+
+    def test_expect(self):
+        # smoke test the expect method of the frozen distribution
+        # only take a gamma w/loc and scale and poisson with loc specified
+        def func(x):
+            return x
+
+        gm = stats.gamma(a=2, loc=3, scale=4)
+        with np.errstate(invalid="ignore", divide="ignore"):
+            gm_val = gm.expect(func, lb=1, ub=2, conditional=True)
+            gamma_val = stats.gamma.expect(func, args=(2,), loc=3, scale=4,
+                                           lb=1, ub=2, conditional=True)
+        assert_allclose(gm_val, gamma_val)
+
+        p = stats.poisson(3, loc=4)
+        p_val = p.expect(func)
+        poisson_val = stats.poisson.expect(func, args=(3,), loc=4)
+        assert_allclose(p_val, poisson_val)
+
+
+class TestExpect:
+    # Test for expect method.
+    #
+    # Uses normal distribution and beta distribution for finite bounds, and
+    # hypergeom for discrete distribution with finite support
+    def test_norm(self):
+        v = stats.norm.expect(lambda x: (x-5)*(x-5), loc=5, scale=2)
+        assert_almost_equal(v, 4, decimal=14)
+
+        m = stats.norm.expect(lambda x: (x), loc=5, scale=2)
+        assert_almost_equal(m, 5, decimal=14)
+
+        lb = stats.norm.ppf(0.05, loc=5, scale=2)
+        ub = stats.norm.ppf(0.95, loc=5, scale=2)
+        prob90 = stats.norm.expect(lambda x: 1, loc=5, scale=2, lb=lb, ub=ub)
+        assert_almost_equal(prob90, 0.9, decimal=14)
+
+        prob90c = stats.norm.expect(lambda x: 1, loc=5, scale=2, lb=lb, ub=ub,
+                                    conditional=True)
+        assert_almost_equal(prob90c, 1., decimal=14)
+
+    def test_beta(self):
+        # case with finite support interval
+        v = stats.beta.expect(lambda x: (x-19/3.)*(x-19/3.), args=(10, 5),
+                              loc=5, scale=2)
+        assert_almost_equal(v, 1./18., decimal=13)
+
+        m = stats.beta.expect(lambda x: x, args=(10, 5), loc=5., scale=2.)
+        assert_almost_equal(m, 19/3., decimal=13)
+
+        ub = stats.beta.ppf(0.95, 10, 10, loc=5, scale=2)
+        lb = stats.beta.ppf(0.05, 10, 10, loc=5, scale=2)
+        prob90 = stats.beta.expect(lambda x: 1., args=(10, 10), loc=5.,
+                                   scale=2., lb=lb, ub=ub, conditional=False)
+        assert_almost_equal(prob90, 0.9, decimal=13)
+
+        prob90c = stats.beta.expect(lambda x: 1, args=(10, 10), loc=5,
+                                    scale=2, lb=lb, ub=ub, conditional=True)
+        assert_almost_equal(prob90c, 1., decimal=13)
+
+    def test_hypergeom(self):
+        # test case with finite bounds
+
+        # without specifying bounds
+        m_true, v_true = stats.hypergeom.stats(20, 10, 8, loc=5.)
+        m = stats.hypergeom.expect(lambda x: x, args=(20, 10, 8), loc=5.)
+        assert_almost_equal(m, m_true, decimal=13)
+
+        v = stats.hypergeom.expect(lambda x: (x-9.)**2, args=(20, 10, 8),
+                                   loc=5.)
+        assert_almost_equal(v, v_true, decimal=14)
+
+        # with bounds, bounds equal to shifted support
+        v_bounds = stats.hypergeom.expect(lambda x: (x-9.)**2,
+                                          args=(20, 10, 8),
+                                          loc=5., lb=5, ub=13)
+        assert_almost_equal(v_bounds, v_true, decimal=14)
+
+        # drop boundary points
+        prob_true = 1-stats.hypergeom.pmf([5, 13], 20, 10, 8, loc=5).sum()
+        prob_bounds = stats.hypergeom.expect(lambda x: 1, args=(20, 10, 8),
+                                             loc=5., lb=6, ub=12)
+        assert_almost_equal(prob_bounds, prob_true, decimal=13)
+
+        # conditional
+        prob_bc = stats.hypergeom.expect(lambda x: 1, args=(20, 10, 8), loc=5.,
+                                         lb=6, ub=12, conditional=True)
+        assert_almost_equal(prob_bc, 1, decimal=14)
+
+        # check simple integral
+        prob_b = stats.hypergeom.expect(lambda x: 1, args=(20, 10, 8),
+                                        lb=0, ub=8)
+        assert_almost_equal(prob_b, 1, decimal=13)
+
+    def test_poisson(self):
+        # poisson, use lower bound only
+        prob_bounds = stats.poisson.expect(lambda x: 1, args=(2,), lb=3,
+                                           conditional=False)
+        prob_b_true = 1-stats.poisson.cdf(2, 2)
+        assert_almost_equal(prob_bounds, prob_b_true, decimal=14)
+
+        prob_lb = stats.poisson.expect(lambda x: 1, args=(2,), lb=2,
+                                       conditional=True)
+        assert_almost_equal(prob_lb, 1, decimal=14)
+
+    def test_genhalflogistic(self):
+        # genhalflogistic, changes upper bound of support in _argcheck
+        # regression test for gh-2622
+        halflog = stats.genhalflogistic
+        # check consistency when calling expect twice with the same input
+        res1 = halflog.expect(args=(1.5,))
+        halflog.expect(args=(0.5,))
+        res2 = halflog.expect(args=(1.5,))
+        assert_almost_equal(res1, res2, decimal=14)
+
+    def test_rice_overflow(self):
+        # rice.pdf(999, 0.74) was inf since special.i0 silently overflows
+        # check that using i0e fixes it
+        assert_(np.isfinite(stats.rice.pdf(999, 0.74)))
+
+        assert_(np.isfinite(stats.rice.expect(lambda x: 1, args=(0.74,))))
+        assert_(np.isfinite(stats.rice.expect(lambda x: 2, args=(0.74,))))
+        assert_(np.isfinite(stats.rice.expect(lambda x: 3, args=(0.74,))))
+
+    def test_logser(self):
+        # test a discrete distribution with infinite support and loc
+        p, loc = 0.3, 3
+        res_0 = stats.logser.expect(lambda k: k, args=(p,))
+        # check against the correct answer (sum of a geom series)
+        assert_allclose(res_0,
+                        p / (p - 1.) / np.log(1. - p), atol=1e-15)
+
+        # now check it with `loc`
+        res_l = stats.logser.expect(lambda k: k, args=(p,), loc=loc)
+        assert_allclose(res_l, res_0 + loc, atol=1e-15)
+
+    def test_skellam(self):
+        # Use a discrete distribution w/ bi-infinite support. Compute two first
+        # moments and compare to known values (cf skellam.stats)
+        p1, p2 = 18, 22
+        m1 = stats.skellam.expect(lambda x: x, args=(p1, p2))
+        m2 = stats.skellam.expect(lambda x: x**2, args=(p1, p2))
+        assert_allclose(m1, p1 - p2, atol=1e-12)
+        assert_allclose(m2 - m1**2, p1 + p2, atol=1e-12)
+
+    def test_randint(self):
+        # Use a discrete distribution w/ parameter-dependent support, which
+        # is larger than the default chunksize
+        lo, hi = 0, 113
+        res = stats.randint.expect(lambda x: x, (lo, hi))
+        assert_allclose(res,
+                        sum(_ for _ in range(lo, hi)) / (hi - lo), atol=1e-15)
+
+    def test_zipf(self):
+        # Test that there is no infinite loop even if the sum diverges
+        with pytest.warns(RuntimeWarning):
+            stats.zipf.expect(lambda x: x**2, (2,))
+
+    def test_discrete_kwds(self):
+        # check that discrete expect accepts keywords to control the summation
+        n0 = stats.poisson.expect(lambda x: 1, args=(2,))
+        n1 = stats.poisson.expect(lambda x: 1, args=(2,),
+                                  maxcount=1001, chunksize=32, tolerance=1e-8)
+        assert_almost_equal(n0, n1, decimal=14)
+
+    def test_moment(self):
+        # test the .moment() method: compute a higher moment and compare to
+        # a known value
+        def poiss_moment5(mu):
+            return mu**5 + 10*mu**4 + 25*mu**3 + 15*mu**2 + mu
+
+        for mu in [5, 7]:
+            m5 = stats.poisson.moment(5, mu)
+            assert_allclose(m5, poiss_moment5(mu), rtol=1e-10)
+
+    def test_challenging_cases_gh8928(self):
+        # Several cases where `expect` failed to produce a correct result were
+        # reported in gh-8928. Check that these cases have been resolved.
+        assert_allclose(stats.norm.expect(loc=36, scale=1.0), 36)
+        assert_allclose(stats.norm.expect(loc=40, scale=1.0), 40)
+        assert_allclose(stats.norm.expect(loc=10, scale=0.1), 10)
+        assert_allclose(stats.gamma.expect(args=(148,)), 148)
+        assert_allclose(stats.logistic.expect(loc=85), 85)
+
+    def test_lb_ub_gh15855(self):
+        # Make sure changes to `expect` made in gh15855 treat lb/ub correctly
+        dist = stats.uniform
+        ref = dist.mean(loc=10, scale=5)  # 12.5
+        # moment over whole distribution
+        assert_allclose(dist.expect(loc=10, scale=5), ref)
+        # moment over whole distribution, lb and ub outside of support
+        assert_allclose(dist.expect(loc=10, scale=5, lb=9, ub=16), ref)
+        # moment over 60% of distribution, [lb, ub] centered within support
+        assert_allclose(dist.expect(loc=10, scale=5, lb=11, ub=14), ref*0.6)
+        # moment over truncated distribution, essentially
+        assert_allclose(dist.expect(loc=10, scale=5, lb=11, ub=14,
+                                    conditional=True), ref)
+        # moment over 40% of distribution, [lb, ub] not centered within support
+        assert_allclose(dist.expect(loc=10, scale=5, lb=11, ub=13), 12*0.4)
+        # moment with lb > ub
+        assert_allclose(dist.expect(loc=10, scale=5, lb=13, ub=11), -12*0.4)
+        # moment with lb > ub, conditional
+        assert_allclose(dist.expect(loc=10, scale=5, lb=13, ub=11,
+                                    conditional=True), 12)
+
+
+class TestNct:
+    def test_nc_parameter(self):
+        # Parameter values c<=0 were not enabled (gh-2402).
+        # For negative values c and for c=0 results of rv.cdf(0) below were nan
+        rv = stats.nct(5, 0)
+        assert_equal(rv.cdf(0), 0.5)
+        rv = stats.nct(5, -1)
+        assert_almost_equal(rv.cdf(0), 0.841344746069, decimal=10)
+
+    def test_broadcasting(self):
+        res = stats.nct.pdf(5, np.arange(4, 7)[:, None],
+                            np.linspace(0.1, 1, 4))
+        expected = array([[0.00321886, 0.00557466, 0.00918418, 0.01442997],
+                          [0.00217142, 0.00395366, 0.00683888, 0.01126276],
+                          [0.00153078, 0.00291093, 0.00525206, 0.00900815]])
+        assert_allclose(res, expected, rtol=1e-5)
+
+    def test_variance_gh_issue_2401(self):
+        # Computation of the variance of a non-central t-distribution resulted
+        # in a TypeError: ufunc 'isinf' not supported for the input types,
+        # and the inputs could not be safely coerced to any supported types
+        # according to the casting rule 'safe'
+        rv = stats.nct(4, 0)
+        assert_equal(rv.var(), 2.0)
+
+    def test_nct_inf_moments(self):
+        # n-th moment of nct only exists for df > n
+        m, v, s, k = stats.nct.stats(df=0.9, nc=0.3, moments='mvsk')
+        assert_equal([m, v, s, k], [np.nan, np.nan, np.nan, np.nan])
+
+        m, v, s, k = stats.nct.stats(df=1.9, nc=0.3, moments='mvsk')
+        assert_(np.isfinite(m))
+        assert_equal([v, s, k], [np.nan, np.nan, np.nan])
+
+        m, v, s, k = stats.nct.stats(df=3.1, nc=0.3, moments='mvsk')
+        assert_(np.isfinite([m, v, s]).all())
+        assert_equal(k, np.nan)
+
+    def test_nct_stats_large_df_values(self):
+        # previously gamma function was used which lost precision at df=345
+        # cf. https://github.com/scipy/scipy/issues/12919 for details
+        nct_mean_df_1000 = stats.nct.mean(1000, 2)
+        nct_stats_df_1000 = stats.nct.stats(1000, 2)
+        # These expected values were computed with mpmath. They were also
+        # verified with the Wolfram Alpha expressions:
+        #     Mean[NoncentralStudentTDistribution[1000, 2]]
+        #     Var[NoncentralStudentTDistribution[1000, 2]]
+        expected_stats_df_1000 = [2.0015015641422464, 1.0040115288163005]
+        assert_allclose(nct_mean_df_1000, expected_stats_df_1000[0],
+                        rtol=1e-10)
+        assert_allclose(nct_stats_df_1000, expected_stats_df_1000,
+                        rtol=1e-10)
+        # and a bigger df value
+        nct_mean = stats.nct.mean(100000, 2)
+        nct_stats = stats.nct.stats(100000, 2)
+        # These expected values were computed with mpmath.
+        expected_stats = [2.0000150001562518, 1.0000400011500288]
+        assert_allclose(nct_mean, expected_stats[0], rtol=1e-10)
+        assert_allclose(nct_stats, expected_stats, rtol=1e-9)
+
+    def test_cdf_large_nc(self):
+        # gh-17916 reported a crash with large `nc` values
+        assert_allclose(stats.nct.cdf(2, 2, float(2**16)), 0)
+
+    # PDF reference values were computed with mpmath
+    # with 100 digits of precision
+
+    # def nct_pdf(x, df, nc):
+    #     x = mp.mpf(x)
+    #     n = mp.mpf(df)
+    #     nc = mp.mpf(nc)
+
+    #     x2 = x*x
+    #     ncx2 = nc*nc*x2
+    #     fac1 = n + x2
+    #     trm1 = (n/2.*mp.log(n) + mp.loggamma(n + mp.one)
+    #             - (n * mp.log(2.) + nc*nc/2 + (n/2)*mp.log(fac1)
+    #                 + mp.loggamma(n/2)))
+    #     Px = mp.exp(trm1)
+    #     valF = ncx2 / (2*fac1)
+    #     trm1 = (mp.sqrt(2)*nc*x*mp.hyp1f1(n/2+1, 1.5, valF)
+    #             / (fac1*mp.gamma((n+1)/2)))
+    #     trm2 = (mp.hyp1f1((n+1)/2, 0.5, valF)
+    #             / (mp.sqrt(fac1)*mp.gamma(n/2 + mp.one)))
+    #     Px *= trm1+trm2
+    #     return float(Px)
+
+    @pytest.mark.parametrize("x, df, nc, expected", [
+        (10000, 10, 16, 3.394646922945872e-30),
+        (-10, 8, 16, 4.282769500264159e-70)
+        ])
+    def test_pdf_large_nc(self, x, df, nc, expected):
+        # gh-#20693 reported zero values for large `nc` values
+        assert_allclose(stats.nct.pdf(x, df, nc), expected, rtol=1e-12)
+
+
+class TestRecipInvGauss:
+
+    def test_pdf_endpoint(self):
+        p = stats.recipinvgauss.pdf(0, 0.6)
+        assert p == 0.0
+
+    def test_logpdf_endpoint(self):
+        logp = stats.recipinvgauss.logpdf(0, 0.6)
+        assert logp == -np.inf
+
+    def test_cdf_small_x(self):
+        # The expected value was computer with mpmath:
+        #
+        # import mpmath
+        #
+        # mpmath.mp.dps = 100
+        #
+        # def recipinvgauss_cdf_mp(x, mu):
+        #     x = mpmath.mpf(x)
+        #     mu = mpmath.mpf(mu)
+        #     trm1 = 1/mu - x
+        #     trm2 = 1/mu + x
+        #     isqx = 1/mpmath.sqrt(x)
+        #     return (mpmath.ncdf(-isqx*trm1)
+        #             - mpmath.exp(2/mu)*mpmath.ncdf(-isqx*trm2))
+        #
+        p = stats.recipinvgauss.cdf(0.05, 0.5)
+        expected = 6.590396159501331e-20
+        assert_allclose(p, expected, rtol=1e-14)
+
+    def test_sf_large_x(self):
+        # The expected value was computed with mpmath; see test_cdf_small.
+        p = stats.recipinvgauss.sf(80, 0.5)
+        expected = 2.699819200556787e-18
+        assert_allclose(p, expected, 5e-15)
+
+
+class TestRice:
+    def setup_method(self):
+        self.rng = np.random.default_rng(666822542)
+
+    def test_rice_zero_b(self):
+        # rice distribution should work with b=0, cf gh-2164
+        x = [0.2, 1., 5.]
+        assert_(np.isfinite(stats.rice.pdf(x, b=0.)).all())
+        assert_(np.isfinite(stats.rice.logpdf(x, b=0.)).all())
+        assert_(np.isfinite(stats.rice.cdf(x, b=0.)).all())
+        assert_(np.isfinite(stats.rice.logcdf(x, b=0.)).all())
+
+        q = [0.1, 0.1, 0.5, 0.9]
+        assert_(np.isfinite(stats.rice.ppf(q, b=0.)).all())
+
+        mvsk = stats.rice.stats(0, moments='mvsk')
+        assert_(np.isfinite(mvsk).all())
+
+        # furthermore, pdf is continuous as b\to 0
+        # rice.pdf(x, b\to 0) = x exp(-x^2/2) + O(b^2)
+        # see e.g. Abramovich & Stegun 9.6.7 & 9.6.10
+        b = 1e-8
+        assert_allclose(stats.rice.pdf(x, 0), stats.rice.pdf(x, b),
+                        atol=b, rtol=0)
+
+    def test_rice_rvs(self):
+        rvs = stats.rice.rvs
+        assert_equal(rvs(b=3., random_state=self.rng).size, 1)
+        assert_equal(rvs(b=3., size=(3, 5), random_state=self.rng).shape, (3, 5))
+
+    def test_rice_gh9836(self):
+        # test that gh-9836 is resolved; previously jumped to 1 at the end
+
+        cdf = stats.rice.cdf(np.arange(10, 160, 10), np.arange(10, 160, 10))
+        # Generated in R
+        # library(VGAM)
+        # options(digits=16)
+        # x = seq(10, 150, 10)
+        # print(price(x, sigma=1, vee=x))
+        cdf_exp = [0.4800278103504522, 0.4900233218590353, 0.4933500379379548,
+                   0.4950128317658719, 0.4960103776798502, 0.4966753655438764,
+                   0.4971503395812474, 0.4975065620443196, 0.4977836197921638,
+                   0.4980052636649550, 0.4981866072661382, 0.4983377260666599,
+                   0.4984655952615694, 0.4985751970541413, 0.4986701850071265]
+        assert_allclose(cdf, cdf_exp)
+
+        probabilities = np.arange(0.1, 1, 0.1)
+        ppf = stats.rice.ppf(probabilities, 500/4, scale=4)
+        # Generated in R
+        # library(VGAM)
+        # options(digits=16)
+        # p = seq(0.1, .9, by = .1)
+        # print(qrice(p, vee = 500, sigma = 4))
+        ppf_exp = [494.8898762347361, 496.6495690858350, 497.9184315188069,
+                   499.0026277378915, 500.0159999146250, 501.0293721352668,
+                   502.1135684981884, 503.3824312270405, 505.1421247157822]
+        assert_allclose(ppf, ppf_exp)
+
+        ppf = scipy.stats.rice.ppf(0.5, np.arange(10, 150, 10))
+        # Generated in R
+        # library(VGAM)
+        # options(digits=16)
+        # b <- seq(10, 140, 10)
+        # print(qrice(0.5, vee = b, sigma = 1))
+        ppf_exp = [10.04995862522287, 20.02499480078302, 30.01666512465732,
+                   40.01249934924363, 50.00999966676032, 60.00833314046875,
+                   70.00714273568241, 80.00624991862573, 90.00555549840364,
+                   100.00499995833597, 110.00454542324384, 120.00416664255323,
+                   130.00384613488120, 140.00357141338748]
+        assert_allclose(ppf, ppf_exp)
+
+
+class TestErlang:
+    def setup_method(self):
+        self.rng = np.random.default_rng(2792245532)
+
+    def test_erlang_runtimewarning(self):
+        # erlang should generate a RuntimeWarning if a non-integer
+        # shape parameter is used.
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", RuntimeWarning)
+
+            # The non-integer shape parameter 1.3 should trigger a
+            # RuntimeWarning
+            assert_raises(RuntimeWarning, stats.erlang.rvs, 1.3, loc=0,
+                          scale=1, size=4, random_state=self.rng)
+
+            # Calling the fit method with `f0` set to an integer should
+            # *not* trigger a RuntimeWarning.  It should return the same
+            # values as gamma.fit(...).
+            data = [0.5, 1.0, 2.0, 4.0]
+            result_erlang = stats.erlang.fit(data, f0=1)
+            result_gamma = stats.gamma.fit(data, f0=1)
+            assert_allclose(result_erlang, result_gamma, rtol=1e-3)
+
+    def test_gh_pr_10949_argcheck(self):
+        assert_equal(stats.erlang.pdf(0.5, a=[1, -1]),
+                     stats.gamma.pdf(0.5, a=[1, -1]))
+
+
+class TestRayleigh:
+    def setup_method(self):
+        self.rng = np.random.default_rng(7186715712)
+
+    # gh-6227
+    def test_logpdf(self):
+        y = stats.rayleigh.logpdf(50)
+        assert_allclose(y, -1246.0879769945718)
+
+    def test_logsf(self):
+        y = stats.rayleigh.logsf(50)
+        assert_allclose(y, -1250)
+
+    @pytest.mark.parametrize("rvs_loc,rvs_scale", [(0.85373171, 0.86932204),
+                                                   (0.20558821, 0.61621008)])
+    def test_fit(self, rvs_loc, rvs_scale):
+        data = stats.rayleigh.rvs(size=250, loc=rvs_loc,
+                                  scale=rvs_scale, random_state=self.rng)
+
+        def scale_mle(data, floc):
+            return (np.sum((data - floc) ** 2) / (2 * len(data))) ** .5
+
+        # when `floc` is provided, `scale` is found with an analytical formula
+        scale_expect = scale_mle(data, rvs_loc)
+        loc, scale = stats.rayleigh.fit(data, floc=rvs_loc)
+        assert_equal(loc, rvs_loc)
+        assert_equal(scale, scale_expect)
+
+        # when `fscale` is fixed, superclass fit is used to determine `loc`.
+        loc, scale = stats.rayleigh.fit(data, fscale=.6)
+        assert_equal(scale, .6)
+
+        # with both parameters free, one dimensional optimization is done
+        # over a new function that takes into account the dependent relation
+        # of `scale` to `loc`.
+        loc, scale = stats.rayleigh.fit(data)
+        # test that `scale` is defined by its relation to `loc`
+        assert_equal(scale, scale_mle(data, loc))
+
+    @pytest.mark.parametrize("rvs_loc,rvs_scale", [[0.74, 0.01],
+                                                   [0.08464463, 0.12069025]])
+    def test_fit_comparison_super_method(self, rvs_loc, rvs_scale):
+        # test that the objective function result of the analytical MLEs is
+        # less than or equal to that of the numerically optimized estimate
+        data = stats.rayleigh.rvs(size=250, loc=rvs_loc,
+                                  scale=rvs_scale, random_state=self.rng)
+        _assert_less_or_close_loglike(stats.rayleigh, data)
+
+    def test_fit_warnings(self):
+        assert_fit_warnings(stats.rayleigh)
+
+    def test_fit_gh17088(self):
+        # `rayleigh.fit` could return a location that was inconsistent with
+        # the data. See gh-17088.
+        rng = np.random.default_rng(456)
+        loc, scale, size = 50, 600, 500
+        rvs = stats.rayleigh.rvs(loc, scale, size=size, random_state=rng)
+        loc_fit, _ = stats.rayleigh.fit(rvs)
+        assert loc_fit < np.min(rvs)
+        loc_fit, scale_fit = stats.rayleigh.fit(rvs, fscale=scale)
+        assert loc_fit < np.min(rvs)
+        assert scale_fit == scale
+
+
+class TestExponWeib:
+
+    def test_pdf_logpdf(self):
+        # Regression test for gh-3508.
+        x = 0.1
+        a = 1.0
+        c = 100.0
+        p = stats.exponweib.pdf(x, a, c)
+        logp = stats.exponweib.logpdf(x, a, c)
+        # Expected values were computed with mpmath.
+        assert_allclose([p, logp],
+                        [1.0000000000000054e-97, -223.35075402042244])
+
+    def test_a_is_1(self):
+        # For issue gh-3508.
+        # Check that when a=1, the pdf and logpdf methods of exponweib are the
+        # same as those of weibull_min.
+        x = np.logspace(-4, -1, 4)
+        a = 1
+        c = 100
+
+        p = stats.exponweib.pdf(x, a, c)
+        expected = stats.weibull_min.pdf(x, c)
+        assert_allclose(p, expected)
+
+        logp = stats.exponweib.logpdf(x, a, c)
+        expected = stats.weibull_min.logpdf(x, c)
+        assert_allclose(logp, expected)
+
+    def test_a_is_1_c_is_1(self):
+        # When a = 1 and c = 1, the distribution is exponential.
+        x = np.logspace(-8, 1, 10)
+        a = 1
+        c = 1
+
+        p = stats.exponweib.pdf(x, a, c)
+        expected = stats.expon.pdf(x)
+        assert_allclose(p, expected)
+
+        logp = stats.exponweib.logpdf(x, a, c)
+        expected = stats.expon.logpdf(x)
+        assert_allclose(logp, expected)
+
+    # Reference values were computed with mpmath, e.g:
+    #
+    #     from mpmath import mp
+    #
+    #     def mp_sf(x, a, c):
+    #         x = mp.mpf(x)
+    #         a = mp.mpf(a)
+    #         c = mp.mpf(c)
+    #         return -mp.powm1(-mp.expm1(-x**c)), a)
+    #
+    #     mp.dps = 100
+    #     print(float(mp_sf(1, 2.5, 0.75)))
+    #
+    # prints
+    #
+    #     0.6823127476985246
+    #
+    @pytest.mark.parametrize(
+        'x, a, c, ref',
+        [(1, 2.5, 0.75, 0.6823127476985246),
+         (50, 2.5, 0.75, 1.7056666054719663e-08),
+         (125, 2.5, 0.75, 1.4534393150714602e-16),
+         (250, 2.5, 0.75, 1.2391389689773512e-27),
+         (250, 0.03125, 0.75, 1.548923711221689e-29),
+         (3, 0.03125, 3.0,  5.873527551689983e-14),
+         (2e80, 10.0, 0.02, 2.9449084156902135e-17)]
+    )
+    def test_sf(self, x, a, c, ref):
+        sf = stats.exponweib.sf(x, a, c)
+        assert_allclose(sf, ref, rtol=1e-14)
+
+    # Reference values were computed with mpmath, e.g.
+    #
+    #     from mpmath import mp
+    #
+    #     def mp_isf(p, a, c):
+    #         p = mp.mpf(p)
+    #         a = mp.mpf(a)
+    #         c = mp.mpf(c)
+    #         return (-mp.log(-mp.expm1(mp.log1p(-p)/a)))**(1/c)
+    #
+    #     mp.dps = 100
+    #     print(float(mp_isf(0.25, 2.5, 0.75)))
+    #
+    # prints
+    #
+    #     2.8946008178158924
+    #
+    @pytest.mark.parametrize(
+        'p, a, c, ref',
+        [(0.25, 2.5, 0.75, 2.8946008178158924),
+         (3e-16, 2.5, 0.75, 121.77966713102938),
+         (1e-12, 1, 2, 5.256521769756932),
+         (2e-13, 0.03125, 3, 2.953915059484589),
+         (5e-14, 10.0, 0.02, 7.57094886384687e+75)]
+    )
+    def test_isf(self, p, a, c, ref):
+        isf = stats.exponweib.isf(p, a, c)
+        assert_allclose(isf, ref, rtol=5e-14)
+
+    # Reference values computed with mpmath.
+    @pytest.mark.parametrize('x, a, c, ref',
+                             [(2, 3, 8, -1.9848783170128456e-111),
+                              (1000, 0.5, 0.75, -2.946296827524972e-78)])
+    def test_logcdf(self, x, a, c, ref):
+        logcdf = stats.exponweib.logcdf(x, a, c)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+
+    # Reference values computed with mpmath.
+    @pytest.mark.parametrize('x, a, c, ref',
+                             [(1e-65, 1.5, 1.25, -1.333521432163324e-122),
+                              (2e-10, 2, 10, -1.0485760000000007e-194)])
+    def test_logsf(self, x, a, c, ref):
+        logsf = stats.exponweib.logsf(x, a, c)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+
+class TestFatigueLife:
+
+    def test_sf_tail(self):
+        # Expected value computed with mpmath:
+        #     import mpmath
+        #     mpmath.mp.dps = 80
+        #     x = mpmath.mpf(800.0)
+        #     c = mpmath.mpf(2.5)
+        #     s = float(1 - mpmath.ncdf(1/c * (mpmath.sqrt(x)
+        #                                      - 1/mpmath.sqrt(x))))
+        #     print(s)
+        # Output:
+        #     6.593376447038406e-30
+        s = stats.fatiguelife.sf(800.0, 2.5)
+        assert_allclose(s, 6.593376447038406e-30, rtol=1e-13)
+
+    def test_isf_tail(self):
+        # See test_sf_tail for the mpmath code.
+        p = 6.593376447038406e-30
+        q = stats.fatiguelife.isf(p, 2.5)
+        assert_allclose(q, 800.0, rtol=1e-13)
+
+
+class TestWeibull:
+
+    def test_logpdf(self):
+        # gh-6217
+        y = stats.weibull_min.logpdf(0, 1)
+        assert_equal(y, 0)
+
+    def test_with_maxima_distrib(self):
+        # Tests for weibull_min and weibull_max.
+        # The expected values were computed using the symbolic algebra
+        # program 'maxima' with the package 'distrib', which has
+        # 'pdf_weibull' and 'cdf_weibull'.  The mapping between the
+        # scipy and maxima functions is as follows:
+        # -----------------------------------------------------------------
+        # scipy                              maxima
+        # ---------------------------------  ------------------------------
+        # weibull_min.pdf(x, a, scale=b)     pdf_weibull(x, a, b)
+        # weibull_min.logpdf(x, a, scale=b)  log(pdf_weibull(x, a, b))
+        # weibull_min.cdf(x, a, scale=b)     cdf_weibull(x, a, b)
+        # weibull_min.logcdf(x, a, scale=b)  log(cdf_weibull(x, a, b))
+        # weibull_min.sf(x, a, scale=b)      1 - cdf_weibull(x, a, b)
+        # weibull_min.logsf(x, a, scale=b)   log(1 - cdf_weibull(x, a, b))
+        #
+        # weibull_max.pdf(x, a, scale=b)     pdf_weibull(-x, a, b)
+        # weibull_max.logpdf(x, a, scale=b)  log(pdf_weibull(-x, a, b))
+        # weibull_max.cdf(x, a, scale=b)     1 - cdf_weibull(-x, a, b)
+        # weibull_max.logcdf(x, a, scale=b)  log(1 - cdf_weibull(-x, a, b))
+        # weibull_max.sf(x, a, scale=b)      cdf_weibull(-x, a, b)
+        # weibull_max.logsf(x, a, scale=b)   log(cdf_weibull(-x, a, b))
+        # -----------------------------------------------------------------
+        x = 1.5
+        a = 2.0
+        b = 3.0
+
+        # weibull_min
+
+        p = stats.weibull_min.pdf(x, a, scale=b)
+        assert_allclose(p, np.exp(-0.25)/3)
+
+        lp = stats.weibull_min.logpdf(x, a, scale=b)
+        assert_allclose(lp, -0.25 - np.log(3))
+
+        c = stats.weibull_min.cdf(x, a, scale=b)
+        assert_allclose(c, -special.expm1(-0.25))
+
+        lc = stats.weibull_min.logcdf(x, a, scale=b)
+        assert_allclose(lc, np.log(-special.expm1(-0.25)))
+
+        s = stats.weibull_min.sf(x, a, scale=b)
+        assert_allclose(s, np.exp(-0.25))
+
+        ls = stats.weibull_min.logsf(x, a, scale=b)
+        assert_allclose(ls, -0.25)
+
+        # Also test using a large value x, for which computing the survival
+        # function using the CDF would result in 0.
+        s = stats.weibull_min.sf(30, 2, scale=3)
+        assert_allclose(s, np.exp(-100))
+
+        ls = stats.weibull_min.logsf(30, 2, scale=3)
+        assert_allclose(ls, -100)
+
+        # weibull_max
+        x = -1.5
+
+        p = stats.weibull_max.pdf(x, a, scale=b)
+        assert_allclose(p, np.exp(-0.25)/3)
+
+        lp = stats.weibull_max.logpdf(x, a, scale=b)
+        assert_allclose(lp, -0.25 - np.log(3))
+
+        c = stats.weibull_max.cdf(x, a, scale=b)
+        assert_allclose(c, np.exp(-0.25))
+
+        lc = stats.weibull_max.logcdf(x, a, scale=b)
+        assert_allclose(lc, -0.25)
+
+        s = stats.weibull_max.sf(x, a, scale=b)
+        assert_allclose(s, -special.expm1(-0.25))
+
+        ls = stats.weibull_max.logsf(x, a, scale=b)
+        assert_allclose(ls, np.log(-special.expm1(-0.25)))
+
+        # Also test using a value of x close to 0, for which computing the
+        # survival function using the CDF would result in 0.
+        s = stats.weibull_max.sf(-1e-9, 2, scale=3)
+        assert_allclose(s, -special.expm1(-1/9000000000000000000))
+
+        ls = stats.weibull_max.logsf(-1e-9, 2, scale=3)
+        assert_allclose(ls, np.log(-special.expm1(-1/9000000000000000000)))
+
+    @pytest.mark.parametrize('scale', [1.0, 0.1])
+    def test_delta_cdf(self, scale):
+        # Expected value computed with mpmath:
+        #
+        # def weibull_min_sf(x, k, scale):
+        #     x = mpmath.mpf(x)
+        #     k = mpmath.mpf(k)
+        #     scale =mpmath.mpf(scale)
+        #     return mpmath.exp(-(x/scale)**k)
+        #
+        # >>> import mpmath
+        # >>> mpmath.mp.dps = 60
+        # >>> sf1 = weibull_min_sf(7.5, 3, 1)
+        # >>> sf2 = weibull_min_sf(8.0, 3, 1)
+        # >>> float(sf1 - sf2)
+        # 6.053624060118734e-184
+        #
+        delta = stats.weibull_min._delta_cdf(scale*7.5, scale*8, 3,
+                                             scale=scale)
+        assert_allclose(delta, 6.053624060118734e-184)
+
+    def test_fit_min(self):
+        rng = np.random.default_rng(5985959307161735394)
+
+        c, loc, scale = 2, 3.5, 0.5  # arbitrary, valid parameters
+        dist = stats.weibull_min(c, loc, scale)
+        rvs = dist.rvs(size=100, random_state=rng)
+
+        # test that MLE still honors guesses and fixed parameters
+        c2, loc2, scale2 = stats.weibull_min.fit(rvs, 1.5, floc=3)
+        c3, loc3, scale3 = stats.weibull_min.fit(rvs, 1.6, floc=3)
+        assert loc2 == loc3 == 3  # fixed parameter is respected
+        assert c2 != c3  # different guess -> (slightly) different outcome
+        # quality of fit is tested elsewhere
+
+        # test that MoM honors fixed parameters, accepts (but ignores) guesses
+        c4, loc4, scale4 = stats.weibull_min.fit(rvs, 3, fscale=3, method='mm')
+        assert scale4 == 3
+        # because scale was fixed, only the mean and skewness will be matched
+        dist4 = stats.weibull_min(c4, loc4, scale4)
+        res = dist4.stats(moments='ms')
+        ref = np.mean(rvs), stats.skew(rvs)
+        assert_allclose(res, ref)
+
+    # reference values were computed via mpmath
+    # from mpmath import mp
+    # def weibull_sf_mpmath(x, c):
+    #     x = mp.mpf(x)
+    #     c = mp.mpf(c)
+    #     return float(mp.exp(-x**c))
+
+    @pytest.mark.parametrize('x, c, ref', [(50, 1, 1.9287498479639178e-22),
+                                           (1000, 0.8,
+                                            8.131269637872743e-110)])
+    def test_sf_isf(self, x, c, ref):
+        assert_allclose(stats.weibull_min.sf(x, c), ref, rtol=5e-14)
+        assert_allclose(stats.weibull_min.isf(ref, c), x, rtol=5e-14)
+
+
+class TestDweibull:
+    def test_entropy(self):
+        # Test that dweibull entropy follows that of weibull_min.
+        # (Generic tests check that the dweibull entropy is consistent
+        #  with its PDF. As for accuracy, dweibull entropy should be just
+        #  as accurate as weibull_min entropy. Checks of accuracy against
+        #  a reference need only be applied to the fundamental distribution -
+        #  weibull_min.)
+        rng = np.random.default_rng(8486259129157041777)
+        c = 10**rng.normal(scale=100, size=10)
+        res = stats.dweibull.entropy(c)
+        ref = stats.weibull_min.entropy(c) - np.log(0.5)
+        assert_allclose(res, ref, rtol=1e-15)
+
+    def test_sf(self):
+        # test that for positive values the dweibull survival function is half
+        # the weibull_min survival function
+        rng = np.random.default_rng(8486259129157041777)
+        c = 10**rng.normal(scale=1, size=10)
+        x = 10 * rng.uniform()
+        res = stats.dweibull.sf(x, c)
+        ref = 0.5 * stats.weibull_min.sf(x, c)
+        assert_allclose(res, ref, rtol=1e-15)
+
+
+class TestTruncWeibull:
+
+    def test_pdf_bounds(self):
+        # test bounds
+        y = stats.truncweibull_min.pdf([0.1, 2.0], 2.0, 0.11, 1.99)
+        assert_equal(y, [0.0, 0.0])
+
+    def test_logpdf(self):
+        y = stats.truncweibull_min.logpdf(2.0, 1.0, 2.0, np.inf)
+        assert_equal(y, 0.0)
+
+        # hand calculation
+        y = stats.truncweibull_min.logpdf(2.0, 1.0, 2.0, 4.0)
+        assert_allclose(y, 0.14541345786885884)
+
+    def test_ppf_bounds(self):
+        # test bounds
+        y = stats.truncweibull_min.ppf([0.0, 1.0], 2.0, 0.1, 2.0)
+        assert_equal(y, [0.1, 2.0])
+
+    def test_cdf_to_ppf(self):
+        q = [0., 0.1, .25, 0.50, 0.75, 0.90, 1.]
+        x = stats.truncweibull_min.ppf(q, 2., 0., 3.)
+        q_out = stats.truncweibull_min.cdf(x, 2., 0., 3.)
+        assert_allclose(q, q_out)
+
+    def test_sf_to_isf(self):
+        q = [0., 0.1, .25, 0.50, 0.75, 0.90, 1.]
+        x = stats.truncweibull_min.isf(q, 2., 0., 3.)
+        q_out = stats.truncweibull_min.sf(x, 2., 0., 3.)
+        assert_allclose(q, q_out)
+
+    def test_munp(self):
+        c = 2.
+        a = 1.
+        b = 3.
+
+        def xnpdf(x, n):
+            return x**n*stats.truncweibull_min.pdf(x, c, a, b)
+
+        m0 = stats.truncweibull_min.moment(0, c, a, b)
+        assert_equal(m0, 1.)
+
+        m1 = stats.truncweibull_min.moment(1, c, a, b)
+        m1_expected, _ = quad(lambda x: xnpdf(x, 1), a, b)
+        assert_allclose(m1, m1_expected)
+
+        m2 = stats.truncweibull_min.moment(2, c, a, b)
+        m2_expected, _ = quad(lambda x: xnpdf(x, 2), a, b)
+        assert_allclose(m2, m2_expected)
+
+        m3 = stats.truncweibull_min.moment(3, c, a, b)
+        m3_expected, _ = quad(lambda x: xnpdf(x, 3), a, b)
+        assert_allclose(m3, m3_expected)
+
+        m4 = stats.truncweibull_min.moment(4, c, a, b)
+        m4_expected, _ = quad(lambda x: xnpdf(x, 4), a, b)
+        assert_allclose(m4, m4_expected)
+
+    def test_reference_values(self):
+        a = 1.
+        b = 3.
+        c = 2.
+        x_med = np.sqrt(1 - np.log(0.5 + np.exp(-(8. + np.log(2.)))))
+
+        cdf = stats.truncweibull_min.cdf(x_med, c, a, b)
+        assert_allclose(cdf, 0.5)
+
+        lc = stats.truncweibull_min.logcdf(x_med, c, a, b)
+        assert_allclose(lc, -np.log(2.))
+
+        ppf = stats.truncweibull_min.ppf(0.5, c, a, b)
+        assert_allclose(ppf, x_med)
+
+        sf = stats.truncweibull_min.sf(x_med, c, a, b)
+        assert_allclose(sf, 0.5)
+
+        ls = stats.truncweibull_min.logsf(x_med, c, a, b)
+        assert_allclose(ls, -np.log(2.))
+
+        isf = stats.truncweibull_min.isf(0.5, c, a, b)
+        assert_allclose(isf, x_med)
+
+    def test_compare_weibull_min(self):
+        # Verify that the truncweibull_min distribution gives the same results
+        # as the original weibull_min
+        x = 1.5
+        c = 2.0
+        a = 0.0
+        b = np.inf
+        scale = 3.0
+
+        p = stats.weibull_min.pdf(x, c, scale=scale)
+        p_trunc = stats.truncweibull_min.pdf(x, c, a, b, scale=scale)
+        assert_allclose(p, p_trunc)
+
+        lp = stats.weibull_min.logpdf(x, c, scale=scale)
+        lp_trunc = stats.truncweibull_min.logpdf(x, c, a, b, scale=scale)
+        assert_allclose(lp, lp_trunc)
+
+        cdf = stats.weibull_min.cdf(x, c, scale=scale)
+        cdf_trunc = stats.truncweibull_min.cdf(x, c, a, b, scale=scale)
+        assert_allclose(cdf, cdf_trunc)
+
+        lc = stats.weibull_min.logcdf(x, c, scale=scale)
+        lc_trunc = stats.truncweibull_min.logcdf(x, c, a, b, scale=scale)
+        assert_allclose(lc, lc_trunc)
+
+        s = stats.weibull_min.sf(x, c, scale=scale)
+        s_trunc = stats.truncweibull_min.sf(x, c, a, b, scale=scale)
+        assert_allclose(s, s_trunc)
+
+        ls = stats.weibull_min.logsf(x, c, scale=scale)
+        ls_trunc = stats.truncweibull_min.logsf(x, c, a, b, scale=scale)
+        assert_allclose(ls, ls_trunc)
+
+        # # Also test using a large value x, for which computing the survival
+        # # function using the CDF would result in 0.
+        s = stats.truncweibull_min.sf(30, 2, a, b, scale=3)
+        assert_allclose(s, np.exp(-100))
+
+        ls = stats.truncweibull_min.logsf(30, 2, a, b, scale=3)
+        assert_allclose(ls, -100)
+
+    def test_compare_weibull_min2(self):
+        # Verify that the truncweibull_min distribution PDF and CDF results
+        # are the same as those calculated from truncating weibull_min
+        c, a, b = 2.5, 0.25, 1.25
+        x = np.linspace(a, b, 100)
+
+        pdf1 = stats.truncweibull_min.pdf(x, c, a, b)
+        cdf1 = stats.truncweibull_min.cdf(x, c, a, b)
+
+        norm = stats.weibull_min.cdf(b, c) - stats.weibull_min.cdf(a, c)
+        pdf2 = stats.weibull_min.pdf(x, c) / norm
+        cdf2 = (stats.weibull_min.cdf(x, c) - stats.weibull_min.cdf(a, c))/norm
+
+        np.testing.assert_allclose(pdf1, pdf2)
+        np.testing.assert_allclose(cdf1, cdf2)
+
+
+class TestRdist:
+    def test_rdist_cdf_gh1285(self):
+        # check workaround in rdist._cdf for issue gh-1285.
+        distfn = stats.rdist
+        values = [0.001, 0.5, 0.999]
+        assert_almost_equal(distfn.cdf(distfn.ppf(values, 541.0), 541.0),
+                            values, decimal=5)
+
+    def test_rdist_beta(self):
+        # rdist is a special case of stats.beta
+        x = np.linspace(-0.99, 0.99, 10)
+        c = 2.7
+        assert_almost_equal(0.5*stats.beta(c/2, c/2).pdf((x + 1)/2),
+                            stats.rdist(c).pdf(x))
+
+    # reference values were computed via mpmath
+    # from mpmath import mp
+    # mp.dps = 200
+    # def rdist_sf_mpmath(x, c):
+    #     x = mp.mpf(x)
+    #     c = mp.mpf(c)
+    #     return float(mp.betainc(c/2, c/2, (x+1)/2, mp.one, regularized=True))
+    @pytest.mark.parametrize(
+        "x, c, ref",
+        [
+            (0.0001, 541, 0.49907251345565845),
+            (0.1, 241, 0.06000788166249205),
+            (0.5, 441, 1.0655898106047832e-29),
+            (0.8, 341, 6.025478373732215e-78),
+        ]
+    )
+    def test_rdist_sf(self, x, c, ref):
+        assert_allclose(stats.rdist.sf(x, c), ref, rtol=5e-14)
+
+
+class TestTrapezoid:
+    def test_reduces_to_triang(self):
+        modes = [0, 0.3, 0.5, 1]
+        for mode in modes:
+            x = [0, mode, 1]
+            assert_almost_equal(stats.trapezoid.pdf(x, mode, mode),
+                                stats.triang.pdf(x, mode))
+            assert_almost_equal(stats.trapezoid.cdf(x, mode, mode),
+                                stats.triang.cdf(x, mode))
+
+    def test_reduces_to_uniform(self):
+        x = np.linspace(0, 1, 10)
+        assert_almost_equal(stats.trapezoid.pdf(x, 0, 1), stats.uniform.pdf(x))
+        assert_almost_equal(stats.trapezoid.cdf(x, 0, 1), stats.uniform.cdf(x))
+
+    def test_cases(self):
+        # edge cases
+        assert_almost_equal(stats.trapezoid.pdf(0, 0, 0), 2)
+        assert_almost_equal(stats.trapezoid.pdf(1, 1, 1), 2)
+        assert_almost_equal(stats.trapezoid.pdf(0.5, 0, 0.8),
+                            1.11111111111111111)
+        assert_almost_equal(stats.trapezoid.pdf(0.5, 0.2, 1.0),
+                            1.11111111111111111)
+
+        # straightforward case
+        assert_almost_equal(stats.trapezoid.pdf(0.1, 0.2, 0.8), 0.625)
+        assert_almost_equal(stats.trapezoid.pdf(0.5, 0.2, 0.8), 1.25)
+        assert_almost_equal(stats.trapezoid.pdf(0.9, 0.2, 0.8), 0.625)
+
+        assert_almost_equal(stats.trapezoid.cdf(0.1, 0.2, 0.8), 0.03125)
+        assert_almost_equal(stats.trapezoid.cdf(0.2, 0.2, 0.8), 0.125)
+        assert_almost_equal(stats.trapezoid.cdf(0.5, 0.2, 0.8), 0.5)
+        assert_almost_equal(stats.trapezoid.cdf(0.9, 0.2, 0.8), 0.96875)
+        assert_almost_equal(stats.trapezoid.cdf(1.0, 0.2, 0.8), 1.0)
+
+    def test_moments_and_entropy(self):
+        # issue #11795: improve precision of trapezoid stats
+        # Apply formulas from Wikipedia for the following parameters:
+        a, b, c, d = -3, -1, 2, 3  # => 1/3, 5/6, -3, 6
+        p1, p2, loc, scale = (b-a) / (d-a), (c-a) / (d-a), a, d-a
+        h = 2 / (d+c-b-a)
+
+        def moment(n):
+            return (h * ((d**(n+2) - c**(n+2)) / (d-c)
+                         - (b**(n+2) - a**(n+2)) / (b-a)) /
+                    (n+1) / (n+2))
+
+        mean = moment(1)
+        var = moment(2) - mean**2
+        entropy = 0.5 * (d-c+b-a) / (d+c-b-a) + np.log(0.5 * (d+c-b-a))
+        assert_almost_equal(stats.trapezoid.mean(p1, p2, loc, scale),
+                            mean, decimal=13)
+        assert_almost_equal(stats.trapezoid.var(p1, p2, loc, scale),
+                            var, decimal=13)
+        assert_almost_equal(stats.trapezoid.entropy(p1, p2, loc, scale),
+                            entropy, decimal=13)
+
+        # Check boundary cases where scipy d=0 or d=1.
+        assert_almost_equal(stats.trapezoid.mean(0, 0, -3, 6), -1, decimal=13)
+        assert_almost_equal(stats.trapezoid.mean(0, 1, -3, 6), 0, decimal=13)
+        assert_almost_equal(stats.trapezoid.var(0, 1, -3, 6), 3, decimal=13)
+
+    def test_trapezoid_vect(self):
+        # test that array-valued shapes and arguments are handled
+        c = np.array([0.1, 0.2, 0.3])
+        d = np.array([0.5, 0.6])[:, None]
+        x = np.array([0.15, 0.25, 0.9])
+        v = stats.trapezoid.pdf(x, c, d)
+
+        cc, dd, xx = np.broadcast_arrays(c, d, x)
+
+        res = np.empty(xx.size, dtype=xx.dtype)
+        ind = np.arange(xx.size)
+        for i, x1, c1, d1 in zip(ind, xx.ravel(), cc.ravel(), dd.ravel()):
+            res[i] = stats.trapezoid.pdf(x1, c1, d1)
+
+        assert_allclose(v, res.reshape(v.shape), atol=1e-15)
+
+        # Check that the stats() method supports vector arguments.
+        v = np.asarray(stats.trapezoid.stats(c, d, moments="mvsk"))
+        cc, dd = np.broadcast_arrays(c, d)
+        res = np.empty((cc.size, 4))  # 4 stats returned per value
+        ind = np.arange(cc.size)
+        for i, c1, d1 in zip(ind, cc.ravel(), dd.ravel()):
+            res[i] = stats.trapezoid.stats(c1, d1, moments="mvsk")
+
+        assert_allclose(v, res.T.reshape(v.shape), atol=1e-15)
+
+    def test_trapezoid_fit_convergence_gh23503(self):
+        # gh-23503 reported that trapezoid.fit would consistently converge to a
+        # triangular distribution unless starting values were provided. Check that this
+        # is resolved.
+
+        # Generate test data from a trapezoidal distribution
+        rng = np.random.default_rng(23842359234598263956)
+        true_args = 0.3, 0.7, -1, 2
+        true_dist = stats.trapezoid(*true_args)
+        x = true_dist.rvs(1000, random_state=rng)
+
+        # fit to data
+        fitted_args = stats.trapezoid.fit(x)
+
+        # Should not converge to triangular distribution (c=d=1)
+        fitted_c, fitted_d = fitted_args[:2]
+        assert not np.allclose(fitted_c, 1, atol=0.1)
+        assert not np.allclose(fitted_d, 1, atol=0.1)
+
+        # objective function is better than with true values of parameters
+        true_llf = stats.trapezoid.nnlf(true_args, x)
+        fitted_llf = stats.trapezoid.nnlf(fitted_args, x)
+        assert fitted_llf < true_llf
+
+
+class TestTriang:
+    def test_edge_cases(self):
+        with np.errstate(all='raise'):
+            assert_equal(stats.triang.pdf(0, 0), 2.)
+            assert_equal(stats.triang.pdf(0.5, 0), 1.)
+            assert_equal(stats.triang.pdf(1, 0), 0.)
+
+            assert_equal(stats.triang.pdf(0, 1), 0)
+            assert_equal(stats.triang.pdf(0.5, 1), 1.)
+            assert_equal(stats.triang.pdf(1, 1), 2)
+
+            assert_equal(stats.triang.cdf(0., 0.), 0.)
+            assert_equal(stats.triang.cdf(0.5, 0.), 0.75)
+            assert_equal(stats.triang.cdf(1.0, 0.), 1.0)
+
+            assert_equal(stats.triang.cdf(0., 1.), 0.)
+            assert_equal(stats.triang.cdf(0.5, 1.), 0.25)
+            assert_equal(stats.triang.cdf(1., 1.), 1)
+
+
+class TestMaxwell:
+
+    # reference values were computed with wolfram alpha
+    # erfc(x/sqrt(2)) + sqrt(2/pi) * x * e^(-x^2/2)
+
+    @pytest.mark.parametrize("x, ref",
+                             [(20, 2.2138865931011177e-86),
+                              (0.01, 0.999999734046458435)])
+    def test_sf(self, x, ref):
+        assert_allclose(stats.maxwell.sf(x), ref, rtol=1e-14)
+
+    # reference values were computed with wolfram alpha
+    # sqrt(2) * sqrt(Q^(-1)(3/2, q))
+
+    @pytest.mark.parametrize("q, ref",
+                             [(0.001, 4.033142223656157022),
+                              (0.9999847412109375, 0.0385743284050381),
+                              (2**-55, 8.95564974719481)])
+    def test_isf(self, q, ref):
+        assert_allclose(stats.maxwell.isf(q), ref, rtol=1e-15)
+
+    def test_logcdf(self):
+        # Reference value computed with mpmath.
+        ref = -1.8729310110194814e-17
+        logcdf = stats.maxwell.logcdf(9)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+
+    def test_logsf(self):
+        # Reference value computed with mpmath.
+        ref = -2.6596152026762177e-25
+        logsf = stats.maxwell.logsf(1e-8)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+
+class TestMielke:
+    def test_moments(self):
+        k, s = 4.642, 0.597
+        # n-th moment exists only if n < s
+        assert_equal(stats.mielke(k, s).moment(1), np.inf)
+        assert_equal(stats.mielke(k, 1.0).moment(1), np.inf)
+        assert_(np.isfinite(stats.mielke(k, 1.01).moment(1)))
+
+    def test_burr_equivalence(self):
+        x = np.linspace(0.01, 100, 50)
+        k, s = 2.45, 5.32
+        assert_allclose(stats.burr.pdf(x, s, k/s), stats.mielke.pdf(x, k, s))
+
+
+class TestBurr:
+    def test_endpoints_7491(self):
+        # gh-7491
+        # Compute the pdf at the left endpoint dst.a.
+        data = [
+            [stats.fisk, (1,), 1],
+            [stats.burr, (0.5, 2), 1],
+            [stats.burr, (1, 1), 1],
+            [stats.burr, (2, 0.5), 1],
+            [stats.burr12, (1, 0.5), 0.5],
+            [stats.burr12, (1, 1), 1.0],
+            [stats.burr12, (1, 2), 2.0]]
+
+        ans = [_f.pdf(_f.a, *_args) for _f, _args, _ in data]
+        correct = [_correct_ for _f, _args, _correct_ in data]
+        assert_array_almost_equal(ans, correct)
+
+        ans = [_f.logpdf(_f.a, *_args) for _f, _args, _ in data]
+        correct = [np.log(_correct_) for _f, _args, _correct_ in data]
+        assert_array_almost_equal(ans, correct)
+
+    def test_burr_stats_9544(self):
+        # gh-9544.  Test from gh-9978
+        c, d = 5.0, 3
+        mean, variance = stats.burr(c, d).stats()
+        # mean = sc.beta(3 + 1/5, 1. - 1/5) * 3  = 1.4110263...
+        # var =  sc.beta(3 + 2 / 5, 1. - 2 / 5) * 3 -
+        #        (sc.beta(3 + 1 / 5, 1. - 1 / 5) * 3) ** 2
+        mean_hc, variance_hc = 1.4110263183925857, 0.22879948026191643
+        assert_allclose(mean, mean_hc)
+        assert_allclose(variance, variance_hc)
+
+    def test_burr_nan_mean_var_9544(self):
+        # gh-9544.  Test from gh-9978
+        c, d = 0.5, 3
+        mean, variance = stats.burr(c, d).stats()
+        assert_(np.isnan(mean))
+        assert_(np.isnan(variance))
+        c, d = 1.5, 3
+        mean, variance = stats.burr(c, d).stats()
+        assert_(np.isfinite(mean))
+        assert_(np.isnan(variance))
+
+        c, d = 0.5, 3
+        e1, e2, e3, e4 = stats.burr._munp(np.array([1, 2, 3, 4]), c, d)
+        assert_(np.isnan(e1))
+        assert_(np.isnan(e2))
+        assert_(np.isnan(e3))
+        assert_(np.isnan(e4))
+        c, d = 1.5, 3
+        e1, e2, e3, e4 = stats.burr._munp([1, 2, 3, 4], c, d)
+        assert_(np.isfinite(e1))
+        assert_(np.isnan(e2))
+        assert_(np.isnan(e3))
+        assert_(np.isnan(e4))
+        c, d = 2.5, 3
+        e1, e2, e3, e4 = stats.burr._munp([1, 2, 3, 4], c, d)
+        assert_(np.isfinite(e1))
+        assert_(np.isfinite(e2))
+        assert_(np.isnan(e3))
+        assert_(np.isnan(e4))
+        c, d = 3.5, 3
+        e1, e2, e3, e4 = stats.burr._munp([1, 2, 3, 4], c, d)
+        assert_(np.isfinite(e1))
+        assert_(np.isfinite(e2))
+        assert_(np.isfinite(e3))
+        assert_(np.isnan(e4))
+        c, d = 4.5, 3
+        e1, e2, e3, e4 = stats.burr._munp([1, 2, 3, 4], c, d)
+        assert_(np.isfinite(e1))
+        assert_(np.isfinite(e2))
+        assert_(np.isfinite(e3))
+        assert_(np.isfinite(e4))
+
+    def test_burr_isf(self):
+        # reference values were computed via the reference distribution, e.g.
+        # mp.dps = 100
+        # Burr(c=5, d=3).isf([0.1, 1e-10, 1e-20, 1e-40])
+        c, d = 5.0, 3.0
+        q = [0.1, 1e-10, 1e-20, 1e-40]
+        ref = [1.9469686558286508, 124.57309395989076, 12457.309396155173,
+               124573093.96155174]
+        assert_allclose(stats.burr.isf(q, c, d), ref, rtol=1e-14)
+
+
+class TestBurr12:
+
+    @pytest.mark.parametrize('scale, expected',
+                             [(1.0, 2.3283064359965952e-170),
+                              (3.5, 5.987114417447875e-153)])
+    def test_delta_cdf(self, scale, expected):
+        # Expected value computed with mpmath:
+        #
+        # def burr12sf(x, c, d, scale):
+        #     x = mpmath.mpf(x)
+        #     c = mpmath.mpf(c)
+        #     d = mpmath.mpf(d)
+        #     scale = mpmath.mpf(scale)
+        #     return (mpmath.mp.one + (x/scale)**c)**(-d)
+        #
+        # >>> import mpmath
+        # >>> mpmath.mp.dps = 60
+        # >>> float(burr12sf(2e5, 4, 8, 1) - burr12sf(4e5, 4, 8, 1))
+        # 2.3283064359965952e-170
+        # >>> float(burr12sf(2e5, 4, 8, 3.5) - burr12sf(4e5, 4, 8, 3.5))
+        # 5.987114417447875e-153
+        #
+        delta = stats.burr12._delta_cdf(2e5, 4e5, 4, 8, scale=scale)
+        assert_allclose(delta, expected, rtol=1e-13)
+
+    def test_moments_edge(self):
+        # gh-18838 reported that burr12 moments could be invalid; see above.
+        # Check that this is resolved in an edge case where c*d == n, and
+        # compare the results against those produced by Mathematica, e.g.
+        # `SinghMaddalaDistribution[2, 2, 1]` at Wolfram Alpha.
+        c, d = 2, 2
+        mean = np.pi/4
+        var = 1 - np.pi**2/16
+        skew = np.pi**3/(32*var**1.5)
+        kurtosis = np.nan
+        ref = [mean, var, skew, kurtosis]
+        res = stats.burr12(c, d).stats('mvsk')
+        assert_allclose(res, ref, rtol=1e-14)
+
+    # Reference values were computed with mpmath using mp.dps = 80
+    # and then cast to float.
+    @pytest.mark.parametrize(
+        'p, c, d, ref',
+        [(1e-12, 20, 0.5, 15.848931924611135),
+         (1e-19, 20, 0.5, 79.43282347242815),
+         (1e-12, 0.25, 35, 2.0888618213462466),
+         (1e-80, 0.25, 35, 1360930951.7972188)]
+    )
+    def test_isf_near_zero(self, p, c, d, ref):
+        x = stats.burr12.isf(p, c, d)
+        assert_allclose(x, ref, rtol=1e-14)
+
+
+class TestStudentizedRange:
+    # For alpha = .05, .01, and .001, and for each value of
+    # v = [1, 3, 10, 20, 120, inf], a Q was picked from each table for
+    # k = [2, 8, 14, 20].
+
+    # these arrays are written with `k` as column, and `v` as rows.
+    # Q values are taken from table 3:
+    # https://www.jstor.org/stable/2237810
+    q05 = [17.97, 45.40, 54.33, 59.56,
+           4.501, 8.853, 10.35, 11.24,
+           3.151, 5.305, 6.028, 6.467,
+           2.950, 4.768, 5.357, 5.714,
+           2.800, 4.363, 4.842, 5.126,
+           2.772, 4.286, 4.743, 5.012]
+    q01 = [90.03, 227.2, 271.8, 298.0,
+           8.261, 15.64, 18.22, 19.77,
+           4.482, 6.875, 7.712, 8.226,
+           4.024, 5.839, 6.450, 6.823,
+           3.702, 5.118, 5.562, 5.827,
+           3.643, 4.987, 5.400, 5.645]
+    q001 = [900.3, 2272, 2718, 2980,
+            18.28, 34.12, 39.69, 43.05,
+            6.487, 9.352, 10.39, 11.03,
+            5.444, 7.313, 7.966, 8.370,
+            4.772, 6.039, 6.448, 6.695,
+            4.654, 5.823, 6.191, 6.411]
+    qs = np.concatenate((q05, q01, q001))
+    ps = [.95, .99, .999]
+    vs = [1, 3, 10, 20, 120, np.inf]
+    ks = [2, 8, 14, 20]
+
+    data = list(zip(product(ps, vs, ks), qs))
+
+    # A small selection of large-v cases generated with R's `ptukey`
+    # Each case is in the format (q, k, v, r_result)
+    r_data = [
+        (0.1, 3, 9001, 0.002752818526842),
+        (1, 10, 1000, 0.000526142388912),
+        (1, 3, np.inf, 0.240712641229283),
+        (4, 3, np.inf, 0.987012338626815),
+        (1, 10, np.inf, 0.000519869467083),
+    ]
+
+    @pytest.mark.slow
+    def test_cdf_against_tables(self):
+        for pvk, q in self.data:
+            p_expected, v, k = pvk
+            res_p = stats.studentized_range.cdf(q, k, v)
+            assert_allclose(res_p, p_expected, rtol=1e-4)
+
+    @pytest.mark.xslow
+    def test_ppf_against_tables(self):
+        for pvk, q_expected in self.data:
+            p, v, k = pvk
+            res_q = stats.studentized_range.ppf(p, k, v)
+            assert_allclose(res_q, q_expected, rtol=5e-4)
+
+    path_prefix = os.path.dirname(__file__)
+    relative_path = "data/studentized_range_mpmath_ref.json"
+    with open(os.path.join(path_prefix, relative_path)) as file:
+        pregenerated_data = json.load(file)
+
+    @pytest.mark.parametrize("case_result", pregenerated_data["cdf_data"])
+    def test_cdf_against_mp(self, case_result):
+        src_case = case_result["src_case"]
+        mp_result = case_result["mp_result"]
+        qkv = src_case["q"], src_case["k"], src_case["v"]
+        res = stats.studentized_range.cdf(*qkv)
+
+        assert_allclose(res, mp_result,
+                        atol=src_case["expected_atol"],
+                        rtol=src_case["expected_rtol"])
+
+    @pytest.mark.parametrize("case_result", pregenerated_data["pdf_data"])
+    def test_pdf_against_mp(self, case_result):
+        src_case = case_result["src_case"]
+        mp_result = case_result["mp_result"]
+        qkv = src_case["q"], src_case["k"], src_case["v"]
+        res = stats.studentized_range.pdf(*qkv)
+
+        assert_allclose(res, mp_result,
+                        atol=src_case["expected_atol"],
+                        rtol=src_case["expected_rtol"])
+
+    @pytest.mark.xslow
+    @pytest.mark.xfail_on_32bit("intermittent RuntimeWarning: invalid value.")
+    @pytest.mark.parametrize("case_result", pregenerated_data["moment_data"])
+    def test_moment_against_mp(self, case_result):
+        src_case = case_result["src_case"]
+        mp_result = case_result["mp_result"]
+        mkv = src_case["m"], src_case["k"], src_case["v"]
+
+        # Silence invalid value encountered warnings. Actual problems will be
+        # caught by the result comparison.
+        with np.errstate(invalid='ignore'):
+            res = stats.studentized_range.moment(*mkv)
+
+        assert_allclose(res, mp_result,
+                        atol=src_case["expected_atol"],
+                        rtol=src_case["expected_rtol"])
+
+    @pytest.mark.slow
+    def test_pdf_integration(self):
+        k, v = 3, 10
+        # Test whether PDF integration is 1 like it should be.
+        res = quad(stats.studentized_range.pdf, 0, np.inf, args=(k, v))
+        assert_allclose(res[0], 1)
+
+    @pytest.mark.xslow
+    def test_pdf_against_cdf(self):
+        k, v = 3, 10
+
+        # Test whether the integrated PDF matches the CDF using cumulative
+        # integration. Use a small step size to reduce error due to the
+        # summation. This is slow, but tests the results well.
+        x = np.arange(0, 10, step=0.01)
+
+        y_cdf = stats.studentized_range.cdf(x, k, v)[1:]
+        y_pdf_raw = stats.studentized_range.pdf(x, k, v)
+        y_pdf_cumulative = cumulative_trapezoid(y_pdf_raw, x)
+
+        # Because of error caused by the summation, use a relatively large rtol
+        assert_allclose(y_pdf_cumulative, y_cdf, rtol=1e-4)
+
+    @pytest.mark.parametrize("r_case_result", r_data)
+    def test_cdf_against_r(self, r_case_result):
+        # Test large `v` values using R
+        q, k, v, r_res = r_case_result
+        with np.errstate(invalid='ignore'):
+            res = stats.studentized_range.cdf(q, k, v)
+        assert_allclose(res, r_res)
+
+    @pytest.mark.xslow
+    @pytest.mark.xfail_on_32bit("intermittent RuntimeWarning: invalid value.")
+    def test_moment_vectorization(self):
+        # Test moment broadcasting. Calls `_munp` directly because
+        # `rv_continuous.moment` is broken at time of writing. See gh-12192
+
+        # Silence invalid value encountered warnings. Actual problems will be
+        # caught by the result comparison.
+        with np.errstate(invalid='ignore'):
+            m = stats.studentized_range._munp([1, 2], [4, 5], [10, 11])
+
+        assert_allclose(m.shape, (2,))
+
+        with pytest.raises(ValueError, match="...could not be broadcast..."):
+            stats.studentized_range._munp(1, [4, 5], [10, 11, 12])
+
+    @pytest.mark.xslow
+    def test_fitstart_valid(self):
+        with warnings.catch_warnings(), np.errstate(invalid="ignore"):
+            # the integration warning message may differ
+            warnings.simplefilter("ignore", IntegrationWarning)
+            k, df, _, _ = stats.studentized_range._fitstart([1, 2, 3])
+        assert_(stats.studentized_range._argcheck(k, df))
+
+    def test_infinite_df(self):
+        # Check that the CDF and PDF infinite and normal integrators
+        # roughly match for a high df case
+        res = stats.studentized_range.pdf(3, 10, np.inf)
+        res_finite = stats.studentized_range.pdf(3, 10, 99999)
+        assert_allclose(res, res_finite, atol=1e-4, rtol=1e-4)
+
+        res = stats.studentized_range.cdf(3, 10, np.inf)
+        res_finite = stats.studentized_range.cdf(3, 10, 99999)
+        assert_allclose(res, res_finite, atol=1e-4, rtol=1e-4)
+
+    def test_df_cutoff(self):
+        # Test that the CDF and PDF properly switch integrators at df=100,000.
+        # The infinite integrator should be different enough that it fails
+        # an allclose assertion. Also sanity check that using the same
+        # integrator does pass the allclose with a 1-df difference, which
+        # should be tiny.
+
+        res = stats.studentized_range.pdf(3, 10, 100000)
+        res_finite = stats.studentized_range.pdf(3, 10, 99999)
+        res_sanity = stats.studentized_range.pdf(3, 10, 99998)
+        assert_raises(AssertionError, assert_allclose, res, res_finite,
+                      atol=1e-6, rtol=1e-6)
+        assert_allclose(res_finite, res_sanity, atol=1e-6, rtol=1e-6)
+
+        res = stats.studentized_range.cdf(3, 10, 100000)
+        res_finite = stats.studentized_range.cdf(3, 10, 99999)
+        res_sanity = stats.studentized_range.cdf(3, 10, 99998)
+        assert_raises(AssertionError, assert_allclose, res, res_finite,
+                      atol=1e-6, rtol=1e-6)
+        assert_allclose(res_finite, res_sanity, atol=1e-6, rtol=1e-6)
+
+    def test_clipping(self):
+        # The result of this computation was -9.9253938401489e-14 on some
+        # systems. The correct result is very nearly zero, but should not be
+        # negative.
+        q, k, v = 34.6413996195345746, 3, 339
+        p = stats.studentized_range.sf(q, k, v)
+        assert_allclose(p, 0, atol=1e-10)
+        assert p >= 0
+
+
+class TestTukeyLambda:
+
+    @pytest.mark.parametrize(
+        'lam',
+        [0.0, -1.0, -2.0, np.array([[-1.0], [0.0], [-2.0]])]
+    )
+    def test_pdf_nonpositive_lambda(self, lam):
+        # Make sure that Tukey-Lambda distribution correctly handles
+        # non-positive lambdas.
+        # This is a crude test--it just checks that all the PDF values
+        # are finite and greater than 0.
+        x = np.linspace(-5.0, 5.0, 101)
+        p = stats.tukeylambda.pdf(x, lam)
+        assert np.isfinite(p).all()
+        assert (p > 0.0).all()
+
+    def test_pdf_mixed_lambda(self):
+        # Another crude test of the behavior of the PDF method.
+        x = np.linspace(-5.0, 5.0, 101)
+        lam = np.array([[-1.0], [0.0], [2.0]])
+        p = stats.tukeylambda.pdf(x, lam)
+        assert np.isfinite(p).all()
+        # For p[0] and p[1], where lam <= 0, the support is (-inf, inf),
+        # so the PDF should be nonzero everywhere (assuming we aren't so
+        # far in the tails that we get underflow).
+        assert (p[:2] > 0.0).all()
+        # For p[2], where lam=2.0, the support is [-0.5, 0.5], so in pdf(x),
+        # some values should be positive and some should be 0.
+        assert (p[2] > 0.0).any()
+        assert (p[2] == 0.0).any()
+
+    def test_support(self):
+        lam = np.array([-1.75, -0.5, 0.0, 0.25, 0.5, 2.0])
+        a, b = stats.tukeylambda.support(lam)
+        expected_b = np.array([np.inf, np.inf, np.inf, 4, 2, 0.5])
+        assert_equal(b, expected_b)
+        assert_equal(a, -expected_b)
+
+    def test_pdf_support_boundary(self):
+        # Verify that tukeylambda.pdf() doesn't generate a
+        # warning when evaluated at the bounds of the support.
+        # For lam=0.5, the support is (-2, 2).
+        p = stats.tukeylambda.pdf([-2.0, 2.0], 0.5)
+        assert_equal(p, [0.0, 0.0])
+
+    def test_tukeylambda_stats_ticket_1545(self):
+        # Some test for the variance and kurtosis of the Tukey Lambda distr.
+        # See test_tukeylamdba_stats.py for more tests.
+
+        mv = stats.tukeylambda.stats(0, moments='mvsk')
+        # Known exact values:
+        expected = [0, np.pi**2/3, 0, 1.2]
+        assert_almost_equal(mv, expected, decimal=10)
+
+        mv = stats.tukeylambda.stats(3.13, moments='mvsk')
+        # 'expected' computed with mpmath.
+        expected = [0, 0.0269220858861465102, 0, -0.898062386219224104]
+        assert_almost_equal(mv, expected, decimal=10)
+
+        mv = stats.tukeylambda.stats(0.14, moments='mvsk')
+        # 'expected' computed with mpmath.
+        expected = [0, 2.11029702221450250, 0, -0.02708377353223019456]
+        assert_almost_equal(mv, expected, decimal=10)
+
+    @pytest.mark.xfail(reason="Precision in tail hasn't been fixed in xsf")
+    def test_large_argument_ticket_21370(self):
+        # Check that survival function goes to 0 as x gets large
+        x = [1e4, 1e6, 1e8, 1e10, 1e12, 1e24]
+        lam = -0.5
+        sf = stats.tukeylambda.sf(x, lam)
+        # Check that the sf is 0 for large x
+        assert sf[-1] == 0.0
+        # Check that the sf is always decreasing except when it is 0
+        assert_array_less(np.diff(sf[sf > 0]), 0)
+
+        cdf = stats.tukeylambda.cdf(x, lam)
+        assert cdf[-1] == 1.0
+        # Check that the cdf is always increasing except when it saturates 
+        # to 1.0
+        assert np.all(np.diff(cdf[cdf < 1]) > 0)
+        
+class TestLevy:
+
+    def test_levy_cdf_ppf(self):
+        # Test levy.cdf, including small arguments.
+        x = np.array([1000, 1.0, 0.5, 0.1, 0.01, 0.001])
+
+        # Expected values were calculated separately with mpmath.
+        # E.g.
+        # >>> mpmath.mp.dps = 100
+        # >>> x = mpmath.mp.mpf('0.01')
+        # >>> cdf = mpmath.erfc(mpmath.sqrt(1/(2*x)))
+        expected = np.array([0.9747728793699604,
+                             0.3173105078629141,
+                             0.1572992070502851,
+                             0.0015654022580025495,
+                             1.523970604832105e-23,
+                             1.795832784800726e-219])
+
+        y = stats.levy.cdf(x)
+        assert_allclose(y, expected, rtol=1e-10)
+
+        # ppf(expected) should get us back to x.
+        xx = stats.levy.ppf(expected)
+        assert_allclose(xx, x, rtol=1e-13)
+
+    def test_levy_sf(self):
+        # Large values, far into the tail of the distribution.
+        x = np.array([1e15, 1e25, 1e35, 1e50])
+        # Expected values were calculated with mpmath.
+        expected = np.array([2.5231325220201597e-08,
+                             2.52313252202016e-13,
+                             2.52313252202016e-18,
+                             7.978845608028653e-26])
+        y = stats.levy.sf(x)
+        assert_allclose(y, expected, rtol=1e-14)
+
+    # The expected values for levy.isf(p) were calculated with mpmath.
+    # For loc=0 and scale=1, the inverse SF can be computed with
+    #
+    #     import mpmath
+    #
+    #     def levy_invsf(p):
+    #         return 1/(2*mpmath.erfinv(p)**2)
+    #
+    # For example, with mpmath.mp.dps set to 60, float(levy_invsf(1e-20))
+    # returns 6.366197723675814e+39.
+    #
+    @pytest.mark.parametrize('p, expected_isf',
+                             [(1e-20, 6.366197723675814e+39),
+                              (1e-8, 6366197723675813.0),
+                              (0.375, 4.185810119346273),
+                              (0.875, 0.42489442055310134),
+                              (0.999, 0.09235685880262713),
+                              (0.9999999962747097, 0.028766845244146945)])
+    def test_levy_isf(self, p, expected_isf):
+        x = stats.levy.isf(p)
+        assert_allclose(x, expected_isf, atol=5e-15)
+
+    def test_levy_logcdf(self):
+        x = 1e50
+        ref = -7.978845608028653e-26
+        logcdf = stats.levy.logcdf(x)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+
+    def test_levy_logsf(self):
+        x = 5e-3
+        ref = -2.0884875837625492e-45
+        logsf = stats.levy.logsf(x)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+
+def test_540_567():
+    # test for nan returned in tickets 540, 567
+    assert_almost_equal(stats.norm.cdf(-1.7624320982), 0.03899815971089126,
+                        decimal=10, err_msg='test_540_567')
+    assert_almost_equal(stats.norm.cdf(-1.7624320983), 0.038998159702449846,
+                        decimal=10, err_msg='test_540_567')
+    assert_almost_equal(stats.norm.cdf(1.38629436112, loc=0.950273420309,
+                                       scale=0.204423758009),
+                        0.98353464004309321,
+                        decimal=10, err_msg='test_540_567')
+
+
+@pytest.mark.skipif(DOCSTRINGS_STRIPPED, reason="docstrings stripped")
+def test_regression_ticket_1421():
+    assert_('pdf(x, mu, loc=0, scale=1)' not in stats.poisson.__doc__)
+    assert_('pmf(x,' in stats.poisson.__doc__)
+
+
+def test_nan_arguments_gh_issue_1362():
+    with np.errstate(invalid='ignore'):
+        assert_(np.isnan(stats.t.logcdf(1, np.nan)))
+        assert_(np.isnan(stats.t.cdf(1, np.nan)))
+        assert_(np.isnan(stats.t.logsf(1, np.nan)))
+        assert_(np.isnan(stats.t.sf(1, np.nan)))
+        assert_(np.isnan(stats.t.pdf(1, np.nan)))
+        assert_(np.isnan(stats.t.logpdf(1, np.nan)))
+        assert_(np.isnan(stats.t.ppf(1, np.nan)))
+        assert_(np.isnan(stats.t.isf(1, np.nan)))
+
+        assert_(np.isnan(stats.bernoulli.logcdf(np.nan, 0.5)))
+        assert_(np.isnan(stats.bernoulli.cdf(np.nan, 0.5)))
+        assert_(np.isnan(stats.bernoulli.logsf(np.nan, 0.5)))
+        assert_(np.isnan(stats.bernoulli.sf(np.nan, 0.5)))
+        assert_(np.isnan(stats.bernoulli.pmf(np.nan, 0.5)))
+        assert_(np.isnan(stats.bernoulli.logpmf(np.nan, 0.5)))
+        assert_(np.isnan(stats.bernoulli.ppf(np.nan, 0.5)))
+        assert_(np.isnan(stats.bernoulli.isf(np.nan, 0.5)))
+
+
+def test_frozen_fit_ticket_1536():
+    rng = np.random.default_rng(5678)
+    true = np.array([0.25, 0., 0.5])
+    x = stats.lognorm.rvs(true[0], true[1], true[2], size=100, random_state=rng)
+
+    with np.errstate(divide='ignore'):
+        params = np.array(stats.lognorm.fit(x, floc=0.))
+
+    assert_almost_equal(params, true, decimal=2)
+
+    params = np.array(stats.lognorm.fit(x, fscale=0.5, loc=0))
+    assert_almost_equal(params, true, decimal=2)
+
+    params = np.array(stats.lognorm.fit(x, f0=0.25, loc=0))
+    assert_almost_equal(params, true, decimal=2)
+
+    params = np.array(stats.lognorm.fit(x, f0=0.25, floc=0))
+    assert_almost_equal(params, true, decimal=2)
+
+    rng = np.random.default_rng(5678)
+    loc = 1
+    floc = 0.9
+    x = stats.norm.rvs(loc, 2., size=100, random_state=rng)
+    params = np.array(stats.norm.fit(x, floc=floc))
+    expected = np.array([floc, np.sqrt(((x-floc)**2).mean())])
+    assert_almost_equal(params, expected, decimal=4)
+
+
+def test_regression_ticket_1530():
+    # Check the starting value works for Cauchy distribution fit.
+    rng = np.random.default_rng(654321)
+    rvs = stats.cauchy.rvs(size=100, random_state=rng)
+    params = stats.cauchy.fit(rvs)
+    expected = (0.045, 1.142)
+    assert_almost_equal(params, expected, decimal=1)
+
+
+def test_gh_pr_4806():
+    # Check starting values for Cauchy distribution fit.
+    rng = np.random.RandomState(1234)
+    x = rng.randn(42)
+    for offset in 10000.0, 1222333444.0:
+        loc, scale = stats.cauchy.fit(x + offset)
+        assert_allclose(loc, offset, atol=1.0)
+        assert_allclose(scale, 0.6, atol=1.0)
+
+
+def test_poisson_logpmf_ticket_1436():
+    assert_(np.isfinite(stats.poisson.logpmf(1500, 200)))
+
+
+def test_powerlaw_stats():
+    """Test the powerlaw stats function.
+
+    This unit test is also a regression test for ticket 1548.
+
+    The exact values are:
+    mean:
+        mu = a / (a + 1)
+    variance:
+        sigma**2 = a / ((a + 2) * (a + 1) ** 2)
+    skewness:
+        One formula (see https://en.wikipedia.org/wiki/Skewness) is
+            gamma_1 = (E[X**3] - 3*mu*E[X**2] + 2*mu**3) / sigma**3
+        A short calculation shows that E[X**k] is a / (a + k), so gamma_1
+        can be implemented as
+            n = a/(a+3) - 3*(a/(a+1))*a/(a+2) + 2*(a/(a+1))**3
+            d = sqrt(a/((a+2)*(a+1)**2)) ** 3
+            gamma_1 = n/d
+        Either by simplifying, or by a direct calculation of mu_3 / sigma**3,
+        one gets the more concise formula:
+            gamma_1 = -2.0 * ((a - 1) / (a + 3)) * sqrt((a + 2) / a)
+    kurtosis: (See https://en.wikipedia.org/wiki/Kurtosis)
+        The excess kurtosis is
+            gamma_2 = mu_4 / sigma**4 - 3
+        A bit of calculus and algebra (sympy helps) shows that
+            mu_4 = 3*a*(3*a**2 - a + 2) / ((a+1)**4 * (a+2) * (a+3) * (a+4))
+        so
+            gamma_2 = 3*(3*a**2 - a + 2) * (a+2) / (a*(a+3)*(a+4)) - 3
+        which can be rearranged to
+            gamma_2 = 6 * (a**3 - a**2 - 6*a + 2) / (a*(a+3)*(a+4))
+    """
+    cases = [(1.0, (0.5, 1./12, 0.0, -1.2)),
+             (2.0, (2./3, 2./36, -0.56568542494924734, -0.6))]
+    for a, exact_mvsk in cases:
+        mvsk = stats.powerlaw.stats(a, moments="mvsk")
+        assert_array_almost_equal(mvsk, exact_mvsk)
+
+
+def test_powerlaw_edge():
+    # Regression test for gh-3986.
+    p = stats.powerlaw.logpdf(0, 1)
+    assert_equal(p, 0.0)
+
+
+def test_exponpow_edge():
+    # Regression test for gh-3982.
+    p = stats.exponpow.logpdf(0, 1)
+    assert_equal(p, 0.0)
+
+    # Check pdf and logpdf at x = 0 for other values of b.
+    p = stats.exponpow.pdf(0, [0.25, 1.0, 1.5])
+    assert_equal(p, [np.inf, 1.0, 0.0])
+    p = stats.exponpow.logpdf(0, [0.25, 1.0, 1.5])
+    assert_equal(p, [np.inf, 0.0, -np.inf])
+
+
+class TestGenGamma:
+    def test_gengamma_edge(self):
+        # Regression test for gh-3985.
+        p = stats.gengamma.pdf(0, 1, 1)
+        assert_equal(p, 1.0)
+
+    @pytest.mark.parametrize("a, c, ref, tol",
+                             [(1500000.0, 1, 8.529426144018633, 1e-15),
+                              (1e+30, 1, 35.95771492811536, 1e-15),
+                              (1e+100, 1, 116.54819318290696, 1e-15),
+                              (3e3, 1, 5.422011196659015, 1e-13),
+                              (3e6, -1e100, -236.29663213396054, 1e-15),
+                              (3e60, 1e-100, 1.3925371786831085e+102, 1e-15)])
+    def test_gengamma_extreme_entropy(self, a, c, ref, tol):
+        # The reference values were calculated with mpmath:
+        # from mpmath import mp
+        # mp.dps = 500
+        #
+        # def gen_entropy(a, c):
+        #     a, c = mp.mpf(a), mp.mpf(c)
+        #     val = mp.digamma(a)
+        #     h = (a * (mp.one - val) + val/c + mp.loggamma(a) - mp.log(abs(c)))
+        #     return float(h)
+        assert_allclose(stats.gengamma.entropy(a, c), ref, rtol=tol)
+
+    def test_gengamma_endpoint_with_neg_c(self):
+        p = stats.gengamma.pdf(0, 1, -1)
+        assert p == 0.0
+        logp = stats.gengamma.logpdf(0, 1, -1)
+        assert logp == -np.inf
+
+    def test_gengamma_munp(self):
+        # Regression tests for gh-4724.
+        p = stats.gengamma._munp(-2, 200, 1.)
+        assert_almost_equal(p, 1./199/198)
+
+        p = stats.gengamma._munp(-2, 10, 1.)
+        assert_almost_equal(p, 1./9/8)
+
+    def test_gengamma_logpdf_broadcasting_gh24574(self):
+        # gh-24574 reported a broadcasting error when `x` included 0s.
+        assert_allclose(stats.gengamma.logpdf([0, 1, 1], 1, -1), [-np.inf, -1, -1])
+
+
+def test_ksone_fit_freeze():
+    # Regression test for ticket #1638.
+    d = np.array(
+        [-0.18879233, 0.15734249, 0.18695107, 0.27908787, -0.248649,
+         -0.2171497, 0.12233512, 0.15126419, 0.03119282, 0.4365294,
+         0.08930393, -0.23509903, 0.28231224, -0.09974875, -0.25196048,
+         0.11102028, 0.1427649, 0.10176452, 0.18754054, 0.25826724,
+         0.05988819, 0.0531668, 0.21906056, 0.32106729, 0.2117662,
+         0.10886442, 0.09375789, 0.24583286, -0.22968366, -0.07842391,
+         -0.31195432, -0.21271196, 0.1114243, -0.13293002, 0.01331725,
+         -0.04330977, -0.09485776, -0.28434547, 0.22245721, -0.18518199,
+         -0.10943985, -0.35243174, 0.06897665, -0.03553363, -0.0701746,
+         -0.06037974, 0.37670779, -0.21684405])
+
+    with np.errstate(invalid='ignore'):
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                "The maximum number of subdivisions .50. has been achieved.",
+                IntegrationWarning,
+            )
+            warnings.filterwarnings(
+                "ignore",
+                "floating point number truncated to an integer",
+                RuntimeWarning,
+            )
+            stats.ksone.fit(d)
+
+
+@pytest.mark.xfail(platform.machine() == "sparc64",
+    reason="complex special functions known to fail on sparc64 (gh-22577)")
+def test_norm_logcdf():
+    # Test precision of the logcdf of the normal distribution.
+    # This precision was enhanced in ticket 1614.
+    x = -np.asarray(list(range(0, 120, 4)))
+    # Values from R
+    expected = [-0.69314718, -10.36010149, -35.01343716, -75.41067300,
+                -131.69539607, -203.91715537, -292.09872100, -396.25241451,
+                -516.38564863, -652.50322759, -804.60844201, -972.70364403,
+                -1156.79057310, -1356.87055173, -1572.94460885, -1805.01356068,
+                -2053.07806561, -2317.13866238, -2597.19579746, -2893.24984493,
+                -3205.30112136, -3533.34989701, -3877.39640444, -4237.44084522,
+                -4613.48339520, -5005.52420869, -5413.56342187, -5837.60115548,
+                -6277.63751711, -6733.67260303]
+
+    assert_allclose(stats.norm().logcdf(x), expected, atol=1e-8)
+
+    # also test the complex-valued code path
+    assert_allclose(stats.norm().logcdf(x + 1e-14j).real, expected, atol=1e-8)
+
+    # test the accuracy: d(logcdf)/dx = pdf / cdf \equiv exp(logpdf - logcdf)
+    deriv = (stats.norm.logcdf(x + 1e-10j)/1e-10).imag
+    deriv_expected = np.exp(stats.norm.logpdf(x) - stats.norm.logcdf(x))
+    assert_allclose(deriv, deriv_expected, atol=1e-10)
+
+
+def test_levy_l_sf():
+    # Test levy_l.sf for small arguments.
+    x = np.array([-0.016, -0.01, -0.005, -0.0015])
+    # Expected values were calculated with mpmath.
+    expected = np.array([2.6644463892359302e-15,
+                         1.523970604832107e-23,
+                         2.0884875837625492e-45,
+                         5.302850374626878e-147])
+    y = stats.levy_l.sf(x)
+    assert_allclose(y, expected, rtol=1e-13)
+
+
+def test_levy_l_isf():
+    # Test roundtrip sf(isf(p)), including a small input value.
+    p = np.array([3.0e-15, 0.25, 0.99])
+    x = stats.levy_l.isf(p)
+    q = stats.levy_l.sf(x)
+    assert_allclose(q, p, rtol=5e-14)
+
+
+def test_hypergeom_interval_1802():
+    # these two had endless loops
+    assert_equal(stats.hypergeom.interval(.95, 187601, 43192, 757),
+                 (152.0, 197.0))
+    assert_equal(stats.hypergeom.interval(.945, 187601, 43192, 757),
+                 (152.0, 197.0))
+    # this was working also before
+    assert_equal(stats.hypergeom.interval(.94, 187601, 43192, 757),
+                 (153.0, 196.0))
+
+    # degenerate case .a == .b
+    assert_equal(stats.hypergeom.ppf(0.02, 100, 100, 8), 8)
+    assert_equal(stats.hypergeom.ppf(1, 100, 100, 8), 8)
+
+
+def test_distribution_too_many_args():
+    rng = np.random.default_rng(5976604568)
+    # Check that a TypeError is raised when too many args are given to a method
+    # Regression test for ticket 1815.
+    x = np.linspace(0.1, 0.7, num=5)
+    assert_raises(TypeError, stats.gamma.pdf, x, 2, 3, loc=1.0)
+    assert_raises(TypeError, stats.gamma.pdf, x, 2, 3, 4, loc=1.0)
+    assert_raises(TypeError, stats.gamma.pdf, x, 2, 3, 4, 5)
+    assert_raises(TypeError, stats.gamma.pdf, x, 2, 3, loc=1.0, scale=0.5)
+    assert_raises(TypeError, stats.gamma.rvs, 2., 3, loc=1.0, scale=0.5,
+                  random_state=rng)
+    assert_raises(TypeError, stats.gamma.cdf, x, 2., 3, loc=1.0, scale=0.5)
+    assert_raises(TypeError, stats.gamma.ppf, x, 2., 3, loc=1.0, scale=0.5)
+    assert_raises(TypeError, stats.gamma.stats, 2., 3, loc=1.0, scale=0.5)
+    assert_raises(TypeError, stats.gamma.entropy, 2., 3, loc=1.0, scale=0.5)
+    assert_raises(TypeError, stats.gamma.fit, x, 2., 3, loc=1.0, scale=0.5)
+
+    # These should not give errors
+    stats.gamma.pdf(x, 2, 3)  # loc=3
+    stats.gamma.pdf(x, 2, 3, 4)  # loc=3, scale=4
+    stats.gamma.stats(2., 3)
+    stats.gamma.stats(2., 3, 4)
+    stats.gamma.stats(2., 3, 4, 'mv')
+    stats.gamma.rvs(2., 3, 4, 5, random_state=rng)
+    stats.gamma.fit(stats.gamma.rvs(2., size=7, random_state=rng), 2.)
+
+    # Also for a discrete distribution
+    stats.geom.pmf(x, 2, loc=3)  # no error, loc=3
+    assert_raises(TypeError, stats.geom.pmf, x, 2, 3, 4)
+    assert_raises(TypeError, stats.geom.pmf, x, 2, 3, loc=4)
+
+    # And for distributions with 0, 2 and 3 args respectively
+    assert_raises(TypeError, stats.expon.pdf, x, 3, loc=1.0)
+    assert_raises(TypeError, stats.exponweib.pdf, x, 3, 4, 5, loc=1.0)
+    assert_raises(TypeError, stats.exponweib.pdf, x, 3, 4, 5, 0.1, 0.1)
+    assert_raises(TypeError, stats.ncf.pdf, x, 3, 4, 5, 6, loc=1.0)
+    assert_raises(TypeError, stats.ncf.pdf, x, 3, 4, 5, 6, 1.0, scale=0.5)
+    stats.ncf.pdf(x, 3, 4, 5, 6, 1.0)  # 3 args, plus loc/scale
+
+
+def test_ncx2_tails_ticket_955():
+    # Trac #955 -- check that the cdf computed by special functions
+    # matches the integrated pdf
+    a = stats.ncx2.cdf(np.arange(20, 25, 0.2), 2, 1.07458615e+02)
+    b = stats.ncx2._cdfvec(np.arange(20, 25, 0.2), 2, 1.07458615e+02)
+    assert_allclose(a, b, rtol=1e-3, atol=0)
+
+
+def test_ncx2_tails_pdf():
+    # ncx2.pdf does not return nans in extreme tails(example from gh-1577)
+    # NB: this is to check that nan_to_num is not needed in ncx2.pdf
+    with warnings.catch_warnings():
+        warnings.simplefilter('error', RuntimeWarning)
+        assert_equal(stats.ncx2.pdf(1, np.arange(340, 350), 2), 0)
+        logval = stats.ncx2.logpdf(1, np.arange(340, 350), 2)
+
+    assert_(np.isneginf(logval).all())
+
+    # Verify logpdf has extended precision when pdf underflows to 0
+    with warnings.catch_warnings():
+        warnings.simplefilter('error', RuntimeWarning)
+        assert_equal(stats.ncx2.pdf(10000, 3, 12), 0)
+        assert_allclose(stats.ncx2.logpdf(10000, 3, 12), -4662.444377524883)
+
+
+@pytest.mark.parametrize('method, expected', [
+    ('cdf', np.array([2.497951336e-09, 3.437288941e-10])),
+    ('pdf', np.array([1.238579980e-07, 1.710041145e-08])),
+    ('logpdf', np.array([-15.90413011, -17.88416331])),
+    ('ppf', np.array([4.865182052, 7.017182271]))
+])
+def test_ncx2_zero_nc(method, expected):
+    # gh-5441
+    # ncx2 with nc=0 is identical to chi2
+    # Comparison to R (v3.5.1)
+    # > options(digits=10)
+    # > pchisq(0.1, df=10, ncp=c(0,4))
+    # > dchisq(0.1, df=10, ncp=c(0,4))
+    # > dchisq(0.1, df=10, ncp=c(0,4), log=TRUE)
+    # > qchisq(0.1, df=10, ncp=c(0,4))
+
+    result = getattr(stats.ncx2, method)(0.1, nc=[0, 4], df=10)
+    assert_allclose(result, expected, atol=1e-15)
+
+
+def test_ncx2_zero_nc_rvs():
+    # gh-5441
+    # ncx2 with nc=0 is identical to chi2
+    result = stats.ncx2.rvs(df=10, nc=0, random_state=1)
+    expected = stats.chi2.rvs(df=10, random_state=1)
+    assert_allclose(result, expected, atol=1e-15)
+
+
+def test_ncx2_gh12731():
+    # test that gh-12731 is resolved; previously these were all 0.5
+    nc = 10**np.arange(5, 10)
+    assert_equal(stats.ncx2.cdf(1e4, df=1, nc=nc), 0)
+
+
+def test_ncx2_gh8665():
+    # test that gh-8665 is resolved; previously this tended to nonzero value
+    x = np.array([4.99515382e+00, 1.07617327e+01, 2.31854502e+01,
+                  4.99515382e+01, 1.07617327e+02, 2.31854502e+02,
+                  4.99515382e+02, 1.07617327e+03, 2.31854502e+03,
+                  4.99515382e+03, 1.07617327e+04, 2.31854502e+04,
+                  4.99515382e+04])
+    nu, lam = 20, 499.51538166556196
+
+    sf = stats.ncx2.sf(x, df=nu, nc=lam)
+    # computed in R. Couldn't find a survival function implementation
+    # options(digits=16)
+    # x <- c(4.99515382e+00, 1.07617327e+01, 2.31854502e+01, 4.99515382e+01,
+    #        1.07617327e+02, 2.31854502e+02, 4.99515382e+02, 1.07617327e+03,
+    #        2.31854502e+03, 4.99515382e+03, 1.07617327e+04, 2.31854502e+04,
+    #        4.99515382e+04)
+    # nu <- 20
+    # lam <- 499.51538166556196
+    # 1 - pchisq(x, df = nu, ncp = lam)
+    sf_expected = [1.0000000000000000, 1.0000000000000000, 1.0000000000000000,
+                   1.0000000000000000, 1.0000000000000000, 0.9999999999999888,
+                   0.6646525582135460, 0.0000000000000000, 0.0000000000000000,
+                   0.0000000000000000, 0.0000000000000000, 0.0000000000000000,
+                   0.0000000000000000]
+    assert_allclose(sf, sf_expected, atol=1e-12)
+
+
+def test_ncx2_gh11777():
+    # regression test for gh-11777:
+    # At high values of degrees of freedom df, ensure the pdf of ncx2 does
+    # not get clipped to zero when the non-centrality parameter is
+    # sufficiently less than df
+    df = 6700
+    nc = 5300
+    x = np.linspace(stats.ncx2.ppf(0.001, df, nc),
+                    stats.ncx2.ppf(0.999, df, nc), num=10000)
+    ncx2_pdf = stats.ncx2.pdf(x, df, nc)
+    gauss_approx = stats.norm.pdf(x, df + nc, np.sqrt(2 * df + 4 * nc))
+    # use huge tolerance as we're only looking for obvious discrepancy
+    assert_allclose(ncx2_pdf, gauss_approx, atol=1e-4)
+
+
+# Expected values for foldnorm.sf were computed with mpmath:
+#
+#    from mpmath import mp
+#    mp.dps = 60
+#    def foldcauchy_sf(x, c):
+#        x = mp.mpf(x)
+#        c = mp.mpf(c)
+#        return mp.one - (mp.atan(x - c) + mp.atan(x + c))/mp.pi
+#
+# E.g.
+#
+#    >>> float(foldcauchy_sf(2, 1))
+#    0.35241638234956674
+#
+@pytest.mark.parametrize('x, c, expected',
+                         [(2, 1, 0.35241638234956674),
+                          (2, 2, 0.5779791303773694),
+                          (1e13, 1, 6.366197723675813e-14),
+                          (2e16, 1, 3.183098861837907e-17),
+                          (1e13, 2e11, 6.368745221764519e-14),
+                          (0.125, 200, 0.999998010612169)])
+def test_foldcauchy_sf(x, c, expected):
+    sf = stats.foldcauchy.sf(x, c)
+    assert_allclose(sf, expected, 2e-15)
+
+
+# The same mpmath code shown in the comments above test_foldcauchy_sf()
+# is used to create these expected values.
+@pytest.mark.parametrize('x, expected',
+                         [(2, 0.2951672353008665),
+                          (1e13, 6.366197723675813e-14),
+                          (2e16, 3.183098861837907e-17),
+                          (5e80, 1.2732395447351629e-81)])
+def test_halfcauchy_sf(x, expected):
+    sf = stats.halfcauchy.sf(x)
+    assert_allclose(sf, expected, 2e-15)
+
+
+# Expected value computed with mpmath:
+#     expected = mp.cot(mp.pi*p/2)
+@pytest.mark.parametrize('p, expected',
+                         [(0.9999995, 7.853981633329977e-07),
+                          (0.975, 0.039290107007669675),
+                          (0.5, 1.0),
+                          (0.01, 63.65674116287158),
+                          (1e-14, 63661977236758.13),
+                          (5e-80, 1.2732395447351627e+79)])
+def test_halfcauchy_isf(p, expected):
+    x = stats.halfcauchy.isf(p)
+    assert_allclose(x, expected)
+
+
+def test_foldnorm_zero():
+    # Parameter value c=0 was not enabled, see gh-2399.
+    rv = stats.foldnorm(0, scale=1)
+    assert_equal(rv.cdf(0), 0)  # rv.cdf(0) previously resulted in: nan
+
+
+# Expected values for foldnorm.sf were computed with mpmath:
+#
+#    from mpmath import mp
+#    mp.dps = 60
+#    def foldnorm_sf(x, c):
+#        x = mp.mpf(x)
+#        c = mp.mpf(c)
+#        return mp.ncdf(-x+c) + mp.ncdf(-x-c)
+#
+# E.g.
+#
+#    >>> float(foldnorm_sf(2, 1))
+#    0.16000515196308715
+#
+@pytest.mark.parametrize('x, c, expected',
+                         [(2, 1, 0.16000515196308715),
+                          (20, 1, 8.527223952630977e-81),
+                          (10, 15, 0.9999997133484281),
+                          (25, 15, 7.619853024160525e-24)])
+def test_foldnorm_sf(x, c, expected):
+    sf = stats.foldnorm.sf(x, c)
+    assert_allclose(sf, expected, 1e-14)
+
+
+def test_stats_shapes_argcheck():
+    # stats method was failing for vector shapes if some of the values
+    # were outside of the allowed range, see gh-2678
+    mv3 = stats.invgamma.stats([0.0, 0.5, 1.0], 1, 0.5)  # 0 is not a legal `a`
+    mv2 = stats.invgamma.stats([0.5, 1.0], 1, 0.5)
+    mv2_augmented = tuple(np.r_[np.nan, _] for _ in mv2)
+    assert_equal(mv2_augmented, mv3)
+
+    # -1 is not a legal shape parameter
+    mv3 = stats.lognorm.stats([2, 2.4, -1])
+    mv2 = stats.lognorm.stats([2, 2.4])
+    mv2_augmented = tuple(np.r_[_, np.nan] for _ in mv2)
+    assert_equal(mv2_augmented, mv3)
+
+    # FIXME: this is only a quick-and-dirty test of a quick-and-dirty bugfix.
+    # stats method with multiple shape parameters is not properly vectorized
+    # anyway, so some distributions may or may not fail.
+
+
+# Test subclassing distributions w/ explicit shapes
+
+class _distr_gen(stats.rv_continuous):
+    def _pdf(self, x, a):
+        return 42
+
+
+class _distr2_gen(stats.rv_continuous):
+    def _cdf(self, x, a):
+        return 42 * a + x
+
+
+class _distr3_gen(stats.rv_continuous):
+    def _pdf(self, x, a, b):
+        return a + b
+
+    def _cdf(self, x, a):
+        # Different # of shape params from _pdf, to be able to check that
+        # inspection catches the inconsistency.
+        return 42 * a + x
+
+
+class _distr6_gen(stats.rv_continuous):
+    # Two shape parameters (both _pdf and _cdf defined, consistent shapes.)
+    def _pdf(self, x, a, b):
+        return a*x + b
+
+    def _cdf(self, x, a, b):
+        return 42 * a + x
+
+
+class TestSubclassingExplicitShapes:
+    # Construct a distribution w/ explicit shapes parameter and test it.
+
+    def test_correct_shapes(self):
+        dummy_distr = _distr_gen(name='dummy', shapes='a')
+        assert_equal(dummy_distr.pdf(1, a=1), 42)
+
+    def test_wrong_shapes_1(self):
+        dummy_distr = _distr_gen(name='dummy', shapes='A')
+        assert_raises(TypeError, dummy_distr.pdf, 1, **dict(a=1))
+
+    def test_wrong_shapes_2(self):
+        dummy_distr = _distr_gen(name='dummy', shapes='a, b, c')
+        dct = dict(a=1, b=2, c=3)
+        assert_raises(TypeError, dummy_distr.pdf, 1, **dct)
+
+    def test_shapes_string(self):
+        # shapes must be a string
+        dct = dict(name='dummy', shapes=42)
+        assert_raises(TypeError, _distr_gen, **dct)
+
+    def test_shapes_identifiers_1(self):
+        # shapes must be a comma-separated list of valid python identifiers
+        dct = dict(name='dummy', shapes='(!)')
+        assert_raises(SyntaxError, _distr_gen, **dct)
+
+    def test_shapes_identifiers_2(self):
+        dct = dict(name='dummy', shapes='4chan')
+        assert_raises(SyntaxError, _distr_gen, **dct)
+
+    def test_shapes_identifiers_3(self):
+        dct = dict(name='dummy', shapes='m(fti)')
+        assert_raises(SyntaxError, _distr_gen, **dct)
+
+    def test_shapes_identifiers_nodefaults(self):
+        dct = dict(name='dummy', shapes='a=2')
+        assert_raises(SyntaxError, _distr_gen, **dct)
+
+    def test_shapes_args(self):
+        dct = dict(name='dummy', shapes='*args')
+        assert_raises(SyntaxError, _distr_gen, **dct)
+
+    def test_shapes_kwargs(self):
+        dct = dict(name='dummy', shapes='**kwargs')
+        assert_raises(SyntaxError, _distr_gen, **dct)
+
+    def test_shapes_keywords(self):
+        # python keywords cannot be used for shape parameters
+        dct = dict(name='dummy', shapes='a, b, c, lambda')
+        assert_raises(SyntaxError, _distr_gen, **dct)
+
+    def test_shapes_signature(self):
+        # test explicit shapes which agree w/ the signature of _pdf
+        class _dist_gen(stats.rv_continuous):
+            def _pdf(self, x, a):
+                return stats.norm._pdf(x) * a
+
+        dist = _dist_gen(shapes='a')
+        assert_equal(dist.pdf(0.5, a=2), stats.norm.pdf(0.5)*2)
+
+    def test_shapes_signature_inconsistent(self):
+        # test explicit shapes which do not agree w/ the signature of _pdf
+        class _dist_gen(stats.rv_continuous):
+            def _pdf(self, x, a):
+                return stats.norm._pdf(x) * a
+
+        dist = _dist_gen(shapes='a, b')
+        assert_raises(TypeError, dist.pdf, 0.5, **dict(a=1, b=2))
+
+    def test_star_args(self):
+        # test _pdf with only starargs
+        # NB: **kwargs of pdf will never reach _pdf
+        class _dist_gen(stats.rv_continuous):
+            def _pdf(self, x, *args):
+                extra_kwarg = args[0]
+                return stats.norm._pdf(x) * extra_kwarg
+
+        dist = _dist_gen(shapes='extra_kwarg')
+        assert_equal(dist.pdf(0.5, extra_kwarg=33), stats.norm.pdf(0.5)*33)
+        assert_equal(dist.pdf(0.5, 33), stats.norm.pdf(0.5)*33)
+        assert_raises(TypeError, dist.pdf, 0.5, **dict(xxx=33))
+
+    def test_star_args_2(self):
+        # test _pdf with named & starargs
+        # NB: **kwargs of pdf will never reach _pdf
+        class _dist_gen(stats.rv_continuous):
+            def _pdf(self, x, offset, *args):
+                extra_kwarg = args[0]
+                return stats.norm._pdf(x) * extra_kwarg + offset
+
+        dist = _dist_gen(shapes='offset, extra_kwarg')
+        assert_equal(dist.pdf(0.5, offset=111, extra_kwarg=33),
+                     stats.norm.pdf(0.5)*33 + 111)
+        assert_equal(dist.pdf(0.5, 111, 33),
+                     stats.norm.pdf(0.5)*33 + 111)
+
+    def test_extra_kwarg(self):
+        # **kwargs to _pdf are ignored.
+        # this is a limitation of the framework (_pdf(x, *goodargs))
+        class _distr_gen(stats.rv_continuous):
+            def _pdf(self, x, *args, **kwargs):
+                # _pdf should handle *args, **kwargs itself.  Here "handling"
+                # is ignoring *args and looking for ``extra_kwarg`` and using
+                # that.
+                extra_kwarg = kwargs.pop('extra_kwarg', 1)
+                return stats.norm._pdf(x) * extra_kwarg
+
+        dist = _distr_gen(shapes='extra_kwarg')
+        assert_equal(dist.pdf(1, extra_kwarg=3), stats.norm.pdf(1))
+
+    def test_shapes_empty_string(self):
+        # shapes='' is equivalent to shapes=None
+        class _dist_gen(stats.rv_continuous):
+            def _pdf(self, x):
+                return stats.norm.pdf(x)
+
+        dist = _dist_gen(shapes='')
+        assert_equal(dist.pdf(0.5), stats.norm.pdf(0.5))
+
+
+class TestSubclassingNoShapes:
+    # Construct a distribution w/o explicit shapes parameter and test it.
+
+    def test_only__pdf(self):
+        dummy_distr = _distr_gen(name='dummy')
+        assert_equal(dummy_distr.pdf(1, a=1), 42)
+
+    def test_only__cdf(self):
+        # _pdf is determined from _cdf by taking numerical derivative
+        dummy_distr = _distr2_gen(name='dummy')
+        assert_almost_equal(dummy_distr.pdf(1, a=1), 1)
+
+    @pytest.mark.skipif(DOCSTRINGS_STRIPPED, reason="docstring stripped")
+    def test_signature_inspection(self):
+        # check that _pdf signature inspection works correctly, and is used in
+        # the class docstring
+        dummy_distr = _distr_gen(name='dummy')
+        assert_equal(dummy_distr.numargs, 1)
+        assert_equal(dummy_distr.shapes, 'a')
+        res = re.findall(r'logpdf\(x, a, loc=0, scale=1\)',
+                         dummy_distr.__doc__)
+        assert_(len(res) == 1)
+
+    @pytest.mark.skipif(DOCSTRINGS_STRIPPED, reason="docstring stripped")
+    def test_signature_inspection_2args(self):
+        # same for 2 shape params and both _pdf and _cdf defined
+        dummy_distr = _distr6_gen(name='dummy')
+        assert_equal(dummy_distr.numargs, 2)
+        assert_equal(dummy_distr.shapes, 'a, b')
+        res = re.findall(r'logpdf\(x, a, b, loc=0, scale=1\)',
+                         dummy_distr.__doc__)
+        assert_(len(res) == 1)
+
+    def test_signature_inspection_2args_incorrect_shapes(self):
+        # both _pdf and _cdf defined, but shapes are inconsistent: raises
+        assert_raises(TypeError, _distr3_gen, name='dummy')
+
+    def test_defaults_raise(self):
+        # default arguments should raise
+        class _dist_gen(stats.rv_continuous):
+            def _pdf(self, x, a=42):
+                return 42
+        assert_raises(TypeError, _dist_gen, **dict(name='dummy'))
+
+    def test_starargs_raise(self):
+        # without explicit shapes, *args are not allowed
+        class _dist_gen(stats.rv_continuous):
+            def _pdf(self, x, a, *args):
+                return 42
+        assert_raises(TypeError, _dist_gen, **dict(name='dummy'))
+
+    def test_kwargs_raise(self):
+        # without explicit shapes, **kwargs are not allowed
+        class _dist_gen(stats.rv_continuous):
+            def _pdf(self, x, a, **kwargs):
+                return 42
+        assert_raises(TypeError, _dist_gen, **dict(name='dummy'))
+
+
+@pytest.mark.skipif(DOCSTRINGS_STRIPPED, reason="docstring stripped")
+def test_docstrings():
+    badones = [r',\s*,', r'\(\s*,', r'^\s*:']
+    for distname in stats.__all__:
+        dist = getattr(stats, distname)
+        if isinstance(dist, (stats.rv_discrete | stats.rv_continuous)):
+            for regex in badones:
+                assert_(re.search(regex, dist.__doc__) is None)
+
+
+def test_infinite_input():
+    assert_almost_equal(stats.skellam.sf(np.inf, 10, 11), 0)
+    assert_almost_equal(stats.ncx2._cdf(np.inf, 8, 0.1), 1)
+
+
+def test_lomax_accuracy():
+    # regression test for gh-4033
+    p = stats.lomax.ppf(stats.lomax.cdf(1e-100, 1), 1)
+    assert_allclose(p, 1e-100)
+
+
+def test_truncexpon_accuracy():
+    # regression test for gh-4035
+    p = stats.truncexpon.ppf(stats.truncexpon.cdf(1e-100, 1), 1)
+    assert_allclose(p, 1e-100)
+
+
+def test_rayleigh_accuracy():
+    # regression test for gh-4034
+    p = stats.rayleigh.isf(stats.rayleigh.sf(9, 1), 1)
+    assert_almost_equal(p, 9.0, decimal=15)
+
+
+def test_genextreme_give_no_warnings():
+    """regression test for gh-6219"""
+
+    with warnings.catch_warnings(record=True) as w:
+        warnings.simplefilter("always")
+
+        stats.genextreme.cdf(.5, 0)
+        stats.genextreme.pdf(.5, 0)
+        stats.genextreme.ppf(.5, 0)
+        stats.genextreme.logpdf(-np.inf, 0.0)
+        number_of_warnings_thrown = len(w)
+        assert_equal(number_of_warnings_thrown, 0)
+
+
+def test_moments_gh22400():
+    # Regression test for gh-22400
+    # Check for correct results at c=0 with no warnings. While we're at it,
+    # check that NaN and sufficiently negative input produce NaNs, and output
+    # with `c=1` also agrees with reference values.
+    res = np.asarray(stats.genextreme.stats([0.0, np.nan, 1, -1.5], moments='mvsk'))
+
+    # Reference values for c=0 (Wikipedia)
+    mean = np.euler_gamma
+    var = np.pi**2 / 6
+    skew = 12 * np.sqrt(6) * special.zeta(3) / np.pi**3
+    kurt = 12 / 5
+    ref_0 = [mean, var, skew, kurt]
+    ref_1 = ref_3 = [np.nan]*4
+    ref_2 = [0, 1, -2, 6]  # Wolfram Alpha, MaxStableDistribution[0, 1, -1]
+
+    assert_allclose(res[:, 0], ref_0, rtol=1e-14)
+    assert_equal(res[:, 1], ref_1)
+    assert_allclose(res[:, 2], ref_2, rtol=1e-14)
+    assert_equal(res[:, 3], ref_3)
+
+
+def test_genextreme_entropy():
+    # regression test for gh-5181
+    euler_gamma = 0.5772156649015329
+
+    h = stats.genextreme.entropy(-1.0)
+    assert_allclose(h, 2*euler_gamma + 1, rtol=1e-14)
+
+    h = stats.genextreme.entropy(0)
+    assert_allclose(h, euler_gamma + 1, rtol=1e-14)
+
+    h = stats.genextreme.entropy(1.0)
+    assert_equal(h, 1)
+
+    h = stats.genextreme.entropy(-2.0, scale=10)
+    assert_allclose(h, euler_gamma*3 + np.log(10) + 1, rtol=1e-14)
+
+    h = stats.genextreme.entropy(10)
+    assert_allclose(h, -9*euler_gamma + 1, rtol=1e-14)
+
+    h = stats.genextreme.entropy(-10)
+    assert_allclose(h, 11*euler_gamma + 1, rtol=1e-14)
+
+
+def test_genextreme_sf_isf():
+    # Expected values were computed using mpmath:
+    #
+    #    import mpmath
+    #
+    #    def mp_genextreme_sf(x, xi, mu=0, sigma=1):
+    #        # Formula from wikipedia, which has a sign convention for xi that
+    #        # is the opposite of scipy's shape parameter.
+    #        if xi != 0:
+    #            t = mpmath.power(1 + ((x - mu)/sigma)*xi, -1/xi)
+    #        else:
+    #            t = mpmath.exp(-(x - mu)/sigma)
+    #        return 1 - mpmath.exp(-t)
+    #
+    # >>> mpmath.mp.dps = 1000
+    # >>> s = mp_genextreme_sf(mpmath.mp.mpf("1e8"), mpmath.mp.mpf("0.125"))
+    # >>> float(s)
+    # 1.6777205262585625e-57
+    # >>> s = mp_genextreme_sf(mpmath.mp.mpf("7.98"), mpmath.mp.mpf("-0.125"))
+    # >>> float(s)
+    # 1.52587890625e-21
+    # >>> s = mp_genextreme_sf(mpmath.mp.mpf("7.98"), mpmath.mp.mpf("0"))
+    # >>> float(s)
+    # 0.00034218086528426593
+
+    x = 1e8
+    s = stats.genextreme.sf(x, -0.125)
+    assert_allclose(s, 1.6777205262585625e-57)
+    x2 = stats.genextreme.isf(s, -0.125)
+    assert_allclose(x2, x)
+
+    x = 7.98
+    s = stats.genextreme.sf(x, 0.125)
+    assert_allclose(s, 1.52587890625e-21)
+    x2 = stats.genextreme.isf(s, 0.125)
+    assert_allclose(x2, x)
+
+    x = 7.98
+    s = stats.genextreme.sf(x, 0)
+    assert_allclose(s, 0.00034218086528426593)
+    x2 = stats.genextreme.isf(s, 0)
+    assert_allclose(x2, x)
+
+
+def test_burr12_ppf_small_arg():
+    prob = 1e-16
+    quantile = stats.burr12.ppf(prob, 2, 3)
+    # The expected quantile was computed using mpmath:
+    #   >>> import mpmath
+    #   >>> mpmath.mp.dps = 100
+    #   >>> prob = mpmath.mpf('1e-16')
+    #   >>> c = mpmath.mpf(2)
+    #   >>> d = mpmath.mpf(3)
+    #   >>> float(((1-prob)**(-1/d) - 1)**(1/c))
+    #   5.7735026918962575e-09
+    assert_allclose(quantile, 5.7735026918962575e-09)
+
+
+def test_invweibull_fit():
+    """
+    Test fitting invweibull to data.
+
+    Here is a the same calculation in R:
+
+    > library(evd)
+    > library(fitdistrplus)
+    > x = c(1, 1.25, 2, 2.5, 2.8,  3, 3.8, 4, 5, 8, 10, 12, 64, 99)
+    > result = fitdist(x, 'frechet', control=list(reltol=1e-13),
+    +                  fix.arg=list(loc=0), start=list(shape=2, scale=3))
+    > result
+    Fitting of the distribution ' frechet ' by maximum likelihood
+    Parameters:
+          estimate Std. Error
+    shape 1.048482  0.2261815
+    scale 3.099456  0.8292887
+    Fixed parameters:
+        value
+    loc     0
+
+    """
+
+    def optimizer(func, x0, args=(), disp=0):
+        return fmin(func, x0, args=args, disp=disp, xtol=1e-12, ftol=1e-12)
+
+    x = np.array([1, 1.25, 2, 2.5, 2.8, 3, 3.8, 4, 5, 8, 10, 12, 64, 99])
+    c, loc, scale = stats.invweibull.fit(x, floc=0, optimizer=optimizer)
+    assert_allclose(c, 1.048482, rtol=5e-6)
+    assert loc == 0
+    assert_allclose(scale, 3.099456, rtol=5e-6)
+
+
+# Expected values were computed with mpmath.
+@pytest.mark.parametrize('x, c, expected',
+                         [(3, 1.5, 0.175064510070713299327),
+                          (2000, 1.5, 1.11802773877318715787e-5),
+                          (2000, 9.25, 2.92060308832269637092e-31),
+                          (1e15, 1.5, 3.16227766016837933199884e-23)])
+def test_invweibull_sf(x, c, expected):
+    computed = stats.invweibull.sf(x, c)
+    assert_allclose(computed, expected, rtol=1e-15)
+
+
+# Expected values were computed with mpmath.
+@pytest.mark.parametrize('p, c, expected',
+                         [(0.5, 2.5, 1.15789669836468183976),
+                          (3e-18, 5, 3195.77171838060906447)])
+def test_invweibull_isf(p, c, expected):
+    computed = stats.invweibull.isf(p, c)
+    assert_allclose(computed, expected, rtol=1e-15)
+
+
+@pytest.mark.parametrize(
+    'df1,df2,x',
+    [(2, 2, [-0.5, 0.2, 1.0, 2.3]),
+     (4, 11, [-0.5, 0.2, 1.0, 2.3]),
+     (7, 17, [1, 2, 3, 4, 5])]
+)
+def test_ncf_edge_case(df1, df2, x):
+    # Test for edge case described in gh-11660.
+    # Non-central Fisher distribution when nc = 0
+    # should be the same as Fisher distribution.
+    nc = 0
+    expected_cdf = stats.f.cdf(x, df1, df2)
+    calculated_cdf = stats.ncf.cdf(x, df1, df2, nc)
+    assert_allclose(expected_cdf, calculated_cdf, rtol=1e-14)
+
+    # when ncf_gen._skip_pdf will be used instead of generic pdf,
+    # this additional test will be useful.
+    expected_pdf = stats.f.pdf(x, df1, df2)
+    calculated_pdf = stats.ncf.pdf(x, df1, df2, nc)
+    assert_allclose(expected_pdf, calculated_pdf, rtol=1e-6)
+
+
+def test_ncf_variance():
+    # Regression test for gh-10658 (incorrect variance formula for ncf).
+    # The correct value of ncf.var(2, 6, 4), 42.75, can be verified with, for
+    # example, Wolfram Alpha with the expression
+    #     Variance[NoncentralFRatioDistribution[2, 6, 4]]
+    # or with the implementation of the noncentral F distribution in the C++
+    # library Boost.
+    v = stats.ncf.var(2, 6, 4)
+    assert_allclose(v, 42.75, rtol=1e-14)
+
+
+def test_ncf_cdf_spotcheck():
+    # Regression test for gh-15582 testing against values from R/MATLAB
+    # Generate check_val from R or MATLAB as follows:
+    #          R: pf(20, df1 = 6, df2 = 33, ncp = 30.4) = 0.998921
+    #     MATLAB: ncfcdf(20, 6, 33, 30.4) = 0.998921
+    scipy_val = stats.ncf.cdf(20, 6, 33, 30.4)
+    check_val = 0.998921
+    assert_allclose(check_val, np.round(scipy_val, decimals=6))
+
+
+def test_ncf_ppf_issue_17026():
+    # Regression test for gh-17026
+    x = np.linspace(0, 1, 600)
+    x[0] = 1e-16
+    par = (0.1, 2, 5, 0, 1)
+    q = stats.ncf.ppf(x, *par)
+    q0 = [stats.ncf.ppf(xi, *par) for xi in x]
+    assert_allclose(q, q0)
+
+
+class TestHistogram:
+    def setup_method(self):
+        self.rng = np.random.default_rng(6561174822)
+
+        # We have 8 bins
+        # [1,2), [2,3), [3,4), [4,5), [5,6), [6,7), [7,8), [8,9)
+        # But actually np.histogram will put the last 9 also in the [8,9) bin!
+        # Therefore there is a slight difference below for the last bin, from
+        # what you might have expected.
+        histogram = np.histogram([1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5,
+                                  6, 6, 6, 6, 7, 7, 7, 8, 8, 9], bins=8)
+        self.template = stats.rv_histogram(histogram)
+
+        data = stats.norm.rvs(loc=1.0, scale=2.5, size=10000, random_state=self.rng)
+        norm_histogram = np.histogram(data, bins=50)
+        self.norm_template = stats.rv_histogram(norm_histogram)
+
+    def test_pdf(self):
+        values = np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5,
+                           5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5])
+        pdf_values = np.asarray([0.0/25.0, 0.0/25.0, 1.0/25.0, 1.0/25.0,
+                                 2.0/25.0, 2.0/25.0, 3.0/25.0, 3.0/25.0,
+                                 4.0/25.0, 4.0/25.0, 5.0/25.0, 5.0/25.0,
+                                 4.0/25.0, 4.0/25.0, 3.0/25.0, 3.0/25.0,
+                                 3.0/25.0, 3.0/25.0, 0.0/25.0, 0.0/25.0])
+        assert_allclose(self.template.pdf(values), pdf_values)
+
+        # Test explicitly the corner cases:
+        # As stated above the pdf in the bin [8,9) is greater than
+        # one would naively expect because np.histogram putted the 9
+        # into the [8,9) bin.
+        assert_almost_equal(self.template.pdf(8.0), 3.0/25.0)
+        assert_almost_equal(self.template.pdf(8.5), 3.0/25.0)
+        # 9 is outside our defined bins [8,9) hence the pdf is already 0
+        # for a continuous distribution this is fine, because a single value
+        # does not have a finite probability!
+        assert_almost_equal(self.template.pdf(9.0), 0.0/25.0)
+        assert_almost_equal(self.template.pdf(10.0), 0.0/25.0)
+
+        x = np.linspace(-2, 2, 10)
+        assert_allclose(self.norm_template.pdf(x),
+                        stats.norm.pdf(x, loc=1.0, scale=2.5), rtol=0.1)
+
+    def test_cdf_ppf(self):
+        values = np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5,
+                           5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5])
+        cdf_values = np.asarray([0.0/25.0, 0.0/25.0, 0.0/25.0, 0.5/25.0,
+                                 1.0/25.0, 2.0/25.0, 3.0/25.0, 4.5/25.0,
+                                 6.0/25.0, 8.0/25.0, 10.0/25.0, 12.5/25.0,
+                                 15.0/25.0, 17.0/25.0, 19.0/25.0, 20.5/25.0,
+                                 22.0/25.0, 23.5/25.0, 25.0/25.0, 25.0/25.0])
+        assert_allclose(self.template.cdf(values), cdf_values)
+        # First three and last two values in cdf_value are not unique
+        assert_allclose(self.template.ppf(cdf_values[2:-1]), values[2:-1])
+
+        # Test of cdf and ppf are inverse functions
+        x = np.linspace(1.0, 9.0, 100)
+        assert_allclose(self.template.ppf(self.template.cdf(x)), x)
+        x = np.linspace(0.0, 1.0, 100)
+        assert_allclose(self.template.cdf(self.template.ppf(x)), x)
+
+        x = np.linspace(-2, 2, 10)
+        assert_allclose(self.norm_template.cdf(x),
+                        stats.norm.cdf(x, loc=1.0, scale=2.5), rtol=0.1)
+
+    def test_rvs(self):
+        N = 10000
+        sample = self.template.rvs(size=N, random_state=self.rng)
+        assert_equal(np.sum(sample < 1.0), 0.0)
+        assert_allclose(np.sum(sample <= 2.0), 1.0/25.0 * N, rtol=0.2)
+        assert_allclose(np.sum(sample <= 2.5), 2.0/25.0 * N, rtol=0.2)
+        assert_allclose(np.sum(sample <= 3.0), 3.0/25.0 * N, rtol=0.1)
+        assert_allclose(np.sum(sample <= 3.5), 4.5/25.0 * N, rtol=0.1)
+        assert_allclose(np.sum(sample <= 4.0), 6.0/25.0 * N, rtol=0.1)
+        assert_allclose(np.sum(sample <= 4.5), 8.0/25.0 * N, rtol=0.1)
+        assert_allclose(np.sum(sample <= 5.0), 10.0/25.0 * N, rtol=0.05)
+        assert_allclose(np.sum(sample <= 5.5), 12.5/25.0 * N, rtol=0.05)
+        assert_allclose(np.sum(sample <= 6.0), 15.0/25.0 * N, rtol=0.05)
+        assert_allclose(np.sum(sample <= 6.5), 17.0/25.0 * N, rtol=0.05)
+        assert_allclose(np.sum(sample <= 7.0), 19.0/25.0 * N, rtol=0.05)
+        assert_allclose(np.sum(sample <= 7.5), 20.5/25.0 * N, rtol=0.05)
+        assert_allclose(np.sum(sample <= 8.0), 22.0/25.0 * N, rtol=0.05)
+        assert_allclose(np.sum(sample <= 8.5), 23.5/25.0 * N, rtol=0.05)
+        assert_allclose(np.sum(sample <= 9.0), 25.0/25.0 * N, rtol=0.05)
+        assert_allclose(np.sum(sample <= 9.0), 25.0/25.0 * N, rtol=0.05)
+        assert_equal(np.sum(sample > 9.0), 0.0)
+
+    def test_munp(self):
+        for n in range(4):
+            assert_allclose(self.norm_template._munp(n),
+                            stats.norm(1.0, 2.5).moment(n), rtol=0.05)
+
+    def test_entropy(self):
+        assert_allclose(self.norm_template.entropy(),
+                        stats.norm.entropy(loc=1.0, scale=2.5), rtol=0.05)
+
+
+def test_histogram_non_uniform():
+    # Tests rv_histogram works even for non-uniform bin widths
+    counts, bins = ([1, 1], [0, 1, 1001])
+
+    dist = stats.rv_histogram((counts, bins), density=False)
+    np.testing.assert_allclose(dist.pdf([0.5, 200]), [0.5, 0.0005])
+    assert dist.median() == 1
+
+    dist = stats.rv_histogram((counts, bins), density=True)
+    np.testing.assert_allclose(dist.pdf([0.5, 200]), 1/1001)
+    assert dist.median() == 1001/2
+
+    # Omitting density produces a warning for non-uniform bins...
+    message = "Bin widths are not constant. Assuming..."
+    with pytest.warns(RuntimeWarning, match=message):
+        dist = stats.rv_histogram((counts, bins))
+        assert dist.median() == 1001/2  # default is like `density=True`
+
+    # ... but not for uniform bins
+    dist = stats.rv_histogram((counts, [0, 1, 2]))
+    assert dist.median() == 1
+
+
+class TestLogUniform:
+    def test_alias(self):
+        # This test makes sure that "reciprocal" and "loguniform" are
+        # aliases of the same distribution and that both are log-uniform
+        rng = np.random.default_rng(98643218961)
+        rv = stats.loguniform(10 ** -3, 10 ** 0)
+        rvs = rv.rvs(size=10000, random_state=rng)
+
+        rng = np.random.default_rng(98643218961)
+        rv2 = stats.reciprocal(10 ** -3, 10 ** 0)
+        rvs2 = rv2.rvs(size=10000, random_state=rng)
+
+        assert_allclose(rvs2, rvs)
+
+        vals, _ = np.histogram(np.log10(rvs), bins=10)
+        assert 900 <= vals.min() <= vals.max() <= 1100
+        assert np.abs(np.median(vals) - 1000) <= 10
+
+    @pytest.mark.parametrize("method", ['mle', 'mm'])
+    def test_fit_override(self, method):
+        # loguniform is overparameterized, so check that fit override enforces
+        # scale=1 unless fscale is provided by the user
+        rng = np.random.default_rng(98643218961)
+        rvs = stats.loguniform.rvs(0.1, 1, size=1000, random_state=rng)
+
+        a, b, loc, scale = stats.loguniform.fit(rvs, method=method)
+        assert scale == 1
+
+        a, b, loc, scale = stats.loguniform.fit(rvs, fscale=2, method=method)
+        assert scale == 2
+
+    def test_overflow(self):
+        # original formulation had overflow issues; check that this is resolved
+        # Extensive accuracy tests elsewhere, no need to test all methods
+        rng = np.random.default_rng(7136519550773909093)
+        a, b = 1e-200, 1e200
+        dist = stats.loguniform(a, b)
+
+        # test roundtrip error
+        cdf = rng.uniform(0, 1, size=1000)
+        assert_allclose(dist.cdf(dist.ppf(cdf)), cdf)
+        rvs = dist.rvs(size=1000, random_state=rng)
+        assert_allclose(dist.ppf(dist.cdf(rvs)), rvs)
+
+        # test a property of the pdf (and that there is no overflow)
+        x = 10.**np.arange(-200, 200)
+        pdf = dist.pdf(x)  # no overflow
+        assert_allclose(pdf[:-1]/pdf[1:], 10)
+
+        # check munp against wikipedia reference
+        mean = (b - a)/(np.log(b) - np.log(a))
+        assert_allclose(dist.mean(), mean)
+
+
+class TestArgus:
+    def test_argus_rvs_large_chi(self):
+        # test that the algorithm can handle large values of chi
+        x = stats.argus.rvs(50, size=500, random_state=325)
+        assert_almost_equal(stats.argus(50).mean(), x.mean(), decimal=4)
+
+    @pytest.mark.parametrize('chi, random_state', [
+            [0.1, 325],   # chi <= 0.5: rejection method case 1
+            [1.3, 155],   # 0.5 < chi <= 1.8: rejection method case 2
+            [3.5, 135]    # chi > 1.8: transform conditional Gamma distribution
+        ])
+    def test_rvs(self, chi, random_state):
+        x = stats.argus.rvs(chi, size=500, random_state=random_state)
+        _, p = stats.kstest(x, "argus", (chi, ))
+        assert_(p > 0.05)
+
+    @pytest.mark.parametrize('chi', [1e-9, 1e-6])
+    def test_rvs_small_chi(self, chi):
+        # test for gh-11699 => rejection method case 1 can even handle chi=0
+        # the CDF of the distribution for chi=0 is 1 - (1 - x**2)**(3/2)
+        # test rvs against distribution of limit chi=0
+        r = stats.argus.rvs(chi, size=500, random_state=890981)
+        _, p = stats.kstest(r, lambda x: 1 - (1 - x**2)**(3/2))
+        assert_(p > 0.05)
+
+    # Expected values were computed with mpmath.
+    @pytest.mark.parametrize('chi, expected_mean',
+                             [(1, 0.6187026683551835),
+                              (10, 0.984805536783744),
+                              (40, 0.9990617659702923),
+                              (60, 0.9995831885165300),
+                              (99, 0.9998469348663028)])
+    def test_mean(self, chi, expected_mean):
+        m = stats.argus.mean(chi, scale=1)
+        assert_allclose(m, expected_mean, rtol=1e-13)
+
+    # Expected values were computed with mpmath.
+    @pytest.mark.parametrize('chi, expected_var, rtol',
+                             [(1, 0.05215651254197807, 1e-13),
+                              (10, 0.00015805472008165595, 1e-11),
+                              (40, 5.877763210262901e-07, 1e-8),
+                              (60, 1.1590179389611416e-07, 1e-8),
+                              (99, 1.5623277006064666e-08, 1e-8)])
+    def test_var(self, chi, expected_var, rtol):
+        v = stats.argus.var(chi, scale=1)
+        assert_allclose(v, expected_var, rtol=rtol)
+
+    # Expected values were computed with mpmath (code: see gh-13370).
+    @pytest.mark.parametrize('chi, expected, rtol',
+                             [(0.9, 0.07646314974436118, 1e-14),
+                              (0.5, 0.015429797891863365, 1e-14),
+                              (0.1, 0.0001325825293278049, 1e-14),
+                              (0.01, 1.3297677078224565e-07, 1e-15),
+                              (1e-3, 1.3298072023958999e-10, 1e-14),
+                              (1e-4, 1.3298075973486862e-13, 1e-14),
+                              (1e-6, 1.32980760133771e-19, 1e-14),
+                              (1e-9, 1.329807601338109e-28, 1e-15)])
+    def test_argus_phi_small_chi(self, chi, expected, rtol):
+        assert_allclose(_argus_phi(chi), expected, rtol=rtol)
+
+    # Expected values were computed with mpmath (code: see gh-13370).
+    @pytest.mark.parametrize(
+        'chi, expected',
+        [(0.5, (0.28414073302940573, 1.2742227939992954, 1.2381254688255896)),
+         (0.2, (0.296172952995264, 1.2951290588110516, 1.1865767100877576)),
+         (0.1, (0.29791447523536274, 1.29806307956989, 1.1793168289857412)),
+         (0.01, (0.2984904104866452, 1.2990283628160553, 1.1769268414080531)),
+         (1e-3, (0.298496172925224, 1.2990380082487925, 1.176902956021053)),
+         (1e-4, (0.29849623054991836, 1.2990381047023793, 1.1769027171686324)),
+         (1e-6, (0.2984962311319278, 1.2990381056765605, 1.1769027147562232)),
+         (1e-9, (0.298496231131986, 1.299038105676658, 1.1769027147559818))])
+    def test_pdf_small_chi(self, chi, expected):
+        x = np.array([0.1, 0.5, 0.9])
+        assert_allclose(stats.argus.pdf(x, chi), expected, rtol=1e-13)
+
+    # Expected values were computed with mpmath (code: see gh-13370).
+    @pytest.mark.parametrize(
+        'chi, expected',
+        [(0.5, (0.9857660526895221, 0.6616565930168475, 0.08796070398429937)),
+         (0.2, (0.9851555052359501, 0.6514666238985464, 0.08362690023746594)),
+         (0.1, (0.9850670974995661, 0.6500061310508574, 0.08302050640683846)),
+         (0.01, (0.9850378582451867, 0.6495239242251358, 0.08282109244852445)),
+         (1e-3, (0.9850375656906663, 0.6495191015522573, 0.08281910005231098)),
+         (1e-4, (0.9850375627651049, 0.6495190533254682, 0.08281908012852317)),
+         (1e-6, (0.9850375627355568, 0.6495190528383777, 0.08281907992729293)),
+         (1e-9, (0.9850375627355538, 0.649519052838329, 0.0828190799272728))])
+    def test_sf_small_chi(self, chi, expected):
+        x = np.array([0.1, 0.5, 0.9])
+        assert_allclose(stats.argus.sf(x, chi), expected, rtol=1e-14)
+
+    # Expected values were computed with mpmath.
+    @pytest.mark.parametrize(
+        'x, chi, expected',
+        [(0.9999999, 0.25, 9.113252974162428e-11),
+         (0.9999999, 3.0, 6.616650419714568e-10),
+         (0.999999999, 2.5, 4.130195911418939e-13),
+         (0.999999999, 10.0, 2.3788319094393724e-11)])
+    def test_sf_near_1(self, x, chi, expected):
+        sf = stats.argus.sf(x, chi)
+        assert_allclose(sf, expected, rtol=5e-15)
+
+    # Expected values were computed with mpmath (code: see gh-13370).
+    @pytest.mark.parametrize(
+        'chi, expected',
+        [(0.5, (0.0142339473104779, 0.3383434069831524, 0.9120392960157007)),
+         (0.2, (0.014844494764049919, 0.34853337610145363, 0.916373099762534)),
+         (0.1, (0.014932902500433911, 0.34999386894914264, 0.9169794935931616)),
+         (0.01, (0.014962141754813293, 0.35047607577486417, 0.9171789075514756)),
+         (1e-3, (0.01496243430933372, 0.35048089844774266, 0.917180899947689)),
+         (1e-4, (0.014962437234895118, 0.3504809466745317, 0.9171809198714769)),
+         (1e-6, (0.01496243726444329, 0.3504809471616223, 0.9171809200727071)),
+         (1e-9, (0.014962437264446245, 0.350480947161671, 0.9171809200727272))])
+    def test_cdf_small_chi(self, chi, expected):
+        x = np.array([0.1, 0.5, 0.9])
+        assert_allclose(stats.argus.cdf(x, chi), expected, rtol=1e-12)
+
+    # Expected values were computed with mpmath (code: see gh-13370).
+    @pytest.mark.parametrize(
+        'chi, expected, rtol',
+        [(0.5, (0.5964284712757741, 0.052890651988588604), 1e-12),
+         (0.101, (0.5893490968089076, 0.053017469847275685), 1e-11),
+         (0.1, (0.5893431757009437, 0.05301755449499372), 1e-13),
+         (0.01, (0.5890515677940915, 0.05302167905837031), 1e-13),
+         (1e-3, (0.5890486520005177, 0.053021719862088104), 1e-13),
+         (1e-4, (0.5890486228426105, 0.0530217202700811), 1e-13),
+         (1e-6, (0.5890486225481156, 0.05302172027420182), 1e-13),
+         (1e-9, (0.5890486225480862, 0.05302172027420224), 1e-13)])
+    def test_stats_small_chi(self, chi, expected, rtol):
+        val = stats.argus.stats(chi, moments='mv')
+        assert_allclose(val, expected, rtol=rtol)
+
+
+class TestNakagami:
+    def setup_method(self):
+        self.rng = np.random.default_rng(9626839526)
+
+    def test_logpdf(self):
+        # Test nakagami logpdf for an input where the PDF is smaller
+        # than can be represented with 64 bit floating point.
+        # The expected value of logpdf was computed with mpmath:
+        #
+        #   def logpdf(x, nu):
+        #       x = mpmath.mpf(x)
+        #       nu = mpmath.mpf(nu)
+        #       return (mpmath.log(2) + nu*mpmath.log(nu) -
+        #               mpmath.loggamma(nu) + (2*nu - 1)*mpmath.log(x) -
+        #               nu*x**2)
+        #
+        nu = 2.5
+        x = 25
+        logp = stats.nakagami.logpdf(x, nu)
+        assert_allclose(logp, -1546.9253055607549)
+
+    def test_sf_isf(self):
+        # Test nakagami sf and isf when the survival function
+        # value is very small.
+        # The expected value of the survival function was computed
+        # with mpmath:
+        #
+        #   def sf(x, nu):
+        #       x = mpmath.mpf(x)
+        #       nu = mpmath.mpf(nu)
+        #       return mpmath.gammainc(nu, nu*x*x, regularized=True)
+        #
+        nu = 2.5
+        x0 = 5.0
+        sf = stats.nakagami.sf(x0, nu)
+        assert_allclose(sf, 2.736273158588307e-25, rtol=1e-13)
+        # Check round trip back to x0.
+        x1 = stats.nakagami.isf(sf, nu)
+        assert_allclose(x1, x0, rtol=1e-13)
+
+    def test_logcdf(self):
+        x = 8
+        nu = 0.5
+        # Reference value computed with mpmath.
+        ref = -1.2441921148543576e-15
+        logcdf = stats.nakagami.logcdf(x, nu)
+        assert_allclose(logcdf, ref, rtol=5e-15)
+
+    def test_logsf(self):
+        x = 0.05
+        nu = 12
+        # Reference value computed with mpmath.
+        ref = -1.0791764722337046e-27
+        logsf = stats.nakagami.logsf(x, nu)
+        assert_allclose(logsf, ref, rtol=5e-15)
+
+    @pytest.mark.parametrize("m, ref",
+        [(5, -0.097341814372152),
+         (0.5, 0.7257913526447274),
+         (10, -0.43426184310934907)])
+    def test_entropy(self, m, ref):
+        # from sympy import *
+        # from mpmath import mp
+        # import numpy as np
+        # v, x = symbols('v, x', real=True, positive=True)
+        # pdf = 2 * v ** v / gamma(v) * x ** (2 * v - 1) * exp(-v * x ** 2)
+        # h = simplify(simplify(integrate(-pdf * log(pdf), (x, 0, oo))))
+        # entropy = lambdify(v, h, 'mpmath')
+        # mp.dps = 200
+        # nu = 5
+        # ref = np.float64(entropy(mp.mpf(nu)))
+        # print(ref)
+        assert_allclose(stats.nakagami.entropy(m), ref, rtol=1.1e-14)
+
+    @pytest.mark.parametrize("m, ref",
+        [(1e-100, -5.0e+99), (1e-10, -4999999965.442979),
+         (9.999e6, -7.333206478668433), (1.001e7, -7.3337562313259825),
+         (1e10, -10.787134112333835), (1e100, -114.40346329705756)])
+    def test_extreme_nu(self, m, ref):
+        assert_allclose(stats.nakagami.entropy(m), ref)
+
+    def test_entropy_overflow(self):
+        assert np.isfinite(stats.nakagami._entropy(1e100))
+        assert np.isfinite(stats.nakagami._entropy(1e-100))
+
+    @pytest.mark.parametrize("nu, ref",
+                             [(1e10, 0.9999999999875),
+                              (1e3, 0.9998750078173821),
+                              (1e-10, 1.772453850659802e-05)])
+    def test_mean(self, nu, ref):
+        # reference values were computed with mpmath
+        # from mpmath import mp
+        # mp.dps = 500
+        # nu = mp.mpf(1e10)
+        # float(mp.rf(nu, mp.mpf(0.5))/mp.sqrt(nu))
+        assert_allclose(stats.nakagami.mean(nu), ref, rtol=1e-12)
+
+    @pytest.mark.xfail(reason="Fit of nakagami not reliable, see gh-10908.")
+    @pytest.mark.parametrize('nu', [1.6, 2.5, 3.9])
+    @pytest.mark.parametrize('loc', [25.0, 10, 35])
+    @pytest.mark.parametrize('scale', [13, 5, 20])
+    def test_fit(self, nu, loc, scale):
+        # Regression test for gh-13396 (21/27 cases failed previously)
+        # The first tuple of the parameters' values is discussed in gh-10908
+        N = 100
+        samples = stats.nakagami.rvs(size=N, nu=nu, loc=loc,
+                                     scale=scale, random_state=self.rng)
+        nu_est, loc_est, scale_est = stats.nakagami.fit(samples)
+        assert_allclose(nu_est, nu, rtol=0.2)
+        assert_allclose(loc_est, loc, rtol=0.2)
+        assert_allclose(scale_est, scale, rtol=0.2)
+
+        def dlogl_dnu(nu, loc, scale):
+            return ((-2*nu + 1) * np.sum(1/(samples - loc))
+                    + 2*nu/scale**2 * np.sum(samples - loc))
+
+        def dlogl_dloc(nu, loc, scale):
+            return (N * (1 + np.log(nu) - polygamma(0, nu)) +
+                    2 * np.sum(np.log((samples - loc) / scale))
+                    - np.sum(((samples - loc) / scale)**2))
+
+        def dlogl_dscale(nu, loc, scale):
+            return (- 2 * N * nu / scale
+                    + 2 * nu / scale ** 3 * np.sum((samples - loc) ** 2))
+
+        assert_allclose(dlogl_dnu(nu_est, loc_est, scale_est), 0, atol=1e-3)
+        assert_allclose(dlogl_dloc(nu_est, loc_est, scale_est), 0, atol=1e-3)
+        assert_allclose(dlogl_dscale(nu_est, loc_est, scale_est), 0, atol=1e-3)
+
+    @pytest.mark.parametrize('loc', [25.0, 10, 35])
+    @pytest.mark.parametrize('scale', [13, 5, 20])
+    def test_fit_nu(self, loc, scale):
+        # For nu = 0.5, we have analytical values for
+        # the MLE of the loc and the scale
+        nu = 0.5
+        n = 100
+        samples = stats.nakagami.rvs(size=n, nu=nu, loc=loc,
+                                     scale=scale, random_state=self.rng)
+        nu_est, loc_est, scale_est = stats.nakagami.fit(samples, f0=nu)
+
+        # Analytical values
+        loc_theo = np.min(samples)
+        scale_theo = np.sqrt(np.mean((samples - loc_est) ** 2))
+
+        assert_allclose(nu_est, nu, rtol=1e-7)
+        assert_allclose(loc_est, loc_theo, rtol=1e-7)
+        assert_allclose(scale_est, scale_theo, rtol=1e-7)
+
+
+class TestWrapCauchy:
+    def setup_method(self):
+        self.rng = np.random.default_rng(2439107123)
+
+    def test_cdf_shape_broadcasting(self):
+        # Regression test for gh-13791.
+        # Check that wrapcauchy.cdf broadcasts the shape parameter
+        # correctly.
+        c = np.array([[0.03, 0.25], [0.5, 0.75]])
+        x = np.array([[1.0], [4.0]])
+        p = stats.wrapcauchy.cdf(x, c)
+        assert p.shape == (2, 2)
+        scalar_values = [stats.wrapcauchy.cdf(x1, c1)
+                         for (x1, c1) in np.nditer((x, c))]
+        assert_allclose(p.ravel(), scalar_values, rtol=1e-13)
+
+    def test_cdf_center(self):
+        p = stats.wrapcauchy.cdf(np.pi, 0.03)
+        assert_allclose(p, 0.5, rtol=1e-14)
+
+    def test_cdf(self):
+        x1 = 1.0  # less than pi
+        x2 = 4.0  # greater than pi
+        c = 0.75
+        p = stats.wrapcauchy.cdf([x1, x2], c)
+        cr = (1 + c)/(1 - c)
+        assert_allclose(p[0], np.arctan(cr*np.tan(x1/2))/np.pi)
+        assert_allclose(p[1], 1 - np.arctan(cr*np.tan(np.pi - x2/2))/np.pi)
+
+    @pytest.mark.parametrize('c', [1e-10, 1e-1])
+    @pytest.mark.parametrize('loc', [-100, -2*np.pi, -np.pi, 0, np.pi, 2*np.pi, 100])
+    @pytest.mark.parametrize('scale', [1e-10, 1, 1e10])
+    def test_rvs_lie_on_circle(self, c, loc, scale):
+        # Check that the random variates lie in range [0, 2*pi]
+        x = stats.wrapcauchy.rvs(c=c, loc=loc, scale=scale,
+                                 size=1000, random_state=self.rng)
+        assert np.all(x >= 0)
+        assert np.all(x <= 2 * np.pi)
+
+
+def test_rvs_no_size_error():
+    # _rvs methods must have parameter `size`; see gh-11394
+    class rvs_no_size_gen(stats.rv_continuous):
+        def _rvs(self):
+            return 1
+
+    rvs_no_size = rvs_no_size_gen(name='rvs_no_size')
+    rng = np.random.default_rng(1334886239)
+    with assert_raises(TypeError, match=r"_rvs\(\) got (an|\d) unexpected"):
+        rvs_no_size.rvs(random_state=rng)
+
+
+@pytest.mark.parametrize('distname, args', invdistdiscrete + invdistcont)
+def test_support_gh13294_regression(distname, args):
+    if distname in skip_test_support_gh13294_regression:
+        pytest.skip(f"skipping test for the support method for "
+                    f"distribution {distname}.")
+    dist = getattr(stats, distname)
+    # test support method with invalid arguments
+    if isinstance(dist, stats.rv_continuous):
+        # test with valid scale
+        if len(args) != 0:
+            a0, b0 = dist.support(*args)
+            assert_equal(a0, np.nan)
+            assert_equal(b0, np.nan)
+        # test with invalid scale
+        # For some distributions, that take no parameters,
+        # the case of only invalid scale occurs and hence,
+        # it is implicitly tested in this test case.
+        loc1, scale1 = 0, -1
+        a1, b1 = dist.support(*args, loc1, scale1)
+        assert_equal(a1, np.nan)
+        assert_equal(b1, np.nan)
+    else:
+        a, b = dist.support(*args)
+        assert_equal(a, np.nan)
+        assert_equal(b, np.nan)
+
+
+def test_support_broadcasting_gh13294_regression():
+    a0, b0 = stats.norm.support([0, 0, 0, 1], [1, 1, 1, -1])
+    ex_a0 = np.array([-np.inf, -np.inf, -np.inf, np.nan])
+    ex_b0 = np.array([np.inf, np.inf, np.inf, np.nan])
+    assert_equal(a0, ex_a0)
+    assert_equal(b0, ex_b0)
+    assert a0.shape == ex_a0.shape
+    assert b0.shape == ex_b0.shape
+
+    a1, b1 = stats.norm.support([], [])
+    ex_a1, ex_b1 = np.array([]), np.array([])
+    assert_equal(a1, ex_a1)
+    assert_equal(b1, ex_b1)
+    assert a1.shape == ex_a1.shape
+    assert b1.shape == ex_b1.shape
+
+    a2, b2 = stats.norm.support([0, 0, 0, 1], [-1])
+    ex_a2 = np.array(4*[np.nan])
+    ex_b2 = np.array(4*[np.nan])
+    assert_equal(a2, ex_a2)
+    assert_equal(b2, ex_b2)
+    assert a2.shape == ex_a2.shape
+    assert b2.shape == ex_b2.shape
+
+
+def test_stats_broadcasting_gh14953_regression():
+    # test case in gh14953
+    loc = [0., 0.]
+    scale = [[1.], [2.], [3.]]
+    assert_equal(stats.norm.var(loc, scale), [[1., 1.], [4., 4.], [9., 9.]])
+    # test some edge cases
+    loc = np.empty((0, ))
+    scale = np.empty((1, 0))
+    assert stats.norm.var(loc, scale).shape == (1, 0)
+
+
+# Check a few values of the cosine distribution's cdf, sf, ppf and
+# isf methods.  Expected values were computed with mpmath.
+
+@pytest.mark.parametrize('x, expected',
+                         [(-3.14159, 4.956444476505336e-19),
+                          (3.14, 0.9999999998928399)])
+def test_cosine_cdf_sf(x, expected):
+    assert_allclose(stats.cosine.cdf(x), expected)
+    assert_allclose(stats.cosine.sf(-x), expected)
+
+
+@pytest.mark.parametrize('p, expected',
+                         [(1e-6, -3.1080612413765905),
+                          (1e-17, -3.141585429601399),
+                          (0.975, 2.1447547020964923)])
+def test_cosine_ppf_isf(p, expected):
+    assert_allclose(stats.cosine.ppf(p), expected)
+    assert_allclose(stats.cosine.isf(p), -expected)
+
+
+def test_cosine_logpdf_endpoints():
+    logp = stats.cosine.logpdf([-np.pi, np.pi])
+    # reference value calculated using mpmath assuming `np.cos(-1)` is four
+    # floating point numbers too high. See gh-18382.
+    assert_array_less(logp, -37.18838327496655)
+
+
+def test_distr_params_lists():
+    # distribution objects are extra distributions added in
+    # test_discrete_basic. All other distributions are strings (names)
+    # and so we only choose those to compare whether both lists match.
+    discrete_distnames = {name for name, _ in distdiscrete
+                          if isinstance(name, str)}
+    invdiscrete_distnames = {name for name, _ in invdistdiscrete}
+    assert discrete_distnames == invdiscrete_distnames
+
+    cont_distnames = {name for name, _ in distcont}
+    invcont_distnames = {name for name, _ in invdistcont}
+    assert cont_distnames == invcont_distnames
+
+
+def test_moment_order_4():
+    # gh-13655 reported that if a distribution has a `_stats` method that
+    # accepts the `moments` parameter, then if the distribution's `moment`
+    # method is called with `order=4`, the faster/more accurate`_stats` gets
+    # called, but the results aren't used, and the generic `_munp` method is
+    # called to calculate the moment anyway. This tests that the issue has
+    # been fixed.
+    # stats.skewnorm._stats accepts the `moments` keyword
+    stats.skewnorm._stats(a=0, moments='k')  # no failure = has `moments`
+    # When `moment` is called, `_stats` is used, so the moment is very accurate
+    # (exactly equal to Pearson's kurtosis of the normal distribution, 3)
+    assert stats.skewnorm.moment(order=4, a=0) == 3.0
+    # At the time of gh-13655, skewnorm._munp() used the generic method
+    # to compute its result, which was inefficient and not very accurate.
+    # At that time, the following assertion would fail.  skewnorm._munp()
+    # has since been made more accurate and efficient, so now this test
+    # is expected to pass.
+    assert stats.skewnorm._munp(4, 0) == 3.0
+
+
+class TestRelativisticBW:
+    @pytest.fixture
+    def ROOT_pdf_sample_data(self):
+        """Sample data points for pdf computed with CERN's ROOT
+
+        See - https://root.cern/
+
+        Uses ROOT.TMath.BreitWignerRelativistic, available in ROOT
+        versions 6.27+
+
+        pdf calculated for Z0 Boson, W Boson, and Higgs Boson for
+        x in `np.linspace(0, 200, 401)`.
+        """
+        data = np.load(
+            Path(__file__).parent /
+            'data/rel_breitwigner_pdf_sample_data_ROOT.npy'
+        )
+        data = np.rec.fromarrays(data.T, names='x,pdf,rho,gamma')
+        return data
+
+    @pytest.mark.parametrize(
+        "rho,gamma,rtol", [
+            (36.545206797050334, 2.4952, 5e-14),  # Z0 Boson
+            (38.55107913669065, 2.085, 1e-14),  # W Boson
+            (96292.3076923077, 0.0013, 5e-13),  # Higgs Boson
+        ]
+    )
+    def test_pdf_against_ROOT(self, ROOT_pdf_sample_data, rho, gamma, rtol):
+        data = ROOT_pdf_sample_data[
+            (ROOT_pdf_sample_data['rho'] == rho)
+            & (ROOT_pdf_sample_data['gamma'] == gamma)
+        ]
+        x, pdf = data['x'], data['pdf']
+        assert_allclose(
+            pdf, stats.rel_breitwigner.pdf(x, rho, scale=gamma), rtol=rtol
+        )
+
+    @pytest.mark.parametrize("rho, Gamma, rtol", [
+              (36.545206797050334, 2.4952, 5e-13),  # Z0 Boson
+              (38.55107913669065, 2.085, 5e-13),  # W Boson
+              (96292.3076923077, 0.0013, 5e-10),  # Higgs Boson
+          ]
+      )
+    def test_pdf_against_simple_implementation(self, rho, Gamma, rtol):
+        # reference implementation straight from formulas on Wikipedia [1]
+        def pdf(E, M, Gamma):
+            gamma = np.sqrt(M**2 * (M**2 + Gamma**2))
+            k = (2 * np.sqrt(2) * M * Gamma * gamma
+                 / (np.pi * np.sqrt(M**2 + gamma)))
+            return k / ((E**2 - M**2)**2 + M**2*Gamma**2)
+        # get reasonable values at which to evaluate the CDF
+        p = np.linspace(0.05, 0.95, 10)
+        x = stats.rel_breitwigner.ppf(p, rho, scale=Gamma)
+        res = stats.rel_breitwigner.pdf(x, rho, scale=Gamma)
+        ref = pdf(x, rho*Gamma, Gamma)
+        assert_allclose(res, ref, rtol=rtol)
+
+    @pytest.mark.xslow
+    @pytest.mark.parametrize(
+        "rho,gamma", [
+            pytest.param(
+                36.545206797050334, 2.4952, marks=pytest.mark.slow
+            ),  # Z0 Boson
+            pytest.param(
+                38.55107913669065, 2.085, marks=pytest.mark.xslow
+            ),  # W Boson
+            pytest.param(
+                96292.3076923077, 0.0013, marks=pytest.mark.xslow
+            ),  # Higgs Boson
+        ]
+    )
+    def test_fit_floc(self, rho, gamma):
+        """Tests fit for cases where floc is set.
+
+        `rel_breitwigner` has special handling for these cases.
+        """
+        seed = 6936804688480013683
+        rng = np.random.default_rng(seed)
+        data = stats.rel_breitwigner.rvs(
+            rho, scale=gamma, size=1000, random_state=rng
+        )
+        fit = stats.rel_breitwigner.fit(data, floc=0)
+        assert_allclose((fit[0], fit[2]), (rho, gamma), rtol=2e-1)
+        assert fit[1] == 0
+        # Check again with fscale set.
+        fit = stats.rel_breitwigner.fit(data, floc=0, fscale=gamma)
+        assert_allclose(fit[0], rho, rtol=1e-2)
+        assert (fit[1], fit[2]) == (0, gamma)
+
+
+class TestJohnsonSU:
+    @pytest.mark.parametrize("case", [  # a, b, loc, scale, m1, m2, g1, g2
+            (-0.01, 1.1, 0.02, 0.0001, 0.02000137427557091,
+             2.1112742956578063e-08, 0.05989781342460999, 20.36324408592951-3),
+            (2.554395574161155, 2.2482281679651965, 0, 1, -1.54215386737391,
+             0.7629882028469993, -1.256656139406788, 6.303058419339775-3)])
+    def test_moment_gh18071(self, case):
+        # gh-18071 reported an IntegrationWarning emitted by johnsonsu.stats
+        # Check that the warning is no longer emitted and that the values
+        # are accurate compared against results from Mathematica.
+        # Reference values from Mathematica, e.g.
+        # Mean[JohnsonDistribution["SU",-0.01, 1.1, 0.02, 0.0001]]
+        res = stats.johnsonsu.stats(*case[:4], moments='mvsk')
+        assert_allclose(res, case[4:], rtol=1e-14)
+
+
+class TestTruncPareto:
+    def test_pdf(self):
+        # PDF is that of the truncated pareto distribution
+        b, c = 1.8, 5.3
+        x = np.linspace(1.8, 5.3)
+        res = stats.truncpareto(b, c).pdf(x)
+        ref = stats.pareto(b).pdf(x) / stats.pareto(b).cdf(c)
+        assert_allclose(res, ref)
+
+    def test_pdf_negative(self):
+        # truncpareto is equivalent to more general powerlaw from gh-23648
+        # exponent of truncpareto is negative in this case
+        a, xmin, xmax = 4, 3, 5
+        x = np.linspace(xmin, xmax)
+
+        # compute reference using PDF from gh-23648
+        C = a / (xmax ** a - xmin ** a)
+        ref = C * x ** (a - 1)
+
+        # compute using `truncpareto` with negative exponent
+        b = -a
+        c = xmax / xmin
+        scale = xmin
+        loc = 0
+        X = stats.truncpareto(b, c, loc, scale)
+
+        assert_allclose(X.pdf(x), ref)
+        assert_allclose(X.logpdf(x), np.log(X.pdf(x)))
+        # indexing avoids RuntimeWarning with `np.log(0)`
+        assert_allclose(X.logcdf(x[1:]), np.log(X.cdf(x[1:])))
+        assert_allclose(X.logsf(x[:-1]), np.log(X.sf(x[:-1])))
+
+    @pytest.mark.parametrize('fix_loc', [True, False])
+    @pytest.mark.parametrize('fix_scale', [True, False])
+    @pytest.mark.parametrize('fix_b', [True, False])
+    @pytest.mark.parametrize('fix_c', [True, False])
+    def test_fit(self, fix_loc, fix_scale, fix_b, fix_c):
+
+        rng = np.random.default_rng(6747363148258237171)
+        b, c, loc, scale = 1.8, 5.3, 1, 2.5
+        dist = stats.truncpareto(b, c, loc=loc, scale=scale)
+        data = dist.rvs(size=500, random_state=rng)
+
+        kwds = {}
+        if fix_loc:
+            kwds['floc'] = loc
+        if fix_scale:
+            kwds['fscale'] = scale
+        if fix_b:
+            kwds['f0'] = b
+        if fix_c:
+            kwds['f1'] = c
+
+        if fix_loc and fix_scale and fix_b and fix_c:
+            message = "All parameters fixed. There is nothing to optimize."
+            with pytest.raises(RuntimeError, match=message):
+                stats.truncpareto.fit(data, **kwds)
+        else:
+            _assert_less_or_close_loglike(stats.truncpareto, data, **kwds)
+
+
+class TestKappa3:
+    def test_sf(self):
+        # During development of gh-18822, we found that the override of
+        # kappa3.sf could experience overflow where the version in main did
+        # not. Check that this does not happen in final implementation.
+        sf0 = 1 - stats.kappa3.cdf(0.5, 1e5)
+        sf1 = stats.kappa3.sf(0.5, 1e5)
+        assert_allclose(sf1, sf0)
+
+
+class TestIrwinHall:
+    unif = stats.uniform(0, 1)
+    ih1 = stats.irwinhall(1)
+    ih10 = stats.irwinhall(10)
+
+    def test_stats_ih10(self):
+        # from Wolfram Alpha "mean variance skew kurtosis UniformSumDistribution[10]"
+        # W|A uses Pearson's definition of kurtosis so subtract 3
+        # should be exact integer division converted to fp64, without any further ops
+        assert_array_max_ulp(self.ih10.stats('mvsk'), (5, 10/12, 0, -3/25))
+
+    def test_moments_ih10(self):
+        # from Wolfram Alpha "values moments UniformSumDistribution[10]"
+        # algo should use integer division converted to fp64, without any further ops
+        # so these should be precise to the ulpm if not exact
+        vals = [5, 155 / 6, 275 / 2, 752, 12650 / 3,
+                677465 / 28, 567325 / 4,
+                15266213 / 18, 10333565 / 2]
+        moments = [self.ih10.moment(n+1) for n in range(len(vals))]
+        assert_array_max_ulp(moments, vals)
+        # also from Wolfram Alpha "50th moment UniformSumDistribution[10]"
+        m50 = self.ih10.moment(50)
+        m50_exact = 17453002755350010529309685557285098151740985685/4862
+        assert_array_max_ulp(m50, m50_exact)
+
+    def test_pdf_ih1_unif(self):
+        # IH(1) PDF is by definition U(0,1)
+        # we should be too, but differences in floating point eval order happen
+        # it's unclear if we can get down to the single ulp for doubles unless
+        # quads are used we're within 6-10 ulps otherwise (across sf/cdf/pdf)
+        # which is pretty good
+
+        pts = np.linspace(0, 1, 100)
+        pdf_unif = self.unif.pdf(pts)
+        pdf_ih1 = self.ih1.pdf(pts)
+        assert_array_max_ulp(pdf_ih1, pdf_unif, maxulp=10)
+
+    def test_pdf_ih2_triangle(self):
+        # IH(2) PDF is a triangle
+        ih2 = stats.irwinhall(2)
+        npts = 101
+        pts = np.linspace(0, 2, npts)
+        expected = np.linspace(0, 2, npts)
+        expected[(npts + 1) // 2:] = 2 - expected[(npts + 1) // 2:]
+        pdf_ih2 = ih2.pdf(pts)
+        assert_array_max_ulp(pdf_ih2, expected, maxulp=10)
+
+    def test_cdf_ih1_unif(self):
+        # CDF of IH(1) should be identical to uniform
+        pts = np.linspace(0, 1, 100)
+        cdf_unif = self.unif.cdf(pts)
+        cdf_ih1 = self.ih1.cdf(pts)
+
+        assert_array_max_ulp(cdf_ih1, cdf_unif, maxulp=10)
+
+    def test_cdf(self):
+        # CDF of IH is symmetric so CDF should be 0.5 at n/2
+        n = np.arange(1, 10)
+        ih = stats.irwinhall(n)
+        ih_cdf = ih.cdf(n / 2)
+        exact = np.repeat(1/2, len(n))
+        # should be identically 1/2 but fp order of eval differences happen
+        assert_array_max_ulp(ih_cdf, exact, maxulp=10)
+
+    def test_cdf_ih10_exact(self):
+        # from Wolfram Alpha "values CDF[UniformSumDistribution[10], x] x=0 to x=10"
+        # symmetric about n/2, i.e., cdf[n-x] = 1-cdf[x] = sf[x]
+        vals = [0, 1 / 3628800, 169 / 604800, 24427 / 1814400,
+                  252023 / 1814400, 1 / 2, 1562377 / 1814400,
+                  1789973 / 1814400, 604631 / 604800,
+                  3628799 / 3628800, 1]
+
+        # essentially a test of bspline evaluation
+        # this and the other ones are mostly to detect regressions
+        assert_array_max_ulp(self.ih10.cdf(np.arange(11)), vals, maxulp=10)
+
+        assert_array_max_ulp(self.ih10.cdf(1/10), 1/36288000000000000, maxulp=10)
+        ref = 36287999999999999/36288000000000000
+        assert_array_max_ulp(self.ih10.cdf(99/10), ref, maxulp=10)
+
+    def test_pdf_ih10_exact(self):
+        # from Wolfram Alpha "values PDF[UniformSumDistribution[10], x] x=0 to x=10"
+        # symmetric about n/2 = 5
+        vals = [0, 1 / 362880, 251 / 181440, 913 / 22680, 44117 / 181440]
+        vals += [15619 / 36288] + vals[::-1]
+        assert_array_max_ulp(self.ih10.pdf(np.arange(11)), vals, maxulp=10)
+
+    def test_sf_ih10_exact(self):
+        assert_allclose(self.ih10.sf(np.arange(11)), 1 - self.ih10.cdf(np.arange(11)))
+        # from Wolfram Alpha "SurvivalFunction[UniformSumDistribution[10],x] at x=1/10"
+        # and symmetry about n/2 = 5
+        # W|A returns 1 for CDF @ x=9.9
+        ref = 36287999999999999/36288000000000000
+        assert_array_max_ulp(self.ih10.sf(1/10), ref, maxulp=10)
+
+
+class TestDParetoLognorm:
+    def test_against_R(self):
+        # Test against R implementation in `distributionsrd`
+        # library(distributionsrd)
+        # options(digits=16)
+        # x = 1.1
+        # b = 2
+        # a = 1.5
+        # m = 3
+        # s = 1.2
+        # ddoubleparetolognormal(x, b, a, m, s)
+        # pdoubleparetolognormal(x, b, a, m, s)
+        x, m, s, a, b = 1.1, 3, 1.2, 1.5, 2
+        dist = stats.dpareto_lognorm(m, s, a, b)
+        np.testing.assert_allclose(dist.pdf(x), 0.02490187219085912)
+        np.testing.assert_allclose(dist.cdf(x), 0.01664024173822796)
+
+
+# Cases are (distribution name, log10 of smallest probability mass to test,
+# log10 of the complement of the largest probability mass to test, atol,
+# rtol). None uses default values.
+@pytest.mark.parametrize("case", [("kappa3", None, None, None, None),
+                                  ("loglaplace", None, None, None, None),
+                                  ("lognorm", None, None, None, None),
+                                  ("lomax", None, None, None, None),
+                                  ("pareto", None, None, None, None),])
+def test_sf_isf_overrides(case):
+    # Test that SF is the inverse of ISF. Supplements
+    # `test_continuous_basic.check_sf_isf` for distributions with overridden
+    # `sf` and `isf` methods.
+    distname, lp1, lp2, atol, rtol = case
+
+    lpm = np.log10(0.5)  # log10 of the probability mass at the median
+    lp1 = lp1 or -290
+    lp2 = lp2 or -14
+    atol = atol or 0
+    rtol = rtol or 1e-12
+    dist = getattr(stats, distname)
+    params = dict(distcont)[distname]
+    dist_frozen = dist(*params)
+
+    # Test (very deep) right tail to median. We can benchmark with random
+    # (loguniform) points, but strictly logspaced points are fine for tests.
+    ref = np.logspace(lp1, lpm)
+    res = dist_frozen.sf(dist_frozen.isf(ref))
+    assert_allclose(res, ref, atol=atol, rtol=rtol)
+
+    # test median to left tail
+    ref = 1 - np.logspace(lp2, lpm, 20)
+    res = dist_frozen.sf(dist_frozen.isf(ref))
+    assert_allclose(res, ref, atol=atol, rtol=rtol)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_entropy.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_entropy.py
new file mode 100644
index 0000000000000000000000000000000000000000..1302d21a8eedf9804176df9d9096a86b325d3558
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_entropy.py
@@ -0,0 +1,317 @@
+import math
+import pytest
+from pytest import raises as assert_raises
+
+import numpy as np
+
+from scipy import stats
+from scipy.stats import norm, expon  # type: ignore[attr-defined]
+from scipy._lib._array_api import make_xp_test_case
+from scipy._lib._array_api_no_0d import (xp_assert_close, xp_assert_equal,
+                                         xp_assert_less)
+
+lazy_xp_modules = [stats]
+skip_xp_backends = pytest.mark.skip_xp_backends
+
+@make_xp_test_case(stats.entropy)
+class TestEntropy:
+    def test_entropy_positive(self, xp):
+        # See ticket #497
+        pk = xp.asarray([0.5, 0.2, 0.3])
+        qk = xp.asarray([0.1, 0.25, 0.65])
+        eself = stats.entropy(pk, pk)
+        edouble = stats.entropy(pk, qk)
+        xp_assert_close(eself, xp.asarray(0.), atol=1e-16)
+        xp_assert_less(-edouble, xp.asarray(0.))
+
+    def test_entropy_base(self, xp):
+        pk = xp.ones(16)
+        S = stats.entropy(pk, base=2.)
+        xp_assert_less(xp.abs(S - 4.), xp.asarray(1.e-5))
+
+        qk = xp.ones(16)
+        qk = xp.where(xp.arange(16) < 8, 2., qk)
+        S = stats.entropy(pk, qk)
+        S2 = stats.entropy(pk, qk, base=2.)
+        xp_assert_less(xp.abs(S/S2 - math.log(2.)), xp.asarray(1.e-5))
+
+    def test_entropy_zero(self, xp):
+        # Test for PR-479
+        x = xp.asarray([0., 1., 2.])
+        xp_assert_close(stats.entropy(x),
+                        xp.asarray(0.63651416829481278))
+
+    def test_entropy_2d(self, xp):
+        pk = xp.asarray([[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]])
+        qk = xp.asarray([[0.2, 0.1], [0.3, 0.6], [0.5, 0.3]])
+        xp_assert_close(stats.entropy(pk, qk),
+                        xp.asarray([0.1933259, 0.18609809]))
+
+    def test_entropy_2d_zero(self, xp):
+        pk = xp.asarray([[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]])
+        qk = xp.asarray([[0.0, 0.1], [0.3, 0.6], [0.5, 0.3]])
+        xp_assert_close(stats.entropy(pk, qk),
+                        xp.asarray([xp.inf, 0.18609809]))
+
+        pk = xp.asarray([[0.0, 0.2], [0.6, 0.3], [0.3, 0.5]])
+        xp_assert_close(stats.entropy(pk, qk),
+                        xp.asarray([0.17403988, 0.18609809]))
+
+    def test_entropy_base_2d_nondefault_axis(self, xp):
+        pk = xp.asarray([[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]])
+        xp_assert_close(stats.entropy(pk, axis=1),
+                        xp.asarray([0.63651417, 0.63651417, 0.66156324]))
+
+    def test_entropy_2d_nondefault_axis(self, xp):
+        pk = xp.asarray([[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]])
+        qk = xp.asarray([[0.2, 0.1], [0.3, 0.6], [0.5, 0.3]])
+        xp_assert_close(stats.entropy(pk, qk, axis=1),
+                        xp.asarray([0.23104906, 0.23104906, 0.12770641]))
+
+    def test_entropy_raises_value_error(self, xp):
+        pk = xp.asarray([[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]])
+        qk = xp.asarray([[0.1, 0.2], [0.6, 0.3]])
+        message = "Array shapes are incompatible for broadcasting."
+        with pytest.raises(ValueError, match=message):
+            stats.entropy(pk, qk)
+
+    def test_base_entropy_with_axis_0_is_equal_to_default(self, xp):
+        pk = xp.asarray([[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]])
+        xp_assert_close(stats.entropy(pk, axis=0),
+                        stats.entropy(pk))
+
+    def test_entropy_with_axis_0_is_equal_to_default(self, xp):
+        pk = xp.asarray([[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]])
+        qk = xp.asarray([[0.2, 0.1], [0.3, 0.6], [0.5, 0.3]])
+        xp_assert_close(stats.entropy(pk, qk, axis=0),
+                        stats.entropy(pk, qk))
+
+    def test_base_entropy_transposed(self, xp):
+        pk = xp.asarray([[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]])
+        xp_assert_close(stats.entropy(pk.T),
+                        stats.entropy(pk, axis=1))
+
+    def test_entropy_transposed(self, xp):
+        pk = xp.asarray([[0.1, 0.2], [0.6, 0.3], [0.3, 0.5]])
+        qk = xp.asarray([[0.2, 0.1], [0.3, 0.6], [0.5, 0.3]])
+        xp_assert_close(stats.entropy(pk.T, qk.T),
+                        stats.entropy(pk, qk, axis=1))
+
+    def test_entropy_broadcasting(self, xp):
+        rng = np.random.default_rng(74187315492831452)
+        x = xp.asarray(rng.random(3))
+        y = xp.asarray(rng.random((2, 1)))
+        res = stats.entropy(x, y, axis=-1)
+        xp_assert_close(res[0], stats.entropy(x, y[0, ...]))
+        xp_assert_close(res[1], stats.entropy(x, y[1, ...]))
+
+    def test_entropy_shape_mismatch(self, xp):
+        x = xp.ones((10, 1, 12))
+        y = xp.ones((11, 2))
+        message = "Array shapes are incompatible for broadcasting."
+        with pytest.raises(ValueError, match=message):
+            stats.entropy(x, y)
+
+    def test_input_validation(self, xp):
+        x = xp.ones(10)
+        message = "`base` must be a positive number."
+        with pytest.raises(ValueError, match=message):
+            stats.entropy(x, base=-2)
+
+
+@make_xp_test_case(stats.differential_entropy)
+class TestDifferentialEntropy:
+    """
+    Vasicek results are compared with the R package vsgoftest.
+
+    # library(vsgoftest)
+    #
+    # samp <- c(<values>)
+    # entropy.estimate(x = samp, window = <window_length>)
+
+    """
+    methods = pytest.mark.parametrize('method', [
+        "vasicek",
+        "van es",
+        pytest.param(
+            "correa",
+            marks=[
+                skip_xp_backends("array_api_strict", reason="Invalid fancy indexing"),
+                skip_xp_backends("dask.array", reason="Invalid fancy indexing"),
+            ],
+        ),
+        "ebrahimi",
+    ])
+
+    def test_differential_entropy_vasicek(self, xp):
+
+        random_state = np.random.RandomState(0)
+        values = random_state.standard_normal(100)
+        values = xp.asarray(values.tolist())
+
+        entropy = stats.differential_entropy(values, method='vasicek')
+        xp_assert_close(entropy, xp.asarray(1.342551187000946))
+
+        entropy = stats.differential_entropy(values, window_length=1,
+                                             method='vasicek')
+        xp_assert_close(entropy, xp.asarray(1.122044177725947))
+
+        entropy = stats.differential_entropy(values, window_length=8,
+                                             method='vasicek')
+        xp_assert_close(entropy, xp.asarray(1.349401487550325))
+
+    def test_differential_entropy_vasicek_2d_nondefault_axis(self, xp):
+        random_state = np.random.RandomState(0)
+        values = random_state.standard_normal((3, 100))
+        values = xp.asarray(values.tolist())
+
+        entropy = stats.differential_entropy(values, axis=1, method='vasicek')
+        ref = xp.asarray([1.342551187000946, 1.341825903922332, 1.293774601883585])
+        xp_assert_close(entropy, ref)
+
+        entropy = stats.differential_entropy(values, axis=1, window_length=1,
+                                             method='vasicek')
+        ref = xp.asarray([1.122044177725947, 1.10294413850758, 1.129615790292772])
+        xp_assert_close(entropy, ref)
+
+        entropy = stats.differential_entropy(values, axis=1, window_length=8,
+                                             method='vasicek')
+        ref = xp.asarray([1.349401487550325, 1.338514126301301, 1.292331889365405])
+        xp_assert_close(entropy, ref)
+
+
+    def test_differential_entropy_raises_value_error(self, xp):
+        random_state = np.random.RandomState(0)
+        values = random_state.standard_normal((3, 100))
+        values = xp.asarray(values.tolist())
+
+        error_str = (
+            r"Window length \({window_length}\) must be positive and less "
+            r"than half the sample size \({sample_size}\)."
+        )
+
+        sample_size = values.shape[1]
+
+        for window_length in {-1, 0, sample_size//2, sample_size}:
+
+            formatted_error_str = error_str.format(
+                window_length=window_length,
+                sample_size=sample_size,
+            )
+
+            with assert_raises(ValueError, match=formatted_error_str):
+                stats.differential_entropy(
+                    values,
+                    window_length=window_length,
+                    axis=1,
+                )
+
+    def test_base_differential_entropy_with_axis_0_is_equal_to_default(self, xp):
+        random_state = np.random.RandomState(0)
+        values = random_state.standard_normal((100, 3))
+        values = xp.asarray(values.tolist())
+
+        entropy = stats.differential_entropy(values, axis=0)
+        default_entropy = stats.differential_entropy(values)
+        xp_assert_close(entropy, default_entropy)
+
+    def test_base_differential_entropy_transposed(self, xp):
+        random_state = np.random.RandomState(0)
+        values = random_state.standard_normal((3, 100))
+        values = xp.asarray(values.tolist())
+
+        xp_assert_close(
+            stats.differential_entropy(values.T),
+            stats.differential_entropy(values, axis=1),
+        )
+
+    def test_input_validation(self, xp):
+        x = np.ones(10)
+        x = xp.asarray(x.tolist())
+
+        message = "`base` must be a positive number or `None`."
+        with pytest.raises(ValueError, match=message):
+            stats.differential_entropy(x, base=-2)
+
+        message = "`method` must be one of..."
+        with pytest.raises(ValueError, match=message):
+            stats.differential_entropy(x, method='ekki-ekki')
+
+    def test_window_length_is_none(self, xp):
+        rng = np.random.default_rng(358923459826738562)
+        x = xp.asarray(rng.random(size=10))
+        ref = stats.differential_entropy(x)
+        res = stats.differential_entropy(x, window_length=None)
+        xp_assert_close(res, ref, rtol=0.005)
+
+    @methods
+    def test_consistency(self, method, xp):
+        # test that method is a consistent estimator
+        n = 10000 if method == 'correa' else 1000000
+        rvs = stats.norm.rvs(size=n, random_state=0)
+        rvs = xp.asarray(rvs.tolist())
+        expected = xp.asarray(float(stats.norm.entropy()))
+        res = stats.differential_entropy(rvs, method=method)
+        xp_assert_close(res, expected, rtol=0.005)
+
+    # values from differential_entropy reference [6], table 1, n=50, m=7
+    norm_rmse_std_cases = {  # method: (RMSE, STD)
+                           'vasicek': (0.198, 0.109),
+                           'van es': (0.212, 0.110),
+                           'correa': (0.135, 0.112),
+                           'ebrahimi': (0.128, 0.109)
+                           }
+
+    # values from differential_entropy reference [6], table 2, n=50, m=7
+    expon_rmse_std_cases = {  # method: (RMSE, STD)
+                            'vasicek': (0.194, 0.148),
+                            'van es': (0.179, 0.149),
+                            'correa': (0.155, 0.152),
+                            'ebrahimi': (0.151, 0.148)
+                            }
+
+    rmse_std_cases = {norm: norm_rmse_std_cases,
+                      expon: expon_rmse_std_cases}
+
+    @methods
+    @pytest.mark.parametrize('dist', [norm, expon])
+    def test_rmse_std(self, method, dist, xp):
+        # test that RMSE and standard deviation of estimators matches values
+        # given in differential_entropy reference [6]. Incidentally, also
+        # tests vectorization.
+        reps, n, m = 10000, 50, 7
+        expected = self.rmse_std_cases[dist][method]
+        rmse_expected, std_expected = xp.asarray(expected[0]), xp.asarray(expected[1])
+        rvs = dist.rvs(size=(reps, n), random_state=0)
+        rvs = xp.asarray(rvs.tolist())
+        true_entropy = xp.asarray(float(dist.entropy()))
+        res = stats.differential_entropy(rvs, window_length=m,
+                                         method=method, axis=-1)
+        xp_assert_close(xp.sqrt(xp.mean((res - true_entropy)**2)),
+                        rmse_expected, atol=0.005)
+        xp_assert_close(xp.std(res, correction=0), std_expected, atol=0.002)
+
+    @pytest.mark.parametrize('n, method', [
+        (8, 'van es'),
+        (12, 'ebrahimi'),
+        (1001, 'vasicek')
+    ])
+    def test_method_auto(self, n, method, xp):
+        rvs = stats.norm.rvs(size=(n,), random_state=0)
+        rvs = xp.asarray(rvs.tolist())
+        res1 = stats.differential_entropy(rvs)
+        res2 = stats.differential_entropy(rvs, method=method)
+        xp_assert_equal(res1, res2)
+
+    @methods
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_dtypes_gh21192(self, method, dtype, xp):
+        # gh-21192 noted a change in the output of method='ebrahimi'
+        # with integer input. Check that the output is consistent regardless
+        # of input dtype.
+        x = [1, 1, 2, 3, 3, 4, 5, 5, 6, 7, 8, 9, 10, 11]
+        dtype_in = getattr(xp, str(dtype), None)
+        dtype_out = getattr(xp, str(dtype), xp.asarray(1.).dtype)
+        res = stats.differential_entropy(xp.asarray(x, dtype=dtype_in), method=method)
+        ref = stats.differential_entropy(xp.asarray(x, dtype=xp.float64), method=method)
+        xp_assert_close(res, xp.asarray(ref, dtype=dtype_out)[()])
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_fast_gen_inversion.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_fast_gen_inversion.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc57dd17d4562909f0a21b23a6ff2768c7373e3a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_fast_gen_inversion.py
@@ -0,0 +1,434 @@
+import pytest
+import warnings
+import numpy as np
+from numpy.testing import assert_array_equal, assert_allclose
+from copy import deepcopy
+from scipy.stats.sampling import FastGeneratorInversion
+from scipy import stats
+from scipy._lib._testutils import IS_MUSL
+
+
+def test_bad_args():
+    # loc and scale must be scalar
+    with pytest.raises(ValueError, match="loc must be scalar"):
+        FastGeneratorInversion(stats.norm(loc=(1.2, 1.3)))
+    with pytest.raises(ValueError, match="scale must be scalar"):
+        FastGeneratorInversion(stats.norm(scale=[1.5, 5.7]))
+
+    with pytest.raises(ValueError, match="'test' cannot be used to seed"):
+        FastGeneratorInversion(stats.norm(), random_state="test")
+
+    msg = "Each of the 1 shape parameters must be a scalar"
+    with pytest.raises(ValueError, match=msg):
+        FastGeneratorInversion(stats.gamma([1.3, 2.5]))
+
+    with pytest.raises(ValueError, match="`dist` must be a frozen"):
+        FastGeneratorInversion("xy")
+
+    with pytest.raises(ValueError, match="Distribution 'truncnorm' is not"):
+        FastGeneratorInversion(stats.truncnorm(1.3, 4.5))
+
+
+def test_random_state():
+    # fixed seed
+    gen = FastGeneratorInversion(stats.norm(), random_state=68734509)
+    x1 = gen.rvs(size=10)
+    gen.random_state = 68734509
+    x2 = gen.rvs(size=10)
+    assert_array_equal(x1, x2)
+
+    # Generator
+    urng = np.random.default_rng(20375857)
+    gen = FastGeneratorInversion(stats.norm(), random_state=urng)
+    x1 = gen.rvs(size=10)
+    gen.random_state = np.random.default_rng(20375857)
+    x2 = gen.rvs(size=10)
+    assert_array_equal(x1, x2)
+
+    # RandomState
+    urng = np.random.RandomState(2364)
+    gen = FastGeneratorInversion(stats.norm(), random_state=urng)
+    x1 = gen.rvs(size=10)
+    gen.random_state = np.random.RandomState(2364)
+    x2 = gen.rvs(size=10)
+    assert_array_equal(x1, x2)
+
+    # if evaluate_error is called, it must not interfere with the random_state
+    # used by rvs
+    gen = FastGeneratorInversion(stats.norm(), random_state=68734509)
+    x1 = gen.rvs(size=10)
+    _ = gen.evaluate_error(size=5)  # this will generate 5 uniform rvs
+    x2 = gen.rvs(size=10)
+    gen.random_state = 68734509
+    x3 = gen.rvs(size=20)
+    assert_array_equal(x2, x3[10:])
+
+
+dists_with_params = [
+    ("alpha", (3.5,)),
+    ("anglit", ()),
+    ("argus", (3.5,)),
+    ("argus", (5.1,)),
+    ("beta", (1.5, 0.9)),
+    ("cosine", ()),
+    ("betaprime", (2.5, 3.3)),
+    ("bradford", (1.2,)),
+    ("burr", (1.3, 2.4)),
+    ("burr12", (0.7, 1.2)),
+    ("cauchy", ()),
+    ("chi2", (3.5,)),
+    ("chi", (4.5,)),
+    ("crystalball", (0.7, 1.2)),
+    ("expon", ()),
+    ("gamma", (1.5,)),
+    ("gennorm", (2.7,)),
+    ("gumbel_l", ()),
+    ("gumbel_r", ()),
+    ("hypsecant", ()),
+    ("invgauss", (3.1,)),
+    ("invweibull", (1.5,)),
+    ("laplace", ()),
+    ("logistic", ()),
+    ("maxwell", ()),
+    ("moyal", ()),
+    ("norm", ()),
+    ("pareto", (1.3,)),
+    ("powerlaw", (7.6,)),
+    ("rayleigh", ()),
+    ("semicircular", ()),
+    ("t", (5.7,)),
+    ("wald", ()),
+    ("weibull_max", (2.4,)),
+    ("weibull_min", (1.2,)),
+]
+
+
+@pytest.mark.parametrize(("distname, args"), dists_with_params)
+def test_rvs_and_ppf(distname, args):
+    # check sample against rvs generated by rv_continuous
+    urng = np.random.default_rng(9807324628097097)
+    rng1 = getattr(stats, distname)(*args)
+    rvs1 = rng1.rvs(size=500, random_state=urng)
+    rng2 = FastGeneratorInversion(rng1, random_state=urng)
+    rvs2 = rng2.rvs(size=500)
+    assert stats.cramervonmises_2samp(rvs1, rvs2).pvalue > 0.01
+
+    # check ppf
+    q = [0.001, 0.1, 0.5, 0.9, 0.999]
+    assert_allclose(rng1.ppf(q), rng2.ppf(q), atol=1e-10)
+
+
+@pytest.mark.parametrize(("distname, args"), dists_with_params)
+def test_u_error(distname, args):
+    # check sample against rvs generated by rv_continuous
+    dist = getattr(stats, distname)(*args)
+    with warnings.catch_warnings():
+        # filter the warnings thrown by UNU.RAN
+        warnings.simplefilter("ignore", RuntimeWarning)
+        rng = FastGeneratorInversion(dist)
+    u_error, x_error = rng.evaluate_error(
+        size=10_000, random_state=9807324628097097, x_error=False
+    )
+    assert u_error <= 1e-10
+
+
+@pytest.mark.xslow
+@pytest.mark.xfail(reason="geninvgauss CDF is not accurate")
+def test_geninvgauss_uerror():
+    dist = stats.geninvgauss(3.2, 1.5)
+    rng = FastGeneratorInversion(dist)
+    err = rng.evaluate_error(size=10_000, random_state=67982)
+    assert err[0] < 1e-10
+
+
+# TODO: add more distributions
+@pytest.mark.skipif(IS_MUSL, reason="Hits RecursionError, see gh-23172")
+@pytest.mark.fail_slow(5)
+@pytest.mark.parametrize(("distname, args"), [("beta", (0.11, 0.11))])
+def test_error_extreme_params(distname, args):
+    # take extreme parameters where u-error might not be below the tolerance
+    # due to limitations of floating point arithmetic
+    with warnings.catch_warnings():
+        # filter the warnings thrown by UNU.RAN for such extreme parameters
+        warnings.simplefilter("ignore", RuntimeWarning)
+        dist = getattr(stats, distname)(*args)
+        rng = FastGeneratorInversion(dist)
+    u_error, x_error = rng.evaluate_error(
+        size=10_000, random_state=980732462809709732623, x_error=True
+    )
+    if u_error >= 2.5 * 1e-10:
+        assert x_error < 1e-9
+
+
+def test_evaluate_error_inputs():
+    gen = FastGeneratorInversion(stats.norm())
+    with pytest.raises(ValueError, match="size must be an integer"):
+        gen.evaluate_error(size=3.5)
+    with pytest.raises(ValueError, match="size must be an integer"):
+        gen.evaluate_error(size=(3, 3))
+
+
+def test_rvs_ppf_loc_scale():
+    loc, scale = 3.5, 2.3
+    dist = stats.norm(loc=loc, scale=scale)
+    rng = FastGeneratorInversion(dist, random_state=1234)
+    r = rng.rvs(size=1000)
+    r_rescaled = (r - loc) / scale
+    assert stats.cramervonmises(r_rescaled, "norm").pvalue > 0.01
+    q = [0.001, 0.1, 0.5, 0.9, 0.999]
+    assert_allclose(rng._ppf(q), rng.ppf(q), atol=1e-10)
+
+
+def test_domain():
+    # only a basic check that the domain argument is passed to the
+    # UNU.RAN generators
+    rng = FastGeneratorInversion(stats.norm(), domain=(-1, 1))
+    r = rng.rvs(size=100)
+    assert -1 <= r.min() < r.max() <= 1
+
+    # if loc and scale are used, new domain is loc + scale*domain
+    loc, scale = 3.5, 1.3
+    dist = stats.norm(loc=loc, scale=scale)
+    rng = FastGeneratorInversion(dist, domain=(-1.5, 2))
+    r = rng.rvs(size=100)
+    lb, ub = loc - scale * 1.5, loc + scale * 2
+    assert lb <= r.min() < r.max() <= ub
+
+
+@pytest.mark.parametrize(("distname, args, expected"),
+                         [("beta", (3.5, 2.5), (0, 1)),
+                          ("norm", (), (-np.inf, np.inf))])
+def test_support(distname, args, expected):
+    # test that the support is updated if truncation and loc/scale are applied
+    # use beta distribution since it is a transformed betaprime distribution,
+    # so it is important that the correct support is considered
+    # (i.e., the support of beta is (0,1), while betaprime is (0, inf))
+    dist = getattr(stats, distname)(*args)
+    rng = FastGeneratorInversion(dist)
+    assert_array_equal(rng.support(), expected)
+    rng.loc = 1
+    rng.scale = 2
+    assert_array_equal(rng.support(), 1 + 2*np.array(expected))
+
+
+@pytest.mark.parametrize(("distname, args"),
+                         [("beta", (3.5, 2.5)), ("norm", ())])
+def test_support_truncation(distname, args):
+    # similar test for truncation
+    dist = getattr(stats, distname)(*args)
+    rng = FastGeneratorInversion(dist, domain=(0.5, 0.7))
+    assert_array_equal(rng.support(), (0.5, 0.7))
+    rng.loc = 1
+    rng.scale = 2
+    assert_array_equal(rng.support(), (1 + 2 * 0.5, 1 + 2 * 0.7))
+
+
+def test_domain_shift_truncation():
+    # center of norm is zero, it should be shifted to the left endpoint of
+    # domain. if this was not the case, PINV in UNURAN would raise a warning
+    # as the center is not inside the domain
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        rng = FastGeneratorInversion(stats.norm(), domain=(1, 2))
+    r = rng.rvs(size=100)
+    assert 1 <= r.min() < r.max() <= 2
+
+
+def test_non_rvs_methods_with_domain():
+    # as a first step, compare truncated normal against stats.truncnorm
+    rng = FastGeneratorInversion(stats.norm(), domain=(2.3, 3.2))
+    trunc_norm = stats.truncnorm(2.3, 3.2)
+    # take values that are inside and outside the domain
+    x = (2.0, 2.4, 3.0, 3.4)
+    p = (0.01, 0.5, 0.99)
+    assert_allclose(rng._cdf(x), trunc_norm.cdf(x))
+    assert_allclose(rng._ppf(p), trunc_norm.ppf(p))
+    loc, scale = 2, 3
+    rng.loc = 2
+    rng.scale = 3
+    trunc_norm = stats.truncnorm(2.3, 3.2, loc=loc, scale=scale)
+    x = np.array(x) * scale + loc
+    assert_allclose(rng._cdf(x), trunc_norm.cdf(x))
+    assert_allclose(rng._ppf(p), trunc_norm.ppf(p))
+
+    # do another sanity check with beta distribution
+    # in that case, it is important to use the correct domain since beta
+    # is a transformation of betaprime which has a different support
+    rng = FastGeneratorInversion(stats.beta(2.5, 3.5), domain=(0.3, 0.7))
+    rng.loc = 2
+    rng.scale = 2.5
+    # the support is 2.75, , 3.75 (2 + 2.5 * 0.3, 2 + 2.5 * 0.7)
+    assert_array_equal(rng.support(), (2.75, 3.75))
+    x = np.array([2.74, 2.76, 3.74, 3.76])
+    # the cdf needs to be zero outside of the domain
+    y_cdf = rng._cdf(x)
+    assert_array_equal((y_cdf[0], y_cdf[3]), (0, 1))
+    assert np.min(y_cdf[1:3]) > 0
+    # ppf needs to map 0 and 1 to the boundaries
+    assert_allclose(rng._ppf(y_cdf), (2.75, 2.76, 3.74, 3.75))
+
+
+def test_non_rvs_methods_without_domain():
+    norm_dist = stats.norm()
+    rng = FastGeneratorInversion(norm_dist)
+    x = np.linspace(-3, 3, num=10)
+    p = (0.01, 0.5, 0.99)
+    assert_allclose(rng._cdf(x), norm_dist.cdf(x))
+    assert_allclose(rng._ppf(p), norm_dist.ppf(p))
+    loc, scale = 0.5, 1.3
+    rng.loc = loc
+    rng.scale = scale
+    norm_dist = stats.norm(loc=loc, scale=scale)
+    assert_allclose(rng._cdf(x), norm_dist.cdf(x))
+    assert_allclose(rng._ppf(p), norm_dist.ppf(p))
+
+@pytest.mark.parametrize(("domain, x"),
+                         [(None, 0.5),
+                         ((0, 1), 0.5),
+                         ((0, 1), 1.5)])
+def test_scalar_inputs(domain, x):
+    """ pdf, cdf etc should map scalar values to scalars. check with and
+    w/o domain since domain impacts pdf, cdf etc
+    Take x inside and outside of domain """
+    rng = FastGeneratorInversion(stats.norm(), domain=domain)
+    assert np.isscalar(rng._cdf(x))
+    assert np.isscalar(rng._ppf(0.5))
+
+
+def test_domain_argus_large_chi():
+    # for large chi, the Gamma distribution is used and the domain has to be
+    # transformed. this is a test to ensure that the transformation works
+    chi, lb, ub = 5.5, 0.25, 0.75
+    rng = FastGeneratorInversion(stats.argus(chi), domain=(lb, ub))
+    rng.random_state = 4574
+    r = rng.rvs(size=500)
+    assert lb <= r.min() < r.max() <= ub
+    # perform goodness of fit test with conditional cdf
+    cdf = stats.argus(chi).cdf
+    prob = cdf(ub) - cdf(lb)
+    assert stats.cramervonmises(r, lambda x: cdf(x) / prob).pvalue > 0.05
+
+
+def test_setting_loc_scale():
+    rng = FastGeneratorInversion(stats.norm(), random_state=765765864)
+    r1 = rng.rvs(size=1000)
+    rng.loc = 3.0
+    rng.scale = 2.5
+    r2 = rng.rvs(1000)
+    # rescaled r2 should be again standard normal
+    assert stats.cramervonmises_2samp(r1, (r2 - 3) / 2.5).pvalue > 0.05
+    # reset values to default loc=0, scale=1
+    rng.loc = 0
+    rng.scale = 1
+    r2 = rng.rvs(1000)
+    assert stats.cramervonmises_2samp(r1, r2).pvalue > 0.05
+
+
+def test_ignore_shape_range():
+    msg = "No generator is defined for the shape parameters"
+    with pytest.raises(ValueError, match=msg):
+        rng = FastGeneratorInversion(stats.t(0.03))
+    rng = FastGeneratorInversion(stats.t(0.03), ignore_shape_range=True)
+    # we can ignore the recommended range of shape parameters
+    # but u-error can be expected to be too large in that case
+    u_err, _ = rng.evaluate_error(size=1000, random_state=234)
+    assert u_err >= 1e-6
+
+@pytest.mark.xfail_on_32bit(
+    "NumericalInversePolynomial.qrvs fails for Win 32-bit"
+)
+class TestQRVS:
+    def test_input_validation(self):
+        gen = FastGeneratorInversion(stats.norm())
+
+        match = "`qmc_engine` must be an instance of..."
+        with pytest.raises(ValueError, match=match):
+            gen.qrvs(qmc_engine=0)
+
+        match = "`d` must be consistent with dimension of `qmc_engine`."
+        with pytest.raises(ValueError, match=match):
+            gen.qrvs(d=3, qmc_engine=stats.qmc.Halton(2))
+
+    qrngs = [None, stats.qmc.Sobol(1, seed=0), stats.qmc.Halton(3, seed=0)]
+    # `size=None` should not add anything to the shape, `size=1` should
+    sizes = [
+        (None, tuple()),
+        (1, (1,)),
+        (4, (4,)),
+        ((4,), (4,)),
+        ((2, 4), (2, 4)),
+    ]
+    # Neither `d=None` nor `d=1` should add anything to the shape
+    ds = [(None, tuple()), (1, tuple()), (3, (3,))]
+
+    @pytest.mark.parametrize("qrng", qrngs)
+    @pytest.mark.parametrize("size_in, size_out", sizes)
+    @pytest.mark.parametrize("d_in, d_out", ds)
+    def test_QRVS_shape_consistency(self, qrng, size_in, size_out,
+                                    d_in, d_out):
+        gen = FastGeneratorInversion(stats.norm())
+
+        # If d and qrng.d are inconsistent, an error is raised
+        if d_in is not None and qrng is not None and qrng.d != d_in:
+            match = "`d` must be consistent with dimension of `qmc_engine`."
+            with pytest.raises(ValueError, match=match):
+                gen.qrvs(size_in, d=d_in, qmc_engine=qrng)
+            return
+
+        # Sometimes d is really determined by qrng
+        if d_in is None and qrng is not None and qrng.d != 1:
+            d_out = (qrng.d,)
+
+        shape_expected = size_out + d_out
+
+        qrng2 = deepcopy(qrng)
+        qrvs = gen.qrvs(size=size_in, d=d_in, qmc_engine=qrng)
+        if size_in is not None:
+            assert qrvs.shape == shape_expected
+
+        if qrng2 is not None:
+            uniform = qrng2.random(np.prod(size_in) or 1)
+            qrvs2 = stats.norm.ppf(uniform).reshape(shape_expected)
+            assert_allclose(qrvs, qrvs2, atol=1e-12)
+
+    def test_QRVS_size_tuple(self):
+        # QMCEngine samples are always of shape (n, d). When `size` is a tuple,
+        # we set `n = prod(size)` in the call to qmc_engine.random, transform
+        # the sample, and reshape it to the final dimensions. When we reshape,
+        # we need to be careful, because the _columns_ of the sample returned
+        # by a QMCEngine are "independent"-ish, but the elements within the
+        # columns are not. We need to make sure that this doesn't get mixed up
+        # by reshaping: qrvs[..., i] should remain "independent"-ish of
+        # qrvs[..., i+1], but the elements within qrvs[..., i] should be
+        # transformed from the same low-discrepancy sequence.
+
+        gen = FastGeneratorInversion(stats.norm())
+
+        size = (3, 4)
+        d = 5
+        qrng = stats.qmc.Halton(d, seed=0)
+        qrng2 = stats.qmc.Halton(d, seed=0)
+
+        uniform = qrng2.random(np.prod(size))
+
+        qrvs = gen.qrvs(size=size, d=d, qmc_engine=qrng)
+        qrvs2 = stats.norm.ppf(uniform)
+
+        for i in range(d):
+            sample = qrvs[..., i]
+            sample2 = qrvs2[:, i].reshape(size)
+            assert_allclose(sample, sample2, atol=1e-12)
+
+
+def test_burr_overflow():
+    # this case leads to an overflow error if math.exp is used
+    # in the definition of the burr pdf instead of np.exp
+    # a direct implementation of the PDF as x**(-c-1) / (1+x**(-c))**(d+1)
+    # also leads to an overflow error in the setup
+    args = (1.89128135, 0.30195177)
+    with warnings.catch_warnings():
+        # filter potential overflow warning
+        warnings.simplefilter("ignore", RuntimeWarning)
+        gen = FastGeneratorInversion(stats.burr(*args))
+    u_error, _ = gen.evaluate_error(random_state=4326)
+    assert u_error <= 1e-10
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_fit.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_fit.py
new file mode 100644
index 0000000000000000000000000000000000000000..e77cb931308948fb48b080ba180e20b7b9e9aefb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_fit.py
@@ -0,0 +1,1092 @@
+import os
+import warnings
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+import pytest
+from scipy import stats
+from scipy.optimize import differential_evolution
+
+from .test_continuous_basic import distcont
+from scipy.stats._distn_infrastructure import FitError
+from scipy.stats._distr_params import distdiscrete
+from scipy.stats import goodness_of_fit
+
+
+# this is not a proper statistical test for convergence, but only
+# verifies that the estimate and true values don't differ by too much
+
+fit_sizes = [1000, 5000, 10000]  # sample sizes to try
+
+thresh_percent = 0.25  # percent of true parameters for fail cut-off
+thresh_min = 0.75  # minimum difference estimate - true to fail test
+
+mle_failing_fits = [
+        'dpareto_lognorm',
+        'gausshyper',
+        'genexpon',
+        'gengamma',
+        'irwinhall',
+        'kappa4',
+        'ksone',
+        'kstwo',
+        'ncf',
+        'ncx2',
+        'truncexpon',
+        'tukeylambda',
+        'vonmises',
+        'levy_stable',
+        'truncpareto',
+        'truncweibull_min',
+        'studentized_range',
+]
+
+# these pass but are XSLOW (>1s)
+mle_Xslow_fits = ['betaprime', 'crystalball', 'exponweib', 'f', 'geninvgauss',
+                  'jf_skew_t', 'nct', 'recipinvgauss', 'rel_breitwigner',
+                  'vonmises_line']
+
+# The MLE fit method of these distributions doesn't perform well when all
+# parameters are fit, so test them with the location fixed at 0.
+mle_use_floc0 = [
+    'burr',
+    'chi',
+    'chi2',
+    'mielke',
+    'pearson3',
+    'genhalflogistic',
+    'rdist',
+    'pareto',
+    'powerlaw',  # distfn.nnlf(est2, rvs) > distfn.nnlf(est1, rvs) otherwise
+    'powerlognorm',
+    'wrapcauchy',
+    'rel_breitwigner',
+]
+
+mm_failing_fits = ['alpha', 'betaprime', 'burr', 'burr12', 'cauchy', 'chi',
+                   'chi2', 'crystalball', 'dgamma', 'dpareto_lognorm', 'dweibull',
+                   'f', 'fatiguelife', 'fisk', 'foldcauchy', 'genextreme',
+                   'gengamma', 'genhyperbolic', 'gennorm', 'genpareto',
+                   'halfcauchy', 'invgamma', 'invweibull', 'irwinhall', 'jf_skew_t',
+                   'johnsonsu', 'kappa3', 'ksone', 'kstwo', 'landau', 'levy', 'levy_l',
+                   'levy_stable', 'loglaplace', 'lomax', 'mielke', 'nakagami',
+                   'ncf', 'nct', 'ncx2', 'pareto', 'powerlognorm', 'powernorm',
+                   'rel_breitwigner', 'skewcauchy', 't', 'triang',
+                   'truncpareto', 'truncweibull_min', 'tukeylambda',
+                   'studentized_range']
+
+# not sure if these fail, but they caused my patience to fail
+mm_XXslow_fits = ['argus', 'exponpow', 'exponweib', 'gausshyper', 'genexpon',
+                  'genhalflogistic', 'gompertz', 'johnsonsb',
+                  'kappa4', 'kstwobign', 'recipinvgauss',
+                  'truncexpon', 'vonmises', 'vonmises_line']
+
+# these pass but are XSLOW (>1s)
+mm_Xslow_fits = ['wrapcauchy']
+
+failing_fits = {"MM": mm_failing_fits + mm_XXslow_fits, "MLE": mle_failing_fits}
+xslow_fits = {"MM": mm_Xslow_fits, "MLE": mle_Xslow_fits}
+fail_interval_censored = {"truncpareto"}
+
+# Don't run the fit test on these:
+skip_fit = [
+    'erlang',  # Subclass of gamma, generates a warning.
+    'genhyperbolic', 'norminvgauss',  # too slow
+]
+
+
+def cases_test_cont_fit():
+    # this tests the closeness of the estimated parameters to the true
+    # parameters with fit method of continuous distributions
+    # Note: is slow, some distributions don't converge with sample
+    # size <= 10000
+    for distname, arg in distcont:
+        if distname not in skip_fit:
+            yield distname, arg
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize('distname,arg', list(cases_test_cont_fit()))
+@pytest.mark.parametrize('method', ["MLE", "MM"])
+def test_cont_fit(distname, arg, method):
+    run_xfail = int(os.getenv('SCIPY_XFAIL', default=False))
+    run_xslow = int(os.getenv('SCIPY_XSLOW', default=False))
+
+    if distname in failing_fits[method] and not run_xfail:
+        # The generic `fit` method can't be expected to work perfectly for all
+        # distributions, data, and guesses. Some failures are expected.
+        msg = "Failure expected; set environment variable SCIPY_XFAIL=1 to run."
+        pytest.xfail(msg)
+
+    if distname in xslow_fits[method] and not run_xslow:
+        msg = "Very slow; set environment variable SCIPY_XSLOW=1 to run."
+        pytest.skip(msg)
+
+    distfn = getattr(stats, distname)
+
+    truearg = np.hstack([arg, [0.0, 1.0]])
+    diffthreshold = np.max(np.vstack([truearg*thresh_percent,
+                                      np.full(distfn.numargs+2, thresh_min)]),
+                           0)
+
+    for fit_size in fit_sizes:
+        # Note that if a fit succeeds, the other fit_sizes are skipped
+        rng = np.random.default_rng(1234)
+
+        with np.errstate(all='ignore'):
+            rvs = distfn.rvs(size=fit_size, *arg, random_state=rng)
+            if method == 'MLE' and distfn.name in mle_use_floc0:
+                kwds = {'floc': 0}
+            else:
+                kwds = {}
+            # start with default values
+            est = distfn.fit(rvs, method=method, **kwds)
+            if method == 'MLE':
+                # Trivial test of the use of CensoredData.  The fit() method
+                # will check that data contains no actual censored data, and
+                # do a regular uncensored fit.
+                data1 = stats.CensoredData(rvs)
+                est1 = distfn.fit(data1, **kwds)
+                msg = ('Different results fitting uncensored data wrapped as'
+                       f' CensoredData: {distfn.name}: est={est} est1={est1}')
+                assert_allclose(est1, est, rtol=1e-10, err_msg=msg)
+            if method == 'MLE' and distname not in fail_interval_censored:
+                # Convert the first `nic` values in rvs to interval-censored
+                # values. The interval is small, so est2 should be close to
+                # est.
+                nic = 15
+                interval = np.column_stack((rvs, rvs))
+                interval[:nic, 0] *= 0.99
+                interval[:nic, 1] *= 1.01
+                interval.sort(axis=1)
+                data2 = stats.CensoredData(interval=interval)
+                est2 = distfn.fit(data2, **kwds)
+                msg = ('Different results fitting interval-censored'
+                       f' data: {distfn.name}: est={est} est2={est2}')
+                assert_allclose(est2, est, rtol=0.05, err_msg=msg)
+
+        diff = est - truearg
+
+        # threshold for location
+        diffthreshold[-2] = np.max([np.abs(rvs.mean())*thresh_percent,
+                                    thresh_min])
+
+        if np.any(np.isnan(est)):
+            raise AssertionError('nan returned in fit')
+        else:
+            if np.all(np.abs(diff) <= diffthreshold):
+                break
+    else:
+        txt = f'parameter: {str(truearg)}\n'
+        txt += f'estimated: {str(est)}\n'
+        txt += f'diff     : {str(diff)}\n'
+        raise AssertionError(f'fit not very good in {distfn.name}\n' + txt)
+
+
+def _check_loc_scale_mle_fit(name, data, desired, atol=None):
+    d = getattr(stats, name)
+    actual = d.fit(data)[-2:]
+    assert_allclose(actual, desired, atol=atol,
+                    err_msg=f'poor mle fit of (loc, scale) in {name}')
+
+
+def test_non_default_loc_scale_mle_fit():
+    data = np.array([1.01, 1.78, 1.78, 1.78, 1.88, 1.88, 1.88, 2.00])
+    _check_loc_scale_mle_fit('uniform', data, [1.01, 0.99], 1e-3)
+    _check_loc_scale_mle_fit('expon', data, [1.01, 0.73875], 1e-3)
+
+
+def test_expon_fit():
+    """gh-6167"""
+    data = [0, 0, 0, 0, 2, 2, 2, 2]
+    phat = stats.expon.fit(data, floc=0)
+    assert_allclose(phat, [0, 1.0], atol=1e-3)
+
+
+def test_fit_error():
+    data = np.concatenate([np.zeros(29), np.ones(21)])
+    message = "Optimization converged to parameters that are..."
+    with pytest.raises(FitError, match=message), \
+            pytest.warns(RuntimeWarning):
+        stats.beta.fit(data)
+
+
+@pytest.mark.parametrize("dist, params",
+                         [(stats.norm, (0.5, 2.5)),  # type: ignore[attr-defined]
+                          (stats.binom, (10, 0.3, 2))])  # type: ignore[attr-defined]
+def test_nnlf_and_related_methods(dist, params):
+    rng = np.random.default_rng(983459824)
+
+    if hasattr(dist, 'pdf'):
+        logpxf = dist.logpdf
+    else:
+        logpxf = dist.logpmf
+
+    x = dist.rvs(*params, size=100, random_state=rng)
+    ref = -logpxf(x, *params).sum()
+    res1 = dist.nnlf(params, x)
+    res2 = dist._penalized_nnlf(params, x)
+    assert_allclose(res1, ref)
+    assert_allclose(res2, ref)
+
+
+def cases_test_fit_mle():
+    # These fail default test or hang
+    skip_basic_fit = {'argus', 'irwinhall', 'foldnorm', 'truncpareto',
+                      'truncweibull_min', 'ksone', 'levy_stable',
+                      'studentized_range', 'kstwo',
+                      'beta', 'nakagami', 'truncnorm', # don't meet tolerance
+                      'poisson_binom'}  # vector-valued shape parameter
+
+    # Please keep this list in alphabetical order...
+    slow_basic_fit = {'alpha', 'arcsine', 'betaprime', 'binom', 'bradford', 'burr12',
+                      'chi', 'crystalball', 'dweibull', 'erlang', 'exponnorm',
+                      'exponpow', 'f', 'fatiguelife', 'fisk', 'foldcauchy', 'gamma',
+                      'genexpon', 'genextreme', 'gennorm', 'genpareto',
+                      'gompertz', 'invgamma', 'invgauss', 'invweibull',
+                      'jf_skew_t', 'johnsonsb', 'johnsonsu', 'kappa3',
+                      'kstwobign', 'loglaplace', 'lognorm', 'lomax', 'mielke',
+                      'nbinom', 'norminvgauss',
+                      'pareto', 'pearson3', 'powerlaw', 'powernorm',
+                      'randint', 'rdist', 'recipinvgauss', 'rice', 'skewnorm',
+                      't', 'uniform', 'weibull_max', 'weibull_min', 'wrapcauchy',
+                      'zipfian'}
+
+    # Please keep this list in alphabetical order...
+    xslow_basic_fit = {'betabinom', 'betanbinom', 'burr', 'dpareto_lognorm',
+                       'exponweib', 'gausshyper', 'gengamma', 'genhalflogistic',
+                       'genhyperbolic', 'geninvgauss',
+                       'hypergeom', 'kappa4', 'loguniform',
+                       'ncf', 'nchypergeom_fisher', 'nchypergeom_wallenius',
+                       'nct', 'ncx2', 'nhypergeom',
+                       'powerlognorm', 'reciprocal', 'rel_breitwigner',
+                       'skellam', 'trapezoid', 'triang',
+                       'tukeylambda', 'vonmises'}
+
+    for dist in dict(distdiscrete + distcont):
+        if dist in skip_basic_fit or not isinstance(dist, str):
+            reason = "tested separately"
+            yield pytest.param(dist, marks=pytest.mark.skip(reason=reason))
+        elif dist in slow_basic_fit:
+            reason = "too slow (>= 0.25s)"
+            yield pytest.param(dist, marks=pytest.mark.slow(reason=reason))
+        elif dist in xslow_basic_fit:
+            reason = "too slow (>= 1.0s)"
+            yield pytest.param(dist, marks=pytest.mark.xslow(reason=reason))
+        else:
+            yield dist
+
+
+def cases_test_fit_mse():
+    # the first four are so slow that I'm not sure whether they would pass
+    skip_basic_fit = {'levy_stable', 'studentized_range', 'ksone', 'skewnorm',
+                      'irwinhall', # hangs
+                      'norminvgauss',  # super slow (~1 hr) but passes
+                      'kstwo',  # very slow (~25 min) but passes
+                      'geninvgauss',  # quite slow (~4 minutes) but passes
+                      'gausshyper', 'genhyperbolic',  # integration warnings
+                      'tukeylambda',  # close, but doesn't meet tolerance
+                      'vonmises',  # can have negative CDF; doesn't play nice
+                      'arcsine', 'argus', 'powerlaw', 'rdist', # don't meet tolerance
+                      'poisson_binom',  # vector-valued shape parameter
+                      }
+
+    # Please keep this list in alphabetical order...
+    slow_basic_fit = {'alpha', 'anglit', 'betabinom', 'bradford',
+                      'chi', 'chi2', 'crystalball', 'dweibull',
+                      'erlang', 'exponnorm', 'exponpow', 'exponweib',
+                      'fatiguelife', 'fisk', 'foldcauchy', 'foldnorm',
+                      'gamma', 'genexpon', 'genextreme', 'genhalflogistic',
+                      'genlogistic', 'genpareto', 'gompertz',
+                      'hypergeom', 'invweibull',
+                      'johnsonsu', 'kappa3', 'kstwobign',
+                      'laplace_asymmetric', 'loggamma', 'loglaplace',
+                      'lognorm', 'lomax',
+                      'maxwell', 'nhypergeom',
+                      'pareto', 'powernorm', 'randint', 'recipinvgauss',
+                      'semicircular',
+                      't', 'triang', 'truncexpon', 'truncpareto',
+                      'uniform',
+                      'wald', 'weibull_max', 'weibull_min', 'wrapcauchy',
+                      'zipfian'}
+
+    # Please keep this list in alphabetical order...
+    xslow_basic_fit = {'argus', 'beta', 'betaprime', 'burr', 'burr12',
+                       'dgamma', 'dpareto_lognorm', 'f', 'gengamma', 'gennorm',
+                       'invgamma', 'invgauss', 'jf_skew_t',
+                       'johnsonsb', 'kappa4', 'loguniform', 'mielke',
+                       'nakagami', 'ncf', 'nchypergeom_fisher',
+                       'nchypergeom_wallenius', 'nct', 'ncx2',
+                       'pearson3', 'powerlognorm',
+                       'reciprocal', 'rel_breitwigner', 'rice',
+                       'trapezoid', 'truncnorm', 'truncweibull_min',
+                       'vonmises_line'}
+
+    warns_basic_fit = {'skellam'}  # can remove mark after gh-14901 is resolved
+
+    for dist in dict(distdiscrete + distcont):
+        if dist in skip_basic_fit or not isinstance(dist, str):
+            reason = "Fails. Oh well."
+            yield pytest.param(dist, marks=pytest.mark.skip(reason=reason))
+        elif dist in slow_basic_fit:
+            reason = "too slow (>= 0.25s)"
+            yield pytest.param(dist, marks=pytest.mark.slow(reason=reason))
+        elif dist in xslow_basic_fit:
+            reason = "too slow (>= 1.0s)"
+            yield pytest.param(dist, marks=pytest.mark.xslow(reason=reason))
+        elif dist in warns_basic_fit:
+            mark = pytest.mark.filterwarnings('ignore::RuntimeWarning')
+            yield pytest.param(dist, marks=mark)
+        else:
+            yield dist
+
+
+def cases_test_fitstart():
+    for distname, shapes in dict(distcont).items():
+        if (not isinstance(distname, str) or
+                distname in {'studentized_range', 'recipinvgauss'}):  # slow
+            continue
+        yield distname, shapes
+
+
+@pytest.mark.parametrize('distname, shapes', list(cases_test_fitstart()))
+def test_fitstart(distname, shapes):
+    dist = getattr(stats, distname)
+    rng = np.random.default_rng(216342614)
+    data = rng.random(10)
+
+    with np.errstate(invalid='ignore', divide='ignore'):  # irrelevant to test
+        guess = dist._fitstart(data)
+
+    assert dist._argcheck(*guess[:-2])
+
+
+def assert_nlff_less_or_close(dist, data, params1, params0, rtol=1e-7, atol=0,
+                              nlff_name='nnlf'):
+    nlff = getattr(dist, nlff_name)
+    nlff1 = nlff(params1, data)
+    nlff0 = nlff(params0, data)
+    if not (nlff1 < nlff0):
+        np.testing.assert_allclose(nlff1, nlff0, rtol=rtol, atol=atol)
+
+
+class TestFit:
+    dist = stats.binom  # type: ignore[attr-defined]
+    seed = 654634816187
+    rng = np.random.default_rng(seed)
+    data = stats.binom.rvs(5, 0.5, size=100, random_state=rng)  # type: ignore[attr-defined]  # noqa: E501
+    shape_bounds_a = [(1, 10), (0, 1)]
+    shape_bounds_d = {'n': (1, 10), 'p': (0, 1)}
+    atol = 5e-2
+    rtol = 1e-2
+    tols = {'atol': atol, 'rtol': rtol}
+
+    def opt(self, *args, rng=1, **kwds):
+        return differential_evolution(*args, rng=rng, **kwds)
+
+    def test_dist_iv(self):
+        message = "`dist` must be an instance of..."
+        with pytest.raises(ValueError, match=message):
+            stats.fit(10, self.data, self.shape_bounds_a)
+
+    def test_data_iv(self):
+        message = "`data` must be exactly one-dimensional."
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, [[1, 2, 3]], self.shape_bounds_a)
+
+        message = "All elements of `data` must be finite numbers."
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, [1, 2, 3, np.nan], self.shape_bounds_a)
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, [1, 2, 3, np.inf], self.shape_bounds_a)
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, ['1', '2', '3'], self.shape_bounds_a)
+
+    def test_bounds_iv(self):
+        message = "Bounds provided for the following unrecognized..."
+        shape_bounds = {'n': (1, 10), 'p': (0, 1), '1': (0, 10)}
+        with pytest.warns(RuntimeWarning, match=message):
+            stats.fit(self.dist, self.data, shape_bounds)
+
+        message = "Each element of a `bounds` sequence must be a tuple..."
+        shape_bounds = [(1, 10, 3), (0, 1)]
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, shape_bounds)
+
+        message = "Each element of `bounds` must be a tuple specifying..."
+        shape_bounds = [(1, 10, 3), (0, 1, 0.5)]
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, shape_bounds)
+        shape_bounds = [1, 0]
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, shape_bounds)
+
+        message = "A `bounds` sequence must contain at least 2 elements..."
+        shape_bounds = [(1, 10)]
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, shape_bounds)
+
+        message = "A `bounds` sequence may not contain more than 3 elements..."
+        bounds = [(1, 10), (1, 10), (1, 10), (1, 10)]
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, bounds)
+
+        message = "There are no values for `p` on the interval..."
+        shape_bounds = {'n': (1, 10), 'p': (1, 0)}
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, shape_bounds)
+
+        message = "There are no values for `n` on the interval..."
+        shape_bounds = [(10, 1), (0, 1)]
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, shape_bounds)
+
+        message = "There are no integer values for `n` on the interval..."
+        shape_bounds = [(1.4, 1.6), (0, 1)]
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, shape_bounds)
+
+        message = "The intersection of user-provided bounds for `n`"
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data)
+        shape_bounds = [(-np.inf, np.inf), (0, 1)]
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, shape_bounds)
+
+    def test_guess_iv(self):
+        message = "Guesses provided for the following unrecognized..."
+        guess = {'n': 1, 'p': 0.5, '1': 255}
+        with pytest.warns(RuntimeWarning, match=message):
+            stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
+
+        message = "Each element of `guess` must be a scalar..."
+        guess = {'n': 1, 'p': 'hi'}
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
+        guess = [1, 'f']
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
+        guess = [[1, 2]]
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
+
+        message = "A `guess` sequence must contain at least 2..."
+        guess = [1]
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
+
+        message = "A `guess` sequence may not contain more than 3..."
+        guess = [1, 2, 3, 4]
+        with pytest.raises(ValueError, match=message):
+            stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
+
+        message = "Guess for parameter `n` rounded.*|Guess for parameter `p` clipped.*"
+        guess = {'n': 4.5, 'p': -0.5}
+        with pytest.warns(RuntimeWarning, match=message):
+            stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
+
+        message = "Guess for parameter `loc` rounded..."
+        guess = [5, 0.5, 0.5]
+        with pytest.warns(RuntimeWarning, match=message):
+            stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
+
+        message = "Guess for parameter `p` clipped..."
+        guess = {'n': 5, 'p': -0.5}
+        with pytest.warns(RuntimeWarning, match=message):
+            stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
+
+        message = "Guess for parameter `loc` clipped..."
+        guess = [5, 0.5, 1]
+        with pytest.warns(RuntimeWarning, match=message):
+            stats.fit(self.dist, self.data, self.shape_bounds_d, guess=guess)
+
+    def basic_fit_test(self, dist_name, method, rng=1):
+
+        N = 5000
+        dist_data = dict(distcont + distdiscrete)
+        rng = np.random.default_rng(self.seed)
+        dist = getattr(stats, dist_name)
+        shapes = np.array(dist_data[dist_name])
+        bounds = np.empty((len(shapes) + 2, 2), dtype=np.float64)
+        bounds[:-2, 0] = shapes/10.**np.sign(shapes)
+        bounds[:-2, 1] = shapes*10.**np.sign(shapes)
+        bounds[-2] = (0, 10)
+        bounds[-1] = (1e-16, 10)
+        loc = rng.uniform(*bounds[-2])
+        scale = rng.uniform(*bounds[-1])
+        ref = list(dist_data[dist_name]) + [loc, scale]
+
+        if getattr(dist, 'pmf', False):
+            ref = ref[:-1]
+            ref[-1] = np.floor(loc)
+            data = dist.rvs(*ref, size=N, random_state=rng)
+            bounds = bounds[:-1]
+        if getattr(dist, 'pdf', False):
+            data = dist.rvs(*ref, size=N, random_state=rng)
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", "overflow encountered", RuntimeWarning)
+            res = stats.fit(dist, data, bounds, method=method,
+                            optimizer=self.opt)
+
+        nlff_names = {'mle': 'nnlf', 'mse': '_penalized_nlpsf'}
+        nlff_name = nlff_names[method]
+        assert_nlff_less_or_close(dist, data, res.params, ref, **self.tols,
+                                  nlff_name=nlff_name)
+
+    @pytest.mark.parametrize("dist_name", list(cases_test_fit_mle()))
+    def test_basic_fit_mle(self, dist_name):
+        self.basic_fit_test(dist_name, "mle", rng=5)
+
+    @pytest.mark.parametrize("dist_name", list(cases_test_fit_mse()))
+    def test_basic_fit_mse(self, dist_name):
+        self.basic_fit_test(dist_name, "mse", rng=2)
+
+    @pytest.mark.slow
+    def test_arcsine(self):
+        # Can't guarantee that all distributions will fit all data with
+        # arbitrary bounds. This distribution just happens to fail above.
+        # Try something slightly different.
+        N = 1000
+        rng = np.random.default_rng(self.seed)
+        dist = stats.arcsine
+        shapes = (1., 2.)
+        data = dist.rvs(*shapes, size=N, random_state=rng)
+        shape_bounds = {'loc': (0.1, 10), 'scale': (0.1, 10)}
+        res = stats.fit(dist, data, shape_bounds, method='mse', optimizer=self.opt)
+        assert_nlff_less_or_close(dist, data, res.params, shapes,
+                                  nlff_name='_penalized_nlpsf', **self.tols)
+
+    @pytest.mark.parametrize("method", ('mle', 'mse'))
+    def test_argus(self, method):
+        # Can't guarantee that all distributions will fit all data with
+        # arbitrary bounds. This distribution just happens to fail above.
+        # Try something slightly different.
+        N = 1000
+        rng = np.random.default_rng(self.seed)
+        dist = stats.argus
+        shapes = (1., 2., 3.)
+        data = dist.rvs(*shapes, size=N, random_state=rng)
+        shape_bounds = {'chi': (0.1, 10), 'loc': (0.1, 10), 'scale': (0.1, 10)}
+        res = stats.fit(dist, data, shape_bounds, optimizer=self.opt, method=method)
+        nlff_name = {'mle': 'nnlf', 'mse': '_penalized_nlpsf'}[method]
+        assert_nlff_less_or_close(dist, data, res.params, shapes, **self.tols,
+                                  nlff_name=nlff_name)
+
+    @pytest.mark.xslow
+    def test_beta(self):
+        # Can't guarantee that all distributions will fit all data with
+        # arbitrary bounds. This distribution just happens to fail above.
+        # Try something slightly different.
+        N = 1000
+        rng = np.random.default_rng(self.seed)
+        dist = stats.beta
+        shapes = (2.3098496451481823, 0.62687954300963677, 1., 2.)
+        data = dist.rvs(*shapes, size=N, random_state=rng)
+        shape_bounds = {'a': (0.1, 10), 'b':(0.1, 10),
+                        'loc': (0.1, 10), 'scale': (0.1, 10)}
+        res = stats.fit(dist, data, shape_bounds, method='mle', optimizer=self.opt)
+        assert_nlff_less_or_close(dist, data, res.params, shapes,
+                                  nlff_name='nnlf', **self.tols)
+
+    def test_foldnorm(self):
+        # Can't guarantee that all distributions will fit all data with
+        # arbitrary bounds. This distribution just happens to fail above.
+        # Try something slightly different.
+        N = 1000
+        rng = np.random.default_rng(self.seed)
+        dist = stats.foldnorm
+        shapes = (1.952125337355587, 2., 3.)
+        data = dist.rvs(*shapes, size=N, random_state=rng)
+        shape_bounds = {'c': (0.1, 10), 'loc': (0.1, 10), 'scale': (0.1, 10)}
+        res = stats.fit(dist, data, shape_bounds, optimizer=self.opt)
+
+        assert_nlff_less_or_close(dist, data, res.params, shapes, **self.tols)
+
+    def test_nakagami(self):
+        # Can't guarantee that all distributions will fit all data with
+        # arbitrary bounds. This distribution just happens to fail above.
+        # Try something slightly different.
+        N = 1000
+        rng = np.random.default_rng(self.seed)
+        dist = stats.nakagami
+        shapes = (4.9673794866666237, 1., 2.)
+        data = dist.rvs(*shapes, size=N, random_state=rng)
+        shape_bounds = {'nu':(0.1, 10), 'loc': (0.1, 10), 'scale': (0.1, 10)}
+        res = stats.fit(dist, data, shape_bounds, method='mle', optimizer=self.opt)
+        assert_nlff_less_or_close(dist, data, res.params, shapes,
+                                  nlff_name='nnlf', **self.tols)
+
+    @pytest.mark.slow
+    def test_powerlaw(self):
+        # Can't guarantee that all distributions will fit all data with
+        # arbitrary bounds. This distribution just happens to fail above.
+        # Try something slightly different.
+        N = 1000
+        rng = np.random.default_rng(self.seed)
+        dist = stats.powerlaw
+        shapes = (1.6591133289905851, 1., 2.)
+        data = dist.rvs(*shapes, size=N, random_state=rng)
+        shape_bounds = {'a': (0.1, 10), 'loc': (0.1, 10), 'scale': (0.1, 10)}
+        res = stats.fit(dist, data, shape_bounds, method='mse', optimizer=self.opt)
+        assert_nlff_less_or_close(dist, data, res.params, shapes,
+                                  nlff_name='_penalized_nlpsf', **self.tols)
+
+    def test_truncpareto(self):
+        # Can't guarantee that all distributions will fit all data with
+        # arbitrary bounds. This distribution just happens to fail above.
+        # Try something slightly different.
+        N = 1000
+        rng = np.random.default_rng(self.seed)
+        dist = stats.truncpareto
+        shapes = (1.8, 5.3, 2.3, 4.1)
+        data = dist.rvs(*shapes, size=N, random_state=rng)
+        shape_bounds = [(0.1, 10)]*4
+        res = stats.fit(dist, data, shape_bounds, optimizer=self.opt)
+
+        assert_nlff_less_or_close(dist, data, res.params, shapes, **self.tols)
+
+    @pytest.mark.slow
+    def test_truncweibull_min(self):
+        # Can't guarantee that all distributions will fit all data with
+        # arbitrary bounds. This distribution just happens to fail above.
+        # Try something slightly different.
+        N = 1000
+        rng = np.random.default_rng(self.seed)
+        dist = stats.truncweibull_min
+        shapes = (2.5, 0.25, 1.75, 2., 3.)
+        data = dist.rvs(*shapes, size=N, random_state=rng)
+        shape_bounds = [(0.1, 10)]*5
+        res = stats.fit(dist, data, shape_bounds, optimizer=self.opt)
+
+        assert_nlff_less_or_close(dist, data, res.params, shapes, **self.tols)
+
+    def test_missing_shape_bounds(self):
+        # some distributions have a small domain w.r.t. a parameter, e.g.
+        # $p \in [0, 1]$ for binomial distribution
+        # User does not need to provide these because the intersection of the
+        # user's bounds (none) and the distribution's domain is finite
+        N = 1000
+        rng = np.random.default_rng(self.seed)
+
+        dist = stats.binom
+        n, p, loc = 10, 0.65, 0
+        data = dist.rvs(n, p, loc=loc, size=N, random_state=rng)
+        shape_bounds = {'n': np.array([0, 20])}  # check arrays are OK, too
+        res = stats.fit(dist, data, shape_bounds, optimizer=self.opt)
+        assert_allclose(res.params, (n, p, loc), **self.tols)
+
+        dist = stats.bernoulli
+        p, loc = 0.314159, 0
+        data = dist.rvs(p, loc=loc, size=N, random_state=rng)
+        res = stats.fit(dist, data, optimizer=self.opt)
+        assert_allclose(res.params, (p, loc), **self.tols)
+
+    def test_fit_only_loc_scale(self):
+        # fit only loc
+        N = 5000
+        rng = np.random.default_rng(self.seed)
+
+        dist = stats.norm
+        loc, scale = 1.5, 1
+        data = dist.rvs(loc=loc, size=N, random_state=rng)
+        loc_bounds = (0, 5)
+        bounds = {'loc': loc_bounds}
+        res = stats.fit(dist, data, bounds, optimizer=self.opt)
+        assert_allclose(res.params, (loc, scale), **self.tols)
+
+        # fit only scale
+        loc, scale = 0, 2.5
+        data = dist.rvs(scale=scale, size=N, random_state=rng)
+        scale_bounds = (0.01, 5)
+        bounds = {'scale': scale_bounds}
+        res = stats.fit(dist, data, bounds, optimizer=self.opt)
+        assert_allclose(res.params, (loc, scale), **self.tols)
+
+        # fit only loc and scale
+        dist = stats.norm
+        loc, scale = 1.5, 2.5
+        data = dist.rvs(loc=loc, scale=scale, size=N, random_state=rng)
+        bounds = {'loc': loc_bounds, 'scale': scale_bounds}
+        res = stats.fit(dist, data, bounds, optimizer=self.opt)
+        assert_allclose(res.params, (loc, scale), **self.tols)
+
+    def test_everything_fixed(self):
+        N = 5000
+        rng = np.random.default_rng(self.seed)
+
+        dist = stats.norm
+        loc, scale = 1.5, 2.5
+        data = dist.rvs(loc=loc, scale=scale, size=N, random_state=rng)
+
+        # loc, scale fixed to 0, 1 by default
+        res = stats.fit(dist, data)
+        assert_allclose(res.params, (0, 1), **self.tols)
+
+        # loc, scale explicitly fixed
+        bounds = {'loc': (loc, loc), 'scale': (scale, scale)}
+        res = stats.fit(dist, data, bounds)
+        assert_allclose(res.params, (loc, scale), **self.tols)
+
+        # `n` gets fixed during polishing
+        dist = stats.binom
+        n, p, loc = 10, 0.65, 0
+        data = dist.rvs(n, p, loc=loc, size=N, random_state=rng)
+        shape_bounds = {'n': (0, 20), 'p': (0.65, 0.65)}
+        res = stats.fit(dist, data, shape_bounds, optimizer=self.opt)
+        assert_allclose(res.params, (n, p, loc), **self.tols)
+
+    def test_failure(self):
+        N = 5000
+        rng = np.random.default_rng(self.seed)
+
+        dist = stats.nbinom
+        shapes = (5, 0.5)
+        data = dist.rvs(*shapes, size=N, random_state=rng)
+
+        assert data.min() == 0
+        # With lower bounds on location at 0.5, likelihood is zero
+        bounds = [(0, 30), (0, 1), (0.5, 10)]
+        res = stats.fit(dist, data, bounds)
+        message = "Optimization converged to parameter values that are"
+        assert res.message.startswith(message)
+        assert res.success is False
+
+    @pytest.mark.xslow
+    def test_guess(self):
+        # Test that guess helps DE find the desired solution
+        N = 2000
+        # With some seeds, `fit` doesn't need a guess
+        rng = np.random.default_rng(196390444561)
+        dist = stats.nhypergeom
+        params = (20, 7, 12, 0)
+        bounds = [(2, 200), (0.7, 70), (1.2, 120), (0, 10)]
+
+        data = dist.rvs(*params, size=N, random_state=rng)
+
+        res = stats.fit(dist, data, bounds, optimizer=self.opt)
+        assert not np.allclose(res.params, params, **self.tols)
+
+        res = stats.fit(dist, data, bounds, guess=params, optimizer=self.opt)
+        assert_allclose(res.params, params, **self.tols)
+
+    def test_mse_accuracy_1(self):
+        # Test maximum spacing estimation against example from Wikipedia
+        # https://en.wikipedia.org/wiki/Maximum_spacing_estimation#Examples
+        data = [2, 4]
+        dist = stats.expon
+        bounds = {'loc': (0, 0), 'scale': (1e-8, 10)}
+        res_mle = stats.fit(dist, data, bounds=bounds, method='mle')
+        assert_allclose(res_mle.params.scale, 3, atol=1e-3)
+        res_mse = stats.fit(dist, data, bounds=bounds, method='mse')
+        assert_allclose(res_mse.params.scale, 3.915, atol=1e-3)
+
+    def test_mse_accuracy_2(self):
+        # Test maximum spacing estimation against example from Wikipedia
+        # https://en.wikipedia.org/wiki/Maximum_spacing_estimation#Examples
+        rng = np.random.default_rng(9843212616816518964)
+
+        dist = stats.uniform
+        n = 10
+        data = dist(3, 6).rvs(size=n, random_state=rng)
+        bounds = {'loc': (0, 10), 'scale': (1e-8, 10)}
+        res = stats.fit(dist, data, bounds=bounds, method='mse')
+        # (loc=3.608118420015416, scale=5.509323262055043)
+
+        x = np.sort(data)
+        a = (n*x[0] - x[-1])/(n - 1)
+        b = (n*x[-1] - x[0])/(n - 1)
+        ref = a, b-a  # (3.6081133632151503, 5.509328130317254)
+        assert_allclose(res.params, ref, rtol=1e-4)
+
+
+# Data from Matlab: https://www.mathworks.com/help/stats/lillietest.html
+examgrades = [65, 61, 81, 88, 69, 89, 55, 84, 86, 84, 71, 81, 84, 81, 78, 67,
+              96, 66, 73, 75, 59, 71, 69, 63, 79, 76, 63, 85, 87, 88, 80, 71,
+              65, 84, 71, 75, 81, 79, 64, 65, 84, 77, 70, 75, 84, 75, 73, 92,
+              90, 79, 80, 71, 73, 71, 58, 79, 73, 64, 77, 82, 81, 59, 54, 82,
+              57, 79, 79, 73, 74, 82, 63, 64, 73, 69, 87, 68, 81, 73, 83, 73,
+              80, 73, 73, 71, 66, 78, 64, 74, 68, 67, 75, 75, 80, 85, 74, 76,
+              80, 77, 93, 70, 86, 80, 81, 83, 68, 60, 85, 64, 74, 82, 81, 77,
+              66, 85, 75, 81, 69, 60, 83, 72]
+
+
+class TestGoodnessOfFit:
+
+    def test_gof_iv(self):
+        dist = stats.norm
+        x = [1, 2, 3]
+
+        message = r"`dist` must be a \(non-frozen\) instance of..."
+        with pytest.raises(TypeError, match=message):
+            goodness_of_fit(stats.norm(), x)
+
+        message = "`data` must be a one-dimensional array of numbers."
+        with pytest.raises(ValueError, match=message):
+            goodness_of_fit(dist, [[1, 2, 3]])
+
+        message = "`statistic` must be one of..."
+        with pytest.raises(ValueError, match=message):
+            goodness_of_fit(dist, x, statistic='mm')
+
+        message = "`n_mc_samples` must be an integer."
+        with pytest.raises(TypeError, match=message):
+            goodness_of_fit(dist, x, n_mc_samples=1000.5)
+
+        message = "SeedSequence expects int or sequence"
+        with pytest.raises(TypeError, match=message):
+            goodness_of_fit(dist, x, rng='herring')
+
+    def test_against_ks(self):
+        rng = np.random.default_rng(8517426291317196949)
+        x = examgrades
+        known_params = {'loc': np.mean(x), 'scale': np.std(x, ddof=1)}
+        res = goodness_of_fit(stats.norm, x, known_params=known_params,
+                              statistic='ks', rng=rng)
+        ref = stats.kstest(x, stats.norm(**known_params).cdf, method='exact')
+        assert_allclose(res.statistic, ref.statistic)  # ~0.0848
+        assert_allclose(res.pvalue, ref.pvalue, atol=5e-3)  # ~0.335
+
+    def test_against_lilliefors(self):
+        rng = np.random.default_rng(2291803665717442724)
+        x = examgrades
+        # preserve use of old random_state during SPEC 7 transition
+        res = goodness_of_fit(stats.norm, x, statistic='ks', random_state=rng)
+        known_params = {'loc': np.mean(x), 'scale': np.std(x, ddof=1)}
+        ref = stats.kstest(x, stats.norm(**known_params).cdf, method='exact')
+        assert_allclose(res.statistic, ref.statistic)  # ~0.0848
+        assert_allclose(res.pvalue, 0.0348, atol=5e-3)
+
+    def test_against_cvm(self):
+        rng = np.random.default_rng(8674330857509546614)
+        x = examgrades
+        known_params = {'loc': np.mean(x), 'scale': np.std(x, ddof=1)}
+        res = goodness_of_fit(stats.norm, x, known_params=known_params,
+                              statistic='cvm', rng=rng)
+        ref = stats.cramervonmises(x, stats.norm(**known_params).cdf)
+        assert_allclose(res.statistic, ref.statistic)  # ~0.090
+        assert_allclose(res.pvalue, ref.pvalue, atol=5e-3)  # ~0.636
+
+    def test_against_anderson_case_0(self):
+        # "Case 0" is where loc and scale are known [1]
+        rng = np.random.default_rng(7384539336846690410)
+        x = np.arange(1, 101)
+        # loc that produced critical value of statistic found w/ root_scalar
+        known_params = {'loc': 45.01575354024957, 'scale': 30}
+        res = goodness_of_fit(stats.norm, x, known_params=known_params,
+                              statistic='ad', rng=rng)
+        assert_allclose(res.statistic, 2.492)  # See [1] Table 1A 1.0
+        assert_allclose(res.pvalue, 0.05, atol=5e-3)
+
+    def test_against_anderson_case_1(self):
+        # "Case 1" is where scale is known and loc is fit [1]
+        rng = np.random.default_rng(5040212485680146248)
+        x = np.arange(1, 101)
+        # scale that produced critical value of statistic found w/ root_scalar
+        known_params = {'scale': 29.957112639101933}
+        res = goodness_of_fit(stats.norm, x, known_params=known_params,
+                              statistic='ad', rng=rng)
+        assert_allclose(res.statistic, 0.908)  # See [1] Table 1B 1.1
+        assert_allclose(res.pvalue, 0.1, atol=5e-3)
+
+    def test_against_anderson_case_2(self):
+        # "Case 2" is where loc is known and scale is fit [1]
+        rng = np.random.default_rng(726693985720914083)
+        x = np.arange(1, 101)
+        # loc that produced critical value of statistic found w/ root_scalar
+        known_params = {'loc': 44.5680212261933}
+        res = goodness_of_fit(stats.norm, x, known_params=known_params,
+                              statistic='ad', rng=rng)
+        assert_allclose(res.statistic, 2.904)  # See [1] Table 1B 1.2
+        assert_allclose(res.pvalue, 0.025, atol=5e-3)
+
+    def test_against_anderson_case_3(self):
+        # "Case 3" is where both loc and scale are fit [1]
+        rng = np.random.default_rng(6763691329830218206)
+        # c that produced critical value of statistic found w/ root_scalar
+        x = stats.skewnorm.rvs(1.4477847789132101, loc=1, scale=2, size=100,
+                               random_state=rng)
+        res = goodness_of_fit(stats.norm, x, statistic='ad', rng=rng)
+        assert_allclose(res.statistic, 0.559)  # See [1] Table 1B 1.2
+        assert_allclose(res.pvalue, 0.15, atol=5e-3)
+
+    @pytest.mark.xslow
+    def test_against_anderson_gumbel_r(self):
+        rng = np.random.default_rng(7302761058217743)
+        # c that produced critical value of statistic found w/ root_scalar
+        x = stats.genextreme(0.051896837188595134, loc=0.5,
+                             scale=1.5).rvs(size=1000, random_state=rng)
+        res = goodness_of_fit(stats.gumbel_r, x, statistic='ad', rng=rng)
+        ref = stats.anderson(x, dist='gumbel_r', method='interpolate')
+        assert_allclose(res.statistic, ref.statistic)
+        assert_allclose(res.pvalue, ref.pvalue, atol=5e-3)
+
+    def test_against_filliben_norm(self):
+        # Test against `stats.fit` ref. [7] Section 8 "Example"
+        rng = np.random.default_rng(8024266430745011915)
+        y = [6, 1, -4, 8, -2, 5, 0]
+        known_params = {'loc': 0, 'scale': 1}
+        res = stats.goodness_of_fit(stats.norm, y, known_params=known_params,
+                                    statistic="filliben", rng=rng)
+        # Slight discrepancy presumably due to roundoff in Filliben's
+        # calculation. Using exact order statistic medians instead of
+        # Filliben's approximation doesn't account for it.
+        assert_allclose(res.statistic, 0.98538, atol=1e-4)
+        assert 0.75 < res.pvalue < 0.9
+
+        # Using R's ppcc library:
+        # library(ppcc)
+        # options(digits=16)
+        # x < - c(6, 1, -4, 8, -2, 5, 0)
+        # set.seed(100)
+        # ppccTest(x, "qnorm", ppos="Filliben")
+        # Discrepancy with
+        assert_allclose(res.statistic, 0.98540957187084, rtol=2e-5)
+        assert_allclose(res.pvalue, 0.8875, rtol=2e-3)
+
+    def test_filliben_property(self):
+        # Filliben's statistic should be independent of data location and scale
+        rng = np.random.default_rng(8535677809395478813)
+        x = rng.normal(loc=10, scale=0.5, size=100)
+        res = stats.goodness_of_fit(stats.norm, x,
+                                    statistic="filliben", rng=rng)
+        known_params = {'loc': 0, 'scale': 1}
+        ref = stats.goodness_of_fit(stats.norm, x, known_params=known_params,
+                                    statistic="filliben", rng=rng)
+        assert_allclose(res.statistic, ref.statistic, rtol=1e-15)
+
+    @pytest.mark.parametrize('case', [(25, [.928, .937, .950, .958, .966]),
+                                      (50, [.959, .965, .972, .977, .981]),
+                                      (95, [.977, .979, .983, .986, .989])])
+    def test_against_filliben_norm_table(self, case):
+        # Test against `stats.fit` ref. [7] Table 1
+        rng = np.random.default_rng(504569995557928957)
+        n, ref = case
+        x = rng.random(n)
+        known_params = {'loc': 0, 'scale': 1}
+        res = stats.goodness_of_fit(stats.norm, x, known_params=known_params,
+                                    statistic="filliben", rng=rng)
+        percentiles = np.array([0.005, 0.01, 0.025, 0.05, 0.1])
+        res = stats.scoreatpercentile(res.null_distribution, percentiles*100)
+        assert_allclose(res, ref, atol=2e-3)
+
+    @pytest.mark.xslow
+    @pytest.mark.parametrize('case', [(5, 0.95772790260469, 0.4755),
+                                      (6, 0.95398832257958, 0.3848),
+                                      (7, 0.9432692889277, 0.2328)])
+    def test_against_ppcc(self, case):
+        # Test against R ppcc, e.g.
+        # library(ppcc)
+        # options(digits=16)
+        # x < - c(0.52325412, 1.06907699, -0.36084066, 0.15305959, 0.99093194)
+        # set.seed(100)
+        # ppccTest(x, "qrayleigh", ppos="Filliben")
+        n, ref_statistic, ref_pvalue = case
+        rng = np.random.default_rng(7777775561439803116)
+        x = rng.normal(size=n)
+        res = stats.goodness_of_fit(stats.rayleigh, x, statistic="filliben",
+                                    rng=rng)
+        assert_allclose(res.statistic, ref_statistic, rtol=1e-4)
+        assert_allclose(res.pvalue, ref_pvalue, atol=1.5e-2)
+
+    def test_params_effects(self):
+        # Ensure that `guessed_params`, `fit_params`, and `known_params` have
+        # the intended effects.
+        rng = np.random.default_rng(9121950977643805391)
+        x = stats.skewnorm.rvs(-5.044559778383153, loc=1, scale=2, size=50,
+                               random_state=rng)
+
+        # Show that `guessed_params` don't fit to the guess,
+        # but `fit_params` and `known_params` respect the provided fit
+        guessed_params = {'c': 13.4}
+        fit_params = {'scale': 13.73}
+        known_params = {'loc': -13.85}
+        rng = np.random.default_rng(9121950977643805391)
+        res1 = goodness_of_fit(stats.weibull_min, x, n_mc_samples=2,
+                               guessed_params=guessed_params,
+                               fit_params=fit_params,
+                               known_params=known_params, rng=rng)
+        assert not np.allclose(res1.fit_result.params.c, 13.4)
+        assert_equal(res1.fit_result.params.scale, 13.73)
+        assert_equal(res1.fit_result.params.loc, -13.85)
+
+        # Show that changing the guess changes the parameter that gets fit,
+        # and it changes the null distribution
+        guessed_params = {'c': 2}
+        rng = np.random.default_rng(9121950977643805391)
+        res2 = goodness_of_fit(stats.weibull_min, x, n_mc_samples=2,
+                               guessed_params=guessed_params,
+                               fit_params=fit_params,
+                               known_params=known_params, rng=rng)
+        assert not np.allclose(res2.fit_result.params.c,
+                               res1.fit_result.params.c, rtol=1e-8)
+        assert not np.allclose(res2.null_distribution,
+                               res1.null_distribution, rtol=1e-8)
+        assert_equal(res2.fit_result.params.scale, 13.73)
+        assert_equal(res2.fit_result.params.loc, -13.85)
+
+        # If we set all parameters as fit_params and known_params,
+        # they're all fixed to those values, but the null distribution
+        # varies.
+        fit_params = {'c': 13.4, 'scale': 13.73}
+        rng = np.random.default_rng(9121950977643805391)
+        res3 = goodness_of_fit(stats.weibull_min, x, n_mc_samples=2,
+                               guessed_params=guessed_params,
+                               fit_params=fit_params,
+                               known_params=known_params, rng=rng)
+        assert_equal(res3.fit_result.params.c, 13.4)
+        assert_equal(res3.fit_result.params.scale, 13.73)
+        assert_equal(res3.fit_result.params.loc, -13.85)
+        assert not np.allclose(res3.null_distribution, res1.null_distribution)
+
+    def test_custom_statistic(self):
+        # Test support for custom statistic function.
+
+        # References:
+        # [1] Pyke, R. (1965).  "Spacings".  Journal of the Royal Statistical
+        #     Society: Series B (Methodological), 27(3): 395-436.
+        # [2] Burrows, P. M. (1979).  "Selected Percentage Points of
+        #     Greenwood's Statistics".  Journal of the Royal Statistical
+        #     Society. Series A (General), 142(2): 256-258.
+
+        # Use the Greenwood statistic for illustration; see [1, p.402].
+        def greenwood(dist, data, *, axis):
+            x = np.sort(data, axis=axis)
+            y = dist.cdf(x)
+            d = np.diff(y, axis=axis, prepend=0, append=1)
+            return np.sum(d ** 2, axis=axis)
+
+        # Run the Monte Carlo test with sample size = 5 on a fully specified
+        # null distribution, and compare the simulated quantiles to the exact
+        # ones given in [2, Table 1, column (n = 5)].
+        rng = np.random.default_rng(9121950977643805391)
+        data = stats.expon.rvs(size=5, random_state=rng)
+        result = goodness_of_fit(stats.expon, data,
+                                 known_params={'loc': 0, 'scale': 1},
+                                 statistic=greenwood, rng=rng)
+        p = [.01, .05, .1, .2, .3, .4, .5, .6, .7, .8, .9, .95, .99]
+        exact_quantiles = [
+            .183863, .199403, .210088, .226040, .239947, .253677, .268422,
+            .285293, .306002, .334447, .382972, .432049, .547468]
+        simulated_quantiles = np.quantile(result.null_distribution, p)
+        assert_allclose(simulated_quantiles, exact_quantiles, atol=0.005)
+
+class TestFitResult:
+    def test_plot_iv(self):
+        rng = np.random.default_rng(1769658657308472721)
+        data = stats.norm.rvs(0, 1, size=100, random_state=rng)
+
+        def optimizer(*args, **kwargs):
+            return differential_evolution(*args, **kwargs, rng=rng)
+
+        bounds = [(0, 30), (0, 1)]
+        res = stats.fit(stats.norm, data, bounds, optimizer=optimizer)
+        try:
+            import matplotlib  # noqa: F401
+            message = r"`plot_type` must be one of \{'..."
+            with pytest.raises(ValueError, match=message):
+                res.plot(plot_type='llama')
+        except (ModuleNotFoundError, ImportError):
+            message = r"matplotlib must be installed to use method `plot`."
+            with pytest.raises(ModuleNotFoundError, match=message):
+                res.plot(plot_type='llama')
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_generation/generate_fisher_exact_results_from_r.R b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_generation/generate_fisher_exact_results_from_r.R
new file mode 100644
index 0000000000000000000000000000000000000000..f49f9fb7ab10701233ebc411dfe2a362f87719a2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_generation/generate_fisher_exact_results_from_r.R
@@ -0,0 +1,84 @@
+# This R script generates the Python file fisher_exact_results_from_r.py.
+# To run this script, use
+# $ Rscript generate_fisher_exact_results_from_r.R
+
+sink("fisher_exact_results_from_r.py")
+
+options(digits=15)
+
+# Note that when converted to a matrix, `byrow=TRUE` is used, so
+# the first two values of each data set become the first row of
+# the contingency  table.  E.g.
+# > matrix(c(100, 2, 1000, 5), nrow=2, ncol=2, byrow=TRUE)
+#      [,1] [,2]
+# [1,]  100    2
+# [2,] 1000    5
+# 
+table_data = list(
+    c(100, 2, 1000, 5),
+    c(2, 7, 8, 2),
+    c(5, 1, 10, 10),
+    c(5, 15, 20, 20),
+    c(5, 16, 16, 25),
+    c(10, 5, 10, 1),
+    c(10, 5, 10, 0),
+    c(5, 0, 1, 4),
+    c(0, 5, 1, 4),
+    c(5, 1, 0, 4),
+    c(0, 1, 3, 2),
+    c(200, 7, 8, 300),
+    c(28, 21, 6, 1957),
+    c(190, 800, 200, 900)
+)
+confidence.levels = c(0.95, 0.99)
+alternatives = c("two.sided", "less", "greater")
+
+cat("# DO NOT EDIT THIS FILE!", fill=TRUE)
+cat("# This file was generated by the R script", fill=TRUE)
+cat("#     generate_fisher_exact_results_from_r.R", fill=TRUE)
+cat("# The script was run with", R.version.string, "at ")
+s = sprintf("%s", Sys.time())
+cat(s, fill=TRUE)
+cat("", fill=TRUE)
+cat("", fill=TRUE)
+cat("from collections import namedtuple", fill=TRUE)
+cat("import numpy as np", fill=TRUE)
+cat("", fill=TRUE)
+cat("", fill=TRUE)
+cat("Inf = np.inf", fill=TRUE)
+cat("", fill=TRUE)
+cat("Parameters = namedtuple('Parameters',", fill=TRUE)
+cat("                        ['table', 'confidence_level', 'alternative'])", fill=TRUE)
+cat("RResults = namedtuple('RResults',", fill=TRUE)
+cat("                      ['pvalue', 'conditional_odds_ratio',", fill=TRUE)
+cat("                       'conditional_odds_ratio_ci'])", fill=TRUE)
+cat("data = [", fill=TRUE)
+for (alternative in alternatives) {
+    for (conf.level in confidence.levels) {
+        for (data in table_data) {
+            table = matrix(data, nrow=2, ncol=2, byrow=TRUE)
+            #result = fisher.test(table, alternative=alternative, conf.level=conf.level)
+            result = fisher.test(table, alternative=alternative, conf.level=conf.level)
+            cat("    (")
+            cat("Parameters(table=")
+            s = sprintf("[[%.16g, %.16g], [%.16g, %.16g]],", table[1, 1], table[1, 2], table[2, 1], table[2, 2])
+            cat(s, fill=TRUE)
+            s = sprintf("                confidence_level=%.16g,", conf.level)
+            cat(s, fill=TRUE)
+            s = sprintf("                alternative='%s'),", alternative)
+            cat(s, fill=TRUE)
+            cat("     RResults(")
+            s = sprintf("pvalue=%.16g,", result$p.value)
+            cat(s, fill=TRUE)
+            s = sprintf("              conditional_odds_ratio=%.16g,", result$estimate)
+            cat(s, fill=TRUE)
+            s = sprintf("              conditional_odds_ratio_ci=(%.16g,", result$conf.int[1])
+            cat(s, fill=TRUE)
+            s = sprintf("                                         %.16g))),", result$conf.int[2])
+            cat(s, fill=TRUE)
+        }
+    }
+}
+cat("]", fill=TRUE)
+
+sink()
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_generation/reference_distribution_infrastructure_tests.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_generation/reference_distribution_infrastructure_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f44d24da8804b8bf9c84685f6892dd11723d933
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_generation/reference_distribution_infrastructure_tests.py
@@ -0,0 +1,90 @@
+# Note: this file is to be run locally, not on CI. It is only for tests of the
+# reference distribution *infrastructure*; unit tests of SciPy distributions
+# do not go here.
+
+import numpy as np
+import pytest
+
+from scipy import stats
+from numpy.testing import assert_allclose
+import scipy.stats.tests.test_generation.reference_distributions as rd
+import mpmath
+from mpmath import mp
+
+def test_basic():
+
+    message = "`mpmath.mp.dps <= 15`. Set a higher precision..."
+    with pytest.raises(RuntimeError, match=message):
+        rd.Normal()
+
+    mpmath.dps = 20
+    message = "`mpmath.dps` has been assigned. This is not intended usage..."
+    with pytest.raises(RuntimeError, match=message):
+        rd.Normal()
+    del mpmath.dps
+
+    mp.dps = 20  # high enough to pass, not unreasonably slow
+
+    # Basic tests of the mpmath distribution infrastructure using a SciPy
+    # distribution as a reference. The intent is just to make sure that the
+    # implementations do not have *mistakes* and that broadcasting is working
+    # as expected. The accuracy is what it is.
+
+    rng = np.random.default_rng(6716188855217730280)
+
+    x = rng.random(size=3)
+    a = rng.random(size=(2, 1))
+    rtol = 1e-15
+
+    dist = rd.SkewNormal(a=a)
+    dist_ref = stats.skewnorm(a)
+
+    assert_allclose(dist.pdf(x), dist_ref.pdf(x), rtol=rtol)
+    assert_allclose(dist.cdf(x), dist_ref.cdf(x), rtol=rtol)
+    assert_allclose(dist.sf(x), dist_ref.sf(x), rtol=rtol)
+    assert_allclose(dist.ppf(x), dist_ref.ppf(x), rtol=rtol)
+    assert_allclose(dist.isf(x), dist_ref.isf(x), rtol=rtol)
+    assert_allclose(dist.logpdf(x), dist_ref.logpdf(x), rtol=rtol)
+    assert_allclose(dist.logcdf(x), dist_ref.logcdf(x), rtol=rtol)
+    assert_allclose(dist.logsf(x), dist_ref.logsf(x), rtol=rtol)
+    assert_allclose(dist.support(), dist_ref.support(), rtol=rtol)
+    assert_allclose(dist.entropy(), dist_ref.entropy(), rtol=rtol)
+    assert_allclose(dist.mean(), dist_ref.mean(), rtol=rtol)
+    assert_allclose(dist.var(), dist_ref.var(), rtol=rtol)
+    assert_allclose(dist.skew(), dist_ref.stats('s'), rtol=rtol)
+    assert_allclose(dist.kurtosis(), dist_ref.stats('k'), rtol=rtol)
+
+
+def test_complementary_method_use():
+    # Show that complementary methods are used as expected.
+    # E.g., use 1 - CDF to compute SF if CDF is overridden but SF is not
+
+    mp.dps = 50
+    x = np.linspace(0, 1, 10)
+    class MyDist(rd.ReferenceDistribution):
+        def _cdf(self, x):
+            return x
+
+    dist = MyDist()
+    assert_allclose(dist.sf(x), 1 - dist.cdf(x))
+
+    class MyDist(rd.ReferenceDistribution):
+        def _sf(self, x):
+            return 1-x
+
+    dist = MyDist()
+    assert_allclose(dist.cdf(x), 1 - dist.sf(x))
+
+    class MyDist(rd.ReferenceDistribution):
+        def _ppf(self, x, guess):
+            return x
+
+    dist = MyDist()
+    assert_allclose(dist.isf(x), dist.ppf(1-x))
+
+    class MyDist(rd.ReferenceDistribution):
+        def _isf(self, x, guess):
+            return 1-x
+
+    dist = MyDist()
+    assert_allclose(dist.ppf(x), dist.isf(1-x))
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_generation/reference_distributions.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_generation/reference_distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..037b16a2d0c88ddf75416fa1324d010d35e0b363
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_generation/reference_distributions.py
@@ -0,0 +1,430 @@
+import numpy as np
+import mpmath
+from mpmath import mp
+
+
+class ReferenceDistribution:
+    """Minimalist distribution infrastructure for generating reference data.
+
+    The purpose is to generate reference values for unit tests of SciPy
+    distribution accuracy and robustness.
+
+    Handles array input with standard broadcasting rules, and method
+    implementations are easily compared against their mathematical definitions.
+    No attempt is made to handle edge cases or be fast, and arbitrary precision
+    arithmetic is trusted for accuracy rather than making the method
+    implementations "smart".
+
+    Notes
+    -----
+
+    In this infrastructure, distributions families are classes, and
+    fully-specified distributions (i.e. with definite values of all family
+    parameters) are instances of these classes. Typically, the public methods
+    accept as input only the argument at which the at which the function is to
+    be evaluated. Unlike SciPy distributions, they never accept values of
+    distribution family shape, location, or scale parameters. A few
+    other parameters are noteworthy:
+
+    - All methods accept `dtype` to control the output data type. The default
+      is `np.float64`, but `object` or `mp.mpf` may be
+      specified to output the full `mpf`.
+    - `ppf`/`isf` accept a `guess` because they use a scalar rootfinder
+      to invert the `cdf`/`sf`. This is passed directly into the `x0` method
+      of `mpmath.findroot`; see its documentation for details.
+    - moment accepts `order`, an integer that specifies the order of the (raw)
+      moment, and `center`, which is the value about which the moment is
+      taken. The default is to calculate the mean and use it to calculate
+      central moments; passing ``0`` results in a noncentral moment. For
+      efficiency, the mean can be passed explicitly if it is already known.
+
+    Follow the example of SkewNormal to generate new reference distributions,
+    overriding only `__init__` and `_pdf`*. Use the reference distributions to
+    generate reference values for unit tests of SciPy distribution method
+    precision and robustness (e.g. for extreme arguments). If the a SciPy
+    methods implementation is independent and yet the output matches reference
+    values generated with this infrastructure, it is unlikely that the SciPy
+    and reference values are both inaccurate.
+
+    * If the SciPy output *doesn't* match and the cause appears to be
+    inaccuracy of the reference values (e.g. due to numerical issues that
+    mpmath's arbitrary precision arithmetic doesn't handle), then it may be
+    appropriate to override a method of the reference distribution rather than
+    relying on the generic implementation. Otherwise, hesitate to override
+    methods: the generic implementations are mathematically correct and easy
+    to verify, whereas an override introduces many possibilities of mistakes,
+    requires more time to write, and requires more time to review.
+
+    In general, do not create custom unit tests to ensure that
+    SciPy distribution methods are *correct* (in the sense of being consistent
+    with the rest of the distribution methods); generic tests take care of
+    that.
+    """
+
+    def __init__(self, **kwargs):
+        try:
+            if mpmath.dps is not None:
+                message = ("`mpmath.dps` has been assigned. This is not "
+                           "intended usage; instead, assign the desired "
+                           "precision to `mpmath.mp.dps` (e.g. `from mpmath "
+                           "as mp; mp.dps = 50.")
+                raise RuntimeError(message)
+        except AttributeError:
+            mpmath.dps = None
+
+        if mp.dps <= 15:
+            message = ("`mpmath.mp.dps <= 15`. Set a higher precision (e.g."
+                       "`50`) to use this distribution.")
+            raise RuntimeError(message)
+
+        self._params = {key:self._make_mpf_array(val)
+                        for key, val in kwargs.items()}
+
+    def _make_mpf_array(self, x):
+        shape = np.shape(x)
+        x = np.asarray(x, dtype=np.float64).ravel()
+        return np.asarray([mp.mpf(xi) for xi in x]).reshape(shape)[()]
+
+    def _pdf(self, x):
+        raise NotImplementedError("_pdf must be overridden.")
+
+    def _cdf(self, x, **kwargs):
+        if ((self._cdf.__func__ is ReferenceDistribution._cdf)
+                and (self._sf.__func__ is not ReferenceDistribution._sf)):
+            return mp.one - self._sf(x, **kwargs)
+
+        a, b = self._support(**kwargs)
+        res = mp.quad(lambda x: self._pdf(x, **kwargs), (a, x))
+        res = res if res < 0.5 else mp.one - self._sf(x, **kwargs)
+        return res
+
+    def _sf(self, x, **kwargs):
+        if ((self._sf.__func__ is ReferenceDistribution._sf)
+                and (self._cdf.__func__ is not ReferenceDistribution._cdf)):
+            return mp.one - self._cdf(x, **kwargs)
+
+        a, b = self._support(**kwargs)
+        res = mp.quad(lambda x: self._pdf(x, **kwargs), (x, b))
+        res = res if res < 0.5 else mp.one - self._cdf(x, **kwargs)
+        return res
+
+    def _ppf(self, p, guess=0, **kwargs):
+        if ((self._ppf.__func__ is ReferenceDistribution._ppf)
+                and (self._isf.__func__ is not ReferenceDistribution._isf)):
+            return self._isf(mp.one - p, guess, **kwargs)
+        def f(x):
+            return self._cdf(x, **kwargs) - p
+        return mp.findroot(f, guess)
+
+    def _isf(self, p, guess=0, **kwargs):
+        if ((self._isf.__func__ is ReferenceDistribution._isf)
+                and (self._ppf.__func__ is not ReferenceDistribution._ppf)):
+            return self._ppf(mp.one - p, guess, **kwargs)
+        def f(x):
+            return self._sf(x, **kwargs) - p
+        return mp.findroot(f, guess)
+
+    def _logpdf(self, x, **kwargs):
+        return mp.log(self._pdf(x, **kwargs))
+
+    def _logcdf(self, x, **kwargs):
+        return mp.log(self._cdf(x, **kwargs))
+
+    def _logsf(self, x, **kwargs):
+        return mp.log(self._sf(x, **kwargs))
+
+    def _support(self, **kwargs):
+        return -mp.inf, mp.inf
+
+    def _entropy(self, **kwargs):
+        def integrand(x):
+            logpdf = self._logpdf(x, **kwargs)
+            pdf = mp.exp(logpdf)
+            return  -pdf*logpdf
+
+        a, b = self._support(**kwargs)
+        return mp.quad(integrand, (a, b))
+
+    def _mean(self, **kwargs):
+        return self._moment(order=1, center=0, **kwargs)
+
+    def _var(self, **kwargs):
+        mu = self._mean(**kwargs)
+        return self._moment(order=2, center=mu, **kwargs)
+
+    def _skew(self, **kwargs):
+        mu = self._mean(**kwargs)
+        u2 = self._moment(order=2, center=mu, **kwargs)
+        sigma = mp.sqrt(u2)
+        u3 = self._moment(order=3, center=mu, **kwargs)
+        return u3 / sigma**3
+
+    def _kurtosis(self, **kwargs):
+        mu = self._mean(**kwargs)
+        u2 = self._moment(order=2, center=mu, **kwargs)
+        u4 = self._moment(order=4, center=mu, **kwargs)
+        return u4 / u2**2 - 3
+
+    def _moment(self, order, center, **kwargs):
+        def integrand(x):
+            return self._pdf(x, **kwargs)*(x - center)**order
+
+        if center is None:
+            center = self._mean(**kwargs)
+
+        a, b = self._support(**kwargs)
+        return mp.quad(integrand, (a, b))
+
+    def pdf(self, x, dtype=np.float64):
+        fun = np.vectorize(self._pdf)
+        x = self._make_mpf_array(x)
+        res = fun(x, **self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def cdf(self, x, dtype=np.float64):
+        fun = np.vectorize(self._cdf)
+        x = self._make_mpf_array(x)
+        res = fun(x, **self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def sf(self, x, dtype=np.float64):
+        fun = np.vectorize(self._sf)
+        x = self._make_mpf_array(x)
+        res = fun(x, **self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def ppf(self, x, guess=0, dtype=np.float64):
+        fun = np.vectorize(self._ppf, excluded={1})  # don't vectorize guess
+        x = self._make_mpf_array(x)
+        res = fun(x, guess, **self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def isf(self, x, guess=0, dtype=np.float64):
+        fun = np.vectorize(self._isf, excluded={1})  # don't vectorize guess
+        x = self._make_mpf_array(x)
+        res = fun(x, guess, **self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def logpdf(self, x, dtype=np.float64):
+        fun = np.vectorize(self._logpdf)
+        x = self._make_mpf_array(x)
+        res = fun(x, **self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def logcdf(self, x, dtype=np.float64):
+        fun = np.vectorize(self._logcdf)
+        x = self._make_mpf_array(x)
+        res = fun(x, **self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def logsf(self, x, dtype=np.float64):
+        fun = np.vectorize(self._logsf)
+        x = self._make_mpf_array(x)
+        res = fun(x, **self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def support(self, dtype=np.float64):
+        fun = np.vectorize(self._support)
+        res = fun(**self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def entropy(self, dtype=np.float64):
+        fun = np.vectorize(self._entropy)
+        res = fun(**self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def mean(self, dtype=np.float64):
+        fun = np.vectorize(self._mean)
+        res = fun(**self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def var(self, dtype=np.float64):
+        fun = np.vectorize(self._var)
+        res = fun(**self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def skew(self, dtype=np.float64):
+        fun = np.vectorize(self._skew)
+        res = fun(**self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def kurtosis(self, dtype=np.float64):
+        fun = np.vectorize(self._kurtosis)
+        res = fun(**self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+    def moment(self, order, center=None, dtype=np.float64):
+        fun = np.vectorize(self._moment)
+        order = self._make_mpf_array(order)
+        res = fun(order, **self._params)
+        return np.asarray(res, dtype=dtype)[()]
+
+
+class SkewNormal(ReferenceDistribution):
+    """Reference implementation of the SkewNormal distribution.
+
+    Follow the example here to generate new reference distributions.
+    Use the reference distributions to generate reference values of
+    distributions functions. For now, copy-paste the output into unit
+    tests. Full code to generate reference values does not need to be
+    included as a comment in the test; just refer to the reference
+    distribution used and the settings (e.g. mp.dps=50).
+    """
+
+    def __init__(self, *, a):
+        # Overriding __init__ is not necessary, but it allows IDEs to hint at
+        # shape parameters. All parameters are keyword only to avoid the
+        # ambiguity inherent in positional arguments. The infrastructure does
+        # not take care of location and scale; nonetheless, assume standard
+        # location and scale. Typically, there is no need to test the SciPy
+        # distribution infrastructure's treatment of location and scale
+        # separately for a specific distribution.
+        super().__init__(a=a)
+
+    def _support(self, a):
+        # Override _support if the support of the distribution is a subset of
+        # the real line
+        return -mp.inf, mp.inf
+
+    def _pdf(self, x, a):
+        # Write PDFs following a scholarly reference as closely as possible.
+        # Trust mpmath for the accuracy, and don't worry about speed. What's
+        # important is the ease of verifying the PDF against the reference. If
+        # the result is inaccurate, it will not match SciPy's output (whether
+        # SciPy is accurate or not). If this occurs, try increasing dps before
+        # implementing a numerically favorable (but presumably more complex)
+        # implementation.
+        return 2 * mp.npdf(x) * mp.ncdf(a * x)
+
+    # Avoid overriding other methods unless the generic implementation is
+    # believed to be inaccurate (e.g. due to numerical difficulties) or it is
+    # too slow. Why? Less code to write, less code to review, and a guarantee
+    # that there is no *mistake* in the implementation (e.g. wrong formula).
+
+
+class BetaPrime(ReferenceDistribution):
+
+    def __init__(self, *, a, b):
+        super().__init__(a=a, b=b)
+
+    def _support(self, **kwargs):
+        return mp.zero, mp.inf
+
+    def _logpdf(self, x, a, b):
+        return (a - mp.one)*mp.log(x) - (a + b)*mp.log1p(x) - mp.log(mp.beta(a, b))
+
+    def _pdf(self, x, a, b):
+        return mp.exp(self._logpdf(x=x, a=a, b=b))
+
+    def _sf(self, x, a, b):
+        return 1.0 - mp.betainc(a, b, 0, x/(1+x), regularized=True)
+
+
+class Burr(ReferenceDistribution):
+
+    def __init__(self, *, c, d):
+        super().__init__(c=c, d=d)
+
+    def _support(self, c, d):
+        return 0, mp.inf
+
+    def _pdf(self, x, c, d):
+        return c * d * x ** (-c - 1) * (1 + x ** (-c)) ** (-d - 1)
+
+    def _ppf(self, p, guess, c, d):
+        return (p**(-1.0/d) - 1)**(-1.0/c)
+
+
+class LogLaplace(ReferenceDistribution):
+
+    def __init__(self, *, c):
+        super().__init__(c=c)
+
+    def _support(self, c):
+        return 0, mp.inf
+
+    def _pdf(self, x, c):
+        if x < mp.one:
+            return c / 2 * x**(c - mp.one)
+        else:
+            return c / 2 * x**(-c - mp.one)
+
+    def _ppf(self, q, guess, c):
+        if q < 0.5:
+            return (2.0 * q)**(mp.one / c)
+        else:
+            return (2 * (mp.one - q))**(-mp.one / c)
+
+
+class LogNormal(ReferenceDistribution):
+
+    def __init__(self, *, s):
+        super().__init__(s=s)
+
+    def _support(self, s):
+        return 0, mp.inf
+
+    def _pdf(self, x, s):
+        return (
+            mp.one / (s * x * mp.sqrt(2 * mp.pi))
+            * mp.exp(-mp.one / 2 * (mp.log(x) / s)**2)
+        )
+
+    def _cdf(self, x, s):
+        return mp.ncdf(mp.log(x) / s)
+
+
+class Normal(ReferenceDistribution):
+
+    def _pdf(self, x):
+        return mp.npdf(x)
+
+
+class NormInvGauss(ReferenceDistribution):
+
+    def __init__(self, *, alpha, beta):
+        super().__init__(alpha=alpha, beta=beta)
+
+    def _pdf(self, x, alpha, beta):
+        # Implemented as described in https://www.jstor.org/stable/4616433
+        # Equations 2.1 - 2.3
+        q = mp.sqrt(1 + x**2)
+        a = mp.pi**-1 * alpha * mp.exp(mp.sqrt(alpha**2 - beta**2))
+        return a * q**-1 * mp.besselk(1, alpha*q) * mp.exp(beta*x)
+
+
+class Pearson3(ReferenceDistribution):
+    def __init__(self, *, skew):
+        super().__init__(skew=skew)
+
+    def _pdf(self, x, skew):
+        b = 2 / skew
+        a = b**2
+        c = -b
+        res = abs(b)/mp.gamma(a) * (b*(x-c))**(a-1) * mp.exp(-b*(x-c))
+        return res if abs(res.real) == res else 0
+
+
+class StudentT(ReferenceDistribution):
+
+    def __init(self, *, df):
+        super().__init__(df=df)
+
+    def _pdf(self, x, df):
+        return (mp.gamma((df + mp.one)/2)/(mp.sqrt(df * mp.pi) * mp.gamma(df/2))
+                * (mp.one + x*x/df)**(-(df + mp.one)/2))
+
+
+class TruncExpon(ReferenceDistribution):
+
+    def __init__(self, *, b):
+        super().__init__(b=b)
+
+    def _support(self, b):
+        return 0, b
+
+    def _pdf(self, x, b):
+        return -mp.exp(-x)/mp.expm1(-b)
+
+    def _sf(self, x, b):
+        return (mp.exp(-b) - mp.exp(-x))/mp.expm1(-b)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_generation/studentized_range_mpmath_ref.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_generation/studentized_range_mpmath_ref.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f67a3453e08561cfab7f2d85b34a58d7e345992
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_generation/studentized_range_mpmath_ref.py
@@ -0,0 +1,252 @@
+# To run this script, run
+# `python studentized_range_mpmath_ref.py`
+# in the "scipy/stats/tests/" directory
+
+# This script generates a JSON file "./data/studentized_range_mpmath_ref.json"
+# that is used to compare the accuracy of `studentized_range` functions against
+# precise (20 DOP) results generated using `mpmath`.
+
+# Equations in this file have been taken from
+# https://en.wikipedia.org/wiki/Studentized_range_distribution
+# and have been checked against the following reference:
+# Lund, R. E., and J. R. Lund. "Algorithm AS 190: Probabilities and
+# Upper Quantiles for the Studentized Range." Journal of the Royal
+# Statistical Society. Series C (Applied Statistics), vol. 32, no. 2,
+# 1983, pp. 204-210. JSTOR, www.jstor.org/stable/2347300. Accessed 18
+# Feb. 2021.
+
+# Note: I would have preferred to use pickle rather than JSON, but -
+# due to security concerns - decided against it.
+import itertools
+from collections import namedtuple
+import json
+import time
+
+import os
+from multiprocessing import Pool, cpu_count
+
+from mpmath import gamma, pi, sqrt, quad, inf, mpf, mp
+from mpmath import npdf as phi
+from mpmath import ncdf as Phi
+
+results_filepath = "data/studentized_range_mpmath_ref.json"
+num_pools = max(cpu_count() - 1, 1)
+
+MPResult = namedtuple("MPResult", ["src_case", "mp_result"])
+
+CdfCase = namedtuple("CdfCase",
+                     ["q", "k", "v", "expected_atol", "expected_rtol"])
+
+MomentCase = namedtuple("MomentCase",
+                        ["m", "k", "v", "expected_atol", "expected_rtol"])
+
+# Load previously generated JSON results, or init a new dict if none exist
+if os.path.isfile(results_filepath):
+    res_dict = json.load(open(results_filepath))
+else:
+    res_dict = dict()
+
+# Frame out data structure. Store data with the function type as a top level
+# key to allow future expansion
+res_dict["COMMENT"] = ("!!!!!! THIS FILE WAS AUTOGENERATED BY RUNNING "
+                       "`python studentized_range_mpmath_ref.py` !!!!!!")
+res_dict.setdefault("cdf_data", [])
+res_dict.setdefault("pdf_data", [])
+res_dict.setdefault("moment_data", [])
+
+general_atol, general_rtol = 1e-11, 1e-11
+
+mp.dps = 24
+
+cp_q = [0.1, 1, 4, 10]
+cp_k = [3, 10, 20]
+cp_nu = [3, 10, 20, 50, 100, 120]
+
+cdf_pdf_cases = [
+    CdfCase(*case,
+            general_atol,
+            general_rtol)
+    for case in
+    itertools.product(cp_q, cp_k, cp_nu)
+]
+
+mom_atol, mom_rtol = 1e-9, 1e-9
+# These are EXTREMELY slow - Multiple days each in worst case.
+moment_cases = [
+    MomentCase(i, 3, 10, mom_atol, mom_rtol)
+    for i in range(5)
+]
+
+
+def write_data():
+    """Writes the current res_dict to the target JSON file"""
+    with open(results_filepath, mode="w") as f:
+        json.dump(res_dict, f, indent=2)
+
+
+def to_dict(named_tuple):
+    """Converts a namedtuple to a dict"""
+    return dict(named_tuple._asdict())
+
+
+def mp_res_to_dict(mp_result):
+    """Formats an MPResult namedtuple into a dict for JSON dumping"""
+    return {
+        "src_case": to_dict(mp_result.src_case),
+
+        # np assert can't handle mpf, so take the accuracy hit here.
+        "mp_result": float(mp_result.mp_result)
+    }
+
+
+def cdf_mp(q, k, nu):
+    """Straightforward implementation of studentized range CDF"""
+    q, k, nu = mpf(q), mpf(k), mpf(nu)
+
+    def inner(s, z):
+        return phi(z) * (Phi(z + q * s) - Phi(z)) ** (k - 1)
+
+    def outer(s, z):
+        return s ** (nu - 1) * phi(sqrt(nu) * s) * inner(s, z)
+
+    def whole(s, z):
+        return (sqrt(2 * pi) * k * nu ** (nu / 2)
+                / (gamma(nu / 2) * 2 ** (nu / 2 - 1)) * outer(s, z))
+
+    res = quad(whole, [0, inf], [-inf, inf],
+               method="gauss-legendre", maxdegree=10)
+    return res
+
+
+def pdf_mp(q, k, nu):
+    """Straightforward implementation of studentized range PDF"""
+    q, k, nu = mpf(q), mpf(k), mpf(nu)
+
+    def inner(s, z):
+        return phi(z + q * s) * phi(z) * (Phi(z + q * s) - Phi(z)) ** (k - 2)
+
+    def outer(s, z):
+        return s ** nu * phi(sqrt(nu) * s) * inner(s, z)
+
+    def whole(s, z):
+        return (sqrt(2 * pi) * k * (k - 1) * nu ** (nu / 2)
+                / (gamma(nu / 2) * 2 ** (nu / 2 - 1)) * outer(s, z))
+
+    res = quad(whole, [0, inf], [-inf, inf],
+               method="gauss-legendre", maxdegree=10)
+    return res
+
+
+def moment_mp(m, k, nu):
+    """Implementation of the studentized range moment"""
+    m, k, nu = mpf(m), mpf(k), mpf(nu)
+
+    def inner(q, s, z):
+        return phi(z + q * s) * phi(z) * (Phi(z + q * s) - Phi(z)) ** (k - 2)
+
+    def outer(q, s, z):
+        return s ** nu * phi(sqrt(nu) * s) * inner(q, s, z)
+
+    def pdf(q, s, z):
+        return (sqrt(2 * pi) * k * (k - 1) * nu ** (nu / 2)
+                / (gamma(nu / 2) * 2 ** (nu / 2 - 1)) * outer(q, s, z))
+
+    def whole(q, s, z):
+        return q ** m * pdf(q, s, z)
+
+    res = quad(whole, [0, inf], [0, inf], [-inf, inf],
+               method="gauss-legendre", maxdegree=10)
+    return res
+
+
+def result_exists(set_key, case):
+    """Searches the results dict for a result in the set that matches a case.
+    Returns True if such a case exists."""
+    if set_key not in res_dict:
+        raise ValueError(f"{set_key} not present in data structure!")
+
+    case_dict = to_dict(case)
+    existing_res = list(filter(
+        lambda res: res["src_case"] == case_dict,  # dict comparison
+        res_dict[set_key]))
+
+    return len(existing_res) > 0
+
+
+def run(case, run_lambda, set_key, index=0, total_cases=0):
+    """Runs the single passed case, returning an mp dictionary and index"""
+    t_start = time.perf_counter()
+
+    res = run_lambda(case)
+
+    print(f"Finished {index + 1}/{total_cases} in batch. "
+          f"(Took {time.perf_counter() - t_start}s)")
+
+    return index, set_key, mp_res_to_dict(MPResult(case, res))
+
+
+def write_result(res):
+    """A callback for completed jobs. Inserts and writes a calculated result
+     to file."""
+    index, set_key, result_dict = res
+    res_dict[set_key].insert(index, result_dict)
+    write_data()
+
+
+def run_cases(cases, run_lambda, set_key):
+    """Runs an array of cases and writes to file"""
+    # Generate jobs to run from cases that do not have a result in
+    # the previously loaded JSON.
+    job_arg = [(case, run_lambda, set_key, index, len(cases))
+               for index, case in enumerate(cases)
+               if not result_exists(set_key, case)]
+
+    print(f"{len(cases) - len(job_arg)}/{len(cases)} cases won't be "
+          f"calculated because their results already exist.")
+
+    jobs = []
+    pool = Pool(num_pools)
+
+    # Run all using multiprocess
+    for case in job_arg:
+        jobs.append(pool.apply_async(run, args=case, callback=write_result))
+
+    pool.close()
+    pool.join()
+
+
+def run_pdf(case):
+    return pdf_mp(case.q, case.k, case.v)
+
+
+def run_cdf(case):
+    return cdf_mp(case.q, case.k, case.v)
+
+
+def run_moment(case):
+    return moment_mp(case.m, case.k, case.v)
+
+
+def main():
+    t_start = time.perf_counter()
+
+    total_cases = 2 * len(cdf_pdf_cases) + len(moment_cases)
+    print(f"Processing {total_cases} test cases")
+
+    print(f"Running 1st batch ({len(cdf_pdf_cases)} PDF cases). "
+          f"These take about 30s each.")
+    run_cases(cdf_pdf_cases, run_pdf, "pdf_data")
+
+    print(f"Running 2nd batch ({len(cdf_pdf_cases)} CDF cases). "
+          f"These take about 30s each.")
+    run_cases(cdf_pdf_cases, run_cdf, "cdf_data")
+
+    print(f"Running 3rd batch ({len(moment_cases)} moment cases). "
+          f"These take about anywhere from a few hours to days each.")
+    run_cases(moment_cases, run_moment, "moment_data")
+
+    print(f"Test data generated in {time.perf_counter() - t_start}s")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_hypotests.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_hypotests.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5762185f7b8b4363e469c3974b20106fad7cb0b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_hypotests.py
@@ -0,0 +1,2248 @@
+from itertools import product
+
+import numpy as np
+import functools
+import pytest
+from numpy.testing import assert_, assert_equal, assert_allclose
+from pytest import raises as assert_raises
+
+from scipy import stats, special
+from scipy.stats import distributions
+from scipy.stats._hypotests import (epps_singleton_2samp, cramervonmises,
+                                    _cdf_cvm, cramervonmises_2samp,
+                                    _pval_cvm_2samp_exact, barnard_exact,
+                                    boschloo_exact)
+from scipy.stats._mannwhitneyu import mannwhitneyu, _mwu_state, _MWU
+from scipy._lib._testutils import _TestPythranFunc
+from scipy._external import array_api_extra as xpx
+from scipy._lib._array_api import (make_xp_test_case, xp_default_dtype, is_numpy,
+                                   eager_warns, xp_ravel, is_jax)
+from scipy._lib._array_api_no_0d import xp_assert_equal, xp_assert_close
+from scipy.stats._axis_nan_policy import SmallSampleWarning, too_small_1d_not_omit
+
+lazy_xp_modules = [stats]
+skip_xp_backends = pytest.mark.skip_xp_backends
+
+
+@make_xp_test_case(epps_singleton_2samp)
+class TestEppsSingleton:
+    def test_epps_singleton_iv(self, xp):
+        # partial input validation test relevant to the addition of JIT support
+        # TODO: complete input validation tests
+        x = xp.asarray([-0.35, 2.55, 1.73, 0.73, 0.35])
+        y = xp.asarray([-1.15, -0.15, 2.48, 3.25, 3.71])
+        t = xp.asarray([0.4, -0.4])
+        if is_jax(xp):
+            w, p = epps_singleton_2samp(x, y, t=t)
+            xp_assert_equal(w, xp.asarray(xp.nan))
+            xp_assert_equal(p, xp.asarray(xp.nan))
+        else:
+            message = "t must contain positive elements only."
+            with pytest.raises(ValueError, match=message):
+                epps_singleton_2samp(x, y, t=t)
+
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_statistic_1(self, dtype, xp):
+        # first example in Goerg & Kaiser, also in original paper of
+        # Epps & Singleton. Note: values do not match exactly, the
+        # value of the interquartile range varies depending on how
+        # quantiles are computed
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        x = xp.asarray([-0.35, 2.55, 1.73, 0.73, 0.35,
+                        2.69, 0.46, -0.94, -0.37, 12.07], dtype=dtype)
+        y = xp.asarray([-1.15, -0.15, 2.48, 3.25, 3.71,
+                     4.29, 5.00, 7.74, 8.38, 8.60], dtype=dtype)
+        w, p = epps_singleton_2samp(x, y)
+        xp_assert_close(w, xp.asarray(15.14, dtype=dtype), atol=0.03)
+        xp_assert_close(p, xp.asarray(0.00442, dtype=dtype), atol=0.0001)
+
+    def test_statistic_2(self, xp):
+        # second example in Goerg & Kaiser, again not a perfect match
+        x = xp.asarray([0, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4,
+                        5, 5, 5, 5, 6, 10, 10, 10, 10])
+        y = xp.asarray([10, 4, 0, 5, 10, 10, 0, 5, 6, 7,
+                        10, 3, 1, 7, 0, 8, 1, 5, 8, 10])
+        w, p = epps_singleton_2samp(x, y)
+        xp_assert_close(w, xp.asarray(8.900), atol=1e-3)
+        xp_assert_close(p, xp.asarray(0.06364), atol=5e-5)
+
+    def test_epps_singleton_array_like(self):  # only relevant for NumPy
+        x, y = np.arange(30), np.arange(28)
+
+        w1, p1 = epps_singleton_2samp(list(x), list(y))
+        w2, p2 = epps_singleton_2samp(tuple(x), tuple(y))
+        w3, p3 = epps_singleton_2samp(x, y)
+
+        assert_(w1 == w2 == w3)
+        assert_(p1 == p2 == p3)
+
+    def test_epps_singleton_size(self, xp):
+        # warns if sample contains fewer than 5 elements
+        x, y = xp.asarray([1, 2, 3, 4]), xp.arange(10)
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = epps_singleton_2samp(x, y)
+            xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+            xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+    def test_epps_singleton_nonfinite(self, xp):
+        rng = np.random.default_rng(83249872384543)
+        x = rng.random(size=(10, 11))
+        y = rng.random(size=(10, 12))
+        i = np.asarray([1, 4, 9])  # arbitrary rows
+
+        w_ref, p_ref = epps_singleton_2samp(x, y, axis=-1)
+        w_ref[i] = np.nan
+        p_ref[i] = np.nan
+
+        x[i[0], 0] = np.nan
+        x[i[1], 1] = np.inf
+        y[i[2], 2] = -np.inf
+
+        x, y = xp.asarray(x), xp.asarray(y)
+        w_res, p_res = epps_singleton_2samp(x, y, axis=-1)
+        xp_assert_close(w_res, xp.asarray(w_ref))
+        xp_assert_close(p_res, xp.asarray(p_ref))
+
+
+@make_xp_test_case(stats.cramervonmises)
+class TestCvm:
+    # the expected values of the cdfs are taken from Table 1 in
+    # Csorgo / Faraway: The Exact and Asymptotic Distribution of
+    # Cramér-von Mises Statistics, 1996.
+    @pytest.mark.parametrize('n, x, ref', [
+        (4, [0.02983, 0.04111, 0.12331, 0.94251], [0.01, 0.05, 0.5, 0.999]),
+        (10, [0.02657, 0.03830, 0.12068, 0.56643], [0.01, 0.05, 0.5, 0.975]),
+        (1000, [0.02481, 0.03658, 0.11889, 1.16120], [0.01, 0.05, 0.5, 0.999]),
+        (None, [0.02480, 0.03656, 0.11888, 1.16204], [0.01, 0.05, 0.5, 0.999]),
+    ])
+    def test_cdf_ref(self, n, x, ref, xp):
+        xp_assert_close(_cdf_cvm(xp.asarray(x), n), xp.asarray(ref), atol=1e-4)
+
+    def test_cdf_support(self, xp):
+        # cdf has support on [1/(12*n), n/3]
+        xp_assert_equal(_cdf_cvm(xp.asarray([1/(12*533), 533/3]), 533),
+                        xp.asarray([0., 1.]))
+        xp_assert_equal(_cdf_cvm(xp.asarray([1/(12*(27 + 1)), (27 + 1)/3]), 27),
+                        xp.asarray([0., 1.]))
+
+    def test_cdf_large_n(self, xp):
+        # test that asymptotic cdf and cdf for large samples are close
+        x = xp.asarray([0.02480, 0.03656, 0.11888, 1.16204, 100])
+        xp_assert_close(_cdf_cvm(x, 10000), _cdf_cvm(x), atol=1e-4)
+
+    def test_large_x(self, xp):
+        # for large values of x and n, the series used to compute the cdf
+        # converges slowly.
+        # this leads to bug in R package goftest and MAPLE code that is
+        # the basis of the implementation in scipy
+        # note: cdf = 1 for x >= 1000/3 and n = 1000
+        x = xp.asarray(333.3, dtype=xp.float64)
+        assert (0.99999 < _cdf_cvm(x, 1000) < 1.0)
+        assert (0.99999 < _cdf_cvm(x) < 1.0)
+
+    def test_low_p(self, xp):
+        # _cdf_cvm can return values larger than 1. In that case, we just
+        # return a p-value of zero.
+        n = 12
+        res = cramervonmises(xp.ones(n)*0.8, special.ndtr)
+        assert _cdf_cvm(res.statistic, n, xp=xp) > 1.0
+        xp_assert_equal(res.pvalue, xp.asarray(0.))
+
+    @pytest.mark.parametrize('x', [(), [1.5]])
+    def test_invalid_input(self, x, xp):
+        with pytest.warns(SmallSampleWarning, match=too_small_1d_not_omit):
+            res = cramervonmises(xp.asarray(x), special.ndtr)
+            xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+            xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_values_R(self, dtype, xp):
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        # compared against R package goftest, version 1.1.1
+        # library(goftest)
+        # options(digits=16)
+        # cvm.test(c(-1.7, 2, 0, 1.3, 4, 0.1, 0.6), "pnorm")
+        res = cramervonmises(xp.asarray([-1.7, 2, 0, 1.3, 4, 0.1, 0.6], dtype=dtype),
+                             special.ndtr)
+        xp_assert_close(res.statistic, xp.asarray(0.28815604599198, dtype=dtype))
+        xp_assert_close(res.pvalue, xp.asarray( 0.1453465252039, dtype=dtype))
+
+        # cvm.test(c(-1.7, 2, 0, 1.3, 4, 0.1, 0.6), "pnorm", mean = 3, sd = 1.5)
+        res = cramervonmises(xp.asarray([-1.7, 2, 0, 1.3, 4, 0.1, 0.6], dtype=dtype),
+                             lambda x: special.ndtr((x - 3)/1.5))
+        xp_assert_close(res.statistic, xp.asarray(0.94266847977072, dtype=dtype))
+        xp_assert_close(res.pvalue, xp.asarray(0.002026416728467, dtype=dtype))
+
+        # cvm.test(c(1, 2, 5, 1.4, 0.14, 11, 13, 0.9, 7.5), "pexp")
+        res = cramervonmises(
+            xp.asarray([1, 2, 5, 1.4, 0.14, 11, 13, 0.9, 7.5], dtype=dtype),
+            lambda x: -xp.expm1(-x))
+        xp_assert_close(res.statistic, xp.asarray(0.84218540922393, dtype=dtype))
+        xp_assert_close(res.pvalue, xp.asarray(0.004433406063014, dtype=dtype))
+
+    def test_str_cdf(self, xp):
+        if not is_numpy(xp):
+            message = "`cdf` must be a callable if `rvs` is a non-NumPy array."
+            with pytest.raises(ValueError, match=message):
+                cramervonmises(xp.asarray([1, 2, 3]), "beta")
+            return
+
+        x, args = np.arange(5), (1.4, 0.7)
+        ref = cramervonmises(x, distributions.expon.cdf)
+        res = cramervonmises(x, "expon")
+        assert_equal((res.statistic, res.pvalue), (ref.statistic, ref.pvalue))
+
+        ref = cramervonmises(x, distributions.beta.cdf, args)
+        res = cramervonmises(x, "beta", args)
+        assert_equal((res.statistic, res.pvalue), (ref.statistic, ref.pvalue))
+
+
+@make_xp_test_case(stats.mannwhitneyu)
+class TestMannWhitneyU:
+
+    # All magic numbers are from R wilcox.test unless otherwise specified
+    # https://rdrr.io/r/stats/wilcox.test.html
+
+    # --- Test Input Validation ---
+
+    def get_zstatistic_from_pvalue(self, p, U, n1, n2, alternative):
+        if alternative == 'less':
+            z = special.ndtri(p)
+        elif alternative == 'greater':
+            z = -special.ndtri(p)
+        else:
+            z = special.ndtri(p/2) if U < n1*n2/2 else -special.ndtri(p/2)
+        return z
+
+    def test_empty(self, xp):
+        x = xp.asarray([1, 2])  # generic, valid inputs
+        y = xp.asarray([3, 4])
+        empty = xp.asarray([], dtype=x.dtype)
+        nan = xp.asarray(xp.nan)
+
+        with pytest.warns(SmallSampleWarning, match=too_small_1d_not_omit):
+            res = mannwhitneyu(x, empty)
+            xp_assert_close(res.statistic, nan)
+            xp_assert_close(res.pvalue, nan)
+
+        with pytest.warns(SmallSampleWarning, match=too_small_1d_not_omit):
+            res = mannwhitneyu(empty, y)
+            xp_assert_close(res.statistic, nan)
+            xp_assert_close(res.pvalue, nan)
+
+        with pytest.warns(SmallSampleWarning, match=too_small_1d_not_omit):
+            res = mannwhitneyu(empty, empty)
+            xp_assert_close(res.statistic, nan)
+            xp_assert_close(res.pvalue, nan)
+
+    def test_input_validation(self, xp):
+        x = xp.asarray([1, 2])  # generic, valid inputs
+        y = xp.asarray([3, 4])
+        with assert_raises(ValueError, match="`use_continuity` must be one"):
+            mannwhitneyu(x, y, use_continuity='ekki')
+        with assert_raises(ValueError, match="`alternative` must be one of"):
+            mannwhitneyu(x, y, alternative='ekki')
+        with assert_raises(ValueError, match="`axis` must be an integer"):
+            mannwhitneyu(x, y, axis=1.5)
+        with assert_raises(ValueError, match="`method` must be one of"):
+            mannwhitneyu(x, y, method='ekki')
+
+    def test_auto(self, xp):
+        # Test that default method ('auto') chooses intended method
+        if is_jax(xp):
+            message = "`method='auto'` is incompatible with JAX arrays."
+            with pytest.raises(ValueError, match=message):
+                mannwhitneyu(xp.arange(10), xp.arange(10))
+            return
+
+        rng = np.random.default_rng(923823782530925934)
+        n = 8  # threshold to switch from exact to asymptotic
+
+        # both inputs are smaller than threshold; should use exact
+        x = xp.asarray(rng.random(n-1))
+        y = xp.asarray(rng.random(n-1))
+        auto = mannwhitneyu(x, y)
+        asymptotic = mannwhitneyu(x, y, method='asymptotic')
+        exact = mannwhitneyu(x, y, method='exact')
+        assert auto.pvalue == exact.pvalue
+        assert auto.pvalue != asymptotic.pvalue
+
+        # one input is smaller than threshold; should use exact
+        x = xp.asarray(rng.random(n-1))
+        y = xp.asarray(rng.random(n+1))
+        auto = mannwhitneyu(x, y)
+        asymptotic = mannwhitneyu(x, y, method='asymptotic')
+        exact = mannwhitneyu(x, y, method='exact')
+        assert auto.pvalue == exact.pvalue
+        assert auto.pvalue != asymptotic.pvalue
+
+        # other input is smaller than threshold; should use exact
+        auto = mannwhitneyu(y, x)
+        asymptotic = mannwhitneyu(x, y, method='asymptotic')
+        exact = mannwhitneyu(x, y, method='exact')
+        assert auto.pvalue == exact.pvalue
+        assert auto.pvalue != asymptotic.pvalue
+
+        # both inputs are larger than threshold; should use asymptotic
+        x = xp.asarray(rng.random(n+1))
+        y = xp.asarray(rng.random(n+1))
+        auto = mannwhitneyu(x, y)
+        asymptotic = mannwhitneyu(x, y, method='asymptotic')
+        exact = mannwhitneyu(x, y, method='exact')
+        assert auto.pvalue != exact.pvalue
+        assert auto.pvalue == asymptotic.pvalue
+
+        # both inputs are smaller than threshold, but there is a tie
+        # should use asymptotic
+        x = xp.asarray(rng.random(n-1))
+        y = xp.asarray(rng.random(n-1))
+        y = xpx.at(y)[3].set(x[3])
+        auto = mannwhitneyu(x, y)
+        asymptotic = mannwhitneyu(x, y, method='asymptotic')
+        exact = mannwhitneyu(x, y, method='exact')
+        assert auto.pvalue != exact.pvalue
+        assert auto.pvalue == asymptotic.pvalue
+
+    # --- Test Basic Functionality ---
+
+    x = [210.052110, 110.190630, 307.918612]
+    y = [436.08811482466416, 416.37397329768191, 179.96975939463582,
+         197.8118754228619, 34.038757281225756, 138.54220550921517,
+         128.7769351470246, 265.92721427951852, 275.6617533155341,
+         592.34083395416258, 448.73177590617018, 300.61495185038905,
+         187.97508449019588]
+
+    # This test was written for mann_whitney_u in gh-4933.
+    # Originally, the p-values for alternatives were swapped;
+    # this has been corrected and the tests have been refactored for
+    # compactness, but otherwise the tests are unchanged.
+    # R code for comparison, e.g.:
+    # options(digits = 16)
+    # x = c(210.052110, 110.190630, 307.918612)
+    # y = c(436.08811482466416, 416.37397329768191, 179.96975939463582,
+    #       197.8118754228619, 34.038757281225756, 138.54220550921517,
+    #       128.7769351470246, 265.92721427951852, 275.6617533155341,
+    #       592.34083395416258, 448.73177590617018, 300.61495185038905,
+    #       187.97508449019588)
+    # wilcox.test(x, y, alternative="g", exact=TRUE)
+    cases_basic = [[{"alternative": 'two-sided', "method": "asymptotic"},
+                    (16., 0.6865041817876)],
+                   [{"alternative": 'less', "method": "asymptotic"},
+                    (16., 0.3432520908938)],
+                   [{"alternative": 'greater', "method": "asymptotic"},
+                    (16., 0.7047591913255)],
+                   [{"alternative": 'two-sided', "method": "exact"},
+                    (16., 0.7035714285714)],
+                   [{"alternative": 'less', "method": "exact"},
+                    (16., 0.3517857142857)],
+                   [{"alternative": 'greater', "method": "exact"},
+                    (16., 0.6946428571429)]]
+
+    @pytest.mark.parametrize(("kwds", "expected"), cases_basic)
+    @pytest.mark.parametrize("dtype", [None, 'float32', 'float64'])
+    def test_basic(self, kwds, expected, dtype, xp):
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        x, y = xp.asarray(self.x, dtype=dtype), xp.asarray(self.y, dtype=dtype)
+        res = mannwhitneyu(x, y, **kwds)
+        xp_assert_close(res.statistic, xp.asarray(expected[0], dtype=dtype))
+        xp_assert_close(res.pvalue, xp.asarray(expected[1], dtype=dtype))
+        if kwds['method'] == 'asymptotic':
+            z = self.get_zstatistic_from_pvalue(res.pvalue, res.statistic,
+                                                x.shape[0], y.shape[0],
+                                                alternative=kwds['alternative'])
+            xp_assert_close(res.zstatistic, xp.asarray(z, dtype=dtype))
+
+    cases_continuity = [[{"alternative": 'two-sided', "use_continuity": True},
+                         (23., 0.6865041817876)],
+                        [{"alternative": 'less', "use_continuity": True},
+                         (23., 0.7047591913255)],
+                        [{"alternative": 'greater', "use_continuity": True},
+                         (23., 0.3432520908938)],
+                        [{"alternative": 'two-sided', "use_continuity": False},
+                         (23., 0.6377328900502)],
+                        [{"alternative": 'less', "use_continuity": False},
+                         (23., 0.6811335549749)],
+                        [{"alternative": 'greater', "use_continuity": False},
+                         (23., 0.3188664450251)]]
+
+    @pytest.mark.parametrize(("kwds", "expected"), cases_continuity)
+    def test_continuity(self, kwds, expected, xp):
+        # When x and y are interchanged, less and greater p-values should
+        # swap (compare to above). This wouldn't happen if the continuity
+        # correction were applied in the wrong direction. Note that less and
+        # greater p-values do not sum to 1 when continuity correction is on,
+        # which is what we'd expect. Also check that results match R when
+        # continuity correction is turned off.
+        # Note that method='asymptotic' -> exact=FALSE
+        # and use_continuity=False -> correct=FALSE, e.g.:
+        # wilcox.test(x, y, alternative="t", exact=FALSE, correct=FALSE)
+        x, y = xp.asarray(self.x), xp.asarray(self.y)
+        res = mannwhitneyu(y, x, method='asymptotic', **kwds)
+        xp_assert_close(res.statistic, xp.asarray(expected[0]))
+        xp_assert_close(res.pvalue, xp.asarray(expected[1]))
+        z = self.get_zstatistic_from_pvalue(res.pvalue, res.statistic,
+                                            x.shape[0], y.shape[0],
+                                            alternative=kwds['alternative'])
+        xp_assert_close(res.zstatistic, xp.asarray(z))
+
+    def test_tie_correct(self, xp):
+        # Test tie correction against R's wilcox.test
+        # options(digits = 16)
+        # x = c(1, 2, 3, 4)
+        # y = c(1, 2, 3, 4, 5)
+        # wilcox.test(x, y, exact=FALSE)
+        x = xp.asarray([1., 2., 3., 4.])
+        y0 = xp.asarray([1., 2., 3., 4., 5.])
+        dy = xp.asarray([0., 1., 0., 1., 0.])*0.01
+        dy2 = xp.asarray([0., 0., 1., 0., 0.])*0.01
+        y = xp.stack([y0-0.01, y0-dy, y0-dy2, y0, y0+dy2, y0+dy, y0+0.01])
+        res = mannwhitneyu(x, y, axis=-1, method="asymptotic")
+        U_expected = [10, 9, 8.5, 8, 7.5, 7, 6]
+        p_expected = [1, 0.9017048037317, 0.804080657472, 0.7086240584439,
+                      0.6197963884941, 0.5368784563079, 0.3912672792826]
+        xp_assert_equal(res.statistic, xp.asarray(U_expected))
+        xp_assert_close(res.pvalue, xp.asarray(p_expected))
+
+    # --- Test Exact Distribution of U ---
+
+    # These are tabulated values of the CDF of the exact distribution of
+    # the test statistic from pg 52 of reference [1] (Mann-Whitney Original)
+    pn3 = {1: [0.25, 0.5, 0.75], 2: [0.1, 0.2, 0.4, 0.6],
+           3: [0.05, .1, 0.2, 0.35, 0.5, 0.65]}
+    pn4 = {1: [0.2, 0.4, 0.6], 2: [0.067, 0.133, 0.267, 0.4, 0.6],
+           3: [0.028, 0.057, 0.114, 0.2, .314, 0.429, 0.571],
+           4: [0.014, 0.029, 0.057, 0.1, 0.171, 0.243, 0.343, 0.443, 0.557]}
+    pm5 = {1: [0.167, 0.333, 0.5, 0.667],
+           2: [0.047, 0.095, 0.19, 0.286, 0.429, 0.571],
+           3: [0.018, 0.036, 0.071, 0.125, 0.196, 0.286, 0.393, 0.5, 0.607],
+           4: [0.008, 0.016, 0.032, 0.056, 0.095, 0.143,
+               0.206, 0.278, 0.365, 0.452, 0.548],
+           5: [0.004, 0.008, 0.016, 0.028, 0.048, 0.075, 0.111,
+               0.155, 0.21, 0.274, 0.345, .421, 0.5, 0.579]}
+    pm6 = {1: [0.143, 0.286, 0.428, 0.571],
+           2: [0.036, 0.071, 0.143, 0.214, 0.321, 0.429, 0.571],
+           3: [0.012, 0.024, 0.048, 0.083, 0.131,
+               0.19, 0.274, 0.357, 0.452, 0.548],
+           4: [0.005, 0.01, 0.019, 0.033, 0.057, 0.086, 0.129,
+               0.176, 0.238, 0.305, 0.381, 0.457, 0.543],  # the last element
+           # of the previous list, 0.543, has been modified from 0.545;
+           # I assume it was a typo
+           5: [0.002, 0.004, 0.009, 0.015, 0.026, 0.041, 0.063, 0.089,
+               0.123, 0.165, 0.214, 0.268, 0.331, 0.396, 0.465, 0.535],
+           6: [0.001, 0.002, 0.004, 0.008, 0.013, 0.021, 0.032, 0.047,
+               0.066, 0.09, 0.12, 0.155, 0.197, 0.242, 0.294, 0.350,
+               0.409, 0.469, 0.531]}
+
+    def test_exact_distribution(self):
+        # I considered parametrize. I decided against it.
+        setattr(_mwu_state, 's', _MWU(0, 0))
+
+        p_tables = {3: self.pn3, 4: self.pn4, 5: self.pm5, 6: self.pm6}
+        for n, table in p_tables.items():
+            for m, p in table.items():
+                # check p-value against table
+                u = np.arange(0, len(p))
+                _mwu_state.s.set_shapes(m, n)
+                assert_allclose(_mwu_state.s.cdf(k=u), p, atol=1e-3)
+
+                # check identity CDF + SF - PMF = 1
+                # ( In this implementation, SF(U) includes PMF(U) )
+                u2 = np.arange(0, m*n+1)
+                assert_allclose(_mwu_state.s.cdf(k=u2)
+                                + _mwu_state.s.sf(k=u2)
+                                - _mwu_state.s.pmf(k=u2), 1)
+
+                # check symmetry about mean of U, i.e. pmf(U) = pmf(m*n-U)
+                pmf = _mwu_state.s.pmf(k=u2)
+                assert_allclose(pmf, pmf[::-1])
+
+                # check symmetry w.r.t. interchange of m, n
+                _mwu_state.s.set_shapes(n, m)
+                pmf2 = _mwu_state.s.pmf(k=u2)
+                assert_allclose(pmf, pmf2)
+
+    def test_asymptotic_behavior(self, xp):
+        rng = np.random.default_rng(12543)
+
+        # for small samples, the asymptotic test is not very accurate
+        x = xp.asarray(rng.random(5))
+        y = xp.asarray(rng.random(5))
+        res1 = mannwhitneyu(x, y, method="exact")
+        res2 = mannwhitneyu(x, y, method="asymptotic")
+        assert res1.statistic == res2.statistic
+        assert xp.abs(res1.pvalue - res2.pvalue) > 1e-2
+
+        # for large samples, they agree reasonably well
+        x = xp.asarray(rng.random(40))
+        y = xp.asarray(rng.random(40))
+        res1 = mannwhitneyu(x, y, method="exact")
+        res2 = mannwhitneyu(x, y, method="asymptotic")
+        assert res1.statistic == res2.statistic
+        assert xp.abs(res1.pvalue - res2.pvalue) < 1e-3
+
+    # --- Test Corner Cases ---
+
+    def test_exact_U_equals_mean(self, xp):
+        # Test U == m*n/2 with exact method
+        # Without special treatment, two-sided p-value > 1 because both
+        # one-sided p-values are > 0.5
+        x, y = xp.asarray([1., 2., 3.]), xp.asarray([1.5, 2.5])
+        res_l = mannwhitneyu(x, y, alternative="less", method="exact")
+        res_g = mannwhitneyu(x, y, alternative="greater", method="exact")
+        xp_assert_equal(res_l.pvalue, res_g.pvalue)
+        assert res_l.pvalue > 0.5
+
+        res = mannwhitneyu(x, y, alternative="two-sided", method="exact")
+        xp_assert_equal(res.statistic, xp.asarray(3.))
+        xp_assert_equal(res.pvalue, xp.asarray(1.))
+        # U == m*n/2 for asymptotic case tested in test_gh_2118
+        # The reason it's tricky for the asymptotic test has to do with
+        # continuity correction.
+
+    cases_scalar = [[{"alternative": 'two-sided', "method": "asymptotic"},
+                     (0., 1.)],
+                    [{"alternative": 'less', "method": "asymptotic"},
+                     (0., 0.5)],
+                    [{"alternative": 'greater', "method": "asymptotic"},
+                     (0., 0.977249868052)],
+                    [{"alternative": 'two-sided', "method": "exact"}, (0., 1)],
+                    [{"alternative": 'less', "method": "exact"}, (0., 0.5)],
+                    [{"alternative": 'greater', "method": "exact"}, (0., 1)]]
+
+    @pytest.mark.parametrize(("kwds", "result"), cases_scalar)
+    def test_scalar_data(self, kwds, result):  # not important to preserve w/ array API
+        # just making sure scalars work
+        assert_allclose(mannwhitneyu(1, 2, **kwds), result)
+
+    def test_equal_scalar_data(self):  # not important to preserve w/ array API
+        # when two scalars are equal, there is an -0.5/0 in the asymptotic
+        # approximation. R gives pvalue=1.0 for alternatives 'less' and
+        # 'greater' but NA for 'two-sided'.
+        assert_equal(mannwhitneyu(1, 1, method="exact"), (0.5, 1))
+        assert_equal(mannwhitneyu(1, 1, method="asymptotic", alternative='less'),
+                     (0.5, 1))
+        assert_equal(mannwhitneyu(1, 1, method="asymptotic", alternative='greater'),
+                     (0.5, 1))
+        assert_equal(mannwhitneyu(1, 1, method="asymptotic"),
+                     (0.5, np.nan))
+
+        # without continuity correction, this definitely becomes 0/0, which really
+        # is undefined
+        assert_equal(mannwhitneyu(1, 1, method="asymptotic", alternative='less',
+                                  use_continuity=False), (0.5, np.nan))
+        assert_equal(mannwhitneyu(1, 1, method="asymptotic", alternative='greater',
+                                  use_continuity=False), (0.5, np.nan))
+        assert_equal(mannwhitneyu(1, 1, method="asymptotic",
+                                  use_continuity=False), (0.5, np.nan))
+
+    # --- Test Enhancements / Bug Reports ---
+
+    @pytest.mark.parametrize("method", ["asymptotic", "exact"])
+    def test_gh_12837_11113(self, method, xp):
+        # Test that behavior for broadcastable nd arrays is appropriate:
+        # output shape is correct and all values are equal to when the test
+        # is performed on one pair of samples at a time.
+        # Tests that gh-12837 and gh-11113 (requests for n-d input)
+        # are resolved
+        rng = np.random.default_rng(6083743794)
+
+        # arrays are broadcastable except for axis = -3
+        axis = -3
+        m, n = 7, 10  # sample sizes
+        x = rng.random((m, 3, 8))
+        y = rng.random((6, n, 1, 8)) + 0.1
+        res = mannwhitneyu(xp.asarray(x), xp.asarray(y), method=method, axis=axis)
+
+        shape = (6, 3, 8)  # appropriate shape of outputs, given inputs
+        assert res.pvalue.shape == shape
+        assert res.statistic.shape == shape
+
+        # move axis of test to end for simplicity
+        x, y = np.moveaxis(x, axis, -1), np.moveaxis(y, axis, -1)
+
+        x = x[None, ...]  # give x a zeroth dimension
+        assert x.ndim == y.ndim
+
+        x = np.broadcast_to(x, shape + (m,))
+        y = np.broadcast_to(y, shape + (n,))
+        assert x.shape[:-1] == shape
+        assert y.shape[:-1] == shape
+
+        # loop over pairs of samples
+        statistics = np.zeros(shape)
+        pvalues = np.zeros(shape)
+        for indices in product(*[range(i) for i in shape]):
+            xi = x[indices]
+            yi = y[indices]
+            temp = mannwhitneyu(xi, yi, method=method)
+            statistics[indices] = temp.statistic
+            pvalues[indices] = temp.pvalue
+
+        xp_assert_close(res.pvalue, xp.asarray(pvalues), atol=1e-16)
+        xp_assert_close(res.statistic, xp.asarray(statistics), atol=1e-16)
+
+    def test_gh_11355(self, xp):
+        # Test for correct behavior with NaN/Inf in input
+        x = [1, 2, 3, 4]
+        y = [3, 6, 7, 8, 9, 3, 2, 1, 4, 4, 5]
+        res1 = mannwhitneyu(xp.asarray(x), xp.asarray(y), method='asymptotic')
+
+        # Inf is not a problem. This is a rank test, and it's the largest value
+        x[0] = 1.  # ensure floating point
+        y[4] = np.inf
+        res2 = mannwhitneyu(xp.asarray(x), xp.asarray(y), method='asymptotic')
+
+        xp_assert_equal(res1.statistic, res2.statistic)
+        xp_assert_equal(res1.pvalue, res2.pvalue)
+
+    @skip_xp_backends("jax.numpy", reason="needs jax-ml/jax#34490")
+    def test_gh11355_nan(self, xp):
+        # NaNs should propagate by default.
+        x = [1., 2., 3., 4.]
+        y = [3, 6, 7, np.nan, 9, 3, 2, 1, 4, 4, 5]
+        res3 = mannwhitneyu(xp.asarray(x), xp.asarray(y))
+        xp_assert_equal(res3.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res3.pvalue, xp.asarray(xp.nan))
+
+    cases_11355 = [([1., 2, 3, 4],
+                    [3, 6, 7, 8, np.inf, 3, 2, 1, 4, 4, 5],
+                    10., 0.1297704873477),
+                   ([1., 2, 3, 4],
+                    [3, 6, 7, 8, np.inf, np.inf, 2, 1, 4, 4, 5],
+                    8.5, 0.08735617507695),
+                   ([1, 2, np.inf, 4],
+                    [3, 6, 7, 8, np.inf, 3, 2, 1, 4, 4, 5],
+                    17.5, 0.5988856695752),
+                   ([1, 2, np.inf, 4],
+                    [3, 6, 7, 8, np.inf, np.inf, 2, 1, 4, 4, 5],
+                    16., 0.4687165824462),
+                   ([1, np.inf, np.inf, 4],
+                    [3, 6, 7, 8, np.inf, np.inf, 2, 1, 4, 4, 5],
+                    24.5, 0.7912517950119)]
+
+    @pytest.mark.parametrize(("x", "y", "statistic", "pvalue"), cases_11355)
+    def test_gh_11355b(self, x, y, statistic, pvalue, xp):
+        # Test for correct behavior with NaN/Inf in input
+        res = mannwhitneyu(xp.asarray(x), xp.asarray(y), method='asymptotic')
+        xp_assert_close(res.statistic, xp.asarray(statistic), atol=1e-12)
+        xp_assert_close(res.pvalue, xp.asarray(pvalue), atol=1e-12)
+
+    cases_9184 = [[True, "less", "asymptotic", 0.900775348204],
+                  [True, "greater", "asymptotic", 0.1223118025635],
+                  [True, "two-sided", "asymptotic", 0.244623605127],
+                  [False, "less", "asymptotic", 0.8896643190401],
+                  [False, "greater", "asymptotic", 0.1103356809599],
+                  [False, "two-sided", "asymptotic", 0.2206713619198],
+                  [True, "less", "exact", 0.8967698967699],
+                  [True, "greater", "exact", 0.1272061272061],
+                  [True, "two-sided", "exact", 0.2544122544123]]
+
+    @pytest.mark.parametrize(("use_continuity", "alternative",
+                              "method", "pvalue_exp"), cases_9184)
+    def test_gh_9184(self, use_continuity, alternative, method, pvalue_exp, xp):
+        # gh-9184 might be considered a doc-only bug. Please see the
+        # documentation to confirm that mannwhitneyu correctly notes
+        # that the output statistic is that of the first sample (x). In any
+        # case, check the case provided there against output from R.
+        # R code:
+        # options(digits=16)
+        # x <- c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46)
+        # y <- c(1.15, 0.88, 0.90, 0.74, 1.21)
+        # wilcox.test(x, y, alternative = "less", exact = FALSE)
+        # wilcox.test(x, y, alternative = "greater", exact = FALSE)
+        # wilcox.test(x, y, alternative = "two.sided", exact = FALSE)
+        # wilcox.test(x, y, alternative = "less", exact = FALSE,
+        #             correct=FALSE)
+        # wilcox.test(x, y, alternative = "greater", exact = FALSE,
+        #             correct=FALSE)
+        # wilcox.test(x, y, alternative = "two.sided", exact = FALSE,
+        #             correct=FALSE)
+        # wilcox.test(x, y, alternative = "less", exact = TRUE)
+        # wilcox.test(x, y, alternative = "greater", exact = TRUE)
+        # wilcox.test(x, y, alternative = "two.sided", exact = TRUE)
+        statistic_exp = 35.
+        x = xp.asarray([0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46])
+        y = xp.asarray([1.15, 0.88, 0.90, 0.74, 1.21])
+        res = mannwhitneyu(x, y, use_continuity=use_continuity,
+                           alternative=alternative, method=method)
+        xp_assert_equal(res.statistic, xp.asarray(statistic_exp))
+        xp_assert_close(res.pvalue, xp.asarray(pvalue_exp))
+
+    @skip_xp_backends("jax.numpy", reason="needs jax-ml/jax#34490")
+    def test_gh_4067(self, xp):
+        # Test for correct behavior with all NaN input - default is propagate
+        nan = xp.asarray(xp.nan)
+        a = xp.stack([nan, nan, nan, nan, nan])
+        b = xp.stack([nan, nan, nan, nan, nan])
+        res = mannwhitneyu(a, b)
+        xp_assert_equal(res.statistic, xp.asarray(nan))
+        xp_assert_equal(res.pvalue, nan)
+
+    # All cases checked against R wilcox.test, e.g.
+    # options(digits=16)
+    # x = c(1, 2, 3)
+    # y = c(1.5, 2.5)
+    # wilcox.test(x, y, exact=FALSE, alternative='less')
+
+    cases_2118 = [[[1., 2., 3.], [1.5, 2.5], "greater", (3., 0.6135850036578)],
+                  [[1., 2., 3.], [1.5, 2.5], "less", (3., 0.6135850036578)],
+                  [[1., 2., 3.], [1.5, 2.5], "two-sided", (3., 1.0)],
+                  [[1, 2, 3], [2], "greater", (1.5, 0.681324055883)],
+                  [[1, 2, 3], [2], "less", (1.5, 0.681324055883)],
+                  [[1, 2, 3], [2], "two-sided", (1.5, 1.)],
+                  [[1, 2], [1, 2], "greater", (2., 0.667497228949)],
+                  [[1, 2], [1, 2], "less", (2., 0.667497228949)],
+                  [[1, 2], [1, 2], "two-sided", (2., 1.)]]
+
+    @pytest.mark.parametrize(["x", "y", "alternative", "expected"], cases_2118)
+    def test_gh_2118(self, x, y, alternative, expected, xp):
+        # test cases in which U == m*n/2 when method is asymptotic
+        # applying continuity correction could result in p-value > 1
+        res = mannwhitneyu(xp.asarray(x), xp.asarray(y), use_continuity=True,
+                           alternative=alternative, method="asymptotic")
+        rtol = 1e-6 if xp_default_dtype(xp) == xp.float32 else 1e-12
+        xp_assert_close(res.statistic, xp.asarray(expected[0]), rtol=rtol)
+        xp_assert_close(res.pvalue, xp.asarray(expected[1]), rtol=rtol)
+
+    def test_gh19692_smaller_table(self, xp):
+        # In gh-19692, we noted that the shape of the cache used in calculating
+        # p-values was dependent on the order of the inputs because the sample
+        # sizes n1 and n2 changed. This was indicative of unnecessary cache
+        # growth and redundant calculation. Check that this is resolved.
+        rng = np.random.default_rng(7600451795963068007)
+        m, n = 5, 11
+        x = xp.asarray(rng.random(size=m).tolist())
+        y = xp.asarray(rng.random(size=n).tolist())
+
+        setattr(_mwu_state, 's', _MWU(0, 0))
+        _mwu_state.s.reset()  # reset cache
+
+        res = stats.mannwhitneyu(x, y, method='exact')
+        shape = _mwu_state.s.configurations.shape
+        assert shape[-1] == min(res.statistic, m*n - res.statistic) + 1
+        stats.mannwhitneyu(y, x, method='exact')
+        assert shape == _mwu_state.s.configurations.shape  # same with reversed sizes
+
+        # Also, we weren't exploiting the symmetry of the null distribution
+        # to its full potential. Ensure that the null distribution is not
+        # evaluated explicitly for `k > m*n/2`.
+        _mwu_state.s.reset()  # reset cache
+        stats.mannwhitneyu(x, 0*y, method='exact', alternative='greater')
+        shape = _mwu_state.s.configurations.shape
+        assert shape[-1] == 1  # k is smallest possible
+        stats.mannwhitneyu(0*x, y, method='exact', alternative='greater')
+        assert shape == _mwu_state.s.configurations.shape
+
+    @pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+    def test_permutation_method(self, alternative, xp):
+        rng = np.random.default_rng(7600451795963068007)
+        x = xp.asarray(rng.random(size=(2, 5)))
+        y = xp.asarray(rng.random(size=(2, 6)))
+        res = stats.mannwhitneyu(x, y, method=stats.PermutationMethod(),
+                                 alternative=alternative, axis=1)
+        res2 = stats.mannwhitneyu(x, y, method='exact',
+                                  alternative=alternative, axis=1)
+        xp_assert_close(res.statistic, res2.statistic, rtol=1e-15)
+        xp_assert_close(res.pvalue, res2.pvalue, rtol=1e-15)
+
+    # Old tests moved from test_stats. Source of magic numbers unknown.
+    X = [19.8958398126694, 19.5452691647182, 19.0577309166425, 21.716543054589,
+         20.3269502208702, 20.0009273294025, 19.3440043632957, 20.4216806548105,
+         19.0649894736528, 18.7808043120398, 19.3680942943298, 19.4848044069953,
+         20.7514611265663, 19.0894948874598, 19.4975522356628, 18.9971170734274,
+         20.3239606288208, 20.6921298083835, 19.0724259532507, 18.9825187935021,
+         19.5144462609601, 19.8256857844223, 20.5174677102032, 21.1122407995892,
+         17.9490854922535, 18.2847521114727, 20.1072217648826, 18.6439891962179,
+         20.4970638083542, 19.5567594734914]
+
+    Y = [19.2790668029091, 16.993808441865, 18.5416338448258, 17.2634018833575,
+         19.1577183624616, 18.5119655377495, 18.6068455037221, 18.8358343362655,
+         19.0366413269742, 18.1135025515417, 19.2201873866958, 17.8344909022841,
+         18.2894380745856, 18.6661374133922, 19.9688601693252, 16.0672254617636,
+         19.00596360572, 19.201561539032, 19.0487501090183, 19.0847908674356]
+
+    rtol = 1e-14
+
+    def test_mannwhitneyu_one_sided(self, xp):
+        X, Y = xp.asarray(self.X), xp.asarray(self.Y)
+        kwargs = dict(method='asymptotic')
+        u1, p1 = stats.mannwhitneyu(X, Y, alternative='less', **kwargs)
+        u2, p2 = stats.mannwhitneyu(Y, X, alternative='greater', **kwargs)
+        u3, p3 = stats.mannwhitneyu(X, Y, alternative='greater', **kwargs)
+        u4, p4 = stats.mannwhitneyu(Y, X, alternative='less', **kwargs)
+
+        xp_assert_equal(p1, p2)
+        xp_assert_equal(p3, p4)
+        assert p1 != p3
+        xp_assert_equal(u1, xp.asarray(498.))
+        xp_assert_equal(u2, xp.asarray(102.))
+        xp_assert_equal(u3, xp.asarray(498.))
+        xp_assert_equal(u4, xp.asarray(102.))
+        assert_allclose(p1, xp.asarray(0.999957683256589), rtol=self.rtol)
+        rtol = self.rtol if X.dtype == xp.float64 else 5e-4
+        assert_allclose(p3, xp.asarray(4.5941632666275e-05), rtol=rtol, atol=1e-16)
+
+    def test_mannwhitneyu_two_sided(self, xp):
+        X, Y = xp.asarray(self.X), xp.asarray(self.Y)
+        kwargs = dict(alternative='two-sided', method='asymptotic')
+        u1, p1 = stats.mannwhitneyu(X, Y, **kwargs)
+        u2, p2 = stats.mannwhitneyu(Y, X, **kwargs)
+
+        xp_assert_equal(p1, p2)
+        xp_assert_equal(u1, xp.asarray(498.))
+        xp_assert_equal(u2, xp.asarray(102.))
+        rtol = self.rtol if X.dtype == xp.float64 else 5e-4
+        xp_assert_close(p1, xp.asarray(9.188326533255e-05), rtol=rtol, atol=1e-16)
+
+    def test_mannwhitneyu_no_correct_one_sided(self, xp):
+        X, Y = xp.asarray(self.X), xp.asarray(self.Y)
+        kwargs = dict(use_continuity=False, method='asymptotic')
+        u1, p1 = stats.mannwhitneyu(X, Y, alternative='less', **kwargs)
+        u2, p2 = stats.mannwhitneyu(Y, X, alternative='greater', **kwargs)
+        u3, p3 = stats.mannwhitneyu(X, Y, alternative='greater', **kwargs)
+        u4, p4 = stats.mannwhitneyu(Y, X, alternative='less', **kwargs)
+
+        xp_assert_equal(p1, p2)
+        xp_assert_equal(p3, p4)
+        assert p1 != p3
+        xp_assert_equal(u1, xp.asarray(498.))
+        xp_assert_equal(u2, xp.asarray(102.))
+        xp_assert_equal(u3, xp.asarray(498.))
+        xp_assert_equal(u4, xp.asarray(102.))
+        rtol = self.rtol if X.dtype == xp.float64 else 5e-4
+        xp_assert_close(p1, xp.asarray(0.999955905990004), rtol=rtol, atol=1e-16)
+        xp_assert_close(p3, xp.asarray(4.40940099958089e-05), rtol=rtol, atol=1e-16)
+
+    def test_mannwhitneyu_no_correct_two_sided(self, xp):
+        X, Y = xp.asarray(self.X), xp.asarray(self.Y)
+        kwargs = dict(use_continuity=False, method='asymptotic')
+        u1, p1 = stats.mannwhitneyu(X, Y, **kwargs)
+        u2, p2 = stats.mannwhitneyu(Y, X, **kwargs)
+
+        xp_assert_equal(p1, p2)
+        xp_assert_equal(u1, xp.asarray(498.))
+        xp_assert_equal(u2, xp.asarray(102.))
+        rtol = self.rtol if X.dtype == xp.float64 else 5e-4
+        xp_assert_close(p1, xp.asarray(8.81880199916178e-05), rtol=rtol, atol=1e-16)
+
+    def test_mannwhitneyu_ones(self, xp):
+        # test for gh-1428
+        x = xp.asarray([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2.,
+                        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 2., 1., 1., 1., 1., 2., 1., 1., 2., 1., 1., 2.,
+                        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 1., 3., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1.])
+
+        y = xp.asarray([1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 1.,
+                        2., 1., 1., 1., 2., 1., 1., 1., 1., 1., 2., 1., 1., 3.,
+                        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1.,
+                        1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 2.,
+                        2., 1., 1., 2., 1., 1., 2., 1., 2., 1., 1., 1., 1., 2.,
+                        2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                        1., 2., 1., 1., 1., 1., 1., 2., 2., 2., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                        2., 1., 1., 2., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1.,
+                        1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 2., 1., 1.,
+                        1., 1., 1., 1.])
+
+        # p-value from R, e.g. wilcox.test(x, y, alternative="g")
+        kwargs = dict(method='asymptotic')
+        res = stats.mannwhitneyu(x, y, alternative='less', **kwargs)
+        xp_assert_close(res.statistic, xp.asarray(16980.5))
+        xp_assert_close(res.pvalue, xp.asarray(2.8214327656317373e-5))
+        res = stats.mannwhitneyu(x, y, alternative='greater', **kwargs)
+        xp_assert_close(res.statistic, xp.asarray(16980.5))
+        xp_assert_close(res.pvalue, xp.asarray(0.9999719954296))
+        res = stats.mannwhitneyu(x, y, alternative='two-sided', **kwargs)
+        xp_assert_close(res.statistic, xp.asarray(16980.5))
+        xp_assert_close(res.pvalue, xp.asarray(5.642865531266e-5))
+
+
+class TestSomersD(_TestPythranFunc):
+    def setup_method(self):
+        self.dtypes = self.ALL_INTEGER + self.ALL_FLOAT
+        self.arguments = {0: (np.arange(10),
+                              self.ALL_INTEGER + self.ALL_FLOAT),
+                          1: (np.arange(10),
+                              self.ALL_INTEGER + self.ALL_FLOAT)}
+        input_array = [self.arguments[idx][0] for idx in self.arguments]
+        # In this case, self.partialfunc can simply be stats.somersd,
+        # since `alternative` is an optional argument. If it is required,
+        # we can use functools.partial to freeze the value, because
+        # we only mainly test various array inputs, not str, etc.
+        self.partialfunc = functools.partial(stats.somersd,
+                                             alternative='two-sided')
+        self.expected = self.partialfunc(*input_array)
+
+    def pythranfunc(self, *args):
+        res = self.partialfunc(*args)
+        assert_allclose(res.statistic, self.expected.statistic, atol=1e-15)
+        assert_allclose(res.pvalue, self.expected.pvalue, atol=1e-15)
+
+    def test_pythranfunc_keywords(self):
+        # Not specifying the optional keyword args
+        table = [[27, 25, 14, 7, 0], [7, 14, 18, 35, 12], [1, 3, 2, 7, 17]]
+        res1 = stats.somersd(table)
+        # Specifying the optional keyword args with default value
+        optional_args = self.get_optional_args(stats.somersd)
+        res2 = stats.somersd(table, **optional_args)
+        # Check if the results are the same in two cases
+        assert_allclose(res1.statistic, res2.statistic, atol=1e-15)
+        assert_allclose(res1.pvalue, res2.pvalue, atol=1e-15)
+
+    def test_like_kendalltau(self):
+        # All tests correspond with one in test_stats.py `test_kendalltau`
+
+        # case without ties, con-dis equal zero
+        x = [5, 2, 1, 3, 6, 4, 7, 8]
+        y = [5, 2, 6, 3, 1, 8, 7, 4]
+        # Cross-check with result from SAS FREQ:
+        expected = (0.000000000000000, 1.000000000000000)
+        res = stats.somersd(x, y)
+        assert_allclose(res.statistic, expected[0], atol=1e-15)
+        assert_allclose(res.pvalue, expected[1], atol=1e-15)
+
+        # case without ties, con-dis equal zero
+        x = [0, 5, 2, 1, 3, 6, 4, 7, 8]
+        y = [5, 2, 0, 6, 3, 1, 8, 7, 4]
+        # Cross-check with result from SAS FREQ:
+        expected = (0.000000000000000, 1.000000000000000)
+        res = stats.somersd(x, y)
+        assert_allclose(res.statistic, expected[0], atol=1e-15)
+        assert_allclose(res.pvalue, expected[1], atol=1e-15)
+
+        # case without ties, con-dis close to zero
+        x = [5, 2, 1, 3, 6, 4, 7]
+        y = [5, 2, 6, 3, 1, 7, 4]
+        # Cross-check with result from SAS FREQ:
+        expected = (-0.142857142857140, 0.630326953157670)
+        res = stats.somersd(x, y)
+        assert_allclose(res.statistic, expected[0], atol=1e-15)
+        assert_allclose(res.pvalue, expected[1], atol=1e-15)
+
+        # simple case without ties
+        x = np.arange(10)
+        y = np.arange(10)
+        # Cross-check with result from SAS FREQ:
+        # SAS p value is not provided.
+        expected = (1.000000000000000, 0)
+        res = stats.somersd(x, y)
+        assert_allclose(res.statistic, expected[0], atol=1e-15)
+        assert_allclose(res.pvalue, expected[1], atol=1e-15)
+
+        # swap a couple values and a couple more
+        x = np.arange(10)
+        y = np.array([0, 2, 1, 3, 4, 6, 5, 7, 8, 9])
+        # Cross-check with result from SAS FREQ:
+        expected = (0.911111111111110, 0.000000000000000)
+        res = stats.somersd(x, y)
+        assert_allclose(res.statistic, expected[0], atol=1e-15)
+        assert_allclose(res.pvalue, expected[1], atol=1e-15)
+
+        # same in opposite direction
+        x = np.arange(10)
+        y = np.arange(10)[::-1]
+        # Cross-check with result from SAS FREQ:
+        # SAS p value is not provided.
+        expected = (-1.000000000000000, 0)
+        res = stats.somersd(x, y)
+        assert_allclose(res.statistic, expected[0], atol=1e-15)
+        assert_allclose(res.pvalue, expected[1], atol=1e-15)
+
+        # swap a couple values and a couple more
+        x = np.arange(10)
+        y = np.array([9, 7, 8, 6, 5, 3, 4, 2, 1, 0])
+        # Cross-check with result from SAS FREQ:
+        expected = (-0.9111111111111111, 0.000000000000000)
+        res = stats.somersd(x, y)
+        assert_allclose(res.statistic, expected[0], atol=1e-15)
+        assert_allclose(res.pvalue, expected[1], atol=1e-15)
+
+        # with some ties
+        x1 = [12, 2, 1, 12, 2]
+        x2 = [1, 4, 7, 1, 0]
+        # Cross-check with result from SAS FREQ:
+        expected = (-0.500000000000000, 0.304901788178780)
+        res = stats.somersd(x1, x2)
+        assert_allclose(res.statistic, expected[0], atol=1e-15)
+        assert_allclose(res.pvalue, expected[1], atol=1e-15)
+
+        # with only ties in one or both inputs
+        # SAS will not produce an output for these:
+        # NOTE: No statistics are computed for x * y because x has fewer
+        # than 2 nonmissing levels.
+        # WARNING: No OUTPUT data set is produced for this table because a
+        # row or column variable has fewer than 2 nonmissing levels and no
+        # statistics are computed.
+
+        res = stats.somersd([2, 2, 2], [2, 2, 2])
+        assert_allclose(res.statistic, np.nan)
+        assert_allclose(res.pvalue, np.nan)
+
+        res = stats.somersd([2, 0, 2], [2, 2, 2])
+        assert_allclose(res.statistic, np.nan)
+        assert_allclose(res.pvalue, np.nan)
+
+        res = stats.somersd([2, 2, 2], [2, 0, 2])
+        assert_allclose(res.statistic, np.nan)
+        assert_allclose(res.pvalue, np.nan)
+
+        res = stats.somersd([0], [0])
+        assert_allclose(res.statistic, np.nan)
+        assert_allclose(res.pvalue, np.nan)
+
+        # empty arrays provided as input
+        res = stats.somersd([], [])
+        assert_allclose(res.statistic, np.nan)
+        assert_allclose(res.pvalue, np.nan)
+
+        # test unequal length inputs
+        x = np.arange(10.)
+        y = np.arange(20.)
+        assert_raises(ValueError, stats.somersd, x, y)
+
+    def test_asymmetry(self):
+        # test that somersd is asymmetric w.r.t. input order and that
+        # convention is as described: first input is row variable & independent
+        # data is from Wikipedia:
+        # https://en.wikipedia.org/wiki/Somers%27_D
+        # but currently that example contradicts itself - it says X is
+        # independent yet take D_XY
+
+        x = [1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 1, 2,
+             2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3]
+        y = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,
+             2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
+        # Cross-check with result from SAS FREQ:
+        d_cr = 0.272727272727270
+        d_rc = 0.342857142857140
+        p = 0.092891940883700  # same p-value for either direction
+        res = stats.somersd(x, y)
+        assert_allclose(res.statistic, d_cr, atol=1e-15)
+        assert_allclose(res.pvalue, p, atol=1e-4)
+        assert_equal(res.table.shape, (3, 2))
+        res = stats.somersd(y, x)
+        assert_allclose(res.statistic, d_rc, atol=1e-15)
+        assert_allclose(res.pvalue, p, atol=1e-15)
+        assert_equal(res.table.shape, (2, 3))
+
+    def test_somers_original(self):
+        # test against Somers' original paper [1]
+
+        # Table 5A
+        # Somers' convention was column IV
+        table = np.array([[8, 2], [6, 5], [3, 4], [1, 3], [2, 3]])
+        # Our convention (and that of SAS FREQ) is row IV
+        table = table.T
+        dyx = 129/340
+        assert_allclose(stats.somersd(table).statistic, dyx)
+
+        # table 7A - d_yx = 1
+        table = np.array([[25, 0], [85, 0], [0, 30]])
+        dxy, dyx = 3300/5425, 3300/3300
+        assert_allclose(stats.somersd(table).statistic, dxy)
+        assert_allclose(stats.somersd(table.T).statistic, dyx)
+
+        # table 7B - d_yx < 0
+        table = np.array([[25, 0], [0, 30], [85, 0]])
+        dyx = -1800/3300
+        assert_allclose(stats.somersd(table.T).statistic, dyx)
+
+    def test_contingency_table_with_zero_rows_cols(self):
+        # test that zero rows/cols in contingency table don't affect result
+
+        N = 100
+        shape = 4, 6
+        size = np.prod(shape)
+
+        rng = np.random.RandomState(0)
+        s = stats.multinomial.rvs(N, p=np.ones(size)/size,
+                                  random_state=rng).reshape(shape)
+        res = stats.somersd(s)
+
+        s2 = np.insert(s, 2, np.zeros(shape[1]), axis=0)
+        res2 = stats.somersd(s2)
+
+        s3 = np.insert(s, 2, np.zeros(shape[0]), axis=1)
+        res3 = stats.somersd(s3)
+
+        s4 = np.insert(s2, 2, np.zeros(shape[0]+1), axis=1)
+        res4 = stats.somersd(s4)
+
+        # Cross-check with result from SAS FREQ:
+        assert_allclose(res.statistic, -0.116981132075470, atol=1e-15)
+        assert_allclose(res.statistic, res2.statistic)
+        assert_allclose(res.statistic, res3.statistic)
+        assert_allclose(res.statistic, res4.statistic)
+
+        assert_allclose(res.pvalue, 0.156376448188150, atol=1e-15)
+        assert_allclose(res.pvalue, res2.pvalue)
+        assert_allclose(res.pvalue, res3.pvalue)
+        assert_allclose(res.pvalue, res4.pvalue)
+
+    def test_invalid_contingency_tables(self):
+        N = 100
+        shape = 4, 6
+        size = np.prod(shape)
+
+        rng = np.random.default_rng(0)
+        # start with a valid contingency table
+        s = stats.multinomial.rvs(N, p=np.ones(size)/size,
+                                  random_state=rng).reshape(shape)
+
+        s5 = s - 2
+        message = "All elements of the contingency table must be non-negative"
+        with assert_raises(ValueError, match=message):
+            stats.somersd(s5)
+
+        s6 = s + 0.01
+        message = "All elements of the contingency table must be integer"
+        with assert_raises(ValueError, match=message):
+            stats.somersd(s6)
+
+        message = ("At least two elements of the contingency "
+                   "table must be nonzero.")
+        with assert_raises(ValueError, match=message):
+            stats.somersd([[]])
+
+        with assert_raises(ValueError, match=message):
+            stats.somersd([[1]])
+
+        s7 = np.zeros((3, 3))
+        with assert_raises(ValueError, match=message):
+            stats.somersd(s7)
+
+        s7[0, 1] = 1
+        with assert_raises(ValueError, match=message):
+            stats.somersd(s7)
+
+    def test_only_ranks_matter(self):
+        # only ranks of input data should matter
+        x = [1, 2, 3]
+        x2 = [-1, 2.1, np.inf]
+        y = [3, 2, 1]
+        y2 = [0, -0.5, -np.inf]
+        res = stats.somersd(x, y)
+        res2 = stats.somersd(x2, y2)
+        assert_equal(res.statistic, res2.statistic)
+        assert_equal(res.pvalue, res2.pvalue)
+
+    def test_contingency_table_return(self):
+        # check that contingency table is returned
+        x = np.arange(10)
+        y = np.arange(10)
+        res = stats.somersd(x, y)
+        assert_equal(res.table, np.eye(10))
+
+    def test_somersd_alternative(self):
+        # Test alternative parameter, asymptotic method (due to tie)
+
+        # Based on scipy.stats.test_stats.TestCorrSpearman2::test_alternative
+        x1 = [1, 2, 3, 4, 5]
+        x2 = [5, 6, 7, 8, 7]
+
+        # strong positive correlation
+        expected = stats.somersd(x1, x2, alternative="two-sided")
+        assert expected.statistic > 0
+
+        # rank correlation > 0 -> large "less" p-value
+        res = stats.somersd(x1, x2, alternative="less")
+        assert_equal(res.statistic, expected.statistic)
+        assert_allclose(res.pvalue, 1 - (expected.pvalue / 2))
+
+        # rank correlation > 0 -> small "greater" p-value
+        res = stats.somersd(x1, x2, alternative="greater")
+        assert_equal(res.statistic, expected.statistic)
+        assert_allclose(res.pvalue, expected.pvalue / 2)
+
+        # reverse the direction of rank correlation
+        x2.reverse()
+
+        # strong negative correlation
+        expected = stats.somersd(x1, x2, alternative="two-sided")
+        assert expected.statistic < 0
+
+        # rank correlation < 0 -> large "greater" p-value
+        res = stats.somersd(x1, x2, alternative="greater")
+        assert_equal(res.statistic, expected.statistic)
+        assert_allclose(res.pvalue, 1 - (expected.pvalue / 2))
+
+        # rank correlation < 0 -> small "less" p-value
+        res = stats.somersd(x1, x2, alternative="less")
+        assert_equal(res.statistic, expected.statistic)
+        assert_allclose(res.pvalue, expected.pvalue / 2)
+
+        with pytest.raises(ValueError, match="`alternative` must be..."):
+            stats.somersd(x1, x2, alternative="ekki-ekki")
+
+    @pytest.mark.parametrize("positive_correlation", (False, True))
+    def test_somersd_perfect_correlation(self, positive_correlation):
+        # Before the addition of `alternative`, perfect correlation was
+        # treated as a special case. Now it is treated like any other case, but
+        # make sure there are no divide by zero warnings or associated errors
+
+        x1 = np.arange(10)
+        x2 = x1 if positive_correlation else np.flip(x1)
+        expected_statistic = 1 if positive_correlation else -1
+
+        # perfect correlation -> small "two-sided" p-value (0)
+        res = stats.somersd(x1, x2, alternative="two-sided")
+        assert res.statistic == expected_statistic
+        assert res.pvalue == 0
+
+        # rank correlation > 0 -> large "less" p-value (1)
+        res = stats.somersd(x1, x2, alternative="less")
+        assert res.statistic == expected_statistic
+        assert res.pvalue == (1 if positive_correlation else 0)
+
+        # rank correlation > 0 -> small "greater" p-value (0)
+        res = stats.somersd(x1, x2, alternative="greater")
+        assert res.statistic == expected_statistic
+        assert res.pvalue == (0 if positive_correlation else 1)
+
+    def test_somersd_large_inputs_gh18132(self):
+        # Test that large inputs where potential overflows could occur give
+        # the expected output. This is tested in the case of binary inputs.
+        # See gh-18126.
+
+        # generate lists of random classes 1-2 (binary)
+        classes = [1, 2]
+        n_samples = 10 ** 6
+        rng = np.random.default_rng(6889320191)
+        x = rng.choice(classes, n_samples)
+        y = rng.choice(classes, n_samples)
+
+        # get value to compare with: sklearn output
+        # from sklearn import metrics
+        # val_auc_sklearn = metrics.roc_auc_score(x, y)
+        # # convert to the Gini coefficient (Gini = (AUC*2)-1)
+        # val_sklearn = 2 * val_auc_sklearn - 1
+        val_sklearn = 0.000624401938730923
+
+        # calculate the Somers' D statistic, which should be equal to the
+        # result of val_sklearn until approximately machine precision
+        val_scipy = stats.somersd(x, y).statistic
+        assert_allclose(val_sklearn, val_scipy, atol=1e-15)
+
+
+class TestBarnardExact:
+    """Some tests to show that barnard_exact() works correctly."""
+
+    @pytest.mark.parametrize(
+        "input_sample,expected",
+        [
+            ([[43, 40], [10, 39]], (3.555406779643, 0.000362832367)),
+            ([[100, 2], [1000, 5]], (-1.776382925679, 0.135126970878)),
+            ([[2, 7], [8, 2]], (-2.518474945157, 0.019210815430)),
+            ([[5, 1], [10, 10]], (1.449486150679, 0.156277546306)),
+            ([[5, 15], [20, 20]], (-1.851640199545, 0.066363501421)),
+            ([[5, 16], [20, 25]], (-1.609639949352, 0.116984852192)),
+            ([[10, 5], [10, 1]], (-1.449486150679, 0.177536588915)),
+            ([[5, 0], [1, 4]], (2.581988897472, 0.013671875000)),
+            ([[0, 1], [3, 2]], (-1.095445115010, 0.509667991877)),
+            ([[0, 2], [6, 4]], (-1.549193338483, 0.197019618792)),
+            ([[2, 7], [8, 2]], (-2.518474945157, 0.019210815430)),
+        ],
+    )
+    def test_precise(self, input_sample, expected):
+        """The expected values have been generated by R, using a resolution
+        for the nuisance parameter of 1e-6 :
+        ```R
+        library(Barnard)
+        options(digits=10)
+        barnard.test(43, 40, 10, 39, dp=1e-6, pooled=TRUE)
+        ```
+        """
+        res = barnard_exact(input_sample)
+        statistic, pvalue = res.statistic, res.pvalue
+        assert_allclose([statistic, pvalue], expected)
+
+    @pytest.mark.parametrize(
+        "input_sample,expected",
+        [
+            ([[43, 40], [10, 39]], (3.920362887717, 0.000289470662)),
+            ([[100, 2], [1000, 5]], (-1.139432816087, 0.950272080594)),
+            ([[2, 7], [8, 2]], (-3.079373904042, 0.020172119141)),
+            ([[5, 1], [10, 10]], (1.622375939458, 0.150599922226)),
+            ([[5, 15], [20, 20]], (-1.974771239528, 0.063038448651)),
+            ([[5, 16], [20, 25]], (-1.722122973346, 0.133329494287)),
+            ([[10, 5], [10, 1]], (-1.765469659009, 0.250566655215)),
+            ([[5, 0], [1, 4]], (5.477225575052, 0.007812500000)),
+            ([[0, 1], [3, 2]], (-1.224744871392, 0.509667991877)),
+            ([[0, 2], [6, 4]], (-1.732050807569, 0.197019618792)),
+            ([[2, 7], [8, 2]], (-3.079373904042, 0.020172119141)),
+        ],
+    )
+    def test_pooled_param(self, input_sample, expected):
+        """The expected values have been generated by R, using a resolution
+        for the nuisance parameter of 1e-6 :
+        ```R
+        library(Barnard)
+        options(digits=10)
+        barnard.test(43, 40, 10, 39, dp=1e-6, pooled=FALSE)
+        ```
+        """
+        res = barnard_exact(input_sample, pooled=False)
+        statistic, pvalue = res.statistic, res.pvalue
+        assert_allclose([statistic, pvalue], expected)
+
+    def test_raises(self):
+        # test we raise an error for wrong input number of nuisances.
+        error_msg = (
+            "Number of points `n` must be strictly positive, found 0"
+        )
+        with assert_raises(ValueError, match=error_msg):
+            barnard_exact([[1, 2], [3, 4]], n=0)
+
+        # test we raise an error for wrong shape of input.
+        error_msg = "The input `table` must be of shape \\(2, 2\\)."
+        with assert_raises(ValueError, match=error_msg):
+            barnard_exact(np.arange(6).reshape(2, 3))
+
+        # Test all values must be positives
+        error_msg = "All values in `table` must be nonnegative."
+        with assert_raises(ValueError, match=error_msg):
+            barnard_exact([[-1, 2], [3, 4]])
+
+        # Test value error on wrong alternative param
+        error_msg = (
+            "`alternative` should be one of {'two-sided', 'less', 'greater'},"
+            " found .*"
+        )
+        with assert_raises(ValueError, match=error_msg):
+            barnard_exact([[1, 2], [3, 4]], "not-correct")
+
+    @pytest.mark.parametrize(
+        "input_sample,expected",
+        [
+            ([[0, 0], [4, 3]], (1.0, 0)),
+        ],
+    )
+    def test_edge_cases(self, input_sample, expected):
+        res = barnard_exact(input_sample)
+        statistic, pvalue = res.statistic, res.pvalue
+        assert_equal(pvalue, expected[0])
+        assert_equal(statistic, expected[1])
+
+    @pytest.mark.parametrize(
+        "input_sample,expected",
+        [
+            ([[0, 5], [0, 10]], (1.0, np.nan)),
+            ([[5, 0], [10, 0]], (1.0, np.nan)),
+        ],
+    )
+    def test_row_or_col_zero(self, input_sample, expected):
+        res = barnard_exact(input_sample)
+        statistic, pvalue = res.statistic, res.pvalue
+        assert_equal(pvalue, expected[0])
+        assert_equal(statistic, expected[1])
+
+    @pytest.mark.parametrize(
+        "input_sample,expected",
+        [
+            ([[2, 7], [8, 2]], (-2.518474945157, 0.009886140845)),
+            ([[7, 200], [300, 8]], (-21.320036698460, 0.0)),
+            ([[21, 28], [1957, 6]], (-30.489638143953, 0.0)),
+        ],
+    )
+    @pytest.mark.parametrize("alternative", ["greater", "less"])
+    def test_less_greater(self, input_sample, expected, alternative):
+        """
+        "The expected values have been generated by R, using a resolution
+        for the nuisance parameter of 1e-6 :
+        ```R
+        library(Barnard)
+        options(digits=10)
+        a = barnard.test(2, 7, 8, 2, dp=1e-6, pooled=TRUE)
+        a$p.value[1]
+        ```
+        In this test, we are using the "one-sided" return value `a$p.value[1]`
+        to test our pvalue.
+        """
+        expected_stat, less_pvalue_expect = expected
+
+        if alternative == "greater":
+            input_sample = np.array(input_sample)[:, ::-1]
+            expected_stat = -expected_stat
+
+        res = barnard_exact(input_sample, alternative=alternative)
+        statistic, pvalue = res.statistic, res.pvalue
+        assert_allclose(
+            [statistic, pvalue], [expected_stat, less_pvalue_expect], atol=1e-7
+        )
+
+
+class TestBoschlooExact:
+    """Some tests to show that boschloo_exact() works correctly."""
+
+    ATOL = 1e-7
+
+    @pytest.mark.parametrize(
+        "input_sample,expected",
+        [
+            ([[2, 7], [8, 2]], (0.01852173, 0.009886142)),
+            ([[5, 1], [10, 10]], (0.9782609, 0.9450994)),
+            ([[5, 16], [20, 25]], (0.08913823, 0.05827348)),
+            ([[10, 5], [10, 1]], (0.1652174, 0.08565611)),
+            ([[5, 0], [1, 4]], (1, 1)),
+            ([[0, 1], [3, 2]], (0.5, 0.34375)),
+            ([[2, 7], [8, 2]], (0.01852173, 0.009886142)),
+            ([[7, 12], [8, 3]], (0.06406797, 0.03410916)),
+            ([[10, 24], [25, 37]], (0.2009359, 0.1512882)),
+        ],
+    )
+    def test_less(self, input_sample, expected):
+        """The expected values have been generated by R, using a resolution
+        for the nuisance parameter of 1e-8 :
+        ```R
+        library(Exact)
+        options(digits=10)
+        data <- matrix(c(43, 10, 40, 39), 2, 2, byrow=TRUE)
+        a = exact.test(data, method="Boschloo", alternative="less",
+                       tsmethod="central", np.interval=TRUE, beta=1e-8)
+        ```
+        """
+        res = boschloo_exact(input_sample, alternative="less")
+        statistic, pvalue = res.statistic, res.pvalue
+        assert_allclose([statistic, pvalue], expected, atol=self.ATOL)
+
+    @pytest.mark.parametrize(
+        "input_sample,expected",
+        [
+            ([[43, 40], [10, 39]], (0.0002875544, 0.0001615562)),
+            ([[2, 7], [8, 2]], (0.9990149, 0.9918327)),
+            ([[5, 1], [10, 10]], (0.1652174, 0.09008534)),
+            ([[5, 15], [20, 20]], (0.9849087, 0.9706997)),
+            ([[5, 16], [20, 25]], (0.972349, 0.9524124)),
+            ([[5, 0], [1, 4]], (0.02380952, 0.006865367)),
+            ([[0, 1], [3, 2]], (1, 1)),
+            ([[0, 2], [6, 4]], (1, 1)),
+            ([[2, 7], [8, 2]], (0.9990149, 0.9918327)),
+            ([[7, 12], [8, 3]], (0.9895302, 0.9771215)),
+            ([[10, 24], [25, 37]], (0.9012936, 0.8633275)),
+        ],
+    )
+    def test_greater(self, input_sample, expected):
+        """The expected values have been generated by R, using a resolution
+        for the nuisance parameter of 1e-8 :
+        ```R
+        library(Exact)
+        options(digits=10)
+        data <- matrix(c(43, 10, 40, 39), 2, 2, byrow=TRUE)
+        a = exact.test(data, method="Boschloo", alternative="greater",
+                       tsmethod="central", np.interval=TRUE, beta=1e-8)
+        ```
+        """
+        res = boschloo_exact(input_sample, alternative="greater")
+        statistic, pvalue = res.statistic, res.pvalue
+        assert_allclose([statistic, pvalue], expected, atol=self.ATOL)
+
+    @pytest.mark.parametrize(
+        "input_sample,expected",
+        [
+            ([[43, 40], [10, 39]], (0.0002875544, 0.0003231115)),
+            ([[2, 7], [8, 2]], (0.01852173, 0.01977228)),
+            ([[5, 1], [10, 10]], (0.1652174, 0.1801707)),
+            ([[5, 16], [20, 25]], (0.08913823, 0.116547)),
+            ([[5, 0], [1, 4]], (0.02380952, 0.01373073)),
+            ([[0, 1], [3, 2]], (0.5, 0.6875)),
+            ([[2, 7], [8, 2]], (0.01852173, 0.01977228)),
+            ([[7, 12], [8, 3]], (0.06406797, 0.06821831)),
+        ],
+    )
+    def test_two_sided(self, input_sample, expected):
+        """The expected values have been generated by R, using a resolution
+        for the nuisance parameter of 1e-8 :
+        ```R
+        library(Exact)
+        options(digits=10)
+        data <- matrix(c(43, 10, 40, 39), 2, 2, byrow=TRUE)
+        a = exact.test(data, method="Boschloo", alternative="two.sided",
+                       tsmethod="central", np.interval=TRUE, beta=1e-8)
+        ```
+        """
+        res = boschloo_exact(input_sample, alternative="two-sided", n=64)
+        # Need n = 64 for python 32-bit
+        statistic, pvalue = res.statistic, res.pvalue
+        assert_allclose([statistic, pvalue], expected, atol=self.ATOL)
+
+    def test_raises(self):
+        # test we raise an error for wrong input number of nuisances.
+        error_msg = (
+            "Number of points `n` must be strictly positive, found 0"
+        )
+        with assert_raises(ValueError, match=error_msg):
+            boschloo_exact([[1, 2], [3, 4]], n=0)
+
+        # test we raise an error for wrong shape of input.
+        error_msg = "The input `table` must be of shape \\(2, 2\\)."
+        with assert_raises(ValueError, match=error_msg):
+            boschloo_exact(np.arange(6).reshape(2, 3))
+
+        # Test all values must be positives
+        error_msg = "All values in `table` must be nonnegative."
+        with assert_raises(ValueError, match=error_msg):
+            boschloo_exact([[-1, 2], [3, 4]])
+
+        # Test value error on wrong alternative param
+        error_msg = (
+            r"`alternative` should be one of \('two-sided', 'less', "
+            r"'greater'\), found .*"
+        )
+        with assert_raises(ValueError, match=error_msg):
+            boschloo_exact([[1, 2], [3, 4]], "not-correct")
+
+    @pytest.mark.parametrize(
+        "input_sample,expected",
+        [
+            ([[0, 5], [0, 10]], (np.nan, np.nan)),
+            ([[5, 0], [10, 0]], (np.nan, np.nan)),
+        ],
+    )
+    def test_row_or_col_zero(self, input_sample, expected):
+        res = boschloo_exact(input_sample)
+        statistic, pvalue = res.statistic, res.pvalue
+        assert_equal(pvalue, expected[0])
+        assert_equal(statistic, expected[1])
+
+    def test_two_sided_gt_1(self):
+        # Check that returned p-value does not exceed 1 even when twice
+        # the minimum of the one-sided p-values does. See gh-15345.
+        tbl = [[1, 1], [13, 12]]
+        pl = boschloo_exact(tbl, alternative='less').pvalue
+        pg = boschloo_exact(tbl, alternative='greater').pvalue
+        assert 2*min(pl, pg) > 1
+        pt = boschloo_exact(tbl, alternative='two-sided').pvalue
+        assert pt == 1.0
+
+    @pytest.mark.parametrize("alternative", ("less", "greater"))
+    def test_against_fisher_exact(self, alternative):
+        # Check that the statistic of `boschloo_exact` is the same as the
+        # p-value of `fisher_exact` (for one-sided tests). See gh-15345.
+        tbl = [[2, 7], [8, 2]]
+        boschloo_stat = boschloo_exact(tbl, alternative=alternative).statistic
+        fisher_p = stats.fisher_exact(tbl, alternative=alternative)[1]
+        assert_allclose(boschloo_stat, fisher_p)
+
+
+@make_xp_test_case(cramervonmises_2samp)
+class TestCvm_2samp:
+    @pytest.mark.parametrize('args', [([], np.arange(5)),
+                                      (np.arange(5), [1])])
+    def test_too_small_input(self, args, xp):
+        args = (xp.asarray(arg, dtype=xp_default_dtype(xp)) for arg in args)
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = cramervonmises_2samp(*args)
+            xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+            xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+    def test_invalid_input(self, xp):
+        y = xp.arange(5)
+        msg = 'method must be either auto, exact or asymptotic'
+        with pytest.raises(ValueError, match=msg):
+            cramervonmises_2samp(y, y, 'xyz')
+
+    def test_list_input(self):  # list input only relevant for NumPy
+        x = [2, 3, 4, 7, 6]
+        y = [0.2, 0.7, 12, 18]
+        r1 = cramervonmises_2samp(x, y)
+        r2 = cramervonmises_2samp(np.array(x), np.array(y))
+        assert_equal((r1.statistic, r1.pvalue), (r2.statistic, r2.pvalue))
+
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_example_conover(self, dtype, xp):
+        # Example 2 in Section 6.2 of W.J. Conover: Practical Nonparametric
+        # Statistics, 1971.
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        x = xp.asarray([7.6, 8.4, 8.6, 8.7, 9.3, 9.9, 10.1, 10.6, 11.2], dtype=dtype)
+        y = xp.asarray([5.2, 5.7, 5.9, 6.5, 6.8, 8.2, 9.1, 9.8,
+                        10.8, 11.3, 11.5, 12.3, 12.5, 13.4, 14.6], dtype=dtype)
+        r = cramervonmises_2samp(x, y)
+        xp_assert_close(r.statistic, xp.asarray(0.262, dtype=dtype), atol=1e-3)
+        xp_assert_close(r.pvalue, xp.asarray(.18, dtype=dtype), atol=1e-2)
+
+    @pytest.mark.parametrize('statistic, m, n, pval',
+                             [(710, 5, 6, 48./462),
+                              (1897, 7, 7, 117./1716),
+                              (576, 4, 6, 2./210),
+                              (1764, 6, 7, 2./1716)])
+    def test_exact_pvalue(self, statistic, m, n, pval):  # only implemented w/ NumPy
+        # the exact values are taken from Anderson: On the distribution of the
+        # two-sample Cramer-von-Mises criterion, 1962.
+        # The values are taken from Table 2, 3, 4 and 5
+        assert_equal(_pval_cvm_2samp_exact(statistic, m, n), pval)
+
+    @pytest.mark.xslow
+    def test_large_sample(self, xp):
+        # for large samples, the statistic U gets very large
+        # do a sanity check that p-value is not 0, 1 or nan
+        rng = np.random.default_rng(4367)
+        x = distributions.norm.rvs(size=1000000, random_state=rng)
+        y = distributions.norm.rvs(size=900000, random_state=rng)
+        x, y = xp.asarray(x), xp.asarray(y)
+        r = cramervonmises_2samp(x, y)
+        assert 0 < r.pvalue < 1
+        r = cramervonmises_2samp(x, y+0.1)
+        assert 0 < r.pvalue < 1
+
+    def test_exact_vs_asymptotic(self, xp):
+        rng = np.random.RandomState(0)
+        x = xp.asarray(rng.random(7))
+        y = xp.asarray(rng.random(8))
+        r1 = cramervonmises_2samp(x, y, method='exact')
+        r2 = cramervonmises_2samp(x, y, method='asymptotic')
+        xp_assert_equal(r1.statistic, r2.statistic)
+        xp_assert_close(r1.pvalue, r2.pvalue, atol=1e-2)
+
+    def test_method_auto(self, xp):
+        x = xp.arange(20.)
+        y = xp.asarray([0.5, 4.7, 13.1])
+        r1 = cramervonmises_2samp(x, y, method='exact')
+        r2 = cramervonmises_2samp(x, y, method='auto')
+        xp_assert_equal(r1.pvalue, r2.pvalue)
+        # switch to asymptotic if one sample has more than 20 observations
+        x = xp.arange(21.)
+        r1 = cramervonmises_2samp(x, y, method='asymptotic')
+        r2 = cramervonmises_2samp(x, y, method='auto')
+        xp_assert_equal(r1.pvalue, r2.pvalue)
+
+    def test_same_input(self, xp):
+        # make sure trivial edge case can be handled
+        # note that _cdf_cvm_inf(0) = nan. implementation avoids nan by
+        # returning pvalue=1 for very small values of the statistic
+        x = xp.arange(15)
+        res = cramervonmises_2samp(x, x)
+        xp_assert_equal(res.statistic, xp.asarray(0.))
+        xp_assert_equal(res.pvalue, xp.asarray(1.))
+        # check exact p-value
+        res = cramervonmises_2samp(x[:4], x[:4])
+        xp_assert_equal(res.statistic, xp.asarray(0.))
+        xp_assert_equal(res.pvalue, xp.asarray(1.))
+
+
+class TestTukeyHSD:
+
+    data_same_size = ([24.5, 23.5, 26.4, 27.1, 29.9],
+                      [28.4, 34.2, 29.5, 32.2, 30.1],
+                      [26.1, 28.3, 24.3, 26.2, 27.8])
+    data_diff_size = ([24.5, 23.5, 26.28, 26.4, 27.1, 29.9, 30.1, 30.1],
+                      [28.4, 34.2, 29.5, 32.2, 30.1],
+                      [26.1, 28.3, 24.3, 26.2, 27.8])
+    extreme_size = ([24.5, 23.5, 26.4],
+                    [28.4, 34.2, 29.5, 32.2, 30.1, 28.4, 34.2, 29.5, 32.2,
+                     30.1],
+                    [26.1, 28.3, 24.3, 26.2, 27.8])
+
+    sas_same_size = """
+    Comparison LowerCL Difference UpperCL Significance
+    2 - 3	0.6908830568	4.34	7.989116943	    1
+    2 - 1	0.9508830568	4.6 	8.249116943 	1
+    3 - 2	-7.989116943	-4.34	-0.6908830568	1
+    3 - 1	-3.389116943	0.26	3.909116943	    0
+    1 - 2	-8.249116943	-4.6	-0.9508830568	1
+    1 - 3	-3.909116943	-0.26	3.389116943	    0
+    """
+
+    sas_diff_size = """
+    Comparison LowerCL Difference UpperCL Significance
+    2 - 1	0.2679292645	3.645	7.022070736	    1
+    2 - 3	0.5934764007	4.34	8.086523599	    1
+    1 - 2	-7.022070736	-3.645	-0.2679292645	1
+    1 - 3	-2.682070736	0.695	4.072070736	    0
+    3 - 2	-8.086523599	-4.34	-0.5934764007	1
+    3 - 1	-4.072070736	-0.695	2.682070736	    0
+    """
+
+    sas_extreme = """
+    Comparison LowerCL Difference UpperCL Significance
+    2 - 3	1.561605075	    4.34	7.118394925	    1
+    2 - 1	2.740784879	    6.08	9.419215121	    1
+    3 - 2	-7.118394925	-4.34	-1.561605075	1
+    3 - 1	-1.964526566	1.74	5.444526566	    0
+    1 - 2	-9.419215121	-6.08	-2.740784879	1
+    1 - 3	-5.444526566	-1.74	1.964526566	    0
+    """
+
+    @pytest.mark.parametrize("data,res_expect_str,atol",
+                             ((data_same_size, sas_same_size, 1e-4),
+                              (data_diff_size, sas_diff_size, 1e-4),
+                              (extreme_size, sas_extreme, 1e-10),
+                              ),
+                             ids=["equal size sample",
+                                  "unequal sample size",
+                                  "extreme sample size differences"])
+    def test_compare_sas(self, data, res_expect_str, atol):
+        '''
+        SAS code used to generate results for each sample:
+        DATA ACHE;
+        INPUT BRAND RELIEF;
+        CARDS;
+        1 24.5
+        ...
+        3 27.8
+        ;
+        ods graphics on;   ODS RTF;ODS LISTING CLOSE;
+           PROC ANOVA DATA=ACHE;
+           CLASS BRAND;
+           MODEL RELIEF=BRAND;
+           MEANS BRAND/TUKEY CLDIFF;
+           TITLE 'COMPARE RELIEF ACROSS MEDICINES  - ANOVA EXAMPLE';
+           ods output  CLDiffs =tc;
+        proc print data=tc;
+            format LowerCL 17.16 UpperCL 17.16 Difference 17.16;
+            title "Output with many digits";
+        RUN;
+        QUIT;
+        ODS RTF close;
+        ODS LISTING;
+        '''
+        res_expect = np.asarray(res_expect_str.replace(" - ", " ").split()[5:],
+                                dtype=float).reshape((6, 6))
+        res_tukey = stats.tukey_hsd(*data)
+        conf = res_tukey.confidence_interval()
+        # loop over the comparisons
+        for i, j, l, s, h, sig in res_expect:
+            i, j = int(i) - 1, int(j) - 1
+            assert_allclose(conf.low[i, j], l, atol=atol)
+            assert_allclose(res_tukey.statistic[i, j], s, atol=atol)
+            assert_allclose(conf.high[i, j], h, atol=atol)
+            assert_allclose((res_tukey.pvalue[i, j] <= .05), sig == 1)
+
+    matlab_sm_siz = """
+        1	2	-8.2491590248597	-4.6	-0.9508409751403	0.0144483269098
+        1	3	-3.9091590248597	-0.26	3.3891590248597	0.9803107240900
+        2	3	0.6908409751403	4.34	7.9891590248597	0.0203311368795
+        """
+
+    matlab_diff_sz = """
+        1	2	-7.02207069748501	-3.645	-0.26792930251500 0.03371498443080
+        1	3	-2.68207069748500	0.695	4.07207069748500 0.85572267328807
+        2	3	0.59347644287720	4.34	8.08652355712281 0.02259047020620
+        """
+
+    @pytest.mark.parametrize("data,res_expect_str,atol",
+                             ((data_same_size, matlab_sm_siz, 1e-12),
+                              (data_diff_size, matlab_diff_sz, 1e-7)),
+                             ids=["equal size sample",
+                                  "unequal size sample"])
+    def test_compare_matlab(self, data, res_expect_str, atol):
+        """
+        vals = [24.5, 23.5,  26.4, 27.1, 29.9, 28.4, 34.2, 29.5, 32.2, 30.1,
+         26.1, 28.3, 24.3, 26.2, 27.8]
+        names = {'zero', 'zero', 'zero', 'zero', 'zero', 'one', 'one', 'one',
+         'one', 'one', 'two', 'two', 'two', 'two', 'two'}
+        [p,t,stats] = anova1(vals,names,"off");
+        [c,m,h,nms] = multcompare(stats, "CType","hsd");
+        """
+        res_expect = np.asarray(res_expect_str.split(),
+                                dtype=float).reshape((3, 6))
+        res_tukey = stats.tukey_hsd(*data)
+        conf = res_tukey.confidence_interval()
+        # loop over the comparisons
+        for i, j, l, s, h, p in res_expect:
+            i, j = int(i) - 1, int(j) - 1
+            assert_allclose(conf.low[i, j], l, atol=atol)
+            assert_allclose(res_tukey.statistic[i, j], s, atol=atol)
+            assert_allclose(conf.high[i, j], h, atol=atol)
+            assert_allclose(res_tukey.pvalue[i, j], p, atol=atol)
+
+    def test_compare_r(self):
+        """
+        Testing against results and p-values from R:
+        from: https://www.rdocumentation.org/packages/stats/versions/3.6.2/
+        topics/TukeyHSD
+        > require(graphics)
+        > summary(fm1 <- aov(breaks ~ tension, data = warpbreaks))
+        > TukeyHSD(fm1, "tension", ordered = TRUE)
+        > plot(TukeyHSD(fm1, "tension"))
+        Tukey multiple comparisons of means
+        95% family-wise confidence level
+        factor levels have been ordered
+        Fit: aov(formula = breaks ~ tension, data = warpbreaks)
+        $tension
+        """
+        str_res = """
+                diff        lwr      upr     p adj
+        2 - 3  4.722222 -4.8376022 14.28205 0.4630831
+        1 - 3 14.722222  5.1623978 24.28205 0.0014315
+        1 - 2 10.000000  0.4401756 19.55982 0.0384598
+        """
+        res_expect = np.asarray(str_res.replace(" - ", " ").split()[5:],
+                                dtype=float).reshape((3, 6))
+        data = ([26, 30, 54, 25, 70, 52, 51, 26, 67,
+                 27, 14, 29, 19, 29, 31, 41, 20, 44],
+                [18, 21, 29, 17, 12, 18, 35, 30, 36,
+                 42, 26, 19, 16, 39, 28, 21, 39, 29],
+                [36, 21, 24, 18, 10, 43, 28, 15, 26,
+                 20, 21, 24, 17, 13, 15, 15, 16, 28])
+
+        res_tukey = stats.tukey_hsd(*data)
+        conf = res_tukey.confidence_interval()
+        # loop over the comparisons
+        for i, j, s, l, h, p in res_expect:
+            i, j = int(i) - 1, int(j) - 1
+            # atols are set to the number of digits present in the r result.
+            assert_allclose(conf.low[i, j], l, atol=1e-7)
+            assert_allclose(res_tukey.statistic[i, j], s, atol=1e-6)
+            assert_allclose(conf.high[i, j], h, atol=1e-5)
+            assert_allclose(res_tukey.pvalue[i, j], p, atol=1e-7)
+
+    def test_engineering_stat_handbook(self):
+        '''
+        Example sourced from:
+        https://www.itl.nist.gov/div898/handbook/prc/section4/prc471.htm
+        '''
+        group1 = [6.9, 5.4, 5.8, 4.6, 4.0]
+        group2 = [8.3, 6.8, 7.8, 9.2, 6.5]
+        group3 = [8.0, 10.5, 8.1, 6.9, 9.3]
+        group4 = [5.8, 3.8, 6.1, 5.6, 6.2]
+        res = stats.tukey_hsd(group1, group2, group3, group4)
+        conf = res.confidence_interval()
+        lower = np.asarray([
+            [0, 0, 0, -2.25],
+            [.29, 0, -2.93, .13],
+            [1.13, 0, 0, .97],
+            [0, 0, 0, 0]])
+        upper = np.asarray([
+            [0, 0, 0, 1.93],
+            [4.47, 0, 1.25, 4.31],
+            [5.31, 0, 0, 5.15],
+            [0, 0, 0, 0]])
+
+        for (i, j) in [(1, 0), (2, 0), (0, 3), (1, 2), (2, 3)]:
+            assert_allclose(conf.low[i, j], lower[i, j], atol=1e-2)
+            assert_allclose(conf.high[i, j], upper[i, j], atol=1e-2)
+
+    def test_rand_symm(self):
+        # test some expected identities of the results
+        rng = np.random.default_rng(2699550179)
+        data = rng.random((3, 100))
+        res = stats.tukey_hsd(*data)
+        conf = res.confidence_interval()
+        # the confidence intervals should be negated symmetric of each other
+        assert_equal(conf.low, -conf.high.T)
+        # the `high` and `low` center diagonals should be the same since the
+        # mean difference in a self comparison is 0.
+        assert_equal(np.diagonal(conf.high), conf.high[0, 0])
+        assert_equal(np.diagonal(conf.low), conf.low[0, 0])
+        # statistic array should be antisymmetric with zeros on the diagonal
+        assert_equal(res.statistic, -res.statistic.T)
+        assert_equal(np.diagonal(res.statistic), 0)
+        # p-values should be symmetric and 1 when compared to itself
+        assert_equal(res.pvalue, res.pvalue.T)
+        assert_equal(np.diagonal(res.pvalue), 1)
+
+    def test_no_inf(self):
+        with assert_raises(ValueError, match="...must be finite."):
+            stats.tukey_hsd([1, 2, 3], [2, np.inf], [6, 7, 3])
+
+    def test_is_1d(self):
+        with assert_raises(ValueError, match="...must be one-dimensional"):
+            stats.tukey_hsd([[1, 2], [2, 3]], [2, 5], [5, 23, 6])
+
+    def test_no_empty(self):
+        with assert_raises(ValueError, match="...must be greater than one"):
+            stats.tukey_hsd([], [2, 5], [4, 5, 6])
+
+    def test_equal_var_input_validation(self):
+        msg = "Expected a boolean value for 'equal_var'"
+        with assert_raises(TypeError, match=msg):
+            stats.tukey_hsd([1, 2, 3], [2, 5], [6, 7], equal_var="False")
+
+    @pytest.mark.parametrize("nargs", (0, 1))
+    def test_not_enough_treatments(self, nargs):
+        with assert_raises(ValueError, match="...more than 1 treatment."):
+            stats.tukey_hsd(*([[23, 7, 3]] * nargs))
+
+    @pytest.mark.parametrize("cl", [-.5, 0, 1, 2])
+    def test_conf_level_invalid(self, cl):
+        with assert_raises(ValueError, match="must be between 0 and 1"):
+            r = stats.tukey_hsd([23, 7, 3], [3, 4], [9, 4])
+            r.confidence_interval(cl)
+
+    def test_2_args_ttest(self):
+        # that with 2 treatments the `pvalue` is equal to that of `ttest_ind`
+        res_tukey = stats.tukey_hsd(*self.data_diff_size[:2])
+        res_ttest = stats.ttest_ind(*self.data_diff_size[:2])
+        assert_allclose(res_ttest.pvalue, res_tukey.pvalue[0, 1])
+        assert_allclose(res_ttest.pvalue, res_tukey.pvalue[1, 0])
+
+
+class TestGamesHowell:
+    # data with unequal variances
+    data_same_size = ([24., 23., 31., 51.],
+                      [34., 18., 18., 26.],
+                      [17., 68., 59.,  7.])
+
+    data_diff_size = ([30., 23., 51.],
+                      [-81., 71., -27., 63.],
+                      [42., 11., 29., 19., 50.],
+                      [23., 22., 20., 18., 9.])
+
+    spss_same_size = """
+            Mean Diff      Lower Bound         Upper Bound         Sig
+    0 - 1   8.25000000    -16.5492749527311    33.0492749527311    0.558733632413559
+    0 - 2  -5.50000000    -63.6702454316458    52.6702454316458    0.941147750599221
+    1 - 2  -13.7500000    -74.3174374251372    46.8174374251372    0.682983914946841
+    """
+
+    spss_diff_size = """
+             Mean Diff       Lower Bound        Upper Bound         Sig
+    0 - 1	 28.16666667    -141.985416377670   198.318749711003	0.8727542747886180
+    0 - 2	 4.466666667	-37.2830676783904   46.2164010117237	0.9752628408671710
+    0 - 3	 16.26666667	-35.0933112382470   67.6266445715803	0.4262506151302880
+    1 - 2	-23.70000000	-195.315617201249   147.915617201249	0.9148950609000590
+    1 - 3	-11.90000000	-188.105478728519   164.305478728519	0.9861432250093960
+    2 - 3	 11.80000000	-16.2894857524254	39.8894857524254    0.4755344436335670
+    """
+
+    @pytest.mark.xslow
+    @pytest.mark.parametrize("data, res_expect_str",
+                            ((data_same_size, spss_same_size),
+                            (data_diff_size, spss_diff_size)),
+                            ids=["equal size sample",
+                                 "unequal sample size"])
+    def test_compare_spss(self, data, res_expect_str):
+        """
+        DATA LIST LIST /Group (F1.0) Value (F8.2).
+        BEGIN DATA
+        0 24
+        0 23
+        0 31
+        0 51
+        1 34
+        1 18
+        1 18
+        1 26
+        2 17
+        2 68
+        2 59
+        2 7
+        END DATA.
+
+        ONEWAY Value BY Group
+            /MISSING ANALYSIS
+            /POSTHOC=GH ALPHA(0.05).
+        """
+        res_expect = np.asarray(
+            res_expect_str.replace(" - ", " ").split()[7:],
+            dtype=float).reshape(-1, 6)
+        res_games = stats.tukey_hsd(*data, equal_var=False)
+        conf = res_games.confidence_interval()
+        # loop over the comparisons
+        for i, j, s, l, h, p in res_expect:
+            i, j = int(i), int(j)
+            assert_allclose(res_games.statistic[i, j], s, atol=1e-8)
+            assert_allclose(res_games.pvalue[i, j], p, atol=1e-8)
+            assert_allclose(conf.low[i, j], l, atol=1e-6)
+            assert_allclose(conf.high[i, j], h, atol=1e-5)
+
+    r_same_size = """
+                  q value             Pr(>|q|)
+    1 - 0 == 0   -1.5467805948856344  0.55873362851759
+    2 - 0 == 0    0.4726721776628535  0.94114775035993
+    2 - 1 == 0    1.246837541297872   0.68298393799782
+    """
+
+    r_diff_size = """
+                 q value             Pr(>|q|)
+    1 - 0 == 0  -1.0589317485313876  0.87275427357438
+    2 - 0 == 0  -0.5716222106144833  0.97526284087419
+    3 - 0 == 0  -2.6209678382077000  0.42625067714691
+    2 - 1 == 0   0.8971899898179028  0.91489506061850
+    3 - 1 == 0   0.4579447210555352  0.98614322544695
+    3 - 2 == 0  -2.198800177874794   0.47553444364614
+    """
+
+    @pytest.mark.parametrize("data, res_expect_str",
+                            ((data_same_size, r_same_size),
+                            (data_diff_size, r_diff_size)),
+                            ids=["equal size sample",
+                                 "unequal sample size"])
+    def test_compare_r(self, data, res_expect_str):
+        """
+        games-howell is provided by PMCMRplus package
+        https://search.r-project.org/CRAN/refmans/PMCMRplus/html/gamesHowellTest.html
+        > library("PMCMRplus")
+        > options(digits=16)
+        > table = data.frame(
+            values = c(24., 23., 31., 51., 34., 18., 18., 26., 17., 68., 59.,  7.),
+            groups = c("0", "0", "0", "0", "1", "1", "1", "1", "2", "2", "2", "2")
+          )
+        > table$groups = as.factor(table$groups)
+        > fit <-aov(values ~ groups, table)
+        > res = gamesHowellTest(fit)
+        > summary(res)
+        """
+        res_expect = np.asarray(
+            res_expect_str.replace(" - ", " ")
+            .replace(" == ", " ").split()[3:],
+            dtype=float).reshape(-1, 5)
+        res_games = stats.tukey_hsd(*data, equal_var=False)
+        # loop over the comparisons
+        # note confidence intervals are not provided by PMCMRplus
+        for j, i, _, _, p in res_expect:
+            i, j = int(i), int(j)
+            assert_allclose(res_games.pvalue[i, j], p, atol=1e-7)
+
+    # Data validation test has been covered by TestTukeyHSD
+    # like empty, 1d, inf, and lack of tretments
+    # because games_howell leverage _tukey_hsd_iv()
+
+class TestPoissonMeansTest:
+    @pytest.mark.parametrize("c1, n1, c2, n2, p_expect", (
+        # example from [1], 6. Illustrative examples: Example 1
+        [0, 100, 3, 100, 0.0884],
+        [2, 100, 6, 100, 0.1749]
+    ))
+    def test_paper_examples(self, c1, n1, c2, n2, p_expect):
+        res = stats.poisson_means_test(c1, n1, c2, n2)
+        assert_allclose(res.pvalue, p_expect, atol=1e-4)
+
+    @pytest.mark.parametrize("c1, n1, c2, n2, p_expect, alt, d", (
+        # These test cases are produced by the wrapped fortran code from the
+        # original authors. Using a slightly modified version of this fortran,
+        # found here, https://github.com/nolanbconaway/poisson-etest,
+        # additional tests were created.
+        [20, 10, 20, 10, 0.9999997568929630, 'two-sided', 0],
+        [10, 10, 10, 10, 0.9999998403241203, 'two-sided', 0],
+        [50, 15, 1, 1, 0.09920321053409643, 'two-sided', .05],
+        [3, 100, 20, 300, 0.12202725450896404, 'two-sided', 0],
+        [3, 12, 4, 20, 0.40416087318539173, 'greater', 0],
+        [4, 20, 3, 100, 0.008053640402974236, 'greater', 0],
+        # publishing paper does not include a `less` alternative,
+        # so it was calculated with switched argument order and
+        # alternative="greater"
+        [4, 20, 3, 10, 0.3083216325432898, 'less', 0],
+        [1, 1, 50, 15, 0.09322998607245102, 'less', 0]
+    ))
+    def test_fortran_authors(self, c1, n1, c2, n2, p_expect, alt, d):
+        res = stats.poisson_means_test(c1, n1, c2, n2, alternative=alt, diff=d)
+        assert_allclose(res.pvalue, p_expect, atol=2e-6, rtol=1e-16)
+
+    def test_different_results(self):
+        # The implementation in Fortran is known to break down at higher
+        # counts and observations, so we expect different results. By
+        # inspection we can infer the p-value to be near one.
+        count1, count2 = 10000, 10000
+        nobs1, nobs2 = 10000, 10000
+        res = stats.poisson_means_test(count1, nobs1, count2, nobs2)
+        assert_allclose(res.pvalue, 1)
+
+    def test_less_than_zero_lambda_hat2(self):
+        # demonstrates behavior that fixes a known fault from original Fortran.
+        # p-value should clearly be near one.
+        count1, count2 = 0, 0
+        nobs1, nobs2 = 1, 1
+        res = stats.poisson_means_test(count1, nobs1, count2, nobs2)
+        assert_allclose(res.pvalue, 1)
+
+    def test_input_validation(self):
+        count1, count2 = 0, 0
+        nobs1, nobs2 = 1, 1
+
+        # test non-integral events
+        message = '`k1` and `k2` must be integers.'
+        with assert_raises(TypeError, match=message):
+            stats.poisson_means_test(.7, nobs1, count2, nobs2)
+        with assert_raises(TypeError, match=message):
+            stats.poisson_means_test(count1, nobs1, .7, nobs2)
+
+        # test negative events
+        message = '`k1` and `k2` must be greater than or equal to 0.'
+        with assert_raises(ValueError, match=message):
+            stats.poisson_means_test(-1, nobs1, count2, nobs2)
+        with assert_raises(ValueError, match=message):
+            stats.poisson_means_test(count1, nobs1, -1, nobs2)
+
+        # test negative sample size
+        message = '`n1` and `n2` must be greater than 0.'
+        with assert_raises(ValueError, match=message):
+            stats.poisson_means_test(count1, -1, count2, nobs2)
+        with assert_raises(ValueError, match=message):
+            stats.poisson_means_test(count1, nobs1, count2, -1)
+
+        # test negative difference
+        message = 'diff must be greater than or equal to 0.'
+        with assert_raises(ValueError, match=message):
+            stats.poisson_means_test(count1, nobs1, count2, nobs2, diff=-1)
+
+        # test invalid alternative
+        message = 'Alternative must be one of ...'
+        with assert_raises(ValueError, match=message):
+            stats.poisson_means_test(1, 2, 1, 2, alternative='error')
+
+
+@make_xp_test_case(stats.bws_test)
+class TestBWSTest:
+
+    def test_bws_input_validation(self, xp):
+        rng = np.random.default_rng(4571775098104213308)
+
+        x, y = rng.random(size=(2, 7))
+        x, y = xp.asarray(x), xp.asarray(y)
+
+        message = '`axis` must be an integer or None.'
+        with pytest.raises(ValueError, match=message):
+            stats.bws_test(x, y, axis=1.5)
+
+        message = '`axis` is not compatible with the shapes of the inputs.'
+        with pytest.raises(ValueError, match=message):
+            stats.bws_test(x, y, axis=3)
+
+        message = '`x` and `y` must contain...'
+        with pytest.raises(ValueError, match=message):
+            stats.bws_test(x, xp.asarray([], dtype=x.dtype))
+
+        message = 'alternative` must be one of...'
+        with pytest.raises(ValueError, match=message):
+            stats.bws_test(x, y, alternative='ekki-ekki')
+
+        message = 'method` must be an instance of...'
+        with pytest.raises(ValueError, match=message):
+            stats.bws_test(x, y, method=42)
+
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_against_published_reference(self, dtype, xp):
+        # Test against Example 2 in bws_test Reference [1], pg 9
+        # https://link.springer.com/content/pdf/10.1007/BF02762032.pdf
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        x = xp.asarray([1, 2, 3, 4, 6, 7, 8], dtype=dtype)
+        y = xp.asarray([5, 9, 10, 11, 12, 13, 14], dtype=dtype)
+        res = stats.bws_test(x, y, alternative='two-sided')
+        xp_assert_close(res.statistic, xp.asarray(5.132, dtype=dtype), atol=1e-3)
+        xp_assert_close(res.pvalue, xp.asarray(10/3432, dtype=dtype))
+
+    @pytest.mark.parametrize(('alternative', 'statistic', 'pvalue'),
+                             [('two-sided', 1.7510204081633, 0.1264422777777),
+                              ('less', -1.7510204081633, 0.05754662004662),
+                              ('greater', -1.7510204081633, 0.9424533799534)])
+    def test_against_R(self, alternative, statistic, pvalue, xp):
+        # Test against R library BWStest function bws_test
+        # library(BWStest)
+        # options(digits=16)
+        # x = c(...)
+        # y = c(...)
+        # bws_test(x, y, alternative='two.sided')
+        rng = np.random.default_rng(4571775098104213308)
+        x, y = rng.random(size=(2, 7))
+        x, y = xp.asarray(x, dtype=xp.float64), xp.asarray(y, dtype=xp.float64)
+        res = stats.bws_test(x, y, alternative=alternative)
+        statistic = xp.asarray(statistic, dtype=xp.float64)
+        pvalue = xp.asarray(pvalue, dtype=xp.float64)
+        xp_assert_close(res.statistic, statistic, rtol=1e-13)
+        xp_assert_close(res.pvalue, pvalue, atol=1e-2, rtol=1e-1)
+
+    @pytest.mark.parametrize(('alternative', 'statistic', 'pvalue'),
+                             [('two-sided', 1.142629265891, 0.2903950180801),
+                              ('less', 0.99629665877411, 0.8545660222131),
+                              ('greater', 0.99629665877411, 0.1454339777869)])
+    def test_against_R_imbalanced(self, alternative, statistic, pvalue, xp):
+        # Test against R library BWStest function bws_test
+        # library(BWStest)
+        # options(digits=16)
+        # x = c(...)
+        # y = c(...)
+        # bws_test(x, y, alternative='two.sided')
+        rng = np.random.default_rng(5429015622386364034)
+        x = rng.random(size=9)
+        y = rng.random(size=8)
+        x, y = xp.asarray(x), xp.asarray(y)
+        res = stats.bws_test(x, y, alternative=alternative)
+        x, y = xp.asarray(x, dtype=xp.float64), xp.asarray(y, dtype=xp.float64)
+        res = stats.bws_test(x, y, alternative=alternative)
+        statistic = xp.asarray(statistic, dtype=xp.float64)
+        pvalue = xp.asarray(pvalue, dtype=xp.float64)
+        xp_assert_close(res.statistic, statistic, rtol=1e-13)
+        xp_assert_close(res.pvalue, pvalue, atol=1e-2, rtol=1e-1)
+
+    def test_method(self, xp):
+        # Test that `method` parameter has the desired effect
+        rng = np.random.default_rng(1520514347193347862)
+        x, y = rng.random(size=(2, 10))
+        x, y = xp.asarray(x), xp.asarray(y)
+
+        rng = np.random.default_rng(1520514347193347862)
+        method = stats.PermutationMethod(n_resamples=10, rng=rng)
+        res1 = stats.bws_test(x, y, method=method)
+
+        assert res1.null_distribution.shape[0] == 10
+
+        rng = np.random.default_rng(1520514347193347862)
+        method = stats.PermutationMethod(n_resamples=10, rng=rng)
+        res2 = stats.bws_test(x, y, method=method)
+
+        xp_assert_close(res1.null_distribution, res2.null_distribution)
+
+        rng = np.random.default_rng(5205143471933478621)
+        method = stats.PermutationMethod(n_resamples=10, rng=rng)
+        res3 = stats.bws_test(x, y, method=method)
+
+        assert xp.all(xp.abs(res3.null_distribution - res1.null_distribution) > 0.1)
+
+    def test_directions(self, xp):
+        # Sanity check of the sign of the one-sided statistic
+        rng = np.random.default_rng(1520514347193347862)
+        x = xp.asarray(rng.random(size=5))
+        y = x - 1.
+
+        pmin = xp.asarray(1 / 252, dtype=xp.float64)
+        pmax = xp.asarray(1, dtype=xp.float64)
+
+        res = stats.bws_test(x, y, alternative='greater')
+        assert res.statistic > 0
+        xp_assert_equal(res.pvalue, pmin)
+
+        res = stats.bws_test(x, y, alternative='less')
+        assert res.statistic > 0
+        xp_assert_equal(res.pvalue, pmax)
+
+        res = stats.bws_test(y, x, alternative='less')
+        assert res.statistic < 0
+        xp_assert_equal(res.pvalue, pmin)
+
+        res = stats.bws_test(y, x, alternative='greater')
+        assert res.statistic < 0
+        xp_assert_equal(res.pvalue, pmax)
+
+    @pytest.mark.parametrize('axis', [0, 1])
+    def test_nd(self, axis, xp):
+        rng = np.random.default_rng(1520514347193347862)
+        z = rng.random(size=(2, 3, 5))
+
+        x, y = z
+        res = stats.bws_test(xp.asarray(x), xp.asarray(y), axis=axis)
+
+        x, y = np.moveaxis(z, axis+1, -1)
+        ref_statistic, ref_pvalue = [], []
+        for x_, y_ in zip(x, y):
+            ref = stats.bws_test(xp.asarray(x_), xp.asarray(y_))
+            ref_statistic.append(ref.statistic)
+            ref_pvalue.append(ref.pvalue)
+        ref_statistic, ref_pvalue = xp.stack(ref_statistic), xp.stack(ref_pvalue)
+
+        xp_assert_close(res.statistic, ref_statistic)
+        xp_assert_close(res.pvalue, ref_pvalue)
+
+    def test_axis_None(self, xp):
+        rng = np.random.default_rng(5205143471933478621)
+        x, y = rng.random(size=(2, 2, 3))
+        x, y = xp.asarray(x), xp.asarray(y)
+        res = stats.bws_test(x, y, axis=None)
+        ref = stats.bws_test(xp_ravel(x), xp_ravel(y))
+        xp_assert_close(res.statistic, ref.statistic)
+        xp_assert_close(res.pvalue, ref.pvalue)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_kdeoth.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_kdeoth.py
new file mode 100644
index 0000000000000000000000000000000000000000..104d31f9ec0c5d93ae89246d8625a14694e64990
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_kdeoth.py
@@ -0,0 +1,678 @@
+from scipy import stats, linalg, integrate
+import numpy as np
+from numpy.testing import (assert_almost_equal, assert_, assert_equal,
+                           assert_array_almost_equal,
+                           assert_array_almost_equal_nulp, assert_allclose)
+import pytest
+from pytest import raises as assert_raises
+
+
+def test_kde_1d():
+    #some basic tests comparing to normal distribution
+    rng = np.random.default_rng(8765678)
+    n_basesample = 500
+    xn = rng.normal(0, 1, n_basesample)
+    xnmean = xn.mean()
+    xnstd = xn.std(ddof=1)
+
+    # get kde for original sample
+    gkde = stats.gaussian_kde(xn)
+
+    # evaluate the density function for the kde for some points
+    xx = np.asarray([0.1, 0.5, 0.9])
+    loc, scale = gkde.dataset, np.sqrt(gkde.covariance)
+    assert_allclose(
+        gkde(xx),
+        stats.norm.pdf(xx[:, None], loc=loc, scale=scale).sum(axis=-1) / gkde.n,
+        rtol=5e-14
+    )
+
+    xs = np.linspace(-7, 7, 501)
+    kdepdf = gkde.evaluate(xs)
+    normpdf = stats.norm.pdf(xs, loc=xnmean, scale=xnstd)
+    intervall = xs[1] - xs[0]
+
+    assert_(np.sum((kdepdf - normpdf)**2)*intervall < 0.01)
+    prob1 = gkde.integrate_box_1d(xnmean, np.inf)
+    prob2 = gkde.integrate_box_1d(-np.inf, xnmean)
+    assert_almost_equal(prob1, 0.5, decimal=1)
+    assert_almost_equal(prob2, 0.5, decimal=1)
+    assert_almost_equal(gkde.integrate_box(xnmean, np.inf), prob1, decimal=13)
+    assert_almost_equal(gkde.integrate_box(-np.inf, xnmean), prob2, decimal=13)
+
+    assert_almost_equal(gkde.integrate_kde(gkde),
+                        (kdepdf**2).sum()*intervall, decimal=2)
+    assert_almost_equal(gkde.integrate_gaussian(xnmean, xnstd**2),
+                        (kdepdf*normpdf).sum()*intervall, decimal=2)
+
+
+def test_kde_1d_weighted():
+    #some basic tests comparing to normal distribution
+    rng = np.random.default_rng(8765678)
+    n_basesample = 500
+    xn = rng.normal(0, 1, n_basesample)
+    wn = rng.random(n_basesample)
+    xnmean = np.average(xn, weights=wn)
+    xnstd = np.sqrt(np.average((xn-xnmean)**2, weights=wn))
+
+    # get kde for original sample
+    gkde = stats.gaussian_kde(xn, weights=wn)
+
+    # evaluate the density function for the kde for some points
+    # evaluate the density function for the kde for some points
+    xx = np.asarray([0.1, 0.5, 0.9])
+    loc, scale = gkde.dataset, np.sqrt(gkde.covariance)
+
+    pdf = stats.norm.pdf
+    assert_allclose(
+        gkde(xx),
+        np.sum(pdf(xx[:, None], loc=loc, scale=scale) * gkde.weights, axis=-1),
+        rtol=5e-14
+    )
+
+    xs = np.linspace(-7, 7, 501)
+    kdepdf = gkde.evaluate(xs)
+    normpdf = stats.norm.pdf(xs, loc=xnmean, scale=xnstd)
+    intervall = xs[1] - xs[0]
+
+    assert_(np.sum((kdepdf - normpdf)**2)*intervall < 0.01)
+    prob1 = gkde.integrate_box_1d(xnmean, np.inf)
+    prob2 = gkde.integrate_box_1d(-np.inf, xnmean)
+    assert_almost_equal(prob1, 0.5, decimal=1)
+    assert_almost_equal(prob2, 0.5, decimal=1)
+    assert_almost_equal(gkde.integrate_box(xnmean, np.inf), prob1, decimal=13)
+    assert_almost_equal(gkde.integrate_box(-np.inf, xnmean), prob2, decimal=13)
+
+    assert_almost_equal(gkde.integrate_kde(gkde),
+                        (kdepdf**2).sum()*intervall, decimal=2)
+    assert_almost_equal(gkde.integrate_gaussian(xnmean, xnstd**2),
+                        (kdepdf*normpdf).sum()*intervall, decimal=2)
+
+
+@pytest.mark.parametrize("n_basesample",
+                         [
+                            20,
+                            pytest.param(500, marks=[pytest.mark.xslow])
+                         ]
+)
+def test_kde_2d(n_basesample):
+    #some basic tests comparing to normal distribution
+    rng = np.random.default_rng(8765678)
+
+    mean = np.array([1.0, 3.0])
+    covariance = np.array([[1.0, 2.0], [2.0, 6.0]])
+
+    # Need transpose (shape (2, 500)) for kde
+    xn = rng.multivariate_normal(mean, covariance, size=n_basesample).T
+
+    # get kde for original sample
+    gkde = stats.gaussian_kde(xn)
+
+    # evaluate vs multivariate normal, using the KDE definition
+    xx = np.asarray([[1, 2], [3, 4], [5, 6]])
+    arg = xx[:, None, :] - gkde.dataset.T
+    pdf = stats.multivariate_normal.pdf
+    assert_allclose(
+        gkde(xx.T),
+        pdf(arg, cov=gkde.covariance).sum(axis=-1) / gkde.n,
+        rtol=5e-14
+    )
+
+    # ... and cdf
+    cdf = stats.multivariate_normal.cdf
+    lo, hi = [-1, -2], [0, 0]
+    lo_, hi_ = lo - gkde.dataset.T, hi - gkde.dataset.T
+    assert_allclose(
+        gkde.integrate_box(lo, hi, rng=rng),
+        cdf(hi_, lower_limit=lo_, cov=gkde.covariance, rng=rng).sum(axis=-1) / gkde.n,
+        rtol=5e-7
+    )
+
+    # evaluate the density function for the kde for some points
+    x, y = np.mgrid[-7:7:500j, -7:7:500j]
+    grid_coords = np.vstack([x.ravel(), y.ravel()])
+    kdepdf = gkde.evaluate(grid_coords)
+    kdepdf = kdepdf.reshape(500, 500)
+
+    normpdf = stats.multivariate_normal.pdf(np.dstack([x, y]),
+                                            mean=mean, cov=covariance)
+    intervall = y.ravel()[1] - y.ravel()[0]
+
+    assert_(np.sum((kdepdf - normpdf)**2) * (intervall**2) < 0.01)
+
+    small = -1e100
+    large = 1e100
+    prob1 = gkde.integrate_box([small, mean[1]], [large, large], rng=rng)
+    prob2 = gkde.integrate_box([small, small], [large, mean[1]], rng=rng)
+
+    assert_almost_equal(prob1, 0.5, decimal=1)
+    assert_almost_equal(prob2, 0.5, decimal=1)
+    assert_almost_equal(gkde.integrate_kde(gkde),
+                        (kdepdf**2).sum()*(intervall**2), decimal=2)
+    assert_almost_equal(gkde.integrate_gaussian(mean, covariance),
+                        (kdepdf*normpdf).sum()*(intervall**2), decimal=2)
+
+
+@pytest.mark.parametrize("n_basesample",
+                         [
+                            20,
+                            pytest.param(500, marks=[pytest.mark.xslow])
+                         ]
+)
+def test_kde_2d_weighted(n_basesample):
+    #some basic tests comparing to normal distribution
+    rng = np.random.RandomState(8765678)
+
+    mean = np.array([1.0, 3.0])
+    covariance = np.array([[1.0, 2.0], [2.0, 6.0]])
+
+    # Need transpose (shape (2, 500)) for kde
+    xn = rng.multivariate_normal(mean, covariance, size=n_basesample).T
+    wn = rng.rand(n_basesample)
+
+    # get kde for original sample
+    gkde = stats.gaussian_kde(xn, weights=wn)
+
+
+    # evaluate vs multivariate normal, using the kde definition
+    xx = np.asarray([[1, 2], [3, 4], [5, 6]])
+    arg = xx[:, None, :] - gkde.dataset.T
+    pdf = stats.multivariate_normal.pdf
+    assert_allclose(
+        gkde(xx.T),
+        np.sum(pdf(arg, cov=gkde.covariance) * gkde.weights, axis=-1),
+        rtol=6e-14
+    )
+
+    # ... and cdf
+    cdf = stats.multivariate_normal.cdf
+    lo, hi = [-1, -2], [0, 0]
+    lo_, hi_ = lo - gkde.dataset.T, hi - gkde.dataset.T
+    assert_allclose(
+        gkde.integrate_box(lo, hi, rng=rng),
+        np.sum(cdf(hi_, lower_limit=lo_, cov=gkde.covariance, rng=rng) *
+               gkde.weights, axis=-1),
+        rtol=5e-6
+    )
+
+    # evaluate the density function for the kde for some points
+    x, y = np.mgrid[-7:7:500j, -7:7:500j]
+    grid_coords = np.vstack([x.ravel(), y.ravel()])
+    kdepdf = gkde.evaluate(grid_coords)
+    kdepdf = kdepdf.reshape(500, 500)
+
+    normpdf = stats.multivariate_normal.pdf(np.dstack([x, y]),
+                                            mean=mean, cov=covariance)
+    intervall = y.ravel()[1] - y.ravel()[0]
+
+    assert_(np.sum((kdepdf - normpdf)**2) * (intervall**2) < 0.01)
+
+    small = -1e100
+    large = 1e100
+    prob1 = gkde.integrate_box([small, mean[1]], [large, large], rng=rng)
+    prob2 = gkde.integrate_box([small, small], [large, mean[1]], rng=rng)
+
+    assert_almost_equal(prob1, 0.5, decimal=1)
+    assert_almost_equal(prob2, 0.5, decimal=1)
+    assert_almost_equal(gkde.integrate_kde(gkde),
+                        (kdepdf**2).sum()*(intervall**2), decimal=2)
+    assert_almost_equal(gkde.integrate_gaussian(mean, covariance),
+                        (kdepdf*normpdf).sum()*(intervall**2), decimal=2)
+
+
+def test_kde_bandwidth_method():
+    def scotts_factor(kde_obj):
+        """Same as default, just check that it works."""
+        return np.power(kde_obj.n, -1./(kde_obj.d+4))
+
+    rng = np.random.default_rng(8765678)
+    n_basesample = 50
+    xn = rng.normal(0, 1, n_basesample)
+
+    # Default
+    gkde = stats.gaussian_kde(xn)
+    # Supply a callable
+    gkde2 = stats.gaussian_kde(xn, bw_method=scotts_factor)
+    # Supply a scalar
+    gkde3 = stats.gaussian_kde(xn, bw_method=gkde.factor)
+
+    xs = np.linspace(-7,7,51)
+    kdepdf = gkde.evaluate(xs)
+    kdepdf2 = gkde2.evaluate(xs)
+    assert_almost_equal(kdepdf, kdepdf2)
+    kdepdf3 = gkde3.evaluate(xs)
+    assert_almost_equal(kdepdf, kdepdf3)
+
+    assert_raises(ValueError, stats.gaussian_kde, xn, bw_method='wrongstring')
+
+
+def test_kde_bandwidth_method_weighted():
+    def scotts_factor(kde_obj):
+        """Same as default, just check that it works."""
+        return np.power(kde_obj.neff, -1./(kde_obj.d+4))
+
+    rng = np.random.default_rng(8765678)
+    n_basesample = 50
+    xn = rng.normal(0, 1, n_basesample)
+
+    # Default
+    gkde = stats.gaussian_kde(xn)
+    # Supply a callable
+    gkde2 = stats.gaussian_kde(xn, bw_method=scotts_factor)
+    # Supply a scalar
+    gkde3 = stats.gaussian_kde(xn, bw_method=gkde.factor)
+
+    xs = np.linspace(-7,7,51)
+    kdepdf = gkde.evaluate(xs)
+    kdepdf2 = gkde2.evaluate(xs)
+    assert_almost_equal(kdepdf, kdepdf2)
+    kdepdf3 = gkde3.evaluate(xs)
+    assert_almost_equal(kdepdf, kdepdf3)
+
+    assert_raises(ValueError, stats.gaussian_kde, xn, bw_method='wrongstring')
+
+
+# Subclasses that should stay working (extracted from various sources).
+# Unfortunately the earlier design of gaussian_kde made it necessary for users
+# to create these kinds of subclasses, or call _compute_covariance() directly.
+
+class _kde_subclass1(stats.gaussian_kde):
+    def __init__(self, dataset):
+        self.dataset = np.atleast_2d(dataset)
+        self.d, self.n = self.dataset.shape
+        self.covariance_factor = self.scotts_factor
+        self._compute_covariance()
+
+
+class _kde_subclass2(stats.gaussian_kde):
+    def __init__(self, dataset):
+        self.covariance_factor = self.scotts_factor
+        super().__init__(dataset)
+
+
+class _kde_subclass4(stats.gaussian_kde):
+    def covariance_factor(self):
+        return 0.5 * self.silverman_factor()
+
+
+def test_gaussian_kde_subclassing():
+    x1 = np.array([-7, -5, 1, 4, 5], dtype=float)
+    xs = np.linspace(-10, 10, num=50)
+
+    # gaussian_kde itself
+    kde = stats.gaussian_kde(x1)
+    ys = kde(xs)
+
+    # subclass 1
+    kde1 = _kde_subclass1(x1)
+    y1 = kde1(xs)
+    assert_array_almost_equal_nulp(ys, y1, nulp=10)
+
+    # subclass 2
+    kde2 = _kde_subclass2(x1)
+    y2 = kde2(xs)
+    assert_array_almost_equal_nulp(ys, y2, nulp=10)
+
+    # subclass 3 was removed because we have no obligation to maintain support
+    # for user invocation of private methods
+
+    # subclass 4
+    kde4 = _kde_subclass4(x1)
+    y4 = kde4(x1)
+    y_expected = [0.06292987, 0.06346938, 0.05860291, 0.08657652, 0.07904017]
+
+    assert_array_almost_equal(y_expected, y4, decimal=6)
+
+    # Not a subclass, but check for use of _compute_covariance()
+    kde5 = kde
+    kde5.covariance_factor = lambda: kde.factor
+    kde5._compute_covariance()
+    y5 = kde5(xs)
+    assert_array_almost_equal_nulp(ys, y5, nulp=10)
+
+
+def test_gaussian_kde_covariance_caching():
+    x1 = np.array([-7, -5, 1, 4, 5], dtype=float)
+    xs = np.linspace(-10, 10, num=5)
+    # These expected values are from scipy 0.10, before some changes to
+    # gaussian_kde.  They were not compared with any external reference.
+    y_expected = [0.02463386, 0.04689208, 0.05395444, 0.05337754, 0.01664475]
+
+    # Set the bandwidth, then reset it to the default.
+    kde = stats.gaussian_kde(x1)
+    kde.set_bandwidth(bw_method=0.5)
+    kde.set_bandwidth(bw_method='scott')
+    y2 = kde(xs)
+
+    assert_array_almost_equal(y_expected, y2, decimal=7)
+
+
+def test_gaussian_kde_monkeypatch():
+    """Ugly, but people may rely on this.  See scipy pull request 123,
+    specifically the linked ML thread "Width of the Gaussian in stats.kde".
+    If it is necessary to break this later on, that is to be discussed on ML.
+    """
+    x1 = np.array([-7, -5, 1, 4, 5], dtype=float)
+    xs = np.linspace(-10, 10, num=50)
+
+    # The old monkeypatched version to get at Silverman's Rule.
+    kde = stats.gaussian_kde(x1)
+    kde.covariance_factor = kde.silverman_factor
+    kde._compute_covariance()
+    y1 = kde(xs)
+
+    # The new saner version.
+    kde2 = stats.gaussian_kde(x1, bw_method='silverman')
+    y2 = kde2(xs)
+
+    assert_array_almost_equal_nulp(y1, y2, nulp=10)
+
+
+def test_kde_integer_input():
+    """Regression test for #1181."""
+    x1 = np.arange(5)
+    kde = stats.gaussian_kde(x1)
+    y_expected = [0.13480721, 0.18222869, 0.19514935, 0.18222869, 0.13480721]
+    assert_array_almost_equal(kde(x1), y_expected, decimal=6)
+
+
+_ftypes = ['float32', 'float64', 'float96', 'float128', 'int32', 'int64']
+
+
+@pytest.mark.filterwarnings("ignore::DeprecationWarning")
+@pytest.mark.parametrize("bw_type", _ftypes + ["scott", "silverman"])
+@pytest.mark.parametrize("dtype", _ftypes)
+def test_kde_output_dtype(dtype, bw_type):
+    # Check whether the datatypes are available
+    dtype = getattr(np, dtype, None)
+
+    if bw_type in ["scott", "silverman"]:
+        bw = bw_type
+    else:
+        bw_type = getattr(np, bw_type, None)
+        bw = bw_type(3) if bw_type else None
+
+    if any(dt is None for dt in [dtype, bw]):
+        pytest.skip()
+
+    weights = np.arange(5, dtype=dtype)
+    dataset = np.arange(5, dtype=dtype)
+    k = stats.gaussian_kde(dataset, bw_method=bw, weights=weights)
+    points = np.arange(5, dtype=dtype)
+    result = k(points)
+    # weights are always cast to float64
+    assert result.dtype == np.result_type(dataset, points, np.float64(weights),
+                                          k.factor)
+
+
+def test_pdf_logpdf_validation():
+    rng = np.random.default_rng(64202298293133848336925499069837723291)
+    xn = rng.standard_normal((2, 10))
+    gkde = stats.gaussian_kde(xn)
+    xs = rng.standard_normal((3, 10))
+
+    msg = "points have dimension 3, dataset has dimension 2"
+    with pytest.raises(ValueError, match=msg):
+        gkde.logpdf(xs)
+
+
+def test_pdf_logpdf():
+    rng = np.random.default_rng(1)
+    n_basesample = 50
+    xn = rng.normal(0, 1, n_basesample)
+
+    # Default
+    gkde = stats.gaussian_kde(xn)
+
+    xs = np.linspace(-15, 12, 25)
+    pdf = gkde.evaluate(xs)
+    pdf2 = gkde.pdf(xs)
+    assert_almost_equal(pdf, pdf2, decimal=12)
+
+    logpdf = np.log(pdf)
+    logpdf2 = gkde.logpdf(xs)
+    assert_almost_equal(logpdf, logpdf2, decimal=12)
+
+    # There are more points than data
+    gkde = stats.gaussian_kde(xs)
+    pdf = np.log(gkde.evaluate(xn))
+    pdf2 = gkde.logpdf(xn)
+    assert_almost_equal(pdf, pdf2, decimal=12)
+
+
+def test_pdf_logpdf_weighted():
+    rng = np.random.default_rng(1)
+    n_basesample = 50
+    xn = rng.normal(0, 1, n_basesample)
+    wn = rng.random(n_basesample)
+
+    # Default
+    gkde = stats.gaussian_kde(xn, weights=wn)
+
+    xs = np.linspace(-15, 12, 25)
+    pdf = gkde.evaluate(xs)
+    pdf2 = gkde.pdf(xs)
+    assert_almost_equal(pdf, pdf2, decimal=12)
+
+    logpdf = np.log(pdf)
+    logpdf2 = gkde.logpdf(xs)
+    assert_almost_equal(logpdf, logpdf2, decimal=12)
+
+    # There are more points than data
+    rng = np.random.default_rng(4531935345)
+    gkde = stats.gaussian_kde(xs, weights=rng.random(len(xs)))
+    pdf = np.log(gkde.evaluate(xn))
+    pdf2 = gkde.logpdf(xn)
+    assert_almost_equal(pdf, pdf2, decimal=12)
+
+
+def test_marginal_1_axis():
+    rng = np.random.default_rng(6111799263660870475)
+    n_data = 50
+    n_dim = 10
+    dataset = rng.normal(size=(n_dim, n_data))
+    points = rng.normal(size=(n_dim, 3))
+
+    dimensions = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])  # dimensions to keep
+
+    kde = stats.gaussian_kde(dataset)
+    marginal = kde.marginal(dimensions)
+    pdf = marginal.pdf(points[dimensions])
+
+    def marginal_pdf_single(point):
+        def f(x):
+            x = np.concatenate(([x], point[dimensions]))
+            return kde.pdf(x)[0]
+        return integrate.quad(f, -np.inf, np.inf)[0]
+
+    def marginal_pdf(points):
+        return np.apply_along_axis(marginal_pdf_single, axis=0, arr=points)
+
+    ref = marginal_pdf(points)
+
+    assert_allclose(pdf, ref, rtol=1e-6)
+
+
+@pytest.mark.xslow
+def test_marginal_2_axis():
+    rng = np.random.default_rng(6111799263660870475)
+    n_data = 30
+    n_dim = 4
+    dataset = rng.normal(size=(n_dim, n_data))
+    points = rng.normal(size=(n_dim, 3))
+
+    dimensions = np.array([1, 3])  # dimensions to keep
+
+    kde = stats.gaussian_kde(dataset)
+    marginal = kde.marginal(dimensions)
+    pdf = marginal.pdf(points[dimensions])
+
+    def marginal_pdf(points):
+        def marginal_pdf_single(point):
+            def f(y, x):
+                w, z = point[dimensions]
+                x = np.array([x, w, y, z])
+                return kde.pdf(x)[0]
+            return integrate.dblquad(f, -np.inf, np.inf, -np.inf, np.inf)[0]
+
+        return np.apply_along_axis(marginal_pdf_single, axis=0, arr=points)
+
+    ref = marginal_pdf(points)
+
+    assert_allclose(pdf, ref, rtol=1e-6)
+
+
+def test_marginal_iv():
+    # test input validation
+    rng = np.random.default_rng(6111799263660870475)
+    n_data = 30
+    n_dim = 4
+    dataset = rng.normal(size=(n_dim, n_data))
+    points = rng.normal(size=(n_dim, 3))
+
+    kde = stats.gaussian_kde(dataset)
+
+    # check that positive and negative indices are equivalent
+    dimensions1 = [-1, 1]
+    marginal1 = kde.marginal(dimensions1)
+    pdf1 = marginal1.pdf(points[dimensions1])
+
+    dimensions2 = [3, -3]
+    marginal2 = kde.marginal(dimensions2)
+    pdf2 = marginal2.pdf(points[dimensions2])
+
+    assert_equal(pdf1, pdf2)
+
+    # IV for non-integer dimensions
+    message = "Elements of `dimensions` must be integers..."
+    with pytest.raises(ValueError, match=message):
+        kde.marginal([1, 2.5])
+
+    # IV for uniqueness
+    message = "All elements of `dimensions` must be unique."
+    with pytest.raises(ValueError, match=message):
+        kde.marginal([1, 2, 2])
+
+    # IV for non-integer dimensions
+    message = (r"Dimensions \[-5  6\] are invalid for a distribution in 4...")
+    with pytest.raises(ValueError, match=message):
+        kde.marginal([1, -5, 6])
+
+
+@pytest.mark.xslow
+def test_logpdf_overflow():
+    # regression test for gh-12988; testing against linalg instability for
+    # very high dimensionality kde
+    rng = np.random.default_rng(1)
+    n_dimensions = 2500
+    n_samples = 5000
+    xn = np.array([rng.normal(0, 1, n_samples) + (n) for n in range(
+        0, n_dimensions)])
+
+    # Default
+    gkde = stats.gaussian_kde(xn)
+
+    logpdf = gkde.logpdf(np.arange(0, n_dimensions))
+    np.testing.assert_equal(np.isneginf(logpdf[0]), False)
+    np.testing.assert_equal(np.isnan(logpdf[0]), False)
+
+
+def test_weights_intact():
+    # regression test for gh-9709: weights are not modified
+    rng = np.random.default_rng(12345)
+    vals = rng.lognormal(size=100)
+    weights = rng.choice([1.0, 10.0, 100], size=vals.size)
+    orig_weights = weights.copy()
+
+    stats.gaussian_kde(np.log10(vals), weights=weights)
+    assert_allclose(weights, orig_weights, atol=1e-14, rtol=1e-14)
+
+
+def test_weights_integer():
+    # integer weights are OK, cf gh-9709 (comment)
+    values = [0.2, 13.5, 21.0, 75.0, 99.0]
+    weights = [1, 2, 4, 8, 16]  # a list of integers
+    pdf_i = stats.gaussian_kde(values, weights=weights)
+    pdf_f = stats.gaussian_kde(values, weights=np.float64(weights))
+
+    xn = [0.3, 11, 88]
+    assert_allclose(pdf_i.evaluate(xn),
+                    pdf_f.evaluate(xn), atol=1e-14, rtol=1e-14)
+
+
+def test_seed():
+    # Test the seed option of the resample method
+    def test_seed_sub(gkde_trail):
+        n_sample = 200
+        # The results should be different without using seed
+        samp1 = gkde_trail.resample(n_sample)
+        samp2 = gkde_trail.resample(n_sample)
+        assert_raises(
+            AssertionError, assert_allclose, samp1, samp2, atol=1e-13
+        )
+        # Use integer seed
+        seed = 831
+        samp1 = gkde_trail.resample(n_sample, seed=seed)
+        samp2 = gkde_trail.resample(n_sample, seed=seed)
+        assert_allclose(samp1, samp2, atol=1e-13)
+        # Use RandomState
+        rstate1 = np.random.RandomState(seed=138)
+        samp1 = gkde_trail.resample(n_sample, seed=rstate1)
+        rstate2 = np.random.RandomState(seed=138)
+        samp2 = gkde_trail.resample(n_sample, seed=rstate2)
+        assert_allclose(samp1, samp2, atol=1e-13)
+
+        # check that np.random.Generator can be used (numpy >= 1.17)
+        if hasattr(np.random, 'default_rng'):
+            # obtain a np.random.Generator object
+            rng = np.random.default_rng(1234)
+            gkde_trail.resample(n_sample, seed=rng)
+
+    rng = np.random.default_rng(8765678)
+    n_basesample = 500
+    wn = rng.random(n_basesample)
+    # Test 1D case
+    xn_1d = rng.normal(0, 1, n_basesample)
+
+    gkde_1d = stats.gaussian_kde(xn_1d)
+    test_seed_sub(gkde_1d)
+    gkde_1d_weighted = stats.gaussian_kde(xn_1d, weights=wn)
+    test_seed_sub(gkde_1d_weighted)
+
+    # Test 2D case
+    mean = np.array([1.0, 3.0])
+    covariance = np.array([[1.0, 2.0], [2.0, 6.0]])
+    xn_2d = rng.multivariate_normal(mean, covariance, size=n_basesample).T
+
+    gkde_2d = stats.gaussian_kde(xn_2d)
+    test_seed_sub(gkde_2d)
+    gkde_2d_weighted = stats.gaussian_kde(xn_2d, weights=wn)
+    test_seed_sub(gkde_2d_weighted)
+
+
+def test_singular_data_covariance_gh10205():
+    # When the data lie in a lower-dimensional subspace and this causes
+    # and exception, check that the error message is informative.
+    rng = np.random.default_rng(2321583144339784787)
+    mu = np.array([1, 10, 20])
+    sigma = np.array([[4, 10, 0], [10, 25, 0], [0, 0, 100]])
+    data = rng.multivariate_normal(mu, sigma, 1000)
+    try:  # doesn't raise any error on some platforms, and that's OK
+        stats.gaussian_kde(data.T)
+    except linalg.LinAlgError:
+        msg = "The data appears to lie in a lower-dimensional subspace..."
+        with assert_raises(linalg.LinAlgError, match=msg):
+            stats.gaussian_kde(data.T)
+
+
+def test_fewer_points_than_dimensions_gh17436():
+    # When the number of points is fewer than the number of dimensions, the
+    # the covariance matrix would be singular, and the exception tested in
+    # test_singular_data_covariance_gh10205 would occur. However, sometimes
+    # this occurs when the user passes in the transpose of what `gaussian_kde`
+    # expects. This can result in a huge covariance matrix, so bail early.
+    rng = np.random.default_rng(2046127537594925772)
+    rvs = rng.multivariate_normal(np.zeros(3), np.eye(3), size=5)
+    message = "Number of dimensions is greater than number of samples..."
+    with pytest.raises(ValueError, match=message):
+        stats.gaussian_kde(rvs)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_marray.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_marray.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbae7321f6acdf1222655ebac391dc874821a66f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_marray.py
@@ -0,0 +1,613 @@
+import numpy as np
+import pytest
+from scipy import stats
+
+from scipy._lib._array_api import xp_assert_close, xp_assert_equal, _count_nonmasked
+from scipy._lib._array_api import make_xp_pytest_param, make_xp_test_case
+from scipy._lib._array_api import SCIPY_ARRAY_API, is_torch
+from scipy.stats._stats_py import _xp_mean, _xp_var
+
+
+marray = pytest.importorskip('marray')
+pytestmark = [
+    pytest.mark.skipif(
+        not SCIPY_ARRAY_API,
+        reason=(
+            "special function dispatch to marray required for these tests"
+            " is hidden behind SCIPY_ARRAY_API flag."
+        ),
+    ),
+]
+
+skip_backend = pytest.mark.skip_xp_backends
+
+def get_arrays(n_arrays, *, dtype='float64', xp=np, shape=(7, 8), all_unique=True,
+               seed=84912165484321):
+    mxp = marray._get_namespace(xp)
+    rng = np.random.default_rng(seed)
+
+    datas, masks = [], []
+    for i in range(n_arrays):
+        data = (rng.random(size=shape) if all_unique
+                else rng.integers(np.min(shape) // 2, size=shape))
+        if dtype.startswith('complex'):
+            data = 10*data * 10j*rng.standard_normal(size=shape)
+        data = data.astype(dtype)
+        datas.append(data)
+        mask = rng.random(size=shape) > 0.75
+        masks.append(mask)
+
+    marrays = []
+    nan_arrays = []
+    for array, mask in zip(datas, masks):
+        marrays.append(mxp.asarray(array, mask=mask))
+        nan_array = array.copy()
+        nan_array[mask] = xp.nan
+        nan_arrays.append(nan_array)
+
+    return mxp, marrays, nan_arrays
+
+
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('fun, kwargs', [
+    make_xp_pytest_param(stats.gmean, {}),
+    make_xp_pytest_param(stats.hmean, {}),
+    make_xp_pytest_param(stats.pmean, {'p': 2}),
+    make_xp_pytest_param(stats.expectile, {'alpha': 0.4}),
+    make_xp_pytest_param(_xp_mean, {}),
+])
+@pytest.mark.parametrize('weighted', [False, True])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_one_sample_weighted_reducing(fun, kwargs, weighted, axis, xp):
+    mxp, marrays, narrays = get_arrays(2, xp=xp)
+    mweights = marrays[1] if weighted else None
+    nweights = narrays[1] if weighted else None
+    res = fun(marrays[0], weights=mweights, axis=axis, **kwargs)
+    ref = fun(narrays[0], weights=nweights, nan_policy='omit', axis=axis, **kwargs)
+    xp_assert_close(res.data, xp.asarray(ref))
+
+
+@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('fun, kwargs',
+    [make_xp_pytest_param(stats.moment, {'order': 2}),
+     make_xp_pytest_param(stats.moment, {'center': 0.5}),
+     make_xp_pytest_param(stats.skew, {}),
+     make_xp_pytest_param(stats.skew, {'bias': False}),
+     make_xp_pytest_param(stats.kurtosis, {}),
+     make_xp_pytest_param(stats.kurtosis, {'fisher': False, 'bias': False}),
+     make_xp_pytest_param(stats.sem, {}),
+     make_xp_pytest_param(stats.sem, {'ddof': 2}),
+     make_xp_pytest_param(stats.kstat, {'n': 1}),
+     make_xp_pytest_param(stats.kstat, {'n': 2}),
+     make_xp_pytest_param(stats.kstat, {'n': 3}),
+     make_xp_pytest_param(stats.kstat, {'n': 4}),
+     make_xp_pytest_param(stats.kstatvar, {'n': 1}),
+     make_xp_pytest_param(stats.kstatvar, {'n': 2}),
+     make_xp_pytest_param(stats.circmean, {}),
+     make_xp_pytest_param(stats.circmean, {'low': 0, 'high': 1}),
+     make_xp_pytest_param(stats.circvar, {}),
+     make_xp_pytest_param(stats.circvar, {'low': 0, 'high': 1}),
+     make_xp_pytest_param(stats.circstd, {}),
+     make_xp_pytest_param(stats.circstd, {'low': 0, 'high': 1, 'normalize':True}),
+     make_xp_pytest_param(stats.gstd, {}),
+     make_xp_pytest_param(stats.gstd, {'ddof': 2}),
+     make_xp_pytest_param(_xp_var, {}),
+     make_xp_pytest_param(_xp_var, {'correction': 1, 'keepdims': True}),
+     make_xp_pytest_param(stats.variation, {}),
+     make_xp_pytest_param(stats.variation, {'ddof': 1}),
+     make_xp_pytest_param(stats.tmean, dict(limits=(0.1, 0.9))),
+     make_xp_pytest_param(stats.tvar, dict(limits=(0.1, 0.9), inclusive=(False, True))),
+     make_xp_pytest_param(stats.tstd, dict(limits=(0.1, 0.9), inclusive=(True, False))),
+     make_xp_pytest_param(stats.tsem, dict(limits=(0.1, 0.9), inclusive=(False,)*2)),
+     make_xp_pytest_param(stats.tmin, dict(lowerlimit=0.5, inclusive=True)),
+     make_xp_pytest_param(stats.tmax, dict(upperlimit=0.5, inclusive=False)),
+     make_xp_pytest_param(stats.iqr, {'interpolation': 'nearest'}),
+     make_xp_pytest_param(stats.iqr, {'rng': (10, 90), 'scale': 'normal'}),
+     make_xp_pytest_param(stats.median_abs_deviation, {}),
+     make_xp_pytest_param(stats.median_abs_deviation, {'scale': 'normal'}),
+     make_xp_pytest_param(stats.trim_mean, {'proportiontocut': 0.1}),
+     ])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_one_sample_reducing(fun, kwargs, axis, xp):
+    mxp, marrays, narrays = get_arrays(1, xp=xp)
+    kwargs = dict(axis=axis) | kwargs
+    res = fun(marrays[0], **kwargs)
+    ref = fun(narrays[0], nan_policy='omit', **kwargs)
+    xp_assert_close(res.data, xp.asarray(ref))
+
+
+@make_xp_test_case(stats.mode)
+@pytest.mark.parametrize('dtype', ['int32', 'float64'])
+@pytest.mark.parametrize('shape', [10, (7, 8)])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_mode(dtype, shape, axis, xp):
+    mxp, marrays, narrays = get_arrays(1, shape=shape, all_unique=False, xp=xp)
+    res = stats.mode(mxp.astype(marrays[0], getattr(mxp, dtype)))
+    ref = stats.mode(*narrays, nan_policy='omit')
+    xp_assert_close(res.mode.data, xp.asarray(ref.mode.astype(dtype)))
+    xp_assert_close(res.count.data, xp.asarray(ref.count))
+
+
+@make_xp_test_case(stats.describe)
+@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('axis', [0, 1, None])
+@pytest.mark.parametrize('kwargs', [{}, {'bias': False}])
+def test_describe(axis, kwargs, xp):
+    mxp, marrays, narrays = get_arrays(1, xp=xp)
+    kwargs = dict(axis=axis) | kwargs
+    res = stats.describe(marrays[0], **kwargs)
+    ref = stats.describe(narrays[0], nan_policy='omit', **kwargs)
+    xp_assert_close(res.nobs.data, xp.asarray(ref.nobs))
+    xp_assert_close(res.minmax[0].data, xp.asarray(ref.minmax[0].data))
+    xp_assert_close(res.minmax[1].data, xp.asarray(ref.minmax[1].data))
+    # copy reference arrays due to torch complaint about non-writeable buffer
+    xp_assert_close(res.variance.data,
+                    xp.asarray(np.asarray(ref.variance.data, copy=True)))
+    xp_assert_close(res.skewness.data,
+                    xp.asarray(np.asarray(ref.skewness.data, copy=True)))
+    xp_assert_close(res.kurtosis.data,
+                    xp.asarray(np.asarray(ref.kurtosis.data, copy=True)))
+
+
+@skip_backend('dask.array', reason='shape (nan,) in _xp_var when ddof=1')
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('fun', [make_xp_pytest_param(stats.zscore),
+                                 make_xp_pytest_param(stats.gzscore),
+                                 make_xp_pytest_param(stats.zmap)])
+@pytest.mark.parametrize('ddof', [0, 1])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_one_sample_non_reducing(fun, ddof, axis, xp):
+    mxp, marrays, narrays = (get_arrays(2, xp=xp) if fun == stats.zmap
+                             else get_arrays(1, xp=xp))
+    res = fun(*marrays, ddof=ddof, axis=axis)
+    ref = xp.asarray(fun(*narrays, ddof=ddof, nan_policy='omit', axis=axis))
+    xp_assert_close(res.data[~res.mask], ref[~xp.isnan(ref)])
+    xp_assert_equal(res.mask, marrays[0].mask)
+
+
+@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('f', [make_xp_pytest_param(stats.ttest_1samp),
+                               make_xp_pytest_param(stats.ttest_rel),
+                               make_xp_pytest_param(stats.ttest_ind)])
+@pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_ttests(f, alternative, axis, xp):
+    f_name = f.__name__
+    mxp, marrays, narrays = get_arrays(2, xp=xp)
+    if f_name == 'ttest_1samp':
+        marrays[1] = mxp.mean(marrays[1], axis=axis, keepdims=axis is not None)
+        narrays[1] = np.nanmean(narrays[1], axis=axis, keepdims=axis is not None)
+    res = f(*marrays, alternative=alternative, axis=axis)
+    ref = f(*narrays, alternative=alternative, nan_policy='omit', axis=axis)
+    xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
+    xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
+    res_ci = res.confidence_interval()
+    ref_ci = ref.confidence_interval()
+    xp_assert_close(res_ci.low.data, xp.asarray(ref_ci.low))
+    xp_assert_close(res_ci.high.data, xp.asarray(ref_ci.high))
+
+
+@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.filterwarnings("ignore::scipy.stats._axis_nan_policy.SmallSampleWarning")
+@pytest.mark.parametrize('f, args', [
+     make_xp_pytest_param(stats.skewtest, tuple()),
+     make_xp_pytest_param(stats.kurtosistest, tuple()),
+     make_xp_pytest_param(stats.normaltest, tuple()),
+     make_xp_pytest_param(stats.jarque_bera, tuple()),
+     make_xp_pytest_param(stats.cramervonmises, (stats.norm.cdf,)),  # type:ignore[attr-defined]
+])
+@pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_goodness_of_fit(f, args, alternative, axis, xp):
+    mxp, marrays, narrays = get_arrays(1, xp=xp, shape=(10, 11))
+
+    if f in {stats.skewtest, stats.kurtosistest}:
+        kwds = {'alternative': alternative}
+    else:
+        if alternative != 'greater':
+            pytest.skip(f'str({f.__name__} does not support multiple alternatives.')
+        kwds = {}
+
+    res = f(*marrays, *args, **kwds, axis=axis)
+    ref = f(*narrays, *args, **kwds, nan_policy='omit', axis=axis)
+
+    xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
+    xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue), atol=1e-15)
+
+
+@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('f, lambda_', [
+    make_xp_pytest_param(stats.chisquare, {}),
+    make_xp_pytest_param(stats.power_divergence, {'lambda_': 'pearson'}),
+    make_xp_pytest_param(stats.power_divergence, {'lambda_': 'log-likelihood'}),
+    make_xp_pytest_param(stats.power_divergence, {'lambda_': 'freeman-tukey'}),
+    make_xp_pytest_param(stats.power_divergence,
+                         {'lambda_': 'mod-log-likelihood'}),
+    make_xp_pytest_param(stats.power_divergence, {'lambda_': 'neyman'}),
+    make_xp_pytest_param(stats.power_divergence, {'lambda_': 'cressie-read'}),
+])
+@pytest.mark.parametrize('ddof', [0, 1])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_power_divergence_chisquare(f, lambda_, ddof, axis, xp):
+    mxp, marrays, narrays = get_arrays(2, xp=xp, shape=(5, 6))
+
+    kwargs = dict(axis=axis, ddof=ddof)
+
+    # test 1-arg
+    res = f(marrays[0], **lambda_, **kwargs)
+    ref = stats.power_divergence(narrays[0], nan_policy='omit', **lambda_, **kwargs)
+
+    xp_assert_close(res.statistic.data, xp.asarray(ref[0]))
+    xp_assert_close(res.pvalue.data, xp.asarray(ref[1]))
+
+    # test 2-arg
+    common_mask = np.isnan(narrays[0]) | np.isnan(narrays[1])
+    normalize = (np.nansum(narrays[1] * ~common_mask, axis=axis, keepdims=True)
+                 / np.nansum(narrays[0] * ~common_mask, axis=axis, keepdims=True))
+    marrays[0] *= xp.asarray(normalize)
+    narrays[0] *= normalize
+
+    res = f(*marrays, **lambda_, **kwargs)
+    ref = stats.power_divergence(*narrays, nan_policy='omit', **lambda_, **kwargs)
+
+    xp_assert_close(res.statistic.data, xp.asarray(ref[0]))
+    xp_assert_close(res.pvalue.data, xp.asarray(ref[1]))
+
+
+@make_xp_test_case(stats.combine_pvalues)
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('method', ['fisher', 'pearson', 'mudholkar_george',
+                                    'tippett', 'stouffer'])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_combine_pvalues(method, axis, xp):
+    mxp, marrays, narrays = get_arrays(2, xp=xp, shape=(10, 11))
+
+    kwargs = dict(method=method, axis=axis)
+    res = stats.combine_pvalues(marrays[0], **kwargs)
+    ref = stats.combine_pvalues(narrays[0], nan_policy='omit', **kwargs)
+
+    xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
+    xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
+
+    if method != 'stouffer':
+        return
+
+    # test method='stouffer' with weights
+    res = stats.combine_pvalues(marrays[0], weights=marrays[1], **kwargs)
+    ref = stats.combine_pvalues(narrays[0], weights=narrays[1],
+                                nan_policy='omit', **kwargs)
+
+    xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
+    xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
+
+
+@make_xp_test_case(stats.ttest_ind_from_stats)
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize("alternative", ['less', 'greater', 'two-sided'])
+@pytest.mark.parametrize("equal_var", [False, True])
+def test_ttest_ind_from_stats(alternative, equal_var, xp):
+    shape = (10, 11)
+    mxp, marrays, narrays = get_arrays(6, xp=xp, shape=shape)
+    mask = np.sum(np.stack([np.isnan(arg) for arg in narrays]), axis=0).astype(bool)
+    narrays = [arg[~mask] for arg in narrays]
+    marrays[2], marrays[5] = marrays[2] * 100, marrays[5] * 100
+    narrays[2], narrays[5] = narrays[2] * 100, narrays[5] * 100
+
+    kwargs = dict(alternative=alternative, equal_var=equal_var)
+    res = stats.ttest_ind_from_stats(*marrays, **kwargs)
+    ref = stats.ttest_ind_from_stats(*narrays, **kwargs)
+
+    mask = xp.asarray(mask)
+    assert xp.any(mask) and xp.any(~mask)
+    xp_assert_close(res.statistic.data[~mask], xp.asarray(ref.statistic))
+    xp_assert_close(res.pvalue.data[~mask], xp.asarray(ref.pvalue))
+    xp_assert_close(res.statistic.mask, mask)
+    xp_assert_close(res.pvalue.mask, mask)
+    assert res.statistic.shape == shape
+    assert res.pvalue.shape == shape
+
+
+def test_length_nonmasked_marray_iterable_axis_raises():
+    xp = marray._get_namespace(np)
+
+    data = [[1.0, 2.0], [3.0, 4.0]]
+    mask = [[False, False], [True, False]]
+    marr = xp.asarray(data, mask=mask)
+
+    # Axis tuples are not currently supported for MArray input.
+    # This test can be removed after support is added.
+    with pytest.raises(NotImplementedError,
+        match="`axis` must be an integer or None for use with `MArray`"):
+        _count_nonmasked(marr, axis=(0, 1), xp=xp)
+
+
+@make_xp_test_case(stats.directional_stats)
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.filterwarnings("ignore::RuntimeWarning")  # mdhaber/marray#120
+@pytest.mark.parametrize('normalize', [False, True])
+@pytest.mark.parametrize('axis', [0, 1])
+def test_directional_stats(normalize, axis, xp):
+    mxp, marrays, narrays = get_arrays(1, shape=(19, 20, 3), xp=xp)
+    res = stats.directional_stats(*marrays, normalize=normalize, axis=axis)
+
+    x, = narrays
+    if axis == 0:
+        x = np.swapaxes(x, 0, 1)
+
+    for i in range(x.shape[0]):
+        xi = x[i]
+        xi = xi[~np.any(np.isnan(xi), axis=-1), :]
+        ref = stats.directional_stats(xi, normalize=normalize)
+        xp_assert_close(res.mean_direction.data[i, ...],
+                        xp.asarray(ref.mean_direction))
+        xp_assert_close(res.mean_resultant_length.data[i],
+                        xp.asarray(ref.mean_resultant_length)[()])
+    assert not xp.any(res.mean_direction.mask)
+    assert not xp.any(res.mean_resultant_length.mask)
+
+
+@make_xp_test_case(stats.wilcoxon)
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('n_samples', [1, 2])
+@pytest.mark.parametrize('zero_method', ['zsplit'])  # TODO: add 'wilcox', 'pratt'
+@pytest.mark.parametrize('correction', [False, True])
+@pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+@pytest.mark.parametrize('method', ['asymptotic'])  # TODO: add 'exact', 'auto'
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_wilcoxon(n_samples, zero_method, correction, alternative, method, axis, xp):
+    mxp, marrays, narrays = get_arrays(n_samples, xp=xp, seed=84912165484322)
+    kwargs = dict(zero_method=zero_method, correction=correction,
+                  alternative=alternative, method=method)
+    res = stats.wilcoxon(*marrays, axis=axis, **kwargs)
+    ref = stats.wilcoxon(*narrays, nan_policy='omit', axis=axis, **kwargs)
+    xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
+    xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
+
+
+@make_xp_test_case(stats.mannwhitneyu)
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('use_continuity', [False, True])
+@pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+@pytest.mark.parametrize('method', ['asymptotic'])  # TODO: add 'exact', 'auto'
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_mannwhitneyu(use_continuity, alternative, method, axis, xp):
+    mxp, marrays, narrays = get_arrays(2, xp=xp, seed=84912165484322)
+    kwargs = dict(use_continuity=use_continuity, alternative=alternative, method=method)
+    res = stats.mannwhitneyu(*marrays, axis=axis, **kwargs)
+    ref = stats.mannwhitneyu(*narrays, nan_policy='omit', axis=axis, **kwargs)
+    xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
+    xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
+
+
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('fun', [make_xp_pytest_param(stats.ks_1samp),
+                                 make_xp_pytest_param(stats.kstest)])
+@pytest.mark.parametrize('method', ['exact', 'approx', 'asymp'])  # auto == exact
+@pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_ks_1samp(fun, method, alternative, axis, xp):
+    mxp, marrays, narrays = get_arrays(1, xp=xp, seed=84912165484322)
+    kwargs = dict(method=method, alternative=alternative, axis=axis)
+    res = fun(*marrays, stats.norm.cdf, **kwargs)
+    ref = stats.ks_1samp(*narrays, stats.norm.cdf, nan_policy='omit', **kwargs)
+    xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
+    xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
+    xp_assert_equal(res.statistic_location.data, xp.asarray(ref.statistic_location))
+    xp_assert_equal(res.statistic_sign.data,
+                    xp.asarray(ref.statistic_sign, dtype=xp.int8))
+
+
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('fun, kwargs', [
+    make_xp_pytest_param(stats.brunnermunzel, {}),
+    make_xp_pytest_param(stats.brunnermunzel, {'distribution': 'normal'}),
+    make_xp_pytest_param(stats.cramervonmises_2samp, {'method': 'exact'}),
+    # make_xp_pytest_param(stats.cramervonmises_2samp, {}),  # TODO: add methods
+])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_two_sample_tests(fun, kwargs, axis, xp):
+    if fun == stats.cramervonmises_2samp and axis is None:
+        pytest.skip("Sample too large for exact method.")
+    mxp, marrays, narrays = get_arrays(2, xp=xp, seed=84912165484322)
+    res = fun(*marrays, axis=axis, **kwargs)
+    ref = fun(*narrays, nan_policy='omit', axis=axis, **kwargs)
+    xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
+    xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
+
+
+@make_xp_test_case(stats.ks_2samp)
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('fun', [make_xp_pytest_param(stats.ks_2samp),
+                                 make_xp_pytest_param(stats.kstest)])
+@pytest.mark.parametrize('method', ['exact', 'asymp', 'auto'])  # auto == exact
+@pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_ks_2samp(fun, method, alternative, axis, xp):
+    mxp, marrays, narrays = get_arrays(2, xp=xp, seed=84912165484322)
+    kwargs = dict(method=method, alternative=alternative, axis=axis)
+    res = fun(*marrays, **kwargs)
+    ref = stats.ks_2samp(*narrays, nan_policy='omit', **kwargs)
+    xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
+    xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
+    # with this random data, there often multiple locations where the statistic assumes
+    # the most extreme value, so we can't expect these to always match
+    # xp_assert_equal(res.statistic_location.data, xp.asarray(ref.statistic_location))
+    xp_assert_equal(res.statistic_sign.data,
+                    xp.asarray(ref.statistic_sign, dtype=xp.int8))
+
+
+@make_xp_test_case(stats.kendalltau)
+class TestKendallTau:
+    @pytest.mark.parametrize('method', ['asymptotic', 'exact'])
+    @pytest.mark.parametrize('variant', ['b', 'c'])
+    @pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+    @pytest.mark.parametrize('axis', [0, 1, None])
+    def test_omit_masked_elements(self, method, variant, alternative, axis, xp):
+        mxp, marrays, narrays = get_arrays(2, shape=(17, 18),
+                                           xp=xp, seed=84912165484322)
+        kwargs = dict(method=method, variant=variant, alternative=alternative)
+        res = stats.kendalltau(*marrays, **kwargs, axis=axis)
+        ref = stats.kendalltau(*narrays, **kwargs, nan_policy='omit', axis=axis)
+        xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
+        xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
+
+    @pytest.mark.parametrize('nan_policy, message', [
+        ('propagate', "`nan_policy='propagate'` is incompatible with MArray input."),
+        ('raise', 'The input contains nan values.')
+    ])
+    def test_input_validation(self, nan_policy, message, xp):
+        mxp = marray._get_namespace(xp)
+        x = mxp.asarray([1, 2, 3, xp.nan])
+        with pytest.raises(ValueError, match=message):
+            stats.kendalltau(x, x, nan_policy=nan_policy)
+
+
+@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('fun, kwargs', [
+    make_xp_pytest_param(stats.bartlett, {}),
+    make_xp_pytest_param(stats.alexandergovern, {}),
+    make_xp_pytest_param(stats.levene, {'center': 'median'}),
+    make_xp_pytest_param(stats.levene, {'center': 'mean'}),
+    make_xp_pytest_param(stats.levene, {'center': 'trimmed'}),
+    make_xp_pytest_param(stats.f_oneway, {'equal_var': True}),
+    make_xp_pytest_param(stats.f_oneway, {'equal_var': False}),
+    make_xp_pytest_param(stats.kruskal, {}),
+    make_xp_pytest_param(stats.fligner, {'center': 'median'}),
+    make_xp_pytest_param(stats.fligner, {'center': 'mean'}),
+    make_xp_pytest_param(stats.fligner, {'center': 'trimmed'}),
+])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_k_sample_tests(fun, kwargs, axis, xp):
+    mxp, marrays, narrays = get_arrays(3, xp=xp)
+    res = fun(*marrays, axis=axis, **kwargs)
+    ref = fun(*narrays, nan_policy='omit', axis=axis, **kwargs)
+    xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
+    xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
+
+
+@skip_backend('jax.numpy', reason="JAX currently incompatible with marray")
+@pytest.mark.parametrize('fun, kwargs', [
+    make_xp_pytest_param(stats.friedmanchisquare, {}),
+])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_k_sample_paired_tests(fun, kwargs, axis, xp):
+    mxp, marrays, narrays = get_arrays(3, shape=(8, 9), xp=xp)
+    res = fun(*marrays, axis=axis, **kwargs)
+    ref = fun(*narrays, nan_policy='omit', axis=axis, **kwargs)
+    xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
+    xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
+
+
+@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('f', [
+    make_xp_pytest_param(stats.pearsonr),
+    make_xp_pytest_param(stats.pointbiserialr),
+    make_xp_pytest_param(stats.spearmanrho),
+])
+@pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_correlation(f, alternative, axis, xp):
+    mxp, marrays, narrays = get_arrays(2, xp=xp)
+
+    kwargs = {} if f == stats.pointbiserialr else {'alternative': alternative}
+    res = f(*marrays, **kwargs, axis=axis)
+
+    # `pearsonr` does not have `axis_nan_policy`, so do this manually
+    x, y = narrays
+    if axis == 0:
+        x, y = x.T, y.T
+    elif axis is None:
+        x, y = x.ravel()[np.newaxis, :], y.ravel()[np.newaxis, :]
+
+    for i in range(x.shape[0]):
+        xi, yi = x[i, ...], y[i, ...]
+        i = () if axis is None else i
+
+        mask = np.isnan(xi) | np.isnan(yi)
+        ref = f(xi[~mask], yi[~mask], **kwargs)
+
+        atol = 1e-7 if (is_torch(xp) and f == stats.spearmanrho) else 0.
+        xp_assert_close(res.statistic.data[i], xp.asarray(ref.statistic)[()], atol=atol)
+        xp_assert_close(res.pvalue.data[i], xp.asarray(ref.pvalue)[()], atol=atol)
+
+        if f == stats.pearsonr:
+            res_ci_low, res_ci_high = res.confidence_interval()
+            ref_ci_low, ref_ci_high = ref.confidence_interval()
+            xp_assert_close(res_ci_low.data[i], xp.asarray(ref_ci_low)[()])
+            xp_assert_close(res_ci_high.data[i], xp.asarray(ref_ci_high)[()])
+
+
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@skip_backend('array_api_strict', reason="issue with clip.")
+@pytest.mark.parametrize('f, method', [
+    make_xp_pytest_param(stats.siegelslopes, {'method':'hierarchical'}),
+    make_xp_pytest_param(stats.siegelslopes, {'method':'separate'}),
+    make_xp_pytest_param(stats.theilslopes, {'method': 'joint'}),
+    make_xp_pytest_param(stats.theilslopes, {'method': 'separate'}),
+    make_xp_pytest_param(stats.linregress, {'alternative': 'less'}),
+    make_xp_pytest_param(stats.linregress, {'alternative': 'greater'}),
+    make_xp_pytest_param(stats.linregress, {'alternative': 'two-sided'}),
+])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_slopes_intercepts(f, method, axis, xp):
+    mxp, marrays, narrays = get_arrays(2, shape=(19, 20), seed=84912165484320, xp=xp)
+    res = f(*marrays, axis=axis, **method)
+    ref = f(*narrays, nan_policy='omit', axis=axis, **method)
+
+    xp_assert_close(res.slope.data, xp.asarray(ref.slope))
+    xp_assert_close(res.intercept.data, xp.asarray(ref.intercept))
+
+    if f == stats.theilslopes:
+        xp_assert_close(res.low_slope.data, xp.asarray(ref.low_slope))
+        xp_assert_close(res.high_slope.data, xp.asarray(ref.high_slope))
+    elif f == stats.linregress:
+        xp_assert_close(res.rvalue.data, xp.asarray(ref.rvalue))
+        xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
+        xp_assert_close(res.stderr.data, xp.asarray(ref.stderr))
+        xp_assert_close(res.intercept_stderr.data, xp.asarray(ref.intercept_stderr))
+
+
+@make_xp_test_case(stats.entropy)
+@skip_backend('dask.array', reason="Dask doesn't like the /= when base is not None.")
+@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
+@pytest.mark.parametrize('qk', [False, True])
+@pytest.mark.parametrize('base', [None, 2])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_entropy(qk, base, axis, xp):
+    mxp, marrays, narrays = get_arrays(2 if qk else 1, xp=xp)
+    res = stats.entropy(*marrays, base=base, axis=axis)
+    ref = stats.entropy(*narrays, base=base, nan_policy='omit', axis=axis)
+    xp_assert_close(res.data, xp.asarray(ref))
+
+
+@make_xp_test_case(stats.rankdata)
+@skip_backend('jax.numpy', reason="JAX currently incompatible with marray")
+@pytest.mark.parametrize('method', ['average', 'min', 'max', 'dense', 'ordinal'])
+@pytest.mark.parametrize('axis', [0, 1, None])
+def test_rankdata(method, axis, xp):
+    mxp, marrays, narrays = get_arrays(1, xp=xp)
+    res = stats.rankdata(*marrays, method=method, axis=axis)
+    ref = stats.rankdata(*narrays, method=method, nan_policy='omit', axis=axis)
+    xp_assert_close(res.data[~res.mask], xp.asarray(ref[~np.isnan(ref)]))
+    xp_assert_close(res.mask, xp.asarray(np.isnan(ref)))
+
+
+@make_xp_test_case(stats.obrientransform)
+@skip_backend('jax.numpy', reason="JAX currently incompatible with marray")
+@pytest.mark.parametrize('dtype', ['float32', 'float64'])
+@pytest.mark.parametrize('n_arrays', [1, 3])
+def test_obrientransform(dtype, n_arrays, xp):
+    # obrientransform is not yet vectorized
+    mxp, marrays, narrays = get_arrays(n_arrays, dtype=dtype, shape=(25,), xp=xp)
+    res = stats.obrientransform(*marrays)
+    ref = stats.obrientransform(*narrays, nan_policy='omit')
+    for res_i, ref_i in zip(res, ref):
+        xp_assert_close(res_i.data[~res_i.mask],
+                        xp.asarray(ref_i[~np.isnan(ref_i)], dtype=getattr(xp, dtype)))
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_mgc.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_mgc.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd4d27d7c1647b0ab72cd668ccd0509f02b4684a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_mgc.py
@@ -0,0 +1,233 @@
+import pytest
+from pytest import raises as assert_raises, warns as assert_warns
+
+import numpy as np
+from numpy.testing import assert_approx_equal, assert_allclose, assert_equal
+
+from scipy.spatial.distance import cdist
+from scipy import stats
+
+class TestMGCErrorWarnings:
+    """ Tests errors and warnings derived from MGC.
+    """
+    def test_error_notndarray(self):
+        # raises error if x or y is not an ndarray
+        x = np.arange(20)
+        y = [5] * 20
+        assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
+        assert_raises(ValueError, stats.multiscale_graphcorr, y, x)
+
+    def test_error_shape(self):
+        # raises error if number of samples different (n)
+        x = np.arange(100).reshape(25, 4)
+        y = x.reshape(10, 10)
+        assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
+
+    def test_error_lowsamples(self):
+        # raises error if samples are low (< 3)
+        x = np.arange(3)
+        y = np.arange(3)
+        assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
+
+    def test_error_nans(self):
+        # raises error if inputs contain NaNs
+        x = np.arange(20, dtype=float)
+        x[0] = np.nan
+        assert_raises(ValueError, stats.multiscale_graphcorr, x, x)
+
+        y = np.arange(20)
+        assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
+
+    def test_error_wrongdisttype(self):
+        # raises error if metric is not a function
+        x = np.arange(20)
+        compute_distance = 0
+        assert_raises(ValueError, stats.multiscale_graphcorr, x, x,
+                      compute_distance=compute_distance)
+
+    @pytest.mark.parametrize("reps", [
+        -1,    # reps is negative
+        '1',   # reps is not integer
+    ])
+    def test_error_reps(self, reps):
+        # raises error if reps is negative
+        x = np.arange(20)
+        assert_raises(ValueError, stats.multiscale_graphcorr, x, x, reps=reps)
+
+    def test_warns_reps(self):
+        # raises warning when reps is less than 1000
+        x = np.arange(20)
+        reps = 100
+        assert_warns(RuntimeWarning, stats.multiscale_graphcorr, x, x, reps=reps)
+
+    def test_error_infty(self):
+        # raises error if input contains infinities
+        x = np.arange(20)
+        y = np.ones(20) * np.inf
+        assert_raises(ValueError, stats.multiscale_graphcorr, x, y)
+
+
+class TestMGCStat:
+    """ Test validity of MGC test statistic
+    """
+    def setup_method(self):
+        self.rng = np.random.default_rng(1266219746)
+
+    def _simulations(self, samps=100, dims=1, sim_type="", rng=None):
+        rng = rng or self.rng
+        # linear simulation
+        if sim_type == "linear":
+            x = rng.uniform(-1, 1, size=(samps, 1))
+            y = x + 0.3 * rng.random(size=(x.size, 1))
+
+        # spiral simulation
+        elif sim_type == "nonlinear":
+            unif = np.array(self.rng.uniform(0, 5, size=(samps, 1)))
+            x = unif * np.cos(np.pi * unif)
+            y = (unif * np.sin(np.pi * unif) +
+                 0.4*self.rng.random(size=(x.size, 1)))
+
+        # independence (tests type I simulation)
+        elif sim_type == "independence":
+            u = self.rng.standard_normal(size=(samps, 1))
+            v = self.rng.standard_normal(size=(samps, 1))
+            u_2 = self.rng.binomial(1, p=0.5, size=(samps, 1))
+            v_2 = self.rng.binomial(1, p=0.5, size=(samps, 1))
+            x = u/3 + 2*u_2 - 1
+            y = v/3 + 2*v_2 - 1
+
+        # raises error if not approved sim_type
+        else:
+            raise ValueError("sim_type must be linear, nonlinear, or "
+                             "independence")
+
+        # add dimensions of noise for higher dimensions
+        if dims > 1:
+            dims_noise = self.rng.standard_normal(size=(samps, dims-1))
+            x = np.concatenate((x, dims_noise), axis=1)
+
+        return x, y
+
+    @pytest.mark.xslow
+    @pytest.mark.parametrize("sim_type, obs_stat, obs_pvalue", [
+        # Reference values produced by `multiscale_graphcorr` ->
+        # this only guards against unintended changes
+        ("linear", 0.970137188683, 0.000999000999),      # test linear simulation
+        ("nonlinear", 0.149843048137, 0.000999000999),   # test spiral simulation
+        ("independence", -0.003965353120, 0.4675324675)  # test independence simulation
+    ])
+    def test_oned(self, sim_type, obs_stat, obs_pvalue):
+        # generate x and y
+        rng = np.random.default_rng(8157117705)
+        x, y = self._simulations(samps=100, dims=1, sim_type=sim_type, rng=rng)
+
+        # test stat and pvalue
+        stat, pvalue, _ = stats.multiscale_graphcorr(x, y, random_state=rng)
+        assert_allclose(stat, obs_stat)
+        assert_allclose(pvalue, obs_pvalue)
+
+    @pytest.mark.xslow
+    @pytest.mark.parametrize("sim_type, obs_stat, obs_pvalue", [
+        # Reference values produced by `multiscale_graphcorr` ->
+        # this only guards against unintended changes
+        ("linear", 0.193063672972, 0.000999000999),     # test linear simulation
+        ("nonlinear", 0.010042844939, 0.216783216783),  # test spiral simulation
+    ])
+    def test_fived(self, sim_type, obs_stat, obs_pvalue):
+        # generate x and y
+        rng = np.random.default_rng(8157117705)
+        x, y = self._simulations(samps=100, dims=5, sim_type=sim_type, rng=rng)
+
+        # test stat and pvalue
+        stat, pvalue, _ = stats.multiscale_graphcorr(x, y, random_state=rng)
+        assert_allclose(stat, obs_stat)
+        assert_allclose(pvalue, obs_pvalue)
+
+    @pytest.mark.xslow
+    def test_twosamp(self):
+        # generate x and y
+        x = self.rng.binomial(100, 0.5, size=(100, 5))
+        y = self.rng.standard_normal(size=(80, 5))
+
+        # test stat and pvalue
+        stat, pvalue, _ = stats.multiscale_graphcorr(x, y, random_state=self.rng)
+        assert_approx_equal(stat, 1.0, significant=1)
+        assert_approx_equal(pvalue, 0.001, significant=1)
+
+        # generate x and y
+        y = self.rng.standard_normal(size=(100, 5))
+
+        # test stat and pvalue
+        stat, pvalue, _ = stats.multiscale_graphcorr(x, y, is_twosamp=True,
+                                                     random_state=self.rng)
+        assert_approx_equal(stat, 1.0, significant=1)
+        assert_approx_equal(pvalue, 0.001, significant=1)
+
+    @pytest.mark.xslow
+    def test_workers(self):
+        # generate x and y
+        x, y = self._simulations(samps=100, dims=1, sim_type="linear")
+
+        # test stat and pvalue
+        stat, pvalue, _ = stats.multiscale_graphcorr(x, y, workers=2,
+                                                     random_state=self.rng)
+        assert_approx_equal(stat, 0.97, significant=1)
+        assert_approx_equal(pvalue, 0.001, significant=1)
+
+    @pytest.mark.xslow
+    def test_random_state(self):
+        # generate x and y
+        x, y = self._simulations(samps=100, dims=1, sim_type="linear")
+
+        # test stat and pvalue
+        stat, pvalue, _ = stats.multiscale_graphcorr(x, y, random_state=1)
+        assert_approx_equal(stat, 0.97, significant=1)
+        assert_approx_equal(pvalue, 0.001, significant=1)
+
+    @pytest.mark.xslow
+    def test_dist_perm(self):
+        # generate x and y
+        x, y = self._simulations(samps=100, dims=1, sim_type="nonlinear")
+        distx = cdist(x, x, metric="euclidean")
+        disty = cdist(y, y, metric="euclidean")
+
+        stat_dist, pvalue_dist, _ = stats.multiscale_graphcorr(distx, disty,
+                                                               compute_distance=None,
+                                                               random_state=1)
+        assert_approx_equal(stat_dist, 0.163, significant=1)
+        assert_approx_equal(pvalue_dist, 0.001, significant=1)
+
+    @pytest.mark.fail_slow(20)  # all other tests are XSLOW; we need at least one to run
+    @pytest.mark.slow
+    def test_pvalue_literature(self):
+        # generate x and y
+        x, y = self._simulations(samps=100, dims=1, sim_type="linear")
+
+        # test stat and pvalue
+        _, pvalue, _ = stats.multiscale_graphcorr(x, y, random_state=1)
+        assert_allclose(pvalue, 1/1001)
+
+    def test_zero_distance_matrix_index_error(self):
+        # regression test for zero distance
+        # matrix IndexError issue; see gh-19769
+        n = 6
+        x = np.arange(n).reshape(-1, 1)
+
+        # valid distance matrix
+        distx = cdist(x, x)
+
+        # degenerate all-zero distance matrix
+        disty = np.zeros((n, n))
+
+        # test stat and pvalue
+        res = stats.multiscale_graphcorr(distx, disty, compute_distance=None)
+        assert_allclose(res.statistic, 0.0)
+        assert_allclose(res.pvalue, 1.0)
+    
+    @pytest.mark.xslow
+    def test_alias(self):
+        # generate x and y
+        x, y = self._simulations(samps=100, dims=1, sim_type="linear")
+
+        res = stats.multiscale_graphcorr(x, y, random_state=1)
+        assert_equal(res.stat, res.statistic)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_morestats.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_morestats.py
new file mode 100644
index 0000000000000000000000000000000000000000..070be8f03d96c62e08364f4e573d7cf2c4705dc2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_morestats.py
@@ -0,0 +1,3916 @@
+# Author:  Travis Oliphant, 2002
+#
+# Further enhancements and tests added by numerous SciPy developers.
+#
+import math
+import re
+import sys
+import warnings
+from functools import partial
+
+import numpy as np
+from numpy.random import RandomState
+from numpy.testing import (assert_array_equal, assert_almost_equal,
+                           assert_array_less, assert_array_almost_equal,
+                           assert_, assert_allclose, assert_equal)
+import pytest
+from pytest import raises as assert_raises
+
+from scipy import optimize, stats, special
+from scipy.stats._morestats import (_abw_state, _get_As_weibull, _Avals_weibull,
+                                    _yeojohnson_transform)
+from .common_tests import check_named_results
+from .._hypotests import _get_wilcoxon_distr, _get_wilcoxon_distr2
+from scipy.stats._ansari_swilk_statistics import swilk
+from scipy.stats._binomtest import _binary_search_for_binom_tst
+from scipy.stats._distr_params import distcont
+from scipy.stats._axis_nan_policy import (SmallSampleWarning, too_small_nd_omit,
+                                          too_small_1d_omit, too_small_1d_not_omit)
+
+import scipy._external.array_api_extra as xpx
+from scipy._lib._array_api import (is_torch, make_xp_test_case, eager_warns, xp_ravel,
+                                   is_numpy, xp_default_dtype, is_array_api_strict,
+                                   is_jax, is_lazy_array)
+from scipy._lib._array_api_no_0d import (
+    xp_assert_close,
+    xp_assert_equal,
+    xp_assert_less,
+)
+
+lazy_xp_modules = [stats]
+skip_xp_backends = pytest.mark.skip_xp_backends
+
+distcont = dict(distcont)  # type: ignore
+
+# Matplotlib is not a scipy dependency but is optionally used in probplot, so
+# check if it's available
+try:
+    import matplotlib
+    matplotlib.rcParams['backend'] = 'Agg'
+    import matplotlib.pyplot as plt
+    have_matplotlib = True
+except Exception:
+    have_matplotlib = False
+
+
+# test data gear.dat from NIST for Levene and Bartlett test
+# https://www.itl.nist.gov/div898/handbook/eda/section3/eda3581.htm
+g1 = [1.006, 0.996, 0.998, 1.000, 0.992, 0.993, 1.002, 0.999, 0.994, 1.000]
+g2 = [0.998, 1.006, 1.000, 1.002, 0.997, 0.998, 0.996, 1.000, 1.006, 0.988]
+g3 = [0.991, 0.987, 0.997, 0.999, 0.995, 0.994, 1.000, 0.999, 0.996, 0.996]
+g4 = [1.005, 1.002, 0.994, 1.000, 0.995, 0.994, 0.998, 0.996, 1.002, 0.996]
+g5 = [0.998, 0.998, 0.982, 0.990, 1.002, 0.984, 0.996, 0.993, 0.980, 0.996]
+g6 = [1.009, 1.013, 1.009, 0.997, 0.988, 1.002, 0.995, 0.998, 0.981, 0.996]
+g7 = [0.990, 1.004, 0.996, 1.001, 0.998, 1.000, 1.018, 1.010, 0.996, 1.002]
+g8 = [0.998, 1.000, 1.006, 1.000, 1.002, 0.996, 0.998, 0.996, 1.002, 1.006]
+g9 = [1.002, 0.998, 0.996, 0.995, 0.996, 1.004, 1.004, 0.998, 0.999, 0.991]
+g10 = [0.991, 0.995, 0.984, 0.994, 0.997, 0.997, 0.991, 0.998, 1.004, 0.997]
+
+
+# The loggamma RVS stream is changing due to gh-13349; this version
+# preserves the old stream so that tests don't change.
+def _old_loggamma_rvs(*args, **kwargs):
+    return np.log(stats.gamma.rvs(*args, **kwargs))
+
+
+class TestBayes_mvs:
+    def test_basic(self):
+        # Expected values in this test simply taken from the function.  For
+        # some checks regarding correctness of implementation, see review in
+        # gh-674
+        data = [6, 9, 12, 7, 8, 8, 13]
+        mean, var, std = stats.bayes_mvs(data)
+        assert_almost_equal(mean.statistic, 9.0)
+        assert_allclose(mean.minmax, (7.103650222492964, 10.896349777507034),
+                        rtol=1e-6)
+
+        assert_almost_equal(var.statistic, 10.0)
+        assert_allclose(var.minmax, (3.1767242068607087, 24.45910381334018),
+                        rtol=1e-09)
+
+        assert_almost_equal(std.statistic, 2.9724954732045084, decimal=14)
+        assert_allclose(std.minmax, (1.7823367265645145, 4.9456146050146312),
+                        rtol=1e-14)
+
+    def test_empty_input(self):
+        assert_raises(ValueError, stats.bayes_mvs, [])
+
+    def test_result_attributes(self):
+        x = np.arange(15)
+        attributes = ('statistic', 'minmax')
+        res = stats.bayes_mvs(x)
+
+        for i in res:
+            check_named_results(i, attributes)
+
+
+class TestMvsdist:
+    def test_basic(self):
+        data = [6, 9, 12, 7, 8, 8, 13]
+        mean, var, std = stats.mvsdist(data)
+        assert_almost_equal(mean.mean(), 9.0)
+        assert_allclose(mean.interval(0.9), (7.103650222492964,
+                                             10.896349777507034), rtol=1e-14)
+
+        assert_almost_equal(var.mean(), 10.0)
+        assert_allclose(var.interval(0.9), (3.1767242068607087,
+                                            24.45910381334018), rtol=1e-09)
+
+        assert_almost_equal(std.mean(), 2.9724954732045084, decimal=14)
+        assert_allclose(std.interval(0.9), (1.7823367265645145,
+                                            4.9456146050146312), rtol=1e-14)
+
+    def test_empty_input(self):
+        assert_raises(ValueError, stats.mvsdist, [])
+
+    def test_bad_arg(self):
+        # Raise ValueError if fewer than two data points are given.
+        data = [1]
+        assert_raises(ValueError, stats.mvsdist, data)
+
+    def test_warns(self):
+        # regression test for gh-5270
+        # make sure there are no spurious divide-by-zero warnings
+        with warnings.catch_warnings():
+            warnings.simplefilter('error', RuntimeWarning)
+            [x.mean() for x in stats.mvsdist([1, 2, 3])]
+            [x.mean() for x in stats.mvsdist([1, 2, 3, 4, 5])]
+
+
+@make_xp_test_case(stats.shapiro)
+class TestShapiro:
+    def test_basic(self, xp):
+        # reference values generated using R shapiro.test, e.g.
+        # options(digits=16)
+        # x = c(0.11, 7.87, 4.61, 10.14, 7.95, 3.14, 0.46,
+        #       4.43, 0.21, 4.75, 0.71, 1.52, 3.24,
+        #       0.93, 0.42, 4.97, 9.53, 4.55, 0.47, 6.66)
+        # shapiro.test(x)
+        x1 = [0.11, 7.87, 4.61, 10.14, 7.95, 3.14, 0.46,
+              4.43, 0.21, 4.75, 0.71, 1.52, 3.24,
+              0.93, 0.42, 4.97, 9.53, 4.55, 0.47, 6.66]
+        shapiro_test = stats.shapiro(xp.asarray(x1))
+        w, pw = shapiro_test
+        xp_assert_close(w, xp.asarray(0.90047287931756))
+        xp_assert_close(shapiro_test.statistic, xp.asarray(0.90047287931756))
+        xp_assert_close(pw, xp.asarray(0.04208957522226))
+        xp_assert_close(shapiro_test.pvalue, xp.asarray(0.04208957522226))
+
+        x2 = [1.36, 1.14, 2.92, 2.55, 1.46, 1.06, 5.27, -1.11,
+              3.48, 1.10, 0.88, -0.51, 1.46, 0.52, 6.20, 1.69,
+              0.08, 3.67, 2.81, 3.49]
+        shapiro_test = stats.shapiro(xp.asarray(x2))
+        xp_assert_close(shapiro_test.statistic, xp.asarray(0.95902694603234))
+        xp_assert_close(shapiro_test.pvalue, xp.asarray(0.5245979304707))
+
+        x3 = stats.norm.rvs(loc=5, scale=3, size=100, random_state=12345678)
+        shapiro_test = stats.shapiro(xp.asarray(x3.tolist()))
+        xp_assert_close(shapiro_test.statistic, xp.asarray(0.97728027037175))
+        xp_assert_close(shapiro_test.pvalue, xp.asarray(0.08143656270016))
+
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_basic2(self, dtype, xp):
+        # # Data from original paper; additional precision from R shapiro.test
+        x4 = [0.139, 0.157, 0.175, 0.256, 0.344, 0.413, 0.503, 0.577, 0.614,
+              0.655, 0.954, 1.392, 1.557, 1.648, 1.690, 1.994, 2.174, 2.206,
+              3.245, 3.510, 3.571, 4.354, 4.980, 6.084, 8.351]
+        dtype = {'dtype': dtype if dtype is None else getattr(xp, dtype)}
+        W_expected = xp.asarray(0.83466627531817, **dtype)
+        p_expected = xp.asarray(0.000913490481813, **dtype)
+        shapiro_test = stats.shapiro(xp.asarray(x4, **dtype))
+        xp_assert_close(shapiro_test.statistic, xp.asarray(W_expected, **dtype))
+        xp_assert_close(shapiro_test.pvalue, xp.asarray(p_expected, **dtype))
+
+    @skip_xp_backends('jax.numpy', reason='lazy backend -> limited input validation')
+    @skip_xp_backends('dask.array', reason='lazy backend -> limited input validation')
+    @pytest.mark.parametrize('x', ([], [1], [1, 2]))
+    def test_not_enough_values(self, x, xp):
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = stats.shapiro(xp.asarray(x))
+            xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+            xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+    def test_nan_input(self, xp):
+        x = xp.arange(10.)
+        x = xpx.at(x)[9].set(xp.nan)
+
+        shapiro_test = stats.shapiro(xp.asarray(x))
+        xp_assert_equal(shapiro_test.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(shapiro_test.pvalue, xp.asarray(xp.nan))
+
+    def test_gh14462(self, xp):
+        # shapiro is theoretically location-invariant, but when the magnitude
+        # of the values is much greater than the variance, there can be
+        # numerical issues. Fixed by subtracting median from the data.
+        # See gh-14462.
+        trans_val, maxlog = stats.boxcox([122500, 474400, 110400])
+        dtype = xp.float64  # we need some precision to work with
+        res = stats.shapiro(xp.asarray(trans_val, dtype=dtype))
+
+        # Reference from R:
+        # options(digits=16)
+        # x = c(0.00000000e+00, 3.39996924e-08, -6.35166875e-09)
+        # shapiro.test(x)
+        ref_statistic = xp.asarray(0.86468431705371, dtype=dtype)
+        ref_pvalue = xp.asarray(0.2805581751566, dtype=dtype)
+
+        xp_assert_close(res.statistic, ref_statistic, rtol=5e-7)
+        xp_assert_close(res.pvalue, ref_pvalue, rtol=5e-7)
+
+    def test_length_3_gh18322(self, xp):
+        # gh-18322 reported that the p-value could be negative for input of
+        # length 3. Check that this is resolved.
+        res = stats.shapiro(xp.asarray([0.6931471805599453, 0.0, 0.0]))
+        assert res.pvalue >= 0
+
+        # R `shapiro.test` doesn't produce an accurate p-value in the case
+        # above. Check that the formula used in `stats.shapiro` is not wrong.
+        # options(digits=16)
+        # x = c(-0.7746653110021126, -0.4344432067942129, 1.8157053280290931)
+        # shapiro.test(x)
+        x = [-0.7746653110021126, -0.4344432067942129, 1.8157053280290931]
+        res = stats.shapiro(xp.asarray(x))
+        xp_assert_close(res.statistic, xp.asarray(0.84658770645509))
+        xp_assert_close(res.pvalue, xp.asarray(0.2313666489882))
+
+    @pytest.mark.parametrize('n', [3, 4, 5, 8, 11, 20, 100, 200, 500, 1000, 2000, 5000])
+    def test_against_as181(self, n, xp):
+        rng = np.random.default_rng(n)
+        x = rng.standard_normal(n)
+
+        a = np.zeros(n // 2, dtype=np.float64)
+        y = np.sort(x) - x[n // 2]
+        ref_statistic, ref_pvalue, _ = swilk(y, a, 0)
+
+        x = xp.asarray(x)
+        res = stats.shapiro(x)
+        ref_statistic = xp.asarray(ref_statistic, dtype=x.dtype)
+        ref_pvalue = xp.asarray(ref_pvalue, dtype=x.dtype)
+
+        xp_assert_close(res.statistic, ref_statistic, rtol=5e-7)
+        xp_assert_close(res.pvalue, ref_pvalue, rtol=5e-7)
+
+
+@pytest.mark.filterwarnings("ignore: As of SciPy 1.17: FutureWarning")
+class TestAnderson:
+    def test_normal(self):
+        rs = RandomState(1234567890)
+        x1 = rs.standard_exponential(size=50)
+        x2 = rs.standard_normal(size=50)
+        A, crit, sig = stats.anderson(x1)
+        assert_array_less(crit[:-1], A)
+        A, crit, sig = stats.anderson(x2)
+        assert_array_less(A, crit[-2:])
+
+        v = np.ones(10)
+        v[0] = 0
+        A, crit, sig = stats.anderson(v)
+        # The expected statistic 3.208057 was computed independently of scipy.
+        # For example, in R:
+        #   > library(nortest)
+        #   > v <- rep(1, 10)
+        #   > v[1] <- 0
+        #   > result <- ad.test(v)
+        #   > result$statistic
+        #          A
+        #   3.208057
+        assert_allclose(A, 3.208057)
+
+    def test_expon(self):
+        rs = RandomState(1234567890)
+        x1 = rs.standard_exponential(size=50)
+        x2 = rs.standard_normal(size=50)
+        A, crit, sig = stats.anderson(x1, 'expon')
+        assert_array_less(A, crit[-2:])
+        with np.errstate(all='ignore'):
+            A, crit, sig = stats.anderson(x2, 'expon')
+        assert_(A > crit[-1])
+
+    def test_gumbel(self):
+        # Regression test for gh-6306.  Before that issue was fixed,
+        # this case would return a2=inf.
+        v = np.ones(100)
+        v[0] = 0.0
+        a2, crit, sig = stats.anderson(v, 'gumbel')
+        # A brief reimplementation of the calculation of the statistic.
+        n = len(v)
+        xbar, s = stats.gumbel_l.fit(v)
+        logcdf = stats.gumbel_l.logcdf(v, xbar, s)
+        logsf = stats.gumbel_l.logsf(v, xbar, s)
+        i = np.arange(1, n+1)
+        expected_a2 = -n - np.mean((2*i - 1) * (logcdf + logsf[::-1]))
+
+        assert_allclose(a2, expected_a2)
+
+    def test_bad_arg(self):
+        assert_raises(ValueError, stats.anderson, [1], dist='plate_of_shrimp')
+
+    def test_result_attributes(self):
+        rs = RandomState(1234567890)
+        x = rs.standard_exponential(size=50)
+        res = stats.anderson(x)
+        attributes = ('statistic', 'critical_values', 'significance_level')
+        check_named_results(res, attributes)
+
+    def test_gumbel_l(self):
+        # gh-2592, gh-6337
+        # Adds support to 'gumbel_r' and 'gumbel_l' as valid inputs for dist.
+        rs = RandomState(1234567890)
+        x = rs.gumbel(size=100)
+        A1, crit1, sig1 = stats.anderson(x, 'gumbel')
+        A2, crit2, sig2 = stats.anderson(x, 'gumbel_l')
+
+        assert_allclose(A2, A1)
+
+    def test_gumbel_r(self):
+        # gh-2592, gh-6337
+        # Adds support to 'gumbel_r' and 'gumbel_l' as valid inputs for dist.
+        rs = RandomState(1234567890)
+        x1 = rs.gumbel(size=100)
+        x2 = np.ones(100)
+        # A constant array is a degenerate case and breaks gumbel_r.fit, so
+        # change one value in x2.
+        x2[0] = 0.996
+        A1, crit1, sig1 = stats.anderson(x1, 'gumbel_r')
+        A2, crit2, sig2 = stats.anderson(x2, 'gumbel_r')
+
+        assert_array_less(A1, crit1[-2:])
+        assert_(A2 > crit2[-1])
+
+    def test_weibull_min_case_A(self):
+        # data and reference values from `anderson` reference [7]
+        x = np.array([225, 171, 198, 189, 189, 135, 162, 135, 117, 162])
+        res = stats.anderson(x, 'weibull_min')
+        m, loc, scale = res.fit_result.params
+        assert_allclose((m, loc, scale), (2.38, 99.02, 78.23), rtol=2e-3)
+        assert_allclose(res.statistic, 0.260, rtol=1e-3)
+        assert res.statistic < res.critical_values[0]
+
+        c = 1 / m  # ~0.42
+        assert_allclose(c, 1/2.38, rtol=2e-3)
+        # interpolate between rows for c=0.4 and c=0.45, indices -3 and -2
+        As40 = _Avals_weibull[-3]
+        As45 = _Avals_weibull[-2]
+        As_ref = As40 + (c - 0.4)/(0.45 - 0.4) * (As45 - As40)
+        # atol=1e-3 because results are rounded up to the next third decimal
+        assert np.all(res.critical_values > As_ref)
+        assert_allclose(res.critical_values, As_ref, atol=1e-3)
+
+    def test_weibull_min_case_B(self):
+        # From `anderson` reference [7]
+        x = np.array([74, 57, 48, 29, 502, 12, 70, 21,
+                      29, 386, 59, 27, 153, 26, 326])
+        message = "Maximum likelihood estimation has converged to "
+        with pytest.raises(ValueError, match=message):
+            stats.anderson(x, 'weibull_min')
+
+    def test_weibull_warning_error(self):
+        # Check for warning message when there are too few observations
+        # This is also an example in which an error occurs during fitting
+        x = -np.array([225, 75, 57, 168, 107, 12, 61, 43, 29])
+        wmessage = "Critical values of the test statistic are given for the..."
+        emessage = "An error occurred while fitting the Weibull distribution..."
+        wcontext = pytest.warns(UserWarning, match=wmessage)
+        econtext = pytest.raises(ValueError, match=emessage)
+        with wcontext, econtext:
+            stats.anderson(x, 'weibull_min')
+
+    @pytest.mark.parametrize('distname',
+                             ['norm', 'expon', 'gumbel_l', 'extreme1',
+                              'gumbel', 'gumbel_r', 'logistic', 'weibull_min'])
+    def test_anderson_fit_params(self, distname):
+        # check that anderson now returns a FitResult
+        rng = np.random.default_rng(330691555377792039)
+        real_distname = ('gumbel_l' if distname in {'extreme1', 'gumbel'}
+                         else distname)
+        dist = getattr(stats, real_distname)
+        params = distcont[real_distname]
+        x = dist.rvs(*params, size=1000, random_state=rng)
+        res = stats.anderson(x, distname)
+        assert res.fit_result.success
+
+    def test_anderson_weibull_As(self):
+        m = 1  # "when mi < 2, so that c > 0.5, the last line...should be used"
+        assert_equal(_get_As_weibull(1/m), _Avals_weibull[-1])
+        m = np.inf
+        assert_equal(_get_As_weibull(1/m), _Avals_weibull[0])
+
+
+class TestAndersonMethod:
+    def test_warning(self):
+        message = "As of SciPy 1.17, users..."
+        with pytest.warns(FutureWarning, match=message):
+            stats.anderson([1, 2, 3], 'norm')
+
+    def test_method_input_validation(self):
+        message = "`method` must be either..."
+        with pytest.raises(ValueError, match=message):
+            stats.anderson([1, 2, 3], 'norm', method='ekki-ekki')
+
+    def test_monte_carlo_method(self):
+        rng = np.random.default_rng(94982389149239)
+
+        message = "The `rvs` attribute..."
+        with pytest.warns(UserWarning, match=message):
+            method = stats.MonteCarloMethod(rvs=rng.random)
+            stats.anderson([1, 2, 3], 'norm', method=method)
+
+        message = "The `batch` attribute..."
+        with pytest.warns(UserWarning, match=message):
+            method = stats.MonteCarloMethod(batch=10)
+            stats.anderson([1, 2, 3], 'norm', method=method)
+
+        method = stats.MonteCarloMethod(n_resamples=9, rng=rng)
+        res = stats.anderson([1, 2, 3], 'norm', method=method)
+        ten_p = res.pvalue * 10
+        # p-value will always be divisible by n_resamples + 1
+        assert np.round(ten_p) == ten_p
+
+        method = stats.MonteCarloMethod(rng=np.random.default_rng(23495984827))
+        ref = stats.anderson([1, 2, 3, 4, 5], 'norm', method=method)
+        method = stats.MonteCarloMethod(rng=np.random.default_rng(23495984827))
+        res = stats.anderson([1, 2, 3, 4, 5], 'norm', method=method)
+        assert res.pvalue == ref.pvalue  # same random state -> same p-value
+        method = stats.MonteCarloMethod(rng=np.random.default_rng(23495984828))
+        res = stats.anderson([1, 2, 3, 4, 5], 'norm', method=method)
+        assert res.pvalue != ref.pvalue  # different random state -> different p-value
+
+    @pytest.mark.parametrize('dist_name, seed',
+        [('norm', 4202165767275),
+         ('expon', 9094400417269),
+         pytest.param('logistic', 3776634590070, marks=pytest.mark.xslow),
+         pytest.param('gumbel_l', 7966588969335, marks=pytest.mark.xslow),
+         pytest.param('gumbel_r', 1886450383828, marks=pytest.mark.xslow)])
+    def test_method_consistency(self, dist_name, seed):
+        dist = getattr(stats, dist_name)
+        rng = np.random.default_rng(seed)
+        x = dist.rvs(size=50, random_state=rng)
+        ref = stats.anderson(x, dist_name, method='interpolate')
+        res = stats.anderson(x, dist_name, method=stats.MonteCarloMethod(rng=rng))
+        np.testing.assert_allclose(res.statistic, ref.statistic)
+        np.testing.assert_allclose(res.pvalue, ref.pvalue, atol=0.005)
+
+    @pytest.mark.parametrize('dist_name',
+        ['norm', 'expon', 'logistic', 'gumbel_l', 'gumbel_r', 'weibull_min'])
+    def test_interpolate_saturation(self, dist_name):
+        dist = getattr(stats, dist_name)
+        rng = np.random.default_rng(4202165767276)
+        args = (3.5,) if dist_name == 'weibull_min' else tuple()
+        x = dist.rvs(*args, size=50, random_state=rng)
+
+        with pytest.warns(FutureWarning):
+            res = stats.anderson(x, dist_name)
+        pvalues = (1 - np.asarray(res.significance_level) if dist_name == 'weibull_min'
+                   else np.asarray(res.significance_level) / 100)
+        pvalue_min = np.min(pvalues)
+        pvalue_max = np.max(pvalues)
+        statistic_min = np.min(res.critical_values)
+        statistic_max = np.max(res.critical_values)
+
+        # data drawn from distribution -> low statistic / high p-value
+        res = stats.anderson(x, dist_name, method='interpolate')
+        assert res.statistic < statistic_min
+        assert res.pvalue == pvalue_max
+
+        # data not from distribution -> high statistic / low p-value
+        res = stats.anderson(rng.random(size=50), dist_name, method='interpolate')
+        assert res.statistic > statistic_max
+        assert res.pvalue == pvalue_min
+
+
+@pytest.mark.filterwarnings("ignore:Parameter `variant`...:UserWarning")
+class TestAndersonKSamp:
+    def test_example1a(self):
+        # Example data from Scholz & Stephens (1987), originally
+        # published in Lehmann (1995, Nonparametrics, Statistical
+        # Methods Based on Ranks, p. 309)
+        # Pass a mixture of lists and arrays
+        t1 = [38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0]
+        t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8])
+        t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0])
+        t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8])
+
+        Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4), midrank=False)
+
+        assert_almost_equal(Tk, 4.449, 3)
+        assert_array_almost_equal([0.4985, 1.3237, 1.9158, 2.4930, 3.2459],
+                                  tm[0:5], 4)
+        assert_allclose(p, 0.0021, atol=0.00025)
+
+    def test_example1b(self):
+        # Example data from Scholz & Stephens (1987), originally
+        # published in Lehmann (1995, Nonparametrics, Statistical
+        # Methods Based on Ranks, p. 309)
+        # Pass arrays
+        t1 = np.array([38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0])
+        t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8])
+        t3 = np.array([34.0, 35.0, 39.0, 40.0, 43.0, 43.0, 44.0, 45.0])
+        t4 = np.array([34.0, 34.8, 34.8, 35.4, 37.2, 37.8, 41.2, 42.8])
+        Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4), midrank=True)
+
+        assert_almost_equal(Tk, 4.480, 3)
+        assert_array_almost_equal([0.4985, 1.3237, 1.9158, 2.4930, 3.2459],
+                                  tm[0:5], 4)
+        assert_allclose(p, 0.0020, atol=0.00025)
+
+    @pytest.mark.xslow
+    def test_example2a(self):
+        # Example data taken from an earlier technical report of
+        # Scholz and Stephens
+        # Pass lists instead of arrays
+        t1 = [194, 15, 41, 29, 33, 181]
+        t2 = [413, 14, 58, 37, 100, 65, 9, 169, 447, 184, 36, 201, 118]
+        t3 = [34, 31, 18, 18, 67, 57, 62, 7, 22, 34]
+        t4 = [90, 10, 60, 186, 61, 49, 14, 24, 56, 20, 79, 84, 44, 59, 29,
+              118, 25, 156, 310, 76, 26, 44, 23, 62]
+        t5 = [130, 208, 70, 101, 208]
+        t6 = [74, 57, 48, 29, 502, 12, 70, 21, 29, 386, 59, 27]
+        t7 = [55, 320, 56, 104, 220, 239, 47, 246, 176, 182, 33]
+        t8 = [23, 261, 87, 7, 120, 14, 62, 47, 225, 71, 246, 21, 42, 20, 5,
+              12, 120, 11, 3, 14, 71, 11, 14, 11, 16, 90, 1, 16, 52, 95]
+        t9 = [97, 51, 11, 4, 141, 18, 142, 68, 77, 80, 1, 16, 106, 206, 82,
+              54, 31, 216, 46, 111, 39, 63, 18, 191, 18, 163, 24]
+        t10 = [50, 44, 102, 72, 22, 39, 3, 15, 197, 188, 79, 88, 46, 5, 5, 36,
+               22, 139, 210, 97, 30, 23, 13, 14]
+        t11 = [359, 9, 12, 270, 603, 3, 104, 2, 438]
+        t12 = [50, 254, 5, 283, 35, 12]
+        t13 = [487, 18, 100, 7, 98, 5, 85, 91, 43, 230, 3, 130]
+        t14 = [102, 209, 14, 57, 54, 32, 67, 59, 134, 152, 27, 14, 230, 66,
+               61, 34]
+
+        samples = (t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11, t12, t13, t14)
+        Tk, tm, p = stats.anderson_ksamp(samples, midrank=False)
+        assert_almost_equal(Tk, 3.288, 3)
+        assert_array_almost_equal([0.5990, 1.3269, 1.8052, 2.2486, 2.8009],
+                                  tm[0:5], 4)
+        assert_allclose(p, 0.0041, atol=0.00025)
+
+        rng = np.random.default_rng(6989860141921615054)
+        method = stats.PermutationMethod(n_resamples=9999, rng=rng)
+        res = stats.anderson_ksamp(samples, midrank=False, method=method)
+        assert_array_equal(res.statistic, Tk)
+        assert_array_equal(res.critical_values, tm)
+        assert_allclose(res.pvalue, p, atol=6e-4)
+
+    def test_example2b(self):
+        # Example data taken from an earlier technical report of
+        # Scholz and Stephens
+        t1 = [194, 15, 41, 29, 33, 181]
+        t2 = [413, 14, 58, 37, 100, 65, 9, 169, 447, 184, 36, 201, 118]
+        t3 = [34, 31, 18, 18, 67, 57, 62, 7, 22, 34]
+        t4 = [90, 10, 60, 186, 61, 49, 14, 24, 56, 20, 79, 84, 44, 59, 29,
+              118, 25, 156, 310, 76, 26, 44, 23, 62]
+        t5 = [130, 208, 70, 101, 208]
+        t6 = [74, 57, 48, 29, 502, 12, 70, 21, 29, 386, 59, 27]
+        t7 = [55, 320, 56, 104, 220, 239, 47, 246, 176, 182, 33]
+        t8 = [23, 261, 87, 7, 120, 14, 62, 47, 225, 71, 246, 21, 42, 20, 5,
+              12, 120, 11, 3, 14, 71, 11, 14, 11, 16, 90, 1, 16, 52, 95]
+        t9 = [97, 51, 11, 4, 141, 18, 142, 68, 77, 80, 1, 16, 106, 206, 82,
+              54, 31, 216, 46, 111, 39, 63, 18, 191, 18, 163, 24]
+        t10 = [50, 44, 102, 72, 22, 39, 3, 15, 197, 188, 79, 88, 46, 5, 5, 36,
+               22, 139, 210, 97, 30, 23, 13, 14]
+        t11 = [359, 9, 12, 270, 603, 3, 104, 2, 438]
+        t12 = [50, 254, 5, 283, 35, 12]
+        t13 = [487, 18, 100, 7, 98, 5, 85, 91, 43, 230, 3, 130]
+        t14 = [102, 209, 14, 57, 54, 32, 67, 59, 134, 152, 27, 14, 230, 66,
+               61, 34]
+
+        Tk, tm, p = stats.anderson_ksamp((t1, t2, t3, t4, t5, t6, t7, t8,
+                                          t9, t10, t11, t12, t13, t14),
+                                         midrank=True)
+
+        assert_almost_equal(Tk, 3.294, 3)
+        assert_array_almost_equal([0.5990, 1.3269, 1.8052, 2.2486, 2.8009],
+                                  tm[0:5], 4)
+        assert_allclose(p, 0.0041, atol=0.00025)
+
+    def test_R_kSamples(self):
+        # test values generates with R package kSamples
+        # package version 1.2-6 (2017-06-14)
+        # r1 = 1:100
+        # continuous case (no ties) --> version  1
+        # res <- kSamples::ad.test(r1, r1 + 40.5)
+        # res$ad[1, "T.AD"] #  41.105
+        # res$ad[1, " asympt. P-value"] #  5.8399e-18
+        #
+        # discrete case (ties allowed) --> version  2 (here: midrank=True)
+        # res$ad[2, "T.AD"] #  41.235
+        #
+        # res <- kSamples::ad.test(r1, r1 + .5)
+        # res$ad[1, "T.AD"] #  -1.2824
+        # res$ad[1, " asympt. P-value"] #  1
+        # res$ad[2, "T.AD"] #  -1.2944
+        #
+        # res <- kSamples::ad.test(r1, r1 + 7.5)
+        # res$ad[1, "T.AD"] # 1.4923
+        # res$ad[1, " asympt. P-value"] # 0.077501
+        #
+        # res <- kSamples::ad.test(r1, r1 + 6)
+        # res$ad[2, "T.AD"] # 0.63892
+        # res$ad[2, " asympt. P-value"] # 0.17981
+        #
+        # res <- kSamples::ad.test(r1, r1 + 11.5)
+        # res$ad[1, "T.AD"] # 4.5042
+        # res$ad[1, " asympt. P-value"] # 0.00545
+        #
+        # res <- kSamples::ad.test(r1, r1 + 13.5)
+        # res$ad[1, "T.AD"] # 6.2982
+        # res$ad[1, " asympt. P-value"] # 0.00118
+
+        x1 = np.linspace(1, 100, 100)
+        # test case: different distributions;p-value floored at 0.001
+        # test case for issue #5493 / #8536
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", 'p-value floored', UserWarning)
+            s, _, p = stats.anderson_ksamp([x1, x1 + 40.5], midrank=False)
+        assert_almost_equal(s, 41.105, 3)
+        assert_equal(p, 0.001)
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", 'p-value floored', UserWarning)
+            s, _, p = stats.anderson_ksamp([x1, x1 + 40.5])
+        assert_almost_equal(s, 41.235, 3)
+        assert_equal(p, 0.001)
+
+        # test case: similar distributions --> p-value capped at 0.25
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", 'p-value capped', UserWarning)
+            s, _, p = stats.anderson_ksamp([x1, x1 + .5], midrank=False)
+        assert_almost_equal(s, -1.2824, 4)
+        assert_equal(p, 0.25)
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", 'p-value capped', UserWarning)
+            s, _, p = stats.anderson_ksamp([x1, x1 + .5])
+        assert_almost_equal(s, -1.2944, 4)
+        assert_equal(p, 0.25)
+
+        # test case: check interpolated p-value in [0.01, 0.25] (no ties)
+        s, _, p = stats.anderson_ksamp([x1, x1 + 7.5], midrank=False)
+        assert_almost_equal(s, 1.4923, 4)
+        assert_allclose(p, 0.0775, atol=0.005, rtol=0)
+
+        # test case: check interpolated p-value in [0.01, 0.25] (w/ ties)
+        s, _, p = stats.anderson_ksamp([x1, x1 + 6])
+        assert_almost_equal(s, 0.6389, 4)
+        assert_allclose(p, 0.1798, atol=0.005, rtol=0)
+
+        # test extended critical values for p=0.001 and p=0.005
+        s, _, p = stats.anderson_ksamp([x1, x1 + 11.5], midrank=False)
+        assert_almost_equal(s, 4.5042, 4)
+        assert_allclose(p, 0.00545, atol=0.0005, rtol=0)
+
+        s, _, p = stats.anderson_ksamp([x1, x1 + 13.5], midrank=False)
+        assert_almost_equal(s, 6.2982, 4)
+        assert_allclose(p, 0.00118, atol=0.0001, rtol=0)
+
+    def test_not_enough_samples(self):
+        assert_raises(ValueError, stats.anderson_ksamp, np.ones(5))
+
+    def test_no_distinct_observations(self):
+        assert_raises(ValueError, stats.anderson_ksamp,
+                      (np.ones(5), np.ones(5)))
+
+    def test_empty_sample(self):
+        assert_raises(ValueError, stats.anderson_ksamp, (np.ones(5), []))
+
+    def test_result_attributes(self):
+        # Pass a mixture of lists and arrays
+        t1 = [38.7, 41.5, 43.8, 44.5, 45.5, 46.0, 47.7, 58.0]
+        t2 = np.array([39.2, 39.3, 39.7, 41.4, 41.8, 42.9, 43.3, 45.8])
+        res = stats.anderson_ksamp((t1, t2), midrank=False)
+
+        attributes = ('statistic', 'critical_values', 'significance_level')
+        check_named_results(res, attributes)
+
+        assert_equal(res.significance_level, res.pvalue)
+
+
+class TestAndersonKSampVariant:
+    def test_variant_values(self):
+        x = [1, 2, 2, 3, 4, 5]
+        y = [1, 2, 3, 4, 4, 5, 6, 6, 6, 7]
+        message = "Parameter `variant` has been introduced..."
+        with pytest.warns(UserWarning, match=message):
+            ref = stats.anderson_ksamp((x, y))
+        assert len(ref) == 3 and hasattr(ref, 'critical_values')
+
+        with pytest.warns(UserWarning, match=message):
+            res = stats.anderson_ksamp((x, y), midrank=True)
+        assert_equal(res.statistic, ref.statistic)
+        assert_equal(res.pvalue, ref.pvalue)
+        assert len(res) == 3 and hasattr(res, 'critical_values')
+
+        with pytest.warns(UserWarning, match=message):
+            res = stats.anderson_ksamp((x, y), midrank=False, variant='midrank')
+        assert_equal(res.statistic, ref.statistic)
+        assert_equal(res.pvalue, ref.pvalue)
+        assert not hasattr(res, 'critical_values')
+
+        res = stats.anderson_ksamp((x, y), variant='midrank')
+        assert_equal(res.statistic, ref.statistic)
+        assert_equal(res.pvalue, ref.pvalue)
+        assert not hasattr(res, 'critical_values')
+
+        with pytest.warns(UserWarning, match=message):
+            ref = stats.anderson_ksamp((x, y), midrank=False)
+        assert len(ref) == 3 and hasattr(ref, 'critical_values')
+
+        with pytest.warns(UserWarning, match=message):
+            res = stats.anderson_ksamp((x, y), midrank=True, variant='right')
+        assert_equal(res.statistic, ref.statistic)
+        assert_equal(res.pvalue, ref.pvalue)
+        assert not hasattr(res, 'critical_values')
+
+        res = stats.anderson_ksamp((x, y), variant='right')
+        assert_equal(res.statistic, ref.statistic)
+        assert_equal(res.pvalue, ref.pvalue)
+        assert not hasattr(res, 'critical_values')
+
+    def test_variant_input_validation(self):
+        x = np.arange(10)
+        message = "`variant` must be one of 'midrank', 'right', or 'continuous'."
+        with pytest.raises(ValueError, match=message):
+            stats.anderson_ksamp((x, x), variant='Camelot')
+
+    @pytest.mark.parametrize('n_samples', [2, 3])
+    def test_variant_continuous(self, n_samples):
+        rng = np.random.default_rng(20182053007)
+        samples = rng.random((n_samples, 15)) + 0.1*np.arange(n_samples)[:, np.newaxis]
+        ref = stats.anderson_ksamp(samples, variant='right')
+        res = stats.anderson_ksamp(samples, variant='continuous')
+        assert_allclose(res.statistic, ref.statistic)
+        assert_allclose(res.pvalue, ref.pvalue)
+
+
+@make_xp_test_case(stats.ansari)
+class TestAnsari:
+
+    def test_small(self, xp):
+        x = xp.asarray([1, 2, 3, 3, 4])
+        y = xp.asarray([3, 2, 6, 1, 6, 1, 4, 1])
+        W, pval = stats.ansari(x, y, method='asymptotic')
+        xp_assert_close(W, xp.asarray(23.5))
+        xp_assert_close(pval, xp.asarray(0.13499256881897437))
+
+    def test_approx(self, xp):
+        ramsay = xp.asarray([111, 107, 100, 99, 102, 106, 109, 108, 104, 99,
+                             101, 96, 97, 102, 107, 113, 116, 113, 110, 98])
+        parekh = xp.asarray([107, 108, 106, 98, 105, 103, 110, 105, 104,
+                             100, 96, 108, 103, 104, 114, 114, 113, 108,
+                             106, 99])
+
+        W, pval = stats.ansari(ramsay, parekh, method='asymptotic')
+        xp_assert_close(W, xp.asarray(185.5))
+        xp_assert_close(pval, xp.asarray(0.18145819972867083))
+
+    def test_exact(self, xp):
+        x, y = xp.asarray([1, 2, 3, 4]), xp.asarray([15, 5, 20, 8, 10, 12])
+        W, pval = stats.ansari(x, y, method='exact')
+        xp_assert_close(W, xp.asarray(10.0))
+        xp_assert_close(pval, xp.asarray(0.533333333333333333))
+
+    @pytest.mark.parametrize('args', [([], [1.]), ([1.], [])])
+    def test_bad_arg(self, args, xp):
+        args = [xp.asarray(arg) for arg in args]
+        with pytest.warns(SmallSampleWarning, match=too_small_1d_not_omit):
+            res = stats.ansari(*args)
+            xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+            xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+    def test_bad_alternative(self, xp):
+        # invalid value for alternative must raise a ValueError
+        x1 = xp.asarray([1, 2, 3, 4])
+        x2 = xp.asarray([5, 6, 7, 8])
+        match = "'alternative' must be 'two-sided'"
+        with assert_raises(ValueError, match=match):
+            stats.ansari(x1, x2, alternative='foo')
+
+    def test_alternative_exact(self, xp):
+        x1 = xp.asarray([-5, 1, 5, 10, 15, 20, 25.])  # high scale, loc=10
+        x2 = xp.asarray([7.5, 8.5, 9.5, 10.5, 11.5, 12.5])  # low scale, loc=10
+        # ratio of scales is greater than 1. So, the
+        # p-value must be high when `alternative='less'`
+        # and low when `alternative='greater'`.
+        statistic, pval = stats.ansari(x1, x2, method='exact')
+        pval_l = stats.ansari(x1, x2, alternative='less', method='exact').pvalue
+        pval_g = stats.ansari(x1, x2, alternative='greater', method='exact').pvalue
+        assert pval_l > 0.95
+        assert pval_g < 0.05  # level of significance.
+        # also check if the p-values sum up to 1 plus the probability
+        # mass under the calculated statistic.
+        prob = _abw_state.a.pmf(float(statistic), x1.shape[0], x2.shape[0])
+        prob = xp.asarray(float(prob))
+        xp_assert_close(pval_g + pval_l, 1 + prob, atol=1e-12)
+        # also check if one of the one-sided p-value equals half the
+        # two-sided p-value and the other one-sided p-value is its
+        # compliment.
+        xp_assert_close(pval_g, pval/2, atol=1e-12)
+        xp_assert_close(pval_l, 1+prob-pval/2, atol=1e-12)
+        # sanity check. The result should flip if
+        # we exchange x and y.
+        pval_l_reverse = stats.ansari(x2, x1, alternative='less',
+                                      method='exact').pvalue
+        pval_g_reverse = stats.ansari(x2, x1, alternative='greater',
+                                      method='exact').pvalue
+        assert pval_l_reverse < 0.05
+        assert pval_g_reverse > 0.95
+
+    @pytest.mark.parametrize(
+        'x, y, alternative, expected',
+        # the tests are designed in such a way that the
+        # if else statement in ansari test for exact
+        # mode is covered.
+        [([1, 2, 3, 4], [5, 6, 7, 8], 'less', 0.6285714285714),
+         ([1, 2, 3, 4], [5, 6, 7, 8], 'greater', 0.6285714285714),
+         ([1, 2, 3], [4, 5, 6, 7, 8], 'less', 0.8928571428571),
+         ([1, 2, 3], [4, 5, 6, 7, 8], 'greater', 0.2857142857143),
+         ([1, 2, 3, 4, 5], [6, 7, 8], 'less', 0.2857142857143),
+         ([1, 2, 3, 4, 5], [6, 7, 8], 'greater', 0.8928571428571)]
+    )
+    def test_alternative_exact_with_R(self, x, y, alternative, expected, xp):
+        # testing with R on arbitrary data
+        # Sample R code used for the third test case above:
+        # ```R
+        # > options(digits=16)
+        # > x <- c(1,2,3)
+        # > y <- c(4,5,6,7,8)
+        # > ansari.test(x, y, alternative='less', exact=TRUE)
+        #
+        #     Ansari-Bradley test
+        #
+        # data:  x and y
+        # AB = 6, p-value = 0.8928571428571
+        # alternative hypothesis: true ratio of scales is less than 1
+        #
+        # ```
+        x, y = xp.asarray(x), xp.asarray(y)
+        pval = stats.ansari(x, y, alternative=alternative, method='exact').pvalue
+        xp_assert_close(pval, xp.asarray(expected), atol=1e-12)
+
+    def test_alternative_approx(self, xp):
+        # intuitive tests for approximation
+        x1 = xp.asarray(stats.norm.rvs(0, 5, size=100, random_state=123))
+        x2 = xp.asarray(stats.norm.rvs(0, 2, size=100, random_state=123))
+        # for m > 55 or n > 55, the test should automatically
+        # switch to approximation.
+        pval_l = stats.ansari(x1, x2, alternative='less', method='asymptotic').pvalue
+        pval_g = stats.ansari(x1, x2, alternative='greater', method='asymptotic').pvalue
+        xp_assert_close(pval_l, xp.asarray(1.0, dtype=xp.float64), atol=1e-12)
+        xp_assert_close(pval_g, xp.asarray(0.0, dtype=xp.float64), atol=1e-12)
+        # also check if one of the one-sided p-value equals half the
+        # two-sided p-value and the other one-sided p-value is its
+        # compliment.
+        x1 = xp.asarray(stats.norm.rvs(0, 2, size=60, random_state=123))
+        x2 = xp.asarray(stats.norm.rvs(0, 1.5, size=60, random_state=123))
+        pval = stats.ansari(x1, x2, method='asymptotic').pvalue
+        pval_l = stats.ansari(x1, x2, alternative='less', method='asymptotic').pvalue
+        pval_g = stats.ansari(x1, x2, alternative='greater', method='asymptotic').pvalue
+        xp_assert_close(pval_g, pval/2, atol=1e-12)
+        xp_assert_close(pval_l, 1-pval/2, atol=1e-12)
+
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    @pytest.mark.parametrize('n', [10, 100])  # affects code path
+    @pytest.mark.parametrize('ties', [False, True])  # affects code path
+    def test_dtypes(self, dtype, n, ties, xp):
+        if is_jax(xp) and dtype != 'float64':
+            pytest.xfail("p-value calculation works natively with 'float64 only")
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        rng = np.random.default_rng(78587342806484)
+        x, y = rng.integers(6, size=(2, n)) if ties else rng.random(size=(2, n))
+        method = {'method': 'exact' if ((n <= 55) and not ties) else 'asymptotic'}
+        ref = stats.ansari(x, y, **method)
+        res = stats.ansari(xp.asarray(x, dtype=dtype), xp.asarray(y, dtype=dtype),
+                           **method)
+        xp_assert_close(res.statistic, xp.asarray(ref.statistic, dtype=dtype))
+        xp_assert_close(res.pvalue, xp.asarray(ref.pvalue, dtype=dtype))
+
+    @pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+    def test_permutation_method(self, alternative, xp):
+        # test that permutation test can reproduce results of method='exact',
+        # but can also be configured to perform randomized test
+        rng = np.random.default_rng(6123643029)
+        x = xp.asarray(rng.random(7).tolist())
+        y = xp.asarray(rng.random(6).tolist())
+        res = stats.ansari(x, y, alternative=alternative,
+                           method=stats.PermutationMethod(rng=rng))
+        ref = stats.ansari(x, y, alternative=alternative, method='exact')
+        xp_assert_close(res.statistic, xp.asarray(ref.statistic))
+        xp_assert_close(res.pvalue, xp.asarray(ref.pvalue))
+
+        if not is_lazy_array(x):  # randomized permutation test has side-effects
+            res = stats.ansari(x, y, alternative=alternative,
+                               method=stats.PermutationMethod(rng=rng, n_resamples=99))
+            xp_assert_close(res.statistic, xp.asarray(ref.statistic))
+            assert res.pvalue != ref.pvalue
+            assert res.pvalue == xp.round(res.pvalue*100)/100  # 99+1 in denominator
+
+    def test_method(self, xp):
+        # test that `method` selection works and 'auto' behaves as expected
+        # expected p-values computed with `ansari.test` (R)
+        # e.g. `ansari.test(x, y, exact=False)`
+        rng = np.random.default_rng(6123643029)
+
+        if is_jax(xp):
+            message = "`method` must be 'exact', 'asymptotic', or an instance..."
+            with pytest.raises(ValueError, match=message):
+                stats.ansari(xp.zeros(4), xp.ones(5))
+            return
+
+        # default is exact, but asymptotic can be selected
+        x = xp.asarray(rng.random(54).tolist())
+        y = xp.asarray(rng.random(54).tolist())
+        res1 = stats.ansari(x, y)
+        res2 = stats.ansari(x, y, method='exact')
+        res3 = stats.ansari(x, y, method='asymptotic')
+        xp_assert_close(res1.pvalue, res2.pvalue)
+        xp_assert_close(res3.pvalue, xp.asarray(0.01259674075933))
+
+        # default is asymptotic, but exact can be selected
+        x = xp.asarray(rng.random(55).tolist())
+        y = xp.asarray(rng.random(55).tolist())
+        res1 = stats.ansari(x, y)
+        res2 = stats.ansari(x, y, method='asymptotic')
+        res3 = stats.ansari(x, y, method='exact')
+        xp_assert_close(res1.pvalue, res2.pvalue)
+        xp_assert_close(res3.pvalue, xp.asarray(0.7613489076855))
+
+        # default is asymptotic because of ties
+        x = xp.asarray(rng.integers(10, size=20).tolist())
+        y = xp.asarray(rng.integers(10, size=20).tolist())
+        res1 = stats.ansari(x, y)
+        res2 = stats.ansari(x, y, method='asymptotic')
+        xp_assert_close(res1.pvalue, res2.pvalue)
+
+
+@make_xp_test_case(stats.bartlett)
+class TestBartlett:
+    def test_data(self, xp):
+        # https://www.itl.nist.gov/div898/handbook/eda/section3/eda357.htm
+        args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
+        args = [xp.asarray(arg) for arg in args]
+        T, pval = stats.bartlett(*args)
+        xp_assert_close(T, xp.asarray(20.78587342806484))
+        xp_assert_close(pval, xp.asarray(0.0136358632781))
+
+    def test_too_few_args(self, xp):
+        message = "Must enter at least two input sample vectors."
+        with pytest.raises(ValueError, match=message):
+            stats.bartlett(xp.asarray([1.]))
+
+    def test_result_attributes(self, xp):
+        args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
+        args = [xp.asarray(arg) for arg in args]
+        res = stats.bartlett(*args)
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, xp=xp)
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered")  # Dask
+    def test_empty_arg(self, xp):
+        args = (g1, g2, g3, g4, g5, g6, g7, g8, g9, g10, [])
+        args = [xp.asarray(arg) for arg in args]
+
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = stats.bartlett(*args)
+
+        NaN = xp.asarray(xp.nan)
+        xp_assert_equal(res.statistic, NaN)
+        xp_assert_equal(res.pvalue, NaN)
+
+    def test_negative_pvalue_gh21152(self, xp):
+        a = xp.asarray([10.1, 10.2, 10.3, 10.4], dtype=xp.float32)
+        b = xp.asarray([10.15, 10.25, 10.35, 10.45], dtype=xp.float32)
+        c = xp.asarray([10.05, 10.15, 10.25, 10.35], dtype=xp.float32)
+        res = stats.bartlett(a, b, c)
+        assert xp.all(res.statistic >= 0)
+
+
+@make_xp_test_case(stats.levene)
+class TestLevene:
+
+    def test_data(self, xp):
+        # https://www.itl.nist.gov/div898/handbook/eda/section3/eda35a.htm
+        args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
+        args = [xp.asarray(arg) for arg in args]
+        W, pval = stats.levene(*args)
+        xp_assert_close(W, xp.asarray(1.7059176930008939))
+        xp_assert_close(pval, xp.asarray(0.0990829755522))
+
+    def test_mean(self, xp):
+        # numbers from R: leveneTest in package car
+        args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
+        args = [xp.asarray(arg) for arg in args]
+        W, pval = stats.levene(*args, center="mean")
+        xp_assert_close(W, xp.asarray(2.15945985647285))
+        xp_assert_close(pval, xp.asarray(0.032236826559783))
+
+    def test_trimmed1(self, xp):
+        # Test that center='trimmed' gives the same result as center='mean'
+        # when proportiontocut=0.
+        args = (xp.asarray(g1), xp.asarray(g2), xp.asarray(g3))
+        W1, pval1 = stats.levene(*args, center='mean')
+        W2, pval2 = stats.levene(*args, center='trimmed', proportiontocut=0.0)
+        xp_assert_close(W1, W2)
+        xp_assert_close(pval1, pval2)
+
+    def test_trimmed2(self, xp):
+        # numbers from R: leveneTest in package car
+        args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
+        args = [xp.asarray(arg) for arg in args]
+        W, pval = stats.levene(*args, center="trimmed", proportiontocut=0.25)
+        xp_assert_close(W, xp.asarray(2.07712845686874))
+        xp_assert_close(pval, xp.asarray(0.0397269688035377))
+
+    def test_equal_mean_median(self, xp):
+        x = np.linspace(-1, 1, 21)
+        rng = np.random.default_rng(4058827756)
+        x2 = rng.permutation(x)
+        y = x**3
+        x, x2, y = xp.asarray(x), xp.asarray(x2), xp.asarray(y)
+        W1, pval1 = stats.levene(x, y, center='mean')
+        W2, pval2 = stats.levene(x2, y, center='median')
+        xp_assert_close(W1, W2)
+        xp_assert_close(pval1, pval2)
+
+    def test_bad_center_value(self, xp):
+        x = xp.linspace(-1, 1, 21)
+        message = "center must be 'mean', 'median' or 'trimmed'."
+        with pytest.raises(ValueError, match=message):
+            stats.levene(x, x, center='trim')
+
+    def test_too_few_args(self, xp):
+        message = "Must provide at least two samples."
+        with pytest.raises(ValueError, match=message):
+            stats.levene(xp.asarray([1]))
+
+    def test_result_attributes(self, xp):
+        args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
+        args = [xp.asarray(arg) for arg in args]
+        res = stats.levene(*args)
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, xp=xp)
+
+
+@make_xp_test_case(stats.binomtest)
+class TestBinomTest:
+    """Tests for stats.binomtest."""
+
+    # Expected results here are from R binom.test, e.g.
+    # options(digits=16)
+    # binom.test(484, 967, p=0.48)
+    @skip_xp_backends("jax.numpy", reason="'two-sided' is incompatible with JAX")
+    @pytest.mark.parametrize("k, n, p, ref, rtol",
+                             # aarch64 observed rtol: 1.5e-11
+                             [(10079999, 21000000, 0.48, 1.0, 1e-10),
+                              (10079990, 21000000, 0.48, 0.9966892187965, 1e-10),
+                              (10080009, 21000000, 0.48, 0.9970377203856, 1e-10),
+                              (10080017, 21000000, 0.48, 0.9940754817328, 1e-9)])
+    def test_two_sided_pvalues1(self, k, n, p, ref, rtol, xp):
+        # `tol` could be stricter on most architectures, but the value
+        # here is limited by accuracy of `binom.cdf` for large inputs on
+        # Linux_Python_37_32bit_full and aarch64
+        dtype = xp.float64  # because this tests precision
+        res = stats.binomtest(k, n, xp.asarray(p, dtype=dtype))
+        xp_assert_close(res.pvalue, xp.asarray(ref, dtype=dtype), rtol=rtol)
+
+    @skip_xp_backends("jax.numpy", reason="'two-sided' is incompatible with JAX")
+    @pytest.mark.parametrize("k, n, p, ref, rtol",
+                             # no aarch64 failure with 1e-15, preemptive bump
+                             [(9, 21, 0.48, 0.6689672431939, 1e-10),
+                              (4, 21, 0.48, 0.0081395634521, 1e-10),
+                              (11, 21, 0.48, 0.8278629664608, 1e-10),
+                              (7, 21, 0.48, 0.1966772901718, 1e-10),
+                              (3, 10, 0.5, 0.34375, 1e-10),
+                              (2, 2, 0.4, 0.16, 1e-10),
+                              (2, 4, 0.3, 0.5884, 1e-10)])
+    def test_two_sided_pvalues2(self, k, n, p, ref, rtol, xp):
+        dtype = xp.float64  # because this tests precision
+        res = stats.binomtest(k, n, xp.asarray(p, dtype=dtype))
+        xp_assert_close(res.pvalue, xp.asarray(ref, dtype=dtype), rtol=rtol)
+
+    @skip_xp_backends("jax.numpy", reason="'two-sided' is incompatible with JAX")
+    @pytest.mark.parametrize("k, n, p, ref, rtol",
+                             # no aarch64 failure with 1e-15, preemptive bump
+                             [(484, 967, 0.5, 1.0, 1e-10),
+                              (3, 47, 3/47, 1.0, 1e-10),
+                              (13, 46, 13/46, 1.0, 1e-10),
+                              (15, 44, 15/44, 1.0, 1e-10),
+                              (7, 13, 0.5, 1.0, 1e-10),
+                              (6, 11, 0.5, 1.0, 1e-10)])
+    def test_edge_cases(self, k, n, p, ref, rtol, xp):
+        dtype = xp.float64
+        res = stats.binomtest(k, n, xp.asarray(p, dtype=dtype))
+        xp_assert_close(res.pvalue, xp.asarray(ref, dtype=dtype), rtol=rtol)
+
+    # tests private function, so leave NumPy-only
+    def test_binary_srch_for_binom_tst(self):
+        # Test that old behavior of binomtest is maintained
+        # by the new binary search method in cases where d
+        # exactly equals the input on one side.
+        n = 10
+        p = 0.5
+        k = 3
+        # First test for the case where k > mode of PMF
+        i = np.arange(np.ceil(p * n), n+1)
+        d = stats.binom.pmf(k, n, p)
+        # Old way of calculating y, probably consistent with R.
+        y1 = np.sum(stats.binom.pmf(i, n, p) <= d, axis=0)
+        # New way with binary search.
+        ix = _binary_search_for_binom_tst(lambda x1:
+                                          -stats.binom.pmf(x1, n, p),
+                                          -d, np.ceil(p * n), n, xp=np)
+        y2 = n - ix + int(d == stats.binom.pmf(ix, n, p))
+        assert_allclose(y1, y2, rtol=1e-9)
+        # Now test for the other side.
+        k = 7
+        i = np.arange(np.floor(p * n) + 1)
+        d = stats.binom.pmf(k, n, p)
+        # Old way of calculating y.
+        y1 = np.sum(stats.binom.pmf(i, n, p) <= d, axis=0)
+        # New way with binary search.
+        ix = _binary_search_for_binom_tst(lambda x1:
+                                          stats.binom.pmf(x1, n, p),
+                                          d, 0, np.floor(p * n), xp=np)
+        y2 = ix + 1
+        assert_allclose(y1, y2, rtol=1e-9)
+
+    # Expected results here are from R 3.6.2 binom.test
+    @skip_xp_backends("array_api_strict", reason="needs find_root")
+    @skip_xp_backends("jax.numpy", reason="needs find_root")
+    @pytest.mark.parametrize('alternative, pval, ci_low, ci_high',
+                             [('less', 0.148831050443,
+                               0.0, 0.2772002496709138),
+                              ('greater', 0.9004695898947,
+                               0.1366613252458672, 1.0),
+                              ('two-sided', 0.2983720970096,
+                               0.1266555521019559, 0.2918426890886281)])
+    def test_confidence_intervals1(self, alternative, pval, ci_low, ci_high, xp):
+        dtype = xp.float64
+        res = stats.binomtest(20, n=100, p=xp.asarray(0.25, dtype=dtype),
+                              alternative=alternative)
+        xp_assert_close(res.pvalue, xp.asarray(pval, dtype=dtype), rtol=1e-12)
+        xp_assert_equal(res.statistic, xp.asarray(0.2, dtype=dtype))
+        ci = res.proportion_ci(confidence_level=xp.asarray(0.95, dtype=dtype))
+        xp_assert_close(ci.low, xp.asarray(ci_low, dtype=dtype), rtol=1e-12)
+        xp_assert_close(ci.high, xp.asarray(ci_high, dtype=dtype), rtol=1e-12)
+
+    # Expected results here are from R 3.6.2 binom.test.
+    @skip_xp_backends("array_api_strict", reason="needs find_root")
+    @skip_xp_backends("jax.numpy", reason="needs find_root")
+    @pytest.mark.parametrize('alternative, pval, ci_low, ci_high',
+                             [('less',
+                               0.005656361, 0.0, 0.1872093),
+                              ('greater',
+                               0.9987146, 0.008860761, 1.0),
+                              ('two-sided',
+                               0.01191714, 0.006872485, 0.202706269)])
+    def test_confidence_intervals2(self, alternative, pval, ci_low, ci_high, xp):
+        dtype = xp.float64
+        res = stats.binomtest(3, n=50, p=xp.asarray(0.2, dtype=dtype),
+                              alternative=alternative)
+        xp_assert_close(res.pvalue, xp.asarray(pval, dtype=dtype), rtol=1e-6)
+        xp_assert_equal(res.statistic, xp.asarray(0.06, dtype=dtype))
+        ci = res.proportion_ci(confidence_level=xp.asarray(0.99, dtype=dtype))
+        xp_assert_close(ci.low, xp.asarray(ci_low, dtype=dtype), rtol=1e-6)
+        xp_assert_close(ci.high, xp.asarray(ci_high, dtype=dtype), rtol=1e-6)
+
+    # Expected results here are from R 3.6.2 binom.test.
+    @skip_xp_backends("array_api_strict", reason="needs find_root")
+    @skip_xp_backends("jax.numpy", reason="needs find_root")
+    @pytest.mark.parametrize('alternative, pval, ci_high',
+                             [('less', 0.05631351, 0.2588656),
+                              ('greater', 1.0, 1.0),
+                              ('two-sided', 0.07604122, 0.3084971)])
+    def test_confidence_interval_exact_k0(self, alternative, pval, ci_high, xp):
+        # Test with k=0, n = 10.
+        dtype = xp.float64
+        res = stats.binomtest(0, n=10, p=xp.asarray(0.25, dtype=dtype),
+                              alternative=alternative)
+        xp_assert_close(res.pvalue, xp.asarray(pval, dtype=dtype), rtol=1e-6)
+        ci = res.proportion_ci(confidence_level=xp.asarray(0.95, dtype=dtype))
+        xp_assert_equal(ci.low, xp.asarray(0.0, dtype=dtype))
+        xp_assert_close(ci.high, xp.asarray(ci_high, dtype=dtype), rtol=1e-6)
+
+    # Expected results here are from R 3.6.2 binom.test.
+    @skip_xp_backends("array_api_strict", reason="needs find_root")
+    @skip_xp_backends("jax.numpy", reason="needs find_root")
+    @pytest.mark.parametrize('alternative, pval, ci_low',
+                             [('less', 1.0, 0.0),
+                              ('greater', 9.536743e-07, 0.7411344),
+                              ('two-sided', 9.536743e-07, 0.6915029)])
+    def test_confidence_interval_exact_k_is_n(self, alternative, pval, ci_low, xp):
+        # Test with k = n = 10.
+        dtype = xp.float64
+        res = stats.binomtest(10, n=10, p=xp.asarray(0.25, dtype=dtype),
+                              alternative=alternative)
+        xp_assert_close(res.pvalue, xp.asarray(pval, dtype=dtype), rtol=1e-6)
+        ci = res.proportion_ci(confidence_level=xp.asarray(0.95, dtype=dtype))
+        xp_assert_equal(ci.high, xp.asarray(1.0, dtype=dtype))
+        xp_assert_close(ci.low, xp.asarray(ci_low, dtype=dtype), rtol=1e-6)
+
+    # Expected results are from the prop.test function in R 3.6.2.
+    @skip_xp_backends("jax.numpy", reason="'two-sided' is incompatible with JAX")
+    @pytest.mark.parametrize(
+        'k, alternative, corr, conf, ci_low, ci_high',
+        [[3, 'two-sided', True, 0.95, 0.08094782, 0.64632928],
+         [3, 'two-sided', True, 0.99, 0.0586329, 0.7169416],
+         [3, 'two-sided', False, 0.95, 0.1077913, 0.6032219],
+         [3, 'two-sided', False, 0.99, 0.07956632, 0.6799753],
+         [3, 'less', True, 0.95, 0.0, 0.6043476],
+         [3, 'less', True, 0.99, 0.0, 0.6901811],
+         [3, 'less', False, 0.95, 0.0, 0.5583002],
+         [3, 'less', False, 0.99, 0.0, 0.6507187],
+         [3, 'greater', True, 0.95, 0.09644904, 1.0],
+         [3, 'greater', True, 0.99, 0.06659141, 1.0],
+         [3, 'greater', False, 0.95, 0.1268766, 1.0],
+         [3, 'greater', False, 0.99, 0.08974147, 1.0],
+
+         [0, 'two-sided', True, 0.95, 0.0, 0.3445372],
+         [0, 'two-sided', False, 0.95, 0.0, 0.2775328],
+         [0, 'less', True, 0.95, 0.0, 0.2847374],
+         [0, 'less', False, 0.95, 0.0, 0.212942],
+         [0, 'greater', True, 0.95, 0.0, 1.0],
+         [0, 'greater', False, 0.95, 0.0, 1.0],
+
+         [10, 'two-sided', True, 0.95, 0.6554628, 1.0],
+         [10, 'two-sided', False, 0.95, 0.7224672, 1.0],
+         [10, 'less', True, 0.95, 0.0, 1.0],
+         [10, 'less', False, 0.95, 0.0, 1.0],
+         [10, 'greater', True, 0.95, 0.7152626, 1.0],
+         [10, 'greater', False, 0.95, 0.787058, 1.0]]
+    )
+    def test_ci_wilson_method(self, k, alternative, corr, conf,
+                              ci_low, ci_high, xp):
+        dtype = xp.float64
+        res = stats.binomtest(k, n=10, p=xp.asarray(0.1, dtype=dtype),
+                              alternative=alternative)
+        if corr:
+            method = 'wilsoncc'
+        else:
+            method = 'wilson'
+        conf = xp.asarray(conf, dtype=dtype)
+        ci = res.proportion_ci(confidence_level=conf, method=method)
+        xp_assert_close(ci.low, xp.asarray(ci_low, dtype=dtype), rtol=1e-6)
+        xp_assert_close(ci.high, xp.asarray(ci_high, dtype=dtype), rtol=1e-6)
+
+    @skip_xp_backends("jax.numpy", reason="'two-sided' is incompatible with JAX")
+    def test_estimate_equals_hypothesized_prop(self, xp):
+        # Test the special case where the estimated proportion equals
+        # the hypothesized proportion.  When alternative is 'two-sided',
+        # the p-value is 1.
+        res = stats.binomtest(4, 16, xp.asarray(0.25))
+        xp_assert_equal(res.statistic, xp.asarray(0.25))
+        xp_assert_equal(res.pvalue, xp.asarray(1.0))
+
+    def test_invalid_confidence_level(self, xp):
+        res = stats.binomtest(3, n=10, p=xp.asarray(0.1), alternative='less')
+        message = r"confidence_level \(-1\) must be in the interval"
+        with pytest.raises(ValueError, match=message):
+            res.proportion_ci(confidence_level=-1)
+
+    def test_invalid_ci_method(self, xp):
+        res = stats.binomtest(3, n=10, p=xp.asarray(0.1), alternative='greater')
+        with pytest.raises(ValueError, match=r"method \('plate of shrimp'\) must be"):
+            res.proportion_ci(method="plate of shrimp")
+
+    def test_invalid_alternative(self, xp):
+        with pytest.raises(ValueError, match=r"alternative \('ekki'\) not..."):
+            stats.binomtest(3, n=10, p=xp.asarray(0.1), alternative='ekki')
+
+    def test_alias(self, xp):
+        res = stats.binomtest(3, n=10, p=xp.asarray(0.1), alternative='greater')
+        xp_assert_equal(res.proportion_estimate, res.statistic)
+
+    @pytest.mark.skipif(sys.maxsize <= 2**32, reason="32-bit does not overflow")
+    def test_boost_overflow_raises(self):  # NumPy-specific
+        # Boost.Math error policy should raise exceptions in Python
+        with pytest.raises(OverflowError, match='Error in function...'):
+            stats.binomtest(5, 6, p=sys.float_info.min)
+
+    @pytest.mark.parametrize("k, n, p",
+        [(-1, 10, 0.5), (11, 10, 0.5), (5.5, 10, 0.5), (np.nan, 10, 0.5),
+         (0, 0, 0.5), (5, 10.5, 0.5), (5, np.nan, 0.5),
+         (5, 10, -0.1), (5, 10, 1.1), (5, 10, np.nan)])
+    def test_invalid(self, k, n, p, xp):
+        res = stats.binomtest(k, n, xp.asarray(p), alternative='less')
+        xp_assert_equal(res.statistic, xp.asarray(np.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(np.nan))
+
+    @pytest.mark.parametrize("alternative", ['less', 'greater', 'two-sided'])
+    @pytest.mark.parametrize("method", ['exact', 'wilson', 'wilsoncc'])
+    def test_scalar_0d_in_scalar_out(self, alternative, method):
+        # Tests that scalar / 0-D NumPy array input are equivalent and that both
+        # result in NumPy scalars (as confirmed by xp_assert_equal).
+        res = stats.binomtest(3, 11, 0.4, alternative=alternative)
+        ref = stats.binomtest(np.asarray(3), np.asarray(11), np.asarray(0.4),
+                              alternative=alternative)
+
+        xp_assert_equal(res.statistic, ref.statistic)
+        xp_assert_equal(res.pvalue, ref.pvalue)
+        xp_assert_equal(res.n, ref.n)
+        xp_assert_equal(res.k, ref.k)
+
+        res = res.proportion_ci(method=method)
+        ref = ref.proportion_ci(method=method)
+
+        xp_assert_equal(res.low, ref.low)
+        xp_assert_equal(res.high, ref.high)
+
+    @pytest.mark.parametrize("dtype", [None, 'float32', 'float64'])
+    @pytest.mark.parametrize("alternative", ['less', 'greater', 'two-sided'])
+    @pytest.mark.parametrize("method", ['exact', 'wilson', 'wilsoncc'])
+    def test_dtype(self, dtype, alternative, method, xp):
+        # Tests that output dtype is as expected
+        dtype = dtype if dtype is None else getattr(xp, dtype)
+        k = xp.asarray(3, dtype=dtype)
+        n = xp.asarray(11, dtype=dtype)
+        p = xp.asarray(0.4, dtype=dtype)
+        if is_jax(xp) and alternative=='two-sided':
+            message = "`alternative='two-sided'` is incompatible with JAX arrays."
+            with pytest.raises(ValueError, match=message):
+                stats.binomtest(k, n, p)
+            return
+
+        res = stats.binomtest(k, n, p, alternative=alternative)
+        ref = stats.binomtest(3, 11, 0.4, alternative=alternative)
+
+        xp_assert_close(res.statistic, xp.asarray(float(ref.statistic), dtype=dtype))
+        xp_assert_close(res.pvalue, xp.asarray(float(ref.pvalue), dtype=dtype))
+
+        if res.statistic.ndim == 0:
+            if not is_lazy_array(res.n):
+                assert isinstance(res.n, int)
+                assert isinstance(res.k, int)
+            assert res.n == ref.n
+            assert res.k == ref.k
+        else:
+            xp_assert_close(res.n, xp.asarray(int(ref.n)))
+            xp_assert_close(res.k, xp.asarray(int(ref.k)))
+
+        if (is_array_api_strict(xp) or is_jax(xp)) and method == 'exact':
+            # array API strict and JAX don't support exact CI right now
+            return
+
+        confidence_level = xp.asarray(0.95, dtype=dtype)
+        res = res.proportion_ci(method=method, confidence_level=confidence_level)
+        ref = ref.proportion_ci(method=method, confidence_level=0.95)
+
+        xp_assert_close(res.low, xp.asarray(float(ref.low), dtype=dtype))
+        xp_assert_close(res.high, xp.asarray(float(ref.high), dtype=dtype))
+
+    @pytest.mark.parametrize("shape", [(), (7, 8, 9)])
+    @pytest.mark.parametrize("alternative", ["less", "greater", "two-sided"])
+    @pytest.mark.parametrize("method", ["exact", "wilson", "wilsoncc"])
+    def test_ndarray(self, shape, alternative, method, xp):
+        rng = np.random.default_rng(2150248640)
+        k = rng.integers(-1, 11, size=shape)
+        n = rng.integers(-1, 11, size=shape)
+        p = rng.uniform(-0.1, 1.1, size=shape)
+
+        if is_jax(xp) and alternative=='two-sided':
+            pytest.skip("`alternative='two-sided'` is incompatible with JAX arrays.")
+
+        res = stats.binomtest(xp.asarray(k), xp.asarray(n), xp.asarray(p),
+                              alternative=alternative)
+
+        @np.vectorize(excluded='alternative')
+        def binomtest_1d(k, n, p, alternative):
+            ref = stats.binomtest(k, n, p, alternative=alternative)
+            ci = ref.proportion_ci(method=method)
+            return ref.k, ref.n, ref.statistic, ref.pvalue, ci.low, ci.high
+
+        ref_k, ref_n, ref_statistic, ref_pvalue, ci_low, ci_high = binomtest_1d(
+            k, n, p, alternative)
+        xp_assert_close(res.k, xp.asarray(ref_k))
+        xp_assert_close(res.n, xp.asarray(ref_n))
+        xp_assert_close(res.statistic, xp.asarray(ref_statistic))
+        xp_assert_close(res.pvalue, xp.asarray(ref_pvalue))
+
+        if (is_array_api_strict(xp) or is_jax(xp)) and method == 'exact':
+            # array API strict and JAX don't support exact CI right now
+            return
+
+        ci = res.proportion_ci(method=method)
+        xp_assert_close(ci.low, xp.asarray(ci_low))
+        xp_assert_close(ci.high, xp.asarray(ci_high))
+
+    @skip_xp_backends('jax.numpy', reason="'two-sided' alternative needs root finder")
+    def test_binomtest(self, xp):
+        # precision tests compared to R for ticket:986
+        pp = xp.concat((xp.linspace(0.1, 0.2, 5),
+                        xp.linspace(0.45, 0.65, 5),
+                        xp.linspace(0.85, 0.95, 5)))
+        n = 501
+        x = 450
+        results = [0.0, 0.0, 1.0159969301994141e-304,
+                   2.9752418572150531e-275, 7.7668382922535275e-250,
+                   2.3381250925167094e-099, 7.8284591587323951e-081,
+                   9.9155947819961383e-065, 2.8729390725176308e-050,
+                   1.7175066298388421e-037, 0.0021070691951093692,
+                   0.12044570587262322, 0.88154763174802508, 0.027120993063129286,
+                   2.6102587134694721e-006]
+
+        xp_assert_close(stats.binomtest(x, n, pp).pvalue, xp.asarray(results))
+
+        xp_assert_close(stats.binomtest(50, 100, xp.asarray(0.1)).pvalue,
+                        xp.asarray(5.8320387857343647e-024))
+
+    @skip_xp_backends('jax.numpy', reason="'two-sided' alternative needs root finder")
+    def test_binomtest2(self, xp):
+        # test added for issue #2384
+        res2 = [
+            [1.0, 1.0],
+            [0.5, 1.0, 0.5],
+            [0.25, 1.00, 1.00, 0.25],
+            [0.125, 0.625, 1.000, 0.625, 0.125],
+            [0.0625, 0.3750, 1.0000, 1.0000, 0.3750, 0.0625],
+            [0.03125, 0.21875, 0.68750, 1.00000, 0.68750, 0.21875, 0.03125],
+            [0.015625, 0.125000, 0.453125, 1.000000, 1.000000, 0.453125, 0.125000,
+             0.015625],
+            [0.0078125, 0.0703125, 0.2890625, 0.7265625, 1.0000000, 0.7265625,
+             0.2890625, 0.0703125, 0.0078125],
+            [0.00390625, 0.03906250, 0.17968750, 0.50781250, 1.00000000,
+             1.00000000, 0.50781250, 0.17968750, 0.03906250, 0.00390625],
+            [0.001953125, 0.021484375, 0.109375000, 0.343750000, 0.753906250,
+             1.000000000, 0.753906250, 0.343750000, 0.109375000, 0.021484375,
+             0.001953125]
+        ]
+        for k in range(1, 11):
+            v = xp.arange(k+1, dtype=xp.float64)
+            res1 = stats.binomtest(v, k, 0.5).pvalue
+            xp_assert_close(res1, xp.asarray(res2[k-1], dtype=xp.float64))
+
+    @skip_xp_backends('jax.numpy', reason="'two-sided' alternative needs root finder")
+    def test_binomtest3(self, xp):
+        # test added for issue #2384
+        # test when x == n*p and neighbors
+        v = xp.arange(1., 11.)[:, xp.newaxis]
+        k = xp.arange(2., 11.)
+        shape = (v.shape[0], k.shape[0])
+
+        res3 = stats.binomtest(v, v*k, 1./k).pvalue
+        xp_assert_close(res3, xp.ones(shape))
+
+        # > bt=c()
+        # > for(i in as.single(1:10)) {
+        # +     for(k in as.single(2:10)) {
+        # +         bt = c(bt, binom.test(i-1, k*i,(1/k))$p.value);
+        # +         print(c(i+1, k*i,(1/k)))
+        # +     }
+        # + }
+        binom_testm1 = xp.asarray([
+             0.5, 0.5555555555555556, 0.578125, 0.5904000000000003,
+             0.5981224279835393, 0.603430543396034, 0.607304096221924,
+             0.610255656871054, 0.612579511000001, 0.625, 0.670781893004115,
+             0.68853759765625, 0.6980101120000006, 0.703906431368616,
+             0.70793209416498, 0.7108561134173507, 0.713076544331419,
+             0.714820192935702, 0.6875, 0.7268709038256367, 0.7418963909149174,
+             0.74986110468096, 0.7548015520398076, 0.7581671424768577,
+             0.760607984787832, 0.762459425024199, 0.7639120677676575, 0.7265625,
+             0.761553963657302, 0.774800934828818, 0.7818005980538996,
+             0.78613491480358, 0.789084353140195, 0.7912217659828884,
+             0.79284214559524, 0.794112956558801, 0.75390625, 0.7856929451142176,
+             0.7976688481430754, 0.8039848974727624, 0.807891868948366,
+             0.8105487660137676, 0.812473307174702, 0.8139318233591120,
+             0.815075399104785, 0.7744140625, 0.8037322594985427,
+             0.814742863657656, 0.8205425178645808, 0.8241275984172285,
+             0.8265645374416, 0.8283292196088257, 0.829666291102775,
+             0.8307144686362666, 0.7905273437499996, 0.8178712053954738,
+             0.828116983756619, 0.833508948940494, 0.8368403871552892,
+             0.839104213210105, 0.840743186196171, 0.84198481438049,
+             0.8429580531563676, 0.803619384765625, 0.829338573944648,
+             0.8389591907548646, 0.84401876783902, 0.84714369697889,
+             0.8492667010581667, 0.850803474598719, 0.851967542858308,
+             0.8528799045949524, 0.8145294189453126, 0.838881732845347,
+             0.847979024541911, 0.852760894015685, 0.8557134656773457,
+             0.8577190131799202, 0.85917058278431, 0.860270010472127,
+             0.861131648404582, 0.823802947998047, 0.846984756807511,
+             0.855635653643743, 0.860180994825685, 0.86298688573253,
+             0.864892525675245, 0.866271647085603, 0.867316125625004,
+             0.8681346531755114
+            ])
+
+        # > bt=c()
+        # > for(i in as.single(1:10)) {
+        # +     for(k in as.single(2:10)) {
+        # +         bt = c(bt, binom.test(i+1, k*i,(1/k))$p.value);
+        # +         print(c(i+1, k*i,(1/k)))
+        # +     }
+        # + }
+
+        binom_testp1 = xp.asarray([
+             0.5, 0.259259259259259, 0.26171875, 0.26272, 0.2632244513031551,
+             0.2635138663069203, 0.2636951804161073, 0.2638162407564354,
+             0.2639010709000002, 0.625, 0.4074074074074074, 0.42156982421875,
+             0.4295746560000003, 0.43473045988554, 0.4383309503172684,
+             0.4409884859402103, 0.4430309389962837, 0.444649849401104, 0.6875,
+             0.4927602499618962, 0.5096031427383425, 0.5189636628480,
+             0.5249280070771274, 0.5290623300865124, 0.5320974248125793,
+             0.5344204730474308, 0.536255847400756, 0.7265625, 0.5496019313526808,
+             0.5669248746708034, 0.576436455045805, 0.5824538812831795,
+             0.5866053321547824, 0.589642781414643, 0.5919618019300193,
+             0.593790427805202, 0.75390625, 0.590868349763505, 0.607983393277209,
+             0.617303847446822, 0.623172512167948, 0.627208862156123,
+             0.6301556891501057, 0.632401894928977, 0.6341708982290303,
+             0.7744140625, 0.622562037497196, 0.639236102912278, 0.648263335014579,
+             0.65392850011132, 0.657816519817211, 0.660650782947676,
+             0.662808780346311, 0.6645068560246006, 0.7905273437499996,
+             0.6478843304312477, 0.6640468318879372, 0.6727589686071775,
+             0.6782129857784873, 0.681950188903695, 0.684671508668418,
+             0.686741824999918, 0.688369886732168, 0.803619384765625,
+             0.668716055304315, 0.684360013879534, 0.6927642396829181,
+             0.6980155964704895, 0.701609591890657, 0.7042244320992127,
+             0.7062125081341817, 0.707775152962577, 0.8145294189453126,
+             0.686243374488305, 0.7013873696358975, 0.709501223328243,
+             0.714563595144314, 0.718024953392931, 0.7205416252126137,
+             0.722454130389843, 0.723956813292035, 0.823802947998047,
+             0.701255953767043, 0.715928221686075, 0.723772209289768,
+             0.7286603031173616, 0.7319999279787631, 0.7344267920995765,
+             0.736270323773157, 0.737718376096348
+            ])
+
+        k, v = xp.asarray(k, dtype=xp.float64), xp.asarray(v, dtype=xp.float64)
+        binom_testp1 = xp.reshape(xp.asarray(binom_testp1, dtype=xp.float64), shape)
+        binom_testm1 = xp.reshape(xp.asarray(binom_testm1, dtype=xp.float64), shape)
+
+        res4_p1 = stats.binomtest(v+1, v*k, 1./k).pvalue
+        res4_m1 = stats.binomtest(v-1, v*k, 1./k).pvalue
+
+        xp_assert_close(res4_p1, binom_testp1)
+        xp_assert_close(res4_m1, binom_testm1)
+
+
+@make_xp_test_case(stats.fligner)
+class TestFligner:
+
+    def _perturb(self, g, rng=124987234782812):
+        # g arrays have ties to which statistic is very sensitive; break them
+        rng = np.random.default_rng(rng)
+        return (np.asarray(g) + 1e-10 * rng.standard_normal(len(g))).tolist()
+
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_data(self, dtype, xp):
+        # numbers from R: fligner.test in package stats
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        x1 = xp.arange(5, dtype=dtype)
+        res = stats.fligner(x1, x1**2)
+        ref = (xp.asarray(3.2282229927203536, dtype=dtype),
+               xp.asarray(0.072379187848207877, dtype=dtype))
+        xp_assert_close(res[0], ref[0])
+        xp_assert_close(res[1], ref[1])
+
+    def test_trimmed1(self, xp):
+        # Perturb input to break ties in the transformed data
+        # See https://github.com/scipy/scipy/pull/8042 for more details
+        rng = np.random.default_rng(9952379681)
+        g1_ = xp.asarray(self._perturb(g1, rng=rng))
+        g2_ = xp.asarray(self._perturb(g2, rng=rng))
+        g3_ = xp.asarray(self._perturb(g3, rng=rng))
+        # Test that center='trimmed' gives the same result as center='mean'
+        # when proportiontocut=0.
+        Xsq1, pval1 = stats.fligner(g1_, g2_, g3_, center='mean')
+        Xsq2, pval2 = stats.fligner(g1_, g2_, g3_, center='trimmed',
+                                    proportiontocut=0.0)
+        xp_assert_close(Xsq1, Xsq2)
+        xp_assert_close(pval1, pval2)
+
+    @skip_xp_backends(np_only=True, reason="inconsistent tie-breaking across backends")
+    def test_trimmed_nonregression(self, xp):
+        # This is a non-regression test
+        # Expected results are *not* from an external gold standard,
+        # we're just making sure the results remain consistent
+        # in the future in case of changes
+        args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
+        args = [xp.asarray(arg, dtype=xp.float64) for arg in args]
+        W, pval = stats.fligner(*args, center="trimmed", proportiontocut=0.25)
+        xp_assert_close(W, xp.asarray(15.953569890010614), rtol=5e-14)
+        xp_assert_close(pval, xp.asarray(0.06785752327432863), rtol=5e-14)
+
+    def test_trimmed_consistency(self, xp):
+        # Tests for consistency across multiple backends when ties are broken
+        rng = np.random.default_rng(4839206199)
+        args = [g1, g2, g3, g4, g5, g6, g7, g8, g9, g10]
+        args = [self._perturb(arg, rng=rng) for arg in args]
+        ref = stats.fligner(*args, center="trimmed", proportiontocut=0.25)
+        args = [xp.asarray(arg, dtype=xp.float64) for arg in args]
+        res = stats.fligner(*args, center="trimmed", proportiontocut=0.25)
+        xp_assert_close(res.statistic, xp.asarray(ref.statistic), rtol=5e-14)
+        xp_assert_close(res.pvalue, xp.asarray(ref.pvalue), rtol=5e-14)
+
+    def test_bad_center_value(self, xp):
+        x = xp.linspace(-1, 1, 21)
+        message = "center must be 'mean', 'median' or 'trimmed'."
+        with pytest.raises(ValueError, match=message):
+            stats.fligner(x, x, center='trim')
+
+    def test_bad_num_args(self, xp):
+        # Too few args raises ValueError.
+        message = "Must provide at least two samples."
+        with pytest.raises(ValueError, match=message):
+            stats.fligner(xp.asarray([1, 2]))
+
+    def test_empty_arg(self, xp):
+        x = xp.arange(5.)
+        with pytest.warns(SmallSampleWarning, match=too_small_1d_not_omit):
+            res = stats.fligner(x, x**2, xp.asarray([]))
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+
+def mood_cases_with_ties():
+    # Generate random `x` and `y` arrays with ties both between and within the
+    # samples. Expected results are (statistic, pvalue) from SAS.
+    expected_results = [(-1.76658511464992, .0386488678399305),
+                        (-.694031428192304, .2438312498647250),
+                        (-1.15093525352151, .1248794365836150)]
+    seeds = [23453254, 1298352315, 987234597]
+    for si, seed in enumerate(seeds):
+        rng = np.random.default_rng(seed)
+        xy = rng.random(100)
+        # Generate random indices to make ties
+        tie_ind = rng.integers(low=0, high=99, size=5)
+        # Generate a random number of ties for each index.
+        num_ties_per_ind = rng.integers(low=1, high=5, size=5)
+        # At each `tie_ind`, mark the next `n` indices equal to that value.
+        for i, n in zip(tie_ind, num_ties_per_ind):
+            for j in range(i + 1, i + n):
+                xy[j] = xy[i]
+        # scramble order of xy before splitting into `x, y`
+        rng.shuffle(xy)
+        x, y = np.split(xy, 2)
+        yield x, y, 'less', *expected_results[si]
+
+
+@make_xp_test_case(stats.mood)
+class TestMood:
+    @pytest.mark.parametrize("x,y,alternative,stat_expect,p_expect",
+                             list(mood_cases_with_ties()))
+    def test_against_SAS(self, x, y, alternative, stat_expect, p_expect, xp):
+        """
+        Example code used to generate SAS output:
+        DATA myData;
+        INPUT X Y;
+        CARDS;
+        1 0
+        1 1
+        1 2
+        1 3
+        1 4
+        2 0
+        2 1
+        2 4
+        2 9
+        2 16
+        ods graphics on;
+        proc npar1way mood data=myData ;
+           class X;
+            ods output  MoodTest=mt;
+        proc contents data=mt;
+        proc print data=mt;
+          format     Prob1 17.16 Prob2 17.16 Statistic 17.16 Z 17.16 ;
+            title "Mood Two-Sample Test";
+        proc print data=myData;
+            title "Data for above results";
+          run;
+        """
+        x, y = xp.asarray(x.tolist()), xp.asarray(y.tolist())
+        statistic, pvalue = stats.mood(x, y, alternative=alternative)
+        xp_assert_close(statistic, xp.asarray(stat_expect), atol=1e-16)
+        xp_assert_close(pvalue, xp.asarray(p_expect), atol=1e-16)
+
+    @pytest.mark.parametrize("dtype", [None, 'float32', 'float64'])
+    @pytest.mark.parametrize("alternative, expected",
+                             [('two-sided', (1.019938533549930,
+                                             .3077576129778760)),
+                              ('less', (1.019938533549930,
+                                        1 - .1538788064889380)),
+                              ('greater', (1.019938533549930,
+                                           .1538788064889380))])
+    def test_against_SAS_2(self, dtype, alternative, expected, xp):
+        # Code to run in SAS in above function
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        x = [111, 107, 100, 99, 102, 106, 109, 108, 104, 99,
+             101, 96, 97, 102, 107, 113, 116, 113, 110, 98]
+        y = [107, 108, 106, 98, 105, 103, 110, 105, 104, 100,
+             96, 108, 103, 104, 114, 114, 113, 108, 106, 99]
+        x, y = xp.asarray(x, dtype=dtype), xp.asarray(y, dtype=dtype)
+        res = stats.mood(x, y, alternative=alternative)
+        xp_assert_close(res.statistic, xp.asarray(expected[0], dtype=dtype))
+        xp_assert_close(res.pvalue, xp.asarray(expected[1], dtype=dtype))
+
+    def test_mood_order_of_args(self, xp):
+        # z should change sign when the order of arguments changes, pvalue
+        # should not change
+        rng = np.random.default_rng(4058827756)
+        x1 = xp.asarray(rng.standard_normal((10, 1)))
+        x2 = xp.asarray(rng.standard_normal((15, 1)))
+        z1, p1 = stats.mood(x1, x2)
+        z2, p2 = stats.mood(x2, x1)
+        xp_assert_close(z2, -z1)
+        xp_assert_close(p2, p1)
+
+    def test_mood_with_axis_none(self, xp):
+        # Test with axis = None, compare with results from R
+        x1 = [-0.626453810742332, 0.183643324222082, -0.835628612410047,
+              1.59528080213779, 0.329507771815361, -0.820468384118015,
+              0.487429052428485, 0.738324705129217, 0.575781351653492,
+              -0.305388387156356, 1.51178116845085, 0.389843236411431,
+              -0.621240580541804, -2.2146998871775, 1.12493091814311,
+              -0.0449336090152309, -0.0161902630989461, 0.943836210685299,
+              0.821221195098089, 0.593901321217509]
+
+        x2 = [-0.896914546624981, 0.184849184646742, 1.58784533120882,
+              -1.13037567424629, -0.0802517565509893, 0.132420284381094,
+              0.707954729271733, -0.23969802417184, 1.98447393665293,
+              -0.138787012119665, 0.417650750792556, 0.981752777463662,
+              -0.392695355503813, -1.03966897694891, 1.78222896030858,
+              -2.31106908460517, 0.878604580921265, 0.035806718015226,
+              1.01282869212708, 0.432265154539617, 2.09081920524915,
+              -1.19992581964387, 1.58963820029007, 1.95465164222325,
+              0.00493777682814261, -2.45170638784613, 0.477237302613617,
+              -0.596558168631403, 0.792203270299649, 0.289636710177348]
+
+        x1 = xp.reshape(xp.asarray(x1), (10, 2))
+        x2 = xp.reshape(xp.asarray(x2), (15, 2))
+        res = stats.mood(x1, x2, axis=None)
+        xp_assert_close(res.statistic, xp.asarray(-1.31716607555))
+        xp_assert_close(res.pvalue, xp.asarray(0.18778296257))
+
+    @pytest.mark.parametrize('rng_method, args', [('standard_normal', tuple()),
+                                                  ('integers', (8,))])
+    def test_mood_2d(self, rng_method, args, xp):
+        # Test if the results of mood test in 2-D vectorized call are consistent
+        # with result when looping over the slices.
+        ny = 5
+        rng = np.random.default_rng(3602349075)
+        rng_method = getattr(rng, rng_method)
+        x1 = rng_method(*args, size=(10, ny))
+        x2 = rng_method(*args, size=(15, ny))
+        dtype = xp.float64
+        res = stats.mood(xp.asarray(x1, dtype=dtype), xp.asarray(x2, dtype=dtype))
+
+        for j in range(ny):
+            ref = stats.mood(x1[:, j], x2[:, j])
+            xp_assert_close(res.statistic[j], xp.asarray(ref.statistic))
+            xp_assert_close(res.pvalue[j], xp.asarray(ref.pvalue))
+
+        # inverse order of dimensions
+        x1 = x1.transpose()
+        x2 = x2.transpose()
+        res = stats.mood(xp.asarray(x1, dtype=dtype), xp.asarray(x2, dtype=dtype),
+                         axis=1)
+
+        for i in range(ny):
+            # check axis handling is self consistent
+            ref = stats.mood(x1[i, :], x2[i, :])
+            xp_assert_close(res.statistic[i], xp.asarray(ref.statistic), atol=1e-14)
+            xp_assert_close(res.pvalue[i], xp.asarray(ref.pvalue))
+
+    @pytest.mark.parametrize('rng_method, args', [('standard_normal', tuple()),
+                                                  ('integers', (8,))])
+    def test_mood_3d(self, rng_method, args, xp):
+        shape = (10, 5, 6)
+        rng = np.random.default_rng(3602349075)
+        rng_method = getattr(rng, rng_method)
+        x1 = xp.asarray(rng_method(*args, size=shape))
+        x2 = xp.asarray(rng_method(*args, size=shape))
+
+        for axis in range(3):
+            res = stats.mood(x1, x2, axis=axis)
+            # Tests that result for 3-D arrays is equal to that for the
+            # same calculation on a set of 1-D arrays taken from the
+            # 3-D array
+            axes_idx = ([1, 2], [0, 2], [0, 1])  # the two axes != axis
+            for i in range(shape[axes_idx[axis][0]]):
+                for j in range(shape[axes_idx[axis][1]]):
+                    if axis == 0:
+                        slice1 = x1[:, i, j]
+                        slice2 = x2[:, i, j]
+                    elif axis == 1:
+                        slice1 = x1[i, :, j]
+                        slice2 = x2[i, :, j]
+                    else:
+                        slice1 = x1[i, j, :]
+                        slice2 = x2[i, j, :]
+
+                    ref = stats.mood(slice1, slice2)
+                    xp_assert_close(res.statistic[i, j], ref.statistic, atol=1e-15)
+                    xp_assert_close(res.pvalue[i, j], ref.pvalue)
+
+    def test_mood_bad_arg(self, xp):
+        # Warns when the sum of the lengths of the args is less than 3
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = stats.mood(xp.asarray([1.]), xp.asarray([]))
+            xp_assert_equal(res.statistic, xp.asarray(np.nan))
+            xp_assert_equal(res.pvalue, xp.asarray(np.nan))
+
+    @pytest.mark.parametrize("dtype", [None, 'float32', 'float64'])
+    def test_mood_alternative(self, dtype, xp):
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+
+        rng = np.random.RandomState(0)
+        x = stats.norm.rvs(scale=0.75, size=100, random_state=rng)
+        y = stats.norm.rvs(scale=1.25, size=100, random_state=rng)
+        x, y = xp.asarray(x, dtype=dtype), xp.asarray(y, dtype=dtype)
+
+        stat1, p1 = stats.mood(x, y, alternative='two-sided')
+        stat2, p2 = stats.mood(x, y, alternative='less')
+        stat3, p3 = stats.mood(x, y, alternative='greater')
+
+        assert stat1 == stat2 == stat3
+        xp_assert_close(p1, xp.asarray(0., dtype=dtype), atol=1e-7)
+        xp_assert_close(p2, xp.asarray(p1/2, dtype=dtype))
+        xp_assert_close(p3, xp.asarray(1 - p1/2, dtype=dtype))
+
+        with pytest.raises(ValueError, match="`alternative` must be..."):
+            stats.mood(x, y, alternative='ekki-ekki')
+
+
+class TestProbplot:
+
+    def test_basic(self):
+        x = stats.norm.rvs(size=20, random_state=12345)
+        osm, osr = stats.probplot(x, fit=False)
+        osm_expected = [-1.8241636, -1.38768012, -1.11829229, -0.91222575,
+                        -0.73908135, -0.5857176, -0.44506467, -0.31273668,
+                        -0.18568928, -0.06158146, 0.06158146, 0.18568928,
+                        0.31273668, 0.44506467, 0.5857176, 0.73908135,
+                        0.91222575, 1.11829229, 1.38768012, 1.8241636]
+        assert_allclose(osr, np.sort(x))
+        assert_allclose(osm, osm_expected)
+
+        res, res_fit = stats.probplot(x, fit=True)
+        res_fit_expected = [1.05361841, 0.31297795, 0.98741609]
+        assert_allclose(res_fit, res_fit_expected)
+
+    def test_sparams_keyword(self):
+        x = stats.norm.rvs(size=100, random_state=123456)
+        # Check that None, () and 0 (loc=0, for normal distribution) all work
+        # and give the same results
+        osm1, osr1 = stats.probplot(x, sparams=None, fit=False)
+        osm2, osr2 = stats.probplot(x, sparams=0, fit=False)
+        osm3, osr3 = stats.probplot(x, sparams=(), fit=False)
+        assert_allclose(osm1, osm2)
+        assert_allclose(osm1, osm3)
+        assert_allclose(osr1, osr2)
+        assert_allclose(osr1, osr3)
+        # Check giving (loc, scale) params for normal distribution
+        osm, osr = stats.probplot(x, sparams=(), fit=False)
+
+    def test_dist_keyword(self):
+        x = stats.norm.rvs(size=20, random_state=12345)
+        osm1, osr1 = stats.probplot(x, fit=False, dist='t', sparams=(3,))
+        osm2, osr2 = stats.probplot(x, fit=False, dist=stats.t, sparams=(3,))
+        assert_allclose(osm1, osm2)
+        assert_allclose(osr1, osr2)
+
+        assert_raises(ValueError, stats.probplot, x, dist='wrong-dist-name')
+        assert_raises(AttributeError, stats.probplot, x, dist=[])
+
+        class custom_dist:
+            """Some class that looks just enough like a distribution."""
+            def ppf(self, q):
+                return stats.norm.ppf(q, loc=2)
+
+        osm1, osr1 = stats.probplot(x, sparams=(2,), fit=False)
+        osm2, osr2 = stats.probplot(x, dist=custom_dist(), fit=False)
+        assert_allclose(osm1, osm2)
+        assert_allclose(osr1, osr2)
+
+    @pytest.mark.skipif(not have_matplotlib, reason="no matplotlib")
+    def test_plot_kwarg(self):
+        fig = plt.figure()
+        fig.add_subplot(111)
+        x = stats.t.rvs(3, size=100, random_state=7654321)
+        res1, fitres1 = stats.probplot(x, plot=plt)
+        plt.close()
+        res2, fitres2 = stats.probplot(x, plot=None)
+        res3 = stats.probplot(x, fit=False, plot=plt)
+        plt.close()
+        res4 = stats.probplot(x, fit=False, plot=None)
+        # Check that results are consistent between combinations of `fit` and
+        # `plot` keywords.
+        assert_(len(res1) == len(res2) == len(res3) == len(res4) == 2)
+        assert_allclose(res1, res2)
+        assert_allclose(res1, res3)
+        assert_allclose(res1, res4)
+        assert_allclose(fitres1, fitres2)
+
+        # Check that a Matplotlib Axes object is accepted
+        fig = plt.figure()
+        ax = fig.add_subplot(111)
+        stats.probplot(x, fit=False, plot=ax)
+        plt.close()
+
+    def test_probplot_bad_args(self):
+        # Raise ValueError when given an invalid distribution.
+        assert_raises(ValueError, stats.probplot, [1], dist="plate_of_shrimp")
+
+    def test_empty(self):
+        assert_equal(stats.probplot([], fit=False),
+                     (np.array([]), np.array([])))
+        assert_equal(stats.probplot([], fit=True),
+                     ((np.array([]), np.array([])),
+                      (np.nan, np.nan, 0.0)))
+
+    def test_array_of_size_one(self):
+        message = "One or more sample arguments is too small..."
+        with (np.errstate(invalid='ignore'),
+              pytest.warns(SmallSampleWarning, match=message)):
+            assert_equal(stats.probplot([1], fit=True),
+                         ((np.array([0.]), np.array([1])),
+                          (np.nan, np.nan, np.nan)))
+
+
+@make_xp_test_case(stats.wilcoxon)
+class TestWilcoxon:
+    def test_wilcoxon_bad_arg(self, xp):
+        # Raise ValueError when two args of different lengths are given or
+        # zero_method is unknown.
+        x = xp.asarray([1, 2])
+        d = xp.asarray([1]*10)
+        message = "`zero_method` must be one of..."
+        with pytest.raises(ValueError, match=message):
+            stats.wilcoxon(x, x, "dummy...")
+        message = "`alternative` must be one of"
+        with pytest.raises(ValueError, match=message):
+            stats.wilcoxon(x, x, alternative="dummy")
+        message = "`method` must be one of..."
+        with pytest.raises(ValueError, match=message):
+            stats.wilcoxon(d, method="xyz")
+
+    def test_zero_diff(self, xp):
+        x = xp.arange(20)
+        # pratt and wilcox do not work if x - y == 0 and method == "asymptotic"
+        # => warning may be emitted and p-value is nan
+        with np.errstate(invalid="ignore"):
+            w, p = stats.wilcoxon(x, x, "wilcox", method="asymptotic")
+            xp_assert_equal(w, xp.asarray(0.0))
+            xp_assert_equal(p, xp.asarray(xp.nan))
+            w, p = stats.wilcoxon(x, x, "pratt", method="asymptotic")
+            xp_assert_equal(w, xp.asarray(0.0))
+            xp_assert_equal(p, xp.asarray(xp.nan))
+        # ranksum is n*(n+1)/2, split in half if zero_method == "zsplit"
+        w, p = stats.wilcoxon(x, x, "zsplit", method="asymptotic")
+        xp_assert_equal(w, xp.asarray(20*21/4))
+        xp_assert_equal(p, xp.asarray(1.0))
+
+    def test_pratt(self, xp):
+        # regression test for gh-6805: p-value matches value from R package
+        # coin (wilcoxsign_test) reported in the issue
+        x = xp.asarray([1, 2, 3, 4])
+        y = xp.asarray([1, 2, 3, 5])
+        res = stats.wilcoxon(x, y, zero_method="pratt", method="asymptotic",
+                             correction=False)
+        xp_assert_close(res.statistic, xp.asarray(0.0))
+        xp_assert_close(res.pvalue, xp.asarray(0.31731050786291415))
+
+    def test_wilcoxon_arg_type(self):
+        # Should be able to accept list as arguments.
+        # Address issue 6070.
+        arr = [1, 2, 3, 0, -1, 3, 1, 2, 1, 1, 2]
+
+        _ = stats.wilcoxon(arr, zero_method="pratt", method="asymptotic")
+        _ = stats.wilcoxon(arr, zero_method="zsplit", method="asymptotic")
+        _ = stats.wilcoxon(arr, zero_method="wilcox", method="asymptotic")
+
+    def test_accuracy_wilcoxon(self, xp):
+        freq = [1, 4, 16, 15, 8, 4, 5, 1, 2]
+        nums = range(-4, 5)
+        x = xp.asarray(np.concatenate([[u] * v for u, v in zip(nums, freq)]))
+        y = xp.zeros_like(x)
+
+        T, p = stats.wilcoxon(x, y, "pratt", method="asymptotic",
+                              correction=False)
+        xp_assert_close(T, xp.asarray(423.))
+        xp_assert_close(p, xp.asarray(0.0031724568006762576))
+
+        T, p = stats.wilcoxon(x, y, "zsplit", method="asymptotic",
+                              correction=False)
+        xp_assert_equal(T, xp.asarray(441.))
+        xp_assert_close(p, xp.asarray(0.0032145343172473055))
+
+        T, p = stats.wilcoxon(x, y, "wilcox", method="asymptotic",
+                              correction=False)
+        xp_assert_equal(T, xp.asarray(327.))
+        xp_assert_close(p, xp.asarray(0.00641346115861))
+
+        # Test the 'correction' option, using values computed in R with:
+        # > options(digits=16)
+        # > wilcox.test(x, y, paired=TRUE, exact=FALSE, correct={FALSE,TRUE})
+        x = xp.asarray([120, 114, 181, 188, 180, 146, 121, 191, 132, 113, 127, 112])
+        y = xp.asarray([133, 143, 119, 189, 112, 199, 198, 113, 115, 121, 142, 187])
+        T, p = stats.wilcoxon(x, y, correction=False, method="asymptotic")
+        xp_assert_equal(T, xp.asarray(34.))
+        xp_assert_close(p, xp.asarray(0.6948866023724735))
+        T, p = stats.wilcoxon(x, y, correction=True, method="asymptotic")
+        xp_assert_equal(T, xp.asarray(34.))
+        xp_assert_close(p, xp.asarray(0.7240816609153895))
+
+    @pytest.mark.parametrize("kwarg",
+        [{"method": "approx"}, {"mode": "approx"}, {"mode": "asymptotic"}])
+    def test_approx_mode(self, kwarg, xp):
+        # Check that `mode` is still an alias of keyword `method`,
+        # and `"approx"` is still an alias of argument `"asymptotic"`
+        x = xp.asarray([3, 5, 23, 7, 243, 58, 98, 2, 8, -3, 9, 11])
+        y = xp.asarray([2, -2, 1, 23, 0, 5, 12, 18, 99, 12, 17, 27])
+        res = stats.wilcoxon(x, y, "wilcox", **kwarg)
+        ref = stats.wilcoxon(x, y, "wilcox", method="asymptotic")
+        xp_assert_equal(res.statistic, ref.statistic)
+        xp_assert_equal(res.pvalue, ref.pvalue)
+
+    def test_wilcoxon_result_attributes(self, xp):
+        x = xp.asarray([120, 114, 181, 188, 180, 146, 121, 191, 132, 113, 127, 112])
+        y = xp.asarray([133, 143, 119, 189, 112, 199, 198, 113, 115, 121, 142, 187])
+        res = stats.wilcoxon(x, y, correction=False, method="asymptotic")
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, xp=xp)
+
+    def test_wilcoxon_has_zstatistic(self, xp):
+        rng = np.random.default_rng(89426135444)
+        x, y = rng.random(15), rng.random(15)
+        x, y = xp.asarray(x), xp.asarray(y)
+
+        res = stats.wilcoxon(x, y, method="asymptotic")
+        ref = special.ndtri(res.pvalue/2)
+        xp_assert_close(res.zstatistic, ref)
+
+        if is_jax(xp):
+            return
+
+        res = stats.wilcoxon(x, y, method="exact")
+        assert not hasattr(res, 'zstatistic')
+
+        res = stats.wilcoxon(x, y)
+        assert not hasattr(res, 'zstatistic')
+
+    def test_wilcoxon_tie(self, xp):
+        # Regression test for gh-2391.
+        # Corresponding R code is:
+        #   > result = wilcox.test(rep(0.1, 10), exact=FALSE, correct=FALSE)
+        #   > result$p.value
+        #   [1] 0.001565402258002551
+        #   > result = wilcox.test(rep(0.1, 10), exact=FALSE, correct=TRUE)
+        #   > result$p.value
+        #   [1] 0.00190419504300439
+        d = xp.asarray([0.1] * 10)
+        expected_stat = xp.asarray(0.0)
+
+        stat, p = stats.wilcoxon(d, method="asymptotic", correction=False)
+        expected_p = xp.asarray(0.001565402258002551)
+        xp_assert_equal(stat, expected_stat)
+        xp_assert_close(p, expected_p)
+
+        stat, p = stats.wilcoxon(d, method="asymptotic", correction=True)
+        expected_p = xp.asarray(0.00190419504300439)
+        xp_assert_equal(stat, expected_stat)
+        xp_assert_close(p, expected_p)
+
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_onesided(self, dtype, xp):
+        # tested against "R version 4.0.3 (2020-10-10)"
+        #
+        # x <- c(125, 115, 130, 140, 140, 115, 140, 125, 140, 135)
+        # y <- c(110, 122, 125, 120, 140, 124, 123, 137, 135, 145)
+        # cfg <- list(x = x, y = y, paired = TRUE, exact = FALSE)
+        # do.call(wilcox.test, c(cfg, list(alternative = "less", correct = FALSE)))
+        # do.call(wilcox.test, c(cfg, list(alternative = "less", correct = TRUE)))
+        # do.call(wilcox.test, c(cfg, list(alternative = "greater", correct = FALSE)))
+        # do.call(wilcox.test, c(cfg, list(alternative = "greater", correct = TRUE)))
+
+        dtype = dtype if dtype is None else getattr(xp, dtype)
+        x = xp.asarray([125, 115, 130, 140, 140, 115, 140, 125, 140, 135], dtype=dtype)
+        y = xp.asarray([110, 122, 125, 120, 140, 124, 123, 137, 135, 145], dtype=dtype)
+
+        w_ref = xp.asarray(27.0, dtype=dtype)
+        w, p = stats.wilcoxon(x, y, alternative="less", method="asymptotic",
+                              correction=False)
+        xp_assert_equal(w, w_ref)
+        xp_assert_close(p, xp.asarray(0.7031847042787, dtype=dtype))
+
+        w, p = stats.wilcoxon(x, y, alternative="less", correction=True,
+                              method="asymptotic")
+        xp_assert_equal(w, w_ref)
+        xp_assert_close(p, xp.asarray(0.72336564289, dtype=dtype))
+
+        w, p = stats.wilcoxon(x, y, alternative="greater",
+                              method="asymptotic", correction=False)
+        xp_assert_equal(w, w_ref)
+        xp_assert_close(p, xp.asarray(0.2968152957213, dtype=dtype))
+
+        w, p = stats.wilcoxon(x, y, alternative="greater",
+                              correction=True, method="asymptotic")
+        xp_assert_equal(w, w_ref)
+        xp_assert_close(p, xp.asarray(0.3176446594176, dtype=dtype))
+
+    def test_exact_basic(self):  # exact distribution is NumPy-only
+        for n in range(1, 51):
+            pmf1 = _get_wilcoxon_distr(n)
+            pmf2 = _get_wilcoxon_distr2(n)
+            assert_equal(n*(n+1)/2 + 1, len(pmf1))
+            assert_equal(sum(pmf1), 1)
+            assert_array_almost_equal(pmf1, pmf2)
+
+    @skip_xp_backends("jax.numpy", reason="`method='exact'` is incompatible with JAX")
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_exact_pval(self, dtype, xp):
+        # expected values computed with "R version 4.0.3 (2020-10-10)"
+        # options(digits=16)
+        # x < - c(1.81, 0.82, 1.56, -0.48, 0.81, 1.28, -1.04, 0.23, -0.75, 0.14)
+        # y < - c(0.71, 0.65, -0.2, 0.85, -1.1, -0.45, -0.84, -0.24, -0.68, -0.76)
+        # result = wilcox.test(x - y, exact=TRUE, alternative='l', correct=TRUE)
+        # result$p.value
+        dtype = dtype if dtype is None else getattr(xp, dtype)
+        x = xp.asarray([1.81, 0.82, 1.56, -0.48, 0.81, 1.28, -1.04, 0.23,
+                        -0.75, 0.14], dtype=dtype)
+        y = xp.asarray([0.71, 0.65, -0.2, 0.85, -1.1, -0.45, -0.84, -0.24,
+                        -0.68, -0.76], dtype=dtype)
+        _, p = stats.wilcoxon(x, y, alternative="two-sided", method="exact")
+        xp_assert_close(p, xp.asarray(0.10546875, dtype=dtype))
+        _, p = stats.wilcoxon(x, y, alternative="less", method="exact")
+        xp_assert_close(p, xp.asarray(0.95800781256, dtype=dtype))
+        _, p = stats.wilcoxon(x, y, alternative="greater", method="exact")
+        xp_assert_close(p, xp.asarray(0.052734375, dtype=dtype))
+
+        x = xp.arange(0., 20., dtype=dtype) + 0.5
+        y = xp.arange(20., 0., -1., dtype=dtype)
+        _, p = stats.wilcoxon(x, y, alternative="two-sided", method="exact")
+        xp_assert_close(p, xp.asarray(0.8694877624511719, dtype=dtype))
+        _, p = stats.wilcoxon(x, y, alternative="less", method="exact")
+        xp_assert_close(p, xp.asarray(0.4347438812255859, dtype=dtype))
+        _, p = stats.wilcoxon(x, y, alternative="greater", method="exact")
+        xp_assert_close(p, xp.asarray(0.5795888900756836, dtype=dtype))
+
+    # These inputs were chosen to give a W statistic that is either the
+    # center of the distribution (when the length of the support is odd), or
+    # the value to the left of the center (when the length of the support is
+    # even).  Also, the numbers are chosen so that the W statistic is the
+    # sum of the positive values.
+
+    @skip_xp_backends("jax.numpy", reason="`method='exact'` is incompatible with JAX")
+    @pytest.mark.parametrize('x', [[-1, -2, 3],
+                                   [-1, 2, -3, -4, 5],
+                                   [-1, -2, 3, -4, -5, -6, 7, 8]])
+    def test_exact_p_1(self, x, xp):
+        w, p = stats.wilcoxon(xp.asarray(x))
+        x = np.array(x)
+        wtrue = x[x > 0].sum()
+        xp_assert_equal(w, xp.asarray(float(wtrue)))
+        xp_assert_equal(p, xp.asarray(1.0))
+
+    def test_auto(self, xp):
+        # auto default to exact if there are no ties and n <= 50
+        x = xp.arange(0., 50.) + 0.5
+        y = xp.arange(50., 0., -1.)
+
+        if is_jax(xp):  #
+            message = "When using `wilcoxon` with `jax.numpy` arrays..."
+            with pytest.raises(ValueError, match=message):
+                stats.wilcoxon(x, y, method="auto")
+            with pytest.raises(ValueError, match=message):
+                stats.wilcoxon(x, y, method="exact")
+            return
+
+        xp_assert_equal(stats.wilcoxon(x, y).pvalue,
+                        stats.wilcoxon(x, y, method="exact").pvalue)
+
+        # n <= 50: if there are zeros in d = x-y, use PermutationMethod
+        pm = stats.PermutationMethod()
+        d = np.arange(-2, 5)
+        w, p = stats.wilcoxon(d)
+        # rerunning the test gives the same results since n_resamples
+        # is large enough to get deterministic results if n <= 13
+        # so we do not need to use a seed. to avoid longer runtimes of the
+        # test, use n=7 only. For n=13, see test_auto_permutation_edge_case
+        assert_equal((w, p), stats.wilcoxon(d, method=pm))
+
+        # for larger vectors (n > 13) with ties/zeros, use asymptotic test
+        d = xp.arange(-5, 9)  # zero
+        _, p = stats.wilcoxon(d)
+        xp_assert_equal(stats.wilcoxon(d, method="asymptotic").pvalue, xp.asarray(p))
+
+        d = xpx.at(d)[d == 0].set(1)  # tie
+        _, p = stats.wilcoxon(d)
+        xp_assert_equal(stats.wilcoxon(d, method="asymptotic").pvalue, xp.asarray(p))
+
+        # use approximation for samples > 50
+        d = xp.arange(1, 52)
+        _, p = stats.wilcoxon(d)
+        xp_assert_equal(stats.wilcoxon(d, method="asymptotic").pvalue, xp.asarray(p))
+
+    @pytest.mark.xslow
+    @skip_xp_backends("jax.numpy", reason="lazy -> limited `method` choices")
+    def test_auto_permutation_edge_case(self, xp):
+        # Check that `PermutationMethod()` is used and results are deterministic when
+        # `method='auto'`, there are zeros or ties in `d = x-y`, and `len(d) <= 13`.
+        d = np.arange(-5, 8)  # zero
+        res = stats.wilcoxon(xp.asarray(d))
+        # generated with stats.wilcoxon(d, method=PermutationMethod())
+        w, p = xp.asarray([27.5, 0.3955078125])
+        xp_assert_equal(res.statistic, w)
+        xp_assert_equal(res.pvalue, p)
+
+        d[d == 0] = 1  # tie
+        res = stats.wilcoxon(xp.asarray(d))
+        # generated with stats.wilcoxon(d, method=PermutationMethod())
+        w, p = xp.asarray([32, 0.3779296875])
+        xp_assert_equal(res.statistic, w)
+        xp_assert_equal(res.pvalue, p)
+
+    @pytest.mark.parametrize('size', [3, 5, 10])
+    def test_permutation_method(self, size, xp):
+        rng = np.random.default_rng(92348034828501345)
+        x = rng.random(size=size).tolist()
+        res = stats.wilcoxon(xp.asarray(x), method=stats.PermutationMethod())
+        ref = stats.wilcoxon(x, method='exact')  # all backends test against NumPy
+        dtype = xp_default_dtype(xp)
+        xp_assert_equal(res.statistic, xp.asarray(ref.statistic, dtype=dtype))
+        xp_assert_equal(res.pvalue, xp.asarray(ref.pvalue, dtype=dtype))
+
+        x = xp.asarray(rng.random(size=size*10))
+        rng = np.random.default_rng(59234803482850134)
+        pm = stats.PermutationMethod(n_resamples=99, rng=rng)
+        ref = stats.wilcoxon(x, method=pm)
+        # preserve use of old random_state during SPEC 7 transition
+        rng = np.random.default_rng(59234803482850134)
+        pm = stats.PermutationMethod(n_resamples=99, random_state=rng)
+        res = stats.wilcoxon(x, method=pm)
+
+        xp_assert_equal(xp.round(res.pvalue*100)/100, res.pvalue)  # n_resamples used
+        xp_assert_equal(res.pvalue, ref.pvalue)  # rng/random_state used
+
+    @skip_xp_backends("jax.numpy", reason="`method='auto'` is incompatible with JAX")
+    def test_method_auto_nan_propagate_ND_length_gt_50_gh20591(self, xp):
+        # When method!='asymptotic', nan_policy='propagate', and a slice of
+        # a >1 dimensional array input contained NaN, the result object of
+        # `wilcoxon` could (under yet other conditions) return `zstatistic`
+        # for some slices but not others. This resulted in an error because
+        # `apply_along_axis` would have to create a ragged array.
+        # Check that this is resolved.
+        rng = np.random.default_rng(235889269872456)
+        A = rng.normal(size=(51, 2))  # length along slice > exact threshold
+        A[5, 1] = np.nan
+        A = xp.asarray(A)
+        res = stats.wilcoxon(A)
+        ref = stats.wilcoxon(A, method='asymptotic')
+        xp_assert_close(res.statistic, ref.statistic)
+        xp_assert_close(res.pvalue, ref.pvalue)
+        assert hasattr(ref, 'zstatistic')
+        assert not hasattr(res, 'zstatistic')
+
+    @pytest.mark.parametrize('method', ['exact', 'asymptotic'])
+    def test_symmetry_gh19872_gh20752(self, method, xp):
+        if is_jax(xp) and method == 'exact':
+            pytest.skip("`method='exact'` is incompatible with JAX")
+        # Check that one-sided exact tests obey required symmetry. Bug reported
+        # in gh-19872 and again in gh-20752; example from gh-19872 is more concise:
+        var1 = xp.asarray([62, 66, 61, 68, 74, 62, 68, 62, 55, 59])
+        var2 = xp.asarray([71, 71, 69, 61, 75, 71, 77, 72, 62, 65])
+        ref = stats.wilcoxon(var1, var2, alternative='less', method=method)
+        res = stats.wilcoxon(var2, var1, alternative='greater', method=method)
+        max_statistic = var1.shape[0] * (var1.shape[0] + 1) / 2
+        assert int(res.statistic) != res.statistic
+        xp_assert_close(max_statistic - res.statistic, ref.statistic)
+        xp_assert_close(res.pvalue, ref.pvalue)
+
+    @pytest.mark.parametrize("method", ('exact', stats.PermutationMethod()))
+    def test_all_zeros_exact(self, method, xp):
+        if is_jax(xp) and method == 'exact':
+            pytest.skip("`method='exact'` is incompatible with JAX")
+        # previously, this raised a RuntimeWarning when calculating Z, even
+        # when the Z value was not needed. Confirm that this no longer
+        # occurs when `method` is 'exact' or a `PermutationMethod`.
+        res = stats.wilcoxon(xp.zeros(5), method=method)
+        xp_assert_close(res.statistic, xp.asarray(0.))
+        xp_assert_close(res.pvalue, xp.asarray(1.))
+
+    def test_wilcoxon_axis_broadcasting_errors_gh22051(self, xp):
+        # In previous versions of SciPy, `wilcoxon` gave an incorrect error
+        # message when `AxisError` was not found in the base NumPy namespace.
+        # Check that this is resolved with and without the ANP decorator.
+        x = xp.asarray([1, 2, 3])
+        y = xp.asarray([4, 5, 6])
+
+        message = "Array shapes are incompatible for broadcasting."
+        with pytest.raises(ValueError, match=message):
+            stats.wilcoxon(x, y[:-1])
+
+        if not is_numpy(xp):
+            return  # hard to generalize the rest
+
+        message = "operands could not be broadcast together with..."
+        with pytest.raises(ValueError, match=message):
+            stats.wilcoxon(x, y[:-1], _no_deco=True)
+
+        AxisError = getattr(np, 'AxisError', None) or np.exceptions.AxisError
+        message = "source: axis 3 is out of bounds for array of dimension 1"
+        with pytest.raises(AxisError, match=message):
+            stats.wilcoxon(x, y, axis=3)
+
+        message = "`axis` must be compatible with the shape..."
+        with pytest.raises(AxisError, match=message):
+            stats.wilcoxon(x, y, axis=3, _no_deco=True)
+
+
+# data for k-statistics tests from
+# https://cran.r-project.org/web/packages/kStatistics/kStatistics.pdf
+# see nKS "Examples"
+x_kstat = [16.34, 10.76, 11.84, 13.55, 15.85, 18.20, 7.51, 10.22, 12.52, 14.68,
+           16.08, 19.43, 8.12, 11.20, 12.95, 14.77, 16.83, 19.80, 8.55, 11.58,
+           12.10, 15.02, 16.83, 16.98, 19.92, 9.47, 11.68, 13.41, 15.35, 19.11]
+
+
+@make_xp_test_case(stats.kstat)
+class TestKstat:
+    def test_moments_normal_distribution(self, xp):
+        rng = np.random.RandomState(32149)
+        data = xp.asarray(rng.randn(12345), dtype=xp.float64)
+        moments = xp.stack([stats.kstat(data, n) for n in [1, 2, 3, 4]])
+
+        expected = xp.asarray([0.011315, 1.017931, 0.05811052, 0.0754134],
+                              dtype=data.dtype)
+        xp_assert_close(moments, expected, rtol=1e-4)
+
+        # test equivalence with `stats.moment`
+        m1 = stats.moment(data, order=1)
+        m2 = stats.moment(data, order=2)
+        m3 = stats.moment(data, order=3)
+        xp_assert_close(xp.stack((m1, m2, m3)), expected[:-1], atol=0.02, rtol=1e-2)
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered")  # Dask
+    def test_empty_input(self, xp):
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = stats.kstat(xp.asarray([]))
+        xp_assert_equal(res, xp.asarray(xp.nan))
+
+    def test_nan_input(self, xp):
+        data = xp.arange(10.)
+        data = xp.where(data == 6, xp.nan, data)
+
+        xp_assert_equal(stats.kstat(data), xp.asarray(xp.nan))
+
+    @pytest.mark.parametrize('n', [0, 4.001])
+    def test_kstat_bad_arg(self, n, xp):
+        # Raise ValueError if n > 4 or n < 1.
+        data = xp.arange(10)
+        message = 'k-statistics only supported for 1<=n<=4'
+        with pytest.raises(ValueError, match=message):
+            stats.kstat(data, n=n)
+
+    @pytest.mark.parametrize('case', [(1, 14.02166666666667),
+                                      (2, 12.65006954022974),
+                                      (3, -1.447059503280798),
+                                      (4, -141.6682291883626)])
+    def test_against_R(self, case, xp):
+        # Test against reference values computed with R kStatistics, e.g.
+        # options(digits=16)
+        # library(kStatistics)
+        # data <-c (16.34, 10.76, 11.84, 13.55, 15.85, 18.20, 7.51, 10.22,
+        #           12.52, 14.68, 16.08, 19.43, 8.12, 11.20, 12.95, 14.77,
+        #           16.83, 19.80, 8.55, 11.58, 12.10, 15.02, 16.83, 16.98,
+        #           19.92, 9.47, 11.68, 13.41, 15.35, 19.11)
+        # nKS(4, data)
+        n, ref = case
+        res = stats.kstat(xp.asarray(x_kstat), n)
+        xp_assert_close(res, xp.asarray(ref))
+
+
+@make_xp_test_case(stats.kstatvar)
+class TestKstatVar:
+    @pytest.mark.filterwarnings("ignore:invalid value encountered")  # Dask
+    def test_empty_input(self, xp):
+        x = xp.asarray([])
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = stats.kstatvar(x)
+        xp_assert_equal(res, xp.asarray(xp.nan))
+
+    def test_nan_input(self, xp):
+        data = xp.arange(10.)
+        data = xp.where(data == 6, xp.nan, data)
+
+        xp_assert_equal(stats.kstat(data), xp.asarray(xp.nan))
+
+    def test_bad_arg(self, xp):
+        # Raise ValueError is n is not 1 or 2.
+        data = xp.asarray([1])
+        n = 10
+        message = 'Only n=1 or n=2 supported.'
+        with pytest.raises(ValueError, match=message):
+            stats.kstatvar(data, n=n)
+
+    def test_against_R_mathworld(self, xp):
+        # Test against reference values computed using formulas exactly as
+        # they appear at https://mathworld.wolfram.com/k-Statistic.html
+        # This is *really* similar to how they appear in the implementation,
+        # but that could change, and this should not.
+        n = len(x_kstat)
+        k2 = 12.65006954022974  # see source code in TestKstat
+        k4 = -141.6682291883626
+
+        res = stats.kstatvar(xp.asarray(x_kstat), 1)
+        ref = k2 / n
+        xp_assert_close(res, xp.asarray(ref))
+
+        res = stats.kstatvar(xp.asarray(x_kstat), 2)
+        # *unbiased estimator* for var(k2)
+        ref = (2*k2**2*n + (n-1)*k4) / (n * (n+1))
+        xp_assert_close(res, xp.asarray(ref))
+
+
+class TestPpccPlot:
+    def setup_method(self):
+        self.x = _old_loggamma_rvs(5, size=500, random_state=7654321) + 5
+
+    def test_basic(self):
+        N = 5
+        svals, ppcc = stats.ppcc_plot(self.x, -10, 10, N=N)
+        ppcc_expected = [0.21139644, 0.21384059, 0.98766719, 0.97980182,
+                         0.93519298]
+        assert_allclose(svals, np.linspace(-10, 10, num=N))
+        assert_allclose(ppcc, ppcc_expected)
+
+    def test_dist(self):
+        # Test that we can specify distributions both by name and as objects.
+        svals1, ppcc1 = stats.ppcc_plot(self.x, -10, 10, dist='tukeylambda')
+        svals2, ppcc2 = stats.ppcc_plot(self.x, -10, 10,
+                                        dist=stats.tukeylambda)
+        assert_allclose(svals1, svals2, rtol=1e-20)
+        assert_allclose(ppcc1, ppcc2, rtol=1e-20)
+        # Test that 'tukeylambda' is the default dist
+        svals3, ppcc3 = stats.ppcc_plot(self.x, -10, 10)
+        assert_allclose(svals1, svals3, rtol=1e-20)
+        assert_allclose(ppcc1, ppcc3, rtol=1e-20)
+
+    @pytest.mark.skipif(not have_matplotlib, reason="no matplotlib")
+    def test_plot_kwarg(self):
+        # Check with the matplotlib.pyplot module
+        fig = plt.figure()
+        ax = fig.add_subplot(111)
+        stats.ppcc_plot(self.x, -20, 20, plot=plt)
+        fig.delaxes(ax)
+
+        # Check that a Matplotlib Axes object is accepted
+        ax = fig.add_subplot(111)
+        stats.ppcc_plot(self.x, -20, 20, plot=ax)
+        plt.close()
+
+    def test_invalid_inputs(self):
+        # `b` has to be larger than `a`
+        assert_raises(ValueError, stats.ppcc_plot, self.x, 1, 0)
+
+        # Raise ValueError when given an invalid distribution.
+        assert_raises(ValueError, stats.ppcc_plot, [1, 2, 3], 0, 1,
+                      dist="plate_of_shrimp")
+
+    def test_empty(self):
+        # For consistency with probplot return for one empty array,
+        # ppcc contains all zeros and svals is the same as for normal array
+        # input.
+        svals, ppcc = stats.ppcc_plot([], 0, 1)
+        assert_allclose(svals, np.linspace(0, 1, num=80))
+        assert_allclose(ppcc, np.zeros(80, dtype=float))
+
+
+class TestPpccMax:
+    def test_ppcc_max_bad_arg(self):
+        # Raise ValueError when given an invalid distribution.
+        data = [1]
+        assert_raises(ValueError, stats.ppcc_max, data, dist="plate_of_shrimp")
+
+    def test_ppcc_max_basic(self):
+        x = stats.tukeylambda.rvs(-0.7, loc=2, scale=0.5, size=10000,
+                                  random_state=1234567) + 1e4
+        assert_almost_equal(stats.ppcc_max(x), -0.71215366521264145, decimal=7)
+
+    def test_dist(self):
+        x = stats.tukeylambda.rvs(-0.7, loc=2, scale=0.5, size=10000,
+                                  random_state=1234567) + 1e4
+
+        # Test that we can specify distributions both by name and as objects.
+        max1 = stats.ppcc_max(x, dist='tukeylambda')
+        max2 = stats.ppcc_max(x, dist=stats.tukeylambda)
+        assert_almost_equal(max1, -0.71215366521264145, decimal=5)
+        assert_almost_equal(max2, -0.71215366521264145, decimal=5)
+
+        # Test that 'tukeylambda' is the default dist
+        max3 = stats.ppcc_max(x)
+        assert_almost_equal(max3, -0.71215366521264145, decimal=5)
+
+    def test_brack(self):
+        x = stats.tukeylambda.rvs(-0.7, loc=2, scale=0.5, size=10000,
+                                  random_state=1234567) + 1e4
+        assert_raises(ValueError, stats.ppcc_max, x, brack=(0.0, 1.0, 0.5))
+
+        assert_almost_equal(stats.ppcc_max(x, brack=(0, 1)),
+                            -0.71215366521264145, decimal=7)
+
+        assert_almost_equal(stats.ppcc_max(x, brack=(-2, 2)),
+                            -0.71215366521264145, decimal=7)
+
+
+@make_xp_test_case(stats.boxcox_llf)
+class TestBoxcox_llf:
+
+    @pytest.mark.parametrize("dtype", ["float32", "float64"])
+    def test_basic(self, dtype, xp):
+        dt = getattr(xp, dtype)
+        x = stats.norm.rvs(size=10000, loc=10, random_state=54321)
+        lmbda = 1
+        llf = stats.boxcox_llf(lmbda, xp.asarray(x, dtype=dt))
+        llf_expected = -x.size / 2. * np.log(np.sum(x.std()**2))
+        xp_assert_close(llf, xp.asarray(llf_expected, dtype=dt))
+
+    @skip_xp_backends(np_only=True,
+                      reason='array-likes only accepted for NumPy backend.')
+    def test_array_like(self, xp):
+        x = stats.norm.rvs(size=100, loc=10, random_state=54321)
+        lmbda = 1
+        llf = stats.boxcox_llf(lmbda, x)
+        llf2 = stats.boxcox_llf(lmbda, list(x))
+        xp_assert_close(llf, llf2, rtol=1e-12)
+
+    def test_2d_input(self, xp):
+        # Note: boxcox_llf() was already working with 2-D input (sort of), so
+        # keep it like that.  boxcox() doesn't work with 2-D input though, due
+        # to brent() returning a scalar.
+        x = stats.norm.rvs(size=100, loc=10, random_state=54321)
+        lmbda = 1
+        llf = stats.boxcox_llf(lmbda, x)
+        llf2 = stats.boxcox_llf(lmbda, np.vstack([x, x]).T)
+        xp_assert_close(xp.asarray([llf, llf]), xp.asarray(llf2), rtol=1e-12)
+
+    def test_empty(self, xp):
+        message = "One or more sample arguments is too small..."
+        with eager_warns(SmallSampleWarning, match=message, xp=xp):
+            assert xp.isnan(xp.asarray(stats.boxcox_llf(1, xp.asarray([]))))
+
+    def test_gh_6873(self, xp):
+        # Regression test for gh-6873.
+        # This example was taken from gh-7534, a duplicate of gh-6873.
+        data = xp.asarray([198.0, 233.0, 233.0, 392.0])
+        llf = stats.boxcox_llf(-8, data)
+        # The expected value was computed with mpmath.
+        xp_assert_close(llf, xp.asarray(-17.93934208579061))
+
+    def test_instability_gh20021(self, xp):
+        data = xp.asarray([2003, 1950, 1997, 2000, 2009], dtype=xp.float64)
+        llf = stats.boxcox_llf(1e-8, data)
+        # The expected value was computed with mpsci, set mpmath.mp.dps=100
+        # expect float64 output for integer input
+        xp_assert_close(llf, xp.asarray(-15.32401272869016598, dtype=xp.float64),
+                        rtol=5e-7)  # bumped tolerance from 1e-7 for Accelerate
+
+    def test_axis(self, xp):
+        data = xp.asarray([[100, 200], [300, 400]])
+        llf_axis_0 = stats.boxcox_llf(1, data, axis=0)
+        llf_0 = xp.stack([
+            stats.boxcox_llf(1, data[:, 0]),
+            stats.boxcox_llf(1, data[:, 1]),
+        ])
+        xp_assert_close(llf_axis_0, llf_0)
+        llf_axis_1 = stats.boxcox_llf(1, data, axis=1)
+        llf_1 = xp.stack([
+            stats.boxcox_llf(1, data[0, :]),
+            stats.boxcox_llf(1, data[1, :]),
+        ])
+        xp_assert_close(llf_axis_1, llf_1)
+
+
+# This is the data from GitHub user Qukaiyi, given as an example
+# of a data set that caused boxcox to fail.
+_boxcox_data = [
+    15957, 112079, 1039553, 711775, 173111, 307382, 183155, 53366, 760875,
+    207500, 160045, 473714, 40194, 440319, 133261, 265444, 155590, 36660,
+    904939, 55108, 138391, 339146, 458053, 63324, 1377727, 1342632, 41575,
+    68685, 172755, 63323, 368161, 199695, 538214, 167760, 388610, 398855,
+    1001873, 364591, 1320518, 194060, 194324, 2318551, 196114, 64225, 272000,
+    198668, 123585, 86420, 1925556, 695798, 88664, 46199, 759135, 28051,
+    345094, 1977752, 51778, 82746, 638126, 2560910, 45830, 140576, 1603787,
+    57371, 548730, 5343629, 2298913, 998813, 2156812, 423966, 68350, 145237,
+    131935, 1600305, 342359, 111398, 1409144, 281007, 60314, 242004, 113418,
+    246211, 61940, 95858, 957805, 40909, 307955, 174159, 124278, 241193,
+    872614, 304180, 146719, 64361, 87478, 509360, 167169, 933479, 620561,
+    483333, 97416, 143518, 286905, 597837, 2556043, 89065, 69944, 196858,
+    88883, 49379, 916265, 1527392, 626954, 54415, 89013, 2883386, 106096,
+    402697, 45578, 349852, 140379, 34648, 757343, 1305442, 2054757, 121232,
+    606048, 101492, 51426, 1820833, 83412, 136349, 1379924, 505977, 1303486,
+    95853, 146451, 285422, 2205423, 259020, 45864, 684547, 182014, 784334,
+    174793, 563068, 170745, 1195531, 63337, 71833, 199978, 2330904, 227335,
+    898280, 75294, 2011361, 116771, 157489, 807147, 1321443, 1148635, 2456524,
+    81839, 1228251, 97488, 1051892, 75397, 3009923, 2732230, 90923, 39735,
+    132433, 225033, 337555, 1204092, 686588, 1062402, 40362, 1361829, 1497217,
+    150074, 551459, 2019128, 39581, 45349, 1117187, 87845, 1877288, 164448,
+    10338362, 24942, 64737, 769946, 2469124, 2366997, 259124, 2667585, 29175,
+    56250, 74450, 96697, 5920978, 838375, 225914, 119494, 206004, 430907,
+    244083, 219495, 322239, 407426, 618748, 2087536, 2242124, 4736149, 124624,
+    406305, 240921, 2675273, 4425340, 821457, 578467, 28040, 348943, 48795,
+    145531, 52110, 1645730, 1768364, 348363, 85042, 2673847, 81935, 169075,
+    367733, 135474, 383327, 1207018, 93481, 5934183, 352190, 636533, 145870,
+    55659, 146215, 73191, 248681, 376907, 1606620, 169381, 81164, 246390,
+    236093, 885778, 335969, 49266, 381430, 307437, 350077, 34346, 49340,
+    84715, 527120, 40163, 46898, 4609439, 617038, 2239574, 159905, 118337,
+    120357, 430778, 3799158, 3516745, 54198, 2970796, 729239, 97848, 6317375,
+    887345, 58198, 88111, 867595, 210136, 1572103, 1420760, 574046, 845988,
+    509743, 397927, 1119016, 189955, 3883644, 291051, 126467, 1239907, 2556229,
+    411058, 657444, 2025234, 1211368, 93151, 577594, 4842264, 1531713, 305084,
+    479251, 20591, 1466166, 137417, 897756, 594767, 3606337, 32844, 82426,
+    1294831, 57174, 290167, 322066, 813146, 5671804, 4425684, 895607, 450598,
+    1048958, 232844, 56871, 46113, 70366, 701618, 97739, 157113, 865047,
+    194810, 1501615, 1765727, 38125, 2733376, 40642, 437590, 127337, 106310,
+    4167579, 665303, 809250, 1210317, 45750, 1853687, 348954, 156786, 90793,
+    1885504, 281501, 3902273, 359546, 797540, 623508, 3672775, 55330, 648221,
+    266831, 90030, 7118372, 735521, 1009925, 283901, 806005, 2434897, 94321,
+    309571, 4213597, 2213280, 120339, 64403, 8155209, 1686948, 4327743,
+    1868312, 135670, 3189615, 1569446, 706058, 58056, 2438625, 520619, 105201,
+    141961, 179990, 1351440, 3148662, 2804457, 2760144, 70775, 33807, 1926518,
+    2362142, 186761, 240941, 97860, 1040429, 1431035, 78892, 484039, 57845,
+    724126, 3166209, 175913, 159211, 1182095, 86734, 1921472, 513546, 326016,
+    1891609
+]
+
+
+class TransformTest:
+    def test_nan_policy(self):
+        transform = getattr(stats, self.transform)
+        normmax = getattr(stats, f"{self.transform}_normmax")
+        special_transform = getattr(special, self.transform, _yeojohnson_transform)
+
+        rng = np.random.default_rng(5908294582456923546)
+        x = rng.random(10)
+        x[3] = np.nan
+
+        # lmb provided
+        lmb = 1.0
+
+        with pytest.raises(ValueError, match="The input contains nan values"):
+            transform(x, lmb, nan_policy='raise')
+
+        res = transform(x, lmb, nan_policy='propagate')
+        np.testing.assert_allclose(res, special_transform(x, 1.))
+
+        res = transform(x, lmb, nan_policy='omit')
+        np.testing.assert_allclose(res, transform(x, lmb, nan_policy='propagate'))
+
+        # lmb not provided
+        with pytest.raises(ValueError, match="The input contains nan values"):
+            transform(x, nan_policy='raise')
+
+        res = transform(x, nan_policy='propagate')
+        np.testing.assert_equal(res[0], special_transform(x, np.nan))
+        np.testing.assert_equal(res[1], np.nan)
+
+        kwarg = {'method': 'mle'} if transform == stats.boxcox else {}
+        res = transform(x, nan_policy='omit')
+        lmb = normmax(x, nan_policy='omit', **kwarg)
+        ref = transform(x, lmb)
+        np.testing.assert_allclose(res[0], ref)
+        np.testing.assert_allclose(res[1], lmb)
+
+
+class TestBoxcox(TransformTest):
+    transform = 'boxcox'
+
+    def test_fixed_lmbda(self):
+        x = _old_loggamma_rvs(5, size=50, random_state=12345) + 5
+        xt = stats.boxcox(x, lmbda=1)
+        assert_allclose(xt, x - 1)
+        xt = stats.boxcox(x, lmbda=-1)
+        assert_allclose(xt, 1 - 1/x)
+
+        xt = stats.boxcox(x, lmbda=0)
+        assert_allclose(xt, np.log(x))
+
+        # Also test that array_like input works
+        xt = stats.boxcox(list(x), lmbda=0)
+        assert_allclose(xt, np.log(x))
+
+        # test that constant input is accepted; see gh-12225
+        xt = stats.boxcox(np.ones(10), 2)
+        assert_equal(xt, np.zeros(10))
+
+    def test_lmbda_None(self):
+        # Start from normal rv's, do inverse transform to check that
+        # optimization function gets close to the right answer.
+        lmbda = 2.5
+        x = stats.norm.rvs(loc=10, size=50000, random_state=1245)
+        x_inv = (x * lmbda + 1)**(-lmbda)
+        xt, maxlog = stats.boxcox(x_inv)
+
+        assert_almost_equal(maxlog, -1 / lmbda, decimal=2)
+
+    def test_alpha(self):
+        rng = np.random.RandomState(1234)
+        x = _old_loggamma_rvs(5, size=50, random_state=rng) + 5
+
+        # Some regular values for alpha, on a small sample size
+        _, _, interval = stats.boxcox(x, alpha=0.75)
+        assert_allclose(interval, [4.004485780226041, 5.138756355035744])
+        _, _, interval = stats.boxcox(x, alpha=0.05)
+        assert_allclose(interval, [1.2138178554857557, 8.209033272375663])
+
+        # Try some extreme values, see we don't hit the N=500 limit
+        x = _old_loggamma_rvs(7, size=500, random_state=rng) + 15
+        _, _, interval = stats.boxcox(x, alpha=0.001)
+        assert_allclose(interval, [0.3988867, 11.40553131])
+        _, _, interval = stats.boxcox(x, alpha=0.999)
+        assert_allclose(interval, [5.83316246, 5.83735292])
+
+    def test_boxcox_bad_arg(self):
+        # Raise ValueError if any data value is negative.
+        x = np.array([-1, 2])
+        assert_raises(ValueError, stats.boxcox, x)
+        # Raise ValueError if data is constant.
+        assert_raises(ValueError, stats.boxcox, np.array([1]))
+        # Raise ValueError if data is not 1-dimensional.
+        assert_raises(ValueError, stats.boxcox, np.array([[1], [2]]))
+
+    def test_empty(self):
+        assert_(stats.boxcox([]).shape == (0,))
+
+    def test_gh_6873(self):
+        # Regression test for gh-6873.
+        y, lam = stats.boxcox(_boxcox_data)
+        # The expected value of lam was computed with the function
+        # powerTransform in the R library 'car'.  I trust that value
+        # to only about five significant digits.
+        assert_allclose(lam, -0.051654, rtol=1e-5)
+
+    @pytest.mark.parametrize("bounds", [(-1, 1), (1.1, 2), (-2, -1.1)])
+    def test_bounded_optimizer_within_bounds(self, bounds):
+        # Define custom optimizer with bounds.
+        def optimizer(fun):
+            return optimize.minimize_scalar(fun, bounds=bounds,
+                                            method="bounded")
+
+        _, lmbda = stats.boxcox(_boxcox_data, lmbda=None, optimizer=optimizer)
+        assert bounds[0] < lmbda < bounds[1]
+
+    def test_bounded_optimizer_against_unbounded_optimizer(self):
+        # Test whether setting bounds on optimizer excludes solution from
+        # unbounded optimizer.
+
+        # Get unbounded solution.
+        _, lmbda = stats.boxcox(_boxcox_data, lmbda=None)
+
+        # Set tolerance and bounds around solution.
+        bounds = (lmbda + 0.1, lmbda + 1)
+        options = {'xatol': 1e-12}
+
+        def optimizer(fun):
+            return optimize.minimize_scalar(fun, bounds=bounds,
+                                            method="bounded", options=options)
+
+        # Check bounded solution. Lower bound should be active.
+        _, lmbda_bounded = stats.boxcox(_boxcox_data, lmbda=None,
+                                        optimizer=optimizer)
+        assert lmbda_bounded != lmbda
+        assert_allclose(lmbda_bounded, bounds[0])
+
+    @pytest.mark.parametrize("optimizer", ["str", (1, 2), 0.1])
+    def test_bad_optimizer_type_raises_error(self, optimizer):
+        # Check if error is raised if string, tuple or float is passed
+        with pytest.raises(ValueError, match="`optimizer` must be a callable"):
+            stats.boxcox(_boxcox_data, lmbda=None, optimizer=optimizer)
+
+    def test_bad_optimizer_value_raises_error(self):
+        # Check if error is raised if `optimizer` function does not return
+        # `OptimizeResult` object
+
+        # Define test function that always returns 1
+        def optimizer(fun):
+            return 1
+
+        message = "return an object containing the optimal `lmbda`"
+        with pytest.raises(ValueError, match=message):
+            stats.boxcox(_boxcox_data, lmbda=None, optimizer=optimizer)
+
+    @pytest.mark.parametrize(
+            "bad_x", [np.array([1, -42, 12345.6]), np.array([np.inf, 42, 1])]
+        )
+    def test_negative_x_value_raises_error(self, bad_x):
+        """Test boxcox_normmax raises ValueError if x contains non-positive values."""
+        message = "only positive, finite, real numbers"
+        with pytest.raises(ValueError, match=message):
+            stats.boxcox_normmax(bad_x)
+
+    @pytest.mark.parametrize('x', [
+        # Attempt to trigger overflow in power expressions.
+        np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0,
+                  2009.0, 1980.0, 1999.0, 2007.0, 1991.0]),
+        # Attempt to trigger overflow with a large optimal lambda.
+        np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0]),
+        # Attempt to trigger overflow with large data.
+        np.array([2003.0e200, 1950.0e200, 1997.0e200, 2000.0e200, 2009.0e200])
+    ])
+    def test_overflow(self, x):
+        with pytest.warns(UserWarning, match="The optimal lambda is"):
+            xt_bc, lam_bc = stats.boxcox(x)
+            assert np.all(np.isfinite(xt_bc))
+
+    def test_confidence_interval_nan_policy(self):
+        # other outputs with `nan_policy` tested in superclass
+        rng = np.random.default_rng(5908294582456923546)
+        x = rng.random(10)
+        x[3] = np.nan
+        alpha = 0.5
+
+        with pytest.raises(ValueError, match="The input contains nan values"):
+            stats.boxcox(x, alpha=alpha, nan_policy='raise')
+
+        res = stats.boxcox(x, alpha=alpha, nan_policy='propagate')[2]
+        np.testing.assert_equal(res, (np.nan, np.nan))
+
+        res = stats.boxcox(x, alpha=alpha, nan_policy='omit')[2]
+        ref = stats.boxcox(x[~np.isnan(x)], alpha=alpha)[2]
+        np.testing.assert_allclose(res, ref)
+
+
+class NormmaxTest:
+    def test_nan_policy(self):
+        transform_normmax = getattr(stats, self.transform_normmax)
+        rng = np.random.default_rng(5908294582456923546)
+        x = rng.random(10)
+        x[3] = np.nan
+
+        with pytest.raises(ValueError, match="The input contains nan values"):
+            transform_normmax(x, nan_policy='raise')
+
+        res = transform_normmax(x, nan_policy='propagate')
+        np.testing.assert_equal(res, np.nan)
+
+        res = transform_normmax(x, nan_policy='omit')
+        ref = transform_normmax(x[~np.isnan(x)])
+        np.testing.assert_allclose(res, ref)
+
+
+class TestBoxcoxNormmax(NormmaxTest):
+    transform_normmax = 'boxcox_normmax'
+
+    def setup_method(self):
+        self.x = _old_loggamma_rvs(5, size=50, random_state=12345) + 5
+
+    def test_pearsonr(self):
+        maxlog = stats.boxcox_normmax(self.x)
+        assert_allclose(maxlog, 1.804465, rtol=1e-6)
+
+    def test_mle(self):
+        maxlog = stats.boxcox_normmax(self.x, method='mle')
+        assert_allclose(maxlog, 1.758101, rtol=1e-6)
+
+        # Check that boxcox() uses 'mle'
+        _, maxlog_boxcox = stats.boxcox(self.x)
+        assert_allclose(maxlog_boxcox, maxlog)
+
+    def test_all(self):
+        maxlog_all = stats.boxcox_normmax(self.x, method='all')
+        assert_allclose(maxlog_all, [1.804465, 1.758101], rtol=1e-6)
+
+    @pytest.mark.parametrize("method", ["mle", "pearsonr", "all"])
+    @pytest.mark.parametrize("bounds", [(-1, 1), (1.1, 2), (-2, -1.1)])
+    def test_bounded_optimizer_within_bounds(self, method, bounds):
+
+        def optimizer(fun):
+            return optimize.minimize_scalar(fun, bounds=bounds,
+                                            method="bounded")
+
+        maxlog = stats.boxcox_normmax(self.x, method=method,
+                                      optimizer=optimizer)
+        assert np.all(bounds[0] < maxlog)
+        assert np.all(maxlog < bounds[1])
+
+    @pytest.mark.slow
+    def test_user_defined_optimizer(self):
+        # tests an optimizer that is not based on scipy.optimize.minimize
+        lmbda = stats.boxcox_normmax(self.x)
+        lmbda_rounded = np.round(lmbda, 5)
+        lmbda_range = np.linspace(lmbda_rounded-0.01, lmbda_rounded+0.01, 1001)
+
+        class MyResult:
+            pass
+
+        def optimizer(fun):
+            # brute force minimum over the range
+            objs = []
+            for lmbda in lmbda_range:
+                objs.append(fun(lmbda))
+            res = MyResult()
+            res.x = lmbda_range[np.argmin(objs)]
+            return res
+
+        lmbda2 = stats.boxcox_normmax(self.x, optimizer=optimizer)
+        assert lmbda2 != lmbda                 # not identical
+        assert_allclose(lmbda2, lmbda, 1e-5)   # but as close as it should be
+
+    def test_user_defined_optimizer_and_brack_raises_error(self):
+        optimizer = optimize.minimize_scalar
+
+        # Using default `brack=None` with user-defined `optimizer` works as
+        # expected.
+        stats.boxcox_normmax(self.x, brack=None, optimizer=optimizer)
+
+        # Using user-defined `brack` with user-defined `optimizer` is expected
+        # to throw an error. Instead, users should specify
+        # optimizer-specific parameters in the optimizer function itself.
+        with pytest.raises(ValueError, match="`brack` must be None if "
+                                             "`optimizer` is given"):
+
+            stats.boxcox_normmax(self.x, brack=(-2.0, 2.0),
+                                 optimizer=optimizer)
+
+    @pytest.mark.parametrize(
+        'x', ([2003.0, 1950.0, 1997.0, 2000.0, 2009.0],
+              [0.50000471, 0.50004979, 0.50005902, 0.50009312, 0.50001632]))
+    def test_overflow(self, x):
+        message = "The optimal lambda is..."
+        with pytest.warns(UserWarning, match=message):
+            lmbda = stats.boxcox_normmax(x, method='mle')
+        assert np.isfinite(special.boxcox(x, lmbda)).all()
+        # 10000 is safety factor used in boxcox_normmax
+        ymax = np.finfo(np.float64).max / 10000
+        x_treme = np.max(x) if lmbda > 0 else np.min(x)
+        y_extreme = special.boxcox(x_treme, lmbda)
+        assert_allclose(y_extreme, ymax * np.sign(lmbda))
+
+    def test_negative_ymax(self):
+        with pytest.raises(ValueError, match="`ymax` must be strictly positive"):
+            stats.boxcox_normmax(self.x, ymax=-1)
+
+    @pytest.mark.parametrize("x", [
+        # positive overflow in float64
+        np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0],
+                 dtype=np.float64),
+        # negative overflow in float64
+        np.array([0.50000471, 0.50004979, 0.50005902, 0.50009312, 0.50001632],
+                 dtype=np.float64),
+        # positive overflow in float32
+        np.array([200.3, 195.0, 199.7, 200.0, 200.9],
+                 dtype=np.float32),
+        # negative overflow in float32
+        np.array([2e-30, 1e-30, 1e-30, 1e-30, 1e-30, 1e-30],
+                 dtype=np.float32),
+    ])
+    @pytest.mark.parametrize("ymax", [1e10, 1e30, None])
+    # TODO: add method "pearsonr" after fix overflow issue
+    @pytest.mark.parametrize("method", ["mle"])
+    def test_user_defined_ymax_input_float64_32(self, x, ymax, method):
+        # Test the maximum of the transformed data close to ymax
+        with pytest.warns(UserWarning, match="The optimal lambda is"):
+            kwarg = {'ymax': ymax} if ymax is not None else {}
+            lmb = stats.boxcox_normmax(x, method=method, **kwarg)
+            x_treme = [np.min(x), np.max(x)]
+            ymax_res = max(abs(stats.boxcox(x_treme, lmb)))
+            if ymax is None:
+                # 10000 is safety factor used in boxcox_normmax
+                ymax = np.finfo(x.dtype).max / 10000
+            assert_allclose(ymax, ymax_res, rtol=1e-5)
+
+    @pytest.mark.parametrize("x", [
+        # positive overflow in float32 but not float64
+        [200.3, 195.0, 199.7, 200.0, 200.9],
+        # negative overflow in float32 but not float64
+        [2e-30, 1e-30, 1e-30, 1e-30, 1e-30, 1e-30],
+    ])
+    # TODO: add method "pearsonr" after fix overflow issue
+    @pytest.mark.parametrize("method", ["mle"])
+    def test_user_defined_ymax_inf(self, x, method):
+        x_32 = np.asarray(x, dtype=np.float32)
+        x_64 = np.asarray(x, dtype=np.float64)
+
+        # assert overflow with float32 but not float64
+        with pytest.warns(UserWarning, match="The optimal lambda is"):
+            stats.boxcox_normmax(x_32, method=method)
+        stats.boxcox_normmax(x_64, method=method)
+
+        # compute the true optimal lambda then compare them
+        lmb_32 = stats.boxcox_normmax(x_32, ymax=np.inf, method=method)
+        lmb_64 = stats.boxcox_normmax(x_64, ymax=np.inf, method=method)
+        assert_allclose(lmb_32, lmb_64, rtol=1e-2)
+
+    def test_nan_policy_all(self):
+        # `nan_policy` with other `method`s tested in superclass
+        rng = np.random.default_rng(5908294582456923546)
+        x = rng.random(10)
+        x[3] = np.nan
+
+        with pytest.raises(ValueError, match="The input contains nan values"):
+            stats.boxcox_normmax(x, method='all', nan_policy='raise')
+
+        res = stats.boxcox_normmax(x, method='all', nan_policy='propagate')
+        np.testing.assert_equal(res, (np.nan, np.nan))
+
+        res = stats.boxcox_normmax(x, method='all', nan_policy='omit')
+        ref = stats.boxcox_normmax(x[~np.isnan(x)], method='all')
+        np.testing.assert_allclose(res, ref, rtol=2e-7)
+
+
+class TestBoxcoxNormplot:
+    def setup_method(self):
+        self.x = _old_loggamma_rvs(5, size=500, random_state=7654321) + 5
+
+    def test_basic(self):
+        N = 5
+        lmbdas, ppcc = stats.boxcox_normplot(self.x, -10, 10, N=N)
+        ppcc_expected = [0.57783375, 0.83610988, 0.97524311, 0.99756057,
+                         0.95843297]
+        assert_allclose(lmbdas, np.linspace(-10, 10, num=N))
+        assert_allclose(ppcc, ppcc_expected)
+
+    @pytest.mark.skipif(not have_matplotlib, reason="no matplotlib")
+    def test_plot_kwarg(self):
+        # Check with the matplotlib.pyplot module
+        fig = plt.figure()
+        ax = fig.add_subplot(111)
+        stats.boxcox_normplot(self.x, -20, 20, plot=plt)
+        fig.delaxes(ax)
+
+        # Check that a Matplotlib Axes object is accepted
+        ax = fig.add_subplot(111)
+        stats.boxcox_normplot(self.x, -20, 20, plot=ax)
+        plt.close()
+
+    def test_invalid_inputs(self):
+        # `lb` has to be larger than `la`
+        assert_raises(ValueError, stats.boxcox_normplot, self.x, 1, 0)
+        # `x` can not contain negative values
+        assert_raises(ValueError, stats.boxcox_normplot, [-1, 1], 0, 1)
+
+    def test_empty(self):
+        assert_(stats.boxcox_normplot([], 0, 1).size == 0)
+
+
+@make_xp_test_case(stats.yeojohnson_llf)
+class TestYeojohnson_llf:
+
+    def test_array_like(self):
+        # array_like not applicable with SCIPY_ARRAY_API=1
+        x = stats.norm.rvs(size=100, loc=0, random_state=54321)
+        lmbda = 1
+        llf = stats.yeojohnson_llf(lmbda, x)
+        llf2 = stats.yeojohnson_llf(lmbda, list(x))
+        assert_allclose(llf, llf2, rtol=1e-12)
+
+    def test_2d_input(self, xp):
+        x = stats.norm.rvs(size=100, loc=10, random_state=54321)
+        x = xp.asarray(x)
+        lmbda = 1
+        ref = stats.yeojohnson_llf(lmbda, x)
+        res = stats.yeojohnson_llf(lmbda, xp.stack([x, x]).T)
+        xp_assert_close(res, xp.stack((ref, ref)), rtol=1e-12)
+
+    def test_empty(self, xp):
+        message = "One or more sample arguments is too small..."
+        with eager_warns(SmallSampleWarning, match=message, xp=xp):
+            assert xp.isnan(stats.yeojohnson_llf(1, xp.asarray([])))
+
+    @skip_xp_backends('jax.numpy', reason="JIT can't special-case for all >0 / <0")
+    def test_gh24172(self, xp):
+        # Test all negative and all positive data
+        data = xp.asarray([10, 10, 10, 9.9], dtype=xp.float64)
+        # The expected value was computed with mpsci, set mpmath.mp.dps=100
+        # 1. Test precision loss
+        xp_assert_close(stats.yeojohnson_llf(-14, data),
+                        xp.asarray(12.418595783381280, dtype=xp.float64), rtol=1e-7)
+        xp_assert_close(stats.yeojohnson_llf(16, -data),
+                        xp.asarray(12.418595783381280, dtype=xp.float64), rtol=1e-7)
+        # 2. Test overflow
+        xp_assert_close(stats.yeojohnson_llf(-20, data),
+                        xp.asarray(12.360966379200528, dtype=xp.float64), rtol=1e-7)
+        xp_assert_close(stats.yeojohnson_llf(20, -data),
+                        xp.asarray(12.380287243698629, dtype=xp.float64), rtol=1e-7)
+
+    @pytest.mark.parametrize("lmb, ref",
+                             [(-1.,  6.757930444097559),
+                              (0., 11.90755408530727),
+                              (1., 14.179952211901629),
+                              (2., 13.040657538043835)])
+    def test_against_reference(self, lmb, ref, xp):
+        # Reference values generated with mpsci, e.g.
+        # import numpy as np
+        # from mpmath import mp
+        # from mpsci.stats import yeojohnson_llf
+        # mp.dps = 100
+        # rng = np.random.default_rng(78435782834992002)
+        # x = rng.uniform(-1, 1, size=25)
+        # float(yeojohnson_llf(-1, x))  # 6.757930444097559
+        rng = np.random.default_rng(78435782834992002)
+        x = rng.uniform(-1, 1, size=25)
+        res = stats.yeojohnson_llf(lmb, xp.asarray(x.tolist()))
+        xp_assert_close(res, xp.asarray(ref))
+
+
+class TestYeojohnson(TransformTest):
+    transform = 'yeojohnson'
+
+    def test_fixed_lmbda(self):
+        rng = np.random.RandomState(12345)
+
+        # Test positive input
+        x = _old_loggamma_rvs(5, size=50, random_state=rng) + 5
+        assert np.all(x > 0)
+        xt = stats.yeojohnson(x, lmbda=1)
+        assert_allclose(xt, x)
+        xt = stats.yeojohnson(x, lmbda=-1)
+        assert_allclose(xt, 1 - 1 / (x + 1))
+        xt = stats.yeojohnson(x, lmbda=0)
+        assert_allclose(xt, np.log(x + 1))
+        xt = stats.yeojohnson(x, lmbda=1)
+        assert_allclose(xt, x)
+
+        # Test negative input
+        x = _old_loggamma_rvs(5, size=50, random_state=rng) - 5
+        assert np.all(x < 0)
+        xt = stats.yeojohnson(x, lmbda=2)
+        assert_allclose(xt, -np.log(-x + 1))
+        xt = stats.yeojohnson(x, lmbda=1)
+        assert_allclose(xt, x)
+        xt = stats.yeojohnson(x, lmbda=3)
+        assert_allclose(xt, 1 / (-x + 1) - 1)
+
+        # test both positive and negative input
+        x = _old_loggamma_rvs(5, size=50, random_state=rng) - 2
+        assert not np.all(x < 0)
+        assert not np.all(x >= 0)
+        pos = x >= 0
+        xt = stats.yeojohnson(x, lmbda=1)
+        assert_allclose(xt[pos], x[pos])
+        xt = stats.yeojohnson(x, lmbda=-1)
+        assert_allclose(xt[pos], 1 - 1 / (x[pos] + 1))
+        xt = stats.yeojohnson(x, lmbda=0)
+        assert_allclose(xt[pos], np.log(x[pos] + 1))
+        xt = stats.yeojohnson(x, lmbda=1)
+        assert_allclose(xt[pos], x[pos])
+
+        neg = ~pos
+        xt = stats.yeojohnson(x, lmbda=2)
+        assert_allclose(xt[neg], -np.log(-x[neg] + 1))
+        xt = stats.yeojohnson(x, lmbda=1)
+        assert_allclose(xt[neg], x[neg])
+        xt = stats.yeojohnson(x, lmbda=3)
+        assert_allclose(xt[neg], 1 / (-x[neg] + 1) - 1)
+
+    @pytest.mark.parametrize('lmbda', [0, .1, .5, 2])
+    def test_lmbda_None(self, lmbda):
+        # Start from normal rv's, do inverse transform to check that
+        # optimization function gets close to the right answer.
+
+        def _inverse_transform(x, lmbda):
+            x_inv = np.zeros(x.shape, dtype=x.dtype)
+            pos = x >= 0
+
+            # when x >= 0
+            if abs(lmbda) < np.spacing(1.):
+                x_inv[pos] = np.exp(x[pos]) - 1
+            else:  # lmbda != 0
+                x_inv[pos] = np.power(x[pos] * lmbda + 1, 1 / lmbda) - 1
+
+            # when x < 0
+            if abs(lmbda - 2) > np.spacing(1.):
+                x_inv[~pos] = 1 - np.power(-(2 - lmbda) * x[~pos] + 1,
+                                           1 / (2 - lmbda))
+            else:  # lmbda == 2
+                x_inv[~pos] = 1 - np.exp(-x[~pos])
+
+            return x_inv
+
+        n_samples = 20000
+        rng = np.random.RandomState(1234567)
+        x = rng.normal(loc=0, scale=1, size=(n_samples))
+
+        x_inv = _inverse_transform(x, lmbda)
+        xt, maxlog = stats.yeojohnson(x_inv)
+
+        assert_allclose(maxlog, lmbda, atol=1e-2)
+
+        assert_almost_equal(0, np.linalg.norm(x - xt) / n_samples, decimal=2)
+        assert_almost_equal(0, xt.mean(), decimal=1)
+        assert_almost_equal(1, xt.std(), decimal=1)
+
+    def test_empty(self):
+        assert_(stats.yeojohnson([]).shape == (0,))
+
+    def test_array_like(self):
+        x = stats.norm.rvs(size=100, loc=0, random_state=54321)
+        xt1, _ = stats.yeojohnson(x)
+        xt2, _ = stats.yeojohnson(list(x))
+        assert_allclose(xt1, xt2, rtol=1e-12)
+
+    @pytest.mark.parametrize('dtype', [np.complex64, np.complex128])
+    def test_input_dtype_complex(self, dtype):
+        x = np.arange(6, dtype=dtype)
+        err_msg = ('Yeo-Johnson transformation is not defined for complex '
+                   'numbers.')
+        with pytest.raises(ValueError, match=err_msg):
+            stats.yeojohnson(x)
+
+    @pytest.mark.parametrize('dtype', [np.int8, np.uint8, np.int16, np.int32])
+    def test_input_dtype_integer(self, dtype):
+        x_int = np.arange(8, dtype=dtype)
+        x_float = np.arange(8, dtype=np.float64)
+        xt_int, lmbda_int = stats.yeojohnson(x_int)
+        xt_float, lmbda_float = stats.yeojohnson(x_float)
+        assert_allclose(xt_int, xt_float, rtol=1e-7)
+        assert_allclose(lmbda_int, lmbda_float, rtol=1e-7)
+
+    def test_input_high_variance(self):
+        # non-regression test for gh-10821
+        x = np.array([3251637.22, 620695.44, 11642969.00, 2223468.22,
+                      85307500.00, 16494389.89, 917215.88, 11642969.00,
+                      2145773.87, 4962000.00, 620695.44, 651234.50,
+                      1907876.71, 4053297.88, 3251637.22, 3259103.08,
+                      9547969.00, 20631286.23, 12807072.08, 2383819.84,
+                      90114500.00, 17209575.46, 12852969.00, 2414609.99,
+                      2170368.23])
+        xt_yeo, lam_yeo = stats.yeojohnson(x)
+        xt_box, lam_box = stats.boxcox(x + 1)
+        assert_allclose(xt_yeo, xt_box, rtol=1e-6)
+        assert_allclose(lam_yeo, lam_box, rtol=1e-6)
+
+    @pytest.mark.parametrize('x', [
+        np.array([1.0, float("inf"), 2.0]),
+        np.array([1.0, -float("inf"), 2.0]),
+        np.array([-1.0, float("inf"), -float("inf"), 1.0])
+    ])
+    def test_nonfinite_input(self, x):
+        with pytest.raises(ValueError, match='Yeo-Johnson input must be finite'):
+            xt_yeo, lam_yeo = stats.yeojohnson(x)
+
+    @pytest.mark.parametrize('x', [
+        # Attempt to trigger overflow in power expressions.
+        np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0,
+                  2009.0, 1980.0, 1999.0, 2007.0, 1991.0]),
+        # Attempt to trigger overflow with a large optimal lambda.
+        np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0]),
+        # Attempt to trigger overflow with large data.
+        np.array([2003.0e200, 1950.0e200, 1997.0e200, 2000.0e200, 2009.0e200])
+    ])
+    def test_overflow(self, x):
+        # non-regression test for gh-18389
+
+        def optimizer(fun, lam_yeo):
+            out = optimize.fminbound(fun, -lam_yeo, lam_yeo, xtol=1.48e-08)
+            result = optimize.OptimizeResult()
+            result.x = out
+            return result
+
+        with np.errstate(all="raise"):
+            xt_yeo, lam_yeo = stats.yeojohnson(x)
+            xt_box, lam_box = stats.boxcox(
+                x + 1, optimizer=partial(optimizer, lam_yeo=lam_yeo))
+            assert np.isfinite(np.var(xt_yeo))
+            assert np.isfinite(np.var(xt_box))
+            assert_allclose(lam_yeo, lam_box, rtol=1e-6)
+            assert_allclose(xt_yeo, xt_box, rtol=1e-4)
+
+    @pytest.mark.parametrize('x', [
+        np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0,
+                  2009.0, 1980.0, 1999.0, 2007.0, 1991.0]),
+        np.array([2003.0, 1950.0, 1997.0, 2000.0, 2009.0])
+    ])
+    @pytest.mark.parametrize('scale', [1, 1e-12, 1e-32, 1e-150, 1e32, 1e200])
+    @pytest.mark.parametrize('sign', [1, -1])
+    def test_overflow_underflow_signed_data(self, x, scale, sign):
+        # non-regression test for gh-18389
+        with np.errstate(all="raise"):
+            xt_yeo, lam_yeo = stats.yeojohnson(sign * x * scale)
+            assert np.all(np.sign(sign * x) == np.sign(xt_yeo))
+            assert np.isfinite(lam_yeo)
+            assert np.isfinite(np.var(xt_yeo))
+
+    @pytest.mark.parametrize('x', [
+        np.array([0, 1, 2, 3]),
+        np.array([0, -1, 2, -3]),
+        np.array([0, 0, 0])
+    ])
+    @pytest.mark.parametrize('sign', [1, -1])
+    @pytest.mark.parametrize('brack', [None, (-2, 2)])
+    def test_integer_signed_data(self, x, sign, brack):
+        with np.errstate(all="raise"):
+            x_int = sign * x
+            x_float = x_int.astype(np.float64)
+            lam_yeo_int = stats.yeojohnson_normmax(x_int, brack=brack)
+            xt_yeo_int = stats.yeojohnson(x_int, lmbda=lam_yeo_int)
+            lam_yeo_float = stats.yeojohnson_normmax(x_float, brack=brack)
+            xt_yeo_float = stats.yeojohnson(x_float, lmbda=lam_yeo_float)
+            assert np.all(np.sign(x_int) == np.sign(xt_yeo_int))
+            assert np.isfinite(lam_yeo_int)
+            assert np.isfinite(np.var(xt_yeo_int))
+            assert lam_yeo_int == lam_yeo_float
+            assert np.all(xt_yeo_int == xt_yeo_float)
+
+
+class TestYeojohnsonNormmax(NormmaxTest):
+    transform_normmax = 'yeojohnson_normmax'
+
+    def setup_method(self):
+        self.x = _old_loggamma_rvs(5, size=50, random_state=12345) + 5
+
+    def test_mle(self):
+        maxlog = stats.yeojohnson_normmax(self.x)
+        assert_allclose(maxlog, 1.876393, rtol=1e-6)
+
+    def test_darwin_example(self):
+        # test from original paper "A new family of power transformations to
+        # improve normality or symmetry" by Yeo and Johnson.
+        x = [6.1, -8.4, 1.0, 2.0, 0.7, 2.9, 3.5, 5.1, 1.8, 3.6, 7.0, 3.0, 9.3,
+             7.5, -6.0]
+        lmbda = stats.yeojohnson_normmax(x)
+        assert np.allclose(lmbda, 1.305, atol=1e-3)
+
+
+@make_xp_test_case(stats.circmean, stats.circvar, stats.circstd)
+class TestCircFuncs:
+    # In gh-5747, the R package `circular` was used to calculate reference
+    # values for the circular variance, e.g.:
+    # library(circular)
+    # options(digits=16)
+    # x = c(0, 2*pi/3, 5*pi/3)
+    # var.circular(x)
+    @pytest.mark.parametrize("test_func,expected",
+                             [(stats.circmean, 0.167690146),
+                              (stats.circvar, 0.006455174000787767),
+                              (stats.circstd, 6.520702116)])
+    def test_circfuncs(self, test_func, expected, xp):
+        x = xp.asarray([355., 5., 2., 359., 10., 350.])
+        xp_assert_close(test_func(x, high=360), xp.asarray(expected))
+
+    def test_circfuncs_small(self, xp):
+        # Default tolerances won't work here because the reference values
+        # are approximations. Ensure all array types work in float64 to
+        # avoid needing separate float32 and float64 tolerances.
+        x = xp.asarray([20, 21, 22, 18, 19, 20.5, 19.2], dtype=xp.float64)
+        M1 = xp.mean(x)
+        M2 = stats.circmean(x, high=360)
+        xp_assert_close(M2, M1, rtol=1e-5)
+
+        V1 = xp.var(x*xp.pi/180, correction=0)
+        # for small variations, circvar is approximately half the
+        # linear variance
+        V1 = V1 / 2.
+        V2 = stats.circvar(x, high=360)
+        xp_assert_close(V2, V1, rtol=1e-4)
+
+        S1 = xp.std(x, correction=0)
+        S2 = stats.circstd(x, high=360)
+        xp_assert_close(S2, S1, rtol=1e-4)
+
+    @pytest.mark.parametrize("test_func, numpy_func",
+                             [(stats.circmean, np.mean),
+                              (stats.circvar, np.var),
+                              (stats.circstd, np.std)])
+    def test_circfuncs_close(self, test_func, numpy_func, xp):
+        # circfuncs should handle very similar inputs (gh-12740)
+        x = np.asarray([0.12675364631578953] * 10 + [0.12675365920187928] * 100)
+        circstat = test_func(xp.asarray(x))
+        normal = xp.asarray(numpy_func(x))
+        xp_assert_close(circstat, normal, atol=2e-8)
+
+    @pytest.mark.parametrize('circfunc', [stats.circmean,
+                                          stats.circvar,
+                                          stats.circstd])
+    def test_circmean_axis(self, circfunc, xp):
+        x = xp.asarray([[355, 5, 2, 359, 10, 350],
+                        [351, 7, 4, 352, 9, 349],
+                        [357, 9, 8, 358, 4, 356.]])
+        res = circfunc(x, high=360)
+        ref = circfunc(xp.reshape(x, (-1,)), high=360)
+        xp_assert_close(res, xp.asarray(ref))
+
+        res = circfunc(x, high=360, axis=1)
+        ref = [circfunc(x[i, :], high=360) for i in range(x.shape[0])]
+        xp_assert_close(res, xp.stack(ref))
+
+        res = circfunc(x, high=360, axis=0)
+        ref = [circfunc(x[:, i], high=360) for i in range(x.shape[1])]
+        xp_assert_close(res, xp.stack(ref))
+
+    @pytest.mark.parametrize("test_func,expected",
+                             [(stats.circmean, 0.167690146),
+                              (stats.circvar, 0.006455174270186603),
+                              (stats.circstd, 6.520702116)])
+    def test_circfuncs_array_like(self, test_func, expected, xp):
+        x = xp.asarray([355, 5, 2, 359, 10, 350.])
+        xp_assert_close(test_func(x, high=360), xp.asarray(expected))
+
+    @pytest.mark.parametrize("test_func", [stats.circmean, stats.circvar,
+                                           stats.circstd])
+    def test_empty(self, test_func, xp):
+        dtype = xp.float64
+        x = xp.asarray([], dtype=dtype)
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = test_func(x)
+
+        xp_assert_equal(res, xp.asarray(xp.nan, dtype=dtype))
+
+    @pytest.mark.parametrize("test_func", [stats.circmean, stats.circvar,
+                                           stats.circstd])
+    def test_nan_propagate(self, test_func, xp):
+        x = xp.asarray([355, 5, 2, 359, 10, 350, np.nan])
+        xp_assert_equal(test_func(x, high=360), xp.asarray(xp.nan))
+
+    @pytest.mark.parametrize("test_func,expected",
+                             [(stats.circmean,
+                               {None: np.nan, 0: 355.66582264, 1: 0.28725053}),
+                              (stats.circvar,
+                               {None: np.nan,
+                                0: 0.002570671054089924,
+                                1: 0.005545914017677123}),
+                              (stats.circstd,
+                               {None: np.nan, 0: 4.11093193, 1: 6.04265394})])
+    def test_nan_propagate_array(self, test_func, expected, xp):
+        x = xp.asarray([[355, 5, 2, 359, 10, 350, 1],
+                        [351, 7, 4, 352, 9, 349, np.nan],
+                        [1, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]])
+        for axis in expected.keys():
+            out = test_func(x, high=360, axis=axis)
+            if axis is None:
+                xp_assert_equal(out, xp.asarray(xp.nan))
+            else:
+                xp_assert_close(out[0], xp.asarray(expected[axis]))
+                xp_assert_equal(out[1:], xp.full_like(out[1:], xp.nan))
+
+    def test_circmean_scalar(self, xp):
+        x = xp.asarray(1.)[()]
+        M1 = x
+        M2 = stats.circmean(x)
+        xp_assert_close(M2, M1, rtol=1e-5)
+
+    def test_circmean_range(self, xp):
+        # regression test for gh-6420: circmean(..., high, low) must be
+        # between `high` and `low`
+        m = stats.circmean(xp.arange(0, 2, 0.1), xp.pi, -xp.pi)
+        xp_assert_less(m, xp.asarray(xp.pi))
+        xp_assert_less(-m, xp.asarray(xp.pi))
+
+    def test_circfuncs_uint8(self, xp):
+        # regression test for gh-7255: overflow when working with
+        # numpy uint8 data type
+        x = xp.asarray([150, 10], dtype=xp.uint8)
+        xp_assert_close(stats.circmean(x, high=180), xp.asarray(170.0))
+        xp_assert_close(stats.circvar(x, high=180), xp.asarray(0.2339555554617))
+        xp_assert_close(stats.circstd(x, high=180), xp.asarray(20.91551378))
+
+    def test_circstd_zero(self, xp):
+        # circstd() of a single number should return positive zero.
+        y = stats.circstd(xp.asarray([0]))
+        assert math.copysign(1.0, y) == 1.0
+
+    def test_circmean_accuracy_tiny_input(self, xp):
+        # For tiny x such that sin(x) == x and cos(x) == 1.0 numerically,
+        # circmean(x) should return x because atan2(sin(x), cos(x)) == x.
+        # This test verifies this.
+        #
+        # The purpose of this test is not to show that circmean() is
+        # accurate in the last digit for certain input, because this is
+        # neither guaranteed not particularly useful.  Rather, it is a
+        # "white-box" sanity check that no undue loss of precision is
+        # introduced by conversion between (high - low) and (2 * pi).
+
+        x = xp.linspace(1e-9, 6e-9, 50)
+        assert xp.all(xp.sin(x) == x) and xp.all(xp.cos(x) == 1.0)
+
+        m = (x * (2 * xp.pi) / (2 * xp.pi)) != x
+        assert xp.any(m)
+        x = x[m]
+
+        y = stats.circmean(x[:, None], axis=1)
+        assert xp.all(y == x)
+
+    def test_circmean_accuracy_huge_input(self, xp):
+        # White-box test that circmean() does not introduce undue loss of
+        # numerical accuracy by eagerly rotating the input.  This is detected
+        # by supplying a huge input x such that (x - low) == x numerically.
+        x = xp.asarray(1e17, dtype=xp.float64)
+        y = math.atan2(xp.sin(x), xp.cos(x))  # -2.6584887370946806
+        expected = xp.asarray(y, dtype=xp.float64)
+        actual = stats.circmean(x, high=xp.pi, low=-xp.pi)
+        xp_assert_close(actual, expected, rtol=1e-15, atol=0.0)
+
+
+class TestCircFuncsNanPolicy:
+    # `nan_policy` is implemented by the `_axis_nan_policy` decorator, which is
+    # not yet array-API compatible. When it is array-API compatible, the generic
+    # tests run on every function will be much stronger than these, so these
+    # will not be necessary. So I don't see a need to make these array-API compatible;
+    # when the time comes, they can just be removed.
+    @pytest.mark.parametrize("test_func,expected",
+                             [(stats.circmean,
+                               {None: 359.4178026893944,
+                                0: np.array([353.0, 6.0, 3.0, 355.5, 9.5,
+                                             349.5]),
+                                1: np.array([0.16769015, 358.66510252])}),
+                              (stats.circvar,
+                               {None: 0.008396678483192477,
+                                0: np.array([1.9997969, 0.4999873, 0.4999873,
+                                             6.1230956, 0.1249992, 0.1249992]
+                                            )*(np.pi/180)**2,
+                                1: np.array([0.006455174270186603,
+                                             0.01016767581393285])}),
+                              (stats.circstd,
+                               {None: 7.440570778057074,
+                                0: np.array([2.00020313, 1.00002539, 1.00002539,
+                                             3.50108929, 0.50000317,
+                                             0.50000317]),
+                                1: np.array([6.52070212, 8.19138093])})])
+    def test_nan_omit_array(self, test_func, expected):
+        x = np.array([[355, 5, 2, 359, 10, 350, np.nan],
+                      [351, 7, 4, 352, 9, 349, np.nan],
+                      [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]])
+        for axis in expected.keys():
+            if axis is None:
+                out = test_func(x, high=360, nan_policy='omit', axis=axis)
+                assert_allclose(out, expected[axis], rtol=1e-7)
+            else:
+                with pytest.warns(SmallSampleWarning, match=too_small_nd_omit):
+                    out = test_func(x, high=360, nan_policy='omit', axis=axis)
+                    assert_allclose(out[:-1], expected[axis], rtol=1e-7)
+                    assert_(np.isnan(out[-1]))
+
+    @pytest.mark.parametrize("test_func,expected",
+                             [(stats.circmean, 0.167690146),
+                              (stats.circvar, 0.006455174270186603),
+                              (stats.circstd, 6.520702116)])
+    def test_nan_omit(self, test_func, expected):
+        x = [355, 5, 2, 359, 10, 350, np.nan]
+        assert_allclose(test_func(x, high=360, nan_policy='omit'),
+                        expected, rtol=1e-7)
+
+    @pytest.mark.parametrize("test_func", [stats.circmean, stats.circvar,
+                                           stats.circstd])
+    def test_nan_omit_all(self, test_func):
+        x = [np.nan, np.nan, np.nan, np.nan, np.nan]
+        with pytest.warns(SmallSampleWarning, match=too_small_1d_omit):
+            assert_(np.isnan(test_func(x, nan_policy='omit')))
+
+    @pytest.mark.parametrize("test_func", [stats.circmean, stats.circvar,
+                                           stats.circstd])
+    def test_nan_omit_all_axis(self, test_func):
+        with pytest.warns(SmallSampleWarning, match=too_small_nd_omit):
+            x = np.array([[np.nan, np.nan, np.nan, np.nan, np.nan],
+                          [np.nan, np.nan, np.nan, np.nan, np.nan]])
+            out = test_func(x, nan_policy='omit', axis=1)
+            assert_(np.isnan(out).all())
+            assert_(len(out) == 2)
+
+    @pytest.mark.parametrize("x",
+                             [[355, 5, 2, 359, 10, 350, np.nan],
+                              np.array([[355, 5, 2, 359, 10, 350, np.nan],
+                                        [351, 7, 4, 352, np.nan, 9, 349]])])
+    @pytest.mark.parametrize("test_func", [stats.circmean, stats.circvar,
+                                           stats.circstd])
+    def test_nan_raise(self, test_func, x):
+        assert_raises(ValueError, test_func, x, high=360, nan_policy='raise')
+
+    @pytest.mark.parametrize("x",
+                             [[355, 5, 2, 359, 10, 350, np.nan],
+                              np.array([[355, 5, 2, 359, 10, 350, np.nan],
+                                        [351, 7, 4, 352, np.nan, 9, 349]])])
+    @pytest.mark.parametrize("test_func", [stats.circmean, stats.circvar,
+                                           stats.circstd])
+    def test_bad_nan_policy(self, test_func, x):
+        assert_raises(ValueError, test_func, x, high=360, nan_policy='foobar')
+
+
+class TestMedianTest:
+
+    def test_bad_n_samples(self):
+        # median_test requires at least two samples.
+        assert_raises(ValueError, stats.median_test, [1, 2, 3])
+
+    def test_empty_sample(self):
+        # Each sample must contain at least one value.
+        assert_raises(ValueError, stats.median_test, [], [1, 2, 3])
+
+    def test_empty_when_ties_ignored(self):
+        # The grand median is 1, and all values in the first argument are
+        # equal to the grand median.  With ties="ignore", those values are
+        # ignored, which results in the first sample being (in effect) empty.
+        # This should raise a ValueError.
+        assert_raises(ValueError, stats.median_test,
+                      [1, 1, 1, 1], [2, 0, 1], [2, 0], ties="ignore")
+
+    def test_empty_contingency_row(self):
+        # The grand median is 1, and with the default ties="below", all the
+        # values in the samples are counted as being below the grand median.
+        # This would result a row of zeros in the contingency table, which is
+        # an error.
+        assert_raises(ValueError, stats.median_test, [1, 1, 1], [1, 1, 1])
+
+        # With ties="above", all the values are counted as above the
+        # grand median.
+        assert_raises(ValueError, stats.median_test, [1, 1, 1], [1, 1, 1],
+                      ties="above")
+
+    def test_bad_ties(self):
+        assert_raises(ValueError, stats.median_test, [1, 2, 3], [4, 5],
+                      ties="foo")
+
+    def test_bad_nan_policy(self):
+        assert_raises(ValueError, stats.median_test, [1, 2, 3], [4, 5],
+                      nan_policy='foobar')
+
+    def test_bad_keyword(self):
+        assert_raises(TypeError, stats.median_test, [1, 2, 3], [4, 5],
+                      foo="foo")
+
+    def test_simple(self):
+        x = [1, 2, 3]
+        y = [1, 2, 3]
+        stat, p, med, tbl = stats.median_test(x, y)
+
+        # The median is floating point, but this equality test should be safe.
+        assert_equal(med, 2.0)
+
+        assert_array_equal(tbl, [[1, 1], [2, 2]])
+
+        # The expected values of the contingency table equal the contingency
+        # table, so the statistic should be 0 and the p-value should be 1.
+        assert_equal(stat, 0)
+        assert_equal(p, 1)
+
+    def test_ties_options(self):
+        # Test the contingency table calculation.
+        x = [1, 2, 3, 4]
+        y = [5, 6]
+        z = [7, 8, 9]
+        # grand median is 5.
+
+        # Default 'ties' option is "below".
+        stat, p, m, tbl = stats.median_test(x, y, z)
+        assert_equal(m, 5)
+        assert_equal(tbl, [[0, 1, 3], [4, 1, 0]])
+
+        stat, p, m, tbl = stats.median_test(x, y, z, ties="ignore")
+        assert_equal(m, 5)
+        assert_equal(tbl, [[0, 1, 3], [4, 0, 0]])
+
+        stat, p, m, tbl = stats.median_test(x, y, z, ties="above")
+        assert_equal(m, 5)
+        assert_equal(tbl, [[0, 2, 3], [4, 0, 0]])
+
+    def test_nan_policy_options(self):
+        x = [1, 2, np.nan]
+        y = [4, 5, 6]
+        mt1 = stats.median_test(x, y, nan_policy='propagate')
+        s, p, m, t = stats.median_test(x, y, nan_policy='omit')
+
+        assert_equal(mt1, (np.nan, np.nan, np.nan, None))
+        assert_allclose(s, 0.31250000000000006)
+        assert_allclose(p, 0.57615012203057869)
+        assert_equal(m, 4.0)
+        assert_equal(t, np.array([[0, 2], [2, 1]]))
+        assert_raises(ValueError, stats.median_test, x, y, nan_policy='raise')
+
+    def test_basic(self):
+        # median_test calls chi2_contingency to compute the test statistic
+        # and p-value.  Make sure it hasn't screwed up the call...
+
+        x = [1, 2, 3, 4, 5]
+        y = [2, 4, 6, 8]
+
+        stat, p, m, tbl = stats.median_test(x, y)
+        assert_equal(m, 4)
+        assert_equal(tbl, [[1, 2], [4, 2]])
+
+        exp_stat, exp_p, dof, e = stats.chi2_contingency(tbl)
+        assert_allclose(stat, exp_stat)
+        assert_allclose(p, exp_p)
+
+        stat, p, m, tbl = stats.median_test(x, y, lambda_=0)
+        assert_equal(m, 4)
+        assert_equal(tbl, [[1, 2], [4, 2]])
+
+        exp_stat, exp_p, dof, e = stats.chi2_contingency(tbl, lambda_=0)
+        assert_allclose(stat, exp_stat)
+        assert_allclose(p, exp_p)
+
+        stat, p, m, tbl = stats.median_test(x, y, correction=False)
+        assert_equal(m, 4)
+        assert_equal(tbl, [[1, 2], [4, 2]])
+
+        exp_stat, exp_p, dof, e = stats.chi2_contingency(tbl, correction=False)
+        assert_allclose(stat, exp_stat)
+        assert_allclose(p, exp_p)
+
+    @pytest.mark.parametrize("correction", [False, True])
+    def test_result(self, correction):
+        x = [1, 2, 3]
+        y = [1, 2, 3]
+
+        res = stats.median_test(x, y, correction=correction)
+        assert_equal((res.statistic, res.pvalue, res.median, res.table), res)
+
+
+@make_xp_test_case(stats.directional_stats)
+class TestDirectionalStats:
+    # Reference implementations are not available
+    def test_directional_stats_correctness(self, xp):
+        # Data from Fisher: Dispersion on a sphere, 1953 and
+        # Mardia and Jupp, Directional Statistics.
+        decl = -np.deg2rad(np.array([343.2, 62., 36.9, 27., 359.,
+                                     5.7, 50.4, 357.6, 44.]))
+        incl = -np.deg2rad(np.array([66.1, 68.7, 70.1, 82.1, 79.5,
+                                     73., 69.3, 58.8, 51.4]))
+        data = np.stack((np.cos(incl) * np.cos(decl),
+                         np.cos(incl) * np.sin(decl),
+                         np.sin(incl)),
+                        axis=1)
+
+        decl = xp.asarray(decl.tolist())
+        incl = xp.asarray(incl.tolist())
+        data = xp.asarray(data.tolist())
+
+        dirstats = stats.directional_stats(data)
+        directional_mean = dirstats.mean_direction
+
+        reference_mean = xp.asarray([0.2984, -0.1346, -0.9449])
+        xp_assert_close(directional_mean, reference_mean, atol=1e-4)
+
+    @pytest.mark.parametrize('angles, ref', [
+        ([-np.pi/2, np.pi/2], 1.),
+        ([0, 2 * np.pi], 0.)
+    ])
+    def test_directional_stats_2d_special_cases(self, angles, ref, xp):
+        angles = xp.asarray(angles)
+        ref = xp.asarray(ref)
+        data = xp.stack([xp.cos(angles), xp.sin(angles)], axis=1)
+        res = 1 - stats.directional_stats(data).mean_resultant_length
+        xp_assert_close(res, ref)
+
+    def test_directional_stats_2d(self, xp):
+        # Test that for circular data directional_stats
+        # yields the same result as circmean/circvar
+        rng = np.random.default_rng(0xec9a6899d5a2830e0d1af479dbe1fd0c)
+        testdata = xp.asarray(2 * xp.pi * rng.random((1000, )))
+        testdata_vector = xp.stack((xp.cos(testdata),
+                                    xp.sin(testdata)),
+                                   axis=1)
+        dirstats = stats.directional_stats(testdata_vector)
+        directional_mean = dirstats.mean_direction
+        directional_mean_angle = xp.atan2(directional_mean[1], directional_mean[0])
+        directional_mean_angle = directional_mean_angle % (2 * xp.pi)
+        circmean = stats.circmean(testdata)
+        xp_assert_close(directional_mean_angle, circmean)
+
+        directional_var = 1. - dirstats.mean_resultant_length
+        circular_var = stats.circvar(testdata)
+        xp_assert_close(directional_var, circular_var)
+
+    def test_directional_mean_higher_dim(self, xp):
+        # test that directional_stats works for higher dimensions
+        # here a 4D array is reduced over axis = 2
+        data = xp.asarray([[0.8660254, 0.5, 0.],
+                           [0.8660254, -0.5, 0.]])
+        full_array = xp.asarray(xp.tile(data, (2, 2, 2, 1)))
+        expected = xp.asarray([[[1., 0., 0.],
+                                [1., 0., 0.]],
+                               [[1., 0., 0.],
+                                [1., 0., 0.]]])
+        dirstats = stats.directional_stats(full_array, axis=2)
+        xp_assert_close(dirstats.mean_direction, expected)
+
+    @skip_xp_backends(np_only=True, reason='checking array-like input')
+    def test_directional_stats_list_ndarray_input(self, xp):
+        # test that list and numpy array inputs yield same results
+        data = [[0.8660254, 0.5, 0.], [0.8660254, -0.5, 0]]
+        data_array = xp.asarray(data, dtype=xp.float64)
+        ref = stats.directional_stats(data)
+        res = stats.directional_stats(data_array)
+        xp_assert_close(res.mean_direction,
+                        xp.asarray(ref.mean_direction))
+        xp_assert_close(res.mean_resultant_length,
+                        xp.asarray(res.mean_resultant_length))
+
+    def test_directional_stats_1d_error(self, xp):
+        # test that one-dimensional data raises ValueError
+        data = xp.ones((5, ))
+        message = (r"samples must at least be two-dimensional. "
+                   r"Instead samples has shape: (5,)")
+        with pytest.raises(ValueError, match=re.escape(message)):
+            stats.directional_stats(data)
+
+    @pytest.mark.parametrize("dtype", ["float32", "float64"])
+    def test_directional_stats_normalize(self, dtype, xp):
+        # test that directional stats calculations yield same results
+        # for unnormalized input with normalize=True and normalized
+        # input with normalize=False
+        data = np.array([[0.8660254, 0.5, 0.],
+                         [1.7320508, -1., 0.]], dtype=dtype)
+        res = stats.directional_stats(xp.asarray(data), normalize=True)
+        normalized_data = data / np.linalg.norm(data, axis=-1,
+                                                keepdims=True)
+        ref = stats.directional_stats(normalized_data, normalize=False)
+        xp_assert_close(res.mean_direction,
+                        xp.asarray(ref.mean_direction))
+        xp_assert_close(res.mean_resultant_length,
+                        xp.asarray(ref.mean_resultant_length))
+
+
+@make_xp_test_case(stats.false_discovery_control)
+class TestFDRControl:
+    @skip_xp_backends(eager_only=True)
+    def test_input_validation_ps(self, xp):
+        message = "All values in `ps` must lie between 0. and 1."
+        with pytest.raises(ValueError, match=message):
+            stats.false_discovery_control(xp.asarray([-1, 0.5, 0.7]))
+        with pytest.raises(ValueError, match=message):
+            stats.false_discovery_control(xp.asarray([0.5, 0.7, 2]))
+        with pytest.raises(ValueError, match=message):
+            stats.false_discovery_control(xp.asarray([0.5, 0.7, xp.nan]))
+
+    def test_input_validation(self, xp):
+        message = "`ps` must contain only real numbers."
+        with pytest.raises(ValueError, match=message):
+            stats.false_discovery_control(xp.asarray([1+1j, 2+2j]))
+
+        message = "Unrecognized `method` 'YAK'"
+        with pytest.raises(ValueError, match=message):
+            stats.false_discovery_control(xp.asarray([0.5, 0.7, 0.9]), method='YAK')
+
+        message = "`axis` must be an integer or `None`"
+        with pytest.raises(ValueError, match=message):
+            stats.false_discovery_control(xp.asarray([0.5, 0.7, 0.9]), axis=1.5)
+        with pytest.raises(ValueError, match=message):
+            stats.false_discovery_control(xp.asarray([0.5, 0.7, 0.9]), axis=(1, 2))
+
+    def test_against_TileStats(self, xp):
+        # See reference [3] of false_discovery_control
+        ps = xp.asarray([0.005, 0.009, 0.019, 0.022, 0.051, 0.101, 0.361, 0.387])
+        res = stats.false_discovery_control(ps)
+        ref = xp.asarray([0.036, 0.036, 0.044, 0.044, 0.082, 0.135, 0.387, 0.387])
+        xp_assert_close(res, ref, atol=1e-3)
+
+    @pytest.mark.parametrize("case",
+                             [([0.24617028, 0.01140030, 0.05652047, 0.06841983,
+                                0.07989886, 0.01841490, 0.17540784, 0.06841983,
+                                0.06841983, 0.25464082], 'bh'),
+                              ([0.72102493, 0.03339112, 0.16554665, 0.20039952,
+                                0.23402122, 0.05393666, 0.51376399, 0.20039952,
+                                0.20039952, 0.74583488], 'by')])
+    def test_against_R(self, case, xp):
+        # Test against p.adjust, e.g.
+        # p = c(0.22155325, 0.00114003,..., 0.0364813 , 0.25464082)
+        # p.adjust(p, "BY")
+        ref, method = case
+        rng = np.random.default_rng(6134137338861652935)
+        ps = stats.loguniform.rvs(1e-3, 0.5, size=10, random_state=rng).tolist()
+        ps[3] = ps[7]  # force a tie
+        res = stats.false_discovery_control(xp.asarray(ps), method=method)
+        xp_assert_close(res, xp.asarray(ref), atol=1e-6)
+
+    def test_axis_None(self, xp):
+        rng = np.random.default_rng(6134137338861652935)
+        ps = stats.loguniform.rvs(1e-3, 0.5, size=(3, 4, 5), random_state=rng)
+        ps = xp.asarray(ps)
+        res = stats.false_discovery_control(ps, axis=None)
+        ref = stats.false_discovery_control(xp_ravel(ps))
+        xp_assert_equal(res, ref)
+
+    @pytest.mark.parametrize("axis", [0, 1, -1])
+    def test_axis(self, axis, xp):
+        rng = np.random.default_rng(6134137338861652935)
+        ps = stats.loguniform.rvs(1e-3, 0.5, size=(3, 4, 5), random_state=rng)
+        res = stats.false_discovery_control(xp.asarray(ps), axis=axis)
+        ref = np.apply_along_axis(stats.false_discovery_control, axis, ps)
+        xp_assert_close(res, xp.asarray(ref))  # torch isn't *equal*
+
+    def test_edge_cases(self, xp):
+        ps = xp.asarray([0.25])
+        xp_assert_equal(stats.false_discovery_control(ps), ps)
+
+        ps = xp.asarray([])
+        xp_assert_equal(stats.false_discovery_control(ps), ps)
+
+        if is_numpy(xp):
+            xp_assert_equal(stats.false_discovery_control(0.25), 0.25)
+
+
+@pytest.mark.uses_xp_capabilities(False, reason="not used in this test yet")
+class TestCommonAxis:
+    # More thorough testing of `axis` in `test_axis_nan_policy`,
+    # but those tests aren't run with array API yet. This class
+    # is in `test_morestats` instead of `test_axis_nan_policy`
+    # because there is no reason to run `test_axis_nan_policy`
+    # with the array API CI job right now.
+
+    @pytest.mark.parametrize('case', [(stats.sem, {}),
+                                      (stats.kstat, {'n': 4}),
+                                      (stats.kstat, {'n': 2}),
+                                      (stats.variation, {})])
+    def test_axis(self, case, xp):
+        if is_torch(xp) and case[0] == stats.variation:
+            pytest.xfail(reason="copysign doesn't accept scalar array-api-compat#271")
+        fun, kwargs = case
+        rng = np.random.default_rng(24598245982345)
+        x = xp.asarray(rng.random((6, 7)))
+
+        res = fun(x, **kwargs, axis=0)
+        ref = xp.stack([fun(x[:, i], **kwargs) for i in range(x.shape[1])])
+        xp_assert_close(res, ref)
+
+        res = fun(x, **kwargs, axis=1)
+        ref = xp.stack([fun(x[i, :], **kwargs) for i in range(x.shape[0])])
+        xp_assert_close(res, ref)
+
+        res = fun(x, **kwargs, axis=None)
+        ref = fun(xp.reshape(x, (-1,)), **kwargs)
+        xp_assert_close(res, ref)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_mstats_basic.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_mstats_basic.py
new file mode 100644
index 0000000000000000000000000000000000000000..475ca50f7dd001d4d04326cc04a258ff739c3154
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_mstats_basic.py
@@ -0,0 +1,2146 @@
+"""
+Tests for the stats.mstats module (support for masked arrays)
+"""
+import warnings
+import platform
+
+import numpy as np
+from numpy import nan
+import numpy.ma as ma
+from numpy.ma import masked, nomask
+
+import scipy.stats.mstats as mstats
+from scipy import stats
+from .common_tests import check_named_results
+import pytest
+from pytest import raises as assert_raises
+from numpy.ma.testutils import (assert_equal, assert_almost_equal,
+                                assert_array_almost_equal,
+                                assert_array_almost_equal_nulp, assert_,
+                                assert_allclose, assert_array_equal)
+from scipy.stats import _mstats_basic, _stats_py
+from scipy.conftest import skip_xp_invalid_arg
+from scipy.stats._axis_nan_policy import SmallSampleWarning, too_small_1d_not_omit
+
+class TestMquantiles:
+    def test_mquantiles_limit_keyword(self):
+        # Regression test for Trac ticket #867
+        data = np.array([[6., 7., 1.],
+                         [47., 15., 2.],
+                         [49., 36., 3.],
+                         [15., 39., 4.],
+                         [42., 40., -999.],
+                         [41., 41., -999.],
+                         [7., -999., -999.],
+                         [39., -999., -999.],
+                         [43., -999., -999.],
+                         [40., -999., -999.],
+                         [36., -999., -999.]])
+        desired = [[19.2, 14.6, 1.45],
+                   [40.0, 37.5, 2.5],
+                   [42.8, 40.05, 3.55]]
+        quants = mstats.mquantiles(data, axis=0, limit=(0, 50))
+        assert_almost_equal(quants, desired)
+
+
+def check_equal_gmean(array_like, desired, axis=None, dtype=None, rtol=1e-7):
+    # Note this doesn't test when axis is not specified
+    x = mstats.gmean(array_like, axis=axis, dtype=dtype)
+    assert_allclose(x, desired, rtol=rtol)
+    assert_equal(x.dtype, dtype)
+
+
+def check_equal_hmean(array_like, desired, axis=None, dtype=None, rtol=1e-7):
+    x = stats.hmean(array_like, axis=axis, dtype=dtype)
+    assert_allclose(x, desired, rtol=rtol)
+    assert_equal(x.dtype, dtype)
+
+
+@skip_xp_invalid_arg
+class TestGeoMean:
+    def test_1d(self):
+        a = [1, 2, 3, 4]
+        desired = np.power(1*2*3*4, 1./4.)
+        check_equal_gmean(a, desired, rtol=1e-14)
+
+    def test_1d_ma(self):
+        #  Test a 1d masked array
+        a = ma.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
+        desired = 45.2872868812
+        check_equal_gmean(a, desired)
+
+        a = ma.array([1, 2, 3, 4], mask=[0, 0, 0, 1])
+        desired = np.power(1*2*3, 1./3.)
+        check_equal_gmean(a, desired, rtol=1e-14)
+
+    def test_1d_ma_value(self):
+        #  Test a 1d masked array with a masked value
+        a = np.ma.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100],
+                        mask=[0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
+        desired = 41.4716627439
+        check_equal_gmean(a, desired)
+
+    def test_1d_ma0(self):
+        #  Test a 1d masked array with zero element
+        a = np.ma.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 0])
+        desired = 0
+        check_equal_gmean(a, desired)
+
+    def test_1d_ma_inf(self):
+        #  Test a 1d masked array with negative element
+        a = np.ma.array([10, 20, 30, 40, 50, 60, 70, 80, 90, -1])
+        desired = np.nan
+        with np.errstate(invalid='ignore'):
+            check_equal_gmean(a, desired)
+
+    @pytest.mark.skipif(not hasattr(np, 'float96'),
+                        reason='cannot find float96 so skipping')
+    def test_1d_float96(self):
+        a = ma.array([1, 2, 3, 4], mask=[0, 0, 0, 1])
+        desired_dt = np.power(1*2*3, 1./3.).astype(np.float96)
+        check_equal_gmean(a, desired_dt, dtype=np.float96, rtol=1e-14)
+
+    def test_2d_ma(self):
+        a = ma.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]],
+                     mask=[[0, 0, 0, 0], [1, 0, 0, 1], [0, 1, 1, 0]])
+        desired = np.array([1, 2, 3, 4])
+        check_equal_gmean(a, desired, axis=0, rtol=1e-14)
+
+        desired = ma.array([np.power(1*2*3*4, 1./4.),
+                            np.power(2*3, 1./2.),
+                            np.power(1*4, 1./2.)])
+        check_equal_gmean(a, desired, axis=-1, rtol=1e-14)
+
+        #  Test a 2d masked array
+        a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
+        desired = 52.8885199
+        check_equal_gmean(np.ma.array(a), desired)
+
+
+@skip_xp_invalid_arg
+class TestHarMean:
+    def test_1d(self):
+        a = ma.array([1, 2, 3, 4], mask=[0, 0, 0, 1])
+        desired = 3. / (1./1 + 1./2 + 1./3)
+        check_equal_hmean(a, desired, rtol=1e-14)
+
+        a = np.ma.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
+        desired = 34.1417152147
+        check_equal_hmean(a, desired)
+
+        a = np.ma.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100],
+                        mask=[0, 0, 0, 0, 0, 0, 0, 0, 0, 1])
+        desired = 31.8137186141
+        check_equal_hmean(a, desired)
+
+    @pytest.mark.skipif(not hasattr(np, 'float96'),
+                        reason='cannot find float96 so skipping')
+    def test_1d_float96(self):
+        a = ma.array([1, 2, 3, 4], mask=[0, 0, 0, 1])
+        desired_dt = np.asarray(3. / (1./1 + 1./2 + 1./3), dtype=np.float96)
+        check_equal_hmean(a, desired_dt, dtype=np.float96)
+
+    def test_2d(self):
+        a = ma.array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]],
+                     mask=[[0, 0, 0, 0], [1, 0, 0, 1], [0, 1, 1, 0]])
+        desired = ma.array([1, 2, 3, 4])
+        check_equal_hmean(a, desired, axis=0, rtol=1e-14)
+
+        desired = [4./(1/1.+1/2.+1/3.+1/4.), 2./(1/2.+1/3.), 2./(1/1.+1/4.)]
+        check_equal_hmean(a, desired, axis=-1, rtol=1e-14)
+
+        a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
+        desired = 38.6696271841
+        check_equal_hmean(np.ma.array(a), desired)
+
+
+class TestRanking:
+    def test_ranking(self):
+        x = ma.array([0,1,1,1,2,3,4,5,5,6,])
+        assert_almost_equal(mstats.rankdata(x),
+                            [1,3,3,3,5,6,7,8.5,8.5,10])
+        x[[3,4]] = masked
+        assert_almost_equal(mstats.rankdata(x),
+                            [1,2.5,2.5,0,0,4,5,6.5,6.5,8])
+        assert_almost_equal(mstats.rankdata(x, use_missing=True),
+                            [1,2.5,2.5,4.5,4.5,4,5,6.5,6.5,8])
+        x = ma.array([0,1,5,1,2,4,3,5,1,6,])
+        assert_almost_equal(mstats.rankdata(x),
+                            [1,3,8.5,3,5,7,6,8.5,3,10])
+        x = ma.array([[0,1,1,1,2], [3,4,5,5,6,]])
+        assert_almost_equal(mstats.rankdata(x),
+                            [[1,3,3,3,5], [6,7,8.5,8.5,10]])
+        assert_almost_equal(mstats.rankdata(x, axis=1),
+                            [[1,3,3,3,5], [1,2,3.5,3.5,5]])
+        assert_almost_equal(mstats.rankdata(x,axis=0),
+                            [[1,1,1,1,1], [2,2,2,2,2,]])
+
+
+class TestCorr:
+    def test_pearsonr(self):
+        # Tests some computations of Pearson's r
+        x = ma.arange(10)
+        with warnings.catch_warnings():
+            # The tests in this context are edge cases, with perfect
+            # correlation or anticorrelation, or totally masked data.
+            # None of these should trigger a RuntimeWarning.
+            warnings.simplefilter("error", RuntimeWarning)
+
+            assert_almost_equal(mstats.pearsonr(x, x)[0], 1.0)
+            assert_almost_equal(mstats.pearsonr(x, x[::-1])[0], -1.0)
+
+            x = ma.array(x, mask=True)
+            pr = mstats.pearsonr(x, x)
+            assert_(pr[0] is masked)
+            assert_(pr[1] is masked)
+
+        x1 = ma.array([-1.0, 0.0, 1.0])
+        y1 = ma.array([0, 0, 3])
+        r, p = mstats.pearsonr(x1, y1)
+        assert_almost_equal(r, np.sqrt(3)/2)
+        assert_almost_equal(p, 1.0/3)
+
+        # (x2, y2) have the same unmasked data as (x1, y1).
+        mask = [False, False, False, True]
+        x2 = ma.array([-1.0, 0.0, 1.0, 99.0], mask=mask)
+        y2 = ma.array([0, 0, 3, -1], mask=mask)
+        r, p = mstats.pearsonr(x2, y2)
+        assert_almost_equal(r, np.sqrt(3)/2)
+        assert_almost_equal(p, 1.0/3)
+
+    def test_pearsonr_misaligned_mask(self):
+        mx = np.ma.masked_array([1, 2, 3, 4, 5, 6], mask=[0, 1, 0, 0, 0, 0])
+        my = np.ma.masked_array([9, 8, 7, 6, 5, 9], mask=[0, 0, 1, 0, 0, 0])
+        x = np.array([1, 4, 5, 6])
+        y = np.array([9, 6, 5, 9])
+        mr, mp = mstats.pearsonr(mx, my)
+        r, p = stats.pearsonr(x, y)
+        assert_equal(mr, r)
+        assert_equal(mp, p)
+
+    def test_spearmanr(self):
+        # Tests some computations of Spearman's rho
+        (x, y) = ([5.05,6.75,3.21,2.66], [1.65,2.64,2.64,6.95])
+        assert_almost_equal(mstats.spearmanr(x,y)[0], -0.6324555)
+        (x, y) = ([5.05,6.75,3.21,2.66,np.nan],[1.65,2.64,2.64,6.95,np.nan])
+        (x, y) = (ma.fix_invalid(x), ma.fix_invalid(y))
+        assert_almost_equal(mstats.spearmanr(x,y)[0], -0.6324555)
+
+        x = [2.0, 47.4, 42.0, 10.8, 60.1, 1.7, 64.0, 63.1,
+             1.0, 1.4, 7.9, 0.3, 3.9, 0.3, 6.7]
+        y = [22.6, 8.3, 44.4, 11.9, 24.6, 0.6, 5.7, 41.6,
+             0.0, 0.6, 6.7, 3.8, 1.0, 1.2, 1.4]
+        assert_almost_equal(mstats.spearmanr(x,y)[0], 0.6887299)
+        x = [2.0, 47.4, 42.0, 10.8, 60.1, 1.7, 64.0, 63.1,
+             1.0, 1.4, 7.9, 0.3, 3.9, 0.3, 6.7, np.nan]
+        y = [22.6, 8.3, 44.4, 11.9, 24.6, 0.6, 5.7, 41.6,
+             0.0, 0.6, 6.7, 3.8, 1.0, 1.2, 1.4, np.nan]
+        (x, y) = (ma.fix_invalid(x), ma.fix_invalid(y))
+        assert_almost_equal(mstats.spearmanr(x,y)[0], 0.6887299)
+        # Next test is to make sure calculation uses sufficient precision.
+        # The denominator's value is ~n^3 and used to be represented as an
+        # int. 2000**3 > 2**32 so these arrays would cause overflow on
+        # some machines.
+        x = list(range(2000))
+        y = list(range(2000))
+        y[0], y[9] = y[9], y[0]
+        y[10], y[434] = y[434], y[10]
+        y[435], y[1509] = y[1509], y[435]
+        # rho = 1 - 6 * (2 * (9^2 + 424^2 + 1074^2))/(2000 * (2000^2 - 1))
+        #     = 1 - (1 / 500)
+        #     = 0.998
+        assert_almost_equal(mstats.spearmanr(x,y)[0], 0.998)
+
+        # test for namedtuple attributes
+        res = mstats.spearmanr(x, y)
+        attributes = ('correlation', 'pvalue')
+        check_named_results(res, attributes, ma=True)
+
+    def test_spearmanr_alternative(self):
+        # check against R
+        # options(digits=16)
+        # cor.test(c(2.0, 47.4, 42.0, 10.8, 60.1, 1.7, 64.0, 63.1,
+        #            1.0, 1.4, 7.9, 0.3, 3.9, 0.3, 6.7),
+        #          c(22.6, 8.3, 44.4, 11.9, 24.6, 0.6, 5.7, 41.6,
+        #            0.0, 0.6, 6.7, 3.8, 1.0, 1.2, 1.4),
+        #          alternative='two.sided', method='spearman')
+        x = [2.0, 47.4, 42.0, 10.8, 60.1, 1.7, 64.0, 63.1,
+             1.0, 1.4, 7.9, 0.3, 3.9, 0.3, 6.7]
+        y = [22.6, 8.3, 44.4, 11.9, 24.6, 0.6, 5.7, 41.6,
+             0.0, 0.6, 6.7, 3.8, 1.0, 1.2, 1.4]
+
+        r_exp = 0.6887298747763864  # from cor.test
+
+        r, p = mstats.spearmanr(x, y)
+        assert_allclose(r, r_exp)
+        assert_allclose(p, 0.004519192910756)
+
+        r, p = mstats.spearmanr(x, y, alternative='greater')
+        assert_allclose(r, r_exp)
+        assert_allclose(p, 0.002259596455378)
+
+        r, p = mstats.spearmanr(x, y, alternative='less')
+        assert_allclose(r, r_exp)
+        assert_allclose(p, 0.9977404035446)
+
+        # intuitive test (with obvious positive correlation)
+        rng = np.random.default_rng(9607138567)
+        n = 100
+        x = np.linspace(0, 5, n)
+        y = 0.1*x + rng.random(n)  # y is positively correlated w/ x
+
+        stat1, p1 = mstats.spearmanr(x, y)
+
+        stat2, p2 = mstats.spearmanr(x, y, alternative="greater")
+        assert_allclose(p2, p1 / 2)  # positive correlation -> small p
+
+        stat3, p3 = mstats.spearmanr(x, y, alternative="less")
+        assert_allclose(p3, 1 - p1 / 2)  # positive correlation -> large p
+
+        assert stat1 == stat2 == stat3
+
+        with pytest.raises(ValueError, match="alternative must be 'less'..."):
+            mstats.spearmanr(x, y, alternative="ekki-ekki")
+
+    @pytest.mark.skipif(platform.machine() == 'ppc64le',
+                        reason="fails/crashes on ppc64le")
+    def test_kendalltau(self):
+        # check case with maximum disorder and p=1
+        x = ma.array(np.array([9, 2, 5, 6]))
+        y = ma.array(np.array([4, 7, 9, 11]))
+        # Cross-check with exact result from R:
+        # cor.test(x,y,method="kendall",exact=1)
+        expected = [0.0, 1.0]
+        assert_almost_equal(np.asarray(mstats.kendalltau(x, y)), expected)
+
+        # simple case without ties
+        x = ma.array(np.arange(10))
+        y = ma.array(np.arange(10))
+        # Cross-check with exact result from R:
+        # cor.test(x,y,method="kendall",exact=1)
+        expected = [1.0, 5.511463844797e-07]
+        assert_almost_equal(np.asarray(mstats.kendalltau(x, y)), expected)
+
+        # check exception in case of invalid method keyword
+        assert_raises(ValueError, mstats.kendalltau, x, y, method='banana')
+
+        # swap a couple of values
+        b = y[1]
+        y[1] = y[2]
+        y[2] = b
+        # Cross-check with exact result from R:
+        # cor.test(x,y,method="kendall",exact=1)
+        expected = [0.9555555555555556, 5.511463844797e-06]
+        assert_almost_equal(np.asarray(mstats.kendalltau(x, y)), expected)
+
+        # swap a couple more
+        b = y[5]
+        y[5] = y[6]
+        y[6] = b
+        # Cross-check with exact result from R:
+        # cor.test(x,y,method="kendall",exact=1)
+        expected = [0.9111111111111111, 2.976190476190e-05]
+        assert_almost_equal(np.asarray(mstats.kendalltau(x, y)), expected)
+
+        # same in opposite direction
+        x = ma.array(np.arange(10))
+        y = ma.array(np.arange(10)[::-1])
+        # Cross-check with exact result from R:
+        # cor.test(x,y,method="kendall",exact=1)
+        expected = [-1.0, 5.511463844797e-07]
+        assert_almost_equal(np.asarray(mstats.kendalltau(x, y)), expected)
+
+        # swap a couple of values
+        b = y[1]
+        y[1] = y[2]
+        y[2] = b
+        # Cross-check with exact result from R:
+        # cor.test(x,y,method="kendall",exact=1)
+        expected = [-0.9555555555555556, 5.511463844797e-06]
+        assert_almost_equal(np.asarray(mstats.kendalltau(x, y)), expected)
+
+        # swap a couple more
+        b = y[5]
+        y[5] = y[6]
+        y[6] = b
+        # Cross-check with exact result from R:
+        # cor.test(x,y,method="kendall",exact=1)
+        expected = [-0.9111111111111111, 2.976190476190e-05]
+        assert_almost_equal(np.asarray(mstats.kendalltau(x, y)), expected)
+
+        # Tests some computations of Kendall's tau
+        x = ma.fix_invalid([5.05, 6.75, 3.21, 2.66, np.nan])
+        y = ma.fix_invalid([1.65, 26.5, -5.93, 7.96, np.nan])
+        z = ma.fix_invalid([1.65, 2.64, 2.64, 6.95, np.nan])
+        assert_almost_equal(np.asarray(mstats.kendalltau(x, y)),
+                            [+0.3333333, 0.75])
+        assert_almost_equal(np.asarray(mstats.kendalltau(x, y, method='asymptotic')),
+                            [+0.3333333, 0.4969059])
+        assert_almost_equal(np.asarray(mstats.kendalltau(x, z)),
+                            [-0.5477226, 0.2785987])
+        #
+        x = ma.fix_invalid([0, 0, 0, 0, 20, 20, 0, 60, 0, 20,
+                            10, 10, 0, 40, 0, 20, 0, 0, 0, 0, 0, np.nan])
+        y = ma.fix_invalid([0, 80, 80, 80, 10, 33, 60, 0, 67, 27,
+                            25, 80, 80, 80, 80, 80, 80, 0, 10, 45, np.nan, 0])
+        result = mstats.kendalltau(x, y)
+        assert_almost_equal(np.asarray(result), [-0.1585188, 0.4128009])
+
+        # test for namedtuple attributes
+        attributes = ('correlation', 'pvalue')
+        check_named_results(result, attributes, ma=True)
+
+    def test_overflow_gh6061(self):
+        # Regression test for GitHub issue #6061 - Overflow on Windows
+        x = np.arange(2000, dtype=float)
+        x = np.ma.masked_greater(x, 1995)
+        y = np.arange(2000, dtype=float)
+        y = np.concatenate((y[1000:], y[:1000]))
+        assert_(np.isfinite(stats.mstats.kendalltau(x,y)[1]))
+
+    def test_kendalltau_vs_mstats_basic(self):
+        rng = np.random.RandomState(42)
+        for s in range(3, 10):
+            a = []
+            # Generate rankings with ties
+            for i in range(s):
+                a += [i]*i
+            b = list(a)
+            rng.shuffle(a)
+            rng.shuffle(b)
+            expected = mstats.kendalltau(a, b)
+            actual = stats.kendalltau(a, b)
+            assert_almost_equal(actual[0], expected[0])
+            assert_almost_equal(actual[1], expected[1])
+
+    @pytest.mark.skipif(platform.machine() == 'ppc64le',
+                        reason="fails/crashes on ppc64le")
+    @pytest.mark.slow
+    def test_kendalltau_large(self):
+        # make sure internal variable use correct precision with
+        # larger arrays
+        x = np.arange(2000, dtype=float)
+        x = ma.masked_greater(x, 1995)
+        y = np.arange(2000, dtype=float)
+        y = np.concatenate((y[1000:], y[:1000]))
+        assert_(np.isfinite(mstats.kendalltau(x, y)[1]))
+
+    def test_kendalltau_seasonal(self):
+        # Tests the seasonal Kendall tau.
+        x = [[nan, nan, 4, 2, 16, 26, 5, 1, 5, 1, 2, 3, 1],
+             [4, 3, 5, 3, 2, 7, 3, 1, 1, 2, 3, 5, 3],
+             [3, 2, 5, 6, 18, 4, 9, 1, 1, nan, 1, 1, nan],
+             [nan, 6, 11, 4, 17, nan, 6, 1, 1, 2, 5, 1, 1]]
+        x = ma.fix_invalid(x).T
+        output = mstats.kendalltau_seasonal(x)
+        assert_almost_equal(output['global p-value (indep)'], 0.008, 3)
+        assert_almost_equal(output['seasonal p-value'].round(2),
+                            [0.18,0.53,0.20,0.04])
+
+    @pytest.mark.parametrize("method", ("exact", "asymptotic"))
+    @pytest.mark.parametrize("alternative", ("two-sided", "greater", "less"))
+    def test_kendalltau_mstats_vs_stats(self, method, alternative):
+        # Test that mstats.kendalltau and stats.kendalltau with
+        # nan_policy='omit' matches behavior of stats.kendalltau
+        # Accuracy of the alternatives is tested in stats/tests/test_stats.py
+
+        rng = np.random.default_rng(8755235945)
+        n = 50
+        x = rng.random(n)
+        y = rng.random(n)
+        mask = rng.random(n) > 0.5
+
+        x_masked = ma.array(x, mask=mask)
+        y_masked = ma.array(y, mask=mask)
+        res_masked = mstats.kendalltau(
+            x_masked, y_masked, method=method, alternative=alternative)
+
+        x_compressed = x_masked.compressed()
+        y_compressed = y_masked.compressed()
+        res_compressed = stats.kendalltau(
+            x_compressed, y_compressed, method=method, alternative=alternative)
+
+        x[mask] = np.nan
+        y[mask] = np.nan
+        res_nan = stats.kendalltau(
+            x, y, method=method, nan_policy='omit', alternative=alternative)
+
+        assert_allclose(res_masked, res_compressed)
+        assert_allclose(res_nan, res_compressed)
+
+    def test_kendall_p_exact_medium(self):
+        # Test for the exact method with medium samples (some n >= 171)
+        # expected values generated using SymPy
+        expectations = {(100, 2393): 0.62822615287956040664,
+                        (101, 2436): 0.60439525773513602669,
+                        (170, 0): 2.755801935583541e-307,
+                        (171, 0): 0.0,
+                        (171, 1): 2.755801935583541e-307,
+                        (172, 1): 0.0,
+                        (200, 9797): 0.74753983745929675209,
+                        (201, 9656): 0.40959218958120363618}
+        for nc, expected in expectations.items():
+            res = _mstats_basic._kendall_p_exact(nc[0], nc[1])
+            assert_almost_equal(res, expected)
+
+    @pytest.mark.xslow
+    def test_kendall_p_exact_large(self):
+        # Test for the exact method with large samples (n >= 171)
+        # expected values generated using SymPy
+        expectations = {(400, 38965): 0.48444283672113314099,
+                        (401, 39516): 0.66363159823474837662,
+                        (800, 156772): 0.42265448483120932055,
+                        (801, 157849): 0.53437553412194416236,
+                        (1600, 637472): 0.84200727400323538419,
+                        (1601, 630304): 0.34465255088058593946}
+
+        for nc, expected in expectations.items():
+            res = _mstats_basic._kendall_p_exact(nc[0], nc[1])
+            assert_almost_equal(res, expected)
+
+    @skip_xp_invalid_arg
+    # mstats.pointbiserialr returns a NumPy float for the statistic, but converts
+    # it to a masked array with no masked elements before calling `special.betainc`,
+    # which won't accept masked arrays when `SCIPY_ARRAY_API=1`.
+    def test_pointbiserial(self):
+        x = [1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0,
+             0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, -1]
+        y = [14.8, 13.8, 12.4, 10.1, 7.1, 6.1, 5.8, 4.6, 4.3, 3.5, 3.3, 3.2,
+             3.0, 2.8, 2.8, 2.5, 2.4, 2.3, 2.1, 1.7, 1.7, 1.5, 1.3, 1.3, 1.2,
+             1.2, 1.1, 0.8, 0.7, 0.6, 0.5, 0.2, 0.2, 0.1, np.nan]
+        assert_almost_equal(mstats.pointbiserialr(x, y)[0], 0.36149, 5)
+
+        # test for namedtuple attributes
+        res = mstats.pointbiserialr(x, y)
+        attributes = ('correlation', 'pvalue')
+        check_named_results(res, attributes, ma=True)
+
+
+@skip_xp_invalid_arg
+class TestTrimming:
+
+    def test_trim(self):
+        a = ma.arange(10)
+        assert_equal(mstats.trim(a), [0,1,2,3,4,5,6,7,8,9])
+        a = ma.arange(10)
+        assert_equal(mstats.trim(a,(2,8)), [None,None,2,3,4,5,6,7,8,None])
+        a = ma.arange(10)
+        assert_equal(mstats.trim(a,limits=(2,8),inclusive=(False,False)),
+                     [None,None,None,3,4,5,6,7,None,None])
+        a = ma.arange(10)
+        assert_equal(mstats.trim(a,limits=(0.1,0.2),relative=True),
+                     [None,1,2,3,4,5,6,7,None,None])
+
+        a = ma.arange(12)
+        a[[0,-1]] = a[5] = masked
+        assert_equal(mstats.trim(a, (2,8)),
+                     [None, None, 2, 3, 4, None, 6, 7, 8, None, None, None])
+
+        x = ma.arange(100).reshape(10, 10)
+        expected = [1]*10 + [0]*70 + [1]*20
+        trimx = mstats.trim(x, (0.1,0.2), relative=True, axis=None)
+        assert_equal(trimx._mask.ravel(), expected)
+        trimx = mstats.trim(x, (0.1,0.2), relative=True, axis=0)
+        assert_equal(trimx._mask.ravel(), expected)
+        trimx = mstats.trim(x, (0.1,0.2), relative=True, axis=-1)
+        assert_equal(trimx._mask.T.ravel(), expected)
+
+        # same as above, but with an extra masked row inserted
+        x = ma.arange(110).reshape(11, 10)
+        x[1] = masked
+        expected = [1]*20 + [0]*70 + [1]*20
+        trimx = mstats.trim(x, (0.1,0.2), relative=True, axis=None)
+        assert_equal(trimx._mask.ravel(), expected)
+        trimx = mstats.trim(x, (0.1,0.2), relative=True, axis=0)
+        assert_equal(trimx._mask.ravel(), expected)
+        trimx = mstats.trim(x.T, (0.1,0.2), relative=True, axis=-1)
+        assert_equal(trimx.T._mask.ravel(), expected)
+
+    def test_trim_old(self):
+        x = ma.arange(100)
+        assert_equal(mstats.trimboth(x).count(), 60)
+        assert_equal(mstats.trimtail(x,tail='r').count(), 80)
+        x[50:70] = masked
+        trimx = mstats.trimboth(x)
+        assert_equal(trimx.count(), 48)
+        assert_equal(trimx._mask, [1]*16 + [0]*34 + [1]*20 + [0]*14 + [1]*16)
+        x._mask = nomask
+        x = x.reshape((10,10))
+        assert_equal(mstats.trimboth(x).count(), 60)
+        assert_equal(mstats.trimtail(x).count(), 80)
+
+    def test_trimr(self):
+        x = ma.arange(10)
+        result = mstats.trimr(x, limits=(0.15, 0.14), inclusive=(False, False))
+        expected = ma.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
+                            mask=[1, 1, 0, 0, 0, 0, 0, 0, 0, 1])
+        assert_equal(result, expected)
+        assert_equal(result.mask, expected.mask)
+
+    def test_trimmedmean(self):
+        data = ma.array([77, 87, 88,114,151,210,219,246,253,262,
+                         296,299,306,376,428,515,666,1310,2611])
+        assert_almost_equal(mstats.trimmed_mean(data,0.1), 343, 0)
+        assert_almost_equal(mstats.trimmed_mean(data,(0.1,0.1)), 343, 0)
+        assert_almost_equal(mstats.trimmed_mean(data,(0.2,0.2)), 283, 0)
+
+    def test_trimmedvar(self):
+        # Basic test. Additional tests of all arguments, edge cases,
+        # input validation, and proper treatment of masked arrays are needed.
+        rng = np.random.default_rng(3262323289434724460)
+        data_orig = rng.random(size=20)
+        data = np.sort(data_orig)
+        data = ma.array(data, mask=[1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+                                    0, 0, 0, 0, 0, 0, 0, 0, 1, 1])
+        assert_allclose(mstats.trimmed_var(data_orig, 0.1), data.var())
+
+    def test_trimmedstd(self):
+        # Basic test. Additional tests of all arguments, edge cases,
+        # input validation, and proper treatment of masked arrays are needed.
+        rng = np.random.default_rng(7121029245207162780)
+        data_orig = rng.random(size=20)
+        data = np.sort(data_orig)
+        data = ma.array(data, mask=[1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+                                    0, 0, 0, 0, 0, 0, 0, 0, 1, 1])
+        assert_allclose(mstats.trimmed_std(data_orig, 0.1), data.std())
+
+    def test_trimmed_stde(self):
+        data = ma.array([77, 87, 88,114,151,210,219,246,253,262,
+                         296,299,306,376,428,515,666,1310,2611])
+        assert_almost_equal(mstats.trimmed_stde(data,(0.2,0.2)), 56.13193, 5)
+        assert_almost_equal(mstats.trimmed_stde(data,0.2), 56.13193, 5)
+
+    def test_winsorization(self):
+        data = ma.array([77, 87, 88,114,151,210,219,246,253,262,
+                         296,299,306,376,428,515,666,1310,2611])
+        assert_almost_equal(mstats.winsorize(data,(0.2,0.2)).var(ddof=1),
+                            21551.4, 1)
+        assert_almost_equal(
+            mstats.winsorize(data, (0.2,0.2),(False,False)).var(ddof=1),
+            11887.3, 1)
+        data[5] = masked
+        winsorized = mstats.winsorize(data)
+        assert_equal(winsorized.mask, data.mask)
+
+    def test_winsorization_nan(self):
+        data = ma.array([np.nan, np.nan, 0, 1, 2])
+        assert_raises(ValueError, mstats.winsorize, data, (0.05, 0.05),
+                      nan_policy='raise')
+        # Testing propagate (default behavior)
+        assert_equal(mstats.winsorize(data, (0.4, 0.4)),
+                     ma.array([2, 2, 2, 2, 2]))
+        assert_equal(mstats.winsorize(data, (0.8, 0.8)),
+                     ma.array([np.nan, np.nan, np.nan, np.nan, np.nan]))
+        assert_equal(mstats.winsorize(data, (0.4, 0.4), nan_policy='omit'),
+                     ma.array([np.nan, np.nan, 2, 2, 2]))
+        assert_equal(mstats.winsorize(data, (0.8, 0.8), nan_policy='omit'),
+                     ma.array([np.nan, np.nan, 2, 2, 2]))
+
+
+@skip_xp_invalid_arg
+class TestMoments:
+    # Comparison numbers are found using R v.1.5.1
+    # note that length(testcase) = 4
+    # testmathworks comes from documentation for the
+    # Statistics Toolbox for Matlab and can be found at both
+    # https://www.mathworks.com/help/stats/kurtosis.html
+    # https://www.mathworks.com/help/stats/skewness.html
+    # Note that both test cases came from here.
+    testcase = [1,2,3,4]
+    testmathworks = ma.fix_invalid([1.165, 0.6268, 0.0751, 0.3516, -0.6965,
+                                    np.nan])
+    testcase_2d = ma.array(
+        np.array([[0.05245846, 0.50344235, 0.86589117, 0.36936353, 0.46961149],
+                  [0.11574073, 0.31299969, 0.45925772, 0.72618805, 0.75194407],
+                  [0.67696689, 0.91878127, 0.09769044, 0.04645137, 0.37615733],
+                  [0.05903624, 0.29908861, 0.34088298, 0.66216337, 0.83160998],
+                  [0.64619526, 0.94894632, 0.27855892, 0.0706151, 0.39962917]]),
+        mask=np.array([[True, False, False, True, False],
+                       [True, True, True, False, True],
+                       [False, False, False, False, False],
+                       [True, True, True, True, True],
+                       [False, False, True, False, False]], dtype=bool))
+
+    def _assert_equal(self, actual, expect, *, shape=None, dtype=None):
+        expect = np.asarray(expect)
+        if shape is not None:
+            expect = np.broadcast_to(expect, shape)
+        assert_array_equal(actual, expect)
+        if dtype is None:
+            dtype = expect.dtype
+        assert actual.dtype == dtype
+
+    def test_moment(self):
+        y = mstats.moment(self.testcase,1)
+        assert_almost_equal(y,0.0,10)
+        y = mstats.moment(self.testcase,2)
+        assert_almost_equal(y,1.25)
+        y = mstats.moment(self.testcase,3)
+        assert_almost_equal(y,0.0)
+        y = mstats.moment(self.testcase,4)
+        assert_almost_equal(y,2.5625)
+
+        # check array_like input for moment
+        y = mstats.moment(self.testcase, [1, 2, 3, 4])
+        assert_allclose(y, [0, 1.25, 0, 2.5625])
+
+        # check moment input consists only of integers
+        y = mstats.moment(self.testcase, 0.0)
+        assert_allclose(y, 1.0)
+        assert_raises(ValueError, mstats.moment, self.testcase, 1.2)
+        y = mstats.moment(self.testcase, [1.0, 2, 3, 4.0])
+        assert_allclose(y, [0, 1.25, 0, 2.5625])
+
+        # test empty input
+        y = mstats.moment([])
+        self._assert_equal(y, np.nan, dtype=np.float64)
+        y = mstats.moment(np.array([], dtype=np.float32))
+        self._assert_equal(y, np.nan, dtype=np.float32)
+        y = mstats.moment(np.zeros((1, 0)), axis=0)
+        self._assert_equal(y, [], shape=(0,), dtype=np.float64)
+        y = mstats.moment([[]], axis=1)
+        self._assert_equal(y, np.nan, shape=(1,), dtype=np.float64)
+        y = mstats.moment([[]], moment=[0, 1], axis=0)
+        self._assert_equal(y, [], shape=(2, 0))
+
+        x = np.arange(10.)
+        x[9] = np.nan
+        assert_equal(mstats.moment(x, 2), ma.masked)  # NaN value is ignored
+
+    def test_variation(self):
+        y = mstats.variation(self.testcase)
+        assert_almost_equal(y,0.44721359549996, 10)
+
+    def test_variation_ddof(self):
+        # test variation with delta degrees of freedom
+        # regression test for gh-13341
+        a = np.array([1, 2, 3, 4, 5])
+        y = mstats.variation(a, ddof=1)
+        assert_almost_equal(y, 0.5270462766947299)
+
+    def test_skewness(self):
+        y = mstats.skew(self.testmathworks)
+        assert_almost_equal(y,-0.29322304336607,10)
+        y = mstats.skew(self.testmathworks,bias=0)
+        assert_almost_equal(y,-0.437111105023940,10)
+        y = mstats.skew(self.testcase)
+        assert_almost_equal(y,0.0,10)
+
+        # test that skew works on multidimensional masked arrays
+        correct_2d = ma.array(
+            np.array([0.6882870394455785, 0, 0.2665647526856708,
+                      0, -0.05211472114254485]),
+            mask=np.array([False, False, False, True, False], dtype=bool)
+        )
+        assert_allclose(mstats.skew(self.testcase_2d, 1), correct_2d)
+        for i, row in enumerate(self.testcase_2d):
+            assert_almost_equal(mstats.skew(row), correct_2d[i])
+
+        correct_2d_bias_corrected = ma.array(
+            np.array([1.685952043212545, 0.0, 0.3973712716070531, 0,
+                      -0.09026534484117164]),
+            mask=np.array([False, False, False, True, False], dtype=bool)
+        )
+        assert_allclose(mstats.skew(self.testcase_2d, 1, bias=False),
+                        correct_2d_bias_corrected)
+        for i, row in enumerate(self.testcase_2d):
+            assert_almost_equal(mstats.skew(row, bias=False),
+                                correct_2d_bias_corrected[i])
+
+        # Check consistency between stats and mstats implementations
+        assert_allclose(mstats.skew(self.testcase_2d[2, :]),
+                        stats.skew(self.testcase_2d[2, :]))
+
+    def test_kurtosis(self):
+        # Set flags for axis = 0 and fisher=0 (Pearson's definition of kurtosis
+        # for compatibility with Matlab)
+        y = mstats.kurtosis(self.testmathworks, 0, fisher=0, bias=1)
+        assert_almost_equal(y, 2.1658856802973, 10)
+        # Note that MATLAB has confusing docs for the following case
+        #  kurtosis(x,0) gives an unbiased estimate of Pearson's skewness
+        #  kurtosis(x) gives a biased estimate of Fisher's skewness (Pearson-3)
+        #  The MATLAB docs imply that both should give Fisher's
+        y = mstats.kurtosis(self.testmathworks, fisher=0, bias=0)
+        assert_almost_equal(y, 3.663542721189047, 10)
+        y = mstats.kurtosis(self.testcase, 0, 0)
+        assert_almost_equal(y, 1.64)
+
+        # test that kurtosis works on multidimensional masked arrays
+        correct_2d = ma.array(np.array([-1.5, -3., -1.47247052385, 0.,
+                                        -1.26979517952]),
+                              mask=np.array([False, False, False, True,
+                                             False], dtype=bool))
+        assert_array_almost_equal(mstats.kurtosis(self.testcase_2d, 1),
+                                  correct_2d)
+        for i, row in enumerate(self.testcase_2d):
+            assert_almost_equal(mstats.kurtosis(row), correct_2d[i])
+
+        correct_2d_bias_corrected = ma.array(
+            np.array([-1.5, -3., -1.88988209538, 0., -0.5234638463918877]),
+            mask=np.array([False, False, False, True, False], dtype=bool))
+        assert_array_almost_equal(mstats.kurtosis(self.testcase_2d, 1,
+                                                  bias=False),
+                                  correct_2d_bias_corrected)
+        for i, row in enumerate(self.testcase_2d):
+            assert_almost_equal(mstats.kurtosis(row, bias=False),
+                                correct_2d_bias_corrected[i])
+
+        # Check consistency between stats and mstats implementations
+        assert_array_almost_equal_nulp(mstats.kurtosis(self.testcase_2d[2, :]),
+                                       stats.kurtosis(self.testcase_2d[2, :]),
+                                       nulp=4)
+
+
+class TestMode:
+    def test_mode(self):
+        a1 = [0,0,0,1,1,1,2,3,3,3,3,4,5,6,7]
+        a2 = np.reshape(a1, (3,5))
+        a3 = np.array([1,2,3,4,5,6])
+        a4 = np.reshape(a3, (3,2))
+        ma1 = ma.masked_where(ma.array(a1) > 2, a1)
+        ma2 = ma.masked_where(a2 > 2, a2)
+        ma3 = ma.masked_where(a3 < 2, a3)
+        ma4 = ma.masked_where(ma.array(a4) < 2, a4)
+        assert_equal(mstats.mode(a1, axis=None), (3,4))
+        assert_equal(mstats.mode(a1, axis=0), (3,4))
+        assert_equal(mstats.mode(ma1, axis=None), (0,3))
+        assert_equal(mstats.mode(a2, axis=None), (3,4))
+        assert_equal(mstats.mode(ma2, axis=None), (0,3))
+        assert_equal(mstats.mode(a3, axis=None), (1,1))
+        assert_equal(mstats.mode(ma3, axis=None), (2,1))
+        assert_equal(mstats.mode(a2, axis=0), ([[0,0,0,1,1]], [[1,1,1,1,1]]))
+        assert_equal(mstats.mode(ma2, axis=0), ([[0,0,0,1,1]], [[1,1,1,1,1]]))
+        assert_equal(mstats.mode(a2, axis=-1), ([[0],[3],[3]], [[3],[3],[1]]))
+        assert_equal(mstats.mode(ma2, axis=-1), ([[0],[1],[0]], [[3],[1],[0]]))
+        assert_equal(mstats.mode(ma4, axis=0), ([[3,2]], [[1,1]]))
+        assert_equal(mstats.mode(ma4, axis=-1), ([[2],[3],[5]], [[1],[1],[1]]))
+
+        a1_res = mstats.mode(a1, axis=None)
+
+        # test for namedtuple attributes
+        attributes = ('mode', 'count')
+        check_named_results(a1_res, attributes, ma=True)
+
+    def test_mode_modifies_input(self):
+        # regression test for gh-6428: mode(..., axis=None) may not modify
+        # the input array
+        im = np.zeros((100, 100))
+        im[:50, :] += 1
+        im[:, :50] += 1
+        cp = im.copy()
+        mstats.mode(im, None)
+        assert_equal(im, cp)
+
+
+class TestPercentile:
+    def setup_method(self):
+        self.a1 = [3, 4, 5, 10, -3, -5, 6]
+        self.a2 = [3, -6, -2, 8, 7, 4, 2, 1]
+        self.a3 = [3., 4, 5, 10, -3, -5, -6, 7.0]
+
+    def test_percentile(self):
+        x = np.arange(8) * 0.5
+        assert_equal(mstats.scoreatpercentile(x, 0), 0.)
+        assert_equal(mstats.scoreatpercentile(x, 100), 3.5)
+        assert_equal(mstats.scoreatpercentile(x, 50), 1.75)
+
+    def test_2D(self):
+        x = ma.array([[1, 1, 1],
+                      [1, 1, 1],
+                      [4, 4, 3],
+                      [1, 1, 1],
+                      [1, 1, 1]])
+        assert_equal(mstats.scoreatpercentile(x, 50), [1, 1, 1])
+
+
+@skip_xp_invalid_arg
+class TestVariability:
+    """  Comparison numbers are found using R v.1.5.1
+         note that length(testcase) = 4
+    """
+    testcase = ma.fix_invalid([1,2,3,4,np.nan])
+
+    def test_sem(self):
+        # This is not in R, so used: sqrt(var(testcase)*3/4) / sqrt(3)
+        y = mstats.sem(self.testcase)
+        assert_almost_equal(y, 0.6454972244)
+        n = self.testcase.count()
+        assert_allclose(mstats.sem(self.testcase, ddof=0) * np.sqrt(n/(n-2)),
+                        mstats.sem(self.testcase, ddof=2))
+
+    def test_zmap(self):
+        # This is not in R, so tested by using:
+        #    (testcase[i]-mean(testcase,axis=0)) / sqrt(var(testcase)*3/4)
+        y = mstats.zmap(self.testcase, self.testcase)
+        desired_unmaskedvals = ([-1.3416407864999, -0.44721359549996,
+                                 0.44721359549996, 1.3416407864999])
+        assert_array_almost_equal(desired_unmaskedvals,
+                                  y.data[y.mask == False], decimal=12)  # noqa: E712
+
+    def test_zscore(self):
+        # This is not in R, so tested by using:
+        #     (testcase[i]-mean(testcase,axis=0)) / sqrt(var(testcase)*3/4)
+        y = mstats.zscore(self.testcase)
+        desired = ma.fix_invalid([-1.3416407864999, -0.44721359549996,
+                                  0.44721359549996, 1.3416407864999, np.nan])
+        assert_almost_equal(desired, y, decimal=12)
+
+
+@skip_xp_invalid_arg
+class TestMisc:
+
+    def test_obrientransform(self):
+        args = [[5]*5+[6]*11+[7]*9+[8]*3+[9]*2+[10]*2,
+                [6]+[7]*2+[8]*4+[9]*9+[10]*16]
+        result = [5*[3.1828]+11*[0.5591]+9*[0.0344]+3*[1.6086]+2*[5.2817]+2*[11.0538],
+                  [10.4352]+2*[4.8599]+4*[1.3836]+9*[0.0061]+16*[0.7277]]
+        assert_almost_equal(np.round(mstats.obrientransform(*args).T, 4),
+                            result, 4)
+
+    def test_ks_2samp(self):
+        x = [[nan,nan, 4, 2, 16, 26, 5, 1, 5, 1, 2, 3, 1],
+             [4, 3, 5, 3, 2, 7, 3, 1, 1, 2, 3, 5, 3],
+             [3, 2, 5, 6, 18, 4, 9, 1, 1, nan, 1, 1, nan],
+             [nan, 6, 11, 4, 17, nan, 6, 1, 1, 2, 5, 1, 1]]
+        x = ma.fix_invalid(x).T
+        (winter, spring, summer, fall) = x.T
+
+        assert_almost_equal(np.round(mstats.ks_2samp(winter, spring), 4),
+                            (0.1818, 0.9628))
+        assert_almost_equal(np.round(mstats.ks_2samp(winter, spring, 'g'), 4),
+                            (0.1469, 0.6886))
+        assert_almost_equal(np.round(mstats.ks_2samp(winter, spring, 'l'), 4),
+                            (0.1818, 0.6011))
+
+    def test_friedmanchisq(self):
+        # No missing values
+        args = ([9.0,9.5,5.0,7.5,9.5,7.5,8.0,7.0,8.5,6.0],
+                [7.0,6.5,7.0,7.5,5.0,8.0,6.0,6.5,7.0,7.0],
+                [6.0,8.0,4.0,6.0,7.0,6.5,6.0,4.0,6.5,3.0])
+        result = mstats.friedmanchisquare(*args)
+        assert_almost_equal(result[0], 10.4737, 4)
+        assert_almost_equal(result[1], 0.005317, 6)
+        # Missing values
+        x = [[nan,nan, 4, 2, 16, 26, 5, 1, 5, 1, 2, 3, 1],
+             [4, 3, 5, 3, 2, 7, 3, 1, 1, 2, 3, 5, 3],
+             [3, 2, 5, 6, 18, 4, 9, 1, 1,nan, 1, 1,nan],
+             [nan, 6, 11, 4, 17,nan, 6, 1, 1, 2, 5, 1, 1]]
+        x = ma.fix_invalid(x)
+        result = mstats.friedmanchisquare(*x)
+        assert_almost_equal(result[0], 2.0156, 4)
+        assert_almost_equal(result[1], 0.5692, 4)
+
+        # test for namedtuple attributes
+        attributes = ('statistic', 'pvalue')
+        check_named_results(result, attributes, ma=True)
+
+        # copied from `test_stats.py`
+        array = np.ma.asarray
+        x1 = [array([0.763, 0.599, 0.954, 0.628, 0.882, 0.936, 0.661,
+                     0.583, 0.775, 1.0, 0.94, 0.619, 0.972, 0.957]),
+              array([0.768, 0.591, 0.971, 0.661, 0.888, 0.931, 0.668,
+                     0.583, 0.838, 1.0, 0.962, 0.666, 0.981, 0.978]),
+              array([0.771, 0.590, 0.968, 0.654, 0.886, 0.916, 0.609,
+                     0.563, 0.866, 1.0, 0.965, 0.614, 0.9751, 0.946]),
+              array([0.798, 0.569, 0.967, 0.657, 0.898, 0.931, 0.685,
+                     0.625, 0.875, 1.0, 0.962, 0.669, 0.975, 0.970])]
+
+        # From "Bioestadistica para las ciencias de la salud" Xf=18.95 p<0.001:
+        # x2 = [array([4, 3, 5, 3, 5, 3, 2, 5, 4, 4, 4, 3]),
+        #       array([2, 2, 1, 2, 3, 1, 2, 3, 2, 1, 1, 3]),
+        #       array([2, 4, 3, 3, 4, 3, 3, 4, 4, 1, 2, 1]),
+        #       array([3, 5, 4, 3, 4, 4, 3, 3, 3, 4, 4, 4])]
+
+        # From Jerrorl H. Zar, "Biostatistical Analysis"(example 12.6),
+        # Xf=10.68, 0.005 < p < 0.01:
+        # Probability from this example is inexact
+        # using Chisquare approximation of Friedman Chisquare.
+        x3 = [array([7.0, 9.9, 8.5, 5.1, 10.3]),
+              array([5.3, 5.7, 4.7, 3.5, 7.7]),
+              array([4.9, 7.6, 5.5, 2.8, 8.4]),
+              array([8.8, 8.9, 8.1, 3.3, 9.1])]
+
+        # test using mstats
+        assert_array_almost_equal(mstats.friedmanchisquare(*x1),
+                                  (10.2283464566929, 0.0167215803284414))
+
+        # the following fails
+        # assert_array_almost_equal(mstats.friedmanchisquare(*x2),
+        #                           (18.9428571428571, 0.000280938375189499))
+
+        assert_array_almost_equal(mstats.friedmanchisquare(*x3),
+                                  (10.68, 0.0135882729582176))
+        assert_raises(ValueError, mstats.friedmanchisquare, x3[0], x3[1])
+
+
+def test_regress_simple():
+    # Regress a line with sinusoidal noise. Test for #1273.
+    x = np.linspace(0, 100, 100)
+    y = 0.2 * np.linspace(0, 100, 100) + 10
+    y += np.sin(np.linspace(0, 20, 100))
+
+    result = mstats.linregress(x, y)
+
+    # Result is of a correct class and with correct fields
+    lr = _stats_py.LinregressResult
+    assert_(isinstance(result, lr))
+    attributes = ('slope', 'intercept', 'rvalue', 'pvalue', 'stderr')
+    check_named_results(result, attributes, ma=True)
+    assert 'intercept_stderr' in dir(result)
+
+    # Slope and intercept are estimated correctly
+    assert_almost_equal(result.slope, 0.19644990055858422)
+    assert_almost_equal(result.intercept, 10.211269918932341)
+    assert_almost_equal(result.stderr, 0.002395781449783862)
+    assert_almost_equal(result.intercept_stderr, 0.13866936078570702)
+
+
+def test_linregress_identical_x():
+    rng = np.random.default_rng(74578245698)
+    x = np.zeros(10)
+    y = rng.random(10)
+    msg = "Cannot calculate a linear regression if all x values are identical"
+    with assert_raises(ValueError, match=msg):
+        mstats.linregress(x, y)
+
+
+class TestTheilslopes:
+    def test_theilslopes(self):
+        # Test for basic slope and intercept.
+        slope, intercept, lower, upper = mstats.theilslopes([0, 1, 1])
+        assert_almost_equal(slope, 0.5)
+        assert_almost_equal(intercept, 0.5)
+
+        slope, intercept, lower, upper = mstats.theilslopes([0, 1, 1],
+                                                            method='joint')
+        assert_almost_equal(slope, 0.5)
+        assert_almost_equal(intercept, 0.0)
+
+        # Test for correct masking.
+        y = np.ma.array([0, 1, 100, 1], mask=[False, False, True, False])
+        slope, intercept, lower, upper = mstats.theilslopes(y)
+        # These reference values are incorrect. With 100 masked, the outputs
+        # should be the same as above.
+        assert_almost_equal(slope, 1./3)
+        assert_almost_equal(intercept, 2./3)
+
+        slope, intercept, lower, upper = mstats.theilslopes(y,
+                                                            method='joint')
+        assert_almost_equal(slope, 1./3)
+        assert_almost_equal(intercept, 0.0)
+
+        # Test of confidence intervals from example in Sen (1968).
+        x = [1, 2, 3, 4, 10, 12, 18]
+        y = [9, 15, 19, 20, 45, 55, 78]
+        slope, intercept, lower, upper = mstats.theilslopes(y, x, 0.07)
+        assert_almost_equal(slope, 4)
+        assert_almost_equal(intercept, 4.0)
+        assert_almost_equal(upper, 4.38, decimal=2)
+        assert_almost_equal(lower, 3.71, decimal=2)
+
+        slope, intercept, lower, upper = mstats.theilslopes(y, x, 0.07,
+                                                            method='joint')
+        assert_almost_equal(slope, 4)
+        assert_almost_equal(intercept, 6.0)
+        assert_almost_equal(upper, 4.38, decimal=2)
+        assert_almost_equal(lower, 3.71, decimal=2)
+
+
+    def test_theilslopes_warnings(self):
+        # Test `theilslopes` with degenerate input; see gh-15943
+        msg = "All `x` coordinates.*|Mean of empty slice|invalid value encountered.*"
+        with pytest.warns(RuntimeWarning, match=msg):
+            res = mstats.theilslopes([0, 1], [0, 0])
+            assert np.all(np.isnan(res))
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "invalid value encountered...", RuntimeWarning)
+            res = mstats.theilslopes([0, 0, 0], [0, 1, 0])
+            assert_allclose(res, (0, 0, np.nan, np.nan))
+
+
+    def test_theilslopes_namedtuple_consistency(self):
+        """
+        Simple test to ensure tuple backwards-compatibility of the returned
+        TheilslopesResult object
+        """
+        y = [1, 2, 4]
+        x = [4, 6, 8]
+        slope, intercept, low_slope, high_slope = mstats.theilslopes(y, x)
+        result = mstats.theilslopes(y, x)
+
+        # note all four returned values are distinct here
+        assert_equal(slope, result.slope)
+        assert_equal(intercept, result.intercept)
+        assert_equal(low_slope, result.low_slope)
+        assert_equal(high_slope, result.high_slope)
+
+    def test_gh19678_uint8(self):
+        # `theilslopes` returned unexpected results when `y` was an unsigned type.
+        # Check that this is resolved.
+        rng = np.random.default_rng(2549824598234528)
+        y = rng.integers(0, 255, size=10, dtype=np.uint8)
+        res = stats.theilslopes(y, y)
+        np.testing.assert_allclose(res.slope, 1)
+
+
+def test_siegelslopes():
+    # method should be exact for straight line
+    y = 2 * np.arange(10) + 0.5
+    assert_equal(mstats.siegelslopes(y), (2.0, 0.5))
+    assert_equal(mstats.siegelslopes(y, method='separate'), (2.0, 0.5))
+
+    x = 2 * np.arange(10)
+    y = 5 * x - 3.0
+    assert_equal(mstats.siegelslopes(y, x), (5.0, -3.0))
+    assert_equal(mstats.siegelslopes(y, x, method='separate'), (5.0, -3.0))
+
+    # method is robust to outliers: brekdown point of 50%
+    y[:4] = 1000
+    assert_equal(mstats.siegelslopes(y, x), (5.0, -3.0))
+
+    # if there are no outliers, results should be comparable to linregress
+    x = np.arange(10)
+    y = -2.3 + 0.3*x + stats.norm.rvs(size=10, random_state=231)
+    slope_ols, intercept_ols, _, _, _ = stats.linregress(x, y)
+
+    slope, intercept = mstats.siegelslopes(y, x)
+    assert_allclose(slope, slope_ols, rtol=0.1)
+    assert_allclose(intercept, intercept_ols, rtol=0.1)
+
+    slope, intercept = mstats.siegelslopes(y, x, method='separate')
+    assert_allclose(slope, slope_ols, rtol=0.1)
+    assert_allclose(intercept, intercept_ols, rtol=0.1)
+
+
+def test_siegelslopes_namedtuple_consistency():
+    """
+    Simple test to ensure tuple backwards-compatibility of the returned
+    SiegelslopesResult object.
+    """
+    y = [1, 2, 4]
+    x = [4, 6, 8]
+    slope, intercept = mstats.siegelslopes(y, x)
+    result = mstats.siegelslopes(y, x)
+
+    # note both returned values are distinct here
+    assert_equal(slope, result.slope)
+    assert_equal(intercept, result.intercept)
+
+
+def test_sen_seasonal_slopes():
+    rng = np.random.default_rng(5765986256978575148)
+    x = rng.random(size=(100, 4))
+    intra_slope, inter_slope = mstats.sen_seasonal_slopes(x)
+
+    # reference implementation from the `sen_seasonal_slopes` documentation
+    def dijk(yi):
+        n = len(yi)
+        x = np.arange(n)
+        dy = yi - yi[:, np.newaxis]
+        dx = x - x[:, np.newaxis]
+        mask = np.triu(np.ones((n, n), dtype=bool), k=1)
+        return dy[mask]/dx[mask]
+
+    for i in range(4):
+        assert_allclose(np.median(dijk(x[:, i])), intra_slope[i])
+
+    all_slopes = np.concatenate([dijk(x[:, i]) for i in range(x.shape[1])])
+    assert_allclose(np.median(all_slopes), inter_slope)
+
+
+def test_plotting_positions():
+    # Regression test for #1256
+    pos = mstats.plotting_positions(np.arange(3), 0, 0)
+    assert_array_almost_equal(pos.data, np.array([0.25, 0.5, 0.75]))
+
+
+@skip_xp_invalid_arg
+class TestNormalitytests:
+
+    def test_vs_nonmasked(self):
+        x = np.array((-2, -1, 0, 1, 2, 3)*4)**2
+        assert_array_almost_equal(mstats.normaltest(x),
+                                  stats.normaltest(x))
+        assert_array_almost_equal(mstats.skewtest(x),
+                                  stats.skewtest(x))
+        assert_array_almost_equal(mstats.kurtosistest(x),
+                                  stats.kurtosistest(x))
+
+        funcs = [stats.normaltest, stats.skewtest, stats.kurtosistest]
+        mfuncs = [mstats.normaltest, mstats.skewtest, mstats.kurtosistest]
+        x = [1, 2, 3, 4]
+        for func, mfunc in zip(funcs, mfuncs):
+            with pytest.warns(SmallSampleWarning, match=too_small_1d_not_omit):
+                res = func(x)
+                assert np.isnan(res.statistic)
+                assert np.isnan(res.pvalue)
+            assert_raises(ValueError, mfunc, x)
+
+    def test_axis_None(self):
+        # Test axis=None (equal to axis=0 for 1-D input)
+        x = np.array((-2,-1,0,1,2,3)*4)**2
+        assert_allclose(mstats.normaltest(x, axis=None), mstats.normaltest(x))
+        assert_allclose(mstats.skewtest(x, axis=None), mstats.skewtest(x))
+        assert_allclose(mstats.kurtosistest(x, axis=None),
+                        mstats.kurtosistest(x))
+
+    def test_maskedarray_input(self):
+        # Add some masked values, test result doesn't change
+        x = np.array((-2, -1, 0, 1, 2, 3)*4)**2
+        xm = np.ma.array(np.r_[np.inf, x, 10],
+                         mask=np.r_[True, [False] * x.size, True])
+        assert_allclose(mstats.normaltest(xm), stats.normaltest(x))
+        assert_allclose(mstats.skewtest(xm), stats.skewtest(x))
+        assert_allclose(mstats.kurtosistest(xm), stats.kurtosistest(x))
+
+    def test_nd_input(self):
+        x = np.array((-2, -1, 0, 1, 2, 3)*4)**2
+        x_2d = np.vstack([x] * 2).T
+        for func in [mstats.normaltest, mstats.skewtest, mstats.kurtosistest]:
+            res_1d = func(x)
+            res_2d = func(x_2d)
+            assert_allclose(res_2d[0], [res_1d[0]] * 2)
+            assert_allclose(res_2d[1], [res_1d[1]] * 2)
+
+    def test_normaltest_result_attributes(self):
+        x = np.array((-2, -1, 0, 1, 2, 3)*4)**2
+        res = mstats.normaltest(x)
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, ma=True)
+
+    def test_kurtosistest_result_attributes(self):
+        x = np.array((-2, -1, 0, 1, 2, 3)*4)**2
+        res = mstats.kurtosistest(x)
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, ma=True)
+
+    def test_regression_9033(self):
+        # x clearly non-normal but power of negative denom needs
+        # to be handled correctly to reject normality
+        counts = [128, 0, 58, 7, 0, 41, 16, 0, 0, 167]
+        x = np.hstack([np.full(c, i) for i, c in enumerate(counts)])
+        assert_equal(mstats.kurtosistest(x)[1] < 0.01, True)
+
+    @pytest.mark.parametrize("test", ["skewtest", "kurtosistest"])
+    @pytest.mark.parametrize("alternative", ["less", "greater"])
+    def test_alternative(self, test, alternative):
+        x = stats.norm.rvs(loc=10, scale=2.5, size=30, random_state=123)
+
+        stats_test = getattr(stats, test)
+        mstats_test = getattr(mstats, test)
+
+        z_ex, p_ex = stats_test(x, alternative=alternative)
+        z, p = mstats_test(x, alternative=alternative)
+        assert_allclose(z, z_ex, atol=1e-12)
+        assert_allclose(p, p_ex, atol=1e-12)
+
+        # test with masked arrays
+        x[1:5] = np.nan
+        x = np.ma.masked_array(x, mask=np.isnan(x))
+        z_ex, p_ex = stats_test(x.compressed(), alternative=alternative)
+        z, p = mstats_test(x, alternative=alternative)
+        assert_allclose(z, z_ex, atol=1e-12)
+        assert_allclose(p, p_ex, atol=1e-12)
+
+    def test_bad_alternative(self):
+        x = stats.norm.rvs(size=20, random_state=123)
+        msg = r"`alternative` must be..."
+
+        with pytest.raises(ValueError, match=msg):
+            mstats.skewtest(x, alternative='error')
+
+        with pytest.raises(ValueError, match=msg):
+            mstats.kurtosistest(x, alternative='error')
+
+
+class TestFOneway:
+    def test_result_attributes(self):
+        a = np.array([655, 788], dtype=np.uint16)
+        b = np.array([789, 772], dtype=np.uint16)
+        res = mstats.f_oneway(a, b)
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, ma=True)
+
+
+class TestMannwhitneyu:
+    # data from gh-1428
+    x = np.array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2.,
+                  1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 2., 1., 1., 1., 1., 2., 1., 1., 2., 1., 1., 2.,
+                  1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 1., 3., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1.])
+
+    y = np.array([1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1., 1., 1., 1.,
+                  2., 1., 1., 1., 2., 1., 1., 1., 1., 1., 2., 1., 1., 3.,
+                  1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 2., 1., 2., 1.,
+                  1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1., 2.,
+                  2., 1., 1., 2., 1., 1., 2., 1., 2., 1., 1., 1., 1., 2.,
+                  2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                  1., 2., 1., 1., 1., 1., 1., 2., 2., 2., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
+                  2., 1., 1., 2., 1., 1., 1., 1., 2., 1., 1., 1., 1., 1.,
+                  1., 1., 1., 1., 1., 1., 1., 2., 1., 1., 1., 2., 1., 1.,
+                  1., 1., 1., 1.])
+
+    def test_result_attributes(self):
+        res = mstats.mannwhitneyu(self.x, self.y)
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, ma=True)
+
+    def test_against_stats(self):
+        # gh-4641 reported that stats.mannwhitneyu returned half the p-value
+        # of mstats.mannwhitneyu. Default alternative of stats.mannwhitneyu
+        # is now two-sided, so they match.
+        res1 = mstats.mannwhitneyu(self.x, self.y)
+        res2 = stats.mannwhitneyu(self.x, self.y)
+        assert res1.statistic == res2.statistic
+        assert_allclose(res1.pvalue, res2.pvalue)
+
+
+class TestKruskal:
+    def test_result_attributes(self):
+        x = [1, 3, 5, 7, 9]
+        y = [2, 4, 6, 8, 10]
+
+        res = mstats.kruskal(x, y)
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, ma=True)
+
+
+# TODO: for all ttest functions, add tests with masked array inputs
+class TestTtest_rel:
+    def setup_method(self):
+        self.rng = np.random.default_rng(9373599542)
+
+    def test_vs_nonmasked(self):
+        outcome = self.rng.standard_normal((20, 4)) + [0, 0, 1, 2]
+
+        # 1-D inputs
+        res1 = stats.ttest_rel(outcome[:, 0], outcome[:, 1])
+        res2 = mstats.ttest_rel(outcome[:, 0], outcome[:, 1])
+        assert_allclose(res1, res2)
+
+        # 2-D inputs
+        res1 = stats.ttest_rel(outcome[:, 0], outcome[:, 1], axis=None)
+        res2 = mstats.ttest_rel(outcome[:, 0], outcome[:, 1], axis=None)
+        assert_allclose(res1, res2)
+        res1 = stats.ttest_rel(outcome[:, :2], outcome[:, 2:], axis=0)
+        res2 = mstats.ttest_rel(outcome[:, :2], outcome[:, 2:], axis=0)
+        assert_allclose(res1, res2)
+
+        # Check default is axis=0
+        res3 = mstats.ttest_rel(outcome[:, :2], outcome[:, 2:])
+        assert_allclose(res2, res3)
+
+    def test_fully_masked(self):
+        outcome = ma.masked_array(self.rng.standard_normal((3, 2)),
+                                  mask=[[1, 1, 1], [0, 0, 0]])
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "invalid value encountered in absolute", RuntimeWarning)
+            for pair in [(outcome[:, 0], outcome[:, 1]),
+                         ([np.nan, np.nan], [1.0, 2.0])]:
+                t, p = mstats.ttest_rel(*pair)
+                assert_array_equal(t, (np.nan, np.nan))
+                assert_array_equal(p, (np.nan, np.nan))
+
+    def test_result_attributes(self):
+        outcome = self.rng.standard_normal((20, 4)) + [0, 0, 1, 2]
+
+        res = mstats.ttest_rel(outcome[:, 0], outcome[:, 1])
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, ma=True)
+
+    def test_invalid_input_size(self):
+        assert_raises(ValueError, mstats.ttest_rel,
+                      np.arange(10), np.arange(11))
+        x = np.arange(24)
+        assert_raises(ValueError, mstats.ttest_rel,
+                      x.reshape(2, 3, 4), x.reshape(2, 4, 3), axis=1)
+        assert_raises(ValueError, mstats.ttest_rel,
+                      x.reshape(2, 3, 4), x.reshape(2, 4, 3), axis=2)
+
+    def test_empty(self):
+        res1 = mstats.ttest_rel([], [])
+        assert_(np.all(np.isnan(res1)))
+
+    def test_zero_division(self):
+        t, p = mstats.ttest_ind([0, 0, 0], [1, 1, 1])
+        assert_equal((np.abs(t), p), (np.inf, 0))
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "invalid value encountered in absolute", RuntimeWarning)
+            t, p = mstats.ttest_ind([0, 0, 0], [0, 0, 0])
+            assert_array_equal(t, np.array([np.nan, np.nan]))
+            assert_array_equal(p, np.array([np.nan, np.nan]))
+
+    def test_bad_alternative(self):
+        msg = r"alternative must be 'less', 'greater' or 'two-sided'"
+        with pytest.raises(ValueError, match=msg):
+            mstats.ttest_ind([1, 2, 3], [4, 5, 6], alternative='foo')
+
+    @pytest.mark.parametrize("alternative", ["less", "greater"])
+    def test_alternative(self, alternative):
+        x = stats.norm.rvs(loc=10, scale=5, size=25, random_state=42)
+        y = stats.norm.rvs(loc=8, scale=2, size=25, random_state=42)
+
+        t_ex, p_ex = stats.ttest_rel(x, y, alternative=alternative)
+        t, p = mstats.ttest_rel(x, y, alternative=alternative)
+        assert_allclose(t, t_ex, rtol=1e-14)
+        assert_allclose(p, p_ex, rtol=1e-14)
+
+        # test with masked arrays
+        x[1:10] = np.nan
+        y[1:10] = np.nan
+        x = np.ma.masked_array(x, mask=np.isnan(x))
+        y = np.ma.masked_array(y, mask=np.isnan(y))
+        t, p = mstats.ttest_rel(x, y, alternative=alternative)
+        t_ex, p_ex = stats.ttest_rel(x.compressed(), y.compressed(),
+                                     alternative=alternative)
+        assert_allclose(t, t_ex, rtol=1e-14)
+        assert_allclose(p, p_ex, rtol=1e-14)
+
+
+class TestTtest_ind:
+    def setup_method(self):
+        self.rng = np.random.default_rng(4776115069)
+
+    def test_vs_nonmasked(self):
+        outcome = self.rng.standard_normal((20, 4)) + [0, 0, 1, 2]
+
+        # 1-D inputs
+        res1 = stats.ttest_ind(outcome[:, 0], outcome[:, 1])
+        res2 = mstats.ttest_ind(outcome[:, 0], outcome[:, 1])
+        assert_allclose(res1, res2)
+
+        # 2-D inputs
+        res1 = stats.ttest_ind(outcome[:, 0], outcome[:, 1], axis=None)
+        res2 = mstats.ttest_ind(outcome[:, 0], outcome[:, 1], axis=None)
+        assert_allclose(res1, res2)
+        res1 = stats.ttest_ind(outcome[:, :2], outcome[:, 2:], axis=0)
+        res2 = mstats.ttest_ind(outcome[:, :2], outcome[:, 2:], axis=0)
+        assert_allclose(res1, res2)
+
+        # Check default is axis=0
+        res3 = mstats.ttest_ind(outcome[:, :2], outcome[:, 2:])
+        assert_allclose(res2, res3)
+
+        # Check equal_var
+        res4 = stats.ttest_ind(outcome[:, 0], outcome[:, 1], equal_var=True)
+        res5 = mstats.ttest_ind(outcome[:, 0], outcome[:, 1], equal_var=True)
+        assert_allclose(res4, res5)
+        res4 = stats.ttest_ind(outcome[:, 0], outcome[:, 1], equal_var=False)
+        res5 = mstats.ttest_ind(outcome[:, 0], outcome[:, 1], equal_var=False)
+        assert_allclose(res4, res5)
+
+    def test_fully_masked(self):
+        outcome = ma.masked_array(self.rng.standard_normal((3, 2)),
+                                  mask=[[1, 1, 1], [0, 0, 0]])
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "invalid value encountered in absolute", RuntimeWarning)
+            for pair in [(outcome[:, 0], outcome[:, 1]),
+                         ([np.nan, np.nan], [1.0, 2.0])]:
+                t, p = mstats.ttest_ind(*pair)
+                assert_array_equal(t, (np.nan, np.nan))
+                assert_array_equal(p, (np.nan, np.nan))
+
+    def test_result_attributes(self):
+        outcome = self.rng.standard_normal((20, 4)) + [0, 0, 1, 2]
+
+        res = mstats.ttest_ind(outcome[:, 0], outcome[:, 1])
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, ma=True)
+
+    def test_empty(self):
+        res1 = mstats.ttest_ind([], [])
+        assert_(np.all(np.isnan(res1)))
+
+    def test_zero_division(self):
+        t, p = mstats.ttest_ind([0, 0, 0], [1, 1, 1])
+        assert_equal((np.abs(t), p), (np.inf, 0))
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "invalid value encountered in absolute", RuntimeWarning)
+            t, p = mstats.ttest_ind([0, 0, 0], [0, 0, 0])
+            assert_array_equal(t, (np.nan, np.nan))
+            assert_array_equal(p, (np.nan, np.nan))
+
+        t, p = mstats.ttest_ind([0, 0, 0], [1, 1, 1], equal_var=False)
+        assert_equal((np.abs(t), p), (np.inf, 0))
+        assert_array_equal(mstats.ttest_ind([0, 0, 0], [0, 0, 0],
+                                            equal_var=False), (np.nan, np.nan))
+
+    def test_bad_alternative(self):
+        msg = r"alternative must be 'less', 'greater' or 'two-sided'"
+        with pytest.raises(ValueError, match=msg):
+            mstats.ttest_ind([1, 2, 3], [4, 5, 6], alternative='foo')
+
+    @pytest.mark.parametrize("alternative", ["less", "greater"])
+    def test_alternative(self, alternative):
+        x = stats.norm.rvs(loc=10, scale=2, size=100, random_state=123)
+        y = stats.norm.rvs(loc=8, scale=2, size=100, random_state=123)
+
+        t_ex, p_ex = stats.ttest_ind(x, y, alternative=alternative)
+        t, p = mstats.ttest_ind(x, y, alternative=alternative)
+        assert_allclose(t, t_ex, rtol=1e-14)
+        assert_allclose(p, p_ex, rtol=1e-14)
+
+        # test with masked arrays
+        x[1:10] = np.nan
+        y[80:90] = np.nan
+        x = np.ma.masked_array(x, mask=np.isnan(x))
+        y = np.ma.masked_array(y, mask=np.isnan(y))
+        t_ex, p_ex = stats.ttest_ind(x.compressed(), y.compressed(),
+                                     alternative=alternative)
+        t, p = mstats.ttest_ind(x, y, alternative=alternative)
+        assert_allclose(t, t_ex, rtol=1e-14)
+        assert_allclose(p, p_ex, rtol=1e-14)
+
+
+class TestTtest_1samp:
+    def setup_method(self):
+        self.rng = np.random.default_rng(6043813830)
+
+    def test_vs_nonmasked(self):
+        outcome = self.rng.standard_normal((20, 4)) + [0, 0, 1, 2]
+
+        # 1-D inputs
+        res1 = stats.ttest_1samp(outcome[:, 0], 1)
+        res2 = mstats.ttest_1samp(outcome[:, 0], 1)
+        assert_allclose(res1, res2)
+
+    def test_fully_masked(self):
+        outcome = ma.masked_array(self.rng.standard_normal(3), mask=[1, 1, 1])
+        expected = (np.nan, np.nan)
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "invalid value encountered in absolute", RuntimeWarning)
+            for pair in [((np.nan, np.nan), 0.0), (outcome, 0.0)]:
+                t, p = mstats.ttest_1samp(*pair)
+                assert_array_equal(p, expected)
+                assert_array_equal(t, expected)
+
+    def test_result_attributes(self):
+        outcome = self.rng.standard_normal((20, 4)) + [0, 0, 1, 2]
+
+        res = mstats.ttest_1samp(outcome[:, 0], 1)
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, ma=True)
+
+    def test_empty(self):
+        res1 = mstats.ttest_1samp([], 1)
+        assert_(np.all(np.isnan(res1)))
+
+    def test_zero_division(self):
+        t, p = mstats.ttest_1samp([0, 0, 0], 1)
+        assert_equal((np.abs(t), p), (np.inf, 0))
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "invalid value encountered in absolute", RuntimeWarning)
+            t, p = mstats.ttest_1samp([0, 0, 0], 0)
+            assert_(np.isnan(t))
+            assert_array_equal(p, (np.nan, np.nan))
+
+    def test_bad_alternative(self):
+        msg = r"alternative must be 'less', 'greater' or 'two-sided'"
+        with pytest.raises(ValueError, match=msg):
+            mstats.ttest_1samp([1, 2, 3], 4, alternative='foo')
+
+    @pytest.mark.parametrize("alternative", ["less", "greater"])
+    def test_alternative(self, alternative):
+        x = stats.norm.rvs(loc=10, scale=2, size=100, random_state=123)
+
+        t_ex, p_ex = stats.ttest_1samp(x, 9, alternative=alternative)
+        t, p = mstats.ttest_1samp(x, 9, alternative=alternative)
+        assert_allclose(t, t_ex, rtol=1e-14)
+        assert_allclose(p, p_ex, rtol=1e-14)
+
+        # test with masked arrays
+        x[1:10] = np.nan
+        x = np.ma.masked_array(x, mask=np.isnan(x))
+        t_ex, p_ex = stats.ttest_1samp(x.compressed(), 9,
+                                       alternative=alternative)
+        t, p = mstats.ttest_1samp(x, 9, alternative=alternative)
+        assert_allclose(t, t_ex, rtol=1e-14)
+        assert_allclose(p, p_ex, rtol=1e-14)
+
+
+class TestDescribe:
+    """
+    Tests for mstats.describe.
+
+    Note that there are also tests for `mstats.describe` in the
+    class TestCompareWithStats.
+    """
+    def test_basic_with_axis(self):
+        # This is a basic test that is also a regression test for gh-7303.
+        a = np.ma.masked_array([[0, 1, 2, 3, 4, 9],
+                                [5, 5, 0, 9, 3, 3]],
+                               mask=[[0, 0, 0, 0, 0, 1],
+                                     [0, 0, 1, 1, 0, 0]])
+        result = mstats.describe(a, axis=1)
+        assert_equal(result.nobs, [5, 4])
+        amin, amax = result.minmax
+        assert_equal(amin, [0, 3])
+        assert_equal(amax, [4, 5])
+        assert_equal(result.mean, [2.0, 4.0])
+        assert_equal(result.variance, [2.0, 1.0])
+        assert_equal(result.skewness, [0.0, 0.0])
+        assert_allclose(result.kurtosis, [-1.3, -2.0])
+
+
+@skip_xp_invalid_arg
+class TestCompareWithStats:
+    """
+    Class to compare mstats results with stats results.
+
+    It is in general assumed that scipy.stats is at a more mature stage than
+    stats.mstats.  If a routine in mstats results in similar results like in
+    scipy.stats, this is considered also as a proper validation of scipy.mstats
+    routine.
+
+    Different sample sizes are used for testing, as some problems between stats
+    and mstats are dependent on sample size.
+
+    Author: Alexander Loew
+
+    NOTE that some tests fail. This might be caused by
+    a) actual differences or bugs between stats and mstats
+    b) numerical inaccuracies
+    c) different definitions of routine interfaces
+
+    These failures need to be checked. Current workaround is to have disabled these
+    tests, but issuing reports on scipy-dev
+
+    """
+    def get_n(self):
+        """ Returns list of sample sizes to be used for comparison. """
+        return [1000, 100, 10, 5]
+
+    def generate_xy_sample(self, n):
+        # This routine generates numpy arrays and corresponding masked arrays
+        # with the same data, but additional masked values
+        rng = np.random.RandomState(1234567)
+        x = rng.randn(n)
+        y = x + rng.randn(n)
+        xm = np.full(len(x) + 5, 1e16)
+        ym = np.full(len(y) + 5, 1e16)
+        xm[0:len(x)] = x
+        ym[0:len(y)] = y
+        mask = xm > 9e15
+        xm = np.ma.array(xm, mask=mask)
+        ym = np.ma.array(ym, mask=mask)
+        return x, y, xm, ym
+
+    def generate_xy_sample2D(self, n, nx):
+        x = np.full((n, nx), np.nan)
+        y = np.full((n, nx), np.nan)
+        xm = np.full((n+5, nx), np.nan)
+        ym = np.full((n+5, nx), np.nan)
+
+        for i in range(nx):
+            x[:, i], y[:, i], dx, dy = self.generate_xy_sample(n)
+
+        xm[0:n, :] = x[0:n]
+        ym[0:n, :] = y[0:n]
+        xm = np.ma.array(xm, mask=np.isnan(xm))
+        ym = np.ma.array(ym, mask=np.isnan(ym))
+        return x, y, xm, ym
+
+    def test_linregress(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            result1 = stats.linregress(x, y)
+            result2 = stats.mstats.linregress(xm, ym)
+            assert_allclose(np.asarray(result1), np.asarray(result2))
+
+    def test_pearsonr(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            r, p = stats.pearsonr(x, y)
+            rm, pm = stats.mstats.pearsonr(xm, ym)
+
+            assert_almost_equal(r, rm, decimal=14)
+            assert_almost_equal(p, pm, decimal=14)
+
+    def test_spearmanr(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            r, p = stats.spearmanr(x, y)
+            rm, pm = stats.mstats.spearmanr(xm, ym)
+            assert_almost_equal(r, rm, 14)
+            assert_almost_equal(p, pm, 14)
+
+    def test_spearmanr_backcompat_useties(self):
+        # A regression test to ensure we don't break backwards compat
+        # more than we have to (see gh-9204).
+        x = np.arange(6)
+        assert_raises(ValueError, mstats.spearmanr, x, x, False)
+
+    def test_gmean(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            r = stats.gmean(abs(x))
+            rm = stats.mstats.gmean(abs(xm))
+            assert_allclose(r, rm, rtol=1e-13)
+
+            r = stats.gmean(abs(y))
+            rm = stats.mstats.gmean(abs(ym))
+            assert_allclose(r, rm, rtol=1e-13)
+
+    def test_hmean(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+
+            r = stats.hmean(abs(x))
+            rm = stats.mstats.hmean(abs(xm))
+            assert_almost_equal(r, rm, 10)
+
+            r = stats.hmean(abs(y))
+            rm = stats.mstats.hmean(abs(ym))
+            assert_almost_equal(r, rm, 10)
+
+    def test_skew(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+
+            r = stats.skew(x)
+            rm = stats.mstats.skew(xm)
+            assert_almost_equal(r, rm, 10)
+
+            r = stats.skew(y)
+            rm = stats.mstats.skew(ym)
+            assert_almost_equal(r, rm, 10)
+
+    def test_moment(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+
+            r = stats.moment(x)
+            rm = stats.mstats.moment(xm)
+            assert_almost_equal(r, rm, 10)
+
+            r = stats.moment(y)
+            rm = stats.mstats.moment(ym)
+            assert_almost_equal(r, rm, 10)
+
+    def test_zscore(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+
+            # reference solution
+            zx = (x - x.mean()) / x.std()
+            zy = (y - y.mean()) / y.std()
+
+            # validate stats
+            assert_allclose(stats.zscore(x), zx, rtol=1e-10)
+            assert_allclose(stats.zscore(y), zy, rtol=1e-10)
+
+            # compare stats and mstats
+            assert_allclose(stats.zscore(x), stats.mstats.zscore(xm[0:len(x)]),
+                            rtol=1e-10)
+            assert_allclose(stats.zscore(y), stats.mstats.zscore(ym[0:len(y)]),
+                            rtol=1e-10)
+
+    def test_kurtosis(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            r = stats.kurtosis(x)
+            rm = stats.mstats.kurtosis(xm)
+            assert_almost_equal(r, rm, 10)
+
+            r = stats.kurtosis(y)
+            rm = stats.mstats.kurtosis(ym)
+            assert_almost_equal(r, rm, 10)
+
+    def test_sem(self):
+        # example from stats.sem doc
+        a = np.arange(20).reshape(5, 4)
+        am = np.ma.array(a)
+        r = stats.sem(a, ddof=1)
+        rm = stats.mstats.sem(am, ddof=1)
+
+        assert_allclose(r, 2.82842712, atol=1e-5)
+        assert_allclose(rm, 2.82842712, atol=1e-5)
+
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            assert_almost_equal(stats.mstats.sem(xm, axis=None, ddof=0),
+                                stats.sem(x, axis=None, ddof=0), decimal=13)
+            assert_almost_equal(stats.mstats.sem(ym, axis=None, ddof=0),
+                                stats.sem(y, axis=None, ddof=0), decimal=13)
+            assert_almost_equal(stats.mstats.sem(xm, axis=None, ddof=1),
+                                stats.sem(x, axis=None, ddof=1), decimal=13)
+            assert_almost_equal(stats.mstats.sem(ym, axis=None, ddof=1),
+                                stats.sem(y, axis=None, ddof=1), decimal=13)
+
+    def test_describe(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            r = stats.describe(x, ddof=1)
+            rm = stats.mstats.describe(xm, ddof=1)
+            for ii in range(6):
+                assert_almost_equal(np.asarray(r[ii]),
+                                    np.asarray(rm[ii]),
+                                    decimal=12)
+
+    def test_describe_result_attributes(self):
+        actual = mstats.describe(np.arange(5))
+        attributes = ('nobs', 'minmax', 'mean', 'variance', 'skewness',
+                      'kurtosis')
+        check_named_results(actual, attributes, ma=True)
+
+    def test_rankdata(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            r = stats.rankdata(x)
+            rm = stats.mstats.rankdata(x)
+            assert_allclose(r, rm)
+
+    def test_tmean(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            assert_almost_equal(stats.tmean(x),stats.mstats.tmean(xm), 14)
+            assert_almost_equal(stats.tmean(y),stats.mstats.tmean(ym), 14)
+
+    def test_tmax(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            assert_almost_equal(stats.tmax(x,2.),
+                                stats.mstats.tmax(xm,2.), 10)
+            assert_almost_equal(stats.tmax(y,2.),
+                                stats.mstats.tmax(ym,2.), 10)
+
+            assert_almost_equal(stats.tmax(x, upperlimit=3.),
+                                stats.mstats.tmax(xm, upperlimit=3.), 10)
+            assert_almost_equal(stats.tmax(y, upperlimit=3.),
+                                stats.mstats.tmax(ym, upperlimit=3.), 10)
+
+    def test_tmin(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            assert_equal(stats.tmin(x), stats.mstats.tmin(xm))
+            assert_equal(stats.tmin(y), stats.mstats.tmin(ym))
+
+            assert_almost_equal(stats.tmin(x, lowerlimit=-1.),
+                                stats.mstats.tmin(xm, lowerlimit=-1.), 10)
+            assert_almost_equal(stats.tmin(y, lowerlimit=-1.),
+                                stats.mstats.tmin(ym, lowerlimit=-1.), 10)
+
+    def test_zmap(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            z = stats.zmap(x, y)
+            zm = stats.mstats.zmap(xm, ym)
+            assert_allclose(z, zm[0:len(z)], atol=1e-10)
+
+    def test_variation(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            assert_almost_equal(stats.variation(x), stats.mstats.variation(xm),
+                                decimal=12)
+            assert_almost_equal(stats.variation(y), stats.mstats.variation(ym),
+                                decimal=12)
+
+    def test_tvar(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            assert_almost_equal(stats.tvar(x), stats.mstats.tvar(xm),
+                                decimal=12)
+            assert_almost_equal(stats.tvar(y), stats.mstats.tvar(ym),
+                                decimal=12)
+
+    def test_trimboth(self):
+        a = np.arange(20)
+        b = stats.trimboth(a, 0.1)
+        bm = stats.mstats.trimboth(a, 0.1)
+        assert_allclose(np.sort(b), bm.data[~bm.mask])
+
+    def test_tsem(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            assert_almost_equal(stats.tsem(x), stats.mstats.tsem(xm),
+                                decimal=14)
+            assert_almost_equal(stats.tsem(y), stats.mstats.tsem(ym),
+                                decimal=14)
+            assert_almost_equal(stats.tsem(x, limits=(-2., 2.)),
+                                stats.mstats.tsem(xm, limits=(-2., 2.)),
+                                decimal=14)
+
+    def test_skewtest(self):
+        # this test is for 1D data
+        for n in self.get_n():
+            if n > 8:
+                x, y, xm, ym = self.generate_xy_sample(n)
+                r = stats.skewtest(x)
+                rm = stats.mstats.skewtest(xm)
+                assert_allclose(r, rm)
+
+    def test_skewtest_result_attributes(self):
+        x = np.array((-2, -1, 0, 1, 2, 3)*4)**2
+        res = mstats.skewtest(x)
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, ma=True)
+
+    def test_skewtest_2D_notmasked(self):
+        # a normal ndarray is passed to the masked function
+        rng = np.random.default_rng(2790153686)
+        x = rng.random((20, 2)) * 20.
+        r = stats.skewtest(x)
+        rm = stats.mstats.skewtest(x)
+        assert_allclose(np.asarray(r), np.asarray(rm))
+
+    def test_skewtest_2D_WithMask(self):
+        nx = 2
+        for n in self.get_n():
+            if n > 8:
+                x, y, xm, ym = self.generate_xy_sample2D(n, nx)
+                r = stats.skewtest(x)
+                rm = stats.mstats.skewtest(xm)
+
+                assert_allclose(r[0][0], rm[0][0], rtol=1e-14)
+                assert_allclose(r[0][1], rm[0][1], rtol=1e-14)
+
+    def test_normaltest(self):
+        with np.errstate(over='raise'), warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "`kurtosistest` p-value may be inaccurate", UserWarning)
+            warnings.filterwarnings(
+                "ignore", "kurtosistest only valid for n>=20", UserWarning)
+            for n in self.get_n():
+                if n > 8:
+                    x, y, xm, ym = self.generate_xy_sample(n)
+                    r = stats.normaltest(x)
+                    rm = stats.mstats.normaltest(xm)
+                    assert_allclose(np.asarray(r), np.asarray(rm))
+
+    def test_find_repeats(self):
+        x = np.asarray([1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 4]).astype('float')
+        tmp = np.asarray([1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5]).astype('float')
+        mask = (tmp == 5.)
+        xm = np.ma.array(tmp, mask=mask)
+        x_orig, xm_orig = x.copy(), xm.copy()
+
+        unique, unique_counts = np.unique(x, return_counts=True)
+        r = unique[unique_counts > 1], unique_counts[unique_counts > 1]
+        rm = stats.mstats.find_repeats(xm)
+
+        assert_equal(r, rm)
+        assert_equal(x, x_orig)
+        assert_equal(xm, xm_orig)
+
+        # This crazy behavior is expected by count_tied_groups, but is not
+        # in the docstring...
+        _, counts = stats.mstats.find_repeats([])
+        assert_equal(counts, np.array(0, dtype=np.intp))
+
+    def test_kendalltau(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            r = stats.kendalltau(x, y)
+            rm = stats.mstats.kendalltau(xm, ym)
+            assert_almost_equal(r[0], rm[0], decimal=10)
+            assert_almost_equal(r[1], rm[1], decimal=7)
+
+    def test_obrientransform(self):
+        for n in self.get_n():
+            x, y, xm, ym = self.generate_xy_sample(n)
+            r = np.stack(stats.obrientransform(x))
+            rm = stats.mstats.obrientransform(xm)
+            assert_almost_equal(r.T, rm[0:len(x)])
+
+    def test_ks_1samp(self):
+        """Checks that mstats.ks_1samp and stats.ks_1samp agree on masked arrays."""
+        for mode in ['auto', 'exact', 'asymp']:
+            with warnings.catch_warnings():
+                for alternative in ['less', 'greater', 'two-sided']:
+                    for n in self.get_n():
+                        x, y, xm, ym = self.generate_xy_sample(n)
+                        res1 = stats.ks_1samp(x, stats.norm.cdf,
+                                              alternative=alternative, mode=mode)
+                        res2 = stats.mstats.ks_1samp(xm, stats.norm.cdf,
+                                                     alternative=alternative, mode=mode)
+                        assert_equal(np.asarray(res1), np.asarray(res2))
+                        res3 = stats.ks_1samp(xm, stats.norm.cdf,
+                                              alternative=alternative, mode=mode)
+                        assert_equal(np.asarray(res1), np.asarray(res3))
+
+    def test_kstest_1samp(self):
+        """
+        Checks that 1-sample mstats.kstest and stats.kstest agree on masked arrays.
+        """
+        for mode in ['auto', 'exact', 'asymp']:
+            with warnings.catch_warnings():
+                for alternative in ['less', 'greater', 'two-sided']:
+                    for n in self.get_n():
+                        x, y, xm, ym = self.generate_xy_sample(n)
+                        res1 = stats.kstest(x, 'norm',
+                                            alternative=alternative, mode=mode)
+                        res2 = stats.mstats.kstest(xm, 'norm',
+                                                   alternative=alternative, mode=mode)
+                        assert_equal(np.asarray(res1), np.asarray(res2))
+                        res3 = stats.kstest(xm, 'norm',
+                                            alternative=alternative, mode=mode)
+                        assert_equal(np.asarray(res1), np.asarray(res3))
+
+    def test_ks_2samp(self):
+        """Checks that mstats.ks_2samp and stats.ks_2samp agree on masked arrays.
+        gh-8431"""
+        for mode in ['auto', 'exact', 'asymp']:
+            with warnings.catch_warnings():
+                if mode in ['auto', 'exact']:
+                    message = "ks_2samp: Exact calculation unsuccessful."
+                    warnings.filterwarnings("ignore", message, RuntimeWarning)
+                for alternative in ['less', 'greater', 'two-sided']:
+                    for n in self.get_n():
+                        x, y, xm, ym = self.generate_xy_sample(n)
+                        res1 = stats.ks_2samp(x, y,
+                                              alternative=alternative, mode=mode)
+                        res2 = stats.mstats.ks_2samp(xm, ym,
+                                                     alternative=alternative, mode=mode)
+                        assert_equal(np.asarray(res1), np.asarray(res2))
+                        res3 = stats.ks_2samp(xm, y,
+                                              alternative=alternative, mode=mode)
+                        assert_equal(np.asarray(res1), np.asarray(res3))
+
+    def test_kstest_2samp(self):
+        """
+        Checks that 2-sample mstats.kstest and stats.kstest agree on masked arrays.
+        """
+        for mode in ['auto', 'exact', 'asymp']:
+            with warnings.catch_warnings():
+                if mode in ['auto', 'exact']:
+                    message = "ks_2samp: Exact calculation unsuccessful."
+                    warnings.filterwarnings("ignore", message, RuntimeWarning)
+                for alternative in ['less', 'greater', 'two-sided']:
+                    for n in self.get_n():
+                        x, y, xm, ym = self.generate_xy_sample(n)
+                        res1 = stats.kstest(x, y,
+                                            alternative=alternative, mode=mode)
+                        res2 = stats.mstats.kstest(xm, ym,
+                                                   alternative=alternative, mode=mode)
+                        assert_equal(np.asarray(res1), np.asarray(res2))
+                        res3 = stats.kstest(xm, y,
+                                            alternative=alternative, mode=mode)
+                        assert_equal(np.asarray(res1), np.asarray(res3))
+
+
+class TestBrunnerMunzel:
+    # Data from (Lumley, 1996)
+    X = np.ma.masked_invalid([1, 2, 1, 1, 1, np.nan, 1, 1,
+                              1, 1, 1, 2, 4, 1, 1, np.nan])
+    Y = np.ma.masked_invalid([3, 3, 4, 3, np.nan, 1, 2, 3, 1, 1, 5, 4])
+    significant = 14
+
+    def test_brunnermunzel_one_sided(self):
+        # Results are compared with R's lawstat package.
+        u1, p1 = mstats.brunnermunzel(self.X, self.Y, alternative='less')
+        u2, p2 = mstats.brunnermunzel(self.Y, self.X, alternative='greater')
+        u3, p3 = mstats.brunnermunzel(self.X, self.Y, alternative='greater')
+        u4, p4 = mstats.brunnermunzel(self.Y, self.X, alternative='less')
+
+        assert_almost_equal(p1, p2, decimal=self.significant)
+        assert_almost_equal(p3, p4, decimal=self.significant)
+        assert_(p1 != p3)
+        assert_almost_equal(u1, 3.1374674823029505,
+                            decimal=self.significant)
+        assert_almost_equal(u2, -3.1374674823029505,
+                            decimal=self.significant)
+        assert_almost_equal(u3, 3.1374674823029505,
+                            decimal=self.significant)
+        assert_almost_equal(u4, -3.1374674823029505,
+                            decimal=self.significant)
+        assert_almost_equal(p1, 0.0028931043330757342,
+                            decimal=self.significant)
+        assert_almost_equal(p3, 0.99710689566692423,
+                            decimal=self.significant)
+
+    def test_brunnermunzel_two_sided(self):
+        # Results are compared with R's lawstat package.
+        u1, p1 = mstats.brunnermunzel(self.X, self.Y, alternative='two-sided')
+        u2, p2 = mstats.brunnermunzel(self.Y, self.X, alternative='two-sided')
+
+        assert_almost_equal(p1, p2, decimal=self.significant)
+        assert_almost_equal(u1, 3.1374674823029505,
+                            decimal=self.significant)
+        assert_almost_equal(u2, -3.1374674823029505,
+                            decimal=self.significant)
+        assert_almost_equal(p1, 0.0057862086661515377,
+                            decimal=self.significant)
+
+    def test_brunnermunzel_default(self):
+        # The default value for alternative is two-sided
+        u1, p1 = mstats.brunnermunzel(self.X, self.Y)
+        u2, p2 = mstats.brunnermunzel(self.Y, self.X)
+
+        assert_almost_equal(p1, p2, decimal=self.significant)
+        assert_almost_equal(u1, 3.1374674823029505,
+                            decimal=self.significant)
+        assert_almost_equal(u2, -3.1374674823029505,
+                            decimal=self.significant)
+        assert_almost_equal(p1, 0.0057862086661515377,
+                            decimal=self.significant)
+
+    def test_brunnermunzel_alternative_error(self):
+        alternative = "error"
+        distribution = "t"
+        assert_(alternative not in ["two-sided", "greater", "less"])
+        assert_raises(ValueError,
+                      mstats.brunnermunzel,
+                      self.X,
+                      self.Y,
+                      alternative,
+                      distribution)
+
+    def test_brunnermunzel_distribution_norm(self):
+        u1, p1 = mstats.brunnermunzel(self.X, self.Y, distribution="normal")
+        u2, p2 = mstats.brunnermunzel(self.Y, self.X, distribution="normal")
+        assert_almost_equal(p1, p2, decimal=self.significant)
+        assert_almost_equal(u1, 3.1374674823029505,
+                            decimal=self.significant)
+        assert_almost_equal(u2, -3.1374674823029505,
+                            decimal=self.significant)
+        assert_almost_equal(p1, 0.0017041417600383024,
+                            decimal=self.significant)
+
+    def test_brunnermunzel_distribution_error(self):
+        alternative = "two-sided"
+        distribution = "error"
+        assert_(alternative not in ["t", "normal"])
+        assert_raises(ValueError,
+                      mstats.brunnermunzel,
+                      self.X,
+                      self.Y,
+                      alternative,
+                      distribution)
+
+    def test_brunnermunzel_empty_imput(self):
+        u1, p1 = mstats.brunnermunzel(self.X, [])
+        u2, p2 = mstats.brunnermunzel([], self.Y)
+        u3, p3 = mstats.brunnermunzel([], [])
+
+        assert_(np.isnan(u1))
+        assert_(np.isnan(p1))
+        assert_(np.isnan(u2))
+        assert_(np.isnan(p2))
+        assert_(np.isnan(u3))
+        assert_(np.isnan(p3))
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_mstats_extras.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_mstats_extras.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b14455b417317a23abda8efe5297e8f4839ffd2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_mstats_extras.py
@@ -0,0 +1,172 @@
+import numpy as np
+import numpy.ma as ma
+import scipy.stats.mstats as ms
+
+from numpy.testing import (assert_equal, assert_almost_equal, assert_,
+                           assert_allclose)
+
+
+def test_compare_medians_ms():
+    x = np.arange(7)
+    y = x + 10
+    assert_almost_equal(ms.compare_medians_ms(x, y), 0)
+
+    y2 = np.linspace(0, 1, num=10)
+    assert_almost_equal(ms.compare_medians_ms(x, y2), 0.017116406778)
+
+
+def test_hdmedian():
+    # 1-D array
+    x = ma.arange(11)
+    assert_allclose(ms.hdmedian(x), 5, rtol=1e-14)
+    x.mask = ma.make_mask(x)
+    x.mask[:7] = False
+    assert_allclose(ms.hdmedian(x), 3, rtol=1e-14)
+
+    # Check that `var` keyword returns a value.  TODO: check whether returned
+    # value is actually correct.
+    assert_(ms.hdmedian(x, var=True).size == 2)
+
+    # 2-D array
+    x2 = ma.arange(22).reshape((11, 2))
+    assert_allclose(ms.hdmedian(x2, axis=0), [10, 11])
+    x2.mask = ma.make_mask(x2)
+    x2.mask[:7, :] = False
+    assert_allclose(ms.hdmedian(x2, axis=0), [6, 7])
+
+
+def test_rsh():
+    rng = np.random.default_rng(806795795)
+    x = rng.standard_normal(100)
+    res = ms.rsh(x)
+    # Just a sanity check that the code runs and output shape is correct.
+    # TODO: check that implementation is correct.
+    assert_(res.shape == x.shape)
+
+    # Check points keyword
+    res = ms.rsh(x, points=[0, 1.])
+    assert_(res.size == 2)
+
+
+def test_mjci():
+    # Tests the Marits-Jarrett estimator
+    data = ma.array([77, 87, 88,114,151,210,219,246,253,262,
+                     296,299,306,376,428,515,666,1310,2611])
+    assert_almost_equal(ms.mjci(data),[55.76819,45.84028,198.87875],5)
+
+
+def test_trimmed_mean_ci():
+    # Tests the confidence intervals of the trimmed mean.
+    data = ma.array([545,555,558,572,575,576,578,580,
+                     594,605,635,651,653,661,666])
+    assert_almost_equal(ms.trimmed_mean(data,0.2), 596.2, 1)
+    assert_equal(np.round(ms.trimmed_mean_ci(data,(0.2,0.2)),1),
+                 [561.8, 630.6])
+
+
+def test_idealfourths():
+    # Tests ideal-fourths
+    test = np.arange(100)
+    assert_almost_equal(np.asarray(ms.idealfourths(test)),
+                        [24.416667,74.583333],6)
+    test_2D = test.repeat(3).reshape(-1,3)
+    assert_almost_equal(ms.idealfourths(test_2D, axis=0),
+                        [[24.416667,24.416667,24.416667],
+                         [74.583333,74.583333,74.583333]],6)
+    assert_almost_equal(ms.idealfourths(test_2D, axis=1),
+                        test.repeat(2).reshape(-1,2))
+    test = [0, 0]
+    _result = ms.idealfourths(test)
+    assert_(np.isnan(_result).all())
+
+
+class TestQuantiles:
+    data = [0.706560797,0.727229578,0.990399276,0.927065621,0.158953014,
+            0.887764025,0.239407086,0.349638551,0.972791145,0.149789972,
+            0.936947700,0.132359948,0.046041972,0.641675031,0.945530547,
+            0.224218684,0.771450991,0.820257774,0.336458052,0.589113496,
+            0.509736129,0.696838829,0.491323573,0.622767425,0.775189248,
+            0.641461450,0.118455200,0.773029450,0.319280007,0.752229111,
+            0.047841438,0.466295911,0.583850781,0.840581845,0.550086491,
+            0.466470062,0.504765074,0.226855960,0.362641207,0.891620942,
+            0.127898691,0.490094097,0.044882048,0.041441695,0.317976349,
+            0.504135618,0.567353033,0.434617473,0.636243375,0.231803616,
+            0.230154113,0.160011327,0.819464108,0.854706985,0.438809221,
+            0.487427267,0.786907310,0.408367937,0.405534192,0.250444460,
+            0.995309248,0.144389588,0.739947527,0.953543606,0.680051621,
+            0.388382017,0.863530727,0.006514031,0.118007779,0.924024803,
+            0.384236354,0.893687694,0.626534881,0.473051932,0.750134705,
+            0.241843555,0.432947602,0.689538104,0.136934797,0.150206859,
+            0.474335206,0.907775349,0.525869295,0.189184225,0.854284286,
+            0.831089744,0.251637345,0.587038213,0.254475554,0.237781276,
+            0.827928620,0.480283781,0.594514455,0.213641488,0.024194386,
+            0.536668589,0.699497811,0.892804071,0.093835427,0.731107772]
+
+    def test_hdquantiles(self):
+        data = self.data
+        assert_almost_equal(ms.hdquantiles(data,[0., 1.]),
+                            [0.006514031, 0.995309248])
+        hdq = ms.hdquantiles(data,[0.25, 0.5, 0.75])
+        assert_almost_equal(hdq, [0.253210762, 0.512847491, 0.762232442,])
+
+        data = np.array(data).reshape(10,10)
+        hdq = ms.hdquantiles(data,[0.25,0.5,0.75],axis=0)
+        assert_almost_equal(hdq[:,0], ms.hdquantiles(data[:,0],[0.25,0.5,0.75]))
+        assert_almost_equal(hdq[:,-1], ms.hdquantiles(data[:,-1],[0.25,0.5,0.75]))
+        hdq = ms.hdquantiles(data,[0.25,0.5,0.75],axis=0,var=True)
+        assert_almost_equal(hdq[...,0],
+                            ms.hdquantiles(data[:,0],[0.25,0.5,0.75],var=True))
+        assert_almost_equal(hdq[...,-1],
+                            ms.hdquantiles(data[:,-1],[0.25,0.5,0.75], var=True))
+
+    def test_hdquantiles_sd(self):
+        # Standard deviation is a jackknife estimator, so we can check if
+        # the efficient version (hdquantiles_sd) matches a rudimentary,
+        # but clear version here.
+
+        hd_std_errs = ms.hdquantiles_sd(self.data)
+
+        # jacknnife standard error, Introduction to the Bootstrap Eq. 11.5
+        n = len(self.data)
+        jdata = np.broadcast_to(self.data, (n, n))
+        jselector = np.logical_not(np.eye(n))  # leave out one sample each row
+        jdata = jdata[jselector].reshape(n, n-1)
+        jdist = ms.hdquantiles(jdata, axis=1)
+        jdist_mean = np.mean(jdist, axis=0)
+        jstd = ((n-1)/n * np.sum((jdist - jdist_mean)**2, axis=0))**.5
+
+        assert_almost_equal(hd_std_errs, jstd)
+        # Test actual values for good measure
+        assert_almost_equal(hd_std_errs, [0.0379258, 0.0380656, 0.0380013])
+
+        two_data_points = ms.hdquantiles_sd([1, 2])
+        assert_almost_equal(two_data_points, [0.5, 0.5, 0.5])
+
+    def test_mquantiles_cimj(self):
+        # Only test that code runs, implementation not checked for correctness
+        ci_lower, ci_upper = ms.mquantiles_cimj(self.data)
+        assert_(ci_lower.size == ci_upper.size == 3)
+
+
+def test_median_cihs():
+    # Basic test against R library EnvStats function `eqnpar`, e.g.
+    # library(EnvStats)
+    # options(digits=8)
+    # x = c(0.88612955, 0.35242375, 0.66240904, 0.94617974, 0.10929913,
+    #       0.76699506, 0.88550655, 0.62763754, 0.76818588, 0.68506508,
+    #       0.88043148, 0.03911248, 0.93805564, 0.95326961, 0.25291112,
+    #       0.16128487, 0.49784577, 0.24588924, 0.6597, 0.92239679)
+    # eqnpar(x, p=0.5,
+    #        ci.method = "interpolate", approx.conf.level = 0.95, ci = TRUE)
+    rng = np.random.default_rng(8824288259505800535)
+    x = rng.random(size=20)
+    assert_allclose(ms.median_cihs(x), (0.38663198, 0.88431272))
+
+    # SciPy's 90% CI upper limit doesn't match that of EnvStats eqnpar. SciPy
+    # doesn't look wrong, and it agrees with a different reference,
+    # `median_confint_hs` from `hoehleatsu/quantileCI`.
+    # In (e.g.) Colab with R runtime:
+    # devtools::install_github("hoehleatsu/quantileCI")
+    # library(quantileCI)
+    # median_confint_hs(x=x, conf.level=0.90, interpolate=TRUE)
+    assert_allclose(ms.median_cihs(x, 0.1), (0.48319773366, 0.88094268050))
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_multicomp.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_multicomp.py
new file mode 100644
index 0000000000000000000000000000000000000000..2860b142c38a339f96c50af92bd452c6de95e84d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_multicomp.py
@@ -0,0 +1,405 @@
+import copy
+
+import numpy as np
+import pytest
+from numpy.testing import assert_allclose
+
+from scipy import stats
+from scipy.stats._multicomp import _pvalue_dunnett, DunnettResult
+
+
+class TestDunnett:
+    # For the following tests, p-values were computed using Matlab, e.g.
+    #     sample = [18.  15.  18.  16.  17.  15.  14.  14.  14.  15.  15....
+    #               14.  15.  14.  22.  18.  21.  21.  10.  10.  11.  9....
+    #               25.  26.  17.5 16.  15.5 14.5 22.  22.  24.  22.5 29....
+    #               24.5 20.  18.  18.5 17.5 26.5 13.  16.5 13.  13.  13....
+    #               28.  27.  34.  31.  29.  27.  24.  23.  38.  36.  25....
+    #               38. 26.  22.  36.  27.  27.  32.  28.  31....
+    #               24.  27.  33.  32.  28.  19. 37.  31.  36.  36....
+    #               34.  38.  32.  38.  32....
+    #               26.  24.  26.  25.  29. 29.5 16.5 36.  44....
+    #               25.  27.  19....
+    #               25.  20....
+    #               28.];
+    #     j = [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
+    #          0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...
+    #          0 0 0 0...
+    #          1 1 1 1 1 1 1 1 1 1 1 1 1 1 1...
+    #          2 2 2 2 2 2 2 2 2...
+    #          3 3 3...
+    #          4 4...
+    #          5];
+    #     [~, ~, stats] = anova1(sample, j, "off");
+    #     [results, ~, ~, gnames] = multcompare(stats, ...
+    #     "CriticalValueType", "dunnett", ...
+    #     "Approximate", false);
+    #     tbl = array2table(results, "VariableNames", ...
+    #     ["Group", "Control Group", "Lower Limit", ...
+    #     "Difference", "Upper Limit", "P-value"]);
+    #     tbl.("Group") = gnames(tbl.("Group"));
+    #     tbl.("Control Group") = gnames(tbl.("Control Group"))
+
+    # Matlab doesn't report the statistic, so the statistics were
+    # computed using R multcomp `glht`, e.g.:
+    #     library(multcomp)
+    #     options(digits=16)
+    #     control < - c(18.0, 15.0, 18.0, 16.0, 17.0, 15.0, 14.0, 14.0, 14.0,
+    #                   15.0, 15.0, 14.0, 15.0, 14.0, 22.0, 18.0, 21.0, 21.0,
+    #                   10.0, 10.0, 11.0, 9.0, 25.0, 26.0, 17.5, 16.0, 15.5,
+    #                   14.5, 22.0, 22.0, 24.0, 22.5, 29.0, 24.5, 20.0, 18.0,
+    #                   18.5, 17.5, 26.5, 13.0, 16.5, 13.0, 13.0, 13.0, 28.0,
+    #                   27.0, 34.0, 31.0, 29.0, 27.0, 24.0, 23.0, 38.0, 36.0,
+    #                   25.0, 38.0, 26.0, 22.0, 36.0, 27.0, 27.0, 32.0, 28.0,
+    #                   31.0)
+    #     t < - c(24.0, 27.0, 33.0, 32.0, 28.0, 19.0, 37.0, 31.0, 36.0, 36.0,
+    #             34.0, 38.0, 32.0, 38.0, 32.0)
+    #     w < - c(26.0, 24.0, 26.0, 25.0, 29.0, 29.5, 16.5, 36.0, 44.0)
+    #     x < - c(25.0, 27.0, 19.0)
+    #     y < - c(25.0, 20.0)
+    #     z < - c(28.0)
+    #
+    #     groups = factor(rep(c("control", "t", "w", "x", "y", "z"),
+    #                         times=c(length(control), length(t), length(w),
+    #                                 length(x), length(y), length(z))))
+    #     df < - data.frame(response=c(control, t, w, x, y, z),
+    #                       group=groups)
+    #     model < - aov(response
+    #     ~group, data = df)
+    #     test < - glht(model=model,
+    #                   linfct=mcp(group="Dunnett"),
+    #                   alternative="g")
+    #     summary(test)
+    #     confint(test)
+    # p-values agreed with those produced by Matlab to at least atol=1e-3
+
+    # From Matlab's documentation on multcompare
+    samples_1 = [
+        [
+            24.0, 27.0, 33.0, 32.0, 28.0, 19.0, 37.0, 31.0, 36.0, 36.0,
+            34.0, 38.0, 32.0, 38.0, 32.0
+        ],
+        [26.0, 24.0, 26.0, 25.0, 29.0, 29.5, 16.5, 36.0, 44.0],
+        [25.0, 27.0, 19.0],
+        [25.0, 20.0],
+        [28.0]
+    ]
+    control_1 = [
+        18.0, 15.0, 18.0, 16.0, 17.0, 15.0, 14.0, 14.0, 14.0, 15.0, 15.0,
+        14.0, 15.0, 14.0, 22.0, 18.0, 21.0, 21.0, 10.0, 10.0, 11.0, 9.0,
+        25.0, 26.0, 17.5, 16.0, 15.5, 14.5, 22.0, 22.0, 24.0, 22.5, 29.0,
+        24.5, 20.0, 18.0, 18.5, 17.5, 26.5, 13.0, 16.5, 13.0, 13.0, 13.0,
+        28.0, 27.0, 34.0, 31.0, 29.0, 27.0, 24.0, 23.0, 38.0, 36.0, 25.0,
+        38.0, 26.0, 22.0, 36.0, 27.0, 27.0, 32.0, 28.0, 31.0
+    ]
+    pvalue_1 = [4.727e-06, 0.022346, 0.97912, 0.99953, 0.86579]  # Matlab
+    # Statistic, alternative p-values, and CIs computed with R multcomp `glht`
+    p_1_twosided = [1e-4, 0.02237, 0.97913, 0.99953, 0.86583]
+    p_1_greater = [1e-4, 0.011217, 0.768500, 0.896991, 0.577211]
+    p_1_less = [1, 1, 0.99660, 0.98398, .99953]
+    statistic_1 = [5.27356, 2.91270, 0.60831, 0.27002, 0.96637]
+    ci_1_twosided = [[5.3633917835622, 0.7296142201217, -8.3879817106607,
+                      -11.9090753452911, -11.7655021543469],
+                     [15.9709832164378, 13.8936496687672, 13.4556900439941,
+                      14.6434503452911, 25.4998771543469]]
+    ci_1_greater = [5.9036402398526, 1.4000632918725, -7.2754756323636,
+                    -10.5567456382391, -9.8675629499576]
+    ci_1_less = [15.4306165948619, 13.2230539537359, 12.3429406339544,
+                 13.2908248513211, 23.6015228251660]
+    pvalues_1 = dict(twosided=p_1_twosided, less=p_1_less, greater=p_1_greater)
+    cis_1 = dict(twosided=ci_1_twosided, less=ci_1_less, greater=ci_1_greater)
+    case_1 = dict(samples=samples_1, control=control_1, statistic=statistic_1,
+                  pvalues=pvalues_1, cis=cis_1)
+
+    # From Dunnett1955 comparing with R's DescTools: DunnettTest
+    samples_2 = [[9.76, 8.80, 7.68, 9.36], [12.80, 9.68, 12.16, 9.20, 10.55]]
+    control_2 = [7.40, 8.50, 7.20, 8.24, 9.84, 8.32]
+    pvalue_2 = [0.6201, 0.0058]
+    # Statistic, alternative p-values, and CIs computed with R multcomp `glht`
+    p_2_twosided = [0.6201020, 0.0058254]
+    p_2_greater = [0.3249776, 0.0029139]
+    p_2_less = [0.91676, 0.99984]
+    statistic_2 = [0.85703, 3.69375]
+    ci_2_twosided = [[-1.2564116462124, 0.8396273539789],
+                     [2.5564116462124, 4.4163726460211]]
+    ci_2_greater = [-0.9588591188156, 1.1187563667543]
+    ci_2_less = [2.2588591188156, 4.1372436332457]
+    pvalues_2 = dict(twosided=p_2_twosided, less=p_2_less, greater=p_2_greater)
+    cis_2 = dict(twosided=ci_2_twosided, less=ci_2_less, greater=ci_2_greater)
+    case_2 = dict(samples=samples_2, control=control_2, statistic=statistic_2,
+                  pvalues=pvalues_2, cis=cis_2)
+
+    samples_3 = [[55, 64, 64], [55, 49, 52], [50, 44, 41]]
+    control_3 = [55, 47, 48]
+    pvalue_3 = [0.0364, 0.8966, 0.4091]
+    # Statistic, alternative p-values, and CIs computed with R multcomp `glht`
+    p_3_twosided = [0.036407, 0.896539, 0.409295]
+    p_3_greater = [0.018277, 0.521109, 0.981892]
+    p_3_less = [0.99944, 0.90054, 0.20974]
+    statistic_3 = [3.09073, 0.56195, -1.40488]
+    ci_3_twosided = [[0.7529028025053, -8.2470971974947, -15.2470971974947],
+                     [21.2470971974947, 12.2470971974947, 5.2470971974947]]
+    ci_3_greater = [2.4023682323149, -6.5976317676851, -13.5976317676851]
+    ci_3_less = [19.5984402363662, 10.5984402363662, 3.5984402363662]
+    pvalues_3 = dict(twosided=p_3_twosided, less=p_3_less, greater=p_3_greater)
+    cis_3 = dict(twosided=ci_3_twosided, less=ci_3_less, greater=ci_3_greater)
+    case_3 = dict(samples=samples_3, control=control_3, statistic=statistic_3,
+                  pvalues=pvalues_3, cis=cis_3)
+
+    # From Thomson and Short,
+    # Mucociliary function in health, chronic obstructive airway disease,
+    # and asbestosis, Journal of Applied Physiology, 1969. Table 1
+    # Comparing with R's DescTools: DunnettTest
+    samples_4 = [[3.8, 2.7, 4.0, 2.4], [2.8, 3.4, 3.7, 2.2, 2.0]]
+    control_4 = [2.9, 3.0, 2.5, 2.6, 3.2]
+    pvalue_4 = [0.5832, 0.9982]
+    # Statistic, alternative p-values, and CIs computed with R multcomp `glht`
+    p_4_twosided = [0.58317, 0.99819]
+    p_4_greater = [0.30225, 0.69115]
+    p_4_less = [0.91929, 0.65212]
+    statistic_4 = [0.90875, -0.05007]
+    ci_4_twosided = [[-0.6898153448579, -1.0333456251632],
+                     [1.4598153448579, 0.9933456251632]]
+    ci_4_greater = [-0.5186459268412, -0.8719655502147 ]
+    ci_4_less = [1.2886459268412, 0.8319655502147]
+    pvalues_4 = dict(twosided=p_4_twosided, less=p_4_less, greater=p_4_greater)
+    cis_4 = dict(twosided=ci_4_twosided, less=ci_4_less, greater=ci_4_greater)
+    case_4 = dict(samples=samples_4, control=control_4, statistic=statistic_4,
+                  pvalues=pvalues_4, cis=cis_4)
+
+    @pytest.mark.parametrize(
+        'rho, n_groups, df, statistic, pvalue, alternative',
+        [
+            # From Dunnett1955
+            # Tables 1a and 1b pages 1117-1118
+            (0.5, 1, 10, 1.81, 0.05, "greater"),  # different than two-sided
+            (0.5, 3, 10, 2.34, 0.05, "greater"),
+            (0.5, 2, 30, 1.99, 0.05, "greater"),
+            (0.5, 5, 30, 2.33, 0.05, "greater"),
+            (0.5, 4, 12, 3.32, 0.01, "greater"),
+            (0.5, 7, 12, 3.56, 0.01, "greater"),
+            (0.5, 2, 60, 2.64, 0.01, "greater"),
+            (0.5, 4, 60, 2.87, 0.01, "greater"),
+            (0.5, 4, 60, [2.87, 2.21], [0.01, 0.05], "greater"),
+            # Tables 2a and 2b pages 1119-1120
+            (0.5, 1, 10, 2.23, 0.05, "two-sided"),  # two-sided
+            (0.5, 3, 10, 2.81, 0.05, "two-sided"),
+            (0.5, 2, 30, 2.32, 0.05, "two-sided"),
+            (0.5, 3, 20, 2.57, 0.05, "two-sided"),
+            (0.5, 4, 12, 3.76, 0.01, "two-sided"),
+            (0.5, 7, 12, 4.08, 0.01, "two-sided"),
+            (0.5, 2, 60, 2.90, 0.01, "two-sided"),
+            (0.5, 4, 60, 3.14, 0.01, "two-sided"),
+            (0.5, 4, 60, [3.14, 2.55], [0.01, 0.05], "two-sided"),
+        ],
+    )
+    def test_critical_values(
+        self, rho, n_groups, df, statistic, pvalue, alternative
+    ):
+        rng = np.random.default_rng(165250594791731684851746311027739134893)
+        rho = np.full((n_groups, n_groups), rho)
+        np.fill_diagonal(rho, 1)
+
+        statistic = np.array(statistic)
+        res = _pvalue_dunnett(
+            rho=rho, df=df, statistic=statistic,
+            alternative=alternative,
+            rng=rng
+        )
+        assert_allclose(res, pvalue, atol=5e-3)
+
+    @pytest.mark.parametrize(
+        'samples, control, pvalue, statistic',
+        [
+            (samples_1, control_1, pvalue_1, statistic_1),
+            (samples_2, control_2, pvalue_2, statistic_2),
+            (samples_3, control_3, pvalue_3, statistic_3),
+            (samples_4, control_4, pvalue_4, statistic_4),
+        ]
+    )
+    def test_basic(self, samples, control, pvalue, statistic):
+        rng = np.random.default_rng(11681140010308601919115036826969764808)
+
+        res = stats.dunnett(*samples, control=control, rng=rng)
+
+        assert isinstance(res, DunnettResult)
+        assert_allclose(res.statistic, statistic, rtol=5e-5)
+        assert_allclose(res.pvalue, pvalue, rtol=1e-2, atol=1e-4)
+
+    @pytest.mark.parametrize(
+        'alternative',
+        ['two-sided', 'less', 'greater']
+    )
+    def test_ttest_ind(self, alternative):
+        # check that `dunnett` agrees with `ttest_ind`
+        # when there are only two groups
+        rng = np.random.default_rng(114184017807316971636137493526995620351)
+
+        for _ in range(10):
+            sample = rng.integers(-100, 100, size=(10,))
+            control = rng.integers(-100, 100, size=(10,))
+
+            # preserve use of old random_state during SPEC 7 transition
+            res = stats.dunnett(
+                sample, control=control,
+                alternative=alternative, random_state=rng
+            )
+            ref = stats.ttest_ind(
+                sample, control,
+                alternative=alternative
+            )
+
+            assert_allclose(res.statistic, ref.statistic, rtol=1e-3, atol=1e-5)
+            assert_allclose(res.pvalue, ref.pvalue, rtol=1e-3, atol=1e-5)
+
+    @pytest.mark.parametrize(
+        'alternative, pvalue',
+        [
+            ('less', [0, 1]),
+            ('greater', [1, 0]),
+            ('two-sided', [0, 0]),
+        ]
+    )
+    def test_alternatives(self, alternative, pvalue):
+        rng = np.random.default_rng(114184017807316971636137493526995620351)
+
+        # width of 20 and min diff between samples/control is 60
+        # and maximal diff would be 100
+        sample_less = rng.integers(0, 20, size=(10,))
+        control = rng.integers(80, 100, size=(10,))
+        sample_greater = rng.integers(160, 180, size=(10,))
+
+        res = stats.dunnett(
+            sample_less, sample_greater, control=control,
+            alternative=alternative, rng=rng
+        )
+        assert_allclose(res.pvalue, pvalue, atol=1e-7)
+
+        ci = res.confidence_interval()
+        # two-sided is comparable for high/low
+        if alternative == 'less':
+            assert np.isneginf(ci.low).all()
+            assert -100 < ci.high[0] < -60
+            assert 60 < ci.high[1] < 100
+        elif alternative == 'greater':
+            assert -100 < ci.low[0] < -60
+            assert 60 < ci.low[1] < 100
+            assert np.isposinf(ci.high).all()
+        elif alternative == 'two-sided':
+            assert -100 < ci.low[0] < -60
+            assert 60 < ci.low[1] < 100
+            assert -100 < ci.high[0] < -60
+            assert 60 < ci.high[1] < 100
+
+    @pytest.mark.parametrize("case", [case_1, case_2, case_3, case_4])
+    @pytest.mark.parametrize("alternative", ['less', 'greater', 'two-sided'])
+    def test_against_R_multicomp_glht(self, case, alternative):
+        rng = np.random.default_rng(189117774084579816190295271136455278291)
+        samples = case['samples']
+        control = case['control']
+        alternatives = {'less': 'less', 'greater': 'greater',
+                        'two-sided': 'twosided'}
+        p_ref = case['pvalues'][alternative.replace('-', '')]
+
+        res = stats.dunnett(*samples, control=control, alternative=alternative,
+                            rng=rng)
+        # atol can't be tighter because R reports some pvalues as "< 1e-4"
+        assert_allclose(res.pvalue, p_ref, rtol=5e-3, atol=1e-4)
+
+        ci_ref = case['cis'][alternatives[alternative]]
+        if alternative == "greater":
+            ci_ref = [ci_ref, np.inf]
+        elif alternative == "less":
+            ci_ref = [-np.inf, ci_ref]
+        assert res._ci is None
+        assert res._ci_cl is None
+        ci = res.confidence_interval(confidence_level=0.95)
+        assert_allclose(ci.low, ci_ref[0], rtol=5e-3, atol=1e-5)
+        assert_allclose(ci.high, ci_ref[1], rtol=5e-3, atol=1e-5)
+
+        # re-run to use the cached value "is" to check id as same object
+        assert res._ci is ci
+        assert res._ci_cl == 0.95
+        ci_ = res.confidence_interval(confidence_level=0.95)
+        assert ci_ is ci
+
+    @pytest.mark.parametrize('alternative', ["two-sided", "less", "greater"])
+    def test_str(self, alternative):
+        rng = np.random.default_rng(189117774084579816190295271136455278291)
+
+        res = stats.dunnett(
+            *self.samples_3, control=self.control_3, alternative=alternative,
+            rng=rng
+        )
+
+        # check some str output
+        res_str = str(res)
+        assert '(Sample 2 - Control)' in res_str
+        assert '95.0%' in res_str
+
+        if alternative == 'less':
+            assert '-inf' in res_str
+            assert '19.' in res_str
+        elif alternative == 'greater':
+            assert 'inf' in res_str
+            assert '-13.' in res_str
+        else:
+            assert 'inf' not in res_str
+            assert '21.' in res_str
+
+    def test_warnings(self):
+        rng = np.random.default_rng(189117774084579816190295271136455278291)
+
+        res = stats.dunnett(
+            *self.samples_3, control=self.control_3, rng=rng
+        )
+        msg = r"Computation of the confidence interval did not converge"
+        with pytest.warns(UserWarning, match=msg):
+            res._allowance(tol=1e-5)
+
+    def test_raises(self):
+        samples, control = self.samples_3, self.control_3
+
+        # alternative
+        with pytest.raises(ValueError, match="alternative must be"):
+            stats.dunnett(*samples, control=control, alternative='bob')
+
+        # 2D for a sample
+        samples_ = copy.deepcopy(samples)
+        samples_[0] = [samples_[0]]
+        with pytest.raises(ValueError, match="must be 1D arrays"):
+            stats.dunnett(*samples_, control=control)
+
+        # 2D for control
+        control_ = copy.deepcopy(control)
+        control_ = [control_]
+        with pytest.raises(ValueError, match="must be 1D arrays"):
+            stats.dunnett(*samples, control=control_)
+
+        # No obs in a sample
+        samples_ = copy.deepcopy(samples)
+        samples_[1] = []
+        with pytest.raises(ValueError, match="at least 1 observation"):
+            stats.dunnett(*samples_, control=control)
+
+        # No obs in control
+        control_ = []
+        with pytest.raises(ValueError, match="at least 1 observation"):
+            stats.dunnett(*samples, control=control_)
+
+        res = stats.dunnett(*samples, control=control)
+        with pytest.raises(ValueError, match="Confidence level must"):
+            res.confidence_interval(confidence_level=3)
+
+    @pytest.mark.filterwarnings("ignore:Computation of the confidence")
+    @pytest.mark.parametrize('n_samples', [1, 2, 3])
+    def test_shapes(self, n_samples):
+        rng = np.random.default_rng(689448934110805334)
+        samples = rng.normal(size=(n_samples, 10))
+        control = rng.normal(size=10)
+        res = stats.dunnett(*samples, control=control, rng=rng)
+        assert res.statistic.shape == (n_samples,)
+        assert res.pvalue.shape == (n_samples,)
+        ci = res.confidence_interval()
+        assert ci.low.shape == (n_samples,)
+        assert ci.high.shape == (n_samples,)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_multivariate.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_multivariate.py
new file mode 100644
index 0000000000000000000000000000000000000000..46690b575a0c56c250d624bad87272f5a1fabd2e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_multivariate.py
@@ -0,0 +1,5052 @@
+"""
+Test functions for multivariate normal, t, and related distributions.
+
+"""
+import pickle
+from dataclasses import dataclass
+
+from numpy.testing import (assert_allclose, assert_almost_equal,
+                           assert_array_almost_equal, assert_equal,
+                           assert_array_less, assert_)
+import pytest
+from pytest import raises as assert_raises
+
+from .test_continuous_basic import check_distribution_rvs
+
+import numpy as np
+
+import scipy.linalg
+
+from scipy.stats._multivariate import (_PSD,
+                                       _lnB,
+                                       multivariate_normal_frozen)
+from scipy.stats import (multivariate_normal, multivariate_hypergeom,
+                         matrix_normal, special_ortho_group, ortho_group,
+                         random_correlation, unitary_group, dirichlet,
+                         beta, wishart, multinomial, invwishart, chi2,
+                         invgamma, norm, uniform, ks_2samp, kstest, binom,
+                         hypergeom, multivariate_t, cauchy, normaltest,
+                         random_table, uniform_direction, vonmises_fisher,
+                         dirichlet_multinomial, vonmises, matrix_t)
+
+from scipy.stats import _covariance, Covariance
+from scipy.stats._continuous_distns import _norm_pdf as norm_pdf
+from scipy import stats
+
+from scipy.integrate import tanhsinh, cubature, quad
+from scipy.integrate import romb, qmc_quad, dblquad, tplquad
+from scipy.special import multigammaln
+import scipy.special as special
+
+from .common_tests import check_random_state_property
+from .data._mvt import _qsimvtv
+
+from unittest.mock import patch
+
+
+def assert_close(res, ref, *args, **kwargs):
+    res, ref = np.asarray(res), np.asarray(ref)
+    assert_allclose(res, ref, *args, **kwargs)
+    assert_equal(res.shape, ref.shape)
+
+
+class TestCovariance:
+
+    def test_input_validation(self):
+
+        message = "The input `precision` must be a square, two-dimensional..."
+        with pytest.raises(ValueError, match=message):
+            _covariance.CovViaPrecision(np.ones(2))
+
+        message = "`precision.shape` must equal `covariance.shape`."
+        with pytest.raises(ValueError, match=message):
+            _covariance.CovViaPrecision(np.eye(3), covariance=np.eye(2))
+
+        message = "The input `diagonal` must be a one-dimensional array..."
+        with pytest.raises(ValueError, match=message):
+            _covariance.CovViaDiagonal("alpaca")
+
+        message = "The input `cholesky` must be a square, two-dimensional..."
+        with pytest.raises(ValueError, match=message):
+            _covariance.CovViaCholesky(np.ones(2))
+
+        message = "The input `eigenvalues` must be a one-dimensional..."
+        with pytest.raises(ValueError, match=message):
+            _covariance.CovViaEigendecomposition(("alpaca", np.eye(2)))
+
+        message = "The input `eigenvectors` must be a square..."
+        with pytest.raises(ValueError, match=message):
+            _covariance.CovViaEigendecomposition((np.ones(2), "alpaca"))
+
+        message = "The shapes of `eigenvalues` and `eigenvectors` must be..."
+        with pytest.raises(ValueError, match=message):
+            _covariance.CovViaEigendecomposition(([1, 2, 3], np.eye(2)))
+
+    _covariance_preprocessing = {"Diagonal": np.diag,
+                                 "Precision": np.linalg.inv,
+                                 "Cholesky": np.linalg.cholesky,
+                                 "Eigendecomposition": np.linalg.eigh,
+                                 "PSD": lambda x:
+                                     _PSD(x, allow_singular=True)}
+    _all_covariance_types = np.array(list(_covariance_preprocessing))
+    _matrices = {"diagonal full rank": np.diag([1, 2, 3]),
+                 "general full rank": [[5, 1, 3], [1, 6, 4], [3, 4, 7]],
+                 "diagonal singular": np.diag([1, 0, 3]),
+                 "general singular": [[5, -1, 0], [-1, 5, 0], [0, 0, 0]]}
+    _cov_types = {"diagonal full rank": _all_covariance_types,
+                  "general full rank": _all_covariance_types[1:],
+                  "diagonal singular": _all_covariance_types[[0, -2, -1]],
+                  "general singular": _all_covariance_types[-2:]}
+
+    @pytest.mark.parametrize("cov_type_name", _all_covariance_types[:-1])
+    def test_factories(self, cov_type_name):
+        A = np.diag([1, 2, 3])
+        x = [-4, 2, 5]
+
+        cov_type = getattr(_covariance, f"CovVia{cov_type_name}")
+        preprocessing = self._covariance_preprocessing[cov_type_name]
+        factory = getattr(Covariance, f"from_{cov_type_name.lower()}")
+
+        res = factory(preprocessing(A))
+        ref = cov_type(preprocessing(A))
+        assert type(res) is type(ref)
+        assert_allclose(res.whiten(x), ref.whiten(x))
+
+    @pytest.mark.parametrize("matrix_type", list(_matrices))
+    @pytest.mark.parametrize("cov_type_name", _all_covariance_types)
+    def test_covariance(self, matrix_type, cov_type_name):
+        message = (f"CovVia{cov_type_name} does not support {matrix_type} "
+                   "matrices")
+        if cov_type_name not in self._cov_types[matrix_type]:
+            pytest.skip(message)
+
+        A = self._matrices[matrix_type]
+        cov_type = getattr(_covariance, f"CovVia{cov_type_name}")
+        preprocessing = self._covariance_preprocessing[cov_type_name]
+
+        psd = _PSD(A, allow_singular=True)
+
+        # test properties
+        cov_object = cov_type(preprocessing(A))
+        assert_close(cov_object.log_pdet, psd.log_pdet)
+        assert_equal(cov_object.rank, psd.rank)
+        assert_equal(cov_object.shape, np.asarray(A).shape)
+        assert_close(cov_object.covariance, np.asarray(A))
+
+        # test whitening/coloring 1D x
+        rng = np.random.default_rng(5292808890472453840)
+        x = rng.random(size=3)
+        res = cov_object.whiten(x)
+        ref = x @ psd.U
+        # res != ref in general; but res @ res == ref @ ref
+        assert_close(res @ res, ref @ ref)
+        if hasattr(cov_object, "_colorize") and "singular" not in matrix_type:
+            # CovViaPSD does not have _colorize
+            assert_close(cov_object.colorize(res), x)
+
+        # test whitening/coloring 3D x
+        x = rng.random(size=(2, 4, 3))
+        res = cov_object.whiten(x)
+        ref = x @ psd.U
+        assert_close((res**2).sum(axis=-1), (ref**2).sum(axis=-1))
+        if hasattr(cov_object, "_colorize") and "singular" not in matrix_type:
+            assert_close(cov_object.colorize(res), x)
+
+        # gh-19197 reported that multivariate normal `rvs` produced incorrect
+        # results when a singular Covariance object was produce using
+        # `from_eigenvalues`. This was due to an issue in `colorize` with
+        # singular covariance matrices. Check this edge case, which is skipped
+        # in the previous tests.
+        if hasattr(cov_object, "_colorize"):
+            res = cov_object.colorize(np.eye(len(A)))
+            assert_close(res.T @ res, A)
+
+    @pytest.mark.parametrize("size", [None, tuple(), 1, (2, 4, 3)])
+    @pytest.mark.parametrize("matrix_type", list(_matrices))
+    @pytest.mark.parametrize("cov_type_name", _all_covariance_types)
+    def test_mvn_with_covariance(self, size, matrix_type, cov_type_name):
+        message = (f"CovVia{cov_type_name} does not support {matrix_type} "
+                   "matrices")
+        if cov_type_name not in self._cov_types[matrix_type]:
+            pytest.skip(message)
+
+        A = self._matrices[matrix_type]
+        cov_type = getattr(_covariance, f"CovVia{cov_type_name}")
+        preprocessing = self._covariance_preprocessing[cov_type_name]
+
+        mean = [0.1, 0.2, 0.3]
+        cov_object = cov_type(preprocessing(A))
+        mvn = multivariate_normal
+        dist0 = multivariate_normal(mean, A, allow_singular=True)
+        dist1 = multivariate_normal(mean, cov_object, allow_singular=True)
+
+        rng = np.random.default_rng(5292808890472453840)
+        x = rng.multivariate_normal(mean, A, size=size)
+        rng = np.random.default_rng(5292808890472453840)
+        x1 = mvn.rvs(mean, cov_object, size=size, random_state=rng)
+        rng = np.random.default_rng(5292808890472453840)
+        x2 = mvn(mean, cov_object, seed=rng).rvs(size=size)
+        if isinstance(cov_object, _covariance.CovViaPSD):
+            assert_close(x1, np.squeeze(x))  # for backward compatibility
+            assert_close(x2, np.squeeze(x))
+        else:
+            assert_equal(x1.shape, x.shape)
+            assert_equal(x2.shape, x.shape)
+            assert_close(x2, x1)
+
+        assert_close(mvn.pdf(x, mean, cov_object), dist0.pdf(x))
+        assert_close(dist1.pdf(x), dist0.pdf(x))
+        assert_close(mvn.logpdf(x, mean, cov_object), dist0.logpdf(x))
+        assert_close(dist1.logpdf(x), dist0.logpdf(x))
+        assert_close(mvn.entropy(mean, cov_object), dist0.entropy())
+        assert_close(dist1.entropy(), dist0.entropy())
+
+    @pytest.mark.parametrize("size", [tuple(), (2, 4, 3)])
+    @pytest.mark.parametrize("cov_type_name", _all_covariance_types)
+    def test_mvn_with_covariance_cdf(self, size, cov_type_name):
+        # This is split from the test above because it's slow to be running
+        # with all matrix types, and there's no need because _mvn.mvnun
+        # does the calculation. All Covariance needs to do is pass is
+        # provide the `covariance` attribute.
+        matrix_type = "diagonal full rank"
+        A = self._matrices[matrix_type]
+        cov_type = getattr(_covariance, f"CovVia{cov_type_name}")
+        preprocessing = self._covariance_preprocessing[cov_type_name]
+
+        mean = [0.1, 0.2, 0.3]
+        cov_object = cov_type(preprocessing(A))
+        mvn = multivariate_normal
+        dist0 = multivariate_normal(mean, A, allow_singular=True)
+        dist1 = multivariate_normal(mean, cov_object, allow_singular=True)
+
+        rng = np.random.default_rng(5292808890472453840)
+        x = rng.multivariate_normal(mean, A, size=size)
+
+        assert_close(mvn.cdf(x, mean, cov_object), dist0.cdf(x))
+        assert_close(dist1.cdf(x), dist0.cdf(x))
+        assert_close(mvn.logcdf(x, mean, cov_object), dist0.logcdf(x))
+        assert_close(dist1.logcdf(x), dist0.logcdf(x))
+
+    def test_covariance_instantiation(self):
+        message = "The `Covariance` class cannot be instantiated directly."
+        with pytest.raises(NotImplementedError, match=message):
+            Covariance()
+
+    @pytest.mark.filterwarnings("ignore::RuntimeWarning")  # matrix not PSD
+    def test_gh9942(self):
+        # Originally there was a mistake in the `multivariate_normal_frozen`
+        # `rvs` method that caused all covariance objects to be processed as
+        # a `_CovViaPSD`. Ensure that this is resolved.
+        A = np.diag([1, 2, -1e-8])
+        n = A.shape[0]
+        mean = np.zeros(n)
+
+        # Error if the matrix is processed as a `_CovViaPSD`
+        with pytest.raises(ValueError, match="The input matrix must be..."):
+            multivariate_normal(mean, A).rvs()
+
+        # No error if it is provided as a `CovViaEigendecomposition`
+        seed = 3562050283508273023
+        rng1 = np.random.default_rng(seed)
+        rng2 = np.random.default_rng(seed)
+        cov = Covariance.from_eigendecomposition(np.linalg.eigh(A))
+        rv = multivariate_normal(mean, cov)
+        res = rv.rvs(random_state=rng1)
+        ref = multivariate_normal.rvs(mean, cov, random_state=rng2)
+        assert_equal(res, ref)
+
+    def test_gh19197(self):
+        # gh-19197 reported that multivariate normal `rvs` produced incorrect
+        # results when a singular Covariance object was produce using
+        # `from_eigenvalues`. Check that this specific issue is resolved;
+        # a more general test is included in `test_covariance`.
+        mean = np.ones(2)
+        cov = Covariance.from_eigendecomposition((np.zeros(2), np.eye(2)))
+        dist = scipy.stats.multivariate_normal(mean=mean, cov=cov)
+        rvs = dist.rvs(size=None)
+        assert_equal(rvs, mean)
+
+        cov = scipy.stats.Covariance.from_eigendecomposition(
+            (np.array([1., 0.]), np.array([[1., 0.], [0., 400.]])))
+        dist = scipy.stats.multivariate_normal(mean=mean, cov=cov)
+        rvs = dist.rvs(size=None)
+        assert rvs[0] != mean[0]
+        assert rvs[1] == mean[1]
+
+
+def _random_covariance(dim, evals, rng, singular=False):
+    # Generates random covariance matrix with dimensionality `dim` and
+    # eigenvalues `evals` using provided Generator `rng`. Randomly sets
+    # some evals to zero if `singular` is True.
+    A = rng.random((dim, dim))
+    A = A @ A.T
+    _, v = np.linalg.eigh(A)
+    if singular:
+        zero_eigs = rng.normal(size=dim) > 0
+        evals[zero_eigs] = 0
+    cov = v @ np.diag(evals) @ v.T
+    return cov
+
+
+def _sample_orthonormal_matrix(n):
+    rng = np.random.default_rng(9086764251)
+    M = rng.standard_normal((n, n))
+    u, s, v = scipy.linalg.svd(M)
+    return u
+
+def marginal_pdf(X, X_ndim, dimensions, x):
+    """Integrate marginalized dimensions of multivariate
+    probability distribution to calculate the marginalized
+    distribution.
+    """
+    # Sort input data based on order of dimensions
+    dimensions = np.asarray(dimensions)
+    dimensions[dimensions < 0] += X_ndim
+    dim_sort_idx = dimensions.argsort()
+    x = x[:, dim_sort_idx]
+
+    i_marginalize = np.ones(X_ndim, dtype=bool)
+    i_marginalize[dimensions] = False
+
+    def g(z):
+        y = np.empty((z.shape[0], x.shape[0], X_ndim))
+        y[..., i_marginalize] = z[:, np.newaxis, :]
+        y[..., ~i_marginalize] = x
+        return X.pdf(y)
+
+    inf = np.full(X_ndim - len(dimensions), np.inf)
+    return cubature(g, -inf, inf).estimate
+
+@dataclass
+class MVNProblem:
+    """Instantiate a multivariate normal integration problem with special structure.
+
+    When covariance matrix is a correlation matrix where the off-diagonal entries
+    ``covar[i, j] == lambdas[i]*lambdas[j]`` for ``i != j``, then the multidimensional
+    integral reduces to a simpler univariate integral that can be numerically integrated
+    easily.
+
+    The ``generate_*()`` classmethods provide a few options for creating variations
+    of this problem.
+
+    References
+    ----------
+    .. [1] Tong, Y.L. "The Multivariate Normal Distribution".
+           Springer-Verlag. p192. 1990.
+    """
+    ndim : int
+    low : np.ndarray
+    high : np.ndarray
+    lambdas : np.ndarray
+    covar : np.ndarray
+    target_val : float
+    target_err : float
+
+    #: The `generator_halves()` case has an analytically-known true value that we'll
+    #:  record here. It remain None for most cases, though.
+    true_val : float | None = None
+
+    def __init__(self, ndim, low, high, lambdas):
+        super().__init__()
+        self.ndim = ndim
+        self.low = low
+        self.high = high
+        self.lambdas = lambdas
+
+        self.covar = np.outer(self.lambdas, self.lambdas)
+        np.fill_diagonal(self.covar, 1.0)
+        self.find_target()
+
+    @classmethod
+    def generate_semigeneral(cls, ndim, rng=None):
+        """Random lambdas, random upper bounds, infinite lower bounds.
+        """
+        rng = np.random.default_rng(rng)
+        low = np.full(ndim, -np.inf)
+        high = rng.uniform(0.0, np.sqrt(ndim), size=ndim)
+        lambdas = rng.uniform(-1.0, 1.0, size=ndim)
+
+        self = cls(
+            ndim=ndim,
+            low=low,
+            high=high,
+            lambdas=lambdas,
+        )
+        return self
+
+    @classmethod
+    def generate_constant(cls, ndim, rng=None):
+        """Constant off-diagonal covariance, random upper bounds, infinite lower bounds.
+        """
+        rng = np.random.default_rng(rng)
+        low = np.full(ndim, -np.inf)
+        high = rng.uniform(0.0, np.sqrt(ndim), size=ndim)
+        sigma = np.sqrt(rng.uniform(0.0, 1.0))
+        lambdas = np.full(ndim, sigma)
+
+        self = cls(
+            ndim=ndim,
+            low=low,
+            high=high,
+            lambdas=lambdas,
+        )
+        return self
+
+    @classmethod
+    def generate_halves(cls, ndim, rng=None):
+        """Off-diagonal covariance of 0.5, negative orthant bounds.
+
+        True analytically-derived answer is 1/(ndim+1).
+        """
+        low = np.full(ndim, -np.inf)
+        high = np.zeros(ndim)
+        lambdas = np.sqrt(0.5)
+
+        self = cls(
+            ndim=ndim,
+            low=low,
+            high=high,
+            lambdas=lambdas,
+        )
+        self.true_val = 1 / (ndim+1)
+        return self
+
+    def find_target(self, **kwds):
+        """Perform the simplified integral and store the results.
+        """
+        d = dict(
+            a=-9.0,
+            b=+9.0,
+        )
+        d.update(kwds)
+        self.target_val, self.target_err = quad(self.univariate_func, **d)
+
+    def _univariate_term(self, t):
+        """The parameter-specific term of the univariate integrand,
+        for separate plotting.
+        """
+        denom = np.sqrt(1 - self.lambdas**2)
+        return np.prod(
+            special.ndtr((self.high + self.lambdas*t[:, np.newaxis]) / denom) -
+            special.ndtr((self.low + self.lambdas*t[:, np.newaxis]) / denom),
+            axis=1,
+        )
+
+    def univariate_func(self, t):
+        """Univariate integrand.
+        """
+        t = np.atleast_1d(t)
+        return np.squeeze(norm_pdf(t) * self._univariate_term(t))
+
+    def plot_integrand(self):
+        """Plot the univariate integrand and its component terms for understanding.
+        """
+        from matplotlib import pyplot as plt
+
+        t = np.linspace(-9.0, 9.0, 1001)
+        plt.plot(t, norm_pdf(t), label=r'$\phi(t)$')
+        plt.plot(t, self._univariate_term(t), label=r'$f(t)$')
+        plt.plot(t, self.univariate_func(t), label=r'$f(t)*phi(t)$')
+        plt.legend()
+
+
+@dataclass
+class SingularMVNProblem:
+    """Instantiate a multivariate normal integration problem with a special singular
+    covariance structure.
+
+    When covariance matrix is a correlation matrix where the off-diagonal entries
+    ``covar[i, j] == -lambdas[i]*lambdas[j]`` for ``i != j``, and
+    ``sum(lambdas**2 / (1+lambdas**2)) == 1``, then the matrix is singular, and
+    the multidimensional integral reduces to a simpler univariate integral that
+    can be numerically integrated fairly easily.
+
+    The lower bound must be infinite, though the upper bounds can be general.
+
+    References
+    ----------
+    .. [1] Kwong, K.-S. (1995). "Evaluation of the one-sided percentage points of the
+           singular multivariate normal distribution." Journal of Statistical
+           Computation and Simulation, 51(2-4), 121-135.
+           :doi:`10.1080/00949659508811627`.
+    """
+    ndim : int
+    low : np.ndarray
+    high : np.ndarray
+    lambdas : np.ndarray
+    covar : np.ndarray
+    target_val : float
+    target_err : float
+
+    def __init__(self, ndim, high, lambdas):
+        self.ndim = ndim
+        self.high = high
+        self.lambdas = lambdas
+
+        self.low = np.full(ndim, -np.inf)
+        self.covar = -np.outer(self.lambdas, self.lambdas)
+        np.fill_diagonal(self.covar, 1.0)
+        self.find_target()
+
+    @classmethod
+    def generate_semiinfinite(cls, ndim, rng=None):
+        """Singular lambdas, random upper bounds.
+        """
+        rng = np.random.default_rng(rng)
+        high = rng.uniform(0.0, np.sqrt(ndim), size=ndim)
+        p = rng.dirichlet(np.full(ndim, 1.0))
+        lambdas = np.sqrt(p / (1-p)) * rng.choice([-1.0, 1.0], size=ndim)
+
+        self = cls(
+            ndim=ndim,
+            high=high,
+            lambdas=lambdas,
+        )
+        return self
+
+    def find_target(self, **kwds):
+        d = dict(
+            a=-9.0,
+            b=+9.0,
+        )
+        d.update(kwds)
+        self.target_val, self.target_err = quad(self.univariate_func, **d)
+
+    def _univariate_term(self, t):
+        denom = np.sqrt(1 + self.lambdas**2)
+        i1 = np.prod(
+            special.ndtr((self.high - 1j*self.lambdas*t[:, np.newaxis]) / denom),
+            axis=1,
+        )
+        i2 = np.prod(
+            special.ndtr((-self.high + 1j*self.lambdas*t[:, np.newaxis]) / denom),
+            axis=1,
+        )
+        # The imaginary part is an odd function, so it can be ignored; it will integrate
+        # out to 0.
+        return (i1 - (-1)**self.ndim * i2).real
+
+    def univariate_func(self, t):
+        t = np.atleast_1d(t)
+        return (norm_pdf(t) * self._univariate_term(t)).squeeze()
+
+    def plot_integrand(self):
+        """Plot the univariate integrand and its component terms for understanding.
+        """
+        from matplotlib import pyplot as plt
+
+        t = np.linspace(-9.0, 9.0, 1001)
+        plt.plot(t, norm_pdf(t), label=r'$\phi(t)$')
+        plt.plot(t, self._univariate_term(t), label=r'$f(t)$')
+        plt.plot(t, self.univariate_func(t), label=r'$f(t)*phi(t)$')
+        plt.ylim(-0.1, 1.1)
+        plt.legend()
+
+
+class TestMultivariateNormal:
+    def test_input_shape(self):
+        mu = np.arange(3)
+        cov = np.identity(2)
+        assert_raises(ValueError, multivariate_normal.pdf, (0, 1), mu, cov)
+        assert_raises(ValueError, multivariate_normal.pdf, (0, 1, 2), mu, cov)
+        assert_raises(ValueError, multivariate_normal.cdf, (0, 1), mu, cov)
+        assert_raises(ValueError, multivariate_normal.cdf, (0, 1, 2), mu, cov)
+
+    def test_scalar_values(self):
+        rng = np.random.default_rng(1234)
+
+        # When evaluated on scalar data, the pdf should return a scalar
+        x, mean, cov = 1.5, 1.7, 2.5
+        pdf = multivariate_normal.pdf(x, mean, cov)
+        assert_equal(pdf.ndim, 0)
+
+        # When evaluated on a single vector, the pdf should return a scalar
+        x = rng.standard_normal(5)
+        mean = rng.standard_normal(5)
+        cov = np.abs(rng.standard_normal(5))  # Diagonal values for cov. matrix
+        pdf = multivariate_normal.pdf(x, mean, cov)
+        assert_equal(pdf.ndim, 0)
+
+        # When evaluated on scalar data, the cdf should return a scalar
+        x, mean, cov = 1.5, 1.7, 2.5
+        cdf = multivariate_normal.cdf(x, mean, cov)
+        assert_equal(cdf.ndim, 0)
+
+        # When evaluated on a single vector, the cdf should return a scalar
+        x = rng.standard_normal(5)
+        mean = rng.standard_normal(5)
+        cov = np.abs(rng.standard_normal(5))  # Diagonal values for cov. matrix
+        cdf = multivariate_normal.cdf(x, mean, cov)
+        assert_equal(cdf.ndim, 0)
+
+    def test_logpdf(self):
+        # Check that the log of the pdf is in fact the logpdf
+        rng = np.random.default_rng(1234)
+        x = rng.standard_normal(5)
+        mean = rng.standard_normal(5)
+        cov = np.abs(rng.standard_normal(5))
+        d1 = multivariate_normal.logpdf(x, mean, cov)
+        d2 = multivariate_normal.pdf(x, mean, cov)
+        assert_allclose(d1, np.log(d2))
+
+    def test_logpdf_default_values(self):
+        # Check that the log of the pdf is in fact the logpdf
+        # with default parameters Mean=None and cov = 1
+        rng = np.random.default_rng(1234)
+        x = rng.standard_normal(5)
+        d1 = multivariate_normal.logpdf(x)
+        d2 = multivariate_normal.pdf(x)
+        # check whether default values are being used
+        d3 = multivariate_normal.logpdf(x, None, 1)
+        d4 = multivariate_normal.pdf(x, None, 1)
+        assert_allclose(d1, np.log(d2))
+        assert_allclose(d3, np.log(d4))
+
+    def test_logcdf(self):
+        # Check that the log of the cdf is in fact the logcdf
+        rng = np.random.default_rng(1234)
+        x = rng.standard_normal(5)
+        mean = rng.standard_normal(5)
+        cov = np.abs(rng.standard_normal(5))
+        d1 = multivariate_normal.logcdf(x, mean, cov)
+        d2 = multivariate_normal.cdf(x, mean, cov)
+        assert_allclose(d1, np.log(d2))
+
+    def test_logcdf_default_values(self):
+        # Check that the log of the cdf is in fact the logcdf
+        # with default parameters Mean=None and cov = 1
+        rng = np.random.default_rng(1234)
+        x = rng.standard_normal(5)
+        d1 = multivariate_normal.logcdf(x)
+        d2 = multivariate_normal.cdf(x)
+        # check whether default values are being used
+        d3 = multivariate_normal.logcdf(x, None, 1)
+        d4 = multivariate_normal.cdf(x, None, 1)
+        assert_allclose(d1, np.log(d2))
+        assert_allclose(d3, np.log(d4))
+
+    def test_rank(self):
+        # Check that the rank is detected correctly.
+        rng = np.random.default_rng(1234)
+        n = 4
+        mean = rng.standard_normal(n)
+        for expected_rank in range(1, n + 1):
+            s = rng.standard_normal((n, expected_rank))
+            cov = np.dot(s, s.T)
+            distn = multivariate_normal(mean, cov, allow_singular=True)
+            assert_equal(distn.cov_object.rank, expected_rank)
+
+    def test_degenerate_distributions(self):
+        rng = np.random.default_rng(1234)
+        for n in range(1, 5):
+            z = rng.standard_normal(n)
+            for k in range(1, n):
+                # Sample a small covariance matrix.
+                s = rng.standard_normal((k, k))
+                cov_kk = np.dot(s, s.T)
+
+                # Embed the small covariance matrix into a larger singular one.
+                cov_nn = np.zeros((n, n))
+                cov_nn[:k, :k] = cov_kk
+
+                # Embed part of the vector in the same way
+                x = np.zeros(n)
+                x[:k] = z[:k]
+
+                # Define a rotation of the larger low rank matrix.
+                u = _sample_orthonormal_matrix(n)
+                cov_rr = np.dot(u, np.dot(cov_nn, u.T))
+                y = np.dot(u, x)
+
+                # Check some identities.
+                distn_kk = multivariate_normal(np.zeros(k), cov_kk,
+                                               allow_singular=True)
+                distn_nn = multivariate_normal(np.zeros(n), cov_nn,
+                                               allow_singular=True)
+                distn_rr = multivariate_normal(np.zeros(n), cov_rr,
+                                               allow_singular=True)
+                assert_equal(distn_kk.cov_object.rank, k)
+                assert_equal(distn_nn.cov_object.rank, k)
+                assert_equal(distn_rr.cov_object.rank, k)
+                pdf_kk = distn_kk.pdf(x[:k])
+                pdf_nn = distn_nn.pdf(x)
+                pdf_rr = distn_rr.pdf(y)
+                assert_allclose(pdf_kk, pdf_nn)
+                assert_allclose(pdf_kk, pdf_rr)
+                logpdf_kk = distn_kk.logpdf(x[:k])
+                logpdf_nn = distn_nn.logpdf(x)
+                logpdf_rr = distn_rr.logpdf(y)
+                assert_allclose(logpdf_kk, logpdf_nn)
+                assert_allclose(logpdf_kk, logpdf_rr)
+
+                # Add an orthogonal component and find the density
+                y_orth = y + u[:, -1]
+                pdf_rr_orth = distn_rr.pdf(y_orth)
+                logpdf_rr_orth = distn_rr.logpdf(y_orth)
+
+                # Ensure that this has zero probability
+                assert_equal(pdf_rr_orth, 0.0)
+                assert_equal(logpdf_rr_orth, -np.inf)
+
+    def test_degenerate_array(self):
+        # Test that we can generate arrays of random variate from a degenerate
+        # multivariate normal, and that the pdf for these samples is non-zero
+        # (i.e. samples from the distribution lie on the subspace)
+        k = 10
+        for n in range(2, 6):
+            for r in range(1, n):
+                mn = np.zeros(n)
+                u = _sample_orthonormal_matrix(n)[:, :r]
+                vr = np.dot(u, u.T)
+                X = multivariate_normal.rvs(mean=mn, cov=vr, size=k)
+
+                pdf = multivariate_normal.pdf(X, mean=mn, cov=vr,
+                                              allow_singular=True)
+                assert_equal(pdf.size, k)
+                assert np.all(pdf > 0.0)
+
+                logpdf = multivariate_normal.logpdf(X, mean=mn, cov=vr,
+                                                    allow_singular=True)
+                assert_equal(logpdf.size, k)
+                assert np.all(logpdf > -np.inf)
+
+    def test_large_pseudo_determinant(self):
+        # Check that large pseudo-determinants are handled appropriately.
+
+        # Construct a singular diagonal covariance matrix
+        # whose pseudo determinant overflows double precision.
+        large_total_log = 1000.0
+        npos = 100
+        nzero = 2
+        large_entry = np.exp(large_total_log / npos)
+        n = npos + nzero
+        cov = np.zeros((n, n), dtype=float)
+        np.fill_diagonal(cov, large_entry)
+        cov[-nzero:, -nzero:] = 0
+
+        # Check some determinants.
+        assert_equal(scipy.linalg.det(cov), 0)
+        assert_equal(scipy.linalg.det(cov[:npos, :npos]), np.inf)
+        assert_allclose(np.linalg.slogdet(cov[:npos, :npos]),
+                        (1, large_total_log))
+
+        # Check the pseudo-determinant.
+        psd = _PSD(cov)
+        assert_allclose(psd.log_pdet, large_total_log)
+
+    def test_broadcasting(self):
+        rng = np.random.RandomState(1234)
+        n = 4
+
+        # Construct a random covariance matrix.
+        data = rng.randn(n, n)
+        cov = np.dot(data, data.T)
+        mean = rng.randn(n)
+
+        # Construct an ndarray which can be interpreted as
+        # a 2x3 array whose elements are random data vectors.
+        X = rng.randn(2, 3, n)
+
+        # Check that multiple data points can be evaluated at once.
+        desired_pdf = multivariate_normal.pdf(X, mean, cov)
+        desired_cdf = multivariate_normal.cdf(X, mean, cov)
+        for i in range(2):
+            for j in range(3):
+                actual = multivariate_normal.pdf(X[i, j], mean, cov)
+                assert_allclose(actual, desired_pdf[i,j])
+                # Repeat for cdf
+                actual = multivariate_normal.cdf(X[i, j], mean, cov)
+                assert_allclose(actual, desired_cdf[i,j], rtol=1e-3)
+
+    def test_normal_1D(self):
+        # The probability density function for a 1D normal variable should
+        # agree with the standard normal distribution in scipy.stats.distributions
+        x = np.linspace(0, 2, 10)
+        mean, cov = 1.2, 0.9
+        scale = cov**0.5
+        d1 = norm.pdf(x, mean, scale)
+        d2 = multivariate_normal.pdf(x, mean, cov)
+        assert_allclose(d1, d2)
+        # The same should hold for the cumulative distribution function
+        d1 = norm.cdf(x, mean, scale)
+        d2 = multivariate_normal.cdf(x, mean, cov)
+        assert_allclose(d1, d2)
+
+    def test_marginalization(self):
+        # Integrating out one of the variables of a 2D Gaussian should
+        # yield a 1D Gaussian
+        mean = np.array([2.5, 3.5])
+        cov = np.array([[.5, 0.2], [0.2, .6]])
+        n = 2 ** 8 + 1  # Number of samples
+        delta = 6 / (n - 1)  # Grid spacing
+
+        v = np.linspace(0, 6, n)
+        xv, yv = np.meshgrid(v, v)
+        pos = np.empty((n, n, 2))
+        pos[:, :, 0] = xv
+        pos[:, :, 1] = yv
+        pdf = multivariate_normal.pdf(pos, mean, cov)
+
+        # Marginalize over x and y axis
+        margin_x = romb(pdf, delta, axis=0)
+        margin_y = romb(pdf, delta, axis=1)
+
+        # Compare with standard normal distribution
+        gauss_x = norm.pdf(v, loc=mean[0], scale=cov[0, 0] ** 0.5)
+        gauss_y = norm.pdf(v, loc=mean[1], scale=cov[1, 1] ** 0.5)
+        assert_allclose(margin_x, gauss_x, rtol=1e-2, atol=1e-2)
+        assert_allclose(margin_y, gauss_y, rtol=1e-2, atol=1e-2)
+
+    def test_frozen(self):
+        # The frozen distribution should agree with the regular one
+        rng = np.random.default_rng(1234)
+        x = rng.standard_normal(5)
+        mean = rng.standard_normal(5)
+        cov = np.abs(rng.standard_normal(5))
+        norm_frozen = multivariate_normal(mean, cov)
+        assert_allclose(norm_frozen.pdf(x), multivariate_normal.pdf(x, mean, cov))
+        assert_allclose(norm_frozen.logpdf(x),
+                        multivariate_normal.logpdf(x, mean, cov))
+        assert_allclose(norm_frozen.cdf(x), multivariate_normal.cdf(x, mean, cov))
+        assert_allclose(norm_frozen.logcdf(x),
+                        multivariate_normal.logcdf(x, mean, cov))
+
+    @pytest.mark.parametrize(
+        'covariance',
+        [
+            np.eye(2),
+            Covariance.from_diagonal([1, 1]),
+        ]
+    )
+    def test_frozen_multivariate_normal_exposes_attributes(self, covariance):
+        mean = np.ones((2,))
+        cov_should_be = np.eye(2)
+        norm_frozen = multivariate_normal(mean, covariance)
+        assert np.allclose(norm_frozen.mean, mean)
+        assert np.allclose(norm_frozen.cov, cov_should_be)
+
+    def test_pseudodet_pinv(self):
+        # Make sure that pseudo-inverse and pseudo-det agree on cutoff
+
+        # Assemble random covariance matrix with large and small eigenvalues
+        rng = np.random.default_rng(1234)
+        n = 7
+        x = rng.standard_normal((n, n))
+        cov = np.dot(x, x.T)
+        s, u = scipy.linalg.eigh(cov)
+        s = np.full(n, 0.5)
+        s[0] = 1.0
+        s[-1] = 1e-7
+        cov = np.dot(u, np.dot(np.diag(s), u.T))
+
+        # Set cond so that the lowest eigenvalue is below the cutoff
+        cond = 1e-5
+        psd = _PSD(cov, cond=cond)
+        psd_pinv = _PSD(psd.pinv, cond=cond)
+
+        # Check that the log pseudo-determinant agrees with the sum
+        # of the logs of all but the smallest eigenvalue
+        assert_allclose(psd.log_pdet, np.sum(np.log(s[:-1])))
+        # Check that the pseudo-determinant of the pseudo-inverse
+        # agrees with 1 / pseudo-determinant
+        assert_allclose(-psd.log_pdet, psd_pinv.log_pdet)
+
+    def test_exception_nonsquare_cov(self):
+        cov = [[1, 2, 3], [4, 5, 6]]
+        assert_raises(ValueError, _PSD, cov)
+
+    def test_exception_nonfinite_cov(self):
+        cov_nan = [[1, 0], [0, np.nan]]
+        assert_raises(ValueError, _PSD, cov_nan)
+        cov_inf = [[1, 0], [0, np.inf]]
+        assert_raises(ValueError, _PSD, cov_inf)
+
+    def test_exception_non_psd_cov(self):
+        cov = [[1, 0], [0, -1]]
+        assert_raises(ValueError, _PSD, cov)
+
+    def test_exception_singular_cov(self):
+        rng = np.random.default_rng(1234)
+        x = rng.standard_normal(5)
+        mean = rng.standard_normal(5)
+        cov = np.ones((5, 5))
+        e = np.linalg.LinAlgError
+        assert_raises(e, multivariate_normal, mean, cov)
+        assert_raises(e, multivariate_normal.pdf, x, mean, cov)
+        assert_raises(e, multivariate_normal.logpdf, x, mean, cov)
+        assert_raises(e, multivariate_normal.cdf, x, mean, cov)
+        assert_raises(e, multivariate_normal.logcdf, x, mean, cov)
+
+        # Message used to be "singular matrix", but this is more accurate.
+        # See gh-15508
+        cov = [[1., 0.], [1., 1.]]
+        msg = "When `allow_singular is False`, the input matrix"
+        with pytest.raises(np.linalg.LinAlgError, match=msg):
+            multivariate_normal(cov=cov)
+
+    def test_R_values(self):
+        # Compare the multivariate pdf with some values precomputed
+        # in R version 3.0.1 (2013-05-16) on Mac OS X 10.6.
+
+        # The values below were generated by the following R-script:
+        # > library(mnormt)
+        # > x <- seq(0, 2, length=5)
+        # > y <- 3*x - 2
+        # > z <- x + cos(y)
+        # > mu <- c(1, 3, 2)
+        # > Sigma <- matrix(c(1,2,0,2,5,0.5,0,0.5,3), 3, 3)
+        # > r_pdf <- dmnorm(cbind(x,y,z), mu, Sigma)
+        r_pdf = np.array([0.0002214706, 0.0013819953, 0.0049138692,
+                          0.0103803050, 0.0140250800])
+
+        x = np.linspace(0, 2, 5)
+        y = 3 * x - 2
+        z = x + np.cos(y)
+        r = np.array([x, y, z]).T
+
+        mean = np.array([1, 3, 2], 'd')
+        cov = np.array([[1, 2, 0], [2, 5, .5], [0, .5, 3]], 'd')
+
+        pdf = multivariate_normal.pdf(r, mean, cov)
+        assert_allclose(pdf, r_pdf, atol=1e-10)
+
+        # Compare the multivariate cdf with some values precomputed
+        # in R version 3.3.2 (2016-10-31) on Debian GNU/Linux.
+
+        # The values below were generated by the following R-script:
+        # > library(mnormt)
+        # > x <- seq(0, 2, length=5)
+        # > y <- 3*x - 2
+        # > z <- x + cos(y)
+        # > mu <- c(1, 3, 2)
+        # > Sigma <- matrix(c(1,2,0,2,5,0.5,0,0.5,3), 3, 3)
+        # > r_cdf <- pmnorm(cbind(x,y,z), mu, Sigma)
+        r_cdf = np.array([0.0017866215, 0.0267142892, 0.0857098761,
+                          0.1063242573, 0.2501068509])
+
+        cdf = multivariate_normal.cdf(r, mean, cov)
+        assert_allclose(cdf, r_cdf, atol=2e-5)
+
+        # Also test bivariate cdf with some values precomputed
+        # in R version 3.3.2 (2016-10-31) on Debian GNU/Linux.
+
+        # The values below were generated by the following R-script:
+        # > library(mnormt)
+        # > x <- seq(0, 2, length=5)
+        # > y <- 3*x - 2
+        # > mu <- c(1, 3)
+        # > Sigma <- matrix(c(1,2,2,5), 2, 2)
+        # > r_cdf2 <- pmnorm(cbind(x,y), mu, Sigma)
+        r_cdf2 = np.array([0.01262147, 0.05838989, 0.18389571,
+                           0.40696599, 0.66470577])
+
+        r2 = np.array([x, y]).T
+
+        mean2 = np.array([1, 3], 'd')
+        cov2 = np.array([[1, 2], [2, 5]], 'd')
+
+        cdf2 = multivariate_normal.cdf(r2, mean2, cov2)
+        assert_allclose(cdf2, r_cdf2, atol=1e-5)
+
+    def test_multivariate_normal_rvs_zero_covariance(self):
+        mean = np.zeros(2)
+        covariance = np.zeros((2, 2))
+        model = multivariate_normal(mean, covariance, allow_singular=True)
+        sample = model.rvs()
+        assert_equal(sample, [0, 0])
+
+    def test_rvs_shape(self):
+        # Check that rvs parses the mean and covariance correctly, and returns
+        # an array of the right shape
+        N = 300
+        d = 4
+        sample = multivariate_normal.rvs(mean=np.zeros(d), cov=1, size=N)
+        assert_equal(sample.shape, (N, d))
+
+        sample = multivariate_normal.rvs(mean=None,
+                                         cov=np.array([[2, .1], [.1, 1]]),
+                                         size=N)
+        assert_equal(sample.shape, (N, 2))
+
+        u = multivariate_normal(mean=0, cov=1)
+        sample = u.rvs(N)
+        assert_equal(sample.shape, (N, ))
+
+    def test_large_sample(self):
+        # Generate large sample and compare sample mean and sample covariance
+        # with mean and covariance matrix.
+
+        rng = np.random.RandomState(2846)
+
+        n = 3
+        mean = rng.randn(n)
+        M = rng.randn(n, n)
+        cov = np.dot(M, M.T)
+        size = 5000
+
+        sample = multivariate_normal.rvs(mean, cov, size, random_state=rng)
+
+        assert_allclose(np.cov(sample.T), cov, rtol=1e-1)
+        assert_allclose(sample.mean(0), mean, rtol=1e-1)
+
+    def test_entropy(self):
+        rng = np.random.RandomState(2846)
+
+        n = 3
+        mean = rng.randn(n)
+        M = rng.randn(n, n)
+        cov = np.dot(M, M.T)
+
+        rv = multivariate_normal(mean, cov)
+
+        # Check that frozen distribution agrees with entropy function
+        assert_almost_equal(rv.entropy(), multivariate_normal.entropy(mean, cov))
+        # Compare entropy with manually computed expression involving
+        # the sum of the logs of the eigenvalues of the covariance matrix
+        eigs = np.linalg.eig(cov)[0]
+        desired = 1 / 2 * (n * (np.log(2 * np.pi) + 1) + np.sum(np.log(eigs)))
+        assert_almost_equal(desired, rv.entropy())
+
+    def test_lnB(self):
+        alpha = np.array([1, 1, 1])
+        desired = .5  # e^lnB = 1/2 for [1, 1, 1]
+
+        assert_almost_equal(np.exp(_lnB(alpha)), desired)
+
+    def test_cdf_with_lower_limit_arrays(self):
+        # test CDF with lower limit in several dimensions
+        rng = np.random.default_rng(2408071309372769818)
+        mean = [0, 0]
+        cov = np.eye(2)
+        a = rng.random((4, 3, 2))*6 - 3
+        b = rng.random((4, 3, 2))*6 - 3
+
+        cdf1 = multivariate_normal.cdf(b, mean, cov, lower_limit=a)
+
+        cdf2a = multivariate_normal.cdf(b, mean, cov)
+        cdf2b = multivariate_normal.cdf(a, mean, cov)
+        ab1 = np.concatenate((a[..., 0:1], b[..., 1:2]), axis=-1)
+        ab2 = np.concatenate((a[..., 1:2], b[..., 0:1]), axis=-1)
+        cdf2ab1 = multivariate_normal.cdf(ab1, mean, cov)
+        cdf2ab2 = multivariate_normal.cdf(ab2, mean, cov)
+        cdf2 = cdf2a + cdf2b - cdf2ab1 - cdf2ab2
+
+        assert_allclose(cdf1, cdf2)
+
+    def test_cdf_with_lower_limit_consistency(self):
+        # check that multivariate normal CDF functions are consistent
+        rng = np.random.default_rng(2408071309372769818)
+        mean = rng.random(3)
+        cov = rng.random((3, 3))
+        cov = cov @ cov.T
+        a = rng.random((2, 3))*6 - 3
+        b = rng.random((2, 3))*6 - 3
+
+        cdf1 = multivariate_normal.cdf(b, mean, cov, lower_limit=a)
+        cdf2 = multivariate_normal(mean, cov).cdf(b, lower_limit=a)
+        cdf3 = np.exp(multivariate_normal.logcdf(b, mean, cov, lower_limit=a))
+        cdf4 = np.exp(multivariate_normal(mean, cov).logcdf(b, lower_limit=a))
+
+        assert_allclose(cdf2, cdf1, rtol=1e-4)
+        assert_allclose(cdf3, cdf1, rtol=1e-4)
+        assert_allclose(cdf4, cdf1, rtol=1e-4)
+
+    def test_cdf_signs(self):
+        # check that sign of output is correct when np.any(lower > x)
+        mean = np.zeros(3)
+        cov = np.eye(3)
+        b = [[1, 1, 1], [0, 0, 0], [1, 0, 1], [0, 1, 0]]
+        a = [[0, 0, 0], [1, 1, 1], [0, 1, 0], [1, 0, 1]]
+        # when odd number of elements of b < a, output is negative
+        expected_signs = np.array([1, -1, -1, 1])
+        cdf = multivariate_normal.cdf(b, mean, cov, lower_limit=a)
+        assert_allclose(cdf, cdf[0]*expected_signs)
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize("ndim", [2, 3])
+    def test_cdf_vs_cubature(self, ndim):
+        rng = np.random.default_rng(123)
+        a = rng.uniform(size=(ndim, ndim))
+        cov = a.T @ a
+        m = rng.uniform(size=ndim)
+        dist = multivariate_normal(mean=m, cov=cov)
+        x = rng.uniform(low=-3, high=3, size=(ndim,))
+        cdf = dist.cdf(x)
+        dist_i = multivariate_normal(mean=[0]*ndim, cov=cov)
+        cdf_i = cubature(dist_i.pdf, [-np.inf]*ndim, x - m).estimate
+        assert_allclose(cdf, cdf_i, atol=5e-6)
+
+    def test_cdf_known(self):
+        # https://github.com/scipy/scipy/pull/17410#issuecomment-1312628547
+        for ndim in range(2, 12):
+            cov = np.full((ndim, ndim), 0.5)
+            np.fill_diagonal(cov, 1.)
+            dist = multivariate_normal([0]*ndim, cov=cov)
+            assert_allclose(
+                dist.cdf([0]*ndim),
+                1. / (1. + ndim),
+                atol=5e-5
+            )
+
+    @pytest.mark.parametrize("ndim", range(2, 10))
+    @pytest.mark.parametrize("seed", [0xdeadbeef, 0xdd24528764c9773579731c6b022b48e2])
+    def test_cdf_vs_univariate(self, seed, ndim):
+        rng = np.random.default_rng(seed)
+        case = MVNProblem.generate_semigeneral(ndim=ndim, rng=rng)
+        assert (case.low == -np.inf).all()
+
+        dist = multivariate_normal(mean=[0]*ndim, cov=case.covar)
+        cdf_val = dist.cdf(case.high, rng=rng)
+        assert_allclose(cdf_val, case.target_val, atol=5e-5)
+
+    @pytest.mark.parametrize("ndim", range(2, 11))
+    @pytest.mark.parametrize("seed", [0xdeadbeef, 0xdd24528764c9773579731c6b022b48e2])
+    def test_cdf_vs_univariate_2(self, seed, ndim):
+        rng = np.random.default_rng(seed)
+        case = MVNProblem.generate_constant(ndim=ndim, rng=rng)
+        assert (case.low == -np.inf).all()
+
+        dist = multivariate_normal(mean=[0]*ndim, cov=case.covar)
+        cdf_val = dist.cdf(case.high, rng=rng)
+        assert_allclose(cdf_val, case.target_val, atol=5e-5)
+
+    @pytest.mark.parametrize("ndim", range(4, 11))
+    @pytest.mark.parametrize("seed", [0xdeadbeef, 0xdd24528764c9773579731c6b022b48e4])
+    def test_cdf_vs_univariate_singular(self, seed, ndim):
+        # NB: ndim = 2, 3 has much poorer accuracy than ndim > 3 for many seeds.
+        # No idea why.
+        rng = np.random.default_rng(seed)
+        case = SingularMVNProblem.generate_semiinfinite(ndim=ndim, rng=rng)
+        assert (case.low == -np.inf).all()
+
+        dist = multivariate_normal(mean=[0]*ndim, cov=case.covar, allow_singular=True,
+                                   # default maxpts is too slow, limit it here
+                                   maxpts=10_000*case.covar.shape[0]
+        )
+        cdf_val = dist.cdf(case.high, rng=rng)
+        assert_allclose(cdf_val, case.target_val, atol=1e-3)
+
+    def test_mean_cov(self):
+        # test the interaction between a Covariance object and mean
+        P = np.diag(1 / np.array([1, 2, 3]))
+        cov_object = _covariance.CovViaPrecision(P)
+
+        message = "`cov` represents a covariance matrix in 3 dimensions..."
+        with pytest.raises(ValueError, match=message):
+            multivariate_normal.entropy([0, 0], cov_object)
+
+        with pytest.raises(ValueError, match=message):
+            multivariate_normal([0, 0], cov_object)
+
+        x = [0.5, 0.5, 0.5]
+        ref = multivariate_normal.pdf(x, [0, 0, 0], cov_object)
+        assert_equal(multivariate_normal.pdf(x, cov=cov_object), ref)
+
+        ref = multivariate_normal.pdf(x, [1, 1, 1], cov_object)
+        assert_equal(multivariate_normal.pdf(x, 1, cov=cov_object), ref)
+
+    def test_fit_wrong_fit_data_shape(self):
+        data = [1, 3]
+        error_msg = "`x` must be two-dimensional."
+        with pytest.raises(ValueError, match=error_msg):
+            multivariate_normal.fit(data)
+
+    @pytest.mark.parametrize('dim', (3, 5))
+    def test_fit_correctness(self, dim):
+        rng = np.random.default_rng(4385269356937404)
+        x = rng.random((100, dim))
+        mean_est, cov_est = multivariate_normal.fit(x)
+        mean_ref, cov_ref = np.mean(x, axis=0), np.cov(x.T, ddof=0)
+        assert_allclose(mean_est, mean_ref, atol=1e-15)
+        assert_allclose(cov_est, cov_ref, rtol=5e-15)
+
+    def test_fit_both_parameters_fixed(self):
+        data = np.full((2, 1), 3)
+        mean_fixed = 1.
+        cov_fixed = np.atleast_2d(1.)
+        mean, cov = multivariate_normal.fit(data, fix_mean=mean_fixed,
+                                            fix_cov=cov_fixed)
+        assert_equal(mean, mean_fixed)
+        assert_equal(cov, cov_fixed)
+
+    @pytest.mark.parametrize('fix_mean', [np.zeros((2, 2)),
+                                          np.zeros((3, ))])
+    def test_fit_fix_mean_input_validation(self, fix_mean):
+        msg = ("`fix_mean` must be a one-dimensional array the same "
+                "length as the dimensionality of the vectors `x`.")
+        with pytest.raises(ValueError, match=msg):
+            multivariate_normal.fit(np.eye(2), fix_mean=fix_mean)
+
+    @pytest.mark.parametrize('fix_cov', [np.zeros((2, )),
+                                         np.zeros((3, 2)),
+                                         np.zeros((4, 4))])
+    def test_fit_fix_cov_input_validation_dimension(self, fix_cov):
+        msg = ("`fix_cov` must be a two-dimensional square array "
+                "of same side length as the dimensionality of the "
+                "vectors `x`.")
+        with pytest.raises(ValueError, match=msg):
+            multivariate_normal.fit(np.eye(3), fix_cov=fix_cov)
+
+    def test_fit_fix_cov_not_positive_semidefinite(self):
+        error_msg = "`fix_cov` must be symmetric positive semidefinite."
+        with pytest.raises(ValueError, match=error_msg):
+            fix_cov = np.array([[1., 0.], [0., -1.]])
+            multivariate_normal.fit(np.eye(2), fix_cov=fix_cov)
+
+    def test_fit_fix_mean(self):
+        rng = np.random.default_rng(4385269356937404)
+        loc = rng.random(3)
+        A = rng.random((3, 3))
+        cov = np.dot(A, A.T)
+        samples = multivariate_normal.rvs(mean=loc, cov=cov, size=100,
+                                          random_state=rng)
+        mean_free, cov_free = multivariate_normal.fit(samples)
+        logp_free = multivariate_normal.logpdf(samples, mean=mean_free,
+                                               cov=cov_free).sum()
+        mean_fix, cov_fix = multivariate_normal.fit(samples, fix_mean=loc)
+        assert_equal(mean_fix, loc)
+        logp_fix = multivariate_normal.logpdf(samples, mean=mean_fix,
+                                              cov=cov_fix).sum()
+        # test that fixed parameters result in lower likelihood than free
+        # parameters
+        assert logp_fix < logp_free
+        # test that a small perturbation of the resulting parameters
+        # has lower likelihood than the estimated parameters
+        A = rng.random((3, 3))
+        m = 1e-8 * np.dot(A, A.T)
+        cov_perturbed = cov_fix + m
+        logp_perturbed = (multivariate_normal.logpdf(samples,
+                                                     mean=mean_fix,
+                                                     cov=cov_perturbed)
+                                                     ).sum()
+        assert logp_perturbed < logp_fix
+
+    def test_fit_fix_cov(self):
+        rng = np.random.default_rng(4385269356937404)
+        loc = rng.random(3)
+        A = rng.random((3, 3))
+        cov = np.dot(A, A.T)
+        samples = multivariate_normal.rvs(mean=loc, cov=cov,
+                                          size=100, random_state=rng)
+        mean_free, cov_free = multivariate_normal.fit(samples)
+        logp_free = multivariate_normal.logpdf(samples, mean=mean_free,
+                                               cov=cov_free).sum()
+        mean_fix, cov_fix = multivariate_normal.fit(samples, fix_cov=cov)
+        assert_equal(mean_fix, np.mean(samples, axis=0))
+        assert_equal(cov_fix, cov)
+        logp_fix = multivariate_normal.logpdf(samples, mean=mean_fix,
+                                              cov=cov_fix).sum()
+        # test that fixed parameters result in lower likelihood than free
+        # parameters
+        assert logp_fix < logp_free
+        # test that a small perturbation of the resulting parameters
+        # has lower likelihood than the estimated parameters
+        mean_perturbed = mean_fix + 1e-8 * rng.random(3)
+        logp_perturbed = (multivariate_normal.logpdf(samples,
+                                                     mean=mean_perturbed,
+                                                     cov=cov_fix)
+                                                     ).sum()
+        assert logp_perturbed < logp_fix
+
+
+class TestMarginal:
+    @pytest.mark.parametrize('dist,kwargs', [(multivariate_normal, {}),
+                                             (multivariate_t, {'df': 4})])
+    @pytest.mark.parametrize('X_ndim', [3])
+    @pytest.mark.parametrize('dimensions', [[1], [-1, 1]])
+    @pytest.mark.parametrize('frozen', [True, False])
+    @pytest.mark.parametrize('cov_object', [True, False])
+    def test_marginal_distribution(self, dist, X_ndim, dimensions, frozen,
+                                   cov_object, kwargs):
+        rng = np.random.default_rng(413911473)
+        loc = rng.standard_normal(X_ndim)
+        A = rng.standard_normal((X_ndim, X_ndim))
+        scale = A @ A.T
+
+        if cov_object and dist == multivariate_t:
+            pytest.skip('`multivariate_t` does not accept a `Covariance` object')
+        elif cov_object:
+            scale = _covariance.CovViaPrecision(scale)
+
+        # number of points at which to evaluate marginal PDF
+        x = np.random.standard_normal((4, len(dimensions)))
+        X = dist(loc, scale, **kwargs)
+
+        if frozen:
+            Y = X.marginal(dimensions)
+            res = Y.pdf(x)
+        else:
+            Y = dist.marginal(dimensions, loc, scale, **kwargs)
+            res = Y.pdf(x)
+
+        ref = marginal_pdf(X, X_ndim, dimensions, x)
+        assert_allclose(ref, res)
+
+    @pytest.mark.parametrize('dist', [multivariate_normal, multivariate_t])
+    def test_marginal_input_validation(self, dist):
+        rng = np.random.default_rng(413911473)
+        mean = rng.standard_normal(3)
+        A = rng.standard_normal((3, 3))
+        cov = A @ A.T
+
+        X = dist(mean, cov)
+
+        msg = r"Dimensions \[3\] are invalid .*"
+        with pytest.raises(ValueError, match=msg):
+            X.marginal(3)
+
+        with pytest.raises(ValueError, match=msg):
+            X.marginal([0, 1, 2, 3])
+
+        msg = r"All elements of `dimensions` must be unique."
+        with pytest.raises(ValueError, match=msg):
+            X.marginal([2, -1])
+
+        with pytest.raises(ValueError, match=msg):
+            X.marginal([[0, 1]])
+
+        msg = r"Elements of `dimensions` must be integers."
+        with pytest.raises(ValueError, match=msg):
+            X.marginal([1.1, 2.0])
+
+    @pytest.mark.parametrize('dist', [multivariate_normal, multivariate_t])
+    def test_marginal_special_cases(self, dist):
+        rng = np.random.default_rng(413911473)
+        loc = rng.standard_normal(3)
+        A = rng.standard_normal((3, 3))
+        scale = A @ A.T
+
+        X = dist(loc, scale)
+
+        msg = r"Cannot marginalize all dimensions."
+        with pytest.raises(ValueError, match=msg):
+            X.marginal([])
+
+
+class TestMatrixNormal:
+
+    def test_bad_input(self):
+        # Check that bad inputs raise errors
+        num_rows = 4
+        num_cols = 3
+        M = np.full((num_rows,num_cols), 0.3)
+        U = 0.5 * np.identity(num_rows) + np.full((num_rows, num_rows), 0.5)
+        V = 0.7 * np.identity(num_cols) + np.full((num_cols, num_cols), 0.3)
+
+        # Incorrect dimensions
+        assert_raises(ValueError, matrix_normal, np.zeros((5,4,3)))
+        assert_raises(ValueError, matrix_normal, M, np.zeros(10), V)
+        assert_raises(ValueError, matrix_normal, M, U, np.zeros(10))
+        assert_raises(ValueError, matrix_normal, M, U, U)
+        assert_raises(ValueError, matrix_normal, M, V, V)
+        assert_raises(ValueError, matrix_normal, M.T, U, V)
+
+        e = np.linalg.LinAlgError
+        # Singular covariance for the rvs method of a non-frozen instance
+        assert_raises(e, matrix_normal.rvs,
+                      M, U, np.ones((num_cols, num_cols)))
+        assert_raises(e, matrix_normal.rvs,
+                      M, np.ones((num_rows, num_rows)), V)
+        # Singular covariance for a frozen instance
+        assert_raises(e, matrix_normal, M, U, np.ones((num_cols, num_cols)))
+        assert_raises(e, matrix_normal, M, np.ones((num_rows, num_rows)), V)
+
+    def test_default_inputs(self):
+        # Check that default argument handling works
+        num_rows = 4
+        num_cols = 3
+        M = np.full((num_rows,num_cols), 0.3)
+        U = 0.5 * np.identity(num_rows) + np.full((num_rows, num_rows), 0.5)
+        V = 0.7 * np.identity(num_cols) + np.full((num_cols, num_cols), 0.3)
+        Z = np.zeros((num_rows, num_cols))
+        Zr = np.zeros((num_rows, 1))
+        Zc = np.zeros((1, num_cols))
+        Ir = np.identity(num_rows)
+        Ic = np.identity(num_cols)
+        I1 = np.identity(1)
+
+        assert_equal(matrix_normal.rvs(mean=M, rowcov=U, colcov=V).shape,
+                     (num_rows, num_cols))
+        assert_equal(matrix_normal.rvs(mean=M).shape,
+                     (num_rows, num_cols))
+        assert_equal(matrix_normal.rvs(rowcov=U).shape,
+                     (num_rows, 1))
+        assert_equal(matrix_normal.rvs(colcov=V).shape,
+                     (1, num_cols))
+        assert_equal(matrix_normal.rvs(mean=M, colcov=V).shape,
+                     (num_rows, num_cols))
+        assert_equal(matrix_normal.rvs(mean=M, rowcov=U).shape,
+                     (num_rows, num_cols))
+        assert_equal(matrix_normal.rvs(rowcov=U, colcov=V).shape,
+                     (num_rows, num_cols))
+
+        assert_equal(matrix_normal(mean=M).rowcov, Ir)
+        assert_equal(matrix_normal(mean=M).colcov, Ic)
+        assert_equal(matrix_normal(rowcov=U).mean, Zr)
+        assert_equal(matrix_normal(rowcov=U).colcov, I1)
+        assert_equal(matrix_normal(colcov=V).mean, Zc)
+        assert_equal(matrix_normal(colcov=V).rowcov, I1)
+        assert_equal(matrix_normal(mean=M, rowcov=U).colcov, Ic)
+        assert_equal(matrix_normal(mean=M, colcov=V).rowcov, Ir)
+        assert_equal(matrix_normal(rowcov=U, colcov=V).mean, Z)
+
+    def test_covariance_expansion(self):
+        # Check that covariance can be specified with scalar or vector
+        num_rows = 4
+        num_cols = 3
+        M = np.full((num_rows, num_cols), 0.3)
+        Uv = np.full(num_rows, 0.2)
+        Us = 0.2
+        Vv = np.full(num_cols, 0.1)
+        Vs = 0.1
+
+        Ir = np.identity(num_rows)
+        Ic = np.identity(num_cols)
+
+        assert_equal(matrix_normal(mean=M, rowcov=Uv, colcov=Vv).rowcov,
+                     0.2*Ir)
+        assert_equal(matrix_normal(mean=M, rowcov=Uv, colcov=Vv).colcov,
+                     0.1*Ic)
+        assert_equal(matrix_normal(mean=M, rowcov=Us, colcov=Vs).rowcov,
+                     0.2*Ir)
+        assert_equal(matrix_normal(mean=M, rowcov=Us, colcov=Vs).colcov,
+                     0.1*Ic)
+
+    def test_frozen_matrix_normal(self):
+        for i in range(1,5):
+            for j in range(1,5):
+                M = np.full((i,j), 0.3)
+                U = 0.5 * np.identity(i) + np.full((i,i), 0.5)
+                V = 0.7 * np.identity(j) + np.full((j,j), 0.3)
+
+                frozen = matrix_normal(mean=M, rowcov=U, colcov=V)
+
+                rvs1 = frozen.rvs(random_state=1234)
+                rvs2 = matrix_normal.rvs(mean=M, rowcov=U, colcov=V,
+                                         random_state=1234)
+                assert_equal(rvs1, rvs2)
+
+                X = frozen.rvs(random_state=1234)
+
+                pdf1 = frozen.pdf(X)
+                pdf2 = matrix_normal.pdf(X, mean=M, rowcov=U, colcov=V)
+                assert_equal(pdf1, pdf2)
+
+                logpdf1 = frozen.logpdf(X)
+                logpdf2 = matrix_normal.logpdf(X, mean=M, rowcov=U, colcov=V)
+                assert_equal(logpdf1, logpdf2)
+
+    def test_matches_multivariate(self):
+        # Check that the pdfs match those obtained by vectorising and
+        # treating as a multivariate normal.
+        for i in range(1,5):
+            for j in range(1,5):
+                M = np.full((i,j), 0.3)
+                U = 0.5 * np.identity(i) + np.full((i,i), 0.5)
+                V = 0.7 * np.identity(j) + np.full((j,j), 0.3)
+
+                frozen = matrix_normal(mean=M, rowcov=U, colcov=V)
+                X = frozen.rvs(random_state=1234)
+                pdf1 = frozen.pdf(X)
+                logpdf1 = frozen.logpdf(X)
+                entropy1 = frozen.entropy()
+
+                vecX = X.T.flatten()
+                vecM = M.T.flatten()
+                cov = np.kron(V,U)
+                pdf2 = multivariate_normal.pdf(vecX, mean=vecM, cov=cov)
+                logpdf2 = multivariate_normal.logpdf(vecX, mean=vecM, cov=cov)
+                entropy2 = multivariate_normal.entropy(mean=vecM, cov=cov)
+
+                assert_allclose(pdf1, pdf2, rtol=1E-10)
+                assert_allclose(logpdf1, logpdf2, rtol=1E-10)
+                assert_allclose(entropy1, entropy2)
+
+    def test_array_input(self):
+        # Check array of inputs has the same output as the separate entries.
+        num_rows = 4
+        num_cols = 3
+        M = np.full((num_rows,num_cols), 0.3)
+        U = 0.5 * np.identity(num_rows) + np.full((num_rows, num_rows), 0.5)
+        V = 0.7 * np.identity(num_cols) + np.full((num_cols, num_cols), 0.3)
+        N = 10
+
+        frozen = matrix_normal(mean=M, rowcov=U, colcov=V)
+        X1 = frozen.rvs(size=N, random_state=1234)
+        X2 = frozen.rvs(size=N, random_state=4321)
+        X = np.concatenate((X1[np.newaxis,:,:,:],X2[np.newaxis,:,:,:]), axis=0)
+        assert_equal(X.shape, (2, N, num_rows, num_cols))
+
+        array_logpdf = frozen.logpdf(X)
+        assert_equal(array_logpdf.shape, (2, N))
+        for i in range(2):
+            for j in range(N):
+                separate_logpdf = matrix_normal.logpdf(X[i,j], mean=M,
+                                                       rowcov=U, colcov=V)
+                assert_allclose(separate_logpdf, array_logpdf[i,j], 1E-10)
+
+    def test_moments(self):
+        # Check that the sample moments match the parameters
+        num_rows = 4
+        num_cols = 3
+        M = np.full((num_rows,num_cols), 0.3)
+        U = 0.5 * np.identity(num_rows) + np.full((num_rows, num_rows), 0.5)
+        V = 0.7 * np.identity(num_cols) + np.full((num_cols, num_cols), 0.3)
+        N = 1000
+
+        frozen = matrix_normal(mean=M, rowcov=U, colcov=V)
+        X = frozen.rvs(size=N, random_state=1234)
+
+        sample_mean = np.mean(X,axis=0)
+        assert_allclose(sample_mean, M, atol=0.1)
+
+        sample_colcov = np.cov(X.reshape(N*num_rows,num_cols).T)
+        assert_allclose(sample_colcov, V, atol=0.1)
+
+        sample_rowcov = np.cov(np.swapaxes(X,1,2).reshape(
+                                                        N*num_cols,num_rows).T)
+        assert_allclose(sample_rowcov, U, atol=0.1)
+
+    def test_samples(self):
+        # Regression test to ensure that we always generate the same stream of
+        # random variates.
+        actual = matrix_normal.rvs(
+            mean=np.array([[1, 2], [3, 4]]),
+            rowcov=np.array([[4, -1], [-1, 2]]),
+            colcov=np.array([[5, 1], [1, 10]]),
+            random_state=np.random.default_rng(0),
+            size=2
+        )
+        expected = np.array(
+            [[[1.56228264238181, -1.24136424071189],
+              [2.46865788392114, 6.22964440489445]],
+             [[3.86405716144353, 10.73714311429529],
+              [2.59428444080606, 5.79987854490876]]]
+        )
+        assert_allclose(actual, expected)
+
+
+class TestMatrixT:
+
+    def test_bad_input(self):
+        # Check that bad inputs raise errors
+        num_rows = 4
+        num_cols = 3
+        df = 5
+        M = np.full((num_rows, num_cols), 0.3)
+        U = 0.5 * np.identity(num_rows) + np.full((num_rows, num_rows), 0.5)
+        V = 0.7 * np.identity(num_cols) + np.full((num_cols, num_cols), 0.3)
+
+        # Nonpositive degrees of freedom
+        with pytest.raises(ValueError, match="Degrees of freedom must be positive."):
+            matrix_t(df=0)
+
+        # Incorrect dimensions
+        with pytest.raises(ValueError, match="Array `mean` must be 2D."):
+            matrix_t(mean=np.zeros((5, 4, 3)))
+
+        with pytest.raises(ValueError, match="Array `mean` has invalid shape."):
+            matrix_t(mean=np.zeros((4, 3, 0)))
+
+        with pytest.raises(ValueError, match="Array `row_spread` has invalid shape."):
+            matrix_t(row_spread=np.ones((1, 0)))
+
+        with pytest.raises(
+            ValueError, match="Array `row_spread` must be a scalar or a 2D array."
+        ):
+            matrix_t(row_spread=np.ones((1, 2, 3)))
+
+        with pytest.raises(ValueError, match="Array `row_spread` must be square."):
+            matrix_t(row_spread=np.ones((1, 2)))
+
+        with pytest.raises(ValueError, match="Array `col_spread` has invalid shape."):
+            matrix_t(col_spread=np.ones((1, 0)))
+
+        with pytest.raises(
+            ValueError, match="Array `col_spread` must be a scalar or a 2D array."
+        ):
+            matrix_t(col_spread=np.ones((1, 2, 3)))
+
+        with pytest.raises(ValueError, match="Array `col_spread` must be square."):
+            matrix_t(col_spread=np.ones((1, 2)))
+
+        with pytest.raises(
+            ValueError,
+            match="Arrays `mean` and `row_spread` must have the same number "
+            "of rows.",
+        ):
+            matrix_t(mean=M, row_spread=V)
+
+        with pytest.raises(
+            ValueError,
+            match="Arrays `mean` and `col_spread` must have the same number "
+            "of columns.",
+        ):
+            matrix_t(mean=M, col_spread=U)
+
+        # Incorrect dimension of input matrix
+        with pytest.raises(
+            ValueError,
+            match="The shape of array `X` is not conformal with "
+            "the distribution parameters.",
+        ):
+            matrix_t.pdf(X=np.zeros((num_rows, num_rows)), mean=M)
+
+        # Singular covariance for a non-frozen instance
+
+        with pytest.raises(
+            np.linalg.LinAlgError,
+            match="Internal potrf return info",
+        ):
+            matrix_t.rvs(M, U, np.ones((num_cols, num_cols)), df)
+
+        with pytest.raises(
+            np.linalg.LinAlgError,
+            match="Internal potrf return info",
+        ):
+            matrix_t.rvs(M, np.ones((num_rows, num_rows)), V, df)
+
+        # Singular covariance for a frozen instance
+
+        with pytest.raises(
+            np.linalg.LinAlgError,
+            match="When `allow_singular is False`, the input matrix must be "
+            "symmetric positive definite.",
+        ):
+            matrix_t(M, U, np.ones((num_cols, num_cols)), df)
+
+        with pytest.raises(
+            np.linalg.LinAlgError,
+            match="When `allow_singular is False`, the input matrix must be "
+            "symmetric positive definite.",
+        ):
+            matrix_t(M, np.ones((num_rows, num_rows)), V, df)
+
+    def test_default_inputs(self):
+        # Check that default argument handling works
+        num_rows = 4
+        num_cols = 3
+        df = 5
+        M = np.full((num_rows, num_cols), 0.3)
+        U = 0.5 * np.identity(num_rows) + np.full((num_rows, num_rows), 0.5)
+        V = 0.7 * np.identity(num_cols) + np.full((num_cols, num_cols), 0.3)
+        Z = np.zeros((num_rows, num_cols))
+        Zr = np.zeros((num_rows, 1))
+        Zc = np.zeros((1, num_cols))
+        Ir = np.identity(num_rows)
+        Ic = np.identity(num_cols)
+        I1 = np.identity(1)
+        dfdefault = 1
+
+        assert_equal(
+            matrix_t.rvs(mean=M, row_spread=U, col_spread=V, df=df).shape,
+            (num_rows, num_cols),
+        )
+        assert_equal(matrix_t.rvs(mean=M).shape, (num_rows, num_cols))
+        assert_equal(matrix_t.rvs(row_spread=U).shape, (num_rows, 1))
+        assert_equal(matrix_t.rvs(col_spread=V).shape, (1, num_cols))
+        assert_equal(matrix_t.rvs(mean=M, col_spread=V).shape, (num_rows, num_cols))
+        assert_equal(matrix_t.rvs(mean=M, row_spread=U).shape, (num_rows, num_cols))
+        assert_equal(
+            matrix_t.rvs(row_spread=U, col_spread=V).shape, (num_rows, num_cols)
+        )
+
+        assert_equal(matrix_t().df, dfdefault)
+        assert_equal(matrix_t(mean=M).row_spread, Ir)
+        assert_equal(matrix_t(mean=M).col_spread, Ic)
+        assert_equal(matrix_t(row_spread=U).mean, Zr)
+        assert_equal(matrix_t(row_spread=U).col_spread, I1)
+        assert_equal(matrix_t(col_spread=V).mean, Zc)
+        assert_equal(matrix_t(col_spread=V).row_spread, I1)
+        assert_equal(matrix_t(mean=M, row_spread=U).col_spread, Ic)
+        assert_equal(matrix_t(mean=M, col_spread=V).row_spread, Ir)
+        assert_equal(matrix_t(row_spread=U, col_spread=V, df=df).mean, Z)
+
+    def test_covariance_expansion(self):
+        # Check that covariance can be specified with scalar or vector
+        num_rows = 4
+        num_cols = 3
+        df = 1
+        M = np.full((num_rows, num_cols), 0.3)
+        Uv = np.full(num_rows, 0.2)
+        Us = 0.2
+        Vv = np.full(num_cols, 0.1)
+        Vs = 0.1
+
+        Ir = np.identity(num_rows)
+        Ic = np.identity(num_cols)
+
+        assert_equal(
+            matrix_t(mean=M, row_spread=Uv, col_spread=Vv, df=df).row_spread, 0.2 * Ir
+        )
+        assert_equal(
+            matrix_t(mean=M, row_spread=Uv, col_spread=Vv, df=df).col_spread, 0.1 * Ic
+        )
+        assert_equal(
+            matrix_t(mean=M, row_spread=Us, col_spread=Vs, df=df).row_spread, 0.2 * Ir
+        )
+        assert_equal(
+            matrix_t(mean=M, row_spread=Us, col_spread=Vs, df=df).col_spread, 0.1 * Ic
+        )
+
+    @pytest.mark.parametrize("i", range(1, 4))
+    @pytest.mark.parametrize("j", range(1, 4))
+    def test_frozen_matrix_t(self, i, j):
+
+        M = np.full((i, j), 0.3)
+        U = 0.5 * np.identity(i) + np.full((i, i), 0.5)
+        V = 0.7 * np.identity(j) + np.full((j, j), 0.3)
+        df = i + j
+
+        frozen = matrix_t(mean=M, row_spread=U, col_spread=V, df=df)
+
+        rvs1 = frozen.rvs(random_state=1234)
+        rvs2 = matrix_t.rvs(
+            mean=M, row_spread=U, col_spread=V, df=df, random_state=1234
+        )
+        assert_equal(rvs1, rvs2)
+
+        X = frozen.rvs(random_state=1234)
+
+        pdf1 = frozen.pdf(X)
+        pdf2 = matrix_t.pdf(X, mean=M, row_spread=U, col_spread=V, df=df)
+        assert_equal(pdf1, pdf2)
+
+        logpdf1 = frozen.logpdf(X)
+        logpdf2 = matrix_t.logpdf(X, mean=M, row_spread=U, col_spread=V, df=df)
+        assert_equal(logpdf1, logpdf2)
+
+    def test_array_input(self):
+        # Check array of inputs has the same output as the separate entries.
+        num_rows = 4
+        num_cols = 3
+        M = np.full((num_rows, num_cols), 0.3)
+        U = 0.5 * np.identity(num_rows) + np.full((num_rows, num_rows), 0.5)
+        V = 0.7 * np.identity(num_cols) + np.full((num_cols, num_cols), 0.3)
+        df = 1
+        N = 10
+
+        frozen = matrix_t(mean=M, row_spread=U, col_spread=V, df=df)
+        X1 = frozen.rvs(size=N, random_state=1234)
+        X2 = frozen.rvs(size=N, random_state=4321)
+        X = np.concatenate((X1[np.newaxis, :, :, :], X2[np.newaxis, :, :, :]), axis=0)
+        assert_equal(X.shape, (2, N, num_rows, num_cols))
+
+        array_logpdf = frozen.logpdf(X)
+        logpdf_shape = array_logpdf.shape
+        assert_equal(logpdf_shape, (2, N))
+        for i in range(2):
+            for j in range(N):
+                separate_logpdf = matrix_t.logpdf(
+                    X[i, j], mean=M, row_spread=U, col_spread=V, df=df
+                )
+                assert_allclose(separate_logpdf, array_logpdf[i, j], 1e-10)
+
+    @staticmethod
+    def relative_error(vec1: np.ndarray, vec2: np.ndarray):
+        numerator = np.linalg.norm(vec1 - vec2) ** 2
+        denominator = np.linalg.norm(vec1) ** 2 + np.linalg.norm(vec2) ** 2
+        return numerator / denominator
+
+    @staticmethod
+    def matrix_divergence(mat_true: np.ndarray, mat_est: np.ndarray) -> float:
+        mat_true_psd = _PSD(mat_true, allow_singular=False)
+        mat_est_psd = _PSD(mat_est, allow_singular=False)
+        if (np.exp(mat_est_psd.log_pdet) <= 0) or (np.exp(mat_true_psd.log_pdet) <= 0):
+            return np.inf
+        trace_term = np.trace(mat_est_psd.pinv @ mat_true)
+        log_detratio = mat_est_psd.log_pdet - mat_true_psd.log_pdet
+        return (trace_term + log_detratio - len(mat_true)) / 2
+
+    @staticmethod
+    def vec(a_mat: np.ndarray) -> np.ndarray:
+        """
+        For an (m,n) array `a_mat` the output `vec(a_mat)` is an (m*n, 1)
+        array formed by stacking the columns of `a_mat` in the order in
+        which they occur in `a_mat`.
+        """
+        assert a_mat.ndim == 2
+        return a_mat.T.reshape((a_mat.size,))
+
+    def test_moments(self):
+        r"""
+        Gupta and Nagar (2000) Theorem 4.3.1 (p.135)
+        --------------------------------------------
+        The covariance of the vectorized matrix variate t-distribution equals
+        $ (V \otimes U) / (\text{df} - 2)$, where $\otimes$
+        denotes the usual Kronecker product.
+        """
+
+        df = 5
+        num_rows = 4
+        num_cols = 3
+        M = np.full((num_rows, num_cols), 0.3)
+        U = 0.5 * np.identity(num_rows) + np.full((num_rows, num_rows), 0.5)
+        V = 0.7 * np.identity(num_cols) + np.full((num_cols, num_cols), 0.3)
+        N = 10**4
+        atol = 1e-1
+
+        frozen = matrix_t(mean=M, row_spread=U, col_spread=V, df=df)
+        X = frozen.rvs(size=N, random_state=42)
+
+        relerr = self.relative_error(M, X.mean(axis=0))
+        assert_close(relerr, 0, atol=atol)
+
+        cov_vec_true = np.kron(V, U) / (df - 2)
+        cov_vec_rvs = np.cov(np.array([self.vec(x) for x in X]), rowvar=False)
+        kl = self.matrix_divergence(cov_vec_true, cov_vec_rvs)
+        assert_close(kl, 0, atol=atol)
+
+    def test_pdf_against_julia(self):
+        """
+        Test values generated from Julia.
+
+        Dockerfile
+        ----------
+        FROM julia:1.11.5
+        RUN julia -e 'using Pkg; Pkg.add("Distributions"); Pkg.add("PDMats")'
+        WORKDIR /usr/src
+
+        Commands
+        --------
+        using DelimitedFiles
+        using Distributions
+        using PDMats
+        using Random
+        Random.seed!(42)
+        ν = 5
+        M = [1 2 3; 4 5 6]
+        Σ = PDMats.PDMat([1 0.5; 0.5 1])
+        Ω = PDMats.PDMat([1 0.3 0.2; 0.3 1 0.4; 0.2 0.4 1])
+        dist = MatrixTDist(ν, M, Σ, Ω)
+        samples = rand(dist, 10)
+        pdfs = [pdf(dist, s) for s in samples]
+        """
+
+        df = 5
+        M = np.array([[1, 2, 3], [4, 5, 6]])
+        U = np.array([[1, 0.5], [0.5, 1]])
+        V = np.array([[1, 0.3, 0.2], [0.3, 1, 0.4], [0.2, 0.4, 1]])
+
+        rtol = 1e-10
+
+        samples_j = np.array(
+            [
+                [
+                    [0.958884881003464, 2.328976673167312, 2.936195396714506],
+                    [3.656388568544394, 5.677549814962506, 6.292509556719057]
+                ],
+                [
+                    [0.830992685140180, 2.588946865508210, 3.310327469315906],
+                    [3.850637198786261, 5.106074165416971, 6.403143979925566]
+                ],
+                [
+                    [1.572053537500711, 1.760828063560249, 2.812123062636012],
+                    [4.156334686390513, 5.075942019982631, 5.827004350136873]
+                ],
+                [
+                    [1.683810860278459, 2.801203900480317, 4.054517744825265],
+                    [4.778239956376877, 5.070613721477604, 6.640349743267192]
+                ],
+                [
+                    [0.443183825511296, 2.072092271247398, 3.045385527559403],
+                    [4.374387994815022, 5.083432151729137, 5.958013783940404]
+                ],
+                [
+                    [0.311591337218329, 1.162836182564980, 2.562167762547456],
+                    [3.079154928756626, 4.202325496476140, 5.485839479663457]
+                ],
+                [
+                    [0.943713128785340, 1.923800464789872, 2.511941262351750],
+                    [4.124882619205123, 4.889406461458511, 5.689675454116582]
+                ],
+                [
+                    [1.487852512870631, 1.933859334657448, 2.681311906634522],
+                    [4.124418827930267, 5.335204598518954, 5.988120342017037]
+                ],
+                [
+                    [1.002470749319751, 1.386785511789551, 2.890832331097640],
+                    [4.372884362128993, 4.729718562700068, 6.732322315921552]
+                ],
+                [
+                    [1.421351511333299, 2.106946903600814, 2.654619331838720],
+                    [4.188693248790616, 5.336439611284261, 5.279121290355546]
+                ]
+            ]
+        )
+
+        pdfs_j = np.array(
+            [
+                0.082798951655369,
+                0.119993852401118,
+                0.151969434727803,
+                0.003620324481841,
+                0.072538716346179,
+                0.027002666410192,
+                0.485180162388507,
+                0.135740468069511,
+                0.013619162593841,
+                0.034813885519299
+            ]
+        )
+
+        pdfs_py = matrix_t.pdf(samples_j, mean=M, row_spread=U, col_spread=V, df=df)
+
+        assert_allclose(pdfs_j, pdfs_py, rtol=rtol)
+
+    def test_pdf_against_mathematica(self):
+        """
+        Test values generated from Mathematica 13.0.0 for Linux x86 (64-bit)
+        Release ID 13.0.0.0 (7522564, 2021120311723), Patch Level 0
+
+        mu={{1,2,3},{4,5,6}};
+        sigma={{1,0.5},{0.5,1}};
+        omega={{1,0.3,0.2},{0.3,1,0.4},{0.2,0.4,1}};
+        df=5;
+        sampleSize=10;
+        SeedRandom[42];
+        dist=MatrixTDistribution[mu,sigma,omega,df];
+        samples=SetPrecision[RandomVariate[dist,sampleSize],15];
+        pdfs=SetPrecision[PDF[dist,#]&/@samples,15];
+        """
+
+        df = 5
+        M = np.array([[1, 2, 3], [4, 5, 6]])
+        U = np.array([[1, 0.5], [0.5, 1]])
+        V = np.array([[1, 0.3, 0.2], [0.3, 1, 0.4], [0.2, 0.4, 1]])
+
+        rtol = 1e-10
+
+        samples_m = np.array(
+            [
+                [
+                    [0.639971699425374, 2.171718671534955, 2.575826093352771],
+                    [4.031082477912233, 5.021680958526638, 6.268126154787008],
+                ],
+                [
+                    [1.164842884206232, 2.526297099993045, 3.781375229865069],
+                    [3.912979114956833, 4.202714884504189, 5.661830748993523],
+                ],
+                [
+                    [1.00461853907369, 2.080028751298565, 3.406489485602410],
+                    [3.993327716320432, 5.655909265966448, 6.578059791357837],
+                ],
+                [
+                    [0.80625209501374, 2.529009560674907, 2.807513313302189],
+                    [3.722896768794995, 5.26987322525995, 5.801155613199776],
+                ],
+                [
+                    [0.445816208657817, 3.224059910964103, 2.954990980541423],
+                    [3.451520519442941, 7.064424621385415, 5.438834195890955],
+                ],
+                [
+                    [0.919232769636664, 2.374572300756703, 3.495118928313048],
+                    [3.924447237903237, 5.627654256287447, 5.806104608153957],
+                ],
+                [
+                    [2.014242004090113, 1.377018127709871, 3.114064311468686],
+                    [3.88881648137925, 4.603482820518904, 5.714205489738063],
+                ],
+                [
+                    [1.322000147426889, 2.602135838377777, 2.558921028724319],
+                    [4.50534702030683, 5.861137323151889, 5.181872548334852],
+                ],
+                [
+                    [1.448743656862261, 2.053847557652242, 3.637321543241769],
+                    [4.097711403906707, 4.506916241403669, 5.68010653497977],
+                ],
+                [
+                    [1.045187318995198, 1.645467189679729, 3.284396214544507],
+                    [3.648493466445393, 5.004212508553601, 6.301624351328048],
+                ],
+            ]
+        )
+
+        pdfs_m = np.array(
+            [
+                0.085671937131824,
+                0.004821273644067,
+                0.105978034029754,
+                0.174250448808208,
+                3.945711836053583e-05,
+                0.027158790350349,
+                0.00299095120309,
+                0.005594546018078,
+                0.025788366971310,
+                0.120210733598845,
+            ]
+        )
+
+        pdfs_py = matrix_t.pdf(samples_m, mean=M, row_spread=U, col_spread=V, df=df)
+
+        assert_allclose(pdfs_m, pdfs_py, rtol=rtol)
+
+    def test_samples(self):
+        df = 5
+        num_rows = 4
+        num_cols = 3
+        M = np.full((num_rows, num_cols), 0.3)
+        U = 0.5 * np.identity(num_rows) + np.full((num_rows, num_rows), 0.5)
+        V = 0.7 * np.identity(num_cols) + np.full((num_cols, num_cols), 0.3)
+        N = 10**4
+        rtol = 0.05
+
+        # `rvs` performs Cholesky-inverse-Wishart sampling on the smaller
+        # dimension of `mean`
+
+        frozen = matrix_t(mean=M, row_spread=U, col_spread=V, df=df)
+        X = frozen.rvs(size=N, random_state=42)  # column-wise rvs
+        m = X.mean(0)
+
+        frozenT = matrix_t(mean=M.T, row_spread=V, col_spread=U, df=df)
+        XT = frozenT.rvs(size=N, random_state=42)  # row-wise rvs
+        mT = XT.mean(0)
+
+        # Gupta and Nagar (2000) Theorem 4.3.3 (p.137)
+        # --------------------------------------------
+        # If T follows a matrix variate t-distribution with mean M and row_spread U
+        # and col_spread V and df degrees of freedom, then its transpose T.T follows
+        # a matrix variate t-distribution with mean M.T and row_spread V and
+        # col_spread U and df degrees of freedom.
+
+        assert_allclose(M, m, rtol=rtol)
+        assert_allclose(M.T, mT, rtol=rtol)
+        assert_allclose(m, mT.T, rtol=rtol)
+        assert_allclose(m.T, mT, rtol=rtol)
+
+    @pytest.mark.parametrize("shape_case", ["row", "col"])
+    def test_against_multivariate_t(self, shape_case):
+        r"""
+        Gupta and Nagar (2000) p.133f
+        When the number of rows or the number of columns equals 1 the
+        matrix t reduces to the multivariate t. But, the matrix t
+        is parameterized by raw 2nd moments whereas the multivariate t is
+        parameterized by a covariance (raw 2nd central moment normalized by df).
+        We can see the difference by comparing the author's notation
+            $t_p(n, \omega, \mathbf{\mu}, \Sigma)$
+        for a matrix t with a single column
+        to the formula (4.1.2) for the PDF of the multivariate t.
+        """
+        rtol = 1e-6
+        df = 5
+
+        if shape_case == "row":
+            num_rows = 1
+            num_cols = 3
+            row_spread = 1
+            col_spread = np.array([[1, 0.3, 0.2], [0.3, 1, 0.4], [0.2, 0.4, 1]])
+            shape = col_spread / df
+        else:  # shape_case == "col"
+            num_rows = 3
+            num_cols = 1
+            row_spread = np.array([[1, 0.3, 0.2], [0.3, 1, 0.4], [0.2, 0.4, 1]])
+            col_spread=1
+            shape = row_spread / df
+
+        M = np.full((num_rows, num_cols), 0.3)
+
+        t_mat = matrix_t(
+            mean=M, row_spread=row_spread, col_spread=col_spread, df=df
+        )
+        t_mvt = multivariate_t(loc=M.squeeze(), shape=shape, df=df)
+
+        X = t_mat.rvs(size=3, random_state=42)
+        t_mat_logpdf = t_mat.logpdf(X)
+        t_mvt_logpdf = t_mvt.logpdf(X.squeeze())
+        assert_allclose(t_mvt_logpdf, t_mat_logpdf, rtol=rtol)
+
+
+class TestDirichlet:
+
+    def test_frozen_dirichlet(self):
+        rng = np.random.default_rng(2846)
+
+        n = rng.integers(1, 32)
+        alpha = rng.uniform(10e-10, 100, n)
+
+        d = dirichlet(alpha)
+
+        assert_equal(d.var(), dirichlet.var(alpha))
+        assert_equal(d.mean(), dirichlet.mean(alpha))
+        assert_equal(d.entropy(), dirichlet.entropy(alpha))
+        num_tests = 10
+        for i in range(num_tests):
+            x = rng.uniform(10e-10, 100, n)
+            x /= np.sum(x)
+            assert_equal(d.pdf(x[:-1]), dirichlet.pdf(x[:-1], alpha))
+            assert_equal(d.logpdf(x[:-1]), dirichlet.logpdf(x[:-1], alpha))
+
+    def test_numpy_rvs_shape_compatibility(self):
+        rng = np.random.default_rng(2846)
+        alpha = np.array([1.0, 2.0, 3.0])
+        x = rng.dirichlet(alpha, size=7)
+        assert_equal(x.shape, (7, 3))
+        assert_raises(ValueError, dirichlet.pdf, x, alpha)
+        assert_raises(ValueError, dirichlet.logpdf, x, alpha)
+        dirichlet.pdf(x.T, alpha)
+        dirichlet.pdf(x.T[:-1], alpha)
+        dirichlet.logpdf(x.T, alpha)
+        dirichlet.logpdf(x.T[:-1], alpha)
+
+    def test_alpha_with_zeros(self):
+        rng = np.random.default_rng(2846)
+        alpha = [1.0, 0.0, 3.0]
+        # don't pass invalid alpha to np.random.dirichlet
+        x = rng.dirichlet(np.maximum(1e-9, alpha), size=7).T
+        assert_raises(ValueError, dirichlet.pdf, x, alpha)
+        assert_raises(ValueError, dirichlet.logpdf, x, alpha)
+
+    def test_alpha_with_negative_entries(self):
+        rng = np.random.default_rng(2846)
+        alpha = [1.0, -2.0, 3.0]
+        # don't pass invalid alpha to np.random.dirichlet
+        x = rng.dirichlet(np.maximum(1e-9, alpha), size=7).T
+        assert_raises(ValueError, dirichlet.pdf, x, alpha)
+        assert_raises(ValueError, dirichlet.logpdf, x, alpha)
+
+    def test_data_with_zeros(self):
+        alpha = np.array([1.0, 2.0, 3.0, 4.0])
+        x = np.array([0.1, 0.0, 0.2, 0.7])
+        dirichlet.pdf(x, alpha)
+        dirichlet.logpdf(x, alpha)
+        alpha = np.array([1.0, 1.0, 1.0, 1.0])
+        assert_almost_equal(dirichlet.pdf(x, alpha), 6)
+        assert_almost_equal(dirichlet.logpdf(x, alpha), np.log(6))
+
+    def test_data_with_zeros_and_small_alpha(self):
+        alpha = np.array([1.0, 0.5, 3.0, 4.0])
+        x = np.array([0.1, 0.0, 0.2, 0.7])
+        assert_raises(ValueError, dirichlet.pdf, x, alpha)
+        assert_raises(ValueError, dirichlet.logpdf, x, alpha)
+
+    def test_data_with_negative_entries(self):
+        alpha = np.array([1.0, 2.0, 3.0, 4.0])
+        x = np.array([0.1, -0.1, 0.3, 0.7])
+        assert_raises(ValueError, dirichlet.pdf, x, alpha)
+        assert_raises(ValueError, dirichlet.logpdf, x, alpha)
+
+    def test_data_with_too_large_entries(self):
+        alpha = np.array([1.0, 2.0, 3.0, 4.0])
+        x = np.array([0.1, 1.1, 0.3, 0.7])
+        assert_raises(ValueError, dirichlet.pdf, x, alpha)
+        assert_raises(ValueError, dirichlet.logpdf, x, alpha)
+
+    def test_data_too_deep_c(self):
+        alpha = np.array([1.0, 2.0, 3.0])
+        x = np.full((2, 7, 7), 1 / 14)
+        assert_raises(ValueError, dirichlet.pdf, x, alpha)
+        assert_raises(ValueError, dirichlet.logpdf, x, alpha)
+
+    def test_alpha_too_deep(self):
+        alpha = np.array([[1.0, 2.0], [3.0, 4.0]])
+        x = np.full((2, 2, 7), 1 / 4)
+        assert_raises(ValueError, dirichlet.pdf, x, alpha)
+        assert_raises(ValueError, dirichlet.logpdf, x, alpha)
+
+    def test_alpha_correct_depth(self):
+        alpha = np.array([1.0, 2.0, 3.0])
+        x = np.full((3, 7), 1 / 3)
+        dirichlet.pdf(x, alpha)
+        dirichlet.logpdf(x, alpha)
+
+    def test_non_simplex_data(self):
+        alpha = np.array([1.0, 2.0, 3.0])
+        x = np.full((3, 7), 1 / 2)
+        assert_raises(ValueError, dirichlet.pdf, x, alpha)
+        assert_raises(ValueError, dirichlet.logpdf, x, alpha)
+
+    def test_data_vector_too_short(self):
+        alpha = np.array([1.0, 2.0, 3.0, 4.0])
+        x = np.full((2, 7), 1 / 2)
+        assert_raises(ValueError, dirichlet.pdf, x, alpha)
+        assert_raises(ValueError, dirichlet.logpdf, x, alpha)
+
+    def test_data_vector_too_long(self):
+        alpha = np.array([1.0, 2.0, 3.0, 4.0])
+        x = np.full((5, 7), 1 / 5)
+        assert_raises(ValueError, dirichlet.pdf, x, alpha)
+        assert_raises(ValueError, dirichlet.logpdf, x, alpha)
+
+    def test_mean_var_cov(self):
+        # Reference values calculated by hand and confirmed with Mathematica, e.g.
+        # `Covariance[DirichletDistribution[{ 1, 0.8, 0.2, 10^-300}]]`
+        alpha = np.array([1., 0.8, 0.2])
+        d = dirichlet(alpha)
+
+        expected_mean = [0.5, 0.4, 0.1]
+        expected_var = [1. / 12., 0.08, 0.03]
+        expected_cov = [
+                [ 1. / 12, -1. / 15, -1. / 60],
+                [-1. / 15,  2. / 25, -1. / 75],
+                [-1. / 60, -1. / 75,  3. / 100],
+        ]
+
+        assert_array_almost_equal(d.mean(), expected_mean)
+        assert_array_almost_equal(d.var(), expected_var)
+        assert_array_almost_equal(d.cov(), expected_cov)
+
+    def test_scalar_values(self):
+        alpha = np.array([0.2])
+        d = dirichlet(alpha)
+
+        # For alpha of length 1, mean and var should be scalar instead of array
+        assert_equal(d.mean().ndim, 0)
+        assert_equal(d.var().ndim, 0)
+
+        assert_equal(d.pdf([1.]).ndim, 0)
+        assert_equal(d.logpdf([1.]).ndim, 0)
+
+    def test_K_and_K_minus_1_calls_equal(self):
+        # Test that calls with K and K-1 entries yield the same results.
+        rng = np.random.default_rng(2846)
+
+        n = rng.integers(1, 32)
+        alpha = rng.uniform(10e-10, 100, n)
+
+        d = dirichlet(alpha)
+        num_tests = 10
+        for i in range(num_tests):
+            x = rng.uniform(10e-10, 100, n)
+            x /= np.sum(x)
+            assert_almost_equal(d.pdf(x[:-1]), d.pdf(x))
+
+    def test_multiple_entry_calls(self):
+        # Test that calls with multiple x vectors as matrix work
+        rng = np.random.default_rng(2846)
+
+        n = rng.integers(1, 32)
+        alpha = rng.uniform(10e-10, 100, n)
+        d = dirichlet(alpha)
+
+        num_tests = 10
+        num_multiple = 5
+        xm = None
+        for i in range(num_tests):
+            for m in range(num_multiple):
+                x = rng.uniform(10e-10, 100, n)
+                x /= np.sum(x)
+                if xm is not None:
+                    xm = np.vstack((xm, x))
+                else:
+                    xm = x
+            rm = d.pdf(xm.T)
+            rs = None
+            for xs in xm:
+                r = d.pdf(xs)
+                if rs is not None:
+                    rs = np.append(rs, r)
+                else:
+                    rs = r
+            assert_array_almost_equal(rm, rs)
+
+    def test_2D_dirichlet_is_beta(self):
+        rng = np.random.default_rng(2846)
+
+        alpha = rng.uniform(10e-10, 100, 2)
+        d = dirichlet(alpha)
+        b = beta(alpha[0], alpha[1])
+
+        num_tests = 10
+        for i in range(num_tests):
+            x = rng.uniform(10e-10, 100, 2)
+            x /= np.sum(x)
+            assert_almost_equal(b.pdf(x), d.pdf([x]))
+
+        assert_almost_equal(b.mean(), d.mean()[0])
+        assert_almost_equal(b.var(), d.var()[0])
+
+
+def test_multivariate_normal_dimensions_mismatch():
+    # Regression test for GH #3493. Check that setting up a PDF with a mean of
+    # length M and a covariance matrix of size (N, N), where M != N, raises a
+    # ValueError with an informative error message.
+    mu = np.array([0.0, 0.0])
+    sigma = np.array([[1.0]])
+
+    assert_raises(ValueError, multivariate_normal, mu, sigma)
+
+    # A simple check that the right error message was passed along. Checking
+    # that the entire message is there, word for word, would be somewhat
+    # fragile, so we just check for the leading part.
+    try:
+        multivariate_normal(mu, sigma)
+    except ValueError as e:
+        msg = "Dimension mismatch"
+        assert_equal(str(e)[:len(msg)], msg)
+
+
+class TestWishart:
+    def test_scale_dimensions(self):
+        # Test that we can call the Wishart with various scale dimensions
+
+        # Test case: dim=1, scale=1
+        true_scale = np.array(1, ndmin=2)
+        scales = [
+            1,                    # scalar
+            [1],                  # iterable
+            np.array(1),          # 0-dim
+            np.r_[1],             # 1-dim
+            np.array(1, ndmin=2)  # 2-dim
+        ]
+        for scale in scales:
+            w = wishart(1, scale)
+            assert_equal(w.scale, true_scale)
+            assert_equal(w.scale.shape, true_scale.shape)
+
+        # Test case: dim=2, scale=[[1,0]
+        #                          [0,2]
+        true_scale = np.array([[1,0],
+                               [0,2]])
+        scales = [
+            [1,2],             # iterable
+            np.r_[1,2],        # 1-dim
+            np.array([[1,0],   # 2-dim
+                      [0,2]])
+        ]
+        for scale in scales:
+            w = wishart(2, scale)
+            assert_equal(w.scale, true_scale)
+            assert_equal(w.scale.shape, true_scale.shape)
+
+        # We cannot call with a df < dim - 1
+        assert_raises(ValueError, wishart, 1, np.eye(2))
+
+        # But we can call with dim - 1 < df < dim
+        wishart(1.1, np.eye(2))  # no error
+        # see gh-5562
+
+        # We cannot call with a 3-dimension array
+        scale = np.array(1, ndmin=3)
+        assert_raises(ValueError, wishart, 1, scale)
+
+    def test_quantile_dimensions(self):
+        # Test that we can call the Wishart rvs with various quantile dimensions
+
+        # If dim == 1, consider x.shape = [1,1,1]
+        X = [
+            1,                      # scalar
+            [1],                    # iterable
+            np.array(1),            # 0-dim
+            np.r_[1],               # 1-dim
+            np.array(1, ndmin=2),   # 2-dim
+            np.array([1], ndmin=3)  # 3-dim
+        ]
+
+        w = wishart(1,1)
+        density = w.pdf(np.array(1, ndmin=3))
+        for x in X:
+            assert_equal(w.pdf(x), density)
+
+        # If dim == 1, consider x.shape = [1,1,*]
+        X = [
+            [1,2,3],                     # iterable
+            np.r_[1,2,3],                # 1-dim
+            np.array([1,2,3], ndmin=3)   # 3-dim
+        ]
+
+        w = wishart(1,1)
+        density = w.pdf(np.array([1,2,3], ndmin=3))
+        for x in X:
+            assert_equal(w.pdf(x), density)
+
+        # If dim == 2, consider x.shape = [2,2,1]
+        # where x[:,:,*] = np.eye(1)*2
+        X = [
+            2,                    # scalar
+            [2,2],                # iterable
+            np.array(2),          # 0-dim
+            np.r_[2,2],           # 1-dim
+            np.array([[2,0],
+                      [0,2]]),    # 2-dim
+            np.array([[2,0],
+                      [0,2]])[:,:,np.newaxis]  # 3-dim
+        ]
+
+        w = wishart(2,np.eye(2))
+        density = w.pdf(np.array([[2,0],
+                                  [0,2]])[:,:,np.newaxis])
+        for x in X:
+            assert_equal(w.pdf(x), density)
+
+    def test_frozen(self):
+        # Test that the frozen and non-frozen Wishart gives the same answers
+
+        # Construct an arbitrary positive definite scale matrix
+        dim = 4
+        scale = np.diag(np.arange(dim)+1)
+        scale[np.tril_indices(dim, k=-1)] = np.arange(dim * (dim-1) // 2)
+        scale = np.dot(scale.T, scale)
+
+        # Construct a collection of positive definite matrices to test the PDF
+        X = []
+        for i in range(5):
+            x = np.diag(np.arange(dim)+(i+1)**2)
+            x[np.tril_indices(dim, k=-1)] = np.arange(dim * (dim-1) // 2)
+            x = np.dot(x.T, x)
+            X.append(x)
+        X = np.array(X).T
+
+        # Construct a 1D and 2D set of parameters
+        parameters = [
+            (10, 1, np.linspace(0.1, 10, 5)),  # 1D case
+            (10, scale, X)
+        ]
+
+        for (df, scale, x) in parameters:
+            w = wishart(df, scale)
+            assert_equal(w.var(), wishart.var(df, scale))
+            assert_equal(w.mean(), wishart.mean(df, scale))
+            assert_equal(w.mode(), wishart.mode(df, scale))
+            assert_equal(w.entropy(), wishart.entropy(df, scale))
+            assert_equal(w.pdf(x), wishart.pdf(x, df, scale))
+
+    def test_wishart_2D_rvs(self):
+        dim = 3
+        df = 10
+
+        # Construct a simple non-diagonal positive definite matrix
+        scale = np.eye(dim)
+        scale[0,1] = 0.5
+        scale[1,0] = 0.5
+
+        # Construct frozen Wishart random variables
+        w = wishart(df, scale)
+
+        # Get the generated random variables from a known seed
+        rng = np.random.RandomState(248042)
+        w_rvs = wishart.rvs(df, scale, random_state=rng)
+        rng = np.random.RandomState(248042)
+        frozen_w_rvs = w.rvs(random_state=rng)
+
+        # Manually calculate what it should be, based on the Bartlett (1933)
+        # decomposition of a Wishart into D A A' D', where D is the Cholesky
+        # factorization of the scale matrix and A is the lower triangular matrix
+        # with the square root of chi^2 variates on the diagonal and N(0,1)
+        # variates in the lower triangle.
+        rng = np.random.RandomState(248042)
+        covariances = rng.normal(size=3)
+        variances = np.r_[
+            rng.chisquare(df),
+            rng.chisquare(df-1),
+            rng.chisquare(df-2),
+        ]**0.5
+
+        # Construct the lower-triangular A matrix
+        A = np.diag(variances)
+        A[np.tril_indices(dim, k=-1)] = covariances
+
+        # Wishart random variate
+        D = np.linalg.cholesky(scale)
+        DA = D.dot(A)
+        manual_w_rvs = np.dot(DA, DA.T)
+
+        # Test for equality
+        assert_allclose(w_rvs, manual_w_rvs)
+        assert_allclose(frozen_w_rvs, manual_w_rvs)
+
+    def test_1D_is_chisquared(self):
+        # The 1-dimensional Wishart with an identity scale matrix is just a
+        # chi-squared distribution.
+        # Test variance, mean, entropy, pdf
+        # Kolgomorov-Smirnov test for rvs
+        rng = np.random.default_rng(482974)
+
+        sn = 500
+        dim = 1
+        scale = np.eye(dim)
+
+        df_range = np.arange(1, 10, 2, dtype=float)
+        X = np.linspace(0.1,10,num=10)
+        for df in df_range:
+            w = wishart(df, scale)
+            c = chi2(df)
+
+            # Statistics
+            assert_allclose(w.var(), c.var())
+            assert_allclose(w.mean(), c.mean())
+            assert_allclose(w.entropy(), c.entropy())
+
+            # PDF
+            assert_allclose(w.pdf(X), c.pdf(X))
+
+            # rvs
+            rvs = w.rvs(size=sn, random_state=rng)
+            args = (df,)
+            alpha = 0.01
+            check_distribution_rvs('chi2', args, alpha, rvs)
+
+    def test_is_scaled_chisquared(self):
+        # The 2-dimensional Wishart with an arbitrary scale matrix can be
+        # transformed to a scaled chi-squared distribution.
+        # For :math:`S \sim W_p(V,n)` and :math:`\lambda \in \mathbb{R}^p` we have
+        # :math:`\lambda' S \lambda \sim \lambda' V \lambda \times \chi^2(n)`
+        rng = np.random.default_rng(482974)
+
+        sn = 500
+        df = 10
+        dim = 4
+        # Construct an arbitrary positive definite matrix
+        scale = np.diag(np.arange(4)+1)
+        scale[np.tril_indices(4, k=-1)] = np.arange(6)
+        scale = np.dot(scale.T, scale)
+        # Use :math:`\lambda = [1, \dots, 1]'`
+        lamda = np.ones((dim,1))
+        sigma_lamda = lamda.T.dot(scale).dot(lamda).squeeze()
+        w = wishart(df, sigma_lamda)
+        c = chi2(df, scale=sigma_lamda)
+
+        # Statistics
+        assert_allclose(w.var(), c.var())
+        assert_allclose(w.mean(), c.mean())
+        assert_allclose(w.entropy(), c.entropy())
+
+        # PDF
+        X = np.linspace(0.1,10,num=10)
+        assert_allclose(w.pdf(X), c.pdf(X))
+
+        # rvs
+        rvs = w.rvs(size=sn, random_state=rng)
+        args = (df,0,sigma_lamda)
+        alpha = 0.01
+        check_distribution_rvs('chi2', args, alpha, rvs)
+
+class TestMultinomial:
+    def test_logpmf(self):
+        vals1 = multinomial.logpmf((3,4), 7, (0.3, 0.7))
+        assert_allclose(vals1, -1.483270127243324, rtol=1e-8)
+
+        vals2 = multinomial.logpmf([3, 4], 0, [.3, .7])
+        assert vals2 == -np.inf
+
+        vals3 = multinomial.logpmf([0, 0], 0, [.3, .7])
+        assert vals3 == 0
+
+        vals4 = multinomial.logpmf([3, 4], 0, [-2, 3])
+        assert_allclose(vals4, np.nan, rtol=1e-8)
+
+    def test_reduces_binomial(self):
+        # test that the multinomial pmf reduces to the binomial pmf in the 2d
+        # case
+        val1 = multinomial.logpmf((3, 4), 7, (0.3, 0.7))
+        val2 = binom.logpmf(3, 7, 0.3)
+        assert_allclose(val1, val2, rtol=1e-8)
+
+        val1 = multinomial.pmf((6, 8), 14, (0.1, 0.9))
+        val2 = binom.pmf(6, 14, 0.1)
+        assert_allclose(val1, val2, rtol=1e-8)
+
+    def test_R(self):
+        # test against the values produced by this R code
+        # (https://stat.ethz.ch/R-manual/R-devel/library/stats/html/Multinom.html)
+        # X <- t(as.matrix(expand.grid(0:3, 0:3))); X <- X[, colSums(X) <= 3]
+        # X <- rbind(X, 3:3 - colSums(X)); dimnames(X) <- list(letters[1:3], NULL)
+        # X
+        # apply(X, 2, function(x) dmultinom(x, prob = c(1,2,5)))
+
+        n, p = 3, [1./8, 2./8, 5./8]
+        r_vals = {(0, 0, 3): 0.244140625, (1, 0, 2): 0.146484375,
+                  (2, 0, 1): 0.029296875, (3, 0, 0): 0.001953125,
+                  (0, 1, 2): 0.292968750, (1, 1, 1): 0.117187500,
+                  (2, 1, 0): 0.011718750, (0, 2, 1): 0.117187500,
+                  (1, 2, 0): 0.023437500, (0, 3, 0): 0.015625000}
+        for x in r_vals:
+            assert_allclose(multinomial.pmf(x, n, p), r_vals[x], atol=1e-14)
+
+    @pytest.mark.parametrize("n", [0, 3])
+    def test_rvs_np(self, n):
+        # test that .rvs agrees w/numpy when `p` is valid
+        rndm = np.random.RandomState(123)
+        sc_rvs = multinomial.rvs(n, [1/3.]*3, size=7, random_state=123)
+        np_rvs = rndm.multinomial(n, [1/3.]*3, size=7)
+        assert_equal(sc_rvs, np_rvs)
+
+        rndm = np.random.RandomState(123)
+        sc_rvs = multinomial.rvs(n, [1/5.]*5, size=7, random_state=123)
+        np_rvs = rndm.multinomial(n, [1/5.]*5, size=7)
+        assert_equal(sc_rvs, np_rvs)
+
+    def test_pmf(self):
+        vals0 = multinomial.pmf((5,), 5, (1,))
+        assert_allclose(vals0, 1, rtol=1e-8)
+
+        vals1 = multinomial.pmf((3,4), 7, (.3, .7))
+        assert_allclose(vals1, .22689449999999994, rtol=1e-8)
+
+        vals2 = multinomial.pmf([[[3,5],[0,8]], [[-1, 9], [1, 1]]], 8,
+                                (.1, .9))
+        assert_allclose(vals2, [[.03306744, .43046721], [0, 0]], rtol=1e-8)
+
+        x = np.empty((0,2), dtype=np.float64)
+        vals3 = multinomial.pmf(x, 4, (.3, .7))
+        assert_equal(vals3, np.empty([], dtype=np.float64))
+
+        vals4 = multinomial.pmf([1,2], 4, (.3, .7))
+        assert_allclose(vals4, 0, rtol=1e-8)
+
+        vals5 = multinomial.pmf([3, 3, 0], 6, [2/3.0, 1/3.0, 0])
+        assert_allclose(vals5, 0.219478737997, rtol=1e-8)
+
+        vals5 = multinomial.pmf([0, 0, 0], 0, [2/3.0, 1/3.0, 0])
+        assert vals5 == 1
+
+        vals6 = multinomial.pmf([2, 1, 0], 0, [2/3.0, 1/3.0, 0])
+        assert vals6 == 0
+
+    def test_pmf_broadcasting(self):
+        vals0 = multinomial.pmf([1, 2], 3, [[.1, .9], [.2, .8]])
+        assert_allclose(vals0, [.243, .384], rtol=1e-8)
+
+        vals1 = multinomial.pmf([1, 2], [3, 4], [.1, .9])
+        assert_allclose(vals1, [.243, 0], rtol=1e-8)
+
+        vals2 = multinomial.pmf([[[1, 2], [1, 1]]], 3, [.1, .9])
+        assert_allclose(vals2, [[.243, 0]], rtol=1e-8)
+
+        vals3 = multinomial.pmf([1, 2], [[[3], [4]]], [.1, .9])
+        assert_allclose(vals3, [[[.243], [0]]], rtol=1e-8)
+
+        vals4 = multinomial.pmf([[1, 2], [1,1]], [[[[3]]]], [.1, .9])
+        assert_allclose(vals4, [[[[.243, 0]]]], rtol=1e-8)
+
+    @pytest.mark.parametrize("n", [0, 5])
+    def test_cov(self, n):
+        cov1 = multinomial.cov(n, (.2, .3, .5))
+        cov2 = [[n*.2*.8, -n*.2*.3, -n*.2*.5],
+                [-n*.3*.2, n*.3*.7, -n*.3*.5],
+                [-n*.5*.2, -n*.5*.3, n*.5*.5]]
+        assert_allclose(cov1, cov2, rtol=1e-8)
+
+    def test_cov_broadcasting(self):
+        cov1 = multinomial.cov(5, [[.1, .9], [.2, .8]])
+        cov2 = [[[.45, -.45],[-.45, .45]], [[.8, -.8], [-.8, .8]]]
+        assert_allclose(cov1, cov2, rtol=1e-8)
+
+        cov3 = multinomial.cov([4, 5], [.1, .9])
+        cov4 = [[[.36, -.36], [-.36, .36]], [[.45, -.45], [-.45, .45]]]
+        assert_allclose(cov3, cov4, rtol=1e-8)
+
+        cov5 = multinomial.cov([4, 5], [[.3, .7], [.4, .6]])
+        cov6 = [[[4*.3*.7, -4*.3*.7], [-4*.3*.7, 4*.3*.7]],
+                [[5*.4*.6, -5*.4*.6], [-5*.4*.6, 5*.4*.6]]]
+        assert_allclose(cov5, cov6, rtol=1e-8)
+
+    @pytest.mark.parametrize("n", [0, 2])
+    def test_entropy(self, n):
+        # this is equivalent to a binomial distribution with n=2, so the
+        # entropy .77899774929 is easily computed "by hand"
+        ent0 = multinomial.entropy(n, [.2, .8])
+        assert_allclose(ent0, binom.entropy(n, .2), rtol=1e-8)
+
+    def test_entropy_broadcasting(self):
+        ent0 = multinomial.entropy([2, 3], [.2, .8])
+        assert_allclose(ent0, [binom.entropy(2, .2), binom.entropy(3, .2)],
+                        rtol=1e-8)
+
+        ent1 = multinomial.entropy([7, 8], [[.3, .7], [.4, .6]])
+        assert_allclose(ent1, [binom.entropy(7, .3), binom.entropy(8, .4)],
+                        rtol=1e-8)
+
+        ent2 = multinomial.entropy([[7], [8]], [[.3, .7], [.4, .6]])
+        assert_allclose(ent2,
+                        [[binom.entropy(7, .3), binom.entropy(7, .4)],
+                         [binom.entropy(8, .3), binom.entropy(8, .4)]],
+                        rtol=1e-8)
+
+    @pytest.mark.parametrize("n", [0, 5])
+    def test_mean(self, n):
+        mean1 = multinomial.mean(n, [.2, .8])
+        assert_allclose(mean1, [n*.2, n*.8], rtol=1e-8)
+
+    def test_mean_broadcasting(self):
+        mean1 = multinomial.mean([5, 6], [.2, .8])
+        assert_allclose(mean1, [[5*.2, 5*.8], [6*.2, 6*.8]], rtol=1e-8)
+
+    def test_frozen(self):
+        # The frozen distribution should agree with the regular one
+        n = 12
+        pvals = (.1, .2, .3, .4)
+        x = [[0,0,0,12],[0,0,1,11],[0,1,1,10],[1,1,1,9],[1,1,2,8]]
+        x = np.asarray(x, dtype=np.float64)
+        mn_frozen = multinomial(n, pvals)
+        assert_allclose(mn_frozen.pmf(x), multinomial.pmf(x, n, pvals))
+        assert_allclose(mn_frozen.logpmf(x), multinomial.logpmf(x, n, pvals))
+        assert_allclose(mn_frozen.entropy(), multinomial.entropy(n, pvals))
+
+    def test_gh_11860(self):
+        # gh-11860 reported cases in which the adjustments made by multinomial
+        # to the last element of `p` can cause `nan`s even when the input is
+        # essentially valid. Check that a pathological case returns a finite,
+        # nonzero result. (This would fail in main before the PR.)
+        n = 88
+        rng = np.random.default_rng(8879715917488330089)
+        p = rng.random(n)
+        p[-1] = 1e-30
+        p /= np.sum(p)
+        x = np.ones(n)
+        logpmf = multinomial.logpmf(x, n, p)
+        assert np.isfinite(logpmf)
+
+    @pytest.mark.parametrize('dtype', [np.float32, np.float64])
+    def test_gh_22565(self, dtype):
+        # Same issue as gh-11860 above, essentially, but the original
+        # fix didn't completely solve the problem.
+        n = 19
+        p = np.asarray([0.2, 0.2, 0.2, 0.2, 0.2], dtype=dtype)
+        res1 = multinomial.pmf(x=[1, 2, 5, 7, 4], n=n, p=p)
+        res2 = multinomial.pmf(x=[1, 2, 4, 5, 7], n=n, p=p)
+        np.testing.assert_allclose(res1, res2, rtol=1e-15)
+
+    @pytest.mark.parametrize('psum', [0.75, 1.25])
+    def test_gh_22585(self, psum):
+        # gh-22585 warned that beginning in SciPy 1.18, rows of p that did not sum to
+        # 1.0 would produce NaNs. Check that this has been implemented.
+        rng = np.random.default_rng(8879715917488330089)
+        d, n = 5, 19  # arbitrary
+        p0 = rng.random(d)
+        p = p0 / np.sum(p0)
+        p_ = p * psum
+        x = multinomial.rvs(n=n, p=p)
+
+        assert np.all(np.isfinite(x))
+        with pytest.raises(ValueError, match="`multinomial.rvs` requires..."):
+            multinomial.rvs(n=n, p=p_)
+
+        assert np.isfinite(multinomial.pmf(x, n=n, p=p))
+        assert np.isnan(multinomial.pmf(x, n=n, p=p_))
+
+        assert np.isfinite(multinomial.pmf(x, n=n, p=p))
+        assert np.isnan(multinomial.pmf(x, n=n, p=p_))
+
+        assert np.isfinite(multinomial.entropy(n=n, p=p))
+        assert np.isnan(multinomial.entropy(n=n, p=p_))
+
+        assert np.all(np.isfinite(multinomial.cov(n=n, p=p)))
+        assert np.all(np.isnan(multinomial.cov(n=n, p=p_)))
+
+
+class TestInvwishart:
+    def test_frozen(self):
+        # Test that the frozen and non-frozen inverse Wishart gives the same
+        # answers
+
+        # Construct an arbitrary positive definite scale matrix
+        dim = 4
+        scale = np.diag(np.arange(dim)+1)
+        scale[np.tril_indices(dim, k=-1)] = np.arange(dim*(dim-1)/2)
+        scale = np.dot(scale.T, scale)
+
+        # Construct a collection of positive definite matrices to test the PDF
+        X = []
+        for i in range(5):
+            x = np.diag(np.arange(dim)+(i+1)**2)
+            x[np.tril_indices(dim, k=-1)] = np.arange(dim*(dim-1)/2)
+            x = np.dot(x.T, x)
+            X.append(x)
+        X = np.array(X).T
+
+        # Construct a 1D and 2D set of parameters
+        parameters = [
+            (10, 1, np.linspace(0.1, 10, 5)),  # 1D case
+            (10, scale, X)
+        ]
+
+        for (df, scale, x) in parameters:
+            iw = invwishart(df, scale)
+            assert_equal(iw.var(), invwishart.var(df, scale))
+            assert_equal(iw.mean(), invwishart.mean(df, scale))
+            assert_equal(iw.mode(), invwishart.mode(df, scale))
+            assert_allclose(iw.pdf(x), invwishart.pdf(x, df, scale))
+
+    def test_1D_is_invgamma(self):
+        # The 1-dimensional inverse Wishart with an identity scale matrix is
+        # just an inverse gamma distribution.
+        # Test variance, mean, pdf, entropy
+        # Kolgomorov-Smirnov test for rvs
+        rng = np.random.RandomState(482974)
+
+        sn = 500
+        dim = 1
+        scale = np.eye(dim)
+
+        df_range = np.arange(5, 20, 2, dtype=float)
+        X = np.linspace(0.1,10,num=10)
+        for df in df_range:
+            iw = invwishart(df, scale)
+            ig = invgamma(df/2, scale=1./2)
+
+            # Statistics
+            assert_allclose(iw.var(), ig.var())
+            assert_allclose(iw.mean(), ig.mean())
+
+            # PDF
+            assert_allclose(iw.pdf(X), ig.pdf(X))
+
+            # rvs
+            rvs = iw.rvs(size=sn, random_state=rng)
+            args = (df/2, 0, 1./2)
+            alpha = 0.01
+            check_distribution_rvs('invgamma', args, alpha, rvs)
+
+            # entropy
+            assert_allclose(iw.entropy(), ig.entropy())
+
+    def test_invwishart_2D_rvs(self):
+        dim = 3
+        df = 10
+
+        # Construct a simple non-diagonal positive definite matrix
+        scale = np.eye(dim)
+        scale[0,1] = 0.5
+        scale[1,0] = 0.5
+
+        # Construct frozen inverse-Wishart random variables
+        iw = invwishart(df, scale)
+
+        # Get the generated random variables from a known seed
+        rng = np.random.RandomState(608072)
+        iw_rvs = invwishart.rvs(df, scale, random_state=rng)
+        rng = np.random.RandomState(608072)
+        frozen_iw_rvs = iw.rvs(random_state=rng)
+
+        # Manually calculate what it should be, based on the decomposition in
+        # https://arxiv.org/abs/2310.15884 of an invers-Wishart into L L',
+        # where L A = D, D is the Cholesky factorization of the scale matrix,
+        # and A is the lower triangular matrix with the square root of chi^2
+        # variates on the diagonal and N(0,1) variates in the lower triangle.
+        # the diagonal chi^2 variates in this A are reversed compared to those
+        # in the Bartlett decomposition A for Wishart rvs.
+        rng = np.random.RandomState(608072)
+        covariances = rng.normal(size=3)
+        variances = np.r_[
+            rng.chisquare(df-2),
+            rng.chisquare(df-1),
+            rng.chisquare(df),
+        ]**0.5
+
+        # Construct the lower-triangular A matrix
+        A = np.diag(variances)
+        A[np.tril_indices(dim, k=-1)] = covariances
+
+        # inverse-Wishart random variate
+        D = np.linalg.cholesky(scale)
+        L = np.linalg.solve(A.T, D.T).T
+        manual_iw_rvs = np.dot(L, L.T)
+
+        # Test for equality
+        assert_allclose(iw_rvs, manual_iw_rvs)
+        assert_allclose(frozen_iw_rvs, manual_iw_rvs)
+
+    def test_sample_mean(self):
+        """Test that sample mean consistent with known mean."""
+        # Construct an arbitrary positive definite scale matrix
+        df = 10
+        sample_size = 20_000
+        for dim in [1, 5]:
+            scale = np.diag(np.arange(dim) + 1)
+            scale[np.tril_indices(dim, k=-1)] = np.arange(dim * (dim - 1) / 2)
+            scale = np.dot(scale.T, scale)
+
+            dist = invwishart(df, scale)
+            Xmean_exp = dist.mean()
+            Xvar_exp = dist.var()
+            Xmean_std = (Xvar_exp / sample_size)**0.5  # asymptotic SE of mean estimate
+
+            X = dist.rvs(size=sample_size, random_state=1234)
+            Xmean_est = X.mean(axis=0)
+
+            ntests = dim*(dim + 1)//2
+            fail_rate = 0.01 / ntests  # correct for multiple tests
+            max_diff = norm.ppf(1 - fail_rate / 2)
+            assert np.allclose(
+                (Xmean_est - Xmean_exp) / Xmean_std,
+                0,
+                atol=max_diff,
+            )
+
+    def test_logpdf_4x4(self):
+        """Regression test for gh-8844."""
+        X = np.array([[2, 1, 0, 0.5],
+                      [1, 2, 0.5, 0.5],
+                      [0, 0.5, 3, 1],
+                      [0.5, 0.5, 1, 2]])
+        Psi = np.array([[9, 7, 3, 1],
+                        [7, 9, 5, 1],
+                        [3, 5, 8, 2],
+                        [1, 1, 2, 9]])
+        nu = 6
+        prob = invwishart.logpdf(X, nu, Psi)
+        # Explicit calculation from the formula on wikipedia.
+        p = X.shape[0]
+        sig, logdetX = np.linalg.slogdet(X)
+        sig, logdetPsi = np.linalg.slogdet(Psi)
+        M = np.linalg.solve(X, Psi)
+        expected = ((nu/2)*logdetPsi
+                    - (nu*p/2)*np.log(2)
+                    - multigammaln(nu/2, p)
+                    - (nu + p + 1)/2*logdetX
+                    - 0.5*M.trace())
+        assert_allclose(prob, expected)
+
+
+class TestSpecialOrthoGroup:
+    def test_reproducibility(self):
+        x = special_ortho_group.rvs(3, random_state=np.random.default_rng(514))
+        expected = np.array([[-0.93200988, 0.01533561, -0.36210826],
+                             [0.35742128, 0.20446501, -0.91128705],
+                             [0.06006333, -0.97875374, -0.19604469]])
+        assert_array_almost_equal(x, expected)
+
+    def test_invalid_dim(self):
+        assert_raises(ValueError, special_ortho_group.rvs, None)
+        assert_raises(ValueError, special_ortho_group.rvs, (2, 2))
+        assert_raises(ValueError, special_ortho_group.rvs, -1)
+        assert_raises(ValueError, special_ortho_group.rvs, 2.5)
+
+    def test_frozen_matrix(self):
+        dim = 7
+        frozen = special_ortho_group(dim)
+
+        rvs1 = frozen.rvs(random_state=1234)
+        rvs2 = special_ortho_group.rvs(dim, random_state=1234)
+
+        assert_equal(rvs1, rvs2)
+
+    def test_det_and_ortho(self):
+        xs = [special_ortho_group.rvs(dim)
+              for dim in range(2,12)
+              for i in range(3)]
+
+        # Test that determinants are always +1
+        dets = [np.linalg.det(x) for x in xs]
+        assert_allclose(dets, [1.]*30, rtol=1e-13)
+
+        # Test that these are orthogonal matrices
+        for x in xs:
+            assert_array_almost_equal(np.dot(x, x.T),
+                                      np.eye(x.shape[0]))
+
+    def test_haar(self):
+        # Test that the distribution is constant under rotation
+        # Every column should have the same distribution
+        # Additionally, the distribution should be invariant under another rotation
+
+        # Generate samples
+        dim = 5
+        samples = 1000  # Not too many, or the test takes too long
+        ks_prob = .05
+        xs = special_ortho_group.rvs(
+            dim, size=samples, random_state=np.random.default_rng(513)
+        )
+
+        # Dot a few rows (0, 1, 2) with unit vectors (0, 2, 4, 3),
+        #   effectively picking off entries in the matrices of xs.
+        #   These projections should all have the same distribution,
+        #     establishing rotational invariance. We use the two-sided
+        #     KS test to confirm this.
+        #   We could instead test that angles between random vectors
+        #     are uniformly distributed, but the below is sufficient.
+        #   It is not feasible to consider all pairs, so pick a few.
+        els = ((0,0), (0,2), (1,4), (2,3))
+        #proj = {(er, ec): [x[er][ec] for x in xs] for er, ec in els}
+        proj = {(er, ec): sorted([x[er][ec] for x in xs]) for er, ec in els}
+        pairs = [(e0, e1) for e0 in els for e1 in els if e0 > e1]
+        ks_tests = [ks_2samp(proj[p0], proj[p1])[1] for (p0, p1) in pairs]
+        assert_array_less([ks_prob]*len(pairs), ks_tests)
+
+    def test_one_by_one(self):
+        # Test that the distribution is a delta function at the identity matrix
+        # when dim=1
+        assert_allclose(special_ortho_group.rvs(1, size=1000), 1, rtol=1e-13)
+
+    def test_zero_by_zero(self):
+        assert_equal(special_ortho_group.rvs(0, size=4).shape, (4, 0, 0))
+
+
+class TestOrthoGroup:
+    def test_reproducibility(self):
+        seed = 514
+        rng = np.random.RandomState(seed)
+        x = ortho_group.rvs(3, random_state=rng)
+        x2 = ortho_group.rvs(3, random_state=seed)
+        # Note this matrix has det -1, distinguishing O(N) from SO(N)
+        assert_almost_equal(np.linalg.det(x), -1)
+        expected = np.array([[0.381686, -0.090374, 0.919863],
+                             [0.905794, -0.161537, -0.391718],
+                             [-0.183993, -0.98272, -0.020204]])
+        assert_array_almost_equal(x, expected)
+        assert_array_almost_equal(x2, expected)
+
+    def test_invalid_dim(self):
+        assert_raises(ValueError, ortho_group.rvs, None)
+        assert_raises(ValueError, ortho_group.rvs, (2, 2))
+        assert_raises(ValueError, ortho_group.rvs, -1)
+        assert_raises(ValueError, ortho_group.rvs, 2.5)
+
+    def test_frozen_matrix(self):
+        dim = 7
+        frozen = ortho_group(dim)
+        frozen_seed = ortho_group(dim, seed=1234)
+
+        rvs1 = frozen.rvs(random_state=1234)
+        rvs2 = ortho_group.rvs(dim, random_state=1234)
+        rvs3 = frozen_seed.rvs(size=1)
+
+        assert_equal(rvs1, rvs2)
+        assert_equal(rvs1, rvs3)
+
+    def test_det_and_ortho(self):
+        xs = [[ortho_group.rvs(dim)
+               for i in range(10)]
+              for dim in range(2,12)]
+
+        # Test that abs determinants are always +1
+        dets = np.array([[np.linalg.det(x) for x in xx] for xx in xs])
+        assert_allclose(np.fabs(dets), np.ones(dets.shape), rtol=1e-13)
+
+        # Test that these are orthogonal matrices
+        for xx in xs:
+            for x in xx:
+                assert_array_almost_equal(np.dot(x, x.T),
+                                          np.eye(x.shape[0]))
+
+    @pytest.mark.parametrize("dim", [2, 5, 10, 20])
+    def test_det_distribution_gh18272(self, dim):
+        # Test that positive and negative determinants are equally likely.
+        rng = np.random.default_rng(6796248956179332344)
+        dist = ortho_group(dim=dim)
+        rvs = dist.rvs(size=5000, random_state=rng)
+        dets = scipy.linalg.det(rvs)
+        k = np.sum(dets > 0)
+        n = len(dets)
+        res = stats.binomtest(k, n)
+        low, high = res.proportion_ci(confidence_level=0.95)
+        assert low < 0.5 < high
+
+    def test_haar(self):
+        # Test that the distribution is constant under rotation
+        # Every column should have the same distribution
+        # Additionally, the distribution should be invariant under another rotation
+
+        # Generate samples
+        dim = 5
+        samples = 1000  # Not too many, or the test takes too long
+        ks_prob = .05
+        rng = np.random.RandomState(518)  # Note that the test is sensitive to seed too
+        xs = ortho_group.rvs(dim, size=samples, random_state=rng)
+
+        # Dot a few rows (0, 1, 2) with unit vectors (0, 2, 4, 3),
+        #   effectively picking off entries in the matrices of xs.
+        #   These projections should all have the same distribution,
+        #     establishing rotational invariance. We use the two-sided
+        #     KS test to confirm this.
+        #   We could instead test that angles between random vectors
+        #     are uniformly distributed, but the below is sufficient.
+        #   It is not feasible to consider all pairs, so pick a few.
+        els = ((0,0), (0,2), (1,4), (2,3))
+        #proj = {(er, ec): [x[er][ec] for x in xs] for er, ec in els}
+        proj = {(er, ec): sorted([x[er][ec] for x in xs]) for er, ec in els}
+        pairs = [(e0, e1) for e0 in els for e1 in els if e0 > e1]
+        ks_tests = [ks_2samp(proj[p0], proj[p1])[1] for (p0, p1) in pairs]
+        assert_array_less([ks_prob]*len(pairs), ks_tests)
+
+    def test_one_by_one(self):
+        # Test that the 1x1 distribution gives ±1 with equal probability.
+        dim = 1
+        xs = ortho_group.rvs(dim, size=5000, random_state=np.random.default_rng(514))
+        assert_allclose(np.abs(xs), 1, rtol=1e-13)
+        k = np.sum(xs > 0)
+        n = len(xs)
+        res = stats.binomtest(k, n)
+        low, high = res.proportion_ci(confidence_level=0.95)
+        assert low < 0.5 < high
+
+    def test_zero_by_zero(self):
+        assert_equal(special_ortho_group.rvs(0, size=4).shape, (4, 0, 0))
+
+    @pytest.mark.slow
+    def test_pairwise_distances(self):
+        # Test that the distribution of pairwise distances is close to correct.
+        rng = np.random.RandomState(514)
+
+        def random_ortho(dim, random_state):
+            u, _s, v = np.linalg.svd(random_state.normal(size=(dim, dim)))
+            return np.dot(u, v)
+
+        for dim in range(2, 6):
+            def generate_test_statistics(rvs, N=1000, eps=1e-10):
+                stats = np.array([
+                    np.sum((rvs(dim=dim, random_state=rng) -
+                            rvs(dim=dim, random_state=rng))**2)
+                    for _ in range(N)
+                ])
+                # Add a bit of noise to account for numeric accuracy.
+                stats += rng.uniform(-eps, eps, size=stats.shape)
+                return stats
+
+            expected = generate_test_statistics(random_ortho)
+            actual = generate_test_statistics(scipy.stats.ortho_group.rvs)
+
+            _D, p = scipy.stats.ks_2samp(expected, actual)
+
+            assert_array_less(.05, p)
+
+
+class TestRandomCorrelation:
+    def test_reproducibility(self):
+        rng = np.random.RandomState(514)
+        eigs = (.5, .8, 1.2, 1.5)
+        x = random_correlation.rvs(eigs, random_state=rng)
+        x2 = random_correlation.rvs(eigs, random_state=514)
+        expected = np.array([[1., -0.184851, 0.109017, -0.227494],
+                             [-0.184851, 1., 0.231236, 0.326669],
+                             [0.109017, 0.231236, 1., -0.178912],
+                             [-0.227494, 0.326669, -0.178912, 1.]])
+        assert_array_almost_equal(x, expected)
+        assert_array_almost_equal(x2, expected)
+
+    def test_invalid_eigs(self):
+        assert_raises(ValueError, random_correlation.rvs, None)
+        assert_raises(ValueError, random_correlation.rvs, 'test')
+        assert_raises(ValueError, random_correlation.rvs, 2.5)
+        assert_raises(ValueError, random_correlation.rvs, [2.5])
+        assert_raises(ValueError, random_correlation.rvs, [[1,2],[3,4]])
+        assert_raises(ValueError, random_correlation.rvs, [2.5, -.5])
+        assert_raises(ValueError, random_correlation.rvs, [1, 2, .1])
+
+    def test_frozen_matrix(self):
+        eigs = (.5, .8, 1.2, 1.5)
+        frozen = random_correlation(eigs)
+        frozen_seed = random_correlation(eigs, seed=514)
+
+        rvs1 = random_correlation.rvs(eigs, random_state=514)
+        rvs2 = frozen.rvs(random_state=514)
+        rvs3 = frozen_seed.rvs()
+
+        assert_equal(rvs1, rvs2)
+        assert_equal(rvs1, rvs3)
+
+    def test_definition(self):
+        # Test the definition of a correlation matrix in several dimensions:
+        #
+        # 1. Det is product of eigenvalues (and positive by construction
+        #    in examples)
+        # 2. 1's on diagonal
+        # 3. Matrix is symmetric
+
+        def norm(i, e):
+            return i*e/sum(e)
+
+        rng = np.random.RandomState(123)
+
+        eigs = [norm(i, rng.uniform(size=i)) for i in range(2, 6)]
+        eigs.append([4,0,0,0])
+
+        ones = [[1.]*len(e) for e in eigs]
+        xs = [random_correlation.rvs(e, random_state=rng) for e in eigs]
+
+        # Test that determinants are products of eigenvalues
+        #   These are positive by construction
+        # Could also test that the eigenvalues themselves are correct,
+        #   but this seems sufficient.
+        dets = [np.fabs(np.linalg.det(x)) for x in xs]
+        dets_known = [np.prod(e) for e in eigs]
+        assert_allclose(dets, dets_known, rtol=1e-13, atol=1e-13)
+
+        # Test for 1's on the diagonal
+        diags = [np.diag(x) for x in xs]
+        for a, b in zip(diags, ones):
+            assert_allclose(a, b, rtol=1e-13)
+
+        # Correlation matrices are symmetric
+        for x in xs:
+            assert_allclose(x, x.T, rtol=1e-13)
+
+    def test_to_corr(self):
+        # Check some corner cases in to_corr
+
+        # ajj == 1
+        m = np.array([[0.1, 0], [0, 1]], dtype=float)
+        m = random_correlation._to_corr(m)
+        assert_allclose(m, np.array([[1, 0], [0, 0.1]]))
+
+        # Floating point overflow; fails to compute the correct
+        # rotation, but should still produce some valid rotation
+        # rather than infs/nans
+        with np.errstate(over='ignore'):
+            g = np.array([[0, 1], [-1, 0]])
+
+            m0 = np.array([[1e300, 0], [0, np.nextafter(1, 0)]], dtype=float)
+            m = random_correlation._to_corr(m0.copy())
+            assert_allclose(m, g.T.dot(m0).dot(g))
+
+            m0 = np.array([[0.9, 1e300], [1e300, 1.1]], dtype=float)
+            m = random_correlation._to_corr(m0.copy())
+            assert_allclose(m, g.T.dot(m0).dot(g))
+
+        # Zero discriminant; should set the first diag entry to 1
+        m0 = np.array([[2, 1], [1, 2]], dtype=float)
+        m = random_correlation._to_corr(m0.copy())
+        assert_allclose(m[0,0], 1)
+
+        # Slightly negative discriminant; should be approx correct still
+        m0 = np.array([[2 + 1e-7, 1], [1, 2]], dtype=float)
+        m = random_correlation._to_corr(m0.copy())
+        assert_allclose(m[0,0], 1)
+
+
+class TestUniformDirection:
+    @pytest.mark.parametrize("dim", [1, 3])
+    @pytest.mark.parametrize("size", [None, 1, 5, (5, 4)])
+    def test_samples(self, dim, size):
+        # test that samples have correct shape and norm 1
+        rng = np.random.default_rng(2777937887058094419)
+        uniform_direction_dist = uniform_direction(dim, seed=rng)
+        samples = uniform_direction_dist.rvs(size)
+        mean, cov = np.zeros(dim), np.eye(dim)
+        expected_shape = rng.multivariate_normal(mean, cov, size=size).shape
+        assert samples.shape == expected_shape
+        norms = np.linalg.norm(samples, axis=-1)
+        assert_allclose(norms, 1.)
+
+    @pytest.mark.parametrize("dim", [None, 0, (2, 2), 2.5])
+    def test_invalid_dim(self, dim):
+        message = ("Dimension of vector must be specified, "
+                   "and must be an integer greater than 0.")
+        with pytest.raises(ValueError, match=message):
+            uniform_direction.rvs(dim)
+
+    def test_frozen_distribution(self):
+        dim = 5
+        frozen = uniform_direction(dim)
+        frozen_seed = uniform_direction(dim, seed=514)
+
+        rvs1 = frozen.rvs(random_state=514)
+        rvs2 = uniform_direction.rvs(dim, random_state=514)
+        rvs3 = frozen_seed.rvs()
+
+        assert_equal(rvs1, rvs2)
+        assert_equal(rvs1, rvs3)
+
+    @pytest.mark.parametrize("dim", [2, 5, 8])
+    def test_uniform(self, dim):
+        rng = np.random.default_rng(1036978481269651776)
+        spherical_dist = uniform_direction(dim, seed=rng)
+        # generate random, orthogonal vectors
+        v1, v2 = spherical_dist.rvs(size=2)
+        v2 -= v1 @ v2 * v1
+        v2 /= np.linalg.norm(v2)
+        assert_allclose(v1 @ v2, 0, atol=1e-14)  # orthogonal
+        # generate data and project onto orthogonal vectors
+        samples = spherical_dist.rvs(size=10000)
+        s1 = samples @ v1
+        s2 = samples @ v2
+        angles = np.arctan2(s1, s2)
+        # test that angles follow a uniform distribution
+        # normalize angles to range [0, 1]
+        angles += np.pi
+        angles /= 2*np.pi
+        # perform KS test
+        uniform_dist = uniform()
+        kstest_result = kstest(angles, uniform_dist.cdf)
+        assert kstest_result.pvalue > 0.05
+
+
+class TestUnitaryGroup:
+    def test_reproducibility(self):
+        rng = np.random.RandomState(514)
+        x = unitary_group.rvs(3, random_state=rng)
+        x2 = unitary_group.rvs(3, random_state=514)
+
+        expected = np.array(
+            [[0.308771+0.360312j, 0.044021+0.622082j, 0.160327+0.600173j],
+             [0.732757+0.297107j, 0.076692-0.4614j, -0.394349+0.022613j],
+             [-0.148844+0.357037j, -0.284602-0.557949j, 0.607051+0.299257j]]
+        )
+
+        assert_array_almost_equal(x, expected)
+        assert_array_almost_equal(x2, expected)
+
+    def test_invalid_dim(self):
+        assert_raises(ValueError, unitary_group.rvs, None)
+        assert_raises(ValueError, unitary_group.rvs, (2, 2))
+        assert_raises(ValueError, unitary_group.rvs, -1)
+        assert_raises(ValueError, unitary_group.rvs, 2.5)
+
+    def test_frozen_matrix(self):
+        dim = 7
+        frozen = unitary_group(dim)
+        frozen_seed = unitary_group(dim, seed=514)
+
+        rvs1 = frozen.rvs(random_state=514)
+        rvs2 = unitary_group.rvs(dim, random_state=514)
+        rvs3 = frozen_seed.rvs(size=1)
+
+        assert_equal(rvs1, rvs2)
+        assert_equal(rvs1, rvs3)
+
+    def test_unitarity(self):
+        xs = [unitary_group.rvs(dim)
+              for dim in range(2,12)
+              for i in range(3)]
+
+        # Test that these are unitary matrices
+        for x in xs:
+            assert_allclose(np.dot(x, x.conj().T), np.eye(x.shape[0]), atol=1e-15)
+
+    def test_haar(self):
+        # Test that the eigenvalues, which lie on the unit circle in
+        # the complex plane, are uncorrelated.
+
+        # Generate samples
+        for dim in (1, 5):
+            samples = 1000  # Not too many, or the test takes too long
+            # Note that the test is sensitive to seed too
+            xs = unitary_group.rvs(
+                dim, size=samples, random_state=np.random.default_rng(514)
+            )
+
+            # The angles "x" of the eigenvalues should be uniformly distributed
+            # Overall this seems to be a necessary but weak test of the distribution.
+            eigs = np.vstack([scipy.linalg.eigvals(x) for x in xs])
+            x = np.arctan2(eigs.imag, eigs.real)
+            res = kstest(x.ravel(), uniform(-np.pi, 2*np.pi).cdf)
+            assert_(res.pvalue > 0.05)
+
+    def test_zero_by_zero(self):
+        assert_equal(unitary_group.rvs(0, size=4).shape, (4, 0, 0))
+
+
+class TestMultivariateT:
+
+    # These tests were created by running vpa(mvtpdf(...)) in MATLAB. The
+    # function takes no `mu` parameter. The tests were run as
+    #
+    # >> ans = vpa(mvtpdf(x - mu, shape, df));
+    #
+    PDF_TESTS = [(
+        # x
+        [
+            [1, 2],
+            [4, 1],
+            [2, 1],
+            [2, 4],
+            [1, 4],
+            [4, 1],
+            [3, 2],
+            [3, 3],
+            [4, 4],
+            [5, 1],
+        ],
+        # loc
+        [0, 0],
+        # shape
+        [
+            [1, 0],
+            [0, 1]
+        ],
+        # df
+        4,
+        # ans
+        [
+            0.013972450422333741737457302178882,
+            0.0010998721906793330026219646100571,
+            0.013972450422333741737457302178882,
+            0.00073682844024025606101402363634634,
+            0.0010998721906793330026219646100571,
+            0.0010998721906793330026219646100571,
+            0.0020732579600816823488240725481546,
+            0.00095660371505271429414668515889275,
+            0.00021831953784896498569831346792114,
+            0.00037725616140301147447000396084604
+        ]
+
+    ), (
+        # x
+        [
+            [0.9718, 0.1298, 0.8134],
+            [0.4922, 0.5522, 0.7185],
+            [0.3010, 0.1491, 0.5008],
+            [0.5971, 0.2585, 0.8940],
+            [0.5434, 0.5287, 0.9507],
+        ],
+        # loc
+        [-1, 1, 50],
+        # shape
+        [
+            [1.0000, 0.5000, 0.2500],
+            [0.5000, 1.0000, -0.1000],
+            [0.2500, -0.1000, 1.0000],
+        ],
+        # df
+        8,
+        # ans
+        [
+            0.00000000000000069609279697467772867405511133763,
+            0.00000000000000073700739052207366474839369535934,
+            0.00000000000000069522909962669171512174435447027,
+            0.00000000000000074212293557998314091880208889767,
+            0.00000000000000077039675154022118593323030449058,
+        ]
+    )]
+
+    @pytest.mark.parametrize("x, loc, shape, df, ans", PDF_TESTS)
+    def test_pdf_correctness(self, x, loc, shape, df, ans):
+        dist = multivariate_t(loc, shape, df, seed=0)
+        val = dist.pdf(x)
+        assert_array_almost_equal(val, ans)
+
+    @pytest.mark.parametrize("x, loc, shape, df, ans", PDF_TESTS)
+    def test_logpdf_correct(self, x, loc, shape, df, ans):
+        dist = multivariate_t(loc, shape, df, seed=0)
+        val1 = dist.pdf(x)
+        val2 = dist.logpdf(x)
+        assert_array_almost_equal(np.log(val1), val2)
+
+    # https://github.com/scipy/scipy/issues/10042#issuecomment-576795195
+    def test_mvt_with_df_one_is_cauchy(self):
+        x = [9, 7, 4, 1, -3, 9, 0, -3, -1, 3]
+        val = multivariate_t.pdf(x, df=1)
+        ans = cauchy.pdf(x)
+        assert_array_almost_equal(val, ans)
+
+    def test_mvt_with_high_df_is_approx_normal(self):
+        # `normaltest` returns the chi-squared statistic and the associated
+        # p-value. The null hypothesis is that `x` came from a normal
+        # distribution, so a low p-value represents rejecting the null, i.e.
+        # that it is unlikely that `x` came a normal distribution.
+        P_VAL_MIN = 0.1
+
+        dist = multivariate_t(0, 1, df=100000, seed=1)
+        samples = dist.rvs(size=100000)
+        _, p = normaltest(samples)
+        assert (p > P_VAL_MIN)
+
+        dist = multivariate_t([-2, 3], [[10, -1], [-1, 10]], df=100000,
+                              seed=42)
+        samples = dist.rvs(size=100000)
+        _, p = normaltest(samples)
+        assert ((p > P_VAL_MIN).all())
+
+    @pytest.mark.thread_unsafe(reason="uses mocking")
+    @patch('scipy.stats.multivariate_normal._logpdf')
+    def test_mvt_with_inf_df_calls_normal(self, mock):
+        dist = multivariate_t(0, 1, df=np.inf, seed=7)
+        assert isinstance(dist, multivariate_normal_frozen)
+        multivariate_t.pdf(0, df=np.inf)
+        assert mock.call_count == 1
+        multivariate_t.logpdf(0, df=np.inf)
+        assert mock.call_count == 2
+
+    def test_shape_correctness(self):
+        # pdf and logpdf should return scalar when the
+        # number of samples in x is one.
+        dim = 4
+        loc = np.zeros(dim)
+        shape = np.eye(dim)
+        df = 4.5
+        x = np.zeros(dim)
+        res = multivariate_t(loc, shape, df).pdf(x)
+        assert np.isscalar(res)
+        res = multivariate_t(loc, shape, df).logpdf(x)
+        assert np.isscalar(res)
+
+        # pdf() and logpdf() should return probabilities of shape
+        # (n_samples,) when x has n_samples.
+        n_samples = 7
+        rng = np.random.default_rng(2767231913)
+        x = rng.random((n_samples, dim))
+        res = multivariate_t(loc, shape, df).pdf(x)
+        assert (res.shape == (n_samples,))
+        res = multivariate_t(loc, shape, df).logpdf(x)
+        assert (res.shape == (n_samples,))
+
+        # rvs() should return scalar unless a size argument is applied.
+        res = multivariate_t(np.zeros(1), np.eye(1), 1).rvs()
+        assert np.isscalar(res)
+
+        # rvs() should return vector of shape (size,) if size argument
+        # is applied.
+        size = 7
+        res = multivariate_t(np.zeros(1), np.eye(1), 1).rvs(size=size)
+        assert (res.shape == (size,))
+
+    def test_default_arguments(self):
+        dist = multivariate_t()
+        assert_equal(dist.loc, [0])
+        assert_equal(dist.shape, [[1]])
+        assert (dist.df == 1)
+
+    DEFAULT_ARGS_TESTS = [
+        (None, None, None, 0, 1, 1),
+        (None, None, 7, 0, 1, 7),
+        (None, [[7, 0], [0, 7]], None, [0, 0], [[7, 0], [0, 7]], 1),
+        (None, [[7, 0], [0, 7]], 7, [0, 0], [[7, 0], [0, 7]], 7),
+        ([7, 7], None, None, [7, 7], [[1, 0], [0, 1]], 1),
+        ([7, 7], None, 7, [7, 7], [[1, 0], [0, 1]], 7),
+        ([7, 7], [[7, 0], [0, 7]], None, [7, 7], [[7, 0], [0, 7]], 1),
+        ([7, 7], [[7, 0], [0, 7]], 7, [7, 7], [[7, 0], [0, 7]], 7)
+    ]
+
+    @pytest.mark.parametrize("loc, shape, df, loc_ans, shape_ans, df_ans",
+                             DEFAULT_ARGS_TESTS)
+    def test_default_args(self, loc, shape, df, loc_ans, shape_ans, df_ans):
+        dist = multivariate_t(loc=loc, shape=shape, df=df)
+        assert_equal(dist.loc, loc_ans)
+        assert_equal(dist.shape, shape_ans)
+        assert (dist.df == df_ans)
+
+    ARGS_SHAPES_TESTS = [
+        (-1, 2, 3, [-1], [[2]], 3),
+        ([-1], [2], 3, [-1], [[2]], 3),
+        (np.array([-1]), np.array([2]), 3, [-1], [[2]], 3)
+    ]
+
+    @pytest.mark.parametrize("loc, shape, df, loc_ans, shape_ans, df_ans",
+                             ARGS_SHAPES_TESTS)
+    def test_scalar_list_and_ndarray_arguments(self, loc, shape, df, loc_ans,
+                                               shape_ans, df_ans):
+        dist = multivariate_t(loc, shape, df)
+        assert_equal(dist.loc, loc_ans)
+        assert_equal(dist.shape, shape_ans)
+        assert_equal(dist.df, df_ans)
+
+    def test_argument_error_handling(self):
+        # `loc` should be a one-dimensional vector.
+        loc = [[1, 1]]
+        assert_raises(ValueError,
+                      multivariate_t,
+                      **dict(loc=loc))
+
+        # `shape` should be scalar or square matrix.
+        shape = [[1, 1], [2, 2], [3, 3]]
+        assert_raises(ValueError,
+                      multivariate_t,
+                      **dict(loc=loc, shape=shape))
+
+        # `df` should be greater than zero.
+        loc = np.zeros(2)
+        shape = np.eye(2)
+        df = -1
+        assert_raises(ValueError,
+                      multivariate_t,
+                      **dict(loc=loc, shape=shape, df=df))
+        df = 0
+        assert_raises(ValueError,
+                      multivariate_t,
+                      **dict(loc=loc, shape=shape, df=df))
+
+    def test_reproducibility(self):
+        rng = np.random.RandomState(4)
+        loc = rng.uniform(size=3)
+        shape = np.eye(3)
+        dist1 = multivariate_t(loc, shape, df=3, seed=2)
+        dist2 = multivariate_t(loc, shape, df=3, seed=2)
+        samples1 = dist1.rvs(size=10)
+        samples2 = dist2.rvs(size=10)
+        assert_equal(samples1, samples2)
+
+    def test_allow_singular(self):
+        # Make shape singular and verify error was raised.
+        args = dict(loc=[0,0], shape=[[0,0],[0,1]], df=1, allow_singular=False)
+        assert_raises(np.linalg.LinAlgError, multivariate_t, **args)
+
+    @pytest.mark.parametrize("size", [(10, 3), (5, 6, 4, 3)])
+    @pytest.mark.parametrize("dim", [2, 3, 4, 5])
+    @pytest.mark.parametrize("df", [1., 2., np.inf])
+    def test_rvs(self, size, dim, df):
+        dist = multivariate_t(np.zeros(dim), np.eye(dim), df)
+        rvs = dist.rvs(size=size)
+        assert rvs.shape == size + (dim, )
+
+    def test_cdf_signs(self):
+        # check that sign of output is correct when np.any(lower > x)
+        mean = np.zeros(3)
+        cov = np.eye(3)
+        df = 10
+        b = [[1, 1, 1], [0, 0, 0], [1, 0, 1], [0, 1, 0]]
+        a = [[0, 0, 0], [1, 1, 1], [0, 1, 0], [1, 0, 1]]
+        # when odd number of elements of b < a, output is negative
+        expected_signs = np.array([1, -1, -1, 1])
+        cdf = multivariate_normal.cdf(b, mean, cov, df, lower_limit=a)
+        assert_allclose(cdf, cdf[0]*expected_signs)
+
+    @pytest.mark.parametrize('dim', [1, 2, 5])
+    def test_cdf_against_multivariate_normal(self, dim):
+        # Check accuracy against MVN randomly-generated cases
+        self.cdf_against_mvn_test(dim)
+
+    @pytest.mark.parametrize('dim', [3, 6, 9])
+    def test_cdf_against_multivariate_normal_singular(self, dim):
+        # Check accuracy against MVN for randomly-generated singular cases
+        self.cdf_against_mvn_test(3, True)
+
+    def cdf_against_mvn_test(self, dim, singular=False):
+        # Check for accuracy in the limit that df -> oo and MVT -> MVN
+        rng = np.random.default_rng(413722918996573)
+        n = 3
+
+        w = 10**rng.uniform(-2, 1, size=dim)
+        cov = _random_covariance(dim, w, rng, singular)
+
+        mean = 10**rng.uniform(-1, 2, size=dim) * np.sign(rng.normal(size=dim))
+        a = -10**rng.uniform(-1, 2, size=(n, dim)) + mean
+        b = 10**rng.uniform(-1, 2, size=(n, dim)) + mean
+
+        res = stats.multivariate_t.cdf(b, mean, cov, df=10000, lower_limit=a,
+                                       allow_singular=True, random_state=rng)
+        ref = stats.multivariate_normal.cdf(b, mean, cov, allow_singular=True,
+                                            lower_limit=a)
+        assert_allclose(res, ref, atol=5e-4)
+
+    def test_cdf_against_univariate_t(self):
+        rng = np.random.default_rng(413722918996573)
+        cov = 2
+        mean = 0
+        x = rng.normal(size=10, scale=np.sqrt(cov))
+        df = 3
+
+        res = stats.multivariate_t.cdf(x, mean, cov, df, lower_limit=-np.inf,
+                                       random_state=rng)
+        ref = stats.t.cdf(x, df, mean, np.sqrt(cov))
+        incorrect = stats.norm.cdf(x, mean, np.sqrt(cov))
+
+        assert_allclose(res, ref, atol=5e-4)  # close to t
+        assert np.all(np.abs(res - incorrect) > 1e-3)  # not close to normal
+
+    @pytest.mark.parametrize("dim", [2, 3, 5, 10])
+    @pytest.mark.parametrize("seed", [3363958638, 7891119608, 3887698049,
+                                      5013150848, 1495033423, 6170824608])
+    @pytest.mark.parametrize("singular", [False, True])
+    def test_cdf_against_qsimvtv(self, dim, seed, singular):
+        if singular and seed != 3363958638:
+            pytest.skip('Agreement with qsimvtv is not great in singular case')
+        rng = np.random.default_rng(seed)
+        w = 10**rng.uniform(-2, 2, size=dim)
+        cov = _random_covariance(dim, w, rng, singular)
+        mean = rng.random(dim)
+        a = -rng.random(dim)
+        b = rng.random(dim)
+        df = rng.random() * 5
+
+        # no lower limit
+        res = stats.multivariate_t.cdf(b, mean, cov, df, random_state=rng,
+                                       allow_singular=True)
+        with np.errstate(invalid='ignore'):
+            ref = _qsimvtv(20000, df, cov, np.inf*a, b - mean, rng)[0]
+        assert_allclose(res, ref, atol=2e-4, rtol=1e-3)
+
+        # with lower limit
+        res = stats.multivariate_t.cdf(b, mean, cov, df, lower_limit=a,
+                                       random_state=rng, allow_singular=True)
+        with np.errstate(invalid='ignore'):
+            ref = _qsimvtv(20000, df, cov, a - mean, b - mean, rng)[0]
+        assert_allclose(res, ref, atol=1e-4, rtol=1e-3)
+
+    @pytest.mark.slow
+    def test_cdf_against_generic_integrators(self):
+        # Compare result against generic numerical integrators
+        dim = 3
+        rng = np.random.default_rng(41372291899657)
+        w = 10 ** rng.uniform(-1, 1, size=dim)
+        cov = _random_covariance(dim, w, rng, singular=True)
+        mean = rng.random(dim)
+        a = -rng.random(dim)
+        b = rng.random(dim)
+        df = rng.random() * 5
+
+        res = stats.multivariate_t.cdf(b, mean, cov, df, random_state=rng,
+                                       lower_limit=a)
+
+        def integrand(x):
+            return stats.multivariate_t.pdf(x.T, mean, cov, df)
+
+        ref = qmc_quad(integrand, a, b, qrng=stats.qmc.Halton(d=dim, seed=rng))
+        assert_allclose(res, ref.integral, rtol=1e-3)
+
+        def integrand(*zyx):
+            return stats.multivariate_t.pdf(zyx[::-1], mean, cov, df)
+
+        ref = tplquad(integrand, a[0], b[0], a[1], b[1], a[2], b[2])
+        assert_allclose(res, ref[0], rtol=1e-3)
+
+    def test_against_matlab(self):
+        # Test against matlab mvtcdf:
+        # C = [6.21786909  0.2333667 7.95506077;
+        #      0.2333667 29.67390923 16.53946426;
+        #      7.95506077 16.53946426 19.17725252]
+        # df = 1.9559939787727658
+        # mvtcdf([0, 0, 0], C, df)  % 0.2523
+        rng = np.random.default_rng(2967390923)
+        cov = np.array([[ 6.21786909,  0.2333667 ,  7.95506077],
+                        [ 0.2333667 , 29.67390923, 16.53946426],
+                        [ 7.95506077, 16.53946426, 19.17725252]])
+        df = 1.9559939787727658
+        dist = stats.multivariate_t(shape=cov, df=df)
+        res = dist.cdf([0, 0, 0], random_state=rng)
+        ref = 0.2523
+        assert_allclose(res, ref, rtol=1e-3)
+
+    def test_frozen(self):
+        seed = 4137229573
+        rng = np.random.default_rng(seed)
+        loc = rng.uniform(size=3)
+        x = rng.uniform(size=3) + loc
+        shape = np.eye(3)
+        df = rng.random()
+        args = (loc, shape, df)
+
+        rng_frozen = np.random.default_rng(seed)
+        rng_unfrozen = np.random.default_rng(seed)
+        dist = stats.multivariate_t(*args, seed=rng_frozen)
+        assert_equal(dist.cdf(x),
+                     multivariate_t.cdf(x, *args, random_state=rng_unfrozen))
+
+    def test_vectorized(self):
+        dim = 4
+        n = (2, 3)
+        rng = np.random.default_rng(413722918996573)
+        A = rng.random(size=(dim, dim))
+        cov = A @ A.T
+        mean = rng.random(dim)
+        x = rng.random(n + (dim,))
+        df = rng.random() * 5
+
+        res = stats.multivariate_t.cdf(x, mean, cov, df, random_state=rng)
+
+        def _cdf_1d(x):
+            return _qsimvtv(10000, df, cov, -np.inf*x, x-mean, rng)[0]
+
+        ref = np.apply_along_axis(_cdf_1d, -1, x)
+        assert_allclose(res, ref, atol=1e-4, rtol=1e-3)
+
+    @pytest.mark.parametrize("dim", (3, 7))
+    def test_against_analytical(self, dim):
+        rng = np.random.default_rng(413722918996573)
+        A = scipy.linalg.toeplitz(c=[1] + [0.5] * (dim - 1))
+        res = stats.multivariate_t(shape=A).cdf([0] * dim, random_state=rng)
+        ref = 1 / (dim + 1)
+        assert_allclose(res, ref, rtol=5e-5)
+
+    def test_entropy_inf_df(self):
+        cov = np.eye(3, 3)
+        df = np.inf
+        mvt_entropy = stats.multivariate_t.entropy(shape=cov, df=df)
+        mvn_entropy = stats.multivariate_normal.entropy(None, cov)
+        assert mvt_entropy == mvn_entropy
+
+    @pytest.mark.parametrize("df", [1, 10, 100])
+    def test_entropy_1d(self, df):
+        mvt_entropy = stats.multivariate_t.entropy(shape=1., df=df)
+        t_entropy = stats.t.entropy(df=df)
+        assert_allclose(mvt_entropy, t_entropy, rtol=1e-13)
+
+    # entropy reference values were computed via numerical integration
+    #
+    # def integrand(x, y, mvt):
+    #     vec = np.array([x, y])
+    #     return mvt.logpdf(vec) * mvt.pdf(vec)
+
+    # def multivariate_t_entropy_quad_2d(df, cov):
+    #     dim = cov.shape[0]
+    #     loc = np.zeros((dim, ))
+    #     mvt = stats.multivariate_t(loc, cov, df)
+    #     limit = 100
+    #     return -integrate.dblquad(integrand, -limit, limit, -limit, limit,
+    #                               args=(mvt, ))[0]
+
+    @pytest.mark.parametrize("df, cov, ref, tol",
+                             [(10, np.eye(2, 2), 3.0378770664093313, 1e-14),
+                              (100, np.array([[0.5, 1], [1, 10]]),
+                               3.55102424550609, 1e-8)])
+    def test_entropy_vs_numerical_integration(self, df, cov, ref, tol):
+        loc = np.zeros((2, ))
+        mvt = stats.multivariate_t(loc, cov, df)
+        assert_allclose(mvt.entropy(), ref, rtol=tol)
+
+    @pytest.mark.parametrize(
+        "df, dim, ref, tol",
+        [
+            (10, 1, 1.5212624929756808, 1e-15),
+            (100, 1, 1.4289633653182439, 1e-13),
+            (500, 1, 1.420939531869349, 1e-14),
+            (1e20, 1, 1.4189385332046727, 1e-15),
+            (1e100, 1, 1.4189385332046727, 1e-15),
+            (10, 10, 15.069150450832911, 1e-15),
+            (1000, 10, 14.19936546446673, 1e-13),
+            (1e20, 10, 14.189385332046728, 1e-15),
+            (1e100, 10, 14.189385332046728, 1e-15),
+            (10, 100, 148.28902883192654, 1e-15),
+            (1000, 100, 141.99155538003762, 1e-14),
+            (1e20, 100, 141.8938533204673, 1e-15),
+            (1e100, 100, 141.8938533204673, 1e-15),
+        ]
+    )
+    def test_extreme_entropy(self, df, dim, ref, tol):
+        # Reference values were calculated with mpmath:
+        # from mpmath import mp
+        # mp.dps = 500
+        #
+        # def mul_t_mpmath_entropy(dim, df=1):
+        #     dim = mp.mpf(dim)
+        #     df = mp.mpf(df)
+        #     halfsum = (dim + df)/2
+        #     half_df = df/2
+        #
+        #     return float(
+        #         -mp.loggamma(halfsum) + mp.loggamma(half_df)
+        #         + dim / 2 * mp.log(df * mp.pi)
+        #         + halfsum * (mp.digamma(halfsum) - mp.digamma(half_df))
+        #         + 0.0
+        #     )
+        mvt = stats.multivariate_t(shape=np.eye(dim), df=df)
+        assert_allclose(mvt.entropy(), ref, rtol=tol)
+
+    def test_entropy_with_covariance(self):
+        # Generated using np.randn(5, 5) and then rounding
+        # to two decimal places
+        _A = np.array([
+            [1.42, 0.09, -0.49, 0.17, 0.74],
+            [-1.13, -0.01,  0.71, 0.4, -0.56],
+            [1.07, 0.44, -0.28, -0.44, 0.29],
+            [-1.5, -0.94, -0.67, 0.73, -1.1],
+            [0.17, -0.08, 1.46, -0.32, 1.36]
+        ])
+        # Set cov to be a symmetric positive semi-definite matrix
+        cov = _A @ _A.T
+
+        # Test the asymptotic case. For large degrees of freedom
+        # the entropy approaches the multivariate normal entropy.
+        df = 1e20
+        mul_t_entropy = stats.multivariate_t.entropy(shape=cov, df=df)
+        mul_norm_entropy = multivariate_normal(None, cov=cov).entropy()
+        assert_allclose(mul_t_entropy, mul_norm_entropy, rtol=1e-15)
+
+        # Test the regular case. For a dim of 5 the threshold comes out
+        # to be approximately 766.45. So using slightly
+        # different dfs on each site of the threshold, the entropies
+        # are being compared.
+        df1 = 765
+        df2 = 768
+        _entropy1 = stats.multivariate_t.entropy(shape=cov, df=df1)
+        _entropy2 = stats.multivariate_t.entropy(shape=cov, df=df2)
+        assert_allclose(_entropy1, _entropy2, rtol=1e-5)
+
+    def test_logpdf_df_inf_gh19930(self):
+        # `multivariate_t._logpdf` (and `logpdf`/`pdf`) was not working with infinite
+        # `df` after an update to `multivariate_normal._logpdf`.
+
+        # Reproducible example from the issue
+        res = multivariate_t.logpdf(1, 1, 1, df=np.inf)
+        ref = multivariate_normal.logpdf(1, 1, 1)
+        assert_allclose(res, ref)
+
+        # More extensive test
+        # Generate a valid multivariate normal distribution and corresponding MVT
+        rng = np.random.default_rng(324893259825)
+        mean = rng.random(3)
+        cov = rng.random((3, 3)) + np.eye(3)*3
+        cov = cov.T + cov
+        X = multivariate_normal(mean=mean, cov=cov)
+        Y = multivariate_t(loc=mean, shape=cov, df=np.inf)
+
+        # compare the pdf and logpdf at 10 random points
+        x = X.rvs(10)
+        assert_allclose(Y.logpdf(x), X.logpdf(x))
+        assert_allclose(Y.pdf(x), X.pdf(x))
+
+
+class TestMultivariateHypergeom:
+    @pytest.mark.parametrize(
+        "x, m, n, expected",
+        [
+            # Ground truth value from R dmvhyper
+            ([3, 4], [5, 10], 7, -1.119814),
+            # test for `n=0`
+            ([3, 4], [5, 10], 0, -np.inf),
+            # test for `x < 0`
+            ([-3, 4], [5, 10], 7, -np.inf),
+            # test for `m < 0` (RuntimeWarning issue)
+            ([3, 4], [-5, 10], 7, np.nan),
+            # test for all `m < 0` and `x.sum() != n`
+            ([[1, 2], [3, 4]], [[-4, -6], [-5, -10]],
+             [3, 7], [np.nan, np.nan]),
+            # test for `x < 0` and `m < 0` (RuntimeWarning issue)
+            ([-3, 4], [-5, 10], 1, np.nan),
+            # test for `x > m`
+            ([1, 11], [10, 1], 12, np.nan),
+            # test for `m < 0` (RuntimeWarning issue)
+            ([1, 11], [10, -1], 12, np.nan),
+            # test for `n < 0`
+            ([3, 4], [5, 10], -7, np.nan),
+            # test for `x.sum() != n`
+            ([3, 3], [5, 10], 7, -np.inf)
+        ]
+    )
+    def test_logpmf(self, x, m, n, expected):
+        vals = multivariate_hypergeom.logpmf(x, m, n)
+        assert_allclose(vals, expected, rtol=1e-6)
+
+    def test_reduces_hypergeom(self):
+        # test that the multivariate_hypergeom pmf reduces to the
+        # hypergeom pmf in the 2d case.
+        val1 = multivariate_hypergeom.pmf(x=[3, 1], m=[10, 5], n=4)
+        val2 = hypergeom.pmf(k=3, M=15, n=4, N=10)
+        assert_allclose(val1, val2, rtol=1e-8)
+
+        val1 = multivariate_hypergeom.pmf(x=[7, 3], m=[15, 10], n=10)
+        val2 = hypergeom.pmf(k=7, M=25, n=10, N=15)
+        assert_allclose(val1, val2, rtol=1e-8)
+
+    def test_rvs(self):
+        # test if `rvs` is unbiased and large sample size converges
+        # to the true mean.
+        rv = multivariate_hypergeom(m=[3, 5], n=4)
+        rvs = rv.rvs(size=1000, random_state=123)
+        assert_allclose(rvs.mean(0), rv.mean(), rtol=1e-2)
+
+    def test_rvs_broadcasting(self):
+        rv = multivariate_hypergeom(m=[[3, 5], [5, 10]], n=[4, 9])
+        rvs = rv.rvs(size=(1000, 2), random_state=123)
+        assert_allclose(rvs.mean(0), rv.mean(), rtol=1e-2)
+
+    @pytest.mark.parametrize('m, n', (
+        ([0, 0, 20, 0, 0], 5), ([0, 0, 0, 0, 0], 0),
+        ([0, 0], 0), ([0], 0)
+    ))
+    def test_rvs_gh16171(self, m, n):
+        res = multivariate_hypergeom.rvs(m, n)
+        m = np.asarray(m)
+        res_ex = m.copy()
+        res_ex[m != 0] = n
+        assert_equal(res, res_ex)
+
+    @pytest.mark.parametrize(
+        "x, m, n, expected",
+        [
+            ([5], [5], 5, 1),
+            ([3, 4], [5, 10], 7, 0.3263403),
+            # Ground truth value from R dmvhyper
+            ([[[3, 5], [0, 8]], [[-1, 9], [1, 1]]],
+             [5, 10], [[8, 8], [8, 2]],
+             [[0.3916084, 0.006993007], [0, 0.4761905]]),
+            # test with empty arrays.
+            (np.array([], dtype=int), np.array([], dtype=int), 0, []),
+            ([1, 2], [4, 5], 5, 0),
+            # Ground truth value from R dmvhyper
+            ([3, 3, 0], [5, 6, 7], 6, 0.01077354)
+        ]
+    )
+    def test_pmf(self, x, m, n, expected):
+        vals = multivariate_hypergeom.pmf(x, m, n)
+        assert_allclose(vals, expected, rtol=1e-7)
+
+    @pytest.mark.parametrize(
+        "x, m, n, expected",
+        [
+            ([3, 4], [[5, 10], [10, 15]], 7, [0.3263403, 0.3407531]),
+            ([[1], [2]], [[3], [4]], [1, 3], [1., 0.]),
+            ([[[1], [2]]], [[3], [4]], [1, 3], [[1., 0.]]),
+            ([[1], [2]], [[[[3]]]], [1, 3], [[[1., 0.]]])
+        ]
+    )
+    def test_pmf_broadcasting(self, x, m, n, expected):
+        vals = multivariate_hypergeom.pmf(x, m, n)
+        assert_allclose(vals, expected, rtol=1e-7)
+
+    def test_cov(self):
+        cov1 = multivariate_hypergeom.cov(m=[3, 7, 10], n=12)
+        cov2 = [[0.64421053, -0.26526316, -0.37894737],
+                [-0.26526316, 1.14947368, -0.88421053],
+                [-0.37894737, -0.88421053, 1.26315789]]
+        assert_allclose(cov1, cov2, rtol=1e-8)
+
+    def test_cov_broadcasting(self):
+        cov1 = multivariate_hypergeom.cov(m=[[7, 9], [10, 15]], n=[8, 12])
+        cov2 = [[[1.05, -1.05], [-1.05, 1.05]],
+                [[1.56, -1.56], [-1.56, 1.56]]]
+        assert_allclose(cov1, cov2, rtol=1e-8)
+
+        cov3 = multivariate_hypergeom.cov(m=[[4], [5]], n=[4, 5])
+        cov4 = [[[0.]], [[0.]]]
+        assert_allclose(cov3, cov4, rtol=1e-8)
+
+        cov5 = multivariate_hypergeom.cov(m=[7, 9], n=[8, 12])
+        cov6 = [[[1.05, -1.05], [-1.05, 1.05]],
+                [[0.7875, -0.7875], [-0.7875, 0.7875]]]
+        assert_allclose(cov5, cov6, rtol=1e-8)
+
+    def test_var(self):
+        # test with hypergeom
+        var0 = multivariate_hypergeom.var(m=[10, 5], n=4)
+        var1 = hypergeom.var(M=15, n=4, N=10)
+        assert_allclose(var0, var1, rtol=1e-8)
+
+    def test_var_broadcasting(self):
+        var0 = multivariate_hypergeom.var(m=[10, 5], n=[4, 8])
+        var1 = multivariate_hypergeom.var(m=[10, 5], n=4)
+        var2 = multivariate_hypergeom.var(m=[10, 5], n=8)
+        assert_allclose(var0[0], var1, rtol=1e-8)
+        assert_allclose(var0[1], var2, rtol=1e-8)
+
+        var3 = multivariate_hypergeom.var(m=[[10, 5], [10, 14]], n=[4, 8])
+        var4 = [[0.6984127, 0.6984127], [1.352657, 1.352657]]
+        assert_allclose(var3, var4, rtol=1e-8)
+
+        var5 = multivariate_hypergeom.var(m=[[5], [10]], n=[5, 10])
+        var6 = [[0.], [0.]]
+        assert_allclose(var5, var6, rtol=1e-8)
+
+    def test_mean(self):
+        # test with hypergeom
+        mean0 = multivariate_hypergeom.mean(m=[10, 5], n=4)
+        mean1 = hypergeom.mean(M=15, n=4, N=10)
+        assert_allclose(mean0[0], mean1, rtol=1e-8)
+
+        mean2 = multivariate_hypergeom.mean(m=[12, 8], n=10)
+        mean3 = [12.*10./20., 8.*10./20.]
+        assert_allclose(mean2, mean3, rtol=1e-8)
+
+    def test_mean_broadcasting(self):
+        mean0 = multivariate_hypergeom.mean(m=[[3, 5], [10, 5]], n=[4, 8])
+        mean1 = [[3.*4./8., 5.*4./8.], [10.*8./15., 5.*8./15.]]
+        assert_allclose(mean0, mean1, rtol=1e-8)
+
+    def test_mean_edge_cases(self):
+        mean0 = multivariate_hypergeom.mean(m=[0, 0, 0], n=0)
+        assert_equal(mean0, [0., 0., 0.])
+
+        mean1 = multivariate_hypergeom.mean(m=[1, 0, 0], n=2)
+        assert_equal(mean1, [np.nan, np.nan, np.nan])
+
+        mean2 = multivariate_hypergeom.mean(m=[[1, 0, 0], [1, 0, 1]], n=2)
+        assert_allclose(mean2, [[np.nan, np.nan, np.nan], [1., 0., 1.]],
+                        rtol=1e-17)
+
+        mean3 = multivariate_hypergeom.mean(m=np.array([], dtype=int), n=0)
+        assert_equal(mean3, [])
+        assert_(mean3.shape == (0, ))
+
+    def test_var_edge_cases(self):
+        var0 = multivariate_hypergeom.var(m=[0, 0, 0], n=0)
+        assert_allclose(var0, [0., 0., 0.], rtol=1e-16)
+
+        var1 = multivariate_hypergeom.var(m=[1, 0, 0], n=2)
+        assert_equal(var1, [np.nan, np.nan, np.nan])
+
+        var2 = multivariate_hypergeom.var(m=[[1, 0, 0], [1, 0, 1]], n=2)
+        assert_allclose(var2, [[np.nan, np.nan, np.nan], [0., 0., 0.]],
+                        rtol=1e-17)
+
+        var3 = multivariate_hypergeom.var(m=np.array([], dtype=int), n=0)
+        assert_equal(var3, [])
+        assert_(var3.shape == (0, ))
+
+    def test_cov_edge_cases(self):
+        cov0 = multivariate_hypergeom.cov(m=[1, 0, 0], n=1)
+        cov1 = [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]
+        assert_allclose(cov0, cov1, rtol=1e-17)
+
+        cov3 = multivariate_hypergeom.cov(m=[0, 0, 0], n=0)
+        cov4 = [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]
+        assert_equal(cov3, cov4)
+
+        cov5 = multivariate_hypergeom.cov(m=np.array([], dtype=int), n=0)
+        cov6 = np.array([], dtype=np.float64).reshape(0, 0)
+        assert_allclose(cov5, cov6, rtol=1e-17)
+        assert_(cov5.shape == (0, 0))
+
+    def test_frozen(self):
+        # The frozen distribution should agree with the regular one
+        n = 12
+        m = [7, 9, 11, 13]
+        x = [[0, 0, 0, 12], [0, 0, 1, 11], [0, 1, 1, 10],
+             [1, 1, 1, 9], [1, 1, 2, 8]]
+        x = np.asarray(x, dtype=int)
+        mhg_frozen = multivariate_hypergeom(m, n)
+        assert_allclose(mhg_frozen.pmf(x),
+                        multivariate_hypergeom.pmf(x, m, n))
+        assert_allclose(mhg_frozen.logpmf(x),
+                        multivariate_hypergeom.logpmf(x, m, n))
+        assert_allclose(mhg_frozen.var(), multivariate_hypergeom.var(m, n))
+        assert_allclose(mhg_frozen.cov(), multivariate_hypergeom.cov(m, n))
+
+    def test_invalid_params(self):
+        assert_raises(ValueError, multivariate_hypergeom.pmf, 5, 10, 5)
+        assert_raises(ValueError, multivariate_hypergeom.pmf, 5, [10], 5)
+        assert_raises(ValueError, multivariate_hypergeom.pmf, [5, 4], [10], 5)
+        assert_raises(TypeError, multivariate_hypergeom.pmf, [5.5, 4.5],
+                      [10, 15], 5)
+        assert_raises(TypeError, multivariate_hypergeom.pmf, [5, 4],
+                      [10.5, 15.5], 5)
+        assert_raises(TypeError, multivariate_hypergeom.pmf, [5, 4],
+                      [10, 15], 5.5)
+
+
+class TestRandomTable:
+    def get_rng(self):
+        return np.random.default_rng(628174795866951638)
+
+    def test_process_parameters(self):
+        message = "`row` must be one-dimensional"
+        with pytest.raises(ValueError, match=message):
+            random_table([[1, 2]], [1, 2])
+
+        message = "`col` must be one-dimensional"
+        with pytest.raises(ValueError, match=message):
+            random_table([1, 2], [[1, 2]])
+
+        message = "each element of `row` must be non-negative"
+        with pytest.raises(ValueError, match=message):
+            random_table([1, -1], [1, 2])
+
+        message = "each element of `col` must be non-negative"
+        with pytest.raises(ValueError, match=message):
+            random_table([1, 2], [1, -2])
+
+        message = "sums over `row` and `col` must be equal"
+        with pytest.raises(ValueError, match=message):
+            random_table([1, 2], [1, 0])
+
+        message = "each element of `row` must be an integer"
+        with pytest.raises(ValueError, match=message):
+            random_table([2.1, 2.1], [1, 1, 2])
+
+        message = "each element of `col` must be an integer"
+        with pytest.raises(ValueError, match=message):
+            random_table([1, 2], [1.1, 1.1, 1])
+
+        row = [1, 3]
+        col = [2, 1, 1]
+        r, c, n = random_table._process_parameters([1, 3], [2, 1, 1])
+        assert_equal(row, r)
+        assert_equal(col, c)
+        assert n == np.sum(row)
+
+    @pytest.mark.parametrize("scale,method",
+                             ((1, "boyett"), (100, "patefield")))
+    def test_process_rvs_method_on_None(self, scale, method):
+        row = np.array([1, 3]) * scale
+        col = np.array([2, 1, 1]) * scale
+
+        ct = random_table
+        expected = ct.rvs(row, col, method=method, random_state=1)
+        got = ct.rvs(row, col, method=None, random_state=1)
+
+        assert_equal(expected, got)
+
+    def test_process_rvs_method_bad_argument(self):
+        row = [1, 3]
+        col = [2, 1, 1]
+
+        # order of items in set is random, so cannot check that
+        message = "'foo' not recognized, must be one of"
+        with pytest.raises(ValueError, match=message):
+            random_table.rvs(row, col, method="foo")
+
+    @pytest.mark.parametrize('frozen', (True, False))
+    @pytest.mark.parametrize('log', (True, False))
+    def test_pmf_logpmf(self, frozen, log):
+        # The pmf is tested through random sample generation
+        # with Boyett's algorithm, whose implementation is simple
+        # enough to verify manually for correctness.
+        rng = self.get_rng()
+        row = [2, 6]
+        col = [1, 3, 4]
+        rvs = random_table.rvs(row, col, size=1000,
+                               method="boyett", random_state=rng)
+
+        obj = random_table(row, col) if frozen else random_table
+        method = getattr(obj, "logpmf" if log else "pmf")
+        if not frozen:
+            original_method = method
+
+            def method(x):
+                return original_method(x, row, col)
+        pmf = (lambda x: np.exp(method(x))) if log else method
+
+        unique_rvs, counts = np.unique(rvs, axis=0, return_counts=True)
+
+        # rough accuracy check
+        p = pmf(unique_rvs)
+        assert_allclose(p * len(rvs), counts, rtol=0.1)
+
+        # accept any iterable
+        p2 = pmf(list(unique_rvs[0]))
+        assert_equal(p2, p[0])
+
+        # accept high-dimensional input and 2d input
+        rvs_nd = rvs.reshape((10, 100) + rvs.shape[1:])
+        p = pmf(rvs_nd)
+        assert p.shape == (10, 100)
+        for i in range(p.shape[0]):
+            for j in range(p.shape[1]):
+                pij = p[i, j]
+                rvij = rvs_nd[i, j]
+                qij = pmf(rvij)
+                assert_equal(pij, qij)
+
+        # probability is zero if column marginal does not match
+        x = [[0, 1, 1], [2, 1, 3]]
+        assert_equal(np.sum(x, axis=-1), row)
+        p = pmf(x)
+        assert p == 0
+
+        # probability is zero if row marginal does not match
+        x = [[0, 1, 2], [1, 2, 2]]
+        assert_equal(np.sum(x, axis=-2), col)
+        p = pmf(x)
+        assert p == 0
+
+        # response to invalid inputs
+        message = "`x` must be at least two-dimensional"
+        with pytest.raises(ValueError, match=message):
+            pmf([1])
+
+        message = "`x` must contain only integral values"
+        with pytest.raises(ValueError, match=message):
+            pmf([[1.1]])
+
+        message = "`x` must contain only integral values"
+        with pytest.raises(ValueError, match=message):
+            pmf([[np.nan]])
+
+        message = "`x` must contain only non-negative values"
+        with pytest.raises(ValueError, match=message):
+            pmf([[-1]])
+
+        message = "shape of `x` must agree with `row`"
+        with pytest.raises(ValueError, match=message):
+            pmf([[1, 2, 3]])
+
+        message = "shape of `x` must agree with `col`"
+        with pytest.raises(ValueError, match=message):
+            pmf([[1, 2],
+                 [3, 4]])
+
+    @pytest.mark.parametrize("method", ("boyett", "patefield"))
+    def test_rvs_mean(self, method):
+        # test if `rvs` is unbiased and large sample size converges
+        # to the true mean.
+        rng = self.get_rng()
+        row = [2, 6]
+        col = [1, 3, 4]
+        rvs = random_table.rvs(row, col, size=1000, method=method,
+                               random_state=rng)
+        mean = random_table.mean(row, col)
+        assert_equal(np.sum(mean), np.sum(row))
+        assert_allclose(rvs.mean(0), mean, atol=0.05)
+        assert_equal(rvs.sum(axis=-1), np.broadcast_to(row, (1000, 2)))
+        assert_equal(rvs.sum(axis=-2), np.broadcast_to(col, (1000, 3)))
+
+    def test_rvs_cov(self):
+        # test if `rvs` generated with patefield and boyett algorithms
+        # produce approximately the same covariance matrix
+        rng = self.get_rng()
+        row = [2, 6]
+        col = [1, 3, 4]
+        rvs1 = random_table.rvs(row, col, size=10000, method="boyett",
+                                random_state=rng)
+        rvs2 = random_table.rvs(row, col, size=10000, method="patefield",
+                                random_state=rng)
+        cov1 = np.var(rvs1, axis=0)
+        cov2 = np.var(rvs2, axis=0)
+        assert_allclose(cov1, cov2, atol=0.02)
+
+    @pytest.mark.parametrize("method", ("boyett", "patefield"))
+    def test_rvs_size(self, method):
+        row = [2, 6]
+        col = [1, 3, 4]
+
+        # test size `None`
+        rv = random_table.rvs(row, col, method=method,
+                              random_state=self.get_rng())
+        assert rv.shape == (2, 3)
+
+        # test size 1
+        rv2 = random_table.rvs(row, col, size=1, method=method,
+                               random_state=self.get_rng())
+        assert rv2.shape == (1, 2, 3)
+        assert_equal(rv, rv2[0])
+
+        # test size 0
+        rv3 = random_table.rvs(row, col, size=0, method=method,
+                               random_state=self.get_rng())
+        assert rv3.shape == (0, 2, 3)
+
+        # test other valid size
+        rv4 = random_table.rvs(row, col, size=20, method=method,
+                               random_state=self.get_rng())
+        assert rv4.shape == (20, 2, 3)
+
+        rv5 = random_table.rvs(row, col, size=(4, 5), method=method,
+                               random_state=self.get_rng())
+        assert rv5.shape == (4, 5, 2, 3)
+
+        assert_allclose(rv5.reshape(20, 2, 3), rv4, rtol=1e-15)
+
+        # test invalid size
+        message = "`size` must be a non-negative integer or `None`"
+        with pytest.raises(ValueError, match=message):
+            random_table.rvs(row, col, size=-1, method=method,
+                             random_state=self.get_rng())
+
+        with pytest.raises(ValueError, match=message):
+            random_table.rvs(row, col, size=np.nan, method=method,
+                             random_state=self.get_rng())
+
+    @pytest.mark.parametrize("method", ("boyett", "patefield"))
+    def test_rvs_method(self, method):
+        # This test assumes that pmf is correct and checks that random samples
+        # follow this probability distribution. This seems like a circular
+        # argument, since pmf is checked in test_pmf_logpmf with random samples
+        # generated with the rvs method. This test is not redundant, because
+        # test_pmf_logpmf intentionally uses rvs generation with Boyett only,
+        # but here we test both Boyett and Patefield.
+        row = [2, 6]
+        col = [1, 3, 4]
+
+        ct = random_table
+        rvs = ct.rvs(row, col, size=100000, method=method,
+                     random_state=self.get_rng())
+
+        unique_rvs, counts = np.unique(rvs, axis=0, return_counts=True)
+
+        # generated frequencies should match expected frequencies
+        p = ct.pmf(unique_rvs, row, col)
+        assert_allclose(p * len(rvs), counts, rtol=0.02)
+
+    @pytest.mark.parametrize("method", ("boyett", "patefield"))
+    def test_rvs_with_zeros_in_col_row(self, method):
+        row = [0, 1, 0]
+        col = [1, 0, 0, 0]
+        d = random_table(row, col)
+        rv = d.rvs(1000, method=method, random_state=self.get_rng())
+        expected = np.zeros((1000, len(row), len(col)))
+        expected[...] = [[0, 0, 0, 0],
+                         [1, 0, 0, 0],
+                         [0, 0, 0, 0]]
+        assert_equal(rv, expected)
+
+    @pytest.mark.parametrize("method", (None, "boyett", "patefield"))
+    @pytest.mark.parametrize("col", ([], [0]))
+    @pytest.mark.parametrize("row", ([], [0]))
+    def test_rvs_with_edge_cases(self, method, row, col):
+        d = random_table(row, col)
+        rv = d.rvs(10, method=method, random_state=self.get_rng())
+        expected = np.zeros((10, len(row), len(col)))
+        assert_equal(rv, expected)
+
+    @pytest.mark.parametrize('v', (1, 2))
+    def test_rvs_rcont(self, v):
+        # This test checks the internal low-level interface.
+        # It is implicitly also checked by the other test_rvs* calls.
+        import scipy.stats._rcont as _rcont
+
+        row = np.array([1, 3], dtype=np.int64)
+        col = np.array([2, 1, 1], dtype=np.int64)
+
+        rvs = getattr(_rcont, f"rvs_rcont{v}")
+
+        ntot = np.sum(row)
+        result = rvs(row, col, ntot, 1, self.get_rng())
+
+        assert result.shape == (1, len(row), len(col))
+        assert np.sum(result) == ntot
+
+    def test_frozen(self):
+        row = [2, 6]
+        col = [1, 3, 4]
+        d = random_table(row, col, seed=self.get_rng())
+
+        sample = d.rvs()
+
+        expected = random_table.mean(row, col)
+        assert_equal(expected, d.mean())
+
+        expected = random_table.pmf(sample, row, col)
+        assert_equal(expected, d.pmf(sample))
+
+        expected = random_table.logpmf(sample, row, col)
+        assert_equal(expected, d.logpmf(sample))
+
+    @pytest.mark.parametrize("method", ("boyett", "patefield"))
+    def test_rvs_frozen(self, method):
+        row = [2, 6]
+        col = [1, 3, 4]
+        d = random_table(row, col, seed=self.get_rng())
+
+        expected = random_table.rvs(row, col, size=10, method=method,
+                                    random_state=self.get_rng())
+        got = d.rvs(size=10, method=method)
+        assert_equal(expected, got)
+
+
+def check_pickling(distfn, args):
+    # check that a distribution instance pickles and unpickles
+    # pay special attention to the random_state property
+
+    # save the random_state (restore later)
+    rndm = distfn.random_state
+
+    distfn.random_state = 1234
+    distfn.rvs(*args, size=8)
+    s = pickle.dumps(distfn)
+    r0 = distfn.rvs(*args, size=8)
+
+    unpickled = pickle.loads(s)
+    r1 = unpickled.rvs(*args, size=8)
+    assert_equal(r0, r1)
+
+    # restore the random_state
+    distfn.random_state = rndm
+
+
+@pytest.mark.thread_unsafe(reason="uses numpy global random state and monkey-patching")
+def test_random_state_property():
+    scale = np.eye(3)
+    scale[0, 1] = 0.5
+    scale[1, 0] = 0.5
+    dists = [
+        [multivariate_normal, ()],
+        [dirichlet, (np.array([1.]), )],
+        [wishart, (10, scale)],
+        [invwishart, (10, scale)],
+        [multinomial, (5, [0.5, 0.4, 0.1])],
+        [ortho_group, (2,)],
+        [special_ortho_group, (2,)]
+    ]
+    for distfn, args in dists:
+        check_random_state_property(distfn, args)
+        check_pickling(distfn, args)
+
+
+class TestVonMises_Fisher:
+    @pytest.mark.parametrize("dim", [2, 3, 4, 6])
+    @pytest.mark.parametrize("size", [None, 1, 5, (5, 4)])
+    def test_samples(self, dim, size):
+        # test that samples have correct shape and norm 1
+        rng = np.random.default_rng(2777937887058094419)
+        mu = np.full((dim, ), 1/np.sqrt(dim))
+        vmf_dist = vonmises_fisher(mu, 1, seed=rng)
+        samples = vmf_dist.rvs(size)
+        mean, cov = np.zeros(dim), np.eye(dim)
+        expected_shape = rng.multivariate_normal(mean, cov, size=size).shape
+        assert samples.shape == expected_shape
+        norms = np.linalg.norm(samples, axis=-1)
+        assert_allclose(norms, 1.)
+
+    @pytest.mark.parametrize("dim", [5, 8])
+    @pytest.mark.parametrize("kappa", [1e15, 1e20, 1e30])
+    def test_sampling_high_concentration(self, dim, kappa):
+        # test that no warnings are encountered for high values
+        rng = np.random.default_rng(2777937887058094419)
+        mu = np.full((dim, ), 1/np.sqrt(dim))
+        vmf_dist = vonmises_fisher(mu, kappa, seed=rng)
+        vmf_dist.rvs(10)
+
+    def test_two_dimensional_mu(self):
+        mu = np.ones((2, 2))
+        msg = "'mu' must have one-dimensional shape."
+        with pytest.raises(ValueError, match=msg):
+            vonmises_fisher(mu, 1)
+
+    def test_wrong_norm_mu(self):
+        mu = np.ones((2, ))
+        msg = "'mu' must be a unit vector of norm 1."
+        with pytest.raises(ValueError, match=msg):
+            vonmises_fisher(mu, 1)
+
+    def test_one_entry_mu(self):
+        mu = np.ones((1, ))
+        msg = "'mu' must have at least two entries."
+        with pytest.raises(ValueError, match=msg):
+            vonmises_fisher(mu, 1)
+
+    @pytest.mark.parametrize("kappa", [-1, (5, 3)])
+    def test_kappa_validation(self, kappa):
+        msg = "'kappa' must be a positive scalar."
+        with pytest.raises(ValueError, match=msg):
+            vonmises_fisher([1, 0], kappa)
+
+    @pytest.mark.parametrize("kappa", [0, 0.])
+    def test_kappa_zero(self, kappa):
+        msg = ("For 'kappa=0' the von Mises-Fisher distribution "
+               "becomes the uniform distribution on the sphere "
+               "surface. Consider using 'scipy.stats.uniform_direction' "
+               "instead.")
+        with pytest.raises(ValueError, match=msg):
+            vonmises_fisher([1, 0], kappa)
+
+
+    @pytest.mark.parametrize("method", [vonmises_fisher.pdf,
+                                        vonmises_fisher.logpdf])
+    def test_invalid_shapes_pdf_logpdf(self, method):
+        x = np.array([1., 0., 0])
+        msg = ("The dimensionality of the last axis of 'x' must "
+               "match the dimensionality of the von Mises Fisher "
+               "distribution.")
+        with pytest.raises(ValueError, match=msg):
+            method(x, [1, 0], 1)
+
+    @pytest.mark.parametrize("method", [vonmises_fisher.pdf,
+                                        vonmises_fisher.logpdf])
+    def test_unnormalized_input(self, method):
+        x = np.array([0.5, 0.])
+        msg = "'x' must be unit vectors of norm 1 along last dimension."
+        with pytest.raises(ValueError, match=msg):
+            method(x, [1, 0], 1)
+
+    # Expected values of the vonmises-fisher logPDF were computed via mpmath
+    # from mpmath import mp
+    # import numpy as np
+    # mp.dps = 50
+    # def logpdf_mpmath(x, mu, kappa):
+    #     dim = mu.size
+    #     halfdim = mp.mpf(0.5 * dim)
+    #     kappa = mp.mpf(kappa)
+    #     const = (kappa**(halfdim - mp.one)/((2*mp.pi)**halfdim * \
+    #              mp.besseli(halfdim -mp.one, kappa)))
+    #     return float(const * mp.exp(kappa*mp.fdot(x, mu)))
+
+    @pytest.mark.parametrize('x, mu, kappa, reference',
+                             [(np.array([1., 0., 0.]), np.array([1., 0., 0.]),
+                               1e-4, 0.0795854295583605),
+                              (np.array([1., 0., 0]), np.array([0., 0., 1.]),
+                               1e-4, 0.07957747141331854),
+                              (np.array([1., 0., 0.]), np.array([1., 0., 0.]),
+                               100, 15.915494309189533),
+                              (np.array([1., 0., 0]), np.array([0., 0., 1.]),
+                               100, 5.920684802611232e-43),
+                              (np.array([1., 0., 0.]),
+                               np.array([np.sqrt(0.98), np.sqrt(0.02), 0.]),
+                               2000, 5.930499050746588e-07),
+                              (np.array([1., 0., 0]), np.array([1., 0., 0.]),
+                               2000, 318.3098861837907),
+                              (np.array([1., 0., 0., 0., 0.]),
+                               np.array([1., 0., 0., 0., 0.]),
+                               2000, 101371.86957712633),
+                              (np.array([1., 0., 0., 0., 0.]),
+                               np.array([np.sqrt(0.98), np.sqrt(0.02), 0.,
+                                         0, 0.]),
+                               2000, 0.00018886808182653578),
+                              (np.array([1., 0., 0., 0., 0.]),
+                               np.array([np.sqrt(0.8), np.sqrt(0.2), 0.,
+                                         0, 0.]),
+                               2000, 2.0255393314603194e-87)])
+    def test_pdf_accuracy(self, x, mu, kappa, reference):
+        pdf = vonmises_fisher(mu, kappa).pdf(x)
+        assert_allclose(pdf, reference, rtol=1e-13)
+
+    # Expected values of the vonmises-fisher logPDF were computed via mpmath
+    # from mpmath import mp
+    # import numpy as np
+    # mp.dps = 50
+    # def logpdf_mpmath(x, mu, kappa):
+    #     dim = mu.size
+    #     halfdim = mp.mpf(0.5 * dim)
+    #     kappa = mp.mpf(kappa)
+    #     two = mp.mpf(2.)
+    #     const = (kappa**(halfdim - mp.one)/((two*mp.pi)**halfdim * \
+    #              mp.besseli(halfdim - mp.one, kappa)))
+    #     return float(mp.log(const * mp.exp(kappa*mp.fdot(x, mu))))
+
+    @pytest.mark.parametrize('x, mu, kappa, reference',
+                             [(np.array([1., 0., 0.]), np.array([1., 0., 0.]),
+                               1e-4, -2.5309242486359573),
+                              (np.array([1., 0., 0]), np.array([0., 0., 1.]),
+                               1e-4, -2.5310242486359575),
+                              (np.array([1., 0., 0.]), np.array([1., 0., 0.]),
+                               100, 2.767293119578746),
+                              (np.array([1., 0., 0]), np.array([0., 0., 1.]),
+                               100, -97.23270688042125),
+                              (np.array([1., 0., 0.]),
+                               np.array([np.sqrt(0.98), np.sqrt(0.02), 0.]),
+                               2000, -14.337987284534103),
+                              (np.array([1., 0., 0]), np.array([1., 0., 0.]),
+                               2000, 5.763025393132737),
+                              (np.array([1., 0., 0., 0., 0.]),
+                               np.array([1., 0., 0., 0., 0.]),
+                               2000, 11.526550911307156),
+                              (np.array([1., 0., 0., 0., 0.]),
+                               np.array([np.sqrt(0.98), np.sqrt(0.02), 0.,
+                                         0, 0.]),
+                               2000, -8.574461766359684),
+                              (np.array([1., 0., 0., 0., 0.]),
+                               np.array([np.sqrt(0.8), np.sqrt(0.2), 0.,
+                                         0, 0.]),
+                               2000, -199.61906708886113)])
+    def test_logpdf_accuracy(self, x, mu, kappa, reference):
+        logpdf = vonmises_fisher(mu, kappa).logpdf(x)
+        assert_allclose(logpdf, reference, rtol=1e-14)
+
+    # Expected values of the vonmises-fisher entropy were computed via mpmath
+    # from mpmath import mp
+    # import numpy as np
+    # mp.dps = 50
+    # def entropy_mpmath(dim, kappa):
+    #     mu = np.full((dim, ), 1/np.sqrt(dim))
+    #     kappa = mp.mpf(kappa)
+    #     halfdim = mp.mpf(0.5 * dim)
+    #     logconstant = (mp.log(kappa**(halfdim - mp.one)
+    #                    /((2*mp.pi)**halfdim
+    #                    * mp.besseli(halfdim -mp.one, kappa)))
+    #     return float(-logconstant - kappa * mp.besseli(halfdim, kappa)/
+    #             mp.besseli(halfdim -1, kappa))
+
+    @pytest.mark.parametrize('dim, kappa, reference',
+                             [(3, 1e-4, 2.531024245302624),
+                              (3, 100, -1.7672931195787458),
+                              (5, 5000, -11.359032310024453),
+                              (8, 1, 3.4189526482545527)])
+    def test_entropy_accuracy(self, dim, kappa, reference):
+        mu = np.full((dim, ), 1/np.sqrt(dim))
+        entropy = vonmises_fisher(mu, kappa).entropy()
+        assert_allclose(entropy, reference, rtol=2e-14)
+
+    @pytest.mark.parametrize("method", [vonmises_fisher.pdf,
+                                        vonmises_fisher.logpdf])
+    def test_broadcasting(self, method):
+        # test that pdf and logpdf values are correctly broadcasted
+        testshape = (2, 2)
+        rng = np.random.default_rng(2777937887058094419)
+        x = uniform_direction(3).rvs(testshape, random_state=rng)
+        mu = np.full((3, ), 1/np.sqrt(3))
+        kappa = 5
+        result_all = method(x, mu, kappa)
+        assert result_all.shape == testshape
+        for i in range(testshape[0]):
+            for j in range(testshape[1]):
+                current_val = method(x[i, j, :], mu, kappa)
+                assert_allclose(current_val, result_all[i, j], rtol=1e-15)
+
+    def test_vs_vonmises_2d(self):
+        # test that in 2D, von Mises-Fisher yields the same results
+        # as the von Mises distribution
+        rng = np.random.default_rng(2777937887058094419)
+        mu = np.array([0, 1])
+        mu_angle = np.arctan2(mu[1], mu[0])
+        kappa = 20
+        vmf = vonmises_fisher(mu, kappa)
+        vonmises_dist = vonmises(loc=mu_angle, kappa=kappa)
+        vectors = uniform_direction(2).rvs(10, random_state=rng)
+        angles = np.arctan2(vectors[:, 1], vectors[:, 0])
+        assert_allclose(vonmises_dist.entropy(), vmf.entropy())
+        assert_allclose(vonmises_dist.pdf(angles), vmf.pdf(vectors))
+        assert_allclose(vonmises_dist.logpdf(angles), vmf.logpdf(vectors))
+
+    @pytest.mark.parametrize("dim", [2, 3, 6])
+    @pytest.mark.parametrize("kappa, mu_tol, kappa_tol",
+                             [(1, 5e-2, 5e-2),
+                              (10, 1e-2, 1e-2),
+                              (100, 5e-3, 2e-2),
+                              (1000, 1e-3, 2e-2)])
+    def test_fit_accuracy(self, dim, kappa, mu_tol, kappa_tol):
+        mu = np.full((dim, ), 1/np.sqrt(dim))
+        vmf_dist = vonmises_fisher(mu, kappa)
+        rng = np.random.default_rng(2777937887058094419)
+        n_samples = 10000
+        samples = vmf_dist.rvs(n_samples, random_state=rng)
+        mu_fit, kappa_fit = vonmises_fisher.fit(samples)
+        angular_error = np.arccos(mu.dot(mu_fit))
+        assert_allclose(angular_error, 0., atol=mu_tol, rtol=0)
+        assert_allclose(kappa, kappa_fit, rtol=kappa_tol)
+
+    def test_fit_error_one_dimensional_data(self):
+        x = np.zeros((3, ))
+        msg = "'x' must be two dimensional."
+        with pytest.raises(ValueError, match=msg):
+            vonmises_fisher.fit(x)
+
+    def test_fit_error_unnormalized_data(self):
+        x = np.ones((3, 3))
+        msg = "'x' must be unit vectors of norm 1 along last dimension."
+        with pytest.raises(ValueError, match=msg):
+            vonmises_fisher.fit(x)
+
+    def test_frozen_distribution(self):
+        mu = np.array([0, 0, 1])
+        kappa = 5
+        frozen = vonmises_fisher(mu, kappa)
+        frozen_seed = vonmises_fisher(mu, kappa, seed=514)
+
+        rvs1 = frozen.rvs(random_state=514)
+        rvs2 = vonmises_fisher.rvs(mu, kappa, random_state=514)
+        rvs3 = frozen_seed.rvs()
+
+        assert_equal(rvs1, rvs2)
+        assert_equal(rvs1, rvs3)
+
+
+class TestDirichletMultinomial:
+    @classmethod
+    def get_params(self, m):
+        rng = np.random.default_rng(28469824356873456)
+        alpha = rng.uniform(0, 100, size=2)
+        x = rng.integers(1, 20, size=(m, 2))
+        n = x.sum(axis=-1)
+        return rng, m, alpha, n, x
+
+    def test_frozen(self):
+        rng = np.random.default_rng(28469824356873456)
+
+        alpha = rng.uniform(0, 100, 10)
+        x = rng.integers(0, 10, 10)
+        n = np.sum(x, axis=-1)
+
+        d = dirichlet_multinomial(alpha, n)
+        assert_equal(d.logpmf(x), dirichlet_multinomial.logpmf(x, alpha, n))
+        assert_equal(d.pmf(x), dirichlet_multinomial.pmf(x, alpha, n))
+        assert_equal(d.mean(), dirichlet_multinomial.mean(alpha, n))
+        assert_equal(d.var(), dirichlet_multinomial.var(alpha, n))
+        assert_equal(d.cov(), dirichlet_multinomial.cov(alpha, n))
+
+    def test_pmf_logpmf_against_R(self):
+        # # Compare PMF against R's extraDistr ddirmnon
+        # # library(extraDistr)
+        # # options(digits=16)
+        # ddirmnom(c(1, 2, 3), 6, c(3, 4, 5))
+        x = np.array([1, 2, 3])
+        n = np.sum(x)
+        alpha = np.array([3, 4, 5])
+        res = dirichlet_multinomial.pmf(x, alpha, n)
+        logres = dirichlet_multinomial.logpmf(x, alpha, n)
+        ref = 0.08484162895927638
+        assert_allclose(res, ref)
+        assert_allclose(logres, np.log(ref))
+        assert res.shape == logres.shape == ()
+
+        # library(extraDistr)
+        # options(digits=16)
+        # ddirmnom(c(4, 3, 2, 0, 2, 3, 5, 7, 4, 7), 37,
+        #          c(45.01025314, 21.98739582, 15.14851365, 80.21588671,
+        #            52.84935481, 25.20905262, 53.85373737, 4.88568118,
+        #            89.06440654, 20.11359466))
+        rng = np.random.default_rng(28469824356873456)
+        alpha = rng.uniform(0, 100, 10)
+        x = rng.integers(0, 10, 10)
+        n = np.sum(x, axis=-1)
+        res = dirichlet_multinomial(alpha, n).pmf(x)
+        logres = dirichlet_multinomial.logpmf(x, alpha, n)
+        ref = 3.65409306285992e-16
+        assert_allclose(res, ref)
+        assert_allclose(logres, np.log(ref))
+
+    def test_pmf_logpmf_support(self):
+        # when the sum of the category counts does not equal the number of
+        # trials, the PMF is zero
+        rng, m, alpha, n, x = self.get_params(1)
+        n += 1
+        assert_equal(dirichlet_multinomial(alpha, n).pmf(x), 0)
+        assert_equal(dirichlet_multinomial(alpha, n).logpmf(x), -np.inf)
+
+        rng, m, alpha, n, x = self.get_params(10)
+        i = rng.random(size=10) > 0.5
+        x[i] = np.round(x[i] * 2)  # sum of these x does not equal n
+        assert_equal(dirichlet_multinomial(alpha, n).pmf(x)[i], 0)
+        assert_equal(dirichlet_multinomial(alpha, n).logpmf(x)[i], -np.inf)
+        assert np.all(dirichlet_multinomial(alpha, n).pmf(x)[~i] > 0)
+        assert np.all(dirichlet_multinomial(alpha, n).logpmf(x)[~i] > -np.inf)
+
+    def test_dimensionality_one(self):
+        # if the dimensionality is one, there is only one possible outcome
+        n = 6  # number of trials
+        alpha = [10]  # concentration parameters
+        x = np.asarray([n])  # counts
+        dist = dirichlet_multinomial(alpha, n)
+
+        assert_equal(dist.pmf(x), 1)
+        assert_equal(dist.pmf(x+1), 0)
+        assert_equal(dist.logpmf(x), 0)
+        assert_equal(dist.logpmf(x+1), -np.inf)
+        assert_equal(dist.mean(), n)
+        assert_equal(dist.var(), 0)
+        assert_equal(dist.cov(), 0)
+
+    def test_n_is_zero(self):
+        # similarly, only one possible outcome if n is zero
+        n = 0
+        alpha = np.asarray([1., 1.])
+        x = np.asarray([0, 0])
+        dist = dirichlet_multinomial(alpha, n)
+
+        assert_equal(dist.pmf(x), 1)
+        assert_equal(dist.pmf(x+1), 0)
+        assert_equal(dist.logpmf(x), 0)
+        assert_equal(dist.logpmf(x+1), -np.inf)
+        assert_equal(dist.mean(), [0, 0])
+        assert_equal(dist.var(), [0, 0])
+        assert_equal(dist.cov(), [[0, 0], [0, 0]])
+
+    @pytest.mark.parametrize('method_name', ['pmf', 'logpmf'])
+    def test_against_betabinom_pmf(self, method_name):
+        rng, m, alpha, n, x = self.get_params(100)
+
+        method = getattr(dirichlet_multinomial(alpha, n), method_name)
+        ref_method = getattr(stats.betabinom(n, *alpha.T), method_name)
+
+        res = method(x)
+        ref = ref_method(x.T[0])
+        assert_allclose(res, ref)
+
+    @pytest.mark.parametrize('method_name', ['mean', 'var'])
+    def test_against_betabinom_moments(self, method_name):
+        rng, m, alpha, n, x = self.get_params(100)
+
+        method = getattr(dirichlet_multinomial(alpha, n), method_name)
+        ref_method = getattr(stats.betabinom(n, *alpha.T), method_name)
+
+        res = method()[:, 0]
+        ref = ref_method()
+        assert_allclose(res, ref)
+
+    def test_moments(self):
+        rng = np.random.default_rng(28469824356873456)
+        dim = 5
+        n = rng.integers(1, 100)
+        alpha = rng.random(size=dim) * 10
+        dist = dirichlet_multinomial(alpha, n)
+
+        # Generate a random sample from the distribution using NumPy
+        m = 100000
+        p = rng.dirichlet(alpha, size=m)
+        x = rng.multinomial(n, p, size=m)
+
+        assert_allclose(dist.mean(), np.mean(x, axis=0), rtol=5e-3)
+        assert_allclose(dist.var(), np.var(x, axis=0), rtol=1e-2)
+        assert dist.mean().shape == dist.var().shape == (dim,)
+
+        cov = dist.cov()
+        assert cov.shape == (dim, dim)
+        assert_allclose(cov, np.cov(x.T), rtol=2e-2)
+        assert_equal(np.diag(cov), dist.var())
+        assert np.all(scipy.linalg.eigh(cov)[0] > 0)  # positive definite
+
+    def test_input_validation(self):
+        # valid inputs
+        x0 = np.array([1, 2, 3])
+        n0 = np.sum(x0)
+        alpha0 = np.array([3, 4, 5])
+
+        text = "`x` must contain only non-negative integers."
+        with assert_raises(ValueError, match=text):
+            dirichlet_multinomial.logpmf([1, -1, 3], alpha0, n0)
+        with assert_raises(ValueError, match=text):
+            dirichlet_multinomial.logpmf([1, 2.1, 3], alpha0, n0)
+
+        text = "`alpha` must contain only positive values."
+        with assert_raises(ValueError, match=text):
+            dirichlet_multinomial.logpmf(x0, [3, 0, 4], n0)
+        with assert_raises(ValueError, match=text):
+            dirichlet_multinomial.logpmf(x0, [3, -1, 4], n0)
+
+        text = "`n` must be a non-negative integer."
+        with assert_raises(ValueError, match=text):
+            dirichlet_multinomial.logpmf(x0, alpha0, 49.1)
+        with assert_raises(ValueError, match=text):
+            dirichlet_multinomial.logpmf(x0, alpha0, -1)
+
+        x = np.array([1, 2, 3, 4])
+        alpha = np.array([3, 4, 5])
+        text = "`x` and `alpha` must be broadcastable."
+        with assert_raises(ValueError, match=text):
+            dirichlet_multinomial.logpmf(x, alpha, x.sum())
+
+    @pytest.mark.parametrize('method', ['pmf', 'logpmf'])
+    def test_broadcasting_pmf(self, method):
+        alpha = np.array([[3, 4, 5], [4, 5, 6], [5, 5, 7], [8, 9, 10]])
+        n = np.array([[6], [7], [8]])
+        x = np.array([[1, 2, 3], [2, 2, 3]]).reshape((2, 1, 1, 3))
+        method = getattr(dirichlet_multinomial, method)
+        res = method(x, alpha, n)
+        assert res.shape == (2, 3, 4)
+        for i in range(len(x)):
+            for j in range(len(n)):
+                for k in range(len(alpha)):
+                    res_ijk = res[i, j, k]
+                    ref = method(x[i].squeeze(), alpha[k].squeeze(), n[j].squeeze())
+                    assert_allclose(res_ijk, ref)
+
+    @pytest.mark.parametrize('method_name', ['mean', 'var', 'cov'])
+    def test_broadcasting_moments(self, method_name):
+        alpha = np.array([[3, 4, 5], [4, 5, 6], [5, 5, 7], [8, 9, 10]])
+        n = np.array([[6], [7], [8]])
+        method = getattr(dirichlet_multinomial, method_name)
+        res = method(alpha, n)
+        assert res.shape == (3, 4, 3) if method_name != 'cov' else (3, 4, 3, 3)
+        for j in range(len(n)):
+            for k in range(len(alpha)):
+                res_ijk = res[j, k]
+                ref = method(alpha[k].squeeze(), n[j].squeeze())
+                assert_allclose(res_ijk, ref)
+
+
+class TestNormalInverseGamma:
+
+    def test_marginal_x(self):
+        # According to [1], sqrt(a * lmbda / b) * (x - u) should follow a t-distribution
+        # with 2*a degrees of freedom. Test that this is true of the PDF and random
+        # variates.
+        rng = np.random.default_rng(8925849245)
+        mu, lmbda, a, b = rng.random(4)
+
+        norm_inv_gamma = stats.normal_inverse_gamma(mu, lmbda, a, b)
+        t = stats.t(2*a, loc=mu, scale=1/np.sqrt(a * lmbda / b))
+
+        # Test PDF
+        x = np.linspace(-5, 5, 11)
+        res = tanhsinh(lambda s2, x: norm_inv_gamma.pdf(x, s2), 0, np.inf, args=(x,))
+        ref = t.pdf(x)
+        assert_allclose(res.integral, ref)
+
+        # Test RVS
+        res = norm_inv_gamma.rvs(size=10000, random_state=rng)
+        _, pvalue = stats.ks_1samp(res[0], t.cdf)
+        assert pvalue > 0.1
+
+    def test_marginal_s2(self):
+        # According to [1], s2 should follow an inverse gamma distribution with
+        # shapes a, b (where b is the scale in our parameterization). Test that
+        # this is true of the PDF and random variates.
+        rng = np.random.default_rng(8925849245)
+        mu, lmbda, a, b = rng.random(4)
+
+        norm_inv_gamma = stats.normal_inverse_gamma(mu, lmbda, a, b)
+        inv_gamma = stats.invgamma(a, scale=b)
+
+        # Test PDF
+        s2 = np.linspace(0.1, 10, 10)
+        res = tanhsinh(lambda x, s2: norm_inv_gamma.pdf(x, s2),
+                       -np.inf, np.inf, args=(s2,))
+        ref = inv_gamma.pdf(s2)
+        assert_allclose(res.integral, ref)
+
+        # Test RVS
+        res = norm_inv_gamma.rvs(size=10000, random_state=rng)
+        _, pvalue = stats.ks_1samp(res[1], inv_gamma.cdf)
+        assert pvalue > 0.1
+
+    def test_pdf_logpdf(self):
+        # Check that PDF and log-PDF are consistent
+        rng = np.random.default_rng(8925849245)
+        mu, lmbda, a, b = rng.random((4, 20)) - 0.25  # make some invalid
+        x, s2 = rng.random(size=(2, 20)) - 0.25
+        res = stats.normal_inverse_gamma(mu, lmbda, a, b).pdf(x, s2)
+        ref = stats.normal_inverse_gamma.logpdf(x, s2, mu, lmbda, a, b)
+        assert_allclose(res, np.exp(ref))
+
+    def test_invalid_and_special_cases(self):
+        # Test cases that are handled by input validation rather than the formulas
+        rng = np.random.default_rng(8925849245)
+        mu, lmbda, a, b = rng.random(4)
+        x, s2 = rng.random(2)
+
+        res = stats.normal_inverse_gamma(np.nan, lmbda, a, b).pdf(x, s2)
+        assert_equal(res, np.nan)
+
+        res = stats.normal_inverse_gamma(mu, -1, a, b).pdf(x, s2)
+        assert_equal(res, np.nan)
+
+        res = stats.normal_inverse_gamma(mu, lmbda, 0, b).pdf(x, s2)
+        assert_equal(res, np.nan)
+
+        res = stats.normal_inverse_gamma(mu, lmbda, a, -1).pdf(x, s2)
+        assert_equal(res, np.nan)
+
+        res = stats.normal_inverse_gamma(mu, lmbda, a, b).pdf(x, -1)
+        assert_equal(res, 0)
+
+        # PDF with out-of-support s2 is not zero if shape parameter is invalid
+        res = stats.normal_inverse_gamma(mu, [-1, np.nan], a, b).pdf(x, -1)
+        assert_equal(res, np.nan)
+
+        res = stats.normal_inverse_gamma(mu, -1, a, b).mean()
+        assert_equal(res, (np.nan, np.nan))
+
+        res = stats.normal_inverse_gamma(mu, lmbda, -1, b).var()
+        assert_equal(res, (np.nan, np.nan))
+
+        with pytest.raises(ValueError, match="Domain error in arguments..."):
+            stats.normal_inverse_gamma(mu, lmbda, a, -1).rvs()
+
+    def test_broadcasting(self):
+        # Test methods with broadcastable array parameters. Roughly speaking, the
+        # shapes should be the broadcasted shapes of all arguments, and the raveled
+        # outputs should be the same as the outputs with raveled inputs.
+        rng = np.random.default_rng(8925849245)
+        b = rng.random(2)
+        a = rng.random((3, 1)) + 2  # for defined moments
+        lmbda = rng.random((4, 1, 1))
+        mu = rng.random((5, 1, 1, 1))
+        s2 = rng.random((6, 1, 1, 1, 1))
+        x = rng.random((7, 1, 1, 1, 1, 1))
+        dist = stats.normal_inverse_gamma(mu, lmbda, a, b)
+
+        # Test PDF and log-PDF
+        broadcasted = np.broadcast_arrays(x, s2, mu, lmbda, a, b)
+        broadcasted_raveled = [np.ravel(arr) for arr in broadcasted]
+
+        res = dist.pdf(x, s2)
+        assert res.shape == broadcasted[0].shape
+        assert_allclose(res.ravel(),
+                        stats.normal_inverse_gamma.pdf(*broadcasted_raveled))
+
+        res = dist.logpdf(x, s2)
+        assert res.shape == broadcasted[0].shape
+        assert_allclose(res.ravel(),
+                        stats.normal_inverse_gamma.logpdf(*broadcasted_raveled))
+
+        # Test moments
+        broadcasted = np.broadcast_arrays(mu, lmbda, a, b)
+        broadcasted_raveled = [np.ravel(arr) for arr in broadcasted]
+
+        res = dist.mean()
+        assert res[0].shape == broadcasted[0].shape
+        assert_allclose((res[0].ravel(), res[1].ravel()),
+                        stats.normal_inverse_gamma.mean(*broadcasted_raveled))
+
+        res = dist.var()
+        assert res[0].shape == broadcasted[0].shape
+        assert_allclose((res[0].ravel(), res[1].ravel()),
+                        stats.normal_inverse_gamma.var(*broadcasted_raveled))
+
+        # Test RVS
+        size = (6, 5, 4, 3, 2)
+        rng = np.random.default_rng(2348923985324)
+        res = dist.rvs(size=size, random_state=rng)
+        rng = np.random.default_rng(2348923985324)
+        shape = 6, 5*4*3*2
+        ref = stats.normal_inverse_gamma.rvs(*broadcasted_raveled, size=shape,
+                                             random_state=rng)
+        assert_allclose((res[0].reshape(shape), res[1].reshape(shape)), ref)
+
+    @pytest.mark.slow
+    @pytest.mark.fail_slow(10)
+    def test_moments(self):
+        # Test moments against quadrature
+        rng = np.random.default_rng(8925849245)
+        mu, lmbda, a, b = rng.random(4)
+        a += 2  # ensure defined
+
+        dist = stats.normal_inverse_gamma(mu, lmbda, a, b)
+        res = dist.mean()
+
+        ref = dblquad(lambda s2, x: dist.pdf(x, s2) * x, -np.inf, np.inf, 0, np.inf)
+        assert_allclose(res[0], ref[0], rtol=1e-6)
+
+        ref = dblquad(lambda s2, x: dist.pdf(x, s2) * s2, -np.inf, np.inf, 0, np.inf)
+        assert_allclose(res[1], ref[0], rtol=1e-6)
+
+    @pytest.mark.parametrize('dtype', [np.int32, np.float16, np.float32, np.float64])
+    def test_dtype(self, dtype):
+        rng = np.random.default_rng(8925849245)
+        x, s2, mu, lmbda, a, b = rng.uniform(3, 10, size=6).astype(dtype)
+        dtype_out = np.result_type(1.0, dtype)
+        dist = stats.normal_inverse_gamma(mu, lmbda, a, b)
+        assert dist.rvs()[0].dtype == dtype_out
+        assert dist.rvs()[1].dtype == dtype_out
+        assert dist.mean()[0].dtype == dtype_out
+        assert dist.mean()[1].dtype == dtype_out
+        assert dist.var()[0].dtype == dtype_out
+        assert dist.var()[1].dtype == dtype_out
+        assert dist.logpdf(x, s2).dtype == dtype_out
+        assert dist.pdf(x, s2).dtype == dtype_out
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_new_distributions.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_new_distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..b266b07b858ee1571e988c59af23e6348a219e4d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_new_distributions.py
@@ -0,0 +1,17 @@
+# file for distribution-specific tests with new infrastructure (UnivariateDistribution)
+import numpy as np
+from numpy.testing import assert_allclose
+from scipy import stats
+
+class TestBinomial:
+    def test_gh23708_binomial_logcdf_method_complement(self):
+        # gh-23708 found that `logcdf` method='complement' was inaccurate in the tails
+        x = np.asarray([0., 18.])
+        X = stats.Binomial(n=np.asarray([18.]), p=np.asarray(0.71022842))
+        assert_allclose(X.logcdf(x, method='complement'), X.logcdf(x), rtol=1e-15)
+        assert_allclose(X.logccdf(x, method='complement'), X.logccdf(x), rtol=1e-15)
+
+        # going even deeper into the tails
+        X = stats.Binomial(n=100, p=0.5)
+        assert_allclose(X.logcdf(0, method='complement'), X.logpmf(0), rtol=1e-15)
+        assert_allclose(X.logccdf(99, method='complement'), X.logpmf(100), rtol=1e-15)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_odds_ratio.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_odds_ratio.py
new file mode 100644
index 0000000000000000000000000000000000000000..14b5ca88d0fca9edb8c3c161c06e52054d594e62
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_odds_ratio.py
@@ -0,0 +1,148 @@
+import pytest
+import numpy as np
+from numpy.testing import assert_equal, assert_allclose
+from .._discrete_distns import nchypergeom_fisher, hypergeom
+from scipy.stats._odds_ratio import odds_ratio
+from .data.fisher_exact_results_from_r import data
+
+
+class TestOddsRatio:
+
+    @pytest.mark.parametrize('parameters, rresult', data)
+    def test_results_from_r(self, parameters, rresult):
+        alternative = parameters.alternative.replace('.', '-')
+        result = odds_ratio(parameters.table)
+        # The results computed by R are not very accurate.
+        if result.statistic < 400:
+            or_rtol = 5e-4
+            ci_rtol = 2e-2
+        else:
+            or_rtol = 5e-2
+            ci_rtol = 1e-1
+        assert_allclose(result.statistic,
+                        rresult.conditional_odds_ratio, rtol=or_rtol)
+        ci = result.confidence_interval(parameters.confidence_level,
+                                        alternative)
+        assert_allclose((ci.low, ci.high), rresult.conditional_odds_ratio_ci,
+                        rtol=ci_rtol)
+
+        # Also do a self-check for the conditional odds ratio.
+        # With the computed conditional odds ratio as the noncentrality
+        # parameter of the noncentral hypergeometric distribution with
+        # parameters table.sum(), table[0].sum(), and table[:,0].sum() as
+        # total, ngood and nsample, respectively, the mean of the distribution
+        # should equal table[0, 0].
+        cor = result.statistic
+        table = np.array(parameters.table)
+        total = table.sum()
+        ngood = table[0].sum()
+        nsample = table[:, 0].sum()
+        # nchypergeom_fisher does not allow the edge cases where the
+        # noncentrality parameter is 0 or inf, so handle those values
+        # separately here.
+        if cor == 0:
+            nchg_mean = hypergeom.support(total, ngood, nsample)[0]
+        elif cor == np.inf:
+            nchg_mean = hypergeom.support(total, ngood, nsample)[1]
+        else:
+            nchg_mean = nchypergeom_fisher.mean(total, ngood, nsample, cor)
+        assert_allclose(nchg_mean, table[0, 0], rtol=1e-13)
+
+        # Check that the confidence interval is correct.
+        alpha = 1 - parameters.confidence_level
+        if alternative == 'two-sided':
+            if ci.low > 0:
+                sf = nchypergeom_fisher.sf(table[0, 0] - 1,
+                                           total, ngood, nsample, ci.low)
+                assert_allclose(sf, alpha/2, rtol=1e-11)
+            if np.isfinite(ci.high):
+                cdf = nchypergeom_fisher.cdf(table[0, 0],
+                                             total, ngood, nsample, ci.high)
+                assert_allclose(cdf, alpha/2, rtol=1e-11)
+        elif alternative == 'less':
+            if np.isfinite(ci.high):
+                cdf = nchypergeom_fisher.cdf(table[0, 0],
+                                             total, ngood, nsample, ci.high)
+                assert_allclose(cdf, alpha, rtol=1e-11)
+        else:
+            # alternative == 'greater'
+            if ci.low > 0:
+                sf = nchypergeom_fisher.sf(table[0, 0] - 1,
+                                           total, ngood, nsample, ci.low)
+                assert_allclose(sf, alpha, rtol=1e-11)
+
+    @pytest.mark.parametrize('table', [
+        [[0, 0], [5, 10]],
+        [[5, 10], [0, 0]],
+        [[0, 5], [0, 10]],
+        [[5, 0], [10, 0]],
+    ])
+    def test_row_or_col_zero(self, table):
+        result = odds_ratio(table)
+        assert_equal(result.statistic, np.nan)
+        ci = result.confidence_interval()
+        assert_equal((ci.low, ci.high), (0, np.inf))
+
+    @pytest.mark.parametrize("case",
+                             [[0.95, 'two-sided', 0.4879913, 2.635883],
+                              [0.90, 'two-sided', 0.5588516, 2.301663]])
+    def test_sample_odds_ratio_ci(self, case):
+        # Compare the sample odds ratio confidence interval to the R function
+        # oddsratio.wald from the epitools package, e.g.
+        # > library(epitools)
+        # > table = matrix(c(10, 20, 41, 93), nrow=2, ncol=2, byrow=TRUE)
+        # > result = oddsratio.wald(table)
+        # > result$measure
+        #           odds ratio with 95% C.I.
+        # Predictor  estimate     lower    upper
+        #   Exposed1 1.000000        NA       NA
+        #   Exposed2 1.134146 0.4879913 2.635883
+
+        confidence_level, alternative, ref_low, ref_high = case
+        table = [[10, 20], [41, 93]]
+        result = odds_ratio(table, kind='sample')
+        assert_allclose(result.statistic, 1.134146, rtol=1e-6)
+        ci = result.confidence_interval(confidence_level, alternative)
+        assert_allclose([ci.low, ci.high], [ref_low, ref_high], rtol=1e-6)
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+    def test_sample_odds_ratio_one_sided_ci(self, alternative):
+        # can't find a good reference for one-sided CI, so bump up the sample
+        # size and compare against the conditional odds ratio CI
+        table = [[1000, 2000], [4100, 9300]]
+        res = odds_ratio(table, kind='sample')
+        ref = odds_ratio(table, kind='conditional')
+        assert_allclose(res.statistic, ref.statistic, atol=1e-5)
+        assert_allclose(res.confidence_interval(alternative=alternative),
+                        ref.confidence_interval(alternative=alternative),
+                        atol=2e-3)
+
+    @pytest.mark.parametrize('kind', ['sample', 'conditional'])
+    @pytest.mark.parametrize('bad_table', [123, "foo", [10, 11, 12]])
+    def test_invalid_table_shape(self, kind, bad_table):
+        with pytest.raises(ValueError, match="Invalid shape"):
+            odds_ratio(bad_table, kind=kind)
+
+    def test_invalid_table_type(self):
+        with pytest.raises(ValueError, match='must be an array of integers'):
+            odds_ratio([[1.0, 3.4], [5.0, 9.9]])
+
+    def test_negative_table_values(self):
+        with pytest.raises(ValueError, match='must be nonnegative'):
+            odds_ratio([[1, 2], [3, -4]])
+
+    def test_invalid_kind(self):
+        with pytest.raises(ValueError, match='`kind` must be'):
+            odds_ratio([[10, 20], [30, 14]], kind='magnetoreluctance')
+
+    def test_invalid_alternative(self):
+        result = odds_ratio([[5, 10], [2, 32]])
+        with pytest.raises(ValueError, match='`alternative` must be'):
+            result.confidence_interval(alternative='depleneration')
+
+    @pytest.mark.parametrize('level', [-0.5, 1.5])
+    def test_invalid_confidence_level(self, level):
+        result = odds_ratio([[5, 10], [2, 32]])
+        with pytest.raises(ValueError, match='must be between 0 and 1'):
+            result.confidence_interval(confidence_level=level)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_qmc.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_qmc.py
new file mode 100644
index 0000000000000000000000000000000000000000..650a16e975c30e6df5b5a2b197d92d2a7f972ed3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_qmc.py
@@ -0,0 +1,1535 @@
+import os
+from collections import Counter
+from itertools import combinations, product
+
+import pytest
+import numpy as np
+from numpy.testing import (assert_allclose, assert_equal, assert_array_equal,
+    assert_array_less)
+
+from scipy.spatial import distance
+from scipy.stats import shapiro
+from scipy.stats._sobol import _test_find_index, _test_low_0_bit
+from scipy.stats import qmc
+from scipy.stats._qmc import (
+    van_der_corput, n_primes, primes_from_2_to,
+    update_discrepancy, QMCEngine, _l1_norm,
+    _perturb_discrepancy, _lloyd_centroidal_voronoi_tessellation
+)
+
+
+class TestUtils:
+    def test_scale(self):
+        # 1d scalar
+        space = [[0], [1], [0.5]]
+        out = [[-2], [6], [2]]
+        scaled_space = qmc.scale(space, l_bounds=-2, u_bounds=6)
+
+        assert_allclose(scaled_space, out)
+
+        # 2d space
+        space = [[0, 0], [1, 1], [0.5, 0.5]]
+        bounds = np.array([[-2, 0], [6, 5]])
+        out = [[-2, 0], [6, 5], [2, 2.5]]
+
+        scaled_space = qmc.scale(space, l_bounds=bounds[0], u_bounds=bounds[1])
+
+        assert_allclose(scaled_space, out)
+
+        scaled_back_space = qmc.scale(scaled_space, l_bounds=bounds[0],
+                                      u_bounds=bounds[1], reverse=True)
+        assert_allclose(scaled_back_space, space)
+
+        # broadcast
+        space = [[0, 0, 0], [1, 1, 1], [0.5, 0.5, 0.5]]
+        l_bounds, u_bounds = 0, [6, 5, 3]
+        out = [[0, 0, 0], [6, 5, 3], [3, 2.5, 1.5]]
+
+        scaled_space = qmc.scale(space, l_bounds=l_bounds, u_bounds=u_bounds)
+
+        assert_allclose(scaled_space, out)
+
+    def test_scale_random(self):
+        rng = np.random.default_rng(317589836511269190194010915937762468165)
+        sample = rng.random((30, 10))
+        a = -rng.random(10) * 10
+        b = rng.random(10) * 10
+        scaled = qmc.scale(sample, a, b, reverse=False)
+        unscaled = qmc.scale(scaled, a, b, reverse=True)
+        assert_allclose(unscaled, sample)
+
+    def test_scale_errors(self):
+        with pytest.raises(ValueError, match=r"Sample is not a 2D array"):
+            space = [0, 1, 0.5]
+            qmc.scale(space, l_bounds=-2, u_bounds=6)
+
+        with pytest.raises(ValueError, match=r"Bounds are not consistent"):
+            space = [[0, 0], [1, 1], [0.5, 0.5]]
+            bounds = np.array([[-2, 6], [6, 5]])
+            qmc.scale(space, l_bounds=bounds[0], u_bounds=bounds[1])
+
+        with pytest.raises(ValueError, match=r"'l_bounds' and 'u_bounds'"
+                                             r" must be broadcastable"):
+            space = [[0, 0], [1, 1], [0.5, 0.5]]
+            l_bounds, u_bounds = [-2, 0, 2], [6, 5]
+            qmc.scale(space, l_bounds=l_bounds, u_bounds=u_bounds)
+
+        with pytest.raises(ValueError, match=r"'l_bounds' and 'u_bounds'"
+                                             r" must be broadcastable"):
+            space = [[0, 0], [1, 1], [0.5, 0.5]]
+            bounds = np.array([[-2, 0, 2], [6, 5, 5]])
+            qmc.scale(space, l_bounds=bounds[0], u_bounds=bounds[1])
+
+        with pytest.raises(ValueError, match=r"Sample is not in unit "
+                                             r"hypercube"):
+            space = [[0, 0], [1, 1.5], [0.5, 0.5]]
+            bounds = np.array([[-2, 0], [6, 5]])
+            qmc.scale(space, l_bounds=bounds[0], u_bounds=bounds[1])
+
+        with pytest.raises(ValueError, match=r"Sample is out of bounds"):
+            out = [[-2, 0], [6, 5], [8, 2.5]]
+            bounds = np.array([[-2, 0], [6, 5]])
+            qmc.scale(out, l_bounds=bounds[0], u_bounds=bounds[1],
+                      reverse=True)
+
+    def test_discrepancy(self):
+        space_1 = np.array([[1, 3], [2, 6], [3, 2], [4, 5], [5, 1], [6, 4]])
+        space_1 = (2.0 * space_1 - 1.0) / (2.0 * 6.0)
+        space_2 = np.array([[1, 5], [2, 4], [3, 3], [4, 2], [5, 1], [6, 6]])
+        space_2 = (2.0 * space_2 - 1.0) / (2.0 * 6.0)
+
+        # From Fang et al. Design and modeling for computer experiments, 2006
+        assert_allclose(qmc.discrepancy(space_1), 0.0081, atol=1e-4)
+        assert_allclose(qmc.discrepancy(space_2), 0.0105, atol=1e-4)
+
+        # From Zhou Y.-D. et al. Mixture discrepancy for quasi-random point
+        # sets. Journal of Complexity, 29 (3-4), pp. 283-301, 2013.
+        # Example 4 on Page 298
+        sample = np.array([[2, 1, 1, 2, 2, 2],
+                           [1, 2, 2, 2, 2, 2],
+                           [2, 1, 1, 1, 1, 1],
+                           [1, 1, 1, 1, 2, 2],
+                           [1, 2, 2, 2, 1, 1],
+                           [2, 2, 2, 2, 1, 1],
+                           [2, 2, 2, 1, 2, 2]])
+        sample = (2.0 * sample - 1.0) / (2.0 * 2.0)
+
+        assert_allclose(qmc.discrepancy(sample, method='MD'), 2.5000,
+                        atol=1e-4)
+        assert_allclose(qmc.discrepancy(sample, method='WD'), 1.3680,
+                        atol=1e-4)
+        assert_allclose(qmc.discrepancy(sample, method='CD'), 0.3172,
+                        atol=1e-4)
+
+        # From Tim P. et al. Minimizing the L2 and Linf star discrepancies
+        # of a single point in the unit hypercube. JCAM, 2005
+        # Table 1 on Page 283
+        for dim in [2, 4, 8, 16, 32, 64]:
+            ref = np.sqrt(3**(-dim))
+            assert_allclose(qmc.discrepancy(np.array([[1]*dim]),
+                                            method='L2-star'), ref)
+
+    def test_discrepancy_errors(self):
+        sample = np.array([[1, 3], [2, 6], [3, 2], [4, 5], [5, 1], [6, 4]])
+
+        with pytest.raises(
+            ValueError, match=r"Sample is not in unit hypercube"
+        ):
+            qmc.discrepancy(sample)
+
+        with pytest.raises(ValueError, match=r"Sample is not a 2D array"):
+            qmc.discrepancy([1, 3])
+
+        sample = [[0, 0], [1, 1], [0.5, 0.5]]
+        with pytest.raises(ValueError, match=r"'toto' is not a valid ..."):
+            qmc.discrepancy(sample, method="toto")
+
+    def test_discrepancy_parallel(self, monkeypatch):
+        sample = np.array([[2, 1, 1, 2, 2, 2],
+                           [1, 2, 2, 2, 2, 2],
+                           [2, 1, 1, 1, 1, 1],
+                           [1, 1, 1, 1, 2, 2],
+                           [1, 2, 2, 2, 1, 1],
+                           [2, 2, 2, 2, 1, 1],
+                           [2, 2, 2, 1, 2, 2]])
+        sample = (2.0 * sample - 1.0) / (2.0 * 2.0)
+
+        assert_allclose(qmc.discrepancy(sample, method='MD', workers=8),
+                        2.5000,
+                        atol=1e-4)
+        assert_allclose(qmc.discrepancy(sample, method='WD', workers=8),
+                        1.3680,
+                        atol=1e-4)
+        assert_allclose(qmc.discrepancy(sample, method='CD', workers=8),
+                        0.3172,
+                        atol=1e-4)
+
+        # From Tim P. et al. Minimizing the L2 and Linf star discrepancies
+        # of a single point in the unit hypercube. JCAM, 2005
+        # Table 1 on Page 283
+        for dim in [2, 4, 8, 16, 32, 64]:
+            ref = np.sqrt(3 ** (-dim))
+            assert_allclose(qmc.discrepancy(np.array([[1] * dim]),
+                                            method='L2-star', workers=-1), ref)
+
+        monkeypatch.setattr(os, 'cpu_count', lambda: None)
+        with pytest.raises(NotImplementedError, match="Cannot determine the"):
+            qmc.discrepancy(sample, workers=-1)
+
+        with pytest.raises(ValueError, match="Invalid number of workers..."):
+            qmc.discrepancy(sample, workers=-2)
+
+    def test_geometric_discrepancy_errors(self):
+        sample = np.array([[1, 3], [2, 6], [3, 2], [4, 5], [5, 1], [6, 4]])
+
+        with pytest.raises(ValueError, match=r"Sample is not in unit hypercube"):
+            qmc.geometric_discrepancy(sample)
+
+        with pytest.raises(ValueError, match=r"Sample is not a 2D array"):
+            qmc.geometric_discrepancy([1, 3])
+
+        sample = [[0, 0], [1, 1], [0.5, 0.5]]
+        with pytest.raises(ValueError, match=r"'toto' is not a valid ..."):
+            qmc.geometric_discrepancy(sample, method="toto")
+
+        sample = np.array([[0, 0], [0, 0], [0, 1]])
+        with pytest.warns(UserWarning, match="Sample contains duplicate points."):
+            qmc.geometric_discrepancy(sample)
+
+        sample = np.array([[0.5, 0.5]])
+        with pytest.raises(ValueError, match="Sample must contain at least two points"):
+            qmc.geometric_discrepancy(sample)
+
+    def test_geometric_discrepancy(self):
+        sample = np.array([[0, 0], [1, 1]])
+        assert_allclose(qmc.geometric_discrepancy(sample), np.sqrt(2))
+        assert_allclose(qmc.geometric_discrepancy(sample, method="mst"), np.sqrt(2))
+
+        sample = np.array([[0, 0], [0, 1], [0.5, 1]])
+        assert_allclose(qmc.geometric_discrepancy(sample), 0.5)
+        assert_allclose(qmc.geometric_discrepancy(sample, method="mst"), 0.75)
+
+        sample = np.array([[0, 0], [0.25, 0.25], [1, 1]])
+        assert_allclose(qmc.geometric_discrepancy(sample), np.sqrt(2) / 4)
+        assert_allclose(qmc.geometric_discrepancy(sample, method="mst"), np.sqrt(2) / 2)
+        assert_allclose(qmc.geometric_discrepancy(sample, metric="chebyshev"), 0.25)
+        assert_allclose(
+            qmc.geometric_discrepancy(sample, method="mst", metric="chebyshev"), 0.5
+        )
+
+        rng = np.random.default_rng(191468432622931918890291693003068437394)
+        sample = qmc.LatinHypercube(d=3, rng=rng).random(50)
+        assert_allclose(qmc.geometric_discrepancy(sample), 0.05106012076093356)
+        assert_allclose(
+            qmc.geometric_discrepancy(sample, method='mst'), 0.19704396643366182
+        )
+
+    @pytest.mark.xfail(
+            reason="minimum_spanning_tree ignores zero distances (#18892)",
+            strict=True,
+    )
+    @pytest.mark.thread_unsafe
+    def test_geometric_discrepancy_mst_with_zero_distances(self):
+        sample = np.array([[0, 0], [0, 0], [0, 1]])
+        assert_allclose(qmc.geometric_discrepancy(sample, method='mst'), 0.5)
+
+    def test_update_discrepancy(self):
+        # From Fang et al. Design and modeling for computer experiments, 2006
+        space_1 = np.array([[1, 3], [2, 6], [3, 2], [4, 5], [5, 1], [6, 4]])
+        space_1 = (2.0 * space_1 - 1.0) / (2.0 * 6.0)
+
+        disc_init = qmc.discrepancy(space_1[:-1], iterative=True)
+        disc_iter = update_discrepancy(space_1[-1], space_1[:-1], disc_init)
+
+        assert_allclose(disc_iter, 0.0081, atol=1e-4)
+
+        # n<d
+        rng = np.random.default_rng(241557431858162136881731220526394276199)
+        space_1 = rng.random((4, 10))
+
+        disc_ref = qmc.discrepancy(space_1)
+        disc_init = qmc.discrepancy(space_1[:-1], iterative=True)
+        disc_iter = update_discrepancy(space_1[-1], space_1[:-1], disc_init)
+
+        assert_allclose(disc_iter, disc_ref, atol=1e-4)
+
+        # errors
+        with pytest.raises(ValueError, match=r"Sample is not in unit "
+                                             r"hypercube"):
+            update_discrepancy(space_1[-1], space_1[:-1] + 1, disc_init)
+
+        with pytest.raises(ValueError, match=r"Sample is not a 2D array"):
+            update_discrepancy(space_1[-1], space_1[0], disc_init)
+
+        x_new = [1, 3]
+        with pytest.raises(ValueError, match=r"x_new is not in unit "
+                                             r"hypercube"):
+            update_discrepancy(x_new, space_1[:-1], disc_init)
+
+        x_new = [[0.5, 0.5]]
+        with pytest.raises(ValueError, match=r"x_new is not a 1D array"):
+            update_discrepancy(x_new, space_1[:-1], disc_init)
+
+        x_new = [0.3, 0.1, 0]
+        with pytest.raises(ValueError, match=r"x_new and sample must be "
+                                             r"broadcastable"):
+            update_discrepancy(x_new, space_1[:-1], disc_init)
+
+    def test_perm_discrepancy(self):
+        rng = np.random.default_rng(46449423132557934943847369749645759997)
+        qmc_gen = qmc.LatinHypercube(5, rng=rng)
+        sample = qmc_gen.random(10)
+        disc = qmc.discrepancy(sample)
+
+        for i in range(100):
+            row_1 = rng.integers(10)
+            row_2 = rng.integers(10)
+            col = rng.integers(5)
+
+            disc = _perturb_discrepancy(sample, row_1, row_2, col, disc)
+            sample[row_1, col], sample[row_2, col] = (
+                sample[row_2, col], sample[row_1, col])
+            disc_reference = qmc.discrepancy(sample)
+            assert_allclose(disc, disc_reference)
+
+    def test_discrepancy_alternative_implementation(self):
+        """Alternative definitions from Matt Haberland."""
+
+        def disc_c2(x):
+            n, s = x.shape
+            xij = x
+            disc1 = np.sum(np.prod((1
+                                    + 1/2*np.abs(xij-0.5)
+                                    - 1/2*np.abs(xij-0.5)**2), axis=1))
+            xij = x[None, :, :]
+            xkj = x[:, None, :]
+            disc2 = np.sum(np.sum(np.prod(1
+                                          + 1/2*np.abs(xij - 0.5)
+                                          + 1/2*np.abs(xkj - 0.5)
+                                          - 1/2*np.abs(xij - xkj), axis=2),
+                                  axis=0))
+            return (13/12)**s - 2/n * disc1 + 1/n**2*disc2
+
+        def disc_wd(x):
+            n, s = x.shape
+            xij = x[None, :, :]
+            xkj = x[:, None, :]
+            disc = np.sum(np.sum(np.prod(3/2
+                                         - np.abs(xij - xkj)
+                                         + np.abs(xij - xkj)**2, axis=2),
+                                 axis=0))
+            return -(4/3)**s + 1/n**2 * disc
+
+        def disc_md(x):
+            n, s = x.shape
+            xij = x
+            disc1 = np.sum(np.prod((5/3
+                                    - 1/4*np.abs(xij-0.5)
+                                    - 1/4*np.abs(xij-0.5)**2), axis=1))
+            xij = x[None, :, :]
+            xkj = x[:, None, :]
+            disc2 = np.sum(np.sum(np.prod(15/8
+                                          - 1/4*np.abs(xij - 0.5)
+                                          - 1/4*np.abs(xkj - 0.5)
+                                          - 3/4*np.abs(xij - xkj)
+                                          + 1/2*np.abs(xij - xkj)**2,
+                                          axis=2), axis=0))
+            return (19/12)**s - 2/n * disc1 + 1/n**2*disc2
+
+        def disc_star_l2(x):
+            n, s = x.shape
+            return np.sqrt(
+                3 ** (-s) - 2 ** (1 - s) / n
+                * np.sum(np.prod(1 - x ** 2, axis=1))
+                + np.sum([
+                    np.prod(1 - np.maximum(x[k, :], x[j, :]))
+                    for k in range(n) for j in range(n)
+                ]) / n ** 2
+            )
+
+        rng = np.random.default_rng(117065081482921065782761407107747179201)
+        sample = rng.random((30, 10))
+
+        disc_curr = qmc.discrepancy(sample, method='CD')
+        disc_alt = disc_c2(sample)
+        assert_allclose(disc_curr, disc_alt)
+
+        disc_curr = qmc.discrepancy(sample, method='WD')
+        disc_alt = disc_wd(sample)
+        assert_allclose(disc_curr, disc_alt)
+
+        disc_curr = qmc.discrepancy(sample, method='MD')
+        disc_alt = disc_md(sample)
+        assert_allclose(disc_curr, disc_alt)
+
+        disc_curr = qmc.discrepancy(sample, method='L2-star')
+        disc_alt = disc_star_l2(sample)
+        assert_allclose(disc_curr, disc_alt)
+
+    def test_n_primes(self):
+        primes = n_primes(10)
+        assert primes[-1] == 29
+
+        primes = n_primes(168)
+        assert primes[-1] == 997
+
+        primes = n_primes(350)
+        assert primes[-1] == 2357
+
+    def test_primes(self):
+        primes = primes_from_2_to(50)
+        out = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]
+        assert_allclose(primes, out)
+
+
+class TestVDC:
+    def test_van_der_corput(self):
+        sample = van_der_corput(10)
+        out = [0.0, 0.5, 0.25, 0.75, 0.125, 0.625,
+               0.375, 0.875, 0.0625, 0.5625]
+        assert_allclose(sample, out)
+
+        sample = van_der_corput(10, workers=4)
+        assert_allclose(sample, out)
+
+        sample = van_der_corput(10, workers=8)
+        assert_allclose(sample, out)
+
+        sample = van_der_corput(7, start_index=3)
+        assert_allclose(sample, out[3:])
+
+    def test_van_der_corput_scramble(self):
+        rng = 338213789010180879520345496831675783177
+        out = van_der_corput(10, scramble=True, rng=rng)
+
+        sample = van_der_corput(7, start_index=3, scramble=True, rng=rng)
+        assert_allclose(sample, out[3:])
+
+        sample = van_der_corput(
+            7, start_index=3, scramble=True, rng=rng, workers=4
+        )
+        assert_allclose(sample, out[3:])
+
+        sample = van_der_corput(
+            7, start_index=3, scramble=True, rng=rng, workers=8
+        )
+        assert_allclose(sample, out[3:])
+
+    def test_invalid_base_error(self):
+        with pytest.raises(ValueError, match=r"'base' must be at least 2"):
+            van_der_corput(10, base=1)
+
+
+class RandomEngine(qmc.QMCEngine):
+    def __init__(self, d, optimization=None, rng=None):
+        super().__init__(d=d, optimization=optimization, rng=rng)
+
+    def _random(self, n=1, *, workers=1):
+        sample = self.rng.random((n, self.d))
+        return sample
+
+
+def test_subclassing_QMCEngine():
+    engine = RandomEngine(2, rng=175180605424926556207367152557812293274)
+
+    sample_1 = engine.random(n=5)
+    sample_2 = engine.random(n=7)
+    assert engine.num_generated == 12
+
+    # reset and re-sample
+    engine.reset()
+    assert engine.num_generated == 0
+
+    sample_1_test = engine.random(n=5)
+    assert_equal(sample_1, sample_1_test)
+
+    # repeat reset and fast forward
+    engine.reset()
+    engine.fast_forward(n=5)
+    sample_2_test = engine.random(n=7)
+
+    assert_equal(sample_2, sample_2_test)
+    assert engine.num_generated == 12
+
+
+def test_raises():
+    # input validation
+    with pytest.raises(ValueError, match=r"d must be a non-negative integer"):
+        RandomEngine((2,))
+
+    with pytest.raises(ValueError, match=r"d must be a non-negative integer"):
+        RandomEngine(-1)
+
+    msg = r"'u_bounds' and 'l_bounds' must be integers"
+    with pytest.raises(ValueError, match=msg):
+        engine = RandomEngine(1)
+        engine.integers(l_bounds=1, u_bounds=1.1)
+
+
+def test_integers():
+    engine = RandomEngine(1, rng=231195739755290648063853336582377368684)
+
+    # basic tests
+    sample = engine.integers(1, n=10)
+    assert_equal(np.unique(sample), [0])
+
+    assert sample.dtype == np.dtype('int64')
+
+    sample = engine.integers(1, n=10, endpoint=True)
+    assert_equal(np.unique(sample), [0, 1])
+
+    low = -5
+    high = 7
+
+    # scaling logic
+    engine.reset()
+    ref_sample = engine.random(20)
+    ref_sample = ref_sample * (high - low) + low
+    ref_sample = np.floor(ref_sample).astype(np.int64)
+
+    engine.reset()
+    sample = engine.integers(low, u_bounds=high, n=20, endpoint=False)
+
+    assert_equal(sample, ref_sample)
+
+    # up to bounds, no less, no more
+    sample = engine.integers(low, u_bounds=high, n=100, endpoint=False)
+    assert_equal((sample.min(), sample.max()), (low, high-1))
+
+    sample = engine.integers(low, u_bounds=high, n=100, endpoint=True)
+    assert_equal((sample.min(), sample.max()), (low, high))
+
+
+def test_integers_nd():
+    d = 10
+    rng = np.random.default_rng(3716505122102428560615700415287450951)
+    low = rng.integers(low=-5, high=-1, size=d)
+    high = rng.integers(low=1, high=5, size=d, endpoint=True)
+    engine = RandomEngine(d, rng=rng)
+
+    sample = engine.integers(low, u_bounds=high, n=100, endpoint=False)
+    assert_equal(sample.min(axis=0), low)
+    assert_equal(sample.max(axis=0), high-1)
+
+    sample = engine.integers(low, u_bounds=high, n=100, endpoint=True)
+    assert_equal(sample.min(axis=0), low)
+    assert_equal(sample.max(axis=0), high)
+
+
+class QMCEngineTests:
+    """Generic tests for QMC engines."""
+    qmce = NotImplemented
+    can_scramble = NotImplemented
+    unscramble_nd = NotImplemented
+    scramble_nd = NotImplemented
+
+    scramble = [True, False]
+    ids = ["Scrambled", "Unscrambled"]
+
+    def engine(
+        self, scramble: bool,
+        rng=170382760648021597650530316304495310428,
+        **kwargs
+    ) -> QMCEngine:
+        # preserve use of `seed` during SPEC 7 transition because
+        # some tests rely on behavior with integer `seed` (which is
+        # different from behavior with integer `rng`)
+        if self.can_scramble:
+            return self.qmce(scramble=scramble, seed=rng, **kwargs)
+        else:
+            if scramble:
+                pytest.skip()
+            else:
+                return self.qmce(seed=rng, **kwargs)
+
+    def reference(self, scramble: bool) -> np.ndarray:
+        return self.scramble_nd if scramble else self.unscramble_nd
+
+    @pytest.mark.parametrize("scramble", scramble, ids=ids)
+    def test_0dim(self, scramble):
+        engine = self.engine(d=0, scramble=scramble)
+        sample = engine.random(4)
+        assert_array_equal(np.empty((4, 0)), sample)
+
+    @pytest.mark.parametrize("scramble", scramble, ids=ids)
+    def test_0sample(self, scramble):
+        engine = self.engine(d=2, scramble=scramble)
+        sample = engine.random(0)
+        assert_array_equal(np.empty((0, 2)), sample)
+
+    @pytest.mark.parametrize("scramble", scramble, ids=ids)
+    def test_1sample(self, scramble):
+        engine = self.engine(d=2, scramble=scramble)
+        sample = engine.random(1)
+        assert (1, 2) == sample.shape
+
+    @pytest.mark.parametrize("scramble", scramble, ids=ids)
+    def test_bounds(self, scramble):
+        engine = self.engine(d=100, scramble=scramble)
+        sample = engine.random(512)
+        assert np.all(sample >= 0)
+        assert np.all(sample <= 1)
+
+    @pytest.mark.parametrize("scramble", scramble, ids=ids)
+    def test_sample(self, scramble):
+        ref_sample = self.reference(scramble=scramble)
+        engine = self.engine(d=2, scramble=scramble)
+        sample = engine.random(n=len(ref_sample))
+
+        assert_allclose(sample, ref_sample, atol=1e-1)
+        assert engine.num_generated == len(ref_sample)
+
+    @pytest.mark.parametrize("scramble", scramble, ids=ids)
+    def test_continuing(self, scramble):
+        engine = self.engine(d=2, scramble=scramble)
+        ref_sample = engine.random(n=8)
+
+        engine = self.engine(d=2, scramble=scramble)
+
+        n_half = len(ref_sample) // 2
+
+        _ = engine.random(n=n_half)
+        sample = engine.random(n=n_half)
+        assert_allclose(sample, ref_sample[n_half:], atol=1e-1)
+
+    @pytest.mark.parametrize("scramble", scramble, ids=ids)
+    @pytest.mark.parametrize(
+        "rng",
+        (
+            170382760648021597650530316304495310428,
+            lambda: np.random.default_rng(170382760648021597650530316304495310428),
+            pytest.param(None, marks=pytest.mark.thread_unsafe),
+        ),
+    )
+    def test_reset(self, scramble, rng):
+        if callable(rng):
+            # Initialize local RNG here to make it thread-local in pytest-run-parallel
+            rng = rng()
+        engine = self.engine(d=2, scramble=scramble, rng=rng)
+        ref_sample = engine.random(n=8)
+
+        engine.reset()
+        assert engine.num_generated == 0
+
+        sample = engine.random(n=8)
+        assert_allclose(sample, ref_sample)
+
+    @pytest.mark.parametrize("scramble", scramble, ids=ids)
+    def test_fast_forward(self, scramble):
+        engine = self.engine(d=2, scramble=scramble)
+        ref_sample = engine.random(n=8)
+
+        engine = self.engine(d=2, scramble=scramble)
+
+        engine.fast_forward(4)
+        sample = engine.random(n=4)
+
+        assert_allclose(sample, ref_sample[4:], atol=1e-1)
+
+        # alternate fast forwarding with sampling
+        engine.reset()
+        even_draws = []
+        for i in range(8):
+            if i % 2 == 0:
+                even_draws.append(engine.random())
+            else:
+                engine.fast_forward(1)
+        assert_allclose(
+            ref_sample[[i for i in range(8) if i % 2 == 0]],
+            np.concatenate(even_draws),
+            atol=1e-5
+        )
+
+    @pytest.mark.parametrize("scramble", [True])
+    def test_distribution(self, scramble):
+        d = 50
+        engine = self.engine(d=d, scramble=scramble)
+        sample = engine.random(1024)
+        assert_allclose(
+            np.mean(sample, axis=0), np.repeat(0.5, d), atol=1e-2
+        )
+        assert_allclose(
+            np.percentile(sample, 25, axis=0), np.repeat(0.25, d), atol=1e-2
+        )
+        assert_allclose(
+            np.percentile(sample, 75, axis=0), np.repeat(0.75, d), atol=1e-2
+        )
+
+    def test_raises_optimizer(self):
+        message = r"'toto' is not a valid optimization method"
+        with pytest.raises(ValueError, match=message):
+            self.engine(d=1, scramble=False, optimization="toto")
+
+    @pytest.mark.parametrize(
+        "optimization,metric",
+        [
+            ("random-CD", qmc.discrepancy),
+            ("lloyd", lambda sample: -_l1_norm(sample))]
+    )
+    def test_optimizers(self, optimization, metric):
+        engine = self.engine(d=2, scramble=False)
+        sample_ref = engine.random(n=64)
+        metric_ref = metric(sample_ref)
+
+        optimal_ = self.engine(d=2, scramble=False, optimization=optimization)
+        sample_ = optimal_.random(n=64)
+        metric_ = metric(sample_)
+
+        assert metric_ < metric_ref
+
+    def test_consume_prng_state(self):
+        rng = np.random.default_rng(0xa29cabb11cfdf44ff6cac8bec254c2a0)
+        sample = []
+        for i in range(3):
+            engine = self.engine(d=2, scramble=True, rng=rng)
+            sample.append(engine.random(4))
+
+        with pytest.raises(AssertionError, match="Arrays are not equal"):
+            assert_equal(sample[0], sample[1])
+        with pytest.raises(AssertionError, match="Arrays are not equal"):
+            assert_equal(sample[0], sample[2])
+
+
+class TestHalton(QMCEngineTests):
+    qmce = qmc.Halton
+    can_scramble = True
+    # theoretical values known from Van der Corput
+    unscramble_nd = np.array([[0, 0], [1 / 2, 1 / 3],
+                              [1 / 4, 2 / 3], [3 / 4, 1 / 9],
+                              [1 / 8, 4 / 9], [5 / 8, 7 / 9],
+                              [3 / 8, 2 / 9], [7 / 8, 5 / 9]])
+    # theoretical values unknown: convergence properties checked
+    scramble_nd = np.array([[0.50246036, 0.93382481],
+                            [0.00246036, 0.26715815],
+                            [0.75246036, 0.60049148],
+                            [0.25246036, 0.8227137 ],
+                            [0.62746036, 0.15604704],
+                            [0.12746036, 0.48938037],
+                            [0.87746036, 0.71160259],
+                            [0.37746036, 0.04493592]])
+
+    def test_workers(self):
+        ref_sample = self.reference(scramble=True)
+        engine = self.engine(d=2, scramble=True)
+        sample = engine.random(n=len(ref_sample), workers=8)
+
+        assert_allclose(sample, ref_sample, atol=1e-3)
+
+        # worker + integers
+        engine.reset()
+        ref_sample = engine.integers(10)
+        engine.reset()
+        sample = engine.integers(10, workers=8)
+        assert_equal(sample, ref_sample)
+
+
+class TestLHS(QMCEngineTests):
+    qmce = qmc.LatinHypercube
+    can_scramble = True
+
+    def test_continuing(self, *args):
+        pytest.skip("Not applicable: not a sequence.")
+
+    def test_fast_forward(self, *args):
+        pytest.skip("Not applicable: not a sequence.")
+
+    def test_sample(self, *args):
+        pytest.skip("Not applicable: the value of reference sample is"
+                    " implementation dependent.")
+
+    @pytest.mark.parametrize("strength", [1, 2])
+    @pytest.mark.parametrize("scramble", [False, True])
+    @pytest.mark.parametrize("optimization", [None, "random-CD"])
+    def test_sample_stratified(self, optimization, scramble, strength):
+        rng = np.random.default_rng(37511836202578819870665127532742111260)
+        p = 5
+        n = p**2
+        d = 6
+
+        engine = qmc.LatinHypercube(d=d, scramble=scramble,
+                                    strength=strength,
+                                    optimization=optimization,
+                                    rng=rng)
+        sample = engine.random(n=n)
+        assert sample.shape == (n, d)
+        assert engine.num_generated == n
+
+        # centering stratifies samples in the middle of equal segments:
+        # * inter-sample distance is constant in 1D sub-projections
+        # * after ordering, columns are equal
+        expected1d = (np.arange(n) + 0.5) / n
+        expected = np.broadcast_to(expected1d, (d, n)).T
+        assert np.any(sample != expected)
+
+        sorted_sample = np.sort(sample, axis=0)
+        tol = 0.5 / n if scramble else 0
+
+        assert_allclose(sorted_sample, expected, atol=tol)
+        assert np.any(sample - expected > tol)
+
+        if strength == 2 and optimization is None:
+            unique_elements = np.arange(p)
+            desired = set(product(unique_elements, unique_elements))
+
+            for i, j in combinations(range(engine.d), 2):
+                samples_2d = sample[:, [i, j]]
+                res = (samples_2d * p).astype(int)
+                res_set = {tuple(row) for row in res}
+                assert_equal(res_set, desired)
+
+    def test_optimizer_1d(self):
+        # discrepancy measures are invariant under permuting factors and runs
+        engine = self.engine(d=1, scramble=False)
+        sample_ref = engine.random(n=64)
+
+        optimal_ = self.engine(d=1, scramble=False, optimization="random-CD")
+        sample_ = optimal_.random(n=64)
+
+        assert_array_equal(sample_ref, sample_)
+
+    def test_raises(self):
+        message = r"not a valid strength"
+        with pytest.raises(ValueError, match=message):
+            qmc.LatinHypercube(1, strength=3)
+
+        message = r"n is not the square of a prime number"
+        with pytest.raises(ValueError, match=message):
+            engine = qmc.LatinHypercube(d=2, strength=2)
+            engine.random(16)
+
+        message = r"n is not the square of a prime number"
+        with pytest.raises(ValueError, match=message):
+            engine = qmc.LatinHypercube(d=2, strength=2)
+            engine.random(5)  # because int(sqrt(5)) would result in 2
+
+        message = r"n is too small for d"
+        with pytest.raises(ValueError, match=message):
+            engine = qmc.LatinHypercube(d=5, strength=2)
+            engine.random(9)
+
+
+class TestSobol(QMCEngineTests):
+    qmce = qmc.Sobol
+    can_scramble = True
+    # theoretical values from Joe Kuo2010
+    unscramble_nd = np.array([[0., 0.],
+                              [0.5, 0.5],
+                              [0.75, 0.25],
+                              [0.25, 0.75],
+                              [0.375, 0.375],
+                              [0.875, 0.875],
+                              [0.625, 0.125],
+                              [0.125, 0.625]])
+
+    # theoretical values unknown: convergence properties checked
+    scramble_nd = np.array([[0.25331921, 0.41371179],
+                            [0.8654213, 0.9821167],
+                            [0.70097554, 0.03664616],
+                            [0.18027647, 0.60895735],
+                            [0.10521339, 0.21897069],
+                            [0.53019685, 0.66619033],
+                            [0.91122276, 0.34580743],
+                            [0.45337471, 0.78912079]])
+
+    def test_warning(self):
+        with pytest.warns(UserWarning, match=r"The balance properties of "
+                                             r"Sobol' points"):
+            engine = qmc.Sobol(1)
+            engine.random(10)
+
+    def test_random_base2(self):
+        engine = qmc.Sobol(2, scramble=False)
+        sample = engine.random_base2(2)
+        assert_array_equal(self.unscramble_nd[:4], sample)
+
+        # resampling still having N=2**n
+        sample = engine.random_base2(2)
+        assert_array_equal(self.unscramble_nd[4:8], sample)
+
+        # resampling again but leading to N!=2**n
+        with pytest.raises(ValueError, match=r"The balance properties of "
+                                             r"Sobol' points"):
+            engine.random_base2(2)
+
+    def test_raise(self):
+        with pytest.raises(ValueError, match=r"Maximum supported "
+                                             r"dimensionality"):
+            qmc.Sobol(qmc.Sobol.MAXDIM + 1)
+
+        with pytest.raises(ValueError, match=r"Maximum supported "
+                                             r"'bits' is 64"):
+            qmc.Sobol(1, bits=65)
+
+    def test_high_dim(self):
+        engine = qmc.Sobol(1111, scramble=False)
+        count1 = Counter(engine.random().flatten().tolist())
+        count2 = Counter(engine.random().flatten().tolist())
+        assert_equal(count1, Counter({0.0: 1111}))
+        assert_equal(count2, Counter({0.5: 1111}))
+
+    @pytest.mark.parametrize("bits", [2, 3])
+    def test_bits(self, bits):
+        engine = qmc.Sobol(2, scramble=False, bits=bits)
+        ns = 2**bits
+        sample = engine.random(ns)
+        assert_array_equal(self.unscramble_nd[:ns], sample)
+
+        with pytest.raises(ValueError, match="increasing `bits`"):
+            engine.random()
+
+    def test_64bits(self):
+        engine = qmc.Sobol(2, scramble=False, bits=64)
+        sample = engine.random(8)
+        assert_array_equal(self.unscramble_nd, sample)
+
+
+class TestLow0Bit:
+    def test_examples(self):
+        test_vector = [
+            # from low_0_bit's docstring
+            (0b0000, 1),
+            (0b0001, 2),
+            (0b0010, 1),
+            (0b0101, 2),
+            (0b0111, 4),
+            # gh-23409
+            (2 ** 32 - 1, 33),
+            (2 ** 32, 1),
+            (2 ** 33 - 1, 34),
+            (2 ** 64 - 1, 65),
+        ]
+        for in_, out in test_vector:
+            assert_equal(_test_low_0_bit(in_), out)
+
+
+class TestPoisson(QMCEngineTests):
+    qmce = qmc.PoissonDisk
+    can_scramble = False
+
+    def test_bounds(self, *args):
+        pytest.skip("Too costly in memory.")
+
+    def test_fast_forward(self, *args):
+        pytest.skip("Not applicable: recursive process.")
+
+    def test_sample(self, *args):
+        pytest.skip("Not applicable: the value of reference sample is"
+                    " implementation dependent.")
+
+    def test_continuing(self, *args):
+        # can continue a sampling, but will not preserve the same order
+        # because candidates are lost, so we will not select the same center
+        radius = 0.05
+        ns = 6
+        engine = self.engine(d=2, radius=radius, scramble=False)
+
+        sample_init = engine.random(n=ns)
+        assert len(sample_init) <= ns
+        assert l2_norm(sample_init) >= radius
+
+        sample_continued = engine.random(n=ns)
+        assert len(sample_continued) <= ns
+        assert l2_norm(sample_continued) >= radius
+
+        sample = np.concatenate([sample_init, sample_continued], axis=0)
+        assert len(sample) <= ns * 2
+        assert l2_norm(sample) >= radius
+
+    def test_mindist(self):
+        rng = np.random.default_rng(132074951149370773672162394161442690287)
+        ns = 50
+
+        low, high = 0.08, 0.2
+        radii = (high - low) * rng.random(5) + low
+
+        dimensions = [1, 3, 4]
+        hypersphere_methods = ["volume", "surface"]
+
+        gen = product(dimensions, radii, hypersphere_methods)
+
+        for d, radius, hypersphere in gen:
+            engine = self.qmce(
+                d=d, radius=radius, hypersphere=hypersphere, rng=rng
+            )
+            sample = engine.random(ns)
+
+            assert len(sample) <= ns
+            assert l2_norm(sample) >= radius
+
+    @pytest.mark.parametrize("l_bounds, u_bounds",  # add bounds to test gh-22819
+        [(None, None), ([-5, -5], [5, 5]),  ([1.1, 1.1], [5.1, 5.1])])
+    def test_fill_space(self, l_bounds, u_bounds):
+        radius = 0.2
+        engine = self.qmce(d=2, radius=radius, l_bounds=l_bounds, u_bounds=u_bounds)
+
+        sample = engine.fill_space()
+        # rtol to accommodate Accelerate; see gh-24486
+        rtol = 1e-6
+        assert l2_norm(sample) >= radius * (1 - rtol)
+
+    def test_bounds_shift_scale(self):
+        # test a reasonable property: as long as shape of region is a hypercube,
+        # bounds just shift and scale the sample.
+        radius = 0.2  # arbitrary parameters
+        shift = np.asarray([-2.1, 3.4])
+        scale = 2.6
+
+        rng = np.random.default_rng(8504196751)
+        engine = self.qmce(d=2, radius=radius*scale, rng=rng,
+                           l_bounds=shift, u_bounds=shift + scale)
+        res = engine.fill_space()
+
+        rng = np.random.default_rng(8504196751)
+        engine = self.qmce(d=2, radius=radius, rng=rng)
+        ref = engine.fill_space()
+
+        # circle packing problem is np complex
+        assert l2_norm(res) >= radius
+        assert_allclose(res, ref*scale + shift)
+
+    @pytest.mark.parametrize("l_bounds", [[-1, -2, -1], [1, 2, 1]])
+    def test_sample_inside_lower_bounds(self, l_bounds):
+        radius = 0.2
+        u_bounds=[3, 3, 2]
+        engine = self.qmce(
+            d=3, radius=radius, l_bounds=l_bounds, u_bounds=u_bounds
+        )
+        sample = engine.random(30)
+
+        for point in sample:
+            assert_array_less(point, u_bounds)
+            assert_array_less(l_bounds, point)
+
+    @pytest.mark.parametrize("u_bounds", [[-1, -2, -1], [1, 2, 1]])
+    def test_sample_inside_upper_bounds(self, u_bounds):
+        radius = 0.2
+        l_bounds=[-3, -3, -2]
+        engine = self.qmce(
+            d=3, radius=radius, l_bounds=l_bounds, u_bounds=u_bounds
+        )
+        sample = engine.random(30)
+
+        for point in sample:
+            assert_array_less(point, u_bounds)
+            assert_array_less(l_bounds, point)
+
+    def test_inconsistent_bound_value(self):
+        radius = 0.2
+        l_bounds=[3, 2, 1]
+        u_bounds=[-1, -2, -1]
+        with pytest.raises(
+            ValueError,
+            match="Bounds are not consistent 'l_bounds' < 'u_bounds'"):
+            self.qmce(d=3, radius=radius, l_bounds=l_bounds, u_bounds=u_bounds)
+
+    @pytest.mark.parametrize("u_bounds", [[-1, -2, -1], [-1, -2]])
+    @pytest.mark.parametrize("l_bounds", [[3, 2]])
+    def test_inconsistent_bounds(self, u_bounds, l_bounds):
+        radius = 0.2
+        with pytest.raises(
+            ValueError,
+            match="'l_bounds' and 'u_bounds' must be broadcastable and respect"
+            " the sample dimension"):
+            self.qmce(
+                d=3, radius=radius,
+                l_bounds=l_bounds, u_bounds=u_bounds
+            )
+
+    def test_raises(self):
+        message = r"'toto' is not a valid hypersphere sampling"
+        with pytest.raises(ValueError, match=message):
+            qmc.PoissonDisk(1, hypersphere="toto")
+
+
+class TestMultinomialQMC:
+    def test_validations(self):
+        # negative Ps
+        p = np.array([0.12, 0.26, -0.05, 0.35, 0.22])
+        with pytest.raises(ValueError, match=r"Elements of pvals must "
+                                             r"be non-negative."):
+            qmc.MultinomialQMC(p, n_trials=10)
+
+        # sum of P too large
+        p = np.array([0.12, 0.26, 0.1, 0.35, 0.22])
+        message = r"Elements of pvals must sum to 1."
+        with pytest.raises(ValueError, match=message):
+            qmc.MultinomialQMC(p, n_trials=10)
+
+        p = np.array([0.12, 0.26, 0.05, 0.35, 0.22])
+
+        message = r"Dimension of `engine` must be 1."
+        with pytest.raises(ValueError, match=message):
+            qmc.MultinomialQMC(p, n_trials=10, engine=qmc.Sobol(d=2))
+
+        message = r"`engine` must be an instance of..."
+        with pytest.raises(ValueError, match=message):
+            qmc.MultinomialQMC(p, n_trials=10, engine=np.random.default_rng())
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_MultinomialBasicDraw(self):
+        rng = np.random.default_rng(6955663962957011631562466584467607969)
+        p = np.array([0.12, 0.26, 0.05, 0.35, 0.22])
+        n_trials = 100
+        expected = np.atleast_2d(n_trials * p).astype(int)
+        # preserve use of legacy keyword during SPEC 7 transition
+        engine = qmc.MultinomialQMC(p, n_trials=n_trials, seed=rng)
+        assert_allclose(engine.random(1), expected, atol=1)
+
+    def test_MultinomialDistribution(self):
+        rng = np.random.default_rng(77797854505813727292048130876699859000)
+        p = np.array([0.12, 0.26, 0.05, 0.35, 0.22])
+        engine = qmc.MultinomialQMC(p, n_trials=8192, rng=rng)
+        draws = engine.random(1)
+        assert_allclose(draws / np.sum(draws), np.atleast_2d(p), atol=1e-4)
+
+    def test_FindIndex(self):
+        p_cumulative = np.array([0.1, 0.4, 0.45, 0.6, 0.75, 0.9, 0.99, 1.0])
+        size = len(p_cumulative)
+        assert_equal(_test_find_index(p_cumulative, size, 0.0), 0)
+        assert_equal(_test_find_index(p_cumulative, size, 0.4), 2)
+        assert_equal(_test_find_index(p_cumulative, size, 0.44999), 2)
+        assert_equal(_test_find_index(p_cumulative, size, 0.45001), 3)
+        assert_equal(_test_find_index(p_cumulative, size, 1.0), size - 1)
+
+    @pytest.mark.filterwarnings('ignore::UserWarning')
+    def test_other_engine(self):
+        # same as test_MultinomialBasicDraw with different engine
+        rng = np.random.default_rng(283753519042773243071753037669078065412)
+        p = np.array([0.12, 0.26, 0.05, 0.35, 0.22])
+        n_trials = 100
+        expected = np.atleast_2d(n_trials * p).astype(int)
+        base_engine = qmc.Sobol(1, scramble=True, rng=rng)
+        engine = qmc.MultinomialQMC(p, n_trials=n_trials, engine=base_engine,
+                                    rng=rng)
+        assert_allclose(engine.random(1), expected, atol=1)
+
+
+class TestNormalQMC:
+    def test_NormalQMC(self):
+        # d = 1
+        engine = qmc.MultivariateNormalQMC(mean=np.zeros(1))
+        samples = engine.random()
+        assert_equal(samples.shape, (1, 1))
+        samples = engine.random(n=5)
+        assert_equal(samples.shape, (5, 1))
+        # d = 2
+        engine = qmc.MultivariateNormalQMC(mean=np.zeros(2))
+        samples = engine.random()
+        assert_equal(samples.shape, (1, 2))
+        samples = engine.random(n=5)
+        assert_equal(samples.shape, (5, 2))
+
+    def test_NormalQMCInvTransform(self):
+        # d = 1
+        engine = qmc.MultivariateNormalQMC(
+            mean=np.zeros(1), inv_transform=True)
+        samples = engine.random()
+        assert_equal(samples.shape, (1, 1))
+        samples = engine.random(n=5)
+        assert_equal(samples.shape, (5, 1))
+        # d = 2
+        engine = qmc.MultivariateNormalQMC(
+            mean=np.zeros(2), inv_transform=True)
+        samples = engine.random()
+        assert_equal(samples.shape, (1, 2))
+        samples = engine.random(n=5)
+        assert_equal(samples.shape, (5, 2))
+
+    def test_NormalQMCSeeded(self):
+        # test even dimension
+        rng = np.random.default_rng(274600237797326520096085022671371676017)
+        # preserve use of legacy keyword during SPEC 7 transition
+        engine = qmc.MultivariateNormalQMC(
+            mean=np.zeros(2), inv_transform=False, seed=rng)
+        samples = engine.random(n=2)
+        samples_expected = np.array([[-0.932001, -0.522923],
+                                     [-1.477655, 0.846851]])
+        assert_allclose(samples, samples_expected, atol=1e-4)
+
+        # test odd dimension
+        rng = np.random.default_rng(274600237797326520096085022671371676017)
+        engine = qmc.MultivariateNormalQMC(
+            mean=np.zeros(3), inv_transform=False, rng=rng)
+        samples = engine.random(n=2)
+        samples_expected = np.array([[-0.932001, -0.522923, 0.036578],
+                                     [-1.778011, 0.912428, -0.065421]])
+        assert_allclose(samples, samples_expected, atol=1e-4)
+
+        # same test with another engine
+        rng = np.random.default_rng(274600237797326520096085022671371676017)
+        base_engine = qmc.Sobol(4, scramble=True, rng=rng)
+        engine = qmc.MultivariateNormalQMC(
+            mean=np.zeros(3), inv_transform=False,
+            engine=base_engine, rng=rng
+        )
+        samples = engine.random(n=2)
+        samples_expected = np.array([[-0.932001, -0.522923, 0.036578],
+                                     [-1.778011, 0.912428, -0.065421]])
+        assert_allclose(samples, samples_expected, atol=1e-4)
+
+    def test_NormalQMCSeededInvTransform(self):
+        # test even dimension
+        rng = np.random.default_rng(288527772707286126646493545351112463929)
+        engine = qmc.MultivariateNormalQMC(
+            mean=np.zeros(2), rng=rng, inv_transform=True)
+        samples = engine.random(n=2)
+        samples_expected = np.array([[-0.913237, -0.964026],
+                                     [0.255904, 0.003068]])
+        assert_allclose(samples, samples_expected, atol=1e-4)
+
+        # test odd dimension
+        rng = np.random.default_rng(288527772707286126646493545351112463929)
+        engine = qmc.MultivariateNormalQMC(
+            mean=np.zeros(3), rng=rng, inv_transform=True)
+        samples = engine.random(n=2)
+        samples_expected = np.array([[-0.913237, -0.964026, 0.355501],
+                                     [0.699261, 2.90213 , -0.6418]])
+        assert_allclose(samples, samples_expected, atol=1e-4)
+
+    def test_other_engine(self):
+        for d in (0, 1, 2):
+            base_engine = qmc.Sobol(d=d, scramble=False)
+            engine = qmc.MultivariateNormalQMC(mean=np.zeros(d),
+                                               engine=base_engine,
+                                               inv_transform=True)
+            samples = engine.random()
+            assert_equal(samples.shape, (1, d))
+
+    def test_NormalQMCShapiro(self):
+        rng = np.random.default_rng(13242)
+        engine = qmc.MultivariateNormalQMC(mean=np.zeros(2), rng=rng)
+        samples = engine.random(n=256)
+        assert all(np.abs(samples.mean(axis=0)) < 1e-2)
+        assert all(np.abs(samples.std(axis=0) - 1) < 1e-2)
+        # perform Shapiro-Wilk test for normality
+        for i in (0, 1):
+            _, pval = shapiro(samples[:, i])
+            assert pval > 0.9
+        # make sure samples are uncorrelated
+        cov = np.cov(samples.transpose())
+        assert np.abs(cov[0, 1]) < 1e-2
+
+    def test_NormalQMCShapiroInvTransform(self):
+        rng = np.random.default_rng(32344554)
+        engine = qmc.MultivariateNormalQMC(
+            mean=np.zeros(2), inv_transform=True, rng=rng)
+        samples = engine.random(n=256)
+        assert all(np.abs(samples.mean(axis=0)) < 1e-2)
+        assert all(np.abs(samples.std(axis=0) - 1) < 1e-2)
+        # perform Shapiro-Wilk test for normality
+        for i in (0, 1):
+            _, pval = shapiro(samples[:, i])
+            assert pval > 0.9
+        # make sure samples are uncorrelated
+        cov = np.cov(samples.transpose())
+        assert np.abs(cov[0, 1]) < 1e-2
+
+
+class TestMultivariateNormalQMC:
+
+    def test_validations(self):
+
+        message = r"Dimension of `engine` must be consistent"
+        with pytest.raises(ValueError, match=message):
+            qmc.MultivariateNormalQMC([0], engine=qmc.Sobol(d=2))
+
+        message = r"Dimension of `engine` must be consistent"
+        with pytest.raises(ValueError, match=message):
+            qmc.MultivariateNormalQMC([0, 0, 0], engine=qmc.Sobol(d=4))
+
+        message = r"`engine` must be an instance of..."
+        with pytest.raises(ValueError, match=message):
+            qmc.MultivariateNormalQMC([0, 0], engine=np.random.default_rng())
+
+        message = r"Covariance matrix not PSD."
+        with pytest.raises(ValueError, match=message):
+            qmc.MultivariateNormalQMC([0, 0], [[1, 2], [2, 1]])
+
+        message = r"Covariance matrix is not symmetric."
+        with pytest.raises(ValueError, match=message):
+            qmc.MultivariateNormalQMC([0, 0], [[1, 0], [2, 1]])
+
+        message = r"Dimension mismatch between mean and covariance."
+        with pytest.raises(ValueError, match=message):
+            qmc.MultivariateNormalQMC([0], [[1, 0], [0, 1]])
+
+    def test_MultivariateNormalQMCNonPD(self):
+        # try with non-pd but psd cov; should work
+        engine = qmc.MultivariateNormalQMC(
+            [0, 0, 0], [[1, 0, 1], [0, 1, 1], [1, 1, 2]],
+        )
+        assert engine._corr_matrix is not None
+
+    def test_MultivariateNormalQMC(self):
+        # d = 1 scalar
+        engine = qmc.MultivariateNormalQMC(mean=0, cov=5)
+        samples = engine.random()
+        assert_equal(samples.shape, (1, 1))
+        samples = engine.random(n=5)
+        assert_equal(samples.shape, (5, 1))
+
+        # d = 2 list
+        engine = qmc.MultivariateNormalQMC(mean=[0, 1], cov=[[1, 0], [0, 1]])
+        samples = engine.random()
+        assert_equal(samples.shape, (1, 2))
+        samples = engine.random(n=5)
+        assert_equal(samples.shape, (5, 2))
+
+        # d = 3 np.array
+        mean = np.array([0, 1, 2])
+        cov = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+        engine = qmc.MultivariateNormalQMC(mean, cov)
+        samples = engine.random()
+        assert_equal(samples.shape, (1, 3))
+        samples = engine.random(n=5)
+        assert_equal(samples.shape, (5, 3))
+
+    def test_MultivariateNormalQMCInvTransform(self):
+        # d = 1 scalar
+        engine = qmc.MultivariateNormalQMC(mean=0, cov=5, inv_transform=True)
+        samples = engine.random()
+        assert_equal(samples.shape, (1, 1))
+        samples = engine.random(n=5)
+        assert_equal(samples.shape, (5, 1))
+
+        # d = 2 list
+        engine = qmc.MultivariateNormalQMC(
+            mean=[0, 1], cov=[[1, 0], [0, 1]], inv_transform=True,
+        )
+        samples = engine.random()
+        assert_equal(samples.shape, (1, 2))
+        samples = engine.random(n=5)
+        assert_equal(samples.shape, (5, 2))
+
+        # d = 3 np.array
+        mean = np.array([0, 1, 2])
+        cov = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+        engine = qmc.MultivariateNormalQMC(mean, cov, inv_transform=True)
+        samples = engine.random()
+        assert_equal(samples.shape, (1, 3))
+        samples = engine.random(n=5)
+        assert_equal(samples.shape, (5, 3))
+
+    def test_MultivariateNormalQMCSeeded(self):
+        # test even dimension
+        rng = np.random.default_rng(180182791534511062935571481899241825000)
+        a = rng.standard_normal((2, 2))
+        A = a @ a.transpose() + np.diag(rng.random(2))
+        engine = qmc.MultivariateNormalQMC(np.array([0, 0]), A,
+                                           inv_transform=False, rng=rng)
+        samples = engine.random(n=2)
+        samples_expected = np.array([[-0.64419, -0.882413],
+                                     [0.837199, 2.045301]])
+        assert_allclose(samples, samples_expected, atol=1e-4)
+
+        # test odd dimension
+        rng = np.random.default_rng(180182791534511062935571481899241825000)
+        a = rng.standard_normal((3, 3))
+        A = a @ a.transpose() + np.diag(rng.random(3))
+        engine = qmc.MultivariateNormalQMC(np.array([0, 0, 0]), A,
+                                           inv_transform=False, rng=rng)
+        samples = engine.random(n=2)
+        samples_expected = np.array([[-0.693853, -1.265338, -0.088024],
+                                     [1.620193, 2.679222, 0.457343]])
+        assert_allclose(samples, samples_expected, atol=1e-4)
+
+    def test_MultivariateNormalQMCSeededInvTransform(self):
+        # test even dimension
+        rng = np.random.default_rng(224125808928297329711992996940871155974)
+        a = rng.standard_normal((2, 2))
+        A = a @ a.transpose() + np.diag(rng.random(2))
+        engine = qmc.MultivariateNormalQMC(
+            np.array([0, 0]), A, rng=rng, inv_transform=True
+        )
+        samples = engine.random(n=2)
+        samples_expected = np.array([[0.682171, -3.114233],
+                                     [-0.098463, 0.668069]])
+        assert_allclose(samples, samples_expected, atol=1e-4)
+
+        # test odd dimension
+        rng = np.random.default_rng(224125808928297329711992996940871155974)
+        a = rng.standard_normal((3, 3))
+        A = a @ a.transpose() + np.diag(rng.random(3))
+        engine = qmc.MultivariateNormalQMC(
+            np.array([0, 0, 0]), A, rng=rng, inv_transform=True
+        )
+        samples = engine.random(n=2)
+        samples_expected = np.array([[0.988061, -1.644089, -0.877035],
+                                     [-1.771731, 1.096988, 2.024744]])
+        assert_allclose(samples, samples_expected, atol=1e-4)
+
+    def test_MultivariateNormalQMCShapiro(self):
+        # test the standard case
+        rng = np.random.default_rng(188960007281846377164494575845971640)
+        engine = qmc.MultivariateNormalQMC(
+            mean=[0, 0], cov=[[1, 0], [0, 1]], rng=rng
+        )
+        samples = engine.random(n=256)
+        assert all(np.abs(samples.mean(axis=0)) < 1e-2)
+        assert all(np.abs(samples.std(axis=0) - 1) < 1e-2)
+        # perform Shapiro-Wilk test for normality
+        for i in (0, 1):
+            _, pval = shapiro(samples[:, i])
+            assert pval > 0.9
+        # make sure samples are uncorrelated
+        cov = np.cov(samples.transpose())
+        assert np.abs(cov[0, 1]) < 1e-2
+
+        # test the correlated, non-zero mean case
+        engine = qmc.MultivariateNormalQMC(
+            mean=[1.0, 2.0], cov=[[1.5, 0.5], [0.5, 1.5]], rng=rng
+        )
+        samples = engine.random(n=256)
+        assert all(np.abs(samples.mean(axis=0) - [1, 2]) < 1e-2)
+        assert all(np.abs(samples.std(axis=0) - np.sqrt(1.5)) < 1e-2)
+        # perform Shapiro-Wilk test for normality
+        for i in (0, 1):
+            _, pval = shapiro(samples[:, i])
+            assert pval > 0.9
+        # check covariance
+        cov = np.cov(samples.transpose())
+        assert np.abs(cov[0, 1] - 0.5) < 1e-2
+
+    def test_MultivariateNormalQMCShapiroInvTransform(self):
+        # test the standard case
+        rng = np.random.default_rng(200089821034563288698994840831440331329)
+        engine = qmc.MultivariateNormalQMC(
+            mean=[0, 0], cov=[[1, 0], [0, 1]], rng=rng, inv_transform=True
+        )
+        samples = engine.random(n=256)
+        assert all(np.abs(samples.mean(axis=0)) < 1e-2)
+        assert all(np.abs(samples.std(axis=0) - 1) < 1e-2)
+        # perform Shapiro-Wilk test for normality
+        for i in (0, 1):
+            _, pval = shapiro(samples[:, i])
+            assert pval > 0.9
+        # make sure samples are uncorrelated
+        cov = np.cov(samples.transpose())
+        assert np.abs(cov[0, 1]) < 1e-2
+
+        # test the correlated, non-zero mean case
+        engine = qmc.MultivariateNormalQMC(
+            mean=[1.0, 2.0],
+            cov=[[1.5, 0.5], [0.5, 1.5]],
+            rng=rng,
+            inv_transform=True,
+        )
+        samples = engine.random(n=256)
+        assert all(np.abs(samples.mean(axis=0) - [1, 2]) < 1e-2)
+        assert all(np.abs(samples.std(axis=0) - np.sqrt(1.5)) < 1e-2)
+        # perform Shapiro-Wilk test for normality
+        for i in (0, 1):
+            _, pval = shapiro(samples[:, i])
+            assert pval > 0.9
+        # check covariance
+        cov = np.cov(samples.transpose())
+        assert np.abs(cov[0, 1] - 0.5) < 1e-2
+
+    def test_MultivariateNormalQMCDegenerate(self):
+        # X, Y iid standard Normal and Z = X + Y, random vector (X, Y, Z)
+        rng = np.random.default_rng(16320637417581448357869821654290448620)
+        engine = qmc.MultivariateNormalQMC(
+            mean=[0.0, 0.0, 0.0],
+            cov=[[1.0, 0.0, 1.0], [0.0, 1.0, 1.0], [1.0, 1.0, 2.0]],
+            rng=rng,
+        )
+        samples = engine.random(n=512)
+        assert all(np.abs(samples.mean(axis=0)) < 1e-2)
+        assert np.abs(np.std(samples[:, 0]) - 1) < 1e-2
+        assert np.abs(np.std(samples[:, 1]) - 1) < 1e-2
+        assert np.abs(np.std(samples[:, 2]) - np.sqrt(2)) < 1e-2
+        for i in (0, 1, 2):
+            _, pval = shapiro(samples[:, i])
+            assert pval > 0.8
+        cov = np.cov(samples.transpose())
+        assert np.abs(cov[0, 1]) < 1e-2
+        assert np.abs(cov[0, 2] - 1) < 1e-2
+        # check to see if X + Y = Z almost exactly
+        assert all(np.abs(samples[:, 0] + samples[:, 1] - samples[:, 2])
+                   < 1e-5)
+
+
+class TestLloyd:
+    def test_lloyd(self):
+        # quite sensible seed as it can go up before going further down
+        rng = np.random.RandomState(1809831)
+        sample = rng.uniform(0, 1, size=(128, 2))
+        base_l1 = _l1_norm(sample)
+        base_l2 = l2_norm(sample)
+
+        for _ in range(4):
+            sample_lloyd = _lloyd_centroidal_voronoi_tessellation(
+                    sample, maxiter=1,
+            )
+            curr_l1 = _l1_norm(sample_lloyd)
+            curr_l2 = l2_norm(sample_lloyd)
+
+            # higher is better for the distance measures
+            assert base_l1 < curr_l1
+            assert base_l2 < curr_l2
+
+            base_l1 = curr_l1
+            base_l2 = curr_l2
+
+            sample = sample_lloyd
+
+    def test_lloyd_non_mutating(self):
+        """
+        Verify that the input samples are not mutated in place and that they do
+        not share memory with the output.
+        """
+        sample_orig = np.array([[0.1, 0.1],
+                                [0.1, 0.2],
+                                [0.2, 0.1],
+                                [0.2, 0.2]])
+        sample_copy = sample_orig.copy()
+        new_sample = _lloyd_centroidal_voronoi_tessellation(
+            sample=sample_orig
+        )
+        assert_allclose(sample_orig, sample_copy)
+        assert not np.may_share_memory(sample_orig, new_sample)
+
+    def test_lloyd_errors(self):
+        with pytest.raises(ValueError, match=r"`sample` is not a 2D array"):
+            sample = [0, 1, 0.5]
+            _lloyd_centroidal_voronoi_tessellation(sample)
+
+        msg = r"`sample` dimension is not >= 2"
+        with pytest.raises(ValueError, match=msg):
+            sample = [[0], [0.4], [1]]
+            _lloyd_centroidal_voronoi_tessellation(sample)
+
+        msg = r"`sample` is not in unit hypercube"
+        with pytest.raises(ValueError, match=msg):
+            sample = [[-1.1, 0], [0.1, 0.4], [1, 2]]
+            _lloyd_centroidal_voronoi_tessellation(sample)
+
+
+# mindist
+def l2_norm(sample):
+    return distance.pdist(sample).min()
+
+
+@pytest.mark.parametrize('engine', [qmc.Halton, qmc.Sobol,
+                                    qmc.LatinHypercube, qmc.PoissonDisk])
+def test_deterministic(engine):
+    seed_number = 2359834584
+
+    rng = np.random.RandomState(seed_number)
+    res1 = engine(d=1, seed=rng).random(4)
+    rng = np.random.RandomState(seed_number)
+    res2 = engine(d=1, seed=rng).random(4)
+    assert_equal(res1, res2)
+
+    rng = np.random.default_rng(seed_number)
+    res1 = engine(d=1, seed=rng).random(4)
+    res2 = engine(d=1, rng=seed_number).random(4)
+    assert_equal(res1, res2)
+    rng = np.random.default_rng(seed_number)
+    res3 = engine(d=1, rng=rng).random(4)
+    assert_equal(res2, res1)
+    assert_equal(res3, res1)
+
+    message = "got multiple values for argument now known as `rng`"
+    with pytest.raises(TypeError, match=message):
+        engine(d=1, rng=seed_number, seed=seed_number)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_quantile.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_quantile.py
new file mode 100644
index 0000000000000000000000000000000000000000..4caa6341adb0123c783a60f324617d0fcaed7090
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_quantile.py
@@ -0,0 +1,774 @@
+import pytest
+import numpy as np
+
+from scipy import stats
+from scipy.stats._quantile import (_xp_searchsorted, _estimated_cdf_methods,
+    _estimated_cdf_discontinuous_methods, _estimated_cdf_continuous_methods)
+from scipy._lib._array_api import (
+    xp_default_dtype,
+    is_numpy,
+    is_torch,
+    is_jax,
+    is_cupy,
+    is_array_api_strict,
+    make_xp_test_case,
+    SCIPY_ARRAY_API,
+    xp_size,
+    xp_copy,
+)
+from scipy._lib._array_api_no_0d import xp_assert_close, xp_assert_equal
+from scipy._lib._util import _apply_over_batch
+import scipy._external.array_api_extra as xpx
+from scipy.stats._axis_nan_policy import _broadcast_arrays
+
+skip_xp_backends = pytest.mark.skip_xp_backends
+
+lazy_xp_modules = [stats]
+
+@_apply_over_batch(('x', 1), ('p', 1))
+def quantile_reference_last_axis(x, p, nan_policy, method):
+    if nan_policy == 'omit':
+        x = x[~np.isnan(x)]
+    p_mask = np.isnan(p)
+    p = p.copy()
+    p[p_mask] = 0.5
+    if method == 'harrell-davis':
+        # hdquantiles returns masked element if length along axis is 1 (bug)
+        res = (np.full_like(p, x[0]) if x.size == 1
+               else stats.mstats.hdquantiles(x, p).data)
+    elif method.startswith('round'):
+        res = winsor_reference_1d(np.sort(x), p, method)
+    else:
+        res = np.quantile(x, p, method=method)
+
+    res = np.asarray(res)
+    if nan_policy == 'propagate' and np.any(np.isnan(x)):
+        res[:] = np.nan
+
+    res[p_mask] = np.nan
+    return res
+
+
+@np.vectorize(excluded={0, 2})  # type: ignore[call-arg]
+def winsor_reference_1d(y, p, method):
+    # Adapted directly from the documentation
+    # Note: `y` is the sorted data array
+    n = len(y)
+    if method == 'round_nearest':
+        j = int(np.round(p * n) if p < 0.5 else np.round(n * p - 1))
+    elif method == 'round_outward':
+        j = int(np.floor(p * n) if p < 0.5 else np.ceil(n * p - 1))
+    elif method == 'round_inward':
+        j = int(np.ceil(p * n) if p < 0.5 else np.floor(n * p - 1))
+    return y[j]
+
+
+def quantile_reference(x, p, *, axis, nan_policy, keepdims, method):
+    x, p = np.moveaxis(x, axis, -1), np.moveaxis(np.atleast_1d(p), axis, -1)
+    res = quantile_reference_last_axis(x, p, nan_policy, method)
+    res = np.moveaxis(res, -1, axis)
+    if not keepdims:
+        res = np.squeeze(res, axis=axis)
+    return res
+
+
+@make_xp_test_case(stats.quantile)
+class TestQuantile:
+
+    def test_input_validation(self, xp):
+        x = xp.asarray([1, 2, 3])
+        p = xp.asarray(0.5)
+
+        message = "`x` must have real dtype."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile(xp.asarray([True, False]), p)
+        with pytest.raises(ValueError):
+            stats.quantile(xp.asarray([1+1j, 2]), p)
+
+        message = "`p` must have real floating dtype."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile(x, xp.asarray([0, 1]))
+
+        message = "`weights` must have real dtype."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile(x, p, weights=xp.astype(x, xp.complex64))
+
+        message = "`axis` must be an integer or None."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile(x, p, axis=0.5)
+        with pytest.raises(ValueError, match=message):
+            stats.quantile(x, p, axis=(0, -1))
+
+        message = "`axis` is not compatible with the shapes of the inputs."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile(x, p, axis=2)
+
+        if not is_jax(xp):  # no data-dependent input validation for lazy arrays
+            message = "The input contains nan values"
+            with pytest.raises(ValueError, match=message):
+                stats.quantile(xp.asarray([xp.nan, 1, 2]), p, nan_policy='raise')
+
+        message = "`method` must be one of..."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile(x, p, method='a duck')
+
+        message = "`method='harrell-davis'` does not support `weights`."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile(x, p, weights=x, method='harrell-davis')
+
+        message = "`method='round_nearest'` does not support `weights`."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile(x, p, weights=x, method='round_nearest')
+
+        message = "If specified, `keepdims` must be True or False."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile(x, p, keepdims=42)
+
+        message = "`keepdims` may be False only if the length of `p` along `axis` is 1."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile(x, xp.asarray([0.5, 0.6]), keepdims=False)
+
+
+    def _get_weights_x_rep(self, x, axis, rng):
+        x = np.swapaxes(x, axis, -1)
+        ndim = x.ndim
+        x = np.atleast_2d(x)
+        counts = rng.integers(10, size=x.shape[-1], dtype=np.int32)
+        x_rep = []
+        weights = []
+        for x_ in x:
+            counts_ = rng.permuted(counts)
+            x_rep.append(np.repeat(x_, counts_))
+            weights.append(counts_)
+        x_rep, weights = np.stack(x_rep), np.stack(weights)
+        if ndim < 2:
+            x_rep, weights = np.squeeze(x_rep, axis=0), np.squeeze(weights, axis=0)
+        x_rep, weights = np.swapaxes(x_rep, -1, axis), np.swapaxes(weights, -1, axis)
+        weights = np.asarray(weights, dtype=x.dtype)
+        return weights, x_rep
+
+
+    @skip_xp_backends(cpu_only=True, reason="PyTorch doesn't have `betainc`.",
+                      exceptions=['cupy'])
+    @pytest.mark.parametrize('method',
+         ['inverted_cdf', 'averaged_inverted_cdf', 'closest_observation',
+          'hazen', 'interpolated_inverted_cdf', 'linear',
+          'median_unbiased', 'normal_unbiased', 'weibull',
+          'harrell-davis', 'round_nearest', 'round_outward', 'round_inward',
+          '_lower', '_higher', '_midpoint', '_nearest'])
+    @pytest.mark.parametrize('shape_x, shape_p, axis',
+        [(10, None, -1), (10, 10, -1), (10, (2, 3), -1), ((10, 2), None, 0)])
+    @pytest.mark.parametrize('weights', [False, True])
+    def test_against_reference(self, method, shape_x, shape_p, axis, weights, xp):
+        # Test all methods with various data shapes
+        if weights and (method.startswith('_') or method.startswith('round')
+                        or method=='harrell-davis'):
+            pytest.skip('`weights` not supported by private (legacy) methods.')
+        dtype = xp_default_dtype(xp)
+        rng = np.random.default_rng(23458924568734956)
+        x = rng.random(size=shape_x)
+        p = rng.random(size=shape_p)
+
+        if weights:
+            weights, x_rep = self._get_weights_x_rep(x, axis, rng)
+        else:
+            weights, x_rep = None, x
+
+        ref = quantile_reference(
+            x_rep, p, method=method[1:] if method.startswith('_') else method,
+            axis=axis, nan_policy='propagate', keepdims=shape_p is not None)
+
+        x, p = xp.asarray(x, dtype=dtype), xp.asarray(p, dtype=dtype)
+        weights = weights if weights is None else xp.asarray(weights, dtype=dtype)
+        res = stats.quantile(x, p, method=method, weights=weights, axis=axis)
+
+        xp_assert_close(res, xp.asarray(ref, dtype=dtype))
+
+    @pytest.mark.filterwarnings("ignore:torch.searchsorted:UserWarning")
+    @skip_xp_backends(cpu_only=True, reason="PyTorch doesn't have `betainc`.",
+                      exceptions=['cupy', 'jax.numpy'])
+    @pytest.mark.parametrize('axis', [0, 1])
+    @pytest.mark.parametrize('keepdims', [False, True])
+    @pytest.mark.parametrize('nan_policy', ['omit', 'propagate', 'marray'])
+    @pytest.mark.parametrize('dtype', ['float32', 'float64'])
+    @pytest.mark.parametrize('method', ['linear', 'harrell-davis', 'round_nearest'])
+    @pytest.mark.parametrize('weights', [False, True])
+    def test_against_reference_2(self, axis, keepdims, nan_policy,
+                                 dtype, method, weights, xp):
+        # Test some methods with various combinations of arguments
+        if is_jax(xp) and nan_policy == 'marray':  # mdhaber/marray#146
+            pytest.skip("`marray` currently incompatible with JAX")
+        if weights and method in {'harrell-davis', 'round_nearest'}:
+            pytest.skip("These methods don't yet support weights")
+        rng = np.random.default_rng(23458924568734956)
+        shape = (5, 6)
+        x = rng.random(size=shape).astype(dtype)
+        p = rng.random(size=shape).astype(dtype)
+        mask = rng.random(size=shape) > 0.8
+        assert np.any(mask)
+        x[mask] = np.nan
+        if not keepdims:
+            p = np.mean(p, axis=axis, keepdims=True)
+
+        # inject p = 0 and p = 1 to test edge cases
+        # Currently would fail with CuPy/JAX (cupy/cupy#8934, jax-ml/jax#21900);
+        # remove the `if` when those are resolved.
+        if is_numpy(xp):
+            p0 = p.ravel()
+            p0[1] = 0.
+            p0[-2] = 1.
+
+        dtype = getattr(xp, dtype)
+
+        if weights:
+            weights, x_rep = self._get_weights_x_rep(x, axis, rng)
+            weights = weights if weights is None else xp.asarray(weights)
+        else:
+            weights, x_rep = None, x
+
+        if nan_policy == 'marray':
+            if not SCIPY_ARRAY_API:
+                pytest.skip("MArray is only available if SCIPY_ARRAY_API=1")
+            if weights is not None:
+                pytest.skip("MArray is not yet compatible with weights")
+            marray = pytest.importorskip('marray')
+            kwargs = dict(axis=axis, keepdims=keepdims, method=method)
+            mxp = marray._get_namespace(xp)
+            x_mp = mxp.asarray(x, mask=mask)
+            weights = weights if weights is None else mxp.asarray(weights)
+            res = stats.quantile(x_mp, mxp.asarray(p), weights=weights, **kwargs)
+            ref = quantile_reference(x_rep, p, nan_policy='omit', **kwargs)
+            xp_assert_close(res.data, xp.asarray(ref, dtype=dtype))
+            return
+
+        kwargs = dict(axis=axis, keepdims=keepdims,
+                      nan_policy=nan_policy, method=method)
+        res = stats.quantile(xp.asarray(x), xp.asarray(p), weights=weights, **kwargs)
+        ref = quantile_reference(x_rep, p, **kwargs)
+        xp_assert_close(res, xp.asarray(ref, dtype=dtype))
+
+    def test_integer_input_output_dtype(self, xp):
+        res = stats.quantile(xp.arange(10, dtype=xp.int64), 0.5)
+        assert res.dtype == xp_default_dtype(xp)
+
+    @pytest.mark.parametrize('x, p, ref, kwargs',
+        [([], 0.5, np.nan, {}),
+         ([1, 2, 3], [-1, 0, 1, 1.5, np.nan], [np.nan, 1, 3, np.nan, np.nan], {}),
+         ([1, 2, 3], [], [], {}),
+         ([[np.nan, 2]], 0.5, [np.nan, 2], {'nan_policy': 'omit'}),
+         ([[], []], 0.5, np.full(2, np.nan), {'axis': -1}),
+         ([[], []], 0.5, np.zeros((0,)), {'axis': 0, 'keepdims': False}),
+         ([[], []], 0.5, np.zeros((1, 0)), {'axis': 0, 'keepdims': True}),
+         ([], [0.5, 0.6], np.full(2, np.nan), {}),
+         (np.arange(1, 28).reshape((3, 3, 3)), 0.5, [[[14.]]],
+          {'axis': None, 'keepdims': True}),
+         ([[1, 2], [3, 4]], [0.25, 0.5, 0.75], [[1.75, 2.5, 3.25]],
+          {'axis': None, 'keepdims': True}),
+         # Known issue:
+         # ([1, 2, 3], 0.5, 2., {'weights': [0, 0, 0]})
+         # See https://github.com/scipy/scipy/pull/23941#issuecomment-3503554361
+         ])
+    def test_edge_cases(self, x, p, ref, kwargs, xp):
+        default_dtype = xp_default_dtype(xp)
+        x, p, ref = xp.asarray(x), xp.asarray(p), xp.asarray(ref, dtype=default_dtype)
+        res = stats.quantile(x, p, **kwargs)
+        xp_assert_equal(res, ref)
+
+    @pytest.mark.parametrize('axis', [0, 1, 2])
+    @pytest.mark.parametrize('keepdims', [False, True])
+    def test_size_0(self, axis, keepdims, xp):
+        shape = [3, 4, 0]
+        out_shape = shape.copy()
+        if keepdims:
+            out_shape[axis] = 1
+        else:
+            out_shape.pop(axis)
+        res = stats.quantile(xp.zeros(tuple(shape)), 0.5, axis=axis, keepdims=keepdims)
+        assert res.shape == tuple(out_shape)
+
+    @pytest.mark.parametrize('method',
+        ['inverted_cdf', 'averaged_inverted_cdf', 'closest_observation',
+         '_lower', '_higher', '_midpoint', '_nearest'])
+    def test_transition(self, method, xp):
+        # test that values of discontinuous estimators are correct when
+        # p*n + m - 1 is integral.
+        if method == 'closest_observation' and np.__version__ < '2.0.1':
+            pytest.skip('Bug in np.quantile (numpy/numpy#26656) fixed in 2.0.1')
+        x = np.arange(8., dtype=np.float64)
+        p = np.arange(0, 1.03125, 0.03125)
+        res = stats.quantile(xp.asarray(x), xp.asarray(p), method=method)
+        ref = np.quantile(x, p, method=method[1:] if method.startswith('_') else method)
+        xp_assert_equal(res, xp.asarray(ref, dtype=xp.float64))
+
+    @pytest.mark.parametrize('zero_weights', [False, True])
+    def test_weights_against_numpy(self, zero_weights, xp):
+        if is_numpy(xp) and xp.__version__ < "2.1.3" and zero_weights:
+            pytest.skip('`Bug in np.quantile (numpy/numpy#27563) fixed in 2.1.3')
+        dtype = xp_default_dtype(xp)
+        rng = np.random.default_rng(85468924398205602)
+        method = 'inverted_cdf'
+        x = rng.random(size=100)
+        weights = rng.random(size=100)
+        if zero_weights:
+            weights[weights < 0.5] = 0
+        p = np.linspace(0., 1., 300)
+        res = stats.quantile(xp.asarray(x, dtype=dtype), xp.asarray(p, dtype=dtype),
+                             method=method, weights=xp.asarray(weights, dtype=dtype))
+        ref = np.quantile(x, p, method=method, weights=weights)
+        xp_assert_close(res, xp.asarray(ref, dtype=dtype))
+
+    @pytest.mark.parametrize('method',
+        ['inverted_cdf', 'averaged_inverted_cdf', 'closest_observation', 'hazen',
+         'interpolated_inverted_cdf', 'linear','median_unbiased', 'normal_unbiased',
+         'weibull'])
+    def test_zero_weights(self, method, xp):
+        rng = np.random.default_rng(85468924398205602)
+
+        # test 1-D versus eliminating zero-weighted values
+        n = 100
+        x = xp.asarray(rng.random(size=n))
+        x0 = xp_copy(x)
+        p = xp.asarray(rng.random(size=n))
+        i_zero = xp.asarray(rng.random(size=n) < 0.1)
+        weights = xp.asarray(rng.random(size=n))
+        weights = xp.where(i_zero, 0., weights)
+        res = stats.quantile(x, p, weights=weights, method=method)
+        ref = stats.quantile(x[~i_zero], p, weights=weights[~i_zero], method=method)
+        xp_assert_close(res, ref)
+        xp_assert_equal(x, x0)  # no input mutation
+
+        # test multi-D versus `nan_policy='omit'`
+        shape = (5, 100)
+        x = xp.asarray(rng.random(size=shape))
+        x0 = xp_copy(x)
+        p = xp.asarray(rng.random(size=shape))
+        i_zero = xp.asarray(rng.random(size=shape) < 0.1)
+        weights = xp.asarray(rng.random(size=shape))
+        x_nanned = xp.where(i_zero, xp.nan, x)
+        weights_zeroed = xp.where(i_zero, 0., weights)
+        res = stats.quantile(x, p, weights=weights_zeroed, method=method, axis=-1)
+        ref = stats.quantile(x_nanned, p, weights=weights,
+                             nan_policy='omit', method=method, axis=-1)
+        xp_assert_close(res, ref)
+        xp_assert_equal(x, x0)  # no input mutation
+
+    @pytest.mark.filterwarnings("ignore:torch.searchsorted:UserWarning")
+    @pytest.mark.parametrize('method',
+        ['inverted_cdf', 'averaged_inverted_cdf', 'closest_observation', 'hazen',
+         'interpolated_inverted_cdf', 'linear','median_unbiased', 'normal_unbiased',
+         'weibull'])
+    @pytest.mark.parametrize('shape', [50, (50, 3)])
+    def test_unity_weights(self, method, shape, xp):
+        # Check that result is unchanged if all weights are `1.0`
+        rng = np.random.default_rng(28546892439820560)
+        x = xp.asarray(rng.random(size=shape))
+        p = xp.asarray(rng.random(size=shape))
+        weights = xp.ones_like(x)
+        res = stats.quantile(x, p, weights=weights, method=method)
+        ref = stats.quantile(x, p, method=method)
+        xp_assert_close(res, ref)
+
+    @skip_xp_backends(cpu_only=True, reason="PyTorch doesn't have `betainc`.",
+                      exceptions=['cupy', 'jax.numpy'])
+    def test_all_nan_harrell_davis_gh24707(self, xp):
+        # While working on gh-24707, there was a case in which if *all* elements of one
+        # slice were NaN, only some elements of another slice were NaN, and
+        # `nan_policy='omit'`, then the NaNs would not be ignored in the other slice.
+        # The same test case could fail with `nan_policy='propagate'` for a different
+        # reason, if the fix were not made carefully. Check that both these cases are
+        # resolved.
+        kwargs = dict(method='harrell-davis', axis=-1)
+        x = xp.asarray([[xp.nan, xp.nan, xp.nan], [xp.nan, 2, 3]])
+
+        res = stats.quantile(x, 0.5, **kwargs, nan_policy='omit')
+        xp_assert_close(res, xp.asarray([xp.nan, 2.5]))
+
+        res = stats.quantile(x, 0.5, **kwargs, nan_policy='propagate')
+        xp_assert_close(res, xp.asarray([xp.nan, xp.nan]))
+
+
+@_apply_over_batch(('a', 1), ('v', 1))
+def np_searchsorted(a, v, side):
+    return np.searchsorted(a, v, side=side)
+
+
+@make_xp_test_case(_xp_searchsorted)
+class Test_XPSearchsorted:
+    @pytest.mark.parametrize('side', ['left', 'right'])
+    @pytest.mark.parametrize('ties', [False, True])
+    @pytest.mark.parametrize('shape', [0, 1, 2, 10, 11, 1000, 10001,
+                                       (2, 0), (0, 2), (2, 10), (2, 3, 11)])
+    @pytest.mark.parametrize('nans_x', [False, True])
+    @pytest.mark.parametrize('infs_x', [False, True])
+    def test_nd(self, side, ties, shape, nans_x, infs_x, xp):
+        if nans_x and is_torch(xp):
+            pytest.skip('torch sorts NaNs differently')
+        rng = np.random.default_rng(945298725498274853)
+        if ties:
+            x = rng.integers(5, size=shape)
+        else:
+            x = rng.random(shape)
+        # float32 is to accommodate JAX - nextafter with `float64` is too small?
+        x = np.asarray(x, dtype=np.float32)
+        xr = np.nextafter(x, np.inf)
+        xl = np.nextafter(x, -np.inf)
+        x_ = np.asarray([-np.inf, np.inf, np.nan])
+        x_ = np.broadcast_to(x_, x.shape[:-1] + (3,))
+        y = rng.permuted(np.concatenate((xl, x, xr, x_), axis=-1), axis=-1)
+        if nans_x:
+            mask = rng.random(shape) < 0.1
+            x[mask] = np.nan
+        if infs_x:
+            mask = rng.random(shape) < 0.1
+            x[mask] = -np.inf
+            mask = rng.random(shape) > 0.9
+            x[mask] = np.inf
+        x = np.sort(x, axis=-1)
+        x, y = np.asarray(x, dtype=np.float64), np.asarray(y, dtype=np.float64)
+        xp_default_int = xp.asarray(1).dtype
+        if xp_size(x) == 0 and x.ndim > 0 and x.shape[-1] != 0:
+            ref = xp.empty(x.shape[:-1] + (y.shape[-1],), dtype=xp_default_int)
+        else:
+            ref = xp.asarray(np_searchsorted(x, y, side=side), dtype=xp_default_int)
+        x, y = xp.asarray(x), xp.asarray(y)
+        res = _xp_searchsorted(x, y, side=side)
+        xp_assert_equal(res, ref)
+
+
+@_apply_over_batch(('x', 1), ('y', 1))
+def estimated_cdf_reference_last_axis(x, y, nan_policy, method):
+    i_nan = np.isnan(x)
+    if nan_policy == 'propagate' and np.any(i_nan):
+        return np.full_like(y, np.nan)
+    elif nan_policy == 'omit':
+        x = x[~i_nan]
+    return stats.estimated_cdf(x, y, keepdims=True, method=method)
+
+
+def estimated_cdf_reference(x, y, *, axis=0, nan_policy='propagate',
+                        keepdims=None, method='linear'):
+    x, y = _broadcast_arrays((x, y), axis=axis)
+    x, y = np.moveaxis(x, axis, -1), np.moveaxis(y, axis, -1)
+    res = estimated_cdf_reference_last_axis(x, y, nan_policy, method)
+    res = np.moveaxis(res, -1, axis)
+    if not keepdims:
+        res = np.squeeze(res, axis=axis)
+    return res
+
+
+# avoid variable collection order issues
+_estimated_cdf_methods_list = sorted(list(_estimated_cdf_methods))
+
+
+@make_xp_test_case(stats.estimated_cdf)
+class TestEstimatedCDF:
+    def test_input_validation(self, xp):
+        x = xp.asarray([1, 2, 3])
+        y = xp.asarray(2)
+
+        message = "`x` must have real dtype."
+        with pytest.raises(ValueError, match=message):
+            stats.estimated_cdf(xp.asarray([True, False]), y)
+        with pytest.raises(ValueError):
+            stats.estimated_cdf(xp.asarray([1+1j, 2]), y)
+
+        message = "`y` must have real dtype."
+        with pytest.raises(ValueError, match=message):
+            stats.estimated_cdf(x, xp.asarray([0+1j, 1]))
+
+        message = "`axis` must be an integer or None."
+        with pytest.raises(ValueError, match=message):
+            stats.estimated_cdf(x, y, axis=0.5)
+        with pytest.raises(ValueError, match=message):
+            stats.estimated_cdf(x, y, axis=(0, -1))
+
+        message = "`axis` is not compatible with the shapes of the inputs."
+        with pytest.raises(ValueError, match=message):
+            stats.estimated_cdf(x, y, axis=2)
+
+        if not is_jax(xp):  # no data-dependent input validation for lazy arrays
+            message = "The input contains nan values"
+            with pytest.raises(ValueError, match=message):
+                stats.estimated_cdf(xp.asarray([xp.nan, 1, 2]), y, nan_policy='raise')
+
+        message = "method` must be one of..."
+        with pytest.raises(ValueError, match=message):
+            stats.estimated_cdf(x, y, method='a duck')
+
+        message = "If specified, `keepdims` must be True or False."
+        with pytest.raises(ValueError, match=message):
+            stats.estimated_cdf(x, y, keepdims=42)
+
+        message = "`keepdims` may be False only if the length of `y` along `axis` is 1."
+        with pytest.raises(ValueError, match=message):
+            stats.estimated_cdf(x, xp.asarray([0.5, 0.6]), keepdims=False)
+
+    @pytest.mark.parametrize('method', _estimated_cdf_methods_list)
+    @pytest.mark.parametrize('x_shape', [2, 10, 11, 100, 1001, (2, 10), (2, 3, 11)])
+    @pytest.mark.parametrize('y_shape', [None, 25])
+    @pytest.mark.parametrize('ties', [False, True])
+    def test_against_quantile(self, method, x_shape, y_shape, ties, xp):
+        discontinuous = method in _estimated_cdf_discontinuous_methods
+        dtype = xp_default_dtype(xp)  # removed parameterization to speed up tests
+        rng = np.random.default_rng(394529872549827485)
+        y_shape = x_shape if y_shape is None else y_shape
+
+        if ties:
+            x = xp.asarray(rng.integers(9, size=x_shape), dtype=dtype)
+        else:
+            x = xp.asarray(rng.standard_normal(size=x_shape), dtype=dtype)
+
+        p = xp.asarray(rng.random(size=y_shape), dtype=dtype)
+        y = stats.quantile(x, p, method=method, axis=-1)
+        res = stats.estimated_cdf(x, y, method=method, axis=-1)
+        ref = xp.broadcast_to(p, (*x.shape[:-1], y.shape[-1]))
+
+        # check that `quantile` is the inverse of `estimated_cdf`
+        # note that for discontinuous methods, res is right on the cusp of a transition,
+        # and there can be a tiny bit of error to the right or left. We shift it left
+        # to ensure we're on the correct side of the transition, producing the same `y2`
+        # as if the probability calculation were exact.
+        res = res - 1e-6 if discontinuous else res
+        y2 = stats.quantile(x, res, method=method, axis=-1)
+        atol = 1e-5 if dtype == xp.float32 else 1e-12
+        xp_assert_close(y2, y, atol=atol)
+
+        # if there are ties or method is discontinuous, `quantile` is not invertible
+        if ties or discontinuous:
+            return
+
+        # `quantile` is not invertible outside this domain
+        a, b = _estimated_cdf_continuous_methods[method]
+        n = x.shape[-1]
+        p_min = (1 - a) / (n + 1 - a - b)
+        p_max = (n - a) / (n + 1 - a - b)
+        i_very_low = y < xp.min(x, axis=-1, keepdims=True)
+        i_very_high = y > xp.max(x, axis=-1, keepdims=True)
+        i_low = (ref <= p_min) & ~i_very_low
+        i_high = (ref >= p_max) & ~i_very_high
+        i_ok = ~(i_low | i_high | i_very_low | i_very_high)
+
+        # check for correct inversion within the domain
+        xp_assert_close(res[i_ok], ref[i_ok])
+
+        # check that all other values get mapped to bottom or top of range
+        kwargs = dict(check_shape=False, check_dtype=False, check_0d=True)
+        xp_assert_close(res[i_low], xp.asarray(p_min), **kwargs)
+        xp_assert_close(res[i_high], xp.asarray(p_max), **kwargs)
+        xp_assert_close(res[i_very_low], xp.asarray(0.0), **kwargs)
+        xp_assert_close(res[i_very_high], xp.asarray(1.0), **kwargs)
+
+    @pytest.mark.filterwarnings("ignore:torch.searchsorted:UserWarning")
+    @pytest.mark.parametrize('axis', [0, 1])
+    @pytest.mark.parametrize('keepdims', [False, True])
+    @pytest.mark.parametrize('nan_policy', ['propagate', 'omit', 'marray'])
+    @pytest.mark.parametrize('dtype', ['float32', 'float64'])
+    @pytest.mark.parametrize('nans', [False, True])
+    @pytest.mark.parametrize('meth', ['linear', 'inverted_cdf'])
+    def test_against_reference(self, axis, keepdims, nan_policy, dtype, nans, meth, xp):
+        if is_jax(xp) and nan_policy == 'marray':  # mdhaber/marray#146
+            pytest.skip("`marray` currently incompatible with JAX")
+        rng = np.random.default_rng(23458924568734956)
+        shape = (5, 6)
+        x = rng.standard_normal(size=shape).astype(dtype)
+        y = rng.standard_normal(size=shape).astype(dtype)
+
+        mask = None
+        if nans:
+            mask = rng.random(size=shape) > 0.8
+            assert np.any(mask)
+            x[mask] = np.nan
+
+        if not keepdims:
+            y = np.mean(y, axis=axis, keepdims=True)
+
+        dtype = getattr(xp, dtype)
+
+        if nan_policy == 'marray':
+            if not SCIPY_ARRAY_API:
+                pytest.skip("MArray is only available if SCIPY_ARRAY_API=1")
+            marray = pytest.importorskip('marray')
+            kwargs = dict(axis=axis, keepdims=keepdims, method=meth)
+            mxp = marray._get_namespace(xp)
+            x_mp = mxp.asarray(x, mask=mask)
+            res = stats.estimated_cdf(x_mp, mxp.asarray(y), **kwargs)
+            ref = estimated_cdf_reference(x, y, nan_policy='omit', **kwargs)
+            xp_assert_close(res.data, xp.asarray(ref, dtype=dtype))
+            return
+
+        kwargs = dict(axis=axis, keepdims=keepdims,
+                      nan_policy=nan_policy, method=meth)
+        res = stats.estimated_cdf(xp.asarray(x), xp.asarray(y), **kwargs)
+        ref = estimated_cdf_reference(x, y, **kwargs)
+        xp_assert_close(res, xp.asarray(ref, dtype=dtype))
+
+    @pytest.mark.skip_xp_backends('torch', reason='issues with sorting NaNs')
+    @pytest.mark.parametrize('n', [50, 500])
+    @pytest.mark.parametrize('method, ab', _estimated_cdf_continuous_methods.items())
+    def test_plotting_positions(self, n, method, ab, xp):
+        a, b = ab
+        rng = np.random.default_rng(539452987254982748)
+        x = rng.standard_normal(n)
+
+        mask = rng.random(n) < 0.1
+        x[mask] = np.nan
+        mask = xp.asarray(mask)
+
+        x_masked = np.ma.masked_invalid(x)
+        ref = stats.mstats.plotting_positions(x_masked, a, b)
+        ref = xp.asarray(ref.data)
+
+        x = xp.asarray(x)
+        res = stats.estimated_cdf(x, x, nan_policy='omit', method=method)
+
+        xp_assert_close(res[~mask], ref[~mask])
+        assert xp.all(xp.isnan(res[mask]))
+
+    @pytest.mark.parametrize('ties', [False, True])
+    def test_against_ecdf_percentileofscore(self, ties, xp):
+        rng = np.random.default_rng(853945298725498274)
+        n = 50
+        dtype = xp_default_dtype(xp)
+        x = rng.integers(10, size=n) if ties else rng.standard_normal(size=n)
+        y = rng.integers(10, size=25) if ties else rng.standard_normal(size=25)
+        ref = stats.ecdf(x).cdf.evaluate(y)
+        ref2 = stats.percentileofscore(x, y, 'weak')
+        x, y = xp.asarray(x, dtype=dtype), xp.asarray(y, dtype=dtype)
+        res = stats.estimated_cdf(x, y, method='inverted_cdf')
+        ref, ref2 = xp.asarray(ref, dtype=dtype), xp.asarray(ref2, dtype=dtype)
+        xp_assert_close(res, ref)
+        xp_assert_close(res, ref2 / 100)
+
+    def test_integer_input_output_dtype(self, xp):
+        x = xp.arange(10, dtype=xp.int64)
+        res = stats.estimated_cdf(x, x)
+        assert res.dtype == xp_default_dtype(xp)
+
+    @pytest.mark.parametrize('nan_policy', ['propagate', 'omit', 'marray'])
+    @pytest.mark.parametrize('method', _estimated_cdf_methods_list)
+    def test_size_one_sample(self, nan_policy, method, xp):
+        discontinuous = method in _estimated_cdf_discontinuous_methods
+        x = xp.arange(10.)
+        y = xp.asarray([0., -1., 1.])
+        n = np.asarray(1.) if is_array_api_strict(xp) else xp.asarray(1.)
+        with np.errstate(divide='ignore', invalid='ignore'):  # for method = 'linear'
+            if discontinuous:
+                ref = xp.asarray([1., 0., 1.])
+            else:
+                a, b = _estimated_cdf_continuous_methods[method]
+                ref = xp.asarray([float((n - a) / (n + 1 - a - b)), 0., 1.])
+
+        if nan_policy == 'propagate':
+            x = x[:1]
+            kwargs = {'nan_policy': 'propagate'}
+        elif nan_policy == 'omit':
+            x = xpx.at(x)[1:].set(xp.nan)
+            kwargs = {'nan_policy': 'omit'}
+        elif nan_policy == 'marray':
+            if is_jax(xp):
+                pytest.skip("JAX currently incompatible with `marray`")
+            if not SCIPY_ARRAY_API:
+                pytest.skip("MArray is only available if SCIPY_ARRAY_API=1")
+            marray = pytest.importorskip('marray')
+            mxp = marray._get_namespace(xp)
+            mask = (x > 0.)
+            x = mxp.asarray(x, mask=mask)
+            y = mxp.asarray(y)
+            kwargs = {}
+
+        with np.errstate(divide='ignore', invalid='ignore'):  # for method = 'linear'
+            res = stats.estimated_cdf(x, y, method=method, **kwargs)
+        res = res.data if nan_policy == 'marray' else res
+        xp_assert_close(res, ref)
+
+    # skipping marray due to mdhaber/marray#24
+    @pytest.mark.parametrize('nan_policy', ['propagate', 'omit'])
+    @pytest.mark.parametrize('method', _estimated_cdf_methods_list)
+    def test_size_zero_sample(self, nan_policy, method, xp):
+        x = xp.arange(10.)
+        y = xp.asarray([0., -1., 1.])  # this should work
+        ref = xp.full_like(y, xp.nan)
+
+        if nan_policy == 'propagate':
+            x = x[0:0]
+            kwargs = {'nan_policy': 'propagate'}
+        elif nan_policy == 'omit':
+            x = xpx.at(x)[:].set(xp.nan)
+            kwargs = {'nan_policy': 'omit'}
+        elif nan_policy == 'marray':
+            if not SCIPY_ARRAY_API:
+                pytest.skip("MArray is only available if SCIPY_ARRAY_API=1")
+            if is_jax(xp):
+                pytest.skip("JAX currently incompatible with `marray`")
+            marray = pytest.importorskip('marray')
+            mxp = marray._get_namespace(xp)
+            mask = (x >= 0.)
+            x = mxp.asarray(x, mask=mask)
+            y = mxp.asarray(y)
+            kwargs = {}
+
+        with np.errstate(divide='ignore', invalid='ignore'):  # for method = 'linear'
+            res = stats.estimated_cdf(x, y, method=method, **kwargs)
+
+        if nan_policy == 'marray':
+            assert xp.all(res.mask)
+        else:
+            xp_assert_close(res, ref)
+
+    @pytest.mark.parametrize('x, y, ref, kwargs',
+        [
+         ([], 0.5, np.nan, {}),
+         ([1, 2, 3], [0.999, 3.001, np.nan], [0., 1., np.nan], {}),
+         ([1, 2, 3], [], [], {}),
+         ([[np.nan, 2]], 2, [np.nan, 0.5], {'nan_policy': 'omit', 'method': 'weibull'}),
+         ([[], []], 0.5, np.full(2, np.nan), {'axis': -1}),
+         ([[], []], 0.5, np.zeros((0,)), {'axis': 0, 'keepdims': False}),
+         ([[], []], 0.5, np.zeros((1, 0)), {'axis': 0, 'keepdims': True}),
+         ([], [0.5, 0.6], np.full(2, np.nan), {}),
+         (np.arange(1, 28).reshape((3, 3, 3)), 14., [[[0.5]]],
+          {'axis': None, 'keepdims': True}),
+         ([[1, 2], [3, 4]], [1.75, 2.5, 3.25], [[0.25, 0.5, 0.75]],
+          {'axis': None, 'keepdims': True}),
+         ([1, 2, 3], [-np.inf, np.inf], [0.0, 1.0], {}),
+         # It is our choice how much effort and computational overhead we want to put
+         # into adjusting for insane edge cases like when `x` contains infinite values,
+         # especially when `y` does, too.
+         # One practical argument would be that y = +/- inf should produce the same
+         # results as an extremely large finite number, in which case the 0th element
+         # of the 'linear' result should be `0`.
+         # Another argument would be for producing NaN below wherever y = +/- inf.
+         # Another would be that it's not appropriate to spend significant computation
+         # correcting these edge cases; we should just document what we do.
+         # In any case, this is the current status.
+         ([-np.inf, -1, 0, 1, np.inf], [-np.inf, -2, -1, 0, 1, 2, np.inf],
+          [0.25, 0.25, 0.25, 0.5 , 0.75, 0.75, np.nan], {'method':'linear'}),
+         ([-np.inf, -1, 0, 1, np.inf], [-np.inf, -2, -1, 0, 1, 2, np.inf],
+          [0.2, 0.2, 0.4, 0.6, 0.8, 0.8, 1.], {'method': 'inverted_cdf'}),
+        ])
+    def test_edge_cases(self, x, y, ref, kwargs, xp):
+        if kwargs.get('method', None) == 'weibull' and is_torch(xp):
+            pytest.skip('data-apis/array-api-compat#360')
+        if kwargs.get('axis', None) == -1 and is_cupy(xp):
+            pytest.skip('Fails; need to investigate.')
+        default_dtype = xp_default_dtype(xp)
+        x, y, ref = xp.asarray(x), xp.asarray(y), xp.asarray(ref, dtype=default_dtype)
+        res = stats.estimated_cdf(x, y, **kwargs)
+        xp_assert_close(res, ref, rtol=1e-15)
+
+    @pytest.mark.skip_xp_backends('jax.numpy', reason="-1e-45 is not less than 0?")
+    @pytest.mark.parametrize('method', _estimated_cdf_discontinuous_methods.keys())
+    def test_transition(self, method, xp):
+        # test that values of discontinuous estimators are as expected around
+        # transition point
+        x = np.arange(8., dtype=np.float64)
+        xl, xr = np.nextafter(x, -np.inf), np.nextafter(x, np.inf)
+        offset = 0.5 if method == 'closest_observation' else 0.0
+        ref_r = (x + 1 + offset) / 8
+        ref_r[-1] = 1.0  # value is greater than or equal to the maximum observation
+        ref_l = (x + offset) / 8
+        ref_l[0] = 0.0  # value is less than the minimum observation
+        x, xl, xr = xp.asarray(x), xp.asarray(xl), xp.asarray(xr)
+        ref_l, ref_r = xp.asarray(ref_l), xp.asarray(ref_r)
+        xp_assert_equal(stats.estimated_cdf(x, x, method=method), ref_r)
+        xp_assert_equal(stats.estimated_cdf(x, xr, method=method), ref_r)
+        xp_assert_equal(stats.estimated_cdf(x, xl, method=method), ref_l)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_rank.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_rank.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d8e89cfea0315e30d1ab0c45e8bdab282360761
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_rank.py
@@ -0,0 +1,350 @@
+import numpy as np
+from numpy.testing import assert_equal, assert_array_equal
+import pytest
+
+from scipy import stats
+from scipy.conftest import skip_xp_invalid_arg
+from scipy.stats import rankdata, tiecorrect
+from scipy._lib._array_api import (xp_assert_equal, make_xp_test_case, xp_result_type,
+                                   xp_default_dtype)
+
+skip_xp_backends = pytest.mark.skip_xp_backends
+
+class TestTieCorrect:
+
+    def test_empty(self):
+        """An empty array requires no correction, should return 1.0."""
+        ranks = np.array([], dtype=np.float64)
+        c = tiecorrect(ranks)
+        assert_equal(c, 1.0)
+
+    def test_one(self):
+        """A single element requires no correction, should return 1.0."""
+        ranks = np.array([1.0], dtype=np.float64)
+        c = tiecorrect(ranks)
+        assert_equal(c, 1.0)
+
+    def test_no_correction(self):
+        """Arrays with no ties require no correction."""
+        ranks = np.arange(2.0)
+        c = tiecorrect(ranks)
+        assert_equal(c, 1.0)
+        ranks = np.arange(3.0)
+        c = tiecorrect(ranks)
+        assert_equal(c, 1.0)
+
+    def test_basic(self):
+        """Check a few basic examples of the tie correction factor."""
+        # One tie of two elements
+        ranks = np.array([1.0, 2.5, 2.5])
+        c = tiecorrect(ranks)
+        T = 2.0
+        N = ranks.size
+        expected = 1.0 - (T**3 - T) / (N**3 - N)
+        assert_equal(c, expected)
+
+        # One tie of two elements (same as above, but tie is not at the end)
+        ranks = np.array([1.5, 1.5, 3.0])
+        c = tiecorrect(ranks)
+        T = 2.0
+        N = ranks.size
+        expected = 1.0 - (T**3 - T) / (N**3 - N)
+        assert_equal(c, expected)
+
+        # One tie of three elements
+        ranks = np.array([1.0, 3.0, 3.0, 3.0])
+        c = tiecorrect(ranks)
+        T = 3.0
+        N = ranks.size
+        expected = 1.0 - (T**3 - T) / (N**3 - N)
+        assert_equal(c, expected)
+
+        # Two ties, lengths 2 and 3.
+        ranks = np.array([1.5, 1.5, 4.0, 4.0, 4.0])
+        c = tiecorrect(ranks)
+        T1 = 2.0
+        T2 = 3.0
+        N = ranks.size
+        expected = 1.0 - ((T1**3 - T1) + (T2**3 - T2)) / (N**3 - N)
+        assert_equal(c, expected)
+
+    def test_overflow(self):
+        ntie, k = 2000, 5
+        a = np.repeat(np.arange(k), ntie)
+        n = a.size  # ntie * k
+        out = tiecorrect(rankdata(a))
+        assert_equal(out, 1.0 - k * (ntie**3 - ntie) / float(n**3 - n))
+
+
+@make_xp_test_case(stats.rankdata)
+class TestRankData:
+
+    def desired_dtype(self, x, *, xp):
+        return xp_result_type(x, force_floating=True, xp=xp)
+
+    def test_empty(self, xp):
+        """stats.rankdata of empty array should return an empty array."""
+        a = xp.asarray([], dtype=xp.int64)
+        r = rankdata(a)
+        xp_assert_equal(r, xp.asarray([], dtype=self.desired_dtype(a, xp=xp)))
+
+    def test_list(self):
+        # test that NumPy still accepts lists
+        r = rankdata([])
+        assert_array_equal(r, np.array([]))
+
+        r = rankdata([40, 10, 30, 10, 50])
+        assert_equal(r, [4.0, 1.5, 3.0, 1.5, 5.0])
+
+    @pytest.mark.parametrize("shape", [(0, 1, 2)])
+    @pytest.mark.parametrize("axis", [None, *range(3)])
+    def test_empty_multidim(self, shape, axis, xp):
+        a = xp.empty(shape, dtype=xp.int64)
+        r = rankdata(a, axis=axis)
+        expected_shape = (0,) if axis is None else shape
+        xp_assert_equal(r, xp.empty(expected_shape, dtype=self.desired_dtype(a, xp=xp)))
+
+    def test_one(self, xp):
+        """Check stats.rankdata with an array of length 1."""
+        data = [100]
+        a = xp.asarray(data, dtype=xp.int64)
+        r = rankdata(a)
+        xp_assert_equal(r, xp.asarray([1.0], dtype=self.desired_dtype(a, xp=xp)))
+
+    def test_basic(self, xp):
+        """Basic tests of stats.rankdata."""
+        desired_dtype = xp_default_dtype(xp)
+
+        data = [100, 10, 50]
+        expected = xp.asarray([3.0, 1.0, 2.0], dtype=desired_dtype)
+        a = xp.asarray(data, dtype=xp.int64)
+        r = rankdata(a)
+        xp_assert_equal(r, expected)
+
+        data = [40, 10, 30, 10, 50]
+        expected = xp.asarray([4.0, 1.5, 3.0, 1.5, 5.0], dtype=desired_dtype)
+        a = xp.asarray(data, dtype=xp.int64)
+        r = rankdata(a)
+        xp_assert_equal(r, expected)
+
+        data = [20, 20, 20, 10, 10, 10]
+        expected = xp.asarray([5.0, 5.0, 5.0, 2.0, 2.0, 2.0], dtype=desired_dtype)
+        a = xp.asarray(data, dtype=xp.int64)
+        r = rankdata(a)
+        xp_assert_equal(r, expected)
+
+        # # The docstring states explicitly that the argument is flattened.
+        a2d = xp.reshape(a, (2, 3))
+        r = rankdata(a2d)
+        xp_assert_equal(r, expected)
+
+    @skip_xp_invalid_arg
+    def test_rankdata_object_string(self):
+
+        def min_rank(a):
+            return [1 + sum(i < j for i in a) for j in a]
+
+        def max_rank(a):
+            return [sum(i <= j for i in a) for j in a]
+
+        def ordinal_rank(a):
+            return min_rank([(x, i) for i, x in enumerate(a)])
+
+        def average_rank(a):
+            return [(i + j) / 2.0 for i, j in zip(min_rank(a), max_rank(a))]
+
+        def dense_rank(a):
+            b = np.unique(a)
+            return [1 + sum(i < j for i in b) for j in a]
+
+        rankf = dict(min=min_rank, max=max_rank, ordinal=ordinal_rank,
+                     average=average_rank, dense=dense_rank)
+
+        def check_ranks(a):
+            for method in 'min', 'max', 'dense', 'ordinal', 'average':
+                out = rankdata(a, method=method)
+                assert_array_equal(out, rankf[method](a))
+
+        val = ['foo', 'bar', 'qux', 'xyz', 'abc', 'efg', 'ace', 'qwe', 'qaz']
+        check_ranks(np.random.choice(val, 200))
+        check_ranks(np.random.choice(val, 200).astype('object'))
+
+        val = np.array([0, 1, 2, 2.718, 3, 3.141], dtype='object')
+        check_ranks(np.random.choice(val, 200).astype('object'))
+
+    @pytest.mark.skip_xp_backends("torch", reason="`take_along_axis` fails with uint64")
+    def test_large_uint(self, xp):
+        data = xp.asarray([2**60, 2**60+1], dtype=xp.uint64)
+        r = rankdata(data)
+        desired_dtype = self.desired_dtype(data, xp=xp)
+        xp_assert_equal(r, xp.asarray([1.0, 2.0], dtype=desired_dtype))
+
+    def test_large_int(self, xp):
+        data = xp.asarray([2**60, 2**60+1], dtype=xp.int64)
+        r = rankdata(data)
+        desired_dtype = self.desired_dtype(data, xp=xp)
+        xp_assert_equal(r, xp.asarray([1.0, 2.0], dtype=desired_dtype))
+
+        data = xp.asarray([2**60, -2**60+1], dtype=xp.int64)
+        r = rankdata(data)
+        xp_assert_equal(r, xp.asarray([2.0, 1.0], dtype=desired_dtype))
+
+    @pytest.mark.parametrize('n', [10000, 100000, 1000000])
+    def test_big_tie(self, n, xp):
+        data = xp.ones(n)
+        r = rankdata(data)
+        expected_rank = 0.5 * (n + 1)
+        ref = xp.asarray(expected_rank * data, dtype=self.desired_dtype(data, xp=xp))
+        xp_assert_equal(r, ref)
+
+    def test_axis(self, xp):
+        data = xp.asarray([[0, 2, 1], [4, 2, 2]])
+
+        expected0 = xp.asarray([[1., 1.5, 1.], [2., 1.5, 2.]])
+        r0 = rankdata(data, axis=0)
+        xp_assert_equal(r0, expected0)
+
+        expected1 = xp.asarray([[1., 3., 2.], [3., 1.5, 1.5]])
+        r1 = rankdata(data, axis=1)
+        xp_assert_equal(r1, expected1)
+
+    methods= ["average", "min", "max", "dense", "ordinal"]
+
+    @pytest.mark.parametrize("axis", [0, 1])
+    @pytest.mark.parametrize("method", methods)
+    def test_size_0_axis(self, axis, method, xp):
+        shape = (3, 0)
+        data = xp.zeros(shape)
+        desired_dtype = self.desired_dtype(data, xp=xp)
+        r = rankdata(data, method=method, axis=axis)
+        assert_equal(r.shape, shape)
+        assert_equal(r.dtype, desired_dtype)
+        xp_assert_equal(r, xp.empty(shape, dtype=desired_dtype))
+
+    @pytest.mark.parametrize('axis', range(3))
+    @pytest.mark.parametrize('method', methods)
+    def test_nan_policy_omit_3d(self, axis, method):
+        shape = (20, 21, 22)
+        rng = np.random.RandomState(23983242)
+
+        a = rng.random(size=shape)
+        i = rng.random(size=shape) < 0.4
+        j = rng.random(size=shape) < 0.1
+        k = rng.random(size=shape) < 0.1
+        a[i] = np.nan
+        a[j] = -np.inf
+        a[k] - np.inf
+
+        def rank_1d_omit(a, method):
+            out = np.zeros_like(a)
+            i = np.isnan(a)
+            a_compressed = a[~i]
+            res = rankdata(a_compressed, method)
+            out[~i] = res
+            out[i] = np.nan
+            return out
+
+        def rank_omit(a, method, axis):
+            return np.apply_along_axis(lambda a: rank_1d_omit(a, method),
+                                       axis, a)
+
+        res = rankdata(a, method, axis=axis, nan_policy='omit')
+        res0 = rank_omit(a, method, axis=axis)
+
+        assert_array_equal(res, res0)
+
+    def test_nan_policy_2d_axis_none(self):
+        # 2 2d-array test with axis=None
+        data = [[0, np.nan, 3],
+                [4, 2, np.nan],
+                [1, 2, 2]]
+        assert_array_equal(rankdata(data, axis=None, nan_policy='omit'),
+                           [1., np.nan, 6., 7., 4., np.nan, 2., 4., 4.])
+        assert_array_equal(rankdata(data, axis=None, nan_policy='propagate'),
+                           [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan,
+                            np.nan, np.nan, np.nan])
+
+    def test_nan_policy_raise(self):
+        # 1 1d-array test
+        data = [0, 2, 3, -2, np.nan, np.nan]
+        with pytest.raises(ValueError, match="The input contains nan"):
+            rankdata(data, nan_policy='raise')
+
+        # 2 2d-array test
+        data = [[0, np.nan, 3],
+                [4, 2, np.nan],
+                [np.nan, 2, 2]]
+
+        with pytest.raises(ValueError, match="The input contains nan"):
+            rankdata(data, axis=0, nan_policy="raise")
+
+        with pytest.raises(ValueError, match="The input contains nan"):
+            rankdata(data, axis=1, nan_policy="raise")
+
+    def test_nan_policy_propagate(self):
+        # 1 1d-array test
+        data = [0, 2, 3, -2, np.nan, np.nan]
+        assert_array_equal(rankdata(data, nan_policy='propagate'),
+                           [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan])
+
+        # 2 2d-array test
+        data = [[0, np.nan, 3],
+                [4, 2, np.nan],
+                [1, 2, 2]]
+        assert_array_equal(rankdata(data, axis=0, nan_policy='propagate'),
+                           [[1, np.nan, np.nan],
+                            [3, np.nan, np.nan],
+                            [2, np.nan, np.nan]])
+        assert_array_equal(rankdata(data, axis=1, nan_policy='propagate'),
+                           [[np.nan, np.nan, np.nan],
+                            [np.nan, np.nan, np.nan],
+                            [1, 2.5, 2.5]])
+
+    _rankdata_cases = (
+        # values, method, expected
+        ([], 'average', []),
+        ([], 'min', []),
+        ([], 'max', []),
+        ([], 'dense', []),
+        ([], 'ordinal', []),
+        #
+        ([100], 'average', [1.0]),
+        ([100], 'min', [1.0]),
+        ([100], 'max', [1.0]),
+        ([100], 'dense', [1.0]),
+        ([100], 'ordinal', [1.0]),
+        #
+        ([100, 100, 100], 'average', [2.0, 2.0, 2.0]),
+        ([100, 100, 100], 'min', [1.0, 1.0, 1.0]),
+        ([100, 100, 100], 'max', [3.0, 3.0, 3.0]),
+        ([100, 100, 100], 'dense', [1.0, 1.0, 1.0]),
+        ([100, 100, 100], 'ordinal', [1.0, 2.0, 3.0]),
+        #
+        ([100, 300, 200], 'average', [1.0, 3.0, 2.0]),
+        ([100, 300, 200], 'min', [1.0, 3.0, 2.0]),
+        ([100, 300, 200], 'max', [1.0, 3.0, 2.0]),
+        ([100, 300, 200], 'dense', [1.0, 3.0, 2.0]),
+        ([100, 300, 200], 'ordinal', [1.0, 3.0, 2.0]),
+        #
+        ([100, 200, 300, 200], 'average', [1.0, 2.5, 4.0, 2.5]),
+        ([100, 200, 300, 200], 'min', [1.0, 2.0, 4.0, 2.0]),
+        ([100, 200, 300, 200], 'max', [1.0, 3.0, 4.0, 3.0]),
+        ([100, 200, 300, 200], 'dense', [1.0, 2.0, 3.0, 2.0]),
+        ([100, 200, 300, 200], 'ordinal', [1.0, 2.0, 4.0, 3.0]),
+        #
+        ([100, 200, 300, 200, 100], 'average', [1.5, 3.5, 5.0, 3.5, 1.5]),
+        ([100, 200, 300, 200, 100], 'min', [1.0, 3.0, 5.0, 3.0, 1.0]),
+        ([100, 200, 300, 200, 100], 'max', [2.0, 4.0, 5.0, 4.0, 2.0]),
+        ([100, 200, 300, 200, 100], 'dense', [1.0, 2.0, 3.0, 2.0, 1.0]),
+        ([100, 200, 300, 200, 100], 'ordinal', [1.0, 3.0, 5.0, 4.0, 2.0]),
+        #
+        ([10] * 30, 'ordinal', np.arange(1.0, 31.0)),
+    )
+
+    @pytest.mark.parametrize('case', _rankdata_cases)
+    def test_cases(self, case, xp):
+        values, method, expected = case
+        values = xp.asarray(values)
+        r = rankdata(xp.asarray(values), method=method)
+        ref = xp.asarray(expected, dtype=self.desired_dtype(values, xp=xp))
+        xp_assert_equal(r, ref)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_relative_risk.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_relative_risk.py
new file mode 100644
index 0000000000000000000000000000000000000000..b75e64d929f319465b1f1d62af4fb2096c2ab2ac
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_relative_risk.py
@@ -0,0 +1,95 @@
+import pytest
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+from scipy.stats.contingency import relative_risk
+
+
+# Test just the calculation of the relative risk, including edge
+# cases that result in a relative risk of 0, inf or nan.
+@pytest.mark.parametrize(
+    'exposed_cases, exposed_total, control_cases, control_total, expected_rr',
+    [(1, 4, 3, 8, 0.25 / 0.375),
+     (0, 10, 5, 20, 0),
+     (0, 10, 0, 20, np.nan),
+     (5, 15, 0, 20, np.inf)]
+)
+def test_relative_risk(exposed_cases, exposed_total,
+                       control_cases, control_total, expected_rr):
+    result = relative_risk(exposed_cases, exposed_total,
+                           control_cases, control_total)
+    assert_allclose(result.relative_risk, expected_rr, rtol=1e-13)
+
+
+def test_relative_risk_confidence_interval():
+    result = relative_risk(exposed_cases=16, exposed_total=128,
+                           control_cases=24, control_total=256)
+    rr = result.relative_risk
+    ci = result.confidence_interval(confidence_level=0.95)
+    # The corresponding calculation in R using the epitools package.
+    #
+    # > library(epitools)
+    # > c <- matrix(c(232, 112, 24, 16), nrow=2)
+    # > result <- riskratio(c)
+    # > result$measure
+    #               risk ratio with 95% C.I.
+    # Predictor  estimate     lower    upper
+    #   Exposed1 1.000000        NA       NA
+    #   Exposed2 1.333333 0.7347317 2.419628
+    #
+    # The last line is the result that we want.
+    assert_allclose(rr, 4/3)
+    assert_allclose((ci.low, ci.high), (0.7347317, 2.419628), rtol=5e-7)
+
+
+def test_relative_risk_ci_conflevel0():
+    result = relative_risk(exposed_cases=4, exposed_total=12,
+                           control_cases=5, control_total=30)
+    rr = result.relative_risk
+    assert_allclose(rr, 2.0, rtol=1e-14)
+    ci = result.confidence_interval(0)
+    assert_allclose((ci.low, ci.high), (2.0, 2.0), rtol=1e-12)
+
+
+def test_relative_risk_ci_conflevel1():
+    result = relative_risk(exposed_cases=4, exposed_total=12,
+                           control_cases=5, control_total=30)
+    ci = result.confidence_interval(1)
+    assert_equal((ci.low, ci.high), (0, np.inf))
+
+
+def test_relative_risk_ci_edge_cases_00():
+    result = relative_risk(exposed_cases=0, exposed_total=12,
+                           control_cases=0, control_total=30)
+    assert_equal(result.relative_risk, np.nan)
+    ci = result.confidence_interval()
+    assert_equal((ci.low, ci.high), (np.nan, np.nan))
+
+
+def test_relative_risk_ci_edge_cases_01():
+    result = relative_risk(exposed_cases=0, exposed_total=12,
+                           control_cases=1, control_total=30)
+    assert_equal(result.relative_risk, 0)
+    ci = result.confidence_interval()
+    assert_equal((ci.low, ci.high), (0.0, np.nan))
+
+
+def test_relative_risk_ci_edge_cases_10():
+    result = relative_risk(exposed_cases=1, exposed_total=12,
+                           control_cases=0, control_total=30)
+    assert_equal(result.relative_risk, np.inf)
+    ci = result.confidence_interval()
+    assert_equal((ci.low, ci.high), (np.nan, np.inf))
+
+
+@pytest.mark.parametrize('ec, et, cc, ct', [(0, 0, 10, 20),
+                                            (-1, 10, 1, 5),
+                                            (1, 10, 0, 0),
+                                            (1, 10, -1, 4)])
+def test_relative_risk_bad_value(ec, et, cc, ct):
+    with pytest.raises(ValueError, match="must be an integer not less than"):
+        relative_risk(ec, et, cc, ct)
+
+
+def test_relative_risk_bad_type():
+    with pytest.raises(TypeError, match="must be an integer"):
+        relative_risk(1, 10, 2.0, 40)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_resampling.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_resampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec900da2d5a05570054596286f82c67f96fbdc0d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_resampling.py
@@ -0,0 +1,2084 @@
+import warnings
+
+import pytest
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+
+from scipy._lib._util import rng_integers
+from scipy._lib._array_api import (is_numpy, make_xp_test_case, xp_default_dtype,
+                                   xp_size, array_namespace, _xp_copy_to_numpy,
+                                   is_lazy_array, eager_warns)
+from scipy._lib._array_api_no_0d import xp_assert_close, xp_assert_equal
+from scipy._external import array_api_extra as xpx
+from scipy import stats, special
+from scipy.fft.tests.test_fftlog import skip_xp_backends
+from scipy.optimize import root
+
+from scipy.stats import bootstrap, monte_carlo_test, permutation_test, power
+import scipy.stats._resampling as _resampling
+
+
+lazy_xp_modules = [stats]
+
+
+@make_xp_test_case(bootstrap)
+class TestBootstrap:
+    @skip_xp_backends('numpy', reason='NumPy does not raise')
+    def test_bootstrap_iv_other(self, xp):
+        message = f"When using array library {xp.__name__}"
+        with pytest.raises(TypeError, match=message):
+            bootstrap((xp.asarray([1, 2, 3]),), lambda x: xp.mean(x))
+
+    def test_bootstrap_iv(self, xp):
+        sample = xp.asarray([1, 2, 3])
+
+        message = "`data` must contain at least one sample."
+        with pytest.raises(ValueError, match=message):
+            bootstrap(tuple(), xp.mean)
+
+        message = "each sample in `data` must contain two or more observations..."
+        with pytest.raises(ValueError, match=message):
+            bootstrap((sample, xp.asarray([1])), xp.mean)
+
+        message = ("When `paired is True`, all samples must have the same length ")
+        with pytest.raises(ValueError, match=message):
+            bootstrap((sample, xp.asarray([1, 2, 3, 4])), xp.mean, paired=True)
+
+        message = "`vectorized` must be `True`, `False`, or `None`."
+        with pytest.raises(ValueError, match=message):
+            bootstrap(sample, xp.mean, vectorized='ekki')
+
+        message = "`axis` must be an integer."
+        with pytest.raises(ValueError, match=message):
+            bootstrap((sample,), xp.mean, axis=1.5)
+
+        message = "could not convert string to float"
+        with pytest.raises(ValueError, match=message):
+            bootstrap((sample,), xp.mean, confidence_level='ni')
+
+        message = "`n_resamples` must be a non-negative integer."
+        with pytest.raises(ValueError, match=message):
+            bootstrap((sample,), xp.mean, n_resamples=-1000)
+
+        message = "`n_resamples` must be a non-negative integer."
+        with pytest.raises(ValueError, match=message):
+            bootstrap((sample,), xp.mean, n_resamples=1000.5)
+
+        message = "`batch` must be a positive integer or None."
+        with pytest.raises(ValueError, match=message):
+            bootstrap((sample,), xp.mean, batch=-1000)
+
+        message = "`batch` must be a positive integer or None."
+        with pytest.raises(ValueError, match=message):
+            bootstrap((sample,), xp.mean, batch=1000.5)
+
+        message = "`method` must be in"
+        with pytest.raises(ValueError, match=message):
+            bootstrap((sample,), xp.mean, method='ekki')
+
+        message = "`bootstrap_result` must have attribute `bootstrap_distribution'"
+        with pytest.raises(ValueError, match=message):
+            bootstrap((sample,), xp.mean, bootstrap_result=10)
+
+        message = "Either `bootstrap_result.bootstrap_distribution.size`"
+        with pytest.raises(ValueError, match=message):
+            bootstrap((sample,), xp.mean, n_resamples=0)
+
+        message = "SeedSequence expects int or sequence of ints"
+        with pytest.raises(TypeError, match=message):
+            bootstrap((sample,), xp.mean, rng='herring')
+
+    @pytest.mark.parametrize("method", ['basic', 'percentile', 'BCa'])
+    @pytest.mark.parametrize("axis", [0, 1, 2])
+    def test_bootstrap_batch(self, method, axis, xp):
+        # for one-sample statistics, batch size shouldn't affect the result
+        rng = np.random.RandomState(0)
+
+        x = rng.rand(10, 11, 12)
+        # SPEC-007 leave one call with random_state to ensure it still works
+        res1 = bootstrap((xp.asarray(x),), xp.mean, batch=None, method=method,
+                         random_state=0, axis=axis, n_resamples=100)
+        rng = np.random.RandomState(0)
+        res2 = bootstrap((xp.asarray(x),), xp.mean, batch=10, method=method,
+                         axis=axis, n_resamples=100, random_state=rng)
+
+        xp_assert_close(res2.confidence_interval.low, res1.confidence_interval.low)
+        xp_assert_close(res2.confidence_interval.high, res1.confidence_interval.high)
+        xp_assert_close(res2.standard_error, res1.standard_error)
+
+    @pytest.mark.parametrize("method", ['basic', 'percentile', 'BCa'])
+    def test_bootstrap_paired(self, method, xp):
+        # test that `paired` works as expected
+        rng = np.random.RandomState(0)
+        n = 100
+        x = xp.asarray(rng.rand(n))
+        y = xp.asarray(rng.rand(n))
+
+        def my_statistic(x, y, axis=-1):
+            return xp.mean((x-y)**2, axis=axis)
+
+        def my_paired_statistic(i, axis=-1):
+            a = x[i]
+            b = y[i]
+            res = my_statistic(a, b)
+            return res
+
+        i = xp.arange(x.shape[0])
+
+        res1 = bootstrap((i,), my_paired_statistic, rng=0)
+        res2 = bootstrap((x, y), my_statistic, paired=True, rng=0)
+
+        xp_assert_close(res1.confidence_interval.low, res2.confidence_interval.low)
+        xp_assert_close(res1.confidence_interval.high, res2.confidence_interval.high)
+        xp_assert_close(res1.standard_error, res2.standard_error)
+
+    @pytest.mark.parametrize("method", ['basic', 'percentile', 'BCa'])
+    @pytest.mark.parametrize("axis", [0, 1, 2])
+    @pytest.mark.parametrize("paired", [True, False])
+    def test_bootstrap_vectorized(self, method, axis, paired, xp):
+        # test that paired is vectorized as expected: when samples are tiled,
+        # CI and standard_error of each axis-slice is the same as those of the
+        # original 1d sample
+
+        rng = np.random.RandomState(0)
+
+        def my_statistic(x, y, z, axis=-1):
+            return xp.mean(x, axis=axis) + xp.mean(y, axis=axis) + xp.mean(z, axis=axis)
+
+        shape = 10, 11, 12
+        n_samples = shape[axis]
+
+        x = rng.rand(n_samples)
+        y = rng.rand(n_samples)
+        z = rng.rand(n_samples)
+        x, y, z = xp.asarray(x), xp.asarray(y), xp.asarray(z)
+
+        res1 = bootstrap((x, y, z), my_statistic, paired=paired, method=method,
+                         rng=0, axis=0, n_resamples=100)
+        assert (res1.bootstrap_distribution.shape
+                == res1.standard_error.shape + (100,))
+
+        reshape = [1, 1, 1]
+        reshape[axis] = n_samples
+        reshape = tuple(reshape)
+        x = xp.broadcast_to(xp.reshape(x, reshape), shape)
+        y = xp.broadcast_to(xp.reshape(y, reshape), shape)
+        z = xp.broadcast_to(xp.reshape(z, reshape), shape)
+        res2 = bootstrap((x, y, z), my_statistic, paired=paired, method=method,
+                         rng=0, axis=axis, n_resamples=100)
+
+        result_shape = list(shape)
+        result_shape.pop(axis)
+
+        ref_ci_low = xp.broadcast_to(res2.confidence_interval.low, result_shape)
+        ref_ci_high = xp.broadcast_to(res2.confidence_interval.high, result_shape)
+        ref_ci_standard_error = xp.broadcast_to(res2.standard_error, result_shape)
+
+        xp_assert_close(res2.confidence_interval.low, ref_ci_low)
+        xp_assert_close(res2.confidence_interval.high, ref_ci_high)
+        xp_assert_close(res2.standard_error, ref_ci_standard_error)
+
+    @pytest.mark.slow
+    @pytest.mark.xfail_on_32bit("MemoryError with BCa observed in CI")
+    @pytest.mark.parametrize("method", ['basic', 'percentile', 'BCa'])
+    def test_bootstrap_against_theory(self, method, xp):
+        # based on https://www.statology.org/confidence-intervals-python/
+        rng = np.random.default_rng(2442101192988600726)
+        data = stats.norm.rvs(loc=5, scale=2, size=5000, random_state=rng)
+        alpha = 0.95
+        dist = stats.t(df=len(data)-1, loc=np.mean(data), scale=stats.sem(data))
+        ref_low, ref_high = dist.interval(confidence=alpha)
+        ref_se = dist.std()
+
+        config = dict(data=(xp.asarray(data),), statistic=xp.mean, n_resamples=5000,
+                      method=method, rng=rng)
+        res = bootstrap(**config, confidence_level=alpha)
+
+        xp_assert_close(res.confidence_interval.low, xp.asarray(ref_low), rtol=5e-4)
+        xp_assert_close(res.confidence_interval.high, xp.asarray(ref_high), rtol=5e-4)
+        xp_assert_close(res.standard_error, xp.asarray(ref_se), atol=3e-4)
+
+        config.update(dict(n_resamples=0, bootstrap_result=res))
+        res = bootstrap(**config, confidence_level=alpha, alternative='less')
+        xp_assert_close(res.confidence_interval.high,
+                        xp.asarray(dist.ppf(alpha)), rtol=5e-4)
+
+        config.update(dict(n_resamples=0, bootstrap_result=res))
+        res = bootstrap(**config, confidence_level=alpha, alternative='greater')
+        xp_assert_close(res.confidence_interval.low,
+                        xp.asarray(dist.ppf(1-alpha)), rtol=5e-4)
+
+    tests_R = [("basic", 23.77, 79.12),
+               ("percentile", 28.86, 84.21),
+               ("BCa", 32.31, 91.43)]
+
+    @pytest.mark.parametrize("method, ref_low, ref_high", tests_R)
+    def test_bootstrap_against_R(self, method, ref_low, ref_high, xp):
+        # Compare against R's "boot" library
+        # library(boot)
+
+        # stat <- function (x, a) {
+        #     mean(x[a])
+        # }
+
+        # x <- c(10, 12, 12.5, 12.5, 13.9, 15, 21, 22,
+        #        23, 34, 50, 81, 89, 121, 134, 213)
+
+        # # Use a large value so we get a few significant digits for the CI.
+        # n = 1000000
+        # bootresult = boot(x, stat, n)
+        # result <- boot.ci(bootresult)
+        # print(result)
+        x = xp.asarray([10, 12, 12.5, 12.5, 13.9, 15, 21, 22,
+                        23, 34, 50, 81, 89, 121, 134, 213])
+        res = bootstrap((x,), xp.mean, n_resamples=1000000, method=method, rng=0)
+        xp_assert_close(res.confidence_interval.low, xp.asarray(ref_low), rtol=0.005)
+        xp_assert_close(res.confidence_interval.high, xp.asarray(ref_high), rtol=0.005)
+
+    def test_multisample_BCa_against_R(self, xp):
+        # Because bootstrap is stochastic, it's tricky to test against reference
+        # behavior. Here, we show that SciPy's BCa CI matches R wboot's BCa CI
+        # much more closely than the other SciPy CIs do.
+
+        # arbitrary skewed data
+        x = xp.asarray([0.75859206, 0.5910282, -0.4419409, -0.36654601,
+                        0.34955357, -1.38835871, 0.76735821])
+        y = xp.asarray([1.41186073, 0.49775975, 0.08275588, 0.24086388,
+                        0.03567057, 0.52024419, 0.31966611, 1.32067634])
+
+        # a multi-sample statistic for which the BCa CI tends to be different
+        # from the other CIs
+        def statistic(x, y, axis):
+            s1 = stats.skew(x, axis=axis)
+            s2 = stats.skew(y, axis=axis)
+            return s1 - s2
+
+        # compute confidence intervals using each method
+        rng = np.random.default_rng(468865032284792692)
+
+        res_basic = stats.bootstrap((x, y), statistic, method='basic',
+                                    batch=100, rng=rng)
+        res_percent = stats.bootstrap((x, y), statistic, method='percentile',
+                                      batch=100, rng=rng)
+        res_bca = stats.bootstrap((x, y), statistic, method='bca',
+                                  batch=100, rng=rng)
+
+        # compute midpoints so we can compare just one number for each
+        mid_basic = xp.mean(xp.stack(res_basic.confidence_interval))
+        mid_percent = xp.mean(xp.stack(res_percent.confidence_interval))
+        mid_bca = xp.mean(xp.stack(res_bca.confidence_interval))
+
+        # reference for BCA CI computed using R wboot package:
+        # library(wBoot)
+        # library(moments)
+
+        # x = c(0.75859206, 0.5910282, -0.4419409, -0.36654601,
+        #       0.34955357, -1.38835871,  0.76735821)
+        # y = c(1.41186073, 0.49775975, 0.08275588, 0.24086388,
+        #       0.03567057, 0.52024419, 0.31966611, 1.32067634)
+
+        # twoskew <- function(x1, y1) {skewness(x1) - skewness(y1)}
+        # boot.two.bca(x, y, skewness, conf.level = 0.95,
+        #              R = 9999, stacked = FALSE)
+        mid_wboot = -1.5519
+
+        # compute percent difference relative to wboot BCA method
+        diff_basic = (mid_basic - mid_wboot)/abs(mid_wboot)
+        diff_percent = (mid_percent - mid_wboot)/abs(mid_wboot)
+        diff_bca = (mid_bca - mid_wboot)/abs(mid_wboot)
+
+        # SciPy's BCa CI midpoint is much closer than that of the other methods
+        assert diff_basic < -0.15
+        assert diff_percent > 0.15
+        assert abs(diff_bca) < 0.03
+
+    def test_BCa_acceleration_against_reference(self, xp):
+        # Compare the (deterministic) acceleration parameter for a multi-sample
+        # problem against a reference value. The example is from [1], but Efron's
+        # value seems inaccurate. Straightforward code for computing the
+        # reference acceleration (0.011008228344026734) is available at:
+        # https://github.com/scipy/scipy/pull/16455#issuecomment-1193400981
+
+        y = xp.asarray([10., 27., 31., 40., 46., 50., 52., 104., 146.])
+        z = xp.asarray([16., 23., 38., 94., 99., 141., 197.])
+
+        def statistic(z, y, axis=0):
+            return xp.mean(z, axis=axis) - xp.mean(y, axis=axis)
+
+        data = [z, y]
+        res = stats.bootstrap(data, statistic)
+
+        axis = -1
+        alpha = 0.95
+        theta_hat_b = res.bootstrap_distribution
+        batch = 100
+        _, _, a_hat = _resampling._bca_interval(data, statistic, axis, alpha,
+                                                theta_hat_b, batch, xp)
+        xp_assert_close(a_hat, xp.asarray(0.011008228344026734))
+
+    tests_against_itself_1samp = {"basic": 1789,
+                                  "percentile": 1790,
+                                  "BCa": 1789}
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize("method, expected",
+                             tests_against_itself_1samp.items())
+    def test_bootstrap_against_itself_1samp(self, method, expected, xp):
+        # The expected values in this test were generated using bootstrap
+        # to check for unintended changes in behavior. The test also makes sure
+        # that bootstrap works with multi-sample statistics and that the
+        # `axis` argument works as expected / function is vectorized.
+        rng = np.random.default_rng(9123847)
+
+        n = 100  # size of sample
+        n_resamples = 999  # number of bootstrap resamples used to form each CI
+        confidence_level = 0.9
+
+        # The true mean is 5
+        dist = stats.norm(loc=5, scale=1)
+        stat_true = float(dist.mean())
+
+        # Do the same thing 2000 times. (The code is fully vectorized.)
+        n_replications = 2000
+        data = dist.rvs(size=(n_replications, n), random_state=rng)
+        res = bootstrap((xp.asarray(data),),
+                        statistic=xp.mean,
+                        confidence_level=confidence_level,
+                        n_resamples=n_resamples,
+                        batch=50,
+                        method=method,
+                        axis=-1,
+                        rng=rng)
+        ci = res.confidence_interval
+
+        # ci contains vectors of lower and upper confidence interval bounds
+        ci_contains_true = xp.count_nonzero((ci[0] < stat_true) & (stat_true < ci[1]))
+        assert ci_contains_true == expected
+
+        # ci_contains_true is not inconsistent with confidence_level
+        pvalue = stats.binomtest(int(ci_contains_true), n_replications,
+                                 confidence_level).pvalue
+        assert pvalue > 0.1
+
+    tests_against_itself_2samp = {"basic": 892,
+                                  "percentile": 890}
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize("method, expected",
+                             tests_against_itself_2samp.items())
+    def test_bootstrap_against_itself_2samp(self, method, expected, xp):
+        # The expected values in this test were generated using bootstrap
+        # to check for unintended changes in behavior. The test also makes sure
+        # that bootstrap works with multi-sample statistics and that the
+        # `axis` argument works as expected / function is vectorized.
+        rng = np.random.RandomState(0)
+
+        n1 = 100  # size of sample 1
+        n2 = 120  # size of sample 2
+        n_resamples = 999  # number of bootstrap resamples used to form each CI
+        confidence_level = 0.9
+
+        # The statistic we're interested in is the difference in means
+        def my_stat(data1, data2, axis=-1):
+            mean1 = xp.mean(data1, axis=axis)
+            mean2 = xp.mean(data2, axis=axis)
+            return mean1 - mean2
+
+        # The true difference in the means is -0.1
+        dist1 = stats.norm(loc=0, scale=1)
+        dist2 = stats.norm(loc=0.1, scale=1)
+        stat_true = float(dist1.mean() - dist2.mean())
+
+        # Do the same thing 1000 times. (The code is fully vectorized.)
+        n_replications = 1000
+        data1 = dist1.rvs(size=(n_replications, n1), random_state=rng)
+        data2 = dist2.rvs(size=(n_replications, n2), random_state=rng)
+        res = bootstrap((xp.asarray(data1), xp.asarray(data2)),
+                        statistic=my_stat,
+                        confidence_level=confidence_level,
+                        n_resamples=n_resamples,
+                        batch=50,
+                        method=method,
+                        axis=-1,
+                        random_state=rng)
+        ci = res.confidence_interval
+
+        # ci contains vectors of lower and upper confidence interval bounds
+        ci_contains_true = xp.count_nonzero((ci[0] < stat_true) & (stat_true < ci[1]))
+        assert ci_contains_true == expected
+
+        # ci_contains_true is not inconsistent with confidence_level
+        pvalue = stats.binomtest(int(ci_contains_true), n_replications,
+                                 confidence_level).pvalue
+        assert pvalue > 0.1
+
+    @pytest.mark.parametrize("method", ["basic", "percentile", "BCa"])
+    @pytest.mark.parametrize("axis", [0, 1])
+    @pytest.mark.parametrize("dtype", ['float32', 'float64'])
+    def test_bootstrap_vectorized_3samp(self, method, axis, dtype, xp):
+        def statistic(*data, axis=0):
+            # an arbitrary, vectorized statistic
+            return sum(xp.mean(sample, axis=axis) for sample in data)
+
+        def statistic_1d(*data):
+            # the same statistic, not vectorized
+            for sample in data:
+                assert sample.ndim == 1
+            return np.asarray(sum(sample.mean() for sample in data), dtype=dtype)
+
+        rng = np.random.RandomState(0)
+        x = rng.rand(4, 5).astype(dtype)
+        y = rng.rand(4, 5).astype(dtype)
+        z = rng.rand(4, 5).astype(dtype)
+        res1 = bootstrap((xp.asarray(x), xp.asarray(y), xp.asarray(z)),
+                         statistic, vectorized=True, axis=axis, n_resamples=100,
+                         method=method, rng=0)
+        res2 = bootstrap((x, y, z), statistic_1d, vectorized=False,
+                         axis=axis, n_resamples=100, method=method, rng=0)
+
+        rtol = 1e-6 if dtype == 'float32' else 1e-14
+        xp_assert_close(res1.confidence_interval.low,
+                        xp.asarray(res2.confidence_interval.low), rtol=rtol)
+        xp_assert_close(res1.confidence_interval.high,
+                        xp.asarray(res2.confidence_interval.high), rtol=rtol)
+        xp_assert_close(res1.standard_error, xp.asarray(res2.standard_error), rtol=rtol)
+
+    @pytest.mark.xfail_on_32bit("Failure is not concerning; see gh-14107")
+    @pytest.mark.parametrize("method", ["basic", "percentile", "BCa"])
+    @pytest.mark.parametrize("axis", [0, 1])
+    def test_bootstrap_vectorized_1samp(self, method, axis, xp):
+        def statistic(x, axis=0):
+            # an arbitrary, vectorized statistic
+            return xp.mean(x, axis=axis)
+
+        def statistic_1d(x):
+            # the same statistic, not vectorized
+            assert x.ndim == 1
+            return x.mean(axis=0)
+
+        rng = np.random.default_rng(7939952824)
+        x = rng.random((2, 3))
+        res1 = bootstrap((xp.asarray(x),), statistic, vectorized=True, axis=axis,
+                         n_resamples=100, batch=None, method=method, rng=0)
+        res2 = bootstrap((x,), statistic_1d, vectorized=False, axis=axis,
+                         n_resamples=100, batch=10, method=method, rng=0)
+        xp_assert_close(res1.confidence_interval.low,
+                        xp.asarray(res2.confidence_interval.low))
+        xp_assert_close(res1.confidence_interval.high,
+                        xp.asarray(res2.confidence_interval.high))
+        xp_assert_close(res1.standard_error, xp.asarray(res2.standard_error))
+
+    @pytest.mark.parametrize("method", ["basic", "percentile", "BCa"])
+    def test_bootstrap_degenerate(self, method, xp):
+        data = xp.full((35,), 10000.)
+        if method == "BCa":
+            with np.errstate(invalid='ignore'):
+                msg = "The BCa confidence interval cannot be calculated"
+                with eager_warns(stats.DegenerateDataWarning, match=msg, xp=xp):
+                    res = bootstrap([data, ], xp.mean, method=method)
+                    xp_assert_equal(res.confidence_interval.low, xp.asarray(xp.nan))
+                    xp_assert_equal(res.confidence_interval.high, xp.asarray(xp.nan))
+        else:
+            res = bootstrap([data, ], xp.mean, method=method)
+            xp_assert_equal(res.confidence_interval.low, xp.asarray(10000.))
+            xp_assert_equal(res.confidence_interval.high, xp.asarray(10000.))
+        xp_assert_close(res.standard_error, xp.asarray(0.), atol=1e-12)
+
+    @pytest.mark.parametrize("method", ["BCa", "basic", "percentile"])
+    def test_bootstrap_gh15678(self, method, xp):
+        # Check that gh-15678 is fixed: when statistic function returned a Python
+        # float, method="BCa" failed when trying to add a dimension to the float
+        rng = np.random.default_rng(354645618886684)
+        dist = stats.norm(loc=2, scale=4)
+        data = dist.rvs(size=100, random_state=rng)
+        res = bootstrap((xp.asarray(data),), stats.skew, method=method, n_resamples=100,
+                        rng=np.random.default_rng(9563))
+        # this always worked because np.apply_along_axis returns NumPy data type
+        ref = bootstrap((data,), stats.skew, method=method, n_resamples=100,
+                        rng=np.random.default_rng(9563), vectorized=False)
+        xp_assert_close(res.confidence_interval.low,
+                        xp.asarray(ref.confidence_interval.low))
+        xp_assert_close(res.confidence_interval.high,
+                        xp.asarray(ref.confidence_interval.high))
+        xp_assert_close(res.standard_error, xp.asarray(ref.standard_error))
+
+    def test_bootstrap_min(self, xp):
+        # Check that gh-15883 is fixed: percentileofscore should
+        # behave according to the 'mean' behavior and not trigger nan for BCa
+        rng = np.random.default_rng(1891289180021102)
+        dist = stats.norm(loc=2, scale=4)
+        data = dist.rvs(size=100, random_state=rng)
+        true_min = np.min(data)
+        data = xp.asarray(data)
+        res = bootstrap((data,), xp.min, method="BCa", n_resamples=100,
+                        rng=np.random.default_rng(3942))
+        xp_assert_equal(res.confidence_interval.low, xp.asarray(true_min))
+        res2 = bootstrap((-data,), xp.max, method="BCa", n_resamples=100,
+                         rng=np.random.default_rng(3942))
+        xp_assert_close(-res.confidence_interval.low, res2.confidence_interval.high)
+        xp_assert_close(-res.confidence_interval.high, res2.confidence_interval.low)
+
+    @pytest.mark.parametrize("additional_resamples", [0, 1000])
+    def test_re_bootstrap(self, additional_resamples, xp):
+        # Test behavior of parameter `bootstrap_result`
+        rng = np.random.default_rng(8958153316228384)
+        x = rng.random(size=100)
+
+        n1 = 1000
+        n2 = additional_resamples
+        n3 = n1 + additional_resamples
+
+        rng = np.random.default_rng(296689032789913033)
+        res = stats.bootstrap((xp.asarray(x),), xp.mean, n_resamples=n1, rng=rng,
+                              confidence_level=0.95, method='percentile')
+        res = stats.bootstrap((xp.asarray(x),), xp.mean, n_resamples=n2, rng=rng,
+                              confidence_level=0.90, method='BCa',
+                              bootstrap_result=res)
+
+        rng = np.random.default_rng(296689032789913033)
+        ref = stats.bootstrap((xp.asarray(x),), xp.mean, n_resamples=n3, rng=rng,
+                              confidence_level=0.90, method='BCa')
+
+        xp_assert_close(res.confidence_interval.low,
+                        xp.asarray(ref.confidence_interval.low),
+                        rtol=1e-14)
+        xp_assert_close(res.confidence_interval.high,
+                        xp.asarray(ref.confidence_interval.high),
+                        rtol=1e-14)
+        xp_assert_close(res.standard_error, xp.asarray(ref.standard_error), rtol=1e-14)
+
+    @pytest.mark.xfail_on_32bit("Sensitive to machine precision")
+    @pytest.mark.parametrize("method", ['basic', 'percentile', 'BCa'])
+    def test_bootstrap_alternative(self, method, xp):
+        rng = np.random.default_rng(5894822712842015040)
+        dist = stats.norm(loc=2, scale=4)
+        data = (xp.asarray(dist.rvs(size=(100), random_state=rng)),)
+
+        config = dict(data=data, statistic=xp.std, rng=rng, axis=-1)
+        t = stats.bootstrap(**config, confidence_level=0.9)
+
+        config.update(dict(n_resamples=0, bootstrap_result=t))
+        l = stats.bootstrap(**config, confidence_level=0.95, alternative='less')
+        g = stats.bootstrap(**config, confidence_level=0.95, alternative='greater')
+
+        xp_assert_close(l.confidence_interval.high, t.confidence_interval.high,
+                        rtol=1e-14)
+        xp_assert_close(g.confidence_interval.low, t.confidence_interval.low,
+                        rtol=1e-14)
+        assert xp.isinf(l.confidence_interval.low) and l.confidence_interval.low < 0
+        assert xp.isinf(g.confidence_interval.high) and g.confidence_interval.high > 0
+
+        with pytest.raises(ValueError, match='`alternative` must be one of'):
+            stats.bootstrap(**config, alternative='ekki-ekki')
+
+    def test_jackknife_resample(self, xp):
+        shape = 3, 4, 5, 6
+        rng = np.random.default_rng(5274950392)
+        x = rng.random(size=shape)
+        y = next(_resampling._jackknife_resample(xp.asarray(x), xp=xp))
+
+        for i in range(shape[-1]):
+            # each resample is indexed along second to last axis
+            # (last axis is the one the statistic will be taken over / consumed)
+            slc = y[..., i, :]
+            expected = np.delete(x, i, axis=-1)
+
+            xp_assert_equal(slc, xp.asarray(expected))
+
+        y2 = list(_resampling._jackknife_resample(xp.asarray(x), batch=2, xp=xp))
+        xp_assert_equal(xp.concat(y2, axis=-2), y)
+
+    @pytest.mark.skip_xp_backends("array_api_strict",
+                                  reason="Test uses ... + fancy indexing")
+    @pytest.mark.parametrize("rng_name", ["RandomState", "default_rng"])
+    def test_bootstrap_resample(self, rng_name, xp):
+        rng_gen = getattr(np.random, rng_name)
+        rng1 = rng_gen(3949441460)
+        rng2 = rng_gen(3949441460)
+
+        n_resamples = 10
+        shape = 3, 4, 5, 6
+
+        rng = np.random.default_rng(5274950392)
+        x = xp.asarray(rng.random(shape))
+        y = _resampling._bootstrap_resample(x, n_resamples, rng=rng1, xp=xp)
+
+        for i in range(n_resamples):
+            # each resample is indexed along second to last axis
+            # (last axis is the one the statistic will be taken over / consumed)
+            slc = y[..., i, :]
+
+            js = xp.asarray(rng_integers(rng2, 0, shape[-1], shape[-1]))
+            expected = x[..., js]
+
+            xp_assert_equal(slc, expected)
+
+    @pytest.mark.parametrize("score", [0, 0.5, 1])
+    @pytest.mark.parametrize("axis", [0, 1, 2])
+    def test_percentile_of_score(self, score, axis, xp):
+        shape = 10, 20, 30
+        rng = np.random.default_rng(5903363153)
+        x = rng.random(shape)
+        dtype = xp_default_dtype(xp)
+        p = _resampling._percentile_of_score(xp.asarray(x, dtype=dtype),
+                                             xp.asarray(score, dtype=dtype),
+                                             axis=-1, xp=xp)
+
+        def vectorized_pos(a, score, axis):
+            return np.apply_along_axis(stats.percentileofscore, axis, a, score)
+
+        p2 = vectorized_pos(x, score, axis=-1)/100
+
+        xp_assert_close(p, xp.asarray(p2, dtype=dtype), rtol=1e-15)
+
+    @pytest.mark.parametrize("axis", [0, 1, 2])
+    def test_vectorize_statistic(self, axis):
+        # test that _vectorize_statistic vectorizes a statistic along `axis`
+        # only used with NumPy arrays
+
+        def statistic(*data, axis):
+            # an arbitrary, vectorized statistic
+            return sum(sample.mean(axis) for sample in data)
+
+        def statistic_1d(*data):
+            # the same statistic, not vectorized
+            for sample in data:
+                assert sample.ndim == 1
+            return statistic(*data, axis=0)
+
+        # vectorize the non-vectorized statistic
+        statistic2 = _resampling._vectorize_statistic(statistic_1d)
+
+        rng = np.random.RandomState(0)
+        x = rng.rand(4, 5, 6)
+        y = rng.rand(4, 1, 6)
+        z = rng.rand(1, 5, 6)
+
+        res1 = statistic(x, y, z, axis=axis)
+        res2 = statistic2(x, y, z, axis=axis)
+        assert_allclose(res1, res2)
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize("method", ["basic", "percentile", "BCa"])
+    def test_vector_valued_statistic(self, method, xp):
+        # Generate 95% confidence interval around MLE of normal distribution
+        # parameters. Repeat 100 times, each time on sample of size 100.
+        # Check that confidence interval contains true parameters ~95 times.
+        # Confidence intervals are estimated and stochastic; a test failure
+        # does not necessarily indicate that something is wrong. More important
+        # than values of `counts` below is that the shapes of the outputs are
+        # correct.
+
+        rng = np.random.default_rng(2196847219)
+        params = 1, 0.5
+        sample = xp.asarray(rng.normal(*params, size=(100, 100)))
+
+        def statistic(data, axis):
+            return xp.stack([xp.mean(data, axis=axis),
+                             xp.std(data, axis=axis, correction=1)])
+
+        res = bootstrap((sample,), statistic, method=method, axis=-1,
+                        n_resamples=9999, batch=200, random_state=rng)
+
+        params = xp.asarray([1, 0.5])
+        counts = xp.count_nonzero((res.confidence_interval.low.T < params)
+                                  & (res.confidence_interval.high.T > params),
+                                  axis=0)
+        assert xp.all(counts >= 90)
+        assert xp.all(counts <= 100)
+        assert res.confidence_interval.low.shape == (2, 100)
+        assert res.confidence_interval.high.shape == (2, 100)
+        assert res.standard_error.shape == (2, 100)
+        assert res.bootstrap_distribution.shape == (2, 100, 9999)
+
+    @pytest.mark.slow
+    @pytest.mark.filterwarnings('ignore::RuntimeWarning')
+    def test_vector_valued_statistic_gh17715(self):
+        # gh-17715 reported a mistake introduced in the extension of BCa to
+        # multi-sample statistics; a `len` should have been `.shape[-1]`. Check
+        # that this is resolved.
+        # If this is resolved for NumPy, it's resolved for the rest,
+        # let's only run this for NumPy.
+
+        rng = np.random.default_rng(141921000979291141)
+
+        def concordance(x, y, axis):
+            xm = x.mean(axis)
+            ym = y.mean(axis)
+            cov = ((x - xm[..., None]) * (y - ym[..., None])).mean(axis)
+            return (2 * cov) / (x.var(axis) + y.var(axis) + (xm - ym) ** 2)
+
+        def statistic(tp, tn, fp, fn, axis):
+            actual = tp + fp
+            expected = tp + fn
+            return np.nan_to_num(concordance(actual, expected, axis))
+
+        def statistic_extradim(*args, axis):
+            return statistic(*args, axis)[np.newaxis, ...]
+
+        data = [[4, 0, 0, 2],  # (tp, tn, fp, fn)
+                [2, 1, 2, 1],
+                [0, 6, 0, 0],
+                [0, 6, 3, 0],
+                [0, 8, 1, 0]]
+        data = np.array(data).T
+
+        res = bootstrap(data, statistic_extradim, rng=rng, paired=True)
+        ref = bootstrap(data, statistic, rng=rng, paired=True)
+        assert_allclose(res.confidence_interval.low[0],
+                        ref.confidence_interval.low, atol=1e-15)
+        assert_allclose(res.confidence_interval.high[0],
+                        ref.confidence_interval.high, atol=1e-15)
+
+    # torch doesn't have T distribution CDF and can't fall back to NumPy on GPU
+    @pytest.mark.skip_xp_backends(np_only=True, exceptions=['cupy'])
+    def test_gh_20850(self, xp):
+        rng = np.random.default_rng(2085020850)
+        x = xp.asarray(rng.random((10, 2)))
+        y = xp.asarray(rng.random((11, 2)))
+        def statistic(x, y, axis):
+            return stats.ttest_ind(x, y, axis=axis).statistic
+
+        # The shapes do *not* need to be the same along axis
+        stats.bootstrap((x, y), statistic)
+        stats.bootstrap((x.T, y.T), statistic, axis=1)
+        # But even when the shapes *are* the same along axis, the lengths
+        # along other dimensions have to be the same (or `bootstrap` warns).
+        message = "Array shapes are incompatible for broadcasting."
+        with pytest.raises(ValueError, match=message):
+            stats.bootstrap((x, y[:10, 0]), statistic)  # this won't work after 1.16
+        stats.bootstrap((x, y[:10, 0:1]), statistic)  # this will
+        stats.bootstrap((x.T, y.T[0:1, :10]), statistic, axis=1)  # this will
+
+# --- Test Monte Carlo Hypothesis Test --- #
+
+@make_xp_test_case(monte_carlo_test)
+class TestMonteCarloHypothesisTest:
+    atol = 2.5e-2  # for comparing p-value
+
+    def get_rvs(self, rvs_in, rs, dtype=None, xp=np):
+        return lambda *args, **kwds: xp.asarray(rvs_in(*args, random_state=rs, **kwds),
+                                                dtype=dtype)
+
+    def get_statistic(self, xp):
+        def statistic(x, axis):
+            m = xp.mean(x, axis=axis)
+            v = xp.var(x, axis=axis, correction=1)
+            n = x.shape[axis]
+            return m / (v/n)**0.5
+            # return stats.ttest_1samp(x, popmean=0., axis=axis).statistic)
+        return statistic
+
+    def test_input_validation(self, xp):
+        # test that the appropriate error messages are raised for invalid input
+
+        data = xp.asarray([1., 2., 3.])
+        def stat(x, axis=None):
+            return xp.mean(x, axis=axis)
+
+        message = "Array shapes are incompatible for broadcasting."
+        temp = (xp.zeros((2, 5)), xp.zeros((3, 5)))
+        rvs = (stats.norm.rvs, stats.norm.rvs)
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(temp, rvs, lambda x, y, axis: 1, axis=-1)
+
+        message = "`axis` must be an integer."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, axis=1.5)
+
+        message = "`vectorized` must be `True`, `False`, or `None`."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, vectorized=1.5)
+
+        message = "`rvs` must be callable or sequence of callables."
+        with pytest.raises(TypeError, match=message):
+            monte_carlo_test(data, None, stat)
+        with pytest.raises(TypeError, match=message):
+            temp = xp.asarray([[1., 2.], [3., 4.]])
+            monte_carlo_test(temp, [lambda x: x, None], stat)
+
+        message = "If `rvs` is a sequence..."
+        with pytest.raises(ValueError, match=message):
+            temp = xp.asarray([[1., 2., 3.]])
+            monte_carlo_test(temp, [lambda x: x, lambda x: x], stat)
+
+        message = "`statistic` must be callable."
+        with pytest.raises(TypeError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, None)
+
+        message = "`n_resamples` must be a positive integer."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, n_resamples=-1000)
+
+        message = "`n_resamples` must be a positive integer."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, n_resamples=1000.5)
+
+        message = "`batch` must be a positive integer or None."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, batch=-1000)
+
+        message = "`batch` must be a positive integer or None."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, batch=1000.5)
+
+        message = "`alternative` must be in..."
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(data, stats.norm.rvs, stat, alternative='ekki')
+
+        # *If* this raises a value error, make sure it has the intended message
+        message = "Signature inspection of statistic"
+        def rvs(size):
+            return xp.asarray(stats.norm.rvs(size=size))
+        try:
+            monte_carlo_test(data, rvs, xp.mean)
+        except ValueError as e:
+            assert str(e).startswith(message)
+
+    def test_input_validation_xp(self, xp):
+        def non_vectorized_statistic(x):
+            return xp.mean(x)
+
+        message = "`statistic` must be vectorized..."
+        sample = xp.asarray([1., 2., 3.])
+        if is_numpy(xp):
+            monte_carlo_test(sample, stats.norm.rvs, non_vectorized_statistic)
+            return
+
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(sample, stats.norm.rvs, non_vectorized_statistic)
+        with pytest.raises(ValueError, match=message):
+            monte_carlo_test(sample, stats.norm.rvs, xp.mean, vectorized=False)
+
+    @pytest.mark.xslow
+    def test_batch(self, xp):
+        # make sure that the `batch` parameter is respected by checking the
+        # maximum batch size provided in calls to `statistic`
+        rng = np.random.default_rng(23492340193)
+        x = xp.asarray(rng.standard_normal(size=10))
+
+        def statistic(x, axis):
+            batch_size = 1 if x.ndim == 1 else x.shape[0]
+            statistic.batch_size = max(batch_size, statistic.batch_size)
+            statistic.counter += 1
+            return self.get_statistic(xp)(x, axis=axis)
+        statistic.counter = 0
+        statistic.batch_size = 0
+
+        kwds = {'sample': x, 'statistic': statistic,
+                'n_resamples': 1000, 'vectorized': True}
+
+        kwds['rvs'] = self.get_rvs(stats.norm.rvs, np.random.default_rng(328423), xp=xp)
+        res1 = monte_carlo_test(batch=1, **kwds)
+        assert_equal(statistic.counter, 1001)
+        assert_equal(statistic.batch_size, 1)
+
+        kwds['rvs'] = self.get_rvs(stats.norm.rvs, np.random.default_rng(328423), xp=xp)
+        statistic.counter = 0
+        res2 = monte_carlo_test(batch=50, **kwds)
+        assert_equal(statistic.counter, 21)
+        assert_equal(statistic.batch_size, 50)
+
+        kwds['rvs'] = self.get_rvs(stats.norm.rvs, np.random.default_rng(328423), xp=xp)
+        statistic.counter = 0
+        res3 = monte_carlo_test(**kwds)
+        assert_equal(statistic.counter, 2)
+        assert_equal(statistic.batch_size, 1000)
+
+        xp_assert_equal(res1.pvalue, res3.pvalue)
+        xp_assert_equal(res2.pvalue, res3.pvalue)
+
+    @pytest.mark.parametrize('axis', range(-3, 3))
+    def test_axis_dtype(self, axis, xp):
+        # test that Nd-array samples are handled correctly for valid values
+        # of the `axis` parameter; also make sure non-default dtype is maintained
+        rng = np.random.default_rng(2389234)
+        size = [2, 3, 4]
+        size[axis] = 100
+
+        # Determine non-default dtype
+        dtype_default = xp.asarray(1.).dtype
+        dtype_str = 'float32'if ("64" in str(dtype_default)) else 'float64'
+        dtype_np = getattr(np, dtype_str)
+        dtype = getattr(xp, dtype_str)
+
+        # ttest_1samp is CPU array-API compatible, but it would be good to
+        # include CuPy in this test. We'll perform ttest_1samp with a
+        # NumPy array, but all the rest with be done with fully array-API
+        # compatible code.
+        x = rng.standard_normal(size=size, dtype=dtype_np)
+        expected = stats.ttest_1samp(x, popmean=0., axis=axis)
+
+        x = xp.asarray(x, dtype=dtype)
+        statistic = self.get_statistic(xp)
+        rvs = self.get_rvs(stats.norm.rvs, rng, dtype=dtype, xp=xp)
+
+        res = monte_carlo_test(x, rvs, statistic, vectorized=True,
+                               n_resamples=20000, axis=axis)
+
+        ref_statistic = xp.asarray(expected.statistic, dtype=dtype)
+        ref_pvalue = xp.asarray(expected.pvalue, dtype=dtype)
+        xp_assert_close(res.statistic, ref_statistic)
+        xp_assert_close(res.pvalue, ref_pvalue, atol=self.atol)
+
+    @pytest.mark.parametrize('alternative', ("two-sided", "less", "greater"))
+    def test_alternative(self, alternative, xp):
+        # test that `alternative` is working as expected
+        rng = np.random.default_rng(65723433)
+
+        x = rng.standard_normal(size=30)
+        ref = stats.ttest_1samp(x, 0., alternative=alternative)
+
+        x = xp.asarray(x)
+        statistic = self.get_statistic(xp)
+        rvs = self.get_rvs(stats.norm.rvs, rng, xp=xp)
+
+        res = monte_carlo_test(x, rvs, statistic, alternative=alternative)
+
+        xp_assert_close(res.statistic, xp.asarray(ref.statistic))
+        xp_assert_close(res.pvalue, xp.asarray(ref.pvalue), atol=self.atol)
+
+
+    # Tests below involve statistics that are not yet array-API compatible.
+    # They can be converted when the statistics are converted.
+    @pytest.mark.slow
+    @pytest.mark.parametrize('alternative', ("less", "greater"))
+    @pytest.mark.parametrize('a', np.linspace(-0.5, 0.5, 5))  # skewness
+    def test_against_ks_1samp(self, alternative, a):
+        # test that monte_carlo_test can reproduce pvalue of ks_1samp
+        rng = np.random.default_rng(65723433)
+
+        x = stats.skewnorm.rvs(a=a, size=30, random_state=rng)
+        expected = stats.ks_1samp(x, stats.norm.cdf, alternative=alternative)
+
+        def statistic1d(x):
+            return stats.ks_1samp(x, stats.norm.cdf, mode='asymp',
+                                  alternative=alternative).statistic
+
+        norm_rvs = self.get_rvs(stats.norm.rvs, rng)
+        res = monte_carlo_test(x, norm_rvs, statistic1d,
+                               n_resamples=1000, vectorized=False,
+                               alternative=alternative)
+
+        assert_allclose(res.statistic, expected.statistic)
+        if alternative == 'greater':
+            assert_allclose(res.pvalue, expected.pvalue, atol=self.atol)
+        elif alternative == 'less':
+            assert_allclose(1-res.pvalue, expected.pvalue, atol=self.atol)
+
+    @pytest.mark.parametrize('hypotest', (stats.skewtest, stats.kurtosistest))
+    @pytest.mark.parametrize('alternative', ("less", "greater", "two-sided"))
+    @pytest.mark.parametrize('a', np.linspace(-2, 2, 5))  # skewness
+    def test_against_normality_tests(self, hypotest, alternative, a):
+        # test that monte_carlo_test can reproduce pvalue of normality tests
+        rng = np.random.default_rng(85723405)
+
+        x = stats.skewnorm.rvs(a=a, size=150, random_state=rng)
+        expected = hypotest(x, alternative=alternative)
+
+        def statistic(x, axis):
+            return hypotest(x, axis=axis).statistic
+
+        norm_rvs = self.get_rvs(stats.norm.rvs, rng)
+        res = monte_carlo_test(x, norm_rvs, statistic, vectorized=True,
+                               alternative=alternative)
+
+        assert_allclose(res.statistic, expected.statistic)
+        assert_allclose(res.pvalue, expected.pvalue, atol=self.atol)
+
+    @pytest.mark.parametrize('a', np.arange(-2, 3))  # skewness parameter
+    def test_against_normaltest(self, a):
+        # test that monte_carlo_test can reproduce pvalue of normaltest
+        rng = np.random.default_rng(12340513)
+
+        x = stats.skewnorm.rvs(a=a, size=150, random_state=rng)
+        expected = stats.normaltest(x)
+
+        def statistic(x, axis):
+            return stats.normaltest(x, axis=axis).statistic
+
+        norm_rvs = self.get_rvs(stats.norm.rvs, rng)
+        res = monte_carlo_test(x, norm_rvs, statistic, vectorized=True,
+                               alternative='greater')
+
+        assert_allclose(res.statistic, expected.statistic)
+        assert_allclose(res.pvalue, expected.pvalue, atol=self.atol)
+
+    @pytest.mark.xslow
+    @pytest.mark.parametrize('a', np.linspace(-0.5, 0.5, 5))  # skewness
+    def test_against_cramervonmises(self, a):
+        # test that monte_carlo_test can reproduce pvalue of cramervonmises
+        rng = np.random.default_rng(234874135)
+
+        x = stats.skewnorm.rvs(a=a, size=30, random_state=rng)
+        expected = stats.cramervonmises(x, stats.norm.cdf)
+
+        def statistic1d(x):
+            return stats.cramervonmises(x, stats.norm.cdf).statistic
+
+        norm_rvs = self.get_rvs(stats.norm.rvs, rng)
+        res = monte_carlo_test(x, norm_rvs, statistic1d,
+                               n_resamples=1000, vectorized=False,
+                               alternative='greater')
+
+        assert_allclose(res.statistic, expected.statistic)
+        assert_allclose(res.pvalue, expected.pvalue, atol=self.atol)
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize('dist_name', ['norm', 'logistic'])
+    @pytest.mark.parametrize('target_statistic', [0.6, 0.7, 0.8])
+    def test_against_anderson(self, dist_name, target_statistic):
+        # test that monte_carlo_test can reproduce results of `anderson`.
+
+        # find the skewness for which the sample statistic is within the range of
+        # values tubulated by `anderson`
+
+        def fun(a):
+            rng = np.random.default_rng(394295467)
+            x = stats.tukeylambda.rvs(a, size=100, random_state=rng)
+            expected = stats.anderson(x, dist_name, method='interpolate')
+            return expected.statistic - target_statistic
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", RuntimeWarning)
+            sol = root(fun, x0=0)
+        assert sol.success
+
+        # get the significance level (p-value) associated with that critical
+        # value
+        a = sol.x[0]
+        rng = np.random.default_rng(394295467)
+        x = stats.tukeylambda.rvs(a, size=100, random_state=rng)
+        expected = stats.anderson(x, dist_name, method='interpolate')
+        expected_stat = expected.statistic
+        expected_p = expected.pvalue
+
+        # perform equivalent Monte Carlo test and compare results
+        def statistic1d(x):
+            return stats.anderson(x, dist_name, method='interpolate').statistic
+
+        dist_rvs = self.get_rvs(getattr(stats, dist_name).rvs, rng)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", RuntimeWarning)
+            res = monte_carlo_test(x, dist_rvs,
+                                   statistic1d, n_resamples=1000,
+                                   vectorized=False, alternative='greater')
+
+        assert_allclose(res.statistic, expected_stat)
+        assert_allclose(res.pvalue, expected_p, atol=2*self.atol)
+
+    def test_p_never_zero(self):
+        # Use biased estimate of p-value to ensure that p-value is never zero
+        # per monte_carlo_test reference [1]
+        rng = np.random.default_rng(2190176673029737545)
+        x = np.zeros(100)
+        res = monte_carlo_test(x, rng.random, np.mean,
+                               vectorized=True, alternative='less')
+        assert res.pvalue == 0.0001
+
+    def test_against_ttest_ind(self):
+        # test that `monte_carlo_test` can reproduce results of `ttest_ind`.
+        rng = np.random.default_rng(219017667302737545)
+        data = rng.random(size=(2, 5)), rng.random(size=7)  # broadcastable
+        rvs = rng.normal, rng.normal
+        def statistic(x, y, axis):
+            return stats.ttest_ind(x, y, axis=axis).statistic
+
+        res = stats.monte_carlo_test(data, rvs, statistic, axis=-1)
+        ref = stats.ttest_ind(data[0], [data[1]], axis=-1)
+        assert_allclose(res.statistic, ref.statistic)
+        assert_allclose(res.pvalue, ref.pvalue, rtol=2e-2)
+
+    def test_against_f_oneway(self):
+        # test that `monte_carlo_test` can reproduce results of `f_oneway`.
+        rng = np.random.default_rng(219017667302737545)
+        data = (rng.random(size=(2, 100)), rng.random(size=(2, 101)),
+                rng.random(size=(2, 102)), rng.random(size=(2, 103)))
+        rvs = rng.normal, rng.normal, rng.normal, rng.normal
+
+        def statistic(*args, axis):
+            return stats.f_oneway(*args, axis=axis).statistic
+
+        res = stats.monte_carlo_test(data, rvs, statistic, axis=-1,
+                                     alternative='greater')
+        ref = stats.f_oneway(*data, axis=-1)
+
+        assert_allclose(res.statistic, ref.statistic)
+        assert_allclose(res.pvalue, ref.pvalue, atol=1e-2)
+
+    @pytest.mark.fail_slow(2)
+    @pytest.mark.xfail_on_32bit("Statistic may not depend on sample order on 32-bit")
+    def test_finite_precision_statistic(self):
+        # Some statistics return numerically distinct values when the values
+        # should be equal in theory. Test that `monte_carlo_test` accounts
+        # for this in some way.
+        rng = np.random.default_rng(2549824598234528)
+        n_resamples = 9999
+        def rvs(size):
+            return 1. * stats.bernoulli(p=0.333).rvs(size=size, random_state=rng)
+
+        x = rvs(100)
+        res = stats.monte_carlo_test(x, rvs, np.var, alternative='less',
+                                     n_resamples=n_resamples)
+        # show that having a tolerance matters
+        c0 = np.sum(res.null_distribution <= res.statistic)
+        c1 = np.sum(res.null_distribution <= res.statistic*(1+1e-15))
+        assert c0 != c1
+        assert res.pvalue == (c1 + 1)/(n_resamples + 1)
+
+
+@make_xp_test_case(power)
+class TestPower:
+    def xp_normal(self, rng, *, xp, dtype=None):
+        dtype = xp_default_dtype(xp) if dtype is None else dtype
+        return lambda size: xp.asarray(rng.normal(size=size), dtype=dtype)
+
+    def test_input_validation(self, xp):
+        # test that the appropriate error messages are raised for invalid input
+        rng = np.random.default_rng(8519895914314711673)
+
+        test = stats.ttest_ind
+        rvs = (self.xp_normal(rng, xp=xp), self.xp_normal(rng, xp=xp))
+        n_observations = (xp.asarray(10), xp.asarray(12))
+
+        message = "`vectorized` must be `True`, `False`, or `None`."
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, n_observations, vectorized=1.5)
+
+        message = "`rvs` must be callable or sequence of callables."
+        with pytest.raises(TypeError, match=message):
+            power(test, None, n_observations)
+        with pytest.raises(TypeError, match=message):
+            power(test, (self.xp_normal(rng, xp=xp), 'ekki'), n_observations)
+
+        message = "If `rvs` is a sequence..."
+        with pytest.raises(ValueError, match=message):
+            power(test, (self.xp_normal(rng, xp=xp),), n_observations)
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, (10,))
+
+        message = "`significance` must be of floating point dtype."
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, n_observations, significance=xp.asarray(True))
+
+        significance = xp.asarray(2.)
+        if not is_lazy_array(significance):
+            message = "All elements of `significance` must be between 0. and 1."
+            with pytest.raises(ValueError, match=message):
+                power(test, rvs, n_observations, significance=significance)
+
+        message = "`kwargs` must be a dictionary"
+        with pytest.raises(TypeError, match=message):
+            power(test, rvs, n_observations, kwargs=(1, 2, 3))
+
+        message = (
+            "not be broadcast"
+            "|Chunks do not add"
+            "|Incompatible shapes"
+            "|Attempting to broadcast"
+        )
+        with pytest.raises((ValueError, RuntimeError), match=message):
+            power(test, rvs, (xp.asarray([10, 11]), xp.asarray([12, 13, 14])))
+        with pytest.raises((ValueError, RuntimeError), match=message):
+            power(test, rvs, (xp.asarray([10, 11]), xp.asarray([12, 13])),
+                  kwargs={'x': xp.asarray([1, 2, 3])})
+
+        message = "`test` must be callable"
+        with pytest.raises(TypeError, match=message):
+            power(None, rvs, n_observations)
+
+        message = "`n_resamples` must be a positive integer"
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, n_observations, n_resamples=-10)
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, n_observations, n_resamples=10.5)
+
+        message = "`batch` must be a positive integer"
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, n_observations, batch=-10)
+        with pytest.raises(ValueError, match=message):
+            power(test, rvs, n_observations, batch=10.5)
+
+    @pytest.mark.slow
+    def test_batch(self, xp):
+        # make sure that the `batch` parameter is respected by checking the
+        # maximum batch size provided in calls to `test`
+        rng = np.random.default_rng(23492340193)
+
+        def test(x, axis):
+            batch_size = 1 if x.ndim == 1 else x.shape[0]
+            test.batch_size = max(batch_size, test.batch_size)
+            test.counter += 1
+            return stats.ttest_1samp(x, xp.asarray(0.), axis=axis).pvalue
+        test.counter = 0
+        test.batch_size = 0
+
+        kwds = dict(test=test,
+                    n_observations=xp.asarray(10),
+                    n_resamples=1000)
+
+        rng = np.random.default_rng(23492340193)
+        res1 = power(**kwds, rvs=self.xp_normal(rng, xp=xp), batch=1)
+        assert test.counter == 1000
+        assert test.batch_size == 1
+
+        rng = np.random.default_rng(23492340193)
+        test.counter = 0
+        res2 = power(**kwds, rvs=self.xp_normal(rng, xp=xp), batch=50)
+        assert test.counter == 20
+        assert test.batch_size == 50
+
+        rng = np.random.default_rng(23492340193)
+        test.counter = 0
+        res3 = power(**kwds, rvs=self.xp_normal(rng, xp=xp), batch=1000)
+        assert test.counter == 1
+        assert test.batch_size == 1000
+
+        xp_assert_equal(res1.power, res3.power)
+        xp_assert_equal(res2.power, res3.power)
+
+    @pytest.mark.slow
+    def test_vectorization(self, xp):
+        # Test that `power` is vectorized as expected
+        rng = np.random.default_rng(25495254834552)
+        alternatives = {-1: 'less', 0:'two-sided', 1: 'greater'}
+
+        # Single vectorized call
+        popmeans = xp.asarray([0, 0.2])
+        def test(x, alternative, axis=-1):
+            # ensure that popmeans axis is zeroth and orthogonal to the rest
+            popmeans_expanded = xpx.expand_dims(popmeans,
+                                                axis=tuple(range(1, x.ndim + 1)))
+            alternative = alternatives[int(alternative)]
+            return stats.ttest_1samp(x, popmeans_expanded, alternative=alternative,
+                                     axis=axis)
+
+        # nx and kwargs broadcast against one another
+        nx = xp.asarray([10, 15, 20, 50, 100])[:, xp.newaxis]
+        kwargs = {'alternative': xp.asarray([-1, 0, 1])}
+
+        # This dimension is added to the beginning
+        significance = xp.asarray([0.01, 0.025, 0.05, 0.1])
+        res = stats.power(test, self.xp_normal(rng, xp=xp), nx,
+                          significance=significance, kwargs=kwargs)
+
+        # Looping over all combinations
+        ref = []
+        for significance_i in significance:
+            for i in range(nx.shape[0]):
+                nx_i = nx[i, ...]
+                for alternative_i in kwargs['alternative']:
+                    for j in range(popmeans.shape[0]):
+                        popmean_j = popmeans[j]
+                        def test2(x, axis=-1):
+                            return stats.ttest_1samp(x, popmean_j, axis=axis,
+                                alternative=alternatives[int(alternative_i)])
+
+                        tmp = stats.power(test2, self.xp_normal(rng, xp=xp), nx_i,
+                                          significance=significance_i)
+                        ref.append(tmp.power)
+        ref = xp.reshape(xp.stack(ref), res.power.shape)
+
+        # Show that results are similar
+        xp_assert_close(res.power, ref, rtol=2e-2, atol=1e-2)
+
+    @pytest.mark.skip_xp_backends(cpu_only=True,
+                                  exceptions=['cupy', 'jax.numpy', 'dask.array'])
+    def test_ttest_ind_null(self, xp):
+        # Check that the p-values of `ttest_ind` are uniformly distributed under
+        # the null hypothesis
+        rng = np.random.default_rng(254952548345528)
+
+        test = stats.ttest_ind
+        n_observations = (xp.asarray(rng.integers(10, 100, size=(10))),
+                          xp.asarray(rng.integers(10, 100, size=(10))))
+        rvs = self.xp_normal(rng, xp=xp), self.xp_normal(rng, xp=xp)
+        significance = xp.asarray([0.01, 0.05, 0.1])
+        res = stats.power(test, rvs, n_observations, significance=significance)
+        significance = xp.broadcast_to(significance[:, xp.newaxis], res.power.shape)
+        xp_assert_close(res.power, significance, atol=1e-2)
+
+    @pytest.mark.skip_xp_backends('array_api_strict',
+                                  reason='currently combines integer and float arrays')
+    @pytest.mark.skip_xp_backends(cpu_only=True,
+                                  exceptions=['cupy', 'jax.numpy', 'dask.array'])
+    def test_ttest_1samp_power(self, xp):
+        # Check simulated ttest_1samp power against reference
+        rng = np.random.default_rng(254952548345528)
+
+        # Reference values computed with statmodels
+        # import numpy as np
+        # from statsmodels.stats.power import tt_solve_power
+        # tt_solve_power = np.vectorize(tt_solve_power)
+        # tt_solve_power([0.1, 0.5, 0.9], [[10], [20]], [[[0.01]], [[0.05]]])
+        ref = [[[0.0126515 , 0.10269751, 0.40415802],
+                [0.01657775, 0.29734608, 0.86228288]],
+               [[0.0592903 , 0.29317561, 0.71718121],
+                [0.07094116, 0.56450441, 0.96815163]]]
+
+        kwargs = {'popmean': xp.asarray([0.1, 0.5, 0.9])}
+        n_observations = xp.asarray([[10], [20]])
+        significance = xp.asarray([0.01, 0.05])
+        res = stats.power(stats.ttest_1samp, self.xp_normal(rng, xp=xp), n_observations,
+                          significance=significance, kwargs=kwargs)
+        xp_assert_close(res.power, xp.asarray(ref), atol=1e-2)
+
+
+@make_xp_test_case(permutation_test)
+class TestPermutationTest:
+
+    rtol = 1e-14
+
+    def setup_method(self):
+        self.rng = np.random.default_rng(7170559330470561044)
+
+    # -- Input validation -- #
+    def test_permutation_test_iv(self, xp):
+
+        def stat(x, y, axis):
+            return stats.ttest_ind((x, y), axis).statistic
+
+        data = (xp.asarray([1, 2, 3]), xp.asarray([1, 2, 3]))
+
+        message = "each sample in `data` must contain two or more ..."
+        with pytest.raises(ValueError, match=message):
+            permutation_test((data[0], xp.asarray([0])), stat)
+
+        message = "`data` must be a tuple containing at least two samples"
+        with pytest.raises(ValueError, match=message):
+            permutation_test((1,), stat)
+        with pytest.raises(TypeError, match=message):
+            permutation_test(1, stat)
+
+        message = "`axis` must be an integer."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(data, stat, axis=1.5)
+
+        message = "`permutation_type` must be in..."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(data, stat,
+                             permutation_type="ekki")
+
+        message = "`vectorized` must be `True`, `False`, or `None`."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(data, stat, vectorized=1.5)
+
+        message = "`n_resamples` must be a positive integer."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(data, stat, n_resamples=-1000)
+
+        message = "`n_resamples` must be a positive integer."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(data, stat, n_resamples=1000.5)
+
+        message = "`batch` must be a positive integer or None."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(data, stat, batch=-1000)
+
+        message = "`batch` must be a positive integer or None."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(data, stat, batch=1000.5)
+
+        message = "`alternative` must be in..."
+        with pytest.raises(ValueError, match=message):
+            permutation_test(data, stat, alternative='ekki')
+
+        message = "SeedSequence expects int or sequence of ints"
+        with pytest.raises(TypeError, match=message):
+            permutation_test(data, stat, rng='herring')
+
+    # -- Test Parameters -- #
+    # SPEC-007 leave one call with seed to check it still works
+    @pytest.mark.parametrize('random_state', [np.random.RandomState,
+                                              np.random.default_rng])
+    @pytest.mark.parametrize('permutation_type',
+                             ['pairings', 'samples', 'independent'])
+    def test_batch(self, permutation_type, random_state, xp):
+        # make sure that the `batch` parameter is respected by checking the
+        # maximum batch size provided in calls to `statistic`
+        x = xp.asarray(self.rng.random(10))
+        y = xp.asarray(self.rng.random(10))
+
+        def statistic(x, y, axis):
+            batch_size = 1 if x.ndim == 1 else x.shape[0]
+            statistic.batch_size = max(batch_size, statistic.batch_size)
+            statistic.counter += 1
+            return xp.mean(x, axis=axis) - xp.mean(y, axis=axis)
+        statistic.counter = 0
+        statistic.batch_size = 0
+
+        kwds = {'n_resamples': 100, 'permutation_type': permutation_type,
+                'vectorized': True}
+        res1 = stats.permutation_test((x, y), statistic, batch=1,
+                                      random_state=random_state(0), **kwds)
+        assert statistic.counter == 101
+        assert statistic.batch_size == 1
+
+        statistic.counter = 0
+        res2 = stats.permutation_test((x, y), statistic, batch=50,
+                                      random_state=random_state(0), **kwds)
+        assert statistic.counter == 3
+        assert statistic.batch_size == 50
+
+        statistic.counter = 0
+        res3 = stats.permutation_test((x, y), statistic, batch=100,
+                                      random_state=random_state(0), **kwds)
+        assert statistic.counter == 2
+        assert statistic.batch_size == 100
+
+        xp_assert_equal(res1.pvalue, res3.pvalue)
+        xp_assert_equal(res2.pvalue, res3.pvalue)
+
+    # SPEC-007 leave at least one call with seed to check it still works
+    @pytest.mark.parametrize('random_state', [np.random.RandomState,
+                                              np.random.default_rng])
+    @pytest.mark.parametrize('permutation_type, exact_size',
+                             [('pairings', special.factorial(3)**2),
+                              ('samples', 2**3),
+                              ('independent', special.binom(6, 3))])
+    def test_permutations(self, permutation_type, exact_size, random_state, xp):
+        # make sure that the `permutations` parameter is respected by checking
+        # the size of the null distribution
+        x = xp.asarray(self.rng.random(3))
+        y = xp.asarray(self.rng.random(3))
+
+        def statistic(x, y, axis):
+            return xp.mean(x, axis=axis) - xp.mean(y, axis=axis)
+
+        kwds = {'permutation_type': permutation_type,
+                'vectorized': True}
+        res = stats.permutation_test((x, y), statistic, n_resamples=3,
+                                     random_state=random_state(0), **kwds)
+        assert xp_size(res.null_distribution) == 3
+
+        res = stats.permutation_test((x, y), statistic, **kwds)
+        assert xp_size(res.null_distribution) == exact_size
+
+    # -- Randomized Permutation Tests -- #
+
+    # To get reasonable accuracy, these next three tests are somewhat slow.
+    # Originally, I had them passing for all combinations of permutation type,
+    # alternative, and RNG, but that takes too long for CI. Instead, split
+    # into three tests, each testing a particular combination of the three
+    # parameters.
+
+    def test_randomized_test_against_exact_both(self, xp):
+        # check that the randomized and exact tests agree to reasonable
+        # precision for permutation_type='both
+
+        alternative, rng = 'less', 0
+
+        nx, ny, permutations = 8, 9, 24000
+        assert special.binom(nx + ny, nx) > permutations
+
+        rng = np.random.default_rng(8235259808)
+        x = xp.asarray(rng.standard_normal(size=nx))
+        y = xp.asarray(rng.standard_normal(size=ny))
+        data = x, y
+
+        def statistic(x, y, axis):
+            return xp.mean(x, axis=axis) - xp.mean(y, axis=axis)
+
+        kwds = {'vectorized': True, 'permutation_type': 'independent',
+                'batch': 100, 'alternative': alternative, 'rng': rng}
+        res = permutation_test(data, statistic, n_resamples=permutations, **kwds)
+        res2 = permutation_test(data, statistic, n_resamples=xp.inf, **kwds)
+
+        assert res.statistic == res2.statistic
+        xp_assert_close(res.pvalue, res2.pvalue, atol=1e-2)
+
+    @pytest.mark.slow()
+    def test_randomized_test_against_exact_samples(self, xp):
+        # check that the randomized and exact tests agree to reasonable
+        # precision for permutation_type='samples'
+
+        alternative, rng = 'greater', None
+
+        nx, ny, permutations = 15, 15, 32000
+        assert 2**nx > permutations
+
+        rng = np.random.default_rng(8235259808)
+        x = xp.asarray(rng.standard_normal(size=nx))
+        y = xp.asarray(rng.standard_normal(size=ny))
+        data = x, y
+
+        def statistic(x, y, axis):
+            return xp.mean(x - y, axis=axis)
+
+        kwds = {'vectorized': True, 'permutation_type': 'samples',
+                'batch': 100, 'alternative': alternative, 'rng': rng}
+        res = permutation_test(data, statistic, n_resamples=permutations, **kwds)
+        res2 = permutation_test(data, statistic, n_resamples=xp.inf, **kwds)
+
+        assert res.statistic == res2.statistic
+        xp_assert_close(res.pvalue, res2.pvalue, atol=1e-2)
+
+    # I only need to skip torch on GPU because it doesn't have betaincc for pearsonr
+    @pytest.mark.skip_xp_backends(cpu_only=True, exceptions=['cupy', 'jax.numpy'])
+    def test_randomized_test_against_exact_pairings(self, xp):
+        # check that the randomized and exact tests agree to reasonable
+        # precision for permutation_type='pairings'
+
+        alternative, rng = 'two-sided', self.rng
+
+        nx, ny, permutations = 8, 8, 40000
+        assert special.factorial(nx) > permutations
+
+        rng = np.random.default_rng(8235259808)
+        x = xp.asarray(rng.standard_normal(size=nx))
+        y = xp.asarray(rng.standard_normal(size=ny))
+        data = [x]
+
+        def statistic(x, axis):
+            return stats.pearsonr(x, y, axis=axis).statistic
+
+        kwds = {'vectorized': True, 'permutation_type': 'samples',
+                'batch': 100, 'alternative': alternative, 'rng': rng}
+        res = permutation_test(data, statistic, n_resamples=permutations, **kwds)
+        res2 = permutation_test(data, statistic, n_resamples=xp.inf, **kwds)
+
+        assert res.statistic == res2.statistic
+        xp_assert_close(res.pvalue, res2.pvalue, atol=1e-2)
+
+    # -- Independent (Unpaired) Sample Tests -- #
+
+    @pytest.mark.skip_xp_backends(eager_only=True)  # TODO: change to jax_jit=False
+    @pytest.mark.parametrize('alternative', ("less", "greater", "two-sided"))
+    def test_against_ks_2samp(self, alternative, xp):
+        x = self.rng.normal(size=4, scale=1)
+        y = self.rng.normal(size=5, loc=3, scale=3)
+
+        expected = stats.ks_2samp(x, y, alternative=alternative, mode='exact')
+
+        def statistic(x, y, axis):
+            # todo: use `xp` as backend when `ks_2samp` supports more backends
+            x, y = _xp_copy_to_numpy(x), _xp_copy_to_numpy(y)
+            res = stats.ks_2samp(x, y, axis=axis, mode='asymp', alternative=alternative)
+            res = xp.asarray(res.statistic)
+            return res[()] if res.ndim == 0 else res
+
+        # ks_2samp is always a one-tailed 'greater' test
+        # it's the statistic that changes (D+ vs D- vs max(D+, D-))
+        x, y = xp.asarray(x), xp.asarray(y)
+        res = permutation_test((x, y), statistic, n_resamples=np.inf,
+                               alternative='greater', rng=self.rng)
+
+        xp_assert_close(res.statistic, xp.asarray(expected.statistic), rtol=self.rtol)
+        xp_assert_close(res.pvalue, xp.asarray(expected.pvalue), rtol=self.rtol)
+
+    @pytest.mark.skip_xp_backends(eager_only=True)  # TODO: change to jax_jit=False
+    @pytest.mark.parametrize('alternative', ("less", "greater", "two-sided"))
+    def test_against_ansari(self, alternative, xp):
+        x = self.rng.normal(size=4, scale=1)
+        y = self.rng.normal(size=5, scale=3)
+
+        # ansari has a different convention for 'alternative'
+        alternative_correspondence = {"less": "greater",
+                                      "greater": "less",
+                                      "two-sided": "two-sided"}
+        alternative_scipy = alternative_correspondence[alternative]
+        expected = stats.ansari(x, y, alternative=alternative_scipy)
+
+        def statistic(x, y, axis):
+            # todo: use `xp` as backend when `ansari` is translated to array API
+            x, y = _xp_copy_to_numpy(x), _xp_copy_to_numpy(y)
+            res = stats.ansari(x, y, axis=axis)
+            res = xp.asarray(res.statistic)
+            return res[()] if res.ndim == 0 else res
+
+        x, y = xp.asarray(x), xp.asarray(y)
+        res = permutation_test((x, y), statistic, n_resamples=np.inf,
+                               alternative=alternative, rng=self.rng)
+
+        xp_assert_close(res.statistic, xp.asarray(expected.statistic), rtol=self.rtol)
+        xp_assert_close(res.pvalue, xp.asarray(expected.pvalue), rtol=self.rtol)
+
+    @skip_xp_backends('cupy', reason='needs mannwhitneyu')
+    @skip_xp_backends(eager_only=True)  # mannwhitneyu does input validation
+    @pytest.mark.parametrize('alternative', ("less", "greater", "two-sided"))
+    def test_against_mannwhitneyu(self, alternative, xp):
+        x = stats.uniform.rvs(size=(3, 5, 2), loc=0, random_state=self.rng)
+        y = stats.uniform.rvs(size=(3, 5, 2), loc=0.05, random_state=self.rng)
+
+        expected = stats.mannwhitneyu(x, y, axis=1, alternative=alternative)
+
+        def statistic(x, y, axis):
+            return stats.mannwhitneyu(x, y, axis=axis, method='asymptotic').statistic
+
+        x, y = xp.asarray(x), xp.asarray(y)
+        res = permutation_test((x, y), statistic, vectorized=True,
+                               n_resamples=xp.inf, alternative=alternative,
+                               axis=1, rng=self.rng)
+
+        xp_assert_close(res.statistic, xp.asarray(expected.statistic), rtol=self.rtol)
+        xp_assert_close(res.pvalue, xp.asarray(expected.pvalue), rtol=self.rtol)
+
+    @skip_xp_backends('cupy', reason='needs cramervonmises_2samp')
+    @skip_xp_backends(eager_only=True)  # cramervonmises_2samp does input validation
+    @skip_xp_backends(cpu_only=True)  # torch doesn't have `kv`
+    def test_against_cvm(self, xp):
+        x = stats.norm.rvs(size=4, scale=1, random_state=self.rng)
+        y = stats.norm.rvs(size=5, loc=3, scale=3, random_state=self.rng)
+
+        expected = stats.cramervonmises_2samp(x, y, method='exact')
+
+        def statistic(x, y, axis):
+            res = stats.cramervonmises_2samp(x, y, axis=axis, method='asymptotic')
+            return res.statistic
+
+        # cramervonmises_2samp has only one alternative, greater
+        x, y = xp.asarray(x), xp.asarray(y)
+        res = permutation_test((x, y), statistic, n_resamples=np.inf,
+                               alternative='greater', rng=self.rng)
+
+        xp_assert_close(res.statistic, xp.asarray(expected.statistic), rtol=self.rtol)
+        xp_assert_close(res.pvalue, xp.asarray(expected.pvalue), rtol=self.rtol)
+
+    @skip_xp_backends(eager_only=True)  # kruskal does input validation
+    @pytest.mark.parametrize('axis', (-1, 2))
+    def test_vectorized_nsamp_ptype_both(self, axis, xp):
+        # statistic only available for NumPy
+
+        # Test that permutation_test with permutation_type='independent' works
+        # properly for a 3-sample statistic with nd array samples of different
+        # (but compatible) shapes and ndims. Show that exact permutation test
+        # and random permutation tests approximate SciPy's asymptotic pvalues
+        # and that exact and random permutation test results are even closer
+        # to one another (than they are to the asymptotic results).
+
+        # Three samples, different (but compatible) shapes with different ndims
+        rng = np.random.default_rng(6709265303529651545)
+        x = rng.random(size=(3))
+        y = rng.random(size=(1, 3, 2))
+        z = rng.random(size=(2, 1, 4))
+        data = (x, y, z)
+
+        expected = stats.kruskal(*data, axis=axis)
+
+        # Define the statistic (and pvalue for comparison)
+        def statistic(*data, axis):
+            return stats.kruskal(*data, axis=axis).statistic
+
+        # Calculate exact and randomized permutation results
+        kwds = {'axis': axis, 'alternative': 'greater',
+                'permutation_type': 'independent', 'rng': rng}
+        data = [xp.asarray(data_) for data_ in data]
+        res = permutation_test(data, statistic, n_resamples=xp.inf, **kwds)
+        res2 = permutation_test(data, statistic, n_resamples=1000, **kwds)
+
+        # Check results
+        xp_assert_close(res.statistic, xp.asarray(expected.statistic), rtol=self.rtol*5)
+        xp_assert_close(res.statistic, res2.statistic, rtol=self.rtol*5)
+        xp_assert_close(res.pvalue, xp.asarray(expected.pvalue), atol=6e-2)
+        xp_assert_close(res.pvalue, res2.pvalue, atol=3e-2)
+
+    # -- Paired-Sample Tests -- #
+
+    @pytest.mark.skip_xp_backends(eager_only=True)  # TODO: change to jax_jit=False
+    @pytest.mark.parametrize('alternative', ("less", "greater", "two-sided"))
+    def test_against_wilcoxon(self, alternative, xp):
+        x = stats.uniform.rvs(size=(3, 6, 2), loc=0, random_state=self.rng)
+        y = stats.uniform.rvs(size=(3, 6, 2), loc=0.05, random_state=self.rng)
+        expected = stats.wilcoxon(x, y, alternative=alternative, axis=1)
+
+        # We'll check both 1- and 2-sample versions of the same test;
+        # we expect identical results to wilcoxon in all cases.
+        def statistic_1samp_1d(z, axis):
+            # todo: use `xp` as backend when `wilcoxon` is translated to array API
+            # 'less' ensures we get the same of two statistics every time
+            z = _xp_copy_to_numpy(z)
+            res = stats.wilcoxon(z, alternative='less', axis=axis)
+            res = xp.asarray(res.statistic)
+            return res[()] if res.ndim == 0 else res
+
+        def statistic_2samp_1d(x, y, axis):
+            # todo: use `xp` as backend when `wilcoxon` is translated to array API
+            x, y = _xp_copy_to_numpy(x), _xp_copy_to_numpy(y)
+            res = stats.wilcoxon(x, y, alternative='less', axis=axis)
+            res = xp.asarray(res.statistic)
+            return res[()] if res.ndim == 0 else res
+
+        x, y = xp.asarray(x), xp.asarray(y)
+        kwds = {'axis': 1, 'alternative': alternative, 'permutation_type': 'samples',
+                'rng': self.rng, 'n_resamples': np.inf}
+        res1 = permutation_test((x-y,), statistic_1samp_1d, **kwds)
+        res2 = permutation_test((x, y), statistic_2samp_1d, **kwds)
+
+        # `wilcoxon` returns a different statistic with 'two-sided'
+        xp_assert_close(res1.statistic, res2.statistic, rtol=self.rtol)
+        if alternative != 'two-sided':
+            xp_assert_close(res2.statistic, xp.asarray(expected.statistic),
+                            rtol=self.rtol)
+
+        xp_assert_close(res2.pvalue, xp.asarray(expected.pvalue), rtol=self.rtol)
+        xp_assert_close(res1.pvalue, res2.pvalue, rtol=self.rtol)
+
+    @pytest.mark.parametrize('alternative', ("less", "greater", "two-sided"))
+    def test_against_binomtest(self, alternative, xp):
+
+        x = self.rng.integers(0, 2, size=10)
+        x[x == 0] = -1
+        # More naturally, the test would flip elements between 0 and one.
+        # However, permutation_test will flip the _signs_ of the elements.
+        # So we have to work with +1/-1 instead of 1/0.
+
+        def statistic(x, axis=0):
+            xp_ = array_namespace(x)
+            return xp_.count_nonzero(x > 0, axis=axis)
+
+        k, n, p = statistic(x), 10, 0.5
+        expected = stats.binomtest(k, n, p, alternative=alternative)
+
+        res = stats.permutation_test((xp.asarray(x, dtype=xp.float64),), statistic,
+                                     vectorized=True,
+                                     permutation_type='samples', n_resamples=xp.inf,
+                                     rng=self.rng, alternative=alternative)
+        xp_assert_close(res.pvalue, xp.asarray(expected.pvalue), rtol=self.rtol)
+
+    # -- Exact Association Tests -- #
+
+    @pytest.mark.skip_xp_backends(eager_only=True)  # TODO: change to jax_jit=False
+    def test_against_kendalltau(self, xp):
+        x = self.rng.normal(size=6)
+        y = x + self.rng.normal(size=6)
+
+        expected = stats.kendalltau(x, y, method='exact')
+
+        def statistic(x, axis):
+            # todo: use `xp` as backend when `kendalltau` is translated to array API
+            x = _xp_copy_to_numpy(x)
+            res = stats.kendalltau(x, y, method='asymptotic', axis=axis)
+            res = xp.asarray(res.statistic)
+            return res[()] if res.ndim == 0 else res
+
+        # kendalltau currently has only one alternative, two-sided
+        x = xp.asarray(x)
+        res = permutation_test((x,), statistic, permutation_type='pairings',
+                               n_resamples=np.inf, rng=self.rng)
+
+        xp_assert_close(res.statistic, xp.asarray(expected.statistic), rtol=self.rtol)
+        xp_assert_close(res.pvalue, xp.asarray(expected.pvalue), rtol=self.rtol)
+
+    @pytest.mark.parametrize('alternative', ('less', 'greater', 'two-sided'))
+    def test_against_fisher_exact(self, alternative, xp):
+        # x and y are binary random variables with some dependence
+        rng = np.random.default_rng(6235696159000529929)
+        x = (rng.random(7) > 0.6).astype(float)
+        y = (rng.random(7) + 0.25*x > 0.6).astype(float)
+        tab = stats.contingency.crosstab(x, y)[1]
+
+        x, y = xp.asarray(x), xp.asarray(y)
+        def statistic(x, axis):
+            return xp.count_nonzero((x == 1) & (y == 1), axis=axis)
+
+        res = permutation_test((x,), statistic, permutation_type='pairings',
+                               n_resamples=xp.inf, alternative=alternative,
+                               rng=rng)
+        res2 = stats.fisher_exact(tab, alternative=alternative)
+
+        xp_assert_close(res.pvalue, xp.asarray(res2.pvalue, dtype=x.dtype))
+
+    @pytest.mark.xslow()
+    @pytest.mark.parametrize('axis', (-2, 1))
+    def test_vectorized_nsamp_ptype_samples(self, axis):
+        # statistic only available for NumPy, and it's a pain to vectorize
+
+        # Test that permutation_test with permutation_type='samples' works
+        # properly for a 3-sample statistic with nd array samples of different
+        # (but compatible) shapes and ndims. Show that exact permutation test
+        # reproduces SciPy's exact pvalue and that random permutation test
+        # approximates it.
+
+        x = self.rng.random(size=(2, 4, 3))
+        y = self.rng.random(size=(1, 4, 3))
+        z = self.rng.random(size=(2, 4, 1))
+        x = stats.rankdata(x, axis=axis)
+        y = stats.rankdata(y, axis=axis)
+        z = stats.rankdata(z, axis=axis)
+        y = y[0]  # to check broadcast with different ndim
+        data = (x, y, z)
+
+        def statistic1d(*data):
+            return stats.page_trend_test(data, ranked=True,
+                                         method='asymptotic').statistic
+
+        def pvalue1d(*data):
+            return stats.page_trend_test(data, ranked=True,
+                                         method='exact').pvalue
+
+        statistic = _resampling._vectorize_statistic(statistic1d)
+        pvalue = _resampling._vectorize_statistic(pvalue1d)
+
+        expected_statistic = statistic(*np.broadcast_arrays(*data), axis=axis)
+        expected_pvalue = pvalue(*np.broadcast_arrays(*data), axis=axis)
+
+        # Let's forgive this use of an integer seed, please.
+        kwds = {'vectorized': False, 'axis': axis, 'alternative': 'greater',
+                'permutation_type': 'pairings', 'rng': 0}
+        res = permutation_test(data, statistic1d, n_resamples=np.inf, **kwds)
+        res2 = permutation_test(data, statistic1d, n_resamples=5000, **kwds)
+
+        assert_allclose(res.statistic, expected_statistic, rtol=self.rtol)
+        assert_allclose(res.statistic, res2.statistic, rtol=self.rtol)
+        assert_allclose(res.pvalue, expected_pvalue, rtol=self.rtol)
+        assert_allclose(res.pvalue, res2.pvalue, atol=3e-2)
+
+    # -- Test Against External References -- #
+
+    tie_case_1 = {'x': [1, 2, 3, 4], 'y': [1.5, 2, 2.5],
+                  'expected_less': 0.2000000000,
+                  'expected_2sided': 0.4,  # 2*expected_less
+                  'expected_Pr_gte_S_mean': 0.3428571429,  # see note below
+                  'expected_statistic': 7.5,
+                  'expected_avg': 9.142857, 'expected_std': 1.40698}
+    tie_case_2 = {'x': [111, 107, 100, 99, 102, 106, 109, 108],
+                  'y': [107, 108, 106, 98, 105, 103, 110, 105, 104],
+                  'expected_less': 0.1555738379,
+                  'expected_2sided': 0.3111476758,
+                  'expected_Pr_gte_S_mean': 0.2969971205,  # see note below
+                  'expected_statistic': 32.5,
+                  'expected_avg': 38.117647, 'expected_std': 5.172124}
+
+    @pytest.mark.skip_xp_backends(eager_only=True)  # TODO: change to jax_jit=False
+    @pytest.mark.xslow()  # only the second case is slow, really
+    @pytest.mark.parametrize('case', (tie_case_1, tie_case_2))
+    def test_with_ties(self, case, xp):
+        """
+        Results above from SAS PROC NPAR1WAY, e.g.
+
+        DATA myData;
+        INPUT X Y;
+        CARDS;
+        1 1
+        1 2
+        1 3
+        1 4
+        2 1.5
+        2 2
+        2 2.5
+        ods graphics on;
+        proc npar1way AB data=myData;
+            class X;
+            EXACT;
+        run;
+        ods graphics off;
+
+        Note: SAS provides Pr >= |S-Mean|, which is different from our
+        definition of a two-sided p-value.
+
+        """
+
+        x = case['x']
+        y = case['y']
+
+        expected_statistic = xp.asarray(case['expected_statistic'])
+        expected_less = xp.asarray(case['expected_less'])
+        expected_2sided = xp.asarray(case['expected_2sided'])
+        expected_Pr_gte_S_mean = xp.asarray(case['expected_Pr_gte_S_mean'])
+        expected_avg = xp.asarray(case['expected_avg'])
+        expected_std = xp.asarray(case['expected_std'])
+
+        def statistic(x, y, axis):
+            # todo: use `xp` as backend when `ansari` is translated to array API
+            x, y = _xp_copy_to_numpy(x), _xp_copy_to_numpy(y)
+            res = stats.ansari(x, y, axis=axis)
+            res = xp.asarray(res.statistic)
+            return res[()] if res.ndim == 0 else res
+
+        dtype = xp_default_dtype(xp)
+        x, y = xp.asarray(x, dtype=dtype), xp.asarray(y, dtype=dtype)
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "Ties preclude use of exact statistic", UserWarning)
+            res = permutation_test((x, y), statistic, n_resamples=np.inf,
+                                   alternative='less')
+            res2 = permutation_test((x, y), statistic, n_resamples=np.inf,
+                                    alternative='two-sided')
+
+        xp_assert_close(res.statistic, expected_statistic, rtol=self.rtol)
+        xp_assert_close(res.pvalue, expected_less, atol=1e-10)
+        xp_assert_close(res2.pvalue, expected_2sided, atol=1e-10)
+        xp_assert_close(xp.mean(res2.null_distribution), expected_avg, rtol=1e-6)
+        xp_assert_close(xp.std(res2.null_distribution), expected_std, rtol=1e-6)
+
+        # SAS provides Pr >= |S-Mean|; might as well check against that, too
+        S = res.statistic
+        mean = xp.mean(res.null_distribution)
+        n = res.null_distribution.shape[0]
+        Pr_gte_S_mean = xp.astype(xp.count_nonzero(
+            xp.abs(res.null_distribution-mean) >= xp.abs(S-mean)), S.dtype) / n
+        xp_assert_close(Pr_gte_S_mean, expected_Pr_gte_S_mean)
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize('alternative, expected_pvalue',
+                             (('less', 0.9708333333333),
+                              ('greater', 0.05138888888889),
+                              ('two-sided', 0.1027777777778)))
+    # I only need to skip torch on GPU because it doesn't have betaincc for pearsonr
+    @pytest.mark.skip_xp_backends(cpu_only=True, exceptions=['cupy', 'jax.numpy'])
+    @pytest.mark.skip_xp_backends(eager_only=True)  # TODO: change to jax_jit=False
+    def test_against_spearmanr_in_R(self, alternative, expected_pvalue, xp):
+        """
+        Results above from R cor.test, e.g.
+
+        options(digits=16)
+        x <- c(1.76405235, 0.40015721, 0.97873798,
+               2.2408932, 1.86755799, -0.97727788)
+        y <- c(2.71414076, 0.2488, 0.87551913,
+               2.6514917, 2.01160156, 0.47699563)
+        cor.test(x, y, method = "spearm", alternative = "t")
+        """
+        # data comes from
+        # np.random.seed(0)
+        # x = stats.norm.rvs(size=6)
+        # y = x + stats.norm.rvs(size=6)
+        x = xp.asarray([1.76405235, 0.40015721, 0.97873798,
+                        2.2408932, 1.86755799, -0.97727788])
+        y = xp.asarray([2.71414076, 0.2488, 0.87551913,
+                        2.6514917, 2.01160156, 0.47699563])
+        expected_statistic = 0.7714285714285715
+
+        y = xp.asarray(stats.rankdata(_xp_copy_to_numpy(y)))
+        def statistic(x, axis):
+            # `spearmanr` is not array api compatible, but `pearsonr` is. So for now
+            # use _xp_copy_to_numpy just for ranking so we can run this test w/ CuPy.
+            # TODO: use `xp` as backend when cupy works with `rankdata`
+            x = xp.asarray(stats.rankdata(_xp_copy_to_numpy(x), axis=axis))
+            return stats.pearsonr(x, y, axis=axis).statistic
+
+        res = permutation_test((x,), statistic, permutation_type='pairings',
+                               n_resamples=xp.inf, alternative=alternative)
+
+        xp_assert_close(res.statistic, xp.asarray(expected_statistic), rtol=self.rtol)
+        xp_assert_close(res.pvalue, xp.asarray(expected_pvalue), atol=1e-13)
+
+    @pytest.mark.parametrize("batch", (-1, 0))
+    def test_batch_generator_iv(self, batch):
+        with pytest.raises(ValueError, match="`batch` must be positive."):
+            list(_resampling._batch_generator([1, 2, 3], batch))
+
+    batch_generator_cases = [(range(0), 3, []),
+                             (range(6), 3, [[0, 1, 2], [3, 4, 5]]),
+                             (range(8), 3, [[0, 1, 2], [3, 4, 5], [6, 7]])]
+
+    @pytest.mark.parametrize("iterable, batch, expected",
+                             batch_generator_cases)
+    def test_batch_generator(self, iterable, batch, expected):
+        got = list(_resampling._batch_generator(iterable, batch))
+        assert got == expected
+
+    @pytest.mark.fail_slow(2)
+    # I only need to skip torch on GPU because it doesn't have betaincc for pearsonr
+    @pytest.mark.skip_xp_backends(cpu_only=True, exceptions=['cupy', 'jax.numpy'])
+    def test_finite_precision_statistic(self, xp):
+        # Some statistics return numerically distinct values when the values
+        # should be equal in theory. Test that `permutation_test` accounts
+        # for this in some way.
+        x = xp.asarray([1., 2., 4., 3.], dtype=xp.float64)
+        y = xp.asarray([2., 4., 6., 8.], dtype=xp.float64)
+
+        def statistic(x, y, axis):
+            return stats.pearsonr(x, y, axis=axis)[0]
+
+        res = stats.permutation_test((x, y), statistic,
+                                     permutation_type='pairings')
+        r, pvalue, null = res.statistic, res.pvalue, res.null_distribution
+
+        correct_p = 2 * float(xp.count_nonzero(null >= r - 1e-14)) / null.shape[0]
+        assert pvalue == correct_p == 1/3
+        # Compare against other exact correlation tests using R corr.test
+        # options(digits=16)
+        # x = c(1, 2, 4, 3)
+        # y = c(2, 4, 6, 8)
+        # cor.test(x, y, alternative = "t", method = "spearman")  # 0.333333333
+        # cor.test(x, y, alternative = "t", method = "kendall")  # 0.333333333
+
+    def test_nan_statistic(self, xp):
+        # Where the observed statistic is NaN, the p-value should be NaN
+        # (Whether a single NaN in the permutation distribution should make the p-value
+        #  NaN is debatable. Users can choose for themselves.)
+        rng = np.random.default_rng(8951482112)
+        dtype = xp_default_dtype(xp)
+        x = xp.asarray(rng.random(5), dtype=dtype)
+        x_nan = xp.asarray([0, 0, 0, xp.nan, 0])
+        y = xp.asarray(rng.random(6), dtype=dtype)
+
+        def statistic(x, y, axis):
+            return xp.mean(x, axis=axis) - xp.mean(y, axis=axis)
+
+        kwds = {'permutation_type': 'independent', 'rng': rng, 'axis': -1}
+
+        ref = permutation_test((x, y), statistic, **kwds)
+
+        data = (xp.stack([x, x + x_nan]), y)
+        res = permutation_test(data, statistic, **kwds)
+
+        NaN = xp.asarray(xp.nan, dtype=dtype)
+        xp_assert_equal(res.statistic[0], ref.statistic)
+        xp_assert_equal(res.pvalue[0], ref.pvalue)
+        xp_assert_equal(res.statistic[1], NaN)
+        xp_assert_equal(res.pvalue[1], NaN)
+
+
+def test_all_partitions_concatenated():
+    # make sure that _all_paritions_concatenated produces the correct number
+    # of partitions of the data into samples of the given sizes and that
+    # all are unique
+    n = np.array([3, 2, 4], dtype=int)
+    nc = np.cumsum(n)
+
+    all_partitions = set()
+    counter = 0
+    for partition_concatenated in _resampling._all_partitions_concatenated(n):
+        counter += 1
+        partitioning = np.split(partition_concatenated, nc[:-1])
+        all_partitions.add(tuple([frozenset(i) for i in partitioning]))
+
+    expected = np.prod([special.binom(sum(n[i:]), sum(n[i+1:]))
+                        for i in range(len(n)-1)])
+
+    assert_equal(counter, expected)
+    assert_equal(len(all_partitions), expected)
+
+
+@pytest.mark.parametrize('fun_name',
+                         ['bootstrap', 'permutation_test', 'monte_carlo_test'])
+def test_parameter_vectorized(fun_name):
+    # Check that parameter `vectorized` is working as desired for all
+    # resampling functions. Results don't matter; just don't fail asserts.
+    rng = np.random.default_rng(75245098234592)
+    sample = rng.random(size=10)
+
+    def rvs(size):  # needed by `monte_carlo_test`
+        return stats.norm.rvs(size=size, random_state=rng)
+
+    fun_options = {'bootstrap': {'data': (sample,), 'rng': rng,
+                                 'method': 'percentile'},
+                   'permutation_test': {'data': (sample,), 'rng': rng,
+                                        'permutation_type': 'samples'},
+                   'monte_carlo_test': {'sample': sample, 'rvs': rvs}}
+    common_options = {'n_resamples': 100}
+
+    fun = getattr(stats, fun_name)
+    options = fun_options[fun_name]
+    options.update(common_options)
+
+    def statistic(x, axis):
+        assert x.ndim > 1 or np.array_equal(x, sample)
+        return np.mean(x, axis=axis)
+    fun(statistic=statistic, vectorized=None, **options)
+    fun(statistic=statistic, vectorized=True, **options)
+
+    def statistic(x):
+        assert x.ndim == 1
+        return np.mean(x)
+    fun(statistic=statistic, vectorized=None, **options)
+    fun(statistic=statistic, vectorized=False, **options)
+
+
+class TestMonteCarloMethod:
+    def test_rvs_and_random_state(self):
+        message = "Use of `rvs` and `rng` are mutually exclusive."
+        rng = np.random.default_rng(34982345)
+        with pytest.raises(ValueError, match=message):
+            stats.MonteCarloMethod(rvs=rng.random, rng=rng)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_sampling.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_sampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d50354d10e6713b6effeede7e92be226df01483
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_sampling.py
@@ -0,0 +1,1455 @@
+import threading
+import pickle
+import pytest
+from copy import deepcopy
+import platform
+import sys
+import math
+import warnings
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+from scipy.stats.sampling import (
+    TransformedDensityRejection,
+    DiscreteAliasUrn,
+    DiscreteGuideTable,
+    NumericalInversePolynomial,
+    NumericalInverseHermite,
+    RatioUniforms,
+    SimpleRatioUniforms,
+    UNURANError
+)
+from pytest import raises as assert_raises
+from scipy import stats
+from scipy import special
+from scipy.stats import chisquare, cramervonmises
+from scipy.stats._distr_params import distdiscrete, distcont
+from scipy._lib._util import check_random_state
+from scipy._lib._gcutils import assert_deallocated
+
+
+# common test data: this data can be shared between all the tests.
+
+
+# Normal distribution shared between all the continuous methods
+class StandardNormal:
+    def pdf(self, x):
+        # normalization constant needed for NumericalInverseHermite
+        return 1./np.sqrt(2.*np.pi) * np.exp(-0.5 * x*x)
+
+    def dpdf(self, x):
+        return 1./np.sqrt(2.*np.pi) * -x * np.exp(-0.5 * x*x)
+
+    def cdf(self, x):
+        return special.ndtr(x)
+
+
+all_methods = [
+    ("TransformedDensityRejection", {"dist": StandardNormal()}),
+    ("DiscreteAliasUrn", {"dist": [0.02, 0.18, 0.8]}),
+    ("DiscreteGuideTable", {"dist": [0.02, 0.18, 0.8]}),
+    ("NumericalInversePolynomial", {"dist": StandardNormal()}),
+    ("NumericalInverseHermite", {"dist": StandardNormal()}),
+    ("SimpleRatioUniforms", {"dist": StandardNormal(), "mode": 0})
+]
+
+floaterr = r"must be real number, not list"
+# Make sure an internal error occurs in UNU.RAN when invalid callbacks are
+# passed. Moreover, different generators throw different error messages.
+# So, in case of an `UNURANError`, we do not validate the error message.
+bad_pdfs_common = [
+    # Negative PDF
+    (lambda x: -x, UNURANError, r"..."),
+    # Returning wrong type
+    (lambda x: [], TypeError, floaterr),
+    # Undefined name inside the function
+    (lambda x: foo, NameError, r"name 'foo' is not defined"),  # type: ignore[name-defined]  # noqa: F821, E501
+    # Infinite value returned => Overflow error.
+    (lambda x: np.inf, UNURANError, r"..."),
+    # NaN value => internal error in UNU.RAN
+    (lambda x: np.nan, UNURANError, r"..."),
+    # signature of PDF wrong
+    (lambda: 1.0, TypeError, r"takes 0 positional arguments but 1 was given")
+]
+
+
+# same approach for dpdf
+bad_dpdf_common = [
+    # Infinite value returned.
+    (lambda x: np.inf, UNURANError, r"..."),
+    # NaN value => internal error in UNU.RAN
+    (lambda x: np.nan, UNURANError, r"..."),
+    # Returning wrong type
+    (lambda x: [], TypeError, floaterr),
+    # Undefined name inside the function
+    (lambda x: foo, NameError, r"name 'foo' is not defined"),  # type: ignore[name-defined]  # noqa: F821, E501
+    # signature of dPDF wrong
+    (lambda: 1.0, TypeError, r"takes 0 positional arguments but 1 was given")
+]
+
+
+# same approach for logpdf
+bad_logpdfs_common = [
+    # Returning wrong type
+    (lambda x: [], TypeError, floaterr),
+    # Undefined name inside the function
+    (lambda x: foo, NameError, r"name 'foo' is not defined"),  # type: ignore[name-defined]  # noqa: F821, E501
+    # Infinite value returned => Overflow error.
+    (lambda x: np.inf, UNURANError, r"..."),
+    # NaN value => internal error in UNU.RAN
+    (lambda x: np.nan, UNURANError, r"..."),
+    # signature of logpdf wrong
+    (lambda: 1.0, TypeError, r"takes 0 positional arguments but 1 was given")
+]
+
+
+bad_pv_common = [
+    ([], r"must contain at least one element"),
+    ([[1.0, 0.0]], r"wrong number of dimensions \(expected 1, got 2\)"),
+    ([0.2, 0.4, np.nan, 0.8], r"must contain only finite / non-nan values"),
+    ([0.2, 0.4, np.inf, 0.8], r"must contain only finite / non-nan values"),
+    ([0.0, 0.0], r"must contain at least one non-zero value"),
+]
+
+
+# size of the domains is incorrect
+bad_sized_domains = [
+    # > 2 elements in the domain
+    ((1, 2, 3), ValueError, r"must be a length 2 tuple"),
+    # empty domain
+    ((), ValueError, r"must be a length 2 tuple")
+]
+
+# domain values are incorrect
+bad_domains = [
+    ((2, 1), UNURANError, r"left >= right"),
+    ((1, 1), UNURANError, r"left >= right"),
+]
+
+# infinite and nan values present in domain.
+inf_nan_domains = [
+    # left >= right
+    ((10, 10), UNURANError, r"left >= right"),
+    ((np.inf, np.inf), UNURANError, r"left >= right"),
+    ((-np.inf, -np.inf), UNURANError, r"left >= right"),
+    ((np.inf, -np.inf), UNURANError, r"left >= right"),
+    # Also include nans in some of the domains.
+    ((-np.inf, np.nan), ValueError, r"only non-nan values"),
+    ((np.nan, np.inf), ValueError, r"only non-nan values")
+]
+
+# `nan` values present in domain. Some distributions don't support
+# infinite tails, so don't mix the nan values with infinities.
+nan_domains = [
+    ((0, np.nan), ValueError, r"only non-nan values"),
+    ((np.nan, np.nan), ValueError, r"only non-nan values")
+]
+
+
+# all the methods should throw errors for nan, bad sized, and bad valued
+# domains.
+@pytest.mark.parametrize("domain, err, msg",
+                         bad_domains + bad_sized_domains +
+                         nan_domains)  # type: ignore[operator]
+@pytest.mark.parametrize("method, kwargs", all_methods)
+def test_bad_domain(domain, err, msg, method, kwargs):
+    Method = getattr(stats.sampling, method)
+    with pytest.raises(err, match=msg):
+        Method(**kwargs, domain=domain)
+
+
+@pytest.mark.parametrize("method, kwargs", all_methods)
+def test_random_state(method, kwargs):
+    Method = getattr(stats.sampling, method)
+
+    # simple seed that works for any version of NumPy
+    seed = 123
+    rng1 = Method(**kwargs, random_state=seed)
+    rng2 = Method(**kwargs, random_state=seed)
+    assert_equal(rng1.rvs(100), rng2.rvs(100))
+
+    # global seed
+    rng = np.random.RandomState(123)
+    rng1 = Method(**kwargs)
+    rvs1 = rng1.rvs(100, random_state=rng)
+    np.random.seed(None)  # valid use of np.random.seed
+    rng2 = Method(**kwargs, random_state=123)
+    rvs2 = rng2.rvs(100)
+    assert_equal(rvs1, rvs2)
+
+    # Generator seed for new NumPy
+    # when a RandomState is given, it should take the bitgen_t
+    # member of the class and create a Generator instance.
+    seed1 = np.random.RandomState(np.random.MT19937(123))
+    seed2 = np.random.Generator(np.random.MT19937(123))
+    rng1 = Method(**kwargs, random_state=seed1)
+    rng2 = Method(**kwargs, random_state=seed2)
+    assert_equal(rng1.rvs(100), rng2.rvs(100))
+
+
+def test_set_random_state():
+    rng1 = TransformedDensityRejection(StandardNormal(), random_state=123)
+    rng2 = TransformedDensityRejection(StandardNormal())
+    rng2.set_random_state(123)
+    assert_equal(rng1.rvs(100), rng2.rvs(100))
+    rng = TransformedDensityRejection(StandardNormal(), random_state=123)
+    rvs1 = rng.rvs(100)
+    rng.set_random_state(123)
+    rvs2 = rng.rvs(100)
+    assert_equal(rvs1, rvs2)
+
+
+def test_threading_behaviour():
+    # Test if the API is thread-safe.
+    # This verifies if the lock mechanism and the use of `PyErr_Occurred`
+    # is correct.
+    errors = {"err1": None, "err2": None}
+
+    class Distribution:
+        def __init__(self, pdf_msg):
+            self.pdf_msg = pdf_msg
+
+        def pdf(self, x):
+            if 49.9 < x < 50.0:
+                raise ValueError(self.pdf_msg)
+            return x
+
+        def dpdf(self, x):
+            return 1
+
+    def func1():
+        dist = Distribution('foo')
+        rng = TransformedDensityRejection(dist, domain=(10, 100),
+                                          random_state=12)
+        try:
+            rng.rvs(100000)
+        except ValueError as e:
+            errors['err1'] = e.args[0]
+
+    def func2():
+        dist = Distribution('bar')
+        rng = TransformedDensityRejection(dist, domain=(10, 100),
+                                          random_state=2)
+        try:
+            rng.rvs(100000)
+        except ValueError as e:
+            errors['err2'] = e.args[0]
+
+    t1 = threading.Thread(target=func1)
+    t2 = threading.Thread(target=func2)
+
+    t1.start()
+    t2.start()
+
+    t1.join()
+    t2.join()
+
+    assert errors['err1'] == 'foo'
+    assert errors['err2'] == 'bar'
+
+
+@pytest.mark.parametrize("method, kwargs", all_methods)
+def test_pickle(method, kwargs):
+    Method = getattr(stats.sampling, method)
+    rng1 = Method(**kwargs, random_state=123)
+    obj = pickle.dumps(rng1)
+    rng2 = pickle.loads(obj)
+    assert_equal(rng1.rvs(100), rng2.rvs(100))
+
+
+@pytest.mark.parametrize("size", [None, 0, (0, ), 1, (10, 3), (2, 3, 4, 5),
+                                  (0, 0), (0, 1)])
+def test_rvs_size(size):
+    # As the `rvs` method is present in the base class and shared between
+    # all the classes, we can just test with one of the methods.
+    rng = TransformedDensityRejection(StandardNormal())
+    if size is None:
+        assert np.isscalar(rng.rvs(size))
+    else:
+        if np.isscalar(size):
+            size = (size, )
+        assert rng.rvs(size).shape == size
+
+
+def test_with_scipy_distribution():
+    # test if the setup works with SciPy's rv_frozen distributions
+    dist = stats.norm()
+    urng = np.random.default_rng(0)
+    rng = NumericalInverseHermite(dist, random_state=urng)
+    u = np.linspace(0, 1, num=100)
+    check_cont_samples(rng, dist, dist.stats())
+    assert_allclose(dist.ppf(u), rng.ppf(u))
+    # test if it works with `loc` and `scale`
+    dist = stats.norm(loc=10., scale=5.)
+    rng = NumericalInverseHermite(dist, random_state=urng)
+    check_cont_samples(rng, dist, dist.stats())
+    assert_allclose(dist.ppf(u), rng.ppf(u))
+    # check for discrete distributions
+    dist = stats.binom(10, 0.2)
+    rng = DiscreteAliasUrn(dist, random_state=urng)
+    domain = dist.support()
+    pv = dist.pmf(np.arange(domain[0], domain[1]+1))
+    check_discr_samples(rng, pv, dist.stats())
+
+
+def test_NumericalInverseHermite_refcycle():
+    # test if NumericalInverseHermite contains a reference cycle
+    dist = stats.norm()
+    urng = np.random.default_rng(0)
+    with assert_deallocated(NumericalInverseHermite, dist, random_state=urng) as rng:
+        u = np.linspace(0, 1, num=100)
+        check_cont_samples(rng, dist, dist.stats())
+        assert_allclose(dist.ppf(u), rng.ppf(u))
+        del rng
+
+
+def check_cont_samples(rng, dist, mv_ex, rtol=1e-7, atol=1e-1):
+    rvs = rng.rvs(100000)
+    mv = rvs.mean(), rvs.var()
+    # test the moments only if the variance is finite
+    if np.isfinite(mv_ex[1]):
+        assert_allclose(mv, mv_ex, rtol=rtol, atol=atol)
+    # Cramer Von Mises test for goodness-of-fit
+    rvs = rng.rvs(500)
+    dist.cdf = np.vectorize(dist.cdf)
+    pval = cramervonmises(rvs, dist.cdf).pvalue
+    assert pval > 0.1
+
+
+def check_discr_samples(rng, pv, mv_ex, rtol=1e-3, atol=1e-1):
+    rvs = rng.rvs(100000)
+    # test if the first few moments match
+    mv = rvs.mean(), rvs.var()
+    assert_allclose(mv, mv_ex, rtol=rtol, atol=atol)
+    # normalize
+    pv = pv / pv.sum()
+    # chi-squared test for goodness-of-fit
+    obs_freqs = np.zeros_like(pv)
+    _, freqs = np.unique(rvs, return_counts=True)
+    freqs = freqs / freqs.sum()
+    obs_freqs[:freqs.size] = freqs
+    pval = chisquare(obs_freqs, pv).pvalue
+    assert pval > 0.1
+
+
+def test_warning_center_not_in_domain():
+    # UNURAN will warn if the center provided or the one computed w/o the
+    # domain is outside of the domain
+    msg = "102 : center moved into domain of distribution"
+    with pytest.warns(RuntimeWarning, match=msg):
+        NumericalInversePolynomial(StandardNormal(), center=0, domain=(3, 5))
+    with pytest.warns(RuntimeWarning, match=msg):
+        NumericalInversePolynomial(StandardNormal(), domain=(3, 5))
+
+
+@pytest.mark.parametrize('method', ["SimpleRatioUniforms",
+                                    "NumericalInversePolynomial",
+                                    "TransformedDensityRejection"])
+def test_error_mode_not_in_domain(method):
+    # UNURAN raises an error if the mode is not in the domain
+    # the behavior is different compared to the case that center is not in the
+    # domain. mode is supposed to be the exact value, center can be an
+    # approximate value
+    Method = getattr(stats.sampling, method)
+    msg = "17 : mode not in domain"
+    with pytest.raises(UNURANError, match=msg):
+        Method(StandardNormal(), mode=0, domain=(3, 5))
+
+
+@pytest.mark.parametrize('method', ["NumericalInverseHermite",
+                                    "NumericalInversePolynomial"])
+class TestQRVS:
+    def test_input_validation(self, method):
+        match = "`qmc_engine` must be an instance of..."
+        with pytest.raises(ValueError, match=match):
+            Method = getattr(stats.sampling, method)
+            gen = Method(StandardNormal())
+            gen.qrvs(qmc_engine=0)
+
+        # issues with QMCEngines and old NumPy
+        Method = getattr(stats.sampling, method)
+        gen = Method(StandardNormal())
+
+        match = "`d` must be consistent with dimension of `qmc_engine`."
+        with pytest.raises(ValueError, match=match):
+            gen.qrvs(d=3, qmc_engine=stats.qmc.Halton(2))
+
+    qrngs = [None, stats.qmc.Sobol(1, seed=0), stats.qmc.Halton(3, seed=0)]
+    # `size=None` should not add anything to the shape, `size=1` should
+    sizes = [(None, tuple()), (1, (1,)), (4, (4,)),
+             ((4,), (4,)), ((2, 4), (2, 4))]  # type: ignore
+    # Neither `d=None` nor `d=1` should add anything to the shape
+    ds = [(None, tuple()), (1, tuple()), (3, (3,))]
+
+    @pytest.mark.parametrize('qrng', qrngs)
+    @pytest.mark.parametrize('size_in, size_out', sizes)
+    @pytest.mark.parametrize('d_in, d_out', ds)
+    @pytest.mark.thread_unsafe(reason="fails in parallel")
+    def test_QRVS_shape_consistency(self, qrng, size_in, size_out,
+                                    d_in, d_out, method):
+        w32 = sys.platform == "win32" and platform.architecture()[0] == "32bit"
+        if w32 and method == "NumericalInversePolynomial":
+            pytest.xfail("NumericalInversePolynomial.qrvs fails for Win "
+                         "32-bit")
+
+        dist = StandardNormal()
+        Method = getattr(stats.sampling, method)
+        gen = Method(dist)
+
+        # If d and qrng.d are inconsistent, an error is raised
+        if d_in is not None and qrng is not None and qrng.d != d_in:
+            match = "`d` must be consistent with dimension of `qmc_engine`."
+            with pytest.raises(ValueError, match=match):
+                gen.qrvs(size_in, d=d_in, qmc_engine=qrng)
+            return
+
+        # Sometimes d is really determined by qrng
+        if d_in is None and qrng is not None and qrng.d != 1:
+            d_out = (qrng.d,)
+
+        shape_expected = size_out + d_out
+
+        qrng2 = deepcopy(qrng)
+        qrvs = gen.qrvs(size=size_in, d=d_in, qmc_engine=qrng)
+        if size_in is not None:
+            assert qrvs.shape == shape_expected
+
+        if qrng2 is not None:
+            uniform = qrng2.random(np.prod(size_in) or 1)
+            qrvs2 = stats.norm.ppf(uniform).reshape(shape_expected)
+            assert_allclose(qrvs, qrvs2, atol=1e-12)
+
+    def test_QRVS_size_tuple(self, method):
+        # QMCEngine samples are always of shape (n, d). When `size` is a tuple,
+        # we set `n = prod(size)` in the call to qmc_engine.random, transform
+        # the sample, and reshape it to the final dimensions. When we reshape,
+        # we need to be careful, because the _columns_ of the sample returned
+        # by a QMCEngine are "independent"-ish, but the elements within the
+        # columns are not. We need to make sure that this doesn't get mixed up
+        # by reshaping: qrvs[..., i] should remain "independent"-ish of
+        # qrvs[..., i+1], but the elements within qrvs[..., i] should be
+        # transformed from the same low-discrepancy sequence.
+
+        dist = StandardNormal()
+        Method = getattr(stats.sampling, method)
+        gen = Method(dist)
+
+        size = (3, 4)
+        d = 5
+        qrng = stats.qmc.Halton(d, seed=0)
+        qrng2 = stats.qmc.Halton(d, seed=0)
+
+        uniform = qrng2.random(np.prod(size))
+
+        qrvs = gen.qrvs(size=size, d=d, qmc_engine=qrng)
+        qrvs2 = stats.norm.ppf(uniform)
+
+        for i in range(d):
+            sample = qrvs[..., i]
+            sample2 = qrvs2[:, i].reshape(size)
+            assert_allclose(sample, sample2, atol=1e-12)
+
+
+class TestTransformedDensityRejection:
+    # Simple Custom Distribution
+    class dist0:
+        def pdf(self, x):
+            return 3/4 * (1-x*x)
+
+        def dpdf(self, x):
+            return 3/4 * (-2*x)
+
+        def cdf(self, x):
+            return 3/4 * (x - x**3/3 + 2/3)
+
+        def support(self):
+            return -1, 1
+
+    # Standard Normal Distribution
+    class dist1:
+        def pdf(self, x):
+            return stats.norm._pdf(x / 0.1)
+
+        def dpdf(self, x):
+            return -x / 0.01 * stats.norm._pdf(x / 0.1)
+
+        def cdf(self, x):
+            return stats.norm._cdf(x / 0.1)
+
+    # pdf with piecewise linear function as transformed density
+    # with T = -1/sqrt with shift. Taken from UNU.RAN test suite
+    # (from file t_tdr_ps.c)
+    class dist2:
+        def __init__(self, shift):
+            self.shift = shift
+
+        def pdf(self, x):
+            x -= self.shift
+            y = 1. / (abs(x) + 1.)
+            return 0.5 * y * y
+
+        def dpdf(self, x):
+            x -= self.shift
+            y = 1. / (abs(x) + 1.)
+            y = y * y * y
+            return y if (x < 0.) else -y
+
+        def cdf(self, x):
+            x -= self.shift
+            if x <= 0.:
+                return 0.5 / (1. - x)
+            else:
+                return 1. - 0.5 / (1. + x)
+
+    dists = [dist0(), dist1(), dist2(0.), dist2(10000.)]
+
+    # exact mean and variance of the distributions in the list dists
+    mv0 = [0., 4./15.]
+    mv1 = [0., 0.01]
+    mv2 = [0., np.inf]
+    mv3 = [10000., np.inf]
+    mvs = [mv0, mv1, mv2, mv3]
+
+    @pytest.mark.parametrize("dist, mv_ex",
+                             list(zip(dists, mvs)))
+    @pytest.mark.thread_unsafe(reason="deadlocks for unknown reasons")
+    def test_basic(self, dist, mv_ex):
+        with warnings.catch_warnings():
+            # filter the warnings thrown by UNU.RAN
+            warnings.simplefilter("ignore", RuntimeWarning)
+            rng = TransformedDensityRejection(dist, random_state=42)
+        check_cont_samples(rng, dist, mv_ex)
+
+    # PDF 0 everywhere => bad construction points
+    bad_pdfs = [(lambda x: 0, UNURANError, r"50 : bad construction points.")]
+    bad_pdfs += bad_pdfs_common  # type: ignore[arg-type]
+
+    @pytest.mark.parametrize("pdf, err, msg", bad_pdfs)
+    def test_bad_pdf(self, pdf, err, msg):
+        class dist:
+            pass
+        dist.pdf = pdf
+        dist.dpdf = lambda x: 1  # an arbitrary dPDF
+        with pytest.raises(err, match=msg):
+            TransformedDensityRejection(dist)
+
+    @pytest.mark.parametrize("dpdf, err, msg", bad_dpdf_common)
+    def test_bad_dpdf(self, dpdf, err, msg):
+        class dist:
+            pass
+        dist.pdf = lambda x: x
+        dist.dpdf = dpdf
+        with pytest.raises(err, match=msg):
+            TransformedDensityRejection(dist, domain=(1, 10))
+
+    # test domains with inf + nan in them. need to write a custom test for
+    # this because not all methods support infinite tails.
+    @pytest.mark.parametrize("domain, err, msg", inf_nan_domains)
+    def test_inf_nan_domains(self, domain, err, msg):
+        with pytest.raises(err, match=msg):
+            TransformedDensityRejection(StandardNormal(), domain=domain)
+
+    @pytest.mark.parametrize("construction_points", [-1, 0, 0.1])
+    def test_bad_construction_points_scalar(self, construction_points):
+        with pytest.raises(ValueError, match=r"`construction_points` must be "
+                                             r"a positive integer."):
+            TransformedDensityRejection(
+                StandardNormal(), construction_points=construction_points
+            )
+
+    def test_bad_construction_points_array(self):
+        # empty array
+        construction_points = []
+        with pytest.raises(ValueError, match=r"`construction_points` must "
+                                             r"either be a "
+                                             r"scalar or a non-empty array."):
+            TransformedDensityRejection(
+                StandardNormal(), construction_points=construction_points
+            )
+
+        # construction_points not monotonically increasing
+        construction_points = [1, 1, 1, 1, 1, 1]
+        with pytest.warns(RuntimeWarning, match=r"33 : starting points not "
+                                                r"strictly monotonically "
+                                                r"increasing"):
+            TransformedDensityRejection(
+                StandardNormal(), construction_points=construction_points
+            )
+
+        # construction_points containing nans
+        construction_points = [np.nan, np.nan, np.nan]
+        with pytest.raises(UNURANError, match=r"50 : bad construction "
+                                              r"points."):
+            TransformedDensityRejection(
+                StandardNormal(), construction_points=construction_points
+            )
+
+        # construction_points out of domain
+        construction_points = [-10, 10]
+        with pytest.warns(RuntimeWarning, match=r"50 : starting point out of "
+                                                r"domain"):
+            TransformedDensityRejection(
+                StandardNormal(), domain=(-3, 3),
+                construction_points=construction_points
+            )
+
+    @pytest.mark.parametrize("c", [-1., np.nan, np.inf, 0.1, 1.])
+    def test_bad_c(self, c):
+        msg = r"`c` must either be -0.5 or 0."
+        with pytest.raises(ValueError, match=msg):
+            TransformedDensityRejection(StandardNormal(), c=-1.)
+
+    u = [np.linspace(0, 1, num=1000), [], [[]], [np.nan],
+         [-np.inf, np.nan, np.inf], 0,
+         [[np.nan, 0.5, 0.1], [0.2, 0.4, np.inf], [-2, 3, 4]]]
+
+    @pytest.mark.parametrize("u", u)
+    def test_ppf_hat(self, u):
+        # Increase the `max_squeeze_hat_ratio` so the ppf_hat is more
+        # accurate.
+        rng = TransformedDensityRejection(StandardNormal(),
+                                          max_squeeze_hat_ratio=0.9999)
+        # Older versions of NumPy throw RuntimeWarnings for comparisons
+        # with nan.
+        with warnings.catch_warnings():
+            msg = "invalid value encountered in "
+            warnings.filterwarnings("ignore", msg + "greater", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "greater_equal", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "less", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "less_equal", RuntimeWarning)
+            res = rng.ppf_hat(u)
+            expected = stats.norm.ppf(u)
+        assert_allclose(res, expected, rtol=1e-3, atol=1e-5)
+        assert res.shape == expected.shape
+
+    def test_bad_dist(self):
+        # Empty distribution
+        class dist:
+            ...
+
+        msg = r"`pdf` required but not found."
+        with pytest.raises(ValueError, match=msg):
+            TransformedDensityRejection(dist)
+
+        # dPDF not present in dist
+        class dist:
+            pdf = lambda x: 1-x*x  # noqa: E731
+
+        msg = r"`dpdf` required but not found."
+        with pytest.raises(ValueError, match=msg):
+            TransformedDensityRejection(dist)
+
+
+class TestDiscreteAliasUrn:
+    # DAU fails on these probably because of large domains and small
+    # computation errors in PMF. Mean/SD match but chi-squared test fails.
+    basic_fail_dists = {
+        'nchypergeom_fisher',  # numerical errors on tails
+        'nchypergeom_wallenius',  # numerical errors on tails
+        'randint'  # fails on 32-bit ubuntu
+    }
+
+    @pytest.mark.parametrize("distname, params", distdiscrete)
+    def test_basic(self, distname, params):
+        if distname in self.basic_fail_dists:
+            msg = ("DAU fails on these probably because of large domains "
+                   "and small computation errors in PMF.")
+            pytest.skip(msg)
+        if not isinstance(distname, str):
+            dist = distname
+        else:
+            dist = getattr(stats, distname)
+        dist = dist(*params)
+        domain = dist.support()
+        if not np.isfinite(domain[1] - domain[0]):
+            # DAU only works with finite domain. So, skip the distributions
+            # with infinite tails.
+            pytest.skip("DAU only works with a finite domain.")
+        k = np.arange(domain[0], domain[1]+1)
+        pv = dist.pmf(k)
+        mv_ex = dist.stats('mv')
+        rng = DiscreteAliasUrn(dist, random_state=42)
+        check_discr_samples(rng, pv, mv_ex)
+
+    # Can't use bad_pmf_common here as we evaluate PMF early on to avoid
+    # unhelpful errors from UNU.RAN.
+    bad_pmf = [
+        # inf returned
+        (lambda x: np.inf, ValueError,
+         r"must contain only finite / non-nan values"),
+        # nan returned
+        (lambda x: np.nan, ValueError,
+         r"must contain only finite / non-nan values"),
+        # all zeros
+        (lambda x: 0.0, ValueError,
+         r"must contain at least one non-zero value"),
+        # Undefined name inside the function
+        (lambda x: foo, NameError,  # type: ignore[name-defined]  # noqa: F821
+         r"name 'foo' is not defined"),
+        # Returning wrong type.
+        (lambda x: [], ValueError,
+         r"setting an array element with a sequence."),
+        # probabilities < 0
+        (lambda x: -x, UNURANError,
+         r"50 : probability < 0"),
+        # signature of PMF wrong
+        (lambda: 1.0, TypeError,
+         r"takes 0 positional arguments but 1 was given")
+    ]
+
+    @pytest.mark.parametrize("pmf, err, msg", bad_pmf)
+    def test_bad_pmf(self, pmf, err, msg):
+        class dist:
+            pass
+        dist.pmf = pmf
+        with pytest.raises(err, match=msg):
+            DiscreteAliasUrn(dist, domain=(1, 10))
+
+    @pytest.mark.parametrize("pv", [[0.18, 0.02, 0.8],
+                                    [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]])
+    def test_sampling_with_pv(self, pv):
+        pv = np.asarray(pv, dtype=np.float64)
+        rng = DiscreteAliasUrn(pv, random_state=123)
+        rng.rvs(100_000)
+        pv = pv / pv.sum()
+        variates = np.arange(0, len(pv))
+        # test if the first few moments match
+        m_expected = np.average(variates, weights=pv)
+        v_expected = np.average((variates - m_expected) ** 2, weights=pv)
+        mv_expected = m_expected, v_expected
+        check_discr_samples(rng, pv, mv_expected)
+
+    @pytest.mark.parametrize("pv, msg", bad_pv_common)
+    def test_bad_pv(self, pv, msg):
+        with pytest.raises(ValueError, match=msg):
+            DiscreteAliasUrn(pv)
+
+    # DAU doesn't support infinite tails. So, it should throw an error when
+    # inf is present in the domain.
+    inf_domain = [(-np.inf, np.inf), (np.inf, np.inf), (-np.inf, -np.inf),
+                  (0, np.inf), (-np.inf, 0)]
+
+    @pytest.mark.parametrize("domain", inf_domain)
+    def test_inf_domain(self, domain):
+        with pytest.raises(ValueError, match=r"must be finite"):
+            DiscreteAliasUrn(stats.binom(10, 0.2), domain=domain)
+
+    def test_bad_urn_factor(self):
+        with pytest.warns(RuntimeWarning, match=r"relative urn size < 1."):
+            DiscreteAliasUrn([0.5, 0.5], urn_factor=-1)
+
+    def test_bad_args(self):
+        msg = (r"`domain` must be provided when the "
+               r"probability vector is not available.")
+
+        class dist:
+            def pmf(self, x):
+                return x
+
+        with pytest.raises(ValueError, match=msg):
+            DiscreteAliasUrn(dist)
+
+    def test_gh19359(self):
+        pv = special.softmax(np.ones((1533,)))
+        rng = DiscreteAliasUrn(pv, random_state=42)
+        # check the correctness
+        check_discr_samples(rng, pv, (1532 / 2, (1532**2 - 1) / 12),
+                            rtol=5e-3)
+
+
+class TestNumericalInversePolynomial:
+    # Simple Custom Distribution
+    class dist0:
+        def pdf(self, x):
+            return 3/4 * (1-x*x)
+
+        def cdf(self, x):
+            return 3/4 * (x - x**3/3 + 2/3)
+
+        def support(self):
+            return -1, 1
+
+    # Standard Normal Distribution
+    class dist1:
+        def pdf(self, x):
+            return stats.norm._pdf(x / 0.1)
+
+        def cdf(self, x):
+            return stats.norm._cdf(x / 0.1)
+
+    # Sin 2 distribution
+    #          /  0.05 + 0.45*(1 +sin(2 Pi x))  if |x| <= 1
+    #  f(x) = <
+    #          \  0        otherwise
+    # Taken from UNU.RAN test suite (from file t_pinv.c)
+    class dist2:
+        def pdf(self, x):
+            return 0.05 + 0.45 * (1 + np.sin(2*np.pi*x))
+
+        def cdf(self, x):
+            return (0.05*(x + 1) +
+                    0.9*(1. + 2.*np.pi*(1 + x) - np.cos(2.*np.pi*x)) /
+                    (4.*np.pi))
+
+        def support(self):
+            return -1, 1
+
+    # Sin 10 distribution
+    #          /  0.05 + 0.45*(1 +sin(2 Pi x))  if |x| <= 5
+    #  f(x) = <
+    #          \  0        otherwise
+    # Taken from UNU.RAN test suite (from file t_pinv.c)
+    class dist3:
+        def pdf(self, x):
+            return 0.2 * (0.05 + 0.45 * (1 + np.sin(2*np.pi*x)))
+
+        def cdf(self, x):
+            return x/10. + 0.5 + 0.09/(2*np.pi) * (np.cos(10*np.pi) -
+                                                   np.cos(2*np.pi*x))
+
+        def support(self):
+            return -5, 5
+
+    dists = [dist0(), dist1(), dist2(), dist3()]
+
+    # exact mean and variance of the distributions in the list dists
+    mv0 = [0., 4./15.]
+    mv1 = [0., 0.01]
+    mv2 = [-0.45/np.pi, 2/3*0.5 - 0.45**2/np.pi**2]
+    mv3 = [-0.45/np.pi, 0.2 * 250/3 * 0.5 - 0.45**2/np.pi**2]
+    mvs = [mv0, mv1, mv2, mv3]
+
+    @pytest.mark.thread_unsafe(reason="deadlocks for unknown reasons")
+    @pytest.mark.parametrize("dist, mv_ex",
+                             list(zip(dists, mvs)))
+    def test_basic(self, dist, mv_ex):
+        rng = NumericalInversePolynomial(dist, random_state=42)
+        check_cont_samples(rng, dist, mv_ex)
+
+    @pytest.mark.xslow
+    @pytest.mark.parametrize("distname, params", distcont)
+    def test_basic_all_scipy_dists(self, distname, params):
+
+        very_slow_dists = ['anglit', 'gausshyper', 'kappa4',
+                           'ksone', 'kstwo', 'levy_l',
+                           'levy_stable', 'studentized_range',
+                           'trapezoid', 'triang', 'vonmises']
+        # for these distributions, some assertions fail due to minor
+        # numerical differences. They can be avoided either by changing
+        # the seed or by increasing the u_resolution.
+        fail_dists = ['chi2', 'fatiguelife', 'gibrat',
+                      'halfgennorm', 'lognorm', 'ncf',
+                      'ncx2', 'pareto', 't']
+        # for these distributions, skip the check for agreement between sample
+        # moments and true moments. We cannot expect them to pass due to the
+        # high variance of sample moments.
+        skip_sample_moment_check = ['rel_breitwigner']
+
+        if distname in very_slow_dists:
+            pytest.skip(f"PINV too slow for {distname}")
+        if distname in fail_dists:
+            pytest.skip(f"PINV fails for {distname}")
+        dist = (getattr(stats, distname)
+                if isinstance(distname, str)
+                else distname)
+        dist = dist(*params)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", RuntimeWarning)
+            rng = NumericalInversePolynomial(dist, random_state=42)
+        if distname in skip_sample_moment_check:
+            return
+        check_cont_samples(rng, dist, [dist.mean(), dist.var()])
+
+    @pytest.mark.parametrize("pdf, err, msg", bad_pdfs_common)
+    def test_bad_pdf(self, pdf, err, msg):
+        class dist:
+            pass
+        dist.pdf = pdf
+        with pytest.raises(err, match=msg):
+            NumericalInversePolynomial(dist, domain=[0, 5])
+
+    @pytest.mark.parametrize("logpdf, err, msg", bad_logpdfs_common)
+    def test_bad_logpdf(self, logpdf, err, msg):
+        class dist:
+            pass
+        dist.logpdf = logpdf
+        with pytest.raises(err, match=msg):
+            NumericalInversePolynomial(dist, domain=[0, 5])
+
+    # test domains with inf + nan in them. need to write a custom test for
+    # this because not all methods support infinite tails.
+    @pytest.mark.parametrize("domain, err, msg", inf_nan_domains)
+    def test_inf_nan_domains(self, domain, err, msg):
+        with pytest.raises(err, match=msg):
+            NumericalInversePolynomial(StandardNormal(), domain=domain)
+
+    u = [
+        # test if quantile 0 and 1 return -inf and inf respectively and check
+        # the correctness of the PPF for equidistant points between 0 and 1.
+        np.linspace(0, 1, num=10000),
+        # test the PPF method for empty arrays
+        [], [[]],
+        # test if nans and infs return nan result.
+        [np.nan], [-np.inf, np.nan, np.inf],
+        # test if a scalar is returned for a scalar input.
+        0,
+        # test for arrays with nans, values greater than 1 and less than 0,
+        # and some valid values.
+        [[np.nan, 0.5, 0.1], [0.2, 0.4, np.inf], [-2, 3, 4]]
+    ]
+
+    @pytest.mark.parametrize("u", u)
+    def test_ppf(self, u):
+        dist = StandardNormal()
+        rng = NumericalInversePolynomial(dist, u_resolution=1e-14)
+        # Older versions of NumPy throw RuntimeWarnings for comparisons
+        # with nan.
+        with warnings.catch_warnings():
+            msg = "invalid value encountered in "
+            warnings.filterwarnings("ignore", msg + "greater", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "greater_equal", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "less", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "less_equal", RuntimeWarning)
+            res = rng.ppf(u)
+            expected = stats.norm.ppf(u)
+        assert_allclose(res, expected, rtol=1e-11, atol=1e-11)
+        assert res.shape == expected.shape
+
+    x = [np.linspace(-10, 10, num=10000), [], [[]], [np.nan],
+         [-np.inf, np.nan, np.inf], 0,
+         [[np.nan, 0.5, 0.1], [0.2, 0.4, np.inf], [-np.inf, 3, 4]]]
+
+    @pytest.mark.parametrize("x", x)
+    def test_cdf(self, x):
+        dist = StandardNormal()
+        rng = NumericalInversePolynomial(dist, u_resolution=1e-14)
+        # Older versions of NumPy throw RuntimeWarnings for comparisons
+        # with nan.
+        with warnings.catch_warnings():
+            msg = "invalid value encountered in "
+            warnings.filterwarnings("ignore", msg + "greater", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "greater_equal", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "less", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "less_equal", RuntimeWarning)
+            res = rng.cdf(x)
+            expected = stats.norm.cdf(x)
+        assert_allclose(res, expected, rtol=1e-11, atol=1e-11)
+        assert res.shape == expected.shape
+
+    @pytest.mark.slow
+    def test_u_error(self):
+        dist = StandardNormal()
+        rng = NumericalInversePolynomial(dist, u_resolution=1e-10)
+        max_error, mae = rng.u_error()
+        assert max_error < 1e-10
+        assert mae <= max_error
+        rng = NumericalInversePolynomial(dist, u_resolution=1e-14)
+        max_error, mae = rng.u_error()
+        assert max_error < 1e-14
+        assert mae <= max_error
+
+    bad_orders = [1, 4.5, 20, np.inf, np.nan]
+    bad_u_resolution = [1e-20, 1e-1, np.inf, np.nan]
+
+    @pytest.mark.parametrize("order", bad_orders)
+    def test_bad_orders(self, order):
+        dist = StandardNormal()
+
+        msg = r"`order` must be an integer in the range \[3, 17\]."
+        with pytest.raises(ValueError, match=msg):
+            NumericalInversePolynomial(dist, order=order)
+
+    @pytest.mark.parametrize("u_resolution", bad_u_resolution)
+    def test_bad_u_resolution(self, u_resolution):
+        msg = r"`u_resolution` must be between 1e-15 and 1e-5."
+        with pytest.raises(ValueError, match=msg):
+            NumericalInversePolynomial(StandardNormal(),
+                                       u_resolution=u_resolution)
+
+    def test_bad_args(self):
+
+        class BadDist:
+            def cdf(self, x):
+                return stats.norm._cdf(x)
+
+        dist = BadDist()
+        msg = r"Either of the methods `pdf` or `logpdf` must be specified"
+        with pytest.raises(ValueError, match=msg):
+            rng = NumericalInversePolynomial(dist)
+
+        dist = StandardNormal()
+        rng = NumericalInversePolynomial(dist)
+        msg = r"`sample_size` must be greater than or equal to 1000."
+        with pytest.raises(ValueError, match=msg):
+            rng.u_error(10)
+
+        class Distribution:
+            def pdf(self, x):
+                return np.exp(-0.5 * x*x)
+
+        dist = Distribution()
+        rng = NumericalInversePolynomial(dist)
+        msg = r"Exact CDF required but not found."
+        with pytest.raises(ValueError, match=msg):
+            rng.u_error()
+
+    def test_logpdf_pdf_consistency(self):
+        # 1. check that PINV works with pdf and logpdf only
+        # 2. check that generated ppf is the same (up to a small tolerance)
+
+        class MyDist:
+            pass
+
+        # create generator from dist with only pdf
+        dist_pdf = MyDist()
+        dist_pdf.pdf = lambda x: math.exp(-x*x/2)
+        rng1 = NumericalInversePolynomial(dist_pdf)
+
+        # create dist with only logpdf
+        dist_logpdf = MyDist()
+        dist_logpdf.logpdf = lambda x: -x*x/2
+        rng2 = NumericalInversePolynomial(dist_logpdf)
+
+        q = np.linspace(1e-5, 1-1e-5, num=100)
+        assert_allclose(rng1.ppf(q), rng2.ppf(q))
+
+
+class TestNumericalInverseHermite:
+    #         /  (1 +sin(2 Pi x))/2  if |x| <= 1
+    # f(x) = <
+    #         \  0        otherwise
+    # Taken from UNU.RAN test suite (from file t_hinv.c)
+    class dist0:
+        def pdf(self, x):
+            return 0.5*(1. + np.sin(2.*np.pi*x))
+
+        def dpdf(self, x):
+            return np.pi*np.cos(2.*np.pi*x)
+
+        def cdf(self, x):
+            return (1. + 2.*np.pi*(1 + x) - np.cos(2.*np.pi*x)) / (4.*np.pi)
+
+        def support(self):
+            return -1, 1
+
+    #         /  Max(sin(2 Pi x)),0)Pi/2  if -1 < x <0.5
+    # f(x) = <
+    #         \  0        otherwise
+    # Taken from UNU.RAN test suite (from file t_hinv.c)
+    class dist1:
+        def pdf(self, x):
+            if (x <= -0.5):
+                return np.sin((2. * np.pi) * x) * 0.5 * np.pi
+            if (x < 0.):
+                return 0.
+            if (x <= 0.5):
+                return np.sin((2. * np.pi) * x) * 0.5 * np.pi
+
+        def dpdf(self, x):
+            if (x <= -0.5):
+                return np.cos((2. * np.pi) * x) * np.pi * np.pi
+            if (x < 0.):
+                return 0.
+            if (x <= 0.5):
+                return np.cos((2. * np.pi) * x) * np.pi * np.pi
+
+        def cdf(self, x):
+            if (x <= -0.5):
+                return 0.25 * (1 - np.cos((2. * np.pi) * x))
+            if (x < 0.):
+                return 0.5
+            if (x <= 0.5):
+                return 0.75 - 0.25 * np.cos((2. * np.pi) * x)
+
+        def support(self):
+            return -1, 0.5
+
+    dists = [dist0(), dist1()]
+
+    # exact mean and variance of the distributions in the list dists
+    mv0 = [-1/(2*np.pi), 1/3 - 1/(4*np.pi*np.pi)]
+    mv1 = [-1/4, 3/8-1/(2*np.pi*np.pi) - 1/16]
+    mvs = [mv0, mv1]
+
+    @pytest.mark.parametrize("dist, mv_ex",
+                             list(zip(dists, mvs)))
+    @pytest.mark.parametrize("order", [3, 5])
+    @pytest.mark.thread_unsafe
+    def test_basic(self, dist, mv_ex, order):
+        rng = NumericalInverseHermite(dist, order=order, random_state=42)
+        check_cont_samples(rng, dist, mv_ex)
+
+    # test domains with inf + nan in them. need to write a custom test for
+    # this because not all methods support infinite tails.
+    @pytest.mark.parametrize("domain, err, msg", inf_nan_domains)
+    def test_inf_nan_domains(self, domain, err, msg):
+        with pytest.raises(err, match=msg):
+            NumericalInverseHermite(StandardNormal(), domain=domain)
+
+    def basic_test_all_scipy_dists(self, distname, shapes):
+        slow_dists = {'ksone', 'kstwo', 'levy_stable', 'skewnorm'}
+        fail_dists = {'beta', 'gausshyper', 'geninvgauss', 'ncf', 'nct',
+                      'norminvgauss', 'genhyperbolic', 'studentized_range',
+                      'vonmises', 'kappa4', 'invgauss', 'wald'}
+
+        if distname in slow_dists:
+            pytest.skip("Distribution is too slow")
+        if distname in fail_dists:
+            # specific reasons documented in gh-13319
+            # https://github.com/scipy/scipy/pull/13319#discussion_r626188955
+            pytest.xfail("Fails - usually due to inaccurate CDF/PDF")
+
+        rng = np.random.default_rng(0)
+
+        dist = getattr(stats, distname)(*shapes)
+        fni = NumericalInverseHermite(dist)
+
+        x = rng.random(10)
+        p_tol = np.max(np.abs(dist.ppf(x)-fni.ppf(x))/np.abs(dist.ppf(x)))
+        u_tol = np.max(np.abs(dist.cdf(fni.ppf(x)) - x))
+
+        assert p_tol < 1e-8
+        assert u_tol < 1e-12
+
+    @pytest.mark.filterwarnings('ignore::RuntimeWarning')
+    @pytest.mark.xslow
+    @pytest.mark.parametrize(("distname", "shapes"), distcont)
+    def test_basic_all_scipy_dists(self, distname, shapes):
+        # if distname == "truncnorm":
+        #     pytest.skip("Tested separately")
+        self.basic_test_all_scipy_dists(distname, shapes)
+
+    @pytest.mark.fail_slow(5)
+    @pytest.mark.filterwarnings('ignore::RuntimeWarning')
+    @pytest.mark.parallel_threads_limit(4)  # Very slow
+    def test_basic_truncnorm_gh17155(self):
+        self.basic_test_all_scipy_dists("truncnorm", (0.1, 2))
+
+    def test_input_validation(self):
+        match = r"`order` must be either 1, 3, or 5."
+        with pytest.raises(ValueError, match=match):
+            NumericalInverseHermite(StandardNormal(), order=2)
+
+        match = "`cdf` required but not found"
+        with pytest.raises(ValueError, match=match):
+            NumericalInverseHermite("norm")
+
+        match = "could not convert string to float"
+        with pytest.raises(ValueError, match=match):
+            NumericalInverseHermite(StandardNormal(),
+                                    u_resolution='ekki')
+
+    rngs = [None, 0, np.random.RandomState(0)]
+    rngs.append(np.random.default_rng(0))  # type: ignore
+    sizes = [(None, tuple()), (8, (8,)), ((4, 5, 6), (4, 5, 6))]
+
+    @pytest.mark.parametrize('rng', rngs)
+    @pytest.mark.parametrize('size_in, size_out', sizes)
+    @pytest.mark.thread_unsafe
+    def test_RVS(self, rng, size_in, size_out):
+        dist = StandardNormal()
+        fni = NumericalInverseHermite(dist)
+
+        rng2 = deepcopy(rng)
+        rvs = fni.rvs(size=size_in, random_state=rng)
+        if size_in is not None:
+            assert rvs.shape == size_out
+
+        if rng2 is not None:
+            rng2 = check_random_state(rng2)
+            uniform = rng2.uniform(size=size_in)
+            rvs2 = stats.norm.ppf(uniform)
+            assert_allclose(rvs, rvs2)
+
+    def test_inaccurate_CDF(self):
+        # CDF function with inaccurate tail cannot be inverted; see gh-13319
+        # https://github.com/scipy/scipy/pull/13319#discussion_r626188955
+        shapes = (2.3098496451481823, 0.6268795430096368)
+        match = ("98 : one or more intervals very short; possibly due to "
+                 "numerical problems with a pole or very flat tail")
+
+        # fails with default tol
+        with pytest.warns(RuntimeWarning, match=match):
+            NumericalInverseHermite(stats.beta(*shapes))
+
+        # no error with coarser tol
+        NumericalInverseHermite(stats.beta(*shapes), u_resolution=1e-8)
+
+    def test_custom_distribution(self):
+        dist1 = StandardNormal()
+        fni1 = NumericalInverseHermite(dist1)
+
+        dist2 = stats.norm()
+        fni2 = NumericalInverseHermite(dist2)
+
+        assert_allclose(fni1.rvs(random_state=0), fni2.rvs(random_state=0))
+
+    u = [
+        # check the correctness of the PPF for equidistant points between
+        # 0.02 and 0.98.
+        np.linspace(0., 1., num=10000),
+        # test the PPF method for empty arrays
+        [], [[]],
+        # test if nans and infs return nan result.
+        [np.nan], [-np.inf, np.nan, np.inf],
+        # test if a scalar is returned for a scalar input.
+        0,
+        # test for arrays with nans, values greater than 1 and less than 0,
+        # and some valid values.
+        [[np.nan, 0.5, 0.1], [0.2, 0.4, np.inf], [-2, 3, 4]]
+    ]
+
+    @pytest.mark.parametrize("u", u)
+    def test_ppf(self, u):
+        dist = StandardNormal()
+        rng = NumericalInverseHermite(dist, u_resolution=1e-12)
+        # Older versions of NumPy throw RuntimeWarnings for comparisons
+        # with nan.
+        with warnings.catch_warnings():
+            msg = "invalid value encountered in "
+            warnings.filterwarnings("ignore", msg + "greater", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "greater_equal", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "less", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "less_equal", RuntimeWarning)
+            res = rng.ppf(u)
+            expected = stats.norm.ppf(u)
+        assert_allclose(res, expected, rtol=1e-9, atol=3e-10)
+        assert res.shape == expected.shape
+
+    @pytest.mark.slow
+    def test_u_error(self):
+        dist = StandardNormal()
+        rng = NumericalInverseHermite(dist, u_resolution=1e-10)
+        max_error, mae = rng.u_error()
+        assert max_error < 1e-10
+        assert mae <= max_error
+        with warnings.catch_warnings():
+            # ignore warning about u-resolution being too small.
+            warnings.simplefilter("ignore", RuntimeWarning)
+            rng = NumericalInverseHermite(dist, u_resolution=1e-14)
+        max_error, mae = rng.u_error()
+        assert max_error < 1e-14
+        assert mae <= max_error
+
+
+class TestDiscreteGuideTable:
+    basic_fail_dists = {
+        'nchypergeom_fisher',  # numerical errors on tails
+        'nchypergeom_wallenius',  # numerical errors on tails
+        'randint'  # fails on 32-bit ubuntu
+    }
+
+    def test_guide_factor_gt3_raises_warning(self):
+        pv = [0.1, 0.3, 0.6]
+        urng = np.random.default_rng()
+        with pytest.warns(RuntimeWarning):
+            DiscreteGuideTable(pv, random_state=urng, guide_factor=7)
+
+    def test_guide_factor_zero_raises_warning(self):
+        pv = [0.1, 0.3, 0.6]
+        urng = np.random.default_rng()
+        with pytest.warns(RuntimeWarning):
+            DiscreteGuideTable(pv, random_state=urng, guide_factor=0)
+
+    def test_negative_guide_factor_raises_warning(self):
+        # This occurs from the UNU.RAN wrapper automatically.
+        # however it already gives a useful warning
+        # Here we just test that a warning is raised.
+        pv = [0.1, 0.3, 0.6]
+        urng = np.random.default_rng()
+        with pytest.warns(RuntimeWarning):
+            DiscreteGuideTable(pv, random_state=urng, guide_factor=-1)
+
+    @pytest.mark.parametrize("distname, params", distdiscrete)
+    def test_basic(self, distname, params):
+        if distname in self.basic_fail_dists:
+            msg = ("DGT fails on these probably because of large domains "
+                   "and small computation errors in PMF.")
+            pytest.skip(msg)
+
+        if not isinstance(distname, str):
+            dist = distname
+        else:
+            dist = getattr(stats, distname)
+
+        dist = dist(*params)
+        domain = dist.support()
+
+        if not np.isfinite(domain[1] - domain[0]):
+            # DGT only works with finite domain. So, skip the distributions
+            # with infinite tails.
+            pytest.skip("DGT only works with a finite domain.")
+
+        k = np.arange(domain[0], domain[1]+1)
+        pv = dist.pmf(k)
+        mv_ex = dist.stats('mv')
+        rng = DiscreteGuideTable(dist, random_state=42)
+        check_discr_samples(rng, pv, mv_ex)
+
+    u = [
+        # the correctness of the PPF for equidistant points between 0 and 1.
+        np.linspace(0, 1, num=10000),
+        # test the PPF method for empty arrays
+        [], [[]],
+        # test if nans and infs return nan result.
+        [np.nan], [-np.inf, np.nan, np.inf],
+        # test if a scalar is returned for a scalar input.
+        0,
+        # test for arrays with nans, values greater than 1 and less than 0,
+        # and some valid values.
+        [[np.nan, 0.5, 0.1], [0.2, 0.4, np.inf], [-2, 3, 4]]
+    ]
+
+    @pytest.mark.parametrize('u', u)
+    def test_ppf(self, u):
+        n, p = 4, 0.1
+        dist = stats.binom(n, p)
+        rng = DiscreteGuideTable(dist, random_state=42)
+
+        # Older versions of NumPy throw RuntimeWarnings for comparisons
+        # with nan.
+        with warnings.catch_warnings():
+            msg = "invalid value encountered in "
+            warnings.filterwarnings("ignore", msg + "greater", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "greater_equal", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "less", RuntimeWarning)
+            warnings.filterwarnings("ignore", msg + "less_equal", RuntimeWarning)
+            res = rng.ppf(u)
+            expected = stats.binom.ppf(u, n, p)
+        assert_equal(res.shape, expected.shape)
+        assert_equal(res, expected)
+
+    @pytest.mark.parametrize("pv, msg", bad_pv_common)
+    def test_bad_pv(self, pv, msg):
+        with pytest.raises(ValueError, match=msg):
+            DiscreteGuideTable(pv)
+
+    # DGT doesn't support infinite tails. So, it should throw an error when
+    # inf is present in the domain.
+    inf_domain = [(-np.inf, np.inf), (np.inf, np.inf), (-np.inf, -np.inf),
+                  (0, np.inf), (-np.inf, 0)]
+
+    @pytest.mark.parametrize("domain", inf_domain)
+    def test_inf_domain(self, domain):
+        with pytest.raises(ValueError, match=r"must be finite"):
+            DiscreteGuideTable(stats.binom(10, 0.2), domain=domain)
+
+
+class TestSimpleRatioUniforms:
+    # pdf with piecewise linear function as transformed density
+    # with T = -1/sqrt with shift. Taken from UNU.RAN test suite
+    # (from file t_srou.c)
+    class dist:
+        def __init__(self, shift):
+            self.shift = shift
+            self.mode = shift
+
+        def pdf(self, x):
+            x -= self.shift
+            y = 1. / (abs(x) + 1.)
+            return 0.5 * y * y
+
+        def cdf(self, x):
+            x -= self.shift
+            if x <= 0.:
+                return 0.5 / (1. - x)
+            else:
+                return 1. - 0.5 / (1. + x)
+
+    dists = [dist(0.), dist(10000.)]
+
+    # exact mean and variance of the distributions in the list dists
+    mv1 = [0., np.inf]
+    mv2 = [10000., np.inf]
+    mvs = [mv1, mv2]
+
+    @pytest.mark.parametrize("dist, mv_ex",
+                             list(zip(dists, mvs)))
+    @pytest.mark.thread_unsafe
+    def test_basic(self, dist, mv_ex):
+        rng = SimpleRatioUniforms(dist, mode=dist.mode, random_state=42)
+        check_cont_samples(rng, dist, mv_ex)
+        rng = SimpleRatioUniforms(dist, mode=dist.mode,
+                                  cdf_at_mode=dist.cdf(dist.mode),
+                                  random_state=42)
+        check_cont_samples(rng, dist, mv_ex)
+
+    # test domains with inf + nan in them. need to write a custom test for
+    # this because not all methods support infinite tails.
+    @pytest.mark.parametrize("domain, err, msg", inf_nan_domains)
+    def test_inf_nan_domains(self, domain, err, msg):
+        with pytest.raises(err, match=msg):
+            SimpleRatioUniforms(StandardNormal(), domain=domain)
+
+    def test_bad_args(self):
+        # pdf_area < 0
+        with pytest.raises(ValueError, match=r"`pdf_area` must be > 0"):
+            SimpleRatioUniforms(StandardNormal(), mode=0, pdf_area=-1)
+
+
+class TestRatioUniforms:
+    def test_rv_generation(self):
+        # use KS test to check distribution of rvs
+        # normal distribution
+        f = stats.norm.pdf
+        v = np.sqrt(f(np.sqrt(2))) * np.sqrt(2)
+        u = np.sqrt(f(0))
+        gen = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=12345)
+        assert_equal(stats.kstest(gen.rvs(2500), 'norm')[1] > 0.25, True)
+
+        # exponential distribution
+        gen = RatioUniforms(lambda x: np.exp(-x), umax=1,
+                            vmin=0, vmax=2*np.exp(-1), random_state=12345)
+        assert_equal(stats.kstest(gen.rvs(1000), 'expon')[1] > 0.25, True)
+
+    def test_shape(self):
+        # test shape of return value depending on size parameter
+        f = stats.norm.pdf
+        v = np.sqrt(f(np.sqrt(2))) * np.sqrt(2)
+        u = np.sqrt(f(0))
+
+        gen1 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=1234)
+        gen2 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=1234)
+        gen3 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=1234)
+        r1, r2, r3 = gen1.rvs(3), gen2.rvs((3,)), gen3.rvs((3, 1))
+        assert_equal(r1, r2)
+        assert_equal(r2, r3.flatten())
+        assert_equal(r1.shape, (3,))
+        assert_equal(r3.shape, (3, 1))
+
+        gen4 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=12)
+        gen5 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=12)
+        r4, r5 = gen4.rvs(size=(3, 3, 3)), gen5.rvs(size=27)
+        assert_equal(r4.flatten(), r5)
+        assert_equal(r4.shape, (3, 3, 3))
+
+        gen6 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=1234)
+        gen7 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=1234)
+        gen8 = RatioUniforms(f, umax=u, vmin=-v, vmax=v, random_state=1234)
+        r6, r7, r8 = gen6.rvs(), gen7.rvs(1), gen8.rvs((1,))
+        assert_equal(r6, r7)
+        assert_equal(r7, r8)
+
+    def test_random_state(self):
+        f = stats.norm.pdf
+        v = np.sqrt(f(np.sqrt(2))) * np.sqrt(2)
+        umax = np.sqrt(f(0))
+        gen1 = RatioUniforms(f, umax=umax, vmin=-v, vmax=v, random_state=1234)
+        r1 = gen1.rvs(10)
+        rng = np.random.RandomState(1234)
+        gen2 = RatioUniforms(f, umax=umax, vmin=-v, vmax=v, random_state=rng)
+        r2 = gen2.rvs(10)
+        assert_equal(r1, r2)
+
+    def test_exceptions(self):
+        f = stats.norm.pdf
+        # need vmin < vmax
+        with assert_raises(ValueError, match="vmin must be smaller than vmax"):
+            RatioUniforms(pdf=f, umax=1, vmin=3, vmax=1)
+        with assert_raises(ValueError, match="vmin must be smaller than vmax"):
+            RatioUniforms(pdf=f, umax=1, vmin=1, vmax=1)
+        # need umax > 0
+        with assert_raises(ValueError, match="umax must be positive"):
+            RatioUniforms(pdf=f, umax=-1, vmin=1, vmax=3)
+        with assert_raises(ValueError, match="umax must be positive"):
+            RatioUniforms(pdf=f, umax=0, vmin=1, vmax=3)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_sensitivity_analysis.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_sensitivity_analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c8d53040380808c03358b905878cc4a8b9b9679
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_sensitivity_analysis.py
@@ -0,0 +1,310 @@
+import numpy as np
+from numpy.testing import assert_allclose, assert_array_less
+import pytest
+
+from scipy import stats
+from scipy.stats import sobol_indices
+from scipy.stats._resampling import BootstrapResult
+from scipy.stats._sensitivity_analysis import (
+    BootstrapSobolResult, f_ishigami, sample_AB, sample_A_B
+)
+
+
+@pytest.fixture(scope='session')
+def ishigami_ref_indices():
+    """Reference values for Ishigami from Saltelli2007.
+
+    Chapter 4, exercise 5 pages 179-182.
+    """
+    a = 7.
+    b = 0.1
+
+    var = 0.5 + a**2/8 + b*np.pi**4/5 + b**2*np.pi**8/18
+    v1 = 0.5 + b*np.pi**4/5 + b**2*np.pi**8/50
+    v2 = a**2/8
+    v3 = 0
+    v12 = 0
+    # v13: mistake in the book, see other derivations e.g. in 10.1002/nme.4856
+    v13 = b**2*np.pi**8*8/225
+    v23 = 0
+
+    s_first = np.array([v1, v2, v3])/var
+    s_second = np.array([
+        [0., 0., v13],
+        [v12, 0., v23],
+        [v13, v23, 0.]
+    ])/var
+    s_total = s_first + s_second.sum(axis=1)
+
+    return s_first, s_total
+
+
+def f_ishigami_vec(x):
+    """Output of shape (2, n)."""
+    res = f_ishigami(x)
+    return res, res
+
+
+class TestSobolIndices:
+
+    dists = [
+        stats.uniform(loc=-np.pi, scale=2*np.pi)  # type: ignore[attr-defined]
+    ] * 3
+
+    def test_sample_AB(self):
+        # (d, n)
+        A = np.array(
+            [[1, 4, 7, 10],
+             [2, 5, 8, 11],
+             [3, 6, 9, 12]]
+        )
+        B = A + 100
+        # (d, d, n)
+        ref = np.array(
+            [[[101, 104, 107, 110],
+              [2, 5, 8, 11],
+              [3, 6, 9, 12]],
+             [[1, 4, 7, 10],
+              [102, 105, 108, 111],
+              [3, 6, 9, 12]],
+             [[1, 4, 7, 10],
+              [2, 5, 8, 11],
+              [103, 106, 109, 112]]]
+        )
+        AB = sample_AB(A=A, B=B)
+        assert_allclose(AB, ref)
+
+    @pytest.mark.xslow
+    @pytest.mark.xfail_on_32bit("Can't create large array for test")
+    @pytest.mark.parametrize(
+        'func',
+        [f_ishigami, pytest.param(f_ishigami_vec, marks=pytest.mark.slow)],
+        ids=['scalar', 'vector']
+    )
+    def test_ishigami(self, ishigami_ref_indices, func):
+        rng = np.random.default_rng(28631265345463262246170309650372465332)
+        res = sobol_indices(
+            func=func, n=4096,
+            dists=self.dists,
+            rng=rng
+        )
+
+        if func.__name__ == 'f_ishigami_vec':
+            ishigami_ref_indices = [
+                    [ishigami_ref_indices[0], ishigami_ref_indices[0]],
+                    [ishigami_ref_indices[1], ishigami_ref_indices[1]]
+            ]
+
+        assert_allclose(res.first_order, ishigami_ref_indices[0], atol=1e-2)
+        assert_allclose(res.total_order, ishigami_ref_indices[1], atol=1e-2)
+
+        assert res._bootstrap_result is None
+        bootstrap_res = res.bootstrap(n_resamples=99)
+        assert isinstance(bootstrap_res, BootstrapSobolResult)
+        assert isinstance(res._bootstrap_result, BootstrapResult)
+
+        assert res._bootstrap_result.confidence_interval.low.shape[0] == 2
+        assert res._bootstrap_result.confidence_interval.low[1].shape \
+               == res.first_order.shape
+
+        assert bootstrap_res.first_order.confidence_interval.low.shape \
+               == res.first_order.shape
+        assert bootstrap_res.total_order.confidence_interval.low.shape \
+               == res.total_order.shape
+
+        assert_array_less(
+            bootstrap_res.first_order.confidence_interval.low, res.first_order
+        )
+        assert_array_less(
+            res.first_order, bootstrap_res.first_order.confidence_interval.high
+        )
+        assert_array_less(
+            bootstrap_res.total_order.confidence_interval.low, res.total_order
+        )
+        assert_array_less(
+            res.total_order, bootstrap_res.total_order.confidence_interval.high
+        )
+
+        # call again to use previous results and change a param
+        assert isinstance(
+            res.bootstrap(confidence_level=0.9, n_resamples=99),
+            BootstrapSobolResult
+        )
+        assert isinstance(res._bootstrap_result, BootstrapResult)
+
+    def test_func_dict(self, ishigami_ref_indices):
+        rng = np.random.default_rng(28631265345463262246170309650372465332)
+        n = 4096
+        dists = [
+            stats.uniform(loc=-np.pi, scale=2*np.pi),
+            stats.uniform(loc=-np.pi, scale=2*np.pi),
+            stats.uniform(loc=-np.pi, scale=2*np.pi)
+        ]
+
+        A, B = sample_A_B(n=n, dists=dists, rng=rng)
+        AB = sample_AB(A=A, B=B)
+
+        func = {
+            'f_A': f_ishigami(A).reshape(1, -1),
+            'f_B': f_ishigami(B).reshape(1, -1),
+            'f_AB': f_ishigami(AB).reshape((3, 1, -1))
+        }
+
+        # preserve use of old random_state during SPEC 7 transition
+        res = sobol_indices(
+            func=func, n=n,
+            dists=dists,
+            rng=rng
+        )
+        assert_allclose(res.first_order, ishigami_ref_indices[0], atol=1e-2)
+
+        res = sobol_indices(
+            func=func, n=n,
+            rng=rng
+        )
+        assert_allclose(res.first_order, ishigami_ref_indices[0], atol=1e-2)
+        # Ideally should be exactly equal but since f_ishigami
+        # uses floating point operations, so exact equality
+        # might not be possible (due to flakiness in computation).
+        # So, assert_allclose is used with default parameters
+        # Regression test for https://github.com/scipy/scipy/issues/21383
+        assert_allclose(f_ishigami(A).reshape(1, -1), func['f_A'])
+        assert_allclose(f_ishigami(B).reshape(1, -1), func['f_B'])
+        assert_allclose(f_ishigami(AB).reshape((3, 1, -1)), func['f_AB'])
+
+    def test_method(self, ishigami_ref_indices):
+        def jansen_sobol(f_A, f_B, f_AB):
+            """Jansen for S and Sobol' for St.
+
+            From Saltelli2010, table 2 formulations (c) and (e)."""
+            var = np.var([f_A, f_B], axis=(0, -1))
+
+            s = (var - 0.5*np.mean((f_B - f_AB)**2, axis=-1)) / var
+            st = np.mean(f_A*(f_A - f_AB), axis=-1) / var
+
+            return s.T, st.T
+
+        rng = np.random.default_rng(28631265345463262246170309650372465332)
+        res = sobol_indices(
+            func=f_ishigami, n=4096,
+            dists=self.dists,
+            method=jansen_sobol,
+            rng=rng
+        )
+
+        assert_allclose(res.first_order, ishigami_ref_indices[0], atol=1e-2)
+        assert_allclose(res.total_order, ishigami_ref_indices[1], atol=1e-2)
+
+        def jansen_sobol_typed(
+            f_A: np.ndarray, f_B: np.ndarray, f_AB: np.ndarray
+        ) -> tuple[np.ndarray, np.ndarray]:
+            return jansen_sobol(f_A, f_B, f_AB)
+
+        _ = sobol_indices(
+            func=f_ishigami, n=8,
+            dists=self.dists,
+            method=jansen_sobol_typed,
+            rng=rng
+        )
+
+    def test_normalization(self, ishigami_ref_indices):
+        rng = np.random.default_rng(28631265345463262246170309650372465332)
+        res = sobol_indices(
+            func=lambda x: f_ishigami(x) + 1000, n=4096,
+            dists=self.dists,
+            rng=rng
+        )
+
+        assert_allclose(res.first_order, ishigami_ref_indices[0], atol=1e-2)
+        assert_allclose(res.total_order, ishigami_ref_indices[1], atol=1e-2)
+
+    def test_constant_function(self, ishigami_ref_indices):
+
+        def f_ishigami_vec_const(x):
+            """Output of shape (3, n)."""
+            res = f_ishigami(x)
+            return res, res * 0 + 10, res
+
+        rng = np.random.default_rng(28631265345463262246170309650372465332)
+        res = sobol_indices(
+            func=f_ishigami_vec_const, n=4096,
+            dists=self.dists,
+            rng=rng
+        )
+
+        ishigami_vec_indices = [
+                [ishigami_ref_indices[0], [0, 0, 0], ishigami_ref_indices[0]],
+                [ishigami_ref_indices[1], [0, 0, 0], ishigami_ref_indices[1]]
+        ]
+
+        assert_allclose(res.first_order, ishigami_vec_indices[0], atol=1e-2)
+        assert_allclose(res.total_order, ishigami_vec_indices[1], atol=1e-2)
+
+    @pytest.mark.xfail_on_32bit("Can't create large array for test")
+    def test_more_converged(self, ishigami_ref_indices):
+        rng = np.random.default_rng(28631265345463262246170309650372465332)
+        res = sobol_indices(
+            func=f_ishigami, n=2**19,  # 524288
+            dists=self.dists,
+            rng=rng
+        )
+
+        assert_allclose(res.first_order, ishigami_ref_indices[0], atol=1e-4)
+        assert_allclose(res.total_order, ishigami_ref_indices[1], atol=1e-4)
+
+    def test_raises(self):
+
+        message = r"Each distribution in `dists` must have method `ppf`"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=0, func=f_ishigami, dists="uniform")
+
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=0, func=f_ishigami, dists=[lambda x: x])
+
+        message = r"The balance properties of Sobol'"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=7, func=f_ishigami, dists=[stats.uniform()])
+
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=4.1, func=f_ishigami, dists=[stats.uniform()])
+
+        message = r"'toto' is not a valid 'method'"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=0, func=f_ishigami, method='toto')
+
+        message = r"must have the following signature"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=0, func=f_ishigami, method=lambda x: x)
+
+        message = r"'dists' must be defined when 'func' is a callable"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(n=0, func=f_ishigami)
+
+        def func_wrong_shape_output(x):
+            return x.reshape(-1, 1)
+
+        message = r"'func' output should have a shape"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(
+                n=2, func=func_wrong_shape_output, dists=[stats.uniform()]
+            )
+
+        message = r"When 'func' is a dictionary"
+        with pytest.raises(ValueError, match=message):
+            sobol_indices(
+                n=2, func={'f_A': [], 'f_AB': []}, dists=[stats.uniform()]
+            )
+
+        with pytest.raises(ValueError, match=message):
+            # f_B malformed
+            sobol_indices(
+                n=2,
+                func={'f_A': [1, 2], 'f_B': [3], 'f_AB': [5, 6, 7, 8]},
+            )
+
+        with pytest.raises(ValueError, match=message):
+            # f_AB malformed
+            sobol_indices(
+                n=2,
+                func={'f_A': [1, 2], 'f_B': [3, 4], 'f_AB': [5, 6, 7]},
+            )
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_stats.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0846f17a0f71699ffb5b654cd2da21dd72d5f74
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_stats.py
@@ -0,0 +1,9890 @@
+""" Test functions for stats module
+
+    WRITTEN BY LOUIS LUANGKESORN <lluang@yahoo.com> FOR THE STATS MODULE
+    BASED ON WILKINSON'S STATISTICS QUIZ
+    https://www.stanford.edu/~clint/bench/wilk.txt
+
+    Additional tests by a host of SciPy developers.
+"""
+import math
+import os
+import warnings
+from collections import namedtuple
+from itertools import product
+import hypothesis.extra.numpy as npst
+import hypothesis
+import contextlib
+
+from numpy.testing import (assert_, assert_equal,
+                           assert_almost_equal, assert_array_almost_equal,
+                           assert_array_equal, assert_approx_equal,
+                           assert_allclose)
+import pytest
+from pytest import raises as assert_raises
+from numpy import array, arange, power
+import numpy as np
+
+import scipy.stats as stats
+from scipy.stats._ksstats import kolmogn
+from scipy.special._testutils import FuncData
+from scipy import optimize, special
+from .common_tests import check_named_results
+from scipy.stats._axis_nan_policy import (SmallSampleWarning,
+                                          too_small_nd_omit, too_small_nd_not_omit,
+                                          too_small_1d_omit, too_small_1d_not_omit)
+from scipy.stats._stats_py import (_chk_asarray, _moment,
+                                   LinregressResult, _xp_mean, _xp_var, _SimpleChi2)
+from scipy._lib._util import AxisError
+from scipy.conftest import skip_xp_invalid_arg
+from scipy._lib._array_api import (array_namespace, eager_warns, is_lazy_array,
+                                   is_numpy, is_torch, xp_default_dtype, xp_size,
+                                   SCIPY_ARRAY_API, make_xp_test_case, xp_ravel,
+                                   xp_swapaxes, xp_result_type, is_jax, is_cupy,
+                                   xp_copy, xp_promote, make_xp_pytest_param)
+from scipy._lib._array_api_no_0d import xp_assert_close, xp_assert_equal, xp_assert_less
+import scipy._external.array_api_extra as xpx
+from scipy._lib._util import _apply_over_batch
+
+
+lazy_xp_modules = [stats]
+skip_xp_backends = pytest.mark.skip_xp_backends
+xfail_xp_backends = pytest.mark.xfail_xp_backends
+
+""" Numbers in docstrings beginning with 'W' refer to the section numbers
+    and headings found in the STATISTICS QUIZ of Leland Wilkinson.  These are
+    considered to be essential functionality.  True testing and
+    evaluation of a statistics package requires use of the
+    NIST Statistical test data.  See McCoullough(1999) Assessing The Reliability
+    of Statistical Software for a test methodology and its
+    implementation in testing SAS, SPSS, and S-Plus
+"""
+
+#  Datasets
+#  These data sets are from the nasty.dat sets used by Wilkinson
+#  For completeness, I should write the relevant tests and count them as failures
+#  Somewhat acceptable, since this is still beta software.  It would count as a
+#  good target for 1.0 status
+X = array([1,2,3,4,5,6,7,8,9], float)
+ZERO = array([0,0,0,0,0,0,0,0,0], float)
+BIG = array([99999991,99999992,99999993,99999994,99999995,99999996,99999997,
+             99999998,99999999], float)
+LITTLE = array([0.99999991,0.99999992,0.99999993,0.99999994,0.99999995,0.99999996,
+                0.99999997,0.99999998,0.99999999], float)
+HUGE = array([1e+12,2e+12,3e+12,4e+12,5e+12,6e+12,7e+12,8e+12,9e+12], float)
+TINY = array([1e-12,2e-12,3e-12,4e-12,5e-12,6e-12,7e-12,8e-12,9e-12], float)
+ROUND = array([0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5], float)
+
+
+class TestTrimmedStats:
+    # TODO: write these tests to handle missing values properly
+    dprec = np.finfo(np.float64).precision
+
+    @make_xp_test_case(stats.tmean)
+    def test_tmean(self, xp):
+        default_dtype = xp_default_dtype(xp)
+        x = xp.asarray(X, dtype=default_dtype)
+
+        y = stats.tmean(x, (2, 8), (True, True))
+        xp_assert_close(y, xp.asarray(5.0))
+
+        y1 = stats.tmean(x, limits=(2, 8), inclusive=(False, False))
+        y2 = stats.tmean(x, limits=None)
+        xp_assert_close(y1, y2)
+
+        x_2d = xp.reshape(xp.arange(63.), (9, 7))
+        y = stats.tmean(x_2d, axis=None)
+        xp_assert_close(y, xp.mean(x_2d))
+
+        y = stats.tmean(x_2d, axis=0)
+        xp_assert_close(y, xp.mean(x_2d, axis=0))
+
+        y = stats.tmean(x_2d, axis=1)
+        xp_assert_close(y, xp.mean(x_2d, axis=1))
+
+        y = stats.tmean(x_2d, limits=(2, 61), axis=None)
+        xp_assert_close(y, xp.asarray(31.5))
+
+        y = stats.tmean(x_2d, limits=(2, 21), axis=0)
+        y_true = [14, 11.5, 9, 10, 11, 12, 13]
+        xp_assert_close(y, xp.asarray(y_true))
+
+        y = stats.tmean(x_2d, limits=(2, 21), inclusive=(True, False), axis=0)
+        y_true = [10.5, 11.5, 9, 10, 11, 12, 13]
+        xp_assert_close(y, xp.asarray(y_true))
+
+        x_2d_with_nan = xpx.at(x_2d)[-1, -3:].set(xp.nan, copy=True)
+        y = stats.tmean(x_2d_with_nan, limits=(1, 13), axis=0)
+        y_true = [7, 4.5, 5.5, 6.5, xp.nan, xp.nan, xp.nan]
+        xp_assert_close(y, xp.asarray(y_true))
+
+        y = stats.tmean(x_2d, limits=(2, 21), axis=1)
+        y_true = [4, 10, 17, 21, xp.nan, xp.nan, xp.nan, xp.nan, xp.nan]
+        xp_assert_close(y, xp.asarray(y_true))
+
+        y = stats.tmean(x_2d, limits=(2, 21),
+                        inclusive=(False, True), axis=1)
+        y_true = [4.5, 10, 17, 21, xp.nan, xp.nan, xp.nan, xp.nan, xp.nan]
+        xp_assert_close(y, xp.asarray(y_true))
+
+    @make_xp_test_case(stats.tvar)
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in divide:RuntimeWarning:dask"
+    )
+    def test_tvar(self, xp):
+        x = xp.asarray(X.tolist())  # use default dtype of xp
+
+        y = stats.tvar(x, limits=(2, 8), inclusive=(True, True))
+        xp_assert_close(y, xp.asarray(4.6666666666666661))
+
+        y = stats.tvar(x, limits=None)
+        xp_assert_close(y, xp.var(x, correction=1))
+
+        x_2d = xp.reshape(xp.arange(63.), (9, 7))
+        y = stats.tvar(x_2d, axis=None)
+        xp_assert_close(y, xp.var(x_2d, correction=1))
+
+        y = stats.tvar(x_2d, axis=0)
+        xp_assert_close(y, xp.full((7,), 367.5))
+
+        y = stats.tvar(x_2d, axis=1)
+        xp_assert_close(y, xp.full((9,), 4.66666667))
+
+        # Limiting some values along one axis
+        y = stats.tvar(x_2d, limits=(1, 5), axis=1, inclusive=(True, True))
+        xp_assert_close(y[0], xp.asarray(2.5))
+
+        # Limiting all values along one axis
+        y = stats.tvar(x_2d, limits=(0, 6), axis=1, inclusive=(True, True))
+        xp_assert_close(y[0], xp.asarray(4.666666666666667))
+        xp_assert_equal(y[1], xp.asarray(xp.nan))
+
+    @make_xp_test_case(stats.tstd)
+    def test_tstd(self, xp):
+        x = xp.asarray(X.tolist())  # use default dtype of xp
+
+        y = stats.tstd(x, (2, 8), (True, True))
+        xp_assert_close(y, xp.asarray(2.1602468994692865))
+
+        y = stats.tstd(x, limits=None)
+        xp_assert_close(y, xp.std(x, correction=1))
+
+    @make_xp_test_case(stats.tmin)
+    def test_tmin(self, xp):
+        x = xp.arange(10.)
+        xp_assert_equal(stats.tmin(x), xp.asarray(0.))
+        xp_assert_equal(stats.tmin(x, lowerlimit=0), xp.asarray(0.))
+        xp_assert_equal(stats.tmin(x, lowerlimit=0, inclusive=False), xp.asarray(1.))
+
+        x = xp.reshape(x, (5, 2))
+        xp_assert_equal(stats.tmin(x, lowerlimit=0, inclusive=False),
+                        xp.asarray([2., 1.]))
+        xp_assert_equal(stats.tmin(x, axis=1), xp.asarray([0., 2., 4., 6., 8.]))
+        xp_assert_equal(stats.tmin(x, axis=None), xp.asarray(0.))
+
+        x = xpx.at(xp.arange(10.), 9).set(xp.nan)
+        xp_assert_equal(stats.tmin(x), xp.asarray(xp.nan))
+
+        # check that if a full slice is masked, the output returns a
+        # nan instead of a garbage value.
+        x = xp.reshape(xp.arange(16), (4, 4))
+        res = stats.tmin(x, lowerlimit=4, axis=1)
+        xp_assert_equal(res, xp.asarray([np.nan, 4, 8, 12]))
+
+    @pytest.mark.parametrize('tfun', [stats.tmin, stats.tmax])
+    def test_tmin_tmax_scalar_input(self, tfun):  # scalar inputs are NumPy only
+        assert_equal(tfun(4), 4)
+
+    @skip_xp_backends(eager_only=True, reason="lazy -> reduced nan_policy capabilities")
+    @pytest.mark.parametrize('tfun, ref', [make_xp_pytest_param(stats.tmin, 0.),
+                                           make_xp_pytest_param(stats.tmax, 9.)])
+    def test_tmin_tmax_nanpolicy(self, tfun, ref, xp):
+        x = xp.arange(10.)
+        x[8] = xp.nan
+        xp_assert_equal(tfun(x, nan_policy='omit'), xp.asarray(ref))
+        msg = "The input contains nan values"
+        with assert_raises(ValueError, match=msg):
+            tfun(x, nan_policy='raise')
+        msg = "nan_policy must be one of..."
+        with assert_raises(ValueError, match=msg):
+            tfun(x, nan_policy='foobar')
+
+    @make_xp_test_case(stats.tmax)
+    def test_tmax(self, xp):
+        x = xp.arange(10.)
+        xp_assert_equal(stats.tmax(x), xp.asarray(9.))
+        xp_assert_equal(stats.tmax(x, upperlimit=9), xp.asarray(9.))
+        xp_assert_equal(stats.tmax(x, upperlimit=9, inclusive=False), xp.asarray(8.))
+
+        x = xp.reshape(x, (5, 2))
+        xp_assert_equal(stats.tmax(x, upperlimit=9, inclusive=False),
+                        xp.asarray([8., 7.]))
+        xp_assert_equal(stats.tmax(x, axis=1), xp.asarray([1., 3., 5., 7., 9.]))
+        xp_assert_equal(stats.tmax(x, axis=None), xp.asarray(9.))
+
+        x = xpx.at(xp.arange(10.), 9).set(xp.nan)
+        xp_assert_equal(stats.tmax(x), xp.asarray(xp.nan))
+
+        # check that if a full slice is masked, the output returns a
+        # nan instead of a garbage value.
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "All-NaN slice encountered", RuntimeWarning)
+            x = xp.reshape(xp.arange(16), (4, 4))
+            res = stats.tmax(x, upperlimit=11, axis=1)
+            xp_assert_equal(res, xp.asarray([3, 7, 11, np.nan]))
+
+    @make_xp_test_case(stats.tmin, stats.tmax)
+    def test_tmin_tmax_int_dtype(self, xp):
+        x = xp.reshape(xp.arange(10, dtype=xp.int16), (2, 5)).T
+
+        # When tmin/tmax don't need to inject any NaNs,
+        # retain the input dtype. Dask/JAX can't inspect
+        # the data so they always return float.
+        expect_dtype = xp_default_dtype(xp) if is_lazy_array(x) else x.dtype
+        xp_assert_equal(stats.tmin(x), xp.asarray([0, 5], dtype=expect_dtype))
+        xp_assert_equal(stats.tmax(x), xp.asarray([4, 9], dtype=expect_dtype))
+
+        # When they do inject NaNs, all backends behave the same.
+        xp_assert_equal(stats.tmin(x, lowerlimit=6), xp.asarray([xp.nan, 6.]))
+        xp_assert_equal(stats.tmax(x, upperlimit=3), xp.asarray([3., xp.nan]))
+
+    @skip_xp_backends(eager_only=True, reason="Only with data-dependent output dtype")
+    @make_xp_test_case(stats.tmin, stats.tmax)
+    def test_gh_22626(self, xp):
+        # Test that `tmin`/`tmax` returns exact result with outrageously large integers
+        x = xp.arange(2**62, 2**62+10)
+        xp_assert_equal(stats.tmin(x[None, :]), x)
+        xp_assert_equal(stats.tmax(x[None, :]), x)
+
+    @make_xp_test_case(stats.tsem)
+    def test_tsem(self, xp):
+        x = xp.asarray(X.tolist())  # use default dtype of xp
+
+        y = stats.tsem(x, limits=(3, 8), inclusive=(False, True))
+        y_ref = xp.asarray([4., 5., 6., 7., 8.])
+        xp_assert_close(y, xp.std(y_ref, correction=1) / xp_size(y_ref)**0.5)
+        xp_assert_close(stats.tsem(x, limits=[-1, 10]), stats.tsem(x, limits=None))
+
+
+class TestPearsonrWilkinson:
+    """ W.II.D. Compute a correlation matrix on all the variables.
+
+        All the correlations, except for ZERO and MISS, should be exactly 1.
+        ZERO and MISS should have undefined or missing correlations with the
+        other variables.  The same should go for SPEARMAN correlations, if
+        your program has them.
+    """
+
+    def test_pXX(self):
+        y = stats.pearsonr(X,X)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pXBIG(self):
+        y = stats.pearsonr(X,BIG)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pXLITTLE(self):
+        y = stats.pearsonr(X,LITTLE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pXHUGE(self):
+        y = stats.pearsonr(X,HUGE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pXTINY(self):
+        y = stats.pearsonr(X,TINY)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pXROUND(self):
+        y = stats.pearsonr(X,ROUND)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pBIGBIG(self):
+        y = stats.pearsonr(BIG,BIG)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pBIGLITTLE(self):
+        y = stats.pearsonr(BIG,LITTLE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pBIGHUGE(self):
+        y = stats.pearsonr(BIG,HUGE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pBIGTINY(self):
+        y = stats.pearsonr(BIG,TINY)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pBIGROUND(self):
+        y = stats.pearsonr(BIG,ROUND)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pLITTLELITTLE(self):
+        y = stats.pearsonr(LITTLE,LITTLE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pLITTLEHUGE(self):
+        y = stats.pearsonr(LITTLE,HUGE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pLITTLETINY(self):
+        y = stats.pearsonr(LITTLE,TINY)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pLITTLEROUND(self):
+        y = stats.pearsonr(LITTLE,ROUND)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pHUGEHUGE(self):
+        y = stats.pearsonr(HUGE,HUGE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pHUGETINY(self):
+        y = stats.pearsonr(HUGE,TINY)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pHUGEROUND(self):
+        y = stats.pearsonr(HUGE,ROUND)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pTINYTINY(self):
+        y = stats.pearsonr(TINY,TINY)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pTINYROUND(self):
+        y = stats.pearsonr(TINY,ROUND)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_pROUNDROUND(self):
+        y = stats.pearsonr(ROUND,ROUND)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+
+@make_xp_test_case(stats.pearsonr)
+class TestPearsonr:
+    def test_pearsonr_result_attributes(self, xp):
+        res = stats.pearsonr(xp.asarray(X), xp.asarray(X))
+        attributes = ('correlation', 'pvalue')
+        check_named_results(res, attributes, xp=xp)
+        xp_assert_equal(res.correlation, res.statistic)
+
+    def test_r_almost_exactly_pos1(self, xp):
+        a = xp.arange(3.0)
+        r, prob = stats.pearsonr(a, a)
+
+        xp_assert_close(r, xp.asarray(1.0), atol=1e-15)
+        # With n = len(a) = 3, the error in prob grows like the
+        # square root of the error in r.
+        xp_assert_close(prob, xp.asarray(0.0), atol=np.sqrt(2*np.spacing(1.0)))
+
+    def test_r_almost_exactly_neg1(self, xp):
+        a = xp.arange(3.0)
+        r, prob = stats.pearsonr(a, -a)
+
+        xp_assert_close(r, xp.asarray(-1.0), atol=1e-15)
+        # With n = len(a) = 3, the error in prob grows like the
+        # square root of the error in r.
+        xp_assert_close(prob, xp.asarray(0.0), atol=np.sqrt(2*np.spacing(1.0)))
+
+    def test_basic(self, xp):
+        # A basic test, with a correlation coefficient
+        # that is not 1 or -1.
+        a = xp.asarray([-1, 0, 1])
+        b = xp.asarray([0, 0, 3])
+        r, prob = stats.pearsonr(a, b)
+        xp_assert_close(r, xp.asarray(3**0.5/2))
+        xp_assert_close(prob, xp.asarray(1/3))
+
+    def test_constant_input(self, xp):
+        # Zero variance input
+        # See https://github.com/scipy/scipy/issues/3728
+        x = xp.asarray([0.667, 0.667, 0.667])
+        y = xp.asarray([0.123, 0.456, 0.789])
+        msg = "An input array is constant"
+        with eager_warns(stats.ConstantInputWarning, match=msg, xp=xp):
+            r, p = stats.pearsonr(x, y)
+            xp_assert_close(r, xp.asarray(xp.nan))
+            xp_assert_close(p, xp.asarray(xp.nan))
+
+    @pytest.mark.parametrize('dtype', ['float32', 'float64'])
+    def test_near_constant_input(self, dtype, xp):
+        npdtype = getattr(np, dtype)
+        dtype = getattr(xp, dtype)
+        # Near constant input (but not constant):
+        x = xp.asarray([2, 2, 2 + np.spacing(2, dtype=npdtype)], dtype=dtype)
+        y = xp.asarray([3, 3, 3 + 6*np.spacing(3, dtype=npdtype)], dtype=dtype)
+        msg = "An input array is nearly constant; the computed"
+        with eager_warns(stats.NearConstantInputWarning, match=msg, xp=xp):
+            # r and p are garbage, so don't bother checking them in this case.
+            # (The exact value of r would be 1.)
+            stats.pearsonr(x, y)
+
+    def test_very_small_input_values(self, xp):
+        # Very small values in an input.  A naive implementation will
+        # suffer from underflow.
+        # See https://github.com/scipy/scipy/issues/9353
+        x = xp.asarray([0.004434375, 0.004756007, 0.003911996, 0.0038005, 0.003409971],
+                       dtype=xp.float64)
+        y = xp.asarray([2.48e-188, 7.41e-181, 4.09e-208, 2.08e-223, 2.66e-245],
+                       dtype=xp.float64)
+        r, p = stats.pearsonr(x, y)
+
+        # The expected values were computed using mpmath with 80 digits
+        # of precision.
+        xp_assert_close(r, xp.asarray(0.7272930540750450, dtype=xp.float64))
+        xp_assert_close(p, xp.asarray(0.1637805429533202, dtype=xp.float64))
+
+    def test_very_large_input_values(self, xp):
+        # Very large values in an input.  A naive implementation will
+        # suffer from overflow.
+        # See https://github.com/scipy/scipy/issues/8980
+        x = 1e90*xp.asarray([0, 0, 0, 1, 1, 1, 1], dtype=xp.float64)
+        y = 1e90*xp.arange(7, dtype=xp.float64)
+
+        r, p = stats.pearsonr(x, y)
+
+        # The expected values were computed using mpmath with 80 digits
+        # of precision.
+        xp_assert_close(r, xp.asarray(0.8660254037844386, dtype=xp.float64))
+        xp_assert_close(p, xp.asarray(0.011724811003954638, dtype=xp.float64))
+
+    def test_extremely_large_input_values(self, xp):
+        # Extremely large values in x and y.  These values would cause the
+        # product sigma_x * sigma_y to overflow if the two factors were
+        # computed independently.
+        x = xp.asarray([2.3e200, 4.5e200, 6.7e200, 8e200], dtype=xp.float64)
+        y = xp.asarray([1.2e199, 5.5e200, 3.3e201, 1.0e200], dtype=xp.float64)
+        r, p = stats.pearsonr(x, y)
+
+        # The expected values were computed using mpmath with 80 digits
+        # of precision.
+        xp_assert_close(r, xp.asarray(0.351312332103289, dtype=xp.float64))
+        xp_assert_close(p, xp.asarray(0.648687667896711, dtype=xp.float64))
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    def test_length_two_pos1(self, xp):
+        # Inputs with length 2.
+        # See https://github.com/scipy/scipy/issues/7730
+        x = xp.asarray([1., 2.])
+        y = xp.asarray([3., 5.])
+        res = stats.pearsonr(x, y)
+        r, p = res
+        one = xp.asarray(1.)
+        xp_assert_equal(r, one)
+        xp_assert_equal(p, one)
+        low, high = res.confidence_interval()
+        xp_assert_equal(low, -one)
+        xp_assert_equal(high, one)
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    def test_length_two_neg1(self, xp):
+        # Inputs with length 2.
+        # See https://github.com/scipy/scipy/issues/7730
+        x = xp.asarray([2., 1.])
+        y = xp.asarray([3., 5.])
+        res = stats.pearsonr(x, y)
+        r, p = res
+        one = xp.asarray(1.)
+        xp_assert_equal(r, -one)
+        xp_assert_equal(p, one)
+        low, high = res.confidence_interval()
+        xp_assert_equal(low, -one)
+        xp_assert_equal(high, one)
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered in divide")
+    def test_length_two_constant_input(self, xp):
+        # Zero variance input
+        # See https://github.com/scipy/scipy/issues/3728
+        # and https://github.com/scipy/scipy/issues/7730
+        x = xp.asarray([0.667, 0.667])
+        y = xp.asarray([0.123, 0.456])
+        msg = "An input array is constant"
+        with eager_warns(stats.ConstantInputWarning, match=msg, xp=xp):
+            r, p = stats.pearsonr(x, y)
+            xp_assert_close(r, xp.asarray(xp.nan))
+            xp_assert_close(p, xp.asarray(xp.nan))
+
+    # Expected values computed with R 3.6.2 cor.test, e.g.
+    # options(digits=16)
+    # x <- c(1, 2, 3, 4)
+    # y <- c(0, 1, 0.5, 1)
+    # cor.test(x, y, method = "pearson", alternative = "g")
+    # correlation coefficient and p-value for alternative='two-sided'
+    # calculated with mpmath agree to 16 digits.
+    @pytest.mark.parametrize('alternative, pval, rlow, rhigh, sign',
+            [('two-sided', 0.325800137536, -0.814938968841, 0.99230697523, 1),
+             ('less', 0.8370999312316, -1., 0.985600937290653, 1),
+             ('greater', 0.1629000687684, -0.6785654158217636, 1., 1),
+             ('two-sided', 0.325800137536, -0.992306975236, 0.81493896884, -1),
+             ('less', 0.1629000687684, -1.0, 0.6785654158217636, -1),
+             ('greater', 0.8370999312316, -0.985600937290653, 1.0, -1)])
+    def test_basic_example(self, alternative, pval, rlow, rhigh, sign, xp):
+        x = xp.asarray([1., 2., 3., 4.])
+        y = xp.asarray([0., 1., 0.5, 1.]) * sign
+        result = stats.pearsonr(x, y, alternative=alternative)
+        xp_assert_close(result.statistic, xp.asarray(0.6741998624632421*sign))
+        xp_assert_close(result.pvalue, xp.asarray(pval))
+        ci = result.confidence_interval()
+        xp_assert_close(ci.low, xp.asarray(rlow))
+        xp_assert_close(ci.high, xp.asarray(rhigh))
+
+    def test_negative_correlation_pvalue_gh17795(self, xp):
+        x = xp.arange(10.)
+        y = -x
+        test_greater = stats.pearsonr(x, y, alternative='greater')
+        test_less = stats.pearsonr(x, y, alternative='less')
+        xp_assert_close(test_greater.pvalue, xp.asarray(1.))
+        xp_assert_close(test_less.pvalue, xp.asarray(0.), atol=1e-20)
+
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    def test_length3_r_exactly_negative_one(self, xp):
+        x = xp.asarray([1., 2., 3.])
+        y = xp.asarray([5., -4., -13.])
+        res = stats.pearsonr(x, y)
+
+        # The expected r and p are exact.
+        r, p = res
+        one = xp.asarray(1.0)
+        xp_assert_close(r, -one)
+        xp_assert_close(p, 0*one, atol=1e-7)
+        low, high = res.confidence_interval()
+        xp_assert_equal(low, -one)
+        xp_assert_equal(high, one)
+
+    def test_input_validation(self):
+        # Arraylike is np only
+        x = [1, 2, 3]
+        y = [4]
+        message = '`x` and `y` must have the same length along `axis`.'
+        with pytest.raises(ValueError, match=message):
+            stats.pearsonr(x, y)
+
+        x = [1, 2, 3]
+        y = [4, 5]
+        message = '`x` and `y` must be broadcastable.'
+        with pytest.raises(ValueError, match=message):
+            stats.pearsonr(x, y)
+
+        x = [1]
+        y = [2]
+        message = '`x` and `y` must have length at least 2.'
+        with pytest.raises(ValueError, match=message):
+            stats.pearsonr(x, y)
+
+        x = [-1j, -2j, -3.0j]
+        y = [-1j, -2j, -3.0j]
+        message = 'This function does not support complex data'
+        with pytest.raises(ValueError, match=message):
+            stats.pearsonr(x, y)
+
+        message = "`method` must be an instance of..."
+        with pytest.raises(ValueError, match=message):
+            stats.pearsonr([1, 2], [3, 4], method="asymptotic")
+
+        res = stats.pearsonr([1, 2], [3, 4])
+        with pytest.raises(ValueError, match=message):
+            res.confidence_interval(method="exact")
+
+    @pytest.mark.fail_slow(10)
+    @pytest.mark.xfail_on_32bit("Monte Carlo method needs > a few kB of memory")
+    @pytest.mark.parametrize('alternative', ('less', 'greater', 'two-sided'))
+    @pytest.mark.parametrize('method_name',
+                             ('permutation', 'monte_carlo', 'monte_carlo2'))
+    def test_resampling_pvalue(self, method_name, alternative):
+        rng = np.random.default_rng(24623935790378923)
+        size = (2, 100) if method_name == 'permutation' else (2, 1000)
+        x = rng.normal(size=size)
+        y = rng.normal(size=size)
+        methods = {'permutation': stats.PermutationMethod(rng=rng),
+                   'monte_carlo': stats.MonteCarloMethod(rvs=(rng.normal,)*2),
+                   'monte_carlo2': stats.MonteCarloMethod(rng=1294)}
+        method = methods[method_name]
+        res = stats.pearsonr(x, y, alternative=alternative, method=method, axis=-1)
+        ref = stats.pearsonr(x, y, alternative=alternative, axis=-1)
+        assert_allclose(res.statistic, ref.statistic, rtol=1e-15)
+        assert_allclose(res.pvalue, ref.pvalue, rtol=1e-2, atol=1e-3)
+
+        if method_name == 'monte_carlo2':
+            method = stats.MonteCarloMethod(rng=1294)
+            res2 = stats.pearsonr(x, y, alternative=alternative, method=method, axis=-1)
+            assert_equal(res2.statistic, res.statistic)
+            assert_equal(res2.pvalue, res.pvalue)
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize('alternative', ('less', 'greater', 'two-sided'))
+    def test_bootstrap_ci(self, alternative):
+        rng = np.random.default_rng(2462935790378923)
+        x = rng.normal(size=(2, 100))
+        y = rng.normal(size=(2, 100))
+        res = stats.pearsonr(x, y, alternative=alternative, axis=-1)
+
+        # preserve use of old random_state during SPEC 7 transition
+        rng = np.random.default_rng(724358723498249852)
+        method = stats.BootstrapMethod(random_state=rng)
+        res_ci = res.confidence_interval(method=method)
+        ref_ci = res.confidence_interval()
+        assert_allclose(res_ci, ref_ci, atol=1.5e-2)
+
+        # `rng` is the new argument name`
+        rng = np.random.default_rng(724358723498249852)
+        method = stats.BootstrapMethod(rng=rng)
+        res_ci2 = res.confidence_interval(method=method)
+        assert_allclose(res_ci2, res_ci)
+
+    @pytest.mark.parametrize('axis', [0, 1])
+    def test_axis01(self, axis):
+        rng = np.random.default_rng(38572345825)
+        shape = (9, 10)
+        x, y = rng.normal(size=(2,) + shape)
+        res = stats.pearsonr(x, y, axis=axis)
+        ci = res.confidence_interval()
+        if axis == 0:
+            x, y = x.T, y.T
+        for i in range(x.shape[0]):
+            res_i = stats.pearsonr(x[i], y[i])
+            ci_i = res_i.confidence_interval()
+            assert_allclose(res.statistic[i], res_i.statistic)
+            assert_allclose(res.pvalue[i], res_i.pvalue)
+            assert_allclose(ci.low[i], ci_i.low)
+            assert_allclose(ci.high[i], ci_i.high)
+
+    def test_axis_None(self):
+        rng = np.random.default_rng(38572345825)
+        shape = (9, 10)
+        x, y = rng.normal(size=(2,) + shape)
+        res = stats.pearsonr(x, y, axis=None)
+        ci = res.confidence_interval()
+        ref = stats.pearsonr(x.ravel(), y.ravel())
+        ci_ref = ref.confidence_interval()
+        assert_allclose(res.statistic, ref.statistic)
+        assert_allclose(res.pvalue, ref.pvalue)
+        assert_allclose(ci, ci_ref)
+
+    def test_nd_input_validation(self, xp):
+        x = y = xp.ones((2, 5))
+        message = '`axis` must be an integer.'
+        with pytest.raises(ValueError, match=message):
+            stats.pearsonr(x, y, axis=1.5)
+
+        message = '`x` and `y` must have the same length along `axis`'
+        with pytest.raises(ValueError, match=message):
+            stats.pearsonr(x, xp.ones((2, 1)), axis=1)
+
+        message = '`x` and `y` must have length at least 2.'
+        with pytest.raises(ValueError, match=message):
+            stats.pearsonr(xp.ones((2, 1)), xp.ones((2, 1)), axis=1)
+
+        message = '`x` and `y` must be broadcastable.'
+        with pytest.raises(ValueError, match=message):
+            stats.pearsonr(x, xp.ones((3, 5)), axis=1)
+
+        message = '`method` must be `None` if arguments are not NumPy arrays.'
+        if not is_numpy(xp):
+            x = xp.arange(10)
+            with pytest.raises(ValueError, match=message):
+                stats.pearsonr(x, x, method=stats.PermutationMethod())
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    def test_nd_special_cases(self, xp):
+        rng = np.random.default_rng(34989235492245)
+
+        x0, y0 = rng.random((4, 5)), rng.random((4, 5))
+        x0[0, ...] = 1
+        y0[1, ...] = 2
+        x, y = xp.asarray(x0), xp.asarray(y0)
+        message = 'An input array is constant'
+        with eager_warns(stats.ConstantInputWarning, match=message, xp=xp):
+            res = stats.pearsonr(x, y, axis=1)
+            ci = res.confidence_interval()
+            nans = xp.asarray([xp.nan, xp.nan], dtype=xp.float64)
+            xp_assert_equal(res.statistic[0:2], nans)
+            xp_assert_equal(res.pvalue[0:2], nans)
+            xp_assert_equal(ci.low[0:2], nans)
+            xp_assert_equal(ci.high[0:2], nans)
+            assert xp.all(xp.isfinite(res.statistic[2:]))
+            assert xp.all(xp.isfinite(res.pvalue[2:]))
+            assert xp.all(xp.isfinite(ci.low[2:]))
+            assert xp.all(xp.isfinite(ci.high[2:]))
+
+        x0[0, 0], y0[1, 1] = 1 + 1e-15, 2 + 1e-15
+        x, y = xp.asarray(x0), xp.asarray(y0)
+        message = 'An input array is nearly constant'
+        with eager_warns(stats.NearConstantInputWarning, match=message, xp=xp):
+            stats.pearsonr(x, y, axis=1)
+
+        # length 2 along axis
+        x = xp.asarray([[1, 2], [1, 2], [2, 1], [2, 1.]])
+        y = xp.asarray([[1, 2], [2, 1], [1, 2], [2, 1.]])
+        ones = xp.ones(4)
+        res = stats.pearsonr(x, y, axis=-1)
+        ci = res.confidence_interval()
+        xp_assert_close(res.statistic, xp.asarray([1, -1, -1, 1.]))
+        xp_assert_close(res.pvalue, ones)
+        xp_assert_close(ci.low, -ones)
+        xp_assert_close(ci.high, ones)
+
+    def test_different_dimensionality(self, xp):
+        # For better or for worse, there is one difference between the broadcasting
+        # behavior of most stats functions and NumPy gufuncs / NEP 5: gufuncs `axis`
+        # refers to the core dimension *before* prepending `1`s to the array shapes
+        # to match dimensionality; SciPy's prepends `1`s first. For instance, in
+        # SciPy, `vecdot` would work just like `xp.sum(x * y, axis=axis)`, but this
+        # is NOT true of NumPy. The discrepancy only arises when there are multiple
+        # arguments with different dimensionality and positive indices are used,
+        # which is probably why it hasn't been a problem. There are pros and cons of
+        # each convention, and we might want to consider changing our behavior in
+        # SciPy 2.0. For now, preserve consistency / backward compatibility.
+        rng = np.random.default_rng(45834598265019344)
+        x = rng.random((3, 10))
+        y = rng.random(10)
+        res = stats.pearsonr(x, y, axis=1)
+        ref = stats.pearsonr(x, y, axis=-1)
+        assert_allclose(res.statistic, ref.statistic, rtol=3e-16)
+
+    @pytest.mark.parametrize('axis', [0, 1, None])
+    @pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+    def test_array_api(self, xp, axis, alternative):
+        rng = np.random.default_rng(2134418962)
+        x, y = rng.normal(size=(2, 10, 11))
+        res = stats.pearsonr(xp.asarray(x), xp.asarray(y),
+                             axis=axis, alternative=alternative)
+        ref = stats.pearsonr(x, y, axis=axis, alternative=alternative)
+        xp_assert_close(res.statistic, xp.asarray(ref.statistic))
+        xp_assert_close(res.pvalue, xp.asarray(ref.pvalue))
+
+        res_ci = res.confidence_interval()
+        ref_ci = ref.confidence_interval()
+        xp_assert_close(res_ci.low, xp.asarray(ref_ci.low))
+        xp_assert_close(res_ci.high, xp.asarray(ref_ci.high))
+
+
+class TestFisherExact:
+    """Some tests to show that fisher_exact() works correctly.
+
+    Note that in SciPy 0.9.0 this was not working well for large numbers due to
+    inaccuracy of the hypergeom distribution (see #1218). Fixed now.
+
+    Also note that R and SciPy have different argument formats for their
+    hypergeometric distribution functions.
+
+    R:
+    > phyper(18999, 99000, 110000, 39000, lower.tail = FALSE)
+    [1] 1.701815e-09
+    """
+
+    def test_basic(self):
+        fisher_exact = stats.fisher_exact
+
+        res = fisher_exact([[14500, 20000], [30000, 40000]])[1]
+        assert_approx_equal(res, 0.01106, significant=4)
+        res = fisher_exact([[100, 2], [1000, 5]])[1]
+        assert_approx_equal(res, 0.1301, significant=4)
+        res = fisher_exact([[2, 7], [8, 2]])[1]
+        assert_approx_equal(res, 0.0230141, significant=6)
+        res = fisher_exact([[5, 1], [10, 10]])[1]
+        assert_approx_equal(res, 0.1973244, significant=6)
+        res = fisher_exact([[5, 15], [20, 20]])[1]
+        assert_approx_equal(res, 0.0958044, significant=6)
+        res = fisher_exact([[5, 16], [20, 25]])[1]
+        assert_approx_equal(res, 0.1725862, significant=6)
+        res = fisher_exact([[10, 5], [10, 1]])[1]
+        assert_approx_equal(res, 0.1973244, significant=6)
+        res = fisher_exact([[5, 0], [1, 4]])[1]
+        assert_approx_equal(res, 0.04761904, significant=6)
+        res = fisher_exact([[0, 1], [3, 2]])[1]
+        assert_approx_equal(res, 1.0)
+        res = fisher_exact([[0, 2], [6, 4]])[1]
+        assert_approx_equal(res, 0.4545454545)
+        res = fisher_exact([[2, 7], [8, 2]])
+        assert_approx_equal(res[1], 0.0230141, significant=6)
+        assert_approx_equal(res[0], 4.0 / 56)
+
+    def test_precise(self):
+        # results from R
+        #
+        # R defines oddsratio differently (see Notes section of fisher_exact
+        # docstring), so those will not match.  We leave them in anyway, in
+        # case they will be useful later on. We test only the p-value.
+        tablist = [
+            ([[100, 2], [1000, 5]], (2.505583993422285e-001, 1.300759363430016e-001)),
+            ([[2, 7], [8, 2]], (8.586235135736206e-002, 2.301413756522114e-002)),
+            ([[5, 1], [10, 10]], (4.725646047336584e+000, 1.973244147157190e-001)),
+            ([[5, 15], [20, 20]], (3.394396617440852e-001, 9.580440012477637e-002)),
+            ([[5, 16], [20, 25]], (3.960558326183334e-001, 1.725864953812994e-001)),
+            ([[10, 5], [10, 1]], (2.116112781158483e-001, 1.973244147157190e-001)),
+            ([[10, 5], [10, 0]], (0.000000000000000e+000, 6.126482213438734e-002)),
+            ([[5, 0], [1, 4]], (np.inf, 4.761904761904762e-002)),
+            ([[0, 5], [1, 4]], (0.000000000000000e+000, 1.000000000000000e+000)),
+            ([[5, 1], [0, 4]], (np.inf, 4.761904761904758e-002)),
+            ([[0, 1], [3, 2]], (0.000000000000000e+000, 1.000000000000000e+000))
+            ]
+        for table, res_r in tablist:
+            res = stats.fisher_exact(np.asarray(table))
+            np.testing.assert_almost_equal(res[1], res_r[1], decimal=11,
+                                           verbose=True)
+
+    def test_gh4130(self):
+        # Previously, a fudge factor used to distinguish between theoretically
+        # and numerically different probability masses was 1e-4; it has been
+        # tightened to fix gh4130. Accuracy checked against R fisher.test.
+        # options(digits=16)
+        # table <- matrix(c(6, 108, 37, 200), nrow = 2)
+        # fisher.test(table, alternative = "t")
+        x = [[6, 37], [108, 200]]
+        res = stats.fisher_exact(x)
+        assert_allclose(res[1], 0.005092697748126)
+
+        # case from https://github.com/brentp/fishers_exact_test/issues/27
+        # That package has an (absolute?) fudge factor of 1e-6; too big
+        x = [[22, 0], [0, 102]]
+        res = stats.fisher_exact(x)
+        assert_allclose(res[1], 7.175066786244549e-25)
+
+        # case from https://github.com/brentp/fishers_exact_test/issues/1
+        x = [[94, 48], [3577, 16988]]
+        res = stats.fisher_exact(x)
+        assert_allclose(res[1], 2.069356340993818e-37)
+
+    def test_gh9231(self):
+        # Previously, fisher_exact was extremely slow for this table
+        # As reported in gh-9231, the p-value should be very nearly zero
+        x = [[5829225, 5692693], [5760959, 5760959]]
+        res = stats.fisher_exact(x)
+        assert_allclose(res[1], 0, atol=1e-170)
+
+    @pytest.mark.slow
+    def test_large_numbers(self):
+        # Test with some large numbers. Regression test for #1401
+        pvals = [5.56e-11, 2.666e-11, 1.363e-11]  # from R
+        for pval, num in zip(pvals, [75, 76, 77]):
+            res = stats.fisher_exact([[17704, 496], [1065, num]])[1]
+            assert_approx_equal(res, pval, significant=4)
+
+        res = stats.fisher_exact([[18000, 80000], [20000, 90000]])[1]
+        assert_approx_equal(res, 0.2751, significant=4)
+
+    def test_raises(self):
+        # test we raise an error for wrong number of dimensions.
+        message = "The input `table` must have two dimensions."
+        with pytest.raises(ValueError, match=message):
+            stats.fisher_exact(np.arange(6))
+
+    def test_row_or_col_zero(self):
+        tables = ([[0, 0], [5, 10]],
+                  [[5, 10], [0, 0]],
+                  [[0, 5], [0, 10]],
+                  [[5, 0], [10, 0]])
+        for table in tables:
+            oddsratio, pval = stats.fisher_exact(table)
+            assert_equal(pval, 1.0)
+            assert_equal(oddsratio, np.nan)
+
+    def test_less_greater(self):
+        tables = (
+            # Some tables to compare with R:
+            [[2, 7], [8, 2]],
+            [[200, 7], [8, 300]],
+            [[28, 21], [6, 1957]],
+            [[190, 800], [200, 900]],
+            # Some tables with simple exact values
+            # (includes regression test for ticket #1568):
+            [[0, 2], [3, 0]],
+            [[1, 1], [2, 1]],
+            [[2, 0], [1, 2]],
+            [[0, 1], [2, 3]],
+            [[1, 0], [1, 4]],
+            )
+        pvals = (
+            # from R:
+            [0.018521725952066501, 0.9990149169715733],
+            [1.0, 2.0056578803889148e-122],
+            [1.0, 5.7284374608319831e-44],
+            [0.7416227, 0.2959826],
+            # Exact:
+            [0.1, 1.0],
+            [0.7, 0.9],
+            [1.0, 0.3],
+            [2./3, 1.0],
+            [1.0, 1./3],
+            )
+        for table, pval in zip(tables, pvals):
+            res = []
+            res.append(stats.fisher_exact(table, alternative="less")[1])
+            res.append(stats.fisher_exact(table, alternative="greater")[1])
+            assert_allclose(res, pval, atol=0, rtol=1e-7)
+
+    def test_gh3014(self):
+        # check if issue #3014 has been fixed.
+        # before, this would have risen a ValueError
+        odds, pvalue = stats.fisher_exact([[1, 2], [9, 84419233]])
+
+    @pytest.mark.parametrize("alternative", ['two-sided', 'less', 'greater'])
+    def test_result(self, alternative):
+        table = np.array([[14500, 20000], [30000, 40000]])
+        res = stats.fisher_exact(table, alternative=alternative)
+        assert_equal((res.statistic, res.pvalue), res)
+
+    def test_input_validation_edge_cases_rxc(self):
+        rng = np.random.default_rng(2345783457834572345)
+        table = np.asarray([[2, 7], [8, 2]])
+
+        message = r"`alternative` must be the default \(None\) unless..."
+        with pytest.raises(ValueError, match=message):
+            method = stats.PermutationMethod(rng=rng)
+            stats.fisher_exact(table, method=method, alternative='less')
+
+        message = "...not recognized; if provided, `method` must be an..."
+        with pytest.raises(ValueError, match=message):
+            method = stats.BootstrapMethod(rng=rng)
+            stats.fisher_exact(table, method=method)
+
+        message = "If the `method` argument of `fisher_exact` is an..."
+        with pytest.raises(ValueError, match=message):
+            method = stats.MonteCarloMethod(rvs=stats.norm.rvs)
+            stats.fisher_exact(table, method=method)
+
+        message = "`table` must have at least one row and one column."
+        with pytest.raises(ValueError, match=message):
+            stats.fisher_exact(np.zeros((0, 1)))
+
+        # Specical case: when there is only one table with given marginals, the
+        # PMF of that case is 1.0, so the p-value is 1.0
+        np.testing.assert_equal(stats.fisher_exact([[1, 2, 3]]), (1, 1))
+        np.testing.assert_equal(stats.fisher_exact([[1], [2], [3]]), (1, 1))
+        np.testing.assert_equal(stats.fisher_exact(np.zeros((2, 3))), (1, 1))
+
+
+
+    @pytest.mark.fail_slow(10)
+    @pytest.mark.slow()
+    def test_resampling_2x2(self):
+        rng = np.random.default_rng(2345783457834572345)
+        table = np.asarray([[2, 7], [8, 2]])
+        ref = stats.fisher_exact(table)
+        ref_pvalue = ref.pvalue
+        ref_stat = stats.random_table(table.sum(axis=1), table.sum(axis=0)).pmf(table)
+
+        method = stats.MonteCarloMethod(rng=rng)
+        res = stats.fisher_exact(table, method=method)
+        assert_allclose(res.pvalue, ref_pvalue, atol=0.0025)
+        assert_equal(res.statistic, ref_stat)
+
+        method = stats.PermutationMethod(rng=rng)
+        res = stats.fisher_exact(table, method=method)
+        assert_allclose(res.pvalue, ref.pvalue, atol=0.0025)
+        assert_equal(res.statistic, ref_stat)
+
+    @pytest.mark.fail_slow(10)
+    @pytest.mark.slow()
+    def test_resampling_rxc(self):
+        # Compare against R fisher.exact
+        # options(digits=16)
+        # MP6 < - rbind(
+        #     c(1, 2, 2, 1, 1, 0, 1),
+        #     c(2, 0, 0, 2, 3, 0, 0),
+        #     c(0, 1, 1, 1, 2, 7, 3),
+        #     c(1, 1, 2, 0, 0, 0, 1),
+        #     c(0, 1, 1, 1, 1, 0, 0))
+        # fisher.test(MP6)
+
+        table = [[1, 2, 2, 1, 1, 0, 1],
+                 [2, 0, 0, 2, 3, 0, 0],
+                 [0, 1, 1, 1, 2, 7, 3],
+                 [1, 1, 2, 0, 0, 0, 1],
+                 [0, 1, 1, 1, 1, 0, 0]]
+        table = np.asarray(table)
+
+        ref_pvalue = 0.03928964365533
+        rng = np.random.default_rng(3928964365533)
+
+        method = stats.PermutationMethod(rng=rng)
+        res = stats.fisher_exact(table, method=method)
+        assert_allclose(res.pvalue, ref_pvalue, atol=5e-4)
+
+        method = stats.MonteCarloMethod(rng=rng, n_resamples=99999)
+        res = stats.fisher_exact(table, method=method)
+        assert_allclose(res.pvalue, ref_pvalue, atol=5e-4)
+
+    @pytest.mark.xslow()
+    def test_resampling_exact_2x2(self):
+        # Test that exact permutation p-value matches result of `fisher_exact`
+        rng = np.random.default_rng(2345783457834572345)
+        method = stats.PermutationMethod(rng=rng)
+
+        for a in range(1, 3):
+            for b in range(1, 3):
+                for c in range(1, 3):
+                    for d in range(1, 4):
+                        table = np.asarray([[a, b], [c, d]])
+                        ref = stats.fisher_exact(table)
+                        res = stats.fisher_exact(table, method=method)
+                        assert_allclose(res.pvalue, ref.pvalue, atol=1e-14)
+
+
+class TestCorrSpearmanr:
+    """ W.II.D. Compute a correlation matrix on all the variables.
+
+        All the correlations, except for ZERO and MISS, should be exactly 1.
+        ZERO and MISS should have undefined or missing correlations with the
+        other variables.  The same should go for SPEARMAN correlations, if
+        your program has them.
+    """
+
+    def setup_method(self):
+        self.rng = np.random.default_rng(228584263)
+
+    def test_scalar(self):
+        y = stats.spearmanr(4., 2.)
+        assert_(np.isnan(y).all())
+
+    def test_uneven_lengths(self):
+        assert_raises(ValueError, stats.spearmanr, [1, 2, 1], [8, 9])
+        assert_raises(ValueError, stats.spearmanr, [1, 2, 1], 8)
+
+    def test_uneven_2d_shapes(self):
+        # Different number of columns should work - those just get concatenated.
+        x = self.rng.standard_normal((4, 3))
+        y = self.rng.standard_normal((4, 2))
+        assert stats.spearmanr(x, y).statistic.shape == (5, 5)
+        assert stats.spearmanr(x.T, y.T, axis=1).pvalue.shape == (5, 5)
+
+        assert_raises(ValueError, stats.spearmanr, x, y, axis=1)
+        assert_raises(ValueError, stats.spearmanr, x.T, y.T)
+
+    def test_ndim_too_high(self):
+        x = self.rng.standard_normal((4, 3, 2))
+        assert_raises(ValueError, stats.spearmanr, x)
+        assert_raises(ValueError, stats.spearmanr, x, x)
+        assert_raises(ValueError, stats.spearmanr, x, None, None)
+        # But should work with axis=None (raveling axes) for two input arrays
+        assert_allclose(stats.spearmanr(x, x, axis=None),
+                        stats.spearmanr(x.flatten(), x.flatten(), axis=0))
+
+    def test_nan_policy(self):
+        x = np.arange(10.)
+        x[9] = np.nan
+        assert_array_equal(stats.spearmanr(x, x), (np.nan, np.nan))
+        assert_array_equal(stats.spearmanr(x, x, nan_policy='omit'),
+                           (1.0, 0.0))
+        assert_raises(ValueError, stats.spearmanr, x, x, nan_policy='raise')
+        assert_raises(ValueError, stats.spearmanr, x, x, nan_policy='foobar')
+
+    def test_nan_policy_bug_12458(self):
+        rng = np.random.default_rng(8119864466)
+        x = rng.random((5, 10))
+        k = 6
+        x[:, k] = np.nan
+        y = np.delete(x, k, axis=1)
+        corx, px = stats.spearmanr(x, nan_policy='omit')
+        cory, py = stats.spearmanr(y)
+        corx = np.delete(np.delete(corx, k, axis=1), k, axis=0)
+        px = np.delete(np.delete(px, k, axis=1), k, axis=0)
+        assert_allclose(corx, cory, atol=1e-14)
+        assert_allclose(px, py, atol=1e-14)
+
+    def test_nan_policy_bug_12411(self):
+        m = 5
+        n = 10
+        x = self.rng.standard_normal((m, n))
+        x[1, 0] = np.nan
+        x[3, -1] = np.nan
+        corr, pvalue = stats.spearmanr(x, axis=1, nan_policy="propagate")
+        res = [[stats.spearmanr(x[i, :], x[j, :]).statistic for i in range(m)]
+               for j in range(m)]
+        assert_allclose(corr, res)
+
+    @pytest.mark.parametrize("axis", [0, 1])
+    def test_constant_input_gh22816(self, axis):
+        X = np.zeros((10, 4))
+        X[:, 2] = np.arange(0, 10)
+        X[:, 3] = -np.arange(0, 10)
+
+        corr = np.array([[np.nan, np.nan, np.nan, np.nan],
+                         [np.nan, np.nan, np.nan, np.nan],
+                         [np.nan, np.nan, 1., -1.],
+                         [np.nan, np.nan, -1., 1.]])
+        if axis == 1:
+            X = X.T
+        with pytest.warns(stats._warnings_errors.ConstantInputWarning):
+            res = stats.spearmanr(X, axis=axis)
+        assert_allclose(res.statistic, corr)
+        assert_allclose(res.pvalue, 0*corr, atol=1e-50)
+
+    def test_sXX(self):
+        y = stats.spearmanr(X,X)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sXBIG(self):
+        y = stats.spearmanr(X,BIG)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sXLITTLE(self):
+        y = stats.spearmanr(X,LITTLE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sXHUGE(self):
+        y = stats.spearmanr(X,HUGE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sXTINY(self):
+        y = stats.spearmanr(X,TINY)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sXROUND(self):
+        y = stats.spearmanr(X,ROUND)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sBIGBIG(self):
+        y = stats.spearmanr(BIG,BIG)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sBIGLITTLE(self):
+        y = stats.spearmanr(BIG,LITTLE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sBIGHUGE(self):
+        y = stats.spearmanr(BIG,HUGE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sBIGTINY(self):
+        y = stats.spearmanr(BIG,TINY)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sBIGROUND(self):
+        y = stats.spearmanr(BIG,ROUND)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sLITTLELITTLE(self):
+        y = stats.spearmanr(LITTLE,LITTLE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sLITTLEHUGE(self):
+        y = stats.spearmanr(LITTLE,HUGE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sLITTLETINY(self):
+        y = stats.spearmanr(LITTLE,TINY)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sLITTLEROUND(self):
+        y = stats.spearmanr(LITTLE,ROUND)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sHUGEHUGE(self):
+        y = stats.spearmanr(HUGE,HUGE)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sHUGETINY(self):
+        y = stats.spearmanr(HUGE,TINY)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sHUGEROUND(self):
+        y = stats.spearmanr(HUGE,ROUND)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sTINYTINY(self):
+        y = stats.spearmanr(TINY,TINY)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sTINYROUND(self):
+        y = stats.spearmanr(TINY,ROUND)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_sROUNDROUND(self):
+        y = stats.spearmanr(ROUND,ROUND)
+        r = y[0]
+        assert_approx_equal(r,1.0)
+
+    def test_spearmanr_result_attributes(self):
+        res = stats.spearmanr(X, X)
+        attributes = ('correlation', 'pvalue')
+        check_named_results(res, attributes)
+        assert_equal(res.correlation, res.statistic)
+
+    def test_1d_vs_2d(self):
+        x1 = [1, 2, 3, 4, 5, 6]
+        x2 = [1, 2, 3, 4, 6, 5]
+        res1 = stats.spearmanr(x1, x2)
+        res2 = stats.spearmanr(np.asarray([x1, x2]).T)
+        assert_allclose(res1, res2)
+
+    def test_1d_vs_2d_nans(self):
+        # Now the same with NaNs present.  Regression test for gh-9103.
+        for nan_policy in ['propagate', 'omit']:
+            x1 = [1, np.nan, 3, 4, 5, 6]
+            x2 = [1, 2, 3, 4, 6, np.nan]
+            res1 = stats.spearmanr(x1, x2, nan_policy=nan_policy)
+            res2 = stats.spearmanr(np.asarray([x1, x2]).T, nan_policy=nan_policy)
+            assert_allclose(res1, res2)
+
+    def test_3cols(self):
+        x1 = np.arange(6)
+        x2 = -x1
+        x3 = np.array([0, 1, 2, 3, 5, 4])
+        x = np.asarray([x1, x2, x3]).T
+        actual = stats.spearmanr(x)
+        expected_corr = np.array([[1, -1, 0.94285714],
+                                  [-1, 1, -0.94285714],
+                                  [0.94285714, -0.94285714, 1]])
+        expected_pvalue = np.zeros((3, 3), dtype=float)
+        expected_pvalue[2, 0:2] = 0.00480466472
+        expected_pvalue[0:2, 2] = 0.00480466472
+
+        assert_allclose(actual.statistic, expected_corr)
+        assert_allclose(actual.pvalue, expected_pvalue)
+
+    def test_gh_9103(self):
+        # Regression test for gh-9103.
+        x = np.array([[np.nan, 3.0, 4.0, 5.0, 5.1, 6.0, 9.2],
+                      [5.0, np.nan, 4.1, 4.8, 4.9, 5.0, 4.1],
+                      [0.5, 4.0, 7.1, 3.8, 8.0, 5.1, 7.6]]).T
+        corr = np.array([[np.nan, np.nan, np.nan],
+                         [np.nan, np.nan, np.nan],
+                         [np.nan, np.nan, 1.]])
+        assert_allclose(stats.spearmanr(x, nan_policy='propagate').statistic,
+                        corr)
+
+        res = stats.spearmanr(x, nan_policy='omit').statistic
+        assert_allclose((res[0][1], res[0][2], res[1][2]),
+                        (0.2051957, 0.4857143, -0.4707919), rtol=1e-6)
+
+    def test_gh_8111(self):
+        # Regression test for gh-8111 (different result for float/int/bool).
+        n = 100
+        rng = np.random.RandomState(234568)
+        x = rng.rand(n)
+        m = rng.rand(n) > 0.7
+
+        # bool against float, no nans
+        a = (x > .5)
+        b = np.array(x)
+        res1 = stats.spearmanr(a, b, nan_policy='omit').statistic
+
+        # bool against float with NaNs
+        b[m] = np.nan
+        res2 = stats.spearmanr(a, b, nan_policy='omit').statistic
+
+        # int against float with NaNs
+        a = a.astype(np.int32)
+        res3 = stats.spearmanr(a, b, nan_policy='omit').statistic
+
+        expected = [0.865895477, 0.866100381, 0.866100381]
+        assert_allclose([res1, res2, res3], expected)
+
+
+class TestCorrSpearmanr2:
+    """Some further tests of the spearmanr function."""
+
+    def test_spearmanr_vs_r(self):
+        # Cross-check with R:
+        # cor.test(c(1,2,3,4,5),c(5,6,7,8,7),method="spearmanr")
+        x1 = [1, 2, 3, 4, 5]
+        x2 = [5, 6, 7, 8, 7]
+        expected = (0.82078268166812329, 0.088587005313543798)
+        res = stats.spearmanr(x1, x2)
+        assert_approx_equal(res[0], expected[0])
+        assert_approx_equal(res[1], expected[1])
+
+    def test_empty_arrays(self):
+        assert_equal(stats.spearmanr([], []), (np.nan, np.nan))
+
+    def test_normal_draws(self):
+        rng = np.random.RandomState(7546)
+        x = np.array([rng.normal(loc=1, scale=1, size=500),
+                      rng.normal(loc=1, scale=1, size=500)])
+        corr = [[1.0, 0.3],
+                [0.3, 1.0]]
+        x = np.dot(np.linalg.cholesky(corr), x)
+        expected = (0.28659685838743354, 6.579862219051161e-11)
+        res = stats.spearmanr(x[0], x[1])
+        assert_approx_equal(res[0], expected[0])
+        assert_approx_equal(res[1], expected[1])
+
+    def test_corr_1(self):
+        assert_approx_equal(stats.spearmanr([1, 1, 2], [1, 1, 2])[0], 1.0)
+
+    def test_nan_policies(self):
+        x = np.arange(10.)
+        x[9] = np.nan
+        assert_array_equal(stats.spearmanr(x, x), (np.nan, np.nan))
+        assert_allclose(stats.spearmanr(x, x, nan_policy='omit'),
+                        (1.0, 0))
+        assert_raises(ValueError, stats.spearmanr, x, x, nan_policy='raise')
+        assert_raises(ValueError, stats.spearmanr, x, x, nan_policy='foobar')
+
+    def test_unequal_lengths(self):
+        x = np.arange(10.)
+        y = np.arange(20.)
+        assert_raises(ValueError, stats.spearmanr, x, y)
+
+    def test_omit_paired_value(self):
+        x1 = [1, 2, 3, 4]
+        x2 = [8, 7, 6, np.nan]
+        res1 = stats.spearmanr(x1, x2, nan_policy='omit')
+        res2 = stats.spearmanr(x1[:3], x2[:3], nan_policy='omit')
+        assert_equal(res1, res2)
+
+    def test_gh_issue_6061_windows_overflow(self):
+        x = list(range(2000))
+        y = list(range(2000))
+        y[0], y[9] = y[9], y[0]
+        y[10], y[434] = y[434], y[10]
+        y[435], y[1509] = y[1509], y[435]
+        # rho = 1 - 6 * (2 * (9^2 + 424^2 + 1074^2))/(2000 * (2000^2 - 1))
+        #     = 1 - (1 / 500)
+        #     = 0.998
+        x.append(np.nan)
+        y.append(3.0)
+        assert_almost_equal(stats.spearmanr(x, y, nan_policy='omit')[0], 0.998)
+
+    def test_tie0(self):
+        # with only ties in one or both inputs
+        warn_msg = "An input array is constant"
+        with pytest.warns(stats.ConstantInputWarning, match=warn_msg):
+            r, p = stats.spearmanr([2, 2, 2], [2, 2, 2])
+            assert_equal(r, np.nan)
+            assert_equal(p, np.nan)
+            r, p = stats.spearmanr([2, 0, 2], [2, 2, 2])
+            assert_equal(r, np.nan)
+            assert_equal(p, np.nan)
+            r, p = stats.spearmanr([2, 2, 2], [2, 0, 2])
+            assert_equal(r, np.nan)
+            assert_equal(p, np.nan)
+
+    def test_tie1(self):
+        # Data
+        x = [1.0, 2.0, 3.0, 4.0]
+        y = [1.0, 2.0, 2.0, 3.0]
+        # Ranks of the data, with tie-handling.
+        xr = [1.0, 2.0, 3.0, 4.0]
+        yr = [1.0, 2.5, 2.5, 4.0]
+        # Result of spearmanr should be the same as applying
+        # pearsonr to the ranks.
+        sr = stats.spearmanr(x, y)
+        pr = stats.pearsonr(xr, yr)
+        assert_almost_equal(sr, pr)
+
+    def test_tie2(self):
+        # Test tie-handling if inputs contain nan's
+        # Data without nan's
+        x1 = [1, 2, 2.5, 2]
+        y1 = [1, 3, 2.5, 4]
+        # Same data with nan's
+        x2 = [1, 2, 2.5, 2, np.nan]
+        y2 = [1, 3, 2.5, 4, np.nan]
+
+        # Results for two data sets should be the same if nan's are ignored
+        sr1 = stats.spearmanr(x1, y1)
+        sr2 = stats.spearmanr(x2, y2, nan_policy='omit')
+        assert_almost_equal(sr1, sr2)
+
+    def test_ties_axis_1(self):
+        z1 = np.array([[1, 1, 1, 1], [1, 2, 3, 4]])
+        z2 = np.array([[1, 2, 3, 4], [1, 1, 1, 1]])
+        z3 = np.array([[1, 1, 1, 1], [1, 1, 1, 1]])
+        warn_msg = "An input array is constant"
+        with pytest.warns(stats.ConstantInputWarning, match=warn_msg):
+            r, p = stats.spearmanr(z1, axis=1)
+            assert_equal(r, np.nan)
+            assert_equal(p, np.nan)
+            r, p = stats.spearmanr(z2, axis=1)
+            assert_equal(r, np.nan)
+            assert_equal(p, np.nan)
+            r, p = stats.spearmanr(z3, axis=1)
+            assert_equal(r, np.nan)
+            assert_equal(p, np.nan)
+
+    def test_gh_11111(self):
+        x = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
+        y = np.array([0, 0.009783728115345005, 0, 0, 0.0019759230121848587,
+                      0.0007535430349118562, 0.0002661781514710257, 0, 0,
+                      0.0007835762419683435])
+        warn_msg = "An input array is constant"
+        with pytest.warns(stats.ConstantInputWarning, match=warn_msg):
+            r, p = stats.spearmanr(x, y)
+            assert_equal(r, np.nan)
+            assert_equal(p, np.nan)
+
+    def test_index_error(self):
+        x = np.array([1.0, 7.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])
+        y = np.array([0, 0.009783728115345005, 0, 0, 0.0019759230121848587,
+                      0.0007535430349118562, 0.0002661781514710257, 0, 0,
+                      0.0007835762419683435])
+        assert_raises(ValueError, stats.spearmanr, x, y, axis=2)
+
+    def test_alternative(self):
+        # Test alternative parameter
+
+        # Simple test - Based on the above ``test_spearmanr_vs_r``
+        x1 = [1, 2, 3, 4, 5]
+        x2 = [5, 6, 7, 8, 7]
+
+        # strong positive correlation
+        expected = (0.82078268166812329, 0.088587005313543798)
+
+        # correlation > 0 -> large "less" p-value
+        res = stats.spearmanr(x1, x2, alternative="less")
+        assert_approx_equal(res[0], expected[0])
+        assert_approx_equal(res[1], 1 - (expected[1] / 2))
+
+        # correlation > 0 -> small "less" p-value
+        res = stats.spearmanr(x1, x2, alternative="greater")
+        assert_approx_equal(res[0], expected[0])
+        assert_approx_equal(res[1], expected[1] / 2)
+
+        with pytest.raises(ValueError, match="`alternative` must be 'less'..."):
+            stats.spearmanr(x1, x2, alternative="ekki-ekki")
+
+    @pytest.mark.parametrize("alternative", ('two-sided', 'less', 'greater'))
+    def test_alternative_nan_policy(self, alternative):
+        # Test nan policies
+        x1 = [1, 2, 3, 4, 5]
+        x2 = [5, 6, 7, 8, 7]
+        x1nan = x1 + [np.nan]
+        x2nan = x2 + [np.nan]
+
+        # test nan_policy="propagate"
+        assert_array_equal(stats.spearmanr(x1nan, x2nan), (np.nan, np.nan))
+
+        # test nan_policy="omit"
+        res_actual = stats.spearmanr(x1nan, x2nan, nan_policy='omit',
+                                     alternative=alternative)
+        res_expected = stats.spearmanr(x1, x2, alternative=alternative)
+        assert_allclose(res_actual, res_expected)
+
+        # test nan_policy="raise"
+        message = 'The input contains nan values'
+        with pytest.raises(ValueError, match=message):
+            stats.spearmanr(x1nan, x2nan, nan_policy='raise',
+                            alternative=alternative)
+
+        # test invalid nan_policy
+        message = "nan_policy must be one of..."
+        with pytest.raises(ValueError, match=message):
+            stats.spearmanr(x1nan, x2nan, nan_policy='ekki-ekki',
+                            alternative=alternative)
+
+
+@make_xp_test_case(stats.kendalltau)
+class TestKendallTau:
+    @pytest.mark.parametrize('x, y, statistic, pvalue', [
+        # For the cases without ties, both variants should give the same
+        # result. Reference values result from R:
+        # cor.test(x, y, method="kendall", exact=1)
+        ([5, 2, 1, 3, 6, 4, 7, 8], [5, 2, 6, 3, 1, 8, 7, 4], 0.0, 1.0),
+        ([0, 5, 2, 1, 3, 6, 4, 7, 8], [5, 2, 0, 6, 3, 1, 8, 7, 4], 0.0, 1.0),
+        ([5, 2, 1, 3, 6, 4, 7], [5, 2, 6, 3, 1, 7, 4], -0.14285714286, 0.77261904762),
+        ([2, 1, 3, 6, 4, 7, 8], [2, 6, 3, 1, 8, 7, 4], 0.047619047619, 1.0),
+        ([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
+         1.0, 5.511463844797e-07),
+        ([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 2, 1, 3, 4, 5, 6, 7, 8, 9],
+         0.9555555555555556, 5.511463844797e-06),
+        ([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [0, 2, 1, 3, 4, 6, 5, 7, 8, 9],
+         0.9111111111111111, 2.976190476190e-05),
+        ([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [9, 8, 7, 6, 5, 4, 3, 2, 1, 0],
+         -1.0, 5.511463844797e-07),
+        ([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [9, 7, 8, 6, 5, 4, 3, 2, 1, 0],
+         -0.9555555555555556, 5.511463844797e-06),
+        ([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [9, 7, 8, 6, 5, 3, 4, 2, 1, 0],
+         -0.9111111111111111, 2.976190476190e-05),
+    ])
+    @pytest.mark.parametrize('variant', ['b', 'c'])
+    def test_kendalltau_no_ties(self, x, y, statistic, pvalue, variant, xp):
+        res = stats.kendalltau(xp.asarray(x), xp.asarray(y), variant=variant)
+        xp_assert_close(res[0], xp.asarray(statistic))
+        xp_assert_close(res[1], xp.asarray(pvalue))
+
+    def test_kendalltau_ties(self, xp):
+        # Check a case where variants are different
+        # Example values found from Kendall (1970).
+        # P-value is the same for the both variants
+        x = xp.asarray([1, 2, 2, 4, 4, 6, 6, 8, 9, 9])
+        y = xp.asarray([1, 2, 4, 4, 4, 4, 8, 8, 8, 10])
+        expected = 0.85895569
+        xp_assert_close(stats.kendalltau(x, y, variant='b')[0], xp.asarray(expected))
+        expected = 0.825
+        xp_assert_close(stats.kendalltau(x, y, variant='c')[0], xp.asarray(expected))
+
+        # tau-b with some ties
+        # Cross-check with R:
+        # cor.test(c(12, 2, 1, 12, 2), c(1, 4, 7, 1, 0), method="kendall", exact=FALSE)
+        x1 = xp.asarray([12, 2, 1, 12, 2])
+        x2 = xp.asarray([1, 4, 7, 1, 0])
+        expected = (-0.47140452079103173, 0.28274545993277478)
+        res = stats.kendalltau(x1, x2)
+        assert_approx_equal(res[0], xp.asarray(expected[0]))
+        assert_approx_equal(res[1], xp.asarray(expected[1]))
+
+        # this should result in 1 for tau-b but not tau-c
+        x = xp.asarray([1, 1, 2])
+        xp_assert_close(stats.kendalltau(x, x, variant='b')[0], xp.asarray(1.0))
+        xp_assert_close(stats.kendalltau(x, x, variant='c')[0], xp.asarray(0.88888888))
+
+    def test_input_validation_edge_cases(self, xp):
+        x = xp.asarray([1, 2, 2, 4, 4, 6, 6, 8, 9, 9])
+        y = xp.asarray([1, 2, 2, 4, 4, 4, 8, 8, 8, 10])
+
+        # check exception in case of ties and method='exact' requested
+        message = "Ties found; exact method cannot be used."
+        with pytest.raises(ValueError, match=message):
+            stats.kendalltau(x, y, method='exact')
+
+        # check exception in case of invalid method keyword
+        message = "Unknown `method` 'banana' specified."
+        with pytest.raises(ValueError, match=message):
+            stats.kendalltau(x, y, method='banana')
+
+        # check exception in case of invalid variant keyword
+        message = "Unknown `variant` 'rms' specified."
+        with pytest.raises(ValueError, match=message):
+            stats.kendalltau(x, y, variant='rms')
+
+        # empty arrays provided as input
+        with eager_warns(SmallSampleWarning, match="One or more sample...", xp=xp):
+            res = stats.kendalltau(xp.asarray([]), xp.asarray([]))
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+        # test too small
+        with pytest.warns(SmallSampleWarning, match="One or more sample..."):
+            tau, p_value = stats.kendalltau(xp.asarray([0]), xp.asarray([0]))
+        xp_assert_equal(tau, xp.asarray(np.nan))
+        xp_assert_equal(p_value, xp.asarray(np.nan))
+
+        # test unequal length inputs
+        x = np.arange(10.)
+        y = np.arange(20.)
+        message = "Array shapes are incompatible for broadcasting."
+        with pytest.raises(ValueError, match=message):
+            stats.kendalltau(x, y)
+
+    @pytest.mark.parametrize('variant', ['b', 'c'])
+    def test_attributes(self, variant, xp):
+        x1 = xp.asarray([1, 2, 2, 4, 4, 6, 6, 8, 9, 9])
+        x2 = xp.asarray([1, 2, 2, 4, 4, 4, 8, 8, 8, 10])
+        # test for namedtuple attribute results
+        attributes = ('correlation', 'pvalue')
+        res = stats.kendalltau(x1, x2, variant=variant)
+        check_named_results(res, attributes, xp=xp)
+        xp_assert_equal(res.correlation, res.statistic)
+
+    @pytest.mark.parametrize('x, y', [
+        ([2, 2, 2], [2, 2, 2]),
+        ([2, 0, 2], [2, 2, 2]),
+        ([2, 2, 2], [2, 0, 2]),
+    ])
+    @pytest.mark.parametrize('variant', ['b', 'c'])
+    def test_degenerate_input(self, x, y, variant, xp):
+        # with only ties in one or both inputs in tau-b or tau-c
+        x, y = xp.asarray(x), xp.asarray(y)
+        res = stats.kendalltau(x, y, variant=variant)
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_large_arrays(self, dtype, xp):
+        # check with larger arrays
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        rng = np.random.RandomState(7546)
+        x = np.array([rng.normal(loc=1, scale=1, size=500),
+                      rng.normal(loc=1, scale=1, size=500)])
+        corr = [[1.0, 0.3],
+                [0.3, 1.0]]
+        x = np.dot(np.linalg.cholesky(corr), x)
+        x0 = xp.asarray(x[0], dtype=dtype)
+        x1 = xp.asarray(x[1], dtype=dtype)
+        expected = (0.19291382765531062, 1.1337095377742629e-10)
+        res = stats.kendalltau(x0, x1)
+        xp_assert_close(res[0], xp.asarray(expected[0], dtype=dtype))
+        xp_assert_close(res[1], xp.asarray(expected[1], dtype=dtype))
+
+    def test_nan_policy(self, xp):
+        # test nan_policy
+        x = np.arange(10.)
+        x[9] = np.nan
+        x = xp.asarray(x.tolist())
+
+        res = stats.kendalltau(x, x)
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+        res = stats.kendalltau(x, x, nan_policy='omit')
+        xp_assert_close(res.statistic, xp.asarray(1.0))
+        xp_assert_close(res.pvalue, xp.asarray(5.5114638e-6), rtol=1e-06)
+
+        res = stats.kendalltau(x, x, nan_policy='omit', method='asymptotic')
+        xp_assert_close(res.statistic, xp.asarray(1.0))
+        xp_assert_close(res.pvalue, xp.asarray(0.00017455009626808976), rtol=1e-06)
+
+        message = "The input contains nan values"
+        with pytest.raises(ValueError, match=message):
+            stats.kendalltau(x, x, nan_policy='raise')
+
+        message = "nan_policy must be one of..."
+        with pytest.raises(ValueError, match=message):
+            stats.kendalltau(x, x, nan_policy='foobar')
+
+    def test_kendalltau_nan_2nd_arg_gh6134(self, xp):
+        # regression test for gh-6134: nans in the second arg were not handled
+        x = xp.asarray([1., 2., 3., 4.])
+        y = xp.asarray([np.nan, 2.4, 3.4, 3.4])
+
+        r1 = stats.kendalltau(x, y, nan_policy='omit')
+        r2 = stats.kendalltau(x[1:], y[1:])
+        xp_assert_close(r1.statistic, r2.statistic, atol=1e-15)
+
+    @pytest.mark.thread_unsafe(reason="fails in parallel")
+    def test_kendalltau_gh18139_overflow(self, xp):
+        # gh-18139 reported an overflow in `kendalltau` that appeared after
+        # SciPy 0.15.1. Check that this particular overflow does not occur.
+        # (Test would fail if warning were emitted.)
+        import random
+        random.seed(6272161)
+        classes = [1, 2, 3, 4, 5, 6, 7]
+        n_samples = 2 * 10 ** 5
+        x = xp.asarray(random.choices(classes, k=n_samples))
+        y = xp.asarray(random.choices(classes, k=n_samples))
+        res = stats.kendalltau(x, y)
+        # Reference value from SciPy 0.15.1
+        xp_assert_close(res.statistic, xp.asarray(0.0011816493905730343))
+        # Reference p-value from `permutation_test` w/ n_resamples=9999 (default).
+        # Expected to be accurate to at least two digits.
+        xp_assert_close(res.pvalue, xp.asarray(0.4894), atol=2e-3)
+
+    def test_kendall_tau_large_nan_policy(self, xp):
+        n = 172
+        # Test omit policy
+        x = np.arange(n + 1).astype(float)
+        y = np.arange(n + 1).astype(float)
+        y[-1] = np.nan
+        x, y = xp.asarray(x.tolist()), xp.asarray(y.tolist())
+        _, pval = stats.kendalltau(x, y, method='exact', nan_policy='omit')
+        xp_assert_equal(pval, xp.asarray(0.0))
+
+
+@make_xp_test_case(stats.kendalltau)
+class TestKendallTauAlternative:
+    def test_kendalltau_alternative_asymptotic(self, xp):
+        # Test alternative parameter, asymptotic method (due to tie)
+
+        # Based on TestCorrSpearman2::test_alternative
+        x1 = [1, 2, 3, 4, 5]
+        x2 = [5, 6, 7, 8, 7]
+
+        # strong positive correlation
+        expected = stats.kendalltau(xp.asarray(x1), xp.asarray(x2),
+                                    alternative="two-sided")
+        assert expected[0] > 0
+
+        # rank correlation > 0 -> large "less" p-value
+        res = stats.kendalltau(xp.asarray(x1), xp.asarray(x2), alternative="less")
+        xp_assert_equal(res[0], xp.asarray(expected[0]))
+        xp_assert_close(res[1], xp.asarray(1 - (expected[1] / 2)))
+
+        # rank correlation > 0 -> small "greater" p-value
+        res = stats.kendalltau(xp.asarray(x1), xp.asarray(x2), alternative="greater")
+        xp_assert_equal(res[0], xp.asarray(expected[0]))
+        xp_assert_close(res[1], xp.asarray(expected[1] / 2))
+
+        # reverse the direction of rank correlation
+        x2.reverse()
+
+        # strong negative correlation
+        expected = stats.kendalltau(xp.asarray(x1), xp.asarray(x2),
+                                    alternative="two-sided")
+        assert expected[0] < 0
+
+        # rank correlation < 0 -> large "greater" p-value
+        res = stats.kendalltau(xp.asarray(x1), xp.asarray(x2), alternative="greater")
+        xp_assert_equal(res[0], xp.asarray(expected[0]))
+        xp_assert_close(res[1], xp.asarray(1 - (expected[1] / 2)))
+
+        # rank correlation < 0 -> small "less" p-value
+        res = stats.kendalltau(xp.asarray(x1), xp.asarray(x2), alternative="less")
+        xp_assert_equal(res[0], xp.asarray(expected[0]))
+        xp_assert_close(res[1], xp.asarray(expected[1] / 2))
+
+        with pytest.raises(ValueError, match="`alternative` must be 'less'..."):
+            stats.kendalltau(xp.asarray(x1), xp.asarray(x2), alternative="ekki-ekki")
+
+    # There are a lot of special cases considered in the calculation of the
+    # exact p-value, so we test each separately. We also need to test
+    # separately when the observed statistic is in the left tail vs the right
+    # tail because the code leverages symmetry of the null distribution; to
+    # do that we use the same test case but negate one of the samples.
+    # Reference values computed using R cor.test, e.g.
+    # options(digits=16)
+    # x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
+    # y <- c( 2.6,  3.1,  2.5,  5.0,  3.6,  4.0,  5.2,  2.8,  3.8)
+    # cor.test(x, y, method = "kendall", alternative = "g")
+
+    alternatives = ('less', 'two-sided', 'greater')
+    p_n1 = [np.nan, np.nan, np.nan]
+    p_n2 = [1.0, 1.0, 0.5]
+    p_c0 = [1.0, 0.3333333333333, 0.1666666666667]
+    p_c1 = [0.9583333333333, 0.3333333333333, 0.1666666666667]
+    p_no_correlation = [0.5916666666667, 1.0, 0.5916666666667]
+    p_no_correlationb = [0.5475694444444, 1.0, 0.5475694444444]
+    p_n_lt_171 = [0.9624118165785, 0.1194389329806, 0.0597194664903]
+    p_n_lt_171b = [0.246236925303, 0.4924738506059, 0.755634083327]
+    p_n_lt_171c = [0.9847475308925, 0.03071385306533, 0.01535692653267]
+
+    def exact_test(self, x, y, alternative, rev, stat_expected, p_expected, xp):
+        x, y = xp.asarray(x), xp.asarray(y)
+        if rev:
+            y = -y
+            stat_expected *= -1
+        res = stats.kendalltau(x, y, method='exact', alternative=alternative)
+        xp_assert_close(res.statistic, xp.asarray(stat_expected))
+        xp_assert_close(res.pvalue, xp.asarray(p_expected))
+
+    case_R_n1 = (list(zip(alternatives, p_n1, [False]*3))
+                 + list(zip(alternatives, reversed(p_n1), [True]*3)))
+
+    @pytest.mark.parametrize("alternative, p_expected, rev", case_R_n1)
+    def test_against_R_n1(self, alternative, p_expected, rev, xp):
+        x, y = [1], [2]
+        stat_expected = np.nan
+        with pytest.warns(SmallSampleWarning, match="One or more sample..."):
+            self.exact_test(x, y, alternative, rev, stat_expected, p_expected, xp=xp)
+
+    case_R_n2 = (list(zip(alternatives, p_n2, [False]*3))
+                 + list(zip(alternatives, reversed(p_n2), [True]*3)))
+
+    @pytest.mark.parametrize("alternative, p_expected, rev", case_R_n2)
+    def test_against_R_n2(self, alternative, p_expected, rev, xp):
+        x, y = [1, 2], [3, 4]
+        stat_expected = 0.9999999999999998
+        self.exact_test(x, y, alternative, rev, stat_expected, p_expected, xp=xp)
+
+    case_R_c0 = (list(zip(alternatives, p_c0, [False]*3))
+                 + list(zip(alternatives, reversed(p_c0), [True]*3)))
+
+    @pytest.mark.parametrize("alternative, p_expected, rev", case_R_c0)
+    def test_against_R_c0(self, alternative, p_expected, rev, xp):
+        x, y = [1, 2, 3], [1, 2, 3]
+        stat_expected = 1.
+        self.exact_test(x, y, alternative, rev, stat_expected, p_expected, xp=xp)
+
+    case_R_c1 = (list(zip(alternatives, p_c1, [False]*3))
+                 + list(zip(alternatives, reversed(p_c1), [True]*3)))
+
+    @pytest.mark.parametrize("alternative, p_expected, rev", case_R_c1)
+    def test_against_R_c1(self, alternative, p_expected, rev, xp):
+        x, y = [1, 2, 3, 4], [1, 2, 4, 3]
+        stat_expected = 0.6666666666666667
+        self.exact_test(x, y, alternative, rev, stat_expected, p_expected, xp=xp)
+
+    case_R_no_corr = (list(zip(alternatives, p_no_correlation, [False]*3))
+                      + list(zip(alternatives, reversed(p_no_correlation),
+                                 [True]*3)))
+
+    @pytest.mark.parametrize("alternative, p_expected, rev", case_R_no_corr)
+    def test_against_R_no_correlation(self, alternative, p_expected, rev, xp):
+        x, y = [1, 2, 3, 4, 5], [1, 5, 4, 2, 3]
+        stat_expected = 0.
+        self.exact_test(x, y, alternative, rev, stat_expected, p_expected, xp=xp)
+
+    case_no_cor_b = (list(zip(alternatives, p_no_correlationb, [False]*3))
+                     + list(zip(alternatives, reversed(p_no_correlationb),
+                                [True]*3)))
+
+    @pytest.mark.parametrize("alternative, p_expected, rev", case_no_cor_b)
+    def test_against_R_no_correlationb(self, alternative, p_expected, rev, xp):
+        x, y = [1, 2, 3, 4, 5, 6, 7, 8], [8, 6, 1, 3, 2, 5, 4, 7]
+        stat_expected = 0.
+        self.exact_test(x, y, alternative, rev, stat_expected, p_expected, xp=xp)
+
+    case_R_lt_171 = (list(zip(alternatives, p_n_lt_171, [False]*3))
+                     + list(zip(alternatives, reversed(p_n_lt_171), [True]*3)))
+
+    @pytest.mark.parametrize("alternative, p_expected, rev", case_R_lt_171)
+    def test_against_R_lt_171(self, alternative, p_expected, rev, xp):
+        # Data from Hollander & Wolfe (1973), p. 187f.
+        # Used from https://rdrr.io/r/stats/cor.test.html
+        x = [44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1]
+        y = [2.6, 3.1, 2.5, 5.0, 3.6, 4.0, 5.2, 2.8, 3.8]
+        stat_expected = 0.4444444444444445
+        self.exact_test(x, y, alternative, rev, stat_expected, p_expected, xp=xp)
+
+    case_R_lt_171b = (list(zip(alternatives, p_n_lt_171b, [False]*3))
+                      + list(zip(alternatives, reversed(p_n_lt_171b),
+                                 [True]*3)))
+
+    @pytest.mark.parametrize("alternative, p_expected, rev", case_R_lt_171b)
+    def test_against_R_lt_171b(self, alternative, p_expected, rev, xp):
+        rng = np.random.RandomState(0)
+        x = rng.rand(100).tolist()
+        y = rng.rand(100).tolist()
+        stat_expected = -0.04686868686868687
+        self.exact_test(x, y, alternative, rev, stat_expected, p_expected, xp=xp)
+
+    case_R_lt_171c = (list(zip(alternatives, p_n_lt_171c, [False]*3))
+                      + list(zip(alternatives, reversed(p_n_lt_171c),
+                                 [True]*3)))
+
+    @pytest.mark.parametrize("alternative, p_expected, rev", case_R_lt_171c)
+    def test_against_R_lt_171c(self, alternative, p_expected, rev, xp):
+        rng = np.random.RandomState(0)
+        x = rng.rand(170).tolist()
+        y = rng.rand(170).tolist()
+        stat_expected = 0.1115906717716673
+        self.exact_test(x, y, alternative, rev, stat_expected, p_expected, xp=xp)
+
+    case_gt_171 = (list(zip(alternatives, [False]*3)) +
+                   list(zip(alternatives, [True]*3)))
+
+    @pytest.mark.parametrize("alternative, rev", case_gt_171)
+    def test_gt_171(self, alternative, rev, xp):
+        rng = np.random.RandomState(0)
+        x = xp.asarray(rng.rand(400).tolist())
+        y = xp.asarray(rng.rand(400).tolist())
+        res0 = stats.kendalltau(x, y, method='exact', alternative=alternative)
+        res1 = stats.kendalltau(x, y, method='asymptotic', alternative=alternative)
+        xp_assert_equal(res0[0], res1[0])
+        xp_assert_close(res0[1], res1[1], rtol=1e-3)
+
+    @pytest.mark.parametrize("method", ('exact', 'asymptotic'))
+    @pytest.mark.parametrize("alternative", ('two-sided', 'less', 'greater'))
+    def test_nan_policy(self, method, alternative, xp):
+        # Test nan policies
+        x1 = [1, 2, 3, 4, 5]
+        x2 = [5, 6, 7, 8, 9]
+        x1nan = x1 + [np.nan]
+        x2nan = x2 + [np.nan]
+        x1, x2 = xp.asarray(x1), xp.asarray(x2)
+        x1nan, x2nan = xp.asarray(x1nan), xp.asarray(x2nan)
+
+        # test nan_policy="propagate"
+        res_actual = stats.kendalltau(x1nan, x2nan,
+                                      method=method, alternative=alternative)
+        xp_assert_equal(res_actual.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res_actual.pvalue, xp.asarray(xp.nan))
+
+        # test nan_policy="omit"
+        res_actual = stats.kendalltau(x1nan, x2nan, nan_policy='omit',
+                                      method=method, alternative=alternative)
+        res_expected = stats.kendalltau(x1, x2, method=method,
+                                        alternative=alternative)
+        xp_assert_equal(res_actual.statistic, res_expected.statistic)
+        xp_assert_equal(res_actual.pvalue, res_expected.pvalue)
+
+        # test nan_policy="raise"
+        message = 'The input contains nan values'
+        with pytest.raises(ValueError, match=message):
+            stats.kendalltau(x1nan, x2nan, nan_policy='raise',
+                             method=method, alternative=alternative)
+
+        # test invalid nan_policy
+        message = "nan_policy must be one of..."
+        with pytest.raises(ValueError, match=message):
+            stats.kendalltau(x1nan, x2nan, nan_policy='ekki-ekki',
+                             method=method, alternative=alternative)
+
+
+class TestWeightedTau:
+    def test_weightedtau(self):
+        x = [12, 2, 1, 12, 2]
+        y = [1, 4, 7, 1, 0]
+        tau, p_value = stats.weightedtau(x, y)
+        assert_approx_equal(tau, -0.56694968153682723)
+        assert_equal(np.nan, p_value)
+        tau, p_value = stats.weightedtau(x, y, additive=False)
+        assert_approx_equal(tau, -0.62205716951801038)
+        assert_equal(np.nan, p_value)
+        # This must be exactly Kendall's tau
+        tau, p_value = stats.weightedtau(x, y, weigher=lambda x: 1)
+        assert_approx_equal(tau, -0.47140452079103173)
+        assert_equal(np.nan, p_value)
+
+        # test for namedtuple attribute results
+        res = stats.weightedtau(x, y)
+        attributes = ('correlation', 'pvalue')
+        check_named_results(res, attributes)
+        assert_equal(res.correlation, res.statistic)
+
+        # Asymmetric, ranked version
+        tau, p_value = stats.weightedtau(x, y, rank=None)
+        assert_approx_equal(tau, -0.4157652301037516)
+        assert_equal(np.nan, p_value)
+        tau, p_value = stats.weightedtau(y, x, rank=None)
+        assert_approx_equal(tau, -0.7181341329699029)
+        assert_equal(np.nan, p_value)
+        tau, p_value = stats.weightedtau(x, y, rank=None, additive=False)
+        assert_approx_equal(tau, -0.40644850966246893)
+        assert_equal(np.nan, p_value)
+        tau, p_value = stats.weightedtau(y, x, rank=None, additive=False)
+        assert_approx_equal(tau, -0.83766582937355172)
+        assert_equal(np.nan, p_value)
+        tau, p_value = stats.weightedtau(x, y, rank=False)
+        assert_approx_equal(tau, -0.51604397940261848)
+        assert_equal(np.nan, p_value)
+        # This must be exactly Kendall's tau
+        tau, p_value = stats.weightedtau(x, y, rank=True, weigher=lambda x: 1)
+        assert_approx_equal(tau, -0.47140452079103173)
+        assert_equal(np.nan, p_value)
+        tau, p_value = stats.weightedtau(y, x, rank=True, weigher=lambda x: 1)
+        assert_approx_equal(tau, -0.47140452079103173)
+        assert_equal(np.nan, p_value)
+        # Test argument conversion
+        tau, p_value = stats.weightedtau(np.asarray(x, dtype=np.float64), y)
+        assert_approx_equal(tau, -0.56694968153682723)
+        tau, p_value = stats.weightedtau(np.asarray(x, dtype=np.int16), y)
+        assert_approx_equal(tau, -0.56694968153682723)
+        tau, p_value = stats.weightedtau(np.asarray(x, dtype=np.float64),
+                                         np.asarray(y, dtype=np.float64))
+        assert_approx_equal(tau, -0.56694968153682723)
+        # All ties
+        with pytest.warns(SmallSampleWarning, match="One or more sample..."):
+            tau, p_value = stats.weightedtau([], [])
+        assert_equal(np.nan, tau)
+        assert_equal(np.nan, p_value)
+        with pytest.warns(SmallSampleWarning, match="One or more sample..."):
+            tau, p_value = stats.weightedtau([0], [0])
+        assert_equal(np.nan, tau)
+        assert_equal(np.nan, p_value)
+        # Size mismatches
+        assert_raises(ValueError, stats.weightedtau, [0, 1], [0, 1, 2])
+        assert_raises(ValueError, stats.weightedtau, [0, 1], [0, 1], [0, 1, 2])
+        # NaNs
+        x = [12, 2, 1, 12, 2]
+        y = [1, 4, 7, 1, np.nan]
+        tau, p_value = stats.weightedtau(x, y)
+        assert_approx_equal(tau, -0.56694968153682723)
+        x = [12, 2, np.nan, 12, 2]
+        tau, p_value = stats.weightedtau(x, y)
+        assert_approx_equal(tau, -0.56694968153682723)
+        # NaNs when the dtype of x and y are all np.float64
+        x = [12.0, 2.0, 1.0, 12.0, 2.0]
+        y = [1.0, 4.0, 7.0, 1.0, np.nan]
+        tau, p_value = stats.weightedtau(x, y)
+        assert_approx_equal(tau, -0.56694968153682723)
+        x = [12.0, 2.0, np.nan, 12.0, 2.0]
+        tau, p_value = stats.weightedtau(x, y)
+        assert_approx_equal(tau, -0.56694968153682723)
+        # NaNs when there are more than one NaN in x or y
+        x = [12.0, 2.0, 1.0, 12.0, 1.0]
+        y = [1.0, 4.0, 7.0, 1.0, 1.0]
+        tau, p_value = stats.weightedtau(x, y)
+        assert_approx_equal(tau, -0.6615242347139803)
+        x = [12.0, 2.0, np.nan, 12.0, np.nan]
+        tau, p_value = stats.weightedtau(x, y)
+        assert_approx_equal(tau, -0.6615242347139803)
+        y = [np.nan, 4.0, 7.0, np.nan, np.nan]
+        tau, p_value = stats.weightedtau(x, y)
+        assert_approx_equal(tau, -0.6615242347139803)
+
+
+    def test_segfault_issue_9710(self):
+        # https://github.com/scipy/scipy/issues/9710
+        # This test was created to check segfault
+        # In issue SEGFAULT only repros in optimized builds after calling
+        # the function twice
+        message = "One or more sample arguments is too small"
+        with pytest.warns(SmallSampleWarning, match=message):
+            stats.weightedtau([1], [1.0])
+            stats.weightedtau([1], [1.0])
+            # The code below also caused SEGFAULT
+            stats.weightedtau([np.nan], [52])
+
+
+    def test_weightedtau_vs_quadratic(self):
+        # Trivial quadratic implementation, all parameters mandatory
+        def wkq(x, y, rank, weigher, add):
+            tot = conc = disc = u = v = 0
+            for (i, j) in product(range(len(x)), range(len(x))):
+                w = weigher(rank[i]) + weigher(rank[j]) if add \
+                    else weigher(rank[i]) * weigher(rank[j])
+                tot += w
+                if x[i] == x[j]:
+                    u += w
+                if y[i] == y[j]:
+                    v += w
+                if x[i] < x[j] and y[i] < y[j] or x[i] > x[j] and y[i] > y[j]:
+                    conc += w
+                elif x[i] < x[j] and y[i] > y[j] or x[i] > x[j] and y[i] < y[j]:
+                    disc += w
+            return (conc - disc) / np.sqrt(tot - u) / np.sqrt(tot - v)
+
+        def weigher(x):
+            return 1. / (x + 1)
+
+        rng = np.random.default_rng(42)
+        for s in range(3,10):
+            a = []
+            # Generate rankings with ties
+            for i in range(s):
+                a += [i]*i
+            b = list(a)
+            rng.shuffle(a)
+            rng.shuffle(b)
+            # First pass: use element indices as ranks
+            rank = np.arange(len(a), dtype=np.intp)
+            for _ in range(2):
+                for add in [True, False]:
+                    expected = wkq(a, b, rank, weigher, add)
+                    actual = stats.weightedtau(a, b, rank, weigher, add).statistic
+                    assert_approx_equal(expected, actual)
+                # Second pass: use a random rank
+                rng.shuffle(rank)
+
+
+@make_xp_test_case(stats.linregress)
+class TestRegression:
+    def test_linregressBIGX(self, xp):
+        # W.II.F.  Regress BIG on X.
+        dtype = xp.float64  # needed for accurate results
+        result = stats.linregress(xp.asarray(X), xp.asarray(BIG))
+        xp_assert_close(result.intercept, xp.asarray(99999990., dtype=dtype))
+        xp_assert_close(result.rvalue, xp.asarray(1.0, dtype=dtype))
+        # The uncertainty ought to be almost zero
+        # since all points lie on a line
+        zero = xp.asarray(0.0, dtype=dtype)
+        xp_assert_close(result.stderr, zero, atol=1e-7)  # atol for JAX
+        xp_assert_close(result.intercept_stderr, zero, atol=1e-7)
+
+    def test_regressXX(self, xp):
+        # W.IV.B.  Regress X on X.
+        # The constant should be exactly 0 and the regression coefficient
+        # should be 1.  This is a perfectly valid regression and the
+        # program should not complain.
+        result = stats.linregress(xp.asarray(X.tolist()), xp.asarray(X.tolist()))
+        xp_assert_close(result.intercept, xp.asarray(0.0), atol=1e-15)
+        xp_assert_close(result.rvalue, xp.asarray(1.0))
+        # The uncertainly on regression through two points ought to be 0
+        xp_assert_close(result.stderr, xp.asarray(0.0), atol=1e-7)
+        xp_assert_close(result.intercept_stderr, xp.asarray(0.0), atol=1e-7)
+
+        # W.IV.C. Regress X on BIG and LITTLE (two predictors).  The program
+        # should tell you that this model is "singular" because BIG and
+        # LITTLE are linear combinations of each other.  Cryptic error
+        # messages are unacceptable here.  Singularity is the most
+        # fundamental regression error.
+        #
+        # Need to figure out how to handle multiple linear regression.
+        # This is not obvious
+
+    def test_regressZEROX(self, xp):
+        # W.IV.D. Regress ZERO on X.
+        # The program should inform you that ZERO has no variance or it should
+        # go ahead and compute the regression and report a correlation and
+        # total sum of squares of exactly 0.
+        result = stats.linregress(xp.asarray(X.tolist()), xp.asarray(ZERO.tolist()))
+        xp_assert_close(result.intercept, xp.asarray(0.0))
+        with eager_warns(stats.ConstantInputWarning, match="An input array...", xp=xp):
+            ref_rvalue = stats.pearsonr(xp.asarray(X.tolist()),
+                                        xp.asarray(ZERO.tolist())).statistic
+        xp_assert_close(result.rvalue, ref_rvalue)
+
+    def test_regress_simple(self, xp):
+        # Regress a line with sinusoidal noise.
+        x = xp.linspace(0, 100, 100)
+        y = 0.2 * xp.linspace(0, 100, 100) + 10
+        y = y + xp.sin(xp.linspace(0, 20, 100))
+
+        result = stats.linregress(x, y)
+        lr = LinregressResult
+        assert isinstance(result, lr)
+        xp_assert_close(result.stderr, xp.asarray(2.3957814497838803e-3))
+
+    def test_regress_alternative(self, xp):
+        # test alternative parameter
+        x = xp.linspace(0, 100, 100)
+        y = 0.2 * xp.linspace(0, 100, 100) + 10  # slope is greater than zero
+        y = y + xp.sin(xp.linspace(0, 20, 100))
+
+        with pytest.raises(ValueError, match="`alternative` must be 'less'..."):
+            stats.linregress(x, y, alternative="ekki-ekki")
+
+        res1 = stats.linregress(x, y, alternative="two-sided")
+
+        # slope is greater than zero, so "less" p-value should be large
+        res2 = stats.linregress(x, y, alternative="less")
+        xp_assert_close(res2.pvalue, 1 - (res1.pvalue / 2))
+
+        # slope is greater than zero, so "greater" p-value should be small
+        res3 = stats.linregress(x, y, alternative="greater")
+        xp_assert_close(res3.pvalue, res1.pvalue / 2)
+
+        assert res1.rvalue == res2.rvalue == res3.rvalue
+
+    @pytest.mark.parametrize("dtype", [None, 'float32', 'float64'])
+    def test_regress_against_R(self, dtype, xp):
+        # test against R `lm`
+        # options(digits=16)
+        # x <- c(151, 174, 138, 186, 128, 136, 179, 163, 152, 131)
+        # y <- c(63, 81, 56, 91, 47, 57, 76, 72, 62, 48)
+        # relation <- lm(y~x)
+        # print(summary(relation))
+        dtype = dtype if dtype is None else getattr(xp, dtype)
+        x = xp.asarray([151, 174, 138, 186, 128, 136, 179, 163, 152, 131], dtype=dtype)
+        y = xp.asarray([63, 81, 56, 91, 47, 57, 76, 72, 62, 48], dtype=dtype)
+        res = stats.linregress(x, y, alternative="two-sided")
+        # expected values from R's `lm` above
+        xp_assert_close(res.slope, xp.asarray(0.6746104491292, dtype=dtype))
+        xp_assert_close(res.intercept, xp.asarray(-38.4550870760770, dtype=dtype))
+        xp_assert_close(res.rvalue, xp.sqrt(xp.asarray(0.95478224775, dtype=dtype)))
+        xp_assert_close(res.pvalue, xp.asarray(1.16440531074e-06, dtype=dtype))
+        xp_assert_close(res.stderr, xp.asarray(0.0519051424731, dtype=dtype))
+        xp_assert_close(res.intercept_stderr, xp.asarray(8.0490133029927, dtype=dtype))
+
+    def test_linregress(self, xp):
+        # compared with multivariate ols with pinv
+        x = np.arange(11)
+        y = np.arange(5, 16)
+        y[[(1), (-2)]] -= 1
+        y[[(0), (-1)]] += 1
+
+        result = stats.linregress(xp.asarray(x), xp.asarray(y))
+
+        # This test used to use 'assert_array_almost_equal' but its
+        # formulation got confusing since LinregressResult became
+        # _lib._bunch._make_tuple_bunch instead of namedtuple
+        # (for backwards compatibility, see PR #12983)
+        def assert_ae(x, y):
+            return xp_assert_close(x, xp.asarray(y), atol=1e-14)
+        assert_ae(result.slope, 1.0)
+        assert_ae(result.intercept, 5.0)
+        assert_ae(result.rvalue, 0.98229948625750)
+        assert_ae(result.pvalue, 7.45259691e-008)
+        assert_ae(result.stderr, 0.063564172616372733)
+        assert_ae(result.intercept_stderr, 0.37605071654517686)
+
+    def test_regress_simple_negative_cor(self, xp):
+        # If the slope of the regression is negative the factor R tend
+        # to -1 not 1.  Sometimes rounding errors makes it < -1
+        # leading to stderr being NaN.
+        a, n = 1e-71, 100000
+        dtype = xp.float64
+        x = xp.linspace(a, 2 * a, n, dtype=dtype)  # doesn't pass with float32
+        y = xp.linspace(2 * a, a, n, dtype=dtype)
+        result = stats.linregress(x, y)
+
+        # Make sure propagated numerical errors
+        # did not bring rvalue below -1 (or were coerced)
+        assert result.rvalue >= -1
+        xp_assert_close(result.rvalue, xp.asarray(-1., dtype=dtype))
+
+        # slope and intercept stderror should stay numeric
+        assert not xp.isnan(result.stderr)
+        assert not xp.isnan(result.intercept_stderr)
+
+    def test_linregress_result_attributes(self, xp):
+        x = xp.linspace(0, 100, 100)
+        y = 0.2 * xp.linspace(0, 100, 100) + 10
+        y = y + xp.sin(xp.linspace(0, 20, 100))
+        result = stats.linregress(x, y)
+
+        # Result is of a correct class
+        lr = LinregressResult
+        assert isinstance(result, lr)
+
+        # LinregressResult elements have correct names
+        attributes = ('slope', 'intercept', 'rvalue', 'pvalue', 'stderr')
+        check_named_results(result, attributes, xp=xp)
+        # Also check that the extra attribute (intercept_stderr) is present
+        assert 'intercept_stderr' in dir(result)
+
+    def test_regress_two_inputs(self, xp):
+        # Regress a simple line formed by two points.
+        x = xp.arange(2)
+        y = xp.arange(3, 5)
+        result = stats.linregress(x, y)
+
+        xp_assert_close(result.slope, xp.asarray(1.0))
+        xp_assert_close(result.intercept, xp.asarray(3.0))
+        xp_assert_close(result.rvalue, xp.asarray(1.0))
+
+        NaN = xp.asarray(xp.nan)
+        xp_assert_equal(result.pvalue, NaN)
+        xp_assert_equal(result.stderr, NaN)
+        xp_assert_equal(result.intercept_stderr, NaN)
+
+    def test_regress_two_inputs_horizontal_line(self, xp):
+        # Regress a horizontal line formed by two points.
+        x = xp.arange(2.)
+        y = xp.ones(2)
+        result = stats.linregress(x, y)
+
+        # Horizontal line
+        xp_assert_close(result.slope, xp.asarray(0.0))
+        xp_assert_close(result.intercept, xp.asarray(1.0))
+
+        NaN = xp.asarray(xp.nan)
+        xp_assert_equal(result.rvalue, NaN)
+        xp_assert_equal(result.pvalue, NaN)
+        xp_assert_equal(result.stderr, NaN)
+        xp_assert_equal(result.intercept_stderr, NaN)
+
+    def test_nist_norris(self, xp):
+        # If this causes a lint failure in the future, please note the history of
+        # requests to allow extra whitespace in table formatting (e.g. gh-12367).
+        # Also see https://github.com/scipy/scipy/wiki/Why-do-we-not-use-an-auto%E2%80%90formatter%3F  # noqa: E501
+        x = [  0.2, 337.4, 118.2, 884.6, 10.1,  226.5,
+             666.3, 996.3, 448.6, 777.0, 558.2,   0.4,
+               0.6, 775.5, 666.9, 338.0, 447.5,  11.6,
+             556.0, 228.1, 995.8, 887.6, 120.2,   0.3,
+               0.3, 556.8, 339.1, 887.2, 999.0, 779.0,
+              11.1, 118.3, 229.2, 669.1, 448.9,   0.5]
+
+        y = [  0.1, 338.8, 118.1, 888.0, 9.2,   228.1,
+             668.5, 998.5, 449.1, 778.9, 559.2,   0.3,
+               0.1, 778.1, 668.8, 339.3, 448.9,  10.8,
+             557.7, 228.3, 998.0, 888.8, 119.6,   0.3,
+               0.6, 557.6, 339.3, 888.0, 998.5, 778.9,
+              10.2, 117.6, 228.9, 668.4, 449.2,   0.2]
+
+        result = stats.linregress(xp.asarray(x), xp.asarray(y))
+
+        xp_assert_close(result.slope, xp.asarray(1.00211681802045))
+        xp_assert_close(result.intercept, xp.asarray(-0.262323073774029))
+        xp_assert_close(result.rvalue**2, xp.asarray(0.999993745883712))
+        xp_assert_close(result.pvalue, xp.asarray(0.0), atol=1e-80)
+        if result.pvalue.dtype == xp.float64:  # these are inaccurate with float32
+            xp_assert_close(result.stderr, xp.asarray(0.00042979684820))
+            xp_assert_close(result.intercept_stderr, xp.asarray(0.23281823430153))
+
+    def test_compare_to_polyfit(self, xp):
+        x = np.linspace(0, 100, 100)
+        y = 0.2 * np.linspace(0, 100, 100) + 10
+        y += np.sin(np.linspace(0, 20, 100))
+        result = stats.linregress(xp.asarray(x), xp.asarray(y))
+        poly = np.polyfit(x, y, 1)  # Fit 1st degree polynomial
+
+        # Make sure linear regression slope and intercept
+        # match with results from numpy polyfit
+        xp_assert_close(result.slope, xp.asarray(poly[0]))
+        xp_assert_close(result.intercept, xp.asarray(poly[1]))
+
+    def test_linregress_two_points_nan_inference(self, xp):
+        # Test for gh-24684
+        x = xp.asarray([0., 1.])
+        y = xp.asarray([0., 1.])
+
+        res = stats.linregress(x, y)
+
+        NaN = xp.asarray(xp.nan)
+        xp_assert_equal(res.pvalue, NaN)
+        xp_assert_equal(res.stderr, NaN)
+        xp_assert_equal(res.intercept_stderr, NaN)
+
+        # Point estimates should still be correct
+        xp_assert_close(res.slope, xp.asarray(1.0))
+        xp_assert_close(res.intercept, xp.asarray(0.0))
+
+    def test_empty_input(self, xp):
+        with eager_warns(SmallSampleWarning, match="One or more sample...", xp=xp):
+            res = stats.linregress(xp.asarray([]), xp.asarray([]))
+            NaN = xp.asarray(xp.nan)
+            xp_assert_equal(res.slope, NaN)
+            xp_assert_equal(res.intercept, NaN)
+            xp_assert_equal(res.rvalue, NaN)
+            xp_assert_equal(res.pvalue, NaN)
+            xp_assert_equal(res.stderr, NaN)
+            xp_assert_equal(res.intercept_stderr, NaN)
+
+    def test_nan_input(self, xp):
+        x = np.arange(10.)
+        x[9] = np.nan
+        x = xp.asarray(x.tolist())
+
+        with np.errstate(invalid="ignore"):
+            result = stats.linregress(x, x)
+
+        # Make sure the result still comes back as `LinregressResult`
+        lr = LinregressResult
+        assert isinstance(result, lr)
+        NaN = xp.asarray(xp.nan)
+        xp_assert_equal(result.slope, NaN)
+        xp_assert_equal(result.intercept, NaN)
+        xp_assert_equal(result.rvalue, NaN)
+        xp_assert_equal(result.pvalue, NaN)
+        xp_assert_equal(result.stderr, NaN)
+        xp_assert_equal(result.intercept_stderr, NaN)
+
+    def test_identical_x(self, xp):
+        rng = np.random.default_rng(7872425088)
+        x = xp.zeros(10)
+        y = xp.asarray(rng.random(10).tolist())
+        with np.errstate(invalid='ignore'):
+            res = stats.linregress(x, y)
+        NaN = xp.asarray(xp.nan)
+        xp_assert_equal(res.slope, NaN)
+        xp_assert_equal(res.intercept, NaN)
+        xp_assert_equal(res.rvalue, NaN)
+        xp_assert_equal(res.pvalue, NaN)
+        xp_assert_equal(res.stderr, NaN)
+        xp_assert_equal(res.intercept_stderr, NaN)
+
+
+class HistFunctionsTest:
+    def test_input_validation(self, xp):
+        histfun = getattr(stats, self.histfun)
+        a = xp.arange(20)
+        numbins = 10
+        defaultreallimits = (0, 20)
+        weights = xp.ones(20)
+
+        message = "If specified, `defaultreallimits` must be..."
+        with pytest.raises(ValueError, match=message):
+            histfun(a, numbins, 10)
+        with pytest.raises(ValueError, match=message):
+            histfun(a, numbins, (1, 2, 3))
+        with pytest.raises(ValueError, match=message):
+            histfun(a, numbins, defaultreallimits[::-1])
+
+        message = r"`a` and \(if specified\) `weights` must have real dtype."
+        with pytest.raises(ValueError, match=message):
+            histfun(xp.astype(a, xp.complex128), numbins, defaultreallimits)
+        with pytest.raises(ValueError, match=message):
+            histfun(a, numbins, defaultreallimits, xp.astype(weights, xp.complex128))
+
+        if not is_lazy_array(weights):
+            message = "All `weights` must be non-negative."
+            with pytest.raises(ValueError, match=message):
+                histfun(a, numbins, defaultreallimits, -weights)
+
+    @pytest.mark.parametrize("dtype", [None, 'float32', 'float64'])
+    @pytest.mark.parametrize("edge_points", [False, True])
+    @pytest.mark.parametrize("specify_limits", [False, True])
+    @pytest.mark.parametrize("specify_weights", [False, True])
+    def test_against_reference(self, dtype, edge_points, specify_limits,
+                               specify_weights, xp):
+        rng = np.random.default_rng(5194553734)
+        nbins = int(rng.integers(10, 15))
+
+        if edge_points and not specify_limits:
+            pytest.skip("Limits must be specified to determine edge points.")
+        if edge_points and (is_jax(xp)
+                            or (dtype != 'float64' and is_torch(xp))
+                            or (dtype != 'float64' and is_cupy(xp))):
+            pytest.skip("Some backends are having trouble with edge point arithmetic.")
+
+        a = np.astype(rng.random(100), dtype)
+
+        if specify_limits:
+            limits = np.astype(np.sort(rng.random(2)), dtype)
+            range_ = limits
+        else:
+            # copy-pasted from documentation to generate reference values
+            s = (1 / 2)*(a.max() - a.min()) / (nbins - 1)
+            range_ = (a.min() - s, a.max() + s)
+            limits = None
+
+        if edge_points:
+            _, edges = np.histogram(a, bins=nbins, range=range_, density=False)
+            a2 = np.repeat(edges, rng.integers(5, size=edges.shape).tolist())
+            a = np.concat([a, a2], dtype=dtype)
+
+        weights = np.astype(rng.random(a.shape), dtype) if specify_weights else None
+
+        bin_counts, ref_bins = np.histogram(a, bins=nbins, range=range_, density=False)
+        ref_hist, _ = np.histogram(a, bins=nbins, range=range_,
+                                   weights=weights, density=False)
+
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        histfun = getattr(stats, self.histfun)
+        weights = weights if weights is None else xp.asarray(weights, dtype=dtype)
+        res = histfun(xp.asarray(a, dtype=dtype), nbins, limits, weights)
+        ref_hist = xp.asarray(ref_hist, dtype=dtype)
+
+        rtol = 1e-12 if dtype == xp.float64 else 2e-5
+        if histfun == stats.cumfreq:
+            ref_cumcount = xp.cumulative_sum(ref_hist)
+            xp_assert_close(res.cumcount, ref_cumcount, rtol=rtol)
+        else:
+            den = a.shape[0] if weights is None else xp.sum(weights)
+            ref_frequency = xp.asarray(ref_hist/den, dtype=dtype)
+            xp_assert_close(res.frequency, ref_frequency, rtol=rtol)
+        xp_assert_close(res.lowerlimit, xp.asarray(ref_bins[0], dtype=dtype))
+        xp_assert_close(res.binsize, xp.asarray(ref_bins[1] - ref_bins[0], dtype=dtype))
+        xp_assert_close(res.extrapoints, xp.asarray(a.size - sum(bin_counts.tolist())))
+
+
+@make_xp_test_case(stats.cumfreq)
+class TestCumfreq(HistFunctionsTest):
+    histfun = 'cumfreq'
+
+    def test_cumfreq(self, xp):
+        x = xp.asarray([1, 4, 2, 1, 3, 1])
+        cumfreqs, lowlim, binsize, extrapoints = stats.cumfreq(x, numbins=4)
+        xp_assert_equal(cumfreqs, xp.asarray([3., 4., 5., 6.]))
+        cumfreqs, lowlim, binsize, extrapoints = stats.cumfreq(
+            x, numbins=4, defaultreallimits=(1.5, 5))
+        assert extrapoints == 3
+
+        # test for namedtuple attribute results
+        attributes = ('cumcount', 'lowerlimit', 'binsize', 'extrapoints')
+        res = stats.cumfreq(x, numbins=4, defaultreallimits=(1.5, 5))
+        check_named_results(res, attributes, xp=xp)
+
+
+@make_xp_test_case(stats.relfreq)
+class TestRelfreq(HistFunctionsTest):
+    histfun = 'relfreq'
+
+    def test_relfreq(self, xp):
+        a = xp.asarray([1, 4, 2, 1, 3, 1])
+        relfreqs, lowlim, binsize, extrapoints = stats.relfreq(a, numbins=4)
+        xp_assert_close(relfreqs, xp.asarray([0.5, 0.16666667, 0.16666667, 0.16666667]))
+
+        # test for namedtuple attribute results
+        attributes = ('frequency', 'lowerlimit', 'binsize', 'extrapoints')
+        res = stats.relfreq(a, numbins=4)
+        check_named_results(res, attributes, xp=xp)
+
+    def test_relfreq_array_like(self):  # array_like is np only
+        # check array_like input is accepted
+        a = [1, 4, 2, 1, 3, 1]
+        res, _, _, _ = stats.relfreq(a, numbins=4)
+        ref, _, _, _ = stats.relfreq(np.asarray(a), numbins=4)
+        xp_assert_equal(res, ref)
+
+
+class TestScoreatpercentile:
+    def setup_method(self):
+        self.a1 = [3, 4, 5, 10, -3, -5, 6]
+        self.a2 = [3, -6, -2, 8, 7, 4, 2, 1]
+        self.a3 = [3., 4, 5, 10, -3, -5, -6, 7.0]
+
+    def test_basic(self):
+        x = arange(8) * 0.5
+        assert_equal(stats.scoreatpercentile(x, 0), 0.)
+        assert_equal(stats.scoreatpercentile(x, 100), 3.5)
+        assert_equal(stats.scoreatpercentile(x, 50), 1.75)
+
+    def test_fraction(self):
+        scoreatperc = stats.scoreatpercentile
+
+        # Test defaults
+        assert_equal(scoreatperc(list(range(10)), 50), 4.5)
+        assert_equal(scoreatperc(list(range(10)), 50, (2,7)), 4.5)
+        assert_equal(scoreatperc(list(range(100)), 50, limit=(1, 8)), 4.5)
+        assert_equal(scoreatperc(np.array([1, 10,100]), 50, (10,100)), 55)
+        assert_equal(scoreatperc(np.array([1, 10,100]), 50, (1,10)), 5.5)
+
+        # explicitly specify interpolation_method 'fraction' (the default)
+        assert_equal(scoreatperc(list(range(10)), 50, interpolation_method='fraction'),
+                     4.5)
+        assert_equal(scoreatperc(list(range(10)), 50, limit=(2, 7),
+                                 interpolation_method='fraction'),
+                     4.5)
+        assert_equal(scoreatperc(list(range(100)), 50, limit=(1, 8),
+                                 interpolation_method='fraction'),
+                     4.5)
+        assert_equal(scoreatperc(np.array([1, 10,100]), 50, (10, 100),
+                                 interpolation_method='fraction'),
+                     55)
+        assert_equal(scoreatperc(np.array([1, 10,100]), 50, (1,10),
+                                 interpolation_method='fraction'),
+                     5.5)
+
+    def test_lower_higher(self):
+        scoreatperc = stats.scoreatpercentile
+
+        # interpolation_method 'lower'/'higher'
+        assert_equal(scoreatperc(list(range(10)), 50,
+                                 interpolation_method='lower'), 4)
+        assert_equal(scoreatperc(list(range(10)), 50,
+                                 interpolation_method='higher'), 5)
+        assert_equal(scoreatperc(list(range(10)), 50, (2,7),
+                                 interpolation_method='lower'), 4)
+        assert_equal(scoreatperc(list(range(10)), 50, limit=(2,7),
+                                 interpolation_method='higher'), 5)
+        assert_equal(scoreatperc(list(range(100)), 50, (1,8),
+                                 interpolation_method='lower'), 4)
+        assert_equal(scoreatperc(list(range(100)), 50, (1,8),
+                                 interpolation_method='higher'), 5)
+        assert_equal(scoreatperc(np.array([1, 10, 100]), 50, (10, 100),
+                                 interpolation_method='lower'), 10)
+        assert_equal(scoreatperc(np.array([1, 10, 100]), 50, limit=(10, 100),
+                                 interpolation_method='higher'), 100)
+        assert_equal(scoreatperc(np.array([1, 10, 100]), 50, (1, 10),
+                                 interpolation_method='lower'), 1)
+        assert_equal(scoreatperc(np.array([1, 10, 100]), 50, limit=(1, 10),
+                                 interpolation_method='higher'), 10)
+
+    def test_sequence_per(self):
+        x = arange(8) * 0.5
+        expected = np.array([0, 3.5, 1.75])
+        res = stats.scoreatpercentile(x, [0, 100, 50])
+        assert_allclose(res, expected)
+        assert_(isinstance(res, np.ndarray))
+        # Test with ndarray.  Regression test for gh-2861
+        assert_allclose(stats.scoreatpercentile(x, np.array([0, 100, 50])),
+                        expected)
+        # Also test combination of 2-D array, axis not None and array-like per
+        res2 = stats.scoreatpercentile(np.arange(12).reshape((3,4)),
+                                       np.array([0, 1, 100, 100]), axis=1)
+        expected2 = array([[0, 4, 8],
+                           [0.03, 4.03, 8.03],
+                           [3, 7, 11],
+                           [3, 7, 11]])
+        assert_allclose(res2, expected2)
+
+    def test_axis(self):
+        scoreatperc = stats.scoreatpercentile
+        x = arange(12).reshape(3, 4)
+
+        assert_equal(scoreatperc(x, (25, 50, 100)), [2.75, 5.5, 11.0])
+
+        r0 = [[2, 3, 4, 5], [4, 5, 6, 7], [8, 9, 10, 11]]
+        assert_equal(scoreatperc(x, (25, 50, 100), axis=0), r0)
+
+        r1 = [[0.75, 4.75, 8.75], [1.5, 5.5, 9.5], [3, 7, 11]]
+        assert_equal(scoreatperc(x, (25, 50, 100), axis=1), r1)
+
+        x = array([[1, 1, 1],
+                   [1, 1, 1],
+                   [4, 4, 3],
+                   [1, 1, 1],
+                   [1, 1, 1]])
+        score = stats.scoreatpercentile(x, 50)
+        assert_equal(score.shape, ())
+        assert_equal(score, 1.0)
+        score = stats.scoreatpercentile(x, 50, axis=0)
+        assert_equal(score.shape, (3,))
+        assert_equal(score, [1, 1, 1])
+
+    def test_exception(self):
+        assert_raises(ValueError, stats.scoreatpercentile, [1, 2], 56,
+                      interpolation_method='foobar')
+        assert_raises(ValueError, stats.scoreatpercentile, [1], 101)
+        assert_raises(ValueError, stats.scoreatpercentile, [1], -1)
+
+    def test_empty(self):
+        assert_equal(stats.scoreatpercentile([], 50), np.nan)
+        assert_equal(stats.scoreatpercentile(np.array([[], []]), 50), np.nan)
+        assert_equal(stats.scoreatpercentile([], [50, 99]), [np.nan, np.nan])
+
+
+@make_xp_test_case(stats.mode)
+class TestMode:
+
+    def test_empty(self, xp):
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            vals, counts = stats.mode(xp.asarray([]))
+        xp_assert_equal(vals, xp.asarray(xp.nan))
+        xp_assert_equal(counts, xp.asarray(0.))
+
+    def test_scalar(self):
+        vals, counts = stats.mode(4.)
+        assert_equal(vals, np.array([4.]))
+        assert_equal(counts, np.array([1]))
+
+    def test_basic(self, xp):
+        data1 = xp.asarray([3, 5, 1, 10, 23, 3, 2, 6, 8, 6, 10, 6])
+        vals = stats.mode(data1)
+        xp_assert_equal(vals[0], xp.asarray(6))
+        xp_assert_equal(vals[1], xp.asarray(3))
+
+    def test_axes_keepdims(self, xp):
+        data1 = [10, 10, 30, 40]
+        data2 = [10, 10, 10, 10]
+        data3 = [20, 10, 20, 20]
+        data4 = [30, 30, 30, 30]
+        data5 = [40, 30, 30, 30]
+        arr = xp.asarray([data1, data2, data3, data4, data5])
+
+        vals = stats.mode(arr, axis=None, keepdims=True)
+        xp_assert_equal(vals[0], xp.asarray([[30]]))
+        xp_assert_equal(vals[1], xp.asarray([[8]]))
+
+        vals = stats.mode(arr, axis=0, keepdims=True)
+        xp_assert_equal(vals[0], xp.asarray([[10, 10, 30, 30]]))
+        xp_assert_equal(vals[1], xp.asarray([[2, 3, 3, 2]]))
+
+        vals = stats.mode(arr, axis=1, keepdims=True)
+        xp_assert_equal(vals[0], xp.asarray([[10], [10], [20], [30], [30]]))
+        xp_assert_equal(vals[1], xp.asarray([[2], [4], [3], [4], [3]]))
+
+    def test_axes(self, xp):
+        data1 = [10, 10, 30, 40]
+        data2 = [10, 10, 10, 10]
+        data3 = [20, 10, 20, 20]
+        data4 = [30, 30, 30, 30]
+        data5 = [40, 30, 30, 30]
+        arr = xp.asarray([data1, data2, data3, data4, data5])
+
+        vals = stats.mode(arr, axis=None)
+        xp_assert_equal(vals[0], xp.asarray(30))
+        xp_assert_equal(vals[1], xp.asarray(8))
+
+        vals = stats.mode(arr, axis=0)
+        xp_assert_equal(vals[0], xp.asarray([10, 10, 30, 30]))
+        xp_assert_equal(vals[1], xp.asarray([2, 3, 3, 2]))
+
+        vals = stats.mode(arr, axis=1)
+        xp_assert_equal(vals[0], xp.asarray([10, 10, 20, 30, 30]))
+        xp_assert_equal(vals[1], xp.asarray([2, 4, 3, 4, 3]))
+
+    @pytest.mark.parametrize('axis', range(-4, 0))
+    def test_negative_axes_gh_15375(self, axis, xp):
+        rng = np.random.default_rng(7090348401)
+        a = xp.asarray(rng.random((10, 11, 12, 13)))
+        res0 = stats.mode(a, axis=a.ndim+axis)
+        res1 = stats.mode(a, axis=axis)
+        xp_assert_equal(res0.mode, res1.mode)
+        xp_assert_equal(res0.count, res1.count)
+
+    def test_mode_result_attributes(self, xp):
+        data1 = xp.asarray([3, 5, 1, 10, 23, 3, 2, 6, 8, 6, 10, 6])
+        data2 = xp.asarray([])
+        actual = stats.mode(data1)
+        attributes = ('mode', 'count')
+        check_named_results(actual, attributes, xp=xp)
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            actual2 = stats.mode(data2)
+        check_named_results(actual2, attributes, xp=xp)
+
+    def test_nan_propagate(self, xp):
+        data1 = xp.asarray([3, np.nan, 5, 1, 10, 23, 3, 2, 6, 8, 6, 10, 6])
+        actual = stats.mode(data1)
+        xp_assert_equal(actual[0], xp.asarray(6, dtype=data1.dtype))
+        xp_assert_equal(actual[1], xp.asarray(3))
+
+    @skip_xp_backends(eager_only=True, reason="lazy arrays don't do 'raise'.")
+    def test_nan_omit(self, xp):
+        data1 = xp.asarray([3, np.nan, 5, 1, 10, 23, 3, 2, 6, 8, 6, 10, 6])
+        res = stats.mode(data1, nan_policy='omit')
+        xp_assert_equal(res.mode, xp.asarray(6.))
+        xp_assert_equal(res.count, xp.asarray(3))
+        assert_raises(ValueError, stats.mode, data1, nan_policy='raise')
+        assert_raises(ValueError, stats.mode, data1, nan_policy='foobar')
+
+    @skip_xp_backends(eager_only=True, reason="lazy arrays don't do 'omit'.")
+    @pytest.mark.parametrize("data", [
+        [3, 5, 1, 1, 3.],
+        [3, np.nan, 5, 1, 1, 3],
+        [3, 5, 1.],
+        [3, np.nan, 5, 1],
+    ])
+    @pytest.mark.parametrize('keepdims', [False, True])
+    def test_smallest_equal(self, data, keepdims, xp):
+        result = stats.mode(xp.asarray(data), nan_policy='omit', keepdims=keepdims)
+        if keepdims:
+            xp_assert_equal(result[0][0], xp.asarray(1.))
+        else:
+            xp_assert_equal(result[0], xp.asarray(1.))
+
+    @pytest.mark.parametrize('axis', range(-3, 3))
+    def test_mode_shape_gh_9955(self, axis, xp):
+        rng = np.random.default_rng(984213899)
+        a = xp.asarray(rng.uniform(size=(3, 4, 5)))
+        res = stats.mode(a, axis=axis, keepdims=False)
+        reference_shape = list(a.shape)
+        reference_shape.pop(axis)
+        np.testing.assert_array_equal(res.mode.shape, reference_shape)
+        np.testing.assert_array_equal(res.count.shape, reference_shape)
+
+    def test_nan_policy_propagate_gh_9815(self, xp):
+        # mode should treat np.nan as it would any other object when
+        # nan_policy='propagate'
+        a = xp.asarray([2, np.nan, 1, np.nan])
+        res = stats.mode(a)
+        assert xp.isnan(res.mode) and res.count == 2
+
+    def test_keepdims_empty(self, xp):
+        # test empty arrays
+        a = xp.zeros((1, 2, 3, 0))
+
+        res = stats.mode(a, axis=1, keepdims=False)
+        assert res.mode.shape == res.count.shape == (1, 3, 0)
+
+        res = stats.mode(a, axis=1, keepdims=True)
+        assert res.mode.shape == res.count.shape == (1, 1, 3, 0)
+
+    def test_keepdims_nonempty(selfself, xp):
+        # test nan_policy='propagate'
+        a = xp.asarray([[1, 3, 3, np.nan], [1, 1, np.nan, 1]])
+
+        res = stats.mode(a, axis=1, keepdims=False)
+        xp_assert_equal(res.mode, xp.asarray([3., 1.]))
+        xp_assert_equal(res.count, xp.asarray([2, 3]))
+
+        res = stats.mode(a, axis=1, keepdims=True)
+        xp_assert_equal(res.mode, xp.asarray([[3.], [1.]]))
+        xp_assert_equal(res.count, xp.asarray([[2], [3]]))
+
+        a = xp.asarray(a)
+        res = stats.mode(a, axis=None, keepdims=False)
+        ref = stats.mode(xp_ravel(a), keepdims=False)
+        xp_assert_equal(res.mode, ref.mode)
+        xp_assert_equal(res.count, ref.count)
+        assert res.mode.shape == ref.mode.shape == ()
+
+        res = stats.mode(a, axis=None, keepdims=True)
+        ref = stats.mode(xp_ravel(a), keepdims=True)
+        xp_assert_equal(xp_ravel(res.mode), xp_ravel(ref.mode))
+        assert res.mode.shape == (1, 1)
+        xp_assert_equal(xp_ravel(res.count), xp_ravel(ref.count))
+        assert res.count.shape == (1, 1)
+
+    def test_keepdims_nan_omit(self):
+        # test nan_policy='omit'
+        a = [[1, np.nan, np.nan, np.nan, 1],
+             [np.nan, np.nan, np.nan, np.nan, 2],
+             [1, 2, np.nan, 5, 5]]
+
+        res = stats.mode(a, axis=1, keepdims=False, nan_policy='omit')
+        assert_array_equal(res.mode, [1, 2, 5])
+        assert_array_equal(res.count, [2, 1, 2])
+
+        res = stats.mode(a, axis=1, keepdims=True, nan_policy='omit')
+        assert_array_equal(res.mode, [[1], [2], [5]])
+        assert_array_equal(res.count, [[2], [1], [2]])
+
+        a = np.array(a)
+        res = stats.mode(a, axis=None, keepdims=False, nan_policy='omit')
+        ref = stats.mode(a.ravel(), keepdims=False, nan_policy='omit')
+        assert_array_equal(res, ref)
+        assert res.mode.shape == ref.mode.shape == ()
+
+        res = stats.mode(a, axis=None, keepdims=True, nan_policy='omit')
+        ref = stats.mode(a.ravel(), keepdims=True, nan_policy='omit')
+        assert_equal(res.mode.ravel(), ref.mode.ravel())
+        assert res.mode.shape == (1, 1)
+        assert_equal(res.count.ravel(), ref.count.ravel())
+        assert res.count.shape == (1, 1)
+
+    @pytest.mark.parametrize("nan_policy", ['propagate', 'omit'])
+    def test_gh16955(self, nan_policy):
+        # Check that bug reported in gh-16955 is resolved
+        shape = (4, 3)
+        data = np.ones(shape)
+        data[0, 0] = np.nan
+        res = stats.mode(a=data, axis=1, keepdims=False, nan_policy=nan_policy)
+        assert_array_equal(res.mode, [1, 1, 1, 1])
+        assert_array_equal(res.count, [2, 3, 3, 3])
+
+        # Test with input from gh-16595. Support for non-numeric input
+        # was deprecated, so check for the appropriate error.
+        my_dtype = np.dtype([('asdf', np.uint8), ('qwer', np.float64, (3,))])
+        test = np.zeros(10, dtype=my_dtype)
+        message = "Argument `a` is not....|An argument has dtype...|The DType..."
+        with pytest.raises(TypeError, match=message):
+            stats.mode(test, nan_policy=nan_policy)
+
+    def test_gh9955(self):
+        # The behavior of mode with empty slices (whether the input was empty
+        # or all elements were omitted) was inconsistent. Test that this is
+        # resolved: the mode of an empty slice is NaN and the count is zero.
+        with pytest.warns(SmallSampleWarning, match=too_small_1d_not_omit):
+            res = stats.mode([])
+        ref = (np.nan, 0)
+        assert_equal(res, ref)
+
+        with pytest.warns(SmallSampleWarning, match=too_small_1d_omit):
+            res = stats.mode([np.nan], nan_policy='omit')
+        assert_equal(res, ref)
+
+        a = [[10., 20., 20.], [np.nan, np.nan, np.nan]]
+        with pytest.warns(SmallSampleWarning, match=too_small_nd_omit):
+            res = stats.mode(a, axis=1, nan_policy='omit')
+        ref = ([20, np.nan], [2, 0])
+        assert_equal(res, ref)
+
+        res = stats.mode(a, axis=1, nan_policy='propagate')
+        ref = ([20, np.nan], [2, 3])
+        assert_equal(res, ref)
+
+        z = np.array([[], []])
+        with pytest.warns(SmallSampleWarning, match=too_small_nd_not_omit):
+            res = stats.mode(z, axis=1)
+        ref = ([np.nan, np.nan], [0, 0])
+        assert_equal(res, ref)
+
+    @pytest.mark.filterwarnings('ignore::RuntimeWarning')  # np.mean warns
+    @pytest.mark.parametrize('z', [np.empty((0, 1, 2)), np.empty((1, 1, 2))])
+    def test_gh17214(self, z, xp):
+        z = xp.asarray(z)
+        if z.size == 0:
+            with pytest.warns(SmallSampleWarning, match=too_small_1d_not_omit):
+                res = stats.mode(z, axis=None, keepdims=True)
+        else:
+            res = stats.mode(z, axis=None, keepdims=True)
+        ref = xp.mean(z, axis=None, keepdims=True)
+        assert res[0].shape == res[1].shape == ref.shape == (1, 1, 1)
+
+    def test_raise_non_numeric_gh18254(self):
+
+        class ArrLike:
+            def __init__(self, x):
+                self._x = x
+
+            def __array__(self, dtype=None, copy=None):
+                return self._x.astype(object)
+
+        message = ("...only boolean and numerical dtypes..." if SCIPY_ARRAY_API
+                   else "Cannot interpret...")
+
+        with pytest.raises(TypeError, match=message):
+            stats.mode(ArrLike(np.arange(3)))
+
+        message = ("...only boolean and numerical dtypes..." if SCIPY_ARRAY_API
+                   else "Argument `a` is not recognized as numeric.")
+        with pytest.raises(TypeError, match=message):
+            stats.mode(np.arange(3, dtype=object))
+
+
+@make_xp_test_case(stats.sem)
+class TestSEM:
+
+    testcase = [1., 2., 3., 4.]
+    scalar_testcase = 4.
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered in divide")
+    def test_sem_scalar(self, xp):
+        # This is not in R, so used:
+        #     sqrt(var(testcase)*3/4)/sqrt(3)
+
+        # y = stats.sem(self.shoes[0])
+        # assert_approx_equal(y,0.775177399)
+        scalar_testcase = xp.asarray(self.scalar_testcase)[()]
+        if is_numpy(xp):
+            with pytest.warns(SmallSampleWarning, match=too_small_1d_not_omit):
+                y = stats.sem(scalar_testcase)
+        else:
+            # Other array types can emit a variety of warnings.
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore", UserWarning)
+                warnings.simplefilter("ignore", RuntimeWarning)
+                y = stats.sem(scalar_testcase)
+        assert xp.isnan(y)
+
+    def test_sem(self, xp):
+        testcase = xp.asarray(self.testcase)
+        y = stats.sem(testcase)
+        xp_assert_close(y, xp.asarray(0.6454972244))
+        n = len(self.testcase)
+        xp_assert_close(stats.sem(testcase, ddof=0) * (n/(n-2))**0.5,
+                        stats.sem(testcase, ddof=2))
+
+        x = xp.arange(10.)
+        x = xp.where(x == 9, xp.nan, x)
+        xp_assert_equal(stats.sem(x), xp.asarray(xp.nan))
+
+    @skip_xp_backends(eager_only=True, reason='lazy -> reduced nan_policy capabilities')
+    def test_sem_nan_policy(self, xp):
+        x = xp.arange(10.)
+        x[9] = xp.nan
+        xp_assert_close(stats.sem(x, nan_policy='omit'), xp.asarray(0.9128709291752769))
+        message = "The input contains nan values"
+        with pytest.raises(ValueError, match=message):
+            stats.sem(x, nan_policy='raise')
+        message = "nan_policy must be one of..."
+        with pytest.raises(ValueError, match=message):
+            stats.sem(x, nan_policy='foobar')
+
+
+@make_xp_test_case(stats.zmap)
+class TestZmap:
+
+    @pytest.mark.parametrize(
+        'x, y',
+        [([1., 2., 3., 4.], [1., 2., 3., 4.]),
+         ([1., 2., 3.], [0., 1., 2., 3., 4.])]
+    )
+    def test_zmap(self, x, y, xp):
+        # For these simple cases, calculate the expected result directly
+        # by using the formula for the z-score.
+        x, y = xp.asarray(x), xp.asarray(y)
+        expected = (x - xp.mean(y)) / xp.std(y, correction=0)
+        z = stats.zmap(x, y)
+        xp_assert_close(z, expected)
+
+    def test_zmap_axis(self, xp):
+        # Test use of 'axis' keyword in zmap.
+        x = xp.asarray([[0.0, 0.0, 1.0, 1.0],
+                        [1.0, 1.0, 1.0, 2.0],
+                        [2.0, 0.0, 2.0, 0.0]])
+
+        t1 = 1.0/(2.0/3)**0.5
+        t2 = 3.**0.5/3
+        t3 = 2.**0.5
+
+        z0 = stats.zmap(x, x, axis=0)
+        z1 = stats.zmap(x, x, axis=1)
+
+        z0_expected = [[-t1, -t3/2, -t3/2, 0.0],
+                       [0.0, t3, -t3/2, t1],
+                       [t1, -t3/2, t3, -t1]]
+        z1_expected = [[-1.0, -1.0, 1.0, 1.0],
+                       [-t2, -t2, -t2, 3.**0.5],
+                       [1.0, -1.0, 1.0, -1.0]]
+        z0_expected = xp.asarray(z0_expected)
+        z1_expected = xp.asarray(z1_expected)
+
+        xp_assert_close(z0, z0_expected)
+        xp_assert_close(z1, z1_expected)
+
+    def test_zmap_ddof(self, xp):
+        # Test use of 'ddof' keyword in zmap.
+        x = xp.asarray([[0.0, 0.0, 1.0, 1.0],
+                        [0.0, 1.0, 2.0, 3.0]])
+
+        z = stats.zmap(x, x, axis=1, ddof=1)
+
+        z0_expected = xp.asarray([-0.5, -0.5, 0.5, 0.5])/(1.0/3**0.5)
+        z1_expected = xp.asarray([-1.5, -0.5, 0.5, 1.5])/(5./3)**0.5
+        xp_assert_close(z[0, :], z0_expected)
+        xp_assert_close(z[1, :], z1_expected)
+
+    @pytest.mark.parametrize('ddof', [0, 2])
+    def test_zmap_nan_policy_omit(self, ddof, xp):
+        # nans in `scores` are propagated, regardless of `nan_policy`.
+        # `nan_policy` only affects how nans in `compare` are handled.
+        scores = xp.asarray([-3, -1, 2, np.nan])
+        compare = xp.asarray([-8, -3, 2, 7, 12, np.nan])
+        z = stats.zmap(scores, compare, ddof=ddof, nan_policy='omit')
+        # exclude nans from compare, don't use isnan + mask since that messes up
+        # dask
+        ref = stats.zmap(scores, compare[:5], ddof=ddof)
+        xp_assert_close(z, ref)
+
+    @pytest.mark.parametrize('ddof', [0, 2])
+    def test_zmap_nan_policy_omit_with_axis(self, ddof, xp):
+        scores = xp.reshape(xp.arange(-5.0, 9.0), (2, -1))
+        compare = np.reshape(np.linspace(-8, 6, 24), (2, -1))
+        compare[0, 4] = np.nan
+        compare[0, 6] = np.nan
+        compare[1, 1] = np.nan
+        # convert from numpy since some libraries like dask
+        # can't handle the data-dependent shapes from the isnan masking
+        compare_0_notna = xp.asarray(compare[0, :][~np.isnan(compare[0, :])])
+        compare_1_notna = xp.asarray(compare[1, :][~np.isnan(compare[1, :])])
+        compare = xp.asarray(compare)
+
+        z = stats.zmap(scores, compare, nan_policy='omit', axis=1, ddof=ddof)
+        res0 = stats.zmap(scores[0, :], compare_0_notna,
+                          ddof=ddof)
+        res1 = stats.zmap(scores[1, :], compare_1_notna,
+                          ddof=ddof)
+        expected = xp.stack((res0, res1))
+        xp_assert_close(z, expected)
+
+    @skip_xp_backends(eager_only=True, reason="lazy arrays don't do 'raise'.")
+    def test_zmap_nan_policy_raise(self, xp):
+        scores = xp.asarray([1, 2, 3])
+        compare = xp.asarray([-8, -3, 2, 7, 12, xp.nan])
+        with pytest.raises(ValueError, match='input contains nan'):
+            stats.zmap(scores, compare, nan_policy='raise')
+
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    def test_degenerate_input(self, xp):
+        scores = xp.arange(3)
+        compare = xp.ones(3)
+        ref = xp.asarray([-xp.inf, xp.nan, xp.inf])
+        with eager_warns(RuntimeWarning, match="Precision loss occurred...", xp=xp):
+            res = stats.zmap(scores, compare)
+        xp_assert_equal(res, ref)
+
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    def test_complex_gh22404(self, xp):
+        res = stats.zmap(xp.asarray([1, 2, 3, 4]), xp.asarray([1, 1j, -1, -1j]))
+        ref = xp.asarray([1.+0.j, 2.+0.j, 3.+0.j, 4.+0.j])
+        xp_assert_close(res, ref)
+
+
+@make_xp_test_case(stats.zscore)
+class TestZscore:
+    def test_zscore(self, xp):
+        # not in R, so tested by using:
+        #    (testcase[i] - mean(testcase, axis=0)) / sqrt(var(testcase) * 3/4)
+        y = stats.zscore(xp.asarray([1, 2, 3, 4]))
+        desired = [-1.3416407864999, -0.44721359549996,
+                   0.44721359549996, 1.3416407864999]
+        xp_assert_close(y, xp.asarray(desired))
+
+    def test_zscore_axis(self, xp):
+        # Test use of 'axis' keyword in zscore.
+        x = xp.asarray([[0.0, 0.0, 1.0, 1.0],
+                        [1.0, 1.0, 1.0, 2.0],
+                        [2.0, 0.0, 2.0, 0.0]])
+
+        t1 = 1.0/(2.0/3)**0.5
+        t2 = 3**0.5/3
+        t3 = 2**0.5
+
+        z0 = stats.zscore(x, axis=0)
+        z1 = stats.zscore(x, axis=1)
+
+        z0_expected = [[-t1, -t3/2, -t3/2, 0.0],
+                       [0.0, t3, -t3/2, t1],
+                       [t1, -t3/2, t3, -t1]]
+        z1_expected = [[-1.0, -1.0, 1.0, 1.0],
+                       [-t2, -t2, -t2, 3**0.5],
+                       [1.0, -1.0, 1.0, -1.0]]
+
+        xp_assert_close(z0, xp.asarray(z0_expected))
+        xp_assert_close(z1, xp.asarray(z1_expected))
+
+    def test_zscore_ddof(self, xp):
+        # Test use of 'ddof' keyword in zscore.
+        x = xp.asarray([[0.0, 0.0, 1.0, 1.0],
+                        [0.0, 1.0, 2.0, 3.0]])
+
+        z = stats.zscore(x, axis=1, ddof=1)
+
+        z0_expected = xp.asarray([-0.5, -0.5, 0.5, 0.5])/(1.0/3**0.5)
+        z1_expected = xp.asarray([-1.5, -0.5, 0.5, 1.5])/((5./3)**0.5)
+        xp_assert_close(z[0, :], z0_expected)
+        xp_assert_close(z[1, :], z1_expected)
+
+    def test_zscore_nan_propagate(self, xp):
+        x = xp.asarray([1, 2, np.nan, 4, 5])
+        z = stats.zscore(x, nan_policy='propagate')
+        xp_assert_equal(z, xp.full(x.shape, xp.nan))
+
+    def test_zscore_nan_omit(self, xp):
+        x = xp.asarray([1, 2, xp.nan, 4, 5])
+
+        z = stats.zscore(x, nan_policy='omit')
+
+        expected = xp.asarray([-1.2649110640673518,
+                               -0.6324555320336759,
+                               xp.nan,
+                               0.6324555320336759,
+                               1.2649110640673518
+                               ])
+        xp_assert_close(z, expected)
+
+    def test_zscore_nan_omit_with_ddof(self, xp):
+        x = xp.asarray([xp.nan, 1.0, 3.0, 5.0, 7.0, 9.0])
+        z = stats.zscore(x, ddof=1, nan_policy='omit')
+        expected = xp.concat([xp.asarray([xp.nan]), stats.zscore(x[1:], ddof=1)])
+        xp_assert_close(z, expected)
+
+    @skip_xp_backends(eager_only=True, reason="lazy arrays don't do 'raise'.")
+    def test_zscore_nan_raise(self, xp):
+        x = xp.asarray([1, 2, xp.nan, 4, 5])
+        with pytest.raises(ValueError, match="The input contains nan..."):
+            stats.zscore(x, nan_policy='raise')
+
+    def test_zscore_constant_input_1d(self, xp):
+        x = xp.asarray([-0.087] * 3)
+        with eager_warns(RuntimeWarning, match="Precision loss occurred...", xp=xp):
+            z = stats.zscore(x)
+        xp_assert_equal(z, xp.full(x.shape, xp.nan))
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    def test_zscore_constant_input_2d(self, xp):
+        x = xp.asarray([[10.0, 10.0, 10.0, 10.0],
+                        [10.0, 11.0, 12.0, 13.0]])
+        with eager_warns(RuntimeWarning, match="Precision loss occurred...", xp=xp):
+            z0 = stats.zscore(x, axis=0)
+        xp_assert_close(z0, xp.asarray([[xp.nan, -1.0, -1.0, -1.0],
+                                        [xp.nan, 1.0, 1.0, 1.0]]))
+
+        with eager_warns(RuntimeWarning, match="Precision loss occurred...", xp=xp):
+            z1 = stats.zscore(x, axis=1)
+        xp_assert_equal(z1, xp.stack([xp.asarray([xp.nan, xp.nan, xp.nan, xp.nan]),
+                                      stats.zscore(x[1, :])]))
+
+        z = stats.zscore(x, axis=None)
+        xp_assert_equal(z, xp.reshape(stats.zscore(xp.reshape(x, (-1,))), x.shape))
+
+        y = xp.ones((3, 6))
+        with eager_warns(RuntimeWarning, match="Precision loss occurred...", xp=xp):
+            z = stats.zscore(y, axis=None)
+        xp_assert_equal(z, xp.full_like(y, xp.nan))
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    def test_zscore_constant_input_2d_nan_policy_omit(self, xp):
+        x = xp.asarray([[10.0, 10.0, 10.0, 10.0],
+                        [10.0, 11.0, 12.0, xp.nan],
+                        [10.0, 12.0, xp.nan, 10.0]])
+        s = (3/2)**0.5
+        s2 = 2**0.5
+
+        with eager_warns(RuntimeWarning, match="Precision loss occurred...", xp=xp):
+            z0 = stats.zscore(x, nan_policy='omit', axis=0)
+        xp_assert_close(z0, xp.asarray([[xp.nan, -s, -1.0, xp.nan],
+                                        [xp.nan, 0, 1.0, xp.nan],
+                                        [xp.nan, s, xp.nan, xp.nan]]))
+
+        with eager_warns(RuntimeWarning, match="Precision loss occurred...", xp=xp):
+            z1 = stats.zscore(x, nan_policy='omit', axis=1)
+        xp_assert_close(z1, xp.asarray([[xp.nan, xp.nan, xp.nan, xp.nan],
+                                        [-s, 0, s, xp.nan],
+                                        [-s2/2, s2, xp.nan, -s2/2]]))
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    def test_zscore_2d_all_nan_row(self, xp):
+        # A row is all nan, and we use axis=1.
+        x = xp.asarray([[np.nan, np.nan, np.nan, np.nan],
+                        [10.0, 10.0, 12.0, 12.0]])
+        z = stats.zscore(x, nan_policy='omit', axis=1)
+        xp_assert_close(z, xp.asarray([[np.nan, np.nan, np.nan, np.nan],
+                                       [-1.0, -1.0, 1.0, 1.0]]))
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    def test_zscore_2d_all_nan(self, xp):
+        # The entire 2d array is nan, and we use axis=None.
+        y = xp.full((2, 3), xp.nan)
+        z = stats.zscore(y, nan_policy='omit', axis=None)
+        xp_assert_equal(z, y)
+
+    @pytest.mark.parametrize('x', [np.array([]), np.zeros((3, 0, 5))])
+    def test_zscore_empty_input(self, x, xp):
+        x = xp.asarray(x)
+        z = stats.zscore(x)
+        xp_assert_equal(z, x)
+
+    @skip_xp_invalid_arg
+    def test_zscore_masked_element_0_gh19039(self, xp):
+        # zscore returned all NaNs when 0th element was masked. See gh-19039.
+        rng = np.random.default_rng(8675309)
+        x = rng.standard_normal(10)
+        mask = np.zeros_like(x)
+        y = np.ma.masked_array(x, mask)
+        y.mask[0] = True
+
+        ref = stats.zscore(x[1:])  # compute reference from non-masked elements
+        assert not np.any(np.isnan(ref))
+        res = stats.zscore(y)
+        assert_allclose(res[1:], ref)
+        res = stats.zscore(y, axis=None)
+        assert_allclose(res[1:], ref)
+
+        y[1:] = y[1]  # when non-masked elements are identical, result is nan
+        with pytest.warns(RuntimeWarning, match="Precision loss occurred..."):
+            res = stats.zscore(y)
+        assert_equal(res[1:], np.nan)
+        with pytest.warns(RuntimeWarning, match="Precision loss occurred..."):
+            res = stats.zscore(y, axis=None)
+        assert_equal(res[1:], np.nan)
+
+
+@make_xp_test_case(stats.gzscore)
+class TestGZscore:
+    def test_gzscore_normal_array(self, xp):
+        x = np.asarray([1, 2, 3, 4])
+        z = stats.gzscore(xp.asarray(x))
+        desired = np.log(x / stats.gmean(x)) / np.log(stats.gstd(x, ddof=0))
+        xp_assert_close(z, xp.asarray(desired, dtype=xp.asarray(1.).dtype))
+
+    @skip_xp_invalid_arg
+    def test_gzscore_masked_array(self):
+        x = np.array([1, 2, -1, 3, 4])
+        mask = [0, 0, 1, 0, 0]
+        mx = np.ma.masked_array(x, mask=mask)
+        z = stats.gzscore(mx)
+        desired = ([-1.526072095151, -0.194700599824, np.inf, 0.584101799472,
+                    1.136670895503])
+        desired = np.ma.masked_array(desired, mask=mask)
+        assert_allclose(z.compressed(), desired.compressed())
+        assert_allclose(z.mask, desired.mask)
+        assert isinstance(z, np.ma.MaskedArray)
+
+
+@make_xp_test_case(stats.median_abs_deviation)
+class TestMedianAbsDeviation:
+    def setup_method(self):
+        self.dat_nan = [2.20, 2.20, 2.4, 2.4, 2.5, 2.7, 2.8, 2.9,
+                        3.03, 3.03, 3.10, 3.37, 3.4, 3.4, 3.4, 3.5,
+                        3.6, 3.7, 3.7, 3.7, 3.7, 3.77, 5.28, np.nan]
+        self.dat = [2.20, 2.20, 2.4, 2.4, 2.5, 2.7, 2.8, 2.9, 3.03,
+                    3.03, 3.10, 3.37, 3.4, 3.4, 3.4, 3.5, 3.6, 3.7,
+                    3.7, 3.7, 3.7, 3.77, 5.28, 28.95]
+
+    def test_median_abs_deviation(self, xp):
+        xp_assert_close(stats.median_abs_deviation(xp.asarray(self.dat), axis=None),
+                        xp.asarray(0.355))
+        dat = xp.reshape(xp.asarray(self.dat), (6, 4))
+        mad = stats.median_abs_deviation(dat, axis=0)
+        mad_expected = xp.asarray([0.435, 0.5, 0.45, 0.4])
+        xp_assert_close(mad, mad_expected)
+
+    @skip_xp_backends('jax.numpy', reason='lazy -> no nan_policy="omit"')
+    def test_mad_nan_omit(self, xp):
+        mad = stats.median_abs_deviation(xp.asarray(self.dat_nan), nan_policy='omit')
+        xp_assert_close(mad, xp.asarray(0.34))
+
+    def test_axis_and_nan(self, xp):
+        x = xp.asarray([[1.0, 2.0, 3.0, 4.0, np.nan],
+                        [1.0, 4.0, 5.0, 8.0, 9.0]])
+        mad = stats.median_abs_deviation(x, axis=1)
+        xp_assert_close(mad, xp.asarray([np.nan, 3.0]))
+
+    @skip_xp_backends('jax.numpy', reason='lazy -> no nan_policy="omit"')
+    def test_nan_policy_omit_with_inf(self, xp):
+        z = xp.asarray([1, 3, 4, 6, 99, np.nan, np.inf])
+        mad = stats.median_abs_deviation(z, nan_policy='omit')
+        xp_assert_close(mad, xp.asarray(3.0))
+
+    @pytest.mark.parametrize('axis', [0, 1, 2, None])
+    def test_size_zero_with_axis(self, axis, xp):
+        x = xp.zeros((3, 0, 4))
+        context = (eager_warns(SmallSampleWarning, match='too small', xp=np)
+                   if axis in {1, None} else contextlib.nullcontext())
+        with context:
+            mad = stats.median_abs_deviation(x, axis=axis)
+        xp_assert_close(mad, xp.full_like(xp.sum(x, axis=axis), fill_value=xp.nan))
+
+    @pytest.mark.parametrize('nan_policy, expected',
+                             [('omit', [np.nan, 1.5, 1.5]),
+                              ('propagate', [np.nan, np.nan, 1.5])])
+    def test_nan_policy_with_axis(self, nan_policy, expected, xp):
+        if nan_policy=='omit' and not is_numpy(xp):
+            pytest.skip("nan_policy='omit' with n-d input only supported by NumPy")
+
+        x = xp.asarray([[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+                        [1, 5, 3, 6, np.nan, np.nan],
+                        [5, 6, 7, 9, 9, 10]])
+
+        context = (eager_warns(SmallSampleWarning, match="too small", xp=np)
+                   if nan_policy == 'omit' else contextlib.nullcontext())
+        with context:
+            mad = stats.median_abs_deviation(x, nan_policy=nan_policy, axis=1)
+            xp_assert_close(mad, xp.asarray(expected))
+
+    @pytest.mark.parametrize('axis, expected',
+                             [(1, [2.5, 2.0, 12.0]), (None, 4.5)])
+    def test_center_mean_with_nan(self, axis, expected):
+        # nan_policy='omit' with multidimensional input only supported by NumPy
+        x = np.array([[1, 2, 4, 9, np.nan],
+                      [0, 1, 1, 1, 12],
+                      [-10, -10, -10, 20, 20]])
+        mad = stats.median_abs_deviation(x, center=np.mean, nan_policy='omit',
+                                         axis=axis)
+        xp_assert_close(mad, expected, rtol=1e-15, atol=1e-15)
+
+    def test_center_not_callable(self, xp):
+        with pytest.raises(TypeError, match='callable'):
+            stats.median_abs_deviation(xp.asarray([1, 2, 3, 5]), center=99)
+
+
+@make_xp_test_case(stats.iqr)
+class TestIQR:
+
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_basic(self, dtype, xp):
+        x = np.arange(8) * 0.5
+        np.random.shuffle(x)
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        xp_assert_equal(stats.iqr(xp.asarray(x, dtype=dtype)),
+                        xp.asarray(1.75, dtype=dtype))
+
+    def test_api(self, xp):
+        d = xp.ones((5, 5))
+        stats.iqr(d)
+        stats.iqr(d, None)
+        stats.iqr(d, 1)
+        stats.iqr(d, (0, 1))
+        stats.iqr(d, None, (10, 90))
+        stats.iqr(d, None, (30, 20), 1.0)
+        stats.iqr(d, None, (25, 75), 1.5, 'propagate')
+
+    @skip_xp_backends('jax.numpy', reason='lazy -> reduced nan_policy capabilities')
+    def test_api_eager(self, xp):
+        d = xp.ones((5, 5))
+        stats.iqr(d, None, (50, 50), 'normal', 'raise', 'linear')
+        stats.iqr(d, None, (25, 75), -0.4, 'omit', 'lower', True)
+
+    @pytest.mark.parametrize('x', [[], np.arange(0)])
+    def test_empty(self, x, xp):
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            xp_assert_equal(stats.iqr(xp.asarray(x)), xp.asarray(xp.nan))
+
+    def test_constant(self, xp):
+        # Constant array always gives 0
+        x = xp.ones((7, 4))
+        zero = xp.asarray(0.0)
+        xp_assert_equal(stats.iqr(x), zero)
+        xp_assert_equal(stats.iqr(x, axis=0), xp.zeros(4))
+        xp_assert_equal(stats.iqr(x, axis=1), xp.zeros(7))
+        xp_assert_equal(stats.iqr(x, interpolation='linear'), zero)
+        xp_assert_equal(stats.iqr(x, interpolation='midpoint'), zero)
+        xp_assert_equal(stats.iqr(x, interpolation='nearest'), zero)
+        xp_assert_equal(stats.iqr(x, interpolation='lower'), zero)
+        xp_assert_equal(stats.iqr(x, interpolation='higher'), zero)
+
+        # 0 only along constant dimensions
+        # This also tests much of `axis`
+        y = xp.ones((4, 5, 6)) * xp.arange(6.)
+        xp_assert_equal(stats.iqr(y, axis=0), xp.zeros((5, 6)))
+        xp_assert_equal(stats.iqr(y, axis=1), xp.zeros((4, 6)))
+        xp_assert_equal(stats.iqr(y, axis=2), xp.full((4, 5), 2.5))
+        xp_assert_equal(stats.iqr(y, axis=(0, 1)), xp.zeros(6))
+        xp_assert_equal(stats.iqr(y, axis=(0, 2)), xp.full(5, 3.))
+        xp_assert_equal(stats.iqr(y, axis=(1, 2)), xp.full(4, 3.))
+
+    def test_scalarlike(self, xp):
+        x = xp.arange(1.) + 7.0
+        xp_assert_equal(stats.iqr(x[0]), xp.asarray(0.0))
+        xp_assert_equal(stats.iqr(x), xp.asarray(0.0))
+        xp_assert_equal(stats.iqr(x, keepdims=True), xp.asarray([0.0]))
+
+    def test_2D(self, xp):
+        x = xp.reshape(xp.arange(15), (3, 5))
+        xp_assert_equal(stats.iqr(x), xp.asarray(7.0))
+        xp_assert_equal(stats.iqr(x, axis=0), xp.full(5, 5.))
+        xp_assert_equal(stats.iqr(x, axis=1), xp.full(3, 2.))
+        xp_assert_equal(stats.iqr(x, axis=(0, 1)), xp.asarray(7.0))
+        xp_assert_equal(stats.iqr(x, axis=(1, 0)), xp.asarray(7.0))
+
+    def test_axis(self, xp):
+        # The `axis` keyword is also put through its paces in `test_keepdims`.
+        o = np.random.normal(size=(71, 23))
+        x = np.dstack([o] * 10)                 # x.shape = (71, 23, 10)
+        o, x = xp.asarray(o), xp.asarray(x)
+        q = xp.broadcast_to(stats.iqr(o), (10,))
+
+        xp_assert_equal(stats.iqr(x, axis=(0, 1)), q)
+        x = xp.moveaxis(x, -1, 0)               # x.shape = (10, 71, 23)
+        xp_assert_equal(stats.iqr(x, axis=(2, 1)), q)
+        x = xp_swapaxes(x,0, 1, xp=xp)                    # x.shape = (71, 10, 23)
+        xp_assert_equal(stats.iqr(x, axis=(0, 2)), q)
+        x = xp_swapaxes(x,0, 1, xp=xp)                    # x.shape = (10, 71, 23)
+
+        xp_assert_equal(stats.iqr(x, axis=(0, 1, 2)),
+                        stats.iqr(x, axis=None))
+        xp_assert_equal(stats.iqr(x, axis=(0,)),
+                        stats.iqr(x, axis=0))
+
+        d = np.arange(3 * 5 * 7 * 11)
+        # Older versions of numpy only shuffle along axis=0.
+        # Not sure about newer, don't care.
+        np.random.shuffle(d)
+        d = d.reshape((3, 5, 7, 11))
+        d = xp.asarray(d)
+        xp_assert_equal(stats.iqr(d, axis=(0, 1, 2))[0],
+                        stats.iqr(xp_ravel(d[:,:,:, 0])))
+        xp_assert_equal(stats.iqr(d, axis=(0, 1, 3))[1],
+                        stats.iqr(xp_ravel(d[:,:, 1,:])))
+        xp_assert_equal(stats.iqr(d, axis=(3, 1, -4))[2],
+                        stats.iqr(xp_ravel(d[:,:, 2,:])))
+        xp_assert_equal(stats.iqr(d, axis=(3, 1, 2))[2],
+                        stats.iqr(xp_ravel(d[2,:,:,:])))
+        xp_assert_equal(stats.iqr(d, axis=(3, 2))[2, 1],
+                        stats.iqr(xp_ravel(d[2, 1,:,:])))
+        xp_assert_equal(stats.iqr(d, axis=(1, -2))[2, 1],
+                        stats.iqr(xp_ravel(d[2, :, :, 1])))
+        xp_assert_equal(stats.iqr(d, axis=(1, 3))[2, 2],
+                        stats.iqr(xp_ravel(d[2, :, 2,:])))
+
+        with pytest.raises(AxisError, match='`axis` is out of bounds...'):
+            stats.iqr(d, axis=4)
+        with pytest.raises(ValueError, match='`axis` must contain only...'):
+            stats.iqr(d, axis=(0, 0))
+
+    def test_rng(self, xp):
+        x = xp.arange(5)
+        xp_assert_close(stats.iqr(x), xp.asarray(2.))
+        xp_assert_close(stats.iqr(x, rng=(25, 87.5)), xp.asarray(2.5))
+        xp_assert_close(stats.iqr(x, rng=(12.5, 75)), xp.asarray(2.5))
+        xp_assert_close(stats.iqr(x, rng=(10, 50)), xp.asarray(1.6))  # 3-1.4
+
+        message = r"Elements of `rng` must be in the range \[0, 100\]."
+        with pytest.raises(ValueError, match=message):
+            stats.iqr(x, rng=(0, 101))
+
+        message = "`rng` must not contain NaNs."
+        with pytest.raises(ValueError, match=message):
+            stats.iqr(x, rng=(np.nan, 25))
+
+        message = "`rng` must be a two element sequence."
+        with pytest.raises(TypeError, match=message):
+            stats.iqr(x, rng=(0, 50, 60))
+
+    def test_interpolation(self, xp):
+        x = xp.arange(5)
+        y = xp.arange(4)
+        # Default
+        xp_assert_equal(stats.iqr(x), xp.asarray(2.))
+        xp_assert_equal(stats.iqr(y), xp.asarray(1.5))
+        # Linear
+        xp_assert_equal(stats.iqr(x, interpolation='linear'), xp.asarray(2.))
+        xp_assert_equal(stats.iqr(y, interpolation='linear'), xp.asarray(1.5))
+        # Higher
+        xp_assert_equal(stats.iqr(x, interpolation='higher'), xp.asarray(2.))
+        xp_assert_equal(stats.iqr(x, rng=(25, 80), interpolation='higher'),
+                        xp.asarray(3.))
+        xp_assert_equal(stats.iqr(y, interpolation='higher'), xp.asarray(2.))
+        # Lower (will generally, but not always be the same as higher)
+        xp_assert_equal(stats.iqr(x, interpolation='lower'), xp.asarray(2.))
+        xp_assert_equal(stats.iqr(x, rng=(25, 80), interpolation='lower'),
+                        xp.asarray(2.))
+        xp_assert_equal(stats.iqr(y, interpolation='lower'), xp.asarray(2.))
+        # Nearest
+        xp_assert_equal(stats.iqr(x, interpolation='nearest'), xp.asarray(2.))
+        xp_assert_equal(stats.iqr(y, interpolation='nearest'), xp.asarray(1.))
+        # Midpoint
+        xp_assert_equal(stats.iqr(x, interpolation='midpoint'), xp.asarray(2.))
+        xp_assert_equal(stats.iqr(x, rng=(25, 80), interpolation='midpoint'),
+                        xp.asarray(2.5))
+        xp_assert_equal(stats.iqr(y, interpolation='midpoint'), xp.asarray(2.))
+
+        # Check all method= values new in numpy 1.22.0 are accepted
+        for method in ('inverted_cdf', 'averaged_inverted_cdf',
+                       'closest_observation', 'interpolated_inverted_cdf',
+                       'hazen', 'weibull', 'median_unbiased',
+                       'normal_unbiased'):
+            stats.iqr(y, interpolation=method)
+
+        with pytest.raises(ValueError, match='`method` must be one of...'):
+            stats.iqr(x, interpolation='foobar')
+
+    def test_keepdims(self, xp):
+        # Also tests most of `axis`
+        x = xp.ones((3, 5, 7, 11))
+        assert_equal(stats.iqr(x, axis=None, keepdims=False).shape, ())
+        assert_equal(stats.iqr(x, axis=2, keepdims=False).shape, (3, 5, 11))
+        assert_equal(stats.iqr(x, axis=(0, 1), keepdims=False).shape, (7, 11))
+        assert_equal(stats.iqr(x, axis=(0, 3), keepdims=False).shape, (5, 7))
+        assert_equal(stats.iqr(x, axis=(1,), keepdims=False).shape, (3, 7, 11))
+        assert_equal(stats.iqr(x, (0, 1, 2, 3), keepdims=False).shape, ())
+        assert_equal(stats.iqr(x, axis=(0, 1, 3), keepdims=False).shape, (7,))
+
+        assert_equal(stats.iqr(x, axis=None, keepdims=True).shape, (1, 1, 1, 1))
+        assert_equal(stats.iqr(x, axis=2, keepdims=True).shape, (3, 5, 1, 11))
+        assert_equal(stats.iqr(x, axis=(0, 1), keepdims=True).shape, (1, 1, 7, 11))
+        assert_equal(stats.iqr(x, axis=(0, 3), keepdims=True).shape, (1, 5, 7, 1))
+        assert_equal(stats.iqr(x, axis=(1,), keepdims=True).shape, (3, 1, 7, 11))
+        assert_equal(stats.iqr(x, (0, 1, 2, 3), keepdims=True).shape, (1, 1, 1, 1))
+        assert_equal(stats.iqr(x, axis=(0, 1, 3), keepdims=True).shape, (1, 1, 7, 1))
+
+    @skip_xp_backends('jax.numpy', reason='lazy -> reduced nan_policy capabilitiesy')
+    def test_nanpolicy(self, xp):
+        x = xp.reshape(xp.arange(15.0), (3, 5))
+
+        # No NaNs
+        xp_assert_equal(stats.iqr(x, nan_policy='propagate'), xp.asarray(7.))
+        xp_assert_equal(stats.iqr(x, nan_policy='omit'), xp.asarray(7.))
+        xp_assert_equal(stats.iqr(x, nan_policy='raise'), xp.asarray(7.))
+
+        # Yes NaNs
+        x = xpx.at(x)[1, 2].set(xp.nan)
+        xp_assert_equal(stats.iqr(x, nan_policy='propagate'),
+                        xp.asarray(xp.nan))
+        xp_assert_equal(stats.iqr(x, axis=0, nan_policy='propagate'),
+                        xp.asarray([5, 5, xp.nan, 5, 5]))
+        xp_assert_equal(stats.iqr(x, axis=1, nan_policy='propagate'),
+                        xp.asarray([2, xp.nan, 2]))
+
+        xp_assert_equal(stats.iqr(x, nan_policy='omit'), xp.asarray(7.5))
+
+        message = "The input contains nan values"
+        with pytest.raises(ValueError, match=message):
+            stats.iqr(x, nan_policy='raise')
+
+        # Bad policy
+        message = "nan_policy must be one of..."
+        with pytest.raises(ValueError, match=message):
+            stats.iqr(x, nan_policy='barfood')
+
+    @skip_xp_backends(np_only=True,
+        reason="nan_policy w/ multidimensional arrays only available w/ NumPy")
+    def test_nanpolicy_nd(self, xp):
+        x = xp.reshape(xp.arange(15.0), (3, 5))
+        x[1, 2] = xp.nan
+        xp_assert_equal(stats.iqr(x, axis=0, nan_policy='omit'),
+                        xp.full(5, 5.))
+        xp_assert_equal(stats.iqr(x, axis=1, nan_policy='omit'),
+                        xp.asarray([2, 2.5, 2]))
+
+        message = "The input contains nan values"
+        with pytest.raises(ValueError, match=message):
+            stats.iqr(x, axis=0, nan_policy='raise')
+        with pytest.raises(ValueError, match=message):
+            stats.iqr(x, axis=1, nan_policy='raise')
+
+    def test_scale_no_nans(self, xp):
+        x = xp.reshape(xp.arange(15.0), (3, 5))
+        xp_assert_equal(stats.iqr(x, scale=1.0), xp.asarray(7.))
+        xp_assert_close(stats.iqr(x, scale='normal'), xp.asarray(7 / 1.3489795))
+        xp_assert_equal(stats.iqr(x, scale=2.0), xp.asarray(3.5))
+
+    def test_scale_with_nans(self, xp):
+        x = xp.reshape(xp.arange(15.0), (3, 5))
+        x = xpx.at(x)[1, 2].set(xp.nan)
+        nan = xp.asarray(xp.nan)
+        xp_assert_equal(stats.iqr(x, scale=1.0, nan_policy='propagate'), nan)
+        xp_assert_equal(stats.iqr(x, scale='normal', nan_policy='propagate'), nan)
+        xp_assert_equal(stats.iqr(x, scale=2.0, nan_policy='propagate'), nan)
+
+    def test_invalid_scale(self, xp):
+        x = xp.arange(15.0)
+        message = "foobar not a valid scale for `iqr`"
+        with pytest.raises(ValueError, match=message):
+            stats.iqr(x, scale='foobar')
+
+    @skip_xp_backends("jax.numpy", reason="lazy -> reduced nan_policy capabilities")
+    def test_scale_nan_policy_omit(self, xp):
+        x = xp.reshape(xp.arange(15.0), (3, 5))
+        x = xpx.at(x)[1, 2].set(xp.nan)
+        xp_assert_equal(stats.iqr(x, scale=1.0, nan_policy='omit'), xp.asarray(7.5))
+        xp_assert_close(stats.iqr(x, scale='normal', nan_policy='omit'),
+                        xp.asarray(7.5 / 1.3489795))
+        xp_assert_equal(stats.iqr(x, scale=2.0, nan_policy='omit'), xp.asarray(3.75))
+
+    def test_scale_nanpolicy_nd(self, xp):
+        # axis=1 chosen to show behavior with both nans and without
+        x = xp.reshape(xp.arange(15.0), (3, 5))
+        x = xpx.at(x)[1, 2].set(xp.nan)
+
+        xp_assert_equal(stats.iqr(x, axis=1, scale=1.0, nan_policy='propagate'),
+                        xp.asarray([2, np.nan, 2]))
+        xp_assert_close(stats.iqr(x, axis=1, scale='normal', nan_policy='propagate'),
+                        xp.asarray([2, np.nan, 2]) / 1.3489795)
+        xp_assert_equal(stats.iqr(x, axis=1, scale=2.0, nan_policy='propagate'),
+                        xp.asarray([1, np.nan, 1]))
+
+    def test_rng_order(self, xp):
+        # test that order of `rng` doesn't matter (as documented)
+        x = xp.arange(8.) * 0.5
+        res = stats.iqr(x, rng=(75, 25))
+        ref = stats.iqr(x)
+        xp_assert_equal(res, ref)
+
+
+@make_xp_test_case(stats.moment)
+class TestMoments:
+    """
+        Comparison numbers are found using R v.1.5.1
+        note that length(testcase) = 4
+        testmathworks comes from documentation for the
+        Statistics Toolbox for Matlab and can be found at both
+        https://www.mathworks.com/help/stats/kurtosis.html
+        https://www.mathworks.com/help/stats/skewness.html
+        Note that both test cases came from here.
+    """
+    testcase = [1., 2., 3., 4.]
+    scalar_testcase = 4.
+    rng = np.random.default_rng(2285049930)
+    testcase_moment_accuracy = rng.random(42)
+
+    @pytest.mark.parametrize('size', [10, (10, 2)])
+    @pytest.mark.parametrize('m, c', list(product((0, 1, 2, 3), (None, 0, 1))))
+    def test_moment_center_scalar_moment(self, size, m, c, xp):
+        rng = np.random.default_rng(6581432544381372042)
+        x = xp.asarray(rng.random(size=size))
+        res = stats.moment(x, m, center=c)
+        c = xp.mean(x, axis=0) if c is None else c
+        ref = xp.sum((x - c)**m, axis=0)/x.shape[0]
+        xp_assert_close(res, ref, atol=1e-16)
+
+    @pytest.mark.parametrize('size', [10, (10, 2)])
+    @pytest.mark.parametrize('c', (None, 0, 1))
+    def test_moment_center_array_moment(self, size, c, xp):
+        rng = np.random.default_rng(1706828300224046506)
+        x = xp.asarray(rng.random(size=size))
+        m = [0, 1, 2, 3]
+        res = stats.moment(x, m, center=c)
+        ref = xp.concat([stats.moment(x, i, center=c)[xp.newaxis, ...] for i in m])
+        xp_assert_close(res, ref)  # JAX JIT doesn't pass with equality
+
+    def test_moment(self, xp):
+        # mean((testcase-mean(testcase))**power,axis=0),axis=0))**power))
+        testcase = xp.asarray(self.testcase)
+
+        y = stats.moment(xp.asarray(self.scalar_testcase))
+        xp_assert_close(y, xp.asarray(0.0))
+
+        y = stats.moment(testcase, 0)
+        xp_assert_close(y, xp.asarray(1.0))
+        y = stats.moment(testcase, 1)
+        xp_assert_close(y, xp.asarray(0.0))
+        y = stats.moment(testcase, 2)
+        xp_assert_close(y, xp.asarray(1.25))
+        y = stats.moment(testcase, 3)
+        xp_assert_close(y, xp.asarray(0.0))
+        y = stats.moment(testcase, 4)
+        xp_assert_close(y, xp.asarray(2.5625))
+
+        # check array_like input for moment
+        y = stats.moment(testcase, [1, 2, 3, 4])
+        xp_assert_close(y, xp.asarray([0, 1.25, 0, 2.5625]))
+
+        # check floating point integer order
+        y = stats.moment(testcase, 0.0)
+        xp_assert_close(y, xp.asarray(1.0))
+
+        y = stats.moment(testcase, [1.0, 2, 3, 4.0])
+        xp_assert_close(y, xp.asarray([0, 1.25, 0, 2.5625]))
+
+    @skip_xp_backends("jax.numpy", reason="array value-dependent error")
+    def test_order_input_validation(self, xp):
+        testcase = xp.asarray(self.testcase)
+        message = 'All elements of `order` must be integral.'
+        with pytest.raises(ValueError, match=message):
+            stats.moment(testcase, 1.2)
+
+    def test_zero_size(self, xp):
+        message = "One or more sample arguments..."
+        with eager_warns(SmallSampleWarning, match=message, xp=xp):
+            y = stats.moment(xp.asarray([]))
+            xp_assert_equal(y, xp.asarray(xp.nan))
+        with eager_warns(SmallSampleWarning, match=message, xp=xp):
+            y = stats.moment(xp.asarray([], dtype=xp.float32))
+            xp_assert_equal(y, xp.asarray(xp.nan, dtype=xp.float32))
+
+        message = "All axis-slices of one or more"
+        with eager_warns(SmallSampleWarning, match=message, xp=xp):
+            y = stats.moment(xp.asarray([[]]), axis=1)
+            xp_assert_equal(y, xp.asarray([xp.nan]))
+
+        y = stats.moment(xp.zeros((1, 0)), axis=0)
+        xp_assert_equal(y, xp.empty((0,)))
+
+        y = stats.moment(xp.asarray([[]]), order=[0, 1], axis=0)
+        xp_assert_equal(y, xp.empty((2, 0)))
+
+    def test_nan_policy(self):
+        x = np.arange(10.)
+        x[9] = np.nan
+        assert_equal(stats.moment(x, 2), np.nan)
+        assert_almost_equal(stats.moment(x, nan_policy='omit'), 0.0)
+        assert_raises(ValueError, stats.moment, x, nan_policy='raise')
+        assert_raises(ValueError, stats.moment, x, nan_policy='foobar')
+
+    @pytest.mark.parametrize('dtype', ['float32', 'float64', 'complex128'])
+    @pytest.mark.parametrize('expect, order', [(0, 1), (1, 0)])
+    def test_constant_moments(self, dtype, expect, order, xp):
+        if dtype=='complex128' and is_torch(xp):
+            message = "torch can't round complex input; data-apis/array-api-compat#407"
+            pytest.skip(message)
+        dtype = getattr(xp, dtype)
+        rng = np.random.default_rng(3824693518)
+        x = xp.asarray(rng.random(5), dtype=dtype)
+        y = stats.moment(x, order=order)
+        xp_assert_equal(y, xp.asarray(expect, dtype=dtype))
+
+        message = "Precision loss occurred in moment calculation"
+        with eager_warns(RuntimeWarning, match=message, xp=xp):
+            y = stats.moment(xp.broadcast_to(x, (6, 5)), axis=0, order=order)
+            xp_assert_equal(y, xp.full((5,), expect, dtype=dtype))
+
+        with eager_warns(RuntimeWarning, match=message, xp=xp):
+            y = stats.moment(xp.broadcast_to(x, (1, 2, 3, 4, 5)), axis=2,
+                             order=order)
+            xp_assert_equal(y, xp.full((1, 2, 4, 5), expect, dtype=dtype))
+
+        y = stats.moment(xp.broadcast_to(x, (1, 2, 3, 4, 5)), axis=None,
+                         order=order)
+        xp_assert_equal(y, xp.full((), expect, dtype=dtype))
+
+    def test_moment_propagate_nan(self, xp):
+        # Check that the shape of the result is the same for inputs
+        # with and without nans, cf gh-5817
+        a = xp.reshape(xp.arange(8.), (2, -1))
+        a = xpx.at(a)[1, 0].set(xp.nan)
+
+        mm = stats.moment(xp.asarray(a), 2, axis=1)
+        xp_assert_close(mm, xp.asarray([1.25, xp.nan]), atol=1e-15)
+
+    def test_moment_empty_order(self, xp):
+        # tests moment with empty `order` list
+        with pytest.raises(ValueError, match=r"`order` must be a scalar or a"
+                                             r" non-empty 1D array."):
+            stats.moment(xp.asarray([1, 2, 3, 4]), order=[])
+
+    def test_rename_moment_order(self, xp):
+        # Parameter 'order' was formerly known as 'moment'. The old name
+        # has not been deprecated, so it must continue to work.
+        x = xp.arange(10)
+        res = stats.moment(x, moment=3)
+        ref = stats.moment(x, order=3)
+        xp_assert_equal(res, ref)
+
+    def test_moment_accuracy(self):
+        # 'moment' must have a small enough error compared to the slower
+        #  but very accurate numpy.power() implementation.
+        tc_no_mean = (self.testcase_moment_accuracy
+                      - np.mean(self.testcase_moment_accuracy))
+        assert_allclose(np.power(tc_no_mean, 42).mean(),
+                        stats.moment(self.testcase_moment_accuracy, 42))
+
+    @pytest.mark.parametrize('order', [0, 1, 2, 3])
+    @pytest.mark.parametrize('axis', [-1, 0, 1])
+    @pytest.mark.parametrize('center', [None, 0.])
+    def test_moment_array_api(self, xp, order, axis, center):
+        rng = np.random.default_rng(34823589259425)
+        x = rng.random(size=(5, 6, 7))
+        res = stats.moment(xp.asarray(x), order, axis=axis, center=center)
+        center = center if center is None else np.asarray(center)
+        ref = xp.asarray(_moment(x, order, axis, center=center)[()])
+        xp_assert_close(res, ref)
+
+
+class SkewKurtosisTest:
+    scalar_testcase = 4.
+    testcase = [1., 2., 3., 4.]
+    testmathworks = [1.165, 0.6268, 0.0751, 0.3516, -0.6965]
+
+    def test_empty_1d(self, xp):
+        x = xp.asarray([])
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = self.stat_fun(x)
+
+        xp_assert_equal(res, xp.asarray(xp.nan))
+
+
+@make_xp_test_case(stats.skew)
+class TestSkew(SkewKurtosisTest):
+    def stat_fun(self, x):
+        return stats.skew(x)
+
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered:RuntimeWarning:dask"
+    )
+    def test_skewness(self, xp):
+        # Scalar test case
+        y = stats.skew(xp.asarray(self.scalar_testcase))
+        xp_assert_close(y, xp.asarray(xp.nan))
+        # sum((testmathworks-mean(testmathworks,axis=0))**3,axis=0) /
+        #     ((sqrt(var(testmathworks)*4/5))**3)/5
+        y = stats.skew(xp.asarray(self.testmathworks))
+        xp_assert_close(y, xp.asarray(-0.29322304336607), atol=1e-10)
+        y = stats.skew(xp.asarray(self.testmathworks), bias=0)
+        xp_assert_close(y, xp.asarray(-0.437111105023940), atol=1e-10)
+        y = stats.skew(xp.asarray(self.testcase))
+        xp_assert_close(y, xp.asarray(0.0), atol=1e-10)
+
+    def test_nan_policy(self):
+        # initially, nan_policy is ignored with alternative backends
+        x = np.arange(10.)
+        x[9] = np.nan
+        with np.errstate(invalid='ignore'):
+            assert_equal(stats.skew(x), np.nan)
+        assert_equal(stats.skew(x, nan_policy='omit'), 0.)
+        assert_raises(ValueError, stats.skew, x, nan_policy='raise')
+        assert_raises(ValueError, stats.skew, x, nan_policy='foobar')
+
+    def test_skewness_scalar(self):
+        # `skew` must return a scalar for 1-dim input (only for NumPy arrays)
+        assert_equal(stats.skew(arange(10)), 0.0)
+
+    def test_skew_propagate_nan(self, xp):
+        # Check that the shape of the result is the same for inputs
+        # with and without nans, cf gh-5817
+        a = xp.arange(8.)
+        a = xp.reshape(a, (2, -1))
+        a = xpx.at(a)[1, 0].set(xp.nan)
+        with np.errstate(invalid='ignore'):
+            s = stats.skew(xp.asarray(a), axis=1)
+        xp_assert_equal(s, xp.asarray([0, xp.nan]))
+
+    def test_skew_constant_value(self, xp):
+        # Skewness of a constant input should be NaN (gh-16061)
+        a = xp.repeat(xp.asarray([-0.27829495]), 10)
+
+        with eager_warns(RuntimeWarning, match="Precision loss occurred", xp=xp):
+            xp_assert_equal(stats.skew(a), xp.asarray(xp.nan))
+            xp_assert_equal(stats.skew(a*2.**50), xp.asarray(xp.nan))
+            xp_assert_equal(stats.skew(a/2.**50), xp.asarray(xp.nan))
+            xp_assert_equal(stats.skew(a, bias=False), xp.asarray(xp.nan))
+
+            # # similarly, from gh-11086:
+            a = xp.asarray([14.3]*7)
+            xp_assert_equal(stats.skew(a), xp.asarray(xp.nan))
+            a = 1. + xp.arange(-3., 4)*1e-16
+            xp_assert_equal(stats.skew(a), xp.asarray(xp.nan))
+
+    def test_precision_loss_gh15554(self, xp):
+        # gh-15554 was one of several issues that have reported problems with
+        # constant or near-constant input. We can't always fix these, but
+        # make sure there's a warning.
+        with eager_warns(RuntimeWarning, match="Precision loss occurred", xp=xp):
+            rng = np.random.default_rng(34095309370)
+            a = rng.random(size=(100, 10))
+            a[:, 0] = 1.01
+            stats.skew(xp.asarray(a))
+
+    @pytest.mark.parametrize('axis', [-1, 0, 2, None])
+    @pytest.mark.parametrize('bias', [False, True])
+    def test_vectorization(self, xp, axis, bias):
+        # Behavior with array input is barely tested above. Compare
+        # against naive implementation.
+        rng = np.random.default_rng(1283413549926)
+        x = xp.asarray(rng.random((3, 4, 5)))
+
+        def skewness(a, axis, bias):
+            # Simple implementation of skewness
+            if axis is None:
+                a = xp.reshape(a, (-1,))
+                axis = 0
+            mean = xp.mean(a, axis=axis, keepdims=True)
+            mu3 = xp.mean((a - mean)**3, axis=axis)
+            std = xp.std(a, axis=axis)
+            res = mu3 / std ** 3
+            if not bias:
+                n = a.shape[axis]
+                res *= ((n - 1.0) * n) ** 0.5 / (n - 2.0)
+            return res
+
+        res = stats.skew(x, axis=axis, bias=bias)
+        ref = skewness(x, axis=axis, bias=bias)
+        xp_assert_close(res, ref)
+
+
+@make_xp_test_case(stats.kurtosis)
+class TestKurtosis(SkewKurtosisTest):
+    def stat_fun(self, x):
+        return stats.kurtosis(x)
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    def test_kurtosis(self, xp):
+        # Scalar test case
+        y = stats.kurtosis(xp.asarray(self.scalar_testcase))
+        assert xp.isnan(y)
+
+        #   sum((testcase-mean(testcase,axis=0))**4,axis=0)
+        #   / ((sqrt(var(testcase)*3/4))**4)
+        #   / 4
+        #
+        #   sum((test2-mean(testmathworks,axis=0))**4,axis=0)
+        #   / ((sqrt(var(testmathworks)*4/5))**4)
+        #   / 5
+        #
+        #   Set flags for axis = 0 and
+        #   fisher=0 (Pearson's defn of kurtosis for compatibility with Matlab)
+        y = stats.kurtosis(xp.asarray(self.testmathworks), 0, fisher=0, bias=1)
+        xp_assert_close(y, xp.asarray(2.1658856802973))
+
+        # Note that MATLAB has confusing docs for the following case
+        #  kurtosis(x,0) gives an unbiased estimate of Pearson's skewness
+        #  kurtosis(x)  gives a biased estimate of Fisher's skewness (Pearson-3)
+        #  The MATLAB docs imply that both should give Fisher's
+        y = stats.kurtosis(xp.asarray(self.testmathworks), fisher=0, bias=0)
+        xp_assert_close(y, xp.asarray(3.663542721189047))
+        y = stats.kurtosis(xp.asarray(self.testcase), 0, 0)
+        xp_assert_close(y, xp.asarray(1.64))
+
+        x = xp.arange(10.)
+        x = xp.where(x == 8, xp.nan, x)
+        xp_assert_equal(stats.kurtosis(x), xp.asarray(xp.nan))
+
+    def test_kurtosis_nan_policy(self):
+        # nan_policy only for NumPy right now
+        x = np.arange(10.)
+        x[9] = np.nan
+        assert_almost_equal(stats.kurtosis(x, nan_policy='omit'), -1.230000)
+        assert_raises(ValueError, stats.kurtosis, x, nan_policy='raise')
+        assert_raises(ValueError, stats.kurtosis, x, nan_policy='foobar')
+
+    def test_kurtosis_array_scalar(self):
+        # "array scalars" do not exist in other backends
+        assert_equal(type(stats.kurtosis([1, 2, 3])), np.float64)
+
+    def test_kurtosis_propagate_nan(self):
+        # nan_policy only for NumPy right now
+        # Check that the shape of the result is the same for inputs
+        # with and without nans, cf gh-5817
+        a = np.arange(8).reshape(2, -1).astype(float)
+        a[1, 0] = np.nan
+        k = stats.kurtosis(a, axis=1, nan_policy="propagate")
+        np.testing.assert_allclose(k, [-1.36, np.nan], atol=1e-15)
+
+    def test_kurtosis_constant_value(self, xp):
+        # Kurtosis of a constant input should be NaN (gh-16061)
+        a = xp.asarray([-0.27829495]*10)
+        with eager_warns(RuntimeWarning, match="Precision loss occurred", xp=xp):
+            assert xp.isnan(stats.kurtosis(a, fisher=False))
+            assert xp.isnan(stats.kurtosis(a * float(2**50), fisher=False))
+            assert xp.isnan(stats.kurtosis(a / float(2**50), fisher=False))
+            assert xp.isnan(stats.kurtosis(a, fisher=False, bias=False))
+
+    @pytest.mark.parametrize('axis', [-1, 0, 2, None])
+    @pytest.mark.parametrize('bias', [False, True])
+    @pytest.mark.parametrize('fisher', [False, True])
+    def test_vectorization(self, xp, axis, bias, fisher):
+        # Behavior with array input is not tested above. Compare
+        # against naive implementation.
+        rng = np.random.default_rng(1283413549926)
+        x = xp.asarray(rng.random((4, 5, 6)))
+
+        def kurtosis(a, axis, bias, fisher):
+            # Simple implementation of kurtosis
+            if axis is None:
+                a = xp.reshape(a, (-1,))
+                axis = 0
+            mean = xp.mean(a, axis=axis, keepdims=True)
+            mu4 = xp.mean((a - mean)**4, axis=axis)
+            mu2 = xp.var(a, axis=axis, correction=0)
+            if bias:
+                res = mu4 / mu2**2 - 3
+            else:
+                n = a.shape[axis]
+                # https://en.wikipedia.org/wiki/Kurtosis#Standard_unbiased_estimator
+                res = (n-1) / ((n-2) * (n-3)) * ((n + 1) * mu4/mu2**2 - 3*(n-1))
+
+            # I know it looks strange to subtract then add 3,
+            # but it is simpler than the alternatives
+            return res if fisher else res + 3
+
+        res = stats.kurtosis(x, axis=axis, bias=bias, fisher=fisher)
+        ref = kurtosis(x, axis=axis, bias=bias, fisher=fisher)
+        xp_assert_close(res, ref)
+
+
+@hypothesis.strategies.composite
+def ttest_data_axis_strategy(draw):
+    # draw an array under shape and value constraints
+    elements = dict(allow_nan=False, allow_infinity=False)
+    shape = npst.array_shapes(min_dims=1, min_side=2)
+    # The test that uses this, `test_pvalue_ci`, uses `float64` to test
+    # extreme `alpha`. It could be adjusted to test a dtype-dependent
+    # range of `alpha` if this strategy is needed to generate other floats.
+    data = draw(npst.arrays(dtype=np.float64, elements=elements, shape=shape))
+
+    # determine axes over which nonzero variance can be computed accurately
+    ok_axes = []
+    # Locally, I don't need catch_warnings or simplefilter, and I can just
+    # suppress RuntimeWarning. I include all that in hope of getting the same
+    # behavior on CI.
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        for axis in range(len(data.shape)):
+            with contextlib.suppress(Exception):
+                var = stats.moment(data, order=2, axis=axis)
+                if np.all(var > 0) and np.all(np.isfinite(var)):
+                    ok_axes.append(axis)
+    # if there are no valid axes, tell hypothesis to try a different example
+    hypothesis.assume(ok_axes)
+
+    # draw one of the valid axes
+    axis = draw(hypothesis.strategies.sampled_from(ok_axes))
+
+    return data, axis
+
+
+@make_xp_test_case(stats.ttest_1samp)
+class TestTTest_1samp:
+    # Preserving original test cases.
+    # Recomputed statistics and p-values with R t.test, e.g.
+    # options(digits=16)
+    # t.test(c(-1., 0., 1.), mu=2)
+    X1 = [-1., 0., 1.]
+    X2 = [0., 1., 2.]
+    T1_0 = 0.
+    P1_0 = 1.
+    T1_1 = -1.7320508075689
+    P1_1 = 0.2254033307585
+    T1_2 = -3.4641016151378
+    P1_2 = 0.07417990022745
+    T2_0 = 1.7320508075689
+    P2_0 = 0.2254033307585
+    P1_1_l = P1_1 / 2
+    P1_1_g = 1 - (P1_1 / 2)
+
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    def test_onesample(self, xp):
+        with warnings.catch_warnings(), \
+                np.errstate(invalid="ignore", divide="ignore"):
+            warnings.filterwarnings(
+                "ignore", "Degrees of freedom <= 0 for slice", RuntimeWarning)
+            a = xp.asarray(4.) if not is_numpy(xp) else 4.
+            t, p = stats.ttest_1samp(a, 3.)
+        xp_assert_equal(t, xp.asarray(xp.nan))
+        xp_assert_equal(p, xp.asarray(xp.nan))
+
+        t, p = stats.ttest_1samp(xp.asarray(self.X1), 0.)
+        xp_assert_close(t, xp.asarray(self.T1_0))
+        xp_assert_close(p, xp.asarray(self.P1_0))
+
+        res = stats.ttest_1samp(xp.asarray(self.X1), 0.)
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, xp=xp)
+
+        t, p = stats.ttest_1samp(xp.asarray(self.X2), 0.)
+
+        xp_assert_close(t, xp.asarray(self.T2_0))
+        xp_assert_close(p, xp.asarray(self.P2_0))
+
+        t, p = stats.ttest_1samp(xp.asarray(self.X1), 1.)
+
+        xp_assert_close(t, xp.asarray(self.T1_1))
+        xp_assert_close(p, xp.asarray(self.P1_1))
+
+        t, p = stats.ttest_1samp(xp.asarray(self.X1), 2.)
+
+        xp_assert_close(t, xp.asarray(self.T1_2))
+        xp_assert_close(p, xp.asarray(self.P1_2))
+
+    def test_onesample_nan_policy_propagate(self, xp):
+        x = stats.norm.rvs(loc=5, scale=10, size=51, random_state=7654567)
+        x[50] = np.nan
+        x = xp.asarray(x, dtype=xp_default_dtype(xp))
+
+        res = stats.ttest_1samp(x, 5.0)
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+    @skip_xp_backends(eager_only=True, reason="lazy arrays don't do 'raise'.")
+    def test_onesample_nan_policy_omit_raise(self, xp):
+        x = stats.norm.rvs(loc=5, scale=10, size=51, random_state=7654567)
+        x[50] = np.nan
+        x = xp.asarray(x, dtype=xp_default_dtype(xp))
+
+        res = stats.ttest_1samp(x, 5.0, nan_policy='omit')
+        xp_assert_close(res.statistic, xp.asarray(-1.6412624074367159))
+        xp_assert_close(res.pvalue, xp.asarray(0.107147027334048005))
+
+        with pytest.raises(ValueError, match="The input contains nan values"):
+            stats.ttest_1samp(x, 5.0, nan_policy='raise')
+
+        with pytest.raises(ValueError, match="nan_policy must be one of"):
+            stats.ttest_1samp(x, 5.0, nan_policy='foobar')
+
+
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered in divide")
+    def test_1samp_alternative(self, xp):
+        message = "`alternative` must be 'less', 'greater', or 'two-sided'."
+        with pytest.raises(ValueError, match=message):
+            stats.ttest_1samp(xp.asarray(self.X1), 0., alternative="error")
+
+        t, p = stats.ttest_1samp(xp.asarray(self.X1), 1., alternative="less")
+        xp_assert_close(p, xp.asarray(self.P1_1_l))
+        xp_assert_close(t, xp.asarray(self.T1_1))
+
+        t, p = stats.ttest_1samp(xp.asarray(self.X1), 1., alternative="greater")
+        xp_assert_close(p, xp.asarray(self.P1_1_g))
+        xp_assert_close(t, xp.asarray(self.T1_1))
+
+    @skip_xp_backends('jax.numpy', reason='Generic stdtrit mutates array.')
+    @pytest.mark.parametrize("alternative", ['two-sided', 'less', 'greater'])
+    def test_1samp_ci_1d(self, xp, alternative):
+        # test confidence interval method against reference values
+        rng = np.random.default_rng(8066178009154342972)
+        n = 10
+        x = rng.normal(size=n, loc=1.5, scale=2)
+        popmean = rng.normal()  # this shouldn't affect confidence interval
+        # Reference values generated with R t.test:
+        # options(digits=16)
+        # x = c(2.75532884,  0.93892217,  0.94835861,  1.49489446, -0.62396595,
+        #      -1.88019867, -1.55684465,  4.88777104,  5.15310979,  4.34656348)
+        # t.test(x, conf.level=0.85, alternative='l')
+        dtype = xp.asarray(1.0).dtype
+        x = xp.asarray(x, dtype=dtype)
+        popmean = xp.asarray(popmean, dtype=dtype)
+
+        ref = {'two-sided': [0.3594423211709136, 2.9333455028290860],
+               'greater': [0.7470806207371626, np.inf],
+               'less': [-np.inf, 2.545707203262837]}
+        res = stats.ttest_1samp(x, popmean=popmean, alternative=alternative)
+        ci = res.confidence_interval(confidence_level=0.85)
+        xp_assert_close(ci.low, xp.asarray(ref[alternative][0]))
+        xp_assert_close(ci.high, xp.asarray(ref[alternative][1]))
+        xp_assert_equal(res.df, xp.asarray(n-1))
+
+    def test_1samp_ci_iv(self, xp):
+        # test `confidence_interval` method input validation
+        res = stats.ttest_1samp(xp.arange(10.), 0.)
+        message = '`confidence_level` must be a number between 0 and 1.'
+        with pytest.raises(ValueError, match=message):
+            res.confidence_interval(confidence_level=10)
+
+    @skip_xp_backends(np_only=True, reason='Too slow.')
+    @pytest.mark.xslow
+    @hypothesis.given(alpha=hypothesis.strategies.floats(1e-15, 1-1e-15),
+                      data_axis=ttest_data_axis_strategy())
+    @pytest.mark.parametrize('alternative', ['less', 'greater'])
+    def test_pvalue_ci(self, alpha, data_axis, alternative, xp):
+        # test relationship between one-sided p-values and confidence intervals
+        data, axis = data_axis
+        data = xp.asarray(data)
+        res = stats.ttest_1samp(data, 0.,
+                                alternative=alternative, axis=axis)
+        l, u = res.confidence_interval(confidence_level=alpha)
+        popmean = l if alternative == 'greater' else u
+        popmean = xp.expand_dims(popmean, axis=axis)
+        res = stats.ttest_1samp(data, popmean, alternative=alternative, axis=axis)
+        shape = list(data.shape)
+        shape.pop(axis)
+        # `float64` is used to correspond with extreme range of `alpha`
+        ref = xp.broadcast_to(xp.asarray(1-alpha, dtype=xp.float64), shape)
+        xp_assert_close(res.pvalue, ref)
+
+
+class TestPercentileOfScore:
+
+    def f(self, *args, **kwargs):
+        return stats.percentileofscore(*args, **kwargs)
+
+    @pytest.mark.parametrize("kind, result", [("rank", 40),
+                                              ("mean", 35),
+                                              ("strict", 30),
+                                              ("weak", 40)])
+    def test_unique(self, kind, result):
+        a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        assert_equal(self.f(a, 4, kind=kind), result)
+
+    @pytest.mark.parametrize("kind, result", [("rank", 45),
+                                              ("mean", 40),
+                                              ("strict", 30),
+                                              ("weak", 50)])
+    def test_multiple2(self, kind, result):
+        a = [1, 2, 3, 4, 4, 5, 6, 7, 8, 9]
+        assert_equal(self.f(a, 4, kind=kind), result)
+
+    @pytest.mark.parametrize("kind, result", [("rank", 50),
+                                              ("mean", 45),
+                                              ("strict", 30),
+                                              ("weak", 60)])
+    def test_multiple3(self, kind, result):
+        a = [1, 2, 3, 4, 4, 4, 5, 6, 7, 8]
+        assert_equal(self.f(a, 4, kind=kind), result)
+
+    @pytest.mark.parametrize("kind, result", [("rank", 30),
+                                              ("mean", 30),
+                                              ("strict", 30),
+                                              ("weak", 30)])
+    def test_missing(self, kind, result):
+        a = [1, 2, 3, 5, 6, 7, 8, 9, 10, 11]
+        assert_equal(self.f(a, 4, kind=kind), result)
+
+    @pytest.mark.parametrize("kind, result", [("rank", 40),
+                                              ("mean", 35),
+                                              ("strict", 30),
+                                              ("weak", 40)])
+    def test_large_numbers(self, kind, result):
+        a = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
+        assert_equal(self.f(a, 40, kind=kind), result)
+
+    @pytest.mark.parametrize("kind, result", [("rank", 50),
+                                              ("mean", 45),
+                                              ("strict", 30),
+                                              ("weak", 60)])
+    def test_large_numbers_multiple3(self, kind, result):
+        a = [10, 20, 30, 40, 40, 40, 50, 60, 70, 80]
+        assert_equal(self.f(a, 40, kind=kind), result)
+
+    @pytest.mark.parametrize("kind, result", [("rank", 30),
+                                              ("mean", 30),
+                                              ("strict", 30),
+                                              ("weak", 30)])
+    def test_large_numbers_missing(self, kind, result):
+        a = [10, 20, 30, 50, 60, 70, 80, 90, 100, 110]
+        assert_equal(self.f(a, 40, kind=kind), result)
+
+    @pytest.mark.parametrize("kind, result", [("rank", [0, 10, 100, 100]),
+                                              ("mean", [0, 5, 95, 100]),
+                                              ("strict", [0, 0, 90, 100]),
+                                              ("weak", [0, 10, 100, 100])])
+    def test_boundaries(self, kind, result):
+        a = [10, 20, 30, 50, 60, 70, 80, 90, 100, 110]
+        assert_equal(self.f(a, [0, 10, 110, 200], kind=kind), result)
+
+    @pytest.mark.parametrize("kind, result", [("rank", [0, 10, 100]),
+                                              ("mean", [0, 5, 95]),
+                                              ("strict", [0, 0, 90]),
+                                              ("weak", [0, 10, 100])])
+    def test_inf(self, kind, result):
+        a = [1, 2, 3, 4, 5, 6, 7, 8, 9, +np.inf]
+        assert_equal(self.f(a, [-np.inf, 1, +np.inf], kind=kind), result)
+
+    cases = [("propagate", [], 1, np.nan),
+             ("propagate", [np.nan], 1, np.nan),
+             ("propagate", [np.nan], [0, 1, 2], [np.nan, np.nan, np.nan]),
+             ("propagate", [1, 2], [1, 2, np.nan], [50, 100, np.nan]),
+             ("omit", [1, 2, np.nan], [0, 1, 2], [0, 50, 100]),
+             ("omit", [1, 2], [0, 1, np.nan], [0, 50, np.nan]),
+             ("omit", [np.nan, np.nan], [0, 1, 2], [np.nan, np.nan, np.nan])]
+
+    @pytest.mark.parametrize("policy, a, score, result", cases)
+    def test_nans_ok(self, policy, a, score, result):
+        assert_equal(self.f(a, score, nan_policy=policy), result)
+
+    cases = [
+        ("raise", [1, 2, 3, np.nan], [1, 2, 3],
+         "The input contains nan values"),
+        ("raise", [1, 2, 3], [1, 2, 3, np.nan],
+         "The input contains nan values"),
+    ]
+
+    @pytest.mark.parametrize("policy, a, score, message", cases)
+    def test_nans_fail(self, policy, a, score, message):
+        with assert_raises(ValueError, match=message):
+            self.f(a, score, nan_policy=policy)
+
+    @pytest.mark.parametrize("shape", [
+        (6, ),
+        (2, 3),
+        (2, 1, 3),
+        (2, 1, 1, 3),
+    ])
+    def test_nd(self, shape):
+        a = np.array([0, 1, 2, 3, 4, 5])
+        scores = a.reshape(shape)
+        results = scores*10
+        a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+        assert_equal(self.f(a, scores, kind="rank"), results)
+
+    def test_multidimensional_error(self):
+        # gh-21563 reported that `percentileofscore` accepted multidimensional
+        # arrays but did not produce meaningful results.
+        message = "`a` must be 1-dimensional."
+        with pytest.raises(ValueError, match=message):
+            stats.percentileofscore(np.ones((3, 3)), 1)
+
+
+PowerDivCase = namedtuple('Case',  # type: ignore[name-match]
+                          ['f_obs', 'f_exp', 'ddof', 'axis',
+                           'chi2',     # Pearson's
+                           'log',      # G-test (log-likelihood)
+                           'mod_log',  # Modified log-likelihood
+                           'cr',       # Cressie-Read (lambda=2/3)
+                           ])
+
+# The details of the first two elements in power_div_1d_cases are used
+# in a test in TestPowerDivergence.  Check that code before making
+# any changes here.
+power_div_1d_cases = [
+    # Use the default f_exp.
+    PowerDivCase(f_obs=[4, 8, 12, 8], f_exp=None, ddof=0, axis=None,
+                 chi2=4,
+                 log=2*(4*np.log(4/8) + 12*np.log(12/8)),
+                 mod_log=2*(8*np.log(8/4) + 8*np.log(8/12)),
+                 cr=(4*((4/8)**(2/3) - 1) + 12*((12/8)**(2/3) - 1))/(5/9)),
+    # Give a non-uniform f_exp.
+    PowerDivCase(f_obs=[4, 8, 12, 8], f_exp=[2, 16, 12, 2], ddof=0, axis=None,
+                 chi2=24,
+                 log=2*(4*np.log(4/2) + 8*np.log(8/16) + 8*np.log(8/2)),
+                 mod_log=2*(2*np.log(2/4) + 16*np.log(16/8) + 2*np.log(2/8)),
+                 cr=(4*((4/2)**(2/3) - 1) + 8*((8/16)**(2/3) - 1) +
+                     8*((8/2)**(2/3) - 1))/(5/9)),
+    # f_exp is a scalar.
+    PowerDivCase(f_obs=[4, 8, 12, 8], f_exp=8, ddof=0, axis=None,
+                 chi2=4,
+                 log=2*(4*np.log(4/8) + 12*np.log(12/8)),
+                 mod_log=2*(8*np.log(8/4) + 8*np.log(8/12)),
+                 cr=(4*((4/8)**(2/3) - 1) + 12*((12/8)**(2/3) - 1))/(5/9)),
+    # f_exp equal to f_obs.
+    PowerDivCase(f_obs=[3, 5, 7, 9], f_exp=[3, 5, 7, 9], ddof=0, axis=0,
+                 chi2=0, log=0, mod_log=0, cr=0),
+]
+
+
+power_div_empty_cases = [
+    # Shape is (0,)--a data set with length 0.  The computed
+    # test statistic should be 0.
+    PowerDivCase(f_obs=[],
+                 f_exp=None, ddof=0, axis=0,
+                 chi2=0, log=0, mod_log=0, cr=0),
+    # Shape is (0, 3).  This is 3 data sets, but each data set has
+    # length 0, so the computed test statistic should be [0, 0, 0].
+    PowerDivCase(f_obs=np.array([[],[],[]]).T,
+                 f_exp=None, ddof=0, axis=0,
+                 chi2=[0, 0, 0],
+                 log=[0, 0, 0],
+                 mod_log=[0, 0, 0],
+                 cr=[0, 0, 0]),
+    # Shape is (3, 0).  This represents an empty collection of
+    # data sets in which each data set has length 3.  The test
+    # statistic should be an empty array.
+    PowerDivCase(f_obs=np.array([[],[],[]]),
+                 f_exp=None, ddof=0, axis=0,
+                 chi2=[],
+                 log=[],
+                 mod_log=[],
+                 cr=[]),
+]
+
+
+@make_xp_test_case(stats.power_divergence)
+class TestPowerDivergence:
+
+    def check_power_divergence(self, f_obs, f_exp, ddof, axis, lambda_,
+                               expected_stat, xp):
+        dtype = xp.asarray(1.).dtype
+
+        f_obs = xp.asarray(f_obs, dtype=dtype)
+        f_exp = xp.asarray(f_exp, dtype=dtype) if f_exp is not None else f_exp
+
+        if axis is None:
+            num_obs = xp_size(f_obs)
+        else:
+            arrays = (xp.broadcast_arrays(f_obs, f_exp) if f_exp is not None
+                      else (f_obs,))
+            num_obs = arrays[0].shape[axis]
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", "Mean of empty slice", RuntimeWarning)
+            stat, p = stats.power_divergence(
+                                f_obs, f_exp=f_exp, ddof=ddof,
+                                axis=axis, lambda_=lambda_)
+            xp_assert_close(stat, xp.asarray(expected_stat, dtype=dtype))
+
+            if lambda_ == 1 or lambda_ == "pearson":
+                # Also test stats.chisquare.
+                stat, p = stats.chisquare(f_obs, f_exp=f_exp, ddof=ddof,
+                                          axis=axis)
+                xp_assert_close(stat, xp.asarray(expected_stat, dtype=dtype))
+
+        ddof = np.asarray(ddof)
+        expected_p = stats.distributions.chi2.sf(expected_stat,
+                                                 num_obs - 1 - ddof)
+        xp_assert_close(p, xp.asarray(expected_p, dtype=dtype))
+
+    @pytest.mark.parametrize('case', power_div_1d_cases)
+    @pytest.mark.parametrize('lambda_stat',
+        [(None, 'chi2'), ('pearson', 'chi2'), (1, 'chi2'),
+         ('log-likelihood', 'log'), ('mod-log-likelihood', 'mod_log'),
+         ('cressie-read', 'cr'), (2/3, 'cr')])
+    def test_basic(self, case, lambda_stat, xp):
+        lambda_, attr = lambda_stat
+        expected_stat = getattr(case, attr)
+        self.check_power_divergence(case.f_obs, case.f_exp, case.ddof, case.axis,
+                                    lambda_, expected_stat, xp)
+
+    def test_axis(self, xp):
+        case0 = power_div_1d_cases[0]
+        case1 = power_div_1d_cases[1]
+        f_obs = np.vstack((case0.f_obs, case1.f_obs))
+        f_exp = np.vstack((np.ones_like(case0.f_obs)*np.mean(case0.f_obs),
+                           case1.f_exp))
+        # Check the four computational code paths in power_divergence
+        # using a 2D array with axis=1.
+        f_obs = xp.asarray(f_obs)
+        f_exp = xp.asarray(f_exp) if f_exp is not None else f_exp
+        self.check_power_divergence(
+               f_obs, f_exp, 0, 1,
+               "pearson", [case0.chi2, case1.chi2], xp=xp)
+        self.check_power_divergence(
+               f_obs, f_exp, 0, 1,
+               "log-likelihood", [case0.log, case1.log], xp=xp)
+        self.check_power_divergence(
+               f_obs, f_exp, 0, 1,
+               "mod-log-likelihood", [case0.mod_log, case1.mod_log], xp=xp)
+        self.check_power_divergence(
+               f_obs, f_exp, 0, 1,
+               "cressie-read", [case0.cr, case1.cr], xp=xp)
+        # Reshape case0.f_obs to shape (2,2), and use axis=None.
+        # The result should be the same.
+        f_obs_reshape = xp.reshape(xp.asarray(case0.f_obs), (2, 2))
+        self.check_power_divergence(
+               f_obs_reshape, None, 0, None,
+               "pearson", case0.chi2, xp=xp)
+
+    def test_ddof_broadcasting(self, xp):
+        # Test that ddof broadcasts correctly.
+        # ddof does not affect the test statistic.  It is broadcast
+        # with the computed test statistic for the computation of
+        # the p value.
+
+        case0 = power_div_1d_cases[0]
+        case1 = power_div_1d_cases[1]
+        # Create 4x2 arrays of observed and expected frequencies.
+        f_obs = np.vstack((case0.f_obs, case1.f_obs)).T
+        f_exp = np.vstack((np.ones_like(case0.f_obs)*np.mean(case0.f_obs),
+                           case1.f_exp)).T
+
+        expected_chi2 = [case0.chi2, case1.chi2]
+
+        dtype = xp.asarray(1.).dtype
+        f_obs = xp.asarray(f_obs, dtype=dtype)
+        f_exp = xp.asarray(f_exp, dtype=dtype)
+        expected_chi2 = xp.asarray(expected_chi2, dtype=dtype)
+
+        # ddof has shape (2, 1).  This is broadcast with the computed
+        # statistic, so p will have shape (2,2).
+        ddof = xp.asarray([[0], [1]])
+
+        stat, p = stats.power_divergence(f_obs, f_exp, ddof=ddof)
+        xp_assert_close(stat, expected_chi2)
+
+        # Compute the p values separately, passing in scalars for ddof.
+        _, p0 = stats.power_divergence(f_obs, f_exp, ddof=ddof[0, 0])
+        _, p1 = stats.power_divergence(f_obs, f_exp, ddof=ddof[1, 0])
+
+        expected_p = xp.concat((p0[xp.newaxis, :], p1[xp.newaxis, :]), axis=0)
+        xp_assert_close(p, expected_p)
+
+    @pytest.mark.parametrize('case', power_div_empty_cases)
+    @pytest.mark.parametrize('lambda_stat',
+        [('pearson', 'chi2'), ('log-likelihood', 'log'),
+         ('mod-log-likelihood', 'mod_log'),
+         ('cressie-read', 'cr'), (2/3, 'cr')])
+    def test_empty_cases(self, case, lambda_stat, xp):
+        lambda_, attr = lambda_stat
+        expected_stat = getattr(case, attr)
+        with warnings.catch_warnings():
+            self.check_power_divergence(
+                case.f_obs, case.f_exp, case.ddof, case.axis,
+                lambda_, expected_stat, xp)
+
+    def test_power_divergence_result_attributes(self, xp):
+        f_obs = power_div_1d_cases[0].f_obs
+        f_exp = power_div_1d_cases[0].f_exp
+        ddof = power_div_1d_cases[0].ddof
+        axis = power_div_1d_cases[0].axis
+        dtype = xp.asarray(1.).dtype
+        f_obs = xp.asarray(f_obs, dtype=dtype)
+        # f_exp is None
+
+        res = stats.power_divergence(f_obs, f_exp=f_exp, ddof=ddof,
+                                     axis=axis, lambda_="pearson")
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, xp=xp)
+
+    def test_power_divergence_gh_12282(self, xp):
+        # The sums of observed and expected frequencies must match
+        f_obs = xp.asarray([[10., 20.], [30., 20.]])
+        f_exp = xp.asarray([[5., 15.], [35., 25.]])
+        stat, pval = stats.power_divergence(f_obs, f_exp=f_exp)
+        xp_assert_close(stat, xp.asarray([5.71428571, 2.66666667]))
+        xp_assert_close(pval, xp.asarray([0.01682741, 0.10247043]))
+
+        if is_lazy_array(f_obs):
+            res = stats.power_divergence(f_obs, f_exp=xp.asarray([30., 60.]))
+            xp_assert_equal(res.statistic, xp.asarray([xp.nan, xp.nan]))
+            xp_assert_equal(res.pvalue, xp.asarray([xp.nan, xp.nan]))
+
+            res = stats.power_divergence(f_obs, f_exp=f_exp, axis=1)
+            xp_assert_equal(res.statistic, xp.asarray([xp.nan, xp.nan]))
+            xp_assert_equal(res.pvalue, xp.asarray([xp.nan, xp.nan]))
+
+            # only slices with unequal sums result in NaN
+            f_exp = xp.asarray([[5., 25.], [35., 25.]])
+            res = stats.power_divergence(f_obs, f_exp=f_exp)
+            xp_assert_close(res.statistic, xp.asarray([stat[0], xp.nan]))
+            xp_assert_close(res.pvalue, xp.asarray([pval[0], xp.nan]))
+        else:
+            message = 'For each axis slice...'
+            with pytest.raises(ValueError, match=message):
+                stats.power_divergence(f_obs, f_exp=xp.asarray([30., 60.]))
+            with pytest.raises(ValueError, match=message):
+                stats.power_divergence(f_obs, f_exp=f_exp, axis=1)
+
+    def test_power_divergence_against_cressie_read_data(self, xp):
+        # Test stats.power_divergence against tables 4 and 5 from
+        # Cressie and Read, "Multimonial Goodness-of-Fit Tests",
+        # J. R. Statist. Soc. B (1984), Vol 46, No. 3, pp. 440-464.
+        # This tests the calculation for several values of lambda.
+
+        # Table 4 data recalculated for greater precision according to:
+        # Shelby J. Haberman, Analysis of Qualitative Data: Volume 1
+        # Introductory Topics, Academic Press, New York, USA (1978).
+        obs = xp.asarray([15., 11., 14., 17., 5., 11., 10., 4., 8.,
+                          10., 7., 9., 11., 3., 6., 1., 1., 4.])
+        beta = -0.083769  # Haberman (1978), p. 15
+        i = xp.arange(1., obs.shape[0] + 1.)
+        alpha = xp.log(xp.sum(obs) / xp.sum(xp.exp(beta*i)))
+        expected_counts = xp.exp(alpha + beta*i)
+
+        # `table4` holds just the second and third columns from Table 4.
+        table4 = xp.concat((obs[xp.newaxis, :],
+                            expected_counts[xp.newaxis, :])).T
+
+        table5 = np.asarray([
+            # lambda, statistic
+            -10.0, 72.2e3,
+            -5.0, 28.9e1,
+            -3.0, 65.6,
+            -2.0, 40.6,
+            -1.5, 34.0,
+            -1.0, 29.5,
+            -0.5, 26.5,
+            0.0, 24.6,
+            0.5, 23.4,
+            0.67, 23.1,
+            1.0, 22.7,
+            1.5, 22.6,
+            2.0, 22.9,
+            3.0, 24.8,
+            5.0, 35.5,
+            10.0, 21.4e1,
+            ])
+        table5 = np.reshape(table5, (-1, 2))
+
+        for i in range(table5.shape[0]):
+            lambda_, expected_stat = float(table5[i, 0]), float(table5[i, 1])
+            stat, p = stats.power_divergence(table4[:, 0], table4[:, 1],
+                                             lambda_=lambda_)
+            xp_assert_close(stat, xp.asarray(expected_stat), rtol=5e-3)
+
+
+@make_xp_test_case(stats.chisquare)
+class TestChisquare:
+    def test_chisquare_12282a(self, xp):
+        # Currently `chisquare` is implemented via power_divergence
+        # in case that ever changes, perform a basic test like
+        # test_power_divergence_gh_12282
+        f_obs = xp.asarray([10., 20.])
+        f_exp = xp.asarray([30., 60.])
+        if is_lazy_array(f_obs):
+            res = stats.chisquare(f_obs, f_exp=f_exp)
+            xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+            xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+        else:
+            with assert_raises(ValueError, match='For each axis slice...'):
+                stats.chisquare(f_obs, f_exp=f_exp)
+
+    def test_chisquare_12282b(self, xp):
+        # Check that users can now disable the sum check tested in
+        # test_chisquare_12282a. Also, confirm that statistic and p-value
+        # are as expected.
+        rng = np.random.default_rng(3843874358728234)
+        n = 10
+        lam = rng.uniform(1000, 2000, size=n)
+        x = rng.poisson(lam)
+        lam = xp.asarray(lam)
+        x = xp.asarray(x, dtype=lam.dtype)
+        res = stats.chisquare(x, f_exp=lam, ddof=-1, sum_check=False)
+        # Poisson is approximately normal with mean and variance lam
+        z = (x - lam) / xp.sqrt(lam)
+        statistic = xp.sum(z**2)
+        xp_assert_close(res.statistic, statistic)
+        # Sum of `n` squared standard normal variables follows chi2 with `n` DoF
+        X2 = _SimpleChi2(xp.asarray(n, dtype=statistic.dtype))
+        xp_assert_close(res.pvalue, X2.sf(statistic))
+
+    @pytest.mark.parametrize("n, dtype", [(200, 'uint8'), (1000000, 'int32')])
+    def test_chiquare_data_types_attributes(self, n, dtype, xp):
+        # Regression test for gh-10159 and gh-18368
+        dtype = getattr(xp, dtype)
+        obs = xp.asarray([n, 0], dtype=dtype)
+        exp = xp.asarray([n // 2, n // 2], dtype=dtype)
+        res = stats.chisquare(obs, exp)
+        stat, p = res
+        xp_assert_close(stat, xp.asarray(n, dtype=xp.asarray(1.).dtype), rtol=1e-13)
+        # check that attributes are identical to unpacked outputs - see gh-18368
+        xp_assert_equal(res.statistic, stat)
+        xp_assert_equal(res.pvalue, p)
+
+
+@make_xp_test_case(stats.friedmanchisquare)
+class TestFriedmanChiSquare:
+    # verified with matlab and R
+    # From Demsar "Statistical Comparisons of Classifiers over Multiple Data Sets"
+    # 2006, Xf=9.28 (no tie handling, tie corrected Xf >=9.28)
+    x1 = [[0.763, 0.599, 0.954, 0.628, 0.882, 0.936, 0.661,
+           0.583, 0.775, 1.0, 0.94, 0.619, 0.972, 0.957],
+          [0.768, 0.591, 0.971, 0.661, 0.888, 0.931, 0.668,
+           0.583, 0.838, 1.0, 0.962, 0.666, 0.981, 0.978],
+          [0.771, 0.590, 0.968, 0.654, 0.886, 0.916, 0.609,
+           0.563, 0.866, 1.0, 0.965, 0.614, 0.9751, 0.946],
+          [0.798, 0.569, 0.967, 0.657, 0.898, 0.931, 0.685,
+           0.625, 0.875, 1.0, 0.962, 0.669, 0.975, 0.970]]
+    ref1 = (10.2283464566929, 0.0167215803284414)
+
+    # From "Bioestadistica para las ciencias de la salud" Xf=18.95 p<0.001:
+    x2 = [[4, 3, 5, 3, 5, 3, 2, 5, 4, 4, 4, 3],
+          [2, 2, 1, 2, 3, 1, 2, 3, 2, 1, 1, 3],
+          [2, 4, 3, 3, 4, 3, 3, 4, 4, 1, 2, 1],
+          [3, 5, 4, 3, 4, 4, 3, 3, 3, 4, 4, 4]]
+    ref2 = (18.9428571428571, 0.000280938375189499)
+
+    # From Jerrorl H. Zar, "Biostatistical Analysis"(example 12.6),
+    # Xf=10.68, 0.005 < p < 0.01:
+    # Probability from this example is inexact
+    # using Chisquare approximation of Friedman Chisquare.
+    x3 = [[7.0, 9.9, 8.5, 5.1, 10.3],
+          [5.3, 5.7, 4.7, 3.5, 7.7],
+          [4.9, 7.6, 5.5, 2.8, 8.4],
+          [8.8, 8.9, 8.1, 3.3, 9.1]]
+    ref3 = (10.68, 0.0135882729582176)
+
+    @pytest.mark.parametrize("dtype", [None, "float32", "float64"])
+    @pytest.mark.parametrize("data, ref", [(x1, ref1), (x2, ref2), (x3, ref3)])
+    def test_against_references(self, dtype, data, ref, xp):
+        dtype = dtype if dtype is None else getattr(xp, dtype)
+        data = [xp.asarray(array, dtype=dtype) for array in data]
+        res = stats.friedmanchisquare(*data)
+        xp_assert_close(res.statistic, xp.asarray(ref[0], dtype=dtype))
+        xp_assert_close(res.pvalue, xp.asarray(ref[1], dtype=dtype))
+
+    def test_too_few_samples(self, xp):
+        message = "At least 3 samples must be given"
+        with pytest.raises(ValueError, match=message):
+            stats.friedmanchisquare(xp.asarray(self.x3[0]), xp.asarray(self.x3[1]))
+
+
+@make_xp_test_case(stats.kstest)
+class TestKSTest:
+    """Tests kstest and ks_1samp agree with K-S various sizes, alternatives, modes."""
+
+    def _test_kstest_and_ks1samp(self, x, alternative, mode='auto', decimal=14):
+        result = stats.kstest(x, 'norm', alternative=alternative, mode=mode)
+        result_1samp = stats.ks_1samp(x, special.ndtr,
+                                      alternative=alternative, mode=mode)
+        xp_assert_close(result.statistic, result_1samp.statistic)
+        xp_assert_close(result.pvalue, result_1samp.pvalue)
+        xp_assert_equal(result.statistic_location, result.statistic_location)
+        xp_assert_equal(result.statistic_sign, result.statistic_sign)
+
+    def test_namedtuple_attributes(self, xp):
+        x = xp.linspace(-1, 1, 9)
+        # test for namedtuple attribute results
+        attributes = ('statistic', 'pvalue')
+        res = stats.kstest(x, special.ndtr)
+        check_named_results(res, attributes, xp=xp)
+
+    def test_agree_with_ks_1samp(self, xp):
+        x = xp.linspace(-1, 1, 9)
+        self._test_kstest_and_ks1samp(x, 'two-sided')
+
+        x = xp.linspace(-15, 15, 9)
+        self._test_kstest_and_ks1samp(x, 'two-sided')
+
+        x = [-1.23, 0.06, -0.60, 0.17, 0.66, -0.17, -0.08, 0.27, -0.98, -0.99]
+        x = xp.asarray(x)
+        self._test_kstest_and_ks1samp(x, 'two-sided')
+        self._test_kstest_and_ks1samp(x, 'greater', mode='exact')
+        self._test_kstest_and_ks1samp(x, 'less', mode='exact')
+
+    # parametrize over function-specific options to make sure they're passed through;
+    # `test_axis_nan_policy` takes care of the rest.
+    @pytest.mark.parametrize("method", ["auto", "exact", "asymp"])
+    @pytest.mark.parametrize("alternative", ['two-sided', 'less', 'greater'])
+    def test_agree_with_ks_2samp(self, method, alternative, xp):
+        rng = np.random.default_rng(236114087)
+        x, y = rng.random((2, 9, 10))
+        x, y = xp.asarray(x), xp.asarray(y)
+        res = stats.kstest(x, y, alternative=alternative, method=method)
+        ref = stats.ks_2samp(x, y, alternative=alternative, method=method)
+        xp_assert_equal(res.statistic, ref.statistic)
+        xp_assert_equal(res.pvalue, ref.pvalue)
+        xp_assert_equal(res.statistic_location, ref.statistic_location)
+        xp_assert_equal(res.statistic_sign, ref.statistic_sign)
+
+    def test_pm_inf_gh20386(self, xp):
+        # Check that gh-20386 is resolved - `kstest` does not
+        # return NaNs when both -inf and inf are in sample.
+        vals = xp.asarray([-xp.inf, 0, 1, xp.inf])
+        def cdf(x):  # Cauchy CDF
+            return xp.atan2(xp.ones_like(x), -x) / xp.pi
+        res = stats.kstest(vals, cdf)
+        ref = stats.kstest(vals, cdf, _no_deco=True)
+        assert xp.isfinite(res.statistic)
+        assert xp.isfinite(res.pvalue)
+        assert xp.isfinite(res.statistic_location)
+        assert xp.isfinite(res.statistic_sign)
+        xp_assert_equal(res.statistic, ref.statistic)
+        xp_assert_equal(res.pvalue, ref.pvalue)
+        xp_assert_equal(res.statistic_location, ref.statistic_location)
+        xp_assert_equal(res.statistic_sign, ref.statistic_sign)
+
+    # missing: no test that uses *args
+
+
+@make_xp_test_case(stats.ks_1samp)
+class TestKSOneSample:
+    """
+    Tests kstest and ks_samp 1-samples with K-S various sizes, alternatives, modes.
+    """
+
+    def _testOne(self, x, alternative, expected_statistic, expected_prob, *,
+                 mode='auto', dtype, xp):
+        rtol = 5e-14 if dtype == xp.float64 else 1e-5
+        res = stats.ks_1samp(x, special.ndtr, alternative=alternative, mode=mode)
+        ref_statistic = xp.asarray(expected_statistic, dtype=dtype)
+        ref_pvalue = xp.asarray(expected_prob, dtype=dtype)
+        xp_assert_close(res.statistic, ref_statistic, rtol=rtol)
+        xp_assert_close(res.pvalue, ref_pvalue, rtol=rtol)
+
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_agree_with_r(self, dtype, xp):
+        # comparing with some values from R
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        x = xp.linspace(-1, 1, 9, dtype=dtype)
+        self._testOne(x, 'two-sided', 0.15865525393145705, 0.95164069201518386,
+                      dtype=dtype, xp=xp)
+
+        x = xp.linspace(-15, 15, 9, dtype=dtype)
+        self._testOne(x, 'two-sided', 0.44435602715924361, 0.038850140086788665,
+                      dtype=dtype, xp=xp)
+
+        x = [-1.23, 0.06, -0.60, 0.17, 0.66, -0.17, -0.08, 0.27, -0.98, -0.99]
+        x = xp.asarray(x, dtype=dtype)
+        self._testOne(x, 'two-sided', 0.293580126801961, 0.293408463684361,
+                      dtype=dtype, xp=xp)
+        self._testOne(x, 'greater', 0.293580126801961, 0.146988835042376, mode='exact',
+                      dtype=dtype, xp=xp)
+        self._testOne(x, 'less', 0.109348552425692, 0.732768892470675, mode='exact',
+                      dtype=dtype, xp=xp)
+
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_known_examples(self, xp, dtype):
+        # the following tests rely on deterministically replicated rvs
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        x = stats.norm.rvs(loc=0.2, size=100, random_state=987654321)
+        x = xp.asarray(x, dtype=dtype)
+        self._testOne(x, 'two-sided', 0.12464329735846891, 0.089444888711820769,
+                      mode='asymp', xp=xp, dtype=dtype)
+        self._testOne(x, 'less', 0.12464329735846891, 0.040989164077641749,
+                      xp=xp, dtype=dtype)
+        self._testOne(x, 'greater', 0.0072115233216310994, 0.98531158590396228,
+                      xp=xp, dtype=dtype)
+
+    # this is a test of the exact p-value calculation, available only with NumPy.
+    def test_ks1samp_allpaths(self):
+        # Check NaN input, output.
+        assert_(np.isnan(kolmogn(np.nan, 1, True)))
+        with assert_raises(ValueError, match='n is not integral: 1.5'):
+            kolmogn(1.5, 1, True)
+        assert_(np.isnan(kolmogn(-1, 1, True)))
+
+        dataset = np.asarray([
+            # Check x out of range
+            (101, 1, True, 1.0),
+            (101, 1.1, True, 1.0),
+            (101, 0, True, 0.0),
+            (101, -0.1, True, 0.0),
+
+            (32, 1.0 / 64, True, 0.0),  # Ruben-Gambino
+            (32, 1.0 / 64, False, 1.0),  # Ruben-Gambino
+
+            # Miller
+            (32, 0.5, True, 0.9999999363163307),
+            # Miller 2 * special.smirnov(32, 0.5)
+            (32, 0.5, False, 6.368366937916623e-08),
+
+            # Check some other paths
+            (32, 1.0 / 8, True, 0.34624229979775223),
+            (32, 1.0 / 4, True, 0.9699508336558085),
+            (1600, 0.49, False, 0.0),
+            # 2 * special.smirnov(1600, 1/16.0)
+            (1600, 1 / 16.0, False, 7.0837876229702195e-06),
+            # _kolmogn_DMTW
+            (1600, 14 / 1600, False, 0.99962357317602),
+            # _kolmogn_PelzGood
+            (1600, 1 / 32, False, 0.08603386296651416),
+        ])
+        FuncData(kolmogn, dataset, (0, 1, 2), 3).check(dtypes=[int, float, bool])
+
+    @pytest.mark.parametrize("ksfunc", [stats.kstest, stats.ks_1samp])
+    @pytest.mark.parametrize("alternative, x6val, ref_location, ref_sign",
+                             [('greater', 6., 6., +1),
+                              ('less', 7., 7., -1),
+                              ('two-sided', 6., 6., +1),
+                              ('two-sided', 7., 7., -1)])
+    def test_location_sign(self, ksfunc, alternative,
+                           x6val, ref_location, ref_sign, xp):
+        # Test that location and sign corresponding with statistic are as
+        # expected. (Test is designed to be easy to predict.)
+        x = xp.arange(10.) + 0.5
+        x = xpx.at(x)[6].set(x6val)
+        # cdf = stats.uniform(scale=10).cdf
+        def cdf(x): return x / 10.
+        res = ksfunc(xp.asarray(x), cdf, alternative=alternative)
+        rtol = 1e-15 if x.dtype == xp.float64 else 1e-6
+        xp_assert_close(res.statistic, xp.asarray(0.1), rtol=rtol)
+        xp_assert_equal(res.statistic_location, xp.asarray(ref_location))
+        xp_assert_equal(res.statistic_sign, xp.asarray(ref_sign, dtype=xp.int8))
+
+    def test_invalid_method_raises(self):
+        # gh-24737: unrecognized method strings must raise ValueError, not
+        # silently fall through to the 'approx' else-clause.
+        rng = np.random.default_rng(42)
+        x = rng.standard_normal(10)
+        with pytest.raises(ValueError, match="Invalid value for method"):
+            stats.ks_1samp(x, special.ndtr, method='any_other_string')
+        # must fire for one-sided alternatives too, not just two-sided
+        with pytest.raises(ValueError, match="Invalid value for method"):
+            stats.ks_1samp(x, special.ndtr, method='typo',
+                           alternative='greater')
+        with pytest.raises(ValueError, match="Invalid value for method"):
+            stats.ks_1samp(x, special.ndtr, method='typo',
+                           alternative='less')
+        # kstest wrapper must also raise
+        with pytest.raises(ValueError, match="Invalid value for method"):
+            stats.kstest(x, stats.norm.cdf, method='any_other_string')
+
+@make_xp_test_case(stats.ks_2samp)
+class TestKSTwoSamples:
+    """Tests 2-samples with K-S various sizes, alternatives, modes."""
+
+    def _check_warnings(self, warn_list, expected_type, expected_len):
+        """
+        Checks that all of the warnings from a list returned by
+        `warnings.catch_all(record=True)` are of the required type and that the list
+        contains expected number of warnings.
+        """
+        assert_equal(len(warn_list), expected_len, "number of warnings")
+        for warn_ in warn_list:
+            assert_(warn_.category is expected_type)
+
+    def _testOne(self, x1, x2, alternative, ref_statistic, ref_pvalue,
+                 mode='auto', *, xp):
+        x1, x2 = xp.asarray(x1), xp.asarray(x2)
+        dtype = xp_result_type(x1, x2, force_floating=True, xp=xp)
+        res_statistic, res_pvalue = stats.ks_2samp(x1, x2, alternative, mode=mode)
+        ref_statistic = xp.asarray(ref_statistic, dtype=dtype)
+        ref_pvalue = xp.asarray(ref_pvalue, dtype=dtype)
+        xp_assert_close(res_statistic, ref_statistic)
+        # The original tests used assert_array_almost_equal, which has an absolute
+        # tolerance by default. It would be good to test with only a relative tolerance,
+        # but that would require computing p-values with another reference. R would be
+        # the natural choice, but it clips tiny p-values.
+        xp_assert_close(res_pvalue, ref_pvalue, atol=1e-14)
+
+    def testSmall(self, xp):
+        self._testOne([0], [1], 'two-sided', 1.0/1, 1.0, xp=xp)
+        self._testOne([0], [1], 'greater', 1.0/1, 0.5, xp=xp)
+        self._testOne([0], [1], 'less', 0.0/1, 1.0, xp=xp)
+        self._testOne([1], [0], 'two-sided', 1.0/1, 1.0, xp=xp)
+        self._testOne([1], [0], 'greater', 0.0/1, 1.0, xp=xp)
+        self._testOne([1], [0], 'less', 1.0/1, 0.5, xp=xp)
+
+    def testTwoVsThree(self, xp):
+        data1 = xp.asarray([1.0, 2.0])
+        data1p = data1 + 0.01
+        data1m = data1 - 0.01
+        data2 = xp.asarray([1.0, 2.0, 3.0])
+        self._testOne(data1p, data2, 'two-sided', 1.0 / 3, 1.0, xp=xp)
+        self._testOne(data1p, data2, 'greater', 1.0 / 3, 0.7, xp=xp)
+        self._testOne(data1p, data2, 'less', 1.0 / 3, 0.7, xp=xp)
+        self._testOne(data1m, data2, 'two-sided', 2.0 / 3, 0.6, xp=xp)
+        self._testOne(data1m, data2, 'greater', 2.0 / 3, 0.3, xp=xp)
+        self._testOne(data1m, data2, 'less', 0, 1.0, xp=xp)
+
+    def testTwoVsFour(self, xp):
+        data1 = xp.asarray([1.0, 2.0])
+        data1p = data1 + 0.01
+        data1m = data1 - 0.01
+        data2 = xp.asarray([1.0, 2.0, 3.0, 4.0])
+        self._testOne(data1p, data2, 'two-sided', 2.0 / 4, 14.0/15, xp=xp)
+        self._testOne(data1p, data2, 'greater', 2.0 / 4, 8.0/15, xp=xp)
+        self._testOne(data1p, data2, 'less', 1.0 / 4, 12.0/15, xp=xp)
+
+        self._testOne(data1m, data2, 'two-sided', 3.0 / 4, 6.0/15, xp=xp)
+        self._testOne(data1m, data2, 'greater', 3.0 / 4, 3.0/15, xp=xp)
+        self._testOne(data1m, data2, 'less', 0, 1.0, xp=xp)
+
+    def test100_100(self, xp):
+        x100 = xp.linspace(1, 100, 100)
+        x100_2_p1 = x100 + 2 + 0.1
+        x100_2_m1 = x100 + 2 - 0.1
+        self._testOne(x100, x100_2_p1, 'two-sided', 3.0 / 100, 0.999999999996206, xp=xp)
+        self._testOne(x100, x100_2_p1, 'greater', 3.0 / 100, 0.9143290114276248, xp=xp)
+        self._testOne(x100, x100_2_p1, 'less', 0, 1.0, xp=xp)
+        self._testOne(x100, x100_2_m1, 'two-sided', 2.0 / 100, 1.0, xp=xp)
+        self._testOne(x100, x100_2_m1, 'greater', 2.0 / 100, 0.960978450786184, xp=xp)
+        self._testOne(x100, x100_2_m1, 'less', 0, 1.0, xp=xp)
+
+    def test100_110(self, xp):
+        x100 = xp.linspace(1, 100, 100)
+        x110 = xp.linspace(1, 100, 110)
+        x110_20_p1 = x110 + 20 + 0.1
+        x110_20_m1 = x110 + 20 - 0.1
+        # 100, 110
+        self._testOne(x100, x110_20_p1, 'two-sided',
+                      232.0 / 1100, 0.015739183865607353, xp=xp)
+        self._testOne(x100, x110_20_p1, 'greater',
+                      232.0 / 1100, 0.007869594319053203, xp=xp)
+        self._testOne(x100, x110_20_p1, 'less',
+                      0, 1, xp=xp)
+        self._testOne(x100, x110_20_m1, 'two-sided',
+                      229.0 / 1100, 0.017803803861026313, xp=xp)
+        self._testOne(x100, x110_20_m1, 'greater',
+                      229.0 / 1100, 0.008901905958245056, xp=xp)
+        self._testOne(x100, x110_20_m1, 'less',
+                      0.0, 1.0, xp=xp)
+
+    def testRepeatedValues(self, xp):
+        x2233 = xp.asarray([2] * 3 + [3] * 4 + [5] * 5 + [6] * 4)
+        x3344 = x2233 + 1
+        x2356 = xp.asarray([2] * 3 + [3] * 4 + [5] * 10 + [6] * 4)
+        x3467 = xp.asarray([3] * 10 + [4] * 2 + [6] * 10 + [7] * 4)
+        self._testOne(x2233, x3344, 'two-sided', 5.0/16, 0.4262934613454952, xp=xp)
+        self._testOne(x2233, x3344, 'greater', 5.0/16, 0.21465428276573786, xp=xp)
+        self._testOne(x2233, x3344, 'less', 0.0/16, 1.0, xp=xp)
+        self._testOne(x2356, x3467, 'two-sided', 190.0/21/26, 0.0919245790168125, xp=xp)
+        self._testOne(x2356, x3467, 'greater', 190.0/21/26, 0.0459633806858544, xp=xp)
+        self._testOne(x2356, x3467, 'less', 70.0/21/26, 0.6121593130022775, xp=xp)
+
+    def testEqualSizes(self, xp):
+        data2 = xp.asarray([1.0, 2.0, 3.0])
+        self._testOne(data2, data2+1, 'two-sided', 1.0/3, 1.0, xp=xp)
+        self._testOne(data2, data2+1, 'greater', 1.0/3, 0.75, xp=xp)
+        self._testOne(data2, data2+1, 'less', 0.0/3, 1., xp=xp)
+        self._testOne(data2, data2+0.5, 'two-sided', 1.0/3, 1.0, xp=xp)
+        self._testOne(data2, data2+0.5, 'greater', 1.0/3, 0.75, xp=xp)
+        self._testOne(data2, data2+0.5, 'less', 0.0/3, 1., xp=xp)
+        self._testOne(data2, data2-0.5, 'two-sided', 1.0/3, 1.0, xp=xp)
+        self._testOne(data2, data2-0.5, 'greater', 0.0/3, 1.0, xp=xp)
+        self._testOne(data2, data2-0.5, 'less', 1.0/3, 0.75, xp=xp)
+
+    @pytest.mark.slow
+    def testMiddlingBoth(self, xp):
+        # 500, 600
+        n1, n2 = 500, 600
+        delta = 1.0/n1/n2/2/2
+        x = xp.linspace(1., 200., n1) - delta
+        y = xp.linspace(2., 200., n2)
+        self._testOne(x, y, 'two-sided', 2000.0 / n1 / n2, 1.0,
+                      mode='auto', xp=xp)
+        self._testOne(x, y, 'two-sided', 2000.0 / n1 / n2, 1.0,
+                      mode='asymp', xp=xp)
+        self._testOne(x, y, 'greater', 2000.0 / n1 / n2, 0.9697596024683929,
+                      mode='asymp', xp=xp)
+        self._testOne(x, y, 'less', 500.0 / n1 / n2, 0.9968735843165021,
+                      mode='asymp', xp=xp)
+        with warnings.catch_warnings():
+            message = "ks_2samp: Exact calculation unsuccessful."
+            warnings.filterwarnings("ignore", message, RuntimeWarning)
+            self._testOne(x, y, 'greater', 2000.0 / n1 / n2, 0.9697596024683929,
+                          mode='exact', xp=xp)
+            self._testOne(x, y, 'less', 500.0 / n1 / n2, 0.9968735843165021,
+                          mode='exact', xp=xp)
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            self._testOne(x, y, 'less', 500.0 / n1 / n2, 0.9968735843165021,
+                          mode='exact', xp=xp)
+            self._check_warnings(w, RuntimeWarning, 1)
+
+    @pytest.mark.slow
+    def testMediumBoth(self, xp):
+        # 1000, 1100
+        n1, n2 = 1000, 1100
+        delta = 1.0/n1/n2/2/2
+        x = xp.linspace(1., 200., n1) - delta
+        y = xp.linspace(2., 200., n2)
+        self._testOne(x, y, 'two-sided', 6600.0 / n1 / n2, 1.0,
+                      mode='asymp', xp=xp)
+        self._testOne(x, y, 'two-sided', 6600.0 / n1 / n2, 1.0,
+                      mode='auto', xp=xp)
+        self._testOne(x, y, 'greater', 6600.0 / n1 / n2, 0.9573185808092622,
+                      mode='asymp', xp=xp)
+        self._testOne(x, y, 'less', 1000.0 / n1 / n2, 0.9982410869433984,
+                      mode='asymp', xp=xp)
+
+        with warnings.catch_warnings():
+            message = "ks_2samp: Exact calculation unsuccessful."
+            warnings.filterwarnings("ignore", message, RuntimeWarning)
+            self._testOne(x, y, 'greater', 6600.0 / n1 / n2, 0.9573185808092622,
+                          mode='exact', xp=xp)
+            self._testOne(x, y, 'less', 1000.0 / n1 / n2, 0.9982410869433984,
+                          mode='exact', xp=xp)
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            self._testOne(x, y, 'less', 1000.0 / n1 / n2, 0.9982410869433984,
+                          mode='exact', xp=xp)
+            self._check_warnings(w, RuntimeWarning, 1)
+
+    def testLarge(self, xp):
+        # 10000, 110
+        n1, n2 = 10000, 110
+        lcm = n1*11.0
+        delta = 1.0/n1/n2/2/2
+        x = xp.linspace(1., 200., n1) - delta
+        y = xp.linspace(2., 100., n2)
+        self._testOne(x, y, 'two-sided', 55275.0 / lcm, 4.2188474935755949e-15, xp=xp)
+        self._testOne(x, y, 'greater', 561.0 / lcm, 0.99115454582047591, xp=xp)
+        self._testOne(x, y, 'less', 55275.0 / lcm, 3.1317328311518713e-26, xp=xp)
+
+    def test_gh11184(self, xp):
+        # 3000, 3001, exact two-sided
+        rng = np.random.RandomState(123456)
+        x = rng.normal(size=3000).tolist()
+        y = rng.normal(size=3001, scale=1.5).tolist()
+        self._testOne(x, y, 'two-sided', 0.11292880151060758, 2.7755575615628914e-15,
+                      mode='asymp', xp=xp)
+        self._testOne(x, y, 'two-sided', 0.11292880151060758, 2.7755575615628914e-15,
+                      mode='exact', xp=xp)
+
+    @pytest.mark.xslow
+    def test_gh11184_bigger(self, xp):
+        # 10000, 10001, exact two-sided
+        rng = np.random.RandomState(123456)
+        x = rng.normal(size=10000).tolist()
+        y = rng.normal(size=10001, scale=1.5).tolist()
+        self._testOne(x, y, 'two-sided', 0.10597913208679133, 3.3149311398483503e-49,
+                      mode='asymp', xp=xp)
+        self._testOne(x, y, 'two-sided', 0.10597913208679133, 2.7755575615628914e-15,
+                      mode='exact', xp=xp)
+        self._testOne(x, y, 'greater', 0.10597913208679133, 2.7947433906389253e-41,
+                      mode='asymp', xp=xp)
+        self._testOne(x, y, 'less', 0.09658002199780022, 2.7947433906389253e-41,
+                      mode='asymp', xp=xp)
+
+    @pytest.mark.xslow
+    def test_gh12999(self, xp):
+        rng = np.random.RandomState(123456)
+        for x in range(1000, 12000, 1000):
+            vals1 = xp.asarray(rng.normal(size=(x)))
+            vals2 = xp.asarray(rng.normal(size=(x + 10), loc=0.5))
+            exact = stats.ks_2samp(vals1, vals2, mode='exact').pvalue
+            asymp = stats.ks_2samp(vals1, vals2, mode='asymp').pvalue
+            # these two p-values should be in line with each other
+            xp_assert_less(exact, 3 * asymp)
+            xp_assert_less(asymp, 3 * exact)
+
+    @pytest.mark.slow
+    def testLargeBoth(self, xp):
+        # 10000, 11000
+        n1, n2 = 10000, 11000
+        lcm = n1*11.0
+        delta = 1.0/n1/n2/2/2
+        x = xp.linspace(1, 200, n1) - delta
+        y = xp.linspace(2, 200, n2)
+        self._testOne(x, y, 'two-sided', 563.0 / lcm, 0.9990660108966576,
+                      mode='asymp', xp=xp)
+        self._testOne(x, y, 'two-sided', 563.0 / lcm, 0.9990456491488628,
+                      mode='exact', xp=xp)
+        self._testOne(x, y, 'two-sided', 563.0 / lcm, 0.9990660108966576,
+                      mode='auto', xp=xp)
+        self._testOne(x, y, 'greater', 563.0 / lcm, 0.7561851877420673, xp=xp)
+        self._testOne(x, y, 'less', 10.0 / lcm, 0.9998239693191724, xp=xp)
+        with warnings.catch_warnings():
+            message = "ks_2samp: Exact calculation unsuccessful."
+            warnings.filterwarnings("ignore", message, RuntimeWarning)
+            self._testOne(x, y, 'greater', 563.0 / lcm, 0.7561851877420673,
+                          mode='exact', xp=xp)
+            self._testOne(x, y, 'less', 10.0 / lcm, 0.9998239693191724,
+                          mode='exact', xp=xp)
+
+    def testNamedAttributes(self, xp):
+        # test for namedtuple attribute results
+        attributes = ('statistic', 'pvalue')
+        res = stats.ks_2samp(xp.asarray([1, 2]), xp.asarray([3]))
+        check_named_results(res, attributes, xp=xp)
+
+    @pytest.mark.slow
+    @skip_xp_backends(np_only=True)
+    def test_some_code_paths(self):
+        # Check that some code paths are executed
+        from scipy.stats._stats_py import (
+            _count_paths_outside_method,
+            _compute_outer_prob_inside_method
+        )
+
+        _compute_outer_prob_inside_method(1, 1, 1, 1)
+        _count_paths_outside_method(1000, 1, 1, 1001)
+
+        with np.errstate(invalid='raise'):
+            assert_raises(FloatingPointError, _count_paths_outside_method,
+                          1100, 1099, 1, 1)
+            assert_raises(FloatingPointError, _count_paths_outside_method,
+                          2000, 1000, 1, 1)
+
+    @pytest.mark.parametrize('case', (([], [1.]), ([1.], []), ([], [])))
+    def test_argument_checking(self, case, xp):
+        # Check that an empty array warns
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = stats.ks_2samp(xp.asarray(case[0]), xp.asarray(case[1]))
+            xp_assert_equal(res.statistic, xp.asarray(np.nan))
+            xp_assert_equal(res.pvalue, xp.asarray(np.nan))
+
+    @pytest.mark.xslow
+    def test_gh12218(self, xp):
+        """Ensure gh-12218 is fixed."""
+        # gh-1228 triggered a TypeError calculating sqrt(n1*n2*(n1+n2)).
+        # n1, n2 both large integers, the product exceeded 2^64
+        rng = np.random.default_rng(8751495592)
+        n1 = 2097152  # 2*^21
+        rvs1 = xp.asarray(stats.uniform.rvs(size=n1, random_state=rng).tolist())
+        rvs2 = rvs1 + 1  # Exact value of rvs2 doesn't matter.
+        stats.ks_2samp(rvs1, rvs2, alternative='greater', mode='asymp')
+        stats.ks_2samp(rvs1, rvs2, alternative='less', mode='asymp')
+        stats.ks_2samp(rvs1, rvs2, alternative='two-sided', mode='asymp')
+
+    @pytest.mark.parametrize("dtype", [None, 'float32', 'float64'])
+    def test_warnings_gh_14019(self, dtype, xp):
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        # Check that RuntimeWarning is raised when method='auto' and exact
+        # p-value calculation fails. See gh-14019.
+        rng = np.random.RandomState(seed=23493549)
+        # random samples of the same size as in the issue
+        data1 = xp.asarray(rng.random(size=881) + 0.5, dtype=dtype)
+        data2 = xp.asarray(rng.random(size=369), dtype=dtype)
+        message = "ks_2samp: Exact calculation unsuccessful"
+        with eager_warns(RuntimeWarning, match=message, xp=xp):
+            res = stats.ks_2samp(data1, data2, alternative='less')
+            xp_assert_close(res.pvalue, xp.asarray(0., dtype=dtype), atol=1e-14)
+
+    @pytest.mark.parametrize("ksfunc", [stats.kstest, stats.ks_2samp])
+    @pytest.mark.parametrize("alternative, x6val, ref_location, ref_sign",
+                             [('greater', 5.9, 5.9, +1),
+                              ('less', 6.1, 6.0, -1),
+                              ('two-sided', 5.9, 5.9, +1),
+                              ('two-sided', 6.1, 6.0, -1)])
+    def test_location_sign(self, ksfunc, alternative,
+                           x6val, ref_location, ref_sign, xp):
+        # Test that location and sign corresponding with statistic are as
+        # expected. (Test is designed to be easy to predict.)
+        x = xp.arange(10., dtype=xp.float64)
+        y = xp_copy(x)
+        x = xpx.at(x)[6].set(x6val)
+        res = stats.ks_2samp(x, y, alternative=alternative)
+        xp_assert_close(res.statistic, xp.asarray(0.1, dtype=xp.float64), rtol=1e-15)
+        assert res.statistic_location == ref_location
+        assert res.statistic_sign == ref_sign
+
+
+@pytest.mark.filterwarnings('ignore:divide by zero:RuntimeWarning:dask')
+@pytest.mark.filterwarnings('ignore:invalid value:RuntimeWarning:dask')
+@make_xp_test_case(stats.ttest_rel)
+class TestTTestRel:
+    def test_ttest_rel_xp(self, xp):
+        # stats.ttest_rel had no tests using the xp fixture. As a temporary
+        # measure to get tools/check_xp_untested.py to pass, a portion of
+        # test_ttest_rel has been converted. It might seem unnecessary to
+        # require tests for a trivial wrapper of a well-tested function, but
+        # this seems simpler than having a way to carve out exceptions to
+        # check_xp_untested.
+        tr = xp.asarray(0.81248591389165692, dtype=xp.float64)
+        pr = xp.asarray(0.41846234511362157, dtype=xp.float64)
+
+        rvs1 = xp.linspace(1, 100, 100, dtype=xp.float64)
+        rvs2 = xp.linspace(1.01, 99.989, 100, dtype=xp.float64)
+        rvs1_2D = xp.stack(
+            [
+                xp.linspace(1,100, 100, dtype=xp.float64),
+                xp.linspace(1.01, 99.989, 100, dtype=xp.float64)
+            ]
+        )
+        rvs2_2D = xp.stack(
+            [
+                xp.linspace(1.01, 99.989, 100, dtype=xp.float64),
+                xp.linspace(1, 100, 100, dtype=xp.float64)
+            ]
+        )
+
+        t, p = stats.ttest_rel(rvs1, rvs2, axis=0)
+        xp_assert_close(t, tr)
+        xp_assert_close(p, pr)
+
+        t, p = stats.ttest_rel(rvs1_2D.T, rvs2_2D.T, axis=0)
+        xp_assert_close(t, xp.stack([tr, -tr]))
+        xp_assert_close(p, xp.stack([pr, pr]))
+
+        t, p = stats.ttest_rel(rvs1_2D, rvs2_2D, axis=1)
+        xp_assert_close(t, xp.stack([tr, -tr]))
+        xp_assert_close(p, xp.stack([pr, pr]))
+
+    def test_ttest_rel(self, xp):
+        dtype = xp.float64  # doesn't pass with default tolerances otherwise
+
+        # regression test
+        tr = xp.asarray(0.81248591389165692, dtype=dtype)
+        pr = xp.asarray(0.41846234511362157, dtype=dtype)
+        tpr = xp.stack([tr, -tr]), xp.stack([pr, pr])
+
+        rvs1 = xp.linspace(1, 100, 100, dtype=dtype)
+        rvs2 = xp.linspace(1.01, 99.989, 100, dtype=dtype)
+        rvs1_2D = xp.stack([xp.linspace(1, 100, 100, dtype=dtype),
+                            xp.linspace(1.01, 99.989, 100, dtype=dtype)])
+        rvs2_2D = xp.stack([xp.linspace(1.01, 99.989, 100, dtype=dtype),
+                            xp.linspace(1, 100, 100, dtype=dtype)])
+
+        t, p = stats.ttest_rel(rvs1, rvs2, axis=0)
+        xp_assert_close(t, tr)
+        xp_assert_close(p, pr)
+        t,p = stats.ttest_rel(rvs1_2D.T, rvs2_2D.T, axis=0)
+        xp_assert_close(t, tpr[0])
+        xp_assert_close(p, tpr[1])
+        t,p = stats.ttest_rel(rvs1_2D, rvs2_2D, axis=1)
+        xp_assert_close(t, tpr[0])
+        xp_assert_close(p, tpr[1])
+
+        # test scalars
+        with warnings.catch_warnings(), \
+                np.errstate(invalid="ignore", divide="ignore"):
+            warnings.filterwarnings(
+                "ignore", "Degrees of freedom <= 0 for slice", RuntimeWarning)
+            t, p = stats.ttest_rel(xp.asarray(4.), xp.asarray(3.))
+        assert xp.isnan(t)
+        assert xp.isnan(p)
+
+        # test for namedtuple attribute results
+        attributes = ('statistic', 'pvalue')
+        res = stats.ttest_rel(rvs1, rvs2, axis=0)
+        check_named_results(res, attributes, xp=xp)
+
+        # test on 3 dimensions
+        rvs1_3D = xp.stack([rvs1_2D, rvs1_2D, rvs1_2D])
+        rvs2_3D = xp.stack([rvs2_2D, rvs2_2D, rvs2_2D])
+        t, p = stats.ttest_rel(rvs1_3D, rvs2_3D, axis=-1)
+        xp_assert_close(t, xp.stack((tpr[0],)*3))
+        xp_assert_close(p, xp.stack((tpr[1],)*3))
+
+        t, p = stats.ttest_rel(xp.moveaxis(rvs1_3D, 2, 1),
+                               xp.moveaxis(rvs2_3D, 2, 1),
+                               axis=1)
+        xp_assert_close(t, xp.stack((tpr[0],)*3))
+        xp_assert_close(p, xp.stack((tpr[1],)*3))
+
+        # test alternative parameter
+        message = "`alternative` must be 'less', 'greater', or 'two-sided'."
+        with pytest.raises(ValueError, match=message):
+            stats.ttest_rel(rvs1, rvs2, alternative="error")
+
+        t, p = stats.ttest_rel(rvs1, rvs2, axis=0, alternative="less")
+        xp_assert_close(p, xp.asarray(1 - pr/2))
+        xp_assert_close(t, xp.asarray(tr))
+
+        t, p = stats.ttest_rel(rvs1, rvs2, axis=0, alternative="greater")
+        xp_assert_close(p, xp.asarray(pr/2))
+        xp_assert_close(t, xp.asarray(tr))
+
+    def test_nan_policy_propagate(self, xp):
+        # check nan policy
+        rng = np.random.RandomState(12345678)
+        x = stats.norm.rvs(loc=5, scale=10, size=501, random_state=rng)
+        x[500] = np.nan
+        y = (stats.norm.rvs(loc=5, scale=10, size=501, random_state=rng) +
+             stats.norm.rvs(scale=0.2, size=501, random_state=rng))
+        y[500] = np.nan
+        x, y = xp.asarray(x.tolist()), xp.asarray(y.tolist())
+
+        res = stats.ttest_rel(x, x)
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+    @skip_xp_backends(eager_only=True, reason='lazy->limited nan_policy support')
+    def test_nan_policy(self, xp):
+        # check nan policy
+        rng = np.random.RandomState(12345678)
+        x = stats.norm.rvs(loc=5, scale=10, size=501, random_state=rng)
+        x[500] = np.nan
+        y = (stats.norm.rvs(loc=5, scale=10, size=501, random_state=rng) +
+             stats.norm.rvs(scale=0.2, size=501, random_state=rng))
+        y[500] = np.nan
+        x, y = xp.asarray(x.tolist()), xp.asarray(y.tolist())
+
+        res = stats.ttest_rel(x, y, nan_policy='omit')
+        xp_assert_close(res.statistic, xp.asarray(0.25299925303978066))
+        xp_assert_close(res.pvalue, xp.asarray(0.8003729814201519))
+
+        message = "The input contains nan values"
+        with pytest.raises(ValueError, match=message):
+            stats.ttest_rel(x, y, nan_policy='raise')
+
+        message = "nan_policy must be one of"
+        with pytest.raises(ValueError, match=message):
+            stats.ttest_rel(x, y, nan_policy='foobar')
+
+    def test_edge_cases(self, xp):
+        # test zero division problem
+        with eager_warns(RuntimeWarning, match="Precision loss occurred", xp=xp):
+            t, p = stats.ttest_rel(xp.asarray([0, 0, 0]), xp.asarray([1, 1, 1]))
+
+        xp_assert_equal(xp.abs(t), xp.asarray(xp.inf))
+        xp_assert_equal(p, xp.asarray(0.0))
+
+        with np.errstate(invalid="ignore"):
+            res = stats.ttest_rel(xp.asarray([0, 0, 0]), xp.asarray([0, 0, 0]))
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+        anan = xp.asarray([[1., np.nan], [-1., 1.]])
+        res = stats.ttest_rel(anan, xp.zeros((2, 2)))
+        xp_assert_equal(res.statistic, xp.asarray([0, np.nan]))
+        xp_assert_equal(res.pvalue, xp.asarray([1, np.nan]))
+
+        # test incorrect input shape raise an error
+        x = xp.arange(24)
+        message = ("Array shapes are incompatible for broadcasting.|"
+                   "operands could not be broadcast together")
+        with pytest.raises(ValueError, match=message):
+            stats.ttest_rel(xp.reshape(x, (8, 3)), xp.reshape(x, (2, 3, 4)))
+
+    @skip_xp_backends(np_only=True, reason="multidimensional 'omit' is np-only")
+    def test_multidimensional_nan_policy_omit(self, xp):
+        rvs1_2D = np.stack([np.linspace(1, 100, 100),
+                            np.linspace(1.01, 99.989, 100)])
+        rvs2_2D = np.stack([np.linspace(1.01, 99.989, 100),
+                            np.linspace(1, 100, 100)])
+        rvs1_2D[:, 20:30] = np.nan
+        rvs2_2D[:, 15:25] = np.nan
+
+        # Convert from two-sided p-values to one sided using T result data.
+        @np.vectorize
+        def converter(t, p, alt):
+            if (t < 0 and alt == "less") or (t > 0 and alt == "greater"):
+                return p / 2
+            return 1 - (p / 2)
+
+        with pytest.warns(SmallSampleWarning, match=too_small_nd_omit):
+            tr, pr = stats.ttest_rel(rvs1_2D, rvs2_2D, 0, nan_policy='omit')
+
+        with pytest.warns(SmallSampleWarning, match=too_small_nd_omit):
+            t, p = stats.ttest_rel(rvs1_2D, rvs2_2D, 0,
+                                   nan_policy='omit', alternative='less')
+        assert_allclose(t, tr, rtol=1e-14)
+        with np.errstate(invalid='ignore'):
+            assert_allclose(p, converter(tr, pr, 'less'), rtol=1e-14)
+
+        with pytest.warns(SmallSampleWarning, match=too_small_nd_omit):
+            t, p = stats.ttest_rel(rvs1_2D, rvs2_2D, 0,
+                                   nan_policy='omit', alternative='greater')
+        assert_allclose(t, tr, rtol=1e-14)
+        with np.errstate(invalid='ignore'):
+            assert_allclose(p, converter(tr, pr, 'greater'), rtol=1e-14)
+
+    @skip_xp_backends(eager_only=True, reason="lazy -> limited nan_policy support")
+    def test_ttest_rel_nan_2nd_arg(self, xp):
+        # regression test for gh-6134: nans in the second arg were not handled
+        x = xp.asarray([xp.nan, 2.0, 3.0, 4.0])
+        y = xp.asarray([1.0, 2.0, 1.0, 2.0])
+
+        r1 = stats.ttest_rel(x, y, nan_policy='omit')
+        r2 = stats.ttest_rel(y, x, nan_policy='omit')
+        xp_assert_close(r2.statistic, -r1.statistic)
+        xp_assert_close(r2.pvalue, r1.pvalue)
+
+        # NB: arguments are paired when NaNs are dropped
+        r3 = stats.ttest_rel(y[1:], x[1:])
+        xp_assert_close(r2.statistic, r3.statistic)
+        xp_assert_close(r2.pvalue, r3.pvalue)
+
+        # .. and this is consistent with R. R code:
+        # x = c(NA, 2.0, 3.0, 4.0)
+        # y = c(1.0, 2.0, 1.0, 2.0)
+        # t.test(x, y, paired=TRUE)),
+        xp_assert_close(r2.statistic, xp.asarray(-2.), atol=1e-4)
+        xp_assert_close(r2.pvalue, xp.asarray(0.1835), atol=1e-4)
+
+    def test_ttest_rel_empty_1d_returns_nan(self, xp):
+        # Two empty inputs should return a TtestResult containing nan
+        # for both values.
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            result = stats.ttest_rel(xp.asarray([]), xp.asarray([]))
+        assert isinstance(result, stats._stats_py.TtestResult)
+        xp_assert_equal(result.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(result.pvalue, xp.asarray(xp.nan))
+
+    @skip_xp_backends("dask.array", reason="dask fails this one")
+    @pytest.mark.parametrize('b, expected_shape',
+                             [(np.empty((1, 5, 0)), (3, 5)),
+                              (np.empty((1, 0, 0)), (3, 0))])
+    def test_ttest_rel_axis_size_zero(self, b, expected_shape, xp):
+        # In this test, the length of the axis dimension is zero.
+        # The results should be arrays containing nan with shape
+        # given by the broadcast nonaxis dimensions.
+        a = xp.empty((3, 1, 0))
+        b = xp.asarray(b, dtype=a.dtype)
+        with warnings.catch_warnings():
+            # first case should warn, second shouldn't?
+            warnings.filterwarnings("ignore", too_small_nd_not_omit, SmallSampleWarning)
+            result = stats.ttest_rel(a, b, axis=-1)
+        assert isinstance(result, stats._stats_py.TtestResult)
+        expected_value = xp.full(expected_shape, fill_value=xp.nan)
+        xp_assert_equal(result.statistic, expected_value)
+        xp_assert_equal(result.pvalue, expected_value)
+
+    def test_ttest_rel_nonaxis_size_zero(self, xp):
+        # In this test, the length of the axis dimension is nonzero,
+        # but one of the nonaxis dimensions has length 0.  Check that
+        # we still get the correctly broadcast shape, which is (5, 0)
+        # in this case.
+        a = xp.empty((1, 8, 0))
+        b = xp.empty((5, 8, 1))
+        result = stats.ttest_rel(a, b, axis=1)
+        assert isinstance(result, stats._stats_py.TtestResult)
+        assert result.statistic.shape == (5, 0)
+        assert result.pvalue.shape == (5, 0)
+
+    @skip_xp_backends("jax.numpy", reason="needs stdtrit; lazy->can't use root finder")
+    @pytest.mark.parametrize("alternative", ['two-sided', 'less', 'greater'])
+    def test_ttest_rel_ci_1d(self, alternative, xp):
+        # test confidence interval method against reference values
+        rng = np.random.default_rng(3749065329432213059)
+        n = 10
+        x = xp.asarray(rng.normal(size=n, loc=1.5, scale=2).tolist())
+        y = xp.asarray(rng.normal(size=n, loc=2, scale=2).tolist())
+        # Reference values generated with R t.test:
+        # options(digits=16)
+        # x = c(1.22825792,  1.63950485,  4.39025641,  0.68609437,  2.03813481,
+        #       -1.20040109,  1.81997937,  1.86854636,  2.94694282,  3.94291373)
+        # y = c(3.49961496, 1.53192536, 5.53620083, 2.91687718, 0.04858043,
+        #       3.78505943, 3.3077496 , 2.30468892, 3.42168074, 0.56797592)
+        # t.test(x, y, paired=TRUE, conf.level=0.85, alternative='l')
+
+        ref = {'two-sided': [-1.912194489914035, 0.400169725914035],
+               'greater': [-1.563944820311475, np.inf],
+               'less': [-np.inf, 0.05192005631147523]}
+        res = stats.ttest_rel(x, y, alternative=alternative)
+        ci = res.confidence_interval(confidence_level=0.85)
+        xp_assert_close(ci.low, xp.asarray(ref[alternative][0]))
+        xp_assert_close(ci.high, xp.asarray(ref[alternative][1]))
+        xp_assert_close(res.df, xp.asarray(n-1))
+
+
+@pytest.mark.parametrize("test_fun, args", [
+    make_xp_pytest_param(stats.ttest_1samp, (np.arange(10), 0)),
+    make_xp_pytest_param(stats.ttest_rel, (np.arange(10), np.arange(10)))])
+def test_ttest_ci_iv(test_fun, args, xp):
+    # test `confidence_interval` method input validation
+    res = test_fun(*(xp.asarray(arg) for arg in args))
+    message = '`confidence_level` must be a number between 0 and 1.'
+    with pytest.raises(ValueError, match=message):
+        res.confidence_interval(confidence_level=10)
+
+
+def _desc_stats(x1, x2, axis=0, *, xp=None):
+    xp = array_namespace(x1, x2) if xp is None else xp
+
+    def _stats(x, axis=0):
+        x = xp.asarray(x)
+        mu = xp.mean(x, axis=axis)
+        std = xp.std(x, axis=axis, correction=1)
+        nobs = x.shape[axis]
+        return mu, std, nobs
+
+    return _stats(x1, axis) + _stats(x2, axis)
+
+
+@make_xp_test_case(stats.ttest_ind, stats.ttest_ind_from_stats)
+class TestTTestInd:
+    def test_ttest_ind(self, xp):
+        # regression test
+        tr = xp.asarray(1.0912746897927283)
+        pr = xp.asarray(0.27647818616351882)
+        tr_2D = xp.stack([tr, -tr])
+        pr_2D = xp.stack([pr, pr])
+
+        rvs1 = xp.linspace(5, 105, 100)
+        rvs2 = xp.linspace(1, 100, 100)
+        rvs1_2D = xp.stack([rvs1, rvs2])
+        rvs2_2D = xp.stack([rvs2, rvs1])
+
+        res = stats.ttest_ind(rvs1, rvs2, axis=0)
+        t, p = res  # check that result object can be unpacked
+        xp_assert_close(t, tr)
+        xp_assert_close(p, pr)
+
+        res = stats.ttest_ind_from_stats(*_desc_stats(rvs1, rvs2))
+        t, p = res  # check that result object can be unpacked
+        xp_assert_close(t, tr)
+        xp_assert_close(p, pr)
+
+        res = stats.ttest_ind(rvs1_2D.T, rvs2_2D.T, axis=0)
+        xp_assert_close(res.statistic, tr_2D)
+        xp_assert_close(res.pvalue, pr_2D)
+
+        res = stats.ttest_ind_from_stats(*_desc_stats(rvs1_2D.T, rvs2_2D.T))
+        xp_assert_close(res.statistic, tr_2D)
+        xp_assert_close(res.pvalue, pr_2D)
+
+        res = stats.ttest_ind(rvs1_2D, rvs2_2D, axis=1)
+        xp_assert_close(res.statistic, tr_2D)
+        xp_assert_close(res.pvalue, pr_2D)
+
+        res = stats.ttest_ind_from_stats(*_desc_stats(rvs1_2D, rvs2_2D, axis=1))
+        xp_assert_close(res.statistic, tr_2D)
+        xp_assert_close(res.pvalue, pr_2D)
+
+        # test on 3 dimensions removed because generic tests in
+        # test_axis_nan_policy are much stronger
+
+        # test alternative parameter
+        message = "`alternative` must be 'less', 'greater', or 'two-sided'."
+        with pytest.raises(ValueError, match=message):
+            stats.ttest_ind(rvs1, rvs2, alternative = "error")
+
+        args = _desc_stats(rvs1_2D.T, rvs2_2D.T)
+        with pytest.raises(ValueError, match=message):
+            stats.ttest_ind_from_stats(*args, alternative = "error")
+
+        t, p = stats.ttest_ind(rvs1, rvs2, alternative="less")
+        xp_assert_close(p, 1 - (pr/2))
+        xp_assert_close(t, tr)
+
+        t, p = stats.ttest_ind(rvs1, rvs2, alternative="greater")
+        xp_assert_close(p, pr/2)
+        xp_assert_close(t, tr)
+
+        # Check that ttest_ind_from_stats agrees with ttest_ind
+        res1 = stats.ttest_ind(rvs1_2D.T, rvs2_2D.T, axis=0, alternative="less")
+        args = _desc_stats(rvs1_2D.T, rvs2_2D.T)
+        res2 = stats.ttest_ind_from_stats(*args, alternative="less")
+        xp_assert_close(res1.statistic, res2.statistic)
+        xp_assert_close(res1.pvalue, res2.pvalue)
+
+        res1 = stats.ttest_ind(rvs1_2D.T, rvs2_2D.T, axis=0, alternative="less")
+        args = _desc_stats(rvs1_2D.T, rvs2_2D.T)
+        res2 = stats.ttest_ind_from_stats(*args, alternative="less")
+        xp_assert_close(res1.statistic, res2.statistic)
+        xp_assert_close(res1.pvalue, res2.pvalue)
+
+        # test NaNs
+        NaN = xp.asarray(xp.nan)
+        rvs1 = xp.where(xp.arange(rvs1.shape[0]) == 0, NaN, rvs1)
+
+        res = stats.ttest_ind(rvs1, rvs2, axis=0)
+        xp_assert_equal(res.statistic, NaN)
+        xp_assert_equal(res.pvalue, NaN)
+
+        res = stats.ttest_ind_from_stats(*_desc_stats(rvs1, rvs2))
+        xp_assert_equal(res.statistic, NaN)
+        xp_assert_equal(res.pvalue, NaN)
+
+    @skip_xp_backends(eager_only=True, reason='lazy -> limited nan_policy support')
+    def test_special_cases(self, xp):
+        # check nan policy
+        rng = np.random.default_rng(22199381)
+        x = stats.norm.rvs(loc=5, scale=10, size=501, random_state=rng)
+        y = stats.norm.rvs(loc=5, scale=10, size=500, random_state=rng)
+        x[500] = np.nan
+        x, y = xp.asarray(x.tolist()), xp.asarray(y.tolist())
+
+        res = stats.ttest_ind(x, y)
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+        # test zero division problem
+        with eager_warns(RuntimeWarning, match="Precision loss occurred", xp=xp):
+            t, p = stats.ttest_ind(xp.asarray([0, 0, 0]), xp.asarray([1, 1, 1]))
+        xp_assert_equal(t, xp.asarray(-xp.inf))
+        xp_assert_equal(p, xp.asarray(0.))
+
+        x = xp.asarray([0, 0, 0])
+        res = stats.ttest_ind(x, x)
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+        # check that nan in input array result in nan output
+        anan = xp.asarray([[1, np.nan], [-1, 1]])
+        res = stats.ttest_ind(anan, xp.zeros((2, 2)))
+        xp_assert_equal(res.statistic, xp.asarray([0, xp.nan]))
+        xp_assert_equal(res.pvalue, xp.asarray([1, xp.nan]))
+
+    @skip_xp_backends(eager_only=True, reason='lazy -> limited nan_policy support')
+    def test_ttest_ind_nan_policy(self, xp):
+        rng = np.random.default_rng(61481957)
+        x = stats.norm.rvs(loc=5, scale=10, size=501, random_state=rng)
+        y = stats.norm.rvs(loc=5, scale=10, size=500, random_state=rng)
+        x[500] = np.nan
+        x, y = xp.asarray(x.tolist()), xp.asarray(y.tolist())
+
+        res = stats.ttest_ind(x, y, nan_policy='omit')
+        ref = stats.ttest_ind(x[~xp.isnan(x)], y, nan_policy='omit')
+        xp_assert_close(res.statistic, ref.statistic)
+        xp_assert_close(res.pvalue, ref.pvalue)
+
+        message = "The input contains nan values"
+        with pytest.raises(ValueError, match=message):
+            stats.ttest_ind(x, y, nan_policy='raise')
+
+        message = "nan_policy must be one of"
+        with pytest.raises(ValueError, match=message):
+            stats.ttest_ind(x, y, nan_policy='foobar')
+
+    def test_3D_nan_policy_omit(self):  # ND nan_policy='omit' is NumPy only
+        rvs1 = np.linspace(5, 105, 100)
+        rvs2 = np.linspace(1, 100, 100)
+        rvs1_2D = np.array([rvs1, rvs2])
+        rvs2_2D = np.array([rvs2, rvs1])
+        rvs1_3D = np.dstack([rvs1_2D, rvs1_2D, rvs1_2D])
+        rvs2_3D = np.dstack([rvs2_2D, rvs2_2D, rvs2_2D])
+
+        rvs1_3D[:, :, 10:15] = np.nan
+        rvs2_3D[:, :, 6:12] = np.nan
+
+        # Convert from two-sided p-values to one sided using T result data.
+        def convert(t, p, alt):
+            if (t < 0 and alt == "less") or (t > 0 and alt == "greater"):
+                return p / 2
+            return 1 - (p / 2)
+        converter = np.vectorize(convert)
+
+        tr, pr = stats.ttest_ind(rvs1_3D, rvs2_3D, axis=0, nan_policy='omit')
+
+        t, p = stats.ttest_ind(rvs1_3D, rvs2_3D, axis=0, nan_policy='omit',
+                               alternative='less')
+        assert_allclose(t, tr, rtol=1e-14)
+        assert_allclose(p, converter(tr, pr, 'less'), rtol=1e-14)
+
+        t, p = stats.ttest_ind(rvs1_3D, rvs2_3D, axis=0, nan_policy='omit',
+                               alternative='greater')
+        assert_allclose(t, tr, rtol=1e-14)
+        assert_allclose(p, converter(tr, pr, 'greater'), rtol=1e-14)
+
+
+    def test_ttest_ind_scalar(self):  # ttest_ind for scalar input is legacy/np-only
+        # test scalars
+        with warnings.catch_warnings(), np.errstate(invalid="ignore"):
+            warnings.filterwarnings(
+                "ignore", "Degrees of freedom <= 0 for slice", RuntimeWarning)
+            t, p = stats.ttest_ind(4., 3.)
+        assert np.isnan(t)
+        assert np.isnan(p)
+
+
+@make_xp_test_case(stats.ttest_ind)
+@skip_xp_backends('dask.array', reason="Dask doesn't support ResamplingMethods")
+@pytest.mark.filterwarnings("ignore:Arguments...:DeprecationWarning")
+class TestTTestIndResampling:
+    N = 20
+
+    # data for most tests
+    rng = np.random.default_rng(169708062)
+    a = np.vstack((np.arange(3*N//4), rng.random(3*N//4)))
+    b = np.vstack((np.arange(N//4) + 100, rng.random(N//4)))
+
+    # data for equal variance tests
+    a2 = np.arange(10)
+    b2 = np.arange(10) + 100
+
+    # data for exact test
+    a3 = [1, 2]
+    b3 = [3, 4]
+
+    # data for bigger test
+    rvs1 = stats.norm.rvs(loc=5, scale=10,  # type: ignore
+                          size=500, random_state=rng).reshape(100, 5).T
+    rvs2 = stats.norm.rvs(loc=8, scale=20, size=100, random_state=rng)  # type: ignore
+
+    p_d = [1/1001, (676+1)/1001]  # desired pvalues
+    p_d_gen = [1/1001, (672 + 1)/1001]  # desired pvalues for Generator seed
+    p_d_big = [(993+1)/1001, (685+1)/1001, (840+1)/1001,
+               (955+1)/1001, (255+1)/1001]
+
+    params = [
+        (a, b, {"axis": 1}, p_d),                     # basic test
+        (a.T, b.T, {'axis': 0}, p_d),                 # along axis 0
+        (a[0, :], b[0, :], {'axis': None}, p_d[0]),   # 1d data
+        (a[0, :].tolist(), b[0, :].tolist(), {'axis': None}, p_d[0]),
+        # different seeds
+        (a, b, {'random_state': 0, "axis": 1}, p_d),
+        (a, b, {'random_state': np.random.RandomState(0), "axis": 1}, p_d),
+        (a2, b2, {'equal_var': True}, 1/1001),  # equal variances
+        (rvs1, rvs2, {'axis': -1, 'random_state': 0}, p_d_big),  # bigger test
+        (a3, b3, {}, 1/3),  # exact test
+        (a, b, {'random_state': np.random.default_rng(0), "axis": 1}, p_d_gen),
+        ]
+
+    @pytest.mark.parametrize("alternative", ['less', 'greater', 'two-sided'])
+    @pytest.mark.parametrize("shape", [(12,), (2, 12)])
+    def test_permutation_method(self, alternative, shape, xp):
+        rng = np.random.default_rng(2348934579834565)
+        x = xp.asarray(rng.random(size=shape).tolist())
+        y = xp.asarray(rng.random(size=13).tolist())
+
+        kwargs = dict(n_resamples=999)
+
+        # Use ttest_ind with `method`
+        rng = np.random.default_rng(348934579834565)
+        method = stats.PermutationMethod(rng=rng, **kwargs)
+        res = stats.ttest_ind(x, y, axis=-1, alternative=alternative, method=method)
+
+        # Use `permutation_test` directly
+        def statistic(x, y, axis): return stats.ttest_ind(x, y, axis=axis).statistic
+        rng = np.random.default_rng(348934579834565)
+        ref = stats.permutation_test((x, y), statistic, axis=-1, rng=rng,
+                                     alternative=alternative, **kwargs)
+
+        xp_assert_close(res.statistic, ref.statistic)
+        xp_assert_close(res.pvalue, ref.pvalue)
+
+        # Sanity check against theoretical t-test
+        ref = stats.ttest_ind(x, y, axis=-1, alternative=alternative)
+        xp_assert_close(res.statistic, ref.statistic)
+        xp_assert_close(res.pvalue, ref.pvalue, rtol=3e-2)
+
+    @pytest.mark.parametrize("alternative", ['less', 'greater', 'two-sided'])
+    @pytest.mark.parametrize("shape", [(12,), (2, 12)])
+    def test_monte_carlo_method(self, alternative, shape, xp):
+        rng = np.random.default_rng(2348934579834565)
+        x = xp.asarray(rng.random(size=shape).tolist())
+        y = xp.asarray(rng.random(size=13).tolist())
+
+        kwargs = dict(n_resamples=999)
+
+        # Use `monte_carlo` directly
+        def statistic(x, y, axis):
+            x, y = xp.asarray(x), xp.asarray(y)
+            return stats.ttest_ind(x, y, axis=axis).statistic
+
+        rng = np.random.default_rng(348934579834565)
+        rvs = [rng.standard_normal, rng.standard_normal]
+        ref = stats.monte_carlo_test((x, y), rvs=rvs, statistic=statistic, axis=-1,
+                                     alternative=alternative, **kwargs)
+
+        # Use ttest_ind with `method`
+        rng = np.random.default_rng(348934579834565)
+        rvs = [rng.standard_normal, rng.standard_normal]
+        method = stats.MonteCarloMethod(rvs=rvs, **kwargs)
+        res = stats.ttest_ind(x, y, axis=-1, alternative=alternative, method=method)
+        xp_assert_close(res.statistic, ref.statistic)
+        xp_assert_close(res.pvalue, ref.pvalue)
+
+        # Passing `rng` instead of `rvs`
+        method = stats.MonteCarloMethod(rng=348934579834565, **kwargs)
+        res = stats.ttest_ind(x, y, axis=-1, alternative=alternative, method=method)
+        xp_assert_close(res.statistic, ref.statistic)
+        xp_assert_close(res.pvalue, ref.pvalue)
+
+        # Sanity check against theoretical t-test
+        ref = stats.ttest_ind(x, y, axis=-1, alternative=alternative)
+        xp_assert_close(res.statistic, ref.statistic)
+        xp_assert_close(res.pvalue, ref.pvalue, rtol=6e-2)
+
+    def test_resampling_input_validation(self, xp):
+        message = "`method` must be an instance of `PermutationMethod`, an instance..."
+        with pytest.raises(ValueError, match=message):
+            x, y = xp.asarray([1, 2, 3]), xp.asarray([4, 5, 6])
+            stats.ttest_ind(x, y, method='migratory')
+
+
+@make_xp_test_case(stats.ttest_ind)
+class TestTTestIndCommon:
+    # for tests that are performed on variations of the t-test (e.g. trimmed)
+    @pytest.mark.parametrize("kwds", [{'trim': .2}, {}],
+                             ids=["trim", "basic"])
+    @pytest.mark.parametrize('equal_var', [True, False],
+                             ids=['equal_var', 'unequal_var'])
+    def test_ttest_many_dims_fast(self, kwds, equal_var, xp):
+        # Test that ttest_ind works on many-dimensional arrays
+        rng = np.random.default_rng(3815288136)
+        a = xp.asarray(rng.random((5, 4, 4, 7, 1, 6)).tolist())
+        b = xp.asarray(rng.random((4, 1, 8, 2, 6)).tolist())
+        res = stats.ttest_ind(a, b, axis=-3, **kwds)
+
+        # compare fully-vectorized t-test against t-test on smaller slice
+        i, j, k = 2, 3, 1
+        a2 = a[i, :, j, :, 0, :]
+        b2 = b[:, 0, :, k, :]
+        res2 = stats.ttest_ind(a2, b2, axis=-2, **kwds)
+        xp_assert_close(res.statistic[i, :, j, k, :], res2.statistic)
+        xp_assert_close(res.pvalue[i, :, j, k, :], res2.pvalue)
+
+    @pytest.mark.xslow
+    @pytest.mark.parametrize("kwds", [{'trim': .2}, {}],
+                             ids=["trim", "basic"])
+    @pytest.mark.parametrize('equal_var', [True, False],
+                             ids=['equal_var', 'unequal_var'])
+    @skip_xp_backends('array_api_strict', reason="indexing in test is too fancy")
+    @skip_xp_backends('jax.numpy', reason="indexing in test is too fancy")
+    @skip_xp_backends('dask.array', reason="too slow")
+    def test_ttest_many_dims_full(self, kwds, equal_var, xp):
+        # compare against t-test on one axis-slice at a time
+        rng = np.random.default_rng(3815288136)
+        a = rng.random((5, 4, 4, 7, 1, 6)).tolist()
+        b = rng.random((4, 1, 8, 2, 6)).tolist()
+        res = stats.ttest_ind(xp.asarray(a), xp.asarray(b), axis=-3, **kwds)
+
+        # manually broadcast with tile; move axis to end to simplify
+        x = np.moveaxis(np.tile(a, (1, 1, 1, 1, 2, 1)), -3, -1)
+        y = np.moveaxis(np.tile(b, (5, 1, 4, 1, 1, 1)), -3, -1)
+        x, y = xp.asarray(x.tolist()), xp.asarray(y.tolist())
+
+        shape = x.shape[:-1]
+        statistics = xp.zeros(shape)
+        pvalues = xp.zeros(shape)
+        for indices in product(*(range(i) for i in shape)):
+            xi = x[indices]  # use tuple to index single axis slice
+            yi = y[indices]
+            res3 = stats.ttest_ind(xi, yi, axis=-1, **kwds)
+            statistics[indices] = res3.statistic
+            pvalues[indices] = res3.pvalue
+
+        xp_assert_close(res.statistic, statistics)
+        xp_assert_close(res.pvalue, pvalues)
+
+    @pytest.mark.parametrize("kwds", [{'trim': .2}, {}],
+                             ids=["trim", "basic"])
+    @pytest.mark.parametrize("axis", [-1, 0])
+    def test_nans_on_axis(self, kwds, axis, xp):
+        # confirm that with `nan_policy='propagate'`, NaN results are returned
+        # on the correct location
+        rng = np.random.default_rng(363836384995579937222)
+        a = rng.integers(10, size=(5, 3, 10)).astype('float')
+        b = rng.integers(10, size=(5, 3, 10)).astype('float')
+        # set some indices in `a` and `b` to be `np.nan`.
+        a[0][2][3] = np.nan
+        b[2][0][6] = np.nan
+        a, b = xp.asarray(a.tolist()), xp.asarray(b.tolist())
+
+        # arbitrarily use `np.sum` as a baseline for which indices should be NaNs
+        expected = xp.isnan(xp.sum(a + b, axis=axis))
+
+        # multidimensional inputs to `t.sf(np.abs(t), df)` with NaNs on some
+        # indices throws an warning. See issue gh-13844
+        with warnings.catch_warnings(), np.errstate(invalid="ignore"):
+            warnings.filterwarnings(
+                "ignore", "invalid value encountered in less_equal", RuntimeWarning)
+            warnings.filterwarnings("ignore", "Precision loss occurred", RuntimeWarning)
+            res = stats.ttest_ind(a, b, axis=axis, **kwds)
+
+        p_nans = xp.isnan(res.pvalue)
+        xp_assert_equal(p_nans, expected)
+        statistic_nans = xp.isnan(res.statistic)
+        xp_assert_equal(statistic_nans, expected)
+
+
+@make_xp_test_case(stats.ttest_ind)
+class TestTTestTrimmed:
+    params = [
+        [[1., 2., 3.], [1.1, 2.9, 4.2], 0.53619490753126731, -0.6864951273557258, .2],
+        [[56., 128.6, 12, 123.8, 64.34, 78, 763.3], [1.1, 2.9, 4.2],
+         0.00998909252078421, 4.591598691181999, .2],
+        [[56., 128.6, 12., 123.8, 64.34, 78., 763.3], [1.1, 2.9, 4.2],
+         0.10512380092302633, 2.832256715395378, .32],
+        [[2.7, 2.7, 1.1, 3.0, 1.9, 3.0, 3.8, 3.8, 0.3, 1.9, 1.9],
+         [6.5, 5.4, 8.1, 3.5, 0.5, 3.8, 6.8, 4.9, 9.5, 6.2, 4.1],
+         0.002878909511344, -4.2461168970325, .2],
+        [[-0.84504783, 0.13366078, 3.53601757, -0.62908581, 0.54119466,
+          -1.16511574, -0.08836614, 1.18495416, 2.48028757, -1.58925028,
+          -1.6706357, 0.3090472, -2.12258305, 0.3697304, -1.0415207,
+          -0.57783497, -0.90997008, 1.09850192, 0.41270579, -1.4927376],
+         [1.2725522, 1.1657899, 2.7509041, 1.2389013, -0.9490494, -1.0752459,
+          1.1038576, 2.9912821, 3.5349111, 0.4171922, 1.0168959, -0.7625041,
+          -0.4300008, 3.0431921, 1.6035947, 0.5285634, -0.7649405, 1.5575896,
+          1.3670797, 1.1726023], 0.005293305834235, -3.0983317739483, .2]]
+
+    @pytest.mark.parametrize("a, b, pr, tr, trim", params)
+    @pytest.mark.parametrize("dtype", [None, 'float32', 'float64'])
+    def test_ttest_compare_r(self, a, b, pr, tr, trim, dtype, xp):
+        '''
+        Using PairedData's yuen.t.test method. Something to note is that there
+        are at least 3 R packages that come with a trimmed t-test method, and
+        comparisons were made between them. It was found that PairedData's
+        method's results match this method, SAS, and one of the other R
+        methods. A notable discrepancy was the DescTools implementation of the
+        function, which only sometimes agreed with SAS, WRS2, PairedData and
+        this implementation. For this reason, most comparisons in R are made
+        against PairedData's method.
+
+        Rather than providing the input and output for all evaluations, here is
+        a representative example:
+        > library(PairedData)
+        > a <- c(1, 2, 3)
+        > b <- c(1.1, 2.9, 4.2)
+        > options(digits=16)
+        > yuen.t.test(a, b, tr=.2)
+
+            Two-sample Yuen test, trim=0.2
+
+        data:  x and y
+        t = -0.68649512735573, df = 3.4104431643464, p-value = 0.5361949075313
+        alternative hypothesis: true difference in trimmed means is not equal
+        to 0
+        95 percent confidence interval:
+         -3.912777195645217  2.446110528978550
+        sample estimates:
+        trimmed mean of x trimmed mean of y
+        2.000000000000000 2.73333333333333
+        '''
+        dtype = dtype if dtype is None else getattr(xp, dtype)
+        a, b = xp.asarray(a, dtype=dtype), xp.asarray(b, dtype=dtype)
+        statistic, pvalue = stats.ttest_ind(a, b, trim=trim, equal_var=False)
+        xp_assert_close(statistic, xp.asarray(tr, dtype=dtype))
+        xp_assert_close(pvalue, xp.asarray(pr, dtype=dtype))
+
+    def test_compare_SAS(self, xp):
+        # Source of the data used in this test:
+        # https://support.sas.com/resources/papers/proceedings14/1660-2014.pdf
+        a = [12, 14, 18, 25, 32, 44, 12, 14, 18, 25, 32, 44]
+        b = [17, 22, 14, 12, 30, 29, 19, 17, 22, 14, 12, 30, 29, 19]
+        a, b = xp.asarray(a), xp.asarray(b)
+        # In this paper, a trimming percentage of 5% is used. However,
+        # in their implementation, the number of values trimmed is rounded to
+        # the nearest whole number. However, consistent with
+        # `scipy.stats.trimmed_mean`, this test truncates to the lower
+        # whole number. In this example, the paper notes that 1 value is
+        # trimmed off of each side. 9% replicates this amount of trimming.
+        statistic, pvalue = stats.ttest_ind(a, b, trim=.09, equal_var=False)
+        xp_assert_close(pvalue, xp.asarray(0.514522), atol=1e-6)
+        xp_assert_close(statistic, xp.asarray(0.669169), atol=1e-6)
+
+    def test_equal_var(self, xp):
+        '''
+        The PairedData library only supports unequal variances. To compare
+        samples with equal variances, the multicon library is used.
+        > library(multicon)
+        > a <- c(2.7, 2.7, 1.1, 3.0, 1.9, 3.0, 3.8, 3.8, 0.3, 1.9, 1.9)
+        > b <- c(6.5, 5.4, 8.1, 3.5, 0.5, 3.8, 6.8, 4.9, 9.5, 6.2, 4.1)
+        > dv = c(a,b)
+        > iv = c(rep('a', length(a)), rep('b', length(b)))
+        > yuenContrast(dv~ iv, EQVAR = TRUE)
+        $Ms
+           N                 M wgt
+        a 11 2.442857142857143   1
+        b 11 5.385714285714286  -1
+
+        $test
+                              stat df              crit                   p
+        results -4.246116897032513 12 2.178812829667228 0.00113508833897713
+        '''
+        a = [2.7, 2.7, 1.1, 3.0, 1.9, 3.0, 3.8, 3.8, 0.3, 1.9, 1.9]
+        b = [6.5, 5.4, 8.1, 3.5, 0.5, 3.8, 6.8, 4.9, 9.5, 6.2, 4.1]
+        a, b = xp.asarray(a), xp.asarray(b)
+        # `equal_var=True` is default
+        statistic, pvalue = stats.ttest_ind(a, b, trim=.2)
+        xp_assert_close(pvalue, xp.asarray(0.00113508833897713))
+        xp_assert_close(statistic, xp.asarray(-4.246116897032513))
+
+    @pytest.mark.parametrize('alt,pr,tr',
+                             (('greater', 0.9985605452443, -4.2461168970325),
+                              ('less', 0.001439454755672, -4.2461168970325),),
+                             )
+    def test_alternatives(self, alt, pr, tr, xp):
+        '''
+        > library(PairedData)
+        > a <- c(2.7,2.7,1.1,3.0,1.9,3.0,3.8,3.8,0.3,1.9,1.9)
+        > b <- c(6.5,5.4,8.1,3.5,0.5,3.8,6.8,4.9,9.5,6.2,4.1)
+        > options(digits=16)
+        > yuen.t.test(a, b, alternative = 'greater')
+        '''
+        a = [2.7, 2.7, 1.1, 3.0, 1.9, 3.0, 3.8, 3.8, 0.3, 1.9, 1.9]
+        b = [6.5, 5.4, 8.1, 3.5, 0.5, 3.8, 6.8, 4.9, 9.5, 6.2, 4.1]
+        a, b = xp.asarray(a), xp.asarray(b)
+        statistic, pvalue = stats.ttest_ind(a, b, trim=.2, equal_var=False,
+                                            alternative=alt)
+        xp_assert_close(pvalue, xp.asarray(pr))
+        xp_assert_close(statistic, xp.asarray(tr))
+
+    @pytest.mark.parametrize("trim", [-.2, .5, 1])
+    def test_trim_bounds_error(self, trim, xp):
+        match = "Trimming percentage should be 0 <= `trim` < .5."
+        with assert_raises(ValueError, match=match):
+            stats.ttest_ind(xp.asarray([1, 2]), xp.asarray([2, 1]), trim=trim)
+
+
+@make_xp_test_case(stats.ttest_ind)
+class TestTTestCI:
+    # indices in order [alternative={two-sided, less, greater},
+    #                   equal_var={False, True}, trim={0, 0.2}]
+    # reference values in order `statistic, df, pvalue, low, high`
+    # equal_var=False reference values computed with R PairedData yuen.t.test:
+    #
+    # library(PairedData)
+    # options(digits=16)
+    # a < - c(0.88236329, 0.97318744, 0.4549262, 0.97893335, 0.0606677,
+    #         0.44013366, 0.55806018, 0.40151434, 0.14453315, 0.25860601,
+    #         0.20202162)
+    # b < - c(0.93455277, 0.42680603, 0.49751939, 0.14152846, 0.711435,
+    #         0.77669667, 0.20507578, 0.78702772, 0.94691855, 0.32464958,
+    #         0.3873582, 0.35187468, 0.21731811)
+    # yuen.t.test(a, b, tr=0, conf.level = 0.9, alternative = 'l')
+    #
+    # equal_var=True reference values computed with R multicon yuenContrast:
+    #
+    # library(multicon)
+    # options(digits=16)
+    # a < - c(0.88236329, 0.97318744, 0.4549262, 0.97893335, 0.0606677,
+    #         0.44013366, 0.55806018, 0.40151434, 0.14453315, 0.25860601,
+    #         0.20202162)
+    # b < - c(0.93455277, 0.42680603, 0.49751939, 0.14152846, 0.711435,
+    #         0.77669667, 0.20507578, 0.78702772, 0.94691855, 0.32464958,
+    #         0.3873582, 0.35187468, 0.21731811)
+    # dv = c(a, b)
+    # iv = c(rep('a', length(a)), rep('b', length(b)))
+    # yuenContrast(dv~iv, EQVAR = FALSE, alternative = 'unequal', tr = 0.2)
+    r = np.empty(shape=(3, 2, 2, 5))
+    r[0, 0, 0] = [-0.2314607, 19.894435, 0.8193209, -0.247220294, 0.188729943]
+    r[1, 0, 0] = [-0.2314607, 19.894435, 0.40966045, -np.inf, 0.1382426469]
+    r[2, 0, 0] = [-0.2314607, 19.894435, 0.5903395, -0.1967329982, np.inf]
+    r[0, 0, 1] = [-0.2452886, 11.427896, 0.8105823, -0.34057446, 0.25847383]
+    r[1, 0, 1] = [-0.2452886, 11.427896, 0.40529115, -np.inf, 0.1865829074]
+    r[2, 0, 1] = [-0.2452886, 11.427896, 0.5947089, -0.268683541, np.inf]
+    # confidence interval not available for equal_var=True
+    r[0, 1, 0] = [-0.2345625322555006, 22, 0.8167175905643815, np.nan, np.nan]
+    r[1, 1, 0] = [-0.2345625322555006, 22, 0.4083587952821908, np.nan, np.nan]
+    r[2, 1, 0] = [-0.2345625322555006, 22, 0.5916412047178092, np.nan, np.nan]
+    r[0, 1, 1] = [-0.2505369406507428, 14, 0.8058115135702835, np.nan, np.nan]
+    r[1, 1, 1] = [-0.2505369406507428, 14, 0.4029057567851417, np.nan, np.nan]
+    r[2, 1, 1] = [-0.2505369406507428, 14, 0.5970942432148583, np.nan, np.nan]
+    @pytest.mark.parametrize('alternative', ['two-sided', 'less', 'greater'])
+    @pytest.mark.parametrize('equal_var', [False, True])
+    @pytest.mark.parametrize('trim', [0, 0.2])
+    @skip_xp_backends('jax.numpy', reason='Generic stdtrit mutates array.')
+    def test_confidence_interval(self, alternative, equal_var, trim, xp):
+        if equal_var and trim:
+            pytest.xfail('Discrepancy in `main`; needs further investigation.')
+
+        if trim and not is_numpy(xp):
+            pytest.skip('`trim` is only compatible with NumPy input')
+
+        rng = np.random.default_rng(3810954496107292580)
+        x = xp.asarray(rng.random(11))
+        y = xp.asarray(rng.random(13))
+
+        res = stats.ttest_ind(x, y, alternative=alternative,
+                              equal_var=equal_var, trim=trim)
+
+        alternatives = {'two-sided': 0, 'less': 1, 'greater': 2}
+        ref = self.r[alternatives[alternative], int(equal_var), int(np.ceil(trim))]
+        statistic, df, pvalue, low, high = ref
+
+        rtol = 1e-7  # only 7 digits in reference
+        xp_assert_close(res.statistic, xp.asarray(statistic), rtol=rtol)
+        xp_assert_close(res.df, xp.asarray(df), rtol=rtol)
+        xp_assert_close(res.pvalue, xp.asarray(pvalue), rtol=rtol)
+
+        if not equal_var:  # CI not available when `equal_var is True`
+            ci = res.confidence_interval(0.9)
+            xp_assert_close(ci.low, xp.asarray(low), rtol=rtol)
+            xp_assert_close(ci.high, xp.asarray(high), rtol=rtol)
+
+
+@make_xp_test_case(stats.ttest_ind)
+class TestTTestIndMore:
+    @make_xp_test_case(stats.ttest_ind_from_stats)
+    def test_ttest_ind_with_uneq_var(self, xp):
+        # check vs. R `t.test`, e.g.
+        # options(digits=20)
+        # a = c(1., 2., 3.)
+        # b = c(1.1, 2.9, 4.2)
+        # t.test(a, b, equal.var=FALSE)
+
+        a = xp.asarray([1., 2., 3.])
+        b = xp.asarray([1.1, 2.9, 4.2])
+        pr = xp.asarray(0.53619490753126686)
+        tr = xp.asarray(-0.686495127355726265)
+
+        t, p = stats.ttest_ind(a, b, equal_var=False)
+        xp_assert_close(t, tr)
+        xp_assert_close(p, pr)
+
+        t, p = stats.ttest_ind_from_stats(*_desc_stats(a, b), equal_var=False)
+        xp_assert_close(t, tr)
+        xp_assert_close(p, pr)
+
+        a = xp.asarray([1., 2., 3., 4.])
+        pr = xp.asarray(0.84354139131608252)
+        tr = xp.asarray(-0.210866331595072315)
+
+        t, p = stats.ttest_ind(a, b, equal_var=False)
+        xp_assert_close(t, tr)
+        xp_assert_close(p, pr)
+
+        t, p = stats.ttest_ind_from_stats(*_desc_stats(a, b), equal_var=False)
+        xp_assert_close(t, tr)
+        xp_assert_close(p, pr)
+
+        # regression test
+        tr = xp.asarray(1.0912746897927283)
+        tr_uneq_n = xp.asarray(0.66745638708050492)
+        pr = xp.asarray(0.27647831993021388)
+        pr_uneq_n = xp.asarray(0.50873585065616544)
+        tr_2D = xp.stack([tr, -tr])
+        pr_2D = xp.stack([pr, pr])
+
+        rvs3 = xp.linspace(1, 100, 25)
+        rvs2 = xp.linspace(1, 100, 100)
+        rvs1 = xp.linspace(5, 105, 100)
+        rvs1_2D = xp.stack([rvs1, rvs2])
+        rvs2_2D = xp.stack([rvs2, rvs1])
+
+        t, p = stats.ttest_ind(rvs1, rvs2, axis=0, equal_var=False)
+        xp_assert_close(t, tr)
+        xp_assert_close(p, pr)
+
+        t, p = stats.ttest_ind_from_stats(*_desc_stats(rvs1, rvs2), equal_var=False)
+        xp_assert_close(t, tr)
+        xp_assert_close(p, pr)
+
+        t, p = stats.ttest_ind(rvs1, rvs3, axis=0, equal_var=False)
+        xp_assert_close(t, tr_uneq_n)
+        xp_assert_close(p, pr_uneq_n)
+
+        t, p = stats.ttest_ind_from_stats(*_desc_stats(rvs1, rvs3), equal_var=False)
+        xp_assert_close(t, tr_uneq_n)
+        xp_assert_close(p, pr_uneq_n)
+
+        res = stats.ttest_ind(rvs1_2D.T, rvs2_2D.T, axis=0, equal_var=False)
+        xp_assert_close(res.statistic, tr_2D)
+        xp_assert_close(res.pvalue, pr_2D)
+
+        args = _desc_stats(rvs1_2D.T, rvs2_2D.T)
+        res = stats.ttest_ind_from_stats(*args, equal_var=False)
+        xp_assert_close(res.statistic, tr_2D)
+        xp_assert_close(res.pvalue, pr_2D)
+
+        res = stats.ttest_ind(rvs1_2D, rvs2_2D, axis=1, equal_var=False)
+        xp_assert_close(res.statistic, tr_2D)
+        xp_assert_close(res.pvalue, pr_2D)
+
+        args = _desc_stats(rvs1_2D, rvs2_2D, axis=1)
+        res = stats.ttest_ind_from_stats(*args, equal_var=False)
+        xp_assert_close(res.statistic, tr_2D)
+        xp_assert_close(res.pvalue, pr_2D)
+
+    @pytest.mark.filterwarnings(
+        "ignore:divide by zero encountered:RuntimeWarning"
+    ) # for dask
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered:RuntimeWarning"
+    ) # for dask
+    def test_ttest_ind_zero_division(self, xp):
+        # test zero division problem
+        x = xp.zeros(3)
+        y = xp.ones(3)
+
+        with eager_warns(RuntimeWarning, match="Precision loss occurred", xp=xp):
+            t, p = stats.ttest_ind(x, y, equal_var=False)
+
+        xp_assert_equal(t, xp.asarray(-xp.inf))
+        xp_assert_equal(p, xp.asarray(0.))
+
+        with np.errstate(all='ignore'):
+            t, p = stats.ttest_ind(x, x, equal_var=False)
+            xp_assert_equal(t, xp.asarray(xp.nan))
+            xp_assert_equal(p, xp.asarray(xp.nan))
+
+            # check that nan in input array result in nan output
+            anan = xp.asarray([[1, xp.nan], [-1, 1]])
+            t, p = stats.ttest_ind(anan, xp.zeros((2, 2)), equal_var=False)
+            xp_assert_equal(t, xp.asarray([0., np.nan]))
+            xp_assert_equal(p, xp.asarray([1., np.nan]))
+
+    def test_ttest_ind_nan_2nd_arg(self):
+        # regression test for gh-6134: nans in the second arg were not handled
+        x = [np.nan, 2.0, 3.0, 4.0]
+        y = [1.0, 2.0, 1.0, 2.0]
+
+        r1 = stats.ttest_ind(x, y, nan_policy='omit')
+        r2 = stats.ttest_ind(y, x, nan_policy='omit')
+        assert_allclose(r2.statistic, -r1.statistic, atol=1e-15)
+        assert_allclose(r2.pvalue, r1.pvalue, atol=1e-15)
+
+        # NB: arguments are not paired when NaNs are dropped
+        r3 = stats.ttest_ind(y, x[1:])
+        assert_allclose(r2, r3, atol=1e-15)
+
+        # .. and this is consistent with R. R code:
+        # x = c(NA, 2.0, 3.0, 4.0)
+        # y = c(1.0, 2.0, 1.0, 2.0)
+        # t.test(x, y, var.equal=TRUE)
+        assert_allclose(r2, (-2.5354627641855498, 0.052181400457057901),
+                        atol=1e-15)
+
+    def test_ttest_ind_empty_1d_returns_nan(self, xp):
+        # Two empty inputs should return a TtestResult containing nan
+        # for both values.
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = stats.ttest_ind(xp.asarray([]), xp.asarray([]))
+        assert isinstance(res, stats._stats_py.TtestResult)
+        NaN = xp.asarray(xp.nan)[()]
+        xp_assert_equal(res.statistic, NaN)
+        xp_assert_equal(res.pvalue, NaN)
+
+    @pytest.mark.parametrize('b, expected_shape',
+                            [(np.empty((1, 5, 0)), (3, 5)),
+                            (np.empty((1, 0, 0)), (3, 0))])
+    def test_ttest_ind_axis_size_zero(self, b, expected_shape, xp):
+        # In this test, the length of the axis dimension is zero.
+        # The results should be arrays containing nan with shape
+        # given by the broadcast nonaxis dimensions.
+        a = xp.empty((3, 1, 0))
+        b = xp.asarray(b, dtype=a.dtype)
+        with warnings.catch_warnings():
+            # first case should warn, second shouldn't?
+            warnings.filterwarnings("ignore", too_small_nd_not_omit, SmallSampleWarning)
+            res = stats.ttest_ind(a, b, axis=-1)
+        assert isinstance(res, stats._stats_py.TtestResult)
+        expected_value = xp.full(expected_shape, fill_value=xp.nan)
+        xp_assert_equal(res.statistic, expected_value)
+        xp_assert_equal(res.pvalue, expected_value)
+
+    def test_ttest_ind_nonaxis_size_zero(self, xp):
+        # In this test, the length of the axis dimension is nonzero,
+        # but one of the nonaxis dimensions has length 0.  Check that
+        # we still get the correctly broadcast shape, which is (5, 0)
+        # in this case.
+        a = xp.empty((1, 8, 0))
+        b = xp.empty((5, 8, 1))
+        res = stats.ttest_ind(a, b, axis=1)
+        assert isinstance(res, stats._stats_py.TtestResult)
+        assert res.statistic.shape ==(5, 0)
+        assert res.pvalue.shape == (5, 0)
+
+    def test_ttest_ind_nonaxis_size_zero_different_lengths(self, xp):
+        # In this test, the length of the axis dimension is nonzero,
+        # and that size is different in the two inputs,
+        # and one of the nonaxis dimensions has length 0.  Check that
+        # we still get the correctly broadcast shape, which is (5, 0)
+        # in this case.
+        a = xp.empty((1, 7, 0))
+        b = xp.empty((5, 8, 1))
+        res = stats.ttest_ind(a, b, axis=1)
+        assert isinstance(res, stats._stats_py.TtestResult)
+        assert res.statistic.shape ==(5, 0)
+        assert res.pvalue.shape == (5, 0)
+
+    @pytest.mark.skip_xp_backends(cpu_only=True, reason='Test uses ks_1samp')
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    @pytest.mark.uses_xp_capabilities(False, reason="not used in this test yet")
+    def test_ttest_uniform_pvalues(self, xp):
+        # test that p-values are uniformly distributed under the null hypothesis
+        rng = np.random.default_rng(246834602926842)
+        x = xp.asarray(rng.normal(size=(10000, 2)))
+        y = xp.asarray(rng.normal(size=(10000, 1)))
+        q = rng.uniform(size=100)
+
+        res = stats.ttest_ind(x, y, equal_var=True, axis=-1)
+        pvalue = np.asarray(res.pvalue)
+        # TODO: isolate use of alt backend to ttest_ind
+        assert stats.ks_1samp(pvalue, stats.uniform().cdf).pvalue > 0.1
+        assert_allclose(np.quantile(pvalue, q), q, atol=1e-2)
+
+        res = stats.ttest_ind(y, x, equal_var=True, axis=-1)
+        pvalue = np.asarray(res.pvalue)
+        assert stats.ks_1samp(pvalue, stats.uniform().cdf).pvalue > 0.1
+        assert_allclose(np.quantile(pvalue, q), q, atol=1e-2)
+
+        # reference values from R:
+        # options(digits=16)
+        # t.test(c(2, 3, 5), c(1.5), var.equal=TRUE)
+        x, y = xp.asarray([2., 3., 5.]), xp.asarray([1.5])
+
+        res = stats.ttest_ind(x, y, equal_var=True)
+        xp_assert_close(res.statistic, xp.asarray(1.0394023007754))
+        xp_assert_close(res.pvalue, xp.asarray(0.407779907736))
+
+
+@make_xp_test_case(stats.ttest_ind_from_stats)
+class TestTTestIndFromStats:
+    def test_gh5686(self, xp):
+        mean1, mean2 = xp.asarray([1, 2]), xp.asarray([3, 4])
+        std1, std2 = xp.asarray([5, 3]), xp.asarray([4, 5])
+        nobs1, nobs2 = xp.asarray([130, 140]), xp.asarray([100, 150])
+        # This will raise a TypeError unless gh-5686 is fixed.
+        stats.ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2)
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning")
+    def test_ttest_ind_from_stats_inputs_zero(self, xp):
+        # Regression test for gh-6409.
+        zero = xp.asarray(0.)
+        six = xp.asarray(6.)
+        NaN = xp.asarray(xp.nan)
+        res = stats.ttest_ind_from_stats(zero, zero, six, zero, zero, six,
+                                         equal_var=False)
+        xp_assert_equal(res.statistic, NaN)
+        xp_assert_equal(res.pvalue, NaN)
+
+
+@make_xp_test_case(stats.ttest_1samp)
+class TestTTest1Samp:
+    def _convert_pvalue_alternative(self, t, p, alt, xp):
+        # test alternative parameter
+        # Convert from two-sided p-values to one sided using T result data.
+        less = xp.asarray(alt == "less")
+        greater = xp.asarray(alt == "greater")
+        i = ((t < 0) & less) | ((t > 0) & greater)
+        return xp.where(i, p/2, 1 - p/2)
+
+    @pytest.mark.slow
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    def test_ttest_1samp_new(self, xp):
+        rng = np.random.default_rng(88123290)
+        n1, n2, n3 = (10, 15, 20)
+        rvn1 = stats.norm.rvs(loc=5, scale=10, size=(n1, n2, n3), random_state=rng)
+        rvn1 = xp.asarray(rvn1)
+
+        # check multidimensional array and correct axis handling
+        # deterministic rvn1 and rvn2 would be better as in test_ttest_rel
+        popmean = xp.ones((1, n2, n3))
+        t1, p1 = stats.ttest_1samp(rvn1, popmean, axis=0)
+        t2, p2 = stats.ttest_1samp(rvn1, 1., axis=0)
+        t3, p3 = stats.ttest_1samp(rvn1[:, 0, 0], 1.)
+        xp_assert_close(t1, t2, rtol=1e-14)
+        xp_assert_close(t1[0, 0], t3, rtol=1e-14)
+        assert_equal(t1.shape, (n2, n3))
+
+        popmean = xp.ones((n1, 1, n3))
+        t1, p1 = stats.ttest_1samp(rvn1, popmean, axis=1)
+        t2, p2 = stats.ttest_1samp(rvn1, 1., axis=1)
+        t3, p3 = stats.ttest_1samp(rvn1[0, :, 0], 1.)
+        xp_assert_close(t1, t2, rtol=1e-14)
+        xp_assert_close(t1[0, 0], t3, rtol=1e-14)
+        assert_equal(t1.shape, (n1, n3))
+
+        popmean = xp.ones((n1, n2, 1))
+        t1, p1 = stats.ttest_1samp(rvn1, popmean, axis=2)
+        t2, p2 = stats.ttest_1samp(rvn1, 1., axis=2)
+        t3, p3 = stats.ttest_1samp(rvn1[0, 0, :], 1.)
+        xp_assert_close(t1, t2, rtol=1e-14)
+        xp_assert_close(t1[0, 0], t3, rtol=1e-14)
+        assert_equal(t1.shape, (n1, n2))
+
+        # test zero division problem
+        t, p = stats.ttest_1samp(xp.asarray([0., 0., 0.]), 1.)
+        xp_assert_equal(xp.abs(t), xp.asarray(xp.inf))
+        xp_assert_equal(p, xp.asarray(0.))
+
+        tr, pr = stats.ttest_1samp(rvn1[:, :, :], 1.)
+
+        t, p = stats.ttest_1samp(rvn1[:, :, :], 1., alternative="greater")
+        pc = self._convert_pvalue_alternative(tr, pr, "greater", xp)
+        xp_assert_close(p, pc)
+        xp_assert_close(t, tr)
+
+        t, p = stats.ttest_1samp(rvn1[:, :, :], 1., alternative="less")
+        pc = self._convert_pvalue_alternative(tr, pr, "less", xp)
+        xp_assert_close(p, pc)
+        xp_assert_close(t, tr)
+
+        with np.errstate(all='ignore'):
+            res = stats.ttest_1samp(xp.asarray([0., 0., 0.]), 0.)
+            xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+            xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+            # check that nan in input array result in nan output
+            anan = xp.asarray([[1., np.nan], [-1., 1.]])
+            res = stats.ttest_1samp(anan, 0.)
+            xp_assert_equal(res.statistic, xp.asarray([0., xp.nan]))
+            xp_assert_equal(res.pvalue, xp.asarray([1., xp.nan]))
+
+    @skip_xp_backends(eager_only=True, reason="lazy -> reduced nan_policy capabilities")
+    def test_ttest_1samp_new_omit(self, xp):
+        rng = np.random.default_rng(4008400329)
+        n1, n2, n3 = (5, 10, 15)
+        rvn1 = stats.norm.rvs(loc=5, scale=10, size=(n1, n2, n3), random_state=rng)
+        rvn1 = xp.asarray(rvn1)
+
+        rvn1[0:2, 1:3, 4:8] = xp.nan
+
+        tr, pr = stats.ttest_1samp(rvn1[:, :, :], 1., nan_policy='omit')
+
+        t, p = stats.ttest_1samp(rvn1[:, :, :], 1., nan_policy='omit',
+                                 alternative="greater")
+        pc = self._convert_pvalue_alternative(tr, pr, "greater", xp)
+        xp_assert_close(p, pc)
+        xp_assert_close(t, tr)
+
+        t, p = stats.ttest_1samp(rvn1[:, :, :], 1., nan_policy='omit',
+                                 alternative="less")
+        pc = self._convert_pvalue_alternative(tr, pr, "less", xp)
+        xp_assert_close(p, pc)
+        xp_assert_close(t, tr)
+
+    @pytest.mark.skip_xp_backends('jax.numpy', reason='Generic stdtrit mutates array.')
+    def test_ttest_1samp_popmean_array(self, xp):
+        # when popmean.shape[axis] != 1, raise an error
+        # if the user wants to test multiple null hypotheses simultaneously,
+        # use standard broadcasting rules
+        rng = np.random.default_rng(2913300596553337193)
+        x = rng.random(size=(1, 15, 20))
+        x = xp.asarray(x)
+
+        message = r"`popmean.shape\[axis\]` must equal 1."
+        popmean = xp.asarray(rng.random(size=(5, 2, 20)))
+        with pytest.raises(ValueError, match=message):
+            stats.ttest_1samp(x, popmean=popmean, axis=-2)
+
+        popmean = xp.asarray(rng.random(size=(5, 1, 20)))
+        res = stats.ttest_1samp(x, popmean=popmean, axis=-2)
+        assert res.statistic.shape == (5, 20)
+
+        l, u = res.confidence_interval()
+        l = xp.expand_dims(l, axis=-2)
+        u = xp.expand_dims(u, axis=-2)
+
+        res = stats.ttest_1samp(x, popmean=l, axis=-2)
+        ref = xp.broadcast_to(xp.asarray(0.05, dtype=xp.float64), res.pvalue.shape)
+        xp_assert_close(res.pvalue, ref)
+
+        res = stats.ttest_1samp(x, popmean=u, axis=-2)
+        xp_assert_close(res.pvalue, ref)
+
+
+@make_xp_test_case(stats.describe)
+class TestDescribe:
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    def test_describe_scalar(self, xp):
+        with warnings.catch_warnings(), \
+              np.errstate(invalid="ignore", divide="ignore"):
+            warnings.filterwarnings(
+                "ignore", "Degrees of freedom <= 0 for slice", RuntimeWarning)
+            n, mm, m, v, sk, kurt = stats.describe(xp.asarray(4.)[()])
+        assert n == 1
+        xp_assert_equal(mm[0], xp.asarray(4.0))
+        xp_assert_equal(mm[1], xp.asarray(4.0))
+        xp_assert_equal(m, xp.asarray(4.0))
+        xp_assert_equal(v ,xp.asarray(xp.nan))
+        xp_assert_equal(sk, xp.asarray(xp.nan))
+        xp_assert_equal(kurt, xp.asarray(xp.nan))
+
+    def test_describe_numbers(self, xp):
+        x = xp.concat((xp.ones((3, 4)), xp.full((2, 4), 2.)))
+        nc = 5
+        mmc = (xp.asarray([1., 1., 1., 1.]), xp.asarray([2., 2., 2., 2.]))
+        mc = xp.asarray([1.4, 1.4, 1.4, 1.4])
+        vc = xp.asarray([0.3, 0.3, 0.3, 0.3])
+        skc = xp.asarray([0.40824829046386357] * 4)
+        kurtc = xp.asarray([-1.833333333333333] * 4)
+        n, mm, m, v, sk, kurt = stats.describe(x)
+        assert n == nc
+        xp_assert_equal(mm[0], mmc[0])
+        xp_assert_equal(mm[1], mmc[1])
+        xp_assert_close(m, mc, rtol=4 * xp.finfo(m.dtype).eps)
+        xp_assert_close(v, vc, rtol=4 * xp.finfo(m.dtype).eps)
+        xp_assert_close(sk, skc)
+        xp_assert_close(kurt, kurtc)
+
+        n, mm, m, v, sk, kurt = stats.describe(x.T, axis=1)
+        assert n == nc
+        xp_assert_equal(mm[0], mmc[0])
+        xp_assert_equal(mm[1], mmc[1])
+        xp_assert_close(m, mc, rtol=4 * xp.finfo(m.dtype).eps)
+        xp_assert_close(v, vc, rtol=4 * xp.finfo(m.dtype).eps)
+        xp_assert_close(sk, skc)
+        xp_assert_close(kurt, kurtc)
+
+    def describe_nan_policy_omit_test(self):
+        x = np.arange(10.)
+        x[9] = np.nan
+
+        nc, mmc = (9, (0.0, 8.0))
+        mc = 4.0
+        vc = 7.5
+        skc = 0.0
+        kurtc = -1.2300000000000002
+        n, mm, m, v, sk, kurt = stats.describe(x, nan_policy='omit')
+        assert_equal(n, nc)
+        assert_equal(mm, mmc)
+        assert_equal(m, mc)
+        assert_equal(v, vc)
+        assert_array_almost_equal(sk, skc)
+        assert_array_almost_equal(kurt, kurtc, decimal=13)
+
+    def test_describe_nan_policy_other(self, xp):
+        x = xp.arange(10.)
+        x = xp.where(x==9, xp.nan, x)
+
+        if is_lazy_array(x):
+            with pytest.raises(TypeError, match='not supported for lazy arrays'):
+                stats.describe(x, nan_policy='raise')
+        else:
+            with pytest.raises(ValueError, match='The input contains nan values'):
+                stats.describe(x, nan_policy='raise')
+
+        n, mm, m, v, sk, kurt = stats.describe(x, nan_policy='propagate')
+        ref = xp.asarray(xp.nan)[()]
+        assert n == 10
+        xp_assert_equal(mm[0], ref)
+        xp_assert_equal(mm[1], ref)
+        xp_assert_equal(m, ref)
+        xp_assert_equal(v, ref)
+        xp_assert_equal(sk, ref)
+        xp_assert_equal(kurt, ref)
+
+        if is_numpy(xp):
+            self.describe_nan_policy_omit_test()
+        elif is_lazy_array(x):
+            with pytest.raises(TypeError, match='not supported for lazy arrays'):
+                stats.describe(x, nan_policy='omit')
+
+        message = 'nan_policy must be one of...'
+        with pytest.raises(ValueError, match=message):
+            stats.describe(x, nan_policy='foobar')
+
+    def test_describe_result_attributes(self):
+        # some result attributes are tuples, which aren't meant to be compared
+        # with `xp_assert_close`
+        actual = stats.describe(np.arange(5.))
+        attributes = ('nobs', 'minmax', 'mean', 'variance', 'skewness', 'kurtosis')
+        check_named_results(actual, attributes)
+
+    def test_describe_ddof(self, xp):
+        x = xp.concat((xp.ones((3, 4)), xp.full((2, 4), 2.)))
+        nc = 5
+        mmc = (xp.asarray([1., 1., 1., 1.]), xp.asarray([2., 2., 2., 2.]))
+        mc = xp.asarray([1.4, 1.4, 1.4, 1.4])
+        vc = xp.asarray([0.24, 0.24, 0.24, 0.24])
+        skc = xp.asarray([0.40824829046386357] * 4)
+        kurtc = xp.asarray([-1.833333333333333] * 4)
+        n, mm, m, v, sk, kurt = stats.describe(x, ddof=0)
+        assert n == nc
+        xp_assert_equal(mm[0], mmc[0])
+        xp_assert_equal(mm[1], mmc[1])
+        xp_assert_close(m, mc)
+        xp_assert_close(v, vc)
+        xp_assert_close(sk, skc)
+        xp_assert_close(kurt, kurtc)
+
+    def test_describe_axis_none(self, xp):
+        x = xp.concat((xp.ones((3, 4)), xp.full((2, 4), 2.)))
+
+        # expected values
+        nc = 20
+        mmc = (xp.asarray(1.0), xp.asarray(2.0))
+        mc = xp.asarray(1.3999999999999999)
+        vc = xp.asarray(0.25263157894736848)
+        skc = xp.asarray(0.4082482904638634)
+        kurtc = xp.asarray(-1.8333333333333333)
+
+        # actual values
+        n, mm, m, v, sk, kurt = stats.describe(x, axis=None)
+
+        assert n == nc
+        xp_assert_equal(mm[0], mmc[0])
+        xp_assert_equal(mm[1], mmc[1])
+        xp_assert_close(m, mc)
+        xp_assert_close(v, vc)
+        xp_assert_close(sk, skc)
+        xp_assert_close(kurt, kurtc)
+
+    def test_describe_empty(self, xp):
+        message = "The input must not be empty."
+        with pytest.raises(ValueError, match=message):
+            stats.describe(xp.asarray([]))
+    
+    @skip_xp_backends("array_api_strict", reason="min/max don't accept boolean input")
+    def test_boolean_input_gh24847(self, xp):
+        # gh-24847 reported that `describe` failed with boolean input
+        rng = np.random.default_rng(3248923598734583)
+        n = 20
+        x = xp.asarray(rng.random(n) < 0.5)
+        res = stats.describe(x)
+        xp_assert_equal(res.nobs, xp.asarray(n, dtype=xp.int64))
+        xp_assert_equal(res.minmax[0], xp.min(x))
+        xp_assert_equal(res.minmax[1], xp.max(x))
+        x = xp_promote(x, force_floating=True, xp=xp)
+        xp_assert_close(res.mean, xp.mean(x))
+        xp_assert_close(res.variance, xp.var(x, correction=1))
+        xp_assert_close(res.skewness, stats.skew(x))
+        xp_assert_close(res.kurtosis, stats.kurtosis(x))
+
+
+class NormalityTests:
+
+    @pytest.mark.parametrize("alternative", ['two-sided', 'less', 'greater'])
+    def test_against_R(self, alternative, xp):
+        # testa against R `dagoTest` from package `fBasics`
+        # library(fBasics)
+        # options(digits=16)
+        # x = c(-2, -1, 0, 1, 2, 3)**2
+        # x = rep(x, times=4)
+        # test_result <- dagoTest(x)
+        # test_result@test$statistic
+        # test_result@test$p.value
+        test_name = self.test_name
+        test_fun = getattr(stats, test_name)
+        ref_statistic= xp.asarray(self.case_ref[0])
+        ref_pvalue = xp.asarray(self.case_ref[1])
+
+        kwargs = {}
+        if alternative in {'less', 'greater'}:
+            if test_name in {'skewtest', 'kurtosistest'}:
+                ref_pvalue = ref_pvalue/2 if alternative == "less" else 1-ref_pvalue/2
+                ref_pvalue = 1-ref_pvalue if test_name == 'skewtest' else ref_pvalue
+                kwargs['alternative'] = alternative
+            else:
+                pytest.skip('`alternative` not available for `normaltest`')
+
+        x = xp.asarray((-2, -1, 0, 1, 2, 3.)*4)**2
+        res = test_fun(x, **kwargs)
+        res_statistic, res_pvalue = res
+        xp_assert_close(res_statistic, ref_statistic)
+        xp_assert_close(res_pvalue, ref_pvalue)
+        check_named_results(res, ('statistic', 'pvalue'), xp=xp)
+
+    def test_nan(self, xp):
+        # nan in input -> nan output (default nan_policy='propagate')
+        test_fun = getattr(stats, self.test_name)
+        x = xp.arange(30.)
+        NaN = xp.asarray(xp.nan, dtype=x.dtype)
+        x = xp.where(x == 29, NaN, x)
+        with np.errstate(invalid="ignore"):
+            res = test_fun(x)
+            xp_assert_equal(res.statistic, NaN)
+            xp_assert_equal(res.pvalue, NaN)
+
+
+@make_xp_test_case(stats.skewtest)
+class TestSkewTest(NormalityTests):
+    test_name = 'skewtest'
+    case_ref = (1.98078826090875881, 0.04761502382843208)  # statistic, pvalue
+
+    def test_intuitive(self, xp):
+        # intuitive tests; see gh-13549. skewnorm with parameter 1 has skew > 0
+        a1 = stats.skewnorm.rvs(a=1, size=10000, random_state=123)
+        a1_xp = xp.asarray(a1)
+        pval = stats.skewtest(a1_xp, alternative='greater').pvalue
+        xp_assert_close(pval, xp.asarray(0.0, dtype=a1_xp.dtype), atol=9e-6)
+
+    def test_skewtest_too_few_observations(self, xp):
+        # Regression test for ticket #1492.
+        # skewtest requires at least 8 observations; 7 should warn and return NaN.
+        stats.skewtest(xp.arange(8.0))
+
+        x = xp.arange(7.0)
+
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = stats.skewtest(x)
+            NaN = xp.asarray(xp.nan)
+            xp_assert_equal(res.statistic, NaN)
+            xp_assert_equal(res.pvalue, NaN)
+
+
+@make_xp_test_case(stats.kurtosistest)
+class TestKurtosisTest(NormalityTests):
+    test_name = 'kurtosistest'
+    case_ref = (-0.01403734404759738, 0.98880018772590561)  # statistic, pvalue
+
+    def test_intuitive(self, xp):
+        # intuitive tests; see gh-13549. excess kurtosis of laplace is 3 > 0
+        a2 = stats.laplace.rvs(size=10000, random_state=123)
+        a2_xp = xp.asarray(a2)
+        pval = stats.kurtosistest(a2_xp, alternative='greater').pvalue
+        xp_assert_close(pval, xp.asarray(0.0, dtype=a2_xp.dtype), atol=1e-15)
+
+    def test_gh9033_regression(self, xp):
+        # regression test for issue gh-9033: x clearly non-normal but power of
+        # negative denom needs to be handled correctly to reject normality
+        counts = [128, 0, 58, 7, 0, 41, 16, 0, 0, 167]
+        x = np.hstack([np.full(c, i) for i, c in enumerate(counts)])
+        x = xp.asarray(x, dtype=xp.float64)
+        assert stats.kurtosistest(x)[1] < 0.01
+
+    def test_kurtosistest_too_few_observations(self, xp):
+        # kurtosistest requires at least 5 observations; 4 should warn and return NaN.
+        # Regression test for ticket #1425.
+        stats.kurtosistest(xp.arange(5.0))
+
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = stats.kurtosistest(xp.arange(4.))
+            NaN = xp.asarray(xp.nan)
+            xp_assert_equal(res.statistic, NaN)
+            xp_assert_equal(res.pvalue, NaN)
+
+
+@make_xp_test_case(stats.normaltest)
+class TestNormalTest(NormalityTests):
+    test_name = 'normaltest'
+    case_ref = (3.92371918158185551, 0.14059672529747502)  # statistic, pvalue
+
+    def test_too_few_observations(self, xp):
+        stats.normaltest(xp.arange(8.))
+
+        # 1D sample has too few observations -> warning / NaN output
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            res = stats.normaltest(xp.arange(7.))
+            NaN = xp.asarray(xp.nan)
+            xp_assert_equal(res.statistic, NaN)
+            xp_assert_equal(res.pvalue, NaN)
+
+
+class TestRankSums:
+
+    @pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+    def test_ranksums_is_mannwhitneyu(self, alternative):
+        rng = np.random.default_rng(3417115752)
+        x, y = rng.random((2, 10))
+
+        # ranksums pval = mannwhitneyu pval w/out continuity or tie correction
+        res1 = stats.ranksums(x, y, alternative=alternative)
+        res2 = stats.mannwhitneyu(x, y, use_continuity=False,
+                                  alternative=alternative, method='asymptotic')
+        assert_allclose(res1.pvalue, res2.pvalue)
+        assert_allclose(res1.statistic, res2.zstatistic)
+
+    def test_ranksums_named_results(self):
+        res = stats.ranksums(np.arange(10), np.arange(10))
+        check_named_results(res, ('statistic', 'pvalue'))
+
+    def test_input_validation(self):
+        with assert_raises(ValueError, match="`alternative` must be 'less'"):
+            stats.ranksums(np.arange(10), np.arange(10), alternative='foobar')
+
+
+@make_xp_test_case(stats.jarque_bera)
+class TestJarqueBera:
+    def test_jarque_bera_against_R(self, xp):
+        # library(tseries)
+        # options(digits=16)
+        # x < - rnorm(5)
+        # jarque.bera.test(x)
+        x = [-0.160104223201523288,  1.131262000934478040, -0.001235254523709458,
+             -0.776440091309490987, -2.072959999533182884]
+        x = xp.asarray(x)
+        ref = xp.asarray([0.17651605223752, 0.9155246169805])
+        res = stats.jarque_bera(x)
+        xp_assert_close(res.statistic, ref[0])
+        xp_assert_close(res.pvalue, ref[1])
+
+    @skip_xp_backends(np_only=True)
+    def test_jarque_bera_array_like(self, xp):
+        # array-like only relevant for NumPy
+        rng = np.random.default_rng(9294968266)
+        x = rng.standard_normal(size=100000)
+
+        jb_test1 = JB1, p1 = stats.jarque_bera(list(x))
+        jb_test2 = JB2, p2 = stats.jarque_bera(tuple(x))
+        jb_test3 = JB3, p3 = stats.jarque_bera(x.reshape(2, 50000))
+
+        assert JB1 == JB2 == JB3 == jb_test1.statistic == jb_test2.statistic == jb_test3.statistic  # noqa: E501
+        assert p1 == p2 == p3 == jb_test1.pvalue == jb_test2.pvalue == jb_test3.pvalue
+
+    def test_jarque_bera_too_few_observations(self, xp):
+        x = xp.asarray([])
+
+        with pytest.warns(SmallSampleWarning, match=too_small_1d_not_omit):
+            res = stats.jarque_bera(x)
+            NaN = xp.asarray(xp.nan)
+            xp_assert_equal(res.statistic, NaN)
+            xp_assert_equal(res.pvalue, NaN)
+
+    def test_axis(self, xp):
+        rng = np.random.RandomState(seed=122398129)
+        x = xp.asarray(rng.random(size=(2, 45)))
+
+        res = stats.jarque_bera(x, axis=None)
+        ref = stats.jarque_bera(xp.reshape(x, (-1,)))
+        xp_assert_equal(res.statistic, ref.statistic)
+        xp_assert_equal(res.pvalue, ref.pvalue)
+
+        res = stats.jarque_bera(x, axis=1)
+        s0, p0 = stats.jarque_bera(x[0, :])
+        s1, p1 = stats.jarque_bera(x[1, :])
+        xp_assert_close(res.statistic, xp.stack([s0, s1]))
+        xp_assert_close(res.pvalue, xp.stack([p0, p1]))
+
+        resT = stats.jarque_bera(x.T, axis=0)
+        xp_assert_close(res.statistic, resT.statistic)
+        xp_assert_close(res.pvalue, resT.pvalue)
+
+
+@make_xp_test_case(stats.pointbiserialr)
+class TestPointBiserialR:
+    def test_pointbiserial(self, xp):
+        # same as mstats test except for the nan
+        # Test data: https://web.archive.org/web/20060504220742/https://support.sas.com/ctx/samples/index.jsp?sid=490&tab=output
+        x = [1,0,1,1,1,1,0,1,0,0,0,1,1,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,
+             0,0,0,0,1.]
+        y = [14.8,13.8,12.4,10.1,7.1,6.1,5.8,4.6,4.3,3.5,3.3,3.2,3.0,
+             2.8,2.8,2.5,2.4,2.3,2.1,1.7,1.7,1.5,1.3,1.3,1.2,1.2,1.1,
+             0.8,0.7,0.6,0.5,0.2,0.2,0.1]
+        res = stats.pointbiserialr(xp.asarray(x), xp.asarray(y))
+        xp_assert_close(res[0], xp.asarray(0.36149), atol=1e-5)
+
+        # test for namedtuple attribute results
+        attributes = ('correlation', 'pvalue')
+        check_named_results(res, attributes, xp=xp)
+        xp_assert_equal(res.correlation, res.statistic)
+
+
+@make_xp_test_case(stats.obrientransform)
+class TestObrientransform:
+    def test_basic(self, xp):
+        # A couple tests calculated by hand.
+        x1 = xp.asarray([0, 2, 4])
+        t1 = stats.obrientransform(x1)
+        expected = xp.asarray([7., -2., 7.])
+        xp_assert_close(t1[0][:], expected)
+
+        x2 = xp.asarray([0, 3, 6, 9])
+        t2 = stats.obrientransform(x2)
+        expected = xp.asarray([30., 0., 0., 30.])
+        xp_assert_close(t2[0][:], expected)
+
+        # Test two arguments.
+        a, b = stats.obrientransform(x1, x2)
+        xp_assert_close(a, t1[0][:])
+        xp_assert_close(b, t2[0][:])
+
+        # Test three arguments.
+        a, b, c = stats.obrientransform(x1, x2, x1)
+        xp_assert_close(a, t1[0][:])
+        xp_assert_close(b, t2[0][:])
+        xp_assert_close(c, t1[0][:])
+
+    def test_something(self, xp):
+        # This is a regression test to check np.var replacement.
+        # The author of this test didn't separately verify the numbers.
+        x1 = xp.arange(5)
+        ref = xp.asarray(
+          [[5.41666667, 1.04166667, -0.41666667, 1.04166667, 5.41666667],
+           [21.66666667, 4.16666667, -1.66666667, 4.16666667, 21.66666667]])
+        res = stats.obrientransform(x1, 2*x1)
+        xp_assert_close(res[0], ref[0, ...])
+        xp_assert_close(res[1], ref[1, ...])
+
+    def test_reference(self, xp):
+        # Example from "O'Brien Test for Homogeneity of Variance" by Herve Abdi.
+        values = np.arange(5, 11)
+        reps = [5, 11, 9, 3, 2, 2]
+        data = np.repeat(values, reps)
+        transformed_values = np.asarray([3.1828, 0.5591, 0.0344,
+                                         1.6086, 5.2817, 11.0538])
+        expected = np.repeat(transformed_values, reps)
+        result = stats.obrientransform(xp.asarray(data))
+        xp_assert_close(result[0][:], xp.asarray(expected.tolist()), rtol=1e-3)
+
+    def test_nan_policy(self, xp):
+        rng = np.random.default_rng(4284359689201882838835)
+        x = rng.random(10)
+        x[3] = np.nan
+        x = xp.asarray(x)
+
+        # nan_policy='raise'
+        if not is_lazy_array(x):
+            with pytest.raises(ValueError, match="The input contains nan values"):
+                stats.obrientransform(x, nan_policy='raise')
+        else:
+            with pytest.raises(TypeError, match="nan_policy='raise' is not supported"):
+                stats.obrientransform(x, nan_policy='raise')
+
+        # nan_policy='propagate'
+        res = stats.obrientransform(x, nan_policy='propagate')[0]
+        xp_assert_equal(res, xp.full_like(x, xp.nan))
+
+        # nan_policy='omit'
+        i = xp.isnan(x)
+        res = stats.obrientransform(x, nan_policy='omit')[0]
+        ref = stats.obrientransform(x[~i])[0]
+        xp_assert_equal(res[i], x[i])
+        xp_assert_close(res[~i], ref)
+
+
+def check_equal_xmean(*args, xp, mean_fun, axis=None, dtype=None,
+                      rtol=None, weights=None):
+    # Note this doesn't test when axis is not specified
+    dtype = dtype or xp.float64
+    if len(args) == 2:
+        array_like, desired = args
+    else:
+        array_like, p, desired = args
+    array_like = xp.asarray(array_like, dtype=dtype)
+    dtype_reference = (xp_result_type(array_like, force_floating=True, xp=xp)
+                       if xp != np.ma else None)
+    desired = xp.asarray(desired, dtype=dtype_reference)
+    weights = xp.asarray(weights, dtype=dtype) if weights is not None else weights
+    args = (array_like,) if len(args) == 2 else (array_like, p)
+    x = mean_fun(*args, axis=axis, weights=weights)
+    xp_assert_close(x, desired, rtol=rtol)
+
+
+def check_equal_gmean(*args, **kwargs):
+    return check_equal_xmean(*args, mean_fun=stats.gmean, **kwargs)
+
+
+def check_equal_hmean(*args, **kwargs):
+    return check_equal_xmean(*args, mean_fun=stats.hmean, **kwargs)
+
+
+def check_equal_pmean(*args, **kwargs):
+    return check_equal_xmean(*args, mean_fun=stats.pmean, **kwargs)
+
+
+@make_xp_test_case(stats.hmean)
+class TestHMean:
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    def test_0(self, xp):
+        a = [1, 0, 2]
+        desired = 0
+        check_equal_hmean(a, desired, xp=xp)
+
+    @pytest.mark.parametrize('dtype', [None, 'int64', 'float32', 'float64'])
+    def test_1d(self, dtype, xp):
+        dtype = dtype if dtype is None else getattr(xp, dtype)
+
+        #  Test a 1d case
+        a = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
+        desired = 34.1417152147
+        check_equal_hmean(a, desired, dtype=dtype, xp=xp)
+
+        a = [1, 2, 3, 4]
+        desired = 4. / (1. / 1 + 1. / 2 + 1. / 3 + 1. / 4)
+        check_equal_hmean(a, desired, dtype=dtype, xp=xp)
+
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    def test_1d_with_zero(self, xp):
+        a = np.array([1, 0])
+        desired = 0.0
+        check_equal_hmean(a, desired, xp=xp, rtol=0.0)
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    def test_1d_with_negative_value(self, xp):
+        a = np.array([1, 0, -1])
+        message = "The harmonic mean is only defined..."
+        with eager_warns(RuntimeWarning, match=message, xp=xp):
+            check_equal_hmean(a, xp.nan, xp=xp, rtol=0.0)
+
+    # Note the next tests use axis=None as default, not axis=0
+    def test_2d(self, xp):
+        #  Test a 2d case
+        a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
+        desired = 38.6696271841
+        check_equal_hmean(np.array(a), desired, xp=xp)
+
+    def test_2d_axis0(self, xp):
+        #  Test a 2d case with axis=0
+        a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
+        desired = np.array([22.88135593, 39.13043478, 52.90076336, 65.45454545])
+        check_equal_hmean(a, desired, axis=0, xp=xp)
+
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    def test_2d_axis0_with_zero(self, xp):
+        a = [[10, 0, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
+        desired = np.array([22.88135593, 0.0, 52.90076336, 65.45454545])
+        check_equal_hmean(a, desired, axis=0, xp=xp)
+
+    def test_2d_axis1(self, xp):
+        #  Test a 2d case with axis=1
+        a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
+        desired = np.array([19.2, 63.03939962, 103.80078637])
+        check_equal_hmean(a, desired, axis=1, xp=xp)
+
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    def test_2d_axis1_with_zero(self, xp):
+        a = [[10, 0, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
+        desired = np.array([0.0, 63.03939962, 103.80078637])
+        check_equal_hmean(a, desired, axis=1, xp=xp)
+
+    @skip_xp_backends(
+        np_only=True,
+        reason='array-likes only supported for NumPy backend',
+    )
+    def test_weights_1d_list(self, xp):
+        # Desired result from:
+        # https://www.hackmath.net/en/math-problem/35871
+        a = [2, 10, 6]
+        weights = [10, 5, 3]
+        desired = 3.
+        # all the other tests use `check_equal_hmean`, which now converts
+        # the input to an xp-array before calling `hmean`. This time, check
+        # that the function still accepts the lists of ints.
+        res = stats.hmean(a, weights=weights)
+        xp_assert_close(res, np.asarray(desired), rtol=1e-5)
+
+    def test_weights_1d(self, xp):
+        # Desired result from:
+        # https://www.hackmath.net/en/math-problem/35871
+        a = np.asarray([2, 10, 6])
+        weights = np.asarray([10, 5, 3])
+        desired = 3
+        check_equal_hmean(a, desired, weights=weights, rtol=1e-5, xp=xp)
+
+    def test_weights_2d_axis0(self, xp):
+        # Desired result from:
+        # https://www.hackmath.net/en/math-problem/35871
+        a = np.array([[2, 5], [10, 5], [6, 5]])
+        weights = np.array([[10, 1], [5, 1], [3, 1]])
+        desired = np.array([3, 5])
+        check_equal_hmean(a, desired, axis=0, weights=weights, rtol=1e-5, xp=xp)
+
+    def test_weights_2d_axis1(self, xp):
+        # Desired result from:
+        # https://www.hackmath.net/en/math-problem/35871
+        a = np.array([[2, 10, 6], [7, 7, 7]])
+        weights = np.array([[10, 5, 3], [1, 1, 1]])
+        desired = np.array([3, 7])
+        check_equal_hmean(a, desired, axis=1, weights=weights, rtol=1e-5, xp=xp)
+
+    @skip_xp_invalid_arg
+    def test_weights_masked_1d_array(self, xp):
+        # Desired result from:
+        # https://www.hackmath.net/en/math-problem/35871
+        a = np.array([2., 10., 6., 42.])
+        weights = np.ma.array([10., 5., 3., 42.], mask=[0, 0, 0, 1])
+        desired = 3.
+        xp = np.ma  # check_equal_hmean uses xp.asarray; this will preserve the mask
+        check_equal_hmean(a, desired, weights=weights, rtol=1e-5,
+                          dtype=np.float64, xp=xp)
+
+
+@make_xp_test_case(stats.gmean)
+class TestGMean:
+    @pytest.mark.filterwarnings(
+        "ignore:divide by zero encountered in log:RuntimeWarning:dask"
+    )
+    def test_0(self, xp):
+        a = [1, 0, 2]
+        desired = 0
+        check_equal_gmean(a, desired, xp=xp)
+
+    @pytest.mark.parametrize('dtype', [None, 'int64', 'float32', 'float64'])
+    def test_1d(self, dtype, xp):
+        dtype = dtype if dtype is None else getattr(xp, dtype)
+
+        #  Test a 1d case
+        a = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
+        desired = 45.2872868812
+        check_equal_gmean(a, desired, dtype=dtype, xp=xp)
+
+        a = [1, 2, 3, 4]
+        desired = power(1 * 2 * 3 * 4, 1. / 4.)
+        check_equal_gmean(a, desired, dtype=dtype, rtol=1e-14, xp=xp)
+
+    # Note the next tests use axis=None as default, not axis=0
+    def test_2d(self, xp):
+        #  Test a 2d case
+        a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
+        desired = 52.8885199
+        check_equal_gmean(a, desired, xp=xp)
+
+    def test_2d_axis0(self, xp):
+        #  Test a 2d case with axis=0
+        a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
+        desired = np.array([35.56893304, 49.32424149, 61.3579244, 72.68482371])
+        check_equal_gmean(a, desired, axis=0, xp=xp)
+
+        a = array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
+        desired = array([1, 2, 3, 4])
+        check_equal_gmean(a, desired, axis=0, rtol=1e-14, xp=xp)
+
+    def test_2d_axis1(self, xp):
+        #  Test a 2d case with axis=1
+        a = [[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]]
+        desired = np.array([22.13363839, 64.02171746, 104.40086817])
+        check_equal_gmean(a, desired, axis=1, xp=xp)
+
+        a = array([[1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4]])
+        v = power(1 * 2 * 3 * 4, 1. / 4.)
+        desired = array([v, v, v])
+        check_equal_gmean(a, desired, axis=1, rtol=1e-14, xp=xp)
+
+    def test_large_values(self, xp):
+        a = array([1e100, 1e200, 1e300])
+        desired = 1e200
+        check_equal_gmean(a, desired, rtol=1e-13, xp=xp)
+
+    @pytest.mark.filterwarnings(
+        "ignore:divide by zero encountered in log:RuntimeWarning:dask"
+    )
+    def test_1d_with_0(self, xp):
+        #  Test a 1d case with zero element
+        a = [10, 20, 30, 40, 50, 60, 70, 80, 90, 0]
+        desired = 0.0  # due to exp(-inf)=0
+        with np.errstate(all='ignore'):
+            check_equal_gmean(a, desired, xp=xp)
+
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered in log:RuntimeWarning:dask"
+    )
+    def test_1d_neg(self, xp):
+        #  Test a 1d case with negative element
+        a = [10, 20, 30, 40, 50, 60, 70, 80, 90, -1]
+        desired = np.nan  # due to log(-1) = nan
+        with np.errstate(invalid='ignore'):
+            check_equal_gmean(a, desired, xp=xp)
+
+    @skip_xp_backends(
+        np_only=True,
+        reason='array-likes only supported for NumPy backend',
+    )
+    def test_weights_1d_list(self, xp):
+        # Desired result from:
+        # https://www.dummies.com/education/math/business-statistics/how-to-find-the-weighted-geometric-mean-of-a-data-set/
+        a = [1, 2, 3, 4, 5]
+        weights = [2, 5, 6, 4, 3]
+        desired = 2.77748
+
+        # all the other tests use `check_equal_gmean`, which now converts
+        # the input to an xp-array before calling `gmean`. This time, check
+        # that the function still accepts the lists of ints.
+        res = stats.gmean(a, weights=weights)
+        xp_assert_close(res, np.asarray(desired), rtol=1e-5)
+
+    def test_weights_1d(self, xp):
+        # Desired result from:
+        # https://www.dummies.com/education/math/business-statistics/how-to-find-the-weighted-geometric-mean-of-a-data-set/
+        a = np.array([1, 2, 3, 4, 5])
+        weights = np.array([2, 5, 6, 4, 3])
+        desired = 2.77748
+        check_equal_gmean(a, desired, weights=weights, rtol=1e-5, xp=xp)
+
+    @skip_xp_invalid_arg
+    def test_weights_masked_1d_array(self, xp):
+        # Desired result from:
+        # https://www.dummies.com/education/math/business-statistics/how-to-find-the-weighted-geometric-mean-of-a-data-set/
+        a = np.array([1., 2., 3., 4., 5., 6.])
+        weights = np.ma.array([2., 5., 6., 4., 3., 5.], mask=[0, 0, 0, 0, 0, 1])
+        desired = 2.77748
+        xp = np.ma  # check_equal_gmean uses xp.asarray; this will preserve the mask
+        check_equal_gmean(a, desired, weights=weights, rtol=1e-5,
+                          dtype=np.float64, xp=xp)
+
+
+@make_xp_test_case(stats.pmean)
+class TestPMean:
+
+    def pmean_reference(a, p):
+        return (np.sum(a**p) / a.size)**(1/p)
+
+    def wpmean_reference(a, p, weights):
+        return (np.sum(weights * a**p) / np.sum(weights))**(1/p)
+
+    def test_bad_exponent(self, xp):
+        with pytest.raises(ValueError, match='Power mean only defined for'):
+            stats.pmean(xp.asarray([1, 2, 3]), xp.asarray([0]))
+
+    @pytest.mark.parametrize('dtype', [None, 'int64', 'float32', 'float64'])
+    def test_1d(self, dtype, xp):
+        dtype = dtype if dtype is None else getattr(xp, dtype)
+
+        a, p = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 3.5
+        desired = TestPMean.pmean_reference(np.array(a), p)
+        check_equal_pmean(a, p, desired, dtype=dtype, xp=xp)
+
+        a, p = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100], -2.5
+        desired = TestPMean.pmean_reference(np.array(a), p)
+        check_equal_pmean(a, p, desired, dtype=dtype, xp=xp)
+
+        a, p = [1, 2, 3, 4], 2
+        desired = np.sqrt((1**2 + 2**2 + 3**2 + 4**2) / 4)
+        check_equal_pmean(a, p, desired, dtype=dtype, xp=xp)
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    def test_1d_with_zero(self, xp):
+        a, p = np.array([1, 0]), -1
+        desired = 0.0
+        check_equal_pmean(a, p, desired, rtol=0.0, xp=xp)
+
+    def test_1d_with_negative_value(self, xp):
+        a, p = np.array([1, 0, -1]), 1.23
+        message = "The power mean is only defined..."
+        with eager_warns(RuntimeWarning, match=message, xp=xp):
+            check_equal_pmean(a, p, xp.nan, xp=xp)
+
+    @pytest.mark.parametrize(
+        ("a", "p"),
+        [([[10, 20], [50, 60], [90, 100]], -0.5),
+         (np.array([[10, 20], [50, 60], [90, 100]]), 0.5)]
+    )
+    def test_2d_axisnone(self, a, p, xp):
+        desired = TestPMean.pmean_reference(np.array(a), p)
+        check_equal_pmean(a, p, desired, xp=xp)
+
+    @pytest.mark.parametrize(
+        ("a", "p"),
+        [([[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]], -0.5),
+         ([[10, 0, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]], 0.5)]
+    )
+    def test_2d_axis0(self, a, p, xp):
+        desired = [
+            TestPMean.pmean_reference(
+                np.array([a[i][j] for i in range(len(a))]), p
+            )
+            for j in range(len(a[0]))
+        ]
+        check_equal_pmean(a, p, desired, axis=0, xp=xp)
+
+    @pytest.mark.parametrize(
+        ("a", "p"),
+        [([[10, 20, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]], -0.5),
+         ([[10, 0, 30, 40], [50, 60, 70, 80], [90, 100, 110, 120]], 0.5)]
+    )
+    def test_2d_axis1(self, a, p, xp):
+        desired = [TestPMean.pmean_reference(np.array(a_), p) for a_ in a]
+        check_equal_pmean(a, p, desired, axis=1, xp=xp)
+
+    def test_weights_1d(self, xp):
+        a, p = [2, 10, 6], -1.23456789
+        weights = [10, 5, 3]
+        desired = TestPMean.wpmean_reference(np.array(a), p, weights)
+        check_equal_pmean(a, p, desired, weights=weights, rtol=1e-5, xp=xp)
+
+    @skip_xp_backends(
+        np_only=True,
+        reason='array-likes only supported for NumPy backend',
+    )
+    def test_weights_1d_list(self, xp):
+        a, p = [2, 10, 6], -1.23456789
+        weights = [10, 5, 3]
+        desired = TestPMean.wpmean_reference(np.array(a), p, weights)
+        # all the other tests use `check_equal_pmean`, which now converts
+        # the input to an xp-array before calling `pmean`. This time, check
+        # that the function still accepts the lists of ints.
+        res = stats.pmean(a, p, weights=weights)
+        xp_assert_close(res, np.asarray(desired), rtol=1e-5)
+
+    @skip_xp_invalid_arg
+    def test_weights_masked_1d_array(self, xp):
+        a, p = np.array([2., 10., 6., 42.]), 1
+        weights = np.ma.array([10., 5., 3., 42.], mask=[0, 0, 0, 1])
+        desired = np.average(a, weights=weights)
+        xp = np.ma  # check_equal_pmean uses xp.asarray; this will preserve the mask
+        check_equal_pmean(a, p, desired, weights=weights, rtol=1e-5,
+                          dtype=np.float64, xp=xp)
+
+    @pytest.mark.parametrize(
+        ("axis", "fun_name", "p"),
+        [(None, "wpmean_reference", 9.87654321),
+         (0, "gmean", 0),
+         (1, "hmean", -1)]
+    )
+    def test_weights_2d(self, axis, fun_name, p, xp):
+        if fun_name == 'wpmean_reference':
+            def fun(a, axis, weights):
+                return TestPMean.wpmean_reference(a, p, weights)
+        else:
+            fun = getattr(stats, fun_name)
+        a = np.array([[2, 5], [10, 5], [6, 5]])
+        weights = np.array([[10, 1], [5, 1], [3, 1]])
+        desired = fun(a, axis=axis, weights=weights)
+        check_equal_pmean(a, p, desired, axis=axis, weights=weights, rtol=1e-5, xp=xp)
+
+    def test_infinite_p_gh23111(self):
+        # gh-23111 reported that `pmean` didn't work properly with infinite `p`;
+        # check that this raises an appropriate error message
+        message = "Power mean only implemented for finite `p`"
+        with pytest.raises(NotImplementedError, match=message):
+            stats.pmean([2], np.inf)
+
+
+    @pytest.mark.parametrize(
+        "p, weights, ref",
+        [
+            # import numpy as np
+            # from mpmath import mp
+            # mp.dps = 1000
+            #
+            # x = [50., 100., 300., 70., 25., 100., 2.]
+            # w = [1., 2., 1., 1., 1., 2., 1.]
+            #
+            # x = np.asarray([mp.mpf(x_) for x_ in x])
+            # w = np.asarray([mp.mpf(w_) for w_ in w])
+            # p = mp.mpf(2e-16)
+            #
+            # res = np.average(x**p)**(1/p)
+            # float(res) # 47.23984586445856
+            #
+            (2e-16, None, 47.23984586445856),
+            #
+            # res = np.average(x**p, weights=w)**(1/p)
+            # float(res) # 55.80644618389137
+            #
+            (2e-16, [1., 2., 1., 1., 1., 2., 1.], 55.80644618389137),
+        ],
+    )
+    def test_small_p_gh23407(self, xp, p, weights, ref):
+        # gh-23407 reported that pmean had
+        # poor numerical behavior for very small nonzero p
+        x = xp.asarray(
+            [50., 100., 300., 70., 25., 100., 2.]
+        )
+        w = None if weights is None else xp.asarray(weights)
+
+        res = stats.pmean(x, p, weights=w)
+        xp_assert_close(res, xp.asarray(ref))
+
+
+@make_xp_test_case(stats.gstd)
+class TestGSTD:
+    # must add 1 as `gstd` is only defined for positive values
+    array_1d = (np.arange(2 * 3 * 4) + 1).tolist()
+    gstd_array_1d = 2.294407613602
+    array_3d = np.reshape(array_1d, (2, 3, 4)).tolist()
+
+    def test_1d_array(self, xp):
+        gstd_actual = stats.gstd(xp.asarray(self.array_1d))
+        xp_assert_close(gstd_actual, xp.asarray(self.gstd_array_1d))
+
+    @skip_xp_backends(np_only=True, reason="Only NumPy supports array-like input")
+    def test_1d_numeric_array_like_input(self, xp):
+        gstd_actual = stats.gstd(tuple(self.array_1d))
+        assert_allclose(gstd_actual, self.gstd_array_1d)
+
+    @skip_xp_invalid_arg
+    def test_raises_error_non_numeric_input(self, xp):
+        message = "could not convert string to float|The DType..."
+        with pytest.raises((ValueError, TypeError), match=message):
+            stats.gstd('You cannot take the logarithm of a string.')
+
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    @pytest.mark.parametrize('bad_value', (0, -1, np.inf, np.nan))
+    def test_returns_nan_invalid_value(self, bad_value, xp):
+        x = xp.asarray(self.array_1d + [bad_value])
+        if np.isfinite(bad_value) and not is_lazy_array(x):
+            message = "The geometric standard deviation is only defined..."
+            with pytest.warns(RuntimeWarning, match=message):
+                res = stats.gstd(x)
+        else:
+            res = stats.gstd(x)
+        xp_assert_equal(res, xp.asarray(np.nan))
+
+    def test_propagates_nan_values(self, xp):
+        a = xp.asarray([[1, 1, 1, 16], [xp.nan, 1, 2, 3]])
+        gstd_actual = stats.gstd(a, axis=1)
+        xp_assert_close(gstd_actual, xp.asarray([4, np.nan]))
+
+    def test_ddof_equal_to_number_of_observations(self, xp):
+        x = xp.asarray(self.array_1d)
+        res = stats.gstd(x, ddof=x.shape[0])
+        xp_assert_equal(res, xp.asarray(xp.nan))
+
+    def test_3d_array(self, xp):
+        x = xp.asarray(self.array_3d)
+        gstd_actual = stats.gstd(x, axis=None)
+        ref = xp.asarray(self.gstd_array_1d)
+        xp_assert_close(gstd_actual, ref)
+
+    def test_3d_array_axis_type_tuple(self, xp):
+        x = xp.asarray(self.array_3d)
+        gstd_actual = stats.gstd(x, axis=(1, 2))
+        ref = xp.asarray([2.12939215, 1.22120169])
+        xp_assert_close(gstd_actual, ref)
+
+    def test_3d_array_axis_0(self, xp):
+        x = xp.asarray(self.array_3d)
+        gstd_actual = stats.gstd(x, axis=0)
+        gstd_desired = xp.asarray([
+            [6.1330555493918, 3.958900210120, 3.1206598248344, 2.6651441426902],
+            [2.3758135028411, 2.174581428192, 2.0260062829505, 1.9115518327308],
+            [1.8205343606803, 1.746342404566, 1.6846557065742, 1.6325269194382]
+        ])
+        xp_assert_close(gstd_actual, gstd_desired)
+
+    def test_3d_array_axis_1(self, xp):
+        x = xp.asarray(self.array_3d)
+        gstd_actual = stats.gstd(x, axis=1)
+        gstd_desired = xp.asarray([
+            [3.118993630946, 2.275985934063, 1.933995977619, 1.742896469724],
+            [1.271693593916, 1.254158641801, 1.238774141609, 1.225164057869]
+        ])
+        xp_assert_close(gstd_actual, gstd_desired)
+
+    def test_3d_array_axis_2(self, xp):
+        x = xp.asarray(self.array_3d)
+        gstd_actual = stats.gstd(x, axis=2)
+        gstd_desired = xp.asarray([
+            [1.8242475707664, 1.2243686572447, 1.1318311657788],
+            [1.0934830582351, 1.0724479791887, 1.0591498540749]
+        ])
+        xp_assert_close(gstd_actual, gstd_desired)
+
+
+class TestTrim:
+    # test trim functions
+    def test_trim1(self):
+        a = np.arange(11)
+        assert_equal(np.sort(stats.trim1(a, 0.1)), np.arange(10))
+        assert_equal(np.sort(stats.trim1(a, 0.2)), np.arange(9))
+        assert_equal(np.sort(stats.trim1(a, 0.2, tail='left')),
+                     np.arange(2, 11))
+        assert_equal(np.sort(stats.trim1(a, 3/11., tail='left')),
+                     np.arange(3, 11))
+        assert_equal(stats.trim1(a, 1.0), [])
+        assert_equal(stats.trim1(a, 1.0, tail='left'), [])
+
+        # empty input
+        assert_equal(stats.trim1([], 0.1), [])
+        assert_equal(stats.trim1([], 3/11., tail='left'), [])
+        assert_equal(stats.trim1([], 4/6.), [])
+
+        # test axis
+        a = np.arange(24).reshape(6, 4)
+        ref = np.arange(4, 24).reshape(5, 4)  # first row trimmed
+
+        axis = 0
+        trimmed = stats.trim1(a, 0.2, tail='left', axis=axis)
+        assert_equal(np.sort(trimmed, axis=axis), ref)
+
+        axis = 1
+        trimmed = stats.trim1(a.T, 0.2, tail='left', axis=axis)
+        assert_equal(np.sort(trimmed, axis=axis), ref.T)
+
+    def test_trimboth(self):
+        a = np.arange(11)
+        assert_equal(np.sort(stats.trimboth(a, 3/11.)), np.arange(3, 8))
+        assert_equal(np.sort(stats.trimboth(a, 0.2)),
+                     np.array([2, 3, 4, 5, 6, 7, 8]))
+        assert_equal(np.sort(stats.trimboth(np.arange(24).reshape(6, 4), 0.2)),
+                     np.arange(4, 20).reshape(4, 4))
+        assert_equal(np.sort(stats.trimboth(np.arange(24).reshape(4, 6).T,
+                                            2/6.)),
+                     np.array([[2, 8, 14, 20], [3, 9, 15, 21]]))
+        assert_raises(ValueError, stats.trimboth,
+                      np.arange(24).reshape(4, 6).T, 4/6.)
+
+        # empty input
+        assert_equal(stats.trimboth([], 0.1), [])
+        assert_equal(stats.trimboth([], 3/11.), [])
+        assert_equal(stats.trimboth([], 4/6.), [])
+
+
+@make_xp_test_case(stats.trim_mean)
+class TestTrimMean:
+    def test_trim_mean(self, xp):
+        # don't use pre-sorted arrays
+        idx = np.array([3, 5, 0, 1, 2, 4])
+        a2 = np.arange(24).reshape(6, 4)[idx, :]
+        a3 = np.arange(24).reshape(6, 4, order='F')[idx, :]
+
+        xp_assert_equal(stats.trim_mean(xp.asarray(a3), 2/6.),
+                        xp.asarray([2.5, 8.5, 14.5, 20.5]))
+        xp_assert_equal(stats.trim_mean(xp.asarray(a2), 2/6.),
+                        xp.asarray([10., 11., 12., 13.]))
+
+        idx4 = np.array([1, 0, 3, 2])
+        a4 = np.arange(24).reshape(4, 6)[idx4, :]
+        xp_assert_equal(stats.trim_mean(xp.asarray(a4), 2/6.),
+                        xp.asarray([9., 10., 11., 12., 13., 14.]))
+
+        # shuffled arange(24) as array_like
+        a = [7, 11, 12, 21, 16, 6, 22, 1, 5, 0, 18, 10, 17, 9, 19, 15, 23,
+             20, 2, 14, 4, 13, 8, 3]
+        xp_assert_equal(stats.trim_mean(xp.asarray(a), 2/6.), xp.asarray(11.5))
+        xp_assert_equal(stats.trim_mean(xp.asarray([5, 4, 3, 1, 2, 0]), 2/6.),
+                        xp.asarray(2.5))
+
+        # check axis argument
+        rng = np.random.default_rng(3417115752)
+        a = rng.integers(20, size=(5, 6, 4, 7))
+        a = xp.asarray(a)
+        for axis in [0, 1, 2, 3, -1]:
+            res1 = stats.trim_mean(a, 2/6., axis=axis)
+            res2 = stats.trim_mean(xp.moveaxis(a, axis, 0), 2/6.)
+            xp_assert_equal(res1, res2)
+
+        res1 = stats.trim_mean(a, 2/6., axis=None)
+        res2 = stats.trim_mean(xp_ravel(a), 2/6.)
+        xp_assert_equal(res1, res2)
+
+        with pytest.raises(ValueError, match="Proportion too big."):
+            stats.trim_mean(a, 0.6)
+
+    @skip_xp_backends('dask.array', reason="lazy -> no _axis_nan_policy")
+    def test_empty_input(self, xp):
+        # empty input
+        with pytest.warns(SmallSampleWarning, match='too small'):
+            xp_assert_equal(stats.trim_mean(xp.asarray([]), 0.0), xp.asarray(xp.nan))
+        with pytest.warns(SmallSampleWarning, match='too small'):
+            xp_assert_equal(stats.trim_mean(xp.asarray([]), 0.6), xp.asarray(xp.nan))
+
+
+@make_xp_test_case(stats.sigmaclip)
+class TestSigmaClip:
+    def test_sigmaclip1(self, xp):
+        a = xp.concat((xp.linspace(9.5, 10.5, 31),
+                       xp.linspace(0., 20., 5)))
+        fact = 4  # default
+        c, low, upp = stats.sigmaclip(a)
+        assert xp.min(c) > low
+        assert xp.max(c) < upp
+        xp_assert_equal(low, xp.mean(c) - fact*xp.std(c))
+        xp_assert_equal(upp, xp.mean(c) + fact*xp.std(c))
+        assert c.shape == a.shape
+
+    def test_sigmaclip2(self, xp):
+        a = xp.concat((xp.linspace(9.5, 10.5, 31),
+                       xp.linspace(0., 20., 5)))
+        fact = 1.5
+        c, low, upp = stats.sigmaclip(a, fact, fact)
+        assert xp.min(c) > low
+        assert xp.max(c) < upp
+        xp_assert_equal(low, xp.mean(c) - fact*xp.std(c))
+        xp_assert_equal(upp, xp.mean(c) + fact*xp.std(c))
+        assert c.shape == (4,)
+        assert a.shape == (36,)
+
+    def test_sigmaclip3(self, xp):
+        a = xp.concat((xp.linspace(9.5, 10.5, 11),
+                       xp.linspace(-100., -50., 3)))
+        fact = 1.8
+        c, low, upp = stats.sigmaclip(a, fact, fact)
+        assert xp.min(c) > low
+        assert xp.max(c) < upp
+        xp_assert_equal(low, xp.mean(c) - fact*xp.std(c))
+        xp_assert_equal(upp, xp.mean(c) + fact*xp.std(c))
+        xp_assert_equal(c, xp.linspace(9.5, 10.5, 11))
+
+    def test_sigmaclip_result_attributes(self, xp):
+        a = xp.concat((xp.linspace(9.5, 10.5, 11),
+                       xp.linspace(-100., -50., 3)))
+        fact = 1.8
+        res = stats.sigmaclip(a, fact, fact)
+        attributes = ('clipped', 'lower', 'upper')
+        check_named_results(res, attributes, xp=xp)
+
+    def test_std_zero(self, xp):
+        # regression test #8632
+        x = xp.ones(10)
+        xp_assert_equal(stats.sigmaclip(x)[0], x)
+
+    @pytest.mark.parametrize("dtype", [None, 'float32', 'float64'])
+    def test_nan_policy(self, dtype, xp):
+        dtype = None if dtype is None else getattr(xp, dtype)
+        rng = np.random.default_rng(243587919486245906119)
+        x = rng.random(10)
+        x[3] = np.nan
+        x = xp.asarray(x.tolist(), dtype=dtype)
+
+        # nan_policy='raise'
+        if not is_lazy_array(x):
+            with pytest.raises(ValueError, match="The input contains nan values"):
+                stats.sigmaclip(x, nan_policy='raise')
+        else:
+            with pytest.raises(TypeError, match="nan_policy='raise' is not supported"):
+                stats.sigmaclip(x, nan_policy='raise')
+
+        # nan_policy='propagate'
+        res = stats.sigmaclip(x, nan_policy='propagate')
+        xp_assert_equal(res.clipped, xp.asarray([], dtype=dtype))
+        xp_assert_equal(res.lower, xp.asarray(xp.nan, dtype=dtype))
+        xp_assert_equal(res.upper, xp.asarray(xp.nan, dtype=dtype))
+
+        # nan_policy='omit'
+        res = stats.sigmaclip(x, nan_policy='omit')
+        ref = stats.sigmaclip(x[~xp.isnan(x)])
+        xp_assert_equal(res.clipped, ref.clipped)
+        xp_assert_equal(res.lower, ref.lower)
+        xp_assert_equal(res.upper, ref.upper)
+
+
+@make_xp_test_case(stats.alexandergovern)
+class TestAlexanderGovern:
+    def test_compare_dtypes(self):
+        # leave this NumPy only
+        args = [[13, 13, 13, 13, 13, 13, 13, 12, 12],
+                [14, 13, 12, 12, 12, 12, 12, 11, 11],
+                [14, 14, 13, 13, 13, 13, 13, 12, 12],
+                [15, 14, 13, 13, 13, 12, 12, 12, 11]]
+        args_int16 = [np.asarray(arg, dtype=np.int16) for arg in args]
+        args_int32 = [np.asarray(arg, dtype=np.int32) for arg in args]
+        args_uint8 = [np.asarray(arg, dtype=np.uint8) for arg in args]
+        args_float64 = [np.asarray(arg, dtype=np.float64) for arg in args]
+
+        res_int16 = stats.alexandergovern(*args_int16)
+        res_int32 = stats.alexandergovern(*args_int32)
+        res_uint8 = stats.alexandergovern(*args_uint8)
+        res_float64 = stats.alexandergovern(*args_float64)
+
+        assert (res_int16.pvalue == res_int32.pvalue ==
+                res_uint8.pvalue == res_float64.pvalue)
+        assert (res_int16.statistic == res_int32.statistic ==
+                res_uint8.statistic == res_float64.statistic)
+
+    @skip_xp_backends('dask.array', reason="Requires `_axis_nan_policy` decorator")
+    @pytest.mark.parametrize('case',[([1, 2], []), ([1, 2], 2), ([1, 2], [2])])
+    def test_too_small_inputs(self, case, xp):
+        # input array is of size zero or too small
+        dtype = xp_default_dtype(xp)
+        case = xp.asarray(case[0], dtype=dtype), xp.asarray(case[1], dtype=dtype)
+        with pytest.warns(SmallSampleWarning, match=too_small_1d_not_omit):
+            res = stats.alexandergovern(*case)
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning")
+    def test_bad_inputs(self, xp):
+        # inputs are not finite (infinity)
+        samples = xp.asarray([1., 2.]), xp.asarray([xp.inf, xp.inf])
+        res = stats.alexandergovern(*samples)
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+    def test_compare_r(self, xp):
+        '''
+        Data generated in R with
+        > set.seed(1)
+        > library("onewaytests")
+        > library("tibble")
+        > y <- c(rnorm(40, sd=10),
+        +        rnorm(30, sd=15),
+        +        rnorm(20, sd=20))
+        > x <- c(rep("one", times=40),
+        +        rep("two", times=30),
+        +        rep("eight", times=20))
+        > x <- factor(x)
+        > ag.test(y ~ x, tibble(y,x))
+
+        Alexander-Govern Test (alpha = 0.05)
+        -------------------------------------------------------------
+        data : y and x
+
+        statistic  : 1.359941
+        parameter  : 2
+        p.value    : 0.5066321
+
+        Result     : Difference is not statistically significant.
+        -------------------------------------------------------------
+        Example adapted from:
+        https://eval-serv2.metpsy.uni-jena.de/wiki-metheval-hp/index.php/R_FUN_Alexander-Govern
+
+        '''
+        one = [-6.264538107423324, 1.8364332422208225, -8.356286124100471,
+               15.952808021377916, 3.295077718153605, -8.204683841180152,
+               4.874290524284853, 7.383247051292173, 5.757813516534923,
+               -3.0538838715635603, 15.11781168450848, 3.898432364114311,
+               -6.2124058054180376, -22.146998871774997, 11.249309181431082,
+               -0.4493360901523085, -0.16190263098946087, 9.438362106852992,
+               8.212211950980885, 5.939013212175088, 9.189773716082183,
+               7.821363007310671, 0.745649833651906, -19.89351695863373,
+               6.198257478947102, -0.5612873952900078, -1.557955067053293,
+               -14.707523838992744, -4.781500551086204, 4.179415601997024,
+               13.58679551529044, -1.0278772734299553, 3.876716115593691,
+               -0.5380504058290512, -13.770595568286065, -4.149945632996798,
+               -3.942899537103493, -0.5931339671118566, 11.000253719838831,
+               7.631757484575442]
+
+        two = [-2.4678539438038034, -3.8004252020476135, 10.454450631071062,
+               8.34994798010486, -10.331335418242798, -10.612427354431794,
+               5.468729432052455, 11.527993867731237, -1.6851931822534207,
+               13.216615896813222, 5.971588205506021, -9.180395898761569,
+               5.116795371366372, -16.94044644121189, 21.495355525515556,
+               29.7059984775879, -5.508322146997636, -15.662019394747961,
+               8.545794411636193, -2.0258190582123654, 36.024266407571645,
+               -0.5886000409975387, 10.346090436761651, 0.4200323817099909,
+               -11.14909813323608, 2.8318844927151434, -27.074379433365568,
+               21.98332292344329, 2.2988000731784655, 32.58917505543229]
+
+        eight = [9.510190577993251, -14.198928618436291, 12.214527069781099,
+                 -18.68195263288503, -25.07266800478204, 5.828924710349257,
+                 -8.86583746436866, 0.02210703263248262, 1.4868264830332811,
+                 -11.79041892376144, -11.37337465637004, -2.7035723024766414,
+                 23.56173993146409, -30.47133600859524, 11.878923752568431,
+                 6.659007424270365, 21.261996745527256, -6.083678472686013,
+                 7.400376198325763, 5.341975815444621]
+
+        one, two, eight = xp.asarray(one), xp.asarray(two), xp.asarray(eight)
+        soln = stats.alexandergovern(one, two, eight)
+        xp_assert_close(soln.statistic, xp.asarray(1.3599405447999450836))
+        xp_assert_close(soln.pvalue, xp.asarray(0.50663205309676440091))
+
+    def test_compare_scholar(self, xp):
+        '''
+        Data taken from 'The Modification and Evaluation of the
+        Alexander-Govern Test in Terms of Power' by Kingsley Ochuko, T.,
+        Abdullah, S., Binti Zain, Z., & Soaad Syed Yahaya, S. (2015).
+        '''
+        young = [482.43, 484.36, 488.84, 495.15, 495.24, 502.69, 504.62,
+                 518.29, 519.1, 524.1, 524.12, 531.18, 548.42, 572.1, 584.68,
+                 609.09, 609.53, 666.63, 676.4]
+        middle = [335.59, 338.43, 353.54, 404.27, 437.5, 469.01, 485.85,
+                  487.3, 493.08, 494.31, 499.1, 886.41]
+        old = [519.01, 528.5, 530.23, 536.03, 538.56, 538.83, 557.24, 558.61,
+               558.95, 565.43, 586.39, 594.69, 629.22, 645.69, 691.84]
+        young, middle, old = xp.asarray(young), xp.asarray(middle), xp.asarray(old)
+        soln = stats.alexandergovern(young, middle, old)
+        xp_assert_close(soln.statistic, xp.asarray(5.3237), atol=1e-3)
+        xp_assert_close(soln.pvalue, xp.asarray(0.06982), atol=1e-4)
+
+        # verify with ag.test in r
+        '''
+        > library("onewaytests")
+        > library("tibble")
+        > young <- c(482.43, 484.36, 488.84, 495.15, 495.24, 502.69, 504.62,
+        +                  518.29, 519.1, 524.1, 524.12, 531.18, 548.42, 572.1,
+        +                  584.68, 609.09, 609.53, 666.63, 676.4)
+        > middle <- c(335.59, 338.43, 353.54, 404.27, 437.5, 469.01, 485.85,
+        +                   487.3, 493.08, 494.31, 499.1, 886.41)
+        > old <- c(519.01, 528.5, 530.23, 536.03, 538.56, 538.83, 557.24,
+        +                   558.61, 558.95, 565.43, 586.39, 594.69, 629.22,
+        +                   645.69, 691.84)
+        > young_fct <- c(rep("young", times=19))
+        > middle_fct <-c(rep("middle", times=12))
+        > old_fct <- c(rep("old", times=15))
+        > ag.test(a ~ b, tibble(a=c(young, middle, old), b=factor(c(young_fct,
+        +                                              middle_fct, old_fct))))
+
+        Alexander-Govern Test (alpha = 0.05)
+        -------------------------------------------------------------
+        data : a and b
+
+        statistic  : 5.324629
+        parameter  : 2
+        p.value    : 0.06978651
+
+        Result     : Difference is not statistically significant.
+        -------------------------------------------------------------
+
+        '''
+        xp_assert_close(soln.statistic, xp.asarray(5.324629), rtol=2e-6)
+        xp_assert_close(soln.pvalue, xp.asarray(0.06978651), rtol=2e-6)
+
+    def test_compare_scholar3(self, xp):
+        '''
+        Data taken from 'Robustness And Comparative Power Of WelchAspin,
+        Alexander-Govern And Yuen Tests Under Non-Normality And Variance
+        Heteroscedasticity', by Ayed A. Almoied. 2017. Page 34-37.
+        https://digitalcommons.wayne.edu/cgi/viewcontent.cgi?article=2775&context=oa_dissertations
+        '''
+        x1 = [-1.77559, -1.4113, -0.69457, -0.54148, -0.18808, -0.07152,
+              0.04696, 0.051183, 0.148695, 0.168052, 0.422561, 0.458555,
+              0.616123, 0.709968, 0.839956, 0.857226, 0.929159, 0.981442,
+              0.999554, 1.642958]
+        x2 = [-1.47973, -1.2722, -0.91914, -0.80916, -0.75977, -0.72253,
+              -0.3601, -0.33273, -0.28859, -0.09637, -0.08969, -0.01824,
+              0.260131, 0.289278, 0.518254, 0.683003, 0.877618, 1.172475,
+              1.33964, 1.576766]
+        x1, x2 = xp.asarray(x1), xp.asarray(x2)
+        soln = stats.alexandergovern(x1, x2)
+        xp_assert_close(soln.statistic, xp.asarray(0.713526), atol=1e-5)
+        xp_assert_close(soln.pvalue, xp.asarray(0.398276), atol=1e-5)
+
+        '''
+        tested in ag.test in R:
+        > library("onewaytests")
+        > library("tibble")
+        > x1 <- c(-1.77559, -1.4113, -0.69457, -0.54148, -0.18808, -0.07152,
+        +          0.04696, 0.051183, 0.148695, 0.168052, 0.422561, 0.458555,
+        +          0.616123, 0.709968, 0.839956, 0.857226, 0.929159, 0.981442,
+        +          0.999554, 1.642958)
+        > x2 <- c(-1.47973, -1.2722, -0.91914, -0.80916, -0.75977, -0.72253,
+        +         -0.3601, -0.33273, -0.28859, -0.09637, -0.08969, -0.01824,
+        +         0.260131, 0.289278, 0.518254, 0.683003, 0.877618, 1.172475,
+        +         1.33964, 1.576766)
+        > x1_fact <- c(rep("x1", times=20))
+        > x2_fact <- c(rep("x2", times=20))
+        > a <- c(x1, x2)
+        > b <- factor(c(x1_fact, x2_fact))
+        > ag.test(a ~ b, tibble(a, b))
+        Alexander-Govern Test (alpha = 0.05)
+        -------------------------------------------------------------
+        data : a and b
+
+        statistic  : 0.7135182
+        parameter  : 1
+        p.value    : 0.3982783
+
+        Result     : Difference is not statistically significant.
+        -------------------------------------------------------------
+        '''
+        xp_assert_close(soln.statistic, xp.asarray(0.7135182), rtol=2e-6)
+        xp_assert_close(soln.pvalue, xp.asarray(0.3982783), rtol=2e-6)
+
+    def test_nan_policy_propagate(self, xp):
+        args = xp.asarray([1., 2., 3., 4.]), xp.asarray([1, xp.nan])
+        # default nan_policy is 'propagate'
+        res = stats.alexandergovern(*args)
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+
+    @skip_xp_backends(eager_only=True, reason="lazy -> reduced nan_policy capabilities")
+    def test_nan_policy_raise(self, xp):
+        args = xp.asarray([1., 2., 3., 4.]), xp.asarray([1., xp.nan])
+        with assert_raises(ValueError, match="The input contains nan values"):
+            stats.alexandergovern(*args, nan_policy='raise')
+
+    @skip_xp_backends(eager_only=True, reason="lazy -> reduced nan_policy capabilities")
+    def test_nan_policy_omit(self, xp):
+        args_nan = xp.asarray([1, 2, 3, xp.nan, 4]), xp.asarray([1, xp.nan, 19, 25])
+        args_no_nan = xp.asarray([1, 2, 3, 4]), xp.asarray([1, 19, 25])
+        res_nan = stats.alexandergovern(*args_nan, nan_policy='omit')
+        res_no_nan = stats.alexandergovern(*args_no_nan)
+        xp_assert_equal(res_nan.pvalue, res_no_nan.pvalue)
+        xp_assert_equal(res_nan.statistic, res_no_nan.statistic)
+
+    @skip_xp_backends(eager_only=True, reason="lazy -> no data-dependent warnings")
+    def test_constant_input(self, xp):
+        # Zero variance input, consistent with `stats.pearsonr`
+        x1 = xp.asarray([0.667, 0.667, 0.667])
+        x2 = xp.asarray([0.123, 0.456, 0.789])
+        with pytest.warns(RuntimeWarning, match="Precision loss occurred..."):
+            res = stats.alexandergovern(x1, x2)
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+    @skip_xp_backends('dask.array', reason="Requires `_axis_nan_policy` decorator")
+    @pytest.mark.parametrize('axis', [0, 1, None])
+    def test_2d_input(self, xp, axis):
+        # much more extensive testing for NumPy in `test_axis_nan_policy.py`
+        rng = np.random.default_rng(23498389754392854235)
+        shape = (11, 12)
+        samples = [rng.random(shape) for i in range(3)]
+        xp_samples = [xp.asarray(sample) for sample in samples]
+        ref = stats.alexandergovern(*samples, axis=axis)
+        res = stats.alexandergovern(*xp_samples, axis=axis)
+        xp_assert_close(res.statistic, xp.asarray(ref.statistic))
+        xp_assert_close(res.pvalue, xp.asarray(ref.pvalue))
+
+
+@make_xp_test_case(stats.f_oneway)
+class TestFOneWay:
+
+    def test_trivial(self, xp):
+        # A trivial test of stats.f_oneway, with F=0.
+        F, p = stats.f_oneway(xp.asarray([0, 2]), xp.asarray([0, 2]))
+        xp_assert_equal(F, xp.asarray(0.0))
+        xp_assert_equal(p, xp.asarray(1.0))
+
+    @pytest.mark.parametrize("dtype", [None, "float32", "float64"])
+    def test_basic(self, dtype, xp):
+        # Despite being a floating point calculation, this data should
+        # result in F being exactly 2.0.
+        dtype = None if dtype is None else getattr(xp, dtype)
+        F, p = stats.f_oneway(xp.asarray([0, 2], dtype=dtype),
+                              xp.asarray([2, 4], dtype=dtype))
+        xp_assert_equal(F, xp.asarray(2.0, dtype=dtype))
+        xp_assert_close(p, xp.asarray(1 - 0.5**0.5, dtype=dtype))
+
+    @pytest.mark.parametrize("dtype", [None, "float32", "float64"])
+    def test_unequal_var(self, dtype, xp):
+        # toy samples with unequal variances and different observations
+        dtype = None if dtype is None else getattr(xp, dtype)
+        samples = [xp.asarray([-50.42, 40.31, -18.09, 35.58, -6.8, 0.22], dtype=dtype),
+                   xp.asarray([23.44, 4.5, 15.1, 9.66], dtype=dtype),
+                   xp.asarray([11.94, 11.1 , 9.87, 9.09, 3.33], dtype=dtype)]
+
+        F, p = stats.f_oneway(*samples, equal_var=False)
+
+        # R language as benchmark
+        # group1 <- c(-50.42, 40.31, -18.09, 35.58, -6.8, 0.22)
+        # group2 <- c(23.44, 4.5, 15.1, 9.66)
+        # group3 <- c(11.94, 11.1 , 9.87, 9.09, 3.33)
+        #
+        # data <- data.frame(
+        #     value = c(group1, group2, group3),
+        #     group = factor(c(rep("G1", length(group1)),
+        #                      rep("G2", length(group2)),
+        #                      rep("G3", length(group3))))
+        # )
+        # welch_anova <- oneway.test(value ~ group, data = data, var.equal = FALSE)
+        # welch_anova$statistic
+        ## F: 0.609740409019517
+        # welch_anova$p.value
+        ## 0.574838941286302
+
+        xp_assert_close(F, xp.asarray(0.609740409019517, dtype=dtype))
+        xp_assert_close(p, xp.asarray(0.574838941286302, dtype=dtype))
+
+    def test_equal_var_input_validation(self, xp):
+        samples = [xp.asarray([-50.42, 40.31, -18.09, 35.58, -6.8, 0.22]),
+                   xp.asarray([23.44, 4.5, 15.1, 9.66]),
+                   xp.asarray([11.94, 11.1 , 9.87, 9.09, 3.33])]
+
+        message = "Expected a boolean value for 'equal_var'"
+        with pytest.raises(TypeError, match=message):
+            stats.f_oneway(*samples, equal_var="False")
+
+    def test_known_exact(self, xp):
+        # Another trivial dataset for which the exact F and p can be
+        # calculated on most platforms
+        F, p = stats.f_oneway(xp.asarray([2]), xp.asarray([2]),
+                              xp.asarray([2, 3, 4]))
+        xp_assert_close(F, xp.asarray(3/5))  # assert_equal fails on some CI platforms
+        xp_assert_close(p, xp.asarray(5/8))
+
+    def test_large_integer_array(self, xp):
+        a = xp.asarray([655, 788], dtype=xp.uint16)
+        b = xp.asarray([789, 772], dtype=xp.uint16)
+        F, p = stats.f_oneway(a, b)
+        # The expected value was verified by computing it with mpmath with
+        # 40 digits of precision.
+        xp_assert_close(F, xp.asarray(0.77450216931805540))
+
+    def test_result_attributes(self, xp):
+        a = xp.asarray([655, 788], dtype=xp.uint16)
+        b = xp.asarray([789, 772], dtype=xp.uint16)
+        res = stats.f_oneway(a, b)
+        attributes = ('statistic', 'pvalue')
+        check_named_results(res, attributes, xp=xp)
+
+    @pytest.mark.parametrize('test_case',
+        ['SiRstv', 'SmLs01', 'SmLs02', 'SmLs03', 'AtmWtAg', 'SmLs04', 'SmLs05',
+         'SmLs06', 'SmLs07', 'SmLs08', 'SmLs09'])
+    def test_nist(self, test_case, xp):
+        # These are the nist ANOVA files. They can be found at:
+        # https://www.itl.nist.gov/div898/strd/anova/anova.html
+        filename = test_case + ".dat"
+        rtol = 1e-7
+        fname = os.path.abspath(os.path.join(os.path.dirname(__file__),
+                                             'data/nist_anova', filename))
+        with open(fname) as f:
+            content = f.read().split('\n')
+        certified = [line.split() for line in content[40:48]
+                     if line.strip()]
+        dataf = np.loadtxt(fname, skiprows=60)
+        y, x = dataf.T
+        y = y.astype(int)
+        caty = np.unique(y)
+        f = float(certified[0][-1])
+
+        xlist = [xp.asarray(x[y == i]) for i in caty]
+        res = stats.f_oneway(*xlist)
+
+        # With the hard test cases we relax the tolerance a bit.
+        hard_tc = ('SmLs07', 'SmLs08', 'SmLs09')
+        if test_case in hard_tc:
+            rtol = 1e-4
+
+        xp_assert_close(res[0], xp.asarray(f, dtype=xp.float64), rtol=rtol)
+
+    @pytest.mark.filterwarnings('ignore')  # Dask emits NumPy warnings
+    @pytest.mark.parametrize("a, b, expected", [
+        (np.array([42, 42, 42]), np.array([7, 7, 7]), (np.inf, 0.)),
+        (np.array([42, 42, 42]), np.array([42, 42, 42]), (np.nan, np.nan))
+        ])
+    def test_constant_input(self, a, b, expected, xp):
+        # For more details, look on https://github.com/scipy/scipy/issues/11669
+        f, p = stats.f_oneway(xp.asarray(a), xp.asarray(b))
+        xp_assert_equal(f, xp.asarray(expected[0]))
+        xp_assert_equal(p, xp.asarray(expected[1]))
+
+    @skip_xp_backends('dask.array', reason='needs _axis_nan_policy')
+    @pytest.mark.parametrize('axis', [-2, -1, 0, 1])
+    def test_2d_inputs(self, axis, xp):
+        a = np.array([[1, 4, 3, 3],
+                      [2, 5, 3, 3],
+                      [3, 6, 3, 3],
+                      [2, 3, 3, 3],
+                      [1, 4, 3, 3]], dtype=np.float64)
+        b = np.array([[3, 1, 5, 3],
+                      [4, 6, 5, 3],
+                      [4, 3, 5, 3],
+                      [1, 5, 5, 3],
+                      [5, 5, 5, 3],
+                      [2, 3, 5, 3],
+                      [8, 2, 5, 3],
+                      [2, 2, 5, 3]], dtype=np.float64)
+        c = np.array([[4, 3, 4, 3],
+                      [4, 2, 4, 3],
+                      [5, 4, 4, 3],
+                      [5, 4, 4, 3]], dtype=np.float64)
+
+        if axis in [-1, 1]:
+            a = a.T
+            b = b.T
+            c = c.T
+            take_axis = 0
+        else:
+            take_axis = 1
+
+        f, p = stats.f_oneway(xp.asarray(a), xp.asarray(b), xp.asarray(c),
+                              axis=axis)
+
+        # Verify that the result computed with the 2d arrays matches
+        # the result of calling f_oneway individually on each slice.
+        for j in [0, 1]:
+            fj, pj = stats.f_oneway(np.take(a, j, take_axis),
+                                    np.take(b, j, take_axis),
+                                    np.take(c, j, take_axis))
+            xp_assert_close(f[j], xp.asarray(fj))
+            xp_assert_close(p[j], xp.asarray(pj))
+        for j in [2, 3]:
+            fj, pj = stats.f_oneway(np.take(a, j, take_axis),
+                                    np.take(b, j, take_axis),
+                                    np.take(c, j, take_axis))
+            xp_assert_close(f[j], xp.asarray(fj))
+            xp_assert_close(p[j], xp.asarray(pj))
+
+    @skip_xp_backends('dask.array', reason='needs _axis_nan_policy')
+    def test_3d_inputs(self, xp):
+        # Some 3-d arrays. (There is nothing special about the values.)
+        a = xp.reshape(1/xp.arange(1.0, 4*5*7 + 1., dtype=xp.float64), (4, 5, 7))
+        b = xp.reshape(2/xp.arange(1.0, 4*8*7 + 1., dtype=xp.float64), (4, 8, 7))
+        c = xp.reshape(1/xp.arange(1.0, 4*4*7 + 1., dtype=xp.float64), (4, 4, 7))
+        c = xp.cos(c)
+
+        f, p = stats.f_oneway(a, b, c, axis=1)
+
+        assert f.shape == (4, 7)
+        assert p.shape == (4, 7)
+
+        for i in range(a.shape[0]):
+            for j in range(a.shape[2]):
+                fij, pij = stats.f_oneway(a[i, :, j], b[i, :, j], c[i, :, j])
+                xp_assert_close(fij, f[i, j])
+                xp_assert_close(pij, p[i, j])
+
+    def test_length0_1d_error(self, xp):
+        # Require at least one value in each group.
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            result = stats.f_oneway(xp.asarray([1., 2., 3.]), xp.asarray([]),
+                                    xp.asarray([4., 5., 6., 7.]))
+            xp_assert_equal(result.statistic, xp.asarray(xp.nan))
+            xp_assert_equal(result.pvalue, xp.asarray(xp.nan))
+
+    @skip_xp_backends('dask.array', reason='needs _axis_nan_policy')
+    def test_length0_2d_error(self, xp):
+        with eager_warns(SmallSampleWarning, match=too_small_nd_not_omit, xp=xp):
+            ncols = 3
+            a = xp.ones((4, ncols))
+            b = xp.ones((0, ncols))
+            c = xp.ones((5, ncols))
+            f, p = stats.f_oneway(a, b, c)
+            nans = xp.full((ncols,), fill_value=xp.nan)
+            xp_assert_equal(f, nans)
+            xp_assert_equal(p, nans)
+
+    def test_all_length_one(self, xp):
+        samples = xp.asarray([10]), xp.asarray([11]), xp.asarray([12]), xp.asarray([13])
+        with eager_warns(SmallSampleWarning, xp=xp):
+            result = stats.f_oneway(*samples)
+            xp_assert_equal(result.statistic, xp.asarray(xp.nan))
+            xp_assert_equal(result.pvalue, xp.asarray(xp.nan))
+
+    @skip_xp_backends('dask.array', reason='lazy->reduced input validation')
+    @pytest.mark.parametrize('args', [(), ([1, 2, 3],)])
+    def test_too_few_inputs(self, args, xp):
+        args = [xp.asarray(arg) for arg in args]
+        message = "At least two samples are required..."
+        with pytest.raises(TypeError, match=message):
+            stats.f_oneway(*args)
+
+    @skip_xp_backends('dask.array', reason='lazy->reduced input validation')
+    def test_axis_error(self, xp):
+        a = xp.ones((3, 4))
+        b = xp.ones((5, 4))
+        with pytest.raises(AxisError):
+            stats.f_oneway(a, b, axis=2)
+
+    def test_bad_shapes(self, xp):
+        a = xp.ones((3, 4))
+        b = xp.ones((5, 4))
+        with pytest.raises(ValueError):
+            stats.f_oneway(a, b, axis=1)
+
+
+@make_xp_test_case(stats.kruskal)
+class TestKruskal:
+    def test_array_like(self):
+        h, p = stats.kruskal([1], [2])
+        assert_equal(h, 1.0)
+        assert_allclose(p, stats.chi2.sf(h, 1))
+
+    def test_simple(self, xp):
+        x = xp.asarray([1])
+        y = xp.asarray([2])
+        h, p = stats.kruskal(x, y)
+        xp_assert_close(h, xp.asarray(1.0))
+        dtype = xp_default_dtype(xp)
+        xp_assert_close(p, xp.asarray(stats.chi2.sf(1, 1), dtype=dtype))
+
+    @pytest.mark.parametrize("dtype", ['float32', 'float64', None])
+    def test_basic(self, xp, dtype):
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        x = xp.asarray([1, 3, 5, 7, 9], dtype=dtype)
+        y = xp.asarray([2, 4, 6, 8, 10], dtype=dtype)
+        h, p = stats.kruskal(x, y)
+        xp_assert_close(h, xp.asarray(3/11, dtype=dtype))
+        xp_assert_close(p, xp.asarray(stats.chi2.sf(3/11, 1), dtype=dtype))
+
+    def test_simple_tie(self, xp):
+        x = [1]
+        y = [1, 2]
+        h, p = stats.kruskal(xp.asarray(x), xp.asarray(y))
+
+        h_uncorr = 1.5**2 + 2*2.25**2 - 12
+        corr = 0.75
+        expected = xp.asarray(h_uncorr / corr)   # 0.5
+
+        # Since the expression is simple and the exact answer is 0.5, it
+        # should be safe to use assert_equal().
+        xp_assert_equal(h, expected)
+        xp_assert_close(p, special.chdtrc(xp.asarray(1.), expected))
+
+    def test_another_tie(self, xp):
+        x = [1, 1, 1, 2]
+        y = [2, 2, 2, 2]
+        h, p = stats.kruskal(xp.asarray(x), xp.asarray(y))
+
+        h_uncorr = (12. / 8. / 9.) * 4 * (3**2 + 6**2) - 3 * 9
+        corr = 1 - float(3**3 - 3 + 5**3 - 5) / (8**3 - 8)
+        expected = xp.asarray(h_uncorr / corr)
+
+        xp_assert_close(h, expected)
+        xp_assert_close(p, special.chdtrc(xp.asarray(1.), expected))
+
+    def test_three_groups(self, xp):
+        # A test of stats.kruskal with three groups, with ties.
+        x = [1, 1, 1]
+        y = [2, 2, 2]
+        z = [2, 2]
+        h, p = stats.kruskal(xp.asarray(x), xp.asarray(y), xp.asarray(z))
+
+        h_uncorr = (12. / 8. / 9.) * (3*2**2 + 3*6**2 + 2*6**2) - 3 * 9  # 5.0
+        corr = 1 - float(3**3 - 3 + 5**3 - 5) / (8**3 - 8)
+        expected = xp.asarray(h_uncorr / corr)  # 7.0
+
+        xp_assert_close(h, expected)
+        xp_assert_close(p, special.chdtrc(xp.asarray(2.), expected))
+
+    def test_empty(self, xp):
+        # A test of stats.kruskal with three groups, with ties.
+        x = xp.asarray([1, 1, 1])
+        y = xp.asarray([2, 2, 2])
+        z = xp.asarray([], dtype=y.dtype)
+        with pytest.warns(SmallSampleWarning, match=too_small_1d_not_omit):
+            res = stats.kruskal(x, y, z)
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+    @skip_xp_backends('jax.numpy', reason='needs jax-ml/jax#34628')
+    def test_nan_policy_propagate(self, xp):
+        x = xp.arange(10.)
+        x = xpx.at(x)[9].set(xp.nan)
+
+        res = stats.kruskal(x, x)
+        xp_assert_equal(res.statistic, xp.asarray(xp.nan))
+        xp_assert_equal(res.pvalue, xp.asarray(xp.nan))
+
+    @skip_xp_backends('jax.numpy', reason='lazy -> reduced nan_policy capabilities')
+    @xfail_xp_backends('cupy', reason='cupy returns small but nonzero res.statistic')
+    def test_nan_policy_omit_raise(self, xp):
+        x = xp.arange(10.)
+        x = xpx.at(x)[9].set(xp.nan)
+
+        res = stats.kruskal(x, x, nan_policy='omit')
+        xp_assert_equal(res.statistic, xp.asarray(0.0))
+        xp_assert_equal(res.pvalue, xp.asarray(1.0))
+
+        with pytest.raises(ValueError, match='The input contains nan values'):
+            stats.kruskal(x, x, nan_policy='raise')
+
+        with pytest.raises(ValueError, match='nan_policy must be one of...'):
+            stats.kruskal(x, x, nan_policy='foobar')
+
+    def test_large_samples(self, xp):
+        # Test to see if large samples are handled correctly.
+        n = 50000
+        rng = np.random.default_rng(1808365978)
+        x = xp.asarray(rng.standard_normal(n))
+        y = xp.asarray(rng.standard_normal(n) + 50)
+        h, p = stats.kruskal(x, y)
+        xp_assert_close(p, xp.asarray(0., dtype=x.dtype))
+
+    def test_no_args_gh20661(self, xp):
+        message = r"Need at least two groups in stats.kruskal\(\)"
+        with pytest.raises(ValueError, match=message):
+            stats.kruskal()
+
+
+@make_xp_test_case(stats.combine_pvalues)
+class TestCombinePvalues:
+    # Reference values computed using the following R code:
+    # options(digits=16)
+    # library(metap)
+    # x = c(0.01, 0.2, 0.3)
+    # sumlog(x)  # fisher
+    # sumz(x)  # stouffer
+    # sumlog(1-x)  # pearson (negative statistic and complement of p-value)
+    # minimump(x)  # tippett
+    @pytest.mark.parametrize(
+        "method, expected_statistic, expected_pvalue",
+        [("fisher", 14.83716180549625 , 0.02156175132483465),
+         ("stouffer", 2.131790594240385, 0.01651203260896294),
+         ("pearson", -1.179737662212887, 1-0.9778736999143087),
+         ("tippett", 0.01, 0.02970100000000002),
+         # mudholkar_george: library(transite); p_combine(x, method="MG")
+         ("mudholkar_george", 6.828712071641684, 0.01654551838539527)])
+    def test_reference_values(self, xp, method, expected_statistic, expected_pvalue):
+        x = [.01, .2, .3]
+        res = stats.combine_pvalues(xp.asarray(x), method=method)
+        xp_assert_close(res.statistic, xp.asarray(expected_statistic))
+        xp_assert_close(res.pvalue, xp.asarray(expected_pvalue))
+
+    @pytest.mark.parametrize(
+        # Reference values computed using R `metap` `sumz`:
+        # options(digits=16)
+        # library(metap)
+        # x = c(0.01, 0.2, 0.3)
+        # sumz(x, weights=c(1., 1., 1.))
+        # sumz(x, weights=c(1., 4., 9.))
+        "weights, expected_statistic, expected_pvalue",
+        [([1., 1., 1.], 2.131790594240385, 0.01651203260896294),
+         ([1., 4., 9.], 1.051815015753598, 0.1464422142261314)])
+    def test_weighted_stouffer(self, xp, weights, expected_statistic, expected_pvalue):
+        x = xp.asarray([.01, .2, .3])
+        res = stats.combine_pvalues(x, method='stouffer', weights=xp.asarray(weights))
+        xp_assert_close(res.statistic, xp.asarray(expected_statistic))
+        xp_assert_close(res.pvalue, xp.asarray(expected_pvalue))
+
+    methods = ["fisher", "pearson", "tippett", "stouffer", "mudholkar_george"]
+
+    @pytest.mark.parametrize("variant", ["single", "all", "random"])
+    @pytest.mark.parametrize("method", methods)
+    def test_monotonicity(self, variant, method, xp):
+        # Test that result increases monotonically with respect to input.
+        m, n = 10, 7
+        rng = np.random.default_rng(278448169958891062669391462690811630763)
+
+        # `pvaluess` is an m × n array of p values. Each row corresponds to
+        # a set of p values to be combined with p values increasing
+        # monotonically down one column (single), simultaneously down each
+        # column (all), or independently down each column (random).
+        if variant == "single":
+            pvaluess = xp.broadcast_to(xp.asarray(rng.random(n)), (m, n))
+            pvaluess = xp.concat([xp.reshape(xp.linspace(0.1, 0.9, m), (-1, 1)),
+                                  pvaluess[:, 1:]], axis=1)
+        elif variant == "all":
+            pvaluess = xp.broadcast_to(xp.linspace(0.1, 0.9, m), (n, m)).T
+        elif variant == "random":
+            pvaluess = xp.sort(xp.asarray(rng.uniform(0, 1, size=(m, n))), axis=0)
+
+        combined_pvalues = xp.asarray([
+            float(stats.combine_pvalues(pvaluess[i, :], method=method)[1])
+            for i in range(pvaluess.shape[0])
+        ])
+        assert xp.all(combined_pvalues[1:] - combined_pvalues[:-1] >= 0)
+
+    @pytest.mark.parametrize("method", methods)
+    def test_result(self, method, xp):
+        res = stats.combine_pvalues(xp.asarray([.01, .2, .3]), method=method)
+        xp_assert_equal(res.statistic, res[0])
+        xp_assert_equal(res.pvalue, res[1])
+
+    @pytest.mark.parametrize("method", methods)
+    @pytest.mark.parametrize("axis", [0, 1, None])
+    def test_axis(self, method, axis, xp):
+        rng = np.random.default_rng(234892349810482)
+        x = xp.asarray(rng.random(size=(2, 10)))
+        x = x.T if (axis == 0) else x
+        res = stats.combine_pvalues(x, axis=axis, method=method)
+
+        if axis is None:
+            x = xp.reshape(x, (-1,))
+            ref = stats.combine_pvalues(x, method=method)
+            xp_assert_close(res.statistic, ref.statistic)
+            xp_assert_close(res.pvalue, ref.pvalue)
+            return
+
+        x = x.T if (axis == 0) else x
+        x0, x1 = x[0, :], x[1, :]
+        ref0 = stats.combine_pvalues(x0, method=method)
+        ref1 = stats.combine_pvalues(x1, method=method)
+
+        xp_assert_close(res.statistic[0], ref0.statistic)
+        xp_assert_close(res.statistic[1], ref1.statistic)
+        xp_assert_close(res.pvalue[0], ref0.pvalue)
+        xp_assert_close(res.pvalue[1], ref1.pvalue)
+
+
+class TestCdfDistanceValidation:
+    """
+    Test that _cdf_distance() (via wasserstein_distance()) raises ValueErrors
+    for bad inputs.
+    """
+
+    def test_distinct_value_and_weight_lengths(self):
+        # When the number of weights does not match the number of values,
+        # a ValueError should be raised.
+        assert_raises(ValueError, stats.wasserstein_distance,
+                      [1], [2], [4], [3, 1])
+        assert_raises(ValueError, stats.wasserstein_distance, [1], [2], [1, 0])
+
+    def test_zero_weight(self):
+        # When a distribution is given zero weight, a ValueError should be
+        # raised.
+        assert_raises(ValueError, stats.wasserstein_distance,
+                      [0, 1], [2], [0, 0])
+        assert_raises(ValueError, stats.wasserstein_distance,
+                      [0, 1], [2], [3, 1], [0])
+
+    def test_negative_weights(self):
+        # A ValueError should be raised if there are any negative weights.
+        assert_raises(ValueError, stats.wasserstein_distance,
+                      [0, 1], [2, 2], [1, 1], [3, -1])
+
+    def test_empty_distribution(self):
+        # A ValueError should be raised when trying to measure the distance
+        # between something and nothing.
+        assert_raises(ValueError, stats.wasserstein_distance, [], [2, 2])
+        assert_raises(ValueError, stats.wasserstein_distance, [1], [])
+
+    def test_inf_weight(self):
+        # An inf weight is not valid.
+        assert_raises(ValueError, stats.wasserstein_distance,
+                      [1, 2, 1], [1, 1], [1, np.inf, 1], [1, 1])
+
+
+class TestWassersteinDistanceND:
+    """ Tests for wasserstein_distance_nd() output values.
+    """
+
+    def test_published_values(self):
+        # Compare against published values and manually computed results.
+        # The values and computed result are posted at James D. McCaffrey's blog,
+        # https://jamesmccaffrey.wordpress.com/2018/03/05/earth-mover-distance
+        # -wasserstein-metric-example-calculation/
+        u = [(1,1), (1,1), (1,1), (1,1), (1,1), (1,1), (1,1), (1,1), (1,1), (1,1),
+             (4,2), (6,1), (6,1)]
+        v = [(2,1), (2,1), (3,2), (3,2), (3,2), (5,1), (5,1), (5,1), (5,1), (5,1),
+             (5,1), (5,1), (7,1)]
+
+        res = stats.wasserstein_distance_nd(u, v)
+        # In original post, the author kept two decimal places for ease of calculation.
+        # This test uses the more precise value of distance to get the precise results.
+        # For comparison, please see the table and figure in the original blog post.
+        flow = np.array([2., 3., 5., 1., 1., 1.])
+        dist = np.array([1.00, 5**0.5, 4.00, 2**0.5, 1.00, 1.00])
+        ref = np.sum(flow * dist)/np.sum(flow)
+        assert_allclose(res, ref)
+
+    @pytest.mark.parametrize('n_value', (4, 15, 35))
+    @pytest.mark.parametrize('ndim', (3, 4, 7))
+    @pytest.mark.parametrize('max_repeats', (5, 10))
+    def test_same_distribution_nD(self, ndim, n_value, max_repeats):
+        # Any distribution moved to itself should have a Wasserstein distance
+        # of zero.
+        rng = np.random.default_rng(363836384995579937222333)
+        repeats = rng.integers(1, max_repeats, size=n_value, dtype=int)
+
+        u_values = rng.random(size=(n_value, ndim))
+        v_values = np.repeat(u_values, repeats, axis=0)
+        v_weights = rng.random(np.sum(repeats))
+        range_repeat = np.repeat(np.arange(len(repeats)), repeats)
+        u_weights = np.bincount(range_repeat, weights=v_weights)
+        index = rng.permutation(len(v_weights))
+        v_values, v_weights = v_values[index], v_weights[index]
+
+        res = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
+        assert_allclose(res, 0, atol=1e-15)
+
+    @pytest.mark.parametrize('nu', (8, 9, 38))
+    @pytest.mark.parametrize('nv', (8, 12, 17))
+    @pytest.mark.parametrize('ndim', (3, 5, 23))
+    def test_collapse_nD(self, nu, nv, ndim):
+        # test collapse for n dimensional values
+        # Collapsing a n-D distribution to a point distribution at zero
+        # is equivalent to taking the average of the norm of data.
+        rng = np.random.default_rng(38573488467338826109)
+        u_values = rng.random(size=(nu, ndim))
+        v_values = np.zeros((nv, ndim))
+        u_weights = rng.random(size=nu)
+        v_weights = rng.random(size=nv)
+        ref = np.average(np.linalg.norm(u_values, axis=1), weights=u_weights)
+        res = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
+        assert_allclose(res, ref)
+
+    @pytest.mark.parametrize('nu', (8, 16, 32))
+    @pytest.mark.parametrize('nv', (8, 16, 32))
+    @pytest.mark.parametrize('ndim', (1, 2, 6))
+    def test_zero_weight_nD(self, nu, nv, ndim):
+        # Values with zero weight have no impact on the Wasserstein distance.
+        rng = np.random.default_rng(38573488467338826109)
+        u_values = rng.random(size=(nu, ndim))
+        v_values = rng.random(size=(nv, ndim))
+        u_weights = rng.random(size=nu)
+        v_weights = rng.random(size=nv)
+        ref = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
+
+        add_row, nrows = rng.integers(0, nu, size=2)
+        add_value = rng.random(size=(nrows, ndim))
+        u_values = np.insert(u_values, add_row, add_value, axis=0)
+        u_weights = np.insert(u_weights, add_row, np.zeros(nrows), axis=0)
+        res = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
+        assert_allclose(res, ref)
+
+    def test_inf_values(self):
+        # Inf values can lead to an inf distance or trigger a RuntimeWarning
+        # (and return NaN) if the distance is undefined.
+        uv, vv, uw = [[1, 1], [2, 1]], [[np.inf, -np.inf]], [1, 1]
+        distance = stats.wasserstein_distance_nd(uv, vv, uw)
+        assert_equal(distance, np.inf)
+        with np.errstate(invalid='ignore'):
+            uv, vv = [[np.inf, np.inf]], [[np.inf, -np.inf]]
+            distance = stats.wasserstein_distance_nd(uv, vv)
+            assert_equal(distance, np.nan)
+
+    @pytest.mark.parametrize('nu', (10, 15, 20))
+    @pytest.mark.parametrize('nv', (10, 15, 20))
+    @pytest.mark.parametrize('ndim', (1, 3, 5))
+    def test_multi_dim_nD(self, nu, nv, ndim):
+        # Adding dimension on distributions do not affect the result
+        rng = np.random.default_rng(2736495738494849509)
+        u_values = rng.random(size=(nu, ndim))
+        v_values = rng.random(size=(nv, ndim))
+        u_weights = rng.random(size=nu)
+        v_weights = rng.random(size=nv)
+        ref = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
+
+        add_dim = rng.integers(0, ndim)
+        add_value = rng.random()
+
+        u_values = np.insert(u_values, add_dim, add_value, axis=1)
+        v_values = np.insert(v_values, add_dim, add_value, axis=1)
+        res = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
+        assert_allclose(res, ref)
+
+    @pytest.mark.parametrize('nu', (7, 13, 19))
+    @pytest.mark.parametrize('nv', (7, 13, 19))
+    @pytest.mark.parametrize('ndim', (2, 4, 7))
+    def test_orthogonal_nD(self, nu, nv, ndim):
+        # orthogonal transformations do not affect the result of the
+        # wasserstein_distance
+        rng = np.random.default_rng(34746837464536)
+        u_values = rng.random(size=(nu, ndim))
+        v_values = rng.random(size=(nv, ndim))
+        u_weights = rng.random(size=nu)
+        v_weights = rng.random(size=nv)
+        ref = stats.wasserstein_distance_nd(u_values, v_values, u_weights, v_weights)
+
+        dist = stats.ortho_group(ndim)
+        transform = dist.rvs(random_state=rng)
+        shift = rng.random(size=ndim)
+        res = stats.wasserstein_distance_nd(u_values @ transform + shift,
+                                         v_values @ transform + shift,
+                                         u_weights, v_weights)
+        assert_allclose(res, ref)
+
+    def test_error_code(self):
+        rng = np.random.default_rng(52473644737485644836320101)
+        with pytest.raises(ValueError, match='Invalid input values. The inputs'):
+            u_values = rng.random(size=(4, 10, 15))
+            v_values = rng.random(size=(6, 2, 7))
+            _ = stats.wasserstein_distance_nd(u_values, v_values)
+        with pytest.raises(ValueError, match='Invalid input values. Dimensions'):
+            u_values = rng.random(size=(15,))
+            v_values = rng.random(size=(3, 15))
+            _ = stats.wasserstein_distance_nd(u_values, v_values)
+        with pytest.raises(ValueError,
+            match='Invalid input values. If two-dimensional'):
+            u_values = rng.random(size=(2, 10))
+            v_values = rng.random(size=(2, 2))
+            _ = stats.wasserstein_distance_nd(u_values, v_values)
+
+    @pytest.mark.parametrize('u_size', [1, 10, 50])
+    @pytest.mark.parametrize('v_size', [1, 10, 50])
+    def test_optimization_vs_analytical(self, u_size, v_size):
+        rng = np.random.default_rng(45634745675)
+        # Test when u_weights = None, v_weights = None
+        u_values = rng.random(size=(u_size, 1))
+        v_values = rng.random(size=(v_size, 1))
+        u_values_flat = u_values.ravel()
+        v_values_flat = v_values.ravel()
+        # These three calculations are done using different backends
+        # but they must be equal
+        d1 = stats.wasserstein_distance(u_values_flat, v_values_flat)
+        d2 = stats.wasserstein_distance_nd(u_values, v_values)
+        d3 = stats.wasserstein_distance_nd(u_values_flat, v_values_flat)
+        assert_allclose(d2, d1)
+        assert_allclose(d3, d1)
+        # Test with u_weights and v_weights specified.
+        u_weights = rng.random(size=u_size)
+        v_weights = rng.random(size=v_size)
+        d1 = stats.wasserstein_distance(u_values_flat, v_values_flat,
+                                        u_weights, v_weights)
+        d2 = stats.wasserstein_distance_nd(u_values, v_values,
+                                        u_weights, v_weights)
+        d3 = stats.wasserstein_distance_nd(u_values_flat, v_values_flat,
+                                        u_weights, v_weights)
+        assert_allclose(d2, d1)
+        assert_allclose(d3, d1)
+
+
+class TestWassersteinDistance:
+    """ Tests for wasserstein_distance() output values.
+    """
+
+    def test_simple(self):
+        # For basic distributions, the value of the Wasserstein distance is
+        # straightforward.
+        assert_allclose(
+            stats.wasserstein_distance([0, 1], [0], [1, 1], [1]),
+            .5)
+        assert_allclose(stats.wasserstein_distance(
+            [0, 1], [0], [3, 1], [1]),
+            .25)
+        assert_allclose(stats.wasserstein_distance(
+            [0, 2], [0], [1, 1], [1]),
+            1)
+        assert_allclose(stats.wasserstein_distance(
+            [0, 1, 2], [1, 2, 3]),
+            1)
+
+    def test_same_distribution(self):
+        # Any distribution moved to itself should have a Wasserstein distance
+        # of zero.
+        assert_equal(stats.wasserstein_distance([1, 2, 3], [2, 1, 3]), 0)
+        assert_equal(
+            stats.wasserstein_distance([1, 1, 1, 4], [4, 1],
+                                       [1, 1, 1, 1], [1, 3]),
+            0)
+
+    def test_shift(self):
+        # If the whole distribution is shifted by x, then the Wasserstein
+        # distance should be the norm of x.
+        assert_allclose(stats.wasserstein_distance([0], [1]), 1)
+        assert_allclose(stats.wasserstein_distance([-5], [5]), 10)
+        assert_allclose(
+            stats.wasserstein_distance([1, 2, 3, 4, 5], [11, 12, 13, 14, 15]),
+            10)
+        assert_allclose(
+            stats.wasserstein_distance([4.5, 6.7, 2.1], [4.6, 7, 9.2],
+                                       [3, 1, 1], [1, 3, 1]),
+            2.5)
+
+    def test_combine_weights(self):
+        # Assigning a weight w to a value is equivalent to including that value
+        # w times in the value array with weight of 1.
+        assert_allclose(
+            stats.wasserstein_distance(
+                [0, 0, 1, 1, 1, 1, 5], [0, 3, 3, 3, 3, 4, 4],
+                [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]),
+            stats.wasserstein_distance([5, 0, 1], [0, 4, 3],
+                                       [1, 2, 4], [1, 2, 4]))
+
+    def test_collapse(self):
+        # Collapsing a distribution to a point distribution at zero is
+        # equivalent to taking the average of the absolute values of the
+        # values.
+        u = np.arange(-10, 30, 0.3)
+        v = np.zeros_like(u)
+        assert_allclose(
+            stats.wasserstein_distance(u, v),
+            np.mean(np.abs(u)))
+
+        u_weights = np.arange(len(u))
+        v_weights = u_weights[::-1]
+        assert_allclose(
+            stats.wasserstein_distance(u, v, u_weights, v_weights),
+            np.average(np.abs(u), weights=u_weights))
+
+    def test_zero_weight(self):
+        # Values with zero weight have no impact on the Wasserstein distance.
+        assert_allclose(
+            stats.wasserstein_distance([1, 2, 100000], [1, 1],
+                                       [1, 1, 0], [1, 1]),
+            stats.wasserstein_distance([1, 2], [1, 1], [1, 1], [1, 1]))
+
+    def test_inf_values(self):
+        # Inf values can lead to an inf distance or trigger a RuntimeWarning
+        # (and return NaN) if the distance is undefined.
+        assert_equal(
+            stats.wasserstein_distance([1, 2, np.inf], [1, 1]),
+            np.inf)
+        assert_equal(
+            stats.wasserstein_distance([1, 2, np.inf], [-np.inf, 1]),
+            np.inf)
+        assert_equal(
+            stats.wasserstein_distance([1, -np.inf, np.inf], [1, 1]),
+            np.inf)
+        with pytest.warns(RuntimeWarning, match="invalid value"):
+            assert_equal(
+                stats.wasserstein_distance([1, 2, np.inf], [np.inf, 1]),
+                np.nan)
+
+
+class TestEnergyDistance:
+    """ Tests for energy_distance() output values.
+    """
+
+    def test_simple(self):
+        # For basic distributions, the value of the energy distance is
+        # straightforward.
+        assert_almost_equal(
+            stats.energy_distance([0, 1], [0], [1, 1], [1]),
+            np.sqrt(2) * .5)
+        assert_almost_equal(stats.energy_distance(
+            [0, 1], [0], [3, 1], [1]),
+            np.sqrt(2) * .25)
+        assert_almost_equal(stats.energy_distance(
+            [0, 2], [0], [1, 1], [1]),
+            2 * .5)
+        assert_almost_equal(
+            stats.energy_distance([0, 1, 2], [1, 2, 3]),
+            np.sqrt(2) * (3*(1./3**2))**.5)
+
+    def test_same_distribution(self):
+        # Any distribution moved to itself should have a energy distance of
+        # zero.
+        assert_equal(stats.energy_distance([1, 2, 3], [2, 1, 3]), 0)
+        assert_equal(
+            stats.energy_distance([1, 1, 1, 4], [4, 1], [1, 1, 1, 1], [1, 3]),
+            0)
+
+    def test_shift(self):
+        # If a single-point distribution is shifted by x, then the energy
+        # distance should be sqrt(2) * sqrt(x).
+        assert_almost_equal(stats.energy_distance([0], [1]), np.sqrt(2))
+        assert_almost_equal(
+            stats.energy_distance([-5], [5]),
+            np.sqrt(2) * 10**.5)
+
+    def test_combine_weights(self):
+        # Assigning a weight w to a value is equivalent to including that value
+        # w times in the value array with weight of 1.
+        assert_almost_equal(
+            stats.energy_distance([0, 0, 1, 1, 1, 1, 5], [0, 3, 3, 3, 3, 4, 4],
+                                  [1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1]),
+            stats.energy_distance([5, 0, 1], [0, 4, 3], [1, 2, 4], [1, 2, 4]))
+
+    def test_zero_weight(self):
+        # Values with zero weight have no impact on the energy distance.
+        assert_almost_equal(
+            stats.energy_distance([1, 2, 100000], [1, 1], [1, 1, 0], [1, 1]),
+            stats.energy_distance([1, 2], [1, 1], [1, 1], [1, 1]))
+
+    def test_inf_values(self):
+        # Inf values can lead to an inf distance or trigger a RuntimeWarning
+        # (and return NaN) if the distance is undefined.
+        assert_equal(stats.energy_distance([1, 2, np.inf], [1, 1]), np.inf)
+        assert_equal(
+            stats.energy_distance([1, 2, np.inf], [-np.inf, 1]),
+            np.inf)
+        assert_equal(
+            stats.energy_distance([1, -np.inf, np.inf], [1, 1]),
+            np.inf)
+        with pytest.warns(RuntimeWarning, match="invalid value"):
+            assert_equal(
+                stats.energy_distance([1, 2, np.inf], [np.inf, 1]),
+                np.nan)
+
+
+@make_xp_test_case(stats.brunnermunzel)
+class TestBrunnerMunzel:
+    # Data from (Lumley, 1996)
+    X = [1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1]
+    Y = [3, 3, 4, 3, 1, 2, 3, 1, 1, 5, 4]
+
+    def test_brunnermunzel_one_sided(self, xp):
+        # Results are compared with R's lawstat package.
+        X, Y = xp.asarray(self.X), xp.asarray(self.Y)
+        u1, p1 = stats.brunnermunzel(X, Y, alternative='less')
+        u2, p2 = stats.brunnermunzel(Y, X, alternative='greater')
+        u3, p3 = stats.brunnermunzel(X, Y, alternative='greater')
+        u4, p4 = stats.brunnermunzel(Y, X, alternative='less')
+
+        xp_assert_close(p1, p2)
+        xp_assert_close(p3, p4)
+        assert p1 != p3
+        xp_assert_close(u1, xp.asarray(3.1374674823029505))
+        xp_assert_close(u2, xp.asarray(-3.1374674823029505))
+        xp_assert_close(u3, xp.asarray(3.1374674823029505))
+        xp_assert_close(u4, xp.asarray(-3.1374674823029505))
+
+        xp_assert_close(p1, xp.asarray(0.0028931043330757342))
+        xp_assert_close(p3, xp.asarray(0.99710689566692423))
+
+    def test_brunnermunzel_two_sided(self, xp):
+        # Results are compared with R's lawstat package.
+        X, Y = xp.asarray(self.X), xp.asarray(self.Y)
+        u1, p1 = stats.brunnermunzel(X, Y, alternative='two-sided')
+        u2, p2 = stats.brunnermunzel(Y, X, alternative='two-sided')
+
+        xp_assert_close(p1, xp.asarray(p2))
+        xp_assert_close(u1, xp.asarray(3.1374674823029505))
+        xp_assert_close(u2, xp.asarray(-3.1374674823029505))
+        xp_assert_close(p1, xp.asarray(0.0057862086661515377))
+
+    def test_brunnermunzel_default(self, xp):
+        # The default value for alternative is two-sided
+        X, Y = xp.asarray(self.X), xp.asarray(self.Y)
+        u1, p1 = stats.brunnermunzel(X, Y)
+        u2, p2 = stats.brunnermunzel(Y, X)
+
+        xp_assert_close(p1, p2)
+        xp_assert_close(u1, xp.asarray(3.1374674823029505))
+        xp_assert_close(u2, xp.asarray(-3.1374674823029505))
+        xp_assert_close(p1, xp.asarray(0.0057862086661515377))
+
+    def test_brunnermunzel_alternative_error(self, xp):
+        alternative = "error"
+        distribution = "t"
+        nan_policy = "propagate"
+        assert alternative not in ["two-sided", "greater", "less"]
+        message = "`alternative` must be 'less', 'greater', or 'two-sided'."
+        with pytest.raises(ValueError, match=message):
+            stats.brunnermunzel(xp.asarray(self.X), xp.asarray(self.Y),
+                                alternative, distribution, nan_policy)
+
+    def test_brunnermunzel_distribution_norm(self, xp):
+        X, Y = xp.asarray(self.X), xp.asarray(self.Y)
+        u1, p1 = stats.brunnermunzel(X, Y, distribution="normal")
+        u2, p2 = stats.brunnermunzel(Y, X, distribution="normal")
+        xp_assert_close(p1, xp.asarray(p2))
+        xp_assert_close(u1, xp.asarray(3.1374674823029505))
+        xp_assert_close(u2, xp.asarray(-3.1374674823029505))
+        xp_assert_close(p1, xp.asarray(0.0017041417600383024))
+
+    def test_brunnermunzel_distribution_error(self, xp):
+        alternative = "two-sided"
+        distribution = "error"
+        nan_policy = "propagate"
+        assert distribution not in ["t", "normal"]
+        message = "distribution should be 't' or 'normal'"
+        with pytest.raises(ValueError, match=message):
+            stats.brunnermunzel(xp.asarray(self.X), xp.asarray(self.Y),
+                                alternative, distribution, nan_policy)
+
+    @pytest.mark.parametrize("kwarg_update", [{'y': []}, {'x': []},
+                                              {'x': [], 'y': []}])
+    def test_brunnermunzel_empty_imput(self, kwarg_update, xp):
+        kwargs = {'x': self.X, 'y': self.Y}
+        kwargs.update(kwarg_update)
+        kwargs = {key:xp.asarray(val, dtype=xp_default_dtype(xp))
+                  for key, val in kwargs.items()}
+        with eager_warns(SmallSampleWarning, match=too_small_1d_not_omit, xp=xp):
+            statistic, pvalue = stats.brunnermunzel(**kwargs)
+        xp_assert_equal(statistic, xp.asarray(xp.nan))
+        xp_assert_equal(pvalue, xp.asarray(xp.nan))
+
+    @skip_xp_backends('jax.numpy', reason='needs jax-ml/jax#34628')
+    def test_brunnermunzel_nan_input_propagate(self, xp):
+        X = xp.asarray([1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1, xp.nan])
+        Y = xp.asarray([3, 3, 4, 3, 1, 2, 3, 1, 1, 5, 4.])
+        u1, p1 = stats.brunnermunzel(X, Y, nan_policy="propagate")
+        u2, p2 = stats.brunnermunzel(Y, X, nan_policy="propagate")
+
+        xp_assert_equal(u1, xp.asarray(xp.nan))
+        xp_assert_equal(p1, xp.asarray(xp.nan))
+        xp_assert_equal(u2, xp.asarray(xp.nan))
+        xp_assert_equal(p2, xp.asarray(xp.nan))
+
+    @skip_xp_backends('jax.numpy', reason='lazy -> reduced nan_policy capabilities')
+    def test_brunnermunzel_nan_input_raise(self, xp):
+        X = xp.asarray([1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1, xp.nan])
+        Y = xp.asarray([3, 3, 4, 3, 1, 2, 3, 1, 1, 5, 4.])
+        alternative = "two-sided"
+        distribution = "t"
+        nan_policy = "raise"
+
+        message = "The input contains nan values"
+        with pytest.raises(ValueError, match=message):
+            stats.brunnermunzel(X, Y, alternative, distribution, nan_policy)
+        with pytest.raises(ValueError, match=message):
+            stats.brunnermunzel(Y, X, alternative, distribution, nan_policy)
+
+    @skip_xp_backends('jax.numpy', reason='lazy -> reduced nan_policy capabilities')
+    def test_brunnermunzel_nan_input_omit(self, xp):
+        X = xp.asarray([1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1, np.nan])
+        Y = xp.asarray([3, 3, 4, 3, 1, 2, 3, 1, 1, 5, 4.])
+        u1, p1 = stats.brunnermunzel(X, Y, nan_policy="omit")
+        u2, p2 = stats.brunnermunzel(Y, X, nan_policy="omit")
+
+        xp_assert_close(p1, p2)
+        xp_assert_close(u1, xp.asarray(3.1374674823029505))
+        xp_assert_close(u2, xp.asarray(-3.1374674823029505))
+        xp_assert_close(p1, xp.asarray(0.0057862086661515377))
+
+    @pytest.mark.parametrize("vectorized_call", [False, True])
+    def test_brunnermunzel_return_nan(self, vectorized_call, xp):
+        """ tests that a warning is emitted when p is nan
+        p-value with t-distributions can be nan (0/0) (see gh-15843)
+        """
+        x = xp.asarray([1., 2., 3.])
+        y = xp.asarray([5., 6., 7., 8., 9.])
+
+        if vectorized_call:
+            x = xp.stack((x, x)).T
+            y = xp.stack((y, y)).T
+
+        msg = "p-value cannot be estimated|divide by zero|invalid value encountered"
+        with eager_warns(RuntimeWarning, match=msg, xp=xp):
+            stats.brunnermunzel(x, y, distribution="t")
+
+    def test_brunnermunzel_normal_dist(self, xp):
+        """ tests that a p is 0 for datasets that cause p->nan
+        when t-distribution is used (see gh-15843)
+        """
+        x = xp.asarray([1., 2., 3.])
+        y = xp.asarray([5., 6., 7., 8., 9.])
+
+        with eager_warns(RuntimeWarning, match='divide by zero', xp=xp):
+            _, p = stats.brunnermunzel(x, y, distribution="normal")
+        xp_assert_equal(p, xp.asarray(0.))
+
+
+@_apply_over_batch(('x', 1), ('q', 1), ('p', 1))
+def quantile_test_reference(x, q, p, alternative):
+    res = stats.quantile_test(x, q=q, p=p, alternative=alternative)
+    return res.statistic, res.pvalue, *res.confidence_interval()
+
+
+@make_xp_test_case(stats.quantile_test)
+class TestQuantileTest:
+    r""" Test the non-parametric quantile test,
+    including the computation of confidence intervals
+    """
+
+    def test_quantile_test_iv(self, xp):
+        x = xp.asarray([1, 2, 3])
+
+        message = "`x` must be an array of numbers."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile_test(xp.astype(x, xp.bool))
+
+        message = "`q` must be a scalar or array of numbers."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile_test(x, q=False)
+
+        message = "`p` must be a scalar or array of floats."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile_test(x, p=2)
+
+        # should p == 0. / p == 1. be valid?
+        p = xp.asarray([-1., 0., 1., 1.5, xp.nan])
+        res = stats.quantile_test(x, p=p)
+        xp_assert_equal(res.statistic, xp.full_like(p, xp.nan))
+        xp_assert_equal(res.statistic_type, xp.full_like(p, xp.nan))
+        xp_assert_equal(res.pvalue, xp.full_like(p, xp.nan))
+
+        message = "`axis` must be an integer or None."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile_test(x, axis=2.5)
+
+        message = "`axis` is not compatible with the shapes of the inputs."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile_test(x, axis=2)
+
+        message = "If specified, `keepdims` must be True or False."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile_test(x, keepdims=10)
+
+        message = ("`keepdims` may be False only if...")
+        with pytest.raises(ValueError, match=message):
+            stats.quantile_test(x, p=xp.asarray([0.1, 0.2]), keepdims=False)
+
+        message = "`alternative` must be one of..."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile_test(x, alternative='one-sided')
+
+        message = "`confidence_level` must be a number between 0 and 1."
+        with pytest.raises(ValueError, match=message):
+            stats.quantile_test(x).confidence_interval(1)
+
+    @pytest.mark.parametrize(
+        'p, alpha, lb, ub, alternative',
+        [[0.3, 0.95, 1.221402758160170, 1.476980793882643, 'two-sided'],
+         [0.5, 0.9, 1.506817785112854, 1.803988415397857, 'two-sided'],
+         [0.25, 0.95, -np.inf, 1.39096812846378, 'less'],
+         [0.8, 0.9, 2.117000016612675, np.inf, 'greater']]
+    )
+    def test_R_ci_quantile(self, p, alpha, lb, ub, alternative, xp):
+        # Test against R library `confintr` function `ci_quantile`, e.g.
+        # library(confintr)
+        # options(digits=16)
+        # x <- exp(seq(0, 1, by = 0.01))
+        # ci_quantile(x, q = 0.3)$interval
+        # ci_quantile(x, q = 0.5, probs = c(0.05, 0.95))$interval
+        # ci_quantile(x, q = 0.25, probs = c(0, 0.95))$interval
+        # ci_quantile(x, q = 0.8, probs = c(0.1, 1))$interval
+        x = xp.exp(xp.arange(0.0, 1.01, 0.01))
+        res = stats.quantile_test(x, p=p, alternative=alternative)
+        ci = res.confidence_interval(alpha)
+        xp_assert_close(ci.low, xp.asarray(lb))
+        xp_assert_close(ci.high, xp.asarray(ub))
+
+    @pytest.mark.parametrize(
+        'q, p, alternative, ref',
+        [[1.2, 0.3, 'two-sided', 0.01515567517648],
+         [1.8, 0.5, 'two-sided', 0.1109183496606]]
+    )
+    def test_R_pvalue(self, q, p, alternative, ref, xp):
+        # Test against R library `snpar` function `quant.test`, e.g.
+        # library(snpar)
+        # options(digits=16)
+        # x < - exp(seq(0, 1, by=0.01))
+        # quant.test(x, q=1.2, p=0.3, exact=TRUE, alternative='t')
+        x = xp.exp(xp.arange(0.0, 1.01, 0.01))
+        res = stats.quantile_test(x, q=q, p=p, alternative=alternative)
+        xp_assert_close(res.pvalue, xp.asarray(ref))
+
+    @pytest.mark.parametrize('case', ['continuous', 'discrete'])
+    @pytest.mark.parametrize('alternative', ['less', 'greater'])
+    @pytest.mark.parametrize('alpha', [0.9, 0.95])
+    def test_pval_ci_match(self, case, alternative, alpha, xp):
+        # Verify that the following statement holds:
+
+        # The 95% confidence interval corresponding with alternative='less'
+        # has -inf as its lower bound, and the upper bound `xu` is the greatest
+        # element from the sample `x` such that:
+        # `stats.quantile_test(x, q=xu, p=p, alternative='less').pvalue``
+        # will be greater than 5%.
+
+        # And the corresponding statement for the alternative='greater' case.
+
+        seed = int((7**len(case) + len(alternative))*alpha)
+        rng = np.random.default_rng(seed)
+        if case == 'continuous':
+            p, q = rng.random(size=2)
+            rvs = rng.random(size=100)
+        else:
+            rvs = rng.integers(1, 11, size=100)
+            p = rng.random()
+            q = rng.integers(1, 11)
+
+        rvs, p, q = xp.asarray(rvs.tolist()), float(p), float(q)
+        res = stats.quantile_test(rvs, q=q, p=p, alternative=alternative)
+        ci = res.confidence_interval(confidence_level=alpha)
+
+        # select elements inside the confidence interval based on alternative
+        if alternative == 'less':
+            i_inside = xp.astype(rvs, ci.high.dtype) <= ci.high
+        else:
+            i_inside = xp.astype(rvs, ci.low.dtype) >= ci.low
+
+        for x in rvs[i_inside]:
+            res = stats.quantile_test(rvs, q=x, p=p, alternative=alternative)
+            assert res.pvalue > 1 - alpha
+
+        for x in rvs[~i_inside]:
+            res = stats.quantile_test(rvs, q=x, p=p, alternative=alternative)
+            assert res.pvalue < 1 - alpha
+
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_match_conover_examples(self, dtype, xp):
+        # Test against the examples in [1] (Conover Practical Nonparametric
+        # Statistics Third Edition) pg 139
+
+        # Example 1
+        # Data is [189, 233, 195, 160, 212, 176, 231, 185, 199, 213, 202, 193,
+        # 174, 166, 248]
+        # Two-sided test of whether the upper quartile (p=0.75) equals 193
+        # (q=193). Conover shows that 7 of the observations are less than or
+        # equal to 193, and "for the binomial random variable Y, P(Y<=7) =
+        # 0.0173", so the two-sided p-value is twice that, 0.0346.
+        dtype = xp_default_dtype(xp) if dtype is None else getattr(xp, dtype)
+        x = xp.asarray([189, 233, 195, 160, 212, 176, 231, 185,
+                        199, 213, 202, 193, 174, 166, 248], dtype=dtype)
+        pvalue_ref = 0.0346
+        res = stats.quantile_test(x, q=193, p=0.75, alternative='two-sided')
+        xp_assert_close(res.pvalue, xp.asarray(pvalue_ref, dtype=dtype), rtol=1e-5)
+
+        # Example 2
+        # Conover doesn't give explicit data, just that 8 out of 112
+        # observations are 60 or less. The test is whether the median time is
+        # equal to 60 against the alternative that the median is greater than
+        # 60. The p-value is calculated as P(Y<=8), where Y is again a binomial
+        # distributed random variable, now with p=0.5 and n=112. Conover uses a
+        # normal approximation, but we can easily calculate the CDF of the
+        # binomial distribution.
+        x = xp.asarray([59]*8 + [61]*(112-8), dtype=dtype)
+        pvalue_ref = float(stats.binom(p=0.5, n=112).pmf(k=8))
+        res = stats.quantile_test(x, q=60, p=0.5, alternative='greater')
+        xp_assert_close(res.pvalue, xp.asarray(pvalue_ref, dtype=dtype), atol=1e-10)
+
+    @skip_xp_backends('array_api_strict', reason='indexing in _apply_over_batch')
+    @pytest.mark.parametrize('alternative', ['less', 'greater', 'two-sided'])
+    @pytest.mark.parametrize('axis', [0, 1])
+    def test_multidimensional(self, alternative, axis, xp):
+        rng = np.random.default_rng(85468924398205602)
+        x = rng.random(size=(2, 20))
+        p = rng.random(size=(1, 3,))
+        q = p + rng.random(size=(2, 1))*0.01
+
+        x, p, q = (xp.asarray(arr.tolist()) for arr in (x, p, q))
+
+        ref = quantile_test_reference(x, q, p, alternative=alternative)
+        ref_statistic, ref_pvalue, ref_low, ref_high = ref
+
+        if axis == 0:
+            x, p, q = x.T, p.T, q.T
+        res = stats.quantile_test(x, q=q, p=p, alternative=alternative, axis=axis)
+        res_statistic, res_pvalue = res.statistic, res.pvalue
+        res_low, res_high = res.confidence_interval()
+        if axis == 0:
+            res_statistic, res_pvalue = res_statistic.T, res_pvalue.T
+            res_low, res_high = res_low.T, res_high.T
+
+        xp_assert_close(res_statistic, ref_statistic)
+        xp_assert_close(res_pvalue, ref_pvalue)
+        xp_assert_close(res_low, res_low)
+        xp_assert_close(res_low, ref_low)
+
+    def test_zero_size(self, xp):
+        rng = np.random.default_rng(883771738488451943)
+        x_shape = (2, 50)
+        qp_shape = (2, 10)
+        x = rng.random(x_shape)
+        q = rng.random(qp_shape)
+        p = q + rng.random(qp_shape) * 1e-2
+
+        x, p, q = (xp.asarray(arr.tolist()) for arr in (x, p, q))
+
+        # case 1: p/q is size zero.
+        qp_zero = xp.empty((0, *qp_shape))
+        out = qp_zero
+        res = stats.quantile_test(x, q=qp_zero, p=qp_zero, axis=-1)
+        ci = res.confidence_interval()
+        # different default int on different platforms
+        xp_assert_equal(res.statistic, xp.asarray(out))
+        xp_assert_equal(res.statistic_type, xp.asarray(out))
+        xp_assert_equal(res.pvalue, out)
+        xp_assert_equal(ci.low, out)
+        xp_assert_equal(ci.high, out)
+
+        # case 2: x is size zero with nonzero length along axis.
+        x_zero = xp.empty((0, *x_shape))
+        out = xp.empty((0, *qp_shape))
+        res = stats.quantile_test(x_zero, q=q, p=p, axis=-1)
+        ci = res.confidence_interval()
+        # different default int on different platforms
+        xp_assert_equal(res.statistic, xp.asarray(out))
+        xp_assert_equal(res.statistic_type, xp.asarray(out))
+        xp_assert_equal(res.pvalue, out)
+        xp_assert_equal(ci.low, out)
+        xp_assert_equal(ci.high, out)
+
+        # case 3: x has zero length along axis.
+        x_zero = xp.empty((x.shape[0], 0))
+        out = xp.full(qp_shape, xp.nan)
+        res = stats.quantile_test(x_zero, q=q, p=p, axis=-1)
+        ci = res.confidence_interval()
+        # different default int on different platforms
+        xp_assert_equal(res.statistic, out)
+        xp_assert_equal(res.statistic_type, out)
+        xp_assert_equal(res.pvalue, out)
+        xp_assert_equal(ci.low, out)
+        xp_assert_equal(ci.high, out)
+
+    def test_nans(self, xp):
+        rng = np.random.default_rng(2920028761208905)
+        x = rng.random((10, 50))
+        q = rng.random((10, 10))
+        p = q + rng.random((10, 10)) * 1e-2
+        x, p, q = (xp.asarray(arr.tolist()) for arr in (x, p, q))
+
+        ref = stats.quantile_test(x, q=q, p=p, axis=-1)
+        ref_low, ref_high = ref.confidence_interval()
+
+        x_ = xp_copy(x)
+        i_nan = xp.asarray(rng.random(x.shape) < 0.01)
+        x_ = xpx.at(x_)[i_nan].set(xp.nan)
+        i_nan_out = xp.any(i_nan, axis=-1)
+        assert not xp.all(i_nan_out)
+        res = stats.quantile_test(x_, q=q, p=p, axis=-1)
+        res_low, res_high = res.confidence_interval()
+
+        assert xp.all(xp.isnan(res.statistic[i_nan_out]))
+        assert xp.all(xp.isnan(res.statistic_type[i_nan_out]))
+        assert xp.all(xp.isnan(res.pvalue[i_nan_out]))
+        assert xp.all(xp.isnan(res_low[i_nan_out]))
+        assert xp.all(xp.isnan(res_high[i_nan_out]))
+        xp_assert_equal(res.statistic[~i_nan_out], ref.statistic[~i_nan_out])
+        xp_assert_equal(res.statistic_type[~i_nan_out], ref.statistic_type[~i_nan_out])
+        xp_assert_equal(res.pvalue[~i_nan_out], ref.pvalue[~i_nan_out])
+        xp_assert_equal(res_low[~i_nan_out], ref_low[~i_nan_out])
+        xp_assert_equal(res_high[~i_nan_out], ref_high[~i_nan_out])
+
+        i_nan_q = xp.asarray(rng.random(q.shape) < 0.01)
+        i_nan_p = xp.asarray(rng.random(p.shape) < 0.01)
+        assert xp.any(i_nan_q)
+        assert xp.any(i_nan_p)
+        q = xpx.at(q)[xp.broadcast_to(i_nan_q, q.shape)].set(xp.nan)
+        p = xpx.at(p)[xp.broadcast_to(i_nan_p, p.shape)].set(xp.nan)
+
+        i_nan_out = xp.asarray(i_nan_q | i_nan_p)
+        assert not xp.all(i_nan_out)
+
+        res = stats.quantile_test(x, q=q, p=p, axis=-1)
+        res_low, res_high = res.confidence_interval()
+        assert xp.all(xp.isnan(res.statistic[i_nan_out]))
+        assert xp.all(xp.isnan(res.statistic_type[i_nan_out]))
+        assert xp.all(xp.isnan(res.pvalue[i_nan_out]))
+        assert xp.all(xp.isnan(res_low[i_nan_out]))
+        assert xp.all(xp.isnan(res_high[i_nan_out]))
+        xp_assert_equal(res.statistic[~i_nan_out], ref.statistic[~i_nan_out])
+        xp_assert_equal(res.statistic_type[~i_nan_out], ref.statistic_type[~i_nan_out])
+        xp_assert_equal(res.pvalue[~i_nan_out], ref.pvalue[~i_nan_out])
+        xp_assert_equal(res_low[~i_nan_out], ref_low[~i_nan_out])
+        xp_assert_equal(res_high[~i_nan_out], ref_high[~i_nan_out])
+
+    def test_axis_none(self, xp):
+        rng = np.random.default_rng(883771738488451943)
+        x_shape = (2, 50)
+        qp_shape = (2, 10)
+        x = rng.random(x_shape)
+        q = rng.random(qp_shape)
+        p = q + rng.random(qp_shape) * 1e-2
+        x, p, q = (xp.asarray(arr.tolist()) for arr in (x, p, q))
+
+        res = stats.quantile_test(x, q=q, p=p, axis=None)
+        res_low, res_high = res.confidence_interval()
+        ref = stats.quantile_test(xp_ravel(x), q=xp_ravel(q), p=xp_ravel(p), axis=None)
+        ref_low, ref_high = ref.confidence_interval()
+        xp_assert_equal(res.statistic, ref.statistic[xp.newaxis, :])
+        xp_assert_equal(res.statistic_type, ref.statistic_type[xp.newaxis, :])
+        xp_assert_equal(res.pvalue, ref.pvalue[xp.newaxis, :])
+        xp_assert_equal(res_low, ref_low[xp.newaxis, :])
+        xp_assert_equal(res_high, ref_high[xp.newaxis, :])
+
+        res = stats.quantile_test(x, q=0.5, p=0.5, axis=None)
+        res_low, res_high = res.confidence_interval()
+        ref = stats.quantile_test(xp_ravel(x), q=0.5, p=0.5, axis=None)
+        ref_low, ref_high = ref.confidence_interval()
+        xp_assert_equal(res.statistic, ref.statistic)
+        xp_assert_equal(res.statistic_type, ref.statistic_type)
+        xp_assert_equal(res.pvalue, ref.pvalue)
+        xp_assert_equal(res_low, ref_low)
+        xp_assert_equal(res_high, ref_high)
+
+    @pytest.mark.filterwarnings("ignore:torch:UserWarning")
+    @pytest.mark.parametrize("x_shape, qp_shape, axis, keepdims, res_shape", [
+        ((1, 3), (1,), None, False, ()),
+        ((1, 3), (1,), None, True, (1, 1)),
+        ((1, 3), (2, 1), -1, False, (2,)),
+        ((1, 3), (2, 1), -1, True, (2, 1)),
+    ])
+    def test_keepdims(self, x_shape, qp_shape, axis, keepdims, res_shape, xp):
+        rng = np.random.default_rng(883771738488451943)
+        x, qp = xp.asarray(rng.random(x_shape)), xp.asarray(rng.random(qp_shape))
+        res = stats.quantile_test(x, q=qp, p=qp, axis=axis, keepdims=keepdims)
+        assert res.statistic.shape == res_shape
+
+
+class TestPageTrendTest:
+    def setup_method(self):
+        self.rng = np.random.default_rng(1808365978)
+
+    # expected statistic and p-values generated using R at
+    # https://rdrr.io/cran/cultevo/, e.g.
+    # library(cultevo)
+    # data = rbind(c(72, 47, 73, 35, 47, 96, 30, 59, 41, 36, 56, 49, 81, 43,
+    #                   70, 47, 28, 28, 62, 20, 61, 20, 80, 24, 50),
+    #              c(68, 52, 60, 34, 44, 20, 65, 88, 21, 81, 48, 31, 31, 67,
+    #                69, 94, 30, 24, 40, 87, 70, 43, 50, 96, 43),
+    #              c(81, 13, 85, 35, 79, 12, 92, 86, 21, 64, 16, 64, 68, 17,
+    #                16, 89, 71, 43, 43, 36, 54, 13, 66, 51, 55))
+    # result = page.test(data, verbose=FALSE)
+    # Most test cases generated to achieve common critical p-values so that
+    # results could be checked (to limited precision) against tables in
+    # scipy.stats.page_trend_test reference [1]
+
+    rng = np.random.default_rng(3113562111)
+    data_3_25 = rng.random((3, 25))
+    rng = np.random.default_rng(3113562111)
+    data_10_26 = rng.random((10, 26))
+
+    ts = [
+          (12949, 0.275539045444, False, 'asymptotic', data_3_25),
+          (47221, 0.5703651063709, False, 'asymptotic', data_10_26),
+          (12332, 0.7722477197436702, False, 'asymptotic',
+           [[72, 47, 73, 35, 47, 96, 30, 59, 41, 36, 56, 49, 81,
+             43, 70, 47, 28, 28, 62, 20, 61, 20, 80, 24, 50],
+            [68, 52, 60, 34, 44, 20, 65, 88, 21, 81, 48, 31, 31,
+             67, 69, 94, 30, 24, 40, 87, 70, 43, 50, 96, 43],
+            [81, 13, 85, 35, 79, 12, 92, 86, 21, 64, 16, 64, 68,
+             17, 16, 89, 71, 43, 43, 36, 54, 13, 66, 51, 55]]),
+          (266, 4.121656378600823e-05, False, 'exact',
+           [[1.5, 4., 8.3, 5, 19, 11],
+            [5, 4, 3.5, 10, 20, 21],
+            [8.4, 3.2, 10, 12, 14, 15]]),
+          (332, 0.9566400920502488, True, 'exact',
+           [[4, 3, 2, 1], [4, 3, 2, 1], [4, 3, 2, 1], [4, 3, 2, 1],
+            [4, 3, 2, 1], [4, 3, 2, 1], [4, 3, 2, 1], [4, 3, 2, 1],
+            [3, 4, 1, 2], [1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3, 4],
+            [1, 2, 3, 4], [1, 2, 3, 4]]),
+          (241, 0.9622210164861476, True, 'exact',
+           [[3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1],
+            [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1],
+            [3, 2, 1], [2, 1, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3],
+            [1, 2, 3], [1, 2, 3], [1, 2, 3]]),
+          (197, 0.9619432897162209, True, 'exact',
+           [[6, 5, 4, 3, 2, 1], [6, 5, 4, 3, 2, 1], [1, 3, 4, 5, 2, 6]]),
+          (423, 0.9590458306880073, True, 'exact',
+           [[5, 4, 3, 2, 1], [5, 4, 3, 2, 1], [5, 4, 3, 2, 1],
+            [5, 4, 3, 2, 1], [5, 4, 3, 2, 1], [5, 4, 3, 2, 1],
+            [4, 1, 3, 2, 5], [1, 2, 3, 4, 5], [1, 2, 3, 4, 5],
+            [1, 2, 3, 4, 5]]),
+          (217, 0.9693058575034678, True, 'exact',
+           [[3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1],
+            [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1],
+            [2, 1, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3],
+            [1, 2, 3]]),
+          (395, 0.991530289351305, True, 'exact',
+           [[7, 6, 5, 4, 3, 2, 1], [7, 6, 5, 4, 3, 2, 1],
+            [6, 5, 7, 4, 3, 2, 1], [1, 2, 3, 4, 5, 6, 7]]),
+          (117, 0.9997817843373017, True, 'exact',
+           [[3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1], [3, 2, 1],
+            [3, 2, 1], [3, 2, 1], [3, 2, 1], [2, 1, 3], [1, 2, 3]]),
+         ]
+
+    @pytest.mark.parametrize("L, p, ranked, method, data", ts)
+    def test_accuracy(self, L, p, ranked, method, data):
+        res = stats.page_trend_test(data, ranked=ranked, method=method)
+        assert_equal(L, res.statistic)
+        assert_allclose(p, res.pvalue)
+        assert_equal(method, res.method)
+
+    ts2 = [
+           (542, 0.9481266260876332, True, 'exact',
+            [[10, 9, 8, 7, 6, 5, 4, 3, 2, 1],
+             [1, 8, 4, 7, 6, 5, 9, 3, 2, 10]]),
+           (1322, 0.9993113928199309, True, 'exact',
+            [[10, 9, 8, 7, 6, 5, 4, 3, 2, 1], [10, 9, 8, 7, 6, 5, 4, 3, 2, 1],
+             [10, 9, 8, 7, 6, 5, 4, 3, 2, 1], [9, 2, 8, 7, 6, 5, 4, 3, 10, 1],
+             [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]),
+           (2286, 0.9908688345484833, True, 'exact',
+            [[8, 7, 6, 5, 4, 3, 2, 1], [8, 7, 6, 5, 4, 3, 2, 1],
+             [8, 7, 6, 5, 4, 3, 2, 1], [8, 7, 6, 5, 4, 3, 2, 1],
+             [8, 7, 6, 5, 4, 3, 2, 1], [8, 7, 6, 5, 4, 3, 2, 1],
+             [8, 7, 6, 5, 4, 3, 2, 1], [8, 7, 6, 5, 4, 3, 2, 1],
+             [8, 7, 6, 5, 4, 3, 2, 1], [1, 3, 5, 6, 4, 7, 2, 8],
+             [1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7, 8],
+             [1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 3, 4, 5, 6, 7, 8],
+             [1, 2, 3, 4, 5, 6, 7, 8]]),
+          ]
+    # only the first of these appears slow because intermediate data are
+    # cached and used on the rest
+    @pytest.mark.parametrize("L, p, ranked, method, data", ts2)
+    @pytest.mark.slow()
+    def test_accuracy2(self, L, p, ranked, method, data):
+        res = stats.page_trend_test(data, ranked=ranked, method=method)
+        assert_equal(L, res.statistic)
+        assert_allclose(p, res.pvalue)
+        assert_equal(method, res.method)
+
+    def test_options(self):
+        rng = np.random.default_rng(183973867)
+        m, n = 10, 20
+        predicted_ranks = np.arange(1, n+1)
+        perm = rng.permutation(np.arange(n))
+        data = rng.random((m, n))
+        ranks = stats.rankdata(data, axis=1)
+        res1 = stats.page_trend_test(ranks)
+        res2 = stats.page_trend_test(ranks, ranked=True)
+        res3 = stats.page_trend_test(data, ranked=False)
+        res4 = stats.page_trend_test(ranks, predicted_ranks=predicted_ranks)
+        res5 = stats.page_trend_test(ranks[:, perm],
+                                     predicted_ranks=predicted_ranks[perm])
+        assert_equal(res1.statistic, res2.statistic)
+        assert_equal(res1.statistic, res3.statistic)
+        assert_equal(res1.statistic, res4.statistic)
+        assert_equal(res1.statistic, res5.statistic)
+
+    def test_Ames_assay(self):
+        # test from _page_trend_test.py [2] page 151; data on page 144
+        data = [[101, 117, 111], [91, 90, 107], [103, 133, 121],
+                [136, 140, 144], [190, 161, 201], [146, 120, 116]]
+        data = np.array(data).T
+        predicted_ranks = np.arange(1, 7)
+
+        res = stats.page_trend_test(data, ranked=False,
+                                    predicted_ranks=predicted_ranks,
+                                    method="asymptotic")
+        assert_equal(res.statistic, 257)
+        assert_almost_equal(res.pvalue, 0.0035, decimal=4)
+
+        res = stats.page_trend_test(data, ranked=False,
+                                    predicted_ranks=predicted_ranks,
+                                    method="exact")
+        assert_equal(res.statistic, 257)
+        assert_almost_equal(res.pvalue, 0.0023, decimal=4)
+
+    def test_input_validation(self):
+        # test data not a 2d array
+        with assert_raises(ValueError, match="`data` must be a 2d array."):
+            stats.page_trend_test(None)
+        with assert_raises(ValueError, match="`data` must be a 2d array."):
+            stats.page_trend_test([])
+        with assert_raises(ValueError, match="`data` must be a 2d array."):
+            stats.page_trend_test([1, 2])
+        with assert_raises(ValueError, match="`data` must be a 2d array."):
+            stats.page_trend_test([[[1]]])
+
+        # test invalid dimensions
+        rng = np.random.default_rng(2482566048)
+        with assert_raises(ValueError, match="Page's L is only appropriate"):
+            stats.page_trend_test(rng.random((1, 3)))
+        with assert_raises(ValueError, match="Page's L is only appropriate"):
+            stats.page_trend_test(rng.random((2, 2)))
+
+        # predicted ranks must include each integer [1, 2, 3] exactly once
+        message = "`predicted_ranks` must include each integer"
+        with assert_raises(ValueError, match=message):
+            stats.page_trend_test(data=[[1, 2, 3], [1, 2, 3]],
+                                  predicted_ranks=[0, 1, 2])
+        with assert_raises(ValueError, match=message):
+            stats.page_trend_test(data=[[1, 2, 3], [1, 2, 3]],
+                                  predicted_ranks=[1.1, 2, 3])
+        with assert_raises(ValueError, match=message):
+            stats.page_trend_test(data=[[1, 2, 3], [1, 2, 3]],
+                                  predicted_ranks=[1, 2, 3, 3])
+        with assert_raises(ValueError, match=message):
+            stats.page_trend_test(data=[[1, 2, 3], [1, 2, 3]],
+                                  predicted_ranks="invalid")
+
+        # test improperly ranked data
+        with assert_raises(ValueError, match="`data` is not properly ranked"):
+            stats.page_trend_test([[0, 2, 3], [1, 2, 3]], True)
+        with assert_raises(ValueError, match="`data` is not properly ranked"):
+            stats.page_trend_test([[1, 2, 3], [1, 2, 4]], True)
+
+        # various
+        with assert_raises(ValueError, match="`data` contains NaNs"):
+            stats.page_trend_test([[1, 2, 3], [1, 2, np.nan]],
+                                  ranked=False)
+        with assert_raises(ValueError, match="`method` must be in"):
+            stats.page_trend_test(data=[[1, 2, 3], [1, 2, 3]],
+                                  method="ekki")
+        with assert_raises(TypeError, match="`ranked` must be boolean."):
+            stats.page_trend_test(data=[[1, 2, 3], [1, 2, 3]],
+                                  ranked="ekki")
+
+
+@make_xp_test_case(stats.expectile)
+class TestExpectile:
+    @pytest.mark.parametrize('dtype', [None, 'float32', 'float64'])
+    def test_same_as_mean(self, dtype, xp):
+        dtype = dtype if dtype is None else getattr(xp, dtype)
+        rng = np.random.default_rng(42)
+
+        x = xp.asarray(rng.random(size=20).tolist(), dtype=dtype)
+        xp_assert_close(stats.expectile(x, 0.5), xp.mean(x))
+
+        w = xp.asarray(rng.random(size=20).tolist(), dtype=dtype)
+        xp_assert_close(stats.expectile(x, 0.5, weights=w), _xp_mean(x, weights=w))
+
+    def test_minimum(self, xp):
+        rng = np.random.default_rng(42)
+        x = xp.asarray(rng.random(size=20))
+        xp_assert_close(stats.expectile(x, alpha=0), xp.min(x))
+
+    def test_maximum(self, xp):
+        rng = np.random.default_rng(42)
+        x = xp.asarray(rng.random(size=20))
+        xp_assert_close(stats.expectile(x, alpha=1), xp.max(x))
+
+    def test_weights(self, xp):
+        # expectile should minimize `fun` defined below; see
+        # F. Sobotka and T. Kneib, "Geoadditive expectile regression",
+        # Computational Statistics and Data Analysis 56 (2012) 755-767
+        # https://doi.org/10.1016/j.csda.2010.11.015
+        rng = np.random.default_rng(1856392524598679138)
+
+        def fun(u, a, alpha, weights):
+            w = np.full_like(a, fill_value=alpha)
+            w[a <= u] = 1 - alpha
+            return np.sum(w * weights * (a - u)**2)
+
+        def expectile2(a, alpha, weights):
+            bracket = np.min(a), np.max(a)
+            return optimize.minimize_scalar(fun, bracket=bracket,
+                                            args=(a, alpha, weights)).x
+
+        n = 10
+        a = rng.random(n)
+        alpha = rng.random()
+        weights = rng.random(n)
+
+        res = stats.expectile(xp.asarray(a), alpha, weights=xp.asarray(weights))
+        ref = expectile2(a, alpha, weights)
+        xp_assert_close(res, xp.asarray(ref))
+
+    @pytest.mark.parametrize(
+        "alpha", [0.2, 0.5 - 1e-12, 0.5, 0.5 + 1e-12, 0.8]
+    )
+    @pytest.mark.parametrize("n", [20, 2000])
+    def test_expectile_properties(self, alpha, n, xp):
+        """
+        See Section 6 of
+        I. Steinwart, C. Pasin, R.C. Williamson & S. Zhang (2014).
+        "Elicitation and Identification of Properties". COLT.
+        http://proceedings.mlr.press/v35/steinwart14.html
+
+        and
+
+        Propositions 5, 6, 7 of
+        F. Bellini, B. Klar, and A. Müller and E. Rosazza Gianin (2013).
+        "Generalized Quantiles as Risk Measures"
+        http://doi.org/10.2139/ssrn.2225751
+        """
+        rng = np.random.default_rng(42)
+        x = xp.asarray(rng.normal(size=n))
+
+        # 0. definite / constancy
+        # Let T(X) denote the expectile of rv X ~ F.
+        # T(c) = c for constant c
+        for c in [-5., 0., 0.5]:
+            xp_assert_close(
+                stats.expectile(xp.full(shape=n, fill_value=c), alpha=alpha),
+                xp.asarray(c)
+            )
+
+        # 1. translation equivariance
+        # T(X + c) = T(X) + c
+        c = xp.asarray(rng.exponential())
+        xp_assert_close(
+            stats.expectile(x + c, alpha=alpha),
+            stats.expectile(x, alpha=alpha) + c,
+        )
+        xp_assert_close(
+            stats.expectile(x - c, alpha=alpha),
+            stats.expectile(x, alpha=alpha) - c,
+        )
+
+        # 2. positively homogeneity
+        # T(cX) = c * T(X) for c > 0
+        xp_assert_close(
+            stats.expectile(c * x, alpha=alpha),
+            c * stats.expectile(x, alpha=alpha),
+        )
+
+        # 3. subadditivity
+        # Note that subadditivity holds for alpha >= 0.5.
+        # T(X + Y) <= T(X) + T(Y)
+        # For alpha = 0.5, i.e. the mean, strict equality holds.
+        # For alpha < 0.5, one can use property 6. to show
+        # T(X + Y) >= T(X) + T(Y)
+        y = xp.asarray(rng.logistic(size=n, loc=10))  # different distribution than x
+        if alpha == 0.5:
+            def assert_op(a, b):
+                xp_assert_close(a, b)
+
+        elif alpha > 0.5:
+            def assert_op(a, b):
+                assert a < b
+
+        else:
+            def assert_op(a, b):
+                assert a > b
+
+        assert_op(
+            stats.expectile(x + y, alpha=alpha),
+            stats.expectile(x, alpha=alpha)
+            + stats.expectile(y, alpha=alpha)
+        )
+
+        # 4. monotonicity
+        # This holds for first order stochastic dominance X:
+        # X >= Y whenever P(X <= x) < P(Y <= x)
+        # T(X) <= T(Y) whenever X <= Y
+        y = xp.asarray(rng.normal(size=n, loc=5))
+        assert (
+            stats.expectile(x, alpha=alpha) <= stats.expectile(y, alpha=alpha)
+        )
+
+        # 5. convexity for alpha > 0.5, concavity for alpha < 0.5
+        # convexity is
+        # T((1 - c) X + c Y) <= (1 - c) T(X) + c T(Y) for 0 <= c <= 1
+        y = xp.asarray(rng.logistic(size=n, loc=10))
+        for c in [0.1, 0.5, 0.8]:
+            assert_op(
+                stats.expectile((1-c)*x + c*y, alpha=alpha),
+                (1-c) * stats.expectile(x, alpha=alpha) +
+                c * stats.expectile(y, alpha=alpha)
+            )
+
+        # 6. negative argument
+        # T_{alpha}(-X) = -T_{1-alpha}(X)
+        xp_assert_close(
+            stats.expectile(-x, alpha=alpha),
+            -stats.expectile(x, alpha=1-alpha),
+        )
+
+    @pytest.mark.parametrize("n", [20, 2000])
+    def test_monotonicity_in_alpha(self, n, xp):
+        rng = np.random.default_rng(42)
+        x = xp.asarray(rng.pareto(a=2, size=n))
+        e_list = []
+        alpha_seq = np.logspace(-15, np.log10(0.5), 100)
+        # sorted list of unique alpha values in interval (0, 1)
+        for alpha in np.r_[0, alpha_seq, 1 - alpha_seq[:-1:-1], 1]:
+            e_list.append(stats.expectile(x, alpha=float(alpha)))
+        assert xp.all(xp.diff(xp.asarray(e_list)) > 0)
+
+
+@make_xp_test_case(stats.lmoment)
+class TestLMoment:
+    # data from https://github.com/scipy/scipy/issues/19460
+    data = [0.87, 0.87, 1.29, 1.5, 1.7, 0.66, 1.5, 0.5, 1., 1.25, 2.3,
+            1.03, 2.85, 0.68, 1.74, 1.94, 0.63, 2.04, 1.2, 0.64, 2.05, 0.97,
+            2.81, 1.02, 2.76, 0.86, 1.36, 1.29, 1.68, 0.72, 1.67, 1.15, 3.26,
+            0.93, 0.83, 0.91, 0.92, 2.32, 1.12, 3.21, 1.23, 1.22, 1.29, 2.08,
+            0.64, 2.83, 2.68, 1.77, 0.69, 1.69, 0.7, 1.83, 2.25, 1.23, 1.17,
+            0.94, 1.22, 0.76, 0.69, 0.48, 1.04, 2.49, 1.38, 1.57, 1.79, 1.59,
+            1.3, 1.54, 1.07, 1.03, 0.76, 2.35, 2.05, 2.02, 2.36, 1.59, 0.97,
+            1.63, 1.66, 0.94, 1.45, 1.26, 1.25, 0.68, 2.96, 0.8, 1.16, 0.82,
+            0.64, 0.87, 1.33, 1.28, 1.26, 1.19, 1.24, 1.12, 1.45, 1.03, 1.37,
+            1.4, 1.35, 1.28, 1.04, 1.31, 0.87, 0.96, 2.55, 1.72, 1.05, 1.15,
+            1.73, 1.03, 1.53, 2.41, 1.36, 2.08, 0.92, 0.73, 1.56, 1.94, 0.78]
+
+    not_integers = [1.5, [1, 2, 3.5], math.nan, math.inf]
+
+    def test_dtype_iv(self, xp):
+        message = '`sample` must be an array of real numbers.'
+        with pytest.raises(ValueError, match=message):
+            stats.lmoment(xp.asarray(self.data, dtype=xp.complex128))
+
+    @skip_xp_invalid_arg
+    def test_dtype_iv_non_numeric(self):
+        message = '`sample` must be an array of real numbers.'
+        with pytest.raises(ValueError, match=message):
+            stats.lmoment(np.array(self.data, dtype=object))
+
+    @pytest.mark.parametrize('order', not_integers + [0, -1, [], [[1, 2, 3]]])
+    def test_order_iv(self, order, xp):
+        if is_jax(xp) and np.isscalar(order) and order < 1:
+            pytest.skip("Order is treated as an array; can't do value-based IV")
+        message = '`order` must be a scalar or a non-empty...'
+        with pytest.raises(ValueError, match=message):
+            stats.lmoment(xp.asarray(self.data), order=order)
+
+    @pytest.mark.parametrize('axis', not_integers)
+    def test_axis_iv(self, axis, xp):
+        message = '`axis` must be an integer'
+        with pytest.raises(ValueError, match=message):
+            stats.lmoment(xp.asarray(self.data), axis=axis)
+
+    @pytest.mark.parametrize('sorted', not_integers)
+    def test_sorted_iv(self, sorted, xp):
+        message = '`sorted` must be True or False.'
+        with pytest.raises(ValueError, match=message):
+            stats.lmoment(xp.asarray(self.data), sorted=sorted)
+
+    @pytest.mark.parametrize('standardize', not_integers)
+    def test_standardize_iv(self, standardize, xp):
+        message = '`standardize` must be True or False.'
+        with pytest.raises(ValueError, match=message):
+            stats.lmoment(xp.asarray(self.data), standardize=standardize)
+
+    @pytest.mark.parametrize('order', [1, 4, [1, 2, 3, 4]])
+    @pytest.mark.parametrize('standardize', [False, True])
+    @pytest.mark.parametrize('presorted', [False, True])
+    def test_lmoment(self, order, standardize, presorted, xp):
+        # Reference values from R package `lmom`
+        # options(digits=16)
+        # library(lmom)
+        # data= c(0.87, 0.87,..., 1.94, 0.78)
+        # samlmu(data)
+        ref = xp.asarray([1.4087603305785130, 0.3415936639118458,
+                          0.2189964482831403, 0.1328186463415905])
+
+        if not standardize:
+            ref = xpx.at(ref)[2:].multiply(ref[1])
+
+        data = sorted(self.data) if presorted else self.data
+        data = xp.asarray(data)
+
+        res = stats.lmoment(data, order, standardize=standardize, sorted=presorted)
+        xp_assert_close(res, ref[xp.asarray(order)-1])
+
+    def test_dtype(self, xp):
+        dtype = xp.float32
+        sample = xp.asarray(self.data)
+        res = stats.lmoment(xp.astype(sample, dtype))
+        ref = xp.astype(stats.lmoment(sample), dtype)
+        xp_assert_close(res, ref, rtol=1e-4)
+
+        dtype = xp.int64
+        sample = xp.asarray([1, 2, 3, 4, 5])
+        res = stats.lmoment(xp.astype(sample, dtype))
+        ref = stats.lmoment(xp.astype(sample, xp_default_dtype(xp)))
+        xp_assert_close(res, ref)
+
+    @pytest.mark.parametrize("axis", [0, 1])
+    def test_axis(self, axis, xp):
+        # nd input is tested extensively in `test_axis_nan_policy`, but only for NumPy
+        rng = np.random.default_rng(234923498149931248151)
+        x = rng.random(size=(10, 11))
+        res = stats.lmoment(xp.asarray(x), axis=axis)
+        ref = xp.asarray(stats.lmoment(x, axis=axis))
+        xp_assert_close(res, ref)
+
+
+@make_xp_test_case(_xp_mean)
+class TestXP_Mean:
+    @pytest.mark.parametrize('axis', [None, 1, -1, (-2, 2)])
+    @pytest.mark.parametrize('weights', [None, True])
+    @pytest.mark.parametrize('keepdims', [False, True])
+    def test_xp_mean_basic(self, xp, axis, weights, keepdims):
+        rng = np.random.default_rng(90359458245906)
+        x = rng.random((3, 4, 5))
+        x_xp = xp.asarray(x)
+        w = w_xp = None
+
+        if weights:
+            w = rng.random((1, 5))
+            w_xp = xp.asarray(w)
+            x, w = np.broadcast_arrays(x, w)
+
+        res = _xp_mean(x_xp, weights=w_xp, axis=axis, keepdims=keepdims)
+        ref = np.average(x, weights=w, axis=axis, keepdims=keepdims)
+
+        xp_assert_close(res, xp.asarray(ref))
+
+    def test_non_broadcastable(self, xp):
+        # non-broadcastable x and weights
+        x, w = xp.arange(10.), xp.zeros(5)
+        message = "Array shapes are incompatible for broadcasting."
+        with pytest.raises(ValueError, match=message):
+            _xp_mean(x, weights=w)
+
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    def test_special_cases(self, xp):
+        # weights sum to zero
+        weights = xp.asarray([-1., 0., 1.])
+
+        res = _xp_mean(xp.asarray([1., 1., 1.]), weights=weights)
+        xp_assert_close(res, xp.asarray(xp.nan))
+
+        res = _xp_mean(xp.asarray([2., 1., 1.]), weights=weights)
+        xp_assert_close(res, xp.asarray(-np.inf))
+
+        res = _xp_mean(xp.asarray([1., 1., 2.]), weights=weights)
+        xp_assert_close(res, xp.asarray(np.inf))
+
+    @pytest.mark.filterwarnings(
+        "ignore:invalid value encountered:RuntimeWarning"
+    ) # for dask
+    def test_nan_policy(self, xp):
+        x = xp.arange(10.)
+        mask = (x == 3)
+        x = xp.where(mask, xp.nan, x)
+
+        # nan_policy='raise' raises an error
+        if is_lazy_array(x):
+            with pytest.raises(TypeError, match='not supported for lazy arrays'):
+                _xp_mean(x, nan_policy='raise')
+        else:
+            with pytest.raises(ValueError, match='The input contains nan values'):
+                _xp_mean(x, nan_policy='raise')
+
+        # `nan_policy='propagate'` is the default, and the result is NaN
+        res1 = _xp_mean(x)
+        res2 = _xp_mean(x, nan_policy='propagate')
+        ref = xp.asarray(xp.nan)
+        xp_assert_equal(res1, ref)
+        xp_assert_equal(res2, ref)
+
+        # `nan_policy='omit'` omits NaNs in `x`
+        res = _xp_mean(x, nan_policy='omit')
+        ref = xp.mean(x[~mask])
+        xp_assert_close(res, ref)
+
+        # `nan_policy='omit'` omits NaNs in `weights`, too
+        weights = xp.ones(10)
+        weights = xp.where(mask, xp.nan, weights)
+        res = _xp_mean(xp.arange(10.), weights=weights, nan_policy='omit')
+        ref = xp.mean(x[~mask])
+        xp_assert_close(res, ref)
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning")
+    def test_nan_policy_warns(self, xp):
+        x = xp.arange(10.)
+        x = xp.where(x == 3, xp.nan, x)
+
+        # Check for warning if omitting NaNs causes empty slice
+        message = 'After omitting NaNs...'
+        with eager_warns(RuntimeWarning, match=message, xp=xp):
+            res = _xp_mean(x * np.nan, nan_policy='omit')
+            ref = xp.asarray(xp.nan)
+            xp_assert_equal(res, ref)
+
+    def test_empty(self, xp):
+        message = 'One or more sample arguments is too small...'
+        with pytest.warns(SmallSampleWarning, match=message):
+            res = _xp_mean(xp.asarray([]))
+            ref = xp.asarray(xp.nan)
+            xp_assert_equal(res, ref)
+
+        message = "All axis-slices of one or more sample arguments..."
+        with pytest.warns(SmallSampleWarning, match=message):
+            res = _xp_mean(xp.asarray([[]]), axis=1)
+            ref = xp.asarray([xp.nan])
+            xp_assert_equal(res, ref)
+
+        res = _xp_mean(xp.asarray([[]]), axis=0)
+        ref = xp.asarray([])
+        xp_assert_equal(res, ref)
+
+    @pytest.mark.filterwarnings(
+        "ignore:overflow encountered in reduce:RuntimeWarning"
+    ) # for dask
+    def test_dtype(self, xp):
+        max = xp.finfo(xp.float32).max
+        x_np = np.asarray([max, max], dtype=np.float32)
+        x_xp = xp.asarray(x_np)
+
+        # Overflow occurs for float32 input
+        with np.errstate(over='ignore'):
+            res = _xp_mean(x_xp)
+            ref = np.mean(x_np)
+            np.testing.assert_equal(ref, np.inf)
+            xp_assert_close(res, xp.asarray(ref))
+
+        # correct result is returned if `float64` is used
+        res = _xp_mean(x_xp, dtype=xp.float64)
+        ref = xp.asarray(np.mean(np.asarray(x_np, dtype=np.float64)))
+        xp_assert_close(res, ref)
+
+    def test_integer(self, xp):
+        # integer inputs are converted to the appropriate float
+        x = xp.arange(10)
+        y = xp.arange(10.)
+        xp_assert_equal(_xp_mean(x), _xp_mean(y))
+        xp_assert_equal(_xp_mean(y, weights=x), _xp_mean(y, weights=y))
+
+    def test_complex_gh22404(self, xp):
+        rng = np.random.default_rng(90359458245906)
+        x, y, wx, wy = rng.random((4, 20))
+        res = _xp_mean(xp.asarray(x + y*1j), weights=xp.asarray(wx + wy*1j))
+        ref = np.average(x + y*1j, weights=wx + wy*1j)
+        xp_assert_close(res, xp.asarray(ref))
+
+
+@make_xp_test_case(_xp_var)
+class TestXP_Var:
+    @pytest.mark.parametrize('axis', [None, 1, -1, (-2, 2)])
+    @pytest.mark.parametrize('keepdims', [False, True])
+    @pytest.mark.parametrize('correction', [0, 1])
+    @pytest.mark.parametrize('nan_policy', ['propagate', 'omit'])
+    def test_xp_var_basic(self, xp, axis, keepdims, correction, nan_policy):
+        rng = np.random.default_rng(90359458245906)
+        x = rng.random((3, 4, 5))
+        var_ref = np.var
+
+        if nan_policy == 'omit':
+            nan_mask = rng.random(size=x.shape) > 0.5
+            x[nan_mask] = np.nan
+            var_ref = np.nanvar
+
+        x_xp = xp.asarray(x)
+
+        res = _xp_var(x_xp, axis=axis, keepdims=keepdims, correction=correction,
+                      nan_policy=nan_policy)
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore", "Degrees of freedom <= 0 for slice", RuntimeWarning)
+            ref = var_ref(x, axis=axis, keepdims=keepdims, ddof=correction)
+
+        xp_assert_close(res, xp.asarray(ref))
+
+    def test_special_cases(self, xp):
+        # correction too big
+        res = _xp_var(xp.asarray([1., 2.]), correction=3)
+        xp_assert_close(res, xp.asarray(xp.nan))
+
+    def test_nan_policy(self, xp):
+        x = xp.arange(10.)
+        mask = (x == 3)
+        x = xp.where(mask, xp.nan, x)
+
+        # `nan_policy='propagate'` is the default, and the result is NaN
+        res1 = _xp_var(x)
+        res2 = _xp_var(x, nan_policy='propagate')
+        ref = xp.asarray(xp.nan)
+        xp_assert_equal(res1, ref)
+        xp_assert_equal(res2, ref)
+
+        # `nan_policy='omit'` omits NaNs in `x`
+        res = _xp_var(x, nan_policy='omit')
+        ref = xp.var(x[~mask])
+        xp_assert_close(res, ref)
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning")
+    def test_nan_policy_warns(self, xp):
+        x = xp.arange(10.)
+        x = xp.where(x == 3, xp.nan, x)
+
+        # Check for warning if omitting NaNs causes empty slice
+        message = 'After omitting NaNs...'
+        with eager_warns(RuntimeWarning, match=message, xp=xp):
+            res = _xp_var(x * np.nan, nan_policy='omit')
+            ref = xp.asarray(xp.nan)
+            xp_assert_equal(res, ref)
+
+    @skip_xp_backends(eager_only=True)
+    def test_nan_policy_raise(self, xp):
+        # nan_policy='raise' raises an error when NaNs are present
+        message = 'The input contains nan values'
+        with pytest.raises(ValueError, match=message):
+            _xp_var(xp.asarray([1, 2, xp.nan]), nan_policy='raise')
+
+    def test_empty(self, xp):
+        message = 'One or more sample arguments is too small...'
+        with pytest.warns(SmallSampleWarning, match=message):
+            res = _xp_var(xp.asarray([]))
+            ref = xp.asarray(xp.nan)
+            xp_assert_equal(res, ref)
+
+        message = "All axis-slices of one or more sample arguments..."
+        with pytest.warns(SmallSampleWarning, match=message):
+            res = _xp_var(xp.asarray([[]]), axis=1)
+            ref = xp.asarray([xp.nan])
+            xp_assert_equal(res, ref)
+
+        res = _xp_var(xp.asarray([[]]), axis=0)
+        ref = xp.asarray([])
+        xp_assert_equal(res, ref)
+
+    @pytest.mark.filterwarnings(
+        "ignore:overflow encountered in reduce:RuntimeWarning"
+    ) # Overflow occurs for float32 input
+    def test_dtype(self, xp):
+        max = xp.finfo(xp.float32).max
+        x_np = np.asarray([max, max/2], dtype=np.float32)
+        x_xp = xp.asarray(x_np)
+
+        res = _xp_var(x_xp)
+        ref = np.var(x_np)
+        np.testing.assert_equal(ref, np.inf)
+        xp_assert_close(res, xp.asarray(ref))
+
+        # correct result is returned if `float64` is used
+        res = _xp_var(x_xp, dtype=xp.float64)
+        ref = xp.asarray(np.var(np.asarray(x_np, dtype=np.float64)))
+        xp_assert_close(res, ref)
+
+    def test_integer(self, xp):
+        # integer inputs are converted to the appropriate float
+        x = xp.arange(10)
+        y = xp.arange(10.)
+        xp_assert_equal(_xp_var(x), _xp_var(y))
+
+    def test_complex_gh22404(self, xp):
+        rng = np.random.default_rng(90359458245906)
+        x, y = rng.random((2, 20))
+        res = _xp_var(xp.asarray(x + y*1j))
+        ref = np.var(x + y*1j)
+        xp_assert_close(res, xp.asarray(ref), check_dtype=False)
+
+
+@pytest.mark.uses_xp_capabilities(False, reason="private")
+def test_chk_asarray(xp):
+    rng = np.random.default_rng(2348923425434)
+    x0 = rng.random(size=(2, 3, 4))
+    x = xp.asarray(x0)
+
+    axis = 1
+    x_out, axis_out = _chk_asarray(x, axis=axis, xp=xp)
+    xp_assert_equal(x_out, xp.asarray(x0))
+    assert_equal(axis_out, axis)
+
+    axis = None
+    x_out, axis_out = _chk_asarray(x, axis=axis, xp=xp)
+    xp_assert_equal(x_out, xp.asarray(x0.ravel()))
+    assert_equal(axis_out, 0)
+
+    axis = 2
+    x_out, axis_out = _chk_asarray(x[0, 0, 0], axis=axis, xp=xp)
+    xp_assert_equal(x_out, xp.asarray(np.atleast_1d(x0[0, 0, 0])))
+    assert_equal(axis_out, axis)
+
+
+@skip_xp_backends("jax.numpy", reason="JAX+wilcoxon+'exact' incompatible")
+@pytest.mark.parametrize("fun", [
+    make_xp_pytest_param(stats.wilcoxon),
+    make_xp_pytest_param(stats.ks_1samp),
+    make_xp_pytest_param(stats.ks_2samp),
+    make_xp_pytest_param(stats.kstest)
+])
+def test_rename_mode_method(fun, xp):
+    rng = np.random.default_rng(23498459284629827814)
+    x = xp.asarray(rng.random(10))
+    y = xp.asarray(rng.random(10))
+
+    if fun == stats.wilcoxon:
+        args = (x,)
+    elif fun == stats.ks_1samp:
+        args = (x, special.ndtr)
+    else:
+        args = (x, y)
+
+    res = fun(*args, method='exact')
+    res2 = fun(*args, mode='exact')
+    xp_assert_equal(res.statistic, res2.statistic)
+    xp_assert_equal(res.pvalue, res2.pvalue)
+
+    err = rf"{fun.__name__}\(\) got multiple values for argument"
+    with pytest.raises(TypeError, match=err):
+        fun(*args, method='exact', mode='exact')
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_survival.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_survival.py
new file mode 100644
index 0000000000000000000000000000000000000000..60a5fef27ae281c028ade8af523bdf1f62f8f664
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_survival.py
@@ -0,0 +1,466 @@
+import pytest
+import numpy as np
+from numpy.testing import assert_equal, assert_allclose
+from scipy import stats
+from scipy.stats import _survival
+
+
+def _kaplan_meier_reference(times, censored):
+    # This is a very straightforward implementation of the Kaplan-Meier
+    # estimator that does almost everything differently from the implementation
+    # in stats.ecdf.
+
+    # Begin by sorting the raw data. Note that the order of death and loss
+    # at a given time matters: death happens first. See [2] page 461:
+    # "These conventions may be paraphrased by saying that deaths recorded as
+    # of an age t are treated as if they occurred slightly before t, and losses
+    # recorded as of an age t are treated as occurring slightly after t."
+    # We implement this by sorting the data first by time, then by `censored`,
+    # (which is 0 when there is a death and 1 when there is only a loss).
+    dtype = [('time', float), ('censored', int)]
+    data = np.array([(t, d) for t, d in zip(times, censored)], dtype=dtype)
+    data = np.sort(data, order=('time', 'censored'))
+    times = data['time']
+    died = np.logical_not(data['censored'])
+
+    m = times.size
+    n = np.arange(m, 0, -1)  # number at risk
+    sf = np.cumprod((n - died) / n)
+
+    # Find the indices of the *last* occurrence of unique times. The
+    # corresponding entries of `times` and `sf` are what we want.
+    _, indices = np.unique(times[::-1], return_index=True)
+    ref_times = times[-indices - 1]
+    ref_sf = sf[-indices - 1]
+    return ref_times, ref_sf
+
+
+class TestSurvival:
+
+    @staticmethod
+    def get_random_sample(rng, n_unique):
+        # generate random sample
+        unique_times = rng.random(n_unique)
+        # convert to `np.int32` to resolve `np.repeat` failure in 32-bit CI
+        repeats = rng.integers(1, 4, n_unique).astype(np.int32)
+        times = rng.permuted(np.repeat(unique_times, repeats))
+        censored = rng.random(size=times.size) > rng.random()
+        sample = stats.CensoredData.right_censored(times, censored)
+        return sample, times, censored
+
+    def test_input_validation(self):
+        message = '`sample` must be a one-dimensional sequence.'
+        with pytest.raises(ValueError, match=message):
+            stats.ecdf([[1]])
+        with pytest.raises(ValueError, match=message):
+            stats.ecdf(1)
+
+        message = '`sample` must not contain nan'
+        with pytest.raises(ValueError, match=message):
+            stats.ecdf([np.nan])
+
+        message = 'Currently, only uncensored and right-censored data...'
+        with pytest.raises(NotImplementedError, match=message):
+            stats.ecdf(stats.CensoredData.left_censored([1], censored=[True]))
+
+        message = 'method` must be one of...'
+        res = stats.ecdf([1, 2, 3])
+        with pytest.raises(ValueError, match=message):
+            res.cdf.confidence_interval(method='ekki-ekki')
+        with pytest.raises(ValueError, match=message):
+            res.sf.confidence_interval(method='shrubbery')
+
+        message = 'confidence_level` must be a scalar between 0 and 1'
+        with pytest.raises(ValueError, match=message):
+            res.cdf.confidence_interval(-1)
+        with pytest.raises(ValueError, match=message):
+            res.sf.confidence_interval([0.5, 0.6])
+
+        message = 'The confidence interval is undefined at some observations.'
+        with pytest.warns(RuntimeWarning, match=message):
+            ci = res.cdf.confidence_interval()
+
+        message = 'Confidence interval bounds do not implement...'
+        with pytest.raises(NotImplementedError, match=message):
+            ci.low.confidence_interval()
+        with pytest.raises(NotImplementedError, match=message):
+            ci.high.confidence_interval()
+
+    def test_edge_cases(self):
+        res = stats.ecdf([])
+        assert_equal(res.cdf.quantiles, [])
+        assert_equal(res.cdf.probabilities, [])
+
+        res = stats.ecdf([1])
+        assert_equal(res.cdf.quantiles, [1])
+        assert_equal(res.cdf.probabilities, [1])
+
+    def test_unique(self):
+        # Example with unique observations; `stats.ecdf` ref. [1] page 80
+        sample = [6.23, 5.58, 7.06, 6.42, 5.20]
+        res = stats.ecdf(sample)
+        ref_x = np.sort(np.unique(sample))
+        ref_cdf = np.arange(1, 6) / 5
+        ref_sf = 1 - ref_cdf
+        assert_equal(res.cdf.quantiles, ref_x)
+        assert_equal(res.cdf.probabilities, ref_cdf)
+        assert_equal(res.sf.quantiles, ref_x)
+        assert_equal(res.sf.probabilities, ref_sf)
+
+    def test_nonunique(self):
+        # Example with non-unique observations; `stats.ecdf` ref. [1] page 82
+        sample = [0, 2, 1, 2, 3, 4]
+        res = stats.ecdf(sample)
+        ref_x = np.sort(np.unique(sample))
+        ref_cdf = np.array([1/6, 2/6, 4/6, 5/6, 1])
+        ref_sf = 1 - ref_cdf
+        assert_equal(res.cdf.quantiles, ref_x)
+        assert_equal(res.cdf.probabilities, ref_cdf)
+        assert_equal(res.sf.quantiles, ref_x)
+        assert_equal(res.sf.probabilities, ref_sf)
+
+    def test_evaluate_methods(self):
+        # Test CDF and SF `evaluate` methods
+        rng = np.random.default_rng(1162729143302572461)
+        sample, _, _ = self.get_random_sample(rng, 15)
+        res = stats.ecdf(sample)
+        x = res.cdf.quantiles
+        xr = x + np.diff(x, append=x[-1]+1)/2  # right shifted points
+
+        assert_equal(res.cdf.evaluate(x), res.cdf.probabilities)
+        assert_equal(res.cdf.evaluate(xr), res.cdf.probabilities)
+        assert_equal(res.cdf.evaluate(x[0]-1), 0)  # CDF starts at 0
+        assert_equal(res.cdf.evaluate([-np.inf, np.inf]), [0, 1])
+
+        assert_equal(res.sf.evaluate(x), res.sf.probabilities)
+        assert_equal(res.sf.evaluate(xr), res.sf.probabilities)
+        assert_equal(res.sf.evaluate(x[0]-1), 1)  # SF starts at 1
+        assert_equal(res.sf.evaluate([-np.inf, np.inf]), [1, 0])
+
+    # ref. [1] page 91
+    t1 = [37, 43, 47, 56, 60, 62, 71, 77, 80, 81]  # times
+    d1 = [0, 0, 1, 1, 0, 0, 0, 1, 1, 1]  # 1 means deaths (not censored)
+    r1 = [1, 1, 0.875, 0.75, 0.75, 0.75, 0.75, 0.5, 0.25, 0]  # reference SF
+
+    # https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_survival/BS704_Survival5.html
+    t2 = [8, 12, 26, 14, 21, 27, 8, 32, 20, 40]
+    d2 = [1, 1, 1, 1, 1, 1, 0, 0, 0, 0]
+    r2 = [0.9, 0.788, 0.675, 0.675, 0.54, 0.405, 0.27, 0.27, 0.27]
+    t3 = [33, 28, 41, 48, 48, 25, 37, 48, 25, 43]
+    d3 = [1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
+    r3 = [1, 0.875, 0.75, 0.75, 0.6, 0.6, 0.6]
+
+    # https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_survival/bs704_survival4.html
+    t4 = [24, 3, 11, 19, 24, 13, 14, 2, 18, 17,
+          24, 21, 12, 1, 10, 23, 6, 5, 9, 17]
+    d4 = [0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1]
+    r4 = [0.95, 0.95, 0.897, 0.844, 0.844, 0.844, 0.844, 0.844, 0.844,
+          0.844, 0.76, 0.676, 0.676, 0.676, 0.676, 0.507, 0.507]
+
+    # https://www.real-statistics.com/survival-analysis/kaplan-meier-procedure/confidence-interval-for-the-survival-function/
+    t5 = [3, 5, 8, 10, 5, 5, 8, 12, 15, 14, 2, 11, 10, 9, 12, 5, 8, 11]
+    d5 = [1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1]
+    r5 = [0.944, 0.889, 0.722, 0.542, 0.542, 0.542, 0.361, 0.181, 0.181, 0.181]
+
+    @pytest.mark.parametrize("case", [(t1, d1, r1), (t2, d2, r2), (t3, d3, r3),
+                                      (t4, d4, r4), (t5, d5, r5)])
+    def test_right_censored_against_examples(self, case):
+        # test `ecdf` against other implementations on example problems
+        times, died, ref = case
+        sample = stats.CensoredData.right_censored(times, np.logical_not(died))
+        res = stats.ecdf(sample)
+        assert_allclose(res.sf.probabilities, ref, atol=1e-3)
+        assert_equal(res.sf.quantiles, np.sort(np.unique(times)))
+
+        # test reference implementation against other implementations
+        res = _kaplan_meier_reference(times, np.logical_not(died))
+        assert_equal(res[0], np.sort(np.unique(times)))
+        assert_allclose(res[1], ref, atol=1e-3)
+
+    @pytest.mark.parametrize('seed', [182746786639392128, 737379171436494115,
+                                      576033618403180168, 308115465002673650])
+    def test_right_censored_against_reference_implementation(self, seed):
+        # test `ecdf` against reference implementation on random problems
+        rng = np.random.default_rng(seed)
+        n_unique = rng.integers(10, 100)
+        sample, times, censored = self.get_random_sample(rng, n_unique)
+        res = stats.ecdf(sample)
+        ref = _kaplan_meier_reference(times, censored)
+        assert_allclose(res.sf.quantiles, ref[0])
+        assert_allclose(res.sf.probabilities, ref[1])
+
+        # If all observations are uncensored, the KM estimate should match
+        # the usual estimate for uncensored data
+        sample = stats.CensoredData(uncensored=times)
+        res = _survival._ecdf_right_censored(sample)  # force Kaplan-Meier
+        ref = stats.ecdf(times)
+        assert_equal(res[0], ref.sf.quantiles)
+        assert_allclose(res[1], ref.cdf.probabilities, rtol=1e-14)
+        assert_allclose(res[2], ref.sf.probabilities, rtol=1e-14)
+
+    def test_right_censored_ci(self):
+        # test "greenwood" confidence interval against example 4 (URL above).
+        times, died = self.t4, self.d4
+        sample = stats.CensoredData.right_censored(times, np.logical_not(died))
+        res = stats.ecdf(sample)
+        ref_allowance = [0.096, 0.096, 0.135, 0.162, 0.162, 0.162, 0.162,
+                         0.162, 0.162, 0.162, 0.214, 0.246, 0.246, 0.246,
+                         0.246, 0.341, 0.341]
+
+        sf_ci = res.sf.confidence_interval()
+        cdf_ci = res.cdf.confidence_interval()
+        allowance = res.sf.probabilities - sf_ci.low.probabilities
+
+        assert_allclose(allowance, ref_allowance, atol=1e-3)
+        assert_allclose(sf_ci.low.probabilities,
+                        np.clip(res.sf.probabilities - allowance, 0, 1))
+        assert_allclose(sf_ci.high.probabilities,
+                        np.clip(res.sf.probabilities + allowance, 0, 1))
+        assert_allclose(cdf_ci.low.probabilities,
+                        np.clip(res.cdf.probabilities - allowance, 0, 1))
+        assert_allclose(cdf_ci.high.probabilities,
+                        np.clip(res.cdf.probabilities + allowance, 0, 1))
+
+        # test "log-log" confidence interval against Mathematica
+        # e = {24, 3, 11, 19, 24, 13, 14, 2, 18, 17, 24, 21, 12, 1, 10, 23, 6, 5,
+        #      9, 17}
+        # ci = {1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0}
+        # R = EventData[e, ci]
+        # S = SurvivalModelFit[R]
+        # S["PointwiseIntervals", ConfidenceLevel->0.95,
+        #   ConfidenceTransform->"LogLog"]
+
+        ref_low = [0.694743, 0.694743, 0.647529, 0.591142, 0.591142, 0.591142,
+                   0.591142, 0.591142, 0.591142, 0.591142, 0.464605, 0.370359,
+                   0.370359, 0.370359, 0.370359, 0.160489, 0.160489]
+        ref_high = [0.992802, 0.992802, 0.973299, 0.947073, 0.947073, 0.947073,
+                    0.947073, 0.947073, 0.947073, 0.947073, 0.906422, 0.856521,
+                    0.856521, 0.856521, 0.856521, 0.776724, 0.776724]
+        sf_ci = res.sf.confidence_interval(method='log-log')
+        assert_allclose(sf_ci.low.probabilities, ref_low, atol=1e-6)
+        assert_allclose(sf_ci.high.probabilities, ref_high, atol=1e-6)
+
+    def test_right_censored_ci_example_5(self):
+        # test "exponential greenwood" confidence interval against example 5
+        times, died = self.t5, self.d5
+        sample = stats.CensoredData.right_censored(times, np.logical_not(died))
+        res = stats.ecdf(sample)
+        lower = np.array([0.66639, 0.624174, 0.456179, 0.287822, 0.287822,
+                          0.287822, 0.128489, 0.030957, 0.030957, 0.030957])
+        upper = np.array([0.991983, 0.970995, 0.87378, 0.739467, 0.739467,
+                          0.739467, 0.603133, 0.430365, 0.430365, 0.430365])
+
+        sf_ci = res.sf.confidence_interval(method='log-log')
+        cdf_ci = res.cdf.confidence_interval(method='log-log')
+
+        assert_allclose(sf_ci.low.probabilities, lower, atol=1e-5)
+        assert_allclose(sf_ci.high.probabilities, upper, atol=1e-5)
+        assert_allclose(cdf_ci.low.probabilities, 1-upper, atol=1e-5)
+        assert_allclose(cdf_ci.high.probabilities, 1-lower, atol=1e-5)
+
+        # Test against R's `survival` library `survfit` function, 90%CI
+        # library(survival)
+        # options(digits=16)
+        # time = c(3, 5, 8, 10, 5, 5, 8, 12, 15, 14, 2, 11, 10, 9, 12, 5, 8, 11)
+        # status = c(1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1)
+        # res = survfit(Surv(time, status)
+        # ~1, conf.type = "log-log", conf.int = 0.90)
+        # res$time; res$lower; res$upper
+        low = [0.74366748406861172, 0.68582332289196246, 0.50596835651480121,
+               0.32913131413336727, 0.32913131413336727, 0.32913131413336727,
+               0.15986912028781664, 0.04499539918147757, 0.04499539918147757,
+               0.04499539918147757]
+        high = [0.9890291867238429, 0.9638835422144144, 0.8560366823086629,
+                0.7130167643978450, 0.7130167643978450, 0.7130167643978450,
+                0.5678602982997164, 0.3887616766886558, 0.3887616766886558,
+                0.3887616766886558]
+        sf_ci = res.sf.confidence_interval(method='log-log',
+                                           confidence_level=0.9)
+        assert_allclose(sf_ci.low.probabilities, low)
+        assert_allclose(sf_ci.high.probabilities, high)
+
+        # And with conf.type = "plain"
+        low = [0.8556383113628162, 0.7670478794850761, 0.5485720663578469,
+               0.3441515412527123, 0.3441515412527123, 0.3441515412527123,
+               0.1449184105424544, 0., 0., 0.]
+        high = [1., 1., 0.8958723780865975, 0.7391817920806210,
+                0.7391817920806210, 0.7391817920806210, 0.5773038116797676,
+                0.3642270254596720, 0.3642270254596720, 0.3642270254596720]
+        sf_ci = res.sf.confidence_interval(confidence_level=0.9)
+        assert_allclose(sf_ci.low.probabilities, low)
+        assert_allclose(sf_ci.high.probabilities, high)
+
+    def test_right_censored_ci_nans(self):
+        # test `ecdf` confidence interval on a problem that results in NaNs
+        times, died = self.t1, self.d1
+        sample = stats.CensoredData.right_censored(times, np.logical_not(died))
+        res = stats.ecdf(sample)
+
+        # Reference values generated with Matlab
+        # format long
+        # t = [37 43 47 56 60 62 71 77 80 81];
+        # d = [0 0 1 1 0 0 0 1 1 1];
+        # censored = ~d1;
+        # [f, x, flo, fup] = ecdf(t, 'Censoring', censored, 'Alpha', 0.05);
+        x = [37, 47, 56, 77, 80, 81]
+        flo = [np.nan, 0, 0, 0.052701464070711, 0.337611126231790, np.nan]
+        fup = [np.nan, 0.35417230377, 0.5500569798, 0.9472985359, 1.0, np.nan]
+        i = np.searchsorted(res.cdf.quantiles, x)
+
+        message = "The confidence interval is undefined at some observations"
+        with pytest.warns(RuntimeWarning, match=message):
+            ci = res.cdf.confidence_interval()
+
+        # Matlab gives NaN as the first element of the CIs. Mathematica agrees,
+        # but R's survfit does not. It makes some sense, but it's not what the
+        # formula gives, so skip that element.
+        assert_allclose(ci.low.probabilities[i][1:], flo[1:])
+        assert_allclose(ci.high.probabilities[i][1:], fup[1:])
+
+        # [f, x, flo, fup] = ecdf(t, 'Censoring', censored, 'Function',
+        #                        'survivor', 'Alpha', 0.05);
+        flo = [np.nan, 0.64582769623, 0.449943020228, 0.05270146407, 0, np.nan]
+        fup = [np.nan, 1.0, 1.0, 0.947298535929289, 0.662388873768210, np.nan]
+        i = np.searchsorted(res.cdf.quantiles, x)
+
+        with pytest.warns(RuntimeWarning, match=message):
+            ci = res.sf.confidence_interval()
+
+        assert_allclose(ci.low.probabilities[i][1:], flo[1:])
+        assert_allclose(ci.high.probabilities[i][1:], fup[1:])
+
+        # With the same data, R's `survival` library `survfit` function
+        # doesn't produce the leading NaN
+        # library(survival)
+        # options(digits=16)
+        # time = c(37, 43, 47, 56, 60, 62, 71, 77, 80, 81)
+        # status = c(0, 0, 1, 1, 0, 0, 0, 1, 1, 1)
+        # res = survfit(Surv(time, status)
+        # ~1, conf.type = "plain", conf.int = 0.95)
+        # res$time
+        # res$lower
+        # res$upper
+        low = [1., 1., 0.64582769623233816, 0.44994302022779326,
+               0.44994302022779326, 0.44994302022779326, 0.44994302022779326,
+               0.05270146407071086, 0., np.nan]
+        high = [1., 1., 1., 1., 1., 1., 1., 0.9472985359292891,
+                0.6623888737682101, np.nan]
+        assert_allclose(ci.low.probabilities, low)
+        assert_allclose(ci.high.probabilities, high)
+
+        # It does with conf.type="log-log", as do we
+        with pytest.warns(RuntimeWarning, match=message):
+            ci = res.sf.confidence_interval(method='log-log')
+        low = [np.nan, np.nan, 0.38700001403202522, 0.31480711370551911,
+               0.31480711370551911, 0.31480711370551911, 0.31480711370551911,
+               0.08048821148507734, 0.01049958986680601, np.nan]
+        high = [np.nan, np.nan, 0.9813929658789660, 0.9308983170906275,
+                0.9308983170906275, 0.9308983170906275, 0.9308983170906275,
+                0.8263946341076415, 0.6558775085110887, np.nan]
+        assert_allclose(ci.low.probabilities, low)
+        assert_allclose(ci.high.probabilities, high)
+
+    def test_right_censored_against_uncensored(self):
+        rng = np.random.default_rng(7463952748044886637)
+        sample = rng.integers(10, 100, size=1000)
+        censored = np.zeros_like(sample)
+        censored[np.argmax(sample)] = True
+        res = stats.ecdf(sample)
+        ref = stats.ecdf(stats.CensoredData.right_censored(sample, censored))
+        assert_equal(res.sf.quantiles, ref.sf.quantiles)
+        assert_equal(res.sf._n, ref.sf._n)
+        assert_equal(res.sf._d[:-1], ref.sf._d[:-1])  # difference @ [-1]
+        assert_allclose(res.sf._sf[:-1], ref.sf._sf[:-1], rtol=1e-14)
+
+    def test_plot_iv(self):
+        rng = np.random.default_rng(1769658657308472721)
+        n_unique = rng.integers(10, 100)
+        sample, _, _ = self.get_random_sample(rng, n_unique)
+        res = stats.ecdf(sample)
+
+        try:
+            import matplotlib.pyplot as plt  # noqa: F401
+            res.sf.plot()  # no other errors occur
+        except (ModuleNotFoundError, ImportError):
+            message = r"matplotlib must be installed to use method `plot`."
+            with pytest.raises(ModuleNotFoundError, match=message):
+                res.sf.plot()
+
+
+class TestLogRank:
+
+    @pytest.mark.parametrize(
+        "x, y, statistic, pvalue",
+        # Results validate with R
+        # library(survival)
+        # options(digits=16)
+        #
+        # futime_1 <- c(8, 12, 26, 14, 21, 27, 8, 32, 20, 40)
+        # fustat_1 <- c(1, 1, 1, 1, 1, 1, 0, 0, 0, 0)
+        # rx_1 <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
+        #
+        # futime_2 <- c(33, 28, 41, 48, 48, 25, 37, 48, 25, 43)
+        # fustat_2 <- c(1, 1, 1, 0, 0, 0, 0, 0, 0, 0)
+        # rx_2 <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
+        #
+        # futime <- c(futime_1, futime_2)
+        # fustat <- c(fustat_1, fustat_2)
+        # rx <- c(rx_1, rx_2)
+        #
+        # survdiff(formula = Surv(futime, fustat) ~ rx)
+        #
+        # Also check against another library which handle alternatives
+        # library(nph)
+        # logrank.test(futime, fustat, rx, alternative = "two.sided")
+        # res["test"]
+        [(
+              # https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_survival/BS704_Survival5.html
+              # uncensored, censored
+              [[8, 12, 26, 14, 21, 27], [8, 32, 20, 40]],
+              [[33, 28, 41], [48, 48, 25, 37, 48, 25, 43]],
+              # chi2, ["two-sided", "less", "greater"]
+              6.91598157449,
+              [0.008542873404, 0.9957285632979385, 0.004271436702061537]
+         ),
+         (
+              # https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_survival/BS704_Survival5.html
+              [[19, 6, 5, 4], [20, 19, 17, 14]],
+              [[16, 21, 7], [21, 15, 18, 18, 5]],
+              0.835004855038,
+              [0.3608293039, 0.8195853480676912, 0.1804146519323088]
+         ),
+         (
+              # Bland, Altman, "The logrank test", BMJ, 2004
+              # https://www.bmj.com/content/328/7447/1073.short
+              [[6, 13, 21, 30, 37, 38, 49, 50, 63, 79, 86, 98, 202, 219],
+               [31, 47, 80, 82, 82, 149]],
+              [[10, 10, 12, 13, 14, 15, 16, 17, 18, 20, 24, 24, 25, 28, 30,
+                33, 35, 37, 40, 40, 46, 48, 76, 81, 82, 91, 112, 181],
+               [34, 40, 70]],
+              7.49659416854,
+              [0.006181578637, 0.003090789318730882, 0.9969092106812691]
+         )]
+    )
+    def test_log_rank(self, x, y, statistic, pvalue):
+        x = stats.CensoredData(uncensored=x[0], right=x[1])
+        y = stats.CensoredData(uncensored=y[0], right=y[1])
+
+        for i, alternative in enumerate(["two-sided", "less", "greater"]):
+            res = stats.logrank(x=x, y=y, alternative=alternative)
+
+            # we return z and use the normal distribution while other framework
+            # return z**2. The p-value are directly comparable, but we have to
+            # square the statistic
+            assert_allclose(res.statistic**2, statistic, atol=1e-10)
+            assert_allclose(res.pvalue, pvalue[i], atol=1e-10)
+
+    def test_raises(self):
+        sample = stats.CensoredData([1, 2])
+
+        msg = r"`y` must be"
+        with pytest.raises(ValueError, match=msg):
+            stats.logrank(x=sample, y=[[1, 2]])
+
+        msg = r"`x` must be"
+        with pytest.raises(ValueError, match=msg):
+            stats.logrank(x=[[1, 2]], y=sample)
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_tukeylambda_stats.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_tukeylambda_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..8041658599a6ed5b2cd70def1a92bffe0d851792
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_tukeylambda_stats.py
@@ -0,0 +1,85 @@
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+
+from scipy.stats._tukeylambda_stats import (tukeylambda_variance,
+                                            tukeylambda_kurtosis)
+
+
+def test_tukeylambda_stats_known_exact():
+    """Compare results with some known exact formulas."""
+    # Some exact values of the Tukey Lambda variance and kurtosis:
+    # lambda   var      kurtosis
+    #   0     pi**2/3     6/5     (logistic distribution)
+    #  0.5    4 - pi    (5/3 - pi/2)/(pi/4 - 1)**2 - 3
+    #   1      1/3       -6/5     (uniform distribution on (-1,1))
+    #   2      1/12      -6/5     (uniform distribution on (-1/2, 1/2))
+
+    # lambda = 0
+    var = tukeylambda_variance(0)
+    assert_allclose(var, np.pi**2 / 3, atol=1e-12)
+    kurt = tukeylambda_kurtosis(0)
+    assert_allclose(kurt, 1.2, atol=1e-10)
+
+    # lambda = 0.5
+    var = tukeylambda_variance(0.5)
+    assert_allclose(var, 4 - np.pi, atol=1e-12)
+    kurt = tukeylambda_kurtosis(0.5)
+    desired = (5./3 - np.pi/2) / (np.pi/4 - 1)**2 - 3
+    assert_allclose(kurt, desired, atol=1e-10)
+
+    # lambda = 1
+    var = tukeylambda_variance(1)
+    assert_allclose(var, 1.0 / 3, atol=1e-12)
+    kurt = tukeylambda_kurtosis(1)
+    assert_allclose(kurt, -1.2, atol=1e-10)
+
+    # lambda = 2
+    var = tukeylambda_variance(2)
+    assert_allclose(var, 1.0 / 12, atol=1e-12)
+    kurt = tukeylambda_kurtosis(2)
+    assert_allclose(kurt, -1.2, atol=1e-10)
+
+
+def test_tukeylambda_stats_mpmath():
+    """Compare results with some values that were computed using mpmath."""
+    a10 = dict(atol=1e-10, rtol=0)
+    a12 = dict(atol=1e-12, rtol=0)
+    data = [
+        # lambda        variance              kurtosis
+        [-0.1, 4.78050217874253547, 3.78559520346454510],
+        [-0.0649, 4.16428023599895777, 2.52019675947435718],
+        [-0.05, 3.93672267890775277, 2.13129793057777277],
+        [-0.001, 3.30128380390964882, 1.21452460083542988],
+        [0.001, 3.27850775649572176, 1.18560634779287585],
+        [0.03125, 2.95927803254615800, 0.804487555161819980],
+        [0.05, 2.78281053405464501, 0.611604043886644327],
+        [0.0649, 2.65282386754100551, 0.476834119532774540],
+        [1.2, 0.242153920578588346, -1.23428047169049726],
+        [10.0, 0.00095237579757703597, 2.37810697355144933],
+        [20.0, 0.00012195121951131043, 7.37654321002709531],
+    ]
+
+    for lam, var_expected, kurt_expected in data:
+        var = tukeylambda_variance(lam)
+        assert_allclose(var, var_expected, **a12)
+        kurt = tukeylambda_kurtosis(lam)
+        assert_allclose(kurt, kurt_expected, **a10)
+
+    # Test with vector arguments (most of the other tests are for single
+    # values).
+    lam, var_expected, kurt_expected = zip(*data)
+    var = tukeylambda_variance(lam)
+    assert_allclose(var, var_expected, **a12)
+    kurt = tukeylambda_kurtosis(lam)
+    assert_allclose(kurt, kurt_expected, **a10)
+
+
+def test_tukeylambda_stats_invalid():
+    """Test values of lambda outside the domains of the functions."""
+    lam = [-1.0, -0.5]
+    var = tukeylambda_variance(lam)
+    assert_equal(var, np.array([np.nan, np.inf]))
+
+    lam = [-1.0, -0.25]
+    kurt = tukeylambda_kurtosis(lam)
+    assert_equal(kurt, np.array([np.nan, np.inf]))
diff --git a/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_variation.py b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_variation.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a07ac1bfcdf85e9be609ff7a5c1445f32e22cba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/stats/tests/test_variation.py
@@ -0,0 +1,205 @@
+import math
+
+import numpy as np
+import pytest
+
+from scipy._lib._util import AxisError
+from scipy._lib._array_api import make_xp_test_case, eager_warns, is_numpy
+from scipy._lib._array_api_no_0d import xp_assert_equal, xp_assert_close
+from scipy._external import array_api_extra as xpx
+
+from scipy import stats
+from scipy.stats import variation
+from scipy.stats._axis_nan_policy import (too_small_nd_omit, too_small_nd_not_omit,
+                                          SmallSampleWarning)
+
+lazy_xp_modules = [stats]
+skip_xp_backends = pytest.mark.skip_xp_backends
+
+
+@make_xp_test_case(variation)
+class TestVariation:
+    """
+    Test class for scipy.stats.variation
+    """
+
+    def test_ddof(self, xp):
+        x = xp.arange(9.0)
+        xp_assert_close(variation(x, ddof=1), xp.asarray(math.sqrt(60/8)/4))
+
+    @pytest.mark.parametrize('sgn', [1, -1])
+    def test_sign(self, sgn, xp):
+        x = xp.asarray([1., 2., 3., 4., 5.])
+        v = variation(sgn*x)
+        expected = xp.asarray(sgn*math.sqrt(2)/3)
+        xp_assert_close(v, expected, rtol=1e-10)
+
+    @skip_xp_backends(np_only=True, reason="test plain python scalar input")
+    def test_scalar(self, xp):
+        # A scalar is treated like a 1-d sequence with length 1.
+        assert variation(4.0) == 0.0
+
+    @pytest.mark.parametrize('nan_policy, expected',
+                             [('propagate', np.nan),
+                              ('omit', math.sqrt(20/3)/4)])
+    @skip_xp_backends(eager_only=True, reason='lazy -> limited nan_policy support')
+    def test_variation_nan(self, nan_policy, expected, xp):
+        x = xp.arange(10.)
+        x[9] = xp.nan
+        xp_assert_close(variation(x, nan_policy=nan_policy), xp.asarray(expected))
+
+    @skip_xp_backends(eager_only=True, reason='lazy -> limited nan_policy support')
+    def test_nan_policy_raise(self, xp):
+        x = xp.asarray([1.0, 2.0, xp.nan, 3.0])
+        with pytest.raises(ValueError, match='input contains nan'):
+            variation(x, nan_policy='raise')
+
+    @skip_xp_backends("dask.array", reason='needs `_axis_nan_policy`')
+    def test_bad_nan_policy(self, xp):
+        with pytest.raises(ValueError, match='must be one of'):
+            variation(xp.asarray([1, 2, 3]), nan_policy='foobar')
+
+    @skip_xp_backends("dask.array", reason='needs `_axis_nan_policy`')
+    def test_keepdims(self, xp):
+        x = xp.reshape(xp.arange(10), (2, 5))
+        y = variation(x, axis=1, keepdims=True)
+        expected = 2**0.5 / xp.asarray([[2.], [7.]])
+        xp_assert_close(y, expected)
+
+    @skip_xp_backends("dask.array", reason='needs `_axis_nan_policy`')
+    @pytest.mark.parametrize('axis, expected',
+                             [(0, np.empty((1, 0)).tolist()),
+                              (1, np.full((5, 1), fill_value=np.nan).tolist())])
+    def test_keepdims_size0(self, axis, expected, xp):
+        x = xp.zeros((5, 0))
+        if axis == 1:
+            with pytest.warns(SmallSampleWarning, match=too_small_nd_not_omit):
+                y = variation(x, axis=axis, keepdims=True)
+        else:
+            y = variation(x, axis=axis, keepdims=True)
+        xp_assert_equal(y, xp.asarray(expected))
+
+    @skip_xp_backends("dask.array", reason='needs `_axis_nan_policy`')
+    @pytest.mark.parametrize('incr, expected_fill', [(0, np.inf), (1, np.nan)])
+    def test_keepdims_and_ddof_eq_len_plus_incr(self, incr, expected_fill, xp):
+        x = xp.asarray([[1, 1, 2, 2], [1, 2, 3, 3]])
+        y = variation(x, axis=1, ddof=x.shape[1] + incr, keepdims=True)
+        xp_assert_equal(y, xp.full((2, 1), fill_value=expected_fill))
+
+    @skip_xp_backends("dask.array", reason='needs `_axis_nan_policy`')
+    def test_propagate_nan(self, xp):
+        # Check that the shape of the result is the same for inputs
+        # with and without nans, cf gh-5817
+        a = xp.reshape(xp.arange(8.), (2, -1))
+        a = xpx.at(a)[1, 0].set(xp.nan)
+        v = variation(a, axis=1, nan_policy="propagate")
+        xp_assert_close(v, xp.asarray([math.sqrt(5/4)/1.5, xp.nan]))
+
+    def test_axis_none(self, xp):
+        # Check that `variation` computes the result on the flattened
+        # input when axis is None.
+        y = variation(xp.asarray([[0, 1], [2, 3]]), axis=None)
+        xp_assert_close(y, xp.asarray(math.sqrt(5/4)/1.5))
+
+    def test_bad_axis(self, xp):
+        # Check that an invalid axis raises np.exceptions.AxisError.
+        x = xp.asarray([[1, 2, 3], [4, 5, 6]])
+        with pytest.raises((AxisError, IndexError)):
+            variation(x, axis=10)
+
+    @pytest.mark.filterwarnings("ignore:divide by zero encountered:RuntimeWarning:dask")
+    def test_mean_zero(self, xp):
+        # Check that `variation` returns inf for a sequence that is not
+        # identically zero but whose mean is zero.
+        x = xp.asarray([10., -3., 1., -4., -4.])
+        y = variation(x)
+        xp_assert_equal(y, xp.asarray(xp.inf))
+
+        x2 = xp.stack([x, -10.*x])
+        y2 = variation(x2, axis=1)
+        xp_assert_equal(y2, xp.asarray([xp.inf, xp.inf]))
+
+    @pytest.mark.filterwarnings("ignore:invalid value encountered:RuntimeWarning:dask")
+    @pytest.mark.parametrize('x', [[0.]*5, [1, 2, np.inf, 9]])
+    def test_return_nan(self, x, xp):
+        x = xp.asarray(x)
+        # Test some cases where `variation` returns nan.
+        y = variation(x)
+        xp_assert_equal(y, xp.asarray(xp.nan, dtype=x.dtype))
+
+    @pytest.mark.filterwarnings('ignore:Invalid value encountered:RuntimeWarning:dask')
+    @pytest.mark.parametrize('axis, expected',
+                             [(0, []), (1, [np.nan]*3), (None, np.nan)])
+    def test_2d_size_zero_with_axis(self, axis, expected, xp):
+        x = xp.empty((3, 0))
+        if axis != 0:
+            # specific message depends on `axis`, and `SmallSampleWarning`
+            # is specific enough.
+            with eager_warns(SmallSampleWarning, xp=xp):
+                y = variation(x, axis=axis)
+        else:
+            y = variation(x, axis=axis)
+        xp_assert_equal(y, xp.asarray(expected))
+
+    @pytest.mark.filterwarnings('ignore:divide by zero encountered:RuntimeWarning:dask')
+    def test_neg_inf(self, xp):
+        # Edge case that produces -inf: ddof equals the number of non-nan
+        # values, the values are not constant, and the mean is negative.
+        x1 = xp.asarray([-3., -5.])
+        xp_assert_equal(variation(x1, ddof=2), xp.asarray(-xp.inf))
+
+    @skip_xp_backends(np_only=True, reason='only NumPy supports ND nan_policy="omit"')
+    def test_neg_inf_nan(self, xp):
+        x2 = xp.asarray([[xp.nan, 1, -10, xp.nan],
+                         [-20, -3, xp.nan, xp.nan]])
+        xp_assert_equal(variation(x2, axis=1, ddof=2, nan_policy='omit'),
+                        xp.asarray([-xp.inf, -xp.inf]))
+
+    @pytest.mark.parametrize("nan_policy", ['propagate', 'omit'])
+    def test_combined_edge_cases(self, nan_policy, xp):
+        if not is_numpy(xp):
+            pytest.skip("Only NumPy supports ND nan_policy='omit'")
+        x = xp.asarray([[0, 10, xp.nan, 1],
+                        [0, -5, xp.nan, 2],
+                        [0, -5, xp.nan, 3]])
+        if nan_policy == 'omit':
+            with pytest.warns(SmallSampleWarning, match=too_small_nd_omit):
+                y = variation(x, axis=0, nan_policy=nan_policy)
+        else:
+            y = variation(x, axis=0, nan_policy=nan_policy)
+        xp_assert_close(y, xp.asarray([xp.nan, xp.inf, xp.nan, math.sqrt(2/3)/2]))
+
+    @skip_xp_backends(np_only=True, reason='only NumPy supports ND nan_policy="omit"')
+    @pytest.mark.parametrize(
+        'ddof, expected',
+        [(0, [np.sqrt(1/6), np.sqrt(5/8), np.inf, 0, np.nan, 0.0, np.nan]),
+         (1, [0.5, np.sqrt(5/6), np.inf, 0, np.nan, 0, np.nan]),
+         (2, [np.sqrt(0.5), np.sqrt(5/4), np.inf, np.nan, np.nan, 0, np.nan])]
+    )
+    def test_more_nan_policy_omit_tests(self, ddof, expected, xp):
+        # The slightly strange formatting in the follow array is my attempt to
+        # maintain a clean tabular arrangement of the data while satisfying
+        # the demands of pycodestyle.  Currently, E201 and E241 are not
+        # disabled by the `noqa` annotation.
+        nan = xp.nan
+        x = xp.asarray([[1.0, 2.0, nan, 3.0],
+                        [0.0, 4.0, 3.0, 1.0],
+                        [nan, -.5, 0.5, nan],
+                        [nan, 9.0, 9.0, nan],
+                        [nan, nan, nan, nan],
+                        [3.0, 3.0, 3.0, 3.0],
+                        [0.0, 0.0, 0.0, 0.0]])
+        with pytest.warns(SmallSampleWarning, match=too_small_nd_omit):
+            v = variation(x, axis=1, ddof=ddof, nan_policy='omit')
+        xp_assert_close(v, expected)
+
+    @skip_xp_backends(eager_only=True, reason='lazy -> limited nan_policy support')
+    def test_variation_ddof(self, xp):
+        # test variation with delta degrees of freedom
+        # regression test for gh-13341
+        a = xp.asarray([1., 2., 3., 4., 5.])
+        nan_a = xp.asarray([1, 2, 3, xp.nan, 4, 5, xp.nan])
+        y = variation(a, ddof=1)
+        nan_y = variation(nan_a, nan_policy="omit", ddof=1)
+        xp_assert_close(y, xp.asarray(math.sqrt(5/2)/3))
+        assert y == nan_y
diff --git a/.venv/lib/python3.12/site-packages/scipy/version.py b/.venv/lib/python3.12/site-packages/scipy/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..4dfc7971da5a34f847e2f075220b053f92123fe5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/scipy/version.py
@@ -0,0 +1,12 @@
+
+"""
+Module to expose more detailed version info for the installed `scipy`
+"""
+version = "1.18.1"
+full_version = version
+short_version = version.split('.dev')[0]
+git_revision = "e4e854eaa8f18d807cd3496028e257e36caa93cc"
+release = 'dev' not in version and '+' not in version
+
+if not release:
+    version = full_version
diff --git a/.venv/lib/python3.12/site-packages/setuptools/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..64464dfaa38a82e9647a095263d70f12961a1f92
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/__init__.py
@@ -0,0 +1,286 @@
+"""Extensions to the 'distutils' for large or complex distributions"""
+# mypy: disable_error_code=override
+# Command.reinitialize_command has an extra **kw param that distutils doesn't have
+# Can't disable on the exact line because distutils doesn't exists on Python 3.12
+# and mypy isn't aware of distutils_hack, causing distutils.core.Command to be Any,
+# and a [unused-ignore] to be raised on 3.12+
+
+from __future__ import annotations
+
+import functools
+import os
+import re
+import sys
+from abc import abstractmethod
+from collections.abc import Mapping
+from typing import TYPE_CHECKING, TypeVar, overload
+
+sys.path.extend(((vendor_path := os.path.join(os.path.dirname(os.path.dirname(__file__)), 'setuptools', '_vendor')) not in sys.path) * [vendor_path])  # fmt: skip
+# workaround for #4476
+sys.modules.pop('backports', None)
+
+import _distutils_hack.override  # noqa: F401
+
+from . import logging, monkey
+from .depends import Require
+from .discovery import PackageFinder, PEP420PackageFinder
+from .dist import Distribution
+from .extension import Extension
+from .version import __version__ as __version__
+from .warnings import SetuptoolsDeprecationWarning
+
+import distutils.core
+from distutils.errors import DistutilsOptionError
+
+__all__ = [
+    'setup',
+    'Distribution',
+    'Command',
+    'Extension',
+    'Require',
+    'SetuptoolsDeprecationWarning',
+    'find_packages',
+    'find_namespace_packages',
+]
+
+_CommandT = TypeVar("_CommandT", bound="_Command")
+
+bootstrap_install_from = None
+
+find_packages = PackageFinder.find
+find_namespace_packages = PEP420PackageFinder.find
+
+
+def _install_setup_requires(attrs):
+    # Note: do not use `setuptools.Distribution` directly, as
+    # our PEP 517 backend patch `distutils.core.Distribution`.
+    class MinimalDistribution(distutils.core.Distribution):
+        """
+        A minimal version of a distribution for supporting the
+        fetch_build_eggs interface.
+        """
+
+        def __init__(self, attrs: Mapping[str, object]) -> None:
+            _incl = 'dependency_links', 'setup_requires'
+            filtered = {k: attrs[k] for k in set(_incl) & set(attrs)}
+            super().__init__(filtered)
+            # Prevent accidentally triggering discovery with incomplete set of attrs
+            self.set_defaults._disable()
+
+        def _get_project_config_files(self, filenames=None):
+            """Ignore ``pyproject.toml``, they are not related to setup_requires"""
+            try:
+                cfg, _toml = super()._split_standard_project_metadata(filenames)
+            except Exception:
+                return filenames, ()
+            return cfg, ()
+
+        def finalize_options(self):
+            """
+            Disable finalize_options to avoid building the working set.
+            Ref #2158.
+            """
+
+    dist = MinimalDistribution(attrs)
+
+    # Honor setup.cfg's options.
+    dist.parse_config_files(ignore_option_errors=True)
+    if dist.setup_requires:
+        _fetch_build_eggs(dist)
+
+
+def _fetch_build_eggs(dist: Distribution):
+    try:
+        dist.fetch_build_eggs(dist.setup_requires)
+    except Exception as ex:
+        msg = """
+        It is possible a package already installed in your system
+        contains an version that is invalid according to PEP 440.
+        You can try `pip install --use-pep517` as a workaround for this problem,
+        or rely on a new virtual environment.
+
+        If the problem refers to a package that is not installed yet,
+        please contact that package's maintainers or distributors.
+        """
+        if "InvalidVersion" in ex.__class__.__name__:
+            if hasattr(ex, "add_note"):
+                ex.add_note(msg)  # PEP 678
+            else:
+                dist.announce(f"\n{msg}\n")
+        raise
+
+
+def setup(**attrs):
+    logging.configure()
+    # Make sure we have any requirements needed to interpret 'attrs'.
+    _install_setup_requires(attrs)
+    return distutils.core.setup(**attrs)
+
+
+setup.__doc__ = distutils.core.setup.__doc__
+
+if TYPE_CHECKING:
+    # Work around a mypy issue where type[T] can't be used as a base: https://github.com/python/mypy/issues/10962
+    from distutils.core import Command as _Command
+else:
+    _Command = monkey.get_unpatched(distutils.core.Command)
+
+
+class Command(_Command):
+    """
+    Setuptools internal actions are organized using a *command design pattern*.
+    This means that each action (or group of closely related actions) executed during
+    the build should be implemented as a ``Command`` subclass.
+
+    These commands are abstractions and do not necessarily correspond to a command that
+    can (or should) be executed via a terminal, in a CLI fashion (although historically
+    they would).
+
+    When creating a new command from scratch, custom defined classes **SHOULD** inherit
+    from ``setuptools.Command`` and implement a few mandatory methods.
+    Between these mandatory methods, are listed:
+    :meth:`initialize_options`, :meth:`finalize_options` and :meth:`run`.
+
+    A useful analogy for command classes is to think of them as subroutines with local
+    variables called "options".  The options are "declared" in :meth:`initialize_options`
+    and "defined" (given their final values, aka "finalized") in :meth:`finalize_options`,
+    both of which must be defined by every command class. The "body" of the subroutine,
+    (where it does all the work) is the :meth:`run` method.
+    Between :meth:`initialize_options` and :meth:`finalize_options`, ``setuptools`` may set
+    the values for options/attributes based on user's input (or circumstance),
+    which means that the implementation should be careful to not overwrite values in
+    :meth:`finalize_options` unless necessary.
+
+    Please note that other commands (or other parts of setuptools) may also overwrite
+    the values of the command's options/attributes multiple times during the build
+    process.
+    Therefore it is important to consistently implement :meth:`initialize_options` and
+    :meth:`finalize_options`. For example, all derived attributes (or attributes that
+    depend on the value of other attributes) **SHOULD** be recomputed in
+    :meth:`finalize_options`.
+
+    When overwriting existing commands, custom defined classes **MUST** abide by the
+    same APIs implemented by the original class. They also **SHOULD** inherit from the
+    original class.
+    """
+
+    command_consumes_arguments = False
+    distribution: Distribution  # override distutils.dist.Distribution with setuptools.dist.Distribution
+
+    def __init__(self, dist: Distribution, **kw) -> None:
+        """
+        Construct the command for dist, updating
+        vars(self) with any keyword parameters.
+        """
+        super().__init__(dist)
+        vars(self).update(kw)
+
+    def _ensure_stringlike(self, option, what, default=None):
+        val = getattr(self, option)
+        if val is None:
+            setattr(self, option, default)
+            return default
+        elif not isinstance(val, str):
+            raise DistutilsOptionError(f"'{option}' must be a {what} (got `{val}`)")
+        return val
+
+    def ensure_string_list(self, option: str) -> None:
+        r"""Ensure that 'option' is a list of strings.  If 'option' is
+        currently a string, we split it either on /,\s*/ or /\s+/, so
+        "foo bar baz", "foo,bar,baz", and "foo,   bar baz" all become
+        ["foo", "bar", "baz"].
+
+        ..
+           TODO: This method seems to be similar to the one in ``distutils.cmd``
+           Probably it is just here for backward compatibility with old Python versions?
+
+        :meta private:
+        """
+        val = getattr(self, option)
+        if val is None:
+            return
+        elif isinstance(val, str):
+            setattr(self, option, re.split(r',\s*|\s+', val))
+        else:
+            if isinstance(val, list):
+                ok = all(isinstance(v, str) for v in val)
+            else:
+                ok = False
+            if not ok:
+                raise DistutilsOptionError(
+                    f"'{option}' must be a list of strings (got {val!r})"
+                )
+
+    @overload
+    def reinitialize_command(
+        self, command: str, reinit_subcommands: bool = False, **kw
+    ) -> _Command: ...
+    @overload
+    def reinitialize_command(
+        self, command: _CommandT, reinit_subcommands: bool = False, **kw
+    ) -> _CommandT: ...
+    def reinitialize_command(
+        self, command: str | _Command, reinit_subcommands: bool = False, **kw
+    ) -> _Command:
+        cmd = _Command.reinitialize_command(self, command, reinit_subcommands)
+        vars(cmd).update(kw)
+        return cmd  # pyright: ignore[reportReturnType] # pypa/distutils#307
+
+    @abstractmethod
+    def initialize_options(self) -> None:
+        """
+        Set or (reset) all options/attributes/caches used by the command
+        to their default values. Note that these values may be overwritten during
+        the build.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def finalize_options(self) -> None:
+        """
+        Set final values for all options/attributes used by the command.
+        Most of the time, each option/attribute/cache should only be set if it does not
+        have any value yet (e.g. ``if self.attr is None: self.attr = val``).
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def run(self) -> None:
+        """
+        Execute the actions intended by the command.
+        (Side effects **SHOULD** only take place when :meth:`run` is executed,
+        for example, creating new files or writing to the terminal output).
+        """
+        raise NotImplementedError
+
+
+def _find_all_simple(path):
+    """
+    Find all files under 'path'
+    """
+    results = (
+        os.path.join(base, file)
+        for base, dirs, files in os.walk(path, followlinks=True)
+        for file in files
+    )
+    return filter(os.path.isfile, results)
+
+
+def findall(dir=os.curdir):
+    """
+    Find all files under 'dir' and return the list of full filenames.
+    Unless dir is '.', return full filenames with dir prepended.
+    """
+    files = _find_all_simple(dir)
+    if dir == os.curdir:
+        make_rel = functools.partial(os.path.relpath, start=dir)
+        files = map(make_rel, files)
+    return list(files)
+
+
+class sic(str):
+    """Treat this string as-is (https://en.wikipedia.org/wiki/Sic)"""
+
+
+# Apply monkey patches
+monkey.patch_all()
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--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_core_metadata.py
@@ -0,0 +1,337 @@
+"""
+Handling of Core Metadata for Python packages (including reading and writing).
+
+See: https://packaging.python.org/en/latest/specifications/core-metadata/
+"""
+
+from __future__ import annotations
+
+import os
+import stat
+import textwrap
+from email import message_from_file
+from email.message import Message
+from tempfile import NamedTemporaryFile
+
+from packaging.markers import Marker
+from packaging.requirements import Requirement
+from packaging.utils import canonicalize_name, canonicalize_version
+from packaging.version import Version
+
+from . import _normalization, _reqs
+from ._static import is_static
+from .warnings import SetuptoolsDeprecationWarning
+
+from distutils.util import rfc822_escape
+
+
+def get_metadata_version(self):
+    mv = getattr(self, 'metadata_version', None)
+    if mv is None:
+        mv = Version('2.4')
+        self.metadata_version = mv
+    return mv
+
+
+def rfc822_unescape(content: str) -> str:
+    """Reverse RFC-822 escaping by removing leading whitespaces from content."""
+    lines = content.splitlines()
+    if len(lines) == 1:
+        return lines[0].lstrip()
+    return '\n'.join((lines[0].lstrip(), textwrap.dedent('\n'.join(lines[1:]))))
+
+
+def _read_field_from_msg(msg: Message, field: str) -> str | None:
+    """Read Message header field."""
+    value = msg[field]
+    if value == 'UNKNOWN':
+        return None
+    return value
+
+
+def _read_field_unescaped_from_msg(msg: Message, field: str) -> str | None:
+    """Read Message header field and apply rfc822_unescape."""
+    value = _read_field_from_msg(msg, field)
+    if value is None:
+        return value
+    return rfc822_unescape(value)
+
+
+def _read_list_from_msg(msg: Message, field: str) -> list[str] | None:
+    """Read Message header field and return all results as list."""
+    values = msg.get_all(field, None)
+    if values == []:
+        return None
+    return values
+
+
+def _read_payload_from_msg(msg: Message) -> str | None:
+    value = str(msg.get_payload()).strip()
+    if value == 'UNKNOWN' or not value:
+        return None
+    return value
+
+
+def read_pkg_file(self, file):
+    """Reads the metadata values from a file object."""
+    msg = message_from_file(file)
+
+    self.metadata_version = Version(msg['metadata-version'])
+    self.name = _read_field_from_msg(msg, 'name')
+    self.version = _read_field_from_msg(msg, 'version')
+    self.description = _read_field_from_msg(msg, 'summary')
+    # we are filling author only.
+    self.author = _read_field_from_msg(msg, 'author')
+    self.maintainer = None
+    self.author_email = _read_field_from_msg(msg, 'author-email')
+    self.maintainer_email = None
+    self.url = _read_field_from_msg(msg, 'home-page')
+    self.download_url = _read_field_from_msg(msg, 'download-url')
+    self.license = _read_field_unescaped_from_msg(msg, 'license')
+    self.license_expression = _read_field_unescaped_from_msg(msg, 'license-expression')
+
+    self.long_description = _read_field_unescaped_from_msg(msg, 'description')
+    if self.long_description is None and self.metadata_version >= Version('2.1'):
+        self.long_description = _read_payload_from_msg(msg)
+    self.description = _read_field_from_msg(msg, 'summary')
+
+    if 'keywords' in msg:
+        self.keywords = _read_field_from_msg(msg, 'keywords').split(',')
+
+    self.platforms = _read_list_from_msg(msg, 'platform')
+    self.classifiers = _read_list_from_msg(msg, 'classifier')
+
+    # PEP 314 - these fields only exist in 1.1
+    if self.metadata_version == Version('1.1'):
+        self.requires = _read_list_from_msg(msg, 'requires')
+        self.provides = _read_list_from_msg(msg, 'provides')
+        self.obsoletes = _read_list_from_msg(msg, 'obsoletes')
+    else:
+        self.requires = None
+        self.provides = None
+        self.obsoletes = None
+
+    self.license_files = _read_list_from_msg(msg, 'license-file')
+
+
+def single_line(val):
+    """
+    Quick and dirty validation for Summary pypa/setuptools#1390.
+    """
+    if '\n' in val:
+        # TODO: Replace with `raise ValueError("newlines not allowed")`
+        # after reviewing #2893.
+        msg = "newlines are not allowed in `summary` and will break in the future"
+        SetuptoolsDeprecationWarning.emit("Invalid config.", msg)
+        # due_date is undefined. Controversial change, there was a lot of push back.
+        val = val.strip().split('\n')[0]
+    return val
+
+
+def write_pkg_info(self, base_dir):
+    """Write the PKG-INFO file into the release tree."""
+    temp = ""
+    final = os.path.join(base_dir, 'PKG-INFO')
+    try:
+        # Use a temporary file while writing to avoid race conditions
+        # (e.g. `importlib.metadata` reading `.egg-info/PKG-INFO`):
+        with NamedTemporaryFile("w", encoding="utf-8", dir=base_dir, delete=False) as f:
+            temp = f.name
+            self.write_pkg_file(f)
+        permissions = stat.S_IMODE(os.lstat(temp).st_mode)
+        os.chmod(temp, permissions | stat.S_IRGRP | stat.S_IROTH)
+        os.replace(temp, final)  # atomic operation.
+    finally:
+        if temp and os.path.exists(temp):
+            os.remove(temp)
+
+
+# Based on Python 3.5 version
+def write_pkg_file(self, file):  # noqa: C901  # is too complex (14)  # FIXME
+    """Write the PKG-INFO format data to a file object."""
+    version = self.get_metadata_version()
+
+    def write_field(key, value):
+        file.write(f"{key}: {value}\n")
+
+    write_field('Metadata-Version', str(version))
+    write_field('Name', self.get_name())
+    write_field('Version', self.get_version())
+
+    summary = self.get_description()
+    if summary:
+        write_field('Summary', single_line(summary))
+
+    optional_fields = (
+        ('Home-page', 'url'),
+        ('Download-URL', 'download_url'),
+        ('Author', 'author'),
+        ('Author-email', 'author_email'),
+        ('Maintainer', 'maintainer'),
+        ('Maintainer-email', 'maintainer_email'),
+    )
+
+    for field, attr in optional_fields:
+        attr_val = getattr(self, attr, None)
+        if attr_val is not None:
+            write_field(field, attr_val)
+
+    if license_expression := self.license_expression:
+        write_field('License-Expression', license_expression)
+    elif license := self.get_license():
+        write_field('License', rfc822_escape(license))
+
+    for label, url in self.project_urls.items():
+        write_field('Project-URL', f'{label}, {url}')
+
+    keywords = ','.join(self.get_keywords())
+    if keywords:
+        write_field('Keywords', keywords)
+
+    platforms = self.get_platforms() or []
+    for platform in platforms:
+        write_field('Platform', platform)
+
+    self._write_list(file, 'Classifier', self.get_classifiers())
+
+    # PEP 314
+    self._write_list(file, 'Requires', self.get_requires())
+    self._write_list(file, 'Provides', self.get_provides())
+    self._write_list(file, 'Obsoletes', self.get_obsoletes())
+
+    # Setuptools specific for PEP 345
+    if hasattr(self, 'python_requires'):
+        write_field('Requires-Python', self.python_requires)
+
+    # PEP 566
+    if self.long_description_content_type:
+        write_field('Description-Content-Type', self.long_description_content_type)
+
+    safe_license_files = map(_safe_license_file, self.license_files or [])
+    self._write_list(file, 'License-File', safe_license_files)
+    _write_requirements(self, file)
+
+    for field, attr in _POSSIBLE_DYNAMIC_FIELDS.items():
+        if (val := getattr(self, attr, None)) and not is_static(val):
+            write_field('Dynamic', field)
+
+    long_description = self.get_long_description()
+    if long_description:
+        file.write(f"\n{long_description}")
+        if not long_description.endswith("\n"):
+            file.write("\n")
+
+
+def _write_requirements(self, file):
+    for req in _reqs.parse(self.install_requires):
+        file.write(f"Requires-Dist: {req}\n")
+
+    processed_extras = {}
+    for augmented_extra, reqs in self.extras_require.items():
+        # Historically, setuptools allows "augmented extras": `<extra>:<condition>`
+        unsafe_extra, _, condition = augmented_extra.partition(":")
+        unsafe_extra = unsafe_extra.strip()
+        extra = _normalization.safe_extra(unsafe_extra)
+
+        if extra:
+            _write_provides_extra(file, processed_extras, extra, unsafe_extra)
+        for req in _reqs.parse_strings(reqs):
+            r = _include_extra(req, extra, condition.strip())
+            file.write(f"Requires-Dist: {r}\n")
+
+    return processed_extras
+
+
+def _include_extra(req: str, extra: str, condition: str) -> Requirement:
+    r = Requirement(req)  # create a fresh object that can be modified
+    parts = (
+        f"({r.marker})" if r.marker else None,
+        f"({condition})" if condition else None,
+        f"extra == {extra!r}" if extra else None,
+    )
+    r.marker = Marker(" and ".join(x for x in parts if x))
+    return r
+
+
+def _write_provides_extra(file, processed_extras, safe, unsafe):
+    previous = processed_extras.get(safe)
+    if previous == unsafe:
+        SetuptoolsDeprecationWarning.emit(
+            'Ambiguity during "extra" normalization for dependencies.',
+            f"""
+            {previous!r} and {unsafe!r} normalize to the same value:\n
+                {safe!r}\n
+            In future versions, setuptools might halt the build process.
+            """,
+            see_url="https://peps.python.org/pep-0685/",
+        )
+    else:
+        processed_extras[safe] = unsafe
+        file.write(f"Provides-Extra: {safe}\n")
+
+
+# from pypa/distutils#244; needed only until that logic is always available
+def get_fullname(self):
+    return _distribution_fullname(self.get_name(), self.get_version())
+
+
+def _distribution_fullname(name: str, version: str) -> str:
+    """
+    >>> _distribution_fullname('setup.tools', '1.0-2')
+    'setup_tools-1.0.post2'
+    >>> _distribution_fullname('setup-tools', '1.2post2')
+    'setup_tools-1.2.post2'
+    >>> _distribution_fullname('setup-tools', '1.0-r2')
+    'setup_tools-1.0.post2'
+    >>> _distribution_fullname('setup.tools', '1.0.post')
+    'setup_tools-1.0.post0'
+    >>> _distribution_fullname('setup.tools', '1.0+ubuntu-1')
+    'setup_tools-1.0+ubuntu.1'
+    """
+    return "{}-{}".format(
+        canonicalize_name(name).replace('-', '_'),
+        canonicalize_version(version, strip_trailing_zero=False),
+    )
+
+
+def _safe_license_file(file):
+    # XXX: Do we need this after the deprecation discussed in #4892, #4896??
+    normalized = os.path.normpath(file).replace(os.sep, "/")
+    if "../" in normalized:
+        return os.path.basename(normalized)  # Temporarily restore pre PEP639 behaviour
+    return normalized
+
+
+_POSSIBLE_DYNAMIC_FIELDS = {
+    # Core Metadata Field x related Distribution attribute
+    "author": "author",
+    "author-email": "author_email",
+    "classifier": "classifiers",
+    "description": "long_description",
+    "description-content-type": "long_description_content_type",
+    "download-url": "download_url",
+    "home-page": "url",
+    "keywords": "keywords",
+    "license": "license",
+    # XXX: License-File is complicated because the user gives globs that are expanded
+    #      during the build. Without special handling it is likely always
+    #      marked as Dynamic, which is an acceptable outcome according to:
+    #      https://github.com/pypa/setuptools/issues/4629#issuecomment-2331233677
+    "license-file": "license_files",
+    "license-expression": "license_expression",  # PEP 639
+    "maintainer": "maintainer",
+    "maintainer-email": "maintainer_email",
+    "obsoletes": "obsoletes",
+    # "obsoletes-dist": "obsoletes_dist",  # NOT USED
+    "platform": "platforms",
+    "project-url": "project_urls",
+    "provides": "provides",
+    # "provides-dist": "provides_dist",  # NOT USED
+    "provides-extra": "extras_require",
+    "requires": "requires",
+    "requires-dist": "install_requires",
+    # "requires-external": "requires_external",  # NOT USED
+    "requires-python": "python_requires",
+    "summary": "description",
+    # "supported-platform": "supported_platforms",  # NOT USED
+}
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e374d5c5604d0eeed555e68b7523bfac72fd9101
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/__init__.py
@@ -0,0 +1,14 @@
+import importlib
+import sys
+
+__version__, _, _ = sys.version.partition(' ')
+
+
+try:
+    # Allow Debian and pkgsrc (only) to customize system
+    # behavior. Ref pypa/distutils#2 and pypa/distutils#16.
+    # This hook is deprecated and no other environments
+    # should use it.
+    importlib.import_module('_distutils_system_mod')
+except ImportError:
+    pass
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/_log.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/_log.py
new file mode 100644
index 0000000000000000000000000000000000000000..0148f157ff3bf8bd728c82f49cd3d1cd9cb5a43e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/_log.py
@@ -0,0 +1,3 @@
+import logging
+
+log = logging.getLogger()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/_macos_compat.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/_macos_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..76ecb96abe478313ce7d09342b2643bf4a765331
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/_macos_compat.py
@@ -0,0 +1,12 @@
+import importlib
+import sys
+
+
+def bypass_compiler_fixup(cmd, args):
+    return cmd
+
+
+if sys.platform == 'darwin':
+    compiler_fixup = importlib.import_module('_osx_support').compiler_fixup
+else:
+    compiler_fixup = bypass_compiler_fixup
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/_modified.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/_modified.py
new file mode 100644
index 0000000000000000000000000000000000000000..f64cab7d61ae20dd0c425b8843b068f39faa6961
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/_modified.py
@@ -0,0 +1,95 @@
+"""Timestamp comparison of files and groups of files."""
+
+from __future__ import annotations
+
+import functools
+import os.path
+from collections.abc import Callable, Iterable
+from typing import Literal, TypeVar
+
+from jaraco.functools import splat
+
+from .compat.py39 import zip_strict
+from .errors import DistutilsFileError
+
+_SourcesT = TypeVar(
+    "_SourcesT", bound="str | bytes | os.PathLike[str] | os.PathLike[bytes]"
+)
+_TargetsT = TypeVar(
+    "_TargetsT", bound="str | bytes | os.PathLike[str] | os.PathLike[bytes]"
+)
+
+
+def _newer(source, target):
+    return not os.path.exists(target) or (
+        os.path.getmtime(source) > os.path.getmtime(target)
+    )
+
+
+def newer(
+    source: str | bytes | os.PathLike[str] | os.PathLike[bytes],
+    target: str | bytes | os.PathLike[str] | os.PathLike[bytes],
+) -> bool:
+    """
+    Is source modified more recently than target.
+
+    Returns True if 'source' is modified more recently than
+    'target' or if 'target' does not exist.
+
+    Raises DistutilsFileError if 'source' does not exist.
+    """
+    if not os.path.exists(source):
+        raise DistutilsFileError(f"file {os.path.abspath(source)!r} does not exist")
+
+    return _newer(source, target)
+
+
+def newer_pairwise(
+    sources: Iterable[_SourcesT],
+    targets: Iterable[_TargetsT],
+    newer: Callable[[_SourcesT, _TargetsT], bool] = newer,
+) -> tuple[list[_SourcesT], list[_TargetsT]]:
+    """
+    Filter filenames where sources are newer than targets.
+
+    Walk two filename iterables in parallel, testing if each source is newer
+    than its corresponding target.  Returns a pair of lists (sources,
+    targets) where source is newer than target, according to the semantics
+    of 'newer()'.
+    """
+    newer_pairs = filter(splat(newer), zip_strict(sources, targets))
+    return tuple(map(list, zip(*newer_pairs))) or ([], [])
+
+
+def newer_group(
+    sources: Iterable[str | bytes | os.PathLike[str] | os.PathLike[bytes]],
+    target: str | bytes | os.PathLike[str] | os.PathLike[bytes],
+    missing: Literal["error", "ignore", "newer"] = "error",
+) -> bool:
+    """
+    Is target out-of-date with respect to any file in sources.
+
+    Return True if 'target' is out-of-date with respect to any file
+    listed in 'sources'. In other words, if 'target' exists and is newer
+    than every file in 'sources', return False; otherwise return True.
+    ``missing`` controls how to handle a missing source file:
+
+    - error (default): allow the ``stat()`` call to fail.
+    - ignore: silently disregard any missing source files.
+    - newer: treat missing source files as "target out of date". This
+      mode is handy in "dry-run" mode: it will pretend to carry out
+      commands that wouldn't work because inputs are missing, but
+      that doesn't matter because dry-run won't run the commands.
+    """
+
+    def missing_as_newer(source):
+        return missing == 'newer' and not os.path.exists(source)
+
+    ignored = os.path.exists if missing == 'ignore' else None
+    return not os.path.exists(target) or any(
+        missing_as_newer(source) or _newer(source, target)
+        for source in filter(ignored, sources)
+    )
+
+
+newer_pairwise_group = functools.partial(newer_pairwise, newer=newer_group)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/_msvccompiler.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/_msvccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..d07c86ef8e41fc31357c989133d51e9618c3e43d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/_msvccompiler.py
@@ -0,0 +1,16 @@
+import warnings
+
+from .compilers.C import msvc
+
+__all__ = ["MSVCCompiler"]
+
+MSVCCompiler = msvc.Compiler
+
+
+def __getattr__(name):
+    if name == '_get_vc_env':
+        warnings.warn(
+            "_get_vc_env is private; find an alternative (pypa/distutils#340)"
+        )
+        return msvc._get_vc_env
+    raise AttributeError(name)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/archive_util.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/archive_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..d860f5527234567a0b726ea07116ae2c8e6bc3d2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/archive_util.py
@@ -0,0 +1,294 @@
+"""distutils.archive_util
+
+Utility functions for creating archive files (tarballs, zip files,
+that sort of thing)."""
+
+from __future__ import annotations
+
+import os
+from typing import Literal, overload
+
+try:
+    import zipfile
+except ImportError:
+    zipfile = None
+
+
+from ._log import log
+from .dir_util import mkpath
+from .errors import DistutilsExecError
+from .spawn import spawn
+
+try:
+    from pwd import getpwnam
+except ImportError:
+    getpwnam = None
+
+try:
+    from grp import getgrnam
+except ImportError:
+    getgrnam = None
+
+
+def _get_gid(name):
+    """Returns a gid, given a group name."""
+    if getgrnam is None or name is None:
+        return None
+    try:
+        result = getgrnam(name)
+    except KeyError:
+        result = None
+    if result is not None:
+        return result[2]
+    return None
+
+
+def _get_uid(name):
+    """Returns an uid, given a user name."""
+    if getpwnam is None or name is None:
+        return None
+    try:
+        result = getpwnam(name)
+    except KeyError:
+        result = None
+    if result is not None:
+        return result[2]
+    return None
+
+
+def make_tarball(
+    base_name: str,
+    base_dir: str | os.PathLike[str],
+    compress: Literal["gzip", "bzip2", "xz"] | None = "gzip",
+    verbose: bool = False,
+    dry_run: bool = False,
+    owner: str | None = None,
+    group: str | None = None,
+) -> str:
+    """Create a (possibly compressed) tar file from all the files under
+    'base_dir'.
+
+    'compress' must be "gzip" (the default), "bzip2", "xz", or None.
+
+    'owner' and 'group' can be used to define an owner and a group for the
+    archive that is being built. If not provided, the current owner and group
+    will be used.
+
+    The output tar file will be named 'base_dir' +  ".tar", possibly plus
+    the appropriate compression extension (".gz", ".bz2", ".xz" or ".Z").
+
+    Returns the output filename.
+    """
+    tar_compression = {
+        'gzip': 'gz',
+        'bzip2': 'bz2',
+        'xz': 'xz',
+        None: '',
+    }
+    compress_ext = {'gzip': '.gz', 'bzip2': '.bz2', 'xz': '.xz'}
+
+    # flags for compression program, each element of list will be an argument
+    if compress is not None and compress not in compress_ext.keys():
+        raise ValueError(
+            "bad value for 'compress': must be None, 'gzip', 'bzip2', 'xz'"
+        )
+
+    archive_name = base_name + '.tar'
+    archive_name += compress_ext.get(compress, '')
+
+    mkpath(os.path.dirname(archive_name), dry_run=dry_run)
+
+    # creating the tarball
+    import tarfile  # late import so Python build itself doesn't break
+
+    log.info('Creating tar archive')
+
+    uid = _get_uid(owner)
+    gid = _get_gid(group)
+
+    def _set_uid_gid(tarinfo):
+        if gid is not None:
+            tarinfo.gid = gid
+            tarinfo.gname = group
+        if uid is not None:
+            tarinfo.uid = uid
+            tarinfo.uname = owner
+        return tarinfo
+
+    if not dry_run:
+        tar = tarfile.open(archive_name, f'w|{tar_compression[compress]}')
+        try:
+            tar.add(base_dir, filter=_set_uid_gid)
+        finally:
+            tar.close()
+
+    return archive_name
+
+
+def make_zipfile(  # noqa: C901
+    base_name: str,
+    base_dir: str | os.PathLike[str],
+    verbose: bool = False,
+    dry_run: bool = False,
+) -> str:
+    """Create a zip file from all the files under 'base_dir'.
+
+    The output zip file will be named 'base_name' + ".zip".  Uses either the
+    "zipfile" Python module (if available) or the InfoZIP "zip" utility
+    (if installed and found on the default search path).  If neither tool is
+    available, raises DistutilsExecError.  Returns the name of the output zip
+    file.
+    """
+    zip_filename = base_name + ".zip"
+    mkpath(os.path.dirname(zip_filename), dry_run=dry_run)
+
+    # If zipfile module is not available, try spawning an external
+    # 'zip' command.
+    if zipfile is None:
+        if verbose:
+            zipoptions = "-r"
+        else:
+            zipoptions = "-rq"
+
+        try:
+            spawn(["zip", zipoptions, zip_filename, base_dir], dry_run=dry_run)
+        except DistutilsExecError:
+            # XXX really should distinguish between "couldn't find
+            # external 'zip' command" and "zip failed".
+            raise DistutilsExecError(
+                f"unable to create zip file '{zip_filename}': "
+                "could neither import the 'zipfile' module nor "
+                "find a standalone zip utility"
+            )
+
+    else:
+        log.info("creating '%s' and adding '%s' to it", zip_filename, base_dir)
+
+        if not dry_run:
+            try:
+                zip = zipfile.ZipFile(
+                    zip_filename, "w", compression=zipfile.ZIP_DEFLATED
+                )
+            except RuntimeError:
+                zip = zipfile.ZipFile(zip_filename, "w", compression=zipfile.ZIP_STORED)
+
+            with zip:
+                if base_dir != os.curdir:
+                    path = os.path.normpath(os.path.join(base_dir, ''))
+                    zip.write(path, path)
+                    log.info("adding '%s'", path)
+                for dirpath, dirnames, filenames in os.walk(base_dir):
+                    for name in dirnames:
+                        path = os.path.normpath(os.path.join(dirpath, name, ''))
+                        zip.write(path, path)
+                        log.info("adding '%s'", path)
+                    for name in filenames:
+                        path = os.path.normpath(os.path.join(dirpath, name))
+                        if os.path.isfile(path):
+                            zip.write(path, path)
+                            log.info("adding '%s'", path)
+
+    return zip_filename
+
+
+ARCHIVE_FORMATS = {
+    'gztar': (make_tarball, [('compress', 'gzip')], "gzip'ed tar-file"),
+    'bztar': (make_tarball, [('compress', 'bzip2')], "bzip2'ed tar-file"),
+    'xztar': (make_tarball, [('compress', 'xz')], "xz'ed tar-file"),
+    'ztar': (make_tarball, [('compress', 'compress')], "compressed tar file"),
+    'tar': (make_tarball, [('compress', None)], "uncompressed tar file"),
+    'zip': (make_zipfile, [], "ZIP file"),
+}
+
+
+def check_archive_formats(formats):
+    """Returns the first format from the 'format' list that is unknown.
+
+    If all formats are known, returns None
+    """
+    for format in formats:
+        if format not in ARCHIVE_FORMATS:
+            return format
+    return None
+
+
+@overload
+def make_archive(
+    base_name: str,
+    format: str,
+    root_dir: str | os.PathLike[str] | bytes | os.PathLike[bytes] | None = None,
+    base_dir: str | None = None,
+    verbose: bool = False,
+    dry_run: bool = False,
+    owner: str | None = None,
+    group: str | None = None,
+) -> str: ...
+@overload
+def make_archive(
+    base_name: str | os.PathLike[str],
+    format: str,
+    root_dir: str | os.PathLike[str] | bytes | os.PathLike[bytes],
+    base_dir: str | None = None,
+    verbose: bool = False,
+    dry_run: bool = False,
+    owner: str | None = None,
+    group: str | None = None,
+) -> str: ...
+def make_archive(
+    base_name: str | os.PathLike[str],
+    format: str,
+    root_dir: str | os.PathLike[str] | bytes | os.PathLike[bytes] | None = None,
+    base_dir: str | None = None,
+    verbose: bool = False,
+    dry_run: bool = False,
+    owner: str | None = None,
+    group: str | None = None,
+) -> str:
+    """Create an archive file (eg. zip or tar).
+
+    'base_name' is the name of the file to create, minus any format-specific
+    extension; 'format' is the archive format: one of "zip", "tar", "gztar",
+    "bztar", "xztar", or "ztar".
+
+    'root_dir' is a directory that will be the root directory of the
+    archive; ie. we typically chdir into 'root_dir' before creating the
+    archive.  'base_dir' is the directory where we start archiving from;
+    ie. 'base_dir' will be the common prefix of all files and
+    directories in the archive.  'root_dir' and 'base_dir' both default
+    to the current directory.  Returns the name of the archive file.
+
+    'owner' and 'group' are used when creating a tar archive. By default,
+    uses the current owner and group.
+    """
+    save_cwd = os.getcwd()
+    if root_dir is not None:
+        log.debug("changing into '%s'", root_dir)
+        base_name = os.path.abspath(base_name)
+        if not dry_run:
+            os.chdir(root_dir)
+
+    if base_dir is None:
+        base_dir = os.curdir
+
+    kwargs = {'dry_run': dry_run}
+
+    try:
+        format_info = ARCHIVE_FORMATS[format]
+    except KeyError:
+        raise ValueError(f"unknown archive format '{format}'")
+
+    func = format_info[0]
+    kwargs.update(format_info[1])
+
+    if format != 'zip':
+        kwargs['owner'] = owner
+        kwargs['group'] = group
+
+    try:
+        filename = func(base_name, base_dir, **kwargs)
+    finally:
+        if root_dir is not None:
+            log.debug("changing back to '%s'", save_cwd)
+            os.chdir(save_cwd)
+
+    return filename
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/ccompiler.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/ccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..58bc6a55e24fd2b0b170e4499c6095e934313827
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/ccompiler.py
@@ -0,0 +1,26 @@
+from .compat.numpy import (  # noqa: F401
+    _default_compilers,
+    compiler_class,
+)
+from .compilers.C import base
+from .compilers.C.base import (
+    gen_lib_options,
+    gen_preprocess_options,
+    get_default_compiler,
+    new_compiler,
+    show_compilers,
+)
+from .compilers.C.errors import CompileError, LinkError
+
+__all__ = [
+    'CompileError',
+    'LinkError',
+    'gen_lib_options',
+    'gen_preprocess_options',
+    'get_default_compiler',
+    'new_compiler',
+    'show_compilers',
+]
+
+
+CCompiler = base.Compiler
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/cmd.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/cmd.py
new file mode 100644
index 0000000000000000000000000000000000000000..241621bd51340819c5216883a0e73929c9fd20b0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/cmd.py
@@ -0,0 +1,554 @@
+"""distutils.cmd
+
+Provides the Command class, the base class for the command classes
+in the distutils.command package.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import re
+import sys
+from abc import abstractmethod
+from collections.abc import Callable, MutableSequence
+from typing import TYPE_CHECKING, Any, ClassVar, TypeVar, overload
+
+from . import _modified, archive_util, dir_util, file_util, util
+from ._log import log
+from .errors import DistutilsOptionError
+
+if TYPE_CHECKING:
+    # type-only import because of mutual dependence between these classes
+    from distutils.dist import Distribution
+
+    from typing_extensions import TypeVarTuple, Unpack
+
+    _Ts = TypeVarTuple("_Ts")
+
+_StrPathT = TypeVar("_StrPathT", bound="str | os.PathLike[str]")
+_BytesPathT = TypeVar("_BytesPathT", bound="bytes | os.PathLike[bytes]")
+_CommandT = TypeVar("_CommandT", bound="Command")
+
+
+class Command:
+    """Abstract base class for defining command classes, the "worker bees"
+    of the Distutils.  A useful analogy for command classes is to think of
+    them as subroutines with local variables called "options".  The options
+    are "declared" in 'initialize_options()' and "defined" (given their
+    final values, aka "finalized") in 'finalize_options()', both of which
+    must be defined by every command class.  The distinction between the
+    two is necessary because option values might come from the outside
+    world (command line, config file, ...), and any options dependent on
+    other options must be computed *after* these outside influences have
+    been processed -- hence 'finalize_options()'.  The "body" of the
+    subroutine, where it does all its work based on the values of its
+    options, is the 'run()' method, which must also be implemented by every
+    command class.
+    """
+
+    # 'sub_commands' formalizes the notion of a "family" of commands,
+    # eg. "install" as the parent with sub-commands "install_lib",
+    # "install_headers", etc.  The parent of a family of commands
+    # defines 'sub_commands' as a class attribute; it's a list of
+    #    (command_name : string, predicate : unbound_method | string | None)
+    # tuples, where 'predicate' is a method of the parent command that
+    # determines whether the corresponding command is applicable in the
+    # current situation.  (Eg. we "install_headers" is only applicable if
+    # we have any C header files to install.)  If 'predicate' is None,
+    # that command is always applicable.
+    #
+    # 'sub_commands' is usually defined at the *end* of a class, because
+    # predicates can be unbound methods, so they must already have been
+    # defined.  The canonical example is the "install" command.
+    sub_commands: ClassVar[  # Any to work around variance issues
+        list[tuple[str, Callable[[Any], bool] | None]]
+    ] = []
+
+    user_options: ClassVar[
+        # Specifying both because list is invariant. Avoids mypy override assignment issues
+        list[tuple[str, str, str]] | list[tuple[str, str | None, str]]
+    ] = []
+
+    # -- Creation/initialization methods -------------------------------
+
+    def __init__(self, dist: Distribution) -> None:
+        """Create and initialize a new Command object.  Most importantly,
+        invokes the 'initialize_options()' method, which is the real
+        initializer and depends on the actual command being
+        instantiated.
+        """
+        # late import because of mutual dependence between these classes
+        from distutils.dist import Distribution
+
+        if not isinstance(dist, Distribution):
+            raise TypeError("dist must be a Distribution instance")
+        if self.__class__ is Command:
+            raise RuntimeError("Command is an abstract class")
+
+        self.distribution = dist
+        self.initialize_options()
+
+        # Per-command versions of the global flags, so that the user can
+        # customize Distutils' behaviour command-by-command and let some
+        # commands fall back on the Distribution's behaviour.  None means
+        # "not defined, check self.distribution's copy", while 0 or 1 mean
+        # false and true (duh).  Note that this means figuring out the real
+        # value of each flag is a touch complicated -- hence "self._dry_run"
+        # will be handled by __getattr__, below.
+        # XXX This needs to be fixed.
+        self._dry_run = None
+
+        # verbose is largely ignored, but needs to be set for
+        # backwards compatibility (I think)?
+        self.verbose = dist.verbose
+
+        # Some commands define a 'self.force' option to ignore file
+        # timestamps, but methods defined *here* assume that
+        # 'self.force' exists for all commands.  So define it here
+        # just to be safe.
+        self.force = None
+
+        # The 'help' flag is just used for command-line parsing, so
+        # none of that complicated bureaucracy is needed.
+        self.help = False
+
+        # 'finalized' records whether or not 'finalize_options()' has been
+        # called.  'finalize_options()' itself should not pay attention to
+        # this flag: it is the business of 'ensure_finalized()', which
+        # always calls 'finalize_options()', to respect/update it.
+        self.finalized = False
+
+    # XXX A more explicit way to customize dry_run would be better.
+    def __getattr__(self, attr):
+        if attr == 'dry_run':
+            myval = getattr(self, "_" + attr)
+            if myval is None:
+                return getattr(self.distribution, attr)
+            else:
+                return myval
+        else:
+            raise AttributeError(attr)
+
+    def ensure_finalized(self) -> None:
+        if not self.finalized:
+            self.finalize_options()
+        self.finalized = True
+
+    # Subclasses must define:
+    #   initialize_options()
+    #     provide default values for all options; may be customized by
+    #     setup script, by options from config file(s), or by command-line
+    #     options
+    #   finalize_options()
+    #     decide on the final values for all options; this is called
+    #     after all possible intervention from the outside world
+    #     (command-line, option file, etc.) has been processed
+    #   run()
+    #     run the command: do whatever it is we're here to do,
+    #     controlled by the command's various option values
+
+    @abstractmethod
+    def initialize_options(self) -> None:
+        """Set default values for all the options that this command
+        supports.  Note that these defaults may be overridden by other
+        commands, by the setup script, by config files, or by the
+        command-line.  Thus, this is not the place to code dependencies
+        between options; generally, 'initialize_options()' implementations
+        are just a bunch of "self.foo = None" assignments.
+
+        This method must be implemented by all command classes.
+        """
+        raise RuntimeError(
+            f"abstract method -- subclass {self.__class__} must override"
+        )
+
+    @abstractmethod
+    def finalize_options(self) -> None:
+        """Set final values for all the options that this command supports.
+        This is always called as late as possible, ie.  after any option
+        assignments from the command-line or from other commands have been
+        done.  Thus, this is the place to code option dependencies: if
+        'foo' depends on 'bar', then it is safe to set 'foo' from 'bar' as
+        long as 'foo' still has the same value it was assigned in
+        'initialize_options()'.
+
+        This method must be implemented by all command classes.
+        """
+        raise RuntimeError(
+            f"abstract method -- subclass {self.__class__} must override"
+        )
+
+    def dump_options(self, header=None, indent=""):
+        from distutils.fancy_getopt import longopt_xlate
+
+        if header is None:
+            header = f"command options for '{self.get_command_name()}':"
+        self.announce(indent + header, level=logging.INFO)
+        indent = indent + "  "
+        for option, _, _ in self.user_options:
+            option = option.translate(longopt_xlate)
+            if option[-1] == "=":
+                option = option[:-1]
+            value = getattr(self, option)
+            self.announce(indent + f"{option} = {value}", level=logging.INFO)
+
+    @abstractmethod
+    def run(self) -> None:
+        """A command's raison d'etre: carry out the action it exists to
+        perform, controlled by the options initialized in
+        'initialize_options()', customized by other commands, the setup
+        script, the command-line, and config files, and finalized in
+        'finalize_options()'.  All terminal output and filesystem
+        interaction should be done by 'run()'.
+
+        This method must be implemented by all command classes.
+        """
+        raise RuntimeError(
+            f"abstract method -- subclass {self.__class__} must override"
+        )
+
+    def announce(self, msg: object, level: int = logging.DEBUG) -> None:
+        log.log(level, msg)
+
+    def debug_print(self, msg: object) -> None:
+        """Print 'msg' to stdout if the global DEBUG (taken from the
+        DISTUTILS_DEBUG environment variable) flag is true.
+        """
+        from distutils.debug import DEBUG
+
+        if DEBUG:
+            print(msg)
+            sys.stdout.flush()
+
+    # -- Option validation methods -------------------------------------
+    # (these are very handy in writing the 'finalize_options()' method)
+    #
+    # NB. the general philosophy here is to ensure that a particular option
+    # value meets certain type and value constraints.  If not, we try to
+    # force it into conformance (eg. if we expect a list but have a string,
+    # split the string on comma and/or whitespace).  If we can't force the
+    # option into conformance, raise DistutilsOptionError.  Thus, command
+    # classes need do nothing more than (eg.)
+    #   self.ensure_string_list('foo')
+    # and they can be guaranteed that thereafter, self.foo will be
+    # a list of strings.
+
+    def _ensure_stringlike(self, option, what, default=None):
+        val = getattr(self, option)
+        if val is None:
+            setattr(self, option, default)
+            return default
+        elif not isinstance(val, str):
+            raise DistutilsOptionError(f"'{option}' must be a {what} (got `{val}`)")
+        return val
+
+    def ensure_string(self, option: str, default: str | None = None) -> None:
+        """Ensure that 'option' is a string; if not defined, set it to
+        'default'.
+        """
+        self._ensure_stringlike(option, "string", default)
+
+    def ensure_string_list(self, option: str) -> None:
+        r"""Ensure that 'option' is a list of strings.  If 'option' is
+        currently a string, we split it either on /,\s*/ or /\s+/, so
+        "foo bar baz", "foo,bar,baz", and "foo,   bar baz" all become
+        ["foo", "bar", "baz"].
+        """
+        val = getattr(self, option)
+        if val is None:
+            return
+        elif isinstance(val, str):
+            setattr(self, option, re.split(r',\s*|\s+', val))
+        else:
+            if isinstance(val, list):
+                ok = all(isinstance(v, str) for v in val)
+            else:
+                ok = False
+            if not ok:
+                raise DistutilsOptionError(
+                    f"'{option}' must be a list of strings (got {val!r})"
+                )
+
+    def _ensure_tested_string(self, option, tester, what, error_fmt, default=None):
+        val = self._ensure_stringlike(option, what, default)
+        if val is not None and not tester(val):
+            raise DistutilsOptionError(
+                ("error in '%s' option: " + error_fmt) % (option, val)
+            )
+
+    def ensure_filename(self, option: str) -> None:
+        """Ensure that 'option' is the name of an existing file."""
+        self._ensure_tested_string(
+            option, os.path.isfile, "filename", "'%s' does not exist or is not a file"
+        )
+
+    def ensure_dirname(self, option: str) -> None:
+        self._ensure_tested_string(
+            option,
+            os.path.isdir,
+            "directory name",
+            "'%s' does not exist or is not a directory",
+        )
+
+    # -- Convenience methods for commands ------------------------------
+
+    def get_command_name(self) -> str:
+        if hasattr(self, 'command_name'):
+            return self.command_name
+        else:
+            return self.__class__.__name__
+
+    def set_undefined_options(
+        self, src_cmd: str, *option_pairs: tuple[str, str]
+    ) -> None:
+        """Set the values of any "undefined" options from corresponding
+        option values in some other command object.  "Undefined" here means
+        "is None", which is the convention used to indicate that an option
+        has not been changed between 'initialize_options()' and
+        'finalize_options()'.  Usually called from 'finalize_options()' for
+        options that depend on some other command rather than another
+        option of the same command.  'src_cmd' is the other command from
+        which option values will be taken (a command object will be created
+        for it if necessary); the remaining arguments are
+        '(src_option,dst_option)' tuples which mean "take the value of
+        'src_option' in the 'src_cmd' command object, and copy it to
+        'dst_option' in the current command object".
+        """
+        # Option_pairs: list of (src_option, dst_option) tuples
+        src_cmd_obj = self.distribution.get_command_obj(src_cmd)
+        src_cmd_obj.ensure_finalized()
+        for src_option, dst_option in option_pairs:
+            if getattr(self, dst_option) is None:
+                setattr(self, dst_option, getattr(src_cmd_obj, src_option))
+
+    # NOTE: Because distutils is private to Setuptools and not all commands are exposed here,
+    # not every possible command is enumerated in the signature.
+    def get_finalized_command(self, command: str, create: bool = True) -> Command:
+        """Wrapper around Distribution's 'get_command_obj()' method: find
+        (create if necessary and 'create' is true) the command object for
+        'command', call its 'ensure_finalized()' method, and return the
+        finalized command object.
+        """
+        cmd_obj = self.distribution.get_command_obj(command, create)
+        cmd_obj.ensure_finalized()
+        return cmd_obj
+
+    # XXX rename to 'get_reinitialized_command()'? (should do the
+    # same in dist.py, if so)
+    @overload
+    def reinitialize_command(
+        self, command: str, reinit_subcommands: bool = False
+    ) -> Command: ...
+    @overload
+    def reinitialize_command(
+        self, command: _CommandT, reinit_subcommands: bool = False
+    ) -> _CommandT: ...
+    def reinitialize_command(
+        self, command: str | Command, reinit_subcommands=False
+    ) -> Command:
+        return self.distribution.reinitialize_command(command, reinit_subcommands)
+
+    def run_command(self, command: str) -> None:
+        """Run some other command: uses the 'run_command()' method of
+        Distribution, which creates and finalizes the command object if
+        necessary and then invokes its 'run()' method.
+        """
+        self.distribution.run_command(command)
+
+    def get_sub_commands(self) -> list[str]:
+        """Determine the sub-commands that are relevant in the current
+        distribution (ie., that need to be run).  This is based on the
+        'sub_commands' class attribute: each tuple in that list may include
+        a method that we call to determine if the subcommand needs to be
+        run for the current distribution.  Return a list of command names.
+        """
+        commands = []
+        for cmd_name, method in self.sub_commands:
+            if method is None or method(self):
+                commands.append(cmd_name)
+        return commands
+
+    # -- External world manipulation -----------------------------------
+
+    def warn(self, msg: object) -> None:
+        log.warning("warning: %s: %s\n", self.get_command_name(), msg)
+
+    def execute(
+        self,
+        func: Callable[[Unpack[_Ts]], object],
+        args: tuple[Unpack[_Ts]],
+        msg: object = None,
+        level: int = 1,
+    ) -> None:
+        util.execute(func, args, msg, dry_run=self.dry_run)
+
+    def mkpath(self, name: str, mode: int = 0o777) -> None:
+        dir_util.mkpath(name, mode, dry_run=self.dry_run)
+
+    @overload
+    def copy_file(
+        self,
+        infile: str | os.PathLike[str],
+        outfile: _StrPathT,
+        preserve_mode: bool = True,
+        preserve_times: bool = True,
+        link: str | None = None,
+        level: int = 1,
+    ) -> tuple[_StrPathT | str, bool]: ...
+    @overload
+    def copy_file(
+        self,
+        infile: bytes | os.PathLike[bytes],
+        outfile: _BytesPathT,
+        preserve_mode: bool = True,
+        preserve_times: bool = True,
+        link: str | None = None,
+        level: int = 1,
+    ) -> tuple[_BytesPathT | bytes, bool]: ...
+    def copy_file(
+        self,
+        infile: str | os.PathLike[str] | bytes | os.PathLike[bytes],
+        outfile: str | os.PathLike[str] | bytes | os.PathLike[bytes],
+        preserve_mode: bool = True,
+        preserve_times: bool = True,
+        link: str | None = None,
+        level: int = 1,
+    ) -> tuple[str | os.PathLike[str] | bytes | os.PathLike[bytes], bool]:
+        """Copy a file respecting verbose, dry-run and force flags.  (The
+        former two default to whatever is in the Distribution object, and
+        the latter defaults to false for commands that don't define it.)"""
+        return file_util.copy_file(
+            infile,
+            outfile,
+            preserve_mode,
+            preserve_times,
+            not self.force,
+            link,
+            dry_run=self.dry_run,
+        )
+
+    def copy_tree(
+        self,
+        infile: str | os.PathLike[str],
+        outfile: str,
+        preserve_mode: bool = True,
+        preserve_times: bool = True,
+        preserve_symlinks: bool = False,
+        level: int = 1,
+    ) -> list[str]:
+        """Copy an entire directory tree respecting verbose, dry-run,
+        and force flags.
+        """
+        return dir_util.copy_tree(
+            infile,
+            outfile,
+            preserve_mode,
+            preserve_times,
+            preserve_symlinks,
+            not self.force,
+            dry_run=self.dry_run,
+        )
+
+    @overload
+    def move_file(
+        self, src: str | os.PathLike[str], dst: _StrPathT, level: int = 1
+    ) -> _StrPathT | str: ...
+    @overload
+    def move_file(
+        self, src: bytes | os.PathLike[bytes], dst: _BytesPathT, level: int = 1
+    ) -> _BytesPathT | bytes: ...
+    def move_file(
+        self,
+        src: str | os.PathLike[str] | bytes | os.PathLike[bytes],
+        dst: str | os.PathLike[str] | bytes | os.PathLike[bytes],
+        level: int = 1,
+    ) -> str | os.PathLike[str] | bytes | os.PathLike[bytes]:
+        """Move a file respecting dry-run flag."""
+        return file_util.move_file(src, dst, dry_run=self.dry_run)
+
+    def spawn(
+        self, cmd: MutableSequence[str], search_path: bool = True, level: int = 1
+    ) -> None:
+        """Spawn an external command respecting dry-run flag."""
+        from distutils.spawn import spawn
+
+        spawn(cmd, search_path, dry_run=self.dry_run)
+
+    @overload
+    def make_archive(
+        self,
+        base_name: str,
+        format: str,
+        root_dir: str | os.PathLike[str] | bytes | os.PathLike[bytes] | None = None,
+        base_dir: str | None = None,
+        owner: str | None = None,
+        group: str | None = None,
+    ) -> str: ...
+    @overload
+    def make_archive(
+        self,
+        base_name: str | os.PathLike[str],
+        format: str,
+        root_dir: str | os.PathLike[str] | bytes | os.PathLike[bytes],
+        base_dir: str | None = None,
+        owner: str | None = None,
+        group: str | None = None,
+    ) -> str: ...
+    def make_archive(
+        self,
+        base_name: str | os.PathLike[str],
+        format: str,
+        root_dir: str | os.PathLike[str] | bytes | os.PathLike[bytes] | None = None,
+        base_dir: str | None = None,
+        owner: str | None = None,
+        group: str | None = None,
+    ) -> str:
+        return archive_util.make_archive(
+            base_name,
+            format,
+            root_dir,
+            base_dir,
+            dry_run=self.dry_run,
+            owner=owner,
+            group=group,
+        )
+
+    def make_file(
+        self,
+        infiles: str | list[str] | tuple[str, ...],
+        outfile: str | os.PathLike[str] | bytes | os.PathLike[bytes],
+        func: Callable[[Unpack[_Ts]], object],
+        args: tuple[Unpack[_Ts]],
+        exec_msg: object = None,
+        skip_msg: object = None,
+        level: int = 1,
+    ) -> None:
+        """Special case of 'execute()' for operations that process one or
+        more input files and generate one output file.  Works just like
+        'execute()', except the operation is skipped and a different
+        message printed if 'outfile' already exists and is newer than all
+        files listed in 'infiles'.  If the command defined 'self.force',
+        and it is true, then the command is unconditionally run -- does no
+        timestamp checks.
+        """
+        if skip_msg is None:
+            skip_msg = f"skipping {outfile} (inputs unchanged)"
+
+        # Allow 'infiles' to be a single string
+        if isinstance(infiles, str):
+            infiles = (infiles,)
+        elif not isinstance(infiles, (list, tuple)):
+            raise TypeError("'infiles' must be a string, or a list or tuple of strings")
+
+        if exec_msg is None:
+            exec_msg = "generating {} from {}".format(outfile, ', '.join(infiles))
+
+        # If 'outfile' must be regenerated (either because it doesn't
+        # exist, is out-of-date, or the 'force' flag is true) then
+        # perform the action that presumably regenerates it
+        if self.force or _modified.newer_group(infiles, outfile):
+            self.execute(func, args, exec_msg, level)
+        # Otherwise, print the "skip" message
+        else:
+            log.debug(skip_msg)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f8a1692ba96f5e03167e7307c1c2ff5776b74d6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/__init__.py
@@ -0,0 +1,23 @@
+"""distutils.command
+
+Package containing implementation of all the standard Distutils
+commands."""
+
+__all__ = [
+    'build',
+    'build_py',
+    'build_ext',
+    'build_clib',
+    'build_scripts',
+    'clean',
+    'install',
+    'install_lib',
+    'install_headers',
+    'install_scripts',
+    'install_data',
+    'sdist',
+    'bdist',
+    'bdist_dumb',
+    'bdist_rpm',
+    'check',
+]
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/_framework_compat.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/_framework_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..00d34bc7d8c85126dbae716ebfa48e35b0833222
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/_framework_compat.py
@@ -0,0 +1,54 @@
+"""
+Backward compatibility for homebrew builds on macOS.
+"""
+
+import functools
+import os
+import subprocess
+import sys
+import sysconfig
+
+
+@functools.lru_cache
+def enabled():
+    """
+    Only enabled for Python 3.9 framework homebrew builds
+    except ensurepip and venv.
+    """
+    PY39 = (3, 9) < sys.version_info < (3, 10)
+    framework = sys.platform == 'darwin' and sys._framework
+    homebrew = "Cellar" in sysconfig.get_config_var('projectbase')
+    venv = sys.prefix != sys.base_prefix
+    ensurepip = os.environ.get("ENSUREPIP_OPTIONS")
+    return PY39 and framework and homebrew and not venv and not ensurepip
+
+
+schemes = dict(
+    osx_framework_library=dict(
+        stdlib='{installed_base}/{platlibdir}/python{py_version_short}',
+        platstdlib='{platbase}/{platlibdir}/python{py_version_short}',
+        purelib='{homebrew_prefix}/lib/python{py_version_short}/site-packages',
+        platlib='{homebrew_prefix}/{platlibdir}/python{py_version_short}/site-packages',
+        include='{installed_base}/include/python{py_version_short}{abiflags}',
+        platinclude='{installed_platbase}/include/python{py_version_short}{abiflags}',
+        scripts='{homebrew_prefix}/bin',
+        data='{homebrew_prefix}',
+    )
+)
+
+
+@functools.lru_cache
+def vars():
+    if not enabled():
+        return {}
+    homebrew_prefix = subprocess.check_output(['brew', '--prefix'], text=True).strip()
+    return locals()
+
+
+def scheme(name):
+    """
+    Override the selected scheme for posix_prefix.
+    """
+    if not enabled() or not name.endswith('_prefix'):
+        return name
+    return 'osx_framework_library'
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/bdist.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/bdist.py
new file mode 100644
index 0000000000000000000000000000000000000000..07811aab27ad7fcd2303883024243e2f52c2bbcb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/bdist.py
@@ -0,0 +1,167 @@
+"""distutils.command.bdist
+
+Implements the Distutils 'bdist' command (create a built [binary]
+distribution)."""
+
+from __future__ import annotations
+
+import os
+import warnings
+from collections.abc import Callable
+from typing import TYPE_CHECKING, ClassVar
+
+from ..core import Command
+from ..errors import DistutilsOptionError, DistutilsPlatformError
+from ..util import get_platform
+
+if TYPE_CHECKING:
+    from typing_extensions import deprecated
+else:
+
+    def deprecated(message):
+        return lambda fn: fn
+
+
+def show_formats():
+    """Print list of available formats (arguments to "--format" option)."""
+    from ..fancy_getopt import FancyGetopt
+
+    formats = [
+        ("formats=" + format, None, bdist.format_commands[format][1])
+        for format in bdist.format_commands
+    ]
+    pretty_printer = FancyGetopt(formats)
+    pretty_printer.print_help("List of available distribution formats:")
+
+
+class ListCompat(dict[str, tuple[str, str]]):
+    # adapter to allow for Setuptools compatibility in format_commands
+    @deprecated("format_commands is now a dict. append is deprecated.")
+    def append(self, item: object) -> None:
+        warnings.warn(
+            "format_commands is now a dict. append is deprecated.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+
+
+class bdist(Command):
+    description = "create a built (binary) distribution"
+
+    user_options = [
+        ('bdist-base=', 'b', "temporary directory for creating built distributions"),
+        (
+            'plat-name=',
+            'p',
+            "platform name to embed in generated filenames "
+            f"[default: {get_platform()}]",
+        ),
+        ('formats=', None, "formats for distribution (comma-separated list)"),
+        (
+            'dist-dir=',
+            'd',
+            "directory to put final built distributions in [default: dist]",
+        ),
+        ('skip-build', None, "skip rebuilding everything (for testing/debugging)"),
+        (
+            'owner=',
+            'u',
+            "Owner name used when creating a tar file [default: current user]",
+        ),
+        (
+            'group=',
+            'g',
+            "Group name used when creating a tar file [default: current group]",
+        ),
+    ]
+
+    boolean_options: ClassVar[list[str]] = ['skip-build']
+
+    help_options: ClassVar[list[tuple[str, str | None, str, Callable[[], object]]]] = [
+        ('help-formats', None, "lists available distribution formats", show_formats),
+    ]
+
+    # The following commands do not take a format option from bdist
+    no_format_option: ClassVar[tuple[str, ...]] = ('bdist_rpm',)
+
+    # This won't do in reality: will need to distinguish RPM-ish Linux,
+    # Debian-ish Linux, Solaris, FreeBSD, ..., Windows, Mac OS.
+    default_format: ClassVar[dict[str, str]] = {'posix': 'gztar', 'nt': 'zip'}
+
+    # Define commands in preferred order for the --help-formats option
+    format_commands = ListCompat({
+        'rpm': ('bdist_rpm', "RPM distribution"),
+        'gztar': ('bdist_dumb', "gzip'ed tar file"),
+        'bztar': ('bdist_dumb', "bzip2'ed tar file"),
+        'xztar': ('bdist_dumb', "xz'ed tar file"),
+        'ztar': ('bdist_dumb', "compressed tar file"),
+        'tar': ('bdist_dumb', "tar file"),
+        'zip': ('bdist_dumb', "ZIP file"),
+    })
+
+    # for compatibility until consumers only reference format_commands
+    format_command = format_commands
+
+    def initialize_options(self):
+        self.bdist_base = None
+        self.plat_name = None
+        self.formats = None
+        self.dist_dir = None
+        self.skip_build = False
+        self.group = None
+        self.owner = None
+
+    def finalize_options(self) -> None:
+        # have to finalize 'plat_name' before 'bdist_base'
+        if self.plat_name is None:
+            if self.skip_build:
+                self.plat_name = get_platform()
+            else:
+                self.plat_name = self.get_finalized_command('build').plat_name
+
+        # 'bdist_base' -- parent of per-built-distribution-format
+        # temporary directories (eg. we'll probably have
+        # "build/bdist.<plat>/dumb", "build/bdist.<plat>/rpm", etc.)
+        if self.bdist_base is None:
+            build_base = self.get_finalized_command('build').build_base
+            self.bdist_base = os.path.join(build_base, 'bdist.' + self.plat_name)
+
+        self.ensure_string_list('formats')
+        if self.formats is None:
+            try:
+                self.formats = [self.default_format[os.name]]
+            except KeyError:
+                raise DistutilsPlatformError(
+                    "don't know how to create built distributions "
+                    f"on platform {os.name}"
+                )
+
+        if self.dist_dir is None:
+            self.dist_dir = "dist"
+
+    def run(self) -> None:
+        # Figure out which sub-commands we need to run.
+        commands = []
+        for format in self.formats:
+            try:
+                commands.append(self.format_commands[format][0])
+            except KeyError:
+                raise DistutilsOptionError(f"invalid format '{format}'")
+
+        # Reinitialize and run each command.
+        for i in range(len(self.formats)):
+            cmd_name = commands[i]
+            sub_cmd = self.reinitialize_command(cmd_name)
+            if cmd_name not in self.no_format_option:
+                sub_cmd.format = self.formats[i]
+
+            # passing the owner and group names for tar archiving
+            if cmd_name == 'bdist_dumb':
+                sub_cmd.owner = self.owner
+                sub_cmd.group = self.group
+
+            # If we're going to need to run this command again, tell it to
+            # keep its temporary files around so subsequent runs go faster.
+            if cmd_name in commands[i + 1 :]:
+                sub_cmd.keep_temp = True
+            self.run_command(cmd_name)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/bdist_dumb.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/bdist_dumb.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccad66f43127cb18b8e592810995a7092f843041
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/bdist_dumb.py
@@ -0,0 +1,141 @@
+"""distutils.command.bdist_dumb
+
+Implements the Distutils 'bdist_dumb' command (create a "dumb" built
+distribution -- i.e., just an archive to be unpacked under $prefix or
+$exec_prefix)."""
+
+import os
+from distutils._log import log
+from typing import ClassVar
+
+from ..core import Command
+from ..dir_util import ensure_relative, remove_tree
+from ..errors import DistutilsPlatformError
+from ..sysconfig import get_python_version
+from ..util import get_platform
+
+
+class bdist_dumb(Command):
+    description = "create a \"dumb\" built distribution"
+
+    user_options = [
+        ('bdist-dir=', 'd', "temporary directory for creating the distribution"),
+        (
+            'plat-name=',
+            'p',
+            "platform name to embed in generated filenames "
+            f"[default: {get_platform()}]",
+        ),
+        (
+            'format=',
+            'f',
+            "archive format to create (tar, gztar, bztar, xztar, ztar, zip)",
+        ),
+        (
+            'keep-temp',
+            'k',
+            "keep the pseudo-installation tree around after creating the distribution archive",
+        ),
+        ('dist-dir=', 'd', "directory to put final built distributions in"),
+        ('skip-build', None, "skip rebuilding everything (for testing/debugging)"),
+        (
+            'relative',
+            None,
+            "build the archive using relative paths [default: false]",
+        ),
+        (
+            'owner=',
+            'u',
+            "Owner name used when creating a tar file [default: current user]",
+        ),
+        (
+            'group=',
+            'g',
+            "Group name used when creating a tar file [default: current group]",
+        ),
+    ]
+
+    boolean_options: ClassVar[list[str]] = ['keep-temp', 'skip-build', 'relative']
+
+    default_format = {'posix': 'gztar', 'nt': 'zip'}
+
+    def initialize_options(self):
+        self.bdist_dir = None
+        self.plat_name = None
+        self.format = None
+        self.keep_temp = False
+        self.dist_dir = None
+        self.skip_build = None
+        self.relative = False
+        self.owner = None
+        self.group = None
+
+    def finalize_options(self):
+        if self.bdist_dir is None:
+            bdist_base = self.get_finalized_command('bdist').bdist_base
+            self.bdist_dir = os.path.join(bdist_base, 'dumb')
+
+        if self.format is None:
+            try:
+                self.format = self.default_format[os.name]
+            except KeyError:
+                raise DistutilsPlatformError(
+                    "don't know how to create dumb built distributions "
+                    f"on platform {os.name}"
+                )
+
+        self.set_undefined_options(
+            'bdist',
+            ('dist_dir', 'dist_dir'),
+            ('plat_name', 'plat_name'),
+            ('skip_build', 'skip_build'),
+        )
+
+    def run(self):
+        if not self.skip_build:
+            self.run_command('build')
+
+        install = self.reinitialize_command('install', reinit_subcommands=True)
+        install.root = self.bdist_dir
+        install.skip_build = self.skip_build
+        install.warn_dir = False
+
+        log.info("installing to %s", self.bdist_dir)
+        self.run_command('install')
+
+        # And make an archive relative to the root of the
+        # pseudo-installation tree.
+        archive_basename = f"{self.distribution.get_fullname()}.{self.plat_name}"
+
+        pseudoinstall_root = os.path.join(self.dist_dir, archive_basename)
+        if not self.relative:
+            archive_root = self.bdist_dir
+        else:
+            if self.distribution.has_ext_modules() and (
+                install.install_base != install.install_platbase
+            ):
+                raise DistutilsPlatformError(
+                    "can't make a dumb built distribution where "
+                    f"base and platbase are different ({install.install_base!r}, {install.install_platbase!r})"
+                )
+            else:
+                archive_root = os.path.join(
+                    self.bdist_dir, ensure_relative(install.install_base)
+                )
+
+        # Make the archive
+        filename = self.make_archive(
+            pseudoinstall_root,
+            self.format,
+            root_dir=archive_root,
+            owner=self.owner,
+            group=self.group,
+        )
+        if self.distribution.has_ext_modules():
+            pyversion = get_python_version()
+        else:
+            pyversion = 'any'
+        self.distribution.dist_files.append(('bdist_dumb', pyversion, filename))
+
+        if not self.keep_temp:
+            remove_tree(self.bdist_dir, dry_run=self.dry_run)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/bdist_rpm.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/bdist_rpm.py
new file mode 100644
index 0000000000000000000000000000000000000000..357b4e861e2e0ec44a0bf1dda6c2dd35db8b9ca8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/bdist_rpm.py
@@ -0,0 +1,598 @@
+"""distutils.command.bdist_rpm
+
+Implements the Distutils 'bdist_rpm' command (create RPM source and binary
+distributions)."""
+
+import os
+import subprocess
+import sys
+from distutils._log import log
+from typing import ClassVar
+
+from ..core import Command
+from ..debug import DEBUG
+from ..errors import (
+    DistutilsExecError,
+    DistutilsFileError,
+    DistutilsOptionError,
+    DistutilsPlatformError,
+)
+from ..file_util import write_file
+from ..sysconfig import get_python_version
+
+
+class bdist_rpm(Command):
+    description = "create an RPM distribution"
+
+    user_options = [
+        ('bdist-base=', None, "base directory for creating built distributions"),
+        (
+            'rpm-base=',
+            None,
+            "base directory for creating RPMs (defaults to \"rpm\" under "
+            "--bdist-base; must be specified for RPM 2)",
+        ),
+        (
+            'dist-dir=',
+            'd',
+            "directory to put final RPM files in (and .spec files if --spec-only)",
+        ),
+        (
+            'python=',
+            None,
+            "path to Python interpreter to hard-code in the .spec file "
+            "[default: \"python\"]",
+        ),
+        (
+            'fix-python',
+            None,
+            "hard-code the exact path to the current Python interpreter in "
+            "the .spec file",
+        ),
+        ('spec-only', None, "only regenerate spec file"),
+        ('source-only', None, "only generate source RPM"),
+        ('binary-only', None, "only generate binary RPM"),
+        ('use-bzip2', None, "use bzip2 instead of gzip to create source distribution"),
+        # More meta-data: too RPM-specific to put in the setup script,
+        # but needs to go in the .spec file -- so we make these options
+        # to "bdist_rpm".  The idea is that packagers would put this
+        # info in setup.cfg, although they are of course free to
+        # supply it on the command line.
+        (
+            'distribution-name=',
+            None,
+            "name of the (Linux) distribution to which this "
+            "RPM applies (*not* the name of the module distribution!)",
+        ),
+        ('group=', None, "package classification [default: \"Development/Libraries\"]"),
+        ('release=', None, "RPM release number"),
+        ('serial=', None, "RPM serial number"),
+        (
+            'vendor=',
+            None,
+            "RPM \"vendor\" (eg. \"Joe Blow <joe@example.com>\") "
+            "[default: maintainer or author from setup script]",
+        ),
+        (
+            'packager=',
+            None,
+            "RPM packager (eg. \"Jane Doe <jane@example.net>\") [default: vendor]",
+        ),
+        ('doc-files=', None, "list of documentation files (space or comma-separated)"),
+        ('changelog=', None, "RPM changelog"),
+        ('icon=', None, "name of icon file"),
+        ('provides=', None, "capabilities provided by this package"),
+        ('requires=', None, "capabilities required by this package"),
+        ('conflicts=', None, "capabilities which conflict with this package"),
+        ('build-requires=', None, "capabilities required to build this package"),
+        ('obsoletes=', None, "capabilities made obsolete by this package"),
+        ('no-autoreq', None, "do not automatically calculate dependencies"),
+        # Actions to take when building RPM
+        ('keep-temp', 'k', "don't clean up RPM build directory"),
+        ('no-keep-temp', None, "clean up RPM build directory [default]"),
+        (
+            'use-rpm-opt-flags',
+            None,
+            "compile with RPM_OPT_FLAGS when building from source RPM",
+        ),
+        ('no-rpm-opt-flags', None, "do not pass any RPM CFLAGS to compiler"),
+        ('rpm3-mode', None, "RPM 3 compatibility mode (default)"),
+        ('rpm2-mode', None, "RPM 2 compatibility mode"),
+        # Add the hooks necessary for specifying custom scripts
+        ('prep-script=', None, "Specify a script for the PREP phase of RPM building"),
+        ('build-script=', None, "Specify a script for the BUILD phase of RPM building"),
+        (
+            'pre-install=',
+            None,
+            "Specify a script for the pre-INSTALL phase of RPM building",
+        ),
+        (
+            'install-script=',
+            None,
+            "Specify a script for the INSTALL phase of RPM building",
+        ),
+        (
+            'post-install=',
+            None,
+            "Specify a script for the post-INSTALL phase of RPM building",
+        ),
+        (
+            'pre-uninstall=',
+            None,
+            "Specify a script for the pre-UNINSTALL phase of RPM building",
+        ),
+        (
+            'post-uninstall=',
+            None,
+            "Specify a script for the post-UNINSTALL phase of RPM building",
+        ),
+        ('clean-script=', None, "Specify a script for the CLEAN phase of RPM building"),
+        (
+            'verify-script=',
+            None,
+            "Specify a script for the VERIFY phase of the RPM build",
+        ),
+        # Allow a packager to explicitly force an architecture
+        ('force-arch=', None, "Force an architecture onto the RPM build process"),
+        ('quiet', 'q', "Run the INSTALL phase of RPM building in quiet mode"),
+    ]
+
+    boolean_options: ClassVar[list[str]] = [
+        'keep-temp',
+        'use-rpm-opt-flags',
+        'rpm3-mode',
+        'no-autoreq',
+        'quiet',
+    ]
+
+    negative_opt: ClassVar[dict[str, str]] = {
+        'no-keep-temp': 'keep-temp',
+        'no-rpm-opt-flags': 'use-rpm-opt-flags',
+        'rpm2-mode': 'rpm3-mode',
+    }
+
+    def initialize_options(self):
+        self.bdist_base = None
+        self.rpm_base = None
+        self.dist_dir = None
+        self.python = None
+        self.fix_python = None
+        self.spec_only = None
+        self.binary_only = None
+        self.source_only = None
+        self.use_bzip2 = None
+
+        self.distribution_name = None
+        self.group = None
+        self.release = None
+        self.serial = None
+        self.vendor = None
+        self.packager = None
+        self.doc_files = None
+        self.changelog = None
+        self.icon = None
+
+        self.prep_script = None
+        self.build_script = None
+        self.install_script = None
+        self.clean_script = None
+        self.verify_script = None
+        self.pre_install = None
+        self.post_install = None
+        self.pre_uninstall = None
+        self.post_uninstall = None
+        self.prep = None
+        self.provides = None
+        self.requires = None
+        self.conflicts = None
+        self.build_requires = None
+        self.obsoletes = None
+
+        self.keep_temp = False
+        self.use_rpm_opt_flags = True
+        self.rpm3_mode = True
+        self.no_autoreq = False
+
+        self.force_arch = None
+        self.quiet = False
+
+    def finalize_options(self) -> None:
+        self.set_undefined_options('bdist', ('bdist_base', 'bdist_base'))
+        if self.rpm_base is None:
+            if not self.rpm3_mode:
+                raise DistutilsOptionError("you must specify --rpm-base in RPM 2 mode")
+            self.rpm_base = os.path.join(self.bdist_base, "rpm")
+
+        if self.python is None:
+            if self.fix_python:
+                self.python = sys.executable
+            else:
+                self.python = "python3"
+        elif self.fix_python:
+            raise DistutilsOptionError(
+                "--python and --fix-python are mutually exclusive options"
+            )
+
+        if os.name != 'posix':
+            raise DistutilsPlatformError(
+                f"don't know how to create RPM distributions on platform {os.name}"
+            )
+        if self.binary_only and self.source_only:
+            raise DistutilsOptionError(
+                "cannot supply both '--source-only' and '--binary-only'"
+            )
+
+        # don't pass CFLAGS to pure python distributions
+        if not self.distribution.has_ext_modules():
+            self.use_rpm_opt_flags = False
+
+        self.set_undefined_options('bdist', ('dist_dir', 'dist_dir'))
+        self.finalize_package_data()
+
+    def finalize_package_data(self) -> None:
+        self.ensure_string('group', "Development/Libraries")
+        self.ensure_string(
+            'vendor',
+            f"{self.distribution.get_contact()} <{self.distribution.get_contact_email()}>",
+        )
+        self.ensure_string('packager')
+        self.ensure_string_list('doc_files')
+        if isinstance(self.doc_files, list):
+            for readme in ('README', 'README.txt'):
+                if os.path.exists(readme) and readme not in self.doc_files:
+                    self.doc_files.append(readme)
+
+        self.ensure_string('release', "1")
+        self.ensure_string('serial')  # should it be an int?
+
+        self.ensure_string('distribution_name')
+
+        self.ensure_string('changelog')
+        # Format changelog correctly
+        self.changelog = self._format_changelog(self.changelog)
+
+        self.ensure_filename('icon')
+
+        self.ensure_filename('prep_script')
+        self.ensure_filename('build_script')
+        self.ensure_filename('install_script')
+        self.ensure_filename('clean_script')
+        self.ensure_filename('verify_script')
+        self.ensure_filename('pre_install')
+        self.ensure_filename('post_install')
+        self.ensure_filename('pre_uninstall')
+        self.ensure_filename('post_uninstall')
+
+        # XXX don't forget we punted on summaries and descriptions -- they
+        # should be handled here eventually!
+
+        # Now *this* is some meta-data that belongs in the setup script...
+        self.ensure_string_list('provides')
+        self.ensure_string_list('requires')
+        self.ensure_string_list('conflicts')
+        self.ensure_string_list('build_requires')
+        self.ensure_string_list('obsoletes')
+
+        self.ensure_string('force_arch')
+
+    def run(self) -> None:  # noqa: C901
+        if DEBUG:
+            print("before _get_package_data():")
+            print("vendor =", self.vendor)
+            print("packager =", self.packager)
+            print("doc_files =", self.doc_files)
+            print("changelog =", self.changelog)
+
+        # make directories
+        if self.spec_only:
+            spec_dir = self.dist_dir
+            self.mkpath(spec_dir)
+        else:
+            rpm_dir = {}
+            for d in ('SOURCES', 'SPECS', 'BUILD', 'RPMS', 'SRPMS'):
+                rpm_dir[d] = os.path.join(self.rpm_base, d)
+                self.mkpath(rpm_dir[d])
+            spec_dir = rpm_dir['SPECS']
+
+        # Spec file goes into 'dist_dir' if '--spec-only specified',
+        # build/rpm.<plat> otherwise.
+        spec_path = os.path.join(spec_dir, f"{self.distribution.get_name()}.spec")
+        self.execute(
+            write_file, (spec_path, self._make_spec_file()), f"writing '{spec_path}'"
+        )
+
+        if self.spec_only:  # stop if requested
+            return
+
+        # Make a source distribution and copy to SOURCES directory with
+        # optional icon.
+        saved_dist_files = self.distribution.dist_files[:]
+        sdist = self.reinitialize_command('sdist')
+        if self.use_bzip2:
+            sdist.formats = ['bztar']
+        else:
+            sdist.formats = ['gztar']
+        self.run_command('sdist')
+        self.distribution.dist_files = saved_dist_files
+
+        source = sdist.get_archive_files()[0]
+        source_dir = rpm_dir['SOURCES']
+        self.copy_file(source, source_dir)
+
+        if self.icon:
+            if os.path.exists(self.icon):
+                self.copy_file(self.icon, source_dir)
+            else:
+                raise DistutilsFileError(f"icon file '{self.icon}' does not exist")
+
+        # build package
+        log.info("building RPMs")
+        rpm_cmd = ['rpmbuild']
+
+        if self.source_only:  # what kind of RPMs?
+            rpm_cmd.append('-bs')
+        elif self.binary_only:
+            rpm_cmd.append('-bb')
+        else:
+            rpm_cmd.append('-ba')
+        rpm_cmd.extend(['--define', f'__python {self.python}'])
+        if self.rpm3_mode:
+            rpm_cmd.extend(['--define', f'_topdir {os.path.abspath(self.rpm_base)}'])
+        if not self.keep_temp:
+            rpm_cmd.append('--clean')
+
+        if self.quiet:
+            rpm_cmd.append('--quiet')
+
+        rpm_cmd.append(spec_path)
+        # Determine the binary rpm names that should be built out of this spec
+        # file
+        # Note that some of these may not be really built (if the file
+        # list is empty)
+        nvr_string = "%{name}-%{version}-%{release}"
+        src_rpm = nvr_string + ".src.rpm"
+        non_src_rpm = "%{arch}/" + nvr_string + ".%{arch}.rpm"
+        q_cmd = rf"rpm -q --qf '{src_rpm} {non_src_rpm}\n' --specfile '{spec_path}'"
+
+        out = os.popen(q_cmd)
+        try:
+            binary_rpms = []
+            source_rpm = None
+            while True:
+                line = out.readline()
+                if not line:
+                    break
+                ell = line.strip().split()
+                assert len(ell) == 2
+                binary_rpms.append(ell[1])
+                # The source rpm is named after the first entry in the spec file
+                if source_rpm is None:
+                    source_rpm = ell[0]
+
+            status = out.close()
+            if status:
+                raise DistutilsExecError(f"Failed to execute: {q_cmd!r}")
+
+        finally:
+            out.close()
+
+        self.spawn(rpm_cmd)
+
+        if not self.dry_run:
+            if self.distribution.has_ext_modules():
+                pyversion = get_python_version()
+            else:
+                pyversion = 'any'
+
+            if not self.binary_only:
+                srpm = os.path.join(rpm_dir['SRPMS'], source_rpm)
+                assert os.path.exists(srpm)
+                self.move_file(srpm, self.dist_dir)
+                filename = os.path.join(self.dist_dir, source_rpm)
+                self.distribution.dist_files.append(('bdist_rpm', pyversion, filename))
+
+            if not self.source_only:
+                for rpm in binary_rpms:
+                    rpm = os.path.join(rpm_dir['RPMS'], rpm)
+                    if os.path.exists(rpm):
+                        self.move_file(rpm, self.dist_dir)
+                        filename = os.path.join(self.dist_dir, os.path.basename(rpm))
+                        self.distribution.dist_files.append((
+                            'bdist_rpm',
+                            pyversion,
+                            filename,
+                        ))
+
+    def _dist_path(self, path):
+        return os.path.join(self.dist_dir, os.path.basename(path))
+
+    def _make_spec_file(self):  # noqa: C901
+        """Generate the text of an RPM spec file and return it as a
+        list of strings (one per line).
+        """
+        # definitions and headers
+        spec_file = [
+            '%define name ' + self.distribution.get_name(),
+            '%define version ' + self.distribution.get_version().replace('-', '_'),
+            '%define unmangled_version ' + self.distribution.get_version(),
+            '%define release ' + self.release.replace('-', '_'),
+            '',
+            'Summary: ' + (self.distribution.get_description() or "UNKNOWN"),
+        ]
+
+        # Workaround for #14443 which affects some RPM based systems such as
+        # RHEL6 (and probably derivatives)
+        vendor_hook = subprocess.getoutput('rpm --eval %{__os_install_post}')
+        # Generate a potential replacement value for __os_install_post (whilst
+        # normalizing the whitespace to simplify the test for whether the
+        # invocation of brp-python-bytecompile passes in __python):
+        vendor_hook = '\n'.join([
+            f'  {line.strip()} \\' for line in vendor_hook.splitlines()
+        ])
+        problem = "brp-python-bytecompile \\\n"
+        fixed = "brp-python-bytecompile %{__python} \\\n"
+        fixed_hook = vendor_hook.replace(problem, fixed)
+        if fixed_hook != vendor_hook:
+            spec_file.append('# Workaround for https://bugs.python.org/issue14443')
+            spec_file.append('%define __os_install_post ' + fixed_hook + '\n')
+
+        # put locale summaries into spec file
+        # XXX not supported for now (hard to put a dictionary
+        # in a config file -- arg!)
+        # for locale in self.summaries.keys():
+        #    spec_file.append('Summary(%s): %s' % (locale,
+        #                                          self.summaries[locale]))
+
+        spec_file.extend([
+            'Name: %{name}',
+            'Version: %{version}',
+            'Release: %{release}',
+        ])
+
+        # XXX yuck! this filename is available from the "sdist" command,
+        # but only after it has run: and we create the spec file before
+        # running "sdist", in case of --spec-only.
+        if self.use_bzip2:
+            spec_file.append('Source0: %{name}-%{unmangled_version}.tar.bz2')
+        else:
+            spec_file.append('Source0: %{name}-%{unmangled_version}.tar.gz')
+
+        spec_file.extend([
+            'License: ' + (self.distribution.get_license() or "UNKNOWN"),
+            'Group: ' + self.group,
+            'BuildRoot: %{_tmppath}/%{name}-%{version}-%{release}-buildroot',
+            'Prefix: %{_prefix}',
+        ])
+
+        if not self.force_arch:
+            # noarch if no extension modules
+            if not self.distribution.has_ext_modules():
+                spec_file.append('BuildArch: noarch')
+        else:
+            spec_file.append(f'BuildArch: {self.force_arch}')
+
+        for field in (
+            'Vendor',
+            'Packager',
+            'Provides',
+            'Requires',
+            'Conflicts',
+            'Obsoletes',
+        ):
+            val = getattr(self, field.lower())
+            if isinstance(val, list):
+                spec_file.append('{}: {}'.format(field, ' '.join(val)))
+            elif val is not None:
+                spec_file.append(f'{field}: {val}')
+
+        if self.distribution.get_url():
+            spec_file.append('Url: ' + self.distribution.get_url())
+
+        if self.distribution_name:
+            spec_file.append('Distribution: ' + self.distribution_name)
+
+        if self.build_requires:
+            spec_file.append('BuildRequires: ' + ' '.join(self.build_requires))
+
+        if self.icon:
+            spec_file.append('Icon: ' + os.path.basename(self.icon))
+
+        if self.no_autoreq:
+            spec_file.append('AutoReq: 0')
+
+        spec_file.extend([
+            '',
+            '%description',
+            self.distribution.get_long_description() or "",
+        ])
+
+        # put locale descriptions into spec file
+        # XXX again, suppressed because config file syntax doesn't
+        # easily support this ;-(
+        # for locale in self.descriptions.keys():
+        #    spec_file.extend([
+        #        '',
+        #        '%description -l ' + locale,
+        #        self.descriptions[locale],
+        #        ])
+
+        # rpm scripts
+        # figure out default build script
+        def_setup_call = f"{self.python} {os.path.basename(sys.argv[0])}"
+        def_build = f"{def_setup_call} build"
+        if self.use_rpm_opt_flags:
+            def_build = 'env CFLAGS="$RPM_OPT_FLAGS" ' + def_build
+
+        # insert contents of files
+
+        # XXX this is kind of misleading: user-supplied options are files
+        # that we open and interpolate into the spec file, but the defaults
+        # are just text that we drop in as-is.  Hmmm.
+
+        install_cmd = f'{def_setup_call} install -O1 --root=$RPM_BUILD_ROOT --record=INSTALLED_FILES'
+
+        script_options = [
+            ('prep', 'prep_script', "%setup -n %{name}-%{unmangled_version}"),
+            ('build', 'build_script', def_build),
+            ('install', 'install_script', install_cmd),
+            ('clean', 'clean_script', "rm -rf $RPM_BUILD_ROOT"),
+            ('verifyscript', 'verify_script', None),
+            ('pre', 'pre_install', None),
+            ('post', 'post_install', None),
+            ('preun', 'pre_uninstall', None),
+            ('postun', 'post_uninstall', None),
+        ]
+
+        for rpm_opt, attr, default in script_options:
+            # Insert contents of file referred to, if no file is referred to
+            # use 'default' as contents of script
+            val = getattr(self, attr)
+            if val or default:
+                spec_file.extend([
+                    '',
+                    '%' + rpm_opt,
+                ])
+                if val:
+                    with open(val) as f:
+                        spec_file.extend(f.read().split('\n'))
+                else:
+                    spec_file.append(default)
+
+        # files section
+        spec_file.extend([
+            '',
+            '%files -f INSTALLED_FILES',
+            '%defattr(-,root,root)',
+        ])
+
+        if self.doc_files:
+            spec_file.append('%doc ' + ' '.join(self.doc_files))
+
+        if self.changelog:
+            spec_file.extend([
+                '',
+                '%changelog',
+            ])
+            spec_file.extend(self.changelog)
+
+        return spec_file
+
+    def _format_changelog(self, changelog):
+        """Format the changelog correctly and convert it to a list of strings"""
+        if not changelog:
+            return changelog
+        new_changelog = []
+        for line in changelog.strip().split('\n'):
+            line = line.strip()
+            if line[0] == '*':
+                new_changelog.extend(['', line])
+            elif line[0] == '-':
+                new_changelog.append(line)
+            else:
+                new_changelog.append('  ' + line)
+
+        # strip trailing newline inserted by first changelog entry
+        if not new_changelog[0]:
+            del new_changelog[0]
+
+        return new_changelog
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a8303a954ea705c547e2014dc6b51d9d987a532
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build.py
@@ -0,0 +1,156 @@
+"""distutils.command.build
+
+Implements the Distutils 'build' command."""
+
+from __future__ import annotations
+
+import os
+import sys
+import sysconfig
+from collections.abc import Callable
+from typing import ClassVar
+
+from ..ccompiler import show_compilers
+from ..core import Command
+from ..errors import DistutilsOptionError
+from ..util import get_platform
+
+
+class build(Command):
+    description = "build everything needed to install"
+
+    user_options = [
+        ('build-base=', 'b', "base directory for build library"),
+        ('build-purelib=', None, "build directory for platform-neutral distributions"),
+        ('build-platlib=', None, "build directory for platform-specific distributions"),
+        (
+            'build-lib=',
+            None,
+            "build directory for all distribution (defaults to either build-purelib or build-platlib",
+        ),
+        ('build-scripts=', None, "build directory for scripts"),
+        ('build-temp=', 't', "temporary build directory"),
+        (
+            'plat-name=',
+            'p',
+            f"platform name to build for, if supported [default: {get_platform()}]",
+        ),
+        ('compiler=', 'c', "specify the compiler type"),
+        ('parallel=', 'j', "number of parallel build jobs"),
+        ('debug', 'g', "compile extensions and libraries with debugging information"),
+        ('force', 'f', "forcibly build everything (ignore file timestamps)"),
+        ('executable=', 'e', "specify final destination interpreter path (build.py)"),
+    ]
+
+    boolean_options: ClassVar[list[str]] = ['debug', 'force']
+
+    help_options: ClassVar[list[tuple[str, str | None, str, Callable[[], object]]]] = [
+        ('help-compiler', None, "list available compilers", show_compilers),
+    ]
+
+    def initialize_options(self):
+        self.build_base = 'build'
+        # these are decided only after 'build_base' has its final value
+        # (unless overridden by the user or client)
+        self.build_purelib = None
+        self.build_platlib = None
+        self.build_lib = None
+        self.build_temp = None
+        self.build_scripts = None
+        self.compiler = None
+        self.plat_name = None
+        self.debug = None
+        self.force = False
+        self.executable = None
+        self.parallel = None
+
+    def finalize_options(self) -> None:  # noqa: C901
+        if self.plat_name is None:
+            self.plat_name = get_platform()
+        else:
+            # plat-name only supported for windows (other platforms are
+            # supported via ./configure flags, if at all).  Avoid misleading
+            # other platforms.
+            if os.name != 'nt':
+                raise DistutilsOptionError(
+                    "--plat-name only supported on Windows (try "
+                    "using './configure --help' on your platform)"
+                )
+
+        plat_specifier = f".{self.plat_name}-{sys.implementation.cache_tag}"
+
+        # Python 3.13+ with --disable-gil shouldn't share build directories
+        if sysconfig.get_config_var('Py_GIL_DISABLED'):
+            plat_specifier += 't'
+
+        # Make it so Python 2.x and Python 2.x with --with-pydebug don't
+        # share the same build directories. Doing so confuses the build
+        # process for C modules
+        if hasattr(sys, 'gettotalrefcount'):
+            plat_specifier += '-pydebug'
+
+        # 'build_purelib' and 'build_platlib' just default to 'lib' and
+        # 'lib.<plat>' under the base build directory.  We only use one of
+        # them for a given distribution, though --
+        if self.build_purelib is None:
+            self.build_purelib = os.path.join(self.build_base, 'lib')
+        if self.build_platlib is None:
+            self.build_platlib = os.path.join(self.build_base, 'lib' + plat_specifier)
+
+        # 'build_lib' is the actual directory that we will use for this
+        # particular module distribution -- if user didn't supply it, pick
+        # one of 'build_purelib' or 'build_platlib'.
+        if self.build_lib is None:
+            if self.distribution.has_ext_modules():
+                self.build_lib = self.build_platlib
+            else:
+                self.build_lib = self.build_purelib
+
+        # 'build_temp' -- temporary directory for compiler turds,
+        # "build/temp.<plat>"
+        if self.build_temp is None:
+            self.build_temp = os.path.join(self.build_base, 'temp' + plat_specifier)
+        if self.build_scripts is None:
+            self.build_scripts = os.path.join(
+                self.build_base,
+                f'scripts-{sys.version_info.major}.{sys.version_info.minor}',
+            )
+
+        if self.executable is None and sys.executable:
+            self.executable = os.path.normpath(sys.executable)
+
+        if isinstance(self.parallel, str):
+            try:
+                self.parallel = int(self.parallel)
+            except ValueError:
+                raise DistutilsOptionError("parallel should be an integer")
+
+    def run(self) -> None:
+        # Run all relevant sub-commands.  This will be some subset of:
+        #  - build_py      - pure Python modules
+        #  - build_clib    - standalone C libraries
+        #  - build_ext     - Python extensions
+        #  - build_scripts - (Python) scripts
+        for cmd_name in self.get_sub_commands():
+            self.run_command(cmd_name)
+
+    # -- Predicates for the sub-command list ---------------------------
+
+    def has_pure_modules(self):
+        return self.distribution.has_pure_modules()
+
+    def has_c_libraries(self):
+        return self.distribution.has_c_libraries()
+
+    def has_ext_modules(self):
+        return self.distribution.has_ext_modules()
+
+    def has_scripts(self):
+        return self.distribution.has_scripts()
+
+    sub_commands = [
+        ('build_py', has_pure_modules),
+        ('build_clib', has_c_libraries),
+        ('build_ext', has_ext_modules),
+        ('build_scripts', has_scripts),
+    ]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build_clib.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build_clib.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b65b3d8ece0253e0c80cfea33160c7741a2435f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build_clib.py
@@ -0,0 +1,201 @@
+"""distutils.command.build_clib
+
+Implements the Distutils 'build_clib' command, to build a C/C++ library
+that is included in the module distribution and needed by an extension
+module."""
+
+# XXX this module has *lots* of code ripped-off quite transparently from
+# build_ext.py -- not surprisingly really, as the work required to build
+# a static library from a collection of C source files is not really all
+# that different from what's required to build a shared object file from
+# a collection of C source files.  Nevertheless, I haven't done the
+# necessary refactoring to account for the overlap in code between the
+# two modules, mainly because a number of subtle details changed in the
+# cut 'n paste.  Sigh.
+from __future__ import annotations
+
+import os
+from collections.abc import Callable
+from distutils._log import log
+from typing import ClassVar
+
+from ..ccompiler import new_compiler, show_compilers
+from ..core import Command
+from ..errors import DistutilsSetupError
+from ..sysconfig import customize_compiler
+
+
+class build_clib(Command):
+    description = "build C/C++ libraries used by Python extensions"
+
+    user_options: ClassVar[list[tuple[str, str, str]]] = [
+        ('build-clib=', 'b', "directory to build C/C++ libraries to"),
+        ('build-temp=', 't', "directory to put temporary build by-products"),
+        ('debug', 'g', "compile with debugging information"),
+        ('force', 'f', "forcibly build everything (ignore file timestamps)"),
+        ('compiler=', 'c', "specify the compiler type"),
+    ]
+
+    boolean_options: ClassVar[list[str]] = ['debug', 'force']
+
+    help_options: ClassVar[list[tuple[str, str | None, str, Callable[[], object]]]] = [
+        ('help-compiler', None, "list available compilers", show_compilers),
+    ]
+
+    def initialize_options(self):
+        self.build_clib = None
+        self.build_temp = None
+
+        # List of libraries to build
+        self.libraries = None
+
+        # Compilation options for all libraries
+        self.include_dirs = None
+        self.define = None
+        self.undef = None
+        self.debug = None
+        self.force = False
+        self.compiler = None
+
+    def finalize_options(self) -> None:
+        # This might be confusing: both build-clib and build-temp default
+        # to build-temp as defined by the "build" command.  This is because
+        # I think that C libraries are really just temporary build
+        # by-products, at least from the point of view of building Python
+        # extensions -- but I want to keep my options open.
+        self.set_undefined_options(
+            'build',
+            ('build_temp', 'build_clib'),
+            ('build_temp', 'build_temp'),
+            ('compiler', 'compiler'),
+            ('debug', 'debug'),
+            ('force', 'force'),
+        )
+
+        self.libraries = self.distribution.libraries
+        if self.libraries:
+            self.check_library_list(self.libraries)
+
+        if self.include_dirs is None:
+            self.include_dirs = self.distribution.include_dirs or []
+        if isinstance(self.include_dirs, str):
+            self.include_dirs = self.include_dirs.split(os.pathsep)
+
+        # XXX same as for build_ext -- what about 'self.define' and
+        # 'self.undef' ?
+
+    def run(self) -> None:
+        if not self.libraries:
+            return
+
+        self.compiler = new_compiler(
+            compiler=self.compiler, dry_run=self.dry_run, force=self.force
+        )
+        customize_compiler(self.compiler)
+
+        if self.include_dirs is not None:
+            self.compiler.set_include_dirs(self.include_dirs)
+        if self.define is not None:
+            # 'define' option is a list of (name,value) tuples
+            for name, value in self.define:
+                self.compiler.define_macro(name, value)
+        if self.undef is not None:
+            for macro in self.undef:
+                self.compiler.undefine_macro(macro)
+
+        self.build_libraries(self.libraries)
+
+    def check_library_list(self, libraries) -> None:
+        """Ensure that the list of libraries is valid.
+
+        `library` is presumably provided as a command option 'libraries'.
+        This method checks that it is a list of 2-tuples, where the tuples
+        are (library_name, build_info_dict).
+
+        Raise DistutilsSetupError if the structure is invalid anywhere;
+        just returns otherwise.
+        """
+        if not isinstance(libraries, list):
+            raise DistutilsSetupError("'libraries' option must be a list of tuples")
+
+        for lib in libraries:
+            if not isinstance(lib, tuple) and len(lib) != 2:
+                raise DistutilsSetupError("each element of 'libraries' must a 2-tuple")
+
+            name, build_info = lib
+
+            if not isinstance(name, str):
+                raise DistutilsSetupError(
+                    "first element of each tuple in 'libraries' "
+                    "must be a string (the library name)"
+                )
+
+            if '/' in name or (os.sep != '/' and os.sep in name):
+                raise DistutilsSetupError(
+                    f"bad library name '{lib[0]}': may not contain directory separators"
+                )
+
+            if not isinstance(build_info, dict):
+                raise DistutilsSetupError(
+                    "second element of each tuple in 'libraries' "
+                    "must be a dictionary (build info)"
+                )
+
+    def get_library_names(self):
+        # Assume the library list is valid -- 'check_library_list()' is
+        # called from 'finalize_options()', so it should be!
+        if not self.libraries:
+            return None
+
+        lib_names = []
+        for lib_name, _build_info in self.libraries:
+            lib_names.append(lib_name)
+        return lib_names
+
+    def get_source_files(self):
+        self.check_library_list(self.libraries)
+        filenames = []
+        for lib_name, build_info in self.libraries:
+            sources = build_info.get('sources')
+            if sources is None or not isinstance(sources, (list, tuple)):
+                raise DistutilsSetupError(
+                    f"in 'libraries' option (library '{lib_name}'), "
+                    "'sources' must be present and must be "
+                    "a list of source filenames"
+                )
+
+            filenames.extend(sources)
+        return filenames
+
+    def build_libraries(self, libraries) -> None:
+        for lib_name, build_info in libraries:
+            sources = build_info.get('sources')
+            if sources is None or not isinstance(sources, (list, tuple)):
+                raise DistutilsSetupError(
+                    f"in 'libraries' option (library '{lib_name}'), "
+                    "'sources' must be present and must be "
+                    "a list of source filenames"
+                )
+            sources = list(sources)
+
+            log.info("building '%s' library", lib_name)
+
+            # First, compile the source code to object files in the library
+            # directory.  (This should probably change to putting object
+            # files in a temporary build directory.)
+            macros = build_info.get('macros')
+            include_dirs = build_info.get('include_dirs')
+            objects = self.compiler.compile(
+                sources,
+                output_dir=self.build_temp,
+                macros=macros,
+                include_dirs=include_dirs,
+                debug=self.debug,
+            )
+
+            # Now "link" the object files together into a static library.
+            # (On Unix at least, this isn't really linking -- it just
+            # builds an archive.  Whatever.)
+            self.compiler.create_static_lib(
+                objects, lib_name, output_dir=self.build_clib, debug=self.debug
+            )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build_ext.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build_ext.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec45b4403e54d3f58459a412746bc86e4135a756
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build_ext.py
@@ -0,0 +1,812 @@
+"""distutils.command.build_ext
+
+Implements the Distutils 'build_ext' command, for building extension
+modules (currently limited to C extensions, should accommodate C++
+extensions ASAP)."""
+
+from __future__ import annotations
+
+import contextlib
+import os
+import re
+import sys
+from collections.abc import Callable
+from distutils._log import log
+from site import USER_BASE
+from typing import ClassVar
+
+from .._modified import newer_group
+from ..ccompiler import new_compiler, show_compilers
+from ..core import Command
+from ..errors import (
+    CCompilerError,
+    CompileError,
+    DistutilsError,
+    DistutilsOptionError,
+    DistutilsPlatformError,
+    DistutilsSetupError,
+)
+from ..extension import Extension
+from ..sysconfig import customize_compiler, get_config_h_filename, get_python_version
+from ..util import get_platform, is_freethreaded, is_mingw
+
+# An extension name is just a dot-separated list of Python NAMEs (ie.
+# the same as a fully-qualified module name).
+extension_name_re = re.compile(r'^[a-zA-Z_][a-zA-Z_0-9]*(\.[a-zA-Z_][a-zA-Z_0-9]*)*$')
+
+
+class build_ext(Command):
+    description = "build C/C++ extensions (compile/link to build directory)"
+
+    # XXX thoughts on how to deal with complex command-line options like
+    # these, i.e. how to make it so fancy_getopt can suck them off the
+    # command line and make it look like setup.py defined the appropriate
+    # lists of tuples of what-have-you.
+    #   - each command needs a callback to process its command-line options
+    #   - Command.__init__() needs access to its share of the whole
+    #     command line (must ultimately come from
+    #     Distribution.parse_command_line())
+    #   - it then calls the current command class' option-parsing
+    #     callback to deal with weird options like -D, which have to
+    #     parse the option text and churn out some custom data
+    #     structure
+    #   - that data structure (in this case, a list of 2-tuples)
+    #     will then be present in the command object by the time
+    #     we get to finalize_options() (i.e. the constructor
+    #     takes care of both command-line and client options
+    #     in between initialize_options() and finalize_options())
+
+    sep_by = f" (separated by '{os.pathsep}')"
+    user_options = [
+        ('build-lib=', 'b', "directory for compiled extension modules"),
+        ('build-temp=', 't', "directory for temporary files (build by-products)"),
+        (
+            'plat-name=',
+            'p',
+            "platform name to cross-compile for, if supported "
+            f"[default: {get_platform()}]",
+        ),
+        (
+            'inplace',
+            'i',
+            "ignore build-lib and put compiled extensions into the source "
+            "directory alongside your pure Python modules",
+        ),
+        (
+            'include-dirs=',
+            'I',
+            "list of directories to search for header files" + sep_by,
+        ),
+        ('define=', 'D', "C preprocessor macros to define"),
+        ('undef=', 'U', "C preprocessor macros to undefine"),
+        ('libraries=', 'l', "external C libraries to link with"),
+        (
+            'library-dirs=',
+            'L',
+            "directories to search for external C libraries" + sep_by,
+        ),
+        ('rpath=', 'R', "directories to search for shared C libraries at runtime"),
+        ('link-objects=', 'O', "extra explicit link objects to include in the link"),
+        ('debug', 'g', "compile/link with debugging information"),
+        ('force', 'f', "forcibly build everything (ignore file timestamps)"),
+        ('compiler=', 'c', "specify the compiler type"),
+        ('parallel=', 'j', "number of parallel build jobs"),
+        ('swig-cpp', None, "make SWIG create C++ files (default is C)"),
+        ('swig-opts=', None, "list of SWIG command line options"),
+        ('swig=', None, "path to the SWIG executable"),
+        ('user', None, "add user include, library and rpath"),
+    ]
+
+    boolean_options: ClassVar[list[str]] = [
+        'inplace',
+        'debug',
+        'force',
+        'swig-cpp',
+        'user',
+    ]
+
+    help_options: ClassVar[list[tuple[str, str | None, str, Callable[[], object]]]] = [
+        ('help-compiler', None, "list available compilers", show_compilers),
+    ]
+
+    def initialize_options(self):
+        self.extensions = None
+        self.build_lib = None
+        self.plat_name = None
+        self.build_temp = None
+        self.inplace = False
+        self.package = None
+
+        self.include_dirs = None
+        self.define = None
+        self.undef = None
+        self.libraries = None
+        self.library_dirs = None
+        self.rpath = None
+        self.link_objects = None
+        self.debug = None
+        self.force = None
+        self.compiler = None
+        self.swig = None
+        self.swig_cpp = None
+        self.swig_opts = None
+        self.user = None
+        self.parallel = None
+
+    @staticmethod
+    def _python_lib_dir(sysconfig):
+        """
+        Resolve Python's library directory for building extensions
+        that rely on a shared Python library.
+
+        See python/cpython#44264 and python/cpython#48686
+        """
+        if not sysconfig.get_config_var('Py_ENABLE_SHARED'):
+            return
+
+        if sysconfig.python_build:
+            yield '.'
+            return
+
+        if sys.platform == 'zos':
+            # On z/OS, a user is not required to install Python to
+            # a predetermined path, but can use Python portably
+            installed_dir = sysconfig.get_config_var('base')
+            lib_dir = sysconfig.get_config_var('platlibdir')
+            yield os.path.join(installed_dir, lib_dir)
+        else:
+            # building third party extensions
+            yield sysconfig.get_config_var('LIBDIR')
+
+    def finalize_options(self) -> None:  # noqa: C901
+        from distutils import sysconfig
+
+        self.set_undefined_options(
+            'build',
+            ('build_lib', 'build_lib'),
+            ('build_temp', 'build_temp'),
+            ('compiler', 'compiler'),
+            ('debug', 'debug'),
+            ('force', 'force'),
+            ('parallel', 'parallel'),
+            ('plat_name', 'plat_name'),
+        )
+
+        if self.package is None:
+            self.package = self.distribution.ext_package
+
+        self.extensions = self.distribution.ext_modules
+
+        # Make sure Python's include directories (for Python.h, pyconfig.h,
+        # etc.) are in the include search path.
+        py_include = sysconfig.get_python_inc()
+        plat_py_include = sysconfig.get_python_inc(plat_specific=True)
+        if self.include_dirs is None:
+            self.include_dirs = self.distribution.include_dirs or []
+        if isinstance(self.include_dirs, str):
+            self.include_dirs = self.include_dirs.split(os.pathsep)
+
+        # If in a virtualenv, add its include directory
+        # Issue 16116
+        if sys.exec_prefix != sys.base_exec_prefix:
+            self.include_dirs.append(os.path.join(sys.exec_prefix, 'include'))
+
+        # Put the Python "system" include dir at the end, so that
+        # any local include dirs take precedence.
+        self.include_dirs.extend(py_include.split(os.path.pathsep))
+        if plat_py_include != py_include:
+            self.include_dirs.extend(plat_py_include.split(os.path.pathsep))
+
+        self.ensure_string_list('libraries')
+        self.ensure_string_list('link_objects')
+
+        # Life is easier if we're not forever checking for None, so
+        # simplify these options to empty lists if unset
+        if self.libraries is None:
+            self.libraries = []
+        if self.library_dirs is None:
+            self.library_dirs = []
+        elif isinstance(self.library_dirs, str):
+            self.library_dirs = self.library_dirs.split(os.pathsep)
+
+        if self.rpath is None:
+            self.rpath = []
+        elif isinstance(self.rpath, str):
+            self.rpath = self.rpath.split(os.pathsep)
+
+        # for extensions under windows use different directories
+        # for Release and Debug builds.
+        # also Python's library directory must be appended to library_dirs
+        if os.name == 'nt' and not is_mingw():
+            # the 'libs' directory is for binary installs - we assume that
+            # must be the *native* platform.  But we don't really support
+            # cross-compiling via a binary install anyway, so we let it go.
+            self.library_dirs.append(os.path.join(sys.exec_prefix, 'libs'))
+            if sys.base_exec_prefix != sys.prefix:  # Issue 16116
+                self.library_dirs.append(os.path.join(sys.base_exec_prefix, 'libs'))
+            if self.debug:
+                self.build_temp = os.path.join(self.build_temp, "Debug")
+            else:
+                self.build_temp = os.path.join(self.build_temp, "Release")
+
+            # Append the source distribution include and library directories,
+            # this allows distutils on windows to work in the source tree
+            self.include_dirs.append(os.path.dirname(get_config_h_filename()))
+            self.library_dirs.append(sys.base_exec_prefix)
+
+            # Use the .lib files for the correct architecture
+            if self.plat_name == 'win32':
+                suffix = 'win32'
+            else:
+                # win-amd64
+                suffix = self.plat_name[4:]
+            new_lib = os.path.join(sys.exec_prefix, 'PCbuild')
+            if suffix:
+                new_lib = os.path.join(new_lib, suffix)
+            self.library_dirs.append(new_lib)
+
+        # For extensions under Cygwin, Python's library directory must be
+        # appended to library_dirs
+        if sys.platform[:6] == 'cygwin':
+            if not sysconfig.python_build:
+                # building third party extensions
+                self.library_dirs.append(
+                    os.path.join(
+                        sys.prefix, "lib", "python" + get_python_version(), "config"
+                    )
+                )
+            else:
+                # building python standard extensions
+                self.library_dirs.append('.')
+
+        self.library_dirs.extend(self._python_lib_dir(sysconfig))
+
+        # The argument parsing will result in self.define being a string, but
+        # it has to be a list of 2-tuples.  All the preprocessor symbols
+        # specified by the 'define' option will be set to '1'.  Multiple
+        # symbols can be separated with commas.
+
+        if self.define:
+            defines = self.define.split(',')
+            self.define = [(symbol, '1') for symbol in defines]
+
+        # The option for macros to undefine is also a string from the
+        # option parsing, but has to be a list.  Multiple symbols can also
+        # be separated with commas here.
+        if self.undef:
+            self.undef = self.undef.split(',')
+
+        if self.swig_opts is None:
+            self.swig_opts = []
+        else:
+            self.swig_opts = self.swig_opts.split(' ')
+
+        # Finally add the user include and library directories if requested
+        if self.user:
+            user_include = os.path.join(USER_BASE, "include")
+            user_lib = os.path.join(USER_BASE, "lib")
+            if os.path.isdir(user_include):
+                self.include_dirs.append(user_include)
+            if os.path.isdir(user_lib):
+                self.library_dirs.append(user_lib)
+                self.rpath.append(user_lib)
+
+        if isinstance(self.parallel, str):
+            try:
+                self.parallel = int(self.parallel)
+            except ValueError:
+                raise DistutilsOptionError("parallel should be an integer")
+
+    def run(self) -> None:  # noqa: C901
+        # 'self.extensions', as supplied by setup.py, is a list of
+        # Extension instances.  See the documentation for Extension (in
+        # distutils.extension) for details.
+        #
+        # For backwards compatibility with Distutils 0.8.2 and earlier, we
+        # also allow the 'extensions' list to be a list of tuples:
+        #    (ext_name, build_info)
+        # where build_info is a dictionary containing everything that
+        # Extension instances do except the name, with a few things being
+        # differently named.  We convert these 2-tuples to Extension
+        # instances as needed.
+
+        if not self.extensions:
+            return
+
+        # If we were asked to build any C/C++ libraries, make sure that the
+        # directory where we put them is in the library search path for
+        # linking extensions.
+        if self.distribution.has_c_libraries():
+            build_clib = self.get_finalized_command('build_clib')
+            self.libraries.extend(build_clib.get_library_names() or [])
+            self.library_dirs.append(build_clib.build_clib)
+
+        # Setup the CCompiler object that we'll use to do all the
+        # compiling and linking
+        self.compiler = new_compiler(
+            compiler=self.compiler,
+            verbose=self.verbose,
+            dry_run=self.dry_run,
+            force=self.force,
+        )
+        customize_compiler(self.compiler)
+        # If we are cross-compiling, init the compiler now (if we are not
+        # cross-compiling, init would not hurt, but people may rely on
+        # late initialization of compiler even if they shouldn't...)
+        if os.name == 'nt' and self.plat_name != get_platform():
+            self.compiler.initialize(self.plat_name)
+
+        # The official Windows free threaded Python installer doesn't set
+        # Py_GIL_DISABLED because its pyconfig.h is shared with the
+        # default build, so define it here (pypa/setuptools#4662).
+        if os.name == 'nt' and is_freethreaded():
+            self.compiler.define_macro('Py_GIL_DISABLED', '1')
+
+        # And make sure that any compile/link-related options (which might
+        # come from the command-line or from the setup script) are set in
+        # that CCompiler object -- that way, they automatically apply to
+        # all compiling and linking done here.
+        if self.include_dirs is not None:
+            self.compiler.set_include_dirs(self.include_dirs)
+        if self.define is not None:
+            # 'define' option is a list of (name,value) tuples
+            for name, value in self.define:
+                self.compiler.define_macro(name, value)
+        if self.undef is not None:
+            for macro in self.undef:
+                self.compiler.undefine_macro(macro)
+        if self.libraries is not None:
+            self.compiler.set_libraries(self.libraries)
+        if self.library_dirs is not None:
+            self.compiler.set_library_dirs(self.library_dirs)
+        if self.rpath is not None:
+            self.compiler.set_runtime_library_dirs(self.rpath)
+        if self.link_objects is not None:
+            self.compiler.set_link_objects(self.link_objects)
+
+        # Now actually compile and link everything.
+        self.build_extensions()
+
+    def check_extensions_list(self, extensions) -> None:  # noqa: C901
+        """Ensure that the list of extensions (presumably provided as a
+        command option 'extensions') is valid, i.e. it is a list of
+        Extension objects.  We also support the old-style list of 2-tuples,
+        where the tuples are (ext_name, build_info), which are converted to
+        Extension instances here.
+
+        Raise DistutilsSetupError if the structure is invalid anywhere;
+        just returns otherwise.
+        """
+        if not isinstance(extensions, list):
+            raise DistutilsSetupError(
+                "'ext_modules' option must be a list of Extension instances"
+            )
+
+        for i, ext in enumerate(extensions):
+            if isinstance(ext, Extension):
+                continue  # OK! (assume type-checking done
+                # by Extension constructor)
+
+            if not isinstance(ext, tuple) or len(ext) != 2:
+                raise DistutilsSetupError(
+                    "each element of 'ext_modules' option must be an "
+                    "Extension instance or 2-tuple"
+                )
+
+            ext_name, build_info = ext
+
+            log.warning(
+                "old-style (ext_name, build_info) tuple found in "
+                "ext_modules for extension '%s' "
+                "-- please convert to Extension instance",
+                ext_name,
+            )
+
+            if not (isinstance(ext_name, str) and extension_name_re.match(ext_name)):
+                raise DistutilsSetupError(
+                    "first element of each tuple in 'ext_modules' "
+                    "must be the extension name (a string)"
+                )
+
+            if not isinstance(build_info, dict):
+                raise DistutilsSetupError(
+                    "second element of each tuple in 'ext_modules' "
+                    "must be a dictionary (build info)"
+                )
+
+            # OK, the (ext_name, build_info) dict is type-safe: convert it
+            # to an Extension instance.
+            ext = Extension(ext_name, build_info['sources'])
+
+            # Easy stuff: one-to-one mapping from dict elements to
+            # instance attributes.
+            for key in (
+                'include_dirs',
+                'library_dirs',
+                'libraries',
+                'extra_objects',
+                'extra_compile_args',
+                'extra_link_args',
+            ):
+                val = build_info.get(key)
+                if val is not None:
+                    setattr(ext, key, val)
+
+            # Medium-easy stuff: same syntax/semantics, different names.
+            ext.runtime_library_dirs = build_info.get('rpath')
+            if 'def_file' in build_info:
+                log.warning("'def_file' element of build info dict no longer supported")
+
+            # Non-trivial stuff: 'macros' split into 'define_macros'
+            # and 'undef_macros'.
+            macros = build_info.get('macros')
+            if macros:
+                ext.define_macros = []
+                ext.undef_macros = []
+                for macro in macros:
+                    if not (isinstance(macro, tuple) and len(macro) in (1, 2)):
+                        raise DistutilsSetupError(
+                            "'macros' element of build info dict must be 1- or 2-tuple"
+                        )
+                    if len(macro) == 1:
+                        ext.undef_macros.append(macro[0])
+                    elif len(macro) == 2:
+                        ext.define_macros.append(macro)
+
+            extensions[i] = ext
+
+    def get_source_files(self):
+        self.check_extensions_list(self.extensions)
+        filenames = []
+
+        # Wouldn't it be neat if we knew the names of header files too...
+        for ext in self.extensions:
+            filenames.extend(ext.sources)
+        return filenames
+
+    def get_outputs(self):
+        # Sanity check the 'extensions' list -- can't assume this is being
+        # done in the same run as a 'build_extensions()' call (in fact, we
+        # can probably assume that it *isn't*!).
+        self.check_extensions_list(self.extensions)
+
+        # And build the list of output (built) filenames.  Note that this
+        # ignores the 'inplace' flag, and assumes everything goes in the
+        # "build" tree.
+        return [self.get_ext_fullpath(ext.name) for ext in self.extensions]
+
+    def build_extensions(self) -> None:
+        # First, sanity-check the 'extensions' list
+        self.check_extensions_list(self.extensions)
+        if self.parallel:
+            self._build_extensions_parallel()
+        else:
+            self._build_extensions_serial()
+
+    def _build_extensions_parallel(self):
+        workers = self.parallel
+        if self.parallel is True:
+            workers = os.cpu_count()  # may return None
+        try:
+            from concurrent.futures import ThreadPoolExecutor
+        except ImportError:
+            workers = None
+
+        if workers is None:
+            self._build_extensions_serial()
+            return
+
+        with ThreadPoolExecutor(max_workers=workers) as executor:
+            futures = [
+                executor.submit(self.build_extension, ext) for ext in self.extensions
+            ]
+            for ext, fut in zip(self.extensions, futures):
+                with self._filter_build_errors(ext):
+                    fut.result()
+
+    def _build_extensions_serial(self):
+        for ext in self.extensions:
+            with self._filter_build_errors(ext):
+                self.build_extension(ext)
+
+    @contextlib.contextmanager
+    def _filter_build_errors(self, ext):
+        try:
+            yield
+        except (CCompilerError, DistutilsError, CompileError) as e:
+            if not ext.optional:
+                raise
+            self.warn(f'building extension "{ext.name}" failed: {e}')
+
+    def build_extension(self, ext) -> None:
+        sources = ext.sources
+        if sources is None or not isinstance(sources, (list, tuple)):
+            raise DistutilsSetupError(
+                f"in 'ext_modules' option (extension '{ext.name}'), "
+                "'sources' must be present and must be "
+                "a list of source filenames"
+            )
+        # sort to make the resulting .so file build reproducible
+        sources = sorted(sources)
+
+        ext_path = self.get_ext_fullpath(ext.name)
+        depends = sources + ext.depends
+        if not (self.force or newer_group(depends, ext_path, 'newer')):
+            log.debug("skipping '%s' extension (up-to-date)", ext.name)
+            return
+        else:
+            log.info("building '%s' extension", ext.name)
+
+        # First, scan the sources for SWIG definition files (.i), run
+        # SWIG on 'em to create .c files, and modify the sources list
+        # accordingly.
+        sources = self.swig_sources(sources, ext)
+
+        # Next, compile the source code to object files.
+
+        # XXX not honouring 'define_macros' or 'undef_macros' -- the
+        # CCompiler API needs to change to accommodate this, and I
+        # want to do one thing at a time!
+
+        # Two possible sources for extra compiler arguments:
+        #   - 'extra_compile_args' in Extension object
+        #   - CFLAGS environment variable (not particularly
+        #     elegant, but people seem to expect it and I
+        #     guess it's useful)
+        # The environment variable should take precedence, and
+        # any sensible compiler will give precedence to later
+        # command line args.  Hence we combine them in order:
+        extra_args = ext.extra_compile_args or []
+
+        macros = ext.define_macros[:]
+        for undef in ext.undef_macros:
+            macros.append((undef,))
+
+        objects = self.compiler.compile(
+            sources,
+            output_dir=self.build_temp,
+            macros=macros,
+            include_dirs=ext.include_dirs,
+            debug=self.debug,
+            extra_postargs=extra_args,
+            depends=ext.depends,
+        )
+
+        # XXX outdated variable, kept here in case third-part code
+        # needs it.
+        self._built_objects = objects[:]
+
+        # Now link the object files together into a "shared object" --
+        # of course, first we have to figure out all the other things
+        # that go into the mix.
+        if ext.extra_objects:
+            objects.extend(ext.extra_objects)
+        extra_args = ext.extra_link_args or []
+
+        # Detect target language, if not provided
+        language = ext.language or self.compiler.detect_language(sources)
+
+        self.compiler.link_shared_object(
+            objects,
+            ext_path,
+            libraries=self.get_libraries(ext),
+            library_dirs=ext.library_dirs,
+            runtime_library_dirs=ext.runtime_library_dirs,
+            extra_postargs=extra_args,
+            export_symbols=self.get_export_symbols(ext),
+            debug=self.debug,
+            build_temp=self.build_temp,
+            target_lang=language,
+        )
+
+    def swig_sources(self, sources, extension):
+        """Walk the list of source files in 'sources', looking for SWIG
+        interface (.i) files.  Run SWIG on all that are found, and
+        return a modified 'sources' list with SWIG source files replaced
+        by the generated C (or C++) files.
+        """
+        new_sources = []
+        swig_sources = []
+        swig_targets = {}
+
+        # XXX this drops generated C/C++ files into the source tree, which
+        # is fine for developers who want to distribute the generated
+        # source -- but there should be an option to put SWIG output in
+        # the temp dir.
+
+        if self.swig_cpp:
+            log.warning("--swig-cpp is deprecated - use --swig-opts=-c++")
+
+        if (
+            self.swig_cpp
+            or ('-c++' in self.swig_opts)
+            or ('-c++' in extension.swig_opts)
+        ):
+            target_ext = '.cpp'
+        else:
+            target_ext = '.c'
+
+        for source in sources:
+            (base, ext) = os.path.splitext(source)
+            if ext == ".i":  # SWIG interface file
+                new_sources.append(base + '_wrap' + target_ext)
+                swig_sources.append(source)
+                swig_targets[source] = new_sources[-1]
+            else:
+                new_sources.append(source)
+
+        if not swig_sources:
+            return new_sources
+
+        swig = self.swig or self.find_swig()
+        swig_cmd = [swig, "-python"]
+        swig_cmd.extend(self.swig_opts)
+        if self.swig_cpp:
+            swig_cmd.append("-c++")
+
+        # Do not override commandline arguments
+        if not self.swig_opts:
+            swig_cmd.extend(extension.swig_opts)
+
+        for source in swig_sources:
+            target = swig_targets[source]
+            log.info("swigging %s to %s", source, target)
+            self.spawn(swig_cmd + ["-o", target, source])
+
+        return new_sources
+
+    def find_swig(self):
+        """Return the name of the SWIG executable.  On Unix, this is
+        just "swig" -- it should be in the PATH.  Tries a bit harder on
+        Windows.
+        """
+        if os.name == "posix":
+            return "swig"
+        elif os.name == "nt":
+            # Look for SWIG in its standard installation directory on
+            # Windows (or so I presume!).  If we find it there, great;
+            # if not, act like Unix and assume it's in the PATH.
+            for vers in ("1.3", "1.2", "1.1"):
+                fn = os.path.join(f"c:\\swig{vers}", "swig.exe")
+                if os.path.isfile(fn):
+                    return fn
+            else:
+                return "swig.exe"
+        else:
+            raise DistutilsPlatformError(
+                f"I don't know how to find (much less run) SWIG on platform '{os.name}'"
+            )
+
+    # -- Name generators -----------------------------------------------
+    # (extension names, filenames, whatever)
+    def get_ext_fullpath(self, ext_name: str) -> str:
+        """Returns the path of the filename for a given extension.
+
+        The file is located in `build_lib` or directly in the package
+        (inplace option).
+        """
+        fullname = self.get_ext_fullname(ext_name)
+        modpath = fullname.split('.')
+        filename = self.get_ext_filename(modpath[-1])
+
+        if not self.inplace:
+            # no further work needed
+            # returning :
+            #   build_dir/package/path/filename
+            filename = os.path.join(*modpath[:-1] + [filename])
+            return os.path.join(self.build_lib, filename)
+
+        # the inplace option requires to find the package directory
+        # using the build_py command for that
+        package = '.'.join(modpath[0:-1])
+        build_py = self.get_finalized_command('build_py')
+        package_dir = os.path.abspath(build_py.get_package_dir(package))
+
+        # returning
+        #   package_dir/filename
+        return os.path.join(package_dir, filename)
+
+    def get_ext_fullname(self, ext_name: str) -> str:
+        """Returns the fullname of a given extension name.
+
+        Adds the `package.` prefix"""
+        if self.package is None:
+            return ext_name
+        else:
+            return self.package + '.' + ext_name
+
+    def get_ext_filename(self, ext_name: str) -> str:
+        r"""Convert the name of an extension (eg. "foo.bar") into the name
+        of the file from which it will be loaded (eg. "foo/bar.so", or
+        "foo\bar.pyd").
+        """
+        from ..sysconfig import get_config_var
+
+        ext_path = ext_name.split('.')
+        ext_suffix = get_config_var('EXT_SUFFIX')
+        return os.path.join(*ext_path) + ext_suffix
+
+    def get_export_symbols(self, ext: Extension) -> list[str]:
+        """Return the list of symbols that a shared extension has to
+        export.  This either uses 'ext.export_symbols' or, if it's not
+        provided, "PyInit_" + module_name.  Only relevant on Windows, where
+        the .pyd file (DLL) must export the module "PyInit_" function.
+        """
+        name = self._get_module_name_for_symbol(ext)
+        try:
+            # Unicode module name support as defined in PEP-489
+            # https://peps.python.org/pep-0489/#export-hook-name
+            name.encode('ascii')
+        except UnicodeEncodeError:
+            suffix = 'U_' + name.encode('punycode').replace(b'-', b'_').decode('ascii')
+        else:
+            suffix = "_" + name
+
+        initfunc_name = "PyInit" + suffix
+        if initfunc_name not in ext.export_symbols:
+            ext.export_symbols.append(initfunc_name)
+        return ext.export_symbols
+
+    def _get_module_name_for_symbol(self, ext):
+        # Package name should be used for `__init__` modules
+        # https://github.com/python/cpython/issues/80074
+        # https://github.com/pypa/setuptools/issues/4826
+        parts = ext.name.split(".")
+        if parts[-1] == "__init__" and len(parts) >= 2:
+            return parts[-2]
+        return parts[-1]
+
+    def get_libraries(self, ext: Extension) -> list[str]:  # noqa: C901
+        """Return the list of libraries to link against when building a
+        shared extension.  On most platforms, this is just 'ext.libraries';
+        on Windows, we add the Python library (eg. python20.dll).
+        """
+        # The python library is always needed on Windows.  For MSVC, this
+        # is redundant, since the library is mentioned in a pragma in
+        # pyconfig.h that MSVC groks.  The other Windows compilers all seem
+        # to need it mentioned explicitly, though, so that's what we do.
+        # Append '_d' to the python import library on debug builds.
+        if sys.platform == "win32" and not is_mingw():
+            from .._msvccompiler import MSVCCompiler
+
+            if not isinstance(self.compiler, MSVCCompiler):
+                template = "python%d%d"
+                if self.debug:
+                    template = template + '_d'
+                pythonlib = template % (
+                    sys.hexversion >> 24,
+                    (sys.hexversion >> 16) & 0xFF,
+                )
+                # don't extend ext.libraries, it may be shared with other
+                # extensions, it is a reference to the original list
+                return ext.libraries + [pythonlib]
+        else:
+            # On Android only the main executable and LD_PRELOADs are considered
+            # to be RTLD_GLOBAL, all the dependencies of the main executable
+            # remain RTLD_LOCAL and so the shared libraries must be linked with
+            # libpython when python is built with a shared python library (issue
+            # bpo-21536).
+            # On Cygwin (and if required, other POSIX-like platforms based on
+            # Windows like MinGW) it is simply necessary that all symbols in
+            # shared libraries are resolved at link time.
+            from ..sysconfig import get_config_var
+
+            link_libpython = False
+            if get_config_var('Py_ENABLE_SHARED'):
+                # A native build on an Android device or on Cygwin
+                if hasattr(sys, 'getandroidapilevel'):
+                    link_libpython = True
+                elif sys.platform == 'cygwin' or is_mingw():
+                    link_libpython = True
+                elif '_PYTHON_HOST_PLATFORM' in os.environ:
+                    # We are cross-compiling for one of the relevant platforms
+                    if get_config_var('ANDROID_API_LEVEL') != 0:
+                        link_libpython = True
+                    elif get_config_var('MACHDEP') == 'cygwin':
+                        link_libpython = True
+
+            if link_libpython:
+                ldversion = get_config_var('LDVERSION')
+                return ext.libraries + ['python' + ldversion]
+
+        return ext.libraries
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build_py.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build_py.py
new file mode 100644
index 0000000000000000000000000000000000000000..a20b076fe7cca9140e05b1cf171b62155995f36e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build_py.py
@@ -0,0 +1,407 @@
+"""distutils.command.build_py
+
+Implements the Distutils 'build_py' command."""
+
+import glob
+import importlib.util
+import os
+import sys
+from distutils._log import log
+from typing import ClassVar
+
+from ..core import Command
+from ..errors import DistutilsFileError, DistutilsOptionError
+from ..util import convert_path
+
+
+class build_py(Command):
+    description = "\"build\" pure Python modules (copy to build directory)"
+
+    user_options = [
+        ('build-lib=', 'd', "directory to \"build\" (copy) to"),
+        ('compile', 'c', "compile .py to .pyc"),
+        ('no-compile', None, "don't compile .py files [default]"),
+        (
+            'optimize=',
+            'O',
+            "also compile with optimization: -O1 for \"python -O\", "
+            "-O2 for \"python -OO\", and -O0 to disable [default: -O0]",
+        ),
+        ('force', 'f', "forcibly build everything (ignore file timestamps)"),
+    ]
+
+    boolean_options: ClassVar[list[str]] = ['compile', 'force']
+    negative_opt: ClassVar[dict[str, str]] = {'no-compile': 'compile'}
+
+    def initialize_options(self):
+        self.build_lib = None
+        self.py_modules = None
+        self.package = None
+        self.package_data = None
+        self.package_dir = None
+        self.compile = False
+        self.optimize = 0
+        self.force = None
+
+    def finalize_options(self) -> None:
+        self.set_undefined_options(
+            'build', ('build_lib', 'build_lib'), ('force', 'force')
+        )
+
+        # Get the distribution options that are aliases for build_py
+        # options -- list of packages and list of modules.
+        self.packages = self.distribution.packages
+        self.py_modules = self.distribution.py_modules
+        self.package_data = self.distribution.package_data
+        self.package_dir = {}
+        if self.distribution.package_dir:
+            for name, path in self.distribution.package_dir.items():
+                self.package_dir[name] = convert_path(path)
+        self.data_files = self.get_data_files()
+
+        # Ick, copied straight from install_lib.py (fancy_getopt needs a
+        # type system!  Hell, *everything* needs a type system!!!)
+        if not isinstance(self.optimize, int):
+            try:
+                self.optimize = int(self.optimize)
+                assert 0 <= self.optimize <= 2
+            except (ValueError, AssertionError):
+                raise DistutilsOptionError("optimize must be 0, 1, or 2")
+
+    def run(self) -> None:
+        # XXX copy_file by default preserves atime and mtime.  IMHO this is
+        # the right thing to do, but perhaps it should be an option -- in
+        # particular, a site administrator might want installed files to
+        # reflect the time of installation rather than the last
+        # modification time before the installed release.
+
+        # XXX copy_file by default preserves mode, which appears to be the
+        # wrong thing to do: if a file is read-only in the working
+        # directory, we want it to be installed read/write so that the next
+        # installation of the same module distribution can overwrite it
+        # without problems.  (This might be a Unix-specific issue.)  Thus
+        # we turn off 'preserve_mode' when copying to the build directory,
+        # since the build directory is supposed to be exactly what the
+        # installation will look like (ie. we preserve mode when
+        # installing).
+
+        # Two options control which modules will be installed: 'packages'
+        # and 'py_modules'.  The former lets us work with whole packages, not
+        # specifying individual modules at all; the latter is for
+        # specifying modules one-at-a-time.
+
+        if self.py_modules:
+            self.build_modules()
+        if self.packages:
+            self.build_packages()
+            self.build_package_data()
+
+        self.byte_compile(self.get_outputs(include_bytecode=False))
+
+    def get_data_files(self):
+        """Generate list of '(package,src_dir,build_dir,filenames)' tuples"""
+        data = []
+        if not self.packages:
+            return data
+        for package in self.packages:
+            # Locate package source directory
+            src_dir = self.get_package_dir(package)
+
+            # Compute package build directory
+            build_dir = os.path.join(*([self.build_lib] + package.split('.')))
+
+            # Length of path to strip from found files
+            plen = 0
+            if src_dir:
+                plen = len(src_dir) + 1
+
+            # Strip directory from globbed filenames
+            filenames = [file[plen:] for file in self.find_data_files(package, src_dir)]
+            data.append((package, src_dir, build_dir, filenames))
+        return data
+
+    def find_data_files(self, package, src_dir):
+        """Return filenames for package's data files in 'src_dir'"""
+        globs = self.package_data.get('', []) + self.package_data.get(package, [])
+        files = []
+        for pattern in globs:
+            # Each pattern has to be converted to a platform-specific path
+            filelist = glob.glob(
+                os.path.join(glob.escape(src_dir), convert_path(pattern))
+            )
+            # Files that match more than one pattern are only added once
+            files.extend([
+                fn for fn in filelist if fn not in files and os.path.isfile(fn)
+            ])
+        return files
+
+    def build_package_data(self) -> None:
+        """Copy data files into build directory"""
+        for _package, src_dir, build_dir, filenames in self.data_files:
+            for filename in filenames:
+                target = os.path.join(build_dir, filename)
+                self.mkpath(os.path.dirname(target))
+                self.copy_file(
+                    os.path.join(src_dir, filename), target, preserve_mode=False
+                )
+
+    def get_package_dir(self, package):
+        """Return the directory, relative to the top of the source
+        distribution, where package 'package' should be found
+        (at least according to the 'package_dir' option, if any)."""
+        path = package.split('.')
+
+        if not self.package_dir:
+            if path:
+                return os.path.join(*path)
+            else:
+                return ''
+        else:
+            tail = []
+            while path:
+                try:
+                    pdir = self.package_dir['.'.join(path)]
+                except KeyError:
+                    tail.insert(0, path[-1])
+                    del path[-1]
+                else:
+                    tail.insert(0, pdir)
+                    return os.path.join(*tail)
+            else:
+                # Oops, got all the way through 'path' without finding a
+                # match in package_dir.  If package_dir defines a directory
+                # for the root (nameless) package, then fallback on it;
+                # otherwise, we might as well have not consulted
+                # package_dir at all, as we just use the directory implied
+                # by 'tail' (which should be the same as the original value
+                # of 'path' at this point).
+                pdir = self.package_dir.get('')
+                if pdir is not None:
+                    tail.insert(0, pdir)
+
+                if tail:
+                    return os.path.join(*tail)
+                else:
+                    return ''
+
+    def check_package(self, package, package_dir):
+        # Empty dir name means current directory, which we can probably
+        # assume exists.  Also, os.path.exists and isdir don't know about
+        # my "empty string means current dir" convention, so we have to
+        # circumvent them.
+        if package_dir != "":
+            if not os.path.exists(package_dir):
+                raise DistutilsFileError(
+                    f"package directory '{package_dir}' does not exist"
+                )
+            if not os.path.isdir(package_dir):
+                raise DistutilsFileError(
+                    f"supposed package directory '{package_dir}' exists, "
+                    "but is not a directory"
+                )
+
+        # Directories without __init__.py are namespace packages (PEP 420).
+        if package:
+            init_py = os.path.join(package_dir, "__init__.py")
+            if os.path.isfile(init_py):
+                return init_py
+
+        # Either not in a package at all (__init__.py not expected), or
+        # __init__.py doesn't exist -- so don't return the filename.
+        return None
+
+    def check_module(self, module, module_file):
+        if not os.path.isfile(module_file):
+            log.warning("file %s (for module %s) not found", module_file, module)
+            return False
+        else:
+            return True
+
+    def find_package_modules(self, package, package_dir):
+        self.check_package(package, package_dir)
+        module_files = glob.glob(os.path.join(glob.escape(package_dir), "*.py"))
+        modules = []
+        setup_script = os.path.abspath(self.distribution.script_name)
+
+        for f in module_files:
+            abs_f = os.path.abspath(f)
+            if abs_f != setup_script:
+                module = os.path.splitext(os.path.basename(f))[0]
+                modules.append((package, module, f))
+            else:
+                self.debug_print(f"excluding {setup_script}")
+        return modules
+
+    def find_modules(self):
+        """Finds individually-specified Python modules, ie. those listed by
+        module name in 'self.py_modules'.  Returns a list of tuples (package,
+        module_base, filename): 'package' is a tuple of the path through
+        package-space to the module; 'module_base' is the bare (no
+        packages, no dots) module name, and 'filename' is the path to the
+        ".py" file (relative to the distribution root) that implements the
+        module.
+        """
+        # Map package names to tuples of useful info about the package:
+        #    (package_dir, checked)
+        # package_dir - the directory where we'll find source files for
+        #   this package
+        # checked - true if we have checked that the package directory
+        #   is valid (exists, contains __init__.py, ... ?)
+        packages = {}
+
+        # List of (package, module, filename) tuples to return
+        modules = []
+
+        # We treat modules-in-packages almost the same as toplevel modules,
+        # just the "package" for a toplevel is empty (either an empty
+        # string or empty list, depending on context).  Differences:
+        #   - don't check for __init__.py in directory for empty package
+        for module in self.py_modules:
+            path = module.split('.')
+            package = '.'.join(path[0:-1])
+            module_base = path[-1]
+
+            try:
+                (package_dir, checked) = packages[package]
+            except KeyError:
+                package_dir = self.get_package_dir(package)
+                checked = False
+
+            if not checked:
+                init_py = self.check_package(package, package_dir)
+                packages[package] = (package_dir, 1)
+                if init_py:
+                    modules.append((package, "__init__", init_py))
+
+            # XXX perhaps we should also check for just .pyc files
+            # (so greedy closed-source bastards can distribute Python
+            # modules too)
+            module_file = os.path.join(package_dir, module_base + ".py")
+            if not self.check_module(module, module_file):
+                continue
+
+            modules.append((package, module_base, module_file))
+
+        return modules
+
+    def find_all_modules(self):
+        """Compute the list of all modules that will be built, whether
+        they are specified one-module-at-a-time ('self.py_modules') or
+        by whole packages ('self.packages').  Return a list of tuples
+        (package, module, module_file), just like 'find_modules()' and
+        'find_package_modules()' do."""
+        modules = []
+        if self.py_modules:
+            modules.extend(self.find_modules())
+        if self.packages:
+            for package in self.packages:
+                package_dir = self.get_package_dir(package)
+                m = self.find_package_modules(package, package_dir)
+                modules.extend(m)
+        return modules
+
+    def get_source_files(self):
+        return [module[-1] for module in self.find_all_modules()]
+
+    def get_module_outfile(self, build_dir, package, module):
+        outfile_path = [build_dir] + list(package) + [module + ".py"]
+        return os.path.join(*outfile_path)
+
+    def get_outputs(self, include_bytecode: bool = True) -> list[str]:
+        modules = self.find_all_modules()
+        outputs = []
+        for package, module, _module_file in modules:
+            package = package.split('.')
+            filename = self.get_module_outfile(self.build_lib, package, module)
+            outputs.append(filename)
+            if include_bytecode:
+                if self.compile:
+                    outputs.append(
+                        importlib.util.cache_from_source(filename, optimization='')
+                    )
+                if self.optimize > 0:
+                    outputs.append(
+                        importlib.util.cache_from_source(
+                            filename, optimization=self.optimize
+                        )
+                    )
+
+        outputs += [
+            os.path.join(build_dir, filename)
+            for package, src_dir, build_dir, filenames in self.data_files
+            for filename in filenames
+        ]
+
+        return outputs
+
+    def build_module(self, module, module_file, package):
+        if isinstance(package, str):
+            package = package.split('.')
+        elif not isinstance(package, (list, tuple)):
+            raise TypeError(
+                "'package' must be a string (dot-separated), list, or tuple"
+            )
+
+        # Now put the module source file into the "build" area -- this is
+        # easy, we just copy it somewhere under self.build_lib (the build
+        # directory for Python source).
+        outfile = self.get_module_outfile(self.build_lib, package, module)
+        dir = os.path.dirname(outfile)
+        self.mkpath(dir)
+        return self.copy_file(module_file, outfile, preserve_mode=False)
+
+    def build_modules(self) -> None:
+        modules = self.find_modules()
+        for package, module, module_file in modules:
+            # Now "build" the module -- ie. copy the source file to
+            # self.build_lib (the build directory for Python source).
+            # (Actually, it gets copied to the directory for this package
+            # under self.build_lib.)
+            self.build_module(module, module_file, package)
+
+    def build_packages(self) -> None:
+        for package in self.packages:
+            # Get list of (package, module, module_file) tuples based on
+            # scanning the package directory.  'package' is only included
+            # in the tuple so that 'find_modules()' and
+            # 'find_package_tuples()' have a consistent interface; it's
+            # ignored here (apart from a sanity check).  Also, 'module' is
+            # the *unqualified* module name (ie. no dots, no package -- we
+            # already know its package!), and 'module_file' is the path to
+            # the .py file, relative to the current directory
+            # (ie. including 'package_dir').
+            package_dir = self.get_package_dir(package)
+            modules = self.find_package_modules(package, package_dir)
+
+            # Now loop over the modules we found, "building" each one (just
+            # copy it to self.build_lib).
+            for package_, module, module_file in modules:
+                assert package == package_
+                self.build_module(module, module_file, package)
+
+    def byte_compile(self, files) -> None:
+        if sys.dont_write_bytecode:
+            self.warn('byte-compiling is disabled, skipping.')
+            return
+
+        from ..util import byte_compile
+
+        prefix = self.build_lib
+        if prefix[-1] != os.sep:
+            prefix = prefix + os.sep
+
+        # XXX this code is essentially the same as the 'byte_compile()
+        # method of the "install_lib" command, except for the determination
+        # of the 'prefix' string.  Hmmm.
+        if self.compile:
+            byte_compile(
+                files, optimize=0, force=self.force, prefix=prefix, dry_run=self.dry_run
+            )
+        if self.optimize > 0:
+            byte_compile(
+                files,
+                optimize=self.optimize,
+                force=self.force,
+                prefix=prefix,
+                dry_run=self.dry_run,
+            )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build_scripts.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build_scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..127c51d8dc2ed5607cf5995ede05f9741c44d2c6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/build_scripts.py
@@ -0,0 +1,160 @@
+"""distutils.command.build_scripts
+
+Implements the Distutils 'build_scripts' command."""
+
+import os
+import re
+import tokenize
+from distutils._log import log
+from stat import ST_MODE
+from typing import ClassVar
+
+from .._modified import newer
+from ..core import Command
+from ..util import convert_path
+
+shebang_pattern = re.compile('^#!.*python[0-9.]*([ \t].*)?$')
+"""
+Pattern matching a Python interpreter indicated in first line of a script.
+"""
+
+# for Setuptools compatibility
+first_line_re = shebang_pattern
+
+
+class build_scripts(Command):
+    description = "\"build\" scripts (copy and fixup #! line)"
+
+    user_options: ClassVar[list[tuple[str, str, str]]] = [
+        ('build-dir=', 'd', "directory to \"build\" (copy) to"),
+        ('force', 'f', "forcibly build everything (ignore file timestamps"),
+        ('executable=', 'e', "specify final destination interpreter path"),
+    ]
+
+    boolean_options: ClassVar[list[str]] = ['force']
+
+    def initialize_options(self):
+        self.build_dir = None
+        self.scripts = None
+        self.force = None
+        self.executable = None
+
+    def finalize_options(self):
+        self.set_undefined_options(
+            'build',
+            ('build_scripts', 'build_dir'),
+            ('force', 'force'),
+            ('executable', 'executable'),
+        )
+        self.scripts = self.distribution.scripts
+
+    def get_source_files(self):
+        return self.scripts
+
+    def run(self):
+        if not self.scripts:
+            return
+        self.copy_scripts()
+
+    def copy_scripts(self):
+        """
+        Copy each script listed in ``self.scripts``.
+
+        If a script is marked as a Python script (first line matches
+        'shebang_pattern', i.e. starts with ``#!`` and contains
+        "python"), then adjust in the copy the first line to refer to
+        the current Python interpreter.
+        """
+        self.mkpath(self.build_dir)
+        outfiles = []
+        updated_files = []
+        for script in self.scripts:
+            self._copy_script(script, outfiles, updated_files)
+
+        self._change_modes(outfiles)
+
+        return outfiles, updated_files
+
+    def _copy_script(self, script, outfiles, updated_files):
+        shebang_match = None
+        script = convert_path(script)
+        outfile = os.path.join(self.build_dir, os.path.basename(script))
+        outfiles.append(outfile)
+
+        if not self.force and not newer(script, outfile):
+            log.debug("not copying %s (up-to-date)", script)
+            return
+
+        # Always open the file, but ignore failures in dry-run mode
+        # in order to attempt to copy directly.
+        try:
+            f = tokenize.open(script)
+        except OSError:
+            if not self.dry_run:
+                raise
+            f = None
+        else:
+            first_line = f.readline()
+            if not first_line:
+                self.warn(f"{script} is an empty file (skipping)")
+                return
+
+            shebang_match = shebang_pattern.match(first_line)
+
+        updated_files.append(outfile)
+        if shebang_match:
+            log.info("copying and adjusting %s -> %s", script, self.build_dir)
+            if not self.dry_run:
+                post_interp = shebang_match.group(1) or ''
+                shebang = f"#!python{post_interp}\n"
+                self._validate_shebang(shebang, f.encoding)
+                with open(outfile, "w", encoding=f.encoding) as outf:
+                    outf.write(shebang)
+                    outf.writelines(f.readlines())
+            if f:
+                f.close()
+        else:
+            if f:
+                f.close()
+            self.copy_file(script, outfile)
+
+    def _change_modes(self, outfiles):
+        if os.name != 'posix':
+            return
+
+        for file in outfiles:
+            self._change_mode(file)
+
+    def _change_mode(self, file):
+        if self.dry_run:
+            log.info("changing mode of %s", file)
+            return
+
+        oldmode = os.stat(file)[ST_MODE] & 0o7777
+        newmode = (oldmode | 0o555) & 0o7777
+        if newmode != oldmode:
+            log.info("changing mode of %s from %o to %o", file, oldmode, newmode)
+            os.chmod(file, newmode)
+
+    @staticmethod
+    def _validate_shebang(shebang, encoding):
+        # Python parser starts to read a script using UTF-8 until
+        # it gets a #coding:xxx cookie. The shebang has to be the
+        # first line of a file, the #coding:xxx cookie cannot be
+        # written before. So the shebang has to be encodable to
+        # UTF-8.
+        try:
+            shebang.encode('utf-8')
+        except UnicodeEncodeError:
+            raise ValueError(f"The shebang ({shebang!r}) is not encodable to utf-8")
+
+        # If the script is encoded to a custom encoding (use a
+        # #coding:xxx cookie), the shebang has to be encodable to
+        # the script encoding too.
+        try:
+            shebang.encode(encoding)
+        except UnicodeEncodeError:
+            raise ValueError(
+                f"The shebang ({shebang!r}) is not encodable "
+                f"to the script encoding ({encoding})"
+            )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/check.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/check.py
new file mode 100644
index 0000000000000000000000000000000000000000..58a823dd3904cf105ed432e458a45d94948fbb4c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/check.py
@@ -0,0 +1,152 @@
+"""distutils.command.check
+
+Implements the Distutils 'check' command.
+"""
+
+import contextlib
+from typing import ClassVar
+
+from ..core import Command
+from ..errors import DistutilsSetupError
+
+with contextlib.suppress(ImportError):
+    import docutils.frontend
+    import docutils.nodes
+    import docutils.parsers.rst
+    import docutils.utils
+
+    class SilentReporter(docutils.utils.Reporter):
+        def __init__(
+            self,
+            source,
+            report_level,
+            halt_level,
+            stream=None,
+            debug=False,
+            encoding='ascii',
+            error_handler='replace',
+        ):
+            self.messages = []
+            super().__init__(
+                source, report_level, halt_level, stream, debug, encoding, error_handler
+            )
+
+        def system_message(self, level, message, *children, **kwargs):
+            self.messages.append((level, message, children, kwargs))
+            return docutils.nodes.system_message(
+                message, *children, level=level, type=self.levels[level], **kwargs
+            )
+
+
+class check(Command):
+    """This command checks the meta-data of the package."""
+
+    description = "perform some checks on the package"
+    user_options: ClassVar[list[tuple[str, str, str]]] = [
+        ('metadata', 'm', 'Verify meta-data'),
+        (
+            'restructuredtext',
+            'r',
+            'Checks if long string meta-data syntax are reStructuredText-compliant',
+        ),
+        ('strict', 's', 'Will exit with an error if a check fails'),
+    ]
+
+    boolean_options: ClassVar[list[str]] = ['metadata', 'restructuredtext', 'strict']
+
+    def initialize_options(self):
+        """Sets default values for options."""
+        self.restructuredtext = False
+        self.metadata = 1
+        self.strict = False
+        self._warnings = 0
+
+    def finalize_options(self):
+        pass
+
+    def warn(self, msg):
+        """Counts the number of warnings that occurs."""
+        self._warnings += 1
+        return Command.warn(self, msg)
+
+    def run(self):
+        """Runs the command."""
+        # perform the various tests
+        if self.metadata:
+            self.check_metadata()
+        if self.restructuredtext:
+            if 'docutils' in globals():
+                try:
+                    self.check_restructuredtext()
+                except TypeError as exc:
+                    raise DistutilsSetupError(str(exc))
+            elif self.strict:
+                raise DistutilsSetupError('The docutils package is needed.')
+
+        # let's raise an error in strict mode, if we have at least
+        # one warning
+        if self.strict and self._warnings > 0:
+            raise DistutilsSetupError('Please correct your package.')
+
+    def check_metadata(self):
+        """Ensures that all required elements of meta-data are supplied.
+
+        Required fields:
+            name, version
+
+        Warns if any are missing.
+        """
+        metadata = self.distribution.metadata
+
+        missing = [
+            attr for attr in ('name', 'version') if not getattr(metadata, attr, None)
+        ]
+
+        if missing:
+            self.warn("missing required meta-data: {}".format(', '.join(missing)))
+
+    def check_restructuredtext(self):
+        """Checks if the long string fields are reST-compliant."""
+        data = self.distribution.get_long_description()
+        for warning in self._check_rst_data(data):
+            line = warning[-1].get('line')
+            if line is None:
+                warning = warning[1]
+            else:
+                warning = f'{warning[1]} (line {line})'
+            self.warn(warning)
+
+    def _check_rst_data(self, data):
+        """Returns warnings when the provided data doesn't compile."""
+        # the include and csv_table directives need this to be a path
+        source_path = self.distribution.script_name or 'setup.py'
+        parser = docutils.parsers.rst.Parser()
+        settings = docutils.frontend.OptionParser(
+            components=(docutils.parsers.rst.Parser,)
+        ).get_default_values()
+        settings.tab_width = 4
+        settings.pep_references = None
+        settings.rfc_references = None
+        reporter = SilentReporter(
+            source_path,
+            settings.report_level,
+            settings.halt_level,
+            stream=settings.warning_stream,
+            debug=settings.debug,
+            encoding=settings.error_encoding,
+            error_handler=settings.error_encoding_error_handler,
+        )
+
+        document = docutils.nodes.document(settings, reporter, source=source_path)
+        document.note_source(source_path, -1)
+        try:
+            parser.parse(data, document)
+        except (AttributeError, TypeError) as e:
+            reporter.messages.append((
+                -1,
+                f'Could not finish the parsing: {e}.',
+                '',
+                {},
+            ))
+
+        return reporter.messages
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/clean.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/clean.py
new file mode 100644
index 0000000000000000000000000000000000000000..23427aba21132ec16122ec17b54794203b02baa5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/clean.py
@@ -0,0 +1,77 @@
+"""distutils.command.clean
+
+Implements the Distutils 'clean' command."""
+
+# contributed by Bastian Kleineidam <calvin@cs.uni-sb.de>, added 2000-03-18
+
+import os
+from distutils._log import log
+from typing import ClassVar
+
+from ..core import Command
+from ..dir_util import remove_tree
+
+
+class clean(Command):
+    description = "clean up temporary files from 'build' command"
+    user_options = [
+        ('build-base=', 'b', "base build directory [default: 'build.build-base']"),
+        (
+            'build-lib=',
+            None,
+            "build directory for all modules [default: 'build.build-lib']",
+        ),
+        ('build-temp=', 't', "temporary build directory [default: 'build.build-temp']"),
+        (
+            'build-scripts=',
+            None,
+            "build directory for scripts [default: 'build.build-scripts']",
+        ),
+        ('bdist-base=', None, "temporary directory for built distributions"),
+        ('all', 'a', "remove all build output, not just temporary by-products"),
+    ]
+
+    boolean_options: ClassVar[list[str]] = ['all']
+
+    def initialize_options(self):
+        self.build_base = None
+        self.build_lib = None
+        self.build_temp = None
+        self.build_scripts = None
+        self.bdist_base = None
+        self.all = None
+
+    def finalize_options(self):
+        self.set_undefined_options(
+            'build',
+            ('build_base', 'build_base'),
+            ('build_lib', 'build_lib'),
+            ('build_scripts', 'build_scripts'),
+            ('build_temp', 'build_temp'),
+        )
+        self.set_undefined_options('bdist', ('bdist_base', 'bdist_base'))
+
+    def run(self):
+        # remove the build/temp.<plat> directory (unless it's already
+        # gone)
+        if os.path.exists(self.build_temp):
+            remove_tree(self.build_temp, dry_run=self.dry_run)
+        else:
+            log.debug("'%s' does not exist -- can't clean it", self.build_temp)
+
+        if self.all:
+            # remove build directories
+            for directory in (self.build_lib, self.bdist_base, self.build_scripts):
+                if os.path.exists(directory):
+                    remove_tree(directory, dry_run=self.dry_run)
+                else:
+                    log.warning("'%s' does not exist -- can't clean it", directory)
+
+        # just for the heck of it, try to remove the base build directory:
+        # we might have emptied it right now, but if not we don't care
+        if not self.dry_run:
+            try:
+                os.rmdir(self.build_base)
+                log.info("removing '%s'", self.build_base)
+            except OSError:
+                pass
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/config.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..44df823388bb1b056480e44efef24dbf85557f8b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/config.py
@@ -0,0 +1,360 @@
+"""distutils.command.config
+
+Implements the Distutils 'config' command, a (mostly) empty command class
+that exists mainly to be sub-classed by specific module distributions and
+applications.  The idea is that while every "config" command is different,
+at least they're all named the same, and users always see "config" in the
+list of standard commands.  Also, this is a good place to put common
+configure-like tasks: "try to compile this C code", or "figure out where
+this header file lives".
+"""
+
+from __future__ import annotations
+
+import os
+import pathlib
+import re
+from collections.abc import Sequence
+from distutils._log import log
+
+from ..ccompiler import CCompiler, CompileError, LinkError, new_compiler
+from ..core import Command
+from ..errors import DistutilsExecError
+from ..sysconfig import customize_compiler
+
+LANG_EXT = {"c": ".c", "c++": ".cxx"}
+
+
+class config(Command):
+    description = "prepare to build"
+
+    user_options = [
+        ('compiler=', None, "specify the compiler type"),
+        ('cc=', None, "specify the compiler executable"),
+        ('include-dirs=', 'I', "list of directories to search for header files"),
+        ('define=', 'D', "C preprocessor macros to define"),
+        ('undef=', 'U', "C preprocessor macros to undefine"),
+        ('libraries=', 'l', "external C libraries to link with"),
+        ('library-dirs=', 'L', "directories to search for external C libraries"),
+        ('noisy', None, "show every action (compile, link, run, ...) taken"),
+        (
+            'dump-source',
+            None,
+            "dump generated source files before attempting to compile them",
+        ),
+    ]
+
+    # The three standard command methods: since the "config" command
+    # does nothing by default, these are empty.
+
+    def initialize_options(self):
+        self.compiler = None
+        self.cc = None
+        self.include_dirs = None
+        self.libraries = None
+        self.library_dirs = None
+
+        # maximal output for now
+        self.noisy = 1
+        self.dump_source = 1
+
+        # list of temporary files generated along-the-way that we have
+        # to clean at some point
+        self.temp_files = []
+
+    def finalize_options(self):
+        if self.include_dirs is None:
+            self.include_dirs = self.distribution.include_dirs or []
+        elif isinstance(self.include_dirs, str):
+            self.include_dirs = self.include_dirs.split(os.pathsep)
+
+        if self.libraries is None:
+            self.libraries = []
+        elif isinstance(self.libraries, str):
+            self.libraries = [self.libraries]
+
+        if self.library_dirs is None:
+            self.library_dirs = []
+        elif isinstance(self.library_dirs, str):
+            self.library_dirs = self.library_dirs.split(os.pathsep)
+
+    def run(self):
+        pass
+
+    # Utility methods for actual "config" commands.  The interfaces are
+    # loosely based on Autoconf macros of similar names.  Sub-classes
+    # may use these freely.
+
+    def _check_compiler(self):
+        """Check that 'self.compiler' really is a CCompiler object;
+        if not, make it one.
+        """
+        # We do this late, and only on-demand, because this is an expensive
+        # import.
+        if not isinstance(self.compiler, CCompiler):
+            self.compiler = new_compiler(
+                compiler=self.compiler, dry_run=self.dry_run, force=True
+            )
+            customize_compiler(self.compiler)
+            if self.include_dirs:
+                self.compiler.set_include_dirs(self.include_dirs)
+            if self.libraries:
+                self.compiler.set_libraries(self.libraries)
+            if self.library_dirs:
+                self.compiler.set_library_dirs(self.library_dirs)
+
+    def _gen_temp_sourcefile(self, body, headers, lang):
+        filename = "_configtest" + LANG_EXT[lang]
+        with open(filename, "w", encoding='utf-8') as file:
+            if headers:
+                for header in headers:
+                    file.write(f"#include <{header}>\n")
+                file.write("\n")
+            file.write(body)
+            if body[-1] != "\n":
+                file.write("\n")
+        return filename
+
+    def _preprocess(self, body, headers, include_dirs, lang):
+        src = self._gen_temp_sourcefile(body, headers, lang)
+        out = "_configtest.i"
+        self.temp_files.extend([src, out])
+        self.compiler.preprocess(src, out, include_dirs=include_dirs)
+        return (src, out)
+
+    def _compile(self, body, headers, include_dirs, lang):
+        src = self._gen_temp_sourcefile(body, headers, lang)
+        if self.dump_source:
+            dump_file(src, f"compiling '{src}':")
+        (obj,) = self.compiler.object_filenames([src])
+        self.temp_files.extend([src, obj])
+        self.compiler.compile([src], include_dirs=include_dirs)
+        return (src, obj)
+
+    def _link(self, body, headers, include_dirs, libraries, library_dirs, lang):
+        (src, obj) = self._compile(body, headers, include_dirs, lang)
+        prog = os.path.splitext(os.path.basename(src))[0]
+        self.compiler.link_executable(
+            [obj],
+            prog,
+            libraries=libraries,
+            library_dirs=library_dirs,
+            target_lang=lang,
+        )
+
+        if self.compiler.exe_extension is not None:
+            prog = prog + self.compiler.exe_extension
+        self.temp_files.append(prog)
+
+        return (src, obj, prog)
+
+    def _clean(self, *filenames):
+        if not filenames:
+            filenames = self.temp_files
+            self.temp_files = []
+        log.info("removing: %s", ' '.join(filenames))
+        for filename in filenames:
+            try:
+                os.remove(filename)
+            except OSError:
+                pass
+
+    # XXX these ignore the dry-run flag: what to do, what to do? even if
+    # you want a dry-run build, you still need some sort of configuration
+    # info.  My inclination is to make it up to the real config command to
+    # consult 'dry_run', and assume a default (minimal) configuration if
+    # true.  The problem with trying to do it here is that you'd have to
+    # return either true or false from all the 'try' methods, neither of
+    # which is correct.
+
+    # XXX need access to the header search path and maybe default macros.
+
+    def try_cpp(self, body=None, headers=None, include_dirs=None, lang="c"):
+        """Construct a source file from 'body' (a string containing lines
+        of C/C++ code) and 'headers' (a list of header files to include)
+        and run it through the preprocessor.  Return true if the
+        preprocessor succeeded, false if there were any errors.
+        ('body' probably isn't of much use, but what the heck.)
+        """
+        self._check_compiler()
+        ok = True
+        try:
+            self._preprocess(body, headers, include_dirs, lang)
+        except CompileError:
+            ok = False
+
+        self._clean()
+        return ok
+
+    def search_cpp(self, pattern, body=None, headers=None, include_dirs=None, lang="c"):
+        """Construct a source file (just like 'try_cpp()'), run it through
+        the preprocessor, and return true if any line of the output matches
+        'pattern'.  'pattern' should either be a compiled regex object or a
+        string containing a regex.  If both 'body' and 'headers' are None,
+        preprocesses an empty file -- which can be useful to determine the
+        symbols the preprocessor and compiler set by default.
+        """
+        self._check_compiler()
+        src, out = self._preprocess(body, headers, include_dirs, lang)
+
+        if isinstance(pattern, str):
+            pattern = re.compile(pattern)
+
+        with open(out, encoding='utf-8') as file:
+            match = any(pattern.search(line) for line in file)
+
+        self._clean()
+        return match
+
+    def try_compile(self, body, headers=None, include_dirs=None, lang="c"):
+        """Try to compile a source file built from 'body' and 'headers'.
+        Return true on success, false otherwise.
+        """
+        self._check_compiler()
+        try:
+            self._compile(body, headers, include_dirs, lang)
+            ok = True
+        except CompileError:
+            ok = False
+
+        log.info(ok and "success!" or "failure.")
+        self._clean()
+        return ok
+
+    def try_link(
+        self,
+        body,
+        headers=None,
+        include_dirs=None,
+        libraries=None,
+        library_dirs=None,
+        lang="c",
+    ):
+        """Try to compile and link a source file, built from 'body' and
+        'headers', to executable form.  Return true on success, false
+        otherwise.
+        """
+        self._check_compiler()
+        try:
+            self._link(body, headers, include_dirs, libraries, library_dirs, lang)
+            ok = True
+        except (CompileError, LinkError):
+            ok = False
+
+        log.info(ok and "success!" or "failure.")
+        self._clean()
+        return ok
+
+    def try_run(
+        self,
+        body,
+        headers=None,
+        include_dirs=None,
+        libraries=None,
+        library_dirs=None,
+        lang="c",
+    ):
+        """Try to compile, link to an executable, and run a program
+        built from 'body' and 'headers'.  Return true on success, false
+        otherwise.
+        """
+        self._check_compiler()
+        try:
+            src, obj, exe = self._link(
+                body, headers, include_dirs, libraries, library_dirs, lang
+            )
+            self.spawn([exe])
+            ok = True
+        except (CompileError, LinkError, DistutilsExecError):
+            ok = False
+
+        log.info(ok and "success!" or "failure.")
+        self._clean()
+        return ok
+
+    # -- High-level methods --------------------------------------------
+    # (these are the ones that are actually likely to be useful
+    # when implementing a real-world config command!)
+
+    def check_func(
+        self,
+        func,
+        headers=None,
+        include_dirs=None,
+        libraries=None,
+        library_dirs=None,
+        decl=False,
+        call=False,
+    ):
+        """Determine if function 'func' is available by constructing a
+        source file that refers to 'func', and compiles and links it.
+        If everything succeeds, returns true; otherwise returns false.
+
+        The constructed source file starts out by including the header
+        files listed in 'headers'.  If 'decl' is true, it then declares
+        'func' (as "int func()"); you probably shouldn't supply 'headers'
+        and set 'decl' true in the same call, or you might get errors about
+        a conflicting declarations for 'func'.  Finally, the constructed
+        'main()' function either references 'func' or (if 'call' is true)
+        calls it.  'libraries' and 'library_dirs' are used when
+        linking.
+        """
+        self._check_compiler()
+        body = []
+        if decl:
+            body.append(f"int {func} ();")
+        body.append("int main () {")
+        if call:
+            body.append(f"  {func}();")
+        else:
+            body.append(f"  {func};")
+        body.append("}")
+        body = "\n".join(body) + "\n"
+
+        return self.try_link(body, headers, include_dirs, libraries, library_dirs)
+
+    def check_lib(
+        self,
+        library,
+        library_dirs=None,
+        headers=None,
+        include_dirs=None,
+        other_libraries: Sequence[str] = [],
+    ):
+        """Determine if 'library' is available to be linked against,
+        without actually checking that any particular symbols are provided
+        by it.  'headers' will be used in constructing the source file to
+        be compiled, but the only effect of this is to check if all the
+        header files listed are available.  Any libraries listed in
+        'other_libraries' will be included in the link, in case 'library'
+        has symbols that depend on other libraries.
+        """
+        self._check_compiler()
+        return self.try_link(
+            "int main (void) { }",
+            headers,
+            include_dirs,
+            [library] + list(other_libraries),
+            library_dirs,
+        )
+
+    def check_header(self, header, include_dirs=None, library_dirs=None, lang="c"):
+        """Determine if the system header file named by 'header_file'
+        exists and can be found by the preprocessor; return true if so,
+        false otherwise.
+        """
+        return self.try_cpp(
+            body="/* No body */", headers=[header], include_dirs=include_dirs
+        )
+
+
+def dump_file(filename, head=None):
+    """Dumps a file content into log.info.
+
+    If head is not None, will be dumped before the file content.
+    """
+    if head is None:
+        log.info('%s', filename)
+    else:
+        log.info(head)
+    log.info(pathlib.Path(filename).read_text(encoding='utf-8'))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc17e56a8070b26f0e3d09c82250e006f1aed826
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install.py
@@ -0,0 +1,805 @@
+"""distutils.command.install
+
+Implements the Distutils 'install' command."""
+
+from __future__ import annotations
+
+import collections
+import contextlib
+import itertools
+import os
+import sys
+import sysconfig
+from distutils._log import log
+from site import USER_BASE, USER_SITE
+from typing import ClassVar
+
+from ..core import Command
+from ..debug import DEBUG
+from ..errors import DistutilsOptionError, DistutilsPlatformError
+from ..file_util import write_file
+from ..sysconfig import get_config_vars
+from ..util import change_root, convert_path, get_platform, subst_vars
+from . import _framework_compat as fw
+
+HAS_USER_SITE = True
+
+WINDOWS_SCHEME = {
+    'purelib': '{base}/Lib/site-packages',
+    'platlib': '{base}/Lib/site-packages',
+    'headers': '{base}/Include/{dist_name}',
+    'scripts': '{base}/Scripts',
+    'data': '{base}',
+}
+
+INSTALL_SCHEMES = {
+    'posix_prefix': {
+        'purelib': '{base}/lib/{implementation_lower}{py_version_short}/site-packages',
+        'platlib': '{platbase}/{platlibdir}/{implementation_lower}'
+        '{py_version_short}/site-packages',
+        'headers': '{base}/include/{implementation_lower}'
+        '{py_version_short}{abiflags}/{dist_name}',
+        'scripts': '{base}/bin',
+        'data': '{base}',
+    },
+    'posix_home': {
+        'purelib': '{base}/lib/{implementation_lower}',
+        'platlib': '{base}/{platlibdir}/{implementation_lower}',
+        'headers': '{base}/include/{implementation_lower}/{dist_name}',
+        'scripts': '{base}/bin',
+        'data': '{base}',
+    },
+    'nt': WINDOWS_SCHEME,
+    'pypy': {
+        'purelib': '{base}/site-packages',
+        'platlib': '{base}/site-packages',
+        'headers': '{base}/include/{dist_name}',
+        'scripts': '{base}/bin',
+        'data': '{base}',
+    },
+    'pypy_nt': {
+        'purelib': '{base}/site-packages',
+        'platlib': '{base}/site-packages',
+        'headers': '{base}/include/{dist_name}',
+        'scripts': '{base}/Scripts',
+        'data': '{base}',
+    },
+}
+
+# user site schemes
+if HAS_USER_SITE:
+    INSTALL_SCHEMES['nt_user'] = {
+        'purelib': '{usersite}',
+        'platlib': '{usersite}',
+        'headers': '{userbase}/{implementation}{py_version_nodot_plat}'
+        '/Include/{dist_name}',
+        'scripts': '{userbase}/{implementation}{py_version_nodot_plat}/Scripts',
+        'data': '{userbase}',
+    }
+
+    INSTALL_SCHEMES['posix_user'] = {
+        'purelib': '{usersite}',
+        'platlib': '{usersite}',
+        'headers': '{userbase}/include/{implementation_lower}'
+        '{py_version_short}{abiflags}/{dist_name}',
+        'scripts': '{userbase}/bin',
+        'data': '{userbase}',
+    }
+
+
+INSTALL_SCHEMES.update(fw.schemes)
+
+
+# The keys to an installation scheme; if any new types of files are to be
+# installed, be sure to add an entry to every installation scheme above,
+# and to SCHEME_KEYS here.
+SCHEME_KEYS = ('purelib', 'platlib', 'headers', 'scripts', 'data')
+
+
+def _load_sysconfig_schemes():
+    with contextlib.suppress(AttributeError):
+        return {
+            scheme: sysconfig.get_paths(scheme, expand=False)
+            for scheme in sysconfig.get_scheme_names()
+        }
+
+
+def _load_schemes():
+    """
+    Extend default schemes with schemes from sysconfig.
+    """
+
+    sysconfig_schemes = _load_sysconfig_schemes() or {}
+
+    return {
+        scheme: {
+            **INSTALL_SCHEMES.get(scheme, {}),
+            **sysconfig_schemes.get(scheme, {}),
+        }
+        for scheme in set(itertools.chain(INSTALL_SCHEMES, sysconfig_schemes))
+    }
+
+
+def _get_implementation():
+    if hasattr(sys, 'pypy_version_info'):
+        return 'PyPy'
+    else:
+        return 'Python'
+
+
+def _select_scheme(ob, name):
+    scheme = _inject_headers(name, _load_scheme(_resolve_scheme(name)))
+    vars(ob).update(_remove_set(ob, _scheme_attrs(scheme)))
+
+
+def _remove_set(ob, attrs):
+    """
+    Include only attrs that are None in ob.
+    """
+    return {key: value for key, value in attrs.items() if getattr(ob, key) is None}
+
+
+def _resolve_scheme(name):
+    os_name, sep, key = name.partition('_')
+    try:
+        resolved = sysconfig.get_preferred_scheme(key)
+    except Exception:
+        resolved = fw.scheme(name)
+    return resolved
+
+
+def _load_scheme(name):
+    return _load_schemes()[name]
+
+
+def _inject_headers(name, scheme):
+    """
+    Given a scheme name and the resolved scheme,
+    if the scheme does not include headers, resolve
+    the fallback scheme for the name and use headers
+    from it. pypa/distutils#88
+    """
+    # Bypass the preferred scheme, which may not
+    # have defined headers.
+    fallback = _load_scheme(name)
+    scheme.setdefault('headers', fallback['headers'])
+    return scheme
+
+
+def _scheme_attrs(scheme):
+    """Resolve install directories by applying the install schemes."""
+    return {f'install_{key}': scheme[key] for key in SCHEME_KEYS}
+
+
+class install(Command):
+    description = "install everything from build directory"
+
+    user_options = [
+        # Select installation scheme and set base director(y|ies)
+        ('prefix=', None, "installation prefix"),
+        ('exec-prefix=', None, "(Unix only) prefix for platform-specific files"),
+        ('home=', None, "(Unix only) home directory to install under"),
+        # Or, just set the base director(y|ies)
+        (
+            'install-base=',
+            None,
+            "base installation directory (instead of --prefix or --home)",
+        ),
+        (
+            'install-platbase=',
+            None,
+            "base installation directory for platform-specific files (instead of --exec-prefix or --home)",
+        ),
+        ('root=', None, "install everything relative to this alternate root directory"),
+        # Or, explicitly set the installation scheme
+        (
+            'install-purelib=',
+            None,
+            "installation directory for pure Python module distributions",
+        ),
+        (
+            'install-platlib=',
+            None,
+            "installation directory for non-pure module distributions",
+        ),
+        (
+            'install-lib=',
+            None,
+            "installation directory for all module distributions (overrides --install-purelib and --install-platlib)",
+        ),
+        ('install-headers=', None, "installation directory for C/C++ headers"),
+        ('install-scripts=', None, "installation directory for Python scripts"),
+        ('install-data=', None, "installation directory for data files"),
+        # Byte-compilation options -- see install_lib.py for details, as
+        # these are duplicated from there (but only install_lib does
+        # anything with them).
+        ('compile', 'c', "compile .py to .pyc [default]"),
+        ('no-compile', None, "don't compile .py files"),
+        (
+            'optimize=',
+            'O',
+            "also compile with optimization: -O1 for \"python -O\", "
+            "-O2 for \"python -OO\", and -O0 to disable [default: -O0]",
+        ),
+        # Miscellaneous control options
+        ('force', 'f', "force installation (overwrite any existing files)"),
+        ('skip-build', None, "skip rebuilding everything (for testing/debugging)"),
+        # Where to install documentation (eventually!)
+        # ('doc-format=', None, "format of documentation to generate"),
+        # ('install-man=', None, "directory for Unix man pages"),
+        # ('install-html=', None, "directory for HTML documentation"),
+        # ('install-info=', None, "directory for GNU info files"),
+        ('record=', None, "filename in which to record list of installed files"),
+    ]
+
+    boolean_options: ClassVar[list[str]] = ['compile', 'force', 'skip-build']
+
+    if HAS_USER_SITE:
+        user_options.append((
+            'user',
+            None,
+            f"install in user site-package '{USER_SITE}'",
+        ))
+        boolean_options.append('user')
+
+    negative_opt: ClassVar[dict[str, str]] = {'no-compile': 'compile'}
+
+    def initialize_options(self) -> None:
+        """Initializes options."""
+        # High-level options: these select both an installation base
+        # and scheme.
+        self.prefix: str | None = None
+        self.exec_prefix: str | None = None
+        self.home: str | None = None
+        self.user = False
+
+        # These select only the installation base; it's up to the user to
+        # specify the installation scheme (currently, that means supplying
+        # the --install-{platlib,purelib,scripts,data} options).
+        self.install_base = None
+        self.install_platbase = None
+        self.root: str | None = None
+
+        # These options are the actual installation directories; if not
+        # supplied by the user, they are filled in using the installation
+        # scheme implied by prefix/exec-prefix/home and the contents of
+        # that installation scheme.
+        self.install_purelib = None  # for pure module distributions
+        self.install_platlib = None  # non-pure (dists w/ extensions)
+        self.install_headers = None  # for C/C++ headers
+        self.install_lib: str | None = None  # set to either purelib or platlib
+        self.install_scripts = None
+        self.install_data = None
+        self.install_userbase = USER_BASE
+        self.install_usersite = USER_SITE
+
+        self.compile = None
+        self.optimize = None
+
+        # Deprecated
+        # These two are for putting non-packagized distributions into their
+        # own directory and creating a .pth file if it makes sense.
+        # 'extra_path' comes from the setup file; 'install_path_file' can
+        # be turned off if it makes no sense to install a .pth file.  (But
+        # better to install it uselessly than to guess wrong and not
+        # install it when it's necessary and would be used!)  Currently,
+        # 'install_path_file' is always true unless some outsider meddles
+        # with it.
+        self.extra_path = None
+        self.install_path_file = True
+
+        # 'force' forces installation, even if target files are not
+        # out-of-date.  'skip_build' skips running the "build" command,
+        # handy if you know it's not necessary.  'warn_dir' (which is *not*
+        # a user option, it's just there so the bdist_* commands can turn
+        # it off) determines whether we warn about installing to a
+        # directory not in sys.path.
+        self.force = False
+        self.skip_build = False
+        self.warn_dir = True
+
+        # These are only here as a conduit from the 'build' command to the
+        # 'install_*' commands that do the real work.  ('build_base' isn't
+        # actually used anywhere, but it might be useful in future.)  They
+        # are not user options, because if the user told the install
+        # command where the build directory is, that wouldn't affect the
+        # build command.
+        self.build_base = None
+        self.build_lib = None
+
+        # Not defined yet because we don't know anything about
+        # documentation yet.
+        # self.install_man = None
+        # self.install_html = None
+        # self.install_info = None
+
+        self.record = None
+
+    # -- Option finalizing methods -------------------------------------
+    # (This is rather more involved than for most commands,
+    # because this is where the policy for installing third-
+    # party Python modules on various platforms given a wide
+    # array of user input is decided.  Yes, it's quite complex!)
+
+    def finalize_options(self) -> None:  # noqa: C901
+        """Finalizes options."""
+        # This method (and its helpers, like 'finalize_unix()',
+        # 'finalize_other()', and 'select_scheme()') is where the default
+        # installation directories for modules, extension modules, and
+        # anything else we care to install from a Python module
+        # distribution.  Thus, this code makes a pretty important policy
+        # statement about how third-party stuff is added to a Python
+        # installation!  Note that the actual work of installation is done
+        # by the relatively simple 'install_*' commands; they just take
+        # their orders from the installation directory options determined
+        # here.
+
+        # Check for errors/inconsistencies in the options; first, stuff
+        # that's wrong on any platform.
+
+        if (self.prefix or self.exec_prefix or self.home) and (
+            self.install_base or self.install_platbase
+        ):
+            raise DistutilsOptionError(
+                "must supply either prefix/exec-prefix/home or install-base/install-platbase -- not both"
+            )
+
+        if self.home and (self.prefix or self.exec_prefix):
+            raise DistutilsOptionError(
+                "must supply either home or prefix/exec-prefix -- not both"
+            )
+
+        if self.user and (
+            self.prefix
+            or self.exec_prefix
+            or self.home
+            or self.install_base
+            or self.install_platbase
+        ):
+            raise DistutilsOptionError(
+                "can't combine user with prefix, "
+                "exec_prefix/home, or install_(plat)base"
+            )
+
+        # Next, stuff that's wrong (or dubious) only on certain platforms.
+        if os.name != "posix":
+            if self.exec_prefix:
+                self.warn("exec-prefix option ignored on this platform")
+                self.exec_prefix = None
+
+        # Now the interesting logic -- so interesting that we farm it out
+        # to other methods.  The goal of these methods is to set the final
+        # values for the install_{lib,scripts,data,...}  options, using as
+        # input a heady brew of prefix, exec_prefix, home, install_base,
+        # install_platbase, user-supplied versions of
+        # install_{purelib,platlib,lib,scripts,data,...}, and the
+        # install schemes.  Phew!
+
+        self.dump_dirs("pre-finalize_{unix,other}")
+
+        if os.name == 'posix':
+            self.finalize_unix()
+        else:
+            self.finalize_other()
+
+        self.dump_dirs("post-finalize_{unix,other}()")
+
+        # Expand configuration variables, tilde, etc. in self.install_base
+        # and self.install_platbase -- that way, we can use $base or
+        # $platbase in the other installation directories and not worry
+        # about needing recursive variable expansion (shudder).
+
+        py_version = sys.version.split()[0]
+        (prefix, exec_prefix) = get_config_vars('prefix', 'exec_prefix')
+        try:
+            abiflags = sys.abiflags
+        except AttributeError:
+            # sys.abiflags may not be defined on all platforms.
+            abiflags = ''
+        local_vars = {
+            'dist_name': self.distribution.get_name(),
+            'dist_version': self.distribution.get_version(),
+            'dist_fullname': self.distribution.get_fullname(),
+            'py_version': py_version,
+            'py_version_short': f'{sys.version_info.major}.{sys.version_info.minor}',
+            'py_version_nodot': f'{sys.version_info.major}{sys.version_info.minor}',
+            'sys_prefix': prefix,
+            'prefix': prefix,
+            'sys_exec_prefix': exec_prefix,
+            'exec_prefix': exec_prefix,
+            'abiflags': abiflags,
+            'platlibdir': getattr(sys, 'platlibdir', 'lib'),
+            'implementation_lower': _get_implementation().lower(),
+            'implementation': _get_implementation(),
+        }
+
+        # vars for compatibility on older Pythons
+        compat_vars = dict(
+            # Python 3.9 and earlier
+            py_version_nodot_plat=getattr(sys, 'winver', '').replace('.', ''),
+        )
+
+        if HAS_USER_SITE:
+            local_vars['userbase'] = self.install_userbase
+            local_vars['usersite'] = self.install_usersite
+
+        self.config_vars = collections.ChainMap(
+            local_vars,
+            sysconfig.get_config_vars(),
+            compat_vars,
+            fw.vars(),
+        )
+
+        self.expand_basedirs()
+
+        self.dump_dirs("post-expand_basedirs()")
+
+        # Now define config vars for the base directories so we can expand
+        # everything else.
+        local_vars['base'] = self.install_base
+        local_vars['platbase'] = self.install_platbase
+
+        if DEBUG:
+            from pprint import pprint
+
+            print("config vars:")
+            pprint(dict(self.config_vars))
+
+        # Expand "~" and configuration variables in the installation
+        # directories.
+        self.expand_dirs()
+
+        self.dump_dirs("post-expand_dirs()")
+
+        # Create directories in the home dir:
+        if self.user:
+            self.create_home_path()
+
+        # Pick the actual directory to install all modules to: either
+        # install_purelib or install_platlib, depending on whether this
+        # module distribution is pure or not.  Of course, if the user
+        # already specified install_lib, use their selection.
+        if self.install_lib is None:
+            if self.distribution.has_ext_modules():  # has extensions: non-pure
+                self.install_lib = self.install_platlib
+            else:
+                self.install_lib = self.install_purelib
+
+        # Convert directories from Unix /-separated syntax to the local
+        # convention.
+        self.convert_paths(
+            'lib',
+            'purelib',
+            'platlib',
+            'scripts',
+            'data',
+            'headers',
+            'userbase',
+            'usersite',
+        )
+
+        # Deprecated
+        # Well, we're not actually fully completely finalized yet: we still
+        # have to deal with 'extra_path', which is the hack for allowing
+        # non-packagized module distributions (hello, Numerical Python!) to
+        # get their own directories.
+        self.handle_extra_path()
+        self.install_libbase = self.install_lib  # needed for .pth file
+        self.install_lib = os.path.join(self.install_lib, self.extra_dirs)
+
+        # If a new root directory was supplied, make all the installation
+        # dirs relative to it.
+        if self.root is not None:
+            self.change_roots(
+                'libbase', 'lib', 'purelib', 'platlib', 'scripts', 'data', 'headers'
+            )
+
+        self.dump_dirs("after prepending root")
+
+        # Find out the build directories, ie. where to install from.
+        self.set_undefined_options(
+            'build', ('build_base', 'build_base'), ('build_lib', 'build_lib')
+        )
+
+        # Punt on doc directories for now -- after all, we're punting on
+        # documentation completely!
+
+    def dump_dirs(self, msg) -> None:
+        """Dumps the list of user options."""
+        if not DEBUG:
+            return
+        from ..fancy_getopt import longopt_xlate
+
+        log.debug(msg + ":")
+        for opt in self.user_options:
+            opt_name = opt[0]
+            if opt_name[-1] == "=":
+                opt_name = opt_name[0:-1]
+            if opt_name in self.negative_opt:
+                opt_name = self.negative_opt[opt_name]
+                opt_name = opt_name.translate(longopt_xlate)
+                val = not getattr(self, opt_name)
+            else:
+                opt_name = opt_name.translate(longopt_xlate)
+                val = getattr(self, opt_name)
+            log.debug("  %s: %s", opt_name, val)
+
+    def finalize_unix(self) -> None:
+        """Finalizes options for posix platforms."""
+        if self.install_base is not None or self.install_platbase is not None:
+            incomplete_scheme = (
+                (
+                    self.install_lib is None
+                    and self.install_purelib is None
+                    and self.install_platlib is None
+                )
+                or self.install_headers is None
+                or self.install_scripts is None
+                or self.install_data is None
+            )
+            if incomplete_scheme:
+                raise DistutilsOptionError(
+                    "install-base or install-platbase supplied, but "
+                    "installation scheme is incomplete"
+                )
+            return
+
+        if self.user:
+            if self.install_userbase is None:
+                raise DistutilsPlatformError("User base directory is not specified")
+            self.install_base = self.install_platbase = self.install_userbase
+            self.select_scheme("posix_user")
+        elif self.home is not None:
+            self.install_base = self.install_platbase = self.home
+            self.select_scheme("posix_home")
+        else:
+            if self.prefix is None:
+                if self.exec_prefix is not None:
+                    raise DistutilsOptionError(
+                        "must not supply exec-prefix without prefix"
+                    )
+
+                # Allow Fedora to add components to the prefix
+                _prefix_addition = getattr(sysconfig, '_prefix_addition', "")
+
+                self.prefix = os.path.normpath(sys.prefix) + _prefix_addition
+                self.exec_prefix = os.path.normpath(sys.exec_prefix) + _prefix_addition
+
+            else:
+                if self.exec_prefix is None:
+                    self.exec_prefix = self.prefix
+
+            self.install_base = self.prefix
+            self.install_platbase = self.exec_prefix
+            self.select_scheme("posix_prefix")
+
+    def finalize_other(self) -> None:
+        """Finalizes options for non-posix platforms"""
+        if self.user:
+            if self.install_userbase is None:
+                raise DistutilsPlatformError("User base directory is not specified")
+            self.install_base = self.install_platbase = self.install_userbase
+            self.select_scheme(os.name + "_user")
+        elif self.home is not None:
+            self.install_base = self.install_platbase = self.home
+            self.select_scheme("posix_home")
+        else:
+            if self.prefix is None:
+                self.prefix = os.path.normpath(sys.prefix)
+
+            self.install_base = self.install_platbase = self.prefix
+            try:
+                self.select_scheme(os.name)
+            except KeyError:
+                raise DistutilsPlatformError(
+                    f"I don't know how to install stuff on '{os.name}'"
+                )
+
+    def select_scheme(self, name) -> None:
+        _select_scheme(self, name)
+
+    def _expand_attrs(self, attrs):
+        for attr in attrs:
+            val = getattr(self, attr)
+            if val is not None:
+                if os.name in ('posix', 'nt'):
+                    val = os.path.expanduser(val)
+                val = subst_vars(val, self.config_vars)
+                setattr(self, attr, val)
+
+    def expand_basedirs(self) -> None:
+        """Calls `os.path.expanduser` on install_base, install_platbase and
+        root."""
+        self._expand_attrs(['install_base', 'install_platbase', 'root'])
+
+    def expand_dirs(self) -> None:
+        """Calls `os.path.expanduser` on install dirs."""
+        self._expand_attrs([
+            'install_purelib',
+            'install_platlib',
+            'install_lib',
+            'install_headers',
+            'install_scripts',
+            'install_data',
+        ])
+
+    def convert_paths(self, *names) -> None:
+        """Call `convert_path` over `names`."""
+        for name in names:
+            attr = "install_" + name
+            setattr(self, attr, convert_path(getattr(self, attr)))
+
+    def handle_extra_path(self) -> None:
+        """Set `path_file` and `extra_dirs` using `extra_path`."""
+        if self.extra_path is None:
+            self.extra_path = self.distribution.extra_path
+
+        if self.extra_path is not None:
+            log.warning(
+                "Distribution option extra_path is deprecated. "
+                "See issue27919 for details."
+            )
+            if isinstance(self.extra_path, str):
+                self.extra_path = self.extra_path.split(',')
+
+            if len(self.extra_path) == 1:
+                path_file = extra_dirs = self.extra_path[0]
+            elif len(self.extra_path) == 2:
+                path_file, extra_dirs = self.extra_path
+            else:
+                raise DistutilsOptionError(
+                    "'extra_path' option must be a list, tuple, or "
+                    "comma-separated string with 1 or 2 elements"
+                )
+
+            # convert to local form in case Unix notation used (as it
+            # should be in setup scripts)
+            extra_dirs = convert_path(extra_dirs)
+        else:
+            path_file = None
+            extra_dirs = ''
+
+        # XXX should we warn if path_file and not extra_dirs? (in which
+        # case the path file would be harmless but pointless)
+        self.path_file = path_file
+        self.extra_dirs = extra_dirs
+
+    def change_roots(self, *names) -> None:
+        """Change the install directories pointed by name using root."""
+        for name in names:
+            attr = "install_" + name
+            setattr(self, attr, change_root(self.root, getattr(self, attr)))
+
+    def create_home_path(self) -> None:
+        """Create directories under ~."""
+        if not self.user:
+            return
+        home = convert_path(os.path.expanduser("~"))
+        for path in self.config_vars.values():
+            if str(path).startswith(home) and not os.path.isdir(path):
+                self.debug_print(f"os.makedirs('{path}', 0o700)")
+                os.makedirs(path, 0o700)
+
+    # -- Command execution methods -------------------------------------
+
+    def run(self):
+        """Runs the command."""
+        # Obviously have to build before we can install
+        if not self.skip_build:
+            self.run_command('build')
+            # If we built for any other platform, we can't install.
+            build_plat = self.distribution.get_command_obj('build').plat_name
+            # check warn_dir - it is a clue that the 'install' is happening
+            # internally, and not to sys.path, so we don't check the platform
+            # matches what we are running.
+            if self.warn_dir and build_plat != get_platform():
+                raise DistutilsPlatformError("Can't install when cross-compiling")
+
+        # Run all sub-commands (at least those that need to be run)
+        for cmd_name in self.get_sub_commands():
+            self.run_command(cmd_name)
+
+        if self.path_file:
+            self.create_path_file()
+
+        # write list of installed files, if requested.
+        if self.record:
+            outputs = self.get_outputs()
+            if self.root:  # strip any package prefix
+                root_len = len(self.root)
+                for counter in range(len(outputs)):
+                    outputs[counter] = outputs[counter][root_len:]
+            self.execute(
+                write_file,
+                (self.record, outputs),
+                f"writing list of installed files to '{self.record}'",
+            )
+
+        sys_path = map(os.path.normpath, sys.path)
+        sys_path = map(os.path.normcase, sys_path)
+        install_lib = os.path.normcase(os.path.normpath(self.install_lib))
+        if (
+            self.warn_dir
+            and not (self.path_file and self.install_path_file)
+            and install_lib not in sys_path
+        ):
+            log.debug(
+                (
+                    "modules installed to '%s', which is not in "
+                    "Python's module search path (sys.path) -- "
+                    "you'll have to change the search path yourself"
+                ),
+                self.install_lib,
+            )
+
+    def create_path_file(self):
+        """Creates the .pth file"""
+        filename = os.path.join(self.install_libbase, self.path_file + ".pth")
+        if self.install_path_file:
+            self.execute(
+                write_file, (filename, [self.extra_dirs]), f"creating {filename}"
+            )
+        else:
+            self.warn(f"path file '{filename}' not created")
+
+    # -- Reporting methods ---------------------------------------------
+
+    def get_outputs(self):
+        """Assembles the outputs of all the sub-commands."""
+        outputs = []
+        for cmd_name in self.get_sub_commands():
+            cmd = self.get_finalized_command(cmd_name)
+            # Add the contents of cmd.get_outputs(), ensuring
+            # that outputs doesn't contain duplicate entries
+            for filename in cmd.get_outputs():
+                if filename not in outputs:
+                    outputs.append(filename)
+
+        if self.path_file and self.install_path_file:
+            outputs.append(os.path.join(self.install_libbase, self.path_file + ".pth"))
+
+        return outputs
+
+    def get_inputs(self):
+        """Returns the inputs of all the sub-commands"""
+        # XXX gee, this looks familiar ;-(
+        inputs = []
+        for cmd_name in self.get_sub_commands():
+            cmd = self.get_finalized_command(cmd_name)
+            inputs.extend(cmd.get_inputs())
+
+        return inputs
+
+    # -- Predicates for sub-command list -------------------------------
+
+    def has_lib(self):
+        """Returns true if the current distribution has any Python
+        modules to install."""
+        return (
+            self.distribution.has_pure_modules() or self.distribution.has_ext_modules()
+        )
+
+    def has_headers(self):
+        """Returns true if the current distribution has any headers to
+        install."""
+        return self.distribution.has_headers()
+
+    def has_scripts(self):
+        """Returns true if the current distribution has any scripts to.
+        install."""
+        return self.distribution.has_scripts()
+
+    def has_data(self):
+        """Returns true if the current distribution has any data to.
+        install."""
+        return self.distribution.has_data_files()
+
+    # 'sub_commands': a list of commands this command might have to run to
+    # get its work done.  See cmd.py for more info.
+    sub_commands = [
+        ('install_lib', has_lib),
+        ('install_headers', has_headers),
+        ('install_scripts', has_scripts),
+        ('install_data', has_data),
+        ('install_egg_info', lambda self: True),
+    ]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_data.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ad186e8ec61b92967ea1c12cdca51d00e82dd15
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_data.py
@@ -0,0 +1,94 @@
+"""distutils.command.install_data
+
+Implements the Distutils 'install_data' command, for installing
+platform-independent data files."""
+
+# contributed by Bastian Kleineidam
+
+from __future__ import annotations
+
+import functools
+import os
+from collections.abc import Iterable
+from typing import ClassVar
+
+from ..core import Command
+from ..util import change_root, convert_path
+
+
+class install_data(Command):
+    description = "install data files"
+
+    user_options = [
+        (
+            'install-dir=',
+            'd',
+            "base directory for installing data files [default: installation base dir]",
+        ),
+        ('root=', None, "install everything relative to this alternate root directory"),
+        ('force', 'f', "force installation (overwrite existing files)"),
+    ]
+
+    boolean_options: ClassVar[list[str]] = ['force']
+
+    def initialize_options(self):
+        self.install_dir = None
+        self.outfiles = []
+        self.root = None
+        self.force = False
+        self.data_files = self.distribution.data_files
+        self.warn_dir = True
+
+    def finalize_options(self) -> None:
+        self.set_undefined_options(
+            'install',
+            ('install_data', 'install_dir'),
+            ('root', 'root'),
+            ('force', 'force'),
+        )
+
+    def run(self) -> None:
+        self.mkpath(self.install_dir)
+        for f in self.data_files:
+            self._copy(f)
+
+    @functools.singledispatchmethod
+    def _copy(self, f: tuple[str | os.PathLike, Iterable[str | os.PathLike]]):
+        # it's a tuple with path to install to and a list of files
+        dir = convert_path(f[0])
+        if not os.path.isabs(dir):
+            dir = os.path.join(self.install_dir, dir)
+        elif self.root:
+            dir = change_root(self.root, dir)
+        self.mkpath(dir)
+
+        if f[1] == []:
+            # If there are no files listed, the user must be
+            # trying to create an empty directory, so add the
+            # directory to the list of output files.
+            self.outfiles.append(dir)
+        else:
+            # Copy files, adding them to the list of output files.
+            for data in f[1]:
+                data = convert_path(data)
+                (out, _) = self.copy_file(data, dir)
+                self.outfiles.append(out)
+
+    @_copy.register(str)
+    @_copy.register(os.PathLike)
+    def _(self, f: str | os.PathLike):
+        # it's a simple file, so copy it
+        f = convert_path(f)
+        if self.warn_dir:
+            self.warn(
+                "setup script did not provide a directory for "
+                f"'{f}' -- installing right in '{self.install_dir}'"
+            )
+        (out, _) = self.copy_file(f, self.install_dir)
+        self.outfiles.append(out)
+
+    def get_inputs(self):
+        return self.data_files or []
+
+    def get_outputs(self):
+        return self.outfiles
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_egg_info.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_egg_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..230e94ab46f0c87f58428b456e9d2fe98eca27a4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_egg_info.py
@@ -0,0 +1,91 @@
+"""
+distutils.command.install_egg_info
+
+Implements the Distutils 'install_egg_info' command, for installing
+a package's PKG-INFO metadata.
+"""
+
+import os
+import re
+import sys
+from typing import ClassVar
+
+from .. import dir_util
+from .._log import log
+from ..cmd import Command
+
+
+class install_egg_info(Command):
+    """Install an .egg-info file for the package"""
+
+    description = "Install package's PKG-INFO metadata as an .egg-info file"
+    user_options: ClassVar[list[tuple[str, str, str]]] = [
+        ('install-dir=', 'd', "directory to install to"),
+    ]
+
+    def initialize_options(self):
+        self.install_dir = None
+
+    @property
+    def basename(self):
+        """
+        Allow basename to be overridden by child class.
+        Ref pypa/distutils#2.
+        """
+        name = to_filename(safe_name(self.distribution.get_name()))
+        version = to_filename(safe_version(self.distribution.get_version()))
+        return f"{name}-{version}-py{sys.version_info.major}.{sys.version_info.minor}.egg-info"
+
+    def finalize_options(self):
+        self.set_undefined_options('install_lib', ('install_dir', 'install_dir'))
+        self.target = os.path.join(self.install_dir, self.basename)
+        self.outputs = [self.target]
+
+    def run(self):
+        target = self.target
+        if os.path.isdir(target) and not os.path.islink(target):
+            dir_util.remove_tree(target, dry_run=self.dry_run)
+        elif os.path.exists(target):
+            self.execute(os.unlink, (self.target,), "Removing " + target)
+        elif not os.path.isdir(self.install_dir):
+            self.execute(
+                os.makedirs, (self.install_dir,), "Creating " + self.install_dir
+            )
+        log.info("Writing %s", target)
+        if not self.dry_run:
+            with open(target, 'w', encoding='UTF-8') as f:
+                self.distribution.metadata.write_pkg_file(f)
+
+    def get_outputs(self):
+        return self.outputs
+
+
+# The following routines are taken from setuptools' pkg_resources module and
+# can be replaced by importing them from pkg_resources once it is included
+# in the stdlib.
+
+
+def safe_name(name):
+    """Convert an arbitrary string to a standard distribution name
+
+    Any runs of non-alphanumeric/. characters are replaced with a single '-'.
+    """
+    return re.sub('[^A-Za-z0-9.]+', '-', name)
+
+
+def safe_version(version):
+    """Convert an arbitrary string to a standard version string
+
+    Spaces become dots, and all other non-alphanumeric characters become
+    dashes, with runs of multiple dashes condensed to a single dash.
+    """
+    version = version.replace(' ', '.')
+    return re.sub('[^A-Za-z0-9.]+', '-', version)
+
+
+def to_filename(name):
+    """Convert a project or version name to its filename-escaped form
+
+    Any '-' characters are currently replaced with '_'.
+    """
+    return name.replace('-', '_')
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_headers.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_headers.py
new file mode 100644
index 0000000000000000000000000000000000000000..97af1371efcc73423e9b91d7b3ced684ff936377
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_headers.py
@@ -0,0 +1,46 @@
+"""distutils.command.install_headers
+
+Implements the Distutils 'install_headers' command, to install C/C++ header
+files to the Python include directory."""
+
+from typing import ClassVar
+
+from ..core import Command
+
+
+# XXX force is never used
+class install_headers(Command):
+    description = "install C/C++ header files"
+
+    user_options: ClassVar[list[tuple[str, str, str]]] = [
+        ('install-dir=', 'd', "directory to install header files to"),
+        ('force', 'f', "force installation (overwrite existing files)"),
+    ]
+
+    boolean_options: ClassVar[list[str]] = ['force']
+
+    def initialize_options(self):
+        self.install_dir = None
+        self.force = False
+        self.outfiles = []
+
+    def finalize_options(self):
+        self.set_undefined_options(
+            'install', ('install_headers', 'install_dir'), ('force', 'force')
+        )
+
+    def run(self):
+        headers = self.distribution.headers
+        if not headers:
+            return
+
+        self.mkpath(self.install_dir)
+        for header in headers:
+            (out, _) = self.copy_file(header, self.install_dir)
+            self.outfiles.append(out)
+
+    def get_inputs(self):
+        return self.distribution.headers or []
+
+    def get_outputs(self):
+        return self.outfiles
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_lib.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_lib.py
new file mode 100644
index 0000000000000000000000000000000000000000..2aababf8004d4636f039dd0609457387a0346ca0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_lib.py
@@ -0,0 +1,238 @@
+"""distutils.command.install_lib
+
+Implements the Distutils 'install_lib' command
+(install all Python modules)."""
+
+from __future__ import annotations
+
+import importlib.util
+import os
+import sys
+from typing import Any, ClassVar
+
+from ..core import Command
+from ..errors import DistutilsOptionError
+
+# Extension for Python source files.
+PYTHON_SOURCE_EXTENSION = ".py"
+
+
+class install_lib(Command):
+    description = "install all Python modules (extensions and pure Python)"
+
+    # The byte-compilation options are a tad confusing.  Here are the
+    # possible scenarios:
+    #   1) no compilation at all (--no-compile --no-optimize)
+    #   2) compile .pyc only (--compile --no-optimize; default)
+    #   3) compile .pyc and "opt-1" .pyc (--compile --optimize)
+    #   4) compile "opt-1" .pyc only (--no-compile --optimize)
+    #   5) compile .pyc and "opt-2" .pyc (--compile --optimize-more)
+    #   6) compile "opt-2" .pyc only (--no-compile --optimize-more)
+    #
+    # The UI for this is two options, 'compile' and 'optimize'.
+    # 'compile' is strictly boolean, and only decides whether to
+    # generate .pyc files.  'optimize' is three-way (0, 1, or 2), and
+    # decides both whether to generate .pyc files and what level of
+    # optimization to use.
+
+    user_options = [
+        ('install-dir=', 'd', "directory to install to"),
+        ('build-dir=', 'b', "build directory (where to install from)"),
+        ('force', 'f', "force installation (overwrite existing files)"),
+        ('compile', 'c', "compile .py to .pyc [default]"),
+        ('no-compile', None, "don't compile .py files"),
+        (
+            'optimize=',
+            'O',
+            "also compile with optimization: -O1 for \"python -O\", "
+            "-O2 for \"python -OO\", and -O0 to disable [default: -O0]",
+        ),
+        ('skip-build', None, "skip the build steps"),
+    ]
+
+    boolean_options: ClassVar[list[str]] = ['force', 'compile', 'skip-build']
+    negative_opt: ClassVar[dict[str, str]] = {'no-compile': 'compile'}
+
+    def initialize_options(self):
+        # let the 'install' command dictate our installation directory
+        self.install_dir = None
+        self.build_dir = None
+        self.force = False
+        self.compile = None
+        self.optimize = None
+        self.skip_build = None
+
+    def finalize_options(self) -> None:
+        # Get all the information we need to install pure Python modules
+        # from the umbrella 'install' command -- build (source) directory,
+        # install (target) directory, and whether to compile .py files.
+        self.set_undefined_options(
+            'install',
+            ('build_lib', 'build_dir'),
+            ('install_lib', 'install_dir'),
+            ('force', 'force'),
+            ('compile', 'compile'),
+            ('optimize', 'optimize'),
+            ('skip_build', 'skip_build'),
+        )
+
+        if self.compile is None:
+            self.compile = True
+        if self.optimize is None:
+            self.optimize = False
+
+        if not isinstance(self.optimize, int):
+            try:
+                self.optimize = int(self.optimize)
+            except ValueError:
+                pass
+            if self.optimize not in (0, 1, 2):
+                raise DistutilsOptionError("optimize must be 0, 1, or 2")
+
+    def run(self) -> None:
+        # Make sure we have built everything we need first
+        self.build()
+
+        # Install everything: simply dump the entire contents of the build
+        # directory to the installation directory (that's the beauty of
+        # having a build directory!)
+        outfiles = self.install()
+
+        # (Optionally) compile .py to .pyc
+        if outfiles is not None and self.distribution.has_pure_modules():
+            self.byte_compile(outfiles)
+
+    # -- Top-level worker functions ------------------------------------
+    # (called from 'run()')
+
+    def build(self) -> None:
+        if not self.skip_build:
+            if self.distribution.has_pure_modules():
+                self.run_command('build_py')
+            if self.distribution.has_ext_modules():
+                self.run_command('build_ext')
+
+    # Any: https://typing.readthedocs.io/en/latest/guides/writing_stubs.html#the-any-trick
+    def install(self) -> list[str] | Any:
+        if os.path.isdir(self.build_dir):
+            outfiles = self.copy_tree(self.build_dir, self.install_dir)
+        else:
+            self.warn(
+                f"'{self.build_dir}' does not exist -- no Python modules to install"
+            )
+            return
+        return outfiles
+
+    def byte_compile(self, files) -> None:
+        if sys.dont_write_bytecode:
+            self.warn('byte-compiling is disabled, skipping.')
+            return
+
+        from ..util import byte_compile
+
+        # Get the "--root" directory supplied to the "install" command,
+        # and use it as a prefix to strip off the purported filename
+        # encoded in bytecode files.  This is far from complete, but it
+        # should at least generate usable bytecode in RPM distributions.
+        install_root = self.get_finalized_command('install').root
+
+        if self.compile:
+            byte_compile(
+                files,
+                optimize=0,
+                force=self.force,
+                prefix=install_root,
+                dry_run=self.dry_run,
+            )
+        if self.optimize > 0:
+            byte_compile(
+                files,
+                optimize=self.optimize,
+                force=self.force,
+                prefix=install_root,
+                verbose=self.verbose,
+                dry_run=self.dry_run,
+            )
+
+    # -- Utility methods -----------------------------------------------
+
+    def _mutate_outputs(self, has_any, build_cmd, cmd_option, output_dir):
+        if not has_any:
+            return []
+
+        build_cmd = self.get_finalized_command(build_cmd)
+        build_files = build_cmd.get_outputs()
+        build_dir = getattr(build_cmd, cmd_option)
+
+        prefix_len = len(build_dir) + len(os.sep)
+        outputs = [os.path.join(output_dir, file[prefix_len:]) for file in build_files]
+
+        return outputs
+
+    def _bytecode_filenames(self, py_filenames):
+        bytecode_files = []
+        for py_file in py_filenames:
+            # Since build_py handles package data installation, the
+            # list of outputs can contain more than just .py files.
+            # Make sure we only report bytecode for the .py files.
+            ext = os.path.splitext(os.path.normcase(py_file))[1]
+            if ext != PYTHON_SOURCE_EXTENSION:
+                continue
+            if self.compile:
+                bytecode_files.append(
+                    importlib.util.cache_from_source(py_file, optimization='')
+                )
+            if self.optimize > 0:
+                bytecode_files.append(
+                    importlib.util.cache_from_source(
+                        py_file, optimization=self.optimize
+                    )
+                )
+
+        return bytecode_files
+
+    # -- External interface --------------------------------------------
+    # (called by outsiders)
+
+    def get_outputs(self):
+        """Return the list of files that would be installed if this command
+        were actually run.  Not affected by the "dry-run" flag or whether
+        modules have actually been built yet.
+        """
+        pure_outputs = self._mutate_outputs(
+            self.distribution.has_pure_modules(),
+            'build_py',
+            'build_lib',
+            self.install_dir,
+        )
+        if self.compile:
+            bytecode_outputs = self._bytecode_filenames(pure_outputs)
+        else:
+            bytecode_outputs = []
+
+        ext_outputs = self._mutate_outputs(
+            self.distribution.has_ext_modules(),
+            'build_ext',
+            'build_lib',
+            self.install_dir,
+        )
+
+        return pure_outputs + bytecode_outputs + ext_outputs
+
+    def get_inputs(self):
+        """Get the list of files that are input to this command, ie. the
+        files that get installed as they are named in the build tree.
+        The files in this list correspond one-to-one to the output
+        filenames returned by 'get_outputs()'.
+        """
+        inputs = []
+
+        if self.distribution.has_pure_modules():
+            build_py = self.get_finalized_command('build_py')
+            inputs.extend(build_py.get_outputs())
+
+        if self.distribution.has_ext_modules():
+            build_ext = self.get_finalized_command('build_ext')
+            inputs.extend(build_ext.get_outputs())
+
+        return inputs
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_scripts.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..92e8694111ee29bd82a33218e1c7f2a9ecbd3a4e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/install_scripts.py
@@ -0,0 +1,62 @@
+"""distutils.command.install_scripts
+
+Implements the Distutils 'install_scripts' command, for installing
+Python scripts."""
+
+# contributed by Bastian Kleineidam
+
+import os
+from distutils._log import log
+from stat import ST_MODE
+from typing import ClassVar
+
+from ..core import Command
+
+
+class install_scripts(Command):
+    description = "install scripts (Python or otherwise)"
+
+    user_options = [
+        ('install-dir=', 'd', "directory to install scripts to"),
+        ('build-dir=', 'b', "build directory (where to install from)"),
+        ('force', 'f', "force installation (overwrite existing files)"),
+        ('skip-build', None, "skip the build steps"),
+    ]
+
+    boolean_options: ClassVar[list[str]] = ['force', 'skip-build']
+
+    def initialize_options(self):
+        self.install_dir = None
+        self.force = False
+        self.build_dir = None
+        self.skip_build = None
+
+    def finalize_options(self) -> None:
+        self.set_undefined_options('build', ('build_scripts', 'build_dir'))
+        self.set_undefined_options(
+            'install',
+            ('install_scripts', 'install_dir'),
+            ('force', 'force'),
+            ('skip_build', 'skip_build'),
+        )
+
+    def run(self) -> None:
+        if not self.skip_build:
+            self.run_command('build_scripts')
+        self.outfiles = self.copy_tree(self.build_dir, self.install_dir)
+        if os.name == 'posix':
+            # Set the executable bits (owner, group, and world) on
+            # all the scripts we just installed.
+            for file in self.get_outputs():
+                if self.dry_run:
+                    log.info("changing mode of %s", file)
+                else:
+                    mode = ((os.stat(file)[ST_MODE]) | 0o555) & 0o7777
+                    log.info("changing mode of %s to %o", file, mode)
+                    os.chmod(file, mode)
+
+    def get_inputs(self):
+        return self.distribution.scripts or []
+
+    def get_outputs(self):
+        return self.outfiles or []
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/sdist.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/sdist.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3bf0c326aa04c00e8da0e82e8c74afa488be055
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/command/sdist.py
@@ -0,0 +1,521 @@
+"""distutils.command.sdist
+
+Implements the Distutils 'sdist' command (create a source distribution)."""
+
+from __future__ import annotations
+
+import os
+import sys
+from collections.abc import Callable
+from distutils import archive_util, dir_util, file_util
+from distutils._log import log
+from glob import glob
+from itertools import filterfalse
+from typing import ClassVar
+
+from ..core import Command
+from ..errors import DistutilsOptionError, DistutilsTemplateError
+from ..filelist import FileList
+from ..text_file import TextFile
+from ..util import convert_path
+
+
+def show_formats():
+    """Print all possible values for the 'formats' option (used by
+    the "--help-formats" command-line option).
+    """
+    from ..archive_util import ARCHIVE_FORMATS
+    from ..fancy_getopt import FancyGetopt
+
+    formats = sorted(
+        ("formats=" + format, None, ARCHIVE_FORMATS[format][2])
+        for format in ARCHIVE_FORMATS.keys()
+    )
+    FancyGetopt(formats).print_help("List of available source distribution formats:")
+
+
+class sdist(Command):
+    description = "create a source distribution (tarball, zip file, etc.)"
+
+    def checking_metadata(self) -> bool:
+        """Callable used for the check sub-command.
+
+        Placed here so user_options can view it"""
+        return self.metadata_check
+
+    user_options = [
+        ('template=', 't', "name of manifest template file [default: MANIFEST.in]"),
+        ('manifest=', 'm', "name of manifest file [default: MANIFEST]"),
+        (
+            'use-defaults',
+            None,
+            "include the default file set in the manifest "
+            "[default; disable with --no-defaults]",
+        ),
+        ('no-defaults', None, "don't include the default file set"),
+        (
+            'prune',
+            None,
+            "specifically exclude files/directories that should not be "
+            "distributed (build tree, RCS/CVS dirs, etc.) "
+            "[default; disable with --no-prune]",
+        ),
+        ('no-prune', None, "don't automatically exclude anything"),
+        (
+            'manifest-only',
+            'o',
+            "just regenerate the manifest and then stop (implies --force-manifest)",
+        ),
+        (
+            'force-manifest',
+            'f',
+            "forcibly regenerate the manifest and carry on as usual. "
+            "Deprecated: now the manifest is always regenerated.",
+        ),
+        ('formats=', None, "formats for source distribution (comma-separated list)"),
+        (
+            'keep-temp',
+            'k',
+            "keep the distribution tree around after creating " + "archive file(s)",
+        ),
+        (
+            'dist-dir=',
+            'd',
+            "directory to put the source distribution archive(s) in [default: dist]",
+        ),
+        (
+            'metadata-check',
+            None,
+            "Ensure that all required elements of meta-data "
+            "are supplied. Warn if any missing. [default]",
+        ),
+        (
+            'owner=',
+            'u',
+            "Owner name used when creating a tar file [default: current user]",
+        ),
+        (
+            'group=',
+            'g',
+            "Group name used when creating a tar file [default: current group]",
+        ),
+    ]
+
+    boolean_options: ClassVar[list[str]] = [
+        'use-defaults',
+        'prune',
+        'manifest-only',
+        'force-manifest',
+        'keep-temp',
+        'metadata-check',
+    ]
+
+    help_options: ClassVar[list[tuple[str, str | None, str, Callable[[], object]]]] = [
+        ('help-formats', None, "list available distribution formats", show_formats),
+    ]
+
+    negative_opt: ClassVar[dict[str, str]] = {
+        'no-defaults': 'use-defaults',
+        'no-prune': 'prune',
+    }
+
+    sub_commands = [('check', checking_metadata)]
+
+    READMES: ClassVar[tuple[str, ...]] = ('README', 'README.txt', 'README.rst')
+
+    def initialize_options(self):
+        # 'template' and 'manifest' are, respectively, the names of
+        # the manifest template and manifest file.
+        self.template = None
+        self.manifest = None
+
+        # 'use_defaults': if true, we will include the default file set
+        # in the manifest
+        self.use_defaults = True
+        self.prune = True
+
+        self.manifest_only = False
+        self.force_manifest = False
+
+        self.formats = ['gztar']
+        self.keep_temp = False
+        self.dist_dir = None
+
+        self.archive_files = None
+        self.metadata_check = True
+        self.owner = None
+        self.group = None
+
+    def finalize_options(self) -> None:
+        if self.manifest is None:
+            self.manifest = "MANIFEST"
+        if self.template is None:
+            self.template = "MANIFEST.in"
+
+        self.ensure_string_list('formats')
+
+        bad_format = archive_util.check_archive_formats(self.formats)
+        if bad_format:
+            raise DistutilsOptionError(f"unknown archive format '{bad_format}'")
+
+        if self.dist_dir is None:
+            self.dist_dir = "dist"
+
+    def run(self) -> None:
+        # 'filelist' contains the list of files that will make up the
+        # manifest
+        self.filelist = FileList()
+
+        # Run sub commands
+        for cmd_name in self.get_sub_commands():
+            self.run_command(cmd_name)
+
+        # Do whatever it takes to get the list of files to process
+        # (process the manifest template, read an existing manifest,
+        # whatever).  File list is accumulated in 'self.filelist'.
+        self.get_file_list()
+
+        # If user just wanted us to regenerate the manifest, stop now.
+        if self.manifest_only:
+            return
+
+        # Otherwise, go ahead and create the source distribution tarball,
+        # or zipfile, or whatever.
+        self.make_distribution()
+
+    def get_file_list(self) -> None:
+        """Figure out the list of files to include in the source
+        distribution, and put it in 'self.filelist'.  This might involve
+        reading the manifest template (and writing the manifest), or just
+        reading the manifest, or just using the default file set -- it all
+        depends on the user's options.
+        """
+        # new behavior when using a template:
+        # the file list is recalculated every time because
+        # even if MANIFEST.in or setup.py are not changed
+        # the user might have added some files in the tree that
+        # need to be included.
+        #
+        #  This makes --force the default and only behavior with templates.
+        template_exists = os.path.isfile(self.template)
+        if not template_exists and self._manifest_is_not_generated():
+            self.read_manifest()
+            self.filelist.sort()
+            self.filelist.remove_duplicates()
+            return
+
+        if not template_exists:
+            self.warn(
+                ("manifest template '%s' does not exist " + "(using default file list)")
+                % self.template
+            )
+        self.filelist.findall()
+
+        if self.use_defaults:
+            self.add_defaults()
+
+        if template_exists:
+            self.read_template()
+
+        if self.prune:
+            self.prune_file_list()
+
+        self.filelist.sort()
+        self.filelist.remove_duplicates()
+        self.write_manifest()
+
+    def add_defaults(self) -> None:
+        """Add all the default files to self.filelist:
+          - README or README.txt
+          - setup.py
+          - tests/test*.py and test/test*.py
+          - all pure Python modules mentioned in setup script
+          - all files pointed by package_data (build_py)
+          - all files defined in data_files.
+          - all files defined as scripts.
+          - all C sources listed as part of extensions or C libraries
+            in the setup script (doesn't catch C headers!)
+        Warns if (README or README.txt) or setup.py are missing; everything
+        else is optional.
+        """
+        self._add_defaults_standards()
+        self._add_defaults_optional()
+        self._add_defaults_python()
+        self._add_defaults_data_files()
+        self._add_defaults_ext()
+        self._add_defaults_c_libs()
+        self._add_defaults_scripts()
+
+    @staticmethod
+    def _cs_path_exists(fspath):
+        """
+        Case-sensitive path existence check
+
+        >>> sdist._cs_path_exists(__file__)
+        True
+        >>> sdist._cs_path_exists(__file__.upper())
+        False
+        """
+        if not os.path.exists(fspath):
+            return False
+        # make absolute so we always have a directory
+        abspath = os.path.abspath(fspath)
+        directory, filename = os.path.split(abspath)
+        return filename in os.listdir(directory)
+
+    def _add_defaults_standards(self):
+        standards = [self.READMES, self.distribution.script_name]
+        for fn in standards:
+            if isinstance(fn, tuple):
+                alts = fn
+                got_it = False
+                for fn in alts:
+                    if self._cs_path_exists(fn):
+                        got_it = True
+                        self.filelist.append(fn)
+                        break
+
+                if not got_it:
+                    self.warn(
+                        "standard file not found: should have one of " + ', '.join(alts)
+                    )
+            else:
+                if self._cs_path_exists(fn):
+                    self.filelist.append(fn)
+                else:
+                    self.warn(f"standard file '{fn}' not found")
+
+    def _add_defaults_optional(self):
+        optional = ['tests/test*.py', 'test/test*.py', 'setup.cfg']
+        for pattern in optional:
+            files = filter(os.path.isfile, glob(pattern))
+            self.filelist.extend(files)
+
+    def _add_defaults_python(self):
+        # build_py is used to get:
+        #  - python modules
+        #  - files defined in package_data
+        build_py = self.get_finalized_command('build_py')
+
+        # getting python files
+        if self.distribution.has_pure_modules():
+            self.filelist.extend(build_py.get_source_files())
+
+        # getting package_data files
+        # (computed in build_py.data_files by build_py.finalize_options)
+        for _pkg, src_dir, _build_dir, filenames in build_py.data_files:
+            for filename in filenames:
+                self.filelist.append(os.path.join(src_dir, filename))
+
+    def _add_defaults_data_files(self):
+        # getting distribution.data_files
+        if self.distribution.has_data_files():
+            for item in self.distribution.data_files:
+                if isinstance(item, str):
+                    # plain file
+                    item = convert_path(item)
+                    if os.path.isfile(item):
+                        self.filelist.append(item)
+                else:
+                    # a (dirname, filenames) tuple
+                    dirname, filenames = item
+                    for f in filenames:
+                        f = convert_path(f)
+                        if os.path.isfile(f):
+                            self.filelist.append(f)
+
+    def _add_defaults_ext(self):
+        if self.distribution.has_ext_modules():
+            build_ext = self.get_finalized_command('build_ext')
+            self.filelist.extend(build_ext.get_source_files())
+
+    def _add_defaults_c_libs(self):
+        if self.distribution.has_c_libraries():
+            build_clib = self.get_finalized_command('build_clib')
+            self.filelist.extend(build_clib.get_source_files())
+
+    def _add_defaults_scripts(self):
+        if self.distribution.has_scripts():
+            build_scripts = self.get_finalized_command('build_scripts')
+            self.filelist.extend(build_scripts.get_source_files())
+
+    def read_template(self) -> None:
+        """Read and parse manifest template file named by self.template.
+
+        (usually "MANIFEST.in") The parsing and processing is done by
+        'self.filelist', which updates itself accordingly.
+        """
+        log.info("reading manifest template '%s'", self.template)
+        template = TextFile(
+            self.template,
+            strip_comments=True,
+            skip_blanks=True,
+            join_lines=True,
+            lstrip_ws=True,
+            rstrip_ws=True,
+            collapse_join=True,
+        )
+
+        try:
+            while True:
+                line = template.readline()
+                if line is None:  # end of file
+                    break
+
+                try:
+                    self.filelist.process_template_line(line)
+                # the call above can raise a DistutilsTemplateError for
+                # malformed lines, or a ValueError from the lower-level
+                # convert_path function
+                except (DistutilsTemplateError, ValueError) as msg:
+                    self.warn(
+                        f"{template.filename}, line {int(template.current_line)}: {msg}"
+                    )
+        finally:
+            template.close()
+
+    def prune_file_list(self) -> None:
+        """Prune off branches that might slip into the file list as created
+        by 'read_template()', but really don't belong there:
+          * the build tree (typically "build")
+          * the release tree itself (only an issue if we ran "sdist"
+            previously with --keep-temp, or it aborted)
+          * any RCS, CVS, .svn, .hg, .git, .bzr, _darcs directories
+        """
+        build = self.get_finalized_command('build')
+        base_dir = self.distribution.get_fullname()
+
+        self.filelist.exclude_pattern(None, prefix=os.fspath(build.build_base))
+        self.filelist.exclude_pattern(None, prefix=base_dir)
+
+        if sys.platform == 'win32':
+            seps = r'/|\\'
+        else:
+            seps = '/'
+
+        vcs_dirs = ['RCS', 'CVS', r'\.svn', r'\.hg', r'\.git', r'\.bzr', '_darcs']
+        vcs_ptrn = r'(^|{})({})({}).*'.format(seps, '|'.join(vcs_dirs), seps)
+        self.filelist.exclude_pattern(vcs_ptrn, is_regex=True)
+
+    def write_manifest(self) -> None:
+        """Write the file list in 'self.filelist' (presumably as filled in
+        by 'add_defaults()' and 'read_template()') to the manifest file
+        named by 'self.manifest'.
+        """
+        if self._manifest_is_not_generated():
+            log.info(
+                f"not writing to manually maintained manifest file '{self.manifest}'"
+            )
+            return
+
+        content = self.filelist.files[:]
+        content.insert(0, '# file GENERATED by distutils, do NOT edit')
+        self.execute(
+            file_util.write_file,
+            (self.manifest, content),
+            f"writing manifest file '{self.manifest}'",
+        )
+
+    def _manifest_is_not_generated(self):
+        # check for special comment used in 3.1.3 and higher
+        if not os.path.isfile(self.manifest):
+            return False
+
+        with open(self.manifest, encoding='utf-8') as fp:
+            first_line = next(fp)
+        return first_line != '# file GENERATED by distutils, do NOT edit\n'
+
+    def read_manifest(self) -> None:
+        """Read the manifest file (named by 'self.manifest') and use it to
+        fill in 'self.filelist', the list of files to include in the source
+        distribution.
+        """
+        log.info("reading manifest file '%s'", self.manifest)
+        with open(self.manifest, encoding='utf-8') as lines:
+            self.filelist.extend(
+                # ignore comments and blank lines
+                filter(None, filterfalse(is_comment, map(str.strip, lines)))
+            )
+
+    def make_release_tree(self, base_dir, files) -> None:
+        """Create the directory tree that will become the source
+        distribution archive.  All directories implied by the filenames in
+        'files' are created under 'base_dir', and then we hard link or copy
+        (if hard linking is unavailable) those files into place.
+        Essentially, this duplicates the developer's source tree, but in a
+        directory named after the distribution, containing only the files
+        to be distributed.
+        """
+        # Create all the directories under 'base_dir' necessary to
+        # put 'files' there; the 'mkpath()' is just so we don't die
+        # if the manifest happens to be empty.
+        self.mkpath(base_dir)
+        dir_util.create_tree(base_dir, files, dry_run=self.dry_run)
+
+        # And walk over the list of files, either making a hard link (if
+        # os.link exists) to each one that doesn't already exist in its
+        # corresponding location under 'base_dir', or copying each file
+        # that's out-of-date in 'base_dir'.  (Usually, all files will be
+        # out-of-date, because by default we blow away 'base_dir' when
+        # we're done making the distribution archives.)
+
+        if hasattr(os, 'link'):  # can make hard links on this system
+            link = 'hard'
+            msg = f"making hard links in {base_dir}..."
+        else:  # nope, have to copy
+            link = None
+            msg = f"copying files to {base_dir}..."
+
+        if not files:
+            log.warning("no files to distribute -- empty manifest?")
+        else:
+            log.info(msg)
+        for file in files:
+            if not os.path.isfile(file):
+                log.warning("'%s' not a regular file -- skipping", file)
+            else:
+                dest = os.path.join(base_dir, file)
+                self.copy_file(file, dest, link=link)
+
+        self.distribution.metadata.write_pkg_info(base_dir)
+
+    def make_distribution(self) -> None:
+        """Create the source distribution(s).  First, we create the release
+        tree with 'make_release_tree()'; then, we create all required
+        archive files (according to 'self.formats') from the release tree.
+        Finally, we clean up by blowing away the release tree (unless
+        'self.keep_temp' is true).  The list of archive files created is
+        stored so it can be retrieved later by 'get_archive_files()'.
+        """
+        # Don't warn about missing meta-data here -- should be (and is!)
+        # done elsewhere.
+        base_dir = self.distribution.get_fullname()
+        base_name = os.path.join(self.dist_dir, base_dir)
+
+        self.make_release_tree(base_dir, self.filelist.files)
+        archive_files = []  # remember names of files we create
+        # tar archive must be created last to avoid overwrite and remove
+        if 'tar' in self.formats:
+            self.formats.append(self.formats.pop(self.formats.index('tar')))
+
+        for fmt in self.formats:
+            file = self.make_archive(
+                base_name, fmt, base_dir=base_dir, owner=self.owner, group=self.group
+            )
+            archive_files.append(file)
+            self.distribution.dist_files.append(('sdist', '', file))
+
+        self.archive_files = archive_files
+
+        if not self.keep_temp:
+            dir_util.remove_tree(base_dir, dry_run=self.dry_run)
+
+    def get_archive_files(self):
+        """Return the list of archive files created when the command
+        was run, or None if the command hasn't run yet.
+        """
+        return self.archive_files
+
+
+def is_comment(line: str) -> bool:
+    return line.startswith('#')
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/compat/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c43729b09ea5e5bf9d8c0708e63c69b5114a279
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compat/__init__.py
@@ -0,0 +1,18 @@
+from __future__ import annotations
+
+from collections.abc import Iterable
+from typing import TypeVar
+
+_IterableT = TypeVar("_IterableT", bound="Iterable[str]")
+
+
+def consolidate_linker_args(args: _IterableT) -> _IterableT | str:
+    """
+    Ensure the return value is a string for backward compatibility.
+
+    Retain until at least 2025-04-31. See pypa/distutils#246
+    """
+
+    if not all(arg.startswith('-Wl,') for arg in args):
+        return args
+    return '-Wl,' + ','.join(arg.removeprefix('-Wl,') for arg in args)
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/compat/__pycache__/py39.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compat/__pycache__/py39.cpython-312.pyc
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/compat/numpy.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compat/numpy.py
new file mode 100644
index 0000000000000000000000000000000000000000..73eca7acb103b7c7ca8e7a9f7f2d772a64aa344b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compat/numpy.py
@@ -0,0 +1,2 @@
+# required for older numpy versions on Pythons prior to 3.12; see pypa/setuptools#4876
+from ..compilers.C.base import _default_compilers, compiler_class  # noqa: F401
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/compat/py39.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compat/py39.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b436d76586cbcf38164548f3fc849ce1a3ff600
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compat/py39.py
@@ -0,0 +1,66 @@
+import functools
+import itertools
+import platform
+import sys
+
+
+def add_ext_suffix_39(vars):
+    """
+    Ensure vars contains 'EXT_SUFFIX'. pypa/distutils#130
+    """
+    import _imp
+
+    ext_suffix = _imp.extension_suffixes()[0]
+    vars.update(
+        EXT_SUFFIX=ext_suffix,
+        # sysconfig sets SO to match EXT_SUFFIX, so maintain
+        # that expectation.
+        # https://github.com/python/cpython/blob/785cc6770588de087d09e89a69110af2542be208/Lib/sysconfig.py#L671-L673
+        SO=ext_suffix,
+    )
+
+
+needs_ext_suffix = sys.version_info < (3, 10) and platform.system() == 'Windows'
+add_ext_suffix = add_ext_suffix_39 if needs_ext_suffix else lambda vars: None
+
+
+# from more_itertools
+class UnequalIterablesError(ValueError):
+    def __init__(self, details=None):
+        msg = 'Iterables have different lengths'
+        if details is not None:
+            msg += (': index 0 has length {}; index {} has length {}').format(*details)
+
+        super().__init__(msg)
+
+
+# from more_itertools
+def _zip_equal_generator(iterables):
+    _marker = object()
+    for combo in itertools.zip_longest(*iterables, fillvalue=_marker):
+        for val in combo:
+            if val is _marker:
+                raise UnequalIterablesError()
+        yield combo
+
+
+# from more_itertools
+def _zip_equal(*iterables):
+    # Check whether the iterables are all the same size.
+    try:
+        first_size = len(iterables[0])
+        for i, it in enumerate(iterables[1:], 1):
+            size = len(it)
+            if size != first_size:
+                raise UnequalIterablesError(details=(first_size, i, size))
+        # All sizes are equal, we can use the built-in zip.
+        return zip(*iterables)
+    # If any one of the iterables didn't have a length, start reading
+    # them until one runs out.
+    except TypeError:
+        return _zip_equal_generator(iterables)
+
+
+zip_strict = (
+    _zip_equal if sys.version_info < (3, 10) else functools.partial(zip, strict=True)
+)
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+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/base.py
@@ -0,0 +1,1394 @@
+"""distutils.ccompiler
+
+Contains Compiler, an abstract base class that defines the interface
+for the Distutils compiler abstraction model."""
+
+from __future__ import annotations
+
+import os
+import pathlib
+import re
+import sys
+import warnings
+from collections.abc import Callable, Iterable, MutableSequence, Sequence
+from typing import (
+    TYPE_CHECKING,
+    ClassVar,
+    Literal,
+    TypeVar,
+    Union,
+    overload,
+)
+
+from more_itertools import always_iterable
+
+from ..._log import log
+from ..._modified import newer_group
+from ...dir_util import mkpath
+from ...errors import (
+    DistutilsModuleError,
+    DistutilsPlatformError,
+)
+from ...file_util import move_file
+from ...spawn import spawn
+from ...util import execute, is_mingw, split_quoted
+from .errors import (
+    CompileError,
+    LinkError,
+    UnknownFileType,
+)
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias, TypeVarTuple, Unpack
+
+    _Ts = TypeVarTuple("_Ts")
+
+_Macro: TypeAlias = Union[tuple[str], tuple[str, Union[str, None]]]
+_StrPathT = TypeVar("_StrPathT", bound="str | os.PathLike[str]")
+_BytesPathT = TypeVar("_BytesPathT", bound="bytes | os.PathLike[bytes]")
+
+
+class Compiler:
+    """Abstract base class to define the interface that must be implemented
+    by real compiler classes.  Also has some utility methods used by
+    several compiler classes.
+
+    The basic idea behind a compiler abstraction class is that each
+    instance can be used for all the compile/link steps in building a
+    single project.  Thus, attributes common to all of those compile and
+    link steps -- include directories, macros to define, libraries to link
+    against, etc. -- are attributes of the compiler instance.  To allow for
+    variability in how individual files are treated, most of those
+    attributes may be varied on a per-compilation or per-link basis.
+    """
+
+    # 'compiler_type' is a class attribute that identifies this class.  It
+    # keeps code that wants to know what kind of compiler it's dealing with
+    # from having to import all possible compiler classes just to do an
+    # 'isinstance'.  In concrete CCompiler subclasses, 'compiler_type'
+    # should really, really be one of the keys of the 'compiler_class'
+    # dictionary (see below -- used by the 'new_compiler()' factory
+    # function) -- authors of new compiler interface classes are
+    # responsible for updating 'compiler_class'!
+    compiler_type: ClassVar[str] = None  # type: ignore[assignment]
+
+    # XXX things not handled by this compiler abstraction model:
+    #   * client can't provide additional options for a compiler,
+    #     e.g. warning, optimization, debugging flags.  Perhaps this
+    #     should be the domain of concrete compiler abstraction classes
+    #     (UnixCCompiler, MSVCCompiler, etc.) -- or perhaps the base
+    #     class should have methods for the common ones.
+    #   * can't completely override the include or library searchg
+    #     path, ie. no "cc -I -Idir1 -Idir2" or "cc -L -Ldir1 -Ldir2".
+    #     I'm not sure how widely supported this is even by Unix
+    #     compilers, much less on other platforms.  And I'm even less
+    #     sure how useful it is; maybe for cross-compiling, but
+    #     support for that is a ways off.  (And anyways, cross
+    #     compilers probably have a dedicated binary with the
+    #     right paths compiled in.  I hope.)
+    #   * can't do really freaky things with the library list/library
+    #     dirs, e.g. "-Ldir1 -lfoo -Ldir2 -lfoo" to link against
+    #     different versions of libfoo.a in different locations.  I
+    #     think this is useless without the ability to null out the
+    #     library search path anyways.
+
+    executables: ClassVar[dict]
+
+    # Subclasses that rely on the standard filename generation methods
+    # implemented below should override these; see the comment near
+    # those methods ('object_filenames()' et. al.) for details:
+    src_extensions: ClassVar[list[str] | None] = None
+    obj_extension: ClassVar[str | None] = None
+    static_lib_extension: ClassVar[str | None] = None
+    shared_lib_extension: ClassVar[str | None] = None
+    static_lib_format: ClassVar[str | None] = None  # format string
+    shared_lib_format: ClassVar[str | None] = None  # prob. same as static_lib_format
+    exe_extension: ClassVar[str | None] = None
+
+    # Default language settings. language_map is used to detect a source
+    # file or Extension target language, checking source filenames.
+    # language_order is used to detect the language precedence, when deciding
+    # what language to use when mixing source types. For example, if some
+    # extension has two files with ".c" extension, and one with ".cpp", it
+    # is still linked as c++.
+    language_map: ClassVar[dict[str, str]] = {
+        ".c": "c",
+        ".cc": "c++",
+        ".cpp": "c++",
+        ".cxx": "c++",
+        ".m": "objc",
+    }
+    language_order: ClassVar[list[str]] = ["c++", "objc", "c"]
+
+    include_dirs: list[str] = []
+    """
+    include dirs specific to this compiler class
+    """
+
+    library_dirs: list[str] = []
+    """
+    library dirs specific to this compiler class
+    """
+
+    def __init__(
+        self, verbose: bool = False, dry_run: bool = False, force: bool = False
+    ) -> None:
+        self.dry_run = dry_run
+        self.force = force
+        self.verbose = verbose
+
+        # 'output_dir': a common output directory for object, library,
+        # shared object, and shared library files
+        self.output_dir: str | None = None
+
+        # 'macros': a list of macro definitions (or undefinitions).  A
+        # macro definition is a 2-tuple (name, value), where the value is
+        # either a string or None (no explicit value).  A macro
+        # undefinition is a 1-tuple (name,).
+        self.macros: list[_Macro] = []
+
+        # 'include_dirs': a list of directories to search for include files
+        self.include_dirs = []
+
+        # 'libraries': a list of libraries to include in any link
+        # (library names, not filenames: eg. "foo" not "libfoo.a")
+        self.libraries: list[str] = []
+
+        # 'library_dirs': a list of directories to search for libraries
+        self.library_dirs = []
+
+        # 'runtime_library_dirs': a list of directories to search for
+        # shared libraries/objects at runtime
+        self.runtime_library_dirs: list[str] = []
+
+        # 'objects': a list of object files (or similar, such as explicitly
+        # named library files) to include on any link
+        self.objects: list[str] = []
+
+        for key in self.executables.keys():
+            self.set_executable(key, self.executables[key])
+
+    def set_executables(self, **kwargs: str) -> None:
+        """Define the executables (and options for them) that will be run
+        to perform the various stages of compilation.  The exact set of
+        executables that may be specified here depends on the compiler
+        class (via the 'executables' class attribute), but most will have:
+          compiler      the C/C++ compiler
+          linker_so     linker used to create shared objects and libraries
+          linker_exe    linker used to create binary executables
+          archiver      static library creator
+
+        On platforms with a command-line (Unix, DOS/Windows), each of these
+        is a string that will be split into executable name and (optional)
+        list of arguments.  (Splitting the string is done similarly to how
+        Unix shells operate: words are delimited by spaces, but quotes and
+        backslashes can override this.  See
+        'distutils.util.split_quoted()'.)
+        """
+
+        # Note that some CCompiler implementation classes will define class
+        # attributes 'cpp', 'cc', etc. with hard-coded executable names;
+        # this is appropriate when a compiler class is for exactly one
+        # compiler/OS combination (eg. MSVCCompiler).  Other compiler
+        # classes (UnixCCompiler, in particular) are driven by information
+        # discovered at run-time, since there are many different ways to do
+        # basically the same things with Unix C compilers.
+
+        for key in kwargs:
+            if key not in self.executables:
+                raise ValueError(
+                    f"unknown executable '{key}' for class {self.__class__.__name__}"
+                )
+            self.set_executable(key, kwargs[key])
+
+    def set_executable(self, key, value):
+        if isinstance(value, str):
+            setattr(self, key, split_quoted(value))
+        else:
+            setattr(self, key, value)
+
+    def _find_macro(self, name):
+        i = 0
+        for defn in self.macros:
+            if defn[0] == name:
+                return i
+            i += 1
+        return None
+
+    def _check_macro_definitions(self, definitions):
+        """Ensure that every element of 'definitions' is valid."""
+        for defn in definitions:
+            self._check_macro_definition(*defn)
+
+    def _check_macro_definition(self, defn):
+        """
+        Raise a TypeError if defn is not valid.
+
+        A valid definition is either a (name, value) 2-tuple or a (name,) tuple.
+        """
+        if not isinstance(defn, tuple) or not self._is_valid_macro(*defn):
+            raise TypeError(
+                f"invalid macro definition '{defn}': "
+                "must be tuple (string,), (string, string), or (string, None)"
+            )
+
+    @staticmethod
+    def _is_valid_macro(name, value=None):
+        """
+        A valid macro is a ``name : str`` and a ``value : str | None``.
+
+        >>> Compiler._is_valid_macro('foo', None)
+        True
+        """
+        return isinstance(name, str) and isinstance(value, (str, type(None)))
+
+    # -- Bookkeeping methods -------------------------------------------
+
+    def define_macro(self, name: str, value: str | None = None) -> None:
+        """Define a preprocessor macro for all compilations driven by this
+        compiler object.  The optional parameter 'value' should be a
+        string; if it is not supplied, then the macro will be defined
+        without an explicit value and the exact outcome depends on the
+        compiler used (XXX true? does ANSI say anything about this?)
+        """
+        # Delete from the list of macro definitions/undefinitions if
+        # already there (so that this one will take precedence).
+        i = self._find_macro(name)
+        if i is not None:
+            del self.macros[i]
+
+        self.macros.append((name, value))
+
+    def undefine_macro(self, name: str) -> None:
+        """Undefine a preprocessor macro for all compilations driven by
+        this compiler object.  If the same macro is defined by
+        'define_macro()' and undefined by 'undefine_macro()' the last call
+        takes precedence (including multiple redefinitions or
+        undefinitions).  If the macro is redefined/undefined on a
+        per-compilation basis (ie. in the call to 'compile()'), then that
+        takes precedence.
+        """
+        # Delete from the list of macro definitions/undefinitions if
+        # already there (so that this one will take precedence).
+        i = self._find_macro(name)
+        if i is not None:
+            del self.macros[i]
+
+        undefn = (name,)
+        self.macros.append(undefn)
+
+    def add_include_dir(self, dir: str) -> None:
+        """Add 'dir' to the list of directories that will be searched for
+        header files.  The compiler is instructed to search directories in
+        the order in which they are supplied by successive calls to
+        'add_include_dir()'.
+        """
+        self.include_dirs.append(dir)
+
+    def set_include_dirs(self, dirs: list[str]) -> None:
+        """Set the list of directories that will be searched to 'dirs' (a
+        list of strings).  Overrides any preceding calls to
+        'add_include_dir()'; subsequence calls to 'add_include_dir()' add
+        to the list passed to 'set_include_dirs()'.  This does not affect
+        any list of standard include directories that the compiler may
+        search by default.
+        """
+        self.include_dirs = dirs[:]
+
+    def add_library(self, libname: str) -> None:
+        """Add 'libname' to the list of libraries that will be included in
+        all links driven by this compiler object.  Note that 'libname'
+        should *not* be the name of a file containing a library, but the
+        name of the library itself: the actual filename will be inferred by
+        the linker, the compiler, or the compiler class (depending on the
+        platform).
+
+        The linker will be instructed to link against libraries in the
+        order they were supplied to 'add_library()' and/or
+        'set_libraries()'.  It is perfectly valid to duplicate library
+        names; the linker will be instructed to link against libraries as
+        many times as they are mentioned.
+        """
+        self.libraries.append(libname)
+
+    def set_libraries(self, libnames: list[str]) -> None:
+        """Set the list of libraries to be included in all links driven by
+        this compiler object to 'libnames' (a list of strings).  This does
+        not affect any standard system libraries that the linker may
+        include by default.
+        """
+        self.libraries = libnames[:]
+
+    def add_library_dir(self, dir: str) -> None:
+        """Add 'dir' to the list of directories that will be searched for
+        libraries specified to 'add_library()' and 'set_libraries()'.  The
+        linker will be instructed to search for libraries in the order they
+        are supplied to 'add_library_dir()' and/or 'set_library_dirs()'.
+        """
+        self.library_dirs.append(dir)
+
+    def set_library_dirs(self, dirs: list[str]) -> None:
+        """Set the list of library search directories to 'dirs' (a list of
+        strings).  This does not affect any standard library search path
+        that the linker may search by default.
+        """
+        self.library_dirs = dirs[:]
+
+    def add_runtime_library_dir(self, dir: str) -> None:
+        """Add 'dir' to the list of directories that will be searched for
+        shared libraries at runtime.
+        """
+        self.runtime_library_dirs.append(dir)
+
+    def set_runtime_library_dirs(self, dirs: list[str]) -> None:
+        """Set the list of directories to search for shared libraries at
+        runtime to 'dirs' (a list of strings).  This does not affect any
+        standard search path that the runtime linker may search by
+        default.
+        """
+        self.runtime_library_dirs = dirs[:]
+
+    def add_link_object(self, object: str) -> None:
+        """Add 'object' to the list of object files (or analogues, such as
+        explicitly named library files or the output of "resource
+        compilers") to be included in every link driven by this compiler
+        object.
+        """
+        self.objects.append(object)
+
+    def set_link_objects(self, objects: list[str]) -> None:
+        """Set the list of object files (or analogues) to be included in
+        every link to 'objects'.  This does not affect any standard object
+        files that the linker may include by default (such as system
+        libraries).
+        """
+        self.objects = objects[:]
+
+    # -- Private utility methods --------------------------------------
+    # (here for the convenience of subclasses)
+
+    # Helper method to prep compiler in subclass compile() methods
+
+    def _setup_compile(
+        self,
+        outdir: str | None,
+        macros: list[_Macro] | None,
+        incdirs: list[str] | tuple[str, ...] | None,
+        sources,
+        depends,
+        extra,
+    ):
+        """Process arguments and decide which source files to compile."""
+        outdir, macros, incdirs = self._fix_compile_args(outdir, macros, incdirs)
+
+        if extra is None:
+            extra = []
+
+        # Get the list of expected output (object) files
+        objects = self.object_filenames(sources, strip_dir=False, output_dir=outdir)
+        assert len(objects) == len(sources)
+
+        pp_opts = gen_preprocess_options(macros, incdirs)
+
+        build = {}
+        for i in range(len(sources)):
+            src = sources[i]
+            obj = objects[i]
+            ext = os.path.splitext(src)[1]
+            self.mkpath(os.path.dirname(obj))
+            build[obj] = (src, ext)
+
+        return macros, objects, extra, pp_opts, build
+
+    def _get_cc_args(self, pp_opts, debug, before):
+        # works for unixccompiler, cygwinccompiler
+        cc_args = pp_opts + ['-c']
+        if debug:
+            cc_args[:0] = ['-g']
+        if before:
+            cc_args[:0] = before
+        return cc_args
+
+    def _fix_compile_args(
+        self,
+        output_dir: str | None,
+        macros: list[_Macro] | None,
+        include_dirs: list[str] | tuple[str, ...] | None,
+    ) -> tuple[str, list[_Macro], list[str]]:
+        """Typecheck and fix-up some of the arguments to the 'compile()'
+        method, and return fixed-up values.  Specifically: if 'output_dir'
+        is None, replaces it with 'self.output_dir'; ensures that 'macros'
+        is a list, and augments it with 'self.macros'; ensures that
+        'include_dirs' is a list, and augments it with 'self.include_dirs'.
+        Guarantees that the returned values are of the correct type,
+        i.e. for 'output_dir' either string or None, and for 'macros' and
+        'include_dirs' either list or None.
+        """
+        if output_dir is None:
+            output_dir = self.output_dir
+        elif not isinstance(output_dir, str):
+            raise TypeError("'output_dir' must be a string or None")
+
+        if macros is None:
+            macros = list(self.macros)
+        elif isinstance(macros, list):
+            macros = macros + (self.macros or [])
+        else:
+            raise TypeError("'macros' (if supplied) must be a list of tuples")
+
+        if include_dirs is None:
+            include_dirs = list(self.include_dirs)
+        elif isinstance(include_dirs, (list, tuple)):
+            include_dirs = list(include_dirs) + (self.include_dirs or [])
+        else:
+            raise TypeError("'include_dirs' (if supplied) must be a list of strings")
+
+        # add include dirs for class
+        include_dirs += self.__class__.include_dirs
+
+        return output_dir, macros, include_dirs
+
+    def _prep_compile(self, sources, output_dir, depends=None):
+        """Decide which source files must be recompiled.
+
+        Determine the list of object files corresponding to 'sources',
+        and figure out which ones really need to be recompiled.
+        Return a list of all object files and a dictionary telling
+        which source files can be skipped.
+        """
+        # Get the list of expected output (object) files
+        objects = self.object_filenames(sources, output_dir=output_dir)
+        assert len(objects) == len(sources)
+
+        # Return an empty dict for the "which source files can be skipped"
+        # return value to preserve API compatibility.
+        return objects, {}
+
+    def _fix_object_args(
+        self, objects: list[str] | tuple[str, ...], output_dir: str | None
+    ) -> tuple[list[str], str]:
+        """Typecheck and fix up some arguments supplied to various methods.
+        Specifically: ensure that 'objects' is a list; if output_dir is
+        None, replace with self.output_dir.  Return fixed versions of
+        'objects' and 'output_dir'.
+        """
+        if not isinstance(objects, (list, tuple)):
+            raise TypeError("'objects' must be a list or tuple of strings")
+        objects = list(objects)
+
+        if output_dir is None:
+            output_dir = self.output_dir
+        elif not isinstance(output_dir, str):
+            raise TypeError("'output_dir' must be a string or None")
+
+        return (objects, output_dir)
+
+    def _fix_lib_args(
+        self,
+        libraries: list[str] | tuple[str, ...] | None,
+        library_dirs: list[str] | tuple[str, ...] | None,
+        runtime_library_dirs: list[str] | tuple[str, ...] | None,
+    ) -> tuple[list[str], list[str], list[str]]:
+        """Typecheck and fix up some of the arguments supplied to the
+        'link_*' methods.  Specifically: ensure that all arguments are
+        lists, and augment them with their permanent versions
+        (eg. 'self.libraries' augments 'libraries').  Return a tuple with
+        fixed versions of all arguments.
+        """
+        if libraries is None:
+            libraries = list(self.libraries)
+        elif isinstance(libraries, (list, tuple)):
+            libraries = list(libraries) + (self.libraries or [])
+        else:
+            raise TypeError("'libraries' (if supplied) must be a list of strings")
+
+        if library_dirs is None:
+            library_dirs = list(self.library_dirs)
+        elif isinstance(library_dirs, (list, tuple)):
+            library_dirs = list(library_dirs) + (self.library_dirs or [])
+        else:
+            raise TypeError("'library_dirs' (if supplied) must be a list of strings")
+
+        # add library dirs for class
+        library_dirs += self.__class__.library_dirs
+
+        if runtime_library_dirs is None:
+            runtime_library_dirs = list(self.runtime_library_dirs)
+        elif isinstance(runtime_library_dirs, (list, tuple)):
+            runtime_library_dirs = list(runtime_library_dirs) + (
+                self.runtime_library_dirs or []
+            )
+        else:
+            raise TypeError(
+                "'runtime_library_dirs' (if supplied) must be a list of strings"
+            )
+
+        return (libraries, library_dirs, runtime_library_dirs)
+
+    def _need_link(self, objects, output_file):
+        """Return true if we need to relink the files listed in 'objects'
+        to recreate 'output_file'.
+        """
+        if self.force:
+            return True
+        else:
+            if self.dry_run:
+                newer = newer_group(objects, output_file, missing='newer')
+            else:
+                newer = newer_group(objects, output_file)
+            return newer
+
+    def detect_language(self, sources: str | list[str]) -> str | None:
+        """Detect the language of a given file, or list of files. Uses
+        language_map, and language_order to do the job.
+        """
+        if not isinstance(sources, list):
+            sources = [sources]
+        lang = None
+        index = len(self.language_order)
+        for source in sources:
+            base, ext = os.path.splitext(source)
+            extlang = self.language_map.get(ext)
+            try:
+                extindex = self.language_order.index(extlang)
+                if extindex < index:
+                    lang = extlang
+                    index = extindex
+            except ValueError:
+                pass
+        return lang
+
+    # -- Worker methods ------------------------------------------------
+    # (must be implemented by subclasses)
+
+    def preprocess(
+        self,
+        source: str | os.PathLike[str],
+        output_file: str | os.PathLike[str] | None = None,
+        macros: list[_Macro] | None = None,
+        include_dirs: list[str] | tuple[str, ...] | None = None,
+        extra_preargs: list[str] | None = None,
+        extra_postargs: Iterable[str] | None = None,
+    ):
+        """Preprocess a single C/C++ source file, named in 'source'.
+        Output will be written to file named 'output_file', or stdout if
+        'output_file' not supplied.  'macros' is a list of macro
+        definitions as for 'compile()', which will augment the macros set
+        with 'define_macro()' and 'undefine_macro()'.  'include_dirs' is a
+        list of directory names that will be added to the default list.
+
+        Raises PreprocessError on failure.
+        """
+        pass
+
+    def compile(
+        self,
+        sources: Sequence[str | os.PathLike[str]],
+        output_dir: str | None = None,
+        macros: list[_Macro] | None = None,
+        include_dirs: list[str] | tuple[str, ...] | None = None,
+        debug: bool = False,
+        extra_preargs: list[str] | None = None,
+        extra_postargs: list[str] | None = None,
+        depends: list[str] | tuple[str, ...] | None = None,
+    ) -> list[str]:
+        """Compile one or more source files.
+
+        'sources' must be a list of filenames, most likely C/C++
+        files, but in reality anything that can be handled by a
+        particular compiler and compiler class (eg. MSVCCompiler can
+        handle resource files in 'sources').  Return a list of object
+        filenames, one per source filename in 'sources'.  Depending on
+        the implementation, not all source files will necessarily be
+        compiled, but all corresponding object filenames will be
+        returned.
+
+        If 'output_dir' is given, object files will be put under it, while
+        retaining their original path component.  That is, "foo/bar.c"
+        normally compiles to "foo/bar.o" (for a Unix implementation); if
+        'output_dir' is "build", then it would compile to
+        "build/foo/bar.o".
+
+        'macros', if given, must be a list of macro definitions.  A macro
+        definition is either a (name, value) 2-tuple or a (name,) 1-tuple.
+        The former defines a macro; if the value is None, the macro is
+        defined without an explicit value.  The 1-tuple case undefines a
+        macro.  Later definitions/redefinitions/ undefinitions take
+        precedence.
+
+        'include_dirs', if given, must be a list of strings, the
+        directories to add to the default include file search path for this
+        compilation only.
+
+        'debug' is a boolean; if true, the compiler will be instructed to
+        output debug symbols in (or alongside) the object file(s).
+
+        'extra_preargs' and 'extra_postargs' are implementation- dependent.
+        On platforms that have the notion of a command-line (e.g. Unix,
+        DOS/Windows), they are most likely lists of strings: extra
+        command-line arguments to prepend/append to the compiler command
+        line.  On other platforms, consult the implementation class
+        documentation.  In any event, they are intended as an escape hatch
+        for those occasions when the abstract compiler framework doesn't
+        cut the mustard.
+
+        'depends', if given, is a list of filenames that all targets
+        depend on.  If a source file is older than any file in
+        depends, then the source file will be recompiled.  This
+        supports dependency tracking, but only at a coarse
+        granularity.
+
+        Raises CompileError on failure.
+        """
+        # A concrete compiler class can either override this method
+        # entirely or implement _compile().
+        macros, objects, extra_postargs, pp_opts, build = self._setup_compile(
+            output_dir, macros, include_dirs, sources, depends, extra_postargs
+        )
+        cc_args = self._get_cc_args(pp_opts, debug, extra_preargs)
+
+        for obj in objects:
+            try:
+                src, ext = build[obj]
+            except KeyError:
+                continue
+            self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts)
+
+        # Return *all* object filenames, not just the ones we just built.
+        return objects
+
+    def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts):
+        """Compile 'src' to product 'obj'."""
+        # A concrete compiler class that does not override compile()
+        # should implement _compile().
+        pass
+
+    def create_static_lib(
+        self,
+        objects: list[str] | tuple[str, ...],
+        output_libname: str,
+        output_dir: str | None = None,
+        debug: bool = False,
+        target_lang: str | None = None,
+    ) -> None:
+        """Link a bunch of stuff together to create a static library file.
+        The "bunch of stuff" consists of the list of object files supplied
+        as 'objects', the extra object files supplied to
+        'add_link_object()' and/or 'set_link_objects()', the libraries
+        supplied to 'add_library()' and/or 'set_libraries()', and the
+        libraries supplied as 'libraries' (if any).
+
+        'output_libname' should be a library name, not a filename; the
+        filename will be inferred from the library name.  'output_dir' is
+        the directory where the library file will be put.
+
+        'debug' is a boolean; if true, debugging information will be
+        included in the library (note that on most platforms, it is the
+        compile step where this matters: the 'debug' flag is included here
+        just for consistency).
+
+        'target_lang' is the target language for which the given objects
+        are being compiled. This allows specific linkage time treatment of
+        certain languages.
+
+        Raises LibError on failure.
+        """
+        pass
+
+    # values for target_desc parameter in link()
+    SHARED_OBJECT = "shared_object"
+    SHARED_LIBRARY = "shared_library"
+    EXECUTABLE = "executable"
+
+    def link(
+        self,
+        target_desc: str,
+        objects: list[str] | tuple[str, ...],
+        output_filename: str,
+        output_dir: str | None = None,
+        libraries: list[str] | tuple[str, ...] | None = None,
+        library_dirs: list[str] | tuple[str, ...] | None = None,
+        runtime_library_dirs: list[str] | tuple[str, ...] | None = None,
+        export_symbols: Iterable[str] | None = None,
+        debug: bool = False,
+        extra_preargs: list[str] | None = None,
+        extra_postargs: list[str] | None = None,
+        build_temp: str | os.PathLike[str] | None = None,
+        target_lang: str | None = None,
+    ):
+        """Link a bunch of stuff together to create an executable or
+        shared library file.
+
+        The "bunch of stuff" consists of the list of object files supplied
+        as 'objects'.  'output_filename' should be a filename.  If
+        'output_dir' is supplied, 'output_filename' is relative to it
+        (i.e. 'output_filename' can provide directory components if
+        needed).
+
+        'libraries' is a list of libraries to link against.  These are
+        library names, not filenames, since they're translated into
+        filenames in a platform-specific way (eg. "foo" becomes "libfoo.a"
+        on Unix and "foo.lib" on DOS/Windows).  However, they can include a
+        directory component, which means the linker will look in that
+        specific directory rather than searching all the normal locations.
+
+        'library_dirs', if supplied, should be a list of directories to
+        search for libraries that were specified as bare library names
+        (ie. no directory component).  These are on top of the system
+        default and those supplied to 'add_library_dir()' and/or
+        'set_library_dirs()'.  'runtime_library_dirs' is a list of
+        directories that will be embedded into the shared library and used
+        to search for other shared libraries that *it* depends on at
+        run-time.  (This may only be relevant on Unix.)
+
+        'export_symbols' is a list of symbols that the shared library will
+        export.  (This appears to be relevant only on Windows.)
+
+        'debug' is as for 'compile()' and 'create_static_lib()', with the
+        slight distinction that it actually matters on most platforms (as
+        opposed to 'create_static_lib()', which includes a 'debug' flag
+        mostly for form's sake).
+
+        'extra_preargs' and 'extra_postargs' are as for 'compile()' (except
+        of course that they supply command-line arguments for the
+        particular linker being used).
+
+        'target_lang' is the target language for which the given objects
+        are being compiled. This allows specific linkage time treatment of
+        certain languages.
+
+        Raises LinkError on failure.
+        """
+        raise NotImplementedError
+
+    # Old 'link_*()' methods, rewritten to use the new 'link()' method.
+
+    def link_shared_lib(
+        self,
+        objects: list[str] | tuple[str, ...],
+        output_libname: str,
+        output_dir: str | None = None,
+        libraries: list[str] | tuple[str, ...] | None = None,
+        library_dirs: list[str] | tuple[str, ...] | None = None,
+        runtime_library_dirs: list[str] | tuple[str, ...] | None = None,
+        export_symbols: Iterable[str] | None = None,
+        debug: bool = False,
+        extra_preargs: list[str] | None = None,
+        extra_postargs: list[str] | None = None,
+        build_temp: str | os.PathLike[str] | None = None,
+        target_lang: str | None = None,
+    ):
+        self.link(
+            Compiler.SHARED_LIBRARY,
+            objects,
+            self.library_filename(output_libname, lib_type='shared'),
+            output_dir,
+            libraries,
+            library_dirs,
+            runtime_library_dirs,
+            export_symbols,
+            debug,
+            extra_preargs,
+            extra_postargs,
+            build_temp,
+            target_lang,
+        )
+
+    def link_shared_object(
+        self,
+        objects: list[str] | tuple[str, ...],
+        output_filename: str,
+        output_dir: str | None = None,
+        libraries: list[str] | tuple[str, ...] | None = None,
+        library_dirs: list[str] | tuple[str, ...] | None = None,
+        runtime_library_dirs: list[str] | tuple[str, ...] | None = None,
+        export_symbols: Iterable[str] | None = None,
+        debug: bool = False,
+        extra_preargs: list[str] | None = None,
+        extra_postargs: list[str] | None = None,
+        build_temp: str | os.PathLike[str] | None = None,
+        target_lang: str | None = None,
+    ):
+        self.link(
+            Compiler.SHARED_OBJECT,
+            objects,
+            output_filename,
+            output_dir,
+            libraries,
+            library_dirs,
+            runtime_library_dirs,
+            export_symbols,
+            debug,
+            extra_preargs,
+            extra_postargs,
+            build_temp,
+            target_lang,
+        )
+
+    def link_executable(
+        self,
+        objects: list[str] | tuple[str, ...],
+        output_progname: str,
+        output_dir: str | None = None,
+        libraries: list[str] | tuple[str, ...] | None = None,
+        library_dirs: list[str] | tuple[str, ...] | None = None,
+        runtime_library_dirs: list[str] | tuple[str, ...] | None = None,
+        debug: bool = False,
+        extra_preargs: list[str] | None = None,
+        extra_postargs: list[str] | None = None,
+        target_lang: str | None = None,
+    ):
+        self.link(
+            Compiler.EXECUTABLE,
+            objects,
+            self.executable_filename(output_progname),
+            output_dir,
+            libraries,
+            library_dirs,
+            runtime_library_dirs,
+            None,
+            debug,
+            extra_preargs,
+            extra_postargs,
+            None,
+            target_lang,
+        )
+
+    # -- Miscellaneous methods -----------------------------------------
+    # These are all used by the 'gen_lib_options() function; there is
+    # no appropriate default implementation so subclasses should
+    # implement all of these.
+
+    def library_dir_option(self, dir: str) -> str:
+        """Return the compiler option to add 'dir' to the list of
+        directories searched for libraries.
+        """
+        raise NotImplementedError
+
+    def runtime_library_dir_option(self, dir: str) -> str:
+        """Return the compiler option to add 'dir' to the list of
+        directories searched for runtime libraries.
+        """
+        raise NotImplementedError
+
+    def library_option(self, lib: str) -> str:
+        """Return the compiler option to add 'lib' to the list of libraries
+        linked into the shared library or executable.
+        """
+        raise NotImplementedError
+
+    def has_function(  # noqa: C901
+        self,
+        funcname: str,
+        includes: Iterable[str] | None = None,
+        include_dirs: list[str] | tuple[str, ...] | None = None,
+        libraries: list[str] | None = None,
+        library_dirs: list[str] | tuple[str, ...] | None = None,
+    ) -> bool:
+        """Return a boolean indicating whether funcname is provided as
+        a symbol on the current platform.  The optional arguments can
+        be used to augment the compilation environment.
+
+        The libraries argument is a list of flags to be passed to the
+        linker to make additional symbol definitions available for
+        linking.
+
+        The includes and include_dirs arguments are deprecated.
+        Usually, supplying include files with function declarations
+        will cause function detection to fail even in cases where the
+        symbol is available for linking.
+
+        """
+        # this can't be included at module scope because it tries to
+        # import math which might not be available at that point - maybe
+        # the necessary logic should just be inlined?
+        import tempfile
+
+        if includes is None:
+            includes = []
+        else:
+            warnings.warn("includes is deprecated", DeprecationWarning)
+        if include_dirs is None:
+            include_dirs = []
+        else:
+            warnings.warn("include_dirs is deprecated", DeprecationWarning)
+        if libraries is None:
+            libraries = []
+        if library_dirs is None:
+            library_dirs = []
+        fd, fname = tempfile.mkstemp(".c", funcname, text=True)
+        with os.fdopen(fd, "w", encoding='utf-8') as f:
+            for incl in includes:
+                f.write(f"""#include "{incl}"\n""")
+            if not includes:
+                # Use "char func(void);" as the prototype to follow
+                # what autoconf does.  This prototype does not match
+                # any well-known function the compiler might recognize
+                # as a builtin, so this ends up as a true link test.
+                # Without a fake prototype, the test would need to
+                # know the exact argument types, and the has_function
+                # interface does not provide that level of information.
+                f.write(
+                    f"""\
+#ifdef __cplusplus
+extern "C"
+#endif
+char {funcname}(void);
+"""
+                )
+            f.write(
+                f"""\
+int main (int argc, char **argv) {{
+    {funcname}();
+    return 0;
+}}
+"""
+            )
+
+        try:
+            objects = self.compile([fname], include_dirs=include_dirs)
+        except CompileError:
+            return False
+        finally:
+            os.remove(fname)
+
+        try:
+            self.link_executable(
+                objects, "a.out", libraries=libraries, library_dirs=library_dirs
+            )
+        except (LinkError, TypeError):
+            return False
+        else:
+            os.remove(
+                self.executable_filename("a.out", output_dir=self.output_dir or '')
+            )
+        finally:
+            for fn in objects:
+                os.remove(fn)
+        return True
+
+    def find_library_file(
+        self, dirs: Iterable[str], lib: str, debug: bool = False
+    ) -> str | None:
+        """Search the specified list of directories for a static or shared
+        library file 'lib' and return the full path to that file.  If
+        'debug' true, look for a debugging version (if that makes sense on
+        the current platform).  Return None if 'lib' wasn't found in any of
+        the specified directories.
+        """
+        raise NotImplementedError
+
+    # -- Filename generation methods -----------------------------------
+
+    # The default implementation of the filename generating methods are
+    # prejudiced towards the Unix/DOS/Windows view of the world:
+    #   * object files are named by replacing the source file extension
+    #     (eg. .c/.cpp -> .o/.obj)
+    #   * library files (shared or static) are named by plugging the
+    #     library name and extension into a format string, eg.
+    #     "lib%s.%s" % (lib_name, ".a") for Unix static libraries
+    #   * executables are named by appending an extension (possibly
+    #     empty) to the program name: eg. progname + ".exe" for
+    #     Windows
+    #
+    # To reduce redundant code, these methods expect to find
+    # several attributes in the current object (presumably defined
+    # as class attributes):
+    #   * src_extensions -
+    #     list of C/C++ source file extensions, eg. ['.c', '.cpp']
+    #   * obj_extension -
+    #     object file extension, eg. '.o' or '.obj'
+    #   * static_lib_extension -
+    #     extension for static library files, eg. '.a' or '.lib'
+    #   * shared_lib_extension -
+    #     extension for shared library/object files, eg. '.so', '.dll'
+    #   * static_lib_format -
+    #     format string for generating static library filenames,
+    #     eg. 'lib%s.%s' or '%s.%s'
+    #   * shared_lib_format
+    #     format string for generating shared library filenames
+    #     (probably same as static_lib_format, since the extension
+    #     is one of the intended parameters to the format string)
+    #   * exe_extension -
+    #     extension for executable files, eg. '' or '.exe'
+
+    def object_filenames(
+        self,
+        source_filenames: Iterable[str | os.PathLike[str]],
+        strip_dir: bool = False,
+        output_dir: str | os.PathLike[str] | None = '',
+    ) -> list[str]:
+        if output_dir is None:
+            output_dir = ''
+        return list(
+            self._make_out_path(output_dir, strip_dir, src_name)
+            for src_name in source_filenames
+        )
+
+    @property
+    def out_extensions(self):
+        return dict.fromkeys(self.src_extensions, self.obj_extension)
+
+    def _make_out_path(self, output_dir, strip_dir, src_name):
+        return self._make_out_path_exts(
+            output_dir, strip_dir, src_name, self.out_extensions
+        )
+
+    @classmethod
+    def _make_out_path_exts(cls, output_dir, strip_dir, src_name, extensions):
+        r"""
+        >>> exts = {'.c': '.o'}
+        >>> Compiler._make_out_path_exts('.', False, '/foo/bar.c', exts).replace('\\', '/')
+        './foo/bar.o'
+        >>> Compiler._make_out_path_exts('.', True, '/foo/bar.c', exts).replace('\\', '/')
+        './bar.o'
+        """
+        src = pathlib.PurePath(src_name)
+        # Ensure base is relative to honor output_dir (python/cpython#37775).
+        base = cls._make_relative(src)
+        try:
+            new_ext = extensions[src.suffix]
+        except LookupError:
+            raise UnknownFileType(f"unknown file type '{src.suffix}' (from '{src}')")
+        if strip_dir:
+            base = pathlib.PurePath(base.name)
+        return os.path.join(output_dir, base.with_suffix(new_ext))
+
+    @staticmethod
+    def _make_relative(base: pathlib.Path):
+        return base.relative_to(base.anchor)
+
+    @overload
+    def shared_object_filename(
+        self,
+        basename: str,
+        strip_dir: Literal[False] = False,
+        output_dir: str | os.PathLike[str] = "",
+    ) -> str: ...
+    @overload
+    def shared_object_filename(
+        self,
+        basename: str | os.PathLike[str],
+        strip_dir: Literal[True],
+        output_dir: str | os.PathLike[str] = "",
+    ) -> str: ...
+    def shared_object_filename(
+        self,
+        basename: str | os.PathLike[str],
+        strip_dir: bool = False,
+        output_dir: str | os.PathLike[str] = '',
+    ) -> str:
+        assert output_dir is not None
+        if strip_dir:
+            basename = os.path.basename(basename)
+        return os.path.join(output_dir, basename + self.shared_lib_extension)
+
+    @overload
+    def executable_filename(
+        self,
+        basename: str,
+        strip_dir: Literal[False] = False,
+        output_dir: str | os.PathLike[str] = "",
+    ) -> str: ...
+    @overload
+    def executable_filename(
+        self,
+        basename: str | os.PathLike[str],
+        strip_dir: Literal[True],
+        output_dir: str | os.PathLike[str] = "",
+    ) -> str: ...
+    def executable_filename(
+        self,
+        basename: str | os.PathLike[str],
+        strip_dir: bool = False,
+        output_dir: str | os.PathLike[str] = '',
+    ) -> str:
+        assert output_dir is not None
+        if strip_dir:
+            basename = os.path.basename(basename)
+        return os.path.join(output_dir, basename + (self.exe_extension or ''))
+
+    def library_filename(
+        self,
+        libname: str,
+        lib_type: str = "static",
+        strip_dir: bool = False,
+        output_dir: str | os.PathLike[str] = "",  # or 'shared'
+    ):
+        assert output_dir is not None
+        expected = '"static", "shared", "dylib", "xcode_stub"'
+        if lib_type not in eval(expected):
+            raise ValueError(f"'lib_type' must be {expected}")
+        fmt = getattr(self, lib_type + "_lib_format")
+        ext = getattr(self, lib_type + "_lib_extension")
+
+        dir, base = os.path.split(libname)
+        filename = fmt % (base, ext)
+        if strip_dir:
+            dir = ''
+
+        return os.path.join(output_dir, dir, filename)
+
+    # -- Utility methods -----------------------------------------------
+
+    def announce(self, msg: object, level: int = 1) -> None:
+        log.debug(msg)
+
+    def debug_print(self, msg: object) -> None:
+        from distutils.debug import DEBUG
+
+        if DEBUG:
+            print(msg)
+
+    def warn(self, msg: object) -> None:
+        sys.stderr.write(f"warning: {msg}\n")
+
+    def execute(
+        self,
+        func: Callable[[Unpack[_Ts]], object],
+        args: tuple[Unpack[_Ts]],
+        msg: object = None,
+        level: int = 1,
+    ) -> None:
+        execute(func, args, msg, self.dry_run)
+
+    def spawn(
+        self, cmd: MutableSequence[bytes | str | os.PathLike[str]], **kwargs
+    ) -> None:
+        spawn(cmd, dry_run=self.dry_run, **kwargs)
+
+    @overload
+    def move_file(
+        self, src: str | os.PathLike[str], dst: _StrPathT
+    ) -> _StrPathT | str: ...
+    @overload
+    def move_file(
+        self, src: bytes | os.PathLike[bytes], dst: _BytesPathT
+    ) -> _BytesPathT | bytes: ...
+    def move_file(
+        self,
+        src: str | os.PathLike[str] | bytes | os.PathLike[bytes],
+        dst: str | os.PathLike[str] | bytes | os.PathLike[bytes],
+    ) -> str | os.PathLike[str] | bytes | os.PathLike[bytes]:
+        return move_file(src, dst, dry_run=self.dry_run)
+
+    def mkpath(self, name, mode=0o777):
+        mkpath(name, mode, dry_run=self.dry_run)
+
+
+# Map a sys.platform/os.name ('posix', 'nt') to the default compiler
+# type for that platform. Keys are interpreted as re match
+# patterns. Order is important; platform mappings are preferred over
+# OS names.
+_default_compilers = (
+    # Platform string mappings
+    # on a cygwin built python we can use gcc like an ordinary UNIXish
+    # compiler
+    ('cygwin.*', 'unix'),
+    ('zos', 'zos'),
+    # OS name mappings
+    ('posix', 'unix'),
+    ('nt', 'msvc'),
+)
+
+
+def get_default_compiler(osname: str | None = None, platform: str | None = None) -> str:
+    """Determine the default compiler to use for the given platform.
+
+    osname should be one of the standard Python OS names (i.e. the
+    ones returned by os.name) and platform the common value
+    returned by sys.platform for the platform in question.
+
+    The default values are os.name and sys.platform in case the
+    parameters are not given.
+    """
+    if osname is None:
+        osname = os.name
+    if platform is None:
+        platform = sys.platform
+    # Mingw is a special case where sys.platform is 'win32' but we
+    # want to use the 'mingw32' compiler, so check it first
+    if is_mingw():
+        return 'mingw32'
+    for pattern, compiler in _default_compilers:
+        if (
+            re.match(pattern, platform) is not None
+            or re.match(pattern, osname) is not None
+        ):
+            return compiler
+    # Default to Unix compiler
+    return 'unix'
+
+
+# Map compiler types to (module_name, class_name) pairs -- ie. where to
+# find the code that implements an interface to this compiler.  (The module
+# is assumed to be in the 'distutils' package.)
+compiler_class = {
+    'unix': ('unixccompiler', 'UnixCCompiler', "standard UNIX-style compiler"),
+    'msvc': ('_msvccompiler', 'MSVCCompiler', "Microsoft Visual C++"),
+    'cygwin': (
+        'cygwinccompiler',
+        'CygwinCCompiler',
+        "Cygwin port of GNU C Compiler for Win32",
+    ),
+    'mingw32': (
+        'cygwinccompiler',
+        'Mingw32CCompiler',
+        "Mingw32 port of GNU C Compiler for Win32",
+    ),
+    'bcpp': ('bcppcompiler', 'BCPPCompiler', "Borland C++ Compiler"),
+    'zos': ('zosccompiler', 'zOSCCompiler', 'IBM XL C/C++ Compilers'),
+}
+
+
+def show_compilers() -> None:
+    """Print list of available compilers (used by the "--help-compiler"
+    options to "build", "build_ext", "build_clib").
+    """
+    # XXX this "knows" that the compiler option it's describing is
+    # "--compiler", which just happens to be the case for the three
+    # commands that use it.
+    from distutils.fancy_getopt import FancyGetopt
+
+    compilers = sorted(
+        ("compiler=" + compiler, None, compiler_class[compiler][2])
+        for compiler in compiler_class.keys()
+    )
+    pretty_printer = FancyGetopt(compilers)
+    pretty_printer.print_help("List of available compilers:")
+
+
+def new_compiler(
+    plat: str | None = None,
+    compiler: str | None = None,
+    verbose: bool = False,
+    dry_run: bool = False,
+    force: bool = False,
+) -> Compiler:
+    """Generate an instance of some CCompiler subclass for the supplied
+    platform/compiler combination.  'plat' defaults to 'os.name'
+    (eg. 'posix', 'nt'), and 'compiler' defaults to the default compiler
+    for that platform.  Currently only 'posix' and 'nt' are supported, and
+    the default compilers are "traditional Unix interface" (UnixCCompiler
+    class) and Visual C++ (MSVCCompiler class).  Note that it's perfectly
+    possible to ask for a Unix compiler object under Windows, and a
+    Microsoft compiler object under Unix -- if you supply a value for
+    'compiler', 'plat' is ignored.
+    """
+    if plat is None:
+        plat = os.name
+
+    try:
+        if compiler is None:
+            compiler = get_default_compiler(plat)
+
+        (module_name, class_name, long_description) = compiler_class[compiler]
+    except KeyError:
+        msg = f"don't know how to compile C/C++ code on platform '{plat}'"
+        if compiler is not None:
+            msg = msg + f" with '{compiler}' compiler"
+        raise DistutilsPlatformError(msg)
+
+    try:
+        module_name = "distutils." + module_name
+        __import__(module_name)
+        module = sys.modules[module_name]
+        klass = vars(module)[class_name]
+    except ImportError:
+        raise DistutilsModuleError(
+            f"can't compile C/C++ code: unable to load module '{module_name}'"
+        )
+    except KeyError:
+        raise DistutilsModuleError(
+            f"can't compile C/C++ code: unable to find class '{class_name}' "
+            f"in module '{module_name}'"
+        )
+
+    # XXX The None is necessary to preserve backwards compatibility
+    # with classes that expect verbose to be the first positional
+    # argument.
+    return klass(None, dry_run, force)
+
+
+def gen_preprocess_options(
+    macros: Iterable[_Macro], include_dirs: Iterable[str]
+) -> list[str]:
+    """Generate C pre-processor options (-D, -U, -I) as used by at least
+    two types of compilers: the typical Unix compiler and Visual C++.
+    'macros' is the usual thing, a list of 1- or 2-tuples, where (name,)
+    means undefine (-U) macro 'name', and (name,value) means define (-D)
+    macro 'name' to 'value'.  'include_dirs' is just a list of directory
+    names to be added to the header file search path (-I).  Returns a list
+    of command-line options suitable for either Unix compilers or Visual
+    C++.
+    """
+    # XXX it would be nice (mainly aesthetic, and so we don't generate
+    # stupid-looking command lines) to go over 'macros' and eliminate
+    # redundant definitions/undefinitions (ie. ensure that only the
+    # latest mention of a particular macro winds up on the command
+    # line).  I don't think it's essential, though, since most (all?)
+    # Unix C compilers only pay attention to the latest -D or -U
+    # mention of a macro on their command line.  Similar situation for
+    # 'include_dirs'.  I'm punting on both for now.  Anyways, weeding out
+    # redundancies like this should probably be the province of
+    # CCompiler, since the data structures used are inherited from it
+    # and therefore common to all CCompiler classes.
+    pp_opts = []
+    for macro in macros:
+        if not (isinstance(macro, tuple) and 1 <= len(macro) <= 2):
+            raise TypeError(
+                f"bad macro definition '{macro}': "
+                "each element of 'macros' list must be a 1- or 2-tuple"
+            )
+
+        if len(macro) == 1:  # undefine this macro
+            pp_opts.append(f"-U{macro[0]}")
+        elif len(macro) == 2:
+            if macro[1] is None:  # define with no explicit value
+                pp_opts.append(f"-D{macro[0]}")
+            else:
+                # XXX *don't* need to be clever about quoting the
+                # macro value here, because we're going to avoid the
+                # shell at all costs when we spawn the command!
+                pp_opts.append("-D{}={}".format(*macro))
+
+    pp_opts.extend(f"-I{dir}" for dir in include_dirs)
+    return pp_opts
+
+
+def gen_lib_options(
+    compiler: Compiler,
+    library_dirs: Iterable[str],
+    runtime_library_dirs: Iterable[str],
+    libraries: Iterable[str],
+) -> list[str]:
+    """Generate linker options for searching library directories and
+    linking with specific libraries.  'libraries' and 'library_dirs' are,
+    respectively, lists of library names (not filenames!) and search
+    directories.  Returns a list of command-line options suitable for use
+    with some compiler (depending on the two format strings passed in).
+    """
+    lib_opts = [compiler.library_dir_option(dir) for dir in library_dirs]
+
+    for dir in runtime_library_dirs:
+        lib_opts.extend(always_iterable(compiler.runtime_library_dir_option(dir)))
+
+    # XXX it's important that we *not* remove redundant library mentions!
+    # sometimes you really do have to say "-lfoo -lbar -lfoo" in order to
+    # resolve all symbols.  I just hope we never have to say "-lfoo obj.o
+    # -lbar" to get things to work -- that's certainly a possibility, but a
+    # pretty nasty way to arrange your C code.
+
+    for lib in libraries:
+        (lib_dir, lib_name) = os.path.split(lib)
+        if lib_dir:
+            lib_file = compiler.find_library_file([lib_dir], lib_name)
+            if lib_file:
+                lib_opts.append(lib_file)
+            else:
+                compiler.warn(
+                    f"no library file corresponding to '{lib}' found (skipping)"
+                )
+        else:
+            lib_opts.append(compiler.library_option(lib))
+    return lib_opts
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/cygwin.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/cygwin.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfabbb306e7e44ed120d35f083304e65d7cecf83
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/cygwin.py
@@ -0,0 +1,340 @@
+"""distutils.cygwinccompiler
+
+Provides the CygwinCCompiler class, a subclass of UnixCCompiler that
+handles the Cygwin port of the GNU C compiler to Windows.  It also contains
+the Mingw32CCompiler class which handles the mingw32 port of GCC (same as
+cygwin in no-cygwin mode).
+"""
+
+import copy
+import os
+import pathlib
+import shlex
+import sys
+import warnings
+from subprocess import check_output
+
+from ...errors import (
+    DistutilsExecError,
+    DistutilsPlatformError,
+)
+from ...file_util import write_file
+from ...sysconfig import get_config_vars
+from ...version import LooseVersion, suppress_known_deprecation
+from . import unix
+from .errors import (
+    CompileError,
+    Error,
+)
+
+
+def get_msvcr():
+    """No longer needed, but kept for backward compatibility."""
+    return []
+
+
+_runtime_library_dirs_msg = (
+    "Unable to set runtime library search path on Windows, "
+    "usually indicated by `runtime_library_dirs` parameter to Extension"
+)
+
+
+class Compiler(unix.Compiler):
+    """Handles the Cygwin port of the GNU C compiler to Windows."""
+
+    compiler_type = 'cygwin'
+    obj_extension = ".o"
+    static_lib_extension = ".a"
+    shared_lib_extension = ".dll.a"
+    dylib_lib_extension = ".dll"
+    static_lib_format = "lib%s%s"
+    shared_lib_format = "lib%s%s"
+    dylib_lib_format = "cyg%s%s"
+    exe_extension = ".exe"
+
+    def __init__(self, verbose=False, dry_run=False, force=False):
+        super().__init__(verbose, dry_run, force)
+
+        status, details = check_config_h()
+        self.debug_print(f"Python's GCC status: {status} (details: {details})")
+        if status is not CONFIG_H_OK:
+            self.warn(
+                "Python's pyconfig.h doesn't seem to support your compiler. "
+                f"Reason: {details}. "
+                "Compiling may fail because of undefined preprocessor macros."
+            )
+
+        self.cc, self.cxx = get_config_vars('CC', 'CXX')
+
+        # Override 'CC' and 'CXX' environment variables for
+        # building using MINGW compiler for MSVC python.
+        self.cc = os.environ.get('CC', self.cc or 'gcc')
+        self.cxx = os.environ.get('CXX', self.cxx or 'g++')
+
+        self.linker_dll = self.cc
+        self.linker_dll_cxx = self.cxx
+        shared_option = "-shared"
+
+        self.set_executables(
+            compiler=f'{self.cc} -mcygwin -O -Wall',
+            compiler_so=f'{self.cc} -mcygwin -mdll -O -Wall',
+            compiler_cxx=f'{self.cxx} -mcygwin -O -Wall',
+            compiler_so_cxx=f'{self.cxx} -mcygwin -mdll -O -Wall',
+            linker_exe=f'{self.cc} -mcygwin',
+            linker_so=f'{self.linker_dll} -mcygwin {shared_option}',
+            linker_exe_cxx=f'{self.cxx} -mcygwin',
+            linker_so_cxx=f'{self.linker_dll_cxx} -mcygwin {shared_option}',
+        )
+
+        self.dll_libraries = get_msvcr()
+
+    @property
+    def gcc_version(self):
+        # Older numpy depended on this existing to check for ancient
+        # gcc versions. This doesn't make much sense with clang etc so
+        # just hardcode to something recent.
+        # https://github.com/numpy/numpy/pull/20333
+        warnings.warn(
+            "gcc_version attribute of CygwinCCompiler is deprecated. "
+            "Instead of returning actual gcc version a fixed value 11.2.0 is returned.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        with suppress_known_deprecation():
+            return LooseVersion("11.2.0")
+
+    def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts):
+        """Compiles the source by spawning GCC and windres if needed."""
+        if ext in ('.rc', '.res'):
+            # gcc needs '.res' and '.rc' compiled to object files !!!
+            try:
+                self.spawn(["windres", "-i", src, "-o", obj])
+            except DistutilsExecError as msg:
+                raise CompileError(msg)
+        else:  # for other files use the C-compiler
+            try:
+                if self.detect_language(src) == 'c++':
+                    self.spawn(
+                        self.compiler_so_cxx
+                        + cc_args
+                        + [src, '-o', obj]
+                        + extra_postargs
+                    )
+                else:
+                    self.spawn(
+                        self.compiler_so + cc_args + [src, '-o', obj] + extra_postargs
+                    )
+            except DistutilsExecError as msg:
+                raise CompileError(msg)
+
+    def link(
+        self,
+        target_desc,
+        objects,
+        output_filename,
+        output_dir=None,
+        libraries=None,
+        library_dirs=None,
+        runtime_library_dirs=None,
+        export_symbols=None,
+        debug=False,
+        extra_preargs=None,
+        extra_postargs=None,
+        build_temp=None,
+        target_lang=None,
+    ):
+        """Link the objects."""
+        # use separate copies, so we can modify the lists
+        extra_preargs = copy.copy(extra_preargs or [])
+        libraries = copy.copy(libraries or [])
+        objects = copy.copy(objects or [])
+
+        if runtime_library_dirs:
+            self.warn(_runtime_library_dirs_msg)
+
+        # Additional libraries
+        libraries.extend(self.dll_libraries)
+
+        # handle export symbols by creating a def-file
+        # with executables this only works with gcc/ld as linker
+        if (export_symbols is not None) and (
+            target_desc != self.EXECUTABLE or self.linker_dll == "gcc"
+        ):
+            # (The linker doesn't do anything if output is up-to-date.
+            # So it would probably better to check if we really need this,
+            # but for this we had to insert some unchanged parts of
+            # UnixCCompiler, and this is not what we want.)
+
+            # we want to put some files in the same directory as the
+            # object files are, build_temp doesn't help much
+            # where are the object files
+            temp_dir = os.path.dirname(objects[0])
+            # name of dll to give the helper files the same base name
+            (dll_name, dll_extension) = os.path.splitext(
+                os.path.basename(output_filename)
+            )
+
+            # generate the filenames for these files
+            def_file = os.path.join(temp_dir, dll_name + ".def")
+
+            # Generate .def file
+            contents = [f"LIBRARY {os.path.basename(output_filename)}", "EXPORTS"]
+            contents.extend(export_symbols)
+            self.execute(write_file, (def_file, contents), f"writing {def_file}")
+
+            # next add options for def-file
+
+            # for gcc/ld the def-file is specified as any object files
+            objects.append(def_file)
+
+        # end: if ((export_symbols is not None) and
+        #        (target_desc != self.EXECUTABLE or self.linker_dll == "gcc")):
+
+        # who wants symbols and a many times larger output file
+        # should explicitly switch the debug mode on
+        # otherwise we let ld strip the output file
+        # (On my machine: 10KiB < stripped_file < ??100KiB
+        #   unstripped_file = stripped_file + XXX KiB
+        #  ( XXX=254 for a typical python extension))
+        if not debug:
+            extra_preargs.append("-s")
+
+        super().link(
+            target_desc,
+            objects,
+            output_filename,
+            output_dir,
+            libraries,
+            library_dirs,
+            runtime_library_dirs,
+            None,  # export_symbols, we do this in our def-file
+            debug,
+            extra_preargs,
+            extra_postargs,
+            build_temp,
+            target_lang,
+        )
+
+    def runtime_library_dir_option(self, dir):
+        # cygwin doesn't support rpath. While in theory we could error
+        # out like MSVC does, code might expect it to work like on Unix, so
+        # just warn and hope for the best.
+        self.warn(_runtime_library_dirs_msg)
+        return []
+
+    # -- Miscellaneous methods -----------------------------------------
+
+    def _make_out_path(self, output_dir, strip_dir, src_name):
+        # use normcase to make sure '.rc' is really '.rc' and not '.RC'
+        norm_src_name = os.path.normcase(src_name)
+        return super()._make_out_path(output_dir, strip_dir, norm_src_name)
+
+    @property
+    def out_extensions(self):
+        """
+        Add support for rc and res files.
+        """
+        return {
+            **super().out_extensions,
+            **{ext: ext + self.obj_extension for ext in ('.res', '.rc')},
+        }
+
+
+# the same as cygwin plus some additional parameters
+class MinGW32Compiler(Compiler):
+    """Handles the Mingw32 port of the GNU C compiler to Windows."""
+
+    compiler_type = 'mingw32'
+
+    def __init__(self, verbose=False, dry_run=False, force=False):
+        super().__init__(verbose, dry_run, force)
+
+        shared_option = "-shared"
+
+        if is_cygwincc(self.cc):
+            raise Error('Cygwin gcc cannot be used with --compiler=mingw32')
+
+        self.set_executables(
+            compiler=f'{self.cc} -O -Wall',
+            compiler_so=f'{self.cc} -shared -O -Wall',
+            compiler_so_cxx=f'{self.cxx} -shared -O -Wall',
+            compiler_cxx=f'{self.cxx} -O -Wall',
+            linker_exe=f'{self.cc}',
+            linker_so=f'{self.linker_dll} {shared_option}',
+            linker_exe_cxx=f'{self.cxx}',
+            linker_so_cxx=f'{self.linker_dll_cxx} {shared_option}',
+        )
+
+    def runtime_library_dir_option(self, dir):
+        raise DistutilsPlatformError(_runtime_library_dirs_msg)
+
+
+# Because these compilers aren't configured in Python's pyconfig.h file by
+# default, we should at least warn the user if he is using an unmodified
+# version.
+
+CONFIG_H_OK = "ok"
+CONFIG_H_NOTOK = "not ok"
+CONFIG_H_UNCERTAIN = "uncertain"
+
+
+def check_config_h():
+    """Check if the current Python installation appears amenable to building
+    extensions with GCC.
+
+    Returns a tuple (status, details), where 'status' is one of the following
+    constants:
+
+    - CONFIG_H_OK: all is well, go ahead and compile
+    - CONFIG_H_NOTOK: doesn't look good
+    - CONFIG_H_UNCERTAIN: not sure -- unable to read pyconfig.h
+
+    'details' is a human-readable string explaining the situation.
+
+    Note there are two ways to conclude "OK": either 'sys.version' contains
+    the string "GCC" (implying that this Python was built with GCC), or the
+    installed "pyconfig.h" contains the string "__GNUC__".
+    """
+
+    # XXX since this function also checks sys.version, it's not strictly a
+    # "pyconfig.h" check -- should probably be renamed...
+
+    from distutils import sysconfig
+
+    # if sys.version contains GCC then python was compiled with GCC, and the
+    # pyconfig.h file should be OK
+    if "GCC" in sys.version:
+        return CONFIG_H_OK, "sys.version mentions 'GCC'"
+
+    # Clang would also work
+    if "Clang" in sys.version:
+        return CONFIG_H_OK, "sys.version mentions 'Clang'"
+
+    # let's see if __GNUC__ is mentioned in python.h
+    fn = sysconfig.get_config_h_filename()
+    try:
+        config_h = pathlib.Path(fn).read_text(encoding='utf-8')
+    except OSError as exc:
+        return (CONFIG_H_UNCERTAIN, f"couldn't read '{fn}': {exc.strerror}")
+    else:
+        substring = '__GNUC__'
+        if substring in config_h:
+            code = CONFIG_H_OK
+            mention_inflected = 'mentions'
+        else:
+            code = CONFIG_H_NOTOK
+            mention_inflected = 'does not mention'
+        return code, f"{fn!r} {mention_inflected} {substring!r}"
+
+
+def is_cygwincc(cc):
+    """Try to determine if the compiler that would be used is from cygwin."""
+    out_string = check_output(shlex.split(cc) + ['-dumpmachine'])
+    return out_string.strip().endswith(b'cygwin')
+
+
+get_versions = None
+"""
+A stand-in for the previous get_versions() function to prevent failures
+when monkeypatched. See pypa/setuptools#2969.
+"""
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/errors.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..01328592b2dad80f4d16c55cd9db9829b3c650f1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/errors.py
@@ -0,0 +1,24 @@
+class Error(Exception):
+    """Some compile/link operation failed."""
+
+
+class PreprocessError(Error):
+    """Failure to preprocess one or more C/C++ files."""
+
+
+class CompileError(Error):
+    """Failure to compile one or more C/C++ source files."""
+
+
+class LibError(Error):
+    """Failure to create a static library from one or more C/C++ object
+    files."""
+
+
+class LinkError(Error):
+    """Failure to link one or more C/C++ object files into an executable
+    or shared library file."""
+
+
+class UnknownFileType(Error):
+    """Attempt to process an unknown file type."""
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/msvc.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/msvc.py
new file mode 100644
index 0000000000000000000000000000000000000000..6db062a9e714d0a95e1550d387abbffe5950547f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/msvc.py
@@ -0,0 +1,614 @@
+"""distutils._msvccompiler
+
+Contains MSVCCompiler, an implementation of the abstract CCompiler class
+for Microsoft Visual Studio 2015.
+
+This module requires VS 2015 or later.
+"""
+
+# Written by Perry Stoll
+# hacked by Robin Becker and Thomas Heller to do a better job of
+#   finding DevStudio (through the registry)
+# ported to VS 2005 and VS 2008 by Christian Heimes
+# ported to VS 2015 by Steve Dower
+from __future__ import annotations
+
+import contextlib
+import os
+import subprocess
+import unittest.mock as mock
+import warnings
+from collections.abc import Iterable
+
+with contextlib.suppress(ImportError):
+    import winreg
+
+from itertools import count
+
+from ..._log import log
+from ...errors import (
+    DistutilsExecError,
+    DistutilsPlatformError,
+)
+from ...util import get_host_platform, get_platform
+from . import base
+from .base import gen_lib_options
+from .errors import (
+    CompileError,
+    LibError,
+    LinkError,
+)
+
+
+def _find_vc2015():
+    try:
+        key = winreg.OpenKeyEx(
+            winreg.HKEY_LOCAL_MACHINE,
+            r"Software\Microsoft\VisualStudio\SxS\VC7",
+            access=winreg.KEY_READ | winreg.KEY_WOW64_32KEY,
+        )
+    except OSError:
+        log.debug("Visual C++ is not registered")
+        return None, None
+
+    best_version = 0
+    best_dir = None
+    with key:
+        for i in count():
+            try:
+                v, vc_dir, vt = winreg.EnumValue(key, i)
+            except OSError:
+                break
+            if v and vt == winreg.REG_SZ and os.path.isdir(vc_dir):
+                try:
+                    version = int(float(v))
+                except (ValueError, TypeError):
+                    continue
+                if version >= 14 and version > best_version:
+                    best_version, best_dir = version, vc_dir
+    return best_version, best_dir
+
+
+def _find_vc2017():
+    """Returns "15, path" based on the result of invoking vswhere.exe
+    If no install is found, returns "None, None"
+
+    The version is returned to avoid unnecessarily changing the function
+    result. It may be ignored when the path is not None.
+
+    If vswhere.exe is not available, by definition, VS 2017 is not
+    installed.
+    """
+    root = os.environ.get("ProgramFiles(x86)") or os.environ.get("ProgramFiles")
+    if not root:
+        return None, None
+
+    variant = 'arm64' if get_platform() == 'win-arm64' else 'x86.x64'
+    suitable_components = (
+        f"Microsoft.VisualStudio.Component.VC.Tools.{variant}",
+        "Microsoft.VisualStudio.Workload.WDExpress",
+    )
+
+    for component in suitable_components:
+        # Workaround for `-requiresAny` (only available on VS 2017 > 15.6)
+        with contextlib.suppress(
+            subprocess.CalledProcessError, OSError, UnicodeDecodeError
+        ):
+            path = (
+                subprocess.check_output([
+                    os.path.join(
+                        root, "Microsoft Visual Studio", "Installer", "vswhere.exe"
+                    ),
+                    "-latest",
+                    "-prerelease",
+                    "-requires",
+                    component,
+                    "-property",
+                    "installationPath",
+                    "-products",
+                    "*",
+                ])
+                .decode(encoding="mbcs", errors="strict")
+                .strip()
+            )
+
+            path = os.path.join(path, "VC", "Auxiliary", "Build")
+            if os.path.isdir(path):
+                return 15, path
+
+    return None, None  # no suitable component found
+
+
+PLAT_SPEC_TO_RUNTIME = {
+    'x86': 'x86',
+    'x86_amd64': 'x64',
+    'x86_arm': 'arm',
+    'x86_arm64': 'arm64',
+}
+
+
+def _find_vcvarsall(plat_spec):
+    # bpo-38597: Removed vcruntime return value
+    _, best_dir = _find_vc2017()
+
+    if not best_dir:
+        best_version, best_dir = _find_vc2015()
+
+    if not best_dir:
+        log.debug("No suitable Visual C++ version found")
+        return None, None
+
+    vcvarsall = os.path.join(best_dir, "vcvarsall.bat")
+    if not os.path.isfile(vcvarsall):
+        log.debug("%s cannot be found", vcvarsall)
+        return None, None
+
+    return vcvarsall, None
+
+
+def _get_vc_env(plat_spec):
+    if os.getenv("DISTUTILS_USE_SDK"):
+        return {key.lower(): value for key, value in os.environ.items()}
+
+    vcvarsall, _ = _find_vcvarsall(plat_spec)
+    if not vcvarsall:
+        raise DistutilsPlatformError(
+            'Microsoft Visual C++ 14.0 or greater is required. '
+            'Get it with "Microsoft C++ Build Tools": '
+            'https://visualstudio.microsoft.com/visual-cpp-build-tools/'
+        )
+
+    try:
+        out = subprocess.check_output(
+            f'cmd /u /c "{vcvarsall}" {plat_spec} && set',
+            stderr=subprocess.STDOUT,
+        ).decode('utf-16le', errors='replace')
+    except subprocess.CalledProcessError as exc:
+        log.error(exc.output)
+        raise DistutilsPlatformError(f"Error executing {exc.cmd}")
+
+    env = {
+        key.lower(): value
+        for key, _, value in (line.partition('=') for line in out.splitlines())
+        if key and value
+    }
+
+    return env
+
+
+def _find_exe(exe, paths=None):
+    """Return path to an MSVC executable program.
+
+    Tries to find the program in several places: first, one of the
+    MSVC program search paths from the registry; next, the directories
+    in the PATH environment variable.  If any of those work, return an
+    absolute path that is known to exist.  If none of them work, just
+    return the original program name, 'exe'.
+    """
+    if not paths:
+        paths = os.getenv('path').split(os.pathsep)
+    for p in paths:
+        fn = os.path.join(os.path.abspath(p), exe)
+        if os.path.isfile(fn):
+            return fn
+    return exe
+
+
+_vcvars_names = {
+    'win32': 'x86',
+    'win-amd64': 'amd64',
+    'win-arm32': 'arm',
+    'win-arm64': 'arm64',
+}
+
+
+def _get_vcvars_spec(host_platform, platform):
+    """
+    Given a host platform and platform, determine the spec for vcvarsall.
+
+    Uses the native MSVC host if the host platform would need expensive
+    emulation for x86.
+
+    >>> _get_vcvars_spec('win-arm64', 'win32')
+    'arm64_x86'
+    >>> _get_vcvars_spec('win-arm64', 'win-amd64')
+    'arm64_amd64'
+
+    Otherwise, always cross-compile from x86 to work with the
+    lighter-weight MSVC installs that do not include native 64-bit tools.
+
+    >>> _get_vcvars_spec('win32', 'win32')
+    'x86'
+    >>> _get_vcvars_spec('win-arm32', 'win-arm32')
+    'x86_arm'
+    >>> _get_vcvars_spec('win-amd64', 'win-arm64')
+    'x86_arm64'
+    """
+    if host_platform != 'win-arm64':
+        host_platform = 'win32'
+    vc_hp = _vcvars_names[host_platform]
+    vc_plat = _vcvars_names[platform]
+    return vc_hp if vc_hp == vc_plat else f'{vc_hp}_{vc_plat}'
+
+
+class Compiler(base.Compiler):
+    """Concrete class that implements an interface to Microsoft Visual C++,
+    as defined by the CCompiler abstract class."""
+
+    compiler_type = 'msvc'
+
+    # Just set this so CCompiler's constructor doesn't barf.  We currently
+    # don't use the 'set_executables()' bureaucracy provided by CCompiler,
+    # as it really isn't necessary for this sort of single-compiler class.
+    # Would be nice to have a consistent interface with UnixCCompiler,
+    # though, so it's worth thinking about.
+    executables = {}
+
+    # Private class data (need to distinguish C from C++ source for compiler)
+    _c_extensions = ['.c']
+    _cpp_extensions = ['.cc', '.cpp', '.cxx']
+    _rc_extensions = ['.rc']
+    _mc_extensions = ['.mc']
+
+    # Needed for the filename generation methods provided by the
+    # base class, CCompiler.
+    src_extensions = _c_extensions + _cpp_extensions + _rc_extensions + _mc_extensions
+    res_extension = '.res'
+    obj_extension = '.obj'
+    static_lib_extension = '.lib'
+    shared_lib_extension = '.dll'
+    static_lib_format = shared_lib_format = '%s%s'
+    exe_extension = '.exe'
+
+    def __init__(self, verbose=False, dry_run=False, force=False) -> None:
+        super().__init__(verbose, dry_run, force)
+        # target platform (.plat_name is consistent with 'bdist')
+        self.plat_name = None
+        self.initialized = False
+
+    @classmethod
+    def _configure(cls, vc_env):
+        """
+        Set class-level include/lib dirs.
+        """
+        cls.include_dirs = cls._parse_path(vc_env.get('include', ''))
+        cls.library_dirs = cls._parse_path(vc_env.get('lib', ''))
+
+    @staticmethod
+    def _parse_path(val):
+        return [dir.rstrip(os.sep) for dir in val.split(os.pathsep) if dir]
+
+    def initialize(self, plat_name: str | None = None) -> None:
+        # multi-init means we would need to check platform same each time...
+        assert not self.initialized, "don't init multiple times"
+        if plat_name is None:
+            plat_name = get_platform()
+        # sanity check for platforms to prevent obscure errors later.
+        if plat_name not in _vcvars_names:
+            raise DistutilsPlatformError(
+                f"--plat-name must be one of {tuple(_vcvars_names)}"
+            )
+
+        plat_spec = _get_vcvars_spec(get_host_platform(), plat_name)
+
+        vc_env = _get_vc_env(plat_spec)
+        if not vc_env:
+            raise DistutilsPlatformError(
+                "Unable to find a compatible Visual Studio installation."
+            )
+        self._configure(vc_env)
+
+        self._paths = vc_env.get('path', '')
+        paths = self._paths.split(os.pathsep)
+        self.cc = _find_exe("cl.exe", paths)
+        self.linker = _find_exe("link.exe", paths)
+        self.lib = _find_exe("lib.exe", paths)
+        self.rc = _find_exe("rc.exe", paths)  # resource compiler
+        self.mc = _find_exe("mc.exe", paths)  # message compiler
+        self.mt = _find_exe("mt.exe", paths)  # message compiler
+
+        self.preprocess_options = None
+        # bpo-38597: Always compile with dynamic linking
+        # Future releases of Python 3.x will include all past
+        # versions of vcruntime*.dll for compatibility.
+        self.compile_options = ['/nologo', '/O2', '/W3', '/GL', '/DNDEBUG', '/MD']
+
+        self.compile_options_debug = [
+            '/nologo',
+            '/Od',
+            '/MDd',
+            '/Zi',
+            '/W3',
+            '/D_DEBUG',
+        ]
+
+        ldflags = ['/nologo', '/INCREMENTAL:NO', '/LTCG']
+
+        ldflags_debug = ['/nologo', '/INCREMENTAL:NO', '/LTCG', '/DEBUG:FULL']
+
+        self.ldflags_exe = [*ldflags, '/MANIFEST:EMBED,ID=1']
+        self.ldflags_exe_debug = [*ldflags_debug, '/MANIFEST:EMBED,ID=1']
+        self.ldflags_shared = [
+            *ldflags,
+            '/DLL',
+            '/MANIFEST:EMBED,ID=2',
+            '/MANIFESTUAC:NO',
+        ]
+        self.ldflags_shared_debug = [
+            *ldflags_debug,
+            '/DLL',
+            '/MANIFEST:EMBED,ID=2',
+            '/MANIFESTUAC:NO',
+        ]
+        self.ldflags_static = [*ldflags]
+        self.ldflags_static_debug = [*ldflags_debug]
+
+        self._ldflags = {
+            (base.Compiler.EXECUTABLE, None): self.ldflags_exe,
+            (base.Compiler.EXECUTABLE, False): self.ldflags_exe,
+            (base.Compiler.EXECUTABLE, True): self.ldflags_exe_debug,
+            (base.Compiler.SHARED_OBJECT, None): self.ldflags_shared,
+            (base.Compiler.SHARED_OBJECT, False): self.ldflags_shared,
+            (base.Compiler.SHARED_OBJECT, True): self.ldflags_shared_debug,
+            (base.Compiler.SHARED_LIBRARY, None): self.ldflags_static,
+            (base.Compiler.SHARED_LIBRARY, False): self.ldflags_static,
+            (base.Compiler.SHARED_LIBRARY, True): self.ldflags_static_debug,
+        }
+
+        self.initialized = True
+
+    # -- Worker methods ------------------------------------------------
+
+    @property
+    def out_extensions(self) -> dict[str, str]:
+        return {
+            **super().out_extensions,
+            **{
+                ext: self.res_extension
+                for ext in self._rc_extensions + self._mc_extensions
+            },
+        }
+
+    def compile(  # noqa: C901
+        self,
+        sources,
+        output_dir=None,
+        macros=None,
+        include_dirs=None,
+        debug=False,
+        extra_preargs=None,
+        extra_postargs=None,
+        depends=None,
+    ):
+        if not self.initialized:
+            self.initialize()
+        compile_info = self._setup_compile(
+            output_dir, macros, include_dirs, sources, depends, extra_postargs
+        )
+        macros, objects, extra_postargs, pp_opts, build = compile_info
+
+        compile_opts = extra_preargs or []
+        compile_opts.append('/c')
+        if debug:
+            compile_opts.extend(self.compile_options_debug)
+        else:
+            compile_opts.extend(self.compile_options)
+
+        add_cpp_opts = False
+
+        for obj in objects:
+            try:
+                src, ext = build[obj]
+            except KeyError:
+                continue
+            if debug:
+                # pass the full pathname to MSVC in debug mode,
+                # this allows the debugger to find the source file
+                # without asking the user to browse for it
+                src = os.path.abspath(src)
+
+            if ext in self._c_extensions:
+                input_opt = f"/Tc{src}"
+            elif ext in self._cpp_extensions:
+                input_opt = f"/Tp{src}"
+                add_cpp_opts = True
+            elif ext in self._rc_extensions:
+                # compile .RC to .RES file
+                input_opt = src
+                output_opt = "/fo" + obj
+                try:
+                    self.spawn([self.rc] + pp_opts + [output_opt, input_opt])
+                except DistutilsExecError as msg:
+                    raise CompileError(msg)
+                continue
+            elif ext in self._mc_extensions:
+                # Compile .MC to .RC file to .RES file.
+                #   * '-h dir' specifies the directory for the
+                #     generated include file
+                #   * '-r dir' specifies the target directory of the
+                #     generated RC file and the binary message resource
+                #     it includes
+                #
+                # For now (since there are no options to change this),
+                # we use the source-directory for the include file and
+                # the build directory for the RC file and message
+                # resources. This works at least for win32all.
+                h_dir = os.path.dirname(src)
+                rc_dir = os.path.dirname(obj)
+                try:
+                    # first compile .MC to .RC and .H file
+                    self.spawn([self.mc, '-h', h_dir, '-r', rc_dir, src])
+                    base, _ = os.path.splitext(os.path.basename(src))
+                    rc_file = os.path.join(rc_dir, base + '.rc')
+                    # then compile .RC to .RES file
+                    self.spawn([self.rc, "/fo" + obj, rc_file])
+
+                except DistutilsExecError as msg:
+                    raise CompileError(msg)
+                continue
+            else:
+                # how to handle this file?
+                raise CompileError(f"Don't know how to compile {src} to {obj}")
+
+            args = [self.cc] + compile_opts + pp_opts
+            if add_cpp_opts:
+                args.append('/EHsc')
+            args.extend((input_opt, "/Fo" + obj))
+            args.extend(extra_postargs)
+
+            try:
+                self.spawn(args)
+            except DistutilsExecError as msg:
+                raise CompileError(msg)
+
+        return objects
+
+    def create_static_lib(
+        self,
+        objects: list[str] | tuple[str, ...],
+        output_libname: str,
+        output_dir: str | None = None,
+        debug: bool = False,
+        target_lang: str | None = None,
+    ) -> None:
+        if not self.initialized:
+            self.initialize()
+        objects, output_dir = self._fix_object_args(objects, output_dir)
+        output_filename = self.library_filename(output_libname, output_dir=output_dir)
+
+        if self._need_link(objects, output_filename):
+            lib_args = objects + ['/OUT:' + output_filename]
+            if debug:
+                pass  # XXX what goes here?
+            try:
+                log.debug('Executing "%s" %s', self.lib, ' '.join(lib_args))
+                self.spawn([self.lib] + lib_args)
+            except DistutilsExecError as msg:
+                raise LibError(msg)
+        else:
+            log.debug("skipping %s (up-to-date)", output_filename)
+
+    def link(
+        self,
+        target_desc: str,
+        objects: list[str] | tuple[str, ...],
+        output_filename: str,
+        output_dir: str | None = None,
+        libraries: list[str] | tuple[str, ...] | None = None,
+        library_dirs: list[str] | tuple[str, ...] | None = None,
+        runtime_library_dirs: list[str] | tuple[str, ...] | None = None,
+        export_symbols: Iterable[str] | None = None,
+        debug: bool = False,
+        extra_preargs: list[str] | None = None,
+        extra_postargs: Iterable[str] | None = None,
+        build_temp: str | os.PathLike[str] | None = None,
+        target_lang: str | None = None,
+    ) -> None:
+        if not self.initialized:
+            self.initialize()
+        objects, output_dir = self._fix_object_args(objects, output_dir)
+        fixed_args = self._fix_lib_args(libraries, library_dirs, runtime_library_dirs)
+        libraries, library_dirs, runtime_library_dirs = fixed_args
+
+        if runtime_library_dirs:
+            self.warn(
+                "I don't know what to do with 'runtime_library_dirs': "
+                + str(runtime_library_dirs)
+            )
+
+        lib_opts = gen_lib_options(self, library_dirs, runtime_library_dirs, libraries)
+        if output_dir is not None:
+            output_filename = os.path.join(output_dir, output_filename)
+
+        if self._need_link(objects, output_filename):
+            ldflags = self._ldflags[target_desc, debug]
+
+            export_opts = ["/EXPORT:" + sym for sym in (export_symbols or [])]
+
+            ld_args = (
+                ldflags + lib_opts + export_opts + objects + ['/OUT:' + output_filename]
+            )
+
+            # The MSVC linker generates .lib and .exp files, which cannot be
+            # suppressed by any linker switches. The .lib files may even be
+            # needed! Make sure they are generated in the temporary build
+            # directory. Since they have different names for debug and release
+            # builds, they can go into the same directory.
+            build_temp = os.path.dirname(objects[0])
+            if export_symbols is not None:
+                (dll_name, dll_ext) = os.path.splitext(
+                    os.path.basename(output_filename)
+                )
+                implib_file = os.path.join(build_temp, self.library_filename(dll_name))
+                ld_args.append('/IMPLIB:' + implib_file)
+
+            if extra_preargs:
+                ld_args[:0] = extra_preargs
+            if extra_postargs:
+                ld_args.extend(extra_postargs)
+
+            output_dir = os.path.dirname(os.path.abspath(output_filename))
+            self.mkpath(output_dir)
+            try:
+                log.debug('Executing "%s" %s', self.linker, ' '.join(ld_args))
+                self.spawn([self.linker] + ld_args)
+            except DistutilsExecError as msg:
+                raise LinkError(msg)
+        else:
+            log.debug("skipping %s (up-to-date)", output_filename)
+
+    def spawn(self, cmd):
+        env = dict(os.environ, PATH=self._paths)
+        with self._fallback_spawn(cmd, env) as fallback:
+            return super().spawn(cmd, env=env)
+        return fallback.value
+
+    @contextlib.contextmanager
+    def _fallback_spawn(self, cmd, env):
+        """
+        Discovered in pypa/distutils#15, some tools monkeypatch the compiler,
+        so the 'env' kwarg causes a TypeError. Detect this condition and
+        restore the legacy, unsafe behavior.
+        """
+        bag = type('Bag', (), {})()
+        try:
+            yield bag
+        except TypeError as exc:
+            if "unexpected keyword argument 'env'" not in str(exc):
+                raise
+        else:
+            return
+        warnings.warn("Fallback spawn triggered. Please update distutils monkeypatch.")
+        with mock.patch.dict('os.environ', env):
+            bag.value = super().spawn(cmd)
+
+    # -- Miscellaneous methods -----------------------------------------
+    # These are all used by the 'gen_lib_options() function, in
+    # ccompiler.py.
+
+    def library_dir_option(self, dir):
+        return "/LIBPATH:" + dir
+
+    def runtime_library_dir_option(self, dir):
+        raise DistutilsPlatformError(
+            "don't know how to set runtime library search path for MSVC"
+        )
+
+    def library_option(self, lib):
+        return self.library_filename(lib)
+
+    def find_library_file(self, dirs, lib, debug=False):
+        # Prefer a debugging library if found (and requested), but deal
+        # with it if we don't have one.
+        if debug:
+            try_names = [lib + "_d", lib]
+        else:
+            try_names = [lib]
+        for dir in dirs:
+            for name in try_names:
+                libfile = os.path.join(dir, self.library_filename(name))
+                if os.path.isfile(libfile):
+                    return libfile
+        else:
+            # Oops, didn't find it in *any* of 'dirs'
+            return None
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--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/tests/test_base.py
@@ -0,0 +1,83 @@
+import platform
+import sysconfig
+import textwrap
+
+import pytest
+
+from .. import base
+
+pytestmark = pytest.mark.usefixtures('suppress_path_mangle')
+
+
+@pytest.fixture
+def c_file(tmp_path):
+    c_file = tmp_path / 'foo.c'
+    gen_headers = ('Python.h',)
+    is_windows = platform.system() == "Windows"
+    plat_headers = ('windows.h',) * is_windows
+    all_headers = gen_headers + plat_headers
+    headers = '\n'.join(f'#include <{header}>\n' for header in all_headers)
+    payload = (
+        textwrap.dedent(
+            """
+        #headers
+        void PyInit_foo(void) {}
+        """
+        )
+        .lstrip()
+        .replace('#headers', headers)
+    )
+    c_file.write_text(payload, encoding='utf-8')
+    return c_file
+
+
+def test_set_include_dirs(c_file):
+    """
+    Extensions should build even if set_include_dirs is invoked.
+    In particular, compiler-specific paths should not be overridden.
+    """
+    compiler = base.new_compiler()
+    python = sysconfig.get_paths()['include']
+    compiler.set_include_dirs([python])
+    compiler.compile([c_file])
+
+    # do it again, setting include dirs after any initialization
+    compiler.set_include_dirs([python])
+    compiler.compile([c_file])
+
+
+def test_has_function_prototype():
+    # Issue https://github.com/pypa/setuptools/issues/3648
+    # Test prototype-generating behavior.
+
+    compiler = base.new_compiler()
+
+    # Every C implementation should have these.
+    assert compiler.has_function('abort')
+    assert compiler.has_function('exit')
+    with pytest.deprecated_call(match='includes is deprecated'):
+        # abort() is a valid expression with the <stdlib.h> prototype.
+        assert compiler.has_function('abort', includes=['stdlib.h'])
+    with pytest.deprecated_call(match='includes is deprecated'):
+        # But exit() is not valid with the actual prototype in scope.
+        assert not compiler.has_function('exit', includes=['stdlib.h'])
+    # And setuptools_does_not_exist is not declared or defined at all.
+    assert not compiler.has_function('setuptools_does_not_exist')
+    with pytest.deprecated_call(match='includes is deprecated'):
+        assert not compiler.has_function(
+            'setuptools_does_not_exist', includes=['stdio.h']
+        )
+
+
+def test_include_dirs_after_multiple_compile_calls(c_file):
+    """
+    Calling compile multiple times should not change the include dirs
+    (regression test for setuptools issue #3591).
+    """
+    compiler = base.new_compiler()
+    python = sysconfig.get_paths()['include']
+    compiler.set_include_dirs([python])
+    compiler.compile([c_file])
+    assert compiler.include_dirs == [python]
+    compiler.compile([c_file])
+    assert compiler.include_dirs == [python]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/tests/test_cygwin.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/tests/test_cygwin.py
new file mode 100644
index 0000000000000000000000000000000000000000..9adf6b8ebf165b4fe92a52d8a14aed21aebba539
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/tests/test_cygwin.py
@@ -0,0 +1,76 @@
+"""Tests for distutils.cygwinccompiler."""
+
+import os
+import sys
+from distutils import sysconfig
+from distutils.tests import support
+
+import pytest
+
+from .. import cygwin
+
+
+@pytest.fixture(autouse=True)
+def stuff(request, monkeypatch, distutils_managed_tempdir):
+    self = request.instance
+    self.python_h = os.path.join(self.mkdtemp(), 'python.h')
+    monkeypatch.setattr(sysconfig, 'get_config_h_filename', self._get_config_h_filename)
+    monkeypatch.setattr(sys, 'version', sys.version)
+
+
+class TestCygwinCCompiler(support.TempdirManager):
+    def _get_config_h_filename(self):
+        return self.python_h
+
+    @pytest.mark.skipif('sys.platform != "cygwin"')
+    @pytest.mark.skipif('not os.path.exists("/usr/lib/libbash.dll.a")')
+    def test_find_library_file(self):
+        from distutils.cygwinccompiler import CygwinCCompiler
+
+        compiler = CygwinCCompiler()
+        link_name = "bash"
+        linkable_file = compiler.find_library_file(["/usr/lib"], link_name)
+        assert linkable_file is not None
+        assert os.path.exists(linkable_file)
+        assert linkable_file == f"/usr/lib/lib{link_name:s}.dll.a"
+
+    @pytest.mark.skipif('sys.platform != "cygwin"')
+    def test_runtime_library_dir_option(self):
+        from distutils.cygwinccompiler import CygwinCCompiler
+
+        compiler = CygwinCCompiler()
+        assert compiler.runtime_library_dir_option('/foo') == []
+
+    def test_check_config_h(self):
+        # check_config_h looks for "GCC" in sys.version first
+        # returns CONFIG_H_OK if found
+        sys.version = (
+            '2.6.1 (r261:67515, Dec  6 2008, 16:42:21) \n[GCC '
+            '4.0.1 (Apple Computer, Inc. build 5370)]'
+        )
+
+        assert cygwin.check_config_h()[0] == cygwin.CONFIG_H_OK
+
+        # then it tries to see if it can find "__GNUC__" in pyconfig.h
+        sys.version = 'something without the *CC word'
+
+        # if the file doesn't exist it returns  CONFIG_H_UNCERTAIN
+        assert cygwin.check_config_h()[0] == cygwin.CONFIG_H_UNCERTAIN
+
+        # if it exists but does not contain __GNUC__, it returns CONFIG_H_NOTOK
+        self.write_file(self.python_h, 'xxx')
+        assert cygwin.check_config_h()[0] == cygwin.CONFIG_H_NOTOK
+
+        # and CONFIG_H_OK if __GNUC__ is found
+        self.write_file(self.python_h, 'xxx __GNUC__ xxx')
+        assert cygwin.check_config_h()[0] == cygwin.CONFIG_H_OK
+
+    def test_get_msvcr(self):
+        assert cygwin.get_msvcr() == []
+
+    @pytest.mark.skipif('sys.platform != "cygwin"')
+    def test_dll_libraries_not_none(self):
+        from distutils.cygwinccompiler import CygwinCCompiler
+
+        compiler = CygwinCCompiler()
+        assert compiler.dll_libraries is not None
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/tests/test_mingw.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/tests/test_mingw.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc45687a917808edbf692ced18b0c99183d21e2d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/tests/test_mingw.py
@@ -0,0 +1,48 @@
+from distutils import sysconfig
+from distutils.errors import DistutilsPlatformError
+from distutils.util import is_mingw, split_quoted
+
+import pytest
+
+from .. import cygwin, errors
+
+
+class TestMinGW32Compiler:
+    @pytest.mark.skipif(not is_mingw(), reason='not on mingw')
+    def test_compiler_type(self):
+        compiler = cygwin.MinGW32Compiler()
+        assert compiler.compiler_type == 'mingw32'
+
+    @pytest.mark.skipif(not is_mingw(), reason='not on mingw')
+    def test_set_executables(self, monkeypatch):
+        monkeypatch.setenv('CC', 'cc')
+        monkeypatch.setenv('CXX', 'c++')
+
+        compiler = cygwin.MinGW32Compiler()
+
+        assert compiler.compiler == split_quoted('cc -O -Wall')
+        assert compiler.compiler_so == split_quoted('cc -shared -O -Wall')
+        assert compiler.compiler_cxx == split_quoted('c++ -O -Wall')
+        assert compiler.linker_exe == split_quoted('cc')
+        assert compiler.linker_so == split_quoted('cc -shared')
+
+    @pytest.mark.skipif(not is_mingw(), reason='not on mingw')
+    def test_runtime_library_dir_option(self):
+        compiler = cygwin.MinGW32Compiler()
+        with pytest.raises(DistutilsPlatformError):
+            compiler.runtime_library_dir_option('/usr/lib')
+
+    @pytest.mark.skipif(not is_mingw(), reason='not on mingw')
+    def test_cygwincc_error(self, monkeypatch):
+        monkeypatch.setattr(cygwin, 'is_cygwincc', lambda _: True)
+
+        with pytest.raises(errors.Error):
+            cygwin.MinGW32Compiler()
+
+    @pytest.mark.skipif('sys.platform == "cygwin"')
+    def test_customize_compiler_with_msvc_python(self):
+        # In case we have an MSVC Python build, but still want to use
+        # MinGW32Compiler, then customize_compiler() shouldn't fail at least.
+        # https://github.com/pypa/setuptools/issues/4456
+        compiler = cygwin.MinGW32Compiler()
+        sysconfig.customize_compiler(compiler)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/tests/test_msvc.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/tests/test_msvc.py
new file mode 100644
index 0000000000000000000000000000000000000000..eca831996adcb07f05560581fe4285cdb29392d3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/tests/test_msvc.py
@@ -0,0 +1,136 @@
+import os
+import sys
+import sysconfig
+import threading
+import unittest.mock as mock
+from distutils.errors import DistutilsPlatformError
+from distutils.tests import support
+from distutils.util import get_platform
+
+import pytest
+
+from .. import msvc
+
+needs_winreg = pytest.mark.skipif('not hasattr(msvc, "winreg")')
+
+
+class Testmsvccompiler(support.TempdirManager):
+    def test_no_compiler(self, monkeypatch):
+        # makes sure query_vcvarsall raises
+        # a DistutilsPlatformError if the compiler
+        # is not found
+        def _find_vcvarsall(plat_spec):
+            return None, None
+
+        monkeypatch.setattr(msvc, '_find_vcvarsall', _find_vcvarsall)
+
+        with pytest.raises(DistutilsPlatformError):
+            msvc._get_vc_env(
+                'wont find this version',
+            )
+
+    @pytest.mark.skipif(
+        not sysconfig.get_platform().startswith("win"),
+        reason="Only run test for non-mingw Windows platforms",
+    )
+    @pytest.mark.parametrize(
+        "plat_name, expected",
+        [
+            ("win-arm64", "win-arm64"),
+            ("win-amd64", "win-amd64"),
+            (None, get_platform()),
+        ],
+    )
+    def test_cross_platform_compilation_paths(self, monkeypatch, plat_name, expected):
+        """
+        Ensure a specified target platform is passed to _get_vcvars_spec.
+        """
+        compiler = msvc.Compiler()
+
+        def _get_vcvars_spec(host_platform, platform):
+            assert platform == expected
+
+        monkeypatch.setattr(msvc, '_get_vcvars_spec', _get_vcvars_spec)
+        compiler.initialize(plat_name)
+
+    @needs_winreg
+    def test_get_vc_env_unicode(self):
+        test_var = 'ṰḖṤṪ┅ṼẨṜ'
+        test_value = '₃⁴₅'
+
+        # Ensure we don't early exit from _get_vc_env
+        old_distutils_use_sdk = os.environ.pop('DISTUTILS_USE_SDK', None)
+        os.environ[test_var] = test_value
+        try:
+            env = msvc._get_vc_env('x86')
+            assert test_var.lower() in env
+            assert test_value == env[test_var.lower()]
+        finally:
+            os.environ.pop(test_var)
+            if old_distutils_use_sdk:
+                os.environ['DISTUTILS_USE_SDK'] = old_distutils_use_sdk
+
+    @needs_winreg
+    @pytest.mark.parametrize('ver', (2015, 2017))
+    def test_get_vc(self, ver):
+        # This function cannot be mocked, so pass if VC is found
+        # and skip otherwise.
+        lookup = getattr(msvc, f'_find_vc{ver}')
+        expected_version = {2015: 14, 2017: 15}[ver]
+        version, path = lookup()
+        if not version:
+            pytest.skip(f"VS {ver} is not installed")
+        assert version >= expected_version
+        assert os.path.isdir(path)
+
+
+class CheckThread(threading.Thread):
+    exc_info = None
+
+    def run(self):
+        try:
+            super().run()
+        except Exception:
+            self.exc_info = sys.exc_info()
+
+    def __bool__(self):
+        return not self.exc_info
+
+
+class TestSpawn:
+    def test_concurrent_safe(self):
+        """
+        Concurrent calls to spawn should have consistent results.
+        """
+        compiler = msvc.Compiler()
+        compiler._paths = "expected"
+        inner_cmd = 'import os; assert os.environ["PATH"] == "expected"'
+        command = [sys.executable, '-c', inner_cmd]
+
+        threads = [
+            CheckThread(target=compiler.spawn, args=[command]) for n in range(100)
+        ]
+        for thread in threads:
+            thread.start()
+        for thread in threads:
+            thread.join()
+        assert all(threads)
+
+    def test_concurrent_safe_fallback(self):
+        """
+        If CCompiler.spawn has been monkey-patched without support
+        for an env, it should still execute.
+        """
+        from distutils import ccompiler
+
+        compiler = msvc.Compiler()
+        compiler._paths = "expected"
+
+        def CCompiler_spawn(self, cmd):
+            "A spawn without an env argument."
+            assert os.environ["PATH"] == "expected"
+
+        with mock.patch.object(ccompiler.CCompiler, 'spawn', CCompiler_spawn):
+            compiler.spawn(["n/a"])
+
+        assert os.environ.get("PATH") != "expected"
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/tests/test_unix.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/tests/test_unix.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4e28984585d49ddc46bc2138cf2109221a62259
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/tests/test_unix.py
@@ -0,0 +1,350 @@
+"""Tests for distutils.unixccompiler."""
+
+import os
+import sys
+import unittest.mock as mock
+from distutils import sysconfig
+from distutils.compat import consolidate_linker_args
+from distutils.errors import DistutilsPlatformError
+from distutils.tests import support
+from distutils.tests.compat.py39 import EnvironmentVarGuard
+from distutils.util import _clear_cached_macosx_ver
+
+import pytest
+
+from .. import unix
+
+
+@pytest.fixture(autouse=True)
+def save_values(monkeypatch):
+    monkeypatch.setattr(sys, 'platform', sys.platform)
+    monkeypatch.setattr(sysconfig, 'get_config_var', sysconfig.get_config_var)
+    monkeypatch.setattr(sysconfig, 'get_config_vars', sysconfig.get_config_vars)
+
+
+@pytest.fixture(autouse=True)
+def compiler_wrapper(request):
+    class CompilerWrapper(unix.Compiler):
+        def rpath_foo(self):
+            return self.runtime_library_dir_option('/foo')
+
+    request.instance.cc = CompilerWrapper()
+
+
+class TestUnixCCompiler(support.TempdirManager):
+    @pytest.mark.skipif('platform.system == "Windows"')
+    def test_runtime_libdir_option(self):  # noqa: C901
+        # Issue #5900; GitHub Issue #37
+        #
+        # Ensure RUNPATH is added to extension modules with RPATH if
+        # GNU ld is used
+
+        # darwin
+        sys.platform = 'darwin'
+        darwin_ver_var = 'MACOSX_DEPLOYMENT_TARGET'
+        darwin_rpath_flag = '-Wl,-rpath,/foo'
+        darwin_lib_flag = '-L/foo'
+
+        # (macOS version from syscfg, macOS version from env var) -> flag
+        # Version value of None generates two tests: as None and as empty string
+        # Expected flag value of None means an mismatch exception is expected
+        darwin_test_cases = [
+            ((None, None), darwin_lib_flag),
+            ((None, '11'), darwin_rpath_flag),
+            (('10', None), darwin_lib_flag),
+            (('10.3', None), darwin_lib_flag),
+            (('10.3.1', None), darwin_lib_flag),
+            (('10.5', None), darwin_rpath_flag),
+            (('10.5.1', None), darwin_rpath_flag),
+            (('10.3', '10.3'), darwin_lib_flag),
+            (('10.3', '10.5'), darwin_rpath_flag),
+            (('10.5', '10.3'), darwin_lib_flag),
+            (('10.5', '11'), darwin_rpath_flag),
+            (('10.4', '10'), None),
+        ]
+
+        def make_darwin_gcv(syscfg_macosx_ver):
+            def gcv(var):
+                if var == darwin_ver_var:
+                    return syscfg_macosx_ver
+                return "xxx"
+
+            return gcv
+
+        def do_darwin_test(syscfg_macosx_ver, env_macosx_ver, expected_flag):
+            env = os.environ
+            msg = f"macOS version = (sysconfig={syscfg_macosx_ver!r}, env={env_macosx_ver!r})"
+
+            # Save
+            old_gcv = sysconfig.get_config_var
+            old_env_macosx_ver = env.get(darwin_ver_var)
+
+            # Setup environment
+            _clear_cached_macosx_ver()
+            sysconfig.get_config_var = make_darwin_gcv(syscfg_macosx_ver)
+            if env_macosx_ver is not None:
+                env[darwin_ver_var] = env_macosx_ver
+            elif darwin_ver_var in env:
+                env.pop(darwin_ver_var)
+
+            # Run the test
+            if expected_flag is not None:
+                assert self.cc.rpath_foo() == expected_flag, msg
+            else:
+                with pytest.raises(
+                    DistutilsPlatformError, match=darwin_ver_var + r' mismatch'
+                ):
+                    self.cc.rpath_foo()
+
+            # Restore
+            if old_env_macosx_ver is not None:
+                env[darwin_ver_var] = old_env_macosx_ver
+            elif darwin_ver_var in env:
+                env.pop(darwin_ver_var)
+            sysconfig.get_config_var = old_gcv
+            _clear_cached_macosx_ver()
+
+        for macosx_vers, expected_flag in darwin_test_cases:
+            syscfg_macosx_ver, env_macosx_ver = macosx_vers
+            do_darwin_test(syscfg_macosx_ver, env_macosx_ver, expected_flag)
+            # Bonus test cases with None interpreted as empty string
+            if syscfg_macosx_ver is None:
+                do_darwin_test("", env_macosx_ver, expected_flag)
+            if env_macosx_ver is None:
+                do_darwin_test(syscfg_macosx_ver, "", expected_flag)
+            if syscfg_macosx_ver is None and env_macosx_ver is None:
+                do_darwin_test("", "", expected_flag)
+
+        old_gcv = sysconfig.get_config_var
+
+        # hp-ux
+        sys.platform = 'hp-ux'
+
+        def gcv(v):
+            return 'xxx'
+
+        sysconfig.get_config_var = gcv
+        assert self.cc.rpath_foo() == ['+s', '-L/foo']
+
+        def gcv(v):
+            return 'gcc'
+
+        sysconfig.get_config_var = gcv
+        assert self.cc.rpath_foo() == ['-Wl,+s', '-L/foo']
+
+        def gcv(v):
+            return 'g++'
+
+        sysconfig.get_config_var = gcv
+        assert self.cc.rpath_foo() == ['-Wl,+s', '-L/foo']
+
+        sysconfig.get_config_var = old_gcv
+
+        # GCC GNULD
+        sys.platform = 'bar'
+
+        def gcv(v):
+            if v == 'CC':
+                return 'gcc'
+            elif v == 'GNULD':
+                return 'yes'
+
+        sysconfig.get_config_var = gcv
+        assert self.cc.rpath_foo() == consolidate_linker_args([
+            '-Wl,--enable-new-dtags',
+            '-Wl,-rpath,/foo',
+        ])
+
+        def gcv(v):
+            if v == 'CC':
+                return 'gcc -pthread -B /bar'
+            elif v == 'GNULD':
+                return 'yes'
+
+        sysconfig.get_config_var = gcv
+        assert self.cc.rpath_foo() == consolidate_linker_args([
+            '-Wl,--enable-new-dtags',
+            '-Wl,-rpath,/foo',
+        ])
+
+        # GCC non-GNULD
+        sys.platform = 'bar'
+
+        def gcv(v):
+            if v == 'CC':
+                return 'gcc'
+            elif v == 'GNULD':
+                return 'no'
+
+        sysconfig.get_config_var = gcv
+        assert self.cc.rpath_foo() == '-Wl,-R/foo'
+
+        # GCC GNULD with fully qualified configuration prefix
+        # see #7617
+        sys.platform = 'bar'
+
+        def gcv(v):
+            if v == 'CC':
+                return 'x86_64-pc-linux-gnu-gcc-4.4.2'
+            elif v == 'GNULD':
+                return 'yes'
+
+        sysconfig.get_config_var = gcv
+        assert self.cc.rpath_foo() == consolidate_linker_args([
+            '-Wl,--enable-new-dtags',
+            '-Wl,-rpath,/foo',
+        ])
+
+        # non-GCC GNULD
+        sys.platform = 'bar'
+
+        def gcv(v):
+            if v == 'CC':
+                return 'cc'
+            elif v == 'GNULD':
+                return 'yes'
+
+        sysconfig.get_config_var = gcv
+        assert self.cc.rpath_foo() == consolidate_linker_args([
+            '-Wl,--enable-new-dtags',
+            '-Wl,-rpath,/foo',
+        ])
+
+        # non-GCC non-GNULD
+        sys.platform = 'bar'
+
+        def gcv(v):
+            if v == 'CC':
+                return 'cc'
+            elif v == 'GNULD':
+                return 'no'
+
+        sysconfig.get_config_var = gcv
+        assert self.cc.rpath_foo() == '-Wl,-R/foo'
+
+    @pytest.mark.skipif('platform.system == "Windows"')
+    def test_cc_overrides_ldshared(self):
+        # Issue #18080:
+        # ensure that setting CC env variable also changes default linker
+        def gcv(v):
+            if v == 'LDSHARED':
+                return 'gcc-4.2 -bundle -undefined dynamic_lookup '
+            return 'gcc-4.2'
+
+        def gcvs(*args, _orig=sysconfig.get_config_vars):
+            if args:
+                return list(map(sysconfig.get_config_var, args))
+            return _orig()
+
+        sysconfig.get_config_var = gcv
+        sysconfig.get_config_vars = gcvs
+        with EnvironmentVarGuard() as env:
+            env['CC'] = 'my_cc'
+            del env['LDSHARED']
+            sysconfig.customize_compiler(self.cc)
+        assert self.cc.linker_so[0] == 'my_cc'
+
+    @pytest.mark.skipif('platform.system == "Windows"')
+    @pytest.mark.usefixtures('disable_macos_customization')
+    def test_cc_overrides_ldshared_for_cxx_correctly(self):
+        """
+        Ensure that setting CC env variable also changes default linker
+        correctly when building C++ extensions.
+
+        pypa/distutils#126
+        """
+
+        def gcv(v):
+            if v == 'LDSHARED':
+                return 'gcc-4.2 -bundle -undefined dynamic_lookup '
+            elif v == 'LDCXXSHARED':
+                return 'g++-4.2 -bundle -undefined dynamic_lookup '
+            elif v == 'CXX':
+                return 'g++-4.2'
+            elif v == 'CC':
+                return 'gcc-4.2'
+            return ''
+
+        def gcvs(*args, _orig=sysconfig.get_config_vars):
+            if args:
+                return list(map(sysconfig.get_config_var, args))
+            return _orig()
+
+        sysconfig.get_config_var = gcv
+        sysconfig.get_config_vars = gcvs
+        with (
+            mock.patch.object(self.cc, 'spawn', return_value=None) as mock_spawn,
+            mock.patch.object(self.cc, '_need_link', return_value=True),
+            mock.patch.object(self.cc, 'mkpath', return_value=None),
+            EnvironmentVarGuard() as env,
+        ):
+            env['CC'] = 'ccache my_cc'
+            env['CXX'] = 'my_cxx'
+            del env['LDSHARED']
+            sysconfig.customize_compiler(self.cc)
+            assert self.cc.linker_so[0:2] == ['ccache', 'my_cc']
+            self.cc.link(None, [], 'a.out', target_lang='c++')
+            call_args = mock_spawn.call_args[0][0]
+            expected = ['my_cxx', '-bundle', '-undefined', 'dynamic_lookup']
+            assert call_args[:4] == expected
+
+    @pytest.mark.skipif('platform.system == "Windows"')
+    def test_explicit_ldshared(self):
+        # Issue #18080:
+        # ensure that setting CC env variable does not change
+        #   explicit LDSHARED setting for linker
+        def gcv(v):
+            if v == 'LDSHARED':
+                return 'gcc-4.2 -bundle -undefined dynamic_lookup '
+            return 'gcc-4.2'
+
+        def gcvs(*args, _orig=sysconfig.get_config_vars):
+            if args:
+                return list(map(sysconfig.get_config_var, args))
+            return _orig()
+
+        sysconfig.get_config_var = gcv
+        sysconfig.get_config_vars = gcvs
+        with EnvironmentVarGuard() as env:
+            env['CC'] = 'my_cc'
+            env['LDSHARED'] = 'my_ld -bundle -dynamic'
+            sysconfig.customize_compiler(self.cc)
+        assert self.cc.linker_so[0] == 'my_ld'
+
+    def test_has_function(self):
+        # Issue https://github.com/pypa/distutils/issues/64:
+        # ensure that setting output_dir does not raise
+        # FileNotFoundError: [Errno 2] No such file or directory: 'a.out'
+        self.cc.output_dir = 'scratch'
+        os.chdir(self.mkdtemp())
+        self.cc.has_function('abort')
+
+    def test_find_library_file(self, monkeypatch):
+        compiler = unix.Compiler()
+        compiler._library_root = lambda dir: dir
+        monkeypatch.setattr(os.path, 'exists', lambda d: 'existing' in d)
+
+        libname = 'libabc.dylib' if sys.platform != 'cygwin' else 'cygabc.dll'
+        dirs = ('/foo/bar/missing', '/foo/bar/existing')
+        assert (
+            compiler.find_library_file(dirs, 'abc').replace('\\', '/')
+            == f'/foo/bar/existing/{libname}'
+        )
+        assert (
+            compiler.find_library_file(reversed(dirs), 'abc').replace('\\', '/')
+            == f'/foo/bar/existing/{libname}'
+        )
+
+        monkeypatch.setattr(
+            os.path,
+            'exists',
+            lambda d: 'existing' in d and '.a' in d and '.dll.a' not in d,
+        )
+        assert (
+            compiler.find_library_file(dirs, 'abc').replace('\\', '/')
+            == '/foo/bar/existing/libabc.a'
+        )
+        assert (
+            compiler.find_library_file(reversed(dirs), 'abc').replace('\\', '/')
+            == '/foo/bar/existing/libabc.a'
+        )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/unix.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/unix.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8a53d452f2d030a2381cca2e17cb98e9f8b47a4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/unix.py
@@ -0,0 +1,423 @@
+"""distutils.unixccompiler
+
+Contains the UnixCCompiler class, a subclass of CCompiler that handles
+the "typical" Unix-style command-line C compiler:
+  * macros defined with -Dname[=value]
+  * macros undefined with -Uname
+  * include search directories specified with -Idir
+  * libraries specified with -lllib
+  * library search directories specified with -Ldir
+  * compile handled by 'cc' (or similar) executable with -c option:
+    compiles .c to .o
+  * link static library handled by 'ar' command (possibly with 'ranlib')
+  * link shared library handled by 'cc -shared'
+"""
+
+from __future__ import annotations
+
+import itertools
+import os
+import re
+import shlex
+import sys
+from collections.abc import Iterable
+
+from ... import sysconfig
+from ..._log import log
+from ..._macos_compat import compiler_fixup
+from ..._modified import newer
+from ...compat import consolidate_linker_args
+from ...errors import DistutilsExecError
+from . import base
+from .base import _Macro, gen_lib_options, gen_preprocess_options
+from .errors import (
+    CompileError,
+    LibError,
+    LinkError,
+)
+
+# XXX Things not currently handled:
+#   * optimization/debug/warning flags; we just use whatever's in Python's
+#     Makefile and live with it.  Is this adequate?  If not, we might
+#     have to have a bunch of subclasses GNUCCompiler, SGICCompiler,
+#     SunCCompiler, and I suspect down that road lies madness.
+#   * even if we don't know a warning flag from an optimization flag,
+#     we need some way for outsiders to feed preprocessor/compiler/linker
+#     flags in to us -- eg. a sysadmin might want to mandate certain flags
+#     via a site config file, or a user might want to set something for
+#     compiling this module distribution only via the setup.py command
+#     line, whatever.  As long as these options come from something on the
+#     current system, they can be as system-dependent as they like, and we
+#     should just happily stuff them into the preprocessor/compiler/linker
+#     options and carry on.
+
+
+def _split_env(cmd):
+    """
+    For macOS, split command into 'env' portion (if any)
+    and the rest of the linker command.
+
+    >>> _split_env(['a', 'b', 'c'])
+    ([], ['a', 'b', 'c'])
+    >>> _split_env(['/usr/bin/env', 'A=3', 'gcc'])
+    (['/usr/bin/env', 'A=3'], ['gcc'])
+    """
+    pivot = 0
+    if os.path.basename(cmd[0]) == "env":
+        pivot = 1
+        while '=' in cmd[pivot]:
+            pivot += 1
+    return cmd[:pivot], cmd[pivot:]
+
+
+def _split_aix(cmd):
+    """
+    AIX platforms prefix the compiler with the ld_so_aix
+    script, so split that from the linker command.
+
+    >>> _split_aix(['a', 'b', 'c'])
+    ([], ['a', 'b', 'c'])
+    >>> _split_aix(['/bin/foo/ld_so_aix', 'gcc'])
+    (['/bin/foo/ld_so_aix'], ['gcc'])
+    """
+    pivot = os.path.basename(cmd[0]) == 'ld_so_aix'
+    return cmd[:pivot], cmd[pivot:]
+
+
+def _linker_params(linker_cmd, compiler_cmd):
+    """
+    The linker command usually begins with the compiler
+    command (possibly multiple elements), followed by zero or more
+    params for shared library building.
+
+    If the LDSHARED env variable overrides the linker command,
+    however, the commands may not match.
+
+    Return the best guess of the linker parameters by stripping
+    the linker command. If the compiler command does not
+    match the linker command, assume the linker command is
+    just the first element.
+
+    >>> _linker_params('gcc foo bar'.split(), ['gcc'])
+    ['foo', 'bar']
+    >>> _linker_params('gcc foo bar'.split(), ['other'])
+    ['foo', 'bar']
+    >>> _linker_params('ccache gcc foo bar'.split(), 'ccache gcc'.split())
+    ['foo', 'bar']
+    >>> _linker_params(['gcc'], ['gcc'])
+    []
+    """
+    c_len = len(compiler_cmd)
+    pivot = c_len if linker_cmd[:c_len] == compiler_cmd else 1
+    return linker_cmd[pivot:]
+
+
+class Compiler(base.Compiler):
+    compiler_type = 'unix'
+
+    # These are used by CCompiler in two places: the constructor sets
+    # instance attributes 'preprocessor', 'compiler', etc. from them, and
+    # 'set_executable()' allows any of these to be set.  The defaults here
+    # are pretty generic; they will probably have to be set by an outsider
+    # (eg. using information discovered by the sysconfig about building
+    # Python extensions).
+    executables = {
+        'preprocessor': None,
+        'compiler': ["cc"],
+        'compiler_so': ["cc"],
+        'compiler_cxx': ["c++"],
+        'compiler_so_cxx': ["c++"],
+        'linker_so': ["cc", "-shared"],
+        'linker_so_cxx': ["c++", "-shared"],
+        'linker_exe': ["cc"],
+        'linker_exe_cxx': ["c++", "-shared"],
+        'archiver': ["ar", "-cr"],
+        'ranlib': None,
+    }
+
+    if sys.platform[:6] == "darwin":
+        executables['ranlib'] = ["ranlib"]
+
+    # Needed for the filename generation methods provided by the base
+    # class, CCompiler.  NB. whoever instantiates/uses a particular
+    # UnixCCompiler instance should set 'shared_lib_ext' -- we set a
+    # reasonable common default here, but it's not necessarily used on all
+    # Unices!
+
+    src_extensions = [".c", ".C", ".cc", ".cxx", ".cpp", ".m"]
+    obj_extension = ".o"
+    static_lib_extension = ".a"
+    shared_lib_extension = ".so"
+    dylib_lib_extension = ".dylib"
+    xcode_stub_lib_extension = ".tbd"
+    static_lib_format = shared_lib_format = dylib_lib_format = "lib%s%s"
+    xcode_stub_lib_format = dylib_lib_format
+    if sys.platform == "cygwin":
+        exe_extension = ".exe"
+        shared_lib_extension = ".dll.a"
+        dylib_lib_extension = ".dll"
+        dylib_lib_format = "cyg%s%s"
+
+    def _fix_lib_args(self, libraries, library_dirs, runtime_library_dirs):
+        """Remove standard library path from rpath"""
+        libraries, library_dirs, runtime_library_dirs = super()._fix_lib_args(
+            libraries, library_dirs, runtime_library_dirs
+        )
+        libdir = sysconfig.get_config_var('LIBDIR')
+        if (
+            runtime_library_dirs
+            and libdir.startswith("/usr/lib")
+            and (libdir in runtime_library_dirs)
+        ):
+            runtime_library_dirs.remove(libdir)
+        return libraries, library_dirs, runtime_library_dirs
+
+    def preprocess(
+        self,
+        source: str | os.PathLike[str],
+        output_file: str | os.PathLike[str] | None = None,
+        macros: list[_Macro] | None = None,
+        include_dirs: list[str] | tuple[str, ...] | None = None,
+        extra_preargs: list[str] | None = None,
+        extra_postargs: Iterable[str] | None = None,
+    ):
+        fixed_args = self._fix_compile_args(None, macros, include_dirs)
+        ignore, macros, include_dirs = fixed_args
+        pp_opts = gen_preprocess_options(macros, include_dirs)
+        pp_args = self.preprocessor + pp_opts
+        if output_file:
+            pp_args.extend(['-o', output_file])
+        if extra_preargs:
+            pp_args[:0] = extra_preargs
+        if extra_postargs:
+            pp_args.extend(extra_postargs)
+        pp_args.append(source)
+
+        # reasons to preprocess:
+        # - force is indicated
+        # - output is directed to stdout
+        # - source file is newer than the target
+        preprocess = self.force or output_file is None or newer(source, output_file)
+        if not preprocess:
+            return
+
+        if output_file:
+            self.mkpath(os.path.dirname(output_file))
+
+        try:
+            self.spawn(pp_args)
+        except DistutilsExecError as msg:
+            raise CompileError(msg)
+
+    def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts):
+        compiler_so = compiler_fixup(self.compiler_so, cc_args + extra_postargs)
+        compiler_so_cxx = compiler_fixup(self.compiler_so_cxx, cc_args + extra_postargs)
+        try:
+            if self.detect_language(src) == 'c++':
+                self.spawn(
+                    compiler_so_cxx + cc_args + [src, '-o', obj] + extra_postargs
+                )
+            else:
+                self.spawn(compiler_so + cc_args + [src, '-o', obj] + extra_postargs)
+        except DistutilsExecError as msg:
+            raise CompileError(msg)
+
+    def create_static_lib(
+        self, objects, output_libname, output_dir=None, debug=False, target_lang=None
+    ):
+        objects, output_dir = self._fix_object_args(objects, output_dir)
+
+        output_filename = self.library_filename(output_libname, output_dir=output_dir)
+
+        if self._need_link(objects, output_filename):
+            self.mkpath(os.path.dirname(output_filename))
+            self.spawn(self.archiver + [output_filename] + objects + self.objects)
+
+            # Not many Unices required ranlib anymore -- SunOS 4.x is, I
+            # think the only major Unix that does.  Maybe we need some
+            # platform intelligence here to skip ranlib if it's not
+            # needed -- or maybe Python's configure script took care of
+            # it for us, hence the check for leading colon.
+            if self.ranlib:
+                try:
+                    self.spawn(self.ranlib + [output_filename])
+                except DistutilsExecError as msg:
+                    raise LibError(msg)
+        else:
+            log.debug("skipping %s (up-to-date)", output_filename)
+
+    def link(
+        self,
+        target_desc,
+        objects: list[str] | tuple[str, ...],
+        output_filename,
+        output_dir: str | None = None,
+        libraries: list[str] | tuple[str, ...] | None = None,
+        library_dirs: list[str] | tuple[str, ...] | None = None,
+        runtime_library_dirs: list[str] | tuple[str, ...] | None = None,
+        export_symbols=None,
+        debug=False,
+        extra_preargs=None,
+        extra_postargs=None,
+        build_temp=None,
+        target_lang=None,
+    ):
+        objects, output_dir = self._fix_object_args(objects, output_dir)
+        fixed_args = self._fix_lib_args(libraries, library_dirs, runtime_library_dirs)
+        libraries, library_dirs, runtime_library_dirs = fixed_args
+
+        lib_opts = gen_lib_options(self, library_dirs, runtime_library_dirs, libraries)
+        if not isinstance(output_dir, (str, type(None))):
+            raise TypeError("'output_dir' must be a string or None")
+        if output_dir is not None:
+            output_filename = os.path.join(output_dir, output_filename)
+
+        if self._need_link(objects, output_filename):
+            ld_args = objects + self.objects + lib_opts + ['-o', output_filename]
+            if debug:
+                ld_args[:0] = ['-g']
+            if extra_preargs:
+                ld_args[:0] = extra_preargs
+            if extra_postargs:
+                ld_args.extend(extra_postargs)
+            self.mkpath(os.path.dirname(output_filename))
+            try:
+                # Select a linker based on context: linker_exe when
+                # building an executable or linker_so (with shared options)
+                # when building a shared library.
+                building_exe = target_desc == base.Compiler.EXECUTABLE
+                linker = (
+                    self.linker_exe
+                    if building_exe
+                    else (
+                        self.linker_so_cxx if target_lang == "c++" else self.linker_so
+                    )
+                )[:]
+
+                if target_lang == "c++" and self.compiler_cxx:
+                    env, linker_ne = _split_env(linker)
+                    aix, linker_na = _split_aix(linker_ne)
+                    _, compiler_cxx_ne = _split_env(self.compiler_cxx)
+                    _, linker_exe_ne = _split_env(self.linker_exe)
+
+                    params = _linker_params(linker_na, linker_exe_ne)
+                    linker = env + aix + compiler_cxx_ne + params
+
+                linker = compiler_fixup(linker, ld_args)
+
+                self.spawn(linker + ld_args)
+            except DistutilsExecError as msg:
+                raise LinkError(msg)
+        else:
+            log.debug("skipping %s (up-to-date)", output_filename)
+
+    # -- Miscellaneous methods -----------------------------------------
+    # These are all used by the 'gen_lib_options() function, in
+    # ccompiler.py.
+
+    def library_dir_option(self, dir):
+        return "-L" + dir
+
+    def _is_gcc(self):
+        cc_var = sysconfig.get_config_var("CC")
+        compiler = os.path.basename(shlex.split(cc_var)[0])
+        return "gcc" in compiler or "g++" in compiler
+
+    def runtime_library_dir_option(self, dir: str) -> str | list[str]:  # type: ignore[override] # Fixed in pypa/distutils#339
+        # XXX Hackish, at the very least.  See Python bug #445902:
+        # https://bugs.python.org/issue445902
+        # Linkers on different platforms need different options to
+        # specify that directories need to be added to the list of
+        # directories searched for dependencies when a dynamic library
+        # is sought.  GCC on GNU systems (Linux, FreeBSD, ...) has to
+        # be told to pass the -R option through to the linker, whereas
+        # other compilers and gcc on other systems just know this.
+        # Other compilers may need something slightly different.  At
+        # this time, there's no way to determine this information from
+        # the configuration data stored in the Python installation, so
+        # we use this hack.
+        if sys.platform[:6] == "darwin":
+            from distutils.util import get_macosx_target_ver, split_version
+
+            macosx_target_ver = get_macosx_target_ver()
+            if macosx_target_ver and split_version(macosx_target_ver) >= [10, 5]:
+                return "-Wl,-rpath," + dir
+            else:  # no support for -rpath on earlier macOS versions
+                return "-L" + dir
+        elif sys.platform[:7] == "freebsd":
+            return "-Wl,-rpath=" + dir
+        elif sys.platform[:5] == "hp-ux":
+            return [
+                "-Wl,+s" if self._is_gcc() else "+s",
+                "-L" + dir,
+            ]
+
+        # For all compilers, `-Wl` is the presumed way to pass a
+        # compiler option to the linker
+        if sysconfig.get_config_var("GNULD") == "yes":
+            return consolidate_linker_args([
+                # Force RUNPATH instead of RPATH
+                "-Wl,--enable-new-dtags",
+                "-Wl,-rpath," + dir,
+            ])
+        else:
+            return "-Wl,-R" + dir
+
+    def library_option(self, lib):
+        return "-l" + lib
+
+    @staticmethod
+    def _library_root(dir):
+        """
+        macOS users can specify an alternate SDK using'-isysroot'.
+        Calculate the SDK root if it is specified.
+
+        Note that, as of Xcode 7, Apple SDKs may contain textual stub
+        libraries with .tbd extensions rather than the normal .dylib
+        shared libraries installed in /.  The Apple compiler tool
+        chain handles this transparently but it can cause problems
+        for programs that are being built with an SDK and searching
+        for specific libraries.  Callers of find_library_file need to
+        keep in mind that the base filename of the returned SDK library
+        file might have a different extension from that of the library
+        file installed on the running system, for example:
+          /Applications/Xcode.app/Contents/Developer/Platforms/
+              MacOSX.platform/Developer/SDKs/MacOSX10.11.sdk/
+              usr/lib/libedit.tbd
+        vs
+          /usr/lib/libedit.dylib
+        """
+        cflags = sysconfig.get_config_var('CFLAGS')
+        match = re.search(r'-isysroot\s*(\S+)', cflags)
+
+        apply_root = (
+            sys.platform == 'darwin'
+            and match
+            and (
+                dir.startswith('/System/')
+                or (dir.startswith('/usr/') and not dir.startswith('/usr/local/'))
+            )
+        )
+
+        return os.path.join(match.group(1), dir[1:]) if apply_root else dir
+
+    def find_library_file(self, dirs, lib, debug=False):
+        """
+        Second-guess the linker with not much hard
+        data to go on: GCC seems to prefer the shared library, so
+        assume that *all* Unix C compilers do,
+        ignoring even GCC's "-static" option.
+        """
+        lib_names = (
+            self.library_filename(lib, lib_type=type)
+            for type in 'dylib xcode_stub shared static'.split()
+        )
+
+        roots = map(self._library_root, dirs)
+
+        searched = itertools.starmap(os.path.join, itertools.product(roots, lib_names))
+
+        found = filter(os.path.exists, searched)
+
+        # Return None if it could not be found in any dir.
+        return next(found, None)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/zos.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/zos.py
new file mode 100644
index 0000000000000000000000000000000000000000..82d017fc90db55b8f7b2517ba6c8a8fd2082cb5e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/compilers/C/zos.py
@@ -0,0 +1,230 @@
+"""distutils.zosccompiler
+
+Contains the selection of the c & c++ compilers on z/OS. There are several
+different c compilers on z/OS, all of them are optional, so the correct
+one needs to be chosen based on the users input. This is compatible with
+the following compilers:
+
+IBM C/C++ For Open Enterprise Languages on z/OS 2.0
+IBM Open XL C/C++ 1.1 for z/OS
+IBM XL C/C++ V2.4.1 for z/OS 2.4 and 2.5
+IBM z/OS XL C/C++
+"""
+
+import os
+
+from ... import sysconfig
+from ...errors import DistutilsExecError
+from . import unix
+from .errors import CompileError
+
+_cc_args = {
+    'ibm-openxl': [
+        '-m64',
+        '-fvisibility=default',
+        '-fzos-le-char-mode=ascii',
+        '-fno-short-enums',
+    ],
+    'ibm-xlclang': [
+        '-q64',
+        '-qexportall',
+        '-qascii',
+        '-qstrict',
+        '-qnocsect',
+        '-Wa,asa,goff',
+        '-Wa,xplink',
+        '-qgonumber',
+        '-qenum=int',
+        '-Wc,DLL',
+    ],
+    'ibm-xlc': [
+        '-q64',
+        '-qexportall',
+        '-qascii',
+        '-qstrict',
+        '-qnocsect',
+        '-Wa,asa,goff',
+        '-Wa,xplink',
+        '-qgonumber',
+        '-qenum=int',
+        '-Wc,DLL',
+        '-qlanglvl=extc99',
+    ],
+}
+
+_cxx_args = {
+    'ibm-openxl': [
+        '-m64',
+        '-fvisibility=default',
+        '-fzos-le-char-mode=ascii',
+        '-fno-short-enums',
+    ],
+    'ibm-xlclang': [
+        '-q64',
+        '-qexportall',
+        '-qascii',
+        '-qstrict',
+        '-qnocsect',
+        '-Wa,asa,goff',
+        '-Wa,xplink',
+        '-qgonumber',
+        '-qenum=int',
+        '-Wc,DLL',
+    ],
+    'ibm-xlc': [
+        '-q64',
+        '-qexportall',
+        '-qascii',
+        '-qstrict',
+        '-qnocsect',
+        '-Wa,asa,goff',
+        '-Wa,xplink',
+        '-qgonumber',
+        '-qenum=int',
+        '-Wc,DLL',
+        '-qlanglvl=extended0x',
+    ],
+}
+
+_asm_args = {
+    'ibm-openxl': ['-fasm', '-fno-integrated-as', '-Wa,--ASA', '-Wa,--GOFF'],
+    'ibm-xlclang': [],
+    'ibm-xlc': [],
+}
+
+_ld_args = {
+    'ibm-openxl': [],
+    'ibm-xlclang': ['-Wl,dll', '-q64'],
+    'ibm-xlc': ['-Wl,dll', '-q64'],
+}
+
+
+# Python on z/OS is built with no compiler specific options in it's CFLAGS.
+# But each compiler requires it's own specific options to build successfully,
+# though some of the options are common between them
+class Compiler(unix.Compiler):
+    src_extensions = ['.c', '.C', '.cc', '.cxx', '.cpp', '.m', '.s']
+    _cpp_extensions = ['.cc', '.cpp', '.cxx', '.C']
+    _asm_extensions = ['.s']
+
+    def _get_zos_compiler_name(self):
+        zos_compiler_names = [
+            os.path.basename(binary)
+            for envvar in ('CC', 'CXX', 'LDSHARED')
+            if (binary := os.environ.get(envvar, None))
+        ]
+        if len(zos_compiler_names) == 0:
+            return 'ibm-openxl'
+
+        zos_compilers = {}
+        for compiler in (
+            'ibm-clang',
+            'ibm-clang64',
+            'ibm-clang++',
+            'ibm-clang++64',
+            'clang',
+            'clang++',
+            'clang-14',
+        ):
+            zos_compilers[compiler] = 'ibm-openxl'
+
+        for compiler in ('xlclang', 'xlclang++', 'njsc', 'njsc++'):
+            zos_compilers[compiler] = 'ibm-xlclang'
+
+        for compiler in ('xlc', 'xlC', 'xlc++'):
+            zos_compilers[compiler] = 'ibm-xlc'
+
+        return zos_compilers.get(zos_compiler_names[0], 'ibm-openxl')
+
+    def __init__(self, verbose=False, dry_run=False, force=False):
+        super().__init__(verbose, dry_run, force)
+        self.zos_compiler = self._get_zos_compiler_name()
+        sysconfig.customize_compiler(self)
+
+    def _compile(self, obj, src, ext, cc_args, extra_postargs, pp_opts):
+        local_args = []
+        if ext in self._cpp_extensions:
+            compiler = self.compiler_cxx
+            local_args.extend(_cxx_args[self.zos_compiler])
+        elif ext in self._asm_extensions:
+            compiler = self.compiler_so
+            local_args.extend(_cc_args[self.zos_compiler])
+            local_args.extend(_asm_args[self.zos_compiler])
+        else:
+            compiler = self.compiler_so
+            local_args.extend(_cc_args[self.zos_compiler])
+        local_args.extend(cc_args)
+
+        try:
+            self.spawn(compiler + local_args + [src, '-o', obj] + extra_postargs)
+        except DistutilsExecError as msg:
+            raise CompileError(msg)
+
+    def runtime_library_dir_option(self, dir):
+        return '-L' + dir
+
+    def link(
+        self,
+        target_desc,
+        objects,
+        output_filename,
+        output_dir=None,
+        libraries=None,
+        library_dirs=None,
+        runtime_library_dirs=None,
+        export_symbols=None,
+        debug=False,
+        extra_preargs=None,
+        extra_postargs=None,
+        build_temp=None,
+        target_lang=None,
+    ):
+        # For a built module to use functions from cpython, it needs to use Pythons
+        # side deck file. The side deck is located beside the libpython3.xx.so
+        ldversion = sysconfig.get_config_var('LDVERSION')
+        if sysconfig.python_build:
+            side_deck_path = os.path.join(
+                sysconfig.get_config_var('abs_builddir'),
+                f'libpython{ldversion}.x',
+            )
+        else:
+            side_deck_path = os.path.join(
+                sysconfig.get_config_var('installed_base'),
+                sysconfig.get_config_var('platlibdir'),
+                f'libpython{ldversion}.x',
+            )
+
+        if os.path.exists(side_deck_path):
+            if extra_postargs:
+                extra_postargs.append(side_deck_path)
+            else:
+                extra_postargs = [side_deck_path]
+
+        # Check and replace libraries included side deck files
+        if runtime_library_dirs:
+            for dir in runtime_library_dirs:
+                for library in libraries[:]:
+                    library_side_deck = os.path.join(dir, f'{library}.x')
+                    if os.path.exists(library_side_deck):
+                        libraries.remove(library)
+                        extra_postargs.append(library_side_deck)
+                        break
+
+        # Any required ld args for the given compiler
+        extra_postargs.extend(_ld_args[self.zos_compiler])
+
+        super().link(
+            target_desc,
+            objects,
+            output_filename,
+            output_dir,
+            libraries,
+            library_dirs,
+            runtime_library_dirs,
+            export_symbols,
+            debug,
+            extra_preargs,
+            extra_postargs,
+            build_temp,
+            target_lang,
+        )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/core.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd62546bdd9d35dcc46e0b46a46b0c61a43fd0ba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/core.py
@@ -0,0 +1,289 @@
+"""distutils.core
+
+The only module that needs to be imported to use the Distutils; provides
+the 'setup' function (which is to be called from the setup script).  Also
+indirectly provides the Distribution and Command classes, although they are
+really defined in distutils.dist and distutils.cmd.
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+import tokenize
+from collections.abc import Iterable
+
+from .cmd import Command
+from .debug import DEBUG
+
+# Mainly import these so setup scripts can "from distutils.core import" them.
+from .dist import Distribution
+from .errors import (
+    CCompilerError,
+    DistutilsArgError,
+    DistutilsError,
+    DistutilsSetupError,
+)
+from .extension import Extension
+
+__all__ = ['Distribution', 'Command', 'Extension', 'setup']
+
+# This is a barebones help message generated displayed when the user
+# runs the setup script with no arguments at all.  More useful help
+# is generated with various --help options: global help, list commands,
+# and per-command help.
+USAGE = """\
+usage: %(script)s [global_opts] cmd1 [cmd1_opts] [cmd2 [cmd2_opts] ...]
+   or: %(script)s --help [cmd1 cmd2 ...]
+   or: %(script)s --help-commands
+   or: %(script)s cmd --help
+"""
+
+
+def gen_usage(script_name):
+    script = os.path.basename(script_name)
+    return USAGE % locals()
+
+
+# Some mild magic to control the behaviour of 'setup()' from 'run_setup()'.
+_setup_stop_after = None
+_setup_distribution = None
+
+# Legal keyword arguments for the setup() function
+setup_keywords = (
+    'distclass',
+    'script_name',
+    'script_args',
+    'options',
+    'name',
+    'version',
+    'author',
+    'author_email',
+    'maintainer',
+    'maintainer_email',
+    'url',
+    'license',
+    'description',
+    'long_description',
+    'keywords',
+    'platforms',
+    'classifiers',
+    'download_url',
+    'requires',
+    'provides',
+    'obsoletes',
+)
+
+# Legal keyword arguments for the Extension constructor
+extension_keywords = (
+    'name',
+    'sources',
+    'include_dirs',
+    'define_macros',
+    'undef_macros',
+    'library_dirs',
+    'libraries',
+    'runtime_library_dirs',
+    'extra_objects',
+    'extra_compile_args',
+    'extra_link_args',
+    'swig_opts',
+    'export_symbols',
+    'depends',
+    'language',
+)
+
+
+def setup(**attrs):  # noqa: C901
+    """The gateway to the Distutils: do everything your setup script needs
+    to do, in a highly flexible and user-driven way.  Briefly: create a
+    Distribution instance; find and parse config files; parse the command
+    line; run each Distutils command found there, customized by the options
+    supplied to 'setup()' (as keyword arguments), in config files, and on
+    the command line.
+
+    The Distribution instance might be an instance of a class supplied via
+    the 'distclass' keyword argument to 'setup'; if no such class is
+    supplied, then the Distribution class (in dist.py) is instantiated.
+    All other arguments to 'setup' (except for 'cmdclass') are used to set
+    attributes of the Distribution instance.
+
+    The 'cmdclass' argument, if supplied, is a dictionary mapping command
+    names to command classes.  Each command encountered on the command line
+    will be turned into a command class, which is in turn instantiated; any
+    class found in 'cmdclass' is used in place of the default, which is
+    (for command 'foo_bar') class 'foo_bar' in module
+    'distutils.command.foo_bar'.  The command class must provide a
+    'user_options' attribute which is a list of option specifiers for
+    'distutils.fancy_getopt'.  Any command-line options between the current
+    and the next command are used to set attributes of the current command
+    object.
+
+    When the entire command-line has been successfully parsed, calls the
+    'run()' method on each command object in turn.  This method will be
+    driven entirely by the Distribution object (which each command object
+    has a reference to, thanks to its constructor), and the
+    command-specific options that became attributes of each command
+    object.
+    """
+
+    global _setup_stop_after, _setup_distribution
+
+    # Determine the distribution class -- either caller-supplied or
+    # our Distribution (see below).
+    klass = attrs.get('distclass')
+    if klass:
+        attrs.pop('distclass')
+    else:
+        klass = Distribution
+
+    if 'script_name' not in attrs:
+        attrs['script_name'] = os.path.basename(sys.argv[0])
+    if 'script_args' not in attrs:
+        attrs['script_args'] = sys.argv[1:]
+
+    # Create the Distribution instance, using the remaining arguments
+    # (ie. everything except distclass) to initialize it
+    try:
+        _setup_distribution = dist = klass(attrs)
+    except DistutilsSetupError as msg:
+        if 'name' not in attrs:
+            raise SystemExit(f"error in setup command: {msg}")
+        else:
+            raise SystemExit("error in {} setup command: {}".format(attrs['name'], msg))
+
+    if _setup_stop_after == "init":
+        return dist
+
+    # Find and parse the config file(s): they will override options from
+    # the setup script, but be overridden by the command line.
+    dist.parse_config_files()
+
+    if DEBUG:
+        print("options (after parsing config files):")
+        dist.dump_option_dicts()
+
+    if _setup_stop_after == "config":
+        return dist
+
+    # Parse the command line and override config files; any
+    # command-line errors are the end user's fault, so turn them into
+    # SystemExit to suppress tracebacks.
+    try:
+        ok = dist.parse_command_line()
+    except DistutilsArgError as msg:
+        raise SystemExit(gen_usage(dist.script_name) + f"\nerror: {msg}")
+
+    if DEBUG:
+        print("options (after parsing command line):")
+        dist.dump_option_dicts()
+
+    if _setup_stop_after == "commandline":
+        return dist
+
+    # And finally, run all the commands found on the command line.
+    if ok:
+        return run_commands(dist)
+
+    return dist
+
+
+# setup ()
+
+
+def run_commands(dist):
+    """Given a Distribution object run all the commands,
+    raising ``SystemExit`` errors in the case of failure.
+
+    This function assumes that either ``sys.argv`` or ``dist.script_args``
+    is already set accordingly.
+    """
+    try:
+        dist.run_commands()
+    except KeyboardInterrupt:
+        raise SystemExit("interrupted")
+    except OSError as exc:
+        if DEBUG:
+            sys.stderr.write(f"error: {exc}\n")
+            raise
+        else:
+            raise SystemExit(f"error: {exc}")
+
+    except (DistutilsError, CCompilerError) as msg:
+        if DEBUG:
+            raise
+        else:
+            raise SystemExit("error: " + str(msg))
+
+    return dist
+
+
+def run_setup(script_name, script_args: Iterable[str] | None = None, stop_after="run"):
+    """Run a setup script in a somewhat controlled environment, and
+    return the Distribution instance that drives things.  This is useful
+    if you need to find out the distribution meta-data (passed as
+    keyword args from 'script' to 'setup()', or the contents of the
+    config files or command-line.
+
+    'script_name' is a file that will be read and run with 'exec()';
+    'sys.argv[0]' will be replaced with 'script' for the duration of the
+    call.  'script_args' is a list of strings; if supplied,
+    'sys.argv[1:]' will be replaced by 'script_args' for the duration of
+    the call.
+
+    'stop_after' tells 'setup()' when to stop processing; possible
+    values:
+      init
+        stop after the Distribution instance has been created and
+        populated with the keyword arguments to 'setup()'
+      config
+        stop after config files have been parsed (and their data
+        stored in the Distribution instance)
+      commandline
+        stop after the command-line ('sys.argv[1:]' or 'script_args')
+        have been parsed (and the data stored in the Distribution)
+      run [default]
+        stop after all commands have been run (the same as if 'setup()'
+        had been called in the usual way
+
+    Returns the Distribution instance, which provides all information
+    used to drive the Distutils.
+    """
+    if stop_after not in ('init', 'config', 'commandline', 'run'):
+        raise ValueError(f"invalid value for 'stop_after': {stop_after!r}")
+
+    global _setup_stop_after, _setup_distribution
+    _setup_stop_after = stop_after
+
+    save_argv = sys.argv.copy()
+    g = {'__file__': script_name, '__name__': '__main__'}
+    try:
+        try:
+            sys.argv[0] = script_name
+            if script_args is not None:
+                sys.argv[1:] = script_args
+            # tokenize.open supports automatic encoding detection
+            with tokenize.open(script_name) as f:
+                code = f.read().replace(r'\r\n', r'\n')
+                exec(code, g)
+        finally:
+            sys.argv = save_argv
+            _setup_stop_after = None
+    except SystemExit:
+        # Hmm, should we do something if exiting with a non-zero code
+        # (ie. error)?
+        pass
+
+    if _setup_distribution is None:
+        raise RuntimeError(
+            "'distutils.core.setup()' was never called -- "
+            f"perhaps '{script_name}' is not a Distutils setup script?"
+        )
+
+    # I wonder if the setup script's namespace -- g and l -- would be of
+    # any interest to callers?
+    # print "_setup_distribution:", _setup_distribution
+    return _setup_distribution
+
+
+# run_setup ()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/cygwinccompiler.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/cygwinccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..de89e3cd8402c9b7048c9e456ff67601934f972b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/cygwinccompiler.py
@@ -0,0 +1,31 @@
+from .compilers.C import cygwin
+from .compilers.C.cygwin import (
+    CONFIG_H_NOTOK,
+    CONFIG_H_OK,
+    CONFIG_H_UNCERTAIN,
+    check_config_h,
+    get_msvcr,
+    is_cygwincc,
+)
+
+__all__ = [
+    'CONFIG_H_NOTOK',
+    'CONFIG_H_OK',
+    'CONFIG_H_UNCERTAIN',
+    'CygwinCCompiler',
+    'Mingw32CCompiler',
+    'check_config_h',
+    'get_msvcr',
+    'is_cygwincc',
+]
+
+
+CygwinCCompiler = cygwin.Compiler
+Mingw32CCompiler = cygwin.MinGW32Compiler
+
+
+get_versions = None
+"""
+A stand-in for the previous get_versions() function to prevent failures
+when monkeypatched. See pypa/setuptools#2969.
+"""
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/debug.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/debug.py
new file mode 100644
index 0000000000000000000000000000000000000000..daf1660f0d821143e388d37532a39ddfd2ca0347
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/debug.py
@@ -0,0 +1,5 @@
+import os
+
+# If DISTUTILS_DEBUG is anything other than the empty string, we run in
+# debug mode.
+DEBUG = os.environ.get('DISTUTILS_DEBUG')
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/dep_util.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/dep_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..09a8a2e126c8bb009dbbe61c5c4bd5e358744996
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/dep_util.py
@@ -0,0 +1,14 @@
+import warnings
+
+from . import _modified
+
+
+def __getattr__(name):
+    if name not in ['newer', 'newer_group', 'newer_pairwise']:
+        raise AttributeError(name)
+    warnings.warn(
+        "dep_util is Deprecated. Use functions from setuptools instead.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    return getattr(_modified, name)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/dir_util.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/dir_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9782602cfe78d4033fb43846965347de8ad9cdf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/dir_util.py
@@ -0,0 +1,244 @@
+"""distutils.dir_util
+
+Utility functions for manipulating directories and directory trees."""
+
+import functools
+import itertools
+import os
+import pathlib
+
+from . import file_util
+from ._log import log
+from .errors import DistutilsFileError, DistutilsInternalError
+
+
+class SkipRepeatAbsolutePaths(set):
+    """
+    Cache for mkpath.
+
+    In addition to cheapening redundant calls, eliminates redundant
+    "creating /foo/bar/baz" messages in dry-run mode.
+    """
+
+    def __init__(self):
+        SkipRepeatAbsolutePaths.instance = self
+
+    @classmethod
+    def clear(cls):
+        super(cls, cls.instance).clear()
+
+    def wrap(self, func):
+        @functools.wraps(func)
+        def wrapper(path, *args, **kwargs):
+            if path.absolute() in self:
+                return
+            result = func(path, *args, **kwargs)
+            self.add(path.absolute())
+            return result
+
+        return wrapper
+
+
+# Python 3.8 compatibility
+wrapper = SkipRepeatAbsolutePaths().wrap
+
+
+@functools.singledispatch
+@wrapper
+def mkpath(name: pathlib.Path, mode=0o777, verbose=True, dry_run=False) -> None:
+    """Create a directory and any missing ancestor directories.
+
+    If the directory already exists (or if 'name' is the empty string, which
+    means the current directory, which of course exists), then do nothing.
+    Raise DistutilsFileError if unable to create some directory along the way
+    (eg. some sub-path exists, but is a file rather than a directory).
+    If 'verbose' is true, log the directory created.
+    """
+    if verbose and not name.is_dir():
+        log.info("creating %s", name)
+
+    try:
+        dry_run or name.mkdir(mode=mode, parents=True, exist_ok=True)
+    except OSError as exc:
+        raise DistutilsFileError(f"could not create '{name}': {exc.args[-1]}")
+
+
+@mkpath.register
+def _(name: str, *args, **kwargs):
+    return mkpath(pathlib.Path(name), *args, **kwargs)
+
+
+@mkpath.register
+def _(name: None, *args, **kwargs):
+    """
+    Detect a common bug -- name is None.
+    """
+    raise DistutilsInternalError(f"mkpath: 'name' must be a string (got {name!r})")
+
+
+def create_tree(base_dir, files, mode=0o777, verbose=True, dry_run=False):
+    """Create all the empty directories under 'base_dir' needed to put 'files'
+    there.
+
+    'base_dir' is just the name of a directory which doesn't necessarily
+    exist yet; 'files' is a list of filenames to be interpreted relative to
+    'base_dir'.  'base_dir' + the directory portion of every file in 'files'
+    will be created if it doesn't already exist.  'mode', 'verbose' and
+    'dry_run' flags are as for 'mkpath()'.
+    """
+    # First get the list of directories to create
+    need_dir = set(os.path.join(base_dir, os.path.dirname(file)) for file in files)
+
+    # Now create them
+    for dir in sorted(need_dir):
+        mkpath(dir, mode, verbose=verbose, dry_run=dry_run)
+
+
+def copy_tree(
+    src,
+    dst,
+    preserve_mode=True,
+    preserve_times=True,
+    preserve_symlinks=False,
+    update=False,
+    verbose=True,
+    dry_run=False,
+):
+    """Copy an entire directory tree 'src' to a new location 'dst'.
+
+    Both 'src' and 'dst' must be directory names.  If 'src' is not a
+    directory, raise DistutilsFileError.  If 'dst' does not exist, it is
+    created with 'mkpath()'.  The end result of the copy is that every
+    file in 'src' is copied to 'dst', and directories under 'src' are
+    recursively copied to 'dst'.  Return the list of files that were
+    copied or might have been copied, using their output name.  The
+    return value is unaffected by 'update' or 'dry_run': it is simply
+    the list of all files under 'src', with the names changed to be
+    under 'dst'.
+
+    'preserve_mode' and 'preserve_times' are the same as for
+    'copy_file'; note that they only apply to regular files, not to
+    directories.  If 'preserve_symlinks' is true, symlinks will be
+    copied as symlinks (on platforms that support them!); otherwise
+    (the default), the destination of the symlink will be copied.
+    'update' and 'verbose' are the same as for 'copy_file'.
+    """
+    if not dry_run and not os.path.isdir(src):
+        raise DistutilsFileError(f"cannot copy tree '{src}': not a directory")
+    try:
+        names = os.listdir(src)
+    except OSError as e:
+        if dry_run:
+            names = []
+        else:
+            raise DistutilsFileError(f"error listing files in '{src}': {e.strerror}")
+
+    if not dry_run:
+        mkpath(dst, verbose=verbose)
+
+    copy_one = functools.partial(
+        _copy_one,
+        src=src,
+        dst=dst,
+        preserve_symlinks=preserve_symlinks,
+        verbose=verbose,
+        dry_run=dry_run,
+        preserve_mode=preserve_mode,
+        preserve_times=preserve_times,
+        update=update,
+    )
+    return list(itertools.chain.from_iterable(map(copy_one, names)))
+
+
+def _copy_one(
+    name,
+    *,
+    src,
+    dst,
+    preserve_symlinks,
+    verbose,
+    dry_run,
+    preserve_mode,
+    preserve_times,
+    update,
+):
+    src_name = os.path.join(src, name)
+    dst_name = os.path.join(dst, name)
+
+    if name.startswith('.nfs'):
+        # skip NFS rename files
+        return
+
+    if preserve_symlinks and os.path.islink(src_name):
+        link_dest = os.readlink(src_name)
+        if verbose >= 1:
+            log.info("linking %s -> %s", dst_name, link_dest)
+        if not dry_run:
+            os.symlink(link_dest, dst_name)
+        yield dst_name
+
+    elif os.path.isdir(src_name):
+        yield from copy_tree(
+            src_name,
+            dst_name,
+            preserve_mode,
+            preserve_times,
+            preserve_symlinks,
+            update,
+            verbose=verbose,
+            dry_run=dry_run,
+        )
+    else:
+        file_util.copy_file(
+            src_name,
+            dst_name,
+            preserve_mode,
+            preserve_times,
+            update,
+            verbose=verbose,
+            dry_run=dry_run,
+        )
+        yield dst_name
+
+
+def _build_cmdtuple(path, cmdtuples):
+    """Helper for remove_tree()."""
+    for f in os.listdir(path):
+        real_f = os.path.join(path, f)
+        if os.path.isdir(real_f) and not os.path.islink(real_f):
+            _build_cmdtuple(real_f, cmdtuples)
+        else:
+            cmdtuples.append((os.remove, real_f))
+    cmdtuples.append((os.rmdir, path))
+
+
+def remove_tree(directory, verbose=True, dry_run=False):
+    """Recursively remove an entire directory tree.
+
+    Any errors are ignored (apart from being reported to stdout if 'verbose'
+    is true).
+    """
+    if verbose >= 1:
+        log.info("removing '%s' (and everything under it)", directory)
+    if dry_run:
+        return
+    cmdtuples = []
+    _build_cmdtuple(directory, cmdtuples)
+    for cmd in cmdtuples:
+        try:
+            cmd[0](cmd[1])
+            # Clear the cache
+            SkipRepeatAbsolutePaths.clear()
+        except OSError as exc:
+            log.warning("error removing %s: %s", directory, exc)
+
+
+def ensure_relative(path):
+    """Take the full path 'path', and make it a relative path.
+
+    This is useful to make 'path' the second argument to os.path.join().
+    """
+    drive, path = os.path.splitdrive(path)
+    if path[0:1] == os.sep:
+        path = drive + path[1:]
+    return path
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/dist.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/dist.py
new file mode 100644
index 0000000000000000000000000000000000000000..37b788df92549877684c2311ed1c71634f14dcd2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/dist.py
@@ -0,0 +1,1386 @@
+"""distutils.dist
+
+Provides the Distribution class, which represents the module distribution
+being built/installed/distributed.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import logging
+import os
+import pathlib
+import re
+import sys
+import warnings
+from collections.abc import Iterable, MutableMapping
+from email import message_from_file
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+    ClassVar,
+    Literal,
+    TypeVar,
+    Union,
+    overload,
+)
+
+from packaging.utils import canonicalize_name, canonicalize_version
+
+from ._log import log
+from .debug import DEBUG
+from .errors import (
+    DistutilsArgError,
+    DistutilsClassError,
+    DistutilsModuleError,
+    DistutilsOptionError,
+)
+from .fancy_getopt import FancyGetopt, translate_longopt
+from .util import check_environ, rfc822_escape, strtobool
+
+if TYPE_CHECKING:
+    from _typeshed import SupportsWrite
+    from typing_extensions import TypeAlias
+
+    # type-only import because of mutual dependence between these modules
+    from .cmd import Command
+
+_CommandT = TypeVar("_CommandT", bound="Command")
+_OptionsList: TypeAlias = list[
+    Union[tuple[str, Union[str, None], str, int], tuple[str, Union[str, None], str]]
+]
+
+
+# Regex to define acceptable Distutils command names.  This is not *quite*
+# the same as a Python NAME -- I don't allow leading underscores.  The fact
+# that they're very similar is no coincidence; the default naming scheme is
+# to look for a Python module named after the command.
+command_re = re.compile(r'^[a-zA-Z]([a-zA-Z0-9_]*)$')
+
+
+def _ensure_list(value: str | Iterable[str], fieldname) -> str | list[str]:
+    if isinstance(value, str):
+        # a string containing comma separated values is okay.  It will
+        # be converted to a list by Distribution.finalize_options().
+        pass
+    elif not isinstance(value, list):
+        # passing a tuple or an iterator perhaps, warn and convert
+        typename = type(value).__name__
+        msg = "Warning: '{fieldname}' should be a list, got type '{typename}'"
+        msg = msg.format(**locals())
+        log.warning(msg)
+        value = list(value)
+    return value
+
+
+class Distribution:
+    """The core of the Distutils.  Most of the work hiding behind 'setup'
+    is really done within a Distribution instance, which farms the work out
+    to the Distutils commands specified on the command line.
+
+    Setup scripts will almost never instantiate Distribution directly,
+    unless the 'setup()' function is totally inadequate to their needs.
+    However, it is conceivable that a setup script might wish to subclass
+    Distribution for some specialized purpose, and then pass the subclass
+    to 'setup()' as the 'distclass' keyword argument.  If so, it is
+    necessary to respect the expectations that 'setup' has of Distribution.
+    See the code for 'setup()', in core.py, for details.
+    """
+
+    # 'global_options' describes the command-line options that may be
+    # supplied to the setup script prior to any actual commands.
+    # Eg. "./setup.py -n" or "./setup.py --quiet" both take advantage of
+    # these global options.  This list should be kept to a bare minimum,
+    # since every global option is also valid as a command option -- and we
+    # don't want to pollute the commands with too many options that they
+    # have minimal control over.
+    # The fourth entry for verbose means that it can be repeated.
+    global_options: ClassVar[_OptionsList] = [
+        ('verbose', 'v', "run verbosely (default)", 1),
+        ('quiet', 'q', "run quietly (turns verbosity off)"),
+        ('dry-run', 'n', "don't actually do anything"),
+        ('help', 'h', "show detailed help message"),
+        ('no-user-cfg', None, 'ignore pydistutils.cfg in your home directory'),
+    ]
+
+    # 'common_usage' is a short (2-3 line) string describing the common
+    # usage of the setup script.
+    common_usage: ClassVar[str] = """\
+Common commands: (see '--help-commands' for more)
+
+  setup.py build      will build the package underneath 'build/'
+  setup.py install    will install the package
+"""
+
+    # options that are not propagated to the commands
+    display_options: ClassVar[_OptionsList] = [
+        ('help-commands', None, "list all available commands"),
+        ('name', None, "print package name"),
+        ('version', 'V', "print package version"),
+        ('fullname', None, "print <package name>-<version>"),
+        ('author', None, "print the author's name"),
+        ('author-email', None, "print the author's email address"),
+        ('maintainer', None, "print the maintainer's name"),
+        ('maintainer-email', None, "print the maintainer's email address"),
+        ('contact', None, "print the maintainer's name if known, else the author's"),
+        (
+            'contact-email',
+            None,
+            "print the maintainer's email address if known, else the author's",
+        ),
+        ('url', None, "print the URL for this package"),
+        ('license', None, "print the license of the package"),
+        ('licence', None, "alias for --license"),
+        ('description', None, "print the package description"),
+        ('long-description', None, "print the long package description"),
+        ('platforms', None, "print the list of platforms"),
+        ('classifiers', None, "print the list of classifiers"),
+        ('keywords', None, "print the list of keywords"),
+        ('provides', None, "print the list of packages/modules provided"),
+        ('requires', None, "print the list of packages/modules required"),
+        ('obsoletes', None, "print the list of packages/modules made obsolete"),
+    ]
+    display_option_names: ClassVar[list[str]] = [
+        translate_longopt(x[0]) for x in display_options
+    ]
+
+    # negative options are options that exclude other options
+    negative_opt: ClassVar[dict[str, str]] = {'quiet': 'verbose'}
+
+    # -- Creation/initialization methods -------------------------------
+
+    # Can't Unpack a TypedDict with optional properties, so using Any instead
+    def __init__(self, attrs: MutableMapping[str, Any] | None = None) -> None:  # noqa: C901
+        """Construct a new Distribution instance: initialize all the
+        attributes of a Distribution, and then use 'attrs' (a dictionary
+        mapping attribute names to values) to assign some of those
+        attributes their "real" values.  (Any attributes not mentioned in
+        'attrs' will be assigned to some null value: 0, None, an empty list
+        or dictionary, etc.)  Most importantly, initialize the
+        'command_obj' attribute to the empty dictionary; this will be
+        filled in with real command objects by 'parse_command_line()'.
+        """
+
+        # Default values for our command-line options
+        self.verbose = True
+        self.dry_run = False
+        self.help = False
+        for attr in self.display_option_names:
+            setattr(self, attr, False)
+
+        # Store the distribution meta-data (name, version, author, and so
+        # forth) in a separate object -- we're getting to have enough
+        # information here (and enough command-line options) that it's
+        # worth it.  Also delegate 'get_XXX()' methods to the 'metadata'
+        # object in a sneaky and underhanded (but efficient!) way.
+        self.metadata = DistributionMetadata()
+        for basename in self.metadata._METHOD_BASENAMES:
+            method_name = "get_" + basename
+            setattr(self, method_name, getattr(self.metadata, method_name))
+
+        # 'cmdclass' maps command names to class objects, so we
+        # can 1) quickly figure out which class to instantiate when
+        # we need to create a new command object, and 2) have a way
+        # for the setup script to override command classes
+        self.cmdclass: dict[str, type[Command]] = {}
+
+        # 'command_packages' is a list of packages in which commands
+        # are searched for.  The factory for command 'foo' is expected
+        # to be named 'foo' in the module 'foo' in one of the packages
+        # named here.  This list is searched from the left; an error
+        # is raised if no named package provides the command being
+        # searched for.  (Always access using get_command_packages().)
+        self.command_packages: str | list[str] | None = None
+
+        # 'script_name' and 'script_args' are usually set to sys.argv[0]
+        # and sys.argv[1:], but they can be overridden when the caller is
+        # not necessarily a setup script run from the command-line.
+        self.script_name: str | os.PathLike[str] | None = None
+        self.script_args: list[str] | None = None
+
+        # 'command_options' is where we store command options between
+        # parsing them (from config files, the command-line, etc.) and when
+        # they are actually needed -- ie. when the command in question is
+        # instantiated.  It is a dictionary of dictionaries of 2-tuples:
+        #   command_options = { command_name : { option : (source, value) } }
+        self.command_options: dict[str, dict[str, tuple[str, str]]] = {}
+
+        # 'dist_files' is the list of (command, pyversion, file) that
+        # have been created by any dist commands run so far. This is
+        # filled regardless of whether the run is dry or not. pyversion
+        # gives sysconfig.get_python_version() if the dist file is
+        # specific to a Python version, 'any' if it is good for all
+        # Python versions on the target platform, and '' for a source
+        # file. pyversion should not be used to specify minimum or
+        # maximum required Python versions; use the metainfo for that
+        # instead.
+        self.dist_files: list[tuple[str, str, str]] = []
+
+        # These options are really the business of various commands, rather
+        # than of the Distribution itself.  We provide aliases for them in
+        # Distribution as a convenience to the developer.
+        self.packages = None
+        self.package_data: dict[str, list[str]] = {}
+        self.package_dir = None
+        self.py_modules = None
+        self.libraries = None
+        self.headers = None
+        self.ext_modules = None
+        self.ext_package = None
+        self.include_dirs = None
+        self.extra_path = None
+        self.scripts = None
+        self.data_files = None
+        self.password = ''
+
+        # And now initialize bookkeeping stuff that can't be supplied by
+        # the caller at all.  'command_obj' maps command names to
+        # Command instances -- that's how we enforce that every command
+        # class is a singleton.
+        self.command_obj: dict[str, Command] = {}
+
+        # 'have_run' maps command names to boolean values; it keeps track
+        # of whether we have actually run a particular command, to make it
+        # cheap to "run" a command whenever we think we might need to -- if
+        # it's already been done, no need for expensive filesystem
+        # operations, we just check the 'have_run' dictionary and carry on.
+        # It's only safe to query 'have_run' for a command class that has
+        # been instantiated -- a false value will be inserted when the
+        # command object is created, and replaced with a true value when
+        # the command is successfully run.  Thus it's probably best to use
+        # '.get()' rather than a straight lookup.
+        self.have_run: dict[str, bool] = {}
+
+        # Now we'll use the attrs dictionary (ultimately, keyword args from
+        # the setup script) to possibly override any or all of these
+        # distribution options.
+
+        if attrs:
+            # Pull out the set of command options and work on them
+            # specifically.  Note that this order guarantees that aliased
+            # command options will override any supplied redundantly
+            # through the general options dictionary.
+            options = attrs.get('options')
+            if options is not None:
+                del attrs['options']
+                for command, cmd_options in options.items():
+                    opt_dict = self.get_option_dict(command)
+                    for opt, val in cmd_options.items():
+                        opt_dict[opt] = ("setup script", val)
+
+            if 'licence' in attrs:
+                attrs['license'] = attrs['licence']
+                del attrs['licence']
+                msg = "'licence' distribution option is deprecated; use 'license'"
+                warnings.warn(msg)
+
+            # Now work on the rest of the attributes.  Any attribute that's
+            # not already defined is invalid!
+            for key, val in attrs.items():
+                if hasattr(self.metadata, "set_" + key):
+                    getattr(self.metadata, "set_" + key)(val)
+                elif hasattr(self.metadata, key):
+                    setattr(self.metadata, key, val)
+                elif hasattr(self, key):
+                    setattr(self, key, val)
+                else:
+                    msg = f"Unknown distribution option: {key!r}"
+                    warnings.warn(msg)
+
+        # no-user-cfg is handled before other command line args
+        # because other args override the config files, and this
+        # one is needed before we can load the config files.
+        # If attrs['script_args'] wasn't passed, assume false.
+        #
+        # This also make sure we just look at the global options
+        self.want_user_cfg = True
+
+        if self.script_args is not None:
+            # Coerce any possible iterable from attrs into a list
+            self.script_args = list(self.script_args)
+            for arg in self.script_args:
+                if not arg.startswith('-'):
+                    break
+                if arg == '--no-user-cfg':
+                    self.want_user_cfg = False
+                    break
+
+        self.finalize_options()
+
+    def get_option_dict(self, command):
+        """Get the option dictionary for a given command.  If that
+        command's option dictionary hasn't been created yet, then create it
+        and return the new dictionary; otherwise, return the existing
+        option dictionary.
+        """
+        dict = self.command_options.get(command)
+        if dict is None:
+            dict = self.command_options[command] = {}
+        return dict
+
+    def dump_option_dicts(self, header=None, commands=None, indent: str = "") -> None:
+        from pprint import pformat
+
+        if commands is None:  # dump all command option dicts
+            commands = sorted(self.command_options.keys())
+
+        if header is not None:
+            self.announce(indent + header)
+            indent = indent + "  "
+
+        if not commands:
+            self.announce(indent + "no commands known yet")
+            return
+
+        for cmd_name in commands:
+            opt_dict = self.command_options.get(cmd_name)
+            if opt_dict is None:
+                self.announce(indent + f"no option dict for '{cmd_name}' command")
+            else:
+                self.announce(indent + f"option dict for '{cmd_name}' command:")
+                out = pformat(opt_dict)
+                for line in out.split('\n'):
+                    self.announce(indent + "  " + line)
+
+    # -- Config file finding/parsing methods ---------------------------
+
+    def find_config_files(self):
+        """Find as many configuration files as should be processed for this
+        platform, and return a list of filenames in the order in which they
+        should be parsed.  The filenames returned are guaranteed to exist
+        (modulo nasty race conditions).
+
+        There are multiple possible config files:
+        - distutils.cfg in the Distutils installation directory (i.e.
+          where the top-level Distutils __inst__.py file lives)
+        - a file in the user's home directory named .pydistutils.cfg
+          on Unix and pydistutils.cfg on Windows/Mac; may be disabled
+          with the ``--no-user-cfg`` option
+        - setup.cfg in the current directory
+        - a file named by an environment variable
+        """
+        check_environ()
+        files = [str(path) for path in self._gen_paths() if os.path.isfile(path)]
+
+        if DEBUG:
+            self.announce("using config files: {}".format(', '.join(files)))
+
+        return files
+
+    def _gen_paths(self):
+        # The system-wide Distutils config file
+        sys_dir = pathlib.Path(sys.modules['distutils'].__file__).parent
+        yield sys_dir / "distutils.cfg"
+
+        # The per-user config file
+        prefix = '.' * (os.name == 'posix')
+        filename = prefix + 'pydistutils.cfg'
+        if self.want_user_cfg:
+            with contextlib.suppress(RuntimeError):
+                yield pathlib.Path('~').expanduser() / filename
+
+        # All platforms support local setup.cfg
+        yield pathlib.Path('setup.cfg')
+
+        # Additional config indicated in the environment
+        with contextlib.suppress(TypeError):
+            yield pathlib.Path(os.getenv("DIST_EXTRA_CONFIG"))
+
+    def parse_config_files(self, filenames=None):  # noqa: C901
+        from configparser import ConfigParser
+
+        # Ignore install directory options if we have a venv
+        if sys.prefix != sys.base_prefix:
+            ignore_options = [
+                'install-base',
+                'install-platbase',
+                'install-lib',
+                'install-platlib',
+                'install-purelib',
+                'install-headers',
+                'install-scripts',
+                'install-data',
+                'prefix',
+                'exec-prefix',
+                'home',
+                'user',
+                'root',
+            ]
+        else:
+            ignore_options = []
+
+        ignore_options = frozenset(ignore_options)
+
+        if filenames is None:
+            filenames = self.find_config_files()
+
+        if DEBUG:
+            self.announce("Distribution.parse_config_files():")
+
+        parser = ConfigParser()
+        for filename in filenames:
+            if DEBUG:
+                self.announce(f"  reading {filename}")
+            parser.read(filename, encoding='utf-8')
+            for section in parser.sections():
+                options = parser.options(section)
+                opt_dict = self.get_option_dict(section)
+
+                for opt in options:
+                    if opt != '__name__' and opt not in ignore_options:
+                        val = parser.get(section, opt)
+                        opt = opt.replace('-', '_')
+                        opt_dict[opt] = (filename, val)
+
+            # Make the ConfigParser forget everything (so we retain
+            # the original filenames that options come from)
+            parser.__init__()
+
+        # If there was a "global" section in the config file, use it
+        # to set Distribution options.
+
+        if 'global' in self.command_options:
+            for opt, (_src, val) in self.command_options['global'].items():
+                alias = self.negative_opt.get(opt)
+                try:
+                    if alias:
+                        setattr(self, alias, not strtobool(val))
+                    elif opt in ('verbose', 'dry_run'):  # ugh!
+                        setattr(self, opt, strtobool(val))
+                    else:
+                        setattr(self, opt, val)
+                except ValueError as msg:
+                    raise DistutilsOptionError(msg)
+
+    # -- Command-line parsing methods ----------------------------------
+
+    def parse_command_line(self):
+        """Parse the setup script's command line, taken from the
+        'script_args' instance attribute (which defaults to 'sys.argv[1:]'
+        -- see 'setup()' in core.py).  This list is first processed for
+        "global options" -- options that set attributes of the Distribution
+        instance.  Then, it is alternately scanned for Distutils commands
+        and options for that command.  Each new command terminates the
+        options for the previous command.  The allowed options for a
+        command are determined by the 'user_options' attribute of the
+        command class -- thus, we have to be able to load command classes
+        in order to parse the command line.  Any error in that 'options'
+        attribute raises DistutilsGetoptError; any error on the
+        command-line raises DistutilsArgError.  If no Distutils commands
+        were found on the command line, raises DistutilsArgError.  Return
+        true if command-line was successfully parsed and we should carry
+        on with executing commands; false if no errors but we shouldn't
+        execute commands (currently, this only happens if user asks for
+        help).
+        """
+        #
+        # We now have enough information to show the Macintosh dialog
+        # that allows the user to interactively specify the "command line".
+        #
+        toplevel_options = self._get_toplevel_options()
+
+        # We have to parse the command line a bit at a time -- global
+        # options, then the first command, then its options, and so on --
+        # because each command will be handled by a different class, and
+        # the options that are valid for a particular class aren't known
+        # until we have loaded the command class, which doesn't happen
+        # until we know what the command is.
+
+        self.commands = []
+        parser = FancyGetopt(toplevel_options + self.display_options)
+        parser.set_negative_aliases(self.negative_opt)
+        parser.set_aliases({'licence': 'license'})
+        args = parser.getopt(args=self.script_args, object=self)
+        option_order = parser.get_option_order()
+        logging.getLogger().setLevel(logging.WARN - 10 * self.verbose)
+
+        # for display options we return immediately
+        if self.handle_display_options(option_order):
+            return
+        while args:
+            args = self._parse_command_opts(parser, args)
+            if args is None:  # user asked for help (and got it)
+                return
+
+        # Handle the cases of --help as a "global" option, ie.
+        # "setup.py --help" and "setup.py --help command ...".  For the
+        # former, we show global options (--verbose, --dry-run, etc.)
+        # and display-only options (--name, --version, etc.); for the
+        # latter, we omit the display-only options and show help for
+        # each command listed on the command line.
+        if self.help:
+            self._show_help(
+                parser, display_options=len(self.commands) == 0, commands=self.commands
+            )
+            return
+
+        # Oops, no commands found -- an end-user error
+        if not self.commands:
+            raise DistutilsArgError("no commands supplied")
+
+        # All is well: return true
+        return True
+
+    def _get_toplevel_options(self):
+        """Return the non-display options recognized at the top level.
+
+        This includes options that are recognized *only* at the top
+        level as well as options recognized for commands.
+        """
+        return self.global_options + [
+            (
+                "command-packages=",
+                None,
+                "list of packages that provide distutils commands",
+            ),
+        ]
+
+    def _parse_command_opts(self, parser, args):  # noqa: C901
+        """Parse the command-line options for a single command.
+        'parser' must be a FancyGetopt instance; 'args' must be the list
+        of arguments, starting with the current command (whose options
+        we are about to parse).  Returns a new version of 'args' with
+        the next command at the front of the list; will be the empty
+        list if there are no more commands on the command line.  Returns
+        None if the user asked for help on this command.
+        """
+        # late import because of mutual dependence between these modules
+        from distutils.cmd import Command
+
+        # Pull the current command from the head of the command line
+        command = args[0]
+        if not command_re.match(command):
+            raise SystemExit(f"invalid command name '{command}'")
+        self.commands.append(command)
+
+        # Dig up the command class that implements this command, so we
+        # 1) know that it's a valid command, and 2) know which options
+        # it takes.
+        try:
+            cmd_class = self.get_command_class(command)
+        except DistutilsModuleError as msg:
+            raise DistutilsArgError(msg)
+
+        # Require that the command class be derived from Command -- want
+        # to be sure that the basic "command" interface is implemented.
+        if not issubclass(cmd_class, Command):
+            raise DistutilsClassError(
+                f"command class {cmd_class} must subclass Command"
+            )
+
+        # Also make sure that the command object provides a list of its
+        # known options.
+        if not (
+            hasattr(cmd_class, 'user_options')
+            and isinstance(cmd_class.user_options, list)
+        ):
+            msg = (
+                "command class %s must provide "
+                "'user_options' attribute (a list of tuples)"
+            )
+            raise DistutilsClassError(msg % cmd_class)
+
+        # If the command class has a list of negative alias options,
+        # merge it in with the global negative aliases.
+        negative_opt = self.negative_opt
+        if hasattr(cmd_class, 'negative_opt'):
+            negative_opt = negative_opt.copy()
+            negative_opt.update(cmd_class.negative_opt)
+
+        # Check for help_options in command class.  They have a different
+        # format (tuple of four) so we need to preprocess them here.
+        if hasattr(cmd_class, 'help_options') and isinstance(
+            cmd_class.help_options, list
+        ):
+            help_options = fix_help_options(cmd_class.help_options)
+        else:
+            help_options = []
+
+        # All commands support the global options too, just by adding
+        # in 'global_options'.
+        parser.set_option_table(
+            self.global_options + cmd_class.user_options + help_options
+        )
+        parser.set_negative_aliases(negative_opt)
+        (args, opts) = parser.getopt(args[1:])
+        if hasattr(opts, 'help') and opts.help:
+            self._show_help(parser, display_options=False, commands=[cmd_class])
+            return
+
+        if hasattr(cmd_class, 'help_options') and isinstance(
+            cmd_class.help_options, list
+        ):
+            help_option_found = 0
+            for help_option, _short, _desc, func in cmd_class.help_options:
+                if hasattr(opts, parser.get_attr_name(help_option)):
+                    help_option_found = 1
+                    if callable(func):
+                        func()
+                    else:
+                        raise DistutilsClassError(
+                            f"invalid help function {func!r} for help option '{help_option}': "
+                            "must be a callable object (function, etc.)"
+                        )
+
+            if help_option_found:
+                return
+
+        # Put the options from the command-line into their official
+        # holding pen, the 'command_options' dictionary.
+        opt_dict = self.get_option_dict(command)
+        for name, value in vars(opts).items():
+            opt_dict[name] = ("command line", value)
+
+        return args
+
+    def finalize_options(self) -> None:
+        """Set final values for all the options on the Distribution
+        instance, analogous to the .finalize_options() method of Command
+        objects.
+        """
+        for attr in ('keywords', 'platforms'):
+            value = getattr(self.metadata, attr)
+            if value is None:
+                continue
+            if isinstance(value, str):
+                value = [elm.strip() for elm in value.split(',')]
+                setattr(self.metadata, attr, value)
+
+    def _show_help(
+        self, parser, global_options=True, display_options=True, commands: Iterable = ()
+    ):
+        """Show help for the setup script command-line in the form of
+        several lists of command-line options.  'parser' should be a
+        FancyGetopt instance; do not expect it to be returned in the
+        same state, as its option table will be reset to make it
+        generate the correct help text.
+
+        If 'global_options' is true, lists the global options:
+        --verbose, --dry-run, etc.  If 'display_options' is true, lists
+        the "display-only" options: --name, --version, etc.  Finally,
+        lists per-command help for every command name or command class
+        in 'commands'.
+        """
+        # late import because of mutual dependence between these modules
+        from distutils.cmd import Command
+        from distutils.core import gen_usage
+
+        if global_options:
+            if display_options:
+                options = self._get_toplevel_options()
+            else:
+                options = self.global_options
+            parser.set_option_table(options)
+            parser.print_help(self.common_usage + "\nGlobal options:")
+            print()
+
+        if display_options:
+            parser.set_option_table(self.display_options)
+            parser.print_help(
+                "Information display options (just display information, ignore any commands)"
+            )
+            print()
+
+        for command in commands:
+            if isinstance(command, type) and issubclass(command, Command):
+                klass = command
+            else:
+                klass = self.get_command_class(command)
+            if hasattr(klass, 'help_options') and isinstance(klass.help_options, list):
+                parser.set_option_table(
+                    klass.user_options + fix_help_options(klass.help_options)
+                )
+            else:
+                parser.set_option_table(klass.user_options)
+            parser.print_help(f"Options for '{klass.__name__}' command:")
+            print()
+
+        print(gen_usage(self.script_name))
+
+    def handle_display_options(self, option_order):
+        """If there were any non-global "display-only" options
+        (--help-commands or the metadata display options) on the command
+        line, display the requested info and return true; else return
+        false.
+        """
+        from distutils.core import gen_usage
+
+        # User just wants a list of commands -- we'll print it out and stop
+        # processing now (ie. if they ran "setup --help-commands foo bar",
+        # we ignore "foo bar").
+        if self.help_commands:
+            self.print_commands()
+            print()
+            print(gen_usage(self.script_name))
+            return 1
+
+        # If user supplied any of the "display metadata" options, then
+        # display that metadata in the order in which the user supplied the
+        # metadata options.
+        any_display_options = 0
+        is_display_option = set()
+        for option in self.display_options:
+            is_display_option.add(option[0])
+
+        for opt, val in option_order:
+            if val and opt in is_display_option:
+                opt = translate_longopt(opt)
+                value = getattr(self.metadata, "get_" + opt)()
+                if opt in ('keywords', 'platforms'):
+                    print(','.join(value))
+                elif opt in ('classifiers', 'provides', 'requires', 'obsoletes'):
+                    print('\n'.join(value))
+                else:
+                    print(value)
+                any_display_options = 1
+
+        return any_display_options
+
+    def print_command_list(self, commands, header, max_length) -> None:
+        """Print a subset of the list of all commands -- used by
+        'print_commands()'.
+        """
+        print(header + ":")
+
+        for cmd in commands:
+            klass = self.cmdclass.get(cmd)
+            if not klass:
+                klass = self.get_command_class(cmd)
+            try:
+                description = klass.description
+            except AttributeError:
+                description = "(no description available)"
+
+            print(f"  {cmd:<{max_length}}  {description}")
+
+    def print_commands(self) -> None:
+        """Print out a help message listing all available commands with a
+        description of each.  The list is divided into "standard commands"
+        (listed in distutils.command.__all__) and "extra commands"
+        (mentioned in self.cmdclass, but not a standard command).  The
+        descriptions come from the command class attribute
+        'description'.
+        """
+        import distutils.command
+
+        std_commands = distutils.command.__all__
+        is_std = set(std_commands)
+
+        extra_commands = [cmd for cmd in self.cmdclass.keys() if cmd not in is_std]
+
+        max_length = 0
+        for cmd in std_commands + extra_commands:
+            if len(cmd) > max_length:
+                max_length = len(cmd)
+
+        self.print_command_list(std_commands, "Standard commands", max_length)
+        if extra_commands:
+            print()
+            self.print_command_list(extra_commands, "Extra commands", max_length)
+
+    def get_command_list(self):
+        """Get a list of (command, description) tuples.
+        The list is divided into "standard commands" (listed in
+        distutils.command.__all__) and "extra commands" (mentioned in
+        self.cmdclass, but not a standard command).  The descriptions come
+        from the command class attribute 'description'.
+        """
+        # Currently this is only used on Mac OS, for the Mac-only GUI
+        # Distutils interface (by Jack Jansen)
+        import distutils.command
+
+        std_commands = distutils.command.__all__
+        is_std = set(std_commands)
+
+        extra_commands = [cmd for cmd in self.cmdclass.keys() if cmd not in is_std]
+
+        rv = []
+        for cmd in std_commands + extra_commands:
+            klass = self.cmdclass.get(cmd)
+            if not klass:
+                klass = self.get_command_class(cmd)
+            try:
+                description = klass.description
+            except AttributeError:
+                description = "(no description available)"
+            rv.append((cmd, description))
+        return rv
+
+    # -- Command class/object methods ----------------------------------
+
+    def get_command_packages(self):
+        """Return a list of packages from which commands are loaded."""
+        pkgs = self.command_packages
+        if not isinstance(pkgs, list):
+            if pkgs is None:
+                pkgs = ''
+            pkgs = [pkg.strip() for pkg in pkgs.split(',') if pkg != '']
+            if "distutils.command" not in pkgs:
+                pkgs.insert(0, "distutils.command")
+            self.command_packages = pkgs
+        return pkgs
+
+    def get_command_class(self, command: str) -> type[Command]:
+        """Return the class that implements the Distutils command named by
+        'command'.  First we check the 'cmdclass' dictionary; if the
+        command is mentioned there, we fetch the class object from the
+        dictionary and return it.  Otherwise we load the command module
+        ("distutils.command." + command) and fetch the command class from
+        the module.  The loaded class is also stored in 'cmdclass'
+        to speed future calls to 'get_command_class()'.
+
+        Raises DistutilsModuleError if the expected module could not be
+        found, or if that module does not define the expected class.
+        """
+        klass = self.cmdclass.get(command)
+        if klass:
+            return klass
+
+        for pkgname in self.get_command_packages():
+            module_name = f"{pkgname}.{command}"
+            klass_name = command
+
+            try:
+                __import__(module_name)
+                module = sys.modules[module_name]
+            except ImportError:
+                continue
+
+            try:
+                klass = getattr(module, klass_name)
+            except AttributeError:
+                raise DistutilsModuleError(
+                    f"invalid command '{command}' (no class '{klass_name}' in module '{module_name}')"
+                )
+
+            self.cmdclass[command] = klass
+            return klass
+
+        raise DistutilsModuleError(f"invalid command '{command}'")
+
+    @overload
+    def get_command_obj(
+        self, command: str, create: Literal[True] = True
+    ) -> Command: ...
+    @overload
+    def get_command_obj(
+        self, command: str, create: Literal[False]
+    ) -> Command | None: ...
+    def get_command_obj(self, command: str, create: bool = True) -> Command | None:
+        """Return the command object for 'command'.  Normally this object
+        is cached on a previous call to 'get_command_obj()'; if no command
+        object for 'command' is in the cache, then we either create and
+        return it (if 'create' is true) or return None.
+        """
+        cmd_obj = self.command_obj.get(command)
+        if not cmd_obj and create:
+            if DEBUG:
+                self.announce(
+                    "Distribution.get_command_obj(): "
+                    f"creating '{command}' command object"
+                )
+
+            klass = self.get_command_class(command)
+            cmd_obj = self.command_obj[command] = klass(self)
+            self.have_run[command] = False
+
+            # Set any options that were supplied in config files
+            # or on the command line.  (NB. support for error
+            # reporting is lame here: any errors aren't reported
+            # until 'finalize_options()' is called, which means
+            # we won't report the source of the error.)
+            options = self.command_options.get(command)
+            if options:
+                self._set_command_options(cmd_obj, options)
+
+        return cmd_obj
+
+    def _set_command_options(self, command_obj, option_dict=None):  # noqa: C901
+        """Set the options for 'command_obj' from 'option_dict'.  Basically
+        this means copying elements of a dictionary ('option_dict') to
+        attributes of an instance ('command').
+
+        'command_obj' must be a Command instance.  If 'option_dict' is not
+        supplied, uses the standard option dictionary for this command
+        (from 'self.command_options').
+        """
+        command_name = command_obj.get_command_name()
+        if option_dict is None:
+            option_dict = self.get_option_dict(command_name)
+
+        if DEBUG:
+            self.announce(f"  setting options for '{command_name}' command:")
+        for option, (source, value) in option_dict.items():
+            if DEBUG:
+                self.announce(f"    {option} = {value} (from {source})")
+            try:
+                bool_opts = [translate_longopt(o) for o in command_obj.boolean_options]
+            except AttributeError:
+                bool_opts = []
+            try:
+                neg_opt = command_obj.negative_opt
+            except AttributeError:
+                neg_opt = {}
+
+            try:
+                is_string = isinstance(value, str)
+                if option in neg_opt and is_string:
+                    setattr(command_obj, neg_opt[option], not strtobool(value))
+                elif option in bool_opts and is_string:
+                    setattr(command_obj, option, strtobool(value))
+                elif hasattr(command_obj, option):
+                    setattr(command_obj, option, value)
+                else:
+                    raise DistutilsOptionError(
+                        f"error in {source}: command '{command_name}' has no such option '{option}'"
+                    )
+            except ValueError as msg:
+                raise DistutilsOptionError(msg)
+
+    @overload
+    def reinitialize_command(
+        self, command: str, reinit_subcommands: bool = False
+    ) -> Command: ...
+    @overload
+    def reinitialize_command(
+        self, command: _CommandT, reinit_subcommands: bool = False
+    ) -> _CommandT: ...
+    def reinitialize_command(
+        self, command: str | Command, reinit_subcommands=False
+    ) -> Command:
+        """Reinitializes a command to the state it was in when first
+        returned by 'get_command_obj()': ie., initialized but not yet
+        finalized.  This provides the opportunity to sneak option
+        values in programmatically, overriding or supplementing
+        user-supplied values from the config files and command line.
+        You'll have to re-finalize the command object (by calling
+        'finalize_options()' or 'ensure_finalized()') before using it for
+        real.
+
+        'command' should be a command name (string) or command object.  If
+        'reinit_subcommands' is true, also reinitializes the command's
+        sub-commands, as declared by the 'sub_commands' class attribute (if
+        it has one).  See the "install" command for an example.  Only
+        reinitializes the sub-commands that actually matter, ie. those
+        whose test predicates return true.
+
+        Returns the reinitialized command object.
+        """
+        from distutils.cmd import Command
+
+        if not isinstance(command, Command):
+            command_name = command
+            command = self.get_command_obj(command_name)
+        else:
+            command_name = command.get_command_name()
+
+        if not command.finalized:
+            return command
+        command.initialize_options()
+        command.finalized = False
+        self.have_run[command_name] = False
+        self._set_command_options(command)
+
+        if reinit_subcommands:
+            for sub in command.get_sub_commands():
+                self.reinitialize_command(sub, reinit_subcommands)
+
+        return command
+
+    # -- Methods that operate on the Distribution ----------------------
+
+    def announce(self, msg, level: int = logging.INFO) -> None:
+        log.log(level, msg)
+
+    def run_commands(self) -> None:
+        """Run each command that was seen on the setup script command line.
+        Uses the list of commands found and cache of command objects
+        created by 'get_command_obj()'.
+        """
+        for cmd in self.commands:
+            self.run_command(cmd)
+
+    # -- Methods that operate on its Commands --------------------------
+
+    def run_command(self, command: str) -> None:
+        """Do whatever it takes to run a command (including nothing at all,
+        if the command has already been run).  Specifically: if we have
+        already created and run the command named by 'command', return
+        silently without doing anything.  If the command named by 'command'
+        doesn't even have a command object yet, create one.  Then invoke
+        'run()' on that command object (or an existing one).
+        """
+        # Already been here, done that? then return silently.
+        if self.have_run.get(command):
+            return
+
+        log.info("running %s", command)
+        cmd_obj = self.get_command_obj(command)
+        cmd_obj.ensure_finalized()
+        cmd_obj.run()
+        self.have_run[command] = True
+
+    # -- Distribution query methods ------------------------------------
+
+    def has_pure_modules(self) -> bool:
+        return len(self.packages or self.py_modules or []) > 0
+
+    def has_ext_modules(self) -> bool:
+        return self.ext_modules and len(self.ext_modules) > 0
+
+    def has_c_libraries(self) -> bool:
+        return self.libraries and len(self.libraries) > 0
+
+    def has_modules(self) -> bool:
+        return self.has_pure_modules() or self.has_ext_modules()
+
+    def has_headers(self) -> bool:
+        return self.headers and len(self.headers) > 0
+
+    def has_scripts(self) -> bool:
+        return self.scripts and len(self.scripts) > 0
+
+    def has_data_files(self) -> bool:
+        return self.data_files and len(self.data_files) > 0
+
+    def is_pure(self) -> bool:
+        return (
+            self.has_pure_modules()
+            and not self.has_ext_modules()
+            and not self.has_c_libraries()
+        )
+
+    # -- Metadata query methods ----------------------------------------
+
+    # If you're looking for 'get_name()', 'get_version()', and so forth,
+    # they are defined in a sneaky way: the constructor binds self.get_XXX
+    # to self.metadata.get_XXX.  The actual code is in the
+    # DistributionMetadata class, below.
+    if TYPE_CHECKING:
+        # Unfortunately this means we need to specify them manually or not expose statically
+        def _(self) -> None:
+            self.get_name = self.metadata.get_name
+            self.get_version = self.metadata.get_version
+            self.get_fullname = self.metadata.get_fullname
+            self.get_author = self.metadata.get_author
+            self.get_author_email = self.metadata.get_author_email
+            self.get_maintainer = self.metadata.get_maintainer
+            self.get_maintainer_email = self.metadata.get_maintainer_email
+            self.get_contact = self.metadata.get_contact
+            self.get_contact_email = self.metadata.get_contact_email
+            self.get_url = self.metadata.get_url
+            self.get_license = self.metadata.get_license
+            self.get_licence = self.metadata.get_licence
+            self.get_description = self.metadata.get_description
+            self.get_long_description = self.metadata.get_long_description
+            self.get_keywords = self.metadata.get_keywords
+            self.get_platforms = self.metadata.get_platforms
+            self.get_classifiers = self.metadata.get_classifiers
+            self.get_download_url = self.metadata.get_download_url
+            self.get_requires = self.metadata.get_requires
+            self.get_provides = self.metadata.get_provides
+            self.get_obsoletes = self.metadata.get_obsoletes
+
+        # Default attributes generated in __init__ from self.display_option_names
+        help_commands: bool
+        name: str | Literal[False]
+        version: str | Literal[False]
+        fullname: str | Literal[False]
+        author: str | Literal[False]
+        author_email: str | Literal[False]
+        maintainer: str | Literal[False]
+        maintainer_email: str | Literal[False]
+        contact: str | Literal[False]
+        contact_email: str | Literal[False]
+        url: str | Literal[False]
+        license: str | Literal[False]
+        licence: str | Literal[False]
+        description: str | Literal[False]
+        long_description: str | Literal[False]
+        platforms: str | list[str] | Literal[False]
+        classifiers: str | list[str] | Literal[False]
+        keywords: str | list[str] | Literal[False]
+        provides: list[str] | Literal[False]
+        requires: list[str] | Literal[False]
+        obsoletes: list[str] | Literal[False]
+
+
+class DistributionMetadata:
+    """Dummy class to hold the distribution meta-data: name, version,
+    author, and so forth.
+    """
+
+    _METHOD_BASENAMES = (
+        "name",
+        "version",
+        "author",
+        "author_email",
+        "maintainer",
+        "maintainer_email",
+        "url",
+        "license",
+        "description",
+        "long_description",
+        "keywords",
+        "platforms",
+        "fullname",
+        "contact",
+        "contact_email",
+        "classifiers",
+        "download_url",
+        # PEP 314
+        "provides",
+        "requires",
+        "obsoletes",
+    )
+
+    def __init__(
+        self, path: str | bytes | os.PathLike[str] | os.PathLike[bytes] | None = None
+    ) -> None:
+        if path is not None:
+            self.read_pkg_file(open(path))
+        else:
+            self.name: str | None = None
+            self.version: str | None = None
+            self.author: str | None = None
+            self.author_email: str | None = None
+            self.maintainer: str | None = None
+            self.maintainer_email: str | None = None
+            self.url: str | None = None
+            self.license: str | None = None
+            self.description: str | None = None
+            self.long_description: str | None = None
+            self.keywords: str | list[str] | None = None
+            self.platforms: str | list[str] | None = None
+            self.classifiers: str | list[str] | None = None
+            self.download_url: str | None = None
+            # PEP 314
+            self.provides: str | list[str] | None = None
+            self.requires: str | list[str] | None = None
+            self.obsoletes: str | list[str] | None = None
+
+    def read_pkg_file(self, file: IO[str]) -> None:
+        """Reads the metadata values from a file object."""
+        msg = message_from_file(file)
+
+        def _read_field(name: str) -> str | None:
+            value = msg[name]
+            if value and value != "UNKNOWN":
+                return value
+            return None
+
+        def _read_list(name):
+            values = msg.get_all(name, None)
+            if values == []:
+                return None
+            return values
+
+        metadata_version = msg['metadata-version']
+        self.name = _read_field('name')
+        self.version = _read_field('version')
+        self.description = _read_field('summary')
+        # we are filling author only.
+        self.author = _read_field('author')
+        self.maintainer = None
+        self.author_email = _read_field('author-email')
+        self.maintainer_email = None
+        self.url = _read_field('home-page')
+        self.license = _read_field('license')
+
+        if 'download-url' in msg:
+            self.download_url = _read_field('download-url')
+        else:
+            self.download_url = None
+
+        self.long_description = _read_field('description')
+        self.description = _read_field('summary')
+
+        if 'keywords' in msg:
+            self.keywords = _read_field('keywords').split(',')
+
+        self.platforms = _read_list('platform')
+        self.classifiers = _read_list('classifier')
+
+        # PEP 314 - these fields only exist in 1.1
+        if metadata_version == '1.1':
+            self.requires = _read_list('requires')
+            self.provides = _read_list('provides')
+            self.obsoletes = _read_list('obsoletes')
+        else:
+            self.requires = None
+            self.provides = None
+            self.obsoletes = None
+
+    def write_pkg_info(self, base_dir: str | os.PathLike[str]) -> None:
+        """Write the PKG-INFO file into the release tree."""
+        with open(
+            os.path.join(base_dir, 'PKG-INFO'), 'w', encoding='UTF-8'
+        ) as pkg_info:
+            self.write_pkg_file(pkg_info)
+
+    def write_pkg_file(self, file: SupportsWrite[str]) -> None:
+        """Write the PKG-INFO format data to a file object."""
+        version = '1.0'
+        if (
+            self.provides
+            or self.requires
+            or self.obsoletes
+            or self.classifiers
+            or self.download_url
+        ):
+            version = '1.1'
+
+        # required fields
+        file.write(f'Metadata-Version: {version}\n')
+        file.write(f'Name: {self.get_name()}\n')
+        file.write(f'Version: {self.get_version()}\n')
+
+        def maybe_write(header, val):
+            if val:
+                file.write(f"{header}: {val}\n")
+
+        # optional fields
+        maybe_write("Summary", self.get_description())
+        maybe_write("Home-page", self.get_url())
+        maybe_write("Author", self.get_contact())
+        maybe_write("Author-email", self.get_contact_email())
+        maybe_write("License", self.get_license())
+        maybe_write("Download-URL", self.download_url)
+        maybe_write("Description", rfc822_escape(self.get_long_description() or ""))
+        maybe_write("Keywords", ",".join(self.get_keywords()))
+
+        self._write_list(file, 'Platform', self.get_platforms())
+        self._write_list(file, 'Classifier', self.get_classifiers())
+
+        # PEP 314
+        self._write_list(file, 'Requires', self.get_requires())
+        self._write_list(file, 'Provides', self.get_provides())
+        self._write_list(file, 'Obsoletes', self.get_obsoletes())
+
+    def _write_list(self, file, name, values):
+        values = values or []
+        for value in values:
+            file.write(f'{name}: {value}\n')
+
+    # -- Metadata query methods ----------------------------------------
+
+    def get_name(self) -> str:
+        return self.name or "UNKNOWN"
+
+    def get_version(self) -> str:
+        return self.version or "0.0.0"
+
+    def get_fullname(self) -> str:
+        return self._fullname(self.get_name(), self.get_version())
+
+    @staticmethod
+    def _fullname(name: str, version: str) -> str:
+        """
+        >>> DistributionMetadata._fullname('setup.tools', '1.0-2')
+        'setup_tools-1.0.post2'
+        >>> DistributionMetadata._fullname('setup-tools', '1.2post2')
+        'setup_tools-1.2.post2'
+        >>> DistributionMetadata._fullname('setup-tools', '1.0-r2')
+        'setup_tools-1.0.post2'
+        >>> DistributionMetadata._fullname('setup.tools', '1.0.post')
+        'setup_tools-1.0.post0'
+        >>> DistributionMetadata._fullname('setup.tools', '1.0+ubuntu-1')
+        'setup_tools-1.0+ubuntu.1'
+        """
+        return "{}-{}".format(
+            canonicalize_name(name).replace('-', '_'),
+            canonicalize_version(version, strip_trailing_zero=False),
+        )
+
+    def get_author(self) -> str | None:
+        return self.author
+
+    def get_author_email(self) -> str | None:
+        return self.author_email
+
+    def get_maintainer(self) -> str | None:
+        return self.maintainer
+
+    def get_maintainer_email(self) -> str | None:
+        return self.maintainer_email
+
+    def get_contact(self) -> str | None:
+        return self.maintainer or self.author
+
+    def get_contact_email(self) -> str | None:
+        return self.maintainer_email or self.author_email
+
+    def get_url(self) -> str | None:
+        return self.url
+
+    def get_license(self) -> str | None:
+        return self.license
+
+    get_licence = get_license
+
+    def get_description(self) -> str | None:
+        return self.description
+
+    def get_long_description(self) -> str | None:
+        return self.long_description
+
+    def get_keywords(self) -> str | list[str]:
+        return self.keywords or []
+
+    def set_keywords(self, value: str | Iterable[str]) -> None:
+        self.keywords = _ensure_list(value, 'keywords')
+
+    def get_platforms(self) -> str | list[str] | None:
+        return self.platforms
+
+    def set_platforms(self, value: str | Iterable[str]) -> None:
+        self.platforms = _ensure_list(value, 'platforms')
+
+    def get_classifiers(self) -> str | list[str]:
+        return self.classifiers or []
+
+    def set_classifiers(self, value: str | Iterable[str]) -> None:
+        self.classifiers = _ensure_list(value, 'classifiers')
+
+    def get_download_url(self) -> str | None:
+        return self.download_url
+
+    # PEP 314
+    def get_requires(self) -> str | list[str]:
+        return self.requires or []
+
+    def set_requires(self, value: Iterable[str]) -> None:
+        import distutils.versionpredicate
+
+        for v in value:
+            distutils.versionpredicate.VersionPredicate(v)
+        self.requires = list(value)
+
+    def get_provides(self) -> str | list[str]:
+        return self.provides or []
+
+    def set_provides(self, value: Iterable[str]) -> None:
+        value = [v.strip() for v in value]
+        for v in value:
+            import distutils.versionpredicate
+
+            distutils.versionpredicate.split_provision(v)
+        self.provides = value
+
+    def get_obsoletes(self) -> str | list[str]:
+        return self.obsoletes or []
+
+    def set_obsoletes(self, value: Iterable[str]) -> None:
+        import distutils.versionpredicate
+
+        for v in value:
+            distutils.versionpredicate.VersionPredicate(v)
+        self.obsoletes = list(value)
+
+
+def fix_help_options(options):
+    """Convert a 4-tuple 'help_options' list as found in various command
+    classes to the 3-tuple form required by FancyGetopt.
+    """
+    return [opt[0:3] for opt in options]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/errors.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..409d21faa2caeb8a53fed10e4266f7cccc764d23
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/errors.py
@@ -0,0 +1,108 @@
+"""
+Exceptions used by the Distutils modules.
+
+Distutils modules may raise these or standard exceptions,
+including :exc:`SystemExit`.
+"""
+
+# compiler exceptions aliased for compatibility
+from .compilers.C.errors import CompileError as CompileError
+from .compilers.C.errors import Error as _Error
+from .compilers.C.errors import LibError as LibError
+from .compilers.C.errors import LinkError as LinkError
+from .compilers.C.errors import PreprocessError as PreprocessError
+from .compilers.C.errors import UnknownFileType as _UnknownFileType
+
+CCompilerError = _Error
+UnknownFileError = _UnknownFileType
+
+
+class DistutilsError(Exception):
+    """The root of all Distutils evil."""
+
+    pass
+
+
+class DistutilsModuleError(DistutilsError):
+    """Unable to load an expected module, or to find an expected class
+    within some module (in particular, command modules and classes)."""
+
+    pass
+
+
+class DistutilsClassError(DistutilsError):
+    """Some command class (or possibly distribution class, if anyone
+    feels a need to subclass Distribution) is found not to be holding
+    up its end of the bargain, ie. implementing some part of the
+    "command "interface."""
+
+    pass
+
+
+class DistutilsGetoptError(DistutilsError):
+    """The option table provided to 'fancy_getopt()' is bogus."""
+
+    pass
+
+
+class DistutilsArgError(DistutilsError):
+    """Raised by fancy_getopt in response to getopt.error -- ie. an
+    error in the command line usage."""
+
+    pass
+
+
+class DistutilsFileError(DistutilsError):
+    """Any problems in the filesystem: expected file not found, etc.
+    Typically this is for problems that we detect before OSError
+    could be raised."""
+
+    pass
+
+
+class DistutilsOptionError(DistutilsError):
+    """Syntactic/semantic errors in command options, such as use of
+    mutually conflicting options, or inconsistent options,
+    badly-spelled values, etc.  No distinction is made between option
+    values originating in the setup script, the command line, config
+    files, or what-have-you -- but if we *know* something originated in
+    the setup script, we'll raise DistutilsSetupError instead."""
+
+    pass
+
+
+class DistutilsSetupError(DistutilsError):
+    """For errors that can be definitely blamed on the setup script,
+    such as invalid keyword arguments to 'setup()'."""
+
+    pass
+
+
+class DistutilsPlatformError(DistutilsError):
+    """We don't know how to do something on the current platform (but
+    we do know how to do it on some platform) -- eg. trying to compile
+    C files on a platform not supported by a CCompiler subclass."""
+
+    pass
+
+
+class DistutilsExecError(DistutilsError):
+    """Any problems executing an external program (such as the C
+    compiler, when compiling C files)."""
+
+    pass
+
+
+class DistutilsInternalError(DistutilsError):
+    """Internal inconsistencies or impossibilities (obviously, this
+    should never be seen if the code is working!)."""
+
+    pass
+
+
+class DistutilsTemplateError(DistutilsError):
+    """Syntax error in a file list template."""
+
+
+class DistutilsByteCompileError(DistutilsError):
+    """Byte compile error."""
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/extension.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/extension.py
new file mode 100644
index 0000000000000000000000000000000000000000..f51411266e4047275280d883b75e7103af21faef
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/extension.py
@@ -0,0 +1,258 @@
+"""distutils.extension
+
+Provides the Extension class, used to describe C/C++ extension
+modules in setup scripts."""
+
+from __future__ import annotations
+
+import os
+import warnings
+from collections.abc import Iterable
+
+# This class is really only used by the "build_ext" command, so it might
+# make sense to put it in distutils.command.build_ext.  However, that
+# module is already big enough, and I want to make this class a bit more
+# complex to simplify some common cases ("foo" module in "foo.c") and do
+# better error-checking ("foo.c" actually exists).
+#
+# Also, putting this in build_ext.py means every setup script would have to
+# import that large-ish module (indirectly, through distutils.core) in
+# order to do anything.
+
+
+class Extension:
+    """Just a collection of attributes that describes an extension
+    module and everything needed to build it (hopefully in a portable
+    way, but there are hooks that let you be as unportable as you need).
+
+    Instance attributes:
+      name : string
+        the full name of the extension, including any packages -- ie.
+        *not* a filename or pathname, but Python dotted name
+      sources : Iterable[string | os.PathLike]
+        iterable of source filenames (except strings, which could be misinterpreted
+        as a single filename), relative to the distribution root (where the setup
+        script lives), in Unix form (slash-separated) for portability. Can be any
+        non-string iterable (list, tuple, set, etc.) containing strings or
+        PathLike objects. Source files may be C, C++, SWIG (.i), platform-specific
+        resource files, or whatever else is recognized by the "build_ext" command
+        as source for a Python extension.
+      include_dirs : [string]
+        list of directories to search for C/C++ header files (in Unix
+        form for portability)
+      define_macros : [(name : string, value : string|None)]
+        list of macros to define; each macro is defined using a 2-tuple,
+        where 'value' is either the string to define it to or None to
+        define it without a particular value (equivalent of "#define
+        FOO" in source or -DFOO on Unix C compiler command line)
+      undef_macros : [string]
+        list of macros to undefine explicitly
+      library_dirs : [string]
+        list of directories to search for C/C++ libraries at link time
+      libraries : [string]
+        list of library names (not filenames or paths) to link against
+      runtime_library_dirs : [string]
+        list of directories to search for C/C++ libraries at run time
+        (for shared extensions, this is when the extension is loaded)
+      extra_objects : [string]
+        list of extra files to link with (eg. object files not implied
+        by 'sources', static library that must be explicitly specified,
+        binary resource files, etc.)
+      extra_compile_args : [string]
+        any extra platform- and compiler-specific information to use
+        when compiling the source files in 'sources'.  For platforms and
+        compilers where "command line" makes sense, this is typically a
+        list of command-line arguments, but for other platforms it could
+        be anything.
+      extra_link_args : [string]
+        any extra platform- and compiler-specific information to use
+        when linking object files together to create the extension (or
+        to create a new static Python interpreter).  Similar
+        interpretation as for 'extra_compile_args'.
+      export_symbols : [string]
+        list of symbols to be exported from a shared extension.  Not
+        used on all platforms, and not generally necessary for Python
+        extensions, which typically export exactly one symbol: "init" +
+        extension_name.
+      swig_opts : [string]
+        any extra options to pass to SWIG if a source file has the .i
+        extension.
+      depends : [string]
+        list of files that the extension depends on
+      language : string
+        extension language (i.e. "c", "c++", "objc"). Will be detected
+        from the source extensions if not provided.
+      optional : boolean
+        specifies that a build failure in the extension should not abort the
+        build process, but simply not install the failing extension.
+    """
+
+    # When adding arguments to this constructor, be sure to update
+    # setup_keywords in core.py.
+    def __init__(
+        self,
+        name: str,
+        sources: Iterable[str | os.PathLike[str]],
+        include_dirs: list[str] | None = None,
+        define_macros: list[tuple[str, str | None]] | None = None,
+        undef_macros: list[str] | None = None,
+        library_dirs: list[str] | None = None,
+        libraries: list[str] | None = None,
+        runtime_library_dirs: list[str] | None = None,
+        extra_objects: list[str] | None = None,
+        extra_compile_args: list[str] | None = None,
+        extra_link_args: list[str] | None = None,
+        export_symbols: list[str] | None = None,
+        swig_opts: list[str] | None = None,
+        depends: list[str] | None = None,
+        language: str | None = None,
+        optional: bool | None = None,
+        **kw,  # To catch unknown keywords
+    ):
+        if not isinstance(name, str):
+            raise TypeError("'name' must be a string")
+
+        # handle the string case first; since strings are iterable, disallow them
+        if isinstance(sources, str):
+            raise TypeError(
+                "'sources' must be an iterable of strings or PathLike objects, not a string"
+            )
+
+        # now we check if it's iterable and contains valid types
+        try:
+            self.sources = list(map(os.fspath, sources))
+        except TypeError:
+            raise TypeError(
+                "'sources' must be an iterable of strings or PathLike objects"
+            )
+
+        self.name = name
+        self.include_dirs = include_dirs or []
+        self.define_macros = define_macros or []
+        self.undef_macros = undef_macros or []
+        self.library_dirs = library_dirs or []
+        self.libraries = libraries or []
+        self.runtime_library_dirs = runtime_library_dirs or []
+        self.extra_objects = extra_objects or []
+        self.extra_compile_args = extra_compile_args or []
+        self.extra_link_args = extra_link_args or []
+        self.export_symbols = export_symbols or []
+        self.swig_opts = swig_opts or []
+        self.depends = depends or []
+        self.language = language
+        self.optional = optional
+
+        # If there are unknown keyword options, warn about them
+        if len(kw) > 0:
+            options = [repr(option) for option in kw]
+            options = ', '.join(sorted(options))
+            msg = f"Unknown Extension options: {options}"
+            warnings.warn(msg)
+
+    def __repr__(self):
+        return f'<{self.__class__.__module__}.{self.__class__.__qualname__}({self.name!r}) at {id(self):#x}>'
+
+
+def read_setup_file(filename):  # noqa: C901
+    """Reads a Setup file and returns Extension instances."""
+    from distutils.sysconfig import _variable_rx, expand_makefile_vars, parse_makefile
+    from distutils.text_file import TextFile
+    from distutils.util import split_quoted
+
+    # First pass over the file to gather "VAR = VALUE" assignments.
+    vars = parse_makefile(filename)
+
+    # Second pass to gobble up the real content: lines of the form
+    #   <module> ... [<sourcefile> ...] [<cpparg> ...] [<library> ...]
+    file = TextFile(
+        filename,
+        strip_comments=True,
+        skip_blanks=True,
+        join_lines=True,
+        lstrip_ws=True,
+        rstrip_ws=True,
+    )
+    try:
+        extensions = []
+
+        while True:
+            line = file.readline()
+            if line is None:  # eof
+                break
+            if _variable_rx.match(line):  # VAR=VALUE, handled in first pass
+                continue
+
+            if line[0] == line[-1] == "*":
+                file.warn(f"'{line}' lines not handled yet")
+                continue
+
+            line = expand_makefile_vars(line, vars)
+            words = split_quoted(line)
+
+            # NB. this parses a slightly different syntax than the old
+            # makesetup script: here, there must be exactly one extension per
+            # line, and it must be the first word of the line.  I have no idea
+            # why the old syntax supported multiple extensions per line, as
+            # they all wind up being the same.
+
+            module = words[0]
+            ext = Extension(module, [])
+            append_next_word = None
+
+            for word in words[1:]:
+                if append_next_word is not None:
+                    append_next_word.append(word)
+                    append_next_word = None
+                    continue
+
+                suffix = os.path.splitext(word)[1]
+                switch = word[0:2]
+                value = word[2:]
+
+                if suffix in (".c", ".cc", ".cpp", ".cxx", ".c++", ".m", ".mm"):
+                    # hmm, should we do something about C vs. C++ sources?
+                    # or leave it up to the CCompiler implementation to
+                    # worry about?
+                    ext.sources.append(word)
+                elif switch == "-I":
+                    ext.include_dirs.append(value)
+                elif switch == "-D":
+                    equals = value.find("=")
+                    if equals == -1:  # bare "-DFOO" -- no value
+                        ext.define_macros.append((value, None))
+                    else:  # "-DFOO=blah"
+                        ext.define_macros.append((value[0:equals], value[equals + 2 :]))
+                elif switch == "-U":
+                    ext.undef_macros.append(value)
+                elif switch == "-C":  # only here 'cause makesetup has it!
+                    ext.extra_compile_args.append(word)
+                elif switch == "-l":
+                    ext.libraries.append(value)
+                elif switch == "-L":
+                    ext.library_dirs.append(value)
+                elif switch == "-R":
+                    ext.runtime_library_dirs.append(value)
+                elif word == "-rpath":
+                    append_next_word = ext.runtime_library_dirs
+                elif word == "-Xlinker":
+                    append_next_word = ext.extra_link_args
+                elif word == "-Xcompiler":
+                    append_next_word = ext.extra_compile_args
+                elif switch == "-u":
+                    ext.extra_link_args.append(word)
+                    if not value:
+                        append_next_word = ext.extra_link_args
+                elif suffix in (".a", ".so", ".sl", ".o", ".dylib"):
+                    # NB. a really faithful emulation of makesetup would
+                    # append a .o file to extra_objects only if it
+                    # had a slash in it; otherwise, it would s/.o/.c/
+                    # and append it to sources.  Hmmmm.
+                    ext.extra_objects.append(word)
+                else:
+                    file.warn(f"unrecognized argument '{word}'")
+
+            extensions.append(ext)
+    finally:
+        file.close()
+
+    return extensions
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/fancy_getopt.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/fancy_getopt.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a1d3a05da544b98ff84d03ecbee1185b2eb45f7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/fancy_getopt.py
@@ -0,0 +1,471 @@
+"""distutils.fancy_getopt
+
+Wrapper around the standard getopt module that provides the following
+additional features:
+  * short and long options are tied together
+  * options have help strings, so fancy_getopt could potentially
+    create a complete usage summary
+  * options set attributes of a passed-in object
+"""
+
+from __future__ import annotations
+
+import getopt
+import re
+import string
+import sys
+from collections.abc import Sequence
+from typing import Any
+
+from .errors import DistutilsArgError, DistutilsGetoptError
+
+# Much like command_re in distutils.core, this is close to but not quite
+# the same as a Python NAME -- except, in the spirit of most GNU
+# utilities, we use '-' in place of '_'.  (The spirit of LISP lives on!)
+# The similarities to NAME are again not a coincidence...
+longopt_pat = r'[a-zA-Z](?:[a-zA-Z0-9-]*)'
+longopt_re = re.compile(rf'^{longopt_pat}$')
+
+# For recognizing "negative alias" options, eg. "quiet=!verbose"
+neg_alias_re = re.compile(f"^({longopt_pat})=!({longopt_pat})$")
+
+# This is used to translate long options to legitimate Python identifiers
+# (for use as attributes of some object).
+longopt_xlate = str.maketrans('-', '_')
+
+
+class FancyGetopt:
+    """Wrapper around the standard 'getopt()' module that provides some
+    handy extra functionality:
+      * short and long options are tied together
+      * options have help strings, and help text can be assembled
+        from them
+      * options set attributes of a passed-in object
+      * boolean options can have "negative aliases" -- eg. if
+        --quiet is the "negative alias" of --verbose, then "--quiet"
+        on the command line sets 'verbose' to false
+    """
+
+    def __init__(self, option_table=None):
+        # The option table is (currently) a list of tuples.  The
+        # tuples may have 3 or four values:
+        #   (long_option, short_option, help_string [, repeatable])
+        # if an option takes an argument, its long_option should have '='
+        # appended; short_option should just be a single character, no ':'
+        # in any case.  If a long_option doesn't have a corresponding
+        # short_option, short_option should be None.  All option tuples
+        # must have long options.
+        self.option_table = option_table
+
+        # 'option_index' maps long option names to entries in the option
+        # table (ie. those 3-tuples).
+        self.option_index = {}
+        if self.option_table:
+            self._build_index()
+
+        # 'alias' records (duh) alias options; {'foo': 'bar'} means
+        # --foo is an alias for --bar
+        self.alias = {}
+
+        # 'negative_alias' keeps track of options that are the boolean
+        # opposite of some other option
+        self.negative_alias = {}
+
+        # These keep track of the information in the option table.  We
+        # don't actually populate these structures until we're ready to
+        # parse the command-line, since the 'option_table' passed in here
+        # isn't necessarily the final word.
+        self.short_opts = []
+        self.long_opts = []
+        self.short2long = {}
+        self.attr_name = {}
+        self.takes_arg = {}
+
+        # And 'option_order' is filled up in 'getopt()'; it records the
+        # original order of options (and their values) on the command-line,
+        # but expands short options, converts aliases, etc.
+        self.option_order = []
+
+    def _build_index(self):
+        self.option_index.clear()
+        for option in self.option_table:
+            self.option_index[option[0]] = option
+
+    def set_option_table(self, option_table):
+        self.option_table = option_table
+        self._build_index()
+
+    def add_option(self, long_option, short_option=None, help_string=None):
+        if long_option in self.option_index:
+            raise DistutilsGetoptError(
+                f"option conflict: already an option '{long_option}'"
+            )
+        else:
+            option = (long_option, short_option, help_string)
+            self.option_table.append(option)
+            self.option_index[long_option] = option
+
+    def has_option(self, long_option):
+        """Return true if the option table for this parser has an
+        option with long name 'long_option'."""
+        return long_option in self.option_index
+
+    def get_attr_name(self, long_option):
+        """Translate long option name 'long_option' to the form it
+        has as an attribute of some object: ie., translate hyphens
+        to underscores."""
+        return long_option.translate(longopt_xlate)
+
+    def _check_alias_dict(self, aliases, what):
+        assert isinstance(aliases, dict)
+        for alias, opt in aliases.items():
+            if alias not in self.option_index:
+                raise DistutilsGetoptError(
+                    f"invalid {what} '{alias}': option '{alias}' not defined"
+                )
+            if opt not in self.option_index:
+                raise DistutilsGetoptError(
+                    f"invalid {what} '{alias}': aliased option '{opt}' not defined"
+                )
+
+    def set_aliases(self, alias):
+        """Set the aliases for this option parser."""
+        self._check_alias_dict(alias, "alias")
+        self.alias = alias
+
+    def set_negative_aliases(self, negative_alias):
+        """Set the negative aliases for this option parser.
+        'negative_alias' should be a dictionary mapping option names to
+        option names, both the key and value must already be defined
+        in the option table."""
+        self._check_alias_dict(negative_alias, "negative alias")
+        self.negative_alias = negative_alias
+
+    def _grok_option_table(self):  # noqa: C901
+        """Populate the various data structures that keep tabs on the
+        option table.  Called by 'getopt()' before it can do anything
+        worthwhile.
+        """
+        self.long_opts = []
+        self.short_opts = []
+        self.short2long.clear()
+        self.repeat = {}
+
+        for option in self.option_table:
+            if len(option) == 3:
+                long, short, help = option
+                repeat = 0
+            elif len(option) == 4:
+                long, short, help, repeat = option
+            else:
+                # the option table is part of the code, so simply
+                # assert that it is correct
+                raise ValueError(f"invalid option tuple: {option!r}")
+
+            # Type- and value-check the option names
+            if not isinstance(long, str) or len(long) < 2:
+                raise DistutilsGetoptError(
+                    f"invalid long option '{long}': must be a string of length >= 2"
+                )
+
+            if not ((short is None) or (isinstance(short, str) and len(short) == 1)):
+                raise DistutilsGetoptError(
+                    f"invalid short option '{short}': must a single character or None"
+                )
+
+            self.repeat[long] = repeat
+            self.long_opts.append(long)
+
+            if long[-1] == '=':  # option takes an argument?
+                if short:
+                    short = short + ':'
+                long = long[0:-1]
+                self.takes_arg[long] = True
+            else:
+                # Is option is a "negative alias" for some other option (eg.
+                # "quiet" == "!verbose")?
+                alias_to = self.negative_alias.get(long)
+                if alias_to is not None:
+                    if self.takes_arg[alias_to]:
+                        raise DistutilsGetoptError(
+                            f"invalid negative alias '{long}': "
+                            f"aliased option '{alias_to}' takes a value"
+                        )
+
+                    self.long_opts[-1] = long  # XXX redundant?!
+                self.takes_arg[long] = False
+
+            # If this is an alias option, make sure its "takes arg" flag is
+            # the same as the option it's aliased to.
+            alias_to = self.alias.get(long)
+            if alias_to is not None:
+                if self.takes_arg[long] != self.takes_arg[alias_to]:
+                    raise DistutilsGetoptError(
+                        f"invalid alias '{long}': inconsistent with "
+                        f"aliased option '{alias_to}' (one of them takes a value, "
+                        "the other doesn't"
+                    )
+
+            # Now enforce some bondage on the long option name, so we can
+            # later translate it to an attribute name on some object.  Have
+            # to do this a bit late to make sure we've removed any trailing
+            # '='.
+            if not longopt_re.match(long):
+                raise DistutilsGetoptError(
+                    f"invalid long option name '{long}' "
+                    "(must be letters, numbers, hyphens only"
+                )
+
+            self.attr_name[long] = self.get_attr_name(long)
+            if short:
+                self.short_opts.append(short)
+                self.short2long[short[0]] = long
+
+    def getopt(self, args: Sequence[str] | None = None, object=None):  # noqa: C901
+        """Parse command-line options in args. Store as attributes on object.
+
+        If 'args' is None or not supplied, uses 'sys.argv[1:]'.  If
+        'object' is None or not supplied, creates a new OptionDummy
+        object, stores option values there, and returns a tuple (args,
+        object).  If 'object' is supplied, it is modified in place and
+        'getopt()' just returns 'args'; in both cases, the returned
+        'args' is a modified copy of the passed-in 'args' list, which
+        is left untouched.
+        """
+        if args is None:
+            args = sys.argv[1:]
+        if object is None:
+            object = OptionDummy()
+            created_object = True
+        else:
+            created_object = False
+
+        self._grok_option_table()
+
+        short_opts = ' '.join(self.short_opts)
+        try:
+            opts, args = getopt.getopt(args, short_opts, self.long_opts)
+        except getopt.error as msg:
+            raise DistutilsArgError(msg)
+
+        for opt, val in opts:
+            if len(opt) == 2 and opt[0] == '-':  # it's a short option
+                opt = self.short2long[opt[1]]
+            else:
+                assert len(opt) > 2 and opt[:2] == '--'
+                opt = opt[2:]
+
+            alias = self.alias.get(opt)
+            if alias:
+                opt = alias
+
+            if not self.takes_arg[opt]:  # boolean option?
+                assert val == '', "boolean option can't have value"
+                alias = self.negative_alias.get(opt)
+                if alias:
+                    opt = alias
+                    val = 0
+                else:
+                    val = 1
+
+            attr = self.attr_name[opt]
+            # The only repeating option at the moment is 'verbose'.
+            # It has a negative option -q quiet, which should set verbose = False.
+            if val and self.repeat.get(attr) is not None:
+                val = getattr(object, attr, 0) + 1
+            setattr(object, attr, val)
+            self.option_order.append((opt, val))
+
+        # for opts
+        if created_object:
+            return args, object
+        else:
+            return args
+
+    def get_option_order(self):
+        """Returns the list of (option, value) tuples processed by the
+        previous run of 'getopt()'.  Raises RuntimeError if
+        'getopt()' hasn't been called yet.
+        """
+        if self.option_order is None:
+            raise RuntimeError("'getopt()' hasn't been called yet")
+        else:
+            return self.option_order
+
+    def generate_help(self, header=None):  # noqa: C901
+        """Generate help text (a list of strings, one per suggested line of
+        output) from the option table for this FancyGetopt object.
+        """
+        # Blithely assume the option table is good: probably wouldn't call
+        # 'generate_help()' unless you've already called 'getopt()'.
+
+        # First pass: determine maximum length of long option names
+        max_opt = 0
+        for option in self.option_table:
+            long = option[0]
+            short = option[1]
+            ell = len(long)
+            if long[-1] == '=':
+                ell = ell - 1
+            if short is not None:
+                ell = ell + 5  # " (-x)" where short == 'x'
+            if ell > max_opt:
+                max_opt = ell
+
+        opt_width = max_opt + 2 + 2 + 2  # room for indent + dashes + gutter
+
+        # Typical help block looks like this:
+        #   --foo       controls foonabulation
+        # Help block for longest option looks like this:
+        #   --flimflam  set the flim-flam level
+        # and with wrapped text:
+        #   --flimflam  set the flim-flam level (must be between
+        #               0 and 100, except on Tuesdays)
+        # Options with short names will have the short name shown (but
+        # it doesn't contribute to max_opt):
+        #   --foo (-f)  controls foonabulation
+        # If adding the short option would make the left column too wide,
+        # we push the explanation off to the next line
+        #   --flimflam (-l)
+        #               set the flim-flam level
+        # Important parameters:
+        #   - 2 spaces before option block start lines
+        #   - 2 dashes for each long option name
+        #   - min. 2 spaces between option and explanation (gutter)
+        #   - 5 characters (incl. space) for short option name
+
+        # Now generate lines of help text.  (If 80 columns were good enough
+        # for Jesus, then 78 columns are good enough for me!)
+        line_width = 78
+        text_width = line_width - opt_width
+        big_indent = ' ' * opt_width
+        if header:
+            lines = [header]
+        else:
+            lines = ['Option summary:']
+
+        for option in self.option_table:
+            long, short, help = option[:3]
+            text = wrap_text(help, text_width)
+            if long[-1] == '=':
+                long = long[0:-1]
+
+            # Case 1: no short option at all (makes life easy)
+            if short is None:
+                if text:
+                    lines.append(f"  --{long:<{max_opt}}  {text[0]}")
+                else:
+                    lines.append(f"  --{long:<{max_opt}}")
+
+            # Case 2: we have a short option, so we have to include it
+            # just after the long option
+            else:
+                opt_names = f"{long} (-{short})"
+                if text:
+                    lines.append(f"  --{opt_names:<{max_opt}}  {text[0]}")
+                else:
+                    lines.append(f"  --{opt_names:<{max_opt}}")
+
+            for ell in text[1:]:
+                lines.append(big_indent + ell)
+        return lines
+
+    def print_help(self, header=None, file=None):
+        if file is None:
+            file = sys.stdout
+        for line in self.generate_help(header):
+            file.write(line + "\n")
+
+
+def fancy_getopt(options, negative_opt, object, args: Sequence[str] | None):
+    parser = FancyGetopt(options)
+    parser.set_negative_aliases(negative_opt)
+    return parser.getopt(args, object)
+
+
+WS_TRANS = {ord(_wschar): ' ' for _wschar in string.whitespace}
+
+
+def wrap_text(text, width):
+    """wrap_text(text : string, width : int) -> [string]
+
+    Split 'text' into multiple lines of no more than 'width' characters
+    each, and return the list of strings that results.
+    """
+    if text is None:
+        return []
+    if len(text) <= width:
+        return [text]
+
+    text = text.expandtabs()
+    text = text.translate(WS_TRANS)
+    chunks = re.split(r'( +|-+)', text)
+    chunks = [ch for ch in chunks if ch]  # ' - ' results in empty strings
+    lines = []
+
+    while chunks:
+        cur_line = []  # list of chunks (to-be-joined)
+        cur_len = 0  # length of current line
+
+        while chunks:
+            ell = len(chunks[0])
+            if cur_len + ell <= width:  # can squeeze (at least) this chunk in
+                cur_line.append(chunks[0])
+                del chunks[0]
+                cur_len = cur_len + ell
+            else:  # this line is full
+                # drop last chunk if all space
+                if cur_line and cur_line[-1][0] == ' ':
+                    del cur_line[-1]
+                break
+
+        if chunks:  # any chunks left to process?
+            # if the current line is still empty, then we had a single
+            # chunk that's too big too fit on a line -- so we break
+            # down and break it up at the line width
+            if cur_len == 0:
+                cur_line.append(chunks[0][0:width])
+                chunks[0] = chunks[0][width:]
+
+            # all-whitespace chunks at the end of a line can be discarded
+            # (and we know from the re.split above that if a chunk has
+            # *any* whitespace, it is *all* whitespace)
+            if chunks[0][0] == ' ':
+                del chunks[0]
+
+        # and store this line in the list-of-all-lines -- as a single
+        # string, of course!
+        lines.append(''.join(cur_line))
+
+    return lines
+
+
+def translate_longopt(opt):
+    """Convert a long option name to a valid Python identifier by
+    changing "-" to "_".
+    """
+    return opt.translate(longopt_xlate)
+
+
+class OptionDummy:
+    """Dummy class just used as a place to hold command-line option
+    values as instance attributes."""
+
+    def __init__(self, options: Sequence[Any] = []):
+        """Create a new OptionDummy instance.  The attributes listed in
+        'options' will be initialized to None."""
+        for opt in options:
+            setattr(self, opt, None)
+
+
+if __name__ == "__main__":
+    text = """\
+Tra-la-la, supercalifragilisticexpialidocious.
+How *do* you spell that odd word, anyways?
+(Someone ask Mary -- she'll know [or she'll
+say, "How should I know?"].)"""
+
+    for w in (10, 20, 30, 40):
+        print(f"width: {w}")
+        print("\n".join(wrap_text(text, w)))
+        print()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/file_util.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/file_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..0acc8cb84bd4d28363c4e33e7ff18d691bebfa48
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/file_util.py
@@ -0,0 +1,236 @@
+"""distutils.file_util
+
+Utility functions for operating on single files.
+"""
+
+import os
+
+from ._log import log
+from .errors import DistutilsFileError
+
+# for generating verbose output in 'copy_file()'
+_copy_action = {None: 'copying', 'hard': 'hard linking', 'sym': 'symbolically linking'}
+
+
+def _copy_file_contents(src, dst, buffer_size=16 * 1024):  # noqa: C901
+    """Copy the file 'src' to 'dst'; both must be filenames.  Any error
+    opening either file, reading from 'src', or writing to 'dst', raises
+    DistutilsFileError.  Data is read/written in chunks of 'buffer_size'
+    bytes (default 16k).  No attempt is made to handle anything apart from
+    regular files.
+    """
+    # Stolen from shutil module in the standard library, but with
+    # custom error-handling added.
+    fsrc = None
+    fdst = None
+    try:
+        try:
+            fsrc = open(src, 'rb')
+        except OSError as e:
+            raise DistutilsFileError(f"could not open '{src}': {e.strerror}")
+
+        if os.path.exists(dst):
+            try:
+                os.unlink(dst)
+            except OSError as e:
+                raise DistutilsFileError(f"could not delete '{dst}': {e.strerror}")
+
+        try:
+            fdst = open(dst, 'wb')
+        except OSError as e:
+            raise DistutilsFileError(f"could not create '{dst}': {e.strerror}")
+
+        while True:
+            try:
+                buf = fsrc.read(buffer_size)
+            except OSError as e:
+                raise DistutilsFileError(f"could not read from '{src}': {e.strerror}")
+
+            if not buf:
+                break
+
+            try:
+                fdst.write(buf)
+            except OSError as e:
+                raise DistutilsFileError(f"could not write to '{dst}': {e.strerror}")
+    finally:
+        if fdst:
+            fdst.close()
+        if fsrc:
+            fsrc.close()
+
+
+def copy_file(  # noqa: C901
+    src,
+    dst,
+    preserve_mode=True,
+    preserve_times=True,
+    update=False,
+    link=None,
+    verbose=True,
+    dry_run=False,
+):
+    """Copy a file 'src' to 'dst'.  If 'dst' is a directory, then 'src' is
+    copied there with the same name; otherwise, it must be a filename.  (If
+    the file exists, it will be ruthlessly clobbered.)  If 'preserve_mode'
+    is true (the default), the file's mode (type and permission bits, or
+    whatever is analogous on the current platform) is copied.  If
+    'preserve_times' is true (the default), the last-modified and
+    last-access times are copied as well.  If 'update' is true, 'src' will
+    only be copied if 'dst' does not exist, or if 'dst' does exist but is
+    older than 'src'.
+
+    'link' allows you to make hard links (os.link) or symbolic links
+    (os.symlink) instead of copying: set it to "hard" or "sym"; if it is
+    None (the default), files are copied.  Don't set 'link' on systems that
+    don't support it: 'copy_file()' doesn't check if hard or symbolic
+    linking is available. If hardlink fails, falls back to
+    _copy_file_contents().
+
+    Under Mac OS, uses the native file copy function in macostools; on
+    other systems, uses '_copy_file_contents()' to copy file contents.
+
+    Return a tuple (dest_name, copied): 'dest_name' is the actual name of
+    the output file, and 'copied' is true if the file was copied (or would
+    have been copied, if 'dry_run' true).
+    """
+    # XXX if the destination file already exists, we clobber it if
+    # copying, but blow up if linking.  Hmmm.  And I don't know what
+    # macostools.copyfile() does.  Should definitely be consistent, and
+    # should probably blow up if destination exists and we would be
+    # changing it (ie. it's not already a hard/soft link to src OR
+    # (not update) and (src newer than dst).
+
+    from distutils._modified import newer
+    from stat import S_IMODE, ST_ATIME, ST_MODE, ST_MTIME
+
+    if not os.path.isfile(src):
+        raise DistutilsFileError(
+            f"can't copy '{src}': doesn't exist or not a regular file"
+        )
+
+    if os.path.isdir(dst):
+        dir = dst
+        dst = os.path.join(dst, os.path.basename(src))
+    else:
+        dir = os.path.dirname(dst)
+
+    if update and not newer(src, dst):
+        if verbose >= 1:
+            log.debug("not copying %s (output up-to-date)", src)
+        return (dst, False)
+
+    try:
+        action = _copy_action[link]
+    except KeyError:
+        raise ValueError(f"invalid value '{link}' for 'link' argument")
+
+    if verbose >= 1:
+        if os.path.basename(dst) == os.path.basename(src):
+            log.info("%s %s -> %s", action, src, dir)
+        else:
+            log.info("%s %s -> %s", action, src, dst)
+
+    if dry_run:
+        return (dst, True)
+
+    # If linking (hard or symbolic), use the appropriate system call
+    # (Unix only, of course, but that's the caller's responsibility)
+    elif link == 'hard':
+        if not (os.path.exists(dst) and os.path.samefile(src, dst)):
+            try:
+                os.link(src, dst)
+            except OSError:
+                # If hard linking fails, fall back on copying file
+                # (some special filesystems don't support hard linking
+                #  even under Unix, see issue #8876).
+                pass
+            else:
+                return (dst, True)
+    elif link == 'sym':
+        if not (os.path.exists(dst) and os.path.samefile(src, dst)):
+            os.symlink(src, dst)
+            return (dst, True)
+
+    # Otherwise (non-Mac, not linking), copy the file contents and
+    # (optionally) copy the times and mode.
+    _copy_file_contents(src, dst)
+    if preserve_mode or preserve_times:
+        st = os.stat(src)
+
+        # According to David Ascher <da@ski.org>, utime() should be done
+        # before chmod() (at least under NT).
+        if preserve_times:
+            os.utime(dst, (st[ST_ATIME], st[ST_MTIME]))
+        if preserve_mode:
+            os.chmod(dst, S_IMODE(st[ST_MODE]))
+
+    return (dst, True)
+
+
+# XXX I suspect this is Unix-specific -- need porting help!
+def move_file(src, dst, verbose=True, dry_run=False):  # noqa: C901
+    """Move a file 'src' to 'dst'.  If 'dst' is a directory, the file will
+    be moved into it with the same name; otherwise, 'src' is just renamed
+    to 'dst'.  Return the new full name of the file.
+
+    Handles cross-device moves on Unix using 'copy_file()'.  What about
+    other systems???
+    """
+    import errno
+    from os.path import basename, dirname, exists, isdir, isfile
+
+    if verbose >= 1:
+        log.info("moving %s -> %s", src, dst)
+
+    if dry_run:
+        return dst
+
+    if not isfile(src):
+        raise DistutilsFileError(f"can't move '{src}': not a regular file")
+
+    if isdir(dst):
+        dst = os.path.join(dst, basename(src))
+    elif exists(dst):
+        raise DistutilsFileError(
+            f"can't move '{src}': destination '{dst}' already exists"
+        )
+
+    if not isdir(dirname(dst)):
+        raise DistutilsFileError(
+            f"can't move '{src}': destination '{dst}' not a valid path"
+        )
+
+    copy_it = False
+    try:
+        os.rename(src, dst)
+    except OSError as e:
+        (num, msg) = e.args
+        if num == errno.EXDEV:
+            copy_it = True
+        else:
+            raise DistutilsFileError(f"couldn't move '{src}' to '{dst}': {msg}")
+
+    if copy_it:
+        copy_file(src, dst, verbose=verbose)
+        try:
+            os.unlink(src)
+        except OSError as e:
+            (num, msg) = e.args
+            try:
+                os.unlink(dst)
+            except OSError:
+                pass
+            raise DistutilsFileError(
+                f"couldn't move '{src}' to '{dst}' by copy/delete: "
+                f"delete '{src}' failed: {msg}"
+            )
+    return dst
+
+
+def write_file(filename, contents):
+    """Create a file with the specified name and write 'contents' (a
+    sequence of strings without line terminators) to it.
+    """
+    with open(filename, 'w', encoding='utf-8') as f:
+        f.writelines(line + '\n' for line in contents)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/filelist.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/filelist.py
new file mode 100644
index 0000000000000000000000000000000000000000..70dc0fdebc3f4200245aa1c6280ab39861dcf8b0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/filelist.py
@@ -0,0 +1,431 @@
+"""distutils.filelist
+
+Provides the FileList class, used for poking about the filesystem
+and building lists of files.
+"""
+
+from __future__ import annotations
+
+import fnmatch
+import functools
+import os
+import re
+from collections.abc import Iterable
+from typing import Literal, overload
+
+from ._log import log
+from .errors import DistutilsInternalError, DistutilsTemplateError
+from .util import convert_path
+
+
+class FileList:
+    """A list of files built by on exploring the filesystem and filtered by
+    applying various patterns to what we find there.
+
+    Instance attributes:
+      dir
+        directory from which files will be taken -- only used if
+        'allfiles' not supplied to constructor
+      files
+        list of filenames currently being built/filtered/manipulated
+      allfiles
+        complete list of files under consideration (ie. without any
+        filtering applied)
+    """
+
+    def __init__(self, warn: object = None, debug_print: object = None) -> None:
+        # ignore argument to FileList, but keep them for backwards
+        # compatibility
+        self.allfiles: Iterable[str] | None = None
+        self.files: list[str] = []
+
+    def set_allfiles(self, allfiles: Iterable[str]) -> None:
+        self.allfiles = allfiles
+
+    def findall(self, dir: str | os.PathLike[str] = os.curdir) -> None:
+        self.allfiles = findall(dir)
+
+    def debug_print(self, msg: object) -> None:
+        """Print 'msg' to stdout if the global DEBUG (taken from the
+        DISTUTILS_DEBUG environment variable) flag is true.
+        """
+        from distutils.debug import DEBUG
+
+        if DEBUG:
+            print(msg)
+
+    # Collection methods
+
+    def append(self, item: str) -> None:
+        self.files.append(item)
+
+    def extend(self, items: Iterable[str]) -> None:
+        self.files.extend(items)
+
+    def sort(self) -> None:
+        # Not a strict lexical sort!
+        sortable_files = sorted(map(os.path.split, self.files))
+        self.files = []
+        for sort_tuple in sortable_files:
+            self.files.append(os.path.join(*sort_tuple))
+
+    # Other miscellaneous utility methods
+
+    def remove_duplicates(self) -> None:
+        # Assumes list has been sorted!
+        for i in range(len(self.files) - 1, 0, -1):
+            if self.files[i] == self.files[i - 1]:
+                del self.files[i]
+
+    # "File template" methods
+
+    def _parse_template_line(self, line):
+        words = line.split()
+        action = words[0]
+
+        patterns = dir = dir_pattern = None
+
+        if action in ('include', 'exclude', 'global-include', 'global-exclude'):
+            if len(words) < 2:
+                raise DistutilsTemplateError(
+                    f"'{action}' expects <pattern1> <pattern2> ..."
+                )
+            patterns = [convert_path(w) for w in words[1:]]
+        elif action in ('recursive-include', 'recursive-exclude'):
+            if len(words) < 3:
+                raise DistutilsTemplateError(
+                    f"'{action}' expects <dir> <pattern1> <pattern2> ..."
+                )
+            dir = convert_path(words[1])
+            patterns = [convert_path(w) for w in words[2:]]
+        elif action in ('graft', 'prune'):
+            if len(words) != 2:
+                raise DistutilsTemplateError(
+                    f"'{action}' expects a single <dir_pattern>"
+                )
+            dir_pattern = convert_path(words[1])
+        else:
+            raise DistutilsTemplateError(f"unknown action '{action}'")
+
+        return (action, patterns, dir, dir_pattern)
+
+    def process_template_line(self, line: str) -> None:  # noqa: C901
+        # Parse the line: split it up, make sure the right number of words
+        # is there, and return the relevant words.  'action' is always
+        # defined: it's the first word of the line.  Which of the other
+        # three are defined depends on the action; it'll be either
+        # patterns, (dir and patterns), or (dir_pattern).
+        (action, patterns, dir, dir_pattern) = self._parse_template_line(line)
+
+        # OK, now we know that the action is valid and we have the
+        # right number of words on the line for that action -- so we
+        # can proceed with minimal error-checking.
+        if action == 'include':
+            self.debug_print("include " + ' '.join(patterns))
+            for pattern in patterns:
+                if not self.include_pattern(pattern, anchor=True):
+                    log.warning("warning: no files found matching '%s'", pattern)
+
+        elif action == 'exclude':
+            self.debug_print("exclude " + ' '.join(patterns))
+            for pattern in patterns:
+                if not self.exclude_pattern(pattern, anchor=True):
+                    log.warning(
+                        "warning: no previously-included files found matching '%s'",
+                        pattern,
+                    )
+
+        elif action == 'global-include':
+            self.debug_print("global-include " + ' '.join(patterns))
+            for pattern in patterns:
+                if not self.include_pattern(pattern, anchor=False):
+                    log.warning(
+                        (
+                            "warning: no files found matching '%s' "
+                            "anywhere in distribution"
+                        ),
+                        pattern,
+                    )
+
+        elif action == 'global-exclude':
+            self.debug_print("global-exclude " + ' '.join(patterns))
+            for pattern in patterns:
+                if not self.exclude_pattern(pattern, anchor=False):
+                    log.warning(
+                        (
+                            "warning: no previously-included files matching "
+                            "'%s' found anywhere in distribution"
+                        ),
+                        pattern,
+                    )
+
+        elif action == 'recursive-include':
+            self.debug_print("recursive-include {} {}".format(dir, ' '.join(patterns)))
+            for pattern in patterns:
+                if not self.include_pattern(pattern, prefix=dir):
+                    msg = "warning: no files found matching '%s' under directory '%s'"
+                    log.warning(msg, pattern, dir)
+
+        elif action == 'recursive-exclude':
+            self.debug_print("recursive-exclude {} {}".format(dir, ' '.join(patterns)))
+            for pattern in patterns:
+                if not self.exclude_pattern(pattern, prefix=dir):
+                    log.warning(
+                        (
+                            "warning: no previously-included files matching "
+                            "'%s' found under directory '%s'"
+                        ),
+                        pattern,
+                        dir,
+                    )
+
+        elif action == 'graft':
+            self.debug_print("graft " + dir_pattern)
+            if not self.include_pattern(None, prefix=dir_pattern):
+                log.warning("warning: no directories found matching '%s'", dir_pattern)
+
+        elif action == 'prune':
+            self.debug_print("prune " + dir_pattern)
+            if not self.exclude_pattern(None, prefix=dir_pattern):
+                log.warning(
+                    ("no previously-included directories found matching '%s'"),
+                    dir_pattern,
+                )
+        else:
+            raise DistutilsInternalError(
+                f"this cannot happen: invalid action '{action}'"
+            )
+
+    # Filtering/selection methods
+    @overload
+    def include_pattern(
+        self,
+        pattern: str,
+        anchor: bool = True,
+        prefix: str | None = None,
+        is_regex: Literal[False] = False,
+    ) -> bool: ...
+    @overload
+    def include_pattern(
+        self,
+        pattern: str | re.Pattern[str],
+        anchor: bool = True,
+        prefix: str | None = None,
+        *,
+        is_regex: Literal[True],
+    ) -> bool: ...
+    @overload
+    def include_pattern(
+        self,
+        pattern: str | re.Pattern[str],
+        anchor: bool,
+        prefix: str | None,
+        is_regex: Literal[True],
+    ) -> bool: ...
+    def include_pattern(
+        self,
+        pattern: str | re.Pattern,
+        anchor: bool = True,
+        prefix: str | None = None,
+        is_regex: bool = False,
+    ) -> bool:
+        """Select strings (presumably filenames) from 'self.files' that
+        match 'pattern', a Unix-style wildcard (glob) pattern.  Patterns
+        are not quite the same as implemented by the 'fnmatch' module: '*'
+        and '?'  match non-special characters, where "special" is platform-
+        dependent: slash on Unix; colon, slash, and backslash on
+        DOS/Windows; and colon on Mac OS.
+
+        If 'anchor' is true (the default), then the pattern match is more
+        stringent: "*.py" will match "foo.py" but not "foo/bar.py".  If
+        'anchor' is false, both of these will match.
+
+        If 'prefix' is supplied, then only filenames starting with 'prefix'
+        (itself a pattern) and ending with 'pattern', with anything in between
+        them, will match.  'anchor' is ignored in this case.
+
+        If 'is_regex' is true, 'anchor' and 'prefix' are ignored, and
+        'pattern' is assumed to be either a string containing a regex or a
+        regex object -- no translation is done, the regex is just compiled
+        and used as-is.
+
+        Selected strings will be added to self.files.
+
+        Return True if files are found, False otherwise.
+        """
+        # XXX docstring lying about what the special chars are?
+        files_found = False
+        pattern_re = translate_pattern(pattern, anchor, prefix, is_regex)
+        self.debug_print(f"include_pattern: applying regex r'{pattern_re.pattern}'")
+
+        # delayed loading of allfiles list
+        if self.allfiles is None:
+            self.findall()
+
+        for name in self.allfiles:
+            if pattern_re.search(name):
+                self.debug_print(" adding " + name)
+                self.files.append(name)
+                files_found = True
+        return files_found
+
+    @overload
+    def exclude_pattern(
+        self,
+        pattern: str,
+        anchor: bool = True,
+        prefix: str | None = None,
+        is_regex: Literal[False] = False,
+    ) -> bool: ...
+    @overload
+    def exclude_pattern(
+        self,
+        pattern: str | re.Pattern[str],
+        anchor: bool = True,
+        prefix: str | None = None,
+        *,
+        is_regex: Literal[True],
+    ) -> bool: ...
+    @overload
+    def exclude_pattern(
+        self,
+        pattern: str | re.Pattern[str],
+        anchor: bool,
+        prefix: str | None,
+        is_regex: Literal[True],
+    ) -> bool: ...
+    def exclude_pattern(
+        self,
+        pattern: str | re.Pattern,
+        anchor: bool = True,
+        prefix: str | None = None,
+        is_regex: bool = False,
+    ) -> bool:
+        """Remove strings (presumably filenames) from 'files' that match
+        'pattern'.  Other parameters are the same as for
+        'include_pattern()', above.
+        The list 'self.files' is modified in place.
+        Return True if files are found, False otherwise.
+        """
+        files_found = False
+        pattern_re = translate_pattern(pattern, anchor, prefix, is_regex)
+        self.debug_print(f"exclude_pattern: applying regex r'{pattern_re.pattern}'")
+        for i in range(len(self.files) - 1, -1, -1):
+            if pattern_re.search(self.files[i]):
+                self.debug_print(" removing " + self.files[i])
+                del self.files[i]
+                files_found = True
+        return files_found
+
+
+# Utility functions
+
+
+def _find_all_simple(path):
+    """
+    Find all files under 'path'
+    """
+    all_unique = _UniqueDirs.filter(os.walk(path, followlinks=True))
+    results = (
+        os.path.join(base, file) for base, dirs, files in all_unique for file in files
+    )
+    return filter(os.path.isfile, results)
+
+
+class _UniqueDirs(set):
+    """
+    Exclude previously-seen dirs from walk results,
+    avoiding infinite recursion.
+    Ref https://bugs.python.org/issue44497.
+    """
+
+    def __call__(self, walk_item):
+        """
+        Given an item from an os.walk result, determine
+        if the item represents a unique dir for this instance
+        and if not, prevent further traversal.
+        """
+        base, dirs, files = walk_item
+        stat = os.stat(base)
+        candidate = stat.st_dev, stat.st_ino
+        found = candidate in self
+        if found:
+            del dirs[:]
+        self.add(candidate)
+        return not found
+
+    @classmethod
+    def filter(cls, items):
+        return filter(cls(), items)
+
+
+def findall(dir: str | os.PathLike[str] = os.curdir):
+    """
+    Find all files under 'dir' and return the list of full filenames.
+    Unless dir is '.', return full filenames with dir prepended.
+    """
+    files = _find_all_simple(dir)
+    if dir == os.curdir:
+        make_rel = functools.partial(os.path.relpath, start=dir)
+        files = map(make_rel, files)
+    return list(files)
+
+
+def glob_to_re(pattern):
+    """Translate a shell-like glob pattern to a regular expression; return
+    a string containing the regex.  Differs from 'fnmatch.translate()' in
+    that '*' does not match "special characters" (which are
+    platform-specific).
+    """
+    pattern_re = fnmatch.translate(pattern)
+
+    # '?' and '*' in the glob pattern become '.' and '.*' in the RE, which
+    # IMHO is wrong -- '?' and '*' aren't supposed to match slash in Unix,
+    # and by extension they shouldn't match such "special characters" under
+    # any OS.  So change all non-escaped dots in the RE to match any
+    # character except the special characters (currently: just os.sep).
+    sep = os.sep
+    if os.sep == '\\':
+        # we're using a regex to manipulate a regex, so we need
+        # to escape the backslash twice
+        sep = r'\\\\'
+    escaped = rf'\1[^{sep}]'
+    pattern_re = re.sub(r'((?<!\\)(\\\\)*)\.', escaped, pattern_re)
+    return pattern_re
+
+
+def translate_pattern(pattern, anchor=True, prefix=None, is_regex=False):
+    """Translate a shell-like wildcard pattern to a compiled regular
+    expression.  Return the compiled regex.  If 'is_regex' true,
+    then 'pattern' is directly compiled to a regex (if it's a string)
+    or just returned as-is (assumes it's a regex object).
+    """
+    if is_regex:
+        if isinstance(pattern, str):
+            return re.compile(pattern)
+        else:
+            return pattern
+
+    # ditch start and end characters
+    start, _, end = glob_to_re('_').partition('_')
+
+    if pattern:
+        pattern_re = glob_to_re(pattern)
+        assert pattern_re.startswith(start) and pattern_re.endswith(end)
+    else:
+        pattern_re = ''
+
+    if prefix is not None:
+        prefix_re = glob_to_re(prefix)
+        assert prefix_re.startswith(start) and prefix_re.endswith(end)
+        prefix_re = prefix_re[len(start) : len(prefix_re) - len(end)]
+        sep = os.sep
+        if os.sep == '\\':
+            sep = r'\\'
+        pattern_re = pattern_re[len(start) : len(pattern_re) - len(end)]
+        pattern_re = rf'{start}\A{prefix_re}{sep}.*{pattern_re}{end}'
+    else:  # no prefix -- respect anchor flag
+        if anchor:
+            pattern_re = rf'{start}\A{pattern_re[len(start) :]}'
+
+    return re.compile(pattern_re)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/log.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/log.py
new file mode 100644
index 0000000000000000000000000000000000000000..8abb09cfa2b8ed70cf67ecd80414dc3a9fd4a158
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/log.py
@@ -0,0 +1,56 @@
+"""
+A simple log mechanism styled after PEP 282.
+
+Retained for compatibility and should not be used.
+"""
+
+import logging
+import warnings
+
+from ._log import log as _global_log
+
+DEBUG = logging.DEBUG
+INFO = logging.INFO
+WARN = logging.WARN
+ERROR = logging.ERROR
+FATAL = logging.FATAL
+
+log = _global_log.log
+debug = _global_log.debug
+info = _global_log.info
+warn = _global_log.warning
+error = _global_log.error
+fatal = _global_log.fatal
+
+
+def set_threshold(level):
+    orig = _global_log.level
+    _global_log.setLevel(level)
+    return orig
+
+
+def set_verbosity(v):
+    if v <= 0:
+        set_threshold(logging.WARN)
+    elif v == 1:
+        set_threshold(logging.INFO)
+    elif v >= 2:
+        set_threshold(logging.DEBUG)
+
+
+class Log(logging.Logger):
+    """distutils.log.Log is deprecated, please use an alternative from `logging`."""
+
+    def __init__(self, threshold=WARN):
+        warnings.warn(Log.__doc__)  # avoid DeprecationWarning to ensure warn is shown
+        super().__init__(__name__, level=threshold)
+
+    @property
+    def threshold(self):
+        return self.level
+
+    @threshold.setter
+    def threshold(self, level):
+        self.setLevel(level)
+
+    warn = logging.Logger.warning
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/spawn.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/spawn.py
new file mode 100644
index 0000000000000000000000000000000000000000..973668f2684054741b109bd8bbb573611309cc2f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/spawn.py
@@ -0,0 +1,134 @@
+"""distutils.spawn
+
+Provides the 'spawn()' function, a front-end to various platform-
+specific functions for launching another program in a sub-process.
+"""
+
+from __future__ import annotations
+
+import os
+import platform
+import shutil
+import subprocess
+import sys
+import warnings
+from collections.abc import Mapping, MutableSequence
+from typing import TYPE_CHECKING, TypeVar, overload
+
+from ._log import log
+from .debug import DEBUG
+from .errors import DistutilsExecError
+
+if TYPE_CHECKING:
+    from subprocess import _ENV
+
+
+_MappingT = TypeVar("_MappingT", bound=Mapping)
+
+
+def _debug(cmd):
+    """
+    Render a subprocess command differently depending on DEBUG.
+    """
+    return cmd if DEBUG else cmd[0]
+
+
+def _inject_macos_ver(env: _MappingT | None) -> _MappingT | dict[str, str | int] | None:
+    if platform.system() != 'Darwin':
+        return env
+
+    from .util import MACOSX_VERSION_VAR, get_macosx_target_ver
+
+    target_ver = get_macosx_target_ver()
+    update = {MACOSX_VERSION_VAR: target_ver} if target_ver else {}
+    return {**_resolve(env), **update}
+
+
+@overload
+def _resolve(env: None) -> os._Environ[str]: ...
+@overload
+def _resolve(env: _MappingT) -> _MappingT: ...
+def _resolve(env: _MappingT | None) -> _MappingT | os._Environ[str]:
+    return os.environ if env is None else env
+
+
+def spawn(
+    cmd: MutableSequence[bytes | str | os.PathLike[str]],
+    search_path: bool = True,
+    verbose: bool = False,
+    dry_run: bool = False,
+    env: _ENV | None = None,
+) -> None:
+    """Run another program, specified as a command list 'cmd', in a new process.
+
+    'cmd' is just the argument list for the new process, ie.
+    cmd[0] is the program to run and cmd[1:] are the rest of its arguments.
+    There is no way to run a program with a name different from that of its
+    executable.
+
+    If 'search_path' is true (the default), the system's executable
+    search path will be used to find the program; otherwise, cmd[0]
+    must be the exact path to the executable.  If 'dry_run' is true,
+    the command will not actually be run.
+
+    Raise DistutilsExecError if running the program fails in any way; just
+    return on success.
+    """
+    log.info(subprocess.list2cmdline(cmd))
+    if dry_run:
+        return
+
+    if search_path:
+        executable = shutil.which(cmd[0])
+        if executable is not None:
+            cmd[0] = executable
+
+    try:
+        subprocess.check_call(cmd, env=_inject_macos_ver(env))
+    except OSError as exc:
+        raise DistutilsExecError(
+            f"command {_debug(cmd)!r} failed: {exc.args[-1]}"
+        ) from exc
+    except subprocess.CalledProcessError as err:
+        raise DistutilsExecError(
+            f"command {_debug(cmd)!r} failed with exit code {err.returncode}"
+        ) from err
+
+
+def find_executable(executable: str, path: str | None = None) -> str | None:
+    """Tries to find 'executable' in the directories listed in 'path'.
+
+    A string listing directories separated by 'os.pathsep'; defaults to
+    os.environ['PATH'].  Returns the complete filename or None if not found.
+    """
+    warnings.warn(
+        'Use shutil.which instead of find_executable', DeprecationWarning, stacklevel=2
+    )
+    _, ext = os.path.splitext(executable)
+    if (sys.platform == 'win32') and (ext != '.exe'):
+        executable = executable + '.exe'
+
+    if os.path.isfile(executable):
+        return executable
+
+    if path is None:
+        path = os.environ.get('PATH', None)
+        # bpo-35755: Don't fall through if PATH is the empty string
+        if path is None:
+            try:
+                path = os.confstr("CS_PATH")
+            except (AttributeError, ValueError):
+                # os.confstr() or CS_PATH is not available
+                path = os.defpath
+
+    # PATH='' doesn't match, whereas PATH=':' looks in the current directory
+    if not path:
+        return None
+
+    paths = path.split(os.pathsep)
+    for p in paths:
+        f = os.path.join(p, executable)
+        if os.path.isfile(f):
+            # the file exists, we have a shot at spawn working
+            return f
+    return None
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/sysconfig.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/sysconfig.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ddc869ab5603d691a8863988da89ec84689de84
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/sysconfig.py
@@ -0,0 +1,598 @@
+"""Provide access to Python's configuration information.  The specific
+configuration variables available depend heavily on the platform and
+configuration.  The values may be retrieved using
+get_config_var(name), and the list of variables is available via
+get_config_vars().keys().  Additional convenience functions are also
+available.
+
+Written by:   Fred L. Drake, Jr.
+Email:        <fdrake@acm.org>
+"""
+
+from __future__ import annotations
+
+import functools
+import os
+import pathlib
+import re
+import sys
+import sysconfig
+from typing import TYPE_CHECKING, Literal, overload
+
+from jaraco.functools import pass_none
+
+from .ccompiler import CCompiler
+from .compat import py39
+from .errors import DistutilsPlatformError
+from .util import is_mingw
+
+if TYPE_CHECKING:
+    from typing_extensions import deprecated
+else:
+
+    def deprecated(message):
+        return lambda fn: fn
+
+
+IS_PYPY = '__pypy__' in sys.builtin_module_names
+
+# These are needed in a couple of spots, so just compute them once.
+PREFIX = os.path.normpath(sys.prefix)
+EXEC_PREFIX = os.path.normpath(sys.exec_prefix)
+BASE_PREFIX = os.path.normpath(sys.base_prefix)
+BASE_EXEC_PREFIX = os.path.normpath(sys.base_exec_prefix)
+
+# Path to the base directory of the project. On Windows the binary may
+# live in project/PCbuild/win32 or project/PCbuild/amd64.
+# set for cross builds
+if "_PYTHON_PROJECT_BASE" in os.environ:
+    project_base = os.path.abspath(os.environ["_PYTHON_PROJECT_BASE"])
+else:
+    if sys.executable:
+        project_base = os.path.dirname(os.path.abspath(sys.executable))
+    else:
+        # sys.executable can be empty if argv[0] has been changed and Python is
+        # unable to retrieve the real program name
+        project_base = os.getcwd()
+
+
+def _is_python_source_dir(d):
+    """
+    Return True if the target directory appears to point to an
+    un-installed Python.
+    """
+    modules = pathlib.Path(d).joinpath('Modules')
+    return any(modules.joinpath(fn).is_file() for fn in ('Setup', 'Setup.local'))
+
+
+_sys_home = getattr(sys, '_home', None)
+
+
+def _is_parent(dir_a, dir_b):
+    """
+    Return True if a is a parent of b.
+    """
+    return os.path.normcase(dir_a).startswith(os.path.normcase(dir_b))
+
+
+if os.name == 'nt':
+
+    @pass_none
+    def _fix_pcbuild(d):
+        # In a venv, sys._home will be inside BASE_PREFIX rather than PREFIX.
+        prefixes = PREFIX, BASE_PREFIX
+        matched = (
+            prefix
+            for prefix in prefixes
+            if _is_parent(d, os.path.join(prefix, "PCbuild"))
+        )
+        return next(matched, d)
+
+    project_base = _fix_pcbuild(project_base)
+    _sys_home = _fix_pcbuild(_sys_home)
+
+
+def _python_build():
+    if _sys_home:
+        return _is_python_source_dir(_sys_home)
+    return _is_python_source_dir(project_base)
+
+
+python_build = _python_build()
+
+
+# Calculate the build qualifier flags if they are defined.  Adding the flags
+# to the include and lib directories only makes sense for an installation, not
+# an in-source build.
+build_flags = ''
+try:
+    if not python_build:
+        build_flags = sys.abiflags
+except AttributeError:
+    # It's not a configure-based build, so the sys module doesn't have
+    # this attribute, which is fine.
+    pass
+
+
+def get_python_version():
+    """Return a string containing the major and minor Python version,
+    leaving off the patchlevel.  Sample return values could be '1.5'
+    or '2.2'.
+    """
+    return f'{sys.version_info.major}.{sys.version_info.minor}'
+
+
+def get_python_inc(plat_specific: bool = False, prefix: str | None = None) -> str:
+    """Return the directory containing installed Python header files.
+
+    If 'plat_specific' is false (the default), this is the path to the
+    non-platform-specific header files, i.e. Python.h and so on;
+    otherwise, this is the path to platform-specific header files
+    (namely pyconfig.h).
+
+    If 'prefix' is supplied, use it instead of sys.base_prefix or
+    sys.base_exec_prefix -- i.e., ignore 'plat_specific'.
+    """
+    default_prefix = BASE_EXEC_PREFIX if plat_specific else BASE_PREFIX
+    resolved_prefix = prefix if prefix is not None else default_prefix
+    # MinGW imitates posix like layout, but os.name != posix
+    os_name = "posix" if is_mingw() else os.name
+    try:
+        getter = globals()[f'_get_python_inc_{os_name}']
+    except KeyError:
+        raise DistutilsPlatformError(
+            "I don't know where Python installs its C header files "
+            f"on platform '{os.name}'"
+        )
+    return getter(resolved_prefix, prefix, plat_specific)
+
+
+@pass_none
+def _extant(path):
+    """
+    Replace path with None if it doesn't exist.
+    """
+    return path if os.path.exists(path) else None
+
+
+def _get_python_inc_posix(prefix, spec_prefix, plat_specific):
+    return (
+        _get_python_inc_posix_python(plat_specific)
+        or _extant(_get_python_inc_from_config(plat_specific, spec_prefix))
+        or _get_python_inc_posix_prefix(prefix)
+    )
+
+
+def _get_python_inc_posix_python(plat_specific):
+    """
+    Assume the executable is in the build directory. The
+    pyconfig.h file should be in the same directory. Since
+    the build directory may not be the source directory,
+    use "srcdir" from the makefile to find the "Include"
+    directory.
+    """
+    if not python_build:
+        return
+    if plat_specific:
+        return _sys_home or project_base
+    incdir = os.path.join(get_config_var('srcdir'), 'Include')
+    return os.path.normpath(incdir)
+
+
+def _get_python_inc_from_config(plat_specific, spec_prefix):
+    """
+    If no prefix was explicitly specified, provide the include
+    directory from the config vars. Useful when
+    cross-compiling, since the config vars may come from
+    the host
+    platform Python installation, while the current Python
+    executable is from the build platform installation.
+
+    >>> monkeypatch = getfixture('monkeypatch')
+    >>> gpifc = _get_python_inc_from_config
+    >>> monkeypatch.setitem(gpifc.__globals__, 'get_config_var', str.lower)
+    >>> gpifc(False, '/usr/bin/')
+    >>> gpifc(False, '')
+    >>> gpifc(False, None)
+    'includepy'
+    >>> gpifc(True, None)
+    'confincludepy'
+    """
+    if spec_prefix is None:
+        return get_config_var('CONF' * plat_specific + 'INCLUDEPY')
+
+
+def _get_python_inc_posix_prefix(prefix):
+    implementation = 'pypy' if IS_PYPY else 'python'
+    python_dir = implementation + get_python_version() + build_flags
+    return os.path.join(prefix, "include", python_dir)
+
+
+def _get_python_inc_nt(prefix, spec_prefix, plat_specific):
+    if python_build:
+        # Include both include dirs to ensure we can find pyconfig.h
+        return (
+            os.path.join(prefix, "include")
+            + os.path.pathsep
+            + os.path.dirname(sysconfig.get_config_h_filename())
+        )
+    return os.path.join(prefix, "include")
+
+
+# allow this behavior to be monkey-patched. Ref pypa/distutils#2.
+def _posix_lib(standard_lib, libpython, early_prefix, prefix):
+    if standard_lib:
+        return libpython
+    else:
+        return os.path.join(libpython, "site-packages")
+
+
+def get_python_lib(
+    plat_specific: bool = False, standard_lib: bool = False, prefix: str | None = None
+) -> str:
+    """Return the directory containing the Python library (standard or
+    site additions).
+
+    If 'plat_specific' is true, return the directory containing
+    platform-specific modules, i.e. any module from a non-pure-Python
+    module distribution; otherwise, return the platform-shared library
+    directory.  If 'standard_lib' is true, return the directory
+    containing standard Python library modules; otherwise, return the
+    directory for site-specific modules.
+
+    If 'prefix' is supplied, use it instead of sys.base_prefix or
+    sys.base_exec_prefix -- i.e., ignore 'plat_specific'.
+    """
+
+    early_prefix = prefix
+
+    if prefix is None:
+        if standard_lib:
+            prefix = plat_specific and BASE_EXEC_PREFIX or BASE_PREFIX
+        else:
+            prefix = plat_specific and EXEC_PREFIX or PREFIX
+
+    if os.name == "posix" or is_mingw():
+        if plat_specific or standard_lib:
+            # Platform-specific modules (any module from a non-pure-Python
+            # module distribution) or standard Python library modules.
+            libdir = getattr(sys, "platlibdir", "lib")
+        else:
+            # Pure Python
+            libdir = "lib"
+        implementation = 'pypy' if IS_PYPY else 'python'
+        libpython = os.path.join(prefix, libdir, implementation + get_python_version())
+        return _posix_lib(standard_lib, libpython, early_prefix, prefix)
+    elif os.name == "nt":
+        if standard_lib:
+            return os.path.join(prefix, "Lib")
+        else:
+            return os.path.join(prefix, "Lib", "site-packages")
+    else:
+        raise DistutilsPlatformError(
+            f"I don't know where Python installs its library on platform '{os.name}'"
+        )
+
+
+@functools.lru_cache
+def _customize_macos():
+    """
+    Perform first-time customization of compiler-related
+    config vars on macOS. Use after a compiler is known
+    to be needed. This customization exists primarily to support Pythons
+    from binary installers. The kind and paths to build tools on
+    the user system may vary significantly from the system
+    that Python itself was built on.  Also the user OS
+    version and build tools may not support the same set
+    of CPU architectures for universal builds.
+    """
+
+    sys.platform == "darwin" and __import__('_osx_support').customize_compiler(
+        get_config_vars()
+    )
+
+
+def customize_compiler(compiler: CCompiler) -> None:
+    """Do any platform-specific customization of a CCompiler instance.
+
+    Mainly needed on Unix, so we can plug in the information that
+    varies across Unices and is stored in Python's Makefile.
+    """
+    if compiler.compiler_type in ["unix", "cygwin"] or (
+        compiler.compiler_type == "mingw32" and is_mingw()
+    ):
+        _customize_macos()
+
+        (
+            cc,
+            cxx,
+            cflags,
+            ccshared,
+            ldshared,
+            ldcxxshared,
+            shlib_suffix,
+            ar,
+            ar_flags,
+        ) = get_config_vars(
+            'CC',
+            'CXX',
+            'CFLAGS',
+            'CCSHARED',
+            'LDSHARED',
+            'LDCXXSHARED',
+            'SHLIB_SUFFIX',
+            'AR',
+            'ARFLAGS',
+        )
+
+        cxxflags = cflags
+
+        if 'CC' in os.environ:
+            newcc = os.environ['CC']
+            if 'LDSHARED' not in os.environ and ldshared.startswith(cc):
+                # If CC is overridden, use that as the default
+                #       command for LDSHARED as well
+                ldshared = newcc + ldshared[len(cc) :]
+            cc = newcc
+        cxx = os.environ.get('CXX', cxx)
+        ldshared = os.environ.get('LDSHARED', ldshared)
+        ldcxxshared = os.environ.get('LDCXXSHARED', ldcxxshared)
+        cpp = os.environ.get(
+            'CPP',
+            cc + " -E",  # not always
+        )
+
+        ldshared = _add_flags(ldshared, 'LD')
+        ldcxxshared = _add_flags(ldcxxshared, 'LD')
+        cflags = os.environ.get('CFLAGS', cflags)
+        ldshared = _add_flags(ldshared, 'C')
+        cxxflags = os.environ.get('CXXFLAGS', cxxflags)
+        ldcxxshared = _add_flags(ldcxxshared, 'CXX')
+        cpp = _add_flags(cpp, 'CPP')
+        cflags = _add_flags(cflags, 'CPP')
+        cxxflags = _add_flags(cxxflags, 'CPP')
+        ldshared = _add_flags(ldshared, 'CPP')
+        ldcxxshared = _add_flags(ldcxxshared, 'CPP')
+
+        ar = os.environ.get('AR', ar)
+
+        archiver = ar + ' ' + os.environ.get('ARFLAGS', ar_flags)
+        cc_cmd = cc + ' ' + cflags
+        cxx_cmd = cxx + ' ' + cxxflags
+
+        compiler.set_executables(
+            preprocessor=cpp,
+            compiler=cc_cmd,
+            compiler_so=cc_cmd + ' ' + ccshared,
+            compiler_cxx=cxx_cmd,
+            compiler_so_cxx=cxx_cmd + ' ' + ccshared,
+            linker_so=ldshared,
+            linker_so_cxx=ldcxxshared,
+            linker_exe=cc,
+            linker_exe_cxx=cxx,
+            archiver=archiver,
+        )
+
+        if 'RANLIB' in os.environ and compiler.executables.get('ranlib', None):
+            compiler.set_executables(ranlib=os.environ['RANLIB'])
+
+        compiler.shared_lib_extension = shlib_suffix
+
+
+def get_config_h_filename() -> str:
+    """Return full pathname of installed pyconfig.h file."""
+    return sysconfig.get_config_h_filename()
+
+
+def get_makefile_filename() -> str:
+    """Return full pathname of installed Makefile from the Python build."""
+    return sysconfig.get_makefile_filename()
+
+
+def parse_config_h(fp, g=None):
+    """Parse a config.h-style file.
+
+    A dictionary containing name/value pairs is returned.  If an
+    optional dictionary is passed in as the second argument, it is
+    used instead of a new dictionary.
+    """
+    return sysconfig.parse_config_h(fp, vars=g)
+
+
+# Regexes needed for parsing Makefile (and similar syntaxes,
+# like old-style Setup files).
+_variable_rx = re.compile(r"([a-zA-Z][a-zA-Z0-9_]+)\s*=\s*(.*)")
+_findvar1_rx = re.compile(r"\$\(([A-Za-z][A-Za-z0-9_]*)\)")
+_findvar2_rx = re.compile(r"\${([A-Za-z][A-Za-z0-9_]*)}")
+
+
+def parse_makefile(fn, g=None):  # noqa: C901
+    """Parse a Makefile-style file.
+
+    A dictionary containing name/value pairs is returned.  If an
+    optional dictionary is passed in as the second argument, it is
+    used instead of a new dictionary.
+    """
+    from distutils.text_file import TextFile
+
+    fp = TextFile(
+        fn,
+        strip_comments=True,
+        skip_blanks=True,
+        join_lines=True,
+        errors="surrogateescape",
+    )
+
+    if g is None:
+        g = {}
+    done = {}
+    notdone = {}
+
+    while True:
+        line = fp.readline()
+        if line is None:  # eof
+            break
+        m = _variable_rx.match(line)
+        if m:
+            n, v = m.group(1, 2)
+            v = v.strip()
+            # `$$' is a literal `$' in make
+            tmpv = v.replace('$$', '')
+
+            if "$" in tmpv:
+                notdone[n] = v
+            else:
+                try:
+                    v = int(v)
+                except ValueError:
+                    # insert literal `$'
+                    done[n] = v.replace('$$', '$')
+                else:
+                    done[n] = v
+
+    # Variables with a 'PY_' prefix in the makefile. These need to
+    # be made available without that prefix through sysconfig.
+    # Special care is needed to ensure that variable expansion works, even
+    # if the expansion uses the name without a prefix.
+    renamed_variables = ('CFLAGS', 'LDFLAGS', 'CPPFLAGS')
+
+    # do variable interpolation here
+    while notdone:
+        for name in list(notdone):
+            value = notdone[name]
+            m = _findvar1_rx.search(value) or _findvar2_rx.search(value)
+            if m:
+                n = m.group(1)
+                found = True
+                if n in done:
+                    item = str(done[n])
+                elif n in notdone:
+                    # get it on a subsequent round
+                    found = False
+                elif n in os.environ:
+                    # do it like make: fall back to environment
+                    item = os.environ[n]
+
+                elif n in renamed_variables:
+                    if name.startswith('PY_') and name[3:] in renamed_variables:
+                        item = ""
+
+                    elif 'PY_' + n in notdone:
+                        found = False
+
+                    else:
+                        item = str(done['PY_' + n])
+                else:
+                    done[n] = item = ""
+                if found:
+                    after = value[m.end() :]
+                    value = value[: m.start()] + item + after
+                    if "$" in after:
+                        notdone[name] = value
+                    else:
+                        try:
+                            value = int(value)
+                        except ValueError:
+                            done[name] = value.strip()
+                        else:
+                            done[name] = value
+                        del notdone[name]
+
+                        if name.startswith('PY_') and name[3:] in renamed_variables:
+                            name = name[3:]
+                            if name not in done:
+                                done[name] = value
+            else:
+                # bogus variable reference; just drop it since we can't deal
+                del notdone[name]
+
+    fp.close()
+
+    # strip spurious spaces
+    for k, v in done.items():
+        if isinstance(v, str):
+            done[k] = v.strip()
+
+    # save the results in the global dictionary
+    g.update(done)
+    return g
+
+
+def expand_makefile_vars(s, vars):
+    """Expand Makefile-style variables -- "${foo}" or "$(foo)" -- in
+    'string' according to 'vars' (a dictionary mapping variable names to
+    values).  Variables not present in 'vars' are silently expanded to the
+    empty string.  The variable values in 'vars' should not contain further
+    variable expansions; if 'vars' is the output of 'parse_makefile()',
+    you're fine.  Returns a variable-expanded version of 's'.
+    """
+
+    # This algorithm does multiple expansion, so if vars['foo'] contains
+    # "${bar}", it will expand ${foo} to ${bar}, and then expand
+    # ${bar}... and so forth.  This is fine as long as 'vars' comes from
+    # 'parse_makefile()', which takes care of such expansions eagerly,
+    # according to make's variable expansion semantics.
+
+    while True:
+        m = _findvar1_rx.search(s) or _findvar2_rx.search(s)
+        if m:
+            (beg, end) = m.span()
+            s = s[0:beg] + vars.get(m.group(1)) + s[end:]
+        else:
+            break
+    return s
+
+
+_config_vars = None
+
+
+@overload
+def get_config_vars() -> dict[str, str | int]: ...
+@overload
+def get_config_vars(arg: str, /, *args: str) -> list[str | int]: ...
+def get_config_vars(*args: str) -> list[str | int] | dict[str, str | int]:
+    """With no arguments, return a dictionary of all configuration
+    variables relevant for the current platform.  Generally this includes
+    everything needed to build extensions and install both pure modules and
+    extensions.  On Unix, this means every variable defined in Python's
+    installed Makefile; on Windows it's a much smaller set.
+
+    With arguments, return a list of values that result from looking up
+    each argument in the configuration variable dictionary.
+    """
+    global _config_vars
+    if _config_vars is None:
+        _config_vars = sysconfig.get_config_vars().copy()
+        py39.add_ext_suffix(_config_vars)
+
+    return [_config_vars.get(name) for name in args] if args else _config_vars
+
+
+@overload
+@deprecated(
+    "SO is deprecated, use EXT_SUFFIX. Support will be removed when this module is synchronized with stdlib Python 3.11"
+)
+def get_config_var(name: Literal["SO"]) -> int | str | None: ...
+@overload
+def get_config_var(name: str) -> int | str | None: ...
+def get_config_var(name: str) -> int | str | None:
+    """Return the value of a single variable using the dictionary
+    returned by 'get_config_vars()'.  Equivalent to
+    get_config_vars().get(name)
+    """
+    if name == 'SO':
+        import warnings
+
+        warnings.warn('SO is deprecated, use EXT_SUFFIX', DeprecationWarning, 2)
+    return get_config_vars().get(name)
+
+
+@pass_none
+def _add_flags(value: str, type: str) -> str:
+    """
+    Add any flags from the environment for the given type.
+
+    type is the prefix to FLAGS in the environment key (e.g. "C" for "CFLAGS").
+    """
+    flags = os.environ.get(f'{type}FLAGS')
+    return f'{value} {flags}' if flags else value
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a8ab061009f1cd87a53ce51eff3bc9e9a632273
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/__init__.py
@@ -0,0 +1,42 @@
+"""
+Test suite for distutils.
+
+Tests for the command classes in the distutils.command package are
+included in distutils.tests as well, instead of using a separate
+distutils.command.tests package, since command identification is done
+by import rather than matching pre-defined names.
+"""
+
+import shutil
+from collections.abc import Sequence
+
+
+def missing_compiler_executable(cmd_names: Sequence[str] = []):  # pragma: no cover
+    """Check if the compiler components used to build the interpreter exist.
+
+    Check for the existence of the compiler executables whose names are listed
+    in 'cmd_names' or all the compiler executables when 'cmd_names' is empty
+    and return the first missing executable or None when none is found
+    missing.
+
+    """
+    from distutils import ccompiler, errors, sysconfig
+
+    compiler = ccompiler.new_compiler()
+    sysconfig.customize_compiler(compiler)
+    if compiler.compiler_type == "msvc":
+        # MSVC has no executables, so check whether initialization succeeds
+        try:
+            compiler.initialize()
+        except errors.DistutilsPlatformError:
+            return "msvc"
+    for name in compiler.executables:
+        if cmd_names and name not in cmd_names:
+            continue
+        cmd = getattr(compiler, name)
+        if cmd_names:
+            assert cmd is not None, f"the '{name}' executable is not configured"
+        elif not cmd:
+            continue
+        if shutil.which(cmd[0]) is None:
+            return cmd[0]
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new file mode 100644
index 0000000000000000000000000000000000000000..aca3939a0cea90dd0a90d7f36fcf83a5167cbfc4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/compat/py39.py
@@ -0,0 +1,40 @@
+import sys
+
+if sys.version_info >= (3, 10):
+    from test.support.import_helper import (
+        CleanImport as CleanImport,
+    )
+    from test.support.import_helper import (
+        DirsOnSysPath as DirsOnSysPath,
+    )
+    from test.support.os_helper import (
+        EnvironmentVarGuard as EnvironmentVarGuard,
+    )
+    from test.support.os_helper import (
+        rmtree as rmtree,
+    )
+    from test.support.os_helper import (
+        skip_unless_symlink as skip_unless_symlink,
+    )
+    from test.support.os_helper import (
+        unlink as unlink,
+    )
+else:
+    from test.support import (
+        CleanImport as CleanImport,
+    )
+    from test.support import (
+        DirsOnSysPath as DirsOnSysPath,
+    )
+    from test.support import (
+        EnvironmentVarGuard as EnvironmentVarGuard,
+    )
+    from test.support import (
+        rmtree as rmtree,
+    )
+    from test.support import (
+        skip_unless_symlink as skip_unless_symlink,
+    )
+    from test.support import (
+        unlink as unlink,
+    )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/support.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/support.py
new file mode 100644
index 0000000000000000000000000000000000000000..9cd2b8a9eedbb9f11a3ed793af57b3290c5febef
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/support.py
@@ -0,0 +1,134 @@
+"""Support code for distutils test cases."""
+
+import itertools
+import os
+import pathlib
+import shutil
+import sys
+import sysconfig
+import tempfile
+from distutils.core import Distribution
+
+import pytest
+from more_itertools import always_iterable
+
+
+@pytest.mark.usefixtures('distutils_managed_tempdir')
+class TempdirManager:
+    """
+    Mix-in class that handles temporary directories for test cases.
+    """
+
+    def mkdtemp(self):
+        """Create a temporary directory that will be cleaned up.
+
+        Returns the path of the directory.
+        """
+        d = tempfile.mkdtemp()
+        self.tempdirs.append(d)
+        return d
+
+    def write_file(self, path, content='xxx'):
+        """Writes a file in the given path.
+
+        path can be a string or a sequence.
+        """
+        pathlib.Path(*always_iterable(path)).write_text(content, encoding='utf-8')
+
+    def create_dist(self, pkg_name='foo', **kw):
+        """Will generate a test environment.
+
+        This function creates:
+         - a Distribution instance using keywords
+         - a temporary directory with a package structure
+
+        It returns the package directory and the distribution
+        instance.
+        """
+        tmp_dir = self.mkdtemp()
+        pkg_dir = os.path.join(tmp_dir, pkg_name)
+        os.mkdir(pkg_dir)
+        dist = Distribution(attrs=kw)
+
+        return pkg_dir, dist
+
+
+class DummyCommand:
+    """Class to store options for retrieval via set_undefined_options()."""
+
+    def __init__(self, **kwargs):
+        vars(self).update(kwargs)
+
+    def ensure_finalized(self):
+        pass
+
+
+def copy_xxmodule_c(directory):
+    """Helper for tests that need the xxmodule.c source file.
+
+    Example use:
+
+        def test_compile(self):
+            copy_xxmodule_c(self.tmpdir)
+            self.assertIn('xxmodule.c', os.listdir(self.tmpdir))
+
+    If the source file can be found, it will be copied to *directory*.  If not,
+    the test will be skipped.  Errors during copy are not caught.
+    """
+    shutil.copy(_get_xxmodule_path(), os.path.join(directory, 'xxmodule.c'))
+
+
+def _get_xxmodule_path():
+    source_name = 'xxmodule.c' if sys.version_info > (3, 9) else 'xxmodule-3.8.c'
+    return os.path.join(os.path.dirname(__file__), source_name)
+
+
+def fixup_build_ext(cmd):
+    """Function needed to make build_ext tests pass.
+
+    When Python was built with --enable-shared on Unix, -L. is not enough to
+    find libpython<blah>.so, because regrtest runs in a tempdir, not in the
+    source directory where the .so lives.
+
+    When Python was built with in debug mode on Windows, build_ext commands
+    need their debug attribute set, and it is not done automatically for
+    some reason.
+
+    This function handles both of these things.  Example use:
+
+        cmd = build_ext(dist)
+        support.fixup_build_ext(cmd)
+        cmd.ensure_finalized()
+
+    Unlike most other Unix platforms, Mac OS X embeds absolute paths
+    to shared libraries into executables, so the fixup is not needed there.
+    """
+    if os.name == 'nt':
+        cmd.debug = sys.executable.endswith('_d.exe')
+    elif sysconfig.get_config_var('Py_ENABLE_SHARED'):
+        # To further add to the shared builds fun on Unix, we can't just add
+        # library_dirs to the Extension() instance because that doesn't get
+        # plumbed through to the final compiler command.
+        runshared = sysconfig.get_config_var('RUNSHARED')
+        if runshared is None:
+            cmd.library_dirs = ['.']
+        else:
+            if sys.platform == 'darwin':
+                cmd.library_dirs = []
+            else:
+                name, equals, value = runshared.partition('=')
+                cmd.library_dirs = [d for d in value.split(os.pathsep) if d]
+
+
+def combine_markers(cls):
+    """
+    pytest will honor markers as found on the class, but when
+    markers are on multiple subclasses, only one appears. Use
+    this decorator to combine those markers.
+    """
+    cls.pytestmark = [
+        mark
+        for base in itertools.chain([cls], cls.__bases__)
+        for mark in getattr(base, 'pytestmark', [])
+    ]
+    return cls
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_archive_util.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_archive_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e4ed75a761dc26fc7dc29066138a02531c6e9c3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_archive_util.py
@@ -0,0 +1,353 @@
+"""Tests for distutils.archive_util."""
+
+import functools
+import operator
+import os
+import pathlib
+import sys
+import tarfile
+from distutils import archive_util
+from distutils.archive_util import (
+    ARCHIVE_FORMATS,
+    check_archive_formats,
+    make_archive,
+    make_tarball,
+    make_zipfile,
+)
+from distutils.spawn import spawn
+from distutils.tests import support
+from os.path import splitdrive
+
+import path
+import pytest
+from test.support import patch
+
+from .unix_compat import UID_0_SUPPORT, grp, pwd, require_uid_0, require_unix_id
+
+
+def can_fs_encode(filename):
+    """
+    Return True if the filename can be saved in the file system.
+    """
+    if os.path.supports_unicode_filenames:
+        return True
+    try:
+        filename.encode(sys.getfilesystemencoding())
+    except UnicodeEncodeError:
+        return False
+    return True
+
+
+def all_equal(values):
+    return functools.reduce(operator.eq, values)
+
+
+def same_drive(*paths):
+    return all_equal(pathlib.Path(path).drive for path in paths)
+
+
+class ArchiveUtilTestCase(support.TempdirManager):
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_make_tarball(self, name='archive'):
+        # creating something to tar
+        tmpdir = self._create_files()
+        self._make_tarball(tmpdir, name, '.tar.gz')
+        # trying an uncompressed one
+        self._make_tarball(tmpdir, name, '.tar', compress=None)
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_make_tarball_gzip(self):
+        tmpdir = self._create_files()
+        self._make_tarball(tmpdir, 'archive', '.tar.gz', compress='gzip')
+
+    def test_make_tarball_bzip2(self):
+        pytest.importorskip('bz2')
+        tmpdir = self._create_files()
+        self._make_tarball(tmpdir, 'archive', '.tar.bz2', compress='bzip2')
+
+    def test_make_tarball_xz(self):
+        pytest.importorskip('lzma')
+        tmpdir = self._create_files()
+        self._make_tarball(tmpdir, 'archive', '.tar.xz', compress='xz')
+
+    @pytest.mark.skipif("not can_fs_encode('årchiv')")
+    def test_make_tarball_latin1(self):
+        """
+        Mirror test_make_tarball, except filename contains latin characters.
+        """
+        self.test_make_tarball('årchiv')  # note this isn't a real word
+
+    @pytest.mark.skipif("not can_fs_encode('のアーカイブ')")
+    def test_make_tarball_extended(self):
+        """
+        Mirror test_make_tarball, except filename contains extended
+        characters outside the latin charset.
+        """
+        self.test_make_tarball('のアーカイブ')  # japanese for archive
+
+    def _make_tarball(self, tmpdir, target_name, suffix, **kwargs):
+        tmpdir2 = self.mkdtemp()
+        if same_drive(tmpdir, tmpdir2):
+            pytest.skip("source and target should be on same drive")
+
+        base_name = os.path.join(tmpdir2, target_name)
+
+        # working with relative paths to avoid tar warnings
+        with path.Path(tmpdir):
+            make_tarball(splitdrive(base_name)[1], 'dist', **kwargs)
+
+        # check if the compressed tarball was created
+        tarball = base_name + suffix
+        assert os.path.exists(tarball)
+        assert self._tarinfo(tarball) == self._created_files
+
+    def _tarinfo(self, path):
+        tar = tarfile.open(path)
+        try:
+            names = tar.getnames()
+            names.sort()
+            return names
+        finally:
+            tar.close()
+
+    _zip_created_files = [
+        'dist/',
+        'dist/file1',
+        'dist/file2',
+        'dist/sub/',
+        'dist/sub/file3',
+        'dist/sub2/',
+    ]
+    _created_files = [p.rstrip('/') for p in _zip_created_files]
+
+    def _create_files(self):
+        # creating something to tar
+        tmpdir = self.mkdtemp()
+        dist = os.path.join(tmpdir, 'dist')
+        os.mkdir(dist)
+        self.write_file([dist, 'file1'], 'xxx')
+        self.write_file([dist, 'file2'], 'xxx')
+        os.mkdir(os.path.join(dist, 'sub'))
+        self.write_file([dist, 'sub', 'file3'], 'xxx')
+        os.mkdir(os.path.join(dist, 'sub2'))
+        return tmpdir
+
+    @pytest.mark.usefixtures('needs_zlib')
+    @pytest.mark.skipif("not (shutil.which('tar') and shutil.which('gzip'))")
+    def test_tarfile_vs_tar(self):
+        tmpdir = self._create_files()
+        tmpdir2 = self.mkdtemp()
+        base_name = os.path.join(tmpdir2, 'archive')
+        old_dir = os.getcwd()
+        os.chdir(tmpdir)
+        try:
+            make_tarball(base_name, 'dist')
+        finally:
+            os.chdir(old_dir)
+
+        # check if the compressed tarball was created
+        tarball = base_name + '.tar.gz'
+        assert os.path.exists(tarball)
+
+        # now create another tarball using `tar`
+        tarball2 = os.path.join(tmpdir, 'archive2.tar.gz')
+        tar_cmd = ['tar', '-cf', 'archive2.tar', 'dist']
+        gzip_cmd = ['gzip', '-f', '-9', 'archive2.tar']
+        old_dir = os.getcwd()
+        os.chdir(tmpdir)
+        try:
+            spawn(tar_cmd)
+            spawn(gzip_cmd)
+        finally:
+            os.chdir(old_dir)
+
+        assert os.path.exists(tarball2)
+        # let's compare both tarballs
+        assert self._tarinfo(tarball) == self._created_files
+        assert self._tarinfo(tarball2) == self._created_files
+
+        # trying an uncompressed one
+        base_name = os.path.join(tmpdir2, 'archive')
+        old_dir = os.getcwd()
+        os.chdir(tmpdir)
+        try:
+            make_tarball(base_name, 'dist', compress=None)
+        finally:
+            os.chdir(old_dir)
+        tarball = base_name + '.tar'
+        assert os.path.exists(tarball)
+
+        # now for a dry_run
+        base_name = os.path.join(tmpdir2, 'archive')
+        old_dir = os.getcwd()
+        os.chdir(tmpdir)
+        try:
+            make_tarball(base_name, 'dist', compress=None, dry_run=True)
+        finally:
+            os.chdir(old_dir)
+        tarball = base_name + '.tar'
+        assert os.path.exists(tarball)
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_make_zipfile(self):
+        zipfile = pytest.importorskip('zipfile')
+        # creating something to tar
+        tmpdir = self._create_files()
+        base_name = os.path.join(self.mkdtemp(), 'archive')
+        with path.Path(tmpdir):
+            make_zipfile(base_name, 'dist')
+
+        # check if the compressed tarball was created
+        tarball = base_name + '.zip'
+        assert os.path.exists(tarball)
+        with zipfile.ZipFile(tarball) as zf:
+            assert sorted(zf.namelist()) == self._zip_created_files
+
+    def test_make_zipfile_no_zlib(self):
+        zipfile = pytest.importorskip('zipfile')
+        patch(self, archive_util.zipfile, 'zlib', None)  # force zlib ImportError
+
+        called = []
+        zipfile_class = zipfile.ZipFile
+
+        def fake_zipfile(*a, **kw):
+            if kw.get('compression', None) == zipfile.ZIP_STORED:
+                called.append((a, kw))
+            return zipfile_class(*a, **kw)
+
+        patch(self, archive_util.zipfile, 'ZipFile', fake_zipfile)
+
+        # create something to tar and compress
+        tmpdir = self._create_files()
+        base_name = os.path.join(self.mkdtemp(), 'archive')
+        with path.Path(tmpdir):
+            make_zipfile(base_name, 'dist')
+
+        tarball = base_name + '.zip'
+        assert called == [((tarball, "w"), {'compression': zipfile.ZIP_STORED})]
+        assert os.path.exists(tarball)
+        with zipfile.ZipFile(tarball) as zf:
+            assert sorted(zf.namelist()) == self._zip_created_files
+
+    def test_check_archive_formats(self):
+        assert check_archive_formats(['gztar', 'xxx', 'zip']) == 'xxx'
+        assert (
+            check_archive_formats(['gztar', 'bztar', 'xztar', 'ztar', 'tar', 'zip'])
+            is None
+        )
+
+    def test_make_archive(self):
+        tmpdir = self.mkdtemp()
+        base_name = os.path.join(tmpdir, 'archive')
+        with pytest.raises(ValueError):
+            make_archive(base_name, 'xxx')
+
+    def test_make_archive_cwd(self):
+        current_dir = os.getcwd()
+
+        def _breaks(*args, **kw):
+            raise RuntimeError()
+
+        ARCHIVE_FORMATS['xxx'] = (_breaks, [], 'xxx file')
+        try:
+            try:
+                make_archive('xxx', 'xxx', root_dir=self.mkdtemp())
+            except Exception:
+                pass
+            assert os.getcwd() == current_dir
+        finally:
+            ARCHIVE_FORMATS.pop('xxx')
+
+    def test_make_archive_tar(self):
+        base_dir = self._create_files()
+        base_name = os.path.join(self.mkdtemp(), 'archive')
+        res = make_archive(base_name, 'tar', base_dir, 'dist')
+        assert os.path.exists(res)
+        assert os.path.basename(res) == 'archive.tar'
+        assert self._tarinfo(res) == self._created_files
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_make_archive_gztar(self):
+        base_dir = self._create_files()
+        base_name = os.path.join(self.mkdtemp(), 'archive')
+        res = make_archive(base_name, 'gztar', base_dir, 'dist')
+        assert os.path.exists(res)
+        assert os.path.basename(res) == 'archive.tar.gz'
+        assert self._tarinfo(res) == self._created_files
+
+    def test_make_archive_bztar(self):
+        pytest.importorskip('bz2')
+        base_dir = self._create_files()
+        base_name = os.path.join(self.mkdtemp(), 'archive')
+        res = make_archive(base_name, 'bztar', base_dir, 'dist')
+        assert os.path.exists(res)
+        assert os.path.basename(res) == 'archive.tar.bz2'
+        assert self._tarinfo(res) == self._created_files
+
+    def test_make_archive_xztar(self):
+        pytest.importorskip('lzma')
+        base_dir = self._create_files()
+        base_name = os.path.join(self.mkdtemp(), 'archive')
+        res = make_archive(base_name, 'xztar', base_dir, 'dist')
+        assert os.path.exists(res)
+        assert os.path.basename(res) == 'archive.tar.xz'
+        assert self._tarinfo(res) == self._created_files
+
+    def test_make_archive_owner_group(self):
+        # testing make_archive with owner and group, with various combinations
+        # this works even if there's not gid/uid support
+        if UID_0_SUPPORT:
+            group = grp.getgrgid(0)[0]
+            owner = pwd.getpwuid(0)[0]
+        else:
+            group = owner = 'root'
+
+        base_dir = self._create_files()
+        root_dir = self.mkdtemp()
+        base_name = os.path.join(self.mkdtemp(), 'archive')
+        res = make_archive(
+            base_name, 'zip', root_dir, base_dir, owner=owner, group=group
+        )
+        assert os.path.exists(res)
+
+        res = make_archive(base_name, 'zip', root_dir, base_dir)
+        assert os.path.exists(res)
+
+        res = make_archive(
+            base_name, 'tar', root_dir, base_dir, owner=owner, group=group
+        )
+        assert os.path.exists(res)
+
+        res = make_archive(
+            base_name, 'tar', root_dir, base_dir, owner='kjhkjhkjg', group='oihohoh'
+        )
+        assert os.path.exists(res)
+
+    @pytest.mark.usefixtures('needs_zlib')
+    @require_unix_id
+    @require_uid_0
+    def test_tarfile_root_owner(self):
+        tmpdir = self._create_files()
+        base_name = os.path.join(self.mkdtemp(), 'archive')
+        old_dir = os.getcwd()
+        os.chdir(tmpdir)
+        group = grp.getgrgid(0)[0]
+        owner = pwd.getpwuid(0)[0]
+        try:
+            archive_name = make_tarball(
+                base_name, 'dist', compress=None, owner=owner, group=group
+            )
+        finally:
+            os.chdir(old_dir)
+
+        # check if the compressed tarball was created
+        assert os.path.exists(archive_name)
+
+        # now checks the rights
+        archive = tarfile.open(archive_name)
+        try:
+            for member in archive.getmembers():
+                assert member.uid == 0
+                assert member.gid == 0
+        finally:
+            archive.close()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_bdist.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_bdist.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5696fc3dcd8081335606dc92badd73a69ceac69
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_bdist.py
@@ -0,0 +1,47 @@
+"""Tests for distutils.command.bdist."""
+
+from distutils.command.bdist import bdist
+from distutils.tests import support
+
+
+class TestBuild(support.TempdirManager):
+    def test_formats(self):
+        # let's create a command and make sure
+        # we can set the format
+        dist = self.create_dist()[1]
+        cmd = bdist(dist)
+        cmd.formats = ['gztar']
+        cmd.ensure_finalized()
+        assert cmd.formats == ['gztar']
+
+        # what formats does bdist offer?
+        formats = [
+            'bztar',
+            'gztar',
+            'rpm',
+            'tar',
+            'xztar',
+            'zip',
+            'ztar',
+        ]
+        found = sorted(cmd.format_commands)
+        assert found == formats
+
+    def test_skip_build(self):
+        # bug #10946: bdist --skip-build should trickle down to subcommands
+        dist = self.create_dist()[1]
+        cmd = bdist(dist)
+        cmd.skip_build = True
+        cmd.ensure_finalized()
+        dist.command_obj['bdist'] = cmd
+
+        names = [
+            'bdist_dumb',
+        ]  # bdist_rpm does not support --skip-build
+
+        for name in names:
+            subcmd = cmd.get_finalized_command(name)
+            if getattr(subcmd, '_unsupported', False):
+                # command is not supported on this build
+                continue
+            assert subcmd.skip_build, f'{name} should take --skip-build from bdist'
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_bdist_dumb.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_bdist_dumb.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fc51d244a78058329c56a618b092e466715e565
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_bdist_dumb.py
@@ -0,0 +1,78 @@
+"""Tests for distutils.command.bdist_dumb."""
+
+import os
+import sys
+import zipfile
+from distutils.command.bdist_dumb import bdist_dumb
+from distutils.core import Distribution
+from distutils.tests import support
+
+import pytest
+
+SETUP_PY = """\
+from distutils.core import setup
+import foo
+
+setup(name='foo', version='0.1', py_modules=['foo'],
+      url='xxx', author='xxx', author_email='xxx')
+
+"""
+
+
+@support.combine_markers
+@pytest.mark.usefixtures('save_env')
+@pytest.mark.usefixtures('save_argv')
+@pytest.mark.usefixtures('save_cwd')
+class TestBuildDumb(
+    support.TempdirManager,
+):
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_simple_built(self):
+        # let's create a simple package
+        tmp_dir = self.mkdtemp()
+        pkg_dir = os.path.join(tmp_dir, 'foo')
+        os.mkdir(pkg_dir)
+        self.write_file((pkg_dir, 'setup.py'), SETUP_PY)
+        self.write_file((pkg_dir, 'foo.py'), '#')
+        self.write_file((pkg_dir, 'MANIFEST.in'), 'include foo.py')
+        self.write_file((pkg_dir, 'README'), '')
+
+        dist = Distribution({
+            'name': 'foo',
+            'version': '0.1',
+            'py_modules': ['foo'],
+            'url': 'xxx',
+            'author': 'xxx',
+            'author_email': 'xxx',
+        })
+        dist.script_name = 'setup.py'
+        os.chdir(pkg_dir)
+
+        sys.argv = ['setup.py']
+        cmd = bdist_dumb(dist)
+
+        # so the output is the same no matter
+        # what is the platform
+        cmd.format = 'zip'
+
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # see what we have
+        dist_created = os.listdir(os.path.join(pkg_dir, 'dist'))
+        base = f"{dist.get_fullname()}.{cmd.plat_name}.zip"
+
+        assert dist_created == [base]
+
+        # now let's check what we have in the zip file
+        fp = zipfile.ZipFile(os.path.join('dist', base))
+        try:
+            contents = fp.namelist()
+        finally:
+            fp.close()
+
+        contents = sorted(filter(None, map(os.path.basename, contents)))
+        wanted = ['foo-0.1-py{}.{}.egg-info'.format(*sys.version_info[:2]), 'foo.py']
+        if not sys.dont_write_bytecode:
+            wanted.append(f'foo.{sys.implementation.cache_tag}.pyc')
+        assert contents == sorted(wanted)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_bdist_rpm.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_bdist_rpm.py
new file mode 100644
index 0000000000000000000000000000000000000000..75051430e224c8908e22601133a40f5f82ee455a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_bdist_rpm.py
@@ -0,0 +1,127 @@
+"""Tests for distutils.command.bdist_rpm."""
+
+import os
+import shutil  # noqa: F401
+import sys
+from distutils.command.bdist_rpm import bdist_rpm
+from distutils.core import Distribution
+from distutils.tests import support
+
+import pytest
+from test.support import requires_zlib
+
+SETUP_PY = """\
+from distutils.core import setup
+import foo
+
+setup(name='foo', version='0.1', py_modules=['foo'],
+      url='xxx', author='xxx', author_email='xxx')
+
+"""
+
+
+@pytest.fixture(autouse=True)
+def sys_executable_encodable():
+    try:
+        sys.executable.encode('UTF-8')
+    except UnicodeEncodeError:
+        pytest.skip("sys.executable is not encodable to UTF-8")
+
+
+mac_woes = pytest.mark.skipif(
+    "not sys.platform.startswith('linux')",
+    reason='spurious sdtout/stderr output under macOS',
+)
+
+
+@pytest.mark.usefixtures('save_env')
+@pytest.mark.usefixtures('save_argv')
+@pytest.mark.usefixtures('save_cwd')
+class TestBuildRpm(
+    support.TempdirManager,
+):
+    @mac_woes
+    @requires_zlib()
+    @pytest.mark.skipif("not shutil.which('rpm')")
+    @pytest.mark.skipif("not shutil.which('rpmbuild')")
+    def test_quiet(self):
+        # let's create a package
+        tmp_dir = self.mkdtemp()
+        os.environ['HOME'] = tmp_dir  # to confine dir '.rpmdb' creation
+        pkg_dir = os.path.join(tmp_dir, 'foo')
+        os.mkdir(pkg_dir)
+        self.write_file((pkg_dir, 'setup.py'), SETUP_PY)
+        self.write_file((pkg_dir, 'foo.py'), '#')
+        self.write_file((pkg_dir, 'MANIFEST.in'), 'include foo.py')
+        self.write_file((pkg_dir, 'README'), '')
+
+        dist = Distribution({
+            'name': 'foo',
+            'version': '0.1',
+            'py_modules': ['foo'],
+            'url': 'xxx',
+            'author': 'xxx',
+            'author_email': 'xxx',
+        })
+        dist.script_name = 'setup.py'
+        os.chdir(pkg_dir)
+
+        sys.argv = ['setup.py']
+        cmd = bdist_rpm(dist)
+        cmd.fix_python = True
+
+        # running in quiet mode
+        cmd.quiet = True
+        cmd.ensure_finalized()
+        cmd.run()
+
+        dist_created = os.listdir(os.path.join(pkg_dir, 'dist'))
+        assert 'foo-0.1-1.noarch.rpm' in dist_created
+
+        # bug #2945: upload ignores bdist_rpm files
+        assert ('bdist_rpm', 'any', 'dist/foo-0.1-1.src.rpm') in dist.dist_files
+        assert ('bdist_rpm', 'any', 'dist/foo-0.1-1.noarch.rpm') in dist.dist_files
+
+    @mac_woes
+    @requires_zlib()
+    # https://bugs.python.org/issue1533164
+    @pytest.mark.skipif("not shutil.which('rpm')")
+    @pytest.mark.skipif("not shutil.which('rpmbuild')")
+    def test_no_optimize_flag(self):
+        # let's create a package that breaks bdist_rpm
+        tmp_dir = self.mkdtemp()
+        os.environ['HOME'] = tmp_dir  # to confine dir '.rpmdb' creation
+        pkg_dir = os.path.join(tmp_dir, 'foo')
+        os.mkdir(pkg_dir)
+        self.write_file((pkg_dir, 'setup.py'), SETUP_PY)
+        self.write_file((pkg_dir, 'foo.py'), '#')
+        self.write_file((pkg_dir, 'MANIFEST.in'), 'include foo.py')
+        self.write_file((pkg_dir, 'README'), '')
+
+        dist = Distribution({
+            'name': 'foo',
+            'version': '0.1',
+            'py_modules': ['foo'],
+            'url': 'xxx',
+            'author': 'xxx',
+            'author_email': 'xxx',
+        })
+        dist.script_name = 'setup.py'
+        os.chdir(pkg_dir)
+
+        sys.argv = ['setup.py']
+        cmd = bdist_rpm(dist)
+        cmd.fix_python = True
+
+        cmd.quiet = True
+        cmd.ensure_finalized()
+        cmd.run()
+
+        dist_created = os.listdir(os.path.join(pkg_dir, 'dist'))
+        assert 'foo-0.1-1.noarch.rpm' in dist_created
+
+        # bug #2945: upload ignores bdist_rpm files
+        assert ('bdist_rpm', 'any', 'dist/foo-0.1-1.src.rpm') in dist.dist_files
+        assert ('bdist_rpm', 'any', 'dist/foo-0.1-1.noarch.rpm') in dist.dist_files
+
+        os.remove(os.path.join(pkg_dir, 'dist', 'foo-0.1-1.noarch.rpm'))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7fe69acd1ad39cf40d277afd6409648b18df146
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build.py
@@ -0,0 +1,49 @@
+"""Tests for distutils.command.build."""
+
+import os
+import sys
+from distutils.command.build import build
+from distutils.tests import support
+from sysconfig import get_config_var, get_platform
+
+
+class TestBuild(support.TempdirManager):
+    def test_finalize_options(self):
+        pkg_dir, dist = self.create_dist()
+        cmd = build(dist)
+        cmd.finalize_options()
+
+        # if not specified, plat_name gets the current platform
+        assert cmd.plat_name == get_platform()
+
+        # build_purelib is build + lib
+        wanted = os.path.join(cmd.build_base, 'lib')
+        assert cmd.build_purelib == wanted
+
+        # build_platlib is 'build/lib.platform-cache_tag[-pydebug]'
+        # examples:
+        #   build/lib.macosx-10.3-i386-cpython39
+        plat_spec = f'.{cmd.plat_name}-{sys.implementation.cache_tag}'
+        if get_config_var('Py_GIL_DISABLED'):
+            plat_spec += 't'
+        if hasattr(sys, 'gettotalrefcount'):
+            assert cmd.build_platlib.endswith('-pydebug')
+            plat_spec += '-pydebug'
+        wanted = os.path.join(cmd.build_base, 'lib' + plat_spec)
+        assert cmd.build_platlib == wanted
+
+        # by default, build_lib = build_purelib
+        assert cmd.build_lib == cmd.build_purelib
+
+        # build_temp is build/temp.<plat>
+        wanted = os.path.join(cmd.build_base, 'temp' + plat_spec)
+        assert cmd.build_temp == wanted
+
+        # build_scripts is build/scripts-x.x
+        wanted = os.path.join(
+            cmd.build_base, f'scripts-{sys.version_info.major}.{sys.version_info.minor}'
+        )
+        assert cmd.build_scripts == wanted
+
+        # executable is os.path.normpath(sys.executable)
+        assert cmd.executable == os.path.normpath(sys.executable)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build_clib.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build_clib.py
new file mode 100644
index 0000000000000000000000000000000000000000..f76f26bcea04ebeaeba20d3c65881198f841c9b5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build_clib.py
@@ -0,0 +1,134 @@
+"""Tests for distutils.command.build_clib."""
+
+import os
+from distutils.command.build_clib import build_clib
+from distutils.errors import DistutilsSetupError
+from distutils.tests import missing_compiler_executable, support
+
+import pytest
+
+
+class TestBuildCLib(support.TempdirManager):
+    def test_check_library_dist(self):
+        pkg_dir, dist = self.create_dist()
+        cmd = build_clib(dist)
+
+        # 'libraries' option must be a list
+        with pytest.raises(DistutilsSetupError):
+            cmd.check_library_list('foo')
+
+        # each element of 'libraries' must a 2-tuple
+        with pytest.raises(DistutilsSetupError):
+            cmd.check_library_list(['foo1', 'foo2'])
+
+        # first element of each tuple in 'libraries'
+        # must be a string (the library name)
+        with pytest.raises(DistutilsSetupError):
+            cmd.check_library_list([(1, 'foo1'), ('name', 'foo2')])
+
+        # library name may not contain directory separators
+        with pytest.raises(DistutilsSetupError):
+            cmd.check_library_list(
+                [('name', 'foo1'), ('another/name', 'foo2')],
+            )
+
+        # second element of each tuple must be a dictionary (build info)
+        with pytest.raises(DistutilsSetupError):
+            cmd.check_library_list(
+                [('name', {}), ('another', 'foo2')],
+            )
+
+        # those work
+        libs = [('name', {}), ('name', {'ok': 'good'})]
+        cmd.check_library_list(libs)
+
+    def test_get_source_files(self):
+        pkg_dir, dist = self.create_dist()
+        cmd = build_clib(dist)
+
+        # "in 'libraries' option 'sources' must be present and must be
+        # a list of source filenames
+        cmd.libraries = [('name', {})]
+        with pytest.raises(DistutilsSetupError):
+            cmd.get_source_files()
+
+        cmd.libraries = [('name', {'sources': 1})]
+        with pytest.raises(DistutilsSetupError):
+            cmd.get_source_files()
+
+        cmd.libraries = [('name', {'sources': ['a', 'b']})]
+        assert cmd.get_source_files() == ['a', 'b']
+
+        cmd.libraries = [('name', {'sources': ('a', 'b')})]
+        assert cmd.get_source_files() == ['a', 'b']
+
+        cmd.libraries = [
+            ('name', {'sources': ('a', 'b')}),
+            ('name2', {'sources': ['c', 'd']}),
+        ]
+        assert cmd.get_source_files() == ['a', 'b', 'c', 'd']
+
+    def test_build_libraries(self):
+        pkg_dir, dist = self.create_dist()
+        cmd = build_clib(dist)
+
+        class FakeCompiler:
+            def compile(*args, **kw):
+                pass
+
+            create_static_lib = compile
+
+        cmd.compiler = FakeCompiler()
+
+        # build_libraries is also doing a bit of typo checking
+        lib = [('name', {'sources': 'notvalid'})]
+        with pytest.raises(DistutilsSetupError):
+            cmd.build_libraries(lib)
+
+        lib = [('name', {'sources': list()})]
+        cmd.build_libraries(lib)
+
+        lib = [('name', {'sources': tuple()})]
+        cmd.build_libraries(lib)
+
+    def test_finalize_options(self):
+        pkg_dir, dist = self.create_dist()
+        cmd = build_clib(dist)
+
+        cmd.include_dirs = 'one-dir'
+        cmd.finalize_options()
+        assert cmd.include_dirs == ['one-dir']
+
+        cmd.include_dirs = None
+        cmd.finalize_options()
+        assert cmd.include_dirs == []
+
+        cmd.distribution.libraries = 'WONTWORK'
+        with pytest.raises(DistutilsSetupError):
+            cmd.finalize_options()
+
+    @pytest.mark.skipif('platform.system() == "Windows"')
+    def test_run(self):
+        pkg_dir, dist = self.create_dist()
+        cmd = build_clib(dist)
+
+        foo_c = os.path.join(pkg_dir, 'foo.c')
+        self.write_file(foo_c, 'int main(void) { return 1;}\n')
+        cmd.libraries = [('foo', {'sources': [foo_c]})]
+
+        build_temp = os.path.join(pkg_dir, 'build')
+        os.mkdir(build_temp)
+        cmd.build_temp = build_temp
+        cmd.build_clib = build_temp
+
+        # Before we run the command, we want to make sure
+        # all commands are present on the system.
+        ccmd = missing_compiler_executable()
+        if ccmd is not None:
+            self.skipTest(f'The {ccmd!r} command is not found')
+
+        # this should work
+        cmd.run()
+
+        # let's check the result
+        assert 'libfoo.a' in os.listdir(build_temp)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build_ext.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build_ext.py
new file mode 100644
index 0000000000000000000000000000000000000000..dab0507f3d3ec104fb4acfe5b15f92661ff33a37
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build_ext.py
@@ -0,0 +1,628 @@
+import contextlib
+import glob
+import importlib
+import os.path
+import platform
+import re
+import shutil
+import site
+import subprocess
+import sys
+import tempfile
+import textwrap
+import time
+from distutils import sysconfig
+from distutils.command.build_ext import build_ext
+from distutils.core import Distribution
+from distutils.errors import (
+    CompileError,
+    DistutilsPlatformError,
+    DistutilsSetupError,
+    UnknownFileError,
+)
+from distutils.extension import Extension
+from distutils.tests import missing_compiler_executable
+from distutils.tests.support import TempdirManager, copy_xxmodule_c, fixup_build_ext
+from io import StringIO
+
+import jaraco.path
+import path
+import pytest
+from test import support
+
+from .compat import py39 as import_helper
+
+
+@pytest.fixture()
+def user_site_dir(request):
+    self = request.instance
+    self.tmp_dir = self.mkdtemp()
+    self.tmp_path = path.Path(self.tmp_dir)
+    from distutils.command import build_ext
+
+    orig_user_base = site.USER_BASE
+
+    site.USER_BASE = self.mkdtemp()
+    build_ext.USER_BASE = site.USER_BASE
+
+    # bpo-30132: On Windows, a .pdb file may be created in the current
+    # working directory. Create a temporary working directory to cleanup
+    # everything at the end of the test.
+    with self.tmp_path:
+        yield
+
+    site.USER_BASE = orig_user_base
+    build_ext.USER_BASE = orig_user_base
+
+    if sys.platform == 'cygwin':
+        time.sleep(1)
+
+
+@contextlib.contextmanager
+def safe_extension_import(name, path):
+    with import_helper.CleanImport(name):
+        with extension_redirect(name, path) as new_path:
+            with import_helper.DirsOnSysPath(new_path):
+                yield
+
+
+@contextlib.contextmanager
+def extension_redirect(mod, path):
+    """
+    Tests will fail to tear down an extension module if it's been imported.
+
+    Before importing, copy the file to a temporary directory that won't
+    be cleaned up. Yield the new path.
+    """
+    if platform.system() != "Windows" and sys.platform != "cygwin":
+        yield path
+        return
+    with import_helper.DirsOnSysPath(path):
+        spec = importlib.util.find_spec(mod)
+    filename = os.path.basename(spec.origin)
+    trash_dir = tempfile.mkdtemp(prefix='deleteme')
+    dest = os.path.join(trash_dir, os.path.basename(filename))
+    shutil.copy(spec.origin, dest)
+    yield trash_dir
+    # TODO: can the file be scheduled for deletion?
+
+
+@pytest.mark.usefixtures('user_site_dir')
+class TestBuildExt(TempdirManager):
+    def build_ext(self, *args, **kwargs):
+        return build_ext(*args, **kwargs)
+
+    @pytest.mark.parametrize("copy_so", [False])
+    def test_build_ext(self, copy_so):
+        missing_compiler_executable()
+        copy_xxmodule_c(self.tmp_dir)
+        xx_c = os.path.join(self.tmp_dir, 'xxmodule.c')
+        xx_ext = Extension('xx', [xx_c])
+        if sys.platform != "win32":
+            if not copy_so:
+                xx_ext = Extension(
+                    'xx',
+                    [xx_c],
+                    library_dirs=['/usr/lib'],
+                    libraries=['z'],
+                    runtime_library_dirs=['/usr/lib'],
+                )
+            elif sys.platform == 'linux':
+                libz_so = {
+                    os.path.realpath(name) for name in glob.iglob('/usr/lib*/libz.so*')
+                }
+                libz_so = sorted(libz_so, key=lambda lib_path: len(lib_path))
+                shutil.copyfile(libz_so[-1], '/tmp/libxx_z.so')
+
+                xx_ext = Extension(
+                    'xx',
+                    [xx_c],
+                    library_dirs=['/tmp'],
+                    libraries=['xx_z'],
+                    runtime_library_dirs=['/tmp'],
+                )
+        dist = Distribution({'name': 'xx', 'ext_modules': [xx_ext]})
+        dist.package_dir = self.tmp_dir
+        cmd = self.build_ext(dist)
+        fixup_build_ext(cmd)
+        cmd.build_lib = self.tmp_dir
+        cmd.build_temp = self.tmp_dir
+
+        old_stdout = sys.stdout
+        if not support.verbose:
+            # silence compiler output
+            sys.stdout = StringIO()
+        try:
+            cmd.ensure_finalized()
+            cmd.run()
+        finally:
+            sys.stdout = old_stdout
+
+        with safe_extension_import('xx', self.tmp_dir):
+            self._test_xx(copy_so)
+
+        if sys.platform == 'linux' and copy_so:
+            os.unlink('/tmp/libxx_z.so')
+
+    @staticmethod
+    def _test_xx(copy_so):
+        import xx  # type: ignore[import-not-found] # Module generated for tests
+
+        for attr in ('error', 'foo', 'new', 'roj'):
+            assert hasattr(xx, attr)
+
+        assert xx.foo(2, 5) == 7
+        assert xx.foo(13, 15) == 28
+        assert xx.new().demo() is None
+        if support.HAVE_DOCSTRINGS:
+            doc = 'This is a template module just for instruction.'
+            assert xx.__doc__ == doc
+        assert isinstance(xx.Null(), xx.Null)
+        assert isinstance(xx.Str(), xx.Str)
+
+        if sys.platform == 'linux':
+            so_headers = subprocess.check_output(
+                ["readelf", "-d", xx.__file__], universal_newlines=True
+            )
+            import pprint
+
+            pprint.pprint(so_headers)
+            rpaths = [
+                rpath
+                for line in so_headers.split("\n")
+                if "RPATH" in line or "RUNPATH" in line
+                for rpath in line.split()[2][1:-1].split(":")
+            ]
+            if not copy_so:
+                pprint.pprint(rpaths)
+                # Linked against a library in /usr/lib{,64}
+                assert "/usr/lib" not in rpaths and "/usr/lib64" not in rpaths
+            else:
+                # Linked against a library in /tmp
+                assert "/tmp" in rpaths
+                # The import is the real test here
+
+    def test_solaris_enable_shared(self):
+        dist = Distribution({'name': 'xx'})
+        cmd = self.build_ext(dist)
+        old = sys.platform
+
+        sys.platform = 'sunos'  # fooling finalize_options
+        from distutils.sysconfig import _config_vars
+
+        old_var = _config_vars.get('Py_ENABLE_SHARED')
+        _config_vars['Py_ENABLE_SHARED'] = True
+        try:
+            cmd.ensure_finalized()
+        finally:
+            sys.platform = old
+            if old_var is None:
+                del _config_vars['Py_ENABLE_SHARED']
+            else:
+                _config_vars['Py_ENABLE_SHARED'] = old_var
+
+        # make sure we get some library dirs under solaris
+        assert len(cmd.library_dirs) > 0
+
+    def test_user_site(self):
+        import site
+
+        dist = Distribution({'name': 'xx'})
+        cmd = self.build_ext(dist)
+
+        # making sure the user option is there
+        options = [name for name, short, label in cmd.user_options]
+        assert 'user' in options
+
+        # setting a value
+        cmd.user = True
+
+        # setting user based lib and include
+        lib = os.path.join(site.USER_BASE, 'lib')
+        incl = os.path.join(site.USER_BASE, 'include')
+        os.mkdir(lib)
+        os.mkdir(incl)
+
+        # let's run finalize
+        cmd.ensure_finalized()
+
+        # see if include_dirs and library_dirs
+        # were set
+        assert lib in cmd.library_dirs
+        assert lib in cmd.rpath
+        assert incl in cmd.include_dirs
+
+    def test_optional_extension(self):
+        # this extension will fail, but let's ignore this failure
+        # with the optional argument.
+        modules = [Extension('foo', ['xxx'], optional=False)]
+        dist = Distribution({'name': 'xx', 'ext_modules': modules})
+        cmd = self.build_ext(dist)
+        cmd.ensure_finalized()
+        with pytest.raises((UnknownFileError, CompileError)):
+            cmd.run()  # should raise an error
+
+        modules = [Extension('foo', ['xxx'], optional=True)]
+        dist = Distribution({'name': 'xx', 'ext_modules': modules})
+        cmd = self.build_ext(dist)
+        cmd.ensure_finalized()
+        cmd.run()  # should pass
+
+    def test_finalize_options(self):
+        # Make sure Python's include directories (for Python.h, pyconfig.h,
+        # etc.) are in the include search path.
+        modules = [Extension('foo', ['xxx'], optional=False)]
+        dist = Distribution({'name': 'xx', 'ext_modules': modules})
+        cmd = self.build_ext(dist)
+        cmd.finalize_options()
+
+        py_include = sysconfig.get_python_inc()
+        for p in py_include.split(os.path.pathsep):
+            assert p in cmd.include_dirs
+
+        plat_py_include = sysconfig.get_python_inc(plat_specific=True)
+        for p in plat_py_include.split(os.path.pathsep):
+            assert p in cmd.include_dirs
+
+        # make sure cmd.libraries is turned into a list
+        # if it's a string
+        cmd = self.build_ext(dist)
+        cmd.libraries = 'my_lib, other_lib lastlib'
+        cmd.finalize_options()
+        assert cmd.libraries == ['my_lib', 'other_lib', 'lastlib']
+
+        # make sure cmd.library_dirs is turned into a list
+        # if it's a string
+        cmd = self.build_ext(dist)
+        cmd.library_dirs = f'my_lib_dir{os.pathsep}other_lib_dir'
+        cmd.finalize_options()
+        assert 'my_lib_dir' in cmd.library_dirs
+        assert 'other_lib_dir' in cmd.library_dirs
+
+        # make sure rpath is turned into a list
+        # if it's a string
+        cmd = self.build_ext(dist)
+        cmd.rpath = f'one{os.pathsep}two'
+        cmd.finalize_options()
+        assert cmd.rpath == ['one', 'two']
+
+        # make sure cmd.link_objects is turned into a list
+        # if it's a string
+        cmd = build_ext(dist)
+        cmd.link_objects = 'one two,three'
+        cmd.finalize_options()
+        assert cmd.link_objects == ['one', 'two', 'three']
+
+        # XXX more tests to perform for win32
+
+        # make sure define is turned into 2-tuples
+        # strings if they are ','-separated strings
+        cmd = self.build_ext(dist)
+        cmd.define = 'one,two'
+        cmd.finalize_options()
+        assert cmd.define == [('one', '1'), ('two', '1')]
+
+        # make sure undef is turned into a list of
+        # strings if they are ','-separated strings
+        cmd = self.build_ext(dist)
+        cmd.undef = 'one,two'
+        cmd.finalize_options()
+        assert cmd.undef == ['one', 'two']
+
+        # make sure swig_opts is turned into a list
+        cmd = self.build_ext(dist)
+        cmd.swig_opts = None
+        cmd.finalize_options()
+        assert cmd.swig_opts == []
+
+        cmd = self.build_ext(dist)
+        cmd.swig_opts = '1 2'
+        cmd.finalize_options()
+        assert cmd.swig_opts == ['1', '2']
+
+    def test_check_extensions_list(self):
+        dist = Distribution()
+        cmd = self.build_ext(dist)
+        cmd.finalize_options()
+
+        # 'extensions' option must be a list of Extension instances
+        with pytest.raises(DistutilsSetupError):
+            cmd.check_extensions_list('foo')
+
+        # each element of 'ext_modules' option must be an
+        # Extension instance or 2-tuple
+        exts = [('bar', 'foo', 'bar'), 'foo']
+        with pytest.raises(DistutilsSetupError):
+            cmd.check_extensions_list(exts)
+
+        # first element of each tuple in 'ext_modules'
+        # must be the extension name (a string) and match
+        # a python dotted-separated name
+        exts = [('foo-bar', '')]
+        with pytest.raises(DistutilsSetupError):
+            cmd.check_extensions_list(exts)
+
+        # second element of each tuple in 'ext_modules'
+        # must be a dictionary (build info)
+        exts = [('foo.bar', '')]
+        with pytest.raises(DistutilsSetupError):
+            cmd.check_extensions_list(exts)
+
+        # ok this one should pass
+        exts = [('foo.bar', {'sources': [''], 'libraries': 'foo', 'some': 'bar'})]
+        cmd.check_extensions_list(exts)
+        ext = exts[0]
+        assert isinstance(ext, Extension)
+
+        # check_extensions_list adds in ext the values passed
+        # when they are in ('include_dirs', 'library_dirs', 'libraries'
+        # 'extra_objects', 'extra_compile_args', 'extra_link_args')
+        assert ext.libraries == 'foo'
+        assert not hasattr(ext, 'some')
+
+        # 'macros' element of build info dict must be 1- or 2-tuple
+        exts = [
+            (
+                'foo.bar',
+                {
+                    'sources': [''],
+                    'libraries': 'foo',
+                    'some': 'bar',
+                    'macros': [('1', '2', '3'), 'foo'],
+                },
+            )
+        ]
+        with pytest.raises(DistutilsSetupError):
+            cmd.check_extensions_list(exts)
+
+        exts[0][1]['macros'] = [('1', '2'), ('3',)]
+        cmd.check_extensions_list(exts)
+        assert exts[0].undef_macros == ['3']
+        assert exts[0].define_macros == [('1', '2')]
+
+    def test_get_source_files(self):
+        modules = [Extension('foo', ['xxx'], optional=False)]
+        dist = Distribution({'name': 'xx', 'ext_modules': modules})
+        cmd = self.build_ext(dist)
+        cmd.ensure_finalized()
+        assert cmd.get_source_files() == ['xxx']
+
+    def test_unicode_module_names(self):
+        modules = [
+            Extension('foo', ['aaa'], optional=False),
+            Extension('föö', ['uuu'], optional=False),
+        ]
+        dist = Distribution({'name': 'xx', 'ext_modules': modules})
+        cmd = self.build_ext(dist)
+        cmd.ensure_finalized()
+        assert re.search(r'foo(_d)?\..*', cmd.get_ext_filename(modules[0].name))
+        assert re.search(r'föö(_d)?\..*', cmd.get_ext_filename(modules[1].name))
+        assert cmd.get_export_symbols(modules[0]) == ['PyInit_foo']
+        assert cmd.get_export_symbols(modules[1]) == ['PyInitU_f_1gaa']
+
+    def test_export_symbols__init__(self):
+        # https://github.com/python/cpython/issues/80074
+        # https://github.com/pypa/setuptools/issues/4826
+        modules = [
+            Extension('foo.__init__', ['aaa']),
+            Extension('föö.__init__', ['uuu']),
+        ]
+        dist = Distribution({'name': 'xx', 'ext_modules': modules})
+        cmd = self.build_ext(dist)
+        cmd.ensure_finalized()
+        assert cmd.get_export_symbols(modules[0]) == ['PyInit_foo']
+        assert cmd.get_export_symbols(modules[1]) == ['PyInitU_f_1gaa']
+
+    def test_compiler_option(self):
+        # cmd.compiler is an option and
+        # should not be overridden by a compiler instance
+        # when the command is run
+        dist = Distribution()
+        cmd = self.build_ext(dist)
+        cmd.compiler = 'unix'
+        cmd.ensure_finalized()
+        cmd.run()
+        assert cmd.compiler == 'unix'
+
+    def test_get_outputs(self):
+        missing_compiler_executable()
+        tmp_dir = self.mkdtemp()
+        c_file = os.path.join(tmp_dir, 'foo.c')
+        self.write_file(c_file, 'void PyInit_foo(void) {}\n')
+        ext = Extension('foo', [c_file], optional=False)
+        dist = Distribution({'name': 'xx', 'ext_modules': [ext]})
+        cmd = self.build_ext(dist)
+        fixup_build_ext(cmd)
+        cmd.ensure_finalized()
+        assert len(cmd.get_outputs()) == 1
+
+        cmd.build_lib = os.path.join(self.tmp_dir, 'build')
+        cmd.build_temp = os.path.join(self.tmp_dir, 'tempt')
+
+        # issue #5977 : distutils build_ext.get_outputs
+        # returns wrong result with --inplace
+        other_tmp_dir = os.path.realpath(self.mkdtemp())
+        old_wd = os.getcwd()
+        os.chdir(other_tmp_dir)
+        try:
+            cmd.inplace = True
+            cmd.run()
+            so_file = cmd.get_outputs()[0]
+        finally:
+            os.chdir(old_wd)
+        assert os.path.exists(so_file)
+        ext_suffix = sysconfig.get_config_var('EXT_SUFFIX')
+        assert so_file.endswith(ext_suffix)
+        so_dir = os.path.dirname(so_file)
+        assert so_dir == other_tmp_dir
+
+        cmd.inplace = False
+        cmd.compiler = None
+        cmd.run()
+        so_file = cmd.get_outputs()[0]
+        assert os.path.exists(so_file)
+        assert so_file.endswith(ext_suffix)
+        so_dir = os.path.dirname(so_file)
+        assert so_dir == cmd.build_lib
+
+        # inplace = False, cmd.package = 'bar'
+        build_py = cmd.get_finalized_command('build_py')
+        build_py.package_dir = {'': 'bar'}
+        path = cmd.get_ext_fullpath('foo')
+        # checking that the last directory is the build_dir
+        path = os.path.split(path)[0]
+        assert path == cmd.build_lib
+
+        # inplace = True, cmd.package = 'bar'
+        cmd.inplace = True
+        other_tmp_dir = os.path.realpath(self.mkdtemp())
+        old_wd = os.getcwd()
+        os.chdir(other_tmp_dir)
+        try:
+            path = cmd.get_ext_fullpath('foo')
+        finally:
+            os.chdir(old_wd)
+        # checking that the last directory is bar
+        path = os.path.split(path)[0]
+        lastdir = os.path.split(path)[-1]
+        assert lastdir == 'bar'
+
+    def test_ext_fullpath(self):
+        ext = sysconfig.get_config_var('EXT_SUFFIX')
+        # building lxml.etree inplace
+        # etree_c = os.path.join(self.tmp_dir, 'lxml.etree.c')
+        # etree_ext = Extension('lxml.etree', [etree_c])
+        # dist = Distribution({'name': 'lxml', 'ext_modules': [etree_ext]})
+        dist = Distribution()
+        cmd = self.build_ext(dist)
+        cmd.inplace = True
+        cmd.distribution.package_dir = {'': 'src'}
+        cmd.distribution.packages = ['lxml', 'lxml.html']
+        curdir = os.getcwd()
+        wanted = os.path.join(curdir, 'src', 'lxml', 'etree' + ext)
+        path = cmd.get_ext_fullpath('lxml.etree')
+        assert wanted == path
+
+        # building lxml.etree not inplace
+        cmd.inplace = False
+        cmd.build_lib = os.path.join(curdir, 'tmpdir')
+        wanted = os.path.join(curdir, 'tmpdir', 'lxml', 'etree' + ext)
+        path = cmd.get_ext_fullpath('lxml.etree')
+        assert wanted == path
+
+        # building twisted.runner.portmap not inplace
+        build_py = cmd.get_finalized_command('build_py')
+        build_py.package_dir = {}
+        cmd.distribution.packages = ['twisted', 'twisted.runner.portmap']
+        path = cmd.get_ext_fullpath('twisted.runner.portmap')
+        wanted = os.path.join(curdir, 'tmpdir', 'twisted', 'runner', 'portmap' + ext)
+        assert wanted == path
+
+        # building twisted.runner.portmap inplace
+        cmd.inplace = True
+        path = cmd.get_ext_fullpath('twisted.runner.portmap')
+        wanted = os.path.join(curdir, 'twisted', 'runner', 'portmap' + ext)
+        assert wanted == path
+
+    @pytest.mark.skipif('platform.system() != "Darwin"')
+    @pytest.mark.usefixtures('save_env')
+    def test_deployment_target_default(self):
+        # Issue 9516: Test that, in the absence of the environment variable,
+        # an extension module is compiled with the same deployment target as
+        #  the interpreter.
+        self._try_compile_deployment_target('==', None)
+
+    @pytest.mark.skipif('platform.system() != "Darwin"')
+    @pytest.mark.usefixtures('save_env')
+    def test_deployment_target_too_low(self):
+        # Issue 9516: Test that an extension module is not allowed to be
+        # compiled with a deployment target less than that of the interpreter.
+        with pytest.raises(DistutilsPlatformError):
+            self._try_compile_deployment_target('>', '10.1')
+
+    @pytest.mark.skipif('platform.system() != "Darwin"')
+    @pytest.mark.usefixtures('save_env')
+    def test_deployment_target_higher_ok(self):  # pragma: no cover
+        # Issue 9516: Test that an extension module can be compiled with a
+        # deployment target higher than that of the interpreter: the ext
+        # module may depend on some newer OS feature.
+        deptarget = sysconfig.get_config_var('MACOSX_DEPLOYMENT_TARGET')
+        if deptarget:
+            # increment the minor version number (i.e. 10.6 -> 10.7)
+            deptarget = [int(x) for x in deptarget.split('.')]
+            deptarget[-1] += 1
+            deptarget = '.'.join(str(i) for i in deptarget)
+            self._try_compile_deployment_target('<', deptarget)
+
+    def _try_compile_deployment_target(self, operator, target):  # pragma: no cover
+        if target is None:
+            if os.environ.get('MACOSX_DEPLOYMENT_TARGET'):
+                del os.environ['MACOSX_DEPLOYMENT_TARGET']
+        else:
+            os.environ['MACOSX_DEPLOYMENT_TARGET'] = target
+
+        jaraco.path.build(
+            {
+                'deptargetmodule.c': textwrap.dedent(f"""\
+                    #include <AvailabilityMacros.h>
+
+                    int dummy;
+
+                    #if TARGET {operator} MAC_OS_X_VERSION_MIN_REQUIRED
+                    #else
+                    #error "Unexpected target"
+                    #endif
+
+                    """),
+            },
+            self.tmp_path,
+        )
+
+        # get the deployment target that the interpreter was built with
+        target = sysconfig.get_config_var('MACOSX_DEPLOYMENT_TARGET')
+        target = tuple(map(int, target.split('.')[0:2]))
+        # format the target value as defined in the Apple
+        # Availability Macros.  We can't use the macro names since
+        # at least one value we test with will not exist yet.
+        if target[:2] < (10, 10):
+            # for 10.1 through 10.9.x -> "10n0"
+            tmpl = '{:02}{:01}0'
+        else:
+            # for 10.10 and beyond -> "10nn00"
+            if len(target) >= 2:
+                tmpl = '{:02}{:02}00'
+            else:
+                # 11 and later can have no minor version (11 instead of 11.0)
+                tmpl = '{:02}0000'
+        target = tmpl.format(*target)
+        deptarget_ext = Extension(
+            'deptarget',
+            [self.tmp_path / 'deptargetmodule.c'],
+            extra_compile_args=[f'-DTARGET={target}'],
+        )
+        dist = Distribution({'name': 'deptarget', 'ext_modules': [deptarget_ext]})
+        dist.package_dir = self.tmp_dir
+        cmd = self.build_ext(dist)
+        cmd.build_lib = self.tmp_dir
+        cmd.build_temp = self.tmp_dir
+
+        try:
+            old_stdout = sys.stdout
+            if not support.verbose:
+                # silence compiler output
+                sys.stdout = StringIO()
+            try:
+                cmd.ensure_finalized()
+                cmd.run()
+            finally:
+                sys.stdout = old_stdout
+
+        except CompileError:
+            self.fail("Wrong deployment target during compilation")
+
+
+class TestParallelBuildExt(TestBuildExt):
+    def build_ext(self, *args, **kwargs):
+        build_ext = super().build_ext(*args, **kwargs)
+        build_ext.parallel = True
+        return build_ext
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build_py.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build_py.py
new file mode 100644
index 0000000000000000000000000000000000000000..b316ed43a11836210caeb7177bd8890a1f61d1bb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build_py.py
@@ -0,0 +1,196 @@
+"""Tests for distutils.command.build_py."""
+
+import os
+import sys
+from distutils.command.build_py import build_py
+from distutils.core import Distribution
+from distutils.errors import DistutilsFileError
+from distutils.tests import support
+
+import jaraco.path
+import pytest
+
+
+@support.combine_markers
+class TestBuildPy(support.TempdirManager):
+    def test_package_data(self):
+        sources = self.mkdtemp()
+        jaraco.path.build(
+            {
+                '__init__.py': "# Pretend this is a package.",
+                'README.txt': 'Info about this package',
+            },
+            sources,
+        )
+
+        destination = self.mkdtemp()
+
+        dist = Distribution({"packages": ["pkg"], "package_dir": {"pkg": sources}})
+        # script_name need not exist, it just need to be initialized
+        dist.script_name = os.path.join(sources, "setup.py")
+        dist.command_obj["build"] = support.DummyCommand(
+            force=False, build_lib=destination
+        )
+        dist.packages = ["pkg"]
+        dist.package_data = {"pkg": ["README.txt"]}
+        dist.package_dir = {"pkg": sources}
+
+        cmd = build_py(dist)
+        cmd.compile = True
+        cmd.ensure_finalized()
+        assert cmd.package_data == dist.package_data
+
+        cmd.run()
+
+        # This makes sure the list of outputs includes byte-compiled
+        # files for Python modules but not for package data files
+        # (there shouldn't *be* byte-code files for those!).
+        assert len(cmd.get_outputs()) == 3
+        pkgdest = os.path.join(destination, "pkg")
+        files = os.listdir(pkgdest)
+        pycache_dir = os.path.join(pkgdest, "__pycache__")
+        assert "__init__.py" in files
+        assert "README.txt" in files
+        if sys.dont_write_bytecode:
+            assert not os.path.exists(pycache_dir)
+        else:
+            pyc_files = os.listdir(pycache_dir)
+            assert f"__init__.{sys.implementation.cache_tag}.pyc" in pyc_files
+
+    def test_empty_package_dir(self):
+        # See bugs #1668596/#1720897
+        sources = self.mkdtemp()
+        jaraco.path.build({'__init__.py': '', 'doc': {'testfile': ''}}, sources)
+
+        os.chdir(sources)
+        dist = Distribution({
+            "packages": ["pkg"],
+            "package_dir": {"pkg": ""},
+            "package_data": {"pkg": ["doc/*"]},
+        })
+        # script_name need not exist, it just need to be initialized
+        dist.script_name = os.path.join(sources, "setup.py")
+        dist.script_args = ["build"]
+        dist.parse_command_line()
+
+        try:
+            dist.run_commands()
+        except DistutilsFileError:
+            self.fail("failed package_data test when package_dir is ''")
+
+    @pytest.mark.skipif('sys.dont_write_bytecode')
+    def test_byte_compile(self):
+        project_dir, dist = self.create_dist(py_modules=['boiledeggs'])
+        os.chdir(project_dir)
+        self.write_file('boiledeggs.py', 'import antigravity')
+        cmd = build_py(dist)
+        cmd.compile = True
+        cmd.build_lib = 'here'
+        cmd.finalize_options()
+        cmd.run()
+
+        found = os.listdir(cmd.build_lib)
+        assert sorted(found) == ['__pycache__', 'boiledeggs.py']
+        found = os.listdir(os.path.join(cmd.build_lib, '__pycache__'))
+        assert found == [f'boiledeggs.{sys.implementation.cache_tag}.pyc']
+
+    @pytest.mark.skipif('sys.dont_write_bytecode')
+    def test_byte_compile_optimized(self):
+        project_dir, dist = self.create_dist(py_modules=['boiledeggs'])
+        os.chdir(project_dir)
+        self.write_file('boiledeggs.py', 'import antigravity')
+        cmd = build_py(dist)
+        cmd.compile = False
+        cmd.optimize = 1
+        cmd.build_lib = 'here'
+        cmd.finalize_options()
+        cmd.run()
+
+        found = os.listdir(cmd.build_lib)
+        assert sorted(found) == ['__pycache__', 'boiledeggs.py']
+        found = os.listdir(os.path.join(cmd.build_lib, '__pycache__'))
+        expect = f'boiledeggs.{sys.implementation.cache_tag}.opt-1.pyc'
+        assert sorted(found) == [expect]
+
+    def test_dir_in_package_data(self):
+        """
+        A directory in package_data should not be added to the filelist.
+        """
+        # See bug 19286
+        sources = self.mkdtemp()
+        jaraco.path.build(
+            {
+                'pkg': {
+                    '__init__.py': '',
+                    'doc': {
+                        'testfile': '',
+                        # create a directory that could be incorrectly detected as a file
+                        'otherdir': {},
+                    },
+                }
+            },
+            sources,
+        )
+
+        os.chdir(sources)
+        dist = Distribution({"packages": ["pkg"], "package_data": {"pkg": ["doc/*"]}})
+        # script_name need not exist, it just need to be initialized
+        dist.script_name = os.path.join(sources, "setup.py")
+        dist.script_args = ["build"]
+        dist.parse_command_line()
+
+        try:
+            dist.run_commands()
+        except DistutilsFileError:
+            self.fail("failed package_data when data dir includes a dir")
+
+    def test_dont_write_bytecode(self, caplog):
+        # makes sure byte_compile is not used
+        dist = self.create_dist()[1]
+        cmd = build_py(dist)
+        cmd.compile = True
+        cmd.optimize = 1
+
+        old_dont_write_bytecode = sys.dont_write_bytecode
+        sys.dont_write_bytecode = True
+        try:
+            cmd.byte_compile([])
+        finally:
+            sys.dont_write_bytecode = old_dont_write_bytecode
+
+        assert 'byte-compiling is disabled' in caplog.records[0].message
+
+    def test_namespace_package_does_not_warn(self, caplog):
+        """
+        Originally distutils implementation did not account for PEP 420
+        and included warns for package directories that did not contain
+        ``__init__.py`` files.
+        After the acceptance of PEP 420, these warnings don't make more sense
+        so we want to ensure there are not displayed to not confuse the users.
+        """
+        # Create a fake project structure with a package namespace:
+        tmp = self.mkdtemp()
+        jaraco.path.build({'ns': {'pkg': {'module.py': ''}}}, tmp)
+        os.chdir(tmp)
+
+        # Configure the package:
+        attrs = {
+            "name": "ns.pkg",
+            "packages": ["ns", "ns.pkg"],
+            "script_name": "setup.py",
+        }
+        dist = Distribution(attrs)
+
+        # Run code paths that would trigger the trap:
+        cmd = dist.get_command_obj("build_py")
+        cmd.finalize_options()
+        modules = cmd.find_all_modules()
+        assert len(modules) == 1
+        module_path = modules[0][-1]
+        assert module_path.replace(os.sep, "/") == "ns/pkg/module.py"
+
+        cmd.run()
+
+        assert not any(
+            "package init file" in msg and "not found" in msg for msg in caplog.messages
+        )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build_scripts.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build_scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..3582f691ef578a419555c6ffbba6a619310517a6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_build_scripts.py
@@ -0,0 +1,96 @@
+"""Tests for distutils.command.build_scripts."""
+
+import os
+import textwrap
+from distutils import sysconfig
+from distutils.command.build_scripts import build_scripts
+from distutils.core import Distribution
+from distutils.tests import support
+
+import jaraco.path
+
+
+class TestBuildScripts(support.TempdirManager):
+    def test_default_settings(self):
+        cmd = self.get_build_scripts_cmd("/foo/bar", [])
+        assert not cmd.force
+        assert cmd.build_dir is None
+
+        cmd.finalize_options()
+
+        assert cmd.force
+        assert cmd.build_dir == "/foo/bar"
+
+    def test_build(self):
+        source = self.mkdtemp()
+        target = self.mkdtemp()
+        expected = self.write_sample_scripts(source)
+
+        cmd = self.get_build_scripts_cmd(
+            target, [os.path.join(source, fn) for fn in expected]
+        )
+        cmd.finalize_options()
+        cmd.run()
+
+        built = os.listdir(target)
+        for name in expected:
+            assert name in built
+
+    def get_build_scripts_cmd(self, target, scripts):
+        import sys
+
+        dist = Distribution()
+        dist.scripts = scripts
+        dist.command_obj["build"] = support.DummyCommand(
+            build_scripts=target, force=True, executable=sys.executable
+        )
+        return build_scripts(dist)
+
+    @staticmethod
+    def write_sample_scripts(dir):
+        spec = {
+            'script1.py': textwrap.dedent("""
+                #! /usr/bin/env python2.3
+                # bogus script w/ Python sh-bang
+                pass
+                """).lstrip(),
+            'script2.py': textwrap.dedent("""
+                #!/usr/bin/python
+                # bogus script w/ Python sh-bang
+                pass
+                """).lstrip(),
+            'shell.sh': textwrap.dedent("""
+                #!/bin/sh
+                # bogus shell script w/ sh-bang
+                exit 0
+                """).lstrip(),
+        }
+        jaraco.path.build(spec, dir)
+        return list(spec)
+
+    def test_version_int(self):
+        source = self.mkdtemp()
+        target = self.mkdtemp()
+        expected = self.write_sample_scripts(source)
+
+        cmd = self.get_build_scripts_cmd(
+            target, [os.path.join(source, fn) for fn in expected]
+        )
+        cmd.finalize_options()
+
+        # https://bugs.python.org/issue4524
+        #
+        # On linux-g++-32 with command line `./configure --enable-ipv6
+        # --with-suffix=3`, python is compiled okay but the build scripts
+        # failed when writing the name of the executable
+        old = sysconfig.get_config_vars().get('VERSION')
+        sysconfig._config_vars['VERSION'] = 4
+        try:
+            cmd.run()
+        finally:
+            if old is not None:
+                sysconfig._config_vars['VERSION'] = old
+
+        built = os.listdir(target)
+        for name in expected:
+            assert name in built
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_check.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_check.py
new file mode 100644
index 0000000000000000000000000000000000000000..b672b1f972b46f399eb5b2ef263da2548d52ef89
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_check.py
@@ -0,0 +1,194 @@
+"""Tests for distutils.command.check."""
+
+import os
+import textwrap
+from distutils.command.check import check
+from distutils.errors import DistutilsSetupError
+from distutils.tests import support
+
+import pytest
+
+try:
+    import pygments
+except ImportError:
+    pygments = None
+
+
+HERE = os.path.dirname(__file__)
+
+
+@support.combine_markers
+class TestCheck(support.TempdirManager):
+    def _run(self, metadata=None, cwd=None, **options):
+        if metadata is None:
+            metadata = {}
+        if cwd is not None:
+            old_dir = os.getcwd()
+            os.chdir(cwd)
+        pkg_info, dist = self.create_dist(**metadata)
+        cmd = check(dist)
+        cmd.initialize_options()
+        for name, value in options.items():
+            setattr(cmd, name, value)
+        cmd.ensure_finalized()
+        cmd.run()
+        if cwd is not None:
+            os.chdir(old_dir)
+        return cmd
+
+    def test_check_metadata(self):
+        # let's run the command with no metadata at all
+        # by default, check is checking the metadata
+        # should have some warnings
+        cmd = self._run()
+        assert cmd._warnings == 1
+
+        # now let's add the required fields
+        # and run it again, to make sure we don't get
+        # any warning anymore
+        metadata = {
+            'url': 'xxx',
+            'author': 'xxx',
+            'author_email': 'xxx',
+            'name': 'xxx',
+            'version': 'xxx',
+        }
+        cmd = self._run(metadata)
+        assert cmd._warnings == 0
+
+        # now with the strict mode, we should
+        # get an error if there are missing metadata
+        with pytest.raises(DistutilsSetupError):
+            self._run({}, **{'strict': 1})
+
+        # and of course, no error when all metadata are present
+        cmd = self._run(metadata, strict=True)
+        assert cmd._warnings == 0
+
+        # now a test with non-ASCII characters
+        metadata = {
+            'url': 'xxx',
+            'author': '\u00c9ric',
+            'author_email': 'xxx',
+            'name': 'xxx',
+            'version': 'xxx',
+            'description': 'Something about esszet \u00df',
+            'long_description': 'More things about esszet \u00df',
+        }
+        cmd = self._run(metadata)
+        assert cmd._warnings == 0
+
+    def test_check_author_maintainer(self):
+        for kind in ("author", "maintainer"):
+            # ensure no warning when author_email or maintainer_email is given
+            # (the spec allows these fields to take the form "Name <email>")
+            metadata = {
+                'url': 'xxx',
+                kind + '_email': 'Name <name@email.com>',
+                'name': 'xxx',
+                'version': 'xxx',
+            }
+            cmd = self._run(metadata)
+            assert cmd._warnings == 0
+
+            # the check should not warn if only email is given
+            metadata[kind + '_email'] = 'name@email.com'
+            cmd = self._run(metadata)
+            assert cmd._warnings == 0
+
+            # the check should not warn if only the name is given
+            metadata[kind] = "Name"
+            del metadata[kind + '_email']
+            cmd = self._run(metadata)
+            assert cmd._warnings == 0
+
+    def test_check_document(self):
+        pytest.importorskip('docutils')
+        pkg_info, dist = self.create_dist()
+        cmd = check(dist)
+
+        # let's see if it detects broken rest
+        broken_rest = 'title\n===\n\ntest'
+        msgs = cmd._check_rst_data(broken_rest)
+        assert len(msgs) == 1
+
+        # and non-broken rest
+        rest = 'title\n=====\n\ntest'
+        msgs = cmd._check_rst_data(rest)
+        assert len(msgs) == 0
+
+    def test_check_restructuredtext(self):
+        pytest.importorskip('docutils')
+        # let's see if it detects broken rest in long_description
+        broken_rest = 'title\n===\n\ntest'
+        pkg_info, dist = self.create_dist(long_description=broken_rest)
+        cmd = check(dist)
+        cmd.check_restructuredtext()
+        assert cmd._warnings == 1
+
+        # let's see if we have an error with strict=True
+        metadata = {
+            'url': 'xxx',
+            'author': 'xxx',
+            'author_email': 'xxx',
+            'name': 'xxx',
+            'version': 'xxx',
+            'long_description': broken_rest,
+        }
+        with pytest.raises(DistutilsSetupError):
+            self._run(metadata, **{'strict': 1, 'restructuredtext': 1})
+
+        # and non-broken rest, including a non-ASCII character to test #12114
+        metadata['long_description'] = 'title\n=====\n\ntest \u00df'
+        cmd = self._run(metadata, strict=True, restructuredtext=True)
+        assert cmd._warnings == 0
+
+        # check that includes work to test #31292
+        metadata['long_description'] = 'title\n=====\n\n.. include:: includetest.rst'
+        cmd = self._run(metadata, cwd=HERE, strict=True, restructuredtext=True)
+        assert cmd._warnings == 0
+
+    def test_check_restructuredtext_with_syntax_highlight(self):
+        pytest.importorskip('docutils')
+        # Don't fail if there is a `code` or `code-block` directive
+
+        example_rst_docs = [
+            textwrap.dedent(
+                """\
+            Here's some code:
+
+            .. code:: python
+
+                def foo():
+                    pass
+            """
+            ),
+            textwrap.dedent(
+                """\
+            Here's some code:
+
+            .. code-block:: python
+
+                def foo():
+                    pass
+            """
+            ),
+        ]
+
+        for rest_with_code in example_rst_docs:
+            pkg_info, dist = self.create_dist(long_description=rest_with_code)
+            cmd = check(dist)
+            cmd.check_restructuredtext()
+            msgs = cmd._check_rst_data(rest_with_code)
+            if pygments is not None:
+                assert len(msgs) == 0
+            else:
+                assert len(msgs) == 1
+                assert (
+                    str(msgs[0][1])
+                    == 'Cannot analyze code. Pygments package not found.'
+                )
+
+    def test_check_all(self):
+        with pytest.raises(DistutilsSetupError):
+            self._run({}, **{'strict': 1, 'restructuredtext': 1})
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_clean.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_clean.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc78f30f34b323845b977b9147c2153390774bd3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_clean.py
@@ -0,0 +1,45 @@
+"""Tests for distutils.command.clean."""
+
+import os
+from distutils.command.clean import clean
+from distutils.tests import support
+
+
+class TestClean(support.TempdirManager):
+    def test_simple_run(self):
+        pkg_dir, dist = self.create_dist()
+        cmd = clean(dist)
+
+        # let's add some elements clean should remove
+        dirs = [
+            (d, os.path.join(pkg_dir, d))
+            for d in (
+                'build_temp',
+                'build_lib',
+                'bdist_base',
+                'build_scripts',
+                'build_base',
+            )
+        ]
+
+        for name, path in dirs:
+            os.mkdir(path)
+            setattr(cmd, name, path)
+            if name == 'build_base':
+                continue
+            for f in ('one', 'two', 'three'):
+                self.write_file(os.path.join(path, f))
+
+        # let's run the command
+        cmd.all = 1
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # make sure the files where removed
+        for _name, path in dirs:
+            assert not os.path.exists(path), f'{path} was not removed'
+
+        # let's run the command again (should spit warnings but succeed)
+        cmd.all = 1
+        cmd.ensure_finalized()
+        cmd.run()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_cmd.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_cmd.py
new file mode 100644
index 0000000000000000000000000000000000000000..76e8f5989b3b102a32239436ff3b65f26e09e029
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_cmd.py
@@ -0,0 +1,107 @@
+"""Tests for distutils.cmd."""
+
+import os
+from distutils import debug
+from distutils.cmd import Command
+from distutils.dist import Distribution
+from distutils.errors import DistutilsOptionError
+
+import pytest
+
+
+class MyCmd(Command):
+    def initialize_options(self):
+        pass
+
+
+@pytest.fixture
+def cmd(request):
+    return MyCmd(Distribution())
+
+
+class TestCommand:
+    def test_ensure_string_list(self, cmd):
+        cmd.not_string_list = ['one', 2, 'three']
+        cmd.yes_string_list = ['one', 'two', 'three']
+        cmd.not_string_list2 = object()
+        cmd.yes_string_list2 = 'ok'
+        cmd.ensure_string_list('yes_string_list')
+        cmd.ensure_string_list('yes_string_list2')
+
+        with pytest.raises(DistutilsOptionError):
+            cmd.ensure_string_list('not_string_list')
+
+        with pytest.raises(DistutilsOptionError):
+            cmd.ensure_string_list('not_string_list2')
+
+        cmd.option1 = 'ok,dok'
+        cmd.ensure_string_list('option1')
+        assert cmd.option1 == ['ok', 'dok']
+
+        cmd.option2 = ['xxx', 'www']
+        cmd.ensure_string_list('option2')
+
+        cmd.option3 = ['ok', 2]
+        with pytest.raises(DistutilsOptionError):
+            cmd.ensure_string_list('option3')
+
+    def test_make_file(self, cmd):
+        # making sure it raises when infiles is not a string or a list/tuple
+        with pytest.raises(TypeError):
+            cmd.make_file(infiles=True, outfile='', func='func', args=())
+
+        # making sure execute gets called properly
+        def _execute(func, args, exec_msg, level):
+            assert exec_msg == 'generating out from in'
+
+        cmd.force = True
+        cmd.execute = _execute
+        cmd.make_file(infiles='in', outfile='out', func='func', args=())
+
+    def test_dump_options(self, cmd):
+        msgs = []
+
+        def _announce(msg, level):
+            msgs.append(msg)
+
+        cmd.announce = _announce
+        cmd.option1 = 1
+        cmd.option2 = 1
+        cmd.user_options = [('option1', '', ''), ('option2', '', '')]
+        cmd.dump_options()
+
+        wanted = ["command options for 'MyCmd':", '  option1 = 1', '  option2 = 1']
+        assert msgs == wanted
+
+    def test_ensure_string(self, cmd):
+        cmd.option1 = 'ok'
+        cmd.ensure_string('option1')
+
+        cmd.option2 = None
+        cmd.ensure_string('option2', 'xxx')
+        assert hasattr(cmd, 'option2')
+
+        cmd.option3 = 1
+        with pytest.raises(DistutilsOptionError):
+            cmd.ensure_string('option3')
+
+    def test_ensure_filename(self, cmd):
+        cmd.option1 = __file__
+        cmd.ensure_filename('option1')
+        cmd.option2 = 'xxx'
+        with pytest.raises(DistutilsOptionError):
+            cmd.ensure_filename('option2')
+
+    def test_ensure_dirname(self, cmd):
+        cmd.option1 = os.path.dirname(__file__) or os.curdir
+        cmd.ensure_dirname('option1')
+        cmd.option2 = 'xxx'
+        with pytest.raises(DistutilsOptionError):
+            cmd.ensure_dirname('option2')
+
+    def test_debug_print(self, cmd, capsys, monkeypatch):
+        cmd.debug_print('xxx')
+        assert capsys.readouterr().out == ''
+        monkeypatch.setattr(debug, 'DEBUG', True)
+        cmd.debug_print('xxx')
+        assert capsys.readouterr().out == 'xxx\n'
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_config_cmd.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_config_cmd.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebee2ef93f0bc9d422984e2e4872665b1be3a1de
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_config_cmd.py
@@ -0,0 +1,87 @@
+"""Tests for distutils.command.config."""
+
+import os
+import sys
+from distutils._log import log
+from distutils.command.config import config, dump_file
+from distutils.tests import missing_compiler_executable, support
+
+import more_itertools
+import path
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def info_log(request, monkeypatch):
+    self = request.instance
+    self._logs = []
+    monkeypatch.setattr(log, 'info', self._info)
+
+
+@support.combine_markers
+class TestConfig(support.TempdirManager):
+    def _info(self, msg, *args):
+        for line in msg.splitlines():
+            self._logs.append(line)
+
+    def test_dump_file(self):
+        this_file = path.Path(__file__).with_suffix('.py')
+        with this_file.open(encoding='utf-8') as f:
+            numlines = more_itertools.ilen(f)
+
+        dump_file(this_file, 'I am the header')
+        assert len(self._logs) == numlines + 1
+
+    @pytest.mark.skipif('platform.system() == "Windows"')
+    def test_search_cpp(self):
+        cmd = missing_compiler_executable(['preprocessor'])
+        if cmd is not None:
+            self.skipTest(f'The {cmd!r} command is not found')
+        pkg_dir, dist = self.create_dist()
+        cmd = config(dist)
+        cmd._check_compiler()
+        compiler = cmd.compiler
+        if sys.platform[:3] == "aix" and "xlc" in compiler.preprocessor[0].lower():
+            self.skipTest(
+                'xlc: The -E option overrides the -P, -o, and -qsyntaxonly options'
+            )
+
+        # simple pattern searches
+        match = cmd.search_cpp(pattern='xxx', body='/* xxx */')
+        assert match == 0
+
+        match = cmd.search_cpp(pattern='_configtest', body='/* xxx */')
+        assert match == 1
+
+    def test_finalize_options(self):
+        # finalize_options does a bit of transformation
+        # on options
+        pkg_dir, dist = self.create_dist()
+        cmd = config(dist)
+        cmd.include_dirs = f'one{os.pathsep}two'
+        cmd.libraries = 'one'
+        cmd.library_dirs = f'three{os.pathsep}four'
+        cmd.ensure_finalized()
+
+        assert cmd.include_dirs == ['one', 'two']
+        assert cmd.libraries == ['one']
+        assert cmd.library_dirs == ['three', 'four']
+
+    def test_clean(self):
+        # _clean removes files
+        tmp_dir = self.mkdtemp()
+        f1 = os.path.join(tmp_dir, 'one')
+        f2 = os.path.join(tmp_dir, 'two')
+
+        self.write_file(f1, 'xxx')
+        self.write_file(f2, 'xxx')
+
+        for f in (f1, f2):
+            assert os.path.exists(f)
+
+        pkg_dir, dist = self.create_dist()
+        cmd = config(dist)
+        cmd._clean(f1, f2)
+
+        for f in (f1, f2):
+            assert not os.path.exists(f)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_core.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_core.py
new file mode 100644
index 0000000000000000000000000000000000000000..bad3fb7e8318d3ee73ecabe20ed98c279f606a77
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_core.py
@@ -0,0 +1,130 @@
+"""Tests for distutils.core."""
+
+import distutils.core
+import io
+import os
+import sys
+from distutils.dist import Distribution
+
+import pytest
+
+# setup script that uses __file__
+setup_using___file__ = """\
+
+__file__
+
+from distutils.core import setup
+setup()
+"""
+
+setup_prints_cwd = """\
+
+import os
+print(os.getcwd())
+
+from distutils.core import setup
+setup()
+"""
+
+setup_does_nothing = """\
+from distutils.core import setup
+setup()
+"""
+
+
+setup_defines_subclass = """\
+from distutils.core import setup
+from distutils.command.install import install as _install
+
+class install(_install):
+    sub_commands = _install.sub_commands + ['cmd']
+
+setup(cmdclass={'install': install})
+"""
+
+setup_within_if_main = """\
+from distutils.core import setup
+
+def main():
+    return setup(name="setup_within_if_main")
+
+if __name__ == "__main__":
+    main()
+"""
+
+
+@pytest.fixture(autouse=True)
+def save_stdout(monkeypatch):
+    monkeypatch.setattr(sys, 'stdout', sys.stdout)
+
+
+@pytest.fixture
+def temp_file(tmp_path):
+    return tmp_path / 'file'
+
+
+@pytest.mark.usefixtures('save_env')
+@pytest.mark.usefixtures('save_argv')
+class TestCore:
+    def test_run_setup_provides_file(self, temp_file):
+        # Make sure the script can use __file__; if that's missing, the test
+        # setup.py script will raise NameError.
+        temp_file.write_text(setup_using___file__, encoding='utf-8')
+        distutils.core.run_setup(temp_file)
+
+    def test_run_setup_preserves_sys_argv(self, temp_file):
+        # Make sure run_setup does not clobber sys.argv
+        argv_copy = sys.argv.copy()
+        temp_file.write_text(setup_does_nothing, encoding='utf-8')
+        distutils.core.run_setup(temp_file)
+        assert sys.argv == argv_copy
+
+    def test_run_setup_defines_subclass(self, temp_file):
+        # Make sure the script can use __file__; if that's missing, the test
+        # setup.py script will raise NameError.
+        temp_file.write_text(setup_defines_subclass, encoding='utf-8')
+        dist = distutils.core.run_setup(temp_file)
+        install = dist.get_command_obj('install')
+        assert 'cmd' in install.sub_commands
+
+    def test_run_setup_uses_current_dir(self, tmp_path):
+        """
+        Test that the setup script is run with the current directory
+        as its own current directory.
+        """
+        sys.stdout = io.StringIO()
+        cwd = os.getcwd()
+
+        # Create a directory and write the setup.py file there:
+        setup_py = tmp_path / 'setup.py'
+        setup_py.write_text(setup_prints_cwd, encoding='utf-8')
+        distutils.core.run_setup(setup_py)
+
+        output = sys.stdout.getvalue()
+        if output.endswith("\n"):
+            output = output[:-1]
+        assert cwd == output
+
+    def test_run_setup_within_if_main(self, temp_file):
+        temp_file.write_text(setup_within_if_main, encoding='utf-8')
+        dist = distutils.core.run_setup(temp_file, stop_after="config")
+        assert isinstance(dist, Distribution)
+        assert dist.get_name() == "setup_within_if_main"
+
+    def test_run_commands(self, temp_file):
+        sys.argv = ['setup.py', 'build']
+        temp_file.write_text(setup_within_if_main, encoding='utf-8')
+        dist = distutils.core.run_setup(temp_file, stop_after="commandline")
+        assert 'build' not in dist.have_run
+        distutils.core.run_commands(dist)
+        assert 'build' in dist.have_run
+
+    def test_debug_mode(self, capsys, monkeypatch):
+        # this covers the code called when DEBUG is set
+        sys.argv = ['setup.py', '--name']
+        distutils.core.setup(name='bar')
+        assert capsys.readouterr().out == 'bar\n'
+        monkeypatch.setattr(distutils.core, 'DEBUG', True)
+        distutils.core.setup(name='bar')
+        wanted = "options (after parsing config files):\n"
+        assert capsys.readouterr().out.startswith(wanted)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_dir_util.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_dir_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..326cb346145ec7e3a1dd0384c33d4125131feaae
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_dir_util.py
@@ -0,0 +1,139 @@
+"""Tests for distutils.dir_util."""
+
+import os
+import pathlib
+import stat
+import sys
+import unittest.mock as mock
+from distutils import dir_util, errors
+from distutils.dir_util import (
+    copy_tree,
+    create_tree,
+    ensure_relative,
+    mkpath,
+    remove_tree,
+)
+from distutils.tests import support
+
+import jaraco.path
+import path
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def stuff(request, monkeypatch, distutils_managed_tempdir):
+    self = request.instance
+    tmp_dir = self.mkdtemp()
+    self.root_target = os.path.join(tmp_dir, 'deep')
+    self.target = os.path.join(self.root_target, 'here')
+    self.target2 = os.path.join(tmp_dir, 'deep2')
+
+
+class TestDirUtil(support.TempdirManager):
+    def test_mkpath_remove_tree_verbosity(self, caplog):
+        mkpath(self.target, verbose=False)
+        assert not caplog.records
+        remove_tree(self.root_target, verbose=False)
+
+        mkpath(self.target, verbose=True)
+        wanted = [f'creating {self.target}']
+        assert caplog.messages == wanted
+        caplog.clear()
+
+        remove_tree(self.root_target, verbose=True)
+        wanted = [f"removing '{self.root_target}' (and everything under it)"]
+        assert caplog.messages == wanted
+
+    @pytest.mark.skipif("platform.system() == 'Windows'")
+    def test_mkpath_with_custom_mode(self):
+        # Get and set the current umask value for testing mode bits.
+        umask = os.umask(0o002)
+        os.umask(umask)
+        mkpath(self.target, 0o700)
+        assert stat.S_IMODE(os.stat(self.target).st_mode) == 0o700 & ~umask
+        mkpath(self.target2, 0o555)
+        assert stat.S_IMODE(os.stat(self.target2).st_mode) == 0o555 & ~umask
+
+    def test_create_tree_verbosity(self, caplog):
+        create_tree(self.root_target, ['one', 'two', 'three'], verbose=False)
+        assert caplog.messages == []
+        remove_tree(self.root_target, verbose=False)
+
+        wanted = [f'creating {self.root_target}']
+        create_tree(self.root_target, ['one', 'two', 'three'], verbose=True)
+        assert caplog.messages == wanted
+
+        remove_tree(self.root_target, verbose=False)
+
+    def test_copy_tree_verbosity(self, caplog):
+        mkpath(self.target, verbose=False)
+
+        copy_tree(self.target, self.target2, verbose=False)
+        assert caplog.messages == []
+
+        remove_tree(self.root_target, verbose=False)
+
+        mkpath(self.target, verbose=False)
+        a_file = path.Path(self.target) / 'ok.txt'
+        jaraco.path.build({'ok.txt': 'some content'}, self.target)
+
+        wanted = [f'copying {a_file} -> {self.target2}']
+        copy_tree(self.target, self.target2, verbose=True)
+        assert caplog.messages == wanted
+
+        remove_tree(self.root_target, verbose=False)
+        remove_tree(self.target2, verbose=False)
+
+    def test_copy_tree_skips_nfs_temp_files(self):
+        mkpath(self.target, verbose=False)
+
+        jaraco.path.build({'ok.txt': 'some content', '.nfs123abc': ''}, self.target)
+
+        copy_tree(self.target, self.target2)
+        assert os.listdir(self.target2) == ['ok.txt']
+
+        remove_tree(self.root_target, verbose=False)
+        remove_tree(self.target2, verbose=False)
+
+    def test_ensure_relative(self):
+        if os.sep == '/':
+            assert ensure_relative('/home/foo') == 'home/foo'
+            assert ensure_relative('some/path') == 'some/path'
+        else:  # \\
+            assert ensure_relative('c:\\home\\foo') == 'c:home\\foo'
+            assert ensure_relative('home\\foo') == 'home\\foo'
+
+    def test_copy_tree_exception_in_listdir(self):
+        """
+        An exception in listdir should raise a DistutilsFileError
+        """
+        with (
+            mock.patch("os.listdir", side_effect=OSError()),
+            pytest.raises(errors.DistutilsFileError),
+        ):
+            src = self.tempdirs[-1]
+            dir_util.copy_tree(src, None)
+
+    def test_mkpath_exception_uncached(self, monkeypatch, tmp_path):
+        """
+        Caching should not remember failed attempts.
+
+        pypa/distutils#304
+        """
+
+        class FailPath(pathlib.Path):
+            def mkdir(self, *args, **kwargs):
+                raise OSError("Failed to create directory")
+
+            if sys.version_info < (3, 12):
+                _flavour = pathlib.Path()._flavour
+
+        target = tmp_path / 'foodir'
+
+        with pytest.raises(errors.DistutilsFileError):
+            mkpath(FailPath(target))
+
+        assert not target.exists()
+
+        mkpath(target)
+        assert target.exists()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_dist.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_dist.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c5beebe64eb9d747088cf117082a546330b29f3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_dist.py
@@ -0,0 +1,552 @@
+"""Tests for distutils.dist."""
+
+import email
+import email.generator
+import email.policy
+import functools
+import io
+import os
+import sys
+import textwrap
+import unittest.mock as mock
+import warnings
+from distutils.cmd import Command
+from distutils.dist import Distribution, fix_help_options
+from distutils.tests import support
+from typing import ClassVar
+
+import jaraco.path
+import pytest
+
+pydistutils_cfg = '.' * (os.name == 'posix') + 'pydistutils.cfg'
+
+
+class test_dist(Command):
+    """Sample distutils extension command."""
+
+    user_options: ClassVar[list[tuple[str, str, str]]] = [
+        ("sample-option=", "S", "help text"),
+    ]
+
+    def initialize_options(self):
+        self.sample_option = None
+
+
+class TestDistribution(Distribution):
+    """Distribution subclasses that avoids the default search for
+    configuration files.
+
+    The ._config_files attribute must be set before
+    .parse_config_files() is called.
+    """
+
+    def find_config_files(self):
+        return self._config_files
+
+
+@pytest.fixture
+def clear_argv():
+    del sys.argv[1:]
+
+
+@support.combine_markers
+@pytest.mark.usefixtures('save_env')
+@pytest.mark.usefixtures('save_argv')
+class TestDistributionBehavior(support.TempdirManager):
+    def create_distribution(self, configfiles=()):
+        d = TestDistribution()
+        d._config_files = configfiles
+        d.parse_config_files()
+        d.parse_command_line()
+        return d
+
+    def test_command_packages_unspecified(self, clear_argv):
+        sys.argv.append("build")
+        d = self.create_distribution()
+        assert d.get_command_packages() == ["distutils.command"]
+
+    def test_command_packages_cmdline(self, clear_argv):
+        from distutils.tests.test_dist import test_dist
+
+        sys.argv.extend([
+            "--command-packages",
+            "foo.bar,distutils.tests",
+            "test_dist",
+            "-Ssometext",
+        ])
+        d = self.create_distribution()
+        # let's actually try to load our test command:
+        assert d.get_command_packages() == [
+            "distutils.command",
+            "foo.bar",
+            "distutils.tests",
+        ]
+        cmd = d.get_command_obj("test_dist")
+        assert isinstance(cmd, test_dist)
+        assert cmd.sample_option == "sometext"
+
+    @pytest.mark.skipif(
+        'distutils' not in Distribution.parse_config_files.__module__,
+        reason='Cannot test when virtualenv has monkey-patched Distribution',
+    )
+    def test_venv_install_options(self, tmp_path, clear_argv):
+        sys.argv.append("install")
+        file = str(tmp_path / 'file')
+
+        fakepath = '/somedir'
+
+        jaraco.path.build({
+            file: f"""
+                    [install]
+                    install-base = {fakepath}
+                    install-platbase = {fakepath}
+                    install-lib = {fakepath}
+                    install-platlib = {fakepath}
+                    install-purelib = {fakepath}
+                    install-headers = {fakepath}
+                    install-scripts = {fakepath}
+                    install-data = {fakepath}
+                    prefix = {fakepath}
+                    exec-prefix = {fakepath}
+                    home = {fakepath}
+                    user = {fakepath}
+                    root = {fakepath}
+                    """,
+        })
+
+        # Base case: Not in a Virtual Environment
+        with mock.patch.multiple(sys, prefix='/a', base_prefix='/a'):
+            d = self.create_distribution([file])
+
+        option_tuple = (file, fakepath)
+
+        result_dict = {
+            'install_base': option_tuple,
+            'install_platbase': option_tuple,
+            'install_lib': option_tuple,
+            'install_platlib': option_tuple,
+            'install_purelib': option_tuple,
+            'install_headers': option_tuple,
+            'install_scripts': option_tuple,
+            'install_data': option_tuple,
+            'prefix': option_tuple,
+            'exec_prefix': option_tuple,
+            'home': option_tuple,
+            'user': option_tuple,
+            'root': option_tuple,
+        }
+
+        assert sorted(d.command_options.get('install').keys()) == sorted(
+            result_dict.keys()
+        )
+
+        for key, value in d.command_options.get('install').items():
+            assert value == result_dict[key]
+
+        # Test case: In a Virtual Environment
+        with mock.patch.multiple(sys, prefix='/a', base_prefix='/b'):
+            d = self.create_distribution([file])
+
+        for key in result_dict.keys():
+            assert key not in d.command_options.get('install', {})
+
+    def test_command_packages_configfile(self, tmp_path, clear_argv):
+        sys.argv.append("build")
+        file = str(tmp_path / "file")
+        jaraco.path.build({
+            file: """
+                    [global]
+                    command_packages = foo.bar, splat
+                    """,
+        })
+
+        d = self.create_distribution([file])
+        assert d.get_command_packages() == ["distutils.command", "foo.bar", "splat"]
+
+        # ensure command line overrides config:
+        sys.argv[1:] = ["--command-packages", "spork", "build"]
+        d = self.create_distribution([file])
+        assert d.get_command_packages() == ["distutils.command", "spork"]
+
+        # Setting --command-packages to '' should cause the default to
+        # be used even if a config file specified something else:
+        sys.argv[1:] = ["--command-packages", "", "build"]
+        d = self.create_distribution([file])
+        assert d.get_command_packages() == ["distutils.command"]
+
+    def test_empty_options(self, request):
+        # an empty options dictionary should not stay in the
+        # list of attributes
+
+        # catching warnings
+        warns = []
+
+        def _warn(msg):
+            warns.append(msg)
+
+        request.addfinalizer(
+            functools.partial(setattr, warnings, 'warn', warnings.warn)
+        )
+        warnings.warn = _warn
+        dist = Distribution(
+            attrs={
+                'author': 'xxx',
+                'name': 'xxx',
+                'version': 'xxx',
+                'url': 'xxxx',
+                'options': {},
+            }
+        )
+
+        assert len(warns) == 0
+        assert 'options' not in dir(dist)
+
+    def test_finalize_options(self):
+        attrs = {'keywords': 'one,two', 'platforms': 'one,two'}
+
+        dist = Distribution(attrs=attrs)
+        dist.finalize_options()
+
+        # finalize_option splits platforms and keywords
+        assert dist.metadata.platforms == ['one', 'two']
+        assert dist.metadata.keywords == ['one', 'two']
+
+        attrs = {'keywords': 'foo bar', 'platforms': 'foo bar'}
+        dist = Distribution(attrs=attrs)
+        dist.finalize_options()
+        assert dist.metadata.platforms == ['foo bar']
+        assert dist.metadata.keywords == ['foo bar']
+
+    def test_get_command_packages(self):
+        dist = Distribution()
+        assert dist.command_packages is None
+        cmds = dist.get_command_packages()
+        assert cmds == ['distutils.command']
+        assert dist.command_packages == ['distutils.command']
+
+        dist.command_packages = 'one,two'
+        cmds = dist.get_command_packages()
+        assert cmds == ['distutils.command', 'one', 'two']
+
+    def test_announce(self):
+        # make sure the level is known
+        dist = Distribution()
+        with pytest.raises(TypeError):
+            dist.announce('ok', level='ok2')
+
+    def test_find_config_files_disable(self, temp_home):
+        # Ticket #1180: Allow user to disable their home config file.
+        jaraco.path.build({pydistutils_cfg: '[distutils]\n'}, temp_home)
+
+        d = Distribution()
+        all_files = d.find_config_files()
+
+        d = Distribution(attrs={'script_args': ['--no-user-cfg']})
+        files = d.find_config_files()
+
+        # make sure --no-user-cfg disables the user cfg file
+        assert len(all_files) - 1 == len(files)
+
+    def test_script_args_list_coercion(self):
+        d = Distribution(attrs={'script_args': ('build', '--no-user-cfg')})
+
+        # make sure script_args is a list even if it started as a different iterable
+        assert d.script_args == ['build', '--no-user-cfg']
+
+    @pytest.mark.skipif(
+        'platform.system() == "Windows"',
+        reason='Windows does not honor chmod 000',
+    )
+    def test_find_config_files_permission_error(self, fake_home):
+        """
+        Finding config files should not fail when directory is inaccessible.
+        """
+        fake_home.joinpath(pydistutils_cfg).write_text('', encoding='utf-8')
+        fake_home.chmod(0o000)
+        Distribution().find_config_files()
+
+
+@pytest.mark.usefixtures('save_env')
+@pytest.mark.usefixtures('save_argv')
+class TestMetadata(support.TempdirManager):
+    def format_metadata(self, dist):
+        sio = io.StringIO()
+        dist.metadata.write_pkg_file(sio)
+        return sio.getvalue()
+
+    def test_simple_metadata(self):
+        attrs = {"name": "package", "version": "1.0"}
+        dist = Distribution(attrs)
+        meta = self.format_metadata(dist)
+        assert "Metadata-Version: 1.0" in meta
+        assert "provides:" not in meta.lower()
+        assert "requires:" not in meta.lower()
+        assert "obsoletes:" not in meta.lower()
+
+    def test_provides(self):
+        attrs = {
+            "name": "package",
+            "version": "1.0",
+            "provides": ["package", "package.sub"],
+        }
+        dist = Distribution(attrs)
+        assert dist.metadata.get_provides() == ["package", "package.sub"]
+        assert dist.get_provides() == ["package", "package.sub"]
+        meta = self.format_metadata(dist)
+        assert "Metadata-Version: 1.1" in meta
+        assert "requires:" not in meta.lower()
+        assert "obsoletes:" not in meta.lower()
+
+    def test_provides_illegal(self):
+        with pytest.raises(ValueError):
+            Distribution(
+                {"name": "package", "version": "1.0", "provides": ["my.pkg (splat)"]},
+            )
+
+    def test_requires(self):
+        attrs = {
+            "name": "package",
+            "version": "1.0",
+            "requires": ["other", "another (==1.0)"],
+        }
+        dist = Distribution(attrs)
+        assert dist.metadata.get_requires() == ["other", "another (==1.0)"]
+        assert dist.get_requires() == ["other", "another (==1.0)"]
+        meta = self.format_metadata(dist)
+        assert "Metadata-Version: 1.1" in meta
+        assert "provides:" not in meta.lower()
+        assert "Requires: other" in meta
+        assert "Requires: another (==1.0)" in meta
+        assert "obsoletes:" not in meta.lower()
+
+    def test_requires_illegal(self):
+        with pytest.raises(ValueError):
+            Distribution(
+                {"name": "package", "version": "1.0", "requires": ["my.pkg (splat)"]},
+            )
+
+    def test_requires_to_list(self):
+        attrs = {"name": "package", "requires": iter(["other"])}
+        dist = Distribution(attrs)
+        assert isinstance(dist.metadata.requires, list)
+
+    def test_obsoletes(self):
+        attrs = {
+            "name": "package",
+            "version": "1.0",
+            "obsoletes": ["other", "another (<1.0)"],
+        }
+        dist = Distribution(attrs)
+        assert dist.metadata.get_obsoletes() == ["other", "another (<1.0)"]
+        assert dist.get_obsoletes() == ["other", "another (<1.0)"]
+        meta = self.format_metadata(dist)
+        assert "Metadata-Version: 1.1" in meta
+        assert "provides:" not in meta.lower()
+        assert "requires:" not in meta.lower()
+        assert "Obsoletes: other" in meta
+        assert "Obsoletes: another (<1.0)" in meta
+
+    def test_obsoletes_illegal(self):
+        with pytest.raises(ValueError):
+            Distribution(
+                {"name": "package", "version": "1.0", "obsoletes": ["my.pkg (splat)"]},
+            )
+
+    def test_obsoletes_to_list(self):
+        attrs = {"name": "package", "obsoletes": iter(["other"])}
+        dist = Distribution(attrs)
+        assert isinstance(dist.metadata.obsoletes, list)
+
+    def test_classifier(self):
+        attrs = {
+            'name': 'Boa',
+            'version': '3.0',
+            'classifiers': ['Programming Language :: Python :: 3'],
+        }
+        dist = Distribution(attrs)
+        assert dist.get_classifiers() == ['Programming Language :: Python :: 3']
+        meta = self.format_metadata(dist)
+        assert 'Metadata-Version: 1.1' in meta
+
+    def test_classifier_invalid_type(self, caplog):
+        attrs = {
+            'name': 'Boa',
+            'version': '3.0',
+            'classifiers': ('Programming Language :: Python :: 3',),
+        }
+        d = Distribution(attrs)
+        # should have warning about passing a non-list
+        assert 'should be a list' in caplog.messages[0]
+        # should be converted to a list
+        assert isinstance(d.metadata.classifiers, list)
+        assert d.metadata.classifiers == list(attrs['classifiers'])
+
+    def test_keywords(self):
+        attrs = {
+            'name': 'Monty',
+            'version': '1.0',
+            'keywords': ['spam', 'eggs', 'life of brian'],
+        }
+        dist = Distribution(attrs)
+        assert dist.get_keywords() == ['spam', 'eggs', 'life of brian']
+
+    def test_keywords_invalid_type(self, caplog):
+        attrs = {
+            'name': 'Monty',
+            'version': '1.0',
+            'keywords': ('spam', 'eggs', 'life of brian'),
+        }
+        d = Distribution(attrs)
+        # should have warning about passing a non-list
+        assert 'should be a list' in caplog.messages[0]
+        # should be converted to a list
+        assert isinstance(d.metadata.keywords, list)
+        assert d.metadata.keywords == list(attrs['keywords'])
+
+    def test_platforms(self):
+        attrs = {
+            'name': 'Monty',
+            'version': '1.0',
+            'platforms': ['GNU/Linux', 'Some Evil Platform'],
+        }
+        dist = Distribution(attrs)
+        assert dist.get_platforms() == ['GNU/Linux', 'Some Evil Platform']
+
+    def test_platforms_invalid_types(self, caplog):
+        attrs = {
+            'name': 'Monty',
+            'version': '1.0',
+            'platforms': ('GNU/Linux', 'Some Evil Platform'),
+        }
+        d = Distribution(attrs)
+        # should have warning about passing a non-list
+        assert 'should be a list' in caplog.messages[0]
+        # should be converted to a list
+        assert isinstance(d.metadata.platforms, list)
+        assert d.metadata.platforms == list(attrs['platforms'])
+
+    def test_download_url(self):
+        attrs = {
+            'name': 'Boa',
+            'version': '3.0',
+            'download_url': 'http://example.org/boa',
+        }
+        dist = Distribution(attrs)
+        meta = self.format_metadata(dist)
+        assert 'Metadata-Version: 1.1' in meta
+
+    def test_long_description(self):
+        long_desc = textwrap.dedent(
+            """\
+        example::
+              We start here
+            and continue here
+          and end here."""
+        )
+        attrs = {"name": "package", "version": "1.0", "long_description": long_desc}
+
+        dist = Distribution(attrs)
+        meta = self.format_metadata(dist)
+        meta = meta.replace('\n' + 8 * ' ', '\n')
+        assert long_desc in meta
+
+    def test_custom_pydistutils(self, temp_home):
+        """
+        pydistutils.cfg is found
+        """
+        jaraco.path.build({pydistutils_cfg: ''}, temp_home)
+        config_path = temp_home / pydistutils_cfg
+
+        assert str(config_path) in Distribution().find_config_files()
+
+    def test_extra_pydistutils(self, monkeypatch, tmp_path):
+        jaraco.path.build({'overrides.cfg': ''}, tmp_path)
+        filename = tmp_path / 'overrides.cfg'
+        monkeypatch.setenv('DIST_EXTRA_CONFIG', str(filename))
+        assert str(filename) in Distribution().find_config_files()
+
+    def test_fix_help_options(self):
+        help_tuples = [('a', 'b', 'c', 'd'), (1, 2, 3, 4)]
+        fancy_options = fix_help_options(help_tuples)
+        assert fancy_options[0] == ('a', 'b', 'c')
+        assert fancy_options[1] == (1, 2, 3)
+
+    def test_show_help(self, request, capsys):
+        # smoke test, just makes sure some help is displayed
+        dist = Distribution()
+        sys.argv = []
+        dist.help = True
+        dist.script_name = 'setup.py'
+        dist.parse_command_line()
+
+        output = [
+            line for line in capsys.readouterr().out.split('\n') if line.strip() != ''
+        ]
+        assert output
+
+    def test_read_metadata(self):
+        attrs = {
+            "name": "package",
+            "version": "1.0",
+            "long_description": "desc",
+            "description": "xxx",
+            "download_url": "http://example.com",
+            "keywords": ['one', 'two'],
+            "requires": ['foo'],
+        }
+
+        dist = Distribution(attrs)
+        metadata = dist.metadata
+
+        # write it then reloads it
+        PKG_INFO = io.StringIO()
+        metadata.write_pkg_file(PKG_INFO)
+        PKG_INFO.seek(0)
+        metadata.read_pkg_file(PKG_INFO)
+
+        assert metadata.name == "package"
+        assert metadata.version == "1.0"
+        assert metadata.description == "xxx"
+        assert metadata.download_url == 'http://example.com'
+        assert metadata.keywords == ['one', 'two']
+        assert metadata.platforms is None
+        assert metadata.obsoletes is None
+        assert metadata.requires == ['foo']
+
+    def test_round_trip_through_email_generator(self):
+        """
+        In pypa/setuptools#4033, it was shown that once PKG-INFO is
+        re-generated using ``email.generator.Generator``, some control
+        characters might cause problems.
+        """
+        # Given a PKG-INFO file ...
+        attrs = {
+            "name": "package",
+            "version": "1.0",
+            "long_description": "hello\x0b\nworld\n",
+        }
+        dist = Distribution(attrs)
+        metadata = dist.metadata
+
+        with io.StringIO() as buffer:
+            metadata.write_pkg_file(buffer)
+            msg = buffer.getvalue()
+
+        # ... when it is read and re-written using stdlib's email library,
+        orig = email.message_from_string(msg)
+        policy = email.policy.EmailPolicy(
+            utf8=True,
+            mangle_from_=False,
+            max_line_length=0,
+        )
+        with io.StringIO() as buffer:
+            email.generator.Generator(buffer, policy=policy).flatten(orig)
+
+            buffer.seek(0)
+            regen = email.message_from_file(buffer)
+
+        # ... then it should be the same as the original
+        # (except for the specific line break characters)
+        orig_desc = set(orig["Description"].splitlines())
+        regen_desc = set(regen["Description"].splitlines())
+        assert regen_desc == orig_desc
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_extension.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_extension.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e8e768223ea40d7a629111db181a946cf8ca5b0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_extension.py
@@ -0,0 +1,117 @@
+"""Tests for distutils.extension."""
+
+import os
+import pathlib
+import warnings
+from distutils.extension import Extension, read_setup_file
+
+import pytest
+from test.support.warnings_helper import check_warnings
+
+
+class TestExtension:
+    def test_read_setup_file(self):
+        # trying to read a Setup file
+        # (sample extracted from the PyGame project)
+        setup = os.path.join(os.path.dirname(__file__), 'Setup.sample')
+
+        exts = read_setup_file(setup)
+        names = [ext.name for ext in exts]
+        names.sort()
+
+        # here are the extensions read_setup_file should have created
+        # out of the file
+        wanted = [
+            '_arraysurfarray',
+            '_camera',
+            '_numericsndarray',
+            '_numericsurfarray',
+            'base',
+            'bufferproxy',
+            'cdrom',
+            'color',
+            'constants',
+            'display',
+            'draw',
+            'event',
+            'fastevent',
+            'font',
+            'gfxdraw',
+            'image',
+            'imageext',
+            'joystick',
+            'key',
+            'mask',
+            'mixer',
+            'mixer_music',
+            'mouse',
+            'movie',
+            'overlay',
+            'pixelarray',
+            'pypm',
+            'rect',
+            'rwobject',
+            'scrap',
+            'surface',
+            'surflock',
+            'time',
+            'transform',
+        ]
+
+        assert names == wanted
+
+    def test_extension_init(self):
+        # the first argument, which is the name, must be a string
+        with pytest.raises(TypeError):
+            Extension(1, [])
+        ext = Extension('name', [])
+        assert ext.name == 'name'
+
+        # the second argument, which is the list of files, must
+        # be an iterable of strings or PathLike objects, and not a string
+        with pytest.raises(TypeError):
+            Extension('name', 'file')
+        with pytest.raises(TypeError):
+            Extension('name', ['file', 1])
+        ext = Extension('name', ['file1', 'file2'])
+        assert ext.sources == ['file1', 'file2']
+        ext = Extension('name', [pathlib.Path('file1'), pathlib.Path('file2')])
+        assert ext.sources == ['file1', 'file2']
+
+        # any non-string iterable of strings or PathLike objects should work
+        ext = Extension('name', ('file1', 'file2'))  # tuple
+        assert ext.sources == ['file1', 'file2']
+        ext = Extension('name', {'file1', 'file2'})  # set
+        assert sorted(ext.sources) == ['file1', 'file2']
+        ext = Extension('name', iter(['file1', 'file2']))  # iterator
+        assert ext.sources == ['file1', 'file2']
+        ext = Extension('name', [pathlib.Path('file1'), 'file2'])  # mixed types
+        assert ext.sources == ['file1', 'file2']
+
+        # others arguments have defaults
+        for attr in (
+            'include_dirs',
+            'define_macros',
+            'undef_macros',
+            'library_dirs',
+            'libraries',
+            'runtime_library_dirs',
+            'extra_objects',
+            'extra_compile_args',
+            'extra_link_args',
+            'export_symbols',
+            'swig_opts',
+            'depends',
+        ):
+            assert getattr(ext, attr) == []
+
+        assert ext.language is None
+        assert ext.optional is None
+
+        # if there are unknown keyword options, warn about them
+        with check_warnings() as w:
+            warnings.simplefilter('always')
+            ext = Extension('name', ['file1', 'file2'], chic=True)
+
+        assert len(w.warnings) == 1
+        assert str(w.warnings[0].message) == "Unknown Extension options: 'chic'"
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_file_util.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_file_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..a75d4a0317dc717c24f67127370aa87089eeba1a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_file_util.py
@@ -0,0 +1,95 @@
+"""Tests for distutils.file_util."""
+
+import errno
+import os
+import unittest.mock as mock
+from distutils.errors import DistutilsFileError
+from distutils.file_util import copy_file, move_file
+
+import jaraco.path
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def stuff(request, tmp_path):
+    self = request.instance
+    self.source = tmp_path / 'f1'
+    self.target = tmp_path / 'f2'
+    self.target_dir = tmp_path / 'd1'
+
+
+class TestFileUtil:
+    def test_move_file_verbosity(self, caplog):
+        jaraco.path.build({self.source: 'some content'})
+
+        move_file(self.source, self.target, verbose=False)
+        assert not caplog.messages
+
+        # back to original state
+        move_file(self.target, self.source, verbose=False)
+
+        move_file(self.source, self.target, verbose=True)
+        wanted = [f'moving {self.source} -> {self.target}']
+        assert caplog.messages == wanted
+
+        # back to original state
+        move_file(self.target, self.source, verbose=False)
+
+        caplog.clear()
+        # now the target is a dir
+        os.mkdir(self.target_dir)
+        move_file(self.source, self.target_dir, verbose=True)
+        wanted = [f'moving {self.source} -> {self.target_dir}']
+        assert caplog.messages == wanted
+
+    def test_move_file_exception_unpacking_rename(self):
+        # see issue 22182
+        with (
+            mock.patch("os.rename", side_effect=OSError("wrong", 1)),
+            pytest.raises(DistutilsFileError),
+        ):
+            jaraco.path.build({self.source: 'spam eggs'})
+            move_file(self.source, self.target, verbose=False)
+
+    def test_move_file_exception_unpacking_unlink(self):
+        # see issue 22182
+        with (
+            mock.patch("os.rename", side_effect=OSError(errno.EXDEV, "wrong")),
+            mock.patch("os.unlink", side_effect=OSError("wrong", 1)),
+            pytest.raises(DistutilsFileError),
+        ):
+            jaraco.path.build({self.source: 'spam eggs'})
+            move_file(self.source, self.target, verbose=False)
+
+    def test_copy_file_hard_link(self):
+        jaraco.path.build({self.source: 'some content'})
+        # Check first that copy_file() will not fall back on copying the file
+        # instead of creating the hard link.
+        try:
+            os.link(self.source, self.target)
+        except OSError as e:
+            self.skipTest(f'os.link: {e}')
+        else:
+            self.target.unlink()
+        st = os.stat(self.source)
+        copy_file(self.source, self.target, link='hard')
+        st2 = os.stat(self.source)
+        st3 = os.stat(self.target)
+        assert os.path.samestat(st, st2), (st, st2)
+        assert os.path.samestat(st2, st3), (st2, st3)
+        assert self.source.read_text(encoding='utf-8') == 'some content'
+
+    def test_copy_file_hard_link_failure(self):
+        # If hard linking fails, copy_file() falls back on copying file
+        # (some special filesystems don't support hard linking even under
+        #  Unix, see issue #8876).
+        jaraco.path.build({self.source: 'some content'})
+        st = os.stat(self.source)
+        with mock.patch("os.link", side_effect=OSError(0, "linking unsupported")):
+            copy_file(self.source, self.target, link='hard')
+        st2 = os.stat(self.source)
+        st3 = os.stat(self.target)
+        assert os.path.samestat(st, st2), (st, st2)
+        assert not os.path.samestat(st2, st3), (st2, st3)
+        for fn in (self.source, self.target):
+            assert fn.read_text(encoding='utf-8') == 'some content'
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_filelist.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_filelist.py
new file mode 100644
index 0000000000000000000000000000000000000000..130e6fb53b0bd9670bdcfe6d4bc7fcf8fd90416e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_filelist.py
@@ -0,0 +1,336 @@
+"""Tests for distutils.filelist."""
+
+import logging
+import os
+import re
+from distutils import debug, filelist
+from distutils.errors import DistutilsTemplateError
+from distutils.filelist import FileList, glob_to_re, translate_pattern
+
+import jaraco.path
+import pytest
+
+from .compat import py39 as os_helper
+
+MANIFEST_IN = """\
+include ok
+include xo
+exclude xo
+include foo.tmp
+include buildout.cfg
+global-include *.x
+global-include *.txt
+global-exclude *.tmp
+recursive-include f *.oo
+recursive-exclude global *.x
+graft dir
+prune dir3
+"""
+
+
+def make_local_path(s):
+    """Converts '/' in a string to os.sep"""
+    return s.replace('/', os.sep)
+
+
+class TestFileList:
+    def assertNoWarnings(self, caplog):
+        warnings = [rec for rec in caplog.records if rec.levelno == logging.WARNING]
+        assert not warnings
+        caplog.clear()
+
+    def assertWarnings(self, caplog):
+        warnings = [rec for rec in caplog.records if rec.levelno == logging.WARNING]
+        assert warnings
+        caplog.clear()
+
+    def test_glob_to_re(self):
+        sep = os.sep
+        if os.sep == '\\':
+            sep = re.escape(os.sep)
+
+        for glob, regex in (
+            # simple cases
+            ('foo*', r'(?s:foo[^%(sep)s]*)\Z'),
+            ('foo?', r'(?s:foo[^%(sep)s])\Z'),
+            ('foo??', r'(?s:foo[^%(sep)s][^%(sep)s])\Z'),
+            # special cases
+            (r'foo\\*', r'(?s:foo\\\\[^%(sep)s]*)\Z'),
+            (r'foo\\\*', r'(?s:foo\\\\\\[^%(sep)s]*)\Z'),
+            ('foo????', r'(?s:foo[^%(sep)s][^%(sep)s][^%(sep)s][^%(sep)s])\Z'),
+            (r'foo\\??', r'(?s:foo\\\\[^%(sep)s][^%(sep)s])\Z'),
+        ):
+            regex = regex % {'sep': sep}
+            assert glob_to_re(glob) == regex
+
+    def test_process_template_line(self):
+        # testing  all MANIFEST.in template patterns
+        file_list = FileList()
+        mlp = make_local_path
+
+        # simulated file list
+        file_list.allfiles = [
+            'foo.tmp',
+            'ok',
+            'xo',
+            'four.txt',
+            'buildout.cfg',
+            # filelist does not filter out VCS directories,
+            # it's sdist that does
+            mlp('.hg/last-message.txt'),
+            mlp('global/one.txt'),
+            mlp('global/two.txt'),
+            mlp('global/files.x'),
+            mlp('global/here.tmp'),
+            mlp('f/o/f.oo'),
+            mlp('dir/graft-one'),
+            mlp('dir/dir2/graft2'),
+            mlp('dir3/ok'),
+            mlp('dir3/sub/ok.txt'),
+        ]
+
+        for line in MANIFEST_IN.split('\n'):
+            if line.strip() == '':
+                continue
+            file_list.process_template_line(line)
+
+        wanted = [
+            'ok',
+            'buildout.cfg',
+            'four.txt',
+            mlp('.hg/last-message.txt'),
+            mlp('global/one.txt'),
+            mlp('global/two.txt'),
+            mlp('f/o/f.oo'),
+            mlp('dir/graft-one'),
+            mlp('dir/dir2/graft2'),
+        ]
+
+        assert file_list.files == wanted
+
+    def test_debug_print(self, capsys, monkeypatch):
+        file_list = FileList()
+        file_list.debug_print('xxx')
+        assert capsys.readouterr().out == ''
+
+        monkeypatch.setattr(debug, 'DEBUG', True)
+        file_list.debug_print('xxx')
+        assert capsys.readouterr().out == 'xxx\n'
+
+    def test_set_allfiles(self):
+        file_list = FileList()
+        files = ['a', 'b', 'c']
+        file_list.set_allfiles(files)
+        assert file_list.allfiles == files
+
+    def test_remove_duplicates(self):
+        file_list = FileList()
+        file_list.files = ['a', 'b', 'a', 'g', 'c', 'g']
+        # files must be sorted beforehand (sdist does it)
+        file_list.sort()
+        file_list.remove_duplicates()
+        assert file_list.files == ['a', 'b', 'c', 'g']
+
+    def test_translate_pattern(self):
+        # not regex
+        assert hasattr(translate_pattern('a', anchor=True, is_regex=False), 'search')
+
+        # is a regex
+        regex = re.compile('a')
+        assert translate_pattern(regex, anchor=True, is_regex=True) == regex
+
+        # plain string flagged as regex
+        assert hasattr(translate_pattern('a', anchor=True, is_regex=True), 'search')
+
+        # glob support
+        assert translate_pattern('*.py', anchor=True, is_regex=False).search(
+            'filelist.py'
+        )
+
+    def test_exclude_pattern(self):
+        # return False if no match
+        file_list = FileList()
+        assert not file_list.exclude_pattern('*.py')
+
+        # return True if files match
+        file_list = FileList()
+        file_list.files = ['a.py', 'b.py']
+        assert file_list.exclude_pattern('*.py')
+
+        # test excludes
+        file_list = FileList()
+        file_list.files = ['a.py', 'a.txt']
+        file_list.exclude_pattern('*.py')
+        assert file_list.files == ['a.txt']
+
+    def test_include_pattern(self):
+        # return False if no match
+        file_list = FileList()
+        file_list.set_allfiles([])
+        assert not file_list.include_pattern('*.py')
+
+        # return True if files match
+        file_list = FileList()
+        file_list.set_allfiles(['a.py', 'b.txt'])
+        assert file_list.include_pattern('*.py')
+
+        # test * matches all files
+        file_list = FileList()
+        assert file_list.allfiles is None
+        file_list.set_allfiles(['a.py', 'b.txt'])
+        file_list.include_pattern('*')
+        assert file_list.allfiles == ['a.py', 'b.txt']
+
+    def test_process_template(self, caplog):
+        mlp = make_local_path
+        # invalid lines
+        file_list = FileList()
+        for action in (
+            'include',
+            'exclude',
+            'global-include',
+            'global-exclude',
+            'recursive-include',
+            'recursive-exclude',
+            'graft',
+            'prune',
+            'blarg',
+        ):
+            with pytest.raises(DistutilsTemplateError):
+                file_list.process_template_line(action)
+
+        # include
+        file_list = FileList()
+        file_list.set_allfiles(['a.py', 'b.txt', mlp('d/c.py')])
+
+        file_list.process_template_line('include *.py')
+        assert file_list.files == ['a.py']
+        self.assertNoWarnings(caplog)
+
+        file_list.process_template_line('include *.rb')
+        assert file_list.files == ['a.py']
+        self.assertWarnings(caplog)
+
+        # exclude
+        file_list = FileList()
+        file_list.files = ['a.py', 'b.txt', mlp('d/c.py')]
+
+        file_list.process_template_line('exclude *.py')
+        assert file_list.files == ['b.txt', mlp('d/c.py')]
+        self.assertNoWarnings(caplog)
+
+        file_list.process_template_line('exclude *.rb')
+        assert file_list.files == ['b.txt', mlp('d/c.py')]
+        self.assertWarnings(caplog)
+
+        # global-include
+        file_list = FileList()
+        file_list.set_allfiles(['a.py', 'b.txt', mlp('d/c.py')])
+
+        file_list.process_template_line('global-include *.py')
+        assert file_list.files == ['a.py', mlp('d/c.py')]
+        self.assertNoWarnings(caplog)
+
+        file_list.process_template_line('global-include *.rb')
+        assert file_list.files == ['a.py', mlp('d/c.py')]
+        self.assertWarnings(caplog)
+
+        # global-exclude
+        file_list = FileList()
+        file_list.files = ['a.py', 'b.txt', mlp('d/c.py')]
+
+        file_list.process_template_line('global-exclude *.py')
+        assert file_list.files == ['b.txt']
+        self.assertNoWarnings(caplog)
+
+        file_list.process_template_line('global-exclude *.rb')
+        assert file_list.files == ['b.txt']
+        self.assertWarnings(caplog)
+
+        # recursive-include
+        file_list = FileList()
+        file_list.set_allfiles(['a.py', mlp('d/b.py'), mlp('d/c.txt'), mlp('d/d/e.py')])
+
+        file_list.process_template_line('recursive-include d *.py')
+        assert file_list.files == [mlp('d/b.py'), mlp('d/d/e.py')]
+        self.assertNoWarnings(caplog)
+
+        file_list.process_template_line('recursive-include e *.py')
+        assert file_list.files == [mlp('d/b.py'), mlp('d/d/e.py')]
+        self.assertWarnings(caplog)
+
+        # recursive-exclude
+        file_list = FileList()
+        file_list.files = ['a.py', mlp('d/b.py'), mlp('d/c.txt'), mlp('d/d/e.py')]
+
+        file_list.process_template_line('recursive-exclude d *.py')
+        assert file_list.files == ['a.py', mlp('d/c.txt')]
+        self.assertNoWarnings(caplog)
+
+        file_list.process_template_line('recursive-exclude e *.py')
+        assert file_list.files == ['a.py', mlp('d/c.txt')]
+        self.assertWarnings(caplog)
+
+        # graft
+        file_list = FileList()
+        file_list.set_allfiles(['a.py', mlp('d/b.py'), mlp('d/d/e.py'), mlp('f/f.py')])
+
+        file_list.process_template_line('graft d')
+        assert file_list.files == [mlp('d/b.py'), mlp('d/d/e.py')]
+        self.assertNoWarnings(caplog)
+
+        file_list.process_template_line('graft e')
+        assert file_list.files == [mlp('d/b.py'), mlp('d/d/e.py')]
+        self.assertWarnings(caplog)
+
+        # prune
+        file_list = FileList()
+        file_list.files = ['a.py', mlp('d/b.py'), mlp('d/d/e.py'), mlp('f/f.py')]
+
+        file_list.process_template_line('prune d')
+        assert file_list.files == ['a.py', mlp('f/f.py')]
+        self.assertNoWarnings(caplog)
+
+        file_list.process_template_line('prune e')
+        assert file_list.files == ['a.py', mlp('f/f.py')]
+        self.assertWarnings(caplog)
+
+
+class TestFindAll:
+    @os_helper.skip_unless_symlink
+    def test_missing_symlink(self, temp_cwd):
+        os.symlink('foo', 'bar')
+        assert filelist.findall() == []
+
+    def test_basic_discovery(self, temp_cwd):
+        """
+        When findall is called with no parameters or with
+        '.' as the parameter, the dot should be omitted from
+        the results.
+        """
+        jaraco.path.build({'foo': {'file1.txt': ''}, 'bar': {'file2.txt': ''}})
+        file1 = os.path.join('foo', 'file1.txt')
+        file2 = os.path.join('bar', 'file2.txt')
+        expected = [file2, file1]
+        assert sorted(filelist.findall()) == expected
+
+    def test_non_local_discovery(self, tmp_path):
+        """
+        When findall is called with another path, the full
+        path name should be returned.
+        """
+        jaraco.path.build({'file1.txt': ''}, tmp_path)
+        expected = [str(tmp_path / 'file1.txt')]
+        assert filelist.findall(tmp_path) == expected
+
+    @os_helper.skip_unless_symlink
+    def test_symlink_loop(self, tmp_path):
+        jaraco.path.build(
+            {
+                'link-to-parent': jaraco.path.Symlink('.'),
+                'somefile': '',
+            },
+            tmp_path,
+        )
+        files = filelist.findall(tmp_path)
+        assert len(files) == 1
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3ffb2e668ac5a042c5a2889d7b51b5755b73157
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install.py
@@ -0,0 +1,245 @@
+"""Tests for distutils.command.install."""
+
+import logging
+import os
+import pathlib
+import site
+import sys
+from distutils import sysconfig
+from distutils.command import install as install_module
+from distutils.command.build_ext import build_ext
+from distutils.command.install import INSTALL_SCHEMES, install
+from distutils.core import Distribution
+from distutils.errors import DistutilsOptionError
+from distutils.extension import Extension
+from distutils.tests import missing_compiler_executable, support
+from distutils.util import is_mingw
+
+import pytest
+
+
+def _make_ext_name(modname):
+    return modname + sysconfig.get_config_var('EXT_SUFFIX')
+
+
+@support.combine_markers
+@pytest.mark.usefixtures('save_env')
+class TestInstall(
+    support.TempdirManager,
+):
+    @pytest.mark.xfail(
+        'platform.system() == "Windows" and sys.version_info > (3, 11)',
+        reason="pypa/distutils#148",
+    )
+    def test_home_installation_scheme(self):
+        # This ensure two things:
+        # - that --home generates the desired set of directory names
+        # - test --home is supported on all platforms
+        builddir = self.mkdtemp()
+        destination = os.path.join(builddir, "installation")
+
+        dist = Distribution({"name": "foopkg"})
+        # script_name need not exist, it just need to be initialized
+        dist.script_name = os.path.join(builddir, "setup.py")
+        dist.command_obj["build"] = support.DummyCommand(
+            build_base=builddir,
+            build_lib=os.path.join(builddir, "lib"),
+        )
+
+        cmd = install(dist)
+        cmd.home = destination
+        cmd.ensure_finalized()
+
+        assert cmd.install_base == destination
+        assert cmd.install_platbase == destination
+
+        def check_path(got, expected):
+            got = os.path.normpath(got)
+            expected = os.path.normpath(expected)
+            assert got == expected
+
+        impl_name = sys.implementation.name.replace("cpython", "python")
+        libdir = os.path.join(destination, "lib", impl_name)
+        check_path(cmd.install_lib, libdir)
+        _platlibdir = getattr(sys, "platlibdir", "lib")
+        platlibdir = os.path.join(destination, _platlibdir, impl_name)
+        check_path(cmd.install_platlib, platlibdir)
+        check_path(cmd.install_purelib, libdir)
+        check_path(
+            cmd.install_headers,
+            os.path.join(destination, "include", impl_name, "foopkg"),
+        )
+        check_path(cmd.install_scripts, os.path.join(destination, "bin"))
+        check_path(cmd.install_data, destination)
+
+    def test_user_site(self, monkeypatch):
+        # test install with --user
+        # preparing the environment for the test
+        self.tmpdir = self.mkdtemp()
+        orig_site = site.USER_SITE
+        orig_base = site.USER_BASE
+        monkeypatch.setattr(site, 'USER_BASE', os.path.join(self.tmpdir, 'B'))
+        monkeypatch.setattr(site, 'USER_SITE', os.path.join(self.tmpdir, 'S'))
+        monkeypatch.setattr(install_module, 'USER_BASE', site.USER_BASE)
+        monkeypatch.setattr(install_module, 'USER_SITE', site.USER_SITE)
+
+        def _expanduser(path):
+            if path.startswith('~'):
+                return os.path.normpath(self.tmpdir + path[1:])
+            return path
+
+        monkeypatch.setattr(os.path, 'expanduser', _expanduser)
+
+        for key in ('nt_user', 'posix_user'):
+            assert key in INSTALL_SCHEMES
+
+        dist = Distribution({'name': 'xx'})
+        cmd = install(dist)
+
+        # making sure the user option is there
+        options = [name for name, short, label in cmd.user_options]
+        assert 'user' in options
+
+        # setting a value
+        cmd.user = True
+
+        # user base and site shouldn't be created yet
+        assert not os.path.exists(site.USER_BASE)
+        assert not os.path.exists(site.USER_SITE)
+
+        # let's run finalize
+        cmd.ensure_finalized()
+
+        # now they should
+        assert os.path.exists(site.USER_BASE)
+        assert os.path.exists(site.USER_SITE)
+
+        assert 'userbase' in cmd.config_vars
+        assert 'usersite' in cmd.config_vars
+
+        actual_headers = os.path.relpath(cmd.install_headers, site.USER_BASE)
+        if os.name == 'nt' and not is_mingw():
+            site_path = os.path.relpath(os.path.dirname(orig_site), orig_base)
+            include = os.path.join(site_path, 'Include')
+        else:
+            include = sysconfig.get_python_inc(0, '')
+        expect_headers = os.path.join(include, 'xx')
+
+        assert os.path.normcase(actual_headers) == os.path.normcase(expect_headers)
+
+    def test_handle_extra_path(self):
+        dist = Distribution({'name': 'xx', 'extra_path': 'path,dirs'})
+        cmd = install(dist)
+
+        # two elements
+        cmd.handle_extra_path()
+        assert cmd.extra_path == ['path', 'dirs']
+        assert cmd.extra_dirs == 'dirs'
+        assert cmd.path_file == 'path'
+
+        # one element
+        cmd.extra_path = ['path']
+        cmd.handle_extra_path()
+        assert cmd.extra_path == ['path']
+        assert cmd.extra_dirs == 'path'
+        assert cmd.path_file == 'path'
+
+        # none
+        dist.extra_path = cmd.extra_path = None
+        cmd.handle_extra_path()
+        assert cmd.extra_path is None
+        assert cmd.extra_dirs == ''
+        assert cmd.path_file is None
+
+        # three elements (no way !)
+        cmd.extra_path = 'path,dirs,again'
+        with pytest.raises(DistutilsOptionError):
+            cmd.handle_extra_path()
+
+    def test_finalize_options(self):
+        dist = Distribution({'name': 'xx'})
+        cmd = install(dist)
+
+        # must supply either prefix/exec-prefix/home or
+        # install-base/install-platbase -- not both
+        cmd.prefix = 'prefix'
+        cmd.install_base = 'base'
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+
+        # must supply either home or prefix/exec-prefix -- not both
+        cmd.install_base = None
+        cmd.home = 'home'
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+
+        # can't combine user with prefix/exec_prefix/home or
+        # install_(plat)base
+        cmd.prefix = None
+        cmd.user = 'user'
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+
+    def test_record(self):
+        install_dir = self.mkdtemp()
+        project_dir, dist = self.create_dist(py_modules=['hello'], scripts=['sayhi'])
+        os.chdir(project_dir)
+        self.write_file('hello.py', "def main(): print('o hai')")
+        self.write_file('sayhi', 'from hello import main; main()')
+
+        cmd = install(dist)
+        dist.command_obj['install'] = cmd
+        cmd.root = install_dir
+        cmd.record = os.path.join(project_dir, 'filelist')
+        cmd.ensure_finalized()
+        cmd.run()
+
+        content = pathlib.Path(cmd.record).read_text(encoding='utf-8')
+
+        found = [pathlib.Path(line).name for line in content.splitlines()]
+        expected = [
+            'hello.py',
+            f'hello.{sys.implementation.cache_tag}.pyc',
+            'sayhi',
+            'UNKNOWN-0.0.0-py{}.{}.egg-info'.format(*sys.version_info[:2]),
+        ]
+        assert found == expected
+
+    def test_record_extensions(self):
+        cmd = missing_compiler_executable()
+        if cmd is not None:
+            pytest.skip(f'The {cmd!r} command is not found')
+        install_dir = self.mkdtemp()
+        project_dir, dist = self.create_dist(
+            ext_modules=[Extension('xx', ['xxmodule.c'])]
+        )
+        os.chdir(project_dir)
+        support.copy_xxmodule_c(project_dir)
+
+        buildextcmd = build_ext(dist)
+        support.fixup_build_ext(buildextcmd)
+        buildextcmd.ensure_finalized()
+
+        cmd = install(dist)
+        dist.command_obj['install'] = cmd
+        dist.command_obj['build_ext'] = buildextcmd
+        cmd.root = install_dir
+        cmd.record = os.path.join(project_dir, 'filelist')
+        cmd.ensure_finalized()
+        cmd.run()
+
+        content = pathlib.Path(cmd.record).read_text(encoding='utf-8')
+
+        found = [pathlib.Path(line).name for line in content.splitlines()]
+        expected = [
+            _make_ext_name('xx'),
+            'UNKNOWN-0.0.0-py{}.{}.egg-info'.format(*sys.version_info[:2]),
+        ]
+        assert found == expected
+
+    def test_debug_mode(self, caplog, monkeypatch):
+        # this covers the code called when DEBUG is set
+        monkeypatch.setattr(install_module, 'DEBUG', True)
+        caplog.set_level(logging.DEBUG)
+        self.test_record()
+        assert any(rec for rec in caplog.records if rec.levelno == logging.DEBUG)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install_data.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..c800f86c645d86fcb11aa3bef1513325ef163132
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install_data.py
@@ -0,0 +1,74 @@
+"""Tests for distutils.command.install_data."""
+
+import os
+import pathlib
+from distutils.command.install_data import install_data
+from distutils.tests import support
+
+import pytest
+
+
+@pytest.mark.usefixtures('save_env')
+class TestInstallData(
+    support.TempdirManager,
+):
+    def test_simple_run(self):
+        pkg_dir, dist = self.create_dist()
+        cmd = install_data(dist)
+        cmd.install_dir = inst = os.path.join(pkg_dir, 'inst')
+
+        # data_files can contain
+        #  - simple files
+        #  - a Path object
+        #  - a tuple with a path, and a list of file
+        one = os.path.join(pkg_dir, 'one')
+        self.write_file(one, 'xxx')
+        inst2 = os.path.join(pkg_dir, 'inst2')
+        two = os.path.join(pkg_dir, 'two')
+        self.write_file(two, 'xxx')
+        three = pathlib.Path(pkg_dir) / 'three'
+        self.write_file(three, 'xxx')
+
+        cmd.data_files = [one, (inst2, [two]), three]
+        assert cmd.get_inputs() == [one, (inst2, [two]), three]
+
+        # let's run the command
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # let's check the result
+        assert len(cmd.get_outputs()) == 3
+        rthree = os.path.split(one)[-1]
+        assert os.path.exists(os.path.join(inst, rthree))
+        rtwo = os.path.split(two)[-1]
+        assert os.path.exists(os.path.join(inst2, rtwo))
+        rone = os.path.split(one)[-1]
+        assert os.path.exists(os.path.join(inst, rone))
+        cmd.outfiles = []
+
+        # let's try with warn_dir one
+        cmd.warn_dir = True
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # let's check the result
+        assert len(cmd.get_outputs()) == 3
+        assert os.path.exists(os.path.join(inst, rthree))
+        assert os.path.exists(os.path.join(inst2, rtwo))
+        assert os.path.exists(os.path.join(inst, rone))
+        cmd.outfiles = []
+
+        # now using root and empty dir
+        cmd.root = os.path.join(pkg_dir, 'root')
+        inst5 = os.path.join(pkg_dir, 'inst5')
+        four = os.path.join(cmd.install_dir, 'four')
+        self.write_file(four, 'xx')
+        cmd.data_files = [one, (inst2, [two]), three, ('inst5', [four]), (inst5, [])]
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # let's check the result
+        assert len(cmd.get_outputs()) == 5
+        assert os.path.exists(os.path.join(inst, rthree))
+        assert os.path.exists(os.path.join(inst2, rtwo))
+        assert os.path.exists(os.path.join(inst, rone))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install_headers.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install_headers.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c74f06b976f3c3ea977667a5c16c0e7f201a558
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install_headers.py
@@ -0,0 +1,33 @@
+"""Tests for distutils.command.install_headers."""
+
+import os
+from distutils.command.install_headers import install_headers
+from distutils.tests import support
+
+import pytest
+
+
+@pytest.mark.usefixtures('save_env')
+class TestInstallHeaders(
+    support.TempdirManager,
+):
+    def test_simple_run(self):
+        # we have two headers
+        header_list = self.mkdtemp()
+        header1 = os.path.join(header_list, 'header1')
+        header2 = os.path.join(header_list, 'header2')
+        self.write_file(header1)
+        self.write_file(header2)
+        headers = [header1, header2]
+
+        pkg_dir, dist = self.create_dist(headers=headers)
+        cmd = install_headers(dist)
+        assert cmd.get_inputs() == headers
+
+        # let's run the command
+        cmd.install_dir = os.path.join(pkg_dir, 'inst')
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # let's check the results
+        assert len(cmd.get_outputs()) == 2
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install_lib.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install_lib.py
new file mode 100644
index 0000000000000000000000000000000000000000..f685a579560ea70bceea221de0e54485975ce180
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install_lib.py
@@ -0,0 +1,110 @@
+"""Tests for distutils.command.install_data."""
+
+import importlib.util
+import os
+import sys
+from distutils.command.install_lib import install_lib
+from distutils.errors import DistutilsOptionError
+from distutils.extension import Extension
+from distutils.tests import support
+
+import pytest
+
+
+@support.combine_markers
+@pytest.mark.usefixtures('save_env')
+class TestInstallLib(
+    support.TempdirManager,
+):
+    def test_finalize_options(self):
+        dist = self.create_dist()[1]
+        cmd = install_lib(dist)
+
+        cmd.finalize_options()
+        assert cmd.compile == 1
+        assert cmd.optimize == 0
+
+        # optimize must be 0, 1, or 2
+        cmd.optimize = 'foo'
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+        cmd.optimize = '4'
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+
+        cmd.optimize = '2'
+        cmd.finalize_options()
+        assert cmd.optimize == 2
+
+    @pytest.mark.skipif('sys.dont_write_bytecode')
+    def test_byte_compile(self):
+        project_dir, dist = self.create_dist()
+        os.chdir(project_dir)
+        cmd = install_lib(dist)
+        cmd.compile = cmd.optimize = 1
+
+        f = os.path.join(project_dir, 'foo.py')
+        self.write_file(f, '# python file')
+        cmd.byte_compile([f])
+        pyc_file = importlib.util.cache_from_source('foo.py', optimization='')
+        pyc_opt_file = importlib.util.cache_from_source(
+            'foo.py', optimization=cmd.optimize
+        )
+        assert os.path.exists(pyc_file)
+        assert os.path.exists(pyc_opt_file)
+
+    def test_get_outputs(self):
+        project_dir, dist = self.create_dist()
+        os.chdir(project_dir)
+        os.mkdir('spam')
+        cmd = install_lib(dist)
+
+        # setting up a dist environment
+        cmd.compile = cmd.optimize = 1
+        cmd.install_dir = self.mkdtemp()
+        f = os.path.join(project_dir, 'spam', '__init__.py')
+        self.write_file(f, '# python package')
+        cmd.distribution.ext_modules = [Extension('foo', ['xxx'])]
+        cmd.distribution.packages = ['spam']
+        cmd.distribution.script_name = 'setup.py'
+
+        # get_outputs should return 4 elements: spam/__init__.py and .pyc,
+        # foo.import-tag-abiflags.so / foo.pyd
+        outputs = cmd.get_outputs()
+        assert len(outputs) == 4, outputs
+
+    def test_get_inputs(self):
+        project_dir, dist = self.create_dist()
+        os.chdir(project_dir)
+        os.mkdir('spam')
+        cmd = install_lib(dist)
+
+        # setting up a dist environment
+        cmd.compile = cmd.optimize = 1
+        cmd.install_dir = self.mkdtemp()
+        f = os.path.join(project_dir, 'spam', '__init__.py')
+        self.write_file(f, '# python package')
+        cmd.distribution.ext_modules = [Extension('foo', ['xxx'])]
+        cmd.distribution.packages = ['spam']
+        cmd.distribution.script_name = 'setup.py'
+
+        # get_inputs should return 2 elements: spam/__init__.py and
+        # foo.import-tag-abiflags.so / foo.pyd
+        inputs = cmd.get_inputs()
+        assert len(inputs) == 2, inputs
+
+    def test_dont_write_bytecode(self, caplog):
+        # makes sure byte_compile is not used
+        dist = self.create_dist()[1]
+        cmd = install_lib(dist)
+        cmd.compile = True
+        cmd.optimize = 1
+
+        old_dont_write_bytecode = sys.dont_write_bytecode
+        sys.dont_write_bytecode = True
+        try:
+            cmd.byte_compile([])
+        finally:
+            sys.dont_write_bytecode = old_dont_write_bytecode
+
+        assert 'byte-compiling is disabled' in caplog.messages[0]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install_scripts.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install_scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..868b1c22521f23a5dd398e4636c94330d4c238ac
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_install_scripts.py
@@ -0,0 +1,52 @@
+"""Tests for distutils.command.install_scripts."""
+
+import os
+from distutils.command.install_scripts import install_scripts
+from distutils.core import Distribution
+from distutils.tests import support
+
+from . import test_build_scripts
+
+
+class TestInstallScripts(support.TempdirManager):
+    def test_default_settings(self):
+        dist = Distribution()
+        dist.command_obj["build"] = support.DummyCommand(build_scripts="/foo/bar")
+        dist.command_obj["install"] = support.DummyCommand(
+            install_scripts="/splat/funk",
+            force=True,
+            skip_build=True,
+        )
+        cmd = install_scripts(dist)
+        assert not cmd.force
+        assert not cmd.skip_build
+        assert cmd.build_dir is None
+        assert cmd.install_dir is None
+
+        cmd.finalize_options()
+
+        assert cmd.force
+        assert cmd.skip_build
+        assert cmd.build_dir == "/foo/bar"
+        assert cmd.install_dir == "/splat/funk"
+
+    def test_installation(self):
+        source = self.mkdtemp()
+
+        expected = test_build_scripts.TestBuildScripts.write_sample_scripts(source)
+
+        target = self.mkdtemp()
+        dist = Distribution()
+        dist.command_obj["build"] = support.DummyCommand(build_scripts=source)
+        dist.command_obj["install"] = support.DummyCommand(
+            install_scripts=target,
+            force=True,
+            skip_build=True,
+        )
+        cmd = install_scripts(dist)
+        cmd.finalize_options()
+        cmd.run()
+
+        installed = os.listdir(target)
+        for name in expected:
+            assert name in installed
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_log.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_log.py
new file mode 100644
index 0000000000000000000000000000000000000000..d67779fc9fa0f58a010929d5a456890c6dde3376
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_log.py
@@ -0,0 +1,12 @@
+"""Tests for distutils.log"""
+
+import logging
+from distutils._log import log
+
+
+class TestLog:
+    def test_non_ascii(self, caplog):
+        caplog.set_level(logging.DEBUG)
+        log.debug('Dεbug\tMėssãge')
+        log.fatal('Fαtal\tÈrrōr')
+        assert caplog.messages == ['Dεbug\tMėssãge', 'Fαtal\tÈrrōr']
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_modified.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_modified.py
new file mode 100644
index 0000000000000000000000000000000000000000..e35cec2d6f1a8d27619e33c253f0a83f0f72a75d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_modified.py
@@ -0,0 +1,126 @@
+"""Tests for distutils._modified."""
+
+import os
+import types
+from distutils._modified import newer, newer_group, newer_pairwise, newer_pairwise_group
+from distutils.errors import DistutilsFileError
+from distutils.tests import support
+
+import pytest
+
+
+class TestDepUtil(support.TempdirManager):
+    def test_newer(self):
+        tmpdir = self.mkdtemp()
+        new_file = os.path.join(tmpdir, 'new')
+        old_file = os.path.abspath(__file__)
+
+        # Raise DistutilsFileError if 'new_file' does not exist.
+        with pytest.raises(DistutilsFileError):
+            newer(new_file, old_file)
+
+        # Return true if 'new_file' exists and is more recently modified than
+        # 'old_file', or if 'new_file' exists and 'old_file' doesn't.
+        self.write_file(new_file)
+        assert newer(new_file, 'I_dont_exist')
+        assert newer(new_file, old_file)
+
+        # Return false if both exist and 'old_file' is the same age or younger
+        # than 'new_file'.
+        assert not newer(old_file, new_file)
+
+    def _setup_1234(self):
+        tmpdir = self.mkdtemp()
+        sources = os.path.join(tmpdir, 'sources')
+        targets = os.path.join(tmpdir, 'targets')
+        os.mkdir(sources)
+        os.mkdir(targets)
+        one = os.path.join(sources, 'one')
+        two = os.path.join(sources, 'two')
+        three = os.path.abspath(__file__)  # I am the old file
+        four = os.path.join(targets, 'four')
+        self.write_file(one)
+        self.write_file(two)
+        self.write_file(four)
+        return one, two, three, four
+
+    def test_newer_pairwise(self):
+        one, two, three, four = self._setup_1234()
+
+        assert newer_pairwise([one, two], [three, four]) == ([one], [three])
+
+    def test_newer_pairwise_mismatch(self):
+        one, two, three, four = self._setup_1234()
+
+        with pytest.raises(ValueError):
+            newer_pairwise([one], [three, four])
+
+        with pytest.raises(ValueError):
+            newer_pairwise([one, two], [three])
+
+    def test_newer_pairwise_empty(self):
+        assert newer_pairwise([], []) == ([], [])
+
+    def test_newer_pairwise_fresh(self):
+        one, two, three, four = self._setup_1234()
+
+        assert newer_pairwise([one, three], [two, four]) == ([], [])
+
+    def test_newer_group(self):
+        tmpdir = self.mkdtemp()
+        sources = os.path.join(tmpdir, 'sources')
+        os.mkdir(sources)
+        one = os.path.join(sources, 'one')
+        two = os.path.join(sources, 'two')
+        three = os.path.join(sources, 'three')
+        old_file = os.path.abspath(__file__)
+
+        # return true if 'old_file' is out-of-date with respect to any file
+        # listed in 'sources'.
+        self.write_file(one)
+        self.write_file(two)
+        self.write_file(three)
+        assert newer_group([one, two, three], old_file)
+        assert not newer_group([one, two, old_file], three)
+
+        # missing handling
+        os.remove(one)
+        with pytest.raises(OSError):
+            newer_group([one, two, old_file], three)
+
+        assert not newer_group([one, two, old_file], three, missing='ignore')
+
+        assert newer_group([one, two, old_file], three, missing='newer')
+
+
+@pytest.fixture
+def groups_target(tmp_path):
+    """
+    Set up some older sources, a target, and newer sources.
+
+    Returns a simple namespace with these values.
+    """
+    filenames = ['older.c', 'older.h', 'target.o', 'newer.c', 'newer.h']
+    paths = [tmp_path / name for name in filenames]
+
+    for mtime, path in enumerate(paths):
+        path.write_text('', encoding='utf-8')
+
+        # make sure modification times are sequential
+        os.utime(path, (mtime, mtime))
+
+    return types.SimpleNamespace(older=paths[:2], target=paths[2], newer=paths[3:])
+
+
+def test_newer_pairwise_group(groups_target):
+    older = newer_pairwise_group([groups_target.older], [groups_target.target])
+    newer = newer_pairwise_group([groups_target.newer], [groups_target.target])
+    assert older == ([], [])
+    assert newer == ([groups_target.newer], [groups_target.target])
+
+
+def test_newer_group_no_sources_no_target(tmp_path):
+    """
+    Consider no sources and no target "newer".
+    """
+    assert newer_group([], str(tmp_path / 'does-not-exist'))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_sdist.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_sdist.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b1a376b262162aa7c8d3d989709c05b38563d6c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_sdist.py
@@ -0,0 +1,470 @@
+"""Tests for distutils.command.sdist."""
+
+import os
+import pathlib
+import shutil  # noqa: F401
+import tarfile
+import zipfile
+from distutils.archive_util import ARCHIVE_FORMATS
+from distutils.command.sdist import sdist, show_formats
+from distutils.core import Distribution
+from distutils.errors import DistutilsOptionError
+from distutils.filelist import FileList
+from os.path import join
+from textwrap import dedent
+
+import jaraco.path
+import path
+import pytest
+from more_itertools import ilen
+
+from . import support
+from .unix_compat import grp, pwd, require_uid_0, require_unix_id
+
+SETUP_PY = """
+from distutils.core import setup
+import somecode
+
+setup(name='fake')
+"""
+
+MANIFEST = """\
+# file GENERATED by distutils, do NOT edit
+README
+buildout.cfg
+inroot.txt
+setup.py
+data%(sep)sdata.dt
+scripts%(sep)sscript.py
+some%(sep)sfile.txt
+some%(sep)sother_file.txt
+somecode%(sep)s__init__.py
+somecode%(sep)sdoc.dat
+somecode%(sep)sdoc.txt
+"""
+
+
+@pytest.fixture(autouse=True)
+def project_dir(request, distutils_managed_tempdir):
+    self = request.instance
+    self.tmp_dir = self.mkdtemp()
+    jaraco.path.build(
+        {
+            'somecode': {
+                '__init__.py': '#',
+            },
+            'README': 'xxx',
+            'setup.py': SETUP_PY,
+        },
+        self.tmp_dir,
+    )
+    with path.Path(self.tmp_dir):
+        yield
+
+
+def clean_lines(filepath):
+    with pathlib.Path(filepath).open(encoding='utf-8') as f:
+        yield from filter(None, map(str.strip, f))
+
+
+class TestSDist(support.TempdirManager):
+    def get_cmd(self, metadata=None):
+        """Returns a cmd"""
+        if metadata is None:
+            metadata = {
+                'name': 'ns.fake--pkg',
+                'version': '1.0',
+                'url': 'xxx',
+                'author': 'xxx',
+                'author_email': 'xxx',
+            }
+        dist = Distribution(metadata)
+        dist.script_name = 'setup.py'
+        dist.packages = ['somecode']
+        dist.include_package_data = True
+        cmd = sdist(dist)
+        cmd.dist_dir = 'dist'
+        return dist, cmd
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_prune_file_list(self):
+        # this test creates a project with some VCS dirs and an NFS rename
+        # file, then launches sdist to check they get pruned on all systems
+
+        # creating VCS directories with some files in them
+        os.mkdir(join(self.tmp_dir, 'somecode', '.svn'))
+        self.write_file((self.tmp_dir, 'somecode', '.svn', 'ok.py'), 'xxx')
+
+        os.mkdir(join(self.tmp_dir, 'somecode', '.hg'))
+        self.write_file((self.tmp_dir, 'somecode', '.hg', 'ok'), 'xxx')
+
+        os.mkdir(join(self.tmp_dir, 'somecode', '.git'))
+        self.write_file((self.tmp_dir, 'somecode', '.git', 'ok'), 'xxx')
+
+        self.write_file((self.tmp_dir, 'somecode', '.nfs0001'), 'xxx')
+
+        # now building a sdist
+        dist, cmd = self.get_cmd()
+
+        # zip is available universally
+        # (tar might not be installed under win32)
+        cmd.formats = ['zip']
+
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # now let's check what we have
+        dist_folder = join(self.tmp_dir, 'dist')
+        files = os.listdir(dist_folder)
+        assert files == ['ns_fake_pkg-1.0.zip']
+
+        zip_file = zipfile.ZipFile(join(dist_folder, 'ns_fake_pkg-1.0.zip'))
+        try:
+            content = zip_file.namelist()
+        finally:
+            zip_file.close()
+
+        # making sure everything has been pruned correctly
+        expected = [
+            '',
+            'PKG-INFO',
+            'README',
+            'setup.py',
+            'somecode/',
+            'somecode/__init__.py',
+        ]
+        assert sorted(content) == ['ns_fake_pkg-1.0/' + x for x in expected]
+
+    @pytest.mark.usefixtures('needs_zlib')
+    @pytest.mark.skipif("not shutil.which('tar')")
+    @pytest.mark.skipif("not shutil.which('gzip')")
+    def test_make_distribution(self):
+        # now building a sdist
+        dist, cmd = self.get_cmd()
+
+        # creating a gztar then a tar
+        cmd.formats = ['gztar', 'tar']
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # making sure we have two files
+        dist_folder = join(self.tmp_dir, 'dist')
+        result = os.listdir(dist_folder)
+        result.sort()
+        assert result == ['ns_fake_pkg-1.0.tar', 'ns_fake_pkg-1.0.tar.gz']
+
+        os.remove(join(dist_folder, 'ns_fake_pkg-1.0.tar'))
+        os.remove(join(dist_folder, 'ns_fake_pkg-1.0.tar.gz'))
+
+        # now trying a tar then a gztar
+        cmd.formats = ['tar', 'gztar']
+
+        cmd.ensure_finalized()
+        cmd.run()
+
+        result = os.listdir(dist_folder)
+        result.sort()
+        assert result == ['ns_fake_pkg-1.0.tar', 'ns_fake_pkg-1.0.tar.gz']
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_add_defaults(self):
+        # https://bugs.python.org/issue2279
+
+        # add_default should also include
+        # data_files and package_data
+        dist, cmd = self.get_cmd()
+
+        # filling data_files by pointing files
+        # in package_data
+        dist.package_data = {'': ['*.cfg', '*.dat'], 'somecode': ['*.txt']}
+        self.write_file((self.tmp_dir, 'somecode', 'doc.txt'), '#')
+        self.write_file((self.tmp_dir, 'somecode', 'doc.dat'), '#')
+
+        # adding some data in data_files
+        data_dir = join(self.tmp_dir, 'data')
+        os.mkdir(data_dir)
+        self.write_file((data_dir, 'data.dt'), '#')
+        some_dir = join(self.tmp_dir, 'some')
+        os.mkdir(some_dir)
+        # make sure VCS directories are pruned (#14004)
+        hg_dir = join(self.tmp_dir, '.hg')
+        os.mkdir(hg_dir)
+        self.write_file((hg_dir, 'last-message.txt'), '#')
+        # a buggy regex used to prevent this from working on windows (#6884)
+        self.write_file((self.tmp_dir, 'buildout.cfg'), '#')
+        self.write_file((self.tmp_dir, 'inroot.txt'), '#')
+        self.write_file((some_dir, 'file.txt'), '#')
+        self.write_file((some_dir, 'other_file.txt'), '#')
+
+        dist.data_files = [
+            ('data', ['data/data.dt', 'buildout.cfg', 'inroot.txt', 'notexisting']),
+            'some/file.txt',
+            'some/other_file.txt',
+        ]
+
+        # adding a script
+        script_dir = join(self.tmp_dir, 'scripts')
+        os.mkdir(script_dir)
+        self.write_file((script_dir, 'script.py'), '#')
+        dist.scripts = [join('scripts', 'script.py')]
+
+        cmd.formats = ['zip']
+        cmd.use_defaults = True
+
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # now let's check what we have
+        dist_folder = join(self.tmp_dir, 'dist')
+        files = os.listdir(dist_folder)
+        assert files == ['ns_fake_pkg-1.0.zip']
+
+        zip_file = zipfile.ZipFile(join(dist_folder, 'ns_fake_pkg-1.0.zip'))
+        try:
+            content = zip_file.namelist()
+        finally:
+            zip_file.close()
+
+        # making sure everything was added
+        expected = [
+            '',
+            'PKG-INFO',
+            'README',
+            'buildout.cfg',
+            'data/',
+            'data/data.dt',
+            'inroot.txt',
+            'scripts/',
+            'scripts/script.py',
+            'setup.py',
+            'some/',
+            'some/file.txt',
+            'some/other_file.txt',
+            'somecode/',
+            'somecode/__init__.py',
+            'somecode/doc.dat',
+            'somecode/doc.txt',
+        ]
+        assert sorted(content) == ['ns_fake_pkg-1.0/' + x for x in expected]
+
+        # checking the MANIFEST
+        manifest = pathlib.Path(self.tmp_dir, 'MANIFEST').read_text(encoding='utf-8')
+        assert manifest == MANIFEST % {'sep': os.sep}
+
+    @staticmethod
+    def warnings(messages, prefix='warning: '):
+        return [msg for msg in messages if msg.startswith(prefix)]
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_metadata_check_option(self, caplog):
+        # testing the `medata-check` option
+        dist, cmd = self.get_cmd(metadata={})
+
+        # this should raise some warnings !
+        # with the `check` subcommand
+        cmd.ensure_finalized()
+        cmd.run()
+        assert len(self.warnings(caplog.messages, 'warning: check: ')) == 1
+
+        # trying with a complete set of metadata
+        caplog.clear()
+        dist, cmd = self.get_cmd()
+        cmd.ensure_finalized()
+        cmd.metadata_check = False
+        cmd.run()
+        assert len(self.warnings(caplog.messages, 'warning: check: ')) == 0
+
+    def test_show_formats(self, capsys):
+        show_formats()
+
+        # the output should be a header line + one line per format
+        num_formats = len(ARCHIVE_FORMATS.keys())
+        output = [
+            line
+            for line in capsys.readouterr().out.split('\n')
+            if line.strip().startswith('--formats=')
+        ]
+        assert len(output) == num_formats
+
+    def test_finalize_options(self):
+        dist, cmd = self.get_cmd()
+        cmd.finalize_options()
+
+        # default options set by finalize
+        assert cmd.manifest == 'MANIFEST'
+        assert cmd.template == 'MANIFEST.in'
+        assert cmd.dist_dir == 'dist'
+
+        # formats has to be a string splitable on (' ', ',') or
+        # a stringlist
+        cmd.formats = 1
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+        cmd.formats = ['zip']
+        cmd.finalize_options()
+
+        # formats has to be known
+        cmd.formats = 'supazipa'
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+
+    # the following tests make sure there is a nice error message instead
+    # of a traceback when parsing an invalid manifest template
+
+    def _check_template(self, content, caplog):
+        dist, cmd = self.get_cmd()
+        os.chdir(self.tmp_dir)
+        self.write_file('MANIFEST.in', content)
+        cmd.ensure_finalized()
+        cmd.filelist = FileList()
+        cmd.read_template()
+        assert len(self.warnings(caplog.messages)) == 1
+
+    def test_invalid_template_unknown_command(self, caplog):
+        self._check_template('taunt knights *', caplog)
+
+    def test_invalid_template_wrong_arguments(self, caplog):
+        # this manifest command takes one argument
+        self._check_template('prune', caplog)
+
+    @pytest.mark.skipif("platform.system() != 'Windows'")
+    def test_invalid_template_wrong_path(self, caplog):
+        # on Windows, trailing slashes are not allowed
+        # this used to crash instead of raising a warning: #8286
+        self._check_template('include examples/', caplog)
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_get_file_list(self):
+        # make sure MANIFEST is recalculated
+        dist, cmd = self.get_cmd()
+
+        # filling data_files by pointing files in package_data
+        dist.package_data = {'somecode': ['*.txt']}
+        self.write_file((self.tmp_dir, 'somecode', 'doc.txt'), '#')
+        cmd.formats = ['gztar']
+        cmd.ensure_finalized()
+        cmd.run()
+
+        assert ilen(clean_lines(cmd.manifest)) == 5
+
+        # adding a file
+        self.write_file((self.tmp_dir, 'somecode', 'doc2.txt'), '#')
+
+        # make sure build_py is reinitialized, like a fresh run
+        build_py = dist.get_command_obj('build_py')
+        build_py.finalized = False
+        build_py.ensure_finalized()
+
+        cmd.run()
+
+        manifest2 = list(clean_lines(cmd.manifest))
+
+        # do we have the new file in MANIFEST ?
+        assert len(manifest2) == 6
+        assert 'doc2.txt' in manifest2[-1]
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_manifest_marker(self):
+        # check that autogenerated MANIFESTs have a marker
+        dist, cmd = self.get_cmd()
+        cmd.ensure_finalized()
+        cmd.run()
+
+        assert (
+            next(clean_lines(cmd.manifest))
+            == '# file GENERATED by distutils, do NOT edit'
+        )
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_manifest_comments(self):
+        # make sure comments don't cause exceptions or wrong includes
+        contents = dedent(
+            """\
+            # bad.py
+            #bad.py
+            good.py
+            """
+        )
+        dist, cmd = self.get_cmd()
+        cmd.ensure_finalized()
+        self.write_file((self.tmp_dir, cmd.manifest), contents)
+        self.write_file((self.tmp_dir, 'good.py'), '# pick me!')
+        self.write_file((self.tmp_dir, 'bad.py'), "# don't pick me!")
+        self.write_file((self.tmp_dir, '#bad.py'), "# don't pick me!")
+        cmd.run()
+        assert cmd.filelist.files == ['good.py']
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_manual_manifest(self):
+        # check that a MANIFEST without a marker is left alone
+        dist, cmd = self.get_cmd()
+        cmd.formats = ['gztar']
+        cmd.ensure_finalized()
+        self.write_file((self.tmp_dir, cmd.manifest), 'README.manual')
+        self.write_file(
+            (self.tmp_dir, 'README.manual'),
+            'This project maintains its MANIFEST file itself.',
+        )
+        cmd.run()
+        assert cmd.filelist.files == ['README.manual']
+
+        assert list(clean_lines(cmd.manifest)) == ['README.manual']
+
+        archive_name = join(self.tmp_dir, 'dist', 'ns_fake_pkg-1.0.tar.gz')
+        archive = tarfile.open(archive_name)
+        try:
+            filenames = [tarinfo.name for tarinfo in archive]
+        finally:
+            archive.close()
+        assert sorted(filenames) == [
+            'ns_fake_pkg-1.0',
+            'ns_fake_pkg-1.0/PKG-INFO',
+            'ns_fake_pkg-1.0/README.manual',
+        ]
+
+    @pytest.mark.usefixtures('needs_zlib')
+    @require_unix_id
+    @require_uid_0
+    @pytest.mark.skipif("not shutil.which('tar')")
+    @pytest.mark.skipif("not shutil.which('gzip')")
+    def test_make_distribution_owner_group(self):
+        # now building a sdist
+        dist, cmd = self.get_cmd()
+
+        # creating a gztar and specifying the owner+group
+        cmd.formats = ['gztar']
+        cmd.owner = pwd.getpwuid(0)[0]
+        cmd.group = grp.getgrgid(0)[0]
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # making sure we have the good rights
+        archive_name = join(self.tmp_dir, 'dist', 'ns_fake_pkg-1.0.tar.gz')
+        archive = tarfile.open(archive_name)
+        try:
+            for member in archive.getmembers():
+                assert member.uid == 0
+                assert member.gid == 0
+        finally:
+            archive.close()
+
+        # building a sdist again
+        dist, cmd = self.get_cmd()
+
+        # creating a gztar
+        cmd.formats = ['gztar']
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # making sure we have the good rights
+        archive_name = join(self.tmp_dir, 'dist', 'ns_fake_pkg-1.0.tar.gz')
+        archive = tarfile.open(archive_name)
+
+        # note that we are not testing the group ownership here
+        # because, depending on the platforms and the container
+        # rights (see #7408)
+        try:
+            for member in archive.getmembers():
+                assert member.uid == os.getuid()
+        finally:
+            archive.close()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_spawn.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_spawn.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b9fc926f6ffc41673630fbf5775e6f4ad638b18
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_spawn.py
@@ -0,0 +1,141 @@
+"""Tests for distutils.spawn."""
+
+import os
+import stat
+import sys
+import unittest.mock as mock
+from distutils.errors import DistutilsExecError
+from distutils.spawn import find_executable, spawn
+from distutils.tests import support
+
+import path
+import pytest
+from test.support import unix_shell
+
+from .compat import py39 as os_helper
+
+
+class TestSpawn(support.TempdirManager):
+    @pytest.mark.skipif("os.name not in ('nt', 'posix')")
+    def test_spawn(self):
+        tmpdir = self.mkdtemp()
+
+        # creating something executable
+        # through the shell that returns 1
+        if sys.platform != 'win32':
+            exe = os.path.join(tmpdir, 'foo.sh')
+            self.write_file(exe, f'#!{unix_shell}\nexit 1')
+        else:
+            exe = os.path.join(tmpdir, 'foo.bat')
+            self.write_file(exe, 'exit 1')
+
+        os.chmod(exe, 0o777)
+        with pytest.raises(DistutilsExecError):
+            spawn([exe])
+
+        # now something that works
+        if sys.platform != 'win32':
+            exe = os.path.join(tmpdir, 'foo.sh')
+            self.write_file(exe, f'#!{unix_shell}\nexit 0')
+        else:
+            exe = os.path.join(tmpdir, 'foo.bat')
+            self.write_file(exe, 'exit 0')
+
+        os.chmod(exe, 0o777)
+        spawn([exe])  # should work without any error
+
+    def test_find_executable(self, tmp_path):
+        program_path = self._make_executable(tmp_path, '.exe')
+        program = program_path.name
+        program_noeext = program_path.with_suffix('').name
+        filename = str(program_path)
+        tmp_dir = path.Path(tmp_path)
+
+        # test path parameter
+        rv = find_executable(program, path=tmp_dir)
+        assert rv == filename
+
+        if sys.platform == 'win32':
+            # test without ".exe" extension
+            rv = find_executable(program_noeext, path=tmp_dir)
+            assert rv == filename
+
+        # test find in the current directory
+        with tmp_dir:
+            rv = find_executable(program)
+            assert rv == program
+
+        # test non-existent program
+        dont_exist_program = "dontexist_" + program
+        rv = find_executable(dont_exist_program, path=tmp_dir)
+        assert rv is None
+
+        # PATH='': no match, except in the current directory
+        with os_helper.EnvironmentVarGuard() as env:
+            env['PATH'] = ''
+            with (
+                mock.patch(
+                    'distutils.spawn.os.confstr', return_value=tmp_dir, create=True
+                ),
+                mock.patch('distutils.spawn.os.defpath', tmp_dir),
+            ):
+                rv = find_executable(program)
+                assert rv is None
+
+                # look in current directory
+                with tmp_dir:
+                    rv = find_executable(program)
+                    assert rv == program
+
+        # PATH=':': explicitly looks in the current directory
+        with os_helper.EnvironmentVarGuard() as env:
+            env['PATH'] = os.pathsep
+            with (
+                mock.patch('distutils.spawn.os.confstr', return_value='', create=True),
+                mock.patch('distutils.spawn.os.defpath', ''),
+            ):
+                rv = find_executable(program)
+                assert rv is None
+
+                # look in current directory
+                with tmp_dir:
+                    rv = find_executable(program)
+                    assert rv == program
+
+        # missing PATH: test os.confstr("CS_PATH") and os.defpath
+        with os_helper.EnvironmentVarGuard() as env:
+            env.pop('PATH', None)
+
+            # without confstr
+            with (
+                mock.patch(
+                    'distutils.spawn.os.confstr', side_effect=ValueError, create=True
+                ),
+                mock.patch('distutils.spawn.os.defpath', tmp_dir),
+            ):
+                rv = find_executable(program)
+                assert rv == filename
+
+            # with confstr
+            with (
+                mock.patch(
+                    'distutils.spawn.os.confstr', return_value=tmp_dir, create=True
+                ),
+                mock.patch('distutils.spawn.os.defpath', ''),
+            ):
+                rv = find_executable(program)
+                assert rv == filename
+
+    @staticmethod
+    def _make_executable(tmp_path, ext):
+        # Give the temporary program a suffix regardless of platform.
+        # It's needed on Windows and not harmful on others.
+        program = tmp_path.joinpath('program').with_suffix(ext)
+        program.write_text("", encoding='utf-8')
+        program.chmod(stat.S_IXUSR)
+        return program
+
+    def test_spawn_missing_exe(self):
+        with pytest.raises(DistutilsExecError) as ctx:
+            spawn(['does-not-exist'])
+        assert "command 'does-not-exist' failed" in str(ctx.value)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_sysconfig.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_sysconfig.py
new file mode 100644
index 0000000000000000000000000000000000000000..43d77c23fad426be237b9f91a6d98d7c36233d84
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_sysconfig.py
@@ -0,0 +1,319 @@
+"""Tests for distutils.sysconfig."""
+
+import contextlib
+import distutils
+import os
+import pathlib
+import subprocess
+import sys
+from distutils import sysconfig
+from distutils.ccompiler import new_compiler  # noqa: F401
+from distutils.unixccompiler import UnixCCompiler
+
+import jaraco.envs
+import path
+import pytest
+from jaraco.text import trim
+from test.support import swap_item
+
+
+def _gen_makefile(root, contents):
+    jaraco.path.build({'Makefile': trim(contents)}, root)
+    return root / 'Makefile'
+
+
+@pytest.mark.usefixtures('save_env')
+class TestSysconfig:
+    def test_get_config_h_filename(self):
+        config_h = sysconfig.get_config_h_filename()
+        assert os.path.isfile(config_h)
+
+    @pytest.mark.skipif("platform.system() == 'Windows'")
+    @pytest.mark.skipif("sys.implementation.name != 'cpython'")
+    def test_get_makefile_filename(self):
+        makefile = sysconfig.get_makefile_filename()
+        assert os.path.isfile(makefile)
+
+    def test_get_python_lib(self, tmp_path):
+        assert sysconfig.get_python_lib() != sysconfig.get_python_lib(prefix=tmp_path)
+
+    def test_get_config_vars(self):
+        cvars = sysconfig.get_config_vars()
+        assert isinstance(cvars, dict)
+        assert cvars
+
+    @pytest.mark.skipif('sysconfig.IS_PYPY')
+    @pytest.mark.skipif('sysconfig.python_build')
+    @pytest.mark.xfail('platform.system() == "Windows"')
+    def test_srcdir_simple(self):
+        # See #15364.
+        srcdir = pathlib.Path(sysconfig.get_config_var('srcdir'))
+
+        assert srcdir.absolute()
+        assert srcdir.is_dir()
+
+        makefile = pathlib.Path(sysconfig.get_makefile_filename())
+        assert makefile.parent.samefile(srcdir)
+
+    @pytest.mark.skipif('sysconfig.IS_PYPY')
+    @pytest.mark.skipif('not sysconfig.python_build')
+    def test_srcdir_python_build(self):
+        # See #15364.
+        srcdir = pathlib.Path(sysconfig.get_config_var('srcdir'))
+
+        # The python executable has not been installed so srcdir
+        # should be a full source checkout.
+        Python_h = srcdir.joinpath('Include', 'Python.h')
+        assert Python_h.is_file()
+        assert sysconfig._is_python_source_dir(srcdir)
+        assert sysconfig._is_python_source_dir(str(srcdir))
+
+    def test_srcdir_independent_of_cwd(self):
+        """
+        srcdir should be independent of the current working directory
+        """
+        # See #15364.
+        srcdir = sysconfig.get_config_var('srcdir')
+        with path.Path('..'):
+            srcdir2 = sysconfig.get_config_var('srcdir')
+        assert srcdir == srcdir2
+
+    def customize_compiler(self):
+        # make sure AR gets caught
+        class compiler:
+            compiler_type = 'unix'
+            executables = UnixCCompiler.executables
+
+            def __init__(self):
+                self.exes = {}
+
+            def set_executables(self, **kw):
+                for k, v in kw.items():
+                    self.exes[k] = v
+
+        sysconfig_vars = {
+            'AR': 'sc_ar',
+            'CC': 'sc_cc',
+            'CXX': 'sc_cxx',
+            'ARFLAGS': '--sc-arflags',
+            'CFLAGS': '--sc-cflags',
+            'CCSHARED': '--sc-ccshared',
+            'LDSHARED': 'sc_ldshared',
+            'SHLIB_SUFFIX': 'sc_shutil_suffix',
+        }
+
+        comp = compiler()
+        with contextlib.ExitStack() as cm:
+            for key, value in sysconfig_vars.items():
+                cm.enter_context(swap_item(sysconfig._config_vars, key, value))
+            sysconfig.customize_compiler(comp)
+
+        return comp
+
+    @pytest.mark.skipif("not isinstance(new_compiler(), UnixCCompiler)")
+    @pytest.mark.usefixtures('disable_macos_customization')
+    def test_customize_compiler(self):
+        # Make sure that sysconfig._config_vars is initialized
+        sysconfig.get_config_vars()
+
+        os.environ['AR'] = 'env_ar'
+        os.environ['CC'] = 'env_cc'
+        os.environ['CPP'] = 'env_cpp'
+        os.environ['CXX'] = 'env_cxx --env-cxx-flags'
+        os.environ['LDSHARED'] = 'env_ldshared'
+        os.environ['LDFLAGS'] = '--env-ldflags'
+        os.environ['ARFLAGS'] = '--env-arflags'
+        os.environ['CFLAGS'] = '--env-cflags'
+        os.environ['CPPFLAGS'] = '--env-cppflags'
+        os.environ['RANLIB'] = 'env_ranlib'
+
+        comp = self.customize_compiler()
+        assert comp.exes['archiver'] == 'env_ar --env-arflags'
+        assert comp.exes['preprocessor'] == 'env_cpp --env-cppflags'
+        assert comp.exes['compiler'] == 'env_cc --env-cflags --env-cppflags'
+        assert comp.exes['compiler_so'] == (
+            'env_cc --env-cflags --env-cppflags --sc-ccshared'
+        )
+        assert (
+            comp.exes['compiler_cxx']
+            == 'env_cxx --env-cxx-flags --sc-cflags --env-cppflags'
+        )
+        assert comp.exes['linker_exe'] == 'env_cc'
+        assert comp.exes['linker_so'] == (
+            'env_ldshared --env-ldflags --env-cflags --env-cppflags'
+        )
+        assert comp.shared_lib_extension == 'sc_shutil_suffix'
+
+        if sys.platform == "darwin":
+            assert comp.exes['ranlib'] == 'env_ranlib'
+        else:
+            assert 'ranlib' not in comp.exes
+
+        del os.environ['AR']
+        del os.environ['CC']
+        del os.environ['CPP']
+        del os.environ['CXX']
+        del os.environ['LDSHARED']
+        del os.environ['LDFLAGS']
+        del os.environ['ARFLAGS']
+        del os.environ['CFLAGS']
+        del os.environ['CPPFLAGS']
+        del os.environ['RANLIB']
+
+        comp = self.customize_compiler()
+        assert comp.exes['archiver'] == 'sc_ar --sc-arflags'
+        assert comp.exes['preprocessor'] == 'sc_cc -E'
+        assert comp.exes['compiler'] == 'sc_cc --sc-cflags'
+        assert comp.exes['compiler_so'] == 'sc_cc --sc-cflags --sc-ccshared'
+        assert comp.exes['compiler_cxx'] == 'sc_cxx --sc-cflags'
+        assert comp.exes['linker_exe'] == 'sc_cc'
+        assert comp.exes['linker_so'] == 'sc_ldshared'
+        assert comp.shared_lib_extension == 'sc_shutil_suffix'
+        assert 'ranlib' not in comp.exes
+
+    def test_parse_makefile_base(self, tmp_path):
+        makefile = _gen_makefile(
+            tmp_path,
+            """
+            CONFIG_ARGS=  '--arg1=optarg1' 'ENV=LIB'
+            VAR=$OTHER
+            OTHER=foo
+            """,
+        )
+        d = sysconfig.parse_makefile(makefile)
+        assert d == {'CONFIG_ARGS': "'--arg1=optarg1' 'ENV=LIB'", 'OTHER': 'foo'}
+
+    def test_parse_makefile_literal_dollar(self, tmp_path):
+        makefile = _gen_makefile(
+            tmp_path,
+            """
+            CONFIG_ARGS=  '--arg1=optarg1' 'ENV=\\$$LIB'
+            VAR=$OTHER
+            OTHER=foo
+            """,
+        )
+        d = sysconfig.parse_makefile(makefile)
+        assert d == {'CONFIG_ARGS': r"'--arg1=optarg1' 'ENV=\$LIB'", 'OTHER': 'foo'}
+
+    def test_sysconfig_module(self):
+        import sysconfig as global_sysconfig
+
+        assert global_sysconfig.get_config_var('CFLAGS') == sysconfig.get_config_var(
+            'CFLAGS'
+        )
+        assert global_sysconfig.get_config_var('LDFLAGS') == sysconfig.get_config_var(
+            'LDFLAGS'
+        )
+
+    # On macOS, binary installers support extension module building on
+    # various levels of the operating system with differing Xcode
+    # configurations, requiring customization of some of the
+    # compiler configuration directives to suit the environment on
+    # the installed machine. Some of these customizations may require
+    # running external programs and are thus deferred until needed by
+    # the first extension module build. Only
+    # the Distutils version of sysconfig is used for extension module
+    # builds, which happens earlier in the Distutils tests. This may
+    # cause the following tests to fail since no tests have caused
+    # the global version of sysconfig to call the customization yet.
+    # The solution for now is to simply skip this test in this case.
+    # The longer-term solution is to only have one version of sysconfig.
+    @pytest.mark.skipif("sysconfig.get_config_var('CUSTOMIZED_OSX_COMPILER')")
+    def test_sysconfig_compiler_vars(self):
+        import sysconfig as global_sysconfig
+
+        if sysconfig.get_config_var('CUSTOMIZED_OSX_COMPILER'):
+            pytest.skip('compiler flags customized')
+        assert global_sysconfig.get_config_var('LDSHARED') == sysconfig.get_config_var(
+            'LDSHARED'
+        )
+        assert global_sysconfig.get_config_var('CC') == sysconfig.get_config_var('CC')
+
+    @pytest.mark.skipif("not sysconfig.get_config_var('EXT_SUFFIX')")
+    def test_SO_deprecation(self):
+        with pytest.warns(DeprecationWarning):
+            sysconfig.get_config_var('SO')
+
+    def test_customize_compiler_before_get_config_vars(self, tmp_path):
+        # Issue #21923: test that a Distribution compiler
+        # instance can be called without an explicit call to
+        # get_config_vars().
+        jaraco.path.build(
+            {
+                'file': trim("""
+                    from distutils.core import Distribution
+                    config = Distribution().get_command_obj('config')
+                    # try_compile may pass or it may fail if no compiler
+                    # is found but it should not raise an exception.
+                    rc = config.try_compile('int x;')
+                    """)
+            },
+            tmp_path,
+        )
+        p = subprocess.Popen(
+            [sys.executable, tmp_path / 'file'],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            universal_newlines=True,
+            encoding='utf-8',
+        )
+        outs, errs = p.communicate()
+        assert 0 == p.returncode, "Subprocess failed: " + outs
+
+    def test_parse_config_h(self):
+        config_h = sysconfig.get_config_h_filename()
+        input = {}
+        with open(config_h, encoding="utf-8") as f:
+            result = sysconfig.parse_config_h(f, g=input)
+        assert input is result
+        with open(config_h, encoding="utf-8") as f:
+            result = sysconfig.parse_config_h(f)
+        assert isinstance(result, dict)
+
+    @pytest.mark.skipif("platform.system() != 'Windows'")
+    @pytest.mark.skipif("sys.implementation.name != 'cpython'")
+    def test_win_ext_suffix(self):
+        assert sysconfig.get_config_var("EXT_SUFFIX").endswith(".pyd")
+        assert sysconfig.get_config_var("EXT_SUFFIX") != ".pyd"
+
+    @pytest.mark.skipif("platform.system() != 'Windows'")
+    @pytest.mark.skipif("sys.implementation.name != 'cpython'")
+    @pytest.mark.skipif(
+        '\\PCbuild\\'.casefold() not in sys.executable.casefold(),
+        reason='Need sys.executable to be in a source tree',
+    )
+    def test_win_build_venv_from_source_tree(self, tmp_path):
+        """Ensure distutils.sysconfig detects venvs from source tree builds."""
+        env = jaraco.envs.VEnv()
+        env.create_opts = env.clean_opts
+        env.root = tmp_path
+        env.ensure_env()
+        cmd = [
+            env.exe(),
+            "-c",
+            "import distutils.sysconfig; print(distutils.sysconfig.python_build)",
+        ]
+        distutils_path = os.path.dirname(os.path.dirname(distutils.__file__))
+        out = subprocess.check_output(
+            cmd, env={**os.environ, "PYTHONPATH": distutils_path}
+        )
+        assert out == "True"
+
+    def test_get_python_inc_missing_config_dir(self, monkeypatch):
+        """
+        In portable Python installations, the sysconfig will be broken,
+        pointing to the directories where the installation was built and
+        not where it currently is. In this case, ensure that the missing
+        directory isn't used for get_python_inc.
+
+        See pypa/distutils#178.
+        """
+
+        def override(name):
+            if name == 'INCLUDEPY':
+                return '/does-not-exist'
+            return sysconfig.get_config_var(name)
+
+        monkeypatch.setattr(sysconfig, 'get_config_var', override)
+
+        assert os.path.exists(sysconfig.get_python_inc())
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_text_file.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_text_file.py
new file mode 100644
index 0000000000000000000000000000000000000000..f51115656135cd3f348a45d4a14f97eeee47fb98
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_text_file.py
@@ -0,0 +1,127 @@
+"""Tests for distutils.text_file."""
+
+from distutils.tests import support
+from distutils.text_file import TextFile
+
+import jaraco.path
+import path
+
+TEST_DATA = """# test file
+
+line 3 \\
+# intervening comment
+  continues on next line
+"""
+
+
+class TestTextFile(support.TempdirManager):
+    def test_class(self):
+        # old tests moved from text_file.__main__
+        # so they are really called by the buildbots
+
+        # result 1: no fancy options
+        result1 = [
+            '# test file\n',
+            '\n',
+            'line 3 \\\n',
+            '# intervening comment\n',
+            '  continues on next line\n',
+        ]
+
+        # result 2: just strip comments
+        result2 = ["\n", "line 3 \\\n", "  continues on next line\n"]
+
+        # result 3: just strip blank lines
+        result3 = [
+            "# test file\n",
+            "line 3 \\\n",
+            "# intervening comment\n",
+            "  continues on next line\n",
+        ]
+
+        # result 4: default, strip comments, blank lines,
+        # and trailing whitespace
+        result4 = ["line 3 \\", "  continues on next line"]
+
+        # result 5: strip comments and blanks, plus join lines (but don't
+        # "collapse" joined lines
+        result5 = ["line 3   continues on next line"]
+
+        # result 6: strip comments and blanks, plus join lines (and
+        # "collapse" joined lines
+        result6 = ["line 3 continues on next line"]
+
+        def test_input(count, description, file, expected_result):
+            result = file.readlines()
+            assert result == expected_result
+
+        tmp_path = path.Path(self.mkdtemp())
+        filename = tmp_path / 'test.txt'
+        jaraco.path.build({filename.name: TEST_DATA}, tmp_path)
+
+        in_file = TextFile(
+            filename,
+            strip_comments=False,
+            skip_blanks=False,
+            lstrip_ws=False,
+            rstrip_ws=False,
+        )
+        try:
+            test_input(1, "no processing", in_file, result1)
+        finally:
+            in_file.close()
+
+        in_file = TextFile(
+            filename,
+            strip_comments=True,
+            skip_blanks=False,
+            lstrip_ws=False,
+            rstrip_ws=False,
+        )
+        try:
+            test_input(2, "strip comments", in_file, result2)
+        finally:
+            in_file.close()
+
+        in_file = TextFile(
+            filename,
+            strip_comments=False,
+            skip_blanks=True,
+            lstrip_ws=False,
+            rstrip_ws=False,
+        )
+        try:
+            test_input(3, "strip blanks", in_file, result3)
+        finally:
+            in_file.close()
+
+        in_file = TextFile(filename)
+        try:
+            test_input(4, "default processing", in_file, result4)
+        finally:
+            in_file.close()
+
+        in_file = TextFile(
+            filename,
+            strip_comments=True,
+            skip_blanks=True,
+            join_lines=True,
+            rstrip_ws=True,
+        )
+        try:
+            test_input(5, "join lines without collapsing", in_file, result5)
+        finally:
+            in_file.close()
+
+        in_file = TextFile(
+            filename,
+            strip_comments=True,
+            skip_blanks=True,
+            join_lines=True,
+            rstrip_ws=True,
+            collapse_join=True,
+        )
+        try:
+            test_input(6, "join lines with collapsing", in_file, result6)
+        finally:
+            in_file.close()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_util.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..00c9743ed0a2664039060ef149d30f165bf2f465
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_util.py
@@ -0,0 +1,243 @@
+"""Tests for distutils.util."""
+
+import email
+import email.generator
+import email.policy
+import io
+import os
+import pathlib
+import sys
+import sysconfig as stdlib_sysconfig
+import unittest.mock as mock
+from copy import copy
+from distutils import sysconfig, util
+from distutils.errors import DistutilsByteCompileError, DistutilsPlatformError
+from distutils.util import (
+    byte_compile,
+    change_root,
+    check_environ,
+    convert_path,
+    get_host_platform,
+    get_platform,
+    grok_environment_error,
+    rfc822_escape,
+    split_quoted,
+    strtobool,
+)
+
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def environment(monkeypatch):
+    monkeypatch.setattr(os, 'name', os.name)
+    monkeypatch.setattr(sys, 'platform', sys.platform)
+    monkeypatch.setattr(sys, 'version', sys.version)
+    monkeypatch.setattr(os, 'sep', os.sep)
+    monkeypatch.setattr(os.path, 'join', os.path.join)
+    monkeypatch.setattr(os.path, 'isabs', os.path.isabs)
+    monkeypatch.setattr(os.path, 'splitdrive', os.path.splitdrive)
+    monkeypatch.setattr(sysconfig, '_config_vars', copy(sysconfig._config_vars))
+
+
+@pytest.mark.usefixtures('save_env')
+class TestUtil:
+    def test_get_host_platform(self):
+        with mock.patch('os.name', 'nt'):
+            with mock.patch('sys.version', '... [... (ARM64)]'):
+                assert get_host_platform() == 'win-arm64'
+            with mock.patch('sys.version', '... [... (ARM)]'):
+                assert get_host_platform() == 'win-arm32'
+
+        with mock.patch('sys.version_info', (3, 9, 0, 'final', 0)):
+            assert get_host_platform() == stdlib_sysconfig.get_platform()
+
+    def test_get_platform(self):
+        with mock.patch('os.name', 'nt'):
+            with mock.patch.dict('os.environ', {'VSCMD_ARG_TGT_ARCH': 'x86'}):
+                assert get_platform() == 'win32'
+            with mock.patch.dict('os.environ', {'VSCMD_ARG_TGT_ARCH': 'x64'}):
+                assert get_platform() == 'win-amd64'
+            with mock.patch.dict('os.environ', {'VSCMD_ARG_TGT_ARCH': 'arm'}):
+                assert get_platform() == 'win-arm32'
+            with mock.patch.dict('os.environ', {'VSCMD_ARG_TGT_ARCH': 'arm64'}):
+                assert get_platform() == 'win-arm64'
+
+    def test_convert_path(self):
+        expected = os.sep.join(('', 'home', 'to', 'my', 'stuff'))
+        assert convert_path('/home/to/my/stuff') == expected
+        assert convert_path(pathlib.Path('/home/to/my/stuff')) == expected
+        assert convert_path('.') == os.curdir
+
+    def test_change_root(self):
+        # linux/mac
+        os.name = 'posix'
+
+        def _isabs(path):
+            return path[0] == '/'
+
+        os.path.isabs = _isabs
+
+        def _join(*path):
+            return '/'.join(path)
+
+        os.path.join = _join
+
+        assert change_root('/root', '/old/its/here') == '/root/old/its/here'
+        assert change_root('/root', 'its/here') == '/root/its/here'
+
+        # windows
+        os.name = 'nt'
+        os.sep = '\\'
+
+        def _isabs(path):
+            return path.startswith('c:\\')
+
+        os.path.isabs = _isabs
+
+        def _splitdrive(path):
+            if path.startswith('c:'):
+                return ('', path.replace('c:', ''))
+            return ('', path)
+
+        os.path.splitdrive = _splitdrive
+
+        def _join(*path):
+            return '\\'.join(path)
+
+        os.path.join = _join
+
+        assert (
+            change_root('c:\\root', 'c:\\old\\its\\here') == 'c:\\root\\old\\its\\here'
+        )
+        assert change_root('c:\\root', 'its\\here') == 'c:\\root\\its\\here'
+
+        # BugsBunny os (it's a great os)
+        os.name = 'BugsBunny'
+        with pytest.raises(DistutilsPlatformError):
+            change_root('c:\\root', 'its\\here')
+
+        # XXX platforms to be covered: mac
+
+    def test_check_environ(self):
+        util.check_environ.cache_clear()
+        os.environ.pop('HOME', None)
+
+        check_environ()
+
+        assert os.environ['PLAT'] == get_platform()
+
+    @pytest.mark.skipif("os.name != 'posix'")
+    def test_check_environ_getpwuid(self):
+        util.check_environ.cache_clear()
+        os.environ.pop('HOME', None)
+
+        import pwd
+
+        # only set pw_dir field, other fields are not used
+        result = pwd.struct_passwd((
+            None,
+            None,
+            None,
+            None,
+            None,
+            '/home/distutils',
+            None,
+        ))
+        with mock.patch.object(pwd, 'getpwuid', return_value=result):
+            check_environ()
+            assert os.environ['HOME'] == '/home/distutils'
+
+        util.check_environ.cache_clear()
+        os.environ.pop('HOME', None)
+
+        # bpo-10496: Catch pwd.getpwuid() error
+        with mock.patch.object(pwd, 'getpwuid', side_effect=KeyError):
+            check_environ()
+            assert 'HOME' not in os.environ
+
+    def test_split_quoted(self):
+        assert split_quoted('""one"" "two" \'three\' \\four') == [
+            'one',
+            'two',
+            'three',
+            'four',
+        ]
+
+    def test_strtobool(self):
+        yes = ('y', 'Y', 'yes', 'True', 't', 'true', 'True', 'On', 'on', '1')
+        no = ('n', 'no', 'f', 'false', 'off', '0', 'Off', 'No', 'N')
+
+        for y in yes:
+            assert strtobool(y)
+
+        for n in no:
+            assert not strtobool(n)
+
+    indent = 8 * ' '
+
+    @pytest.mark.parametrize(
+        "given,wanted",
+        [
+            # 0x0b, 0x0c, ..., etc are also considered a line break by Python
+            ("hello\x0b\nworld\n", f"hello\x0b{indent}\n{indent}world\n{indent}"),
+            ("hello\x1eworld", f"hello\x1e{indent}world"),
+            ("", ""),
+            (
+                "I am a\npoor\nlonesome\nheader\n",
+                f"I am a\n{indent}poor\n{indent}lonesome\n{indent}header\n{indent}",
+            ),
+        ],
+    )
+    def test_rfc822_escape(self, given, wanted):
+        """
+        We want to ensure a multi-line header parses correctly.
+
+        For interoperability, the escaped value should also "round-trip" over
+        `email.generator.Generator.flatten` and `email.message_from_*`
+        (see pypa/setuptools#4033).
+
+        The main issue is that internally `email.policy.EmailPolicy` uses
+        `splitlines` which will split on some control chars. If all the new lines
+        are not prefixed with spaces, the parser will interrupt reading
+        the current header and produce an incomplete value, while
+        incorrectly interpreting the rest of the headers as part of the payload.
+        """
+        res = rfc822_escape(given)
+
+        policy = email.policy.EmailPolicy(
+            utf8=True,
+            mangle_from_=False,
+            max_line_length=0,
+        )
+        with io.StringIO() as buffer:
+            raw = f"header: {res}\nother-header: 42\n\npayload\n"
+            orig = email.message_from_string(raw)
+            email.generator.Generator(buffer, policy=policy).flatten(orig)
+            buffer.seek(0)
+            regen = email.message_from_file(buffer)
+
+        for msg in (orig, regen):
+            assert msg.get_payload() == "payload\n"
+            assert msg["other-header"] == "42"
+            # Generator may replace control chars with `\n`
+            assert set(msg["header"].splitlines()) == set(res.splitlines())
+
+        assert res == wanted
+
+    def test_dont_write_bytecode(self):
+        # makes sure byte_compile raise a DistutilsError
+        # if sys.dont_write_bytecode is True
+        old_dont_write_bytecode = sys.dont_write_bytecode
+        sys.dont_write_bytecode = True
+        try:
+            with pytest.raises(DistutilsByteCompileError):
+                byte_compile([])
+        finally:
+            sys.dont_write_bytecode = old_dont_write_bytecode
+
+    def test_grok_environment_error(self):
+        # test obsolete function to ensure backward compat (#4931)
+        exc = OSError("Unable to find batch file")
+        msg = grok_environment_error(exc)
+        assert msg == "error: Unable to find batch file"
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_version.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..b68f0977249aa23aafbf31cf74a0e4127f777d05
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_version.py
@@ -0,0 +1,80 @@
+"""Tests for distutils.version."""
+
+import distutils
+from distutils.version import LooseVersion, StrictVersion
+
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def suppress_deprecation():
+    with distutils.version.suppress_known_deprecation():
+        yield
+
+
+class TestVersion:
+    def test_prerelease(self):
+        version = StrictVersion('1.2.3a1')
+        assert version.version == (1, 2, 3)
+        assert version.prerelease == ('a', 1)
+        assert str(version) == '1.2.3a1'
+
+        version = StrictVersion('1.2.0')
+        assert str(version) == '1.2'
+
+    def test_cmp_strict(self):
+        versions = (
+            ('1.5.1', '1.5.2b2', -1),
+            ('161', '3.10a', ValueError),
+            ('8.02', '8.02', 0),
+            ('3.4j', '1996.07.12', ValueError),
+            ('3.2.pl0', '3.1.1.6', ValueError),
+            ('2g6', '11g', ValueError),
+            ('0.9', '2.2', -1),
+            ('1.2.1', '1.2', 1),
+            ('1.1', '1.2.2', -1),
+            ('1.2', '1.1', 1),
+            ('1.2.1', '1.2.2', -1),
+            ('1.2.2', '1.2', 1),
+            ('1.2', '1.2.2', -1),
+            ('0.4.0', '0.4', 0),
+            ('1.13++', '5.5.kw', ValueError),
+        )
+
+        for v1, v2, wanted in versions:
+            try:
+                res = StrictVersion(v1)._cmp(StrictVersion(v2))
+            except ValueError:
+                if wanted is ValueError:
+                    continue
+                else:
+                    raise AssertionError(f"cmp({v1}, {v2}) shouldn't raise ValueError")
+            assert res == wanted, f'cmp({v1}, {v2}) should be {wanted}, got {res}'
+            res = StrictVersion(v1)._cmp(v2)
+            assert res == wanted, f'cmp({v1}, {v2}) should be {wanted}, got {res}'
+            res = StrictVersion(v1)._cmp(object())
+            assert res is NotImplemented, (
+                f'cmp({v1}, {v2}) should be NotImplemented, got {res}'
+            )
+
+    def test_cmp(self):
+        versions = (
+            ('1.5.1', '1.5.2b2', -1),
+            ('161', '3.10a', 1),
+            ('8.02', '8.02', 0),
+            ('3.4j', '1996.07.12', -1),
+            ('3.2.pl0', '3.1.1.6', 1),
+            ('2g6', '11g', -1),
+            ('0.960923', '2.2beta29', -1),
+            ('1.13++', '5.5.kw', -1),
+        )
+
+        for v1, v2, wanted in versions:
+            res = LooseVersion(v1)._cmp(LooseVersion(v2))
+            assert res == wanted, f'cmp({v1}, {v2}) should be {wanted}, got {res}'
+            res = LooseVersion(v1)._cmp(v2)
+            assert res == wanted, f'cmp({v1}, {v2}) should be {wanted}, got {res}'
+            res = LooseVersion(v1)._cmp(object())
+            assert res is NotImplemented, (
+                f'cmp({v1}, {v2}) should be NotImplemented, got {res}'
+            )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_versionpredicate.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/test_versionpredicate.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/unix_compat.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/unix_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5d9ee45ccc639794d29ba0a5fd24f781ee5ecf5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/tests/unix_compat.py
@@ -0,0 +1,17 @@
+import sys
+
+try:
+    import grp
+    import pwd
+except ImportError:
+    grp = pwd = None
+
+import pytest
+
+UNIX_ID_SUPPORT = grp and pwd
+UID_0_SUPPORT = UNIX_ID_SUPPORT and sys.platform != "cygwin"
+
+require_unix_id = pytest.mark.skipif(
+    not UNIX_ID_SUPPORT, reason="Requires grp and pwd support"
+)
+require_uid_0 = pytest.mark.skipif(not UID_0_SUPPORT, reason="Requires UID 0 support")
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/text_file.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/text_file.py
new file mode 100644
index 0000000000000000000000000000000000000000..89d9048d5902950516c3541ad212bff7b6cb2045
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/text_file.py
@@ -0,0 +1,286 @@
+"""text_file
+
+provides the TextFile class, which gives an interface to text files
+that (optionally) takes care of stripping comments, ignoring blank
+lines, and joining lines with backslashes."""
+
+import sys
+
+
+class TextFile:
+    """Provides a file-like object that takes care of all the things you
+    commonly want to do when processing a text file that has some
+    line-by-line syntax: strip comments (as long as "#" is your
+    comment character), skip blank lines, join adjacent lines by
+    escaping the newline (ie. backslash at end of line), strip
+    leading and/or trailing whitespace.  All of these are optional
+    and independently controllable.
+
+    Provides a 'warn()' method so you can generate warning messages that
+    report physical line number, even if the logical line in question
+    spans multiple physical lines.  Also provides 'unreadline()' for
+    implementing line-at-a-time lookahead.
+
+    Constructor is called as:
+
+        TextFile (filename=None, file=None, **options)
+
+    It bombs (RuntimeError) if both 'filename' and 'file' are None;
+    'filename' should be a string, and 'file' a file object (or
+    something that provides 'readline()' and 'close()' methods).  It is
+    recommended that you supply at least 'filename', so that TextFile
+    can include it in warning messages.  If 'file' is not supplied,
+    TextFile creates its own using 'io.open()'.
+
+    The options are all boolean, and affect the value returned by
+    'readline()':
+      strip_comments [default: true]
+        strip from "#" to end-of-line, as well as any whitespace
+        leading up to the "#" -- unless it is escaped by a backslash
+      lstrip_ws [default: false]
+        strip leading whitespace from each line before returning it
+      rstrip_ws [default: true]
+        strip trailing whitespace (including line terminator!) from
+        each line before returning it
+      skip_blanks [default: true}
+        skip lines that are empty *after* stripping comments and
+        whitespace.  (If both lstrip_ws and rstrip_ws are false,
+        then some lines may consist of solely whitespace: these will
+        *not* be skipped, even if 'skip_blanks' is true.)
+      join_lines [default: false]
+        if a backslash is the last non-newline character on a line
+        after stripping comments and whitespace, join the following line
+        to it to form one "logical line"; if N consecutive lines end
+        with a backslash, then N+1 physical lines will be joined to
+        form one logical line.
+      collapse_join [default: false]
+        strip leading whitespace from lines that are joined to their
+        predecessor; only matters if (join_lines and not lstrip_ws)
+      errors [default: 'strict']
+        error handler used to decode the file content
+
+    Note that since 'rstrip_ws' can strip the trailing newline, the
+    semantics of 'readline()' must differ from those of the builtin file
+    object's 'readline()' method!  In particular, 'readline()' returns
+    None for end-of-file: an empty string might just be a blank line (or
+    an all-whitespace line), if 'rstrip_ws' is true but 'skip_blanks' is
+    not."""
+
+    default_options = {
+        'strip_comments': 1,
+        'skip_blanks': 1,
+        'lstrip_ws': 0,
+        'rstrip_ws': 1,
+        'join_lines': 0,
+        'collapse_join': 0,
+        'errors': 'strict',
+    }
+
+    def __init__(self, filename=None, file=None, **options):
+        """Construct a new TextFile object.  At least one of 'filename'
+        (a string) and 'file' (a file-like object) must be supplied.
+        They keyword argument options are described above and affect
+        the values returned by 'readline()'."""
+        if filename is None and file is None:
+            raise RuntimeError(
+                "you must supply either or both of 'filename' and 'file'"
+            )
+
+        # set values for all options -- either from client option hash
+        # or fallback to default_options
+        for opt in self.default_options.keys():
+            if opt in options:
+                setattr(self, opt, options[opt])
+            else:
+                setattr(self, opt, self.default_options[opt])
+
+        # sanity check client option hash
+        for opt in options.keys():
+            if opt not in self.default_options:
+                raise KeyError(f"invalid TextFile option '{opt}'")
+
+        if file is None:
+            self.open(filename)
+        else:
+            self.filename = filename
+            self.file = file
+            self.current_line = 0  # assuming that file is at BOF!
+
+        # 'linebuf' is a stack of lines that will be emptied before we
+        # actually read from the file; it's only populated by an
+        # 'unreadline()' operation
+        self.linebuf = []
+
+    def open(self, filename):
+        """Open a new file named 'filename'.  This overrides both the
+        'filename' and 'file' arguments to the constructor."""
+        self.filename = filename
+        self.file = open(self.filename, errors=self.errors, encoding='utf-8')
+        self.current_line = 0
+
+    def close(self):
+        """Close the current file and forget everything we know about it
+        (filename, current line number)."""
+        file = self.file
+        self.file = None
+        self.filename = None
+        self.current_line = None
+        file.close()
+
+    def gen_error(self, msg, line=None):
+        outmsg = []
+        if line is None:
+            line = self.current_line
+        outmsg.append(self.filename + ", ")
+        if isinstance(line, (list, tuple)):
+            outmsg.append("lines {}-{}: ".format(*line))
+        else:
+            outmsg.append(f"line {int(line)}: ")
+        outmsg.append(str(msg))
+        return "".join(outmsg)
+
+    def error(self, msg, line=None):
+        raise ValueError("error: " + self.gen_error(msg, line))
+
+    def warn(self, msg, line=None):
+        """Print (to stderr) a warning message tied to the current logical
+        line in the current file.  If the current logical line in the
+        file spans multiple physical lines, the warning refers to the
+        whole range, eg. "lines 3-5".  If 'line' supplied, it overrides
+        the current line number; it may be a list or tuple to indicate a
+        range of physical lines, or an integer for a single physical
+        line."""
+        sys.stderr.write("warning: " + self.gen_error(msg, line) + "\n")
+
+    def readline(self):  # noqa: C901
+        """Read and return a single logical line from the current file (or
+        from an internal buffer if lines have previously been "unread"
+        with 'unreadline()').  If the 'join_lines' option is true, this
+        may involve reading multiple physical lines concatenated into a
+        single string.  Updates the current line number, so calling
+        'warn()' after 'readline()' emits a warning about the physical
+        line(s) just read.  Returns None on end-of-file, since the empty
+        string can occur if 'rstrip_ws' is true but 'strip_blanks' is
+        not."""
+        # If any "unread" lines waiting in 'linebuf', return the top
+        # one.  (We don't actually buffer read-ahead data -- lines only
+        # get put in 'linebuf' if the client explicitly does an
+        # 'unreadline()'.
+        if self.linebuf:
+            line = self.linebuf[-1]
+            del self.linebuf[-1]
+            return line
+
+        buildup_line = ''
+
+        while True:
+            # read the line, make it None if EOF
+            line = self.file.readline()
+            if line == '':
+                line = None
+
+            if self.strip_comments and line:
+                # Look for the first "#" in the line.  If none, never
+                # mind.  If we find one and it's the first character, or
+                # is not preceded by "\", then it starts a comment --
+                # strip the comment, strip whitespace before it, and
+                # carry on.  Otherwise, it's just an escaped "#", so
+                # unescape it (and any other escaped "#"'s that might be
+                # lurking in there) and otherwise leave the line alone.
+
+                pos = line.find("#")
+                if pos == -1:  # no "#" -- no comments
+                    pass
+
+                # It's definitely a comment -- either "#" is the first
+                # character, or it's elsewhere and unescaped.
+                elif pos == 0 or line[pos - 1] != "\\":
+                    # Have to preserve the trailing newline, because it's
+                    # the job of a later step (rstrip_ws) to remove it --
+                    # and if rstrip_ws is false, we'd better preserve it!
+                    # (NB. this means that if the final line is all comment
+                    # and has no trailing newline, we will think that it's
+                    # EOF; I think that's OK.)
+                    eol = (line[-1] == '\n') and '\n' or ''
+                    line = line[0:pos] + eol
+
+                    # If all that's left is whitespace, then skip line
+                    # *now*, before we try to join it to 'buildup_line' --
+                    # that way constructs like
+                    #   hello \\
+                    #   # comment that should be ignored
+                    #   there
+                    # result in "hello there".
+                    if line.strip() == "":
+                        continue
+                else:  # it's an escaped "#"
+                    line = line.replace("\\#", "#")
+
+            # did previous line end with a backslash? then accumulate
+            if self.join_lines and buildup_line:
+                # oops: end of file
+                if line is None:
+                    self.warn("continuation line immediately precedes end-of-file")
+                    return buildup_line
+
+                if self.collapse_join:
+                    line = line.lstrip()
+                line = buildup_line + line
+
+                # careful: pay attention to line number when incrementing it
+                if isinstance(self.current_line, list):
+                    self.current_line[1] = self.current_line[1] + 1
+                else:
+                    self.current_line = [self.current_line, self.current_line + 1]
+            # just an ordinary line, read it as usual
+            else:
+                if line is None:  # eof
+                    return None
+
+                # still have to be careful about incrementing the line number!
+                if isinstance(self.current_line, list):
+                    self.current_line = self.current_line[1] + 1
+                else:
+                    self.current_line = self.current_line + 1
+
+            # strip whitespace however the client wants (leading and
+            # trailing, or one or the other, or neither)
+            if self.lstrip_ws and self.rstrip_ws:
+                line = line.strip()
+            elif self.lstrip_ws:
+                line = line.lstrip()
+            elif self.rstrip_ws:
+                line = line.rstrip()
+
+            # blank line (whether we rstrip'ed or not)? skip to next line
+            # if appropriate
+            if line in ('', '\n') and self.skip_blanks:
+                continue
+
+            if self.join_lines:
+                if line[-1] == '\\':
+                    buildup_line = line[:-1]
+                    continue
+
+                if line[-2:] == '\\\n':
+                    buildup_line = line[0:-2] + '\n'
+                    continue
+
+            # well, I guess there's some actual content there: return it
+            return line
+
+    def readlines(self):
+        """Read and return the list of all logical lines remaining in the
+        current file."""
+        lines = []
+        while True:
+            line = self.readline()
+            if line is None:
+                return lines
+            lines.append(line)
+
+    def unreadline(self, line):
+        """Push 'line' (a string) onto an internal buffer that will be
+        checked by future 'readline()' calls.  Handy for implementing
+        a parser with line-at-a-time lookahead."""
+        self.linebuf.append(line)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/unixccompiler.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/unixccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..20b8ce6b9bba21bd709b77249bdcae5b4119e9ca
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/unixccompiler.py
@@ -0,0 +1,9 @@
+import importlib
+
+from .compilers.C import unix
+
+UnixCCompiler = unix.Compiler
+
+# ensure import of unixccompiler implies ccompiler imported
+# (pypa/setuptools#4871)
+importlib.import_module('distutils.ccompiler')
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/util.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dbe049f4274f7177295491d7f433e8ca303df48
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/util.py
@@ -0,0 +1,518 @@
+"""distutils.util
+
+Miscellaneous utility functions -- anything that doesn't fit into
+one of the other *util.py modules.
+"""
+
+from __future__ import annotations
+
+import functools
+import importlib.util
+import os
+import pathlib
+import re
+import string
+import subprocess
+import sys
+import sysconfig
+import tempfile
+from collections.abc import Callable, Iterable, Mapping
+from typing import TYPE_CHECKING, AnyStr
+
+from jaraco.functools import pass_none
+
+from ._log import log
+from ._modified import newer
+from .errors import DistutilsByteCompileError, DistutilsPlatformError
+from .spawn import spawn
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeVarTuple, Unpack
+
+    _Ts = TypeVarTuple("_Ts")
+
+
+def get_host_platform() -> str:
+    """
+    Return a string that identifies the current platform. Use this
+    function to distinguish platform-specific build directories and
+    platform-specific built distributions.
+    """
+
+    # This function initially exposed platforms as defined in Python 3.9
+    # even with older Python versions when distutils was split out.
+    # Now it delegates to stdlib sysconfig.
+
+    return sysconfig.get_platform()
+
+
+def get_platform() -> str:
+    if os.name == 'nt':
+        TARGET_TO_PLAT = {
+            'x86': 'win32',
+            'x64': 'win-amd64',
+            'arm': 'win-arm32',
+            'arm64': 'win-arm64',
+        }
+        target = os.environ.get('VSCMD_ARG_TGT_ARCH')
+        return TARGET_TO_PLAT.get(target) or get_host_platform()
+    return get_host_platform()
+
+
+if sys.platform == 'darwin':
+    _syscfg_macosx_ver = None  # cache the version pulled from sysconfig
+MACOSX_VERSION_VAR = 'MACOSX_DEPLOYMENT_TARGET'
+
+
+def _clear_cached_macosx_ver():
+    """For testing only. Do not call."""
+    global _syscfg_macosx_ver
+    _syscfg_macosx_ver = None
+
+
+def get_macosx_target_ver_from_syscfg():
+    """Get the version of macOS latched in the Python interpreter configuration.
+    Returns the version as a string or None if can't obtain one. Cached."""
+    global _syscfg_macosx_ver
+    if _syscfg_macosx_ver is None:
+        from distutils import sysconfig
+
+        ver = sysconfig.get_config_var(MACOSX_VERSION_VAR) or ''
+        if ver:
+            _syscfg_macosx_ver = ver
+    return _syscfg_macosx_ver
+
+
+def get_macosx_target_ver():
+    """Return the version of macOS for which we are building.
+
+    The target version defaults to the version in sysconfig latched at time
+    the Python interpreter was built, unless overridden by an environment
+    variable. If neither source has a value, then None is returned"""
+
+    syscfg_ver = get_macosx_target_ver_from_syscfg()
+    env_ver = os.environ.get(MACOSX_VERSION_VAR)
+
+    if env_ver:
+        # Validate overridden version against sysconfig version, if have both.
+        # Ensure that the deployment target of the build process is not less
+        # than 10.3 if the interpreter was built for 10.3 or later.  This
+        # ensures extension modules are built with correct compatibility
+        # values, specifically LDSHARED which can use
+        # '-undefined dynamic_lookup' which only works on >= 10.3.
+        if (
+            syscfg_ver
+            and split_version(syscfg_ver) >= [10, 3]
+            and split_version(env_ver) < [10, 3]
+        ):
+            my_msg = (
+                '$' + MACOSX_VERSION_VAR + ' mismatch: '
+                f'now "{env_ver}" but "{syscfg_ver}" during configure; '
+                'must use 10.3 or later'
+            )
+            raise DistutilsPlatformError(my_msg)
+        return env_ver
+    return syscfg_ver
+
+
+def split_version(s: str) -> list[int]:
+    """Convert a dot-separated string into a list of numbers for comparisons"""
+    return [int(n) for n in s.split('.')]
+
+
+@pass_none
+def convert_path(pathname: str | os.PathLike[str]) -> str:
+    r"""
+    Allow for pathlib.Path inputs, coax to a native path string.
+
+    If None is passed, will just pass it through as
+    Setuptools relies on this behavior.
+
+    >>> convert_path(None) is None
+    True
+
+    Removes empty paths.
+
+    >>> convert_path('foo/./bar').replace('\\', '/')
+    'foo/bar'
+    """
+    return os.fspath(pathlib.PurePath(pathname))
+
+
+def change_root(
+    new_root: AnyStr | os.PathLike[AnyStr], pathname: AnyStr | os.PathLike[AnyStr]
+) -> AnyStr:
+    """Return 'pathname' with 'new_root' prepended.  If 'pathname' is
+    relative, this is equivalent to "os.path.join(new_root,pathname)".
+    Otherwise, it requires making 'pathname' relative and then joining the
+    two, which is tricky on DOS/Windows and Mac OS.
+    """
+    if os.name == 'posix':
+        if not os.path.isabs(pathname):
+            return os.path.join(new_root, pathname)
+        else:
+            return os.path.join(new_root, pathname[1:])
+
+    elif os.name == 'nt':
+        (drive, path) = os.path.splitdrive(pathname)
+        if path[0] == os.sep:
+            path = path[1:]
+        return os.path.join(new_root, path)
+
+    raise DistutilsPlatformError(f"nothing known about platform '{os.name}'")
+
+
+@functools.lru_cache
+def check_environ() -> None:
+    """Ensure that 'os.environ' has all the environment variables we
+    guarantee that users can use in config files, command-line options,
+    etc.  Currently this includes:
+      HOME - user's home directory (Unix only)
+      PLAT - description of the current platform, including hardware
+             and OS (see 'get_platform()')
+    """
+    if os.name == 'posix' and 'HOME' not in os.environ:
+        try:
+            import pwd
+
+            os.environ['HOME'] = pwd.getpwuid(os.getuid())[5]
+        except (ImportError, KeyError):
+            # bpo-10496: if the current user identifier doesn't exist in the
+            # password database, do nothing
+            pass
+
+    if 'PLAT' not in os.environ:
+        os.environ['PLAT'] = get_platform()
+
+
+def subst_vars(s, local_vars: Mapping[str, object]) -> str:
+    """
+    Perform variable substitution on 'string'.
+    Variables are indicated by format-style braces ("{var}").
+    Variable is substituted by the value found in the 'local_vars'
+    dictionary or in 'os.environ' if it's not in 'local_vars'.
+    'os.environ' is first checked/augmented to guarantee that it contains
+    certain values: see 'check_environ()'.  Raise ValueError for any
+    variables not found in either 'local_vars' or 'os.environ'.
+    """
+    check_environ()
+    lookup = dict(os.environ)
+    lookup.update((name, str(value)) for name, value in local_vars.items())
+    try:
+        return _subst_compat(s).format_map(lookup)
+    except KeyError as var:
+        raise ValueError(f"invalid variable {var}")
+
+
+def _subst_compat(s):
+    """
+    Replace shell/Perl-style variable substitution with
+    format-style. For compatibility.
+    """
+
+    def _subst(match):
+        return f'{{{match.group(1)}}}'
+
+    repl = re.sub(r'\$([a-zA-Z_][a-zA-Z_0-9]*)', _subst, s)
+    if repl != s:
+        import warnings
+
+        warnings.warn(
+            "shell/Perl-style substitutions are deprecated",
+            DeprecationWarning,
+        )
+    return repl
+
+
+def grok_environment_error(exc: object, prefix: str = "error: ") -> str:
+    # Function kept for backward compatibility.
+    # Used to try clever things with EnvironmentErrors,
+    # but nowadays str(exception) produces good messages.
+    return prefix + str(exc)
+
+
+# Needed by 'split_quoted()'
+_wordchars_re = _squote_re = _dquote_re = None
+
+
+def _init_regex():
+    global _wordchars_re, _squote_re, _dquote_re
+    _wordchars_re = re.compile(rf'[^\\\'\"{string.whitespace} ]*')
+    _squote_re = re.compile(r"'(?:[^'\\]|\\.)*'")
+    _dquote_re = re.compile(r'"(?:[^"\\]|\\.)*"')
+
+
+def split_quoted(s: str) -> list[str]:
+    """Split a string up according to Unix shell-like rules for quotes and
+    backslashes.  In short: words are delimited by spaces, as long as those
+    spaces are not escaped by a backslash, or inside a quoted string.
+    Single and double quotes are equivalent, and the quote characters can
+    be backslash-escaped.  The backslash is stripped from any two-character
+    escape sequence, leaving only the escaped character.  The quote
+    characters are stripped from any quoted string.  Returns a list of
+    words.
+    """
+
+    # This is a nice algorithm for splitting up a single string, since it
+    # doesn't require character-by-character examination.  It was a little
+    # bit of a brain-bender to get it working right, though...
+    if _wordchars_re is None:
+        _init_regex()
+
+    s = s.strip()
+    words = []
+    pos = 0
+
+    while s:
+        m = _wordchars_re.match(s, pos)
+        end = m.end()
+        if end == len(s):
+            words.append(s[:end])
+            break
+
+        if s[end] in string.whitespace:
+            # unescaped, unquoted whitespace: now
+            # we definitely have a word delimiter
+            words.append(s[:end])
+            s = s[end:].lstrip()
+            pos = 0
+
+        elif s[end] == '\\':
+            # preserve whatever is being escaped;
+            # will become part of the current word
+            s = s[:end] + s[end + 1 :]
+            pos = end + 1
+
+        else:
+            if s[end] == "'":  # slurp singly-quoted string
+                m = _squote_re.match(s, end)
+            elif s[end] == '"':  # slurp doubly-quoted string
+                m = _dquote_re.match(s, end)
+            else:
+                raise RuntimeError(f"this can't happen (bad char '{s[end]}')")
+
+            if m is None:
+                raise ValueError(f"bad string (mismatched {s[end]} quotes?)")
+
+            (beg, end) = m.span()
+            s = s[:beg] + s[beg + 1 : end - 1] + s[end:]
+            pos = m.end() - 2
+
+        if pos >= len(s):
+            words.append(s)
+            break
+
+    return words
+
+
+# split_quoted ()
+
+
+def execute(
+    func: Callable[[Unpack[_Ts]], object],
+    args: tuple[Unpack[_Ts]],
+    msg: object = None,
+    verbose: bool = False,
+    dry_run: bool = False,
+) -> None:
+    """
+    Perform some action that affects the outside world (e.g. by
+    writing to the filesystem). Such actions are special because they
+    are disabled by the 'dry_run' flag. This method handles that
+    complication; simply supply the
+    function to call and an argument tuple for it (to embody the
+    "external action" being performed) and an optional message to
+    emit.
+    """
+    if msg is None:
+        msg = f"{func.__name__}{args!r}"
+        if msg[-2:] == ',)':  # correct for singleton tuple
+            msg = msg[0:-2] + ')'
+
+    log.info(msg)
+    if not dry_run:
+        func(*args)
+
+
+def strtobool(val: str) -> bool:
+    """Convert a string representation of truth to true (1) or false (0).
+
+    True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values
+    are 'n', 'no', 'f', 'false', 'off', and '0'.  Raises ValueError if
+    'val' is anything else.
+    """
+    val = val.lower()
+    if val in ('y', 'yes', 't', 'true', 'on', '1'):
+        return True
+    elif val in ('n', 'no', 'f', 'false', 'off', '0'):
+        return False
+    else:
+        raise ValueError(f"invalid truth value {val!r}")
+
+
+def byte_compile(  # noqa: C901
+    py_files: Iterable[str],
+    optimize: int = 0,
+    force: bool = False,
+    prefix: str | None = None,
+    base_dir: str | None = None,
+    verbose: bool = True,
+    dry_run: bool = False,
+    direct: bool | None = None,
+) -> None:
+    """Byte-compile a collection of Python source files to .pyc
+    files in a __pycache__ subdirectory.  'py_files' is a list
+    of files to compile; any files that don't end in ".py" are silently
+    skipped.  'optimize' must be one of the following:
+      0 - don't optimize
+      1 - normal optimization (like "python -O")
+      2 - extra optimization (like "python -OO")
+    If 'force' is true, all files are recompiled regardless of
+    timestamps.
+
+    The source filename encoded in each bytecode file defaults to the
+    filenames listed in 'py_files'; you can modify these with 'prefix' and
+    'basedir'.  'prefix' is a string that will be stripped off of each
+    source filename, and 'base_dir' is a directory name that will be
+    prepended (after 'prefix' is stripped).  You can supply either or both
+    (or neither) of 'prefix' and 'base_dir', as you wish.
+
+    If 'dry_run' is true, doesn't actually do anything that would
+    affect the filesystem.
+
+    Byte-compilation is either done directly in this interpreter process
+    with the standard py_compile module, or indirectly by writing a
+    temporary script and executing it.  Normally, you should let
+    'byte_compile()' figure out to use direct compilation or not (see
+    the source for details).  The 'direct' flag is used by the script
+    generated in indirect mode; unless you know what you're doing, leave
+    it set to None.
+    """
+
+    # nothing is done if sys.dont_write_bytecode is True
+    if sys.dont_write_bytecode:
+        raise DistutilsByteCompileError('byte-compiling is disabled.')
+
+    # First, if the caller didn't force us into direct or indirect mode,
+    # figure out which mode we should be in.  We take a conservative
+    # approach: choose direct mode *only* if the current interpreter is
+    # in debug mode and optimize is 0.  If we're not in debug mode (-O
+    # or -OO), we don't know which level of optimization this
+    # interpreter is running with, so we can't do direct
+    # byte-compilation and be certain that it's the right thing.  Thus,
+    # always compile indirectly if the current interpreter is in either
+    # optimize mode, or if either optimization level was requested by
+    # the caller.
+    if direct is None:
+        direct = __debug__ and optimize == 0
+
+    # "Indirect" byte-compilation: write a temporary script and then
+    # run it with the appropriate flags.
+    if not direct:
+        (script_fd, script_name) = tempfile.mkstemp(".py")
+        log.info("writing byte-compilation script '%s'", script_name)
+        if not dry_run:
+            script = os.fdopen(script_fd, "w", encoding='utf-8')
+
+            with script:
+                script.write(
+                    """\
+from distutils.util import byte_compile
+files = [
+"""
+                )
+
+                # XXX would be nice to write absolute filenames, just for
+                # safety's sake (script should be more robust in the face of
+                # chdir'ing before running it).  But this requires abspath'ing
+                # 'prefix' as well, and that breaks the hack in build_lib's
+                # 'byte_compile()' method that carefully tacks on a trailing
+                # slash (os.sep really) to make sure the prefix here is "just
+                # right".  This whole prefix business is rather delicate -- the
+                # problem is that it's really a directory, but I'm treating it
+                # as a dumb string, so trailing slashes and so forth matter.
+
+                script.write(",\n".join(map(repr, py_files)) + "]\n")
+                script.write(
+                    f"""
+byte_compile(files, optimize={optimize!r}, force={force!r},
+             prefix={prefix!r}, base_dir={base_dir!r},
+             verbose={verbose!r}, dry_run=False,
+             direct=True)
+"""
+                )
+
+        cmd = [sys.executable]
+        cmd.extend(subprocess._optim_args_from_interpreter_flags())
+        cmd.append(script_name)
+        spawn(cmd, dry_run=dry_run)
+        execute(os.remove, (script_name,), f"removing {script_name}", dry_run=dry_run)
+
+    # "Direct" byte-compilation: use the py_compile module to compile
+    # right here, right now.  Note that the script generated in indirect
+    # mode simply calls 'byte_compile()' in direct mode, a weird sort of
+    # cross-process recursion.  Hey, it works!
+    else:
+        from py_compile import compile
+
+        for file in py_files:
+            if file[-3:] != ".py":
+                # This lets us be lazy and not filter filenames in
+                # the "install_lib" command.
+                continue
+
+            # Terminology from the py_compile module:
+            #   cfile - byte-compiled file
+            #   dfile - purported source filename (same as 'file' by default)
+            if optimize >= 0:
+                opt = '' if optimize == 0 else optimize
+                cfile = importlib.util.cache_from_source(file, optimization=opt)
+            else:
+                cfile = importlib.util.cache_from_source(file)
+            dfile = file
+            if prefix:
+                if file[: len(prefix)] != prefix:
+                    raise ValueError(
+                        f"invalid prefix: filename {file!r} doesn't start with {prefix!r}"
+                    )
+                dfile = dfile[len(prefix) :]
+            if base_dir:
+                dfile = os.path.join(base_dir, dfile)
+
+            cfile_base = os.path.basename(cfile)
+            if direct:
+                if force or newer(file, cfile):
+                    log.info("byte-compiling %s to %s", file, cfile_base)
+                    if not dry_run:
+                        compile(file, cfile, dfile)
+                else:
+                    log.debug("skipping byte-compilation of %s to %s", file, cfile_base)
+
+
+def rfc822_escape(header: str) -> str:
+    """Return a version of the string escaped for inclusion in an
+    RFC-822 header, by ensuring there are 8 spaces space after each newline.
+    """
+    indent = 8 * " "
+    lines = header.splitlines(keepends=True)
+
+    # Emulate the behaviour of `str.split`
+    # (the terminal line break in `splitlines` does not result in an extra line):
+    ends_in_newline = lines and lines[-1].splitlines()[0] != lines[-1]
+    suffix = indent if ends_in_newline else ""
+
+    return indent.join(lines) + suffix
+
+
+def is_mingw() -> bool:
+    """Returns True if the current platform is mingw.
+
+    Python compiled with Mingw-w64 has sys.platform == 'win32' and
+    get_platform() starts with 'mingw'.
+    """
+    return sys.platform == 'win32' and get_platform().startswith('mingw')
+
+
+def is_freethreaded():
+    """Return True if the Python interpreter is built with free threading support."""
+    return bool(sysconfig.get_config_var('Py_GIL_DISABLED'))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/version.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..2223ee9c8cab2495034a57d4585625feb81a8330
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/version.py
@@ -0,0 +1,348 @@
+#
+# distutils/version.py
+#
+# Implements multiple version numbering conventions for the
+# Python Module Distribution Utilities.
+#
+# $Id$
+#
+
+"""Provides classes to represent module version numbers (one class for
+each style of version numbering).  There are currently two such classes
+implemented: StrictVersion and LooseVersion.
+
+Every version number class implements the following interface:
+  * the 'parse' method takes a string and parses it to some internal
+    representation; if the string is an invalid version number,
+    'parse' raises a ValueError exception
+  * the class constructor takes an optional string argument which,
+    if supplied, is passed to 'parse'
+  * __str__ reconstructs the string that was passed to 'parse' (or
+    an equivalent string -- ie. one that will generate an equivalent
+    version number instance)
+  * __repr__ generates Python code to recreate the version number instance
+  * _cmp compares the current instance with either another instance
+    of the same class or a string (which will be parsed to an instance
+    of the same class, thus must follow the same rules)
+"""
+
+import contextlib
+import re
+import warnings
+
+
+@contextlib.contextmanager
+def suppress_known_deprecation():
+    with warnings.catch_warnings(record=True) as ctx:
+        warnings.filterwarnings(
+            action='default',
+            category=DeprecationWarning,
+            message="distutils Version classes are deprecated.",
+        )
+        yield ctx
+
+
+class Version:
+    """Abstract base class for version numbering classes.  Just provides
+    constructor (__init__) and reproducer (__repr__), because those
+    seem to be the same for all version numbering classes; and route
+    rich comparisons to _cmp.
+    """
+
+    def __init__(self, vstring=None):
+        if vstring:
+            self.parse(vstring)
+        warnings.warn(
+            "distutils Version classes are deprecated. Use packaging.version instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+
+    def __repr__(self):
+        return f"{self.__class__.__name__} ('{self}')"
+
+    def __eq__(self, other):
+        c = self._cmp(other)
+        if c is NotImplemented:
+            return c
+        return c == 0
+
+    def __lt__(self, other):
+        c = self._cmp(other)
+        if c is NotImplemented:
+            return c
+        return c < 0
+
+    def __le__(self, other):
+        c = self._cmp(other)
+        if c is NotImplemented:
+            return c
+        return c <= 0
+
+    def __gt__(self, other):
+        c = self._cmp(other)
+        if c is NotImplemented:
+            return c
+        return c > 0
+
+    def __ge__(self, other):
+        c = self._cmp(other)
+        if c is NotImplemented:
+            return c
+        return c >= 0
+
+
+# Interface for version-number classes -- must be implemented
+# by the following classes (the concrete ones -- Version should
+# be treated as an abstract class).
+#    __init__ (string) - create and take same action as 'parse'
+#                        (string parameter is optional)
+#    parse (string)    - convert a string representation to whatever
+#                        internal representation is appropriate for
+#                        this style of version numbering
+#    __str__ (self)    - convert back to a string; should be very similar
+#                        (if not identical to) the string supplied to parse
+#    __repr__ (self)   - generate Python code to recreate
+#                        the instance
+#    _cmp (self, other) - compare two version numbers ('other' may
+#                        be an unparsed version string, or another
+#                        instance of your version class)
+
+
+class StrictVersion(Version):
+    """Version numbering for anal retentives and software idealists.
+    Implements the standard interface for version number classes as
+    described above.  A version number consists of two or three
+    dot-separated numeric components, with an optional "pre-release" tag
+    on the end.  The pre-release tag consists of the letter 'a' or 'b'
+    followed by a number.  If the numeric components of two version
+    numbers are equal, then one with a pre-release tag will always
+    be deemed earlier (lesser) than one without.
+
+    The following are valid version numbers (shown in the order that
+    would be obtained by sorting according to the supplied cmp function):
+
+        0.4       0.4.0  (these two are equivalent)
+        0.4.1
+        0.5a1
+        0.5b3
+        0.5
+        0.9.6
+        1.0
+        1.0.4a3
+        1.0.4b1
+        1.0.4
+
+    The following are examples of invalid version numbers:
+
+        1
+        2.7.2.2
+        1.3.a4
+        1.3pl1
+        1.3c4
+
+    The rationale for this version numbering system will be explained
+    in the distutils documentation.
+    """
+
+    version_re = re.compile(
+        r'^(\d+) \. (\d+) (\. (\d+))? ([ab](\d+))?$', re.VERBOSE | re.ASCII
+    )
+
+    def parse(self, vstring):
+        match = self.version_re.match(vstring)
+        if not match:
+            raise ValueError(f"invalid version number '{vstring}'")
+
+        (major, minor, patch, prerelease, prerelease_num) = match.group(1, 2, 4, 5, 6)
+
+        if patch:
+            self.version = tuple(map(int, [major, minor, patch]))
+        else:
+            self.version = tuple(map(int, [major, minor])) + (0,)
+
+        if prerelease:
+            self.prerelease = (prerelease[0], int(prerelease_num))
+        else:
+            self.prerelease = None
+
+    def __str__(self):
+        if self.version[2] == 0:
+            vstring = '.'.join(map(str, self.version[0:2]))
+        else:
+            vstring = '.'.join(map(str, self.version))
+
+        if self.prerelease:
+            vstring = vstring + self.prerelease[0] + str(self.prerelease[1])
+
+        return vstring
+
+    def _cmp(self, other):
+        if isinstance(other, str):
+            with suppress_known_deprecation():
+                other = StrictVersion(other)
+        elif not isinstance(other, StrictVersion):
+            return NotImplemented
+
+        if self.version == other.version:
+            # versions match; pre-release drives the comparison
+            return self._cmp_prerelease(other)
+
+        return -1 if self.version < other.version else 1
+
+    def _cmp_prerelease(self, other):
+        """
+        case 1: self has prerelease, other doesn't; other is greater
+        case 2: self doesn't have prerelease, other does: self is greater
+        case 3: both or neither have prerelease: compare them!
+        """
+        if self.prerelease and not other.prerelease:
+            return -1
+        elif not self.prerelease and other.prerelease:
+            return 1
+
+        if self.prerelease == other.prerelease:
+            return 0
+        elif self.prerelease < other.prerelease:
+            return -1
+        else:
+            return 1
+
+
+# end class StrictVersion
+
+
+# The rules according to Greg Stein:
+# 1) a version number has 1 or more numbers separated by a period or by
+#    sequences of letters. If only periods, then these are compared
+#    left-to-right to determine an ordering.
+# 2) sequences of letters are part of the tuple for comparison and are
+#    compared lexicographically
+# 3) recognize the numeric components may have leading zeroes
+#
+# The LooseVersion class below implements these rules: a version number
+# string is split up into a tuple of integer and string components, and
+# comparison is a simple tuple comparison.  This means that version
+# numbers behave in a predictable and obvious way, but a way that might
+# not necessarily be how people *want* version numbers to behave.  There
+# wouldn't be a problem if people could stick to purely numeric version
+# numbers: just split on period and compare the numbers as tuples.
+# However, people insist on putting letters into their version numbers;
+# the most common purpose seems to be:
+#   - indicating a "pre-release" version
+#     ('alpha', 'beta', 'a', 'b', 'pre', 'p')
+#   - indicating a post-release patch ('p', 'pl', 'patch')
+# but of course this can't cover all version number schemes, and there's
+# no way to know what a programmer means without asking him.
+#
+# The problem is what to do with letters (and other non-numeric
+# characters) in a version number.  The current implementation does the
+# obvious and predictable thing: keep them as strings and compare
+# lexically within a tuple comparison.  This has the desired effect if
+# an appended letter sequence implies something "post-release":
+# eg. "0.99" < "0.99pl14" < "1.0", and "5.001" < "5.001m" < "5.002".
+#
+# However, if letters in a version number imply a pre-release version,
+# the "obvious" thing isn't correct.  Eg. you would expect that
+# "1.5.1" < "1.5.2a2" < "1.5.2", but under the tuple/lexical comparison
+# implemented here, this just isn't so.
+#
+# Two possible solutions come to mind.  The first is to tie the
+# comparison algorithm to a particular set of semantic rules, as has
+# been done in the StrictVersion class above.  This works great as long
+# as everyone can go along with bondage and discipline.  Hopefully a
+# (large) subset of Python module programmers will agree that the
+# particular flavour of bondage and discipline provided by StrictVersion
+# provides enough benefit to be worth using, and will submit their
+# version numbering scheme to its domination.  The free-thinking
+# anarchists in the lot will never give in, though, and something needs
+# to be done to accommodate them.
+#
+# Perhaps a "moderately strict" version class could be implemented that
+# lets almost anything slide (syntactically), and makes some heuristic
+# assumptions about non-digits in version number strings.  This could
+# sink into special-case-hell, though; if I was as talented and
+# idiosyncratic as Larry Wall, I'd go ahead and implement a class that
+# somehow knows that "1.2.1" < "1.2.2a2" < "1.2.2" < "1.2.2pl3", and is
+# just as happy dealing with things like "2g6" and "1.13++".  I don't
+# think I'm smart enough to do it right though.
+#
+# In any case, I've coded the test suite for this module (see
+# ../test/test_version.py) specifically to fail on things like comparing
+# "1.2a2" and "1.2".  That's not because the *code* is doing anything
+# wrong, it's because the simple, obvious design doesn't match my
+# complicated, hairy expectations for real-world version numbers.  It
+# would be a snap to fix the test suite to say, "Yep, LooseVersion does
+# the Right Thing" (ie. the code matches the conception).  But I'd rather
+# have a conception that matches common notions about version numbers.
+
+
+class LooseVersion(Version):
+    """Version numbering for anarchists and software realists.
+    Implements the standard interface for version number classes as
+    described above.  A version number consists of a series of numbers,
+    separated by either periods or strings of letters.  When comparing
+    version numbers, the numeric components will be compared
+    numerically, and the alphabetic components lexically.  The following
+    are all valid version numbers, in no particular order:
+
+        1.5.1
+        1.5.2b2
+        161
+        3.10a
+        8.02
+        3.4j
+        1996.07.12
+        3.2.pl0
+        3.1.1.6
+        2g6
+        11g
+        0.960923
+        2.2beta29
+        1.13++
+        5.5.kw
+        2.0b1pl0
+
+    In fact, there is no such thing as an invalid version number under
+    this scheme; the rules for comparison are simple and predictable,
+    but may not always give the results you want (for some definition
+    of "want").
+    """
+
+    component_re = re.compile(r'(\d+ | [a-z]+ | \.)', re.VERBOSE)
+
+    def parse(self, vstring):
+        # I've given up on thinking I can reconstruct the version string
+        # from the parsed tuple -- so I just store the string here for
+        # use by __str__
+        self.vstring = vstring
+        components = [x for x in self.component_re.split(vstring) if x and x != '.']
+        for i, obj in enumerate(components):
+            try:
+                components[i] = int(obj)
+            except ValueError:
+                pass
+
+        self.version = components
+
+    def __str__(self):
+        return self.vstring
+
+    def __repr__(self):
+        return f"LooseVersion ('{self}')"
+
+    def _cmp(self, other):
+        if isinstance(other, str):
+            other = LooseVersion(other)
+        elif not isinstance(other, LooseVersion):
+            return NotImplemented
+
+        if self.version == other.version:
+            return 0
+        if self.version < other.version:
+            return -1
+        if self.version > other.version:
+            return 1
+
+
+# end class LooseVersion
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/versionpredicate.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/versionpredicate.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe31b0ed8ef843080ea64df9f58a398d317434ad
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/versionpredicate.py
@@ -0,0 +1,175 @@
+"""Module for parsing and testing package version predicate strings."""
+
+import operator
+import re
+
+from . import version
+
+re_validPackage = re.compile(r"(?i)^\s*([a-z_]\w*(?:\.[a-z_]\w*)*)(.*)", re.ASCII)
+# (package) (rest)
+
+re_paren = re.compile(r"^\s*\((.*)\)\s*$")  # (list) inside of parentheses
+re_splitComparison = re.compile(r"^\s*(<=|>=|<|>|!=|==)\s*([^\s,]+)\s*$")
+# (comp) (version)
+
+
+def splitUp(pred):
+    """Parse a single version comparison.
+
+    Return (comparison string, StrictVersion)
+    """
+    res = re_splitComparison.match(pred)
+    if not res:
+        raise ValueError(f"bad package restriction syntax: {pred!r}")
+    comp, verStr = res.groups()
+    with version.suppress_known_deprecation():
+        other = version.StrictVersion(verStr)
+    return (comp, other)
+
+
+compmap = {
+    "<": operator.lt,
+    "<=": operator.le,
+    "==": operator.eq,
+    ">": operator.gt,
+    ">=": operator.ge,
+    "!=": operator.ne,
+}
+
+
+class VersionPredicate:
+    """Parse and test package version predicates.
+
+    >>> v = VersionPredicate('pyepat.abc (>1.0, <3333.3a1, !=1555.1b3)')
+
+    The `name` attribute provides the full dotted name that is given::
+
+    >>> v.name
+    'pyepat.abc'
+
+    The str() of a `VersionPredicate` provides a normalized
+    human-readable version of the expression::
+
+    >>> print(v)
+    pyepat.abc (> 1.0, < 3333.3a1, != 1555.1b3)
+
+    The `satisfied_by()` method can be used to determine with a given
+    version number is included in the set described by the version
+    restrictions::
+
+    >>> v.satisfied_by('1.1')
+    True
+    >>> v.satisfied_by('1.4')
+    True
+    >>> v.satisfied_by('1.0')
+    False
+    >>> v.satisfied_by('4444.4')
+    False
+    >>> v.satisfied_by('1555.1b3')
+    False
+
+    `VersionPredicate` is flexible in accepting extra whitespace::
+
+    >>> v = VersionPredicate(' pat( ==  0.1  )  ')
+    >>> v.name
+    'pat'
+    >>> v.satisfied_by('0.1')
+    True
+    >>> v.satisfied_by('0.2')
+    False
+
+    If any version numbers passed in do not conform to the
+    restrictions of `StrictVersion`, a `ValueError` is raised::
+
+    >>> v = VersionPredicate('p1.p2.p3.p4(>=1.0, <=1.3a1, !=1.2zb3)')
+    Traceback (most recent call last):
+      ...
+    ValueError: invalid version number '1.2zb3'
+
+    It the module or package name given does not conform to what's
+    allowed as a legal module or package name, `ValueError` is
+    raised::
+
+    >>> v = VersionPredicate('foo-bar')
+    Traceback (most recent call last):
+      ...
+    ValueError: expected parenthesized list: '-bar'
+
+    >>> v = VersionPredicate('foo bar (12.21)')
+    Traceback (most recent call last):
+      ...
+    ValueError: expected parenthesized list: 'bar (12.21)'
+
+    """
+
+    def __init__(self, versionPredicateStr):
+        """Parse a version predicate string."""
+        # Fields:
+        #    name:  package name
+        #    pred:  list of (comparison string, StrictVersion)
+
+        versionPredicateStr = versionPredicateStr.strip()
+        if not versionPredicateStr:
+            raise ValueError("empty package restriction")
+        match = re_validPackage.match(versionPredicateStr)
+        if not match:
+            raise ValueError(f"bad package name in {versionPredicateStr!r}")
+        self.name, paren = match.groups()
+        paren = paren.strip()
+        if paren:
+            match = re_paren.match(paren)
+            if not match:
+                raise ValueError(f"expected parenthesized list: {paren!r}")
+            str = match.groups()[0]
+            self.pred = [splitUp(aPred) for aPred in str.split(",")]
+            if not self.pred:
+                raise ValueError(f"empty parenthesized list in {versionPredicateStr!r}")
+        else:
+            self.pred = []
+
+    def __str__(self):
+        if self.pred:
+            seq = [cond + " " + str(ver) for cond, ver in self.pred]
+            return self.name + " (" + ", ".join(seq) + ")"
+        else:
+            return self.name
+
+    def satisfied_by(self, version):
+        """True if version is compatible with all the predicates in self.
+        The parameter version must be acceptable to the StrictVersion
+        constructor.  It may be either a string or StrictVersion.
+        """
+        for cond, ver in self.pred:
+            if not compmap[cond](version, ver):
+                return False
+        return True
+
+
+_provision_rx = None
+
+
+def split_provision(value):
+    """Return the name and optional version number of a provision.
+
+    The version number, if given, will be returned as a `StrictVersion`
+    instance, otherwise it will be `None`.
+
+    >>> split_provision('mypkg')
+    ('mypkg', None)
+    >>> split_provision(' mypkg( 1.2 ) ')
+    ('mypkg', StrictVersion ('1.2'))
+    """
+    global _provision_rx
+    if _provision_rx is None:
+        _provision_rx = re.compile(
+            r"([a-zA-Z_]\w*(?:\.[a-zA-Z_]\w*)*)(?:\s*\(\s*([^)\s]+)\s*\))?$", re.ASCII
+        )
+    value = value.strip()
+    m = _provision_rx.match(value)
+    if not m:
+        raise ValueError(f"illegal provides specification: {value!r}")
+    ver = m.group(2) or None
+    if ver:
+        with version.suppress_known_deprecation():
+            ver = version.StrictVersion(ver)
+    return m.group(1), ver
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_distutils/zosccompiler.py b/.venv/lib/python3.12/site-packages/setuptools/_distutils/zosccompiler.py
new file mode 100644
index 0000000000000000000000000000000000000000..e49630ac6ed8cad62c4d2ff8361758519e6dc83b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_distutils/zosccompiler.py
@@ -0,0 +1,3 @@
+from .compilers.C import zos
+
+zOSCCompiler = zos.Compiler
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_entry_points.py b/.venv/lib/python3.12/site-packages/setuptools/_entry_points.py
new file mode 100644
index 0000000000000000000000000000000000000000..e785fc7df8d51241a38675902fe5e5b78a0cc29c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_entry_points.py
@@ -0,0 +1,90 @@
+import functools
+import itertools
+import operator
+
+from jaraco.functools import pass_none
+from jaraco.text import yield_lines
+from more_itertools import consume
+
+from ._importlib import metadata
+from ._itertools import ensure_unique
+from .errors import OptionError
+
+
+def ensure_valid(ep):
+    """
+    Exercise one of the dynamic properties to trigger
+    the pattern match.
+    """
+    try:
+        ep.extras
+    except (AttributeError, AssertionError) as ex:
+        # Why both? See https://github.com/python/importlib_metadata/issues/488
+        msg = (
+            f"Problems to parse {ep}.\nPlease ensure entry-point follows the spec: "
+            "https://packaging.python.org/en/latest/specifications/entry-points/"
+        )
+        raise OptionError(msg) from ex
+
+
+def load_group(value, group):
+    """
+    Given a value of an entry point or series of entry points,
+    return each as an EntryPoint.
+    """
+    # normalize to a single sequence of lines
+    lines = yield_lines(value)
+    text = f'[{group}]\n' + '\n'.join(lines)
+    return metadata.EntryPoints._from_text(text)
+
+
+def by_group_and_name(ep):
+    return ep.group, ep.name
+
+
+def validate(eps: metadata.EntryPoints):
+    """
+    Ensure entry points are unique by group and name and validate each.
+    """
+    consume(map(ensure_valid, ensure_unique(eps, key=by_group_and_name)))
+    return eps
+
+
+@functools.singledispatch
+def load(eps):
+    """
+    Given a Distribution.entry_points, produce EntryPoints.
+    """
+    groups = itertools.chain.from_iterable(
+        load_group(value, group) for group, value in eps.items()
+    )
+    return validate(metadata.EntryPoints(groups))
+
+
+@load.register(str)
+def _(eps):
+    r"""
+    >>> ep, = load('[console_scripts]\nfoo=bar')
+    >>> ep.group
+    'console_scripts'
+    >>> ep.name
+    'foo'
+    >>> ep.value
+    'bar'
+    """
+    return validate(metadata.EntryPoints(metadata.EntryPoints._from_text(eps)))
+
+
+load.register(type(None), lambda x: x)
+
+
+@pass_none
+def render(eps: metadata.EntryPoints):
+    by_group = operator.attrgetter('group')
+    groups = itertools.groupby(sorted(eps, key=by_group), by_group)
+
+    return '\n'.join(f'[{group}]\n{render_items(items)}\n' for group, items in groups)
+
+
+def render_items(eps):
+    return '\n'.join(f'{ep.name} = {ep.value}' for ep in sorted(eps))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_imp.py b/.venv/lib/python3.12/site-packages/setuptools/_imp.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1d9f29218987d4f830f2d57aca9e3f74d00a095
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_imp.py
@@ -0,0 +1,87 @@
+"""
+Re-implementation of find_module and get_frozen_object
+from the deprecated imp module.
+"""
+
+import importlib.machinery
+import importlib.util
+import os
+import tokenize
+from importlib.util import module_from_spec
+
+PY_SOURCE = 1
+PY_COMPILED = 2
+C_EXTENSION = 3
+C_BUILTIN = 6
+PY_FROZEN = 7
+
+
+def find_spec(module, paths):
+    finder = (
+        importlib.machinery.PathFinder().find_spec
+        if isinstance(paths, list)
+        else importlib.util.find_spec
+    )
+    return finder(module, paths)
+
+
+def find_module(module, paths=None):
+    """Just like 'imp.find_module()', but with package support"""
+    spec = find_spec(module, paths)
+    if spec is None:
+        raise ImportError(f"Can't find {module}")
+    if not spec.has_location and hasattr(spec, 'submodule_search_locations'):
+        spec = importlib.util.spec_from_loader('__init__.py', spec.loader)
+
+    kind = -1
+    file = None
+    static = isinstance(spec.loader, type)
+    if (
+        spec.origin == 'frozen'
+        or static
+        and issubclass(spec.loader, importlib.machinery.FrozenImporter)
+    ):
+        kind = PY_FROZEN
+        path = None  # imp compabilty
+        suffix = mode = ''  # imp compatibility
+    elif (
+        spec.origin == 'built-in'
+        or static
+        and issubclass(spec.loader, importlib.machinery.BuiltinImporter)
+    ):
+        kind = C_BUILTIN
+        path = None  # imp compabilty
+        suffix = mode = ''  # imp compatibility
+    elif spec.has_location:
+        path = spec.origin
+        suffix = os.path.splitext(path)[1]
+        mode = 'r' if suffix in importlib.machinery.SOURCE_SUFFIXES else 'rb'
+
+        if suffix in importlib.machinery.SOURCE_SUFFIXES:
+            kind = PY_SOURCE
+            file = tokenize.open(path)
+        elif suffix in importlib.machinery.BYTECODE_SUFFIXES:
+            kind = PY_COMPILED
+            file = open(path, 'rb')
+        elif suffix in importlib.machinery.EXTENSION_SUFFIXES:
+            kind = C_EXTENSION
+
+    else:
+        path = None
+        suffix = mode = ''
+
+    return file, path, (suffix, mode, kind)
+
+
+def get_frozen_object(module, paths=None):
+    spec = find_spec(module, paths)
+    if not spec:
+        raise ImportError(f"Can't find {module}")
+    return spec.loader.get_code(module)
+
+
+def get_module(module, paths, info):
+    spec = find_spec(module, paths)
+    if not spec:
+        raise ImportError(f"Can't find {module}")
+    return module_from_spec(spec)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_importlib.py b/.venv/lib/python3.12/site-packages/setuptools/_importlib.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce0fd52653b56c9c2cb2b2c7bfb35e3ec3c61408
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_importlib.py
@@ -0,0 +1,9 @@
+import sys
+
+if sys.version_info < (3, 10):
+    import importlib_metadata as metadata  # pragma: no cover
+else:
+    import importlib.metadata as metadata  # noqa: F401
+
+
+import importlib.resources as resources  # noqa: F401
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_itertools.py b/.venv/lib/python3.12/site-packages/setuptools/_itertools.py
new file mode 100644
index 0000000000000000000000000000000000000000..d6ca841353ce39ac4361013f5c8160d69028d0d8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_itertools.py
@@ -0,0 +1,23 @@
+from more_itertools import consume  # noqa: F401
+
+
+# copied from jaraco.itertools 6.1
+def ensure_unique(iterable, key=lambda x: x):
+    """
+    Wrap an iterable to raise a ValueError if non-unique values are encountered.
+
+    >>> list(ensure_unique('abc'))
+    ['a', 'b', 'c']
+    >>> consume(ensure_unique('abca'))
+    Traceback (most recent call last):
+    ...
+    ValueError: Duplicate element 'a' encountered.
+    """
+    seen = set()
+    seen_add = seen.add
+    for element in iterable:
+        k = key(element)
+        if k in seen:
+            raise ValueError(f"Duplicate element {element!r} encountered.")
+        seen_add(k)
+        yield element
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_normalization.py b/.venv/lib/python3.12/site-packages/setuptools/_normalization.py
new file mode 100644
index 0000000000000000000000000000000000000000..0937a4faf8cf2358ed1ec8a4f12b8260e493fd24
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_normalization.py
@@ -0,0 +1,179 @@
+"""
+Helpers for normalization as expected in wheel/sdist/module file names
+and core metadata
+"""
+
+import re
+from typing import TYPE_CHECKING
+
+import packaging
+
+# https://packaging.python.org/en/latest/specifications/core-metadata/#name
+_VALID_NAME = re.compile(r"^([A-Z0-9]|[A-Z0-9][A-Z0-9._-]*[A-Z0-9])$", re.I)
+_UNSAFE_NAME_CHARS = re.compile(r"[^A-Z0-9._-]+", re.I)
+_NON_ALPHANUMERIC = re.compile(r"[^A-Z0-9]+", re.I)
+_PEP440_FALLBACK = re.compile(r"^v?(?P<safe>(?:[0-9]+!)?[0-9]+(?:\.[0-9]+)*)", re.I)
+
+
+def safe_identifier(name: str) -> str:
+    """Make a string safe to be used as Python identifier.
+    >>> safe_identifier("12abc")
+    '_12abc'
+    >>> safe_identifier("__editable__.myns.pkg-78.9.3_local")
+    '__editable___myns_pkg_78_9_3_local'
+    """
+    safe = re.sub(r'\W|^(?=\d)', '_', name)
+    assert safe.isidentifier()
+    return safe
+
+
+def safe_name(component: str) -> str:
+    """Escape a component used as a project name according to Core Metadata.
+    >>> safe_name("hello world")
+    'hello-world'
+    >>> safe_name("hello?world")
+    'hello-world'
+    >>> safe_name("hello_world")
+    'hello_world'
+    """
+    # See pkg_resources.safe_name
+    return _UNSAFE_NAME_CHARS.sub("-", component)
+
+
+def safe_version(version: str) -> str:
+    """Convert an arbitrary string into a valid version string.
+    Can still raise an ``InvalidVersion`` exception.
+    To avoid exceptions use ``best_effort_version``.
+    >>> safe_version("1988 12 25")
+    '1988.12.25'
+    >>> safe_version("v0.2.1")
+    '0.2.1'
+    >>> safe_version("v0.2?beta")
+    '0.2b0'
+    >>> safe_version("v0.2 beta")
+    '0.2b0'
+    >>> safe_version("ubuntu lts")
+    Traceback (most recent call last):
+    ...
+    packaging.version.InvalidVersion: Invalid version: 'ubuntu.lts'
+    """
+    v = version.replace(' ', '.')
+    try:
+        return str(packaging.version.Version(v))
+    except packaging.version.InvalidVersion:
+        attempt = _UNSAFE_NAME_CHARS.sub("-", v)
+        return str(packaging.version.Version(attempt))
+
+
+def best_effort_version(version: str) -> str:
+    """Convert an arbitrary string into a version-like string.
+    Fallback when ``safe_version`` is not safe enough.
+    >>> best_effort_version("v0.2 beta")
+    '0.2b0'
+    >>> best_effort_version("ubuntu lts")
+    '0.dev0+sanitized.ubuntu.lts'
+    >>> best_effort_version("0.23ubuntu1")
+    '0.23.dev0+sanitized.ubuntu1'
+    >>> best_effort_version("0.23-")
+    '0.23.dev0+sanitized'
+    >>> best_effort_version("0.-_")
+    '0.dev0+sanitized'
+    >>> best_effort_version("42.+?1")
+    '42.dev0+sanitized.1'
+    """
+    # See pkg_resources._forgiving_version
+    try:
+        return safe_version(version)
+    except packaging.version.InvalidVersion:
+        v = version.replace(' ', '.')
+        match = _PEP440_FALLBACK.search(v)
+        if match:
+            safe = match["safe"]
+            rest = v[len(safe) :]
+        else:
+            safe = "0"
+            rest = version
+        safe_rest = _NON_ALPHANUMERIC.sub(".", rest).strip(".")
+        local = f"sanitized.{safe_rest}".strip(".")
+        return safe_version(f"{safe}.dev0+{local}")
+
+
+def safe_extra(extra: str) -> str:
+    """Normalize extra name according to PEP 685
+    >>> safe_extra("_FrIeNdLy-._.-bArD")
+    'friendly-bard'
+    >>> safe_extra("FrIeNdLy-._.-bArD__._-")
+    'friendly-bard'
+    """
+    return _NON_ALPHANUMERIC.sub("-", extra).strip("-").lower()
+
+
+def filename_component(value: str) -> str:
+    """Normalize each component of a filename (e.g. distribution/version part of wheel)
+    Note: ``value`` needs to be already normalized.
+    >>> filename_component("my-pkg")
+    'my_pkg'
+    """
+    return value.replace("-", "_").strip("_")
+
+
+def filename_component_broken(value: str) -> str:
+    """
+    Produce the incorrect filename component for compatibility.
+
+    See pypa/setuptools#4167 for detailed analysis.
+
+    TODO: replace this with filename_component after pip 24 is
+    nearly-ubiquitous.
+
+    >>> filename_component_broken('foo_bar-baz')
+    'foo-bar-baz'
+    """
+    return value.replace('_', '-')
+
+
+def safer_name(value: str) -> str:
+    """Like ``safe_name`` but can be used as filename component for wheel"""
+    # See bdist_wheel.safer_name
+    return (
+        # Per https://packaging.python.org/en/latest/specifications/name-normalization/#name-normalization
+        re.sub(r"[-_.]+", "-", safe_name(value))
+        .lower()
+        # Per https://packaging.python.org/en/latest/specifications/binary-distribution-format/#escaping-and-unicode
+        .replace("-", "_")
+    )
+
+
+def safer_best_effort_version(value: str) -> str:
+    """Like ``best_effort_version`` but can be used as filename component for wheel"""
+    # See bdist_wheel.safer_verion
+    # TODO: Replace with only safe_version in the future (no need for best effort)
+    return filename_component(best_effort_version(value))
+
+
+def _missing_canonicalize_license_expression(expression: str) -> str:
+    """
+    Defer import error to affect only users that actually use it
+    https://github.com/pypa/setuptools/issues/4894
+    >>> _missing_canonicalize_license_expression("a OR b")
+    Traceback (most recent call last):
+    ...
+    ImportError: ...Cannot import `packaging.licenses`...
+    """
+    raise ImportError(
+        "Cannot import `packaging.licenses`."
+        """
+        Setuptools>=77.0.0 requires "packaging>=24.2" to work properly.
+        Please make sure you have a suitable version installed.
+        """
+    )
+
+
+try:
+    from packaging.licenses import (
+        canonicalize_license_expression as _canonicalize_license_expression,
+    )
+except ImportError:  # pragma: nocover
+    if not TYPE_CHECKING:
+        # XXX: pyright is still upset even with # pyright: ignore[reportAssignmentType]
+        _canonicalize_license_expression = _missing_canonicalize_license_expression
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_path.py b/.venv/lib/python3.12/site-packages/setuptools/_path.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d99b0f539ff5f819b167013c48726180cd83d49
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_path.py
@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+import contextlib
+import os
+import sys
+from typing import TYPE_CHECKING, TypeVar, Union
+
+from more_itertools import unique_everseen
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+StrPath: TypeAlias = Union[str, os.PathLike[str]]  #  Same as _typeshed.StrPath
+StrPathT = TypeVar("StrPathT", bound=Union[str, os.PathLike[str]])
+
+
+def ensure_directory(path):
+    """Ensure that the parent directory of `path` exists"""
+    dirname = os.path.dirname(path)
+    os.makedirs(dirname, exist_ok=True)
+
+
+def same_path(p1: StrPath, p2: StrPath) -> bool:
+    """Differs from os.path.samefile because it does not require paths to exist.
+    Purely string based (no comparison between i-nodes).
+    >>> same_path("a/b", "./a/b")
+    True
+    >>> same_path("a/b", "a/./b")
+    True
+    >>> same_path("a/b", "././a/b")
+    True
+    >>> same_path("a/b", "./a/b/c/..")
+    True
+    >>> same_path("a/b", "../a/b/c")
+    False
+    >>> same_path("a", "a/b")
+    False
+    """
+    return normpath(p1) == normpath(p2)
+
+
+def normpath(filename: StrPath) -> str:
+    """Normalize a file/dir name for comparison purposes."""
+    # See pkg_resources.normalize_path for notes about cygwin
+    file = os.path.abspath(filename) if sys.platform == 'cygwin' else filename
+    return os.path.normcase(os.path.realpath(os.path.normpath(file)))
+
+
+@contextlib.contextmanager
+def paths_on_pythonpath(paths):
+    """
+    Add the indicated paths to the head of the PYTHONPATH environment
+    variable so that subprocesses will also see the packages at
+    these paths.
+
+    Do this in a context that restores the value on exit.
+
+    >>> getfixture('monkeypatch').setenv('PYTHONPATH', 'anything')
+    >>> with paths_on_pythonpath(['foo', 'bar']):
+    ...     assert 'foo' in os.environ['PYTHONPATH']
+    ...     assert 'anything' in os.environ['PYTHONPATH']
+    >>> os.environ['PYTHONPATH']
+    'anything'
+
+    >>> getfixture('monkeypatch').delenv('PYTHONPATH')
+    >>> with paths_on_pythonpath(['foo', 'bar']):
+    ...     assert 'foo' in os.environ['PYTHONPATH']
+    >>> os.environ.get('PYTHONPATH')
+    """
+    nothing = object()
+    orig_pythonpath = os.environ.get('PYTHONPATH', nothing)
+    current_pythonpath = os.environ.get('PYTHONPATH', '')
+    try:
+        prefix = os.pathsep.join(unique_everseen(paths))
+        to_join = filter(None, [prefix, current_pythonpath])
+        new_path = os.pathsep.join(to_join)
+        if new_path:
+            os.environ['PYTHONPATH'] = new_path
+        yield
+    finally:
+        if orig_pythonpath is nothing:
+            os.environ.pop('PYTHONPATH', None)
+        else:
+            os.environ['PYTHONPATH'] = orig_pythonpath
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_reqs.py b/.venv/lib/python3.12/site-packages/setuptools/_reqs.py
new file mode 100644
index 0000000000000000000000000000000000000000..c793be4d6eb3991d7b4ada615201a01cbfbbefd5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_reqs.py
@@ -0,0 +1,42 @@
+from __future__ import annotations
+
+from collections.abc import Iterable, Iterator
+from functools import lru_cache
+from typing import TYPE_CHECKING, Callable, TypeVar, Union, overload
+
+import jaraco.text as text
+from packaging.requirements import Requirement
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+_T = TypeVar("_T")
+_StrOrIter: TypeAlias = Union[str, Iterable[str]]
+
+
+parse_req: Callable[[str], Requirement] = lru_cache()(Requirement)
+# Setuptools parses the same requirement many times
+# (e.g. first for validation than for normalisation),
+# so it might be worth to cache.
+
+
+def parse_strings(strs: _StrOrIter) -> Iterator[str]:
+    """
+    Yield requirement strings for each specification in `strs`.
+
+    `strs` must be a string, or a (possibly-nested) iterable thereof.
+    """
+    return text.join_continuation(map(text.drop_comment, text.yield_lines(strs)))
+
+
+# These overloads are only needed because of a mypy false-positive, pyright gets it right
+# https://github.com/python/mypy/issues/3737
+@overload
+def parse(strs: _StrOrIter) -> Iterator[Requirement]: ...
+@overload
+def parse(strs: _StrOrIter, parser: Callable[[str], _T]) -> Iterator[_T]: ...
+def parse(strs: _StrOrIter, parser: Callable[[str], _T] = parse_req) -> Iterator[_T]:  # type: ignore[assignment]
+    """
+    Replacement for ``pkg_resources.parse_requirements`` that uses ``packaging``.
+    """
+    return map(parser, parse_strings(strs))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_shutil.py b/.venv/lib/python3.12/site-packages/setuptools/_shutil.py
new file mode 100644
index 0000000000000000000000000000000000000000..6acbb4281fc986587f52a83395dc63912a863caf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_shutil.py
@@ -0,0 +1,53 @@
+"""Convenience layer on top of stdlib's shutil and os"""
+
+import os
+import stat
+from typing import Callable, TypeVar
+
+from .compat import py311
+
+from distutils import log
+
+try:
+    from os import chmod  # pyright: ignore[reportAssignmentType]
+    # Losing type-safety w/ pyright, but that's ok
+except ImportError:  # pragma: no cover
+    # Jython compatibility
+    def chmod(*args: object, **kwargs: object) -> None:  # type: ignore[misc] # Mypy reuses the imported definition anyway
+        pass
+
+
+_T = TypeVar("_T")
+
+
+def attempt_chmod_verbose(path, mode):
+    log.debug("changing mode of %s to %o", path, mode)
+    try:
+        chmod(path, mode)
+    except OSError as e:  # pragma: no cover
+        log.debug("chmod failed: %s", e)
+
+
+# Must match shutil._OnExcCallback
+def _auto_chmod(
+    func: Callable[..., _T], arg: str, exc: BaseException
+) -> _T:  # pragma: no cover
+    """shutils onexc callback to automatically call chmod for certain functions."""
+    # Only retry for scenarios known to have an issue
+    if func in [os.unlink, os.remove] and os.name == 'nt':
+        attempt_chmod_verbose(arg, stat.S_IWRITE)
+        return func(arg)
+    raise exc
+
+
+def rmtree(path, ignore_errors=False, onexc=_auto_chmod):
+    """
+    Similar to ``shutil.rmtree`` but automatically executes ``chmod``
+    for well know Windows failure scenarios.
+    """
+    return py311.shutil_rmtree(path, ignore_errors, onexc)
+
+
+def rmdir(path, **opts):
+    if os.path.isdir(path):
+        rmtree(path, **opts)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_static.py b/.venv/lib/python3.12/site-packages/setuptools/_static.py
new file mode 100644
index 0000000000000000000000000000000000000000..af35862cf8b759a0e60110ce9e92bfdb1b49bc5f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_static.py
@@ -0,0 +1,188 @@
+from functools import wraps
+from typing import TypeVar
+
+import packaging.specifiers
+
+from .warnings import SetuptoolsDeprecationWarning
+
+
+class Static:
+    """
+    Wrapper for built-in object types that are allow setuptools to identify
+    static core metadata (in opposition to ``Dynamic``, as defined :pep:`643`).
+
+    The trick is to mark values with :class:`Static` when they come from
+    ``pyproject.toml`` or ``setup.cfg``, so if any plugin overwrite the value
+    with a built-in, setuptools will be able to recognise the change.
+
+    We inherit from built-in classes, so that we don't need to change the existing
+    code base to deal with the new types.
+    We also should strive for immutability objects to avoid changes after the
+    initial parsing.
+    """
+
+    _mutated_: bool = False  # TODO: Remove after deprecation warning is solved
+
+
+def _prevent_modification(target: type, method: str, copying: str) -> None:
+    """
+    Because setuptools is very flexible we cannot fully prevent
+    plugins and user customizations from modifying static values that were
+    parsed from config files.
+    But we can attempt to block "in-place" mutations and identify when they
+    were done.
+    """
+    fn = getattr(target, method, None)
+    if fn is None:
+        return
+
+    @wraps(fn)
+    def _replacement(self: Static, *args, **kwargs):
+        # TODO: After deprecation period raise NotImplementedError instead of warning
+        #       which obviated the existence and checks of the `_mutated_` attribute.
+        self._mutated_ = True
+        SetuptoolsDeprecationWarning.emit(
+            "Direct modification of value will be disallowed",
+            f"""
+            In an effort to implement PEP 643, direct/in-place changes of static values
+            that come from configuration files are deprecated.
+            If you need to modify this value, please first create a copy with {copying}
+            and make sure conform to all relevant standards when overriding setuptools
+            functionality (https://packaging.python.org/en/latest/specifications/).
+            """,
+            due_date=(2025, 10, 10),  # Initially introduced in 2024-09-06
+        )
+        return fn(self, *args, **kwargs)
+
+    _replacement.__doc__ = ""  # otherwise doctest may fail.
+    setattr(target, method, _replacement)
+
+
+class Str(str, Static):
+    pass
+
+
+class Tuple(tuple, Static):
+    pass
+
+
+class List(list, Static):
+    """
+    :meta private:
+    >>> x = List([1, 2, 3])
+    >>> is_static(x)
+    True
+    >>> x += [0]  # doctest: +IGNORE_EXCEPTION_DETAIL
+    Traceback (most recent call last):
+    SetuptoolsDeprecationWarning: Direct modification ...
+    >>> is_static(x)  # no longer static after modification
+    False
+    >>> y = list(x)
+    >>> y.clear()
+    >>> y
+    []
+    >>> y == x
+    False
+    >>> is_static(List(y))
+    True
+    """
+
+
+# Make `List` immutable-ish
+# (certain places of setuptools/distutils issue a warn if we use tuple instead of list)
+for _method in (
+    '__delitem__',
+    '__iadd__',
+    '__setitem__',
+    'append',
+    'clear',
+    'extend',
+    'insert',
+    'remove',
+    'reverse',
+    'pop',
+):
+    _prevent_modification(List, _method, "`list(value)`")
+
+
+class Dict(dict, Static):
+    """
+    :meta private:
+    >>> x = Dict({'a': 1, 'b': 2})
+    >>> is_static(x)
+    True
+    >>> x['c'] = 0  # doctest: +IGNORE_EXCEPTION_DETAIL
+    Traceback (most recent call last):
+    SetuptoolsDeprecationWarning: Direct modification ...
+    >>> x._mutated_
+    True
+    >>> is_static(x)  # no longer static after modification
+    False
+    >>> y = dict(x)
+    >>> y.popitem()
+    ('b', 2)
+    >>> y == x
+    False
+    >>> is_static(Dict(y))
+    True
+    """
+
+
+# Make `Dict` immutable-ish (we cannot inherit from types.MappingProxyType):
+for _method in (
+    '__delitem__',
+    '__ior__',
+    '__setitem__',
+    'clear',
+    'pop',
+    'popitem',
+    'setdefault',
+    'update',
+):
+    _prevent_modification(Dict, _method, "`dict(value)`")
+
+
+class SpecifierSet(packaging.specifiers.SpecifierSet, Static):
+    """Not exactly a built-in type but useful for ``requires-python``"""
+
+
+T = TypeVar("T")
+
+
+def noop(value: T) -> T:
+    """
+    >>> noop(42)
+    42
+    """
+    return value
+
+
+_CONVERSIONS = {str: Str, tuple: Tuple, list: List, dict: Dict}
+
+
+def attempt_conversion(value: T) -> T:
+    """
+    >>> is_static(attempt_conversion("hello"))
+    True
+    >>> is_static(object())
+    False
+    """
+    return _CONVERSIONS.get(type(value), noop)(value)  # type: ignore[call-overload]
+
+
+def is_static(value: object) -> bool:
+    """
+    >>> is_static(a := Dict({'a': 1}))
+    True
+    >>> is_static(dict(a))
+    False
+    >>> is_static(b := List([1, 2, 3]))
+    True
+    >>> is_static(list(b))
+    False
+    """
+    return isinstance(value, Static) and not value._mutated_
+
+
+EMPTY_LIST = List()
+EMPTY_DICT = Dict()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/__pycache__/typing_extensions.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/__pycache__/typing_extensions.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4b3aa28850c8368046d3f32d70485509cffec5a6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/__pycache__/typing_extensions.cpython-312.pyc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b27cc3e90b8ac1cd2540b7da10d65fdb3cbfdfbe39eb5d081940ba7c94327e1
+size 139507
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..b49c3af060ce6326b8d91b9b73e76456ea0d9d2b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/LICENSE
@@ -0,0 +1,166 @@
+GNU LESSER GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+
+  This version of the GNU Lesser General Public License incorporates
+the terms and conditions of version 3 of the GNU General Public
+License, supplemented by the additional permissions listed below.
+
+  0. Additional Definitions.
+
+  As used herein, "this License" refers to version 3 of the GNU Lesser
+General Public License, and the "GNU GPL" refers to version 3 of the GNU
+General Public License.
+
+  "The Library" refers to a covered work governed by this License,
+other than an Application or a Combined Work as defined below.
+
+  An "Application" is any work that makes use of an interface provided
+by the Library, but which is not otherwise based on the Library.
+Defining a subclass of a class defined by the Library is deemed a mode
+of using an interface provided by the Library.
+
+  A "Combined Work" is a work produced by combining or linking an
+Application with the Library.  The particular version of the Library
+with which the Combined Work was made is also called the "Linked
+Version".
+
+  The "Minimal Corresponding Source" for a Combined Work means the
+Corresponding Source for the Combined Work, excluding any source code
+for portions of the Combined Work that, considered in isolation, are
+based on the Application, and not on the Linked Version.
+
+  The "Corresponding Application Code" for a Combined Work means the
+object code and/or source code for the Application, including any data
+and utility programs needed for reproducing the Combined Work from the
+Application, but excluding the System Libraries of the Combined Work.
+
+  1. Exception to Section 3 of the GNU GPL.
+
+  You may convey a covered work under sections 3 and 4 of this License
+without being bound by section 3 of the GNU GPL.
+
+  2. Conveying Modified Versions.
+
+  If you modify a copy of the Library, and, in your modifications, a
+facility refers to a function or data to be supplied by an Application
+that uses the facility (other than as an argument passed when the
+facility is invoked), then you may convey a copy of the modified
+version:
+
+   a) under this License, provided that you make a good faith effort to
+   ensure that, in the event an Application does not supply the
+   function or data, the facility still operates, and performs
+   whatever part of its purpose remains meaningful, or
+
+   b) under the GNU GPL, with none of the additional permissions of
+   this License applicable to that copy.
+
+  3. Object Code Incorporating Material from Library Header Files.
+
+  The object code form of an Application may incorporate material from
+a header file that is part of the Library.  You may convey such object
+code under terms of your choice, provided that, if the incorporated
+material is not limited to numerical parameters, data structure
+layouts and accessors, or small macros, inline functions and templates
+(ten or fewer lines in length), you do both of the following:
+
+   a) Give prominent notice with each copy of the object code that the
+   Library is used in it and that the Library and its use are
+   covered by this License.
+
+   b) Accompany the object code with a copy of the GNU GPL and this license
+   document.
+
+  4. Combined Works.
+
+  You may convey a Combined Work under terms of your choice that,
+taken together, effectively do not restrict modification of the
+portions of the Library contained in the Combined Work and reverse
+engineering for debugging such modifications, if you also do each of
+the following:
+
+   a) Give prominent notice with each copy of the Combined Work that
+   the Library is used in it and that the Library and its use are
+   covered by this License.
+
+   b) Accompany the Combined Work with a copy of the GNU GPL and this license
+   document.
+
+   c) For a Combined Work that displays copyright notices during
+   execution, include the copyright notice for the Library among
+   these notices, as well as a reference directing the user to the
+   copies of the GNU GPL and this license document.
+
+   d) Do one of the following:
+
+       0) Convey the Minimal Corresponding Source under the terms of this
+       License, and the Corresponding Application Code in a form
+       suitable for, and under terms that permit, the user to
+       recombine or relink the Application with a modified version of
+       the Linked Version to produce a modified Combined Work, in the
+       manner specified by section 6 of the GNU GPL for conveying
+       Corresponding Source.
+
+       1) Use a suitable shared library mechanism for linking with the
+       Library.  A suitable mechanism is one that (a) uses at run time
+       a copy of the Library already present on the user's computer
+       system, and (b) will operate properly with a modified version
+       of the Library that is interface-compatible with the Linked
+       Version.
+
+   e) Provide Installation Information, but only if you would otherwise
+   be required to provide such information under section 6 of the
+   GNU GPL, and only to the extent that such information is
+   necessary to install and execute a modified version of the
+   Combined Work produced by recombining or relinking the
+   Application with a modified version of the Linked Version. (If
+   you use option 4d0, the Installation Information must accompany
+   the Minimal Corresponding Source and Corresponding Application
+   Code. If you use option 4d1, you must provide the Installation
+   Information in the manner specified by section 6 of the GNU GPL
+   for conveying Corresponding Source.)
+
+  5. Combined Libraries.
+
+  You may place library facilities that are a work based on the
+Library side by side in a single library together with other library
+facilities that are not Applications and are not covered by this
+License, and convey such a combined library under terms of your
+choice, if you do both of the following:
+
+   a) Accompany the combined library with a copy of the same work based
+   on the Library, uncombined with any other library facilities,
+   conveyed under the terms of this License.
+
+   b) Give prominent notice with the combined library that part of it
+   is a work based on the Library, and explaining where to find the
+   accompanying uncombined form of the same work.
+
+  6. Revised Versions of the GNU Lesser General Public License.
+
+  The Free Software Foundation may publish revised and/or new versions
+of the GNU Lesser General Public License from time to time. Such new
+versions will be similar in spirit to the present version, but may
+differ in detail to address new problems or concerns.
+
+  Each version is given a distinguishing version number. If the
+Library as you received it specifies that a certain numbered version
+of the GNU Lesser General Public License "or any later version"
+applies to it, you have the option of following the terms and
+conditions either of that published version or of any later version
+published by the Free Software Foundation. If the Library as you
+received it does not specify a version number of the GNU Lesser
+General Public License, you may choose any version of the GNU Lesser
+General Public License ever published by the Free Software Foundation.
+
+  If the Library as you received it specifies that a proxy can decide
+whether future versions of the GNU Lesser General Public License shall
+apply, that proxy's public statement of acceptance of any version is
+permanent authorization for you to choose that version for the
+Library.
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..32214fb4400035788bfe5c0de42dad106a4f54f1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/METADATA
@@ -0,0 +1,420 @@
+Metadata-Version: 2.1
+Name: autocommand
+Version: 2.2.2
+Summary: A library to create a command-line program from a function
+Home-page: https://github.com/Lucretiel/autocommand
+Author: Nathan West
+License: LGPLv3
+Project-URL: Homepage, https://github.com/Lucretiel/autocommand
+Project-URL: Bug Tracker, https://github.com/Lucretiel/autocommand/issues
+Platform: any
+Classifier: Development Status :: 6 - Mature
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Topic :: Software Development
+Classifier: Topic :: Software Development :: Libraries
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Requires-Python: >=3.7
+Description-Content-Type: text/markdown
+License-File: LICENSE
+
+[![PyPI version](https://badge.fury.io/py/autocommand.svg)](https://badge.fury.io/py/autocommand)
+
+# autocommand
+
+A library to automatically generate and run simple argparse parsers from function signatures.
+
+## Installation
+
+Autocommand is installed via pip:
+
+```
+$ pip install autocommand
+```
+
+## Usage
+
+Autocommand turns a function into a command-line program. It converts the function's parameter signature into command-line arguments, and automatically runs the function if the module was called as `__main__`. In effect, it lets your create a smart main function.
+
+```python
+from autocommand import autocommand
+
+# This program takes exactly one argument and echos it.
+@autocommand(__name__)
+def echo(thing):
+    print(thing)
+```
+
+```
+$ python echo.py hello
+hello
+$ python echo.py -h
+usage: echo [-h] thing
+
+positional arguments:
+  thing
+
+optional arguments:
+  -h, --help  show this help message and exit
+$ python echo.py hello world  # too many arguments
+usage: echo.py [-h] thing
+echo.py: error: unrecognized arguments: world
+```
+
+As you can see, autocommand converts the signature of the function into an argument spec. When you run the file as a program, autocommand collects the command-line arguments and turns them into function arguments. The function is executed with these arguments, and then the program exits with the return value of the function, via `sys.exit`. Autocommand also automatically creates a usage message, which can be invoked with `-h` or `--help`, and automatically prints an error message when provided with invalid arguments.
+
+### Types
+
+You can use a type annotation to give an argument a type. Any type (or in fact any callable) that returns an object when given a string argument can be used, though there are a few special cases that are described later.
+
+```python
+@autocommand(__name__)
+def net_client(host, port: int):
+    ...
+```
+
+Autocommand will catch `TypeErrors` raised by the type during argument parsing, so you can supply a callable and do some basic argument validation as well.
+
+### Trailing Arguments
+
+You can add a `*args` parameter to your function to give it trailing arguments. The command will collect 0 or more trailing arguments and supply them to `args` as a tuple. If a type annotation is supplied, the type is applied to each argument.
+
+```python
+# Write the contents of each file, one by one
+@autocommand(__name__)
+def cat(*files):
+    for filename in files:
+        with open(filename) as file:
+            for line in file:
+                print(line.rstrip())
+```
+
+```
+$ python cat.py -h
+usage: ipython [-h] [file [file ...]]
+
+positional arguments:
+  file
+
+optional arguments:
+  -h, --help  show this help message and exit
+```
+
+### Options
+
+To create `--option` switches, just assign a default. Autocommand will automatically create `--long` and `-s`hort switches.
+
+```python
+@autocommand(__name__)
+def do_with_config(argument, config='~/foo.conf'):
+    pass
+```
+
+```
+$ python example.py -h
+usage: example.py [-h] [-c CONFIG] argument
+
+positional arguments:
+  argument
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -c CONFIG, --config CONFIG
+```
+
+The option's type is automatically deduced from the default, unless one is explicitly given in an annotation:
+
+```python
+@autocommand(__name__)
+def http_connect(host, port=80):
+    print('{}:{}'.format(host, port))
+```
+
+```
+$ python http.py -h
+usage: http.py [-h] [-p PORT] host
+
+positional arguments:
+  host
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -p PORT, --port PORT
+$ python http.py localhost
+localhost:80
+$ python http.py localhost -p 8080
+localhost:8080
+$ python http.py localhost -p blah
+usage: http.py [-h] [-p PORT] host
+http.py: error: argument -p/--port: invalid int value: 'blah'
+```
+
+#### None
+
+If an option is given a default value of `None`, it reads in a value as normal, but supplies `None` if the option isn't provided.
+
+#### Switches
+
+If an argument is given a default value of `True` or `False`, or
+given an explicit `bool` type, it becomes an option switch.
+
+```python
+    @autocommand(__name__)
+    def example(verbose=False, quiet=False):
+        pass
+```
+
+```
+$ python example.py -h
+usage: example.py [-h] [-v] [-q]
+
+optional arguments:
+  -h, --help     show this help message and exit
+  -v, --verbose
+  -q, --quiet
+```
+
+Autocommand attempts to do the "correct thing" in these cases- if the default is `True`, then supplying the switch makes the argument `False`; if the type is `bool` and the default is some other `True` value, then supplying the switch makes the argument `False`, while not supplying the switch makes the argument the default value.
+
+Autocommand also supports the creation of switch inverters. Pass `add_nos=True` to `autocommand` to enable this.
+
+```
+    @autocommand(__name__, add_nos=True)
+    def example(verbose=False):
+        pass
+```
+
+```
+$ python example.py -h
+usage: ipython [-h] [-v] [--no-verbose]
+
+optional arguments:
+  -h, --help     show this help message and exit
+  -v, --verbose
+  --no-verbose
+```
+
+Using the `--no-` version of a switch will pass the opposite value in as a function argument. If multiple switches are present, the last one takes precedence.
+
+#### Files
+
+If the default value is a file object, such as `sys.stdout`, then autocommand just looks for a string, for a file path. It doesn't do any special checking on the string, though (such as checking if the file exists); it's better to let the client decide how to handle errors in this case. Instead, it provides a special context manager called `smart_open`, which behaves exactly like `open` if a filename or other openable type is provided, but also lets you use already open files:
+
+```python
+from autocommand import autocommand, smart_open
+import sys
+
+# Write the contents of stdin, or a file, to stdout
+@autocommand(__name__)
+def write_out(infile=sys.stdin):
+    with smart_open(infile) as f:
+        for line in f:
+            print(line.rstrip())
+    # If a file was opened, it is closed here. If it was just stdin, it is untouched.
+```
+
+```
+$ echo "Hello World!" | python write_out.py | tee hello.txt
+Hello World!
+$ python write_out.py --infile hello.txt
+Hello World!
+```
+
+### Descriptions and docstrings
+
+The `autocommand` decorator accepts `description` and `epilog` kwargs, corresponding to the `description <https://docs.python.org/3/library/argparse.html#description>`_ and `epilog <https://docs.python.org/3/library/argparse.html#epilog>`_ of the `ArgumentParser`. If no description is given, but the decorated function has a docstring, then it is taken as the `description` for the `ArgumentParser`. You can also provide both the description and epilog in the docstring by splitting it into two sections with 4 or more - characters.
+
+```python
+@autocommand(__name__)
+def copy(infile=sys.stdin, outfile=sys.stdout):
+    '''
+    Copy an the contents of a file (or stdin) to another file (or stdout)
+    ----------
+    Some extra documentation in the epilog
+    '''
+    with smart_open(infile) as istr:
+        with smart_open(outfile, 'w') as ostr:
+            for line in istr:
+                ostr.write(line)
+```
+
+```
+$ python copy.py -h
+usage: copy.py [-h] [-i INFILE] [-o OUTFILE]
+
+Copy an the contents of a file (or stdin) to another file (or stdout)
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -i INFILE, --infile INFILE
+  -o OUTFILE, --outfile OUTFILE
+
+Some extra documentation in the epilog
+$ echo "Hello World" | python copy.py --outfile hello.txt
+$ python copy.py --infile hello.txt --outfile hello2.txt
+$ python copy.py --infile hello2.txt
+Hello World
+```
+
+### Parameter descriptions
+
+You can also attach description text to individual parameters in the annotation. To attach both a type and a description, supply them both in any order in a tuple
+
+```python
+@autocommand(__name__)
+def copy_net(
+    infile: 'The name of the file to send',
+    host: 'The host to send the file to',
+    port: (int, 'The port to connect to')):
+
+    '''
+    Copy a file over raw TCP to a remote destination.
+    '''
+    # Left as an exercise to the reader
+```
+
+### Decorators and wrappers
+
+Autocommand automatically follows wrapper chains created by `@functools.wraps`. This means that you can apply other wrapping decorators to your main function, and autocommand will still correctly detect the signature.
+
+```python
+from functools import wraps
+from autocommand import autocommand
+
+def print_yielded(func):
+    '''
+    Convert a generator into a function that prints all yielded elements
+    '''
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        for thing in func(*args, **kwargs):
+            print(thing)
+    return wrapper
+
+@autocommand(__name__,
+    description= 'Print all the values from START to STOP, inclusive, in steps of STEP',
+    epilog=      'STOP and STEP default to 1')
+@print_yielded
+def seq(stop, start=1, step=1):
+    for i in range(start, stop + 1, step):
+        yield i
+```
+
+```
+$ seq.py -h
+usage: seq.py [-h] [-s START] [-S STEP] stop
+
+Print all the values from START to STOP, inclusive, in steps of STEP
+
+positional arguments:
+  stop
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -s START, --start START
+  -S STEP, --step STEP
+
+STOP and STEP default to 1
+```
+
+Even though autocommand is being applied to the `wrapper` returned by `print_yielded`, it still retreives the signature of the underlying `seq` function to create the argument parsing.
+
+### Custom Parser
+
+While autocommand's automatic parser generator is a powerful convenience, it doesn't cover all of the different features that argparse provides. If you need these features, you can provide your own parser as a kwarg to `autocommand`:
+
+```python
+from argparse import ArgumentParser
+from autocommand import autocommand
+
+parser = ArgumentParser()
+# autocommand can't do optional positonal parameters
+parser.add_argument('arg', nargs='?')
+# or mutually exclusive options
+group = parser.add_mutually_exclusive_group()
+group.add_argument('-v', '--verbose', action='store_true')
+group.add_argument('-q', '--quiet', action='store_true')
+
+@autocommand(__name__, parser=parser)
+def main(arg, verbose, quiet):
+    print(arg, verbose, quiet)
+```
+
+```
+$ python parser.py -h
+usage: write_file.py [-h] [-v | -q] [arg]
+
+positional arguments:
+  arg
+
+optional arguments:
+  -h, --help     show this help message and exit
+  -v, --verbose
+  -q, --quiet
+$ python parser.py
+None False False
+$ python parser.py hello
+hello False False
+$ python parser.py -v
+None True False
+$ python parser.py -q
+None False True
+$ python parser.py -vq
+usage: parser.py [-h] [-v | -q] [arg]
+parser.py: error: argument -q/--quiet: not allowed with argument -v/--verbose
+```
+
+Any parser should work fine, so long as each of the parser's arguments has a corresponding parameter in the decorated main function. The order of parameters doesn't matter, as long as they are all present. Note that when using a custom parser, autocommand doesn't modify the parser or the retrieved arguments. This means that no description/epilog will be added, and the function's type annotations and defaults (if present) will be ignored.
+
+## Testing and Library use
+
+The decorated function is only called and exited from if the first argument to `autocommand` is `'__main__'` or `True`. If it is neither of these values, or no argument is given, then a new main function is created by the decorator. This function has the signature `main(argv=None)`, and is intended to be called with arguments as if via `main(sys.argv[1:])`. The function has the attributes `parser` and `main`, which are the generated `ArgumentParser` and the original main function that was decorated. This is to facilitate testing and library use of your main. Calling the function triggers a `parse_args()` with the supplied arguments, and returns the result of the main function. Note that, while it returns instead of calling `sys.exit`, the `parse_args()` function will raise a `SystemExit` in the event of a parsing error or `-h/--help` argument.
+
+```python
+    @autocommand()
+    def test_prog(arg1, arg2: int, quiet=False, verbose=False):
+        if not quiet:
+            print(arg1, arg2)
+            if verbose:
+                print("LOUD NOISES")
+
+        return 0
+
+    print(test_prog(['-v', 'hello', '80']))
+```
+
+```
+$ python test_prog.py
+hello 80
+LOUD NOISES
+0
+```
+
+If the function is called with no arguments, `sys.argv[1:]` is used. This is to allow the autocommand function to be used as a setuptools entry point.
+
+## Exceptions and limitations
+
+- There are a few possible exceptions that `autocommand` can raise. All of them derive from `autocommand.AutocommandError`.
+
+  - If an invalid annotation is given (that is, it isn't a `type`, `str`, `(type, str)`, or `(str, type)`, an `AnnotationError` is raised. The `type` may be any callable, as described in the `Types`_ section.
+  - If the function has a `**kwargs` parameter, a `KWargError` is raised.
+  - If, somehow, the function has a positional-only parameter, a `PositionalArgError` is raised. This means that the argument doesn't have a name, which is currently not possible with a plain `def` or `lambda`, though many built-in functions have this kind of parameter.
+
+- There are a few argparse features that are not supported by autocommand.
+
+  - It isn't possible to have an optional positional argument (as opposed to a `--option`). POSIX thinks this is bad form anyway.
+  - It isn't possible to have mutually exclusive arguments or options
+  - It isn't possible to have subcommands or subparsers, though I'm working on a few solutions involving classes or nested function definitions to allow this.
+
+## Development
+
+Autocommand cannot be important from the project root; this is to enforce separation of concerns and prevent accidental importing of `setup.py` or tests. To develop, install the project in editable mode:
+
+```
+$ python setup.py develop
+```
+
+This will create a link to the source files in the deployment directory, so that any source changes are reflected when it is imported.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..e6e12ea51e18b270be6c5103c8cb1f6ef62f9e4a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/RECORD
@@ -0,0 +1,18 @@
+autocommand-2.2.2.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+autocommand-2.2.2.dist-info/LICENSE,sha256=reeNBJgtaZctREqOFKlPh6IzTdOFXMgDSOqOJAqg3y0,7634
+autocommand-2.2.2.dist-info/METADATA,sha256=OADZuR3O6iBlpu1ieTgzYul6w4uOVrk0P0BO5TGGAJk,15006
+autocommand-2.2.2.dist-info/RECORD,,
+autocommand-2.2.2.dist-info/WHEEL,sha256=2wepM1nk4DS4eFpYrW1TTqPcoGNfHhhO_i5m4cOimbo,92
+autocommand-2.2.2.dist-info/top_level.txt,sha256=AzfhgKKS8EdAwWUTSF8mgeVQbXOY9kokHB6kSqwwqu0,12
+autocommand/__init__.py,sha256=zko5Rnvolvb-UXjCx_2ArPTGBWwUK5QY4LIQIKYR7As,1037
+autocommand/__pycache__/__init__.cpython-312.pyc,,
+autocommand/__pycache__/autoasync.cpython-312.pyc,,
+autocommand/__pycache__/autocommand.cpython-312.pyc,,
+autocommand/__pycache__/automain.cpython-312.pyc,,
+autocommand/__pycache__/autoparse.cpython-312.pyc,,
+autocommand/__pycache__/errors.cpython-312.pyc,,
+autocommand/autoasync.py,sha256=AMdyrxNS4pqWJfP_xuoOcImOHWD-qT7x06wmKN1Vp-U,5680
+autocommand/autocommand.py,sha256=hmkEmQ72HtL55gnURVjDOnsfYlGd5lLXbvT4KG496Qw,2505
+autocommand/automain.py,sha256=A2b8i754Mxc_DjU9WFr6vqYDWlhz0cn8miu8d8EsxV8,2076
+autocommand/autoparse.py,sha256=WVWmZJPcbzUKXP40raQw_0HD8qPJ2V9VG1eFFmmnFxw,11642
+autocommand/errors.py,sha256=7aa3roh9Herd6nIKpQHNWEslWE8oq7GiHYVUuRqORnA,886
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..57e3d840d59a650ac5bccbad5baeec47d155f0ad
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.38.4)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/top_level.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dda5158ff6d263927861b19ef5a5d183d2aa77ed
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand-2.2.2.dist-info/top_level.txt
@@ -0,0 +1 @@
+autocommand
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..73fbfca6b36927c6371839df3dfe733bebb2066f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2014-2016 Nathan West
+#
+# This file is part of autocommand.
+#
+# autocommand is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# autocommand is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with autocommand.  If not, see <http://www.gnu.org/licenses/>.
+
+# flake8 flags all these imports as unused, hence the NOQAs everywhere.
+
+from .automain import automain  # NOQA
+from .autoparse import autoparse, smart_open  # NOQA
+from .autocommand import autocommand  # NOQA
+
+try:
+    from .autoasync import autoasync  # NOQA
+except ImportError:  # pragma: no cover
+    pass
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/autoasync.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/autoasync.py
new file mode 100644
index 0000000000000000000000000000000000000000..688f7e05544bb5b4914d290c9bfe73bb99f4e06a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/autoasync.py
@@ -0,0 +1,142 @@
+# Copyright 2014-2015 Nathan West
+#
+# This file is part of autocommand.
+#
+# autocommand is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# autocommand is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with autocommand.  If not, see <http://www.gnu.org/licenses/>.
+
+from asyncio import get_event_loop, iscoroutine
+from functools import wraps
+from inspect import signature
+
+
+async def _run_forever_coro(coro, args, kwargs, loop):
+    '''
+    This helper function launches an async main function that was tagged with
+    forever=True. There are two possibilities:
+
+    - The function is a normal function, which handles initializing the event
+      loop, which is then run forever
+    - The function is a coroutine, which needs to be scheduled in the event
+      loop, which is then run forever
+      - There is also the possibility that the function is a normal function
+        wrapping a coroutine function
+
+    The function is therefore called unconditionally and scheduled in the event
+    loop if the return value is a coroutine object.
+
+    The reason this is a separate function is to make absolutely sure that all
+    the objects created are garbage collected after all is said and done; we
+    do this to ensure that any exceptions raised in the tasks are collected
+    ASAP.
+    '''
+
+    # Personal note: I consider this an antipattern, as it relies on the use of
+    # unowned resources. The setup function dumps some stuff into the event
+    # loop where it just whirls in the ether without a well defined owner or
+    # lifetime. For this reason, there's a good chance I'll remove the
+    # forever=True feature from autoasync at some point in the future.
+    thing = coro(*args, **kwargs)
+    if iscoroutine(thing):
+        await thing
+
+
+def autoasync(coro=None, *, loop=None, forever=False, pass_loop=False):
+    '''
+    Convert an asyncio coroutine into a function which, when called, is
+    evaluted in an event loop, and the return value returned. This is intented
+    to make it easy to write entry points into asyncio coroutines, which
+    otherwise need to be explictly evaluted with an event loop's
+    run_until_complete.
+
+    If `loop` is given, it is used as the event loop to run the coro in. If it
+    is None (the default), the loop is retreived using asyncio.get_event_loop.
+    This call is defered until the decorated function is called, so that
+    callers can install custom event loops or event loop policies after
+    @autoasync is applied.
+
+    If `forever` is True, the loop is run forever after the decorated coroutine
+    is finished. Use this for servers created with asyncio.start_server and the
+    like.
+
+    If `pass_loop` is True, the event loop object is passed into the coroutine
+    as the `loop` kwarg when the wrapper function is called. In this case, the
+    wrapper function's __signature__ is updated to remove this parameter, so
+    that autoparse can still be used on it without generating a parameter for
+    `loop`.
+
+    This coroutine can be called with ( @autoasync(...) ) or without
+    ( @autoasync ) arguments.
+
+    Examples:
+
+    @autoasync
+    def get_file(host, port):
+        reader, writer = yield from asyncio.open_connection(host, port)
+        data = reader.read()
+        sys.stdout.write(data.decode())
+
+    get_file(host, port)
+
+    @autoasync(forever=True, pass_loop=True)
+    def server(host, port, loop):
+        yield_from loop.create_server(Proto, host, port)
+
+    server('localhost', 8899)
+
+    '''
+    if coro is None:
+        return lambda c: autoasync(
+            c, loop=loop,
+            forever=forever,
+            pass_loop=pass_loop)
+
+    # The old and new signatures are required to correctly bind the loop
+    # parameter in 100% of cases, even if it's a positional parameter.
+    # NOTE: A future release will probably require the loop parameter to be
+    # a kwonly parameter.
+    if pass_loop:
+        old_sig = signature(coro)
+        new_sig = old_sig.replace(parameters=(
+            param for name, param in old_sig.parameters.items()
+            if name != "loop"))
+
+    @wraps(coro)
+    def autoasync_wrapper(*args, **kwargs):
+        # Defer the call to get_event_loop so that, if a custom policy is
+        # installed after the autoasync decorator, it is respected at call time
+        local_loop = get_event_loop() if loop is None else loop
+
+        # Inject the 'loop' argument. We have to use this signature binding to
+        # ensure it's injected in the correct place (positional, keyword, etc)
+        if pass_loop:
+            bound_args = old_sig.bind_partial()
+            bound_args.arguments.update(
+                loop=local_loop,
+                **new_sig.bind(*args, **kwargs).arguments)
+            args, kwargs = bound_args.args, bound_args.kwargs
+
+        if forever:
+            local_loop.create_task(_run_forever_coro(
+                coro, args, kwargs, local_loop
+            ))
+            local_loop.run_forever()
+        else:
+            return local_loop.run_until_complete(coro(*args, **kwargs))
+
+    # Attach the updated signature. This allows 'pass_loop' to be used with
+    # autoparse
+    if pass_loop:
+        autoasync_wrapper.__signature__ = new_sig
+
+    return autoasync_wrapper
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/autocommand.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/autocommand.py
new file mode 100644
index 0000000000000000000000000000000000000000..097e86de0720126a1390d728f7e50a4f89cac77f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/autocommand.py
@@ -0,0 +1,70 @@
+# Copyright 2014-2015 Nathan West
+#
+# This file is part of autocommand.
+#
+# autocommand is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# autocommand is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with autocommand.  If not, see <http://www.gnu.org/licenses/>.
+
+from .autoparse import autoparse
+from .automain import automain
+try:
+    from .autoasync import autoasync
+except ImportError:  # pragma: no cover
+    pass
+
+
+def autocommand(
+        module, *,
+        description=None,
+        epilog=None,
+        add_nos=False,
+        parser=None,
+        loop=None,
+        forever=False,
+        pass_loop=False):
+
+    if callable(module):
+        raise TypeError('autocommand requires a module name argument')
+
+    def autocommand_decorator(func):
+        # Step 1: if requested, run it all in an asyncio event loop. autoasync
+        # patches the __signature__ of the decorated function, so that in the
+        # event that pass_loop is True, the `loop` parameter of the original
+        # function will *not* be interpreted as a command-line argument by
+        # autoparse
+        if loop is not None or forever or pass_loop:
+            func = autoasync(
+                func,
+                loop=None if loop is True else loop,
+                pass_loop=pass_loop,
+                forever=forever)
+
+        # Step 2: create parser. We do this second so that the arguments are
+        # parsed and passed *before* entering the asyncio event loop, if it
+        # exists. This simplifies the stack trace and ensures errors are
+        # reported earlier. It also ensures that errors raised during parsing &
+        # passing are still raised if `forever` is True.
+        func = autoparse(
+            func,
+            description=description,
+            epilog=epilog,
+            add_nos=add_nos,
+            parser=parser)
+
+        # Step 3: call the function automatically if __name__ == '__main__' (or
+        # if True was provided)
+        func = automain(module)(func)
+
+        return func
+
+    return autocommand_decorator
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/automain.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/automain.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cc45db66a1c1dbf1bedb993683b0700d0510643
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/automain.py
@@ -0,0 +1,59 @@
+# Copyright 2014-2015 Nathan West
+#
+# This file is part of autocommand.
+#
+# autocommand is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# autocommand is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with autocommand.  If not, see <http://www.gnu.org/licenses/>.
+
+import sys
+from .errors import AutocommandError
+
+
+class AutomainRequiresModuleError(AutocommandError, TypeError):
+    pass
+
+
+def automain(module, *, args=(), kwargs=None):
+    '''
+    This decorator automatically invokes a function if the module is being run
+    as the "__main__" module. Optionally, provide args or kwargs with which to
+    call the function. If `module` is "__main__", the function is called, and
+    the program is `sys.exit`ed with the return value. You can also pass `True`
+    to cause the function to be called unconditionally. If the function is not
+    called, it is returned unchanged by the decorator.
+
+    Usage:
+
+    @automain(__name__)  # Pass __name__ to check __name__=="__main__"
+    def main():
+        ...
+
+    If __name__ is "__main__" here, the main function is called, and then
+    sys.exit called with the return value.
+    '''
+
+    # Check that @automain(...) was called, rather than @automain
+    if callable(module):
+        raise AutomainRequiresModuleError(module)
+
+    if module == '__main__' or module is True:
+        if kwargs is None:
+            kwargs = {}
+
+        # Use a function definition instead of a lambda for a neater traceback
+        def automain_decorator(main):
+            sys.exit(main(*args, **kwargs))
+
+        return automain_decorator
+    else:
+        return lambda main: main
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/autoparse.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/autoparse.py
new file mode 100644
index 0000000000000000000000000000000000000000..0276a3fae10eb367af326543bcd69e62432b2f7b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/autoparse.py
@@ -0,0 +1,333 @@
+# Copyright 2014-2015 Nathan West
+#
+# This file is part of autocommand.
+#
+# autocommand is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# autocommand is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with autocommand.  If not, see <http://www.gnu.org/licenses/>.
+
+import sys
+from re import compile as compile_regex
+from inspect import signature, getdoc, Parameter
+from argparse import ArgumentParser
+from contextlib import contextmanager
+from functools import wraps
+from io import IOBase
+from autocommand.errors import AutocommandError
+
+
+_empty = Parameter.empty
+
+
+class AnnotationError(AutocommandError):
+    '''Annotation error: annotation must be a string, type, or tuple of both'''
+
+
+class PositionalArgError(AutocommandError):
+    '''
+    Postional Arg Error: autocommand can't handle postional-only parameters
+    '''
+
+
+class KWArgError(AutocommandError):
+    '''kwarg Error: autocommand can't handle a **kwargs parameter'''
+
+
+class DocstringError(AutocommandError):
+    '''Docstring error'''
+
+
+class TooManySplitsError(DocstringError):
+    '''
+    The docstring had too many ---- section splits. Currently we only support
+    using up to a single split, to split the docstring into description and
+    epilog parts.
+    '''
+
+
+def _get_type_description(annotation):
+    '''
+    Given an annotation, return the (type, description) for the parameter.
+    If you provide an annotation that is somehow both a string and a callable,
+    the behavior is undefined.
+    '''
+    if annotation is _empty:
+        return None, None
+    elif callable(annotation):
+        return annotation, None
+    elif isinstance(annotation, str):
+        return None, annotation
+    elif isinstance(annotation, tuple):
+        try:
+            arg1, arg2 = annotation
+        except ValueError as e:
+            raise AnnotationError(annotation) from e
+        else:
+            if callable(arg1) and isinstance(arg2, str):
+                return arg1, arg2
+            elif isinstance(arg1, str) and callable(arg2):
+                return arg2, arg1
+
+    raise AnnotationError(annotation)
+
+
+def _add_arguments(param, parser, used_char_args, add_nos):
+    '''
+    Add the argument(s) to an ArgumentParser (using add_argument) for a given
+    parameter. used_char_args is the set of -short options currently already in
+    use, and is updated (if necessary) by this function. If add_nos is True,
+    this will also add an inverse switch for all boolean options. For
+    instance, for the boolean parameter "verbose", this will create --verbose
+    and --no-verbose.
+    '''
+
+    # Impl note: This function is kept separate from make_parser because it's
+    # already very long and I wanted to separate out as much as possible into
+    # its own call scope, to prevent even the possibility of suble mutation
+    # bugs.
+    if param.kind is param.POSITIONAL_ONLY:
+        raise PositionalArgError(param)
+    elif param.kind is param.VAR_KEYWORD:
+        raise KWArgError(param)
+
+    # These are the kwargs for the add_argument function.
+    arg_spec = {}
+    is_option = False
+
+    # Get the type and default from the annotation.
+    arg_type, description = _get_type_description(param.annotation)
+
+    # Get the default value
+    default = param.default
+
+    # If there is no explicit type, and the default is present and not None,
+    # infer the type from the default.
+    if arg_type is None and default not in {_empty, None}:
+        arg_type = type(default)
+
+    # Add default. The presence of a default means this is an option, not an
+    # argument.
+    if default is not _empty:
+        arg_spec['default'] = default
+        is_option = True
+
+    # Add the type
+    if arg_type is not None:
+        # Special case for bool: make it just a --switch
+        if arg_type is bool:
+            if not default or default is _empty:
+                arg_spec['action'] = 'store_true'
+            else:
+                arg_spec['action'] = 'store_false'
+
+            # Switches are always options
+            is_option = True
+
+        # Special case for file types: make it a string type, for filename
+        elif isinstance(default, IOBase):
+            arg_spec['type'] = str
+
+        # TODO: special case for list type.
+        #   - How to specificy type of list members?
+        #       - param: [int]
+        #       - param: int =[]
+        #   - action='append' vs nargs='*'
+
+        else:
+            arg_spec['type'] = arg_type
+
+    # nargs: if the signature includes *args, collect them as trailing CLI
+    # arguments in a list. *args can't have a default value, so it can never be
+    # an option.
+    if param.kind is param.VAR_POSITIONAL:
+        # TODO: consider depluralizing metavar/name here.
+        arg_spec['nargs'] = '*'
+
+    # Add description.
+    if description is not None:
+        arg_spec['help'] = description
+
+    # Get the --flags
+    flags = []
+    name = param.name
+
+    if is_option:
+        # Add the first letter as a -short option.
+        for letter in name[0], name[0].swapcase():
+            if letter not in used_char_args:
+                used_char_args.add(letter)
+                flags.append('-{}'.format(letter))
+                break
+
+        # If the parameter is a --long option, or is a -short option that
+        # somehow failed to get a flag, add it.
+        if len(name) > 1 or not flags:
+            flags.append('--{}'.format(name))
+
+        arg_spec['dest'] = name
+    else:
+        flags.append(name)
+
+    parser.add_argument(*flags, **arg_spec)
+
+    # Create the --no- version for boolean switches
+    if add_nos and arg_type is bool:
+        parser.add_argument(
+            '--no-{}'.format(name),
+            action='store_const',
+            dest=name,
+            const=default if default is not _empty else False)
+
+
+def make_parser(func_sig, description, epilog, add_nos):
+    '''
+    Given the signature of a function, create an ArgumentParser
+    '''
+    parser = ArgumentParser(description=description, epilog=epilog)
+
+    used_char_args = {'h'}
+
+    # Arange the params so that single-character arguments are first. This
+    # esnures they don't have to get --long versions. sorted is stable, so the
+    # parameters will otherwise still be in relative order.
+    params = sorted(
+        func_sig.parameters.values(),
+        key=lambda param: len(param.name) > 1)
+
+    for param in params:
+        _add_arguments(param, parser, used_char_args, add_nos)
+
+    return parser
+
+
+_DOCSTRING_SPLIT = compile_regex(r'\n\s*-{4,}\s*\n')
+
+
+def parse_docstring(docstring):
+    '''
+    Given a docstring, parse it into a description and epilog part
+    '''
+    if docstring is None:
+        return '', ''
+
+    parts = _DOCSTRING_SPLIT.split(docstring)
+
+    if len(parts) == 1:
+        return docstring, ''
+    elif len(parts) == 2:
+        return parts[0], parts[1]
+    else:
+        raise TooManySplitsError()
+
+
+def autoparse(
+        func=None, *,
+        description=None,
+        epilog=None,
+        add_nos=False,
+        parser=None):
+    '''
+    This decorator converts a function that takes normal arguments into a
+    function which takes a single optional argument, argv, parses it using an
+    argparse.ArgumentParser, and calls the underlying function with the parsed
+    arguments. If it is not given, sys.argv[1:] is used. This is so that the
+    function can be used as a setuptools entry point, as well as a normal main
+    function. sys.argv[1:] is not evaluated until the function is called, to
+    allow injecting different arguments for testing.
+
+    It uses the argument signature of the function to create an
+    ArgumentParser. Parameters without defaults become positional parameters,
+    while parameters *with* defaults become --options. Use annotations to set
+    the type of the parameter.
+
+    The `desctiption` and `epilog` parameters corrospond to the same respective
+    argparse parameters. If no description is given, it defaults to the
+    decorated functions's docstring, if present.
+
+    If add_nos is True, every boolean option (that is, every parameter with a
+    default of True/False or a type of bool) will have a --no- version created
+    as well, which inverts the option. For instance, the --verbose option will
+    have a --no-verbose counterpart. These are not mutually exclusive-
+    whichever one appears last in the argument list will have precedence.
+
+    If a parser is given, it is used instead of one generated from the function
+    signature. In this case, no parser is created; instead, the given parser is
+    used to parse the argv argument. The parser's results' argument names must
+    match up with the parameter names of the decorated function.
+
+    The decorated function is attached to the result as the `func` attribute,
+    and the parser is attached as the `parser` attribute.
+    '''
+
+    # If @autoparse(...) is used instead of @autoparse
+    if func is None:
+        return lambda f: autoparse(
+            f, description=description,
+            epilog=epilog,
+            add_nos=add_nos,
+            parser=parser)
+
+    func_sig = signature(func)
+
+    docstr_description, docstr_epilog = parse_docstring(getdoc(func))
+
+    if parser is None:
+        parser = make_parser(
+            func_sig,
+            description or docstr_description,
+            epilog or docstr_epilog,
+            add_nos)
+
+    @wraps(func)
+    def autoparse_wrapper(argv=None):
+        if argv is None:
+            argv = sys.argv[1:]
+
+        # Get empty argument binding, to fill with parsed arguments. This
+        # object does all the heavy lifting of turning named arguments into
+        # into correctly bound *args and **kwargs.
+        parsed_args = func_sig.bind_partial()
+        parsed_args.arguments.update(vars(parser.parse_args(argv)))
+
+        return func(*parsed_args.args, **parsed_args.kwargs)
+
+    # TODO: attach an updated __signature__ to autoparse_wrapper, just in case.
+
+    # Attach the wrapped function and parser, and return the wrapper.
+    autoparse_wrapper.func = func
+    autoparse_wrapper.parser = parser
+    return autoparse_wrapper
+
+
+@contextmanager
+def smart_open(filename_or_file, *args, **kwargs):
+    '''
+    This context manager allows you to open a filename, if you want to default
+    some already-existing file object, like sys.stdout, which shouldn't be
+    closed at the end of the context. If the filename argument is a str, bytes,
+    or int, the file object is created via a call to open with the given *args
+    and **kwargs, sent to the context, and closed at the end of the context,
+    just like "with open(filename) as f:". If it isn't one of the openable
+    types, the object simply sent to the context unchanged, and left unclosed
+    at the end of the context. Example:
+
+        def work_with_file(name=sys.stdout):
+            with smart_open(name) as f:
+                # Works correctly if name is a str filename or sys.stdout
+                print("Some stuff", file=f)
+                # If it was a filename, f is closed at the end here.
+    '''
+    if isinstance(filename_or_file, (str, bytes, int)):
+        with open(filename_or_file, *args, **kwargs) as file:
+            yield file
+    else:
+        yield filename_or_file
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/errors.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..2570607399a3ae13cb92db65a9171d955d3248c6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/autocommand/errors.py
@@ -0,0 +1,23 @@
+# Copyright 2014-2016 Nathan West
+#
+# This file is part of autocommand.
+#
+# autocommand is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Lesser General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# autocommand is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public License
+# along with autocommand.  If not, see <http://www.gnu.org/licenses/>.
+
+
+class AutocommandError(Exception):
+    '''Base class for autocommand exceptions'''
+    pass
+
+# Individual modules will define errors specific to that module.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..1bb5a44356f00884a71ceeefd24ded6caaba2418
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/LICENSE
@@ -0,0 +1,17 @@
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to
+deal in the Software without restriction, including without limitation the
+rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+sell copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+IN THE SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..db0a2dcdbe5f55f5815b2eaa243c507d0bdad002
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/METADATA
@@ -0,0 +1,46 @@
+Metadata-Version: 2.1
+Name: backports.tarfile
+Version: 1.2.0
+Summary: Backport of CPython tarfile module
+Author-email: "Jason R. Coombs" <jaraco@jaraco.com>
+Project-URL: Homepage, https://github.com/jaraco/backports.tarfile
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Provides-Extra: docs
+Requires-Dist: sphinx >=3.5 ; extra == 'docs'
+Requires-Dist: jaraco.packaging >=9.3 ; extra == 'docs'
+Requires-Dist: rst.linker >=1.9 ; extra == 'docs'
+Requires-Dist: furo ; extra == 'docs'
+Requires-Dist: sphinx-lint ; extra == 'docs'
+Provides-Extra: testing
+Requires-Dist: pytest !=8.1.*,>=6 ; extra == 'testing'
+Requires-Dist: pytest-checkdocs >=2.4 ; extra == 'testing'
+Requires-Dist: pytest-cov ; extra == 'testing'
+Requires-Dist: pytest-enabler >=2.2 ; extra == 'testing'
+Requires-Dist: jaraco.test ; extra == 'testing'
+Requires-Dist: pytest !=8.0.* ; extra == 'testing'
+
+.. image:: https://img.shields.io/pypi/v/backports.tarfile.svg
+   :target: https://pypi.org/project/backports.tarfile
+
+.. image:: https://img.shields.io/pypi/pyversions/backports.tarfile.svg
+
+.. image:: https://github.com/jaraco/backports.tarfile/actions/workflows/main.yml/badge.svg
+   :target: https://github.com/jaraco/backports.tarfile/actions?query=workflow%3A%22tests%22
+   :alt: tests
+
+.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
+    :target: https://github.com/astral-sh/ruff
+    :alt: Ruff
+
+.. .. image:: https://readthedocs.org/projects/backportstarfile/badge/?version=latest
+..    :target: https://backportstarfile.readthedocs.io/en/latest/?badge=latest
+
+.. image:: https://img.shields.io/badge/skeleton-2024-informational
+   :target: https://blog.jaraco.com/skeleton
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..536dc2f09ed8a2e9b04f98b82625101bb9f74715
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/RECORD
@@ -0,0 +1,17 @@
+backports.tarfile-1.2.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+backports.tarfile-1.2.0.dist-info/LICENSE,sha256=htoPAa6uRjSKPD1GUZXcHOzN55956HdppkuNoEsqR0E,1023
+backports.tarfile-1.2.0.dist-info/METADATA,sha256=ghXFTq132dxaEIolxr3HK1mZqm9iyUmaRANZQSr6WlE,2020
+backports.tarfile-1.2.0.dist-info/RECORD,,
+backports.tarfile-1.2.0.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+backports.tarfile-1.2.0.dist-info/WHEEL,sha256=GJ7t_kWBFywbagK5eo9IoUwLW6oyOeTKmQ-9iHFVNxQ,92
+backports.tarfile-1.2.0.dist-info/top_level.txt,sha256=cGjaLMOoBR1FK0ApojtzWVmViTtJ7JGIK_HwXiEsvtU,10
+backports/__init__.py,sha256=iOEMwnlORWezdO8-2vxBIPSR37D7JGjluZ8f55vzxls,81
+backports/__pycache__/__init__.cpython-312.pyc,,
+backports/tarfile/__init__.py,sha256=Pwf2qUIfB0SolJPCKcx3vz3UEu_aids4g4sAfxy94qg,108491
+backports/tarfile/__main__.py,sha256=Yw2oGT1afrz2eBskzdPYL8ReB_3liApmhFkN2EbDmc4,59
+backports/tarfile/__pycache__/__init__.cpython-312.pyc,,
+backports/tarfile/__pycache__/__main__.cpython-312.pyc,,
+backports/tarfile/compat/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+backports/tarfile/compat/__pycache__/__init__.cpython-312.pyc,,
+backports/tarfile/compat/__pycache__/py38.cpython-312.pyc,,
+backports/tarfile/compat/py38.py,sha256=iYkyt_gvWjLzGUTJD9TuTfMMjOk-ersXZmRlvQYN2qE,568
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/REQUESTED b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/REQUESTED
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..bab98d675883cc7567a79df485cd7b4f015e376f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.43.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/top_level.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..99d2be5b64d7dc414f8ab9c002de06456b4bada2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports.tarfile-1.2.0.dist-info/top_level.txt
@@ -0,0 +1 @@
+backports
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d1f7edf5dc6343538563a8caa7de6829752c4f9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/__init__.py
@@ -0,0 +1 @@
+__path__ = __import__('pkgutil').extend_path(__path__, __name__)  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/__pycache__/__init__.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..21473c5c92dd978a8d96aaf7470d3ab65e3009ac
Binary files /dev/null and b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/__pycache__/__init__.cpython-312.pyc differ
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c16881cb3ae1ea6e729b62bb2e5d625faa3f5fe
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/__init__.py
@@ -0,0 +1,2937 @@
+#-------------------------------------------------------------------
+# tarfile.py
+#-------------------------------------------------------------------
+# Copyright (C) 2002 Lars Gustaebel <lars@gustaebel.de>
+# All rights reserved.
+#
+# Permission  is  hereby granted,  free  of charge,  to  any person
+# obtaining a  copy of  this software  and associated documentation
+# files  (the  "Software"),  to   deal  in  the  Software   without
+# restriction,  including  without limitation  the  rights to  use,
+# copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies  of  the  Software,  and to  permit  persons  to  whom the
+# Software  is  furnished  to  do  so,  subject  to  the  following
+# conditions:
+#
+# The above copyright  notice and this  permission notice shall  be
+# included in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS  IS", WITHOUT WARRANTY OF ANY  KIND,
+# EXPRESS OR IMPLIED, INCLUDING  BUT NOT LIMITED TO  THE WARRANTIES
+# OF  MERCHANTABILITY,  FITNESS   FOR  A  PARTICULAR   PURPOSE  AND
+# NONINFRINGEMENT.  IN  NO  EVENT SHALL  THE  AUTHORS  OR COPYRIGHT
+# HOLDERS  BE LIABLE  FOR ANY  CLAIM, DAMAGES  OR OTHER  LIABILITY,
+# WHETHER  IN AN  ACTION OF  CONTRACT, TORT  OR OTHERWISE,  ARISING
+# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+# OTHER DEALINGS IN THE SOFTWARE.
+#
+"""Read from and write to tar format archives.
+"""
+
+version     = "0.9.0"
+__author__  = "Lars Gust\u00e4bel (lars@gustaebel.de)"
+__credits__ = "Gustavo Niemeyer, Niels Gust\u00e4bel, Richard Townsend."
+
+#---------
+# Imports
+#---------
+from builtins import open as bltn_open
+import sys
+import os
+import io
+import shutil
+import stat
+import time
+import struct
+import copy
+import re
+
+from .compat.py38 import removesuffix
+
+try:
+    import pwd
+except ImportError:
+    pwd = None
+try:
+    import grp
+except ImportError:
+    grp = None
+
+# os.symlink on Windows prior to 6.0 raises NotImplementedError
+# OSError (winerror=1314) will be raised if the caller does not hold the
+# SeCreateSymbolicLinkPrivilege privilege
+symlink_exception = (AttributeError, NotImplementedError, OSError)
+
+# from tarfile import *
+__all__ = ["TarFile", "TarInfo", "is_tarfile", "TarError", "ReadError",
+           "CompressionError", "StreamError", "ExtractError", "HeaderError",
+           "ENCODING", "USTAR_FORMAT", "GNU_FORMAT", "PAX_FORMAT",
+           "DEFAULT_FORMAT", "open","fully_trusted_filter", "data_filter",
+           "tar_filter", "FilterError", "AbsoluteLinkError",
+           "OutsideDestinationError", "SpecialFileError", "AbsolutePathError",
+           "LinkOutsideDestinationError"]
+
+
+#---------------------------------------------------------
+# tar constants
+#---------------------------------------------------------
+NUL = b"\0"                     # the null character
+BLOCKSIZE = 512                 # length of processing blocks
+RECORDSIZE = BLOCKSIZE * 20     # length of records
+GNU_MAGIC = b"ustar  \0"        # magic gnu tar string
+POSIX_MAGIC = b"ustar\x0000"    # magic posix tar string
+
+LENGTH_NAME = 100               # maximum length of a filename
+LENGTH_LINK = 100               # maximum length of a linkname
+LENGTH_PREFIX = 155             # maximum length of the prefix field
+
+REGTYPE = b"0"                  # regular file
+AREGTYPE = b"\0"                # regular file
+LNKTYPE = b"1"                  # link (inside tarfile)
+SYMTYPE = b"2"                  # symbolic link
+CHRTYPE = b"3"                  # character special device
+BLKTYPE = b"4"                  # block special device
+DIRTYPE = b"5"                  # directory
+FIFOTYPE = b"6"                 # fifo special device
+CONTTYPE = b"7"                 # contiguous file
+
+GNUTYPE_LONGNAME = b"L"         # GNU tar longname
+GNUTYPE_LONGLINK = b"K"         # GNU tar longlink
+GNUTYPE_SPARSE = b"S"           # GNU tar sparse file
+
+XHDTYPE = b"x"                  # POSIX.1-2001 extended header
+XGLTYPE = b"g"                  # POSIX.1-2001 global header
+SOLARIS_XHDTYPE = b"X"          # Solaris extended header
+
+USTAR_FORMAT = 0                # POSIX.1-1988 (ustar) format
+GNU_FORMAT = 1                  # GNU tar format
+PAX_FORMAT = 2                  # POSIX.1-2001 (pax) format
+DEFAULT_FORMAT = PAX_FORMAT
+
+#---------------------------------------------------------
+# tarfile constants
+#---------------------------------------------------------
+# File types that tarfile supports:
+SUPPORTED_TYPES = (REGTYPE, AREGTYPE, LNKTYPE,
+                   SYMTYPE, DIRTYPE, FIFOTYPE,
+                   CONTTYPE, CHRTYPE, BLKTYPE,
+                   GNUTYPE_LONGNAME, GNUTYPE_LONGLINK,
+                   GNUTYPE_SPARSE)
+
+# File types that will be treated as a regular file.
+REGULAR_TYPES = (REGTYPE, AREGTYPE,
+                 CONTTYPE, GNUTYPE_SPARSE)
+
+# File types that are part of the GNU tar format.
+GNU_TYPES = (GNUTYPE_LONGNAME, GNUTYPE_LONGLINK,
+             GNUTYPE_SPARSE)
+
+# Fields from a pax header that override a TarInfo attribute.
+PAX_FIELDS = ("path", "linkpath", "size", "mtime",
+              "uid", "gid", "uname", "gname")
+
+# Fields from a pax header that are affected by hdrcharset.
+PAX_NAME_FIELDS = {"path", "linkpath", "uname", "gname"}
+
+# Fields in a pax header that are numbers, all other fields
+# are treated as strings.
+PAX_NUMBER_FIELDS = {
+    "atime": float,
+    "ctime": float,
+    "mtime": float,
+    "uid": int,
+    "gid": int,
+    "size": int
+}
+
+#---------------------------------------------------------
+# initialization
+#---------------------------------------------------------
+if os.name == "nt":
+    ENCODING = "utf-8"
+else:
+    ENCODING = sys.getfilesystemencoding()
+
+#---------------------------------------------------------
+# Some useful functions
+#---------------------------------------------------------
+
+def stn(s, length, encoding, errors):
+    """Convert a string to a null-terminated bytes object.
+    """
+    if s is None:
+        raise ValueError("metadata cannot contain None")
+    s = s.encode(encoding, errors)
+    return s[:length] + (length - len(s)) * NUL
+
+def nts(s, encoding, errors):
+    """Convert a null-terminated bytes object to a string.
+    """
+    p = s.find(b"\0")
+    if p != -1:
+        s = s[:p]
+    return s.decode(encoding, errors)
+
+def nti(s):
+    """Convert a number field to a python number.
+    """
+    # There are two possible encodings for a number field, see
+    # itn() below.
+    if s[0] in (0o200, 0o377):
+        n = 0
+        for i in range(len(s) - 1):
+            n <<= 8
+            n += s[i + 1]
+        if s[0] == 0o377:
+            n = -(256 ** (len(s) - 1) - n)
+    else:
+        try:
+            s = nts(s, "ascii", "strict")
+            n = int(s.strip() or "0", 8)
+        except ValueError:
+            raise InvalidHeaderError("invalid header")
+    return n
+
+def itn(n, digits=8, format=DEFAULT_FORMAT):
+    """Convert a python number to a number field.
+    """
+    # POSIX 1003.1-1988 requires numbers to be encoded as a string of
+    # octal digits followed by a null-byte, this allows values up to
+    # (8**(digits-1))-1. GNU tar allows storing numbers greater than
+    # that if necessary. A leading 0o200 or 0o377 byte indicate this
+    # particular encoding, the following digits-1 bytes are a big-endian
+    # base-256 representation. This allows values up to (256**(digits-1))-1.
+    # A 0o200 byte indicates a positive number, a 0o377 byte a negative
+    # number.
+    original_n = n
+    n = int(n)
+    if 0 <= n < 8 ** (digits - 1):
+        s = bytes("%0*o" % (digits - 1, n), "ascii") + NUL
+    elif format == GNU_FORMAT and -256 ** (digits - 1) <= n < 256 ** (digits - 1):
+        if n >= 0:
+            s = bytearray([0o200])
+        else:
+            s = bytearray([0o377])
+            n = 256 ** digits + n
+
+        for i in range(digits - 1):
+            s.insert(1, n & 0o377)
+            n >>= 8
+    else:
+        raise ValueError("overflow in number field")
+
+    return s
+
+def calc_chksums(buf):
+    """Calculate the checksum for a member's header by summing up all
+       characters except for the chksum field which is treated as if
+       it was filled with spaces. According to the GNU tar sources,
+       some tars (Sun and NeXT) calculate chksum with signed char,
+       which will be different if there are chars in the buffer with
+       the high bit set. So we calculate two checksums, unsigned and
+       signed.
+    """
+    unsigned_chksum = 256 + sum(struct.unpack_from("148B8x356B", buf))
+    signed_chksum = 256 + sum(struct.unpack_from("148b8x356b", buf))
+    return unsigned_chksum, signed_chksum
+
+def copyfileobj(src, dst, length=None, exception=OSError, bufsize=None):
+    """Copy length bytes from fileobj src to fileobj dst.
+       If length is None, copy the entire content.
+    """
+    bufsize = bufsize or 16 * 1024
+    if length == 0:
+        return
+    if length is None:
+        shutil.copyfileobj(src, dst, bufsize)
+        return
+
+    blocks, remainder = divmod(length, bufsize)
+    for b in range(blocks):
+        buf = src.read(bufsize)
+        if len(buf) < bufsize:
+            raise exception("unexpected end of data")
+        dst.write(buf)
+
+    if remainder != 0:
+        buf = src.read(remainder)
+        if len(buf) < remainder:
+            raise exception("unexpected end of data")
+        dst.write(buf)
+    return
+
+def _safe_print(s):
+    encoding = getattr(sys.stdout, 'encoding', None)
+    if encoding is not None:
+        s = s.encode(encoding, 'backslashreplace').decode(encoding)
+    print(s, end=' ')
+
+
+class TarError(Exception):
+    """Base exception."""
+    pass
+class ExtractError(TarError):
+    """General exception for extract errors."""
+    pass
+class ReadError(TarError):
+    """Exception for unreadable tar archives."""
+    pass
+class CompressionError(TarError):
+    """Exception for unavailable compression methods."""
+    pass
+class StreamError(TarError):
+    """Exception for unsupported operations on stream-like TarFiles."""
+    pass
+class HeaderError(TarError):
+    """Base exception for header errors."""
+    pass
+class EmptyHeaderError(HeaderError):
+    """Exception for empty headers."""
+    pass
+class TruncatedHeaderError(HeaderError):
+    """Exception for truncated headers."""
+    pass
+class EOFHeaderError(HeaderError):
+    """Exception for end of file headers."""
+    pass
+class InvalidHeaderError(HeaderError):
+    """Exception for invalid headers."""
+    pass
+class SubsequentHeaderError(HeaderError):
+    """Exception for missing and invalid extended headers."""
+    pass
+
+#---------------------------
+# internal stream interface
+#---------------------------
+class _LowLevelFile:
+    """Low-level file object. Supports reading and writing.
+       It is used instead of a regular file object for streaming
+       access.
+    """
+
+    def __init__(self, name, mode):
+        mode = {
+            "r": os.O_RDONLY,
+            "w": os.O_WRONLY | os.O_CREAT | os.O_TRUNC,
+        }[mode]
+        if hasattr(os, "O_BINARY"):
+            mode |= os.O_BINARY
+        self.fd = os.open(name, mode, 0o666)
+
+    def close(self):
+        os.close(self.fd)
+
+    def read(self, size):
+        return os.read(self.fd, size)
+
+    def write(self, s):
+        os.write(self.fd, s)
+
+class _Stream:
+    """Class that serves as an adapter between TarFile and
+       a stream-like object.  The stream-like object only
+       needs to have a read() or write() method that works with bytes,
+       and the method is accessed blockwise.
+       Use of gzip or bzip2 compression is possible.
+       A stream-like object could be for example: sys.stdin.buffer,
+       sys.stdout.buffer, a socket, a tape device etc.
+
+       _Stream is intended to be used only internally.
+    """
+
+    def __init__(self, name, mode, comptype, fileobj, bufsize,
+                 compresslevel):
+        """Construct a _Stream object.
+        """
+        self._extfileobj = True
+        if fileobj is None:
+            fileobj = _LowLevelFile(name, mode)
+            self._extfileobj = False
+
+        if comptype == '*':
+            # Enable transparent compression detection for the
+            # stream interface
+            fileobj = _StreamProxy(fileobj)
+            comptype = fileobj.getcomptype()
+
+        self.name     = name or ""
+        self.mode     = mode
+        self.comptype = comptype
+        self.fileobj  = fileobj
+        self.bufsize  = bufsize
+        self.buf      = b""
+        self.pos      = 0
+        self.closed   = False
+
+        try:
+            if comptype == "gz":
+                try:
+                    import zlib
+                except ImportError:
+                    raise CompressionError("zlib module is not available") from None
+                self.zlib = zlib
+                self.crc = zlib.crc32(b"")
+                if mode == "r":
+                    self.exception = zlib.error
+                    self._init_read_gz()
+                else:
+                    self._init_write_gz(compresslevel)
+
+            elif comptype == "bz2":
+                try:
+                    import bz2
+                except ImportError:
+                    raise CompressionError("bz2 module is not available") from None
+                if mode == "r":
+                    self.dbuf = b""
+                    self.cmp = bz2.BZ2Decompressor()
+                    self.exception = OSError
+                else:
+                    self.cmp = bz2.BZ2Compressor(compresslevel)
+
+            elif comptype == "xz":
+                try:
+                    import lzma
+                except ImportError:
+                    raise CompressionError("lzma module is not available") from None
+                if mode == "r":
+                    self.dbuf = b""
+                    self.cmp = lzma.LZMADecompressor()
+                    self.exception = lzma.LZMAError
+                else:
+                    self.cmp = lzma.LZMACompressor()
+
+            elif comptype != "tar":
+                raise CompressionError("unknown compression type %r" % comptype)
+
+        except:
+            if not self._extfileobj:
+                self.fileobj.close()
+            self.closed = True
+            raise
+
+    def __del__(self):
+        if hasattr(self, "closed") and not self.closed:
+            self.close()
+
+    def _init_write_gz(self, compresslevel):
+        """Initialize for writing with gzip compression.
+        """
+        self.cmp = self.zlib.compressobj(compresslevel,
+                                         self.zlib.DEFLATED,
+                                         -self.zlib.MAX_WBITS,
+                                         self.zlib.DEF_MEM_LEVEL,
+                                         0)
+        timestamp = struct.pack("<L", int(time.time()))
+        self.__write(b"\037\213\010\010" + timestamp + b"\002\377")
+        if self.name.endswith(".gz"):
+            self.name = self.name[:-3]
+        # Honor "directory components removed" from RFC1952
+        self.name = os.path.basename(self.name)
+        # RFC1952 says we must use ISO-8859-1 for the FNAME field.
+        self.__write(self.name.encode("iso-8859-1", "replace") + NUL)
+
+    def write(self, s):
+        """Write string s to the stream.
+        """
+        if self.comptype == "gz":
+            self.crc = self.zlib.crc32(s, self.crc)
+        self.pos += len(s)
+        if self.comptype != "tar":
+            s = self.cmp.compress(s)
+        self.__write(s)
+
+    def __write(self, s):
+        """Write string s to the stream if a whole new block
+           is ready to be written.
+        """
+        self.buf += s
+        while len(self.buf) > self.bufsize:
+            self.fileobj.write(self.buf[:self.bufsize])
+            self.buf = self.buf[self.bufsize:]
+
+    def close(self):
+        """Close the _Stream object. No operation should be
+           done on it afterwards.
+        """
+        if self.closed:
+            return
+
+        self.closed = True
+        try:
+            if self.mode == "w" and self.comptype != "tar":
+                self.buf += self.cmp.flush()
+
+            if self.mode == "w" and self.buf:
+                self.fileobj.write(self.buf)
+                self.buf = b""
+                if self.comptype == "gz":
+                    self.fileobj.write(struct.pack("<L", self.crc))
+                    self.fileobj.write(struct.pack("<L", self.pos & 0xffffFFFF))
+        finally:
+            if not self._extfileobj:
+                self.fileobj.close()
+
+    def _init_read_gz(self):
+        """Initialize for reading a gzip compressed fileobj.
+        """
+        self.cmp = self.zlib.decompressobj(-self.zlib.MAX_WBITS)
+        self.dbuf = b""
+
+        # taken from gzip.GzipFile with some alterations
+        if self.__read(2) != b"\037\213":
+            raise ReadError("not a gzip file")
+        if self.__read(1) != b"\010":
+            raise CompressionError("unsupported compression method")
+
+        flag = ord(self.__read(1))
+        self.__read(6)
+
+        if flag & 4:
+            xlen = ord(self.__read(1)) + 256 * ord(self.__read(1))
+            self.read(xlen)
+        if flag & 8:
+            while True:
+                s = self.__read(1)
+                if not s or s == NUL:
+                    break
+        if flag & 16:
+            while True:
+                s = self.__read(1)
+                if not s or s == NUL:
+                    break
+        if flag & 2:
+            self.__read(2)
+
+    def tell(self):
+        """Return the stream's file pointer position.
+        """
+        return self.pos
+
+    def seek(self, pos=0):
+        """Set the stream's file pointer to pos. Negative seeking
+           is forbidden.
+        """
+        if pos - self.pos >= 0:
+            blocks, remainder = divmod(pos - self.pos, self.bufsize)
+            for i in range(blocks):
+                self.read(self.bufsize)
+            self.read(remainder)
+        else:
+            raise StreamError("seeking backwards is not allowed")
+        return self.pos
+
+    def read(self, size):
+        """Return the next size number of bytes from the stream."""
+        assert size is not None
+        buf = self._read(size)
+        self.pos += len(buf)
+        return buf
+
+    def _read(self, size):
+        """Return size bytes from the stream.
+        """
+        if self.comptype == "tar":
+            return self.__read(size)
+
+        c = len(self.dbuf)
+        t = [self.dbuf]
+        while c < size:
+            # Skip underlying buffer to avoid unaligned double buffering.
+            if self.buf:
+                buf = self.buf
+                self.buf = b""
+            else:
+                buf = self.fileobj.read(self.bufsize)
+                if not buf:
+                    break
+            try:
+                buf = self.cmp.decompress(buf)
+            except self.exception as e:
+                raise ReadError("invalid compressed data") from e
+            t.append(buf)
+            c += len(buf)
+        t = b"".join(t)
+        self.dbuf = t[size:]
+        return t[:size]
+
+    def __read(self, size):
+        """Return size bytes from stream. If internal buffer is empty,
+           read another block from the stream.
+        """
+        c = len(self.buf)
+        t = [self.buf]
+        while c < size:
+            buf = self.fileobj.read(self.bufsize)
+            if not buf:
+                break
+            t.append(buf)
+            c += len(buf)
+        t = b"".join(t)
+        self.buf = t[size:]
+        return t[:size]
+# class _Stream
+
+class _StreamProxy(object):
+    """Small proxy class that enables transparent compression
+       detection for the Stream interface (mode 'r|*').
+    """
+
+    def __init__(self, fileobj):
+        self.fileobj = fileobj
+        self.buf = self.fileobj.read(BLOCKSIZE)
+
+    def read(self, size):
+        self.read = self.fileobj.read
+        return self.buf
+
+    def getcomptype(self):
+        if self.buf.startswith(b"\x1f\x8b\x08"):
+            return "gz"
+        elif self.buf[0:3] == b"BZh" and self.buf[4:10] == b"1AY&SY":
+            return "bz2"
+        elif self.buf.startswith((b"\x5d\x00\x00\x80", b"\xfd7zXZ")):
+            return "xz"
+        else:
+            return "tar"
+
+    def close(self):
+        self.fileobj.close()
+# class StreamProxy
+
+#------------------------
+# Extraction file object
+#------------------------
+class _FileInFile(object):
+    """A thin wrapper around an existing file object that
+       provides a part of its data as an individual file
+       object.
+    """
+
+    def __init__(self, fileobj, offset, size, name, blockinfo=None):
+        self.fileobj = fileobj
+        self.offset = offset
+        self.size = size
+        self.position = 0
+        self.name = name
+        self.closed = False
+
+        if blockinfo is None:
+            blockinfo = [(0, size)]
+
+        # Construct a map with data and zero blocks.
+        self.map_index = 0
+        self.map = []
+        lastpos = 0
+        realpos = self.offset
+        for offset, size in blockinfo:
+            if offset > lastpos:
+                self.map.append((False, lastpos, offset, None))
+            self.map.append((True, offset, offset + size, realpos))
+            realpos += size
+            lastpos = offset + size
+        if lastpos < self.size:
+            self.map.append((False, lastpos, self.size, None))
+
+    def flush(self):
+        pass
+
+    @property
+    def mode(self):
+        return 'rb'
+
+    def readable(self):
+        return True
+
+    def writable(self):
+        return False
+
+    def seekable(self):
+        return self.fileobj.seekable()
+
+    def tell(self):
+        """Return the current file position.
+        """
+        return self.position
+
+    def seek(self, position, whence=io.SEEK_SET):
+        """Seek to a position in the file.
+        """
+        if whence == io.SEEK_SET:
+            self.position = min(max(position, 0), self.size)
+        elif whence == io.SEEK_CUR:
+            if position < 0:
+                self.position = max(self.position + position, 0)
+            else:
+                self.position = min(self.position + position, self.size)
+        elif whence == io.SEEK_END:
+            self.position = max(min(self.size + position, self.size), 0)
+        else:
+            raise ValueError("Invalid argument")
+        return self.position
+
+    def read(self, size=None):
+        """Read data from the file.
+        """
+        if size is None:
+            size = self.size - self.position
+        else:
+            size = min(size, self.size - self.position)
+
+        buf = b""
+        while size > 0:
+            while True:
+                data, start, stop, offset = self.map[self.map_index]
+                if start <= self.position < stop:
+                    break
+                else:
+                    self.map_index += 1
+                    if self.map_index == len(self.map):
+                        self.map_index = 0
+            length = min(size, stop - self.position)
+            if data:
+                self.fileobj.seek(offset + (self.position - start))
+                b = self.fileobj.read(length)
+                if len(b) != length:
+                    raise ReadError("unexpected end of data")
+                buf += b
+            else:
+                buf += NUL * length
+            size -= length
+            self.position += length
+        return buf
+
+    def readinto(self, b):
+        buf = self.read(len(b))
+        b[:len(buf)] = buf
+        return len(buf)
+
+    def close(self):
+        self.closed = True
+#class _FileInFile
+
+class ExFileObject(io.BufferedReader):
+
+    def __init__(self, tarfile, tarinfo):
+        fileobj = _FileInFile(tarfile.fileobj, tarinfo.offset_data,
+                tarinfo.size, tarinfo.name, tarinfo.sparse)
+        super().__init__(fileobj)
+#class ExFileObject
+
+
+#-----------------------------
+# extraction filters (PEP 706)
+#-----------------------------
+
+class FilterError(TarError):
+    pass
+
+class AbsolutePathError(FilterError):
+    def __init__(self, tarinfo):
+        self.tarinfo = tarinfo
+        super().__init__(f'member {tarinfo.name!r} has an absolute path')
+
+class OutsideDestinationError(FilterError):
+    def __init__(self, tarinfo, path):
+        self.tarinfo = tarinfo
+        self._path = path
+        super().__init__(f'{tarinfo.name!r} would be extracted to {path!r}, '
+                         + 'which is outside the destination')
+
+class SpecialFileError(FilterError):
+    def __init__(self, tarinfo):
+        self.tarinfo = tarinfo
+        super().__init__(f'{tarinfo.name!r} is a special file')
+
+class AbsoluteLinkError(FilterError):
+    def __init__(self, tarinfo):
+        self.tarinfo = tarinfo
+        super().__init__(f'{tarinfo.name!r} is a link to an absolute path')
+
+class LinkOutsideDestinationError(FilterError):
+    def __init__(self, tarinfo, path):
+        self.tarinfo = tarinfo
+        self._path = path
+        super().__init__(f'{tarinfo.name!r} would link to {path!r}, '
+                         + 'which is outside the destination')
+
+def _get_filtered_attrs(member, dest_path, for_data=True):
+    new_attrs = {}
+    name = member.name
+    dest_path = os.path.realpath(dest_path)
+    # Strip leading / (tar's directory separator) from filenames.
+    # Include os.sep (target OS directory separator) as well.
+    if name.startswith(('/', os.sep)):
+        name = new_attrs['name'] = member.path.lstrip('/' + os.sep)
+    if os.path.isabs(name):
+        # Path is absolute even after stripping.
+        # For example, 'C:/foo' on Windows.
+        raise AbsolutePathError(member)
+    # Ensure we stay in the destination
+    target_path = os.path.realpath(os.path.join(dest_path, name))
+    if os.path.commonpath([target_path, dest_path]) != dest_path:
+        raise OutsideDestinationError(member, target_path)
+    # Limit permissions (no high bits, and go-w)
+    mode = member.mode
+    if mode is not None:
+        # Strip high bits & group/other write bits
+        mode = mode & 0o755
+        if for_data:
+            # For data, handle permissions & file types
+            if member.isreg() or member.islnk():
+                if not mode & 0o100:
+                    # Clear executable bits if not executable by user
+                    mode &= ~0o111
+                # Ensure owner can read & write
+                mode |= 0o600
+            elif member.isdir() or member.issym():
+                # Ignore mode for directories & symlinks
+                mode = None
+            else:
+                # Reject special files
+                raise SpecialFileError(member)
+        if mode != member.mode:
+            new_attrs['mode'] = mode
+    if for_data:
+        # Ignore ownership for 'data'
+        if member.uid is not None:
+            new_attrs['uid'] = None
+        if member.gid is not None:
+            new_attrs['gid'] = None
+        if member.uname is not None:
+            new_attrs['uname'] = None
+        if member.gname is not None:
+            new_attrs['gname'] = None
+        # Check link destination for 'data'
+        if member.islnk() or member.issym():
+            if os.path.isabs(member.linkname):
+                raise AbsoluteLinkError(member)
+            if member.issym():
+                target_path = os.path.join(dest_path,
+                                           os.path.dirname(name),
+                                           member.linkname)
+            else:
+                target_path = os.path.join(dest_path,
+                                           member.linkname)
+            target_path = os.path.realpath(target_path)
+            if os.path.commonpath([target_path, dest_path]) != dest_path:
+                raise LinkOutsideDestinationError(member, target_path)
+    return new_attrs
+
+def fully_trusted_filter(member, dest_path):
+    return member
+
+def tar_filter(member, dest_path):
+    new_attrs = _get_filtered_attrs(member, dest_path, False)
+    if new_attrs:
+        return member.replace(**new_attrs, deep=False)
+    return member
+
+def data_filter(member, dest_path):
+    new_attrs = _get_filtered_attrs(member, dest_path, True)
+    if new_attrs:
+        return member.replace(**new_attrs, deep=False)
+    return member
+
+_NAMED_FILTERS = {
+    "fully_trusted": fully_trusted_filter,
+    "tar": tar_filter,
+    "data": data_filter,
+}
+
+#------------------
+# Exported Classes
+#------------------
+
+# Sentinel for replace() defaults, meaning "don't change the attribute"
+_KEEP = object()
+
+class TarInfo(object):
+    """Informational class which holds the details about an
+       archive member given by a tar header block.
+       TarInfo objects are returned by TarFile.getmember(),
+       TarFile.getmembers() and TarFile.gettarinfo() and are
+       usually created internally.
+    """
+
+    __slots__ = dict(
+        name = 'Name of the archive member.',
+        mode = 'Permission bits.',
+        uid = 'User ID of the user who originally stored this member.',
+        gid = 'Group ID of the user who originally stored this member.',
+        size = 'Size in bytes.',
+        mtime = 'Time of last modification.',
+        chksum = 'Header checksum.',
+        type = ('File type. type is usually one of these constants: '
+                'REGTYPE, AREGTYPE, LNKTYPE, SYMTYPE, DIRTYPE, FIFOTYPE, '
+                'CONTTYPE, CHRTYPE, BLKTYPE, GNUTYPE_SPARSE.'),
+        linkname = ('Name of the target file name, which is only present '
+                    'in TarInfo objects of type LNKTYPE and SYMTYPE.'),
+        uname = 'User name.',
+        gname = 'Group name.',
+        devmajor = 'Device major number.',
+        devminor = 'Device minor number.',
+        offset = 'The tar header starts here.',
+        offset_data = "The file's data starts here.",
+        pax_headers = ('A dictionary containing key-value pairs of an '
+                       'associated pax extended header.'),
+        sparse = 'Sparse member information.',
+        _tarfile = None,
+        _sparse_structs = None,
+        _link_target = None,
+        )
+
+    def __init__(self, name=""):
+        """Construct a TarInfo object. name is the optional name
+           of the member.
+        """
+        self.name = name        # member name
+        self.mode = 0o644       # file permissions
+        self.uid = 0            # user id
+        self.gid = 0            # group id
+        self.size = 0           # file size
+        self.mtime = 0          # modification time
+        self.chksum = 0         # header checksum
+        self.type = REGTYPE     # member type
+        self.linkname = ""      # link name
+        self.uname = ""         # user name
+        self.gname = ""         # group name
+        self.devmajor = 0       # device major number
+        self.devminor = 0       # device minor number
+
+        self.offset = 0         # the tar header starts here
+        self.offset_data = 0    # the file's data starts here
+
+        self.sparse = None      # sparse member information
+        self.pax_headers = {}   # pax header information
+
+    @property
+    def tarfile(self):
+        import warnings
+        warnings.warn(
+            'The undocumented "tarfile" attribute of TarInfo objects '
+            + 'is deprecated and will be removed in Python 3.16',
+            DeprecationWarning, stacklevel=2)
+        return self._tarfile
+
+    @tarfile.setter
+    def tarfile(self, tarfile):
+        import warnings
+        warnings.warn(
+            'The undocumented "tarfile" attribute of TarInfo objects '
+            + 'is deprecated and will be removed in Python 3.16',
+            DeprecationWarning, stacklevel=2)
+        self._tarfile = tarfile
+
+    @property
+    def path(self):
+        'In pax headers, "name" is called "path".'
+        return self.name
+
+    @path.setter
+    def path(self, name):
+        self.name = name
+
+    @property
+    def linkpath(self):
+        'In pax headers, "linkname" is called "linkpath".'
+        return self.linkname
+
+    @linkpath.setter
+    def linkpath(self, linkname):
+        self.linkname = linkname
+
+    def __repr__(self):
+        return "<%s %r at %#x>" % (self.__class__.__name__,self.name,id(self))
+
+    def replace(self, *,
+                name=_KEEP, mtime=_KEEP, mode=_KEEP, linkname=_KEEP,
+                uid=_KEEP, gid=_KEEP, uname=_KEEP, gname=_KEEP,
+                deep=True, _KEEP=_KEEP):
+        """Return a deep copy of self with the given attributes replaced.
+        """
+        if deep:
+            result = copy.deepcopy(self)
+        else:
+            result = copy.copy(self)
+        if name is not _KEEP:
+            result.name = name
+        if mtime is not _KEEP:
+            result.mtime = mtime
+        if mode is not _KEEP:
+            result.mode = mode
+        if linkname is not _KEEP:
+            result.linkname = linkname
+        if uid is not _KEEP:
+            result.uid = uid
+        if gid is not _KEEP:
+            result.gid = gid
+        if uname is not _KEEP:
+            result.uname = uname
+        if gname is not _KEEP:
+            result.gname = gname
+        return result
+
+    def get_info(self):
+        """Return the TarInfo's attributes as a dictionary.
+        """
+        if self.mode is None:
+            mode = None
+        else:
+            mode = self.mode & 0o7777
+        info = {
+            "name":     self.name,
+            "mode":     mode,
+            "uid":      self.uid,
+            "gid":      self.gid,
+            "size":     self.size,
+            "mtime":    self.mtime,
+            "chksum":   self.chksum,
+            "type":     self.type,
+            "linkname": self.linkname,
+            "uname":    self.uname,
+            "gname":    self.gname,
+            "devmajor": self.devmajor,
+            "devminor": self.devminor
+        }
+
+        if info["type"] == DIRTYPE and not info["name"].endswith("/"):
+            info["name"] += "/"
+
+        return info
+
+    def tobuf(self, format=DEFAULT_FORMAT, encoding=ENCODING, errors="surrogateescape"):
+        """Return a tar header as a string of 512 byte blocks.
+        """
+        info = self.get_info()
+        for name, value in info.items():
+            if value is None:
+                raise ValueError("%s may not be None" % name)
+
+        if format == USTAR_FORMAT:
+            return self.create_ustar_header(info, encoding, errors)
+        elif format == GNU_FORMAT:
+            return self.create_gnu_header(info, encoding, errors)
+        elif format == PAX_FORMAT:
+            return self.create_pax_header(info, encoding)
+        else:
+            raise ValueError("invalid format")
+
+    def create_ustar_header(self, info, encoding, errors):
+        """Return the object as a ustar header block.
+        """
+        info["magic"] = POSIX_MAGIC
+
+        if len(info["linkname"].encode(encoding, errors)) > LENGTH_LINK:
+            raise ValueError("linkname is too long")
+
+        if len(info["name"].encode(encoding, errors)) > LENGTH_NAME:
+            info["prefix"], info["name"] = self._posix_split_name(info["name"], encoding, errors)
+
+        return self._create_header(info, USTAR_FORMAT, encoding, errors)
+
+    def create_gnu_header(self, info, encoding, errors):
+        """Return the object as a GNU header block sequence.
+        """
+        info["magic"] = GNU_MAGIC
+
+        buf = b""
+        if len(info["linkname"].encode(encoding, errors)) > LENGTH_LINK:
+            buf += self._create_gnu_long_header(info["linkname"], GNUTYPE_LONGLINK, encoding, errors)
+
+        if len(info["name"].encode(encoding, errors)) > LENGTH_NAME:
+            buf += self._create_gnu_long_header(info["name"], GNUTYPE_LONGNAME, encoding, errors)
+
+        return buf + self._create_header(info, GNU_FORMAT, encoding, errors)
+
+    def create_pax_header(self, info, encoding):
+        """Return the object as a ustar header block. If it cannot be
+           represented this way, prepend a pax extended header sequence
+           with supplement information.
+        """
+        info["magic"] = POSIX_MAGIC
+        pax_headers = self.pax_headers.copy()
+
+        # Test string fields for values that exceed the field length or cannot
+        # be represented in ASCII encoding.
+        for name, hname, length in (
+                ("name", "path", LENGTH_NAME), ("linkname", "linkpath", LENGTH_LINK),
+                ("uname", "uname", 32), ("gname", "gname", 32)):
+
+            if hname in pax_headers:
+                # The pax header has priority.
+                continue
+
+            # Try to encode the string as ASCII.
+            try:
+                info[name].encode("ascii", "strict")
+            except UnicodeEncodeError:
+                pax_headers[hname] = info[name]
+                continue
+
+            if len(info[name]) > length:
+                pax_headers[hname] = info[name]
+
+        # Test number fields for values that exceed the field limit or values
+        # that like to be stored as float.
+        for name, digits in (("uid", 8), ("gid", 8), ("size", 12), ("mtime", 12)):
+            needs_pax = False
+
+            val = info[name]
+            val_is_float = isinstance(val, float)
+            val_int = round(val) if val_is_float else val
+            if not 0 <= val_int < 8 ** (digits - 1):
+                # Avoid overflow.
+                info[name] = 0
+                needs_pax = True
+            elif val_is_float:
+                # Put rounded value in ustar header, and full
+                # precision value in pax header.
+                info[name] = val_int
+                needs_pax = True
+
+            # The existing pax header has priority.
+            if needs_pax and name not in pax_headers:
+                pax_headers[name] = str(val)
+
+        # Create a pax extended header if necessary.
+        if pax_headers:
+            buf = self._create_pax_generic_header(pax_headers, XHDTYPE, encoding)
+        else:
+            buf = b""
+
+        return buf + self._create_header(info, USTAR_FORMAT, "ascii", "replace")
+
+    @classmethod
+    def create_pax_global_header(cls, pax_headers):
+        """Return the object as a pax global header block sequence.
+        """
+        return cls._create_pax_generic_header(pax_headers, XGLTYPE, "utf-8")
+
+    def _posix_split_name(self, name, encoding, errors):
+        """Split a name longer than 100 chars into a prefix
+           and a name part.
+        """
+        components = name.split("/")
+        for i in range(1, len(components)):
+            prefix = "/".join(components[:i])
+            name = "/".join(components[i:])
+            if len(prefix.encode(encoding, errors)) <= LENGTH_PREFIX and \
+                    len(name.encode(encoding, errors)) <= LENGTH_NAME:
+                break
+        else:
+            raise ValueError("name is too long")
+
+        return prefix, name
+
+    @staticmethod
+    def _create_header(info, format, encoding, errors):
+        """Return a header block. info is a dictionary with file
+           information, format must be one of the *_FORMAT constants.
+        """
+        has_device_fields = info.get("type") in (CHRTYPE, BLKTYPE)
+        if has_device_fields:
+            devmajor = itn(info.get("devmajor", 0), 8, format)
+            devminor = itn(info.get("devminor", 0), 8, format)
+        else:
+            devmajor = stn("", 8, encoding, errors)
+            devminor = stn("", 8, encoding, errors)
+
+        # None values in metadata should cause ValueError.
+        # itn()/stn() do this for all fields except type.
+        filetype = info.get("type", REGTYPE)
+        if filetype is None:
+            raise ValueError("TarInfo.type must not be None")
+
+        parts = [
+            stn(info.get("name", ""), 100, encoding, errors),
+            itn(info.get("mode", 0) & 0o7777, 8, format),
+            itn(info.get("uid", 0), 8, format),
+            itn(info.get("gid", 0), 8, format),
+            itn(info.get("size", 0), 12, format),
+            itn(info.get("mtime", 0), 12, format),
+            b"        ", # checksum field
+            filetype,
+            stn(info.get("linkname", ""), 100, encoding, errors),
+            info.get("magic", POSIX_MAGIC),
+            stn(info.get("uname", ""), 32, encoding, errors),
+            stn(info.get("gname", ""), 32, encoding, errors),
+            devmajor,
+            devminor,
+            stn(info.get("prefix", ""), 155, encoding, errors)
+        ]
+
+        buf = struct.pack("%ds" % BLOCKSIZE, b"".join(parts))
+        chksum = calc_chksums(buf[-BLOCKSIZE:])[0]
+        buf = buf[:-364] + bytes("%06o\0" % chksum, "ascii") + buf[-357:]
+        return buf
+
+    @staticmethod
+    def _create_payload(payload):
+        """Return the string payload filled with zero bytes
+           up to the next 512 byte border.
+        """
+        blocks, remainder = divmod(len(payload), BLOCKSIZE)
+        if remainder > 0:
+            payload += (BLOCKSIZE - remainder) * NUL
+        return payload
+
+    @classmethod
+    def _create_gnu_long_header(cls, name, type, encoding, errors):
+        """Return a GNUTYPE_LONGNAME or GNUTYPE_LONGLINK sequence
+           for name.
+        """
+        name = name.encode(encoding, errors) + NUL
+
+        info = {}
+        info["name"] = "././@LongLink"
+        info["type"] = type
+        info["size"] = len(name)
+        info["magic"] = GNU_MAGIC
+
+        # create extended header + name blocks.
+        return cls._create_header(info, USTAR_FORMAT, encoding, errors) + \
+                cls._create_payload(name)
+
+    @classmethod
+    def _create_pax_generic_header(cls, pax_headers, type, encoding):
+        """Return a POSIX.1-2008 extended or global header sequence
+           that contains a list of keyword, value pairs. The values
+           must be strings.
+        """
+        # Check if one of the fields contains surrogate characters and thereby
+        # forces hdrcharset=BINARY, see _proc_pax() for more information.
+        binary = False
+        for keyword, value in pax_headers.items():
+            try:
+                value.encode("utf-8", "strict")
+            except UnicodeEncodeError:
+                binary = True
+                break
+
+        records = b""
+        if binary:
+            # Put the hdrcharset field at the beginning of the header.
+            records += b"21 hdrcharset=BINARY\n"
+
+        for keyword, value in pax_headers.items():
+            keyword = keyword.encode("utf-8")
+            if binary:
+                # Try to restore the original byte representation of 'value'.
+                # Needless to say, that the encoding must match the string.
+                value = value.encode(encoding, "surrogateescape")
+            else:
+                value = value.encode("utf-8")
+
+            l = len(keyword) + len(value) + 3   # ' ' + '=' + '\n'
+            n = p = 0
+            while True:
+                n = l + len(str(p))
+                if n == p:
+                    break
+                p = n
+            records += bytes(str(p), "ascii") + b" " + keyword + b"=" + value + b"\n"
+
+        # We use a hardcoded "././@PaxHeader" name like star does
+        # instead of the one that POSIX recommends.
+        info = {}
+        info["name"] = "././@PaxHeader"
+        info["type"] = type
+        info["size"] = len(records)
+        info["magic"] = POSIX_MAGIC
+
+        # Create pax header + record blocks.
+        return cls._create_header(info, USTAR_FORMAT, "ascii", "replace") + \
+                cls._create_payload(records)
+
+    @classmethod
+    def frombuf(cls, buf, encoding, errors):
+        """Construct a TarInfo object from a 512 byte bytes object.
+        """
+        if len(buf) == 0:
+            raise EmptyHeaderError("empty header")
+        if len(buf) != BLOCKSIZE:
+            raise TruncatedHeaderError("truncated header")
+        if buf.count(NUL) == BLOCKSIZE:
+            raise EOFHeaderError("end of file header")
+
+        chksum = nti(buf[148:156])
+        if chksum not in calc_chksums(buf):
+            raise InvalidHeaderError("bad checksum")
+
+        obj = cls()
+        obj.name = nts(buf[0:100], encoding, errors)
+        obj.mode = nti(buf[100:108])
+        obj.uid = nti(buf[108:116])
+        obj.gid = nti(buf[116:124])
+        obj.size = nti(buf[124:136])
+        obj.mtime = nti(buf[136:148])
+        obj.chksum = chksum
+        obj.type = buf[156:157]
+        obj.linkname = nts(buf[157:257], encoding, errors)
+        obj.uname = nts(buf[265:297], encoding, errors)
+        obj.gname = nts(buf[297:329], encoding, errors)
+        obj.devmajor = nti(buf[329:337])
+        obj.devminor = nti(buf[337:345])
+        prefix = nts(buf[345:500], encoding, errors)
+
+        # Old V7 tar format represents a directory as a regular
+        # file with a trailing slash.
+        if obj.type == AREGTYPE and obj.name.endswith("/"):
+            obj.type = DIRTYPE
+
+        # The old GNU sparse format occupies some of the unused
+        # space in the buffer for up to 4 sparse structures.
+        # Save them for later processing in _proc_sparse().
+        if obj.type == GNUTYPE_SPARSE:
+            pos = 386
+            structs = []
+            for i in range(4):
+                try:
+                    offset = nti(buf[pos:pos + 12])
+                    numbytes = nti(buf[pos + 12:pos + 24])
+                except ValueError:
+                    break
+                structs.append((offset, numbytes))
+                pos += 24
+            isextended = bool(buf[482])
+            origsize = nti(buf[483:495])
+            obj._sparse_structs = (structs, isextended, origsize)
+
+        # Remove redundant slashes from directories.
+        if obj.isdir():
+            obj.name = obj.name.rstrip("/")
+
+        # Reconstruct a ustar longname.
+        if prefix and obj.type not in GNU_TYPES:
+            obj.name = prefix + "/" + obj.name
+        return obj
+
+    @classmethod
+    def fromtarfile(cls, tarfile):
+        """Return the next TarInfo object from TarFile object
+           tarfile.
+        """
+        buf = tarfile.fileobj.read(BLOCKSIZE)
+        obj = cls.frombuf(buf, tarfile.encoding, tarfile.errors)
+        obj.offset = tarfile.fileobj.tell() - BLOCKSIZE
+        return obj._proc_member(tarfile)
+
+    #--------------------------------------------------------------------------
+    # The following are methods that are called depending on the type of a
+    # member. The entry point is _proc_member() which can be overridden in a
+    # subclass to add custom _proc_*() methods. A _proc_*() method MUST
+    # implement the following
+    # operations:
+    # 1. Set self.offset_data to the position where the data blocks begin,
+    #    if there is data that follows.
+    # 2. Set tarfile.offset to the position where the next member's header will
+    #    begin.
+    # 3. Return self or another valid TarInfo object.
+    def _proc_member(self, tarfile):
+        """Choose the right processing method depending on
+           the type and call it.
+        """
+        if self.type in (GNUTYPE_LONGNAME, GNUTYPE_LONGLINK):
+            return self._proc_gnulong(tarfile)
+        elif self.type == GNUTYPE_SPARSE:
+            return self._proc_sparse(tarfile)
+        elif self.type in (XHDTYPE, XGLTYPE, SOLARIS_XHDTYPE):
+            return self._proc_pax(tarfile)
+        else:
+            return self._proc_builtin(tarfile)
+
+    def _proc_builtin(self, tarfile):
+        """Process a builtin type or an unknown type which
+           will be treated as a regular file.
+        """
+        self.offset_data = tarfile.fileobj.tell()
+        offset = self.offset_data
+        if self.isreg() or self.type not in SUPPORTED_TYPES:
+            # Skip the following data blocks.
+            offset += self._block(self.size)
+        tarfile.offset = offset
+
+        # Patch the TarInfo object with saved global
+        # header information.
+        self._apply_pax_info(tarfile.pax_headers, tarfile.encoding, tarfile.errors)
+
+        # Remove redundant slashes from directories. This is to be consistent
+        # with frombuf().
+        if self.isdir():
+            self.name = self.name.rstrip("/")
+
+        return self
+
+    def _proc_gnulong(self, tarfile):
+        """Process the blocks that hold a GNU longname
+           or longlink member.
+        """
+        buf = tarfile.fileobj.read(self._block(self.size))
+
+        # Fetch the next header and process it.
+        try:
+            next = self.fromtarfile(tarfile)
+        except HeaderError as e:
+            raise SubsequentHeaderError(str(e)) from None
+
+        # Patch the TarInfo object from the next header with
+        # the longname information.
+        next.offset = self.offset
+        if self.type == GNUTYPE_LONGNAME:
+            next.name = nts(buf, tarfile.encoding, tarfile.errors)
+        elif self.type == GNUTYPE_LONGLINK:
+            next.linkname = nts(buf, tarfile.encoding, tarfile.errors)
+
+        # Remove redundant slashes from directories. This is to be consistent
+        # with frombuf().
+        if next.isdir():
+            next.name = removesuffix(next.name, "/")
+
+        return next
+
+    def _proc_sparse(self, tarfile):
+        """Process a GNU sparse header plus extra headers.
+        """
+        # We already collected some sparse structures in frombuf().
+        structs, isextended, origsize = self._sparse_structs
+        del self._sparse_structs
+
+        # Collect sparse structures from extended header blocks.
+        while isextended:
+            buf = tarfile.fileobj.read(BLOCKSIZE)
+            pos = 0
+            for i in range(21):
+                try:
+                    offset = nti(buf[pos:pos + 12])
+                    numbytes = nti(buf[pos + 12:pos + 24])
+                except ValueError:
+                    break
+                if offset and numbytes:
+                    structs.append((offset, numbytes))
+                pos += 24
+            isextended = bool(buf[504])
+        self.sparse = structs
+
+        self.offset_data = tarfile.fileobj.tell()
+        tarfile.offset = self.offset_data + self._block(self.size)
+        self.size = origsize
+        return self
+
+    def _proc_pax(self, tarfile):
+        """Process an extended or global header as described in
+           POSIX.1-2008.
+        """
+        # Read the header information.
+        buf = tarfile.fileobj.read(self._block(self.size))
+
+        # A pax header stores supplemental information for either
+        # the following file (extended) or all following files
+        # (global).
+        if self.type == XGLTYPE:
+            pax_headers = tarfile.pax_headers
+        else:
+            pax_headers = tarfile.pax_headers.copy()
+
+        # Check if the pax header contains a hdrcharset field. This tells us
+        # the encoding of the path, linkpath, uname and gname fields. Normally,
+        # these fields are UTF-8 encoded but since POSIX.1-2008 tar
+        # implementations are allowed to store them as raw binary strings if
+        # the translation to UTF-8 fails.
+        match = re.search(br"\d+ hdrcharset=([^\n]+)\n", buf)
+        if match is not None:
+            pax_headers["hdrcharset"] = match.group(1).decode("utf-8")
+
+        # For the time being, we don't care about anything other than "BINARY".
+        # The only other value that is currently allowed by the standard is
+        # "ISO-IR 10646 2000 UTF-8" in other words UTF-8.
+        hdrcharset = pax_headers.get("hdrcharset")
+        if hdrcharset == "BINARY":
+            encoding = tarfile.encoding
+        else:
+            encoding = "utf-8"
+
+        # Parse pax header information. A record looks like that:
+        # "%d %s=%s\n" % (length, keyword, value). length is the size
+        # of the complete record including the length field itself and
+        # the newline. keyword and value are both UTF-8 encoded strings.
+        regex = re.compile(br"(\d+) ([^=]+)=")
+        pos = 0
+        while match := regex.match(buf, pos):
+            length, keyword = match.groups()
+            length = int(length)
+            if length == 0:
+                raise InvalidHeaderError("invalid header")
+            value = buf[match.end(2) + 1:match.start(1) + length - 1]
+
+            # Normally, we could just use "utf-8" as the encoding and "strict"
+            # as the error handler, but we better not take the risk. For
+            # example, GNU tar <= 1.23 is known to store filenames it cannot
+            # translate to UTF-8 as raw strings (unfortunately without a
+            # hdrcharset=BINARY header).
+            # We first try the strict standard encoding, and if that fails we
+            # fall back on the user's encoding and error handler.
+            keyword = self._decode_pax_field(keyword, "utf-8", "utf-8",
+                    tarfile.errors)
+            if keyword in PAX_NAME_FIELDS:
+                value = self._decode_pax_field(value, encoding, tarfile.encoding,
+                        tarfile.errors)
+            else:
+                value = self._decode_pax_field(value, "utf-8", "utf-8",
+                        tarfile.errors)
+
+            pax_headers[keyword] = value
+            pos += length
+
+        # Fetch the next header.
+        try:
+            next = self.fromtarfile(tarfile)
+        except HeaderError as e:
+            raise SubsequentHeaderError(str(e)) from None
+
+        # Process GNU sparse information.
+        if "GNU.sparse.map" in pax_headers:
+            # GNU extended sparse format version 0.1.
+            self._proc_gnusparse_01(next, pax_headers)
+
+        elif "GNU.sparse.size" in pax_headers:
+            # GNU extended sparse format version 0.0.
+            self._proc_gnusparse_00(next, pax_headers, buf)
+
+        elif pax_headers.get("GNU.sparse.major") == "1" and pax_headers.get("GNU.sparse.minor") == "0":
+            # GNU extended sparse format version 1.0.
+            self._proc_gnusparse_10(next, pax_headers, tarfile)
+
+        if self.type in (XHDTYPE, SOLARIS_XHDTYPE):
+            # Patch the TarInfo object with the extended header info.
+            next._apply_pax_info(pax_headers, tarfile.encoding, tarfile.errors)
+            next.offset = self.offset
+
+            if "size" in pax_headers:
+                # If the extended header replaces the size field,
+                # we need to recalculate the offset where the next
+                # header starts.
+                offset = next.offset_data
+                if next.isreg() or next.type not in SUPPORTED_TYPES:
+                    offset += next._block(next.size)
+                tarfile.offset = offset
+
+        return next
+
+    def _proc_gnusparse_00(self, next, pax_headers, buf):
+        """Process a GNU tar extended sparse header, version 0.0.
+        """
+        offsets = []
+        for match in re.finditer(br"\d+ GNU.sparse.offset=(\d+)\n", buf):
+            offsets.append(int(match.group(1)))
+        numbytes = []
+        for match in re.finditer(br"\d+ GNU.sparse.numbytes=(\d+)\n", buf):
+            numbytes.append(int(match.group(1)))
+        next.sparse = list(zip(offsets, numbytes))
+
+    def _proc_gnusparse_01(self, next, pax_headers):
+        """Process a GNU tar extended sparse header, version 0.1.
+        """
+        sparse = [int(x) for x in pax_headers["GNU.sparse.map"].split(",")]
+        next.sparse = list(zip(sparse[::2], sparse[1::2]))
+
+    def _proc_gnusparse_10(self, next, pax_headers, tarfile):
+        """Process a GNU tar extended sparse header, version 1.0.
+        """
+        fields = None
+        sparse = []
+        buf = tarfile.fileobj.read(BLOCKSIZE)
+        fields, buf = buf.split(b"\n", 1)
+        fields = int(fields)
+        while len(sparse) < fields * 2:
+            if b"\n" not in buf:
+                buf += tarfile.fileobj.read(BLOCKSIZE)
+            number, buf = buf.split(b"\n", 1)
+            sparse.append(int(number))
+        next.offset_data = tarfile.fileobj.tell()
+        next.sparse = list(zip(sparse[::2], sparse[1::2]))
+
+    def _apply_pax_info(self, pax_headers, encoding, errors):
+        """Replace fields with supplemental information from a previous
+           pax extended or global header.
+        """
+        for keyword, value in pax_headers.items():
+            if keyword == "GNU.sparse.name":
+                setattr(self, "path", value)
+            elif keyword == "GNU.sparse.size":
+                setattr(self, "size", int(value))
+            elif keyword == "GNU.sparse.realsize":
+                setattr(self, "size", int(value))
+            elif keyword in PAX_FIELDS:
+                if keyword in PAX_NUMBER_FIELDS:
+                    try:
+                        value = PAX_NUMBER_FIELDS[keyword](value)
+                    except ValueError:
+                        value = 0
+                if keyword == "path":
+                    value = value.rstrip("/")
+                setattr(self, keyword, value)
+
+        self.pax_headers = pax_headers.copy()
+
+    def _decode_pax_field(self, value, encoding, fallback_encoding, fallback_errors):
+        """Decode a single field from a pax record.
+        """
+        try:
+            return value.decode(encoding, "strict")
+        except UnicodeDecodeError:
+            return value.decode(fallback_encoding, fallback_errors)
+
+    def _block(self, count):
+        """Round up a byte count by BLOCKSIZE and return it,
+           e.g. _block(834) => 1024.
+        """
+        blocks, remainder = divmod(count, BLOCKSIZE)
+        if remainder:
+            blocks += 1
+        return blocks * BLOCKSIZE
+
+    def isreg(self):
+        'Return True if the Tarinfo object is a regular file.'
+        return self.type in REGULAR_TYPES
+
+    def isfile(self):
+        'Return True if the Tarinfo object is a regular file.'
+        return self.isreg()
+
+    def isdir(self):
+        'Return True if it is a directory.'
+        return self.type == DIRTYPE
+
+    def issym(self):
+        'Return True if it is a symbolic link.'
+        return self.type == SYMTYPE
+
+    def islnk(self):
+        'Return True if it is a hard link.'
+        return self.type == LNKTYPE
+
+    def ischr(self):
+        'Return True if it is a character device.'
+        return self.type == CHRTYPE
+
+    def isblk(self):
+        'Return True if it is a block device.'
+        return self.type == BLKTYPE
+
+    def isfifo(self):
+        'Return True if it is a FIFO.'
+        return self.type == FIFOTYPE
+
+    def issparse(self):
+        return self.sparse is not None
+
+    def isdev(self):
+        'Return True if it is one of character device, block device or FIFO.'
+        return self.type in (CHRTYPE, BLKTYPE, FIFOTYPE)
+# class TarInfo
+
+class TarFile(object):
+    """The TarFile Class provides an interface to tar archives.
+    """
+
+    debug = 0                   # May be set from 0 (no msgs) to 3 (all msgs)
+
+    dereference = False         # If true, add content of linked file to the
+                                # tar file, else the link.
+
+    ignore_zeros = False        # If true, skips empty or invalid blocks and
+                                # continues processing.
+
+    errorlevel = 1              # If 0, fatal errors only appear in debug
+                                # messages (if debug >= 0). If > 0, errors
+                                # are passed to the caller as exceptions.
+
+    format = DEFAULT_FORMAT     # The format to use when creating an archive.
+
+    encoding = ENCODING         # Encoding for 8-bit character strings.
+
+    errors = None               # Error handler for unicode conversion.
+
+    tarinfo = TarInfo           # The default TarInfo class to use.
+
+    fileobject = ExFileObject   # The file-object for extractfile().
+
+    extraction_filter = None    # The default filter for extraction.
+
+    def __init__(self, name=None, mode="r", fileobj=None, format=None,
+            tarinfo=None, dereference=None, ignore_zeros=None, encoding=None,
+            errors="surrogateescape", pax_headers=None, debug=None,
+            errorlevel=None, copybufsize=None, stream=False):
+        """Open an (uncompressed) tar archive 'name'. 'mode' is either 'r' to
+           read from an existing archive, 'a' to append data to an existing
+           file or 'w' to create a new file overwriting an existing one. 'mode'
+           defaults to 'r'.
+           If 'fileobj' is given, it is used for reading or writing data. If it
+           can be determined, 'mode' is overridden by 'fileobj's mode.
+           'fileobj' is not closed, when TarFile is closed.
+        """
+        modes = {"r": "rb", "a": "r+b", "w": "wb", "x": "xb"}
+        if mode not in modes:
+            raise ValueError("mode must be 'r', 'a', 'w' or 'x'")
+        self.mode = mode
+        self._mode = modes[mode]
+
+        if not fileobj:
+            if self.mode == "a" and not os.path.exists(name):
+                # Create nonexistent files in append mode.
+                self.mode = "w"
+                self._mode = "wb"
+            fileobj = bltn_open(name, self._mode)
+            self._extfileobj = False
+        else:
+            if (name is None and hasattr(fileobj, "name") and
+                isinstance(fileobj.name, (str, bytes))):
+                name = fileobj.name
+            if hasattr(fileobj, "mode"):
+                self._mode = fileobj.mode
+            self._extfileobj = True
+        self.name = os.path.abspath(name) if name else None
+        self.fileobj = fileobj
+
+        self.stream = stream
+
+        # Init attributes.
+        if format is not None:
+            self.format = format
+        if tarinfo is not None:
+            self.tarinfo = tarinfo
+        if dereference is not None:
+            self.dereference = dereference
+        if ignore_zeros is not None:
+            self.ignore_zeros = ignore_zeros
+        if encoding is not None:
+            self.encoding = encoding
+        self.errors = errors
+
+        if pax_headers is not None and self.format == PAX_FORMAT:
+            self.pax_headers = pax_headers
+        else:
+            self.pax_headers = {}
+
+        if debug is not None:
+            self.debug = debug
+        if errorlevel is not None:
+            self.errorlevel = errorlevel
+
+        # Init datastructures.
+        self.copybufsize = copybufsize
+        self.closed = False
+        self.members = []       # list of members as TarInfo objects
+        self._loaded = False    # flag if all members have been read
+        self.offset = self.fileobj.tell()
+                                # current position in the archive file
+        self.inodes = {}        # dictionary caching the inodes of
+                                # archive members already added
+
+        try:
+            if self.mode == "r":
+                self.firstmember = None
+                self.firstmember = self.next()
+
+            if self.mode == "a":
+                # Move to the end of the archive,
+                # before the first empty block.
+                while True:
+                    self.fileobj.seek(self.offset)
+                    try:
+                        tarinfo = self.tarinfo.fromtarfile(self)
+                        self.members.append(tarinfo)
+                    except EOFHeaderError:
+                        self.fileobj.seek(self.offset)
+                        break
+                    except HeaderError as e:
+                        raise ReadError(str(e)) from None
+
+            if self.mode in ("a", "w", "x"):
+                self._loaded = True
+
+                if self.pax_headers:
+                    buf = self.tarinfo.create_pax_global_header(self.pax_headers.copy())
+                    self.fileobj.write(buf)
+                    self.offset += len(buf)
+        except:
+            if not self._extfileobj:
+                self.fileobj.close()
+            self.closed = True
+            raise
+
+    #--------------------------------------------------------------------------
+    # Below are the classmethods which act as alternate constructors to the
+    # TarFile class. The open() method is the only one that is needed for
+    # public use; it is the "super"-constructor and is able to select an
+    # adequate "sub"-constructor for a particular compression using the mapping
+    # from OPEN_METH.
+    #
+    # This concept allows one to subclass TarFile without losing the comfort of
+    # the super-constructor. A sub-constructor is registered and made available
+    # by adding it to the mapping in OPEN_METH.
+
+    @classmethod
+    def open(cls, name=None, mode="r", fileobj=None, bufsize=RECORDSIZE, **kwargs):
+        r"""Open a tar archive for reading, writing or appending. Return
+           an appropriate TarFile class.
+
+           mode:
+           'r' or 'r:\*' open for reading with transparent compression
+           'r:'         open for reading exclusively uncompressed
+           'r:gz'       open for reading with gzip compression
+           'r:bz2'      open for reading with bzip2 compression
+           'r:xz'       open for reading with lzma compression
+           'a' or 'a:'  open for appending, creating the file if necessary
+           'w' or 'w:'  open for writing without compression
+           'w:gz'       open for writing with gzip compression
+           'w:bz2'      open for writing with bzip2 compression
+           'w:xz'       open for writing with lzma compression
+
+           'x' or 'x:'  create a tarfile exclusively without compression, raise
+                        an exception if the file is already created
+           'x:gz'       create a gzip compressed tarfile, raise an exception
+                        if the file is already created
+           'x:bz2'      create a bzip2 compressed tarfile, raise an exception
+                        if the file is already created
+           'x:xz'       create an lzma compressed tarfile, raise an exception
+                        if the file is already created
+
+           'r|\*'        open a stream of tar blocks with transparent compression
+           'r|'         open an uncompressed stream of tar blocks for reading
+           'r|gz'       open a gzip compressed stream of tar blocks
+           'r|bz2'      open a bzip2 compressed stream of tar blocks
+           'r|xz'       open an lzma compressed stream of tar blocks
+           'w|'         open an uncompressed stream for writing
+           'w|gz'       open a gzip compressed stream for writing
+           'w|bz2'      open a bzip2 compressed stream for writing
+           'w|xz'       open an lzma compressed stream for writing
+        """
+
+        if not name and not fileobj:
+            raise ValueError("nothing to open")
+
+        if mode in ("r", "r:*"):
+            # Find out which *open() is appropriate for opening the file.
+            def not_compressed(comptype):
+                return cls.OPEN_METH[comptype] == 'taropen'
+            error_msgs = []
+            for comptype in sorted(cls.OPEN_METH, key=not_compressed):
+                func = getattr(cls, cls.OPEN_METH[comptype])
+                if fileobj is not None:
+                    saved_pos = fileobj.tell()
+                try:
+                    return func(name, "r", fileobj, **kwargs)
+                except (ReadError, CompressionError) as e:
+                    error_msgs.append(f'- method {comptype}: {e!r}')
+                    if fileobj is not None:
+                        fileobj.seek(saved_pos)
+                    continue
+            error_msgs_summary = '\n'.join(error_msgs)
+            raise ReadError(f"file could not be opened successfully:\n{error_msgs_summary}")
+
+        elif ":" in mode:
+            filemode, comptype = mode.split(":", 1)
+            filemode = filemode or "r"
+            comptype = comptype or "tar"
+
+            # Select the *open() function according to
+            # given compression.
+            if comptype in cls.OPEN_METH:
+                func = getattr(cls, cls.OPEN_METH[comptype])
+            else:
+                raise CompressionError("unknown compression type %r" % comptype)
+            return func(name, filemode, fileobj, **kwargs)
+
+        elif "|" in mode:
+            filemode, comptype = mode.split("|", 1)
+            filemode = filemode or "r"
+            comptype = comptype or "tar"
+
+            if filemode not in ("r", "w"):
+                raise ValueError("mode must be 'r' or 'w'")
+
+            compresslevel = kwargs.pop("compresslevel", 9)
+            stream = _Stream(name, filemode, comptype, fileobj, bufsize,
+                             compresslevel)
+            try:
+                t = cls(name, filemode, stream, **kwargs)
+            except:
+                stream.close()
+                raise
+            t._extfileobj = False
+            return t
+
+        elif mode in ("a", "w", "x"):
+            return cls.taropen(name, mode, fileobj, **kwargs)
+
+        raise ValueError("undiscernible mode")
+
+    @classmethod
+    def taropen(cls, name, mode="r", fileobj=None, **kwargs):
+        """Open uncompressed tar archive name for reading or writing.
+        """
+        if mode not in ("r", "a", "w", "x"):
+            raise ValueError("mode must be 'r', 'a', 'w' or 'x'")
+        return cls(name, mode, fileobj, **kwargs)
+
+    @classmethod
+    def gzopen(cls, name, mode="r", fileobj=None, compresslevel=9, **kwargs):
+        """Open gzip compressed tar archive name for reading or writing.
+           Appending is not allowed.
+        """
+        if mode not in ("r", "w", "x"):
+            raise ValueError("mode must be 'r', 'w' or 'x'")
+
+        try:
+            from gzip import GzipFile
+        except ImportError:
+            raise CompressionError("gzip module is not available") from None
+
+        try:
+            fileobj = GzipFile(name, mode + "b", compresslevel, fileobj)
+        except OSError as e:
+            if fileobj is not None and mode == 'r':
+                raise ReadError("not a gzip file") from e
+            raise
+
+        try:
+            t = cls.taropen(name, mode, fileobj, **kwargs)
+        except OSError as e:
+            fileobj.close()
+            if mode == 'r':
+                raise ReadError("not a gzip file") from e
+            raise
+        except:
+            fileobj.close()
+            raise
+        t._extfileobj = False
+        return t
+
+    @classmethod
+    def bz2open(cls, name, mode="r", fileobj=None, compresslevel=9, **kwargs):
+        """Open bzip2 compressed tar archive name for reading or writing.
+           Appending is not allowed.
+        """
+        if mode not in ("r", "w", "x"):
+            raise ValueError("mode must be 'r', 'w' or 'x'")
+
+        try:
+            from bz2 import BZ2File
+        except ImportError:
+            raise CompressionError("bz2 module is not available") from None
+
+        fileobj = BZ2File(fileobj or name, mode, compresslevel=compresslevel)
+
+        try:
+            t = cls.taropen(name, mode, fileobj, **kwargs)
+        except (OSError, EOFError) as e:
+            fileobj.close()
+            if mode == 'r':
+                raise ReadError("not a bzip2 file") from e
+            raise
+        except:
+            fileobj.close()
+            raise
+        t._extfileobj = False
+        return t
+
+    @classmethod
+    def xzopen(cls, name, mode="r", fileobj=None, preset=None, **kwargs):
+        """Open lzma compressed tar archive name for reading or writing.
+           Appending is not allowed.
+        """
+        if mode not in ("r", "w", "x"):
+            raise ValueError("mode must be 'r', 'w' or 'x'")
+
+        try:
+            from lzma import LZMAFile, LZMAError
+        except ImportError:
+            raise CompressionError("lzma module is not available") from None
+
+        fileobj = LZMAFile(fileobj or name, mode, preset=preset)
+
+        try:
+            t = cls.taropen(name, mode, fileobj, **kwargs)
+        except (LZMAError, EOFError) as e:
+            fileobj.close()
+            if mode == 'r':
+                raise ReadError("not an lzma file") from e
+            raise
+        except:
+            fileobj.close()
+            raise
+        t._extfileobj = False
+        return t
+
+    # All *open() methods are registered here.
+    OPEN_METH = {
+        "tar": "taropen",   # uncompressed tar
+        "gz":  "gzopen",    # gzip compressed tar
+        "bz2": "bz2open",   # bzip2 compressed tar
+        "xz":  "xzopen"     # lzma compressed tar
+    }
+
+    #--------------------------------------------------------------------------
+    # The public methods which TarFile provides:
+
+    def close(self):
+        """Close the TarFile. In write-mode, two finishing zero blocks are
+           appended to the archive.
+        """
+        if self.closed:
+            return
+
+        self.closed = True
+        try:
+            if self.mode in ("a", "w", "x"):
+                self.fileobj.write(NUL * (BLOCKSIZE * 2))
+                self.offset += (BLOCKSIZE * 2)
+                # fill up the end with zero-blocks
+                # (like option -b20 for tar does)
+                blocks, remainder = divmod(self.offset, RECORDSIZE)
+                if remainder > 0:
+                    self.fileobj.write(NUL * (RECORDSIZE - remainder))
+        finally:
+            if not self._extfileobj:
+                self.fileobj.close()
+
+    def getmember(self, name):
+        """Return a TarInfo object for member 'name'. If 'name' can not be
+           found in the archive, KeyError is raised. If a member occurs more
+           than once in the archive, its last occurrence is assumed to be the
+           most up-to-date version.
+        """
+        tarinfo = self._getmember(name.rstrip('/'))
+        if tarinfo is None:
+            raise KeyError("filename %r not found" % name)
+        return tarinfo
+
+    def getmembers(self):
+        """Return the members of the archive as a list of TarInfo objects. The
+           list has the same order as the members in the archive.
+        """
+        self._check()
+        if not self._loaded:    # if we want to obtain a list of
+            self._load()        # all members, we first have to
+                                # scan the whole archive.
+        return self.members
+
+    def getnames(self):
+        """Return the members of the archive as a list of their names. It has
+           the same order as the list returned by getmembers().
+        """
+        return [tarinfo.name for tarinfo in self.getmembers()]
+
+    def gettarinfo(self, name=None, arcname=None, fileobj=None):
+        """Create a TarInfo object from the result of os.stat or equivalent
+           on an existing file. The file is either named by 'name', or
+           specified as a file object 'fileobj' with a file descriptor. If
+           given, 'arcname' specifies an alternative name for the file in the
+           archive, otherwise, the name is taken from the 'name' attribute of
+           'fileobj', or the 'name' argument. The name should be a text
+           string.
+        """
+        self._check("awx")
+
+        # When fileobj is given, replace name by
+        # fileobj's real name.
+        if fileobj is not None:
+            name = fileobj.name
+
+        # Building the name of the member in the archive.
+        # Backward slashes are converted to forward slashes,
+        # Absolute paths are turned to relative paths.
+        if arcname is None:
+            arcname = name
+        drv, arcname = os.path.splitdrive(arcname)
+        arcname = arcname.replace(os.sep, "/")
+        arcname = arcname.lstrip("/")
+
+        # Now, fill the TarInfo object with
+        # information specific for the file.
+        tarinfo = self.tarinfo()
+        tarinfo._tarfile = self  # To be removed in 3.16.
+
+        # Use os.stat or os.lstat, depending on if symlinks shall be resolved.
+        if fileobj is None:
+            if not self.dereference:
+                statres = os.lstat(name)
+            else:
+                statres = os.stat(name)
+        else:
+            statres = os.fstat(fileobj.fileno())
+        linkname = ""
+
+        stmd = statres.st_mode
+        if stat.S_ISREG(stmd):
+            inode = (statres.st_ino, statres.st_dev)
+            if not self.dereference and statres.st_nlink > 1 and \
+                    inode in self.inodes and arcname != self.inodes[inode]:
+                # Is it a hardlink to an already
+                # archived file?
+                type = LNKTYPE
+                linkname = self.inodes[inode]
+            else:
+                # The inode is added only if its valid.
+                # For win32 it is always 0.
+                type = REGTYPE
+                if inode[0]:
+                    self.inodes[inode] = arcname
+        elif stat.S_ISDIR(stmd):
+            type = DIRTYPE
+        elif stat.S_ISFIFO(stmd):
+            type = FIFOTYPE
+        elif stat.S_ISLNK(stmd):
+            type = SYMTYPE
+            linkname = os.readlink(name)
+        elif stat.S_ISCHR(stmd):
+            type = CHRTYPE
+        elif stat.S_ISBLK(stmd):
+            type = BLKTYPE
+        else:
+            return None
+
+        # Fill the TarInfo object with all
+        # information we can get.
+        tarinfo.name = arcname
+        tarinfo.mode = stmd
+        tarinfo.uid = statres.st_uid
+        tarinfo.gid = statres.st_gid
+        if type == REGTYPE:
+            tarinfo.size = statres.st_size
+        else:
+            tarinfo.size = 0
+        tarinfo.mtime = statres.st_mtime
+        tarinfo.type = type
+        tarinfo.linkname = linkname
+        if pwd:
+            try:
+                tarinfo.uname = pwd.getpwuid(tarinfo.uid)[0]
+            except KeyError:
+                pass
+        if grp:
+            try:
+                tarinfo.gname = grp.getgrgid(tarinfo.gid)[0]
+            except KeyError:
+                pass
+
+        if type in (CHRTYPE, BLKTYPE):
+            if hasattr(os, "major") and hasattr(os, "minor"):
+                tarinfo.devmajor = os.major(statres.st_rdev)
+                tarinfo.devminor = os.minor(statres.st_rdev)
+        return tarinfo
+
+    def list(self, verbose=True, *, members=None):
+        """Print a table of contents to sys.stdout. If 'verbose' is False, only
+           the names of the members are printed. If it is True, an 'ls -l'-like
+           output is produced. 'members' is optional and must be a subset of the
+           list returned by getmembers().
+        """
+        # Convert tarinfo type to stat type.
+        type2mode = {REGTYPE: stat.S_IFREG, SYMTYPE: stat.S_IFLNK,
+                     FIFOTYPE: stat.S_IFIFO, CHRTYPE: stat.S_IFCHR,
+                     DIRTYPE: stat.S_IFDIR, BLKTYPE: stat.S_IFBLK}
+        self._check()
+
+        if members is None:
+            members = self
+        for tarinfo in members:
+            if verbose:
+                if tarinfo.mode is None:
+                    _safe_print("??????????")
+                else:
+                    modetype = type2mode.get(tarinfo.type, 0)
+                    _safe_print(stat.filemode(modetype | tarinfo.mode))
+                _safe_print("%s/%s" % (tarinfo.uname or tarinfo.uid,
+                                       tarinfo.gname or tarinfo.gid))
+                if tarinfo.ischr() or tarinfo.isblk():
+                    _safe_print("%10s" %
+                            ("%d,%d" % (tarinfo.devmajor, tarinfo.devminor)))
+                else:
+                    _safe_print("%10d" % tarinfo.size)
+                if tarinfo.mtime is None:
+                    _safe_print("????-??-?? ??:??:??")
+                else:
+                    _safe_print("%d-%02d-%02d %02d:%02d:%02d" \
+                                % time.localtime(tarinfo.mtime)[:6])
+
+            _safe_print(tarinfo.name + ("/" if tarinfo.isdir() else ""))
+
+            if verbose:
+                if tarinfo.issym():
+                    _safe_print("-> " + tarinfo.linkname)
+                if tarinfo.islnk():
+                    _safe_print("link to " + tarinfo.linkname)
+            print()
+
+    def add(self, name, arcname=None, recursive=True, *, filter=None):
+        """Add the file 'name' to the archive. 'name' may be any type of file
+           (directory, fifo, symbolic link, etc.). If given, 'arcname'
+           specifies an alternative name for the file in the archive.
+           Directories are added recursively by default. This can be avoided by
+           setting 'recursive' to False. 'filter' is a function
+           that expects a TarInfo object argument and returns the changed
+           TarInfo object, if it returns None the TarInfo object will be
+           excluded from the archive.
+        """
+        self._check("awx")
+
+        if arcname is None:
+            arcname = name
+
+        # Skip if somebody tries to archive the archive...
+        if self.name is not None and os.path.abspath(name) == self.name:
+            self._dbg(2, "tarfile: Skipped %r" % name)
+            return
+
+        self._dbg(1, name)
+
+        # Create a TarInfo object from the file.
+        tarinfo = self.gettarinfo(name, arcname)
+
+        if tarinfo is None:
+            self._dbg(1, "tarfile: Unsupported type %r" % name)
+            return
+
+        # Change or exclude the TarInfo object.
+        if filter is not None:
+            tarinfo = filter(tarinfo)
+            if tarinfo is None:
+                self._dbg(2, "tarfile: Excluded %r" % name)
+                return
+
+        # Append the tar header and data to the archive.
+        if tarinfo.isreg():
+            with bltn_open(name, "rb") as f:
+                self.addfile(tarinfo, f)
+
+        elif tarinfo.isdir():
+            self.addfile(tarinfo)
+            if recursive:
+                for f in sorted(os.listdir(name)):
+                    self.add(os.path.join(name, f), os.path.join(arcname, f),
+                            recursive, filter=filter)
+
+        else:
+            self.addfile(tarinfo)
+
+    def addfile(self, tarinfo, fileobj=None):
+        """Add the TarInfo object 'tarinfo' to the archive. If 'tarinfo' represents
+           a non zero-size regular file, the 'fileobj' argument should be a binary file,
+           and tarinfo.size bytes are read from it and added to the archive.
+           You can create TarInfo objects directly, or by using gettarinfo().
+        """
+        self._check("awx")
+
+        if fileobj is None and tarinfo.isreg() and tarinfo.size != 0:
+            raise ValueError("fileobj not provided for non zero-size regular file")
+
+        tarinfo = copy.copy(tarinfo)
+
+        buf = tarinfo.tobuf(self.format, self.encoding, self.errors)
+        self.fileobj.write(buf)
+        self.offset += len(buf)
+        bufsize=self.copybufsize
+        # If there's data to follow, append it.
+        if fileobj is not None:
+            copyfileobj(fileobj, self.fileobj, tarinfo.size, bufsize=bufsize)
+            blocks, remainder = divmod(tarinfo.size, BLOCKSIZE)
+            if remainder > 0:
+                self.fileobj.write(NUL * (BLOCKSIZE - remainder))
+                blocks += 1
+            self.offset += blocks * BLOCKSIZE
+
+        self.members.append(tarinfo)
+
+    def _get_filter_function(self, filter):
+        if filter is None:
+            filter = self.extraction_filter
+            if filter is None:
+                import warnings
+                warnings.warn(
+                    'Python 3.14 will, by default, filter extracted tar '
+                    + 'archives and reject files or modify their metadata. '
+                    + 'Use the filter argument to control this behavior.',
+                    DeprecationWarning, stacklevel=3)
+                return fully_trusted_filter
+            if isinstance(filter, str):
+                raise TypeError(
+                    'String names are not supported for '
+                    + 'TarFile.extraction_filter. Use a function such as '
+                    + 'tarfile.data_filter directly.')
+            return filter
+        if callable(filter):
+            return filter
+        try:
+            return _NAMED_FILTERS[filter]
+        except KeyError:
+            raise ValueError(f"filter {filter!r} not found") from None
+
+    def extractall(self, path=".", members=None, *, numeric_owner=False,
+                   filter=None):
+        """Extract all members from the archive to the current working
+           directory and set owner, modification time and permissions on
+           directories afterwards. 'path' specifies a different directory
+           to extract to. 'members' is optional and must be a subset of the
+           list returned by getmembers(). If 'numeric_owner' is True, only
+           the numbers for user/group names are used and not the names.
+
+           The 'filter' function will be called on each member just
+           before extraction.
+           It can return a changed TarInfo or None to skip the member.
+           String names of common filters are accepted.
+        """
+        directories = []
+
+        filter_function = self._get_filter_function(filter)
+        if members is None:
+            members = self
+
+        for member in members:
+            tarinfo = self._get_extract_tarinfo(member, filter_function, path)
+            if tarinfo is None:
+                continue
+            if tarinfo.isdir():
+                # For directories, delay setting attributes until later,
+                # since permissions can interfere with extraction and
+                # extracting contents can reset mtime.
+                directories.append(tarinfo)
+            self._extract_one(tarinfo, path, set_attrs=not tarinfo.isdir(),
+                              numeric_owner=numeric_owner)
+
+        # Reverse sort directories.
+        directories.sort(key=lambda a: a.name, reverse=True)
+
+        # Set correct owner, mtime and filemode on directories.
+        for tarinfo in directories:
+            dirpath = os.path.join(path, tarinfo.name)
+            try:
+                self.chown(tarinfo, dirpath, numeric_owner=numeric_owner)
+                self.utime(tarinfo, dirpath)
+                self.chmod(tarinfo, dirpath)
+            except ExtractError as e:
+                self._handle_nonfatal_error(e)
+
+    def extract(self, member, path="", set_attrs=True, *, numeric_owner=False,
+                filter=None):
+        """Extract a member from the archive to the current working directory,
+           using its full name. Its file information is extracted as accurately
+           as possible. 'member' may be a filename or a TarInfo object. You can
+           specify a different directory using 'path'. File attributes (owner,
+           mtime, mode) are set unless 'set_attrs' is False. If 'numeric_owner'
+           is True, only the numbers for user/group names are used and not
+           the names.
+
+           The 'filter' function will be called before extraction.
+           It can return a changed TarInfo or None to skip the member.
+           String names of common filters are accepted.
+        """
+        filter_function = self._get_filter_function(filter)
+        tarinfo = self._get_extract_tarinfo(member, filter_function, path)
+        if tarinfo is not None:
+            self._extract_one(tarinfo, path, set_attrs, numeric_owner)
+
+    def _get_extract_tarinfo(self, member, filter_function, path):
+        """Get filtered TarInfo (or None) from member, which might be a str"""
+        if isinstance(member, str):
+            tarinfo = self.getmember(member)
+        else:
+            tarinfo = member
+
+        unfiltered = tarinfo
+        try:
+            tarinfo = filter_function(tarinfo, path)
+        except (OSError, FilterError) as e:
+            self._handle_fatal_error(e)
+        except ExtractError as e:
+            self._handle_nonfatal_error(e)
+        if tarinfo is None:
+            self._dbg(2, "tarfile: Excluded %r" % unfiltered.name)
+            return None
+        # Prepare the link target for makelink().
+        if tarinfo.islnk():
+            tarinfo = copy.copy(tarinfo)
+            tarinfo._link_target = os.path.join(path, tarinfo.linkname)
+        return tarinfo
+
+    def _extract_one(self, tarinfo, path, set_attrs, numeric_owner):
+        """Extract from filtered tarinfo to disk"""
+        self._check("r")
+
+        try:
+            self._extract_member(tarinfo, os.path.join(path, tarinfo.name),
+                                 set_attrs=set_attrs,
+                                 numeric_owner=numeric_owner)
+        except OSError as e:
+            self._handle_fatal_error(e)
+        except ExtractError as e:
+            self._handle_nonfatal_error(e)
+
+    def _handle_nonfatal_error(self, e):
+        """Handle non-fatal error (ExtractError) according to errorlevel"""
+        if self.errorlevel > 1:
+            raise
+        else:
+            self._dbg(1, "tarfile: %s" % e)
+
+    def _handle_fatal_error(self, e):
+        """Handle "fatal" error according to self.errorlevel"""
+        if self.errorlevel > 0:
+            raise
+        elif isinstance(e, OSError):
+            if e.filename is None:
+                self._dbg(1, "tarfile: %s" % e.strerror)
+            else:
+                self._dbg(1, "tarfile: %s %r" % (e.strerror, e.filename))
+        else:
+            self._dbg(1, "tarfile: %s %s" % (type(e).__name__, e))
+
+    def extractfile(self, member):
+        """Extract a member from the archive as a file object. 'member' may be
+           a filename or a TarInfo object. If 'member' is a regular file or
+           a link, an io.BufferedReader object is returned. For all other
+           existing members, None is returned. If 'member' does not appear
+           in the archive, KeyError is raised.
+        """
+        self._check("r")
+
+        if isinstance(member, str):
+            tarinfo = self.getmember(member)
+        else:
+            tarinfo = member
+
+        if tarinfo.isreg() or tarinfo.type not in SUPPORTED_TYPES:
+            # Members with unknown types are treated as regular files.
+            return self.fileobject(self, tarinfo)
+
+        elif tarinfo.islnk() or tarinfo.issym():
+            if isinstance(self.fileobj, _Stream):
+                # A small but ugly workaround for the case that someone tries
+                # to extract a (sym)link as a file-object from a non-seekable
+                # stream of tar blocks.
+                raise StreamError("cannot extract (sym)link as file object")
+            else:
+                # A (sym)link's file object is its target's file object.
+                return self.extractfile(self._find_link_target(tarinfo))
+        else:
+            # If there's no data associated with the member (directory, chrdev,
+            # blkdev, etc.), return None instead of a file object.
+            return None
+
+    def _extract_member(self, tarinfo, targetpath, set_attrs=True,
+                        numeric_owner=False):
+        """Extract the TarInfo object tarinfo to a physical
+           file called targetpath.
+        """
+        # Fetch the TarInfo object for the given name
+        # and build the destination pathname, replacing
+        # forward slashes to platform specific separators.
+        targetpath = targetpath.rstrip("/")
+        targetpath = targetpath.replace("/", os.sep)
+
+        # Create all upper directories.
+        upperdirs = os.path.dirname(targetpath)
+        if upperdirs and not os.path.exists(upperdirs):
+            # Create directories that are not part of the archive with
+            # default permissions.
+            os.makedirs(upperdirs, exist_ok=True)
+
+        if tarinfo.islnk() or tarinfo.issym():
+            self._dbg(1, "%s -> %s" % (tarinfo.name, tarinfo.linkname))
+        else:
+            self._dbg(1, tarinfo.name)
+
+        if tarinfo.isreg():
+            self.makefile(tarinfo, targetpath)
+        elif tarinfo.isdir():
+            self.makedir(tarinfo, targetpath)
+        elif tarinfo.isfifo():
+            self.makefifo(tarinfo, targetpath)
+        elif tarinfo.ischr() or tarinfo.isblk():
+            self.makedev(tarinfo, targetpath)
+        elif tarinfo.islnk() or tarinfo.issym():
+            self.makelink(tarinfo, targetpath)
+        elif tarinfo.type not in SUPPORTED_TYPES:
+            self.makeunknown(tarinfo, targetpath)
+        else:
+            self.makefile(tarinfo, targetpath)
+
+        if set_attrs:
+            self.chown(tarinfo, targetpath, numeric_owner)
+            if not tarinfo.issym():
+                self.chmod(tarinfo, targetpath)
+                self.utime(tarinfo, targetpath)
+
+    #--------------------------------------------------------------------------
+    # Below are the different file methods. They are called via
+    # _extract_member() when extract() is called. They can be replaced in a
+    # subclass to implement other functionality.
+
+    def makedir(self, tarinfo, targetpath):
+        """Make a directory called targetpath.
+        """
+        try:
+            if tarinfo.mode is None:
+                # Use the system's default mode
+                os.mkdir(targetpath)
+            else:
+                # Use a safe mode for the directory, the real mode is set
+                # later in _extract_member().
+                os.mkdir(targetpath, 0o700)
+        except FileExistsError:
+            if not os.path.isdir(targetpath):
+                raise
+
+    def makefile(self, tarinfo, targetpath):
+        """Make a file called targetpath.
+        """
+        source = self.fileobj
+        source.seek(tarinfo.offset_data)
+        bufsize = self.copybufsize
+        with bltn_open(targetpath, "wb") as target:
+            if tarinfo.sparse is not None:
+                for offset, size in tarinfo.sparse:
+                    target.seek(offset)
+                    copyfileobj(source, target, size, ReadError, bufsize)
+                target.seek(tarinfo.size)
+                target.truncate()
+            else:
+                copyfileobj(source, target, tarinfo.size, ReadError, bufsize)
+
+    def makeunknown(self, tarinfo, targetpath):
+        """Make a file from a TarInfo object with an unknown type
+           at targetpath.
+        """
+        self.makefile(tarinfo, targetpath)
+        self._dbg(1, "tarfile: Unknown file type %r, " \
+                     "extracted as regular file." % tarinfo.type)
+
+    def makefifo(self, tarinfo, targetpath):
+        """Make a fifo called targetpath.
+        """
+        if hasattr(os, "mkfifo"):
+            os.mkfifo(targetpath)
+        else:
+            raise ExtractError("fifo not supported by system")
+
+    def makedev(self, tarinfo, targetpath):
+        """Make a character or block device called targetpath.
+        """
+        if not hasattr(os, "mknod") or not hasattr(os, "makedev"):
+            raise ExtractError("special devices not supported by system")
+
+        mode = tarinfo.mode
+        if mode is None:
+            # Use mknod's default
+            mode = 0o600
+        if tarinfo.isblk():
+            mode |= stat.S_IFBLK
+        else:
+            mode |= stat.S_IFCHR
+
+        os.mknod(targetpath, mode,
+                 os.makedev(tarinfo.devmajor, tarinfo.devminor))
+
+    def makelink(self, tarinfo, targetpath):
+        """Make a (symbolic) link called targetpath. If it cannot be created
+          (platform limitation), we try to make a copy of the referenced file
+          instead of a link.
+        """
+        try:
+            # For systems that support symbolic and hard links.
+            if tarinfo.issym():
+                if os.path.lexists(targetpath):
+                    # Avoid FileExistsError on following os.symlink.
+                    os.unlink(targetpath)
+                os.symlink(tarinfo.linkname, targetpath)
+            else:
+                if os.path.exists(tarinfo._link_target):
+                    os.link(tarinfo._link_target, targetpath)
+                else:
+                    self._extract_member(self._find_link_target(tarinfo),
+                                         targetpath)
+        except symlink_exception:
+            try:
+                self._extract_member(self._find_link_target(tarinfo),
+                                     targetpath)
+            except KeyError:
+                raise ExtractError("unable to resolve link inside archive") from None
+
+    def chown(self, tarinfo, targetpath, numeric_owner):
+        """Set owner of targetpath according to tarinfo. If numeric_owner
+           is True, use .gid/.uid instead of .gname/.uname. If numeric_owner
+           is False, fall back to .gid/.uid when the search based on name
+           fails.
+        """
+        if hasattr(os, "geteuid") and os.geteuid() == 0:
+            # We have to be root to do so.
+            g = tarinfo.gid
+            u = tarinfo.uid
+            if not numeric_owner:
+                try:
+                    if grp and tarinfo.gname:
+                        g = grp.getgrnam(tarinfo.gname)[2]
+                except KeyError:
+                    pass
+                try:
+                    if pwd and tarinfo.uname:
+                        u = pwd.getpwnam(tarinfo.uname)[2]
+                except KeyError:
+                    pass
+            if g is None:
+                g = -1
+            if u is None:
+                u = -1
+            try:
+                if tarinfo.issym() and hasattr(os, "lchown"):
+                    os.lchown(targetpath, u, g)
+                else:
+                    os.chown(targetpath, u, g)
+            except (OSError, OverflowError) as e:
+                # OverflowError can be raised if an ID doesn't fit in 'id_t'
+                raise ExtractError("could not change owner") from e
+
+    def chmod(self, tarinfo, targetpath):
+        """Set file permissions of targetpath according to tarinfo.
+        """
+        if tarinfo.mode is None:
+            return
+        try:
+            os.chmod(targetpath, tarinfo.mode)
+        except OSError as e:
+            raise ExtractError("could not change mode") from e
+
+    def utime(self, tarinfo, targetpath):
+        """Set modification time of targetpath according to tarinfo.
+        """
+        mtime = tarinfo.mtime
+        if mtime is None:
+            return
+        if not hasattr(os, 'utime'):
+            return
+        try:
+            os.utime(targetpath, (mtime, mtime))
+        except OSError as e:
+            raise ExtractError("could not change modification time") from e
+
+    #--------------------------------------------------------------------------
+    def next(self):
+        """Return the next member of the archive as a TarInfo object, when
+           TarFile is opened for reading. Return None if there is no more
+           available.
+        """
+        self._check("ra")
+        if self.firstmember is not None:
+            m = self.firstmember
+            self.firstmember = None
+            return m
+
+        # Advance the file pointer.
+        if self.offset != self.fileobj.tell():
+            if self.offset == 0:
+                return None
+            self.fileobj.seek(self.offset - 1)
+            if not self.fileobj.read(1):
+                raise ReadError("unexpected end of data")
+
+        # Read the next block.
+        tarinfo = None
+        while True:
+            try:
+                tarinfo = self.tarinfo.fromtarfile(self)
+            except EOFHeaderError as e:
+                if self.ignore_zeros:
+                    self._dbg(2, "0x%X: %s" % (self.offset, e))
+                    self.offset += BLOCKSIZE
+                    continue
+            except InvalidHeaderError as e:
+                if self.ignore_zeros:
+                    self._dbg(2, "0x%X: %s" % (self.offset, e))
+                    self.offset += BLOCKSIZE
+                    continue
+                elif self.offset == 0:
+                    raise ReadError(str(e)) from None
+            except EmptyHeaderError:
+                if self.offset == 0:
+                    raise ReadError("empty file") from None
+            except TruncatedHeaderError as e:
+                if self.offset == 0:
+                    raise ReadError(str(e)) from None
+            except SubsequentHeaderError as e:
+                raise ReadError(str(e)) from None
+            except Exception as e:
+                try:
+                    import zlib
+                    if isinstance(e, zlib.error):
+                        raise ReadError(f'zlib error: {e}') from None
+                    else:
+                        raise e
+                except ImportError:
+                    raise e
+            break
+
+        if tarinfo is not None:
+            # if streaming the file we do not want to cache the tarinfo
+            if not self.stream:
+                self.members.append(tarinfo)
+        else:
+            self._loaded = True
+
+        return tarinfo
+
+    #--------------------------------------------------------------------------
+    # Little helper methods:
+
+    def _getmember(self, name, tarinfo=None, normalize=False):
+        """Find an archive member by name from bottom to top.
+           If tarinfo is given, it is used as the starting point.
+        """
+        # Ensure that all members have been loaded.
+        members = self.getmembers()
+
+        # Limit the member search list up to tarinfo.
+        skipping = False
+        if tarinfo is not None:
+            try:
+                index = members.index(tarinfo)
+            except ValueError:
+                # The given starting point might be a (modified) copy.
+                # We'll later skip members until we find an equivalent.
+                skipping = True
+            else:
+                # Happy fast path
+                members = members[:index]
+
+        if normalize:
+            name = os.path.normpath(name)
+
+        for member in reversed(members):
+            if skipping:
+                if tarinfo.offset == member.offset:
+                    skipping = False
+                continue
+            if normalize:
+                member_name = os.path.normpath(member.name)
+            else:
+                member_name = member.name
+
+            if name == member_name:
+                return member
+
+        if skipping:
+            # Starting point was not found
+            raise ValueError(tarinfo)
+
+    def _load(self):
+        """Read through the entire archive file and look for readable
+           members. This should not run if the file is set to stream.
+        """
+        if not self.stream:
+            while self.next() is not None:
+                pass
+            self._loaded = True
+
+    def _check(self, mode=None):
+        """Check if TarFile is still open, and if the operation's mode
+           corresponds to TarFile's mode.
+        """
+        if self.closed:
+            raise OSError("%s is closed" % self.__class__.__name__)
+        if mode is not None and self.mode not in mode:
+            raise OSError("bad operation for mode %r" % self.mode)
+
+    def _find_link_target(self, tarinfo):
+        """Find the target member of a symlink or hardlink member in the
+           archive.
+        """
+        if tarinfo.issym():
+            # Always search the entire archive.
+            linkname = "/".join(filter(None, (os.path.dirname(tarinfo.name), tarinfo.linkname)))
+            limit = None
+        else:
+            # Search the archive before the link, because a hard link is
+            # just a reference to an already archived file.
+            linkname = tarinfo.linkname
+            limit = tarinfo
+
+        member = self._getmember(linkname, tarinfo=limit, normalize=True)
+        if member is None:
+            raise KeyError("linkname %r not found" % linkname)
+        return member
+
+    def __iter__(self):
+        """Provide an iterator object.
+        """
+        if self._loaded:
+            yield from self.members
+            return
+
+        # Yield items using TarFile's next() method.
+        # When all members have been read, set TarFile as _loaded.
+        index = 0
+        # Fix for SF #1100429: Under rare circumstances it can
+        # happen that getmembers() is called during iteration,
+        # which will have already exhausted the next() method.
+        if self.firstmember is not None:
+            tarinfo = self.next()
+            index += 1
+            yield tarinfo
+
+        while True:
+            if index < len(self.members):
+                tarinfo = self.members[index]
+            elif not self._loaded:
+                tarinfo = self.next()
+                if not tarinfo:
+                    self._loaded = True
+                    return
+            else:
+                return
+            index += 1
+            yield tarinfo
+
+    def _dbg(self, level, msg):
+        """Write debugging output to sys.stderr.
+        """
+        if level <= self.debug:
+            print(msg, file=sys.stderr)
+
+    def __enter__(self):
+        self._check()
+        return self
+
+    def __exit__(self, type, value, traceback):
+        if type is None:
+            self.close()
+        else:
+            # An exception occurred. We must not call close() because
+            # it would try to write end-of-archive blocks and padding.
+            if not self._extfileobj:
+                self.fileobj.close()
+            self.closed = True
+
+#--------------------
+# exported functions
+#--------------------
+
+def is_tarfile(name):
+    """Return True if name points to a tar archive that we
+       are able to handle, else return False.
+
+       'name' should be a string, file, or file-like object.
+    """
+    try:
+        if hasattr(name, "read"):
+            pos = name.tell()
+            t = open(fileobj=name)
+            name.seek(pos)
+        else:
+            t = open(name)
+        t.close()
+        return True
+    except TarError:
+        return False
+
+open = TarFile.open
+
+
+def main():
+    import argparse
+
+    description = 'A simple command-line interface for tarfile module.'
+    parser = argparse.ArgumentParser(description=description)
+    parser.add_argument('-v', '--verbose', action='store_true', default=False,
+                        help='Verbose output')
+    parser.add_argument('--filter', metavar='<filtername>',
+                        choices=_NAMED_FILTERS,
+                        help='Filter for extraction')
+
+    group = parser.add_mutually_exclusive_group(required=True)
+    group.add_argument('-l', '--list', metavar='<tarfile>',
+                       help='Show listing of a tarfile')
+    group.add_argument('-e', '--extract', nargs='+',
+                       metavar=('<tarfile>', '<output_dir>'),
+                       help='Extract tarfile into target dir')
+    group.add_argument('-c', '--create', nargs='+',
+                       metavar=('<name>', '<file>'),
+                       help='Create tarfile from sources')
+    group.add_argument('-t', '--test', metavar='<tarfile>',
+                       help='Test if a tarfile is valid')
+
+    args = parser.parse_args()
+
+    if args.filter and args.extract is None:
+        parser.exit(1, '--filter is only valid for extraction\n')
+
+    if args.test is not None:
+        src = args.test
+        if is_tarfile(src):
+            with open(src, 'r') as tar:
+                tar.getmembers()
+                print(tar.getmembers(), file=sys.stderr)
+            if args.verbose:
+                print('{!r} is a tar archive.'.format(src))
+        else:
+            parser.exit(1, '{!r} is not a tar archive.\n'.format(src))
+
+    elif args.list is not None:
+        src = args.list
+        if is_tarfile(src):
+            with TarFile.open(src, 'r:*') as tf:
+                tf.list(verbose=args.verbose)
+        else:
+            parser.exit(1, '{!r} is not a tar archive.\n'.format(src))
+
+    elif args.extract is not None:
+        if len(args.extract) == 1:
+            src = args.extract[0]
+            curdir = os.curdir
+        elif len(args.extract) == 2:
+            src, curdir = args.extract
+        else:
+            parser.exit(1, parser.format_help())
+
+        if is_tarfile(src):
+            with TarFile.open(src, 'r:*') as tf:
+                tf.extractall(path=curdir, filter=args.filter)
+            if args.verbose:
+                if curdir == '.':
+                    msg = '{!r} file is extracted.'.format(src)
+                else:
+                    msg = ('{!r} file is extracted '
+                           'into {!r} directory.').format(src, curdir)
+                print(msg)
+        else:
+            parser.exit(1, '{!r} is not a tar archive.\n'.format(src))
+
+    elif args.create is not None:
+        tar_name = args.create.pop(0)
+        _, ext = os.path.splitext(tar_name)
+        compressions = {
+            # gz
+            '.gz': 'gz',
+            '.tgz': 'gz',
+            # xz
+            '.xz': 'xz',
+            '.txz': 'xz',
+            # bz2
+            '.bz2': 'bz2',
+            '.tbz': 'bz2',
+            '.tbz2': 'bz2',
+            '.tb2': 'bz2',
+        }
+        tar_mode = 'w:' + compressions[ext] if ext in compressions else 'w'
+        tar_files = args.create
+
+        with TarFile.open(tar_name, tar_mode) as tf:
+            for file_name in tar_files:
+                tf.add(file_name)
+
+        if args.verbose:
+            print('{!r} file created.'.format(tar_name))
+
+if __name__ == '__main__':
+    main()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/__main__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..daf55090862ae43283b6552073549ac3ab2ad50a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/__main__.py
@@ -0,0 +1,5 @@
+from . import main
+
+
+if __name__ == '__main__':
+    main()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/__pycache__/__init__.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7721458be8f2e5a7253e31c326e084daca073625
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/__pycache__/__init__.cpython-312.pyc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1a46580c284ed36ea1f8370dc1aab2209c5949211bc3a781ef7dd727d78d3bbf
+size 121270
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/__pycache__/__main__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/__pycache__/__main__.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..9b100a1cdc01d3db8bb8828499c3a38c621796f8
Binary files /dev/null and b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/__pycache__/__main__.cpython-312.pyc differ
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/compat/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/compat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/compat/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/compat/__pycache__/__init__.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f40c9b951e40503ac5991d7146420caee4525913
Binary files /dev/null and b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/compat/__pycache__/__init__.cpython-312.pyc differ
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/compat/__pycache__/py38.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/compat/__pycache__/py38.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f87254741d37e9bea60a985b639d3b7fee804c40
Binary files /dev/null and b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/compat/__pycache__/py38.cpython-312.pyc differ
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/compat/py38.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/compat/py38.py
new file mode 100644
index 0000000000000000000000000000000000000000..20fbbfc1c095baf9f8c72902b24296b50ad3ab9d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/backports/tarfile/compat/py38.py
@@ -0,0 +1,24 @@
+import sys
+
+
+if sys.version_info < (3, 9):
+
+    def removesuffix(self, suffix):
+        # suffix='' should not call self[:-0].
+        if suffix and self.endswith(suffix):
+            return self[: -len(suffix)]
+        else:
+            return self[:]
+
+    def removeprefix(self, prefix):
+        if self.startswith(prefix):
+            return self[len(prefix) :]
+        else:
+            return self[:]
+else:
+
+    def removesuffix(self, suffix):
+        return self.removesuffix(suffix)
+
+    def removeprefix(self, prefix):
+        return self.removeprefix(prefix)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..d645695673349e3947e8e5ae42332d0ac3164cd7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/LICENSE
@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
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+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
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+      To apply the Apache License to your work, attach the following
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+   Unless required by applicable law or agreed to in writing, software
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..85513e8a9f651c99f4a1fa372ba7768255bfa1c9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/METADATA
@@ -0,0 +1,129 @@
+Metadata-Version: 2.1
+Name: importlib_metadata
+Version: 8.0.0
+Summary: Read metadata from Python packages
+Author-email: "Jason R. Coombs" <jaraco@jaraco.com>
+Project-URL: Source, https://github.com/python/importlib_metadata
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: zipp >=0.5
+Requires-Dist: typing-extensions >=3.6.4 ; python_version < "3.8"
+Provides-Extra: doc
+Requires-Dist: sphinx >=3.5 ; extra == 'doc'
+Requires-Dist: jaraco.packaging >=9.3 ; extra == 'doc'
+Requires-Dist: rst.linker >=1.9 ; extra == 'doc'
+Requires-Dist: furo ; extra == 'doc'
+Requires-Dist: sphinx-lint ; extra == 'doc'
+Requires-Dist: jaraco.tidelift >=1.4 ; extra == 'doc'
+Provides-Extra: perf
+Requires-Dist: ipython ; extra == 'perf'
+Provides-Extra: test
+Requires-Dist: pytest !=8.1.*,>=6 ; extra == 'test'
+Requires-Dist: pytest-checkdocs >=2.4 ; extra == 'test'
+Requires-Dist: pytest-cov ; extra == 'test'
+Requires-Dist: pytest-mypy ; extra == 'test'
+Requires-Dist: pytest-enabler >=2.2 ; extra == 'test'
+Requires-Dist: pytest-ruff >=0.2.1 ; extra == 'test'
+Requires-Dist: packaging ; extra == 'test'
+Requires-Dist: pyfakefs ; extra == 'test'
+Requires-Dist: flufl.flake8 ; extra == 'test'
+Requires-Dist: pytest-perf >=0.9.2 ; extra == 'test'
+Requires-Dist: jaraco.test >=5.4 ; extra == 'test'
+Requires-Dist: importlib-resources >=1.3 ; (python_version < "3.9") and extra == 'test'
+
+.. image:: https://img.shields.io/pypi/v/importlib_metadata.svg
+   :target: https://pypi.org/project/importlib_metadata
+
+.. image:: https://img.shields.io/pypi/pyversions/importlib_metadata.svg
+
+.. image:: https://github.com/python/importlib_metadata/actions/workflows/main.yml/badge.svg
+   :target: https://github.com/python/importlib_metadata/actions?query=workflow%3A%22tests%22
+   :alt: tests
+
+.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
+    :target: https://github.com/astral-sh/ruff
+    :alt: Ruff
+
+.. image:: https://readthedocs.org/projects/importlib-metadata/badge/?version=latest
+   :target: https://importlib-metadata.readthedocs.io/en/latest/?badge=latest
+
+.. image:: https://img.shields.io/badge/skeleton-2024-informational
+   :target: https://blog.jaraco.com/skeleton
+
+.. image:: https://tidelift.com/badges/package/pypi/importlib-metadata
+   :target: https://tidelift.com/subscription/pkg/pypi-importlib-metadata?utm_source=pypi-importlib-metadata&utm_medium=readme
+
+Library to access the metadata for a Python package.
+
+This package supplies third-party access to the functionality of
+`importlib.metadata <https://docs.python.org/3/library/importlib.metadata.html>`_
+including improvements added to subsequent Python versions.
+
+
+Compatibility
+=============
+
+New features are introduced in this third-party library and later merged
+into CPython. The following table indicates which versions of this library
+were contributed to different versions in the standard library:
+
+.. list-table::
+   :header-rows: 1
+
+   * - importlib_metadata
+     - stdlib
+   * - 7.0
+     - 3.13
+   * - 6.5
+     - 3.12
+   * - 4.13
+     - 3.11
+   * - 4.6
+     - 3.10
+   * - 1.4
+     - 3.8
+
+
+Usage
+=====
+
+See the `online documentation <https://importlib-metadata.readthedocs.io/>`_
+for usage details.
+
+`Finder authors
+<https://docs.python.org/3/reference/import.html#finders-and-loaders>`_ can
+also add support for custom package installers.  See the above documentation
+for details.
+
+
+Caveats
+=======
+
+This project primarily supports third-party packages installed by PyPA
+tools (or other conforming packages). It does not support:
+
+- Packages in the stdlib.
+- Packages installed without metadata.
+
+Project details
+===============
+
+ * Project home: https://github.com/python/importlib_metadata
+ * Report bugs at: https://github.com/python/importlib_metadata/issues
+ * Code hosting: https://github.com/python/importlib_metadata
+ * Documentation: https://importlib-metadata.readthedocs.io/
+
+For Enterprise
+==============
+
+Available as part of the Tidelift Subscription.
+
+This project and the maintainers of thousands of other packages are working with Tidelift to deliver one enterprise subscription that covers all of the open source you use.
+
+`Learn more <https://tidelift.com/subscription/pkg/pypi-importlib-metadata?utm_source=pypi-importlib-metadata&utm_medium=referral&utm_campaign=github>`_.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..07b7dc51dbf8da033f8cd0eb05a52ecfe26dcbeb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/RECORD
@@ -0,0 +1,32 @@
+importlib_metadata-8.0.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+importlib_metadata-8.0.0.dist-info/LICENSE,sha256=z8d0m5b2O9McPEK1xHG_dWgUBT6EfBDz6wA0F7xSPTA,11358
+importlib_metadata-8.0.0.dist-info/METADATA,sha256=anuQ7_7h4J1bSEzfcjIBakPi2cyVQ7y7jklLHsBeH1k,4648
+importlib_metadata-8.0.0.dist-info/RECORD,,
+importlib_metadata-8.0.0.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+importlib_metadata-8.0.0.dist-info/WHEEL,sha256=mguMlWGMX-VHnMpKOjjQidIo1ssRlCFu4a4mBpz1s2M,91
+importlib_metadata-8.0.0.dist-info/top_level.txt,sha256=CO3fD9yylANiXkrMo4qHLV_mqXL2sC5JFKgt1yWAT-A,19
+importlib_metadata/__init__.py,sha256=tZNB-23h8Bixi9uCrQqj9Yf0aeC--Josdy3IZRIQeB0,33798
+importlib_metadata/__pycache__/__init__.cpython-312.pyc,,
+importlib_metadata/__pycache__/_adapters.cpython-312.pyc,,
+importlib_metadata/__pycache__/_collections.cpython-312.pyc,,
+importlib_metadata/__pycache__/_compat.cpython-312.pyc,,
+importlib_metadata/__pycache__/_functools.cpython-312.pyc,,
+importlib_metadata/__pycache__/_itertools.cpython-312.pyc,,
+importlib_metadata/__pycache__/_meta.cpython-312.pyc,,
+importlib_metadata/__pycache__/_text.cpython-312.pyc,,
+importlib_metadata/__pycache__/diagnose.cpython-312.pyc,,
+importlib_metadata/_adapters.py,sha256=rIhWTwBvYA1bV7i-5FfVX38qEXDTXFeS5cb5xJtP3ks,2317
+importlib_metadata/_collections.py,sha256=CJ0OTCHIjWA0ZIVS4voORAsn2R4R2cQBEtPsZEJpASY,743
+importlib_metadata/_compat.py,sha256=73QKrN9KNoaZzhbX5yPCCZa-FaALwXe8TPlDR72JgBU,1314
+importlib_metadata/_functools.py,sha256=PsY2-4rrKX4RVeRC1oGp1lB1pmC9eKN88_f-bD9uOoA,2895
+importlib_metadata/_itertools.py,sha256=cvr_2v8BRbxcIl5x5ldfqdHjhI8Yi8s8yk50G_nm6jQ,2068
+importlib_metadata/_meta.py,sha256=nxZ7C8GVlcBFAKWyVOn_dn7ot_twBcbm1NmvjIetBHI,1801
+importlib_metadata/_text.py,sha256=HCsFksZpJLeTP3NEk_ngrAeXVRRtTrtyh9eOABoRP4A,2166
+importlib_metadata/compat/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+importlib_metadata/compat/__pycache__/__init__.cpython-312.pyc,,
+importlib_metadata/compat/__pycache__/py311.cpython-312.pyc,,
+importlib_metadata/compat/__pycache__/py39.cpython-312.pyc,,
+importlib_metadata/compat/py311.py,sha256=uqm-K-uohyj1042TH4a9Er_I5o7667DvulcD-gC_fSA,608
+importlib_metadata/compat/py39.py,sha256=cPkMv6-0ilK-0Jw_Tkn0xYbOKJZc4WJKQHow0c2T44w,1102
+importlib_metadata/diagnose.py,sha256=nkSRMiowlmkhLYhKhvCg9glmt_11Cox-EmLzEbqYTa8,379
+importlib_metadata/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/REQUESTED b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/REQUESTED
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..edf4ec7c70d7dbfc16600ff1b368daf1097c5dc7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (70.1.1)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/top_level.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bbb07547a19c30031d13c45cf01cba61dc434e47
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata-8.0.0.dist-info/top_level.txt
@@ -0,0 +1 @@
+importlib_metadata
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed4813551ac238bfb9b5a48f4476463355415d27
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/__init__.py
@@ -0,0 +1,1083 @@
+from __future__ import annotations
+
+import os
+import re
+import abc
+import sys
+import json
+import zipp
+import email
+import types
+import inspect
+import pathlib
+import operator
+import textwrap
+import functools
+import itertools
+import posixpath
+import collections
+
+from . import _meta
+from .compat import py39, py311
+from ._collections import FreezableDefaultDict, Pair
+from ._compat import (
+    NullFinder,
+    install,
+)
+from ._functools import method_cache, pass_none
+from ._itertools import always_iterable, unique_everseen
+from ._meta import PackageMetadata, SimplePath
+
+from contextlib import suppress
+from importlib import import_module
+from importlib.abc import MetaPathFinder
+from itertools import starmap
+from typing import Any, Iterable, List, Mapping, Match, Optional, Set, cast
+
+__all__ = [
+    'Distribution',
+    'DistributionFinder',
+    'PackageMetadata',
+    'PackageNotFoundError',
+    'distribution',
+    'distributions',
+    'entry_points',
+    'files',
+    'metadata',
+    'packages_distributions',
+    'requires',
+    'version',
+]
+
+
+class PackageNotFoundError(ModuleNotFoundError):
+    """The package was not found."""
+
+    def __str__(self) -> str:
+        return f"No package metadata was found for {self.name}"
+
+    @property
+    def name(self) -> str:  # type: ignore[override]
+        (name,) = self.args
+        return name
+
+
+class Sectioned:
+    """
+    A simple entry point config parser for performance
+
+    >>> for item in Sectioned.read(Sectioned._sample):
+    ...     print(item)
+    Pair(name='sec1', value='# comments ignored')
+    Pair(name='sec1', value='a = 1')
+    Pair(name='sec1', value='b = 2')
+    Pair(name='sec2', value='a = 2')
+
+    >>> res = Sectioned.section_pairs(Sectioned._sample)
+    >>> item = next(res)
+    >>> item.name
+    'sec1'
+    >>> item.value
+    Pair(name='a', value='1')
+    >>> item = next(res)
+    >>> item.value
+    Pair(name='b', value='2')
+    >>> item = next(res)
+    >>> item.name
+    'sec2'
+    >>> item.value
+    Pair(name='a', value='2')
+    >>> list(res)
+    []
+    """
+
+    _sample = textwrap.dedent(
+        """
+        [sec1]
+        # comments ignored
+        a = 1
+        b = 2
+
+        [sec2]
+        a = 2
+        """
+    ).lstrip()
+
+    @classmethod
+    def section_pairs(cls, text):
+        return (
+            section._replace(value=Pair.parse(section.value))
+            for section in cls.read(text, filter_=cls.valid)
+            if section.name is not None
+        )
+
+    @staticmethod
+    def read(text, filter_=None):
+        lines = filter(filter_, map(str.strip, text.splitlines()))
+        name = None
+        for value in lines:
+            section_match = value.startswith('[') and value.endswith(']')
+            if section_match:
+                name = value.strip('[]')
+                continue
+            yield Pair(name, value)
+
+    @staticmethod
+    def valid(line: str):
+        return line and not line.startswith('#')
+
+
+class EntryPoint:
+    """An entry point as defined by Python packaging conventions.
+
+    See `the packaging docs on entry points
+    <https://packaging.python.org/specifications/entry-points/>`_
+    for more information.
+
+    >>> ep = EntryPoint(
+    ...     name=None, group=None, value='package.module:attr [extra1, extra2]')
+    >>> ep.module
+    'package.module'
+    >>> ep.attr
+    'attr'
+    >>> ep.extras
+    ['extra1', 'extra2']
+    """
+
+    pattern = re.compile(
+        r'(?P<module>[\w.]+)\s*'
+        r'(:\s*(?P<attr>[\w.]+)\s*)?'
+        r'((?P<extras>\[.*\])\s*)?$'
+    )
+    """
+    A regular expression describing the syntax for an entry point,
+    which might look like:
+
+        - module
+        - package.module
+        - package.module:attribute
+        - package.module:object.attribute
+        - package.module:attr [extra1, extra2]
+
+    Other combinations are possible as well.
+
+    The expression is lenient about whitespace around the ':',
+    following the attr, and following any extras.
+    """
+
+    name: str
+    value: str
+    group: str
+
+    dist: Optional[Distribution] = None
+
+    def __init__(self, name: str, value: str, group: str) -> None:
+        vars(self).update(name=name, value=value, group=group)
+
+    def load(self) -> Any:
+        """Load the entry point from its definition. If only a module
+        is indicated by the value, return that module. Otherwise,
+        return the named object.
+        """
+        match = cast(Match, self.pattern.match(self.value))
+        module = import_module(match.group('module'))
+        attrs = filter(None, (match.group('attr') or '').split('.'))
+        return functools.reduce(getattr, attrs, module)
+
+    @property
+    def module(self) -> str:
+        match = self.pattern.match(self.value)
+        assert match is not None
+        return match.group('module')
+
+    @property
+    def attr(self) -> str:
+        match = self.pattern.match(self.value)
+        assert match is not None
+        return match.group('attr')
+
+    @property
+    def extras(self) -> List[str]:
+        match = self.pattern.match(self.value)
+        assert match is not None
+        return re.findall(r'\w+', match.group('extras') or '')
+
+    def _for(self, dist):
+        vars(self).update(dist=dist)
+        return self
+
+    def matches(self, **params):
+        """
+        EntryPoint matches the given parameters.
+
+        >>> ep = EntryPoint(group='foo', name='bar', value='bing:bong [extra1, extra2]')
+        >>> ep.matches(group='foo')
+        True
+        >>> ep.matches(name='bar', value='bing:bong [extra1, extra2]')
+        True
+        >>> ep.matches(group='foo', name='other')
+        False
+        >>> ep.matches()
+        True
+        >>> ep.matches(extras=['extra1', 'extra2'])
+        True
+        >>> ep.matches(module='bing')
+        True
+        >>> ep.matches(attr='bong')
+        True
+        """
+        attrs = (getattr(self, param) for param in params)
+        return all(map(operator.eq, params.values(), attrs))
+
+    def _key(self):
+        return self.name, self.value, self.group
+
+    def __lt__(self, other):
+        return self._key() < other._key()
+
+    def __eq__(self, other):
+        return self._key() == other._key()
+
+    def __setattr__(self, name, value):
+        raise AttributeError("EntryPoint objects are immutable.")
+
+    def __repr__(self):
+        return (
+            f'EntryPoint(name={self.name!r}, value={self.value!r}, '
+            f'group={self.group!r})'
+        )
+
+    def __hash__(self) -> int:
+        return hash(self._key())
+
+
+class EntryPoints(tuple):
+    """
+    An immutable collection of selectable EntryPoint objects.
+    """
+
+    __slots__ = ()
+
+    def __getitem__(self, name: str) -> EntryPoint:  # type: ignore[override]
+        """
+        Get the EntryPoint in self matching name.
+        """
+        try:
+            return next(iter(self.select(name=name)))
+        except StopIteration:
+            raise KeyError(name)
+
+    def __repr__(self):
+        """
+        Repr with classname and tuple constructor to
+        signal that we deviate from regular tuple behavior.
+        """
+        return '%s(%r)' % (self.__class__.__name__, tuple(self))
+
+    def select(self, **params) -> EntryPoints:
+        """
+        Select entry points from self that match the
+        given parameters (typically group and/or name).
+        """
+        return EntryPoints(ep for ep in self if py39.ep_matches(ep, **params))
+
+    @property
+    def names(self) -> Set[str]:
+        """
+        Return the set of all names of all entry points.
+        """
+        return {ep.name for ep in self}
+
+    @property
+    def groups(self) -> Set[str]:
+        """
+        Return the set of all groups of all entry points.
+        """
+        return {ep.group for ep in self}
+
+    @classmethod
+    def _from_text_for(cls, text, dist):
+        return cls(ep._for(dist) for ep in cls._from_text(text))
+
+    @staticmethod
+    def _from_text(text):
+        return (
+            EntryPoint(name=item.value.name, value=item.value.value, group=item.name)
+            for item in Sectioned.section_pairs(text or '')
+        )
+
+
+class PackagePath(pathlib.PurePosixPath):
+    """A reference to a path in a package"""
+
+    hash: Optional[FileHash]
+    size: int
+    dist: Distribution
+
+    def read_text(self, encoding: str = 'utf-8') -> str:  # type: ignore[override]
+        return self.locate().read_text(encoding=encoding)
+
+    def read_binary(self) -> bytes:
+        return self.locate().read_bytes()
+
+    def locate(self) -> SimplePath:
+        """Return a path-like object for this path"""
+        return self.dist.locate_file(self)
+
+
+class FileHash:
+    def __init__(self, spec: str) -> None:
+        self.mode, _, self.value = spec.partition('=')
+
+    def __repr__(self) -> str:
+        return f'<FileHash mode: {self.mode} value: {self.value}>'
+
+
+class Distribution(metaclass=abc.ABCMeta):
+    """
+    An abstract Python distribution package.
+
+    Custom providers may derive from this class and define
+    the abstract methods to provide a concrete implementation
+    for their environment. Some providers may opt to override
+    the default implementation of some properties to bypass
+    the file-reading mechanism.
+    """
+
+    @abc.abstractmethod
+    def read_text(self, filename) -> Optional[str]:
+        """Attempt to load metadata file given by the name.
+
+        Python distribution metadata is organized by blobs of text
+        typically represented as "files" in the metadata directory
+        (e.g. package-1.0.dist-info). These files include things
+        like:
+
+        - METADATA: The distribution metadata including fields
+          like Name and Version and Description.
+        - entry_points.txt: A series of entry points as defined in
+          `the entry points spec <https://packaging.python.org/en/latest/specifications/entry-points/#file-format>`_.
+        - RECORD: A record of files according to
+          `this recording spec <https://packaging.python.org/en/latest/specifications/recording-installed-packages/#the-record-file>`_.
+
+        A package may provide any set of files, including those
+        not listed here or none at all.
+
+        :param filename: The name of the file in the distribution info.
+        :return: The text if found, otherwise None.
+        """
+
+    @abc.abstractmethod
+    def locate_file(self, path: str | os.PathLike[str]) -> SimplePath:
+        """
+        Given a path to a file in this distribution, return a SimplePath
+        to it.
+        """
+
+    @classmethod
+    def from_name(cls, name: str) -> Distribution:
+        """Return the Distribution for the given package name.
+
+        :param name: The name of the distribution package to search for.
+        :return: The Distribution instance (or subclass thereof) for the named
+            package, if found.
+        :raises PackageNotFoundError: When the named package's distribution
+            metadata cannot be found.
+        :raises ValueError: When an invalid value is supplied for name.
+        """
+        if not name:
+            raise ValueError("A distribution name is required.")
+        try:
+            return next(iter(cls.discover(name=name)))
+        except StopIteration:
+            raise PackageNotFoundError(name)
+
+    @classmethod
+    def discover(
+        cls, *, context: Optional[DistributionFinder.Context] = None, **kwargs
+    ) -> Iterable[Distribution]:
+        """Return an iterable of Distribution objects for all packages.
+
+        Pass a ``context`` or pass keyword arguments for constructing
+        a context.
+
+        :context: A ``DistributionFinder.Context`` object.
+        :return: Iterable of Distribution objects for packages matching
+          the context.
+        """
+        if context and kwargs:
+            raise ValueError("cannot accept context and kwargs")
+        context = context or DistributionFinder.Context(**kwargs)
+        return itertools.chain.from_iterable(
+            resolver(context) for resolver in cls._discover_resolvers()
+        )
+
+    @staticmethod
+    def at(path: str | os.PathLike[str]) -> Distribution:
+        """Return a Distribution for the indicated metadata path.
+
+        :param path: a string or path-like object
+        :return: a concrete Distribution instance for the path
+        """
+        return PathDistribution(pathlib.Path(path))
+
+    @staticmethod
+    def _discover_resolvers():
+        """Search the meta_path for resolvers (MetadataPathFinders)."""
+        declared = (
+            getattr(finder, 'find_distributions', None) for finder in sys.meta_path
+        )
+        return filter(None, declared)
+
+    @property
+    def metadata(self) -> _meta.PackageMetadata:
+        """Return the parsed metadata for this Distribution.
+
+        The returned object will have keys that name the various bits of
+        metadata per the
+        `Core metadata specifications <https://packaging.python.org/en/latest/specifications/core-metadata/#core-metadata>`_.
+
+        Custom providers may provide the METADATA file or override this
+        property.
+        """
+        # deferred for performance (python/cpython#109829)
+        from . import _adapters
+
+        opt_text = (
+            self.read_text('METADATA')
+            or self.read_text('PKG-INFO')
+            # This last clause is here to support old egg-info files.  Its
+            # effect is to just end up using the PathDistribution's self._path
+            # (which points to the egg-info file) attribute unchanged.
+            or self.read_text('')
+        )
+        text = cast(str, opt_text)
+        return _adapters.Message(email.message_from_string(text))
+
+    @property
+    def name(self) -> str:
+        """Return the 'Name' metadata for the distribution package."""
+        return self.metadata['Name']
+
+    @property
+    def _normalized_name(self):
+        """Return a normalized version of the name."""
+        return Prepared.normalize(self.name)
+
+    @property
+    def version(self) -> str:
+        """Return the 'Version' metadata for the distribution package."""
+        return self.metadata['Version']
+
+    @property
+    def entry_points(self) -> EntryPoints:
+        """
+        Return EntryPoints for this distribution.
+
+        Custom providers may provide the ``entry_points.txt`` file
+        or override this property.
+        """
+        return EntryPoints._from_text_for(self.read_text('entry_points.txt'), self)
+
+    @property
+    def files(self) -> Optional[List[PackagePath]]:
+        """Files in this distribution.
+
+        :return: List of PackagePath for this distribution or None
+
+        Result is `None` if the metadata file that enumerates files
+        (i.e. RECORD for dist-info, or installed-files.txt or
+        SOURCES.txt for egg-info) is missing.
+        Result may be empty if the metadata exists but is empty.
+
+        Custom providers are recommended to provide a "RECORD" file (in
+        ``read_text``) or override this property to allow for callers to be
+        able to resolve filenames provided by the package.
+        """
+
+        def make_file(name, hash=None, size_str=None):
+            result = PackagePath(name)
+            result.hash = FileHash(hash) if hash else None
+            result.size = int(size_str) if size_str else None
+            result.dist = self
+            return result
+
+        @pass_none
+        def make_files(lines):
+            # Delay csv import, since Distribution.files is not as widely used
+            # as other parts of importlib.metadata
+            import csv
+
+            return starmap(make_file, csv.reader(lines))
+
+        @pass_none
+        def skip_missing_files(package_paths):
+            return list(filter(lambda path: path.locate().exists(), package_paths))
+
+        return skip_missing_files(
+            make_files(
+                self._read_files_distinfo()
+                or self._read_files_egginfo_installed()
+                or self._read_files_egginfo_sources()
+            )
+        )
+
+    def _read_files_distinfo(self):
+        """
+        Read the lines of RECORD.
+        """
+        text = self.read_text('RECORD')
+        return text and text.splitlines()
+
+    def _read_files_egginfo_installed(self):
+        """
+        Read installed-files.txt and return lines in a similar
+        CSV-parsable format as RECORD: each file must be placed
+        relative to the site-packages directory and must also be
+        quoted (since file names can contain literal commas).
+
+        This file is written when the package is installed by pip,
+        but it might not be written for other installation methods.
+        Assume the file is accurate if it exists.
+        """
+        text = self.read_text('installed-files.txt')
+        # Prepend the .egg-info/ subdir to the lines in this file.
+        # But this subdir is only available from PathDistribution's
+        # self._path.
+        subdir = getattr(self, '_path', None)
+        if not text or not subdir:
+            return
+
+        paths = (
+            py311.relative_fix((subdir / name).resolve())
+            .relative_to(self.locate_file('').resolve(), walk_up=True)
+            .as_posix()
+            for name in text.splitlines()
+        )
+        return map('"{}"'.format, paths)
+
+    def _read_files_egginfo_sources(self):
+        """
+        Read SOURCES.txt and return lines in a similar CSV-parsable
+        format as RECORD: each file name must be quoted (since it
+        might contain literal commas).
+
+        Note that SOURCES.txt is not a reliable source for what
+        files are installed by a package. This file is generated
+        for a source archive, and the files that are present
+        there (e.g. setup.py) may not correctly reflect the files
+        that are present after the package has been installed.
+        """
+        text = self.read_text('SOURCES.txt')
+        return text and map('"{}"'.format, text.splitlines())
+
+    @property
+    def requires(self) -> Optional[List[str]]:
+        """Generated requirements specified for this Distribution"""
+        reqs = self._read_dist_info_reqs() or self._read_egg_info_reqs()
+        return reqs and list(reqs)
+
+    def _read_dist_info_reqs(self):
+        return self.metadata.get_all('Requires-Dist')
+
+    def _read_egg_info_reqs(self):
+        source = self.read_text('requires.txt')
+        return pass_none(self._deps_from_requires_text)(source)
+
+    @classmethod
+    def _deps_from_requires_text(cls, source):
+        return cls._convert_egg_info_reqs_to_simple_reqs(Sectioned.read(source))
+
+    @staticmethod
+    def _convert_egg_info_reqs_to_simple_reqs(sections):
+        """
+        Historically, setuptools would solicit and store 'extra'
+        requirements, including those with environment markers,
+        in separate sections. More modern tools expect each
+        dependency to be defined separately, with any relevant
+        extras and environment markers attached directly to that
+        requirement. This method converts the former to the
+        latter. See _test_deps_from_requires_text for an example.
+        """
+
+        def make_condition(name):
+            return name and f'extra == "{name}"'
+
+        def quoted_marker(section):
+            section = section or ''
+            extra, sep, markers = section.partition(':')
+            if extra and markers:
+                markers = f'({markers})'
+            conditions = list(filter(None, [markers, make_condition(extra)]))
+            return '; ' + ' and '.join(conditions) if conditions else ''
+
+        def url_req_space(req):
+            """
+            PEP 508 requires a space between the url_spec and the quoted_marker.
+            Ref python/importlib_metadata#357.
+            """
+            # '@' is uniquely indicative of a url_req.
+            return ' ' * ('@' in req)
+
+        for section in sections:
+            space = url_req_space(section.value)
+            yield section.value + space + quoted_marker(section.name)
+
+    @property
+    def origin(self):
+        return self._load_json('direct_url.json')
+
+    def _load_json(self, filename):
+        return pass_none(json.loads)(
+            self.read_text(filename),
+            object_hook=lambda data: types.SimpleNamespace(**data),
+        )
+
+
+class DistributionFinder(MetaPathFinder):
+    """
+    A MetaPathFinder capable of discovering installed distributions.
+
+    Custom providers should implement this interface in order to
+    supply metadata.
+    """
+
+    class Context:
+        """
+        Keyword arguments presented by the caller to
+        ``distributions()`` or ``Distribution.discover()``
+        to narrow the scope of a search for distributions
+        in all DistributionFinders.
+
+        Each DistributionFinder may expect any parameters
+        and should attempt to honor the canonical
+        parameters defined below when appropriate.
+
+        This mechanism gives a custom provider a means to
+        solicit additional details from the caller beyond
+        "name" and "path" when searching distributions.
+        For example, imagine a provider that exposes suites
+        of packages in either a "public" or "private" ``realm``.
+        A caller may wish to query only for distributions in
+        a particular realm and could call
+        ``distributions(realm="private")`` to signal to the
+        custom provider to only include distributions from that
+        realm.
+        """
+
+        name = None
+        """
+        Specific name for which a distribution finder should match.
+        A name of ``None`` matches all distributions.
+        """
+
+        def __init__(self, **kwargs):
+            vars(self).update(kwargs)
+
+        @property
+        def path(self) -> List[str]:
+            """
+            The sequence of directory path that a distribution finder
+            should search.
+
+            Typically refers to Python installed package paths such as
+            "site-packages" directories and defaults to ``sys.path``.
+            """
+            return vars(self).get('path', sys.path)
+
+    @abc.abstractmethod
+    def find_distributions(self, context=Context()) -> Iterable[Distribution]:
+        """
+        Find distributions.
+
+        Return an iterable of all Distribution instances capable of
+        loading the metadata for packages matching the ``context``,
+        a DistributionFinder.Context instance.
+        """
+
+
+class FastPath:
+    """
+    Micro-optimized class for searching a root for children.
+
+    Root is a path on the file system that may contain metadata
+    directories either as natural directories or within a zip file.
+
+    >>> FastPath('').children()
+    ['...']
+
+    FastPath objects are cached and recycled for any given root.
+
+    >>> FastPath('foobar') is FastPath('foobar')
+    True
+    """
+
+    @functools.lru_cache()  # type: ignore
+    def __new__(cls, root):
+        return super().__new__(cls)
+
+    def __init__(self, root):
+        self.root = root
+
+    def joinpath(self, child):
+        return pathlib.Path(self.root, child)
+
+    def children(self):
+        with suppress(Exception):
+            return os.listdir(self.root or '.')
+        with suppress(Exception):
+            return self.zip_children()
+        return []
+
+    def zip_children(self):
+        zip_path = zipp.Path(self.root)
+        names = zip_path.root.namelist()
+        self.joinpath = zip_path.joinpath
+
+        return dict.fromkeys(child.split(posixpath.sep, 1)[0] for child in names)
+
+    def search(self, name):
+        return self.lookup(self.mtime).search(name)
+
+    @property
+    def mtime(self):
+        with suppress(OSError):
+            return os.stat(self.root).st_mtime
+        self.lookup.cache_clear()
+
+    @method_cache
+    def lookup(self, mtime):
+        return Lookup(self)
+
+
+class Lookup:
+    """
+    A micro-optimized class for searching a (fast) path for metadata.
+    """
+
+    def __init__(self, path: FastPath):
+        """
+        Calculate all of the children representing metadata.
+
+        From the children in the path, calculate early all of the
+        children that appear to represent metadata (infos) or legacy
+        metadata (eggs).
+        """
+
+        base = os.path.basename(path.root).lower()
+        base_is_egg = base.endswith(".egg")
+        self.infos = FreezableDefaultDict(list)
+        self.eggs = FreezableDefaultDict(list)
+
+        for child in path.children():
+            low = child.lower()
+            if low.endswith((".dist-info", ".egg-info")):
+                # rpartition is faster than splitext and suitable for this purpose.
+                name = low.rpartition(".")[0].partition("-")[0]
+                normalized = Prepared.normalize(name)
+                self.infos[normalized].append(path.joinpath(child))
+            elif base_is_egg and low == "egg-info":
+                name = base.rpartition(".")[0].partition("-")[0]
+                legacy_normalized = Prepared.legacy_normalize(name)
+                self.eggs[legacy_normalized].append(path.joinpath(child))
+
+        self.infos.freeze()
+        self.eggs.freeze()
+
+    def search(self, prepared: Prepared):
+        """
+        Yield all infos and eggs matching the Prepared query.
+        """
+        infos = (
+            self.infos[prepared.normalized]
+            if prepared
+            else itertools.chain.from_iterable(self.infos.values())
+        )
+        eggs = (
+            self.eggs[prepared.legacy_normalized]
+            if prepared
+            else itertools.chain.from_iterable(self.eggs.values())
+        )
+        return itertools.chain(infos, eggs)
+
+
+class Prepared:
+    """
+    A prepared search query for metadata on a possibly-named package.
+
+    Pre-calculates the normalization to prevent repeated operations.
+
+    >>> none = Prepared(None)
+    >>> none.normalized
+    >>> none.legacy_normalized
+    >>> bool(none)
+    False
+    >>> sample = Prepared('Sample__Pkg-name.foo')
+    >>> sample.normalized
+    'sample_pkg_name_foo'
+    >>> sample.legacy_normalized
+    'sample__pkg_name.foo'
+    >>> bool(sample)
+    True
+    """
+
+    normalized = None
+    legacy_normalized = None
+
+    def __init__(self, name: Optional[str]):
+        self.name = name
+        if name is None:
+            return
+        self.normalized = self.normalize(name)
+        self.legacy_normalized = self.legacy_normalize(name)
+
+    @staticmethod
+    def normalize(name):
+        """
+        PEP 503 normalization plus dashes as underscores.
+        """
+        return re.sub(r"[-_.]+", "-", name).lower().replace('-', '_')
+
+    @staticmethod
+    def legacy_normalize(name):
+        """
+        Normalize the package name as found in the convention in
+        older packaging tools versions and specs.
+        """
+        return name.lower().replace('-', '_')
+
+    def __bool__(self):
+        return bool(self.name)
+
+
+@install
+class MetadataPathFinder(NullFinder, DistributionFinder):
+    """A degenerate finder for distribution packages on the file system.
+
+    This finder supplies only a find_distributions() method for versions
+    of Python that do not have a PathFinder find_distributions().
+    """
+
+    @classmethod
+    def find_distributions(
+        cls, context=DistributionFinder.Context()
+    ) -> Iterable[PathDistribution]:
+        """
+        Find distributions.
+
+        Return an iterable of all Distribution instances capable of
+        loading the metadata for packages matching ``context.name``
+        (or all names if ``None`` indicated) along the paths in the list
+        of directories ``context.path``.
+        """
+        found = cls._search_paths(context.name, context.path)
+        return map(PathDistribution, found)
+
+    @classmethod
+    def _search_paths(cls, name, paths):
+        """Find metadata directories in paths heuristically."""
+        prepared = Prepared(name)
+        return itertools.chain.from_iterable(
+            path.search(prepared) for path in map(FastPath, paths)
+        )
+
+    @classmethod
+    def invalidate_caches(cls) -> None:
+        FastPath.__new__.cache_clear()
+
+
+class PathDistribution(Distribution):
+    def __init__(self, path: SimplePath) -> None:
+        """Construct a distribution.
+
+        :param path: SimplePath indicating the metadata directory.
+        """
+        self._path = path
+
+    def read_text(self, filename: str | os.PathLike[str]) -> Optional[str]:
+        with suppress(
+            FileNotFoundError,
+            IsADirectoryError,
+            KeyError,
+            NotADirectoryError,
+            PermissionError,
+        ):
+            return self._path.joinpath(filename).read_text(encoding='utf-8')
+
+        return None
+
+    read_text.__doc__ = Distribution.read_text.__doc__
+
+    def locate_file(self, path: str | os.PathLike[str]) -> SimplePath:
+        return self._path.parent / path
+
+    @property
+    def _normalized_name(self):
+        """
+        Performance optimization: where possible, resolve the
+        normalized name from the file system path.
+        """
+        stem = os.path.basename(str(self._path))
+        return (
+            pass_none(Prepared.normalize)(self._name_from_stem(stem))
+            or super()._normalized_name
+        )
+
+    @staticmethod
+    def _name_from_stem(stem):
+        """
+        >>> PathDistribution._name_from_stem('foo-3.0.egg-info')
+        'foo'
+        >>> PathDistribution._name_from_stem('CherryPy-3.0.dist-info')
+        'CherryPy'
+        >>> PathDistribution._name_from_stem('face.egg-info')
+        'face'
+        >>> PathDistribution._name_from_stem('foo.bar')
+        """
+        filename, ext = os.path.splitext(stem)
+        if ext not in ('.dist-info', '.egg-info'):
+            return
+        name, sep, rest = filename.partition('-')
+        return name
+
+
+def distribution(distribution_name: str) -> Distribution:
+    """Get the ``Distribution`` instance for the named package.
+
+    :param distribution_name: The name of the distribution package as a string.
+    :return: A ``Distribution`` instance (or subclass thereof).
+    """
+    return Distribution.from_name(distribution_name)
+
+
+def distributions(**kwargs) -> Iterable[Distribution]:
+    """Get all ``Distribution`` instances in the current environment.
+
+    :return: An iterable of ``Distribution`` instances.
+    """
+    return Distribution.discover(**kwargs)
+
+
+def metadata(distribution_name: str) -> _meta.PackageMetadata:
+    """Get the metadata for the named package.
+
+    :param distribution_name: The name of the distribution package to query.
+    :return: A PackageMetadata containing the parsed metadata.
+    """
+    return Distribution.from_name(distribution_name).metadata
+
+
+def version(distribution_name: str) -> str:
+    """Get the version string for the named package.
+
+    :param distribution_name: The name of the distribution package to query.
+    :return: The version string for the package as defined in the package's
+        "Version" metadata key.
+    """
+    return distribution(distribution_name).version
+
+
+_unique = functools.partial(
+    unique_everseen,
+    key=py39.normalized_name,
+)
+"""
+Wrapper for ``distributions`` to return unique distributions by name.
+"""
+
+
+def entry_points(**params) -> EntryPoints:
+    """Return EntryPoint objects for all installed packages.
+
+    Pass selection parameters (group or name) to filter the
+    result to entry points matching those properties (see
+    EntryPoints.select()).
+
+    :return: EntryPoints for all installed packages.
+    """
+    eps = itertools.chain.from_iterable(
+        dist.entry_points for dist in _unique(distributions())
+    )
+    return EntryPoints(eps).select(**params)
+
+
+def files(distribution_name: str) -> Optional[List[PackagePath]]:
+    """Return a list of files for the named package.
+
+    :param distribution_name: The name of the distribution package to query.
+    :return: List of files composing the distribution.
+    """
+    return distribution(distribution_name).files
+
+
+def requires(distribution_name: str) -> Optional[List[str]]:
+    """
+    Return a list of requirements for the named package.
+
+    :return: An iterable of requirements, suitable for
+        packaging.requirement.Requirement.
+    """
+    return distribution(distribution_name).requires
+
+
+def packages_distributions() -> Mapping[str, List[str]]:
+    """
+    Return a mapping of top-level packages to their
+    distributions.
+
+    >>> import collections.abc
+    >>> pkgs = packages_distributions()
+    >>> all(isinstance(dist, collections.abc.Sequence) for dist in pkgs.values())
+    True
+    """
+    pkg_to_dist = collections.defaultdict(list)
+    for dist in distributions():
+        for pkg in _top_level_declared(dist) or _top_level_inferred(dist):
+            pkg_to_dist[pkg].append(dist.metadata['Name'])
+    return dict(pkg_to_dist)
+
+
+def _top_level_declared(dist):
+    return (dist.read_text('top_level.txt') or '').split()
+
+
+def _topmost(name: PackagePath) -> Optional[str]:
+    """
+    Return the top-most parent as long as there is a parent.
+    """
+    top, *rest = name.parts
+    return top if rest else None
+
+
+def _get_toplevel_name(name: PackagePath) -> str:
+    """
+    Infer a possibly importable module name from a name presumed on
+    sys.path.
+
+    >>> _get_toplevel_name(PackagePath('foo.py'))
+    'foo'
+    >>> _get_toplevel_name(PackagePath('foo'))
+    'foo'
+    >>> _get_toplevel_name(PackagePath('foo.pyc'))
+    'foo'
+    >>> _get_toplevel_name(PackagePath('foo/__init__.py'))
+    'foo'
+    >>> _get_toplevel_name(PackagePath('foo.pth'))
+    'foo.pth'
+    >>> _get_toplevel_name(PackagePath('foo.dist-info'))
+    'foo.dist-info'
+    """
+    return _topmost(name) or (
+        # python/typeshed#10328
+        inspect.getmodulename(name)  # type: ignore
+        or str(name)
+    )
+
+
+def _top_level_inferred(dist):
+    opt_names = set(map(_get_toplevel_name, always_iterable(dist.files)))
+
+    def importable_name(name):
+        return '.' not in name
+
+    return filter(importable_name, opt_names)
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--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_adapters.py
@@ -0,0 +1,83 @@
+import re
+import textwrap
+import email.message
+
+from ._text import FoldedCase
+
+
+class Message(email.message.Message):
+    multiple_use_keys = set(
+        map(
+            FoldedCase,
+            [
+                'Classifier',
+                'Obsoletes-Dist',
+                'Platform',
+                'Project-URL',
+                'Provides-Dist',
+                'Provides-Extra',
+                'Requires-Dist',
+                'Requires-External',
+                'Supported-Platform',
+                'Dynamic',
+            ],
+        )
+    )
+    """
+    Keys that may be indicated multiple times per PEP 566.
+    """
+
+    def __new__(cls, orig: email.message.Message):
+        res = super().__new__(cls)
+        vars(res).update(vars(orig))
+        return res
+
+    def __init__(self, *args, **kwargs):
+        self._headers = self._repair_headers()
+
+    # suppress spurious error from mypy
+    def __iter__(self):
+        return super().__iter__()
+
+    def __getitem__(self, item):
+        """
+        Override parent behavior to typical dict behavior.
+
+        ``email.message.Message`` will emit None values for missing
+        keys. Typical mappings, including this ``Message``, will raise
+        a key error for missing keys.
+
+        Ref python/importlib_metadata#371.
+        """
+        res = super().__getitem__(item)
+        if res is None:
+            raise KeyError(item)
+        return res
+
+    def _repair_headers(self):
+        def redent(value):
+            "Correct for RFC822 indentation"
+            if not value or '\n' not in value:
+                return value
+            return textwrap.dedent(' ' * 8 + value)
+
+        headers = [(key, redent(value)) for key, value in vars(self)['_headers']]
+        if self._payload:
+            headers.append(('Description', self.get_payload()))
+        return headers
+
+    @property
+    def json(self):
+        """
+        Convert PackageMetadata to a JSON-compatible format
+        per PEP 0566.
+        """
+
+        def transform(key):
+            value = self.get_all(key) if key in self.multiple_use_keys else self[key]
+            if key == 'Keywords':
+                value = re.split(r'\s+', value)
+            tk = key.lower().replace('-', '_')
+            return tk, value
+
+        return dict(map(transform, map(FoldedCase, self)))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_collections.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_collections.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf0954e1a30546d781bf25781ec716ef92a77e32
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_collections.py
@@ -0,0 +1,30 @@
+import collections
+
+
+# from jaraco.collections 3.3
+class FreezableDefaultDict(collections.defaultdict):
+    """
+    Often it is desirable to prevent the mutation of
+    a default dict after its initial construction, such
+    as to prevent mutation during iteration.
+
+    >>> dd = FreezableDefaultDict(list)
+    >>> dd[0].append('1')
+    >>> dd.freeze()
+    >>> dd[1]
+    []
+    >>> len(dd)
+    1
+    """
+
+    def __missing__(self, key):
+        return getattr(self, '_frozen', super().__missing__)(key)
+
+    def freeze(self):
+        self._frozen = lambda key: self.default_factory()
+
+
+class Pair(collections.namedtuple('Pair', 'name value')):
+    @classmethod
+    def parse(cls, text):
+        return cls(*map(str.strip, text.split("=", 1)))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_compat.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..df312b1cbbf18a337278df0e618fb9e8c862e5f6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_compat.py
@@ -0,0 +1,57 @@
+import sys
+import platform
+
+
+__all__ = ['install', 'NullFinder']
+
+
+def install(cls):
+    """
+    Class decorator for installation on sys.meta_path.
+
+    Adds the backport DistributionFinder to sys.meta_path and
+    attempts to disable the finder functionality of the stdlib
+    DistributionFinder.
+    """
+    sys.meta_path.append(cls())
+    disable_stdlib_finder()
+    return cls
+
+
+def disable_stdlib_finder():
+    """
+    Give the backport primacy for discovering path-based distributions
+    by monkey-patching the stdlib O_O.
+
+    See #91 for more background for rationale on this sketchy
+    behavior.
+    """
+
+    def matches(finder):
+        return getattr(
+            finder, '__module__', None
+        ) == '_frozen_importlib_external' and hasattr(finder, 'find_distributions')
+
+    for finder in filter(matches, sys.meta_path):  # pragma: nocover
+        del finder.find_distributions
+
+
+class NullFinder:
+    """
+    A "Finder" (aka "MetaPathFinder") that never finds any modules,
+    but may find distributions.
+    """
+
+    @staticmethod
+    def find_spec(*args, **kwargs):
+        return None
+
+
+def pypy_partial(val):
+    """
+    Adjust for variable stacklevel on partial under PyPy.
+
+    Workaround for #327.
+    """
+    is_pypy = platform.python_implementation() == 'PyPy'
+    return val + is_pypy
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_functools.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_functools.py
new file mode 100644
index 0000000000000000000000000000000000000000..71f66bd03cb713a2190853bdf7170c4ea80d2425
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_functools.py
@@ -0,0 +1,104 @@
+import types
+import functools
+
+
+# from jaraco.functools 3.3
+def method_cache(method, cache_wrapper=None):
+    """
+    Wrap lru_cache to support storing the cache data in the object instances.
+
+    Abstracts the common paradigm where the method explicitly saves an
+    underscore-prefixed protected property on first call and returns that
+    subsequently.
+
+    >>> class MyClass:
+    ...     calls = 0
+    ...
+    ...     @method_cache
+    ...     def method(self, value):
+    ...         self.calls += 1
+    ...         return value
+
+    >>> a = MyClass()
+    >>> a.method(3)
+    3
+    >>> for x in range(75):
+    ...     res = a.method(x)
+    >>> a.calls
+    75
+
+    Note that the apparent behavior will be exactly like that of lru_cache
+    except that the cache is stored on each instance, so values in one
+    instance will not flush values from another, and when an instance is
+    deleted, so are the cached values for that instance.
+
+    >>> b = MyClass()
+    >>> for x in range(35):
+    ...     res = b.method(x)
+    >>> b.calls
+    35
+    >>> a.method(0)
+    0
+    >>> a.calls
+    75
+
+    Note that if method had been decorated with ``functools.lru_cache()``,
+    a.calls would have been 76 (due to the cached value of 0 having been
+    flushed by the 'b' instance).
+
+    Clear the cache with ``.cache_clear()``
+
+    >>> a.method.cache_clear()
+
+    Same for a method that hasn't yet been called.
+
+    >>> c = MyClass()
+    >>> c.method.cache_clear()
+
+    Another cache wrapper may be supplied:
+
+    >>> cache = functools.lru_cache(maxsize=2)
+    >>> MyClass.method2 = method_cache(lambda self: 3, cache_wrapper=cache)
+    >>> a = MyClass()
+    >>> a.method2()
+    3
+
+    Caution - do not subsequently wrap the method with another decorator, such
+    as ``@property``, which changes the semantics of the function.
+
+    See also
+    http://code.activestate.com/recipes/577452-a-memoize-decorator-for-instance-methods/
+    for another implementation and additional justification.
+    """
+    cache_wrapper = cache_wrapper or functools.lru_cache()
+
+    def wrapper(self, *args, **kwargs):
+        # it's the first call, replace the method with a cached, bound method
+        bound_method = types.MethodType(method, self)
+        cached_method = cache_wrapper(bound_method)
+        setattr(self, method.__name__, cached_method)
+        return cached_method(*args, **kwargs)
+
+    # Support cache clear even before cache has been created.
+    wrapper.cache_clear = lambda: None
+
+    return wrapper
+
+
+# From jaraco.functools 3.3
+def pass_none(func):
+    """
+    Wrap func so it's not called if its first param is None
+
+    >>> print_text = pass_none(print)
+    >>> print_text('text')
+    text
+    >>> print_text(None)
+    """
+
+    @functools.wraps(func)
+    def wrapper(param, *args, **kwargs):
+        if param is not None:
+            return func(param, *args, **kwargs)
+
+    return wrapper
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_itertools.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_itertools.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ca9b9140e3f085b36609bb8dfdaea79c78e144
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_itertools.py
@@ -0,0 +1,73 @@
+from itertools import filterfalse
+
+
+def unique_everseen(iterable, key=None):
+    "List unique elements, preserving order. Remember all elements ever seen."
+    # unique_everseen('AAAABBBCCDAABBB') --> A B C D
+    # unique_everseen('ABBCcAD', str.lower) --> A B C D
+    seen = set()
+    seen_add = seen.add
+    if key is None:
+        for element in filterfalse(seen.__contains__, iterable):
+            seen_add(element)
+            yield element
+    else:
+        for element in iterable:
+            k = key(element)
+            if k not in seen:
+                seen_add(k)
+                yield element
+
+
+# copied from more_itertools 8.8
+def always_iterable(obj, base_type=(str, bytes)):
+    """If *obj* is iterable, return an iterator over its items::
+
+        >>> obj = (1, 2, 3)
+        >>> list(always_iterable(obj))
+        [1, 2, 3]
+
+    If *obj* is not iterable, return a one-item iterable containing *obj*::
+
+        >>> obj = 1
+        >>> list(always_iterable(obj))
+        [1]
+
+    If *obj* is ``None``, return an empty iterable:
+
+        >>> obj = None
+        >>> list(always_iterable(None))
+        []
+
+    By default, binary and text strings are not considered iterable::
+
+        >>> obj = 'foo'
+        >>> list(always_iterable(obj))
+        ['foo']
+
+    If *base_type* is set, objects for which ``isinstance(obj, base_type)``
+    returns ``True`` won't be considered iterable.
+
+        >>> obj = {'a': 1}
+        >>> list(always_iterable(obj))  # Iterate over the dict's keys
+        ['a']
+        >>> list(always_iterable(obj, base_type=dict))  # Treat dicts as a unit
+        [{'a': 1}]
+
+    Set *base_type* to ``None`` to avoid any special handling and treat objects
+    Python considers iterable as iterable:
+
+        >>> obj = 'foo'
+        >>> list(always_iterable(obj, base_type=None))
+        ['f', 'o', 'o']
+    """
+    if obj is None:
+        return iter(())
+
+    if (base_type is not None) and isinstance(obj, base_type):
+        return iter((obj,))
+
+    try:
+        return iter(obj)
+    except TypeError:
+        return iter((obj,))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_meta.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_meta.py
new file mode 100644
index 0000000000000000000000000000000000000000..1927d0f624d82f2fa12f81c80cce91279f039e84
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_meta.py
@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+import os
+from typing import Protocol
+from typing import Any, Dict, Iterator, List, Optional, TypeVar, Union, overload
+
+
+_T = TypeVar("_T")
+
+
+class PackageMetadata(Protocol):
+    def __len__(self) -> int: ...  # pragma: no cover
+
+    def __contains__(self, item: str) -> bool: ...  # pragma: no cover
+
+    def __getitem__(self, key: str) -> str: ...  # pragma: no cover
+
+    def __iter__(self) -> Iterator[str]: ...  # pragma: no cover
+
+    @overload
+    def get(
+        self, name: str, failobj: None = None
+    ) -> Optional[str]: ...  # pragma: no cover
+
+    @overload
+    def get(self, name: str, failobj: _T) -> Union[str, _T]: ...  # pragma: no cover
+
+    # overload per python/importlib_metadata#435
+    @overload
+    def get_all(
+        self, name: str, failobj: None = None
+    ) -> Optional[List[Any]]: ...  # pragma: no cover
+
+    @overload
+    def get_all(self, name: str, failobj: _T) -> Union[List[Any], _T]:
+        """
+        Return all values associated with a possibly multi-valued key.
+        """
+
+    @property
+    def json(self) -> Dict[str, Union[str, List[str]]]:
+        """
+        A JSON-compatible form of the metadata.
+        """
+
+
+class SimplePath(Protocol):
+    """
+    A minimal subset of pathlib.Path required by Distribution.
+    """
+
+    def joinpath(
+        self, other: Union[str, os.PathLike[str]]
+    ) -> SimplePath: ...  # pragma: no cover
+
+    def __truediv__(
+        self, other: Union[str, os.PathLike[str]]
+    ) -> SimplePath: ...  # pragma: no cover
+
+    @property
+    def parent(self) -> SimplePath: ...  # pragma: no cover
+
+    def read_text(self, encoding=None) -> str: ...  # pragma: no cover
+
+    def read_bytes(self) -> bytes: ...  # pragma: no cover
+
+    def exists(self) -> bool: ...  # pragma: no cover
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_text.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_text.py
new file mode 100644
index 0000000000000000000000000000000000000000..c88cfbb2349c6401336bc5ba6623f51afd1eb59d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/_text.py
@@ -0,0 +1,99 @@
+import re
+
+from ._functools import method_cache
+
+
+# from jaraco.text 3.5
+class FoldedCase(str):
+    """
+    A case insensitive string class; behaves just like str
+    except compares equal when the only variation is case.
+
+    >>> s = FoldedCase('hello world')
+
+    >>> s == 'Hello World'
+    True
+
+    >>> 'Hello World' == s
+    True
+
+    >>> s != 'Hello World'
+    False
+
+    >>> s.index('O')
+    4
+
+    >>> s.split('O')
+    ['hell', ' w', 'rld']
+
+    >>> sorted(map(FoldedCase, ['GAMMA', 'alpha', 'Beta']))
+    ['alpha', 'Beta', 'GAMMA']
+
+    Sequence membership is straightforward.
+
+    >>> "Hello World" in [s]
+    True
+    >>> s in ["Hello World"]
+    True
+
+    You may test for set inclusion, but candidate and elements
+    must both be folded.
+
+    >>> FoldedCase("Hello World") in {s}
+    True
+    >>> s in {FoldedCase("Hello World")}
+    True
+
+    String inclusion works as long as the FoldedCase object
+    is on the right.
+
+    >>> "hello" in FoldedCase("Hello World")
+    True
+
+    But not if the FoldedCase object is on the left:
+
+    >>> FoldedCase('hello') in 'Hello World'
+    False
+
+    In that case, use in_:
+
+    >>> FoldedCase('hello').in_('Hello World')
+    True
+
+    >>> FoldedCase('hello') > FoldedCase('Hello')
+    False
+    """
+
+    def __lt__(self, other):
+        return self.lower() < other.lower()
+
+    def __gt__(self, other):
+        return self.lower() > other.lower()
+
+    def __eq__(self, other):
+        return self.lower() == other.lower()
+
+    def __ne__(self, other):
+        return self.lower() != other.lower()
+
+    def __hash__(self):
+        return hash(self.lower())
+
+    def __contains__(self, other):
+        return super().lower().__contains__(other.lower())
+
+    def in_(self, other):
+        "Does self appear in other?"
+        return self in FoldedCase(other)
+
+    # cache lower since it's likely to be called frequently.
+    @method_cache
+    def lower(self):
+        return super().lower()
+
+    def index(self, sub):
+        return self.lower().index(sub.lower())
+
+    def split(self, splitter=' ', maxsplit=0):
+        pattern = re.compile(re.escape(splitter), re.I)
+        return pattern.split(self, maxsplit)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/compat/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/compat/__init__.py
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/compat/__pycache__/py39.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/compat/__pycache__/py39.cpython-312.pyc
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/compat/py311.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/compat/py311.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a5327436f9b1d9eae371e321c491a270634b3cf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/compat/py311.py
@@ -0,0 +1,22 @@
+import os
+import pathlib
+import sys
+import types
+
+
+def wrap(path):  # pragma: no cover
+    """
+    Workaround for https://github.com/python/cpython/issues/84538
+    to add backward compatibility for walk_up=True.
+    An example affected package is dask-labextension, which uses
+    jupyter-packaging to install JupyterLab javascript files outside
+    of site-packages.
+    """
+
+    def relative_to(root, *, walk_up=False):
+        return pathlib.Path(os.path.relpath(path, root))
+
+    return types.SimpleNamespace(relative_to=relative_to)
+
+
+relative_fix = wrap if sys.version_info < (3, 12) else lambda x: x
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/compat/py39.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/compat/py39.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f15bd97e6aa028d3e86734dd08c0eb5c06d79bc
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/compat/py39.py
@@ -0,0 +1,36 @@
+"""
+Compatibility layer with Python 3.8/3.9
+"""
+
+from typing import TYPE_CHECKING, Any, Optional
+
+if TYPE_CHECKING:  # pragma: no cover
+    # Prevent circular imports on runtime.
+    from .. import Distribution, EntryPoint
+else:
+    Distribution = EntryPoint = Any
+
+
+def normalized_name(dist: Distribution) -> Optional[str]:
+    """
+    Honor name normalization for distributions that don't provide ``_normalized_name``.
+    """
+    try:
+        return dist._normalized_name
+    except AttributeError:
+        from .. import Prepared  # -> delay to prevent circular imports.
+
+        return Prepared.normalize(getattr(dist, "name", None) or dist.metadata['Name'])
+
+
+def ep_matches(ep: EntryPoint, **params) -> bool:
+    """
+    Workaround for ``EntryPoint`` objects without the ``matches`` method.
+    """
+    try:
+        return ep.matches(**params)
+    except AttributeError:
+        from .. import EntryPoint  # -> delay to prevent circular imports.
+
+        # Reconstruct the EntryPoint object to make sure it is compatible.
+        return EntryPoint(ep.name, ep.value, ep.group).matches(**params)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/diagnose.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/diagnose.py
new file mode 100644
index 0000000000000000000000000000000000000000..e405471ac4d94371b1ee9b1622227ff76b337180
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/diagnose.py
@@ -0,0 +1,21 @@
+import sys
+
+from . import Distribution
+
+
+def inspect(path):
+    print("Inspecting", path)
+    dists = list(Distribution.discover(path=[path]))
+    if not dists:
+        return
+    print("Found", len(dists), "packages:", end=' ')
+    print(', '.join(dist.name for dist in dists))
+
+
+def run():
+    for path in sys.path:
+        inspect(path)
+
+
+if __name__ == '__main__':
+    run()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/py.typed b/.venv/lib/python3.12/site-packages/setuptools/_vendor/importlib_metadata/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..1bb5a44356f00884a71ceeefd24ded6caaba2418
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/LICENSE
@@ -0,0 +1,17 @@
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to
+deal in the Software without restriction, including without limitation the
+rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+sell copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+IN THE SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..9a2097a54aaaf21e7f46235a3ccf362213032ca9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/METADATA
@@ -0,0 +1,591 @@
+Metadata-Version: 2.1
+Name: inflect
+Version: 7.3.1
+Summary: Correctly generate plurals, singular nouns, ordinals, indefinite articles
+Author-email: Paul Dyson <pwdyson@yahoo.com>
+Maintainer-email: "Jason R. Coombs" <jaraco@jaraco.com>
+Project-URL: Source, https://github.com/jaraco/inflect
+Keywords: plural,inflect,participle
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Natural Language :: English
+Classifier: Operating System :: OS Independent
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Topic :: Text Processing :: Linguistic
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: more-itertools >=8.5.0
+Requires-Dist: typeguard >=4.0.1
+Requires-Dist: typing-extensions ; python_version < "3.9"
+Provides-Extra: doc
+Requires-Dist: sphinx >=3.5 ; extra == 'doc'
+Requires-Dist: jaraco.packaging >=9.3 ; extra == 'doc'
+Requires-Dist: rst.linker >=1.9 ; extra == 'doc'
+Requires-Dist: furo ; extra == 'doc'
+Requires-Dist: sphinx-lint ; extra == 'doc'
+Requires-Dist: jaraco.tidelift >=1.4 ; extra == 'doc'
+Provides-Extra: test
+Requires-Dist: pytest !=8.1.*,>=6 ; extra == 'test'
+Requires-Dist: pytest-checkdocs >=2.4 ; extra == 'test'
+Requires-Dist: pytest-cov ; extra == 'test'
+Requires-Dist: pytest-mypy ; extra == 'test'
+Requires-Dist: pytest-enabler >=2.2 ; extra == 'test'
+Requires-Dist: pytest-ruff >=0.2.1 ; extra == 'test'
+Requires-Dist: pygments ; extra == 'test'
+
+.. image:: https://img.shields.io/pypi/v/inflect.svg
+   :target: https://pypi.org/project/inflect
+
+.. image:: https://img.shields.io/pypi/pyversions/inflect.svg
+
+.. image:: https://github.com/jaraco/inflect/actions/workflows/main.yml/badge.svg
+   :target: https://github.com/jaraco/inflect/actions?query=workflow%3A%22tests%22
+   :alt: tests
+
+.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
+    :target: https://github.com/astral-sh/ruff
+    :alt: Ruff
+
+.. image:: https://readthedocs.org/projects/inflect/badge/?version=latest
+   :target: https://inflect.readthedocs.io/en/latest/?badge=latest
+
+.. image:: https://img.shields.io/badge/skeleton-2024-informational
+   :target: https://blog.jaraco.com/skeleton
+
+.. image:: https://tidelift.com/badges/package/pypi/inflect
+   :target: https://tidelift.com/subscription/pkg/pypi-inflect?utm_source=pypi-inflect&utm_medium=readme
+
+NAME
+====
+
+inflect.py - Correctly generate plurals, singular nouns, ordinals, indefinite articles; convert numbers to words.
+
+SYNOPSIS
+========
+
+.. code-block:: python
+
+    import inflect
+
+    p = inflect.engine()
+
+    # METHODS:
+
+    # plural plural_noun plural_verb plural_adj singular_noun no num
+    # compare compare_nouns compare_nouns compare_adjs
+    # a an
+    # present_participle
+    # ordinal number_to_words
+    # join
+    # inflect classical gender
+    # defnoun defverb defadj defa defan
+
+
+    # UNCONDITIONALLY FORM THE PLURAL
+
+    print("The plural of ", word, " is ", p.plural(word))
+
+
+    # CONDITIONALLY FORM THE PLURAL
+
+    print("I saw", cat_count, p.plural("cat", cat_count))
+
+
+    # FORM PLURALS FOR SPECIFIC PARTS OF SPEECH
+
+    print(
+        p.plural_noun("I", N1),
+        p.plural_verb("saw", N1),
+        p.plural_adj("my", N2),
+        p.plural_noun("saw", N2),
+    )
+
+
+    # FORM THE SINGULAR OF PLURAL NOUNS
+
+    print("The singular of ", word, " is ", p.singular_noun(word))
+
+    # SELECT THE GENDER OF SINGULAR PRONOUNS
+
+    print(p.singular_noun("they"))  # 'it'
+    p.gender("feminine")
+    print(p.singular_noun("they"))  # 'she'
+
+
+    # DEAL WITH "0/1/N" -> "no/1/N" TRANSLATION:
+
+    print("There ", p.plural_verb("was", errors), p.no(" error", errors))
+
+
+    # USE DEFAULT COUNTS:
+
+    print(
+        p.num(N1, ""),
+        p.plural("I"),
+        p.plural_verb(" saw"),
+        p.num(N2),
+        p.plural_noun(" saw"),
+    )
+    print("There ", p.num(errors, ""), p.plural_verb("was"), p.no(" error"))
+
+
+    # COMPARE TWO WORDS "NUMBER-INSENSITIVELY":
+
+    if p.compare(word1, word2):
+        print("same")
+    if p.compare_nouns(word1, word2):
+        print("same noun")
+    if p.compare_verbs(word1, word2):
+        print("same verb")
+    if p.compare_adjs(word1, word2):
+        print("same adj.")
+
+
+    # ADD CORRECT "a" OR "an" FOR A GIVEN WORD:
+
+    print("Did you want ", p.a(thing), " or ", p.an(idea))
+
+
+    # CONVERT NUMERALS INTO ORDINALS (i.e. 1->1st, 2->2nd, 3->3rd, etc.)
+
+    print("It was", p.ordinal(position), " from the left\n")
+
+    # CONVERT NUMERALS TO WORDS (i.e. 1->"one", 101->"one hundred and one", etc.)
+    # RETURNS A SINGLE STRING...
+
+    words = p.number_to_words(1234)
+    # "one thousand, two hundred and thirty-four"
+    words = p.number_to_words(p.ordinal(1234))
+    # "one thousand, two hundred and thirty-fourth"
+
+
+    # GET BACK A LIST OF STRINGS, ONE FOR EACH "CHUNK"...
+
+    words = p.number_to_words(1234, wantlist=True)
+    # ("one thousand","two hundred and thirty-four")
+
+
+    # OPTIONAL PARAMETERS CHANGE TRANSLATION:
+
+    words = p.number_to_words(12345, group=1)
+    # "one, two, three, four, five"
+
+    words = p.number_to_words(12345, group=2)
+    # "twelve, thirty-four, five"
+
+    words = p.number_to_words(12345, group=3)
+    # "one twenty-three, forty-five"
+
+    words = p.number_to_words(1234, andword="")
+    # "one thousand, two hundred thirty-four"
+
+    words = p.number_to_words(1234, andword=", plus")
+    # "one thousand, two hundred, plus thirty-four"
+    # TODO: I get no comma before plus: check perl
+
+    words = p.number_to_words(555_1202, group=1, zero="oh")
+    # "five, five, five, one, two, oh, two"
+
+    words = p.number_to_words(555_1202, group=1, one="unity")
+    # "five, five, five, unity, two, oh, two"
+
+    words = p.number_to_words(123.456, group=1, decimal="mark")
+    # "one two three mark four five six"
+    # TODO: DOCBUG: perl gives commas here as do I
+
+    # LITERAL STYLE ONLY NAMES NUMBERS LESS THAN A CERTAIN THRESHOLD...
+
+    words = p.number_to_words(9, threshold=10)  # "nine"
+    words = p.number_to_words(10, threshold=10)  # "ten"
+    words = p.number_to_words(11, threshold=10)  # "11"
+    words = p.number_to_words(1000, threshold=10)  # "1,000"
+
+    # JOIN WORDS INTO A LIST:
+
+    mylist = p.join(("apple", "banana", "carrot"))
+    # "apple, banana, and carrot"
+
+    mylist = p.join(("apple", "banana"))
+    # "apple and banana"
+
+    mylist = p.join(("apple", "banana", "carrot"), final_sep="")
+    # "apple, banana and carrot"
+
+
+    # REQUIRE "CLASSICAL" PLURALS (EG: "focus"->"foci", "cherub"->"cherubim")
+
+    p.classical()  # USE ALL CLASSICAL PLURALS
+
+    p.classical(all=True)  # USE ALL CLASSICAL PLURALS
+    p.classical(all=False)  # SWITCH OFF CLASSICAL MODE
+
+    p.classical(zero=True)  #  "no error" INSTEAD OF "no errors"
+    p.classical(zero=False)  #  "no errors" INSTEAD OF "no error"
+
+    p.classical(herd=True)  #  "2 buffalo" INSTEAD OF "2 buffalos"
+    p.classical(herd=False)  #  "2 buffalos" INSTEAD OF "2 buffalo"
+
+    p.classical(persons=True)  # "2 chairpersons" INSTEAD OF "2 chairpeople"
+    p.classical(persons=False)  # "2 chairpeople" INSTEAD OF "2 chairpersons"
+
+    p.classical(ancient=True)  # "2 formulae" INSTEAD OF "2 formulas"
+    p.classical(ancient=False)  # "2 formulas" INSTEAD OF "2 formulae"
+
+
+    # INTERPOLATE "plural()", "plural_noun()", "plural_verb()", "plural_adj()", "singular_noun()",
+    # a()", "an()", "num()" AND "ordinal()" WITHIN STRINGS:
+
+    print(p.inflect("The plural of {0} is plural('{0}')".format(word)))
+    print(p.inflect("The singular of {0} is singular_noun('{0}')".format(word)))
+    print(p.inflect("I saw {0} plural('cat',{0})".format(cat_count)))
+    print(
+        p.inflect(
+            "plural('I',{0}) "
+            "plural_verb('saw',{0}) "
+            "plural('a',{1}) "
+            "plural_noun('saw',{1})".format(N1, N2)
+        )
+    )
+    print(
+        p.inflect(
+            "num({0}, False)plural('I') "
+            "plural_verb('saw') "
+            "num({1}, False)plural('a') "
+            "plural_noun('saw')".format(N1, N2)
+        )
+    )
+    print(p.inflect("I saw num({0}) plural('cat')\nnum()".format(cat_count)))
+    print(p.inflect("There plural_verb('was',{0}) no('error',{0})".format(errors)))
+    print(p.inflect("There num({0}, False)plural_verb('was') no('error')".format(errors)))
+    print(p.inflect("Did you want a('{0}') or an('{1}')".format(thing, idea)))
+    print(p.inflect("It was ordinal('{0}') from the left".format(position)))
+
+
+    # ADD USER-DEFINED INFLECTIONS (OVERRIDING INBUILT RULES):
+
+    p.defnoun("VAX", "VAXen")  # SINGULAR => PLURAL
+
+    p.defverb(
+        "will",  # 1ST PERSON SINGULAR
+        "shall",  # 1ST PERSON PLURAL
+        "will",  # 2ND PERSON SINGULAR
+        "will",  # 2ND PERSON PLURAL
+        "will",  # 3RD PERSON SINGULAR
+        "will",  # 3RD PERSON PLURAL
+    )
+
+    p.defadj("hir", "their")  # SINGULAR => PLURAL
+
+    p.defa("h")  # "AY HALWAYS SEZ 'HAITCH'!"
+
+    p.defan("horrendous.*")  # "AN HORRENDOUS AFFECTATION"
+
+
+DESCRIPTION
+===========
+
+The methods of the class ``engine`` in module ``inflect.py`` provide plural
+inflections, singular noun inflections, "a"/"an" selection for English words,
+and manipulation of numbers as words.
+
+Plural forms of all nouns, most verbs, and some adjectives are
+provided. Where appropriate, "classical" variants (for example: "brother" ->
+"brethren", "dogma" -> "dogmata", etc.) are also provided.
+
+Single forms of nouns are also provided. The gender of singular pronouns
+can be chosen (for example "they" -> "it" or "she" or "he" or "they").
+
+Pronunciation-based "a"/"an" selection is provided for all English
+words, and most initialisms.
+
+It is also possible to inflect numerals (1,2,3) to ordinals (1st, 2nd, 3rd)
+and to English words ("one", "two", "three").
+
+In generating these inflections, ``inflect.py`` follows the Oxford
+English Dictionary and the guidelines in Fowler's Modern English
+Usage, preferring the former where the two disagree.
+
+The module is built around standard British spelling, but is designed
+to cope with common American variants as well. Slang, jargon, and
+other English dialects are *not* explicitly catered for.
+
+Where two or more inflected forms exist for a single word (typically a
+"classical" form and a "modern" form), ``inflect.py`` prefers the
+more common form (typically the "modern" one), unless "classical"
+processing has been specified
+(see `MODERN VS CLASSICAL INFLECTIONS`).
+
+FORMING PLURALS AND SINGULARS
+=============================
+
+Inflecting Plurals and Singulars
+--------------------------------
+
+All of the ``plural...`` plural inflection methods take the word to be
+inflected as their first argument and return the corresponding inflection.
+Note that all such methods expect the *singular* form of the word. The
+results of passing a plural form are undefined (and unlikely to be correct).
+Similarly, the ``si...`` singular inflection method expects the *plural*
+form of the word.
+
+The ``plural...`` methods also take an optional second argument,
+which indicates the grammatical "number" of the word (or of another word
+with which the word being inflected must agree). If the "number" argument is
+supplied and is not ``1`` (or ``"one"`` or ``"a"``, or some other adjective that
+implies the singular), the plural form of the word is returned. If the
+"number" argument *does* indicate singularity, the (uninflected) word
+itself is returned. If the number argument is omitted, the plural form
+is returned unconditionally.
+
+The ``si...`` method takes a second argument in a similar fashion. If it is
+some form of the number ``1``, or is omitted, the singular form is returned.
+Otherwise the plural is returned unaltered.
+
+
+The various methods of ``inflect.engine`` are:
+
+
+
+``plural_noun(word, count=None)``
+
+ The method ``plural_noun()`` takes a *singular* English noun or
+ pronoun and returns its plural. Pronouns in the nominative ("I" ->
+ "we") and accusative ("me" -> "us") cases are handled, as are
+ possessive pronouns ("mine" -> "ours").
+
+
+``plural_verb(word, count=None)``
+
+ The method ``plural_verb()`` takes the *singular* form of a
+ conjugated verb (that is, one which is already in the correct "person"
+ and "mood") and returns the corresponding plural conjugation.
+
+
+``plural_adj(word, count=None)``
+
+ The method ``plural_adj()`` takes the *singular* form of
+ certain types of adjectives and returns the corresponding plural form.
+ Adjectives that are correctly handled include: "numerical" adjectives
+ ("a" -> "some"), demonstrative adjectives ("this" -> "these", "that" ->
+ "those"), and possessives ("my" -> "our", "cat's" -> "cats'", "child's"
+ -> "childrens'", etc.)
+
+
+``plural(word, count=None)``
+
+ The method ``plural()`` takes a *singular* English noun,
+ pronoun, verb, or adjective and returns its plural form. Where a word
+ has more than one inflection depending on its part of speech (for
+ example, the noun "thought" inflects to "thoughts", the verb "thought"
+ to "thought"), the (singular) noun sense is preferred to the (singular)
+ verb sense.
+
+ Hence ``plural("knife")`` will return "knives" ("knife" having been treated
+ as a singular noun), whereas ``plural("knifes")`` will return "knife"
+ ("knifes" having been treated as a 3rd person singular verb).
+
+ The inherent ambiguity of such cases suggests that,
+ where the part of speech is known, ``plural_noun``, ``plural_verb``, and
+ ``plural_adj`` should be used in preference to ``plural``.
+
+
+``singular_noun(word, count=None)``
+
+ The method ``singular_noun()`` takes a *plural* English noun or
+ pronoun and returns its singular. Pronouns in the nominative ("we" ->
+ "I") and accusative ("us" -> "me") cases are handled, as are
+ possessive pronouns ("ours" -> "mine"). When third person
+ singular pronouns are returned they take the neuter gender by default
+ ("they" -> "it"), not ("they"-> "she") nor ("they" -> "he"). This can be
+ changed with ``gender()``.
+
+Note that all these methods ignore any whitespace surrounding the
+word being inflected, but preserve that whitespace when the result is
+returned. For example, ``plural(" cat  ")`` returns " cats  ".
+
+
+``gender(genderletter)``
+
+ The third person plural pronoun takes the same form for the female, male and
+ neuter (e.g. "they"). The singular however, depends upon gender (e.g. "she",
+ "he", "it" and "they" -- "they" being the gender neutral form.) By default
+ ``singular_noun`` returns the neuter form, however, the gender can be selected with
+ the ``gender`` method. Pass the first letter of the gender to
+ ``gender`` to return the f(eminine), m(asculine), n(euter) or t(hey)
+ form of the singular. e.g.
+ gender('f') followed by singular_noun('themselves') returns 'herself'.
+
+Numbered plurals
+----------------
+
+The ``plural...`` methods return only the inflected word, not the count that
+was used to inflect it. Thus, in order to produce "I saw 3 ducks", it
+is necessary to use:
+
+.. code-block:: python
+
+    print("I saw", N, p.plural_noun(animal, N))
+
+Since the usual purpose of producing a plural is to make it agree with
+a preceding count, inflect.py provides a method
+(``no(word, count)``) which, given a word and a(n optional) count, returns the
+count followed by the correctly inflected word. Hence the previous
+example can be rewritten:
+
+.. code-block:: python
+
+    print("I saw ", p.no(animal, N))
+
+In addition, if the count is zero (or some other term which implies
+zero, such as ``"zero"``, ``"nil"``, etc.) the count is replaced by the
+word "no". Hence, if ``N`` had the value zero, the previous example
+would print (the somewhat more elegant)::
+
+    I saw no animals
+
+rather than::
+
+    I saw 0 animals
+
+Note that the name of the method is a pun: the method
+returns either a number (a *No.*) or a ``"no"``, in front of the
+inflected word.
+
+
+Reducing the number of counts required
+--------------------------------------
+
+In some contexts, the need to supply an explicit count to the various
+``plural...`` methods makes for tiresome repetition. For example:
+
+.. code-block:: python
+
+    print(
+        plural_adj("This", errors),
+        plural_noun(" error", errors),
+        plural_verb(" was", errors),
+        " fatal.",
+    )
+
+inflect.py therefore provides a method
+(``num(count=None, show=None)``) which may be used to set a persistent "default number"
+value. If such a value is set, it is subsequently used whenever an
+optional second "number" argument is omitted. The default value thus set
+can subsequently be removed by calling ``num()`` with no arguments.
+Hence we could rewrite the previous example:
+
+.. code-block:: python
+
+    p.num(errors)
+    print(p.plural_adj("This"), p.plural_noun(" error"), p.plural_verb(" was"), "fatal.")
+    p.num()
+
+Normally, ``num()`` returns its first argument, so that it may also
+be "inlined" in contexts like:
+
+.. code-block:: python
+
+    print(p.num(errors), p.plural_noun(" error"), p.plural_verb(" was"), " detected.")
+    if severity > 1:
+        print(
+            p.plural_adj("This"), p.plural_noun(" error"), p.plural_verb(" was"), "fatal."
+        )
+
+However, in certain contexts (see `INTERPOLATING INFLECTIONS IN STRINGS`)
+it is preferable that ``num()`` return an empty string. Hence ``num()``
+provides an optional second argument. If that argument is supplied (that is, if
+it is defined) and evaluates to false, ``num`` returns an empty string
+instead of its first argument. For example:
+
+.. code-block:: python
+
+    print(p.num(errors, 0), p.no("error"), p.plural_verb(" was"), " detected.")
+    if severity > 1:
+        print(
+            p.plural_adj("This"), p.plural_noun(" error"), p.plural_verb(" was"), "fatal."
+        )
+
+
+
+Number-insensitive equality
+---------------------------
+
+inflect.py also provides a solution to the problem
+of comparing words of differing plurality through the methods
+``compare(word1, word2)``, ``compare_nouns(word1, word2)``,
+``compare_verbs(word1, word2)``, and ``compare_adjs(word1, word2)``.
+Each  of these methods takes two strings, and  compares them
+using the corresponding plural-inflection method (``plural()``, ``plural_noun()``,
+``plural_verb()``, and ``plural_adj()`` respectively).
+
+The comparison returns true if:
+
+- the strings are equal, or
+- one string is equal to a plural form of the other, or
+- the strings are two different plural forms of the one word.
+
+
+Hence all of the following return true:
+
+.. code-block:: python
+
+    p.compare("index", "index")  # RETURNS "eq"
+    p.compare("index", "indexes")  # RETURNS "s:p"
+    p.compare("index", "indices")  # RETURNS "s:p"
+    p.compare("indexes", "index")  # RETURNS "p:s"
+    p.compare("indices", "index")  # RETURNS "p:s"
+    p.compare("indices", "indexes")  # RETURNS "p:p"
+    p.compare("indexes", "indices")  # RETURNS "p:p"
+    p.compare("indices", "indices")  # RETURNS "eq"
+
+As indicated by the comments in the previous example, the actual value
+returned by the various ``compare`` methods encodes which of the
+three equality rules succeeded: "eq" is returned if the strings were
+identical, "s:p" if the strings were singular and plural respectively,
+"p:s" for plural and singular, and "p:p" for two distinct plurals.
+Inequality is indicated by returning an empty string.
+
+It should be noted that two distinct singular words which happen to take
+the same plural form are *not* considered equal, nor are cases where
+one (singular) word's plural is the other (plural) word's singular.
+Hence all of the following return false:
+
+.. code-block:: python
+
+    p.compare("base", "basis")  # ALTHOUGH BOTH -> "bases"
+    p.compare("syrinx", "syringe")  # ALTHOUGH BOTH -> "syringes"
+    p.compare("she", "he")  # ALTHOUGH BOTH -> "they"
+
+    p.compare("opus", "operas")  # ALTHOUGH "opus" -> "opera" -> "operas"
+    p.compare("taxi", "taxes")  # ALTHOUGH "taxi" -> "taxis" -> "taxes"
+
+Note too that, although the comparison is "number-insensitive" it is *not*
+case-insensitive (that is, ``plural("time","Times")`` returns false. To obtain
+both number and case insensitivity, use the ``lower()`` method on both strings
+(that is, ``plural("time".lower(), "Times".lower())`` returns true).
+
+Related Functionality
+=====================
+
+Shout out to these libraries that provide related functionality:
+
+* `WordSet <https://jaracotext.readthedocs.io/en/latest/#jaraco.text.WordSet>`_
+  parses identifiers like variable names into sets of words suitable for re-assembling
+  in another form.
+
+* `word2number <https://pypi.org/project/word2number/>`_ converts words to
+  a number.
+
+
+For Enterprise
+==============
+
+Available as part of the Tidelift Subscription.
+
+This project and the maintainers of thousands of other packages are working with Tidelift to deliver one enterprise subscription that covers all of the open source you use.
+
+`Learn more <https://tidelift.com/subscription/pkg/pypi-PROJECT?utm_source=pypi-PROJECT&utm_medium=referral&utm_campaign=github>`_.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/RECORD
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@@ -0,0 +1,13 @@
+inflect-7.3.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+inflect-7.3.1.dist-info/LICENSE,sha256=htoPAa6uRjSKPD1GUZXcHOzN55956HdppkuNoEsqR0E,1023
+inflect-7.3.1.dist-info/METADATA,sha256=ZgMNY0WAZRs-U8wZiV2SMfjSKqBrMngXyDMs_CAwMwg,21079
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+inflect-7.3.1.dist-info/top_level.txt,sha256=m52ujdp10CqT6jh1XQxZT6kEntcnv-7Tl7UiGNTzWZA,8
+inflect/__init__.py,sha256=Jxy1HJXZiZ85kHeLAhkmvz6EMTdFqBe-duvt34R6IOc,103796
+inflect/__pycache__/__init__.cpython-312.pyc,,
+inflect/compat/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
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+inflect/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..564c6724e4bdf48d3dc10e804a73f100414c6533
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (70.2.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/top_level.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0fd75fab3e9e94881f61ee5eb66aef862e637a36
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect-7.3.1.dist-info/top_level.txt
@@ -0,0 +1 @@
+inflect
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3eec27f4c67f24db167393895a6eaf0a9aa16d35
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/__init__.py
@@ -0,0 +1,3986 @@
+"""
+inflect: english language inflection
+ - correctly generate plurals, ordinals, indefinite articles
+ - convert numbers to words
+
+Copyright (C) 2010 Paul Dyson
+
+Based upon the Perl module
+`Lingua::EN::Inflect <https://metacpan.org/pod/Lingua::EN::Inflect>`_.
+
+methods:
+    classical inflect
+    plural plural_noun plural_verb plural_adj singular_noun no num a an
+    compare compare_nouns compare_verbs compare_adjs
+    present_participle
+    ordinal
+    number_to_words
+    join
+    defnoun defverb defadj defa defan
+
+INFLECTIONS:
+    classical inflect
+    plural plural_noun plural_verb plural_adj singular_noun compare
+    no num a an present_participle
+
+PLURALS:
+    classical inflect
+    plural plural_noun plural_verb plural_adj singular_noun no num
+    compare compare_nouns compare_verbs compare_adjs
+
+COMPARISONS:
+    classical
+    compare compare_nouns compare_verbs compare_adjs
+
+ARTICLES:
+    classical inflect num a an
+
+NUMERICAL:
+    ordinal number_to_words
+
+USER_DEFINED:
+    defnoun defverb defadj defa defan
+
+Exceptions:
+ UnknownClassicalModeError
+ BadNumValueError
+ BadChunkingOptionError
+ NumOutOfRangeError
+ BadUserDefinedPatternError
+ BadRcFileError
+ BadGenderError
+
+"""
+
+from __future__ import annotations
+
+import ast
+import collections
+import contextlib
+import functools
+import itertools
+import re
+from numbers import Number
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Literal,
+    Match,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+    cast,
+)
+
+from more_itertools import windowed_complete
+from typeguard import typechecked
+
+from .compat.py38 import Annotated
+
+
+class UnknownClassicalModeError(Exception):
+    pass
+
+
+class BadNumValueError(Exception):
+    pass
+
+
+class BadChunkingOptionError(Exception):
+    pass
+
+
+class NumOutOfRangeError(Exception):
+    pass
+
+
+class BadUserDefinedPatternError(Exception):
+    pass
+
+
+class BadRcFileError(Exception):
+    pass
+
+
+class BadGenderError(Exception):
+    pass
+
+
+def enclose(s: str) -> str:
+    return f"(?:{s})"
+
+
+def joinstem(cutpoint: Optional[int] = 0, words: Optional[Iterable[str]] = None) -> str:
+    """
+    Join stem of each word in words into a string for regex.
+
+    Each word is truncated at cutpoint.
+
+    Cutpoint is usually negative indicating the number of letters to remove
+    from the end of each word.
+
+    >>> joinstem(-2, ["ephemeris", "iris", ".*itis"])
+    '(?:ephemer|ir|.*it)'
+
+    >>> joinstem(None, ["ephemeris"])
+    '(?:ephemeris)'
+
+    >>> joinstem(5, None)
+    '(?:)'
+    """
+    return enclose("|".join(w[:cutpoint] for w in words or []))
+
+
+def bysize(words: Iterable[str]) -> Dict[int, set]:
+    """
+    From a list of words, return a dict of sets sorted by word length.
+
+    >>> words = ['ant', 'cat', 'dog', 'pig', 'frog', 'goat', 'horse', 'elephant']
+    >>> ret = bysize(words)
+    >>> sorted(ret[3])
+    ['ant', 'cat', 'dog', 'pig']
+    >>> ret[5]
+    {'horse'}
+    """
+    res: Dict[int, set] = collections.defaultdict(set)
+    for w in words:
+        res[len(w)].add(w)
+    return res
+
+
+def make_pl_si_lists(
+    lst: Iterable[str],
+    plending: str,
+    siendingsize: Optional[int],
+    dojoinstem: bool = True,
+):
+    """
+    given a list of singular words: lst
+
+    an ending to append to make the plural: plending
+
+    the number of characters to remove from the singular
+    before appending plending: siendingsize
+
+    a flag whether to create a joinstem: dojoinstem
+
+    return:
+    a list of pluralised words: si_list (called si because this is what you need to
+    look for to make the singular)
+
+    the pluralised words as a dict of sets sorted by word length: si_bysize
+    the singular words as a dict of sets sorted by word length: pl_bysize
+    if dojoinstem is True: a regular expression that matches any of the stems: stem
+    """
+    if siendingsize is not None:
+        siendingsize = -siendingsize
+    si_list = [w[:siendingsize] + plending for w in lst]
+    pl_bysize = bysize(lst)
+    si_bysize = bysize(si_list)
+    if dojoinstem:
+        stem = joinstem(siendingsize, lst)
+        return si_list, si_bysize, pl_bysize, stem
+    else:
+        return si_list, si_bysize, pl_bysize
+
+
+# 1. PLURALS
+
+pl_sb_irregular_s = {
+    "corpus": "corpuses|corpora",
+    "opus": "opuses|opera",
+    "genus": "genera",
+    "mythos": "mythoi",
+    "penis": "penises|penes",
+    "testis": "testes",
+    "atlas": "atlases|atlantes",
+    "yes": "yeses",
+}
+
+pl_sb_irregular = {
+    "child": "children",
+    "chili": "chilis|chilies",
+    "brother": "brothers|brethren",
+    "infinity": "infinities|infinity",
+    "loaf": "loaves",
+    "lore": "lores|lore",
+    "hoof": "hoofs|hooves",
+    "beef": "beefs|beeves",
+    "thief": "thiefs|thieves",
+    "money": "monies",
+    "mongoose": "mongooses",
+    "ox": "oxen",
+    "cow": "cows|kine",
+    "graffito": "graffiti",
+    "octopus": "octopuses|octopodes",
+    "genie": "genies|genii",
+    "ganglion": "ganglions|ganglia",
+    "trilby": "trilbys",
+    "turf": "turfs|turves",
+    "numen": "numina",
+    "atman": "atmas",
+    "occiput": "occiputs|occipita",
+    "sabretooth": "sabretooths",
+    "sabertooth": "sabertooths",
+    "lowlife": "lowlifes",
+    "flatfoot": "flatfoots",
+    "tenderfoot": "tenderfoots",
+    "romany": "romanies",
+    "jerry": "jerries",
+    "mary": "maries",
+    "talouse": "talouses",
+    "rom": "roma",
+    "carmen": "carmina",
+}
+
+pl_sb_irregular.update(pl_sb_irregular_s)
+# pl_sb_irregular_keys = enclose('|'.join(pl_sb_irregular.keys()))
+
+pl_sb_irregular_caps = {
+    "Romany": "Romanies",
+    "Jerry": "Jerrys",
+    "Mary": "Marys",
+    "Rom": "Roma",
+}
+
+pl_sb_irregular_compound = {"prima donna": "prima donnas|prime donne"}
+
+si_sb_irregular = {v: k for (k, v) in pl_sb_irregular.items()}
+for k in list(si_sb_irregular):
+    if "|" in k:
+        k1, k2 = k.split("|")
+        si_sb_irregular[k1] = si_sb_irregular[k2] = si_sb_irregular[k]
+        del si_sb_irregular[k]
+si_sb_irregular_caps = {v: k for (k, v) in pl_sb_irregular_caps.items()}
+si_sb_irregular_compound = {v: k for (k, v) in pl_sb_irregular_compound.items()}
+for k in list(si_sb_irregular_compound):
+    if "|" in k:
+        k1, k2 = k.split("|")
+        si_sb_irregular_compound[k1] = si_sb_irregular_compound[k2] = (
+            si_sb_irregular_compound[k]
+        )
+        del si_sb_irregular_compound[k]
+
+# si_sb_irregular_keys = enclose('|'.join(si_sb_irregular.keys()))
+
+# Z's that don't double
+
+pl_sb_z_zes_list = ("quartz", "topaz")
+pl_sb_z_zes_bysize = bysize(pl_sb_z_zes_list)
+
+pl_sb_ze_zes_list = ("snooze",)
+pl_sb_ze_zes_bysize = bysize(pl_sb_ze_zes_list)
+
+
+# CLASSICAL "..is" -> "..ides"
+
+pl_sb_C_is_ides_complete = [
+    # GENERAL WORDS...
+    "ephemeris",
+    "iris",
+    "clitoris",
+    "chrysalis",
+    "epididymis",
+]
+
+pl_sb_C_is_ides_endings = [
+    # INFLAMATIONS...
+    "itis"
+]
+
+pl_sb_C_is_ides = joinstem(
+    -2, pl_sb_C_is_ides_complete + [f".*{w}" for w in pl_sb_C_is_ides_endings]
+)
+
+pl_sb_C_is_ides_list = pl_sb_C_is_ides_complete + pl_sb_C_is_ides_endings
+
+(
+    si_sb_C_is_ides_list,
+    si_sb_C_is_ides_bysize,
+    pl_sb_C_is_ides_bysize,
+) = make_pl_si_lists(pl_sb_C_is_ides_list, "ides", 2, dojoinstem=False)
+
+
+# CLASSICAL "..a" -> "..ata"
+
+pl_sb_C_a_ata_list = (
+    "anathema",
+    "bema",
+    "carcinoma",
+    "charisma",
+    "diploma",
+    "dogma",
+    "drama",
+    "edema",
+    "enema",
+    "enigma",
+    "lemma",
+    "lymphoma",
+    "magma",
+    "melisma",
+    "miasma",
+    "oedema",
+    "sarcoma",
+    "schema",
+    "soma",
+    "stigma",
+    "stoma",
+    "trauma",
+    "gumma",
+    "pragma",
+)
+
+(
+    si_sb_C_a_ata_list,
+    si_sb_C_a_ata_bysize,
+    pl_sb_C_a_ata_bysize,
+    pl_sb_C_a_ata,
+) = make_pl_si_lists(pl_sb_C_a_ata_list, "ata", 1)
+
+# UNCONDITIONAL "..a" -> "..ae"
+
+pl_sb_U_a_ae_list = (
+    "alumna",
+    "alga",
+    "vertebra",
+    "persona",
+    "vita",
+)
+(
+    si_sb_U_a_ae_list,
+    si_sb_U_a_ae_bysize,
+    pl_sb_U_a_ae_bysize,
+    pl_sb_U_a_ae,
+) = make_pl_si_lists(pl_sb_U_a_ae_list, "e", None)
+
+# CLASSICAL "..a" -> "..ae"
+
+pl_sb_C_a_ae_list = (
+    "amoeba",
+    "antenna",
+    "formula",
+    "hyperbola",
+    "medusa",
+    "nebula",
+    "parabola",
+    "abscissa",
+    "hydra",
+    "nova",
+    "lacuna",
+    "aurora",
+    "umbra",
+    "flora",
+    "fauna",
+)
+(
+    si_sb_C_a_ae_list,
+    si_sb_C_a_ae_bysize,
+    pl_sb_C_a_ae_bysize,
+    pl_sb_C_a_ae,
+) = make_pl_si_lists(pl_sb_C_a_ae_list, "e", None)
+
+
+# CLASSICAL "..en" -> "..ina"
+
+pl_sb_C_en_ina_list = ("stamen", "foramen", "lumen")
+
+(
+    si_sb_C_en_ina_list,
+    si_sb_C_en_ina_bysize,
+    pl_sb_C_en_ina_bysize,
+    pl_sb_C_en_ina,
+) = make_pl_si_lists(pl_sb_C_en_ina_list, "ina", 2)
+
+
+# UNCONDITIONAL "..um" -> "..a"
+
+pl_sb_U_um_a_list = (
+    "bacterium",
+    "agendum",
+    "desideratum",
+    "erratum",
+    "stratum",
+    "datum",
+    "ovum",
+    "extremum",
+    "candelabrum",
+)
+(
+    si_sb_U_um_a_list,
+    si_sb_U_um_a_bysize,
+    pl_sb_U_um_a_bysize,
+    pl_sb_U_um_a,
+) = make_pl_si_lists(pl_sb_U_um_a_list, "a", 2)
+
+# CLASSICAL "..um" -> "..a"
+
+pl_sb_C_um_a_list = (
+    "maximum",
+    "minimum",
+    "momentum",
+    "optimum",
+    "quantum",
+    "cranium",
+    "curriculum",
+    "dictum",
+    "phylum",
+    "aquarium",
+    "compendium",
+    "emporium",
+    "encomium",
+    "gymnasium",
+    "honorarium",
+    "interregnum",
+    "lustrum",
+    "memorandum",
+    "millennium",
+    "rostrum",
+    "spectrum",
+    "speculum",
+    "stadium",
+    "trapezium",
+    "ultimatum",
+    "medium",
+    "vacuum",
+    "velum",
+    "consortium",
+    "arboretum",
+)
+
+(
+    si_sb_C_um_a_list,
+    si_sb_C_um_a_bysize,
+    pl_sb_C_um_a_bysize,
+    pl_sb_C_um_a,
+) = make_pl_si_lists(pl_sb_C_um_a_list, "a", 2)
+
+
+# UNCONDITIONAL "..us" -> "i"
+
+pl_sb_U_us_i_list = (
+    "alumnus",
+    "alveolus",
+    "bacillus",
+    "bronchus",
+    "locus",
+    "nucleus",
+    "stimulus",
+    "meniscus",
+    "sarcophagus",
+)
+(
+    si_sb_U_us_i_list,
+    si_sb_U_us_i_bysize,
+    pl_sb_U_us_i_bysize,
+    pl_sb_U_us_i,
+) = make_pl_si_lists(pl_sb_U_us_i_list, "i", 2)
+
+# CLASSICAL "..us" -> "..i"
+
+pl_sb_C_us_i_list = (
+    "focus",
+    "radius",
+    "genius",
+    "incubus",
+    "succubus",
+    "nimbus",
+    "fungus",
+    "nucleolus",
+    "stylus",
+    "torus",
+    "umbilicus",
+    "uterus",
+    "hippopotamus",
+    "cactus",
+)
+
+(
+    si_sb_C_us_i_list,
+    si_sb_C_us_i_bysize,
+    pl_sb_C_us_i_bysize,
+    pl_sb_C_us_i,
+) = make_pl_si_lists(pl_sb_C_us_i_list, "i", 2)
+
+
+# CLASSICAL "..us" -> "..us"  (ASSIMILATED 4TH DECLENSION LATIN NOUNS)
+
+pl_sb_C_us_us = (
+    "status",
+    "apparatus",
+    "prospectus",
+    "sinus",
+    "hiatus",
+    "impetus",
+    "plexus",
+)
+pl_sb_C_us_us_bysize = bysize(pl_sb_C_us_us)
+
+# UNCONDITIONAL "..on" -> "a"
+
+pl_sb_U_on_a_list = (
+    "criterion",
+    "perihelion",
+    "aphelion",
+    "phenomenon",
+    "prolegomenon",
+    "noumenon",
+    "organon",
+    "asyndeton",
+    "hyperbaton",
+)
+(
+    si_sb_U_on_a_list,
+    si_sb_U_on_a_bysize,
+    pl_sb_U_on_a_bysize,
+    pl_sb_U_on_a,
+) = make_pl_si_lists(pl_sb_U_on_a_list, "a", 2)
+
+# CLASSICAL "..on" -> "..a"
+
+pl_sb_C_on_a_list = ("oxymoron",)
+
+(
+    si_sb_C_on_a_list,
+    si_sb_C_on_a_bysize,
+    pl_sb_C_on_a_bysize,
+    pl_sb_C_on_a,
+) = make_pl_si_lists(pl_sb_C_on_a_list, "a", 2)
+
+
+# CLASSICAL "..o" -> "..i"  (BUT NORMALLY -> "..os")
+
+pl_sb_C_o_i = [
+    "solo",
+    "soprano",
+    "basso",
+    "alto",
+    "contralto",
+    "tempo",
+    "piano",
+    "virtuoso",
+]  # list not tuple so can concat for pl_sb_U_o_os
+
+pl_sb_C_o_i_bysize = bysize(pl_sb_C_o_i)
+si_sb_C_o_i_bysize = bysize([f"{w[:-1]}i" for w in pl_sb_C_o_i])
+
+pl_sb_C_o_i_stems = joinstem(-1, pl_sb_C_o_i)
+
+# ALWAYS "..o" -> "..os"
+
+pl_sb_U_o_os_complete = {"ado", "ISO", "NATO", "NCO", "NGO", "oto"}
+si_sb_U_o_os_complete = {f"{w}s" for w in pl_sb_U_o_os_complete}
+
+
+pl_sb_U_o_os_endings = [
+    "aficionado",
+    "aggro",
+    "albino",
+    "allegro",
+    "ammo",
+    "Antananarivo",
+    "archipelago",
+    "armadillo",
+    "auto",
+    "avocado",
+    "Bamako",
+    "Barquisimeto",
+    "bimbo",
+    "bingo",
+    "Biro",
+    "bolero",
+    "Bolzano",
+    "bongo",
+    "Boto",
+    "burro",
+    "Cairo",
+    "canto",
+    "cappuccino",
+    "casino",
+    "cello",
+    "Chicago",
+    "Chimango",
+    "cilantro",
+    "cochito",
+    "coco",
+    "Colombo",
+    "Colorado",
+    "commando",
+    "concertino",
+    "contango",
+    "credo",
+    "crescendo",
+    "cyano",
+    "demo",
+    "ditto",
+    "Draco",
+    "dynamo",
+    "embryo",
+    "Esperanto",
+    "espresso",
+    "euro",
+    "falsetto",
+    "Faro",
+    "fiasco",
+    "Filipino",
+    "flamenco",
+    "furioso",
+    "generalissimo",
+    "Gestapo",
+    "ghetto",
+    "gigolo",
+    "gizmo",
+    "Greensboro",
+    "gringo",
+    "Guaiabero",
+    "guano",
+    "gumbo",
+    "gyro",
+    "hairdo",
+    "hippo",
+    "Idaho",
+    "impetigo",
+    "inferno",
+    "info",
+    "intermezzo",
+    "intertrigo",
+    "Iquico",
+    "jumbo",
+    "junto",
+    "Kakapo",
+    "kilo",
+    "Kinkimavo",
+    "Kokako",
+    "Kosovo",
+    "Lesotho",
+    "libero",
+    "libido",
+    "libretto",
+    "lido",
+    "Lilo",
+    "limbo",
+    "limo",
+    "lineno",
+    "lingo",
+    "lino",
+    "livedo",
+    "loco",
+    "logo",
+    "lumbago",
+    "macho",
+    "macro",
+    "mafioso",
+    "magneto",
+    "magnifico",
+    "Majuro",
+    "Malabo",
+    "manifesto",
+    "Maputo",
+    "Maracaibo",
+    "medico",
+    "memo",
+    "metro",
+    "Mexico",
+    "micro",
+    "Milano",
+    "Monaco",
+    "mono",
+    "Montenegro",
+    "Morocco",
+    "Muqdisho",
+    "myo",
+    "neutrino",
+    "Ningbo",
+    "octavo",
+    "oregano",
+    "Orinoco",
+    "Orlando",
+    "Oslo",
+    "panto",
+    "Paramaribo",
+    "Pardusco",
+    "pedalo",
+    "photo",
+    "pimento",
+    "pinto",
+    "pleco",
+    "Pluto",
+    "pogo",
+    "polo",
+    "poncho",
+    "Porto-Novo",
+    "Porto",
+    "pro",
+    "psycho",
+    "pueblo",
+    "quarto",
+    "Quito",
+    "repo",
+    "rhino",
+    "risotto",
+    "rococo",
+    "rondo",
+    "Sacramento",
+    "saddo",
+    "sago",
+    "salvo",
+    "Santiago",
+    "Sapporo",
+    "Sarajevo",
+    "scherzando",
+    "scherzo",
+    "silo",
+    "sirocco",
+    "sombrero",
+    "staccato",
+    "sterno",
+    "stucco",
+    "stylo",
+    "sumo",
+    "Taiko",
+    "techno",
+    "terrazzo",
+    "testudo",
+    "timpano",
+    "tiro",
+    "tobacco",
+    "Togo",
+    "Tokyo",
+    "torero",
+    "Torino",
+    "Toronto",
+    "torso",
+    "tremolo",
+    "typo",
+    "tyro",
+    "ufo",
+    "UNESCO",
+    "vaquero",
+    "vermicello",
+    "verso",
+    "vibrato",
+    "violoncello",
+    "Virgo",
+    "weirdo",
+    "WHO",
+    "WTO",
+    "Yamoussoukro",
+    "yo-yo",
+    "zero",
+    "Zibo",
+] + pl_sb_C_o_i
+
+pl_sb_U_o_os_bysize = bysize(pl_sb_U_o_os_endings)
+si_sb_U_o_os_bysize = bysize([f"{w}s" for w in pl_sb_U_o_os_endings])
+
+
+# UNCONDITIONAL "..ch" -> "..chs"
+
+pl_sb_U_ch_chs_list = ("czech", "eunuch", "stomach")
+
+(
+    si_sb_U_ch_chs_list,
+    si_sb_U_ch_chs_bysize,
+    pl_sb_U_ch_chs_bysize,
+    pl_sb_U_ch_chs,
+) = make_pl_si_lists(pl_sb_U_ch_chs_list, "s", None)
+
+
+# UNCONDITIONAL "..[ei]x" -> "..ices"
+
+pl_sb_U_ex_ices_list = ("codex", "murex", "silex")
+(
+    si_sb_U_ex_ices_list,
+    si_sb_U_ex_ices_bysize,
+    pl_sb_U_ex_ices_bysize,
+    pl_sb_U_ex_ices,
+) = make_pl_si_lists(pl_sb_U_ex_ices_list, "ices", 2)
+
+pl_sb_U_ix_ices_list = ("radix", "helix")
+(
+    si_sb_U_ix_ices_list,
+    si_sb_U_ix_ices_bysize,
+    pl_sb_U_ix_ices_bysize,
+    pl_sb_U_ix_ices,
+) = make_pl_si_lists(pl_sb_U_ix_ices_list, "ices", 2)
+
+# CLASSICAL "..[ei]x" -> "..ices"
+
+pl_sb_C_ex_ices_list = (
+    "vortex",
+    "vertex",
+    "cortex",
+    "latex",
+    "pontifex",
+    "apex",
+    "index",
+    "simplex",
+)
+
+(
+    si_sb_C_ex_ices_list,
+    si_sb_C_ex_ices_bysize,
+    pl_sb_C_ex_ices_bysize,
+    pl_sb_C_ex_ices,
+) = make_pl_si_lists(pl_sb_C_ex_ices_list, "ices", 2)
+
+
+pl_sb_C_ix_ices_list = ("appendix",)
+
+(
+    si_sb_C_ix_ices_list,
+    si_sb_C_ix_ices_bysize,
+    pl_sb_C_ix_ices_bysize,
+    pl_sb_C_ix_ices,
+) = make_pl_si_lists(pl_sb_C_ix_ices_list, "ices", 2)
+
+
+# ARABIC: ".." -> "..i"
+
+pl_sb_C_i_list = ("afrit", "afreet", "efreet")
+
+(si_sb_C_i_list, si_sb_C_i_bysize, pl_sb_C_i_bysize, pl_sb_C_i) = make_pl_si_lists(
+    pl_sb_C_i_list, "i", None
+)
+
+
+# HEBREW: ".." -> "..im"
+
+pl_sb_C_im_list = ("goy", "seraph", "cherub")
+
+(si_sb_C_im_list, si_sb_C_im_bysize, pl_sb_C_im_bysize, pl_sb_C_im) = make_pl_si_lists(
+    pl_sb_C_im_list, "im", None
+)
+
+
+# UNCONDITIONAL "..man" -> "..mans"
+
+pl_sb_U_man_mans_list = """
+    ataman caiman cayman ceriman
+    desman dolman farman harman hetman
+    human leman ottoman shaman talisman
+""".split()
+pl_sb_U_man_mans_caps_list = """
+    Alabaman Bahaman Burman German
+    Hiroshiman Liman Nakayaman Norman Oklahoman
+    Panaman Roman Selman Sonaman Tacoman Yakiman
+    Yokohaman Yuman
+""".split()
+
+(
+    si_sb_U_man_mans_list,
+    si_sb_U_man_mans_bysize,
+    pl_sb_U_man_mans_bysize,
+) = make_pl_si_lists(pl_sb_U_man_mans_list, "s", None, dojoinstem=False)
+(
+    si_sb_U_man_mans_caps_list,
+    si_sb_U_man_mans_caps_bysize,
+    pl_sb_U_man_mans_caps_bysize,
+) = make_pl_si_lists(pl_sb_U_man_mans_caps_list, "s", None, dojoinstem=False)
+
+# UNCONDITIONAL "..louse" -> "..lice"
+pl_sb_U_louse_lice_list = ("booklouse", "grapelouse", "louse", "woodlouse")
+
+(
+    si_sb_U_louse_lice_list,
+    si_sb_U_louse_lice_bysize,
+    pl_sb_U_louse_lice_bysize,
+) = make_pl_si_lists(pl_sb_U_louse_lice_list, "lice", 5, dojoinstem=False)
+
+pl_sb_uninflected_s_complete = [
+    # PAIRS OR GROUPS SUBSUMED TO A SINGULAR...
+    "breeches",
+    "britches",
+    "pajamas",
+    "pyjamas",
+    "clippers",
+    "gallows",
+    "hijinks",
+    "headquarters",
+    "pliers",
+    "scissors",
+    "testes",
+    "herpes",
+    "pincers",
+    "shears",
+    "proceedings",
+    "trousers",
+    # UNASSIMILATED LATIN 4th DECLENSION
+    "cantus",
+    "coitus",
+    "nexus",
+    # RECENT IMPORTS...
+    "contretemps",
+    "corps",
+    "debris",
+    "siemens",
+    # DISEASES
+    "mumps",
+    # MISCELLANEOUS OTHERS...
+    "diabetes",
+    "jackanapes",
+    "series",
+    "species",
+    "subspecies",
+    "rabies",
+    "chassis",
+    "innings",
+    "news",
+    "mews",
+    "haggis",
+]
+
+pl_sb_uninflected_s_endings = [
+    # RECENT IMPORTS...
+    "ois",
+    # DISEASES
+    "measles",
+]
+
+pl_sb_uninflected_s = pl_sb_uninflected_s_complete + [
+    f".*{w}" for w in pl_sb_uninflected_s_endings
+]
+
+pl_sb_uninflected_herd = (
+    # DON'T INFLECT IN CLASSICAL MODE, OTHERWISE NORMAL INFLECTION
+    "wildebeest",
+    "swine",
+    "eland",
+    "bison",
+    "buffalo",
+    "cattle",
+    "elk",
+    "rhinoceros",
+    "zucchini",
+    "caribou",
+    "dace",
+    "grouse",
+    "guinea fowl",
+    "guinea-fowl",
+    "haddock",
+    "hake",
+    "halibut",
+    "herring",
+    "mackerel",
+    "pickerel",
+    "pike",
+    "roe",
+    "seed",
+    "shad",
+    "snipe",
+    "teal",
+    "turbot",
+    "water fowl",
+    "water-fowl",
+)
+
+pl_sb_uninflected_complete = [
+    # SOME FISH AND HERD ANIMALS
+    "tuna",
+    "salmon",
+    "mackerel",
+    "trout",
+    "bream",
+    "sea-bass",
+    "sea bass",
+    "carp",
+    "cod",
+    "flounder",
+    "whiting",
+    "moose",
+    # OTHER ODDITIES
+    "graffiti",
+    "djinn",
+    "samuri",
+    "offspring",
+    "pence",
+    "quid",
+    "hertz",
+] + pl_sb_uninflected_s_complete
+# SOME WORDS ENDING IN ...s (OFTEN PAIRS TAKEN AS A WHOLE)
+
+pl_sb_uninflected_caps = [
+    # ALL NATIONALS ENDING IN -ese
+    "Portuguese",
+    "Amoyese",
+    "Borghese",
+    "Congoese",
+    "Faroese",
+    "Foochowese",
+    "Genevese",
+    "Genoese",
+    "Gilbertese",
+    "Hottentotese",
+    "Kiplingese",
+    "Kongoese",
+    "Lucchese",
+    "Maltese",
+    "Nankingese",
+    "Niasese",
+    "Pekingese",
+    "Piedmontese",
+    "Pistoiese",
+    "Sarawakese",
+    "Shavese",
+    "Vermontese",
+    "Wenchowese",
+    "Yengeese",
+]
+
+
+pl_sb_uninflected_endings = [
+    # UNCOUNTABLE NOUNS
+    "butter",
+    "cash",
+    "furniture",
+    "information",
+    # SOME FISH AND HERD ANIMALS
+    "fish",
+    "deer",
+    "sheep",
+    # ALL NATIONALS ENDING IN -ese
+    "nese",
+    "rese",
+    "lese",
+    "mese",
+    # DISEASES
+    "pox",
+    # OTHER ODDITIES
+    "craft",
+] + pl_sb_uninflected_s_endings
+# SOME WORDS ENDING IN ...s (OFTEN PAIRS TAKEN AS A WHOLE)
+
+
+pl_sb_uninflected_bysize = bysize(pl_sb_uninflected_endings)
+
+
+# SINGULAR WORDS ENDING IN ...s (ALL INFLECT WITH ...es)
+
+pl_sb_singular_s_complete = [
+    "acropolis",
+    "aegis",
+    "alias",
+    "asbestos",
+    "bathos",
+    "bias",
+    "bronchitis",
+    "bursitis",
+    "caddis",
+    "cannabis",
+    "canvas",
+    "chaos",
+    "cosmos",
+    "dais",
+    "digitalis",
+    "epidermis",
+    "ethos",
+    "eyas",
+    "gas",
+    "glottis",
+    "hubris",
+    "ibis",
+    "lens",
+    "mantis",
+    "marquis",
+    "metropolis",
+    "pathos",
+    "pelvis",
+    "polis",
+    "rhinoceros",
+    "sassafras",
+    "trellis",
+] + pl_sb_C_is_ides_complete
+
+
+pl_sb_singular_s_endings = ["ss", "us"] + pl_sb_C_is_ides_endings
+
+pl_sb_singular_s_bysize = bysize(pl_sb_singular_s_endings)
+
+si_sb_singular_s_complete = [f"{w}es" for w in pl_sb_singular_s_complete]
+si_sb_singular_s_endings = [f"{w}es" for w in pl_sb_singular_s_endings]
+si_sb_singular_s_bysize = bysize(si_sb_singular_s_endings)
+
+pl_sb_singular_s_es = ["[A-Z].*es"]
+
+pl_sb_singular_s = enclose(
+    "|".join(
+        pl_sb_singular_s_complete
+        + [f".*{w}" for w in pl_sb_singular_s_endings]
+        + pl_sb_singular_s_es
+    )
+)
+
+
+# PLURALS ENDING IN uses -> use
+
+
+si_sb_ois_oi_case = ("Bolshois", "Hanois")
+
+si_sb_uses_use_case = ("Betelgeuses", "Duses", "Meuses", "Syracuses", "Toulouses")
+
+si_sb_uses_use = (
+    "abuses",
+    "applauses",
+    "blouses",
+    "carouses",
+    "causes",
+    "chartreuses",
+    "clauses",
+    "contuses",
+    "douses",
+    "excuses",
+    "fuses",
+    "grouses",
+    "hypotenuses",
+    "masseuses",
+    "menopauses",
+    "misuses",
+    "muses",
+    "overuses",
+    "pauses",
+    "peruses",
+    "profuses",
+    "recluses",
+    "reuses",
+    "ruses",
+    "souses",
+    "spouses",
+    "suffuses",
+    "transfuses",
+    "uses",
+)
+
+si_sb_ies_ie_case = (
+    "Addies",
+    "Aggies",
+    "Allies",
+    "Amies",
+    "Angies",
+    "Annies",
+    "Annmaries",
+    "Archies",
+    "Arties",
+    "Aussies",
+    "Barbies",
+    "Barries",
+    "Basies",
+    "Bennies",
+    "Bernies",
+    "Berties",
+    "Bessies",
+    "Betties",
+    "Billies",
+    "Blondies",
+    "Bobbies",
+    "Bonnies",
+    "Bowies",
+    "Brandies",
+    "Bries",
+    "Brownies",
+    "Callies",
+    "Carnegies",
+    "Carries",
+    "Cassies",
+    "Charlies",
+    "Cheries",
+    "Christies",
+    "Connies",
+    "Curies",
+    "Dannies",
+    "Debbies",
+    "Dixies",
+    "Dollies",
+    "Donnies",
+    "Drambuies",
+    "Eddies",
+    "Effies",
+    "Ellies",
+    "Elsies",
+    "Eries",
+    "Ernies",
+    "Essies",
+    "Eugenies",
+    "Fannies",
+    "Flossies",
+    "Frankies",
+    "Freddies",
+    "Gillespies",
+    "Goldies",
+    "Gracies",
+    "Guthries",
+    "Hallies",
+    "Hatties",
+    "Hetties",
+    "Hollies",
+    "Jackies",
+    "Jamies",
+    "Janies",
+    "Jannies",
+    "Jeanies",
+    "Jeannies",
+    "Jennies",
+    "Jessies",
+    "Jimmies",
+    "Jodies",
+    "Johnies",
+    "Johnnies",
+    "Josies",
+    "Julies",
+    "Kalgoorlies",
+    "Kathies",
+    "Katies",
+    "Kellies",
+    "Kewpies",
+    "Kristies",
+    "Laramies",
+    "Lassies",
+    "Lauries",
+    "Leslies",
+    "Lessies",
+    "Lillies",
+    "Lizzies",
+    "Lonnies",
+    "Lories",
+    "Lorries",
+    "Lotties",
+    "Louies",
+    "Mackenzies",
+    "Maggies",
+    "Maisies",
+    "Mamies",
+    "Marcies",
+    "Margies",
+    "Maries",
+    "Marjories",
+    "Matties",
+    "McKenzies",
+    "Melanies",
+    "Mickies",
+    "Millies",
+    "Minnies",
+    "Mollies",
+    "Mounties",
+    "Nannies",
+    "Natalies",
+    "Nellies",
+    "Netties",
+    "Ollies",
+    "Ozzies",
+    "Pearlies",
+    "Pottawatomies",
+    "Reggies",
+    "Richies",
+    "Rickies",
+    "Robbies",
+    "Ronnies",
+    "Rosalies",
+    "Rosemaries",
+    "Rosies",
+    "Roxies",
+    "Rushdies",
+    "Ruthies",
+    "Sadies",
+    "Sallies",
+    "Sammies",
+    "Scotties",
+    "Selassies",
+    "Sherries",
+    "Sophies",
+    "Stacies",
+    "Stefanies",
+    "Stephanies",
+    "Stevies",
+    "Susies",
+    "Sylvies",
+    "Tammies",
+    "Terries",
+    "Tessies",
+    "Tommies",
+    "Tracies",
+    "Trekkies",
+    "Valaries",
+    "Valeries",
+    "Valkyries",
+    "Vickies",
+    "Virgies",
+    "Willies",
+    "Winnies",
+    "Wylies",
+    "Yorkies",
+)
+
+si_sb_ies_ie = (
+    "aeries",
+    "baggies",
+    "belies",
+    "biggies",
+    "birdies",
+    "bogies",
+    "bonnies",
+    "boogies",
+    "bookies",
+    "bourgeoisies",
+    "brownies",
+    "budgies",
+    "caddies",
+    "calories",
+    "camaraderies",
+    "cockamamies",
+    "collies",
+    "cookies",
+    "coolies",
+    "cooties",
+    "coteries",
+    "crappies",
+    "curies",
+    "cutesies",
+    "dogies",
+    "eyries",
+    "floozies",
+    "footsies",
+    "freebies",
+    "genies",
+    "goalies",
+    "groupies",
+    "hies",
+    "jalousies",
+    "junkies",
+    "kiddies",
+    "laddies",
+    "lassies",
+    "lies",
+    "lingeries",
+    "magpies",
+    "menageries",
+    "mommies",
+    "movies",
+    "neckties",
+    "newbies",
+    "nighties",
+    "oldies",
+    "organdies",
+    "overlies",
+    "pies",
+    "pinkies",
+    "pixies",
+    "potpies",
+    "prairies",
+    "quickies",
+    "reveries",
+    "rookies",
+    "rotisseries",
+    "softies",
+    "sorties",
+    "species",
+    "stymies",
+    "sweeties",
+    "ties",
+    "underlies",
+    "unties",
+    "veggies",
+    "vies",
+    "yuppies",
+    "zombies",
+)
+
+
+si_sb_oes_oe_case = (
+    "Chloes",
+    "Crusoes",
+    "Defoes",
+    "Faeroes",
+    "Ivanhoes",
+    "Joes",
+    "McEnroes",
+    "Moes",
+    "Monroes",
+    "Noes",
+    "Poes",
+    "Roscoes",
+    "Tahoes",
+    "Tippecanoes",
+    "Zoes",
+)
+
+si_sb_oes_oe = (
+    "aloes",
+    "backhoes",
+    "canoes",
+    "does",
+    "floes",
+    "foes",
+    "hoes",
+    "mistletoes",
+    "oboes",
+    "pekoes",
+    "roes",
+    "sloes",
+    "throes",
+    "tiptoes",
+    "toes",
+    "woes",
+)
+
+si_sb_z_zes = ("quartzes", "topazes")
+
+si_sb_zzes_zz = ("buzzes", "fizzes", "frizzes", "razzes")
+
+si_sb_ches_che_case = (
+    "Andromaches",
+    "Apaches",
+    "Blanches",
+    "Comanches",
+    "Nietzsches",
+    "Porsches",
+    "Roches",
+)
+
+si_sb_ches_che = (
+    "aches",
+    "avalanches",
+    "backaches",
+    "bellyaches",
+    "caches",
+    "cloches",
+    "creches",
+    "douches",
+    "earaches",
+    "fiches",
+    "headaches",
+    "heartaches",
+    "microfiches",
+    "niches",
+    "pastiches",
+    "psyches",
+    "quiches",
+    "stomachaches",
+    "toothaches",
+    "tranches",
+)
+
+si_sb_xes_xe = ("annexes", "axes", "deluxes", "pickaxes")
+
+si_sb_sses_sse_case = ("Hesses", "Jesses", "Larousses", "Matisses")
+si_sb_sses_sse = (
+    "bouillabaisses",
+    "crevasses",
+    "demitasses",
+    "impasses",
+    "mousses",
+    "posses",
+)
+
+si_sb_ves_ve_case = (
+    # *[nwl]ives -> [nwl]live
+    "Clives",
+    "Palmolives",
+)
+si_sb_ves_ve = (
+    # *[^d]eaves -> eave
+    "interweaves",
+    "weaves",
+    # *[nwl]ives -> [nwl]live
+    "olives",
+    # *[eoa]lves -> [eoa]lve
+    "bivalves",
+    "dissolves",
+    "resolves",
+    "salves",
+    "twelves",
+    "valves",
+)
+
+
+plverb_special_s = enclose(
+    "|".join(
+        [pl_sb_singular_s]
+        + pl_sb_uninflected_s
+        + list(pl_sb_irregular_s)
+        + ["(.*[csx])is", "(.*)ceps", "[A-Z].*s"]
+    )
+)
+
+_pl_sb_postfix_adj_defn = (
+    ("general", enclose(r"(?!major|lieutenant|brigadier|adjutant|.*star)\S+")),
+    ("martial", enclose("court")),
+    ("force", enclose("pound")),
+)
+
+pl_sb_postfix_adj: Iterable[str] = (
+    enclose(val + f"(?=(?:-|\\s+){key})") for key, val in _pl_sb_postfix_adj_defn
+)
+
+pl_sb_postfix_adj_stems = f"({'|'.join(pl_sb_postfix_adj)})(.*)"
+
+
+# PLURAL WORDS ENDING IS es GO TO SINGULAR is
+
+si_sb_es_is = (
+    "amanuenses",
+    "amniocenteses",
+    "analyses",
+    "antitheses",
+    "apotheoses",
+    "arterioscleroses",
+    "atheroscleroses",
+    "axes",
+    # 'bases', # bases -> basis
+    "catalyses",
+    "catharses",
+    "chasses",
+    "cirrhoses",
+    "cocces",
+    "crises",
+    "diagnoses",
+    "dialyses",
+    "diereses",
+    "electrolyses",
+    "emphases",
+    "exegeses",
+    "geneses",
+    "halitoses",
+    "hydrolyses",
+    "hypnoses",
+    "hypotheses",
+    "hystereses",
+    "metamorphoses",
+    "metastases",
+    "misdiagnoses",
+    "mitoses",
+    "mononucleoses",
+    "narcoses",
+    "necroses",
+    "nemeses",
+    "neuroses",
+    "oases",
+    "osmoses",
+    "osteoporoses",
+    "paralyses",
+    "parentheses",
+    "parthenogeneses",
+    "periphrases",
+    "photosyntheses",
+    "probosces",
+    "prognoses",
+    "prophylaxes",
+    "prostheses",
+    "preces",
+    "psoriases",
+    "psychoanalyses",
+    "psychokineses",
+    "psychoses",
+    "scleroses",
+    "scolioses",
+    "sepses",
+    "silicoses",
+    "symbioses",
+    "synopses",
+    "syntheses",
+    "taxes",
+    "telekineses",
+    "theses",
+    "thromboses",
+    "tuberculoses",
+    "urinalyses",
+)
+
+pl_prep_list = """
+    about above across after among around at athwart before behind
+    below beneath beside besides between betwixt beyond but by
+    during except for from in into near of off on onto out over
+    since till to under until unto upon with""".split()
+
+pl_prep_list_da = pl_prep_list + ["de", "du", "da"]
+
+pl_prep_bysize = bysize(pl_prep_list_da)
+
+pl_prep = enclose("|".join(pl_prep_list_da))
+
+pl_sb_prep_dual_compound = rf"(.*?)((?:-|\s+)(?:{pl_prep})(?:-|\s+))a(?:-|\s+)(.*)"
+
+
+singular_pronoun_genders = {
+    "neuter",
+    "feminine",
+    "masculine",
+    "gender-neutral",
+    "feminine or masculine",
+    "masculine or feminine",
+}
+
+pl_pron_nom = {
+    # NOMINATIVE    REFLEXIVE
+    "i": "we",
+    "myself": "ourselves",
+    "you": "you",
+    "yourself": "yourselves",
+    "she": "they",
+    "herself": "themselves",
+    "he": "they",
+    "himself": "themselves",
+    "it": "they",
+    "itself": "themselves",
+    "they": "they",
+    "themself": "themselves",
+    #   POSSESSIVE
+    "mine": "ours",
+    "yours": "yours",
+    "hers": "theirs",
+    "his": "theirs",
+    "its": "theirs",
+    "theirs": "theirs",
+}
+
+si_pron: Dict[str, Dict[str, Union[str, Dict[str, str]]]] = {
+    "nom": {v: k for (k, v) in pl_pron_nom.items()}
+}
+si_pron["nom"]["we"] = "I"
+
+
+pl_pron_acc = {
+    # ACCUSATIVE    REFLEXIVE
+    "me": "us",
+    "myself": "ourselves",
+    "you": "you",
+    "yourself": "yourselves",
+    "her": "them",
+    "herself": "themselves",
+    "him": "them",
+    "himself": "themselves",
+    "it": "them",
+    "itself": "themselves",
+    "them": "them",
+    "themself": "themselves",
+}
+
+pl_pron_acc_keys = enclose("|".join(pl_pron_acc))
+pl_pron_acc_keys_bysize = bysize(pl_pron_acc)
+
+si_pron["acc"] = {v: k for (k, v) in pl_pron_acc.items()}
+
+for _thecase, _plur, _gend, _sing in (
+    ("nom", "they", "neuter", "it"),
+    ("nom", "they", "feminine", "she"),
+    ("nom", "they", "masculine", "he"),
+    ("nom", "they", "gender-neutral", "they"),
+    ("nom", "they", "feminine or masculine", "she or he"),
+    ("nom", "they", "masculine or feminine", "he or she"),
+    ("nom", "themselves", "neuter", "itself"),
+    ("nom", "themselves", "feminine", "herself"),
+    ("nom", "themselves", "masculine", "himself"),
+    ("nom", "themselves", "gender-neutral", "themself"),
+    ("nom", "themselves", "feminine or masculine", "herself or himself"),
+    ("nom", "themselves", "masculine or feminine", "himself or herself"),
+    ("nom", "theirs", "neuter", "its"),
+    ("nom", "theirs", "feminine", "hers"),
+    ("nom", "theirs", "masculine", "his"),
+    ("nom", "theirs", "gender-neutral", "theirs"),
+    ("nom", "theirs", "feminine or masculine", "hers or his"),
+    ("nom", "theirs", "masculine or feminine", "his or hers"),
+    ("acc", "them", "neuter", "it"),
+    ("acc", "them", "feminine", "her"),
+    ("acc", "them", "masculine", "him"),
+    ("acc", "them", "gender-neutral", "them"),
+    ("acc", "them", "feminine or masculine", "her or him"),
+    ("acc", "them", "masculine or feminine", "him or her"),
+    ("acc", "themselves", "neuter", "itself"),
+    ("acc", "themselves", "feminine", "herself"),
+    ("acc", "themselves", "masculine", "himself"),
+    ("acc", "themselves", "gender-neutral", "themself"),
+    ("acc", "themselves", "feminine or masculine", "herself or himself"),
+    ("acc", "themselves", "masculine or feminine", "himself or herself"),
+):
+    try:
+        si_pron[_thecase][_plur][_gend] = _sing  # type: ignore
+    except TypeError:
+        si_pron[_thecase][_plur] = {}
+        si_pron[_thecase][_plur][_gend] = _sing  # type: ignore
+
+
+si_pron_acc_keys = enclose("|".join(si_pron["acc"]))
+si_pron_acc_keys_bysize = bysize(si_pron["acc"])
+
+
+def get_si_pron(thecase, word, gender) -> str:
+    try:
+        sing = si_pron[thecase][word]
+    except KeyError:
+        raise  # not a pronoun
+    try:
+        return sing[gender]  # has several types due to gender
+    except TypeError:
+        return cast(str, sing)  # answer independent of gender
+
+
+# These dictionaries group verbs by first, second and third person
+# conjugations.
+
+plverb_irregular_pres = {
+    "am": "are",
+    "are": "are",
+    "is": "are",
+    "was": "were",
+    "were": "were",
+    "have": "have",
+    "has": "have",
+    "do": "do",
+    "does": "do",
+}
+
+plverb_ambiguous_pres = {
+    "act": "act",
+    "acts": "act",
+    "blame": "blame",
+    "blames": "blame",
+    "can": "can",
+    "must": "must",
+    "fly": "fly",
+    "flies": "fly",
+    "copy": "copy",
+    "copies": "copy",
+    "drink": "drink",
+    "drinks": "drink",
+    "fight": "fight",
+    "fights": "fight",
+    "fire": "fire",
+    "fires": "fire",
+    "like": "like",
+    "likes": "like",
+    "look": "look",
+    "looks": "look",
+    "make": "make",
+    "makes": "make",
+    "reach": "reach",
+    "reaches": "reach",
+    "run": "run",
+    "runs": "run",
+    "sink": "sink",
+    "sinks": "sink",
+    "sleep": "sleep",
+    "sleeps": "sleep",
+    "view": "view",
+    "views": "view",
+}
+
+plverb_ambiguous_pres_keys = re.compile(
+    rf"^({enclose('|'.join(plverb_ambiguous_pres))})((\s.*)?)$", re.IGNORECASE
+)
+
+
+plverb_irregular_non_pres = (
+    "did",
+    "had",
+    "ate",
+    "made",
+    "put",
+    "spent",
+    "fought",
+    "sank",
+    "gave",
+    "sought",
+    "shall",
+    "could",
+    "ought",
+    "should",
+)
+
+plverb_ambiguous_non_pres = re.compile(
+    r"^((?:thought|saw|bent|will|might|cut))((\s.*)?)$", re.IGNORECASE
+)
+
+# "..oes" -> "..oe" (the rest are "..oes" -> "o")
+
+pl_v_oes_oe = ("canoes", "floes", "oboes", "roes", "throes", "woes")
+pl_v_oes_oe_endings_size4 = ("hoes", "toes")
+pl_v_oes_oe_endings_size5 = ("shoes",)
+
+
+pl_count_zero = ("0", "no", "zero", "nil")
+
+
+pl_count_one = ("1", "a", "an", "one", "each", "every", "this", "that")
+
+pl_adj_special = {"a": "some", "an": "some", "this": "these", "that": "those"}
+
+pl_adj_special_keys = re.compile(
+    rf"^({enclose('|'.join(pl_adj_special))})$", re.IGNORECASE
+)
+
+pl_adj_poss = {
+    "my": "our",
+    "your": "your",
+    "its": "their",
+    "her": "their",
+    "his": "their",
+    "their": "their",
+}
+
+pl_adj_poss_keys = re.compile(rf"^({enclose('|'.join(pl_adj_poss))})$", re.IGNORECASE)
+
+
+# 2. INDEFINITE ARTICLES
+
+# THIS PATTERN MATCHES STRINGS OF CAPITALS STARTING WITH A "VOWEL-SOUND"
+# CONSONANT FOLLOWED BY ANOTHER CONSONANT, AND WHICH ARE NOT LIKELY
+# TO BE REAL WORDS (OH, ALL RIGHT THEN, IT'S JUST MAGIC!)
+
+A_abbrev = re.compile(
+    r"""
+^(?! FJO | [HLMNS]Y.  | RY[EO] | SQU
+  | ( F[LR]? | [HL] | MN? | N | RH? | S[CHKLMNPTVW]? | X(YL)?) [AEIOU])
+[FHLMNRSX][A-Z]
+""",
+    re.VERBOSE,
+)
+
+# THIS PATTERN CODES THE BEGINNINGS OF ALL ENGLISH WORDS BEGINING WITH A
+# 'y' FOLLOWED BY A CONSONANT. ANY OTHER Y-CONSONANT PREFIX THEREFORE
+# IMPLIES AN ABBREVIATION.
+
+A_y_cons = re.compile(r"^(y(b[lor]|cl[ea]|fere|gg|p[ios]|rou|tt))", re.IGNORECASE)
+
+# EXCEPTIONS TO EXCEPTIONS
+
+A_explicit_a = re.compile(r"^((?:unabomber|unanimous|US))", re.IGNORECASE)
+
+A_explicit_an = re.compile(
+    r"^((?:euler|hour(?!i)|heir|honest|hono[ur]|mpeg))", re.IGNORECASE
+)
+
+A_ordinal_an = re.compile(r"^([aefhilmnorsx]-?th)", re.IGNORECASE)
+
+A_ordinal_a = re.compile(r"^([bcdgjkpqtuvwyz]-?th)", re.IGNORECASE)
+
+
+# NUMERICAL INFLECTIONS
+
+nth = {
+    0: "th",
+    1: "st",
+    2: "nd",
+    3: "rd",
+    4: "th",
+    5: "th",
+    6: "th",
+    7: "th",
+    8: "th",
+    9: "th",
+    11: "th",
+    12: "th",
+    13: "th",
+}
+nth_suff = set(nth.values())
+
+ordinal = dict(
+    ty="tieth",
+    one="first",
+    two="second",
+    three="third",
+    five="fifth",
+    eight="eighth",
+    nine="ninth",
+    twelve="twelfth",
+)
+
+ordinal_suff = re.compile(rf"({'|'.join(ordinal)})\Z")
+
+
+# NUMBERS
+
+unit = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
+teen = [
+    "ten",
+    "eleven",
+    "twelve",
+    "thirteen",
+    "fourteen",
+    "fifteen",
+    "sixteen",
+    "seventeen",
+    "eighteen",
+    "nineteen",
+]
+ten = [
+    "",
+    "",
+    "twenty",
+    "thirty",
+    "forty",
+    "fifty",
+    "sixty",
+    "seventy",
+    "eighty",
+    "ninety",
+]
+mill = [
+    " ",
+    " thousand",
+    " million",
+    " billion",
+    " trillion",
+    " quadrillion",
+    " quintillion",
+    " sextillion",
+    " septillion",
+    " octillion",
+    " nonillion",
+    " decillion",
+]
+
+
+# SUPPORT CLASSICAL PLURALIZATIONS
+
+def_classical = dict(
+    all=False, zero=False, herd=False, names=True, persons=False, ancient=False
+)
+
+all_classical = {k: True for k in def_classical}
+no_classical = {k: False for k in def_classical}
+
+
+# Maps strings to built-in constant types
+string_to_constant = {"True": True, "False": False, "None": None}
+
+
+# Pre-compiled regular expression objects
+DOLLAR_DIGITS = re.compile(r"\$(\d+)")
+FUNCTION_CALL = re.compile(r"((\w+)\([^)]*\)*)", re.IGNORECASE)
+PARTITION_WORD = re.compile(r"\A(\s*)(.+?)(\s*)\Z")
+PL_SB_POSTFIX_ADJ_STEMS_RE = re.compile(
+    rf"^(?:{pl_sb_postfix_adj_stems})$", re.IGNORECASE
+)
+PL_SB_PREP_DUAL_COMPOUND_RE = re.compile(
+    rf"^(?:{pl_sb_prep_dual_compound})$", re.IGNORECASE
+)
+DENOMINATOR = re.compile(r"(?P<denominator>.+)( (per|a) .+)")
+PLVERB_SPECIAL_S_RE = re.compile(rf"^({plverb_special_s})$")
+WHITESPACE = re.compile(r"\s")
+ENDS_WITH_S = re.compile(r"^(.*[^s])s$", re.IGNORECASE)
+ENDS_WITH_APOSTROPHE_S = re.compile(r"^(.*)'s?$")
+INDEFINITE_ARTICLE_TEST = re.compile(r"\A(\s*)(?:an?\s+)?(.+?)(\s*)\Z", re.IGNORECASE)
+SPECIAL_AN = re.compile(r"^[aefhilmnorsx]$", re.IGNORECASE)
+SPECIAL_A = re.compile(r"^[bcdgjkpqtuvwyz]$", re.IGNORECASE)
+SPECIAL_ABBREV_AN = re.compile(r"^[aefhilmnorsx][.-]", re.IGNORECASE)
+SPECIAL_ABBREV_A = re.compile(r"^[a-z][.-]", re.IGNORECASE)
+CONSONANTS = re.compile(r"^[^aeiouy]", re.IGNORECASE)
+ARTICLE_SPECIAL_EU = re.compile(r"^e[uw]", re.IGNORECASE)
+ARTICLE_SPECIAL_ONCE = re.compile(r"^onc?e\b", re.IGNORECASE)
+ARTICLE_SPECIAL_ONETIME = re.compile(r"^onetime\b", re.IGNORECASE)
+ARTICLE_SPECIAL_UNIT = re.compile(r"^uni([^nmd]|mo)", re.IGNORECASE)
+ARTICLE_SPECIAL_UBA = re.compile(r"^u[bcfghjkqrst][aeiou]", re.IGNORECASE)
+ARTICLE_SPECIAL_UKR = re.compile(r"^ukr", re.IGNORECASE)
+SPECIAL_CAPITALS = re.compile(r"^U[NK][AIEO]?")
+VOWELS = re.compile(r"^[aeiou]", re.IGNORECASE)
+
+DIGIT_GROUP = re.compile(r"(\d)")
+TWO_DIGITS = re.compile(r"(\d)(\d)")
+THREE_DIGITS = re.compile(r"(\d)(\d)(\d)")
+THREE_DIGITS_WORD = re.compile(r"(\d)(\d)(\d)(?=\D*\Z)")
+TWO_DIGITS_WORD = re.compile(r"(\d)(\d)(?=\D*\Z)")
+ONE_DIGIT_WORD = re.compile(r"(\d)(?=\D*\Z)")
+
+FOUR_DIGIT_COMMA = re.compile(r"(\d)(\d{3}(?:,|\Z))")
+NON_DIGIT = re.compile(r"\D")
+WHITESPACES_COMMA = re.compile(r"\s+,")
+COMMA_WORD = re.compile(r", (\S+)\s+\Z")
+WHITESPACES = re.compile(r"\s+")
+
+
+PRESENT_PARTICIPLE_REPLACEMENTS = (
+    (re.compile(r"ie$"), r"y"),
+    (
+        re.compile(r"ue$"),
+        r"u",
+    ),  # TODO: isn't ue$ -> u encompassed in the following rule?
+    (re.compile(r"([auy])e$"), r"\g<1>"),
+    (re.compile(r"ski$"), r"ski"),
+    (re.compile(r"[^b]i$"), r""),
+    (re.compile(r"^(are|were)$"), r"be"),
+    (re.compile(r"^(had)$"), r"hav"),
+    (re.compile(r"^(hoe)$"), r"\g<1>"),
+    (re.compile(r"([^e])e$"), r"\g<1>"),
+    (re.compile(r"er$"), r"er"),
+    (re.compile(r"([^aeiou][aeiouy]([bdgmnprst]))$"), r"\g<1>\g<2>"),
+)
+
+DIGIT = re.compile(r"\d")
+
+
+class Words(str):
+    lowered: str
+    split_: List[str]
+    first: str
+    last: str
+
+    def __init__(self, orig) -> None:
+        self.lowered = self.lower()
+        self.split_ = self.split()
+        self.first = self.split_[0]
+        self.last = self.split_[-1]
+
+
+Falsish = Any  # ideally, falsish would only validate on bool(value) is False
+
+
+_STATIC_TYPE_CHECKING = TYPE_CHECKING
+# ^-- Workaround for typeguard AST manipulation:
+#     https://github.com/agronholm/typeguard/issues/353#issuecomment-1556306554
+
+if _STATIC_TYPE_CHECKING:  # pragma: no cover
+    Word = Annotated[str, "String with at least 1 character"]
+else:
+
+    class _WordMeta(type):  # Too dynamic to be supported by mypy...
+        def __instancecheck__(self, instance: Any) -> bool:
+            return isinstance(instance, str) and len(instance) >= 1
+
+    class Word(metaclass=_WordMeta):  # type: ignore[no-redef]
+        """String with at least 1 character"""
+
+
+class engine:
+    def __init__(self) -> None:
+        self.classical_dict = def_classical.copy()
+        self.persistent_count: Optional[int] = None
+        self.mill_count = 0
+        self.pl_sb_user_defined: List[Optional[Word]] = []
+        self.pl_v_user_defined: List[Optional[Word]] = []
+        self.pl_adj_user_defined: List[Optional[Word]] = []
+        self.si_sb_user_defined: List[Optional[Word]] = []
+        self.A_a_user_defined: List[Optional[Word]] = []
+        self.thegender = "neuter"
+        self.__number_args: Optional[Dict[str, str]] = None
+
+    @property
+    def _number_args(self):
+        return cast(Dict[str, str], self.__number_args)
+
+    @_number_args.setter
+    def _number_args(self, val):
+        self.__number_args = val
+
+    @typechecked
+    def defnoun(self, singular: Optional[Word], plural: Optional[Word]) -> int:
+        """
+        Set the noun plural of singular to plural.
+
+        """
+        self.checkpat(singular)
+        self.checkpatplural(plural)
+        self.pl_sb_user_defined.extend((singular, plural))
+        self.si_sb_user_defined.extend((plural, singular))
+        return 1
+
+    @typechecked
+    def defverb(
+        self,
+        s1: Optional[Word],
+        p1: Optional[Word],
+        s2: Optional[Word],
+        p2: Optional[Word],
+        s3: Optional[Word],
+        p3: Optional[Word],
+    ) -> int:
+        """
+        Set the verb plurals for s1, s2 and s3 to p1, p2 and p3 respectively.
+
+        Where 1, 2 and 3 represent the 1st, 2nd and 3rd person forms of the verb.
+
+        """
+        self.checkpat(s1)
+        self.checkpat(s2)
+        self.checkpat(s3)
+        self.checkpatplural(p1)
+        self.checkpatplural(p2)
+        self.checkpatplural(p3)
+        self.pl_v_user_defined.extend((s1, p1, s2, p2, s3, p3))
+        return 1
+
+    @typechecked
+    def defadj(self, singular: Optional[Word], plural: Optional[Word]) -> int:
+        """
+        Set the adjective plural of singular to plural.
+
+        """
+        self.checkpat(singular)
+        self.checkpatplural(plural)
+        self.pl_adj_user_defined.extend((singular, plural))
+        return 1
+
+    @typechecked
+    def defa(self, pattern: Optional[Word]) -> int:
+        """
+        Define the indefinite article as 'a' for words matching pattern.
+
+        """
+        self.checkpat(pattern)
+        self.A_a_user_defined.extend((pattern, "a"))
+        return 1
+
+    @typechecked
+    def defan(self, pattern: Optional[Word]) -> int:
+        """
+        Define the indefinite article as 'an' for words matching pattern.
+
+        """
+        self.checkpat(pattern)
+        self.A_a_user_defined.extend((pattern, "an"))
+        return 1
+
+    def checkpat(self, pattern: Optional[Word]) -> None:
+        """
+        check for errors in a regex pattern
+        """
+        if pattern is None:
+            return
+        try:
+            re.match(pattern, "")
+        except re.error as err:
+            raise BadUserDefinedPatternError(pattern) from err
+
+    def checkpatplural(self, pattern: Optional[Word]) -> None:
+        """
+        check for errors in a regex replace pattern
+        """
+        return
+
+    @typechecked
+    def ud_match(self, word: Word, wordlist: Sequence[Optional[Word]]) -> Optional[str]:
+        for i in range(len(wordlist) - 2, -2, -2):  # backwards through even elements
+            mo = re.search(rf"^{wordlist[i]}$", word, re.IGNORECASE)
+            if mo:
+                if wordlist[i + 1] is None:
+                    return None
+                pl = DOLLAR_DIGITS.sub(
+                    r"\\1", cast(Word, wordlist[i + 1])
+                )  # change $n to \n for expand
+                return mo.expand(pl)
+        return None
+
+    def classical(self, **kwargs) -> None:
+        """
+        turn classical mode on and off for various categories
+
+        turn on all classical modes:
+        classical()
+        classical(all=True)
+
+        turn on or off specific claassical modes:
+        e.g.
+        classical(herd=True)
+        classical(names=False)
+
+        By default all classical modes are off except names.
+
+        unknown value in args or key in kwargs raises
+        exception: UnknownClasicalModeError
+
+        """
+        if not kwargs:
+            self.classical_dict = all_classical.copy()
+            return
+        if "all" in kwargs:
+            if kwargs["all"]:
+                self.classical_dict = all_classical.copy()
+            else:
+                self.classical_dict = no_classical.copy()
+
+        for k, v in kwargs.items():
+            if k in def_classical:
+                self.classical_dict[k] = v
+            else:
+                raise UnknownClassicalModeError
+
+    def num(
+        self, count: Optional[int] = None, show: Optional[int] = None
+    ) -> str:  # (;$count,$show)
+        """
+        Set the number to be used in other method calls.
+
+        Returns count.
+
+        Set show to False to return '' instead.
+
+        """
+        if count is not None:
+            try:
+                self.persistent_count = int(count)
+            except ValueError as err:
+                raise BadNumValueError from err
+            if (show is None) or show:
+                return str(count)
+        else:
+            self.persistent_count = None
+        return ""
+
+    def gender(self, gender: str) -> None:
+        """
+        set the gender for the singular of plural pronouns
+
+        can be one of:
+        'neuter'                ('they' -> 'it')
+        'feminine'              ('they' -> 'she')
+        'masculine'             ('they' -> 'he')
+        'gender-neutral'        ('they' -> 'they')
+        'feminine or masculine' ('they' -> 'she or he')
+        'masculine or feminine' ('they' -> 'he or she')
+        """
+        if gender in singular_pronoun_genders:
+            self.thegender = gender
+        else:
+            raise BadGenderError
+
+    def _get_value_from_ast(self, obj):
+        """
+        Return the value of the ast object.
+        """
+        if isinstance(obj, ast.Num):
+            return obj.n
+        elif isinstance(obj, ast.Str):
+            return obj.s
+        elif isinstance(obj, ast.List):
+            return [self._get_value_from_ast(e) for e in obj.elts]
+        elif isinstance(obj, ast.Tuple):
+            return tuple([self._get_value_from_ast(e) for e in obj.elts])
+
+        # None, True and False are NameConstants in Py3.4 and above.
+        elif isinstance(obj, ast.NameConstant):
+            return obj.value
+
+        # Probably passed a variable name.
+        # Or passed a single word without wrapping it in quotes as an argument
+        # ex: p.inflect("I plural(see)") instead of p.inflect("I plural('see')")
+        raise NameError(f"name '{obj.id}' is not defined")
+
+    def _string_to_substitute(
+        self, mo: Match, methods_dict: Dict[str, Callable]
+    ) -> str:
+        """
+        Return the string to be substituted for the match.
+        """
+        matched_text, f_name = mo.groups()
+        # matched_text is the complete match string. e.g. plural_noun(cat)
+        # f_name is the function name. e.g. plural_noun
+
+        # Return matched_text if function name is not in methods_dict
+        if f_name not in methods_dict:
+            return matched_text
+
+        # Parse the matched text
+        a_tree = ast.parse(matched_text)
+
+        # get the args and kwargs from ast objects
+        args_list = [
+            self._get_value_from_ast(a)
+            for a in a_tree.body[0].value.args  # type: ignore[attr-defined]
+        ]
+        kwargs_list = {
+            kw.arg: self._get_value_from_ast(kw.value)
+            for kw in a_tree.body[0].value.keywords  # type: ignore[attr-defined]
+        }
+
+        # Call the corresponding function
+        return methods_dict[f_name](*args_list, **kwargs_list)
+
+    # 0. PERFORM GENERAL INFLECTIONS IN A STRING
+
+    @typechecked
+    def inflect(self, text: Word) -> str:
+        """
+        Perform inflections in a string.
+
+        e.g. inflect('The plural of cat is plural(cat)') returns
+        'The plural of cat is cats'
+
+        can use plural, plural_noun, plural_verb, plural_adj,
+        singular_noun, a, an, no, ordinal, number_to_words,
+        and prespart
+
+        """
+        save_persistent_count = self.persistent_count
+
+        # Dictionary of allowed methods
+        methods_dict: Dict[str, Callable] = {
+            "plural": self.plural,
+            "plural_adj": self.plural_adj,
+            "plural_noun": self.plural_noun,
+            "plural_verb": self.plural_verb,
+            "singular_noun": self.singular_noun,
+            "a": self.a,
+            "an": self.a,
+            "no": self.no,
+            "ordinal": self.ordinal,
+            "number_to_words": self.number_to_words,
+            "present_participle": self.present_participle,
+            "num": self.num,
+        }
+
+        # Regular expression to find Python's function call syntax
+        output = FUNCTION_CALL.sub(
+            lambda mo: self._string_to_substitute(mo, methods_dict), text
+        )
+        self.persistent_count = save_persistent_count
+        return output
+
+    # ## PLURAL SUBROUTINES
+
+    def postprocess(self, orig: str, inflected) -> str:
+        inflected = str(inflected)
+        if "|" in inflected:
+            word_options = inflected.split("|")
+            # When two parts of a noun need to be pluralized
+            if len(word_options[0].split(" ")) == len(word_options[1].split(" ")):
+                result = inflected.split("|")[self.classical_dict["all"]].split(" ")
+            # When only the last part of the noun needs to be pluralized
+            else:
+                result = inflected.split(" ")
+                for index, word in enumerate(result):
+                    if "|" in word:
+                        result[index] = word.split("|")[self.classical_dict["all"]]
+        else:
+            result = inflected.split(" ")
+
+        # Try to fix word wise capitalization
+        for index, word in enumerate(orig.split(" ")):
+            if word == "I":
+                # Is this the only word for exceptions like this
+                # Where the original is fully capitalized
+                # without 'meaning' capitalization?
+                # Also this fails to handle a capitalizaion in context
+                continue
+            if word.capitalize() == word:
+                result[index] = result[index].capitalize()
+            if word == word.upper():
+                result[index] = result[index].upper()
+        return " ".join(result)
+
+    def partition_word(self, text: str) -> Tuple[str, str, str]:
+        mo = PARTITION_WORD.search(text)
+        if mo:
+            return mo.group(1), mo.group(2), mo.group(3)
+        else:
+            return "", "", ""
+
+    @typechecked
+    def plural(self, text: Word, count: Optional[Union[str, int, Any]] = None) -> str:
+        """
+        Return the plural of text.
+
+        If count supplied, then return text if count is one of:
+            1, a, an, one, each, every, this, that
+
+        otherwise return the plural.
+
+        Whitespace at the start and end is preserved.
+
+        """
+        pre, word, post = self.partition_word(text)
+        if not word:
+            return text
+        plural = self.postprocess(
+            word,
+            self._pl_special_adjective(word, count)
+            or self._pl_special_verb(word, count)
+            or self._plnoun(word, count),
+        )
+        return f"{pre}{plural}{post}"
+
+    @typechecked
+    def plural_noun(
+        self, text: Word, count: Optional[Union[str, int, Any]] = None
+    ) -> str:
+        """
+        Return the plural of text, where text is a noun.
+
+        If count supplied, then return text if count is one of:
+            1, a, an, one, each, every, this, that
+
+        otherwise return the plural.
+
+        Whitespace at the start and end is preserved.
+
+        """
+        pre, word, post = self.partition_word(text)
+        if not word:
+            return text
+        plural = self.postprocess(word, self._plnoun(word, count))
+        return f"{pre}{plural}{post}"
+
+    @typechecked
+    def plural_verb(
+        self, text: Word, count: Optional[Union[str, int, Any]] = None
+    ) -> str:
+        """
+        Return the plural of text, where text is a verb.
+
+        If count supplied, then return text if count is one of:
+            1, a, an, one, each, every, this, that
+
+        otherwise return the plural.
+
+        Whitespace at the start and end is preserved.
+
+        """
+        pre, word, post = self.partition_word(text)
+        if not word:
+            return text
+        plural = self.postprocess(
+            word,
+            self._pl_special_verb(word, count) or self._pl_general_verb(word, count),
+        )
+        return f"{pre}{plural}{post}"
+
+    @typechecked
+    def plural_adj(
+        self, text: Word, count: Optional[Union[str, int, Any]] = None
+    ) -> str:
+        """
+        Return the plural of text, where text is an adjective.
+
+        If count supplied, then return text if count is one of:
+            1, a, an, one, each, every, this, that
+
+        otherwise return the plural.
+
+        Whitespace at the start and end is preserved.
+
+        """
+        pre, word, post = self.partition_word(text)
+        if not word:
+            return text
+        plural = self.postprocess(word, self._pl_special_adjective(word, count) or word)
+        return f"{pre}{plural}{post}"
+
+    @typechecked
+    def compare(self, word1: Word, word2: Word) -> Union[str, bool]:
+        """
+        compare word1 and word2 for equality regardless of plurality
+
+        return values:
+        eq - the strings are equal
+        p:s - word1 is the plural of word2
+        s:p - word2 is the plural of word1
+        p:p - word1 and word2 are two different plural forms of the one word
+        False - otherwise
+
+        >>> compare = engine().compare
+        >>> compare("egg", "eggs")
+        's:p'
+        >>> compare('egg', 'egg')
+        'eq'
+
+        Words should not be empty.
+
+        >>> compare('egg', '')
+        Traceback (most recent call last):
+        ...
+        typeguard.TypeCheckError:...is not an instance of inflect.Word
+        """
+        norms = self.plural_noun, self.plural_verb, self.plural_adj
+        results = (self._plequal(word1, word2, norm) for norm in norms)
+        return next(filter(None, results), False)
+
+    @typechecked
+    def compare_nouns(self, word1: Word, word2: Word) -> Union[str, bool]:
+        """
+        compare word1 and word2 for equality regardless of plurality
+        word1 and word2 are to be treated as nouns
+
+        return values:
+        eq - the strings are equal
+        p:s - word1 is the plural of word2
+        s:p - word2 is the plural of word1
+        p:p - word1 and word2 are two different plural forms of the one word
+        False - otherwise
+
+        """
+        return self._plequal(word1, word2, self.plural_noun)
+
+    @typechecked
+    def compare_verbs(self, word1: Word, word2: Word) -> Union[str, bool]:
+        """
+        compare word1 and word2 for equality regardless of plurality
+        word1 and word2 are to be treated as verbs
+
+        return values:
+        eq - the strings are equal
+        p:s - word1 is the plural of word2
+        s:p - word2 is the plural of word1
+        p:p - word1 and word2 are two different plural forms of the one word
+        False - otherwise
+
+        """
+        return self._plequal(word1, word2, self.plural_verb)
+
+    @typechecked
+    def compare_adjs(self, word1: Word, word2: Word) -> Union[str, bool]:
+        """
+        compare word1 and word2 for equality regardless of plurality
+        word1 and word2 are to be treated as adjectives
+
+        return values:
+        eq - the strings are equal
+        p:s - word1 is the plural of word2
+        s:p - word2 is the plural of word1
+        p:p - word1 and word2 are two different plural forms of the one word
+        False - otherwise
+
+        """
+        return self._plequal(word1, word2, self.plural_adj)
+
+    @typechecked
+    def singular_noun(
+        self,
+        text: Word,
+        count: Optional[Union[int, str, Any]] = None,
+        gender: Optional[str] = None,
+    ) -> Union[str, Literal[False]]:
+        """
+        Return the singular of text, where text is a plural noun.
+
+        If count supplied, then return the singular if count is one of:
+            1, a, an, one, each, every, this, that or if count is None
+
+        otherwise return text unchanged.
+
+        Whitespace at the start and end is preserved.
+
+        >>> p = engine()
+        >>> p.singular_noun('horses')
+        'horse'
+        >>> p.singular_noun('knights')
+        'knight'
+
+        Returns False when a singular noun is passed.
+
+        >>> p.singular_noun('horse')
+        False
+        >>> p.singular_noun('knight')
+        False
+        >>> p.singular_noun('soldier')
+        False
+
+        """
+        pre, word, post = self.partition_word(text)
+        if not word:
+            return text
+        sing = self._sinoun(word, count=count, gender=gender)
+        if sing is not False:
+            plural = self.postprocess(word, sing)
+            return f"{pre}{plural}{post}"
+        return False
+
+    def _plequal(self, word1: str, word2: str, pl) -> Union[str, bool]:  # noqa: C901
+        classval = self.classical_dict.copy()
+        self.classical_dict = all_classical.copy()
+        if word1 == word2:
+            return "eq"
+        if word1 == pl(word2):
+            return "p:s"
+        if pl(word1) == word2:
+            return "s:p"
+        self.classical_dict = no_classical.copy()
+        if word1 == pl(word2):
+            return "p:s"
+        if pl(word1) == word2:
+            return "s:p"
+        self.classical_dict = classval.copy()
+
+        if pl == self.plural or pl == self.plural_noun:
+            if self._pl_check_plurals_N(word1, word2):
+                return "p:p"
+            if self._pl_check_plurals_N(word2, word1):
+                return "p:p"
+        if pl == self.plural or pl == self.plural_adj:
+            if self._pl_check_plurals_adj(word1, word2):
+                return "p:p"
+        return False
+
+    def _pl_reg_plurals(self, pair: str, stems: str, end1: str, end2: str) -> bool:
+        pattern = rf"({stems})({end1}\|\1{end2}|{end2}\|\1{end1})"
+        return bool(re.search(pattern, pair))
+
+    def _pl_check_plurals_N(self, word1: str, word2: str) -> bool:
+        stem_endings = (
+            (pl_sb_C_a_ata, "as", "ata"),
+            (pl_sb_C_is_ides, "is", "ides"),
+            (pl_sb_C_a_ae, "s", "e"),
+            (pl_sb_C_en_ina, "ens", "ina"),
+            (pl_sb_C_um_a, "ums", "a"),
+            (pl_sb_C_us_i, "uses", "i"),
+            (pl_sb_C_on_a, "ons", "a"),
+            (pl_sb_C_o_i_stems, "os", "i"),
+            (pl_sb_C_ex_ices, "exes", "ices"),
+            (pl_sb_C_ix_ices, "ixes", "ices"),
+            (pl_sb_C_i, "s", "i"),
+            (pl_sb_C_im, "s", "im"),
+            (".*eau", "s", "x"),
+            (".*ieu", "s", "x"),
+            (".*tri", "xes", "ces"),
+            (".{2,}[yia]n", "xes", "ges"),
+        )
+
+        words = map(Words, (word1, word2))
+        pair = "|".join(word.last for word in words)
+
+        return (
+            pair in pl_sb_irregular_s.values()
+            or pair in pl_sb_irregular.values()
+            or pair in pl_sb_irregular_caps.values()
+            or any(
+                self._pl_reg_plurals(pair, stems, end1, end2)
+                for stems, end1, end2 in stem_endings
+            )
+        )
+
+    def _pl_check_plurals_adj(self, word1: str, word2: str) -> bool:
+        word1a = word1[: word1.rfind("'")] if word1.endswith(("'s", "'")) else ""
+        word2a = word2[: word2.rfind("'")] if word2.endswith(("'s", "'")) else ""
+
+        return (
+            bool(word1a)
+            and bool(word2a)
+            and (
+                self._pl_check_plurals_N(word1a, word2a)
+                or self._pl_check_plurals_N(word2a, word1a)
+            )
+        )
+
+    def get_count(self, count: Optional[Union[str, int]] = None) -> Union[str, int]:
+        if count is None and self.persistent_count is not None:
+            count = self.persistent_count
+
+        if count is not None:
+            count = (
+                1
+                if (
+                    (str(count) in pl_count_one)
+                    or (
+                        self.classical_dict["zero"]
+                        and str(count).lower() in pl_count_zero
+                    )
+                )
+                else 2
+            )
+        else:
+            count = ""
+        return count
+
+    # @profile
+    def _plnoun(  # noqa: C901
+        self, word: str, count: Optional[Union[str, int]] = None
+    ) -> str:
+        count = self.get_count(count)
+
+        # DEFAULT TO PLURAL
+
+        if count == 1:
+            return word
+
+        # HANDLE USER-DEFINED NOUNS
+
+        value = self.ud_match(word, self.pl_sb_user_defined)
+        if value is not None:
+            return value
+
+        # HANDLE EMPTY WORD, SINGULAR COUNT AND UNINFLECTED PLURALS
+
+        if word == "":
+            return word
+
+        word = Words(word)
+
+        if word.last.lower() in pl_sb_uninflected_complete:
+            if len(word.split_) >= 3:
+                return self._handle_long_compounds(word, count=2) or word
+            return word
+
+        if word in pl_sb_uninflected_caps:
+            return word
+
+        for k, v in pl_sb_uninflected_bysize.items():
+            if word.lowered[-k:] in v:
+                return word
+
+        if self.classical_dict["herd"] and word.last.lower() in pl_sb_uninflected_herd:
+            return word
+
+        # HANDLE COMPOUNDS ("Governor General", "mother-in-law", "aide-de-camp", ETC.)
+
+        mo = PL_SB_POSTFIX_ADJ_STEMS_RE.search(word)
+        if mo and mo.group(2) != "":
+            return f"{self._plnoun(mo.group(1), 2)}{mo.group(2)}"
+
+        if " a " in word.lowered or "-a-" in word.lowered:
+            mo = PL_SB_PREP_DUAL_COMPOUND_RE.search(word)
+            if mo and mo.group(2) != "" and mo.group(3) != "":
+                return (
+                    f"{self._plnoun(mo.group(1), 2)}"
+                    f"{mo.group(2)}"
+                    f"{self._plnoun(mo.group(3))}"
+                )
+
+        if len(word.split_) >= 3:
+            handled_words = self._handle_long_compounds(word, count=2)
+            if handled_words is not None:
+                return handled_words
+
+        # only pluralize denominators in units
+        mo = DENOMINATOR.search(word.lowered)
+        if mo:
+            index = len(mo.group("denominator"))
+            return f"{self._plnoun(word[:index])}{word[index:]}"
+
+        # handle units given in degrees (only accept if
+        # there is no more than one word following)
+        # degree Celsius => degrees Celsius but degree
+        # fahrenheit hour => degree fahrenheit hours
+        if len(word.split_) >= 2 and word.split_[-2] == "degree":
+            return " ".join([self._plnoun(word.first)] + word.split_[1:])
+
+        with contextlib.suppress(ValueError):
+            return self._handle_prepositional_phrase(
+                word.lowered,
+                functools.partial(self._plnoun, count=2),
+                '-',
+            )
+
+        # HANDLE PRONOUNS
+
+        for k, v in pl_pron_acc_keys_bysize.items():
+            if word.lowered[-k:] in v:  # ends with accusative pronoun
+                for pk, pv in pl_prep_bysize.items():
+                    if word.lowered[:pk] in pv:  # starts with a prep
+                        if word.lowered.split() == [
+                            word.lowered[:pk],
+                            word.lowered[-k:],
+                        ]:
+                            # only whitespace in between
+                            return word.lowered[:-k] + pl_pron_acc[word.lowered[-k:]]
+
+        try:
+            return pl_pron_nom[word.lowered]
+        except KeyError:
+            pass
+
+        try:
+            return pl_pron_acc[word.lowered]
+        except KeyError:
+            pass
+
+        # HANDLE ISOLATED IRREGULAR PLURALS
+
+        if word.last in pl_sb_irregular_caps:
+            llen = len(word.last)
+            return f"{word[:-llen]}{pl_sb_irregular_caps[word.last]}"
+
+        lowered_last = word.last.lower()
+        if lowered_last in pl_sb_irregular:
+            llen = len(lowered_last)
+            return f"{word[:-llen]}{pl_sb_irregular[lowered_last]}"
+
+        dash_split = word.lowered.split('-')
+        if (" ".join(dash_split[-2:])).lower() in pl_sb_irregular_compound:
+            llen = len(
+                " ".join(dash_split[-2:])
+            )  # TODO: what if 2 spaces between these words?
+            return (
+                f"{word[:-llen]}"
+                f"{pl_sb_irregular_compound[(' '.join(dash_split[-2:])).lower()]}"
+            )
+
+        if word.lowered[-3:] == "quy":
+            return f"{word[:-1]}ies"
+
+        if word.lowered[-6:] == "person":
+            if self.classical_dict["persons"]:
+                return f"{word}s"
+            else:
+                return f"{word[:-4]}ople"
+
+        # HANDLE FAMILIES OF IRREGULAR PLURALS
+
+        if word.lowered[-3:] == "man":
+            for k, v in pl_sb_U_man_mans_bysize.items():
+                if word.lowered[-k:] in v:
+                    return f"{word}s"
+            for k, v in pl_sb_U_man_mans_caps_bysize.items():
+                if word[-k:] in v:
+                    return f"{word}s"
+            return f"{word[:-3]}men"
+        if word.lowered[-5:] == "mouse":
+            return f"{word[:-5]}mice"
+        if word.lowered[-5:] == "louse":
+            v = pl_sb_U_louse_lice_bysize.get(len(word))
+            if v and word.lowered in v:
+                return f"{word[:-5]}lice"
+            return f"{word}s"
+        if word.lowered[-5:] == "goose":
+            return f"{word[:-5]}geese"
+        if word.lowered[-5:] == "tooth":
+            return f"{word[:-5]}teeth"
+        if word.lowered[-4:] == "foot":
+            return f"{word[:-4]}feet"
+        if word.lowered[-4:] == "taco":
+            return f"{word[:-5]}tacos"
+
+        if word.lowered == "die":
+            return "dice"
+
+        # HANDLE UNASSIMILATED IMPORTS
+
+        if word.lowered[-4:] == "ceps":
+            return word
+        if word.lowered[-4:] == "zoon":
+            return f"{word[:-2]}a"
+        if word.lowered[-3:] in ("cis", "sis", "xis"):
+            return f"{word[:-2]}es"
+
+        for lastlet, d, numend, post in (
+            ("h", pl_sb_U_ch_chs_bysize, None, "s"),
+            ("x", pl_sb_U_ex_ices_bysize, -2, "ices"),
+            ("x", pl_sb_U_ix_ices_bysize, -2, "ices"),
+            ("m", pl_sb_U_um_a_bysize, -2, "a"),
+            ("s", pl_sb_U_us_i_bysize, -2, "i"),
+            ("n", pl_sb_U_on_a_bysize, -2, "a"),
+            ("a", pl_sb_U_a_ae_bysize, None, "e"),
+        ):
+            if word.lowered[-1] == lastlet:  # this test to add speed
+                for k, v in d.items():
+                    if word.lowered[-k:] in v:
+                        return word[:numend] + post
+
+        # HANDLE INCOMPLETELY ASSIMILATED IMPORTS
+
+        if self.classical_dict["ancient"]:
+            if word.lowered[-4:] == "trix":
+                return f"{word[:-1]}ces"
+            if word.lowered[-3:] in ("eau", "ieu"):
+                return f"{word}x"
+            if word.lowered[-3:] in ("ynx", "inx", "anx") and len(word) > 4:
+                return f"{word[:-1]}ges"
+
+            for lastlet, d, numend, post in (
+                ("n", pl_sb_C_en_ina_bysize, -2, "ina"),
+                ("x", pl_sb_C_ex_ices_bysize, -2, "ices"),
+                ("x", pl_sb_C_ix_ices_bysize, -2, "ices"),
+                ("m", pl_sb_C_um_a_bysize, -2, "a"),
+                ("s", pl_sb_C_us_i_bysize, -2, "i"),
+                ("s", pl_sb_C_us_us_bysize, None, ""),
+                ("a", pl_sb_C_a_ae_bysize, None, "e"),
+                ("a", pl_sb_C_a_ata_bysize, None, "ta"),
+                ("s", pl_sb_C_is_ides_bysize, -1, "des"),
+                ("o", pl_sb_C_o_i_bysize, -1, "i"),
+                ("n", pl_sb_C_on_a_bysize, -2, "a"),
+            ):
+                if word.lowered[-1] == lastlet:  # this test to add speed
+                    for k, v in d.items():
+                        if word.lowered[-k:] in v:
+                            return word[:numend] + post
+
+            for d, numend, post in (
+                (pl_sb_C_i_bysize, None, "i"),
+                (pl_sb_C_im_bysize, None, "im"),
+            ):
+                for k, v in d.items():
+                    if word.lowered[-k:] in v:
+                        return word[:numend] + post
+
+        # HANDLE SINGULAR NOUNS ENDING IN ...s OR OTHER SILIBANTS
+
+        if lowered_last in pl_sb_singular_s_complete:
+            return f"{word}es"
+
+        for k, v in pl_sb_singular_s_bysize.items():
+            if word.lowered[-k:] in v:
+                return f"{word}es"
+
+        if word.lowered[-2:] == "es" and word[0] == word[0].upper():
+            return f"{word}es"
+
+        if word.lowered[-1] == "z":
+            for k, v in pl_sb_z_zes_bysize.items():
+                if word.lowered[-k:] in v:
+                    return f"{word}es"
+
+            if word.lowered[-2:-1] != "z":
+                return f"{word}zes"
+
+        if word.lowered[-2:] == "ze":
+            for k, v in pl_sb_ze_zes_bysize.items():
+                if word.lowered[-k:] in v:
+                    return f"{word}s"
+
+        if word.lowered[-2:] in ("ch", "sh", "zz", "ss") or word.lowered[-1] == "x":
+            return f"{word}es"
+
+        # HANDLE ...f -> ...ves
+
+        if word.lowered[-3:] in ("elf", "alf", "olf"):
+            return f"{word[:-1]}ves"
+        if word.lowered[-3:] == "eaf" and word.lowered[-4:-3] != "d":
+            return f"{word[:-1]}ves"
+        if word.lowered[-4:] in ("nife", "life", "wife"):
+            return f"{word[:-2]}ves"
+        if word.lowered[-3:] == "arf":
+            return f"{word[:-1]}ves"
+
+        # HANDLE ...y
+
+        if word.lowered[-1] == "y":
+            if word.lowered[-2:-1] in "aeiou" or len(word) == 1:
+                return f"{word}s"
+
+            if self.classical_dict["names"]:
+                if word.lowered[-1] == "y" and word[0] == word[0].upper():
+                    return f"{word}s"
+
+            return f"{word[:-1]}ies"
+
+        # HANDLE ...o
+
+        if lowered_last in pl_sb_U_o_os_complete:
+            return f"{word}s"
+
+        for k, v in pl_sb_U_o_os_bysize.items():
+            if word.lowered[-k:] in v:
+                return f"{word}s"
+
+        if word.lowered[-2:] in ("ao", "eo", "io", "oo", "uo"):
+            return f"{word}s"
+
+        if word.lowered[-1] == "o":
+            return f"{word}es"
+
+        # OTHERWISE JUST ADD ...s
+
+        return f"{word}s"
+
+    @classmethod
+    def _handle_prepositional_phrase(cls, phrase, transform, sep):
+        """
+        Given a word or phrase possibly separated by sep, parse out
+        the prepositional phrase and apply the transform to the word
+        preceding the prepositional phrase.
+
+        Raise ValueError if the pivot is not found or if at least two
+        separators are not found.
+
+        >>> engine._handle_prepositional_phrase("man-of-war", str.upper, '-')
+        'MAN-of-war'
+        >>> engine._handle_prepositional_phrase("man of war", str.upper, ' ')
+        'MAN of war'
+        """
+        parts = phrase.split(sep)
+        if len(parts) < 3:
+            raise ValueError("Cannot handle words with fewer than two separators")
+
+        pivot = cls._find_pivot(parts, pl_prep_list_da)
+
+        transformed = transform(parts[pivot - 1]) or parts[pivot - 1]
+        return " ".join(
+            parts[: pivot - 1] + [sep.join([transformed, parts[pivot], ''])]
+        ) + " ".join(parts[(pivot + 1) :])
+
+    def _handle_long_compounds(self, word: Words, count: int) -> Union[str, None]:
+        """
+        Handles the plural and singular for compound `Words` that
+        have three or more words, based on the given count.
+
+        >>> engine()._handle_long_compounds(Words("pair of scissors"), 2)
+        'pairs of scissors'
+        >>> engine()._handle_long_compounds(Words("men beyond hills"), 1)
+        'man beyond hills'
+        """
+        inflection = self._sinoun if count == 1 else self._plnoun
+        solutions = (  # type: ignore
+            " ".join(
+                itertools.chain(
+                    leader,
+                    [inflection(cand, count), prep],  # type: ignore
+                    trailer,
+                )
+            )
+            for leader, (cand, prep), trailer in windowed_complete(word.split_, 2)
+            if prep in pl_prep_list_da  # type: ignore
+        )
+        return next(solutions, None)
+
+    @staticmethod
+    def _find_pivot(words, candidates):
+        pivots = (
+            index for index in range(1, len(words) - 1) if words[index] in candidates
+        )
+        try:
+            return next(pivots)
+        except StopIteration:
+            raise ValueError("No pivot found") from None
+
+    def _pl_special_verb(  # noqa: C901
+        self, word: str, count: Optional[Union[str, int]] = None
+    ) -> Union[str, bool]:
+        if self.classical_dict["zero"] and str(count).lower() in pl_count_zero:
+            return False
+        count = self.get_count(count)
+
+        if count == 1:
+            return word
+
+        # HANDLE USER-DEFINED VERBS
+
+        value = self.ud_match(word, self.pl_v_user_defined)
+        if value is not None:
+            return value
+
+        # HANDLE IRREGULAR PRESENT TENSE (SIMPLE AND COMPOUND)
+
+        try:
+            words = Words(word)
+        except IndexError:
+            return False  # word is ''
+
+        if words.first in plverb_irregular_pres:
+            return f"{plverb_irregular_pres[words.first]}{words[len(words.first) :]}"
+
+        # HANDLE IRREGULAR FUTURE, PRETERITE AND PERFECT TENSES
+
+        if words.first in plverb_irregular_non_pres:
+            return word
+
+        # HANDLE PRESENT NEGATIONS (SIMPLE AND COMPOUND)
+
+        if words.first.endswith("n't") and words.first[:-3] in plverb_irregular_pres:
+            return (
+                f"{plverb_irregular_pres[words.first[:-3]]}n't"
+                f"{words[len(words.first) :]}"
+            )
+
+        if words.first.endswith("n't"):
+            return word
+
+        # HANDLE SPECIAL CASES
+
+        mo = PLVERB_SPECIAL_S_RE.search(word)
+        if mo:
+            return False
+        if WHITESPACE.search(word):
+            return False
+
+        if words.lowered == "quizzes":
+            return "quiz"
+
+        # HANDLE STANDARD 3RD PERSON (CHOP THE ...(e)s OFF SINGLE WORDS)
+
+        if (
+            words.lowered[-4:] in ("ches", "shes", "zzes", "sses")
+            or words.lowered[-3:] == "xes"
+        ):
+            return words[:-2]
+
+        if words.lowered[-3:] == "ies" and len(words) > 3:
+            return words.lowered[:-3] + "y"
+
+        if (
+            words.last.lower() in pl_v_oes_oe
+            or words.lowered[-4:] in pl_v_oes_oe_endings_size4
+            or words.lowered[-5:] in pl_v_oes_oe_endings_size5
+        ):
+            return words[:-1]
+
+        if words.lowered.endswith("oes") and len(words) > 3:
+            return words.lowered[:-2]
+
+        mo = ENDS_WITH_S.search(words)
+        if mo:
+            return mo.group(1)
+
+        # OTHERWISE, A REGULAR VERB (HANDLE ELSEWHERE)
+
+        return False
+
+    def _pl_general_verb(
+        self, word: str, count: Optional[Union[str, int]] = None
+    ) -> str:
+        count = self.get_count(count)
+
+        if count == 1:
+            return word
+
+        # HANDLE AMBIGUOUS PRESENT TENSES  (SIMPLE AND COMPOUND)
+
+        mo = plverb_ambiguous_pres_keys.search(word)
+        if mo:
+            return f"{plverb_ambiguous_pres[mo.group(1).lower()]}{mo.group(2)}"
+
+        # HANDLE AMBIGUOUS PRETERITE AND PERFECT TENSES
+
+        mo = plverb_ambiguous_non_pres.search(word)
+        if mo:
+            return word
+
+        # OTHERWISE, 1st OR 2ND PERSON IS UNINFLECTED
+
+        return word
+
+    def _pl_special_adjective(
+        self, word: str, count: Optional[Union[str, int]] = None
+    ) -> Union[str, bool]:
+        count = self.get_count(count)
+
+        if count == 1:
+            return word
+
+        # HANDLE USER-DEFINED ADJECTIVES
+
+        value = self.ud_match(word, self.pl_adj_user_defined)
+        if value is not None:
+            return value
+
+        # HANDLE KNOWN CASES
+
+        mo = pl_adj_special_keys.search(word)
+        if mo:
+            return pl_adj_special[mo.group(1).lower()]
+
+        # HANDLE POSSESSIVES
+
+        mo = pl_adj_poss_keys.search(word)
+        if mo:
+            return pl_adj_poss[mo.group(1).lower()]
+
+        mo = ENDS_WITH_APOSTROPHE_S.search(word)
+        if mo:
+            pl = self.plural_noun(mo.group(1))
+            trailing_s = "" if pl[-1] == "s" else "s"
+            return f"{pl}'{trailing_s}"
+
+        # OTHERWISE, NO IDEA
+
+        return False
+
+    # @profile
+    def _sinoun(  # noqa: C901
+        self,
+        word: str,
+        count: Optional[Union[str, int]] = None,
+        gender: Optional[str] = None,
+    ) -> Union[str, bool]:
+        count = self.get_count(count)
+
+        # DEFAULT TO PLURAL
+
+        if count == 2:
+            return word
+
+        # SET THE GENDER
+
+        try:
+            if gender is None:
+                gender = self.thegender
+            elif gender not in singular_pronoun_genders:
+                raise BadGenderError
+        except (TypeError, IndexError) as err:
+            raise BadGenderError from err
+
+        # HANDLE USER-DEFINED NOUNS
+
+        value = self.ud_match(word, self.si_sb_user_defined)
+        if value is not None:
+            return value
+
+        # HANDLE EMPTY WORD, SINGULAR COUNT AND UNINFLECTED PLURALS
+
+        if word == "":
+            return word
+
+        if word in si_sb_ois_oi_case:
+            return word[:-1]
+
+        words = Words(word)
+
+        if words.last.lower() in pl_sb_uninflected_complete:
+            if len(words.split_) >= 3:
+                return self._handle_long_compounds(words, count=1) or word
+            return word
+
+        if word in pl_sb_uninflected_caps:
+            return word
+
+        for k, v in pl_sb_uninflected_bysize.items():
+            if words.lowered[-k:] in v:
+                return word
+
+        if self.classical_dict["herd"] and words.last.lower() in pl_sb_uninflected_herd:
+            return word
+
+        if words.last.lower() in pl_sb_C_us_us:
+            return word if self.classical_dict["ancient"] else False
+
+        # HANDLE COMPOUNDS ("Governor General", "mother-in-law", "aide-de-camp", ETC.)
+
+        mo = PL_SB_POSTFIX_ADJ_STEMS_RE.search(word)
+        if mo and mo.group(2) != "":
+            return f"{self._sinoun(mo.group(1), 1, gender=gender)}{mo.group(2)}"
+
+        with contextlib.suppress(ValueError):
+            return self._handle_prepositional_phrase(
+                words.lowered,
+                functools.partial(self._sinoun, count=1, gender=gender),
+                ' ',
+            )
+
+        with contextlib.suppress(ValueError):
+            return self._handle_prepositional_phrase(
+                words.lowered,
+                functools.partial(self._sinoun, count=1, gender=gender),
+                '-',
+            )
+
+        # HANDLE PRONOUNS
+
+        for k, v in si_pron_acc_keys_bysize.items():
+            if words.lowered[-k:] in v:  # ends with accusative pronoun
+                for pk, pv in pl_prep_bysize.items():
+                    if words.lowered[:pk] in pv:  # starts with a prep
+                        if words.lowered.split() == [
+                            words.lowered[:pk],
+                            words.lowered[-k:],
+                        ]:
+                            # only whitespace in between
+                            return words.lowered[:-k] + get_si_pron(
+                                "acc", words.lowered[-k:], gender
+                            )
+
+        try:
+            return get_si_pron("nom", words.lowered, gender)
+        except KeyError:
+            pass
+
+        try:
+            return get_si_pron("acc", words.lowered, gender)
+        except KeyError:
+            pass
+
+        # HANDLE ISOLATED IRREGULAR PLURALS
+
+        if words.last in si_sb_irregular_caps:
+            llen = len(words.last)
+            return f"{word[:-llen]}{si_sb_irregular_caps[words.last]}"
+
+        if words.last.lower() in si_sb_irregular:
+            llen = len(words.last.lower())
+            return f"{word[:-llen]}{si_sb_irregular[words.last.lower()]}"
+
+        dash_split = words.lowered.split("-")
+        if (" ".join(dash_split[-2:])).lower() in si_sb_irregular_compound:
+            llen = len(
+                " ".join(dash_split[-2:])
+            )  # TODO: what if 2 spaces between these words?
+            return "{}{}".format(
+                word[:-llen],
+                si_sb_irregular_compound[(" ".join(dash_split[-2:])).lower()],
+            )
+
+        if words.lowered[-5:] == "quies":
+            return word[:-3] + "y"
+
+        if words.lowered[-7:] == "persons":
+            return word[:-1]
+        if words.lowered[-6:] == "people":
+            return word[:-4] + "rson"
+
+        # HANDLE FAMILIES OF IRREGULAR PLURALS
+
+        if words.lowered[-4:] == "mans":
+            for k, v in si_sb_U_man_mans_bysize.items():
+                if words.lowered[-k:] in v:
+                    return word[:-1]
+            for k, v in si_sb_U_man_mans_caps_bysize.items():
+                if word[-k:] in v:
+                    return word[:-1]
+        if words.lowered[-3:] == "men":
+            return word[:-3] + "man"
+        if words.lowered[-4:] == "mice":
+            return word[:-4] + "mouse"
+        if words.lowered[-4:] == "lice":
+            v = si_sb_U_louse_lice_bysize.get(len(word))
+            if v and words.lowered in v:
+                return word[:-4] + "louse"
+        if words.lowered[-5:] == "geese":
+            return word[:-5] + "goose"
+        if words.lowered[-5:] == "teeth":
+            return word[:-5] + "tooth"
+        if words.lowered[-4:] == "feet":
+            return word[:-4] + "foot"
+
+        if words.lowered == "dice":
+            return "die"
+
+        # HANDLE UNASSIMILATED IMPORTS
+
+        if words.lowered[-4:] == "ceps":
+            return word
+        if words.lowered[-3:] == "zoa":
+            return word[:-1] + "on"
+
+        for lastlet, d, unass_numend, post in (
+            ("s", si_sb_U_ch_chs_bysize, -1, ""),
+            ("s", si_sb_U_ex_ices_bysize, -4, "ex"),
+            ("s", si_sb_U_ix_ices_bysize, -4, "ix"),
+            ("a", si_sb_U_um_a_bysize, -1, "um"),
+            ("i", si_sb_U_us_i_bysize, -1, "us"),
+            ("a", si_sb_U_on_a_bysize, -1, "on"),
+            ("e", si_sb_U_a_ae_bysize, -1, ""),
+        ):
+            if words.lowered[-1] == lastlet:  # this test to add speed
+                for k, v in d.items():
+                    if words.lowered[-k:] in v:
+                        return word[:unass_numend] + post
+
+        # HANDLE INCOMPLETELY ASSIMILATED IMPORTS
+
+        if self.classical_dict["ancient"]:
+            if words.lowered[-6:] == "trices":
+                return word[:-3] + "x"
+            if words.lowered[-4:] in ("eaux", "ieux"):
+                return word[:-1]
+            if words.lowered[-5:] in ("ynges", "inges", "anges") and len(word) > 6:
+                return word[:-3] + "x"
+
+            for lastlet, d, class_numend, post in (
+                ("a", si_sb_C_en_ina_bysize, -3, "en"),
+                ("s", si_sb_C_ex_ices_bysize, -4, "ex"),
+                ("s", si_sb_C_ix_ices_bysize, -4, "ix"),
+                ("a", si_sb_C_um_a_bysize, -1, "um"),
+                ("i", si_sb_C_us_i_bysize, -1, "us"),
+                ("s", pl_sb_C_us_us_bysize, None, ""),
+                ("e", si_sb_C_a_ae_bysize, -1, ""),
+                ("a", si_sb_C_a_ata_bysize, -2, ""),
+                ("s", si_sb_C_is_ides_bysize, -3, "s"),
+                ("i", si_sb_C_o_i_bysize, -1, "o"),
+                ("a", si_sb_C_on_a_bysize, -1, "on"),
+                ("m", si_sb_C_im_bysize, -2, ""),
+                ("i", si_sb_C_i_bysize, -1, ""),
+            ):
+                if words.lowered[-1] == lastlet:  # this test to add speed
+                    for k, v in d.items():
+                        if words.lowered[-k:] in v:
+                            return word[:class_numend] + post
+
+        # HANDLE PLURLS ENDING IN uses -> use
+
+        if (
+            words.lowered[-6:] == "houses"
+            or word in si_sb_uses_use_case
+            or words.last.lower() in si_sb_uses_use
+        ):
+            return word[:-1]
+
+        # HANDLE PLURLS ENDING IN ies -> ie
+
+        if word in si_sb_ies_ie_case or words.last.lower() in si_sb_ies_ie:
+            return word[:-1]
+
+        # HANDLE PLURLS ENDING IN oes -> oe
+
+        if (
+            words.lowered[-5:] == "shoes"
+            or word in si_sb_oes_oe_case
+            or words.last.lower() in si_sb_oes_oe
+        ):
+            return word[:-1]
+
+        # HANDLE SINGULAR NOUNS ENDING IN ...s OR OTHER SILIBANTS
+
+        if word in si_sb_sses_sse_case or words.last.lower() in si_sb_sses_sse:
+            return word[:-1]
+
+        if words.last.lower() in si_sb_singular_s_complete:
+            return word[:-2]
+
+        for k, v in si_sb_singular_s_bysize.items():
+            if words.lowered[-k:] in v:
+                return word[:-2]
+
+        if words.lowered[-4:] == "eses" and word[0] == word[0].upper():
+            return word[:-2]
+
+        if words.last.lower() in si_sb_z_zes:
+            return word[:-2]
+
+        if words.last.lower() in si_sb_zzes_zz:
+            return word[:-2]
+
+        if words.lowered[-4:] == "zzes":
+            return word[:-3]
+
+        if word in si_sb_ches_che_case or words.last.lower() in si_sb_ches_che:
+            return word[:-1]
+
+        if words.lowered[-4:] in ("ches", "shes"):
+            return word[:-2]
+
+        if words.last.lower() in si_sb_xes_xe:
+            return word[:-1]
+
+        if words.lowered[-3:] == "xes":
+            return word[:-2]
+
+        # HANDLE ...f -> ...ves
+
+        if word in si_sb_ves_ve_case or words.last.lower() in si_sb_ves_ve:
+            return word[:-1]
+
+        if words.lowered[-3:] == "ves":
+            if words.lowered[-5:-3] in ("el", "al", "ol"):
+                return word[:-3] + "f"
+            if words.lowered[-5:-3] == "ea" and word[-6:-5] != "d":
+                return word[:-3] + "f"
+            if words.lowered[-5:-3] in ("ni", "li", "wi"):
+                return word[:-3] + "fe"
+            if words.lowered[-5:-3] == "ar":
+                return word[:-3] + "f"
+
+        # HANDLE ...y
+
+        if words.lowered[-2:] == "ys":
+            if len(words.lowered) > 2 and words.lowered[-3] in "aeiou":
+                return word[:-1]
+
+            if self.classical_dict["names"]:
+                if words.lowered[-2:] == "ys" and word[0] == word[0].upper():
+                    return word[:-1]
+
+        if words.lowered[-3:] == "ies":
+            return word[:-3] + "y"
+
+        # HANDLE ...o
+
+        if words.lowered[-2:] == "os":
+            if words.last.lower() in si_sb_U_o_os_complete:
+                return word[:-1]
+
+            for k, v in si_sb_U_o_os_bysize.items():
+                if words.lowered[-k:] in v:
+                    return word[:-1]
+
+            if words.lowered[-3:] in ("aos", "eos", "ios", "oos", "uos"):
+                return word[:-1]
+
+        if words.lowered[-3:] == "oes":
+            return word[:-2]
+
+        # UNASSIMILATED IMPORTS FINAL RULE
+
+        if word in si_sb_es_is:
+            return word[:-2] + "is"
+
+        # OTHERWISE JUST REMOVE ...s
+
+        if words.lowered[-1] == "s":
+            return word[:-1]
+
+        # COULD NOT FIND SINGULAR
+
+        return False
+
+    # ADJECTIVES
+
+    @typechecked
+    def a(self, text: Word, count: Optional[Union[int, str, Any]] = 1) -> str:
+        """
+        Return the appropriate indefinite article followed by text.
+
+        The indefinite article is either 'a' or 'an'.
+
+        If count is not one, then return count followed by text
+        instead of 'a' or 'an'.
+
+        Whitespace at the start and end is preserved.
+
+        """
+        mo = INDEFINITE_ARTICLE_TEST.search(text)
+        if mo:
+            word = mo.group(2)
+            if not word:
+                return text
+            pre = mo.group(1)
+            post = mo.group(3)
+            result = self._indef_article(word, count)
+            return f"{pre}{result}{post}"
+        return ""
+
+    an = a
+
+    _indef_article_cases = (
+        # HANDLE ORDINAL FORMS
+        (A_ordinal_a, "a"),
+        (A_ordinal_an, "an"),
+        # HANDLE SPECIAL CASES
+        (A_explicit_an, "an"),
+        (SPECIAL_AN, "an"),
+        (SPECIAL_A, "a"),
+        # HANDLE ABBREVIATIONS
+        (A_abbrev, "an"),
+        (SPECIAL_ABBREV_AN, "an"),
+        (SPECIAL_ABBREV_A, "a"),
+        # HANDLE CONSONANTS
+        (CONSONANTS, "a"),
+        # HANDLE SPECIAL VOWEL-FORMS
+        (ARTICLE_SPECIAL_EU, "a"),
+        (ARTICLE_SPECIAL_ONCE, "a"),
+        (ARTICLE_SPECIAL_ONETIME, "a"),
+        (ARTICLE_SPECIAL_UNIT, "a"),
+        (ARTICLE_SPECIAL_UBA, "a"),
+        (ARTICLE_SPECIAL_UKR, "a"),
+        (A_explicit_a, "a"),
+        # HANDLE SPECIAL CAPITALS
+        (SPECIAL_CAPITALS, "a"),
+        # HANDLE VOWELS
+        (VOWELS, "an"),
+        # HANDLE y...
+        # (BEFORE CERTAIN CONSONANTS IMPLIES (UNNATURALIZED) "i.." SOUND)
+        (A_y_cons, "an"),
+    )
+
+    def _indef_article(self, word: str, count: Union[int, str, Any]) -> str:
+        mycount = self.get_count(count)
+
+        if mycount != 1:
+            return f"{count} {word}"
+
+        # HANDLE USER-DEFINED VARIANTS
+
+        value = self.ud_match(word, self.A_a_user_defined)
+        if value is not None:
+            return f"{value} {word}"
+
+        matches = (
+            f'{article} {word}'
+            for regexen, article in self._indef_article_cases
+            if regexen.search(word)
+        )
+
+        # OTHERWISE, GUESS "a"
+        fallback = f'a {word}'
+        return next(matches, fallback)
+
+    # 2. TRANSLATE ZERO-QUANTIFIED $word TO "no plural($word)"
+
+    @typechecked
+    def no(self, text: Word, count: Optional[Union[int, str]] = None) -> str:
+        """
+        If count is 0, no, zero or nil, return 'no' followed by the plural
+        of text.
+
+        If count is one of:
+            1, a, an, one, each, every, this, that
+            return count followed by text.
+
+        Otherwise return count follow by the plural of text.
+
+        In the return value count is always followed by a space.
+
+        Whitespace at the start and end is preserved.
+
+        """
+        if count is None and self.persistent_count is not None:
+            count = self.persistent_count
+
+        if count is None:
+            count = 0
+        mo = PARTITION_WORD.search(text)
+        if mo:
+            pre = mo.group(1)
+            word = mo.group(2)
+            post = mo.group(3)
+        else:
+            pre = ""
+            word = ""
+            post = ""
+
+        if str(count).lower() in pl_count_zero:
+            count = 'no'
+        return f"{pre}{count} {self.plural(word, count)}{post}"
+
+    # PARTICIPLES
+
+    @typechecked
+    def present_participle(self, word: Word) -> str:
+        """
+        Return the present participle for word.
+
+        word is the 3rd person singular verb.
+
+        """
+        plv = self.plural_verb(word, 2)
+        ans = plv
+
+        for regexen, repl in PRESENT_PARTICIPLE_REPLACEMENTS:
+            ans, num = regexen.subn(repl, plv)
+            if num:
+                return f"{ans}ing"
+        return f"{ans}ing"
+
+    # NUMERICAL INFLECTIONS
+
+    @typechecked
+    def ordinal(self, num: Union[Number, Word]) -> str:
+        """
+        Return the ordinal of num.
+
+        >>> ordinal = engine().ordinal
+        >>> ordinal(1)
+        '1st'
+        >>> ordinal('one')
+        'first'
+        """
+        if DIGIT.match(str(num)):
+            if isinstance(num, (float, int)) and int(num) == num:
+                n = int(num)
+            else:
+                if "." in str(num):
+                    try:
+                        # numbers after decimal,
+                        # so only need last one for ordinal
+                        n = int(str(num)[-1])
+
+                    except ValueError:  # ends with '.', so need to use whole string
+                        n = int(str(num)[:-1])
+                else:
+                    n = int(num)  # type: ignore
+            try:
+                post = nth[n % 100]
+            except KeyError:
+                post = nth[n % 10]
+            return f"{num}{post}"
+        else:
+            return self._sub_ord(num)
+
+    def millfn(self, ind: int = 0) -> str:
+        if ind > len(mill) - 1:
+            raise NumOutOfRangeError
+        return mill[ind]
+
+    def unitfn(self, units: int, mindex: int = 0) -> str:
+        return f"{unit[units]}{self.millfn(mindex)}"
+
+    def tenfn(self, tens, units, mindex=0) -> str:
+        if tens != 1:
+            tens_part = ten[tens]
+            if tens and units:
+                hyphen = "-"
+            else:
+                hyphen = ""
+            unit_part = unit[units]
+            mill_part = self.millfn(mindex)
+            return f"{tens_part}{hyphen}{unit_part}{mill_part}"
+        return f"{teen[units]}{mill[mindex]}"
+
+    def hundfn(self, hundreds: int, tens: int, units: int, mindex: int) -> str:
+        if hundreds:
+            andword = f" {self._number_args['andword']} " if tens or units else ""
+            # use unit not unitfn as simpler
+            return (
+                f"{unit[hundreds]} hundred{andword}"
+                f"{self.tenfn(tens, units)}{self.millfn(mindex)}, "
+            )
+        if tens or units:
+            return f"{self.tenfn(tens, units)}{self.millfn(mindex)}, "
+        return ""
+
+    def group1sub(self, mo: Match) -> str:
+        units = int(mo.group(1))
+        if units == 1:
+            return f" {self._number_args['one']}, "
+        elif units:
+            return f"{unit[units]}, "
+        else:
+            return f" {self._number_args['zero']}, "
+
+    def group1bsub(self, mo: Match) -> str:
+        units = int(mo.group(1))
+        if units:
+            return f"{unit[units]}, "
+        else:
+            return f" {self._number_args['zero']}, "
+
+    def group2sub(self, mo: Match) -> str:
+        tens = int(mo.group(1))
+        units = int(mo.group(2))
+        if tens:
+            return f"{self.tenfn(tens, units)}, "
+        if units:
+            return f" {self._number_args['zero']} {unit[units]}, "
+        return f" {self._number_args['zero']} {self._number_args['zero']}, "
+
+    def group3sub(self, mo: Match) -> str:
+        hundreds = int(mo.group(1))
+        tens = int(mo.group(2))
+        units = int(mo.group(3))
+        if hundreds == 1:
+            hunword = f" {self._number_args['one']}"
+        elif hundreds:
+            hunword = str(unit[hundreds])
+        else:
+            hunword = f" {self._number_args['zero']}"
+        if tens:
+            tenword = self.tenfn(tens, units)
+        elif units:
+            tenword = f" {self._number_args['zero']} {unit[units]}"
+        else:
+            tenword = f" {self._number_args['zero']} {self._number_args['zero']}"
+        return f"{hunword} {tenword}, "
+
+    def hundsub(self, mo: Match) -> str:
+        ret = self.hundfn(
+            int(mo.group(1)), int(mo.group(2)), int(mo.group(3)), self.mill_count
+        )
+        self.mill_count += 1
+        return ret
+
+    def tensub(self, mo: Match) -> str:
+        return f"{self.tenfn(int(mo.group(1)), int(mo.group(2)), self.mill_count)}, "
+
+    def unitsub(self, mo: Match) -> str:
+        return f"{self.unitfn(int(mo.group(1)), self.mill_count)}, "
+
+    def enword(self, num: str, group: int) -> str:
+        # import pdb
+        # pdb.set_trace()
+
+        if group == 1:
+            num = DIGIT_GROUP.sub(self.group1sub, num)
+        elif group == 2:
+            num = TWO_DIGITS.sub(self.group2sub, num)
+            num = DIGIT_GROUP.sub(self.group1bsub, num, 1)
+        elif group == 3:
+            num = THREE_DIGITS.sub(self.group3sub, num)
+            num = TWO_DIGITS.sub(self.group2sub, num, 1)
+            num = DIGIT_GROUP.sub(self.group1sub, num, 1)
+        elif int(num) == 0:
+            num = self._number_args["zero"]
+        elif int(num) == 1:
+            num = self._number_args["one"]
+        else:
+            num = num.lstrip().lstrip("0")
+            self.mill_count = 0
+            # surely there's a better way to do the next bit
+            mo = THREE_DIGITS_WORD.search(num)
+            while mo:
+                num = THREE_DIGITS_WORD.sub(self.hundsub, num, 1)
+                mo = THREE_DIGITS_WORD.search(num)
+            num = TWO_DIGITS_WORD.sub(self.tensub, num, 1)
+            num = ONE_DIGIT_WORD.sub(self.unitsub, num, 1)
+        return num
+
+    @staticmethod
+    def _sub_ord(val):
+        new = ordinal_suff.sub(lambda match: ordinal[match.group(1)], val)
+        return new + "th" * (new == val)
+
+    @classmethod
+    def _chunk_num(cls, num, decimal, group):
+        if decimal:
+            max_split = -1 if group != 0 else 1
+            chunks = num.split(".", max_split)
+        else:
+            chunks = [num]
+        return cls._remove_last_blank(chunks)
+
+    @staticmethod
+    def _remove_last_blank(chunks):
+        """
+        Remove the last item from chunks if it's a blank string.
+
+        Return the resultant chunks and whether the last item was removed.
+        """
+        removed = chunks[-1] == ""
+        result = chunks[:-1] if removed else chunks
+        return result, removed
+
+    @staticmethod
+    def _get_sign(num):
+        return {'+': 'plus', '-': 'minus'}.get(num.lstrip()[0], '')
+
+    @typechecked
+    def number_to_words(  # noqa: C901
+        self,
+        num: Union[Number, Word],
+        wantlist: bool = False,
+        group: int = 0,
+        comma: Union[Falsish, str] = ",",
+        andword: str = "and",
+        zero: str = "zero",
+        one: str = "one",
+        decimal: Union[Falsish, str] = "point",
+        threshold: Optional[int] = None,
+    ) -> Union[str, List[str]]:
+        """
+        Return a number in words.
+
+        group = 1, 2 or 3 to group numbers before turning into words
+        comma: define comma
+
+        andword:
+            word for 'and'. Can be set to ''.
+            e.g. "one hundred and one" vs "one hundred one"
+
+        zero: word for '0'
+        one: word for '1'
+        decimal: word for decimal point
+        threshold: numbers above threshold not turned into words
+
+        parameters not remembered from last call. Departure from Perl version.
+        """
+        self._number_args = {"andword": andword, "zero": zero, "one": one}
+        num = str(num)
+
+        # Handle "stylistic" conversions (up to a given threshold)...
+        if threshold is not None and float(num) > threshold:
+            spnum = num.split(".", 1)
+            while comma:
+                (spnum[0], n) = FOUR_DIGIT_COMMA.subn(r"\1,\2", spnum[0])
+                if n == 0:
+                    break
+            try:
+                return f"{spnum[0]}.{spnum[1]}"
+            except IndexError:
+                return str(spnum[0])
+
+        if group < 0 or group > 3:
+            raise BadChunkingOptionError
+
+        sign = self._get_sign(num)
+
+        if num in nth_suff:
+            num = zero
+
+        myord = num[-2:] in nth_suff
+        if myord:
+            num = num[:-2]
+
+        chunks, finalpoint = self._chunk_num(num, decimal, group)
+
+        loopstart = chunks[0] == ""
+        first: bool | None = not loopstart
+
+        def _handle_chunk(chunk):
+            nonlocal first
+
+            # remove all non numeric \D
+            chunk = NON_DIGIT.sub("", chunk)
+            if chunk == "":
+                chunk = "0"
+
+            if group == 0 and not first:
+                chunk = self.enword(chunk, 1)
+            else:
+                chunk = self.enword(chunk, group)
+
+            if chunk[-2:] == ", ":
+                chunk = chunk[:-2]
+            chunk = WHITESPACES_COMMA.sub(",", chunk)
+
+            if group == 0 and first:
+                chunk = COMMA_WORD.sub(f" {andword} \\1", chunk)
+            chunk = WHITESPACES.sub(" ", chunk)
+            # chunk = re.sub(r"(\A\s|\s\Z)", self.blankfn, chunk)
+            chunk = chunk.strip()
+            if first:
+                first = None
+            return chunk
+
+        chunks[loopstart:] = map(_handle_chunk, chunks[loopstart:])
+
+        numchunks = []
+        if first != 0:
+            numchunks = chunks[0].split(f"{comma} ")
+
+        if myord and numchunks:
+            numchunks[-1] = self._sub_ord(numchunks[-1])
+
+        for chunk in chunks[1:]:
+            numchunks.append(decimal)
+            numchunks.extend(chunk.split(f"{comma} "))
+
+        if finalpoint:
+            numchunks.append(decimal)
+
+        if wantlist:
+            return [sign] * bool(sign) + numchunks
+
+        signout = f"{sign} " if sign else ""
+        valout = (
+            ', '.join(numchunks)
+            if group
+            else ''.join(self._render(numchunks, decimal, comma))
+        )
+        return signout + valout
+
+    @staticmethod
+    def _render(chunks, decimal, comma):
+        first_item = chunks.pop(0)
+        yield first_item
+        first = decimal is None or not first_item.endswith(decimal)
+        for nc in chunks:
+            if nc == decimal:
+                first = False
+            elif first:
+                yield comma
+            yield f" {nc}"
+
+    @typechecked
+    def join(
+        self,
+        words: Optional[Sequence[Word]],
+        sep: Optional[str] = None,
+        sep_spaced: bool = True,
+        final_sep: Optional[str] = None,
+        conj: str = "and",
+        conj_spaced: bool = True,
+    ) -> str:
+        """
+        Join words into a list.
+
+        e.g. join(['ant', 'bee', 'fly']) returns 'ant, bee, and fly'
+
+        options:
+        conj: replacement for 'and'
+        sep: separator. default ',', unless ',' is in the list then ';'
+        final_sep: final separator. default ',', unless ',' is in the list then ';'
+        conj_spaced: boolean. Should conj have spaces around it
+
+        """
+        if not words:
+            return ""
+        if len(words) == 1:
+            return words[0]
+
+        if conj_spaced:
+            if conj == "":
+                conj = " "
+            else:
+                conj = f" {conj} "
+
+        if len(words) == 2:
+            return f"{words[0]}{conj}{words[1]}"
+
+        if sep is None:
+            if "," in "".join(words):
+                sep = ";"
+            else:
+                sep = ","
+        if final_sep is None:
+            final_sep = sep
+
+        final_sep = f"{final_sep}{conj}"
+
+        if sep_spaced:
+            sep += " "
+
+        return f"{sep.join(words[0:-1])}{final_sep}{words[-1]}"
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/__pycache__/__init__.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..11ac46673db517ad774bf4815b8d7266712a0b13
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/__pycache__/__init__.cpython-312.pyc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95cdee3a265244a9efe06c223421be025fac77c383ce607ed777f872bbaa2d80
+size 113085
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/compat/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/compat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/compat/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/compat/__pycache__/__init__.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b9f24f49780c549dff4d200e0f6c2d1500137a29
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/compat/__pycache__/py38.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/compat/__pycache__/py38.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b85cf161e5a3c24ddd40c5926b4614d6f2dca7c6
Binary files /dev/null and b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/compat/__pycache__/py38.cpython-312.pyc differ
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/compat/py38.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/compat/py38.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2d01bd98f4ae3d9236d0e6ec3b89faa9ca706ae
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/compat/py38.py
@@ -0,0 +1,7 @@
+import sys
+
+
+if sys.version_info > (3, 9):
+    from typing import Annotated
+else:  # pragma: no cover
+    from typing_extensions import Annotated  # noqa: F401
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/py.typed b/.venv/lib/python3.12/site-packages/setuptools/_vendor/inflect/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..1bb5a44356f00884a71ceeefd24ded6caaba2418
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/LICENSE
@@ -0,0 +1,17 @@
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to
+deal in the Software without restriction, including without limitation the
+rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+sell copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+IN THE SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..fe6ca5ad880ff2a77c81a94ed84ee60c742e298f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/METADATA
@@ -0,0 +1,85 @@
+Metadata-Version: 2.1
+Name: jaraco.collections
+Version: 5.1.0
+Summary: Collection objects similar to those in stdlib by jaraco
+Author-email: "Jason R. Coombs" <jaraco@jaraco.com>
+Project-URL: Source, https://github.com/jaraco/jaraco.collections
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: jaraco.text
+Provides-Extra: check
+Requires-Dist: pytest-checkdocs >=2.4 ; extra == 'check'
+Requires-Dist: pytest-ruff >=0.2.1 ; (sys_platform != "cygwin") and extra == 'check'
+Provides-Extra: cover
+Requires-Dist: pytest-cov ; extra == 'cover'
+Provides-Extra: doc
+Requires-Dist: sphinx >=3.5 ; extra == 'doc'
+Requires-Dist: jaraco.packaging >=9.3 ; extra == 'doc'
+Requires-Dist: rst.linker >=1.9 ; extra == 'doc'
+Requires-Dist: furo ; extra == 'doc'
+Requires-Dist: sphinx-lint ; extra == 'doc'
+Requires-Dist: jaraco.tidelift >=1.4 ; extra == 'doc'
+Provides-Extra: enabler
+Requires-Dist: pytest-enabler >=2.2 ; extra == 'enabler'
+Provides-Extra: test
+Requires-Dist: pytest !=8.1.*,>=6 ; extra == 'test'
+Provides-Extra: type
+Requires-Dist: pytest-mypy ; extra == 'type'
+
+.. image:: https://img.shields.io/pypi/v/jaraco.collections.svg
+   :target: https://pypi.org/project/jaraco.collections
+
+.. image:: https://img.shields.io/pypi/pyversions/jaraco.collections.svg
+
+.. image:: https://github.com/jaraco/jaraco.collections/actions/workflows/main.yml/badge.svg
+   :target: https://github.com/jaraco/jaraco.collections/actions?query=workflow%3A%22tests%22
+   :alt: tests
+
+.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
+    :target: https://github.com/astral-sh/ruff
+    :alt: Ruff
+
+.. image:: https://readthedocs.org/projects/jaracocollections/badge/?version=latest
+   :target: https://jaracocollections.readthedocs.io/en/latest/?badge=latest
+
+.. image:: https://img.shields.io/badge/skeleton-2024-informational
+   :target: https://blog.jaraco.com/skeleton
+
+.. image:: https://tidelift.com/badges/package/pypi/jaraco.collections
+   :target: https://tidelift.com/subscription/pkg/pypi-jaraco.collections?utm_source=pypi-jaraco.collections&utm_medium=readme
+
+Models and classes to supplement the stdlib 'collections' module.
+
+See the docs, linked above, for descriptions and usage examples.
+
+Highlights include:
+
+- RangeMap: A mapping that accepts a range of values for keys.
+- Projection: A subset over an existing mapping.
+- KeyTransformingDict: Generalized mapping with keys transformed by a function.
+- FoldedCaseKeyedDict: A dict whose string keys are case-insensitive.
+- BijectiveMap: A map where keys map to values and values back to their keys.
+- ItemsAsAttributes: A mapping mix-in exposing items as attributes.
+- IdentityOverrideMap: A map whose keys map by default to themselves unless overridden.
+- FrozenDict: A hashable, immutable map.
+- Enumeration: An object whose keys are enumerated.
+- Everything: A container that contains all things.
+- Least, Greatest: Objects that are always less than or greater than any other.
+- pop_all: Return all items from the mutable sequence and remove them from that sequence.
+- DictStack: A stack of dicts, great for sharing scopes.
+- WeightedLookup: A specialized RangeMap for selecting an item by weights.
+
+For Enterprise
+==============
+
+Available as part of the Tidelift Subscription.
+
+This project and the maintainers of thousands of other packages are working with Tidelift to deliver one enterprise subscription that covers all of the open source you use.
+
+`Learn more <https://tidelift.com/subscription/pkg/pypi-jaraco.collections?utm_source=pypi-jaraco.collections&utm_medium=referral&utm_campaign=github>`_.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..48b957ec88588e68cf23d25ebf6f5f6b1cfcf43b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/RECORD
@@ -0,0 +1,10 @@
+jaraco.collections-5.1.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+jaraco.collections-5.1.0.dist-info/LICENSE,sha256=htoPAa6uRjSKPD1GUZXcHOzN55956HdppkuNoEsqR0E,1023
+jaraco.collections-5.1.0.dist-info/METADATA,sha256=IMUaliNsA5X1Ox9MXUWOagch5R4Wwb_3M7erp29dBtg,3933
+jaraco.collections-5.1.0.dist-info/RECORD,,
+jaraco.collections-5.1.0.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+jaraco.collections-5.1.0.dist-info/WHEEL,sha256=Mdi9PDNwEZptOjTlUcAth7XJDFtKrHYaQMPulZeBCiQ,91
+jaraco.collections-5.1.0.dist-info/top_level.txt,sha256=0JnN3LfXH4LIRfXL-QFOGCJzQWZO3ELx4R1d_louoQM,7
+jaraco/collections/__init__.py,sha256=Pc1-SqjWm81ad1P0-GttpkwO_LWlnaY6gUq8gcKh2v0,26640
+jaraco/collections/__pycache__/__init__.cpython-312.pyc,,
+jaraco/collections/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/REQUESTED b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/REQUESTED
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new file mode 100644
index 0000000000000000000000000000000000000000..50e1e84e4a3fa44387f2798f8f465963bc3fc406
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (73.0.1)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/top_level.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f6205a5f19a533fd30f90a433e610325ff02f989
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.collections-5.1.0.dist-info/top_level.txt
@@ -0,0 +1 @@
+jaraco
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..1bb5a44356f00884a71ceeefd24ded6caaba2418
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/LICENSE
@@ -0,0 +1,17 @@
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to
+deal in the Software without restriction, including without limitation the
+rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+sell copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+IN THE SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..a36f7c5e82d796eadf667ad461fae3bb5cea0b4a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/METADATA
@@ -0,0 +1,75 @@
+Metadata-Version: 2.1
+Name: jaraco.context
+Version: 5.3.0
+Summary: Useful decorators and context managers
+Home-page: https://github.com/jaraco/jaraco.context
+Author: Jason R. Coombs
+Author-email: jaraco@jaraco.com
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Requires-Python: >=3.8
+License-File: LICENSE
+Requires-Dist: backports.tarfile ; python_version < "3.12"
+Provides-Extra: docs
+Requires-Dist: sphinx >=3.5 ; extra == 'docs'
+Requires-Dist: jaraco.packaging >=9.3 ; extra == 'docs'
+Requires-Dist: rst.linker >=1.9 ; extra == 'docs'
+Requires-Dist: furo ; extra == 'docs'
+Requires-Dist: sphinx-lint ; extra == 'docs'
+Requires-Dist: jaraco.tidelift >=1.4 ; extra == 'docs'
+Provides-Extra: testing
+Requires-Dist: pytest !=8.1.1,>=6 ; extra == 'testing'
+Requires-Dist: pytest-checkdocs >=2.4 ; extra == 'testing'
+Requires-Dist: pytest-cov ; extra == 'testing'
+Requires-Dist: pytest-mypy ; extra == 'testing'
+Requires-Dist: pytest-enabler >=2.2 ; extra == 'testing'
+Requires-Dist: pytest-ruff >=0.2.1 ; extra == 'testing'
+Requires-Dist: portend ; extra == 'testing'
+
+.. image:: https://img.shields.io/pypi/v/jaraco.context.svg
+   :target: https://pypi.org/project/jaraco.context
+
+.. image:: https://img.shields.io/pypi/pyversions/jaraco.context.svg
+
+.. image:: https://github.com/jaraco/jaraco.context/actions/workflows/main.yml/badge.svg
+   :target: https://github.com/jaraco/jaraco.context/actions?query=workflow%3A%22tests%22
+   :alt: tests
+
+.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
+    :target: https://github.com/astral-sh/ruff
+    :alt: Ruff
+
+.. image:: https://readthedocs.org/projects/jaracocontext/badge/?version=latest
+   :target: https://jaracocontext.readthedocs.io/en/latest/?badge=latest
+
+.. image:: https://img.shields.io/badge/skeleton-2024-informational
+   :target: https://blog.jaraco.com/skeleton
+
+.. image:: https://tidelift.com/badges/package/pypi/jaraco.context
+   :target: https://tidelift.com/subscription/pkg/pypi-jaraco.context?utm_source=pypi-jaraco.context&utm_medium=readme
+
+
+Highlights
+==========
+
+See the docs linked from the badge above for the full details, but here are some features that may be of interest.
+
+- ``ExceptionTrap`` provides a general-purpose wrapper for trapping exceptions and then acting on the outcome. Includes ``passes`` and ``raises`` decorators to replace the result of a wrapped function by a boolean indicating the outcome of the exception trap. See `this keyring commit <https://github.com/jaraco/keyring/commit/a85a7cbc6c909f8121660ed1f7b487f99a1c2bf7>`_ for an example of it in production.
+- ``suppress`` simply enables ``contextlib.suppress`` as a decorator.
+- ``on_interrupt`` is a decorator used by CLI entry points to affect the handling of a ``KeyboardInterrupt``. Inspired by `Lucretiel/autocommand#18 <https://github.com/Lucretiel/autocommand/issues/18>`_.
+- ``pushd`` is similar to pytest's ``monkeypatch.chdir`` or path's `default context <https://path.readthedocs.io/en/latest/api.html>`_, changes the current working directory for the duration of the context.
+- ``tarball`` will download a tarball, extract it, change directory, yield, then clean up after. Convenient when working with web assets.
+- ``null`` is there for those times when one code branch needs a context and the other doesn't; this null context provides symmetry across those branches.
+
+
+For Enterprise
+==============
+
+Available as part of the Tidelift Subscription.
+
+This project and the maintainers of thousands of other packages are working with Tidelift to deliver one enterprise subscription that covers all of the open source you use.
+
+`Learn more <https://tidelift.com/subscription/pkg/pypi-jaraco.context?utm_source=pypi-jaraco.context&utm_medium=referral&utm_campaign=github>`_.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..09d191f214a1b83403df2ba891b21f5be94134dc
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/RECORD
@@ -0,0 +1,8 @@
+jaraco.context-5.3.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+jaraco.context-5.3.0.dist-info/LICENSE,sha256=htoPAa6uRjSKPD1GUZXcHOzN55956HdppkuNoEsqR0E,1023
+jaraco.context-5.3.0.dist-info/METADATA,sha256=xDtguJej0tN9iEXCUvxEJh2a7xceIRVBEakBLSr__tY,4020
+jaraco.context-5.3.0.dist-info/RECORD,,
+jaraco.context-5.3.0.dist-info/WHEEL,sha256=GJ7t_kWBFywbagK5eo9IoUwLW6oyOeTKmQ-9iHFVNxQ,92
+jaraco.context-5.3.0.dist-info/top_level.txt,sha256=0JnN3LfXH4LIRfXL-QFOGCJzQWZO3ELx4R1d_louoQM,7
+jaraco/__pycache__/context.cpython-312.pyc,,
+jaraco/context.py,sha256=REoLIxDkO5MfEYowt_WoupNCRoxBS5v7YX2PbW8lIcs,9552
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..bab98d675883cc7567a79df485cd7b4f015e376f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.43.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/top_level.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f6205a5f19a533fd30f90a433e610325ff02f989
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.context-5.3.0.dist-info/top_level.txt
@@ -0,0 +1 @@
+jaraco
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..1bb5a44356f00884a71ceeefd24ded6caaba2418
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/LICENSE
@@ -0,0 +1,17 @@
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to
+deal in the Software without restriction, including without limitation the
+rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+sell copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+IN THE SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..c865140ab2971d078e8605fa22d8e4a27c2fecff
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/METADATA
@@ -0,0 +1,64 @@
+Metadata-Version: 2.1
+Name: jaraco.functools
+Version: 4.0.1
+Summary: Functools like those found in stdlib
+Author-email: "Jason R. Coombs" <jaraco@jaraco.com>
+Project-URL: Homepage, https://github.com/jaraco/jaraco.functools
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: more-itertools
+Provides-Extra: docs
+Requires-Dist: sphinx >=3.5 ; extra == 'docs'
+Requires-Dist: sphinx <7.2.5 ; extra == 'docs'
+Requires-Dist: jaraco.packaging >=9.3 ; extra == 'docs'
+Requires-Dist: rst.linker >=1.9 ; extra == 'docs'
+Requires-Dist: furo ; extra == 'docs'
+Requires-Dist: sphinx-lint ; extra == 'docs'
+Requires-Dist: jaraco.tidelift >=1.4 ; extra == 'docs'
+Provides-Extra: testing
+Requires-Dist: pytest >=6 ; extra == 'testing'
+Requires-Dist: pytest-checkdocs >=2.4 ; extra == 'testing'
+Requires-Dist: pytest-cov ; extra == 'testing'
+Requires-Dist: pytest-enabler >=2.2 ; extra == 'testing'
+Requires-Dist: pytest-ruff >=0.2.1 ; extra == 'testing'
+Requires-Dist: jaraco.classes ; extra == 'testing'
+Requires-Dist: pytest-mypy ; (platform_python_implementation != "PyPy") and extra == 'testing'
+
+.. image:: https://img.shields.io/pypi/v/jaraco.functools.svg
+   :target: https://pypi.org/project/jaraco.functools
+
+.. image:: https://img.shields.io/pypi/pyversions/jaraco.functools.svg
+
+.. image:: https://github.com/jaraco/jaraco.functools/actions/workflows/main.yml/badge.svg
+   :target: https://github.com/jaraco/jaraco.functools/actions?query=workflow%3A%22tests%22
+   :alt: tests
+
+.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
+    :target: https://github.com/astral-sh/ruff
+    :alt: Ruff
+
+.. image:: https://readthedocs.org/projects/jaracofunctools/badge/?version=latest
+   :target: https://jaracofunctools.readthedocs.io/en/latest/?badge=latest
+
+.. image:: https://img.shields.io/badge/skeleton-2024-informational
+   :target: https://blog.jaraco.com/skeleton
+
+.. image:: https://tidelift.com/badges/package/pypi/jaraco.functools
+   :target: https://tidelift.com/subscription/pkg/pypi-jaraco.functools?utm_source=pypi-jaraco.functools&utm_medium=readme
+
+Additional functools in the spirit of stdlib's functools.
+
+For Enterprise
+==============
+
+Available as part of the Tidelift Subscription.
+
+This project and the maintainers of thousands of other packages are working with Tidelift to deliver one enterprise subscription that covers all of the open source you use.
+
+`Learn more <https://tidelift.com/subscription/pkg/pypi-jaraco.functools?utm_source=pypi-jaraco.functools&utm_medium=referral&utm_campaign=github>`_.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..ef3bc21e929e6881fc560bdef1505ed44bf838e1
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+jaraco/functools/__init__.py,sha256=hEAJaS2uSZRuF_JY4CxCHIYh79ZpxaPp9OiHyr9EJ1w,16642
+jaraco/functools/__init__.pyi,sha256=gk3dsgHzo5F_U74HzAvpNivFAPCkPJ1b2-yCd62dfnw,3878
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+jaraco/functools/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/WHEEL
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index 0000000000000000000000000000000000000000..bab98d675883cc7567a79df485cd7b4f015e376f
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+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.43.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/top_level.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f6205a5f19a533fd30f90a433e610325ff02f989
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.functools-4.0.1.dist-info/top_level.txt
@@ -0,0 +1 @@
+jaraco
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..1bb5a44356f00884a71ceeefd24ded6caaba2418
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/LICENSE
@@ -0,0 +1,17 @@
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to
+deal in the Software without restriction, including without limitation the
+rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+sell copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+IN THE SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..0258a380f4acb6abb3b9e8d1bea70836b69a2559
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/METADATA
@@ -0,0 +1,95 @@
+Metadata-Version: 2.1
+Name: jaraco.text
+Version: 3.12.1
+Summary: Module for text manipulation
+Author-email: "Jason R. Coombs" <jaraco@jaraco.com>
+Project-URL: Homepage, https://github.com/jaraco/jaraco.text
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: jaraco.functools
+Requires-Dist: jaraco.context >=4.1
+Requires-Dist: autocommand
+Requires-Dist: inflect
+Requires-Dist: more-itertools
+Requires-Dist: importlib-resources ; python_version < "3.9"
+Provides-Extra: doc
+Requires-Dist: sphinx >=3.5 ; extra == 'doc'
+Requires-Dist: jaraco.packaging >=9.3 ; extra == 'doc'
+Requires-Dist: rst.linker >=1.9 ; extra == 'doc'
+Requires-Dist: furo ; extra == 'doc'
+Requires-Dist: sphinx-lint ; extra == 'doc'
+Requires-Dist: jaraco.tidelift >=1.4 ; extra == 'doc'
+Provides-Extra: test
+Requires-Dist: pytest !=8.1.*,>=6 ; extra == 'test'
+Requires-Dist: pytest-checkdocs >=2.4 ; extra == 'test'
+Requires-Dist: pytest-cov ; extra == 'test'
+Requires-Dist: pytest-mypy ; extra == 'test'
+Requires-Dist: pytest-enabler >=2.2 ; extra == 'test'
+Requires-Dist: pytest-ruff >=0.2.1 ; extra == 'test'
+Requires-Dist: pathlib2 ; (python_version < "3.10") and extra == 'test'
+
+.. image:: https://img.shields.io/pypi/v/jaraco.text.svg
+   :target: https://pypi.org/project/jaraco.text
+
+.. image:: https://img.shields.io/pypi/pyversions/jaraco.text.svg
+
+.. image:: https://github.com/jaraco/jaraco.text/actions/workflows/main.yml/badge.svg
+   :target: https://github.com/jaraco/jaraco.text/actions?query=workflow%3A%22tests%22
+   :alt: tests
+
+.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
+    :target: https://github.com/astral-sh/ruff
+    :alt: Ruff
+
+.. image:: https://readthedocs.org/projects/jaracotext/badge/?version=latest
+   :target: https://jaracotext.readthedocs.io/en/latest/?badge=latest
+
+.. image:: https://img.shields.io/badge/skeleton-2024-informational
+   :target: https://blog.jaraco.com/skeleton
+
+.. image:: https://tidelift.com/badges/package/pypi/jaraco.text
+   :target: https://tidelift.com/subscription/pkg/pypi-jaraco.text?utm_source=pypi-jaraco.text&utm_medium=readme
+
+
+This package provides handy routines for dealing with text, such as
+wrapping, substitution, trimming, stripping, prefix and suffix removal,
+line continuation, indentation, comment processing, identifier processing,
+values parsing, case insensitive comparison, and more. See the docs
+(linked in the badge above) for the detailed documentation and examples.
+
+Layouts
+=======
+
+One of the features of this package is the layouts module, which
+provides a simple example of translating keystrokes from one keyboard
+layout to another::
+
+    echo qwerty | python -m jaraco.text.to-dvorak
+    ',.pyf
+    echo  "',.pyf" | python -m jaraco.text.to-qwerty
+    qwerty
+
+Newline Reporting
+=================
+
+Need to know what newlines appear in a file?
+
+::
+
+    $ python -m jaraco.text.show-newlines README.rst
+    newline is '\n'
+
+For Enterprise
+==============
+
+Available as part of the Tidelift Subscription.
+
+This project and the maintainers of thousands of other packages are working with Tidelift to deliver one enterprise subscription that covers all of the open source you use.
+
+`Learn more <https://tidelift.com/subscription/pkg/pypi-jaraco.text?utm_source=pypi-jaraco.text&utm_medium=referral&utm_campaign=github>`_.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..19e2d8402af9356a13a1335ef2b71222b0aadeec
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/RECORD
@@ -0,0 +1,20 @@
+jaraco.text-3.12.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+jaraco.text-3.12.1.dist-info/LICENSE,sha256=htoPAa6uRjSKPD1GUZXcHOzN55956HdppkuNoEsqR0E,1023
+jaraco.text-3.12.1.dist-info/METADATA,sha256=AzWdm6ViMfDOPoQMfLWn2zgBQSGJScyqeN29TcuWXVI,3658
+jaraco.text-3.12.1.dist-info/RECORD,,
+jaraco.text-3.12.1.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+jaraco.text-3.12.1.dist-info/WHEEL,sha256=GJ7t_kWBFywbagK5eo9IoUwLW6oyOeTKmQ-9iHFVNxQ,92
+jaraco.text-3.12.1.dist-info/top_level.txt,sha256=0JnN3LfXH4LIRfXL-QFOGCJzQWZO3ELx4R1d_louoQM,7
+jaraco/text/Lorem ipsum.txt,sha256=N_7c_79zxOufBY9HZ3yzMgOkNv-TkOTTio4BydrSjgs,1335
+jaraco/text/__init__.py,sha256=Y2YUqXR_orUoDaY4SkPRe6ZZhb5HUHB_Ah9RCNsVyho,16250
+jaraco/text/__pycache__/__init__.cpython-312.pyc,,
+jaraco/text/__pycache__/layouts.cpython-312.pyc,,
+jaraco/text/__pycache__/show-newlines.cpython-312.pyc,,
+jaraco/text/__pycache__/strip-prefix.cpython-312.pyc,,
+jaraco/text/__pycache__/to-dvorak.cpython-312.pyc,,
+jaraco/text/__pycache__/to-qwerty.cpython-312.pyc,,
+jaraco/text/layouts.py,sha256=HTC8aSTLZ7uXipyOXapRMC158juecjK6RVwitfmZ9_w,643
+jaraco/text/show-newlines.py,sha256=WGQa65e8lyhb92LUOLqVn6KaCtoeVgVws6WtSRmLk6w,904
+jaraco/text/strip-prefix.py,sha256=NfVXV8JVNo6nqcuYASfMV7_y4Eo8zMQqlCOGvAnRIVw,412
+jaraco/text/to-dvorak.py,sha256=1SNcbSsvISpXXg-LnybIHHY-RUFOQr36zcHkY1pWFqw,119
+jaraco/text/to-qwerty.py,sha256=s4UMQUnPwFn_dB5uZC27BurHOQcYondBfzIpVL5pEzw,119
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/REQUESTED b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/REQUESTED
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..bab98d675883cc7567a79df485cd7b4f015e376f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.43.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/top_level.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f6205a5f19a533fd30f90a433e610325ff02f989
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco.text-3.12.1.dist-info/top_level.txt
@@ -0,0 +1 @@
+jaraco
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/__pycache__/context.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/__pycache__/context.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..f612da530be960b7e60faf790198f9152772ef07
Binary files /dev/null and b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/__pycache__/context.cpython-312.pyc differ
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/collections/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/collections/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d501cf9e9cef5859f3c1bf844bc7280ce1e7aaa
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/collections/__init__.py
@@ -0,0 +1,1091 @@
+from __future__ import annotations
+
+import collections.abc
+import copy
+import functools
+import itertools
+import operator
+import random
+import re
+from collections.abc import Container, Iterable, Mapping
+from typing import TYPE_CHECKING, Any, Callable, Dict, TypeVar, Union, overload
+
+import jaraco.text
+
+if TYPE_CHECKING:
+    from _operator import _SupportsComparison
+
+    from _typeshed import SupportsKeysAndGetItem
+    from typing_extensions import Self
+
+    _RangeMapKT = TypeVar('_RangeMapKT', bound=_SupportsComparison)
+else:
+    # _SupportsComparison doesn't exist at runtime,
+    # but _RangeMapKT is used in RangeMap's superclass' type parameters
+    _RangeMapKT = TypeVar('_RangeMapKT')
+
+_T = TypeVar('_T')
+_VT = TypeVar('_VT')
+
+_Matchable = Union[Callable, Container, Iterable, re.Pattern]
+
+
+def _dispatch(obj: _Matchable) -> Callable:
+    # can't rely on singledispatch for Union[Container, Iterable]
+    # due to ambiguity
+    # (https://peps.python.org/pep-0443/#abstract-base-classes).
+    if isinstance(obj, re.Pattern):
+        return obj.fullmatch
+    # mypy issue: https://github.com/python/mypy/issues/11071
+    if not isinstance(obj, Callable):  # type: ignore[arg-type]
+        if not isinstance(obj, Container):
+            obj = set(obj)  # type: ignore[arg-type]
+        obj = obj.__contains__
+    return obj  # type: ignore[return-value]
+
+
+class Projection(collections.abc.Mapping):
+    """
+    Project a set of keys over a mapping
+
+    >>> sample = {'a': 1, 'b': 2, 'c': 3}
+    >>> prj = Projection(['a', 'c', 'd'], sample)
+    >>> dict(prj)
+    {'a': 1, 'c': 3}
+
+    Projection also accepts an iterable or callable or pattern.
+
+    >>> iter_prj = Projection(iter('acd'), sample)
+    >>> call_prj = Projection(lambda k: ord(k) in (97, 99, 100), sample)
+    >>> pat_prj = Projection(re.compile(r'[acd]'), sample)
+    >>> prj == iter_prj == call_prj == pat_prj
+    True
+
+    Keys should only appear if they were specified and exist in the space.
+    Order is retained.
+
+    >>> list(prj)
+    ['a', 'c']
+
+    Attempting to access a key not in the projection
+    results in a KeyError.
+
+    >>> prj['b']
+    Traceback (most recent call last):
+    ...
+    KeyError: 'b'
+
+    Use the projection to update another dict.
+
+    >>> target = {'a': 2, 'b': 2}
+    >>> target.update(prj)
+    >>> target
+    {'a': 1, 'b': 2, 'c': 3}
+
+    Projection keeps a reference to the original dict, so
+    modifying the original dict may modify the Projection.
+
+    >>> del sample['a']
+    >>> dict(prj)
+    {'c': 3}
+    """
+
+    def __init__(self, keys: _Matchable, space: Mapping):
+        self._match = _dispatch(keys)
+        self._space = space
+
+    def __getitem__(self, key):
+        if not self._match(key):
+            raise KeyError(key)
+        return self._space[key]
+
+    def _keys_resolved(self):
+        return filter(self._match, self._space)
+
+    def __iter__(self):
+        return self._keys_resolved()
+
+    def __len__(self):
+        return len(tuple(self._keys_resolved()))
+
+
+class Mask(Projection):
+    """
+    The inverse of a :class:`Projection`, masking out keys.
+
+    >>> sample = {'a': 1, 'b': 2, 'c': 3}
+    >>> msk = Mask(['a', 'c', 'd'], sample)
+    >>> dict(msk)
+    {'b': 2}
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # self._match = compose(operator.not_, self._match)
+        self._match = lambda key, orig=self._match: not orig(key)
+
+
+def dict_map(function, dictionary):
+    """
+    Return a new dict with function applied to values of dictionary.
+
+    >>> dict_map(lambda x: x+1, dict(a=1, b=2))
+    {'a': 2, 'b': 3}
+    """
+    return dict((key, function(value)) for key, value in dictionary.items())
+
+
+class RangeMap(Dict[_RangeMapKT, _VT]):
+    """
+    A dictionary-like object that uses the keys as bounds for a range.
+    Inclusion of the value for that range is determined by the
+    key_match_comparator, which defaults to less-than-or-equal.
+    A value is returned for a key if it is the first key that matches in
+    the sorted list of keys.
+
+    One may supply keyword parameters to be passed to the sort function used
+    to sort keys (i.e. key, reverse) as sort_params.
+
+    Create a map that maps 1-3 -> 'a', 4-6 -> 'b'
+
+    >>> r = RangeMap({3: 'a', 6: 'b'})  # boy, that was easy
+    >>> r[1], r[2], r[3], r[4], r[5], r[6]
+    ('a', 'a', 'a', 'b', 'b', 'b')
+
+    Even float values should work so long as the comparison operator
+    supports it.
+
+    >>> r[4.5]
+    'b'
+
+    Notice that the way rangemap is defined, it must be open-ended
+    on one side.
+
+    >>> r[0]
+    'a'
+    >>> r[-1]
+    'a'
+
+    One can close the open-end of the RangeMap by using undefined_value
+
+    >>> r = RangeMap({0: RangeMap.undefined_value, 3: 'a', 6: 'b'})
+    >>> r[0]
+    Traceback (most recent call last):
+    ...
+    KeyError: 0
+
+    One can get the first or last elements in the range by using RangeMap.Item
+
+    >>> last_item = RangeMap.Item(-1)
+    >>> r[last_item]
+    'b'
+
+    .last_item is a shortcut for Item(-1)
+
+    >>> r[RangeMap.last_item]
+    'b'
+
+    Sometimes it's useful to find the bounds for a RangeMap
+
+    >>> r.bounds()
+    (0, 6)
+
+    RangeMap supports .get(key, default)
+
+    >>> r.get(0, 'not found')
+    'not found'
+
+    >>> r.get(7, 'not found')
+    'not found'
+
+    One often wishes to define the ranges by their left-most values,
+    which requires use of sort params and a key_match_comparator.
+
+    >>> r = RangeMap({1: 'a', 4: 'b'},
+    ...     sort_params=dict(reverse=True),
+    ...     key_match_comparator=operator.ge)
+    >>> r[1], r[2], r[3], r[4], r[5], r[6]
+    ('a', 'a', 'a', 'b', 'b', 'b')
+
+    That wasn't nearly as easy as before, so an alternate constructor
+    is provided:
+
+    >>> r = RangeMap.left({1: 'a', 4: 'b', 7: RangeMap.undefined_value})
+    >>> r[1], r[2], r[3], r[4], r[5], r[6]
+    ('a', 'a', 'a', 'b', 'b', 'b')
+
+    """
+
+    def __init__(
+        self,
+        source: (
+            SupportsKeysAndGetItem[_RangeMapKT, _VT] | Iterable[tuple[_RangeMapKT, _VT]]
+        ),
+        sort_params: Mapping[str, Any] = {},
+        key_match_comparator: Callable[[_RangeMapKT, _RangeMapKT], bool] = operator.le,
+    ):
+        dict.__init__(self, source)
+        self.sort_params = sort_params
+        self.match = key_match_comparator
+
+    @classmethod
+    def left(
+        cls,
+        source: (
+            SupportsKeysAndGetItem[_RangeMapKT, _VT] | Iterable[tuple[_RangeMapKT, _VT]]
+        ),
+    ) -> Self:
+        return cls(
+            source, sort_params=dict(reverse=True), key_match_comparator=operator.ge
+        )
+
+    def __getitem__(self, item: _RangeMapKT) -> _VT:
+        sorted_keys = sorted(self.keys(), **self.sort_params)
+        if isinstance(item, RangeMap.Item):
+            result = self.__getitem__(sorted_keys[item])
+        else:
+            key = self._find_first_match_(sorted_keys, item)
+            result = dict.__getitem__(self, key)
+            if result is RangeMap.undefined_value:
+                raise KeyError(key)
+        return result
+
+    @overload  # type: ignore[override] # Signature simplified over dict and Mapping
+    def get(self, key: _RangeMapKT, default: _T) -> _VT | _T: ...
+    @overload
+    def get(self, key: _RangeMapKT, default: None = None) -> _VT | None: ...
+    def get(self, key: _RangeMapKT, default: _T | None = None) -> _VT | _T | None:
+        """
+        Return the value for key if key is in the dictionary, else default.
+        If default is not given, it defaults to None, so that this method
+        never raises a KeyError.
+        """
+        try:
+            return self[key]
+        except KeyError:
+            return default
+
+    def _find_first_match_(
+        self, keys: Iterable[_RangeMapKT], item: _RangeMapKT
+    ) -> _RangeMapKT:
+        is_match = functools.partial(self.match, item)
+        matches = filter(is_match, keys)
+        try:
+            return next(matches)
+        except StopIteration:
+            raise KeyError(item) from None
+
+    def bounds(self) -> tuple[_RangeMapKT, _RangeMapKT]:
+        sorted_keys = sorted(self.keys(), **self.sort_params)
+        return (sorted_keys[RangeMap.first_item], sorted_keys[RangeMap.last_item])
+
+    # some special values for the RangeMap
+    undefined_value = type('RangeValueUndefined', (), {})()
+
+    class Item(int):
+        """RangeMap Item"""
+
+    first_item = Item(0)
+    last_item = Item(-1)
+
+
+def __identity(x):
+    return x
+
+
+def sorted_items(d, key=__identity, reverse=False):
+    """
+    Return the items of the dictionary sorted by the keys.
+
+    >>> sample = dict(foo=20, bar=42, baz=10)
+    >>> tuple(sorted_items(sample))
+    (('bar', 42), ('baz', 10), ('foo', 20))
+
+    >>> reverse_string = lambda s: ''.join(reversed(s))
+    >>> tuple(sorted_items(sample, key=reverse_string))
+    (('foo', 20), ('bar', 42), ('baz', 10))
+
+    >>> tuple(sorted_items(sample, reverse=True))
+    (('foo', 20), ('baz', 10), ('bar', 42))
+    """
+
+    # wrap the key func so it operates on the first element of each item
+    def pairkey_key(item):
+        return key(item[0])
+
+    return sorted(d.items(), key=pairkey_key, reverse=reverse)
+
+
+class KeyTransformingDict(dict):
+    """
+    A dict subclass that transforms the keys before they're used.
+    Subclasses may override the default transform_key to customize behavior.
+    """
+
+    @staticmethod
+    def transform_key(key):  # pragma: nocover
+        return key
+
+    def __init__(self, *args, **kargs):
+        super().__init__()
+        # build a dictionary using the default constructs
+        d = dict(*args, **kargs)
+        # build this dictionary using transformed keys.
+        for item in d.items():
+            self.__setitem__(*item)
+
+    def __setitem__(self, key, val):
+        key = self.transform_key(key)
+        super().__setitem__(key, val)
+
+    def __getitem__(self, key):
+        key = self.transform_key(key)
+        return super().__getitem__(key)
+
+    def __contains__(self, key):
+        key = self.transform_key(key)
+        return super().__contains__(key)
+
+    def __delitem__(self, key):
+        key = self.transform_key(key)
+        return super().__delitem__(key)
+
+    def get(self, key, *args, **kwargs):
+        key = self.transform_key(key)
+        return super().get(key, *args, **kwargs)
+
+    def setdefault(self, key, *args, **kwargs):
+        key = self.transform_key(key)
+        return super().setdefault(key, *args, **kwargs)
+
+    def pop(self, key, *args, **kwargs):
+        key = self.transform_key(key)
+        return super().pop(key, *args, **kwargs)
+
+    def matching_key_for(self, key):
+        """
+        Given a key, return the actual key stored in self that matches.
+        Raise KeyError if the key isn't found.
+        """
+        try:
+            return next(e_key for e_key in self.keys() if e_key == key)
+        except StopIteration as err:
+            raise KeyError(key) from err
+
+
+class FoldedCaseKeyedDict(KeyTransformingDict):
+    """
+    A case-insensitive dictionary (keys are compared as insensitive
+    if they are strings).
+
+    >>> d = FoldedCaseKeyedDict()
+    >>> d['heLlo'] = 'world'
+    >>> list(d.keys()) == ['heLlo']
+    True
+    >>> list(d.values()) == ['world']
+    True
+    >>> d['hello'] == 'world'
+    True
+    >>> 'hello' in d
+    True
+    >>> 'HELLO' in d
+    True
+    >>> print(repr(FoldedCaseKeyedDict({'heLlo': 'world'})))
+    {'heLlo': 'world'}
+    >>> d = FoldedCaseKeyedDict({'heLlo': 'world'})
+    >>> print(d['hello'])
+    world
+    >>> print(d['Hello'])
+    world
+    >>> list(d.keys())
+    ['heLlo']
+    >>> d = FoldedCaseKeyedDict({'heLlo': 'world', 'Hello': 'world'})
+    >>> list(d.values())
+    ['world']
+    >>> key, = d.keys()
+    >>> key in ['heLlo', 'Hello']
+    True
+    >>> del d['HELLO']
+    >>> d
+    {}
+
+    get should work
+
+    >>> d['Sumthin'] = 'else'
+    >>> d.get('SUMTHIN')
+    'else'
+    >>> d.get('OTHER', 'thing')
+    'thing'
+    >>> del d['sumthin']
+
+    setdefault should also work
+
+    >>> d['This'] = 'that'
+    >>> print(d.setdefault('this', 'other'))
+    that
+    >>> len(d)
+    1
+    >>> print(d['this'])
+    that
+    >>> print(d.setdefault('That', 'other'))
+    other
+    >>> print(d['THAT'])
+    other
+
+    Make it pop!
+
+    >>> print(d.pop('THAT'))
+    other
+
+    To retrieve the key in its originally-supplied form, use matching_key_for
+
+    >>> print(d.matching_key_for('this'))
+    This
+
+    >>> d.matching_key_for('missing')
+    Traceback (most recent call last):
+    ...
+    KeyError: 'missing'
+    """
+
+    @staticmethod
+    def transform_key(key):
+        return jaraco.text.FoldedCase(key)
+
+
+class DictAdapter:
+    """
+    Provide a getitem interface for attributes of an object.
+
+    Let's say you want to get at the string.lowercase property in a formatted
+    string. It's easy with DictAdapter.
+
+    >>> import string
+    >>> print("lowercase is %(ascii_lowercase)s" % DictAdapter(string))
+    lowercase is abcdefghijklmnopqrstuvwxyz
+    """
+
+    def __init__(self, wrapped_ob):
+        self.object = wrapped_ob
+
+    def __getitem__(self, name):
+        return getattr(self.object, name)
+
+
+class ItemsAsAttributes:
+    """
+    Mix-in class to enable a mapping object to provide items as
+    attributes.
+
+    >>> C = type('C', (dict, ItemsAsAttributes), dict())
+    >>> i = C()
+    >>> i['foo'] = 'bar'
+    >>> i.foo
+    'bar'
+
+    Natural attribute access takes precedence
+
+    >>> i.foo = 'henry'
+    >>> i.foo
+    'henry'
+
+    But as you might expect, the mapping functionality is preserved.
+
+    >>> i['foo']
+    'bar'
+
+    A normal attribute error should be raised if an attribute is
+    requested that doesn't exist.
+
+    >>> i.missing
+    Traceback (most recent call last):
+    ...
+    AttributeError: 'C' object has no attribute 'missing'
+
+    It also works on dicts that customize __getitem__
+
+    >>> missing_func = lambda self, key: 'missing item'
+    >>> C = type(
+    ...     'C',
+    ...     (dict, ItemsAsAttributes),
+    ...     dict(__missing__ = missing_func),
+    ... )
+    >>> i = C()
+    >>> i.missing
+    'missing item'
+    >>> i.foo
+    'missing item'
+    """
+
+    def __getattr__(self, key):
+        try:
+            return getattr(super(), key)
+        except AttributeError as e:
+            # attempt to get the value from the mapping (return self[key])
+            #  but be careful not to lose the original exception context.
+            noval = object()
+
+            def _safe_getitem(cont, key, missing_result):
+                try:
+                    return cont[key]
+                except KeyError:
+                    return missing_result
+
+            result = _safe_getitem(self, key, noval)
+            if result is not noval:
+                return result
+            # raise the original exception, but use the original class
+            #  name, not 'super'.
+            (message,) = e.args
+            message = message.replace('super', self.__class__.__name__, 1)
+            e.args = (message,)
+            raise
+
+
+def invert_map(map):
+    """
+    Given a dictionary, return another dictionary with keys and values
+    switched. If any of the values resolve to the same key, raises
+    a ValueError.
+
+    >>> numbers = dict(a=1, b=2, c=3)
+    >>> letters = invert_map(numbers)
+    >>> letters[1]
+    'a'
+    >>> numbers['d'] = 3
+    >>> invert_map(numbers)
+    Traceback (most recent call last):
+    ...
+    ValueError: Key conflict in inverted mapping
+    """
+    res = dict((v, k) for k, v in map.items())
+    if not len(res) == len(map):
+        raise ValueError('Key conflict in inverted mapping')
+    return res
+
+
+class IdentityOverrideMap(dict):
+    """
+    A dictionary that by default maps each key to itself, but otherwise
+    acts like a normal dictionary.
+
+    >>> d = IdentityOverrideMap()
+    >>> d[42]
+    42
+    >>> d['speed'] = 'speedo'
+    >>> print(d['speed'])
+    speedo
+    """
+
+    def __missing__(self, key):
+        return key
+
+
+class DictStack(list, collections.abc.MutableMapping):
+    """
+    A stack of dictionaries that behaves as a view on those dictionaries,
+    giving preference to the last.
+
+    >>> stack = DictStack([dict(a=1, c=2), dict(b=2, a=2)])
+    >>> stack['a']
+    2
+    >>> stack['b']
+    2
+    >>> stack['c']
+    2
+    >>> len(stack)
+    3
+    >>> stack.push(dict(a=3))
+    >>> stack['a']
+    3
+    >>> stack['a'] = 4
+    >>> set(stack.keys()) == set(['a', 'b', 'c'])
+    True
+    >>> set(stack.items()) == set([('a', 4), ('b', 2), ('c', 2)])
+    True
+    >>> dict(**stack) == dict(stack) == dict(a=4, c=2, b=2)
+    True
+    >>> d = stack.pop()
+    >>> stack['a']
+    2
+    >>> d = stack.pop()
+    >>> stack['a']
+    1
+    >>> stack.get('b', None)
+    >>> 'c' in stack
+    True
+    >>> del stack['c']
+    >>> dict(stack)
+    {'a': 1}
+    """
+
+    def __iter__(self):
+        dicts = list.__iter__(self)
+        return iter(set(itertools.chain.from_iterable(c.keys() for c in dicts)))
+
+    def __getitem__(self, key):
+        for scope in reversed(tuple(list.__iter__(self))):
+            if key in scope:
+                return scope[key]
+        raise KeyError(key)
+
+    push = list.append
+
+    def __contains__(self, other):
+        return collections.abc.Mapping.__contains__(self, other)
+
+    def __len__(self):
+        return len(list(iter(self)))
+
+    def __setitem__(self, key, item):
+        last = list.__getitem__(self, -1)
+        return last.__setitem__(key, item)
+
+    def __delitem__(self, key):
+        last = list.__getitem__(self, -1)
+        return last.__delitem__(key)
+
+    # workaround for mypy confusion
+    def pop(self, *args, **kwargs):
+        return list.pop(self, *args, **kwargs)
+
+
+class BijectiveMap(dict):
+    """
+    A Bijective Map (two-way mapping).
+
+    Implemented as a simple dictionary of 2x the size, mapping values back
+    to keys.
+
+    Note, this implementation may be incomplete. If there's not a test for
+    your use case below, it's likely to fail, so please test and send pull
+    requests or patches for additional functionality needed.
+
+
+    >>> m = BijectiveMap()
+    >>> m['a'] = 'b'
+    >>> m == {'a': 'b', 'b': 'a'}
+    True
+    >>> print(m['b'])
+    a
+
+    >>> m['c'] = 'd'
+    >>> len(m)
+    2
+
+    Some weird things happen if you map an item to itself or overwrite a
+    single key of a pair, so it's disallowed.
+
+    >>> m['e'] = 'e'
+    Traceback (most recent call last):
+    ValueError: Key cannot map to itself
+
+    >>> m['d'] = 'e'
+    Traceback (most recent call last):
+    ValueError: Key/Value pairs may not overlap
+
+    >>> m['e'] = 'd'
+    Traceback (most recent call last):
+    ValueError: Key/Value pairs may not overlap
+
+    >>> print(m.pop('d'))
+    c
+
+    >>> 'c' in m
+    False
+
+    >>> m = BijectiveMap(dict(a='b'))
+    >>> len(m)
+    1
+    >>> print(m['b'])
+    a
+
+    >>> m = BijectiveMap()
+    >>> m.update(a='b')
+    >>> m['b']
+    'a'
+
+    >>> del m['b']
+    >>> len(m)
+    0
+    >>> 'a' in m
+    False
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+        self.update(*args, **kwargs)
+
+    def __setitem__(self, item, value):
+        if item == value:
+            raise ValueError("Key cannot map to itself")
+        overlap = (
+            item in self
+            and self[item] != value
+            or value in self
+            and self[value] != item
+        )
+        if overlap:
+            raise ValueError("Key/Value pairs may not overlap")
+        super().__setitem__(item, value)
+        super().__setitem__(value, item)
+
+    def __delitem__(self, item):
+        self.pop(item)
+
+    def __len__(self):
+        return super().__len__() // 2
+
+    def pop(self, key, *args, **kwargs):
+        mirror = self[key]
+        super().__delitem__(mirror)
+        return super().pop(key, *args, **kwargs)
+
+    def update(self, *args, **kwargs):
+        # build a dictionary using the default constructs
+        d = dict(*args, **kwargs)
+        # build this dictionary using transformed keys.
+        for item in d.items():
+            self.__setitem__(*item)
+
+
+class FrozenDict(collections.abc.Mapping, collections.abc.Hashable):
+    """
+    An immutable mapping.
+
+    >>> a = FrozenDict(a=1, b=2)
+    >>> b = FrozenDict(a=1, b=2)
+    >>> a == b
+    True
+
+    >>> a == dict(a=1, b=2)
+    True
+    >>> dict(a=1, b=2) == a
+    True
+    >>> 'a' in a
+    True
+    >>> type(hash(a)) is type(0)
+    True
+    >>> set(iter(a)) == {'a', 'b'}
+    True
+    >>> len(a)
+    2
+    >>> a['a'] == a.get('a') == 1
+    True
+
+    >>> a['c'] = 3
+    Traceback (most recent call last):
+    ...
+    TypeError: 'FrozenDict' object does not support item assignment
+
+    >>> a.update(y=3)
+    Traceback (most recent call last):
+    ...
+    AttributeError: 'FrozenDict' object has no attribute 'update'
+
+    Copies should compare equal
+
+    >>> copy.copy(a) == a
+    True
+
+    Copies should be the same type
+
+    >>> isinstance(copy.copy(a), FrozenDict)
+    True
+
+    FrozenDict supplies .copy(), even though
+    collections.abc.Mapping doesn't demand it.
+
+    >>> a.copy() == a
+    True
+    >>> a.copy() is not a
+    True
+    """
+
+    __slots__ = ['__data']
+
+    def __new__(cls, *args, **kwargs):
+        self = super().__new__(cls)
+        self.__data = dict(*args, **kwargs)
+        return self
+
+    # Container
+    def __contains__(self, key):
+        return key in self.__data
+
+    # Hashable
+    def __hash__(self):
+        return hash(tuple(sorted(self.__data.items())))
+
+    # Mapping
+    def __iter__(self):
+        return iter(self.__data)
+
+    def __len__(self):
+        return len(self.__data)
+
+    def __getitem__(self, key):
+        return self.__data[key]
+
+    # override get for efficiency provided by dict
+    def get(self, *args, **kwargs):
+        return self.__data.get(*args, **kwargs)
+
+    # override eq to recognize underlying implementation
+    def __eq__(self, other):
+        if isinstance(other, FrozenDict):
+            other = other.__data
+        return self.__data.__eq__(other)
+
+    def copy(self):
+        "Return a shallow copy of self"
+        return copy.copy(self)
+
+
+class Enumeration(ItemsAsAttributes, BijectiveMap):
+    """
+    A convenient way to provide enumerated values
+
+    >>> e = Enumeration('a b c')
+    >>> e['a']
+    0
+
+    >>> e.a
+    0
+
+    >>> e[1]
+    'b'
+
+    >>> set(e.names) == set('abc')
+    True
+
+    >>> set(e.codes) == set(range(3))
+    True
+
+    >>> e.get('d') is None
+    True
+
+    Codes need not start with 0
+
+    >>> e = Enumeration('a b c', range(1, 4))
+    >>> e['a']
+    1
+
+    >>> e[3]
+    'c'
+    """
+
+    def __init__(self, names, codes=None):
+        if isinstance(names, str):
+            names = names.split()
+        if codes is None:
+            codes = itertools.count()
+        super().__init__(zip(names, codes))
+
+    @property
+    def names(self):
+        return (key for key in self if isinstance(key, str))
+
+    @property
+    def codes(self):
+        return (self[name] for name in self.names)
+
+
+class Everything:
+    """
+    A collection "containing" every possible thing.
+
+    >>> 'foo' in Everything()
+    True
+
+    >>> import random
+    >>> random.randint(1, 999) in Everything()
+    True
+
+    >>> random.choice([None, 'foo', 42, ('a', 'b', 'c')]) in Everything()
+    True
+    """
+
+    def __contains__(self, other):
+        return True
+
+
+class InstrumentedDict(collections.UserDict):
+    """
+    Instrument an existing dictionary with additional
+    functionality, but always reference and mutate
+    the original dictionary.
+
+    >>> orig = {'a': 1, 'b': 2}
+    >>> inst = InstrumentedDict(orig)
+    >>> inst['a']
+    1
+    >>> inst['c'] = 3
+    >>> orig['c']
+    3
+    >>> inst.keys() == orig.keys()
+    True
+    """
+
+    def __init__(self, data):
+        super().__init__()
+        self.data = data
+
+
+class Least:
+    """
+    A value that is always lesser than any other
+
+    >>> least = Least()
+    >>> 3 < least
+    False
+    >>> 3 > least
+    True
+    >>> least < 3
+    True
+    >>> least <= 3
+    True
+    >>> least > 3
+    False
+    >>> 'x' > least
+    True
+    >>> None > least
+    True
+    """
+
+    def __le__(self, other):
+        return True
+
+    __lt__ = __le__
+
+    def __ge__(self, other):
+        return False
+
+    __gt__ = __ge__
+
+
+class Greatest:
+    """
+    A value that is always greater than any other
+
+    >>> greatest = Greatest()
+    >>> 3 < greatest
+    True
+    >>> 3 > greatest
+    False
+    >>> greatest < 3
+    False
+    >>> greatest > 3
+    True
+    >>> greatest >= 3
+    True
+    >>> 'x' > greatest
+    False
+    >>> None > greatest
+    False
+    """
+
+    def __ge__(self, other):
+        return True
+
+    __gt__ = __ge__
+
+    def __le__(self, other):
+        return False
+
+    __lt__ = __le__
+
+
+def pop_all(items):
+    """
+    Clear items in place and return a copy of items.
+
+    >>> items = [1, 2, 3]
+    >>> popped = pop_all(items)
+    >>> popped is items
+    False
+    >>> popped
+    [1, 2, 3]
+    >>> items
+    []
+    """
+    result, items[:] = items[:], []
+    return result
+
+
+class FreezableDefaultDict(collections.defaultdict):
+    """
+    Often it is desirable to prevent the mutation of
+    a default dict after its initial construction, such
+    as to prevent mutation during iteration.
+
+    >>> dd = FreezableDefaultDict(list)
+    >>> dd[0].append('1')
+    >>> dd.freeze()
+    >>> dd[1]
+    []
+    >>> len(dd)
+    1
+    """
+
+    def __missing__(self, key):
+        return getattr(self, '_frozen', super().__missing__)(key)
+
+    def freeze(self):
+        self._frozen = lambda key: self.default_factory()
+
+
+class Accumulator:
+    def __init__(self, initial=0):
+        self.val = initial
+
+    def __call__(self, val):
+        self.val += val
+        return self.val
+
+
+class WeightedLookup(RangeMap):
+    """
+    Given parameters suitable for a dict representing keys
+    and a weighted proportion, return a RangeMap representing
+    spans of values proportial to the weights:
+
+    >>> even = WeightedLookup(a=1, b=1)
+
+    [0, 1) -> a
+    [1, 2) -> b
+
+    >>> lk = WeightedLookup(a=1, b=2)
+
+    [0, 1) -> a
+    [1, 3) -> b
+
+    >>> lk[.5]
+    'a'
+    >>> lk[1.5]
+    'b'
+
+    Adds ``.random()`` to select a random weighted value:
+
+    >>> lk.random() in ['a', 'b']
+    True
+
+    >>> choices = [lk.random() for x in range(1000)]
+
+    Statistically speaking, choices should be .5 a:b
+    >>> ratio = choices.count('a') / choices.count('b')
+    >>> .4 < ratio < .6
+    True
+    """
+
+    def __init__(self, *args, **kwargs):
+        raw = dict(*args, **kwargs)
+
+        # allocate keys by weight
+        indexes = map(Accumulator(), raw.values())
+        super().__init__(zip(indexes, raw.keys()), key_match_comparator=operator.lt)
+
+    def random(self):
+        lower, upper = self.bounds()
+        selector = random.random() * upper
+        return self[selector]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/collections/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/collections/__pycache__/__init__.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..70d863052221343bc63fe5e6add6e27579a57db0
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/collections/py.typed b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/collections/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/context.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/context.py
new file mode 100644
index 0000000000000000000000000000000000000000..61b27135df1fc5504622ad69875249c87947e453
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/context.py
@@ -0,0 +1,361 @@
+from __future__ import annotations
+
+import contextlib
+import functools
+import operator
+import os
+import shutil
+import subprocess
+import sys
+import tempfile
+import urllib.request
+import warnings
+from typing import Iterator
+
+
+if sys.version_info < (3, 12):
+    from backports import tarfile
+else:
+    import tarfile
+
+
+@contextlib.contextmanager
+def pushd(dir: str | os.PathLike) -> Iterator[str | os.PathLike]:
+    """
+    >>> tmp_path = getfixture('tmp_path')
+    >>> with pushd(tmp_path):
+    ...     assert os.getcwd() == os.fspath(tmp_path)
+    >>> assert os.getcwd() != os.fspath(tmp_path)
+    """
+
+    orig = os.getcwd()
+    os.chdir(dir)
+    try:
+        yield dir
+    finally:
+        os.chdir(orig)
+
+
+@contextlib.contextmanager
+def tarball(
+    url, target_dir: str | os.PathLike | None = None
+) -> Iterator[str | os.PathLike]:
+    """
+    Get a tarball, extract it, yield, then clean up.
+
+    >>> import urllib.request
+    >>> url = getfixture('tarfile_served')
+    >>> target = getfixture('tmp_path') / 'out'
+    >>> tb = tarball(url, target_dir=target)
+    >>> import pathlib
+    >>> with tb as extracted:
+    ...     contents = pathlib.Path(extracted, 'contents.txt').read_text(encoding='utf-8')
+    >>> assert not os.path.exists(extracted)
+    """
+    if target_dir is None:
+        target_dir = os.path.basename(url).replace('.tar.gz', '').replace('.tgz', '')
+    # In the tar command, use --strip-components=1 to strip the first path and
+    #  then
+    #  use -C to cause the files to be extracted to {target_dir}. This ensures
+    #  that we always know where the files were extracted.
+    os.mkdir(target_dir)
+    try:
+        req = urllib.request.urlopen(url)
+        with tarfile.open(fileobj=req, mode='r|*') as tf:
+            tf.extractall(path=target_dir, filter=strip_first_component)
+        yield target_dir
+    finally:
+        shutil.rmtree(target_dir)
+
+
+def strip_first_component(
+    member: tarfile.TarInfo,
+    path,
+) -> tarfile.TarInfo:
+    _, member.name = member.name.split('/', 1)
+    return member
+
+
+def _compose(*cmgrs):
+    """
+    Compose any number of dependent context managers into a single one.
+
+    The last, innermost context manager may take arbitrary arguments, but
+    each successive context manager should accept the result from the
+    previous as a single parameter.
+
+    Like :func:`jaraco.functools.compose`, behavior works from right to
+    left, so the context manager should be indicated from outermost to
+    innermost.
+
+    Example, to create a context manager to change to a temporary
+    directory:
+
+    >>> temp_dir_as_cwd = _compose(pushd, temp_dir)
+    >>> with temp_dir_as_cwd() as dir:
+    ...     assert os.path.samefile(os.getcwd(), dir)
+    """
+
+    def compose_two(inner, outer):
+        def composed(*args, **kwargs):
+            with inner(*args, **kwargs) as saved, outer(saved) as res:
+                yield res
+
+        return contextlib.contextmanager(composed)
+
+    return functools.reduce(compose_two, reversed(cmgrs))
+
+
+tarball_cwd = _compose(pushd, tarball)
+
+
+@contextlib.contextmanager
+def tarball_context(*args, **kwargs):
+    warnings.warn(
+        "tarball_context is deprecated. Use tarball or tarball_cwd instead.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    pushd_ctx = kwargs.pop('pushd', pushd)
+    with tarball(*args, **kwargs) as tball, pushd_ctx(tball) as dir:
+        yield dir
+
+
+def infer_compression(url):
+    """
+    Given a URL or filename, infer the compression code for tar.
+
+    >>> infer_compression('http://foo/bar.tar.gz')
+    'z'
+    >>> infer_compression('http://foo/bar.tgz')
+    'z'
+    >>> infer_compression('file.bz')
+    'j'
+    >>> infer_compression('file.xz')
+    'J'
+    """
+    warnings.warn(
+        "infer_compression is deprecated with no replacement",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    # cheat and just assume it's the last two characters
+    compression_indicator = url[-2:]
+    mapping = dict(gz='z', bz='j', xz='J')
+    # Assume 'z' (gzip) if no match
+    return mapping.get(compression_indicator, 'z')
+
+
+@contextlib.contextmanager
+def temp_dir(remover=shutil.rmtree):
+    """
+    Create a temporary directory context. Pass a custom remover
+    to override the removal behavior.
+
+    >>> import pathlib
+    >>> with temp_dir() as the_dir:
+    ...     assert os.path.isdir(the_dir)
+    ...     _ = pathlib.Path(the_dir).joinpath('somefile').write_text('contents', encoding='utf-8')
+    >>> assert not os.path.exists(the_dir)
+    """
+    temp_dir = tempfile.mkdtemp()
+    try:
+        yield temp_dir
+    finally:
+        remover(temp_dir)
+
+
+@contextlib.contextmanager
+def repo_context(url, branch=None, quiet=True, dest_ctx=temp_dir):
+    """
+    Check out the repo indicated by url.
+
+    If dest_ctx is supplied, it should be a context manager
+    to yield the target directory for the check out.
+    """
+    exe = 'git' if 'git' in url else 'hg'
+    with dest_ctx() as repo_dir:
+        cmd = [exe, 'clone', url, repo_dir]
+        if branch:
+            cmd.extend(['--branch', branch])
+        devnull = open(os.path.devnull, 'w')
+        stdout = devnull if quiet else None
+        subprocess.check_call(cmd, stdout=stdout)
+        yield repo_dir
+
+
+def null():
+    """
+    A null context suitable to stand in for a meaningful context.
+
+    >>> with null() as value:
+    ...     assert value is None
+
+    This context is most useful when dealing with two or more code
+    branches but only some need a context. Wrap the others in a null
+    context to provide symmetry across all options.
+    """
+    warnings.warn(
+        "null is deprecated. Use contextlib.nullcontext",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    return contextlib.nullcontext()
+
+
+class ExceptionTrap:
+    """
+    A context manager that will catch certain exceptions and provide an
+    indication they occurred.
+
+    >>> with ExceptionTrap() as trap:
+    ...     raise Exception()
+    >>> bool(trap)
+    True
+
+    >>> with ExceptionTrap() as trap:
+    ...     pass
+    >>> bool(trap)
+    False
+
+    >>> with ExceptionTrap(ValueError) as trap:
+    ...     raise ValueError("1 + 1 is not 3")
+    >>> bool(trap)
+    True
+    >>> trap.value
+    ValueError('1 + 1 is not 3')
+    >>> trap.tb
+    <traceback object at ...>
+
+    >>> with ExceptionTrap(ValueError) as trap:
+    ...     raise Exception()
+    Traceback (most recent call last):
+    ...
+    Exception
+
+    >>> bool(trap)
+    False
+    """
+
+    exc_info = None, None, None
+
+    def __init__(self, exceptions=(Exception,)):
+        self.exceptions = exceptions
+
+    def __enter__(self):
+        return self
+
+    @property
+    def type(self):
+        return self.exc_info[0]
+
+    @property
+    def value(self):
+        return self.exc_info[1]
+
+    @property
+    def tb(self):
+        return self.exc_info[2]
+
+    def __exit__(self, *exc_info):
+        type = exc_info[0]
+        matches = type and issubclass(type, self.exceptions)
+        if matches:
+            self.exc_info = exc_info
+        return matches
+
+    def __bool__(self):
+        return bool(self.type)
+
+    def raises(self, func, *, _test=bool):
+        """
+        Wrap func and replace the result with the truth
+        value of the trap (True if an exception occurred).
+
+        First, give the decorator an alias to support Python 3.8
+        Syntax.
+
+        >>> raises = ExceptionTrap(ValueError).raises
+
+        Now decorate a function that always fails.
+
+        >>> @raises
+        ... def fail():
+        ...     raise ValueError('failed')
+        >>> fail()
+        True
+        """
+
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            with ExceptionTrap(self.exceptions) as trap:
+                func(*args, **kwargs)
+            return _test(trap)
+
+        return wrapper
+
+    def passes(self, func):
+        """
+        Wrap func and replace the result with the truth
+        value of the trap (True if no exception).
+
+        First, give the decorator an alias to support Python 3.8
+        Syntax.
+
+        >>> passes = ExceptionTrap(ValueError).passes
+
+        Now decorate a function that always fails.
+
+        >>> @passes
+        ... def fail():
+        ...     raise ValueError('failed')
+
+        >>> fail()
+        False
+        """
+        return self.raises(func, _test=operator.not_)
+
+
+class suppress(contextlib.suppress, contextlib.ContextDecorator):
+    """
+    A version of contextlib.suppress with decorator support.
+
+    >>> @suppress(KeyError)
+    ... def key_error():
+    ...     {}['']
+    >>> key_error()
+    """
+
+
+class on_interrupt(contextlib.ContextDecorator):
+    """
+    Replace a KeyboardInterrupt with SystemExit(1)
+
+    >>> def do_interrupt():
+    ...     raise KeyboardInterrupt()
+    >>> on_interrupt('error')(do_interrupt)()
+    Traceback (most recent call last):
+    ...
+    SystemExit: 1
+    >>> on_interrupt('error', code=255)(do_interrupt)()
+    Traceback (most recent call last):
+    ...
+    SystemExit: 255
+    >>> on_interrupt('suppress')(do_interrupt)()
+    >>> with __import__('pytest').raises(KeyboardInterrupt):
+    ...     on_interrupt('ignore')(do_interrupt)()
+    """
+
+    def __init__(self, action='error', /, code=1):
+        self.action = action
+        self.code = code
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exctype, excinst, exctb):
+        if exctype is not KeyboardInterrupt or self.action == 'ignore':
+            return
+        elif self.action == 'error':
+            raise SystemExit(self.code) from excinst
+        return self.action == 'suppress'
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/functools/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/functools/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca6c22fa9ba2777507118fbfa38bc29ac2d84964
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/functools/__init__.py
@@ -0,0 +1,633 @@
+import collections.abc
+import functools
+import inspect
+import itertools
+import operator
+import time
+import types
+import warnings
+
+import more_itertools
+
+
+def compose(*funcs):
+    """
+    Compose any number of unary functions into a single unary function.
+
+    >>> import textwrap
+    >>> expected = str.strip(textwrap.dedent(compose.__doc__))
+    >>> strip_and_dedent = compose(str.strip, textwrap.dedent)
+    >>> strip_and_dedent(compose.__doc__) == expected
+    True
+
+    Compose also allows the innermost function to take arbitrary arguments.
+
+    >>> round_three = lambda x: round(x, ndigits=3)
+    >>> f = compose(round_three, int.__truediv__)
+    >>> [f(3*x, x+1) for x in range(1,10)]
+    [1.5, 2.0, 2.25, 2.4, 2.5, 2.571, 2.625, 2.667, 2.7]
+    """
+
+    def compose_two(f1, f2):
+        return lambda *args, **kwargs: f1(f2(*args, **kwargs))
+
+    return functools.reduce(compose_two, funcs)
+
+
+def once(func):
+    """
+    Decorate func so it's only ever called the first time.
+
+    This decorator can ensure that an expensive or non-idempotent function
+    will not be expensive on subsequent calls and is idempotent.
+
+    >>> add_three = once(lambda a: a+3)
+    >>> add_three(3)
+    6
+    >>> add_three(9)
+    6
+    >>> add_three('12')
+    6
+
+    To reset the stored value, simply clear the property ``saved_result``.
+
+    >>> del add_three.saved_result
+    >>> add_three(9)
+    12
+    >>> add_three(8)
+    12
+
+    Or invoke 'reset()' on it.
+
+    >>> add_three.reset()
+    >>> add_three(-3)
+    0
+    >>> add_three(0)
+    0
+    """
+
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        if not hasattr(wrapper, 'saved_result'):
+            wrapper.saved_result = func(*args, **kwargs)
+        return wrapper.saved_result
+
+    wrapper.reset = lambda: vars(wrapper).__delitem__('saved_result')
+    return wrapper
+
+
+def method_cache(method, cache_wrapper=functools.lru_cache()):
+    """
+    Wrap lru_cache to support storing the cache data in the object instances.
+
+    Abstracts the common paradigm where the method explicitly saves an
+    underscore-prefixed protected property on first call and returns that
+    subsequently.
+
+    >>> class MyClass:
+    ...     calls = 0
+    ...
+    ...     @method_cache
+    ...     def method(self, value):
+    ...         self.calls += 1
+    ...         return value
+
+    >>> a = MyClass()
+    >>> a.method(3)
+    3
+    >>> for x in range(75):
+    ...     res = a.method(x)
+    >>> a.calls
+    75
+
+    Note that the apparent behavior will be exactly like that of lru_cache
+    except that the cache is stored on each instance, so values in one
+    instance will not flush values from another, and when an instance is
+    deleted, so are the cached values for that instance.
+
+    >>> b = MyClass()
+    >>> for x in range(35):
+    ...     res = b.method(x)
+    >>> b.calls
+    35
+    >>> a.method(0)
+    0
+    >>> a.calls
+    75
+
+    Note that if method had been decorated with ``functools.lru_cache()``,
+    a.calls would have been 76 (due to the cached value of 0 having been
+    flushed by the 'b' instance).
+
+    Clear the cache with ``.cache_clear()``
+
+    >>> a.method.cache_clear()
+
+    Same for a method that hasn't yet been called.
+
+    >>> c = MyClass()
+    >>> c.method.cache_clear()
+
+    Another cache wrapper may be supplied:
+
+    >>> cache = functools.lru_cache(maxsize=2)
+    >>> MyClass.method2 = method_cache(lambda self: 3, cache_wrapper=cache)
+    >>> a = MyClass()
+    >>> a.method2()
+    3
+
+    Caution - do not subsequently wrap the method with another decorator, such
+    as ``@property``, which changes the semantics of the function.
+
+    See also
+    http://code.activestate.com/recipes/577452-a-memoize-decorator-for-instance-methods/
+    for another implementation and additional justification.
+    """
+
+    def wrapper(self, *args, **kwargs):
+        # it's the first call, replace the method with a cached, bound method
+        bound_method = types.MethodType(method, self)
+        cached_method = cache_wrapper(bound_method)
+        setattr(self, method.__name__, cached_method)
+        return cached_method(*args, **kwargs)
+
+    # Support cache clear even before cache has been created.
+    wrapper.cache_clear = lambda: None
+
+    return _special_method_cache(method, cache_wrapper) or wrapper
+
+
+def _special_method_cache(method, cache_wrapper):
+    """
+    Because Python treats special methods differently, it's not
+    possible to use instance attributes to implement the cached
+    methods.
+
+    Instead, install the wrapper method under a different name
+    and return a simple proxy to that wrapper.
+
+    https://github.com/jaraco/jaraco.functools/issues/5
+    """
+    name = method.__name__
+    special_names = '__getattr__', '__getitem__'
+
+    if name not in special_names:
+        return None
+
+    wrapper_name = '__cached' + name
+
+    def proxy(self, /, *args, **kwargs):
+        if wrapper_name not in vars(self):
+            bound = types.MethodType(method, self)
+            cache = cache_wrapper(bound)
+            setattr(self, wrapper_name, cache)
+        else:
+            cache = getattr(self, wrapper_name)
+        return cache(*args, **kwargs)
+
+    return proxy
+
+
+def apply(transform):
+    """
+    Decorate a function with a transform function that is
+    invoked on results returned from the decorated function.
+
+    >>> @apply(reversed)
+    ... def get_numbers(start):
+    ...     "doc for get_numbers"
+    ...     return range(start, start+3)
+    >>> list(get_numbers(4))
+    [6, 5, 4]
+    >>> get_numbers.__doc__
+    'doc for get_numbers'
+    """
+
+    def wrap(func):
+        return functools.wraps(func)(compose(transform, func))
+
+    return wrap
+
+
+def result_invoke(action):
+    r"""
+    Decorate a function with an action function that is
+    invoked on the results returned from the decorated
+    function (for its side effect), then return the original
+    result.
+
+    >>> @result_invoke(print)
+    ... def add_two(a, b):
+    ...     return a + b
+    >>> x = add_two(2, 3)
+    5
+    >>> x
+    5
+    """
+
+    def wrap(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            result = func(*args, **kwargs)
+            action(result)
+            return result
+
+        return wrapper
+
+    return wrap
+
+
+def invoke(f, /, *args, **kwargs):
+    """
+    Call a function for its side effect after initialization.
+
+    The benefit of using the decorator instead of simply invoking a function
+    after defining it is that it makes explicit the author's intent for the
+    function to be called immediately. Whereas if one simply calls the
+    function immediately, it's less obvious if that was intentional or
+    incidental. It also avoids repeating the name - the two actions, defining
+    the function and calling it immediately are modeled separately, but linked
+    by the decorator construct.
+
+    The benefit of having a function construct (opposed to just invoking some
+    behavior inline) is to serve as a scope in which the behavior occurs. It
+    avoids polluting the global namespace with local variables, provides an
+    anchor on which to attach documentation (docstring), keeps the behavior
+    logically separated (instead of conceptually separated or not separated at
+    all), and provides potential to re-use the behavior for testing or other
+    purposes.
+
+    This function is named as a pithy way to communicate, "call this function
+    primarily for its side effect", or "while defining this function, also
+    take it aside and call it". It exists because there's no Python construct
+    for "define and call" (nor should there be, as decorators serve this need
+    just fine). The behavior happens immediately and synchronously.
+
+    >>> @invoke
+    ... def func(): print("called")
+    called
+    >>> func()
+    called
+
+    Use functools.partial to pass parameters to the initial call
+
+    >>> @functools.partial(invoke, name='bingo')
+    ... def func(name): print('called with', name)
+    called with bingo
+    """
+    f(*args, **kwargs)
+    return f
+
+
+class Throttler:
+    """Rate-limit a function (or other callable)."""
+
+    def __init__(self, func, max_rate=float('Inf')):
+        if isinstance(func, Throttler):
+            func = func.func
+        self.func = func
+        self.max_rate = max_rate
+        self.reset()
+
+    def reset(self):
+        self.last_called = 0
+
+    def __call__(self, *args, **kwargs):
+        self._wait()
+        return self.func(*args, **kwargs)
+
+    def _wait(self):
+        """Ensure at least 1/max_rate seconds from last call."""
+        elapsed = time.time() - self.last_called
+        must_wait = 1 / self.max_rate - elapsed
+        time.sleep(max(0, must_wait))
+        self.last_called = time.time()
+
+    def __get__(self, obj, owner=None):
+        return first_invoke(self._wait, functools.partial(self.func, obj))
+
+
+def first_invoke(func1, func2):
+    """
+    Return a function that when invoked will invoke func1 without
+    any parameters (for its side effect) and then invoke func2
+    with whatever parameters were passed, returning its result.
+    """
+
+    def wrapper(*args, **kwargs):
+        func1()
+        return func2(*args, **kwargs)
+
+    return wrapper
+
+
+method_caller = first_invoke(
+    lambda: warnings.warn(
+        '`jaraco.functools.method_caller` is deprecated, '
+        'use `operator.methodcaller` instead',
+        DeprecationWarning,
+        stacklevel=3,
+    ),
+    operator.methodcaller,
+)
+
+
+def retry_call(func, cleanup=lambda: None, retries=0, trap=()):
+    """
+    Given a callable func, trap the indicated exceptions
+    for up to 'retries' times, invoking cleanup on the
+    exception. On the final attempt, allow any exceptions
+    to propagate.
+    """
+    attempts = itertools.count() if retries == float('inf') else range(retries)
+    for _ in attempts:
+        try:
+            return func()
+        except trap:
+            cleanup()
+
+    return func()
+
+
+def retry(*r_args, **r_kwargs):
+    """
+    Decorator wrapper for retry_call. Accepts arguments to retry_call
+    except func and then returns a decorator for the decorated function.
+
+    Ex:
+
+    >>> @retry(retries=3)
+    ... def my_func(a, b):
+    ...     "this is my funk"
+    ...     print(a, b)
+    >>> my_func.__doc__
+    'this is my funk'
+    """
+
+    def decorate(func):
+        @functools.wraps(func)
+        def wrapper(*f_args, **f_kwargs):
+            bound = functools.partial(func, *f_args, **f_kwargs)
+            return retry_call(bound, *r_args, **r_kwargs)
+
+        return wrapper
+
+    return decorate
+
+
+def print_yielded(func):
+    """
+    Convert a generator into a function that prints all yielded elements.
+
+    >>> @print_yielded
+    ... def x():
+    ...     yield 3; yield None
+    >>> x()
+    3
+    None
+    """
+    print_all = functools.partial(map, print)
+    print_results = compose(more_itertools.consume, print_all, func)
+    return functools.wraps(func)(print_results)
+
+
+def pass_none(func):
+    """
+    Wrap func so it's not called if its first param is None.
+
+    >>> print_text = pass_none(print)
+    >>> print_text('text')
+    text
+    >>> print_text(None)
+    """
+
+    @functools.wraps(func)
+    def wrapper(param, /, *args, **kwargs):
+        if param is not None:
+            return func(param, *args, **kwargs)
+        return None
+
+    return wrapper
+
+
+def assign_params(func, namespace):
+    """
+    Assign parameters from namespace where func solicits.
+
+    >>> def func(x, y=3):
+    ...     print(x, y)
+    >>> assigned = assign_params(func, dict(x=2, z=4))
+    >>> assigned()
+    2 3
+
+    The usual errors are raised if a function doesn't receive
+    its required parameters:
+
+    >>> assigned = assign_params(func, dict(y=3, z=4))
+    >>> assigned()
+    Traceback (most recent call last):
+    TypeError: func() ...argument...
+
+    It even works on methods:
+
+    >>> class Handler:
+    ...     def meth(self, arg):
+    ...         print(arg)
+    >>> assign_params(Handler().meth, dict(arg='crystal', foo='clear'))()
+    crystal
+    """
+    sig = inspect.signature(func)
+    params = sig.parameters.keys()
+    call_ns = {k: namespace[k] for k in params if k in namespace}
+    return functools.partial(func, **call_ns)
+
+
+def save_method_args(method):
+    """
+    Wrap a method such that when it is called, the args and kwargs are
+    saved on the method.
+
+    >>> class MyClass:
+    ...     @save_method_args
+    ...     def method(self, a, b):
+    ...         print(a, b)
+    >>> my_ob = MyClass()
+    >>> my_ob.method(1, 2)
+    1 2
+    >>> my_ob._saved_method.args
+    (1, 2)
+    >>> my_ob._saved_method.kwargs
+    {}
+    >>> my_ob.method(a=3, b='foo')
+    3 foo
+    >>> my_ob._saved_method.args
+    ()
+    >>> my_ob._saved_method.kwargs == dict(a=3, b='foo')
+    True
+
+    The arguments are stored on the instance, allowing for
+    different instance to save different args.
+
+    >>> your_ob = MyClass()
+    >>> your_ob.method({str('x'): 3}, b=[4])
+    {'x': 3} [4]
+    >>> your_ob._saved_method.args
+    ({'x': 3},)
+    >>> my_ob._saved_method.args
+    ()
+    """
+    args_and_kwargs = collections.namedtuple('args_and_kwargs', 'args kwargs')
+
+    @functools.wraps(method)
+    def wrapper(self, /, *args, **kwargs):
+        attr_name = '_saved_' + method.__name__
+        attr = args_and_kwargs(args, kwargs)
+        setattr(self, attr_name, attr)
+        return method(self, *args, **kwargs)
+
+    return wrapper
+
+
+def except_(*exceptions, replace=None, use=None):
+    """
+    Replace the indicated exceptions, if raised, with the indicated
+    literal replacement or evaluated expression (if present).
+
+    >>> safe_int = except_(ValueError)(int)
+    >>> safe_int('five')
+    >>> safe_int('5')
+    5
+
+    Specify a literal replacement with ``replace``.
+
+    >>> safe_int_r = except_(ValueError, replace=0)(int)
+    >>> safe_int_r('five')
+    0
+
+    Provide an expression to ``use`` to pass through particular parameters.
+
+    >>> safe_int_pt = except_(ValueError, use='args[0]')(int)
+    >>> safe_int_pt('five')
+    'five'
+
+    """
+
+    def decorate(func):
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            try:
+                return func(*args, **kwargs)
+            except exceptions:
+                try:
+                    return eval(use)
+                except TypeError:
+                    return replace
+
+        return wrapper
+
+    return decorate
+
+
+def identity(x):
+    """
+    Return the argument.
+
+    >>> o = object()
+    >>> identity(o) is o
+    True
+    """
+    return x
+
+
+def bypass_when(check, *, _op=identity):
+    """
+    Decorate a function to return its parameter when ``check``.
+
+    >>> bypassed = []  # False
+
+    >>> @bypass_when(bypassed)
+    ... def double(x):
+    ...     return x * 2
+    >>> double(2)
+    4
+    >>> bypassed[:] = [object()]  # True
+    >>> double(2)
+    2
+    """
+
+    def decorate(func):
+        @functools.wraps(func)
+        def wrapper(param, /):
+            return param if _op(check) else func(param)
+
+        return wrapper
+
+    return decorate
+
+
+def bypass_unless(check):
+    """
+    Decorate a function to return its parameter unless ``check``.
+
+    >>> enabled = [object()]  # True
+
+    >>> @bypass_unless(enabled)
+    ... def double(x):
+    ...     return x * 2
+    >>> double(2)
+    4
+    >>> del enabled[:]  # False
+    >>> double(2)
+    2
+    """
+    return bypass_when(check, _op=operator.not_)
+
+
+@functools.singledispatch
+def _splat_inner(args, func):
+    """Splat args to func."""
+    return func(*args)
+
+
+@_splat_inner.register
+def _(args: collections.abc.Mapping, func):
+    """Splat kargs to func as kwargs."""
+    return func(**args)
+
+
+def splat(func):
+    """
+    Wrap func to expect its parameters to be passed positionally in a tuple.
+
+    Has a similar effect to that of ``itertools.starmap`` over
+    simple ``map``.
+
+    >>> pairs = [(-1, 1), (0, 2)]
+    >>> more_itertools.consume(itertools.starmap(print, pairs))
+    -1 1
+    0 2
+    >>> more_itertools.consume(map(splat(print), pairs))
+    -1 1
+    0 2
+
+    The approach generalizes to other iterators that don't have a "star"
+    equivalent, such as a "starfilter".
+
+    >>> list(filter(splat(operator.add), pairs))
+    [(0, 2)]
+
+    Splat also accepts a mapping argument.
+
+    >>> def is_nice(msg, code):
+    ...     return "smile" in msg or code == 0
+    >>> msgs = [
+    ...     dict(msg='smile!', code=20),
+    ...     dict(msg='error :(', code=1),
+    ...     dict(msg='unknown', code=0),
+    ... ]
+    >>> for msg in filter(splat(is_nice), msgs):
+    ...     print(msg)
+    {'msg': 'smile!', 'code': 20}
+    {'msg': 'unknown', 'code': 0}
+    """
+    return functools.wraps(func)(functools.partial(_splat_inner, func=func))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/functools/__init__.pyi b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/functools/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..19191bf93eea22a55c8d47a57ec24de370caca27
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/functools/__init__.pyi
@@ -0,0 +1,125 @@
+from collections.abc import Callable, Hashable, Iterator
+from functools import partial
+from operator import methodcaller
+import sys
+from typing import (
+    Any,
+    Generic,
+    Protocol,
+    TypeVar,
+    overload,
+)
+
+if sys.version_info >= (3, 10):
+    from typing import Concatenate, ParamSpec
+else:
+    from typing_extensions import Concatenate, ParamSpec
+
+_P = ParamSpec('_P')
+_R = TypeVar('_R')
+_T = TypeVar('_T')
+_R1 = TypeVar('_R1')
+_R2 = TypeVar('_R2')
+_V = TypeVar('_V')
+_S = TypeVar('_S')
+_R_co = TypeVar('_R_co', covariant=True)
+
+class _OnceCallable(Protocol[_P, _R]):
+    saved_result: _R
+    reset: Callable[[], None]
+    def __call__(self, *args: _P.args, **kwargs: _P.kwargs) -> _R: ...
+
+class _ProxyMethodCacheWrapper(Protocol[_R_co]):
+    cache_clear: Callable[[], None]
+    def __call__(self, *args: Hashable, **kwargs: Hashable) -> _R_co: ...
+
+class _MethodCacheWrapper(Protocol[_R_co]):
+    def cache_clear(self) -> None: ...
+    def __call__(self, *args: Hashable, **kwargs: Hashable) -> _R_co: ...
+
+# `compose()` overloads below will cover most use cases.
+
+@overload
+def compose(
+    __func1: Callable[[_R], _T],
+    __func2: Callable[_P, _R],
+    /,
+) -> Callable[_P, _T]: ...
+@overload
+def compose(
+    __func1: Callable[[_R], _T],
+    __func2: Callable[[_R1], _R],
+    __func3: Callable[_P, _R1],
+    /,
+) -> Callable[_P, _T]: ...
+@overload
+def compose(
+    __func1: Callable[[_R], _T],
+    __func2: Callable[[_R2], _R],
+    __func3: Callable[[_R1], _R2],
+    __func4: Callable[_P, _R1],
+    /,
+) -> Callable[_P, _T]: ...
+def once(func: Callable[_P, _R]) -> _OnceCallable[_P, _R]: ...
+def method_cache(
+    method: Callable[..., _R],
+    cache_wrapper: Callable[[Callable[..., _R]], _MethodCacheWrapper[_R]] = ...,
+) -> _MethodCacheWrapper[_R] | _ProxyMethodCacheWrapper[_R]: ...
+def apply(
+    transform: Callable[[_R], _T]
+) -> Callable[[Callable[_P, _R]], Callable[_P, _T]]: ...
+def result_invoke(
+    action: Callable[[_R], Any]
+) -> Callable[[Callable[_P, _R]], Callable[_P, _R]]: ...
+def invoke(
+    f: Callable[_P, _R], /, *args: _P.args, **kwargs: _P.kwargs
+) -> Callable[_P, _R]: ...
+
+class Throttler(Generic[_R]):
+    last_called: float
+    func: Callable[..., _R]
+    max_rate: float
+    def __init__(
+        self, func: Callable[..., _R] | Throttler[_R], max_rate: float = ...
+    ) -> None: ...
+    def reset(self) -> None: ...
+    def __call__(self, *args: Any, **kwargs: Any) -> _R: ...
+    def __get__(self, obj: Any, owner: type[Any] | None = ...) -> Callable[..., _R]: ...
+
+def first_invoke(
+    func1: Callable[..., Any], func2: Callable[_P, _R]
+) -> Callable[_P, _R]: ...
+
+method_caller: Callable[..., methodcaller]
+
+def retry_call(
+    func: Callable[..., _R],
+    cleanup: Callable[..., None] = ...,
+    retries: int | float = ...,
+    trap: type[BaseException] | tuple[type[BaseException], ...] = ...,
+) -> _R: ...
+def retry(
+    cleanup: Callable[..., None] = ...,
+    retries: int | float = ...,
+    trap: type[BaseException] | tuple[type[BaseException], ...] = ...,
+) -> Callable[[Callable[..., _R]], Callable[..., _R]]: ...
+def print_yielded(func: Callable[_P, Iterator[Any]]) -> Callable[_P, None]: ...
+def pass_none(
+    func: Callable[Concatenate[_T, _P], _R]
+) -> Callable[Concatenate[_T, _P], _R]: ...
+def assign_params(
+    func: Callable[..., _R], namespace: dict[str, Any]
+) -> partial[_R]: ...
+def save_method_args(
+    method: Callable[Concatenate[_S, _P], _R]
+) -> Callable[Concatenate[_S, _P], _R]: ...
+def except_(
+    *exceptions: type[BaseException], replace: Any = ..., use: Any = ...
+) -> Callable[[Callable[_P, Any]], Callable[_P, Any]]: ...
+def identity(x: _T) -> _T: ...
+def bypass_when(
+    check: _V, *, _op: Callable[[_V], Any] = ...
+) -> Callable[[Callable[[_T], _R]], Callable[[_T], _T | _R]]: ...
+def bypass_unless(
+    check: Any,
+) -> Callable[[Callable[[_T], _R]], Callable[[_T], _T | _R]]: ...
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/functools/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/functools/__pycache__/__init__.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..40ca007ed44a94e302beec6a3ca68572f381b82e
Binary files /dev/null and b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/functools/__pycache__/__init__.cpython-312.pyc differ
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/functools/py.typed b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/functools/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/Lorem ipsum.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/Lorem ipsum.txt
new file mode 100644
index 0000000000000000000000000000000000000000..986f944b60b9900c22464a0c027d713854cc204e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/Lorem ipsum.txt	
@@ -0,0 +1,2 @@
+Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
+Curabitur pretium tincidunt lacus. Nulla gravida orci a odio. Nullam varius, turpis et commodo pharetra, est eros bibendum elit, nec luctus magna felis sollicitudin mauris. Integer in mauris eu nibh euismod gravida. Duis ac tellus et risus vulputate vehicula. Donec lobortis risus a elit. Etiam tempor. Ut ullamcorper, ligula eu tempor congue, eros est euismod turpis, id tincidunt sapien risus a quam. Maecenas fermentum consequat mi. Donec fermentum. Pellentesque malesuada nulla a mi. Duis sapien sem, aliquet nec, commodo eget, consequat quis, neque. Aliquam faucibus, elit ut dictum aliquet, felis nisl adipiscing sapien, sed malesuada diam lacus eget erat. Cras mollis scelerisque nunc. Nullam arcu. Aliquam consequat. Curabitur augue lorem, dapibus quis, laoreet et, pretium ac, nisi. Aenean magna nisl, mollis quis, molestie eu, feugiat in, orci. In hac habitasse platea dictumst.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fabd0c3f036d47f2cd9b2a66828ba33a00ab7b3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/__init__.py
@@ -0,0 +1,624 @@
+import re
+import itertools
+import textwrap
+import functools
+
+try:
+    from importlib.resources import files  # type: ignore
+except ImportError:  # pragma: nocover
+    from importlib_resources import files  # type: ignore
+
+from jaraco.functools import compose, method_cache
+from jaraco.context import ExceptionTrap
+
+
+def substitution(old, new):
+    """
+    Return a function that will perform a substitution on a string
+    """
+    return lambda s: s.replace(old, new)
+
+
+def multi_substitution(*substitutions):
+    """
+    Take a sequence of pairs specifying substitutions, and create
+    a function that performs those substitutions.
+
+    >>> multi_substitution(('foo', 'bar'), ('bar', 'baz'))('foo')
+    'baz'
+    """
+    substitutions = itertools.starmap(substitution, substitutions)
+    # compose function applies last function first, so reverse the
+    #  substitutions to get the expected order.
+    substitutions = reversed(tuple(substitutions))
+    return compose(*substitutions)
+
+
+class FoldedCase(str):
+    """
+    A case insensitive string class; behaves just like str
+    except compares equal when the only variation is case.
+
+    >>> s = FoldedCase('hello world')
+
+    >>> s == 'Hello World'
+    True
+
+    >>> 'Hello World' == s
+    True
+
+    >>> s != 'Hello World'
+    False
+
+    >>> s.index('O')
+    4
+
+    >>> s.split('O')
+    ['hell', ' w', 'rld']
+
+    >>> sorted(map(FoldedCase, ['GAMMA', 'alpha', 'Beta']))
+    ['alpha', 'Beta', 'GAMMA']
+
+    Sequence membership is straightforward.
+
+    >>> "Hello World" in [s]
+    True
+    >>> s in ["Hello World"]
+    True
+
+    Allows testing for set inclusion, but candidate and elements
+    must both be folded.
+
+    >>> FoldedCase("Hello World") in {s}
+    True
+    >>> s in {FoldedCase("Hello World")}
+    True
+
+    String inclusion works as long as the FoldedCase object
+    is on the right.
+
+    >>> "hello" in FoldedCase("Hello World")
+    True
+
+    But not if the FoldedCase object is on the left:
+
+    >>> FoldedCase('hello') in 'Hello World'
+    False
+
+    In that case, use ``in_``:
+
+    >>> FoldedCase('hello').in_('Hello World')
+    True
+
+    >>> FoldedCase('hello') > FoldedCase('Hello')
+    False
+
+    >>> FoldedCase('ß') == FoldedCase('ss')
+    True
+    """
+
+    def __lt__(self, other):
+        return self.casefold() < other.casefold()
+
+    def __gt__(self, other):
+        return self.casefold() > other.casefold()
+
+    def __eq__(self, other):
+        return self.casefold() == other.casefold()
+
+    def __ne__(self, other):
+        return self.casefold() != other.casefold()
+
+    def __hash__(self):
+        return hash(self.casefold())
+
+    def __contains__(self, other):
+        return super().casefold().__contains__(other.casefold())
+
+    def in_(self, other):
+        "Does self appear in other?"
+        return self in FoldedCase(other)
+
+    # cache casefold since it's likely to be called frequently.
+    @method_cache
+    def casefold(self):
+        return super().casefold()
+
+    def index(self, sub):
+        return self.casefold().index(sub.casefold())
+
+    def split(self, splitter=' ', maxsplit=0):
+        pattern = re.compile(re.escape(splitter), re.I)
+        return pattern.split(self, maxsplit)
+
+
+# Python 3.8 compatibility
+_unicode_trap = ExceptionTrap(UnicodeDecodeError)
+
+
+@_unicode_trap.passes
+def is_decodable(value):
+    r"""
+    Return True if the supplied value is decodable (using the default
+    encoding).
+
+    >>> is_decodable(b'\xff')
+    False
+    >>> is_decodable(b'\x32')
+    True
+    """
+    value.decode()
+
+
+def is_binary(value):
+    r"""
+    Return True if the value appears to be binary (that is, it's a byte
+    string and isn't decodable).
+
+    >>> is_binary(b'\xff')
+    True
+    >>> is_binary('\xff')
+    False
+    """
+    return isinstance(value, bytes) and not is_decodable(value)
+
+
+def trim(s):
+    r"""
+    Trim something like a docstring to remove the whitespace that
+    is common due to indentation and formatting.
+
+    >>> trim("\n\tfoo = bar\n\t\tbar = baz\n")
+    'foo = bar\n\tbar = baz'
+    """
+    return textwrap.dedent(s).strip()
+
+
+def wrap(s):
+    """
+    Wrap lines of text, retaining existing newlines as
+    paragraph markers.
+
+    >>> print(wrap(lorem_ipsum))
+    Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do
+    eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad
+    minim veniam, quis nostrud exercitation ullamco laboris nisi ut
+    aliquip ex ea commodo consequat. Duis aute irure dolor in
+    reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla
+    pariatur. Excepteur sint occaecat cupidatat non proident, sunt in
+    culpa qui officia deserunt mollit anim id est laborum.
+    <BLANKLINE>
+    Curabitur pretium tincidunt lacus. Nulla gravida orci a odio. Nullam
+    varius, turpis et commodo pharetra, est eros bibendum elit, nec luctus
+    magna felis sollicitudin mauris. Integer in mauris eu nibh euismod
+    gravida. Duis ac tellus et risus vulputate vehicula. Donec lobortis
+    risus a elit. Etiam tempor. Ut ullamcorper, ligula eu tempor congue,
+    eros est euismod turpis, id tincidunt sapien risus a quam. Maecenas
+    fermentum consequat mi. Donec fermentum. Pellentesque malesuada nulla
+    a mi. Duis sapien sem, aliquet nec, commodo eget, consequat quis,
+    neque. Aliquam faucibus, elit ut dictum aliquet, felis nisl adipiscing
+    sapien, sed malesuada diam lacus eget erat. Cras mollis scelerisque
+    nunc. Nullam arcu. Aliquam consequat. Curabitur augue lorem, dapibus
+    quis, laoreet et, pretium ac, nisi. Aenean magna nisl, mollis quis,
+    molestie eu, feugiat in, orci. In hac habitasse platea dictumst.
+    """
+    paragraphs = s.splitlines()
+    wrapped = ('\n'.join(textwrap.wrap(para)) for para in paragraphs)
+    return '\n\n'.join(wrapped)
+
+
+def unwrap(s):
+    r"""
+    Given a multi-line string, return an unwrapped version.
+
+    >>> wrapped = wrap(lorem_ipsum)
+    >>> wrapped.count('\n')
+    20
+    >>> unwrapped = unwrap(wrapped)
+    >>> unwrapped.count('\n')
+    1
+    >>> print(unwrapped)
+    Lorem ipsum dolor sit amet, consectetur adipiscing ...
+    Curabitur pretium tincidunt lacus. Nulla gravida orci ...
+
+    """
+    paragraphs = re.split(r'\n\n+', s)
+    cleaned = (para.replace('\n', ' ') for para in paragraphs)
+    return '\n'.join(cleaned)
+
+
+lorem_ipsum: str = (
+    files(__name__).joinpath('Lorem ipsum.txt').read_text(encoding='utf-8')
+)
+
+
+class Splitter:
+    """object that will split a string with the given arguments for each call
+
+    >>> s = Splitter(',')
+    >>> s('hello, world, this is your, master calling')
+    ['hello', ' world', ' this is your', ' master calling']
+    """
+
+    def __init__(self, *args):
+        self.args = args
+
+    def __call__(self, s):
+        return s.split(*self.args)
+
+
+def indent(string, prefix=' ' * 4):
+    """
+    >>> indent('foo')
+    '    foo'
+    """
+    return prefix + string
+
+
+class WordSet(tuple):
+    """
+    Given an identifier, return the words that identifier represents,
+    whether in camel case, underscore-separated, etc.
+
+    >>> WordSet.parse("camelCase")
+    ('camel', 'Case')
+
+    >>> WordSet.parse("under_sep")
+    ('under', 'sep')
+
+    Acronyms should be retained
+
+    >>> WordSet.parse("firstSNL")
+    ('first', 'SNL')
+
+    >>> WordSet.parse("you_and_I")
+    ('you', 'and', 'I')
+
+    >>> WordSet.parse("A simple test")
+    ('A', 'simple', 'test')
+
+    Multiple caps should not interfere with the first cap of another word.
+
+    >>> WordSet.parse("myABCClass")
+    ('my', 'ABC', 'Class')
+
+    The result is a WordSet, providing access to other forms.
+
+    >>> WordSet.parse("myABCClass").underscore_separated()
+    'my_ABC_Class'
+
+    >>> WordSet.parse('a-command').camel_case()
+    'ACommand'
+
+    >>> WordSet.parse('someIdentifier').lowered().space_separated()
+    'some identifier'
+
+    Slices of the result should return another WordSet.
+
+    >>> WordSet.parse('taken-out-of-context')[1:].underscore_separated()
+    'out_of_context'
+
+    >>> WordSet.from_class_name(WordSet()).lowered().space_separated()
+    'word set'
+
+    >>> example = WordSet.parse('figured it out')
+    >>> example.headless_camel_case()
+    'figuredItOut'
+    >>> example.dash_separated()
+    'figured-it-out'
+
+    """
+
+    _pattern = re.compile('([A-Z]?[a-z]+)|([A-Z]+(?![a-z]))')
+
+    def capitalized(self):
+        return WordSet(word.capitalize() for word in self)
+
+    def lowered(self):
+        return WordSet(word.lower() for word in self)
+
+    def camel_case(self):
+        return ''.join(self.capitalized())
+
+    def headless_camel_case(self):
+        words = iter(self)
+        first = next(words).lower()
+        new_words = itertools.chain((first,), WordSet(words).camel_case())
+        return ''.join(new_words)
+
+    def underscore_separated(self):
+        return '_'.join(self)
+
+    def dash_separated(self):
+        return '-'.join(self)
+
+    def space_separated(self):
+        return ' '.join(self)
+
+    def trim_right(self, item):
+        """
+        Remove the item from the end of the set.
+
+        >>> WordSet.parse('foo bar').trim_right('foo')
+        ('foo', 'bar')
+        >>> WordSet.parse('foo bar').trim_right('bar')
+        ('foo',)
+        >>> WordSet.parse('').trim_right('bar')
+        ()
+        """
+        return self[:-1] if self and self[-1] == item else self
+
+    def trim_left(self, item):
+        """
+        Remove the item from the beginning of the set.
+
+        >>> WordSet.parse('foo bar').trim_left('foo')
+        ('bar',)
+        >>> WordSet.parse('foo bar').trim_left('bar')
+        ('foo', 'bar')
+        >>> WordSet.parse('').trim_left('bar')
+        ()
+        """
+        return self[1:] if self and self[0] == item else self
+
+    def trim(self, item):
+        """
+        >>> WordSet.parse('foo bar').trim('foo')
+        ('bar',)
+        """
+        return self.trim_left(item).trim_right(item)
+
+    def __getitem__(self, item):
+        result = super().__getitem__(item)
+        if isinstance(item, slice):
+            result = WordSet(result)
+        return result
+
+    @classmethod
+    def parse(cls, identifier):
+        matches = cls._pattern.finditer(identifier)
+        return WordSet(match.group(0) for match in matches)
+
+    @classmethod
+    def from_class_name(cls, subject):
+        return cls.parse(subject.__class__.__name__)
+
+
+# for backward compatibility
+words = WordSet.parse
+
+
+def simple_html_strip(s):
+    r"""
+    Remove HTML from the string `s`.
+
+    >>> str(simple_html_strip(''))
+    ''
+
+    >>> print(simple_html_strip('A <bold>stormy</bold> day in paradise'))
+    A stormy day in paradise
+
+    >>> print(simple_html_strip('Somebody <!-- do not --> tell the truth.'))
+    Somebody  tell the truth.
+
+    >>> print(simple_html_strip('What about<br/>\nmultiple lines?'))
+    What about
+    multiple lines?
+    """
+    html_stripper = re.compile('(<!--.*?-->)|(<[^>]*>)|([^<]+)', re.DOTALL)
+    texts = (match.group(3) or '' for match in html_stripper.finditer(s))
+    return ''.join(texts)
+
+
+class SeparatedValues(str):
+    """
+    A string separated by a separator. Overrides __iter__ for getting
+    the values.
+
+    >>> list(SeparatedValues('a,b,c'))
+    ['a', 'b', 'c']
+
+    Whitespace is stripped and empty values are discarded.
+
+    >>> list(SeparatedValues(' a,   b   , c,  '))
+    ['a', 'b', 'c']
+    """
+
+    separator = ','
+
+    def __iter__(self):
+        parts = self.split(self.separator)
+        return filter(None, (part.strip() for part in parts))
+
+
+class Stripper:
+    r"""
+    Given a series of lines, find the common prefix and strip it from them.
+
+    >>> lines = [
+    ...     'abcdefg\n',
+    ...     'abc\n',
+    ...     'abcde\n',
+    ... ]
+    >>> res = Stripper.strip_prefix(lines)
+    >>> res.prefix
+    'abc'
+    >>> list(res.lines)
+    ['defg\n', '\n', 'de\n']
+
+    If no prefix is common, nothing should be stripped.
+
+    >>> lines = [
+    ...     'abcd\n',
+    ...     '1234\n',
+    ... ]
+    >>> res = Stripper.strip_prefix(lines)
+    >>> res.prefix = ''
+    >>> list(res.lines)
+    ['abcd\n', '1234\n']
+    """
+
+    def __init__(self, prefix, lines):
+        self.prefix = prefix
+        self.lines = map(self, lines)
+
+    @classmethod
+    def strip_prefix(cls, lines):
+        prefix_lines, lines = itertools.tee(lines)
+        prefix = functools.reduce(cls.common_prefix, prefix_lines)
+        return cls(prefix, lines)
+
+    def __call__(self, line):
+        if not self.prefix:
+            return line
+        null, prefix, rest = line.partition(self.prefix)
+        return rest
+
+    @staticmethod
+    def common_prefix(s1, s2):
+        """
+        Return the common prefix of two lines.
+        """
+        index = min(len(s1), len(s2))
+        while s1[:index] != s2[:index]:
+            index -= 1
+        return s1[:index]
+
+
+def remove_prefix(text, prefix):
+    """
+    Remove the prefix from the text if it exists.
+
+    >>> remove_prefix('underwhelming performance', 'underwhelming ')
+    'performance'
+
+    >>> remove_prefix('something special', 'sample')
+    'something special'
+    """
+    null, prefix, rest = text.rpartition(prefix)
+    return rest
+
+
+def remove_suffix(text, suffix):
+    """
+    Remove the suffix from the text if it exists.
+
+    >>> remove_suffix('name.git', '.git')
+    'name'
+
+    >>> remove_suffix('something special', 'sample')
+    'something special'
+    """
+    rest, suffix, null = text.partition(suffix)
+    return rest
+
+
+def normalize_newlines(text):
+    r"""
+    Replace alternate newlines with the canonical newline.
+
+    >>> normalize_newlines('Lorem Ipsum\u2029')
+    'Lorem Ipsum\n'
+    >>> normalize_newlines('Lorem Ipsum\r\n')
+    'Lorem Ipsum\n'
+    >>> normalize_newlines('Lorem Ipsum\x85')
+    'Lorem Ipsum\n'
+    """
+    newlines = ['\r\n', '\r', '\n', '\u0085', '\u2028', '\u2029']
+    pattern = '|'.join(newlines)
+    return re.sub(pattern, '\n', text)
+
+
+def _nonblank(str):
+    return str and not str.startswith('#')
+
+
+@functools.singledispatch
+def yield_lines(iterable):
+    r"""
+    Yield valid lines of a string or iterable.
+
+    >>> list(yield_lines(''))
+    []
+    >>> list(yield_lines(['foo', 'bar']))
+    ['foo', 'bar']
+    >>> list(yield_lines('foo\nbar'))
+    ['foo', 'bar']
+    >>> list(yield_lines('\nfoo\n#bar\nbaz #comment'))
+    ['foo', 'baz #comment']
+    >>> list(yield_lines(['foo\nbar', 'baz', 'bing\n\n\n']))
+    ['foo', 'bar', 'baz', 'bing']
+    """
+    return itertools.chain.from_iterable(map(yield_lines, iterable))
+
+
+@yield_lines.register(str)
+def _(text):
+    return filter(_nonblank, map(str.strip, text.splitlines()))
+
+
+def drop_comment(line):
+    """
+    Drop comments.
+
+    >>> drop_comment('foo # bar')
+    'foo'
+
+    A hash without a space may be in a URL.
+
+    >>> drop_comment('http://example.com/foo#bar')
+    'http://example.com/foo#bar'
+    """
+    return line.partition(' #')[0]
+
+
+def join_continuation(lines):
+    r"""
+    Join lines continued by a trailing backslash.
+
+    >>> list(join_continuation(['foo \\', 'bar', 'baz']))
+    ['foobar', 'baz']
+    >>> list(join_continuation(['foo \\', 'bar', 'baz']))
+    ['foobar', 'baz']
+    >>> list(join_continuation(['foo \\', 'bar \\', 'baz']))
+    ['foobarbaz']
+
+    Not sure why, but...
+    The character preceding the backslash is also elided.
+
+    >>> list(join_continuation(['goo\\', 'dly']))
+    ['godly']
+
+    A terrible idea, but...
+    If no line is available to continue, suppress the lines.
+
+    >>> list(join_continuation(['foo', 'bar\\', 'baz\\']))
+    ['foo']
+    """
+    lines = iter(lines)
+    for item in lines:
+        while item.endswith('\\'):
+            try:
+                item = item[:-2].strip() + next(lines)
+            except StopIteration:
+                return
+        yield item
+
+
+def read_newlines(filename, limit=1024):
+    r"""
+    >>> tmp_path = getfixture('tmp_path')
+    >>> filename = tmp_path / 'out.txt'
+    >>> _ = filename.write_text('foo\n', newline='', encoding='utf-8')
+    >>> read_newlines(filename)
+    '\n'
+    >>> _ = filename.write_text('foo\r\n', newline='', encoding='utf-8')
+    >>> read_newlines(filename)
+    '\r\n'
+    >>> _ = filename.write_text('foo\r\nbar\nbing\r', newline='', encoding='utf-8')
+    >>> read_newlines(filename)
+    ('\r', '\n', '\r\n')
+    """
+    with open(filename, encoding='utf-8') as fp:
+        fp.read(limit)
+    return fp.newlines
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/__pycache__/__init__.cpython-312.pyc
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/__pycache__/to-dvorak.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/__pycache__/to-dvorak.cpython-312.pyc
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/__pycache__/to-qwerty.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/__pycache__/to-qwerty.cpython-312.pyc
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/layouts.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/layouts.py
new file mode 100644
index 0000000000000000000000000000000000000000..9636f0f7b53cb4d1ea5842e1eb62c998ddb91070
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/layouts.py
@@ -0,0 +1,25 @@
+qwerty = "-=qwertyuiop[]asdfghjkl;'zxcvbnm,./_+QWERTYUIOP{}ASDFGHJKL:\"ZXCVBNM<>?"
+dvorak = "[]',.pyfgcrl/=aoeuidhtns-;qjkxbmwvz{}\"<>PYFGCRL?+AOEUIDHTNS_:QJKXBMWVZ"
+
+
+to_dvorak = str.maketrans(qwerty, dvorak)
+to_qwerty = str.maketrans(dvorak, qwerty)
+
+
+def translate(input, translation):
+    """
+    >>> translate('dvorak', to_dvorak)
+    'ekrpat'
+    >>> translate('qwerty', to_qwerty)
+    'x,dokt'
+    """
+    return input.translate(translation)
+
+
+def _translate_stream(stream, translation):
+    """
+    >>> import io
+    >>> _translate_stream(io.StringIO('foo'), to_dvorak)
+    urr
+    """
+    print(translate(stream.read(), translation))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/show-newlines.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/show-newlines.py
new file mode 100644
index 0000000000000000000000000000000000000000..e11d1ba4280e9a882ca788c27f56034e90987004
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/show-newlines.py
@@ -0,0 +1,33 @@
+import autocommand
+import inflect
+
+from more_itertools import always_iterable
+
+import jaraco.text
+
+
+def report_newlines(filename):
+    r"""
+    Report the newlines in the indicated file.
+
+    >>> tmp_path = getfixture('tmp_path')
+    >>> filename = tmp_path / 'out.txt'
+    >>> _ = filename.write_text('foo\nbar\n', newline='', encoding='utf-8')
+    >>> report_newlines(filename)
+    newline is '\n'
+    >>> filename = tmp_path / 'out.txt'
+    >>> _ = filename.write_text('foo\nbar\r\n', newline='', encoding='utf-8')
+    >>> report_newlines(filename)
+    newlines are ('\n', '\r\n')
+    """
+    newlines = jaraco.text.read_newlines(filename)
+    count = len(tuple(always_iterable(newlines)))
+    engine = inflect.engine()
+    print(
+        engine.plural_noun("newline", count),
+        engine.plural_verb("is", count),
+        repr(newlines),
+    )
+
+
+autocommand.autocommand(__name__)(report_newlines)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/strip-prefix.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/strip-prefix.py
new file mode 100644
index 0000000000000000000000000000000000000000..761717a9b9e1f837eeacf0e888822f6fad881361
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/strip-prefix.py
@@ -0,0 +1,21 @@
+import sys
+
+import autocommand
+
+from jaraco.text import Stripper
+
+
+def strip_prefix():
+    r"""
+    Strip any common prefix from stdin.
+
+    >>> import io, pytest
+    >>> getfixture('monkeypatch').setattr('sys.stdin', io.StringIO('abcdef\nabc123'))
+    >>> strip_prefix()
+    def
+    123
+    """
+    sys.stdout.writelines(Stripper.strip_prefix(sys.stdin).lines)
+
+
+autocommand.autocommand(__name__)(strip_prefix)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/to-dvorak.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/to-dvorak.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6d5da80b3b565b956b523a5e0aaea7b6274cfcb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/to-dvorak.py
@@ -0,0 +1,6 @@
+import sys
+
+from . import layouts
+
+
+__name__ == '__main__' and layouts._translate_stream(sys.stdin, layouts.to_dvorak)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/to-qwerty.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/to-qwerty.py
new file mode 100644
index 0000000000000000000000000000000000000000..abe27286629cd3c1d95a984ad4a172957927af9b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/jaraco/text/to-qwerty.py
@@ -0,0 +1,6 @@
+import sys
+
+from . import layouts
+
+
+__name__ == '__main__' and layouts._translate_stream(sys.stdin, layouts.to_qwerty)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..0a523bece3e50519653c4d7a38399baa487fefa1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/LICENSE
@@ -0,0 +1,19 @@
+Copyright (c) 2012 Erik Rose
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..fb41b0cfe69bd01a045de31597a3f6d176604d25
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/METADATA
@@ -0,0 +1,266 @@
+Metadata-Version: 2.1
+Name: more-itertools
+Version: 10.3.0
+Summary: More routines for operating on iterables, beyond itertools
+Keywords: itertools,iterator,iteration,filter,peek,peekable,chunk,chunked
+Author-email: Erik Rose <erikrose@grinchcentral.com>
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Natural Language :: English
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Software Development :: Libraries
+Project-URL: Homepage, https://github.com/more-itertools/more-itertools
+
+==============
+More Itertools
+==============
+
+.. image:: https://readthedocs.org/projects/more-itertools/badge/?version=latest
+  :target: https://more-itertools.readthedocs.io/en/stable/
+
+Python's ``itertools`` library is a gem - you can compose elegant solutions
+for a variety of problems with the functions it provides. In ``more-itertools``
+we collect additional building blocks, recipes, and routines for working with
+Python iterables.
+
++------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Grouping               | `chunked <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.chunked>`_,                                                                               |
+|                        | `ichunked <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.ichunked>`_,                                                                             |
+|                        | `chunked_even <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.chunked_even>`_,                                                                     |
+|                        | `sliced <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.sliced>`_,                                                                                 |
+|                        | `constrained_batches <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.constrained_batches>`_,                                                       |
+|                        | `distribute <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.distribute>`_,                                                                         |
+|                        | `divide <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.divide>`_,                                                                                 |
+|                        | `split_at <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.split_at>`_,                                                                             |
+|                        | `split_before <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.split_before>`_,                                                                     |
+|                        | `split_after <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.split_after>`_,                                                                       |
+|                        | `split_into <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.split_into>`_,                                                                         |
+|                        | `split_when <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.split_when>`_,                                                                         |
+|                        | `bucket <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.bucket>`_,                                                                                 |
+|                        | `unzip <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.unzip>`_,                                                                                   |
+|                        | `batched <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.batched>`_,                                                                               |
+|                        | `grouper <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.grouper>`_,                                                                               |
+|                        | `partition <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.partition>`_,                                                                           |
+|                        | `transpose <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.transpose>`_                                                                            |
++------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Lookahead and lookback | `spy <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.spy>`_,                                                                                       |
+|                        | `peekable <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.peekable>`_,                                                                             |
+|                        | `seekable <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.seekable>`_                                                                              |
++------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Windowing              | `windowed <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.windowed>`_,                                                                             |
+|                        | `substrings <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.substrings>`_,                                                                         |
+|                        | `substrings_indexes <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.substrings_indexes>`_,                                                         |
+|                        | `stagger <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.stagger>`_,                                                                               |
+|                        | `windowed_complete <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.windowed_complete>`_,                                                           |
+|                        | `pairwise <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.pairwise>`_,                                                                             |
+|                        | `triplewise <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.triplewise>`_,                                                                         |
+|                        | `sliding_window <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.sliding_window>`_,                                                                 |
+|                        | `subslices <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.subslices>`_                                                                            |
++------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Augmenting             | `count_cycle <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.count_cycle>`_,                                                                       |
+|                        | `intersperse <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.intersperse>`_,                                                                       |
+|                        | `padded <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.padded>`_,                                                                                 |
+|                        | `repeat_each <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.repeat_each>`_,                                                                       |
+|                        | `mark_ends <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.mark_ends>`_,                                                                           |
+|                        | `repeat_last <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.repeat_last>`_,                                                                       |
+|                        | `adjacent <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.adjacent>`_,                                                                             |
+|                        | `groupby_transform <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.groupby_transform>`_,                                                           |
+|                        | `pad_none <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.pad_none>`_,                                                                             |
+|                        | `ncycles <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.ncycles>`_                                                                                |
++------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Combining              | `collapse <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.collapse>`_,                                                                             |
+|                        | `sort_together <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.sort_together>`_,                                                                   |
+|                        | `interleave <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.interleave>`_,                                                                         |
+|                        | `interleave_longest <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.interleave_longest>`_,                                                         |
+|                        | `interleave_evenly <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.interleave_evenly>`_,                                                           |
+|                        | `zip_offset <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.zip_offset>`_,                                                                         |
+|                        | `zip_equal <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.zip_equal>`_,                                                                           |
+|                        | `zip_broadcast <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.zip_broadcast>`_,                                                                   |
+|                        | `flatten <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.flatten>`_,                                                                               |
+|                        | `roundrobin <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.roundrobin>`_,                                                                         |
+|                        | `prepend <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.prepend>`_,                                                                               |
+|                        | `value_chain <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.value_chain>`_,                                                                       |
+|                        | `partial_product <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.partial_product>`_                                                                |
++------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Summarizing            | `ilen <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.ilen>`_,                                                                                     |
+|                        | `unique_to_each <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.unique_to_each>`_,                                                                 |
+|                        | `sample <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.sample>`_,                                                                                 |
+|                        | `consecutive_groups <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.consecutive_groups>`_,                                                         |
+|                        | `run_length <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.run_length>`_,                                                                         |
+|                        | `map_reduce <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.map_reduce>`_,                                                                         |
+|                        | `join_mappings <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.join_mappings>`_,                                                                   |
+|                        | `exactly_n <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.exactly_n>`_,                                                                           |
+|                        | `is_sorted <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.is_sorted>`_,                                                                           |
+|                        | `all_equal <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.all_equal>`_,                                                                           |
+|                        | `all_unique <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.all_unique>`_,                                                                         |
+|                        | `minmax <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.minmax>`_,                                                                                 |
+|                        | `first_true <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.first_true>`_,                                                                         |
+|                        | `quantify <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.quantify>`_,                                                                             |
+|                        | `iequals <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.iequals>`_                                                                                |
++------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Selecting              | `islice_extended <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.islice_extended>`_,                                                               |
+|                        | `first <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.first>`_,                                                                                   |
+|                        | `last <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.last>`_,                                                                                     |
+|                        | `one <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.one>`_,                                                                                       |
+|                        | `only <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.only>`_,                                                                                     |
+|                        | `strictly_n <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.strictly_n>`_,                                                                         |
+|                        | `strip <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.strip>`_,                                                                                   |
+|                        | `lstrip <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.lstrip>`_,                                                                                 |
+|                        | `rstrip <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.rstrip>`_,                                                                                 |
+|                        | `filter_except <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.filter_except>`_,                                                                   |
+|                        | `map_except <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.map_except>`_,                                                                         |
+|                        | `filter_map <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.filter_map>`_,                                                                         |
+|                        | `iter_suppress <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.iter_suppress>`_,                                                                   |
+|                        | `nth_or_last <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.nth_or_last>`_,                                                                       |
+|                        | `unique_in_window <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.unique_in_window>`_,                                                             |
+|                        | `before_and_after <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.before_and_after>`_,                                                             |
+|                        | `nth <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.nth>`_,                                                                                       |
+|                        | `take <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.take>`_,                                                                                     |
+|                        | `tail <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.tail>`_,                                                                                     |
+|                        | `unique_everseen <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.unique_everseen>`_,                                                               |
+|                        | `unique_justseen <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.unique_justseen>`_,                                                               |
+|                        | `unique <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.unique>`_,                                                                                 |
+|                        | `duplicates_everseen <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.duplicates_everseen>`_,                                                       |
+|                        | `duplicates_justseen <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.duplicates_justseen>`_,                                                       |
+|                        | `classify_unique <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.classify_unique>`_,                                                               |
+|                        | `longest_common_prefix <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.longest_common_prefix>`_,                                                   |
+|                        | `takewhile_inclusive <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.takewhile_inclusive>`_                                                        |
++------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Math                   | `dft <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.dft>`_,                                                                                       |
+|                        | `idft <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.idft>`_,                                                                                     |
+|                        | `convolve <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.convolve>`_,                                                                             |
+|                        | `dotproduct <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.dotproduct>`_,                                                                         |
+|                        | `factor <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.factor>`_,                                                                                 |
+|                        | `matmul <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.matmul>`_,                                                                                 |
+|                        | `polynomial_from_roots <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.polynomial_from_roots>`_,                                                   |
+|                        | `polynomial_derivative <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.polynomial_derivative>`_,                                                   |
+|                        | `polynomial_eval <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.polynomial_eval>`_,                                                               |
+|                        | `sieve <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.sieve>`_,                                                                                   |
+|                        | `sum_of_squares <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.sum_of_squares>`_,                                                                 |
+|                        | `totient <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.totient>`_                                                                                |
++------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Combinatorics          | `distinct_permutations <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.distinct_permutations>`_,                                                   |
+|                        | `distinct_combinations <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.distinct_combinations>`_,                                                   |
+|                        | `circular_shifts <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.circular_shifts>`_,                                                               |
+|                        | `partitions <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.partitions>`_,                                                                         |
+|                        | `set_partitions <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.set_partitions>`_,                                                                 |
+|                        | `product_index <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.product_index>`_,                                                                   |
+|                        | `combination_index <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.combination_index>`_,                                                           |
+|                        | `permutation_index <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.permutation_index>`_,                                                           |
+|                        | `combination_with_replacement_index <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.combination_with_replacement_index>`_,                         |
+|                        | `gray_product  <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.gray_product>`_,                                                                    |
+|                        | `outer_product  <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.outer_product>`_,                                                                  |
+|                        | `powerset <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.powerset>`_,                                                                             |
+|                        | `powerset_of_sets <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.powerset_of_sets>`_,                                                             |
+|                        | `random_product <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.random_product>`_,                                                                 |
+|                        | `random_permutation <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.random_permutation>`_,                                                         |
+|                        | `random_combination <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.random_combination>`_,                                                         |
+|                        | `random_combination_with_replacement <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.random_combination_with_replacement>`_,                       |
+|                        | `nth_product <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.nth_product>`_,                                                                       |
+|                        | `nth_permutation <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.nth_permutation>`_,                                                               |
+|                        | `nth_combination <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.nth_combination>`_,                                                               |
+|                        | `nth_combination_with_replacement <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.nth_combination_with_replacement>`_                              |
++------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Wrapping               | `always_iterable <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.always_iterable>`_,                                                               |
+|                        | `always_reversible <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.always_reversible>`_,                                                           |
+|                        | `countable <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.countable>`_,                                                                           |
+|                        | `consumer <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.consumer>`_,                                                                             |
+|                        | `with_iter <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.with_iter>`_,                                                                           |
+|                        | `iter_except <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.iter_except>`_                                                                        |
++------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Others                 | `locate <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.locate>`_,                                                                                 |
+|                        | `rlocate <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.rlocate>`_,                                                                               |
+|                        | `replace <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.replace>`_,                                                                               |
+|                        | `numeric_range <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.numeric_range>`_,                                                                   |
+|                        | `side_effect <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.side_effect>`_,                                                                       |
+|                        | `iterate <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.iterate>`_,                                                                               |
+|                        | `difference <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.difference>`_,                                                                         |
+|                        | `make_decorator <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.make_decorator>`_,                                                                 |
+|                        | `SequenceView <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.SequenceView>`_,                                                                     |
+|                        | `time_limited <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.time_limited>`_,                                                                     |
+|                        | `map_if <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.map_if>`_,                                                                                 |
+|                        | `iter_index <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.iter_index>`_,                                                                         |
+|                        | `consume <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.consume>`_,                                                                               |
+|                        | `tabulate <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.tabulate>`_,                                                                             |
+|                        | `repeatfunc <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.repeatfunc>`_,                                                                         |
+|                        | `reshape <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.reshape>`_                                                                                |
+|                        | `doublestarmap <https://more-itertools.readthedocs.io/en/stable/api.html#more_itertools.doublestarmap>`_                                                                    |
++------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+
+
+Getting started
+===============
+
+To get started, install the library with `pip <https://pip.pypa.io/en/stable/>`_:
+
+.. code-block:: shell
+
+    pip install more-itertools
+
+The recipes from the `itertools docs <https://docs.python.org/3/library/itertools.html#itertools-recipes>`_
+are included in the top-level package:
+
+.. code-block:: python
+
+    >>> from more_itertools import flatten
+    >>> iterable = [(0, 1), (2, 3)]
+    >>> list(flatten(iterable))
+    [0, 1, 2, 3]
+
+Several new recipes are available as well:
+
+.. code-block:: python
+
+    >>> from more_itertools import chunked
+    >>> iterable = [0, 1, 2, 3, 4, 5, 6, 7, 8]
+    >>> list(chunked(iterable, 3))
+    [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+
+    >>> from more_itertools import spy
+    >>> iterable = (x * x for x in range(1, 6))
+    >>> head, iterable = spy(iterable, n=3)
+    >>> list(head)
+    [1, 4, 9]
+    >>> list(iterable)
+    [1, 4, 9, 16, 25]
+
+
+
+For the full listing of functions, see the `API documentation <https://more-itertools.readthedocs.io/en/stable/api.html>`_.
+
+
+Links elsewhere
+===============
+
+Blog posts about ``more-itertools``:
+
+* `Yo, I heard you like decorators <https://www.bbayles.com/index/decorator_factory>`__
+* `Tour of Python Itertools <https://martinheinz.dev/blog/16>`__ (`Alternate <https://dev.to/martinheinz/tour-of-python-itertools-4122>`__)
+* `Real-World Python More Itertools <https://www.gidware.com/real-world-more-itertools/>`_
+
+
+Development
+===========
+
+``more-itertools`` is maintained by `@erikrose <https://github.com/erikrose>`_
+and `@bbayles <https://github.com/bbayles>`_, with help from `many others <https://github.com/more-itertools/more-itertools/graphs/contributors>`_.
+If you have a problem or suggestion, please file a bug or pull request in this
+repository. Thanks for contributing!
+
+
+Version History
+===============
+
+The version history can be found in `documentation <https://more-itertools.readthedocs.io/en/stable/versions.html>`_.
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..f15f3fcdc5c751048d3c9eac077a3d11c861df36
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/RECORD
@@ -0,0 +1,16 @@
+more_itertools-10.3.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+more_itertools-10.3.0.dist-info/LICENSE,sha256=CfHIyelBrz5YTVlkHqm4fYPAyw_QB-te85Gn4mQ8GkY,1053
+more_itertools-10.3.0.dist-info/METADATA,sha256=BFO90O-fLNiVQMpj7oIS5ztzgJUUQZ3TA32P5HH3N-A,36293
+more_itertools-10.3.0.dist-info/RECORD,,
+more_itertools-10.3.0.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+more_itertools-10.3.0.dist-info/WHEEL,sha256=rSgq_JpHF9fHR1lx53qwg_1-2LypZE_qmcuXbVUq948,81
+more_itertools/__init__.py,sha256=dtAbGjTDmn_ghiU5YXfhyDy0phAlXVdt5klZA5fUa-Q,149
+more_itertools/__init__.pyi,sha256=5B3eTzON1BBuOLob1vCflyEb2lSd6usXQQ-Cv-hXkeA,43
+more_itertools/__pycache__/__init__.cpython-312.pyc,,
+more_itertools/__pycache__/more.cpython-312.pyc,,
+more_itertools/__pycache__/recipes.cpython-312.pyc,,
+more_itertools/more.py,sha256=1E5kzFncRKTDw0cYv1yRXMgDdunstLQd1QStcnL6U90,148370
+more_itertools/more.pyi,sha256=iXXeqt48Nxe8VGmIWpkVXuKpR2FYNuu2DU8nQLWCCu0,21484
+more_itertools/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+more_itertools/recipes.py,sha256=WedhhfhGVgr6zii8fIbGJVmRTw0ZKRiLKnYBDGJv4nY,28591
+more_itertools/recipes.pyi,sha256=T_mdGpcFdfrP3JSWbwzYP9JyNV-Go-7RPfpxfftAWlA,4617
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/REQUESTED b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/REQUESTED
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..db4a255f3a271083b66f205eeaa81f74c97bb8ac
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools-10.3.0.dist-info/WHEEL
@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: flit 3.8.0
+Root-Is-Purelib: true
+Tag: py3-none-any
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/__init__.py
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index 0000000000000000000000000000000000000000..9c4662fc31a75cd576f6f110a345f5e2f3cb2958
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/__init__.py
@@ -0,0 +1,6 @@
+"""More routines for operating on iterables, beyond itertools"""
+
+from .more import *  # noqa
+from .recipes import *  # noqa
+
+__version__ = '10.3.0'
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/__init__.pyi b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..96f6e36c7f4ac9ea0aebdcd9e11b8d1ff092d2ef
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/__init__.pyi
@@ -0,0 +1,2 @@
+from .more import *
+from .recipes import *
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/__pycache__/__init__.cpython-312.pyc
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index 0000000000000000000000000000000000000000..83e8846e16fbbf29daa5745e2f5d1863ef5229dc
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+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/__pycache__/more.cpython-312.pyc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e58539f19196ca8842554b8b8c0be3d9dd849ed6ca8bf54619a2901587b8024
+size 174210
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/__pycache__/recipes.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/__pycache__/recipes.cpython-312.pyc
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/more.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/more.py
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index 0000000000000000000000000000000000000000..7b481907daee16c08b1e6fac8cec3b87ea4c8bd8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/more.py
@@ -0,0 +1,4806 @@
+import math
+import warnings
+
+from collections import Counter, defaultdict, deque, abc
+from collections.abc import Sequence
+from functools import cached_property, partial, reduce, wraps
+from heapq import heapify, heapreplace, heappop
+from itertools import (
+    chain,
+    combinations,
+    compress,
+    count,
+    cycle,
+    dropwhile,
+    groupby,
+    islice,
+    repeat,
+    starmap,
+    takewhile,
+    tee,
+    zip_longest,
+    product,
+)
+from math import comb, e, exp, factorial, floor, fsum, log, perm, tau
+from queue import Empty, Queue
+from random import random, randrange, uniform
+from operator import itemgetter, mul, sub, gt, lt, ge, le
+from sys import hexversion, maxsize
+from time import monotonic
+
+from .recipes import (
+    _marker,
+    _zip_equal,
+    UnequalIterablesError,
+    consume,
+    flatten,
+    pairwise,
+    powerset,
+    take,
+    unique_everseen,
+    all_equal,
+    batched,
+)
+
+__all__ = [
+    'AbortThread',
+    'SequenceView',
+    'UnequalIterablesError',
+    'adjacent',
+    'all_unique',
+    'always_iterable',
+    'always_reversible',
+    'bucket',
+    'callback_iter',
+    'chunked',
+    'chunked_even',
+    'circular_shifts',
+    'collapse',
+    'combination_index',
+    'combination_with_replacement_index',
+    'consecutive_groups',
+    'constrained_batches',
+    'consumer',
+    'count_cycle',
+    'countable',
+    'dft',
+    'difference',
+    'distinct_combinations',
+    'distinct_permutations',
+    'distribute',
+    'divide',
+    'doublestarmap',
+    'duplicates_everseen',
+    'duplicates_justseen',
+    'classify_unique',
+    'exactly_n',
+    'filter_except',
+    'filter_map',
+    'first',
+    'gray_product',
+    'groupby_transform',
+    'ichunked',
+    'iequals',
+    'idft',
+    'ilen',
+    'interleave',
+    'interleave_evenly',
+    'interleave_longest',
+    'intersperse',
+    'is_sorted',
+    'islice_extended',
+    'iterate',
+    'iter_suppress',
+    'join_mappings',
+    'last',
+    'locate',
+    'longest_common_prefix',
+    'lstrip',
+    'make_decorator',
+    'map_except',
+    'map_if',
+    'map_reduce',
+    'mark_ends',
+    'minmax',
+    'nth_or_last',
+    'nth_permutation',
+    'nth_product',
+    'nth_combination_with_replacement',
+    'numeric_range',
+    'one',
+    'only',
+    'outer_product',
+    'padded',
+    'partial_product',
+    'partitions',
+    'peekable',
+    'permutation_index',
+    'powerset_of_sets',
+    'product_index',
+    'raise_',
+    'repeat_each',
+    'repeat_last',
+    'replace',
+    'rlocate',
+    'rstrip',
+    'run_length',
+    'sample',
+    'seekable',
+    'set_partitions',
+    'side_effect',
+    'sliced',
+    'sort_together',
+    'split_after',
+    'split_at',
+    'split_before',
+    'split_into',
+    'split_when',
+    'spy',
+    'stagger',
+    'strip',
+    'strictly_n',
+    'substrings',
+    'substrings_indexes',
+    'takewhile_inclusive',
+    'time_limited',
+    'unique_in_window',
+    'unique_to_each',
+    'unzip',
+    'value_chain',
+    'windowed',
+    'windowed_complete',
+    'with_iter',
+    'zip_broadcast',
+    'zip_equal',
+    'zip_offset',
+]
+
+# math.sumprod is available for Python 3.12+
+_fsumprod = getattr(math, 'sumprod', lambda x, y: fsum(map(mul, x, y)))
+
+
+def chunked(iterable, n, strict=False):
+    """Break *iterable* into lists of length *n*:
+
+        >>> list(chunked([1, 2, 3, 4, 5, 6], 3))
+        [[1, 2, 3], [4, 5, 6]]
+
+    By the default, the last yielded list will have fewer than *n* elements
+    if the length of *iterable* is not divisible by *n*:
+
+        >>> list(chunked([1, 2, 3, 4, 5, 6, 7, 8], 3))
+        [[1, 2, 3], [4, 5, 6], [7, 8]]
+
+    To use a fill-in value instead, see the :func:`grouper` recipe.
+
+    If the length of *iterable* is not divisible by *n* and *strict* is
+    ``True``, then ``ValueError`` will be raised before the last
+    list is yielded.
+
+    """
+    iterator = iter(partial(take, n, iter(iterable)), [])
+    if strict:
+        if n is None:
+            raise ValueError('n must not be None when using strict mode.')
+
+        def ret():
+            for chunk in iterator:
+                if len(chunk) != n:
+                    raise ValueError('iterable is not divisible by n.')
+                yield chunk
+
+        return iter(ret())
+    else:
+        return iterator
+
+
+def first(iterable, default=_marker):
+    """Return the first item of *iterable*, or *default* if *iterable* is
+    empty.
+
+        >>> first([0, 1, 2, 3])
+        0
+        >>> first([], 'some default')
+        'some default'
+
+    If *default* is not provided and there are no items in the iterable,
+    raise ``ValueError``.
+
+    :func:`first` is useful when you have a generator of expensive-to-retrieve
+    values and want any arbitrary one. It is marginally shorter than
+    ``next(iter(iterable), default)``.
+
+    """
+    for item in iterable:
+        return item
+    if default is _marker:
+        raise ValueError(
+            'first() was called on an empty iterable, and no '
+            'default value was provided.'
+        )
+    return default
+
+
+def last(iterable, default=_marker):
+    """Return the last item of *iterable*, or *default* if *iterable* is
+    empty.
+
+        >>> last([0, 1, 2, 3])
+        3
+        >>> last([], 'some default')
+        'some default'
+
+    If *default* is not provided and there are no items in the iterable,
+    raise ``ValueError``.
+    """
+    try:
+        if isinstance(iterable, Sequence):
+            return iterable[-1]
+        # Work around https://bugs.python.org/issue38525
+        elif hasattr(iterable, '__reversed__') and (hexversion != 0x030800F0):
+            return next(reversed(iterable))
+        else:
+            return deque(iterable, maxlen=1)[-1]
+    except (IndexError, TypeError, StopIteration):
+        if default is _marker:
+            raise ValueError(
+                'last() was called on an empty iterable, and no default was '
+                'provided.'
+            )
+        return default
+
+
+def nth_or_last(iterable, n, default=_marker):
+    """Return the nth or the last item of *iterable*,
+    or *default* if *iterable* is empty.
+
+        >>> nth_or_last([0, 1, 2, 3], 2)
+        2
+        >>> nth_or_last([0, 1], 2)
+        1
+        >>> nth_or_last([], 0, 'some default')
+        'some default'
+
+    If *default* is not provided and there are no items in the iterable,
+    raise ``ValueError``.
+    """
+    return last(islice(iterable, n + 1), default=default)
+
+
+class peekable:
+    """Wrap an iterator to allow lookahead and prepending elements.
+
+    Call :meth:`peek` on the result to get the value that will be returned
+    by :func:`next`. This won't advance the iterator:
+
+        >>> p = peekable(['a', 'b'])
+        >>> p.peek()
+        'a'
+        >>> next(p)
+        'a'
+
+    Pass :meth:`peek` a default value to return that instead of raising
+    ``StopIteration`` when the iterator is exhausted.
+
+        >>> p = peekable([])
+        >>> p.peek('hi')
+        'hi'
+
+    peekables also offer a :meth:`prepend` method, which "inserts" items
+    at the head of the iterable:
+
+        >>> p = peekable([1, 2, 3])
+        >>> p.prepend(10, 11, 12)
+        >>> next(p)
+        10
+        >>> p.peek()
+        11
+        >>> list(p)
+        [11, 12, 1, 2, 3]
+
+    peekables can be indexed. Index 0 is the item that will be returned by
+    :func:`next`, index 1 is the item after that, and so on:
+    The values up to the given index will be cached.
+
+        >>> p = peekable(['a', 'b', 'c', 'd'])
+        >>> p[0]
+        'a'
+        >>> p[1]
+        'b'
+        >>> next(p)
+        'a'
+
+    Negative indexes are supported, but be aware that they will cache the
+    remaining items in the source iterator, which may require significant
+    storage.
+
+    To check whether a peekable is exhausted, check its truth value:
+
+        >>> p = peekable(['a', 'b'])
+        >>> if p:  # peekable has items
+        ...     list(p)
+        ['a', 'b']
+        >>> if not p:  # peekable is exhausted
+        ...     list(p)
+        []
+
+    """
+
+    def __init__(self, iterable):
+        self._it = iter(iterable)
+        self._cache = deque()
+
+    def __iter__(self):
+        return self
+
+    def __bool__(self):
+        try:
+            self.peek()
+        except StopIteration:
+            return False
+        return True
+
+    def peek(self, default=_marker):
+        """Return the item that will be next returned from ``next()``.
+
+        Return ``default`` if there are no items left. If ``default`` is not
+        provided, raise ``StopIteration``.
+
+        """
+        if not self._cache:
+            try:
+                self._cache.append(next(self._it))
+            except StopIteration:
+                if default is _marker:
+                    raise
+                return default
+        return self._cache[0]
+
+    def prepend(self, *items):
+        """Stack up items to be the next ones returned from ``next()`` or
+        ``self.peek()``. The items will be returned in
+        first in, first out order::
+
+            >>> p = peekable([1, 2, 3])
+            >>> p.prepend(10, 11, 12)
+            >>> next(p)
+            10
+            >>> list(p)
+            [11, 12, 1, 2, 3]
+
+        It is possible, by prepending items, to "resurrect" a peekable that
+        previously raised ``StopIteration``.
+
+            >>> p = peekable([])
+            >>> next(p)
+            Traceback (most recent call last):
+              ...
+            StopIteration
+            >>> p.prepend(1)
+            >>> next(p)
+            1
+            >>> next(p)
+            Traceback (most recent call last):
+              ...
+            StopIteration
+
+        """
+        self._cache.extendleft(reversed(items))
+
+    def __next__(self):
+        if self._cache:
+            return self._cache.popleft()
+
+        return next(self._it)
+
+    def _get_slice(self, index):
+        # Normalize the slice's arguments
+        step = 1 if (index.step is None) else index.step
+        if step > 0:
+            start = 0 if (index.start is None) else index.start
+            stop = maxsize if (index.stop is None) else index.stop
+        elif step < 0:
+            start = -1 if (index.start is None) else index.start
+            stop = (-maxsize - 1) if (index.stop is None) else index.stop
+        else:
+            raise ValueError('slice step cannot be zero')
+
+        # If either the start or stop index is negative, we'll need to cache
+        # the rest of the iterable in order to slice from the right side.
+        if (start < 0) or (stop < 0):
+            self._cache.extend(self._it)
+        # Otherwise we'll need to find the rightmost index and cache to that
+        # point.
+        else:
+            n = min(max(start, stop) + 1, maxsize)
+            cache_len = len(self._cache)
+            if n >= cache_len:
+                self._cache.extend(islice(self._it, n - cache_len))
+
+        return list(self._cache)[index]
+
+    def __getitem__(self, index):
+        if isinstance(index, slice):
+            return self._get_slice(index)
+
+        cache_len = len(self._cache)
+        if index < 0:
+            self._cache.extend(self._it)
+        elif index >= cache_len:
+            self._cache.extend(islice(self._it, index + 1 - cache_len))
+
+        return self._cache[index]
+
+
+def consumer(func):
+    """Decorator that automatically advances a PEP-342-style "reverse iterator"
+    to its first yield point so you don't have to call ``next()`` on it
+    manually.
+
+        >>> @consumer
+        ... def tally():
+        ...     i = 0
+        ...     while True:
+        ...         print('Thing number %s is %s.' % (i, (yield)))
+        ...         i += 1
+        ...
+        >>> t = tally()
+        >>> t.send('red')
+        Thing number 0 is red.
+        >>> t.send('fish')
+        Thing number 1 is fish.
+
+    Without the decorator, you would have to call ``next(t)`` before
+    ``t.send()`` could be used.
+
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        gen = func(*args, **kwargs)
+        next(gen)
+        return gen
+
+    return wrapper
+
+
+def ilen(iterable):
+    """Return the number of items in *iterable*.
+
+        >>> ilen(x for x in range(1000000) if x % 3 == 0)
+        333334
+
+    This consumes the iterable, so handle with care.
+
+    """
+    # This approach was selected because benchmarks showed it's likely the
+    # fastest of the known implementations at the time of writing.
+    # See GitHub tracker: #236, #230.
+    counter = count()
+    deque(zip(iterable, counter), maxlen=0)
+    return next(counter)
+
+
+def iterate(func, start):
+    """Return ``start``, ``func(start)``, ``func(func(start))``, ...
+
+    >>> from itertools import islice
+    >>> list(islice(iterate(lambda x: 2*x, 1), 10))
+    [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]
+
+    """
+    while True:
+        yield start
+        try:
+            start = func(start)
+        except StopIteration:
+            break
+
+
+def with_iter(context_manager):
+    """Wrap an iterable in a ``with`` statement, so it closes once exhausted.
+
+    For example, this will close the file when the iterator is exhausted::
+
+        upper_lines = (line.upper() for line in with_iter(open('foo')))
+
+    Any context manager which returns an iterable is a candidate for
+    ``with_iter``.
+
+    """
+    with context_manager as iterable:
+        yield from iterable
+
+
+def one(iterable, too_short=None, too_long=None):
+    """Return the first item from *iterable*, which is expected to contain only
+    that item. Raise an exception if *iterable* is empty or has more than one
+    item.
+
+    :func:`one` is useful for ensuring that an iterable contains only one item.
+    For example, it can be used to retrieve the result of a database query
+    that is expected to return a single row.
+
+    If *iterable* is empty, ``ValueError`` will be raised. You may specify a
+    different exception with the *too_short* keyword:
+
+        >>> it = []
+        >>> one(it)  # doctest: +IGNORE_EXCEPTION_DETAIL
+        Traceback (most recent call last):
+        ...
+        ValueError: too many items in iterable (expected 1)'
+        >>> too_short = IndexError('too few items')
+        >>> one(it, too_short=too_short)  # doctest: +IGNORE_EXCEPTION_DETAIL
+        Traceback (most recent call last):
+        ...
+        IndexError: too few items
+
+    Similarly, if *iterable* contains more than one item, ``ValueError`` will
+    be raised. You may specify a different exception with the *too_long*
+    keyword:
+
+        >>> it = ['too', 'many']
+        >>> one(it)  # doctest: +IGNORE_EXCEPTION_DETAIL
+        Traceback (most recent call last):
+        ...
+        ValueError: Expected exactly one item in iterable, but got 'too',
+        'many', and perhaps more.
+        >>> too_long = RuntimeError
+        >>> one(it, too_long=too_long)  # doctest: +IGNORE_EXCEPTION_DETAIL
+        Traceback (most recent call last):
+        ...
+        RuntimeError
+
+    Note that :func:`one` attempts to advance *iterable* twice to ensure there
+    is only one item. See :func:`spy` or :func:`peekable` to check iterable
+    contents less destructively.
+
+    """
+    it = iter(iterable)
+
+    try:
+        first_value = next(it)
+    except StopIteration as exc:
+        raise (
+            too_short or ValueError('too few items in iterable (expected 1)')
+        ) from exc
+
+    try:
+        second_value = next(it)
+    except StopIteration:
+        pass
+    else:
+        msg = (
+            'Expected exactly one item in iterable, but got {!r}, {!r}, '
+            'and perhaps more.'.format(first_value, second_value)
+        )
+        raise too_long or ValueError(msg)
+
+    return first_value
+
+
+def raise_(exception, *args):
+    raise exception(*args)
+
+
+def strictly_n(iterable, n, too_short=None, too_long=None):
+    """Validate that *iterable* has exactly *n* items and return them if
+    it does. If it has fewer than *n* items, call function *too_short*
+    with those items. If it has more than *n* items, call function
+    *too_long* with the first ``n + 1`` items.
+
+        >>> iterable = ['a', 'b', 'c', 'd']
+        >>> n = 4
+        >>> list(strictly_n(iterable, n))
+        ['a', 'b', 'c', 'd']
+
+    Note that the returned iterable must be consumed in order for the check to
+    be made.
+
+    By default, *too_short* and *too_long* are functions that raise
+    ``ValueError``.
+
+        >>> list(strictly_n('ab', 3))  # doctest: +IGNORE_EXCEPTION_DETAIL
+        Traceback (most recent call last):
+        ...
+        ValueError: too few items in iterable (got 2)
+
+        >>> list(strictly_n('abc', 2))  # doctest: +IGNORE_EXCEPTION_DETAIL
+        Traceback (most recent call last):
+        ...
+        ValueError: too many items in iterable (got at least 3)
+
+    You can instead supply functions that do something else.
+    *too_short* will be called with the number of items in *iterable*.
+    *too_long* will be called with `n + 1`.
+
+        >>> def too_short(item_count):
+        ...     raise RuntimeError
+        >>> it = strictly_n('abcd', 6, too_short=too_short)
+        >>> list(it)  # doctest: +IGNORE_EXCEPTION_DETAIL
+        Traceback (most recent call last):
+        ...
+        RuntimeError
+
+        >>> def too_long(item_count):
+        ...     print('The boss is going to hear about this')
+        >>> it = strictly_n('abcdef', 4, too_long=too_long)
+        >>> list(it)
+        The boss is going to hear about this
+        ['a', 'b', 'c', 'd']
+
+    """
+    if too_short is None:
+        too_short = lambda item_count: raise_(
+            ValueError,
+            'Too few items in iterable (got {})'.format(item_count),
+        )
+
+    if too_long is None:
+        too_long = lambda item_count: raise_(
+            ValueError,
+            'Too many items in iterable (got at least {})'.format(item_count),
+        )
+
+    it = iter(iterable)
+    for i in range(n):
+        try:
+            item = next(it)
+        except StopIteration:
+            too_short(i)
+            return
+        else:
+            yield item
+
+    try:
+        next(it)
+    except StopIteration:
+        pass
+    else:
+        too_long(n + 1)
+
+
+def distinct_permutations(iterable, r=None):
+    """Yield successive distinct permutations of the elements in *iterable*.
+
+        >>> sorted(distinct_permutations([1, 0, 1]))
+        [(0, 1, 1), (1, 0, 1), (1, 1, 0)]
+
+    Equivalent to ``set(permutations(iterable))``, except duplicates are not
+    generated and thrown away. For larger input sequences this is much more
+    efficient.
+
+    Duplicate permutations arise when there are duplicated elements in the
+    input iterable. The number of items returned is
+    `n! / (x_1! * x_2! * ... * x_n!)`, where `n` is the total number of
+    items input, and each `x_i` is the count of a distinct item in the input
+    sequence.
+
+    If *r* is given, only the *r*-length permutations are yielded.
+
+        >>> sorted(distinct_permutations([1, 0, 1], r=2))
+        [(0, 1), (1, 0), (1, 1)]
+        >>> sorted(distinct_permutations(range(3), r=2))
+        [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+
+    """
+
+    # Algorithm: https://w.wiki/Qai
+    def _full(A):
+        while True:
+            # Yield the permutation we have
+            yield tuple(A)
+
+            # Find the largest index i such that A[i] < A[i + 1]
+            for i in range(size - 2, -1, -1):
+                if A[i] < A[i + 1]:
+                    break
+            #  If no such index exists, this permutation is the last one
+            else:
+                return
+
+            # Find the largest index j greater than j such that A[i] < A[j]
+            for j in range(size - 1, i, -1):
+                if A[i] < A[j]:
+                    break
+
+            # Swap the value of A[i] with that of A[j], then reverse the
+            # sequence from A[i + 1] to form the new permutation
+            A[i], A[j] = A[j], A[i]
+            A[i + 1 :] = A[: i - size : -1]  # A[i + 1:][::-1]
+
+    # Algorithm: modified from the above
+    def _partial(A, r):
+        # Split A into the first r items and the last r items
+        head, tail = A[:r], A[r:]
+        right_head_indexes = range(r - 1, -1, -1)
+        left_tail_indexes = range(len(tail))
+
+        while True:
+            # Yield the permutation we have
+            yield tuple(head)
+
+            # Starting from the right, find the first index of the head with
+            # value smaller than the maximum value of the tail - call it i.
+            pivot = tail[-1]
+            for i in right_head_indexes:
+                if head[i] < pivot:
+                    break
+                pivot = head[i]
+            else:
+                return
+
+            # Starting from the left, find the first value of the tail
+            # with a value greater than head[i] and swap.
+            for j in left_tail_indexes:
+                if tail[j] > head[i]:
+                    head[i], tail[j] = tail[j], head[i]
+                    break
+            # If we didn't find one, start from the right and find the first
+            # index of the head with a value greater than head[i] and swap.
+            else:
+                for j in right_head_indexes:
+                    if head[j] > head[i]:
+                        head[i], head[j] = head[j], head[i]
+                        break
+
+            # Reverse head[i + 1:] and swap it with tail[:r - (i + 1)]
+            tail += head[: i - r : -1]  # head[i + 1:][::-1]
+            i += 1
+            head[i:], tail[:] = tail[: r - i], tail[r - i :]
+
+    items = sorted(iterable)
+
+    size = len(items)
+    if r is None:
+        r = size
+
+    if 0 < r <= size:
+        return _full(items) if (r == size) else _partial(items, r)
+
+    return iter(() if r else ((),))
+
+
+def intersperse(e, iterable, n=1):
+    """Intersperse filler element *e* among the items in *iterable*, leaving
+    *n* items between each filler element.
+
+        >>> list(intersperse('!', [1, 2, 3, 4, 5]))
+        [1, '!', 2, '!', 3, '!', 4, '!', 5]
+
+        >>> list(intersperse(None, [1, 2, 3, 4, 5], n=2))
+        [1, 2, None, 3, 4, None, 5]
+
+    """
+    if n == 0:
+        raise ValueError('n must be > 0')
+    elif n == 1:
+        # interleave(repeat(e), iterable) -> e, x_0, e, x_1, e, x_2...
+        # islice(..., 1, None) -> x_0, e, x_1, e, x_2...
+        return islice(interleave(repeat(e), iterable), 1, None)
+    else:
+        # interleave(filler, chunks) -> [e], [x_0, x_1], [e], [x_2, x_3]...
+        # islice(..., 1, None) -> [x_0, x_1], [e], [x_2, x_3]...
+        # flatten(...) -> x_0, x_1, e, x_2, x_3...
+        filler = repeat([e])
+        chunks = chunked(iterable, n)
+        return flatten(islice(interleave(filler, chunks), 1, None))
+
+
+def unique_to_each(*iterables):
+    """Return the elements from each of the input iterables that aren't in the
+    other input iterables.
+
+    For example, suppose you have a set of packages, each with a set of
+    dependencies::
+
+        {'pkg_1': {'A', 'B'}, 'pkg_2': {'B', 'C'}, 'pkg_3': {'B', 'D'}}
+
+    If you remove one package, which dependencies can also be removed?
+
+    If ``pkg_1`` is removed, then ``A`` is no longer necessary - it is not
+    associated with ``pkg_2`` or ``pkg_3``. Similarly, ``C`` is only needed for
+    ``pkg_2``, and ``D`` is only needed for ``pkg_3``::
+
+        >>> unique_to_each({'A', 'B'}, {'B', 'C'}, {'B', 'D'})
+        [['A'], ['C'], ['D']]
+
+    If there are duplicates in one input iterable that aren't in the others
+    they will be duplicated in the output. Input order is preserved::
+
+        >>> unique_to_each("mississippi", "missouri")
+        [['p', 'p'], ['o', 'u', 'r']]
+
+    It is assumed that the elements of each iterable are hashable.
+
+    """
+    pool = [list(it) for it in iterables]
+    counts = Counter(chain.from_iterable(map(set, pool)))
+    uniques = {element for element in counts if counts[element] == 1}
+    return [list(filter(uniques.__contains__, it)) for it in pool]
+
+
+def windowed(seq, n, fillvalue=None, step=1):
+    """Return a sliding window of width *n* over the given iterable.
+
+        >>> all_windows = windowed([1, 2, 3, 4, 5], 3)
+        >>> list(all_windows)
+        [(1, 2, 3), (2, 3, 4), (3, 4, 5)]
+
+    When the window is larger than the iterable, *fillvalue* is used in place
+    of missing values:
+
+        >>> list(windowed([1, 2, 3], 4))
+        [(1, 2, 3, None)]
+
+    Each window will advance in increments of *step*:
+
+        >>> list(windowed([1, 2, 3, 4, 5, 6], 3, fillvalue='!', step=2))
+        [(1, 2, 3), (3, 4, 5), (5, 6, '!')]
+
+    To slide into the iterable's items, use :func:`chain` to add filler items
+    to the left:
+
+        >>> iterable = [1, 2, 3, 4]
+        >>> n = 3
+        >>> padding = [None] * (n - 1)
+        >>> list(windowed(chain(padding, iterable), 3))
+        [(None, None, 1), (None, 1, 2), (1, 2, 3), (2, 3, 4)]
+    """
+    if n < 0:
+        raise ValueError('n must be >= 0')
+    if n == 0:
+        yield ()
+        return
+    if step < 1:
+        raise ValueError('step must be >= 1')
+
+    iterable = iter(seq)
+
+    # Generate first window
+    window = deque(islice(iterable, n), maxlen=n)
+
+    # Deal with the first window not being full
+    if not window:
+        return
+    if len(window) < n:
+        yield tuple(window) + ((fillvalue,) * (n - len(window)))
+        return
+    yield tuple(window)
+
+    # Create the filler for the next windows. The padding ensures
+    # we have just enough elements to fill the last window.
+    padding = (fillvalue,) * (n - 1 if step >= n else step - 1)
+    filler = map(window.append, chain(iterable, padding))
+
+    # Generate the rest of the windows
+    for _ in islice(filler, step - 1, None, step):
+        yield tuple(window)
+
+
+def substrings(iterable):
+    """Yield all of the substrings of *iterable*.
+
+        >>> [''.join(s) for s in substrings('more')]
+        ['m', 'o', 'r', 'e', 'mo', 'or', 're', 'mor', 'ore', 'more']
+
+    Note that non-string iterables can also be subdivided.
+
+        >>> list(substrings([0, 1, 2]))
+        [(0,), (1,), (2,), (0, 1), (1, 2), (0, 1, 2)]
+
+    """
+    # The length-1 substrings
+    seq = []
+    for item in iter(iterable):
+        seq.append(item)
+        yield (item,)
+    seq = tuple(seq)
+    item_count = len(seq)
+
+    # And the rest
+    for n in range(2, item_count + 1):
+        for i in range(item_count - n + 1):
+            yield seq[i : i + n]
+
+
+def substrings_indexes(seq, reverse=False):
+    """Yield all substrings and their positions in *seq*
+
+    The items yielded will be a tuple of the form ``(substr, i, j)``, where
+    ``substr == seq[i:j]``.
+
+    This function only works for iterables that support slicing, such as
+    ``str`` objects.
+
+    >>> for item in substrings_indexes('more'):
+    ...    print(item)
+    ('m', 0, 1)
+    ('o', 1, 2)
+    ('r', 2, 3)
+    ('e', 3, 4)
+    ('mo', 0, 2)
+    ('or', 1, 3)
+    ('re', 2, 4)
+    ('mor', 0, 3)
+    ('ore', 1, 4)
+    ('more', 0, 4)
+
+    Set *reverse* to ``True`` to yield the same items in the opposite order.
+
+
+    """
+    r = range(1, len(seq) + 1)
+    if reverse:
+        r = reversed(r)
+    return (
+        (seq[i : i + L], i, i + L) for L in r for i in range(len(seq) - L + 1)
+    )
+
+
+class bucket:
+    """Wrap *iterable* and return an object that buckets the iterable into
+    child iterables based on a *key* function.
+
+        >>> iterable = ['a1', 'b1', 'c1', 'a2', 'b2', 'c2', 'b3']
+        >>> s = bucket(iterable, key=lambda x: x[0])  # Bucket by 1st character
+        >>> sorted(list(s))  # Get the keys
+        ['a', 'b', 'c']
+        >>> a_iterable = s['a']
+        >>> next(a_iterable)
+        'a1'
+        >>> next(a_iterable)
+        'a2'
+        >>> list(s['b'])
+        ['b1', 'b2', 'b3']
+
+    The original iterable will be advanced and its items will be cached until
+    they are used by the child iterables. This may require significant storage.
+
+    By default, attempting to select a bucket to which no items belong  will
+    exhaust the iterable and cache all values.
+    If you specify a *validator* function, selected buckets will instead be
+    checked against it.
+
+        >>> from itertools import count
+        >>> it = count(1, 2)  # Infinite sequence of odd numbers
+        >>> key = lambda x: x % 10  # Bucket by last digit
+        >>> validator = lambda x: x in {1, 3, 5, 7, 9}  # Odd digits only
+        >>> s = bucket(it, key=key, validator=validator)
+        >>> 2 in s
+        False
+        >>> list(s[2])
+        []
+
+    """
+
+    def __init__(self, iterable, key, validator=None):
+        self._it = iter(iterable)
+        self._key = key
+        self._cache = defaultdict(deque)
+        self._validator = validator or (lambda x: True)
+
+    def __contains__(self, value):
+        if not self._validator(value):
+            return False
+
+        try:
+            item = next(self[value])
+        except StopIteration:
+            return False
+        else:
+            self._cache[value].appendleft(item)
+
+        return True
+
+    def _get_values(self, value):
+        """
+        Helper to yield items from the parent iterator that match *value*.
+        Items that don't match are stored in the local cache as they
+        are encountered.
+        """
+        while True:
+            # If we've cached some items that match the target value, emit
+            # the first one and evict it from the cache.
+            if self._cache[value]:
+                yield self._cache[value].popleft()
+            # Otherwise we need to advance the parent iterator to search for
+            # a matching item, caching the rest.
+            else:
+                while True:
+                    try:
+                        item = next(self._it)
+                    except StopIteration:
+                        return
+                    item_value = self._key(item)
+                    if item_value == value:
+                        yield item
+                        break
+                    elif self._validator(item_value):
+                        self._cache[item_value].append(item)
+
+    def __iter__(self):
+        for item in self._it:
+            item_value = self._key(item)
+            if self._validator(item_value):
+                self._cache[item_value].append(item)
+
+        yield from self._cache.keys()
+
+    def __getitem__(self, value):
+        if not self._validator(value):
+            return iter(())
+
+        return self._get_values(value)
+
+
+def spy(iterable, n=1):
+    """Return a 2-tuple with a list containing the first *n* elements of
+    *iterable*, and an iterator with the same items as *iterable*.
+    This allows you to "look ahead" at the items in the iterable without
+    advancing it.
+
+    There is one item in the list by default:
+
+        >>> iterable = 'abcdefg'
+        >>> head, iterable = spy(iterable)
+        >>> head
+        ['a']
+        >>> list(iterable)
+        ['a', 'b', 'c', 'd', 'e', 'f', 'g']
+
+    You may use unpacking to retrieve items instead of lists:
+
+        >>> (head,), iterable = spy('abcdefg')
+        >>> head
+        'a'
+        >>> (first, second), iterable = spy('abcdefg', 2)
+        >>> first
+        'a'
+        >>> second
+        'b'
+
+    The number of items requested can be larger than the number of items in
+    the iterable:
+
+        >>> iterable = [1, 2, 3, 4, 5]
+        >>> head, iterable = spy(iterable, 10)
+        >>> head
+        [1, 2, 3, 4, 5]
+        >>> list(iterable)
+        [1, 2, 3, 4, 5]
+
+    """
+    it = iter(iterable)
+    head = take(n, it)
+
+    return head.copy(), chain(head, it)
+
+
+def interleave(*iterables):
+    """Return a new iterable yielding from each iterable in turn,
+    until the shortest is exhausted.
+
+        >>> list(interleave([1, 2, 3], [4, 5], [6, 7, 8]))
+        [1, 4, 6, 2, 5, 7]
+
+    For a version that doesn't terminate after the shortest iterable is
+    exhausted, see :func:`interleave_longest`.
+
+    """
+    return chain.from_iterable(zip(*iterables))
+
+
+def interleave_longest(*iterables):
+    """Return a new iterable yielding from each iterable in turn,
+    skipping any that are exhausted.
+
+        >>> list(interleave_longest([1, 2, 3], [4, 5], [6, 7, 8]))
+        [1, 4, 6, 2, 5, 7, 3, 8]
+
+    This function produces the same output as :func:`roundrobin`, but may
+    perform better for some inputs (in particular when the number of iterables
+    is large).
+
+    """
+    i = chain.from_iterable(zip_longest(*iterables, fillvalue=_marker))
+    return (x for x in i if x is not _marker)
+
+
+def interleave_evenly(iterables, lengths=None):
+    """
+    Interleave multiple iterables so that their elements are evenly distributed
+    throughout the output sequence.
+
+    >>> iterables = [1, 2, 3, 4, 5], ['a', 'b']
+    >>> list(interleave_evenly(iterables))
+    [1, 2, 'a', 3, 4, 'b', 5]
+
+    >>> iterables = [[1, 2, 3], [4, 5], [6, 7, 8]]
+    >>> list(interleave_evenly(iterables))
+    [1, 6, 4, 2, 7, 3, 8, 5]
+
+    This function requires iterables of known length. Iterables without
+    ``__len__()`` can be used by manually specifying lengths with *lengths*:
+
+    >>> from itertools import combinations, repeat
+    >>> iterables = [combinations(range(4), 2), ['a', 'b', 'c']]
+    >>> lengths = [4 * (4 - 1) // 2, 3]
+    >>> list(interleave_evenly(iterables, lengths=lengths))
+    [(0, 1), (0, 2), 'a', (0, 3), (1, 2), 'b', (1, 3), (2, 3), 'c']
+
+    Based on Bresenham's algorithm.
+    """
+    if lengths is None:
+        try:
+            lengths = [len(it) for it in iterables]
+        except TypeError:
+            raise ValueError(
+                'Iterable lengths could not be determined automatically. '
+                'Specify them with the lengths keyword.'
+            )
+    elif len(iterables) != len(lengths):
+        raise ValueError('Mismatching number of iterables and lengths.')
+
+    dims = len(lengths)
+
+    # sort iterables by length, descending
+    lengths_permute = sorted(
+        range(dims), key=lambda i: lengths[i], reverse=True
+    )
+    lengths_desc = [lengths[i] for i in lengths_permute]
+    iters_desc = [iter(iterables[i]) for i in lengths_permute]
+
+    # the longest iterable is the primary one (Bresenham: the longest
+    # distance along an axis)
+    delta_primary, deltas_secondary = lengths_desc[0], lengths_desc[1:]
+    iter_primary, iters_secondary = iters_desc[0], iters_desc[1:]
+    errors = [delta_primary // dims] * len(deltas_secondary)
+
+    to_yield = sum(lengths)
+    while to_yield:
+        yield next(iter_primary)
+        to_yield -= 1
+        # update errors for each secondary iterable
+        errors = [e - delta for e, delta in zip(errors, deltas_secondary)]
+
+        # those iterables for which the error is negative are yielded
+        # ("diagonal step" in Bresenham)
+        for i, e_ in enumerate(errors):
+            if e_ < 0:
+                yield next(iters_secondary[i])
+                to_yield -= 1
+                errors[i] += delta_primary
+
+
+def collapse(iterable, base_type=None, levels=None):
+    """Flatten an iterable with multiple levels of nesting (e.g., a list of
+    lists of tuples) into non-iterable types.
+
+        >>> iterable = [(1, 2), ([3, 4], [[5], [6]])]
+        >>> list(collapse(iterable))
+        [1, 2, 3, 4, 5, 6]
+
+    Binary and text strings are not considered iterable and
+    will not be collapsed.
+
+    To avoid collapsing other types, specify *base_type*:
+
+        >>> iterable = ['ab', ('cd', 'ef'), ['gh', 'ij']]
+        >>> list(collapse(iterable, base_type=tuple))
+        ['ab', ('cd', 'ef'), 'gh', 'ij']
+
+    Specify *levels* to stop flattening after a certain level:
+
+    >>> iterable = [('a', ['b']), ('c', ['d'])]
+    >>> list(collapse(iterable))  # Fully flattened
+    ['a', 'b', 'c', 'd']
+    >>> list(collapse(iterable, levels=1))  # Only one level flattened
+    ['a', ['b'], 'c', ['d']]
+
+    """
+    stack = deque()
+    # Add our first node group, treat the iterable as a single node
+    stack.appendleft((0, repeat(iterable, 1)))
+
+    while stack:
+        node_group = stack.popleft()
+        level, nodes = node_group
+
+        # Check if beyond max level
+        if levels is not None and level > levels:
+            yield from nodes
+            continue
+
+        for node in nodes:
+            # Check if done iterating
+            if isinstance(node, (str, bytes)) or (
+                (base_type is not None) and isinstance(node, base_type)
+            ):
+                yield node
+            # Otherwise try to create child nodes
+            else:
+                try:
+                    tree = iter(node)
+                except TypeError:
+                    yield node
+                else:
+                    # Save our current location
+                    stack.appendleft(node_group)
+                    # Append the new child node
+                    stack.appendleft((level + 1, tree))
+                    # Break to process child node
+                    break
+
+
+def side_effect(func, iterable, chunk_size=None, before=None, after=None):
+    """Invoke *func* on each item in *iterable* (or on each *chunk_size* group
+    of items) before yielding the item.
+
+    `func` must be a function that takes a single argument. Its return value
+    will be discarded.
+
+    *before* and *after* are optional functions that take no arguments. They
+    will be executed before iteration starts and after it ends, respectively.
+
+    `side_effect` can be used for logging, updating progress bars, or anything
+    that is not functionally "pure."
+
+    Emitting a status message:
+
+        >>> from more_itertools import consume
+        >>> func = lambda item: print('Received {}'.format(item))
+        >>> consume(side_effect(func, range(2)))
+        Received 0
+        Received 1
+
+    Operating on chunks of items:
+
+        >>> pair_sums = []
+        >>> func = lambda chunk: pair_sums.append(sum(chunk))
+        >>> list(side_effect(func, [0, 1, 2, 3, 4, 5], 2))
+        [0, 1, 2, 3, 4, 5]
+        >>> list(pair_sums)
+        [1, 5, 9]
+
+    Writing to a file-like object:
+
+        >>> from io import StringIO
+        >>> from more_itertools import consume
+        >>> f = StringIO()
+        >>> func = lambda x: print(x, file=f)
+        >>> before = lambda: print(u'HEADER', file=f)
+        >>> after = f.close
+        >>> it = [u'a', u'b', u'c']
+        >>> consume(side_effect(func, it, before=before, after=after))
+        >>> f.closed
+        True
+
+    """
+    try:
+        if before is not None:
+            before()
+
+        if chunk_size is None:
+            for item in iterable:
+                func(item)
+                yield item
+        else:
+            for chunk in chunked(iterable, chunk_size):
+                func(chunk)
+                yield from chunk
+    finally:
+        if after is not None:
+            after()
+
+
+def sliced(seq, n, strict=False):
+    """Yield slices of length *n* from the sequence *seq*.
+
+    >>> list(sliced((1, 2, 3, 4, 5, 6), 3))
+    [(1, 2, 3), (4, 5, 6)]
+
+    By the default, the last yielded slice will have fewer than *n* elements
+    if the length of *seq* is not divisible by *n*:
+
+    >>> list(sliced((1, 2, 3, 4, 5, 6, 7, 8), 3))
+    [(1, 2, 3), (4, 5, 6), (7, 8)]
+
+    If the length of *seq* is not divisible by *n* and *strict* is
+    ``True``, then ``ValueError`` will be raised before the last
+    slice is yielded.
+
+    This function will only work for iterables that support slicing.
+    For non-sliceable iterables, see :func:`chunked`.
+
+    """
+    iterator = takewhile(len, (seq[i : i + n] for i in count(0, n)))
+    if strict:
+
+        def ret():
+            for _slice in iterator:
+                if len(_slice) != n:
+                    raise ValueError("seq is not divisible by n.")
+                yield _slice
+
+        return iter(ret())
+    else:
+        return iterator
+
+
+def split_at(iterable, pred, maxsplit=-1, keep_separator=False):
+    """Yield lists of items from *iterable*, where each list is delimited by
+    an item where callable *pred* returns ``True``.
+
+        >>> list(split_at('abcdcba', lambda x: x == 'b'))
+        [['a'], ['c', 'd', 'c'], ['a']]
+
+        >>> list(split_at(range(10), lambda n: n % 2 == 1))
+        [[0], [2], [4], [6], [8], []]
+
+    At most *maxsplit* splits are done. If *maxsplit* is not specified or -1,
+    then there is no limit on the number of splits:
+
+        >>> list(split_at(range(10), lambda n: n % 2 == 1, maxsplit=2))
+        [[0], [2], [4, 5, 6, 7, 8, 9]]
+
+    By default, the delimiting items are not included in the output.
+    To include them, set *keep_separator* to ``True``.
+
+        >>> list(split_at('abcdcba', lambda x: x == 'b', keep_separator=True))
+        [['a'], ['b'], ['c', 'd', 'c'], ['b'], ['a']]
+
+    """
+    if maxsplit == 0:
+        yield list(iterable)
+        return
+
+    buf = []
+    it = iter(iterable)
+    for item in it:
+        if pred(item):
+            yield buf
+            if keep_separator:
+                yield [item]
+            if maxsplit == 1:
+                yield list(it)
+                return
+            buf = []
+            maxsplit -= 1
+        else:
+            buf.append(item)
+    yield buf
+
+
+def split_before(iterable, pred, maxsplit=-1):
+    """Yield lists of items from *iterable*, where each list ends just before
+    an item for which callable *pred* returns ``True``:
+
+        >>> list(split_before('OneTwo', lambda s: s.isupper()))
+        [['O', 'n', 'e'], ['T', 'w', 'o']]
+
+        >>> list(split_before(range(10), lambda n: n % 3 == 0))
+        [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
+
+    At most *maxsplit* splits are done. If *maxsplit* is not specified or -1,
+    then there is no limit on the number of splits:
+
+        >>> list(split_before(range(10), lambda n: n % 3 == 0, maxsplit=2))
+        [[0, 1, 2], [3, 4, 5], [6, 7, 8, 9]]
+    """
+    if maxsplit == 0:
+        yield list(iterable)
+        return
+
+    buf = []
+    it = iter(iterable)
+    for item in it:
+        if pred(item) and buf:
+            yield buf
+            if maxsplit == 1:
+                yield [item] + list(it)
+                return
+            buf = []
+            maxsplit -= 1
+        buf.append(item)
+    if buf:
+        yield buf
+
+
+def split_after(iterable, pred, maxsplit=-1):
+    """Yield lists of items from *iterable*, where each list ends with an
+    item where callable *pred* returns ``True``:
+
+        >>> list(split_after('one1two2', lambda s: s.isdigit()))
+        [['o', 'n', 'e', '1'], ['t', 'w', 'o', '2']]
+
+        >>> list(split_after(range(10), lambda n: n % 3 == 0))
+        [[0], [1, 2, 3], [4, 5, 6], [7, 8, 9]]
+
+    At most *maxsplit* splits are done. If *maxsplit* is not specified or -1,
+    then there is no limit on the number of splits:
+
+        >>> list(split_after(range(10), lambda n: n % 3 == 0, maxsplit=2))
+        [[0], [1, 2, 3], [4, 5, 6, 7, 8, 9]]
+
+    """
+    if maxsplit == 0:
+        yield list(iterable)
+        return
+
+    buf = []
+    it = iter(iterable)
+    for item in it:
+        buf.append(item)
+        if pred(item) and buf:
+            yield buf
+            if maxsplit == 1:
+                buf = list(it)
+                if buf:
+                    yield buf
+                return
+            buf = []
+            maxsplit -= 1
+    if buf:
+        yield buf
+
+
+def split_when(iterable, pred, maxsplit=-1):
+    """Split *iterable* into pieces based on the output of *pred*.
+    *pred* should be a function that takes successive pairs of items and
+    returns ``True`` if the iterable should be split in between them.
+
+    For example, to find runs of increasing numbers, split the iterable when
+    element ``i`` is larger than element ``i + 1``:
+
+        >>> list(split_when([1, 2, 3, 3, 2, 5, 2, 4, 2], lambda x, y: x > y))
+        [[1, 2, 3, 3], [2, 5], [2, 4], [2]]
+
+    At most *maxsplit* splits are done. If *maxsplit* is not specified or -1,
+    then there is no limit on the number of splits:
+
+        >>> list(split_when([1, 2, 3, 3, 2, 5, 2, 4, 2],
+        ...                 lambda x, y: x > y, maxsplit=2))
+        [[1, 2, 3, 3], [2, 5], [2, 4, 2]]
+
+    """
+    if maxsplit == 0:
+        yield list(iterable)
+        return
+
+    it = iter(iterable)
+    try:
+        cur_item = next(it)
+    except StopIteration:
+        return
+
+    buf = [cur_item]
+    for next_item in it:
+        if pred(cur_item, next_item):
+            yield buf
+            if maxsplit == 1:
+                yield [next_item] + list(it)
+                return
+            buf = []
+            maxsplit -= 1
+
+        buf.append(next_item)
+        cur_item = next_item
+
+    yield buf
+
+
+def split_into(iterable, sizes):
+    """Yield a list of sequential items from *iterable* of length 'n' for each
+    integer 'n' in *sizes*.
+
+        >>> list(split_into([1,2,3,4,5,6], [1,2,3]))
+        [[1], [2, 3], [4, 5, 6]]
+
+    If the sum of *sizes* is smaller than the length of *iterable*, then the
+    remaining items of *iterable* will not be returned.
+
+        >>> list(split_into([1,2,3,4,5,6], [2,3]))
+        [[1, 2], [3, 4, 5]]
+
+    If the sum of *sizes* is larger than the length of *iterable*, fewer items
+    will be returned in the iteration that overruns *iterable* and further
+    lists will be empty:
+
+        >>> list(split_into([1,2,3,4], [1,2,3,4]))
+        [[1], [2, 3], [4], []]
+
+    When a ``None`` object is encountered in *sizes*, the returned list will
+    contain items up to the end of *iterable* the same way that itertools.slice
+    does:
+
+        >>> list(split_into([1,2,3,4,5,6,7,8,9,0], [2,3,None]))
+        [[1, 2], [3, 4, 5], [6, 7, 8, 9, 0]]
+
+    :func:`split_into` can be useful for grouping a series of items where the
+    sizes of the groups are not uniform. An example would be where in a row
+    from a table, multiple columns represent elements of the same feature
+    (e.g. a point represented by x,y,z) but, the format is not the same for
+    all columns.
+    """
+    # convert the iterable argument into an iterator so its contents can
+    # be consumed by islice in case it is a generator
+    it = iter(iterable)
+
+    for size in sizes:
+        if size is None:
+            yield list(it)
+            return
+        else:
+            yield list(islice(it, size))
+
+
+def padded(iterable, fillvalue=None, n=None, next_multiple=False):
+    """Yield the elements from *iterable*, followed by *fillvalue*, such that
+    at least *n* items are emitted.
+
+        >>> list(padded([1, 2, 3], '?', 5))
+        [1, 2, 3, '?', '?']
+
+    If *next_multiple* is ``True``, *fillvalue* will be emitted until the
+    number of items emitted is a multiple of *n*:
+
+        >>> list(padded([1, 2, 3, 4], n=3, next_multiple=True))
+        [1, 2, 3, 4, None, None]
+
+    If *n* is ``None``, *fillvalue* will be emitted indefinitely.
+
+    To create an *iterable* of exactly size *n*, you can truncate with
+    :func:`islice`.
+
+        >>> list(islice(padded([1, 2, 3], '?'), 5))
+        [1, 2, 3, '?', '?']
+        >>> list(islice(padded([1, 2, 3, 4, 5, 6, 7, 8], '?'), 5))
+        [1, 2, 3, 4, 5]
+
+    """
+    iterable = iter(iterable)
+    iterable_with_repeat = chain(iterable, repeat(fillvalue))
+
+    if n is None:
+        return iterable_with_repeat
+    elif n < 1:
+        raise ValueError('n must be at least 1')
+    elif next_multiple:
+
+        def slice_generator():
+            for first in iterable:
+                yield (first,)
+                yield islice(iterable_with_repeat, n - 1)
+
+        # While elements exist produce slices of size n
+        return chain.from_iterable(slice_generator())
+    else:
+        # Ensure the first batch is at least size n then iterate
+        return chain(islice(iterable_with_repeat, n), iterable)
+
+
+def repeat_each(iterable, n=2):
+    """Repeat each element in *iterable* *n* times.
+
+    >>> list(repeat_each('ABC', 3))
+    ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C']
+    """
+    return chain.from_iterable(map(repeat, iterable, repeat(n)))
+
+
+def repeat_last(iterable, default=None):
+    """After the *iterable* is exhausted, keep yielding its last element.
+
+        >>> list(islice(repeat_last(range(3)), 5))
+        [0, 1, 2, 2, 2]
+
+    If the iterable is empty, yield *default* forever::
+
+        >>> list(islice(repeat_last(range(0), 42), 5))
+        [42, 42, 42, 42, 42]
+
+    """
+    item = _marker
+    for item in iterable:
+        yield item
+    final = default if item is _marker else item
+    yield from repeat(final)
+
+
+def distribute(n, iterable):
+    """Distribute the items from *iterable* among *n* smaller iterables.
+
+        >>> group_1, group_2 = distribute(2, [1, 2, 3, 4, 5, 6])
+        >>> list(group_1)
+        [1, 3, 5]
+        >>> list(group_2)
+        [2, 4, 6]
+
+    If the length of *iterable* is not evenly divisible by *n*, then the
+    length of the returned iterables will not be identical:
+
+        >>> children = distribute(3, [1, 2, 3, 4, 5, 6, 7])
+        >>> [list(c) for c in children]
+        [[1, 4, 7], [2, 5], [3, 6]]
+
+    If the length of *iterable* is smaller than *n*, then the last returned
+    iterables will be empty:
+
+        >>> children = distribute(5, [1, 2, 3])
+        >>> [list(c) for c in children]
+        [[1], [2], [3], [], []]
+
+    This function uses :func:`itertools.tee` and may require significant
+    storage.
+
+    If you need the order items in the smaller iterables to match the
+    original iterable, see :func:`divide`.
+
+    """
+    if n < 1:
+        raise ValueError('n must be at least 1')
+
+    children = tee(iterable, n)
+    return [islice(it, index, None, n) for index, it in enumerate(children)]
+
+
+def stagger(iterable, offsets=(-1, 0, 1), longest=False, fillvalue=None):
+    """Yield tuples whose elements are offset from *iterable*.
+    The amount by which the `i`-th item in each tuple is offset is given by
+    the `i`-th item in *offsets*.
+
+        >>> list(stagger([0, 1, 2, 3]))
+        [(None, 0, 1), (0, 1, 2), (1, 2, 3)]
+        >>> list(stagger(range(8), offsets=(0, 2, 4)))
+        [(0, 2, 4), (1, 3, 5), (2, 4, 6), (3, 5, 7)]
+
+    By default, the sequence will end when the final element of a tuple is the
+    last item in the iterable. To continue until the first element of a tuple
+    is the last item in the iterable, set *longest* to ``True``::
+
+        >>> list(stagger([0, 1, 2, 3], longest=True))
+        [(None, 0, 1), (0, 1, 2), (1, 2, 3), (2, 3, None), (3, None, None)]
+
+    By default, ``None`` will be used to replace offsets beyond the end of the
+    sequence. Specify *fillvalue* to use some other value.
+
+    """
+    children = tee(iterable, len(offsets))
+
+    return zip_offset(
+        *children, offsets=offsets, longest=longest, fillvalue=fillvalue
+    )
+
+
+def zip_equal(*iterables):
+    """``zip`` the input *iterables* together, but raise
+    ``UnequalIterablesError`` if they aren't all the same length.
+
+        >>> it_1 = range(3)
+        >>> it_2 = iter('abc')
+        >>> list(zip_equal(it_1, it_2))
+        [(0, 'a'), (1, 'b'), (2, 'c')]
+
+        >>> it_1 = range(3)
+        >>> it_2 = iter('abcd')
+        >>> list(zip_equal(it_1, it_2)) # doctest: +IGNORE_EXCEPTION_DETAIL
+        Traceback (most recent call last):
+        ...
+        more_itertools.more.UnequalIterablesError: Iterables have different
+        lengths
+
+    """
+    if hexversion >= 0x30A00A6:
+        warnings.warn(
+            (
+                'zip_equal will be removed in a future version of '
+                'more-itertools. Use the builtin zip function with '
+                'strict=True instead.'
+            ),
+            DeprecationWarning,
+        )
+
+    return _zip_equal(*iterables)
+
+
+def zip_offset(*iterables, offsets, longest=False, fillvalue=None):
+    """``zip`` the input *iterables* together, but offset the `i`-th iterable
+    by the `i`-th item in *offsets*.
+
+        >>> list(zip_offset('0123', 'abcdef', offsets=(0, 1)))
+        [('0', 'b'), ('1', 'c'), ('2', 'd'), ('3', 'e')]
+
+    This can be used as a lightweight alternative to SciPy or pandas to analyze
+    data sets in which some series have a lead or lag relationship.
+
+    By default, the sequence will end when the shortest iterable is exhausted.
+    To continue until the longest iterable is exhausted, set *longest* to
+    ``True``.
+
+        >>> list(zip_offset('0123', 'abcdef', offsets=(0, 1), longest=True))
+        [('0', 'b'), ('1', 'c'), ('2', 'd'), ('3', 'e'), (None, 'f')]
+
+    By default, ``None`` will be used to replace offsets beyond the end of the
+    sequence. Specify *fillvalue* to use some other value.
+
+    """
+    if len(iterables) != len(offsets):
+        raise ValueError("Number of iterables and offsets didn't match")
+
+    staggered = []
+    for it, n in zip(iterables, offsets):
+        if n < 0:
+            staggered.append(chain(repeat(fillvalue, -n), it))
+        elif n > 0:
+            staggered.append(islice(it, n, None))
+        else:
+            staggered.append(it)
+
+    if longest:
+        return zip_longest(*staggered, fillvalue=fillvalue)
+
+    return zip(*staggered)
+
+
+def sort_together(iterables, key_list=(0,), key=None, reverse=False):
+    """Return the input iterables sorted together, with *key_list* as the
+    priority for sorting. All iterables are trimmed to the length of the
+    shortest one.
+
+    This can be used like the sorting function in a spreadsheet. If each
+    iterable represents a column of data, the key list determines which
+    columns are used for sorting.
+
+    By default, all iterables are sorted using the ``0``-th iterable::
+
+        >>> iterables = [(4, 3, 2, 1), ('a', 'b', 'c', 'd')]
+        >>> sort_together(iterables)
+        [(1, 2, 3, 4), ('d', 'c', 'b', 'a')]
+
+    Set a different key list to sort according to another iterable.
+    Specifying multiple keys dictates how ties are broken::
+
+        >>> iterables = [(3, 1, 2), (0, 1, 0), ('c', 'b', 'a')]
+        >>> sort_together(iterables, key_list=(1, 2))
+        [(2, 3, 1), (0, 0, 1), ('a', 'c', 'b')]
+
+    To sort by a function of the elements of the iterable, pass a *key*
+    function. Its arguments are the elements of the iterables corresponding to
+    the key list::
+
+        >>> names = ('a', 'b', 'c')
+        >>> lengths = (1, 2, 3)
+        >>> widths = (5, 2, 1)
+        >>> def area(length, width):
+        ...     return length * width
+        >>> sort_together([names, lengths, widths], key_list=(1, 2), key=area)
+        [('c', 'b', 'a'), (3, 2, 1), (1, 2, 5)]
+
+    Set *reverse* to ``True`` to sort in descending order.
+
+        >>> sort_together([(1, 2, 3), ('c', 'b', 'a')], reverse=True)
+        [(3, 2, 1), ('a', 'b', 'c')]
+
+    """
+    if key is None:
+        # if there is no key function, the key argument to sorted is an
+        # itemgetter
+        key_argument = itemgetter(*key_list)
+    else:
+        # if there is a key function, call it with the items at the offsets
+        # specified by the key function as arguments
+        key_list = list(key_list)
+        if len(key_list) == 1:
+            # if key_list contains a single item, pass the item at that offset
+            # as the only argument to the key function
+            key_offset = key_list[0]
+            key_argument = lambda zipped_items: key(zipped_items[key_offset])
+        else:
+            # if key_list contains multiple items, use itemgetter to return a
+            # tuple of items, which we pass as *args to the key function
+            get_key_items = itemgetter(*key_list)
+            key_argument = lambda zipped_items: key(
+                *get_key_items(zipped_items)
+            )
+
+    return list(
+        zip(*sorted(zip(*iterables), key=key_argument, reverse=reverse))
+    )
+
+
+def unzip(iterable):
+    """The inverse of :func:`zip`, this function disaggregates the elements
+    of the zipped *iterable*.
+
+    The ``i``-th iterable contains the ``i``-th element from each element
+    of the zipped iterable. The first element is used to determine the
+    length of the remaining elements.
+
+        >>> iterable = [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
+        >>> letters, numbers = unzip(iterable)
+        >>> list(letters)
+        ['a', 'b', 'c', 'd']
+        >>> list(numbers)
+        [1, 2, 3, 4]
+
+    This is similar to using ``zip(*iterable)``, but it avoids reading
+    *iterable* into memory. Note, however, that this function uses
+    :func:`itertools.tee` and thus may require significant storage.
+
+    """
+    head, iterable = spy(iter(iterable))
+    if not head:
+        # empty iterable, e.g. zip([], [], [])
+        return ()
+    # spy returns a one-length iterable as head
+    head = head[0]
+    iterables = tee(iterable, len(head))
+
+    def itemgetter(i):
+        def getter(obj):
+            try:
+                return obj[i]
+            except IndexError:
+                # basically if we have an iterable like
+                # iter([(1, 2, 3), (4, 5), (6,)])
+                # the second unzipped iterable would fail at the third tuple
+                # since it would try to access tup[1]
+                # same with the third unzipped iterable and the second tuple
+                # to support these "improperly zipped" iterables,
+                # we create a custom itemgetter
+                # which just stops the unzipped iterables
+                # at first length mismatch
+                raise StopIteration
+
+        return getter
+
+    return tuple(map(itemgetter(i), it) for i, it in enumerate(iterables))
+
+
+def divide(n, iterable):
+    """Divide the elements from *iterable* into *n* parts, maintaining
+    order.
+
+        >>> group_1, group_2 = divide(2, [1, 2, 3, 4, 5, 6])
+        >>> list(group_1)
+        [1, 2, 3]
+        >>> list(group_2)
+        [4, 5, 6]
+
+    If the length of *iterable* is not evenly divisible by *n*, then the
+    length of the returned iterables will not be identical:
+
+        >>> children = divide(3, [1, 2, 3, 4, 5, 6, 7])
+        >>> [list(c) for c in children]
+        [[1, 2, 3], [4, 5], [6, 7]]
+
+    If the length of the iterable is smaller than n, then the last returned
+    iterables will be empty:
+
+        >>> children = divide(5, [1, 2, 3])
+        >>> [list(c) for c in children]
+        [[1], [2], [3], [], []]
+
+    This function will exhaust the iterable before returning.
+    If order is not important, see :func:`distribute`, which does not first
+    pull the iterable into memory.
+
+    """
+    if n < 1:
+        raise ValueError('n must be at least 1')
+
+    try:
+        iterable[:0]
+    except TypeError:
+        seq = tuple(iterable)
+    else:
+        seq = iterable
+
+    q, r = divmod(len(seq), n)
+
+    ret = []
+    stop = 0
+    for i in range(1, n + 1):
+        start = stop
+        stop += q + 1 if i <= r else q
+        ret.append(iter(seq[start:stop]))
+
+    return ret
+
+
+def always_iterable(obj, base_type=(str, bytes)):
+    """If *obj* is iterable, return an iterator over its items::
+
+        >>> obj = (1, 2, 3)
+        >>> list(always_iterable(obj))
+        [1, 2, 3]
+
+    If *obj* is not iterable, return a one-item iterable containing *obj*::
+
+        >>> obj = 1
+        >>> list(always_iterable(obj))
+        [1]
+
+    If *obj* is ``None``, return an empty iterable:
+
+        >>> obj = None
+        >>> list(always_iterable(None))
+        []
+
+    By default, binary and text strings are not considered iterable::
+
+        >>> obj = 'foo'
+        >>> list(always_iterable(obj))
+        ['foo']
+
+    If *base_type* is set, objects for which ``isinstance(obj, base_type)``
+    returns ``True`` won't be considered iterable.
+
+        >>> obj = {'a': 1}
+        >>> list(always_iterable(obj))  # Iterate over the dict's keys
+        ['a']
+        >>> list(always_iterable(obj, base_type=dict))  # Treat dicts as a unit
+        [{'a': 1}]
+
+    Set *base_type* to ``None`` to avoid any special handling and treat objects
+    Python considers iterable as iterable:
+
+        >>> obj = 'foo'
+        >>> list(always_iterable(obj, base_type=None))
+        ['f', 'o', 'o']
+    """
+    if obj is None:
+        return iter(())
+
+    if (base_type is not None) and isinstance(obj, base_type):
+        return iter((obj,))
+
+    try:
+        return iter(obj)
+    except TypeError:
+        return iter((obj,))
+
+
+def adjacent(predicate, iterable, distance=1):
+    """Return an iterable over `(bool, item)` tuples where the `item` is
+    drawn from *iterable* and the `bool` indicates whether
+    that item satisfies the *predicate* or is adjacent to an item that does.
+
+    For example, to find whether items are adjacent to a ``3``::
+
+        >>> list(adjacent(lambda x: x == 3, range(6)))
+        [(False, 0), (False, 1), (True, 2), (True, 3), (True, 4), (False, 5)]
+
+    Set *distance* to change what counts as adjacent. For example, to find
+    whether items are two places away from a ``3``:
+
+        >>> list(adjacent(lambda x: x == 3, range(6), distance=2))
+        [(False, 0), (True, 1), (True, 2), (True, 3), (True, 4), (True, 5)]
+
+    This is useful for contextualizing the results of a search function.
+    For example, a code comparison tool might want to identify lines that
+    have changed, but also surrounding lines to give the viewer of the diff
+    context.
+
+    The predicate function will only be called once for each item in the
+    iterable.
+
+    See also :func:`groupby_transform`, which can be used with this function
+    to group ranges of items with the same `bool` value.
+
+    """
+    # Allow distance=0 mainly for testing that it reproduces results with map()
+    if distance < 0:
+        raise ValueError('distance must be at least 0')
+
+    i1, i2 = tee(iterable)
+    padding = [False] * distance
+    selected = chain(padding, map(predicate, i1), padding)
+    adjacent_to_selected = map(any, windowed(selected, 2 * distance + 1))
+    return zip(adjacent_to_selected, i2)
+
+
+def groupby_transform(iterable, keyfunc=None, valuefunc=None, reducefunc=None):
+    """An extension of :func:`itertools.groupby` that can apply transformations
+    to the grouped data.
+
+    * *keyfunc* is a function computing a key value for each item in *iterable*
+    * *valuefunc* is a function that transforms the individual items from
+      *iterable* after grouping
+    * *reducefunc* is a function that transforms each group of items
+
+    >>> iterable = 'aAAbBBcCC'
+    >>> keyfunc = lambda k: k.upper()
+    >>> valuefunc = lambda v: v.lower()
+    >>> reducefunc = lambda g: ''.join(g)
+    >>> list(groupby_transform(iterable, keyfunc, valuefunc, reducefunc))
+    [('A', 'aaa'), ('B', 'bbb'), ('C', 'ccc')]
+
+    Each optional argument defaults to an identity function if not specified.
+
+    :func:`groupby_transform` is useful when grouping elements of an iterable
+    using a separate iterable as the key. To do this, :func:`zip` the iterables
+    and pass a *keyfunc* that extracts the first element and a *valuefunc*
+    that extracts the second element::
+
+        >>> from operator import itemgetter
+        >>> keys = [0, 0, 1, 1, 1, 2, 2, 2, 3]
+        >>> values = 'abcdefghi'
+        >>> iterable = zip(keys, values)
+        >>> grouper = groupby_transform(iterable, itemgetter(0), itemgetter(1))
+        >>> [(k, ''.join(g)) for k, g in grouper]
+        [(0, 'ab'), (1, 'cde'), (2, 'fgh'), (3, 'i')]
+
+    Note that the order of items in the iterable is significant.
+    Only adjacent items are grouped together, so if you don't want any
+    duplicate groups, you should sort the iterable by the key function.
+
+    """
+    ret = groupby(iterable, keyfunc)
+    if valuefunc:
+        ret = ((k, map(valuefunc, g)) for k, g in ret)
+    if reducefunc:
+        ret = ((k, reducefunc(g)) for k, g in ret)
+
+    return ret
+
+
+class numeric_range(abc.Sequence, abc.Hashable):
+    """An extension of the built-in ``range()`` function whose arguments can
+    be any orderable numeric type.
+
+    With only *stop* specified, *start* defaults to ``0`` and *step*
+    defaults to ``1``. The output items will match the type of *stop*:
+
+        >>> list(numeric_range(3.5))
+        [0.0, 1.0, 2.0, 3.0]
+
+    With only *start* and *stop* specified, *step* defaults to ``1``. The
+    output items will match the type of *start*:
+
+        >>> from decimal import Decimal
+        >>> start = Decimal('2.1')
+        >>> stop = Decimal('5.1')
+        >>> list(numeric_range(start, stop))
+        [Decimal('2.1'), Decimal('3.1'), Decimal('4.1')]
+
+    With *start*, *stop*, and *step*  specified the output items will match
+    the type of ``start + step``:
+
+        >>> from fractions import Fraction
+        >>> start = Fraction(1, 2)  # Start at 1/2
+        >>> stop = Fraction(5, 2)  # End at 5/2
+        >>> step = Fraction(1, 2)  # Count by 1/2
+        >>> list(numeric_range(start, stop, step))
+        [Fraction(1, 2), Fraction(1, 1), Fraction(3, 2), Fraction(2, 1)]
+
+    If *step* is zero, ``ValueError`` is raised. Negative steps are supported:
+
+        >>> list(numeric_range(3, -1, -1.0))
+        [3.0, 2.0, 1.0, 0.0]
+
+    Be aware of the limitations of floating point numbers; the representation
+    of the yielded numbers may be surprising.
+
+    ``datetime.datetime`` objects can be used for *start* and *stop*, if *step*
+    is a ``datetime.timedelta`` object:
+
+        >>> import datetime
+        >>> start = datetime.datetime(2019, 1, 1)
+        >>> stop = datetime.datetime(2019, 1, 3)
+        >>> step = datetime.timedelta(days=1)
+        >>> items = iter(numeric_range(start, stop, step))
+        >>> next(items)
+        datetime.datetime(2019, 1, 1, 0, 0)
+        >>> next(items)
+        datetime.datetime(2019, 1, 2, 0, 0)
+
+    """
+
+    _EMPTY_HASH = hash(range(0, 0))
+
+    def __init__(self, *args):
+        argc = len(args)
+        if argc == 1:
+            (self._stop,) = args
+            self._start = type(self._stop)(0)
+            self._step = type(self._stop - self._start)(1)
+        elif argc == 2:
+            self._start, self._stop = args
+            self._step = type(self._stop - self._start)(1)
+        elif argc == 3:
+            self._start, self._stop, self._step = args
+        elif argc == 0:
+            raise TypeError(
+                'numeric_range expected at least '
+                '1 argument, got {}'.format(argc)
+            )
+        else:
+            raise TypeError(
+                'numeric_range expected at most '
+                '3 arguments, got {}'.format(argc)
+            )
+
+        self._zero = type(self._step)(0)
+        if self._step == self._zero:
+            raise ValueError('numeric_range() arg 3 must not be zero')
+        self._growing = self._step > self._zero
+
+    def __bool__(self):
+        if self._growing:
+            return self._start < self._stop
+        else:
+            return self._start > self._stop
+
+    def __contains__(self, elem):
+        if self._growing:
+            if self._start <= elem < self._stop:
+                return (elem - self._start) % self._step == self._zero
+        else:
+            if self._start >= elem > self._stop:
+                return (self._start - elem) % (-self._step) == self._zero
+
+        return False
+
+    def __eq__(self, other):
+        if isinstance(other, numeric_range):
+            empty_self = not bool(self)
+            empty_other = not bool(other)
+            if empty_self or empty_other:
+                return empty_self and empty_other  # True if both empty
+            else:
+                return (
+                    self._start == other._start
+                    and self._step == other._step
+                    and self._get_by_index(-1) == other._get_by_index(-1)
+                )
+        else:
+            return False
+
+    def __getitem__(self, key):
+        if isinstance(key, int):
+            return self._get_by_index(key)
+        elif isinstance(key, slice):
+            step = self._step if key.step is None else key.step * self._step
+
+            if key.start is None or key.start <= -self._len:
+                start = self._start
+            elif key.start >= self._len:
+                start = self._stop
+            else:  # -self._len < key.start < self._len
+                start = self._get_by_index(key.start)
+
+            if key.stop is None or key.stop >= self._len:
+                stop = self._stop
+            elif key.stop <= -self._len:
+                stop = self._start
+            else:  # -self._len < key.stop < self._len
+                stop = self._get_by_index(key.stop)
+
+            return numeric_range(start, stop, step)
+        else:
+            raise TypeError(
+                'numeric range indices must be '
+                'integers or slices, not {}'.format(type(key).__name__)
+            )
+
+    def __hash__(self):
+        if self:
+            return hash((self._start, self._get_by_index(-1), self._step))
+        else:
+            return self._EMPTY_HASH
+
+    def __iter__(self):
+        values = (self._start + (n * self._step) for n in count())
+        if self._growing:
+            return takewhile(partial(gt, self._stop), values)
+        else:
+            return takewhile(partial(lt, self._stop), values)
+
+    def __len__(self):
+        return self._len
+
+    @cached_property
+    def _len(self):
+        if self._growing:
+            start = self._start
+            stop = self._stop
+            step = self._step
+        else:
+            start = self._stop
+            stop = self._start
+            step = -self._step
+        distance = stop - start
+        if distance <= self._zero:
+            return 0
+        else:  # distance > 0 and step > 0: regular euclidean division
+            q, r = divmod(distance, step)
+            return int(q) + int(r != self._zero)
+
+    def __reduce__(self):
+        return numeric_range, (self._start, self._stop, self._step)
+
+    def __repr__(self):
+        if self._step == 1:
+            return "numeric_range({}, {})".format(
+                repr(self._start), repr(self._stop)
+            )
+        else:
+            return "numeric_range({}, {}, {})".format(
+                repr(self._start), repr(self._stop), repr(self._step)
+            )
+
+    def __reversed__(self):
+        return iter(
+            numeric_range(
+                self._get_by_index(-1), self._start - self._step, -self._step
+            )
+        )
+
+    def count(self, value):
+        return int(value in self)
+
+    def index(self, value):
+        if self._growing:
+            if self._start <= value < self._stop:
+                q, r = divmod(value - self._start, self._step)
+                if r == self._zero:
+                    return int(q)
+        else:
+            if self._start >= value > self._stop:
+                q, r = divmod(self._start - value, -self._step)
+                if r == self._zero:
+                    return int(q)
+
+        raise ValueError("{} is not in numeric range".format(value))
+
+    def _get_by_index(self, i):
+        if i < 0:
+            i += self._len
+        if i < 0 or i >= self._len:
+            raise IndexError("numeric range object index out of range")
+        return self._start + i * self._step
+
+
+def count_cycle(iterable, n=None):
+    """Cycle through the items from *iterable* up to *n* times, yielding
+    the number of completed cycles along with each item. If *n* is omitted the
+    process repeats indefinitely.
+
+    >>> list(count_cycle('AB', 3))
+    [(0, 'A'), (0, 'B'), (1, 'A'), (1, 'B'), (2, 'A'), (2, 'B')]
+
+    """
+    iterable = tuple(iterable)
+    if not iterable:
+        return iter(())
+    counter = count() if n is None else range(n)
+    return ((i, item) for i in counter for item in iterable)
+
+
+def mark_ends(iterable):
+    """Yield 3-tuples of the form ``(is_first, is_last, item)``.
+
+    >>> list(mark_ends('ABC'))
+    [(True, False, 'A'), (False, False, 'B'), (False, True, 'C')]
+
+    Use this when looping over an iterable to take special action on its first
+    and/or last items:
+
+    >>> iterable = ['Header', 100, 200, 'Footer']
+    >>> total = 0
+    >>> for is_first, is_last, item in mark_ends(iterable):
+    ...     if is_first:
+    ...         continue  # Skip the header
+    ...     if is_last:
+    ...         continue  # Skip the footer
+    ...     total += item
+    >>> print(total)
+    300
+    """
+    it = iter(iterable)
+
+    try:
+        b = next(it)
+    except StopIteration:
+        return
+
+    try:
+        for i in count():
+            a = b
+            b = next(it)
+            yield i == 0, False, a
+
+    except StopIteration:
+        yield i == 0, True, a
+
+
+def locate(iterable, pred=bool, window_size=None):
+    """Yield the index of each item in *iterable* for which *pred* returns
+    ``True``.
+
+    *pred* defaults to :func:`bool`, which will select truthy items:
+
+        >>> list(locate([0, 1, 1, 0, 1, 0, 0]))
+        [1, 2, 4]
+
+    Set *pred* to a custom function to, e.g., find the indexes for a particular
+    item.
+
+        >>> list(locate(['a', 'b', 'c', 'b'], lambda x: x == 'b'))
+        [1, 3]
+
+    If *window_size* is given, then the *pred* function will be called with
+    that many items. This enables searching for sub-sequences:
+
+        >>> iterable = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
+        >>> pred = lambda *args: args == (1, 2, 3)
+        >>> list(locate(iterable, pred=pred, window_size=3))
+        [1, 5, 9]
+
+    Use with :func:`seekable` to find indexes and then retrieve the associated
+    items:
+
+        >>> from itertools import count
+        >>> from more_itertools import seekable
+        >>> source = (3 * n + 1 if (n % 2) else n // 2 for n in count())
+        >>> it = seekable(source)
+        >>> pred = lambda x: x > 100
+        >>> indexes = locate(it, pred=pred)
+        >>> i = next(indexes)
+        >>> it.seek(i)
+        >>> next(it)
+        106
+
+    """
+    if window_size is None:
+        return compress(count(), map(pred, iterable))
+
+    if window_size < 1:
+        raise ValueError('window size must be at least 1')
+
+    it = windowed(iterable, window_size, fillvalue=_marker)
+    return compress(count(), starmap(pred, it))
+
+
+def longest_common_prefix(iterables):
+    """Yield elements of the longest common prefix amongst given *iterables*.
+
+    >>> ''.join(longest_common_prefix(['abcd', 'abc', 'abf']))
+    'ab'
+
+    """
+    return (c[0] for c in takewhile(all_equal, zip(*iterables)))
+
+
+def lstrip(iterable, pred):
+    """Yield the items from *iterable*, but strip any from the beginning
+    for which *pred* returns ``True``.
+
+    For example, to remove a set of items from the start of an iterable:
+
+        >>> iterable = (None, False, None, 1, 2, None, 3, False, None)
+        >>> pred = lambda x: x in {None, False, ''}
+        >>> list(lstrip(iterable, pred))
+        [1, 2, None, 3, False, None]
+
+    This function is analogous to to :func:`str.lstrip`, and is essentially
+    an wrapper for :func:`itertools.dropwhile`.
+
+    """
+    return dropwhile(pred, iterable)
+
+
+def rstrip(iterable, pred):
+    """Yield the items from *iterable*, but strip any from the end
+    for which *pred* returns ``True``.
+
+    For example, to remove a set of items from the end of an iterable:
+
+        >>> iterable = (None, False, None, 1, 2, None, 3, False, None)
+        >>> pred = lambda x: x in {None, False, ''}
+        >>> list(rstrip(iterable, pred))
+        [None, False, None, 1, 2, None, 3]
+
+    This function is analogous to :func:`str.rstrip`.
+
+    """
+    cache = []
+    cache_append = cache.append
+    cache_clear = cache.clear
+    for x in iterable:
+        if pred(x):
+            cache_append(x)
+        else:
+            yield from cache
+            cache_clear()
+            yield x
+
+
+def strip(iterable, pred):
+    """Yield the items from *iterable*, but strip any from the
+    beginning and end for which *pred* returns ``True``.
+
+    For example, to remove a set of items from both ends of an iterable:
+
+        >>> iterable = (None, False, None, 1, 2, None, 3, False, None)
+        >>> pred = lambda x: x in {None, False, ''}
+        >>> list(strip(iterable, pred))
+        [1, 2, None, 3]
+
+    This function is analogous to :func:`str.strip`.
+
+    """
+    return rstrip(lstrip(iterable, pred), pred)
+
+
+class islice_extended:
+    """An extension of :func:`itertools.islice` that supports negative values
+    for *stop*, *start*, and *step*.
+
+        >>> iterable = iter('abcdefgh')
+        >>> list(islice_extended(iterable, -4, -1))
+        ['e', 'f', 'g']
+
+    Slices with negative values require some caching of *iterable*, but this
+    function takes care to minimize the amount of memory required.
+
+    For example, you can use a negative step with an infinite iterator:
+
+        >>> from itertools import count
+        >>> list(islice_extended(count(), 110, 99, -2))
+        [110, 108, 106, 104, 102, 100]
+
+    You can also use slice notation directly:
+
+        >>> iterable = map(str, count())
+        >>> it = islice_extended(iterable)[10:20:2]
+        >>> list(it)
+        ['10', '12', '14', '16', '18']
+
+    """
+
+    def __init__(self, iterable, *args):
+        it = iter(iterable)
+        if args:
+            self._iterable = _islice_helper(it, slice(*args))
+        else:
+            self._iterable = it
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        return next(self._iterable)
+
+    def __getitem__(self, key):
+        if isinstance(key, slice):
+            return islice_extended(_islice_helper(self._iterable, key))
+
+        raise TypeError('islice_extended.__getitem__ argument must be a slice')
+
+
+def _islice_helper(it, s):
+    start = s.start
+    stop = s.stop
+    if s.step == 0:
+        raise ValueError('step argument must be a non-zero integer or None.')
+    step = s.step or 1
+
+    if step > 0:
+        start = 0 if (start is None) else start
+
+        if start < 0:
+            # Consume all but the last -start items
+            cache = deque(enumerate(it, 1), maxlen=-start)
+            len_iter = cache[-1][0] if cache else 0
+
+            # Adjust start to be positive
+            i = max(len_iter + start, 0)
+
+            # Adjust stop to be positive
+            if stop is None:
+                j = len_iter
+            elif stop >= 0:
+                j = min(stop, len_iter)
+            else:
+                j = max(len_iter + stop, 0)
+
+            # Slice the cache
+            n = j - i
+            if n <= 0:
+                return
+
+            for index, item in islice(cache, 0, n, step):
+                yield item
+        elif (stop is not None) and (stop < 0):
+            # Advance to the start position
+            next(islice(it, start, start), None)
+
+            # When stop is negative, we have to carry -stop items while
+            # iterating
+            cache = deque(islice(it, -stop), maxlen=-stop)
+
+            for index, item in enumerate(it):
+                cached_item = cache.popleft()
+                if index % step == 0:
+                    yield cached_item
+                cache.append(item)
+        else:
+            # When both start and stop are positive we have the normal case
+            yield from islice(it, start, stop, step)
+    else:
+        start = -1 if (start is None) else start
+
+        if (stop is not None) and (stop < 0):
+            # Consume all but the last items
+            n = -stop - 1
+            cache = deque(enumerate(it, 1), maxlen=n)
+            len_iter = cache[-1][0] if cache else 0
+
+            # If start and stop are both negative they are comparable and
+            # we can just slice. Otherwise we can adjust start to be negative
+            # and then slice.
+            if start < 0:
+                i, j = start, stop
+            else:
+                i, j = min(start - len_iter, -1), None
+
+            for index, item in list(cache)[i:j:step]:
+                yield item
+        else:
+            # Advance to the stop position
+            if stop is not None:
+                m = stop + 1
+                next(islice(it, m, m), None)
+
+            # stop is positive, so if start is negative they are not comparable
+            # and we need the rest of the items.
+            if start < 0:
+                i = start
+                n = None
+            # stop is None and start is positive, so we just need items up to
+            # the start index.
+            elif stop is None:
+                i = None
+                n = start + 1
+            # Both stop and start are positive, so they are comparable.
+            else:
+                i = None
+                n = start - stop
+                if n <= 0:
+                    return
+
+            cache = list(islice(it, n))
+
+            yield from cache[i::step]
+
+
+def always_reversible(iterable):
+    """An extension of :func:`reversed` that supports all iterables, not
+    just those which implement the ``Reversible`` or ``Sequence`` protocols.
+
+        >>> print(*always_reversible(x for x in range(3)))
+        2 1 0
+
+    If the iterable is already reversible, this function returns the
+    result of :func:`reversed()`. If the iterable is not reversible,
+    this function will cache the remaining items in the iterable and
+    yield them in reverse order, which may require significant storage.
+    """
+    try:
+        return reversed(iterable)
+    except TypeError:
+        return reversed(list(iterable))
+
+
+def consecutive_groups(iterable, ordering=lambda x: x):
+    """Yield groups of consecutive items using :func:`itertools.groupby`.
+    The *ordering* function determines whether two items are adjacent by
+    returning their position.
+
+    By default, the ordering function is the identity function. This is
+    suitable for finding runs of numbers:
+
+        >>> iterable = [1, 10, 11, 12, 20, 30, 31, 32, 33, 40]
+        >>> for group in consecutive_groups(iterable):
+        ...     print(list(group))
+        [1]
+        [10, 11, 12]
+        [20]
+        [30, 31, 32, 33]
+        [40]
+
+    For finding runs of adjacent letters, try using the :meth:`index` method
+    of a string of letters:
+
+        >>> from string import ascii_lowercase
+        >>> iterable = 'abcdfgilmnop'
+        >>> ordering = ascii_lowercase.index
+        >>> for group in consecutive_groups(iterable, ordering):
+        ...     print(list(group))
+        ['a', 'b', 'c', 'd']
+        ['f', 'g']
+        ['i']
+        ['l', 'm', 'n', 'o', 'p']
+
+    Each group of consecutive items is an iterator that shares it source with
+    *iterable*. When an an output group is advanced, the previous group is
+    no longer available unless its elements are copied (e.g., into a ``list``).
+
+        >>> iterable = [1, 2, 11, 12, 21, 22]
+        >>> saved_groups = []
+        >>> for group in consecutive_groups(iterable):
+        ...     saved_groups.append(list(group))  # Copy group elements
+        >>> saved_groups
+        [[1, 2], [11, 12], [21, 22]]
+
+    """
+    for k, g in groupby(
+        enumerate(iterable), key=lambda x: x[0] - ordering(x[1])
+    ):
+        yield map(itemgetter(1), g)
+
+
+def difference(iterable, func=sub, *, initial=None):
+    """This function is the inverse of :func:`itertools.accumulate`. By default
+    it will compute the first difference of *iterable* using
+    :func:`operator.sub`:
+
+        >>> from itertools import accumulate
+        >>> iterable = accumulate([0, 1, 2, 3, 4])  # produces 0, 1, 3, 6, 10
+        >>> list(difference(iterable))
+        [0, 1, 2, 3, 4]
+
+    *func* defaults to :func:`operator.sub`, but other functions can be
+    specified. They will be applied as follows::
+
+        A, B, C, D, ... --> A, func(B, A), func(C, B), func(D, C), ...
+
+    For example, to do progressive division:
+
+        >>> iterable = [1, 2, 6, 24, 120]
+        >>> func = lambda x, y: x // y
+        >>> list(difference(iterable, func))
+        [1, 2, 3, 4, 5]
+
+    If the *initial* keyword is set, the first element will be skipped when
+    computing successive differences.
+
+        >>> it = [10, 11, 13, 16]  # from accumulate([1, 2, 3], initial=10)
+        >>> list(difference(it, initial=10))
+        [1, 2, 3]
+
+    """
+    a, b = tee(iterable)
+    try:
+        first = [next(b)]
+    except StopIteration:
+        return iter([])
+
+    if initial is not None:
+        first = []
+
+    return chain(first, map(func, b, a))
+
+
+class SequenceView(Sequence):
+    """Return a read-only view of the sequence object *target*.
+
+    :class:`SequenceView` objects are analogous to Python's built-in
+    "dictionary view" types. They provide a dynamic view of a sequence's items,
+    meaning that when the sequence updates, so does the view.
+
+        >>> seq = ['0', '1', '2']
+        >>> view = SequenceView(seq)
+        >>> view
+        SequenceView(['0', '1', '2'])
+        >>> seq.append('3')
+        >>> view
+        SequenceView(['0', '1', '2', '3'])
+
+    Sequence views support indexing, slicing, and length queries. They act
+    like the underlying sequence, except they don't allow assignment:
+
+        >>> view[1]
+        '1'
+        >>> view[1:-1]
+        ['1', '2']
+        >>> len(view)
+        4
+
+    Sequence views are useful as an alternative to copying, as they don't
+    require (much) extra storage.
+
+    """
+
+    def __init__(self, target):
+        if not isinstance(target, Sequence):
+            raise TypeError
+        self._target = target
+
+    def __getitem__(self, index):
+        return self._target[index]
+
+    def __len__(self):
+        return len(self._target)
+
+    def __repr__(self):
+        return '{}({})'.format(self.__class__.__name__, repr(self._target))
+
+
+class seekable:
+    """Wrap an iterator to allow for seeking backward and forward. This
+    progressively caches the items in the source iterable so they can be
+    re-visited.
+
+    Call :meth:`seek` with an index to seek to that position in the source
+    iterable.
+
+    To "reset" an iterator, seek to ``0``:
+
+        >>> from itertools import count
+        >>> it = seekable((str(n) for n in count()))
+        >>> next(it), next(it), next(it)
+        ('0', '1', '2')
+        >>> it.seek(0)
+        >>> next(it), next(it), next(it)
+        ('0', '1', '2')
+        >>> next(it)
+        '3'
+
+    You can also seek forward:
+
+        >>> it = seekable((str(n) for n in range(20)))
+        >>> it.seek(10)
+        >>> next(it)
+        '10'
+        >>> it.relative_seek(-2)  # Seeking relative to the current position
+        >>> next(it)
+        '9'
+        >>> it.seek(20)  # Seeking past the end of the source isn't a problem
+        >>> list(it)
+        []
+        >>> it.seek(0)  # Resetting works even after hitting the end
+        >>> next(it), next(it), next(it)
+        ('0', '1', '2')
+
+    Call :meth:`peek` to look ahead one item without advancing the iterator:
+
+        >>> it = seekable('1234')
+        >>> it.peek()
+        '1'
+        >>> list(it)
+        ['1', '2', '3', '4']
+        >>> it.peek(default='empty')
+        'empty'
+
+    Before the iterator is at its end, calling :func:`bool` on it will return
+    ``True``. After it will return ``False``:
+
+        >>> it = seekable('5678')
+        >>> bool(it)
+        True
+        >>> list(it)
+        ['5', '6', '7', '8']
+        >>> bool(it)
+        False
+
+    You may view the contents of the cache with the :meth:`elements` method.
+    That returns a :class:`SequenceView`, a view that updates automatically:
+
+        >>> it = seekable((str(n) for n in range(10)))
+        >>> next(it), next(it), next(it)
+        ('0', '1', '2')
+        >>> elements = it.elements()
+        >>> elements
+        SequenceView(['0', '1', '2'])
+        >>> next(it)
+        '3'
+        >>> elements
+        SequenceView(['0', '1', '2', '3'])
+
+    By default, the cache grows as the source iterable progresses, so beware of
+    wrapping very large or infinite iterables. Supply *maxlen* to limit the
+    size of the cache (this of course limits how far back you can seek).
+
+        >>> from itertools import count
+        >>> it = seekable((str(n) for n in count()), maxlen=2)
+        >>> next(it), next(it), next(it), next(it)
+        ('0', '1', '2', '3')
+        >>> list(it.elements())
+        ['2', '3']
+        >>> it.seek(0)
+        >>> next(it), next(it), next(it), next(it)
+        ('2', '3', '4', '5')
+        >>> next(it)
+        '6'
+
+    """
+
+    def __init__(self, iterable, maxlen=None):
+        self._source = iter(iterable)
+        if maxlen is None:
+            self._cache = []
+        else:
+            self._cache = deque([], maxlen)
+        self._index = None
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self._index is not None:
+            try:
+                item = self._cache[self._index]
+            except IndexError:
+                self._index = None
+            else:
+                self._index += 1
+                return item
+
+        item = next(self._source)
+        self._cache.append(item)
+        return item
+
+    def __bool__(self):
+        try:
+            self.peek()
+        except StopIteration:
+            return False
+        return True
+
+    def peek(self, default=_marker):
+        try:
+            peeked = next(self)
+        except StopIteration:
+            if default is _marker:
+                raise
+            return default
+        if self._index is None:
+            self._index = len(self._cache)
+        self._index -= 1
+        return peeked
+
+    def elements(self):
+        return SequenceView(self._cache)
+
+    def seek(self, index):
+        self._index = index
+        remainder = index - len(self._cache)
+        if remainder > 0:
+            consume(self, remainder)
+
+    def relative_seek(self, count):
+        index = len(self._cache)
+        self.seek(max(index + count, 0))
+
+
+class run_length:
+    """
+    :func:`run_length.encode` compresses an iterable with run-length encoding.
+    It yields groups of repeated items with the count of how many times they
+    were repeated:
+
+        >>> uncompressed = 'abbcccdddd'
+        >>> list(run_length.encode(uncompressed))
+        [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
+
+    :func:`run_length.decode` decompresses an iterable that was previously
+    compressed with run-length encoding. It yields the items of the
+    decompressed iterable:
+
+        >>> compressed = [('a', 1), ('b', 2), ('c', 3), ('d', 4)]
+        >>> list(run_length.decode(compressed))
+        ['a', 'b', 'b', 'c', 'c', 'c', 'd', 'd', 'd', 'd']
+
+    """
+
+    @staticmethod
+    def encode(iterable):
+        return ((k, ilen(g)) for k, g in groupby(iterable))
+
+    @staticmethod
+    def decode(iterable):
+        return chain.from_iterable(repeat(k, n) for k, n in iterable)
+
+
+def exactly_n(iterable, n, predicate=bool):
+    """Return ``True`` if exactly ``n`` items in the iterable are ``True``
+    according to the *predicate* function.
+
+        >>> exactly_n([True, True, False], 2)
+        True
+        >>> exactly_n([True, True, False], 1)
+        False
+        >>> exactly_n([0, 1, 2, 3, 4, 5], 3, lambda x: x < 3)
+        True
+
+    The iterable will be advanced until ``n + 1`` truthy items are encountered,
+    so avoid calling it on infinite iterables.
+
+    """
+    return len(take(n + 1, filter(predicate, iterable))) == n
+
+
+def circular_shifts(iterable):
+    """Return a list of circular shifts of *iterable*.
+
+    >>> circular_shifts(range(4))
+    [(0, 1, 2, 3), (1, 2, 3, 0), (2, 3, 0, 1), (3, 0, 1, 2)]
+    """
+    lst = list(iterable)
+    return take(len(lst), windowed(cycle(lst), len(lst)))
+
+
+def make_decorator(wrapping_func, result_index=0):
+    """Return a decorator version of *wrapping_func*, which is a function that
+    modifies an iterable. *result_index* is the position in that function's
+    signature where the iterable goes.
+
+    This lets you use itertools on the "production end," i.e. at function
+    definition. This can augment what the function returns without changing the
+    function's code.
+
+    For example, to produce a decorator version of :func:`chunked`:
+
+        >>> from more_itertools import chunked
+        >>> chunker = make_decorator(chunked, result_index=0)
+        >>> @chunker(3)
+        ... def iter_range(n):
+        ...     return iter(range(n))
+        ...
+        >>> list(iter_range(9))
+        [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+
+    To only allow truthy items to be returned:
+
+        >>> truth_serum = make_decorator(filter, result_index=1)
+        >>> @truth_serum(bool)
+        ... def boolean_test():
+        ...     return [0, 1, '', ' ', False, True]
+        ...
+        >>> list(boolean_test())
+        [1, ' ', True]
+
+    The :func:`peekable` and :func:`seekable` wrappers make for practical
+    decorators:
+
+        >>> from more_itertools import peekable
+        >>> peekable_function = make_decorator(peekable)
+        >>> @peekable_function()
+        ... def str_range(*args):
+        ...     return (str(x) for x in range(*args))
+        ...
+        >>> it = str_range(1, 20, 2)
+        >>> next(it), next(it), next(it)
+        ('1', '3', '5')
+        >>> it.peek()
+        '7'
+        >>> next(it)
+        '7'
+
+    """
+
+    # See https://sites.google.com/site/bbayles/index/decorator_factory for
+    # notes on how this works.
+    def decorator(*wrapping_args, **wrapping_kwargs):
+        def outer_wrapper(f):
+            def inner_wrapper(*args, **kwargs):
+                result = f(*args, **kwargs)
+                wrapping_args_ = list(wrapping_args)
+                wrapping_args_.insert(result_index, result)
+                return wrapping_func(*wrapping_args_, **wrapping_kwargs)
+
+            return inner_wrapper
+
+        return outer_wrapper
+
+    return decorator
+
+
+def map_reduce(iterable, keyfunc, valuefunc=None, reducefunc=None):
+    """Return a dictionary that maps the items in *iterable* to categories
+    defined by *keyfunc*, transforms them with *valuefunc*, and
+    then summarizes them by category with *reducefunc*.
+
+    *valuefunc* defaults to the identity function if it is unspecified.
+    If *reducefunc* is unspecified, no summarization takes place:
+
+        >>> keyfunc = lambda x: x.upper()
+        >>> result = map_reduce('abbccc', keyfunc)
+        >>> sorted(result.items())
+        [('A', ['a']), ('B', ['b', 'b']), ('C', ['c', 'c', 'c'])]
+
+    Specifying *valuefunc* transforms the categorized items:
+
+        >>> keyfunc = lambda x: x.upper()
+        >>> valuefunc = lambda x: 1
+        >>> result = map_reduce('abbccc', keyfunc, valuefunc)
+        >>> sorted(result.items())
+        [('A', [1]), ('B', [1, 1]), ('C', [1, 1, 1])]
+
+    Specifying *reducefunc* summarizes the categorized items:
+
+        >>> keyfunc = lambda x: x.upper()
+        >>> valuefunc = lambda x: 1
+        >>> reducefunc = sum
+        >>> result = map_reduce('abbccc', keyfunc, valuefunc, reducefunc)
+        >>> sorted(result.items())
+        [('A', 1), ('B', 2), ('C', 3)]
+
+    You may want to filter the input iterable before applying the map/reduce
+    procedure:
+
+        >>> all_items = range(30)
+        >>> items = [x for x in all_items if 10 <= x <= 20]  # Filter
+        >>> keyfunc = lambda x: x % 2  # Evens map to 0; odds to 1
+        >>> categories = map_reduce(items, keyfunc=keyfunc)
+        >>> sorted(categories.items())
+        [(0, [10, 12, 14, 16, 18, 20]), (1, [11, 13, 15, 17, 19])]
+        >>> summaries = map_reduce(items, keyfunc=keyfunc, reducefunc=sum)
+        >>> sorted(summaries.items())
+        [(0, 90), (1, 75)]
+
+    Note that all items in the iterable are gathered into a list before the
+    summarization step, which may require significant storage.
+
+    The returned object is a :obj:`collections.defaultdict` with the
+    ``default_factory`` set to ``None``, such that it behaves like a normal
+    dictionary.
+
+    """
+    valuefunc = (lambda x: x) if (valuefunc is None) else valuefunc
+
+    ret = defaultdict(list)
+    for item in iterable:
+        key = keyfunc(item)
+        value = valuefunc(item)
+        ret[key].append(value)
+
+    if reducefunc is not None:
+        for key, value_list in ret.items():
+            ret[key] = reducefunc(value_list)
+
+    ret.default_factory = None
+    return ret
+
+
+def rlocate(iterable, pred=bool, window_size=None):
+    """Yield the index of each item in *iterable* for which *pred* returns
+    ``True``, starting from the right and moving left.
+
+    *pred* defaults to :func:`bool`, which will select truthy items:
+
+        >>> list(rlocate([0, 1, 1, 0, 1, 0, 0]))  # Truthy at 1, 2, and 4
+        [4, 2, 1]
+
+    Set *pred* to a custom function to, e.g., find the indexes for a particular
+    item:
+
+        >>> iterable = iter('abcb')
+        >>> pred = lambda x: x == 'b'
+        >>> list(rlocate(iterable, pred))
+        [3, 1]
+
+    If *window_size* is given, then the *pred* function will be called with
+    that many items. This enables searching for sub-sequences:
+
+        >>> iterable = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
+        >>> pred = lambda *args: args == (1, 2, 3)
+        >>> list(rlocate(iterable, pred=pred, window_size=3))
+        [9, 5, 1]
+
+    Beware, this function won't return anything for infinite iterables.
+    If *iterable* is reversible, ``rlocate`` will reverse it and search from
+    the right. Otherwise, it will search from the left and return the results
+    in reverse order.
+
+    See :func:`locate` to for other example applications.
+
+    """
+    if window_size is None:
+        try:
+            len_iter = len(iterable)
+            return (len_iter - i - 1 for i in locate(reversed(iterable), pred))
+        except TypeError:
+            pass
+
+    return reversed(list(locate(iterable, pred, window_size)))
+
+
+def replace(iterable, pred, substitutes, count=None, window_size=1):
+    """Yield the items from *iterable*, replacing the items for which *pred*
+    returns ``True`` with the items from the iterable *substitutes*.
+
+        >>> iterable = [1, 1, 0, 1, 1, 0, 1, 1]
+        >>> pred = lambda x: x == 0
+        >>> substitutes = (2, 3)
+        >>> list(replace(iterable, pred, substitutes))
+        [1, 1, 2, 3, 1, 1, 2, 3, 1, 1]
+
+    If *count* is given, the number of replacements will be limited:
+
+        >>> iterable = [1, 1, 0, 1, 1, 0, 1, 1, 0]
+        >>> pred = lambda x: x == 0
+        >>> substitutes = [None]
+        >>> list(replace(iterable, pred, substitutes, count=2))
+        [1, 1, None, 1, 1, None, 1, 1, 0]
+
+    Use *window_size* to control the number of items passed as arguments to
+    *pred*. This allows for locating and replacing subsequences.
+
+        >>> iterable = [0, 1, 2, 5, 0, 1, 2, 5]
+        >>> window_size = 3
+        >>> pred = lambda *args: args == (0, 1, 2)  # 3 items passed to pred
+        >>> substitutes = [3, 4] # Splice in these items
+        >>> list(replace(iterable, pred, substitutes, window_size=window_size))
+        [3, 4, 5, 3, 4, 5]
+
+    """
+    if window_size < 1:
+        raise ValueError('window_size must be at least 1')
+
+    # Save the substitutes iterable, since it's used more than once
+    substitutes = tuple(substitutes)
+
+    # Add padding such that the number of windows matches the length of the
+    # iterable
+    it = chain(iterable, [_marker] * (window_size - 1))
+    windows = windowed(it, window_size)
+
+    n = 0
+    for w in windows:
+        # If the current window matches our predicate (and we haven't hit
+        # our maximum number of replacements), splice in the substitutes
+        # and then consume the following windows that overlap with this one.
+        # For example, if the iterable is (0, 1, 2, 3, 4...)
+        # and the window size is 2, we have (0, 1), (1, 2), (2, 3)...
+        # If the predicate matches on (0, 1), we need to zap (0, 1) and (1, 2)
+        if pred(*w):
+            if (count is None) or (n < count):
+                n += 1
+                yield from substitutes
+                consume(windows, window_size - 1)
+                continue
+
+        # If there was no match (or we've reached the replacement limit),
+        # yield the first item from the window.
+        if w and (w[0] is not _marker):
+            yield w[0]
+
+
+def partitions(iterable):
+    """Yield all possible order-preserving partitions of *iterable*.
+
+    >>> iterable = 'abc'
+    >>> for part in partitions(iterable):
+    ...     print([''.join(p) for p in part])
+    ['abc']
+    ['a', 'bc']
+    ['ab', 'c']
+    ['a', 'b', 'c']
+
+    This is unrelated to :func:`partition`.
+
+    """
+    sequence = list(iterable)
+    n = len(sequence)
+    for i in powerset(range(1, n)):
+        yield [sequence[i:j] for i, j in zip((0,) + i, i + (n,))]
+
+
+def set_partitions(iterable, k=None):
+    """
+    Yield the set partitions of *iterable* into *k* parts. Set partitions are
+    not order-preserving.
+
+    >>> iterable = 'abc'
+    >>> for part in set_partitions(iterable, 2):
+    ...     print([''.join(p) for p in part])
+    ['a', 'bc']
+    ['ab', 'c']
+    ['b', 'ac']
+
+
+    If *k* is not given, every set partition is generated.
+
+    >>> iterable = 'abc'
+    >>> for part in set_partitions(iterable):
+    ...     print([''.join(p) for p in part])
+    ['abc']
+    ['a', 'bc']
+    ['ab', 'c']
+    ['b', 'ac']
+    ['a', 'b', 'c']
+
+    """
+    L = list(iterable)
+    n = len(L)
+    if k is not None:
+        if k < 1:
+            raise ValueError(
+                "Can't partition in a negative or zero number of groups"
+            )
+        elif k > n:
+            return
+
+    def set_partitions_helper(L, k):
+        n = len(L)
+        if k == 1:
+            yield [L]
+        elif n == k:
+            yield [[s] for s in L]
+        else:
+            e, *M = L
+            for p in set_partitions_helper(M, k - 1):
+                yield [[e], *p]
+            for p in set_partitions_helper(M, k):
+                for i in range(len(p)):
+                    yield p[:i] + [[e] + p[i]] + p[i + 1 :]
+
+    if k is None:
+        for k in range(1, n + 1):
+            yield from set_partitions_helper(L, k)
+    else:
+        yield from set_partitions_helper(L, k)
+
+
+class time_limited:
+    """
+    Yield items from *iterable* until *limit_seconds* have passed.
+    If the time limit expires before all items have been yielded, the
+    ``timed_out`` parameter will be set to ``True``.
+
+    >>> from time import sleep
+    >>> def generator():
+    ...     yield 1
+    ...     yield 2
+    ...     sleep(0.2)
+    ...     yield 3
+    >>> iterable = time_limited(0.1, generator())
+    >>> list(iterable)
+    [1, 2]
+    >>> iterable.timed_out
+    True
+
+    Note that the time is checked before each item is yielded, and iteration
+    stops if  the time elapsed is greater than *limit_seconds*. If your time
+    limit is 1 second, but it takes 2 seconds to generate the first item from
+    the iterable, the function will run for 2 seconds and not yield anything.
+    As a special case, when *limit_seconds* is zero, the iterator never
+    returns anything.
+
+    """
+
+    def __init__(self, limit_seconds, iterable):
+        if limit_seconds < 0:
+            raise ValueError('limit_seconds must be positive')
+        self.limit_seconds = limit_seconds
+        self._iterable = iter(iterable)
+        self._start_time = monotonic()
+        self.timed_out = False
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.limit_seconds == 0:
+            self.timed_out = True
+            raise StopIteration
+        item = next(self._iterable)
+        if monotonic() - self._start_time > self.limit_seconds:
+            self.timed_out = True
+            raise StopIteration
+
+        return item
+
+
+def only(iterable, default=None, too_long=None):
+    """If *iterable* has only one item, return it.
+    If it has zero items, return *default*.
+    If it has more than one item, raise the exception given by *too_long*,
+    which is ``ValueError`` by default.
+
+    >>> only([], default='missing')
+    'missing'
+    >>> only([1])
+    1
+    >>> only([1, 2])  # doctest: +IGNORE_EXCEPTION_DETAIL
+    Traceback (most recent call last):
+    ...
+    ValueError: Expected exactly one item in iterable, but got 1, 2,
+     and perhaps more.'
+    >>> only([1, 2], too_long=TypeError)  # doctest: +IGNORE_EXCEPTION_DETAIL
+    Traceback (most recent call last):
+    ...
+    TypeError
+
+    Note that :func:`only` attempts to advance *iterable* twice to ensure there
+    is only one item.  See :func:`spy` or :func:`peekable` to check
+    iterable contents less destructively.
+    """
+    it = iter(iterable)
+    first_value = next(it, default)
+
+    try:
+        second_value = next(it)
+    except StopIteration:
+        pass
+    else:
+        msg = (
+            'Expected exactly one item in iterable, but got {!r}, {!r}, '
+            'and perhaps more.'.format(first_value, second_value)
+        )
+        raise too_long or ValueError(msg)
+
+    return first_value
+
+
+def _ichunk(iterable, n):
+    cache = deque()
+    chunk = islice(iterable, n)
+
+    def generator():
+        while True:
+            if cache:
+                yield cache.popleft()
+            else:
+                try:
+                    item = next(chunk)
+                except StopIteration:
+                    return
+                else:
+                    yield item
+
+    def materialize_next(n=1):
+        # if n not specified materialize everything
+        if n is None:
+            cache.extend(chunk)
+            return len(cache)
+
+        to_cache = n - len(cache)
+
+        # materialize up to n
+        if to_cache > 0:
+            cache.extend(islice(chunk, to_cache))
+
+        # return number materialized up to n
+        return min(n, len(cache))
+
+    return (generator(), materialize_next)
+
+
+def ichunked(iterable, n):
+    """Break *iterable* into sub-iterables with *n* elements each.
+    :func:`ichunked` is like :func:`chunked`, but it yields iterables
+    instead of lists.
+
+    If the sub-iterables are read in order, the elements of *iterable*
+    won't be stored in memory.
+    If they are read out of order, :func:`itertools.tee` is used to cache
+    elements as necessary.
+
+    >>> from itertools import count
+    >>> all_chunks = ichunked(count(), 4)
+    >>> c_1, c_2, c_3 = next(all_chunks), next(all_chunks), next(all_chunks)
+    >>> list(c_2)  # c_1's elements have been cached; c_3's haven't been
+    [4, 5, 6, 7]
+    >>> list(c_1)
+    [0, 1, 2, 3]
+    >>> list(c_3)
+    [8, 9, 10, 11]
+
+    """
+    iterable = iter(iterable)
+    while True:
+        # Create new chunk
+        chunk, materialize_next = _ichunk(iterable, n)
+
+        # Check to see whether we're at the end of the source iterable
+        if not materialize_next():
+            return
+
+        yield chunk
+
+        # Fill previous chunk's cache
+        materialize_next(None)
+
+
+def iequals(*iterables):
+    """Return ``True`` if all given *iterables* are equal to each other,
+    which means that they contain the same elements in the same order.
+
+    The function is useful for comparing iterables of different data types
+    or iterables that do not support equality checks.
+
+    >>> iequals("abc", ['a', 'b', 'c'], ('a', 'b', 'c'), iter("abc"))
+    True
+
+    >>> iequals("abc", "acb")
+    False
+
+    Not to be confused with :func:`all_equal`, which checks whether all
+    elements of iterable are equal to each other.
+
+    """
+    return all(map(all_equal, zip_longest(*iterables, fillvalue=object())))
+
+
+def distinct_combinations(iterable, r):
+    """Yield the distinct combinations of *r* items taken from *iterable*.
+
+        >>> list(distinct_combinations([0, 0, 1], 2))
+        [(0, 0), (0, 1)]
+
+    Equivalent to ``set(combinations(iterable))``, except duplicates are not
+    generated and thrown away. For larger input sequences this is much more
+    efficient.
+
+    """
+    if r < 0:
+        raise ValueError('r must be non-negative')
+    elif r == 0:
+        yield ()
+        return
+    pool = tuple(iterable)
+    generators = [unique_everseen(enumerate(pool), key=itemgetter(1))]
+    current_combo = [None] * r
+    level = 0
+    while generators:
+        try:
+            cur_idx, p = next(generators[-1])
+        except StopIteration:
+            generators.pop()
+            level -= 1
+            continue
+        current_combo[level] = p
+        if level + 1 == r:
+            yield tuple(current_combo)
+        else:
+            generators.append(
+                unique_everseen(
+                    enumerate(pool[cur_idx + 1 :], cur_idx + 1),
+                    key=itemgetter(1),
+                )
+            )
+            level += 1
+
+
+def filter_except(validator, iterable, *exceptions):
+    """Yield the items from *iterable* for which the *validator* function does
+    not raise one of the specified *exceptions*.
+
+    *validator* is called for each item in *iterable*.
+    It should be a function that accepts one argument and raises an exception
+    if that item is not valid.
+
+    >>> iterable = ['1', '2', 'three', '4', None]
+    >>> list(filter_except(int, iterable, ValueError, TypeError))
+    ['1', '2', '4']
+
+    If an exception other than one given by *exceptions* is raised by
+    *validator*, it is raised like normal.
+    """
+    for item in iterable:
+        try:
+            validator(item)
+        except exceptions:
+            pass
+        else:
+            yield item
+
+
+def map_except(function, iterable, *exceptions):
+    """Transform each item from *iterable* with *function* and yield the
+    result, unless *function* raises one of the specified *exceptions*.
+
+    *function* is called to transform each item in *iterable*.
+    It should accept one argument.
+
+    >>> iterable = ['1', '2', 'three', '4', None]
+    >>> list(map_except(int, iterable, ValueError, TypeError))
+    [1, 2, 4]
+
+    If an exception other than one given by *exceptions* is raised by
+    *function*, it is raised like normal.
+    """
+    for item in iterable:
+        try:
+            yield function(item)
+        except exceptions:
+            pass
+
+
+def map_if(iterable, pred, func, func_else=lambda x: x):
+    """Evaluate each item from *iterable* using *pred*. If the result is
+    equivalent to ``True``, transform the item with *func* and yield it.
+    Otherwise, transform the item with *func_else* and yield it.
+
+    *pred*, *func*, and *func_else* should each be functions that accept
+    one argument. By default, *func_else* is the identity function.
+
+    >>> from math import sqrt
+    >>> iterable = list(range(-5, 5))
+    >>> iterable
+    [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
+    >>> list(map_if(iterable, lambda x: x > 3, lambda x: 'toobig'))
+    [-5, -4, -3, -2, -1, 0, 1, 2, 3, 'toobig']
+    >>> list(map_if(iterable, lambda x: x >= 0,
+    ... lambda x: f'{sqrt(x):.2f}', lambda x: None))
+    [None, None, None, None, None, '0.00', '1.00', '1.41', '1.73', '2.00']
+    """
+    for item in iterable:
+        yield func(item) if pred(item) else func_else(item)
+
+
+def _sample_unweighted(iterable, k):
+    # Implementation of "Algorithm L" from the 1994 paper by Kim-Hung Li:
+    # "Reservoir-Sampling Algorithms of Time Complexity O(n(1+log(N/n)))".
+
+    # Fill up the reservoir (collection of samples) with the first `k` samples
+    reservoir = take(k, iterable)
+
+    # Generate random number that's the largest in a sample of k U(0,1) numbers
+    # Largest order statistic: https://en.wikipedia.org/wiki/Order_statistic
+    W = exp(log(random()) / k)
+
+    # The number of elements to skip before changing the reservoir is a random
+    # number with a geometric distribution. Sample it using random() and logs.
+    next_index = k + floor(log(random()) / log(1 - W))
+
+    for index, element in enumerate(iterable, k):
+        if index == next_index:
+            reservoir[randrange(k)] = element
+            # The new W is the largest in a sample of k U(0, `old_W`) numbers
+            W *= exp(log(random()) / k)
+            next_index += floor(log(random()) / log(1 - W)) + 1
+
+    return reservoir
+
+
+def _sample_weighted(iterable, k, weights):
+    # Implementation of "A-ExpJ" from the 2006 paper by Efraimidis et al. :
+    # "Weighted random sampling with a reservoir".
+
+    # Log-transform for numerical stability for weights that are small/large
+    weight_keys = (log(random()) / weight for weight in weights)
+
+    # Fill up the reservoir (collection of samples) with the first `k`
+    # weight-keys and elements, then heapify the list.
+    reservoir = take(k, zip(weight_keys, iterable))
+    heapify(reservoir)
+
+    # The number of jumps before changing the reservoir is a random variable
+    # with an exponential distribution. Sample it using random() and logs.
+    smallest_weight_key, _ = reservoir[0]
+    weights_to_skip = log(random()) / smallest_weight_key
+
+    for weight, element in zip(weights, iterable):
+        if weight >= weights_to_skip:
+            # The notation here is consistent with the paper, but we store
+            # the weight-keys in log-space for better numerical stability.
+            smallest_weight_key, _ = reservoir[0]
+            t_w = exp(weight * smallest_weight_key)
+            r_2 = uniform(t_w, 1)  # generate U(t_w, 1)
+            weight_key = log(r_2) / weight
+            heapreplace(reservoir, (weight_key, element))
+            smallest_weight_key, _ = reservoir[0]
+            weights_to_skip = log(random()) / smallest_weight_key
+        else:
+            weights_to_skip -= weight
+
+    # Equivalent to [element for weight_key, element in sorted(reservoir)]
+    return [heappop(reservoir)[1] for _ in range(k)]
+
+
+def sample(iterable, k, weights=None):
+    """Return a *k*-length list of elements chosen (without replacement)
+    from the *iterable*. Like :func:`random.sample`, but works on iterables
+    of unknown length.
+
+    >>> iterable = range(100)
+    >>> sample(iterable, 5)  # doctest: +SKIP
+    [81, 60, 96, 16, 4]
+
+    An iterable with *weights* may also be given:
+
+    >>> iterable = range(100)
+    >>> weights = (i * i + 1 for i in range(100))
+    >>> sampled = sample(iterable, 5, weights=weights)  # doctest: +SKIP
+    [79, 67, 74, 66, 78]
+
+    The algorithm can also be used to generate weighted random permutations.
+    The relative weight of each item determines the probability that it
+    appears late in the permutation.
+
+    >>> data = "abcdefgh"
+    >>> weights = range(1, len(data) + 1)
+    >>> sample(data, k=len(data), weights=weights)  # doctest: +SKIP
+    ['c', 'a', 'b', 'e', 'g', 'd', 'h', 'f']
+    """
+    if k == 0:
+        return []
+
+    iterable = iter(iterable)
+    if weights is None:
+        return _sample_unweighted(iterable, k)
+    else:
+        weights = iter(weights)
+        return _sample_weighted(iterable, k, weights)
+
+
+def is_sorted(iterable, key=None, reverse=False, strict=False):
+    """Returns ``True`` if the items of iterable are in sorted order, and
+    ``False`` otherwise. *key* and *reverse* have the same meaning that they do
+    in the built-in :func:`sorted` function.
+
+    >>> is_sorted(['1', '2', '3', '4', '5'], key=int)
+    True
+    >>> is_sorted([5, 4, 3, 1, 2], reverse=True)
+    False
+
+    If *strict*, tests for strict sorting, that is, returns ``False`` if equal
+    elements are found:
+
+    >>> is_sorted([1, 2, 2])
+    True
+    >>> is_sorted([1, 2, 2], strict=True)
+    False
+
+    The function returns ``False`` after encountering the first out-of-order
+    item. If there are no out-of-order items, the iterable is exhausted.
+    """
+
+    compare = (le if reverse else ge) if strict else (lt if reverse else gt)
+    it = iterable if key is None else map(key, iterable)
+    return not any(starmap(compare, pairwise(it)))
+
+
+class AbortThread(BaseException):
+    pass
+
+
+class callback_iter:
+    """Convert a function that uses callbacks to an iterator.
+
+    Let *func* be a function that takes a `callback` keyword argument.
+    For example:
+
+    >>> def func(callback=None):
+    ...     for i, c in [(1, 'a'), (2, 'b'), (3, 'c')]:
+    ...         if callback:
+    ...             callback(i, c)
+    ...     return 4
+
+
+    Use ``with callback_iter(func)`` to get an iterator over the parameters
+    that are delivered to the callback.
+
+    >>> with callback_iter(func) as it:
+    ...     for args, kwargs in it:
+    ...         print(args)
+    (1, 'a')
+    (2, 'b')
+    (3, 'c')
+
+    The function will be called in a background thread. The ``done`` property
+    indicates whether it has completed execution.
+
+    >>> it.done
+    True
+
+    If it completes successfully, its return value will be available
+    in the ``result`` property.
+
+    >>> it.result
+    4
+
+    Notes:
+
+    * If the function uses some keyword argument besides ``callback``, supply
+      *callback_kwd*.
+    * If it finished executing, but raised an exception, accessing the
+      ``result`` property will raise the same exception.
+    * If it hasn't finished executing, accessing the ``result``
+      property from within the ``with`` block will raise ``RuntimeError``.
+    * If it hasn't finished executing, accessing the ``result`` property from
+      outside the ``with`` block will raise a
+      ``more_itertools.AbortThread`` exception.
+    * Provide *wait_seconds* to adjust how frequently the it is polled for
+      output.
+
+    """
+
+    def __init__(self, func, callback_kwd='callback', wait_seconds=0.1):
+        self._func = func
+        self._callback_kwd = callback_kwd
+        self._aborted = False
+        self._future = None
+        self._wait_seconds = wait_seconds
+        # Lazily import concurrent.future
+        self._executor = __import__(
+            'concurrent.futures'
+        ).futures.ThreadPoolExecutor(max_workers=1)
+        self._iterator = self._reader()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self._aborted = True
+        self._executor.shutdown()
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        return next(self._iterator)
+
+    @property
+    def done(self):
+        if self._future is None:
+            return False
+        return self._future.done()
+
+    @property
+    def result(self):
+        if not self.done:
+            raise RuntimeError('Function has not yet completed')
+
+        return self._future.result()
+
+    def _reader(self):
+        q = Queue()
+
+        def callback(*args, **kwargs):
+            if self._aborted:
+                raise AbortThread('canceled by user')
+
+            q.put((args, kwargs))
+
+        self._future = self._executor.submit(
+            self._func, **{self._callback_kwd: callback}
+        )
+
+        while True:
+            try:
+                item = q.get(timeout=self._wait_seconds)
+            except Empty:
+                pass
+            else:
+                q.task_done()
+                yield item
+
+            if self._future.done():
+                break
+
+        remaining = []
+        while True:
+            try:
+                item = q.get_nowait()
+            except Empty:
+                break
+            else:
+                q.task_done()
+                remaining.append(item)
+        q.join()
+        yield from remaining
+
+
+def windowed_complete(iterable, n):
+    """
+    Yield ``(beginning, middle, end)`` tuples, where:
+
+    * Each ``middle`` has *n* items from *iterable*
+    * Each ``beginning`` has the items before the ones in ``middle``
+    * Each ``end`` has the items after the ones in ``middle``
+
+    >>> iterable = range(7)
+    >>> n = 3
+    >>> for beginning, middle, end in windowed_complete(iterable, n):
+    ...     print(beginning, middle, end)
+    () (0, 1, 2) (3, 4, 5, 6)
+    (0,) (1, 2, 3) (4, 5, 6)
+    (0, 1) (2, 3, 4) (5, 6)
+    (0, 1, 2) (3, 4, 5) (6,)
+    (0, 1, 2, 3) (4, 5, 6) ()
+
+    Note that *n* must be at least 0 and most equal to the length of
+    *iterable*.
+
+    This function will exhaust the iterable and may require significant
+    storage.
+    """
+    if n < 0:
+        raise ValueError('n must be >= 0')
+
+    seq = tuple(iterable)
+    size = len(seq)
+
+    if n > size:
+        raise ValueError('n must be <= len(seq)')
+
+    for i in range(size - n + 1):
+        beginning = seq[:i]
+        middle = seq[i : i + n]
+        end = seq[i + n :]
+        yield beginning, middle, end
+
+
+def all_unique(iterable, key=None):
+    """
+    Returns ``True`` if all the elements of *iterable* are unique (no two
+    elements are equal).
+
+        >>> all_unique('ABCB')
+        False
+
+    If a *key* function is specified, it will be used to make comparisons.
+
+        >>> all_unique('ABCb')
+        True
+        >>> all_unique('ABCb', str.lower)
+        False
+
+    The function returns as soon as the first non-unique element is
+    encountered. Iterables with a mix of hashable and unhashable items can
+    be used, but the function will be slower for unhashable items.
+    """
+    seenset = set()
+    seenset_add = seenset.add
+    seenlist = []
+    seenlist_add = seenlist.append
+    for element in map(key, iterable) if key else iterable:
+        try:
+            if element in seenset:
+                return False
+            seenset_add(element)
+        except TypeError:
+            if element in seenlist:
+                return False
+            seenlist_add(element)
+    return True
+
+
+def nth_product(index, *args):
+    """Equivalent to ``list(product(*args))[index]``.
+
+    The products of *args* can be ordered lexicographically.
+    :func:`nth_product` computes the product at sort position *index* without
+    computing the previous products.
+
+        >>> nth_product(8, range(2), range(2), range(2), range(2))
+        (1, 0, 0, 0)
+
+    ``IndexError`` will be raised if the given *index* is invalid.
+    """
+    pools = list(map(tuple, reversed(args)))
+    ns = list(map(len, pools))
+
+    c = reduce(mul, ns)
+
+    if index < 0:
+        index += c
+
+    if not 0 <= index < c:
+        raise IndexError
+
+    result = []
+    for pool, n in zip(pools, ns):
+        result.append(pool[index % n])
+        index //= n
+
+    return tuple(reversed(result))
+
+
+def nth_permutation(iterable, r, index):
+    """Equivalent to ``list(permutations(iterable, r))[index]```
+
+    The subsequences of *iterable* that are of length *r* where order is
+    important can be ordered lexicographically. :func:`nth_permutation`
+    computes the subsequence at sort position *index* directly, without
+    computing the previous subsequences.
+
+        >>> nth_permutation('ghijk', 2, 5)
+        ('h', 'i')
+
+    ``ValueError`` will be raised If *r* is negative or greater than the length
+    of *iterable*.
+    ``IndexError`` will be raised if the given *index* is invalid.
+    """
+    pool = list(iterable)
+    n = len(pool)
+
+    if r is None or r == n:
+        r, c = n, factorial(n)
+    elif not 0 <= r < n:
+        raise ValueError
+    else:
+        c = perm(n, r)
+    assert c > 0  # factortial(n)>0, and r<n so perm(n,r) is never zero
+
+    if index < 0:
+        index += c
+
+    if not 0 <= index < c:
+        raise IndexError
+
+    result = [0] * r
+    q = index * factorial(n) // c if r < n else index
+    for d in range(1, n + 1):
+        q, i = divmod(q, d)
+        if 0 <= n - d < r:
+            result[n - d] = i
+        if q == 0:
+            break
+
+    return tuple(map(pool.pop, result))
+
+
+def nth_combination_with_replacement(iterable, r, index):
+    """Equivalent to
+    ``list(combinations_with_replacement(iterable, r))[index]``.
+
+
+    The subsequences with repetition of *iterable* that are of length *r* can
+    be ordered lexicographically. :func:`nth_combination_with_replacement`
+    computes the subsequence at sort position *index* directly, without
+    computing the previous subsequences with replacement.
+
+        >>> nth_combination_with_replacement(range(5), 3, 5)
+        (0, 1, 1)
+
+    ``ValueError`` will be raised If *r* is negative or greater than the length
+    of *iterable*.
+    ``IndexError`` will be raised if the given *index* is invalid.
+    """
+    pool = tuple(iterable)
+    n = len(pool)
+    if (r < 0) or (r > n):
+        raise ValueError
+
+    c = comb(n + r - 1, r)
+
+    if index < 0:
+        index += c
+
+    if (index < 0) or (index >= c):
+        raise IndexError
+
+    result = []
+    i = 0
+    while r:
+        r -= 1
+        while n >= 0:
+            num_combs = comb(n + r - 1, r)
+            if index < num_combs:
+                break
+            n -= 1
+            i += 1
+            index -= num_combs
+        result.append(pool[i])
+
+    return tuple(result)
+
+
+def value_chain(*args):
+    """Yield all arguments passed to the function in the same order in which
+    they were passed. If an argument itself is iterable then iterate over its
+    values.
+
+        >>> list(value_chain(1, 2, 3, [4, 5, 6]))
+        [1, 2, 3, 4, 5, 6]
+
+    Binary and text strings are not considered iterable and are emitted
+    as-is:
+
+        >>> list(value_chain('12', '34', ['56', '78']))
+        ['12', '34', '56', '78']
+
+    Pre- or postpend a single element to an iterable:
+
+        >>> list(value_chain(1, [2, 3, 4, 5, 6]))
+        [1, 2, 3, 4, 5, 6]
+        >>> list(value_chain([1, 2, 3, 4, 5], 6))
+        [1, 2, 3, 4, 5, 6]
+
+    Multiple levels of nesting are not flattened.
+
+    """
+    for value in args:
+        if isinstance(value, (str, bytes)):
+            yield value
+            continue
+        try:
+            yield from value
+        except TypeError:
+            yield value
+
+
+def product_index(element, *args):
+    """Equivalent to ``list(product(*args)).index(element)``
+
+    The products of *args* can be ordered lexicographically.
+    :func:`product_index` computes the first index of *element* without
+    computing the previous products.
+
+        >>> product_index([8, 2], range(10), range(5))
+        42
+
+    ``ValueError`` will be raised if the given *element* isn't in the product
+    of *args*.
+    """
+    index = 0
+
+    for x, pool in zip_longest(element, args, fillvalue=_marker):
+        if x is _marker or pool is _marker:
+            raise ValueError('element is not a product of args')
+
+        pool = tuple(pool)
+        index = index * len(pool) + pool.index(x)
+
+    return index
+
+
+def combination_index(element, iterable):
+    """Equivalent to ``list(combinations(iterable, r)).index(element)``
+
+    The subsequences of *iterable* that are of length *r* can be ordered
+    lexicographically. :func:`combination_index` computes the index of the
+    first *element*, without computing the previous combinations.
+
+        >>> combination_index('adf', 'abcdefg')
+        10
+
+    ``ValueError`` will be raised if the given *element* isn't one of the
+    combinations of *iterable*.
+    """
+    element = enumerate(element)
+    k, y = next(element, (None, None))
+    if k is None:
+        return 0
+
+    indexes = []
+    pool = enumerate(iterable)
+    for n, x in pool:
+        if x == y:
+            indexes.append(n)
+            tmp, y = next(element, (None, None))
+            if tmp is None:
+                break
+            else:
+                k = tmp
+    else:
+        raise ValueError('element is not a combination of iterable')
+
+    n, _ = last(pool, default=(n, None))
+
+    # Python versions below 3.8 don't have math.comb
+    index = 1
+    for i, j in enumerate(reversed(indexes), start=1):
+        j = n - j
+        if i <= j:
+            index += comb(j, i)
+
+    return comb(n + 1, k + 1) - index
+
+
+def combination_with_replacement_index(element, iterable):
+    """Equivalent to
+    ``list(combinations_with_replacement(iterable, r)).index(element)``
+
+    The subsequences with repetition of *iterable* that are of length *r* can
+    be ordered lexicographically. :func:`combination_with_replacement_index`
+    computes the index of the first *element*, without computing the previous
+    combinations with replacement.
+
+        >>> combination_with_replacement_index('adf', 'abcdefg')
+        20
+
+    ``ValueError`` will be raised if the given *element* isn't one of the
+    combinations with replacement of *iterable*.
+    """
+    element = tuple(element)
+    l = len(element)
+    element = enumerate(element)
+
+    k, y = next(element, (None, None))
+    if k is None:
+        return 0
+
+    indexes = []
+    pool = tuple(iterable)
+    for n, x in enumerate(pool):
+        while x == y:
+            indexes.append(n)
+            tmp, y = next(element, (None, None))
+            if tmp is None:
+                break
+            else:
+                k = tmp
+        if y is None:
+            break
+    else:
+        raise ValueError(
+            'element is not a combination with replacement of iterable'
+        )
+
+    n = len(pool)
+    occupations = [0] * n
+    for p in indexes:
+        occupations[p] += 1
+
+    index = 0
+    cumulative_sum = 0
+    for k in range(1, n):
+        cumulative_sum += occupations[k - 1]
+        j = l + n - 1 - k - cumulative_sum
+        i = n - k
+        if i <= j:
+            index += comb(j, i)
+
+    return index
+
+
+def permutation_index(element, iterable):
+    """Equivalent to ``list(permutations(iterable, r)).index(element)```
+
+    The subsequences of *iterable* that are of length *r* where order is
+    important can be ordered lexicographically. :func:`permutation_index`
+    computes the index of the first *element* directly, without computing
+    the previous permutations.
+
+        >>> permutation_index([1, 3, 2], range(5))
+        19
+
+    ``ValueError`` will be raised if the given *element* isn't one of the
+    permutations of *iterable*.
+    """
+    index = 0
+    pool = list(iterable)
+    for i, x in zip(range(len(pool), -1, -1), element):
+        r = pool.index(x)
+        index = index * i + r
+        del pool[r]
+
+    return index
+
+
+class countable:
+    """Wrap *iterable* and keep a count of how many items have been consumed.
+
+    The ``items_seen`` attribute starts at ``0`` and increments as the iterable
+    is consumed:
+
+        >>> iterable = map(str, range(10))
+        >>> it = countable(iterable)
+        >>> it.items_seen
+        0
+        >>> next(it), next(it)
+        ('0', '1')
+        >>> list(it)
+        ['2', '3', '4', '5', '6', '7', '8', '9']
+        >>> it.items_seen
+        10
+    """
+
+    def __init__(self, iterable):
+        self._it = iter(iterable)
+        self.items_seen = 0
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        item = next(self._it)
+        self.items_seen += 1
+
+        return item
+
+
+def chunked_even(iterable, n):
+    """Break *iterable* into lists of approximately length *n*.
+    Items are distributed such the lengths of the lists differ by at most
+    1 item.
+
+    >>> iterable = [1, 2, 3, 4, 5, 6, 7]
+    >>> n = 3
+    >>> list(chunked_even(iterable, n))  # List lengths: 3, 2, 2
+    [[1, 2, 3], [4, 5], [6, 7]]
+    >>> list(chunked(iterable, n))  # List lengths: 3, 3, 1
+    [[1, 2, 3], [4, 5, 6], [7]]
+
+    """
+    iterable = iter(iterable)
+
+    # Initialize a buffer to process the chunks while keeping
+    # some back to fill any underfilled chunks
+    min_buffer = (n - 1) * (n - 2)
+    buffer = list(islice(iterable, min_buffer))
+
+    # Append items until we have a completed chunk
+    for _ in islice(map(buffer.append, iterable), n, None, n):
+        yield buffer[:n]
+        del buffer[:n]
+
+    # Check if any chunks need addition processing
+    if not buffer:
+        return
+    length = len(buffer)
+
+    # Chunks are either size `full_size <= n` or `partial_size = full_size - 1`
+    q, r = divmod(length, n)
+    num_lists = q + (1 if r > 0 else 0)
+    q, r = divmod(length, num_lists)
+    full_size = q + (1 if r > 0 else 0)
+    partial_size = full_size - 1
+    num_full = length - partial_size * num_lists
+
+    # Yield chunks of full size
+    partial_start_idx = num_full * full_size
+    if full_size > 0:
+        for i in range(0, partial_start_idx, full_size):
+            yield buffer[i : i + full_size]
+
+    # Yield chunks of partial size
+    if partial_size > 0:
+        for i in range(partial_start_idx, length, partial_size):
+            yield buffer[i : i + partial_size]
+
+
+def zip_broadcast(*objects, scalar_types=(str, bytes), strict=False):
+    """A version of :func:`zip` that "broadcasts" any scalar
+    (i.e., non-iterable) items into output tuples.
+
+    >>> iterable_1 = [1, 2, 3]
+    >>> iterable_2 = ['a', 'b', 'c']
+    >>> scalar = '_'
+    >>> list(zip_broadcast(iterable_1, iterable_2, scalar))
+    [(1, 'a', '_'), (2, 'b', '_'), (3, 'c', '_')]
+
+    The *scalar_types* keyword argument determines what types are considered
+    scalar. It is set to ``(str, bytes)`` by default. Set it to ``None`` to
+    treat strings and byte strings as iterable:
+
+    >>> list(zip_broadcast('abc', 0, 'xyz', scalar_types=None))
+    [('a', 0, 'x'), ('b', 0, 'y'), ('c', 0, 'z')]
+
+    If the *strict* keyword argument is ``True``, then
+    ``UnequalIterablesError`` will be raised if any of the iterables have
+    different lengths.
+    """
+
+    def is_scalar(obj):
+        if scalar_types and isinstance(obj, scalar_types):
+            return True
+        try:
+            iter(obj)
+        except TypeError:
+            return True
+        else:
+            return False
+
+    size = len(objects)
+    if not size:
+        return
+
+    new_item = [None] * size
+    iterables, iterable_positions = [], []
+    for i, obj in enumerate(objects):
+        if is_scalar(obj):
+            new_item[i] = obj
+        else:
+            iterables.append(iter(obj))
+            iterable_positions.append(i)
+
+    if not iterables:
+        yield tuple(objects)
+        return
+
+    zipper = _zip_equal if strict else zip
+    for item in zipper(*iterables):
+        for i, new_item[i] in zip(iterable_positions, item):
+            pass
+        yield tuple(new_item)
+
+
+def unique_in_window(iterable, n, key=None):
+    """Yield the items from *iterable* that haven't been seen recently.
+    *n* is the size of the lookback window.
+
+        >>> iterable = [0, 1, 0, 2, 3, 0]
+        >>> n = 3
+        >>> list(unique_in_window(iterable, n))
+        [0, 1, 2, 3, 0]
+
+    The *key* function, if provided, will be used to determine uniqueness:
+
+        >>> list(unique_in_window('abAcda', 3, key=lambda x: x.lower()))
+        ['a', 'b', 'c', 'd', 'a']
+
+    The items in *iterable* must be hashable.
+
+    """
+    if n <= 0:
+        raise ValueError('n must be greater than 0')
+
+    window = deque(maxlen=n)
+    counts = defaultdict(int)
+    use_key = key is not None
+
+    for item in iterable:
+        if len(window) == n:
+            to_discard = window[0]
+            if counts[to_discard] == 1:
+                del counts[to_discard]
+            else:
+                counts[to_discard] -= 1
+
+        k = key(item) if use_key else item
+        if k not in counts:
+            yield item
+        counts[k] += 1
+        window.append(k)
+
+
+def duplicates_everseen(iterable, key=None):
+    """Yield duplicate elements after their first appearance.
+
+    >>> list(duplicates_everseen('mississippi'))
+    ['s', 'i', 's', 's', 'i', 'p', 'i']
+    >>> list(duplicates_everseen('AaaBbbCccAaa', str.lower))
+    ['a', 'a', 'b', 'b', 'c', 'c', 'A', 'a', 'a']
+
+    This function is analogous to :func:`unique_everseen` and is subject to
+    the same performance considerations.
+
+    """
+    seen_set = set()
+    seen_list = []
+    use_key = key is not None
+
+    for element in iterable:
+        k = key(element) if use_key else element
+        try:
+            if k not in seen_set:
+                seen_set.add(k)
+            else:
+                yield element
+        except TypeError:
+            if k not in seen_list:
+                seen_list.append(k)
+            else:
+                yield element
+
+
+def duplicates_justseen(iterable, key=None):
+    """Yields serially-duplicate elements after their first appearance.
+
+    >>> list(duplicates_justseen('mississippi'))
+    ['s', 's', 'p']
+    >>> list(duplicates_justseen('AaaBbbCccAaa', str.lower))
+    ['a', 'a', 'b', 'b', 'c', 'c', 'a', 'a']
+
+    This function is analogous to :func:`unique_justseen`.
+
+    """
+    return flatten(g for _, g in groupby(iterable, key) for _ in g)
+
+
+def classify_unique(iterable, key=None):
+    """Classify each element in terms of its uniqueness.
+
+    For each element in the input iterable, return a 3-tuple consisting of:
+
+    1. The element itself
+    2. ``False`` if the element is equal to the one preceding it in the input,
+       ``True`` otherwise (i.e. the equivalent of :func:`unique_justseen`)
+    3. ``False`` if this element has been seen anywhere in the input before,
+       ``True`` otherwise (i.e. the equivalent of :func:`unique_everseen`)
+
+    >>> list(classify_unique('otto'))    # doctest: +NORMALIZE_WHITESPACE
+    [('o', True,  True),
+     ('t', True,  True),
+     ('t', False, False),
+     ('o', True,  False)]
+
+    This function is analogous to :func:`unique_everseen` and is subject to
+    the same performance considerations.
+
+    """
+    seen_set = set()
+    seen_list = []
+    use_key = key is not None
+    previous = None
+
+    for i, element in enumerate(iterable):
+        k = key(element) if use_key else element
+        is_unique_justseen = not i or previous != k
+        previous = k
+        is_unique_everseen = False
+        try:
+            if k not in seen_set:
+                seen_set.add(k)
+                is_unique_everseen = True
+        except TypeError:
+            if k not in seen_list:
+                seen_list.append(k)
+                is_unique_everseen = True
+        yield element, is_unique_justseen, is_unique_everseen
+
+
+def minmax(iterable_or_value, *others, key=None, default=_marker):
+    """Returns both the smallest and largest items in an iterable
+    or the largest of two or more arguments.
+
+        >>> minmax([3, 1, 5])
+        (1, 5)
+
+        >>> minmax(4, 2, 6)
+        (2, 6)
+
+    If a *key* function is provided, it will be used to transform the input
+    items for comparison.
+
+        >>> minmax([5, 30], key=str)  # '30' sorts before '5'
+        (30, 5)
+
+    If a *default* value is provided, it will be returned if there are no
+    input items.
+
+        >>> minmax([], default=(0, 0))
+        (0, 0)
+
+    Otherwise ``ValueError`` is raised.
+
+    This function is based on the
+    `recipe <http://code.activestate.com/recipes/577916/>`__ by
+    Raymond Hettinger and takes care to minimize the number of comparisons
+    performed.
+    """
+    iterable = (iterable_or_value, *others) if others else iterable_or_value
+
+    it = iter(iterable)
+
+    try:
+        lo = hi = next(it)
+    except StopIteration as exc:
+        if default is _marker:
+            raise ValueError(
+                '`minmax()` argument is an empty iterable. '
+                'Provide a `default` value to suppress this error.'
+            ) from exc
+        return default
+
+    # Different branches depending on the presence of key. This saves a lot
+    # of unimportant copies which would slow the "key=None" branch
+    # significantly down.
+    if key is None:
+        for x, y in zip_longest(it, it, fillvalue=lo):
+            if y < x:
+                x, y = y, x
+            if x < lo:
+                lo = x
+            if hi < y:
+                hi = y
+
+    else:
+        lo_key = hi_key = key(lo)
+
+        for x, y in zip_longest(it, it, fillvalue=lo):
+            x_key, y_key = key(x), key(y)
+
+            if y_key < x_key:
+                x, y, x_key, y_key = y, x, y_key, x_key
+            if x_key < lo_key:
+                lo, lo_key = x, x_key
+            if hi_key < y_key:
+                hi, hi_key = y, y_key
+
+    return lo, hi
+
+
+def constrained_batches(
+    iterable, max_size, max_count=None, get_len=len, strict=True
+):
+    """Yield batches of items from *iterable* with a combined size limited by
+    *max_size*.
+
+    >>> iterable = [b'12345', b'123', b'12345678', b'1', b'1', b'12', b'1']
+    >>> list(constrained_batches(iterable, 10))
+    [(b'12345', b'123'), (b'12345678', b'1', b'1'), (b'12', b'1')]
+
+    If a *max_count* is supplied, the number of items per batch is also
+    limited:
+
+    >>> iterable = [b'12345', b'123', b'12345678', b'1', b'1', b'12', b'1']
+    >>> list(constrained_batches(iterable, 10, max_count = 2))
+    [(b'12345', b'123'), (b'12345678', b'1'), (b'1', b'12'), (b'1',)]
+
+    If a *get_len* function is supplied, use that instead of :func:`len` to
+    determine item size.
+
+    If *strict* is ``True``, raise ``ValueError`` if any single item is bigger
+    than *max_size*. Otherwise, allow single items to exceed *max_size*.
+    """
+    if max_size <= 0:
+        raise ValueError('maximum size must be greater than zero')
+
+    batch = []
+    batch_size = 0
+    batch_count = 0
+    for item in iterable:
+        item_len = get_len(item)
+        if strict and item_len > max_size:
+            raise ValueError('item size exceeds maximum size')
+
+        reached_count = batch_count == max_count
+        reached_size = item_len + batch_size > max_size
+        if batch_count and (reached_size or reached_count):
+            yield tuple(batch)
+            batch.clear()
+            batch_size = 0
+            batch_count = 0
+
+        batch.append(item)
+        batch_size += item_len
+        batch_count += 1
+
+    if batch:
+        yield tuple(batch)
+
+
+def gray_product(*iterables):
+    """Like :func:`itertools.product`, but return tuples in an order such
+    that only one element in the generated tuple changes from one iteration
+    to the next.
+
+        >>> list(gray_product('AB','CD'))
+        [('A', 'C'), ('B', 'C'), ('B', 'D'), ('A', 'D')]
+
+    This function consumes all of the input iterables before producing output.
+    If any of the input iterables have fewer than two items, ``ValueError``
+    is raised.
+
+    For information on the algorithm, see
+    `this section <https://www-cs-faculty.stanford.edu/~knuth/fasc2a.ps.gz>`__
+    of Donald Knuth's *The Art of Computer Programming*.
+    """
+    all_iterables = tuple(tuple(x) for x in iterables)
+    iterable_count = len(all_iterables)
+    for iterable in all_iterables:
+        if len(iterable) < 2:
+            raise ValueError("each iterable must have two or more items")
+
+    # This is based on "Algorithm H" from section 7.2.1.1, page 20.
+    # a holds the indexes of the source iterables for the n-tuple to be yielded
+    # f is the array of "focus pointers"
+    # o is the array of "directions"
+    a = [0] * iterable_count
+    f = list(range(iterable_count + 1))
+    o = [1] * iterable_count
+    while True:
+        yield tuple(all_iterables[i][a[i]] for i in range(iterable_count))
+        j = f[0]
+        f[0] = 0
+        if j == iterable_count:
+            break
+        a[j] = a[j] + o[j]
+        if a[j] == 0 or a[j] == len(all_iterables[j]) - 1:
+            o[j] = -o[j]
+            f[j] = f[j + 1]
+            f[j + 1] = j + 1
+
+
+def partial_product(*iterables):
+    """Yields tuples containing one item from each iterator, with subsequent
+    tuples changing a single item at a time by advancing each iterator until it
+    is exhausted. This sequence guarantees every value in each iterable is
+    output at least once without generating all possible combinations.
+
+    This may be useful, for example, when testing an expensive function.
+
+        >>> list(partial_product('AB', 'C', 'DEF'))
+        [('A', 'C', 'D'), ('B', 'C', 'D'), ('B', 'C', 'E'), ('B', 'C', 'F')]
+    """
+
+    iterators = list(map(iter, iterables))
+
+    try:
+        prod = [next(it) for it in iterators]
+    except StopIteration:
+        return
+    yield tuple(prod)
+
+    for i, it in enumerate(iterators):
+        for prod[i] in it:
+            yield tuple(prod)
+
+
+def takewhile_inclusive(predicate, iterable):
+    """A variant of :func:`takewhile` that yields one additional element.
+
+        >>> list(takewhile_inclusive(lambda x: x < 5, [1, 4, 6, 4, 1]))
+        [1, 4, 6]
+
+    :func:`takewhile` would return ``[1, 4]``.
+    """
+    for x in iterable:
+        yield x
+        if not predicate(x):
+            break
+
+
+def outer_product(func, xs, ys, *args, **kwargs):
+    """A generalized outer product that applies a binary function to all
+    pairs of items. Returns a 2D matrix with ``len(xs)`` rows and ``len(ys)``
+    columns.
+    Also accepts ``*args`` and ``**kwargs`` that are passed to ``func``.
+
+    Multiplication table:
+
+    >>> list(outer_product(mul, range(1, 4), range(1, 6)))
+    [(1, 2, 3, 4, 5), (2, 4, 6, 8, 10), (3, 6, 9, 12, 15)]
+
+    Cross tabulation:
+
+    >>> xs = ['A', 'B', 'A', 'A', 'B', 'B', 'A', 'A', 'B', 'B']
+    >>> ys = ['X', 'X', 'X', 'Y', 'Z', 'Z', 'Y', 'Y', 'Z', 'Z']
+    >>> rows = list(zip(xs, ys))
+    >>> count_rows = lambda x, y: rows.count((x, y))
+    >>> list(outer_product(count_rows, sorted(set(xs)), sorted(set(ys))))
+    [(2, 3, 0), (1, 0, 4)]
+
+    Usage with ``*args`` and ``**kwargs``:
+
+    >>> animals = ['cat', 'wolf', 'mouse']
+    >>> list(outer_product(min, animals, animals, key=len))
+    [('cat', 'cat', 'cat'), ('cat', 'wolf', 'wolf'), ('cat', 'wolf', 'mouse')]
+    """
+    ys = tuple(ys)
+    return batched(
+        starmap(lambda x, y: func(x, y, *args, **kwargs), product(xs, ys)),
+        n=len(ys),
+    )
+
+
+def iter_suppress(iterable, *exceptions):
+    """Yield each of the items from *iterable*. If the iteration raises one of
+    the specified *exceptions*, that exception will be suppressed and iteration
+    will stop.
+
+    >>> from itertools import chain
+    >>> def breaks_at_five(x):
+    ...     while True:
+    ...         if x >= 5:
+    ...             raise RuntimeError
+    ...         yield x
+    ...         x += 1
+    >>> it_1 = iter_suppress(breaks_at_five(1), RuntimeError)
+    >>> it_2 = iter_suppress(breaks_at_five(2), RuntimeError)
+    >>> list(chain(it_1, it_2))
+    [1, 2, 3, 4, 2, 3, 4]
+    """
+    try:
+        yield from iterable
+    except exceptions:
+        return
+
+
+def filter_map(func, iterable):
+    """Apply *func* to every element of *iterable*, yielding only those which
+    are not ``None``.
+
+    >>> elems = ['1', 'a', '2', 'b', '3']
+    >>> list(filter_map(lambda s: int(s) if s.isnumeric() else None, elems))
+    [1, 2, 3]
+    """
+    for x in iterable:
+        y = func(x)
+        if y is not None:
+            yield y
+
+
+def powerset_of_sets(iterable):
+    """Yields all possible subsets of the iterable.
+
+        >>> list(powerset_of_sets([1, 2, 3]))  # doctest: +SKIP
+        [set(), {1}, {2}, {3}, {1, 2}, {1, 3}, {2, 3}, {1, 2, 3}]
+        >>> list(powerset_of_sets([1, 1, 0]))  # doctest: +SKIP
+        [set(), {1}, {0}, {0, 1}]
+
+    :func:`powerset_of_sets` takes care to minimize the number
+    of hash operations performed.
+    """
+    sets = tuple(map(set, dict.fromkeys(map(frozenset, zip(iterable)))))
+    for r in range(len(sets) + 1):
+        yield from starmap(set().union, combinations(sets, r))
+
+
+def join_mappings(**field_to_map):
+    """
+    Joins multiple mappings together using their common keys.
+
+    >>> user_scores = {'elliot': 50, 'claris': 60}
+    >>> user_times = {'elliot': 30, 'claris': 40}
+    >>> join_mappings(score=user_scores, time=user_times)
+    {'elliot': {'score': 50, 'time': 30}, 'claris': {'score': 60, 'time': 40}}
+    """
+    ret = defaultdict(dict)
+
+    for field_name, mapping in field_to_map.items():
+        for key, value in mapping.items():
+            ret[key][field_name] = value
+
+    return dict(ret)
+
+
+def _complex_sumprod(v1, v2):
+    """High precision sumprod() for complex numbers.
+    Used by :func:`dft` and :func:`idft`.
+    """
+
+    r1 = chain((p.real for p in v1), (-p.imag for p in v1))
+    r2 = chain((q.real for q in v2), (q.imag for q in v2))
+    i1 = chain((p.real for p in v1), (p.imag for p in v1))
+    i2 = chain((q.imag for q in v2), (q.real for q in v2))
+    return complex(_fsumprod(r1, r2), _fsumprod(i1, i2))
+
+
+def dft(xarr):
+    """Discrete Fourier Tranform. *xarr* is a sequence of complex numbers.
+    Yields the components of the corresponding transformed output vector.
+
+    >>> import cmath
+    >>> xarr = [1, 2-1j, -1j, -1+2j]
+    >>> Xarr = [2, -2-2j, -2j, 4+4j]
+    >>> all(map(cmath.isclose, dft(xarr), Xarr))
+    True
+
+    See :func:`idft` for the inverse Discrete Fourier Transform.
+    """
+    N = len(xarr)
+    roots_of_unity = [e ** (n / N * tau * -1j) for n in range(N)]
+    for k in range(N):
+        coeffs = [roots_of_unity[k * n % N] for n in range(N)]
+        yield _complex_sumprod(xarr, coeffs)
+
+
+def idft(Xarr):
+    """Inverse Discrete Fourier Tranform. *Xarr* is a sequence of
+    complex numbers. Yields the components of the corresponding
+    inverse-transformed output vector.
+
+    >>> import cmath
+    >>> xarr = [1, 2-1j, -1j, -1+2j]
+    >>> Xarr = [2, -2-2j, -2j, 4+4j]
+    >>> all(map(cmath.isclose, idft(Xarr), xarr))
+    True
+
+    See :func:`dft` for the Discrete Fourier Transform.
+    """
+    N = len(Xarr)
+    roots_of_unity = [e ** (n / N * tau * 1j) for n in range(N)]
+    for k in range(N):
+        coeffs = [roots_of_unity[k * n % N] for n in range(N)]
+        yield _complex_sumprod(Xarr, coeffs) / N
+
+
+def doublestarmap(func, iterable):
+    """Apply *func* to every item of *iterable* by dictionary unpacking
+    the item into *func*.
+
+    The difference between :func:`itertools.starmap` and :func:`doublestarmap`
+    parallels the distinction between ``func(*a)`` and ``func(**a)``.
+
+    >>> iterable = [{'a': 1, 'b': 2}, {'a': 40, 'b': 60}]
+    >>> list(doublestarmap(lambda a, b: a + b, iterable))
+    [3, 100]
+
+    ``TypeError`` will be raised if *func*'s signature doesn't match the
+    mapping contained in *iterable* or if *iterable* does not contain mappings.
+    """
+    for item in iterable:
+        yield func(**item)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/more.pyi b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/more.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e94602325935c27066f916356b8b8a3e180e8b6f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/more.pyi
@@ -0,0 +1,709 @@
+"""Stubs for more_itertools.more"""
+
+from __future__ import annotations
+
+from types import TracebackType
+from typing import (
+    Any,
+    Callable,
+    Container,
+    ContextManager,
+    Generic,
+    Hashable,
+    Mapping,
+    Iterable,
+    Iterator,
+    Mapping,
+    overload,
+    Reversible,
+    Sequence,
+    Sized,
+    Type,
+    TypeVar,
+    type_check_only,
+)
+from typing_extensions import Protocol
+
+# Type and type variable definitions
+_T = TypeVar('_T')
+_T1 = TypeVar('_T1')
+_T2 = TypeVar('_T2')
+_U = TypeVar('_U')
+_V = TypeVar('_V')
+_W = TypeVar('_W')
+_T_co = TypeVar('_T_co', covariant=True)
+_GenFn = TypeVar('_GenFn', bound=Callable[..., Iterator[Any]])
+_Raisable = BaseException | Type[BaseException]
+
+@type_check_only
+class _SizedIterable(Protocol[_T_co], Sized, Iterable[_T_co]): ...
+
+@type_check_only
+class _SizedReversible(Protocol[_T_co], Sized, Reversible[_T_co]): ...
+
+@type_check_only
+class _SupportsSlicing(Protocol[_T_co]):
+    def __getitem__(self, __k: slice) -> _T_co: ...
+
+def chunked(
+    iterable: Iterable[_T], n: int | None, strict: bool = ...
+) -> Iterator[list[_T]]: ...
+@overload
+def first(iterable: Iterable[_T]) -> _T: ...
+@overload
+def first(iterable: Iterable[_T], default: _U) -> _T | _U: ...
+@overload
+def last(iterable: Iterable[_T]) -> _T: ...
+@overload
+def last(iterable: Iterable[_T], default: _U) -> _T | _U: ...
+@overload
+def nth_or_last(iterable: Iterable[_T], n: int) -> _T: ...
+@overload
+def nth_or_last(iterable: Iterable[_T], n: int, default: _U) -> _T | _U: ...
+
+class peekable(Generic[_T], Iterator[_T]):
+    def __init__(self, iterable: Iterable[_T]) -> None: ...
+    def __iter__(self) -> peekable[_T]: ...
+    def __bool__(self) -> bool: ...
+    @overload
+    def peek(self) -> _T: ...
+    @overload
+    def peek(self, default: _U) -> _T | _U: ...
+    def prepend(self, *items: _T) -> None: ...
+    def __next__(self) -> _T: ...
+    @overload
+    def __getitem__(self, index: int) -> _T: ...
+    @overload
+    def __getitem__(self, index: slice) -> list[_T]: ...
+
+def consumer(func: _GenFn) -> _GenFn: ...
+def ilen(iterable: Iterable[_T]) -> int: ...
+def iterate(func: Callable[[_T], _T], start: _T) -> Iterator[_T]: ...
+def with_iter(
+    context_manager: ContextManager[Iterable[_T]],
+) -> Iterator[_T]: ...
+def one(
+    iterable: Iterable[_T],
+    too_short: _Raisable | None = ...,
+    too_long: _Raisable | None = ...,
+) -> _T: ...
+def raise_(exception: _Raisable, *args: Any) -> None: ...
+def strictly_n(
+    iterable: Iterable[_T],
+    n: int,
+    too_short: _GenFn | None = ...,
+    too_long: _GenFn | None = ...,
+) -> list[_T]: ...
+def distinct_permutations(
+    iterable: Iterable[_T], r: int | None = ...
+) -> Iterator[tuple[_T, ...]]: ...
+def intersperse(
+    e: _U, iterable: Iterable[_T], n: int = ...
+) -> Iterator[_T | _U]: ...
+def unique_to_each(*iterables: Iterable[_T]) -> list[list[_T]]: ...
+@overload
+def windowed(
+    seq: Iterable[_T], n: int, *, step: int = ...
+) -> Iterator[tuple[_T | None, ...]]: ...
+@overload
+def windowed(
+    seq: Iterable[_T], n: int, fillvalue: _U, step: int = ...
+) -> Iterator[tuple[_T | _U, ...]]: ...
+def substrings(iterable: Iterable[_T]) -> Iterator[tuple[_T, ...]]: ...
+def substrings_indexes(
+    seq: Sequence[_T], reverse: bool = ...
+) -> Iterator[tuple[Sequence[_T], int, int]]: ...
+
+class bucket(Generic[_T, _U], Container[_U]):
+    def __init__(
+        self,
+        iterable: Iterable[_T],
+        key: Callable[[_T], _U],
+        validator: Callable[[_U], object] | None = ...,
+    ) -> None: ...
+    def __contains__(self, value: object) -> bool: ...
+    def __iter__(self) -> Iterator[_U]: ...
+    def __getitem__(self, value: object) -> Iterator[_T]: ...
+
+def spy(
+    iterable: Iterable[_T], n: int = ...
+) -> tuple[list[_T], Iterator[_T]]: ...
+def interleave(*iterables: Iterable[_T]) -> Iterator[_T]: ...
+def interleave_longest(*iterables: Iterable[_T]) -> Iterator[_T]: ...
+def interleave_evenly(
+    iterables: list[Iterable[_T]], lengths: list[int] | None = ...
+) -> Iterator[_T]: ...
+def collapse(
+    iterable: Iterable[Any],
+    base_type: type | None = ...,
+    levels: int | None = ...,
+) -> Iterator[Any]: ...
+@overload
+def side_effect(
+    func: Callable[[_T], object],
+    iterable: Iterable[_T],
+    chunk_size: None = ...,
+    before: Callable[[], object] | None = ...,
+    after: Callable[[], object] | None = ...,
+) -> Iterator[_T]: ...
+@overload
+def side_effect(
+    func: Callable[[list[_T]], object],
+    iterable: Iterable[_T],
+    chunk_size: int,
+    before: Callable[[], object] | None = ...,
+    after: Callable[[], object] | None = ...,
+) -> Iterator[_T]: ...
+def sliced(
+    seq: _SupportsSlicing[_T], n: int, strict: bool = ...
+) -> Iterator[_T]: ...
+def split_at(
+    iterable: Iterable[_T],
+    pred: Callable[[_T], object],
+    maxsplit: int = ...,
+    keep_separator: bool = ...,
+) -> Iterator[list[_T]]: ...
+def split_before(
+    iterable: Iterable[_T], pred: Callable[[_T], object], maxsplit: int = ...
+) -> Iterator[list[_T]]: ...
+def split_after(
+    iterable: Iterable[_T], pred: Callable[[_T], object], maxsplit: int = ...
+) -> Iterator[list[_T]]: ...
+def split_when(
+    iterable: Iterable[_T],
+    pred: Callable[[_T, _T], object],
+    maxsplit: int = ...,
+) -> Iterator[list[_T]]: ...
+def split_into(
+    iterable: Iterable[_T], sizes: Iterable[int | None]
+) -> Iterator[list[_T]]: ...
+@overload
+def padded(
+    iterable: Iterable[_T],
+    *,
+    n: int | None = ...,
+    next_multiple: bool = ...,
+) -> Iterator[_T | None]: ...
+@overload
+def padded(
+    iterable: Iterable[_T],
+    fillvalue: _U,
+    n: int | None = ...,
+    next_multiple: bool = ...,
+) -> Iterator[_T | _U]: ...
+@overload
+def repeat_last(iterable: Iterable[_T]) -> Iterator[_T]: ...
+@overload
+def repeat_last(iterable: Iterable[_T], default: _U) -> Iterator[_T | _U]: ...
+def distribute(n: int, iterable: Iterable[_T]) -> list[Iterator[_T]]: ...
+@overload
+def stagger(
+    iterable: Iterable[_T],
+    offsets: _SizedIterable[int] = ...,
+    longest: bool = ...,
+) -> Iterator[tuple[_T | None, ...]]: ...
+@overload
+def stagger(
+    iterable: Iterable[_T],
+    offsets: _SizedIterable[int] = ...,
+    longest: bool = ...,
+    fillvalue: _U = ...,
+) -> Iterator[tuple[_T | _U, ...]]: ...
+
+class UnequalIterablesError(ValueError):
+    def __init__(self, details: tuple[int, int, int] | None = ...) -> None: ...
+
+@overload
+def zip_equal(__iter1: Iterable[_T1]) -> Iterator[tuple[_T1]]: ...
+@overload
+def zip_equal(
+    __iter1: Iterable[_T1], __iter2: Iterable[_T2]
+) -> Iterator[tuple[_T1, _T2]]: ...
+@overload
+def zip_equal(
+    __iter1: Iterable[_T],
+    __iter2: Iterable[_T],
+    __iter3: Iterable[_T],
+    *iterables: Iterable[_T],
+) -> Iterator[tuple[_T, ...]]: ...
+@overload
+def zip_offset(
+    __iter1: Iterable[_T1],
+    *,
+    offsets: _SizedIterable[int],
+    longest: bool = ...,
+    fillvalue: None = None,
+) -> Iterator[tuple[_T1 | None]]: ...
+@overload
+def zip_offset(
+    __iter1: Iterable[_T1],
+    __iter2: Iterable[_T2],
+    *,
+    offsets: _SizedIterable[int],
+    longest: bool = ...,
+    fillvalue: None = None,
+) -> Iterator[tuple[_T1 | None, _T2 | None]]: ...
+@overload
+def zip_offset(
+    __iter1: Iterable[_T],
+    __iter2: Iterable[_T],
+    __iter3: Iterable[_T],
+    *iterables: Iterable[_T],
+    offsets: _SizedIterable[int],
+    longest: bool = ...,
+    fillvalue: None = None,
+) -> Iterator[tuple[_T | None, ...]]: ...
+@overload
+def zip_offset(
+    __iter1: Iterable[_T1],
+    *,
+    offsets: _SizedIterable[int],
+    longest: bool = ...,
+    fillvalue: _U,
+) -> Iterator[tuple[_T1 | _U]]: ...
+@overload
+def zip_offset(
+    __iter1: Iterable[_T1],
+    __iter2: Iterable[_T2],
+    *,
+    offsets: _SizedIterable[int],
+    longest: bool = ...,
+    fillvalue: _U,
+) -> Iterator[tuple[_T1 | _U, _T2 | _U]]: ...
+@overload
+def zip_offset(
+    __iter1: Iterable[_T],
+    __iter2: Iterable[_T],
+    __iter3: Iterable[_T],
+    *iterables: Iterable[_T],
+    offsets: _SizedIterable[int],
+    longest: bool = ...,
+    fillvalue: _U,
+) -> Iterator[tuple[_T | _U, ...]]: ...
+def sort_together(
+    iterables: Iterable[Iterable[_T]],
+    key_list: Iterable[int] = ...,
+    key: Callable[..., Any] | None = ...,
+    reverse: bool = ...,
+) -> list[tuple[_T, ...]]: ...
+def unzip(iterable: Iterable[Sequence[_T]]) -> tuple[Iterator[_T], ...]: ...
+def divide(n: int, iterable: Iterable[_T]) -> list[Iterator[_T]]: ...
+def always_iterable(
+    obj: object,
+    base_type: type | tuple[type | tuple[Any, ...], ...] | None = ...,
+) -> Iterator[Any]: ...
+def adjacent(
+    predicate: Callable[[_T], bool],
+    iterable: Iterable[_T],
+    distance: int = ...,
+) -> Iterator[tuple[bool, _T]]: ...
+@overload
+def groupby_transform(
+    iterable: Iterable[_T],
+    keyfunc: None = None,
+    valuefunc: None = None,
+    reducefunc: None = None,
+) -> Iterator[tuple[_T, Iterator[_T]]]: ...
+@overload
+def groupby_transform(
+    iterable: Iterable[_T],
+    keyfunc: Callable[[_T], _U],
+    valuefunc: None,
+    reducefunc: None,
+) -> Iterator[tuple[_U, Iterator[_T]]]: ...
+@overload
+def groupby_transform(
+    iterable: Iterable[_T],
+    keyfunc: None,
+    valuefunc: Callable[[_T], _V],
+    reducefunc: None,
+) -> Iterable[tuple[_T, Iterable[_V]]]: ...
+@overload
+def groupby_transform(
+    iterable: Iterable[_T],
+    keyfunc: Callable[[_T], _U],
+    valuefunc: Callable[[_T], _V],
+    reducefunc: None,
+) -> Iterable[tuple[_U, Iterator[_V]]]: ...
+@overload
+def groupby_transform(
+    iterable: Iterable[_T],
+    keyfunc: None,
+    valuefunc: None,
+    reducefunc: Callable[[Iterator[_T]], _W],
+) -> Iterable[tuple[_T, _W]]: ...
+@overload
+def groupby_transform(
+    iterable: Iterable[_T],
+    keyfunc: Callable[[_T], _U],
+    valuefunc: None,
+    reducefunc: Callable[[Iterator[_T]], _W],
+) -> Iterable[tuple[_U, _W]]: ...
+@overload
+def groupby_transform(
+    iterable: Iterable[_T],
+    keyfunc: None,
+    valuefunc: Callable[[_T], _V],
+    reducefunc: Callable[[Iterable[_V]], _W],
+) -> Iterable[tuple[_T, _W]]: ...
+@overload
+def groupby_transform(
+    iterable: Iterable[_T],
+    keyfunc: Callable[[_T], _U],
+    valuefunc: Callable[[_T], _V],
+    reducefunc: Callable[[Iterable[_V]], _W],
+) -> Iterable[tuple[_U, _W]]: ...
+
+class numeric_range(Generic[_T, _U], Sequence[_T], Hashable, Reversible[_T]):
+    @overload
+    def __init__(self, __stop: _T) -> None: ...
+    @overload
+    def __init__(self, __start: _T, __stop: _T) -> None: ...
+    @overload
+    def __init__(self, __start: _T, __stop: _T, __step: _U) -> None: ...
+    def __bool__(self) -> bool: ...
+    def __contains__(self, elem: object) -> bool: ...
+    def __eq__(self, other: object) -> bool: ...
+    @overload
+    def __getitem__(self, key: int) -> _T: ...
+    @overload
+    def __getitem__(self, key: slice) -> numeric_range[_T, _U]: ...
+    def __hash__(self) -> int: ...
+    def __iter__(self) -> Iterator[_T]: ...
+    def __len__(self) -> int: ...
+    def __reduce__(
+        self,
+    ) -> tuple[Type[numeric_range[_T, _U]], tuple[_T, _T, _U]]: ...
+    def __repr__(self) -> str: ...
+    def __reversed__(self) -> Iterator[_T]: ...
+    def count(self, value: _T) -> int: ...
+    def index(self, value: _T) -> int: ...  # type: ignore
+
+def count_cycle(
+    iterable: Iterable[_T], n: int | None = ...
+) -> Iterable[tuple[int, _T]]: ...
+def mark_ends(
+    iterable: Iterable[_T],
+) -> Iterable[tuple[bool, bool, _T]]: ...
+def locate(
+    iterable: Iterable[_T],
+    pred: Callable[..., Any] = ...,
+    window_size: int | None = ...,
+) -> Iterator[int]: ...
+def lstrip(
+    iterable: Iterable[_T], pred: Callable[[_T], object]
+) -> Iterator[_T]: ...
+def rstrip(
+    iterable: Iterable[_T], pred: Callable[[_T], object]
+) -> Iterator[_T]: ...
+def strip(
+    iterable: Iterable[_T], pred: Callable[[_T], object]
+) -> Iterator[_T]: ...
+
+class islice_extended(Generic[_T], Iterator[_T]):
+    def __init__(self, iterable: Iterable[_T], *args: int | None) -> None: ...
+    def __iter__(self) -> islice_extended[_T]: ...
+    def __next__(self) -> _T: ...
+    def __getitem__(self, index: slice) -> islice_extended[_T]: ...
+
+def always_reversible(iterable: Iterable[_T]) -> Iterator[_T]: ...
+def consecutive_groups(
+    iterable: Iterable[_T], ordering: Callable[[_T], int] = ...
+) -> Iterator[Iterator[_T]]: ...
+@overload
+def difference(
+    iterable: Iterable[_T],
+    func: Callable[[_T, _T], _U] = ...,
+    *,
+    initial: None = ...,
+) -> Iterator[_T | _U]: ...
+@overload
+def difference(
+    iterable: Iterable[_T], func: Callable[[_T, _T], _U] = ..., *, initial: _U
+) -> Iterator[_U]: ...
+
+class SequenceView(Generic[_T], Sequence[_T]):
+    def __init__(self, target: Sequence[_T]) -> None: ...
+    @overload
+    def __getitem__(self, index: int) -> _T: ...
+    @overload
+    def __getitem__(self, index: slice) -> Sequence[_T]: ...
+    def __len__(self) -> int: ...
+
+class seekable(Generic[_T], Iterator[_T]):
+    def __init__(
+        self, iterable: Iterable[_T], maxlen: int | None = ...
+    ) -> None: ...
+    def __iter__(self) -> seekable[_T]: ...
+    def __next__(self) -> _T: ...
+    def __bool__(self) -> bool: ...
+    @overload
+    def peek(self) -> _T: ...
+    @overload
+    def peek(self, default: _U) -> _T | _U: ...
+    def elements(self) -> SequenceView[_T]: ...
+    def seek(self, index: int) -> None: ...
+    def relative_seek(self, count: int) -> None: ...
+
+class run_length:
+    @staticmethod
+    def encode(iterable: Iterable[_T]) -> Iterator[tuple[_T, int]]: ...
+    @staticmethod
+    def decode(iterable: Iterable[tuple[_T, int]]) -> Iterator[_T]: ...
+
+def exactly_n(
+    iterable: Iterable[_T], n: int, predicate: Callable[[_T], object] = ...
+) -> bool: ...
+def circular_shifts(iterable: Iterable[_T]) -> list[tuple[_T, ...]]: ...
+def make_decorator(
+    wrapping_func: Callable[..., _U], result_index: int = ...
+) -> Callable[..., Callable[[Callable[..., Any]], Callable[..., _U]]]: ...
+@overload
+def map_reduce(
+    iterable: Iterable[_T],
+    keyfunc: Callable[[_T], _U],
+    valuefunc: None = ...,
+    reducefunc: None = ...,
+) -> dict[_U, list[_T]]: ...
+@overload
+def map_reduce(
+    iterable: Iterable[_T],
+    keyfunc: Callable[[_T], _U],
+    valuefunc: Callable[[_T], _V],
+    reducefunc: None = ...,
+) -> dict[_U, list[_V]]: ...
+@overload
+def map_reduce(
+    iterable: Iterable[_T],
+    keyfunc: Callable[[_T], _U],
+    valuefunc: None = ...,
+    reducefunc: Callable[[list[_T]], _W] = ...,
+) -> dict[_U, _W]: ...
+@overload
+def map_reduce(
+    iterable: Iterable[_T],
+    keyfunc: Callable[[_T], _U],
+    valuefunc: Callable[[_T], _V],
+    reducefunc: Callable[[list[_V]], _W],
+) -> dict[_U, _W]: ...
+def rlocate(
+    iterable: Iterable[_T],
+    pred: Callable[..., object] = ...,
+    window_size: int | None = ...,
+) -> Iterator[int]: ...
+def replace(
+    iterable: Iterable[_T],
+    pred: Callable[..., object],
+    substitutes: Iterable[_U],
+    count: int | None = ...,
+    window_size: int = ...,
+) -> Iterator[_T | _U]: ...
+def partitions(iterable: Iterable[_T]) -> Iterator[list[list[_T]]]: ...
+def set_partitions(
+    iterable: Iterable[_T], k: int | None = ...
+) -> Iterator[list[list[_T]]]: ...
+
+class time_limited(Generic[_T], Iterator[_T]):
+    def __init__(
+        self, limit_seconds: float, iterable: Iterable[_T]
+    ) -> None: ...
+    def __iter__(self) -> islice_extended[_T]: ...
+    def __next__(self) -> _T: ...
+
+@overload
+def only(
+    iterable: Iterable[_T], *, too_long: _Raisable | None = ...
+) -> _T | None: ...
+@overload
+def only(
+    iterable: Iterable[_T], default: _U, too_long: _Raisable | None = ...
+) -> _T | _U: ...
+def ichunked(iterable: Iterable[_T], n: int) -> Iterator[Iterator[_T]]: ...
+def distinct_combinations(
+    iterable: Iterable[_T], r: int
+) -> Iterator[tuple[_T, ...]]: ...
+def filter_except(
+    validator: Callable[[Any], object],
+    iterable: Iterable[_T],
+    *exceptions: Type[BaseException],
+) -> Iterator[_T]: ...
+def map_except(
+    function: Callable[[Any], _U],
+    iterable: Iterable[_T],
+    *exceptions: Type[BaseException],
+) -> Iterator[_U]: ...
+def map_if(
+    iterable: Iterable[Any],
+    pred: Callable[[Any], bool],
+    func: Callable[[Any], Any],
+    func_else: Callable[[Any], Any] | None = ...,
+) -> Iterator[Any]: ...
+def sample(
+    iterable: Iterable[_T],
+    k: int,
+    weights: Iterable[float] | None = ...,
+) -> list[_T]: ...
+def is_sorted(
+    iterable: Iterable[_T],
+    key: Callable[[_T], _U] | None = ...,
+    reverse: bool = False,
+    strict: bool = False,
+) -> bool: ...
+
+class AbortThread(BaseException):
+    pass
+
+class callback_iter(Generic[_T], Iterator[_T]):
+    def __init__(
+        self,
+        func: Callable[..., Any],
+        callback_kwd: str = ...,
+        wait_seconds: float = ...,
+    ) -> None: ...
+    def __enter__(self) -> callback_iter[_T]: ...
+    def __exit__(
+        self,
+        exc_type: Type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> bool | None: ...
+    def __iter__(self) -> callback_iter[_T]: ...
+    def __next__(self) -> _T: ...
+    def _reader(self) -> Iterator[_T]: ...
+    @property
+    def done(self) -> bool: ...
+    @property
+    def result(self) -> Any: ...
+
+def windowed_complete(
+    iterable: Iterable[_T], n: int
+) -> Iterator[tuple[_T, ...]]: ...
+def all_unique(
+    iterable: Iterable[_T], key: Callable[[_T], _U] | None = ...
+) -> bool: ...
+def nth_product(index: int, *args: Iterable[_T]) -> tuple[_T, ...]: ...
+def nth_combination_with_replacement(
+    iterable: Iterable[_T], r: int, index: int
+) -> tuple[_T, ...]: ...
+def nth_permutation(
+    iterable: Iterable[_T], r: int, index: int
+) -> tuple[_T, ...]: ...
+def value_chain(*args: _T | Iterable[_T]) -> Iterable[_T]: ...
+def product_index(element: Iterable[_T], *args: Iterable[_T]) -> int: ...
+def combination_index(
+    element: Iterable[_T], iterable: Iterable[_T]
+) -> int: ...
+def combination_with_replacement_index(
+    element: Iterable[_T], iterable: Iterable[_T]
+) -> int: ...
+def permutation_index(
+    element: Iterable[_T], iterable: Iterable[_T]
+) -> int: ...
+def repeat_each(iterable: Iterable[_T], n: int = ...) -> Iterator[_T]: ...
+
+class countable(Generic[_T], Iterator[_T]):
+    def __init__(self, iterable: Iterable[_T]) -> None: ...
+    def __iter__(self) -> countable[_T]: ...
+    def __next__(self) -> _T: ...
+    items_seen: int
+
+def chunked_even(iterable: Iterable[_T], n: int) -> Iterator[list[_T]]: ...
+def zip_broadcast(
+    *objects: _T | Iterable[_T],
+    scalar_types: type | tuple[type | tuple[Any, ...], ...] | None = ...,
+    strict: bool = ...,
+) -> Iterable[tuple[_T, ...]]: ...
+def unique_in_window(
+    iterable: Iterable[_T], n: int, key: Callable[[_T], _U] | None = ...
+) -> Iterator[_T]: ...
+def duplicates_everseen(
+    iterable: Iterable[_T], key: Callable[[_T], _U] | None = ...
+) -> Iterator[_T]: ...
+def duplicates_justseen(
+    iterable: Iterable[_T], key: Callable[[_T], _U] | None = ...
+) -> Iterator[_T]: ...
+def classify_unique(
+    iterable: Iterable[_T], key: Callable[[_T], _U] | None = ...
+) -> Iterator[tuple[_T, bool, bool]]: ...
+
+class _SupportsLessThan(Protocol):
+    def __lt__(self, __other: Any) -> bool: ...
+
+_SupportsLessThanT = TypeVar("_SupportsLessThanT", bound=_SupportsLessThan)
+
+@overload
+def minmax(
+    iterable_or_value: Iterable[_SupportsLessThanT], *, key: None = None
+) -> tuple[_SupportsLessThanT, _SupportsLessThanT]: ...
+@overload
+def minmax(
+    iterable_or_value: Iterable[_T], *, key: Callable[[_T], _SupportsLessThan]
+) -> tuple[_T, _T]: ...
+@overload
+def minmax(
+    iterable_or_value: Iterable[_SupportsLessThanT],
+    *,
+    key: None = None,
+    default: _U,
+) -> _U | tuple[_SupportsLessThanT, _SupportsLessThanT]: ...
+@overload
+def minmax(
+    iterable_or_value: Iterable[_T],
+    *,
+    key: Callable[[_T], _SupportsLessThan],
+    default: _U,
+) -> _U | tuple[_T, _T]: ...
+@overload
+def minmax(
+    iterable_or_value: _SupportsLessThanT,
+    __other: _SupportsLessThanT,
+    *others: _SupportsLessThanT,
+) -> tuple[_SupportsLessThanT, _SupportsLessThanT]: ...
+@overload
+def minmax(
+    iterable_or_value: _T,
+    __other: _T,
+    *others: _T,
+    key: Callable[[_T], _SupportsLessThan],
+) -> tuple[_T, _T]: ...
+def longest_common_prefix(
+    iterables: Iterable[Iterable[_T]],
+) -> Iterator[_T]: ...
+def iequals(*iterables: Iterable[Any]) -> bool: ...
+def constrained_batches(
+    iterable: Iterable[_T],
+    max_size: int,
+    max_count: int | None = ...,
+    get_len: Callable[[_T], object] = ...,
+    strict: bool = ...,
+) -> Iterator[tuple[_T]]: ...
+def gray_product(*iterables: Iterable[_T]) -> Iterator[tuple[_T, ...]]: ...
+def partial_product(*iterables: Iterable[_T]) -> Iterator[tuple[_T, ...]]: ...
+def takewhile_inclusive(
+    predicate: Callable[[_T], bool], iterable: Iterable[_T]
+) -> Iterator[_T]: ...
+def outer_product(
+    func: Callable[[_T, _U], _V],
+    xs: Iterable[_T],
+    ys: Iterable[_U],
+    *args: Any,
+    **kwargs: Any,
+) -> Iterator[tuple[_V, ...]]: ...
+def iter_suppress(
+    iterable: Iterable[_T],
+    *exceptions: Type[BaseException],
+) -> Iterator[_T]: ...
+def filter_map(
+    func: Callable[[_T], _V | None],
+    iterable: Iterable[_T],
+) -> Iterator[_V]: ...
+def powerset_of_sets(iterable: Iterable[_T]) -> Iterator[set[_T]]: ...
+def join_mappings(
+    **field_to_map: Mapping[_T, _V]
+) -> dict[_T, dict[str, _V]]: ...
+def doublestarmap(
+    func: Callable[..., _T],
+    iterable: Iterable[Mapping[str, Any]],
+) -> Iterator[_T]: ...
+def dft(xarr: Sequence[complex]) -> Iterator[complex]: ...
+def idft(Xarr: Sequence[complex]) -> Iterator[complex]: ...
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/py.typed b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/recipes.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/recipes.py
new file mode 100644
index 0000000000000000000000000000000000000000..b32fa955339a07c2a789a15fd2287e70fc94d867
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/recipes.py
@@ -0,0 +1,1046 @@
+"""Imported from the recipes section of the itertools documentation.
+
+All functions taken from the recipes section of the itertools library docs
+[1]_.
+Some backward-compatible usability improvements have been made.
+
+.. [1] http://docs.python.org/library/itertools.html#recipes
+
+"""
+
+import math
+import operator
+
+from collections import deque
+from collections.abc import Sized
+from functools import partial, reduce
+from itertools import (
+    chain,
+    combinations,
+    compress,
+    count,
+    cycle,
+    groupby,
+    islice,
+    product,
+    repeat,
+    starmap,
+    tee,
+    zip_longest,
+)
+from random import randrange, sample, choice
+from sys import hexversion
+
+__all__ = [
+    'all_equal',
+    'batched',
+    'before_and_after',
+    'consume',
+    'convolve',
+    'dotproduct',
+    'first_true',
+    'factor',
+    'flatten',
+    'grouper',
+    'iter_except',
+    'iter_index',
+    'matmul',
+    'ncycles',
+    'nth',
+    'nth_combination',
+    'padnone',
+    'pad_none',
+    'pairwise',
+    'partition',
+    'polynomial_eval',
+    'polynomial_from_roots',
+    'polynomial_derivative',
+    'powerset',
+    'prepend',
+    'quantify',
+    'reshape',
+    'random_combination_with_replacement',
+    'random_combination',
+    'random_permutation',
+    'random_product',
+    'repeatfunc',
+    'roundrobin',
+    'sieve',
+    'sliding_window',
+    'subslices',
+    'sum_of_squares',
+    'tabulate',
+    'tail',
+    'take',
+    'totient',
+    'transpose',
+    'triplewise',
+    'unique',
+    'unique_everseen',
+    'unique_justseen',
+]
+
+_marker = object()
+
+
+# zip with strict is available for Python 3.10+
+try:
+    zip(strict=True)
+except TypeError:
+    _zip_strict = zip
+else:
+    _zip_strict = partial(zip, strict=True)
+
+# math.sumprod is available for Python 3.12+
+_sumprod = getattr(math, 'sumprod', lambda x, y: dotproduct(x, y))
+
+
+def take(n, iterable):
+    """Return first *n* items of the iterable as a list.
+
+        >>> take(3, range(10))
+        [0, 1, 2]
+
+    If there are fewer than *n* items in the iterable, all of them are
+    returned.
+
+        >>> take(10, range(3))
+        [0, 1, 2]
+
+    """
+    return list(islice(iterable, n))
+
+
+def tabulate(function, start=0):
+    """Return an iterator over the results of ``func(start)``,
+    ``func(start + 1)``, ``func(start + 2)``...
+
+    *func* should be a function that accepts one integer argument.
+
+    If *start* is not specified it defaults to 0. It will be incremented each
+    time the iterator is advanced.
+
+        >>> square = lambda x: x ** 2
+        >>> iterator = tabulate(square, -3)
+        >>> take(4, iterator)
+        [9, 4, 1, 0]
+
+    """
+    return map(function, count(start))
+
+
+def tail(n, iterable):
+    """Return an iterator over the last *n* items of *iterable*.
+
+    >>> t = tail(3, 'ABCDEFG')
+    >>> list(t)
+    ['E', 'F', 'G']
+
+    """
+    # If the given iterable has a length, then we can use islice to get its
+    # final elements. Note that if the iterable is not actually Iterable,
+    # either islice or deque will throw a TypeError. This is why we don't
+    # check if it is Iterable.
+    if isinstance(iterable, Sized):
+        yield from islice(iterable, max(0, len(iterable) - n), None)
+    else:
+        yield from iter(deque(iterable, maxlen=n))
+
+
+def consume(iterator, n=None):
+    """Advance *iterable* by *n* steps. If *n* is ``None``, consume it
+    entirely.
+
+    Efficiently exhausts an iterator without returning values. Defaults to
+    consuming the whole iterator, but an optional second argument may be
+    provided to limit consumption.
+
+        >>> i = (x for x in range(10))
+        >>> next(i)
+        0
+        >>> consume(i, 3)
+        >>> next(i)
+        4
+        >>> consume(i)
+        >>> next(i)
+        Traceback (most recent call last):
+          File "<stdin>", line 1, in <module>
+        StopIteration
+
+    If the iterator has fewer items remaining than the provided limit, the
+    whole iterator will be consumed.
+
+        >>> i = (x for x in range(3))
+        >>> consume(i, 5)
+        >>> next(i)
+        Traceback (most recent call last):
+          File "<stdin>", line 1, in <module>
+        StopIteration
+
+    """
+    # Use functions that consume iterators at C speed.
+    if n is None:
+        # feed the entire iterator into a zero-length deque
+        deque(iterator, maxlen=0)
+    else:
+        # advance to the empty slice starting at position n
+        next(islice(iterator, n, n), None)
+
+
+def nth(iterable, n, default=None):
+    """Returns the nth item or a default value.
+
+    >>> l = range(10)
+    >>> nth(l, 3)
+    3
+    >>> nth(l, 20, "zebra")
+    'zebra'
+
+    """
+    return next(islice(iterable, n, None), default)
+
+
+def all_equal(iterable, key=None):
+    """
+    Returns ``True`` if all the elements are equal to each other.
+
+        >>> all_equal('aaaa')
+        True
+        >>> all_equal('aaab')
+        False
+
+    A function that accepts a single argument and returns a transformed version
+    of each input item can be specified with *key*:
+
+        >>> all_equal('AaaA', key=str.casefold)
+        True
+        >>> all_equal([1, 2, 3], key=lambda x: x < 10)
+        True
+
+    """
+    return len(list(islice(groupby(iterable, key), 2))) <= 1
+
+
+def quantify(iterable, pred=bool):
+    """Return the how many times the predicate is true.
+
+    >>> quantify([True, False, True])
+    2
+
+    """
+    return sum(map(pred, iterable))
+
+
+def pad_none(iterable):
+    """Returns the sequence of elements and then returns ``None`` indefinitely.
+
+        >>> take(5, pad_none(range(3)))
+        [0, 1, 2, None, None]
+
+    Useful for emulating the behavior of the built-in :func:`map` function.
+
+    See also :func:`padded`.
+
+    """
+    return chain(iterable, repeat(None))
+
+
+padnone = pad_none
+
+
+def ncycles(iterable, n):
+    """Returns the sequence elements *n* times
+
+    >>> list(ncycles(["a", "b"], 3))
+    ['a', 'b', 'a', 'b', 'a', 'b']
+
+    """
+    return chain.from_iterable(repeat(tuple(iterable), n))
+
+
+def dotproduct(vec1, vec2):
+    """Returns the dot product of the two iterables.
+
+    >>> dotproduct([10, 10], [20, 20])
+    400
+
+    """
+    return sum(map(operator.mul, vec1, vec2))
+
+
+def flatten(listOfLists):
+    """Return an iterator flattening one level of nesting in a list of lists.
+
+        >>> list(flatten([[0, 1], [2, 3]]))
+        [0, 1, 2, 3]
+
+    See also :func:`collapse`, which can flatten multiple levels of nesting.
+
+    """
+    return chain.from_iterable(listOfLists)
+
+
+def repeatfunc(func, times=None, *args):
+    """Call *func* with *args* repeatedly, returning an iterable over the
+    results.
+
+    If *times* is specified, the iterable will terminate after that many
+    repetitions:
+
+        >>> from operator import add
+        >>> times = 4
+        >>> args = 3, 5
+        >>> list(repeatfunc(add, times, *args))
+        [8, 8, 8, 8]
+
+    If *times* is ``None`` the iterable will not terminate:
+
+        >>> from random import randrange
+        >>> times = None
+        >>> args = 1, 11
+        >>> take(6, repeatfunc(randrange, times, *args))  # doctest:+SKIP
+        [2, 4, 8, 1, 8, 4]
+
+    """
+    if times is None:
+        return starmap(func, repeat(args))
+    return starmap(func, repeat(args, times))
+
+
+def _pairwise(iterable):
+    """Returns an iterator of paired items, overlapping, from the original
+
+    >>> take(4, pairwise(count()))
+    [(0, 1), (1, 2), (2, 3), (3, 4)]
+
+    On Python 3.10 and above, this is an alias for :func:`itertools.pairwise`.
+
+    """
+    a, b = tee(iterable)
+    next(b, None)
+    return zip(a, b)
+
+
+try:
+    from itertools import pairwise as itertools_pairwise
+except ImportError:
+    pairwise = _pairwise
+else:
+
+    def pairwise(iterable):
+        return itertools_pairwise(iterable)
+
+    pairwise.__doc__ = _pairwise.__doc__
+
+
+class UnequalIterablesError(ValueError):
+    def __init__(self, details=None):
+        msg = 'Iterables have different lengths'
+        if details is not None:
+            msg += (': index 0 has length {}; index {} has length {}').format(
+                *details
+            )
+
+        super().__init__(msg)
+
+
+def _zip_equal_generator(iterables):
+    for combo in zip_longest(*iterables, fillvalue=_marker):
+        for val in combo:
+            if val is _marker:
+                raise UnequalIterablesError()
+        yield combo
+
+
+def _zip_equal(*iterables):
+    # Check whether the iterables are all the same size.
+    try:
+        first_size = len(iterables[0])
+        for i, it in enumerate(iterables[1:], 1):
+            size = len(it)
+            if size != first_size:
+                raise UnequalIterablesError(details=(first_size, i, size))
+        # All sizes are equal, we can use the built-in zip.
+        return zip(*iterables)
+    # If any one of the iterables didn't have a length, start reading
+    # them until one runs out.
+    except TypeError:
+        return _zip_equal_generator(iterables)
+
+
+def grouper(iterable, n, incomplete='fill', fillvalue=None):
+    """Group elements from *iterable* into fixed-length groups of length *n*.
+
+    >>> list(grouper('ABCDEF', 3))
+    [('A', 'B', 'C'), ('D', 'E', 'F')]
+
+    The keyword arguments *incomplete* and *fillvalue* control what happens for
+    iterables whose length is not a multiple of *n*.
+
+    When *incomplete* is `'fill'`, the last group will contain instances of
+    *fillvalue*.
+
+    >>> list(grouper('ABCDEFG', 3, incomplete='fill', fillvalue='x'))
+    [('A', 'B', 'C'), ('D', 'E', 'F'), ('G', 'x', 'x')]
+
+    When *incomplete* is `'ignore'`, the last group will not be emitted.
+
+    >>> list(grouper('ABCDEFG', 3, incomplete='ignore', fillvalue='x'))
+    [('A', 'B', 'C'), ('D', 'E', 'F')]
+
+    When *incomplete* is `'strict'`, a subclass of `ValueError` will be raised.
+
+    >>> it = grouper('ABCDEFG', 3, incomplete='strict')
+    >>> list(it)  # doctest: +IGNORE_EXCEPTION_DETAIL
+    Traceback (most recent call last):
+    ...
+    UnequalIterablesError
+
+    """
+    args = [iter(iterable)] * n
+    if incomplete == 'fill':
+        return zip_longest(*args, fillvalue=fillvalue)
+    if incomplete == 'strict':
+        return _zip_equal(*args)
+    if incomplete == 'ignore':
+        return zip(*args)
+    else:
+        raise ValueError('Expected fill, strict, or ignore')
+
+
+def roundrobin(*iterables):
+    """Yields an item from each iterable, alternating between them.
+
+        >>> list(roundrobin('ABC', 'D', 'EF'))
+        ['A', 'D', 'E', 'B', 'F', 'C']
+
+    This function produces the same output as :func:`interleave_longest`, but
+    may perform better for some inputs (in particular when the number of
+    iterables is small).
+
+    """
+    # Algorithm credited to George Sakkis
+    iterators = map(iter, iterables)
+    for num_active in range(len(iterables), 0, -1):
+        iterators = cycle(islice(iterators, num_active))
+        yield from map(next, iterators)
+
+
+def partition(pred, iterable):
+    """
+    Returns a 2-tuple of iterables derived from the input iterable.
+    The first yields the items that have ``pred(item) == False``.
+    The second yields the items that have ``pred(item) == True``.
+
+        >>> is_odd = lambda x: x % 2 != 0
+        >>> iterable = range(10)
+        >>> even_items, odd_items = partition(is_odd, iterable)
+        >>> list(even_items), list(odd_items)
+        ([0, 2, 4, 6, 8], [1, 3, 5, 7, 9])
+
+    If *pred* is None, :func:`bool` is used.
+
+        >>> iterable = [0, 1, False, True, '', ' ']
+        >>> false_items, true_items = partition(None, iterable)
+        >>> list(false_items), list(true_items)
+        ([0, False, ''], [1, True, ' '])
+
+    """
+    if pred is None:
+        pred = bool
+
+    t1, t2, p = tee(iterable, 3)
+    p1, p2 = tee(map(pred, p))
+    return (compress(t1, map(operator.not_, p1)), compress(t2, p2))
+
+
+def powerset(iterable):
+    """Yields all possible subsets of the iterable.
+
+        >>> list(powerset([1, 2, 3]))
+        [(), (1,), (2,), (3,), (1, 2), (1, 3), (2, 3), (1, 2, 3)]
+
+    :func:`powerset` will operate on iterables that aren't :class:`set`
+    instances, so repeated elements in the input will produce repeated elements
+    in the output.
+
+        >>> seq = [1, 1, 0]
+        >>> list(powerset(seq))
+        [(), (1,), (1,), (0,), (1, 1), (1, 0), (1, 0), (1, 1, 0)]
+
+    For a variant that efficiently yields actual :class:`set` instances, see
+    :func:`powerset_of_sets`.
+    """
+    s = list(iterable)
+    return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1))
+
+
+def unique_everseen(iterable, key=None):
+    """
+    Yield unique elements, preserving order.
+
+        >>> list(unique_everseen('AAAABBBCCDAABBB'))
+        ['A', 'B', 'C', 'D']
+        >>> list(unique_everseen('ABBCcAD', str.lower))
+        ['A', 'B', 'C', 'D']
+
+    Sequences with a mix of hashable and unhashable items can be used.
+    The function will be slower (i.e., `O(n^2)`) for unhashable items.
+
+    Remember that ``list`` objects are unhashable - you can use the *key*
+    parameter to transform the list to a tuple (which is hashable) to
+    avoid a slowdown.
+
+        >>> iterable = ([1, 2], [2, 3], [1, 2])
+        >>> list(unique_everseen(iterable))  # Slow
+        [[1, 2], [2, 3]]
+        >>> list(unique_everseen(iterable, key=tuple))  # Faster
+        [[1, 2], [2, 3]]
+
+    Similarly, you may want to convert unhashable ``set`` objects with
+    ``key=frozenset``. For ``dict`` objects,
+    ``key=lambda x: frozenset(x.items())`` can be used.
+
+    """
+    seenset = set()
+    seenset_add = seenset.add
+    seenlist = []
+    seenlist_add = seenlist.append
+    use_key = key is not None
+
+    for element in iterable:
+        k = key(element) if use_key else element
+        try:
+            if k not in seenset:
+                seenset_add(k)
+                yield element
+        except TypeError:
+            if k not in seenlist:
+                seenlist_add(k)
+                yield element
+
+
+def unique_justseen(iterable, key=None):
+    """Yields elements in order, ignoring serial duplicates
+
+    >>> list(unique_justseen('AAAABBBCCDAABBB'))
+    ['A', 'B', 'C', 'D', 'A', 'B']
+    >>> list(unique_justseen('ABBCcAD', str.lower))
+    ['A', 'B', 'C', 'A', 'D']
+
+    """
+    if key is None:
+        return map(operator.itemgetter(0), groupby(iterable))
+
+    return map(next, map(operator.itemgetter(1), groupby(iterable, key)))
+
+
+def unique(iterable, key=None, reverse=False):
+    """Yields unique elements in sorted order.
+
+    >>> list(unique([[1, 2], [3, 4], [1, 2]]))
+    [[1, 2], [3, 4]]
+
+    *key* and *reverse* are passed to :func:`sorted`.
+
+    >>> list(unique('ABBcCAD', str.casefold))
+    ['A', 'B', 'c', 'D']
+    >>> list(unique('ABBcCAD', str.casefold, reverse=True))
+    ['D', 'c', 'B', 'A']
+
+    The elements in *iterable* need not be hashable, but they must be
+    comparable for sorting to work.
+    """
+    return unique_justseen(sorted(iterable, key=key, reverse=reverse), key=key)
+
+
+def iter_except(func, exception, first=None):
+    """Yields results from a function repeatedly until an exception is raised.
+
+    Converts a call-until-exception interface to an iterator interface.
+    Like ``iter(func, sentinel)``, but uses an exception instead of a sentinel
+    to end the loop.
+
+        >>> l = [0, 1, 2]
+        >>> list(iter_except(l.pop, IndexError))
+        [2, 1, 0]
+
+    Multiple exceptions can be specified as a stopping condition:
+
+        >>> l = [1, 2, 3, '...', 4, 5, 6]
+        >>> list(iter_except(lambda: 1 + l.pop(), (IndexError, TypeError)))
+        [7, 6, 5]
+        >>> list(iter_except(lambda: 1 + l.pop(), (IndexError, TypeError)))
+        [4, 3, 2]
+        >>> list(iter_except(lambda: 1 + l.pop(), (IndexError, TypeError)))
+        []
+
+    """
+    try:
+        if first is not None:
+            yield first()
+        while 1:
+            yield func()
+    except exception:
+        pass
+
+
+def first_true(iterable, default=None, pred=None):
+    """
+    Returns the first true value in the iterable.
+
+    If no true value is found, returns *default*
+
+    If *pred* is not None, returns the first item for which
+    ``pred(item) == True`` .
+
+        >>> first_true(range(10))
+        1
+        >>> first_true(range(10), pred=lambda x: x > 5)
+        6
+        >>> first_true(range(10), default='missing', pred=lambda x: x > 9)
+        'missing'
+
+    """
+    return next(filter(pred, iterable), default)
+
+
+def random_product(*args, repeat=1):
+    """Draw an item at random from each of the input iterables.
+
+        >>> random_product('abc', range(4), 'XYZ')  # doctest:+SKIP
+        ('c', 3, 'Z')
+
+    If *repeat* is provided as a keyword argument, that many items will be
+    drawn from each iterable.
+
+        >>> random_product('abcd', range(4), repeat=2)  # doctest:+SKIP
+        ('a', 2, 'd', 3)
+
+    This equivalent to taking a random selection from
+    ``itertools.product(*args, **kwarg)``.
+
+    """
+    pools = [tuple(pool) for pool in args] * repeat
+    return tuple(choice(pool) for pool in pools)
+
+
+def random_permutation(iterable, r=None):
+    """Return a random *r* length permutation of the elements in *iterable*.
+
+    If *r* is not specified or is ``None``, then *r* defaults to the length of
+    *iterable*.
+
+        >>> random_permutation(range(5))  # doctest:+SKIP
+        (3, 4, 0, 1, 2)
+
+    This equivalent to taking a random selection from
+    ``itertools.permutations(iterable, r)``.
+
+    """
+    pool = tuple(iterable)
+    r = len(pool) if r is None else r
+    return tuple(sample(pool, r))
+
+
+def random_combination(iterable, r):
+    """Return a random *r* length subsequence of the elements in *iterable*.
+
+        >>> random_combination(range(5), 3)  # doctest:+SKIP
+        (2, 3, 4)
+
+    This equivalent to taking a random selection from
+    ``itertools.combinations(iterable, r)``.
+
+    """
+    pool = tuple(iterable)
+    n = len(pool)
+    indices = sorted(sample(range(n), r))
+    return tuple(pool[i] for i in indices)
+
+
+def random_combination_with_replacement(iterable, r):
+    """Return a random *r* length subsequence of elements in *iterable*,
+    allowing individual elements to be repeated.
+
+        >>> random_combination_with_replacement(range(3), 5) # doctest:+SKIP
+        (0, 0, 1, 2, 2)
+
+    This equivalent to taking a random selection from
+    ``itertools.combinations_with_replacement(iterable, r)``.
+
+    """
+    pool = tuple(iterable)
+    n = len(pool)
+    indices = sorted(randrange(n) for i in range(r))
+    return tuple(pool[i] for i in indices)
+
+
+def nth_combination(iterable, r, index):
+    """Equivalent to ``list(combinations(iterable, r))[index]``.
+
+    The subsequences of *iterable* that are of length *r* can be ordered
+    lexicographically. :func:`nth_combination` computes the subsequence at
+    sort position *index* directly, without computing the previous
+    subsequences.
+
+        >>> nth_combination(range(5), 3, 5)
+        (0, 3, 4)
+
+    ``ValueError`` will be raised If *r* is negative or greater than the length
+    of *iterable*.
+    ``IndexError`` will be raised if the given *index* is invalid.
+    """
+    pool = tuple(iterable)
+    n = len(pool)
+    if (r < 0) or (r > n):
+        raise ValueError
+
+    c = 1
+    k = min(r, n - r)
+    for i in range(1, k + 1):
+        c = c * (n - k + i) // i
+
+    if index < 0:
+        index += c
+
+    if (index < 0) or (index >= c):
+        raise IndexError
+
+    result = []
+    while r:
+        c, n, r = c * r // n, n - 1, r - 1
+        while index >= c:
+            index -= c
+            c, n = c * (n - r) // n, n - 1
+        result.append(pool[-1 - n])
+
+    return tuple(result)
+
+
+def prepend(value, iterator):
+    """Yield *value*, followed by the elements in *iterator*.
+
+        >>> value = '0'
+        >>> iterator = ['1', '2', '3']
+        >>> list(prepend(value, iterator))
+        ['0', '1', '2', '3']
+
+    To prepend multiple values, see :func:`itertools.chain`
+    or :func:`value_chain`.
+
+    """
+    return chain([value], iterator)
+
+
+def convolve(signal, kernel):
+    """Convolve the iterable *signal* with the iterable *kernel*.
+
+        >>> signal = (1, 2, 3, 4, 5)
+        >>> kernel = [3, 2, 1]
+        >>> list(convolve(signal, kernel))
+        [3, 8, 14, 20, 26, 14, 5]
+
+    Note: the input arguments are not interchangeable, as the *kernel*
+    is immediately consumed and stored.
+
+    """
+    # This implementation intentionally doesn't match the one in the itertools
+    # documentation.
+    kernel = tuple(kernel)[::-1]
+    n = len(kernel)
+    window = deque([0], maxlen=n) * n
+    for x in chain(signal, repeat(0, n - 1)):
+        window.append(x)
+        yield _sumprod(kernel, window)
+
+
+def before_and_after(predicate, it):
+    """A variant of :func:`takewhile` that allows complete access to the
+    remainder of the iterator.
+
+         >>> it = iter('ABCdEfGhI')
+         >>> all_upper, remainder = before_and_after(str.isupper, it)
+         >>> ''.join(all_upper)
+         'ABC'
+         >>> ''.join(remainder) # takewhile() would lose the 'd'
+         'dEfGhI'
+
+    Note that the first iterator must be fully consumed before the second
+    iterator can generate valid results.
+    """
+    it = iter(it)
+    transition = []
+
+    def true_iterator():
+        for elem in it:
+            if predicate(elem):
+                yield elem
+            else:
+                transition.append(elem)
+                return
+
+    # Note: this is different from itertools recipes to allow nesting
+    # before_and_after remainders into before_and_after again. See tests
+    # for an example.
+    remainder_iterator = chain(transition, it)
+
+    return true_iterator(), remainder_iterator
+
+
+def triplewise(iterable):
+    """Return overlapping triplets from *iterable*.
+
+    >>> list(triplewise('ABCDE'))
+    [('A', 'B', 'C'), ('B', 'C', 'D'), ('C', 'D', 'E')]
+
+    """
+    for (a, _), (b, c) in pairwise(pairwise(iterable)):
+        yield a, b, c
+
+
+def sliding_window(iterable, n):
+    """Return a sliding window of width *n* over *iterable*.
+
+        >>> list(sliding_window(range(6), 4))
+        [(0, 1, 2, 3), (1, 2, 3, 4), (2, 3, 4, 5)]
+
+    If *iterable* has fewer than *n* items, then nothing is yielded:
+
+        >>> list(sliding_window(range(3), 4))
+        []
+
+    For a variant with more features, see :func:`windowed`.
+    """
+    it = iter(iterable)
+    window = deque(islice(it, n - 1), maxlen=n)
+    for x in it:
+        window.append(x)
+        yield tuple(window)
+
+
+def subslices(iterable):
+    """Return all contiguous non-empty subslices of *iterable*.
+
+        >>> list(subslices('ABC'))
+        [['A'], ['A', 'B'], ['A', 'B', 'C'], ['B'], ['B', 'C'], ['C']]
+
+    This is similar to :func:`substrings`, but emits items in a different
+    order.
+    """
+    seq = list(iterable)
+    slices = starmap(slice, combinations(range(len(seq) + 1), 2))
+    return map(operator.getitem, repeat(seq), slices)
+
+
+def polynomial_from_roots(roots):
+    """Compute a polynomial's coefficients from its roots.
+
+    >>> roots = [5, -4, 3]  # (x - 5) * (x + 4) * (x - 3)
+    >>> polynomial_from_roots(roots)  # x^3 - 4 * x^2 - 17 * x + 60
+    [1, -4, -17, 60]
+    """
+    factors = zip(repeat(1), map(operator.neg, roots))
+    return list(reduce(convolve, factors, [1]))
+
+
+def iter_index(iterable, value, start=0, stop=None):
+    """Yield the index of each place in *iterable* that *value* occurs,
+    beginning with index *start* and ending before index *stop*.
+
+
+    >>> list(iter_index('AABCADEAF', 'A'))
+    [0, 1, 4, 7]
+    >>> list(iter_index('AABCADEAF', 'A', 1))  # start index is inclusive
+    [1, 4, 7]
+    >>> list(iter_index('AABCADEAF', 'A', 1, 7))  # stop index is not inclusive
+    [1, 4]
+
+    The behavior for non-scalar *values* matches the built-in Python types.
+
+    >>> list(iter_index('ABCDABCD', 'AB'))
+    [0, 4]
+    >>> list(iter_index([0, 1, 2, 3, 0, 1, 2, 3], [0, 1]))
+    []
+    >>> list(iter_index([[0, 1], [2, 3], [0, 1], [2, 3]], [0, 1]))
+    [0, 2]
+
+    See :func:`locate` for a more general means of finding the indexes
+    associated with particular values.
+
+    """
+    seq_index = getattr(iterable, 'index', None)
+    if seq_index is None:
+        # Slow path for general iterables
+        it = islice(iterable, start, stop)
+        for i, element in enumerate(it, start):
+            if element is value or element == value:
+                yield i
+    else:
+        # Fast path for sequences
+        stop = len(iterable) if stop is None else stop
+        i = start - 1
+        try:
+            while True:
+                yield (i := seq_index(value, i + 1, stop))
+        except ValueError:
+            pass
+
+
+def sieve(n):
+    """Yield the primes less than n.
+
+    >>> list(sieve(30))
+    [2, 3, 5, 7, 11, 13, 17, 19, 23, 29]
+    """
+    if n > 2:
+        yield 2
+    start = 3
+    data = bytearray((0, 1)) * (n // 2)
+    limit = math.isqrt(n) + 1
+    for p in iter_index(data, 1, start, limit):
+        yield from iter_index(data, 1, start, p * p)
+        data[p * p : n : p + p] = bytes(len(range(p * p, n, p + p)))
+        start = p * p
+    yield from iter_index(data, 1, start)
+
+
+def _batched(iterable, n, *, strict=False):
+    """Batch data into tuples of length *n*. If the number of items in
+    *iterable* is not divisible by *n*:
+    * The last batch will be shorter if *strict* is ``False``.
+    * :exc:`ValueError` will be raised if *strict* is ``True``.
+
+    >>> list(batched('ABCDEFG', 3))
+    [('A', 'B', 'C'), ('D', 'E', 'F'), ('G',)]
+
+    On Python 3.13 and above, this is an alias for :func:`itertools.batched`.
+    """
+    if n < 1:
+        raise ValueError('n must be at least one')
+    it = iter(iterable)
+    while batch := tuple(islice(it, n)):
+        if strict and len(batch) != n:
+            raise ValueError('batched(): incomplete batch')
+        yield batch
+
+
+if hexversion >= 0x30D00A2:
+    from itertools import batched as itertools_batched
+
+    def batched(iterable, n, *, strict=False):
+        return itertools_batched(iterable, n, strict=strict)
+
+else:
+    batched = _batched
+
+    batched.__doc__ = _batched.__doc__
+
+
+def transpose(it):
+    """Swap the rows and columns of the input matrix.
+
+    >>> list(transpose([(1, 2, 3), (11, 22, 33)]))
+    [(1, 11), (2, 22), (3, 33)]
+
+    The caller should ensure that the dimensions of the input are compatible.
+    If the input is empty, no output will be produced.
+    """
+    return _zip_strict(*it)
+
+
+def reshape(matrix, cols):
+    """Reshape the 2-D input *matrix* to have a column count given by *cols*.
+
+    >>> matrix = [(0, 1), (2, 3), (4, 5)]
+    >>> cols = 3
+    >>> list(reshape(matrix, cols))
+    [(0, 1, 2), (3, 4, 5)]
+    """
+    return batched(chain.from_iterable(matrix), cols)
+
+
+def matmul(m1, m2):
+    """Multiply two matrices.
+
+    >>> list(matmul([(7, 5), (3, 5)], [(2, 5), (7, 9)]))
+    [(49, 80), (41, 60)]
+
+    The caller should ensure that the dimensions of the input matrices are
+    compatible with each other.
+    """
+    n = len(m2[0])
+    return batched(starmap(_sumprod, product(m1, transpose(m2))), n)
+
+
+def factor(n):
+    """Yield the prime factors of n.
+
+    >>> list(factor(360))
+    [2, 2, 2, 3, 3, 5]
+    """
+    for prime in sieve(math.isqrt(n) + 1):
+        while not n % prime:
+            yield prime
+            n //= prime
+            if n == 1:
+                return
+    if n > 1:
+        yield n
+
+
+def polynomial_eval(coefficients, x):
+    """Evaluate a polynomial at a specific value.
+
+    Example: evaluating x^3 - 4 * x^2 - 17 * x + 60 at x = 2.5:
+
+    >>> coefficients = [1, -4, -17, 60]
+    >>> x = 2.5
+    >>> polynomial_eval(coefficients, x)
+    8.125
+    """
+    n = len(coefficients)
+    if n == 0:
+        return x * 0  # coerce zero to the type of x
+    powers = map(pow, repeat(x), reversed(range(n)))
+    return _sumprod(coefficients, powers)
+
+
+def sum_of_squares(it):
+    """Return the sum of the squares of the input values.
+
+    >>> sum_of_squares([10, 20, 30])
+    1400
+    """
+    return _sumprod(*tee(it))
+
+
+def polynomial_derivative(coefficients):
+    """Compute the first derivative of a polynomial.
+
+    Example: evaluating the derivative of x^3 - 4 * x^2 - 17 * x + 60
+
+    >>> coefficients = [1, -4, -17, 60]
+    >>> derivative_coefficients = polynomial_derivative(coefficients)
+    >>> derivative_coefficients
+    [3, -8, -17]
+    """
+    n = len(coefficients)
+    powers = reversed(range(1, n))
+    return list(map(operator.mul, coefficients, powers))
+
+
+def totient(n):
+    """Return the count of natural numbers up to *n* that are coprime with *n*.
+
+    >>> totient(9)
+    6
+    >>> totient(12)
+    4
+    """
+    # The itertools docs use unique_justseen instead of set; see
+    # https://github.com/more-itertools/more-itertools/issues/823
+    for p in set(factor(n)):
+        n = n // p * (p - 1)
+
+    return n
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/recipes.pyi b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/recipes.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..739acec05fb3fdd5778aa27db82face3442c3c3f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/more_itertools/recipes.pyi
@@ -0,0 +1,136 @@
+"""Stubs for more_itertools.recipes"""
+
+from __future__ import annotations
+
+from typing import (
+    Any,
+    Callable,
+    Iterable,
+    Iterator,
+    overload,
+    Sequence,
+    Type,
+    TypeVar,
+)
+
+# Type and type variable definitions
+_T = TypeVar('_T')
+_T1 = TypeVar('_T1')
+_T2 = TypeVar('_T2')
+_U = TypeVar('_U')
+
+def take(n: int, iterable: Iterable[_T]) -> list[_T]: ...
+def tabulate(
+    function: Callable[[int], _T], start: int = ...
+) -> Iterator[_T]: ...
+def tail(n: int, iterable: Iterable[_T]) -> Iterator[_T]: ...
+def consume(iterator: Iterable[_T], n: int | None = ...) -> None: ...
+@overload
+def nth(iterable: Iterable[_T], n: int) -> _T | None: ...
+@overload
+def nth(iterable: Iterable[_T], n: int, default: _U) -> _T | _U: ...
+def all_equal(
+    iterable: Iterable[_T], key: Callable[[_T], _U] | None = ...
+) -> bool: ...
+def quantify(
+    iterable: Iterable[_T], pred: Callable[[_T], bool] = ...
+) -> int: ...
+def pad_none(iterable: Iterable[_T]) -> Iterator[_T | None]: ...
+def padnone(iterable: Iterable[_T]) -> Iterator[_T | None]: ...
+def ncycles(iterable: Iterable[_T], n: int) -> Iterator[_T]: ...
+def dotproduct(vec1: Iterable[_T1], vec2: Iterable[_T2]) -> Any: ...
+def flatten(listOfLists: Iterable[Iterable[_T]]) -> Iterator[_T]: ...
+def repeatfunc(
+    func: Callable[..., _U], times: int | None = ..., *args: Any
+) -> Iterator[_U]: ...
+def pairwise(iterable: Iterable[_T]) -> Iterator[tuple[_T, _T]]: ...
+def grouper(
+    iterable: Iterable[_T],
+    n: int,
+    incomplete: str = ...,
+    fillvalue: _U = ...,
+) -> Iterator[tuple[_T | _U, ...]]: ...
+def roundrobin(*iterables: Iterable[_T]) -> Iterator[_T]: ...
+def partition(
+    pred: Callable[[_T], object] | None, iterable: Iterable[_T]
+) -> tuple[Iterator[_T], Iterator[_T]]: ...
+def powerset(iterable: Iterable[_T]) -> Iterator[tuple[_T, ...]]: ...
+def unique_everseen(
+    iterable: Iterable[_T], key: Callable[[_T], _U] | None = ...
+) -> Iterator[_T]: ...
+def unique_justseen(
+    iterable: Iterable[_T], key: Callable[[_T], object] | None = ...
+) -> Iterator[_T]: ...
+def unique(
+    iterable: Iterable[_T],
+    key: Callable[[_T], object] | None = ...,
+    reverse: bool = False,
+) -> Iterator[_T]: ...
+@overload
+def iter_except(
+    func: Callable[[], _T],
+    exception: Type[BaseException] | tuple[Type[BaseException], ...],
+    first: None = ...,
+) -> Iterator[_T]: ...
+@overload
+def iter_except(
+    func: Callable[[], _T],
+    exception: Type[BaseException] | tuple[Type[BaseException], ...],
+    first: Callable[[], _U],
+) -> Iterator[_T | _U]: ...
+@overload
+def first_true(
+    iterable: Iterable[_T], *, pred: Callable[[_T], object] | None = ...
+) -> _T | None: ...
+@overload
+def first_true(
+    iterable: Iterable[_T],
+    default: _U,
+    pred: Callable[[_T], object] | None = ...,
+) -> _T | _U: ...
+def random_product(
+    *args: Iterable[_T], repeat: int = ...
+) -> tuple[_T, ...]: ...
+def random_permutation(
+    iterable: Iterable[_T], r: int | None = ...
+) -> tuple[_T, ...]: ...
+def random_combination(iterable: Iterable[_T], r: int) -> tuple[_T, ...]: ...
+def random_combination_with_replacement(
+    iterable: Iterable[_T], r: int
+) -> tuple[_T, ...]: ...
+def nth_combination(
+    iterable: Iterable[_T], r: int, index: int
+) -> tuple[_T, ...]: ...
+def prepend(value: _T, iterator: Iterable[_U]) -> Iterator[_T | _U]: ...
+def convolve(signal: Iterable[_T], kernel: Iterable[_T]) -> Iterator[_T]: ...
+def before_and_after(
+    predicate: Callable[[_T], bool], it: Iterable[_T]
+) -> tuple[Iterator[_T], Iterator[_T]]: ...
+def triplewise(iterable: Iterable[_T]) -> Iterator[tuple[_T, _T, _T]]: ...
+def sliding_window(
+    iterable: Iterable[_T], n: int
+) -> Iterator[tuple[_T, ...]]: ...
+def subslices(iterable: Iterable[_T]) -> Iterator[list[_T]]: ...
+def polynomial_from_roots(roots: Sequence[_T]) -> list[_T]: ...
+def iter_index(
+    iterable: Iterable[_T],
+    value: Any,
+    start: int | None = ...,
+    stop: int | None = ...,
+) -> Iterator[int]: ...
+def sieve(n: int) -> Iterator[int]: ...
+def batched(
+    iterable: Iterable[_T], n: int, *, strict: bool = False
+) -> Iterator[tuple[_T]]: ...
+def transpose(
+    it: Iterable[Iterable[_T]],
+) -> Iterator[tuple[_T, ...]]: ...
+def reshape(
+    matrix: Iterable[Iterable[_T]], cols: int
+) -> Iterator[tuple[_T, ...]]: ...
+def matmul(m1: Sequence[_T], m2: Sequence[_T]) -> Iterator[tuple[_T]]: ...
+def factor(n: int) -> Iterator[int]: ...
+def polynomial_eval(coefficients: Sequence[_T], x: _U) -> _U: ...
+def sum_of_squares(it: Iterable[_T]) -> _T: ...
+def polynomial_derivative(coefficients: Sequence[_T]) -> list[_T]: ...
+def totient(n: int) -> int: ...
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..5c69047b2eb8235994febeeae1da4a82365a240a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/INSTALLER
@@ -0,0 +1 @@
+uv
\ No newline at end of file
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..6f62d44e4ef733c0e713afcd2371fed7f2b3de67
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/LICENSE
@@ -0,0 +1,3 @@
+This software is made available under the terms of *either* of the licenses
+found in LICENSE.APACHE or LICENSE.BSD. Contributions to this software is made
+under the terms of *both* these licenses.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/LICENSE.APACHE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/LICENSE.APACHE
new file mode 100644
index 0000000000000000000000000000000000000000..f433b1a53f5b830a205fd2df78e2b34974656c7b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/LICENSE.APACHE
@@ -0,0 +1,177 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
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+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
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+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
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+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
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+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
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+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
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+      on Your own behalf and on Your sole responsibility, not on behalf
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+
+   END OF TERMS AND CONDITIONS
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/LICENSE.BSD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/LICENSE.BSD
new file mode 100644
index 0000000000000000000000000000000000000000..42ce7b75c92fb01a3f6ed17eea363f756b7da582
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/LICENSE.BSD
@@ -0,0 +1,23 @@
+Copyright (c) Donald Stufft and individual contributors.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+    1. Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..1479c8694bfbd583a896dbe9bd33cdb6d7e7371e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/METADATA
@@ -0,0 +1,102 @@
+Metadata-Version: 2.3
+Name: packaging
+Version: 24.2
+Summary: Core utilities for Python packages
+Author-email: Donald Stufft <donald@stufft.io>
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Typing :: Typed
+Project-URL: Documentation, https://packaging.pypa.io/
+Project-URL: Source, https://github.com/pypa/packaging
+
+packaging
+=========
+
+.. start-intro
+
+Reusable core utilities for various Python Packaging
+`interoperability specifications <https://packaging.python.org/specifications/>`_.
+
+This library provides utilities that implement the interoperability
+specifications which have clearly one correct behaviour (eg: :pep:`440`)
+or benefit greatly from having a single shared implementation (eg: :pep:`425`).
+
+.. end-intro
+
+The ``packaging`` project includes the following: version handling, specifiers,
+markers, requirements, tags, utilities.
+
+Documentation
+-------------
+
+The `documentation`_ provides information and the API for the following:
+
+- Version Handling
+- Specifiers
+- Markers
+- Requirements
+- Tags
+- Utilities
+
+Installation
+------------
+
+Use ``pip`` to install these utilities::
+
+    pip install packaging
+
+The ``packaging`` library uses calendar-based versioning (``YY.N``).
+
+Discussion
+----------
+
+If you run into bugs, you can file them in our `issue tracker`_.
+
+You can also join ``#pypa`` on Freenode to ask questions or get involved.
+
+
+.. _`documentation`: https://packaging.pypa.io/
+.. _`issue tracker`: https://github.com/pypa/packaging/issues
+
+
+Code of Conduct
+---------------
+
+Everyone interacting in the packaging project's codebases, issue trackers, chat
+rooms, and mailing lists is expected to follow the `PSF Code of Conduct`_.
+
+.. _PSF Code of Conduct: https://github.com/pypa/.github/blob/main/CODE_OF_CONDUCT.md
+
+Contributing
+------------
+
+The ``CONTRIBUTING.rst`` file outlines how to contribute to this project as
+well as how to report a potential security issue. The documentation for this
+project also covers information about `project development`_ and `security`_.
+
+.. _`project development`: https://packaging.pypa.io/en/latest/development/
+.. _`security`: https://packaging.pypa.io/en/latest/security/
+
+Project History
+---------------
+
+Please review the ``CHANGELOG.rst`` file or the `Changelog documentation`_ for
+recent changes and project history.
+
+.. _`Changelog documentation`: https://packaging.pypa.io/en/latest/changelog/
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/RECORD
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+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/RECORD
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+packaging-24.2.dist-info/INSTALLER,sha256=5hhM4Q4mYTT9z6QB6PGpUAW81PGNFrYrdXMj4oM_6ak,2
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+packaging-24.2.dist-info/METADATA,sha256=ohH86s6k5mIfQxY2TS0LcSfADeOFa4BiCC-bxZV-pNs,3204
+packaging-24.2.dist-info/RECORD,,
+packaging-24.2.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+packaging-24.2.dist-info/WHEEL,sha256=CpUCUxeHQbRN5UGRQHYRJorO5Af-Qy_fHMctcQ8DSGI,82
+packaging/__init__.py,sha256=dk4Ta_vmdVJxYHDcfyhvQNw8V3PgSBomKNXqg-D2JDY,494
+packaging/_elffile.py,sha256=cflAQAkE25tzhYmq_aCi72QfbT_tn891tPzfpbeHOwE,3306
+packaging/_manylinux.py,sha256=vl5OCoz4kx80H5rwXKeXWjl9WNISGmr4ZgTpTP9lU9c,9612
+packaging/_musllinux.py,sha256=p9ZqNYiOItGee8KcZFeHF_YcdhVwGHdK6r-8lgixvGQ,2694
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+packaging/_structures.py,sha256=q3eVNmbWJGG_S0Dit_S3Ao8qQqz_5PYTXFAKBZe5yr4,1431
+packaging/_tokenizer.py,sha256=J6v5H7Jzvb-g81xp_2QACKwO7LxHQA6ikryMU7zXwN8,5273
+packaging/licenses/__init__.py,sha256=1x5M1nEYjcgwEbLt0dXwz2ukjr18DiCzC0sraQqJ-Ww,5715
+packaging/licenses/_spdx.py,sha256=oAm1ztPFwlsmCKe7lAAsv_OIOfS1cWDu9bNBkeu-2ns,48398
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+packaging/specifiers.py,sha256=GG1wPNMcL0fMJO68vF53wKMdwnfehDcaI-r9NpTfilA,40074
+packaging/tags.py,sha256=CFqrJzAzc2XNGexerH__T-Y5Iwq7WbsYXsiLERLWxY0,21014
+packaging/utils.py,sha256=0F3Hh9OFuRgrhTgGZUl5K22Fv1YP2tZl1z_2gO6kJiA,5050
+packaging/version.py,sha256=olfyuk_DPbflNkJ4wBWetXQ17c74x3DB501degUv7DY,16676
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/REQUESTED b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/REQUESTED
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..e3c6feefa22927866e3fd5575379ea972b432aaf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging-24.2.dist-info/WHEEL
@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: flit 3.10.1
+Root-Is-Purelib: true
+Tag: py3-none-any
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d79f73c574ffc759ef5d2145b1ec742d85c2500b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/__init__.py
@@ -0,0 +1,15 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+__title__ = "packaging"
+__summary__ = "Core utilities for Python packages"
+__uri__ = "https://github.com/pypa/packaging"
+
+__version__ = "24.2"
+
+__author__ = "Donald Stufft and individual contributors"
+__email__ = "donald@stufft.io"
+
+__license__ = "BSD-2-Clause or Apache-2.0"
+__copyright__ = f"2014 {__author__}"
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_elffile.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_elffile.py
new file mode 100644
index 0000000000000000000000000000000000000000..25f4282cc29cb03d7be881f03dee841d7dbc215a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_elffile.py
@@ -0,0 +1,110 @@
+"""
+ELF file parser.
+
+This provides a class ``ELFFile`` that parses an ELF executable in a similar
+interface to ``ZipFile``. Only the read interface is implemented.
+
+Based on: https://gist.github.com/lyssdod/f51579ae8d93c8657a5564aefc2ffbca
+ELF header: https://refspecs.linuxfoundation.org/elf/gabi4+/ch4.eheader.html
+"""
+
+from __future__ import annotations
+
+import enum
+import os
+import struct
+from typing import IO
+
+
+class ELFInvalid(ValueError):
+    pass
+
+
+class EIClass(enum.IntEnum):
+    C32 = 1
+    C64 = 2
+
+
+class EIData(enum.IntEnum):
+    Lsb = 1
+    Msb = 2
+
+
+class EMachine(enum.IntEnum):
+    I386 = 3
+    S390 = 22
+    Arm = 40
+    X8664 = 62
+    AArc64 = 183
+
+
+class ELFFile:
+    """
+    Representation of an ELF executable.
+    """
+
+    def __init__(self, f: IO[bytes]) -> None:
+        self._f = f
+
+        try:
+            ident = self._read("16B")
+        except struct.error as e:
+            raise ELFInvalid("unable to parse identification") from e
+        magic = bytes(ident[:4])
+        if magic != b"\x7fELF":
+            raise ELFInvalid(f"invalid magic: {magic!r}")
+
+        self.capacity = ident[4]  # Format for program header (bitness).
+        self.encoding = ident[5]  # Data structure encoding (endianness).
+
+        try:
+            # e_fmt: Format for program header.
+            # p_fmt: Format for section header.
+            # p_idx: Indexes to find p_type, p_offset, and p_filesz.
+            e_fmt, self._p_fmt, self._p_idx = {
+                (1, 1): ("<HHIIIIIHHH", "<IIIIIIII", (0, 1, 4)),  # 32-bit LSB.
+                (1, 2): (">HHIIIIIHHH", ">IIIIIIII", (0, 1, 4)),  # 32-bit MSB.
+                (2, 1): ("<HHIQQQIHHH", "<IIQQQQQQ", (0, 2, 5)),  # 64-bit LSB.
+                (2, 2): (">HHIQQQIHHH", ">IIQQQQQQ", (0, 2, 5)),  # 64-bit MSB.
+            }[(self.capacity, self.encoding)]
+        except KeyError as e:
+            raise ELFInvalid(
+                f"unrecognized capacity ({self.capacity}) or "
+                f"encoding ({self.encoding})"
+            ) from e
+
+        try:
+            (
+                _,
+                self.machine,  # Architecture type.
+                _,
+                _,
+                self._e_phoff,  # Offset of program header.
+                _,
+                self.flags,  # Processor-specific flags.
+                _,
+                self._e_phentsize,  # Size of section.
+                self._e_phnum,  # Number of sections.
+            ) = self._read(e_fmt)
+        except struct.error as e:
+            raise ELFInvalid("unable to parse machine and section information") from e
+
+    def _read(self, fmt: str) -> tuple[int, ...]:
+        return struct.unpack(fmt, self._f.read(struct.calcsize(fmt)))
+
+    @property
+    def interpreter(self) -> str | None:
+        """
+        The path recorded in the ``PT_INTERP`` section header.
+        """
+        for index in range(self._e_phnum):
+            self._f.seek(self._e_phoff + self._e_phentsize * index)
+            try:
+                data = self._read(self._p_fmt)
+            except struct.error:
+                continue
+            if data[self._p_idx[0]] != 3:  # Not PT_INTERP.
+                continue
+            self._f.seek(data[self._p_idx[1]])
+            return os.fsdecode(self._f.read(data[self._p_idx[2]])).strip("\0")
+        return None
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_manylinux.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_manylinux.py
new file mode 100644
index 0000000000000000000000000000000000000000..61339a6fcc1b82803136f3bf980e0c8f574b2220
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_manylinux.py
@@ -0,0 +1,263 @@
+from __future__ import annotations
+
+import collections
+import contextlib
+import functools
+import os
+import re
+import sys
+import warnings
+from typing import Generator, Iterator, NamedTuple, Sequence
+
+from ._elffile import EIClass, EIData, ELFFile, EMachine
+
+EF_ARM_ABIMASK = 0xFF000000
+EF_ARM_ABI_VER5 = 0x05000000
+EF_ARM_ABI_FLOAT_HARD = 0x00000400
+
+
+# `os.PathLike` not a generic type until Python 3.9, so sticking with `str`
+# as the type for `path` until then.
+@contextlib.contextmanager
+def _parse_elf(path: str) -> Generator[ELFFile | None, None, None]:
+    try:
+        with open(path, "rb") as f:
+            yield ELFFile(f)
+    except (OSError, TypeError, ValueError):
+        yield None
+
+
+def _is_linux_armhf(executable: str) -> bool:
+    # hard-float ABI can be detected from the ELF header of the running
+    # process
+    # https://static.docs.arm.com/ihi0044/g/aaelf32.pdf
+    with _parse_elf(executable) as f:
+        return (
+            f is not None
+            and f.capacity == EIClass.C32
+            and f.encoding == EIData.Lsb
+            and f.machine == EMachine.Arm
+            and f.flags & EF_ARM_ABIMASK == EF_ARM_ABI_VER5
+            and f.flags & EF_ARM_ABI_FLOAT_HARD == EF_ARM_ABI_FLOAT_HARD
+        )
+
+
+def _is_linux_i686(executable: str) -> bool:
+    with _parse_elf(executable) as f:
+        return (
+            f is not None
+            and f.capacity == EIClass.C32
+            and f.encoding == EIData.Lsb
+            and f.machine == EMachine.I386
+        )
+
+
+def _have_compatible_abi(executable: str, archs: Sequence[str]) -> bool:
+    if "armv7l" in archs:
+        return _is_linux_armhf(executable)
+    if "i686" in archs:
+        return _is_linux_i686(executable)
+    allowed_archs = {
+        "x86_64",
+        "aarch64",
+        "ppc64",
+        "ppc64le",
+        "s390x",
+        "loongarch64",
+        "riscv64",
+    }
+    return any(arch in allowed_archs for arch in archs)
+
+
+# If glibc ever changes its major version, we need to know what the last
+# minor version was, so we can build the complete list of all versions.
+# For now, guess what the highest minor version might be, assume it will
+# be 50 for testing. Once this actually happens, update the dictionary
+# with the actual value.
+_LAST_GLIBC_MINOR: dict[int, int] = collections.defaultdict(lambda: 50)
+
+
+class _GLibCVersion(NamedTuple):
+    major: int
+    minor: int
+
+
+def _glibc_version_string_confstr() -> str | None:
+    """
+    Primary implementation of glibc_version_string using os.confstr.
+    """
+    # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
+    # to be broken or missing. This strategy is used in the standard library
+    # platform module.
+    # https://github.com/python/cpython/blob/fcf1d003bf4f0100c/Lib/platform.py#L175-L183
+    try:
+        # Should be a string like "glibc 2.17".
+        version_string: str | None = os.confstr("CS_GNU_LIBC_VERSION")
+        assert version_string is not None
+        _, version = version_string.rsplit()
+    except (AssertionError, AttributeError, OSError, ValueError):
+        # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
+        return None
+    return version
+
+
+def _glibc_version_string_ctypes() -> str | None:
+    """
+    Fallback implementation of glibc_version_string using ctypes.
+    """
+    try:
+        import ctypes
+    except ImportError:
+        return None
+
+    # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
+    # manpage says, "If filename is NULL, then the returned handle is for the
+    # main program". This way we can let the linker do the work to figure out
+    # which libc our process is actually using.
+    #
+    # We must also handle the special case where the executable is not a
+    # dynamically linked executable. This can occur when using musl libc,
+    # for example. In this situation, dlopen() will error, leading to an
+    # OSError. Interestingly, at least in the case of musl, there is no
+    # errno set on the OSError. The single string argument used to construct
+    # OSError comes from libc itself and is therefore not portable to
+    # hard code here. In any case, failure to call dlopen() means we
+    # can proceed, so we bail on our attempt.
+    try:
+        process_namespace = ctypes.CDLL(None)
+    except OSError:
+        return None
+
+    try:
+        gnu_get_libc_version = process_namespace.gnu_get_libc_version
+    except AttributeError:
+        # Symbol doesn't exist -> therefore, we are not linked to
+        # glibc.
+        return None
+
+    # Call gnu_get_libc_version, which returns a string like "2.5"
+    gnu_get_libc_version.restype = ctypes.c_char_p
+    version_str: str = gnu_get_libc_version()
+    # py2 / py3 compatibility:
+    if not isinstance(version_str, str):
+        version_str = version_str.decode("ascii")
+
+    return version_str
+
+
+def _glibc_version_string() -> str | None:
+    """Returns glibc version string, or None if not using glibc."""
+    return _glibc_version_string_confstr() or _glibc_version_string_ctypes()
+
+
+def _parse_glibc_version(version_str: str) -> tuple[int, int]:
+    """Parse glibc version.
+
+    We use a regexp instead of str.split because we want to discard any
+    random junk that might come after the minor version -- this might happen
+    in patched/forked versions of glibc (e.g. Linaro's version of glibc
+    uses version strings like "2.20-2014.11"). See gh-3588.
+    """
+    m = re.match(r"(?P<major>[0-9]+)\.(?P<minor>[0-9]+)", version_str)
+    if not m:
+        warnings.warn(
+            f"Expected glibc version with 2 components major.minor,"
+            f" got: {version_str}",
+            RuntimeWarning,
+            stacklevel=2,
+        )
+        return -1, -1
+    return int(m.group("major")), int(m.group("minor"))
+
+
+@functools.lru_cache
+def _get_glibc_version() -> tuple[int, int]:
+    version_str = _glibc_version_string()
+    if version_str is None:
+        return (-1, -1)
+    return _parse_glibc_version(version_str)
+
+
+# From PEP 513, PEP 600
+def _is_compatible(arch: str, version: _GLibCVersion) -> bool:
+    sys_glibc = _get_glibc_version()
+    if sys_glibc < version:
+        return False
+    # Check for presence of _manylinux module.
+    try:
+        import _manylinux
+    except ImportError:
+        return True
+    if hasattr(_manylinux, "manylinux_compatible"):
+        result = _manylinux.manylinux_compatible(version[0], version[1], arch)
+        if result is not None:
+            return bool(result)
+        return True
+    if version == _GLibCVersion(2, 5):
+        if hasattr(_manylinux, "manylinux1_compatible"):
+            return bool(_manylinux.manylinux1_compatible)
+    if version == _GLibCVersion(2, 12):
+        if hasattr(_manylinux, "manylinux2010_compatible"):
+            return bool(_manylinux.manylinux2010_compatible)
+    if version == _GLibCVersion(2, 17):
+        if hasattr(_manylinux, "manylinux2014_compatible"):
+            return bool(_manylinux.manylinux2014_compatible)
+    return True
+
+
+_LEGACY_MANYLINUX_MAP = {
+    # CentOS 7 w/ glibc 2.17 (PEP 599)
+    (2, 17): "manylinux2014",
+    # CentOS 6 w/ glibc 2.12 (PEP 571)
+    (2, 12): "manylinux2010",
+    # CentOS 5 w/ glibc 2.5 (PEP 513)
+    (2, 5): "manylinux1",
+}
+
+
+def platform_tags(archs: Sequence[str]) -> Iterator[str]:
+    """Generate manylinux tags compatible to the current platform.
+
+    :param archs: Sequence of compatible architectures.
+        The first one shall be the closest to the actual architecture and be the part of
+        platform tag after the ``linux_`` prefix, e.g. ``x86_64``.
+        The ``linux_`` prefix is assumed as a prerequisite for the current platform to
+        be manylinux-compatible.
+
+    :returns: An iterator of compatible manylinux tags.
+    """
+    if not _have_compatible_abi(sys.executable, archs):
+        return
+    # Oldest glibc to be supported regardless of architecture is (2, 17).
+    too_old_glibc2 = _GLibCVersion(2, 16)
+    if set(archs) & {"x86_64", "i686"}:
+        # On x86/i686 also oldest glibc to be supported is (2, 5).
+        too_old_glibc2 = _GLibCVersion(2, 4)
+    current_glibc = _GLibCVersion(*_get_glibc_version())
+    glibc_max_list = [current_glibc]
+    # We can assume compatibility across glibc major versions.
+    # https://sourceware.org/bugzilla/show_bug.cgi?id=24636
+    #
+    # Build a list of maximum glibc versions so that we can
+    # output the canonical list of all glibc from current_glibc
+    # down to too_old_glibc2, including all intermediary versions.
+    for glibc_major in range(current_glibc.major - 1, 1, -1):
+        glibc_minor = _LAST_GLIBC_MINOR[glibc_major]
+        glibc_max_list.append(_GLibCVersion(glibc_major, glibc_minor))
+    for arch in archs:
+        for glibc_max in glibc_max_list:
+            if glibc_max.major == too_old_glibc2.major:
+                min_minor = too_old_glibc2.minor
+            else:
+                # For other glibc major versions oldest supported is (x, 0).
+                min_minor = -1
+            for glibc_minor in range(glibc_max.minor, min_minor, -1):
+                glibc_version = _GLibCVersion(glibc_max.major, glibc_minor)
+                tag = "manylinux_{}_{}".format(*glibc_version)
+                if _is_compatible(arch, glibc_version):
+                    yield f"{tag}_{arch}"
+                # Handle the legacy manylinux1, manylinux2010, manylinux2014 tags.
+                if glibc_version in _LEGACY_MANYLINUX_MAP:
+                    legacy_tag = _LEGACY_MANYLINUX_MAP[glibc_version]
+                    if _is_compatible(arch, glibc_version):
+                        yield f"{legacy_tag}_{arch}"
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_musllinux.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_musllinux.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2bf30b56319ba862c5c9a1a39a87c6d1cb68718
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_musllinux.py
@@ -0,0 +1,85 @@
+"""PEP 656 support.
+
+This module implements logic to detect if the currently running Python is
+linked against musl, and what musl version is used.
+"""
+
+from __future__ import annotations
+
+import functools
+import re
+import subprocess
+import sys
+from typing import Iterator, NamedTuple, Sequence
+
+from ._elffile import ELFFile
+
+
+class _MuslVersion(NamedTuple):
+    major: int
+    minor: int
+
+
+def _parse_musl_version(output: str) -> _MuslVersion | None:
+    lines = [n for n in (n.strip() for n in output.splitlines()) if n]
+    if len(lines) < 2 or lines[0][:4] != "musl":
+        return None
+    m = re.match(r"Version (\d+)\.(\d+)", lines[1])
+    if not m:
+        return None
+    return _MuslVersion(major=int(m.group(1)), minor=int(m.group(2)))
+
+
+@functools.lru_cache
+def _get_musl_version(executable: str) -> _MuslVersion | None:
+    """Detect currently-running musl runtime version.
+
+    This is done by checking the specified executable's dynamic linking
+    information, and invoking the loader to parse its output for a version
+    string. If the loader is musl, the output would be something like::
+
+        musl libc (x86_64)
+        Version 1.2.2
+        Dynamic Program Loader
+    """
+    try:
+        with open(executable, "rb") as f:
+            ld = ELFFile(f).interpreter
+    except (OSError, TypeError, ValueError):
+        return None
+    if ld is None or "musl" not in ld:
+        return None
+    proc = subprocess.run([ld], stderr=subprocess.PIPE, text=True)
+    return _parse_musl_version(proc.stderr)
+
+
+def platform_tags(archs: Sequence[str]) -> Iterator[str]:
+    """Generate musllinux tags compatible to the current platform.
+
+    :param archs: Sequence of compatible architectures.
+        The first one shall be the closest to the actual architecture and be the part of
+        platform tag after the ``linux_`` prefix, e.g. ``x86_64``.
+        The ``linux_`` prefix is assumed as a prerequisite for the current platform to
+        be musllinux-compatible.
+
+    :returns: An iterator of compatible musllinux tags.
+    """
+    sys_musl = _get_musl_version(sys.executable)
+    if sys_musl is None:  # Python not dynamically linked against musl.
+        return
+    for arch in archs:
+        for minor in range(sys_musl.minor, -1, -1):
+            yield f"musllinux_{sys_musl.major}_{minor}_{arch}"
+
+
+if __name__ == "__main__":  # pragma: no cover
+    import sysconfig
+
+    plat = sysconfig.get_platform()
+    assert plat.startswith("linux-"), "not linux"
+
+    print("plat:", plat)
+    print("musl:", _get_musl_version(sys.executable))
+    print("tags:", end=" ")
+    for t in platform_tags(re.sub(r"[.-]", "_", plat.split("-", 1)[-1])):
+        print(t, end="\n      ")
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_parser.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1238c06eab95f8c90c393383a703aa3b8c366a5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_parser.py
@@ -0,0 +1,354 @@
+"""Handwritten parser of dependency specifiers.
+
+The docstring for each __parse_* function contains EBNF-inspired grammar representing
+the implementation.
+"""
+
+from __future__ import annotations
+
+import ast
+from typing import NamedTuple, Sequence, Tuple, Union
+
+from ._tokenizer import DEFAULT_RULES, Tokenizer
+
+
+class Node:
+    def __init__(self, value: str) -> None:
+        self.value = value
+
+    def __str__(self) -> str:
+        return self.value
+
+    def __repr__(self) -> str:
+        return f"<{self.__class__.__name__}('{self}')>"
+
+    def serialize(self) -> str:
+        raise NotImplementedError
+
+
+class Variable(Node):
+    def serialize(self) -> str:
+        return str(self)
+
+
+class Value(Node):
+    def serialize(self) -> str:
+        return f'"{self}"'
+
+
+class Op(Node):
+    def serialize(self) -> str:
+        return str(self)
+
+
+MarkerVar = Union[Variable, Value]
+MarkerItem = Tuple[MarkerVar, Op, MarkerVar]
+MarkerAtom = Union[MarkerItem, Sequence["MarkerAtom"]]
+MarkerList = Sequence[Union["MarkerList", MarkerAtom, str]]
+
+
+class ParsedRequirement(NamedTuple):
+    name: str
+    url: str
+    extras: list[str]
+    specifier: str
+    marker: MarkerList | None
+
+
+# --------------------------------------------------------------------------------------
+# Recursive descent parser for dependency specifier
+# --------------------------------------------------------------------------------------
+def parse_requirement(source: str) -> ParsedRequirement:
+    return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES))
+
+
+def _parse_requirement(tokenizer: Tokenizer) -> ParsedRequirement:
+    """
+    requirement = WS? IDENTIFIER WS? extras WS? requirement_details
+    """
+    tokenizer.consume("WS")
+
+    name_token = tokenizer.expect(
+        "IDENTIFIER", expected="package name at the start of dependency specifier"
+    )
+    name = name_token.text
+    tokenizer.consume("WS")
+
+    extras = _parse_extras(tokenizer)
+    tokenizer.consume("WS")
+
+    url, specifier, marker = _parse_requirement_details(tokenizer)
+    tokenizer.expect("END", expected="end of dependency specifier")
+
+    return ParsedRequirement(name, url, extras, specifier, marker)
+
+
+def _parse_requirement_details(
+    tokenizer: Tokenizer,
+) -> tuple[str, str, MarkerList | None]:
+    """
+    requirement_details = AT URL (WS requirement_marker?)?
+                        | specifier WS? (requirement_marker)?
+    """
+
+    specifier = ""
+    url = ""
+    marker = None
+
+    if tokenizer.check("AT"):
+        tokenizer.read()
+        tokenizer.consume("WS")
+
+        url_start = tokenizer.position
+        url = tokenizer.expect("URL", expected="URL after @").text
+        if tokenizer.check("END", peek=True):
+            return (url, specifier, marker)
+
+        tokenizer.expect("WS", expected="whitespace after URL")
+
+        # The input might end after whitespace.
+        if tokenizer.check("END", peek=True):
+            return (url, specifier, marker)
+
+        marker = _parse_requirement_marker(
+            tokenizer, span_start=url_start, after="URL and whitespace"
+        )
+    else:
+        specifier_start = tokenizer.position
+        specifier = _parse_specifier(tokenizer)
+        tokenizer.consume("WS")
+
+        if tokenizer.check("END", peek=True):
+            return (url, specifier, marker)
+
+        marker = _parse_requirement_marker(
+            tokenizer,
+            span_start=specifier_start,
+            after=(
+                "version specifier"
+                if specifier
+                else "name and no valid version specifier"
+            ),
+        )
+
+    return (url, specifier, marker)
+
+
+def _parse_requirement_marker(
+    tokenizer: Tokenizer, *, span_start: int, after: str
+) -> MarkerList:
+    """
+    requirement_marker = SEMICOLON marker WS?
+    """
+
+    if not tokenizer.check("SEMICOLON"):
+        tokenizer.raise_syntax_error(
+            f"Expected end or semicolon (after {after})",
+            span_start=span_start,
+        )
+    tokenizer.read()
+
+    marker = _parse_marker(tokenizer)
+    tokenizer.consume("WS")
+
+    return marker
+
+
+def _parse_extras(tokenizer: Tokenizer) -> list[str]:
+    """
+    extras = (LEFT_BRACKET wsp* extras_list? wsp* RIGHT_BRACKET)?
+    """
+    if not tokenizer.check("LEFT_BRACKET", peek=True):
+        return []
+
+    with tokenizer.enclosing_tokens(
+        "LEFT_BRACKET",
+        "RIGHT_BRACKET",
+        around="extras",
+    ):
+        tokenizer.consume("WS")
+        extras = _parse_extras_list(tokenizer)
+        tokenizer.consume("WS")
+
+    return extras
+
+
+def _parse_extras_list(tokenizer: Tokenizer) -> list[str]:
+    """
+    extras_list = identifier (wsp* ',' wsp* identifier)*
+    """
+    extras: list[str] = []
+
+    if not tokenizer.check("IDENTIFIER"):
+        return extras
+
+    extras.append(tokenizer.read().text)
+
+    while True:
+        tokenizer.consume("WS")
+        if tokenizer.check("IDENTIFIER", peek=True):
+            tokenizer.raise_syntax_error("Expected comma between extra names")
+        elif not tokenizer.check("COMMA"):
+            break
+
+        tokenizer.read()
+        tokenizer.consume("WS")
+
+        extra_token = tokenizer.expect("IDENTIFIER", expected="extra name after comma")
+        extras.append(extra_token.text)
+
+    return extras
+
+
+def _parse_specifier(tokenizer: Tokenizer) -> str:
+    """
+    specifier = LEFT_PARENTHESIS WS? version_many WS? RIGHT_PARENTHESIS
+              | WS? version_many WS?
+    """
+    with tokenizer.enclosing_tokens(
+        "LEFT_PARENTHESIS",
+        "RIGHT_PARENTHESIS",
+        around="version specifier",
+    ):
+        tokenizer.consume("WS")
+        parsed_specifiers = _parse_version_many(tokenizer)
+        tokenizer.consume("WS")
+
+    return parsed_specifiers
+
+
+def _parse_version_many(tokenizer: Tokenizer) -> str:
+    """
+    version_many = (SPECIFIER (WS? COMMA WS? SPECIFIER)*)?
+    """
+    parsed_specifiers = ""
+    while tokenizer.check("SPECIFIER"):
+        span_start = tokenizer.position
+        parsed_specifiers += tokenizer.read().text
+        if tokenizer.check("VERSION_PREFIX_TRAIL", peek=True):
+            tokenizer.raise_syntax_error(
+                ".* suffix can only be used with `==` or `!=` operators",
+                span_start=span_start,
+                span_end=tokenizer.position + 1,
+            )
+        if tokenizer.check("VERSION_LOCAL_LABEL_TRAIL", peek=True):
+            tokenizer.raise_syntax_error(
+                "Local version label can only be used with `==` or `!=` operators",
+                span_start=span_start,
+                span_end=tokenizer.position,
+            )
+        tokenizer.consume("WS")
+        if not tokenizer.check("COMMA"):
+            break
+        parsed_specifiers += tokenizer.read().text
+        tokenizer.consume("WS")
+
+    return parsed_specifiers
+
+
+# --------------------------------------------------------------------------------------
+# Recursive descent parser for marker expression
+# --------------------------------------------------------------------------------------
+def parse_marker(source: str) -> MarkerList:
+    return _parse_full_marker(Tokenizer(source, rules=DEFAULT_RULES))
+
+
+def _parse_full_marker(tokenizer: Tokenizer) -> MarkerList:
+    retval = _parse_marker(tokenizer)
+    tokenizer.expect("END", expected="end of marker expression")
+    return retval
+
+
+def _parse_marker(tokenizer: Tokenizer) -> MarkerList:
+    """
+    marker = marker_atom (BOOLOP marker_atom)+
+    """
+    expression = [_parse_marker_atom(tokenizer)]
+    while tokenizer.check("BOOLOP"):
+        token = tokenizer.read()
+        expr_right = _parse_marker_atom(tokenizer)
+        expression.extend((token.text, expr_right))
+    return expression
+
+
+def _parse_marker_atom(tokenizer: Tokenizer) -> MarkerAtom:
+    """
+    marker_atom = WS? LEFT_PARENTHESIS WS? marker WS? RIGHT_PARENTHESIS WS?
+                | WS? marker_item WS?
+    """
+
+    tokenizer.consume("WS")
+    if tokenizer.check("LEFT_PARENTHESIS", peek=True):
+        with tokenizer.enclosing_tokens(
+            "LEFT_PARENTHESIS",
+            "RIGHT_PARENTHESIS",
+            around="marker expression",
+        ):
+            tokenizer.consume("WS")
+            marker: MarkerAtom = _parse_marker(tokenizer)
+            tokenizer.consume("WS")
+    else:
+        marker = _parse_marker_item(tokenizer)
+    tokenizer.consume("WS")
+    return marker
+
+
+def _parse_marker_item(tokenizer: Tokenizer) -> MarkerItem:
+    """
+    marker_item = WS? marker_var WS? marker_op WS? marker_var WS?
+    """
+    tokenizer.consume("WS")
+    marker_var_left = _parse_marker_var(tokenizer)
+    tokenizer.consume("WS")
+    marker_op = _parse_marker_op(tokenizer)
+    tokenizer.consume("WS")
+    marker_var_right = _parse_marker_var(tokenizer)
+    tokenizer.consume("WS")
+    return (marker_var_left, marker_op, marker_var_right)
+
+
+def _parse_marker_var(tokenizer: Tokenizer) -> MarkerVar:
+    """
+    marker_var = VARIABLE | QUOTED_STRING
+    """
+    if tokenizer.check("VARIABLE"):
+        return process_env_var(tokenizer.read().text.replace(".", "_"))
+    elif tokenizer.check("QUOTED_STRING"):
+        return process_python_str(tokenizer.read().text)
+    else:
+        tokenizer.raise_syntax_error(
+            message="Expected a marker variable or quoted string"
+        )
+
+
+def process_env_var(env_var: str) -> Variable:
+    if env_var in ("platform_python_implementation", "python_implementation"):
+        return Variable("platform_python_implementation")
+    else:
+        return Variable(env_var)
+
+
+def process_python_str(python_str: str) -> Value:
+    value = ast.literal_eval(python_str)
+    return Value(str(value))
+
+
+def _parse_marker_op(tokenizer: Tokenizer) -> Op:
+    """
+    marker_op = IN | NOT IN | OP
+    """
+    if tokenizer.check("IN"):
+        tokenizer.read()
+        return Op("in")
+    elif tokenizer.check("NOT"):
+        tokenizer.read()
+        tokenizer.expect("WS", expected="whitespace after 'not'")
+        tokenizer.expect("IN", expected="'in' after 'not'")
+        return Op("not in")
+    elif tokenizer.check("OP"):
+        return Op(tokenizer.read().text)
+    else:
+        return tokenizer.raise_syntax_error(
+            "Expected marker operator, one of "
+            "<=, <, !=, ==, >=, >, ~=, ===, in, not in"
+        )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_structures.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_structures.py
new file mode 100644
index 0000000000000000000000000000000000000000..90a6465f9682c886363eea5327dac64bf623a6ff
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_structures.py
@@ -0,0 +1,61 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+
+class InfinityType:
+    def __repr__(self) -> str:
+        return "Infinity"
+
+    def __hash__(self) -> int:
+        return hash(repr(self))
+
+    def __lt__(self, other: object) -> bool:
+        return False
+
+    def __le__(self, other: object) -> bool:
+        return False
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, self.__class__)
+
+    def __gt__(self, other: object) -> bool:
+        return True
+
+    def __ge__(self, other: object) -> bool:
+        return True
+
+    def __neg__(self: object) -> "NegativeInfinityType":
+        return NegativeInfinity
+
+
+Infinity = InfinityType()
+
+
+class NegativeInfinityType:
+    def __repr__(self) -> str:
+        return "-Infinity"
+
+    def __hash__(self) -> int:
+        return hash(repr(self))
+
+    def __lt__(self, other: object) -> bool:
+        return True
+
+    def __le__(self, other: object) -> bool:
+        return True
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, self.__class__)
+
+    def __gt__(self, other: object) -> bool:
+        return False
+
+    def __ge__(self, other: object) -> bool:
+        return False
+
+    def __neg__(self: object) -> InfinityType:
+        return Infinity
+
+
+NegativeInfinity = NegativeInfinityType()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_tokenizer.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..89d041605c006e326a67f399a58a1fec8eb24acf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/_tokenizer.py
@@ -0,0 +1,194 @@
+from __future__ import annotations
+
+import contextlib
+import re
+from dataclasses import dataclass
+from typing import Iterator, NoReturn
+
+from .specifiers import Specifier
+
+
+@dataclass
+class Token:
+    name: str
+    text: str
+    position: int
+
+
+class ParserSyntaxError(Exception):
+    """The provided source text could not be parsed correctly."""
+
+    def __init__(
+        self,
+        message: str,
+        *,
+        source: str,
+        span: tuple[int, int],
+    ) -> None:
+        self.span = span
+        self.message = message
+        self.source = source
+
+        super().__init__()
+
+    def __str__(self) -> str:
+        marker = " " * self.span[0] + "~" * (self.span[1] - self.span[0]) + "^"
+        return "\n    ".join([self.message, self.source, marker])
+
+
+DEFAULT_RULES: dict[str, str | re.Pattern[str]] = {
+    "LEFT_PARENTHESIS": r"\(",
+    "RIGHT_PARENTHESIS": r"\)",
+    "LEFT_BRACKET": r"\[",
+    "RIGHT_BRACKET": r"\]",
+    "SEMICOLON": r";",
+    "COMMA": r",",
+    "QUOTED_STRING": re.compile(
+        r"""
+            (
+                ('[^']*')
+                |
+                ("[^"]*")
+            )
+        """,
+        re.VERBOSE,
+    ),
+    "OP": r"(===|==|~=|!=|<=|>=|<|>)",
+    "BOOLOP": r"\b(or|and)\b",
+    "IN": r"\bin\b",
+    "NOT": r"\bnot\b",
+    "VARIABLE": re.compile(
+        r"""
+            \b(
+                python_version
+                |python_full_version
+                |os[._]name
+                |sys[._]platform
+                |platform_(release|system)
+                |platform[._](version|machine|python_implementation)
+                |python_implementation
+                |implementation_(name|version)
+                |extra
+            )\b
+        """,
+        re.VERBOSE,
+    ),
+    "SPECIFIER": re.compile(
+        Specifier._operator_regex_str + Specifier._version_regex_str,
+        re.VERBOSE | re.IGNORECASE,
+    ),
+    "AT": r"\@",
+    "URL": r"[^ \t]+",
+    "IDENTIFIER": r"\b[a-zA-Z0-9][a-zA-Z0-9._-]*\b",
+    "VERSION_PREFIX_TRAIL": r"\.\*",
+    "VERSION_LOCAL_LABEL_TRAIL": r"\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*",
+    "WS": r"[ \t]+",
+    "END": r"$",
+}
+
+
+class Tokenizer:
+    """Context-sensitive token parsing.
+
+    Provides methods to examine the input stream to check whether the next token
+    matches.
+    """
+
+    def __init__(
+        self,
+        source: str,
+        *,
+        rules: dict[str, str | re.Pattern[str]],
+    ) -> None:
+        self.source = source
+        self.rules: dict[str, re.Pattern[str]] = {
+            name: re.compile(pattern) for name, pattern in rules.items()
+        }
+        self.next_token: Token | None = None
+        self.position = 0
+
+    def consume(self, name: str) -> None:
+        """Move beyond provided token name, if at current position."""
+        if self.check(name):
+            self.read()
+
+    def check(self, name: str, *, peek: bool = False) -> bool:
+        """Check whether the next token has the provided name.
+
+        By default, if the check succeeds, the token *must* be read before
+        another check. If `peek` is set to `True`, the token is not loaded and
+        would need to be checked again.
+        """
+        assert (
+            self.next_token is None
+        ), f"Cannot check for {name!r}, already have {self.next_token!r}"
+        assert name in self.rules, f"Unknown token name: {name!r}"
+
+        expression = self.rules[name]
+
+        match = expression.match(self.source, self.position)
+        if match is None:
+            return False
+        if not peek:
+            self.next_token = Token(name, match[0], self.position)
+        return True
+
+    def expect(self, name: str, *, expected: str) -> Token:
+        """Expect a certain token name next, failing with a syntax error otherwise.
+
+        The token is *not* read.
+        """
+        if not self.check(name):
+            raise self.raise_syntax_error(f"Expected {expected}")
+        return self.read()
+
+    def read(self) -> Token:
+        """Consume the next token and return it."""
+        token = self.next_token
+        assert token is not None
+
+        self.position += len(token.text)
+        self.next_token = None
+
+        return token
+
+    def raise_syntax_error(
+        self,
+        message: str,
+        *,
+        span_start: int | None = None,
+        span_end: int | None = None,
+    ) -> NoReturn:
+        """Raise ParserSyntaxError at the given position."""
+        span = (
+            self.position if span_start is None else span_start,
+            self.position if span_end is None else span_end,
+        )
+        raise ParserSyntaxError(
+            message,
+            source=self.source,
+            span=span,
+        )
+
+    @contextlib.contextmanager
+    def enclosing_tokens(
+        self, open_token: str, close_token: str, *, around: str
+    ) -> Iterator[None]:
+        if self.check(open_token):
+            open_position = self.position
+            self.read()
+        else:
+            open_position = None
+
+        yield
+
+        if open_position is None:
+            return
+
+        if not self.check(close_token):
+            self.raise_syntax_error(
+                f"Expected matching {close_token} for {open_token}, after {around}",
+                span_start=open_position,
+            )
+
+        self.read()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/licenses/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/licenses/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..569156d6ca47719f49b753a4781a86a924de173b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/licenses/__init__.py
@@ -0,0 +1,145 @@
+#######################################################################################
+#
+# Adapted from:
+#  https://github.com/pypa/hatch/blob/5352e44/backend/src/hatchling/licenses/parse.py
+#
+# MIT License
+#
+# Copyright (c) 2017-present Ofek Lev <oss@ofek.dev>
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this
+# software and associated documentation files (the "Software"), to deal in the Software
+# without restriction, including without limitation the rights to use, copy, modify,
+# merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
+# permit persons to whom the Software is furnished to do so, subject to the following
+# conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies
+# or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+# PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE
+# OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#
+#
+# With additional allowance of arbitrary `LicenseRef-` identifiers, not just
+# `LicenseRef-Public-Domain` and `LicenseRef-Proprietary`.
+#
+#######################################################################################
+from __future__ import annotations
+
+import re
+from typing import NewType, cast
+
+from packaging.licenses._spdx import EXCEPTIONS, LICENSES
+
+__all__ = [
+    "NormalizedLicenseExpression",
+    "InvalidLicenseExpression",
+    "canonicalize_license_expression",
+]
+
+license_ref_allowed = re.compile("^[A-Za-z0-9.-]*$")
+
+NormalizedLicenseExpression = NewType("NormalizedLicenseExpression", str)
+
+
+class InvalidLicenseExpression(ValueError):
+    """Raised when a license-expression string is invalid
+
+    >>> canonicalize_license_expression("invalid")
+    Traceback (most recent call last):
+        ...
+    packaging.licenses.InvalidLicenseExpression: Invalid license expression: 'invalid'
+    """
+
+
+def canonicalize_license_expression(
+    raw_license_expression: str,
+) -> NormalizedLicenseExpression:
+    if not raw_license_expression:
+        message = f"Invalid license expression: {raw_license_expression!r}"
+        raise InvalidLicenseExpression(message)
+
+    # Pad any parentheses so tokenization can be achieved by merely splitting on
+    # whitespace.
+    license_expression = raw_license_expression.replace("(", " ( ").replace(")", " ) ")
+    licenseref_prefix = "LicenseRef-"
+    license_refs = {
+        ref.lower(): "LicenseRef-" + ref[len(licenseref_prefix) :]
+        for ref in license_expression.split()
+        if ref.lower().startswith(licenseref_prefix.lower())
+    }
+
+    # Normalize to lower case so we can look up licenses/exceptions
+    # and so boolean operators are Python-compatible.
+    license_expression = license_expression.lower()
+
+    tokens = license_expression.split()
+
+    # Rather than implementing boolean logic, we create an expression that Python can
+    # parse. Everything that is not involved with the grammar itself is treated as
+    # `False` and the expression should evaluate as such.
+    python_tokens = []
+    for token in tokens:
+        if token not in {"or", "and", "with", "(", ")"}:
+            python_tokens.append("False")
+        elif token == "with":
+            python_tokens.append("or")
+        elif token == "(" and python_tokens and python_tokens[-1] not in {"or", "and"}:
+            message = f"Invalid license expression: {raw_license_expression!r}"
+            raise InvalidLicenseExpression(message)
+        else:
+            python_tokens.append(token)
+
+    python_expression = " ".join(python_tokens)
+    try:
+        invalid = eval(python_expression, globals(), locals())
+    except Exception:
+        invalid = True
+
+    if invalid is not False:
+        message = f"Invalid license expression: {raw_license_expression!r}"
+        raise InvalidLicenseExpression(message) from None
+
+    # Take a final pass to check for unknown licenses/exceptions.
+    normalized_tokens = []
+    for token in tokens:
+        if token in {"or", "and", "with", "(", ")"}:
+            normalized_tokens.append(token.upper())
+            continue
+
+        if normalized_tokens and normalized_tokens[-1] == "WITH":
+            if token not in EXCEPTIONS:
+                message = f"Unknown license exception: {token!r}"
+                raise InvalidLicenseExpression(message)
+
+            normalized_tokens.append(EXCEPTIONS[token]["id"])
+        else:
+            if token.endswith("+"):
+                final_token = token[:-1]
+                suffix = "+"
+            else:
+                final_token = token
+                suffix = ""
+
+            if final_token.startswith("licenseref-"):
+                if not license_ref_allowed.match(final_token):
+                    message = f"Invalid licenseref: {final_token!r}"
+                    raise InvalidLicenseExpression(message)
+                normalized_tokens.append(license_refs[final_token] + suffix)
+            else:
+                if final_token not in LICENSES:
+                    message = f"Unknown license: {final_token!r}"
+                    raise InvalidLicenseExpression(message)
+                normalized_tokens.append(LICENSES[final_token]["id"] + suffix)
+
+    normalized_expression = " ".join(normalized_tokens)
+
+    return cast(
+        NormalizedLicenseExpression,
+        normalized_expression.replace("( ", "(").replace(" )", ")"),
+    )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/licenses/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/licenses/__pycache__/__init__.cpython-312.pyc
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/licenses/__pycache__/_spdx.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/licenses/__pycache__/_spdx.cpython-312.pyc
new file mode 100644
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/licenses/_spdx.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/licenses/_spdx.py
new file mode 100644
index 0000000000000000000000000000000000000000..eac22276a34ccd73fc9d70c67ca318a49eb11e77
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/licenses/_spdx.py
@@ -0,0 +1,759 @@
+
+from __future__ import annotations
+
+from typing import TypedDict
+
+class SPDXLicense(TypedDict):
+    id: str
+    deprecated: bool
+
+class SPDXException(TypedDict):
+    id: str
+    deprecated: bool
+
+
+VERSION = '3.25.0'
+
+LICENSES: dict[str, SPDXLicense] = {
+    '0bsd': {'id': '0BSD', 'deprecated': False},
+    '3d-slicer-1.0': {'id': '3D-Slicer-1.0', 'deprecated': False},
+    'aal': {'id': 'AAL', 'deprecated': False},
+    'abstyles': {'id': 'Abstyles', 'deprecated': False},
+    'adacore-doc': {'id': 'AdaCore-doc', 'deprecated': False},
+    'adobe-2006': {'id': 'Adobe-2006', 'deprecated': False},
+    'adobe-display-postscript': {'id': 'Adobe-Display-PostScript', 'deprecated': False},
+    'adobe-glyph': {'id': 'Adobe-Glyph', 'deprecated': False},
+    'adobe-utopia': {'id': 'Adobe-Utopia', 'deprecated': False},
+    'adsl': {'id': 'ADSL', 'deprecated': False},
+    'afl-1.1': {'id': 'AFL-1.1', 'deprecated': False},
+    'afl-1.2': {'id': 'AFL-1.2', 'deprecated': False},
+    'afl-2.0': {'id': 'AFL-2.0', 'deprecated': False},
+    'afl-2.1': {'id': 'AFL-2.1', 'deprecated': False},
+    'afl-3.0': {'id': 'AFL-3.0', 'deprecated': False},
+    'afmparse': {'id': 'Afmparse', 'deprecated': False},
+    'agpl-1.0': {'id': 'AGPL-1.0', 'deprecated': True},
+    'agpl-1.0-only': {'id': 'AGPL-1.0-only', 'deprecated': False},
+    'agpl-1.0-or-later': {'id': 'AGPL-1.0-or-later', 'deprecated': False},
+    'agpl-3.0': {'id': 'AGPL-3.0', 'deprecated': True},
+    'agpl-3.0-only': {'id': 'AGPL-3.0-only', 'deprecated': False},
+    'agpl-3.0-or-later': {'id': 'AGPL-3.0-or-later', 'deprecated': False},
+    'aladdin': {'id': 'Aladdin', 'deprecated': False},
+    'amd-newlib': {'id': 'AMD-newlib', 'deprecated': False},
+    'amdplpa': {'id': 'AMDPLPA', 'deprecated': False},
+    'aml': {'id': 'AML', 'deprecated': False},
+    'aml-glslang': {'id': 'AML-glslang', 'deprecated': False},
+    'ampas': {'id': 'AMPAS', 'deprecated': False},
+    'antlr-pd': {'id': 'ANTLR-PD', 'deprecated': False},
+    'antlr-pd-fallback': {'id': 'ANTLR-PD-fallback', 'deprecated': False},
+    'any-osi': {'id': 'any-OSI', 'deprecated': False},
+    'apache-1.0': {'id': 'Apache-1.0', 'deprecated': False},
+    'apache-1.1': {'id': 'Apache-1.1', 'deprecated': False},
+    'apache-2.0': {'id': 'Apache-2.0', 'deprecated': False},
+    'apafml': {'id': 'APAFML', 'deprecated': False},
+    'apl-1.0': {'id': 'APL-1.0', 'deprecated': False},
+    'app-s2p': {'id': 'App-s2p', 'deprecated': False},
+    'apsl-1.0': {'id': 'APSL-1.0', 'deprecated': False},
+    'apsl-1.1': {'id': 'APSL-1.1', 'deprecated': False},
+    'apsl-1.2': {'id': 'APSL-1.2', 'deprecated': False},
+    'apsl-2.0': {'id': 'APSL-2.0', 'deprecated': False},
+    'arphic-1999': {'id': 'Arphic-1999', 'deprecated': False},
+    'artistic-1.0': {'id': 'Artistic-1.0', 'deprecated': False},
+    'artistic-1.0-cl8': {'id': 'Artistic-1.0-cl8', 'deprecated': False},
+    'artistic-1.0-perl': {'id': 'Artistic-1.0-Perl', 'deprecated': False},
+    'artistic-2.0': {'id': 'Artistic-2.0', 'deprecated': False},
+    'aswf-digital-assets-1.0': {'id': 'ASWF-Digital-Assets-1.0', 'deprecated': False},
+    'aswf-digital-assets-1.1': {'id': 'ASWF-Digital-Assets-1.1', 'deprecated': False},
+    'baekmuk': {'id': 'Baekmuk', 'deprecated': False},
+    'bahyph': {'id': 'Bahyph', 'deprecated': False},
+    'barr': {'id': 'Barr', 'deprecated': False},
+    'bcrypt-solar-designer': {'id': 'bcrypt-Solar-Designer', 'deprecated': False},
+    'beerware': {'id': 'Beerware', 'deprecated': False},
+    'bitstream-charter': {'id': 'Bitstream-Charter', 'deprecated': False},
+    'bitstream-vera': {'id': 'Bitstream-Vera', 'deprecated': False},
+    'bittorrent-1.0': {'id': 'BitTorrent-1.0', 'deprecated': False},
+    'bittorrent-1.1': {'id': 'BitTorrent-1.1', 'deprecated': False},
+    'blessing': {'id': 'blessing', 'deprecated': False},
+    'blueoak-1.0.0': {'id': 'BlueOak-1.0.0', 'deprecated': False},
+    'boehm-gc': {'id': 'Boehm-GC', 'deprecated': False},
+    'borceux': {'id': 'Borceux', 'deprecated': False},
+    'brian-gladman-2-clause': {'id': 'Brian-Gladman-2-Clause', 'deprecated': False},
+    'brian-gladman-3-clause': {'id': 'Brian-Gladman-3-Clause', 'deprecated': False},
+    'bsd-1-clause': {'id': 'BSD-1-Clause', 'deprecated': False},
+    'bsd-2-clause': {'id': 'BSD-2-Clause', 'deprecated': False},
+    'bsd-2-clause-darwin': {'id': 'BSD-2-Clause-Darwin', 'deprecated': False},
+    'bsd-2-clause-first-lines': {'id': 'BSD-2-Clause-first-lines', 'deprecated': False},
+    'bsd-2-clause-freebsd': {'id': 'BSD-2-Clause-FreeBSD', 'deprecated': True},
+    'bsd-2-clause-netbsd': {'id': 'BSD-2-Clause-NetBSD', 'deprecated': True},
+    'bsd-2-clause-patent': {'id': 'BSD-2-Clause-Patent', 'deprecated': False},
+    'bsd-2-clause-views': {'id': 'BSD-2-Clause-Views', 'deprecated': False},
+    'bsd-3-clause': {'id': 'BSD-3-Clause', 'deprecated': False},
+    'bsd-3-clause-acpica': {'id': 'BSD-3-Clause-acpica', 'deprecated': False},
+    'bsd-3-clause-attribution': {'id': 'BSD-3-Clause-Attribution', 'deprecated': False},
+    'bsd-3-clause-clear': {'id': 'BSD-3-Clause-Clear', 'deprecated': False},
+    'bsd-3-clause-flex': {'id': 'BSD-3-Clause-flex', 'deprecated': False},
+    'bsd-3-clause-hp': {'id': 'BSD-3-Clause-HP', 'deprecated': False},
+    'bsd-3-clause-lbnl': {'id': 'BSD-3-Clause-LBNL', 'deprecated': False},
+    'bsd-3-clause-modification': {'id': 'BSD-3-Clause-Modification', 'deprecated': False},
+    'bsd-3-clause-no-military-license': {'id': 'BSD-3-Clause-No-Military-License', 'deprecated': False},
+    'bsd-3-clause-no-nuclear-license': {'id': 'BSD-3-Clause-No-Nuclear-License', 'deprecated': False},
+    'bsd-3-clause-no-nuclear-license-2014': {'id': 'BSD-3-Clause-No-Nuclear-License-2014', 'deprecated': False},
+    'bsd-3-clause-no-nuclear-warranty': {'id': 'BSD-3-Clause-No-Nuclear-Warranty', 'deprecated': False},
+    'bsd-3-clause-open-mpi': {'id': 'BSD-3-Clause-Open-MPI', 'deprecated': False},
+    'bsd-3-clause-sun': {'id': 'BSD-3-Clause-Sun', 'deprecated': False},
+    'bsd-4-clause': {'id': 'BSD-4-Clause', 'deprecated': False},
+    'bsd-4-clause-shortened': {'id': 'BSD-4-Clause-Shortened', 'deprecated': False},
+    'bsd-4-clause-uc': {'id': 'BSD-4-Clause-UC', 'deprecated': False},
+    'bsd-4.3reno': {'id': 'BSD-4.3RENO', 'deprecated': False},
+    'bsd-4.3tahoe': {'id': 'BSD-4.3TAHOE', 'deprecated': False},
+    'bsd-advertising-acknowledgement': {'id': 'BSD-Advertising-Acknowledgement', 'deprecated': False},
+    'bsd-attribution-hpnd-disclaimer': {'id': 'BSD-Attribution-HPND-disclaimer', 'deprecated': False},
+    'bsd-inferno-nettverk': {'id': 'BSD-Inferno-Nettverk', 'deprecated': False},
+    'bsd-protection': {'id': 'BSD-Protection', 'deprecated': False},
+    'bsd-source-beginning-file': {'id': 'BSD-Source-beginning-file', 'deprecated': False},
+    'bsd-source-code': {'id': 'BSD-Source-Code', 'deprecated': False},
+    'bsd-systemics': {'id': 'BSD-Systemics', 'deprecated': False},
+    'bsd-systemics-w3works': {'id': 'BSD-Systemics-W3Works', 'deprecated': False},
+    'bsl-1.0': {'id': 'BSL-1.0', 'deprecated': False},
+    'busl-1.1': {'id': 'BUSL-1.1', 'deprecated': False},
+    'bzip2-1.0.5': {'id': 'bzip2-1.0.5', 'deprecated': True},
+    'bzip2-1.0.6': {'id': 'bzip2-1.0.6', 'deprecated': False},
+    'c-uda-1.0': {'id': 'C-UDA-1.0', 'deprecated': False},
+    'cal-1.0': {'id': 'CAL-1.0', 'deprecated': False},
+    'cal-1.0-combined-work-exception': {'id': 'CAL-1.0-Combined-Work-Exception', 'deprecated': False},
+    'caldera': {'id': 'Caldera', 'deprecated': False},
+    'caldera-no-preamble': {'id': 'Caldera-no-preamble', 'deprecated': False},
+    'catharon': {'id': 'Catharon', 'deprecated': False},
+    'catosl-1.1': {'id': 'CATOSL-1.1', 'deprecated': False},
+    'cc-by-1.0': {'id': 'CC-BY-1.0', 'deprecated': False},
+    'cc-by-2.0': {'id': 'CC-BY-2.0', 'deprecated': False},
+    'cc-by-2.5': {'id': 'CC-BY-2.5', 'deprecated': False},
+    'cc-by-2.5-au': {'id': 'CC-BY-2.5-AU', 'deprecated': False},
+    'cc-by-3.0': {'id': 'CC-BY-3.0', 'deprecated': False},
+    'cc-by-3.0-at': {'id': 'CC-BY-3.0-AT', 'deprecated': False},
+    'cc-by-3.0-au': {'id': 'CC-BY-3.0-AU', 'deprecated': False},
+    'cc-by-3.0-de': {'id': 'CC-BY-3.0-DE', 'deprecated': False},
+    'cc-by-3.0-igo': {'id': 'CC-BY-3.0-IGO', 'deprecated': False},
+    'cc-by-3.0-nl': {'id': 'CC-BY-3.0-NL', 'deprecated': False},
+    'cc-by-3.0-us': {'id': 'CC-BY-3.0-US', 'deprecated': False},
+    'cc-by-4.0': {'id': 'CC-BY-4.0', 'deprecated': False},
+    'cc-by-nc-1.0': {'id': 'CC-BY-NC-1.0', 'deprecated': False},
+    'cc-by-nc-2.0': {'id': 'CC-BY-NC-2.0', 'deprecated': False},
+    'cc-by-nc-2.5': {'id': 'CC-BY-NC-2.5', 'deprecated': False},
+    'cc-by-nc-3.0': {'id': 'CC-BY-NC-3.0', 'deprecated': False},
+    'cc-by-nc-3.0-de': {'id': 'CC-BY-NC-3.0-DE', 'deprecated': False},
+    'cc-by-nc-4.0': {'id': 'CC-BY-NC-4.0', 'deprecated': False},
+    'cc-by-nc-nd-1.0': {'id': 'CC-BY-NC-ND-1.0', 'deprecated': False},
+    'cc-by-nc-nd-2.0': {'id': 'CC-BY-NC-ND-2.0', 'deprecated': False},
+    'cc-by-nc-nd-2.5': {'id': 'CC-BY-NC-ND-2.5', 'deprecated': False},
+    'cc-by-nc-nd-3.0': {'id': 'CC-BY-NC-ND-3.0', 'deprecated': False},
+    'cc-by-nc-nd-3.0-de': {'id': 'CC-BY-NC-ND-3.0-DE', 'deprecated': False},
+    'cc-by-nc-nd-3.0-igo': {'id': 'CC-BY-NC-ND-3.0-IGO', 'deprecated': False},
+    'cc-by-nc-nd-4.0': {'id': 'CC-BY-NC-ND-4.0', 'deprecated': False},
+    'cc-by-nc-sa-1.0': {'id': 'CC-BY-NC-SA-1.0', 'deprecated': False},
+    'cc-by-nc-sa-2.0': {'id': 'CC-BY-NC-SA-2.0', 'deprecated': False},
+    'cc-by-nc-sa-2.0-de': {'id': 'CC-BY-NC-SA-2.0-DE', 'deprecated': False},
+    'cc-by-nc-sa-2.0-fr': {'id': 'CC-BY-NC-SA-2.0-FR', 'deprecated': False},
+    'cc-by-nc-sa-2.0-uk': {'id': 'CC-BY-NC-SA-2.0-UK', 'deprecated': False},
+    'cc-by-nc-sa-2.5': {'id': 'CC-BY-NC-SA-2.5', 'deprecated': False},
+    'cc-by-nc-sa-3.0': {'id': 'CC-BY-NC-SA-3.0', 'deprecated': False},
+    'cc-by-nc-sa-3.0-de': {'id': 'CC-BY-NC-SA-3.0-DE', 'deprecated': False},
+    'cc-by-nc-sa-3.0-igo': {'id': 'CC-BY-NC-SA-3.0-IGO', 'deprecated': False},
+    'cc-by-nc-sa-4.0': {'id': 'CC-BY-NC-SA-4.0', 'deprecated': False},
+    'cc-by-nd-1.0': {'id': 'CC-BY-ND-1.0', 'deprecated': False},
+    'cc-by-nd-2.0': {'id': 'CC-BY-ND-2.0', 'deprecated': False},
+    'cc-by-nd-2.5': {'id': 'CC-BY-ND-2.5', 'deprecated': False},
+    'cc-by-nd-3.0': {'id': 'CC-BY-ND-3.0', 'deprecated': False},
+    'cc-by-nd-3.0-de': {'id': 'CC-BY-ND-3.0-DE', 'deprecated': False},
+    'cc-by-nd-4.0': {'id': 'CC-BY-ND-4.0', 'deprecated': False},
+    'cc-by-sa-1.0': {'id': 'CC-BY-SA-1.0', 'deprecated': False},
+    'cc-by-sa-2.0': {'id': 'CC-BY-SA-2.0', 'deprecated': False},
+    'cc-by-sa-2.0-uk': {'id': 'CC-BY-SA-2.0-UK', 'deprecated': False},
+    'cc-by-sa-2.1-jp': {'id': 'CC-BY-SA-2.1-JP', 'deprecated': False},
+    'cc-by-sa-2.5': {'id': 'CC-BY-SA-2.5', 'deprecated': False},
+    'cc-by-sa-3.0': {'id': 'CC-BY-SA-3.0', 'deprecated': False},
+    'cc-by-sa-3.0-at': {'id': 'CC-BY-SA-3.0-AT', 'deprecated': False},
+    'cc-by-sa-3.0-de': {'id': 'CC-BY-SA-3.0-DE', 'deprecated': False},
+    'cc-by-sa-3.0-igo': {'id': 'CC-BY-SA-3.0-IGO', 'deprecated': False},
+    'cc-by-sa-4.0': {'id': 'CC-BY-SA-4.0', 'deprecated': False},
+    'cc-pddc': {'id': 'CC-PDDC', 'deprecated': False},
+    'cc0-1.0': {'id': 'CC0-1.0', 'deprecated': False},
+    'cddl-1.0': {'id': 'CDDL-1.0', 'deprecated': False},
+    'cddl-1.1': {'id': 'CDDL-1.1', 'deprecated': False},
+    'cdl-1.0': {'id': 'CDL-1.0', 'deprecated': False},
+    'cdla-permissive-1.0': {'id': 'CDLA-Permissive-1.0', 'deprecated': False},
+    'cdla-permissive-2.0': {'id': 'CDLA-Permissive-2.0', 'deprecated': False},
+    'cdla-sharing-1.0': {'id': 'CDLA-Sharing-1.0', 'deprecated': False},
+    'cecill-1.0': {'id': 'CECILL-1.0', 'deprecated': False},
+    'cecill-1.1': {'id': 'CECILL-1.1', 'deprecated': False},
+    'cecill-2.0': {'id': 'CECILL-2.0', 'deprecated': False},
+    'cecill-2.1': {'id': 'CECILL-2.1', 'deprecated': False},
+    'cecill-b': {'id': 'CECILL-B', 'deprecated': False},
+    'cecill-c': {'id': 'CECILL-C', 'deprecated': False},
+    'cern-ohl-1.1': {'id': 'CERN-OHL-1.1', 'deprecated': False},
+    'cern-ohl-1.2': {'id': 'CERN-OHL-1.2', 'deprecated': False},
+    'cern-ohl-p-2.0': {'id': 'CERN-OHL-P-2.0', 'deprecated': False},
+    'cern-ohl-s-2.0': {'id': 'CERN-OHL-S-2.0', 'deprecated': False},
+    'cern-ohl-w-2.0': {'id': 'CERN-OHL-W-2.0', 'deprecated': False},
+    'cfitsio': {'id': 'CFITSIO', 'deprecated': False},
+    'check-cvs': {'id': 'check-cvs', 'deprecated': False},
+    'checkmk': {'id': 'checkmk', 'deprecated': False},
+    'clartistic': {'id': 'ClArtistic', 'deprecated': False},
+    'clips': {'id': 'Clips', 'deprecated': False},
+    'cmu-mach': {'id': 'CMU-Mach', 'deprecated': False},
+    'cmu-mach-nodoc': {'id': 'CMU-Mach-nodoc', 'deprecated': False},
+    'cnri-jython': {'id': 'CNRI-Jython', 'deprecated': False},
+    'cnri-python': {'id': 'CNRI-Python', 'deprecated': False},
+    'cnri-python-gpl-compatible': {'id': 'CNRI-Python-GPL-Compatible', 'deprecated': False},
+    'coil-1.0': {'id': 'COIL-1.0', 'deprecated': False},
+    'community-spec-1.0': {'id': 'Community-Spec-1.0', 'deprecated': False},
+    'condor-1.1': {'id': 'Condor-1.1', 'deprecated': False},
+    'copyleft-next-0.3.0': {'id': 'copyleft-next-0.3.0', 'deprecated': False},
+    'copyleft-next-0.3.1': {'id': 'copyleft-next-0.3.1', 'deprecated': False},
+    'cornell-lossless-jpeg': {'id': 'Cornell-Lossless-JPEG', 'deprecated': False},
+    'cpal-1.0': {'id': 'CPAL-1.0', 'deprecated': False},
+    'cpl-1.0': {'id': 'CPL-1.0', 'deprecated': False},
+    'cpol-1.02': {'id': 'CPOL-1.02', 'deprecated': False},
+    'cronyx': {'id': 'Cronyx', 'deprecated': False},
+    'crossword': {'id': 'Crossword', 'deprecated': False},
+    'crystalstacker': {'id': 'CrystalStacker', 'deprecated': False},
+    'cua-opl-1.0': {'id': 'CUA-OPL-1.0', 'deprecated': False},
+    'cube': {'id': 'Cube', 'deprecated': False},
+    'curl': {'id': 'curl', 'deprecated': False},
+    'cve-tou': {'id': 'cve-tou', 'deprecated': False},
+    'd-fsl-1.0': {'id': 'D-FSL-1.0', 'deprecated': False},
+    'dec-3-clause': {'id': 'DEC-3-Clause', 'deprecated': False},
+    'diffmark': {'id': 'diffmark', 'deprecated': False},
+    'dl-de-by-2.0': {'id': 'DL-DE-BY-2.0', 'deprecated': False},
+    'dl-de-zero-2.0': {'id': 'DL-DE-ZERO-2.0', 'deprecated': False},
+    'doc': {'id': 'DOC', 'deprecated': False},
+    'docbook-schema': {'id': 'DocBook-Schema', 'deprecated': False},
+    'docbook-xml': {'id': 'DocBook-XML', 'deprecated': False},
+    'dotseqn': {'id': 'Dotseqn', 'deprecated': False},
+    'drl-1.0': {'id': 'DRL-1.0', 'deprecated': False},
+    'drl-1.1': {'id': 'DRL-1.1', 'deprecated': False},
+    'dsdp': {'id': 'DSDP', 'deprecated': False},
+    'dtoa': {'id': 'dtoa', 'deprecated': False},
+    'dvipdfm': {'id': 'dvipdfm', 'deprecated': False},
+    'ecl-1.0': {'id': 'ECL-1.0', 'deprecated': False},
+    'ecl-2.0': {'id': 'ECL-2.0', 'deprecated': False},
+    'ecos-2.0': {'id': 'eCos-2.0', 'deprecated': True},
+    'efl-1.0': {'id': 'EFL-1.0', 'deprecated': False},
+    'efl-2.0': {'id': 'EFL-2.0', 'deprecated': False},
+    'egenix': {'id': 'eGenix', 'deprecated': False},
+    'elastic-2.0': {'id': 'Elastic-2.0', 'deprecated': False},
+    'entessa': {'id': 'Entessa', 'deprecated': False},
+    'epics': {'id': 'EPICS', 'deprecated': False},
+    'epl-1.0': {'id': 'EPL-1.0', 'deprecated': False},
+    'epl-2.0': {'id': 'EPL-2.0', 'deprecated': False},
+    'erlpl-1.1': {'id': 'ErlPL-1.1', 'deprecated': False},
+    'etalab-2.0': {'id': 'etalab-2.0', 'deprecated': False},
+    'eudatagrid': {'id': 'EUDatagrid', 'deprecated': False},
+    'eupl-1.0': {'id': 'EUPL-1.0', 'deprecated': False},
+    'eupl-1.1': {'id': 'EUPL-1.1', 'deprecated': False},
+    'eupl-1.2': {'id': 'EUPL-1.2', 'deprecated': False},
+    'eurosym': {'id': 'Eurosym', 'deprecated': False},
+    'fair': {'id': 'Fair', 'deprecated': False},
+    'fbm': {'id': 'FBM', 'deprecated': False},
+    'fdk-aac': {'id': 'FDK-AAC', 'deprecated': False},
+    'ferguson-twofish': {'id': 'Ferguson-Twofish', 'deprecated': False},
+    'frameworx-1.0': {'id': 'Frameworx-1.0', 'deprecated': False},
+    'freebsd-doc': {'id': 'FreeBSD-DOC', 'deprecated': False},
+    'freeimage': {'id': 'FreeImage', 'deprecated': False},
+    'fsfap': {'id': 'FSFAP', 'deprecated': False},
+    'fsfap-no-warranty-disclaimer': {'id': 'FSFAP-no-warranty-disclaimer', 'deprecated': False},
+    'fsful': {'id': 'FSFUL', 'deprecated': False},
+    'fsfullr': {'id': 'FSFULLR', 'deprecated': False},
+    'fsfullrwd': {'id': 'FSFULLRWD', 'deprecated': False},
+    'ftl': {'id': 'FTL', 'deprecated': False},
+    'furuseth': {'id': 'Furuseth', 'deprecated': False},
+    'fwlw': {'id': 'fwlw', 'deprecated': False},
+    'gcr-docs': {'id': 'GCR-docs', 'deprecated': False},
+    'gd': {'id': 'GD', 'deprecated': False},
+    'gfdl-1.1': {'id': 'GFDL-1.1', 'deprecated': True},
+    'gfdl-1.1-invariants-only': {'id': 'GFDL-1.1-invariants-only', 'deprecated': False},
+    'gfdl-1.1-invariants-or-later': {'id': 'GFDL-1.1-invariants-or-later', 'deprecated': False},
+    'gfdl-1.1-no-invariants-only': {'id': 'GFDL-1.1-no-invariants-only', 'deprecated': False},
+    'gfdl-1.1-no-invariants-or-later': {'id': 'GFDL-1.1-no-invariants-or-later', 'deprecated': False},
+    'gfdl-1.1-only': {'id': 'GFDL-1.1-only', 'deprecated': False},
+    'gfdl-1.1-or-later': {'id': 'GFDL-1.1-or-later', 'deprecated': False},
+    'gfdl-1.2': {'id': 'GFDL-1.2', 'deprecated': True},
+    'gfdl-1.2-invariants-only': {'id': 'GFDL-1.2-invariants-only', 'deprecated': False},
+    'gfdl-1.2-invariants-or-later': {'id': 'GFDL-1.2-invariants-or-later', 'deprecated': False},
+    'gfdl-1.2-no-invariants-only': {'id': 'GFDL-1.2-no-invariants-only', 'deprecated': False},
+    'gfdl-1.2-no-invariants-or-later': {'id': 'GFDL-1.2-no-invariants-or-later', 'deprecated': False},
+    'gfdl-1.2-only': {'id': 'GFDL-1.2-only', 'deprecated': False},
+    'gfdl-1.2-or-later': {'id': 'GFDL-1.2-or-later', 'deprecated': False},
+    'gfdl-1.3': {'id': 'GFDL-1.3', 'deprecated': True},
+    'gfdl-1.3-invariants-only': {'id': 'GFDL-1.3-invariants-only', 'deprecated': False},
+    'gfdl-1.3-invariants-or-later': {'id': 'GFDL-1.3-invariants-or-later', 'deprecated': False},
+    'gfdl-1.3-no-invariants-only': {'id': 'GFDL-1.3-no-invariants-only', 'deprecated': False},
+    'gfdl-1.3-no-invariants-or-later': {'id': 'GFDL-1.3-no-invariants-or-later', 'deprecated': False},
+    'gfdl-1.3-only': {'id': 'GFDL-1.3-only', 'deprecated': False},
+    'gfdl-1.3-or-later': {'id': 'GFDL-1.3-or-later', 'deprecated': False},
+    'giftware': {'id': 'Giftware', 'deprecated': False},
+    'gl2ps': {'id': 'GL2PS', 'deprecated': False},
+    'glide': {'id': 'Glide', 'deprecated': False},
+    'glulxe': {'id': 'Glulxe', 'deprecated': False},
+    'glwtpl': {'id': 'GLWTPL', 'deprecated': False},
+    'gnuplot': {'id': 'gnuplot', 'deprecated': False},
+    'gpl-1.0': {'id': 'GPL-1.0', 'deprecated': True},
+    'gpl-1.0+': {'id': 'GPL-1.0+', 'deprecated': True},
+    'gpl-1.0-only': {'id': 'GPL-1.0-only', 'deprecated': False},
+    'gpl-1.0-or-later': {'id': 'GPL-1.0-or-later', 'deprecated': False},
+    'gpl-2.0': {'id': 'GPL-2.0', 'deprecated': True},
+    'gpl-2.0+': {'id': 'GPL-2.0+', 'deprecated': True},
+    'gpl-2.0-only': {'id': 'GPL-2.0-only', 'deprecated': False},
+    'gpl-2.0-or-later': {'id': 'GPL-2.0-or-later', 'deprecated': False},
+    'gpl-2.0-with-autoconf-exception': {'id': 'GPL-2.0-with-autoconf-exception', 'deprecated': True},
+    'gpl-2.0-with-bison-exception': {'id': 'GPL-2.0-with-bison-exception', 'deprecated': True},
+    'gpl-2.0-with-classpath-exception': {'id': 'GPL-2.0-with-classpath-exception', 'deprecated': True},
+    'gpl-2.0-with-font-exception': {'id': 'GPL-2.0-with-font-exception', 'deprecated': True},
+    'gpl-2.0-with-gcc-exception': {'id': 'GPL-2.0-with-GCC-exception', 'deprecated': True},
+    'gpl-3.0': {'id': 'GPL-3.0', 'deprecated': True},
+    'gpl-3.0+': {'id': 'GPL-3.0+', 'deprecated': True},
+    'gpl-3.0-only': {'id': 'GPL-3.0-only', 'deprecated': False},
+    'gpl-3.0-or-later': {'id': 'GPL-3.0-or-later', 'deprecated': False},
+    'gpl-3.0-with-autoconf-exception': {'id': 'GPL-3.0-with-autoconf-exception', 'deprecated': True},
+    'gpl-3.0-with-gcc-exception': {'id': 'GPL-3.0-with-GCC-exception', 'deprecated': True},
+    'graphics-gems': {'id': 'Graphics-Gems', 'deprecated': False},
+    'gsoap-1.3b': {'id': 'gSOAP-1.3b', 'deprecated': False},
+    'gtkbook': {'id': 'gtkbook', 'deprecated': False},
+    'gutmann': {'id': 'Gutmann', 'deprecated': False},
+    'haskellreport': {'id': 'HaskellReport', 'deprecated': False},
+    'hdparm': {'id': 'hdparm', 'deprecated': False},
+    'hidapi': {'id': 'HIDAPI', 'deprecated': False},
+    'hippocratic-2.1': {'id': 'Hippocratic-2.1', 'deprecated': False},
+    'hp-1986': {'id': 'HP-1986', 'deprecated': False},
+    'hp-1989': {'id': 'HP-1989', 'deprecated': False},
+    'hpnd': {'id': 'HPND', 'deprecated': False},
+    'hpnd-dec': {'id': 'HPND-DEC', 'deprecated': False},
+    'hpnd-doc': {'id': 'HPND-doc', 'deprecated': False},
+    'hpnd-doc-sell': {'id': 'HPND-doc-sell', 'deprecated': False},
+    'hpnd-export-us': {'id': 'HPND-export-US', 'deprecated': False},
+    'hpnd-export-us-acknowledgement': {'id': 'HPND-export-US-acknowledgement', 'deprecated': False},
+    'hpnd-export-us-modify': {'id': 'HPND-export-US-modify', 'deprecated': False},
+    'hpnd-export2-us': {'id': 'HPND-export2-US', 'deprecated': False},
+    'hpnd-fenneberg-livingston': {'id': 'HPND-Fenneberg-Livingston', 'deprecated': False},
+    'hpnd-inria-imag': {'id': 'HPND-INRIA-IMAG', 'deprecated': False},
+    'hpnd-intel': {'id': 'HPND-Intel', 'deprecated': False},
+    'hpnd-kevlin-henney': {'id': 'HPND-Kevlin-Henney', 'deprecated': False},
+    'hpnd-markus-kuhn': {'id': 'HPND-Markus-Kuhn', 'deprecated': False},
+    'hpnd-merchantability-variant': {'id': 'HPND-merchantability-variant', 'deprecated': False},
+    'hpnd-mit-disclaimer': {'id': 'HPND-MIT-disclaimer', 'deprecated': False},
+    'hpnd-netrek': {'id': 'HPND-Netrek', 'deprecated': False},
+    'hpnd-pbmplus': {'id': 'HPND-Pbmplus', 'deprecated': False},
+    'hpnd-sell-mit-disclaimer-xserver': {'id': 'HPND-sell-MIT-disclaimer-xserver', 'deprecated': False},
+    'hpnd-sell-regexpr': {'id': 'HPND-sell-regexpr', 'deprecated': False},
+    'hpnd-sell-variant': {'id': 'HPND-sell-variant', 'deprecated': False},
+    'hpnd-sell-variant-mit-disclaimer': {'id': 'HPND-sell-variant-MIT-disclaimer', 'deprecated': False},
+    'hpnd-sell-variant-mit-disclaimer-rev': {'id': 'HPND-sell-variant-MIT-disclaimer-rev', 'deprecated': False},
+    'hpnd-uc': {'id': 'HPND-UC', 'deprecated': False},
+    'hpnd-uc-export-us': {'id': 'HPND-UC-export-US', 'deprecated': False},
+    'htmltidy': {'id': 'HTMLTIDY', 'deprecated': False},
+    'ibm-pibs': {'id': 'IBM-pibs', 'deprecated': False},
+    'icu': {'id': 'ICU', 'deprecated': False},
+    'iec-code-components-eula': {'id': 'IEC-Code-Components-EULA', 'deprecated': False},
+    'ijg': {'id': 'IJG', 'deprecated': False},
+    'ijg-short': {'id': 'IJG-short', 'deprecated': False},
+    'imagemagick': {'id': 'ImageMagick', 'deprecated': False},
+    'imatix': {'id': 'iMatix', 'deprecated': False},
+    'imlib2': {'id': 'Imlib2', 'deprecated': False},
+    'info-zip': {'id': 'Info-ZIP', 'deprecated': False},
+    'inner-net-2.0': {'id': 'Inner-Net-2.0', 'deprecated': False},
+    'intel': {'id': 'Intel', 'deprecated': False},
+    'intel-acpi': {'id': 'Intel-ACPI', 'deprecated': False},
+    'interbase-1.0': {'id': 'Interbase-1.0', 'deprecated': False},
+    'ipa': {'id': 'IPA', 'deprecated': False},
+    'ipl-1.0': {'id': 'IPL-1.0', 'deprecated': False},
+    'isc': {'id': 'ISC', 'deprecated': False},
+    'isc-veillard': {'id': 'ISC-Veillard', 'deprecated': False},
+    'jam': {'id': 'Jam', 'deprecated': False},
+    'jasper-2.0': {'id': 'JasPer-2.0', 'deprecated': False},
+    'jpl-image': {'id': 'JPL-image', 'deprecated': False},
+    'jpnic': {'id': 'JPNIC', 'deprecated': False},
+    'json': {'id': 'JSON', 'deprecated': False},
+    'kastrup': {'id': 'Kastrup', 'deprecated': False},
+    'kazlib': {'id': 'Kazlib', 'deprecated': False},
+    'knuth-ctan': {'id': 'Knuth-CTAN', 'deprecated': False},
+    'lal-1.2': {'id': 'LAL-1.2', 'deprecated': False},
+    'lal-1.3': {'id': 'LAL-1.3', 'deprecated': False},
+    'latex2e': {'id': 'Latex2e', 'deprecated': False},
+    'latex2e-translated-notice': {'id': 'Latex2e-translated-notice', 'deprecated': False},
+    'leptonica': {'id': 'Leptonica', 'deprecated': False},
+    'lgpl-2.0': {'id': 'LGPL-2.0', 'deprecated': True},
+    'lgpl-2.0+': {'id': 'LGPL-2.0+', 'deprecated': True},
+    'lgpl-2.0-only': {'id': 'LGPL-2.0-only', 'deprecated': False},
+    'lgpl-2.0-or-later': {'id': 'LGPL-2.0-or-later', 'deprecated': False},
+    'lgpl-2.1': {'id': 'LGPL-2.1', 'deprecated': True},
+    'lgpl-2.1+': {'id': 'LGPL-2.1+', 'deprecated': True},
+    'lgpl-2.1-only': {'id': 'LGPL-2.1-only', 'deprecated': False},
+    'lgpl-2.1-or-later': {'id': 'LGPL-2.1-or-later', 'deprecated': False},
+    'lgpl-3.0': {'id': 'LGPL-3.0', 'deprecated': True},
+    'lgpl-3.0+': {'id': 'LGPL-3.0+', 'deprecated': True},
+    'lgpl-3.0-only': {'id': 'LGPL-3.0-only', 'deprecated': False},
+    'lgpl-3.0-or-later': {'id': 'LGPL-3.0-or-later', 'deprecated': False},
+    'lgpllr': {'id': 'LGPLLR', 'deprecated': False},
+    'libpng': {'id': 'Libpng', 'deprecated': False},
+    'libpng-2.0': {'id': 'libpng-2.0', 'deprecated': False},
+    'libselinux-1.0': {'id': 'libselinux-1.0', 'deprecated': False},
+    'libtiff': {'id': 'libtiff', 'deprecated': False},
+    'libutil-david-nugent': {'id': 'libutil-David-Nugent', 'deprecated': False},
+    'liliq-p-1.1': {'id': 'LiLiQ-P-1.1', 'deprecated': False},
+    'liliq-r-1.1': {'id': 'LiLiQ-R-1.1', 'deprecated': False},
+    'liliq-rplus-1.1': {'id': 'LiLiQ-Rplus-1.1', 'deprecated': False},
+    'linux-man-pages-1-para': {'id': 'Linux-man-pages-1-para', 'deprecated': False},
+    'linux-man-pages-copyleft': {'id': 'Linux-man-pages-copyleft', 'deprecated': False},
+    'linux-man-pages-copyleft-2-para': {'id': 'Linux-man-pages-copyleft-2-para', 'deprecated': False},
+    'linux-man-pages-copyleft-var': {'id': 'Linux-man-pages-copyleft-var', 'deprecated': False},
+    'linux-openib': {'id': 'Linux-OpenIB', 'deprecated': False},
+    'loop': {'id': 'LOOP', 'deprecated': False},
+    'lpd-document': {'id': 'LPD-document', 'deprecated': False},
+    'lpl-1.0': {'id': 'LPL-1.0', 'deprecated': False},
+    'lpl-1.02': {'id': 'LPL-1.02', 'deprecated': False},
+    'lppl-1.0': {'id': 'LPPL-1.0', 'deprecated': False},
+    'lppl-1.1': {'id': 'LPPL-1.1', 'deprecated': False},
+    'lppl-1.2': {'id': 'LPPL-1.2', 'deprecated': False},
+    'lppl-1.3a': {'id': 'LPPL-1.3a', 'deprecated': False},
+    'lppl-1.3c': {'id': 'LPPL-1.3c', 'deprecated': False},
+    'lsof': {'id': 'lsof', 'deprecated': False},
+    'lucida-bitmap-fonts': {'id': 'Lucida-Bitmap-Fonts', 'deprecated': False},
+    'lzma-sdk-9.11-to-9.20': {'id': 'LZMA-SDK-9.11-to-9.20', 'deprecated': False},
+    'lzma-sdk-9.22': {'id': 'LZMA-SDK-9.22', 'deprecated': False},
+    'mackerras-3-clause': {'id': 'Mackerras-3-Clause', 'deprecated': False},
+    'mackerras-3-clause-acknowledgment': {'id': 'Mackerras-3-Clause-acknowledgment', 'deprecated': False},
+    'magaz': {'id': 'magaz', 'deprecated': False},
+    'mailprio': {'id': 'mailprio', 'deprecated': False},
+    'makeindex': {'id': 'MakeIndex', 'deprecated': False},
+    'martin-birgmeier': {'id': 'Martin-Birgmeier', 'deprecated': False},
+    'mcphee-slideshow': {'id': 'McPhee-slideshow', 'deprecated': False},
+    'metamail': {'id': 'metamail', 'deprecated': False},
+    'minpack': {'id': 'Minpack', 'deprecated': False},
+    'miros': {'id': 'MirOS', 'deprecated': False},
+    'mit': {'id': 'MIT', 'deprecated': False},
+    'mit-0': {'id': 'MIT-0', 'deprecated': False},
+    'mit-advertising': {'id': 'MIT-advertising', 'deprecated': False},
+    'mit-cmu': {'id': 'MIT-CMU', 'deprecated': False},
+    'mit-enna': {'id': 'MIT-enna', 'deprecated': False},
+    'mit-feh': {'id': 'MIT-feh', 'deprecated': False},
+    'mit-festival': {'id': 'MIT-Festival', 'deprecated': False},
+    'mit-khronos-old': {'id': 'MIT-Khronos-old', 'deprecated': False},
+    'mit-modern-variant': {'id': 'MIT-Modern-Variant', 'deprecated': False},
+    'mit-open-group': {'id': 'MIT-open-group', 'deprecated': False},
+    'mit-testregex': {'id': 'MIT-testregex', 'deprecated': False},
+    'mit-wu': {'id': 'MIT-Wu', 'deprecated': False},
+    'mitnfa': {'id': 'MITNFA', 'deprecated': False},
+    'mmixware': {'id': 'MMIXware', 'deprecated': False},
+    'motosoto': {'id': 'Motosoto', 'deprecated': False},
+    'mpeg-ssg': {'id': 'MPEG-SSG', 'deprecated': False},
+    'mpi-permissive': {'id': 'mpi-permissive', 'deprecated': False},
+    'mpich2': {'id': 'mpich2', 'deprecated': False},
+    'mpl-1.0': {'id': 'MPL-1.0', 'deprecated': False},
+    'mpl-1.1': {'id': 'MPL-1.1', 'deprecated': False},
+    'mpl-2.0': {'id': 'MPL-2.0', 'deprecated': False},
+    'mpl-2.0-no-copyleft-exception': {'id': 'MPL-2.0-no-copyleft-exception', 'deprecated': False},
+    'mplus': {'id': 'mplus', 'deprecated': False},
+    'ms-lpl': {'id': 'MS-LPL', 'deprecated': False},
+    'ms-pl': {'id': 'MS-PL', 'deprecated': False},
+    'ms-rl': {'id': 'MS-RL', 'deprecated': False},
+    'mtll': {'id': 'MTLL', 'deprecated': False},
+    'mulanpsl-1.0': {'id': 'MulanPSL-1.0', 'deprecated': False},
+    'mulanpsl-2.0': {'id': 'MulanPSL-2.0', 'deprecated': False},
+    'multics': {'id': 'Multics', 'deprecated': False},
+    'mup': {'id': 'Mup', 'deprecated': False},
+    'naist-2003': {'id': 'NAIST-2003', 'deprecated': False},
+    'nasa-1.3': {'id': 'NASA-1.3', 'deprecated': False},
+    'naumen': {'id': 'Naumen', 'deprecated': False},
+    'nbpl-1.0': {'id': 'NBPL-1.0', 'deprecated': False},
+    'ncbi-pd': {'id': 'NCBI-PD', 'deprecated': False},
+    'ncgl-uk-2.0': {'id': 'NCGL-UK-2.0', 'deprecated': False},
+    'ncl': {'id': 'NCL', 'deprecated': False},
+    'ncsa': {'id': 'NCSA', 'deprecated': False},
+    'net-snmp': {'id': 'Net-SNMP', 'deprecated': True},
+    'netcdf': {'id': 'NetCDF', 'deprecated': False},
+    'newsletr': {'id': 'Newsletr', 'deprecated': False},
+    'ngpl': {'id': 'NGPL', 'deprecated': False},
+    'nicta-1.0': {'id': 'NICTA-1.0', 'deprecated': False},
+    'nist-pd': {'id': 'NIST-PD', 'deprecated': False},
+    'nist-pd-fallback': {'id': 'NIST-PD-fallback', 'deprecated': False},
+    'nist-software': {'id': 'NIST-Software', 'deprecated': False},
+    'nlod-1.0': {'id': 'NLOD-1.0', 'deprecated': False},
+    'nlod-2.0': {'id': 'NLOD-2.0', 'deprecated': False},
+    'nlpl': {'id': 'NLPL', 'deprecated': False},
+    'nokia': {'id': 'Nokia', 'deprecated': False},
+    'nosl': {'id': 'NOSL', 'deprecated': False},
+    'noweb': {'id': 'Noweb', 'deprecated': False},
+    'npl-1.0': {'id': 'NPL-1.0', 'deprecated': False},
+    'npl-1.1': {'id': 'NPL-1.1', 'deprecated': False},
+    'nposl-3.0': {'id': 'NPOSL-3.0', 'deprecated': False},
+    'nrl': {'id': 'NRL', 'deprecated': False},
+    'ntp': {'id': 'NTP', 'deprecated': False},
+    'ntp-0': {'id': 'NTP-0', 'deprecated': False},
+    'nunit': {'id': 'Nunit', 'deprecated': True},
+    'o-uda-1.0': {'id': 'O-UDA-1.0', 'deprecated': False},
+    'oar': {'id': 'OAR', 'deprecated': False},
+    'occt-pl': {'id': 'OCCT-PL', 'deprecated': False},
+    'oclc-2.0': {'id': 'OCLC-2.0', 'deprecated': False},
+    'odbl-1.0': {'id': 'ODbL-1.0', 'deprecated': False},
+    'odc-by-1.0': {'id': 'ODC-By-1.0', 'deprecated': False},
+    'offis': {'id': 'OFFIS', 'deprecated': False},
+    'ofl-1.0': {'id': 'OFL-1.0', 'deprecated': False},
+    'ofl-1.0-no-rfn': {'id': 'OFL-1.0-no-RFN', 'deprecated': False},
+    'ofl-1.0-rfn': {'id': 'OFL-1.0-RFN', 'deprecated': False},
+    'ofl-1.1': {'id': 'OFL-1.1', 'deprecated': False},
+    'ofl-1.1-no-rfn': {'id': 'OFL-1.1-no-RFN', 'deprecated': False},
+    'ofl-1.1-rfn': {'id': 'OFL-1.1-RFN', 'deprecated': False},
+    'ogc-1.0': {'id': 'OGC-1.0', 'deprecated': False},
+    'ogdl-taiwan-1.0': {'id': 'OGDL-Taiwan-1.0', 'deprecated': False},
+    'ogl-canada-2.0': {'id': 'OGL-Canada-2.0', 'deprecated': False},
+    'ogl-uk-1.0': {'id': 'OGL-UK-1.0', 'deprecated': False},
+    'ogl-uk-2.0': {'id': 'OGL-UK-2.0', 'deprecated': False},
+    'ogl-uk-3.0': {'id': 'OGL-UK-3.0', 'deprecated': False},
+    'ogtsl': {'id': 'OGTSL', 'deprecated': False},
+    'oldap-1.1': {'id': 'OLDAP-1.1', 'deprecated': False},
+    'oldap-1.2': {'id': 'OLDAP-1.2', 'deprecated': False},
+    'oldap-1.3': {'id': 'OLDAP-1.3', 'deprecated': False},
+    'oldap-1.4': {'id': 'OLDAP-1.4', 'deprecated': False},
+    'oldap-2.0': {'id': 'OLDAP-2.0', 'deprecated': False},
+    'oldap-2.0.1': {'id': 'OLDAP-2.0.1', 'deprecated': False},
+    'oldap-2.1': {'id': 'OLDAP-2.1', 'deprecated': False},
+    'oldap-2.2': {'id': 'OLDAP-2.2', 'deprecated': False},
+    'oldap-2.2.1': {'id': 'OLDAP-2.2.1', 'deprecated': False},
+    'oldap-2.2.2': {'id': 'OLDAP-2.2.2', 'deprecated': False},
+    'oldap-2.3': {'id': 'OLDAP-2.3', 'deprecated': False},
+    'oldap-2.4': {'id': 'OLDAP-2.4', 'deprecated': False},
+    'oldap-2.5': {'id': 'OLDAP-2.5', 'deprecated': False},
+    'oldap-2.6': {'id': 'OLDAP-2.6', 'deprecated': False},
+    'oldap-2.7': {'id': 'OLDAP-2.7', 'deprecated': False},
+    'oldap-2.8': {'id': 'OLDAP-2.8', 'deprecated': False},
+    'olfl-1.3': {'id': 'OLFL-1.3', 'deprecated': False},
+    'oml': {'id': 'OML', 'deprecated': False},
+    'openpbs-2.3': {'id': 'OpenPBS-2.3', 'deprecated': False},
+    'openssl': {'id': 'OpenSSL', 'deprecated': False},
+    'openssl-standalone': {'id': 'OpenSSL-standalone', 'deprecated': False},
+    'openvision': {'id': 'OpenVision', 'deprecated': False},
+    'opl-1.0': {'id': 'OPL-1.0', 'deprecated': False},
+    'opl-uk-3.0': {'id': 'OPL-UK-3.0', 'deprecated': False},
+    'opubl-1.0': {'id': 'OPUBL-1.0', 'deprecated': False},
+    'oset-pl-2.1': {'id': 'OSET-PL-2.1', 'deprecated': False},
+    'osl-1.0': {'id': 'OSL-1.0', 'deprecated': False},
+    'osl-1.1': {'id': 'OSL-1.1', 'deprecated': False},
+    'osl-2.0': {'id': 'OSL-2.0', 'deprecated': False},
+    'osl-2.1': {'id': 'OSL-2.1', 'deprecated': False},
+    'osl-3.0': {'id': 'OSL-3.0', 'deprecated': False},
+    'padl': {'id': 'PADL', 'deprecated': False},
+    'parity-6.0.0': {'id': 'Parity-6.0.0', 'deprecated': False},
+    'parity-7.0.0': {'id': 'Parity-7.0.0', 'deprecated': False},
+    'pddl-1.0': {'id': 'PDDL-1.0', 'deprecated': False},
+    'php-3.0': {'id': 'PHP-3.0', 'deprecated': False},
+    'php-3.01': {'id': 'PHP-3.01', 'deprecated': False},
+    'pixar': {'id': 'Pixar', 'deprecated': False},
+    'pkgconf': {'id': 'pkgconf', 'deprecated': False},
+    'plexus': {'id': 'Plexus', 'deprecated': False},
+    'pnmstitch': {'id': 'pnmstitch', 'deprecated': False},
+    'polyform-noncommercial-1.0.0': {'id': 'PolyForm-Noncommercial-1.0.0', 'deprecated': False},
+    'polyform-small-business-1.0.0': {'id': 'PolyForm-Small-Business-1.0.0', 'deprecated': False},
+    'postgresql': {'id': 'PostgreSQL', 'deprecated': False},
+    'ppl': {'id': 'PPL', 'deprecated': False},
+    'psf-2.0': {'id': 'PSF-2.0', 'deprecated': False},
+    'psfrag': {'id': 'psfrag', 'deprecated': False},
+    'psutils': {'id': 'psutils', 'deprecated': False},
+    'python-2.0': {'id': 'Python-2.0', 'deprecated': False},
+    'python-2.0.1': {'id': 'Python-2.0.1', 'deprecated': False},
+    'python-ldap': {'id': 'python-ldap', 'deprecated': False},
+    'qhull': {'id': 'Qhull', 'deprecated': False},
+    'qpl-1.0': {'id': 'QPL-1.0', 'deprecated': False},
+    'qpl-1.0-inria-2004': {'id': 'QPL-1.0-INRIA-2004', 'deprecated': False},
+    'radvd': {'id': 'radvd', 'deprecated': False},
+    'rdisc': {'id': 'Rdisc', 'deprecated': False},
+    'rhecos-1.1': {'id': 'RHeCos-1.1', 'deprecated': False},
+    'rpl-1.1': {'id': 'RPL-1.1', 'deprecated': False},
+    'rpl-1.5': {'id': 'RPL-1.5', 'deprecated': False},
+    'rpsl-1.0': {'id': 'RPSL-1.0', 'deprecated': False},
+    'rsa-md': {'id': 'RSA-MD', 'deprecated': False},
+    'rscpl': {'id': 'RSCPL', 'deprecated': False},
+    'ruby': {'id': 'Ruby', 'deprecated': False},
+    'ruby-pty': {'id': 'Ruby-pty', 'deprecated': False},
+    'sax-pd': {'id': 'SAX-PD', 'deprecated': False},
+    'sax-pd-2.0': {'id': 'SAX-PD-2.0', 'deprecated': False},
+    'saxpath': {'id': 'Saxpath', 'deprecated': False},
+    'scea': {'id': 'SCEA', 'deprecated': False},
+    'schemereport': {'id': 'SchemeReport', 'deprecated': False},
+    'sendmail': {'id': 'Sendmail', 'deprecated': False},
+    'sendmail-8.23': {'id': 'Sendmail-8.23', 'deprecated': False},
+    'sgi-b-1.0': {'id': 'SGI-B-1.0', 'deprecated': False},
+    'sgi-b-1.1': {'id': 'SGI-B-1.1', 'deprecated': False},
+    'sgi-b-2.0': {'id': 'SGI-B-2.0', 'deprecated': False},
+    'sgi-opengl': {'id': 'SGI-OpenGL', 'deprecated': False},
+    'sgp4': {'id': 'SGP4', 'deprecated': False},
+    'shl-0.5': {'id': 'SHL-0.5', 'deprecated': False},
+    'shl-0.51': {'id': 'SHL-0.51', 'deprecated': False},
+    'simpl-2.0': {'id': 'SimPL-2.0', 'deprecated': False},
+    'sissl': {'id': 'SISSL', 'deprecated': False},
+    'sissl-1.2': {'id': 'SISSL-1.2', 'deprecated': False},
+    'sl': {'id': 'SL', 'deprecated': False},
+    'sleepycat': {'id': 'Sleepycat', 'deprecated': False},
+    'smlnj': {'id': 'SMLNJ', 'deprecated': False},
+    'smppl': {'id': 'SMPPL', 'deprecated': False},
+    'snia': {'id': 'SNIA', 'deprecated': False},
+    'snprintf': {'id': 'snprintf', 'deprecated': False},
+    'softsurfer': {'id': 'softSurfer', 'deprecated': False},
+    'soundex': {'id': 'Soundex', 'deprecated': False},
+    'spencer-86': {'id': 'Spencer-86', 'deprecated': False},
+    'spencer-94': {'id': 'Spencer-94', 'deprecated': False},
+    'spencer-99': {'id': 'Spencer-99', 'deprecated': False},
+    'spl-1.0': {'id': 'SPL-1.0', 'deprecated': False},
+    'ssh-keyscan': {'id': 'ssh-keyscan', 'deprecated': False},
+    'ssh-openssh': {'id': 'SSH-OpenSSH', 'deprecated': False},
+    'ssh-short': {'id': 'SSH-short', 'deprecated': False},
+    'ssleay-standalone': {'id': 'SSLeay-standalone', 'deprecated': False},
+    'sspl-1.0': {'id': 'SSPL-1.0', 'deprecated': False},
+    'standardml-nj': {'id': 'StandardML-NJ', 'deprecated': True},
+    'sugarcrm-1.1.3': {'id': 'SugarCRM-1.1.3', 'deprecated': False},
+    'sun-ppp': {'id': 'Sun-PPP', 'deprecated': False},
+    'sun-ppp-2000': {'id': 'Sun-PPP-2000', 'deprecated': False},
+    'sunpro': {'id': 'SunPro', 'deprecated': False},
+    'swl': {'id': 'SWL', 'deprecated': False},
+    'swrule': {'id': 'swrule', 'deprecated': False},
+    'symlinks': {'id': 'Symlinks', 'deprecated': False},
+    'tapr-ohl-1.0': {'id': 'TAPR-OHL-1.0', 'deprecated': False},
+    'tcl': {'id': 'TCL', 'deprecated': False},
+    'tcp-wrappers': {'id': 'TCP-wrappers', 'deprecated': False},
+    'termreadkey': {'id': 'TermReadKey', 'deprecated': False},
+    'tgppl-1.0': {'id': 'TGPPL-1.0', 'deprecated': False},
+    'threeparttable': {'id': 'threeparttable', 'deprecated': False},
+    'tmate': {'id': 'TMate', 'deprecated': False},
+    'torque-1.1': {'id': 'TORQUE-1.1', 'deprecated': False},
+    'tosl': {'id': 'TOSL', 'deprecated': False},
+    'tpdl': {'id': 'TPDL', 'deprecated': False},
+    'tpl-1.0': {'id': 'TPL-1.0', 'deprecated': False},
+    'ttwl': {'id': 'TTWL', 'deprecated': False},
+    'ttyp0': {'id': 'TTYP0', 'deprecated': False},
+    'tu-berlin-1.0': {'id': 'TU-Berlin-1.0', 'deprecated': False},
+    'tu-berlin-2.0': {'id': 'TU-Berlin-2.0', 'deprecated': False},
+    'ubuntu-font-1.0': {'id': 'Ubuntu-font-1.0', 'deprecated': False},
+    'ucar': {'id': 'UCAR', 'deprecated': False},
+    'ucl-1.0': {'id': 'UCL-1.0', 'deprecated': False},
+    'ulem': {'id': 'ulem', 'deprecated': False},
+    'umich-merit': {'id': 'UMich-Merit', 'deprecated': False},
+    'unicode-3.0': {'id': 'Unicode-3.0', 'deprecated': False},
+    'unicode-dfs-2015': {'id': 'Unicode-DFS-2015', 'deprecated': False},
+    'unicode-dfs-2016': {'id': 'Unicode-DFS-2016', 'deprecated': False},
+    'unicode-tou': {'id': 'Unicode-TOU', 'deprecated': False},
+    'unixcrypt': {'id': 'UnixCrypt', 'deprecated': False},
+    'unlicense': {'id': 'Unlicense', 'deprecated': False},
+    'upl-1.0': {'id': 'UPL-1.0', 'deprecated': False},
+    'urt-rle': {'id': 'URT-RLE', 'deprecated': False},
+    'vim': {'id': 'Vim', 'deprecated': False},
+    'vostrom': {'id': 'VOSTROM', 'deprecated': False},
+    'vsl-1.0': {'id': 'VSL-1.0', 'deprecated': False},
+    'w3c': {'id': 'W3C', 'deprecated': False},
+    'w3c-19980720': {'id': 'W3C-19980720', 'deprecated': False},
+    'w3c-20150513': {'id': 'W3C-20150513', 'deprecated': False},
+    'w3m': {'id': 'w3m', 'deprecated': False},
+    'watcom-1.0': {'id': 'Watcom-1.0', 'deprecated': False},
+    'widget-workshop': {'id': 'Widget-Workshop', 'deprecated': False},
+    'wsuipa': {'id': 'Wsuipa', 'deprecated': False},
+    'wtfpl': {'id': 'WTFPL', 'deprecated': False},
+    'wxwindows': {'id': 'wxWindows', 'deprecated': True},
+    'x11': {'id': 'X11', 'deprecated': False},
+    'x11-distribute-modifications-variant': {'id': 'X11-distribute-modifications-variant', 'deprecated': False},
+    'x11-swapped': {'id': 'X11-swapped', 'deprecated': False},
+    'xdebug-1.03': {'id': 'Xdebug-1.03', 'deprecated': False},
+    'xerox': {'id': 'Xerox', 'deprecated': False},
+    'xfig': {'id': 'Xfig', 'deprecated': False},
+    'xfree86-1.1': {'id': 'XFree86-1.1', 'deprecated': False},
+    'xinetd': {'id': 'xinetd', 'deprecated': False},
+    'xkeyboard-config-zinoviev': {'id': 'xkeyboard-config-Zinoviev', 'deprecated': False},
+    'xlock': {'id': 'xlock', 'deprecated': False},
+    'xnet': {'id': 'Xnet', 'deprecated': False},
+    'xpp': {'id': 'xpp', 'deprecated': False},
+    'xskat': {'id': 'XSkat', 'deprecated': False},
+    'xzoom': {'id': 'xzoom', 'deprecated': False},
+    'ypl-1.0': {'id': 'YPL-1.0', 'deprecated': False},
+    'ypl-1.1': {'id': 'YPL-1.1', 'deprecated': False},
+    'zed': {'id': 'Zed', 'deprecated': False},
+    'zeeff': {'id': 'Zeeff', 'deprecated': False},
+    'zend-2.0': {'id': 'Zend-2.0', 'deprecated': False},
+    'zimbra-1.3': {'id': 'Zimbra-1.3', 'deprecated': False},
+    'zimbra-1.4': {'id': 'Zimbra-1.4', 'deprecated': False},
+    'zlib': {'id': 'Zlib', 'deprecated': False},
+    'zlib-acknowledgement': {'id': 'zlib-acknowledgement', 'deprecated': False},
+    'zpl-1.1': {'id': 'ZPL-1.1', 'deprecated': False},
+    'zpl-2.0': {'id': 'ZPL-2.0', 'deprecated': False},
+    'zpl-2.1': {'id': 'ZPL-2.1', 'deprecated': False},
+}
+
+EXCEPTIONS: dict[str, SPDXException] = {
+    '389-exception': {'id': '389-exception', 'deprecated': False},
+    'asterisk-exception': {'id': 'Asterisk-exception', 'deprecated': False},
+    'asterisk-linking-protocols-exception': {'id': 'Asterisk-linking-protocols-exception', 'deprecated': False},
+    'autoconf-exception-2.0': {'id': 'Autoconf-exception-2.0', 'deprecated': False},
+    'autoconf-exception-3.0': {'id': 'Autoconf-exception-3.0', 'deprecated': False},
+    'autoconf-exception-generic': {'id': 'Autoconf-exception-generic', 'deprecated': False},
+    'autoconf-exception-generic-3.0': {'id': 'Autoconf-exception-generic-3.0', 'deprecated': False},
+    'autoconf-exception-macro': {'id': 'Autoconf-exception-macro', 'deprecated': False},
+    'bison-exception-1.24': {'id': 'Bison-exception-1.24', 'deprecated': False},
+    'bison-exception-2.2': {'id': 'Bison-exception-2.2', 'deprecated': False},
+    'bootloader-exception': {'id': 'Bootloader-exception', 'deprecated': False},
+    'classpath-exception-2.0': {'id': 'Classpath-exception-2.0', 'deprecated': False},
+    'clisp-exception-2.0': {'id': 'CLISP-exception-2.0', 'deprecated': False},
+    'cryptsetup-openssl-exception': {'id': 'cryptsetup-OpenSSL-exception', 'deprecated': False},
+    'digirule-foss-exception': {'id': 'DigiRule-FOSS-exception', 'deprecated': False},
+    'ecos-exception-2.0': {'id': 'eCos-exception-2.0', 'deprecated': False},
+    'erlang-otp-linking-exception': {'id': 'erlang-otp-linking-exception', 'deprecated': False},
+    'fawkes-runtime-exception': {'id': 'Fawkes-Runtime-exception', 'deprecated': False},
+    'fltk-exception': {'id': 'FLTK-exception', 'deprecated': False},
+    'fmt-exception': {'id': 'fmt-exception', 'deprecated': False},
+    'font-exception-2.0': {'id': 'Font-exception-2.0', 'deprecated': False},
+    'freertos-exception-2.0': {'id': 'freertos-exception-2.0', 'deprecated': False},
+    'gcc-exception-2.0': {'id': 'GCC-exception-2.0', 'deprecated': False},
+    'gcc-exception-2.0-note': {'id': 'GCC-exception-2.0-note', 'deprecated': False},
+    'gcc-exception-3.1': {'id': 'GCC-exception-3.1', 'deprecated': False},
+    'gmsh-exception': {'id': 'Gmsh-exception', 'deprecated': False},
+    'gnat-exception': {'id': 'GNAT-exception', 'deprecated': False},
+    'gnome-examples-exception': {'id': 'GNOME-examples-exception', 'deprecated': False},
+    'gnu-compiler-exception': {'id': 'GNU-compiler-exception', 'deprecated': False},
+    'gnu-javamail-exception': {'id': 'gnu-javamail-exception', 'deprecated': False},
+    'gpl-3.0-interface-exception': {'id': 'GPL-3.0-interface-exception', 'deprecated': False},
+    'gpl-3.0-linking-exception': {'id': 'GPL-3.0-linking-exception', 'deprecated': False},
+    'gpl-3.0-linking-source-exception': {'id': 'GPL-3.0-linking-source-exception', 'deprecated': False},
+    'gpl-cc-1.0': {'id': 'GPL-CC-1.0', 'deprecated': False},
+    'gstreamer-exception-2005': {'id': 'GStreamer-exception-2005', 'deprecated': False},
+    'gstreamer-exception-2008': {'id': 'GStreamer-exception-2008', 'deprecated': False},
+    'i2p-gpl-java-exception': {'id': 'i2p-gpl-java-exception', 'deprecated': False},
+    'kicad-libraries-exception': {'id': 'KiCad-libraries-exception', 'deprecated': False},
+    'lgpl-3.0-linking-exception': {'id': 'LGPL-3.0-linking-exception', 'deprecated': False},
+    'libpri-openh323-exception': {'id': 'libpri-OpenH323-exception', 'deprecated': False},
+    'libtool-exception': {'id': 'Libtool-exception', 'deprecated': False},
+    'linux-syscall-note': {'id': 'Linux-syscall-note', 'deprecated': False},
+    'llgpl': {'id': 'LLGPL', 'deprecated': False},
+    'llvm-exception': {'id': 'LLVM-exception', 'deprecated': False},
+    'lzma-exception': {'id': 'LZMA-exception', 'deprecated': False},
+    'mif-exception': {'id': 'mif-exception', 'deprecated': False},
+    'nokia-qt-exception-1.1': {'id': 'Nokia-Qt-exception-1.1', 'deprecated': True},
+    'ocaml-lgpl-linking-exception': {'id': 'OCaml-LGPL-linking-exception', 'deprecated': False},
+    'occt-exception-1.0': {'id': 'OCCT-exception-1.0', 'deprecated': False},
+    'openjdk-assembly-exception-1.0': {'id': 'OpenJDK-assembly-exception-1.0', 'deprecated': False},
+    'openvpn-openssl-exception': {'id': 'openvpn-openssl-exception', 'deprecated': False},
+    'pcre2-exception': {'id': 'PCRE2-exception', 'deprecated': False},
+    'ps-or-pdf-font-exception-20170817': {'id': 'PS-or-PDF-font-exception-20170817', 'deprecated': False},
+    'qpl-1.0-inria-2004-exception': {'id': 'QPL-1.0-INRIA-2004-exception', 'deprecated': False},
+    'qt-gpl-exception-1.0': {'id': 'Qt-GPL-exception-1.0', 'deprecated': False},
+    'qt-lgpl-exception-1.1': {'id': 'Qt-LGPL-exception-1.1', 'deprecated': False},
+    'qwt-exception-1.0': {'id': 'Qwt-exception-1.0', 'deprecated': False},
+    'romic-exception': {'id': 'romic-exception', 'deprecated': False},
+    'rrdtool-floss-exception-2.0': {'id': 'RRDtool-FLOSS-exception-2.0', 'deprecated': False},
+    'sane-exception': {'id': 'SANE-exception', 'deprecated': False},
+    'shl-2.0': {'id': 'SHL-2.0', 'deprecated': False},
+    'shl-2.1': {'id': 'SHL-2.1', 'deprecated': False},
+    'stunnel-exception': {'id': 'stunnel-exception', 'deprecated': False},
+    'swi-exception': {'id': 'SWI-exception', 'deprecated': False},
+    'swift-exception': {'id': 'Swift-exception', 'deprecated': False},
+    'texinfo-exception': {'id': 'Texinfo-exception', 'deprecated': False},
+    'u-boot-exception-2.0': {'id': 'u-boot-exception-2.0', 'deprecated': False},
+    'ubdl-exception': {'id': 'UBDL-exception', 'deprecated': False},
+    'universal-foss-exception-1.0': {'id': 'Universal-FOSS-exception-1.0', 'deprecated': False},
+    'vsftpd-openssl-exception': {'id': 'vsftpd-openssl-exception', 'deprecated': False},
+    'wxwindows-exception-3.1': {'id': 'WxWindows-exception-3.1', 'deprecated': False},
+    'x11vnc-openssl-exception': {'id': 'x11vnc-openssl-exception', 'deprecated': False},
+}
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/markers.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/markers.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb7f49cf8cd43ffae71e3e8d15174d7536f9da02
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/markers.py
@@ -0,0 +1,331 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+from __future__ import annotations
+
+import operator
+import os
+import platform
+import sys
+from typing import Any, Callable, TypedDict, cast
+
+from ._parser import MarkerAtom, MarkerList, Op, Value, Variable
+from ._parser import parse_marker as _parse_marker
+from ._tokenizer import ParserSyntaxError
+from .specifiers import InvalidSpecifier, Specifier
+from .utils import canonicalize_name
+
+__all__ = [
+    "InvalidMarker",
+    "Marker",
+    "UndefinedComparison",
+    "UndefinedEnvironmentName",
+    "default_environment",
+]
+
+Operator = Callable[[str, str], bool]
+
+
+class InvalidMarker(ValueError):
+    """
+    An invalid marker was found, users should refer to PEP 508.
+    """
+
+
+class UndefinedComparison(ValueError):
+    """
+    An invalid operation was attempted on a value that doesn't support it.
+    """
+
+
+class UndefinedEnvironmentName(ValueError):
+    """
+    A name was attempted to be used that does not exist inside of the
+    environment.
+    """
+
+
+class Environment(TypedDict):
+    implementation_name: str
+    """The implementation's identifier, e.g. ``'cpython'``."""
+
+    implementation_version: str
+    """
+    The implementation's version, e.g. ``'3.13.0a2'`` for CPython 3.13.0a2, or
+    ``'7.3.13'`` for PyPy3.10 v7.3.13.
+    """
+
+    os_name: str
+    """
+    The value of :py:data:`os.name`. The name of the operating system dependent module
+    imported, e.g. ``'posix'``.
+    """
+
+    platform_machine: str
+    """
+    Returns the machine type, e.g. ``'i386'``.
+
+    An empty string if the value cannot be determined.
+    """
+
+    platform_release: str
+    """
+    The system's release, e.g. ``'2.2.0'`` or ``'NT'``.
+
+    An empty string if the value cannot be determined.
+    """
+
+    platform_system: str
+    """
+    The system/OS name, e.g. ``'Linux'``, ``'Windows'`` or ``'Java'``.
+
+    An empty string if the value cannot be determined.
+    """
+
+    platform_version: str
+    """
+    The system's release version, e.g. ``'#3 on degas'``.
+
+    An empty string if the value cannot be determined.
+    """
+
+    python_full_version: str
+    """
+    The Python version as string ``'major.minor.patchlevel'``.
+
+    Note that unlike the Python :py:data:`sys.version`, this value will always include
+    the patchlevel (it defaults to 0).
+    """
+
+    platform_python_implementation: str
+    """
+    A string identifying the Python implementation, e.g. ``'CPython'``.
+    """
+
+    python_version: str
+    """The Python version as string ``'major.minor'``."""
+
+    sys_platform: str
+    """
+    This string contains a platform identifier that can be used to append
+    platform-specific components to :py:data:`sys.path`, for instance.
+
+    For Unix systems, except on Linux and AIX, this is the lowercased OS name as
+    returned by ``uname -s`` with the first part of the version as returned by
+    ``uname -r`` appended, e.g. ``'sunos5'`` or ``'freebsd8'``, at the time when Python
+    was built.
+    """
+
+
+def _normalize_extra_values(results: Any) -> Any:
+    """
+    Normalize extra values.
+    """
+    if isinstance(results[0], tuple):
+        lhs, op, rhs = results[0]
+        if isinstance(lhs, Variable) and lhs.value == "extra":
+            normalized_extra = canonicalize_name(rhs.value)
+            rhs = Value(normalized_extra)
+        elif isinstance(rhs, Variable) and rhs.value == "extra":
+            normalized_extra = canonicalize_name(lhs.value)
+            lhs = Value(normalized_extra)
+        results[0] = lhs, op, rhs
+    return results
+
+
+def _format_marker(
+    marker: list[str] | MarkerAtom | str, first: bool | None = True
+) -> str:
+    assert isinstance(marker, (list, tuple, str))
+
+    # Sometimes we have a structure like [[...]] which is a single item list
+    # where the single item is itself it's own list. In that case we want skip
+    # the rest of this function so that we don't get extraneous () on the
+    # outside.
+    if (
+        isinstance(marker, list)
+        and len(marker) == 1
+        and isinstance(marker[0], (list, tuple))
+    ):
+        return _format_marker(marker[0])
+
+    if isinstance(marker, list):
+        inner = (_format_marker(m, first=False) for m in marker)
+        if first:
+            return " ".join(inner)
+        else:
+            return "(" + " ".join(inner) + ")"
+    elif isinstance(marker, tuple):
+        return " ".join([m.serialize() for m in marker])
+    else:
+        return marker
+
+
+_operators: dict[str, Operator] = {
+    "in": lambda lhs, rhs: lhs in rhs,
+    "not in": lambda lhs, rhs: lhs not in rhs,
+    "<": operator.lt,
+    "<=": operator.le,
+    "==": operator.eq,
+    "!=": operator.ne,
+    ">=": operator.ge,
+    ">": operator.gt,
+}
+
+
+def _eval_op(lhs: str, op: Op, rhs: str) -> bool:
+    try:
+        spec = Specifier("".join([op.serialize(), rhs]))
+    except InvalidSpecifier:
+        pass
+    else:
+        return spec.contains(lhs, prereleases=True)
+
+    oper: Operator | None = _operators.get(op.serialize())
+    if oper is None:
+        raise UndefinedComparison(f"Undefined {op!r} on {lhs!r} and {rhs!r}.")
+
+    return oper(lhs, rhs)
+
+
+def _normalize(*values: str, key: str) -> tuple[str, ...]:
+    # PEP 685 – Comparison of extra names for optional distribution dependencies
+    # https://peps.python.org/pep-0685/
+    # > When comparing extra names, tools MUST normalize the names being
+    # > compared using the semantics outlined in PEP 503 for names
+    if key == "extra":
+        return tuple(canonicalize_name(v) for v in values)
+
+    # other environment markers don't have such standards
+    return values
+
+
+def _evaluate_markers(markers: MarkerList, environment: dict[str, str]) -> bool:
+    groups: list[list[bool]] = [[]]
+
+    for marker in markers:
+        assert isinstance(marker, (list, tuple, str))
+
+        if isinstance(marker, list):
+            groups[-1].append(_evaluate_markers(marker, environment))
+        elif isinstance(marker, tuple):
+            lhs, op, rhs = marker
+
+            if isinstance(lhs, Variable):
+                environment_key = lhs.value
+                lhs_value = environment[environment_key]
+                rhs_value = rhs.value
+            else:
+                lhs_value = lhs.value
+                environment_key = rhs.value
+                rhs_value = environment[environment_key]
+
+            lhs_value, rhs_value = _normalize(lhs_value, rhs_value, key=environment_key)
+            groups[-1].append(_eval_op(lhs_value, op, rhs_value))
+        else:
+            assert marker in ["and", "or"]
+            if marker == "or":
+                groups.append([])
+
+    return any(all(item) for item in groups)
+
+
+def format_full_version(info: sys._version_info) -> str:
+    version = f"{info.major}.{info.minor}.{info.micro}"
+    kind = info.releaselevel
+    if kind != "final":
+        version += kind[0] + str(info.serial)
+    return version
+
+
+def default_environment() -> Environment:
+    iver = format_full_version(sys.implementation.version)
+    implementation_name = sys.implementation.name
+    return {
+        "implementation_name": implementation_name,
+        "implementation_version": iver,
+        "os_name": os.name,
+        "platform_machine": platform.machine(),
+        "platform_release": platform.release(),
+        "platform_system": platform.system(),
+        "platform_version": platform.version(),
+        "python_full_version": platform.python_version(),
+        "platform_python_implementation": platform.python_implementation(),
+        "python_version": ".".join(platform.python_version_tuple()[:2]),
+        "sys_platform": sys.platform,
+    }
+
+
+class Marker:
+    def __init__(self, marker: str) -> None:
+        # Note: We create a Marker object without calling this constructor in
+        #       packaging.requirements.Requirement. If any additional logic is
+        #       added here, make sure to mirror/adapt Requirement.
+        try:
+            self._markers = _normalize_extra_values(_parse_marker(marker))
+            # The attribute `_markers` can be described in terms of a recursive type:
+            # MarkerList = List[Union[Tuple[Node, ...], str, MarkerList]]
+            #
+            # For example, the following expression:
+            # python_version > "3.6" or (python_version == "3.6" and os_name == "unix")
+            #
+            # is parsed into:
+            # [
+            #     (<Variable('python_version')>, <Op('>')>, <Value('3.6')>),
+            #     'and',
+            #     [
+            #         (<Variable('python_version')>, <Op('==')>, <Value('3.6')>),
+            #         'or',
+            #         (<Variable('os_name')>, <Op('==')>, <Value('unix')>)
+            #     ]
+            # ]
+        except ParserSyntaxError as e:
+            raise InvalidMarker(str(e)) from e
+
+    def __str__(self) -> str:
+        return _format_marker(self._markers)
+
+    def __repr__(self) -> str:
+        return f"<Marker('{self}')>"
+
+    def __hash__(self) -> int:
+        return hash((self.__class__.__name__, str(self)))
+
+    def __eq__(self, other: Any) -> bool:
+        if not isinstance(other, Marker):
+            return NotImplemented
+
+        return str(self) == str(other)
+
+    def evaluate(self, environment: dict[str, str] | None = None) -> bool:
+        """Evaluate a marker.
+
+        Return the boolean from evaluating the given marker against the
+        environment. environment is an optional argument to override all or
+        part of the determined environment.
+
+        The environment is determined from the current Python process.
+        """
+        current_environment = cast("dict[str, str]", default_environment())
+        current_environment["extra"] = ""
+        if environment is not None:
+            current_environment.update(environment)
+            # The API used to allow setting extra to None. We need to handle this
+            # case for backwards compatibility.
+            if current_environment["extra"] is None:
+                current_environment["extra"] = ""
+
+        return _evaluate_markers(
+            self._markers, _repair_python_full_version(current_environment)
+        )
+
+
+def _repair_python_full_version(env: dict[str, str]) -> dict[str, str]:
+    """
+    Work around platform.python_version() returning something that is not PEP 440
+    compliant for non-tagged Python builds.
+    """
+    if env["python_full_version"].endswith("+"):
+        env["python_full_version"] += "local"
+    return env
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/metadata.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..721f411cfc44f6d24c13112e4246b5ad776a5e0b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/metadata.py
@@ -0,0 +1,863 @@
+from __future__ import annotations
+
+import email.feedparser
+import email.header
+import email.message
+import email.parser
+import email.policy
+import pathlib
+import sys
+import typing
+from typing import (
+    Any,
+    Callable,
+    Generic,
+    Literal,
+    TypedDict,
+    cast,
+)
+
+from . import licenses, requirements, specifiers, utils
+from . import version as version_module
+from .licenses import NormalizedLicenseExpression
+
+T = typing.TypeVar("T")
+
+
+if sys.version_info >= (3, 11):  # pragma: no cover
+    ExceptionGroup = ExceptionGroup
+else:  # pragma: no cover
+
+    class ExceptionGroup(Exception):
+        """A minimal implementation of :external:exc:`ExceptionGroup` from Python 3.11.
+
+        If :external:exc:`ExceptionGroup` is already defined by Python itself,
+        that version is used instead.
+        """
+
+        message: str
+        exceptions: list[Exception]
+
+        def __init__(self, message: str, exceptions: list[Exception]) -> None:
+            self.message = message
+            self.exceptions = exceptions
+
+        def __repr__(self) -> str:
+            return f"{self.__class__.__name__}({self.message!r}, {self.exceptions!r})"
+
+
+class InvalidMetadata(ValueError):
+    """A metadata field contains invalid data."""
+
+    field: str
+    """The name of the field that contains invalid data."""
+
+    def __init__(self, field: str, message: str) -> None:
+        self.field = field
+        super().__init__(message)
+
+
+# The RawMetadata class attempts to make as few assumptions about the underlying
+# serialization formats as possible. The idea is that as long as a serialization
+# formats offer some very basic primitives in *some* way then we can support
+# serializing to and from that format.
+class RawMetadata(TypedDict, total=False):
+    """A dictionary of raw core metadata.
+
+    Each field in core metadata maps to a key of this dictionary (when data is
+    provided). The key is lower-case and underscores are used instead of dashes
+    compared to the equivalent core metadata field. Any core metadata field that
+    can be specified multiple times or can hold multiple values in a single
+    field have a key with a plural name. See :class:`Metadata` whose attributes
+    match the keys of this dictionary.
+
+    Core metadata fields that can be specified multiple times are stored as a
+    list or dict depending on which is appropriate for the field. Any fields
+    which hold multiple values in a single field are stored as a list.
+
+    """
+
+    # Metadata 1.0 - PEP 241
+    metadata_version: str
+    name: str
+    version: str
+    platforms: list[str]
+    summary: str
+    description: str
+    keywords: list[str]
+    home_page: str
+    author: str
+    author_email: str
+    license: str
+
+    # Metadata 1.1 - PEP 314
+    supported_platforms: list[str]
+    download_url: str
+    classifiers: list[str]
+    requires: list[str]
+    provides: list[str]
+    obsoletes: list[str]
+
+    # Metadata 1.2 - PEP 345
+    maintainer: str
+    maintainer_email: str
+    requires_dist: list[str]
+    provides_dist: list[str]
+    obsoletes_dist: list[str]
+    requires_python: str
+    requires_external: list[str]
+    project_urls: dict[str, str]
+
+    # Metadata 2.0
+    # PEP 426 attempted to completely revamp the metadata format
+    # but got stuck without ever being able to build consensus on
+    # it and ultimately ended up withdrawn.
+    #
+    # However, a number of tools had started emitting METADATA with
+    # `2.0` Metadata-Version, so for historical reasons, this version
+    # was skipped.
+
+    # Metadata 2.1 - PEP 566
+    description_content_type: str
+    provides_extra: list[str]
+
+    # Metadata 2.2 - PEP 643
+    dynamic: list[str]
+
+    # Metadata 2.3 - PEP 685
+    # No new fields were added in PEP 685, just some edge case were
+    # tightened up to provide better interoptability.
+
+    # Metadata 2.4 - PEP 639
+    license_expression: str
+    license_files: list[str]
+
+
+_STRING_FIELDS = {
+    "author",
+    "author_email",
+    "description",
+    "description_content_type",
+    "download_url",
+    "home_page",
+    "license",
+    "license_expression",
+    "maintainer",
+    "maintainer_email",
+    "metadata_version",
+    "name",
+    "requires_python",
+    "summary",
+    "version",
+}
+
+_LIST_FIELDS = {
+    "classifiers",
+    "dynamic",
+    "license_files",
+    "obsoletes",
+    "obsoletes_dist",
+    "platforms",
+    "provides",
+    "provides_dist",
+    "provides_extra",
+    "requires",
+    "requires_dist",
+    "requires_external",
+    "supported_platforms",
+}
+
+_DICT_FIELDS = {
+    "project_urls",
+}
+
+
+def _parse_keywords(data: str) -> list[str]:
+    """Split a string of comma-separated keywords into a list of keywords."""
+    return [k.strip() for k in data.split(",")]
+
+
+def _parse_project_urls(data: list[str]) -> dict[str, str]:
+    """Parse a list of label/URL string pairings separated by a comma."""
+    urls = {}
+    for pair in data:
+        # Our logic is slightly tricky here as we want to try and do
+        # *something* reasonable with malformed data.
+        #
+        # The main thing that we have to worry about, is data that does
+        # not have a ',' at all to split the label from the Value. There
+        # isn't a singular right answer here, and we will fail validation
+        # later on (if the caller is validating) so it doesn't *really*
+        # matter, but since the missing value has to be an empty str
+        # and our return value is dict[str, str], if we let the key
+        # be the missing value, then they'd have multiple '' values that
+        # overwrite each other in a accumulating dict.
+        #
+        # The other potentional issue is that it's possible to have the
+        # same label multiple times in the metadata, with no solid "right"
+        # answer with what to do in that case. As such, we'll do the only
+        # thing we can, which is treat the field as unparseable and add it
+        # to our list of unparsed fields.
+        parts = [p.strip() for p in pair.split(",", 1)]
+        parts.extend([""] * (max(0, 2 - len(parts))))  # Ensure 2 items
+
+        # TODO: The spec doesn't say anything about if the keys should be
+        #       considered case sensitive or not... logically they should
+        #       be case-preserving and case-insensitive, but doing that
+        #       would open up more cases where we might have duplicate
+        #       entries.
+        label, url = parts
+        if label in urls:
+            # The label already exists in our set of urls, so this field
+            # is unparseable, and we can just add the whole thing to our
+            # unparseable data and stop processing it.
+            raise KeyError("duplicate labels in project urls")
+        urls[label] = url
+
+    return urls
+
+
+def _get_payload(msg: email.message.Message, source: bytes | str) -> str:
+    """Get the body of the message."""
+    # If our source is a str, then our caller has managed encodings for us,
+    # and we don't need to deal with it.
+    if isinstance(source, str):
+        payload = msg.get_payload()
+        assert isinstance(payload, str)
+        return payload
+    # If our source is a bytes, then we're managing the encoding and we need
+    # to deal with it.
+    else:
+        bpayload = msg.get_payload(decode=True)
+        assert isinstance(bpayload, bytes)
+        try:
+            return bpayload.decode("utf8", "strict")
+        except UnicodeDecodeError as exc:
+            raise ValueError("payload in an invalid encoding") from exc
+
+
+# The various parse_FORMAT functions here are intended to be as lenient as
+# possible in their parsing, while still returning a correctly typed
+# RawMetadata.
+#
+# To aid in this, we also generally want to do as little touching of the
+# data as possible, except where there are possibly some historic holdovers
+# that make valid data awkward to work with.
+#
+# While this is a lower level, intermediate format than our ``Metadata``
+# class, some light touch ups can make a massive difference in usability.
+
+# Map METADATA fields to RawMetadata.
+_EMAIL_TO_RAW_MAPPING = {
+    "author": "author",
+    "author-email": "author_email",
+    "classifier": "classifiers",
+    "description": "description",
+    "description-content-type": "description_content_type",
+    "download-url": "download_url",
+    "dynamic": "dynamic",
+    "home-page": "home_page",
+    "keywords": "keywords",
+    "license": "license",
+    "license-expression": "license_expression",
+    "license-file": "license_files",
+    "maintainer": "maintainer",
+    "maintainer-email": "maintainer_email",
+    "metadata-version": "metadata_version",
+    "name": "name",
+    "obsoletes": "obsoletes",
+    "obsoletes-dist": "obsoletes_dist",
+    "platform": "platforms",
+    "project-url": "project_urls",
+    "provides": "provides",
+    "provides-dist": "provides_dist",
+    "provides-extra": "provides_extra",
+    "requires": "requires",
+    "requires-dist": "requires_dist",
+    "requires-external": "requires_external",
+    "requires-python": "requires_python",
+    "summary": "summary",
+    "supported-platform": "supported_platforms",
+    "version": "version",
+}
+_RAW_TO_EMAIL_MAPPING = {raw: email for email, raw in _EMAIL_TO_RAW_MAPPING.items()}
+
+
+def parse_email(data: bytes | str) -> tuple[RawMetadata, dict[str, list[str]]]:
+    """Parse a distribution's metadata stored as email headers (e.g. from ``METADATA``).
+
+    This function returns a two-item tuple of dicts. The first dict is of
+    recognized fields from the core metadata specification. Fields that can be
+    parsed and translated into Python's built-in types are converted
+    appropriately. All other fields are left as-is. Fields that are allowed to
+    appear multiple times are stored as lists.
+
+    The second dict contains all other fields from the metadata. This includes
+    any unrecognized fields. It also includes any fields which are expected to
+    be parsed into a built-in type but were not formatted appropriately. Finally,
+    any fields that are expected to appear only once but are repeated are
+    included in this dict.
+
+    """
+    raw: dict[str, str | list[str] | dict[str, str]] = {}
+    unparsed: dict[str, list[str]] = {}
+
+    if isinstance(data, str):
+        parsed = email.parser.Parser(policy=email.policy.compat32).parsestr(data)
+    else:
+        parsed = email.parser.BytesParser(policy=email.policy.compat32).parsebytes(data)
+
+    # We have to wrap parsed.keys() in a set, because in the case of multiple
+    # values for a key (a list), the key will appear multiple times in the
+    # list of keys, but we're avoiding that by using get_all().
+    for name in frozenset(parsed.keys()):
+        # Header names in RFC are case insensitive, so we'll normalize to all
+        # lower case to make comparisons easier.
+        name = name.lower()
+
+        # We use get_all() here, even for fields that aren't multiple use,
+        # because otherwise someone could have e.g. two Name fields, and we
+        # would just silently ignore it rather than doing something about it.
+        headers = parsed.get_all(name) or []
+
+        # The way the email module works when parsing bytes is that it
+        # unconditionally decodes the bytes as ascii using the surrogateescape
+        # handler. When you pull that data back out (such as with get_all() ),
+        # it looks to see if the str has any surrogate escapes, and if it does
+        # it wraps it in a Header object instead of returning the string.
+        #
+        # As such, we'll look for those Header objects, and fix up the encoding.
+        value = []
+        # Flag if we have run into any issues processing the headers, thus
+        # signalling that the data belongs in 'unparsed'.
+        valid_encoding = True
+        for h in headers:
+            # It's unclear if this can return more types than just a Header or
+            # a str, so we'll just assert here to make sure.
+            assert isinstance(h, (email.header.Header, str))
+
+            # If it's a header object, we need to do our little dance to get
+            # the real data out of it. In cases where there is invalid data
+            # we're going to end up with mojibake, but there's no obvious, good
+            # way around that without reimplementing parts of the Header object
+            # ourselves.
+            #
+            # That should be fine since, if mojibacked happens, this key is
+            # going into the unparsed dict anyways.
+            if isinstance(h, email.header.Header):
+                # The Header object stores it's data as chunks, and each chunk
+                # can be independently encoded, so we'll need to check each
+                # of them.
+                chunks: list[tuple[bytes, str | None]] = []
+                for bin, encoding in email.header.decode_header(h):
+                    try:
+                        bin.decode("utf8", "strict")
+                    except UnicodeDecodeError:
+                        # Enable mojibake.
+                        encoding = "latin1"
+                        valid_encoding = False
+                    else:
+                        encoding = "utf8"
+                    chunks.append((bin, encoding))
+
+                # Turn our chunks back into a Header object, then let that
+                # Header object do the right thing to turn them into a
+                # string for us.
+                value.append(str(email.header.make_header(chunks)))
+            # This is already a string, so just add it.
+            else:
+                value.append(h)
+
+        # We've processed all of our values to get them into a list of str,
+        # but we may have mojibake data, in which case this is an unparsed
+        # field.
+        if not valid_encoding:
+            unparsed[name] = value
+            continue
+
+        raw_name = _EMAIL_TO_RAW_MAPPING.get(name)
+        if raw_name is None:
+            # This is a bit of a weird situation, we've encountered a key that
+            # we don't know what it means, so we don't know whether it's meant
+            # to be a list or not.
+            #
+            # Since we can't really tell one way or another, we'll just leave it
+            # as a list, even though it may be a single item list, because that's
+            # what makes the most sense for email headers.
+            unparsed[name] = value
+            continue
+
+        # If this is one of our string fields, then we'll check to see if our
+        # value is a list of a single item. If it is then we'll assume that
+        # it was emitted as a single string, and unwrap the str from inside
+        # the list.
+        #
+        # If it's any other kind of data, then we haven't the faintest clue
+        # what we should parse it as, and we have to just add it to our list
+        # of unparsed stuff.
+        if raw_name in _STRING_FIELDS and len(value) == 1:
+            raw[raw_name] = value[0]
+        # If this is one of our list of string fields, then we can just assign
+        # the value, since email *only* has strings, and our get_all() call
+        # above ensures that this is a list.
+        elif raw_name in _LIST_FIELDS:
+            raw[raw_name] = value
+        # Special Case: Keywords
+        # The keywords field is implemented in the metadata spec as a str,
+        # but it conceptually is a list of strings, and is serialized using
+        # ", ".join(keywords), so we'll do some light data massaging to turn
+        # this into what it logically is.
+        elif raw_name == "keywords" and len(value) == 1:
+            raw[raw_name] = _parse_keywords(value[0])
+        # Special Case: Project-URL
+        # The project urls is implemented in the metadata spec as a list of
+        # specially-formatted strings that represent a key and a value, which
+        # is fundamentally a mapping, however the email format doesn't support
+        # mappings in a sane way, so it was crammed into a list of strings
+        # instead.
+        #
+        # We will do a little light data massaging to turn this into a map as
+        # it logically should be.
+        elif raw_name == "project_urls":
+            try:
+                raw[raw_name] = _parse_project_urls(value)
+            except KeyError:
+                unparsed[name] = value
+        # Nothing that we've done has managed to parse this, so it'll just
+        # throw it in our unparseable data and move on.
+        else:
+            unparsed[name] = value
+
+    # We need to support getting the Description from the message payload in
+    # addition to getting it from the the headers. This does mean, though, there
+    # is the possibility of it being set both ways, in which case we put both
+    # in 'unparsed' since we don't know which is right.
+    try:
+        payload = _get_payload(parsed, data)
+    except ValueError:
+        unparsed.setdefault("description", []).append(
+            parsed.get_payload(decode=isinstance(data, bytes))  # type: ignore[call-overload]
+        )
+    else:
+        if payload:
+            # Check to see if we've already got a description, if so then both
+            # it, and this body move to unparseable.
+            if "description" in raw:
+                description_header = cast(str, raw.pop("description"))
+                unparsed.setdefault("description", []).extend(
+                    [description_header, payload]
+                )
+            elif "description" in unparsed:
+                unparsed["description"].append(payload)
+            else:
+                raw["description"] = payload
+
+    # We need to cast our `raw` to a metadata, because a TypedDict only support
+    # literal key names, but we're computing our key names on purpose, but the
+    # way this function is implemented, our `TypedDict` can only have valid key
+    # names.
+    return cast(RawMetadata, raw), unparsed
+
+
+_NOT_FOUND = object()
+
+
+# Keep the two values in sync.
+_VALID_METADATA_VERSIONS = ["1.0", "1.1", "1.2", "2.1", "2.2", "2.3", "2.4"]
+_MetadataVersion = Literal["1.0", "1.1", "1.2", "2.1", "2.2", "2.3", "2.4"]
+
+_REQUIRED_ATTRS = frozenset(["metadata_version", "name", "version"])
+
+
+class _Validator(Generic[T]):
+    """Validate a metadata field.
+
+    All _process_*() methods correspond to a core metadata field. The method is
+    called with the field's raw value. If the raw value is valid it is returned
+    in its "enriched" form (e.g. ``version.Version`` for the ``Version`` field).
+    If the raw value is invalid, :exc:`InvalidMetadata` is raised (with a cause
+    as appropriate).
+    """
+
+    name: str
+    raw_name: str
+    added: _MetadataVersion
+
+    def __init__(
+        self,
+        *,
+        added: _MetadataVersion = "1.0",
+    ) -> None:
+        self.added = added
+
+    def __set_name__(self, _owner: Metadata, name: str) -> None:
+        self.name = name
+        self.raw_name = _RAW_TO_EMAIL_MAPPING[name]
+
+    def __get__(self, instance: Metadata, _owner: type[Metadata]) -> T:
+        # With Python 3.8, the caching can be replaced with functools.cached_property().
+        # No need to check the cache as attribute lookup will resolve into the
+        # instance's __dict__ before __get__ is called.
+        cache = instance.__dict__
+        value = instance._raw.get(self.name)
+
+        # To make the _process_* methods easier, we'll check if the value is None
+        # and if this field is NOT a required attribute, and if both of those
+        # things are true, we'll skip the the converter. This will mean that the
+        # converters never have to deal with the None union.
+        if self.name in _REQUIRED_ATTRS or value is not None:
+            try:
+                converter: Callable[[Any], T] = getattr(self, f"_process_{self.name}")
+            except AttributeError:
+                pass
+            else:
+                value = converter(value)
+
+        cache[self.name] = value
+        try:
+            del instance._raw[self.name]  # type: ignore[misc]
+        except KeyError:
+            pass
+
+        return cast(T, value)
+
+    def _invalid_metadata(
+        self, msg: str, cause: Exception | None = None
+    ) -> InvalidMetadata:
+        exc = InvalidMetadata(
+            self.raw_name, msg.format_map({"field": repr(self.raw_name)})
+        )
+        exc.__cause__ = cause
+        return exc
+
+    def _process_metadata_version(self, value: str) -> _MetadataVersion:
+        # Implicitly makes Metadata-Version required.
+        if value not in _VALID_METADATA_VERSIONS:
+            raise self._invalid_metadata(f"{value!r} is not a valid metadata version")
+        return cast(_MetadataVersion, value)
+
+    def _process_name(self, value: str) -> str:
+        if not value:
+            raise self._invalid_metadata("{field} is a required field")
+        # Validate the name as a side-effect.
+        try:
+            utils.canonicalize_name(value, validate=True)
+        except utils.InvalidName as exc:
+            raise self._invalid_metadata(
+                f"{value!r} is invalid for {{field}}", cause=exc
+            ) from exc
+        else:
+            return value
+
+    def _process_version(self, value: str) -> version_module.Version:
+        if not value:
+            raise self._invalid_metadata("{field} is a required field")
+        try:
+            return version_module.parse(value)
+        except version_module.InvalidVersion as exc:
+            raise self._invalid_metadata(
+                f"{value!r} is invalid for {{field}}", cause=exc
+            ) from exc
+
+    def _process_summary(self, value: str) -> str:
+        """Check the field contains no newlines."""
+        if "\n" in value:
+            raise self._invalid_metadata("{field} must be a single line")
+        return value
+
+    def _process_description_content_type(self, value: str) -> str:
+        content_types = {"text/plain", "text/x-rst", "text/markdown"}
+        message = email.message.EmailMessage()
+        message["content-type"] = value
+
+        content_type, parameters = (
+            # Defaults to `text/plain` if parsing failed.
+            message.get_content_type().lower(),
+            message["content-type"].params,
+        )
+        # Check if content-type is valid or defaulted to `text/plain` and thus was
+        # not parseable.
+        if content_type not in content_types or content_type not in value.lower():
+            raise self._invalid_metadata(
+                f"{{field}} must be one of {list(content_types)}, not {value!r}"
+            )
+
+        charset = parameters.get("charset", "UTF-8")
+        if charset != "UTF-8":
+            raise self._invalid_metadata(
+                f"{{field}} can only specify the UTF-8 charset, not {list(charset)}"
+            )
+
+        markdown_variants = {"GFM", "CommonMark"}
+        variant = parameters.get("variant", "GFM")  # Use an acceptable default.
+        if content_type == "text/markdown" and variant not in markdown_variants:
+            raise self._invalid_metadata(
+                f"valid Markdown variants for {{field}} are {list(markdown_variants)}, "
+                f"not {variant!r}",
+            )
+        return value
+
+    def _process_dynamic(self, value: list[str]) -> list[str]:
+        for dynamic_field in map(str.lower, value):
+            if dynamic_field in {"name", "version", "metadata-version"}:
+                raise self._invalid_metadata(
+                    f"{dynamic_field!r} is not allowed as a dynamic field"
+                )
+            elif dynamic_field not in _EMAIL_TO_RAW_MAPPING:
+                raise self._invalid_metadata(
+                    f"{dynamic_field!r} is not a valid dynamic field"
+                )
+        return list(map(str.lower, value))
+
+    def _process_provides_extra(
+        self,
+        value: list[str],
+    ) -> list[utils.NormalizedName]:
+        normalized_names = []
+        try:
+            for name in value:
+                normalized_names.append(utils.canonicalize_name(name, validate=True))
+        except utils.InvalidName as exc:
+            raise self._invalid_metadata(
+                f"{name!r} is invalid for {{field}}", cause=exc
+            ) from exc
+        else:
+            return normalized_names
+
+    def _process_requires_python(self, value: str) -> specifiers.SpecifierSet:
+        try:
+            return specifiers.SpecifierSet(value)
+        except specifiers.InvalidSpecifier as exc:
+            raise self._invalid_metadata(
+                f"{value!r} is invalid for {{field}}", cause=exc
+            ) from exc
+
+    def _process_requires_dist(
+        self,
+        value: list[str],
+    ) -> list[requirements.Requirement]:
+        reqs = []
+        try:
+            for req in value:
+                reqs.append(requirements.Requirement(req))
+        except requirements.InvalidRequirement as exc:
+            raise self._invalid_metadata(
+                f"{req!r} is invalid for {{field}}", cause=exc
+            ) from exc
+        else:
+            return reqs
+
+    def _process_license_expression(
+        self, value: str
+    ) -> NormalizedLicenseExpression | None:
+        try:
+            return licenses.canonicalize_license_expression(value)
+        except ValueError as exc:
+            raise self._invalid_metadata(
+                f"{value!r} is invalid for {{field}}", cause=exc
+            ) from exc
+
+    def _process_license_files(self, value: list[str]) -> list[str]:
+        paths = []
+        for path in value:
+            if ".." in path:
+                raise self._invalid_metadata(
+                    f"{path!r} is invalid for {{field}}, "
+                    "parent directory indicators are not allowed"
+                )
+            if "*" in path:
+                raise self._invalid_metadata(
+                    f"{path!r} is invalid for {{field}}, paths must be resolved"
+                )
+            if (
+                pathlib.PurePosixPath(path).is_absolute()
+                or pathlib.PureWindowsPath(path).is_absolute()
+            ):
+                raise self._invalid_metadata(
+                    f"{path!r} is invalid for {{field}}, paths must be relative"
+                )
+            if pathlib.PureWindowsPath(path).as_posix() != path:
+                raise self._invalid_metadata(
+                    f"{path!r} is invalid for {{field}}, "
+                    "paths must use '/' delimiter"
+                )
+            paths.append(path)
+        return paths
+
+
+class Metadata:
+    """Representation of distribution metadata.
+
+    Compared to :class:`RawMetadata`, this class provides objects representing
+    metadata fields instead of only using built-in types. Any invalid metadata
+    will cause :exc:`InvalidMetadata` to be raised (with a
+    :py:attr:`~BaseException.__cause__` attribute as appropriate).
+    """
+
+    _raw: RawMetadata
+
+    @classmethod
+    def from_raw(cls, data: RawMetadata, *, validate: bool = True) -> Metadata:
+        """Create an instance from :class:`RawMetadata`.
+
+        If *validate* is true, all metadata will be validated. All exceptions
+        related to validation will be gathered and raised as an :class:`ExceptionGroup`.
+        """
+        ins = cls()
+        ins._raw = data.copy()  # Mutations occur due to caching enriched values.
+
+        if validate:
+            exceptions: list[Exception] = []
+            try:
+                metadata_version = ins.metadata_version
+                metadata_age = _VALID_METADATA_VERSIONS.index(metadata_version)
+            except InvalidMetadata as metadata_version_exc:
+                exceptions.append(metadata_version_exc)
+                metadata_version = None
+
+            # Make sure to check for the fields that are present, the required
+            # fields (so their absence can be reported).
+            fields_to_check = frozenset(ins._raw) | _REQUIRED_ATTRS
+            # Remove fields that have already been checked.
+            fields_to_check -= {"metadata_version"}
+
+            for key in fields_to_check:
+                try:
+                    if metadata_version:
+                        # Can't use getattr() as that triggers descriptor protocol which
+                        # will fail due to no value for the instance argument.
+                        try:
+                            field_metadata_version = cls.__dict__[key].added
+                        except KeyError:
+                            exc = InvalidMetadata(key, f"unrecognized field: {key!r}")
+                            exceptions.append(exc)
+                            continue
+                        field_age = _VALID_METADATA_VERSIONS.index(
+                            field_metadata_version
+                        )
+                        if field_age > metadata_age:
+                            field = _RAW_TO_EMAIL_MAPPING[key]
+                            exc = InvalidMetadata(
+                                field,
+                                f"{field} introduced in metadata version "
+                                f"{field_metadata_version}, not {metadata_version}",
+                            )
+                            exceptions.append(exc)
+                            continue
+                    getattr(ins, key)
+                except InvalidMetadata as exc:
+                    exceptions.append(exc)
+
+            if exceptions:
+                raise ExceptionGroup("invalid metadata", exceptions)
+
+        return ins
+
+    @classmethod
+    def from_email(cls, data: bytes | str, *, validate: bool = True) -> Metadata:
+        """Parse metadata from email headers.
+
+        If *validate* is true, the metadata will be validated. All exceptions
+        related to validation will be gathered and raised as an :class:`ExceptionGroup`.
+        """
+        raw, unparsed = parse_email(data)
+
+        if validate:
+            exceptions: list[Exception] = []
+            for unparsed_key in unparsed:
+                if unparsed_key in _EMAIL_TO_RAW_MAPPING:
+                    message = f"{unparsed_key!r} has invalid data"
+                else:
+                    message = f"unrecognized field: {unparsed_key!r}"
+                exceptions.append(InvalidMetadata(unparsed_key, message))
+
+            if exceptions:
+                raise ExceptionGroup("unparsed", exceptions)
+
+        try:
+            return cls.from_raw(raw, validate=validate)
+        except ExceptionGroup as exc_group:
+            raise ExceptionGroup(
+                "invalid or unparsed metadata", exc_group.exceptions
+            ) from None
+
+    metadata_version: _Validator[_MetadataVersion] = _Validator()
+    """:external:ref:`core-metadata-metadata-version`
+    (required; validated to be a valid metadata version)"""
+    # `name` is not normalized/typed to NormalizedName so as to provide access to
+    # the original/raw name.
+    name: _Validator[str] = _Validator()
+    """:external:ref:`core-metadata-name`
+    (required; validated using :func:`~packaging.utils.canonicalize_name` and its
+    *validate* parameter)"""
+    version: _Validator[version_module.Version] = _Validator()
+    """:external:ref:`core-metadata-version` (required)"""
+    dynamic: _Validator[list[str] | None] = _Validator(
+        added="2.2",
+    )
+    """:external:ref:`core-metadata-dynamic`
+    (validated against core metadata field names and lowercased)"""
+    platforms: _Validator[list[str] | None] = _Validator()
+    """:external:ref:`core-metadata-platform`"""
+    supported_platforms: _Validator[list[str] | None] = _Validator(added="1.1")
+    """:external:ref:`core-metadata-supported-platform`"""
+    summary: _Validator[str | None] = _Validator()
+    """:external:ref:`core-metadata-summary` (validated to contain no newlines)"""
+    description: _Validator[str | None] = _Validator()  # TODO 2.1: can be in body
+    """:external:ref:`core-metadata-description`"""
+    description_content_type: _Validator[str | None] = _Validator(added="2.1")
+    """:external:ref:`core-metadata-description-content-type` (validated)"""
+    keywords: _Validator[list[str] | None] = _Validator()
+    """:external:ref:`core-metadata-keywords`"""
+    home_page: _Validator[str | None] = _Validator()
+    """:external:ref:`core-metadata-home-page`"""
+    download_url: _Validator[str | None] = _Validator(added="1.1")
+    """:external:ref:`core-metadata-download-url`"""
+    author: _Validator[str | None] = _Validator()
+    """:external:ref:`core-metadata-author`"""
+    author_email: _Validator[str | None] = _Validator()
+    """:external:ref:`core-metadata-author-email`"""
+    maintainer: _Validator[str | None] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-maintainer`"""
+    maintainer_email: _Validator[str | None] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-maintainer-email`"""
+    license: _Validator[str | None] = _Validator()
+    """:external:ref:`core-metadata-license`"""
+    license_expression: _Validator[NormalizedLicenseExpression | None] = _Validator(
+        added="2.4"
+    )
+    """:external:ref:`core-metadata-license-expression`"""
+    license_files: _Validator[list[str] | None] = _Validator(added="2.4")
+    """:external:ref:`core-metadata-license-file`"""
+    classifiers: _Validator[list[str] | None] = _Validator(added="1.1")
+    """:external:ref:`core-metadata-classifier`"""
+    requires_dist: _Validator[list[requirements.Requirement] | None] = _Validator(
+        added="1.2"
+    )
+    """:external:ref:`core-metadata-requires-dist`"""
+    requires_python: _Validator[specifiers.SpecifierSet | None] = _Validator(
+        added="1.2"
+    )
+    """:external:ref:`core-metadata-requires-python`"""
+    # Because `Requires-External` allows for non-PEP 440 version specifiers, we
+    # don't do any processing on the values.
+    requires_external: _Validator[list[str] | None] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-requires-external`"""
+    project_urls: _Validator[dict[str, str] | None] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-project-url`"""
+    # PEP 685 lets us raise an error if an extra doesn't pass `Name` validation
+    # regardless of metadata version.
+    provides_extra: _Validator[list[utils.NormalizedName] | None] = _Validator(
+        added="2.1",
+    )
+    """:external:ref:`core-metadata-provides-extra`"""
+    provides_dist: _Validator[list[str] | None] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-provides-dist`"""
+    obsoletes_dist: _Validator[list[str] | None] = _Validator(added="1.2")
+    """:external:ref:`core-metadata-obsoletes-dist`"""
+    requires: _Validator[list[str] | None] = _Validator(added="1.1")
+    """``Requires`` (deprecated)"""
+    provides: _Validator[list[str] | None] = _Validator(added="1.1")
+    """``Provides`` (deprecated)"""
+    obsoletes: _Validator[list[str] | None] = _Validator(added="1.1")
+    """``Obsoletes`` (deprecated)"""
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/py.typed b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/requirements.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/requirements.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e068c9567def3564f238a76fe7ab46b569f33e5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/requirements.py
@@ -0,0 +1,91 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+from __future__ import annotations
+
+from typing import Any, Iterator
+
+from ._parser import parse_requirement as _parse_requirement
+from ._tokenizer import ParserSyntaxError
+from .markers import Marker, _normalize_extra_values
+from .specifiers import SpecifierSet
+from .utils import canonicalize_name
+
+
+class InvalidRequirement(ValueError):
+    """
+    An invalid requirement was found, users should refer to PEP 508.
+    """
+
+
+class Requirement:
+    """Parse a requirement.
+
+    Parse a given requirement string into its parts, such as name, specifier,
+    URL, and extras. Raises InvalidRequirement on a badly-formed requirement
+    string.
+    """
+
+    # TODO: Can we test whether something is contained within a requirement?
+    #       If so how do we do that? Do we need to test against the _name_ of
+    #       the thing as well as the version? What about the markers?
+    # TODO: Can we normalize the name and extra name?
+
+    def __init__(self, requirement_string: str) -> None:
+        try:
+            parsed = _parse_requirement(requirement_string)
+        except ParserSyntaxError as e:
+            raise InvalidRequirement(str(e)) from e
+
+        self.name: str = parsed.name
+        self.url: str | None = parsed.url or None
+        self.extras: set[str] = set(parsed.extras or [])
+        self.specifier: SpecifierSet = SpecifierSet(parsed.specifier)
+        self.marker: Marker | None = None
+        if parsed.marker is not None:
+            self.marker = Marker.__new__(Marker)
+            self.marker._markers = _normalize_extra_values(parsed.marker)
+
+    def _iter_parts(self, name: str) -> Iterator[str]:
+        yield name
+
+        if self.extras:
+            formatted_extras = ",".join(sorted(self.extras))
+            yield f"[{formatted_extras}]"
+
+        if self.specifier:
+            yield str(self.specifier)
+
+        if self.url:
+            yield f"@ {self.url}"
+            if self.marker:
+                yield " "
+
+        if self.marker:
+            yield f"; {self.marker}"
+
+    def __str__(self) -> str:
+        return "".join(self._iter_parts(self.name))
+
+    def __repr__(self) -> str:
+        return f"<Requirement('{self}')>"
+
+    def __hash__(self) -> int:
+        return hash(
+            (
+                self.__class__.__name__,
+                *self._iter_parts(canonicalize_name(self.name)),
+            )
+        )
+
+    def __eq__(self, other: Any) -> bool:
+        if not isinstance(other, Requirement):
+            return NotImplemented
+
+        return (
+            canonicalize_name(self.name) == canonicalize_name(other.name)
+            and self.extras == other.extras
+            and self.specifier == other.specifier
+            and self.url == other.url
+            and self.marker == other.marker
+        )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/specifiers.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/specifiers.py
new file mode 100644
index 0000000000000000000000000000000000000000..b30926af8bf4f47efe98eea44d5ded4cb6f7e07d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/specifiers.py
@@ -0,0 +1,1020 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+"""
+.. testsetup::
+
+    from packaging.specifiers import Specifier, SpecifierSet, InvalidSpecifier
+    from packaging.version import Version
+"""
+
+from __future__ import annotations
+
+import abc
+import itertools
+import re
+from typing import Callable, Iterable, Iterator, TypeVar, Union
+
+from .utils import canonicalize_version
+from .version import Version
+
+UnparsedVersion = Union[Version, str]
+UnparsedVersionVar = TypeVar("UnparsedVersionVar", bound=UnparsedVersion)
+CallableOperator = Callable[[Version, str], bool]
+
+
+def _coerce_version(version: UnparsedVersion) -> Version:
+    if not isinstance(version, Version):
+        version = Version(version)
+    return version
+
+
+class InvalidSpecifier(ValueError):
+    """
+    Raised when attempting to create a :class:`Specifier` with a specifier
+    string that is invalid.
+
+    >>> Specifier("lolwat")
+    Traceback (most recent call last):
+        ...
+    packaging.specifiers.InvalidSpecifier: Invalid specifier: 'lolwat'
+    """
+
+
+class BaseSpecifier(metaclass=abc.ABCMeta):
+    @abc.abstractmethod
+    def __str__(self) -> str:
+        """
+        Returns the str representation of this Specifier-like object. This
+        should be representative of the Specifier itself.
+        """
+
+    @abc.abstractmethod
+    def __hash__(self) -> int:
+        """
+        Returns a hash value for this Specifier-like object.
+        """
+
+    @abc.abstractmethod
+    def __eq__(self, other: object) -> bool:
+        """
+        Returns a boolean representing whether or not the two Specifier-like
+        objects are equal.
+
+        :param other: The other object to check against.
+        """
+
+    @property
+    @abc.abstractmethod
+    def prereleases(self) -> bool | None:
+        """Whether or not pre-releases as a whole are allowed.
+
+        This can be set to either ``True`` or ``False`` to explicitly enable or disable
+        prereleases or it can be set to ``None`` (the default) to use default semantics.
+        """
+
+    @prereleases.setter
+    def prereleases(self, value: bool) -> None:
+        """Setter for :attr:`prereleases`.
+
+        :param value: The value to set.
+        """
+
+    @abc.abstractmethod
+    def contains(self, item: str, prereleases: bool | None = None) -> bool:
+        """
+        Determines if the given item is contained within this specifier.
+        """
+
+    @abc.abstractmethod
+    def filter(
+        self, iterable: Iterable[UnparsedVersionVar], prereleases: bool | None = None
+    ) -> Iterator[UnparsedVersionVar]:
+        """
+        Takes an iterable of items and filters them so that only items which
+        are contained within this specifier are allowed in it.
+        """
+
+
+class Specifier(BaseSpecifier):
+    """This class abstracts handling of version specifiers.
+
+    .. tip::
+
+        It is generally not required to instantiate this manually. You should instead
+        prefer to work with :class:`SpecifierSet` instead, which can parse
+        comma-separated version specifiers (which is what package metadata contains).
+    """
+
+    _operator_regex_str = r"""
+        (?P<operator>(~=|==|!=|<=|>=|<|>|===))
+        """
+    _version_regex_str = r"""
+        (?P<version>
+            (?:
+                # The identity operators allow for an escape hatch that will
+                # do an exact string match of the version you wish to install.
+                # This will not be parsed by PEP 440 and we cannot determine
+                # any semantic meaning from it. This operator is discouraged
+                # but included entirely as an escape hatch.
+                (?<====)  # Only match for the identity operator
+                \s*
+                [^\s;)]*  # The arbitrary version can be just about anything,
+                          # we match everything except for whitespace, a
+                          # semi-colon for marker support, and a closing paren
+                          # since versions can be enclosed in them.
+            )
+            |
+            (?:
+                # The (non)equality operators allow for wild card and local
+                # versions to be specified so we have to define these two
+                # operators separately to enable that.
+                (?<===|!=)            # Only match for equals and not equals
+
+                \s*
+                v?
+                (?:[0-9]+!)?          # epoch
+                [0-9]+(?:\.[0-9]+)*   # release
+
+                # You cannot use a wild card and a pre-release, post-release, a dev or
+                # local version together so group them with a | and make them optional.
+                (?:
+                    \.\*  # Wild card syntax of .*
+                    |
+                    (?:                                  # pre release
+                        [-_\.]?
+                        (alpha|beta|preview|pre|a|b|c|rc)
+                        [-_\.]?
+                        [0-9]*
+                    )?
+                    (?:                                  # post release
+                        (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
+                    )?
+                    (?:[-_\.]?dev[-_\.]?[0-9]*)?         # dev release
+                    (?:\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*)? # local
+                )?
+            )
+            |
+            (?:
+                # The compatible operator requires at least two digits in the
+                # release segment.
+                (?<=~=)               # Only match for the compatible operator
+
+                \s*
+                v?
+                (?:[0-9]+!)?          # epoch
+                [0-9]+(?:\.[0-9]+)+   # release  (We have a + instead of a *)
+                (?:                   # pre release
+                    [-_\.]?
+                    (alpha|beta|preview|pre|a|b|c|rc)
+                    [-_\.]?
+                    [0-9]*
+                )?
+                (?:                                   # post release
+                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
+                )?
+                (?:[-_\.]?dev[-_\.]?[0-9]*)?          # dev release
+            )
+            |
+            (?:
+                # All other operators only allow a sub set of what the
+                # (non)equality operators do. Specifically they do not allow
+                # local versions to be specified nor do they allow the prefix
+                # matching wild cards.
+                (?<!==|!=|~=)         # We have special cases for these
+                                      # operators so we want to make sure they
+                                      # don't match here.
+
+                \s*
+                v?
+                (?:[0-9]+!)?          # epoch
+                [0-9]+(?:\.[0-9]+)*   # release
+                (?:                   # pre release
+                    [-_\.]?
+                    (alpha|beta|preview|pre|a|b|c|rc)
+                    [-_\.]?
+                    [0-9]*
+                )?
+                (?:                                   # post release
+                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
+                )?
+                (?:[-_\.]?dev[-_\.]?[0-9]*)?          # dev release
+            )
+        )
+        """
+
+    _regex = re.compile(
+        r"^\s*" + _operator_regex_str + _version_regex_str + r"\s*$",
+        re.VERBOSE | re.IGNORECASE,
+    )
+
+    _operators = {
+        "~=": "compatible",
+        "==": "equal",
+        "!=": "not_equal",
+        "<=": "less_than_equal",
+        ">=": "greater_than_equal",
+        "<": "less_than",
+        ">": "greater_than",
+        "===": "arbitrary",
+    }
+
+    def __init__(self, spec: str = "", prereleases: bool | None = None) -> None:
+        """Initialize a Specifier instance.
+
+        :param spec:
+            The string representation of a specifier which will be parsed and
+            normalized before use.
+        :param prereleases:
+            This tells the specifier if it should accept prerelease versions if
+            applicable or not. The default of ``None`` will autodetect it from the
+            given specifiers.
+        :raises InvalidSpecifier:
+            If the given specifier is invalid (i.e. bad syntax).
+        """
+        match = self._regex.search(spec)
+        if not match:
+            raise InvalidSpecifier(f"Invalid specifier: {spec!r}")
+
+        self._spec: tuple[str, str] = (
+            match.group("operator").strip(),
+            match.group("version").strip(),
+        )
+
+        # Store whether or not this Specifier should accept prereleases
+        self._prereleases = prereleases
+
+    # https://github.com/python/mypy/pull/13475#pullrequestreview-1079784515
+    @property  # type: ignore[override]
+    def prereleases(self) -> bool:
+        # If there is an explicit prereleases set for this, then we'll just
+        # blindly use that.
+        if self._prereleases is not None:
+            return self._prereleases
+
+        # Look at all of our specifiers and determine if they are inclusive
+        # operators, and if they are if they are including an explicit
+        # prerelease.
+        operator, version = self._spec
+        if operator in ["==", ">=", "<=", "~=", "===", ">", "<"]:
+            # The == specifier can include a trailing .*, if it does we
+            # want to remove before parsing.
+            if operator == "==" and version.endswith(".*"):
+                version = version[:-2]
+
+            # Parse the version, and if it is a pre-release than this
+            # specifier allows pre-releases.
+            if Version(version).is_prerelease:
+                return True
+
+        return False
+
+    @prereleases.setter
+    def prereleases(self, value: bool) -> None:
+        self._prereleases = value
+
+    @property
+    def operator(self) -> str:
+        """The operator of this specifier.
+
+        >>> Specifier("==1.2.3").operator
+        '=='
+        """
+        return self._spec[0]
+
+    @property
+    def version(self) -> str:
+        """The version of this specifier.
+
+        >>> Specifier("==1.2.3").version
+        '1.2.3'
+        """
+        return self._spec[1]
+
+    def __repr__(self) -> str:
+        """A representation of the Specifier that shows all internal state.
+
+        >>> Specifier('>=1.0.0')
+        <Specifier('>=1.0.0')>
+        >>> Specifier('>=1.0.0', prereleases=False)
+        <Specifier('>=1.0.0', prereleases=False)>
+        >>> Specifier('>=1.0.0', prereleases=True)
+        <Specifier('>=1.0.0', prereleases=True)>
+        """
+        pre = (
+            f", prereleases={self.prereleases!r}"
+            if self._prereleases is not None
+            else ""
+        )
+
+        return f"<{self.__class__.__name__}({str(self)!r}{pre})>"
+
+    def __str__(self) -> str:
+        """A string representation of the Specifier that can be round-tripped.
+
+        >>> str(Specifier('>=1.0.0'))
+        '>=1.0.0'
+        >>> str(Specifier('>=1.0.0', prereleases=False))
+        '>=1.0.0'
+        """
+        return "{}{}".format(*self._spec)
+
+    @property
+    def _canonical_spec(self) -> tuple[str, str]:
+        canonical_version = canonicalize_version(
+            self._spec[1],
+            strip_trailing_zero=(self._spec[0] != "~="),
+        )
+        return self._spec[0], canonical_version
+
+    def __hash__(self) -> int:
+        return hash(self._canonical_spec)
+
+    def __eq__(self, other: object) -> bool:
+        """Whether or not the two Specifier-like objects are equal.
+
+        :param other: The other object to check against.
+
+        The value of :attr:`prereleases` is ignored.
+
+        >>> Specifier("==1.2.3") == Specifier("== 1.2.3.0")
+        True
+        >>> (Specifier("==1.2.3", prereleases=False) ==
+        ...  Specifier("==1.2.3", prereleases=True))
+        True
+        >>> Specifier("==1.2.3") == "==1.2.3"
+        True
+        >>> Specifier("==1.2.3") == Specifier("==1.2.4")
+        False
+        >>> Specifier("==1.2.3") == Specifier("~=1.2.3")
+        False
+        """
+        if isinstance(other, str):
+            try:
+                other = self.__class__(str(other))
+            except InvalidSpecifier:
+                return NotImplemented
+        elif not isinstance(other, self.__class__):
+            return NotImplemented
+
+        return self._canonical_spec == other._canonical_spec
+
+    def _get_operator(self, op: str) -> CallableOperator:
+        operator_callable: CallableOperator = getattr(
+            self, f"_compare_{self._operators[op]}"
+        )
+        return operator_callable
+
+    def _compare_compatible(self, prospective: Version, spec: str) -> bool:
+        # Compatible releases have an equivalent combination of >= and ==. That
+        # is that ~=2.2 is equivalent to >=2.2,==2.*. This allows us to
+        # implement this in terms of the other specifiers instead of
+        # implementing it ourselves. The only thing we need to do is construct
+        # the other specifiers.
+
+        # We want everything but the last item in the version, but we want to
+        # ignore suffix segments.
+        prefix = _version_join(
+            list(itertools.takewhile(_is_not_suffix, _version_split(spec)))[:-1]
+        )
+
+        # Add the prefix notation to the end of our string
+        prefix += ".*"
+
+        return self._get_operator(">=")(prospective, spec) and self._get_operator("==")(
+            prospective, prefix
+        )
+
+    def _compare_equal(self, prospective: Version, spec: str) -> bool:
+        # We need special logic to handle prefix matching
+        if spec.endswith(".*"):
+            # In the case of prefix matching we want to ignore local segment.
+            normalized_prospective = canonicalize_version(
+                prospective.public, strip_trailing_zero=False
+            )
+            # Get the normalized version string ignoring the trailing .*
+            normalized_spec = canonicalize_version(spec[:-2], strip_trailing_zero=False)
+            # Split the spec out by bangs and dots, and pretend that there is
+            # an implicit dot in between a release segment and a pre-release segment.
+            split_spec = _version_split(normalized_spec)
+
+            # Split the prospective version out by bangs and dots, and pretend
+            # that there is an implicit dot in between a release segment and
+            # a pre-release segment.
+            split_prospective = _version_split(normalized_prospective)
+
+            # 0-pad the prospective version before shortening it to get the correct
+            # shortened version.
+            padded_prospective, _ = _pad_version(split_prospective, split_spec)
+
+            # Shorten the prospective version to be the same length as the spec
+            # so that we can determine if the specifier is a prefix of the
+            # prospective version or not.
+            shortened_prospective = padded_prospective[: len(split_spec)]
+
+            return shortened_prospective == split_spec
+        else:
+            # Convert our spec string into a Version
+            spec_version = Version(spec)
+
+            # If the specifier does not have a local segment, then we want to
+            # act as if the prospective version also does not have a local
+            # segment.
+            if not spec_version.local:
+                prospective = Version(prospective.public)
+
+            return prospective == spec_version
+
+    def _compare_not_equal(self, prospective: Version, spec: str) -> bool:
+        return not self._compare_equal(prospective, spec)
+
+    def _compare_less_than_equal(self, prospective: Version, spec: str) -> bool:
+        # NB: Local version identifiers are NOT permitted in the version
+        # specifier, so local version labels can be universally removed from
+        # the prospective version.
+        return Version(prospective.public) <= Version(spec)
+
+    def _compare_greater_than_equal(self, prospective: Version, spec: str) -> bool:
+        # NB: Local version identifiers are NOT permitted in the version
+        # specifier, so local version labels can be universally removed from
+        # the prospective version.
+        return Version(prospective.public) >= Version(spec)
+
+    def _compare_less_than(self, prospective: Version, spec_str: str) -> bool:
+        # Convert our spec to a Version instance, since we'll want to work with
+        # it as a version.
+        spec = Version(spec_str)
+
+        # Check to see if the prospective version is less than the spec
+        # version. If it's not we can short circuit and just return False now
+        # instead of doing extra unneeded work.
+        if not prospective < spec:
+            return False
+
+        # This special case is here so that, unless the specifier itself
+        # includes is a pre-release version, that we do not accept pre-release
+        # versions for the version mentioned in the specifier (e.g. <3.1 should
+        # not match 3.1.dev0, but should match 3.0.dev0).
+        if not spec.is_prerelease and prospective.is_prerelease:
+            if Version(prospective.base_version) == Version(spec.base_version):
+                return False
+
+        # If we've gotten to here, it means that prospective version is both
+        # less than the spec version *and* it's not a pre-release of the same
+        # version in the spec.
+        return True
+
+    def _compare_greater_than(self, prospective: Version, spec_str: str) -> bool:
+        # Convert our spec to a Version instance, since we'll want to work with
+        # it as a version.
+        spec = Version(spec_str)
+
+        # Check to see if the prospective version is greater than the spec
+        # version. If it's not we can short circuit and just return False now
+        # instead of doing extra unneeded work.
+        if not prospective > spec:
+            return False
+
+        # This special case is here so that, unless the specifier itself
+        # includes is a post-release version, that we do not accept
+        # post-release versions for the version mentioned in the specifier
+        # (e.g. >3.1 should not match 3.0.post0, but should match 3.2.post0).
+        if not spec.is_postrelease and prospective.is_postrelease:
+            if Version(prospective.base_version) == Version(spec.base_version):
+                return False
+
+        # Ensure that we do not allow a local version of the version mentioned
+        # in the specifier, which is technically greater than, to match.
+        if prospective.local is not None:
+            if Version(prospective.base_version) == Version(spec.base_version):
+                return False
+
+        # If we've gotten to here, it means that prospective version is both
+        # greater than the spec version *and* it's not a pre-release of the
+        # same version in the spec.
+        return True
+
+    def _compare_arbitrary(self, prospective: Version, spec: str) -> bool:
+        return str(prospective).lower() == str(spec).lower()
+
+    def __contains__(self, item: str | Version) -> bool:
+        """Return whether or not the item is contained in this specifier.
+
+        :param item: The item to check for.
+
+        This is used for the ``in`` operator and behaves the same as
+        :meth:`contains` with no ``prereleases`` argument passed.
+
+        >>> "1.2.3" in Specifier(">=1.2.3")
+        True
+        >>> Version("1.2.3") in Specifier(">=1.2.3")
+        True
+        >>> "1.0.0" in Specifier(">=1.2.3")
+        False
+        >>> "1.3.0a1" in Specifier(">=1.2.3")
+        False
+        >>> "1.3.0a1" in Specifier(">=1.2.3", prereleases=True)
+        True
+        """
+        return self.contains(item)
+
+    def contains(self, item: UnparsedVersion, prereleases: bool | None = None) -> bool:
+        """Return whether or not the item is contained in this specifier.
+
+        :param item:
+            The item to check for, which can be a version string or a
+            :class:`Version` instance.
+        :param prereleases:
+            Whether or not to match prereleases with this Specifier. If set to
+            ``None`` (the default), it uses :attr:`prereleases` to determine
+            whether or not prereleases are allowed.
+
+        >>> Specifier(">=1.2.3").contains("1.2.3")
+        True
+        >>> Specifier(">=1.2.3").contains(Version("1.2.3"))
+        True
+        >>> Specifier(">=1.2.3").contains("1.0.0")
+        False
+        >>> Specifier(">=1.2.3").contains("1.3.0a1")
+        False
+        >>> Specifier(">=1.2.3", prereleases=True).contains("1.3.0a1")
+        True
+        >>> Specifier(">=1.2.3").contains("1.3.0a1", prereleases=True)
+        True
+        """
+
+        # Determine if prereleases are to be allowed or not.
+        if prereleases is None:
+            prereleases = self.prereleases
+
+        # Normalize item to a Version, this allows us to have a shortcut for
+        # "2.0" in Specifier(">=2")
+        normalized_item = _coerce_version(item)
+
+        # Determine if we should be supporting prereleases in this specifier
+        # or not, if we do not support prereleases than we can short circuit
+        # logic if this version is a prereleases.
+        if normalized_item.is_prerelease and not prereleases:
+            return False
+
+        # Actually do the comparison to determine if this item is contained
+        # within this Specifier or not.
+        operator_callable: CallableOperator = self._get_operator(self.operator)
+        return operator_callable(normalized_item, self.version)
+
+    def filter(
+        self, iterable: Iterable[UnparsedVersionVar], prereleases: bool | None = None
+    ) -> Iterator[UnparsedVersionVar]:
+        """Filter items in the given iterable, that match the specifier.
+
+        :param iterable:
+            An iterable that can contain version strings and :class:`Version` instances.
+            The items in the iterable will be filtered according to the specifier.
+        :param prereleases:
+            Whether or not to allow prereleases in the returned iterator. If set to
+            ``None`` (the default), it will be intelligently decide whether to allow
+            prereleases or not (based on the :attr:`prereleases` attribute, and
+            whether the only versions matching are prereleases).
+
+        This method is smarter than just ``filter(Specifier().contains, [...])``
+        because it implements the rule from :pep:`440` that a prerelease item
+        SHOULD be accepted if no other versions match the given specifier.
+
+        >>> list(Specifier(">=1.2.3").filter(["1.2", "1.3", "1.5a1"]))
+        ['1.3']
+        >>> list(Specifier(">=1.2.3").filter(["1.2", "1.2.3", "1.3", Version("1.4")]))
+        ['1.2.3', '1.3', <Version('1.4')>]
+        >>> list(Specifier(">=1.2.3").filter(["1.2", "1.5a1"]))
+        ['1.5a1']
+        >>> list(Specifier(">=1.2.3").filter(["1.3", "1.5a1"], prereleases=True))
+        ['1.3', '1.5a1']
+        >>> list(Specifier(">=1.2.3", prereleases=True).filter(["1.3", "1.5a1"]))
+        ['1.3', '1.5a1']
+        """
+
+        yielded = False
+        found_prereleases = []
+
+        kw = {"prereleases": prereleases if prereleases is not None else True}
+
+        # Attempt to iterate over all the values in the iterable and if any of
+        # them match, yield them.
+        for version in iterable:
+            parsed_version = _coerce_version(version)
+
+            if self.contains(parsed_version, **kw):
+                # If our version is a prerelease, and we were not set to allow
+                # prereleases, then we'll store it for later in case nothing
+                # else matches this specifier.
+                if parsed_version.is_prerelease and not (
+                    prereleases or self.prereleases
+                ):
+                    found_prereleases.append(version)
+                # Either this is not a prerelease, or we should have been
+                # accepting prereleases from the beginning.
+                else:
+                    yielded = True
+                    yield version
+
+        # Now that we've iterated over everything, determine if we've yielded
+        # any values, and if we have not and we have any prereleases stored up
+        # then we will go ahead and yield the prereleases.
+        if not yielded and found_prereleases:
+            for version in found_prereleases:
+                yield version
+
+
+_prefix_regex = re.compile(r"^([0-9]+)((?:a|b|c|rc)[0-9]+)$")
+
+
+def _version_split(version: str) -> list[str]:
+    """Split version into components.
+
+    The split components are intended for version comparison. The logic does
+    not attempt to retain the original version string, so joining the
+    components back with :func:`_version_join` may not produce the original
+    version string.
+    """
+    result: list[str] = []
+
+    epoch, _, rest = version.rpartition("!")
+    result.append(epoch or "0")
+
+    for item in rest.split("."):
+        match = _prefix_regex.search(item)
+        if match:
+            result.extend(match.groups())
+        else:
+            result.append(item)
+    return result
+
+
+def _version_join(components: list[str]) -> str:
+    """Join split version components into a version string.
+
+    This function assumes the input came from :func:`_version_split`, where the
+    first component must be the epoch (either empty or numeric), and all other
+    components numeric.
+    """
+    epoch, *rest = components
+    return f"{epoch}!{'.'.join(rest)}"
+
+
+def _is_not_suffix(segment: str) -> bool:
+    return not any(
+        segment.startswith(prefix) for prefix in ("dev", "a", "b", "rc", "post")
+    )
+
+
+def _pad_version(left: list[str], right: list[str]) -> tuple[list[str], list[str]]:
+    left_split, right_split = [], []
+
+    # Get the release segment of our versions
+    left_split.append(list(itertools.takewhile(lambda x: x.isdigit(), left)))
+    right_split.append(list(itertools.takewhile(lambda x: x.isdigit(), right)))
+
+    # Get the rest of our versions
+    left_split.append(left[len(left_split[0]) :])
+    right_split.append(right[len(right_split[0]) :])
+
+    # Insert our padding
+    left_split.insert(1, ["0"] * max(0, len(right_split[0]) - len(left_split[0])))
+    right_split.insert(1, ["0"] * max(0, len(left_split[0]) - len(right_split[0])))
+
+    return (
+        list(itertools.chain.from_iterable(left_split)),
+        list(itertools.chain.from_iterable(right_split)),
+    )
+
+
+class SpecifierSet(BaseSpecifier):
+    """This class abstracts handling of a set of version specifiers.
+
+    It can be passed a single specifier (``>=3.0``), a comma-separated list of
+    specifiers (``>=3.0,!=3.1``), or no specifier at all.
+    """
+
+    def __init__(
+        self,
+        specifiers: str | Iterable[Specifier] = "",
+        prereleases: bool | None = None,
+    ) -> None:
+        """Initialize a SpecifierSet instance.
+
+        :param specifiers:
+            The string representation of a specifier or a comma-separated list of
+            specifiers which will be parsed and normalized before use.
+            May also be an iterable of ``Specifier`` instances, which will be used
+            as is.
+        :param prereleases:
+            This tells the SpecifierSet if it should accept prerelease versions if
+            applicable or not. The default of ``None`` will autodetect it from the
+            given specifiers.
+
+        :raises InvalidSpecifier:
+            If the given ``specifiers`` are not parseable than this exception will be
+            raised.
+        """
+
+        if isinstance(specifiers, str):
+            # Split on `,` to break each individual specifier into its own item, and
+            # strip each item to remove leading/trailing whitespace.
+            split_specifiers = [s.strip() for s in specifiers.split(",") if s.strip()]
+
+            # Make each individual specifier a Specifier and save in a frozen set
+            # for later.
+            self._specs = frozenset(map(Specifier, split_specifiers))
+        else:
+            # Save the supplied specifiers in a frozen set.
+            self._specs = frozenset(specifiers)
+
+        # Store our prereleases value so we can use it later to determine if
+        # we accept prereleases or not.
+        self._prereleases = prereleases
+
+    @property
+    def prereleases(self) -> bool | None:
+        # If we have been given an explicit prerelease modifier, then we'll
+        # pass that through here.
+        if self._prereleases is not None:
+            return self._prereleases
+
+        # If we don't have any specifiers, and we don't have a forced value,
+        # then we'll just return None since we don't know if this should have
+        # pre-releases or not.
+        if not self._specs:
+            return None
+
+        # Otherwise we'll see if any of the given specifiers accept
+        # prereleases, if any of them do we'll return True, otherwise False.
+        return any(s.prereleases for s in self._specs)
+
+    @prereleases.setter
+    def prereleases(self, value: bool) -> None:
+        self._prereleases = value
+
+    def __repr__(self) -> str:
+        """A representation of the specifier set that shows all internal state.
+
+        Note that the ordering of the individual specifiers within the set may not
+        match the input string.
+
+        >>> SpecifierSet('>=1.0.0,!=2.0.0')
+        <SpecifierSet('!=2.0.0,>=1.0.0')>
+        >>> SpecifierSet('>=1.0.0,!=2.0.0', prereleases=False)
+        <SpecifierSet('!=2.0.0,>=1.0.0', prereleases=False)>
+        >>> SpecifierSet('>=1.0.0,!=2.0.0', prereleases=True)
+        <SpecifierSet('!=2.0.0,>=1.0.0', prereleases=True)>
+        """
+        pre = (
+            f", prereleases={self.prereleases!r}"
+            if self._prereleases is not None
+            else ""
+        )
+
+        return f"<SpecifierSet({str(self)!r}{pre})>"
+
+    def __str__(self) -> str:
+        """A string representation of the specifier set that can be round-tripped.
+
+        Note that the ordering of the individual specifiers within the set may not
+        match the input string.
+
+        >>> str(SpecifierSet(">=1.0.0,!=1.0.1"))
+        '!=1.0.1,>=1.0.0'
+        >>> str(SpecifierSet(">=1.0.0,!=1.0.1", prereleases=False))
+        '!=1.0.1,>=1.0.0'
+        """
+        return ",".join(sorted(str(s) for s in self._specs))
+
+    def __hash__(self) -> int:
+        return hash(self._specs)
+
+    def __and__(self, other: SpecifierSet | str) -> SpecifierSet:
+        """Return a SpecifierSet which is a combination of the two sets.
+
+        :param other: The other object to combine with.
+
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") & '<=2.0.0,!=2.0.1'
+        <SpecifierSet('!=1.0.1,!=2.0.1,<=2.0.0,>=1.0.0')>
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") & SpecifierSet('<=2.0.0,!=2.0.1')
+        <SpecifierSet('!=1.0.1,!=2.0.1,<=2.0.0,>=1.0.0')>
+        """
+        if isinstance(other, str):
+            other = SpecifierSet(other)
+        elif not isinstance(other, SpecifierSet):
+            return NotImplemented
+
+        specifier = SpecifierSet()
+        specifier._specs = frozenset(self._specs | other._specs)
+
+        if self._prereleases is None and other._prereleases is not None:
+            specifier._prereleases = other._prereleases
+        elif self._prereleases is not None and other._prereleases is None:
+            specifier._prereleases = self._prereleases
+        elif self._prereleases == other._prereleases:
+            specifier._prereleases = self._prereleases
+        else:
+            raise ValueError(
+                "Cannot combine SpecifierSets with True and False prerelease "
+                "overrides."
+            )
+
+        return specifier
+
+    def __eq__(self, other: object) -> bool:
+        """Whether or not the two SpecifierSet-like objects are equal.
+
+        :param other: The other object to check against.
+
+        The value of :attr:`prereleases` is ignored.
+
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0,!=1.0.1")
+        True
+        >>> (SpecifierSet(">=1.0.0,!=1.0.1", prereleases=False) ==
+        ...  SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True))
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") == ">=1.0.0,!=1.0.1"
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0")
+        False
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0,!=1.0.2")
+        False
+        """
+        if isinstance(other, (str, Specifier)):
+            other = SpecifierSet(str(other))
+        elif not isinstance(other, SpecifierSet):
+            return NotImplemented
+
+        return self._specs == other._specs
+
+    def __len__(self) -> int:
+        """Returns the number of specifiers in this specifier set."""
+        return len(self._specs)
+
+    def __iter__(self) -> Iterator[Specifier]:
+        """
+        Returns an iterator over all the underlying :class:`Specifier` instances
+        in this specifier set.
+
+        >>> sorted(SpecifierSet(">=1.0.0,!=1.0.1"), key=str)
+        [<Specifier('!=1.0.1')>, <Specifier('>=1.0.0')>]
+        """
+        return iter(self._specs)
+
+    def __contains__(self, item: UnparsedVersion) -> bool:
+        """Return whether or not the item is contained in this specifier.
+
+        :param item: The item to check for.
+
+        This is used for the ``in`` operator and behaves the same as
+        :meth:`contains` with no ``prereleases`` argument passed.
+
+        >>> "1.2.3" in SpecifierSet(">=1.0.0,!=1.0.1")
+        True
+        >>> Version("1.2.3") in SpecifierSet(">=1.0.0,!=1.0.1")
+        True
+        >>> "1.0.1" in SpecifierSet(">=1.0.0,!=1.0.1")
+        False
+        >>> "1.3.0a1" in SpecifierSet(">=1.0.0,!=1.0.1")
+        False
+        >>> "1.3.0a1" in SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True)
+        True
+        """
+        return self.contains(item)
+
+    def contains(
+        self,
+        item: UnparsedVersion,
+        prereleases: bool | None = None,
+        installed: bool | None = None,
+    ) -> bool:
+        """Return whether or not the item is contained in this SpecifierSet.
+
+        :param item:
+            The item to check for, which can be a version string or a
+            :class:`Version` instance.
+        :param prereleases:
+            Whether or not to match prereleases with this SpecifierSet. If set to
+            ``None`` (the default), it uses :attr:`prereleases` to determine
+            whether or not prereleases are allowed.
+
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.2.3")
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains(Version("1.2.3"))
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.0.1")
+        False
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.3.0a1")
+        False
+        >>> SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True).contains("1.3.0a1")
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.3.0a1", prereleases=True)
+        True
+        """
+        # Ensure that our item is a Version instance.
+        if not isinstance(item, Version):
+            item = Version(item)
+
+        # Determine if we're forcing a prerelease or not, if we're not forcing
+        # one for this particular filter call, then we'll use whatever the
+        # SpecifierSet thinks for whether or not we should support prereleases.
+        if prereleases is None:
+            prereleases = self.prereleases
+
+        # We can determine if we're going to allow pre-releases by looking to
+        # see if any of the underlying items supports them. If none of them do
+        # and this item is a pre-release then we do not allow it and we can
+        # short circuit that here.
+        # Note: This means that 1.0.dev1 would not be contained in something
+        #       like >=1.0.devabc however it would be in >=1.0.debabc,>0.0.dev0
+        if not prereleases and item.is_prerelease:
+            return False
+
+        if installed and item.is_prerelease:
+            item = Version(item.base_version)
+
+        # We simply dispatch to the underlying specs here to make sure that the
+        # given version is contained within all of them.
+        # Note: This use of all() here means that an empty set of specifiers
+        #       will always return True, this is an explicit design decision.
+        return all(s.contains(item, prereleases=prereleases) for s in self._specs)
+
+    def filter(
+        self, iterable: Iterable[UnparsedVersionVar], prereleases: bool | None = None
+    ) -> Iterator[UnparsedVersionVar]:
+        """Filter items in the given iterable, that match the specifiers in this set.
+
+        :param iterable:
+            An iterable that can contain version strings and :class:`Version` instances.
+            The items in the iterable will be filtered according to the specifier.
+        :param prereleases:
+            Whether or not to allow prereleases in the returned iterator. If set to
+            ``None`` (the default), it will be intelligently decide whether to allow
+            prereleases or not (based on the :attr:`prereleases` attribute, and
+            whether the only versions matching are prereleases).
+
+        This method is smarter than just ``filter(SpecifierSet(...).contains, [...])``
+        because it implements the rule from :pep:`440` that a prerelease item
+        SHOULD be accepted if no other versions match the given specifier.
+
+        >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.3", "1.5a1"]))
+        ['1.3']
+        >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.3", Version("1.4")]))
+        ['1.3', <Version('1.4')>]
+        >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.5a1"]))
+        []
+        >>> list(SpecifierSet(">=1.2.3").filter(["1.3", "1.5a1"], prereleases=True))
+        ['1.3', '1.5a1']
+        >>> list(SpecifierSet(">=1.2.3", prereleases=True).filter(["1.3", "1.5a1"]))
+        ['1.3', '1.5a1']
+
+        An "empty" SpecifierSet will filter items based on the presence of prerelease
+        versions in the set.
+
+        >>> list(SpecifierSet("").filter(["1.3", "1.5a1"]))
+        ['1.3']
+        >>> list(SpecifierSet("").filter(["1.5a1"]))
+        ['1.5a1']
+        >>> list(SpecifierSet("", prereleases=True).filter(["1.3", "1.5a1"]))
+        ['1.3', '1.5a1']
+        >>> list(SpecifierSet("").filter(["1.3", "1.5a1"], prereleases=True))
+        ['1.3', '1.5a1']
+        """
+        # Determine if we're forcing a prerelease or not, if we're not forcing
+        # one for this particular filter call, then we'll use whatever the
+        # SpecifierSet thinks for whether or not we should support prereleases.
+        if prereleases is None:
+            prereleases = self.prereleases
+
+        # If we have any specifiers, then we want to wrap our iterable in the
+        # filter method for each one, this will act as a logical AND amongst
+        # each specifier.
+        if self._specs:
+            for spec in self._specs:
+                iterable = spec.filter(iterable, prereleases=bool(prereleases))
+            return iter(iterable)
+        # If we do not have any specifiers, then we need to have a rough filter
+        # which will filter out any pre-releases, unless there are no final
+        # releases.
+        else:
+            filtered: list[UnparsedVersionVar] = []
+            found_prereleases: list[UnparsedVersionVar] = []
+
+            for item in iterable:
+                parsed_version = _coerce_version(item)
+
+                # Store any item which is a pre-release for later unless we've
+                # already found a final version or we are accepting prereleases
+                if parsed_version.is_prerelease and not prereleases:
+                    if not filtered:
+                        found_prereleases.append(item)
+                else:
+                    filtered.append(item)
+
+            # If we've found no items except for pre-releases, then we'll go
+            # ahead and use the pre-releases
+            if not filtered and found_prereleases and prereleases is None:
+                return iter(found_prereleases)
+
+            return iter(filtered)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/tags.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/tags.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5903402abb5a0aed37bb23914f678ef7e34a554
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/tags.py
@@ -0,0 +1,617 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+from __future__ import annotations
+
+import logging
+import platform
+import re
+import struct
+import subprocess
+import sys
+import sysconfig
+from importlib.machinery import EXTENSION_SUFFIXES
+from typing import (
+    Iterable,
+    Iterator,
+    Sequence,
+    Tuple,
+    cast,
+)
+
+from . import _manylinux, _musllinux
+
+logger = logging.getLogger(__name__)
+
+PythonVersion = Sequence[int]
+AppleVersion = Tuple[int, int]
+
+INTERPRETER_SHORT_NAMES: dict[str, str] = {
+    "python": "py",  # Generic.
+    "cpython": "cp",
+    "pypy": "pp",
+    "ironpython": "ip",
+    "jython": "jy",
+}
+
+
+_32_BIT_INTERPRETER = struct.calcsize("P") == 4
+
+
+class Tag:
+    """
+    A representation of the tag triple for a wheel.
+
+    Instances are considered immutable and thus are hashable. Equality checking
+    is also supported.
+    """
+
+    __slots__ = ["_abi", "_hash", "_interpreter", "_platform"]
+
+    def __init__(self, interpreter: str, abi: str, platform: str) -> None:
+        self._interpreter = interpreter.lower()
+        self._abi = abi.lower()
+        self._platform = platform.lower()
+        # The __hash__ of every single element in a Set[Tag] will be evaluated each time
+        # that a set calls its `.disjoint()` method, which may be called hundreds of
+        # times when scanning a page of links for packages with tags matching that
+        # Set[Tag]. Pre-computing the value here produces significant speedups for
+        # downstream consumers.
+        self._hash = hash((self._interpreter, self._abi, self._platform))
+
+    @property
+    def interpreter(self) -> str:
+        return self._interpreter
+
+    @property
+    def abi(self) -> str:
+        return self._abi
+
+    @property
+    def platform(self) -> str:
+        return self._platform
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Tag):
+            return NotImplemented
+
+        return (
+            (self._hash == other._hash)  # Short-circuit ASAP for perf reasons.
+            and (self._platform == other._platform)
+            and (self._abi == other._abi)
+            and (self._interpreter == other._interpreter)
+        )
+
+    def __hash__(self) -> int:
+        return self._hash
+
+    def __str__(self) -> str:
+        return f"{self._interpreter}-{self._abi}-{self._platform}"
+
+    def __repr__(self) -> str:
+        return f"<{self} @ {id(self)}>"
+
+
+def parse_tag(tag: str) -> frozenset[Tag]:
+    """
+    Parses the provided tag (e.g. `py3-none-any`) into a frozenset of Tag instances.
+
+    Returning a set is required due to the possibility that the tag is a
+    compressed tag set.
+    """
+    tags = set()
+    interpreters, abis, platforms = tag.split("-")
+    for interpreter in interpreters.split("."):
+        for abi in abis.split("."):
+            for platform_ in platforms.split("."):
+                tags.add(Tag(interpreter, abi, platform_))
+    return frozenset(tags)
+
+
+def _get_config_var(name: str, warn: bool = False) -> int | str | None:
+    value: int | str | None = sysconfig.get_config_var(name)
+    if value is None and warn:
+        logger.debug(
+            "Config variable '%s' is unset, Python ABI tag may be incorrect", name
+        )
+    return value
+
+
+def _normalize_string(string: str) -> str:
+    return string.replace(".", "_").replace("-", "_").replace(" ", "_")
+
+
+def _is_threaded_cpython(abis: list[str]) -> bool:
+    """
+    Determine if the ABI corresponds to a threaded (`--disable-gil`) build.
+
+    The threaded builds are indicated by a "t" in the abiflags.
+    """
+    if len(abis) == 0:
+        return False
+    # expect e.g., cp313
+    m = re.match(r"cp\d+(.*)", abis[0])
+    if not m:
+        return False
+    abiflags = m.group(1)
+    return "t" in abiflags
+
+
+def _abi3_applies(python_version: PythonVersion, threading: bool) -> bool:
+    """
+    Determine if the Python version supports abi3.
+
+    PEP 384 was first implemented in Python 3.2. The threaded (`--disable-gil`)
+    builds do not support abi3.
+    """
+    return len(python_version) > 1 and tuple(python_version) >= (3, 2) and not threading
+
+
+def _cpython_abis(py_version: PythonVersion, warn: bool = False) -> list[str]:
+    py_version = tuple(py_version)  # To allow for version comparison.
+    abis = []
+    version = _version_nodot(py_version[:2])
+    threading = debug = pymalloc = ucs4 = ""
+    with_debug = _get_config_var("Py_DEBUG", warn)
+    has_refcount = hasattr(sys, "gettotalrefcount")
+    # Windows doesn't set Py_DEBUG, so checking for support of debug-compiled
+    # extension modules is the best option.
+    # https://github.com/pypa/pip/issues/3383#issuecomment-173267692
+    has_ext = "_d.pyd" in EXTENSION_SUFFIXES
+    if with_debug or (with_debug is None and (has_refcount or has_ext)):
+        debug = "d"
+    if py_version >= (3, 13) and _get_config_var("Py_GIL_DISABLED", warn):
+        threading = "t"
+    if py_version < (3, 8):
+        with_pymalloc = _get_config_var("WITH_PYMALLOC", warn)
+        if with_pymalloc or with_pymalloc is None:
+            pymalloc = "m"
+        if py_version < (3, 3):
+            unicode_size = _get_config_var("Py_UNICODE_SIZE", warn)
+            if unicode_size == 4 or (
+                unicode_size is None and sys.maxunicode == 0x10FFFF
+            ):
+                ucs4 = "u"
+    elif debug:
+        # Debug builds can also load "normal" extension modules.
+        # We can also assume no UCS-4 or pymalloc requirement.
+        abis.append(f"cp{version}{threading}")
+    abis.insert(0, f"cp{version}{threading}{debug}{pymalloc}{ucs4}")
+    return abis
+
+
+def cpython_tags(
+    python_version: PythonVersion | None = None,
+    abis: Iterable[str] | None = None,
+    platforms: Iterable[str] | None = None,
+    *,
+    warn: bool = False,
+) -> Iterator[Tag]:
+    """
+    Yields the tags for a CPython interpreter.
+
+    The tags consist of:
+    - cp<python_version>-<abi>-<platform>
+    - cp<python_version>-abi3-<platform>
+    - cp<python_version>-none-<platform>
+    - cp<less than python_version>-abi3-<platform>  # Older Python versions down to 3.2.
+
+    If python_version only specifies a major version then user-provided ABIs and
+    the 'none' ABItag will be used.
+
+    If 'abi3' or 'none' are specified in 'abis' then they will be yielded at
+    their normal position and not at the beginning.
+    """
+    if not python_version:
+        python_version = sys.version_info[:2]
+
+    interpreter = f"cp{_version_nodot(python_version[:2])}"
+
+    if abis is None:
+        if len(python_version) > 1:
+            abis = _cpython_abis(python_version, warn)
+        else:
+            abis = []
+    abis = list(abis)
+    # 'abi3' and 'none' are explicitly handled later.
+    for explicit_abi in ("abi3", "none"):
+        try:
+            abis.remove(explicit_abi)
+        except ValueError:
+            pass
+
+    platforms = list(platforms or platform_tags())
+    for abi in abis:
+        for platform_ in platforms:
+            yield Tag(interpreter, abi, platform_)
+
+    threading = _is_threaded_cpython(abis)
+    use_abi3 = _abi3_applies(python_version, threading)
+    if use_abi3:
+        yield from (Tag(interpreter, "abi3", platform_) for platform_ in platforms)
+    yield from (Tag(interpreter, "none", platform_) for platform_ in platforms)
+
+    if use_abi3:
+        for minor_version in range(python_version[1] - 1, 1, -1):
+            for platform_ in platforms:
+                version = _version_nodot((python_version[0], minor_version))
+                interpreter = f"cp{version}"
+                yield Tag(interpreter, "abi3", platform_)
+
+
+def _generic_abi() -> list[str]:
+    """
+    Return the ABI tag based on EXT_SUFFIX.
+    """
+    # The following are examples of `EXT_SUFFIX`.
+    # We want to keep the parts which are related to the ABI and remove the
+    # parts which are related to the platform:
+    # - linux:   '.cpython-310-x86_64-linux-gnu.so' => cp310
+    # - mac:     '.cpython-310-darwin.so'           => cp310
+    # - win:     '.cp310-win_amd64.pyd'             => cp310
+    # - win:     '.pyd'                             => cp37 (uses _cpython_abis())
+    # - pypy:    '.pypy38-pp73-x86_64-linux-gnu.so' => pypy38_pp73
+    # - graalpy: '.graalpy-38-native-x86_64-darwin.dylib'
+    #                                               => graalpy_38_native
+
+    ext_suffix = _get_config_var("EXT_SUFFIX", warn=True)
+    if not isinstance(ext_suffix, str) or ext_suffix[0] != ".":
+        raise SystemError("invalid sysconfig.get_config_var('EXT_SUFFIX')")
+    parts = ext_suffix.split(".")
+    if len(parts) < 3:
+        # CPython3.7 and earlier uses ".pyd" on Windows.
+        return _cpython_abis(sys.version_info[:2])
+    soabi = parts[1]
+    if soabi.startswith("cpython"):
+        # non-windows
+        abi = "cp" + soabi.split("-")[1]
+    elif soabi.startswith("cp"):
+        # windows
+        abi = soabi.split("-")[0]
+    elif soabi.startswith("pypy"):
+        abi = "-".join(soabi.split("-")[:2])
+    elif soabi.startswith("graalpy"):
+        abi = "-".join(soabi.split("-")[:3])
+    elif soabi:
+        # pyston, ironpython, others?
+        abi = soabi
+    else:
+        return []
+    return [_normalize_string(abi)]
+
+
+def generic_tags(
+    interpreter: str | None = None,
+    abis: Iterable[str] | None = None,
+    platforms: Iterable[str] | None = None,
+    *,
+    warn: bool = False,
+) -> Iterator[Tag]:
+    """
+    Yields the tags for a generic interpreter.
+
+    The tags consist of:
+    - <interpreter>-<abi>-<platform>
+
+    The "none" ABI will be added if it was not explicitly provided.
+    """
+    if not interpreter:
+        interp_name = interpreter_name()
+        interp_version = interpreter_version(warn=warn)
+        interpreter = "".join([interp_name, interp_version])
+    if abis is None:
+        abis = _generic_abi()
+    else:
+        abis = list(abis)
+    platforms = list(platforms or platform_tags())
+    if "none" not in abis:
+        abis.append("none")
+    for abi in abis:
+        for platform_ in platforms:
+            yield Tag(interpreter, abi, platform_)
+
+
+def _py_interpreter_range(py_version: PythonVersion) -> Iterator[str]:
+    """
+    Yields Python versions in descending order.
+
+    After the latest version, the major-only version will be yielded, and then
+    all previous versions of that major version.
+    """
+    if len(py_version) > 1:
+        yield f"py{_version_nodot(py_version[:2])}"
+    yield f"py{py_version[0]}"
+    if len(py_version) > 1:
+        for minor in range(py_version[1] - 1, -1, -1):
+            yield f"py{_version_nodot((py_version[0], minor))}"
+
+
+def compatible_tags(
+    python_version: PythonVersion | None = None,
+    interpreter: str | None = None,
+    platforms: Iterable[str] | None = None,
+) -> Iterator[Tag]:
+    """
+    Yields the sequence of tags that are compatible with a specific version of Python.
+
+    The tags consist of:
+    - py*-none-<platform>
+    - <interpreter>-none-any  # ... if `interpreter` is provided.
+    - py*-none-any
+    """
+    if not python_version:
+        python_version = sys.version_info[:2]
+    platforms = list(platforms or platform_tags())
+    for version in _py_interpreter_range(python_version):
+        for platform_ in platforms:
+            yield Tag(version, "none", platform_)
+    if interpreter:
+        yield Tag(interpreter, "none", "any")
+    for version in _py_interpreter_range(python_version):
+        yield Tag(version, "none", "any")
+
+
+def _mac_arch(arch: str, is_32bit: bool = _32_BIT_INTERPRETER) -> str:
+    if not is_32bit:
+        return arch
+
+    if arch.startswith("ppc"):
+        return "ppc"
+
+    return "i386"
+
+
+def _mac_binary_formats(version: AppleVersion, cpu_arch: str) -> list[str]:
+    formats = [cpu_arch]
+    if cpu_arch == "x86_64":
+        if version < (10, 4):
+            return []
+        formats.extend(["intel", "fat64", "fat32"])
+
+    elif cpu_arch == "i386":
+        if version < (10, 4):
+            return []
+        formats.extend(["intel", "fat32", "fat"])
+
+    elif cpu_arch == "ppc64":
+        # TODO: Need to care about 32-bit PPC for ppc64 through 10.2?
+        if version > (10, 5) or version < (10, 4):
+            return []
+        formats.append("fat64")
+
+    elif cpu_arch == "ppc":
+        if version > (10, 6):
+            return []
+        formats.extend(["fat32", "fat"])
+
+    if cpu_arch in {"arm64", "x86_64"}:
+        formats.append("universal2")
+
+    if cpu_arch in {"x86_64", "i386", "ppc64", "ppc", "intel"}:
+        formats.append("universal")
+
+    return formats
+
+
+def mac_platforms(
+    version: AppleVersion | None = None, arch: str | None = None
+) -> Iterator[str]:
+    """
+    Yields the platform tags for a macOS system.
+
+    The `version` parameter is a two-item tuple specifying the macOS version to
+    generate platform tags for. The `arch` parameter is the CPU architecture to
+    generate platform tags for. Both parameters default to the appropriate value
+    for the current system.
+    """
+    version_str, _, cpu_arch = platform.mac_ver()
+    if version is None:
+        version = cast("AppleVersion", tuple(map(int, version_str.split(".")[:2])))
+        if version == (10, 16):
+            # When built against an older macOS SDK, Python will report macOS 10.16
+            # instead of the real version.
+            version_str = subprocess.run(
+                [
+                    sys.executable,
+                    "-sS",
+                    "-c",
+                    "import platform; print(platform.mac_ver()[0])",
+                ],
+                check=True,
+                env={"SYSTEM_VERSION_COMPAT": "0"},
+                stdout=subprocess.PIPE,
+                text=True,
+            ).stdout
+            version = cast("AppleVersion", tuple(map(int, version_str.split(".")[:2])))
+    else:
+        version = version
+    if arch is None:
+        arch = _mac_arch(cpu_arch)
+    else:
+        arch = arch
+
+    if (10, 0) <= version and version < (11, 0):
+        # Prior to Mac OS 11, each yearly release of Mac OS bumped the
+        # "minor" version number.  The major version was always 10.
+        major_version = 10
+        for minor_version in range(version[1], -1, -1):
+            compat_version = major_version, minor_version
+            binary_formats = _mac_binary_formats(compat_version, arch)
+            for binary_format in binary_formats:
+                yield f"macosx_{major_version}_{minor_version}_{binary_format}"
+
+    if version >= (11, 0):
+        # Starting with Mac OS 11, each yearly release bumps the major version
+        # number.   The minor versions are now the midyear updates.
+        minor_version = 0
+        for major_version in range(version[0], 10, -1):
+            compat_version = major_version, minor_version
+            binary_formats = _mac_binary_formats(compat_version, arch)
+            for binary_format in binary_formats:
+                yield f"macosx_{major_version}_{minor_version}_{binary_format}"
+
+    if version >= (11, 0):
+        # Mac OS 11 on x86_64 is compatible with binaries from previous releases.
+        # Arm64 support was introduced in 11.0, so no Arm binaries from previous
+        # releases exist.
+        #
+        # However, the "universal2" binary format can have a
+        # macOS version earlier than 11.0 when the x86_64 part of the binary supports
+        # that version of macOS.
+        major_version = 10
+        if arch == "x86_64":
+            for minor_version in range(16, 3, -1):
+                compat_version = major_version, minor_version
+                binary_formats = _mac_binary_formats(compat_version, arch)
+                for binary_format in binary_formats:
+                    yield f"macosx_{major_version}_{minor_version}_{binary_format}"
+        else:
+            for minor_version in range(16, 3, -1):
+                compat_version = major_version, minor_version
+                binary_format = "universal2"
+                yield f"macosx_{major_version}_{minor_version}_{binary_format}"
+
+
+def ios_platforms(
+    version: AppleVersion | None = None, multiarch: str | None = None
+) -> Iterator[str]:
+    """
+    Yields the platform tags for an iOS system.
+
+    :param version: A two-item tuple specifying the iOS version to generate
+        platform tags for. Defaults to the current iOS version.
+    :param multiarch: The CPU architecture+ABI to generate platform tags for -
+        (the value used by `sys.implementation._multiarch` e.g.,
+        `arm64_iphoneos` or `x84_64_iphonesimulator`). Defaults to the current
+        multiarch value.
+    """
+    if version is None:
+        # if iOS is the current platform, ios_ver *must* be defined. However,
+        # it won't exist for CPython versions before 3.13, which causes a mypy
+        # error.
+        _, release, _, _ = platform.ios_ver()  # type: ignore[attr-defined, unused-ignore]
+        version = cast("AppleVersion", tuple(map(int, release.split(".")[:2])))
+
+    if multiarch is None:
+        multiarch = sys.implementation._multiarch
+    multiarch = multiarch.replace("-", "_")
+
+    ios_platform_template = "ios_{major}_{minor}_{multiarch}"
+
+    # Consider any iOS major.minor version from the version requested, down to
+    # 12.0. 12.0 is the first iOS version that is known to have enough features
+    # to support CPython. Consider every possible minor release up to X.9. There
+    # highest the minor has ever gone is 8 (14.8 and 15.8) but having some extra
+    # candidates that won't ever match doesn't really hurt, and it saves us from
+    # having to keep an explicit list of known iOS versions in the code. Return
+    # the results descending order of version number.
+
+    # If the requested major version is less than 12, there won't be any matches.
+    if version[0] < 12:
+        return
+
+    # Consider the actual X.Y version that was requested.
+    yield ios_platform_template.format(
+        major=version[0], minor=version[1], multiarch=multiarch
+    )
+
+    # Consider every minor version from X.0 to the minor version prior to the
+    # version requested by the platform.
+    for minor in range(version[1] - 1, -1, -1):
+        yield ios_platform_template.format(
+            major=version[0], minor=minor, multiarch=multiarch
+        )
+
+    for major in range(version[0] - 1, 11, -1):
+        for minor in range(9, -1, -1):
+            yield ios_platform_template.format(
+                major=major, minor=minor, multiarch=multiarch
+            )
+
+
+def _linux_platforms(is_32bit: bool = _32_BIT_INTERPRETER) -> Iterator[str]:
+    linux = _normalize_string(sysconfig.get_platform())
+    if not linux.startswith("linux_"):
+        # we should never be here, just yield the sysconfig one and return
+        yield linux
+        return
+    if is_32bit:
+        if linux == "linux_x86_64":
+            linux = "linux_i686"
+        elif linux == "linux_aarch64":
+            linux = "linux_armv8l"
+    _, arch = linux.split("_", 1)
+    archs = {"armv8l": ["armv8l", "armv7l"]}.get(arch, [arch])
+    yield from _manylinux.platform_tags(archs)
+    yield from _musllinux.platform_tags(archs)
+    for arch in archs:
+        yield f"linux_{arch}"
+
+
+def _generic_platforms() -> Iterator[str]:
+    yield _normalize_string(sysconfig.get_platform())
+
+
+def platform_tags() -> Iterator[str]:
+    """
+    Provides the platform tags for this installation.
+    """
+    if platform.system() == "Darwin":
+        return mac_platforms()
+    elif platform.system() == "iOS":
+        return ios_platforms()
+    elif platform.system() == "Linux":
+        return _linux_platforms()
+    else:
+        return _generic_platforms()
+
+
+def interpreter_name() -> str:
+    """
+    Returns the name of the running interpreter.
+
+    Some implementations have a reserved, two-letter abbreviation which will
+    be returned when appropriate.
+    """
+    name = sys.implementation.name
+    return INTERPRETER_SHORT_NAMES.get(name) or name
+
+
+def interpreter_version(*, warn: bool = False) -> str:
+    """
+    Returns the version of the running interpreter.
+    """
+    version = _get_config_var("py_version_nodot", warn=warn)
+    if version:
+        version = str(version)
+    else:
+        version = _version_nodot(sys.version_info[:2])
+    return version
+
+
+def _version_nodot(version: PythonVersion) -> str:
+    return "".join(map(str, version))
+
+
+def sys_tags(*, warn: bool = False) -> Iterator[Tag]:
+    """
+    Returns the sequence of tag triples for the running interpreter.
+
+    The order of the sequence corresponds to priority order for the
+    interpreter, from most to least important.
+    """
+
+    interp_name = interpreter_name()
+    if interp_name == "cp":
+        yield from cpython_tags(warn=warn)
+    else:
+        yield from generic_tags()
+
+    if interp_name == "pp":
+        interp = "pp3"
+    elif interp_name == "cp":
+        interp = "cp" + interpreter_version(warn=warn)
+    else:
+        interp = None
+    yield from compatible_tags(interpreter=interp)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/utils.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..23450953df74eccd9c13cd2a955ce09d1f968565
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/utils.py
@@ -0,0 +1,163 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+from __future__ import annotations
+
+import functools
+import re
+from typing import NewType, Tuple, Union, cast
+
+from .tags import Tag, parse_tag
+from .version import InvalidVersion, Version, _TrimmedRelease
+
+BuildTag = Union[Tuple[()], Tuple[int, str]]
+NormalizedName = NewType("NormalizedName", str)
+
+
+class InvalidName(ValueError):
+    """
+    An invalid distribution name; users should refer to the packaging user guide.
+    """
+
+
+class InvalidWheelFilename(ValueError):
+    """
+    An invalid wheel filename was found, users should refer to PEP 427.
+    """
+
+
+class InvalidSdistFilename(ValueError):
+    """
+    An invalid sdist filename was found, users should refer to the packaging user guide.
+    """
+
+
+# Core metadata spec for `Name`
+_validate_regex = re.compile(
+    r"^([A-Z0-9]|[A-Z0-9][A-Z0-9._-]*[A-Z0-9])$", re.IGNORECASE
+)
+_canonicalize_regex = re.compile(r"[-_.]+")
+_normalized_regex = re.compile(r"^([a-z0-9]|[a-z0-9]([a-z0-9-](?!--))*[a-z0-9])$")
+# PEP 427: The build number must start with a digit.
+_build_tag_regex = re.compile(r"(\d+)(.*)")
+
+
+def canonicalize_name(name: str, *, validate: bool = False) -> NormalizedName:
+    if validate and not _validate_regex.match(name):
+        raise InvalidName(f"name is invalid: {name!r}")
+    # This is taken from PEP 503.
+    value = _canonicalize_regex.sub("-", name).lower()
+    return cast(NormalizedName, value)
+
+
+def is_normalized_name(name: str) -> bool:
+    return _normalized_regex.match(name) is not None
+
+
+@functools.singledispatch
+def canonicalize_version(
+    version: Version | str, *, strip_trailing_zero: bool = True
+) -> str:
+    """
+    Return a canonical form of a version as a string.
+
+    >>> canonicalize_version('1.0.1')
+    '1.0.1'
+
+    Per PEP 625, versions may have multiple canonical forms, differing
+    only by trailing zeros.
+
+    >>> canonicalize_version('1.0.0')
+    '1'
+    >>> canonicalize_version('1.0.0', strip_trailing_zero=False)
+    '1.0.0'
+
+    Invalid versions are returned unaltered.
+
+    >>> canonicalize_version('foo bar baz')
+    'foo bar baz'
+    """
+    return str(_TrimmedRelease(str(version)) if strip_trailing_zero else version)
+
+
+@canonicalize_version.register
+def _(version: str, *, strip_trailing_zero: bool = True) -> str:
+    try:
+        parsed = Version(version)
+    except InvalidVersion:
+        # Legacy versions cannot be normalized
+        return version
+    return canonicalize_version(parsed, strip_trailing_zero=strip_trailing_zero)
+
+
+def parse_wheel_filename(
+    filename: str,
+) -> tuple[NormalizedName, Version, BuildTag, frozenset[Tag]]:
+    if not filename.endswith(".whl"):
+        raise InvalidWheelFilename(
+            f"Invalid wheel filename (extension must be '.whl'): {filename!r}"
+        )
+
+    filename = filename[:-4]
+    dashes = filename.count("-")
+    if dashes not in (4, 5):
+        raise InvalidWheelFilename(
+            f"Invalid wheel filename (wrong number of parts): {filename!r}"
+        )
+
+    parts = filename.split("-", dashes - 2)
+    name_part = parts[0]
+    # See PEP 427 for the rules on escaping the project name.
+    if "__" in name_part or re.match(r"^[\w\d._]*$", name_part, re.UNICODE) is None:
+        raise InvalidWheelFilename(f"Invalid project name: {filename!r}")
+    name = canonicalize_name(name_part)
+
+    try:
+        version = Version(parts[1])
+    except InvalidVersion as e:
+        raise InvalidWheelFilename(
+            f"Invalid wheel filename (invalid version): {filename!r}"
+        ) from e
+
+    if dashes == 5:
+        build_part = parts[2]
+        build_match = _build_tag_regex.match(build_part)
+        if build_match is None:
+            raise InvalidWheelFilename(
+                f"Invalid build number: {build_part} in {filename!r}"
+            )
+        build = cast(BuildTag, (int(build_match.group(1)), build_match.group(2)))
+    else:
+        build = ()
+    tags = parse_tag(parts[-1])
+    return (name, version, build, tags)
+
+
+def parse_sdist_filename(filename: str) -> tuple[NormalizedName, Version]:
+    if filename.endswith(".tar.gz"):
+        file_stem = filename[: -len(".tar.gz")]
+    elif filename.endswith(".zip"):
+        file_stem = filename[: -len(".zip")]
+    else:
+        raise InvalidSdistFilename(
+            f"Invalid sdist filename (extension must be '.tar.gz' or '.zip'):"
+            f" {filename!r}"
+        )
+
+    # We are requiring a PEP 440 version, which cannot contain dashes,
+    # so we split on the last dash.
+    name_part, sep, version_part = file_stem.rpartition("-")
+    if not sep:
+        raise InvalidSdistFilename(f"Invalid sdist filename: {filename!r}")
+
+    name = canonicalize_name(name_part)
+
+    try:
+        version = Version(version_part)
+    except InvalidVersion as e:
+        raise InvalidSdistFilename(
+            f"Invalid sdist filename (invalid version): {filename!r}"
+        ) from e
+
+    return (name, version)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/version.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9bbda20e463b8d9389ecd65f74af33810a02bdd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/packaging/version.py
@@ -0,0 +1,582 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+"""
+.. testsetup::
+
+    from packaging.version import parse, Version
+"""
+
+from __future__ import annotations
+
+import itertools
+import re
+from typing import Any, Callable, NamedTuple, SupportsInt, Tuple, Union
+
+from ._structures import Infinity, InfinityType, NegativeInfinity, NegativeInfinityType
+
+__all__ = ["VERSION_PATTERN", "InvalidVersion", "Version", "parse"]
+
+LocalType = Tuple[Union[int, str], ...]
+
+CmpPrePostDevType = Union[InfinityType, NegativeInfinityType, Tuple[str, int]]
+CmpLocalType = Union[
+    NegativeInfinityType,
+    Tuple[Union[Tuple[int, str], Tuple[NegativeInfinityType, Union[int, str]]], ...],
+]
+CmpKey = Tuple[
+    int,
+    Tuple[int, ...],
+    CmpPrePostDevType,
+    CmpPrePostDevType,
+    CmpPrePostDevType,
+    CmpLocalType,
+]
+VersionComparisonMethod = Callable[[CmpKey, CmpKey], bool]
+
+
+class _Version(NamedTuple):
+    epoch: int
+    release: tuple[int, ...]
+    dev: tuple[str, int] | None
+    pre: tuple[str, int] | None
+    post: tuple[str, int] | None
+    local: LocalType | None
+
+
+def parse(version: str) -> Version:
+    """Parse the given version string.
+
+    >>> parse('1.0.dev1')
+    <Version('1.0.dev1')>
+
+    :param version: The version string to parse.
+    :raises InvalidVersion: When the version string is not a valid version.
+    """
+    return Version(version)
+
+
+class InvalidVersion(ValueError):
+    """Raised when a version string is not a valid version.
+
+    >>> Version("invalid")
+    Traceback (most recent call last):
+        ...
+    packaging.version.InvalidVersion: Invalid version: 'invalid'
+    """
+
+
+class _BaseVersion:
+    _key: tuple[Any, ...]
+
+    def __hash__(self) -> int:
+        return hash(self._key)
+
+    # Please keep the duplicated `isinstance` check
+    # in the six comparisons hereunder
+    # unless you find a way to avoid adding overhead function calls.
+    def __lt__(self, other: _BaseVersion) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key < other._key
+
+    def __le__(self, other: _BaseVersion) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key <= other._key
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key == other._key
+
+    def __ge__(self, other: _BaseVersion) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key >= other._key
+
+    def __gt__(self, other: _BaseVersion) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key > other._key
+
+    def __ne__(self, other: object) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key != other._key
+
+
+# Deliberately not anchored to the start and end of the string, to make it
+# easier for 3rd party code to reuse
+_VERSION_PATTERN = r"""
+    v?
+    (?:
+        (?:(?P<epoch>[0-9]+)!)?                           # epoch
+        (?P<release>[0-9]+(?:\.[0-9]+)*)                  # release segment
+        (?P<pre>                                          # pre-release
+            [-_\.]?
+            (?P<pre_l>alpha|a|beta|b|preview|pre|c|rc)
+            [-_\.]?
+            (?P<pre_n>[0-9]+)?
+        )?
+        (?P<post>                                         # post release
+            (?:-(?P<post_n1>[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?P<post_l>post|rev|r)
+                [-_\.]?
+                (?P<post_n2>[0-9]+)?
+            )
+        )?
+        (?P<dev>                                          # dev release
+            [-_\.]?
+            (?P<dev_l>dev)
+            [-_\.]?
+            (?P<dev_n>[0-9]+)?
+        )?
+    )
+    (?:\+(?P<local>[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+VERSION_PATTERN = _VERSION_PATTERN
+"""
+A string containing the regular expression used to match a valid version.
+
+The pattern is not anchored at either end, and is intended for embedding in larger
+expressions (for example, matching a version number as part of a file name). The
+regular expression should be compiled with the ``re.VERBOSE`` and ``re.IGNORECASE``
+flags set.
+
+:meta hide-value:
+"""
+
+
+class Version(_BaseVersion):
+    """This class abstracts handling of a project's versions.
+
+    A :class:`Version` instance is comparison aware and can be compared and
+    sorted using the standard Python interfaces.
+
+    >>> v1 = Version("1.0a5")
+    >>> v2 = Version("1.0")
+    >>> v1
+    <Version('1.0a5')>
+    >>> v2
+    <Version('1.0')>
+    >>> v1 < v2
+    True
+    >>> v1 == v2
+    False
+    >>> v1 > v2
+    False
+    >>> v1 >= v2
+    False
+    >>> v1 <= v2
+    True
+    """
+
+    _regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
+    _key: CmpKey
+
+    def __init__(self, version: str) -> None:
+        """Initialize a Version object.
+
+        :param version:
+            The string representation of a version which will be parsed and normalized
+            before use.
+        :raises InvalidVersion:
+            If the ``version`` does not conform to PEP 440 in any way then this
+            exception will be raised.
+        """
+
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion(f"Invalid version: {version!r}")
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(match.group("pre_l"), match.group("pre_n")),
+            post=_parse_letter_version(
+                match.group("post_l"), match.group("post_n1") or match.group("post_n2")
+            ),
+            dev=_parse_letter_version(match.group("dev_l"), match.group("dev_n")),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self) -> str:
+        """A representation of the Version that shows all internal state.
+
+        >>> Version('1.0.0')
+        <Version('1.0.0')>
+        """
+        return f"<Version('{self}')>"
+
+    def __str__(self) -> str:
+        """A string representation of the version that can be round-tripped.
+
+        >>> str(Version("1.0a5"))
+        '1.0a5'
+        """
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        # Pre-release
+        if self.pre is not None:
+            parts.append("".join(str(x) for x in self.pre))
+
+        # Post-release
+        if self.post is not None:
+            parts.append(f".post{self.post}")
+
+        # Development release
+        if self.dev is not None:
+            parts.append(f".dev{self.dev}")
+
+        # Local version segment
+        if self.local is not None:
+            parts.append(f"+{self.local}")
+
+        return "".join(parts)
+
+    @property
+    def epoch(self) -> int:
+        """The epoch of the version.
+
+        >>> Version("2.0.0").epoch
+        0
+        >>> Version("1!2.0.0").epoch
+        1
+        """
+        return self._version.epoch
+
+    @property
+    def release(self) -> tuple[int, ...]:
+        """The components of the "release" segment of the version.
+
+        >>> Version("1.2.3").release
+        (1, 2, 3)
+        >>> Version("2.0.0").release
+        (2, 0, 0)
+        >>> Version("1!2.0.0.post0").release
+        (2, 0, 0)
+
+        Includes trailing zeroes but not the epoch or any pre-release / development /
+        post-release suffixes.
+        """
+        return self._version.release
+
+    @property
+    def pre(self) -> tuple[str, int] | None:
+        """The pre-release segment of the version.
+
+        >>> print(Version("1.2.3").pre)
+        None
+        >>> Version("1.2.3a1").pre
+        ('a', 1)
+        >>> Version("1.2.3b1").pre
+        ('b', 1)
+        >>> Version("1.2.3rc1").pre
+        ('rc', 1)
+        """
+        return self._version.pre
+
+    @property
+    def post(self) -> int | None:
+        """The post-release number of the version.
+
+        >>> print(Version("1.2.3").post)
+        None
+        >>> Version("1.2.3.post1").post
+        1
+        """
+        return self._version.post[1] if self._version.post else None
+
+    @property
+    def dev(self) -> int | None:
+        """The development number of the version.
+
+        >>> print(Version("1.2.3").dev)
+        None
+        >>> Version("1.2.3.dev1").dev
+        1
+        """
+        return self._version.dev[1] if self._version.dev else None
+
+    @property
+    def local(self) -> str | None:
+        """The local version segment of the version.
+
+        >>> print(Version("1.2.3").local)
+        None
+        >>> Version("1.2.3+abc").local
+        'abc'
+        """
+        if self._version.local:
+            return ".".join(str(x) for x in self._version.local)
+        else:
+            return None
+
+    @property
+    def public(self) -> str:
+        """The public portion of the version.
+
+        >>> Version("1.2.3").public
+        '1.2.3'
+        >>> Version("1.2.3+abc").public
+        '1.2.3'
+        >>> Version("1!1.2.3dev1+abc").public
+        '1!1.2.3.dev1'
+        """
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self) -> str:
+        """The "base version" of the version.
+
+        >>> Version("1.2.3").base_version
+        '1.2.3'
+        >>> Version("1.2.3+abc").base_version
+        '1.2.3'
+        >>> Version("1!1.2.3dev1+abc").base_version
+        '1!1.2.3'
+
+        The "base version" is the public version of the project without any pre or post
+        release markers.
+        """
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        return "".join(parts)
+
+    @property
+    def is_prerelease(self) -> bool:
+        """Whether this version is a pre-release.
+
+        >>> Version("1.2.3").is_prerelease
+        False
+        >>> Version("1.2.3a1").is_prerelease
+        True
+        >>> Version("1.2.3b1").is_prerelease
+        True
+        >>> Version("1.2.3rc1").is_prerelease
+        True
+        >>> Version("1.2.3dev1").is_prerelease
+        True
+        """
+        return self.dev is not None or self.pre is not None
+
+    @property
+    def is_postrelease(self) -> bool:
+        """Whether this version is a post-release.
+
+        >>> Version("1.2.3").is_postrelease
+        False
+        >>> Version("1.2.3.post1").is_postrelease
+        True
+        """
+        return self.post is not None
+
+    @property
+    def is_devrelease(self) -> bool:
+        """Whether this version is a development release.
+
+        >>> Version("1.2.3").is_devrelease
+        False
+        >>> Version("1.2.3.dev1").is_devrelease
+        True
+        """
+        return self.dev is not None
+
+    @property
+    def major(self) -> int:
+        """The first item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").major
+        1
+        """
+        return self.release[0] if len(self.release) >= 1 else 0
+
+    @property
+    def minor(self) -> int:
+        """The second item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").minor
+        2
+        >>> Version("1").minor
+        0
+        """
+        return self.release[1] if len(self.release) >= 2 else 0
+
+    @property
+    def micro(self) -> int:
+        """The third item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").micro
+        3
+        >>> Version("1").micro
+        0
+        """
+        return self.release[2] if len(self.release) >= 3 else 0
+
+
+class _TrimmedRelease(Version):
+    @property
+    def release(self) -> tuple[int, ...]:
+        """
+        Release segment without any trailing zeros.
+
+        >>> _TrimmedRelease('1.0.0').release
+        (1,)
+        >>> _TrimmedRelease('0.0').release
+        (0,)
+        """
+        rel = super().release
+        nonzeros = (index for index, val in enumerate(rel) if val)
+        last_nonzero = max(nonzeros, default=0)
+        return rel[: last_nonzero + 1]
+
+
+def _parse_letter_version(
+    letter: str | None, number: str | bytes | SupportsInt | None
+) -> tuple[str, int] | None:
+    if letter:
+        # We consider there to be an implicit 0 in a pre-release if there is
+        # not a numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+
+    assert not letter
+    if number:
+        # We assume if we are given a number, but we are not given a letter
+        # then this is using the implicit post release syntax (e.g. 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+    return None
+
+
+_local_version_separators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local: str | None) -> LocalType | None:
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_separators.split(local)
+        )
+    return None
+
+
+def _cmpkey(
+    epoch: int,
+    release: tuple[int, ...],
+    pre: tuple[str, int] | None,
+    post: tuple[str, int] | None,
+    dev: tuple[str, int] | None,
+    local: LocalType | None,
+) -> CmpKey:
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non zero, then take the rest
+    # re-reverse it back into the correct order and make it a tuple and use
+    # that for our sorting key.
+    _release = tuple(
+        reversed(list(itertools.dropwhile(lambda x: x == 0, reversed(release))))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre segment, but we _only_ want to do this
+    # if there is not a pre or a post segment. If we have one of those then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        _pre: CmpPrePostDevType = NegativeInfinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        _pre = Infinity
+    else:
+        _pre = pre
+
+    # Versions without a post segment should sort before those with one.
+    if post is None:
+        _post: CmpPrePostDevType = NegativeInfinity
+
+    else:
+        _post = post
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        _dev: CmpPrePostDevType = Infinity
+
+    else:
+        _dev = dev
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        _local: CmpLocalType = NegativeInfinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alpha numeric segments sort before numeric segments
+        # - Alpha numeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        _local = tuple(
+            (i, "") if isinstance(i, int) else (NegativeInfinity, i) for i in local
+        )
+
+    return epoch, _release, _pre, _post, _dev, _local
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..ab51ef36add104ccb48652a207a3667ba9b582c7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/METADATA
@@ -0,0 +1,319 @@
+Metadata-Version: 2.3
+Name: platformdirs
+Version: 4.2.2
+Summary: A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`.
+Project-URL: Documentation, https://platformdirs.readthedocs.io
+Project-URL: Homepage, https://github.com/platformdirs/platformdirs
+Project-URL: Source, https://github.com/platformdirs/platformdirs
+Project-URL: Tracker, https://github.com/platformdirs/platformdirs/issues
+Maintainer-email: Bernát Gábor <gaborjbernat@gmail.com>, Julian Berman <Julian@GrayVines.com>, Ofek Lev <oss@ofek.dev>, Ronny Pfannschmidt <opensource@ronnypfannschmidt.de>
+License-Expression: MIT
+License-File: LICENSE
+Keywords: appdirs,application,cache,directory,log,user
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Requires-Python: >=3.8
+Provides-Extra: docs
+Requires-Dist: furo>=2023.9.10; extra == 'docs'
+Requires-Dist: proselint>=0.13; extra == 'docs'
+Requires-Dist: sphinx-autodoc-typehints>=1.25.2; extra == 'docs'
+Requires-Dist: sphinx>=7.2.6; extra == 'docs'
+Provides-Extra: test
+Requires-Dist: appdirs==1.4.4; extra == 'test'
+Requires-Dist: covdefaults>=2.3; extra == 'test'
+Requires-Dist: pytest-cov>=4.1; extra == 'test'
+Requires-Dist: pytest-mock>=3.12; extra == 'test'
+Requires-Dist: pytest>=7.4.3; extra == 'test'
+Provides-Extra: type
+Requires-Dist: mypy>=1.8; extra == 'type'
+Description-Content-Type: text/x-rst
+
+The problem
+===========
+
+.. image:: https://github.com/platformdirs/platformdirs/actions/workflows/check.yml/badge.svg
+   :target: https://github.com/platformdirs/platformdirs/actions
+
+When writing desktop application, finding the right location to store user data
+and configuration varies per platform. Even for single-platform apps, there
+may by plenty of nuances in figuring out the right location.
+
+For example, if running on macOS, you should use::
+
+    ~/Library/Application Support/<AppName>
+
+If on Windows (at least English Win) that should be::
+
+    C:\Documents and Settings\<User>\Application Data\Local Settings\<AppAuthor>\<AppName>
+
+or possibly::
+
+    C:\Documents and Settings\<User>\Application Data\<AppAuthor>\<AppName>
+
+for `roaming profiles <https://docs.microsoft.com/en-us/previous-versions/windows/it-pro/windows-vista/cc766489(v=ws.10)>`_ but that is another story.
+
+On Linux (and other Unices), according to the `XDG Basedir Spec`_, it should be::
+
+    ~/.local/share/<AppName>
+
+.. _XDG Basedir Spec: https://specifications.freedesktop.org/basedir-spec/basedir-spec-latest.html
+
+``platformdirs`` to the rescue
+==============================
+
+This kind of thing is what the ``platformdirs`` package is for.
+``platformdirs`` will help you choose an appropriate:
+
+- user data dir (``user_data_dir``)
+- user config dir (``user_config_dir``)
+- user cache dir (``user_cache_dir``)
+- site data dir (``site_data_dir``)
+- site config dir (``site_config_dir``)
+- user log dir (``user_log_dir``)
+- user documents dir (``user_documents_dir``)
+- user downloads dir (``user_downloads_dir``)
+- user pictures dir (``user_pictures_dir``)
+- user videos dir (``user_videos_dir``)
+- user music dir (``user_music_dir``)
+- user desktop dir (``user_desktop_dir``)
+- user runtime dir (``user_runtime_dir``)
+
+And also:
+
+- Is slightly opinionated on the directory names used. Look for "OPINION" in
+  documentation and code for when an opinion is being applied.
+
+Example output
+==============
+
+On macOS:
+
+.. code-block:: pycon
+
+    >>> from platformdirs import *
+    >>> appname = "SuperApp"
+    >>> appauthor = "Acme"
+    >>> user_data_dir(appname, appauthor)
+    '/Users/trentm/Library/Application Support/SuperApp'
+    >>> site_data_dir(appname, appauthor)
+    '/Library/Application Support/SuperApp'
+    >>> user_cache_dir(appname, appauthor)
+    '/Users/trentm/Library/Caches/SuperApp'
+    >>> user_log_dir(appname, appauthor)
+    '/Users/trentm/Library/Logs/SuperApp'
+    >>> user_documents_dir()
+    '/Users/trentm/Documents'
+    >>> user_downloads_dir()
+    '/Users/trentm/Downloads'
+    >>> user_pictures_dir()
+    '/Users/trentm/Pictures'
+    >>> user_videos_dir()
+    '/Users/trentm/Movies'
+    >>> user_music_dir()
+    '/Users/trentm/Music'
+    >>> user_desktop_dir()
+    '/Users/trentm/Desktop'
+    >>> user_runtime_dir(appname, appauthor)
+    '/Users/trentm/Library/Caches/TemporaryItems/SuperApp'
+
+On Windows:
+
+.. code-block:: pycon
+
+    >>> from platformdirs import *
+    >>> appname = "SuperApp"
+    >>> appauthor = "Acme"
+    >>> user_data_dir(appname, appauthor)
+    'C:\\Users\\trentm\\AppData\\Local\\Acme\\SuperApp'
+    >>> user_data_dir(appname, appauthor, roaming=True)
+    'C:\\Users\\trentm\\AppData\\Roaming\\Acme\\SuperApp'
+    >>> user_cache_dir(appname, appauthor)
+    'C:\\Users\\trentm\\AppData\\Local\\Acme\\SuperApp\\Cache'
+    >>> user_log_dir(appname, appauthor)
+    'C:\\Users\\trentm\\AppData\\Local\\Acme\\SuperApp\\Logs'
+    >>> user_documents_dir()
+    'C:\\Users\\trentm\\Documents'
+    >>> user_downloads_dir()
+    'C:\\Users\\trentm\\Downloads'
+    >>> user_pictures_dir()
+    'C:\\Users\\trentm\\Pictures'
+    >>> user_videos_dir()
+    'C:\\Users\\trentm\\Videos'
+    >>> user_music_dir()
+    'C:\\Users\\trentm\\Music'
+    >>> user_desktop_dir()
+    'C:\\Users\\trentm\\Desktop'
+    >>> user_runtime_dir(appname, appauthor)
+    'C:\\Users\\trentm\\AppData\\Local\\Temp\\Acme\\SuperApp'
+
+On Linux:
+
+.. code-block:: pycon
+
+    >>> from platformdirs import *
+    >>> appname = "SuperApp"
+    >>> appauthor = "Acme"
+    >>> user_data_dir(appname, appauthor)
+    '/home/trentm/.local/share/SuperApp'
+    >>> site_data_dir(appname, appauthor)
+    '/usr/local/share/SuperApp'
+    >>> site_data_dir(appname, appauthor, multipath=True)
+    '/usr/local/share/SuperApp:/usr/share/SuperApp'
+    >>> user_cache_dir(appname, appauthor)
+    '/home/trentm/.cache/SuperApp'
+    >>> user_log_dir(appname, appauthor)
+    '/home/trentm/.local/state/SuperApp/log'
+    >>> user_config_dir(appname)
+    '/home/trentm/.config/SuperApp'
+    >>> user_documents_dir()
+    '/home/trentm/Documents'
+    >>> user_downloads_dir()
+    '/home/trentm/Downloads'
+    >>> user_pictures_dir()
+    '/home/trentm/Pictures'
+    >>> user_videos_dir()
+    '/home/trentm/Videos'
+    >>> user_music_dir()
+    '/home/trentm/Music'
+    >>> user_desktop_dir()
+    '/home/trentm/Desktop'
+    >>> user_runtime_dir(appname, appauthor)
+    '/run/user/{os.getuid()}/SuperApp'
+    >>> site_config_dir(appname)
+    '/etc/xdg/SuperApp'
+    >>> os.environ["XDG_CONFIG_DIRS"] = "/etc:/usr/local/etc"
+    >>> site_config_dir(appname, multipath=True)
+    '/etc/SuperApp:/usr/local/etc/SuperApp'
+
+On Android::
+
+    >>> from platformdirs import *
+    >>> appname = "SuperApp"
+    >>> appauthor = "Acme"
+    >>> user_data_dir(appname, appauthor)
+    '/data/data/com.myApp/files/SuperApp'
+    >>> user_cache_dir(appname, appauthor)
+    '/data/data/com.myApp/cache/SuperApp'
+    >>> user_log_dir(appname, appauthor)
+    '/data/data/com.myApp/cache/SuperApp/log'
+    >>> user_config_dir(appname)
+    '/data/data/com.myApp/shared_prefs/SuperApp'
+    >>> user_documents_dir()
+    '/storage/emulated/0/Documents'
+    >>> user_downloads_dir()
+    '/storage/emulated/0/Downloads'
+    >>> user_pictures_dir()
+    '/storage/emulated/0/Pictures'
+    >>> user_videos_dir()
+    '/storage/emulated/0/DCIM/Camera'
+    >>> user_music_dir()
+    '/storage/emulated/0/Music'
+    >>> user_desktop_dir()
+    '/storage/emulated/0/Desktop'
+    >>> user_runtime_dir(appname, appauthor)
+    '/data/data/com.myApp/cache/SuperApp/tmp'
+
+Note: Some android apps like Termux and Pydroid are used as shells. These
+apps are used by the end user to emulate Linux environment. Presence of
+``SHELL`` environment variable is used by Platformdirs to differentiate
+between general android apps and android apps used as shells. Shell android
+apps also support ``XDG_*`` environment variables.
+
+
+``PlatformDirs`` for convenience
+================================
+
+.. code-block:: pycon
+
+    >>> from platformdirs import PlatformDirs
+    >>> dirs = PlatformDirs("SuperApp", "Acme")
+    >>> dirs.user_data_dir
+    '/Users/trentm/Library/Application Support/SuperApp'
+    >>> dirs.site_data_dir
+    '/Library/Application Support/SuperApp'
+    >>> dirs.user_cache_dir
+    '/Users/trentm/Library/Caches/SuperApp'
+    >>> dirs.user_log_dir
+    '/Users/trentm/Library/Logs/SuperApp'
+    >>> dirs.user_documents_dir
+    '/Users/trentm/Documents'
+    >>> dirs.user_downloads_dir
+    '/Users/trentm/Downloads'
+    >>> dirs.user_pictures_dir
+    '/Users/trentm/Pictures'
+    >>> dirs.user_videos_dir
+    '/Users/trentm/Movies'
+    >>> dirs.user_music_dir
+    '/Users/trentm/Music'
+    >>> dirs.user_desktop_dir
+    '/Users/trentm/Desktop'
+    >>> dirs.user_runtime_dir
+    '/Users/trentm/Library/Caches/TemporaryItems/SuperApp'
+
+Per-version isolation
+=====================
+
+If you have multiple versions of your app in use that you want to be
+able to run side-by-side, then you may want version-isolation for these
+dirs::
+
+    >>> from platformdirs import PlatformDirs
+    >>> dirs = PlatformDirs("SuperApp", "Acme", version="1.0")
+    >>> dirs.user_data_dir
+    '/Users/trentm/Library/Application Support/SuperApp/1.0'
+    >>> dirs.site_data_dir
+    '/Library/Application Support/SuperApp/1.0'
+    >>> dirs.user_cache_dir
+    '/Users/trentm/Library/Caches/SuperApp/1.0'
+    >>> dirs.user_log_dir
+    '/Users/trentm/Library/Logs/SuperApp/1.0'
+    >>> dirs.user_documents_dir
+    '/Users/trentm/Documents'
+    >>> dirs.user_downloads_dir
+    '/Users/trentm/Downloads'
+    >>> dirs.user_pictures_dir
+    '/Users/trentm/Pictures'
+    >>> dirs.user_videos_dir
+    '/Users/trentm/Movies'
+    >>> dirs.user_music_dir
+    '/Users/trentm/Music'
+    >>> dirs.user_desktop_dir
+    '/Users/trentm/Desktop'
+    >>> dirs.user_runtime_dir
+    '/Users/trentm/Library/Caches/TemporaryItems/SuperApp/1.0'
+
+Be wary of using this for configuration files though; you'll need to handle
+migrating configuration files manually.
+
+Why this Fork?
+==============
+
+This repository is a friendly fork of the wonderful work started by
+`ActiveState <https://github.com/ActiveState/appdirs>`_ who created
+``appdirs``, this package's ancestor.
+
+Maintaining an open source project is no easy task, particularly
+from within an organization, and the Python community is indebted
+to ``appdirs`` (and to Trent Mick and Jeff Rouse in particular) for
+creating an incredibly useful simple module, as evidenced by the wide
+number of users it has attracted over the years.
+
+Nonetheless, given the number of long-standing open issues
+and pull requests, and no clear path towards `ensuring
+that maintenance of the package would continue or grow
+<https://github.com/ActiveState/appdirs/issues/79>`_, this fork was
+created.
+
+Contributions are most welcome.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..64c0c8ea2eeca2768ee87d653fa3539517b0d6bb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/RECORD
@@ -0,0 +1,23 @@
+platformdirs-4.2.2.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+platformdirs-4.2.2.dist-info/METADATA,sha256=zmsie01G1MtXR0wgIv5XpVeTO7idr0WWvfmxKsKWuGk,11429
+platformdirs-4.2.2.dist-info/RECORD,,
+platformdirs-4.2.2.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+platformdirs-4.2.2.dist-info/WHEEL,sha256=zEMcRr9Kr03x1ozGwg5v9NQBKn3kndp6LSoSlVg-jhU,87
+platformdirs-4.2.2.dist-info/licenses/LICENSE,sha256=KeD9YukphQ6G6yjD_czwzv30-pSHkBHP-z0NS-1tTbY,1089
+platformdirs/__init__.py,sha256=EMGE8qeHRR9CzDFr8kL3tA8hdZZniYjXBVZd0UGTWK0,22225
+platformdirs/__main__.py,sha256=HnsUQHpiBaiTxwcmwVw-nFaPdVNZtQIdi1eWDtI-MzI,1493
+platformdirs/__pycache__/__init__.cpython-312.pyc,,
+platformdirs/__pycache__/__main__.cpython-312.pyc,,
+platformdirs/__pycache__/android.cpython-312.pyc,,
+platformdirs/__pycache__/api.cpython-312.pyc,,
+platformdirs/__pycache__/macos.cpython-312.pyc,,
+platformdirs/__pycache__/unix.cpython-312.pyc,,
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+platformdirs/__pycache__/windows.cpython-312.pyc,,
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+platformdirs/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+platformdirs/unix.py,sha256=Cci9Wqt35dAMsg6HT9nRGHSBW5obb0pR3AE1JJnsCXg,10643
+platformdirs/version.py,sha256=r7F76tZRjgQKzrpx_I0_ZMQOMU-PS7eGnHD7zEK3KB0,411
+platformdirs/windows.py,sha256=IFpiohUBwxPtCzlyKwNtxyW4Jk8haa6W8o59mfrDXVo,10125
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/REQUESTED b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/REQUESTED
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..516596c76787b10928cbab24f22c0ea00433b15d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/WHEEL
@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: hatchling 1.24.2
+Root-Is-Purelib: true
+Tag: py3-none-any
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/licenses/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/licenses/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..f35fed9191b1142ddaada8a96de4a9461c5d796c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs-4.2.2.dist-info/licenses/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2010-202x The platformdirs developers
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f7d9490d12b7224fc5d5348823619f262a0aae5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/__init__.py
@@ -0,0 +1,627 @@
+"""
+Utilities for determining application-specific dirs.
+
+See <https://github.com/platformdirs/platformdirs> for details and usage.
+
+"""
+
+from __future__ import annotations
+
+import os
+import sys
+from typing import TYPE_CHECKING
+
+from .api import PlatformDirsABC
+from .version import __version__
+from .version import __version_tuple__ as __version_info__
+
+if TYPE_CHECKING:
+    from pathlib import Path
+    from typing import Literal
+
+
+def _set_platform_dir_class() -> type[PlatformDirsABC]:
+    if sys.platform == "win32":
+        from platformdirs.windows import Windows as Result  # noqa: PLC0415
+    elif sys.platform == "darwin":
+        from platformdirs.macos import MacOS as Result  # noqa: PLC0415
+    else:
+        from platformdirs.unix import Unix as Result  # noqa: PLC0415
+
+    if os.getenv("ANDROID_DATA") == "/data" and os.getenv("ANDROID_ROOT") == "/system":
+        if os.getenv("SHELL") or os.getenv("PREFIX"):
+            return Result
+
+        from platformdirs.android import _android_folder  # noqa: PLC0415
+
+        if _android_folder() is not None:
+            from platformdirs.android import Android  # noqa: PLC0415
+
+            return Android  # return to avoid redefinition of a result
+
+    return Result
+
+
+PlatformDirs = _set_platform_dir_class()  #: Currently active platform
+AppDirs = PlatformDirs  #: Backwards compatibility with appdirs
+
+
+def user_data_dir(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    roaming: bool = False,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> str:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param roaming: See `roaming <platformdirs.api.PlatformDirsABC.roaming>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: data directory tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        roaming=roaming,
+        ensure_exists=ensure_exists,
+    ).user_data_dir
+
+
+def site_data_dir(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    multipath: bool = False,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> str:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param multipath: See `roaming <platformdirs.api.PlatformDirsABC.multipath>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: data directory shared by users
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        multipath=multipath,
+        ensure_exists=ensure_exists,
+    ).site_data_dir
+
+
+def user_config_dir(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    roaming: bool = False,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> str:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param roaming: See `roaming <platformdirs.api.PlatformDirsABC.roaming>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: config directory tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        roaming=roaming,
+        ensure_exists=ensure_exists,
+    ).user_config_dir
+
+
+def site_config_dir(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    multipath: bool = False,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> str:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param multipath: See `roaming <platformdirs.api.PlatformDirsABC.multipath>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: config directory shared by the users
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        multipath=multipath,
+        ensure_exists=ensure_exists,
+    ).site_config_dir
+
+
+def user_cache_dir(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    opinion: bool = True,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> str:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param opinion: See `roaming <platformdirs.api.PlatformDirsABC.opinion>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: cache directory tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        opinion=opinion,
+        ensure_exists=ensure_exists,
+    ).user_cache_dir
+
+
+def site_cache_dir(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    opinion: bool = True,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> str:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param opinion: See `opinion <platformdirs.api.PlatformDirsABC.opinion>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: cache directory tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        opinion=opinion,
+        ensure_exists=ensure_exists,
+    ).site_cache_dir
+
+
+def user_state_dir(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    roaming: bool = False,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> str:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param roaming: See `roaming <platformdirs.api.PlatformDirsABC.roaming>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: state directory tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        roaming=roaming,
+        ensure_exists=ensure_exists,
+    ).user_state_dir
+
+
+def user_log_dir(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    opinion: bool = True,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> str:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param opinion: See `roaming <platformdirs.api.PlatformDirsABC.opinion>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: log directory tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        opinion=opinion,
+        ensure_exists=ensure_exists,
+    ).user_log_dir
+
+
+def user_documents_dir() -> str:
+    """:returns: documents directory tied to the user"""
+    return PlatformDirs().user_documents_dir
+
+
+def user_downloads_dir() -> str:
+    """:returns: downloads directory tied to the user"""
+    return PlatformDirs().user_downloads_dir
+
+
+def user_pictures_dir() -> str:
+    """:returns: pictures directory tied to the user"""
+    return PlatformDirs().user_pictures_dir
+
+
+def user_videos_dir() -> str:
+    """:returns: videos directory tied to the user"""
+    return PlatformDirs().user_videos_dir
+
+
+def user_music_dir() -> str:
+    """:returns: music directory tied to the user"""
+    return PlatformDirs().user_music_dir
+
+
+def user_desktop_dir() -> str:
+    """:returns: desktop directory tied to the user"""
+    return PlatformDirs().user_desktop_dir
+
+
+def user_runtime_dir(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    opinion: bool = True,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> str:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param opinion: See `opinion <platformdirs.api.PlatformDirsABC.opinion>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: runtime directory tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        opinion=opinion,
+        ensure_exists=ensure_exists,
+    ).user_runtime_dir
+
+
+def site_runtime_dir(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    opinion: bool = True,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> str:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param opinion: See `opinion <platformdirs.api.PlatformDirsABC.opinion>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: runtime directory shared by users
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        opinion=opinion,
+        ensure_exists=ensure_exists,
+    ).site_runtime_dir
+
+
+def user_data_path(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    roaming: bool = False,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> Path:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param roaming: See `roaming <platformdirs.api.PlatformDirsABC.roaming>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: data path tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        roaming=roaming,
+        ensure_exists=ensure_exists,
+    ).user_data_path
+
+
+def site_data_path(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    multipath: bool = False,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> Path:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param multipath: See `multipath <platformdirs.api.PlatformDirsABC.multipath>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: data path shared by users
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        multipath=multipath,
+        ensure_exists=ensure_exists,
+    ).site_data_path
+
+
+def user_config_path(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    roaming: bool = False,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> Path:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param roaming: See `roaming <platformdirs.api.PlatformDirsABC.roaming>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: config path tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        roaming=roaming,
+        ensure_exists=ensure_exists,
+    ).user_config_path
+
+
+def site_config_path(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    multipath: bool = False,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> Path:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param multipath: See `roaming <platformdirs.api.PlatformDirsABC.multipath>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: config path shared by the users
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        multipath=multipath,
+        ensure_exists=ensure_exists,
+    ).site_config_path
+
+
+def site_cache_path(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    opinion: bool = True,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> Path:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param opinion: See `opinion <platformdirs.api.PlatformDirsABC.opinion>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: cache directory tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        opinion=opinion,
+        ensure_exists=ensure_exists,
+    ).site_cache_path
+
+
+def user_cache_path(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    opinion: bool = True,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> Path:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param opinion: See `roaming <platformdirs.api.PlatformDirsABC.opinion>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: cache path tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        opinion=opinion,
+        ensure_exists=ensure_exists,
+    ).user_cache_path
+
+
+def user_state_path(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    roaming: bool = False,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> Path:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param roaming: See `roaming <platformdirs.api.PlatformDirsABC.roaming>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: state path tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        roaming=roaming,
+        ensure_exists=ensure_exists,
+    ).user_state_path
+
+
+def user_log_path(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    opinion: bool = True,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> Path:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param opinion: See `roaming <platformdirs.api.PlatformDirsABC.opinion>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: log path tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        opinion=opinion,
+        ensure_exists=ensure_exists,
+    ).user_log_path
+
+
+def user_documents_path() -> Path:
+    """:returns: documents a path tied to the user"""
+    return PlatformDirs().user_documents_path
+
+
+def user_downloads_path() -> Path:
+    """:returns: downloads path tied to the user"""
+    return PlatformDirs().user_downloads_path
+
+
+def user_pictures_path() -> Path:
+    """:returns: pictures path tied to the user"""
+    return PlatformDirs().user_pictures_path
+
+
+def user_videos_path() -> Path:
+    """:returns: videos path tied to the user"""
+    return PlatformDirs().user_videos_path
+
+
+def user_music_path() -> Path:
+    """:returns: music path tied to the user"""
+    return PlatformDirs().user_music_path
+
+
+def user_desktop_path() -> Path:
+    """:returns: desktop path tied to the user"""
+    return PlatformDirs().user_desktop_path
+
+
+def user_runtime_path(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    opinion: bool = True,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> Path:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param opinion: See `opinion <platformdirs.api.PlatformDirsABC.opinion>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: runtime path tied to the user
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        opinion=opinion,
+        ensure_exists=ensure_exists,
+    ).user_runtime_path
+
+
+def site_runtime_path(
+    appname: str | None = None,
+    appauthor: str | None | Literal[False] = None,
+    version: str | None = None,
+    opinion: bool = True,  # noqa: FBT001, FBT002
+    ensure_exists: bool = False,  # noqa: FBT001, FBT002
+) -> Path:
+    """
+    :param appname: See `appname <platformdirs.api.PlatformDirsABC.appname>`.
+    :param appauthor: See `appauthor <platformdirs.api.PlatformDirsABC.appauthor>`.
+    :param version: See `version <platformdirs.api.PlatformDirsABC.version>`.
+    :param opinion: See `opinion <platformdirs.api.PlatformDirsABC.opinion>`.
+    :param ensure_exists: See `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+    :returns: runtime path shared by users
+    """
+    return PlatformDirs(
+        appname=appname,
+        appauthor=appauthor,
+        version=version,
+        opinion=opinion,
+        ensure_exists=ensure_exists,
+    ).site_runtime_path
+
+
+__all__ = [
+    "AppDirs",
+    "PlatformDirs",
+    "PlatformDirsABC",
+    "__version__",
+    "__version_info__",
+    "site_cache_dir",
+    "site_cache_path",
+    "site_config_dir",
+    "site_config_path",
+    "site_data_dir",
+    "site_data_path",
+    "site_runtime_dir",
+    "site_runtime_path",
+    "user_cache_dir",
+    "user_cache_path",
+    "user_config_dir",
+    "user_config_path",
+    "user_data_dir",
+    "user_data_path",
+    "user_desktop_dir",
+    "user_desktop_path",
+    "user_documents_dir",
+    "user_documents_path",
+    "user_downloads_dir",
+    "user_downloads_path",
+    "user_log_dir",
+    "user_log_path",
+    "user_music_dir",
+    "user_music_path",
+    "user_pictures_dir",
+    "user_pictures_path",
+    "user_runtime_dir",
+    "user_runtime_path",
+    "user_state_dir",
+    "user_state_path",
+    "user_videos_dir",
+    "user_videos_path",
+]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/__main__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..922c521358e349470ec48d6372b8f8ee8641128a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/__main__.py
@@ -0,0 +1,55 @@
+"""Main entry point."""
+
+from __future__ import annotations
+
+from platformdirs import PlatformDirs, __version__
+
+PROPS = (
+    "user_data_dir",
+    "user_config_dir",
+    "user_cache_dir",
+    "user_state_dir",
+    "user_log_dir",
+    "user_documents_dir",
+    "user_downloads_dir",
+    "user_pictures_dir",
+    "user_videos_dir",
+    "user_music_dir",
+    "user_runtime_dir",
+    "site_data_dir",
+    "site_config_dir",
+    "site_cache_dir",
+    "site_runtime_dir",
+)
+
+
+def main() -> None:
+    """Run the main entry point."""
+    app_name = "MyApp"
+    app_author = "MyCompany"
+
+    print(f"-- platformdirs {__version__} --")  # noqa: T201
+
+    print("-- app dirs (with optional 'version')")  # noqa: T201
+    dirs = PlatformDirs(app_name, app_author, version="1.0")
+    for prop in PROPS:
+        print(f"{prop}: {getattr(dirs, prop)}")  # noqa: T201
+
+    print("\n-- app dirs (without optional 'version')")  # noqa: T201
+    dirs = PlatformDirs(app_name, app_author)
+    for prop in PROPS:
+        print(f"{prop}: {getattr(dirs, prop)}")  # noqa: T201
+
+    print("\n-- app dirs (without optional 'appauthor')")  # noqa: T201
+    dirs = PlatformDirs(app_name)
+    for prop in PROPS:
+        print(f"{prop}: {getattr(dirs, prop)}")  # noqa: T201
+
+    print("\n-- app dirs (with disabled 'appauthor')")  # noqa: T201
+    dirs = PlatformDirs(app_name, appauthor=False)
+    for prop in PROPS:
+        print(f"{prop}: {getattr(dirs, prop)}")  # noqa: T201
+
+
+if __name__ == "__main__":
+    main()
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/android.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/android.py
new file mode 100644
index 0000000000000000000000000000000000000000..afd3141c725873847bdecf88222c73ff7d5afc39
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/android.py
@@ -0,0 +1,249 @@
+"""Android."""
+
+from __future__ import annotations
+
+import os
+import re
+import sys
+from functools import lru_cache
+from typing import TYPE_CHECKING, cast
+
+from .api import PlatformDirsABC
+
+
+class Android(PlatformDirsABC):
+    """
+    Follows the guidance `from here <https://android.stackexchange.com/a/216132>`_.
+
+    Makes use of the `appname <platformdirs.api.PlatformDirsABC.appname>`, `version
+    <platformdirs.api.PlatformDirsABC.version>`, `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+
+    """
+
+    @property
+    def user_data_dir(self) -> str:
+        """:return: data directory tied to the user, e.g. ``/data/user/<userid>/<packagename>/files/<AppName>``"""
+        return self._append_app_name_and_version(cast(str, _android_folder()), "files")
+
+    @property
+    def site_data_dir(self) -> str:
+        """:return: data directory shared by users, same as `user_data_dir`"""
+        return self.user_data_dir
+
+    @property
+    def user_config_dir(self) -> str:
+        """
+        :return: config directory tied to the user, e.g. \
+        ``/data/user/<userid>/<packagename>/shared_prefs/<AppName>``
+        """
+        return self._append_app_name_and_version(cast(str, _android_folder()), "shared_prefs")
+
+    @property
+    def site_config_dir(self) -> str:
+        """:return: config directory shared by the users, same as `user_config_dir`"""
+        return self.user_config_dir
+
+    @property
+    def user_cache_dir(self) -> str:
+        """:return: cache directory tied to the user, e.g.,``/data/user/<userid>/<packagename>/cache/<AppName>``"""
+        return self._append_app_name_and_version(cast(str, _android_folder()), "cache")
+
+    @property
+    def site_cache_dir(self) -> str:
+        """:return: cache directory shared by users, same as `user_cache_dir`"""
+        return self.user_cache_dir
+
+    @property
+    def user_state_dir(self) -> str:
+        """:return: state directory tied to the user, same as `user_data_dir`"""
+        return self.user_data_dir
+
+    @property
+    def user_log_dir(self) -> str:
+        """
+        :return: log directory tied to the user, same as `user_cache_dir` if not opinionated else ``log`` in it,
+          e.g. ``/data/user/<userid>/<packagename>/cache/<AppName>/log``
+        """
+        path = self.user_cache_dir
+        if self.opinion:
+            path = os.path.join(path, "log")  # noqa: PTH118
+        return path
+
+    @property
+    def user_documents_dir(self) -> str:
+        """:return: documents directory tied to the user e.g. ``/storage/emulated/0/Documents``"""
+        return _android_documents_folder()
+
+    @property
+    def user_downloads_dir(self) -> str:
+        """:return: downloads directory tied to the user e.g. ``/storage/emulated/0/Downloads``"""
+        return _android_downloads_folder()
+
+    @property
+    def user_pictures_dir(self) -> str:
+        """:return: pictures directory tied to the user e.g. ``/storage/emulated/0/Pictures``"""
+        return _android_pictures_folder()
+
+    @property
+    def user_videos_dir(self) -> str:
+        """:return: videos directory tied to the user e.g. ``/storage/emulated/0/DCIM/Camera``"""
+        return _android_videos_folder()
+
+    @property
+    def user_music_dir(self) -> str:
+        """:return: music directory tied to the user e.g. ``/storage/emulated/0/Music``"""
+        return _android_music_folder()
+
+    @property
+    def user_desktop_dir(self) -> str:
+        """:return: desktop directory tied to the user e.g. ``/storage/emulated/0/Desktop``"""
+        return "/storage/emulated/0/Desktop"
+
+    @property
+    def user_runtime_dir(self) -> str:
+        """
+        :return: runtime directory tied to the user, same as `user_cache_dir` if not opinionated else ``tmp`` in it,
+          e.g. ``/data/user/<userid>/<packagename>/cache/<AppName>/tmp``
+        """
+        path = self.user_cache_dir
+        if self.opinion:
+            path = os.path.join(path, "tmp")  # noqa: PTH118
+        return path
+
+    @property
+    def site_runtime_dir(self) -> str:
+        """:return: runtime directory shared by users, same as `user_runtime_dir`"""
+        return self.user_runtime_dir
+
+
+@lru_cache(maxsize=1)
+def _android_folder() -> str | None:  # noqa: C901, PLR0912
+    """:return: base folder for the Android OS or None if it cannot be found"""
+    result: str | None = None
+    # type checker isn't happy with our "import android", just don't do this when type checking see
+    # https://stackoverflow.com/a/61394121
+    if not TYPE_CHECKING:
+        try:
+            # First try to get a path to android app using python4android (if available)...
+            from android import mActivity  # noqa: PLC0415
+
+            context = cast("android.content.Context", mActivity.getApplicationContext())  # noqa: F821
+            result = context.getFilesDir().getParentFile().getAbsolutePath()
+        except Exception:  # noqa: BLE001
+            result = None
+    if result is None:
+        try:
+            # ...and fall back to using plain pyjnius, if python4android isn't available or doesn't deliver any useful
+            # result...
+            from jnius import autoclass  # noqa: PLC0415
+
+            context = autoclass("android.content.Context")
+            result = context.getFilesDir().getParentFile().getAbsolutePath()
+        except Exception:  # noqa: BLE001
+            result = None
+    if result is None:
+        # and if that fails, too, find an android folder looking at path on the sys.path
+        # warning: only works for apps installed under /data, not adopted storage etc.
+        pattern = re.compile(r"/data/(data|user/\d+)/(.+)/files")
+        for path in sys.path:
+            if pattern.match(path):
+                result = path.split("/files")[0]
+                break
+        else:
+            result = None
+    if result is None:
+        # one last try: find an android folder looking at path on the sys.path taking adopted storage paths into
+        # account
+        pattern = re.compile(r"/mnt/expand/[a-fA-F0-9-]{36}/(data|user/\d+)/(.+)/files")
+        for path in sys.path:
+            if pattern.match(path):
+                result = path.split("/files")[0]
+                break
+        else:
+            result = None
+    return result
+
+
+@lru_cache(maxsize=1)
+def _android_documents_folder() -> str:
+    """:return: documents folder for the Android OS"""
+    # Get directories with pyjnius
+    try:
+        from jnius import autoclass  # noqa: PLC0415
+
+        context = autoclass("android.content.Context")
+        environment = autoclass("android.os.Environment")
+        documents_dir: str = context.getExternalFilesDir(environment.DIRECTORY_DOCUMENTS).getAbsolutePath()
+    except Exception:  # noqa: BLE001
+        documents_dir = "/storage/emulated/0/Documents"
+
+    return documents_dir
+
+
+@lru_cache(maxsize=1)
+def _android_downloads_folder() -> str:
+    """:return: downloads folder for the Android OS"""
+    # Get directories with pyjnius
+    try:
+        from jnius import autoclass  # noqa: PLC0415
+
+        context = autoclass("android.content.Context")
+        environment = autoclass("android.os.Environment")
+        downloads_dir: str = context.getExternalFilesDir(environment.DIRECTORY_DOWNLOADS).getAbsolutePath()
+    except Exception:  # noqa: BLE001
+        downloads_dir = "/storage/emulated/0/Downloads"
+
+    return downloads_dir
+
+
+@lru_cache(maxsize=1)
+def _android_pictures_folder() -> str:
+    """:return: pictures folder for the Android OS"""
+    # Get directories with pyjnius
+    try:
+        from jnius import autoclass  # noqa: PLC0415
+
+        context = autoclass("android.content.Context")
+        environment = autoclass("android.os.Environment")
+        pictures_dir: str = context.getExternalFilesDir(environment.DIRECTORY_PICTURES).getAbsolutePath()
+    except Exception:  # noqa: BLE001
+        pictures_dir = "/storage/emulated/0/Pictures"
+
+    return pictures_dir
+
+
+@lru_cache(maxsize=1)
+def _android_videos_folder() -> str:
+    """:return: videos folder for the Android OS"""
+    # Get directories with pyjnius
+    try:
+        from jnius import autoclass  # noqa: PLC0415
+
+        context = autoclass("android.content.Context")
+        environment = autoclass("android.os.Environment")
+        videos_dir: str = context.getExternalFilesDir(environment.DIRECTORY_DCIM).getAbsolutePath()
+    except Exception:  # noqa: BLE001
+        videos_dir = "/storage/emulated/0/DCIM/Camera"
+
+    return videos_dir
+
+
+@lru_cache(maxsize=1)
+def _android_music_folder() -> str:
+    """:return: music folder for the Android OS"""
+    # Get directories with pyjnius
+    try:
+        from jnius import autoclass  # noqa: PLC0415
+
+        context = autoclass("android.content.Context")
+        environment = autoclass("android.os.Environment")
+        music_dir: str = context.getExternalFilesDir(environment.DIRECTORY_MUSIC).getAbsolutePath()
+    except Exception:  # noqa: BLE001
+        music_dir = "/storage/emulated/0/Music"
+
+    return music_dir
+
+
+__all__ = [
+    "Android",
+]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/api.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..c50caa648a6d18f5b515404ac5a4d0e41a072739
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/api.py
@@ -0,0 +1,292 @@
+"""Base API."""
+
+from __future__ import annotations
+
+import os
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Iterator, Literal
+
+
+class PlatformDirsABC(ABC):  # noqa: PLR0904
+    """Abstract base class for platform directories."""
+
+    def __init__(  # noqa: PLR0913, PLR0917
+        self,
+        appname: str | None = None,
+        appauthor: str | None | Literal[False] = None,
+        version: str | None = None,
+        roaming: bool = False,  # noqa: FBT001, FBT002
+        multipath: bool = False,  # noqa: FBT001, FBT002
+        opinion: bool = True,  # noqa: FBT001, FBT002
+        ensure_exists: bool = False,  # noqa: FBT001, FBT002
+    ) -> None:
+        """
+        Create a new platform directory.
+
+        :param appname: See `appname`.
+        :param appauthor: See `appauthor`.
+        :param version: See `version`.
+        :param roaming: See `roaming`.
+        :param multipath: See `multipath`.
+        :param opinion: See `opinion`.
+        :param ensure_exists: See `ensure_exists`.
+
+        """
+        self.appname = appname  #: The name of application.
+        self.appauthor = appauthor
+        """
+        The name of the app author or distributing body for this application.
+
+        Typically, it is the owning company name. Defaults to `appname`. You may pass ``False`` to disable it.
+
+        """
+        self.version = version
+        """
+        An optional version path element to append to the path.
+
+        You might want to use this if you want multiple versions of your app to be able to run independently. If used,
+        this would typically be ``<major>.<minor>``.
+
+        """
+        self.roaming = roaming
+        """
+        Whether to use the roaming appdata directory on Windows.
+
+        That means that for users on a Windows network setup for roaming profiles, this user data will be synced on
+        login (see
+        `here <https://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>`_).
+
+        """
+        self.multipath = multipath
+        """
+        An optional parameter which indicates that the entire list of data dirs should be returned.
+
+        By default, the first item would only be returned.
+
+        """
+        self.opinion = opinion  #: A flag to indicating to use opinionated values.
+        self.ensure_exists = ensure_exists
+        """
+        Optionally create the directory (and any missing parents) upon access if it does not exist.
+
+        By default, no directories are created.
+
+        """
+
+    def _append_app_name_and_version(self, *base: str) -> str:
+        params = list(base[1:])
+        if self.appname:
+            params.append(self.appname)
+            if self.version:
+                params.append(self.version)
+        path = os.path.join(base[0], *params)  # noqa: PTH118
+        self._optionally_create_directory(path)
+        return path
+
+    def _optionally_create_directory(self, path: str) -> None:
+        if self.ensure_exists:
+            Path(path).mkdir(parents=True, exist_ok=True)
+
+    @property
+    @abstractmethod
+    def user_data_dir(self) -> str:
+        """:return: data directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def site_data_dir(self) -> str:
+        """:return: data directory shared by users"""
+
+    @property
+    @abstractmethod
+    def user_config_dir(self) -> str:
+        """:return: config directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def site_config_dir(self) -> str:
+        """:return: config directory shared by the users"""
+
+    @property
+    @abstractmethod
+    def user_cache_dir(self) -> str:
+        """:return: cache directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def site_cache_dir(self) -> str:
+        """:return: cache directory shared by users"""
+
+    @property
+    @abstractmethod
+    def user_state_dir(self) -> str:
+        """:return: state directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_log_dir(self) -> str:
+        """:return: log directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_documents_dir(self) -> str:
+        """:return: documents directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_downloads_dir(self) -> str:
+        """:return: downloads directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_pictures_dir(self) -> str:
+        """:return: pictures directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_videos_dir(self) -> str:
+        """:return: videos directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_music_dir(self) -> str:
+        """:return: music directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_desktop_dir(self) -> str:
+        """:return: desktop directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def user_runtime_dir(self) -> str:
+        """:return: runtime directory tied to the user"""
+
+    @property
+    @abstractmethod
+    def site_runtime_dir(self) -> str:
+        """:return: runtime directory shared by users"""
+
+    @property
+    def user_data_path(self) -> Path:
+        """:return: data path tied to the user"""
+        return Path(self.user_data_dir)
+
+    @property
+    def site_data_path(self) -> Path:
+        """:return: data path shared by users"""
+        return Path(self.site_data_dir)
+
+    @property
+    def user_config_path(self) -> Path:
+        """:return: config path tied to the user"""
+        return Path(self.user_config_dir)
+
+    @property
+    def site_config_path(self) -> Path:
+        """:return: config path shared by the users"""
+        return Path(self.site_config_dir)
+
+    @property
+    def user_cache_path(self) -> Path:
+        """:return: cache path tied to the user"""
+        return Path(self.user_cache_dir)
+
+    @property
+    def site_cache_path(self) -> Path:
+        """:return: cache path shared by users"""
+        return Path(self.site_cache_dir)
+
+    @property
+    def user_state_path(self) -> Path:
+        """:return: state path tied to the user"""
+        return Path(self.user_state_dir)
+
+    @property
+    def user_log_path(self) -> Path:
+        """:return: log path tied to the user"""
+        return Path(self.user_log_dir)
+
+    @property
+    def user_documents_path(self) -> Path:
+        """:return: documents a path tied to the user"""
+        return Path(self.user_documents_dir)
+
+    @property
+    def user_downloads_path(self) -> Path:
+        """:return: downloads path tied to the user"""
+        return Path(self.user_downloads_dir)
+
+    @property
+    def user_pictures_path(self) -> Path:
+        """:return: pictures path tied to the user"""
+        return Path(self.user_pictures_dir)
+
+    @property
+    def user_videos_path(self) -> Path:
+        """:return: videos path tied to the user"""
+        return Path(self.user_videos_dir)
+
+    @property
+    def user_music_path(self) -> Path:
+        """:return: music path tied to the user"""
+        return Path(self.user_music_dir)
+
+    @property
+    def user_desktop_path(self) -> Path:
+        """:return: desktop path tied to the user"""
+        return Path(self.user_desktop_dir)
+
+    @property
+    def user_runtime_path(self) -> Path:
+        """:return: runtime path tied to the user"""
+        return Path(self.user_runtime_dir)
+
+    @property
+    def site_runtime_path(self) -> Path:
+        """:return: runtime path shared by users"""
+        return Path(self.site_runtime_dir)
+
+    def iter_config_dirs(self) -> Iterator[str]:
+        """:yield: all user and site configuration directories."""
+        yield self.user_config_dir
+        yield self.site_config_dir
+
+    def iter_data_dirs(self) -> Iterator[str]:
+        """:yield: all user and site data directories."""
+        yield self.user_data_dir
+        yield self.site_data_dir
+
+    def iter_cache_dirs(self) -> Iterator[str]:
+        """:yield: all user and site cache directories."""
+        yield self.user_cache_dir
+        yield self.site_cache_dir
+
+    def iter_runtime_dirs(self) -> Iterator[str]:
+        """:yield: all user and site runtime directories."""
+        yield self.user_runtime_dir
+        yield self.site_runtime_dir
+
+    def iter_config_paths(self) -> Iterator[Path]:
+        """:yield: all user and site configuration paths."""
+        for path in self.iter_config_dirs():
+            yield Path(path)
+
+    def iter_data_paths(self) -> Iterator[Path]:
+        """:yield: all user and site data paths."""
+        for path in self.iter_data_dirs():
+            yield Path(path)
+
+    def iter_cache_paths(self) -> Iterator[Path]:
+        """:yield: all user and site cache paths."""
+        for path in self.iter_cache_dirs():
+            yield Path(path)
+
+    def iter_runtime_paths(self) -> Iterator[Path]:
+        """:yield: all user and site runtime paths."""
+        for path in self.iter_runtime_dirs():
+            yield Path(path)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/macos.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/macos.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb1ba5df1dacfb4cbedc66e0ddd81a4057d4ebbe
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/macos.py
@@ -0,0 +1,130 @@
+"""macOS."""
+
+from __future__ import annotations
+
+import os.path
+import sys
+
+from .api import PlatformDirsABC
+
+
+class MacOS(PlatformDirsABC):
+    """
+    Platform directories for the macOS operating system.
+
+    Follows the guidance from
+    `Apple documentation <https://developer.apple.com/library/archive/documentation/FileManagement/Conceptual/FileSystemProgrammingGuide/MacOSXDirectories/MacOSXDirectories.html>`_.
+    Makes use of the `appname <platformdirs.api.PlatformDirsABC.appname>`,
+    `version <platformdirs.api.PlatformDirsABC.version>`,
+    `ensure_exists <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+
+    """
+
+    @property
+    def user_data_dir(self) -> str:
+        """:return: data directory tied to the user, e.g. ``~/Library/Application Support/$appname/$version``"""
+        return self._append_app_name_and_version(os.path.expanduser("~/Library/Application Support"))  # noqa: PTH111
+
+    @property
+    def site_data_dir(self) -> str:
+        """
+        :return: data directory shared by users, e.g. ``/Library/Application Support/$appname/$version``.
+          If we're using a Python binary managed by `Homebrew <https://brew.sh>`_, the directory
+          will be under the Homebrew prefix, e.g. ``/opt/homebrew/share/$appname/$version``.
+          If `multipath <platformdirs.api.PlatformDirsABC.multipath>` is enabled, and we're in Homebrew,
+          the response is a multi-path string separated by ":", e.g.
+          ``/opt/homebrew/share/$appname/$version:/Library/Application Support/$appname/$version``
+        """
+        is_homebrew = sys.prefix.startswith("/opt/homebrew")
+        path_list = [self._append_app_name_and_version("/opt/homebrew/share")] if is_homebrew else []
+        path_list.append(self._append_app_name_and_version("/Library/Application Support"))
+        if self.multipath:
+            return os.pathsep.join(path_list)
+        return path_list[0]
+
+    @property
+    def user_config_dir(self) -> str:
+        """:return: config directory tied to the user, same as `user_data_dir`"""
+        return self.user_data_dir
+
+    @property
+    def site_config_dir(self) -> str:
+        """:return: config directory shared by the users, same as `site_data_dir`"""
+        return self.site_data_dir
+
+    @property
+    def user_cache_dir(self) -> str:
+        """:return: cache directory tied to the user, e.g. ``~/Library/Caches/$appname/$version``"""
+        return self._append_app_name_and_version(os.path.expanduser("~/Library/Caches"))  # noqa: PTH111
+
+    @property
+    def site_cache_dir(self) -> str:
+        """
+        :return: cache directory shared by users, e.g. ``/Library/Caches/$appname/$version``.
+          If we're using a Python binary managed by `Homebrew <https://brew.sh>`_, the directory
+          will be under the Homebrew prefix, e.g. ``/opt/homebrew/var/cache/$appname/$version``.
+          If `multipath <platformdirs.api.PlatformDirsABC.multipath>` is enabled, and we're in Homebrew,
+          the response is a multi-path string separated by ":", e.g.
+          ``/opt/homebrew/var/cache/$appname/$version:/Library/Caches/$appname/$version``
+        """
+        is_homebrew = sys.prefix.startswith("/opt/homebrew")
+        path_list = [self._append_app_name_and_version("/opt/homebrew/var/cache")] if is_homebrew else []
+        path_list.append(self._append_app_name_and_version("/Library/Caches"))
+        if self.multipath:
+            return os.pathsep.join(path_list)
+        return path_list[0]
+
+    @property
+    def user_state_dir(self) -> str:
+        """:return: state directory tied to the user, same as `user_data_dir`"""
+        return self.user_data_dir
+
+    @property
+    def user_log_dir(self) -> str:
+        """:return: log directory tied to the user, e.g. ``~/Library/Logs/$appname/$version``"""
+        return self._append_app_name_and_version(os.path.expanduser("~/Library/Logs"))  # noqa: PTH111
+
+    @property
+    def user_documents_dir(self) -> str:
+        """:return: documents directory tied to the user, e.g. ``~/Documents``"""
+        return os.path.expanduser("~/Documents")  # noqa: PTH111
+
+    @property
+    def user_downloads_dir(self) -> str:
+        """:return: downloads directory tied to the user, e.g. ``~/Downloads``"""
+        return os.path.expanduser("~/Downloads")  # noqa: PTH111
+
+    @property
+    def user_pictures_dir(self) -> str:
+        """:return: pictures directory tied to the user, e.g. ``~/Pictures``"""
+        return os.path.expanduser("~/Pictures")  # noqa: PTH111
+
+    @property
+    def user_videos_dir(self) -> str:
+        """:return: videos directory tied to the user, e.g. ``~/Movies``"""
+        return os.path.expanduser("~/Movies")  # noqa: PTH111
+
+    @property
+    def user_music_dir(self) -> str:
+        """:return: music directory tied to the user, e.g. ``~/Music``"""
+        return os.path.expanduser("~/Music")  # noqa: PTH111
+
+    @property
+    def user_desktop_dir(self) -> str:
+        """:return: desktop directory tied to the user, e.g. ``~/Desktop``"""
+        return os.path.expanduser("~/Desktop")  # noqa: PTH111
+
+    @property
+    def user_runtime_dir(self) -> str:
+        """:return: runtime directory tied to the user, e.g. ``~/Library/Caches/TemporaryItems/$appname/$version``"""
+        return self._append_app_name_and_version(os.path.expanduser("~/Library/Caches/TemporaryItems"))  # noqa: PTH111
+
+    @property
+    def site_runtime_dir(self) -> str:
+        """:return: runtime directory shared by users, same as `user_runtime_dir`"""
+        return self.user_runtime_dir
+
+
+__all__ = [
+    "MacOS",
+]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/py.typed b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/unix.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/unix.py
new file mode 100644
index 0000000000000000000000000000000000000000..9500ade614ce4e698ce02e23c8d7b52988a2b1a8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/unix.py
@@ -0,0 +1,275 @@
+"""Unix."""
+
+from __future__ import annotations
+
+import os
+import sys
+from configparser import ConfigParser
+from pathlib import Path
+from typing import Iterator, NoReturn
+
+from .api import PlatformDirsABC
+
+if sys.platform == "win32":
+
+    def getuid() -> NoReturn:
+        msg = "should only be used on Unix"
+        raise RuntimeError(msg)
+
+else:
+    from os import getuid
+
+
+class Unix(PlatformDirsABC):  # noqa: PLR0904
+    """
+    On Unix/Linux, we follow the `XDG Basedir Spec <https://specifications.freedesktop.org/basedir-spec/basedir-spec-
+    latest.html>`_.
+
+    The spec allows overriding directories with environment variables. The examples shown are the default values,
+    alongside the name of the environment variable that overrides them. Makes use of the `appname
+    <platformdirs.api.PlatformDirsABC.appname>`, `version <platformdirs.api.PlatformDirsABC.version>`, `multipath
+    <platformdirs.api.PlatformDirsABC.multipath>`, `opinion <platformdirs.api.PlatformDirsABC.opinion>`, `ensure_exists
+    <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+
+    """
+
+    @property
+    def user_data_dir(self) -> str:
+        """
+        :return: data directory tied to the user, e.g. ``~/.local/share/$appname/$version`` or
+         ``$XDG_DATA_HOME/$appname/$version``
+        """
+        path = os.environ.get("XDG_DATA_HOME", "")
+        if not path.strip():
+            path = os.path.expanduser("~/.local/share")  # noqa: PTH111
+        return self._append_app_name_and_version(path)
+
+    @property
+    def _site_data_dirs(self) -> list[str]:
+        path = os.environ.get("XDG_DATA_DIRS", "")
+        if not path.strip():
+            path = f"/usr/local/share{os.pathsep}/usr/share"
+        return [self._append_app_name_and_version(p) for p in path.split(os.pathsep)]
+
+    @property
+    def site_data_dir(self) -> str:
+        """
+        :return: data directories shared by users (if `multipath <platformdirs.api.PlatformDirsABC.multipath>` is
+         enabled and ``XDG_DATA_DIRS`` is set and a multi path the response is also a multi path separated by the
+         OS path separator), e.g. ``/usr/local/share/$appname/$version`` or ``/usr/share/$appname/$version``
+        """
+        # XDG default for $XDG_DATA_DIRS; only first, if multipath is False
+        dirs = self._site_data_dirs
+        if not self.multipath:
+            return dirs[0]
+        return os.pathsep.join(dirs)
+
+    @property
+    def user_config_dir(self) -> str:
+        """
+        :return: config directory tied to the user, e.g. ``~/.config/$appname/$version`` or
+         ``$XDG_CONFIG_HOME/$appname/$version``
+        """
+        path = os.environ.get("XDG_CONFIG_HOME", "")
+        if not path.strip():
+            path = os.path.expanduser("~/.config")  # noqa: PTH111
+        return self._append_app_name_and_version(path)
+
+    @property
+    def _site_config_dirs(self) -> list[str]:
+        path = os.environ.get("XDG_CONFIG_DIRS", "")
+        if not path.strip():
+            path = "/etc/xdg"
+        return [self._append_app_name_and_version(p) for p in path.split(os.pathsep)]
+
+    @property
+    def site_config_dir(self) -> str:
+        """
+        :return: config directories shared by users (if `multipath <platformdirs.api.PlatformDirsABC.multipath>`
+         is enabled and ``XDG_CONFIG_DIRS`` is set and a multi path the response is also a multi path separated by
+         the OS path separator), e.g. ``/etc/xdg/$appname/$version``
+        """
+        # XDG default for $XDG_CONFIG_DIRS only first, if multipath is False
+        dirs = self._site_config_dirs
+        if not self.multipath:
+            return dirs[0]
+        return os.pathsep.join(dirs)
+
+    @property
+    def user_cache_dir(self) -> str:
+        """
+        :return: cache directory tied to the user, e.g. ``~/.cache/$appname/$version`` or
+         ``~/$XDG_CACHE_HOME/$appname/$version``
+        """
+        path = os.environ.get("XDG_CACHE_HOME", "")
+        if not path.strip():
+            path = os.path.expanduser("~/.cache")  # noqa: PTH111
+        return self._append_app_name_and_version(path)
+
+    @property
+    def site_cache_dir(self) -> str:
+        """:return: cache directory shared by users, e.g. ``/var/cache/$appname/$version``"""
+        return self._append_app_name_and_version("/var/cache")
+
+    @property
+    def user_state_dir(self) -> str:
+        """
+        :return: state directory tied to the user, e.g. ``~/.local/state/$appname/$version`` or
+         ``$XDG_STATE_HOME/$appname/$version``
+        """
+        path = os.environ.get("XDG_STATE_HOME", "")
+        if not path.strip():
+            path = os.path.expanduser("~/.local/state")  # noqa: PTH111
+        return self._append_app_name_and_version(path)
+
+    @property
+    def user_log_dir(self) -> str:
+        """:return: log directory tied to the user, same as `user_state_dir` if not opinionated else ``log`` in it"""
+        path = self.user_state_dir
+        if self.opinion:
+            path = os.path.join(path, "log")  # noqa: PTH118
+            self._optionally_create_directory(path)
+        return path
+
+    @property
+    def user_documents_dir(self) -> str:
+        """:return: documents directory tied to the user, e.g. ``~/Documents``"""
+        return _get_user_media_dir("XDG_DOCUMENTS_DIR", "~/Documents")
+
+    @property
+    def user_downloads_dir(self) -> str:
+        """:return: downloads directory tied to the user, e.g. ``~/Downloads``"""
+        return _get_user_media_dir("XDG_DOWNLOAD_DIR", "~/Downloads")
+
+    @property
+    def user_pictures_dir(self) -> str:
+        """:return: pictures directory tied to the user, e.g. ``~/Pictures``"""
+        return _get_user_media_dir("XDG_PICTURES_DIR", "~/Pictures")
+
+    @property
+    def user_videos_dir(self) -> str:
+        """:return: videos directory tied to the user, e.g. ``~/Videos``"""
+        return _get_user_media_dir("XDG_VIDEOS_DIR", "~/Videos")
+
+    @property
+    def user_music_dir(self) -> str:
+        """:return: music directory tied to the user, e.g. ``~/Music``"""
+        return _get_user_media_dir("XDG_MUSIC_DIR", "~/Music")
+
+    @property
+    def user_desktop_dir(self) -> str:
+        """:return: desktop directory tied to the user, e.g. ``~/Desktop``"""
+        return _get_user_media_dir("XDG_DESKTOP_DIR", "~/Desktop")
+
+    @property
+    def user_runtime_dir(self) -> str:
+        """
+        :return: runtime directory tied to the user, e.g. ``/run/user/$(id -u)/$appname/$version`` or
+         ``$XDG_RUNTIME_DIR/$appname/$version``.
+
+         For FreeBSD/OpenBSD/NetBSD, it would return ``/var/run/user/$(id -u)/$appname/$version`` if
+         exists, otherwise ``/tmp/runtime-$(id -u)/$appname/$version``, if``$XDG_RUNTIME_DIR``
+         is not set.
+        """
+        path = os.environ.get("XDG_RUNTIME_DIR", "")
+        if not path.strip():
+            if sys.platform.startswith(("freebsd", "openbsd", "netbsd")):
+                path = f"/var/run/user/{getuid()}"
+                if not Path(path).exists():
+                    path = f"/tmp/runtime-{getuid()}"  # noqa: S108
+            else:
+                path = f"/run/user/{getuid()}"
+        return self._append_app_name_and_version(path)
+
+    @property
+    def site_runtime_dir(self) -> str:
+        """
+        :return: runtime directory shared by users, e.g. ``/run/$appname/$version`` or \
+        ``$XDG_RUNTIME_DIR/$appname/$version``.
+
+        Note that this behaves almost exactly like `user_runtime_dir` if ``$XDG_RUNTIME_DIR`` is set, but will
+        fall back to paths associated to the root user instead of a regular logged-in user if it's not set.
+
+        If you wish to ensure that a logged-in root user path is returned e.g. ``/run/user/0``, use `user_runtime_dir`
+        instead.
+
+        For FreeBSD/OpenBSD/NetBSD, it would return ``/var/run/$appname/$version`` if ``$XDG_RUNTIME_DIR`` is not set.
+        """
+        path = os.environ.get("XDG_RUNTIME_DIR", "")
+        if not path.strip():
+            if sys.platform.startswith(("freebsd", "openbsd", "netbsd")):
+                path = "/var/run"
+            else:
+                path = "/run"
+        return self._append_app_name_and_version(path)
+
+    @property
+    def site_data_path(self) -> Path:
+        """:return: data path shared by users. Only return the first item, even if ``multipath`` is set to ``True``"""
+        return self._first_item_as_path_if_multipath(self.site_data_dir)
+
+    @property
+    def site_config_path(self) -> Path:
+        """:return: config path shared by the users, returns the first item, even if ``multipath`` is set to ``True``"""
+        return self._first_item_as_path_if_multipath(self.site_config_dir)
+
+    @property
+    def site_cache_path(self) -> Path:
+        """:return: cache path shared by users. Only return the first item, even if ``multipath`` is set to ``True``"""
+        return self._first_item_as_path_if_multipath(self.site_cache_dir)
+
+    def _first_item_as_path_if_multipath(self, directory: str) -> Path:
+        if self.multipath:
+            # If multipath is True, the first path is returned.
+            directory = directory.split(os.pathsep)[0]
+        return Path(directory)
+
+    def iter_config_dirs(self) -> Iterator[str]:
+        """:yield: all user and site configuration directories."""
+        yield self.user_config_dir
+        yield from self._site_config_dirs
+
+    def iter_data_dirs(self) -> Iterator[str]:
+        """:yield: all user and site data directories."""
+        yield self.user_data_dir
+        yield from self._site_data_dirs
+
+
+def _get_user_media_dir(env_var: str, fallback_tilde_path: str) -> str:
+    media_dir = _get_user_dirs_folder(env_var)
+    if media_dir is None:
+        media_dir = os.environ.get(env_var, "").strip()
+        if not media_dir:
+            media_dir = os.path.expanduser(fallback_tilde_path)  # noqa: PTH111
+
+    return media_dir
+
+
+def _get_user_dirs_folder(key: str) -> str | None:
+    """
+    Return directory from user-dirs.dirs config file.
+
+    See https://freedesktop.org/wiki/Software/xdg-user-dirs/.
+
+    """
+    user_dirs_config_path = Path(Unix().user_config_dir) / "user-dirs.dirs"
+    if user_dirs_config_path.exists():
+        parser = ConfigParser()
+
+        with user_dirs_config_path.open() as stream:
+            # Add fake section header, so ConfigParser doesn't complain
+            parser.read_string(f"[top]\n{stream.read()}")
+
+        if key not in parser["top"]:
+            return None
+
+        path = parser["top"][key].strip('"')
+        # Handle relative home paths
+        return path.replace("$HOME", os.path.expanduser("~"))  # noqa: PTH111
+
+    return None
+
+
+__all__ = [
+    "Unix",
+]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/version.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..6483ddce0bcfbaa01a5b3dc3f8b864de20ef0683
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/version.py
@@ -0,0 +1,16 @@
+# file generated by setuptools_scm
+# don't change, don't track in version control
+TYPE_CHECKING = False
+if TYPE_CHECKING:
+    from typing import Tuple, Union
+    VERSION_TUPLE = Tuple[Union[int, str], ...]
+else:
+    VERSION_TUPLE = object
+
+version: str
+__version__: str
+__version_tuple__: VERSION_TUPLE
+version_tuple: VERSION_TUPLE
+
+__version__ = version = '4.2.2'
+__version_tuple__ = version_tuple = (4, 2, 2)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/windows.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/windows.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7bc96091a2b1cd078a0847519cb5dd50a5d8898
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/platformdirs/windows.py
@@ -0,0 +1,272 @@
+"""Windows."""
+
+from __future__ import annotations
+
+import os
+import sys
+from functools import lru_cache
+from typing import TYPE_CHECKING
+
+from .api import PlatformDirsABC
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+
+class Windows(PlatformDirsABC):
+    """
+    `MSDN on where to store app data files <https://learn.microsoft.com/en-us/windows/win32/shell/knownfolderid>`_.
+
+    Makes use of the `appname <platformdirs.api.PlatformDirsABC.appname>`, `appauthor
+    <platformdirs.api.PlatformDirsABC.appauthor>`, `version <platformdirs.api.PlatformDirsABC.version>`, `roaming
+    <platformdirs.api.PlatformDirsABC.roaming>`, `opinion <platformdirs.api.PlatformDirsABC.opinion>`, `ensure_exists
+    <platformdirs.api.PlatformDirsABC.ensure_exists>`.
+
+    """
+
+    @property
+    def user_data_dir(self) -> str:
+        """
+        :return: data directory tied to the user, e.g.
+         ``%USERPROFILE%\\AppData\\Local\\$appauthor\\$appname`` (not roaming) or
+         ``%USERPROFILE%\\AppData\\Roaming\\$appauthor\\$appname`` (roaming)
+        """
+        const = "CSIDL_APPDATA" if self.roaming else "CSIDL_LOCAL_APPDATA"
+        path = os.path.normpath(get_win_folder(const))
+        return self._append_parts(path)
+
+    def _append_parts(self, path: str, *, opinion_value: str | None = None) -> str:
+        params = []
+        if self.appname:
+            if self.appauthor is not False:
+                author = self.appauthor or self.appname
+                params.append(author)
+            params.append(self.appname)
+            if opinion_value is not None and self.opinion:
+                params.append(opinion_value)
+            if self.version:
+                params.append(self.version)
+        path = os.path.join(path, *params)  # noqa: PTH118
+        self._optionally_create_directory(path)
+        return path
+
+    @property
+    def site_data_dir(self) -> str:
+        """:return: data directory shared by users, e.g. ``C:\\ProgramData\\$appauthor\\$appname``"""
+        path = os.path.normpath(get_win_folder("CSIDL_COMMON_APPDATA"))
+        return self._append_parts(path)
+
+    @property
+    def user_config_dir(self) -> str:
+        """:return: config directory tied to the user, same as `user_data_dir`"""
+        return self.user_data_dir
+
+    @property
+    def site_config_dir(self) -> str:
+        """:return: config directory shared by the users, same as `site_data_dir`"""
+        return self.site_data_dir
+
+    @property
+    def user_cache_dir(self) -> str:
+        """
+        :return: cache directory tied to the user (if opinionated with ``Cache`` folder within ``$appname``) e.g.
+         ``%USERPROFILE%\\AppData\\Local\\$appauthor\\$appname\\Cache\\$version``
+        """
+        path = os.path.normpath(get_win_folder("CSIDL_LOCAL_APPDATA"))
+        return self._append_parts(path, opinion_value="Cache")
+
+    @property
+    def site_cache_dir(self) -> str:
+        """:return: cache directory shared by users, e.g. ``C:\\ProgramData\\$appauthor\\$appname\\Cache\\$version``"""
+        path = os.path.normpath(get_win_folder("CSIDL_COMMON_APPDATA"))
+        return self._append_parts(path, opinion_value="Cache")
+
+    @property
+    def user_state_dir(self) -> str:
+        """:return: state directory tied to the user, same as `user_data_dir`"""
+        return self.user_data_dir
+
+    @property
+    def user_log_dir(self) -> str:
+        """:return: log directory tied to the user, same as `user_data_dir` if not opinionated else ``Logs`` in it"""
+        path = self.user_data_dir
+        if self.opinion:
+            path = os.path.join(path, "Logs")  # noqa: PTH118
+            self._optionally_create_directory(path)
+        return path
+
+    @property
+    def user_documents_dir(self) -> str:
+        """:return: documents directory tied to the user e.g. ``%USERPROFILE%\\Documents``"""
+        return os.path.normpath(get_win_folder("CSIDL_PERSONAL"))
+
+    @property
+    def user_downloads_dir(self) -> str:
+        """:return: downloads directory tied to the user e.g. ``%USERPROFILE%\\Downloads``"""
+        return os.path.normpath(get_win_folder("CSIDL_DOWNLOADS"))
+
+    @property
+    def user_pictures_dir(self) -> str:
+        """:return: pictures directory tied to the user e.g. ``%USERPROFILE%\\Pictures``"""
+        return os.path.normpath(get_win_folder("CSIDL_MYPICTURES"))
+
+    @property
+    def user_videos_dir(self) -> str:
+        """:return: videos directory tied to the user e.g. ``%USERPROFILE%\\Videos``"""
+        return os.path.normpath(get_win_folder("CSIDL_MYVIDEO"))
+
+    @property
+    def user_music_dir(self) -> str:
+        """:return: music directory tied to the user e.g. ``%USERPROFILE%\\Music``"""
+        return os.path.normpath(get_win_folder("CSIDL_MYMUSIC"))
+
+    @property
+    def user_desktop_dir(self) -> str:
+        """:return: desktop directory tied to the user, e.g. ``%USERPROFILE%\\Desktop``"""
+        return os.path.normpath(get_win_folder("CSIDL_DESKTOPDIRECTORY"))
+
+    @property
+    def user_runtime_dir(self) -> str:
+        """
+        :return: runtime directory tied to the user, e.g.
+         ``%USERPROFILE%\\AppData\\Local\\Temp\\$appauthor\\$appname``
+        """
+        path = os.path.normpath(os.path.join(get_win_folder("CSIDL_LOCAL_APPDATA"), "Temp"))  # noqa: PTH118
+        return self._append_parts(path)
+
+    @property
+    def site_runtime_dir(self) -> str:
+        """:return: runtime directory shared by users, same as `user_runtime_dir`"""
+        return self.user_runtime_dir
+
+
+def get_win_folder_from_env_vars(csidl_name: str) -> str:
+    """Get folder from environment variables."""
+    result = get_win_folder_if_csidl_name_not_env_var(csidl_name)
+    if result is not None:
+        return result
+
+    env_var_name = {
+        "CSIDL_APPDATA": "APPDATA",
+        "CSIDL_COMMON_APPDATA": "ALLUSERSPROFILE",
+        "CSIDL_LOCAL_APPDATA": "LOCALAPPDATA",
+    }.get(csidl_name)
+    if env_var_name is None:
+        msg = f"Unknown CSIDL name: {csidl_name}"
+        raise ValueError(msg)
+    result = os.environ.get(env_var_name)
+    if result is None:
+        msg = f"Unset environment variable: {env_var_name}"
+        raise ValueError(msg)
+    return result
+
+
+def get_win_folder_if_csidl_name_not_env_var(csidl_name: str) -> str | None:
+    """Get a folder for a CSIDL name that does not exist as an environment variable."""
+    if csidl_name == "CSIDL_PERSONAL":
+        return os.path.join(os.path.normpath(os.environ["USERPROFILE"]), "Documents")  # noqa: PTH118
+
+    if csidl_name == "CSIDL_DOWNLOADS":
+        return os.path.join(os.path.normpath(os.environ["USERPROFILE"]), "Downloads")  # noqa: PTH118
+
+    if csidl_name == "CSIDL_MYPICTURES":
+        return os.path.join(os.path.normpath(os.environ["USERPROFILE"]), "Pictures")  # noqa: PTH118
+
+    if csidl_name == "CSIDL_MYVIDEO":
+        return os.path.join(os.path.normpath(os.environ["USERPROFILE"]), "Videos")  # noqa: PTH118
+
+    if csidl_name == "CSIDL_MYMUSIC":
+        return os.path.join(os.path.normpath(os.environ["USERPROFILE"]), "Music")  # noqa: PTH118
+    return None
+
+
+def get_win_folder_from_registry(csidl_name: str) -> str:
+    """
+    Get folder from the registry.
+
+    This is a fallback technique at best. I'm not sure if using the registry for these guarantees us the correct answer
+    for all CSIDL_* names.
+
+    """
+    shell_folder_name = {
+        "CSIDL_APPDATA": "AppData",
+        "CSIDL_COMMON_APPDATA": "Common AppData",
+        "CSIDL_LOCAL_APPDATA": "Local AppData",
+        "CSIDL_PERSONAL": "Personal",
+        "CSIDL_DOWNLOADS": "{374DE290-123F-4565-9164-39C4925E467B}",
+        "CSIDL_MYPICTURES": "My Pictures",
+        "CSIDL_MYVIDEO": "My Video",
+        "CSIDL_MYMUSIC": "My Music",
+    }.get(csidl_name)
+    if shell_folder_name is None:
+        msg = f"Unknown CSIDL name: {csidl_name}"
+        raise ValueError(msg)
+    if sys.platform != "win32":  # only needed for mypy type checker to know that this code runs only on Windows
+        raise NotImplementedError
+    import winreg  # noqa: PLC0415
+
+    key = winreg.OpenKey(winreg.HKEY_CURRENT_USER, r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders")
+    directory, _ = winreg.QueryValueEx(key, shell_folder_name)
+    return str(directory)
+
+
+def get_win_folder_via_ctypes(csidl_name: str) -> str:
+    """Get folder with ctypes."""
+    # There is no 'CSIDL_DOWNLOADS'.
+    # Use 'CSIDL_PROFILE' (40) and append the default folder 'Downloads' instead.
+    # https://learn.microsoft.com/en-us/windows/win32/shell/knownfolderid
+
+    import ctypes  # noqa: PLC0415
+
+    csidl_const = {
+        "CSIDL_APPDATA": 26,
+        "CSIDL_COMMON_APPDATA": 35,
+        "CSIDL_LOCAL_APPDATA": 28,
+        "CSIDL_PERSONAL": 5,
+        "CSIDL_MYPICTURES": 39,
+        "CSIDL_MYVIDEO": 14,
+        "CSIDL_MYMUSIC": 13,
+        "CSIDL_DOWNLOADS": 40,
+        "CSIDL_DESKTOPDIRECTORY": 16,
+    }.get(csidl_name)
+    if csidl_const is None:
+        msg = f"Unknown CSIDL name: {csidl_name}"
+        raise ValueError(msg)
+
+    buf = ctypes.create_unicode_buffer(1024)
+    windll = getattr(ctypes, "windll")  # noqa: B009 # using getattr to avoid false positive with mypy type checker
+    windll.shell32.SHGetFolderPathW(None, csidl_const, None, 0, buf)
+
+    # Downgrade to short path name if it has high-bit chars.
+    if any(ord(c) > 255 for c in buf):  # noqa: PLR2004
+        buf2 = ctypes.create_unicode_buffer(1024)
+        if windll.kernel32.GetShortPathNameW(buf.value, buf2, 1024):
+            buf = buf2
+
+    if csidl_name == "CSIDL_DOWNLOADS":
+        return os.path.join(buf.value, "Downloads")  # noqa: PTH118
+
+    return buf.value
+
+
+def _pick_get_win_folder() -> Callable[[str], str]:
+    try:
+        import ctypes  # noqa: PLC0415
+    except ImportError:
+        pass
+    else:
+        if hasattr(ctypes, "windll"):
+            return get_win_folder_via_ctypes
+    try:
+        import winreg  # noqa: PLC0415, F401
+    except ImportError:
+        return get_win_folder_from_env_vars
+    else:
+        return get_win_folder_from_registry
+
+
+get_win_folder = lru_cache(maxsize=None)(_pick_get_win_folder())
+
+__all__ = [
+    "Windows",
+]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..e859590f886cd78344206af1a8ccb3080d4385e0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Taneli Hukkinen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..efd87ecc1659a43c0a6e37bdc77954f352172acc
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/METADATA
@@ -0,0 +1,206 @@
+Metadata-Version: 2.1
+Name: tomli
+Version: 2.0.1
+Summary: A lil' TOML parser
+Keywords: toml
+Author-email: Taneli Hukkinen <hukkin@users.noreply.github.com>
+Requires-Python: >=3.7
+Description-Content-Type: text/markdown
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: MacOS
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX :: Linux
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Typing :: Typed
+Project-URL: Changelog, https://github.com/hukkin/tomli/blob/master/CHANGELOG.md
+Project-URL: Homepage, https://github.com/hukkin/tomli
+
+[![Build Status](https://github.com/hukkin/tomli/workflows/Tests/badge.svg?branch=master)](https://github.com/hukkin/tomli/actions?query=workflow%3ATests+branch%3Amaster+event%3Apush)
+[![codecov.io](https://codecov.io/gh/hukkin/tomli/branch/master/graph/badge.svg)](https://codecov.io/gh/hukkin/tomli)
+[![PyPI version](https://img.shields.io/pypi/v/tomli)](https://pypi.org/project/tomli)
+
+# Tomli
+
+> A lil' TOML parser
+
+**Table of Contents**  *generated with [mdformat-toc](https://github.com/hukkin/mdformat-toc)*
+
+<!-- mdformat-toc start --slug=github --maxlevel=6 --minlevel=2 -->
+
+- [Intro](#intro)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Parse a TOML string](#parse-a-toml-string)
+  - [Parse a TOML file](#parse-a-toml-file)
+  - [Handle invalid TOML](#handle-invalid-toml)
+  - [Construct `decimal.Decimal`s from TOML floats](#construct-decimaldecimals-from-toml-floats)
+- [FAQ](#faq)
+  - [Why this parser?](#why-this-parser)
+  - [Is comment preserving round-trip parsing supported?](#is-comment-preserving-round-trip-parsing-supported)
+  - [Is there a `dumps`, `write` or `encode` function?](#is-there-a-dumps-write-or-encode-function)
+  - [How do TOML types map into Python types?](#how-do-toml-types-map-into-python-types)
+- [Performance](#performance)
+
+<!-- mdformat-toc end -->
+
+## Intro<a name="intro"></a>
+
+Tomli is a Python library for parsing [TOML](https://toml.io).
+Tomli is fully compatible with [TOML v1.0.0](https://toml.io/en/v1.0.0).
+
+## Installation<a name="installation"></a>
+
+```bash
+pip install tomli
+```
+
+## Usage<a name="usage"></a>
+
+### Parse a TOML string<a name="parse-a-toml-string"></a>
+
+```python
+import tomli
+
+toml_str = """
+           gretzky = 99
+
+           [kurri]
+           jari = 17
+           """
+
+toml_dict = tomli.loads(toml_str)
+assert toml_dict == {"gretzky": 99, "kurri": {"jari": 17}}
+```
+
+### Parse a TOML file<a name="parse-a-toml-file"></a>
+
+```python
+import tomli
+
+with open("path_to_file/conf.toml", "rb") as f:
+    toml_dict = tomli.load(f)
+```
+
+The file must be opened in binary mode (with the `"rb"` flag).
+Binary mode will enforce decoding the file as UTF-8 with universal newlines disabled,
+both of which are required to correctly parse TOML.
+
+### Handle invalid TOML<a name="handle-invalid-toml"></a>
+
+```python
+import tomli
+
+try:
+    toml_dict = tomli.loads("]] this is invalid TOML [[")
+except tomli.TOMLDecodeError:
+    print("Yep, definitely not valid.")
+```
+
+Note that error messages are considered informational only.
+They should not be assumed to stay constant across Tomli versions.
+
+### Construct `decimal.Decimal`s from TOML floats<a name="construct-decimaldecimals-from-toml-floats"></a>
+
+```python
+from decimal import Decimal
+import tomli
+
+toml_dict = tomli.loads("precision-matters = 0.982492", parse_float=Decimal)
+assert toml_dict["precision-matters"] == Decimal("0.982492")
+```
+
+Note that `decimal.Decimal` can be replaced with another callable that converts a TOML float from string to a Python type.
+The `decimal.Decimal` is, however, a practical choice for use cases where float inaccuracies can not be tolerated.
+
+Illegal types are `dict` and `list`, and their subtypes.
+A `ValueError` will be raised if `parse_float` produces illegal types.
+
+## FAQ<a name="faq"></a>
+
+### Why this parser?<a name="why-this-parser"></a>
+
+- it's lil'
+- pure Python with zero dependencies
+- the fastest pure Python parser [\*](#performance):
+  15x as fast as [tomlkit](https://pypi.org/project/tomlkit/),
+  2.4x as fast as [toml](https://pypi.org/project/toml/)
+- outputs [basic data types](#how-do-toml-types-map-into-python-types) only
+- 100% spec compliant: passes all tests in
+  [a test set](https://github.com/toml-lang/compliance/pull/8)
+  soon to be merged to the official
+  [compliance tests for TOML](https://github.com/toml-lang/compliance)
+  repository
+- thoroughly tested: 100% branch coverage
+
+### Is comment preserving round-trip parsing supported?<a name="is-comment-preserving-round-trip-parsing-supported"></a>
+
+No.
+
+The `tomli.loads` function returns a plain `dict` that is populated with builtin types and types from the standard library only.
+Preserving comments requires a custom type to be returned so will not be supported,
+at least not by the `tomli.loads` and `tomli.load` functions.
+
+Look into [TOML Kit](https://github.com/sdispater/tomlkit) if preservation of style is what you need.
+
+### Is there a `dumps`, `write` or `encode` function?<a name="is-there-a-dumps-write-or-encode-function"></a>
+
+[Tomli-W](https://github.com/hukkin/tomli-w) is the write-only counterpart of Tomli, providing `dump` and `dumps` functions.
+
+The core library does not include write capability, as most TOML use cases are read-only, and Tomli intends to be minimal.
+
+### How do TOML types map into Python types?<a name="how-do-toml-types-map-into-python-types"></a>
+
+| TOML type        | Python type         | Details                                                      |
+| ---------------- | ------------------- | ------------------------------------------------------------ |
+| Document Root    | `dict`              |                                                              |
+| Key              | `str`               |                                                              |
+| String           | `str`               |                                                              |
+| Integer          | `int`               |                                                              |
+| Float            | `float`             |                                                              |
+| Boolean          | `bool`              |                                                              |
+| Offset Date-Time | `datetime.datetime` | `tzinfo` attribute set to an instance of `datetime.timezone` |
+| Local Date-Time  | `datetime.datetime` | `tzinfo` attribute set to `None`                             |
+| Local Date       | `datetime.date`     |                                                              |
+| Local Time       | `datetime.time`     |                                                              |
+| Array            | `list`              |                                                              |
+| Table            | `dict`              |                                                              |
+| Inline Table     | `dict`              |                                                              |
+
+## Performance<a name="performance"></a>
+
+The `benchmark/` folder in this repository contains a performance benchmark for comparing the various Python TOML parsers.
+The benchmark can be run with `tox -e benchmark-pypi`.
+Running the benchmark on my personal computer output the following:
+
+```console
+foo@bar:~/dev/tomli$ tox -e benchmark-pypi
+benchmark-pypi installed: attrs==19.3.0,click==7.1.2,pytomlpp==1.0.2,qtoml==0.3.0,rtoml==0.7.0,toml==0.10.2,tomli==1.1.0,tomlkit==0.7.2
+benchmark-pypi run-test-pre: PYTHONHASHSEED='2658546909'
+benchmark-pypi run-test: commands[0] | python -c 'import datetime; print(datetime.date.today())'
+2021-07-23
+benchmark-pypi run-test: commands[1] | python --version
+Python 3.8.10
+benchmark-pypi run-test: commands[2] | python benchmark/run.py
+Parsing data.toml 5000 times:
+------------------------------------------------------
+    parser |  exec time | performance (more is better)
+-----------+------------+-----------------------------
+     rtoml |    0.901 s | baseline (100%)
+  pytomlpp |     1.08 s | 83.15%
+     tomli |     3.89 s | 23.15%
+      toml |     9.36 s | 9.63%
+     qtoml |     11.5 s | 7.82%
+   tomlkit |     56.8 s | 1.59%
+```
+
+The parsers are ordered from fastest to slowest, using the fastest parser as baseline.
+Tomli performed the best out of all pure Python TOML parsers,
+losing only to pytomlpp (wraps C++) and rtoml (wraps Rust).
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..1db8063ec5c3ba925e681030347e8d396eb934be
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/RECORD
@@ -0,0 +1,15 @@
+tomli-2.0.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+tomli-2.0.1.dist-info/LICENSE,sha256=uAgWsNUwuKzLTCIReDeQmEpuO2GSLCte6S8zcqsnQv4,1072
+tomli-2.0.1.dist-info/METADATA,sha256=zPDceKmPwJGLWtZykrHixL7WVXWmJGzZ1jyRT5lCoPI,8875
+tomli-2.0.1.dist-info/RECORD,,
+tomli-2.0.1.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+tomli-2.0.1.dist-info/WHEEL,sha256=jPMR_Dzkc4X4icQtmz81lnNY_kAsfog7ry7qoRvYLXw,81
+tomli/__init__.py,sha256=JhUwV66DB1g4Hvt1UQCVMdfCu-IgAV8FXmvDU9onxd4,396
+tomli/__pycache__/__init__.cpython-312.pyc,,
+tomli/__pycache__/_parser.cpython-312.pyc,,
+tomli/__pycache__/_re.cpython-312.pyc,,
+tomli/__pycache__/_types.cpython-312.pyc,,
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+tomli/_types.py,sha256=-GTG2VUqkpxwMqzmVO4F7ybKddIbAnuAHXfmWQcTi3Q,254
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/REQUESTED b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/REQUESTED
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..c727d148239a36b0e8de2b97fdb23fb96da78869
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli-2.0.1.dist-info/WHEEL
@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: flit 3.6.0
+Root-Is-Purelib: true
+Tag: py3-none-any
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c6ec97ec6961bcf184b6e0b2437b9924db0b9de
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/__init__.py
@@ -0,0 +1,11 @@
+# SPDX-License-Identifier: MIT
+# SPDX-FileCopyrightText: 2021 Taneli Hukkinen
+# Licensed to PSF under a Contributor Agreement.
+
+__all__ = ("loads", "load", "TOMLDecodeError")
+__version__ = "2.0.1"  # DO NOT EDIT THIS LINE MANUALLY. LET bump2version UTILITY DO IT
+
+from ._parser import TOMLDecodeError, load, loads
+
+# Pretend this exception was created here.
+TOMLDecodeError.__module__ = __name__
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/_parser.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/_parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1bb0aa19a556725aa2ae2b8cea95489c99a9078
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/_parser.py
@@ -0,0 +1,691 @@
+# SPDX-License-Identifier: MIT
+# SPDX-FileCopyrightText: 2021 Taneli Hukkinen
+# Licensed to PSF under a Contributor Agreement.
+
+from __future__ import annotations
+
+from collections.abc import Iterable
+import string
+from types import MappingProxyType
+from typing import Any, BinaryIO, NamedTuple
+
+from ._re import (
+    RE_DATETIME,
+    RE_LOCALTIME,
+    RE_NUMBER,
+    match_to_datetime,
+    match_to_localtime,
+    match_to_number,
+)
+from ._types import Key, ParseFloat, Pos
+
+ASCII_CTRL = frozenset(chr(i) for i in range(32)) | frozenset(chr(127))
+
+# Neither of these sets include quotation mark or backslash. They are
+# currently handled as separate cases in the parser functions.
+ILLEGAL_BASIC_STR_CHARS = ASCII_CTRL - frozenset("\t")
+ILLEGAL_MULTILINE_BASIC_STR_CHARS = ASCII_CTRL - frozenset("\t\n")
+
+ILLEGAL_LITERAL_STR_CHARS = ILLEGAL_BASIC_STR_CHARS
+ILLEGAL_MULTILINE_LITERAL_STR_CHARS = ILLEGAL_MULTILINE_BASIC_STR_CHARS
+
+ILLEGAL_COMMENT_CHARS = ILLEGAL_BASIC_STR_CHARS
+
+TOML_WS = frozenset(" \t")
+TOML_WS_AND_NEWLINE = TOML_WS | frozenset("\n")
+BARE_KEY_CHARS = frozenset(string.ascii_letters + string.digits + "-_")
+KEY_INITIAL_CHARS = BARE_KEY_CHARS | frozenset("\"'")
+HEXDIGIT_CHARS = frozenset(string.hexdigits)
+
+BASIC_STR_ESCAPE_REPLACEMENTS = MappingProxyType(
+    {
+        "\\b": "\u0008",  # backspace
+        "\\t": "\u0009",  # tab
+        "\\n": "\u000A",  # linefeed
+        "\\f": "\u000C",  # form feed
+        "\\r": "\u000D",  # carriage return
+        '\\"': "\u0022",  # quote
+        "\\\\": "\u005C",  # backslash
+    }
+)
+
+
+class TOMLDecodeError(ValueError):
+    """An error raised if a document is not valid TOML."""
+
+
+def load(__fp: BinaryIO, *, parse_float: ParseFloat = float) -> dict[str, Any]:
+    """Parse TOML from a binary file object."""
+    b = __fp.read()
+    try:
+        s = b.decode()
+    except AttributeError:
+        raise TypeError(
+            "File must be opened in binary mode, e.g. use `open('foo.toml', 'rb')`"
+        ) from None
+    return loads(s, parse_float=parse_float)
+
+
+def loads(__s: str, *, parse_float: ParseFloat = float) -> dict[str, Any]:  # noqa: C901
+    """Parse TOML from a string."""
+
+    # The spec allows converting "\r\n" to "\n", even in string
+    # literals. Let's do so to simplify parsing.
+    src = __s.replace("\r\n", "\n")
+    pos = 0
+    out = Output(NestedDict(), Flags())
+    header: Key = ()
+    parse_float = make_safe_parse_float(parse_float)
+
+    # Parse one statement at a time
+    # (typically means one line in TOML source)
+    while True:
+        # 1. Skip line leading whitespace
+        pos = skip_chars(src, pos, TOML_WS)
+
+        # 2. Parse rules. Expect one of the following:
+        #    - end of file
+        #    - end of line
+        #    - comment
+        #    - key/value pair
+        #    - append dict to list (and move to its namespace)
+        #    - create dict (and move to its namespace)
+        # Skip trailing whitespace when applicable.
+        try:
+            char = src[pos]
+        except IndexError:
+            break
+        if char == "\n":
+            pos += 1
+            continue
+        if char in KEY_INITIAL_CHARS:
+            pos = key_value_rule(src, pos, out, header, parse_float)
+            pos = skip_chars(src, pos, TOML_WS)
+        elif char == "[":
+            try:
+                second_char: str | None = src[pos + 1]
+            except IndexError:
+                second_char = None
+            out.flags.finalize_pending()
+            if second_char == "[":
+                pos, header = create_list_rule(src, pos, out)
+            else:
+                pos, header = create_dict_rule(src, pos, out)
+            pos = skip_chars(src, pos, TOML_WS)
+        elif char != "#":
+            raise suffixed_err(src, pos, "Invalid statement")
+
+        # 3. Skip comment
+        pos = skip_comment(src, pos)
+
+        # 4. Expect end of line or end of file
+        try:
+            char = src[pos]
+        except IndexError:
+            break
+        if char != "\n":
+            raise suffixed_err(
+                src, pos, "Expected newline or end of document after a statement"
+            )
+        pos += 1
+
+    return out.data.dict
+
+
+class Flags:
+    """Flags that map to parsed keys/namespaces."""
+
+    # Marks an immutable namespace (inline array or inline table).
+    FROZEN = 0
+    # Marks a nest that has been explicitly created and can no longer
+    # be opened using the "[table]" syntax.
+    EXPLICIT_NEST = 1
+
+    def __init__(self) -> None:
+        self._flags: dict[str, dict] = {}
+        self._pending_flags: set[tuple[Key, int]] = set()
+
+    def add_pending(self, key: Key, flag: int) -> None:
+        self._pending_flags.add((key, flag))
+
+    def finalize_pending(self) -> None:
+        for key, flag in self._pending_flags:
+            self.set(key, flag, recursive=False)
+        self._pending_flags.clear()
+
+    def unset_all(self, key: Key) -> None:
+        cont = self._flags
+        for k in key[:-1]:
+            if k not in cont:
+                return
+            cont = cont[k]["nested"]
+        cont.pop(key[-1], None)
+
+    def set(self, key: Key, flag: int, *, recursive: bool) -> None:  # noqa: A003
+        cont = self._flags
+        key_parent, key_stem = key[:-1], key[-1]
+        for k in key_parent:
+            if k not in cont:
+                cont[k] = {"flags": set(), "recursive_flags": set(), "nested": {}}
+            cont = cont[k]["nested"]
+        if key_stem not in cont:
+            cont[key_stem] = {"flags": set(), "recursive_flags": set(), "nested": {}}
+        cont[key_stem]["recursive_flags" if recursive else "flags"].add(flag)
+
+    def is_(self, key: Key, flag: int) -> bool:
+        if not key:
+            return False  # document root has no flags
+        cont = self._flags
+        for k in key[:-1]:
+            if k not in cont:
+                return False
+            inner_cont = cont[k]
+            if flag in inner_cont["recursive_flags"]:
+                return True
+            cont = inner_cont["nested"]
+        key_stem = key[-1]
+        if key_stem in cont:
+            cont = cont[key_stem]
+            return flag in cont["flags"] or flag in cont["recursive_flags"]
+        return False
+
+
+class NestedDict:
+    def __init__(self) -> None:
+        # The parsed content of the TOML document
+        self.dict: dict[str, Any] = {}
+
+    def get_or_create_nest(
+        self,
+        key: Key,
+        *,
+        access_lists: bool = True,
+    ) -> dict:
+        cont: Any = self.dict
+        for k in key:
+            if k not in cont:
+                cont[k] = {}
+            cont = cont[k]
+            if access_lists and isinstance(cont, list):
+                cont = cont[-1]
+            if not isinstance(cont, dict):
+                raise KeyError("There is no nest behind this key")
+        return cont
+
+    def append_nest_to_list(self, key: Key) -> None:
+        cont = self.get_or_create_nest(key[:-1])
+        last_key = key[-1]
+        if last_key in cont:
+            list_ = cont[last_key]
+            if not isinstance(list_, list):
+                raise KeyError("An object other than list found behind this key")
+            list_.append({})
+        else:
+            cont[last_key] = [{}]
+
+
+class Output(NamedTuple):
+    data: NestedDict
+    flags: Flags
+
+
+def skip_chars(src: str, pos: Pos, chars: Iterable[str]) -> Pos:
+    try:
+        while src[pos] in chars:
+            pos += 1
+    except IndexError:
+        pass
+    return pos
+
+
+def skip_until(
+    src: str,
+    pos: Pos,
+    expect: str,
+    *,
+    error_on: frozenset[str],
+    error_on_eof: bool,
+) -> Pos:
+    try:
+        new_pos = src.index(expect, pos)
+    except ValueError:
+        new_pos = len(src)
+        if error_on_eof:
+            raise suffixed_err(src, new_pos, f"Expected {expect!r}") from None
+
+    if not error_on.isdisjoint(src[pos:new_pos]):
+        while src[pos] not in error_on:
+            pos += 1
+        raise suffixed_err(src, pos, f"Found invalid character {src[pos]!r}")
+    return new_pos
+
+
+def skip_comment(src: str, pos: Pos) -> Pos:
+    try:
+        char: str | None = src[pos]
+    except IndexError:
+        char = None
+    if char == "#":
+        return skip_until(
+            src, pos + 1, "\n", error_on=ILLEGAL_COMMENT_CHARS, error_on_eof=False
+        )
+    return pos
+
+
+def skip_comments_and_array_ws(src: str, pos: Pos) -> Pos:
+    while True:
+        pos_before_skip = pos
+        pos = skip_chars(src, pos, TOML_WS_AND_NEWLINE)
+        pos = skip_comment(src, pos)
+        if pos == pos_before_skip:
+            return pos
+
+
+def create_dict_rule(src: str, pos: Pos, out: Output) -> tuple[Pos, Key]:
+    pos += 1  # Skip "["
+    pos = skip_chars(src, pos, TOML_WS)
+    pos, key = parse_key(src, pos)
+
+    if out.flags.is_(key, Flags.EXPLICIT_NEST) or out.flags.is_(key, Flags.FROZEN):
+        raise suffixed_err(src, pos, f"Cannot declare {key} twice")
+    out.flags.set(key, Flags.EXPLICIT_NEST, recursive=False)
+    try:
+        out.data.get_or_create_nest(key)
+    except KeyError:
+        raise suffixed_err(src, pos, "Cannot overwrite a value") from None
+
+    if not src.startswith("]", pos):
+        raise suffixed_err(src, pos, "Expected ']' at the end of a table declaration")
+    return pos + 1, key
+
+
+def create_list_rule(src: str, pos: Pos, out: Output) -> tuple[Pos, Key]:
+    pos += 2  # Skip "[["
+    pos = skip_chars(src, pos, TOML_WS)
+    pos, key = parse_key(src, pos)
+
+    if out.flags.is_(key, Flags.FROZEN):
+        raise suffixed_err(src, pos, f"Cannot mutate immutable namespace {key}")
+    # Free the namespace now that it points to another empty list item...
+    out.flags.unset_all(key)
+    # ...but this key precisely is still prohibited from table declaration
+    out.flags.set(key, Flags.EXPLICIT_NEST, recursive=False)
+    try:
+        out.data.append_nest_to_list(key)
+    except KeyError:
+        raise suffixed_err(src, pos, "Cannot overwrite a value") from None
+
+    if not src.startswith("]]", pos):
+        raise suffixed_err(src, pos, "Expected ']]' at the end of an array declaration")
+    return pos + 2, key
+
+
+def key_value_rule(
+    src: str, pos: Pos, out: Output, header: Key, parse_float: ParseFloat
+) -> Pos:
+    pos, key, value = parse_key_value_pair(src, pos, parse_float)
+    key_parent, key_stem = key[:-1], key[-1]
+    abs_key_parent = header + key_parent
+
+    relative_path_cont_keys = (header + key[:i] for i in range(1, len(key)))
+    for cont_key in relative_path_cont_keys:
+        # Check that dotted key syntax does not redefine an existing table
+        if out.flags.is_(cont_key, Flags.EXPLICIT_NEST):
+            raise suffixed_err(src, pos, f"Cannot redefine namespace {cont_key}")
+        # Containers in the relative path can't be opened with the table syntax or
+        # dotted key/value syntax in following table sections.
+        out.flags.add_pending(cont_key, Flags.EXPLICIT_NEST)
+
+    if out.flags.is_(abs_key_parent, Flags.FROZEN):
+        raise suffixed_err(
+            src, pos, f"Cannot mutate immutable namespace {abs_key_parent}"
+        )
+
+    try:
+        nest = out.data.get_or_create_nest(abs_key_parent)
+    except KeyError:
+        raise suffixed_err(src, pos, "Cannot overwrite a value") from None
+    if key_stem in nest:
+        raise suffixed_err(src, pos, "Cannot overwrite a value")
+    # Mark inline table and array namespaces recursively immutable
+    if isinstance(value, (dict, list)):
+        out.flags.set(header + key, Flags.FROZEN, recursive=True)
+    nest[key_stem] = value
+    return pos
+
+
+def parse_key_value_pair(
+    src: str, pos: Pos, parse_float: ParseFloat
+) -> tuple[Pos, Key, Any]:
+    pos, key = parse_key(src, pos)
+    try:
+        char: str | None = src[pos]
+    except IndexError:
+        char = None
+    if char != "=":
+        raise suffixed_err(src, pos, "Expected '=' after a key in a key/value pair")
+    pos += 1
+    pos = skip_chars(src, pos, TOML_WS)
+    pos, value = parse_value(src, pos, parse_float)
+    return pos, key, value
+
+
+def parse_key(src: str, pos: Pos) -> tuple[Pos, Key]:
+    pos, key_part = parse_key_part(src, pos)
+    key: Key = (key_part,)
+    pos = skip_chars(src, pos, TOML_WS)
+    while True:
+        try:
+            char: str | None = src[pos]
+        except IndexError:
+            char = None
+        if char != ".":
+            return pos, key
+        pos += 1
+        pos = skip_chars(src, pos, TOML_WS)
+        pos, key_part = parse_key_part(src, pos)
+        key += (key_part,)
+        pos = skip_chars(src, pos, TOML_WS)
+
+
+def parse_key_part(src: str, pos: Pos) -> tuple[Pos, str]:
+    try:
+        char: str | None = src[pos]
+    except IndexError:
+        char = None
+    if char in BARE_KEY_CHARS:
+        start_pos = pos
+        pos = skip_chars(src, pos, BARE_KEY_CHARS)
+        return pos, src[start_pos:pos]
+    if char == "'":
+        return parse_literal_str(src, pos)
+    if char == '"':
+        return parse_one_line_basic_str(src, pos)
+    raise suffixed_err(src, pos, "Invalid initial character for a key part")
+
+
+def parse_one_line_basic_str(src: str, pos: Pos) -> tuple[Pos, str]:
+    pos += 1
+    return parse_basic_str(src, pos, multiline=False)
+
+
+def parse_array(src: str, pos: Pos, parse_float: ParseFloat) -> tuple[Pos, list]:
+    pos += 1
+    array: list = []
+
+    pos = skip_comments_and_array_ws(src, pos)
+    if src.startswith("]", pos):
+        return pos + 1, array
+    while True:
+        pos, val = parse_value(src, pos, parse_float)
+        array.append(val)
+        pos = skip_comments_and_array_ws(src, pos)
+
+        c = src[pos : pos + 1]
+        if c == "]":
+            return pos + 1, array
+        if c != ",":
+            raise suffixed_err(src, pos, "Unclosed array")
+        pos += 1
+
+        pos = skip_comments_and_array_ws(src, pos)
+        if src.startswith("]", pos):
+            return pos + 1, array
+
+
+def parse_inline_table(src: str, pos: Pos, parse_float: ParseFloat) -> tuple[Pos, dict]:
+    pos += 1
+    nested_dict = NestedDict()
+    flags = Flags()
+
+    pos = skip_chars(src, pos, TOML_WS)
+    if src.startswith("}", pos):
+        return pos + 1, nested_dict.dict
+    while True:
+        pos, key, value = parse_key_value_pair(src, pos, parse_float)
+        key_parent, key_stem = key[:-1], key[-1]
+        if flags.is_(key, Flags.FROZEN):
+            raise suffixed_err(src, pos, f"Cannot mutate immutable namespace {key}")
+        try:
+            nest = nested_dict.get_or_create_nest(key_parent, access_lists=False)
+        except KeyError:
+            raise suffixed_err(src, pos, "Cannot overwrite a value") from None
+        if key_stem in nest:
+            raise suffixed_err(src, pos, f"Duplicate inline table key {key_stem!r}")
+        nest[key_stem] = value
+        pos = skip_chars(src, pos, TOML_WS)
+        c = src[pos : pos + 1]
+        if c == "}":
+            return pos + 1, nested_dict.dict
+        if c != ",":
+            raise suffixed_err(src, pos, "Unclosed inline table")
+        if isinstance(value, (dict, list)):
+            flags.set(key, Flags.FROZEN, recursive=True)
+        pos += 1
+        pos = skip_chars(src, pos, TOML_WS)
+
+
+def parse_basic_str_escape(
+    src: str, pos: Pos, *, multiline: bool = False
+) -> tuple[Pos, str]:
+    escape_id = src[pos : pos + 2]
+    pos += 2
+    if multiline and escape_id in {"\\ ", "\\\t", "\\\n"}:
+        # Skip whitespace until next non-whitespace character or end of
+        # the doc. Error if non-whitespace is found before newline.
+        if escape_id != "\\\n":
+            pos = skip_chars(src, pos, TOML_WS)
+            try:
+                char = src[pos]
+            except IndexError:
+                return pos, ""
+            if char != "\n":
+                raise suffixed_err(src, pos, "Unescaped '\\' in a string")
+            pos += 1
+        pos = skip_chars(src, pos, TOML_WS_AND_NEWLINE)
+        return pos, ""
+    if escape_id == "\\u":
+        return parse_hex_char(src, pos, 4)
+    if escape_id == "\\U":
+        return parse_hex_char(src, pos, 8)
+    try:
+        return pos, BASIC_STR_ESCAPE_REPLACEMENTS[escape_id]
+    except KeyError:
+        raise suffixed_err(src, pos, "Unescaped '\\' in a string") from None
+
+
+def parse_basic_str_escape_multiline(src: str, pos: Pos) -> tuple[Pos, str]:
+    return parse_basic_str_escape(src, pos, multiline=True)
+
+
+def parse_hex_char(src: str, pos: Pos, hex_len: int) -> tuple[Pos, str]:
+    hex_str = src[pos : pos + hex_len]
+    if len(hex_str) != hex_len or not HEXDIGIT_CHARS.issuperset(hex_str):
+        raise suffixed_err(src, pos, "Invalid hex value")
+    pos += hex_len
+    hex_int = int(hex_str, 16)
+    if not is_unicode_scalar_value(hex_int):
+        raise suffixed_err(src, pos, "Escaped character is not a Unicode scalar value")
+    return pos, chr(hex_int)
+
+
+def parse_literal_str(src: str, pos: Pos) -> tuple[Pos, str]:
+    pos += 1  # Skip starting apostrophe
+    start_pos = pos
+    pos = skip_until(
+        src, pos, "'", error_on=ILLEGAL_LITERAL_STR_CHARS, error_on_eof=True
+    )
+    return pos + 1, src[start_pos:pos]  # Skip ending apostrophe
+
+
+def parse_multiline_str(src: str, pos: Pos, *, literal: bool) -> tuple[Pos, str]:
+    pos += 3
+    if src.startswith("\n", pos):
+        pos += 1
+
+    if literal:
+        delim = "'"
+        end_pos = skip_until(
+            src,
+            pos,
+            "'''",
+            error_on=ILLEGAL_MULTILINE_LITERAL_STR_CHARS,
+            error_on_eof=True,
+        )
+        result = src[pos:end_pos]
+        pos = end_pos + 3
+    else:
+        delim = '"'
+        pos, result = parse_basic_str(src, pos, multiline=True)
+
+    # Add at maximum two extra apostrophes/quotes if the end sequence
+    # is 4 or 5 chars long instead of just 3.
+    if not src.startswith(delim, pos):
+        return pos, result
+    pos += 1
+    if not src.startswith(delim, pos):
+        return pos, result + delim
+    pos += 1
+    return pos, result + (delim * 2)
+
+
+def parse_basic_str(src: str, pos: Pos, *, multiline: bool) -> tuple[Pos, str]:
+    if multiline:
+        error_on = ILLEGAL_MULTILINE_BASIC_STR_CHARS
+        parse_escapes = parse_basic_str_escape_multiline
+    else:
+        error_on = ILLEGAL_BASIC_STR_CHARS
+        parse_escapes = parse_basic_str_escape
+    result = ""
+    start_pos = pos
+    while True:
+        try:
+            char = src[pos]
+        except IndexError:
+            raise suffixed_err(src, pos, "Unterminated string") from None
+        if char == '"':
+            if not multiline:
+                return pos + 1, result + src[start_pos:pos]
+            if src.startswith('"""', pos):
+                return pos + 3, result + src[start_pos:pos]
+            pos += 1
+            continue
+        if char == "\\":
+            result += src[start_pos:pos]
+            pos, parsed_escape = parse_escapes(src, pos)
+            result += parsed_escape
+            start_pos = pos
+            continue
+        if char in error_on:
+            raise suffixed_err(src, pos, f"Illegal character {char!r}")
+        pos += 1
+
+
+def parse_value(  # noqa: C901
+    src: str, pos: Pos, parse_float: ParseFloat
+) -> tuple[Pos, Any]:
+    try:
+        char: str | None = src[pos]
+    except IndexError:
+        char = None
+
+    # IMPORTANT: order conditions based on speed of checking and likelihood
+
+    # Basic strings
+    if char == '"':
+        if src.startswith('"""', pos):
+            return parse_multiline_str(src, pos, literal=False)
+        return parse_one_line_basic_str(src, pos)
+
+    # Literal strings
+    if char == "'":
+        if src.startswith("'''", pos):
+            return parse_multiline_str(src, pos, literal=True)
+        return parse_literal_str(src, pos)
+
+    # Booleans
+    if char == "t":
+        if src.startswith("true", pos):
+            return pos + 4, True
+    if char == "f":
+        if src.startswith("false", pos):
+            return pos + 5, False
+
+    # Arrays
+    if char == "[":
+        return parse_array(src, pos, parse_float)
+
+    # Inline tables
+    if char == "{":
+        return parse_inline_table(src, pos, parse_float)
+
+    # Dates and times
+    datetime_match = RE_DATETIME.match(src, pos)
+    if datetime_match:
+        try:
+            datetime_obj = match_to_datetime(datetime_match)
+        except ValueError as e:
+            raise suffixed_err(src, pos, "Invalid date or datetime") from e
+        return datetime_match.end(), datetime_obj
+    localtime_match = RE_LOCALTIME.match(src, pos)
+    if localtime_match:
+        return localtime_match.end(), match_to_localtime(localtime_match)
+
+    # Integers and "normal" floats.
+    # The regex will greedily match any type starting with a decimal
+    # char, so needs to be located after handling of dates and times.
+    number_match = RE_NUMBER.match(src, pos)
+    if number_match:
+        return number_match.end(), match_to_number(number_match, parse_float)
+
+    # Special floats
+    first_three = src[pos : pos + 3]
+    if first_three in {"inf", "nan"}:
+        return pos + 3, parse_float(first_three)
+    first_four = src[pos : pos + 4]
+    if first_four in {"-inf", "+inf", "-nan", "+nan"}:
+        return pos + 4, parse_float(first_four)
+
+    raise suffixed_err(src, pos, "Invalid value")
+
+
+def suffixed_err(src: str, pos: Pos, msg: str) -> TOMLDecodeError:
+    """Return a `TOMLDecodeError` where error message is suffixed with
+    coordinates in source."""
+
+    def coord_repr(src: str, pos: Pos) -> str:
+        if pos >= len(src):
+            return "end of document"
+        line = src.count("\n", 0, pos) + 1
+        if line == 1:
+            column = pos + 1
+        else:
+            column = pos - src.rindex("\n", 0, pos)
+        return f"line {line}, column {column}"
+
+    return TOMLDecodeError(f"{msg} (at {coord_repr(src, pos)})")
+
+
+def is_unicode_scalar_value(codepoint: int) -> bool:
+    return (0 <= codepoint <= 55295) or (57344 <= codepoint <= 1114111)
+
+
+def make_safe_parse_float(parse_float: ParseFloat) -> ParseFloat:
+    """A decorator to make `parse_float` safe.
+
+    `parse_float` must not return dicts or lists, because these types
+    would be mixed with parsed TOML tables and arrays, thus confusing
+    the parser. The returned decorated callable raises `ValueError`
+    instead of returning illegal types.
+    """
+    # The default `float` callable never returns illegal types. Optimize it.
+    if parse_float is float:  # type: ignore[comparison-overlap]
+        return float
+
+    def safe_parse_float(float_str: str) -> Any:
+        float_value = parse_float(float_str)
+        if isinstance(float_value, (dict, list)):
+            raise ValueError("parse_float must not return dicts or lists")
+        return float_value
+
+    return safe_parse_float
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/_re.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/_re.py
new file mode 100644
index 0000000000000000000000000000000000000000..994bb7493fd92865e6ab87c277ba5741b44c31a9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/_re.py
@@ -0,0 +1,107 @@
+# SPDX-License-Identifier: MIT
+# SPDX-FileCopyrightText: 2021 Taneli Hukkinen
+# Licensed to PSF under a Contributor Agreement.
+
+from __future__ import annotations
+
+from datetime import date, datetime, time, timedelta, timezone, tzinfo
+from functools import lru_cache
+import re
+from typing import Any
+
+from ._types import ParseFloat
+
+# E.g.
+# - 00:32:00.999999
+# - 00:32:00
+_TIME_RE_STR = r"([01][0-9]|2[0-3]):([0-5][0-9]):([0-5][0-9])(?:\.([0-9]{1,6})[0-9]*)?"
+
+RE_NUMBER = re.compile(
+    r"""
+0
+(?:
+    x[0-9A-Fa-f](?:_?[0-9A-Fa-f])*   # hex
+    |
+    b[01](?:_?[01])*                 # bin
+    |
+    o[0-7](?:_?[0-7])*               # oct
+)
+|
+[+-]?(?:0|[1-9](?:_?[0-9])*)         # dec, integer part
+(?P<floatpart>
+    (?:\.[0-9](?:_?[0-9])*)?         # optional fractional part
+    (?:[eE][+-]?[0-9](?:_?[0-9])*)?  # optional exponent part
+)
+""",
+    flags=re.VERBOSE,
+)
+RE_LOCALTIME = re.compile(_TIME_RE_STR)
+RE_DATETIME = re.compile(
+    rf"""
+([0-9]{{4}})-(0[1-9]|1[0-2])-(0[1-9]|[12][0-9]|3[01])  # date, e.g. 1988-10-27
+(?:
+    [Tt ]
+    {_TIME_RE_STR}
+    (?:([Zz])|([+-])([01][0-9]|2[0-3]):([0-5][0-9]))?  # optional time offset
+)?
+""",
+    flags=re.VERBOSE,
+)
+
+
+def match_to_datetime(match: re.Match) -> datetime | date:
+    """Convert a `RE_DATETIME` match to `datetime.datetime` or `datetime.date`.
+
+    Raises ValueError if the match does not correspond to a valid date
+    or datetime.
+    """
+    (
+        year_str,
+        month_str,
+        day_str,
+        hour_str,
+        minute_str,
+        sec_str,
+        micros_str,
+        zulu_time,
+        offset_sign_str,
+        offset_hour_str,
+        offset_minute_str,
+    ) = match.groups()
+    year, month, day = int(year_str), int(month_str), int(day_str)
+    if hour_str is None:
+        return date(year, month, day)
+    hour, minute, sec = int(hour_str), int(minute_str), int(sec_str)
+    micros = int(micros_str.ljust(6, "0")) if micros_str else 0
+    if offset_sign_str:
+        tz: tzinfo | None = cached_tz(
+            offset_hour_str, offset_minute_str, offset_sign_str
+        )
+    elif zulu_time:
+        tz = timezone.utc
+    else:  # local date-time
+        tz = None
+    return datetime(year, month, day, hour, minute, sec, micros, tzinfo=tz)
+
+
+@lru_cache(maxsize=None)
+def cached_tz(hour_str: str, minute_str: str, sign_str: str) -> timezone:
+    sign = 1 if sign_str == "+" else -1
+    return timezone(
+        timedelta(
+            hours=sign * int(hour_str),
+            minutes=sign * int(minute_str),
+        )
+    )
+
+
+def match_to_localtime(match: re.Match) -> time:
+    hour_str, minute_str, sec_str, micros_str = match.groups()
+    micros = int(micros_str.ljust(6, "0")) if micros_str else 0
+    return time(int(hour_str), int(minute_str), int(sec_str), micros)
+
+
+def match_to_number(match: re.Match, parse_float: ParseFloat) -> Any:
+    if match.group("floatpart"):
+        return parse_float(match.group())
+    return int(match.group(), 0)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/_types.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..d949412e03b29d70592c7721fe747e5085c2e280
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/_types.py
@@ -0,0 +1,10 @@
+# SPDX-License-Identifier: MIT
+# SPDX-FileCopyrightText: 2021 Taneli Hukkinen
+# Licensed to PSF under a Contributor Agreement.
+
+from typing import Any, Callable, Tuple
+
+# Type annotations
+ParseFloat = Callable[[str], Any]
+Key = Tuple[str, ...]
+Pos = int
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/py.typed b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..7632ecf77545c5e5501cb3fc5719df0761104ca2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/tomli/py.typed
@@ -0,0 +1 @@
+# Marker file for PEP 561
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..07806f8af9dd3253ebdb5710b67e73124aea4941
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/LICENSE
@@ -0,0 +1,19 @@
+This is the MIT license: http://www.opensource.org/licenses/mit-license.php
+
+Copyright (c) Alex Grönholm
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this
+software and associated documentation files (the "Software"), to deal in the Software
+without restriction, including without limitation the rights to use, copy, modify, merge,
+publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons
+to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or
+substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
+PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE
+FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..6e5750b485fd5cbdb25a9ac9b03bc494357546c7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/METADATA
@@ -0,0 +1,81 @@
+Metadata-Version: 2.1
+Name: typeguard
+Version: 4.3.0
+Summary: Run-time type checker for Python
+Author-email: Alex Grönholm <alex.gronholm@nextday.fi>
+License: MIT
+Project-URL: Documentation, https://typeguard.readthedocs.io/en/latest/
+Project-URL: Change log, https://typeguard.readthedocs.io/en/latest/versionhistory.html
+Project-URL: Source code, https://github.com/agronholm/typeguard
+Project-URL: Issue tracker, https://github.com/agronholm/typeguard/issues
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: typing-extensions >=4.10.0
+Requires-Dist: importlib-metadata >=3.6 ; python_version < "3.10"
+Provides-Extra: doc
+Requires-Dist: packaging ; extra == 'doc'
+Requires-Dist: Sphinx >=7 ; extra == 'doc'
+Requires-Dist: sphinx-autodoc-typehints >=1.2.0 ; extra == 'doc'
+Requires-Dist: sphinx-rtd-theme >=1.3.0 ; extra == 'doc'
+Provides-Extra: test
+Requires-Dist: coverage[toml] >=7 ; extra == 'test'
+Requires-Dist: pytest >=7 ; extra == 'test'
+Requires-Dist: mypy >=1.2.0 ; (platform_python_implementation != "PyPy") and extra == 'test'
+
+.. image:: https://github.com/agronholm/typeguard/actions/workflows/test.yml/badge.svg
+  :target: https://github.com/agronholm/typeguard/actions/workflows/test.yml
+  :alt: Build Status
+.. image:: https://coveralls.io/repos/agronholm/typeguard/badge.svg?branch=master&service=github
+  :target: https://coveralls.io/github/agronholm/typeguard?branch=master
+  :alt: Code Coverage
+.. image:: https://readthedocs.org/projects/typeguard/badge/?version=latest
+  :target: https://typeguard.readthedocs.io/en/latest/?badge=latest
+  :alt: Documentation
+
+This library provides run-time type checking for functions defined with
+`PEP 484 <https://www.python.org/dev/peps/pep-0484/>`_ argument (and return) type
+annotations, and any arbitrary objects. It can be used together with static type
+checkers as an additional layer of type safety, to catch type violations that could only
+be detected at run time.
+
+Two principal ways to do type checking are provided:
+
+#. The ``check_type`` function:
+
+   * like ``isinstance()``, but supports arbitrary type annotations (within limits)
+   * can be used as a ``cast()`` replacement, but with actual checking of the value
+#. Code instrumentation:
+
+   * entire modules, or individual functions (via ``@typechecked``) are recompiled, with
+     type checking code injected into them
+   * automatically checks function arguments, return values and assignments to annotated
+     local variables
+   * for generator functions (regular and async), checks yield and send values
+   * requires the original source code of the instrumented module(s) to be accessible
+
+Two options are provided for code instrumentation:
+
+#. the ``@typechecked`` function:
+
+   * can be applied to functions individually
+#. the import hook (``typeguard.install_import_hook()``):
+
+   * automatically instruments targeted modules on import
+   * no manual code changes required in the target modules
+   * requires the import hook to be installed before the targeted modules are imported
+   * may clash with other import hooks
+
+See the documentation_ for further information.
+
+.. _documentation: https://typeguard.readthedocs.io/en/latest/
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..801e73347c4dfdd6c275f53295de748d0aa7049d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/RECORD
@@ -0,0 +1,34 @@
+typeguard-4.3.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+typeguard-4.3.0.dist-info/LICENSE,sha256=YWP3mH37ONa8MgzitwsvArhivEESZRbVUu8c1DJH51g,1130
+typeguard-4.3.0.dist-info/METADATA,sha256=z2dcHAp0TwhYCFU5Deh8x31nazElgujUz9tbuP0pjSE,3717
+typeguard-4.3.0.dist-info/RECORD,,
+typeguard-4.3.0.dist-info/WHEEL,sha256=GJ7t_kWBFywbagK5eo9IoUwLW6oyOeTKmQ-9iHFVNxQ,92
+typeguard-4.3.0.dist-info/entry_points.txt,sha256=qp7NQ1aLtiSgMQqo6gWlfGpy0IIXzoMJmeQTLpzqFZQ,48
+typeguard-4.3.0.dist-info/top_level.txt,sha256=4z28AhuDodwRS_c1J_l8H51t5QuwfTseskYzlxp6grs,10
+typeguard/__init__.py,sha256=Onh4w38elPCjtlcU3JY9k3h70NjsxXIkAflmQn-Z0FY,2071
+typeguard/__pycache__/__init__.cpython-312.pyc,,
+typeguard/__pycache__/_checkers.cpython-312.pyc,,
+typeguard/__pycache__/_config.cpython-312.pyc,,
+typeguard/__pycache__/_decorators.cpython-312.pyc,,
+typeguard/__pycache__/_exceptions.cpython-312.pyc,,
+typeguard/__pycache__/_functions.cpython-312.pyc,,
+typeguard/__pycache__/_importhook.cpython-312.pyc,,
+typeguard/__pycache__/_memo.cpython-312.pyc,,
+typeguard/__pycache__/_pytest_plugin.cpython-312.pyc,,
+typeguard/__pycache__/_suppression.cpython-312.pyc,,
+typeguard/__pycache__/_transformer.cpython-312.pyc,,
+typeguard/__pycache__/_union_transformer.cpython-312.pyc,,
+typeguard/__pycache__/_utils.cpython-312.pyc,,
+typeguard/_checkers.py,sha256=JRrgKicdOEfIBoNEtegYCEIlhpad-a1u1Em7GCj0WCI,31360
+typeguard/_config.py,sha256=nIz8QwDa-oFO3L9O8_6srzlmd99pSby2wOM4Wb7F_B0,2846
+typeguard/_decorators.py,sha256=v6dsIeWvPhExGLP_wXF-RmDUyjZf_Ak28g7gBJ_v0-0,9033
+typeguard/_exceptions.py,sha256=ZIPeiV-FBd5Emw2EaWd2Fvlsrwi4ocwT2fVGBIAtHcQ,1121
+typeguard/_functions.py,sha256=ibgSAKa5ptIm1eR9ARG0BSozAFJPFNASZqhPVyQeqig,10393
+typeguard/_importhook.py,sha256=ugjCDvFcdWMU7UugqlJG91IpVNpEIxtRr-99s0h1k7M,6389
+typeguard/_memo.py,sha256=1juQV_vxnD2JYKbSrebiQuj4oKHz6n67v9pYA-CCISg,1303
+typeguard/_pytest_plugin.py,sha256=-fcSqkv54rIfIF8pDavY5YQPkj4OX8GMt_lL7CQSD4I,4416
+typeguard/_suppression.py,sha256=VQfzxcwIbu3if0f7VBkKM7hkYOA7tNFw9a7jMBsmMg4,2266
+typeguard/_transformer.py,sha256=9Ha7_QhdwoUni_6hvdY-hZbuEergowHrNL2vzHIakFY,44937
+typeguard/_union_transformer.py,sha256=v_42r7-6HuRX2SoFwnyJ-E5PlxXpVeUJPJR1-HU9qSo,1354
+typeguard/_utils.py,sha256=5HhO1rPn5f1M6ymkVAEv7Xmlz1cX-j0OnTMlyHqqrR8,5270
+typeguard/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..bab98d675883cc7567a79df485cd7b4f015e376f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.43.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/entry_points.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/entry_points.txt
new file mode 100644
index 0000000000000000000000000000000000000000..47c9d0bd913e50da44b12013bde2a1df636ef684
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/entry_points.txt
@@ -0,0 +1,2 @@
+[pytest11]
+typeguard = typeguard._pytest_plugin
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/top_level.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..be5ec23ea2016f69345eaf80c7714777d04988f0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard-4.3.0.dist-info/top_level.txt
@@ -0,0 +1 @@
+typeguard
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6781cad094b29db0959df4fb0c29c0fa9d480c0c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/__init__.py
@@ -0,0 +1,48 @@
+import os
+from typing import Any
+
+from ._checkers import TypeCheckerCallable as TypeCheckerCallable
+from ._checkers import TypeCheckLookupCallback as TypeCheckLookupCallback
+from ._checkers import check_type_internal as check_type_internal
+from ._checkers import checker_lookup_functions as checker_lookup_functions
+from ._checkers import load_plugins as load_plugins
+from ._config import CollectionCheckStrategy as CollectionCheckStrategy
+from ._config import ForwardRefPolicy as ForwardRefPolicy
+from ._config import TypeCheckConfiguration as TypeCheckConfiguration
+from ._decorators import typechecked as typechecked
+from ._decorators import typeguard_ignore as typeguard_ignore
+from ._exceptions import InstrumentationWarning as InstrumentationWarning
+from ._exceptions import TypeCheckError as TypeCheckError
+from ._exceptions import TypeCheckWarning as TypeCheckWarning
+from ._exceptions import TypeHintWarning as TypeHintWarning
+from ._functions import TypeCheckFailCallback as TypeCheckFailCallback
+from ._functions import check_type as check_type
+from ._functions import warn_on_error as warn_on_error
+from ._importhook import ImportHookManager as ImportHookManager
+from ._importhook import TypeguardFinder as TypeguardFinder
+from ._importhook import install_import_hook as install_import_hook
+from ._memo import TypeCheckMemo as TypeCheckMemo
+from ._suppression import suppress_type_checks as suppress_type_checks
+from ._utils import Unset as Unset
+
+# Re-export imports so they look like they live directly in this package
+for value in list(locals().values()):
+    if getattr(value, "__module__", "").startswith(f"{__name__}."):
+        value.__module__ = __name__
+
+
+config: TypeCheckConfiguration
+
+
+def __getattr__(name: str) -> Any:
+    if name == "config":
+        from ._config import global_config
+
+        return global_config
+
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+# Automatically load checker lookup functions unless explicitly disabled
+if "TYPEGUARD_DISABLE_PLUGIN_AUTOLOAD" not in os.environ:
+    load_plugins()
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_checkers.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_checkers.py
new file mode 100644
index 0000000000000000000000000000000000000000..67dd5ad4dcbda70353b06fc08db962d1a41ba266
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_checkers.py
@@ -0,0 +1,993 @@
+from __future__ import annotations
+
+import collections.abc
+import inspect
+import sys
+import types
+import typing
+import warnings
+from enum import Enum
+from inspect import Parameter, isclass, isfunction
+from io import BufferedIOBase, IOBase, RawIOBase, TextIOBase
+from textwrap import indent
+from typing import (
+    IO,
+    AbstractSet,
+    Any,
+    BinaryIO,
+    Callable,
+    Dict,
+    ForwardRef,
+    List,
+    Mapping,
+    MutableMapping,
+    NewType,
+    Optional,
+    Sequence,
+    Set,
+    TextIO,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+)
+from unittest.mock import Mock
+from weakref import WeakKeyDictionary
+
+try:
+    import typing_extensions
+except ImportError:
+    typing_extensions = None  # type: ignore[assignment]
+
+# Must use this because typing.is_typeddict does not recognize
+# TypedDict from typing_extensions, and as of version 4.12.0
+# typing_extensions.TypedDict is different from typing.TypedDict
+# on all versions.
+from typing_extensions import is_typeddict
+
+from ._config import ForwardRefPolicy
+from ._exceptions import TypeCheckError, TypeHintWarning
+from ._memo import TypeCheckMemo
+from ._utils import evaluate_forwardref, get_stacklevel, get_type_name, qualified_name
+
+if sys.version_info >= (3, 11):
+    from typing import (
+        Annotated,
+        NotRequired,
+        TypeAlias,
+        get_args,
+        get_origin,
+    )
+
+    SubclassableAny = Any
+else:
+    from typing_extensions import (
+        Annotated,
+        NotRequired,
+        TypeAlias,
+        get_args,
+        get_origin,
+    )
+    from typing_extensions import Any as SubclassableAny
+
+if sys.version_info >= (3, 10):
+    from importlib.metadata import entry_points
+    from typing import ParamSpec
+else:
+    from importlib_metadata import entry_points
+    from typing_extensions import ParamSpec
+
+TypeCheckerCallable: TypeAlias = Callable[
+    [Any, Any, Tuple[Any, ...], TypeCheckMemo], Any
+]
+TypeCheckLookupCallback: TypeAlias = Callable[
+    [Any, Tuple[Any, ...], Tuple[Any, ...]], Optional[TypeCheckerCallable]
+]
+
+checker_lookup_functions: list[TypeCheckLookupCallback] = []
+generic_alias_types: tuple[type, ...] = (type(List), type(List[Any]))
+if sys.version_info >= (3, 9):
+    generic_alias_types += (types.GenericAlias,)
+
+protocol_check_cache: WeakKeyDictionary[
+    type[Any], dict[type[Any], TypeCheckError | None]
+] = WeakKeyDictionary()
+
+# Sentinel
+_missing = object()
+
+# Lifted from mypy.sharedparse
+BINARY_MAGIC_METHODS = {
+    "__add__",
+    "__and__",
+    "__cmp__",
+    "__divmod__",
+    "__div__",
+    "__eq__",
+    "__floordiv__",
+    "__ge__",
+    "__gt__",
+    "__iadd__",
+    "__iand__",
+    "__idiv__",
+    "__ifloordiv__",
+    "__ilshift__",
+    "__imatmul__",
+    "__imod__",
+    "__imul__",
+    "__ior__",
+    "__ipow__",
+    "__irshift__",
+    "__isub__",
+    "__itruediv__",
+    "__ixor__",
+    "__le__",
+    "__lshift__",
+    "__lt__",
+    "__matmul__",
+    "__mod__",
+    "__mul__",
+    "__ne__",
+    "__or__",
+    "__pow__",
+    "__radd__",
+    "__rand__",
+    "__rdiv__",
+    "__rfloordiv__",
+    "__rlshift__",
+    "__rmatmul__",
+    "__rmod__",
+    "__rmul__",
+    "__ror__",
+    "__rpow__",
+    "__rrshift__",
+    "__rshift__",
+    "__rsub__",
+    "__rtruediv__",
+    "__rxor__",
+    "__sub__",
+    "__truediv__",
+    "__xor__",
+}
+
+
+def check_callable(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if not callable(value):
+        raise TypeCheckError("is not callable")
+
+    if args:
+        try:
+            signature = inspect.signature(value)
+        except (TypeError, ValueError):
+            return
+
+        argument_types = args[0]
+        if isinstance(argument_types, list) and not any(
+            type(item) is ParamSpec for item in argument_types
+        ):
+            # The callable must not have keyword-only arguments without defaults
+            unfulfilled_kwonlyargs = [
+                param.name
+                for param in signature.parameters.values()
+                if param.kind == Parameter.KEYWORD_ONLY
+                and param.default == Parameter.empty
+            ]
+            if unfulfilled_kwonlyargs:
+                raise TypeCheckError(
+                    f"has mandatory keyword-only arguments in its declaration: "
+                    f'{", ".join(unfulfilled_kwonlyargs)}'
+                )
+
+            num_positional_args = num_mandatory_pos_args = 0
+            has_varargs = False
+            for param in signature.parameters.values():
+                if param.kind in (
+                    Parameter.POSITIONAL_ONLY,
+                    Parameter.POSITIONAL_OR_KEYWORD,
+                ):
+                    num_positional_args += 1
+                    if param.default is Parameter.empty:
+                        num_mandatory_pos_args += 1
+                elif param.kind == Parameter.VAR_POSITIONAL:
+                    has_varargs = True
+
+            if num_mandatory_pos_args > len(argument_types):
+                raise TypeCheckError(
+                    f"has too many mandatory positional arguments in its declaration; "
+                    f"expected {len(argument_types)} but {num_mandatory_pos_args} "
+                    f"mandatory positional argument(s) declared"
+                )
+            elif not has_varargs and num_positional_args < len(argument_types):
+                raise TypeCheckError(
+                    f"has too few arguments in its declaration; expected "
+                    f"{len(argument_types)} but {num_positional_args} argument(s) "
+                    f"declared"
+                )
+
+
+def check_mapping(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if origin_type is Dict or origin_type is dict:
+        if not isinstance(value, dict):
+            raise TypeCheckError("is not a dict")
+    if origin_type is MutableMapping or origin_type is collections.abc.MutableMapping:
+        if not isinstance(value, collections.abc.MutableMapping):
+            raise TypeCheckError("is not a mutable mapping")
+    elif not isinstance(value, collections.abc.Mapping):
+        raise TypeCheckError("is not a mapping")
+
+    if args:
+        key_type, value_type = args
+        if key_type is not Any or value_type is not Any:
+            samples = memo.config.collection_check_strategy.iterate_samples(
+                value.items()
+            )
+            for k, v in samples:
+                try:
+                    check_type_internal(k, key_type, memo)
+                except TypeCheckError as exc:
+                    exc.append_path_element(f"key {k!r}")
+                    raise
+
+                try:
+                    check_type_internal(v, value_type, memo)
+                except TypeCheckError as exc:
+                    exc.append_path_element(f"value of key {k!r}")
+                    raise
+
+
+def check_typed_dict(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if not isinstance(value, dict):
+        raise TypeCheckError("is not a dict")
+
+    declared_keys = frozenset(origin_type.__annotations__)
+    if hasattr(origin_type, "__required_keys__"):
+        required_keys = set(origin_type.__required_keys__)
+    else:  # py3.8 and lower
+        required_keys = set(declared_keys) if origin_type.__total__ else set()
+
+    existing_keys = set(value)
+    extra_keys = existing_keys - declared_keys
+    if extra_keys:
+        keys_formatted = ", ".join(f'"{key}"' for key in sorted(extra_keys, key=repr))
+        raise TypeCheckError(f"has unexpected extra key(s): {keys_formatted}")
+
+    # Detect NotRequired fields which are hidden by get_type_hints()
+    type_hints: dict[str, type] = {}
+    for key, annotation in origin_type.__annotations__.items():
+        if isinstance(annotation, ForwardRef):
+            annotation = evaluate_forwardref(annotation, memo)
+            if get_origin(annotation) is NotRequired:
+                required_keys.discard(key)
+                annotation = get_args(annotation)[0]
+
+        type_hints[key] = annotation
+
+    missing_keys = required_keys - existing_keys
+    if missing_keys:
+        keys_formatted = ", ".join(f'"{key}"' for key in sorted(missing_keys, key=repr))
+        raise TypeCheckError(f"is missing required key(s): {keys_formatted}")
+
+    for key, argtype in type_hints.items():
+        argvalue = value.get(key, _missing)
+        if argvalue is not _missing:
+            try:
+                check_type_internal(argvalue, argtype, memo)
+            except TypeCheckError as exc:
+                exc.append_path_element(f"value of key {key!r}")
+                raise
+
+
+def check_list(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if not isinstance(value, list):
+        raise TypeCheckError("is not a list")
+
+    if args and args != (Any,):
+        samples = memo.config.collection_check_strategy.iterate_samples(value)
+        for i, v in enumerate(samples):
+            try:
+                check_type_internal(v, args[0], memo)
+            except TypeCheckError as exc:
+                exc.append_path_element(f"item {i}")
+                raise
+
+
+def check_sequence(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if not isinstance(value, collections.abc.Sequence):
+        raise TypeCheckError("is not a sequence")
+
+    if args and args != (Any,):
+        samples = memo.config.collection_check_strategy.iterate_samples(value)
+        for i, v in enumerate(samples):
+            try:
+                check_type_internal(v, args[0], memo)
+            except TypeCheckError as exc:
+                exc.append_path_element(f"item {i}")
+                raise
+
+
+def check_set(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if origin_type is frozenset:
+        if not isinstance(value, frozenset):
+            raise TypeCheckError("is not a frozenset")
+    elif not isinstance(value, AbstractSet):
+        raise TypeCheckError("is not a set")
+
+    if args and args != (Any,):
+        samples = memo.config.collection_check_strategy.iterate_samples(value)
+        for v in samples:
+            try:
+                check_type_internal(v, args[0], memo)
+            except TypeCheckError as exc:
+                exc.append_path_element(f"[{v}]")
+                raise
+
+
+def check_tuple(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    # Specialized check for NamedTuples
+    if field_types := getattr(origin_type, "__annotations__", None):
+        if not isinstance(value, origin_type):
+            raise TypeCheckError(
+                f"is not a named tuple of type {qualified_name(origin_type)}"
+            )
+
+        for name, field_type in field_types.items():
+            try:
+                check_type_internal(getattr(value, name), field_type, memo)
+            except TypeCheckError as exc:
+                exc.append_path_element(f"attribute {name!r}")
+                raise
+
+        return
+    elif not isinstance(value, tuple):
+        raise TypeCheckError("is not a tuple")
+
+    if args:
+        use_ellipsis = args[-1] is Ellipsis
+        tuple_params = args[: -1 if use_ellipsis else None]
+    else:
+        # Unparametrized Tuple or plain tuple
+        return
+
+    if use_ellipsis:
+        element_type = tuple_params[0]
+        samples = memo.config.collection_check_strategy.iterate_samples(value)
+        for i, element in enumerate(samples):
+            try:
+                check_type_internal(element, element_type, memo)
+            except TypeCheckError as exc:
+                exc.append_path_element(f"item {i}")
+                raise
+    elif tuple_params == ((),):
+        if value != ():
+            raise TypeCheckError("is not an empty tuple")
+    else:
+        if len(value) != len(tuple_params):
+            raise TypeCheckError(
+                f"has wrong number of elements (expected {len(tuple_params)}, got "
+                f"{len(value)} instead)"
+            )
+
+        for i, (element, element_type) in enumerate(zip(value, tuple_params)):
+            try:
+                check_type_internal(element, element_type, memo)
+            except TypeCheckError as exc:
+                exc.append_path_element(f"item {i}")
+                raise
+
+
+def check_union(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    errors: dict[str, TypeCheckError] = {}
+    try:
+        for type_ in args:
+            try:
+                check_type_internal(value, type_, memo)
+                return
+            except TypeCheckError as exc:
+                errors[get_type_name(type_)] = exc
+
+        formatted_errors = indent(
+            "\n".join(f"{key}: {error}" for key, error in errors.items()), "  "
+        )
+    finally:
+        del errors  # avoid creating ref cycle
+    raise TypeCheckError(f"did not match any element in the union:\n{formatted_errors}")
+
+
+def check_uniontype(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    errors: dict[str, TypeCheckError] = {}
+    for type_ in args:
+        try:
+            check_type_internal(value, type_, memo)
+            return
+        except TypeCheckError as exc:
+            errors[get_type_name(type_)] = exc
+
+    formatted_errors = indent(
+        "\n".join(f"{key}: {error}" for key, error in errors.items()), "  "
+    )
+    raise TypeCheckError(f"did not match any element in the union:\n{formatted_errors}")
+
+
+def check_class(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if not isclass(value) and not isinstance(value, generic_alias_types):
+        raise TypeCheckError("is not a class")
+
+    if not args:
+        return
+
+    if isinstance(args[0], ForwardRef):
+        expected_class = evaluate_forwardref(args[0], memo)
+    else:
+        expected_class = args[0]
+
+    if expected_class is Any:
+        return
+    elif getattr(expected_class, "_is_protocol", False):
+        check_protocol(value, expected_class, (), memo)
+    elif isinstance(expected_class, TypeVar):
+        check_typevar(value, expected_class, (), memo, subclass_check=True)
+    elif get_origin(expected_class) is Union:
+        errors: dict[str, TypeCheckError] = {}
+        for arg in get_args(expected_class):
+            if arg is Any:
+                return
+
+            try:
+                check_class(value, type, (arg,), memo)
+                return
+            except TypeCheckError as exc:
+                errors[get_type_name(arg)] = exc
+        else:
+            formatted_errors = indent(
+                "\n".join(f"{key}: {error}" for key, error in errors.items()), "  "
+            )
+            raise TypeCheckError(
+                f"did not match any element in the union:\n{formatted_errors}"
+            )
+    elif not issubclass(value, expected_class):  # type: ignore[arg-type]
+        raise TypeCheckError(f"is not a subclass of {qualified_name(expected_class)}")
+
+
+def check_newtype(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    check_type_internal(value, origin_type.__supertype__, memo)
+
+
+def check_instance(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if not isinstance(value, origin_type):
+        raise TypeCheckError(f"is not an instance of {qualified_name(origin_type)}")
+
+
+def check_typevar(
+    value: Any,
+    origin_type: TypeVar,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+    *,
+    subclass_check: bool = False,
+) -> None:
+    if origin_type.__bound__ is not None:
+        annotation = (
+            Type[origin_type.__bound__] if subclass_check else origin_type.__bound__
+        )
+        check_type_internal(value, annotation, memo)
+    elif origin_type.__constraints__:
+        for constraint in origin_type.__constraints__:
+            annotation = Type[constraint] if subclass_check else constraint
+            try:
+                check_type_internal(value, annotation, memo)
+            except TypeCheckError:
+                pass
+            else:
+                break
+        else:
+            formatted_constraints = ", ".join(
+                get_type_name(constraint) for constraint in origin_type.__constraints__
+            )
+            raise TypeCheckError(
+                f"does not match any of the constraints " f"({formatted_constraints})"
+            )
+
+
+if typing_extensions is None:
+
+    def _is_literal_type(typ: object) -> bool:
+        return typ is typing.Literal
+
+else:
+
+    def _is_literal_type(typ: object) -> bool:
+        return typ is typing.Literal or typ is typing_extensions.Literal
+
+
+def check_literal(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    def get_literal_args(literal_args: tuple[Any, ...]) -> tuple[Any, ...]:
+        retval: list[Any] = []
+        for arg in literal_args:
+            if _is_literal_type(get_origin(arg)):
+                retval.extend(get_literal_args(arg.__args__))
+            elif arg is None or isinstance(arg, (int, str, bytes, bool, Enum)):
+                retval.append(arg)
+            else:
+                raise TypeError(
+                    f"Illegal literal value: {arg}"
+                )  # TypeError here is deliberate
+
+        return tuple(retval)
+
+    final_args = tuple(get_literal_args(args))
+    try:
+        index = final_args.index(value)
+    except ValueError:
+        pass
+    else:
+        if type(final_args[index]) is type(value):
+            return
+
+    formatted_args = ", ".join(repr(arg) for arg in final_args)
+    raise TypeCheckError(f"is not any of ({formatted_args})") from None
+
+
+def check_literal_string(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    check_type_internal(value, str, memo)
+
+
+def check_typeguard(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    check_type_internal(value, bool, memo)
+
+
+def check_none(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if value is not None:
+        raise TypeCheckError("is not None")
+
+
+def check_number(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if origin_type is complex and not isinstance(value, (complex, float, int)):
+        raise TypeCheckError("is neither complex, float or int")
+    elif origin_type is float and not isinstance(value, (float, int)):
+        raise TypeCheckError("is neither float or int")
+
+
+def check_io(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if origin_type is TextIO or (origin_type is IO and args == (str,)):
+        if not isinstance(value, TextIOBase):
+            raise TypeCheckError("is not a text based I/O object")
+    elif origin_type is BinaryIO or (origin_type is IO and args == (bytes,)):
+        if not isinstance(value, (RawIOBase, BufferedIOBase)):
+            raise TypeCheckError("is not a binary I/O object")
+    elif not isinstance(value, IOBase):
+        raise TypeCheckError("is not an I/O object")
+
+
+def check_protocol(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    subject: type[Any] = value if isclass(value) else type(value)
+
+    if subject in protocol_check_cache:
+        result_map = protocol_check_cache[subject]
+        if origin_type in result_map:
+            if exc := result_map[origin_type]:
+                raise exc
+            else:
+                return
+
+    # Collect a set of methods and non-method attributes present in the protocol
+    ignored_attrs = set(dir(typing.Protocol)) | {
+        "__annotations__",
+        "__non_callable_proto_members__",
+    }
+    expected_methods: dict[str, tuple[Any, Any]] = {}
+    expected_noncallable_members: dict[str, Any] = {}
+    for attrname in dir(origin_type):
+        # Skip attributes present in typing.Protocol
+        if attrname in ignored_attrs:
+            continue
+
+        member = getattr(origin_type, attrname)
+        if callable(member):
+            signature = inspect.signature(member)
+            argtypes = [
+                (p.annotation if p.annotation is not Parameter.empty else Any)
+                for p in signature.parameters.values()
+                if p.kind is not Parameter.KEYWORD_ONLY
+            ] or Ellipsis
+            return_annotation = (
+                signature.return_annotation
+                if signature.return_annotation is not Parameter.empty
+                else Any
+            )
+            expected_methods[attrname] = argtypes, return_annotation
+        else:
+            expected_noncallable_members[attrname] = member
+
+    for attrname, annotation in typing.get_type_hints(origin_type).items():
+        expected_noncallable_members[attrname] = annotation
+
+    subject_annotations = typing.get_type_hints(subject)
+
+    # Check that all required methods are present and their signatures are compatible
+    result_map = protocol_check_cache.setdefault(subject, {})
+    try:
+        for attrname, callable_args in expected_methods.items():
+            try:
+                method = getattr(subject, attrname)
+            except AttributeError:
+                if attrname in subject_annotations:
+                    raise TypeCheckError(
+                        f"is not compatible with the {origin_type.__qualname__} protocol "
+                        f"because its {attrname!r} attribute is not a method"
+                    ) from None
+                else:
+                    raise TypeCheckError(
+                        f"is not compatible with the {origin_type.__qualname__} protocol "
+                        f"because it has no method named {attrname!r}"
+                    ) from None
+
+            if not callable(method):
+                raise TypeCheckError(
+                    f"is not compatible with the {origin_type.__qualname__} protocol "
+                    f"because its {attrname!r} attribute is not a callable"
+                )
+
+            # TODO: raise exception on added keyword-only arguments without defaults
+            try:
+                check_callable(method, Callable, callable_args, memo)
+            except TypeCheckError as exc:
+                raise TypeCheckError(
+                    f"is not compatible with the {origin_type.__qualname__} protocol "
+                    f"because its {attrname!r} method {exc}"
+                ) from None
+
+        # Check that all required non-callable members are present
+        for attrname in expected_noncallable_members:
+            # TODO: implement assignability checks for non-callable members
+            if attrname not in subject_annotations and not hasattr(subject, attrname):
+                raise TypeCheckError(
+                    f"is not compatible with the {origin_type.__qualname__} protocol "
+                    f"because it has no attribute named {attrname!r}"
+                )
+    except TypeCheckError as exc:
+        result_map[origin_type] = exc
+        raise
+    else:
+        result_map[origin_type] = None
+
+
+def check_byteslike(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if not isinstance(value, (bytearray, bytes, memoryview)):
+        raise TypeCheckError("is not bytes-like")
+
+
+def check_self(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if memo.self_type is None:
+        raise TypeCheckError("cannot be checked against Self outside of a method call")
+
+    if isclass(value):
+        if not issubclass(value, memo.self_type):
+            raise TypeCheckError(
+                f"is not an instance of the self type "
+                f"({qualified_name(memo.self_type)})"
+            )
+    elif not isinstance(value, memo.self_type):
+        raise TypeCheckError(
+            f"is not an instance of the self type ({qualified_name(memo.self_type)})"
+        )
+
+
+def check_paramspec(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    pass  # No-op for now
+
+
+def check_instanceof(
+    value: Any,
+    origin_type: Any,
+    args: tuple[Any, ...],
+    memo: TypeCheckMemo,
+) -> None:
+    if not isinstance(value, origin_type):
+        raise TypeCheckError(f"is not an instance of {qualified_name(origin_type)}")
+
+
+def check_type_internal(
+    value: Any,
+    annotation: Any,
+    memo: TypeCheckMemo,
+) -> None:
+    """
+    Check that the given object is compatible with the given type annotation.
+
+    This function should only be used by type checker callables. Applications should use
+    :func:`~.check_type` instead.
+
+    :param value: the value to check
+    :param annotation: the type annotation to check against
+    :param memo: a memo object containing configuration and information necessary for
+        looking up forward references
+    """
+
+    if isinstance(annotation, ForwardRef):
+        try:
+            annotation = evaluate_forwardref(annotation, memo)
+        except NameError:
+            if memo.config.forward_ref_policy is ForwardRefPolicy.ERROR:
+                raise
+            elif memo.config.forward_ref_policy is ForwardRefPolicy.WARN:
+                warnings.warn(
+                    f"Cannot resolve forward reference {annotation.__forward_arg__!r}",
+                    TypeHintWarning,
+                    stacklevel=get_stacklevel(),
+                )
+
+            return
+
+    if annotation is Any or annotation is SubclassableAny or isinstance(value, Mock):
+        return
+
+    # Skip type checks if value is an instance of a class that inherits from Any
+    if not isclass(value) and SubclassableAny in type(value).__bases__:
+        return
+
+    extras: tuple[Any, ...]
+    origin_type = get_origin(annotation)
+    if origin_type is Annotated:
+        annotation, *extras_ = get_args(annotation)
+        extras = tuple(extras_)
+        origin_type = get_origin(annotation)
+    else:
+        extras = ()
+
+    if origin_type is not None:
+        args = get_args(annotation)
+
+        # Compatibility hack to distinguish between unparametrized and empty tuple
+        # (tuple[()]), necessary due to https://github.com/python/cpython/issues/91137
+        if origin_type in (tuple, Tuple) and annotation is not Tuple and not args:
+            args = ((),)
+    else:
+        origin_type = annotation
+        args = ()
+
+    for lookup_func in checker_lookup_functions:
+        checker = lookup_func(origin_type, args, extras)
+        if checker:
+            checker(value, origin_type, args, memo)
+            return
+
+    if isclass(origin_type):
+        if not isinstance(value, origin_type):
+            raise TypeCheckError(f"is not an instance of {qualified_name(origin_type)}")
+    elif type(origin_type) is str:  # noqa: E721
+        warnings.warn(
+            f"Skipping type check against {origin_type!r}; this looks like a "
+            f"string-form forward reference imported from another module",
+            TypeHintWarning,
+            stacklevel=get_stacklevel(),
+        )
+
+
+# Equality checks are applied to these
+origin_type_checkers = {
+    bytes: check_byteslike,
+    AbstractSet: check_set,
+    BinaryIO: check_io,
+    Callable: check_callable,
+    collections.abc.Callable: check_callable,
+    complex: check_number,
+    dict: check_mapping,
+    Dict: check_mapping,
+    float: check_number,
+    frozenset: check_set,
+    IO: check_io,
+    list: check_list,
+    List: check_list,
+    typing.Literal: check_literal,
+    Mapping: check_mapping,
+    MutableMapping: check_mapping,
+    None: check_none,
+    collections.abc.Mapping: check_mapping,
+    collections.abc.MutableMapping: check_mapping,
+    Sequence: check_sequence,
+    collections.abc.Sequence: check_sequence,
+    collections.abc.Set: check_set,
+    set: check_set,
+    Set: check_set,
+    TextIO: check_io,
+    tuple: check_tuple,
+    Tuple: check_tuple,
+    type: check_class,
+    Type: check_class,
+    Union: check_union,
+}
+if sys.version_info >= (3, 10):
+    origin_type_checkers[types.UnionType] = check_uniontype
+    origin_type_checkers[typing.TypeGuard] = check_typeguard
+if sys.version_info >= (3, 11):
+    origin_type_checkers.update(
+        {typing.LiteralString: check_literal_string, typing.Self: check_self}
+    )
+if typing_extensions is not None:
+    # On some Python versions, these may simply be re-exports from typing,
+    # but exactly which Python versions is subject to change,
+    # so it's best to err on the safe side
+    # and update the dictionary on all Python versions
+    # if typing_extensions is installed
+    origin_type_checkers[typing_extensions.Literal] = check_literal
+    origin_type_checkers[typing_extensions.LiteralString] = check_literal_string
+    origin_type_checkers[typing_extensions.Self] = check_self
+    origin_type_checkers[typing_extensions.TypeGuard] = check_typeguard
+
+
+def builtin_checker_lookup(
+    origin_type: Any, args: tuple[Any, ...], extras: tuple[Any, ...]
+) -> TypeCheckerCallable | None:
+    checker = origin_type_checkers.get(origin_type)
+    if checker is not None:
+        return checker
+    elif is_typeddict(origin_type):
+        return check_typed_dict
+    elif isclass(origin_type) and issubclass(
+        origin_type,
+        Tuple,  # type: ignore[arg-type]
+    ):
+        # NamedTuple
+        return check_tuple
+    elif getattr(origin_type, "_is_protocol", False):
+        return check_protocol
+    elif isinstance(origin_type, ParamSpec):
+        return check_paramspec
+    elif isinstance(origin_type, TypeVar):
+        return check_typevar
+    elif origin_type.__class__ is NewType:
+        # typing.NewType on Python 3.10+
+        return check_newtype
+    elif (
+        isfunction(origin_type)
+        and getattr(origin_type, "__module__", None) == "typing"
+        and getattr(origin_type, "__qualname__", "").startswith("NewType.")
+        and hasattr(origin_type, "__supertype__")
+    ):
+        # typing.NewType on Python 3.9 and below
+        return check_newtype
+
+    return None
+
+
+checker_lookup_functions.append(builtin_checker_lookup)
+
+
+def load_plugins() -> None:
+    """
+    Load all type checker lookup functions from entry points.
+
+    All entry points from the ``typeguard.checker_lookup`` group are loaded, and the
+    returned lookup functions are added to :data:`typeguard.checker_lookup_functions`.
+
+    .. note:: This function is called implicitly on import, unless the
+        ``TYPEGUARD_DISABLE_PLUGIN_AUTOLOAD`` environment variable is present.
+    """
+
+    for ep in entry_points(group="typeguard.checker_lookup"):
+        try:
+            plugin = ep.load()
+        except Exception as exc:
+            warnings.warn(
+                f"Failed to load plugin {ep.name!r}: " f"{qualified_name(exc)}: {exc}",
+                stacklevel=2,
+            )
+            continue
+
+        if not callable(plugin):
+            warnings.warn(
+                f"Plugin {ep} returned a non-callable object: {plugin!r}", stacklevel=2
+            )
+            continue
+
+        checker_lookup_functions.insert(0, plugin)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_config.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..36efad53965488e7e003c265262db8d5d902a930
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_config.py
@@ -0,0 +1,108 @@
+from __future__ import annotations
+
+from collections.abc import Iterable
+from dataclasses import dataclass
+from enum import Enum, auto
+from typing import TYPE_CHECKING, TypeVar
+
+if TYPE_CHECKING:
+    from ._functions import TypeCheckFailCallback
+
+T = TypeVar("T")
+
+
+class ForwardRefPolicy(Enum):
+    """
+    Defines how unresolved forward references are handled.
+
+    Members:
+
+    * ``ERROR``: propagate the :exc:`NameError` when the forward reference lookup fails
+    * ``WARN``: emit a :class:`~.TypeHintWarning` if the forward reference lookup fails
+    * ``IGNORE``: silently skip checks for unresolveable forward references
+    """
+
+    ERROR = auto()
+    WARN = auto()
+    IGNORE = auto()
+
+
+class CollectionCheckStrategy(Enum):
+    """
+    Specifies how thoroughly the contents of collections are type checked.
+
+    This has an effect on the following built-in checkers:
+
+    * ``AbstractSet``
+    * ``Dict``
+    * ``List``
+    * ``Mapping``
+    * ``Set``
+    * ``Tuple[<type>, ...]`` (arbitrarily sized tuples)
+
+    Members:
+
+    * ``FIRST_ITEM``: check only the first item
+    * ``ALL_ITEMS``: check all items
+    """
+
+    FIRST_ITEM = auto()
+    ALL_ITEMS = auto()
+
+    def iterate_samples(self, collection: Iterable[T]) -> Iterable[T]:
+        if self is CollectionCheckStrategy.FIRST_ITEM:
+            try:
+                return [next(iter(collection))]
+            except StopIteration:
+                return ()
+        else:
+            return collection
+
+
+@dataclass
+class TypeCheckConfiguration:
+    """
+     You can change Typeguard's behavior with these settings.
+
+    .. attribute:: typecheck_fail_callback
+       :type: Callable[[TypeCheckError, TypeCheckMemo], Any]
+
+         Callable that is called when type checking fails.
+
+         Default: ``None`` (the :exc:`~.TypeCheckError` is raised directly)
+
+    .. attribute:: forward_ref_policy
+       :type: ForwardRefPolicy
+
+         Specifies what to do when a forward reference fails to resolve.
+
+         Default: ``WARN``
+
+    .. attribute:: collection_check_strategy
+       :type: CollectionCheckStrategy
+
+         Specifies how thoroughly the contents of collections (list, dict, etc.) are
+         type checked.
+
+         Default: ``FIRST_ITEM``
+
+    .. attribute:: debug_instrumentation
+       :type: bool
+
+         If set to ``True``, the code of modules or functions instrumented by typeguard
+         is printed to ``sys.stderr`` after the instrumentation is done
+
+         Requires Python 3.9 or newer.
+
+         Default: ``False``
+    """
+
+    forward_ref_policy: ForwardRefPolicy = ForwardRefPolicy.WARN
+    typecheck_fail_callback: TypeCheckFailCallback | None = None
+    collection_check_strategy: CollectionCheckStrategy = (
+        CollectionCheckStrategy.FIRST_ITEM
+    )
+    debug_instrumentation: bool = False
+
+
+global_config = TypeCheckConfiguration()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_decorators.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_decorators.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf3253351fe3b07af0b79f7c1ffac7b2d6127a19
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_decorators.py
@@ -0,0 +1,235 @@
+from __future__ import annotations
+
+import ast
+import inspect
+import sys
+from collections.abc import Sequence
+from functools import partial
+from inspect import isclass, isfunction
+from types import CodeType, FrameType, FunctionType
+from typing import TYPE_CHECKING, Any, Callable, ForwardRef, TypeVar, cast, overload
+from warnings import warn
+
+from ._config import CollectionCheckStrategy, ForwardRefPolicy, global_config
+from ._exceptions import InstrumentationWarning
+from ._functions import TypeCheckFailCallback
+from ._transformer import TypeguardTransformer
+from ._utils import Unset, function_name, get_stacklevel, is_method_of, unset
+
+if TYPE_CHECKING:
+    from typeshed.stdlib.types import _Cell
+
+    _F = TypeVar("_F")
+
+    def typeguard_ignore(f: _F) -> _F:
+        """This decorator is a noop during static type-checking."""
+        return f
+
+else:
+    from typing import no_type_check as typeguard_ignore  # noqa: F401
+
+T_CallableOrType = TypeVar("T_CallableOrType", bound=Callable[..., Any])
+
+
+def make_cell(value: object) -> _Cell:
+    return (lambda: value).__closure__[0]  # type: ignore[index]
+
+
+def find_target_function(
+    new_code: CodeType, target_path: Sequence[str], firstlineno: int
+) -> CodeType | None:
+    target_name = target_path[0]
+    for const in new_code.co_consts:
+        if isinstance(const, CodeType):
+            if const.co_name == target_name:
+                if const.co_firstlineno == firstlineno:
+                    return const
+                elif len(target_path) > 1:
+                    target_code = find_target_function(
+                        const, target_path[1:], firstlineno
+                    )
+                    if target_code:
+                        return target_code
+
+    return None
+
+
+def instrument(f: T_CallableOrType) -> FunctionType | str:
+    if not getattr(f, "__code__", None):
+        return "no code associated"
+    elif not getattr(f, "__module__", None):
+        return "__module__ attribute is not set"
+    elif f.__code__.co_filename == "<stdin>":
+        return "cannot instrument functions defined in a REPL"
+    elif hasattr(f, "__wrapped__"):
+        return (
+            "@typechecked only supports instrumenting functions wrapped with "
+            "@classmethod, @staticmethod or @property"
+        )
+
+    target_path = [item for item in f.__qualname__.split(".") if item != "<locals>"]
+    module_source = inspect.getsource(sys.modules[f.__module__])
+    module_ast = ast.parse(module_source)
+    instrumentor = TypeguardTransformer(target_path, f.__code__.co_firstlineno)
+    instrumentor.visit(module_ast)
+
+    if not instrumentor.target_node or instrumentor.target_lineno is None:
+        return "instrumentor did not find the target function"
+
+    module_code = compile(module_ast, f.__code__.co_filename, "exec", dont_inherit=True)
+    new_code = find_target_function(
+        module_code, target_path, instrumentor.target_lineno
+    )
+    if not new_code:
+        return "cannot find the target function in the AST"
+
+    if global_config.debug_instrumentation and sys.version_info >= (3, 9):
+        # Find the matching AST node, then unparse it to source and print to stdout
+        print(
+            f"Source code of {f.__qualname__}() after instrumentation:"
+            "\n----------------------------------------------",
+            file=sys.stderr,
+        )
+        print(ast.unparse(instrumentor.target_node), file=sys.stderr)
+        print(
+            "----------------------------------------------",
+            file=sys.stderr,
+        )
+
+    closure = f.__closure__
+    if new_code.co_freevars != f.__code__.co_freevars:
+        # Create a new closure and find values for the new free variables
+        frame = cast(FrameType, inspect.currentframe())
+        frame = cast(FrameType, frame.f_back)
+        frame_locals = cast(FrameType, frame.f_back).f_locals
+        cells: list[_Cell] = []
+        for key in new_code.co_freevars:
+            if key in instrumentor.names_used_in_annotations:
+                # Find the value and make a new cell from it
+                value = frame_locals.get(key) or ForwardRef(key)
+                cells.append(make_cell(value))
+            else:
+                # Reuse the cell from the existing closure
+                assert f.__closure__
+                cells.append(f.__closure__[f.__code__.co_freevars.index(key)])
+
+        closure = tuple(cells)
+
+    new_function = FunctionType(new_code, f.__globals__, f.__name__, closure=closure)
+    new_function.__module__ = f.__module__
+    new_function.__name__ = f.__name__
+    new_function.__qualname__ = f.__qualname__
+    new_function.__annotations__ = f.__annotations__
+    new_function.__doc__ = f.__doc__
+    new_function.__defaults__ = f.__defaults__
+    new_function.__kwdefaults__ = f.__kwdefaults__
+    return new_function
+
+
+@overload
+def typechecked(
+    *,
+    forward_ref_policy: ForwardRefPolicy | Unset = unset,
+    typecheck_fail_callback: TypeCheckFailCallback | Unset = unset,
+    collection_check_strategy: CollectionCheckStrategy | Unset = unset,
+    debug_instrumentation: bool | Unset = unset,
+) -> Callable[[T_CallableOrType], T_CallableOrType]: ...
+
+
+@overload
+def typechecked(target: T_CallableOrType) -> T_CallableOrType: ...
+
+
+def typechecked(
+    target: T_CallableOrType | None = None,
+    *,
+    forward_ref_policy: ForwardRefPolicy | Unset = unset,
+    typecheck_fail_callback: TypeCheckFailCallback | Unset = unset,
+    collection_check_strategy: CollectionCheckStrategy | Unset = unset,
+    debug_instrumentation: bool | Unset = unset,
+) -> Any:
+    """
+    Instrument the target function to perform run-time type checking.
+
+    This decorator recompiles the target function, injecting code to type check
+    arguments, return values, yield values (excluding ``yield from``) and assignments to
+    annotated local variables.
+
+    This can also be used as a class decorator. This will instrument all type annotated
+    methods, including :func:`@classmethod <classmethod>`,
+    :func:`@staticmethod <staticmethod>`,  and :class:`@property <property>` decorated
+    methods in the class.
+
+    .. note:: When Python is run in optimized mode (``-O`` or ``-OO``, this decorator
+        is a no-op). This is a feature meant for selectively introducing type checking
+        into a code base where the checks aren't meant to be run in production.
+
+    :param target: the function or class to enable type checking for
+    :param forward_ref_policy: override for
+        :attr:`.TypeCheckConfiguration.forward_ref_policy`
+    :param typecheck_fail_callback: override for
+        :attr:`.TypeCheckConfiguration.typecheck_fail_callback`
+    :param collection_check_strategy: override for
+        :attr:`.TypeCheckConfiguration.collection_check_strategy`
+    :param debug_instrumentation: override for
+        :attr:`.TypeCheckConfiguration.debug_instrumentation`
+
+    """
+    if target is None:
+        return partial(
+            typechecked,
+            forward_ref_policy=forward_ref_policy,
+            typecheck_fail_callback=typecheck_fail_callback,
+            collection_check_strategy=collection_check_strategy,
+            debug_instrumentation=debug_instrumentation,
+        )
+
+    if not __debug__:
+        return target
+
+    if isclass(target):
+        for key, attr in target.__dict__.items():
+            if is_method_of(attr, target):
+                retval = instrument(attr)
+                if isfunction(retval):
+                    setattr(target, key, retval)
+            elif isinstance(attr, (classmethod, staticmethod)):
+                if is_method_of(attr.__func__, target):
+                    retval = instrument(attr.__func__)
+                    if isfunction(retval):
+                        wrapper = attr.__class__(retval)
+                        setattr(target, key, wrapper)
+            elif isinstance(attr, property):
+                kwargs: dict[str, Any] = dict(doc=attr.__doc__)
+                for name in ("fset", "fget", "fdel"):
+                    property_func = kwargs[name] = getattr(attr, name)
+                    if is_method_of(property_func, target):
+                        retval = instrument(property_func)
+                        if isfunction(retval):
+                            kwargs[name] = retval
+
+                setattr(target, key, attr.__class__(**kwargs))
+
+        return target
+
+    # Find either the first Python wrapper or the actual function
+    wrapper_class: (
+        type[classmethod[Any, Any, Any]] | type[staticmethod[Any, Any]] | None
+    ) = None
+    if isinstance(target, (classmethod, staticmethod)):
+        wrapper_class = target.__class__
+        target = target.__func__
+
+    retval = instrument(target)
+    if isinstance(retval, str):
+        warn(
+            f"{retval} -- not typechecking {function_name(target)}",
+            InstrumentationWarning,
+            stacklevel=get_stacklevel(),
+        )
+        return target
+
+    if wrapper_class is None:
+        return retval
+    else:
+        return wrapper_class(retval)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_exceptions.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_exceptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..625437a64990b5fae898c9c98fe9a7bd6be90cfb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_exceptions.py
@@ -0,0 +1,42 @@
+from collections import deque
+from typing import Deque
+
+
+class TypeHintWarning(UserWarning):
+    """
+    A warning that is emitted when a type hint in string form could not be resolved to
+    an actual type.
+    """
+
+
+class TypeCheckWarning(UserWarning):
+    """Emitted by typeguard's type checkers when a type mismatch is detected."""
+
+    def __init__(self, message: str):
+        super().__init__(message)
+
+
+class InstrumentationWarning(UserWarning):
+    """Emitted when there's a problem with instrumenting a function for type checks."""
+
+    def __init__(self, message: str):
+        super().__init__(message)
+
+
+class TypeCheckError(Exception):
+    """
+    Raised by typeguard's type checkers when a type mismatch is detected.
+    """
+
+    def __init__(self, message: str):
+        super().__init__(message)
+        self._path: Deque[str] = deque()
+
+    def append_path_element(self, element: str) -> None:
+        self._path.append(element)
+
+    def __str__(self) -> str:
+        if self._path:
+            return " of ".join(self._path) + " " + str(self.args[0])
+        else:
+            return str(self.args[0])
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_functions.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..28497856a325e1b00dbe4aadfd2ed1c6082cd23a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_functions.py
@@ -0,0 +1,308 @@
+from __future__ import annotations
+
+import sys
+import warnings
+from typing import Any, Callable, NoReturn, TypeVar, Union, overload
+
+from . import _suppression
+from ._checkers import BINARY_MAGIC_METHODS, check_type_internal
+from ._config import (
+    CollectionCheckStrategy,
+    ForwardRefPolicy,
+    TypeCheckConfiguration,
+)
+from ._exceptions import TypeCheckError, TypeCheckWarning
+from ._memo import TypeCheckMemo
+from ._utils import get_stacklevel, qualified_name
+
+if sys.version_info >= (3, 11):
+    from typing import Literal, Never, TypeAlias
+else:
+    from typing_extensions import Literal, Never, TypeAlias
+
+T = TypeVar("T")
+TypeCheckFailCallback: TypeAlias = Callable[[TypeCheckError, TypeCheckMemo], Any]
+
+
+@overload
+def check_type(
+    value: object,
+    expected_type: type[T],
+    *,
+    forward_ref_policy: ForwardRefPolicy = ...,
+    typecheck_fail_callback: TypeCheckFailCallback | None = ...,
+    collection_check_strategy: CollectionCheckStrategy = ...,
+) -> T: ...
+
+
+@overload
+def check_type(
+    value: object,
+    expected_type: Any,
+    *,
+    forward_ref_policy: ForwardRefPolicy = ...,
+    typecheck_fail_callback: TypeCheckFailCallback | None = ...,
+    collection_check_strategy: CollectionCheckStrategy = ...,
+) -> Any: ...
+
+
+def check_type(
+    value: object,
+    expected_type: Any,
+    *,
+    forward_ref_policy: ForwardRefPolicy = TypeCheckConfiguration().forward_ref_policy,
+    typecheck_fail_callback: TypeCheckFailCallback | None = (
+        TypeCheckConfiguration().typecheck_fail_callback
+    ),
+    collection_check_strategy: CollectionCheckStrategy = (
+        TypeCheckConfiguration().collection_check_strategy
+    ),
+) -> Any:
+    """
+    Ensure that ``value`` matches ``expected_type``.
+
+    The types from the :mod:`typing` module do not support :func:`isinstance` or
+    :func:`issubclass` so a number of type specific checks are required. This function
+    knows which checker to call for which type.
+
+    This function wraps :func:`~.check_type_internal` in the following ways:
+
+    * Respects type checking suppression (:func:`~.suppress_type_checks`)
+    * Forms a :class:`~.TypeCheckMemo` from the current stack frame
+    * Calls the configured type check fail callback if the check fails
+
+    Note that this function is independent of the globally shared configuration in
+    :data:`typeguard.config`. This means that usage within libraries is safe from being
+    affected configuration changes made by other libraries or by the integrating
+    application. Instead, configuration options have the same default values as their
+    corresponding fields in :class:`TypeCheckConfiguration`.
+
+    :param value: value to be checked against ``expected_type``
+    :param expected_type: a class or generic type instance, or a tuple of such things
+    :param forward_ref_policy: see :attr:`TypeCheckConfiguration.forward_ref_policy`
+    :param typecheck_fail_callback:
+        see :attr`TypeCheckConfiguration.typecheck_fail_callback`
+    :param collection_check_strategy:
+        see :attr:`TypeCheckConfiguration.collection_check_strategy`
+    :return: ``value``, unmodified
+    :raises TypeCheckError: if there is a type mismatch
+
+    """
+    if type(expected_type) is tuple:
+        expected_type = Union[expected_type]
+
+    config = TypeCheckConfiguration(
+        forward_ref_policy=forward_ref_policy,
+        typecheck_fail_callback=typecheck_fail_callback,
+        collection_check_strategy=collection_check_strategy,
+    )
+
+    if _suppression.type_checks_suppressed or expected_type is Any:
+        return value
+
+    frame = sys._getframe(1)
+    memo = TypeCheckMemo(frame.f_globals, frame.f_locals, config=config)
+    try:
+        check_type_internal(value, expected_type, memo)
+    except TypeCheckError as exc:
+        exc.append_path_element(qualified_name(value, add_class_prefix=True))
+        if config.typecheck_fail_callback:
+            config.typecheck_fail_callback(exc, memo)
+        else:
+            raise
+
+    return value
+
+
+def check_argument_types(
+    func_name: str,
+    arguments: dict[str, tuple[Any, Any]],
+    memo: TypeCheckMemo,
+) -> Literal[True]:
+    if _suppression.type_checks_suppressed:
+        return True
+
+    for argname, (value, annotation) in arguments.items():
+        if annotation is NoReturn or annotation is Never:
+            exc = TypeCheckError(
+                f"{func_name}() was declared never to be called but it was"
+            )
+            if memo.config.typecheck_fail_callback:
+                memo.config.typecheck_fail_callback(exc, memo)
+            else:
+                raise exc
+
+        try:
+            check_type_internal(value, annotation, memo)
+        except TypeCheckError as exc:
+            qualname = qualified_name(value, add_class_prefix=True)
+            exc.append_path_element(f'argument "{argname}" ({qualname})')
+            if memo.config.typecheck_fail_callback:
+                memo.config.typecheck_fail_callback(exc, memo)
+            else:
+                raise
+
+    return True
+
+
+def check_return_type(
+    func_name: str,
+    retval: T,
+    annotation: Any,
+    memo: TypeCheckMemo,
+) -> T:
+    if _suppression.type_checks_suppressed:
+        return retval
+
+    if annotation is NoReturn or annotation is Never:
+        exc = TypeCheckError(f"{func_name}() was declared never to return but it did")
+        if memo.config.typecheck_fail_callback:
+            memo.config.typecheck_fail_callback(exc, memo)
+        else:
+            raise exc
+
+    try:
+        check_type_internal(retval, annotation, memo)
+    except TypeCheckError as exc:
+        # Allow NotImplemented if this is a binary magic method (__eq__() et al)
+        if retval is NotImplemented and annotation is bool:
+            # This does (and cannot) not check if it's actually a method
+            func_name = func_name.rsplit(".", 1)[-1]
+            if func_name in BINARY_MAGIC_METHODS:
+                return retval
+
+        qualname = qualified_name(retval, add_class_prefix=True)
+        exc.append_path_element(f"the return value ({qualname})")
+        if memo.config.typecheck_fail_callback:
+            memo.config.typecheck_fail_callback(exc, memo)
+        else:
+            raise
+
+    return retval
+
+
+def check_send_type(
+    func_name: str,
+    sendval: T,
+    annotation: Any,
+    memo: TypeCheckMemo,
+) -> T:
+    if _suppression.type_checks_suppressed:
+        return sendval
+
+    if annotation is NoReturn or annotation is Never:
+        exc = TypeCheckError(
+            f"{func_name}() was declared never to be sent a value to but it was"
+        )
+        if memo.config.typecheck_fail_callback:
+            memo.config.typecheck_fail_callback(exc, memo)
+        else:
+            raise exc
+
+    try:
+        check_type_internal(sendval, annotation, memo)
+    except TypeCheckError as exc:
+        qualname = qualified_name(sendval, add_class_prefix=True)
+        exc.append_path_element(f"the value sent to generator ({qualname})")
+        if memo.config.typecheck_fail_callback:
+            memo.config.typecheck_fail_callback(exc, memo)
+        else:
+            raise
+
+    return sendval
+
+
+def check_yield_type(
+    func_name: str,
+    yieldval: T,
+    annotation: Any,
+    memo: TypeCheckMemo,
+) -> T:
+    if _suppression.type_checks_suppressed:
+        return yieldval
+
+    if annotation is NoReturn or annotation is Never:
+        exc = TypeCheckError(f"{func_name}() was declared never to yield but it did")
+        if memo.config.typecheck_fail_callback:
+            memo.config.typecheck_fail_callback(exc, memo)
+        else:
+            raise exc
+
+    try:
+        check_type_internal(yieldval, annotation, memo)
+    except TypeCheckError as exc:
+        qualname = qualified_name(yieldval, add_class_prefix=True)
+        exc.append_path_element(f"the yielded value ({qualname})")
+        if memo.config.typecheck_fail_callback:
+            memo.config.typecheck_fail_callback(exc, memo)
+        else:
+            raise
+
+    return yieldval
+
+
+def check_variable_assignment(
+    value: object, varname: str, annotation: Any, memo: TypeCheckMemo
+) -> Any:
+    if _suppression.type_checks_suppressed:
+        return value
+
+    try:
+        check_type_internal(value, annotation, memo)
+    except TypeCheckError as exc:
+        qualname = qualified_name(value, add_class_prefix=True)
+        exc.append_path_element(f"value assigned to {varname} ({qualname})")
+        if memo.config.typecheck_fail_callback:
+            memo.config.typecheck_fail_callback(exc, memo)
+        else:
+            raise
+
+    return value
+
+
+def check_multi_variable_assignment(
+    value: Any, targets: list[dict[str, Any]], memo: TypeCheckMemo
+) -> Any:
+    if max(len(target) for target in targets) == 1:
+        iterated_values = [value]
+    else:
+        iterated_values = list(value)
+
+    if not _suppression.type_checks_suppressed:
+        for expected_types in targets:
+            value_index = 0
+            for ann_index, (varname, expected_type) in enumerate(
+                expected_types.items()
+            ):
+                if varname.startswith("*"):
+                    varname = varname[1:]
+                    keys_left = len(expected_types) - 1 - ann_index
+                    next_value_index = len(iterated_values) - keys_left
+                    obj: object = iterated_values[value_index:next_value_index]
+                    value_index = next_value_index
+                else:
+                    obj = iterated_values[value_index]
+                    value_index += 1
+
+                try:
+                    check_type_internal(obj, expected_type, memo)
+                except TypeCheckError as exc:
+                    qualname = qualified_name(obj, add_class_prefix=True)
+                    exc.append_path_element(f"value assigned to {varname} ({qualname})")
+                    if memo.config.typecheck_fail_callback:
+                        memo.config.typecheck_fail_callback(exc, memo)
+                    else:
+                        raise
+
+    return iterated_values[0] if len(iterated_values) == 1 else iterated_values
+
+
+def warn_on_error(exc: TypeCheckError, memo: TypeCheckMemo) -> None:
+    """
+    Emit a warning on a type mismatch.
+
+    This is intended to be used as an error handler in
+    :attr:`TypeCheckConfiguration.typecheck_fail_callback`.
+
+    """
+    warnings.warn(TypeCheckWarning(str(exc)), stacklevel=get_stacklevel())
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_importhook.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_importhook.py
new file mode 100644
index 0000000000000000000000000000000000000000..8590540a5a2ace23bbfc8272aca8e066740474a6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_importhook.py
@@ -0,0 +1,213 @@
+from __future__ import annotations
+
+import ast
+import sys
+import types
+from collections.abc import Callable, Iterable
+from importlib.abc import MetaPathFinder
+from importlib.machinery import ModuleSpec, SourceFileLoader
+from importlib.util import cache_from_source, decode_source
+from inspect import isclass
+from os import PathLike
+from types import CodeType, ModuleType, TracebackType
+from typing import Sequence, TypeVar
+from unittest.mock import patch
+
+from ._config import global_config
+from ._transformer import TypeguardTransformer
+
+if sys.version_info >= (3, 12):
+    from collections.abc import Buffer
+else:
+    from typing_extensions import Buffer
+
+if sys.version_info >= (3, 11):
+    from typing import ParamSpec
+else:
+    from typing_extensions import ParamSpec
+
+if sys.version_info >= (3, 10):
+    from importlib.metadata import PackageNotFoundError, version
+else:
+    from importlib_metadata import PackageNotFoundError, version
+
+try:
+    OPTIMIZATION = "typeguard" + "".join(version("typeguard").split(".")[:3])
+except PackageNotFoundError:
+    OPTIMIZATION = "typeguard"
+
+P = ParamSpec("P")
+T = TypeVar("T")
+
+
+# The name of this function is magical
+def _call_with_frames_removed(
+    f: Callable[P, T], *args: P.args, **kwargs: P.kwargs
+) -> T:
+    return f(*args, **kwargs)
+
+
+def optimized_cache_from_source(path: str, debug_override: bool | None = None) -> str:
+    return cache_from_source(path, debug_override, optimization=OPTIMIZATION)
+
+
+class TypeguardLoader(SourceFileLoader):
+    @staticmethod
+    def source_to_code(
+        data: Buffer | str | ast.Module | ast.Expression | ast.Interactive,
+        path: Buffer | str | PathLike[str] = "<string>",
+    ) -> CodeType:
+        if isinstance(data, (ast.Module, ast.Expression, ast.Interactive)):
+            tree = data
+        else:
+            if isinstance(data, str):
+                source = data
+            else:
+                source = decode_source(data)
+
+            tree = _call_with_frames_removed(
+                ast.parse,
+                source,
+                path,
+                "exec",
+            )
+
+        tree = TypeguardTransformer().visit(tree)
+        ast.fix_missing_locations(tree)
+
+        if global_config.debug_instrumentation and sys.version_info >= (3, 9):
+            print(
+                f"Source code of {path!r} after instrumentation:\n"
+                "----------------------------------------------",
+                file=sys.stderr,
+            )
+            print(ast.unparse(tree), file=sys.stderr)
+            print("----------------------------------------------", file=sys.stderr)
+
+        return _call_with_frames_removed(
+            compile, tree, path, "exec", 0, dont_inherit=True
+        )
+
+    def exec_module(self, module: ModuleType) -> None:
+        # Use a custom optimization marker – the import lock should make this monkey
+        # patch safe
+        with patch(
+            "importlib._bootstrap_external.cache_from_source",
+            optimized_cache_from_source,
+        ):
+            super().exec_module(module)
+
+
+class TypeguardFinder(MetaPathFinder):
+    """
+    Wraps another path finder and instruments the module with
+    :func:`@typechecked <typeguard.typechecked>` if :meth:`should_instrument` returns
+    ``True``.
+
+    Should not be used directly, but rather via :func:`~.install_import_hook`.
+
+    .. versionadded:: 2.6
+    """
+
+    def __init__(self, packages: list[str] | None, original_pathfinder: MetaPathFinder):
+        self.packages = packages
+        self._original_pathfinder = original_pathfinder
+
+    def find_spec(
+        self,
+        fullname: str,
+        path: Sequence[str] | None,
+        target: types.ModuleType | None = None,
+    ) -> ModuleSpec | None:
+        if self.should_instrument(fullname):
+            spec = self._original_pathfinder.find_spec(fullname, path, target)
+            if spec is not None and isinstance(spec.loader, SourceFileLoader):
+                spec.loader = TypeguardLoader(spec.loader.name, spec.loader.path)
+                return spec
+
+        return None
+
+    def should_instrument(self, module_name: str) -> bool:
+        """
+        Determine whether the module with the given name should be instrumented.
+
+        :param module_name: full name of the module that is about to be imported (e.g.
+            ``xyz.abc``)
+
+        """
+        if self.packages is None:
+            return True
+
+        for package in self.packages:
+            if module_name == package or module_name.startswith(package + "."):
+                return True
+
+        return False
+
+
+class ImportHookManager:
+    """
+    A handle that can be used to uninstall the Typeguard import hook.
+    """
+
+    def __init__(self, hook: MetaPathFinder):
+        self.hook = hook
+
+    def __enter__(self) -> None:
+        pass
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException],
+        exc_val: BaseException,
+        exc_tb: TracebackType,
+    ) -> None:
+        self.uninstall()
+
+    def uninstall(self) -> None:
+        """Uninstall the import hook."""
+        try:
+            sys.meta_path.remove(self.hook)
+        except ValueError:
+            pass  # already removed
+
+
+def install_import_hook(
+    packages: Iterable[str] | None = None,
+    *,
+    cls: type[TypeguardFinder] = TypeguardFinder,
+) -> ImportHookManager:
+    """
+    Install an import hook that instruments functions for automatic type checking.
+
+    This only affects modules loaded **after** this hook has been installed.
+
+    :param packages: an iterable of package names to instrument, or ``None`` to
+        instrument all packages
+    :param cls: a custom meta path finder class
+    :return: a context manager that uninstalls the hook on exit (or when you call
+        ``.uninstall()``)
+
+    .. versionadded:: 2.6
+
+    """
+    if packages is None:
+        target_packages: list[str] | None = None
+    elif isinstance(packages, str):
+        target_packages = [packages]
+    else:
+        target_packages = list(packages)
+
+    for finder in sys.meta_path:
+        if (
+            isclass(finder)
+            and finder.__name__ == "PathFinder"
+            and hasattr(finder, "find_spec")
+        ):
+            break
+    else:
+        raise RuntimeError("Cannot find a PathFinder in sys.meta_path")
+
+    hook = cls(target_packages, finder)
+    sys.meta_path.insert(0, hook)
+    return ImportHookManager(hook)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_memo.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_memo.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d0d80c66d788e2f8f5e24ce14249319c54054c0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_memo.py
@@ -0,0 +1,48 @@
+from __future__ import annotations
+
+from typing import Any
+
+from typeguard._config import TypeCheckConfiguration, global_config
+
+
+class TypeCheckMemo:
+    """
+    Contains information necessary for type checkers to do their work.
+
+    .. attribute:: globals
+       :type: dict[str, Any]
+
+        Dictionary of global variables to use for resolving forward references.
+
+    .. attribute:: locals
+       :type: dict[str, Any]
+
+        Dictionary of local variables to use for resolving forward references.
+
+    .. attribute:: self_type
+       :type: type | None
+
+        When running type checks within an instance method or class method, this is the
+        class object that the first argument (usually named ``self`` or ``cls``) refers
+        to.
+
+    .. attribute:: config
+       :type: TypeCheckConfiguration
+
+         Contains the configuration for a particular set of type checking operations.
+    """
+
+    __slots__ = "globals", "locals", "self_type", "config"
+
+    def __init__(
+        self,
+        globals: dict[str, Any],
+        locals: dict[str, Any],
+        *,
+        self_type: type | None = None,
+        config: TypeCheckConfiguration = global_config,
+    ):
+        self.globals = globals
+        self.locals = locals
+        self.self_type = self_type
+        self.config = config
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_pytest_plugin.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_pytest_plugin.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b2f494ec7dbe5333c2798c0fcebb83cc3ac9907
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_pytest_plugin.py
@@ -0,0 +1,127 @@
+from __future__ import annotations
+
+import sys
+import warnings
+from typing import TYPE_CHECKING, Any, Literal
+
+from typeguard._config import CollectionCheckStrategy, ForwardRefPolicy, global_config
+from typeguard._exceptions import InstrumentationWarning
+from typeguard._importhook import install_import_hook
+from typeguard._utils import qualified_name, resolve_reference
+
+if TYPE_CHECKING:
+    from pytest import Config, Parser
+
+
+def pytest_addoption(parser: Parser) -> None:
+    def add_ini_option(
+        opt_type: (
+            Literal["string", "paths", "pathlist", "args", "linelist", "bool"] | None
+        ),
+    ) -> None:
+        parser.addini(
+            group.options[-1].names()[0][2:],
+            group.options[-1].attrs()["help"],
+            opt_type,
+        )
+
+    group = parser.getgroup("typeguard")
+    group.addoption(
+        "--typeguard-packages",
+        action="store",
+        help="comma separated name list of packages and modules to instrument for "
+        "type checking, or :all: to instrument all modules loaded after typeguard",
+    )
+    add_ini_option("linelist")
+
+    group.addoption(
+        "--typeguard-debug-instrumentation",
+        action="store_true",
+        help="print all instrumented code to stderr",
+    )
+    add_ini_option("bool")
+
+    group.addoption(
+        "--typeguard-typecheck-fail-callback",
+        action="store",
+        help=(
+            "a module:varname (e.g. typeguard:warn_on_error) reference to a function "
+            "that is called (with the exception, and memo object as arguments) to "
+            "handle a TypeCheckError"
+        ),
+    )
+    add_ini_option("string")
+
+    group.addoption(
+        "--typeguard-forward-ref-policy",
+        action="store",
+        choices=list(ForwardRefPolicy.__members__),
+        help=(
+            "determines how to deal with unresolveable forward references in type "
+            "annotations"
+        ),
+    )
+    add_ini_option("string")
+
+    group.addoption(
+        "--typeguard-collection-check-strategy",
+        action="store",
+        choices=list(CollectionCheckStrategy.__members__),
+        help="determines how thoroughly to check collections (list, dict, etc)",
+    )
+    add_ini_option("string")
+
+
+def pytest_configure(config: Config) -> None:
+    def getoption(name: str) -> Any:
+        return config.getoption(name.replace("-", "_")) or config.getini(name)
+
+    packages: list[str] | None = []
+    if packages_option := config.getoption("typeguard_packages"):
+        packages = [pkg.strip() for pkg in packages_option.split(",")]
+    elif packages_ini := config.getini("typeguard-packages"):
+        packages = packages_ini
+
+    if packages:
+        if packages == [":all:"]:
+            packages = None
+        else:
+            already_imported_packages = sorted(
+                package for package in packages if package in sys.modules
+            )
+            if already_imported_packages:
+                warnings.warn(
+                    f"typeguard cannot check these packages because they are already "
+                    f"imported: {', '.join(already_imported_packages)}",
+                    InstrumentationWarning,
+                    stacklevel=1,
+                )
+
+        install_import_hook(packages=packages)
+
+    debug_option = getoption("typeguard-debug-instrumentation")
+    if debug_option:
+        global_config.debug_instrumentation = True
+
+    fail_callback_option = getoption("typeguard-typecheck-fail-callback")
+    if fail_callback_option:
+        callback = resolve_reference(fail_callback_option)
+        if not callable(callback):
+            raise TypeError(
+                f"{fail_callback_option} ({qualified_name(callback.__class__)}) is not "
+                f"a callable"
+            )
+
+        global_config.typecheck_fail_callback = callback
+
+    forward_ref_policy_option = getoption("typeguard-forward-ref-policy")
+    if forward_ref_policy_option:
+        forward_ref_policy = ForwardRefPolicy.__members__[forward_ref_policy_option]
+        global_config.forward_ref_policy = forward_ref_policy
+
+    collection_check_strategy_option = getoption("typeguard-collection-check-strategy")
+    if collection_check_strategy_option:
+        collection_check_strategy = CollectionCheckStrategy.__members__[
+            collection_check_strategy_option
+        ]
+        global_config.collection_check_strategy = collection_check_strategy
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_suppression.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_suppression.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbbfbfbe8ef364d45a1db581562c73b323819b84
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_suppression.py
@@ -0,0 +1,86 @@
+from __future__ import annotations
+
+import sys
+from collections.abc import Callable, Generator
+from contextlib import contextmanager
+from functools import update_wrapper
+from threading import Lock
+from typing import ContextManager, TypeVar, overload
+
+if sys.version_info >= (3, 10):
+    from typing import ParamSpec
+else:
+    from typing_extensions import ParamSpec
+
+P = ParamSpec("P")
+T = TypeVar("T")
+
+type_checks_suppressed = 0
+type_checks_suppress_lock = Lock()
+
+
+@overload
+def suppress_type_checks(func: Callable[P, T]) -> Callable[P, T]: ...
+
+
+@overload
+def suppress_type_checks() -> ContextManager[None]: ...
+
+
+def suppress_type_checks(
+    func: Callable[P, T] | None = None,
+) -> Callable[P, T] | ContextManager[None]:
+    """
+    Temporarily suppress all type checking.
+
+    This function has two operating modes, based on how it's used:
+
+    #. as a context manager (``with suppress_type_checks(): ...``)
+    #. as a decorator (``@suppress_type_checks``)
+
+    When used as a context manager, :func:`check_type` and any automatically
+    instrumented functions skip the actual type checking. These context managers can be
+    nested.
+
+    When used as a decorator, all type checking is suppressed while the function is
+    running.
+
+    Type checking will resume once no more context managers are active and no decorated
+    functions are running.
+
+    Both operating modes are thread-safe.
+
+    """
+
+    def wrapper(*args: P.args, **kwargs: P.kwargs) -> T:
+        global type_checks_suppressed
+
+        with type_checks_suppress_lock:
+            type_checks_suppressed += 1
+
+        assert func is not None
+        try:
+            return func(*args, **kwargs)
+        finally:
+            with type_checks_suppress_lock:
+                type_checks_suppressed -= 1
+
+    def cm() -> Generator[None, None, None]:
+        global type_checks_suppressed
+
+        with type_checks_suppress_lock:
+            type_checks_suppressed += 1
+
+        try:
+            yield
+        finally:
+            with type_checks_suppress_lock:
+                type_checks_suppressed -= 1
+
+    if func is None:
+        # Context manager mode
+        return contextmanager(cm)()
+    else:
+        # Decorator mode
+        update_wrapper(wrapper, func)
+        return wrapper
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_transformer.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..13ac3630e6c29ca32b2a84a3e6a85665b439cedf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_transformer.py
@@ -0,0 +1,1229 @@
+from __future__ import annotations
+
+import ast
+import builtins
+import sys
+import typing
+from ast import (
+    AST,
+    Add,
+    AnnAssign,
+    Assign,
+    AsyncFunctionDef,
+    Attribute,
+    AugAssign,
+    BinOp,
+    BitAnd,
+    BitOr,
+    BitXor,
+    Call,
+    ClassDef,
+    Constant,
+    Dict,
+    Div,
+    Expr,
+    Expression,
+    FloorDiv,
+    FunctionDef,
+    If,
+    Import,
+    ImportFrom,
+    Index,
+    List,
+    Load,
+    LShift,
+    MatMult,
+    Mod,
+    Module,
+    Mult,
+    Name,
+    NamedExpr,
+    NodeTransformer,
+    NodeVisitor,
+    Pass,
+    Pow,
+    Return,
+    RShift,
+    Starred,
+    Store,
+    Sub,
+    Subscript,
+    Tuple,
+    Yield,
+    YieldFrom,
+    alias,
+    copy_location,
+    expr,
+    fix_missing_locations,
+    keyword,
+    walk,
+)
+from collections import defaultdict
+from collections.abc import Generator, Sequence
+from contextlib import contextmanager
+from copy import deepcopy
+from dataclasses import dataclass, field
+from typing import Any, ClassVar, cast, overload
+
+generator_names = (
+    "typing.Generator",
+    "collections.abc.Generator",
+    "typing.Iterator",
+    "collections.abc.Iterator",
+    "typing.Iterable",
+    "collections.abc.Iterable",
+    "typing.AsyncIterator",
+    "collections.abc.AsyncIterator",
+    "typing.AsyncIterable",
+    "collections.abc.AsyncIterable",
+    "typing.AsyncGenerator",
+    "collections.abc.AsyncGenerator",
+)
+anytype_names = (
+    "typing.Any",
+    "typing_extensions.Any",
+)
+literal_names = (
+    "typing.Literal",
+    "typing_extensions.Literal",
+)
+annotated_names = (
+    "typing.Annotated",
+    "typing_extensions.Annotated",
+)
+ignore_decorators = (
+    "typing.no_type_check",
+    "typeguard.typeguard_ignore",
+)
+aug_assign_functions = {
+    Add: "iadd",
+    Sub: "isub",
+    Mult: "imul",
+    MatMult: "imatmul",
+    Div: "itruediv",
+    FloorDiv: "ifloordiv",
+    Mod: "imod",
+    Pow: "ipow",
+    LShift: "ilshift",
+    RShift: "irshift",
+    BitAnd: "iand",
+    BitXor: "ixor",
+    BitOr: "ior",
+}
+
+
+@dataclass
+class TransformMemo:
+    node: Module | ClassDef | FunctionDef | AsyncFunctionDef | None
+    parent: TransformMemo | None
+    path: tuple[str, ...]
+    joined_path: Constant = field(init=False)
+    return_annotation: expr | None = None
+    yield_annotation: expr | None = None
+    send_annotation: expr | None = None
+    is_async: bool = False
+    local_names: set[str] = field(init=False, default_factory=set)
+    imported_names: dict[str, str] = field(init=False, default_factory=dict)
+    ignored_names: set[str] = field(init=False, default_factory=set)
+    load_names: defaultdict[str, dict[str, Name]] = field(
+        init=False, default_factory=lambda: defaultdict(dict)
+    )
+    has_yield_expressions: bool = field(init=False, default=False)
+    has_return_expressions: bool = field(init=False, default=False)
+    memo_var_name: Name | None = field(init=False, default=None)
+    should_instrument: bool = field(init=False, default=True)
+    variable_annotations: dict[str, expr] = field(init=False, default_factory=dict)
+    configuration_overrides: dict[str, Any] = field(init=False, default_factory=dict)
+    code_inject_index: int = field(init=False, default=0)
+
+    def __post_init__(self) -> None:
+        elements: list[str] = []
+        memo = self
+        while isinstance(memo.node, (ClassDef, FunctionDef, AsyncFunctionDef)):
+            elements.insert(0, memo.node.name)
+            if not memo.parent:
+                break
+
+            memo = memo.parent
+            if isinstance(memo.node, (FunctionDef, AsyncFunctionDef)):
+                elements.insert(0, "<locals>")
+
+        self.joined_path = Constant(".".join(elements))
+
+        # Figure out where to insert instrumentation code
+        if self.node:
+            for index, child in enumerate(self.node.body):
+                if isinstance(child, ImportFrom) and child.module == "__future__":
+                    # (module only) __future__ imports must come first
+                    continue
+                elif (
+                    isinstance(child, Expr)
+                    and isinstance(child.value, Constant)
+                    and isinstance(child.value.value, str)
+                ):
+                    continue  # docstring
+
+                self.code_inject_index = index
+                break
+
+    def get_unused_name(self, name: str) -> str:
+        memo: TransformMemo | None = self
+        while memo is not None:
+            if name in memo.local_names:
+                memo = self
+                name += "_"
+            else:
+                memo = memo.parent
+
+        self.local_names.add(name)
+        return name
+
+    def is_ignored_name(self, expression: expr | Expr | None) -> bool:
+        top_expression = (
+            expression.value if isinstance(expression, Expr) else expression
+        )
+
+        if isinstance(top_expression, Attribute) and isinstance(
+            top_expression.value, Name
+        ):
+            name = top_expression.value.id
+        elif isinstance(top_expression, Name):
+            name = top_expression.id
+        else:
+            return False
+
+        memo: TransformMemo | None = self
+        while memo is not None:
+            if name in memo.ignored_names:
+                return True
+
+            memo = memo.parent
+
+        return False
+
+    def get_memo_name(self) -> Name:
+        if not self.memo_var_name:
+            self.memo_var_name = Name(id="memo", ctx=Load())
+
+        return self.memo_var_name
+
+    def get_import(self, module: str, name: str) -> Name:
+        if module in self.load_names and name in self.load_names[module]:
+            return self.load_names[module][name]
+
+        qualified_name = f"{module}.{name}"
+        if name in self.imported_names and self.imported_names[name] == qualified_name:
+            return Name(id=name, ctx=Load())
+
+        alias = self.get_unused_name(name)
+        node = self.load_names[module][name] = Name(id=alias, ctx=Load())
+        self.imported_names[name] = qualified_name
+        return node
+
+    def insert_imports(self, node: Module | FunctionDef | AsyncFunctionDef) -> None:
+        """Insert imports needed by injected code."""
+        if not self.load_names:
+            return
+
+        # Insert imports after any "from __future__ ..." imports and any docstring
+        for modulename, names in self.load_names.items():
+            aliases = [
+                alias(orig_name, new_name.id if orig_name != new_name.id else None)
+                for orig_name, new_name in sorted(names.items())
+            ]
+            node.body.insert(self.code_inject_index, ImportFrom(modulename, aliases, 0))
+
+    def name_matches(self, expression: expr | Expr | None, *names: str) -> bool:
+        if expression is None:
+            return False
+
+        path: list[str] = []
+        top_expression = (
+            expression.value if isinstance(expression, Expr) else expression
+        )
+
+        if isinstance(top_expression, Subscript):
+            top_expression = top_expression.value
+        elif isinstance(top_expression, Call):
+            top_expression = top_expression.func
+
+        while isinstance(top_expression, Attribute):
+            path.insert(0, top_expression.attr)
+            top_expression = top_expression.value
+
+        if not isinstance(top_expression, Name):
+            return False
+
+        if top_expression.id in self.imported_names:
+            translated = self.imported_names[top_expression.id]
+        elif hasattr(builtins, top_expression.id):
+            translated = "builtins." + top_expression.id
+        else:
+            translated = top_expression.id
+
+        path.insert(0, translated)
+        joined_path = ".".join(path)
+        if joined_path in names:
+            return True
+        elif self.parent:
+            return self.parent.name_matches(expression, *names)
+        else:
+            return False
+
+    def get_config_keywords(self) -> list[keyword]:
+        if self.parent and isinstance(self.parent.node, ClassDef):
+            overrides = self.parent.configuration_overrides.copy()
+        else:
+            overrides = {}
+
+        overrides.update(self.configuration_overrides)
+        return [keyword(key, value) for key, value in overrides.items()]
+
+
+class NameCollector(NodeVisitor):
+    def __init__(self) -> None:
+        self.names: set[str] = set()
+
+    def visit_Import(self, node: Import) -> None:
+        for name in node.names:
+            self.names.add(name.asname or name.name)
+
+    def visit_ImportFrom(self, node: ImportFrom) -> None:
+        for name in node.names:
+            self.names.add(name.asname or name.name)
+
+    def visit_Assign(self, node: Assign) -> None:
+        for target in node.targets:
+            if isinstance(target, Name):
+                self.names.add(target.id)
+
+    def visit_NamedExpr(self, node: NamedExpr) -> Any:
+        if isinstance(node.target, Name):
+            self.names.add(node.target.id)
+
+    def visit_FunctionDef(self, node: FunctionDef) -> None:
+        pass
+
+    def visit_ClassDef(self, node: ClassDef) -> None:
+        pass
+
+
+class GeneratorDetector(NodeVisitor):
+    """Detects if a function node is a generator function."""
+
+    contains_yields: bool = False
+    in_root_function: bool = False
+
+    def visit_Yield(self, node: Yield) -> Any:
+        self.contains_yields = True
+
+    def visit_YieldFrom(self, node: YieldFrom) -> Any:
+        self.contains_yields = True
+
+    def visit_ClassDef(self, node: ClassDef) -> Any:
+        pass
+
+    def visit_FunctionDef(self, node: FunctionDef | AsyncFunctionDef) -> Any:
+        if not self.in_root_function:
+            self.in_root_function = True
+            self.generic_visit(node)
+            self.in_root_function = False
+
+    def visit_AsyncFunctionDef(self, node: AsyncFunctionDef) -> Any:
+        self.visit_FunctionDef(node)
+
+
+class AnnotationTransformer(NodeTransformer):
+    type_substitutions: ClassVar[dict[str, tuple[str, str]]] = {
+        "builtins.dict": ("typing", "Dict"),
+        "builtins.list": ("typing", "List"),
+        "builtins.tuple": ("typing", "Tuple"),
+        "builtins.set": ("typing", "Set"),
+        "builtins.frozenset": ("typing", "FrozenSet"),
+    }
+
+    def __init__(self, transformer: TypeguardTransformer):
+        self.transformer = transformer
+        self._memo = transformer._memo
+        self._level = 0
+
+    def visit(self, node: AST) -> Any:
+        # Don't process Literals
+        if isinstance(node, expr) and self._memo.name_matches(node, *literal_names):
+            return node
+
+        self._level += 1
+        new_node = super().visit(node)
+        self._level -= 1
+
+        if isinstance(new_node, Expression) and not hasattr(new_node, "body"):
+            return None
+
+        # Return None if this new node matches a variation of typing.Any
+        if (
+            self._level == 0
+            and isinstance(new_node, expr)
+            and self._memo.name_matches(new_node, *anytype_names)
+        ):
+            return None
+
+        return new_node
+
+    def visit_BinOp(self, node: BinOp) -> Any:
+        self.generic_visit(node)
+
+        if isinstance(node.op, BitOr):
+            # If either branch of the BinOp has been transformed to `None`, it means
+            # that a type in the union was ignored, so the entire annotation should e
+            # ignored
+            if not hasattr(node, "left") or not hasattr(node, "right"):
+                return None
+
+            # Return Any if either side is Any
+            if self._memo.name_matches(node.left, *anytype_names):
+                return node.left
+            elif self._memo.name_matches(node.right, *anytype_names):
+                return node.right
+
+            if sys.version_info < (3, 10):
+                union_name = self.transformer._get_import("typing", "Union")
+                return Subscript(
+                    value=union_name,
+                    slice=Index(
+                        Tuple(elts=[node.left, node.right], ctx=Load()), ctx=Load()
+                    ),
+                    ctx=Load(),
+                )
+
+        return node
+
+    def visit_Attribute(self, node: Attribute) -> Any:
+        if self._memo.is_ignored_name(node):
+            return None
+
+        return node
+
+    def visit_Subscript(self, node: Subscript) -> Any:
+        if self._memo.is_ignored_name(node.value):
+            return None
+
+        # The subscript of typing(_extensions).Literal can be any arbitrary string, so
+        # don't try to evaluate it as code
+        if node.slice:
+            if isinstance(node.slice, Index):
+                # Python 3.8
+                slice_value = node.slice.value  # type: ignore[attr-defined]
+            else:
+                slice_value = node.slice
+
+            if isinstance(slice_value, Tuple):
+                if self._memo.name_matches(node.value, *annotated_names):
+                    # Only treat the first argument to typing.Annotated as a potential
+                    # forward reference
+                    items = cast(
+                        typing.List[expr],
+                        [self.visit(slice_value.elts[0])] + slice_value.elts[1:],
+                    )
+                else:
+                    items = cast(
+                        typing.List[expr],
+                        [self.visit(item) for item in slice_value.elts],
+                    )
+
+                # If this is a Union and any of the items is Any, erase the entire
+                # annotation
+                if self._memo.name_matches(node.value, "typing.Union") and any(
+                    item is None
+                    or (
+                        isinstance(item, expr)
+                        and self._memo.name_matches(item, *anytype_names)
+                    )
+                    for item in items
+                ):
+                    return None
+
+                # If all items in the subscript were Any, erase the subscript entirely
+                if all(item is None for item in items):
+                    return node.value
+
+                for index, item in enumerate(items):
+                    if item is None:
+                        items[index] = self.transformer._get_import("typing", "Any")
+
+                slice_value.elts = items
+            else:
+                self.generic_visit(node)
+
+                # If the transformer erased the slice entirely, just return the node
+                # value without the subscript (unless it's Optional, in which case erase
+                # the node entirely
+                if self._memo.name_matches(
+                    node.value, "typing.Optional"
+                ) and not hasattr(node, "slice"):
+                    return None
+                if sys.version_info >= (3, 9) and not hasattr(node, "slice"):
+                    return node.value
+                elif sys.version_info < (3, 9) and not hasattr(node.slice, "value"):
+                    return node.value
+
+        return node
+
+    def visit_Name(self, node: Name) -> Any:
+        if self._memo.is_ignored_name(node):
+            return None
+
+        if sys.version_info < (3, 9):
+            for typename, substitute in self.type_substitutions.items():
+                if self._memo.name_matches(node, typename):
+                    new_node = self.transformer._get_import(*substitute)
+                    return copy_location(new_node, node)
+
+        return node
+
+    def visit_Call(self, node: Call) -> Any:
+        # Don't recurse into calls
+        return node
+
+    def visit_Constant(self, node: Constant) -> Any:
+        if isinstance(node.value, str):
+            expression = ast.parse(node.value, mode="eval")
+            new_node = self.visit(expression)
+            if new_node:
+                return copy_location(new_node.body, node)
+            else:
+                return None
+
+        return node
+
+
+class TypeguardTransformer(NodeTransformer):
+    def __init__(
+        self, target_path: Sequence[str] | None = None, target_lineno: int | None = None
+    ) -> None:
+        self._target_path = tuple(target_path) if target_path else None
+        self._memo = self._module_memo = TransformMemo(None, None, ())
+        self.names_used_in_annotations: set[str] = set()
+        self.target_node: FunctionDef | AsyncFunctionDef | None = None
+        self.target_lineno = target_lineno
+
+    def generic_visit(self, node: AST) -> AST:
+        has_non_empty_body_initially = bool(getattr(node, "body", None))
+        initial_type = type(node)
+
+        node = super().generic_visit(node)
+
+        if (
+            type(node) is initial_type
+            and has_non_empty_body_initially
+            and hasattr(node, "body")
+            and not node.body
+        ):
+            # If we have still the same node type after transformation
+            # but we've optimised it's body away, we add a `pass` statement.
+            node.body = [Pass()]
+
+        return node
+
+    @contextmanager
+    def _use_memo(
+        self, node: ClassDef | FunctionDef | AsyncFunctionDef
+    ) -> Generator[None, Any, None]:
+        new_memo = TransformMemo(node, self._memo, self._memo.path + (node.name,))
+        old_memo = self._memo
+        self._memo = new_memo
+
+        if isinstance(node, (FunctionDef, AsyncFunctionDef)):
+            new_memo.should_instrument = (
+                self._target_path is None or new_memo.path == self._target_path
+            )
+            if new_memo.should_instrument:
+                # Check if the function is a generator function
+                detector = GeneratorDetector()
+                detector.visit(node)
+
+                # Extract yield, send and return types where possible from a subscripted
+                # annotation like Generator[int, str, bool]
+                return_annotation = deepcopy(node.returns)
+                if detector.contains_yields and new_memo.name_matches(
+                    return_annotation, *generator_names
+                ):
+                    if isinstance(return_annotation, Subscript):
+                        annotation_slice = return_annotation.slice
+
+                        # Python < 3.9
+                        if isinstance(annotation_slice, Index):
+                            annotation_slice = (
+                                annotation_slice.value  # type: ignore[attr-defined]
+                            )
+
+                        if isinstance(annotation_slice, Tuple):
+                            items = annotation_slice.elts
+                        else:
+                            items = [annotation_slice]
+
+                        if len(items) > 0:
+                            new_memo.yield_annotation = self._convert_annotation(
+                                items[0]
+                            )
+
+                        if len(items) > 1:
+                            new_memo.send_annotation = self._convert_annotation(
+                                items[1]
+                            )
+
+                        if len(items) > 2:
+                            new_memo.return_annotation = self._convert_annotation(
+                                items[2]
+                            )
+                else:
+                    new_memo.return_annotation = self._convert_annotation(
+                        return_annotation
+                    )
+
+        if isinstance(node, AsyncFunctionDef):
+            new_memo.is_async = True
+
+        yield
+        self._memo = old_memo
+
+    def _get_import(self, module: str, name: str) -> Name:
+        memo = self._memo if self._target_path else self._module_memo
+        return memo.get_import(module, name)
+
+    @overload
+    def _convert_annotation(self, annotation: None) -> None: ...
+
+    @overload
+    def _convert_annotation(self, annotation: expr) -> expr: ...
+
+    def _convert_annotation(self, annotation: expr | None) -> expr | None:
+        if annotation is None:
+            return None
+
+        # Convert PEP 604 unions (x | y) and generic built-in collections where
+        # necessary, and undo forward references
+        new_annotation = cast(expr, AnnotationTransformer(self).visit(annotation))
+        if isinstance(new_annotation, expr):
+            new_annotation = ast.copy_location(new_annotation, annotation)
+
+            # Store names used in the annotation
+            names = {node.id for node in walk(new_annotation) if isinstance(node, Name)}
+            self.names_used_in_annotations.update(names)
+
+        return new_annotation
+
+    def visit_Name(self, node: Name) -> Name:
+        self._memo.local_names.add(node.id)
+        return node
+
+    def visit_Module(self, node: Module) -> Module:
+        self._module_memo = self._memo = TransformMemo(node, None, ())
+        self.generic_visit(node)
+        self._module_memo.insert_imports(node)
+
+        fix_missing_locations(node)
+        return node
+
+    def visit_Import(self, node: Import) -> Import:
+        for name in node.names:
+            self._memo.local_names.add(name.asname or name.name)
+            self._memo.imported_names[name.asname or name.name] = name.name
+
+        return node
+
+    def visit_ImportFrom(self, node: ImportFrom) -> ImportFrom:
+        for name in node.names:
+            if name.name != "*":
+                alias = name.asname or name.name
+                self._memo.local_names.add(alias)
+                self._memo.imported_names[alias] = f"{node.module}.{name.name}"
+
+        return node
+
+    def visit_ClassDef(self, node: ClassDef) -> ClassDef | None:
+        self._memo.local_names.add(node.name)
+
+        # Eliminate top level classes not belonging to the target path
+        if (
+            self._target_path is not None
+            and not self._memo.path
+            and node.name != self._target_path[0]
+        ):
+            return None
+
+        with self._use_memo(node):
+            for decorator in node.decorator_list.copy():
+                if self._memo.name_matches(decorator, "typeguard.typechecked"):
+                    # Remove the decorator to prevent duplicate instrumentation
+                    node.decorator_list.remove(decorator)
+
+                    # Store any configuration overrides
+                    if isinstance(decorator, Call) and decorator.keywords:
+                        self._memo.configuration_overrides.update(
+                            {kw.arg: kw.value for kw in decorator.keywords if kw.arg}
+                        )
+
+            self.generic_visit(node)
+            return node
+
+    def visit_FunctionDef(
+        self, node: FunctionDef | AsyncFunctionDef
+    ) -> FunctionDef | AsyncFunctionDef | None:
+        """
+        Injects type checks for function arguments, and for a return of None if the
+        function is annotated to return something else than Any or None, and the body
+        ends without an explicit "return".
+
+        """
+        self._memo.local_names.add(node.name)
+
+        # Eliminate top level functions not belonging to the target path
+        if (
+            self._target_path is not None
+            and not self._memo.path
+            and node.name != self._target_path[0]
+        ):
+            return None
+
+        # Skip instrumentation if we're instrumenting the whole module and the function
+        # contains either @no_type_check or @typeguard_ignore
+        if self._target_path is None:
+            for decorator in node.decorator_list:
+                if self._memo.name_matches(decorator, *ignore_decorators):
+                    return node
+
+        with self._use_memo(node):
+            arg_annotations: dict[str, Any] = {}
+            if self._target_path is None or self._memo.path == self._target_path:
+                # Find line number we're supposed to match against
+                if node.decorator_list:
+                    first_lineno = node.decorator_list[0].lineno
+                else:
+                    first_lineno = node.lineno
+
+                for decorator in node.decorator_list.copy():
+                    if self._memo.name_matches(decorator, "typing.overload"):
+                        # Remove overloads entirely
+                        return None
+                    elif self._memo.name_matches(decorator, "typeguard.typechecked"):
+                        # Remove the decorator to prevent duplicate instrumentation
+                        node.decorator_list.remove(decorator)
+
+                        # Store any configuration overrides
+                        if isinstance(decorator, Call) and decorator.keywords:
+                            self._memo.configuration_overrides = {
+                                kw.arg: kw.value for kw in decorator.keywords if kw.arg
+                            }
+
+                if self.target_lineno == first_lineno:
+                    assert self.target_node is None
+                    self.target_node = node
+                    if node.decorator_list:
+                        self.target_lineno = node.decorator_list[0].lineno
+                    else:
+                        self.target_lineno = node.lineno
+
+                all_args = node.args.args + node.args.kwonlyargs + node.args.posonlyargs
+
+                # Ensure that any type shadowed by the positional or keyword-only
+                # argument names are ignored in this function
+                for arg in all_args:
+                    self._memo.ignored_names.add(arg.arg)
+
+                # Ensure that any type shadowed by the variable positional argument name
+                # (e.g. "args" in *args) is ignored this function
+                if node.args.vararg:
+                    self._memo.ignored_names.add(node.args.vararg.arg)
+
+                # Ensure that any type shadowed by the variable keywrod argument name
+                # (e.g. "kwargs" in *kwargs) is ignored this function
+                if node.args.kwarg:
+                    self._memo.ignored_names.add(node.args.kwarg.arg)
+
+                for arg in all_args:
+                    annotation = self._convert_annotation(deepcopy(arg.annotation))
+                    if annotation:
+                        arg_annotations[arg.arg] = annotation
+
+                if node.args.vararg:
+                    annotation_ = self._convert_annotation(node.args.vararg.annotation)
+                    if annotation_:
+                        if sys.version_info >= (3, 9):
+                            container = Name("tuple", ctx=Load())
+                        else:
+                            container = self._get_import("typing", "Tuple")
+
+                        subscript_slice: Tuple | Index = Tuple(
+                            [
+                                annotation_,
+                                Constant(Ellipsis),
+                            ],
+                            ctx=Load(),
+                        )
+                        if sys.version_info < (3, 9):
+                            subscript_slice = Index(subscript_slice, ctx=Load())
+
+                        arg_annotations[node.args.vararg.arg] = Subscript(
+                            container, subscript_slice, ctx=Load()
+                        )
+
+                if node.args.kwarg:
+                    annotation_ = self._convert_annotation(node.args.kwarg.annotation)
+                    if annotation_:
+                        if sys.version_info >= (3, 9):
+                            container = Name("dict", ctx=Load())
+                        else:
+                            container = self._get_import("typing", "Dict")
+
+                        subscript_slice = Tuple(
+                            [
+                                Name("str", ctx=Load()),
+                                annotation_,
+                            ],
+                            ctx=Load(),
+                        )
+                        if sys.version_info < (3, 9):
+                            subscript_slice = Index(subscript_slice, ctx=Load())
+
+                        arg_annotations[node.args.kwarg.arg] = Subscript(
+                            container, subscript_slice, ctx=Load()
+                        )
+
+                if arg_annotations:
+                    self._memo.variable_annotations.update(arg_annotations)
+
+            self.generic_visit(node)
+
+            if arg_annotations:
+                annotations_dict = Dict(
+                    keys=[Constant(key) for key in arg_annotations.keys()],
+                    values=[
+                        Tuple([Name(key, ctx=Load()), annotation], ctx=Load())
+                        for key, annotation in arg_annotations.items()
+                    ],
+                )
+                func_name = self._get_import(
+                    "typeguard._functions", "check_argument_types"
+                )
+                args = [
+                    self._memo.joined_path,
+                    annotations_dict,
+                    self._memo.get_memo_name(),
+                ]
+                node.body.insert(
+                    self._memo.code_inject_index, Expr(Call(func_name, args, []))
+                )
+
+            # Add a checked "return None" to the end if there's no explicit return
+            # Skip if the return annotation is None or Any
+            if (
+                self._memo.return_annotation
+                and (not self._memo.is_async or not self._memo.has_yield_expressions)
+                and not isinstance(node.body[-1], Return)
+                and (
+                    not isinstance(self._memo.return_annotation, Constant)
+                    or self._memo.return_annotation.value is not None
+                )
+            ):
+                func_name = self._get_import(
+                    "typeguard._functions", "check_return_type"
+                )
+                return_node = Return(
+                    Call(
+                        func_name,
+                        [
+                            self._memo.joined_path,
+                            Constant(None),
+                            self._memo.return_annotation,
+                            self._memo.get_memo_name(),
+                        ],
+                        [],
+                    )
+                )
+
+                # Replace a placeholder "pass" at the end
+                if isinstance(node.body[-1], Pass):
+                    copy_location(return_node, node.body[-1])
+                    del node.body[-1]
+
+                node.body.append(return_node)
+
+            # Insert code to create the call memo, if it was ever needed for this
+            # function
+            if self._memo.memo_var_name:
+                memo_kwargs: dict[str, Any] = {}
+                if self._memo.parent and isinstance(self._memo.parent.node, ClassDef):
+                    for decorator in node.decorator_list:
+                        if (
+                            isinstance(decorator, Name)
+                            and decorator.id == "staticmethod"
+                        ):
+                            break
+                        elif (
+                            isinstance(decorator, Name)
+                            and decorator.id == "classmethod"
+                        ):
+                            memo_kwargs["self_type"] = Name(
+                                id=node.args.args[0].arg, ctx=Load()
+                            )
+                            break
+                    else:
+                        if node.args.args:
+                            if node.name == "__new__":
+                                memo_kwargs["self_type"] = Name(
+                                    id=node.args.args[0].arg, ctx=Load()
+                                )
+                            else:
+                                memo_kwargs["self_type"] = Attribute(
+                                    Name(id=node.args.args[0].arg, ctx=Load()),
+                                    "__class__",
+                                    ctx=Load(),
+                                )
+
+                # Construct the function reference
+                # Nested functions get special treatment: the function name is added
+                # to free variables (and the closure of the resulting function)
+                names: list[str] = [node.name]
+                memo = self._memo.parent
+                while memo:
+                    if isinstance(memo.node, (FunctionDef, AsyncFunctionDef)):
+                        # This is a nested function. Use the function name as-is.
+                        del names[:-1]
+                        break
+                    elif not isinstance(memo.node, ClassDef):
+                        break
+
+                    names.insert(0, memo.node.name)
+                    memo = memo.parent
+
+                config_keywords = self._memo.get_config_keywords()
+                if config_keywords:
+                    memo_kwargs["config"] = Call(
+                        self._get_import("dataclasses", "replace"),
+                        [self._get_import("typeguard._config", "global_config")],
+                        config_keywords,
+                    )
+
+                self._memo.memo_var_name.id = self._memo.get_unused_name("memo")
+                memo_store_name = Name(id=self._memo.memo_var_name.id, ctx=Store())
+                globals_call = Call(Name(id="globals", ctx=Load()), [], [])
+                locals_call = Call(Name(id="locals", ctx=Load()), [], [])
+                memo_expr = Call(
+                    self._get_import("typeguard", "TypeCheckMemo"),
+                    [globals_call, locals_call],
+                    [keyword(key, value) for key, value in memo_kwargs.items()],
+                )
+                node.body.insert(
+                    self._memo.code_inject_index,
+                    Assign([memo_store_name], memo_expr),
+                )
+
+                self._memo.insert_imports(node)
+
+                # Special case the __new__() method to create a local alias from the
+                # class name to the first argument (usually "cls")
+                if (
+                    isinstance(node, FunctionDef)
+                    and node.args
+                    and self._memo.parent is not None
+                    and isinstance(self._memo.parent.node, ClassDef)
+                    and node.name == "__new__"
+                ):
+                    first_args_expr = Name(node.args.args[0].arg, ctx=Load())
+                    cls_name = Name(self._memo.parent.node.name, ctx=Store())
+                    node.body.insert(
+                        self._memo.code_inject_index,
+                        Assign([cls_name], first_args_expr),
+                    )
+
+                # Rmove any placeholder "pass" at the end
+                if isinstance(node.body[-1], Pass):
+                    del node.body[-1]
+
+        return node
+
+    def visit_AsyncFunctionDef(
+        self, node: AsyncFunctionDef
+    ) -> FunctionDef | AsyncFunctionDef | None:
+        return self.visit_FunctionDef(node)
+
+    def visit_Return(self, node: Return) -> Return:
+        """This injects type checks into "return" statements."""
+        self.generic_visit(node)
+        if (
+            self._memo.return_annotation
+            and self._memo.should_instrument
+            and not self._memo.is_ignored_name(self._memo.return_annotation)
+        ):
+            func_name = self._get_import("typeguard._functions", "check_return_type")
+            old_node = node
+            retval = old_node.value or Constant(None)
+            node = Return(
+                Call(
+                    func_name,
+                    [
+                        self._memo.joined_path,
+                        retval,
+                        self._memo.return_annotation,
+                        self._memo.get_memo_name(),
+                    ],
+                    [],
+                )
+            )
+            copy_location(node, old_node)
+
+        return node
+
+    def visit_Yield(self, node: Yield) -> Yield | Call:
+        """
+        This injects type checks into "yield" expressions, checking both the yielded
+        value and the value sent back to the generator, when appropriate.
+
+        """
+        self._memo.has_yield_expressions = True
+        self.generic_visit(node)
+
+        if (
+            self._memo.yield_annotation
+            and self._memo.should_instrument
+            and not self._memo.is_ignored_name(self._memo.yield_annotation)
+        ):
+            func_name = self._get_import("typeguard._functions", "check_yield_type")
+            yieldval = node.value or Constant(None)
+            node.value = Call(
+                func_name,
+                [
+                    self._memo.joined_path,
+                    yieldval,
+                    self._memo.yield_annotation,
+                    self._memo.get_memo_name(),
+                ],
+                [],
+            )
+
+        if (
+            self._memo.send_annotation
+            and self._memo.should_instrument
+            and not self._memo.is_ignored_name(self._memo.send_annotation)
+        ):
+            func_name = self._get_import("typeguard._functions", "check_send_type")
+            old_node = node
+            call_node = Call(
+                func_name,
+                [
+                    self._memo.joined_path,
+                    old_node,
+                    self._memo.send_annotation,
+                    self._memo.get_memo_name(),
+                ],
+                [],
+            )
+            copy_location(call_node, old_node)
+            return call_node
+
+        return node
+
+    def visit_AnnAssign(self, node: AnnAssign) -> Any:
+        """
+        This injects a type check into a local variable annotation-assignment within a
+        function body.
+
+        """
+        self.generic_visit(node)
+
+        if (
+            isinstance(self._memo.node, (FunctionDef, AsyncFunctionDef))
+            and node.annotation
+            and isinstance(node.target, Name)
+        ):
+            self._memo.ignored_names.add(node.target.id)
+            annotation = self._convert_annotation(deepcopy(node.annotation))
+            if annotation:
+                self._memo.variable_annotations[node.target.id] = annotation
+                if node.value:
+                    func_name = self._get_import(
+                        "typeguard._functions", "check_variable_assignment"
+                    )
+                    node.value = Call(
+                        func_name,
+                        [
+                            node.value,
+                            Constant(node.target.id),
+                            annotation,
+                            self._memo.get_memo_name(),
+                        ],
+                        [],
+                    )
+
+        return node
+
+    def visit_Assign(self, node: Assign) -> Any:
+        """
+        This injects a type check into a local variable assignment within a function
+        body. The variable must have been annotated earlier in the function body.
+
+        """
+        self.generic_visit(node)
+
+        # Only instrument function-local assignments
+        if isinstance(self._memo.node, (FunctionDef, AsyncFunctionDef)):
+            targets: list[dict[Constant, expr | None]] = []
+            check_required = False
+            for target in node.targets:
+                elts: Sequence[expr]
+                if isinstance(target, Name):
+                    elts = [target]
+                elif isinstance(target, Tuple):
+                    elts = target.elts
+                else:
+                    continue
+
+                annotations_: dict[Constant, expr | None] = {}
+                for exp in elts:
+                    prefix = ""
+                    if isinstance(exp, Starred):
+                        exp = exp.value
+                        prefix = "*"
+
+                    if isinstance(exp, Name):
+                        self._memo.ignored_names.add(exp.id)
+                        name = prefix + exp.id
+                        annotation = self._memo.variable_annotations.get(exp.id)
+                        if annotation:
+                            annotations_[Constant(name)] = annotation
+                            check_required = True
+                        else:
+                            annotations_[Constant(name)] = None
+
+                targets.append(annotations_)
+
+            if check_required:
+                # Replace missing annotations with typing.Any
+                for item in targets:
+                    for key, expression in item.items():
+                        if expression is None:
+                            item[key] = self._get_import("typing", "Any")
+
+                if len(targets) == 1 and len(targets[0]) == 1:
+                    func_name = self._get_import(
+                        "typeguard._functions", "check_variable_assignment"
+                    )
+                    target_varname = next(iter(targets[0]))
+                    node.value = Call(
+                        func_name,
+                        [
+                            node.value,
+                            target_varname,
+                            targets[0][target_varname],
+                            self._memo.get_memo_name(),
+                        ],
+                        [],
+                    )
+                elif targets:
+                    func_name = self._get_import(
+                        "typeguard._functions", "check_multi_variable_assignment"
+                    )
+                    targets_arg = List(
+                        [
+                            Dict(keys=list(target), values=list(target.values()))
+                            for target in targets
+                        ],
+                        ctx=Load(),
+                    )
+                    node.value = Call(
+                        func_name,
+                        [node.value, targets_arg, self._memo.get_memo_name()],
+                        [],
+                    )
+
+        return node
+
+    def visit_NamedExpr(self, node: NamedExpr) -> Any:
+        """This injects a type check into an assignment expression (a := foo())."""
+        self.generic_visit(node)
+
+        # Only instrument function-local assignments
+        if isinstance(self._memo.node, (FunctionDef, AsyncFunctionDef)) and isinstance(
+            node.target, Name
+        ):
+            self._memo.ignored_names.add(node.target.id)
+
+            # Bail out if no matching annotation is found
+            annotation = self._memo.variable_annotations.get(node.target.id)
+            if annotation is None:
+                return node
+
+            func_name = self._get_import(
+                "typeguard._functions", "check_variable_assignment"
+            )
+            node.value = Call(
+                func_name,
+                [
+                    node.value,
+                    Constant(node.target.id),
+                    annotation,
+                    self._memo.get_memo_name(),
+                ],
+                [],
+            )
+
+        return node
+
+    def visit_AugAssign(self, node: AugAssign) -> Any:
+        """
+        This injects a type check into an augmented assignment expression (a += 1).
+
+        """
+        self.generic_visit(node)
+
+        # Only instrument function-local assignments
+        if isinstance(self._memo.node, (FunctionDef, AsyncFunctionDef)) and isinstance(
+            node.target, Name
+        ):
+            # Bail out if no matching annotation is found
+            annotation = self._memo.variable_annotations.get(node.target.id)
+            if annotation is None:
+                return node
+
+            # Bail out if the operator is not found (newer Python version?)
+            try:
+                operator_func_name = aug_assign_functions[node.op.__class__]
+            except KeyError:
+                return node
+
+            operator_func = self._get_import("operator", operator_func_name)
+            operator_call = Call(
+                operator_func, [Name(node.target.id, ctx=Load()), node.value], []
+            )
+            check_call = Call(
+                self._get_import("typeguard._functions", "check_variable_assignment"),
+                [
+                    operator_call,
+                    Constant(node.target.id),
+                    annotation,
+                    self._memo.get_memo_name(),
+                ],
+                [],
+            )
+            return Assign(targets=[node.target], value=check_call)
+
+        return node
+
+    def visit_If(self, node: If) -> Any:
+        """
+        This blocks names from being collected from a module-level
+        "if typing.TYPE_CHECKING:" block, so that they won't be type checked.
+
+        """
+        self.generic_visit(node)
+
+        if (
+            self._memo is self._module_memo
+            and isinstance(node.test, Name)
+            and self._memo.name_matches(node.test, "typing.TYPE_CHECKING")
+        ):
+            collector = NameCollector()
+            collector.visit(node)
+            self._memo.ignored_names.update(collector.names)
+
+        return node
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_union_transformer.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_union_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..19617e6af5a0fd691b9d3793f9486ade2868d5bf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_union_transformer.py
@@ -0,0 +1,55 @@
+"""
+Transforms lazily evaluated PEP 604 unions into typing.Unions, for compatibility with
+Python versions older than 3.10.
+"""
+
+from __future__ import annotations
+
+from ast import (
+    BinOp,
+    BitOr,
+    Index,
+    Load,
+    Name,
+    NodeTransformer,
+    Subscript,
+    fix_missing_locations,
+    parse,
+)
+from ast import Tuple as ASTTuple
+from types import CodeType
+from typing import Any, Dict, FrozenSet, List, Set, Tuple, Union
+
+type_substitutions = {
+    "dict": Dict,
+    "list": List,
+    "tuple": Tuple,
+    "set": Set,
+    "frozenset": FrozenSet,
+    "Union": Union,
+}
+
+
+class UnionTransformer(NodeTransformer):
+    def __init__(self, union_name: Name | None = None):
+        self.union_name = union_name or Name(id="Union", ctx=Load())
+
+    def visit_BinOp(self, node: BinOp) -> Any:
+        self.generic_visit(node)
+        if isinstance(node.op, BitOr):
+            return Subscript(
+                value=self.union_name,
+                slice=Index(
+                    ASTTuple(elts=[node.left, node.right], ctx=Load()), ctx=Load()
+                ),
+                ctx=Load(),
+            )
+
+        return node
+
+
+def compile_type_hint(hint: str) -> CodeType:
+    parsed = parse(hint, "<string>", "eval")
+    UnionTransformer().visit(parsed)
+    fix_missing_locations(parsed)
+    return compile(parsed, "<string>", "eval", flags=0)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_utils.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bcc8417f8773115de078d307682b457af848052
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/_utils.py
@@ -0,0 +1,173 @@
+from __future__ import annotations
+
+import inspect
+import sys
+from importlib import import_module
+from inspect import currentframe
+from types import CodeType, FrameType, FunctionType
+from typing import TYPE_CHECKING, Any, Callable, ForwardRef, Union, cast, final
+from weakref import WeakValueDictionary
+
+if TYPE_CHECKING:
+    from ._memo import TypeCheckMemo
+
+if sys.version_info >= (3, 13):
+    from typing import get_args, get_origin
+
+    def evaluate_forwardref(forwardref: ForwardRef, memo: TypeCheckMemo) -> Any:
+        return forwardref._evaluate(
+            memo.globals, memo.locals, type_params=(), recursive_guard=frozenset()
+        )
+
+elif sys.version_info >= (3, 10):
+    from typing import get_args, get_origin
+
+    def evaluate_forwardref(forwardref: ForwardRef, memo: TypeCheckMemo) -> Any:
+        return forwardref._evaluate(
+            memo.globals, memo.locals, recursive_guard=frozenset()
+        )
+
+else:
+    from typing_extensions import get_args, get_origin
+
+    evaluate_extra_args: tuple[frozenset[Any], ...] = (
+        (frozenset(),) if sys.version_info >= (3, 9) else ()
+    )
+
+    def evaluate_forwardref(forwardref: ForwardRef, memo: TypeCheckMemo) -> Any:
+        from ._union_transformer import compile_type_hint, type_substitutions
+
+        if not forwardref.__forward_evaluated__:
+            forwardref.__forward_code__ = compile_type_hint(forwardref.__forward_arg__)
+
+        try:
+            return forwardref._evaluate(memo.globals, memo.locals, *evaluate_extra_args)
+        except NameError:
+            if sys.version_info < (3, 10):
+                # Try again, with the type substitutions (list -> List etc.) in place
+                new_globals = memo.globals.copy()
+                new_globals.setdefault("Union", Union)
+                if sys.version_info < (3, 9):
+                    new_globals.update(type_substitutions)
+
+                return forwardref._evaluate(
+                    new_globals, memo.locals or new_globals, *evaluate_extra_args
+                )
+
+            raise
+
+
+_functions_map: WeakValueDictionary[CodeType, FunctionType] = WeakValueDictionary()
+
+
+def get_type_name(type_: Any) -> str:
+    name: str
+    for attrname in "__name__", "_name", "__forward_arg__":
+        candidate = getattr(type_, attrname, None)
+        if isinstance(candidate, str):
+            name = candidate
+            break
+    else:
+        origin = get_origin(type_)
+        candidate = getattr(origin, "_name", None)
+        if candidate is None:
+            candidate = type_.__class__.__name__.strip("_")
+
+        if isinstance(candidate, str):
+            name = candidate
+        else:
+            return "(unknown)"
+
+    args = get_args(type_)
+    if args:
+        if name == "Literal":
+            formatted_args = ", ".join(repr(arg) for arg in args)
+        else:
+            formatted_args = ", ".join(get_type_name(arg) for arg in args)
+
+        name += f"[{formatted_args}]"
+
+    module = getattr(type_, "__module__", None)
+    if module and module not in (None, "typing", "typing_extensions", "builtins"):
+        name = module + "." + name
+
+    return name
+
+
+def qualified_name(obj: Any, *, add_class_prefix: bool = False) -> str:
+    """
+    Return the qualified name (e.g. package.module.Type) for the given object.
+
+    Builtins and types from the :mod:`typing` package get special treatment by having
+    the module name stripped from the generated name.
+
+    """
+    if obj is None:
+        return "None"
+    elif inspect.isclass(obj):
+        prefix = "class " if add_class_prefix else ""
+        type_ = obj
+    else:
+        prefix = ""
+        type_ = type(obj)
+
+    module = type_.__module__
+    qualname = type_.__qualname__
+    name = qualname if module in ("typing", "builtins") else f"{module}.{qualname}"
+    return prefix + name
+
+
+def function_name(func: Callable[..., Any]) -> str:
+    """
+    Return the qualified name of the given function.
+
+    Builtins and types from the :mod:`typing` package get special treatment by having
+    the module name stripped from the generated name.
+
+    """
+    # For partial functions and objects with __call__ defined, __qualname__ does not
+    # exist
+    module = getattr(func, "__module__", "")
+    qualname = (module + ".") if module not in ("builtins", "") else ""
+    return qualname + getattr(func, "__qualname__", repr(func))
+
+
+def resolve_reference(reference: str) -> Any:
+    modulename, varname = reference.partition(":")[::2]
+    if not modulename or not varname:
+        raise ValueError(f"{reference!r} is not a module:varname reference")
+
+    obj = import_module(modulename)
+    for attr in varname.split("."):
+        obj = getattr(obj, attr)
+
+    return obj
+
+
+def is_method_of(obj: object, cls: type) -> bool:
+    return (
+        inspect.isfunction(obj)
+        and obj.__module__ == cls.__module__
+        and obj.__qualname__.startswith(cls.__qualname__ + ".")
+    )
+
+
+def get_stacklevel() -> int:
+    level = 1
+    frame = cast(FrameType, currentframe()).f_back
+    while frame and frame.f_globals.get("__name__", "").startswith("typeguard."):
+        level += 1
+        frame = frame.f_back
+
+    return level
+
+
+@final
+class Unset:
+    __slots__ = ()
+
+    def __repr__(self) -> str:
+        return "<unset>"
+
+
+unset = Unset()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/py.typed b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typeguard/py.typed
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..f26bcf4d2de6eb136e31006ca3ab447d5e488adf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/LICENSE
@@ -0,0 +1,279 @@
+A. HISTORY OF THE SOFTWARE
+==========================
+
+Python was created in the early 1990s by Guido van Rossum at Stichting
+Mathematisch Centrum (CWI, see https://www.cwi.nl) in the Netherlands
+as a successor of a language called ABC.  Guido remains Python's
+principal author, although it includes many contributions from others.
+
+In 1995, Guido continued his work on Python at the Corporation for
+National Research Initiatives (CNRI, see https://www.cnri.reston.va.us)
+in Reston, Virginia where he released several versions of the
+software.
+
+In May 2000, Guido and the Python core development team moved to
+BeOpen.com to form the BeOpen PythonLabs team.  In October of the same
+year, the PythonLabs team moved to Digital Creations, which became
+Zope Corporation.  In 2001, the Python Software Foundation (PSF, see
+https://www.python.org/psf/) was formed, a non-profit organization
+created specifically to own Python-related Intellectual Property.
+Zope Corporation was a sponsoring member of the PSF.
+
+All Python releases are Open Source (see https://opensource.org for
+the Open Source Definition).  Historically, most, but not all, Python
+releases have also been GPL-compatible; the table below summarizes
+the various releases.
+
+    Release         Derived     Year        Owner       GPL-
+                    from                                compatible? (1)
+
+    0.9.0 thru 1.2              1991-1995   CWI         yes
+    1.3 thru 1.5.2  1.2         1995-1999   CNRI        yes
+    1.6             1.5.2       2000        CNRI        no
+    2.0             1.6         2000        BeOpen.com  no
+    1.6.1           1.6         2001        CNRI        yes (2)
+    2.1             2.0+1.6.1   2001        PSF         no
+    2.0.1           2.0+1.6.1   2001        PSF         yes
+    2.1.1           2.1+2.0.1   2001        PSF         yes
+    2.1.2           2.1.1       2002        PSF         yes
+    2.1.3           2.1.2       2002        PSF         yes
+    2.2 and above   2.1.1       2001-now    PSF         yes
+
+Footnotes:
+
+(1) GPL-compatible doesn't mean that we're distributing Python under
+    the GPL.  All Python licenses, unlike the GPL, let you distribute
+    a modified version without making your changes open source.  The
+    GPL-compatible licenses make it possible to combine Python with
+    other software that is released under the GPL; the others don't.
+
+(2) According to Richard Stallman, 1.6.1 is not GPL-compatible,
+    because its license has a choice of law clause.  According to
+    CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1
+    is "not incompatible" with the GPL.
+
+Thanks to the many outside volunteers who have worked under Guido's
+direction to make these releases possible.
+
+
+B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON
+===============================================================
+
+Python software and documentation are licensed under the
+Python Software Foundation License Version 2.
+
+Starting with Python 3.8.6, examples, recipes, and other code in
+the documentation are dual licensed under the PSF License Version 2
+and the Zero-Clause BSD license.
+
+Some software incorporated into Python is under different licenses.
+The licenses are listed with code falling under that license.
+
+
+PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2
+--------------------------------------------
+
+1. This LICENSE AGREEMENT is between the Python Software Foundation
+("PSF"), and the Individual or Organization ("Licensee") accessing and
+otherwise using this software ("Python") in source or binary form and
+its associated documentation.
+
+2. Subject to the terms and conditions of this License Agreement, PSF hereby
+grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
+analyze, test, perform and/or display publicly, prepare derivative works,
+distribute, and otherwise use Python alone or in any derivative version,
+provided, however, that PSF's License Agreement and PSF's notice of copyright,
+i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,
+2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023 Python Software Foundation;
+All Rights Reserved" are retained in Python alone or in any derivative version
+prepared by Licensee.
+
+3. In the event Licensee prepares a derivative work that is based on
+or incorporates Python or any part thereof, and wants to make
+the derivative work available to others as provided herein, then
+Licensee hereby agrees to include in any such work a brief summary of
+the changes made to Python.
+
+4. PSF is making Python available to Licensee on an "AS IS"
+basis.  PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND
+DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON WILL NOT
+INFRINGE ANY THIRD PARTY RIGHTS.
+
+5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
+FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
+A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON,
+OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+
+6. This License Agreement will automatically terminate upon a material
+breach of its terms and conditions.
+
+7. Nothing in this License Agreement shall be deemed to create any
+relationship of agency, partnership, or joint venture between PSF and
+Licensee.  This License Agreement does not grant permission to use PSF
+trademarks or trade name in a trademark sense to endorse or promote
+products or services of Licensee, or any third party.
+
+8. By copying, installing or otherwise using Python, Licensee
+agrees to be bound by the terms and conditions of this License
+Agreement.
+
+
+BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0
+-------------------------------------------
+
+BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1
+
+1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an
+office at 160 Saratoga Avenue, Santa Clara, CA 95051, and the
+Individual or Organization ("Licensee") accessing and otherwise using
+this software in source or binary form and its associated
+documentation ("the Software").
+
+2. Subject to the terms and conditions of this BeOpen Python License
+Agreement, BeOpen hereby grants Licensee a non-exclusive,
+royalty-free, world-wide license to reproduce, analyze, test, perform
+and/or display publicly, prepare derivative works, distribute, and
+otherwise use the Software alone or in any derivative version,
+provided, however, that the BeOpen Python License is retained in the
+Software, alone or in any derivative version prepared by Licensee.
+
+3. BeOpen is making the Software available to Licensee on an "AS IS"
+basis.  BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND
+DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE WILL NOT
+INFRINGE ANY THIRD PARTY RIGHTS.
+
+4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE
+SOFTWARE FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS
+AS A RESULT OF USING, MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY
+DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+
+5. This License Agreement will automatically terminate upon a material
+breach of its terms and conditions.
+
+6. This License Agreement shall be governed by and interpreted in all
+respects by the law of the State of California, excluding conflict of
+law provisions.  Nothing in this License Agreement shall be deemed to
+create any relationship of agency, partnership, or joint venture
+between BeOpen and Licensee.  This License Agreement does not grant
+permission to use BeOpen trademarks or trade names in a trademark
+sense to endorse or promote products or services of Licensee, or any
+third party.  As an exception, the "BeOpen Python" logos available at
+http://www.pythonlabs.com/logos.html may be used according to the
+permissions granted on that web page.
+
+7. By copying, installing or otherwise using the software, Licensee
+agrees to be bound by the terms and conditions of this License
+Agreement.
+
+
+CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1
+---------------------------------------
+
+1. This LICENSE AGREEMENT is between the Corporation for National
+Research Initiatives, having an office at 1895 Preston White Drive,
+Reston, VA 20191 ("CNRI"), and the Individual or Organization
+("Licensee") accessing and otherwise using Python 1.6.1 software in
+source or binary form and its associated documentation.
+
+2. Subject to the terms and conditions of this License Agreement, CNRI
+hereby grants Licensee a nonexclusive, royalty-free, world-wide
+license to reproduce, analyze, test, perform and/or display publicly,
+prepare derivative works, distribute, and otherwise use Python 1.6.1
+alone or in any derivative version, provided, however, that CNRI's
+License Agreement and CNRI's notice of copyright, i.e., "Copyright (c)
+1995-2001 Corporation for National Research Initiatives; All Rights
+Reserved" are retained in Python 1.6.1 alone or in any derivative
+version prepared by Licensee.  Alternately, in lieu of CNRI's License
+Agreement, Licensee may substitute the following text (omitting the
+quotes): "Python 1.6.1 is made available subject to the terms and
+conditions in CNRI's License Agreement.  This Agreement together with
+Python 1.6.1 may be located on the internet using the following
+unique, persistent identifier (known as a handle): 1895.22/1013.  This
+Agreement may also be obtained from a proxy server on the internet
+using the following URL: http://hdl.handle.net/1895.22/1013".
+
+3. In the event Licensee prepares a derivative work that is based on
+or incorporates Python 1.6.1 or any part thereof, and wants to make
+the derivative work available to others as provided herein, then
+Licensee hereby agrees to include in any such work a brief summary of
+the changes made to Python 1.6.1.
+
+4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS"
+basis.  CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
+IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND
+DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS
+FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT
+INFRINGE ANY THIRD PARTY RIGHTS.
+
+5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON
+1.6.1 FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS
+A RESULT OF MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1,
+OR ANY DERIVATIVE THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+
+6. This License Agreement will automatically terminate upon a material
+breach of its terms and conditions.
+
+7. This License Agreement shall be governed by the federal
+intellectual property law of the United States, including without
+limitation the federal copyright law, and, to the extent such
+U.S. federal law does not apply, by the law of the Commonwealth of
+Virginia, excluding Virginia's conflict of law provisions.
+Notwithstanding the foregoing, with regard to derivative works based
+on Python 1.6.1 that incorporate non-separable material that was
+previously distributed under the GNU General Public License (GPL), the
+law of the Commonwealth of Virginia shall govern this License
+Agreement only as to issues arising under or with respect to
+Paragraphs 4, 5, and 7 of this License Agreement.  Nothing in this
+License Agreement shall be deemed to create any relationship of
+agency, partnership, or joint venture between CNRI and Licensee.  This
+License Agreement does not grant permission to use CNRI trademarks or
+trade name in a trademark sense to endorse or promote products or
+services of Licensee, or any third party.
+
+8. By clicking on the "ACCEPT" button where indicated, or by copying,
+installing or otherwise using Python 1.6.1, Licensee agrees to be
+bound by the terms and conditions of this License Agreement.
+
+        ACCEPT
+
+
+CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2
+--------------------------------------------------
+
+Copyright (c) 1991 - 1995, Stichting Mathematisch Centrum Amsterdam,
+The Netherlands.  All rights reserved.
+
+Permission to use, copy, modify, and distribute this software and its
+documentation for any purpose and without fee is hereby granted,
+provided that the above copyright notice appear in all copies and that
+both that copyright notice and this permission notice appear in
+supporting documentation, and that the name of Stichting Mathematisch
+Centrum or CWI not be used in advertising or publicity pertaining to
+distribution of the software without specific, written prior
+permission.
+
+STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO
+THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
+FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE
+FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
+ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
+OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+
+ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON DOCUMENTATION
+----------------------------------------------------------------------
+
+Permission to use, copy, modify, and/or distribute this software for any
+purpose with or without fee is hereby granted.
+
+THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
+REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
+AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
+INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
+LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
+OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
+PERFORMANCE OF THIS SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..f15e2b387737b14e013a83b32a766fe4eb0da611
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/METADATA
@@ -0,0 +1,67 @@
+Metadata-Version: 2.1
+Name: typing_extensions
+Version: 4.12.2
+Summary: Backported and Experimental Type Hints for Python 3.8+
+Keywords: annotations,backport,checker,checking,function,hinting,hints,type,typechecking,typehinting,typehints,typing
+Author-email: "Guido van Rossum, Jukka Lehtosalo, Łukasz Langa, Michael Lee" <levkivskyi@gmail.com>
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Environment :: Console
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: Python Software Foundation License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Topic :: Software Development
+Project-URL: Bug Tracker, https://github.com/python/typing_extensions/issues
+Project-URL: Changes, https://github.com/python/typing_extensions/blob/main/CHANGELOG.md
+Project-URL: Documentation, https://typing-extensions.readthedocs.io/
+Project-URL: Home, https://github.com/python/typing_extensions
+Project-URL: Q & A, https://github.com/python/typing/discussions
+Project-URL: Repository, https://github.com/python/typing_extensions
+
+# Typing Extensions
+
+[![Chat at https://gitter.im/python/typing](https://badges.gitter.im/python/typing.svg)](https://gitter.im/python/typing)
+
+[Documentation](https://typing-extensions.readthedocs.io/en/latest/#) –
+[PyPI](https://pypi.org/project/typing-extensions/)
+
+## Overview
+
+The `typing_extensions` module serves two related purposes:
+
+- Enable use of new type system features on older Python versions. For example,
+  `typing.TypeGuard` is new in Python 3.10, but `typing_extensions` allows
+  users on previous Python versions to use it too.
+- Enable experimentation with new type system PEPs before they are accepted and
+  added to the `typing` module.
+
+`typing_extensions` is treated specially by static type checkers such as
+mypy and pyright. Objects defined in `typing_extensions` are treated the same
+way as equivalent forms in `typing`.
+
+`typing_extensions` uses
+[Semantic Versioning](https://semver.org/). The
+major version will be incremented only for backwards-incompatible changes.
+Therefore, it's safe to depend
+on `typing_extensions` like this: `typing_extensions >=x.y, <(x+1)`,
+where `x.y` is the first version that includes all features you need.
+
+## Included items
+
+See [the documentation](https://typing-extensions.readthedocs.io/en/latest/#) for a
+complete listing of module contents.
+
+## Contributing
+
+See [CONTRIBUTING.md](https://github.com/python/typing_extensions/blob/main/CONTRIBUTING.md)
+for how to contribute to `typing_extensions`.
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..bc7b45334dd59e2c386632c238648ed3cb5031bb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/RECORD
@@ -0,0 +1,7 @@
+__pycache__/typing_extensions.cpython-312.pyc,,
+typing_extensions-4.12.2.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+typing_extensions-4.12.2.dist-info/LICENSE,sha256=Oy-B_iHRgcSZxZolbI4ZaEVdZonSaaqFNzv7avQdo78,13936
+typing_extensions-4.12.2.dist-info/METADATA,sha256=BeUQIa8cnYbrjWx-N8TOznM9UGW5Gm2DicVpDtRA8W0,3018
+typing_extensions-4.12.2.dist-info/RECORD,,
+typing_extensions-4.12.2.dist-info/WHEEL,sha256=EZbGkh7Ie4PoZfRQ8I0ZuP9VklN_TvcZ6DSE5Uar4z4,81
+typing_extensions.py,sha256=gwekpyG9DVG3lxWKX4ni8u7nk3We5slG98mA9F3DJQw,134451
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..3b5e64b5e6c4a210201d1676a891fd57b15cda99
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions-4.12.2.dist-info/WHEEL
@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: flit 3.9.0
+Root-Is-Purelib: true
+Tag: py3-none-any
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions.py
new file mode 100644
index 0000000000000000000000000000000000000000..dec429ca8723950097217d2140dd8daccb064e2d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/typing_extensions.py
@@ -0,0 +1,3641 @@
+import abc
+import collections
+import collections.abc
+import contextlib
+import functools
+import inspect
+import operator
+import sys
+import types as _types
+import typing
+import warnings
+
+__all__ = [
+    # Super-special typing primitives.
+    'Any',
+    'ClassVar',
+    'Concatenate',
+    'Final',
+    'LiteralString',
+    'ParamSpec',
+    'ParamSpecArgs',
+    'ParamSpecKwargs',
+    'Self',
+    'Type',
+    'TypeVar',
+    'TypeVarTuple',
+    'Unpack',
+
+    # ABCs (from collections.abc).
+    'Awaitable',
+    'AsyncIterator',
+    'AsyncIterable',
+    'Coroutine',
+    'AsyncGenerator',
+    'AsyncContextManager',
+    'Buffer',
+    'ChainMap',
+
+    # Concrete collection types.
+    'ContextManager',
+    'Counter',
+    'Deque',
+    'DefaultDict',
+    'NamedTuple',
+    'OrderedDict',
+    'TypedDict',
+
+    # Structural checks, a.k.a. protocols.
+    'SupportsAbs',
+    'SupportsBytes',
+    'SupportsComplex',
+    'SupportsFloat',
+    'SupportsIndex',
+    'SupportsInt',
+    'SupportsRound',
+
+    # One-off things.
+    'Annotated',
+    'assert_never',
+    'assert_type',
+    'clear_overloads',
+    'dataclass_transform',
+    'deprecated',
+    'Doc',
+    'get_overloads',
+    'final',
+    'get_args',
+    'get_origin',
+    'get_original_bases',
+    'get_protocol_members',
+    'get_type_hints',
+    'IntVar',
+    'is_protocol',
+    'is_typeddict',
+    'Literal',
+    'NewType',
+    'overload',
+    'override',
+    'Protocol',
+    'reveal_type',
+    'runtime',
+    'runtime_checkable',
+    'Text',
+    'TypeAlias',
+    'TypeAliasType',
+    'TypeGuard',
+    'TypeIs',
+    'TYPE_CHECKING',
+    'Never',
+    'NoReturn',
+    'ReadOnly',
+    'Required',
+    'NotRequired',
+
+    # Pure aliases, have always been in typing
+    'AbstractSet',
+    'AnyStr',
+    'BinaryIO',
+    'Callable',
+    'Collection',
+    'Container',
+    'Dict',
+    'ForwardRef',
+    'FrozenSet',
+    'Generator',
+    'Generic',
+    'Hashable',
+    'IO',
+    'ItemsView',
+    'Iterable',
+    'Iterator',
+    'KeysView',
+    'List',
+    'Mapping',
+    'MappingView',
+    'Match',
+    'MutableMapping',
+    'MutableSequence',
+    'MutableSet',
+    'NoDefault',
+    'Optional',
+    'Pattern',
+    'Reversible',
+    'Sequence',
+    'Set',
+    'Sized',
+    'TextIO',
+    'Tuple',
+    'Union',
+    'ValuesView',
+    'cast',
+    'no_type_check',
+    'no_type_check_decorator',
+]
+
+# for backward compatibility
+PEP_560 = True
+GenericMeta = type
+_PEP_696_IMPLEMENTED = sys.version_info >= (3, 13, 0, "beta")
+
+# The functions below are modified copies of typing internal helpers.
+# They are needed by _ProtocolMeta and they provide support for PEP 646.
+
+
+class _Sentinel:
+    def __repr__(self):
+        return "<sentinel>"
+
+
+_marker = _Sentinel()
+
+
+if sys.version_info >= (3, 10):
+    def _should_collect_from_parameters(t):
+        return isinstance(
+            t, (typing._GenericAlias, _types.GenericAlias, _types.UnionType)
+        )
+elif sys.version_info >= (3, 9):
+    def _should_collect_from_parameters(t):
+        return isinstance(t, (typing._GenericAlias, _types.GenericAlias))
+else:
+    def _should_collect_from_parameters(t):
+        return isinstance(t, typing._GenericAlias) and not t._special
+
+
+NoReturn = typing.NoReturn
+
+# Some unconstrained type variables.  These are used by the container types.
+# (These are not for export.)
+T = typing.TypeVar('T')  # Any type.
+KT = typing.TypeVar('KT')  # Key type.
+VT = typing.TypeVar('VT')  # Value type.
+T_co = typing.TypeVar('T_co', covariant=True)  # Any type covariant containers.
+T_contra = typing.TypeVar('T_contra', contravariant=True)  # Ditto contravariant.
+
+
+if sys.version_info >= (3, 11):
+    from typing import Any
+else:
+
+    class _AnyMeta(type):
+        def __instancecheck__(self, obj):
+            if self is Any:
+                raise TypeError("typing_extensions.Any cannot be used with isinstance()")
+            return super().__instancecheck__(obj)
+
+        def __repr__(self):
+            if self is Any:
+                return "typing_extensions.Any"
+            return super().__repr__()
+
+    class Any(metaclass=_AnyMeta):
+        """Special type indicating an unconstrained type.
+        - Any is compatible with every type.
+        - Any assumed to have all methods.
+        - All values assumed to be instances of Any.
+        Note that all the above statements are true from the point of view of
+        static type checkers. At runtime, Any should not be used with instance
+        checks.
+        """
+        def __new__(cls, *args, **kwargs):
+            if cls is Any:
+                raise TypeError("Any cannot be instantiated")
+            return super().__new__(cls, *args, **kwargs)
+
+
+ClassVar = typing.ClassVar
+
+
+class _ExtensionsSpecialForm(typing._SpecialForm, _root=True):
+    def __repr__(self):
+        return 'typing_extensions.' + self._name
+
+
+Final = typing.Final
+
+if sys.version_info >= (3, 11):
+    final = typing.final
+else:
+    # @final exists in 3.8+, but we backport it for all versions
+    # before 3.11 to keep support for the __final__ attribute.
+    # See https://bugs.python.org/issue46342
+    def final(f):
+        """This decorator can be used to indicate to type checkers that
+        the decorated method cannot be overridden, and decorated class
+        cannot be subclassed. For example:
+
+            class Base:
+                @final
+                def done(self) -> None:
+                    ...
+            class Sub(Base):
+                def done(self) -> None:  # Error reported by type checker
+                    ...
+            @final
+            class Leaf:
+                ...
+            class Other(Leaf):  # Error reported by type checker
+                ...
+
+        There is no runtime checking of these properties. The decorator
+        sets the ``__final__`` attribute to ``True`` on the decorated object
+        to allow runtime introspection.
+        """
+        try:
+            f.__final__ = True
+        except (AttributeError, TypeError):
+            # Skip the attribute silently if it is not writable.
+            # AttributeError happens if the object has __slots__ or a
+            # read-only property, TypeError if it's a builtin class.
+            pass
+        return f
+
+
+def IntVar(name):
+    return typing.TypeVar(name)
+
+
+# A Literal bug was fixed in 3.11.0, 3.10.1 and 3.9.8
+if sys.version_info >= (3, 10, 1):
+    Literal = typing.Literal
+else:
+    def _flatten_literal_params(parameters):
+        """An internal helper for Literal creation: flatten Literals among parameters"""
+        params = []
+        for p in parameters:
+            if isinstance(p, _LiteralGenericAlias):
+                params.extend(p.__args__)
+            else:
+                params.append(p)
+        return tuple(params)
+
+    def _value_and_type_iter(params):
+        for p in params:
+            yield p, type(p)
+
+    class _LiteralGenericAlias(typing._GenericAlias, _root=True):
+        def __eq__(self, other):
+            if not isinstance(other, _LiteralGenericAlias):
+                return NotImplemented
+            these_args_deduped = set(_value_and_type_iter(self.__args__))
+            other_args_deduped = set(_value_and_type_iter(other.__args__))
+            return these_args_deduped == other_args_deduped
+
+        def __hash__(self):
+            return hash(frozenset(_value_and_type_iter(self.__args__)))
+
+    class _LiteralForm(_ExtensionsSpecialForm, _root=True):
+        def __init__(self, doc: str):
+            self._name = 'Literal'
+            self._doc = self.__doc__ = doc
+
+        def __getitem__(self, parameters):
+            if not isinstance(parameters, tuple):
+                parameters = (parameters,)
+
+            parameters = _flatten_literal_params(parameters)
+
+            val_type_pairs = list(_value_and_type_iter(parameters))
+            try:
+                deduped_pairs = set(val_type_pairs)
+            except TypeError:
+                # unhashable parameters
+                pass
+            else:
+                # similar logic to typing._deduplicate on Python 3.9+
+                if len(deduped_pairs) < len(val_type_pairs):
+                    new_parameters = []
+                    for pair in val_type_pairs:
+                        if pair in deduped_pairs:
+                            new_parameters.append(pair[0])
+                            deduped_pairs.remove(pair)
+                    assert not deduped_pairs, deduped_pairs
+                    parameters = tuple(new_parameters)
+
+            return _LiteralGenericAlias(self, parameters)
+
+    Literal = _LiteralForm(doc="""\
+                           A type that can be used to indicate to type checkers
+                           that the corresponding value has a value literally equivalent
+                           to the provided parameter. For example:
+
+                               var: Literal[4] = 4
+
+                           The type checker understands that 'var' is literally equal to
+                           the value 4 and no other value.
+
+                           Literal[...] cannot be subclassed. There is no runtime
+                           checking verifying that the parameter is actually a value
+                           instead of a type.""")
+
+
+_overload_dummy = typing._overload_dummy
+
+
+if hasattr(typing, "get_overloads"):  # 3.11+
+    overload = typing.overload
+    get_overloads = typing.get_overloads
+    clear_overloads = typing.clear_overloads
+else:
+    # {module: {qualname: {firstlineno: func}}}
+    _overload_registry = collections.defaultdict(
+        functools.partial(collections.defaultdict, dict)
+    )
+
+    def overload(func):
+        """Decorator for overloaded functions/methods.
+
+        In a stub file, place two or more stub definitions for the same
+        function in a row, each decorated with @overload.  For example:
+
+        @overload
+        def utf8(value: None) -> None: ...
+        @overload
+        def utf8(value: bytes) -> bytes: ...
+        @overload
+        def utf8(value: str) -> bytes: ...
+
+        In a non-stub file (i.e. a regular .py file), do the same but
+        follow it with an implementation.  The implementation should *not*
+        be decorated with @overload.  For example:
+
+        @overload
+        def utf8(value: None) -> None: ...
+        @overload
+        def utf8(value: bytes) -> bytes: ...
+        @overload
+        def utf8(value: str) -> bytes: ...
+        def utf8(value):
+            # implementation goes here
+
+        The overloads for a function can be retrieved at runtime using the
+        get_overloads() function.
+        """
+        # classmethod and staticmethod
+        f = getattr(func, "__func__", func)
+        try:
+            _overload_registry[f.__module__][f.__qualname__][
+                f.__code__.co_firstlineno
+            ] = func
+        except AttributeError:
+            # Not a normal function; ignore.
+            pass
+        return _overload_dummy
+
+    def get_overloads(func):
+        """Return all defined overloads for *func* as a sequence."""
+        # classmethod and staticmethod
+        f = getattr(func, "__func__", func)
+        if f.__module__ not in _overload_registry:
+            return []
+        mod_dict = _overload_registry[f.__module__]
+        if f.__qualname__ not in mod_dict:
+            return []
+        return list(mod_dict[f.__qualname__].values())
+
+    def clear_overloads():
+        """Clear all overloads in the registry."""
+        _overload_registry.clear()
+
+
+# This is not a real generic class.  Don't use outside annotations.
+Type = typing.Type
+
+# Various ABCs mimicking those in collections.abc.
+# A few are simply re-exported for completeness.
+Awaitable = typing.Awaitable
+Coroutine = typing.Coroutine
+AsyncIterable = typing.AsyncIterable
+AsyncIterator = typing.AsyncIterator
+Deque = typing.Deque
+DefaultDict = typing.DefaultDict
+OrderedDict = typing.OrderedDict
+Counter = typing.Counter
+ChainMap = typing.ChainMap
+Text = typing.Text
+TYPE_CHECKING = typing.TYPE_CHECKING
+
+
+if sys.version_info >= (3, 13, 0, "beta"):
+    from typing import AsyncContextManager, AsyncGenerator, ContextManager, Generator
+else:
+    def _is_dunder(attr):
+        return attr.startswith('__') and attr.endswith('__')
+
+    # Python <3.9 doesn't have typing._SpecialGenericAlias
+    _special_generic_alias_base = getattr(
+        typing, "_SpecialGenericAlias", typing._GenericAlias
+    )
+
+    class _SpecialGenericAlias(_special_generic_alias_base, _root=True):
+        def __init__(self, origin, nparams, *, inst=True, name=None, defaults=()):
+            if _special_generic_alias_base is typing._GenericAlias:
+                # Python <3.9
+                self.__origin__ = origin
+                self._nparams = nparams
+                super().__init__(origin, nparams, special=True, inst=inst, name=name)
+            else:
+                # Python >= 3.9
+                super().__init__(origin, nparams, inst=inst, name=name)
+            self._defaults = defaults
+
+        def __setattr__(self, attr, val):
+            allowed_attrs = {'_name', '_inst', '_nparams', '_defaults'}
+            if _special_generic_alias_base is typing._GenericAlias:
+                # Python <3.9
+                allowed_attrs.add("__origin__")
+            if _is_dunder(attr) or attr in allowed_attrs:
+                object.__setattr__(self, attr, val)
+            else:
+                setattr(self.__origin__, attr, val)
+
+        @typing._tp_cache
+        def __getitem__(self, params):
+            if not isinstance(params, tuple):
+                params = (params,)
+            msg = "Parameters to generic types must be types."
+            params = tuple(typing._type_check(p, msg) for p in params)
+            if (
+                self._defaults
+                and len(params) < self._nparams
+                and len(params) + len(self._defaults) >= self._nparams
+            ):
+                params = (*params, *self._defaults[len(params) - self._nparams:])
+            actual_len = len(params)
+
+            if actual_len != self._nparams:
+                if self._defaults:
+                    expected = f"at least {self._nparams - len(self._defaults)}"
+                else:
+                    expected = str(self._nparams)
+                if not self._nparams:
+                    raise TypeError(f"{self} is not a generic class")
+                raise TypeError(
+                    f"Too {'many' if actual_len > self._nparams else 'few'}"
+                    f" arguments for {self};"
+                    f" actual {actual_len}, expected {expected}"
+                )
+            return self.copy_with(params)
+
+    _NoneType = type(None)
+    Generator = _SpecialGenericAlias(
+        collections.abc.Generator, 3, defaults=(_NoneType, _NoneType)
+    )
+    AsyncGenerator = _SpecialGenericAlias(
+        collections.abc.AsyncGenerator, 2, defaults=(_NoneType,)
+    )
+    ContextManager = _SpecialGenericAlias(
+        contextlib.AbstractContextManager,
+        2,
+        name="ContextManager",
+        defaults=(typing.Optional[bool],)
+    )
+    AsyncContextManager = _SpecialGenericAlias(
+        contextlib.AbstractAsyncContextManager,
+        2,
+        name="AsyncContextManager",
+        defaults=(typing.Optional[bool],)
+    )
+
+
+_PROTO_ALLOWLIST = {
+    'collections.abc': [
+        'Callable', 'Awaitable', 'Iterable', 'Iterator', 'AsyncIterable',
+        'Hashable', 'Sized', 'Container', 'Collection', 'Reversible', 'Buffer',
+    ],
+    'contextlib': ['AbstractContextManager', 'AbstractAsyncContextManager'],
+    'typing_extensions': ['Buffer'],
+}
+
+
+_EXCLUDED_ATTRS = frozenset(typing.EXCLUDED_ATTRIBUTES) | {
+    "__match_args__", "__protocol_attrs__", "__non_callable_proto_members__",
+    "__final__",
+}
+
+
+def _get_protocol_attrs(cls):
+    attrs = set()
+    for base in cls.__mro__[:-1]:  # without object
+        if base.__name__ in {'Protocol', 'Generic'}:
+            continue
+        annotations = getattr(base, '__annotations__', {})
+        for attr in (*base.__dict__, *annotations):
+            if (not attr.startswith('_abc_') and attr not in _EXCLUDED_ATTRS):
+                attrs.add(attr)
+    return attrs
+
+
+def _caller(depth=2):
+    try:
+        return sys._getframe(depth).f_globals.get('__name__', '__main__')
+    except (AttributeError, ValueError):  # For platforms without _getframe()
+        return None
+
+
+# `__match_args__` attribute was removed from protocol members in 3.13,
+# we want to backport this change to older Python versions.
+if sys.version_info >= (3, 13):
+    Protocol = typing.Protocol
+else:
+    def _allow_reckless_class_checks(depth=3):
+        """Allow instance and class checks for special stdlib modules.
+        The abc and functools modules indiscriminately call isinstance() and
+        issubclass() on the whole MRO of a user class, which may contain protocols.
+        """
+        return _caller(depth) in {'abc', 'functools', None}
+
+    def _no_init(self, *args, **kwargs):
+        if type(self)._is_protocol:
+            raise TypeError('Protocols cannot be instantiated')
+
+    def _type_check_issubclass_arg_1(arg):
+        """Raise TypeError if `arg` is not an instance of `type`
+        in `issubclass(arg, <protocol>)`.
+
+        In most cases, this is verified by type.__subclasscheck__.
+        Checking it again unnecessarily would slow down issubclass() checks,
+        so, we don't perform this check unless we absolutely have to.
+
+        For various error paths, however,
+        we want to ensure that *this* error message is shown to the user
+        where relevant, rather than a typing.py-specific error message.
+        """
+        if not isinstance(arg, type):
+            # Same error message as for issubclass(1, int).
+            raise TypeError('issubclass() arg 1 must be a class')
+
+    # Inheriting from typing._ProtocolMeta isn't actually desirable,
+    # but is necessary to allow typing.Protocol and typing_extensions.Protocol
+    # to mix without getting TypeErrors about "metaclass conflict"
+    class _ProtocolMeta(type(typing.Protocol)):
+        # This metaclass is somewhat unfortunate,
+        # but is necessary for several reasons...
+        #
+        # NOTE: DO NOT call super() in any methods in this class
+        # That would call the methods on typing._ProtocolMeta on Python 3.8-3.11
+        # and those are slow
+        def __new__(mcls, name, bases, namespace, **kwargs):
+            if name == "Protocol" and len(bases) < 2:
+                pass
+            elif {Protocol, typing.Protocol} & set(bases):
+                for base in bases:
+                    if not (
+                        base in {object, typing.Generic, Protocol, typing.Protocol}
+                        or base.__name__ in _PROTO_ALLOWLIST.get(base.__module__, [])
+                        or is_protocol(base)
+                    ):
+                        raise TypeError(
+                            f"Protocols can only inherit from other protocols, "
+                            f"got {base!r}"
+                        )
+            return abc.ABCMeta.__new__(mcls, name, bases, namespace, **kwargs)
+
+        def __init__(cls, *args, **kwargs):
+            abc.ABCMeta.__init__(cls, *args, **kwargs)
+            if getattr(cls, "_is_protocol", False):
+                cls.__protocol_attrs__ = _get_protocol_attrs(cls)
+
+        def __subclasscheck__(cls, other):
+            if cls is Protocol:
+                return type.__subclasscheck__(cls, other)
+            if (
+                getattr(cls, '_is_protocol', False)
+                and not _allow_reckless_class_checks()
+            ):
+                if not getattr(cls, '_is_runtime_protocol', False):
+                    _type_check_issubclass_arg_1(other)
+                    raise TypeError(
+                        "Instance and class checks can only be used with "
+                        "@runtime_checkable protocols"
+                    )
+                if (
+                    # this attribute is set by @runtime_checkable:
+                    cls.__non_callable_proto_members__
+                    and cls.__dict__.get("__subclasshook__") is _proto_hook
+                ):
+                    _type_check_issubclass_arg_1(other)
+                    non_method_attrs = sorted(cls.__non_callable_proto_members__)
+                    raise TypeError(
+                        "Protocols with non-method members don't support issubclass()."
+                        f" Non-method members: {str(non_method_attrs)[1:-1]}."
+                    )
+            return abc.ABCMeta.__subclasscheck__(cls, other)
+
+        def __instancecheck__(cls, instance):
+            # We need this method for situations where attributes are
+            # assigned in __init__.
+            if cls is Protocol:
+                return type.__instancecheck__(cls, instance)
+            if not getattr(cls, "_is_protocol", False):
+                # i.e., it's a concrete subclass of a protocol
+                return abc.ABCMeta.__instancecheck__(cls, instance)
+
+            if (
+                not getattr(cls, '_is_runtime_protocol', False) and
+                not _allow_reckless_class_checks()
+            ):
+                raise TypeError("Instance and class checks can only be used with"
+                                " @runtime_checkable protocols")
+
+            if abc.ABCMeta.__instancecheck__(cls, instance):
+                return True
+
+            for attr in cls.__protocol_attrs__:
+                try:
+                    val = inspect.getattr_static(instance, attr)
+                except AttributeError:
+                    break
+                # this attribute is set by @runtime_checkable:
+                if val is None and attr not in cls.__non_callable_proto_members__:
+                    break
+            else:
+                return True
+
+            return False
+
+        def __eq__(cls, other):
+            # Hack so that typing.Generic.__class_getitem__
+            # treats typing_extensions.Protocol
+            # as equivalent to typing.Protocol
+            if abc.ABCMeta.__eq__(cls, other) is True:
+                return True
+            return cls is Protocol and other is typing.Protocol
+
+        # This has to be defined, or the abc-module cache
+        # complains about classes with this metaclass being unhashable,
+        # if we define only __eq__!
+        def __hash__(cls) -> int:
+            return type.__hash__(cls)
+
+    @classmethod
+    def _proto_hook(cls, other):
+        if not cls.__dict__.get('_is_protocol', False):
+            return NotImplemented
+
+        for attr in cls.__protocol_attrs__:
+            for base in other.__mro__:
+                # Check if the members appears in the class dictionary...
+                if attr in base.__dict__:
+                    if base.__dict__[attr] is None:
+                        return NotImplemented
+                    break
+
+                # ...or in annotations, if it is a sub-protocol.
+                annotations = getattr(base, '__annotations__', {})
+                if (
+                    isinstance(annotations, collections.abc.Mapping)
+                    and attr in annotations
+                    and is_protocol(other)
+                ):
+                    break
+            else:
+                return NotImplemented
+        return True
+
+    class Protocol(typing.Generic, metaclass=_ProtocolMeta):
+        __doc__ = typing.Protocol.__doc__
+        __slots__ = ()
+        _is_protocol = True
+        _is_runtime_protocol = False
+
+        def __init_subclass__(cls, *args, **kwargs):
+            super().__init_subclass__(*args, **kwargs)
+
+            # Determine if this is a protocol or a concrete subclass.
+            if not cls.__dict__.get('_is_protocol', False):
+                cls._is_protocol = any(b is Protocol for b in cls.__bases__)
+
+            # Set (or override) the protocol subclass hook.
+            if '__subclasshook__' not in cls.__dict__:
+                cls.__subclasshook__ = _proto_hook
+
+            # Prohibit instantiation for protocol classes
+            if cls._is_protocol and cls.__init__ is Protocol.__init__:
+                cls.__init__ = _no_init
+
+
+if sys.version_info >= (3, 13):
+    runtime_checkable = typing.runtime_checkable
+else:
+    def runtime_checkable(cls):
+        """Mark a protocol class as a runtime protocol.
+
+        Such protocol can be used with isinstance() and issubclass().
+        Raise TypeError if applied to a non-protocol class.
+        This allows a simple-minded structural check very similar to
+        one trick ponies in collections.abc such as Iterable.
+
+        For example::
+
+            @runtime_checkable
+            class Closable(Protocol):
+                def close(self): ...
+
+            assert isinstance(open('/some/file'), Closable)
+
+        Warning: this will check only the presence of the required methods,
+        not their type signatures!
+        """
+        if not issubclass(cls, typing.Generic) or not getattr(cls, '_is_protocol', False):
+            raise TypeError(f'@runtime_checkable can be only applied to protocol classes,'
+                            f' got {cls!r}')
+        cls._is_runtime_protocol = True
+
+        # typing.Protocol classes on <=3.11 break if we execute this block,
+        # because typing.Protocol classes on <=3.11 don't have a
+        # `__protocol_attrs__` attribute, and this block relies on the
+        # `__protocol_attrs__` attribute. Meanwhile, typing.Protocol classes on 3.12.2+
+        # break if we *don't* execute this block, because *they* assume that all
+        # protocol classes have a `__non_callable_proto_members__` attribute
+        # (which this block sets)
+        if isinstance(cls, _ProtocolMeta) or sys.version_info >= (3, 12, 2):
+            # PEP 544 prohibits using issubclass()
+            # with protocols that have non-method members.
+            # See gh-113320 for why we compute this attribute here,
+            # rather than in `_ProtocolMeta.__init__`
+            cls.__non_callable_proto_members__ = set()
+            for attr in cls.__protocol_attrs__:
+                try:
+                    is_callable = callable(getattr(cls, attr, None))
+                except Exception as e:
+                    raise TypeError(
+                        f"Failed to determine whether protocol member {attr!r} "
+                        "is a method member"
+                    ) from e
+                else:
+                    if not is_callable:
+                        cls.__non_callable_proto_members__.add(attr)
+
+        return cls
+
+
+# The "runtime" alias exists for backwards compatibility.
+runtime = runtime_checkable
+
+
+# Our version of runtime-checkable protocols is faster on Python 3.8-3.11
+if sys.version_info >= (3, 12):
+    SupportsInt = typing.SupportsInt
+    SupportsFloat = typing.SupportsFloat
+    SupportsComplex = typing.SupportsComplex
+    SupportsBytes = typing.SupportsBytes
+    SupportsIndex = typing.SupportsIndex
+    SupportsAbs = typing.SupportsAbs
+    SupportsRound = typing.SupportsRound
+else:
+    @runtime_checkable
+    class SupportsInt(Protocol):
+        """An ABC with one abstract method __int__."""
+        __slots__ = ()
+
+        @abc.abstractmethod
+        def __int__(self) -> int:
+            pass
+
+    @runtime_checkable
+    class SupportsFloat(Protocol):
+        """An ABC with one abstract method __float__."""
+        __slots__ = ()
+
+        @abc.abstractmethod
+        def __float__(self) -> float:
+            pass
+
+    @runtime_checkable
+    class SupportsComplex(Protocol):
+        """An ABC with one abstract method __complex__."""
+        __slots__ = ()
+
+        @abc.abstractmethod
+        def __complex__(self) -> complex:
+            pass
+
+    @runtime_checkable
+    class SupportsBytes(Protocol):
+        """An ABC with one abstract method __bytes__."""
+        __slots__ = ()
+
+        @abc.abstractmethod
+        def __bytes__(self) -> bytes:
+            pass
+
+    @runtime_checkable
+    class SupportsIndex(Protocol):
+        __slots__ = ()
+
+        @abc.abstractmethod
+        def __index__(self) -> int:
+            pass
+
+    @runtime_checkable
+    class SupportsAbs(Protocol[T_co]):
+        """
+        An ABC with one abstract method __abs__ that is covariant in its return type.
+        """
+        __slots__ = ()
+
+        @abc.abstractmethod
+        def __abs__(self) -> T_co:
+            pass
+
+    @runtime_checkable
+    class SupportsRound(Protocol[T_co]):
+        """
+        An ABC with one abstract method __round__ that is covariant in its return type.
+        """
+        __slots__ = ()
+
+        @abc.abstractmethod
+        def __round__(self, ndigits: int = 0) -> T_co:
+            pass
+
+
+def _ensure_subclassable(mro_entries):
+    def inner(func):
+        if sys.implementation.name == "pypy" and sys.version_info < (3, 9):
+            cls_dict = {
+                "__call__": staticmethod(func),
+                "__mro_entries__": staticmethod(mro_entries)
+            }
+            t = type(func.__name__, (), cls_dict)
+            return functools.update_wrapper(t(), func)
+        else:
+            func.__mro_entries__ = mro_entries
+            return func
+    return inner
+
+
+# Update this to something like >=3.13.0b1 if and when
+# PEP 728 is implemented in CPython
+_PEP_728_IMPLEMENTED = False
+
+if _PEP_728_IMPLEMENTED:
+    # The standard library TypedDict in Python 3.8 does not store runtime information
+    # about which (if any) keys are optional.  See https://bugs.python.org/issue38834
+    # The standard library TypedDict in Python 3.9.0/1 does not honour the "total"
+    # keyword with old-style TypedDict().  See https://bugs.python.org/issue42059
+    # The standard library TypedDict below Python 3.11 does not store runtime
+    # information about optional and required keys when using Required or NotRequired.
+    # Generic TypedDicts are also impossible using typing.TypedDict on Python <3.11.
+    # Aaaand on 3.12 we add __orig_bases__ to TypedDict
+    # to enable better runtime introspection.
+    # On 3.13 we deprecate some odd ways of creating TypedDicts.
+    # Also on 3.13, PEP 705 adds the ReadOnly[] qualifier.
+    # PEP 728 (still pending) makes more changes.
+    TypedDict = typing.TypedDict
+    _TypedDictMeta = typing._TypedDictMeta
+    is_typeddict = typing.is_typeddict
+else:
+    # 3.10.0 and later
+    _TAKES_MODULE = "module" in inspect.signature(typing._type_check).parameters
+
+    def _get_typeddict_qualifiers(annotation_type):
+        while True:
+            annotation_origin = get_origin(annotation_type)
+            if annotation_origin is Annotated:
+                annotation_args = get_args(annotation_type)
+                if annotation_args:
+                    annotation_type = annotation_args[0]
+                else:
+                    break
+            elif annotation_origin is Required:
+                yield Required
+                annotation_type, = get_args(annotation_type)
+            elif annotation_origin is NotRequired:
+                yield NotRequired
+                annotation_type, = get_args(annotation_type)
+            elif annotation_origin is ReadOnly:
+                yield ReadOnly
+                annotation_type, = get_args(annotation_type)
+            else:
+                break
+
+    class _TypedDictMeta(type):
+        def __new__(cls, name, bases, ns, *, total=True, closed=False):
+            """Create new typed dict class object.
+
+            This method is called when TypedDict is subclassed,
+            or when TypedDict is instantiated. This way
+            TypedDict supports all three syntax forms described in its docstring.
+            Subclasses and instances of TypedDict return actual dictionaries.
+            """
+            for base in bases:
+                if type(base) is not _TypedDictMeta and base is not typing.Generic:
+                    raise TypeError('cannot inherit from both a TypedDict type '
+                                    'and a non-TypedDict base class')
+
+            if any(issubclass(b, typing.Generic) for b in bases):
+                generic_base = (typing.Generic,)
+            else:
+                generic_base = ()
+
+            # typing.py generally doesn't let you inherit from plain Generic, unless
+            # the name of the class happens to be "Protocol"
+            tp_dict = type.__new__(_TypedDictMeta, "Protocol", (*generic_base, dict), ns)
+            tp_dict.__name__ = name
+            if tp_dict.__qualname__ == "Protocol":
+                tp_dict.__qualname__ = name
+
+            if not hasattr(tp_dict, '__orig_bases__'):
+                tp_dict.__orig_bases__ = bases
+
+            annotations = {}
+            if "__annotations__" in ns:
+                own_annotations = ns["__annotations__"]
+            elif "__annotate__" in ns:
+                # TODO: Use inspect.VALUE here, and make the annotations lazily evaluated
+                own_annotations = ns["__annotate__"](1)
+            else:
+                own_annotations = {}
+            msg = "TypedDict('Name', {f0: t0, f1: t1, ...}); each t must be a type"
+            if _TAKES_MODULE:
+                own_annotations = {
+                    n: typing._type_check(tp, msg, module=tp_dict.__module__)
+                    for n, tp in own_annotations.items()
+                }
+            else:
+                own_annotations = {
+                    n: typing._type_check(tp, msg)
+                    for n, tp in own_annotations.items()
+                }
+            required_keys = set()
+            optional_keys = set()
+            readonly_keys = set()
+            mutable_keys = set()
+            extra_items_type = None
+
+            for base in bases:
+                base_dict = base.__dict__
+
+                annotations.update(base_dict.get('__annotations__', {}))
+                required_keys.update(base_dict.get('__required_keys__', ()))
+                optional_keys.update(base_dict.get('__optional_keys__', ()))
+                readonly_keys.update(base_dict.get('__readonly_keys__', ()))
+                mutable_keys.update(base_dict.get('__mutable_keys__', ()))
+                base_extra_items_type = base_dict.get('__extra_items__', None)
+                if base_extra_items_type is not None:
+                    extra_items_type = base_extra_items_type
+
+            if closed and extra_items_type is None:
+                extra_items_type = Never
+            if closed and "__extra_items__" in own_annotations:
+                annotation_type = own_annotations.pop("__extra_items__")
+                qualifiers = set(_get_typeddict_qualifiers(annotation_type))
+                if Required in qualifiers:
+                    raise TypeError(
+                        "Special key __extra_items__ does not support "
+                        "Required"
+                    )
+                if NotRequired in qualifiers:
+                    raise TypeError(
+                        "Special key __extra_items__ does not support "
+                        "NotRequired"
+                    )
+                extra_items_type = annotation_type
+
+            annotations.update(own_annotations)
+            for annotation_key, annotation_type in own_annotations.items():
+                qualifiers = set(_get_typeddict_qualifiers(annotation_type))
+
+                if Required in qualifiers:
+                    required_keys.add(annotation_key)
+                elif NotRequired in qualifiers:
+                    optional_keys.add(annotation_key)
+                elif total:
+                    required_keys.add(annotation_key)
+                else:
+                    optional_keys.add(annotation_key)
+                if ReadOnly in qualifiers:
+                    mutable_keys.discard(annotation_key)
+                    readonly_keys.add(annotation_key)
+                else:
+                    mutable_keys.add(annotation_key)
+                    readonly_keys.discard(annotation_key)
+
+            tp_dict.__annotations__ = annotations
+            tp_dict.__required_keys__ = frozenset(required_keys)
+            tp_dict.__optional_keys__ = frozenset(optional_keys)
+            tp_dict.__readonly_keys__ = frozenset(readonly_keys)
+            tp_dict.__mutable_keys__ = frozenset(mutable_keys)
+            if not hasattr(tp_dict, '__total__'):
+                tp_dict.__total__ = total
+            tp_dict.__closed__ = closed
+            tp_dict.__extra_items__ = extra_items_type
+            return tp_dict
+
+        __call__ = dict  # static method
+
+        def __subclasscheck__(cls, other):
+            # Typed dicts are only for static structural subtyping.
+            raise TypeError('TypedDict does not support instance and class checks')
+
+        __instancecheck__ = __subclasscheck__
+
+    _TypedDict = type.__new__(_TypedDictMeta, 'TypedDict', (), {})
+
+    @_ensure_subclassable(lambda bases: (_TypedDict,))
+    def TypedDict(typename, fields=_marker, /, *, total=True, closed=False, **kwargs):
+        """A simple typed namespace. At runtime it is equivalent to a plain dict.
+
+        TypedDict creates a dictionary type such that a type checker will expect all
+        instances to have a certain set of keys, where each key is
+        associated with a value of a consistent type. This expectation
+        is not checked at runtime.
+
+        Usage::
+
+            class Point2D(TypedDict):
+                x: int
+                y: int
+                label: str
+
+            a: Point2D = {'x': 1, 'y': 2, 'label': 'good'}  # OK
+            b: Point2D = {'z': 3, 'label': 'bad'}           # Fails type check
+
+            assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')
+
+        The type info can be accessed via the Point2D.__annotations__ dict, and
+        the Point2D.__required_keys__ and Point2D.__optional_keys__ frozensets.
+        TypedDict supports an additional equivalent form::
+
+            Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
+
+        By default, all keys must be present in a TypedDict. It is possible
+        to override this by specifying totality::
+
+            class Point2D(TypedDict, total=False):
+                x: int
+                y: int
+
+        This means that a Point2D TypedDict can have any of the keys omitted. A type
+        checker is only expected to support a literal False or True as the value of
+        the total argument. True is the default, and makes all items defined in the
+        class body be required.
+
+        The Required and NotRequired special forms can also be used to mark
+        individual keys as being required or not required::
+
+            class Point2D(TypedDict):
+                x: int  # the "x" key must always be present (Required is the default)
+                y: NotRequired[int]  # the "y" key can be omitted
+
+        See PEP 655 for more details on Required and NotRequired.
+        """
+        if fields is _marker or fields is None:
+            if fields is _marker:
+                deprecated_thing = "Failing to pass a value for the 'fields' parameter"
+            else:
+                deprecated_thing = "Passing `None` as the 'fields' parameter"
+
+            example = f"`{typename} = TypedDict({typename!r}, {{}})`"
+            deprecation_msg = (
+                f"{deprecated_thing} is deprecated and will be disallowed in "
+                "Python 3.15. To create a TypedDict class with 0 fields "
+                "using the functional syntax, pass an empty dictionary, e.g. "
+            ) + example + "."
+            warnings.warn(deprecation_msg, DeprecationWarning, stacklevel=2)
+            if closed is not False and closed is not True:
+                kwargs["closed"] = closed
+                closed = False
+            fields = kwargs
+        elif kwargs:
+            raise TypeError("TypedDict takes either a dict or keyword arguments,"
+                            " but not both")
+        if kwargs:
+            if sys.version_info >= (3, 13):
+                raise TypeError("TypedDict takes no keyword arguments")
+            warnings.warn(
+                "The kwargs-based syntax for TypedDict definitions is deprecated "
+                "in Python 3.11, will be removed in Python 3.13, and may not be "
+                "understood by third-party type checkers.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
+        ns = {'__annotations__': dict(fields)}
+        module = _caller()
+        if module is not None:
+            # Setting correct module is necessary to make typed dict classes pickleable.
+            ns['__module__'] = module
+
+        td = _TypedDictMeta(typename, (), ns, total=total, closed=closed)
+        td.__orig_bases__ = (TypedDict,)
+        return td
+
+    if hasattr(typing, "_TypedDictMeta"):
+        _TYPEDDICT_TYPES = (typing._TypedDictMeta, _TypedDictMeta)
+    else:
+        _TYPEDDICT_TYPES = (_TypedDictMeta,)
+
+    def is_typeddict(tp):
+        """Check if an annotation is a TypedDict class
+
+        For example::
+            class Film(TypedDict):
+                title: str
+                year: int
+
+            is_typeddict(Film)  # => True
+            is_typeddict(Union[list, str])  # => False
+        """
+        # On 3.8, this would otherwise return True
+        if hasattr(typing, "TypedDict") and tp is typing.TypedDict:
+            return False
+        return isinstance(tp, _TYPEDDICT_TYPES)
+
+
+if hasattr(typing, "assert_type"):
+    assert_type = typing.assert_type
+
+else:
+    def assert_type(val, typ, /):
+        """Assert (to the type checker) that the value is of the given type.
+
+        When the type checker encounters a call to assert_type(), it
+        emits an error if the value is not of the specified type::
+
+            def greet(name: str) -> None:
+                assert_type(name, str)  # ok
+                assert_type(name, int)  # type checker error
+
+        At runtime this returns the first argument unchanged and otherwise
+        does nothing.
+        """
+        return val
+
+
+if hasattr(typing, "ReadOnly"):  # 3.13+
+    get_type_hints = typing.get_type_hints
+else:  # <=3.13
+    # replaces _strip_annotations()
+    def _strip_extras(t):
+        """Strips Annotated, Required and NotRequired from a given type."""
+        if isinstance(t, _AnnotatedAlias):
+            return _strip_extras(t.__origin__)
+        if hasattr(t, "__origin__") and t.__origin__ in (Required, NotRequired, ReadOnly):
+            return _strip_extras(t.__args__[0])
+        if isinstance(t, typing._GenericAlias):
+            stripped_args = tuple(_strip_extras(a) for a in t.__args__)
+            if stripped_args == t.__args__:
+                return t
+            return t.copy_with(stripped_args)
+        if hasattr(_types, "GenericAlias") and isinstance(t, _types.GenericAlias):
+            stripped_args = tuple(_strip_extras(a) for a in t.__args__)
+            if stripped_args == t.__args__:
+                return t
+            return _types.GenericAlias(t.__origin__, stripped_args)
+        if hasattr(_types, "UnionType") and isinstance(t, _types.UnionType):
+            stripped_args = tuple(_strip_extras(a) for a in t.__args__)
+            if stripped_args == t.__args__:
+                return t
+            return functools.reduce(operator.or_, stripped_args)
+
+        return t
+
+    def get_type_hints(obj, globalns=None, localns=None, include_extras=False):
+        """Return type hints for an object.
+
+        This is often the same as obj.__annotations__, but it handles
+        forward references encoded as string literals, adds Optional[t] if a
+        default value equal to None is set and recursively replaces all
+        'Annotated[T, ...]', 'Required[T]' or 'NotRequired[T]' with 'T'
+        (unless 'include_extras=True').
+
+        The argument may be a module, class, method, or function. The annotations
+        are returned as a dictionary. For classes, annotations include also
+        inherited members.
+
+        TypeError is raised if the argument is not of a type that can contain
+        annotations, and an empty dictionary is returned if no annotations are
+        present.
+
+        BEWARE -- the behavior of globalns and localns is counterintuitive
+        (unless you are familiar with how eval() and exec() work).  The
+        search order is locals first, then globals.
+
+        - If no dict arguments are passed, an attempt is made to use the
+          globals from obj (or the respective module's globals for classes),
+          and these are also used as the locals.  If the object does not appear
+          to have globals, an empty dictionary is used.
+
+        - If one dict argument is passed, it is used for both globals and
+          locals.
+
+        - If two dict arguments are passed, they specify globals and
+          locals, respectively.
+        """
+        if hasattr(typing, "Annotated"):  # 3.9+
+            hint = typing.get_type_hints(
+                obj, globalns=globalns, localns=localns, include_extras=True
+            )
+        else:  # 3.8
+            hint = typing.get_type_hints(obj, globalns=globalns, localns=localns)
+        if include_extras:
+            return hint
+        return {k: _strip_extras(t) for k, t in hint.items()}
+
+
+# Python 3.9+ has PEP 593 (Annotated)
+if hasattr(typing, 'Annotated'):
+    Annotated = typing.Annotated
+    # Not exported and not a public API, but needed for get_origin() and get_args()
+    # to work.
+    _AnnotatedAlias = typing._AnnotatedAlias
+# 3.8
+else:
+    class _AnnotatedAlias(typing._GenericAlias, _root=True):
+        """Runtime representation of an annotated type.
+
+        At its core 'Annotated[t, dec1, dec2, ...]' is an alias for the type 't'
+        with extra annotations. The alias behaves like a normal typing alias,
+        instantiating is the same as instantiating the underlying type, binding
+        it to types is also the same.
+        """
+        def __init__(self, origin, metadata):
+            if isinstance(origin, _AnnotatedAlias):
+                metadata = origin.__metadata__ + metadata
+                origin = origin.__origin__
+            super().__init__(origin, origin)
+            self.__metadata__ = metadata
+
+        def copy_with(self, params):
+            assert len(params) == 1
+            new_type = params[0]
+            return _AnnotatedAlias(new_type, self.__metadata__)
+
+        def __repr__(self):
+            return (f"typing_extensions.Annotated[{typing._type_repr(self.__origin__)}, "
+                    f"{', '.join(repr(a) for a in self.__metadata__)}]")
+
+        def __reduce__(self):
+            return operator.getitem, (
+                Annotated, (self.__origin__, *self.__metadata__)
+            )
+
+        def __eq__(self, other):
+            if not isinstance(other, _AnnotatedAlias):
+                return NotImplemented
+            if self.__origin__ != other.__origin__:
+                return False
+            return self.__metadata__ == other.__metadata__
+
+        def __hash__(self):
+            return hash((self.__origin__, self.__metadata__))
+
+    class Annotated:
+        """Add context specific metadata to a type.
+
+        Example: Annotated[int, runtime_check.Unsigned] indicates to the
+        hypothetical runtime_check module that this type is an unsigned int.
+        Every other consumer of this type can ignore this metadata and treat
+        this type as int.
+
+        The first argument to Annotated must be a valid type (and will be in
+        the __origin__ field), the remaining arguments are kept as a tuple in
+        the __extra__ field.
+
+        Details:
+
+        - It's an error to call `Annotated` with less than two arguments.
+        - Nested Annotated are flattened::
+
+            Annotated[Annotated[T, Ann1, Ann2], Ann3] == Annotated[T, Ann1, Ann2, Ann3]
+
+        - Instantiating an annotated type is equivalent to instantiating the
+        underlying type::
+
+            Annotated[C, Ann1](5) == C(5)
+
+        - Annotated can be used as a generic type alias::
+
+            Optimized = Annotated[T, runtime.Optimize()]
+            Optimized[int] == Annotated[int, runtime.Optimize()]
+
+            OptimizedList = Annotated[List[T], runtime.Optimize()]
+            OptimizedList[int] == Annotated[List[int], runtime.Optimize()]
+        """
+
+        __slots__ = ()
+
+        def __new__(cls, *args, **kwargs):
+            raise TypeError("Type Annotated cannot be instantiated.")
+
+        @typing._tp_cache
+        def __class_getitem__(cls, params):
+            if not isinstance(params, tuple) or len(params) < 2:
+                raise TypeError("Annotated[...] should be used "
+                                "with at least two arguments (a type and an "
+                                "annotation).")
+            allowed_special_forms = (ClassVar, Final)
+            if get_origin(params[0]) in allowed_special_forms:
+                origin = params[0]
+            else:
+                msg = "Annotated[t, ...]: t must be a type."
+                origin = typing._type_check(params[0], msg)
+            metadata = tuple(params[1:])
+            return _AnnotatedAlias(origin, metadata)
+
+        def __init_subclass__(cls, *args, **kwargs):
+            raise TypeError(
+                f"Cannot subclass {cls.__module__}.Annotated"
+            )
+
+# Python 3.8 has get_origin() and get_args() but those implementations aren't
+# Annotated-aware, so we can't use those. Python 3.9's versions don't support
+# ParamSpecArgs and ParamSpecKwargs, so only Python 3.10's versions will do.
+if sys.version_info[:2] >= (3, 10):
+    get_origin = typing.get_origin
+    get_args = typing.get_args
+# 3.8-3.9
+else:
+    try:
+        # 3.9+
+        from typing import _BaseGenericAlias
+    except ImportError:
+        _BaseGenericAlias = typing._GenericAlias
+    try:
+        # 3.9+
+        from typing import GenericAlias as _typing_GenericAlias
+    except ImportError:
+        _typing_GenericAlias = typing._GenericAlias
+
+    def get_origin(tp):
+        """Get the unsubscripted version of a type.
+
+        This supports generic types, Callable, Tuple, Union, Literal, Final, ClassVar
+        and Annotated. Return None for unsupported types. Examples::
+
+            get_origin(Literal[42]) is Literal
+            get_origin(int) is None
+            get_origin(ClassVar[int]) is ClassVar
+            get_origin(Generic) is Generic
+            get_origin(Generic[T]) is Generic
+            get_origin(Union[T, int]) is Union
+            get_origin(List[Tuple[T, T]][int]) == list
+            get_origin(P.args) is P
+        """
+        if isinstance(tp, _AnnotatedAlias):
+            return Annotated
+        if isinstance(tp, (typing._GenericAlias, _typing_GenericAlias, _BaseGenericAlias,
+                           ParamSpecArgs, ParamSpecKwargs)):
+            return tp.__origin__
+        if tp is typing.Generic:
+            return typing.Generic
+        return None
+
+    def get_args(tp):
+        """Get type arguments with all substitutions performed.
+
+        For unions, basic simplifications used by Union constructor are performed.
+        Examples::
+            get_args(Dict[str, int]) == (str, int)
+            get_args(int) == ()
+            get_args(Union[int, Union[T, int], str][int]) == (int, str)
+            get_args(Union[int, Tuple[T, int]][str]) == (int, Tuple[str, int])
+            get_args(Callable[[], T][int]) == ([], int)
+        """
+        if isinstance(tp, _AnnotatedAlias):
+            return (tp.__origin__, *tp.__metadata__)
+        if isinstance(tp, (typing._GenericAlias, _typing_GenericAlias)):
+            if getattr(tp, "_special", False):
+                return ()
+            res = tp.__args__
+            if get_origin(tp) is collections.abc.Callable and res[0] is not Ellipsis:
+                res = (list(res[:-1]), res[-1])
+            return res
+        return ()
+
+
+# 3.10+
+if hasattr(typing, 'TypeAlias'):
+    TypeAlias = typing.TypeAlias
+# 3.9
+elif sys.version_info[:2] >= (3, 9):
+    @_ExtensionsSpecialForm
+    def TypeAlias(self, parameters):
+        """Special marker indicating that an assignment should
+        be recognized as a proper type alias definition by type
+        checkers.
+
+        For example::
+
+            Predicate: TypeAlias = Callable[..., bool]
+
+        It's invalid when used anywhere except as in the example above.
+        """
+        raise TypeError(f"{self} is not subscriptable")
+# 3.8
+else:
+    TypeAlias = _ExtensionsSpecialForm(
+        'TypeAlias',
+        doc="""Special marker indicating that an assignment should
+        be recognized as a proper type alias definition by type
+        checkers.
+
+        For example::
+
+            Predicate: TypeAlias = Callable[..., bool]
+
+        It's invalid when used anywhere except as in the example
+        above."""
+    )
+
+
+if hasattr(typing, "NoDefault"):
+    NoDefault = typing.NoDefault
+else:
+    class NoDefaultTypeMeta(type):
+        def __setattr__(cls, attr, value):
+            # TypeError is consistent with the behavior of NoneType
+            raise TypeError(
+                f"cannot set {attr!r} attribute of immutable type {cls.__name__!r}"
+            )
+
+    class NoDefaultType(metaclass=NoDefaultTypeMeta):
+        """The type of the NoDefault singleton."""
+
+        __slots__ = ()
+
+        def __new__(cls):
+            return globals().get("NoDefault") or object.__new__(cls)
+
+        def __repr__(self):
+            return "typing_extensions.NoDefault"
+
+        def __reduce__(self):
+            return "NoDefault"
+
+    NoDefault = NoDefaultType()
+    del NoDefaultType, NoDefaultTypeMeta
+
+
+def _set_default(type_param, default):
+    type_param.has_default = lambda: default is not NoDefault
+    type_param.__default__ = default
+
+
+def _set_module(typevarlike):
+    # for pickling:
+    def_mod = _caller(depth=3)
+    if def_mod != 'typing_extensions':
+        typevarlike.__module__ = def_mod
+
+
+class _DefaultMixin:
+    """Mixin for TypeVarLike defaults."""
+
+    __slots__ = ()
+    __init__ = _set_default
+
+
+# Classes using this metaclass must provide a _backported_typevarlike ClassVar
+class _TypeVarLikeMeta(type):
+    def __instancecheck__(cls, __instance: Any) -> bool:
+        return isinstance(__instance, cls._backported_typevarlike)
+
+
+if _PEP_696_IMPLEMENTED:
+    from typing import TypeVar
+else:
+    # Add default and infer_variance parameters from PEP 696 and 695
+    class TypeVar(metaclass=_TypeVarLikeMeta):
+        """Type variable."""
+
+        _backported_typevarlike = typing.TypeVar
+
+        def __new__(cls, name, *constraints, bound=None,
+                    covariant=False, contravariant=False,
+                    default=NoDefault, infer_variance=False):
+            if hasattr(typing, "TypeAliasType"):
+                # PEP 695 implemented (3.12+), can pass infer_variance to typing.TypeVar
+                typevar = typing.TypeVar(name, *constraints, bound=bound,
+                                         covariant=covariant, contravariant=contravariant,
+                                         infer_variance=infer_variance)
+            else:
+                typevar = typing.TypeVar(name, *constraints, bound=bound,
+                                         covariant=covariant, contravariant=contravariant)
+                if infer_variance and (covariant or contravariant):
+                    raise ValueError("Variance cannot be specified with infer_variance.")
+                typevar.__infer_variance__ = infer_variance
+
+            _set_default(typevar, default)
+            _set_module(typevar)
+
+            def _tvar_prepare_subst(alias, args):
+                if (
+                    typevar.has_default()
+                    and alias.__parameters__.index(typevar) == len(args)
+                ):
+                    args += (typevar.__default__,)
+                return args
+
+            typevar.__typing_prepare_subst__ = _tvar_prepare_subst
+            return typevar
+
+        def __init_subclass__(cls) -> None:
+            raise TypeError(f"type '{__name__}.TypeVar' is not an acceptable base type")
+
+
+# Python 3.10+ has PEP 612
+if hasattr(typing, 'ParamSpecArgs'):
+    ParamSpecArgs = typing.ParamSpecArgs
+    ParamSpecKwargs = typing.ParamSpecKwargs
+# 3.8-3.9
+else:
+    class _Immutable:
+        """Mixin to indicate that object should not be copied."""
+        __slots__ = ()
+
+        def __copy__(self):
+            return self
+
+        def __deepcopy__(self, memo):
+            return self
+
+    class ParamSpecArgs(_Immutable):
+        """The args for a ParamSpec object.
+
+        Given a ParamSpec object P, P.args is an instance of ParamSpecArgs.
+
+        ParamSpecArgs objects have a reference back to their ParamSpec:
+
+        P.args.__origin__ is P
+
+        This type is meant for runtime introspection and has no special meaning to
+        static type checkers.
+        """
+        def __init__(self, origin):
+            self.__origin__ = origin
+
+        def __repr__(self):
+            return f"{self.__origin__.__name__}.args"
+
+        def __eq__(self, other):
+            if not isinstance(other, ParamSpecArgs):
+                return NotImplemented
+            return self.__origin__ == other.__origin__
+
+    class ParamSpecKwargs(_Immutable):
+        """The kwargs for a ParamSpec object.
+
+        Given a ParamSpec object P, P.kwargs is an instance of ParamSpecKwargs.
+
+        ParamSpecKwargs objects have a reference back to their ParamSpec:
+
+        P.kwargs.__origin__ is P
+
+        This type is meant for runtime introspection and has no special meaning to
+        static type checkers.
+        """
+        def __init__(self, origin):
+            self.__origin__ = origin
+
+        def __repr__(self):
+            return f"{self.__origin__.__name__}.kwargs"
+
+        def __eq__(self, other):
+            if not isinstance(other, ParamSpecKwargs):
+                return NotImplemented
+            return self.__origin__ == other.__origin__
+
+
+if _PEP_696_IMPLEMENTED:
+    from typing import ParamSpec
+
+# 3.10+
+elif hasattr(typing, 'ParamSpec'):
+
+    # Add default parameter - PEP 696
+    class ParamSpec(metaclass=_TypeVarLikeMeta):
+        """Parameter specification."""
+
+        _backported_typevarlike = typing.ParamSpec
+
+        def __new__(cls, name, *, bound=None,
+                    covariant=False, contravariant=False,
+                    infer_variance=False, default=NoDefault):
+            if hasattr(typing, "TypeAliasType"):
+                # PEP 695 implemented, can pass infer_variance to typing.TypeVar
+                paramspec = typing.ParamSpec(name, bound=bound,
+                                             covariant=covariant,
+                                             contravariant=contravariant,
+                                             infer_variance=infer_variance)
+            else:
+                paramspec = typing.ParamSpec(name, bound=bound,
+                                             covariant=covariant,
+                                             contravariant=contravariant)
+                paramspec.__infer_variance__ = infer_variance
+
+            _set_default(paramspec, default)
+            _set_module(paramspec)
+
+            def _paramspec_prepare_subst(alias, args):
+                params = alias.__parameters__
+                i = params.index(paramspec)
+                if i == len(args) and paramspec.has_default():
+                    args = [*args, paramspec.__default__]
+                if i >= len(args):
+                    raise TypeError(f"Too few arguments for {alias}")
+                # Special case where Z[[int, str, bool]] == Z[int, str, bool] in PEP 612.
+                if len(params) == 1 and not typing._is_param_expr(args[0]):
+                    assert i == 0
+                    args = (args,)
+                # Convert lists to tuples to help other libraries cache the results.
+                elif isinstance(args[i], list):
+                    args = (*args[:i], tuple(args[i]), *args[i + 1:])
+                return args
+
+            paramspec.__typing_prepare_subst__ = _paramspec_prepare_subst
+            return paramspec
+
+        def __init_subclass__(cls) -> None:
+            raise TypeError(f"type '{__name__}.ParamSpec' is not an acceptable base type")
+
+# 3.8-3.9
+else:
+
+    # Inherits from list as a workaround for Callable checks in Python < 3.9.2.
+    class ParamSpec(list, _DefaultMixin):
+        """Parameter specification variable.
+
+        Usage::
+
+           P = ParamSpec('P')
+
+        Parameter specification variables exist primarily for the benefit of static
+        type checkers.  They are used to forward the parameter types of one
+        callable to another callable, a pattern commonly found in higher order
+        functions and decorators.  They are only valid when used in ``Concatenate``,
+        or s the first argument to ``Callable``. In Python 3.10 and higher,
+        they are also supported in user-defined Generics at runtime.
+        See class Generic for more information on generic types.  An
+        example for annotating a decorator::
+
+           T = TypeVar('T')
+           P = ParamSpec('P')
+
+           def add_logging(f: Callable[P, T]) -> Callable[P, T]:
+               '''A type-safe decorator to add logging to a function.'''
+               def inner(*args: P.args, **kwargs: P.kwargs) -> T:
+                   logging.info(f'{f.__name__} was called')
+                   return f(*args, **kwargs)
+               return inner
+
+           @add_logging
+           def add_two(x: float, y: float) -> float:
+               '''Add two numbers together.'''
+               return x + y
+
+        Parameter specification variables defined with covariant=True or
+        contravariant=True can be used to declare covariant or contravariant
+        generic types.  These keyword arguments are valid, but their actual semantics
+        are yet to be decided.  See PEP 612 for details.
+
+        Parameter specification variables can be introspected. e.g.:
+
+           P.__name__ == 'T'
+           P.__bound__ == None
+           P.__covariant__ == False
+           P.__contravariant__ == False
+
+        Note that only parameter specification variables defined in global scope can
+        be pickled.
+        """
+
+        # Trick Generic __parameters__.
+        __class__ = typing.TypeVar
+
+        @property
+        def args(self):
+            return ParamSpecArgs(self)
+
+        @property
+        def kwargs(self):
+            return ParamSpecKwargs(self)
+
+        def __init__(self, name, *, bound=None, covariant=False, contravariant=False,
+                     infer_variance=False, default=NoDefault):
+            list.__init__(self, [self])
+            self.__name__ = name
+            self.__covariant__ = bool(covariant)
+            self.__contravariant__ = bool(contravariant)
+            self.__infer_variance__ = bool(infer_variance)
+            if bound:
+                self.__bound__ = typing._type_check(bound, 'Bound must be a type.')
+            else:
+                self.__bound__ = None
+            _DefaultMixin.__init__(self, default)
+
+            # for pickling:
+            def_mod = _caller()
+            if def_mod != 'typing_extensions':
+                self.__module__ = def_mod
+
+        def __repr__(self):
+            if self.__infer_variance__:
+                prefix = ''
+            elif self.__covariant__:
+                prefix = '+'
+            elif self.__contravariant__:
+                prefix = '-'
+            else:
+                prefix = '~'
+            return prefix + self.__name__
+
+        def __hash__(self):
+            return object.__hash__(self)
+
+        def __eq__(self, other):
+            return self is other
+
+        def __reduce__(self):
+            return self.__name__
+
+        # Hack to get typing._type_check to pass.
+        def __call__(self, *args, **kwargs):
+            pass
+
+
+# 3.8-3.9
+if not hasattr(typing, 'Concatenate'):
+    # Inherits from list as a workaround for Callable checks in Python < 3.9.2.
+    class _ConcatenateGenericAlias(list):
+
+        # Trick Generic into looking into this for __parameters__.
+        __class__ = typing._GenericAlias
+
+        # Flag in 3.8.
+        _special = False
+
+        def __init__(self, origin, args):
+            super().__init__(args)
+            self.__origin__ = origin
+            self.__args__ = args
+
+        def __repr__(self):
+            _type_repr = typing._type_repr
+            return (f'{_type_repr(self.__origin__)}'
+                    f'[{", ".join(_type_repr(arg) for arg in self.__args__)}]')
+
+        def __hash__(self):
+            return hash((self.__origin__, self.__args__))
+
+        # Hack to get typing._type_check to pass in Generic.
+        def __call__(self, *args, **kwargs):
+            pass
+
+        @property
+        def __parameters__(self):
+            return tuple(
+                tp for tp in self.__args__ if isinstance(tp, (typing.TypeVar, ParamSpec))
+            )
+
+
+# 3.8-3.9
+@typing._tp_cache
+def _concatenate_getitem(self, parameters):
+    if parameters == ():
+        raise TypeError("Cannot take a Concatenate of no types.")
+    if not isinstance(parameters, tuple):
+        parameters = (parameters,)
+    if not isinstance(parameters[-1], ParamSpec):
+        raise TypeError("The last parameter to Concatenate should be a "
+                        "ParamSpec variable.")
+    msg = "Concatenate[arg, ...]: each arg must be a type."
+    parameters = tuple(typing._type_check(p, msg) for p in parameters)
+    return _ConcatenateGenericAlias(self, parameters)
+
+
+# 3.10+
+if hasattr(typing, 'Concatenate'):
+    Concatenate = typing.Concatenate
+    _ConcatenateGenericAlias = typing._ConcatenateGenericAlias
+# 3.9
+elif sys.version_info[:2] >= (3, 9):
+    @_ExtensionsSpecialForm
+    def Concatenate(self, parameters):
+        """Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a
+        higher order function which adds, removes or transforms parameters of a
+        callable.
+
+        For example::
+
+           Callable[Concatenate[int, P], int]
+
+        See PEP 612 for detailed information.
+        """
+        return _concatenate_getitem(self, parameters)
+# 3.8
+else:
+    class _ConcatenateForm(_ExtensionsSpecialForm, _root=True):
+        def __getitem__(self, parameters):
+            return _concatenate_getitem(self, parameters)
+
+    Concatenate = _ConcatenateForm(
+        'Concatenate',
+        doc="""Used in conjunction with ``ParamSpec`` and ``Callable`` to represent a
+        higher order function which adds, removes or transforms parameters of a
+        callable.
+
+        For example::
+
+           Callable[Concatenate[int, P], int]
+
+        See PEP 612 for detailed information.
+        """)
+
+# 3.10+
+if hasattr(typing, 'TypeGuard'):
+    TypeGuard = typing.TypeGuard
+# 3.9
+elif sys.version_info[:2] >= (3, 9):
+    @_ExtensionsSpecialForm
+    def TypeGuard(self, parameters):
+        """Special typing form used to annotate the return type of a user-defined
+        type guard function.  ``TypeGuard`` only accepts a single type argument.
+        At runtime, functions marked this way should return a boolean.
+
+        ``TypeGuard`` aims to benefit *type narrowing* -- a technique used by static
+        type checkers to determine a more precise type of an expression within a
+        program's code flow.  Usually type narrowing is done by analyzing
+        conditional code flow and applying the narrowing to a block of code.  The
+        conditional expression here is sometimes referred to as a "type guard".
+
+        Sometimes it would be convenient to use a user-defined boolean function
+        as a type guard.  Such a function should use ``TypeGuard[...]`` as its
+        return type to alert static type checkers to this intention.
+
+        Using  ``-> TypeGuard`` tells the static type checker that for a given
+        function:
+
+        1. The return value is a boolean.
+        2. If the return value is ``True``, the type of its argument
+        is the type inside ``TypeGuard``.
+
+        For example::
+
+            def is_str(val: Union[str, float]):
+                # "isinstance" type guard
+                if isinstance(val, str):
+                    # Type of ``val`` is narrowed to ``str``
+                    ...
+                else:
+                    # Else, type of ``val`` is narrowed to ``float``.
+                    ...
+
+        Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower
+        form of ``TypeA`` (it can even be a wider form) and this may lead to
+        type-unsafe results.  The main reason is to allow for things like
+        narrowing ``List[object]`` to ``List[str]`` even though the latter is not
+        a subtype of the former, since ``List`` is invariant.  The responsibility of
+        writing type-safe type guards is left to the user.
+
+        ``TypeGuard`` also works with type variables.  For more information, see
+        PEP 647 (User-Defined Type Guards).
+        """
+        item = typing._type_check(parameters, f'{self} accepts only a single type.')
+        return typing._GenericAlias(self, (item,))
+# 3.8
+else:
+    class _TypeGuardForm(_ExtensionsSpecialForm, _root=True):
+        def __getitem__(self, parameters):
+            item = typing._type_check(parameters,
+                                      f'{self._name} accepts only a single type')
+            return typing._GenericAlias(self, (item,))
+
+    TypeGuard = _TypeGuardForm(
+        'TypeGuard',
+        doc="""Special typing form used to annotate the return type of a user-defined
+        type guard function.  ``TypeGuard`` only accepts a single type argument.
+        At runtime, functions marked this way should return a boolean.
+
+        ``TypeGuard`` aims to benefit *type narrowing* -- a technique used by static
+        type checkers to determine a more precise type of an expression within a
+        program's code flow.  Usually type narrowing is done by analyzing
+        conditional code flow and applying the narrowing to a block of code.  The
+        conditional expression here is sometimes referred to as a "type guard".
+
+        Sometimes it would be convenient to use a user-defined boolean function
+        as a type guard.  Such a function should use ``TypeGuard[...]`` as its
+        return type to alert static type checkers to this intention.
+
+        Using  ``-> TypeGuard`` tells the static type checker that for a given
+        function:
+
+        1. The return value is a boolean.
+        2. If the return value is ``True``, the type of its argument
+        is the type inside ``TypeGuard``.
+
+        For example::
+
+            def is_str(val: Union[str, float]):
+                # "isinstance" type guard
+                if isinstance(val, str):
+                    # Type of ``val`` is narrowed to ``str``
+                    ...
+                else:
+                    # Else, type of ``val`` is narrowed to ``float``.
+                    ...
+
+        Strict type narrowing is not enforced -- ``TypeB`` need not be a narrower
+        form of ``TypeA`` (it can even be a wider form) and this may lead to
+        type-unsafe results.  The main reason is to allow for things like
+        narrowing ``List[object]`` to ``List[str]`` even though the latter is not
+        a subtype of the former, since ``List`` is invariant.  The responsibility of
+        writing type-safe type guards is left to the user.
+
+        ``TypeGuard`` also works with type variables.  For more information, see
+        PEP 647 (User-Defined Type Guards).
+        """)
+
+# 3.13+
+if hasattr(typing, 'TypeIs'):
+    TypeIs = typing.TypeIs
+# 3.9
+elif sys.version_info[:2] >= (3, 9):
+    @_ExtensionsSpecialForm
+    def TypeIs(self, parameters):
+        """Special typing form used to annotate the return type of a user-defined
+        type narrower function.  ``TypeIs`` only accepts a single type argument.
+        At runtime, functions marked this way should return a boolean.
+
+        ``TypeIs`` aims to benefit *type narrowing* -- a technique used by static
+        type checkers to determine a more precise type of an expression within a
+        program's code flow.  Usually type narrowing is done by analyzing
+        conditional code flow and applying the narrowing to a block of code.  The
+        conditional expression here is sometimes referred to as a "type guard".
+
+        Sometimes it would be convenient to use a user-defined boolean function
+        as a type guard.  Such a function should use ``TypeIs[...]`` as its
+        return type to alert static type checkers to this intention.
+
+        Using  ``-> TypeIs`` tells the static type checker that for a given
+        function:
+
+        1. The return value is a boolean.
+        2. If the return value is ``True``, the type of its argument
+        is the intersection of the type inside ``TypeGuard`` and the argument's
+        previously known type.
+
+        For example::
+
+            def is_awaitable(val: object) -> TypeIs[Awaitable[Any]]:
+                return hasattr(val, '__await__')
+
+            def f(val: Union[int, Awaitable[int]]) -> int:
+                if is_awaitable(val):
+                    assert_type(val, Awaitable[int])
+                else:
+                    assert_type(val, int)
+
+        ``TypeIs`` also works with type variables.  For more information, see
+        PEP 742 (Narrowing types with TypeIs).
+        """
+        item = typing._type_check(parameters, f'{self} accepts only a single type.')
+        return typing._GenericAlias(self, (item,))
+# 3.8
+else:
+    class _TypeIsForm(_ExtensionsSpecialForm, _root=True):
+        def __getitem__(self, parameters):
+            item = typing._type_check(parameters,
+                                      f'{self._name} accepts only a single type')
+            return typing._GenericAlias(self, (item,))
+
+    TypeIs = _TypeIsForm(
+        'TypeIs',
+        doc="""Special typing form used to annotate the return type of a user-defined
+        type narrower function.  ``TypeIs`` only accepts a single type argument.
+        At runtime, functions marked this way should return a boolean.
+
+        ``TypeIs`` aims to benefit *type narrowing* -- a technique used by static
+        type checkers to determine a more precise type of an expression within a
+        program's code flow.  Usually type narrowing is done by analyzing
+        conditional code flow and applying the narrowing to a block of code.  The
+        conditional expression here is sometimes referred to as a "type guard".
+
+        Sometimes it would be convenient to use a user-defined boolean function
+        as a type guard.  Such a function should use ``TypeIs[...]`` as its
+        return type to alert static type checkers to this intention.
+
+        Using  ``-> TypeIs`` tells the static type checker that for a given
+        function:
+
+        1. The return value is a boolean.
+        2. If the return value is ``True``, the type of its argument
+        is the intersection of the type inside ``TypeGuard`` and the argument's
+        previously known type.
+
+        For example::
+
+            def is_awaitable(val: object) -> TypeIs[Awaitable[Any]]:
+                return hasattr(val, '__await__')
+
+            def f(val: Union[int, Awaitable[int]]) -> int:
+                if is_awaitable(val):
+                    assert_type(val, Awaitable[int])
+                else:
+                    assert_type(val, int)
+
+        ``TypeIs`` also works with type variables.  For more information, see
+        PEP 742 (Narrowing types with TypeIs).
+        """)
+
+
+# Vendored from cpython typing._SpecialFrom
+class _SpecialForm(typing._Final, _root=True):
+    __slots__ = ('_name', '__doc__', '_getitem')
+
+    def __init__(self, getitem):
+        self._getitem = getitem
+        self._name = getitem.__name__
+        self.__doc__ = getitem.__doc__
+
+    def __getattr__(self, item):
+        if item in {'__name__', '__qualname__'}:
+            return self._name
+
+        raise AttributeError(item)
+
+    def __mro_entries__(self, bases):
+        raise TypeError(f"Cannot subclass {self!r}")
+
+    def __repr__(self):
+        return f'typing_extensions.{self._name}'
+
+    def __reduce__(self):
+        return self._name
+
+    def __call__(self, *args, **kwds):
+        raise TypeError(f"Cannot instantiate {self!r}")
+
+    def __or__(self, other):
+        return typing.Union[self, other]
+
+    def __ror__(self, other):
+        return typing.Union[other, self]
+
+    def __instancecheck__(self, obj):
+        raise TypeError(f"{self} cannot be used with isinstance()")
+
+    def __subclasscheck__(self, cls):
+        raise TypeError(f"{self} cannot be used with issubclass()")
+
+    @typing._tp_cache
+    def __getitem__(self, parameters):
+        return self._getitem(self, parameters)
+
+
+if hasattr(typing, "LiteralString"):  # 3.11+
+    LiteralString = typing.LiteralString
+else:
+    @_SpecialForm
+    def LiteralString(self, params):
+        """Represents an arbitrary literal string.
+
+        Example::
+
+          from typing_extensions import LiteralString
+
+          def query(sql: LiteralString) -> ...:
+              ...
+
+          query("SELECT * FROM table")  # ok
+          query(f"SELECT * FROM {input()}")  # not ok
+
+        See PEP 675 for details.
+
+        """
+        raise TypeError(f"{self} is not subscriptable")
+
+
+if hasattr(typing, "Self"):  # 3.11+
+    Self = typing.Self
+else:
+    @_SpecialForm
+    def Self(self, params):
+        """Used to spell the type of "self" in classes.
+
+        Example::
+
+          from typing import Self
+
+          class ReturnsSelf:
+              def parse(self, data: bytes) -> Self:
+                  ...
+                  return self
+
+        """
+
+        raise TypeError(f"{self} is not subscriptable")
+
+
+if hasattr(typing, "Never"):  # 3.11+
+    Never = typing.Never
+else:
+    @_SpecialForm
+    def Never(self, params):
+        """The bottom type, a type that has no members.
+
+        This can be used to define a function that should never be
+        called, or a function that never returns::
+
+            from typing_extensions import Never
+
+            def never_call_me(arg: Never) -> None:
+                pass
+
+            def int_or_str(arg: int | str) -> None:
+                never_call_me(arg)  # type checker error
+                match arg:
+                    case int():
+                        print("It's an int")
+                    case str():
+                        print("It's a str")
+                    case _:
+                        never_call_me(arg)  # ok, arg is of type Never
+
+        """
+
+        raise TypeError(f"{self} is not subscriptable")
+
+
+if hasattr(typing, 'Required'):  # 3.11+
+    Required = typing.Required
+    NotRequired = typing.NotRequired
+elif sys.version_info[:2] >= (3, 9):  # 3.9-3.10
+    @_ExtensionsSpecialForm
+    def Required(self, parameters):
+        """A special typing construct to mark a key of a total=False TypedDict
+        as required. For example:
+
+            class Movie(TypedDict, total=False):
+                title: Required[str]
+                year: int
+
+            m = Movie(
+                title='The Matrix',  # typechecker error if key is omitted
+                year=1999,
+            )
+
+        There is no runtime checking that a required key is actually provided
+        when instantiating a related TypedDict.
+        """
+        item = typing._type_check(parameters, f'{self._name} accepts only a single type.')
+        return typing._GenericAlias(self, (item,))
+
+    @_ExtensionsSpecialForm
+    def NotRequired(self, parameters):
+        """A special typing construct to mark a key of a TypedDict as
+        potentially missing. For example:
+
+            class Movie(TypedDict):
+                title: str
+                year: NotRequired[int]
+
+            m = Movie(
+                title='The Matrix',  # typechecker error if key is omitted
+                year=1999,
+            )
+        """
+        item = typing._type_check(parameters, f'{self._name} accepts only a single type.')
+        return typing._GenericAlias(self, (item,))
+
+else:  # 3.8
+    class _RequiredForm(_ExtensionsSpecialForm, _root=True):
+        def __getitem__(self, parameters):
+            item = typing._type_check(parameters,
+                                      f'{self._name} accepts only a single type.')
+            return typing._GenericAlias(self, (item,))
+
+    Required = _RequiredForm(
+        'Required',
+        doc="""A special typing construct to mark a key of a total=False TypedDict
+        as required. For example:
+
+            class Movie(TypedDict, total=False):
+                title: Required[str]
+                year: int
+
+            m = Movie(
+                title='The Matrix',  # typechecker error if key is omitted
+                year=1999,
+            )
+
+        There is no runtime checking that a required key is actually provided
+        when instantiating a related TypedDict.
+        """)
+    NotRequired = _RequiredForm(
+        'NotRequired',
+        doc="""A special typing construct to mark a key of a TypedDict as
+        potentially missing. For example:
+
+            class Movie(TypedDict):
+                title: str
+                year: NotRequired[int]
+
+            m = Movie(
+                title='The Matrix',  # typechecker error if key is omitted
+                year=1999,
+            )
+        """)
+
+
+if hasattr(typing, 'ReadOnly'):
+    ReadOnly = typing.ReadOnly
+elif sys.version_info[:2] >= (3, 9):  # 3.9-3.12
+    @_ExtensionsSpecialForm
+    def ReadOnly(self, parameters):
+        """A special typing construct to mark an item of a TypedDict as read-only.
+
+        For example:
+
+            class Movie(TypedDict):
+                title: ReadOnly[str]
+                year: int
+
+            def mutate_movie(m: Movie) -> None:
+                m["year"] = 1992  # allowed
+                m["title"] = "The Matrix"  # typechecker error
+
+        There is no runtime checking for this property.
+        """
+        item = typing._type_check(parameters, f'{self._name} accepts only a single type.')
+        return typing._GenericAlias(self, (item,))
+
+else:  # 3.8
+    class _ReadOnlyForm(_ExtensionsSpecialForm, _root=True):
+        def __getitem__(self, parameters):
+            item = typing._type_check(parameters,
+                                      f'{self._name} accepts only a single type.')
+            return typing._GenericAlias(self, (item,))
+
+    ReadOnly = _ReadOnlyForm(
+        'ReadOnly',
+        doc="""A special typing construct to mark a key of a TypedDict as read-only.
+
+        For example:
+
+            class Movie(TypedDict):
+                title: ReadOnly[str]
+                year: int
+
+            def mutate_movie(m: Movie) -> None:
+                m["year"] = 1992  # allowed
+                m["title"] = "The Matrix"  # typechecker error
+
+        There is no runtime checking for this propery.
+        """)
+
+
+_UNPACK_DOC = """\
+Type unpack operator.
+
+The type unpack operator takes the child types from some container type,
+such as `tuple[int, str]` or a `TypeVarTuple`, and 'pulls them out'. For
+example:
+
+  # For some generic class `Foo`:
+  Foo[Unpack[tuple[int, str]]]  # Equivalent to Foo[int, str]
+
+  Ts = TypeVarTuple('Ts')
+  # Specifies that `Bar` is generic in an arbitrary number of types.
+  # (Think of `Ts` as a tuple of an arbitrary number of individual
+  #  `TypeVar`s, which the `Unpack` is 'pulling out' directly into the
+  #  `Generic[]`.)
+  class Bar(Generic[Unpack[Ts]]): ...
+  Bar[int]  # Valid
+  Bar[int, str]  # Also valid
+
+From Python 3.11, this can also be done using the `*` operator:
+
+    Foo[*tuple[int, str]]
+    class Bar(Generic[*Ts]): ...
+
+The operator can also be used along with a `TypedDict` to annotate
+`**kwargs` in a function signature. For instance:
+
+  class Movie(TypedDict):
+    name: str
+    year: int
+
+  # This function expects two keyword arguments - *name* of type `str` and
+  # *year* of type `int`.
+  def foo(**kwargs: Unpack[Movie]): ...
+
+Note that there is only some runtime checking of this operator. Not
+everything the runtime allows may be accepted by static type checkers.
+
+For more information, see PEP 646 and PEP 692.
+"""
+
+
+if sys.version_info >= (3, 12):  # PEP 692 changed the repr of Unpack[]
+    Unpack = typing.Unpack
+
+    def _is_unpack(obj):
+        return get_origin(obj) is Unpack
+
+elif sys.version_info[:2] >= (3, 9):  # 3.9+
+    class _UnpackSpecialForm(_ExtensionsSpecialForm, _root=True):
+        def __init__(self, getitem):
+            super().__init__(getitem)
+            self.__doc__ = _UNPACK_DOC
+
+    class _UnpackAlias(typing._GenericAlias, _root=True):
+        __class__ = typing.TypeVar
+
+        @property
+        def __typing_unpacked_tuple_args__(self):
+            assert self.__origin__ is Unpack
+            assert len(self.__args__) == 1
+            arg, = self.__args__
+            if isinstance(arg, (typing._GenericAlias, _types.GenericAlias)):
+                if arg.__origin__ is not tuple:
+                    raise TypeError("Unpack[...] must be used with a tuple type")
+                return arg.__args__
+            return None
+
+    @_UnpackSpecialForm
+    def Unpack(self, parameters):
+        item = typing._type_check(parameters, f'{self._name} accepts only a single type.')
+        return _UnpackAlias(self, (item,))
+
+    def _is_unpack(obj):
+        return isinstance(obj, _UnpackAlias)
+
+else:  # 3.8
+    class _UnpackAlias(typing._GenericAlias, _root=True):
+        __class__ = typing.TypeVar
+
+    class _UnpackForm(_ExtensionsSpecialForm, _root=True):
+        def __getitem__(self, parameters):
+            item = typing._type_check(parameters,
+                                      f'{self._name} accepts only a single type.')
+            return _UnpackAlias(self, (item,))
+
+    Unpack = _UnpackForm('Unpack', doc=_UNPACK_DOC)
+
+    def _is_unpack(obj):
+        return isinstance(obj, _UnpackAlias)
+
+
+if _PEP_696_IMPLEMENTED:
+    from typing import TypeVarTuple
+
+elif hasattr(typing, "TypeVarTuple"):  # 3.11+
+
+    def _unpack_args(*args):
+        newargs = []
+        for arg in args:
+            subargs = getattr(arg, '__typing_unpacked_tuple_args__', None)
+            if subargs is not None and not (subargs and subargs[-1] is ...):
+                newargs.extend(subargs)
+            else:
+                newargs.append(arg)
+        return newargs
+
+    # Add default parameter - PEP 696
+    class TypeVarTuple(metaclass=_TypeVarLikeMeta):
+        """Type variable tuple."""
+
+        _backported_typevarlike = typing.TypeVarTuple
+
+        def __new__(cls, name, *, default=NoDefault):
+            tvt = typing.TypeVarTuple(name)
+            _set_default(tvt, default)
+            _set_module(tvt)
+
+            def _typevartuple_prepare_subst(alias, args):
+                params = alias.__parameters__
+                typevartuple_index = params.index(tvt)
+                for param in params[typevartuple_index + 1:]:
+                    if isinstance(param, TypeVarTuple):
+                        raise TypeError(
+                            f"More than one TypeVarTuple parameter in {alias}"
+                        )
+
+                alen = len(args)
+                plen = len(params)
+                left = typevartuple_index
+                right = plen - typevartuple_index - 1
+                var_tuple_index = None
+                fillarg = None
+                for k, arg in enumerate(args):
+                    if not isinstance(arg, type):
+                        subargs = getattr(arg, '__typing_unpacked_tuple_args__', None)
+                        if subargs and len(subargs) == 2 and subargs[-1] is ...:
+                            if var_tuple_index is not None:
+                                raise TypeError(
+                                    "More than one unpacked "
+                                    "arbitrary-length tuple argument"
+                                )
+                            var_tuple_index = k
+                            fillarg = subargs[0]
+                if var_tuple_index is not None:
+                    left = min(left, var_tuple_index)
+                    right = min(right, alen - var_tuple_index - 1)
+                elif left + right > alen:
+                    raise TypeError(f"Too few arguments for {alias};"
+                                    f" actual {alen}, expected at least {plen - 1}")
+                if left == alen - right and tvt.has_default():
+                    replacement = _unpack_args(tvt.__default__)
+                else:
+                    replacement = args[left: alen - right]
+
+                return (
+                    *args[:left],
+                    *([fillarg] * (typevartuple_index - left)),
+                    replacement,
+                    *([fillarg] * (plen - right - left - typevartuple_index - 1)),
+                    *args[alen - right:],
+                )
+
+            tvt.__typing_prepare_subst__ = _typevartuple_prepare_subst
+            return tvt
+
+        def __init_subclass__(self, *args, **kwds):
+            raise TypeError("Cannot subclass special typing classes")
+
+else:  # <=3.10
+    class TypeVarTuple(_DefaultMixin):
+        """Type variable tuple.
+
+        Usage::
+
+            Ts = TypeVarTuple('Ts')
+
+        In the same way that a normal type variable is a stand-in for a single
+        type such as ``int``, a type variable *tuple* is a stand-in for a *tuple*
+        type such as ``Tuple[int, str]``.
+
+        Type variable tuples can be used in ``Generic`` declarations.
+        Consider the following example::
+
+            class Array(Generic[*Ts]): ...
+
+        The ``Ts`` type variable tuple here behaves like ``tuple[T1, T2]``,
+        where ``T1`` and ``T2`` are type variables. To use these type variables
+        as type parameters of ``Array``, we must *unpack* the type variable tuple using
+        the star operator: ``*Ts``. The signature of ``Array`` then behaves
+        as if we had simply written ``class Array(Generic[T1, T2]): ...``.
+        In contrast to ``Generic[T1, T2]``, however, ``Generic[*Shape]`` allows
+        us to parameterise the class with an *arbitrary* number of type parameters.
+
+        Type variable tuples can be used anywhere a normal ``TypeVar`` can.
+        This includes class definitions, as shown above, as well as function
+        signatures and variable annotations::
+
+            class Array(Generic[*Ts]):
+
+                def __init__(self, shape: Tuple[*Ts]):
+                    self._shape: Tuple[*Ts] = shape
+
+                def get_shape(self) -> Tuple[*Ts]:
+                    return self._shape
+
+            shape = (Height(480), Width(640))
+            x: Array[Height, Width] = Array(shape)
+            y = abs(x)  # Inferred type is Array[Height, Width]
+            z = x + x   #        ...    is Array[Height, Width]
+            x.get_shape()  #     ...    is tuple[Height, Width]
+
+        """
+
+        # Trick Generic __parameters__.
+        __class__ = typing.TypeVar
+
+        def __iter__(self):
+            yield self.__unpacked__
+
+        def __init__(self, name, *, default=NoDefault):
+            self.__name__ = name
+            _DefaultMixin.__init__(self, default)
+
+            # for pickling:
+            def_mod = _caller()
+            if def_mod != 'typing_extensions':
+                self.__module__ = def_mod
+
+            self.__unpacked__ = Unpack[self]
+
+        def __repr__(self):
+            return self.__name__
+
+        def __hash__(self):
+            return object.__hash__(self)
+
+        def __eq__(self, other):
+            return self is other
+
+        def __reduce__(self):
+            return self.__name__
+
+        def __init_subclass__(self, *args, **kwds):
+            if '_root' not in kwds:
+                raise TypeError("Cannot subclass special typing classes")
+
+
+if hasattr(typing, "reveal_type"):  # 3.11+
+    reveal_type = typing.reveal_type
+else:  # <=3.10
+    def reveal_type(obj: T, /) -> T:
+        """Reveal the inferred type of a variable.
+
+        When a static type checker encounters a call to ``reveal_type()``,
+        it will emit the inferred type of the argument::
+
+            x: int = 1
+            reveal_type(x)
+
+        Running a static type checker (e.g., ``mypy``) on this example
+        will produce output similar to 'Revealed type is "builtins.int"'.
+
+        At runtime, the function prints the runtime type of the
+        argument and returns it unchanged.
+
+        """
+        print(f"Runtime type is {type(obj).__name__!r}", file=sys.stderr)
+        return obj
+
+
+if hasattr(typing, "_ASSERT_NEVER_REPR_MAX_LENGTH"):  # 3.11+
+    _ASSERT_NEVER_REPR_MAX_LENGTH = typing._ASSERT_NEVER_REPR_MAX_LENGTH
+else:  # <=3.10
+    _ASSERT_NEVER_REPR_MAX_LENGTH = 100
+
+
+if hasattr(typing, "assert_never"):  # 3.11+
+    assert_never = typing.assert_never
+else:  # <=3.10
+    def assert_never(arg: Never, /) -> Never:
+        """Assert to the type checker that a line of code is unreachable.
+
+        Example::
+
+            def int_or_str(arg: int | str) -> None:
+                match arg:
+                    case int():
+                        print("It's an int")
+                    case str():
+                        print("It's a str")
+                    case _:
+                        assert_never(arg)
+
+        If a type checker finds that a call to assert_never() is
+        reachable, it will emit an error.
+
+        At runtime, this throws an exception when called.
+
+        """
+        value = repr(arg)
+        if len(value) > _ASSERT_NEVER_REPR_MAX_LENGTH:
+            value = value[:_ASSERT_NEVER_REPR_MAX_LENGTH] + '...'
+        raise AssertionError(f"Expected code to be unreachable, but got: {value}")
+
+
+if sys.version_info >= (3, 12):  # 3.12+
+    # dataclass_transform exists in 3.11 but lacks the frozen_default parameter
+    dataclass_transform = typing.dataclass_transform
+else:  # <=3.11
+    def dataclass_transform(
+        *,
+        eq_default: bool = True,
+        order_default: bool = False,
+        kw_only_default: bool = False,
+        frozen_default: bool = False,
+        field_specifiers: typing.Tuple[
+            typing.Union[typing.Type[typing.Any], typing.Callable[..., typing.Any]],
+            ...
+        ] = (),
+        **kwargs: typing.Any,
+    ) -> typing.Callable[[T], T]:
+        """Decorator that marks a function, class, or metaclass as providing
+        dataclass-like behavior.
+
+        Example:
+
+            from typing_extensions import dataclass_transform
+
+            _T = TypeVar("_T")
+
+            # Used on a decorator function
+            @dataclass_transform()
+            def create_model(cls: type[_T]) -> type[_T]:
+                ...
+                return cls
+
+            @create_model
+            class CustomerModel:
+                id: int
+                name: str
+
+            # Used on a base class
+            @dataclass_transform()
+            class ModelBase: ...
+
+            class CustomerModel(ModelBase):
+                id: int
+                name: str
+
+            # Used on a metaclass
+            @dataclass_transform()
+            class ModelMeta(type): ...
+
+            class ModelBase(metaclass=ModelMeta): ...
+
+            class CustomerModel(ModelBase):
+                id: int
+                name: str
+
+        Each of the ``CustomerModel`` classes defined in this example will now
+        behave similarly to a dataclass created with the ``@dataclasses.dataclass``
+        decorator. For example, the type checker will synthesize an ``__init__``
+        method.
+
+        The arguments to this decorator can be used to customize this behavior:
+        - ``eq_default`` indicates whether the ``eq`` parameter is assumed to be
+          True or False if it is omitted by the caller.
+        - ``order_default`` indicates whether the ``order`` parameter is
+          assumed to be True or False if it is omitted by the caller.
+        - ``kw_only_default`` indicates whether the ``kw_only`` parameter is
+          assumed to be True or False if it is omitted by the caller.
+        - ``frozen_default`` indicates whether the ``frozen`` parameter is
+          assumed to be True or False if it is omitted by the caller.
+        - ``field_specifiers`` specifies a static list of supported classes
+          or functions that describe fields, similar to ``dataclasses.field()``.
+
+        At runtime, this decorator records its arguments in the
+        ``__dataclass_transform__`` attribute on the decorated object.
+
+        See PEP 681 for details.
+
+        """
+        def decorator(cls_or_fn):
+            cls_or_fn.__dataclass_transform__ = {
+                "eq_default": eq_default,
+                "order_default": order_default,
+                "kw_only_default": kw_only_default,
+                "frozen_default": frozen_default,
+                "field_specifiers": field_specifiers,
+                "kwargs": kwargs,
+            }
+            return cls_or_fn
+        return decorator
+
+
+if hasattr(typing, "override"):  # 3.12+
+    override = typing.override
+else:  # <=3.11
+    _F = typing.TypeVar("_F", bound=typing.Callable[..., typing.Any])
+
+    def override(arg: _F, /) -> _F:
+        """Indicate that a method is intended to override a method in a base class.
+
+        Usage:
+
+            class Base:
+                def method(self) -> None:
+                    pass
+
+            class Child(Base):
+                @override
+                def method(self) -> None:
+                    super().method()
+
+        When this decorator is applied to a method, the type checker will
+        validate that it overrides a method with the same name on a base class.
+        This helps prevent bugs that may occur when a base class is changed
+        without an equivalent change to a child class.
+
+        There is no runtime checking of these properties. The decorator
+        sets the ``__override__`` attribute to ``True`` on the decorated object
+        to allow runtime introspection.
+
+        See PEP 698 for details.
+
+        """
+        try:
+            arg.__override__ = True
+        except (AttributeError, TypeError):
+            # Skip the attribute silently if it is not writable.
+            # AttributeError happens if the object has __slots__ or a
+            # read-only property, TypeError if it's a builtin class.
+            pass
+        return arg
+
+
+if hasattr(warnings, "deprecated"):
+    deprecated = warnings.deprecated
+else:
+    _T = typing.TypeVar("_T")
+
+    class deprecated:
+        """Indicate that a class, function or overload is deprecated.
+
+        When this decorator is applied to an object, the type checker
+        will generate a diagnostic on usage of the deprecated object.
+
+        Usage:
+
+            @deprecated("Use B instead")
+            class A:
+                pass
+
+            @deprecated("Use g instead")
+            def f():
+                pass
+
+            @overload
+            @deprecated("int support is deprecated")
+            def g(x: int) -> int: ...
+            @overload
+            def g(x: str) -> int: ...
+
+        The warning specified by *category* will be emitted at runtime
+        on use of deprecated objects. For functions, that happens on calls;
+        for classes, on instantiation and on creation of subclasses.
+        If the *category* is ``None``, no warning is emitted at runtime.
+        The *stacklevel* determines where the
+        warning is emitted. If it is ``1`` (the default), the warning
+        is emitted at the direct caller of the deprecated object; if it
+        is higher, it is emitted further up the stack.
+        Static type checker behavior is not affected by the *category*
+        and *stacklevel* arguments.
+
+        The deprecation message passed to the decorator is saved in the
+        ``__deprecated__`` attribute on the decorated object.
+        If applied to an overload, the decorator
+        must be after the ``@overload`` decorator for the attribute to
+        exist on the overload as returned by ``get_overloads()``.
+
+        See PEP 702 for details.
+
+        """
+        def __init__(
+            self,
+            message: str,
+            /,
+            *,
+            category: typing.Optional[typing.Type[Warning]] = DeprecationWarning,
+            stacklevel: int = 1,
+        ) -> None:
+            if not isinstance(message, str):
+                raise TypeError(
+                    "Expected an object of type str for 'message', not "
+                    f"{type(message).__name__!r}"
+                )
+            self.message = message
+            self.category = category
+            self.stacklevel = stacklevel
+
+        def __call__(self, arg: _T, /) -> _T:
+            # Make sure the inner functions created below don't
+            # retain a reference to self.
+            msg = self.message
+            category = self.category
+            stacklevel = self.stacklevel
+            if category is None:
+                arg.__deprecated__ = msg
+                return arg
+            elif isinstance(arg, type):
+                import functools
+                from types import MethodType
+
+                original_new = arg.__new__
+
+                @functools.wraps(original_new)
+                def __new__(cls, *args, **kwargs):
+                    if cls is arg:
+                        warnings.warn(msg, category=category, stacklevel=stacklevel + 1)
+                    if original_new is not object.__new__:
+                        return original_new(cls, *args, **kwargs)
+                    # Mirrors a similar check in object.__new__.
+                    elif cls.__init__ is object.__init__ and (args or kwargs):
+                        raise TypeError(f"{cls.__name__}() takes no arguments")
+                    else:
+                        return original_new(cls)
+
+                arg.__new__ = staticmethod(__new__)
+
+                original_init_subclass = arg.__init_subclass__
+                # We need slightly different behavior if __init_subclass__
+                # is a bound method (likely if it was implemented in Python)
+                if isinstance(original_init_subclass, MethodType):
+                    original_init_subclass = original_init_subclass.__func__
+
+                    @functools.wraps(original_init_subclass)
+                    def __init_subclass__(*args, **kwargs):
+                        warnings.warn(msg, category=category, stacklevel=stacklevel + 1)
+                        return original_init_subclass(*args, **kwargs)
+
+                    arg.__init_subclass__ = classmethod(__init_subclass__)
+                # Or otherwise, which likely means it's a builtin such as
+                # object's implementation of __init_subclass__.
+                else:
+                    @functools.wraps(original_init_subclass)
+                    def __init_subclass__(*args, **kwargs):
+                        warnings.warn(msg, category=category, stacklevel=stacklevel + 1)
+                        return original_init_subclass(*args, **kwargs)
+
+                    arg.__init_subclass__ = __init_subclass__
+
+                arg.__deprecated__ = __new__.__deprecated__ = msg
+                __init_subclass__.__deprecated__ = msg
+                return arg
+            elif callable(arg):
+                import functools
+
+                @functools.wraps(arg)
+                def wrapper(*args, **kwargs):
+                    warnings.warn(msg, category=category, stacklevel=stacklevel + 1)
+                    return arg(*args, **kwargs)
+
+                arg.__deprecated__ = wrapper.__deprecated__ = msg
+                return wrapper
+            else:
+                raise TypeError(
+                    "@deprecated decorator with non-None category must be applied to "
+                    f"a class or callable, not {arg!r}"
+                )
+
+
+# We have to do some monkey patching to deal with the dual nature of
+# Unpack/TypeVarTuple:
+# - We want Unpack to be a kind of TypeVar so it gets accepted in
+#   Generic[Unpack[Ts]]
+# - We want it to *not* be treated as a TypeVar for the purposes of
+#   counting generic parameters, so that when we subscript a generic,
+#   the runtime doesn't try to substitute the Unpack with the subscripted type.
+if not hasattr(typing, "TypeVarTuple"):
+    def _check_generic(cls, parameters, elen=_marker):
+        """Check correct count for parameters of a generic cls (internal helper).
+
+        This gives a nice error message in case of count mismatch.
+        """
+        if not elen:
+            raise TypeError(f"{cls} is not a generic class")
+        if elen is _marker:
+            if not hasattr(cls, "__parameters__") or not cls.__parameters__:
+                raise TypeError(f"{cls} is not a generic class")
+            elen = len(cls.__parameters__)
+        alen = len(parameters)
+        if alen != elen:
+            expect_val = elen
+            if hasattr(cls, "__parameters__"):
+                parameters = [p for p in cls.__parameters__ if not _is_unpack(p)]
+                num_tv_tuples = sum(isinstance(p, TypeVarTuple) for p in parameters)
+                if (num_tv_tuples > 0) and (alen >= elen - num_tv_tuples):
+                    return
+
+                # deal with TypeVarLike defaults
+                # required TypeVarLikes cannot appear after a defaulted one.
+                if alen < elen:
+                    # since we validate TypeVarLike default in _collect_type_vars
+                    # or _collect_parameters we can safely check parameters[alen]
+                    if (
+                        getattr(parameters[alen], '__default__', NoDefault)
+                        is not NoDefault
+                    ):
+                        return
+
+                    num_default_tv = sum(getattr(p, '__default__', NoDefault)
+                                         is not NoDefault for p in parameters)
+
+                    elen -= num_default_tv
+
+                    expect_val = f"at least {elen}"
+
+            things = "arguments" if sys.version_info >= (3, 10) else "parameters"
+            raise TypeError(f"Too {'many' if alen > elen else 'few'} {things}"
+                            f" for {cls}; actual {alen}, expected {expect_val}")
+else:
+    # Python 3.11+
+
+    def _check_generic(cls, parameters, elen):
+        """Check correct count for parameters of a generic cls (internal helper).
+
+        This gives a nice error message in case of count mismatch.
+        """
+        if not elen:
+            raise TypeError(f"{cls} is not a generic class")
+        alen = len(parameters)
+        if alen != elen:
+            expect_val = elen
+            if hasattr(cls, "__parameters__"):
+                parameters = [p for p in cls.__parameters__ if not _is_unpack(p)]
+
+                # deal with TypeVarLike defaults
+                # required TypeVarLikes cannot appear after a defaulted one.
+                if alen < elen:
+                    # since we validate TypeVarLike default in _collect_type_vars
+                    # or _collect_parameters we can safely check parameters[alen]
+                    if (
+                        getattr(parameters[alen], '__default__', NoDefault)
+                        is not NoDefault
+                    ):
+                        return
+
+                    num_default_tv = sum(getattr(p, '__default__', NoDefault)
+                                         is not NoDefault for p in parameters)
+
+                    elen -= num_default_tv
+
+                    expect_val = f"at least {elen}"
+
+            raise TypeError(f"Too {'many' if alen > elen else 'few'} arguments"
+                            f" for {cls}; actual {alen}, expected {expect_val}")
+
+if not _PEP_696_IMPLEMENTED:
+    typing._check_generic = _check_generic
+
+
+def _has_generic_or_protocol_as_origin() -> bool:
+    try:
+        frame = sys._getframe(2)
+    # - Catch AttributeError: not all Python implementations have sys._getframe()
+    # - Catch ValueError: maybe we're called from an unexpected module
+    #   and the call stack isn't deep enough
+    except (AttributeError, ValueError):
+        return False  # err on the side of leniency
+    else:
+        # If we somehow get invoked from outside typing.py,
+        # also err on the side of leniency
+        if frame.f_globals.get("__name__") != "typing":
+            return False
+        origin = frame.f_locals.get("origin")
+        # Cannot use "in" because origin may be an object with a buggy __eq__ that
+        # throws an error.
+        return origin is typing.Generic or origin is Protocol or origin is typing.Protocol
+
+
+_TYPEVARTUPLE_TYPES = {TypeVarTuple, getattr(typing, "TypeVarTuple", None)}
+
+
+def _is_unpacked_typevartuple(x) -> bool:
+    if get_origin(x) is not Unpack:
+        return False
+    args = get_args(x)
+    return (
+        bool(args)
+        and len(args) == 1
+        and type(args[0]) in _TYPEVARTUPLE_TYPES
+    )
+
+
+# Python 3.11+ _collect_type_vars was renamed to _collect_parameters
+if hasattr(typing, '_collect_type_vars'):
+    def _collect_type_vars(types, typevar_types=None):
+        """Collect all type variable contained in types in order of
+        first appearance (lexicographic order). For example::
+
+            _collect_type_vars((T, List[S, T])) == (T, S)
+        """
+        if typevar_types is None:
+            typevar_types = typing.TypeVar
+        tvars = []
+
+        # A required TypeVarLike cannot appear after a TypeVarLike with a default
+        # if it was a direct call to `Generic[]` or `Protocol[]`
+        enforce_default_ordering = _has_generic_or_protocol_as_origin()
+        default_encountered = False
+
+        # Also, a TypeVarLike with a default cannot appear after a TypeVarTuple
+        type_var_tuple_encountered = False
+
+        for t in types:
+            if _is_unpacked_typevartuple(t):
+                type_var_tuple_encountered = True
+            elif isinstance(t, typevar_types) and t not in tvars:
+                if enforce_default_ordering:
+                    has_default = getattr(t, '__default__', NoDefault) is not NoDefault
+                    if has_default:
+                        if type_var_tuple_encountered:
+                            raise TypeError('Type parameter with a default'
+                                            ' follows TypeVarTuple')
+                        default_encountered = True
+                    elif default_encountered:
+                        raise TypeError(f'Type parameter {t!r} without a default'
+                                        ' follows type parameter with a default')
+
+                tvars.append(t)
+            if _should_collect_from_parameters(t):
+                tvars.extend([t for t in t.__parameters__ if t not in tvars])
+        return tuple(tvars)
+
+    typing._collect_type_vars = _collect_type_vars
+else:
+    def _collect_parameters(args):
+        """Collect all type variables and parameter specifications in args
+        in order of first appearance (lexicographic order).
+
+        For example::
+
+            assert _collect_parameters((T, Callable[P, T])) == (T, P)
+        """
+        parameters = []
+
+        # A required TypeVarLike cannot appear after a TypeVarLike with default
+        # if it was a direct call to `Generic[]` or `Protocol[]`
+        enforce_default_ordering = _has_generic_or_protocol_as_origin()
+        default_encountered = False
+
+        # Also, a TypeVarLike with a default cannot appear after a TypeVarTuple
+        type_var_tuple_encountered = False
+
+        for t in args:
+            if isinstance(t, type):
+                # We don't want __parameters__ descriptor of a bare Python class.
+                pass
+            elif isinstance(t, tuple):
+                # `t` might be a tuple, when `ParamSpec` is substituted with
+                # `[T, int]`, or `[int, *Ts]`, etc.
+                for x in t:
+                    for collected in _collect_parameters([x]):
+                        if collected not in parameters:
+                            parameters.append(collected)
+            elif hasattr(t, '__typing_subst__'):
+                if t not in parameters:
+                    if enforce_default_ordering:
+                        has_default = (
+                            getattr(t, '__default__', NoDefault) is not NoDefault
+                        )
+
+                        if type_var_tuple_encountered and has_default:
+                            raise TypeError('Type parameter with a default'
+                                            ' follows TypeVarTuple')
+
+                        if has_default:
+                            default_encountered = True
+                        elif default_encountered:
+                            raise TypeError(f'Type parameter {t!r} without a default'
+                                            ' follows type parameter with a default')
+
+                    parameters.append(t)
+            else:
+                if _is_unpacked_typevartuple(t):
+                    type_var_tuple_encountered = True
+                for x in getattr(t, '__parameters__', ()):
+                    if x not in parameters:
+                        parameters.append(x)
+
+        return tuple(parameters)
+
+    if not _PEP_696_IMPLEMENTED:
+        typing._collect_parameters = _collect_parameters
+
+# Backport typing.NamedTuple as it exists in Python 3.13.
+# In 3.11, the ability to define generic `NamedTuple`s was supported.
+# This was explicitly disallowed in 3.9-3.10, and only half-worked in <=3.8.
+# On 3.12, we added __orig_bases__ to call-based NamedTuples
+# On 3.13, we deprecated kwargs-based NamedTuples
+if sys.version_info >= (3, 13):
+    NamedTuple = typing.NamedTuple
+else:
+    def _make_nmtuple(name, types, module, defaults=()):
+        fields = [n for n, t in types]
+        annotations = {n: typing._type_check(t, f"field {n} annotation must be a type")
+                       for n, t in types}
+        nm_tpl = collections.namedtuple(name, fields,
+                                        defaults=defaults, module=module)
+        nm_tpl.__annotations__ = nm_tpl.__new__.__annotations__ = annotations
+        # The `_field_types` attribute was removed in 3.9;
+        # in earlier versions, it is the same as the `__annotations__` attribute
+        if sys.version_info < (3, 9):
+            nm_tpl._field_types = annotations
+        return nm_tpl
+
+    _prohibited_namedtuple_fields = typing._prohibited
+    _special_namedtuple_fields = frozenset({'__module__', '__name__', '__annotations__'})
+
+    class _NamedTupleMeta(type):
+        def __new__(cls, typename, bases, ns):
+            assert _NamedTuple in bases
+            for base in bases:
+                if base is not _NamedTuple and base is not typing.Generic:
+                    raise TypeError(
+                        'can only inherit from a NamedTuple type and Generic')
+            bases = tuple(tuple if base is _NamedTuple else base for base in bases)
+            if "__annotations__" in ns:
+                types = ns["__annotations__"]
+            elif "__annotate__" in ns:
+                # TODO: Use inspect.VALUE here, and make the annotations lazily evaluated
+                types = ns["__annotate__"](1)
+            else:
+                types = {}
+            default_names = []
+            for field_name in types:
+                if field_name in ns:
+                    default_names.append(field_name)
+                elif default_names:
+                    raise TypeError(f"Non-default namedtuple field {field_name} "
+                                    f"cannot follow default field"
+                                    f"{'s' if len(default_names) > 1 else ''} "
+                                    f"{', '.join(default_names)}")
+            nm_tpl = _make_nmtuple(
+                typename, types.items(),
+                defaults=[ns[n] for n in default_names],
+                module=ns['__module__']
+            )
+            nm_tpl.__bases__ = bases
+            if typing.Generic in bases:
+                if hasattr(typing, '_generic_class_getitem'):  # 3.12+
+                    nm_tpl.__class_getitem__ = classmethod(typing._generic_class_getitem)
+                else:
+                    class_getitem = typing.Generic.__class_getitem__.__func__
+                    nm_tpl.__class_getitem__ = classmethod(class_getitem)
+            # update from user namespace without overriding special namedtuple attributes
+            for key, val in ns.items():
+                if key in _prohibited_namedtuple_fields:
+                    raise AttributeError("Cannot overwrite NamedTuple attribute " + key)
+                elif key not in _special_namedtuple_fields:
+                    if key not in nm_tpl._fields:
+                        setattr(nm_tpl, key, ns[key])
+                    try:
+                        set_name = type(val).__set_name__
+                    except AttributeError:
+                        pass
+                    else:
+                        try:
+                            set_name(val, nm_tpl, key)
+                        except BaseException as e:
+                            msg = (
+                                f"Error calling __set_name__ on {type(val).__name__!r} "
+                                f"instance {key!r} in {typename!r}"
+                            )
+                            # BaseException.add_note() existed on py311,
+                            # but the __set_name__ machinery didn't start
+                            # using add_note() until py312.
+                            # Making sure exceptions are raised in the same way
+                            # as in "normal" classes seems most important here.
+                            if sys.version_info >= (3, 12):
+                                e.add_note(msg)
+                                raise
+                            else:
+                                raise RuntimeError(msg) from e
+
+            if typing.Generic in bases:
+                nm_tpl.__init_subclass__()
+            return nm_tpl
+
+    _NamedTuple = type.__new__(_NamedTupleMeta, 'NamedTuple', (), {})
+
+    def _namedtuple_mro_entries(bases):
+        assert NamedTuple in bases
+        return (_NamedTuple,)
+
+    @_ensure_subclassable(_namedtuple_mro_entries)
+    def NamedTuple(typename, fields=_marker, /, **kwargs):
+        """Typed version of namedtuple.
+
+        Usage::
+
+            class Employee(NamedTuple):
+                name: str
+                id: int
+
+        This is equivalent to::
+
+            Employee = collections.namedtuple('Employee', ['name', 'id'])
+
+        The resulting class has an extra __annotations__ attribute, giving a
+        dict that maps field names to types.  (The field names are also in
+        the _fields attribute, which is part of the namedtuple API.)
+        An alternative equivalent functional syntax is also accepted::
+
+            Employee = NamedTuple('Employee', [('name', str), ('id', int)])
+        """
+        if fields is _marker:
+            if kwargs:
+                deprecated_thing = "Creating NamedTuple classes using keyword arguments"
+                deprecation_msg = (
+                    "{name} is deprecated and will be disallowed in Python {remove}. "
+                    "Use the class-based or functional syntax instead."
+                )
+            else:
+                deprecated_thing = "Failing to pass a value for the 'fields' parameter"
+                example = f"`{typename} = NamedTuple({typename!r}, [])`"
+                deprecation_msg = (
+                    "{name} is deprecated and will be disallowed in Python {remove}. "
+                    "To create a NamedTuple class with 0 fields "
+                    "using the functional syntax, "
+                    "pass an empty list, e.g. "
+                ) + example + "."
+        elif fields is None:
+            if kwargs:
+                raise TypeError(
+                    "Cannot pass `None` as the 'fields' parameter "
+                    "and also specify fields using keyword arguments"
+                )
+            else:
+                deprecated_thing = "Passing `None` as the 'fields' parameter"
+                example = f"`{typename} = NamedTuple({typename!r}, [])`"
+                deprecation_msg = (
+                    "{name} is deprecated and will be disallowed in Python {remove}. "
+                    "To create a NamedTuple class with 0 fields "
+                    "using the functional syntax, "
+                    "pass an empty list, e.g. "
+                ) + example + "."
+        elif kwargs:
+            raise TypeError("Either list of fields or keywords"
+                            " can be provided to NamedTuple, not both")
+        if fields is _marker or fields is None:
+            warnings.warn(
+                deprecation_msg.format(name=deprecated_thing, remove="3.15"),
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            fields = kwargs.items()
+        nt = _make_nmtuple(typename, fields, module=_caller())
+        nt.__orig_bases__ = (NamedTuple,)
+        return nt
+
+
+if hasattr(collections.abc, "Buffer"):
+    Buffer = collections.abc.Buffer
+else:
+    class Buffer(abc.ABC):  # noqa: B024
+        """Base class for classes that implement the buffer protocol.
+
+        The buffer protocol allows Python objects to expose a low-level
+        memory buffer interface. Before Python 3.12, it is not possible
+        to implement the buffer protocol in pure Python code, or even
+        to check whether a class implements the buffer protocol. In
+        Python 3.12 and higher, the ``__buffer__`` method allows access
+        to the buffer protocol from Python code, and the
+        ``collections.abc.Buffer`` ABC allows checking whether a class
+        implements the buffer protocol.
+
+        To indicate support for the buffer protocol in earlier versions,
+        inherit from this ABC, either in a stub file or at runtime,
+        or use ABC registration. This ABC provides no methods, because
+        there is no Python-accessible methods shared by pre-3.12 buffer
+        classes. It is useful primarily for static checks.
+
+        """
+
+    # As a courtesy, register the most common stdlib buffer classes.
+    Buffer.register(memoryview)
+    Buffer.register(bytearray)
+    Buffer.register(bytes)
+
+
+# Backport of types.get_original_bases, available on 3.12+ in CPython
+if hasattr(_types, "get_original_bases"):
+    get_original_bases = _types.get_original_bases
+else:
+    def get_original_bases(cls, /):
+        """Return the class's "original" bases prior to modification by `__mro_entries__`.
+
+        Examples::
+
+            from typing import TypeVar, Generic
+            from typing_extensions import NamedTuple, TypedDict
+
+            T = TypeVar("T")
+            class Foo(Generic[T]): ...
+            class Bar(Foo[int], float): ...
+            class Baz(list[str]): ...
+            Eggs = NamedTuple("Eggs", [("a", int), ("b", str)])
+            Spam = TypedDict("Spam", {"a": int, "b": str})
+
+            assert get_original_bases(Bar) == (Foo[int], float)
+            assert get_original_bases(Baz) == (list[str],)
+            assert get_original_bases(Eggs) == (NamedTuple,)
+            assert get_original_bases(Spam) == (TypedDict,)
+            assert get_original_bases(int) == (object,)
+        """
+        try:
+            return cls.__dict__.get("__orig_bases__", cls.__bases__)
+        except AttributeError:
+            raise TypeError(
+                f'Expected an instance of type, not {type(cls).__name__!r}'
+            ) from None
+
+
+# NewType is a class on Python 3.10+, making it pickleable
+# The error message for subclassing instances of NewType was improved on 3.11+
+if sys.version_info >= (3, 11):
+    NewType = typing.NewType
+else:
+    class NewType:
+        """NewType creates simple unique types with almost zero
+        runtime overhead. NewType(name, tp) is considered a subtype of tp
+        by static type checkers. At runtime, NewType(name, tp) returns
+        a dummy callable that simply returns its argument. Usage::
+            UserId = NewType('UserId', int)
+            def name_by_id(user_id: UserId) -> str:
+                ...
+            UserId('user')          # Fails type check
+            name_by_id(42)          # Fails type check
+            name_by_id(UserId(42))  # OK
+            num = UserId(5) + 1     # type: int
+        """
+
+        def __call__(self, obj, /):
+            return obj
+
+        def __init__(self, name, tp):
+            self.__qualname__ = name
+            if '.' in name:
+                name = name.rpartition('.')[-1]
+            self.__name__ = name
+            self.__supertype__ = tp
+            def_mod = _caller()
+            if def_mod != 'typing_extensions':
+                self.__module__ = def_mod
+
+        def __mro_entries__(self, bases):
+            # We defined __mro_entries__ to get a better error message
+            # if a user attempts to subclass a NewType instance. bpo-46170
+            supercls_name = self.__name__
+
+            class Dummy:
+                def __init_subclass__(cls):
+                    subcls_name = cls.__name__
+                    raise TypeError(
+                        f"Cannot subclass an instance of NewType. "
+                        f"Perhaps you were looking for: "
+                        f"`{subcls_name} = NewType({subcls_name!r}, {supercls_name})`"
+                    )
+
+            return (Dummy,)
+
+        def __repr__(self):
+            return f'{self.__module__}.{self.__qualname__}'
+
+        def __reduce__(self):
+            return self.__qualname__
+
+        if sys.version_info >= (3, 10):
+            # PEP 604 methods
+            # It doesn't make sense to have these methods on Python <3.10
+
+            def __or__(self, other):
+                return typing.Union[self, other]
+
+            def __ror__(self, other):
+                return typing.Union[other, self]
+
+
+if hasattr(typing, "TypeAliasType"):
+    TypeAliasType = typing.TypeAliasType
+else:
+    def _is_unionable(obj):
+        """Corresponds to is_unionable() in unionobject.c in CPython."""
+        return obj is None or isinstance(obj, (
+            type,
+            _types.GenericAlias,
+            _types.UnionType,
+            TypeAliasType,
+        ))
+
+    class TypeAliasType:
+        """Create named, parameterized type aliases.
+
+        This provides a backport of the new `type` statement in Python 3.12:
+
+            type ListOrSet[T] = list[T] | set[T]
+
+        is equivalent to:
+
+            T = TypeVar("T")
+            ListOrSet = TypeAliasType("ListOrSet", list[T] | set[T], type_params=(T,))
+
+        The name ListOrSet can then be used as an alias for the type it refers to.
+
+        The type_params argument should contain all the type parameters used
+        in the value of the type alias. If the alias is not generic, this
+        argument is omitted.
+
+        Static type checkers should only support type aliases declared using
+        TypeAliasType that follow these rules:
+
+        - The first argument (the name) must be a string literal.
+        - The TypeAliasType instance must be immediately assigned to a variable
+          of the same name. (For example, 'X = TypeAliasType("Y", int)' is invalid,
+          as is 'X, Y = TypeAliasType("X", int), TypeAliasType("Y", int)').
+
+        """
+
+        def __init__(self, name: str, value, *, type_params=()):
+            if not isinstance(name, str):
+                raise TypeError("TypeAliasType name must be a string")
+            self.__value__ = value
+            self.__type_params__ = type_params
+
+            parameters = []
+            for type_param in type_params:
+                if isinstance(type_param, TypeVarTuple):
+                    parameters.extend(type_param)
+                else:
+                    parameters.append(type_param)
+            self.__parameters__ = tuple(parameters)
+            def_mod = _caller()
+            if def_mod != 'typing_extensions':
+                self.__module__ = def_mod
+            # Setting this attribute closes the TypeAliasType from further modification
+            self.__name__ = name
+
+        def __setattr__(self, name: str, value: object, /) -> None:
+            if hasattr(self, "__name__"):
+                self._raise_attribute_error(name)
+            super().__setattr__(name, value)
+
+        def __delattr__(self, name: str, /) -> Never:
+            self._raise_attribute_error(name)
+
+        def _raise_attribute_error(self, name: str) -> Never:
+            # Match the Python 3.12 error messages exactly
+            if name == "__name__":
+                raise AttributeError("readonly attribute")
+            elif name in {"__value__", "__type_params__", "__parameters__", "__module__"}:
+                raise AttributeError(
+                    f"attribute '{name}' of 'typing.TypeAliasType' objects "
+                    "is not writable"
+                )
+            else:
+                raise AttributeError(
+                    f"'typing.TypeAliasType' object has no attribute '{name}'"
+                )
+
+        def __repr__(self) -> str:
+            return self.__name__
+
+        def __getitem__(self, parameters):
+            if not isinstance(parameters, tuple):
+                parameters = (parameters,)
+            parameters = [
+                typing._type_check(
+                    item, f'Subscripting {self.__name__} requires a type.'
+                )
+                for item in parameters
+            ]
+            return typing._GenericAlias(self, tuple(parameters))
+
+        def __reduce__(self):
+            return self.__name__
+
+        def __init_subclass__(cls, *args, **kwargs):
+            raise TypeError(
+                "type 'typing_extensions.TypeAliasType' is not an acceptable base type"
+            )
+
+        # The presence of this method convinces typing._type_check
+        # that TypeAliasTypes are types.
+        def __call__(self):
+            raise TypeError("Type alias is not callable")
+
+        if sys.version_info >= (3, 10):
+            def __or__(self, right):
+                # For forward compatibility with 3.12, reject Unions
+                # that are not accepted by the built-in Union.
+                if not _is_unionable(right):
+                    return NotImplemented
+                return typing.Union[self, right]
+
+            def __ror__(self, left):
+                if not _is_unionable(left):
+                    return NotImplemented
+                return typing.Union[left, self]
+
+
+if hasattr(typing, "is_protocol"):
+    is_protocol = typing.is_protocol
+    get_protocol_members = typing.get_protocol_members
+else:
+    def is_protocol(tp: type, /) -> bool:
+        """Return True if the given type is a Protocol.
+
+        Example::
+
+            >>> from typing_extensions import Protocol, is_protocol
+            >>> class P(Protocol):
+            ...     def a(self) -> str: ...
+            ...     b: int
+            >>> is_protocol(P)
+            True
+            >>> is_protocol(int)
+            False
+        """
+        return (
+            isinstance(tp, type)
+            and getattr(tp, '_is_protocol', False)
+            and tp is not Protocol
+            and tp is not typing.Protocol
+        )
+
+    def get_protocol_members(tp: type, /) -> typing.FrozenSet[str]:
+        """Return the set of members defined in a Protocol.
+
+        Example::
+
+            >>> from typing_extensions import Protocol, get_protocol_members
+            >>> class P(Protocol):
+            ...     def a(self) -> str: ...
+            ...     b: int
+            >>> get_protocol_members(P)
+            frozenset({'a', 'b'})
+
+        Raise a TypeError for arguments that are not Protocols.
+        """
+        if not is_protocol(tp):
+            raise TypeError(f'{tp!r} is not a Protocol')
+        if hasattr(tp, '__protocol_attrs__'):
+            return frozenset(tp.__protocol_attrs__)
+        return frozenset(_get_protocol_attrs(tp))
+
+
+if hasattr(typing, "Doc"):
+    Doc = typing.Doc
+else:
+    class Doc:
+        """Define the documentation of a type annotation using ``Annotated``, to be
+         used in class attributes, function and method parameters, return values,
+         and variables.
+
+        The value should be a positional-only string literal to allow static tools
+        like editors and documentation generators to use it.
+
+        This complements docstrings.
+
+        The string value passed is available in the attribute ``documentation``.
+
+        Example::
+
+            >>> from typing_extensions import Annotated, Doc
+            >>> def hi(to: Annotated[str, Doc("Who to say hi to")]) -> None: ...
+        """
+        def __init__(self, documentation: str, /) -> None:
+            self.documentation = documentation
+
+        def __repr__(self) -> str:
+            return f"Doc({self.documentation!r})"
+
+        def __hash__(self) -> int:
+            return hash(self.documentation)
+
+        def __eq__(self, other: object) -> bool:
+            if not isinstance(other, Doc):
+                return NotImplemented
+            return self.documentation == other.documentation
+
+
+_CapsuleType = getattr(_types, "CapsuleType", None)
+
+if _CapsuleType is None:
+    try:
+        import _socket
+    except ImportError:
+        pass
+    else:
+        _CAPI = getattr(_socket, "CAPI", None)
+        if _CAPI is not None:
+            _CapsuleType = type(_CAPI)
+
+if _CapsuleType is not None:
+    CapsuleType = _CapsuleType
+    __all__.append("CapsuleType")
+
+
+# Aliases for items that have always been in typing.
+# Explicitly assign these (rather than using `from typing import *` at the top),
+# so that we get a CI error if one of these is deleted from typing.py
+# in a future version of Python
+AbstractSet = typing.AbstractSet
+AnyStr = typing.AnyStr
+BinaryIO = typing.BinaryIO
+Callable = typing.Callable
+Collection = typing.Collection
+Container = typing.Container
+Dict = typing.Dict
+ForwardRef = typing.ForwardRef
+FrozenSet = typing.FrozenSet
+Generic = typing.Generic
+Hashable = typing.Hashable
+IO = typing.IO
+ItemsView = typing.ItemsView
+Iterable = typing.Iterable
+Iterator = typing.Iterator
+KeysView = typing.KeysView
+List = typing.List
+Mapping = typing.Mapping
+MappingView = typing.MappingView
+Match = typing.Match
+MutableMapping = typing.MutableMapping
+MutableSequence = typing.MutableSequence
+MutableSet = typing.MutableSet
+Optional = typing.Optional
+Pattern = typing.Pattern
+Reversible = typing.Reversible
+Sequence = typing.Sequence
+Set = typing.Set
+Sized = typing.Sized
+TextIO = typing.TextIO
+Tuple = typing.Tuple
+Union = typing.Union
+ValuesView = typing.ValuesView
+cast = typing.cast
+no_type_check = typing.no_type_check
+no_type_check_decorator = typing.no_type_check_decorator
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/LICENSE.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a31470f14c5978d5fcc3bc173b8399b6c9a6443f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/LICENSE.txt
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2012 Daniel Holth <dholth@fastmail.fm> and contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a
+copy of this software and associated documentation files (the "Software"),
+to deal in the Software without restriction, including without limitation
+the rights to use, copy, modify, merge, publish, distribute, sublicense,
+and/or sell copies of the Software, and to permit persons to whom the
+Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included
+in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+OTHER DEALINGS IN THE SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..f645dcb673aaa7e3e7e054d57af5e8c0992b70e8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/METADATA
@@ -0,0 +1,66 @@
+Metadata-Version: 2.3
+Name: wheel
+Version: 0.45.1
+Summary: A built-package format for Python
+Keywords: wheel,packaging
+Author-email: Daniel Holth <dholth@fastmail.fm>
+Maintainer-email: Alex Grönholm <alex.gronholm@nextday.fi>
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Topic :: System :: Archiving :: Packaging
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Requires-Dist: pytest >= 6.0.0 ; extra == "test"
+Requires-Dist: setuptools >= 65 ; extra == "test"
+Project-URL: Changelog, https://wheel.readthedocs.io/en/stable/news.html
+Project-URL: Documentation, https://wheel.readthedocs.io/
+Project-URL: Issue Tracker, https://github.com/pypa/wheel/issues
+Project-URL: Source, https://github.com/pypa/wheel
+Provides-Extra: test
+
+wheel
+=====
+
+This is a command line tool for manipulating Python wheel files, as defined in
+`PEP 427`_. It contains the following functionality:
+
+* Convert ``.egg`` archives into ``.whl``
+* Unpack wheel archives
+* Repack wheel archives
+* Add or remove tags in existing wheel archives
+
+.. _PEP 427: https://www.python.org/dev/peps/pep-0427/
+
+Historical note
+---------------
+
+This project used to contain the implementation of the setuptools_ ``bdist_wheel``
+command, but as of setuptools v70.1, it no longer needs ``wheel`` installed for that to
+work. Thus, you should install this **only** if you intend to use the ``wheel`` command
+line tool!
+
+.. _setuptools: https://pypi.org/project/setuptools/
+
+Documentation
+-------------
+
+The documentation_ can be found on Read The Docs.
+
+.. _documentation: https://wheel.readthedocs.io/
+
+Code of Conduct
+---------------
+
+Everyone interacting in the wheel project's codebases, issue trackers, chat
+rooms, and mailing lists is expected to follow the `PSF Code of Conduct`_.
+
+.. _PSF Code of Conduct: https://github.com/pypa/.github/blob/main/CODE_OF_CONDUCT.md
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..c1535b697f8dd60212541f637c69c9dfb4f1c8f1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/RECORD
@@ -0,0 +1,68 @@
+../../bin/wheel,sha256=pBhV19bQIgjS-r541fG3kLU6QtcyKaKdQ2RE9YIzeiU,249
+wheel-0.45.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+wheel-0.45.1.dist-info/LICENSE.txt,sha256=MMI2GGeRCPPo6h0qZYx8pBe9_IkcmO8aifpP8MmChlQ,1107
+wheel-0.45.1.dist-info/METADATA,sha256=mKz84H7m7jsxJyzeIcTVORiTb0NPMV39KvOIYhGgmjA,2313
+wheel-0.45.1.dist-info/RECORD,,
+wheel-0.45.1.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+wheel-0.45.1.dist-info/WHEEL,sha256=CpUCUxeHQbRN5UGRQHYRJorO5Af-Qy_fHMctcQ8DSGI,82
+wheel-0.45.1.dist-info/entry_points.txt,sha256=rTY1BbkPHhkGMm4Q3F0pIzJBzW2kMxoG1oriffvGdA0,104
+wheel/__init__.py,sha256=mrxMnvdXACur_LWegbUfh5g5ysWZrd63UJn890wvGNk,59
+wheel/__main__.py,sha256=NkMUnuTCGcOkgY0IBLgBCVC_BGGcWORx2K8jYGS12UE,455
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+wheel/vendored/packaging/__pycache__/tags.cpython-311.pyc,,
+wheel/vendored/packaging/__pycache__/utils.cpython-311.pyc,,
+wheel/vendored/packaging/__pycache__/version.cpython-311.pyc,,
+wheel/vendored/packaging/_elffile.py,sha256=hbmK8OD6Z7fY6hwinHEUcD1by7czkGiNYu7ShnFEk2k,3266
+wheel/vendored/packaging/_manylinux.py,sha256=P7sdR5_7XBY09LVYYPhHmydMJIIwPXWsh4olk74Uuj4,9588
+wheel/vendored/packaging/_musllinux.py,sha256=z1s8To2hQ0vpn_d-O2i5qxGwEK8WmGlLt3d_26V7NeY,2674
+wheel/vendored/packaging/_parser.py,sha256=4tT4emSl2qTaU7VTQE1Xa9o1jMPCsBezsYBxyNMUN-s,10347
+wheel/vendored/packaging/_structures.py,sha256=q3eVNmbWJGG_S0Dit_S3Ao8qQqz_5PYTXFAKBZe5yr4,1431
+wheel/vendored/packaging/_tokenizer.py,sha256=alCtbwXhOFAmFGZ6BQ-wCTSFoRAJ2z-ysIf7__MTJ_k,5292
+wheel/vendored/packaging/markers.py,sha256=_TSPI1BhJYO7Bp9AzTmHQxIqHEVXaTjmDh9G-w8qzPA,8232
+wheel/vendored/packaging/requirements.py,sha256=dgoBeVprPu2YE6Q8nGfwOPTjATHbRa_ZGLyXhFEln6Q,2933
+wheel/vendored/packaging/specifiers.py,sha256=IWSt0SrLSP72heWhAC8UL0eGvas7XIQHjqiViVfmPKE,39778
+wheel/vendored/packaging/tags.py,sha256=fedHXiOHkBxNZTXotXv8uXPmMFU9ae-TKBujgYHigcA,18950
+wheel/vendored/packaging/utils.py,sha256=XgdmP3yx9-wQEFjO7OvMj9RjEf5JlR5HFFR69v7SQ9E,5268
+wheel/vendored/packaging/version.py,sha256=PFJaYZDxBgyxkfYhH3SQw4qfE9ICCWrTmitvq14y3bs,16234
+wheel/vendored/vendor.txt,sha256=Z2ENjB1i5prfez8CdM1Sdr3c6Zxv2rRRolMpLmBncAE,16
+wheel/wheelfile.py,sha256=USCttNlJwafxt51YYFFKG7jnxz8dfhbyqAZL6jMTA9s,8411
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/REQUESTED b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/REQUESTED
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..e3c6feefa22927866e3fd5575379ea972b432aaf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/WHEEL
@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: flit 3.10.1
+Root-Is-Purelib: true
+Tag: py3-none-any
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/entry_points.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/entry_points.txt
new file mode 100644
index 0000000000000000000000000000000000000000..06c9f69debd05e53c3b6053a905805e616c61fad
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel-0.45.1.dist-info/entry_points.txt
@@ -0,0 +1,6 @@
+[console_scripts]
+wheel=wheel.cli:main
+
+[distutils.commands]
+bdist_wheel=wheel.bdist_wheel:bdist_wheel
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ab8f72d8b2e31b1cb8456ee1c4b8e62d4aba252
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/__init__.py
@@ -0,0 +1,3 @@
+from __future__ import annotations
+
+__version__ = "0.45.1"
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/__main__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0be74537494dc2cf18c2e3b318ffd22b886aef6b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/__main__.py
@@ -0,0 +1,23 @@
+"""
+Wheel command line tool (enable python -m wheel syntax)
+"""
+
+from __future__ import annotations
+
+import sys
+
+
+def main():  # needed for console script
+    if __package__ == "":
+        # To be able to run 'python wheel-0.9.whl/wheel':
+        import os.path
+
+        path = os.path.dirname(os.path.dirname(__file__))
+        sys.path[0:0] = [path]
+    import wheel.cli
+
+    sys.exit(wheel.cli.main())
+
+
+if __name__ == "__main__":
+    sys.exit(main())
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/_bdist_wheel.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/_bdist_wheel.py
new file mode 100644
index 0000000000000000000000000000000000000000..88973ebfb88f9521e5f0613b5cedf1204d10a8f1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/_bdist_wheel.py
@@ -0,0 +1,613 @@
+"""
+Create a wheel (.whl) distribution.
+
+A wheel is a built archive format.
+"""
+
+from __future__ import annotations
+
+import os
+import re
+import shutil
+import stat
+import struct
+import sys
+import sysconfig
+import warnings
+from email.generator import BytesGenerator, Generator
+from email.policy import EmailPolicy
+from glob import iglob
+from shutil import rmtree
+from typing import TYPE_CHECKING, Callable, Iterable, Literal, Sequence, cast
+from zipfile import ZIP_DEFLATED, ZIP_STORED
+
+import setuptools
+from setuptools import Command
+
+from . import __version__ as wheel_version
+from .metadata import pkginfo_to_metadata
+from .util import log
+from .vendored.packaging import tags
+from .vendored.packaging import version as _packaging_version
+from .wheelfile import WheelFile
+
+if TYPE_CHECKING:
+    import types
+
+# ensure Python logging is configured
+try:
+    __import__("setuptools.logging")
+except ImportError:
+    # setuptools < ??
+    from . import _setuptools_logging
+
+    _setuptools_logging.configure()
+
+
+def safe_name(name: str) -> str:
+    """Convert an arbitrary string to a standard distribution name
+    Any runs of non-alphanumeric/. characters are replaced with a single '-'.
+    """
+    return re.sub("[^A-Za-z0-9.]+", "-", name)
+
+
+def safe_version(version: str) -> str:
+    """
+    Convert an arbitrary string to a standard version string
+    """
+    try:
+        # normalize the version
+        return str(_packaging_version.Version(version))
+    except _packaging_version.InvalidVersion:
+        version = version.replace(" ", ".")
+        return re.sub("[^A-Za-z0-9.]+", "-", version)
+
+
+setuptools_major_version = int(setuptools.__version__.split(".")[0])
+
+PY_LIMITED_API_PATTERN = r"cp3\d"
+
+
+def _is_32bit_interpreter() -> bool:
+    return struct.calcsize("P") == 4
+
+
+def python_tag() -> str:
+    return f"py{sys.version_info[0]}"
+
+
+def get_platform(archive_root: str | None) -> str:
+    """Return our platform name 'win32', 'linux_x86_64'"""
+    result = sysconfig.get_platform()
+    if result.startswith("macosx") and archive_root is not None:
+        from .macosx_libfile import calculate_macosx_platform_tag
+
+        result = calculate_macosx_platform_tag(archive_root, result)
+    elif _is_32bit_interpreter():
+        if result == "linux-x86_64":
+            # pip pull request #3497
+            result = "linux-i686"
+        elif result == "linux-aarch64":
+            # packaging pull request #234
+            # TODO armv8l, packaging pull request #690 => this did not land
+            # in pip/packaging yet
+            result = "linux-armv7l"
+
+    return result.replace("-", "_")
+
+
+def get_flag(
+    var: str, fallback: bool, expected: bool = True, warn: bool = True
+) -> bool:
+    """Use a fallback value for determining SOABI flags if the needed config
+    var is unset or unavailable."""
+    val = sysconfig.get_config_var(var)
+    if val is None:
+        if warn:
+            warnings.warn(
+                f"Config variable '{var}' is unset, Python ABI tag may be incorrect",
+                RuntimeWarning,
+                stacklevel=2,
+            )
+        return fallback
+    return val == expected
+
+
+def get_abi_tag() -> str | None:
+    """Return the ABI tag based on SOABI (if available) or emulate SOABI (PyPy2)."""
+    soabi: str = sysconfig.get_config_var("SOABI")
+    impl = tags.interpreter_name()
+    if not soabi and impl in ("cp", "pp") and hasattr(sys, "maxunicode"):
+        d = ""
+        m = ""
+        u = ""
+        if get_flag("Py_DEBUG", hasattr(sys, "gettotalrefcount"), warn=(impl == "cp")):
+            d = "d"
+
+        if get_flag(
+            "WITH_PYMALLOC",
+            impl == "cp",
+            warn=(impl == "cp" and sys.version_info < (3, 8)),
+        ) and sys.version_info < (3, 8):
+            m = "m"
+
+        abi = f"{impl}{tags.interpreter_version()}{d}{m}{u}"
+    elif soabi and impl == "cp" and soabi.startswith("cpython"):
+        # non-Windows
+        abi = "cp" + soabi.split("-")[1]
+    elif soabi and impl == "cp" and soabi.startswith("cp"):
+        # Windows
+        abi = soabi.split("-")[0]
+    elif soabi and impl == "pp":
+        # we want something like pypy36-pp73
+        abi = "-".join(soabi.split("-")[:2])
+        abi = abi.replace(".", "_").replace("-", "_")
+    elif soabi and impl == "graalpy":
+        abi = "-".join(soabi.split("-")[:3])
+        abi = abi.replace(".", "_").replace("-", "_")
+    elif soabi:
+        abi = soabi.replace(".", "_").replace("-", "_")
+    else:
+        abi = None
+
+    return abi
+
+
+def safer_name(name: str) -> str:
+    return safe_name(name).replace("-", "_")
+
+
+def safer_version(version: str) -> str:
+    return safe_version(version).replace("-", "_")
+
+
+def remove_readonly(
+    func: Callable[..., object],
+    path: str,
+    excinfo: tuple[type[Exception], Exception, types.TracebackType],
+) -> None:
+    remove_readonly_exc(func, path, excinfo[1])
+
+
+def remove_readonly_exc(func: Callable[..., object], path: str, exc: Exception) -> None:
+    os.chmod(path, stat.S_IWRITE)
+    func(path)
+
+
+class bdist_wheel(Command):
+    description = "create a wheel distribution"
+
+    supported_compressions = {
+        "stored": ZIP_STORED,
+        "deflated": ZIP_DEFLATED,
+    }
+
+    user_options = [
+        ("bdist-dir=", "b", "temporary directory for creating the distribution"),
+        (
+            "plat-name=",
+            "p",
+            "platform name to embed in generated filenames "
+            f"(default: {get_platform(None)})",
+        ),
+        (
+            "keep-temp",
+            "k",
+            "keep the pseudo-installation tree around after "
+            "creating the distribution archive",
+        ),
+        ("dist-dir=", "d", "directory to put final built distributions in"),
+        ("skip-build", None, "skip rebuilding everything (for testing/debugging)"),
+        (
+            "relative",
+            None,
+            "build the archive using relative paths (default: false)",
+        ),
+        (
+            "owner=",
+            "u",
+            "Owner name used when creating a tar file [default: current user]",
+        ),
+        (
+            "group=",
+            "g",
+            "Group name used when creating a tar file [default: current group]",
+        ),
+        ("universal", None, "make a universal wheel (default: false)"),
+        (
+            "compression=",
+            None,
+            "zipfile compression (one of: {}) (default: 'deflated')".format(
+                ", ".join(supported_compressions)
+            ),
+        ),
+        (
+            "python-tag=",
+            None,
+            f"Python implementation compatibility tag (default: '{python_tag()}')",
+        ),
+        (
+            "build-number=",
+            None,
+            "Build number for this particular version. "
+            "As specified in PEP-0427, this must start with a digit. "
+            "[default: None]",
+        ),
+        (
+            "py-limited-api=",
+            None,
+            "Python tag (cp32|cp33|cpNN) for abi3 wheel tag (default: false)",
+        ),
+    ]
+
+    boolean_options = ["keep-temp", "skip-build", "relative", "universal"]
+
+    def initialize_options(self):
+        self.bdist_dir: str = None
+        self.data_dir = None
+        self.plat_name: str | None = None
+        self.plat_tag = None
+        self.format = "zip"
+        self.keep_temp = False
+        self.dist_dir: str | None = None
+        self.egginfo_dir = None
+        self.root_is_pure: bool | None = None
+        self.skip_build = None
+        self.relative = False
+        self.owner = None
+        self.group = None
+        self.universal: bool = False
+        self.compression: str | int = "deflated"
+        self.python_tag: str = python_tag()
+        self.build_number: str | None = None
+        self.py_limited_api: str | Literal[False] = False
+        self.plat_name_supplied = False
+
+    def finalize_options(self):
+        if self.bdist_dir is None:
+            bdist_base = self.get_finalized_command("bdist").bdist_base
+            self.bdist_dir = os.path.join(bdist_base, "wheel")
+
+        egg_info = self.distribution.get_command_obj("egg_info")
+        egg_info.ensure_finalized()  # needed for correct `wheel_dist_name`
+
+        self.data_dir = self.wheel_dist_name + ".data"
+        self.plat_name_supplied = self.plat_name is not None
+
+        try:
+            self.compression = self.supported_compressions[self.compression]
+        except KeyError:
+            raise ValueError(f"Unsupported compression: {self.compression}") from None
+
+        need_options = ("dist_dir", "plat_name", "skip_build")
+
+        self.set_undefined_options("bdist", *zip(need_options, need_options))
+
+        self.root_is_pure = not (
+            self.distribution.has_ext_modules() or self.distribution.has_c_libraries()
+        )
+
+        if self.py_limited_api and not re.match(
+            PY_LIMITED_API_PATTERN, self.py_limited_api
+        ):
+            raise ValueError(f"py-limited-api must match '{PY_LIMITED_API_PATTERN}'")
+
+        # Support legacy [wheel] section for setting universal
+        wheel = self.distribution.get_option_dict("wheel")
+        if "universal" in wheel:
+            # please don't define this in your global configs
+            log.warning(
+                "The [wheel] section is deprecated. Use [bdist_wheel] instead.",
+            )
+            val = wheel["universal"][1].strip()
+            if val.lower() in ("1", "true", "yes"):
+                self.universal = True
+
+        if self.build_number is not None and not self.build_number[:1].isdigit():
+            raise ValueError("Build tag (build-number) must start with a digit.")
+
+    @property
+    def wheel_dist_name(self):
+        """Return distribution full name with - replaced with _"""
+        components = (
+            safer_name(self.distribution.get_name()),
+            safer_version(self.distribution.get_version()),
+        )
+        if self.build_number:
+            components += (self.build_number,)
+        return "-".join(components)
+
+    def get_tag(self) -> tuple[str, str, str]:
+        # bdist sets self.plat_name if unset, we should only use it for purepy
+        # wheels if the user supplied it.
+        if self.plat_name_supplied:
+            plat_name = cast(str, self.plat_name)
+        elif self.root_is_pure:
+            plat_name = "any"
+        else:
+            # macosx contains system version in platform name so need special handle
+            if self.plat_name and not self.plat_name.startswith("macosx"):
+                plat_name = self.plat_name
+            else:
+                # on macosx always limit the platform name to comply with any
+                # c-extension modules in bdist_dir, since the user can specify
+                # a higher MACOSX_DEPLOYMENT_TARGET via tools like CMake
+
+                # on other platforms, and on macosx if there are no c-extension
+                # modules, use the default platform name.
+                plat_name = get_platform(self.bdist_dir)
+
+            if _is_32bit_interpreter():
+                if plat_name in ("linux-x86_64", "linux_x86_64"):
+                    plat_name = "linux_i686"
+                if plat_name in ("linux-aarch64", "linux_aarch64"):
+                    # TODO armv8l, packaging pull request #690 => this did not land
+                    # in pip/packaging yet
+                    plat_name = "linux_armv7l"
+
+        plat_name = (
+            plat_name.lower().replace("-", "_").replace(".", "_").replace(" ", "_")
+        )
+
+        if self.root_is_pure:
+            if self.universal:
+                impl = "py2.py3"
+            else:
+                impl = self.python_tag
+            tag = (impl, "none", plat_name)
+        else:
+            impl_name = tags.interpreter_name()
+            impl_ver = tags.interpreter_version()
+            impl = impl_name + impl_ver
+            # We don't work on CPython 3.1, 3.0.
+            if self.py_limited_api and (impl_name + impl_ver).startswith("cp3"):
+                impl = self.py_limited_api
+                abi_tag = "abi3"
+            else:
+                abi_tag = str(get_abi_tag()).lower()
+            tag = (impl, abi_tag, plat_name)
+            # issue gh-374: allow overriding plat_name
+            supported_tags = [
+                (t.interpreter, t.abi, plat_name) for t in tags.sys_tags()
+            ]
+            assert (
+                tag in supported_tags
+            ), f"would build wheel with unsupported tag {tag}"
+        return tag
+
+    def run(self):
+        build_scripts = self.reinitialize_command("build_scripts")
+        build_scripts.executable = "python"
+        build_scripts.force = True
+
+        build_ext = self.reinitialize_command("build_ext")
+        build_ext.inplace = False
+
+        if not self.skip_build:
+            self.run_command("build")
+
+        install = self.reinitialize_command("install", reinit_subcommands=True)
+        install.root = self.bdist_dir
+        install.compile = False
+        install.skip_build = self.skip_build
+        install.warn_dir = False
+
+        # A wheel without setuptools scripts is more cross-platform.
+        # Use the (undocumented) `no_ep` option to setuptools'
+        # install_scripts command to avoid creating entry point scripts.
+        install_scripts = self.reinitialize_command("install_scripts")
+        install_scripts.no_ep = True
+
+        # Use a custom scheme for the archive, because we have to decide
+        # at installation time which scheme to use.
+        for key in ("headers", "scripts", "data", "purelib", "platlib"):
+            setattr(install, "install_" + key, os.path.join(self.data_dir, key))
+
+        basedir_observed = ""
+
+        if os.name == "nt":
+            # win32 barfs if any of these are ''; could be '.'?
+            # (distutils.command.install:change_roots bug)
+            basedir_observed = os.path.normpath(os.path.join(self.data_dir, ".."))
+            self.install_libbase = self.install_lib = basedir_observed
+
+        setattr(
+            install,
+            "install_purelib" if self.root_is_pure else "install_platlib",
+            basedir_observed,
+        )
+
+        log.info(f"installing to {self.bdist_dir}")
+
+        self.run_command("install")
+
+        impl_tag, abi_tag, plat_tag = self.get_tag()
+        archive_basename = f"{self.wheel_dist_name}-{impl_tag}-{abi_tag}-{plat_tag}"
+        if not self.relative:
+            archive_root = self.bdist_dir
+        else:
+            archive_root = os.path.join(
+                self.bdist_dir, self._ensure_relative(install.install_base)
+            )
+
+        self.set_undefined_options("install_egg_info", ("target", "egginfo_dir"))
+        distinfo_dirname = (
+            f"{safer_name(self.distribution.get_name())}-"
+            f"{safer_version(self.distribution.get_version())}.dist-info"
+        )
+        distinfo_dir = os.path.join(self.bdist_dir, distinfo_dirname)
+        self.egg2dist(self.egginfo_dir, distinfo_dir)
+
+        self.write_wheelfile(distinfo_dir)
+
+        # Make the archive
+        if not os.path.exists(self.dist_dir):
+            os.makedirs(self.dist_dir)
+
+        wheel_path = os.path.join(self.dist_dir, archive_basename + ".whl")
+        with WheelFile(wheel_path, "w", self.compression) as wf:
+            wf.write_files(archive_root)
+
+        # Add to 'Distribution.dist_files' so that the "upload" command works
+        getattr(self.distribution, "dist_files", []).append(
+            (
+                "bdist_wheel",
+                "{}.{}".format(*sys.version_info[:2]),  # like 3.7
+                wheel_path,
+            )
+        )
+
+        if not self.keep_temp:
+            log.info(f"removing {self.bdist_dir}")
+            if not self.dry_run:
+                if sys.version_info < (3, 12):
+                    rmtree(self.bdist_dir, onerror=remove_readonly)
+                else:
+                    rmtree(self.bdist_dir, onexc=remove_readonly_exc)
+
+    def write_wheelfile(
+        self, wheelfile_base: str, generator: str = f"bdist_wheel ({wheel_version})"
+    ):
+        from email.message import Message
+
+        msg = Message()
+        msg["Wheel-Version"] = "1.0"  # of the spec
+        msg["Generator"] = generator
+        msg["Root-Is-Purelib"] = str(self.root_is_pure).lower()
+        if self.build_number is not None:
+            msg["Build"] = self.build_number
+
+        # Doesn't work for bdist_wininst
+        impl_tag, abi_tag, plat_tag = self.get_tag()
+        for impl in impl_tag.split("."):
+            for abi in abi_tag.split("."):
+                for plat in plat_tag.split("."):
+                    msg["Tag"] = "-".join((impl, abi, plat))
+
+        wheelfile_path = os.path.join(wheelfile_base, "WHEEL")
+        log.info(f"creating {wheelfile_path}")
+        with open(wheelfile_path, "wb") as f:
+            BytesGenerator(f, maxheaderlen=0).flatten(msg)
+
+    def _ensure_relative(self, path: str) -> str:
+        # copied from dir_util, deleted
+        drive, path = os.path.splitdrive(path)
+        if path[0:1] == os.sep:
+            path = drive + path[1:]
+        return path
+
+    @property
+    def license_paths(self) -> Iterable[str]:
+        if setuptools_major_version >= 57:
+            # Setuptools has resolved any patterns to actual file names
+            return self.distribution.metadata.license_files or ()
+
+        files: set[str] = set()
+        metadata = self.distribution.get_option_dict("metadata")
+        if setuptools_major_version >= 42:
+            # Setuptools recognizes the license_files option but does not do globbing
+            patterns = cast(Sequence[str], self.distribution.metadata.license_files)
+        else:
+            # Prior to those, wheel is entirely responsible for handling license files
+            if "license_files" in metadata:
+                patterns = metadata["license_files"][1].split()
+            else:
+                patterns = ()
+
+        if "license_file" in metadata:
+            warnings.warn(
+                'The "license_file" option is deprecated. Use "license_files" instead.',
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            files.add(metadata["license_file"][1])
+
+        if not files and not patterns and not isinstance(patterns, list):
+            patterns = ("LICEN[CS]E*", "COPYING*", "NOTICE*", "AUTHORS*")
+
+        for pattern in patterns:
+            for path in iglob(pattern):
+                if path.endswith("~"):
+                    log.debug(
+                        f'ignoring license file "{path}" as it looks like a backup'
+                    )
+                    continue
+
+                if path not in files and os.path.isfile(path):
+                    log.info(
+                        f'adding license file "{path}" (matched pattern "{pattern}")'
+                    )
+                    files.add(path)
+
+        return files
+
+    def egg2dist(self, egginfo_path: str, distinfo_path: str):
+        """Convert an .egg-info directory into a .dist-info directory"""
+
+        def adios(p: str) -> None:
+            """Appropriately delete directory, file or link."""
+            if os.path.exists(p) and not os.path.islink(p) and os.path.isdir(p):
+                shutil.rmtree(p)
+            elif os.path.exists(p):
+                os.unlink(p)
+
+        adios(distinfo_path)
+
+        if not os.path.exists(egginfo_path):
+            # There is no egg-info. This is probably because the egg-info
+            # file/directory is not named matching the distribution name used
+            # to name the archive file. Check for this case and report
+            # accordingly.
+            import glob
+
+            pat = os.path.join(os.path.dirname(egginfo_path), "*.egg-info")
+            possible = glob.glob(pat)
+            err = f"Egg metadata expected at {egginfo_path} but not found"
+            if possible:
+                alt = os.path.basename(possible[0])
+                err += f" ({alt} found - possible misnamed archive file?)"
+
+            raise ValueError(err)
+
+        if os.path.isfile(egginfo_path):
+            # .egg-info is a single file
+            pkg_info = pkginfo_to_metadata(egginfo_path, egginfo_path)
+            os.mkdir(distinfo_path)
+        else:
+            # .egg-info is a directory
+            pkginfo_path = os.path.join(egginfo_path, "PKG-INFO")
+            pkg_info = pkginfo_to_metadata(egginfo_path, pkginfo_path)
+
+            # ignore common egg metadata that is useless to wheel
+            shutil.copytree(
+                egginfo_path,
+                distinfo_path,
+                ignore=lambda x, y: {
+                    "PKG-INFO",
+                    "requires.txt",
+                    "SOURCES.txt",
+                    "not-zip-safe",
+                },
+            )
+
+            # delete dependency_links if it is only whitespace
+            dependency_links_path = os.path.join(distinfo_path, "dependency_links.txt")
+            with open(dependency_links_path, encoding="utf-8") as dependency_links_file:
+                dependency_links = dependency_links_file.read().strip()
+            if not dependency_links:
+                adios(dependency_links_path)
+
+        pkg_info_path = os.path.join(distinfo_path, "METADATA")
+        serialization_policy = EmailPolicy(
+            utf8=True,
+            mangle_from_=False,
+            max_line_length=0,
+        )
+        with open(pkg_info_path, "w", encoding="utf-8") as out:
+            Generator(out, policy=serialization_policy).flatten(pkg_info)
+
+        for license_path in self.license_paths:
+            filename = os.path.basename(license_path)
+            shutil.copy(license_path, os.path.join(distinfo_path, filename))
+
+        adios(egginfo_path)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/_setuptools_logging.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/_setuptools_logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1a2482ba29ac5290c8f7d7688452ec3faf59332
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/_setuptools_logging.py
@@ -0,0 +1,26 @@
+# copied from setuptools.logging, omitting monkeypatching
+from __future__ import annotations
+
+import logging
+import sys
+
+
+def _not_warning(record: logging.LogRecord) -> bool:
+    return record.levelno < logging.WARNING
+
+
+def configure() -> None:
+    """
+    Configure logging to emit warning and above to stderr
+    and everything else to stdout. This behavior is provided
+    for compatibility with distutils.log but may change in
+    the future.
+    """
+    err_handler = logging.StreamHandler()
+    err_handler.setLevel(logging.WARNING)
+    out_handler = logging.StreamHandler(sys.stdout)
+    out_handler.addFilter(_not_warning)
+    handlers = err_handler, out_handler
+    logging.basicConfig(
+        format="{message}", style="{", handlers=handlers, level=logging.DEBUG
+    )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/bdist_wheel.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/bdist_wheel.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd7b8629e5087fe35a2ea7ab6d3cbbb4cacf0031
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/bdist_wheel.py
@@ -0,0 +1,26 @@
+from typing import TYPE_CHECKING
+from warnings import warn
+
+warn(
+    "The 'wheel' package is no longer the canonical location of the 'bdist_wheel' "
+    "command, and will be removed in a future release. Please update to setuptools "
+    "v70.1 or later which contains an integrated version of this command.",
+    DeprecationWarning,
+    stacklevel=1,
+)
+
+if TYPE_CHECKING:
+    from ._bdist_wheel import bdist_wheel as bdist_wheel
+else:
+    try:
+        # Better integration/compatibility with setuptools:
+        # in the case new fixes or PEPs are implemented in setuptools
+        # there is no need to backport them to the deprecated code base.
+        # This is useful in the case of old packages in the ecosystem
+        # that are still used but have low maintenance.
+        from setuptools.command.bdist_wheel import bdist_wheel
+    except ImportError:
+        # Only used in the case of old setuptools versions.
+        # If the user wants to get the latest fixes/PEPs,
+        # they are encouraged to address the deprecation warning.
+        from ._bdist_wheel import bdist_wheel as bdist_wheel
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ba1217f5bdb6106c37ecc2be74d53ef2237b717
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/__init__.py
@@ -0,0 +1,155 @@
+"""
+Wheel command-line utility.
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import sys
+from argparse import ArgumentTypeError
+
+
+class WheelError(Exception):
+    pass
+
+
+def unpack_f(args: argparse.Namespace) -> None:
+    from .unpack import unpack
+
+    unpack(args.wheelfile, args.dest)
+
+
+def pack_f(args: argparse.Namespace) -> None:
+    from .pack import pack
+
+    pack(args.directory, args.dest_dir, args.build_number)
+
+
+def convert_f(args: argparse.Namespace) -> None:
+    from .convert import convert
+
+    convert(args.files, args.dest_dir, args.verbose)
+
+
+def tags_f(args: argparse.Namespace) -> None:
+    from .tags import tags
+
+    names = (
+        tags(
+            wheel,
+            args.python_tag,
+            args.abi_tag,
+            args.platform_tag,
+            args.build,
+            args.remove,
+        )
+        for wheel in args.wheel
+    )
+
+    for name in names:
+        print(name)
+
+
+def version_f(args: argparse.Namespace) -> None:
+    from .. import __version__
+
+    print(f"wheel {__version__}")
+
+
+def parse_build_tag(build_tag: str) -> str:
+    if build_tag and not build_tag[0].isdigit():
+        raise ArgumentTypeError("build tag must begin with a digit")
+    elif "-" in build_tag:
+        raise ArgumentTypeError("invalid character ('-') in build tag")
+
+    return build_tag
+
+
+TAGS_HELP = """\
+Make a new wheel with given tags. Any tags unspecified will remain the same.
+Starting the tags with a "+" will append to the existing tags. Starting with a
+"-" will remove a tag (use --option=-TAG syntax). Multiple tags can be
+separated by ".". The original file will remain unless --remove is given.  The
+output filename(s) will be displayed on stdout for further processing.
+"""
+
+
+def parser():
+    p = argparse.ArgumentParser()
+    s = p.add_subparsers(help="commands")
+
+    unpack_parser = s.add_parser("unpack", help="Unpack wheel")
+    unpack_parser.add_argument(
+        "--dest", "-d", help="Destination directory", default="."
+    )
+    unpack_parser.add_argument("wheelfile", help="Wheel file")
+    unpack_parser.set_defaults(func=unpack_f)
+
+    repack_parser = s.add_parser("pack", help="Repack wheel")
+    repack_parser.add_argument("directory", help="Root directory of the unpacked wheel")
+    repack_parser.add_argument(
+        "--dest-dir",
+        "-d",
+        default=os.path.curdir,
+        help="Directory to store the wheel (default %(default)s)",
+    )
+    repack_parser.add_argument(
+        "--build-number", help="Build tag to use in the wheel name"
+    )
+    repack_parser.set_defaults(func=pack_f)
+
+    convert_parser = s.add_parser("convert", help="Convert egg or wininst to wheel")
+    convert_parser.add_argument("files", nargs="*", help="Files to convert")
+    convert_parser.add_argument(
+        "--dest-dir",
+        "-d",
+        default=os.path.curdir,
+        help="Directory to store wheels (default %(default)s)",
+    )
+    convert_parser.add_argument("--verbose", "-v", action="store_true")
+    convert_parser.set_defaults(func=convert_f)
+
+    tags_parser = s.add_parser(
+        "tags", help="Add or replace the tags on a wheel", description=TAGS_HELP
+    )
+    tags_parser.add_argument("wheel", nargs="*", help="Existing wheel(s) to retag")
+    tags_parser.add_argument(
+        "--remove",
+        action="store_true",
+        help="Remove the original files, keeping only the renamed ones",
+    )
+    tags_parser.add_argument(
+        "--python-tag", metavar="TAG", help="Specify an interpreter tag(s)"
+    )
+    tags_parser.add_argument("--abi-tag", metavar="TAG", help="Specify an ABI tag(s)")
+    tags_parser.add_argument(
+        "--platform-tag", metavar="TAG", help="Specify a platform tag(s)"
+    )
+    tags_parser.add_argument(
+        "--build", type=parse_build_tag, metavar="BUILD", help="Specify a build tag"
+    )
+    tags_parser.set_defaults(func=tags_f)
+
+    version_parser = s.add_parser("version", help="Print version and exit")
+    version_parser.set_defaults(func=version_f)
+
+    help_parser = s.add_parser("help", help="Show this help")
+    help_parser.set_defaults(func=lambda args: p.print_help())
+
+    return p
+
+
+def main():
+    p = parser()
+    args = p.parse_args()
+    if not hasattr(args, "func"):
+        p.print_help()
+    else:
+        try:
+            args.func(args)
+            return 0
+        except WheelError as e:
+            print(e, file=sys.stderr)
+
+    return 1
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/convert.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..61d4775c58596ec12a0c4d4a548de430dee579bf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/convert.py
@@ -0,0 +1,332 @@
+from __future__ import annotations
+
+import os.path
+import re
+from abc import ABCMeta, abstractmethod
+from collections import defaultdict
+from collections.abc import Iterator
+from email.message import Message
+from email.parser import Parser
+from email.policy import EmailPolicy
+from glob import iglob
+from pathlib import Path
+from textwrap import dedent
+from zipfile import ZipFile
+
+from .. import __version__
+from ..metadata import generate_requirements
+from ..vendored.packaging.tags import parse_tag
+from ..wheelfile import WheelFile
+
+egg_filename_re = re.compile(
+    r"""
+    (?P<name>.+?)-(?P<ver>.+?)
+    (-(?P<pyver>py\d\.\d+)
+     (-(?P<arch>.+?))?
+    )?.egg$""",
+    re.VERBOSE,
+)
+egg_info_re = re.compile(
+    r"""
+    ^(?P<name>.+?)-(?P<ver>.+?)
+    (-(?P<pyver>py\d\.\d+)
+    )?.egg-info/""",
+    re.VERBOSE,
+)
+wininst_re = re.compile(
+    r"\.(?P<platform>win32|win-amd64)(?:-(?P<pyver>py\d\.\d))?\.exe$"
+)
+pyd_re = re.compile(r"\.(?P<abi>[a-z0-9]+)-(?P<platform>win32|win_amd64)\.pyd$")
+serialization_policy = EmailPolicy(
+    utf8=True,
+    mangle_from_=False,
+    max_line_length=0,
+)
+GENERATOR = f"wheel {__version__}"
+
+
+def convert_requires(requires: str, metadata: Message) -> None:
+    extra: str | None = None
+    requirements: dict[str | None, list[str]] = defaultdict(list)
+    for line in requires.splitlines():
+        line = line.strip()
+        if not line:
+            continue
+
+        if line.startswith("[") and line.endswith("]"):
+            extra = line[1:-1]
+            continue
+
+        requirements[extra].append(line)
+
+    for key, value in generate_requirements(requirements):
+        metadata.add_header(key, value)
+
+
+def convert_pkg_info(pkginfo: str, metadata: Message):
+    parsed_message = Parser().parsestr(pkginfo)
+    for key, value in parsed_message.items():
+        key_lower = key.lower()
+        if value == "UNKNOWN":
+            continue
+
+        if key_lower == "description":
+            description_lines = value.splitlines()
+            value = "\n".join(
+                (
+                    description_lines[0].lstrip(),
+                    dedent("\n".join(description_lines[1:])),
+                    "\n",
+                )
+            )
+            metadata.set_payload(value)
+        elif key_lower == "home-page":
+            metadata.add_header("Project-URL", f"Homepage, {value}")
+        elif key_lower == "download-url":
+            metadata.add_header("Project-URL", f"Download, {value}")
+        else:
+            metadata.add_header(key, value)
+
+    metadata.replace_header("Metadata-Version", "2.4")
+
+
+def normalize(name: str) -> str:
+    return re.sub(r"[-_.]+", "-", name).lower().replace("-", "_")
+
+
+class ConvertSource(metaclass=ABCMeta):
+    name: str
+    version: str
+    pyver: str = "py2.py3"
+    abi: str = "none"
+    platform: str = "any"
+    metadata: Message
+
+    @property
+    def dist_info_dir(self) -> str:
+        return f"{self.name}-{self.version}.dist-info"
+
+    @abstractmethod
+    def generate_contents(self) -> Iterator[tuple[str, bytes]]:
+        pass
+
+
+class EggFileSource(ConvertSource):
+    def __init__(self, path: Path):
+        if not (match := egg_filename_re.match(path.name)):
+            raise ValueError(f"Invalid egg file name: {path.name}")
+
+        # Binary wheels are assumed to be for CPython
+        self.path = path
+        self.name = normalize(match.group("name"))
+        self.version = match.group("ver")
+        if pyver := match.group("pyver"):
+            self.pyver = pyver.replace(".", "")
+            if arch := match.group("arch"):
+                self.abi = self.pyver.replace("py", "cp")
+                self.platform = normalize(arch)
+
+        self.metadata = Message()
+
+    def generate_contents(self) -> Iterator[tuple[str, bytes]]:
+        with ZipFile(self.path, "r") as zip_file:
+            for filename in sorted(zip_file.namelist()):
+                # Skip pure directory entries
+                if filename.endswith("/"):
+                    continue
+
+                # Handle files in the egg-info directory specially, selectively moving
+                # them to the dist-info directory while converting as needed
+                if filename.startswith("EGG-INFO/"):
+                    if filename == "EGG-INFO/requires.txt":
+                        requires = zip_file.read(filename).decode("utf-8")
+                        convert_requires(requires, self.metadata)
+                    elif filename == "EGG-INFO/PKG-INFO":
+                        pkginfo = zip_file.read(filename).decode("utf-8")
+                        convert_pkg_info(pkginfo, self.metadata)
+                    elif filename == "EGG-INFO/entry_points.txt":
+                        yield (
+                            f"{self.dist_info_dir}/entry_points.txt",
+                            zip_file.read(filename),
+                        )
+
+                    continue
+
+                # For any other file, just pass it through
+                yield filename, zip_file.read(filename)
+
+
+class EggDirectorySource(EggFileSource):
+    def generate_contents(self) -> Iterator[tuple[str, bytes]]:
+        for dirpath, _, filenames in os.walk(self.path):
+            for filename in sorted(filenames):
+                path = Path(dirpath, filename)
+                if path.parent.name == "EGG-INFO":
+                    if path.name == "requires.txt":
+                        requires = path.read_text("utf-8")
+                        convert_requires(requires, self.metadata)
+                    elif path.name == "PKG-INFO":
+                        pkginfo = path.read_text("utf-8")
+                        convert_pkg_info(pkginfo, self.metadata)
+                        if name := self.metadata.get("Name"):
+                            self.name = normalize(name)
+
+                        if version := self.metadata.get("Version"):
+                            self.version = version
+                    elif path.name == "entry_points.txt":
+                        yield (
+                            f"{self.dist_info_dir}/entry_points.txt",
+                            path.read_bytes(),
+                        )
+
+                    continue
+
+                # For any other file, just pass it through
+                yield str(path.relative_to(self.path)), path.read_bytes()
+
+
+class WininstFileSource(ConvertSource):
+    """
+    Handles distributions created with ``bdist_wininst``.
+
+    The egginfo filename has the format::
+
+        name-ver(-pyver)(-arch).egg-info
+
+    The installer filename has the format::
+
+        name-ver.arch(-pyver).exe
+
+    Some things to note:
+
+    1. The installer filename is not definitive. An installer can be renamed
+       and work perfectly well as an installer. So more reliable data should
+       be used whenever possible.
+    2. The egg-info data should be preferred for the name and version, because
+       these come straight from the distutils metadata, and are mandatory.
+    3. The pyver from the egg-info data should be ignored, as it is
+       constructed from the version of Python used to build the installer,
+       which is irrelevant - the installer filename is correct here (even to
+       the point that when it's not there, any version is implied).
+    4. The architecture must be taken from the installer filename, as it is
+       not included in the egg-info data.
+    5. Architecture-neutral installers still have an architecture because the
+       installer format itself (being executable) is architecture-specific. We
+       should therefore ignore the architecture if the content is pure-python.
+    """
+
+    def __init__(self, path: Path):
+        self.path = path
+        self.metadata = Message()
+
+        # Determine the initial architecture and Python version from the file name
+        # (if possible)
+        if match := wininst_re.search(path.name):
+            self.platform = normalize(match.group("platform"))
+            if pyver := match.group("pyver"):
+                self.pyver = pyver.replace(".", "")
+
+        # Look for an .egg-info directory and any .pyd files for more precise info
+        egg_info_found = pyd_found = False
+        with ZipFile(self.path) as zip_file:
+            for filename in zip_file.namelist():
+                prefix, filename = filename.split("/", 1)
+                if not egg_info_found and (match := egg_info_re.match(filename)):
+                    egg_info_found = True
+                    self.name = normalize(match.group("name"))
+                    self.version = match.group("ver")
+                    if pyver := match.group("pyver"):
+                        self.pyver = pyver.replace(".", "")
+                elif not pyd_found and (match := pyd_re.search(filename)):
+                    pyd_found = True
+                    self.abi = match.group("abi")
+                    self.platform = match.group("platform")
+
+                if egg_info_found and pyd_found:
+                    break
+
+    def generate_contents(self) -> Iterator[tuple[str, bytes]]:
+        dist_info_dir = f"{self.name}-{self.version}.dist-info"
+        data_dir = f"{self.name}-{self.version}.data"
+        with ZipFile(self.path, "r") as zip_file:
+            for filename in sorted(zip_file.namelist()):
+                # Skip pure directory entries
+                if filename.endswith("/"):
+                    continue
+
+                # Handle files in the egg-info directory specially, selectively moving
+                # them to the dist-info directory while converting as needed
+                prefix, target_filename = filename.split("/", 1)
+                if egg_info_re.search(target_filename):
+                    basename = target_filename.rsplit("/", 1)[-1]
+                    if basename == "requires.txt":
+                        requires = zip_file.read(filename).decode("utf-8")
+                        convert_requires(requires, self.metadata)
+                    elif basename == "PKG-INFO":
+                        pkginfo = zip_file.read(filename).decode("utf-8")
+                        convert_pkg_info(pkginfo, self.metadata)
+                    elif basename == "entry_points.txt":
+                        yield (
+                            f"{dist_info_dir}/entry_points.txt",
+                            zip_file.read(filename),
+                        )
+
+                    continue
+                elif prefix == "SCRIPTS":
+                    target_filename = f"{data_dir}/scripts/{target_filename}"
+
+                # For any other file, just pass it through
+                yield target_filename, zip_file.read(filename)
+
+
+def convert(files: list[str], dest_dir: str, verbose: bool) -> None:
+    for pat in files:
+        for archive in iglob(pat):
+            path = Path(archive)
+            if path.suffix == ".egg":
+                if path.is_dir():
+                    source: ConvertSource = EggDirectorySource(path)
+                else:
+                    source = EggFileSource(path)
+            else:
+                source = WininstFileSource(path)
+
+            if verbose:
+                print(f"{archive}...", flush=True, end="")
+
+            dest_path = Path(dest_dir) / (
+                f"{source.name}-{source.version}-{source.pyver}-{source.abi}"
+                f"-{source.platform}.whl"
+            )
+            with WheelFile(dest_path, "w") as wheelfile:
+                for name_or_zinfo, contents in source.generate_contents():
+                    wheelfile.writestr(name_or_zinfo, contents)
+
+                # Write the METADATA file
+                wheelfile.writestr(
+                    f"{source.dist_info_dir}/METADATA",
+                    source.metadata.as_string(policy=serialization_policy).encode(
+                        "utf-8"
+                    ),
+                )
+
+                # Write the WHEEL file
+                wheel_message = Message()
+                wheel_message.add_header("Wheel-Version", "1.0")
+                wheel_message.add_header("Generator", GENERATOR)
+                wheel_message.add_header(
+                    "Root-Is-Purelib", str(source.platform == "any").lower()
+                )
+                tags = parse_tag(f"{source.pyver}-{source.abi}-{source.platform}")
+                for tag in sorted(tags, key=lambda tag: tag.interpreter):
+                    wheel_message.add_header("Tag", str(tag))
+
+                wheelfile.writestr(
+                    f"{source.dist_info_dir}/WHEEL",
+                    wheel_message.as_string(policy=serialization_policy).encode(
+                        "utf-8"
+                    ),
+                )
+
+            if verbose:
+                print("OK")
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/pack.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/pack.py
new file mode 100644
index 0000000000000000000000000000000000000000..64469c0c730c24449e77c23f46d0e3f68d647d67
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/pack.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+import email.policy
+import os.path
+import re
+from email.generator import BytesGenerator
+from email.parser import BytesParser
+
+from wheel.cli import WheelError
+from wheel.wheelfile import WheelFile
+
+DIST_INFO_RE = re.compile(r"^(?P<namever>(?P<name>.+?)-(?P<ver>\d.*?))\.dist-info$")
+
+
+def pack(directory: str, dest_dir: str, build_number: str | None) -> None:
+    """Repack a previously unpacked wheel directory into a new wheel file.
+
+    The .dist-info/WHEEL file must contain one or more tags so that the target
+    wheel file name can be determined.
+
+    :param directory: The unpacked wheel directory
+    :param dest_dir: Destination directory (defaults to the current directory)
+    """
+    # Find the .dist-info directory
+    dist_info_dirs = [
+        fn
+        for fn in os.listdir(directory)
+        if os.path.isdir(os.path.join(directory, fn)) and DIST_INFO_RE.match(fn)
+    ]
+    if len(dist_info_dirs) > 1:
+        raise WheelError(f"Multiple .dist-info directories found in {directory}")
+    elif not dist_info_dirs:
+        raise WheelError(f"No .dist-info directories found in {directory}")
+
+    # Determine the target wheel filename
+    dist_info_dir = dist_info_dirs[0]
+    name_version = DIST_INFO_RE.match(dist_info_dir).group("namever")
+
+    # Read the tags and the existing build number from .dist-info/WHEEL
+    wheel_file_path = os.path.join(directory, dist_info_dir, "WHEEL")
+    with open(wheel_file_path, "rb") as f:
+        info = BytesParser(policy=email.policy.compat32).parse(f)
+        tags: list[str] = info.get_all("Tag", [])
+        existing_build_number = info.get("Build")
+
+        if not tags:
+            raise WheelError(
+                f"No tags present in {dist_info_dir}/WHEEL; cannot determine target "
+                f"wheel filename"
+            )
+
+    # Set the wheel file name and add/replace/remove the Build tag in .dist-info/WHEEL
+    build_number = build_number if build_number is not None else existing_build_number
+    if build_number is not None:
+        del info["Build"]
+        if build_number:
+            info["Build"] = build_number
+            name_version += "-" + build_number
+
+        if build_number != existing_build_number:
+            with open(wheel_file_path, "wb") as f:
+                BytesGenerator(f, maxheaderlen=0).flatten(info)
+
+    # Reassemble the tags for the wheel file
+    tagline = compute_tagline(tags)
+
+    # Repack the wheel
+    wheel_path = os.path.join(dest_dir, f"{name_version}-{tagline}.whl")
+    with WheelFile(wheel_path, "w") as wf:
+        print(f"Repacking wheel as {wheel_path}...", end="", flush=True)
+        wf.write_files(directory)
+
+    print("OK")
+
+
+def compute_tagline(tags: list[str]) -> str:
+    """Compute a tagline from a list of tags.
+
+    :param tags: A list of tags
+    :return: A tagline
+    """
+    impls = sorted({tag.split("-")[0] for tag in tags})
+    abivers = sorted({tag.split("-")[1] for tag in tags})
+    platforms = sorted({tag.split("-")[2] for tag in tags})
+    return "-".join([".".join(impls), ".".join(abivers), ".".join(platforms)])
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/tags.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/tags.py
new file mode 100644
index 0000000000000000000000000000000000000000..88da72e9ec4a31e2427bdb2bcf2b3e0ce3c0beb0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/tags.py
@@ -0,0 +1,139 @@
+from __future__ import annotations
+
+import email.policy
+import itertools
+import os
+from collections.abc import Iterable
+from email.parser import BytesParser
+
+from ..wheelfile import WheelFile
+
+
+def _compute_tags(original_tags: Iterable[str], new_tags: str | None) -> set[str]:
+    """Add or replace tags. Supports dot-separated tags"""
+    if new_tags is None:
+        return set(original_tags)
+
+    if new_tags.startswith("+"):
+        return {*original_tags, *new_tags[1:].split(".")}
+
+    if new_tags.startswith("-"):
+        return set(original_tags) - set(new_tags[1:].split("."))
+
+    return set(new_tags.split("."))
+
+
+def tags(
+    wheel: str,
+    python_tags: str | None = None,
+    abi_tags: str | None = None,
+    platform_tags: str | None = None,
+    build_tag: str | None = None,
+    remove: bool = False,
+) -> str:
+    """Change the tags on a wheel file.
+
+    The tags are left unchanged if they are not specified. To specify "none",
+    use ["none"]. To append to the previous tags, a tag should start with a
+    "+".  If a tag starts with "-", it will be removed from existing tags.
+    Processing is done left to right.
+
+    :param wheel: The paths to the wheels
+    :param python_tags: The Python tags to set
+    :param abi_tags: The ABI tags to set
+    :param platform_tags: The platform tags to set
+    :param build_tag: The build tag to set
+    :param remove: Remove the original wheel
+    """
+    with WheelFile(wheel, "r") as f:
+        assert f.filename, f"{f.filename} must be available"
+
+        wheel_info = f.read(f.dist_info_path + "/WHEEL")
+        info = BytesParser(policy=email.policy.compat32).parsebytes(wheel_info)
+
+        original_wheel_name = os.path.basename(f.filename)
+        namever = f.parsed_filename.group("namever")
+        build = f.parsed_filename.group("build")
+        original_python_tags = f.parsed_filename.group("pyver").split(".")
+        original_abi_tags = f.parsed_filename.group("abi").split(".")
+        original_plat_tags = f.parsed_filename.group("plat").split(".")
+
+    tags: list[str] = info.get_all("Tag", [])
+    existing_build_tag = info.get("Build")
+
+    impls = {tag.split("-")[0] for tag in tags}
+    abivers = {tag.split("-")[1] for tag in tags}
+    platforms = {tag.split("-")[2] for tag in tags}
+
+    if impls != set(original_python_tags):
+        msg = f"Wheel internal tags {impls!r} != filename tags {original_python_tags!r}"
+        raise AssertionError(msg)
+
+    if abivers != set(original_abi_tags):
+        msg = f"Wheel internal tags {abivers!r} != filename tags {original_abi_tags!r}"
+        raise AssertionError(msg)
+
+    if platforms != set(original_plat_tags):
+        msg = (
+            f"Wheel internal tags {platforms!r} != filename tags {original_plat_tags!r}"
+        )
+        raise AssertionError(msg)
+
+    if existing_build_tag != build:
+        msg = (
+            f"Incorrect filename '{build}' "
+            f"& *.dist-info/WHEEL '{existing_build_tag}' build numbers"
+        )
+        raise AssertionError(msg)
+
+    # Start changing as needed
+    if build_tag is not None:
+        build = build_tag
+
+    final_python_tags = sorted(_compute_tags(original_python_tags, python_tags))
+    final_abi_tags = sorted(_compute_tags(original_abi_tags, abi_tags))
+    final_plat_tags = sorted(_compute_tags(original_plat_tags, platform_tags))
+
+    final_tags = [
+        namever,
+        ".".join(final_python_tags),
+        ".".join(final_abi_tags),
+        ".".join(final_plat_tags),
+    ]
+    if build:
+        final_tags.insert(1, build)
+
+    final_wheel_name = "-".join(final_tags) + ".whl"
+
+    if original_wheel_name != final_wheel_name:
+        del info["Tag"], info["Build"]
+        for a, b, c in itertools.product(
+            final_python_tags, final_abi_tags, final_plat_tags
+        ):
+            info["Tag"] = f"{a}-{b}-{c}"
+        if build:
+            info["Build"] = build
+
+        original_wheel_path = os.path.join(
+            os.path.dirname(f.filename), original_wheel_name
+        )
+        final_wheel_path = os.path.join(os.path.dirname(f.filename), final_wheel_name)
+
+        with WheelFile(original_wheel_path, "r") as fin, WheelFile(
+            final_wheel_path, "w"
+        ) as fout:
+            fout.comment = fin.comment  # preserve the comment
+            for item in fin.infolist():
+                if item.is_dir():
+                    continue
+                if item.filename == f.dist_info_path + "/RECORD":
+                    continue
+                if item.filename == f.dist_info_path + "/WHEEL":
+                    fout.writestr(item, info.as_bytes())
+                else:
+                    fout.writestr(item, fin.read(item))
+
+        if remove:
+            os.remove(original_wheel_path)
+
+    return final_wheel_name
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/unpack.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/unpack.py
new file mode 100644
index 0000000000000000000000000000000000000000..d48840e6ec0512225233bf02d1d7ce203415b04c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/cli/unpack.py
@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+from ..wheelfile import WheelFile
+
+
+def unpack(path: str, dest: str = ".") -> None:
+    """Unpack a wheel.
+
+    Wheel content will be unpacked to {dest}/{name}-{ver}, where {name}
+    is the package name and {ver} its version.
+
+    :param path: The path to the wheel.
+    :param dest: Destination directory (default to current directory).
+    """
+    with WheelFile(path) as wf:
+        namever = wf.parsed_filename.group("namever")
+        destination = Path(dest) / namever
+        print(f"Unpacking to: {destination}...", end="", flush=True)
+        for zinfo in wf.filelist:
+            wf.extract(zinfo, destination)
+
+            # Set permissions to the same values as they were set in the archive
+            # We have to do this manually due to
+            # https://github.com/python/cpython/issues/59999
+            permissions = zinfo.external_attr >> 16 & 0o777
+            destination.joinpath(zinfo.filename).chmod(permissions)
+
+    print("OK")
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/macosx_libfile.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/macosx_libfile.py
new file mode 100644
index 0000000000000000000000000000000000000000..abdfc9eda1d4c1f33270c155e1610fe73bd54263
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/macosx_libfile.py
@@ -0,0 +1,482 @@
+"""
+This module contains function to analyse dynamic library
+headers to extract system information
+
+Currently only for MacOSX
+
+Library file on macosx system starts with Mach-O or Fat field.
+This can be distinguish by first 32 bites and it is called magic number.
+Proper value of magic number is with suffix _MAGIC. Suffix _CIGAM means
+reversed bytes order.
+Both fields can occur in two types: 32 and 64 bytes.
+
+FAT field inform that this library contains few version of library
+(typically for different types version). It contains
+information where Mach-O headers starts.
+
+Each section started with Mach-O header contains one library
+(So if file starts with this field it contains only one version).
+
+After filed Mach-O there are section fields.
+Each of them starts with two fields:
+cmd - magic number for this command
+cmdsize - total size occupied by this section information.
+
+In this case only sections LC_VERSION_MIN_MACOSX (for macosx 10.13 and earlier)
+and LC_BUILD_VERSION (for macosx 10.14 and newer) are interesting,
+because them contains information about minimal system version.
+
+Important remarks:
+- For fat files this implementation looks for maximum number version.
+  It not check if it is 32 or 64 and do not compare it with currently built package.
+  So it is possible to false report higher version that needed.
+- All structures signatures are taken form macosx header files.
+- I think that binary format will be more stable than `otool` output.
+  and if apple introduce some changes both implementation will need to be updated.
+- The system compile will set the deployment target no lower than
+  11.0 for arm64 builds. For "Universal 2" builds use the x86_64 deployment
+  target when the arm64 target is 11.0.
+"""
+
+from __future__ import annotations
+
+import ctypes
+import os
+import sys
+from io import BufferedIOBase
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from typing import Union
+
+    StrPath = Union[str, os.PathLike[str]]
+
+"""here the needed const and struct from mach-o header files"""
+
+FAT_MAGIC = 0xCAFEBABE
+FAT_CIGAM = 0xBEBAFECA
+FAT_MAGIC_64 = 0xCAFEBABF
+FAT_CIGAM_64 = 0xBFBAFECA
+MH_MAGIC = 0xFEEDFACE
+MH_CIGAM = 0xCEFAEDFE
+MH_MAGIC_64 = 0xFEEDFACF
+MH_CIGAM_64 = 0xCFFAEDFE
+
+LC_VERSION_MIN_MACOSX = 0x24
+LC_BUILD_VERSION = 0x32
+
+CPU_TYPE_ARM64 = 0x0100000C
+
+mach_header_fields = [
+    ("magic", ctypes.c_uint32),
+    ("cputype", ctypes.c_int),
+    ("cpusubtype", ctypes.c_int),
+    ("filetype", ctypes.c_uint32),
+    ("ncmds", ctypes.c_uint32),
+    ("sizeofcmds", ctypes.c_uint32),
+    ("flags", ctypes.c_uint32),
+]
+"""
+struct mach_header {
+    uint32_t	magic;		/* mach magic number identifier */
+    cpu_type_t	cputype;	/* cpu specifier */
+    cpu_subtype_t	cpusubtype;	/* machine specifier */
+    uint32_t	filetype;	/* type of file */
+    uint32_t	ncmds;		/* number of load commands */
+    uint32_t	sizeofcmds;	/* the size of all the load commands */
+    uint32_t	flags;		/* flags */
+};
+typedef integer_t cpu_type_t;
+typedef integer_t cpu_subtype_t;
+"""
+
+mach_header_fields_64 = mach_header_fields + [("reserved", ctypes.c_uint32)]
+"""
+struct mach_header_64 {
+    uint32_t	magic;		/* mach magic number identifier */
+    cpu_type_t	cputype;	/* cpu specifier */
+    cpu_subtype_t	cpusubtype;	/* machine specifier */
+    uint32_t	filetype;	/* type of file */
+    uint32_t	ncmds;		/* number of load commands */
+    uint32_t	sizeofcmds;	/* the size of all the load commands */
+    uint32_t	flags;		/* flags */
+    uint32_t	reserved;	/* reserved */
+};
+"""
+
+fat_header_fields = [("magic", ctypes.c_uint32), ("nfat_arch", ctypes.c_uint32)]
+"""
+struct fat_header {
+    uint32_t	magic;		/* FAT_MAGIC or FAT_MAGIC_64 */
+    uint32_t	nfat_arch;	/* number of structs that follow */
+};
+"""
+
+fat_arch_fields = [
+    ("cputype", ctypes.c_int),
+    ("cpusubtype", ctypes.c_int),
+    ("offset", ctypes.c_uint32),
+    ("size", ctypes.c_uint32),
+    ("align", ctypes.c_uint32),
+]
+"""
+struct fat_arch {
+    cpu_type_t	cputype;	/* cpu specifier (int) */
+    cpu_subtype_t	cpusubtype;	/* machine specifier (int) */
+    uint32_t	offset;		/* file offset to this object file */
+    uint32_t	size;		/* size of this object file */
+    uint32_t	align;		/* alignment as a power of 2 */
+};
+"""
+
+fat_arch_64_fields = [
+    ("cputype", ctypes.c_int),
+    ("cpusubtype", ctypes.c_int),
+    ("offset", ctypes.c_uint64),
+    ("size", ctypes.c_uint64),
+    ("align", ctypes.c_uint32),
+    ("reserved", ctypes.c_uint32),
+]
+"""
+struct fat_arch_64 {
+    cpu_type_t	cputype;	/* cpu specifier (int) */
+    cpu_subtype_t	cpusubtype;	/* machine specifier (int) */
+    uint64_t	offset;		/* file offset to this object file */
+    uint64_t	size;		/* size of this object file */
+    uint32_t	align;		/* alignment as a power of 2 */
+    uint32_t	reserved;	/* reserved */
+};
+"""
+
+segment_base_fields = [("cmd", ctypes.c_uint32), ("cmdsize", ctypes.c_uint32)]
+"""base for reading segment info"""
+
+segment_command_fields = [
+    ("cmd", ctypes.c_uint32),
+    ("cmdsize", ctypes.c_uint32),
+    ("segname", ctypes.c_char * 16),
+    ("vmaddr", ctypes.c_uint32),
+    ("vmsize", ctypes.c_uint32),
+    ("fileoff", ctypes.c_uint32),
+    ("filesize", ctypes.c_uint32),
+    ("maxprot", ctypes.c_int),
+    ("initprot", ctypes.c_int),
+    ("nsects", ctypes.c_uint32),
+    ("flags", ctypes.c_uint32),
+]
+"""
+struct segment_command { /* for 32-bit architectures */
+    uint32_t	cmd;		/* LC_SEGMENT */
+    uint32_t	cmdsize;	/* includes sizeof section structs */
+    char		segname[16];	/* segment name */
+    uint32_t	vmaddr;		/* memory address of this segment */
+    uint32_t	vmsize;		/* memory size of this segment */
+    uint32_t	fileoff;	/* file offset of this segment */
+    uint32_t	filesize;	/* amount to map from the file */
+    vm_prot_t	maxprot;	/* maximum VM protection */
+    vm_prot_t	initprot;	/* initial VM protection */
+    uint32_t	nsects;		/* number of sections in segment */
+    uint32_t	flags;		/* flags */
+};
+typedef int vm_prot_t;
+"""
+
+segment_command_fields_64 = [
+    ("cmd", ctypes.c_uint32),
+    ("cmdsize", ctypes.c_uint32),
+    ("segname", ctypes.c_char * 16),
+    ("vmaddr", ctypes.c_uint64),
+    ("vmsize", ctypes.c_uint64),
+    ("fileoff", ctypes.c_uint64),
+    ("filesize", ctypes.c_uint64),
+    ("maxprot", ctypes.c_int),
+    ("initprot", ctypes.c_int),
+    ("nsects", ctypes.c_uint32),
+    ("flags", ctypes.c_uint32),
+]
+"""
+struct segment_command_64 { /* for 64-bit architectures */
+    uint32_t	cmd;		/* LC_SEGMENT_64 */
+    uint32_t	cmdsize;	/* includes sizeof section_64 structs */
+    char		segname[16];	/* segment name */
+    uint64_t	vmaddr;		/* memory address of this segment */
+    uint64_t	vmsize;		/* memory size of this segment */
+    uint64_t	fileoff;	/* file offset of this segment */
+    uint64_t	filesize;	/* amount to map from the file */
+    vm_prot_t	maxprot;	/* maximum VM protection */
+    vm_prot_t	initprot;	/* initial VM protection */
+    uint32_t	nsects;		/* number of sections in segment */
+    uint32_t	flags;		/* flags */
+};
+"""
+
+version_min_command_fields = segment_base_fields + [
+    ("version", ctypes.c_uint32),
+    ("sdk", ctypes.c_uint32),
+]
+"""
+struct version_min_command {
+    uint32_t	cmd;		/* LC_VERSION_MIN_MACOSX or
+                               LC_VERSION_MIN_IPHONEOS or
+                               LC_VERSION_MIN_WATCHOS or
+                               LC_VERSION_MIN_TVOS */
+    uint32_t	cmdsize;	/* sizeof(struct min_version_command) */
+    uint32_t	version;	/* X.Y.Z is encoded in nibbles xxxx.yy.zz */
+    uint32_t	sdk;		/* X.Y.Z is encoded in nibbles xxxx.yy.zz */
+};
+"""
+
+build_version_command_fields = segment_base_fields + [
+    ("platform", ctypes.c_uint32),
+    ("minos", ctypes.c_uint32),
+    ("sdk", ctypes.c_uint32),
+    ("ntools", ctypes.c_uint32),
+]
+"""
+struct build_version_command {
+    uint32_t	cmd;		/* LC_BUILD_VERSION */
+    uint32_t	cmdsize;	/* sizeof(struct build_version_command) plus */
+                                /* ntools * sizeof(struct build_tool_version) */
+    uint32_t	platform;	/* platform */
+    uint32_t	minos;		/* X.Y.Z is encoded in nibbles xxxx.yy.zz */
+    uint32_t	sdk;		/* X.Y.Z is encoded in nibbles xxxx.yy.zz */
+    uint32_t	ntools;		/* number of tool entries following this */
+};
+"""
+
+
+def swap32(x: int) -> int:
+    return (
+        ((x << 24) & 0xFF000000)
+        | ((x << 8) & 0x00FF0000)
+        | ((x >> 8) & 0x0000FF00)
+        | ((x >> 24) & 0x000000FF)
+    )
+
+
+def get_base_class_and_magic_number(
+    lib_file: BufferedIOBase,
+    seek: int | None = None,
+) -> tuple[type[ctypes.Structure], int]:
+    if seek is None:
+        seek = lib_file.tell()
+    else:
+        lib_file.seek(seek)
+    magic_number = ctypes.c_uint32.from_buffer_copy(
+        lib_file.read(ctypes.sizeof(ctypes.c_uint32))
+    ).value
+
+    # Handle wrong byte order
+    if magic_number in [FAT_CIGAM, FAT_CIGAM_64, MH_CIGAM, MH_CIGAM_64]:
+        if sys.byteorder == "little":
+            BaseClass = ctypes.BigEndianStructure
+        else:
+            BaseClass = ctypes.LittleEndianStructure
+
+        magic_number = swap32(magic_number)
+    else:
+        BaseClass = ctypes.Structure
+
+    lib_file.seek(seek)
+    return BaseClass, magic_number
+
+
+def read_data(struct_class: type[ctypes.Structure], lib_file: BufferedIOBase):
+    return struct_class.from_buffer_copy(lib_file.read(ctypes.sizeof(struct_class)))
+
+
+def extract_macosx_min_system_version(path_to_lib: str):
+    with open(path_to_lib, "rb") as lib_file:
+        BaseClass, magic_number = get_base_class_and_magic_number(lib_file, 0)
+        if magic_number not in [FAT_MAGIC, FAT_MAGIC_64, MH_MAGIC, MH_MAGIC_64]:
+            return
+
+        if magic_number in [FAT_MAGIC, FAT_CIGAM_64]:
+
+            class FatHeader(BaseClass):
+                _fields_ = fat_header_fields
+
+            fat_header = read_data(FatHeader, lib_file)
+            if magic_number == FAT_MAGIC:
+
+                class FatArch(BaseClass):
+                    _fields_ = fat_arch_fields
+
+            else:
+
+                class FatArch(BaseClass):
+                    _fields_ = fat_arch_64_fields
+
+            fat_arch_list = [
+                read_data(FatArch, lib_file) for _ in range(fat_header.nfat_arch)
+            ]
+
+            versions_list: list[tuple[int, int, int]] = []
+            for el in fat_arch_list:
+                try:
+                    version = read_mach_header(lib_file, el.offset)
+                    if version is not None:
+                        if el.cputype == CPU_TYPE_ARM64 and len(fat_arch_list) != 1:
+                            # Xcode will not set the deployment target below 11.0.0
+                            # for the arm64 architecture. Ignore the arm64 deployment
+                            # in fat binaries when the target is 11.0.0, that way
+                            # the other architectures can select a lower deployment
+                            # target.
+                            # This is safe because there is no arm64 variant for
+                            # macOS 10.15 or earlier.
+                            if version == (11, 0, 0):
+                                continue
+                        versions_list.append(version)
+                except ValueError:
+                    pass
+
+            if len(versions_list) > 0:
+                return max(versions_list)
+            else:
+                return None
+
+        else:
+            try:
+                return read_mach_header(lib_file, 0)
+            except ValueError:
+                """when some error during read library files"""
+                return None
+
+
+def read_mach_header(
+    lib_file: BufferedIOBase,
+    seek: int | None = None,
+) -> tuple[int, int, int] | None:
+    """
+    This function parses a Mach-O header and extracts
+    information about the minimal macOS version.
+
+    :param lib_file: reference to opened library file with pointer
+    """
+    base_class, magic_number = get_base_class_and_magic_number(lib_file, seek)
+    arch = "32" if magic_number == MH_MAGIC else "64"
+
+    class SegmentBase(base_class):
+        _fields_ = segment_base_fields
+
+    if arch == "32":
+
+        class MachHeader(base_class):
+            _fields_ = mach_header_fields
+
+    else:
+
+        class MachHeader(base_class):
+            _fields_ = mach_header_fields_64
+
+    mach_header = read_data(MachHeader, lib_file)
+    for _i in range(mach_header.ncmds):
+        pos = lib_file.tell()
+        segment_base = read_data(SegmentBase, lib_file)
+        lib_file.seek(pos)
+        if segment_base.cmd == LC_VERSION_MIN_MACOSX:
+
+            class VersionMinCommand(base_class):
+                _fields_ = version_min_command_fields
+
+            version_info = read_data(VersionMinCommand, lib_file)
+            return parse_version(version_info.version)
+        elif segment_base.cmd == LC_BUILD_VERSION:
+
+            class VersionBuild(base_class):
+                _fields_ = build_version_command_fields
+
+            version_info = read_data(VersionBuild, lib_file)
+            return parse_version(version_info.minos)
+        else:
+            lib_file.seek(pos + segment_base.cmdsize)
+            continue
+
+
+def parse_version(version: int) -> tuple[int, int, int]:
+    x = (version & 0xFFFF0000) >> 16
+    y = (version & 0x0000FF00) >> 8
+    z = version & 0x000000FF
+    return x, y, z
+
+
+def calculate_macosx_platform_tag(archive_root: StrPath, platform_tag: str) -> str:
+    """
+    Calculate proper macosx platform tag basing on files which are included to wheel
+
+    Example platform tag `macosx-10.14-x86_64`
+    """
+    prefix, base_version, suffix = platform_tag.split("-")
+    base_version = tuple(int(x) for x in base_version.split("."))
+    base_version = base_version[:2]
+    if base_version[0] > 10:
+        base_version = (base_version[0], 0)
+    assert len(base_version) == 2
+    if "MACOSX_DEPLOYMENT_TARGET" in os.environ:
+        deploy_target = tuple(
+            int(x) for x in os.environ["MACOSX_DEPLOYMENT_TARGET"].split(".")
+        )
+        deploy_target = deploy_target[:2]
+        if deploy_target[0] > 10:
+            deploy_target = (deploy_target[0], 0)
+        if deploy_target < base_version:
+            sys.stderr.write(
+                "[WARNING] MACOSX_DEPLOYMENT_TARGET is set to a lower value ({}) than "
+                "the version on which the Python interpreter was compiled ({}), and "
+                "will be ignored.\n".format(
+                    ".".join(str(x) for x in deploy_target),
+                    ".".join(str(x) for x in base_version),
+                )
+            )
+        else:
+            base_version = deploy_target
+
+    assert len(base_version) == 2
+    start_version = base_version
+    versions_dict: dict[str, tuple[int, int]] = {}
+    for dirpath, _dirnames, filenames in os.walk(archive_root):
+        for filename in filenames:
+            if filename.endswith(".dylib") or filename.endswith(".so"):
+                lib_path = os.path.join(dirpath, filename)
+                min_ver = extract_macosx_min_system_version(lib_path)
+                if min_ver is not None:
+                    min_ver = min_ver[0:2]
+                    if min_ver[0] > 10:
+                        min_ver = (min_ver[0], 0)
+                    versions_dict[lib_path] = min_ver
+
+    if len(versions_dict) > 0:
+        base_version = max(base_version, max(versions_dict.values()))
+
+    # macosx platform tag do not support minor bugfix release
+    fin_base_version = "_".join([str(x) for x in base_version])
+    if start_version < base_version:
+        problematic_files = [k for k, v in versions_dict.items() if v > start_version]
+        problematic_files = "\n".join(problematic_files)
+        if len(problematic_files) == 1:
+            files_form = "this file"
+        else:
+            files_form = "these files"
+        error_message = (
+            "[WARNING] This wheel needs a higher macOS version than {}  "
+            "To silence this warning, set MACOSX_DEPLOYMENT_TARGET to at least "
+            + fin_base_version
+            + " or recreate "
+            + files_form
+            + " with lower "
+            "MACOSX_DEPLOYMENT_TARGET:  \n" + problematic_files
+        )
+
+        if "MACOSX_DEPLOYMENT_TARGET" in os.environ:
+            error_message = error_message.format(
+                "is set in MACOSX_DEPLOYMENT_TARGET variable."
+            )
+        else:
+            error_message = error_message.format(
+                "the version your Python interpreter is compiled against."
+            )
+
+        sys.stderr.write(error_message)
+
+    platform_tag = prefix + "_" + fin_base_version + "_" + suffix
+    return platform_tag
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/metadata.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8098fa8599a1f142ee33ee551b7a874f5f98840
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/metadata.py
@@ -0,0 +1,183 @@
+"""
+Tools for converting old- to new-style metadata.
+"""
+
+from __future__ import annotations
+
+import functools
+import itertools
+import os.path
+import re
+import textwrap
+from email.message import Message
+from email.parser import Parser
+from typing import Generator, Iterable, Iterator, Literal
+
+from .vendored.packaging.requirements import Requirement
+
+
+def _nonblank(str: str) -> bool | Literal[""]:
+    return str and not str.startswith("#")
+
+
+@functools.singledispatch
+def yield_lines(iterable: Iterable[str]) -> Iterator[str]:
+    r"""
+    Yield valid lines of a string or iterable.
+    >>> list(yield_lines(''))
+    []
+    >>> list(yield_lines(['foo', 'bar']))
+    ['foo', 'bar']
+    >>> list(yield_lines('foo\nbar'))
+    ['foo', 'bar']
+    >>> list(yield_lines('\nfoo\n#bar\nbaz #comment'))
+    ['foo', 'baz #comment']
+    >>> list(yield_lines(['foo\nbar', 'baz', 'bing\n\n\n']))
+    ['foo', 'bar', 'baz', 'bing']
+    """
+    return itertools.chain.from_iterable(map(yield_lines, iterable))
+
+
+@yield_lines.register(str)
+def _(text: str) -> Iterator[str]:
+    return filter(_nonblank, map(str.strip, text.splitlines()))
+
+
+def split_sections(
+    s: str | Iterator[str],
+) -> Generator[tuple[str | None, list[str]], None, None]:
+    """Split a string or iterable thereof into (section, content) pairs
+    Each ``section`` is a stripped version of the section header ("[section]")
+    and each ``content`` is a list of stripped lines excluding blank lines and
+    comment-only lines.  If there are any such lines before the first section
+    header, they're returned in a first ``section`` of ``None``.
+    """
+    section = None
+    content: list[str] = []
+    for line in yield_lines(s):
+        if line.startswith("["):
+            if line.endswith("]"):
+                if section or content:
+                    yield section, content
+                section = line[1:-1].strip()
+                content = []
+            else:
+                raise ValueError("Invalid section heading", line)
+        else:
+            content.append(line)
+
+    # wrap up last segment
+    yield section, content
+
+
+def safe_extra(extra: str) -> str:
+    """Convert an arbitrary string to a standard 'extra' name
+    Any runs of non-alphanumeric characters are replaced with a single '_',
+    and the result is always lowercased.
+    """
+    return re.sub("[^A-Za-z0-9.-]+", "_", extra).lower()
+
+
+def safe_name(name: str) -> str:
+    """Convert an arbitrary string to a standard distribution name
+    Any runs of non-alphanumeric/. characters are replaced with a single '-'.
+    """
+    return re.sub("[^A-Za-z0-9.]+", "-", name)
+
+
+def requires_to_requires_dist(requirement: Requirement) -> str:
+    """Return the version specifier for a requirement in PEP 345/566 fashion."""
+    if requirement.url:
+        return " @ " + requirement.url
+
+    requires_dist: list[str] = []
+    for spec in requirement.specifier:
+        requires_dist.append(spec.operator + spec.version)
+
+    if requires_dist:
+        return " " + ",".join(sorted(requires_dist))
+    else:
+        return ""
+
+
+def convert_requirements(requirements: list[str]) -> Iterator[str]:
+    """Yield Requires-Dist: strings for parsed requirements strings."""
+    for req in requirements:
+        parsed_requirement = Requirement(req)
+        spec = requires_to_requires_dist(parsed_requirement)
+        extras = ",".join(sorted(safe_extra(e) for e in parsed_requirement.extras))
+        if extras:
+            extras = f"[{extras}]"
+
+        yield safe_name(parsed_requirement.name) + extras + spec
+
+
+def generate_requirements(
+    extras_require: dict[str | None, list[str]],
+) -> Iterator[tuple[str, str]]:
+    """
+    Convert requirements from a setup()-style dictionary to
+    ('Requires-Dist', 'requirement') and ('Provides-Extra', 'extra') tuples.
+
+    extras_require is a dictionary of {extra: [requirements]} as passed to setup(),
+    using the empty extra {'': [requirements]} to hold install_requires.
+    """
+    for extra, depends in extras_require.items():
+        condition = ""
+        extra = extra or ""
+        if ":" in extra:  # setuptools extra:condition syntax
+            extra, condition = extra.split(":", 1)
+
+        extra = safe_extra(extra)
+        if extra:
+            yield "Provides-Extra", extra
+            if condition:
+                condition = "(" + condition + ") and "
+            condition += f"extra == '{extra}'"
+
+        if condition:
+            condition = " ; " + condition
+
+        for new_req in convert_requirements(depends):
+            canonical_req = str(Requirement(new_req + condition))
+            yield "Requires-Dist", canonical_req
+
+
+def pkginfo_to_metadata(egg_info_path: str, pkginfo_path: str) -> Message:
+    """
+    Convert .egg-info directory with PKG-INFO to the Metadata 2.1 format
+    """
+    with open(pkginfo_path, encoding="utf-8") as headers:
+        pkg_info = Parser().parse(headers)
+
+    pkg_info.replace_header("Metadata-Version", "2.1")
+    # Those will be regenerated from `requires.txt`.
+    del pkg_info["Provides-Extra"]
+    del pkg_info["Requires-Dist"]
+    requires_path = os.path.join(egg_info_path, "requires.txt")
+    if os.path.exists(requires_path):
+        with open(requires_path, encoding="utf-8") as requires_file:
+            requires = requires_file.read()
+
+        parsed_requirements = sorted(split_sections(requires), key=lambda x: x[0] or "")
+        for extra, reqs in parsed_requirements:
+            for key, value in generate_requirements({extra: reqs}):
+                if (key, value) not in pkg_info.items():
+                    pkg_info[key] = value
+
+    description = pkg_info["Description"]
+    if description:
+        description_lines = pkg_info["Description"].splitlines()
+        dedented_description = "\n".join(
+            # if the first line of long_description is blank,
+            # the first line here will be indented.
+            (
+                description_lines[0].lstrip(),
+                textwrap.dedent("\n".join(description_lines[1:])),
+                "\n",
+            )
+        )
+        pkg_info.set_payload(dedented_description)
+        del pkg_info["Description"]
+
+    return pkg_info
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/util.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..c928aa403b75e1ae392b4af5fe13147a095bbd30
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/util.py
@@ -0,0 +1,17 @@
+from __future__ import annotations
+
+import base64
+import logging
+
+log = logging.getLogger("wheel")
+
+
+def urlsafe_b64encode(data: bytes) -> bytes:
+    """urlsafe_b64encode without padding"""
+    return base64.urlsafe_b64encode(data).rstrip(b"=")
+
+
+def urlsafe_b64decode(data: bytes) -> bytes:
+    """urlsafe_b64decode without padding"""
+    pad = b"=" * (4 - (len(data) & 3))
+    return base64.urlsafe_b64decode(data + pad)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/__pycache__/__init__.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..5ea332a76e71a5b09a3ce0734edafb1ec15459ef
Binary files /dev/null and b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/__pycache__/__init__.cpython-312.pyc differ
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..6f62d44e4ef733c0e713afcd2371fed7f2b3de67
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/LICENSE
@@ -0,0 +1,3 @@
+This software is made available under the terms of *either* of the licenses
+found in LICENSE.APACHE or LICENSE.BSD. Contributions to this software is made
+under the terms of *both* these licenses.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/LICENSE.APACHE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/LICENSE.APACHE
new file mode 100644
index 0000000000000000000000000000000000000000..f433b1a53f5b830a205fd2df78e2b34974656c7b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/LICENSE.APACHE
@@ -0,0 +1,177 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
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+
+      "You" (or "Your") shall mean an individual or Legal Entity
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+
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+   5. Submission of Contributions. Unless You explicitly state otherwise,
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+   6. Trademarks. This License does not grant permission to use the trade
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+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
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+      negligent acts) or agreed to in writing, shall any Contributor be
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+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/LICENSE.BSD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/LICENSE.BSD
new file mode 100644
index 0000000000000000000000000000000000000000..42ce7b75c92fb01a3f6ed17eea363f756b7da582
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/LICENSE.BSD
@@ -0,0 +1,23 @@
+Copyright (c) Donald Stufft and individual contributors.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+    1. Redistributions of source code must retain the above copyright notice,
+       this list of conditions and the following disclaimer.
+
+    2. Redistributions in binary form must reproduce the above copyright
+       notice, this list of conditions and the following disclaimer in the
+       documentation and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_elffile.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_elffile.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fb19b30bb53c18f38a9ef02dd7c4478670fb962
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_elffile.py
@@ -0,0 +1,108 @@
+"""
+ELF file parser.
+
+This provides a class ``ELFFile`` that parses an ELF executable in a similar
+interface to ``ZipFile``. Only the read interface is implemented.
+
+Based on: https://gist.github.com/lyssdod/f51579ae8d93c8657a5564aefc2ffbca
+ELF header: https://refspecs.linuxfoundation.org/elf/gabi4+/ch4.eheader.html
+"""
+
+import enum
+import os
+import struct
+from typing import IO, Optional, Tuple
+
+
+class ELFInvalid(ValueError):
+    pass
+
+
+class EIClass(enum.IntEnum):
+    C32 = 1
+    C64 = 2
+
+
+class EIData(enum.IntEnum):
+    Lsb = 1
+    Msb = 2
+
+
+class EMachine(enum.IntEnum):
+    I386 = 3
+    S390 = 22
+    Arm = 40
+    X8664 = 62
+    AArc64 = 183
+
+
+class ELFFile:
+    """
+    Representation of an ELF executable.
+    """
+
+    def __init__(self, f: IO[bytes]) -> None:
+        self._f = f
+
+        try:
+            ident = self._read("16B")
+        except struct.error:
+            raise ELFInvalid("unable to parse identification")
+        magic = bytes(ident[:4])
+        if magic != b"\x7fELF":
+            raise ELFInvalid(f"invalid magic: {magic!r}")
+
+        self.capacity = ident[4]  # Format for program header (bitness).
+        self.encoding = ident[5]  # Data structure encoding (endianness).
+
+        try:
+            # e_fmt: Format for program header.
+            # p_fmt: Format for section header.
+            # p_idx: Indexes to find p_type, p_offset, and p_filesz.
+            e_fmt, self._p_fmt, self._p_idx = {
+                (1, 1): ("<HHIIIIIHHH", "<IIIIIIII", (0, 1, 4)),  # 32-bit LSB.
+                (1, 2): (">HHIIIIIHHH", ">IIIIIIII", (0, 1, 4)),  # 32-bit MSB.
+                (2, 1): ("<HHIQQQIHHH", "<IIQQQQQQ", (0, 2, 5)),  # 64-bit LSB.
+                (2, 2): (">HHIQQQIHHH", ">IIQQQQQQ", (0, 2, 5)),  # 64-bit MSB.
+            }[(self.capacity, self.encoding)]
+        except KeyError:
+            raise ELFInvalid(
+                f"unrecognized capacity ({self.capacity}) or "
+                f"encoding ({self.encoding})"
+            )
+
+        try:
+            (
+                _,
+                self.machine,  # Architecture type.
+                _,
+                _,
+                self._e_phoff,  # Offset of program header.
+                _,
+                self.flags,  # Processor-specific flags.
+                _,
+                self._e_phentsize,  # Size of section.
+                self._e_phnum,  # Number of sections.
+            ) = self._read(e_fmt)
+        except struct.error as e:
+            raise ELFInvalid("unable to parse machine and section information") from e
+
+    def _read(self, fmt: str) -> Tuple[int, ...]:
+        return struct.unpack(fmt, self._f.read(struct.calcsize(fmt)))
+
+    @property
+    def interpreter(self) -> Optional[str]:
+        """
+        The path recorded in the ``PT_INTERP`` section header.
+        """
+        for index in range(self._e_phnum):
+            self._f.seek(self._e_phoff + self._e_phentsize * index)
+            try:
+                data = self._read(self._p_fmt)
+            except struct.error:
+                continue
+            if data[self._p_idx[0]] != 3:  # Not PT_INTERP.
+                continue
+            self._f.seek(data[self._p_idx[1]])
+            return os.fsdecode(self._f.read(data[self._p_idx[2]])).strip("\0")
+        return None
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_manylinux.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_manylinux.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f5f4ab3e514d1846d3bd189bf081fdb1528ba08
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_manylinux.py
@@ -0,0 +1,260 @@
+import collections
+import contextlib
+import functools
+import os
+import re
+import sys
+import warnings
+from typing import Dict, Generator, Iterator, NamedTuple, Optional, Sequence, Tuple
+
+from ._elffile import EIClass, EIData, ELFFile, EMachine
+
+EF_ARM_ABIMASK = 0xFF000000
+EF_ARM_ABI_VER5 = 0x05000000
+EF_ARM_ABI_FLOAT_HARD = 0x00000400
+
+
+# `os.PathLike` not a generic type until Python 3.9, so sticking with `str`
+# as the type for `path` until then.
+@contextlib.contextmanager
+def _parse_elf(path: str) -> Generator[Optional[ELFFile], None, None]:
+    try:
+        with open(path, "rb") as f:
+            yield ELFFile(f)
+    except (OSError, TypeError, ValueError):
+        yield None
+
+
+def _is_linux_armhf(executable: str) -> bool:
+    # hard-float ABI can be detected from the ELF header of the running
+    # process
+    # https://static.docs.arm.com/ihi0044/g/aaelf32.pdf
+    with _parse_elf(executable) as f:
+        return (
+            f is not None
+            and f.capacity == EIClass.C32
+            and f.encoding == EIData.Lsb
+            and f.machine == EMachine.Arm
+            and f.flags & EF_ARM_ABIMASK == EF_ARM_ABI_VER5
+            and f.flags & EF_ARM_ABI_FLOAT_HARD == EF_ARM_ABI_FLOAT_HARD
+        )
+
+
+def _is_linux_i686(executable: str) -> bool:
+    with _parse_elf(executable) as f:
+        return (
+            f is not None
+            and f.capacity == EIClass.C32
+            and f.encoding == EIData.Lsb
+            and f.machine == EMachine.I386
+        )
+
+
+def _have_compatible_abi(executable: str, archs: Sequence[str]) -> bool:
+    if "armv7l" in archs:
+        return _is_linux_armhf(executable)
+    if "i686" in archs:
+        return _is_linux_i686(executable)
+    allowed_archs = {
+        "x86_64",
+        "aarch64",
+        "ppc64",
+        "ppc64le",
+        "s390x",
+        "loongarch64",
+        "riscv64",
+    }
+    return any(arch in allowed_archs for arch in archs)
+
+
+# If glibc ever changes its major version, we need to know what the last
+# minor version was, so we can build the complete list of all versions.
+# For now, guess what the highest minor version might be, assume it will
+# be 50 for testing. Once this actually happens, update the dictionary
+# with the actual value.
+_LAST_GLIBC_MINOR: Dict[int, int] = collections.defaultdict(lambda: 50)
+
+
+class _GLibCVersion(NamedTuple):
+    major: int
+    minor: int
+
+
+def _glibc_version_string_confstr() -> Optional[str]:
+    """
+    Primary implementation of glibc_version_string using os.confstr.
+    """
+    # os.confstr is quite a bit faster than ctypes.DLL. It's also less likely
+    # to be broken or missing. This strategy is used in the standard library
+    # platform module.
+    # https://github.com/python/cpython/blob/fcf1d003bf4f0100c/Lib/platform.py#L175-L183
+    try:
+        # Should be a string like "glibc 2.17".
+        version_string: Optional[str] = os.confstr("CS_GNU_LIBC_VERSION")
+        assert version_string is not None
+        _, version = version_string.rsplit()
+    except (AssertionError, AttributeError, OSError, ValueError):
+        # os.confstr() or CS_GNU_LIBC_VERSION not available (or a bad value)...
+        return None
+    return version
+
+
+def _glibc_version_string_ctypes() -> Optional[str]:
+    """
+    Fallback implementation of glibc_version_string using ctypes.
+    """
+    try:
+        import ctypes
+    except ImportError:
+        return None
+
+    # ctypes.CDLL(None) internally calls dlopen(NULL), and as the dlopen
+    # manpage says, "If filename is NULL, then the returned handle is for the
+    # main program". This way we can let the linker do the work to figure out
+    # which libc our process is actually using.
+    #
+    # We must also handle the special case where the executable is not a
+    # dynamically linked executable. This can occur when using musl libc,
+    # for example. In this situation, dlopen() will error, leading to an
+    # OSError. Interestingly, at least in the case of musl, there is no
+    # errno set on the OSError. The single string argument used to construct
+    # OSError comes from libc itself and is therefore not portable to
+    # hard code here. In any case, failure to call dlopen() means we
+    # can proceed, so we bail on our attempt.
+    try:
+        process_namespace = ctypes.CDLL(None)
+    except OSError:
+        return None
+
+    try:
+        gnu_get_libc_version = process_namespace.gnu_get_libc_version
+    except AttributeError:
+        # Symbol doesn't exist -> therefore, we are not linked to
+        # glibc.
+        return None
+
+    # Call gnu_get_libc_version, which returns a string like "2.5"
+    gnu_get_libc_version.restype = ctypes.c_char_p
+    version_str: str = gnu_get_libc_version()
+    # py2 / py3 compatibility:
+    if not isinstance(version_str, str):
+        version_str = version_str.decode("ascii")
+
+    return version_str
+
+
+def _glibc_version_string() -> Optional[str]:
+    """Returns glibc version string, or None if not using glibc."""
+    return _glibc_version_string_confstr() or _glibc_version_string_ctypes()
+
+
+def _parse_glibc_version(version_str: str) -> Tuple[int, int]:
+    """Parse glibc version.
+
+    We use a regexp instead of str.split because we want to discard any
+    random junk that might come after the minor version -- this might happen
+    in patched/forked versions of glibc (e.g. Linaro's version of glibc
+    uses version strings like "2.20-2014.11"). See gh-3588.
+    """
+    m = re.match(r"(?P<major>[0-9]+)\.(?P<minor>[0-9]+)", version_str)
+    if not m:
+        warnings.warn(
+            f"Expected glibc version with 2 components major.minor,"
+            f" got: {version_str}",
+            RuntimeWarning,
+        )
+        return -1, -1
+    return int(m.group("major")), int(m.group("minor"))
+
+
+@functools.lru_cache
+def _get_glibc_version() -> Tuple[int, int]:
+    version_str = _glibc_version_string()
+    if version_str is None:
+        return (-1, -1)
+    return _parse_glibc_version(version_str)
+
+
+# From PEP 513, PEP 600
+def _is_compatible(arch: str, version: _GLibCVersion) -> bool:
+    sys_glibc = _get_glibc_version()
+    if sys_glibc < version:
+        return False
+    # Check for presence of _manylinux module.
+    try:
+        import _manylinux
+    except ImportError:
+        return True
+    if hasattr(_manylinux, "manylinux_compatible"):
+        result = _manylinux.manylinux_compatible(version[0], version[1], arch)
+        if result is not None:
+            return bool(result)
+        return True
+    if version == _GLibCVersion(2, 5):
+        if hasattr(_manylinux, "manylinux1_compatible"):
+            return bool(_manylinux.manylinux1_compatible)
+    if version == _GLibCVersion(2, 12):
+        if hasattr(_manylinux, "manylinux2010_compatible"):
+            return bool(_manylinux.manylinux2010_compatible)
+    if version == _GLibCVersion(2, 17):
+        if hasattr(_manylinux, "manylinux2014_compatible"):
+            return bool(_manylinux.manylinux2014_compatible)
+    return True
+
+
+_LEGACY_MANYLINUX_MAP = {
+    # CentOS 7 w/ glibc 2.17 (PEP 599)
+    (2, 17): "manylinux2014",
+    # CentOS 6 w/ glibc 2.12 (PEP 571)
+    (2, 12): "manylinux2010",
+    # CentOS 5 w/ glibc 2.5 (PEP 513)
+    (2, 5): "manylinux1",
+}
+
+
+def platform_tags(archs: Sequence[str]) -> Iterator[str]:
+    """Generate manylinux tags compatible to the current platform.
+
+    :param archs: Sequence of compatible architectures.
+        The first one shall be the closest to the actual architecture and be the part of
+        platform tag after the ``linux_`` prefix, e.g. ``x86_64``.
+        The ``linux_`` prefix is assumed as a prerequisite for the current platform to
+        be manylinux-compatible.
+
+    :returns: An iterator of compatible manylinux tags.
+    """
+    if not _have_compatible_abi(sys.executable, archs):
+        return
+    # Oldest glibc to be supported regardless of architecture is (2, 17).
+    too_old_glibc2 = _GLibCVersion(2, 16)
+    if set(archs) & {"x86_64", "i686"}:
+        # On x86/i686 also oldest glibc to be supported is (2, 5).
+        too_old_glibc2 = _GLibCVersion(2, 4)
+    current_glibc = _GLibCVersion(*_get_glibc_version())
+    glibc_max_list = [current_glibc]
+    # We can assume compatibility across glibc major versions.
+    # https://sourceware.org/bugzilla/show_bug.cgi?id=24636
+    #
+    # Build a list of maximum glibc versions so that we can
+    # output the canonical list of all glibc from current_glibc
+    # down to too_old_glibc2, including all intermediary versions.
+    for glibc_major in range(current_glibc.major - 1, 1, -1):
+        glibc_minor = _LAST_GLIBC_MINOR[glibc_major]
+        glibc_max_list.append(_GLibCVersion(glibc_major, glibc_minor))
+    for arch in archs:
+        for glibc_max in glibc_max_list:
+            if glibc_max.major == too_old_glibc2.major:
+                min_minor = too_old_glibc2.minor
+            else:
+                # For other glibc major versions oldest supported is (x, 0).
+                min_minor = -1
+            for glibc_minor in range(glibc_max.minor, min_minor, -1):
+                glibc_version = _GLibCVersion(glibc_max.major, glibc_minor)
+                tag = "manylinux_{}_{}".format(*glibc_version)
+                if _is_compatible(arch, glibc_version):
+                    yield f"{tag}_{arch}"
+                # Handle the legacy manylinux1, manylinux2010, manylinux2014 tags.
+                if glibc_version in _LEGACY_MANYLINUX_MAP:
+                    legacy_tag = _LEGACY_MANYLINUX_MAP[glibc_version]
+                    if _is_compatible(arch, glibc_version):
+                        yield f"{legacy_tag}_{arch}"
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_musllinux.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_musllinux.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb4251b5c1e82772b2b0ea539943da5141fd55ec
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_musllinux.py
@@ -0,0 +1,83 @@
+"""PEP 656 support.
+
+This module implements logic to detect if the currently running Python is
+linked against musl, and what musl version is used.
+"""
+
+import functools
+import re
+import subprocess
+import sys
+from typing import Iterator, NamedTuple, Optional, Sequence
+
+from ._elffile import ELFFile
+
+
+class _MuslVersion(NamedTuple):
+    major: int
+    minor: int
+
+
+def _parse_musl_version(output: str) -> Optional[_MuslVersion]:
+    lines = [n for n in (n.strip() for n in output.splitlines()) if n]
+    if len(lines) < 2 or lines[0][:4] != "musl":
+        return None
+    m = re.match(r"Version (\d+)\.(\d+)", lines[1])
+    if not m:
+        return None
+    return _MuslVersion(major=int(m.group(1)), minor=int(m.group(2)))
+
+
+@functools.lru_cache
+def _get_musl_version(executable: str) -> Optional[_MuslVersion]:
+    """Detect currently-running musl runtime version.
+
+    This is done by checking the specified executable's dynamic linking
+    information, and invoking the loader to parse its output for a version
+    string. If the loader is musl, the output would be something like::
+
+        musl libc (x86_64)
+        Version 1.2.2
+        Dynamic Program Loader
+    """
+    try:
+        with open(executable, "rb") as f:
+            ld = ELFFile(f).interpreter
+    except (OSError, TypeError, ValueError):
+        return None
+    if ld is None or "musl" not in ld:
+        return None
+    proc = subprocess.run([ld], stderr=subprocess.PIPE, text=True)
+    return _parse_musl_version(proc.stderr)
+
+
+def platform_tags(archs: Sequence[str]) -> Iterator[str]:
+    """Generate musllinux tags compatible to the current platform.
+
+    :param archs: Sequence of compatible architectures.
+        The first one shall be the closest to the actual architecture and be the part of
+        platform tag after the ``linux_`` prefix, e.g. ``x86_64``.
+        The ``linux_`` prefix is assumed as a prerequisite for the current platform to
+        be musllinux-compatible.
+
+    :returns: An iterator of compatible musllinux tags.
+    """
+    sys_musl = _get_musl_version(sys.executable)
+    if sys_musl is None:  # Python not dynamically linked against musl.
+        return
+    for arch in archs:
+        for minor in range(sys_musl.minor, -1, -1):
+            yield f"musllinux_{sys_musl.major}_{minor}_{arch}"
+
+
+if __name__ == "__main__":  # pragma: no cover
+    import sysconfig
+
+    plat = sysconfig.get_platform()
+    assert plat.startswith("linux-"), "not linux"
+
+    print("plat:", plat)
+    print("musl:", _get_musl_version(sys.executable))
+    print("tags:", end=" ")
+    for t in platform_tags(re.sub(r"[.-]", "_", plat.split("-", 1)[-1])):
+        print(t, end="\n      ")
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_parser.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..513686a2190f9911c08ca1ca263f37b799f44702
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_parser.py
@@ -0,0 +1,356 @@
+"""Handwritten parser of dependency specifiers.
+
+The docstring for each __parse_* function contains EBNF-inspired grammar representing
+the implementation.
+"""
+
+import ast
+from typing import Any, List, NamedTuple, Optional, Tuple, Union
+
+from ._tokenizer import DEFAULT_RULES, Tokenizer
+
+
+class Node:
+    def __init__(self, value: str) -> None:
+        self.value = value
+
+    def __str__(self) -> str:
+        return self.value
+
+    def __repr__(self) -> str:
+        return f"<{self.__class__.__name__}('{self}')>"
+
+    def serialize(self) -> str:
+        raise NotImplementedError
+
+
+class Variable(Node):
+    def serialize(self) -> str:
+        return str(self)
+
+
+class Value(Node):
+    def serialize(self) -> str:
+        return f'"{self}"'
+
+
+class Op(Node):
+    def serialize(self) -> str:
+        return str(self)
+
+
+MarkerVar = Union[Variable, Value]
+MarkerItem = Tuple[MarkerVar, Op, MarkerVar]
+# MarkerAtom = Union[MarkerItem, List["MarkerAtom"]]
+# MarkerList = List[Union["MarkerList", MarkerAtom, str]]
+# mypy does not support recursive type definition
+# https://github.com/python/mypy/issues/731
+MarkerAtom = Any
+MarkerList = List[Any]
+
+
+class ParsedRequirement(NamedTuple):
+    name: str
+    url: str
+    extras: List[str]
+    specifier: str
+    marker: Optional[MarkerList]
+
+
+# --------------------------------------------------------------------------------------
+# Recursive descent parser for dependency specifier
+# --------------------------------------------------------------------------------------
+def parse_requirement(source: str) -> ParsedRequirement:
+    return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES))
+
+
+def _parse_requirement(tokenizer: Tokenizer) -> ParsedRequirement:
+    """
+    requirement = WS? IDENTIFIER WS? extras WS? requirement_details
+    """
+    tokenizer.consume("WS")
+
+    name_token = tokenizer.expect(
+        "IDENTIFIER", expected="package name at the start of dependency specifier"
+    )
+    name = name_token.text
+    tokenizer.consume("WS")
+
+    extras = _parse_extras(tokenizer)
+    tokenizer.consume("WS")
+
+    url, specifier, marker = _parse_requirement_details(tokenizer)
+    tokenizer.expect("END", expected="end of dependency specifier")
+
+    return ParsedRequirement(name, url, extras, specifier, marker)
+
+
+def _parse_requirement_details(
+    tokenizer: Tokenizer,
+) -> Tuple[str, str, Optional[MarkerList]]:
+    """
+    requirement_details = AT URL (WS requirement_marker?)?
+                        | specifier WS? (requirement_marker)?
+    """
+
+    specifier = ""
+    url = ""
+    marker = None
+
+    if tokenizer.check("AT"):
+        tokenizer.read()
+        tokenizer.consume("WS")
+
+        url_start = tokenizer.position
+        url = tokenizer.expect("URL", expected="URL after @").text
+        if tokenizer.check("END", peek=True):
+            return (url, specifier, marker)
+
+        tokenizer.expect("WS", expected="whitespace after URL")
+
+        # The input might end after whitespace.
+        if tokenizer.check("END", peek=True):
+            return (url, specifier, marker)
+
+        marker = _parse_requirement_marker(
+            tokenizer, span_start=url_start, after="URL and whitespace"
+        )
+    else:
+        specifier_start = tokenizer.position
+        specifier = _parse_specifier(tokenizer)
+        tokenizer.consume("WS")
+
+        if tokenizer.check("END", peek=True):
+            return (url, specifier, marker)
+
+        marker = _parse_requirement_marker(
+            tokenizer,
+            span_start=specifier_start,
+            after=(
+                "version specifier"
+                if specifier
+                else "name and no valid version specifier"
+            ),
+        )
+
+    return (url, specifier, marker)
+
+
+def _parse_requirement_marker(
+    tokenizer: Tokenizer, *, span_start: int, after: str
+) -> MarkerList:
+    """
+    requirement_marker = SEMICOLON marker WS?
+    """
+
+    if not tokenizer.check("SEMICOLON"):
+        tokenizer.raise_syntax_error(
+            f"Expected end or semicolon (after {after})",
+            span_start=span_start,
+        )
+    tokenizer.read()
+
+    marker = _parse_marker(tokenizer)
+    tokenizer.consume("WS")
+
+    return marker
+
+
+def _parse_extras(tokenizer: Tokenizer) -> List[str]:
+    """
+    extras = (LEFT_BRACKET wsp* extras_list? wsp* RIGHT_BRACKET)?
+    """
+    if not tokenizer.check("LEFT_BRACKET", peek=True):
+        return []
+
+    with tokenizer.enclosing_tokens(
+        "LEFT_BRACKET",
+        "RIGHT_BRACKET",
+        around="extras",
+    ):
+        tokenizer.consume("WS")
+        extras = _parse_extras_list(tokenizer)
+        tokenizer.consume("WS")
+
+    return extras
+
+
+def _parse_extras_list(tokenizer: Tokenizer) -> List[str]:
+    """
+    extras_list = identifier (wsp* ',' wsp* identifier)*
+    """
+    extras: List[str] = []
+
+    if not tokenizer.check("IDENTIFIER"):
+        return extras
+
+    extras.append(tokenizer.read().text)
+
+    while True:
+        tokenizer.consume("WS")
+        if tokenizer.check("IDENTIFIER", peek=True):
+            tokenizer.raise_syntax_error("Expected comma between extra names")
+        elif not tokenizer.check("COMMA"):
+            break
+
+        tokenizer.read()
+        tokenizer.consume("WS")
+
+        extra_token = tokenizer.expect("IDENTIFIER", expected="extra name after comma")
+        extras.append(extra_token.text)
+
+    return extras
+
+
+def _parse_specifier(tokenizer: Tokenizer) -> str:
+    """
+    specifier = LEFT_PARENTHESIS WS? version_many WS? RIGHT_PARENTHESIS
+              | WS? version_many WS?
+    """
+    with tokenizer.enclosing_tokens(
+        "LEFT_PARENTHESIS",
+        "RIGHT_PARENTHESIS",
+        around="version specifier",
+    ):
+        tokenizer.consume("WS")
+        parsed_specifiers = _parse_version_many(tokenizer)
+        tokenizer.consume("WS")
+
+    return parsed_specifiers
+
+
+def _parse_version_many(tokenizer: Tokenizer) -> str:
+    """
+    version_many = (SPECIFIER (WS? COMMA WS? SPECIFIER)*)?
+    """
+    parsed_specifiers = ""
+    while tokenizer.check("SPECIFIER"):
+        span_start = tokenizer.position
+        parsed_specifiers += tokenizer.read().text
+        if tokenizer.check("VERSION_PREFIX_TRAIL", peek=True):
+            tokenizer.raise_syntax_error(
+                ".* suffix can only be used with `==` or `!=` operators",
+                span_start=span_start,
+                span_end=tokenizer.position + 1,
+            )
+        if tokenizer.check("VERSION_LOCAL_LABEL_TRAIL", peek=True):
+            tokenizer.raise_syntax_error(
+                "Local version label can only be used with `==` or `!=` operators",
+                span_start=span_start,
+                span_end=tokenizer.position,
+            )
+        tokenizer.consume("WS")
+        if not tokenizer.check("COMMA"):
+            break
+        parsed_specifiers += tokenizer.read().text
+        tokenizer.consume("WS")
+
+    return parsed_specifiers
+
+
+# --------------------------------------------------------------------------------------
+# Recursive descent parser for marker expression
+# --------------------------------------------------------------------------------------
+def parse_marker(source: str) -> MarkerList:
+    return _parse_full_marker(Tokenizer(source, rules=DEFAULT_RULES))
+
+
+def _parse_full_marker(tokenizer: Tokenizer) -> MarkerList:
+    retval = _parse_marker(tokenizer)
+    tokenizer.expect("END", expected="end of marker expression")
+    return retval
+
+
+def _parse_marker(tokenizer: Tokenizer) -> MarkerList:
+    """
+    marker = marker_atom (BOOLOP marker_atom)+
+    """
+    expression = [_parse_marker_atom(tokenizer)]
+    while tokenizer.check("BOOLOP"):
+        token = tokenizer.read()
+        expr_right = _parse_marker_atom(tokenizer)
+        expression.extend((token.text, expr_right))
+    return expression
+
+
+def _parse_marker_atom(tokenizer: Tokenizer) -> MarkerAtom:
+    """
+    marker_atom = WS? LEFT_PARENTHESIS WS? marker WS? RIGHT_PARENTHESIS WS?
+                | WS? marker_item WS?
+    """
+
+    tokenizer.consume("WS")
+    if tokenizer.check("LEFT_PARENTHESIS", peek=True):
+        with tokenizer.enclosing_tokens(
+            "LEFT_PARENTHESIS",
+            "RIGHT_PARENTHESIS",
+            around="marker expression",
+        ):
+            tokenizer.consume("WS")
+            marker: MarkerAtom = _parse_marker(tokenizer)
+            tokenizer.consume("WS")
+    else:
+        marker = _parse_marker_item(tokenizer)
+    tokenizer.consume("WS")
+    return marker
+
+
+def _parse_marker_item(tokenizer: Tokenizer) -> MarkerItem:
+    """
+    marker_item = WS? marker_var WS? marker_op WS? marker_var WS?
+    """
+    tokenizer.consume("WS")
+    marker_var_left = _parse_marker_var(tokenizer)
+    tokenizer.consume("WS")
+    marker_op = _parse_marker_op(tokenizer)
+    tokenizer.consume("WS")
+    marker_var_right = _parse_marker_var(tokenizer)
+    tokenizer.consume("WS")
+    return (marker_var_left, marker_op, marker_var_right)
+
+
+def _parse_marker_var(tokenizer: Tokenizer) -> MarkerVar:
+    """
+    marker_var = VARIABLE | QUOTED_STRING
+    """
+    if tokenizer.check("VARIABLE"):
+        return process_env_var(tokenizer.read().text.replace(".", "_"))
+    elif tokenizer.check("QUOTED_STRING"):
+        return process_python_str(tokenizer.read().text)
+    else:
+        tokenizer.raise_syntax_error(
+            message="Expected a marker variable or quoted string"
+        )
+
+
+def process_env_var(env_var: str) -> Variable:
+    if env_var in ("platform_python_implementation", "python_implementation"):
+        return Variable("platform_python_implementation")
+    else:
+        return Variable(env_var)
+
+
+def process_python_str(python_str: str) -> Value:
+    value = ast.literal_eval(python_str)
+    return Value(str(value))
+
+
+def _parse_marker_op(tokenizer: Tokenizer) -> Op:
+    """
+    marker_op = IN | NOT IN | OP
+    """
+    if tokenizer.check("IN"):
+        tokenizer.read()
+        return Op("in")
+    elif tokenizer.check("NOT"):
+        tokenizer.read()
+        tokenizer.expect("WS", expected="whitespace after 'not'")
+        tokenizer.expect("IN", expected="'in' after 'not'")
+        return Op("not in")
+    elif tokenizer.check("OP"):
+        return Op(tokenizer.read().text)
+    else:
+        return tokenizer.raise_syntax_error(
+            "Expected marker operator, one of "
+            "<=, <, !=, ==, >=, >, ~=, ===, in, not in"
+        )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_structures.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_structures.py
new file mode 100644
index 0000000000000000000000000000000000000000..90a6465f9682c886363eea5327dac64bf623a6ff
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_structures.py
@@ -0,0 +1,61 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+
+class InfinityType:
+    def __repr__(self) -> str:
+        return "Infinity"
+
+    def __hash__(self) -> int:
+        return hash(repr(self))
+
+    def __lt__(self, other: object) -> bool:
+        return False
+
+    def __le__(self, other: object) -> bool:
+        return False
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, self.__class__)
+
+    def __gt__(self, other: object) -> bool:
+        return True
+
+    def __ge__(self, other: object) -> bool:
+        return True
+
+    def __neg__(self: object) -> "NegativeInfinityType":
+        return NegativeInfinity
+
+
+Infinity = InfinityType()
+
+
+class NegativeInfinityType:
+    def __repr__(self) -> str:
+        return "-Infinity"
+
+    def __hash__(self) -> int:
+        return hash(repr(self))
+
+    def __lt__(self, other: object) -> bool:
+        return True
+
+    def __le__(self, other: object) -> bool:
+        return True
+
+    def __eq__(self, other: object) -> bool:
+        return isinstance(other, self.__class__)
+
+    def __gt__(self, other: object) -> bool:
+        return False
+
+    def __ge__(self, other: object) -> bool:
+        return False
+
+    def __neg__(self: object) -> InfinityType:
+        return Infinity
+
+
+NegativeInfinity = NegativeInfinityType()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_tokenizer.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd0d648d49a7c1a62d25ce5c9107aa448a8a22d1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/_tokenizer.py
@@ -0,0 +1,192 @@
+import contextlib
+import re
+from dataclasses import dataclass
+from typing import Dict, Iterator, NoReturn, Optional, Tuple, Union
+
+from .specifiers import Specifier
+
+
+@dataclass
+class Token:
+    name: str
+    text: str
+    position: int
+
+
+class ParserSyntaxError(Exception):
+    """The provided source text could not be parsed correctly."""
+
+    def __init__(
+        self,
+        message: str,
+        *,
+        source: str,
+        span: Tuple[int, int],
+    ) -> None:
+        self.span = span
+        self.message = message
+        self.source = source
+
+        super().__init__()
+
+    def __str__(self) -> str:
+        marker = " " * self.span[0] + "~" * (self.span[1] - self.span[0]) + "^"
+        return "\n    ".join([self.message, self.source, marker])
+
+
+DEFAULT_RULES: "Dict[str, Union[str, re.Pattern[str]]]" = {
+    "LEFT_PARENTHESIS": r"\(",
+    "RIGHT_PARENTHESIS": r"\)",
+    "LEFT_BRACKET": r"\[",
+    "RIGHT_BRACKET": r"\]",
+    "SEMICOLON": r";",
+    "COMMA": r",",
+    "QUOTED_STRING": re.compile(
+        r"""
+            (
+                ('[^']*')
+                |
+                ("[^"]*")
+            )
+        """,
+        re.VERBOSE,
+    ),
+    "OP": r"(===|==|~=|!=|<=|>=|<|>)",
+    "BOOLOP": r"\b(or|and)\b",
+    "IN": r"\bin\b",
+    "NOT": r"\bnot\b",
+    "VARIABLE": re.compile(
+        r"""
+            \b(
+                python_version
+                |python_full_version
+                |os[._]name
+                |sys[._]platform
+                |platform_(release|system)
+                |platform[._](version|machine|python_implementation)
+                |python_implementation
+                |implementation_(name|version)
+                |extra
+            )\b
+        """,
+        re.VERBOSE,
+    ),
+    "SPECIFIER": re.compile(
+        Specifier._operator_regex_str + Specifier._version_regex_str,
+        re.VERBOSE | re.IGNORECASE,
+    ),
+    "AT": r"\@",
+    "URL": r"[^ \t]+",
+    "IDENTIFIER": r"\b[a-zA-Z0-9][a-zA-Z0-9._-]*\b",
+    "VERSION_PREFIX_TRAIL": r"\.\*",
+    "VERSION_LOCAL_LABEL_TRAIL": r"\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*",
+    "WS": r"[ \t]+",
+    "END": r"$",
+}
+
+
+class Tokenizer:
+    """Context-sensitive token parsing.
+
+    Provides methods to examine the input stream to check whether the next token
+    matches.
+    """
+
+    def __init__(
+        self,
+        source: str,
+        *,
+        rules: "Dict[str, Union[str, re.Pattern[str]]]",
+    ) -> None:
+        self.source = source
+        self.rules: Dict[str, re.Pattern[str]] = {
+            name: re.compile(pattern) for name, pattern in rules.items()
+        }
+        self.next_token: Optional[Token] = None
+        self.position = 0
+
+    def consume(self, name: str) -> None:
+        """Move beyond provided token name, if at current position."""
+        if self.check(name):
+            self.read()
+
+    def check(self, name: str, *, peek: bool = False) -> bool:
+        """Check whether the next token has the provided name.
+
+        By default, if the check succeeds, the token *must* be read before
+        another check. If `peek` is set to `True`, the token is not loaded and
+        would need to be checked again.
+        """
+        assert (
+            self.next_token is None
+        ), f"Cannot check for {name!r}, already have {self.next_token!r}"
+        assert name in self.rules, f"Unknown token name: {name!r}"
+
+        expression = self.rules[name]
+
+        match = expression.match(self.source, self.position)
+        if match is None:
+            return False
+        if not peek:
+            self.next_token = Token(name, match[0], self.position)
+        return True
+
+    def expect(self, name: str, *, expected: str) -> Token:
+        """Expect a certain token name next, failing with a syntax error otherwise.
+
+        The token is *not* read.
+        """
+        if not self.check(name):
+            raise self.raise_syntax_error(f"Expected {expected}")
+        return self.read()
+
+    def read(self) -> Token:
+        """Consume the next token and return it."""
+        token = self.next_token
+        assert token is not None
+
+        self.position += len(token.text)
+        self.next_token = None
+
+        return token
+
+    def raise_syntax_error(
+        self,
+        message: str,
+        *,
+        span_start: Optional[int] = None,
+        span_end: Optional[int] = None,
+    ) -> NoReturn:
+        """Raise ParserSyntaxError at the given position."""
+        span = (
+            self.position if span_start is None else span_start,
+            self.position if span_end is None else span_end,
+        )
+        raise ParserSyntaxError(
+            message,
+            source=self.source,
+            span=span,
+        )
+
+    @contextlib.contextmanager
+    def enclosing_tokens(
+        self, open_token: str, close_token: str, *, around: str
+    ) -> Iterator[None]:
+        if self.check(open_token):
+            open_position = self.position
+            self.read()
+        else:
+            open_position = None
+
+        yield
+
+        if open_position is None:
+            return
+
+        if not self.check(close_token):
+            self.raise_syntax_error(
+                f"Expected matching {close_token} for {open_token}, after {around}",
+                span_start=open_position,
+            )
+
+        self.read()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/markers.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/markers.py
new file mode 100644
index 0000000000000000000000000000000000000000..c96d22a5a445e7353cd2454dec4255d3785c07b3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/markers.py
@@ -0,0 +1,253 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+import operator
+import os
+import platform
+import sys
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+from ._parser import (
+    MarkerAtom,
+    MarkerList,
+    Op,
+    Value,
+    Variable,
+)
+from ._parser import (
+    parse_marker as _parse_marker,
+)
+from ._tokenizer import ParserSyntaxError
+from .specifiers import InvalidSpecifier, Specifier
+from .utils import canonicalize_name
+
+__all__ = [
+    "InvalidMarker",
+    "UndefinedComparison",
+    "UndefinedEnvironmentName",
+    "Marker",
+    "default_environment",
+]
+
+Operator = Callable[[str, str], bool]
+
+
+class InvalidMarker(ValueError):
+    """
+    An invalid marker was found, users should refer to PEP 508.
+    """
+
+
+class UndefinedComparison(ValueError):
+    """
+    An invalid operation was attempted on a value that doesn't support it.
+    """
+
+
+class UndefinedEnvironmentName(ValueError):
+    """
+    A name was attempted to be used that does not exist inside of the
+    environment.
+    """
+
+
+def _normalize_extra_values(results: Any) -> Any:
+    """
+    Normalize extra values.
+    """
+    if isinstance(results[0], tuple):
+        lhs, op, rhs = results[0]
+        if isinstance(lhs, Variable) and lhs.value == "extra":
+            normalized_extra = canonicalize_name(rhs.value)
+            rhs = Value(normalized_extra)
+        elif isinstance(rhs, Variable) and rhs.value == "extra":
+            normalized_extra = canonicalize_name(lhs.value)
+            lhs = Value(normalized_extra)
+        results[0] = lhs, op, rhs
+    return results
+
+
+def _format_marker(
+    marker: Union[List[str], MarkerAtom, str], first: Optional[bool] = True
+) -> str:
+    assert isinstance(marker, (list, tuple, str))
+
+    # Sometimes we have a structure like [[...]] which is a single item list
+    # where the single item is itself it's own list. In that case we want skip
+    # the rest of this function so that we don't get extraneous () on the
+    # outside.
+    if (
+        isinstance(marker, list)
+        and len(marker) == 1
+        and isinstance(marker[0], (list, tuple))
+    ):
+        return _format_marker(marker[0])
+
+    if isinstance(marker, list):
+        inner = (_format_marker(m, first=False) for m in marker)
+        if first:
+            return " ".join(inner)
+        else:
+            return "(" + " ".join(inner) + ")"
+    elif isinstance(marker, tuple):
+        return " ".join([m.serialize() for m in marker])
+    else:
+        return marker
+
+
+_operators: Dict[str, Operator] = {
+    "in": lambda lhs, rhs: lhs in rhs,
+    "not in": lambda lhs, rhs: lhs not in rhs,
+    "<": operator.lt,
+    "<=": operator.le,
+    "==": operator.eq,
+    "!=": operator.ne,
+    ">=": operator.ge,
+    ">": operator.gt,
+}
+
+
+def _eval_op(lhs: str, op: Op, rhs: str) -> bool:
+    try:
+        spec = Specifier("".join([op.serialize(), rhs]))
+    except InvalidSpecifier:
+        pass
+    else:
+        return spec.contains(lhs, prereleases=True)
+
+    oper: Optional[Operator] = _operators.get(op.serialize())
+    if oper is None:
+        raise UndefinedComparison(f"Undefined {op!r} on {lhs!r} and {rhs!r}.")
+
+    return oper(lhs, rhs)
+
+
+def _normalize(*values: str, key: str) -> Tuple[str, ...]:
+    # PEP 685 – Comparison of extra names for optional distribution dependencies
+    # https://peps.python.org/pep-0685/
+    # > When comparing extra names, tools MUST normalize the names being
+    # > compared using the semantics outlined in PEP 503 for names
+    if key == "extra":
+        return tuple(canonicalize_name(v) for v in values)
+
+    # other environment markers don't have such standards
+    return values
+
+
+def _evaluate_markers(markers: MarkerList, environment: Dict[str, str]) -> bool:
+    groups: List[List[bool]] = [[]]
+
+    for marker in markers:
+        assert isinstance(marker, (list, tuple, str))
+
+        if isinstance(marker, list):
+            groups[-1].append(_evaluate_markers(marker, environment))
+        elif isinstance(marker, tuple):
+            lhs, op, rhs = marker
+
+            if isinstance(lhs, Variable):
+                environment_key = lhs.value
+                lhs_value = environment[environment_key]
+                rhs_value = rhs.value
+            else:
+                lhs_value = lhs.value
+                environment_key = rhs.value
+                rhs_value = environment[environment_key]
+
+            lhs_value, rhs_value = _normalize(lhs_value, rhs_value, key=environment_key)
+            groups[-1].append(_eval_op(lhs_value, op, rhs_value))
+        else:
+            assert marker in ["and", "or"]
+            if marker == "or":
+                groups.append([])
+
+    return any(all(item) for item in groups)
+
+
+def format_full_version(info: "sys._version_info") -> str:
+    version = "{0.major}.{0.minor}.{0.micro}".format(info)
+    kind = info.releaselevel
+    if kind != "final":
+        version += kind[0] + str(info.serial)
+    return version
+
+
+def default_environment() -> Dict[str, str]:
+    iver = format_full_version(sys.implementation.version)
+    implementation_name = sys.implementation.name
+    return {
+        "implementation_name": implementation_name,
+        "implementation_version": iver,
+        "os_name": os.name,
+        "platform_machine": platform.machine(),
+        "platform_release": platform.release(),
+        "platform_system": platform.system(),
+        "platform_version": platform.version(),
+        "python_full_version": platform.python_version(),
+        "platform_python_implementation": platform.python_implementation(),
+        "python_version": ".".join(platform.python_version_tuple()[:2]),
+        "sys_platform": sys.platform,
+    }
+
+
+class Marker:
+    def __init__(self, marker: str) -> None:
+        # Note: We create a Marker object without calling this constructor in
+        #       packaging.requirements.Requirement. If any additional logic is
+        #       added here, make sure to mirror/adapt Requirement.
+        try:
+            self._markers = _normalize_extra_values(_parse_marker(marker))
+            # The attribute `_markers` can be described in terms of a recursive type:
+            # MarkerList = List[Union[Tuple[Node, ...], str, MarkerList]]
+            #
+            # For example, the following expression:
+            # python_version > "3.6" or (python_version == "3.6" and os_name == "unix")
+            #
+            # is parsed into:
+            # [
+            #     (<Variable('python_version')>, <Op('>')>, <Value('3.6')>),
+            #     'and',
+            #     [
+            #         (<Variable('python_version')>, <Op('==')>, <Value('3.6')>),
+            #         'or',
+            #         (<Variable('os_name')>, <Op('==')>, <Value('unix')>)
+            #     ]
+            # ]
+        except ParserSyntaxError as e:
+            raise InvalidMarker(str(e)) from e
+
+    def __str__(self) -> str:
+        return _format_marker(self._markers)
+
+    def __repr__(self) -> str:
+        return f"<Marker('{self}')>"
+
+    def __hash__(self) -> int:
+        return hash((self.__class__.__name__, str(self)))
+
+    def __eq__(self, other: Any) -> bool:
+        if not isinstance(other, Marker):
+            return NotImplemented
+
+        return str(self) == str(other)
+
+    def evaluate(self, environment: Optional[Dict[str, str]] = None) -> bool:
+        """Evaluate a marker.
+
+        Return the boolean from evaluating the given marker against the
+        environment. environment is an optional argument to override all or
+        part of the determined environment.
+
+        The environment is determined from the current Python process.
+        """
+        current_environment = default_environment()
+        current_environment["extra"] = ""
+        if environment is not None:
+            current_environment.update(environment)
+            # The API used to allow setting extra to None. We need to handle this
+            # case for backwards compatibility.
+            if current_environment["extra"] is None:
+                current_environment["extra"] = ""
+
+        return _evaluate_markers(self._markers, current_environment)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/requirements.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/requirements.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdc43a7e98d87dba0c2069bfb4554f71d228cad4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/requirements.py
@@ -0,0 +1,90 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+from typing import Any, Iterator, Optional, Set
+
+from ._parser import parse_requirement as _parse_requirement
+from ._tokenizer import ParserSyntaxError
+from .markers import Marker, _normalize_extra_values
+from .specifiers import SpecifierSet
+from .utils import canonicalize_name
+
+
+class InvalidRequirement(ValueError):
+    """
+    An invalid requirement was found, users should refer to PEP 508.
+    """
+
+
+class Requirement:
+    """Parse a requirement.
+
+    Parse a given requirement string into its parts, such as name, specifier,
+    URL, and extras. Raises InvalidRequirement on a badly-formed requirement
+    string.
+    """
+
+    # TODO: Can we test whether something is contained within a requirement?
+    #       If so how do we do that? Do we need to test against the _name_ of
+    #       the thing as well as the version? What about the markers?
+    # TODO: Can we normalize the name and extra name?
+
+    def __init__(self, requirement_string: str) -> None:
+        try:
+            parsed = _parse_requirement(requirement_string)
+        except ParserSyntaxError as e:
+            raise InvalidRequirement(str(e)) from e
+
+        self.name: str = parsed.name
+        self.url: Optional[str] = parsed.url or None
+        self.extras: Set[str] = set(parsed.extras or [])
+        self.specifier: SpecifierSet = SpecifierSet(parsed.specifier)
+        self.marker: Optional[Marker] = None
+        if parsed.marker is not None:
+            self.marker = Marker.__new__(Marker)
+            self.marker._markers = _normalize_extra_values(parsed.marker)
+
+    def _iter_parts(self, name: str) -> Iterator[str]:
+        yield name
+
+        if self.extras:
+            formatted_extras = ",".join(sorted(self.extras))
+            yield f"[{formatted_extras}]"
+
+        if self.specifier:
+            yield str(self.specifier)
+
+        if self.url:
+            yield f"@ {self.url}"
+            if self.marker:
+                yield " "
+
+        if self.marker:
+            yield f"; {self.marker}"
+
+    def __str__(self) -> str:
+        return "".join(self._iter_parts(self.name))
+
+    def __repr__(self) -> str:
+        return f"<Requirement('{self}')>"
+
+    def __hash__(self) -> int:
+        return hash(
+            (
+                self.__class__.__name__,
+                *self._iter_parts(canonicalize_name(self.name)),
+            )
+        )
+
+    def __eq__(self, other: Any) -> bool:
+        if not isinstance(other, Requirement):
+            return NotImplemented
+
+        return (
+            canonicalize_name(self.name) == canonicalize_name(other.name)
+            and self.extras == other.extras
+            and self.specifier == other.specifier
+            and self.url == other.url
+            and self.marker == other.marker
+        )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/specifiers.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/specifiers.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d4066ae2770a3112f37d0e30d9a98fe59c4861f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/specifiers.py
@@ -0,0 +1,1011 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+"""
+.. testsetup::
+
+    from packaging.specifiers import Specifier, SpecifierSet, InvalidSpecifier
+    from packaging.version import Version
+"""
+
+import abc
+import itertools
+import re
+from typing import Callable, Iterable, Iterator, List, Optional, Tuple, TypeVar, Union
+
+from .utils import canonicalize_version
+from .version import Version
+
+UnparsedVersion = Union[Version, str]
+UnparsedVersionVar = TypeVar("UnparsedVersionVar", bound=UnparsedVersion)
+CallableOperator = Callable[[Version, str], bool]
+
+
+def _coerce_version(version: UnparsedVersion) -> Version:
+    if not isinstance(version, Version):
+        version = Version(version)
+    return version
+
+
+class InvalidSpecifier(ValueError):
+    """
+    Raised when attempting to create a :class:`Specifier` with a specifier
+    string that is invalid.
+
+    >>> Specifier("lolwat")
+    Traceback (most recent call last):
+        ...
+    packaging.specifiers.InvalidSpecifier: Invalid specifier: 'lolwat'
+    """
+
+
+class BaseSpecifier(metaclass=abc.ABCMeta):
+    @abc.abstractmethod
+    def __str__(self) -> str:
+        """
+        Returns the str representation of this Specifier-like object. This
+        should be representative of the Specifier itself.
+        """
+
+    @abc.abstractmethod
+    def __hash__(self) -> int:
+        """
+        Returns a hash value for this Specifier-like object.
+        """
+
+    @abc.abstractmethod
+    def __eq__(self, other: object) -> bool:
+        """
+        Returns a boolean representing whether or not the two Specifier-like
+        objects are equal.
+
+        :param other: The other object to check against.
+        """
+
+    @property
+    @abc.abstractmethod
+    def prereleases(self) -> Optional[bool]:
+        """Whether or not pre-releases as a whole are allowed.
+
+        This can be set to either ``True`` or ``False`` to explicitly enable or disable
+        prereleases or it can be set to ``None`` (the default) to use default semantics.
+        """
+
+    @prereleases.setter
+    def prereleases(self, value: bool) -> None:
+        """Setter for :attr:`prereleases`.
+
+        :param value: The value to set.
+        """
+
+    @abc.abstractmethod
+    def contains(self, item: str, prereleases: Optional[bool] = None) -> bool:
+        """
+        Determines if the given item is contained within this specifier.
+        """
+
+    @abc.abstractmethod
+    def filter(
+        self, iterable: Iterable[UnparsedVersionVar], prereleases: Optional[bool] = None
+    ) -> Iterator[UnparsedVersionVar]:
+        """
+        Takes an iterable of items and filters them so that only items which
+        are contained within this specifier are allowed in it.
+        """
+
+
+class Specifier(BaseSpecifier):
+    """This class abstracts handling of version specifiers.
+
+    .. tip::
+
+        It is generally not required to instantiate this manually. You should instead
+        prefer to work with :class:`SpecifierSet` instead, which can parse
+        comma-separated version specifiers (which is what package metadata contains).
+    """
+
+    _operator_regex_str = r"""
+        (?P<operator>(~=|==|!=|<=|>=|<|>|===))
+        """
+    _version_regex_str = r"""
+        (?P<version>
+            (?:
+                # The identity operators allow for an escape hatch that will
+                # do an exact string match of the version you wish to install.
+                # This will not be parsed by PEP 440 and we cannot determine
+                # any semantic meaning from it. This operator is discouraged
+                # but included entirely as an escape hatch.
+                (?<====)  # Only match for the identity operator
+                \s*
+                [^\s;)]*  # The arbitrary version can be just about anything,
+                          # we match everything except for whitespace, a
+                          # semi-colon for marker support, and a closing paren
+                          # since versions can be enclosed in them.
+            )
+            |
+            (?:
+                # The (non)equality operators allow for wild card and local
+                # versions to be specified so we have to define these two
+                # operators separately to enable that.
+                (?<===|!=)            # Only match for equals and not equals
+
+                \s*
+                v?
+                (?:[0-9]+!)?          # epoch
+                [0-9]+(?:\.[0-9]+)*   # release
+
+                # You cannot use a wild card and a pre-release, post-release, a dev or
+                # local version together so group them with a | and make them optional.
+                (?:
+                    \.\*  # Wild card syntax of .*
+                    |
+                    (?:                                  # pre release
+                        [-_\.]?
+                        (alpha|beta|preview|pre|a|b|c|rc)
+                        [-_\.]?
+                        [0-9]*
+                    )?
+                    (?:                                  # post release
+                        (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
+                    )?
+                    (?:[-_\.]?dev[-_\.]?[0-9]*)?         # dev release
+                    (?:\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*)? # local
+                )?
+            )
+            |
+            (?:
+                # The compatible operator requires at least two digits in the
+                # release segment.
+                (?<=~=)               # Only match for the compatible operator
+
+                \s*
+                v?
+                (?:[0-9]+!)?          # epoch
+                [0-9]+(?:\.[0-9]+)+   # release  (We have a + instead of a *)
+                (?:                   # pre release
+                    [-_\.]?
+                    (alpha|beta|preview|pre|a|b|c|rc)
+                    [-_\.]?
+                    [0-9]*
+                )?
+                (?:                                   # post release
+                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
+                )?
+                (?:[-_\.]?dev[-_\.]?[0-9]*)?          # dev release
+            )
+            |
+            (?:
+                # All other operators only allow a sub set of what the
+                # (non)equality operators do. Specifically they do not allow
+                # local versions to be specified nor do they allow the prefix
+                # matching wild cards.
+                (?<!==|!=|~=)         # We have special cases for these
+                                      # operators so we want to make sure they
+                                      # don't match here.
+
+                \s*
+                v?
+                (?:[0-9]+!)?          # epoch
+                [0-9]+(?:\.[0-9]+)*   # release
+                (?:                   # pre release
+                    [-_\.]?
+                    (alpha|beta|preview|pre|a|b|c|rc)
+                    [-_\.]?
+                    [0-9]*
+                )?
+                (?:                                   # post release
+                    (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
+                )?
+                (?:[-_\.]?dev[-_\.]?[0-9]*)?          # dev release
+            )
+        )
+        """
+
+    _regex = re.compile(
+        r"^\s*" + _operator_regex_str + _version_regex_str + r"\s*$",
+        re.VERBOSE | re.IGNORECASE,
+    )
+
+    _operators = {
+        "~=": "compatible",
+        "==": "equal",
+        "!=": "not_equal",
+        "<=": "less_than_equal",
+        ">=": "greater_than_equal",
+        "<": "less_than",
+        ">": "greater_than",
+        "===": "arbitrary",
+    }
+
+    def __init__(self, spec: str = "", prereleases: Optional[bool] = None) -> None:
+        """Initialize a Specifier instance.
+
+        :param spec:
+            The string representation of a specifier which will be parsed and
+            normalized before use.
+        :param prereleases:
+            This tells the specifier if it should accept prerelease versions if
+            applicable or not. The default of ``None`` will autodetect it from the
+            given specifiers.
+        :raises InvalidSpecifier:
+            If the given specifier is invalid (i.e. bad syntax).
+        """
+        match = self._regex.search(spec)
+        if not match:
+            raise InvalidSpecifier(f"Invalid specifier: '{spec}'")
+
+        self._spec: Tuple[str, str] = (
+            match.group("operator").strip(),
+            match.group("version").strip(),
+        )
+
+        # Store whether or not this Specifier should accept prereleases
+        self._prereleases = prereleases
+
+    # https://github.com/python/mypy/pull/13475#pullrequestreview-1079784515
+    @property  # type: ignore[override]
+    def prereleases(self) -> bool:
+        # If there is an explicit prereleases set for this, then we'll just
+        # blindly use that.
+        if self._prereleases is not None:
+            return self._prereleases
+
+        # Look at all of our specifiers and determine if they are inclusive
+        # operators, and if they are if they are including an explicit
+        # prerelease.
+        operator, version = self._spec
+        if operator in ["==", ">=", "<=", "~=", "==="]:
+            # The == specifier can include a trailing .*, if it does we
+            # want to remove before parsing.
+            if operator == "==" and version.endswith(".*"):
+                version = version[:-2]
+
+            # Parse the version, and if it is a pre-release than this
+            # specifier allows pre-releases.
+            if Version(version).is_prerelease:
+                return True
+
+        return False
+
+    @prereleases.setter
+    def prereleases(self, value: bool) -> None:
+        self._prereleases = value
+
+    @property
+    def operator(self) -> str:
+        """The operator of this specifier.
+
+        >>> Specifier("==1.2.3").operator
+        '=='
+        """
+        return self._spec[0]
+
+    @property
+    def version(self) -> str:
+        """The version of this specifier.
+
+        >>> Specifier("==1.2.3").version
+        '1.2.3'
+        """
+        return self._spec[1]
+
+    def __repr__(self) -> str:
+        """A representation of the Specifier that shows all internal state.
+
+        >>> Specifier('>=1.0.0')
+        <Specifier('>=1.0.0')>
+        >>> Specifier('>=1.0.0', prereleases=False)
+        <Specifier('>=1.0.0', prereleases=False)>
+        >>> Specifier('>=1.0.0', prereleases=True)
+        <Specifier('>=1.0.0', prereleases=True)>
+        """
+        pre = (
+            f", prereleases={self.prereleases!r}"
+            if self._prereleases is not None
+            else ""
+        )
+
+        return f"<{self.__class__.__name__}({str(self)!r}{pre})>"
+
+    def __str__(self) -> str:
+        """A string representation of the Specifier that can be round-tripped.
+
+        >>> str(Specifier('>=1.0.0'))
+        '>=1.0.0'
+        >>> str(Specifier('>=1.0.0', prereleases=False))
+        '>=1.0.0'
+        """
+        return "{}{}".format(*self._spec)
+
+    @property
+    def _canonical_spec(self) -> Tuple[str, str]:
+        canonical_version = canonicalize_version(
+            self._spec[1],
+            strip_trailing_zero=(self._spec[0] != "~="),
+        )
+        return self._spec[0], canonical_version
+
+    def __hash__(self) -> int:
+        return hash(self._canonical_spec)
+
+    def __eq__(self, other: object) -> bool:
+        """Whether or not the two Specifier-like objects are equal.
+
+        :param other: The other object to check against.
+
+        The value of :attr:`prereleases` is ignored.
+
+        >>> Specifier("==1.2.3") == Specifier("== 1.2.3.0")
+        True
+        >>> (Specifier("==1.2.3", prereleases=False) ==
+        ...  Specifier("==1.2.3", prereleases=True))
+        True
+        >>> Specifier("==1.2.3") == "==1.2.3"
+        True
+        >>> Specifier("==1.2.3") == Specifier("==1.2.4")
+        False
+        >>> Specifier("==1.2.3") == Specifier("~=1.2.3")
+        False
+        """
+        if isinstance(other, str):
+            try:
+                other = self.__class__(str(other))
+            except InvalidSpecifier:
+                return NotImplemented
+        elif not isinstance(other, self.__class__):
+            return NotImplemented
+
+        return self._canonical_spec == other._canonical_spec
+
+    def _get_operator(self, op: str) -> CallableOperator:
+        operator_callable: CallableOperator = getattr(
+            self, f"_compare_{self._operators[op]}"
+        )
+        return operator_callable
+
+    def _compare_compatible(self, prospective: Version, spec: str) -> bool:
+        # Compatible releases have an equivalent combination of >= and ==. That
+        # is that ~=2.2 is equivalent to >=2.2,==2.*. This allows us to
+        # implement this in terms of the other specifiers instead of
+        # implementing it ourselves. The only thing we need to do is construct
+        # the other specifiers.
+
+        # We want everything but the last item in the version, but we want to
+        # ignore suffix segments.
+        prefix = _version_join(
+            list(itertools.takewhile(_is_not_suffix, _version_split(spec)))[:-1]
+        )
+
+        # Add the prefix notation to the end of our string
+        prefix += ".*"
+
+        return self._get_operator(">=")(prospective, spec) and self._get_operator("==")(
+            prospective, prefix
+        )
+
+    def _compare_equal(self, prospective: Version, spec: str) -> bool:
+        # We need special logic to handle prefix matching
+        if spec.endswith(".*"):
+            # In the case of prefix matching we want to ignore local segment.
+            normalized_prospective = canonicalize_version(
+                prospective.public, strip_trailing_zero=False
+            )
+            # Get the normalized version string ignoring the trailing .*
+            normalized_spec = canonicalize_version(spec[:-2], strip_trailing_zero=False)
+            # Split the spec out by bangs and dots, and pretend that there is
+            # an implicit dot in between a release segment and a pre-release segment.
+            split_spec = _version_split(normalized_spec)
+
+            # Split the prospective version out by bangs and dots, and pretend
+            # that there is an implicit dot in between a release segment and
+            # a pre-release segment.
+            split_prospective = _version_split(normalized_prospective)
+
+            # 0-pad the prospective version before shortening it to get the correct
+            # shortened version.
+            padded_prospective, _ = _pad_version(split_prospective, split_spec)
+
+            # Shorten the prospective version to be the same length as the spec
+            # so that we can determine if the specifier is a prefix of the
+            # prospective version or not.
+            shortened_prospective = padded_prospective[: len(split_spec)]
+
+            return shortened_prospective == split_spec
+        else:
+            # Convert our spec string into a Version
+            spec_version = Version(spec)
+
+            # If the specifier does not have a local segment, then we want to
+            # act as if the prospective version also does not have a local
+            # segment.
+            if not spec_version.local:
+                prospective = Version(prospective.public)
+
+            return prospective == spec_version
+
+    def _compare_not_equal(self, prospective: Version, spec: str) -> bool:
+        return not self._compare_equal(prospective, spec)
+
+    def _compare_less_than_equal(self, prospective: Version, spec: str) -> bool:
+        # NB: Local version identifiers are NOT permitted in the version
+        # specifier, so local version labels can be universally removed from
+        # the prospective version.
+        return Version(prospective.public) <= Version(spec)
+
+    def _compare_greater_than_equal(self, prospective: Version, spec: str) -> bool:
+        # NB: Local version identifiers are NOT permitted in the version
+        # specifier, so local version labels can be universally removed from
+        # the prospective version.
+        return Version(prospective.public) >= Version(spec)
+
+    def _compare_less_than(self, prospective: Version, spec_str: str) -> bool:
+        # Convert our spec to a Version instance, since we'll want to work with
+        # it as a version.
+        spec = Version(spec_str)
+
+        # Check to see if the prospective version is less than the spec
+        # version. If it's not we can short circuit and just return False now
+        # instead of doing extra unneeded work.
+        if not prospective < spec:
+            return False
+
+        # This special case is here so that, unless the specifier itself
+        # includes is a pre-release version, that we do not accept pre-release
+        # versions for the version mentioned in the specifier (e.g. <3.1 should
+        # not match 3.1.dev0, but should match 3.0.dev0).
+        if not spec.is_prerelease and prospective.is_prerelease:
+            if Version(prospective.base_version) == Version(spec.base_version):
+                return False
+
+        # If we've gotten to here, it means that prospective version is both
+        # less than the spec version *and* it's not a pre-release of the same
+        # version in the spec.
+        return True
+
+    def _compare_greater_than(self, prospective: Version, spec_str: str) -> bool:
+        # Convert our spec to a Version instance, since we'll want to work with
+        # it as a version.
+        spec = Version(spec_str)
+
+        # Check to see if the prospective version is greater than the spec
+        # version. If it's not we can short circuit and just return False now
+        # instead of doing extra unneeded work.
+        if not prospective > spec:
+            return False
+
+        # This special case is here so that, unless the specifier itself
+        # includes is a post-release version, that we do not accept
+        # post-release versions for the version mentioned in the specifier
+        # (e.g. >3.1 should not match 3.0.post0, but should match 3.2.post0).
+        if not spec.is_postrelease and prospective.is_postrelease:
+            if Version(prospective.base_version) == Version(spec.base_version):
+                return False
+
+        # Ensure that we do not allow a local version of the version mentioned
+        # in the specifier, which is technically greater than, to match.
+        if prospective.local is not None:
+            if Version(prospective.base_version) == Version(spec.base_version):
+                return False
+
+        # If we've gotten to here, it means that prospective version is both
+        # greater than the spec version *and* it's not a pre-release of the
+        # same version in the spec.
+        return True
+
+    def _compare_arbitrary(self, prospective: Version, spec: str) -> bool:
+        return str(prospective).lower() == str(spec).lower()
+
+    def __contains__(self, item: Union[str, Version]) -> bool:
+        """Return whether or not the item is contained in this specifier.
+
+        :param item: The item to check for.
+
+        This is used for the ``in`` operator and behaves the same as
+        :meth:`contains` with no ``prereleases`` argument passed.
+
+        >>> "1.2.3" in Specifier(">=1.2.3")
+        True
+        >>> Version("1.2.3") in Specifier(">=1.2.3")
+        True
+        >>> "1.0.0" in Specifier(">=1.2.3")
+        False
+        >>> "1.3.0a1" in Specifier(">=1.2.3")
+        False
+        >>> "1.3.0a1" in Specifier(">=1.2.3", prereleases=True)
+        True
+        """
+        return self.contains(item)
+
+    def contains(
+        self, item: UnparsedVersion, prereleases: Optional[bool] = None
+    ) -> bool:
+        """Return whether or not the item is contained in this specifier.
+
+        :param item:
+            The item to check for, which can be a version string or a
+            :class:`Version` instance.
+        :param prereleases:
+            Whether or not to match prereleases with this Specifier. If set to
+            ``None`` (the default), it uses :attr:`prereleases` to determine
+            whether or not prereleases are allowed.
+
+        >>> Specifier(">=1.2.3").contains("1.2.3")
+        True
+        >>> Specifier(">=1.2.3").contains(Version("1.2.3"))
+        True
+        >>> Specifier(">=1.2.3").contains("1.0.0")
+        False
+        >>> Specifier(">=1.2.3").contains("1.3.0a1")
+        False
+        >>> Specifier(">=1.2.3", prereleases=True).contains("1.3.0a1")
+        True
+        >>> Specifier(">=1.2.3").contains("1.3.0a1", prereleases=True)
+        True
+        """
+
+        # Determine if prereleases are to be allowed or not.
+        if prereleases is None:
+            prereleases = self.prereleases
+
+        # Normalize item to a Version, this allows us to have a shortcut for
+        # "2.0" in Specifier(">=2")
+        normalized_item = _coerce_version(item)
+
+        # Determine if we should be supporting prereleases in this specifier
+        # or not, if we do not support prereleases than we can short circuit
+        # logic if this version is a prereleases.
+        if normalized_item.is_prerelease and not prereleases:
+            return False
+
+        # Actually do the comparison to determine if this item is contained
+        # within this Specifier or not.
+        operator_callable: CallableOperator = self._get_operator(self.operator)
+        return operator_callable(normalized_item, self.version)
+
+    def filter(
+        self, iterable: Iterable[UnparsedVersionVar], prereleases: Optional[bool] = None
+    ) -> Iterator[UnparsedVersionVar]:
+        """Filter items in the given iterable, that match the specifier.
+
+        :param iterable:
+            An iterable that can contain version strings and :class:`Version` instances.
+            The items in the iterable will be filtered according to the specifier.
+        :param prereleases:
+            Whether or not to allow prereleases in the returned iterator. If set to
+            ``None`` (the default), it will be intelligently decide whether to allow
+            prereleases or not (based on the :attr:`prereleases` attribute, and
+            whether the only versions matching are prereleases).
+
+        This method is smarter than just ``filter(Specifier().contains, [...])``
+        because it implements the rule from :pep:`440` that a prerelease item
+        SHOULD be accepted if no other versions match the given specifier.
+
+        >>> list(Specifier(">=1.2.3").filter(["1.2", "1.3", "1.5a1"]))
+        ['1.3']
+        >>> list(Specifier(">=1.2.3").filter(["1.2", "1.2.3", "1.3", Version("1.4")]))
+        ['1.2.3', '1.3', <Version('1.4')>]
+        >>> list(Specifier(">=1.2.3").filter(["1.2", "1.5a1"]))
+        ['1.5a1']
+        >>> list(Specifier(">=1.2.3").filter(["1.3", "1.5a1"], prereleases=True))
+        ['1.3', '1.5a1']
+        >>> list(Specifier(">=1.2.3", prereleases=True).filter(["1.3", "1.5a1"]))
+        ['1.3', '1.5a1']
+        """
+
+        yielded = False
+        found_prereleases = []
+
+        kw = {"prereleases": prereleases if prereleases is not None else True}
+
+        # Attempt to iterate over all the values in the iterable and if any of
+        # them match, yield them.
+        for version in iterable:
+            parsed_version = _coerce_version(version)
+
+            if self.contains(parsed_version, **kw):
+                # If our version is a prerelease, and we were not set to allow
+                # prereleases, then we'll store it for later in case nothing
+                # else matches this specifier.
+                if parsed_version.is_prerelease and not (
+                    prereleases or self.prereleases
+                ):
+                    found_prereleases.append(version)
+                # Either this is not a prerelease, or we should have been
+                # accepting prereleases from the beginning.
+                else:
+                    yielded = True
+                    yield version
+
+        # Now that we've iterated over everything, determine if we've yielded
+        # any values, and if we have not and we have any prereleases stored up
+        # then we will go ahead and yield the prereleases.
+        if not yielded and found_prereleases:
+            for version in found_prereleases:
+                yield version
+
+
+_prefix_regex = re.compile(r"^([0-9]+)((?:a|b|c|rc)[0-9]+)$")
+
+
+def _version_split(version: str) -> List[str]:
+    """Split version into components.
+
+    The split components are intended for version comparison. The logic does
+    not attempt to retain the original version string, so joining the
+    components back with :func:`_version_join` may not produce the original
+    version string.
+    """
+    result: List[str] = []
+
+    epoch, _, rest = version.rpartition("!")
+    result.append(epoch or "0")
+
+    for item in rest.split("."):
+        match = _prefix_regex.search(item)
+        if match:
+            result.extend(match.groups())
+        else:
+            result.append(item)
+    return result
+
+
+def _version_join(components: List[str]) -> str:
+    """Join split version components into a version string.
+
+    This function assumes the input came from :func:`_version_split`, where the
+    first component must be the epoch (either empty or numeric), and all other
+    components numeric.
+    """
+    epoch, *rest = components
+    return f"{epoch}!{'.'.join(rest)}"
+
+
+def _is_not_suffix(segment: str) -> bool:
+    return not any(
+        segment.startswith(prefix) for prefix in ("dev", "a", "b", "rc", "post")
+    )
+
+
+def _pad_version(left: List[str], right: List[str]) -> Tuple[List[str], List[str]]:
+    left_split, right_split = [], []
+
+    # Get the release segment of our versions
+    left_split.append(list(itertools.takewhile(lambda x: x.isdigit(), left)))
+    right_split.append(list(itertools.takewhile(lambda x: x.isdigit(), right)))
+
+    # Get the rest of our versions
+    left_split.append(left[len(left_split[0]) :])
+    right_split.append(right[len(right_split[0]) :])
+
+    # Insert our padding
+    left_split.insert(1, ["0"] * max(0, len(right_split[0]) - len(left_split[0])))
+    right_split.insert(1, ["0"] * max(0, len(left_split[0]) - len(right_split[0])))
+
+    return (
+        list(itertools.chain.from_iterable(left_split)),
+        list(itertools.chain.from_iterable(right_split)),
+    )
+
+
+class SpecifierSet(BaseSpecifier):
+    """This class abstracts handling of a set of version specifiers.
+
+    It can be passed a single specifier (``>=3.0``), a comma-separated list of
+    specifiers (``>=3.0,!=3.1``), or no specifier at all.
+    """
+
+    def __init__(
+        self, specifiers: str = "", prereleases: Optional[bool] = None
+    ) -> None:
+        """Initialize a SpecifierSet instance.
+
+        :param specifiers:
+            The string representation of a specifier or a comma-separated list of
+            specifiers which will be parsed and normalized before use.
+        :param prereleases:
+            This tells the SpecifierSet if it should accept prerelease versions if
+            applicable or not. The default of ``None`` will autodetect it from the
+            given specifiers.
+
+        :raises InvalidSpecifier:
+            If the given ``specifiers`` are not parseable than this exception will be
+            raised.
+        """
+
+        # Split on `,` to break each individual specifier into it's own item, and
+        # strip each item to remove leading/trailing whitespace.
+        split_specifiers = [s.strip() for s in specifiers.split(",") if s.strip()]
+
+        # Make each individual specifier a Specifier and save in a frozen set for later.
+        self._specs = frozenset(map(Specifier, split_specifiers))
+
+        # Store our prereleases value so we can use it later to determine if
+        # we accept prereleases or not.
+        self._prereleases = prereleases
+
+    @property
+    def prereleases(self) -> Optional[bool]:
+        # If we have been given an explicit prerelease modifier, then we'll
+        # pass that through here.
+        if self._prereleases is not None:
+            return self._prereleases
+
+        # If we don't have any specifiers, and we don't have a forced value,
+        # then we'll just return None since we don't know if this should have
+        # pre-releases or not.
+        if not self._specs:
+            return None
+
+        # Otherwise we'll see if any of the given specifiers accept
+        # prereleases, if any of them do we'll return True, otherwise False.
+        return any(s.prereleases for s in self._specs)
+
+    @prereleases.setter
+    def prereleases(self, value: bool) -> None:
+        self._prereleases = value
+
+    def __repr__(self) -> str:
+        """A representation of the specifier set that shows all internal state.
+
+        Note that the ordering of the individual specifiers within the set may not
+        match the input string.
+
+        >>> SpecifierSet('>=1.0.0,!=2.0.0')
+        <SpecifierSet('!=2.0.0,>=1.0.0')>
+        >>> SpecifierSet('>=1.0.0,!=2.0.0', prereleases=False)
+        <SpecifierSet('!=2.0.0,>=1.0.0', prereleases=False)>
+        >>> SpecifierSet('>=1.0.0,!=2.0.0', prereleases=True)
+        <SpecifierSet('!=2.0.0,>=1.0.0', prereleases=True)>
+        """
+        pre = (
+            f", prereleases={self.prereleases!r}"
+            if self._prereleases is not None
+            else ""
+        )
+
+        return f"<SpecifierSet({str(self)!r}{pre})>"
+
+    def __str__(self) -> str:
+        """A string representation of the specifier set that can be round-tripped.
+
+        Note that the ordering of the individual specifiers within the set may not
+        match the input string.
+
+        >>> str(SpecifierSet(">=1.0.0,!=1.0.1"))
+        '!=1.0.1,>=1.0.0'
+        >>> str(SpecifierSet(">=1.0.0,!=1.0.1", prereleases=False))
+        '!=1.0.1,>=1.0.0'
+        """
+        return ",".join(sorted(str(s) for s in self._specs))
+
+    def __hash__(self) -> int:
+        return hash(self._specs)
+
+    def __and__(self, other: Union["SpecifierSet", str]) -> "SpecifierSet":
+        """Return a SpecifierSet which is a combination of the two sets.
+
+        :param other: The other object to combine with.
+
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") & '<=2.0.0,!=2.0.1'
+        <SpecifierSet('!=1.0.1,!=2.0.1,<=2.0.0,>=1.0.0')>
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") & SpecifierSet('<=2.0.0,!=2.0.1')
+        <SpecifierSet('!=1.0.1,!=2.0.1,<=2.0.0,>=1.0.0')>
+        """
+        if isinstance(other, str):
+            other = SpecifierSet(other)
+        elif not isinstance(other, SpecifierSet):
+            return NotImplemented
+
+        specifier = SpecifierSet()
+        specifier._specs = frozenset(self._specs | other._specs)
+
+        if self._prereleases is None and other._prereleases is not None:
+            specifier._prereleases = other._prereleases
+        elif self._prereleases is not None and other._prereleases is None:
+            specifier._prereleases = self._prereleases
+        elif self._prereleases == other._prereleases:
+            specifier._prereleases = self._prereleases
+        else:
+            raise ValueError(
+                "Cannot combine SpecifierSets with True and False prerelease "
+                "overrides."
+            )
+
+        return specifier
+
+    def __eq__(self, other: object) -> bool:
+        """Whether or not the two SpecifierSet-like objects are equal.
+
+        :param other: The other object to check against.
+
+        The value of :attr:`prereleases` is ignored.
+
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0,!=1.0.1")
+        True
+        >>> (SpecifierSet(">=1.0.0,!=1.0.1", prereleases=False) ==
+        ...  SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True))
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") == ">=1.0.0,!=1.0.1"
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0")
+        False
+        >>> SpecifierSet(">=1.0.0,!=1.0.1") == SpecifierSet(">=1.0.0,!=1.0.2")
+        False
+        """
+        if isinstance(other, (str, Specifier)):
+            other = SpecifierSet(str(other))
+        elif not isinstance(other, SpecifierSet):
+            return NotImplemented
+
+        return self._specs == other._specs
+
+    def __len__(self) -> int:
+        """Returns the number of specifiers in this specifier set."""
+        return len(self._specs)
+
+    def __iter__(self) -> Iterator[Specifier]:
+        """
+        Returns an iterator over all the underlying :class:`Specifier` instances
+        in this specifier set.
+
+        >>> sorted(SpecifierSet(">=1.0.0,!=1.0.1"), key=str)
+        [<Specifier('!=1.0.1')>, <Specifier('>=1.0.0')>]
+        """
+        return iter(self._specs)
+
+    def __contains__(self, item: UnparsedVersion) -> bool:
+        """Return whether or not the item is contained in this specifier.
+
+        :param item: The item to check for.
+
+        This is used for the ``in`` operator and behaves the same as
+        :meth:`contains` with no ``prereleases`` argument passed.
+
+        >>> "1.2.3" in SpecifierSet(">=1.0.0,!=1.0.1")
+        True
+        >>> Version("1.2.3") in SpecifierSet(">=1.0.0,!=1.0.1")
+        True
+        >>> "1.0.1" in SpecifierSet(">=1.0.0,!=1.0.1")
+        False
+        >>> "1.3.0a1" in SpecifierSet(">=1.0.0,!=1.0.1")
+        False
+        >>> "1.3.0a1" in SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True)
+        True
+        """
+        return self.contains(item)
+
+    def contains(
+        self,
+        item: UnparsedVersion,
+        prereleases: Optional[bool] = None,
+        installed: Optional[bool] = None,
+    ) -> bool:
+        """Return whether or not the item is contained in this SpecifierSet.
+
+        :param item:
+            The item to check for, which can be a version string or a
+            :class:`Version` instance.
+        :param prereleases:
+            Whether or not to match prereleases with this SpecifierSet. If set to
+            ``None`` (the default), it uses :attr:`prereleases` to determine
+            whether or not prereleases are allowed.
+
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.2.3")
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains(Version("1.2.3"))
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.0.1")
+        False
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.3.0a1")
+        False
+        >>> SpecifierSet(">=1.0.0,!=1.0.1", prereleases=True).contains("1.3.0a1")
+        True
+        >>> SpecifierSet(">=1.0.0,!=1.0.1").contains("1.3.0a1", prereleases=True)
+        True
+        """
+        # Ensure that our item is a Version instance.
+        if not isinstance(item, Version):
+            item = Version(item)
+
+        # Determine if we're forcing a prerelease or not, if we're not forcing
+        # one for this particular filter call, then we'll use whatever the
+        # SpecifierSet thinks for whether or not we should support prereleases.
+        if prereleases is None:
+            prereleases = self.prereleases
+
+        # We can determine if we're going to allow pre-releases by looking to
+        # see if any of the underlying items supports them. If none of them do
+        # and this item is a pre-release then we do not allow it and we can
+        # short circuit that here.
+        # Note: This means that 1.0.dev1 would not be contained in something
+        #       like >=1.0.devabc however it would be in >=1.0.debabc,>0.0.dev0
+        if not prereleases and item.is_prerelease:
+            return False
+
+        if installed and item.is_prerelease:
+            item = Version(item.base_version)
+
+        # We simply dispatch to the underlying specs here to make sure that the
+        # given version is contained within all of them.
+        # Note: This use of all() here means that an empty set of specifiers
+        #       will always return True, this is an explicit design decision.
+        return all(s.contains(item, prereleases=prereleases) for s in self._specs)
+
+    def filter(
+        self, iterable: Iterable[UnparsedVersionVar], prereleases: Optional[bool] = None
+    ) -> Iterator[UnparsedVersionVar]:
+        """Filter items in the given iterable, that match the specifiers in this set.
+
+        :param iterable:
+            An iterable that can contain version strings and :class:`Version` instances.
+            The items in the iterable will be filtered according to the specifier.
+        :param prereleases:
+            Whether or not to allow prereleases in the returned iterator. If set to
+            ``None`` (the default), it will be intelligently decide whether to allow
+            prereleases or not (based on the :attr:`prereleases` attribute, and
+            whether the only versions matching are prereleases).
+
+        This method is smarter than just ``filter(SpecifierSet(...).contains, [...])``
+        because it implements the rule from :pep:`440` that a prerelease item
+        SHOULD be accepted if no other versions match the given specifier.
+
+        >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.3", "1.5a1"]))
+        ['1.3']
+        >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.3", Version("1.4")]))
+        ['1.3', <Version('1.4')>]
+        >>> list(SpecifierSet(">=1.2.3").filter(["1.2", "1.5a1"]))
+        []
+        >>> list(SpecifierSet(">=1.2.3").filter(["1.3", "1.5a1"], prereleases=True))
+        ['1.3', '1.5a1']
+        >>> list(SpecifierSet(">=1.2.3", prereleases=True).filter(["1.3", "1.5a1"]))
+        ['1.3', '1.5a1']
+
+        An "empty" SpecifierSet will filter items based on the presence of prerelease
+        versions in the set.
+
+        >>> list(SpecifierSet("").filter(["1.3", "1.5a1"]))
+        ['1.3']
+        >>> list(SpecifierSet("").filter(["1.5a1"]))
+        ['1.5a1']
+        >>> list(SpecifierSet("", prereleases=True).filter(["1.3", "1.5a1"]))
+        ['1.3', '1.5a1']
+        >>> list(SpecifierSet("").filter(["1.3", "1.5a1"], prereleases=True))
+        ['1.3', '1.5a1']
+        """
+        # Determine if we're forcing a prerelease or not, if we're not forcing
+        # one for this particular filter call, then we'll use whatever the
+        # SpecifierSet thinks for whether or not we should support prereleases.
+        if prereleases is None:
+            prereleases = self.prereleases
+
+        # If we have any specifiers, then we want to wrap our iterable in the
+        # filter method for each one, this will act as a logical AND amongst
+        # each specifier.
+        if self._specs:
+            for spec in self._specs:
+                iterable = spec.filter(iterable, prereleases=bool(prereleases))
+            return iter(iterable)
+        # If we do not have any specifiers, then we need to have a rough filter
+        # which will filter out any pre-releases, unless there are no final
+        # releases.
+        else:
+            filtered: List[UnparsedVersionVar] = []
+            found_prereleases: List[UnparsedVersionVar] = []
+
+            for item in iterable:
+                parsed_version = _coerce_version(item)
+
+                # Store any item which is a pre-release for later unless we've
+                # already found a final version or we are accepting prereleases
+                if parsed_version.is_prerelease and not prereleases:
+                    if not filtered:
+                        found_prereleases.append(item)
+                else:
+                    filtered.append(item)
+
+            # If we've found no items except for pre-releases, then we'll go
+            # ahead and use the pre-releases
+            if not filtered and found_prereleases and prereleases is None:
+                return iter(found_prereleases)
+
+            return iter(filtered)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/tags.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/tags.py
new file mode 100644
index 0000000000000000000000000000000000000000..89f1926137dd2d2a6bd63616bf5b9f722fc8d584
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/tags.py
@@ -0,0 +1,571 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+import logging
+import platform
+import re
+import struct
+import subprocess
+import sys
+import sysconfig
+from importlib.machinery import EXTENSION_SUFFIXES
+from typing import (
+    Dict,
+    FrozenSet,
+    Iterable,
+    Iterator,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+    cast,
+)
+
+from . import _manylinux, _musllinux
+
+logger = logging.getLogger(__name__)
+
+PythonVersion = Sequence[int]
+MacVersion = Tuple[int, int]
+
+INTERPRETER_SHORT_NAMES: Dict[str, str] = {
+    "python": "py",  # Generic.
+    "cpython": "cp",
+    "pypy": "pp",
+    "ironpython": "ip",
+    "jython": "jy",
+}
+
+
+_32_BIT_INTERPRETER = struct.calcsize("P") == 4
+
+
+class Tag:
+    """
+    A representation of the tag triple for a wheel.
+
+    Instances are considered immutable and thus are hashable. Equality checking
+    is also supported.
+    """
+
+    __slots__ = ["_interpreter", "_abi", "_platform", "_hash"]
+
+    def __init__(self, interpreter: str, abi: str, platform: str) -> None:
+        self._interpreter = interpreter.lower()
+        self._abi = abi.lower()
+        self._platform = platform.lower()
+        # The __hash__ of every single element in a Set[Tag] will be evaluated each time
+        # that a set calls its `.disjoint()` method, which may be called hundreds of
+        # times when scanning a page of links for packages with tags matching that
+        # Set[Tag]. Pre-computing the value here produces significant speedups for
+        # downstream consumers.
+        self._hash = hash((self._interpreter, self._abi, self._platform))
+
+    @property
+    def interpreter(self) -> str:
+        return self._interpreter
+
+    @property
+    def abi(self) -> str:
+        return self._abi
+
+    @property
+    def platform(self) -> str:
+        return self._platform
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Tag):
+            return NotImplemented
+
+        return (
+            (self._hash == other._hash)  # Short-circuit ASAP for perf reasons.
+            and (self._platform == other._platform)
+            and (self._abi == other._abi)
+            and (self._interpreter == other._interpreter)
+        )
+
+    def __hash__(self) -> int:
+        return self._hash
+
+    def __str__(self) -> str:
+        return f"{self._interpreter}-{self._abi}-{self._platform}"
+
+    def __repr__(self) -> str:
+        return f"<{self} @ {id(self)}>"
+
+
+def parse_tag(tag: str) -> FrozenSet[Tag]:
+    """
+    Parses the provided tag (e.g. `py3-none-any`) into a frozenset of Tag instances.
+
+    Returning a set is required due to the possibility that the tag is a
+    compressed tag set.
+    """
+    tags = set()
+    interpreters, abis, platforms = tag.split("-")
+    for interpreter in interpreters.split("."):
+        for abi in abis.split("."):
+            for platform_ in platforms.split("."):
+                tags.add(Tag(interpreter, abi, platform_))
+    return frozenset(tags)
+
+
+def _get_config_var(name: str, warn: bool = False) -> Union[int, str, None]:
+    value: Union[int, str, None] = sysconfig.get_config_var(name)
+    if value is None and warn:
+        logger.debug(
+            "Config variable '%s' is unset, Python ABI tag may be incorrect", name
+        )
+    return value
+
+
+def _normalize_string(string: str) -> str:
+    return string.replace(".", "_").replace("-", "_").replace(" ", "_")
+
+
+def _is_threaded_cpython(abis: List[str]) -> bool:
+    """
+    Determine if the ABI corresponds to a threaded (`--disable-gil`) build.
+
+    The threaded builds are indicated by a "t" in the abiflags.
+    """
+    if len(abis) == 0:
+        return False
+    # expect e.g., cp313
+    m = re.match(r"cp\d+(.*)", abis[0])
+    if not m:
+        return False
+    abiflags = m.group(1)
+    return "t" in abiflags
+
+
+def _abi3_applies(python_version: PythonVersion, threading: bool) -> bool:
+    """
+    Determine if the Python version supports abi3.
+
+    PEP 384 was first implemented in Python 3.2. The threaded (`--disable-gil`)
+    builds do not support abi3.
+    """
+    return len(python_version) > 1 and tuple(python_version) >= (3, 2) and not threading
+
+
+def _cpython_abis(py_version: PythonVersion, warn: bool = False) -> List[str]:
+    py_version = tuple(py_version)  # To allow for version comparison.
+    abis = []
+    version = _version_nodot(py_version[:2])
+    threading = debug = pymalloc = ucs4 = ""
+    with_debug = _get_config_var("Py_DEBUG", warn)
+    has_refcount = hasattr(sys, "gettotalrefcount")
+    # Windows doesn't set Py_DEBUG, so checking for support of debug-compiled
+    # extension modules is the best option.
+    # https://github.com/pypa/pip/issues/3383#issuecomment-173267692
+    has_ext = "_d.pyd" in EXTENSION_SUFFIXES
+    if with_debug or (with_debug is None and (has_refcount or has_ext)):
+        debug = "d"
+    if py_version >= (3, 13) and _get_config_var("Py_GIL_DISABLED", warn):
+        threading = "t"
+    if py_version < (3, 8):
+        with_pymalloc = _get_config_var("WITH_PYMALLOC", warn)
+        if with_pymalloc or with_pymalloc is None:
+            pymalloc = "m"
+        if py_version < (3, 3):
+            unicode_size = _get_config_var("Py_UNICODE_SIZE", warn)
+            if unicode_size == 4 or (
+                unicode_size is None and sys.maxunicode == 0x10FFFF
+            ):
+                ucs4 = "u"
+    elif debug:
+        # Debug builds can also load "normal" extension modules.
+        # We can also assume no UCS-4 or pymalloc requirement.
+        abis.append(f"cp{version}{threading}")
+    abis.insert(0, f"cp{version}{threading}{debug}{pymalloc}{ucs4}")
+    return abis
+
+
+def cpython_tags(
+    python_version: Optional[PythonVersion] = None,
+    abis: Optional[Iterable[str]] = None,
+    platforms: Optional[Iterable[str]] = None,
+    *,
+    warn: bool = False,
+) -> Iterator[Tag]:
+    """
+    Yields the tags for a CPython interpreter.
+
+    The tags consist of:
+    - cp<python_version>-<abi>-<platform>
+    - cp<python_version>-abi3-<platform>
+    - cp<python_version>-none-<platform>
+    - cp<less than python_version>-abi3-<platform>  # Older Python versions down to 3.2.
+
+    If python_version only specifies a major version then user-provided ABIs and
+    the 'none' ABItag will be used.
+
+    If 'abi3' or 'none' are specified in 'abis' then they will be yielded at
+    their normal position and not at the beginning.
+    """
+    if not python_version:
+        python_version = sys.version_info[:2]
+
+    interpreter = f"cp{_version_nodot(python_version[:2])}"
+
+    if abis is None:
+        if len(python_version) > 1:
+            abis = _cpython_abis(python_version, warn)
+        else:
+            abis = []
+    abis = list(abis)
+    # 'abi3' and 'none' are explicitly handled later.
+    for explicit_abi in ("abi3", "none"):
+        try:
+            abis.remove(explicit_abi)
+        except ValueError:
+            pass
+
+    platforms = list(platforms or platform_tags())
+    for abi in abis:
+        for platform_ in platforms:
+            yield Tag(interpreter, abi, platform_)
+
+    threading = _is_threaded_cpython(abis)
+    use_abi3 = _abi3_applies(python_version, threading)
+    if use_abi3:
+        yield from (Tag(interpreter, "abi3", platform_) for platform_ in platforms)
+    yield from (Tag(interpreter, "none", platform_) for platform_ in platforms)
+
+    if use_abi3:
+        for minor_version in range(python_version[1] - 1, 1, -1):
+            for platform_ in platforms:
+                interpreter = "cp{version}".format(
+                    version=_version_nodot((python_version[0], minor_version))
+                )
+                yield Tag(interpreter, "abi3", platform_)
+
+
+def _generic_abi() -> List[str]:
+    """
+    Return the ABI tag based on EXT_SUFFIX.
+    """
+    # The following are examples of `EXT_SUFFIX`.
+    # We want to keep the parts which are related to the ABI and remove the
+    # parts which are related to the platform:
+    # - linux:   '.cpython-310-x86_64-linux-gnu.so' => cp310
+    # - mac:     '.cpython-310-darwin.so'           => cp310
+    # - win:     '.cp310-win_amd64.pyd'             => cp310
+    # - win:     '.pyd'                             => cp37 (uses _cpython_abis())
+    # - pypy:    '.pypy38-pp73-x86_64-linux-gnu.so' => pypy38_pp73
+    # - graalpy: '.graalpy-38-native-x86_64-darwin.dylib'
+    #                                               => graalpy_38_native
+
+    ext_suffix = _get_config_var("EXT_SUFFIX", warn=True)
+    if not isinstance(ext_suffix, str) or ext_suffix[0] != ".":
+        raise SystemError("invalid sysconfig.get_config_var('EXT_SUFFIX')")
+    parts = ext_suffix.split(".")
+    if len(parts) < 3:
+        # CPython3.7 and earlier uses ".pyd" on Windows.
+        return _cpython_abis(sys.version_info[:2])
+    soabi = parts[1]
+    if soabi.startswith("cpython"):
+        # non-windows
+        abi = "cp" + soabi.split("-")[1]
+    elif soabi.startswith("cp"):
+        # windows
+        abi = soabi.split("-")[0]
+    elif soabi.startswith("pypy"):
+        abi = "-".join(soabi.split("-")[:2])
+    elif soabi.startswith("graalpy"):
+        abi = "-".join(soabi.split("-")[:3])
+    elif soabi:
+        # pyston, ironpython, others?
+        abi = soabi
+    else:
+        return []
+    return [_normalize_string(abi)]
+
+
+def generic_tags(
+    interpreter: Optional[str] = None,
+    abis: Optional[Iterable[str]] = None,
+    platforms: Optional[Iterable[str]] = None,
+    *,
+    warn: bool = False,
+) -> Iterator[Tag]:
+    """
+    Yields the tags for a generic interpreter.
+
+    The tags consist of:
+    - <interpreter>-<abi>-<platform>
+
+    The "none" ABI will be added if it was not explicitly provided.
+    """
+    if not interpreter:
+        interp_name = interpreter_name()
+        interp_version = interpreter_version(warn=warn)
+        interpreter = "".join([interp_name, interp_version])
+    if abis is None:
+        abis = _generic_abi()
+    else:
+        abis = list(abis)
+    platforms = list(platforms or platform_tags())
+    if "none" not in abis:
+        abis.append("none")
+    for abi in abis:
+        for platform_ in platforms:
+            yield Tag(interpreter, abi, platform_)
+
+
+def _py_interpreter_range(py_version: PythonVersion) -> Iterator[str]:
+    """
+    Yields Python versions in descending order.
+
+    After the latest version, the major-only version will be yielded, and then
+    all previous versions of that major version.
+    """
+    if len(py_version) > 1:
+        yield f"py{_version_nodot(py_version[:2])}"
+    yield f"py{py_version[0]}"
+    if len(py_version) > 1:
+        for minor in range(py_version[1] - 1, -1, -1):
+            yield f"py{_version_nodot((py_version[0], minor))}"
+
+
+def compatible_tags(
+    python_version: Optional[PythonVersion] = None,
+    interpreter: Optional[str] = None,
+    platforms: Optional[Iterable[str]] = None,
+) -> Iterator[Tag]:
+    """
+    Yields the sequence of tags that are compatible with a specific version of Python.
+
+    The tags consist of:
+    - py*-none-<platform>
+    - <interpreter>-none-any  # ... if `interpreter` is provided.
+    - py*-none-any
+    """
+    if not python_version:
+        python_version = sys.version_info[:2]
+    platforms = list(platforms or platform_tags())
+    for version in _py_interpreter_range(python_version):
+        for platform_ in platforms:
+            yield Tag(version, "none", platform_)
+    if interpreter:
+        yield Tag(interpreter, "none", "any")
+    for version in _py_interpreter_range(python_version):
+        yield Tag(version, "none", "any")
+
+
+def _mac_arch(arch: str, is_32bit: bool = _32_BIT_INTERPRETER) -> str:
+    if not is_32bit:
+        return arch
+
+    if arch.startswith("ppc"):
+        return "ppc"
+
+    return "i386"
+
+
+def _mac_binary_formats(version: MacVersion, cpu_arch: str) -> List[str]:
+    formats = [cpu_arch]
+    if cpu_arch == "x86_64":
+        if version < (10, 4):
+            return []
+        formats.extend(["intel", "fat64", "fat32"])
+
+    elif cpu_arch == "i386":
+        if version < (10, 4):
+            return []
+        formats.extend(["intel", "fat32", "fat"])
+
+    elif cpu_arch == "ppc64":
+        # TODO: Need to care about 32-bit PPC for ppc64 through 10.2?
+        if version > (10, 5) or version < (10, 4):
+            return []
+        formats.append("fat64")
+
+    elif cpu_arch == "ppc":
+        if version > (10, 6):
+            return []
+        formats.extend(["fat32", "fat"])
+
+    if cpu_arch in {"arm64", "x86_64"}:
+        formats.append("universal2")
+
+    if cpu_arch in {"x86_64", "i386", "ppc64", "ppc", "intel"}:
+        formats.append("universal")
+
+    return formats
+
+
+def mac_platforms(
+    version: Optional[MacVersion] = None, arch: Optional[str] = None
+) -> Iterator[str]:
+    """
+    Yields the platform tags for a macOS system.
+
+    The `version` parameter is a two-item tuple specifying the macOS version to
+    generate platform tags for. The `arch` parameter is the CPU architecture to
+    generate platform tags for. Both parameters default to the appropriate value
+    for the current system.
+    """
+    version_str, _, cpu_arch = platform.mac_ver()
+    if version is None:
+        version = cast("MacVersion", tuple(map(int, version_str.split(".")[:2])))
+        if version == (10, 16):
+            # When built against an older macOS SDK, Python will report macOS 10.16
+            # instead of the real version.
+            version_str = subprocess.run(
+                [
+                    sys.executable,
+                    "-sS",
+                    "-c",
+                    "import platform; print(platform.mac_ver()[0])",
+                ],
+                check=True,
+                env={"SYSTEM_VERSION_COMPAT": "0"},
+                stdout=subprocess.PIPE,
+                text=True,
+            ).stdout
+            version = cast("MacVersion", tuple(map(int, version_str.split(".")[:2])))
+    else:
+        version = version
+    if arch is None:
+        arch = _mac_arch(cpu_arch)
+    else:
+        arch = arch
+
+    if (10, 0) <= version and version < (11, 0):
+        # Prior to Mac OS 11, each yearly release of Mac OS bumped the
+        # "minor" version number.  The major version was always 10.
+        for minor_version in range(version[1], -1, -1):
+            compat_version = 10, minor_version
+            binary_formats = _mac_binary_formats(compat_version, arch)
+            for binary_format in binary_formats:
+                yield "macosx_{major}_{minor}_{binary_format}".format(
+                    major=10, minor=minor_version, binary_format=binary_format
+                )
+
+    if version >= (11, 0):
+        # Starting with Mac OS 11, each yearly release bumps the major version
+        # number.   The minor versions are now the midyear updates.
+        for major_version in range(version[0], 10, -1):
+            compat_version = major_version, 0
+            binary_formats = _mac_binary_formats(compat_version, arch)
+            for binary_format in binary_formats:
+                yield "macosx_{major}_{minor}_{binary_format}".format(
+                    major=major_version, minor=0, binary_format=binary_format
+                )
+
+    if version >= (11, 0):
+        # Mac OS 11 on x86_64 is compatible with binaries from previous releases.
+        # Arm64 support was introduced in 11.0, so no Arm binaries from previous
+        # releases exist.
+        #
+        # However, the "universal2" binary format can have a
+        # macOS version earlier than 11.0 when the x86_64 part of the binary supports
+        # that version of macOS.
+        if arch == "x86_64":
+            for minor_version in range(16, 3, -1):
+                compat_version = 10, minor_version
+                binary_formats = _mac_binary_formats(compat_version, arch)
+                for binary_format in binary_formats:
+                    yield "macosx_{major}_{minor}_{binary_format}".format(
+                        major=compat_version[0],
+                        minor=compat_version[1],
+                        binary_format=binary_format,
+                    )
+        else:
+            for minor_version in range(16, 3, -1):
+                compat_version = 10, minor_version
+                binary_format = "universal2"
+                yield "macosx_{major}_{minor}_{binary_format}".format(
+                    major=compat_version[0],
+                    minor=compat_version[1],
+                    binary_format=binary_format,
+                )
+
+
+def _linux_platforms(is_32bit: bool = _32_BIT_INTERPRETER) -> Iterator[str]:
+    linux = _normalize_string(sysconfig.get_platform())
+    if not linux.startswith("linux_"):
+        # we should never be here, just yield the sysconfig one and return
+        yield linux
+        return
+    if is_32bit:
+        if linux == "linux_x86_64":
+            linux = "linux_i686"
+        elif linux == "linux_aarch64":
+            linux = "linux_armv8l"
+    _, arch = linux.split("_", 1)
+    archs = {"armv8l": ["armv8l", "armv7l"]}.get(arch, [arch])
+    yield from _manylinux.platform_tags(archs)
+    yield from _musllinux.platform_tags(archs)
+    for arch in archs:
+        yield f"linux_{arch}"
+
+
+def _generic_platforms() -> Iterator[str]:
+    yield _normalize_string(sysconfig.get_platform())
+
+
+def platform_tags() -> Iterator[str]:
+    """
+    Provides the platform tags for this installation.
+    """
+    if platform.system() == "Darwin":
+        return mac_platforms()
+    elif platform.system() == "Linux":
+        return _linux_platforms()
+    else:
+        return _generic_platforms()
+
+
+def interpreter_name() -> str:
+    """
+    Returns the name of the running interpreter.
+
+    Some implementations have a reserved, two-letter abbreviation which will
+    be returned when appropriate.
+    """
+    name = sys.implementation.name
+    return INTERPRETER_SHORT_NAMES.get(name) or name
+
+
+def interpreter_version(*, warn: bool = False) -> str:
+    """
+    Returns the version of the running interpreter.
+    """
+    version = _get_config_var("py_version_nodot", warn=warn)
+    if version:
+        version = str(version)
+    else:
+        version = _version_nodot(sys.version_info[:2])
+    return version
+
+
+def _version_nodot(version: PythonVersion) -> str:
+    return "".join(map(str, version))
+
+
+def sys_tags(*, warn: bool = False) -> Iterator[Tag]:
+    """
+    Returns the sequence of tag triples for the running interpreter.
+
+    The order of the sequence corresponds to priority order for the
+    interpreter, from most to least important.
+    """
+
+    interp_name = interpreter_name()
+    if interp_name == "cp":
+        yield from cpython_tags(warn=warn)
+    else:
+        yield from generic_tags()
+
+    if interp_name == "pp":
+        interp = "pp3"
+    elif interp_name == "cp":
+        interp = "cp" + interpreter_version(warn=warn)
+    else:
+        interp = None
+    yield from compatible_tags(interpreter=interp)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/utils.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2c2f75aa806282d322c76c2117c0f0fdfb09d25
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/utils.py
@@ -0,0 +1,172 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+
+import re
+from typing import FrozenSet, NewType, Tuple, Union, cast
+
+from .tags import Tag, parse_tag
+from .version import InvalidVersion, Version
+
+BuildTag = Union[Tuple[()], Tuple[int, str]]
+NormalizedName = NewType("NormalizedName", str)
+
+
+class InvalidName(ValueError):
+    """
+    An invalid distribution name; users should refer to the packaging user guide.
+    """
+
+
+class InvalidWheelFilename(ValueError):
+    """
+    An invalid wheel filename was found, users should refer to PEP 427.
+    """
+
+
+class InvalidSdistFilename(ValueError):
+    """
+    An invalid sdist filename was found, users should refer to the packaging user guide.
+    """
+
+
+# Core metadata spec for `Name`
+_validate_regex = re.compile(
+    r"^([A-Z0-9]|[A-Z0-9][A-Z0-9._-]*[A-Z0-9])$", re.IGNORECASE
+)
+_canonicalize_regex = re.compile(r"[-_.]+")
+_normalized_regex = re.compile(r"^([a-z0-9]|[a-z0-9]([a-z0-9-](?!--))*[a-z0-9])$")
+# PEP 427: The build number must start with a digit.
+_build_tag_regex = re.compile(r"(\d+)(.*)")
+
+
+def canonicalize_name(name: str, *, validate: bool = False) -> NormalizedName:
+    if validate and not _validate_regex.match(name):
+        raise InvalidName(f"name is invalid: {name!r}")
+    # This is taken from PEP 503.
+    value = _canonicalize_regex.sub("-", name).lower()
+    return cast(NormalizedName, value)
+
+
+def is_normalized_name(name: str) -> bool:
+    return _normalized_regex.match(name) is not None
+
+
+def canonicalize_version(
+    version: Union[Version, str], *, strip_trailing_zero: bool = True
+) -> str:
+    """
+    This is very similar to Version.__str__, but has one subtle difference
+    with the way it handles the release segment.
+    """
+    if isinstance(version, str):
+        try:
+            parsed = Version(version)
+        except InvalidVersion:
+            # Legacy versions cannot be normalized
+            return version
+    else:
+        parsed = version
+
+    parts = []
+
+    # Epoch
+    if parsed.epoch != 0:
+        parts.append(f"{parsed.epoch}!")
+
+    # Release segment
+    release_segment = ".".join(str(x) for x in parsed.release)
+    if strip_trailing_zero:
+        # NB: This strips trailing '.0's to normalize
+        release_segment = re.sub(r"(\.0)+$", "", release_segment)
+    parts.append(release_segment)
+
+    # Pre-release
+    if parsed.pre is not None:
+        parts.append("".join(str(x) for x in parsed.pre))
+
+    # Post-release
+    if parsed.post is not None:
+        parts.append(f".post{parsed.post}")
+
+    # Development release
+    if parsed.dev is not None:
+        parts.append(f".dev{parsed.dev}")
+
+    # Local version segment
+    if parsed.local is not None:
+        parts.append(f"+{parsed.local}")
+
+    return "".join(parts)
+
+
+def parse_wheel_filename(
+    filename: str,
+) -> Tuple[NormalizedName, Version, BuildTag, FrozenSet[Tag]]:
+    if not filename.endswith(".whl"):
+        raise InvalidWheelFilename(
+            f"Invalid wheel filename (extension must be '.whl'): {filename}"
+        )
+
+    filename = filename[:-4]
+    dashes = filename.count("-")
+    if dashes not in (4, 5):
+        raise InvalidWheelFilename(
+            f"Invalid wheel filename (wrong number of parts): {filename}"
+        )
+
+    parts = filename.split("-", dashes - 2)
+    name_part = parts[0]
+    # See PEP 427 for the rules on escaping the project name.
+    if "__" in name_part or re.match(r"^[\w\d._]*$", name_part, re.UNICODE) is None:
+        raise InvalidWheelFilename(f"Invalid project name: {filename}")
+    name = canonicalize_name(name_part)
+
+    try:
+        version = Version(parts[1])
+    except InvalidVersion as e:
+        raise InvalidWheelFilename(
+            f"Invalid wheel filename (invalid version): {filename}"
+        ) from e
+
+    if dashes == 5:
+        build_part = parts[2]
+        build_match = _build_tag_regex.match(build_part)
+        if build_match is None:
+            raise InvalidWheelFilename(
+                f"Invalid build number: {build_part} in '{filename}'"
+            )
+        build = cast(BuildTag, (int(build_match.group(1)), build_match.group(2)))
+    else:
+        build = ()
+    tags = parse_tag(parts[-1])
+    return (name, version, build, tags)
+
+
+def parse_sdist_filename(filename: str) -> Tuple[NormalizedName, Version]:
+    if filename.endswith(".tar.gz"):
+        file_stem = filename[: -len(".tar.gz")]
+    elif filename.endswith(".zip"):
+        file_stem = filename[: -len(".zip")]
+    else:
+        raise InvalidSdistFilename(
+            f"Invalid sdist filename (extension must be '.tar.gz' or '.zip'):"
+            f" {filename}"
+        )
+
+    # We are requiring a PEP 440 version, which cannot contain dashes,
+    # so we split on the last dash.
+    name_part, sep, version_part = file_stem.rpartition("-")
+    if not sep:
+        raise InvalidSdistFilename(f"Invalid sdist filename: {filename}")
+
+    name = canonicalize_name(name_part)
+
+    try:
+        version = Version(version_part)
+    except InvalidVersion as e:
+        raise InvalidSdistFilename(
+            f"Invalid sdist filename (invalid version): {filename}"
+        ) from e
+
+    return (name, version)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/version.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..cda8e99935c8d92010b84437ae83d75031245d61
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/packaging/version.py
@@ -0,0 +1,561 @@
+# This file is dual licensed under the terms of the Apache License, Version
+# 2.0, and the BSD License. See the LICENSE file in the root of this repository
+# for complete details.
+"""
+.. testsetup::
+
+    from packaging.version import parse, Version
+"""
+
+import itertools
+import re
+from typing import Any, Callable, NamedTuple, Optional, SupportsInt, Tuple, Union
+
+from ._structures import Infinity, InfinityType, NegativeInfinity, NegativeInfinityType
+
+__all__ = ["VERSION_PATTERN", "parse", "Version", "InvalidVersion"]
+
+LocalType = Tuple[Union[int, str], ...]
+
+CmpPrePostDevType = Union[InfinityType, NegativeInfinityType, Tuple[str, int]]
+CmpLocalType = Union[
+    NegativeInfinityType,
+    Tuple[Union[Tuple[int, str], Tuple[NegativeInfinityType, Union[int, str]]], ...],
+]
+CmpKey = Tuple[
+    int,
+    Tuple[int, ...],
+    CmpPrePostDevType,
+    CmpPrePostDevType,
+    CmpPrePostDevType,
+    CmpLocalType,
+]
+VersionComparisonMethod = Callable[[CmpKey, CmpKey], bool]
+
+
+class _Version(NamedTuple):
+    epoch: int
+    release: Tuple[int, ...]
+    dev: Optional[Tuple[str, int]]
+    pre: Optional[Tuple[str, int]]
+    post: Optional[Tuple[str, int]]
+    local: Optional[LocalType]
+
+
+def parse(version: str) -> "Version":
+    """Parse the given version string.
+
+    >>> parse('1.0.dev1')
+    <Version('1.0.dev1')>
+
+    :param version: The version string to parse.
+    :raises InvalidVersion: When the version string is not a valid version.
+    """
+    return Version(version)
+
+
+class InvalidVersion(ValueError):
+    """Raised when a version string is not a valid version.
+
+    >>> Version("invalid")
+    Traceback (most recent call last):
+        ...
+    packaging.version.InvalidVersion: Invalid version: 'invalid'
+    """
+
+
+class _BaseVersion:
+    _key: Tuple[Any, ...]
+
+    def __hash__(self) -> int:
+        return hash(self._key)
+
+    # Please keep the duplicated `isinstance` check
+    # in the six comparisons hereunder
+    # unless you find a way to avoid adding overhead function calls.
+    def __lt__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key < other._key
+
+    def __le__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key <= other._key
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key == other._key
+
+    def __ge__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key >= other._key
+
+    def __gt__(self, other: "_BaseVersion") -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key > other._key
+
+    def __ne__(self, other: object) -> bool:
+        if not isinstance(other, _BaseVersion):
+            return NotImplemented
+
+        return self._key != other._key
+
+
+# Deliberately not anchored to the start and end of the string, to make it
+# easier for 3rd party code to reuse
+_VERSION_PATTERN = r"""
+    v?
+    (?:
+        (?:(?P<epoch>[0-9]+)!)?                           # epoch
+        (?P<release>[0-9]+(?:\.[0-9]+)*)                  # release segment
+        (?P<pre>                                          # pre-release
+            [-_\.]?
+            (?P<pre_l>alpha|a|beta|b|preview|pre|c|rc)
+            [-_\.]?
+            (?P<pre_n>[0-9]+)?
+        )?
+        (?P<post>                                         # post release
+            (?:-(?P<post_n1>[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?P<post_l>post|rev|r)
+                [-_\.]?
+                (?P<post_n2>[0-9]+)?
+            )
+        )?
+        (?P<dev>                                          # dev release
+            [-_\.]?
+            (?P<dev_l>dev)
+            [-_\.]?
+            (?P<dev_n>[0-9]+)?
+        )?
+    )
+    (?:\+(?P<local>[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+VERSION_PATTERN = _VERSION_PATTERN
+"""
+A string containing the regular expression used to match a valid version.
+
+The pattern is not anchored at either end, and is intended for embedding in larger
+expressions (for example, matching a version number as part of a file name). The
+regular expression should be compiled with the ``re.VERBOSE`` and ``re.IGNORECASE``
+flags set.
+
+:meta hide-value:
+"""
+
+
+class Version(_BaseVersion):
+    """This class abstracts handling of a project's versions.
+
+    A :class:`Version` instance is comparison aware and can be compared and
+    sorted using the standard Python interfaces.
+
+    >>> v1 = Version("1.0a5")
+    >>> v2 = Version("1.0")
+    >>> v1
+    <Version('1.0a5')>
+    >>> v2
+    <Version('1.0')>
+    >>> v1 < v2
+    True
+    >>> v1 == v2
+    False
+    >>> v1 > v2
+    False
+    >>> v1 >= v2
+    False
+    >>> v1 <= v2
+    True
+    """
+
+    _regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
+    _key: CmpKey
+
+    def __init__(self, version: str) -> None:
+        """Initialize a Version object.
+
+        :param version:
+            The string representation of a version which will be parsed and normalized
+            before use.
+        :raises InvalidVersion:
+            If the ``version`` does not conform to PEP 440 in any way then this
+            exception will be raised.
+        """
+
+        # Validate the version and parse it into pieces
+        match = self._regex.search(version)
+        if not match:
+            raise InvalidVersion(f"Invalid version: '{version}'")
+
+        # Store the parsed out pieces of the version
+        self._version = _Version(
+            epoch=int(match.group("epoch")) if match.group("epoch") else 0,
+            release=tuple(int(i) for i in match.group("release").split(".")),
+            pre=_parse_letter_version(match.group("pre_l"), match.group("pre_n")),
+            post=_parse_letter_version(
+                match.group("post_l"), match.group("post_n1") or match.group("post_n2")
+            ),
+            dev=_parse_letter_version(match.group("dev_l"), match.group("dev_n")),
+            local=_parse_local_version(match.group("local")),
+        )
+
+        # Generate a key which will be used for sorting
+        self._key = _cmpkey(
+            self._version.epoch,
+            self._version.release,
+            self._version.pre,
+            self._version.post,
+            self._version.dev,
+            self._version.local,
+        )
+
+    def __repr__(self) -> str:
+        """A representation of the Version that shows all internal state.
+
+        >>> Version('1.0.0')
+        <Version('1.0.0')>
+        """
+        return f"<Version('{self}')>"
+
+    def __str__(self) -> str:
+        """A string representation of the version that can be rounded-tripped.
+
+        >>> str(Version("1.0a5"))
+        '1.0a5'
+        """
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        # Pre-release
+        if self.pre is not None:
+            parts.append("".join(str(x) for x in self.pre))
+
+        # Post-release
+        if self.post is not None:
+            parts.append(f".post{self.post}")
+
+        # Development release
+        if self.dev is not None:
+            parts.append(f".dev{self.dev}")
+
+        # Local version segment
+        if self.local is not None:
+            parts.append(f"+{self.local}")
+
+        return "".join(parts)
+
+    @property
+    def epoch(self) -> int:
+        """The epoch of the version.
+
+        >>> Version("2.0.0").epoch
+        0
+        >>> Version("1!2.0.0").epoch
+        1
+        """
+        return self._version.epoch
+
+    @property
+    def release(self) -> Tuple[int, ...]:
+        """The components of the "release" segment of the version.
+
+        >>> Version("1.2.3").release
+        (1, 2, 3)
+        >>> Version("2.0.0").release
+        (2, 0, 0)
+        >>> Version("1!2.0.0.post0").release
+        (2, 0, 0)
+
+        Includes trailing zeroes but not the epoch or any pre-release / development /
+        post-release suffixes.
+        """
+        return self._version.release
+
+    @property
+    def pre(self) -> Optional[Tuple[str, int]]:
+        """The pre-release segment of the version.
+
+        >>> print(Version("1.2.3").pre)
+        None
+        >>> Version("1.2.3a1").pre
+        ('a', 1)
+        >>> Version("1.2.3b1").pre
+        ('b', 1)
+        >>> Version("1.2.3rc1").pre
+        ('rc', 1)
+        """
+        return self._version.pre
+
+    @property
+    def post(self) -> Optional[int]:
+        """The post-release number of the version.
+
+        >>> print(Version("1.2.3").post)
+        None
+        >>> Version("1.2.3.post1").post
+        1
+        """
+        return self._version.post[1] if self._version.post else None
+
+    @property
+    def dev(self) -> Optional[int]:
+        """The development number of the version.
+
+        >>> print(Version("1.2.3").dev)
+        None
+        >>> Version("1.2.3.dev1").dev
+        1
+        """
+        return self._version.dev[1] if self._version.dev else None
+
+    @property
+    def local(self) -> Optional[str]:
+        """The local version segment of the version.
+
+        >>> print(Version("1.2.3").local)
+        None
+        >>> Version("1.2.3+abc").local
+        'abc'
+        """
+        if self._version.local:
+            return ".".join(str(x) for x in self._version.local)
+        else:
+            return None
+
+    @property
+    def public(self) -> str:
+        """The public portion of the version.
+
+        >>> Version("1.2.3").public
+        '1.2.3'
+        >>> Version("1.2.3+abc").public
+        '1.2.3'
+        >>> Version("1.2.3+abc.dev1").public
+        '1.2.3'
+        """
+        return str(self).split("+", 1)[0]
+
+    @property
+    def base_version(self) -> str:
+        """The "base version" of the version.
+
+        >>> Version("1.2.3").base_version
+        '1.2.3'
+        >>> Version("1.2.3+abc").base_version
+        '1.2.3'
+        >>> Version("1!1.2.3+abc.dev1").base_version
+        '1!1.2.3'
+
+        The "base version" is the public version of the project without any pre or post
+        release markers.
+        """
+        parts = []
+
+        # Epoch
+        if self.epoch != 0:
+            parts.append(f"{self.epoch}!")
+
+        # Release segment
+        parts.append(".".join(str(x) for x in self.release))
+
+        return "".join(parts)
+
+    @property
+    def is_prerelease(self) -> bool:
+        """Whether this version is a pre-release.
+
+        >>> Version("1.2.3").is_prerelease
+        False
+        >>> Version("1.2.3a1").is_prerelease
+        True
+        >>> Version("1.2.3b1").is_prerelease
+        True
+        >>> Version("1.2.3rc1").is_prerelease
+        True
+        >>> Version("1.2.3dev1").is_prerelease
+        True
+        """
+        return self.dev is not None or self.pre is not None
+
+    @property
+    def is_postrelease(self) -> bool:
+        """Whether this version is a post-release.
+
+        >>> Version("1.2.3").is_postrelease
+        False
+        >>> Version("1.2.3.post1").is_postrelease
+        True
+        """
+        return self.post is not None
+
+    @property
+    def is_devrelease(self) -> bool:
+        """Whether this version is a development release.
+
+        >>> Version("1.2.3").is_devrelease
+        False
+        >>> Version("1.2.3.dev1").is_devrelease
+        True
+        """
+        return self.dev is not None
+
+    @property
+    def major(self) -> int:
+        """The first item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").major
+        1
+        """
+        return self.release[0] if len(self.release) >= 1 else 0
+
+    @property
+    def minor(self) -> int:
+        """The second item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").minor
+        2
+        >>> Version("1").minor
+        0
+        """
+        return self.release[1] if len(self.release) >= 2 else 0
+
+    @property
+    def micro(self) -> int:
+        """The third item of :attr:`release` or ``0`` if unavailable.
+
+        >>> Version("1.2.3").micro
+        3
+        >>> Version("1").micro
+        0
+        """
+        return self.release[2] if len(self.release) >= 3 else 0
+
+
+def _parse_letter_version(
+    letter: Optional[str], number: Union[str, bytes, SupportsInt, None]
+) -> Optional[Tuple[str, int]]:
+    if letter:
+        # We consider there to be an implicit 0 in a pre-release if there is
+        # not a numeral associated with it.
+        if number is None:
+            number = 0
+
+        # We normalize any letters to their lower case form
+        letter = letter.lower()
+
+        # We consider some words to be alternate spellings of other words and
+        # in those cases we want to normalize the spellings to our preferred
+        # spelling.
+        if letter == "alpha":
+            letter = "a"
+        elif letter == "beta":
+            letter = "b"
+        elif letter in ["c", "pre", "preview"]:
+            letter = "rc"
+        elif letter in ["rev", "r"]:
+            letter = "post"
+
+        return letter, int(number)
+    if not letter and number:
+        # We assume if we are given a number, but we are not given a letter
+        # then this is using the implicit post release syntax (e.g. 1.0-1)
+        letter = "post"
+
+        return letter, int(number)
+
+    return None
+
+
+_local_version_separators = re.compile(r"[\._-]")
+
+
+def _parse_local_version(local: Optional[str]) -> Optional[LocalType]:
+    """
+    Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
+    """
+    if local is not None:
+        return tuple(
+            part.lower() if not part.isdigit() else int(part)
+            for part in _local_version_separators.split(local)
+        )
+    return None
+
+
+def _cmpkey(
+    epoch: int,
+    release: Tuple[int, ...],
+    pre: Optional[Tuple[str, int]],
+    post: Optional[Tuple[str, int]],
+    dev: Optional[Tuple[str, int]],
+    local: Optional[LocalType],
+) -> CmpKey:
+    # When we compare a release version, we want to compare it with all of the
+    # trailing zeros removed. So we'll use a reverse the list, drop all the now
+    # leading zeros until we come to something non zero, then take the rest
+    # re-reverse it back into the correct order and make it a tuple and use
+    # that for our sorting key.
+    _release = tuple(
+        reversed(list(itertools.dropwhile(lambda x: x == 0, reversed(release))))
+    )
+
+    # We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
+    # We'll do this by abusing the pre segment, but we _only_ want to do this
+    # if there is not a pre or a post segment. If we have one of those then
+    # the normal sorting rules will handle this case correctly.
+    if pre is None and post is None and dev is not None:
+        _pre: CmpPrePostDevType = NegativeInfinity
+    # Versions without a pre-release (except as noted above) should sort after
+    # those with one.
+    elif pre is None:
+        _pre = Infinity
+    else:
+        _pre = pre
+
+    # Versions without a post segment should sort before those with one.
+    if post is None:
+        _post: CmpPrePostDevType = NegativeInfinity
+
+    else:
+        _post = post
+
+    # Versions without a development segment should sort after those with one.
+    if dev is None:
+        _dev: CmpPrePostDevType = Infinity
+
+    else:
+        _dev = dev
+
+    if local is None:
+        # Versions without a local segment should sort before those with one.
+        _local: CmpLocalType = NegativeInfinity
+    else:
+        # Versions with a local segment need that segment parsed to implement
+        # the sorting rules in PEP440.
+        # - Alpha numeric segments sort before numeric segments
+        # - Alpha numeric segments sort lexicographically
+        # - Numeric segments sort numerically
+        # - Shorter versions sort before longer versions when the prefixes
+        #   match exactly
+        _local = tuple(
+            (i, "") if isinstance(i, int) else (NegativeInfinity, i) for i in local
+        )
+
+    return epoch, _release, _pre, _post, _dev, _local
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/vendor.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/vendor.txt
new file mode 100644
index 0000000000000000000000000000000000000000..14666103a82ea7fced3d8cffb8a9b2a9e03fb492
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/vendored/vendor.txt
@@ -0,0 +1 @@
+packaging==24.0
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/wheelfile.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/wheelfile.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a0f4596c566420135991713ee75ca29c4cff3ed
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/wheel/wheelfile.py
@@ -0,0 +1,227 @@
+from __future__ import annotations
+
+import csv
+import hashlib
+import os.path
+import re
+import stat
+import time
+from io import StringIO, TextIOWrapper
+from typing import IO, TYPE_CHECKING, Literal
+from zipfile import ZIP_DEFLATED, ZipFile, ZipInfo
+
+from wheel.cli import WheelError
+from wheel.util import log, urlsafe_b64decode, urlsafe_b64encode
+
+if TYPE_CHECKING:
+    from typing import Protocol, Sized, Union
+
+    from typing_extensions import Buffer
+
+    StrPath = Union[str, os.PathLike[str]]
+
+    class SizedBuffer(Sized, Buffer, Protocol): ...
+
+
+# Non-greedy matching of an optional build number may be too clever (more
+# invalid wheel filenames will match). Separate regex for .dist-info?
+WHEEL_INFO_RE = re.compile(
+    r"""^(?P<namever>(?P<name>[^\s-]+?)-(?P<ver>[^\s-]+?))(-(?P<build>\d[^\s-]*))?
+     -(?P<pyver>[^\s-]+?)-(?P<abi>[^\s-]+?)-(?P<plat>\S+)\.whl$""",
+    re.VERBOSE,
+)
+MINIMUM_TIMESTAMP = 315532800  # 1980-01-01 00:00:00 UTC
+
+
+def get_zipinfo_datetime(timestamp: float | None = None):
+    # Some applications need reproducible .whl files, but they can't do this without
+    # forcing the timestamp of the individual ZipInfo objects. See issue #143.
+    timestamp = int(os.environ.get("SOURCE_DATE_EPOCH", timestamp or time.time()))
+    timestamp = max(timestamp, MINIMUM_TIMESTAMP)
+    return time.gmtime(timestamp)[0:6]
+
+
+class WheelFile(ZipFile):
+    """A ZipFile derivative class that also reads SHA-256 hashes from
+    .dist-info/RECORD and checks any read files against those.
+    """
+
+    _default_algorithm = hashlib.sha256
+
+    def __init__(
+        self,
+        file: StrPath,
+        mode: Literal["r", "w", "x", "a"] = "r",
+        compression: int = ZIP_DEFLATED,
+    ):
+        basename = os.path.basename(file)
+        self.parsed_filename = WHEEL_INFO_RE.match(basename)
+        if not basename.endswith(".whl") or self.parsed_filename is None:
+            raise WheelError(f"Bad wheel filename {basename!r}")
+
+        ZipFile.__init__(self, file, mode, compression=compression, allowZip64=True)
+
+        self.dist_info_path = "{}.dist-info".format(
+            self.parsed_filename.group("namever")
+        )
+        self.record_path = self.dist_info_path + "/RECORD"
+        self._file_hashes: dict[str, tuple[None, None] | tuple[int, bytes]] = {}
+        self._file_sizes = {}
+        if mode == "r":
+            # Ignore RECORD and any embedded wheel signatures
+            self._file_hashes[self.record_path] = None, None
+            self._file_hashes[self.record_path + ".jws"] = None, None
+            self._file_hashes[self.record_path + ".p7s"] = None, None
+
+            # Fill in the expected hashes by reading them from RECORD
+            try:
+                record = self.open(self.record_path)
+            except KeyError:
+                raise WheelError(f"Missing {self.record_path} file") from None
+
+            with record:
+                for line in csv.reader(
+                    TextIOWrapper(record, newline="", encoding="utf-8")
+                ):
+                    path, hash_sum, size = line
+                    if not hash_sum:
+                        continue
+
+                    algorithm, hash_sum = hash_sum.split("=")
+                    try:
+                        hashlib.new(algorithm)
+                    except ValueError:
+                        raise WheelError(
+                            f"Unsupported hash algorithm: {algorithm}"
+                        ) from None
+
+                    if algorithm.lower() in {"md5", "sha1"}:
+                        raise WheelError(
+                            f"Weak hash algorithm ({algorithm}) is not permitted by "
+                            f"PEP 427"
+                        )
+
+                    self._file_hashes[path] = (
+                        algorithm,
+                        urlsafe_b64decode(hash_sum.encode("ascii")),
+                    )
+
+    def open(
+        self,
+        name_or_info: str | ZipInfo,
+        mode: Literal["r", "w"] = "r",
+        pwd: bytes | None = None,
+    ) -> IO[bytes]:
+        def _update_crc(newdata: bytes) -> None:
+            eof = ef._eof
+            update_crc_orig(newdata)
+            running_hash.update(newdata)
+            if eof and running_hash.digest() != expected_hash:
+                raise WheelError(f"Hash mismatch for file '{ef_name}'")
+
+        ef_name = (
+            name_or_info.filename if isinstance(name_or_info, ZipInfo) else name_or_info
+        )
+        if (
+            mode == "r"
+            and not ef_name.endswith("/")
+            and ef_name not in self._file_hashes
+        ):
+            raise WheelError(f"No hash found for file '{ef_name}'")
+
+        ef = ZipFile.open(self, name_or_info, mode, pwd)
+        if mode == "r" and not ef_name.endswith("/"):
+            algorithm, expected_hash = self._file_hashes[ef_name]
+            if expected_hash is not None:
+                # Monkey patch the _update_crc method to also check for the hash from
+                # RECORD
+                running_hash = hashlib.new(algorithm)
+                update_crc_orig, ef._update_crc = ef._update_crc, _update_crc
+
+        return ef
+
+    def write_files(self, base_dir: str):
+        log.info(f"creating '{self.filename}' and adding '{base_dir}' to it")
+        deferred: list[tuple[str, str]] = []
+        for root, dirnames, filenames in os.walk(base_dir):
+            # Sort the directory names so that `os.walk` will walk them in a
+            # defined order on the next iteration.
+            dirnames.sort()
+            for name in sorted(filenames):
+                path = os.path.normpath(os.path.join(root, name))
+                if os.path.isfile(path):
+                    arcname = os.path.relpath(path, base_dir).replace(os.path.sep, "/")
+                    if arcname == self.record_path:
+                        pass
+                    elif root.endswith(".dist-info"):
+                        deferred.append((path, arcname))
+                    else:
+                        self.write(path, arcname)
+
+        deferred.sort()
+        for path, arcname in deferred:
+            self.write(path, arcname)
+
+    def write(
+        self,
+        filename: str,
+        arcname: str | None = None,
+        compress_type: int | None = None,
+    ) -> None:
+        with open(filename, "rb") as f:
+            st = os.fstat(f.fileno())
+            data = f.read()
+
+        zinfo = ZipInfo(
+            arcname or filename, date_time=get_zipinfo_datetime(st.st_mtime)
+        )
+        zinfo.external_attr = (stat.S_IMODE(st.st_mode) | stat.S_IFMT(st.st_mode)) << 16
+        zinfo.compress_type = compress_type or self.compression
+        self.writestr(zinfo, data, compress_type)
+
+    def writestr(
+        self,
+        zinfo_or_arcname: str | ZipInfo,
+        data: SizedBuffer | str,
+        compress_type: int | None = None,
+    ):
+        if isinstance(zinfo_or_arcname, str):
+            zinfo_or_arcname = ZipInfo(
+                zinfo_or_arcname, date_time=get_zipinfo_datetime()
+            )
+            zinfo_or_arcname.compress_type = self.compression
+            zinfo_or_arcname.external_attr = (0o664 | stat.S_IFREG) << 16
+
+        if isinstance(data, str):
+            data = data.encode("utf-8")
+
+        ZipFile.writestr(self, zinfo_or_arcname, data, compress_type)
+        fname = (
+            zinfo_or_arcname.filename
+            if isinstance(zinfo_or_arcname, ZipInfo)
+            else zinfo_or_arcname
+        )
+        log.info(f"adding '{fname}'")
+        if fname != self.record_path:
+            hash_ = self._default_algorithm(data)
+            self._file_hashes[fname] = (
+                hash_.name,
+                urlsafe_b64encode(hash_.digest()).decode("ascii"),
+            )
+            self._file_sizes[fname] = len(data)
+
+    def close(self):
+        # Write RECORD
+        if self.fp is not None and self.mode == "w" and self._file_hashes:
+            data = StringIO()
+            writer = csv.writer(data, delimiter=",", quotechar='"', lineterminator="\n")
+            writer.writerows(
+                (
+                    (fname, algorithm + "=" + hash_, self._file_sizes[fname])
+                    for fname, (algorithm, hash_) in self._file_hashes.items()
+                )
+            )
+            writer.writerow((format(self.record_path), "", ""))
+            self.writestr(self.record_path, data.getvalue())
+
+        ZipFile.close(self)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/INSTALLER
new file mode 100644
index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/INSTALLER
@@ -0,0 +1 @@
+pip
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..1bb5a44356f00884a71ceeefd24ded6caaba2418
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/LICENSE
@@ -0,0 +1,17 @@
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to
+deal in the Software without restriction, including without limitation the
+rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+sell copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
+IN THE SOFTWARE.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/METADATA b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..1399281717f23d7a37cb8c56153afedf0c73c860
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/METADATA
@@ -0,0 +1,102 @@
+Metadata-Version: 2.1
+Name: zipp
+Version: 3.19.2
+Summary: Backport of pathlib-compatible object wrapper for zip files
+Author-email: "Jason R. Coombs" <jaraco@jaraco.com>
+Project-URL: Homepage, https://github.com/jaraco/zipp
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Provides-Extra: doc
+Requires-Dist: sphinx >=3.5 ; extra == 'doc'
+Requires-Dist: jaraco.packaging >=9.3 ; extra == 'doc'
+Requires-Dist: rst.linker >=1.9 ; extra == 'doc'
+Requires-Dist: furo ; extra == 'doc'
+Requires-Dist: sphinx-lint ; extra == 'doc'
+Requires-Dist: jaraco.tidelift >=1.4 ; extra == 'doc'
+Provides-Extra: test
+Requires-Dist: pytest !=8.1.*,>=6 ; extra == 'test'
+Requires-Dist: pytest-checkdocs >=2.4 ; extra == 'test'
+Requires-Dist: pytest-cov ; extra == 'test'
+Requires-Dist: pytest-mypy ; extra == 'test'
+Requires-Dist: pytest-enabler >=2.2 ; extra == 'test'
+Requires-Dist: pytest-ruff >=0.2.1 ; extra == 'test'
+Requires-Dist: jaraco.itertools ; extra == 'test'
+Requires-Dist: jaraco.functools ; extra == 'test'
+Requires-Dist: more-itertools ; extra == 'test'
+Requires-Dist: big-O ; extra == 'test'
+Requires-Dist: pytest-ignore-flaky ; extra == 'test'
+Requires-Dist: jaraco.test ; extra == 'test'
+Requires-Dist: importlib-resources ; (python_version < "3.9") and extra == 'test'
+
+.. image:: https://img.shields.io/pypi/v/zipp.svg
+   :target: https://pypi.org/project/zipp
+
+.. image:: https://img.shields.io/pypi/pyversions/zipp.svg
+
+.. image:: https://github.com/jaraco/zipp/actions/workflows/main.yml/badge.svg
+   :target: https://github.com/jaraco/zipp/actions?query=workflow%3A%22tests%22
+   :alt: tests
+
+.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
+    :target: https://github.com/astral-sh/ruff
+    :alt: Ruff
+
+.. .. image:: https://readthedocs.org/projects/PROJECT_RTD/badge/?version=latest
+..    :target: https://PROJECT_RTD.readthedocs.io/en/latest/?badge=latest
+
+.. image:: https://img.shields.io/badge/skeleton-2024-informational
+   :target: https://blog.jaraco.com/skeleton
+
+.. image:: https://tidelift.com/badges/package/pypi/zipp
+   :target: https://tidelift.com/subscription/pkg/pypi-zipp?utm_source=pypi-zipp&utm_medium=readme
+
+
+A pathlib-compatible Zipfile object wrapper. Official backport of the standard library
+`Path object <https://docs.python.org/3.8/library/zipfile.html#path-objects>`_.
+
+
+Compatibility
+=============
+
+New features are introduced in this third-party library and later merged
+into CPython. The following table indicates which versions of this library
+were contributed to different versions in the standard library:
+
+.. list-table::
+   :header-rows: 1
+
+   * - zipp
+     - stdlib
+   * - 3.18
+     - 3.13
+   * - 3.16
+     - 3.12
+   * - 3.5
+     - 3.11
+   * - 3.2
+     - 3.10
+   * - 3.3 ??
+     - 3.9
+   * - 1.0
+     - 3.8
+
+
+Usage
+=====
+
+Use ``zipp.Path`` in place of ``zipfile.Path`` on any Python.
+
+For Enterprise
+==============
+
+Available as part of the Tidelift Subscription.
+
+This project and the maintainers of thousands of other packages are working with Tidelift to deliver one enterprise subscription that covers all of the open source you use.
+
+`Learn more <https://tidelift.com/subscription/pkg/pypi-zipp?utm_source=pypi-zipp&utm_medium=referral&utm_campaign=github>`_.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/RECORD b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..77c02835d891989ded4fbb04d1fdbdb92c208c10
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/RECORD
@@ -0,0 +1,15 @@
+zipp-3.19.2.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+zipp-3.19.2.dist-info/LICENSE,sha256=htoPAa6uRjSKPD1GUZXcHOzN55956HdppkuNoEsqR0E,1023
+zipp-3.19.2.dist-info/METADATA,sha256=UIrk_kMIHGSwsKKChYizqMw0MMZpPRZ2ZiVpQAsN_bE,3575
+zipp-3.19.2.dist-info/RECORD,,
+zipp-3.19.2.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+zipp-3.19.2.dist-info/WHEEL,sha256=GJ7t_kWBFywbagK5eo9IoUwLW6oyOeTKmQ-9iHFVNxQ,92
+zipp-3.19.2.dist-info/top_level.txt,sha256=iAbdoSHfaGqBfVb2XuR9JqSQHCoOsOtG6y9C_LSpqFw,5
+zipp/__init__.py,sha256=QuI1g00G4fRAcGt-HqbV0oWIkmSgedCGGYsHHYzNa8A,13412
+zipp/__pycache__/__init__.cpython-312.pyc,,
+zipp/__pycache__/glob.cpython-312.pyc,,
+zipp/compat/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+zipp/compat/__pycache__/__init__.cpython-312.pyc,,
+zipp/compat/__pycache__/py310.cpython-312.pyc,,
+zipp/compat/py310.py,sha256=eZpkW0zRtunkhEh8jjX3gCGe22emoKCBJw72Zt4RkhA,219
+zipp/glob.py,sha256=etWpnfEoRyfUvrUsi6sTiGmErvPwe6HzY6pT8jg_lUI,3082
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/REQUESTED b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/REQUESTED
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..bab98d675883cc7567a79df485cd7b4f015e376f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.43.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/top_level.txt b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e82f676f82a3381fa909d1e6578c7a22044fafca
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp-3.19.2.dist-info/top_level.txt
@@ -0,0 +1 @@
+zipp
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d65297b8354b934f687de1ab79c1f8cdf30050ff
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp/__init__.py
@@ -0,0 +1,501 @@
+import io
+import posixpath
+import zipfile
+import itertools
+import contextlib
+import pathlib
+import re
+import stat
+import sys
+
+from .compat.py310 import text_encoding
+from .glob import Translator
+
+
+__all__ = ['Path']
+
+
+def _parents(path):
+    """
+    Given a path with elements separated by
+    posixpath.sep, generate all parents of that path.
+
+    >>> list(_parents('b/d'))
+    ['b']
+    >>> list(_parents('/b/d/'))
+    ['/b']
+    >>> list(_parents('b/d/f/'))
+    ['b/d', 'b']
+    >>> list(_parents('b'))
+    []
+    >>> list(_parents(''))
+    []
+    """
+    return itertools.islice(_ancestry(path), 1, None)
+
+
+def _ancestry(path):
+    """
+    Given a path with elements separated by
+    posixpath.sep, generate all elements of that path
+
+    >>> list(_ancestry('b/d'))
+    ['b/d', 'b']
+    >>> list(_ancestry('/b/d/'))
+    ['/b/d', '/b']
+    >>> list(_ancestry('b/d/f/'))
+    ['b/d/f', 'b/d', 'b']
+    >>> list(_ancestry('b'))
+    ['b']
+    >>> list(_ancestry(''))
+    []
+    """
+    path = path.rstrip(posixpath.sep)
+    while path and path != posixpath.sep:
+        yield path
+        path, tail = posixpath.split(path)
+
+
+_dedupe = dict.fromkeys
+"""Deduplicate an iterable in original order"""
+
+
+def _difference(minuend, subtrahend):
+    """
+    Return items in minuend not in subtrahend, retaining order
+    with O(1) lookup.
+    """
+    return itertools.filterfalse(set(subtrahend).__contains__, minuend)
+
+
+class InitializedState:
+    """
+    Mix-in to save the initialization state for pickling.
+    """
+
+    def __init__(self, *args, **kwargs):
+        self.__args = args
+        self.__kwargs = kwargs
+        super().__init__(*args, **kwargs)
+
+    def __getstate__(self):
+        return self.__args, self.__kwargs
+
+    def __setstate__(self, state):
+        args, kwargs = state
+        super().__init__(*args, **kwargs)
+
+
+class SanitizedNames:
+    """
+    ZipFile mix-in to ensure names are sanitized.
+    """
+
+    def namelist(self):
+        return list(map(self._sanitize, super().namelist()))
+
+    @staticmethod
+    def _sanitize(name):
+        r"""
+        Ensure a relative path with posix separators and no dot names.
+
+        Modeled after
+        https://github.com/python/cpython/blob/bcc1be39cb1d04ad9fc0bd1b9193d3972835a57c/Lib/zipfile/__init__.py#L1799-L1813
+        but provides consistent cross-platform behavior.
+
+        >>> san = SanitizedNames._sanitize
+        >>> san('/foo/bar')
+        'foo/bar'
+        >>> san('//foo.txt')
+        'foo.txt'
+        >>> san('foo/.././bar.txt')
+        'foo/bar.txt'
+        >>> san('foo../.bar.txt')
+        'foo../.bar.txt'
+        >>> san('\\foo\\bar.txt')
+        'foo/bar.txt'
+        >>> san('D:\\foo.txt')
+        'D/foo.txt'
+        >>> san('\\\\server\\share\\file.txt')
+        'server/share/file.txt'
+        >>> san('\\\\?\\GLOBALROOT\\Volume3')
+        '?/GLOBALROOT/Volume3'
+        >>> san('\\\\.\\PhysicalDrive1\\root')
+        'PhysicalDrive1/root'
+
+        Retain any trailing slash.
+        >>> san('abc/')
+        'abc/'
+
+        Raises a ValueError if the result is empty.
+        >>> san('../..')
+        Traceback (most recent call last):
+        ...
+        ValueError: Empty filename
+        """
+
+        def allowed(part):
+            return part and part not in {'..', '.'}
+
+        # Remove the drive letter.
+        # Don't use ntpath.splitdrive, because that also strips UNC paths
+        bare = re.sub('^([A-Z]):', r'\1', name, flags=re.IGNORECASE)
+        clean = bare.replace('\\', '/')
+        parts = clean.split('/')
+        joined = '/'.join(filter(allowed, parts))
+        if not joined:
+            raise ValueError("Empty filename")
+        return joined + '/' * name.endswith('/')
+
+
+class CompleteDirs(InitializedState, SanitizedNames, zipfile.ZipFile):
+    """
+    A ZipFile subclass that ensures that implied directories
+    are always included in the namelist.
+
+    >>> list(CompleteDirs._implied_dirs(['foo/bar.txt', 'foo/bar/baz.txt']))
+    ['foo/', 'foo/bar/']
+    >>> list(CompleteDirs._implied_dirs(['foo/bar.txt', 'foo/bar/baz.txt', 'foo/bar/']))
+    ['foo/']
+    """
+
+    @staticmethod
+    def _implied_dirs(names):
+        parents = itertools.chain.from_iterable(map(_parents, names))
+        as_dirs = (p + posixpath.sep for p in parents)
+        return _dedupe(_difference(as_dirs, names))
+
+    def namelist(self):
+        names = super().namelist()
+        return names + list(self._implied_dirs(names))
+
+    def _name_set(self):
+        return set(self.namelist())
+
+    def resolve_dir(self, name):
+        """
+        If the name represents a directory, return that name
+        as a directory (with the trailing slash).
+        """
+        names = self._name_set()
+        dirname = name + '/'
+        dir_match = name not in names and dirname in names
+        return dirname if dir_match else name
+
+    def getinfo(self, name):
+        """
+        Supplement getinfo for implied dirs.
+        """
+        try:
+            return super().getinfo(name)
+        except KeyError:
+            if not name.endswith('/') or name not in self._name_set():
+                raise
+            return zipfile.ZipInfo(filename=name)
+
+    @classmethod
+    def make(cls, source):
+        """
+        Given a source (filename or zipfile), return an
+        appropriate CompleteDirs subclass.
+        """
+        if isinstance(source, CompleteDirs):
+            return source
+
+        if not isinstance(source, zipfile.ZipFile):
+            return cls(source)
+
+        # Only allow for FastLookup when supplied zipfile is read-only
+        if 'r' not in source.mode:
+            cls = CompleteDirs
+
+        source.__class__ = cls
+        return source
+
+    @classmethod
+    def inject(cls, zf: zipfile.ZipFile) -> zipfile.ZipFile:
+        """
+        Given a writable zip file zf, inject directory entries for
+        any directories implied by the presence of children.
+        """
+        for name in cls._implied_dirs(zf.namelist()):
+            zf.writestr(name, b"")
+        return zf
+
+
+class FastLookup(CompleteDirs):
+    """
+    ZipFile subclass to ensure implicit
+    dirs exist and are resolved rapidly.
+    """
+
+    def namelist(self):
+        with contextlib.suppress(AttributeError):
+            return self.__names
+        self.__names = super().namelist()
+        return self.__names
+
+    def _name_set(self):
+        with contextlib.suppress(AttributeError):
+            return self.__lookup
+        self.__lookup = super()._name_set()
+        return self.__lookup
+
+
+def _extract_text_encoding(encoding=None, *args, **kwargs):
+    # compute stack level so that the caller of the caller sees any warning.
+    is_pypy = sys.implementation.name == 'pypy'
+    stack_level = 3 + is_pypy
+    return text_encoding(encoding, stack_level), args, kwargs
+
+
+class Path:
+    """
+    A :class:`importlib.resources.abc.Traversable` interface for zip files.
+
+    Implements many of the features users enjoy from
+    :class:`pathlib.Path`.
+
+    Consider a zip file with this structure::
+
+        .
+        ├── a.txt
+        └── b
+            ├── c.txt
+            └── d
+                └── e.txt
+
+    >>> data = io.BytesIO()
+    >>> zf = zipfile.ZipFile(data, 'w')
+    >>> zf.writestr('a.txt', 'content of a')
+    >>> zf.writestr('b/c.txt', 'content of c')
+    >>> zf.writestr('b/d/e.txt', 'content of e')
+    >>> zf.filename = 'mem/abcde.zip'
+
+    Path accepts the zipfile object itself or a filename
+
+    >>> path = Path(zf)
+
+    From there, several path operations are available.
+
+    Directory iteration (including the zip file itself):
+
+    >>> a, b = path.iterdir()
+    >>> a
+    Path('mem/abcde.zip', 'a.txt')
+    >>> b
+    Path('mem/abcde.zip', 'b/')
+
+    name property:
+
+    >>> b.name
+    'b'
+
+    join with divide operator:
+
+    >>> c = b / 'c.txt'
+    >>> c
+    Path('mem/abcde.zip', 'b/c.txt')
+    >>> c.name
+    'c.txt'
+
+    Read text:
+
+    >>> c.read_text(encoding='utf-8')
+    'content of c'
+
+    existence:
+
+    >>> c.exists()
+    True
+    >>> (b / 'missing.txt').exists()
+    False
+
+    Coercion to string:
+
+    >>> import os
+    >>> str(c).replace(os.sep, posixpath.sep)
+    'mem/abcde.zip/b/c.txt'
+
+    At the root, ``name``, ``filename``, and ``parent``
+    resolve to the zipfile.
+
+    >>> str(path)
+    'mem/abcde.zip/'
+    >>> path.name
+    'abcde.zip'
+    >>> path.filename == pathlib.Path('mem/abcde.zip')
+    True
+    >>> str(path.parent)
+    'mem'
+
+    If the zipfile has no filename, such attributes are not
+    valid and accessing them will raise an Exception.
+
+    >>> zf.filename = None
+    >>> path.name
+    Traceback (most recent call last):
+    ...
+    TypeError: ...
+
+    >>> path.filename
+    Traceback (most recent call last):
+    ...
+    TypeError: ...
+
+    >>> path.parent
+    Traceback (most recent call last):
+    ...
+    TypeError: ...
+
+    # workaround python/cpython#106763
+    >>> pass
+    """
+
+    __repr = "{self.__class__.__name__}({self.root.filename!r}, {self.at!r})"
+
+    def __init__(self, root, at=""):
+        """
+        Construct a Path from a ZipFile or filename.
+
+        Note: When the source is an existing ZipFile object,
+        its type (__class__) will be mutated to a
+        specialized type. If the caller wishes to retain the
+        original type, the caller should either create a
+        separate ZipFile object or pass a filename.
+        """
+        self.root = FastLookup.make(root)
+        self.at = at
+
+    def __eq__(self, other):
+        """
+        >>> Path(zipfile.ZipFile(io.BytesIO(), 'w')) == 'foo'
+        False
+        """
+        if self.__class__ is not other.__class__:
+            return NotImplemented
+        return (self.root, self.at) == (other.root, other.at)
+
+    def __hash__(self):
+        return hash((self.root, self.at))
+
+    def open(self, mode='r', *args, pwd=None, **kwargs):
+        """
+        Open this entry as text or binary following the semantics
+        of ``pathlib.Path.open()`` by passing arguments through
+        to io.TextIOWrapper().
+        """
+        if self.is_dir():
+            raise IsADirectoryError(self)
+        zip_mode = mode[0]
+        if not self.exists() and zip_mode == 'r':
+            raise FileNotFoundError(self)
+        stream = self.root.open(self.at, zip_mode, pwd=pwd)
+        if 'b' in mode:
+            if args or kwargs:
+                raise ValueError("encoding args invalid for binary operation")
+            return stream
+        # Text mode:
+        encoding, args, kwargs = _extract_text_encoding(*args, **kwargs)
+        return io.TextIOWrapper(stream, encoding, *args, **kwargs)
+
+    def _base(self):
+        return pathlib.PurePosixPath(self.at or self.root.filename)
+
+    @property
+    def name(self):
+        return self._base().name
+
+    @property
+    def suffix(self):
+        return self._base().suffix
+
+    @property
+    def suffixes(self):
+        return self._base().suffixes
+
+    @property
+    def stem(self):
+        return self._base().stem
+
+    @property
+    def filename(self):
+        return pathlib.Path(self.root.filename).joinpath(self.at)
+
+    def read_text(self, *args, **kwargs):
+        encoding, args, kwargs = _extract_text_encoding(*args, **kwargs)
+        with self.open('r', encoding, *args, **kwargs) as strm:
+            return strm.read()
+
+    def read_bytes(self):
+        with self.open('rb') as strm:
+            return strm.read()
+
+    def _is_child(self, path):
+        return posixpath.dirname(path.at.rstrip("/")) == self.at.rstrip("/")
+
+    def _next(self, at):
+        return self.__class__(self.root, at)
+
+    def is_dir(self):
+        return not self.at or self.at.endswith("/")
+
+    def is_file(self):
+        return self.exists() and not self.is_dir()
+
+    def exists(self):
+        return self.at in self.root._name_set()
+
+    def iterdir(self):
+        if not self.is_dir():
+            raise ValueError("Can't listdir a file")
+        subs = map(self._next, self.root.namelist())
+        return filter(self._is_child, subs)
+
+    def match(self, path_pattern):
+        return pathlib.PurePosixPath(self.at).match(path_pattern)
+
+    def is_symlink(self):
+        """
+        Return whether this path is a symlink.
+        """
+        info = self.root.getinfo(self.at)
+        mode = info.external_attr >> 16
+        return stat.S_ISLNK(mode)
+
+    def glob(self, pattern):
+        if not pattern:
+            raise ValueError(f"Unacceptable pattern: {pattern!r}")
+
+        prefix = re.escape(self.at)
+        tr = Translator(seps='/')
+        matches = re.compile(prefix + tr.translate(pattern)).fullmatch
+        names = (data.filename for data in self.root.filelist)
+        return map(self._next, filter(matches, names))
+
+    def rglob(self, pattern):
+        return self.glob(f'**/{pattern}')
+
+    def relative_to(self, other, *extra):
+        return posixpath.relpath(str(self), str(other.joinpath(*extra)))
+
+    def __str__(self):
+        return posixpath.join(self.root.filename, self.at)
+
+    def __repr__(self):
+        return self.__repr.format(self=self)
+
+    def joinpath(self, *other):
+        next = posixpath.join(self.at, *other)
+        return self._next(self.root.resolve_dir(next))
+
+    __truediv__ = joinpath
+
+    @property
+    def parent(self):
+        if not self.at:
+            return self.filename.parent
+        parent_at = posixpath.dirname(self.at.rstrip('/'))
+        if parent_at:
+            parent_at += '/'
+        return self._next(parent_at)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp/__pycache__/__init__.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp/__pycache__/__init__.cpython-312.pyc
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp/compat/py310.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp/compat/py310.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5ca53e037b267ff1b196b3b208dea0acdebe4c5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp/compat/py310.py
@@ -0,0 +1,11 @@
+import sys
+import io
+
+
+def _text_encoding(encoding, stacklevel=2, /):  # pragma: no cover
+    return encoding
+
+
+text_encoding = (
+    io.text_encoding if sys.version_info > (3, 10) else _text_encoding  # type: ignore
+)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp/glob.py b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp/glob.py
new file mode 100644
index 0000000000000000000000000000000000000000..69c41d77c3f6549bd58d778dfe978a6ed728ede5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/_vendor/zipp/glob.py
@@ -0,0 +1,106 @@
+import os
+import re
+
+
+_default_seps = os.sep + str(os.altsep) * bool(os.altsep)
+
+
+class Translator:
+    """
+    >>> Translator('xyz')
+    Traceback (most recent call last):
+    ...
+    AssertionError: Invalid separators
+
+    >>> Translator('')
+    Traceback (most recent call last):
+    ...
+    AssertionError: Invalid separators
+    """
+
+    seps: str
+
+    def __init__(self, seps: str = _default_seps):
+        assert seps and set(seps) <= set(_default_seps), "Invalid separators"
+        self.seps = seps
+
+    def translate(self, pattern):
+        """
+        Given a glob pattern, produce a regex that matches it.
+        """
+        return self.extend(self.translate_core(pattern))
+
+    def extend(self, pattern):
+        r"""
+        Extend regex for pattern-wide concerns.
+
+        Apply '(?s:)' to create a non-matching group that
+        matches newlines (valid on Unix).
+
+        Append '\Z' to imply fullmatch even when match is used.
+        """
+        return rf'(?s:{pattern})\Z'
+
+    def translate_core(self, pattern):
+        r"""
+        Given a glob pattern, produce a regex that matches it.
+
+        >>> t = Translator()
+        >>> t.translate_core('*.txt').replace('\\\\', '')
+        '[^/]*\\.txt'
+        >>> t.translate_core('a?txt')
+        'a[^/]txt'
+        >>> t.translate_core('**/*').replace('\\\\', '')
+        '.*/[^/][^/]*'
+        """
+        self.restrict_rglob(pattern)
+        return ''.join(map(self.replace, separate(self.star_not_empty(pattern))))
+
+    def replace(self, match):
+        """
+        Perform the replacements for a match from :func:`separate`.
+        """
+        return match.group('set') or (
+            re.escape(match.group(0))
+            .replace('\\*\\*', r'.*')
+            .replace('\\*', rf'[^{re.escape(self.seps)}]*')
+            .replace('\\?', r'[^/]')
+        )
+
+    def restrict_rglob(self, pattern):
+        """
+        Raise ValueError if ** appears in anything but a full path segment.
+
+        >>> Translator().translate('**foo')
+        Traceback (most recent call last):
+        ...
+        ValueError: ** must appear alone in a path segment
+        """
+        seps_pattern = rf'[{re.escape(self.seps)}]+'
+        segments = re.split(seps_pattern, pattern)
+        if any('**' in segment and segment != '**' for segment in segments):
+            raise ValueError("** must appear alone in a path segment")
+
+    def star_not_empty(self, pattern):
+        """
+        Ensure that * will not match an empty segment.
+        """
+
+        def handle_segment(match):
+            segment = match.group(0)
+            return '?*' if segment == '*' else segment
+
+        not_seps_pattern = rf'[^{re.escape(self.seps)}]+'
+        return re.sub(not_seps_pattern, handle_segment, pattern)
+
+
+def separate(pattern):
+    """
+    Separate out character sets to avoid translating their contents.
+
+    >>> [m.group(0) for m in separate('*.txt')]
+    ['*.txt']
+    >>> [m.group(0) for m in separate('a[?]txt')]
+    ['a', '[?]', 'txt']
+    """
+    return re.finditer(r'([^\[]+)|(?P<set>[\[].*?[\]])|([\[][^\]]*$)', pattern)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/archive_util.py b/.venv/lib/python3.12/site-packages/setuptools/archive_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a02010bb2af2be0487730d6a32080877b9ac220
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/archive_util.py
@@ -0,0 +1,219 @@
+"""Utilities for extracting common archive formats"""
+
+import contextlib
+import os
+import posixpath
+import shutil
+import tarfile
+import zipfile
+
+from ._path import ensure_directory
+
+from distutils.errors import DistutilsError
+
+__all__ = [
+    "unpack_archive",
+    "unpack_zipfile",
+    "unpack_tarfile",
+    "default_filter",
+    "UnrecognizedFormat",
+    "extraction_drivers",
+    "unpack_directory",
+]
+
+
+class UnrecognizedFormat(DistutilsError):
+    """Couldn't recognize the archive type"""
+
+
+def default_filter(src, dst):
+    """The default progress/filter callback; returns True for all files"""
+    return dst
+
+
+def unpack_archive(
+    filename, extract_dir, progress_filter=default_filter, drivers=None
+) -> None:
+    """Unpack `filename` to `extract_dir`, or raise ``UnrecognizedFormat``
+
+    `progress_filter` is a function taking two arguments: a source path
+    internal to the archive ('/'-separated), and a filesystem path where it
+    will be extracted.  The callback must return the desired extract path
+    (which may be the same as the one passed in), or else ``None`` to skip
+    that file or directory.  The callback can thus be used to report on the
+    progress of the extraction, as well as to filter the items extracted or
+    alter their extraction paths.
+
+    `drivers`, if supplied, must be a non-empty sequence of functions with the
+    same signature as this function (minus the `drivers` argument), that raise
+    ``UnrecognizedFormat`` if they do not support extracting the designated
+    archive type.  The `drivers` are tried in sequence until one is found that
+    does not raise an error, or until all are exhausted (in which case
+    ``UnrecognizedFormat`` is raised).  If you do not supply a sequence of
+    drivers, the module's ``extraction_drivers`` constant will be used, which
+    means that ``unpack_zipfile`` and ``unpack_tarfile`` will be tried, in that
+    order.
+    """
+    for driver in drivers or extraction_drivers:
+        try:
+            driver(filename, extract_dir, progress_filter)
+        except UnrecognizedFormat:
+            continue
+        else:
+            return
+    else:
+        raise UnrecognizedFormat(f"Not a recognized archive type: {filename}")
+
+
+def unpack_directory(filename, extract_dir, progress_filter=default_filter) -> None:
+    """ "Unpack" a directory, using the same interface as for archives
+
+    Raises ``UnrecognizedFormat`` if `filename` is not a directory
+    """
+    if not os.path.isdir(filename):
+        raise UnrecognizedFormat(f"{filename} is not a directory")
+
+    paths = {
+        filename: ('', extract_dir),
+    }
+    for base, dirs, files in os.walk(filename):
+        src, dst = paths[base]
+        for d in dirs:
+            paths[os.path.join(base, d)] = src + d + '/', os.path.join(dst, d)
+        for f in files:
+            target = os.path.join(dst, f)
+            target = progress_filter(src + f, target)
+            if not target:
+                # skip non-files
+                continue
+            ensure_directory(target)
+            f = os.path.join(base, f)
+            shutil.copyfile(f, target)
+            shutil.copystat(f, target)
+
+
+def unpack_zipfile(filename, extract_dir, progress_filter=default_filter) -> None:
+    """Unpack zip `filename` to `extract_dir`
+
+    Raises ``UnrecognizedFormat`` if `filename` is not a zipfile (as determined
+    by ``zipfile.is_zipfile()``).  See ``unpack_archive()`` for an explanation
+    of the `progress_filter` argument.
+    """
+
+    if not zipfile.is_zipfile(filename):
+        raise UnrecognizedFormat(f"{filename} is not a zip file")
+
+    with zipfile.ZipFile(filename) as z:
+        _unpack_zipfile_obj(z, extract_dir, progress_filter)
+
+
+def _unpack_zipfile_obj(zipfile_obj, extract_dir, progress_filter=default_filter):
+    """Internal/private API used by other parts of setuptools.
+    Similar to ``unpack_zipfile``, but receives an already opened :obj:`zipfile.ZipFile`
+    object instead of a filename.
+    """
+    for info in zipfile_obj.infolist():
+        name = info.filename
+
+        # don't extract absolute paths or ones with .. in them
+        if name.startswith('/') or '..' in name.split('/'):
+            continue
+
+        target = os.path.join(extract_dir, *name.split('/'))
+        target = progress_filter(name, target)
+        if not target:
+            continue
+        if name.endswith('/'):
+            # directory
+            ensure_directory(target)
+        else:
+            # file
+            ensure_directory(target)
+            data = zipfile_obj.read(info.filename)
+            with open(target, 'wb') as f:
+                f.write(data)
+        unix_attributes = info.external_attr >> 16
+        if unix_attributes:
+            os.chmod(target, unix_attributes)
+
+
+def _resolve_tar_file_or_dir(tar_obj, tar_member_obj):
+    """Resolve any links and extract link targets as normal files."""
+    while tar_member_obj is not None and (
+        tar_member_obj.islnk() or tar_member_obj.issym()
+    ):
+        linkpath = tar_member_obj.linkname
+        if tar_member_obj.issym():
+            base = posixpath.dirname(tar_member_obj.name)
+            linkpath = posixpath.join(base, linkpath)
+            linkpath = posixpath.normpath(linkpath)
+        tar_member_obj = tar_obj._getmember(linkpath)
+
+    is_file_or_dir = tar_member_obj is not None and (
+        tar_member_obj.isfile() or tar_member_obj.isdir()
+    )
+    if is_file_or_dir:
+        return tar_member_obj
+
+    raise LookupError('Got unknown file type')
+
+
+def _iter_open_tar(tar_obj, extract_dir, progress_filter):
+    """Emit member-destination pairs from a tar archive."""
+    # don't do any chowning!
+    tar_obj.chown = lambda *args: None
+
+    with contextlib.closing(tar_obj):
+        for member in tar_obj:
+            name = member.name
+            # don't extract absolute paths or ones with .. in them
+            if name.startswith('/') or '..' in name.split('/'):
+                continue
+
+            prelim_dst = os.path.join(extract_dir, *name.split('/'))
+
+            try:
+                member = _resolve_tar_file_or_dir(tar_obj, member)
+            except LookupError:
+                continue
+
+            final_dst = progress_filter(name, prelim_dst)
+            if not final_dst:
+                continue
+
+            if final_dst.endswith(os.sep):
+                final_dst = final_dst[:-1]
+
+            yield member, final_dst
+
+
+def unpack_tarfile(filename, extract_dir, progress_filter=default_filter) -> bool:
+    """Unpack tar/tar.gz/tar.bz2 `filename` to `extract_dir`
+
+    Raises ``UnrecognizedFormat`` if `filename` is not a tarfile (as determined
+    by ``tarfile.open()``).  See ``unpack_archive()`` for an explanation
+    of the `progress_filter` argument.
+    """
+    try:
+        tarobj = tarfile.open(filename)
+    except tarfile.TarError as e:
+        raise UnrecognizedFormat(
+            f"{filename} is not a compressed or uncompressed tar file"
+        ) from e
+
+    for member, final_dst in _iter_open_tar(
+        tarobj,
+        extract_dir,
+        progress_filter,
+    ):
+        try:
+            # XXX Ugh
+            tarobj._extract_member(member, final_dst)
+        except tarfile.ExtractError:
+            # chown/chmod/mkfifo/mknode/makedev failed
+            pass
+
+    return True
+
+
+extraction_drivers = unpack_directory, unpack_zipfile, unpack_tarfile
diff --git a/.venv/lib/python3.12/site-packages/setuptools/build_meta.py b/.venv/lib/python3.12/site-packages/setuptools/build_meta.py
new file mode 100644
index 0000000000000000000000000000000000000000..00fa5e1f7048c8781cfc4838f705e122fd547825
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/build_meta.py
@@ -0,0 +1,560 @@
+"""A PEP 517 interface to setuptools
+
+Previously, when a user or a command line tool (let's call it a "frontend")
+needed to make a request of setuptools to take a certain action, for
+example, generating a list of installation requirements, the frontend
+would call "setup.py egg_info" or "setup.py bdist_wheel" on the command line.
+
+PEP 517 defines a different method of interfacing with setuptools. Rather
+than calling "setup.py" directly, the frontend should:
+
+  1. Set the current directory to the directory with a setup.py file
+  2. Import this module into a safe python interpreter (one in which
+     setuptools can potentially set global variables or crash hard).
+  3. Call one of the functions defined in PEP 517.
+
+What each function does is defined in PEP 517. However, here is a "casual"
+definition of the functions (this definition should not be relied on for
+bug reports or API stability):
+
+  - `build_wheel`: build a wheel in the folder and return the basename
+  - `get_requires_for_build_wheel`: get the `setup_requires` to build
+  - `prepare_metadata_for_build_wheel`: get the `install_requires`
+  - `build_sdist`: build an sdist in the folder and return the basename
+  - `get_requires_for_build_sdist`: get the `setup_requires` to build
+
+Again, this is not a formal definition! Just a "taste" of the module.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import io
+import os
+import shlex
+import shutil
+import sys
+import tempfile
+import tokenize
+import warnings
+from collections.abc import Iterable, Iterator, Mapping
+from pathlib import Path
+from typing import TYPE_CHECKING, Union
+
+import setuptools
+
+from . import errors
+from ._path import StrPath, same_path
+from ._reqs import parse_strings
+from .warnings import SetuptoolsDeprecationWarning
+
+import distutils
+from distutils.util import strtobool
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+__all__ = [
+    'get_requires_for_build_sdist',
+    'get_requires_for_build_wheel',
+    'prepare_metadata_for_build_wheel',
+    'build_wheel',
+    'build_sdist',
+    'get_requires_for_build_editable',
+    'prepare_metadata_for_build_editable',
+    'build_editable',
+    '__legacy__',
+    'SetupRequirementsError',
+]
+
+SETUPTOOLS_ENABLE_FEATURES = os.getenv("SETUPTOOLS_ENABLE_FEATURES", "").lower()
+LEGACY_EDITABLE = "legacy-editable" in SETUPTOOLS_ENABLE_FEATURES.replace("_", "-")
+
+
+class SetupRequirementsError(BaseException):
+    def __init__(self, specifiers) -> None:
+        self.specifiers = specifiers
+
+
+class Distribution(setuptools.dist.Distribution):
+    def fetch_build_eggs(self, specifiers):
+        specifier_list = list(parse_strings(specifiers))
+
+        raise SetupRequirementsError(specifier_list)
+
+    @classmethod
+    @contextlib.contextmanager
+    def patch(cls):
+        """
+        Replace
+        distutils.dist.Distribution with this class
+        for the duration of this context.
+        """
+        orig = distutils.core.Distribution
+        distutils.core.Distribution = cls  # type: ignore[misc] # monkeypatching
+        try:
+            yield
+        finally:
+            distutils.core.Distribution = orig  # type: ignore[misc] # monkeypatching
+
+
+@contextlib.contextmanager
+def no_install_setup_requires():
+    """Temporarily disable installing setup_requires
+
+    Under PEP 517, the backend reports build dependencies to the frontend,
+    and the frontend is responsible for ensuring they're installed.
+    So setuptools (acting as a backend) should not try to install them.
+    """
+    orig = setuptools._install_setup_requires
+    setuptools._install_setup_requires = lambda attrs: None
+    try:
+        yield
+    finally:
+        setuptools._install_setup_requires = orig
+
+
+def _get_immediate_subdirectories(a_dir):
+    return [
+        name for name in os.listdir(a_dir) if os.path.isdir(os.path.join(a_dir, name))
+    ]
+
+
+def _file_with_extension(directory: StrPath, extension: str | tuple[str, ...]):
+    matching = (f for f in os.listdir(directory) if f.endswith(extension))
+    try:
+        (file,) = matching
+    except ValueError:
+        raise ValueError(
+            'No distribution was found. Ensure that `setup.py` '
+            'is not empty and that it calls `setup()`.'
+        ) from None
+    return file
+
+
+def _open_setup_script(setup_script):
+    if not os.path.exists(setup_script):
+        # Supply a default setup.py
+        return io.StringIO("from setuptools import setup; setup()")
+
+    return tokenize.open(setup_script)
+
+
+@contextlib.contextmanager
+def suppress_known_deprecation():
+    with warnings.catch_warnings():
+        warnings.filterwarnings('ignore', 'setup.py install is deprecated')
+        yield
+
+
+_ConfigSettings: TypeAlias = Union[Mapping[str, Union[str, list[str], None]], None]
+"""
+Currently the user can run::
+
+    pip install -e . --config-settings key=value
+    python -m build -C--key=value -C key=value
+
+- pip will pass both key and value as strings and overwriting repeated keys
+  (pypa/pip#11059).
+- build will accumulate values associated with repeated keys in a list.
+  It will also accept keys with no associated value.
+  This means that an option passed by build can be ``str | list[str] | None``.
+- PEP 517 specifies that ``config_settings`` is an optional dict.
+"""
+
+
+class _ConfigSettingsTranslator:
+    """Translate ``config_settings`` into distutils-style command arguments.
+    Only a limited number of options is currently supported.
+    """
+
+    # See pypa/setuptools#1928 pypa/setuptools#2491
+
+    def _get_config(self, key: str, config_settings: _ConfigSettings) -> list[str]:
+        """
+        Get the value of a specific key in ``config_settings`` as a list of strings.
+
+        >>> fn = _ConfigSettingsTranslator()._get_config
+        >>> fn("--global-option", None)
+        []
+        >>> fn("--global-option", {})
+        []
+        >>> fn("--global-option", {'--global-option': 'foo'})
+        ['foo']
+        >>> fn("--global-option", {'--global-option': ['foo']})
+        ['foo']
+        >>> fn("--global-option", {'--global-option': 'foo'})
+        ['foo']
+        >>> fn("--global-option", {'--global-option': 'foo bar'})
+        ['foo', 'bar']
+        """
+        cfg = config_settings or {}
+        opts = cfg.get(key) or []
+        return shlex.split(opts) if isinstance(opts, str) else opts
+
+    def _global_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
+        """
+        Let the user specify ``verbose`` or ``quiet`` + escape hatch via
+        ``--global-option``.
+        Note: ``-v``, ``-vv``, ``-vvv`` have similar effects in setuptools,
+        so we just have to cover the basic scenario ``-v``.
+
+        >>> fn = _ConfigSettingsTranslator()._global_args
+        >>> list(fn(None))
+        []
+        >>> list(fn({"verbose": "False"}))
+        ['-q']
+        >>> list(fn({"verbose": "1"}))
+        ['-v']
+        >>> list(fn({"--verbose": None}))
+        ['-v']
+        >>> list(fn({"verbose": "true", "--global-option": "-q --no-user-cfg"}))
+        ['-v', '-q', '--no-user-cfg']
+        >>> list(fn({"--quiet": None}))
+        ['-q']
+        """
+        cfg = config_settings or {}
+        falsey = {"false", "no", "0", "off"}
+        if "verbose" in cfg or "--verbose" in cfg:
+            level = str(cfg.get("verbose") or cfg.get("--verbose") or "1")
+            yield ("-q" if level.lower() in falsey else "-v")
+        if "quiet" in cfg or "--quiet" in cfg:
+            level = str(cfg.get("quiet") or cfg.get("--quiet") or "1")
+            yield ("-v" if level.lower() in falsey else "-q")
+
+        yield from self._get_config("--global-option", config_settings)
+
+    def __dist_info_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
+        """
+        The ``dist_info`` command accepts ``tag-date`` and ``tag-build``.
+
+        .. warning::
+           We cannot use this yet as it requires the ``sdist`` and ``bdist_wheel``
+           commands run in ``build_sdist`` and ``build_wheel`` to reuse the egg-info
+           directory created in ``prepare_metadata_for_build_wheel``.
+
+        >>> fn = _ConfigSettingsTranslator()._ConfigSettingsTranslator__dist_info_args
+        >>> list(fn(None))
+        []
+        >>> list(fn({"tag-date": "False"}))
+        ['--no-date']
+        >>> list(fn({"tag-date": None}))
+        ['--no-date']
+        >>> list(fn({"tag-date": "true", "tag-build": ".a"}))
+        ['--tag-date', '--tag-build', '.a']
+        """
+        cfg = config_settings or {}
+        if "tag-date" in cfg:
+            val = strtobool(str(cfg["tag-date"] or "false"))
+            yield ("--tag-date" if val else "--no-date")
+        if "tag-build" in cfg:
+            yield from ["--tag-build", str(cfg["tag-build"])]
+
+    def _editable_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
+        """
+        The ``editable_wheel`` command accepts ``editable-mode=strict``.
+
+        >>> fn = _ConfigSettingsTranslator()._editable_args
+        >>> list(fn(None))
+        []
+        >>> list(fn({"editable-mode": "strict"}))
+        ['--mode', 'strict']
+        """
+        cfg = config_settings or {}
+        mode = cfg.get("editable-mode") or cfg.get("editable_mode")
+        if not mode:
+            return
+        yield from ["--mode", str(mode)]
+
+    def _arbitrary_args(self, config_settings: _ConfigSettings) -> Iterator[str]:
+        """
+        Users may expect to pass arbitrary lists of arguments to a command
+        via "--global-option" (example provided in PEP 517 of a "escape hatch").
+
+        >>> fn = _ConfigSettingsTranslator()._arbitrary_args
+        >>> list(fn(None))
+        []
+        >>> list(fn({}))
+        []
+        >>> list(fn({'--build-option': 'foo'}))
+        ['foo']
+        >>> list(fn({'--build-option': ['foo']}))
+        ['foo']
+        >>> list(fn({'--build-option': 'foo'}))
+        ['foo']
+        >>> list(fn({'--build-option': 'foo bar'}))
+        ['foo', 'bar']
+        >>> list(fn({'--global-option': 'foo'}))
+        []
+        """
+        yield from self._get_config("--build-option", config_settings)
+
+
+class _BuildMetaBackend(_ConfigSettingsTranslator):
+    def _get_build_requires(
+        self, config_settings: _ConfigSettings, requirements: list[str]
+    ):
+        sys.argv = [
+            *sys.argv[:1],
+            *self._global_args(config_settings),
+            "egg_info",
+        ]
+        try:
+            with Distribution.patch():
+                self.run_setup()
+        except SetupRequirementsError as e:
+            requirements += e.specifiers
+
+        return requirements
+
+    def run_setup(self, setup_script: str = 'setup.py'):
+        # Note that we can reuse our build directory between calls
+        # Correctness comes first, then optimization later
+        __file__ = os.path.abspath(setup_script)
+        __name__ = '__main__'
+
+        with _open_setup_script(__file__) as f:
+            code = f.read().replace(r'\r\n', r'\n')
+
+        try:
+            exec(code, locals())
+        except SystemExit as e:
+            if e.code:
+                raise
+            # We ignore exit code indicating success
+            SetuptoolsDeprecationWarning.emit(
+                "Running `setup.py` directly as CLI tool is deprecated.",
+                "Please avoid using `sys.exit(0)` or similar statements "
+                "that don't fit in the paradigm of a configuration file.",
+                see_url="https://blog.ganssle.io/articles/2021/10/"
+                "setup-py-deprecated.html",
+            )
+
+    def get_requires_for_build_wheel(self, config_settings: _ConfigSettings = None):
+        return self._get_build_requires(config_settings, requirements=[])
+
+    def get_requires_for_build_sdist(self, config_settings: _ConfigSettings = None):
+        return self._get_build_requires(config_settings, requirements=[])
+
+    def _bubble_up_info_directory(
+        self, metadata_directory: StrPath, suffix: str
+    ) -> str:
+        """
+        PEP 517 requires that the .dist-info directory be placed in the
+        metadata_directory. To comply, we MUST copy the directory to the root.
+
+        Returns the basename of the info directory, e.g. `proj-0.0.0.dist-info`.
+        """
+        info_dir = self._find_info_directory(metadata_directory, suffix)
+        if not same_path(info_dir.parent, metadata_directory):
+            shutil.move(str(info_dir), metadata_directory)
+            # PEP 517 allow other files and dirs to exist in metadata_directory
+        return info_dir.name
+
+    def _find_info_directory(self, metadata_directory: StrPath, suffix: str) -> Path:
+        for parent, dirs, _ in os.walk(metadata_directory):
+            candidates = [f for f in dirs if f.endswith(suffix)]
+
+            if len(candidates) != 0 or len(dirs) != 1:
+                assert len(candidates) == 1, f"Multiple {suffix} directories found"
+                return Path(parent, candidates[0])
+
+        msg = f"No {suffix} directory found in {metadata_directory}"
+        raise errors.InternalError(msg)
+
+    def prepare_metadata_for_build_wheel(
+        self, metadata_directory: StrPath, config_settings: _ConfigSettings = None
+    ):
+        sys.argv = [
+            *sys.argv[:1],
+            *self._global_args(config_settings),
+            "dist_info",
+            "--output-dir",
+            str(metadata_directory),
+            "--keep-egg-info",
+        ]
+        with no_install_setup_requires():
+            self.run_setup()
+
+        self._bubble_up_info_directory(metadata_directory, ".egg-info")
+        return self._bubble_up_info_directory(metadata_directory, ".dist-info")
+
+    def _build_with_temp_dir(
+        self,
+        setup_command: Iterable[str],
+        result_extension: str | tuple[str, ...],
+        result_directory: StrPath,
+        config_settings: _ConfigSettings,
+        arbitrary_args: Iterable[str] = (),
+    ):
+        result_directory = os.path.abspath(result_directory)
+
+        # Build in a temporary directory, then copy to the target.
+        os.makedirs(result_directory, exist_ok=True)
+
+        with tempfile.TemporaryDirectory(
+            prefix=".tmp-", dir=result_directory
+        ) as tmp_dist_dir:
+            sys.argv = [
+                *sys.argv[:1],
+                *self._global_args(config_settings),
+                *setup_command,
+                "--dist-dir",
+                tmp_dist_dir,
+                *arbitrary_args,
+            ]
+            with no_install_setup_requires():
+                self.run_setup()
+
+            result_basename = _file_with_extension(tmp_dist_dir, result_extension)
+            result_path = os.path.join(result_directory, result_basename)
+            if os.path.exists(result_path):
+                # os.rename will fail overwriting on non-Unix.
+                os.remove(result_path)
+            os.rename(os.path.join(tmp_dist_dir, result_basename), result_path)
+
+        return result_basename
+
+    def build_wheel(
+        self,
+        wheel_directory: StrPath,
+        config_settings: _ConfigSettings = None,
+        metadata_directory: StrPath | None = None,
+    ):
+        def _build(cmd: list[str]):
+            with suppress_known_deprecation():
+                return self._build_with_temp_dir(
+                    cmd,
+                    '.whl',
+                    wheel_directory,
+                    config_settings,
+                    self._arbitrary_args(config_settings),
+                )
+
+        if metadata_directory is None:
+            return _build(['bdist_wheel'])
+
+        try:
+            return _build(['bdist_wheel', '--dist-info-dir', str(metadata_directory)])
+        except SystemExit as ex:  # pragma: nocover
+            # pypa/setuptools#4683
+            if "--dist-info-dir not recognized" not in str(ex):
+                raise
+            _IncompatibleBdistWheel.emit()
+            return _build(['bdist_wheel'])
+
+    def build_sdist(
+        self, sdist_directory: StrPath, config_settings: _ConfigSettings = None
+    ):
+        return self._build_with_temp_dir(
+            ['sdist', '--formats', 'gztar'], '.tar.gz', sdist_directory, config_settings
+        )
+
+    def _get_dist_info_dir(self, metadata_directory: StrPath | None) -> str | None:
+        if not metadata_directory:
+            return None
+        dist_info_candidates = list(Path(metadata_directory).glob("*.dist-info"))
+        assert len(dist_info_candidates) <= 1
+        return str(dist_info_candidates[0]) if dist_info_candidates else None
+
+    if not LEGACY_EDITABLE:
+        # PEP660 hooks:
+        # build_editable
+        # get_requires_for_build_editable
+        # prepare_metadata_for_build_editable
+        def build_editable(
+            self,
+            wheel_directory: StrPath,
+            config_settings: _ConfigSettings = None,
+            metadata_directory: StrPath | None = None,
+        ):
+            # XXX can or should we hide our editable_wheel command normally?
+            info_dir = self._get_dist_info_dir(metadata_directory)
+            opts = ["--dist-info-dir", info_dir] if info_dir else []
+            cmd = ["editable_wheel", *opts, *self._editable_args(config_settings)]
+            with suppress_known_deprecation():
+                return self._build_with_temp_dir(
+                    cmd, ".whl", wheel_directory, config_settings
+                )
+
+        def get_requires_for_build_editable(
+            self, config_settings: _ConfigSettings = None
+        ):
+            return self.get_requires_for_build_wheel(config_settings)
+
+        def prepare_metadata_for_build_editable(
+            self, metadata_directory: StrPath, config_settings: _ConfigSettings = None
+        ):
+            return self.prepare_metadata_for_build_wheel(
+                metadata_directory, config_settings
+            )
+
+
+class _BuildMetaLegacyBackend(_BuildMetaBackend):
+    """Compatibility backend for setuptools
+
+    This is a version of setuptools.build_meta that endeavors
+    to maintain backwards
+    compatibility with pre-PEP 517 modes of invocation. It
+    exists as a temporary
+    bridge between the old packaging mechanism and the new
+    packaging mechanism,
+    and will eventually be removed.
+    """
+
+    def run_setup(self, setup_script: str = 'setup.py'):
+        # In order to maintain compatibility with scripts assuming that
+        # the setup.py script is in a directory on the PYTHONPATH, inject
+        # '' into sys.path. (pypa/setuptools#1642)
+        sys_path = list(sys.path)  # Save the original path
+
+        script_dir = os.path.dirname(os.path.abspath(setup_script))
+        if script_dir not in sys.path:
+            sys.path.insert(0, script_dir)
+
+        # Some setup.py scripts (e.g. in pygame and numpy) use sys.argv[0] to
+        # get the directory of the source code. They expect it to refer to the
+        # setup.py script.
+        sys_argv_0 = sys.argv[0]
+        sys.argv[0] = setup_script
+
+        try:
+            super().run_setup(setup_script=setup_script)
+        finally:
+            # While PEP 517 frontends should be calling each hook in a fresh
+            # subprocess according to the standard (and thus it should not be
+            # strictly necessary to restore the old sys.path), we'll restore
+            # the original path so that the path manipulation does not persist
+            # within the hook after run_setup is called.
+            sys.path[:] = sys_path
+            sys.argv[0] = sys_argv_0
+
+
+class _IncompatibleBdistWheel(SetuptoolsDeprecationWarning):
+    _SUMMARY = "wheel.bdist_wheel is deprecated, please import it from setuptools"
+    _DETAILS = """
+    Ensure that any custom bdist_wheel implementation is a subclass of
+    setuptools.command.bdist_wheel.bdist_wheel.
+    """
+    _DUE_DATE = (2025, 10, 15)
+    # Initially introduced in 2024/10/15, but maybe too disruptive to be enforced?
+    _SEE_URL = "https://github.com/pypa/wheel/pull/631"
+
+
+# The primary backend
+_BACKEND = _BuildMetaBackend()
+
+get_requires_for_build_wheel = _BACKEND.get_requires_for_build_wheel
+get_requires_for_build_sdist = _BACKEND.get_requires_for_build_sdist
+prepare_metadata_for_build_wheel = _BACKEND.prepare_metadata_for_build_wheel
+build_wheel = _BACKEND.build_wheel
+build_sdist = _BACKEND.build_sdist
+
+if not LEGACY_EDITABLE:
+    get_requires_for_build_editable = _BACKEND.get_requires_for_build_editable
+    prepare_metadata_for_build_editable = _BACKEND.prepare_metadata_for_build_editable
+    build_editable = _BACKEND.build_editable
+
+
+# The legacy backend
+__legacy__ = _BuildMetaLegacyBackend()
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new file mode 100644
index 0000000000000000000000000000000000000000..50e6c2f54f38db5bf5175a7a4db219815785f1bc
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/__init__.py
@@ -0,0 +1,21 @@
+# mypy: disable_error_code=call-overload
+# pyright: reportCallIssue=false, reportArgumentType=false
+# Can't disable on the exact line because distutils doesn't exists on Python 3.12
+# and type-checkers aren't aware of distutils_hack,
+# causing distutils.command.bdist.bdist.format_commands to be Any.
+
+import sys
+
+from distutils.command.bdist import bdist
+
+if 'egg' not in bdist.format_commands:
+    try:
+        # format_commands is a dict in vendored distutils
+        # It used to be a list in older (stdlib) distutils
+        # We support both for backwards compatibility
+        bdist.format_commands['egg'] = ('bdist_egg', "Python .egg file")
+    except TypeError:
+        bdist.format_command['egg'] = ('bdist_egg', "Python .egg file")
+        bdist.format_commands.append('egg')
+
+del bdist, sys
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4a51707fb7e04e5bf4a1d0eaf7590f576a3cafbdb8ef42a25d3ebf5660a05368
+size 110553
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/_requirestxt.py b/.venv/lib/python3.12/site-packages/setuptools/command/_requirestxt.py
new file mode 100644
index 0000000000000000000000000000000000000000..9029b125141158d4b12de1db8215819ce6ccf437
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/_requirestxt.py
@@ -0,0 +1,131 @@
+"""Helper code used to generate ``requires.txt`` files in the egg-info directory.
+
+The ``requires.txt`` file has an specific format:
+    - Environment markers need to be part of the section headers and
+      should not be part of the requirement spec itself.
+
+See https://setuptools.pypa.io/en/latest/deprecated/python_eggs.html#requires-txt
+"""
+
+from __future__ import annotations
+
+import io
+from collections import defaultdict
+from collections.abc import Mapping
+from itertools import filterfalse
+from typing import TypeVar
+
+from jaraco.text import yield_lines
+from packaging.requirements import Requirement
+
+from .. import _reqs
+from .._reqs import _StrOrIter
+
+# dict can work as an ordered set
+_T = TypeVar("_T")
+_Ordered = dict[_T, None]
+
+
+def _prepare(
+    install_requires: _StrOrIter, extras_require: Mapping[str, _StrOrIter]
+) -> tuple[list[str], dict[str, list[str]]]:
+    """Given values for ``install_requires`` and ``extras_require``
+    create modified versions in a way that can be written in ``requires.txt``
+    """
+    extras = _convert_extras_requirements(extras_require)
+    return _move_install_requirements_markers(install_requires, extras)
+
+
+def _convert_extras_requirements(
+    extras_require: Mapping[str, _StrOrIter],
+) -> defaultdict[str, _Ordered[Requirement]]:
+    """
+    Convert requirements in `extras_require` of the form
+    `"extra": ["barbazquux; {marker}"]` to
+    `"extra:{marker}": ["barbazquux"]`.
+    """
+    output = defaultdict[str, _Ordered[Requirement]](dict)
+    for section, v in extras_require.items():
+        # Do not strip empty sections.
+        output[section]
+        for r in _reqs.parse(v):
+            output[section + _suffix_for(r)].setdefault(r)
+
+    return output
+
+
+def _move_install_requirements_markers(
+    install_requires: _StrOrIter, extras_require: Mapping[str, _Ordered[Requirement]]
+) -> tuple[list[str], dict[str, list[str]]]:
+    """
+    The ``requires.txt`` file has an specific format:
+        - Environment markers need to be part of the section headers and
+          should not be part of the requirement spec itself.
+
+    Move requirements in ``install_requires`` that are using environment
+    markers ``extras_require``.
+    """
+
+    # divide the install_requires into two sets, simple ones still
+    # handled by install_requires and more complex ones handled by extras_require.
+
+    inst_reqs = list(_reqs.parse(install_requires))
+    simple_reqs = filter(_no_marker, inst_reqs)
+    complex_reqs = filterfalse(_no_marker, inst_reqs)
+    simple_install_requires = list(map(str, simple_reqs))
+
+    for r in complex_reqs:
+        extras_require[':' + str(r.marker)].setdefault(r)
+
+    expanded_extras = dict(
+        # list(dict.fromkeys(...))  ensures a list of unique strings
+        (k, list(dict.fromkeys(str(r) for r in map(_clean_req, v))))
+        for k, v in extras_require.items()
+    )
+
+    return simple_install_requires, expanded_extras
+
+
+def _suffix_for(req):
+    """Return the 'extras_require' suffix for a given requirement."""
+    return ':' + str(req.marker) if req.marker else ''
+
+
+def _clean_req(req):
+    """Given a Requirement, remove environment markers and return it"""
+    r = Requirement(str(req))  # create a copy before modifying
+    r.marker = None
+    return r
+
+
+def _no_marker(req):
+    return not req.marker
+
+
+def _write_requirements(stream, reqs):
+    lines = yield_lines(reqs or ())
+
+    def append_cr(line):
+        return line + '\n'
+
+    lines = map(append_cr, lines)
+    stream.writelines(lines)
+
+
+def write_requirements(cmd, basename, filename):
+    dist = cmd.distribution
+    data = io.StringIO()
+    install_requires, extras_require = _prepare(
+        dist.install_requires or (), dist.extras_require or {}
+    )
+    _write_requirements(data, install_requires)
+    for extra in sorted(extras_require):
+        data.write('\n[{extra}]\n'.format(**vars()))
+        _write_requirements(data, extras_require[extra])
+    cmd.write_or_delete_file("requirements", filename, data.getvalue())
+
+
+def write_setup_requirements(cmd, basename, filename):
+    data = io.StringIO()
+    _write_requirements(data, cmd.distribution.setup_requires)
+    cmd.write_or_delete_file("setup-requirements", filename, data.getvalue())
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/alias.py b/.venv/lib/python3.12/site-packages/setuptools/command/alias.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8d74af71d4464abf899b9f872b5741f23167165
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/alias.py
@@ -0,0 +1,77 @@
+from setuptools.command.setopt import config_file, edit_config, option_base
+
+from distutils.errors import DistutilsOptionError
+
+
+def shquote(arg):
+    """Quote an argument for later parsing by shlex.split()"""
+    for c in '"', "'", "\\", "#":
+        if c in arg:
+            return repr(arg)
+    if arg.split() != [arg]:
+        return repr(arg)
+    return arg
+
+
+class alias(option_base):
+    """Define a shortcut that invokes one or more commands"""
+
+    description = "define a shortcut to invoke one or more commands"
+    command_consumes_arguments = True
+
+    user_options = [
+        ('remove', 'r', 'remove (unset) the alias'),
+    ] + option_base.user_options
+
+    boolean_options = option_base.boolean_options + ['remove']
+
+    def initialize_options(self):
+        option_base.initialize_options(self)
+        self.args = None
+        self.remove = None
+
+    def finalize_options(self) -> None:
+        option_base.finalize_options(self)
+        if self.remove and len(self.args) != 1:
+            raise DistutilsOptionError(
+                "Must specify exactly one argument (the alias name) when using --remove"
+            )
+
+    def run(self) -> None:
+        aliases = self.distribution.get_option_dict('aliases')
+
+        if not self.args:
+            print("Command Aliases")
+            print("---------------")
+            for alias in aliases:
+                print("setup.py alias", format_alias(alias, aliases))
+            return
+
+        elif len(self.args) == 1:
+            (alias,) = self.args
+            if self.remove:
+                command = None
+            elif alias in aliases:
+                print("setup.py alias", format_alias(alias, aliases))
+                return
+            else:
+                print(f"No alias definition found for {alias!r}")
+                return
+        else:
+            alias = self.args[0]
+            command = ' '.join(map(shquote, self.args[1:]))
+
+        edit_config(self.filename, {'aliases': {alias: command}}, self.dry_run)
+
+
+def format_alias(name, aliases):
+    source, command = aliases[name]
+    if source == config_file('global'):
+        source = '--global-config '
+    elif source == config_file('user'):
+        source = '--user-config '
+    elif source == config_file('local'):
+        source = ''
+    else:
+        source = f'--filename={source!r}'
+    return source + name + ' ' + command
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/bdist_egg.py b/.venv/lib/python3.12/site-packages/setuptools/command/bdist_egg.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f66c3ba6a7f554e910800a21c5ccc3733abf07d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/bdist_egg.py
@@ -0,0 +1,479 @@
+"""setuptools.command.bdist_egg
+
+Build .egg distributions"""
+
+from __future__ import annotations
+
+import marshal
+import os
+import re
+import sys
+import textwrap
+from sysconfig import get_path, get_python_version
+from types import CodeType
+from typing import TYPE_CHECKING, Literal
+
+from setuptools import Command
+from setuptools.extension import Library
+
+from .._path import StrPathT, ensure_directory
+
+from distutils import log
+from distutils.dir_util import mkpath, remove_tree
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+# Same as zipfile._ZipFileMode from typeshed
+_ZipFileMode: TypeAlias = Literal["r", "w", "x", "a"]
+
+
+def _get_purelib():
+    return get_path("purelib")
+
+
+def strip_module(filename):
+    if '.' in filename:
+        filename = os.path.splitext(filename)[0]
+    if filename.endswith('module'):
+        filename = filename[:-6]
+    return filename
+
+
+def sorted_walk(dir):
+    """Do os.walk in a reproducible way,
+    independent of indeterministic filesystem readdir order
+    """
+    for base, dirs, files in os.walk(dir):
+        dirs.sort()
+        files.sort()
+        yield base, dirs, files
+
+
+def write_stub(resource, pyfile) -> None:
+    _stub_template = textwrap.dedent(
+        """
+        def __bootstrap__():
+            global __bootstrap__, __loader__, __file__
+            import sys, pkg_resources, importlib.util
+            __file__ = pkg_resources.resource_filename(__name__, %r)
+            __loader__ = None; del __bootstrap__, __loader__
+            spec = importlib.util.spec_from_file_location(__name__,__file__)
+            mod = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(mod)
+        __bootstrap__()
+        """
+    ).lstrip()
+    with open(pyfile, 'w', encoding="utf-8") as f:
+        f.write(_stub_template % resource)
+
+
+class bdist_egg(Command):
+    description = 'create an "egg" distribution'
+
+    user_options = [
+        ('bdist-dir=', 'b', "temporary directory for creating the distribution"),
+        (
+            'plat-name=',
+            'p',
+            "platform name to embed in generated filenames "
+            "(by default uses `pkg_resources.get_build_platform()`)",
+        ),
+        ('exclude-source-files', None, "remove all .py files from the generated egg"),
+        (
+            'keep-temp',
+            'k',
+            "keep the pseudo-installation tree around after "
+            "creating the distribution archive",
+        ),
+        ('dist-dir=', 'd', "directory to put final built distributions in"),
+        ('skip-build', None, "skip rebuilding everything (for testing/debugging)"),
+    ]
+
+    boolean_options = ['keep-temp', 'skip-build', 'exclude-source-files']
+
+    def initialize_options(self):
+        self.bdist_dir = None
+        self.plat_name = None
+        self.keep_temp = False
+        self.dist_dir = None
+        self.skip_build = False
+        self.egg_output = None
+        self.exclude_source_files = None
+
+    def finalize_options(self) -> None:
+        ei_cmd = self.ei_cmd = self.get_finalized_command("egg_info")
+        self.egg_info = ei_cmd.egg_info
+
+        if self.bdist_dir is None:
+            bdist_base = self.get_finalized_command('bdist').bdist_base
+            self.bdist_dir = os.path.join(bdist_base, 'egg')
+
+        if self.plat_name is None:
+            from pkg_resources import get_build_platform
+
+            self.plat_name = get_build_platform()
+
+        self.set_undefined_options('bdist', ('dist_dir', 'dist_dir'))
+
+        if self.egg_output is None:
+            # Compute filename of the output egg
+            basename = ei_cmd._get_egg_basename(
+                py_version=get_python_version(),
+                platform=self.distribution.has_ext_modules() and self.plat_name,
+            )
+
+            self.egg_output = os.path.join(self.dist_dir, basename + '.egg')
+
+    def do_install_data(self) -> None:
+        # Hack for packages that install data to install's --install-lib
+        self.get_finalized_command('install').install_lib = self.bdist_dir
+
+        site_packages = os.path.normcase(os.path.realpath(_get_purelib()))
+        old, self.distribution.data_files = self.distribution.data_files, []
+
+        for item in old:
+            if isinstance(item, tuple) and len(item) == 2:
+                if os.path.isabs(item[0]):
+                    realpath = os.path.realpath(item[0])
+                    normalized = os.path.normcase(realpath)
+                    if normalized == site_packages or normalized.startswith(
+                        site_packages + os.sep
+                    ):
+                        item = realpath[len(site_packages) + 1 :], item[1]
+                        # XXX else: raise ???
+            self.distribution.data_files.append(item)
+
+        try:
+            log.info("installing package data to %s", self.bdist_dir)
+            self.call_command('install_data', force=False, root=None)
+        finally:
+            self.distribution.data_files = old
+
+    def get_outputs(self):
+        return [self.egg_output]
+
+    def call_command(self, cmdname, **kw):
+        """Invoke reinitialized command `cmdname` with keyword args"""
+        for dirname in INSTALL_DIRECTORY_ATTRS:
+            kw.setdefault(dirname, self.bdist_dir)
+        kw.setdefault('skip_build', self.skip_build)
+        kw.setdefault('dry_run', self.dry_run)
+        cmd = self.reinitialize_command(cmdname, **kw)
+        self.run_command(cmdname)
+        return cmd
+
+    def run(self):  # noqa: C901  # is too complex (14)  # FIXME
+        # Generate metadata first
+        self.run_command("egg_info")
+        # We run install_lib before install_data, because some data hacks
+        # pull their data path from the install_lib command.
+        log.info("installing library code to %s", self.bdist_dir)
+        instcmd = self.get_finalized_command('install')
+        old_root = instcmd.root
+        instcmd.root = None
+        if self.distribution.has_c_libraries() and not self.skip_build:
+            self.run_command('build_clib')
+        cmd = self.call_command('install_lib', warn_dir=False)
+        instcmd.root = old_root
+
+        all_outputs, ext_outputs = self.get_ext_outputs()
+        self.stubs = []
+        to_compile = []
+        for p, ext_name in enumerate(ext_outputs):
+            filename, _ext = os.path.splitext(ext_name)
+            pyfile = os.path.join(self.bdist_dir, strip_module(filename) + '.py')
+            self.stubs.append(pyfile)
+            log.info("creating stub loader for %s", ext_name)
+            if not self.dry_run:
+                write_stub(os.path.basename(ext_name), pyfile)
+            to_compile.append(pyfile)
+            ext_outputs[p] = ext_name.replace(os.sep, '/')
+
+        if to_compile:
+            cmd.byte_compile(to_compile)
+        if self.distribution.data_files:
+            self.do_install_data()
+
+        # Make the EGG-INFO directory
+        archive_root = self.bdist_dir
+        egg_info = os.path.join(archive_root, 'EGG-INFO')
+        self.mkpath(egg_info)
+        if self.distribution.scripts:
+            script_dir = os.path.join(egg_info, 'scripts')
+            log.info("installing scripts to %s", script_dir)
+            self.call_command('install_scripts', install_dir=script_dir, no_ep=True)
+
+        self.copy_metadata_to(egg_info)
+        native_libs = os.path.join(egg_info, "native_libs.txt")
+        if all_outputs:
+            log.info("writing %s", native_libs)
+            if not self.dry_run:
+                ensure_directory(native_libs)
+                with open(native_libs, 'wt', encoding="utf-8") as libs_file:
+                    libs_file.write('\n'.join(all_outputs))
+                    libs_file.write('\n')
+        elif os.path.isfile(native_libs):
+            log.info("removing %s", native_libs)
+            if not self.dry_run:
+                os.unlink(native_libs)
+
+        write_safety_flag(os.path.join(archive_root, 'EGG-INFO'), self.zip_safe())
+
+        if os.path.exists(os.path.join(self.egg_info, 'depends.txt')):
+            log.warn(
+                "WARNING: 'depends.txt' will not be used by setuptools 0.6!\n"
+                "Use the install_requires/extras_require setup() args instead."
+            )
+
+        if self.exclude_source_files:
+            self.zap_pyfiles()
+
+        # Make the archive
+        make_zipfile(
+            self.egg_output,
+            archive_root,
+            verbose=self.verbose,
+            dry_run=self.dry_run,
+            mode=self.gen_header(),
+        )
+        if not self.keep_temp:
+            remove_tree(self.bdist_dir, dry_run=self.dry_run)
+
+        # Add to 'Distribution.dist_files' so that the "upload" command works
+        getattr(self.distribution, 'dist_files', []).append((
+            'bdist_egg',
+            get_python_version(),
+            self.egg_output,
+        ))
+
+    def zap_pyfiles(self):
+        log.info("Removing .py files from temporary directory")
+        for base, dirs, files in walk_egg(self.bdist_dir):
+            for name in files:
+                path = os.path.join(base, name)
+
+                if name.endswith('.py'):
+                    log.debug("Deleting %s", path)
+                    os.unlink(path)
+
+                if base.endswith('__pycache__'):
+                    path_old = path
+
+                    pattern = r'(?P<name>.+)\.(?P<magic>[^.]+)\.pyc'
+                    m = re.match(pattern, name)
+                    path_new = os.path.join(base, os.pardir, m.group('name') + '.pyc')
+                    log.info(f"Renaming file from [{path_old}] to [{path_new}]")
+                    try:
+                        os.remove(path_new)
+                    except OSError:
+                        pass
+                    os.rename(path_old, path_new)
+
+    def zip_safe(self):
+        safe = getattr(self.distribution, 'zip_safe', None)
+        if safe is not None:
+            return safe
+        log.warn("zip_safe flag not set; analyzing archive contents...")
+        return analyze_egg(self.bdist_dir, self.stubs)
+
+    def gen_header(self) -> Literal["w"]:
+        return 'w'
+
+    def copy_metadata_to(self, target_dir) -> None:
+        "Copy metadata (egg info) to the target_dir"
+        # normalize the path (so that a forward-slash in egg_info will
+        # match using startswith below)
+        norm_egg_info = os.path.normpath(self.egg_info)
+        prefix = os.path.join(norm_egg_info, '')
+        for path in self.ei_cmd.filelist.files:
+            if path.startswith(prefix):
+                target = os.path.join(target_dir, path[len(prefix) :])
+                ensure_directory(target)
+                self.copy_file(path, target)
+
+    def get_ext_outputs(self):
+        """Get a list of relative paths to C extensions in the output distro"""
+
+        all_outputs = []
+        ext_outputs = []
+
+        paths = {self.bdist_dir: ''}
+        for base, dirs, files in sorted_walk(self.bdist_dir):
+            all_outputs.extend(
+                paths[base] + filename
+                for filename in files
+                if os.path.splitext(filename)[1].lower() in NATIVE_EXTENSIONS
+            )
+            for filename in dirs:
+                paths[os.path.join(base, filename)] = paths[base] + filename + '/'
+
+        if self.distribution.has_ext_modules():
+            build_cmd = self.get_finalized_command('build_ext')
+            for ext in build_cmd.extensions:
+                if isinstance(ext, Library):
+                    continue
+                fullname = build_cmd.get_ext_fullname(ext.name)
+                filename = build_cmd.get_ext_filename(fullname)
+                if not os.path.basename(filename).startswith('dl-'):
+                    if os.path.exists(os.path.join(self.bdist_dir, filename)):
+                        ext_outputs.append(filename)
+
+        return all_outputs, ext_outputs
+
+
+NATIVE_EXTENSIONS: dict[str, None] = dict.fromkeys('.dll .so .dylib .pyd'.split())
+
+
+def walk_egg(egg_dir):
+    """Walk an unpacked egg's contents, skipping the metadata directory"""
+    walker = sorted_walk(egg_dir)
+    base, dirs, files = next(walker)
+    if 'EGG-INFO' in dirs:
+        dirs.remove('EGG-INFO')
+    yield base, dirs, files
+    yield from walker
+
+
+def analyze_egg(egg_dir, stubs):
+    # check for existing flag in EGG-INFO
+    for flag, fn in safety_flags.items():
+        if os.path.exists(os.path.join(egg_dir, 'EGG-INFO', fn)):
+            return flag
+    if not can_scan():
+        return False
+    safe = True
+    for base, dirs, files in walk_egg(egg_dir):
+        for name in files:
+            if name.endswith('.py') or name.endswith('.pyw'):
+                continue
+            elif name.endswith('.pyc') or name.endswith('.pyo'):
+                # always scan, even if we already know we're not safe
+                safe = scan_module(egg_dir, base, name, stubs) and safe
+    return safe
+
+
+def write_safety_flag(egg_dir, safe) -> None:
+    # Write or remove zip safety flag file(s)
+    for flag, fn in safety_flags.items():
+        fn = os.path.join(egg_dir, fn)
+        if os.path.exists(fn):
+            if safe is None or bool(safe) != flag:
+                os.unlink(fn)
+        elif safe is not None and bool(safe) == flag:
+            with open(fn, 'wt', encoding="utf-8") as f:
+                f.write('\n')
+
+
+safety_flags = {
+    True: 'zip-safe',
+    False: 'not-zip-safe',
+}
+
+
+def scan_module(egg_dir, base, name, stubs):
+    """Check whether module possibly uses unsafe-for-zipfile stuff"""
+
+    filename = os.path.join(base, name)
+    if filename[:-1] in stubs:
+        return True  # Extension module
+    pkg = base[len(egg_dir) + 1 :].replace(os.sep, '.')
+    module = pkg + (pkg and '.' or '') + os.path.splitext(name)[0]
+    skip = 16  # skip magic & reserved? & date & file size
+    f = open(filename, 'rb')
+    f.read(skip)
+    code = marshal.load(f)
+    f.close()
+    safe = True
+    symbols = dict.fromkeys(iter_symbols(code))
+    for bad in ['__file__', '__path__']:
+        if bad in symbols:
+            log.warn("%s: module references %s", module, bad)
+            safe = False
+    if 'inspect' in symbols:
+        for bad in [
+            'getsource',
+            'getabsfile',
+            'getfile',
+            'getsourcefile',
+            'getsourcelines',
+            'findsource',
+            'getcomments',
+            'getframeinfo',
+            'getinnerframes',
+            'getouterframes',
+            'stack',
+            'trace',
+        ]:
+            if bad in symbols:
+                log.warn("%s: module MAY be using inspect.%s", module, bad)
+                safe = False
+    return safe
+
+
+def iter_symbols(code):
+    """Yield names and strings used by `code` and its nested code objects"""
+    yield from code.co_names
+    for const in code.co_consts:
+        if isinstance(const, str):
+            yield const
+        elif isinstance(const, CodeType):
+            yield from iter_symbols(const)
+
+
+def can_scan() -> bool:
+    if not sys.platform.startswith('java') and sys.platform != 'cli':
+        # CPython, PyPy, etc.
+        return True
+    log.warn("Unable to analyze compiled code on this platform.")
+    log.warn(
+        "Please ask the author to include a 'zip_safe'"
+        " setting (either True or False) in the package's setup.py"
+    )
+    return False
+
+
+# Attribute names of options for commands that might need to be convinced to
+# install to the egg build directory
+
+INSTALL_DIRECTORY_ATTRS = ['install_lib', 'install_dir', 'install_data', 'install_base']
+
+
+def make_zipfile(
+    zip_filename: StrPathT,
+    base_dir,
+    verbose: bool = False,
+    dry_run: bool = False,
+    compress=True,
+    mode: _ZipFileMode = 'w',
+) -> StrPathT:
+    """Create a zip file from all the files under 'base_dir'.  The output
+    zip file will be named 'base_dir' + ".zip".  Uses either the "zipfile"
+    Python module (if available) or the InfoZIP "zip" utility (if installed
+    and found on the default search path).  If neither tool is available,
+    raises DistutilsExecError.  Returns the name of the output zip file.
+    """
+    import zipfile
+
+    mkpath(os.path.dirname(zip_filename), dry_run=dry_run)  # type: ignore[arg-type] # python/mypy#18075
+    log.info("creating '%s' and adding '%s' to it", zip_filename, base_dir)
+
+    def visit(z, dirname, names):
+        for name in names:
+            path = os.path.normpath(os.path.join(dirname, name))
+            if os.path.isfile(path):
+                p = path[len(base_dir) + 1 :]
+                if not dry_run:
+                    z.write(path, p)
+                log.debug("adding '%s'", p)
+
+    compression = zipfile.ZIP_DEFLATED if compress else zipfile.ZIP_STORED
+    if not dry_run:
+        z = zipfile.ZipFile(zip_filename, mode, compression=compression)
+        for dirname, dirs, files in sorted_walk(base_dir):
+            visit(z, dirname, files)
+        z.close()
+    else:
+        for dirname, dirs, files in sorted_walk(base_dir):
+            visit(None, dirname, files)
+    return zip_filename
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/bdist_rpm.py b/.venv/lib/python3.12/site-packages/setuptools/command/bdist_rpm.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dbb27002a8cc7f1cafd4f5cbb75ba39aa7430e2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/bdist_rpm.py
@@ -0,0 +1,42 @@
+from ..dist import Distribution
+from ..warnings import SetuptoolsDeprecationWarning
+
+import distutils.command.bdist_rpm as orig
+
+
+class bdist_rpm(orig.bdist_rpm):
+    """
+    Override the default bdist_rpm behavior to do the following:
+
+    1. Run egg_info to ensure the name and version are properly calculated.
+    2. Always run 'install' using --single-version-externally-managed to
+       disable eggs in RPM distributions.
+    """
+
+    distribution: Distribution  # override distutils.dist.Distribution with setuptools.dist.Distribution
+
+    def run(self) -> None:
+        SetuptoolsDeprecationWarning.emit(
+            "Deprecated command",
+            """
+            bdist_rpm is deprecated and will be removed in a future version.
+            Use bdist_wheel (wheel packages) instead.
+            """,
+            see_url="https://github.com/pypa/setuptools/issues/1988",
+            due_date=(2023, 10, 30),  # Deprecation introduced in 22 Oct 2021.
+        )
+
+        # ensure distro name is up-to-date
+        self.run_command('egg_info')
+
+        orig.bdist_rpm.run(self)
+
+    def _make_spec_file(self):
+        spec = orig.bdist_rpm._make_spec_file(self)
+        return [
+            line.replace(
+                "setup.py install ",
+                "setup.py install --single-version-externally-managed ",
+            ).replace("%setup", "%setup -n %{name}-%{unmangled_version}")
+            for line in spec
+        ]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/bdist_wheel.py b/.venv/lib/python3.12/site-packages/setuptools/command/bdist_wheel.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e3f637bcc31e6b49f2f9415934f553e9cf55c4a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/bdist_wheel.py
@@ -0,0 +1,604 @@
+"""
+Create a wheel (.whl) distribution.
+
+A wheel is a built archive format.
+"""
+
+from __future__ import annotations
+
+import os
+import re
+import shutil
+import struct
+import sys
+import sysconfig
+import warnings
+from collections.abc import Iterable, Sequence
+from email.generator import BytesGenerator
+from glob import iglob
+from typing import Literal, cast
+from zipfile import ZIP_DEFLATED, ZIP_STORED
+
+from packaging import tags, version as _packaging_version
+from wheel.wheelfile import WheelFile
+
+from .. import Command, __version__, _shutil
+from .._core_metadata import _safe_license_file
+from .._normalization import safer_name
+from ..warnings import SetuptoolsDeprecationWarning
+from .egg_info import egg_info as egg_info_cls
+
+from distutils import log
+
+
+def safe_version(version: str) -> str:
+    """
+    Convert an arbitrary string to a standard version string
+    """
+    try:
+        # normalize the version
+        return str(_packaging_version.Version(version))
+    except _packaging_version.InvalidVersion:
+        version = version.replace(" ", ".")
+        return re.sub("[^A-Za-z0-9.]+", "-", version)
+
+
+setuptools_major_version = int(__version__.split(".")[0])
+
+PY_LIMITED_API_PATTERN = r"cp3\d"
+
+
+def _is_32bit_interpreter() -> bool:
+    return struct.calcsize("P") == 4
+
+
+def python_tag() -> str:
+    return f"py{sys.version_info.major}"
+
+
+def get_platform(archive_root: str | None) -> str:
+    """Return our platform name 'win32', 'linux_x86_64'"""
+    result = sysconfig.get_platform()
+    if result.startswith("macosx") and archive_root is not None:  # pragma: no cover
+        from wheel.macosx_libfile import calculate_macosx_platform_tag
+
+        result = calculate_macosx_platform_tag(archive_root, result)
+    elif _is_32bit_interpreter():
+        if result == "linux-x86_64":
+            # pip pull request #3497
+            result = "linux-i686"
+        elif result == "linux-aarch64":
+            # packaging pull request #234
+            # TODO armv8l, packaging pull request #690 => this did not land
+            # in pip/packaging yet
+            result = "linux-armv7l"
+
+    return result.replace("-", "_")
+
+
+def get_flag(
+    var: str, fallback: bool, expected: bool = True, warn: bool = True
+) -> bool:
+    """Use a fallback value for determining SOABI flags if the needed config
+    var is unset or unavailable."""
+    val = sysconfig.get_config_var(var)
+    if val is None:
+        if warn:
+            warnings.warn(
+                f"Config variable '{var}' is unset, Python ABI tag may be incorrect",
+                RuntimeWarning,
+                stacklevel=2,
+            )
+        return fallback
+    return val == expected
+
+
+def get_abi_tag() -> str | None:
+    """Return the ABI tag based on SOABI (if available) or emulate SOABI (PyPy2)."""
+    soabi: str = sysconfig.get_config_var("SOABI")
+    impl = tags.interpreter_name()
+    if not soabi and impl in ("cp", "pp") and hasattr(sys, "maxunicode"):
+        d = ""
+        u = ""
+        if get_flag("Py_DEBUG", hasattr(sys, "gettotalrefcount"), warn=(impl == "cp")):
+            d = "d"
+
+        abi = f"{impl}{tags.interpreter_version()}{d}{u}"
+    elif soabi and impl == "cp" and soabi.startswith("cpython"):
+        # non-Windows
+        abi = "cp" + soabi.split("-")[1]
+    elif soabi and impl == "cp" and soabi.startswith("cp"):
+        # Windows
+        abi = soabi.split("-")[0]
+        if hasattr(sys, "gettotalrefcount"):
+            # using debug build; append "d" flag
+            abi += "d"
+    elif soabi and impl == "pp":
+        # we want something like pypy36-pp73
+        abi = "-".join(soabi.split("-")[:2])
+        abi = abi.replace(".", "_").replace("-", "_")
+    elif soabi and impl == "graalpy":
+        abi = "-".join(soabi.split("-")[:3])
+        abi = abi.replace(".", "_").replace("-", "_")
+    elif soabi:
+        abi = soabi.replace(".", "_").replace("-", "_")
+    else:
+        abi = None
+
+    return abi
+
+
+def safer_version(version: str) -> str:
+    return safe_version(version).replace("-", "_")
+
+
+class bdist_wheel(Command):
+    description = "create a wheel distribution"
+
+    supported_compressions = {
+        "stored": ZIP_STORED,
+        "deflated": ZIP_DEFLATED,
+    }
+
+    user_options = [
+        ("bdist-dir=", "b", "temporary directory for creating the distribution"),
+        (
+            "plat-name=",
+            "p",
+            "platform name to embed in generated filenames "
+            f"[default: {get_platform(None)}]",
+        ),
+        (
+            "keep-temp",
+            "k",
+            "keep the pseudo-installation tree around after "
+            "creating the distribution archive",
+        ),
+        ("dist-dir=", "d", "directory to put final built distributions in"),
+        ("skip-build", None, "skip rebuilding everything (for testing/debugging)"),
+        (
+            "relative",
+            None,
+            "build the archive using relative paths [default: false]",
+        ),
+        (
+            "owner=",
+            "u",
+            "Owner name used when creating a tar file [default: current user]",
+        ),
+        (
+            "group=",
+            "g",
+            "Group name used when creating a tar file [default: current group]",
+        ),
+        ("universal", None, "*DEPRECATED* make a universal wheel [default: false]"),
+        (
+            "compression=",
+            None,
+            f"zipfile compression (one of: {', '.join(supported_compressions)}) [default: 'deflated']",
+        ),
+        (
+            "python-tag=",
+            None,
+            f"Python implementation compatibility tag [default: '{python_tag()}']",
+        ),
+        (
+            "build-number=",
+            None,
+            "Build number for this particular version. "
+            "As specified in PEP-0427, this must start with a digit. "
+            "[default: None]",
+        ),
+        (
+            "py-limited-api=",
+            None,
+            "Python tag (cp32|cp33|cpNN) for abi3 wheel tag [default: false]",
+        ),
+        (
+            "dist-info-dir=",
+            None,
+            "directory where a pre-generated dist-info can be found (e.g. as a "
+            "result of calling the PEP517 'prepare_metadata_for_build_wheel' "
+            "method)",
+        ),
+    ]
+
+    boolean_options = ["keep-temp", "skip-build", "relative", "universal"]
+
+    def initialize_options(self) -> None:
+        self.bdist_dir: str | None = None
+        self.data_dir = ""
+        self.plat_name: str | None = None
+        self.plat_tag: str | None = None
+        self.format = "zip"
+        self.keep_temp = False
+        self.dist_dir: str | None = None
+        self.dist_info_dir = None
+        self.egginfo_dir: str | None = None
+        self.root_is_pure: bool | None = None
+        self.skip_build = False
+        self.relative = False
+        self.owner = None
+        self.group = None
+        self.universal = False
+        self.compression: str | int = "deflated"
+        self.python_tag = python_tag()
+        self.build_number: str | None = None
+        self.py_limited_api: str | Literal[False] = False
+        self.plat_name_supplied = False
+
+    def finalize_options(self) -> None:
+        if not self.bdist_dir:
+            bdist_base = self.get_finalized_command("bdist").bdist_base
+            self.bdist_dir = os.path.join(bdist_base, "wheel")
+
+        if self.dist_info_dir is None:
+            egg_info = cast(egg_info_cls, self.distribution.get_command_obj("egg_info"))
+            egg_info.ensure_finalized()  # needed for correct `wheel_dist_name`
+
+        self.data_dir = self.wheel_dist_name + ".data"
+        self.plat_name_supplied = bool(self.plat_name)
+
+        need_options = ("dist_dir", "plat_name", "skip_build")
+
+        self.set_undefined_options("bdist", *zip(need_options, need_options))
+
+        self.root_is_pure = not (
+            self.distribution.has_ext_modules() or self.distribution.has_c_libraries()
+        )
+
+        self._validate_py_limited_api()
+
+        # Support legacy [wheel] section for setting universal
+        wheel = self.distribution.get_option_dict("wheel")
+        if "universal" in wheel:  # pragma: no cover
+            # please don't define this in your global configs
+            log.warn("The [wheel] section is deprecated. Use [bdist_wheel] instead.")
+            val = wheel["universal"][1].strip()
+            if val.lower() in ("1", "true", "yes"):
+                self.universal = True
+
+        if self.universal:
+            SetuptoolsDeprecationWarning.emit(
+                "bdist_wheel.universal is deprecated",
+                """
+                With Python 2.7 end-of-life, support for building universal wheels
+                (i.e., wheels that support both Python 2 and Python 3)
+                is being obviated.
+                Please discontinue using this option, or if you still need it,
+                file an issue with pypa/setuptools describing your use case.
+                """,
+                due_date=(2025, 8, 30),  # Introduced in 2024-08-30
+            )
+
+        if self.build_number is not None and not self.build_number[:1].isdigit():
+            raise ValueError("Build tag (build-number) must start with a digit.")
+
+    def _validate_py_limited_api(self) -> None:
+        if not self.py_limited_api:
+            return
+
+        if not re.match(PY_LIMITED_API_PATTERN, self.py_limited_api):
+            raise ValueError(f"py-limited-api must match '{PY_LIMITED_API_PATTERN}'")
+
+        if sysconfig.get_config_var("Py_GIL_DISABLED"):
+            raise ValueError(
+                f"`py_limited_api={self.py_limited_api!r}` not supported. "
+                "`Py_LIMITED_API` is currently incompatible with "
+                "`Py_GIL_DISABLED`."
+                "See https://github.com/python/cpython/issues/111506."
+            )
+
+    @property
+    def wheel_dist_name(self) -> str:
+        """Return distribution full name with - replaced with _"""
+        components = [
+            safer_name(self.distribution.get_name()),
+            safer_version(self.distribution.get_version()),
+        ]
+        if self.build_number:
+            components.append(self.build_number)
+        return "-".join(components)
+
+    def get_tag(self) -> tuple[str, str, str]:
+        # bdist sets self.plat_name if unset, we should only use it for purepy
+        # wheels if the user supplied it.
+        if self.plat_name_supplied and self.plat_name:
+            plat_name = self.plat_name
+        elif self.root_is_pure:
+            plat_name = "any"
+        else:
+            # macosx contains system version in platform name so need special handle
+            if self.plat_name and not self.plat_name.startswith("macosx"):
+                plat_name = self.plat_name
+            else:
+                # on macosx always limit the platform name to comply with any
+                # c-extension modules in bdist_dir, since the user can specify
+                # a higher MACOSX_DEPLOYMENT_TARGET via tools like CMake
+
+                # on other platforms, and on macosx if there are no c-extension
+                # modules, use the default platform name.
+                plat_name = get_platform(self.bdist_dir)
+
+            if _is_32bit_interpreter():
+                if plat_name in ("linux-x86_64", "linux_x86_64"):
+                    plat_name = "linux_i686"
+                if plat_name in ("linux-aarch64", "linux_aarch64"):
+                    # TODO armv8l, packaging pull request #690 => this did not land
+                    # in pip/packaging yet
+                    plat_name = "linux_armv7l"
+
+        plat_name = (
+            plat_name.lower().replace("-", "_").replace(".", "_").replace(" ", "_")
+        )
+
+        if self.root_is_pure:
+            if self.universal:
+                impl = "py2.py3"
+            else:
+                impl = self.python_tag
+            tag = (impl, "none", plat_name)
+        else:
+            impl_name = tags.interpreter_name()
+            impl_ver = tags.interpreter_version()
+            impl = impl_name + impl_ver
+            # We don't work on CPython 3.1, 3.0.
+            if self.py_limited_api and (impl_name + impl_ver).startswith("cp3"):
+                impl = self.py_limited_api
+                abi_tag = "abi3"
+            else:
+                abi_tag = str(get_abi_tag()).lower()
+            tag = (impl, abi_tag, plat_name)
+            # issue gh-374: allow overriding plat_name
+            supported_tags = [
+                (t.interpreter, t.abi, plat_name) for t in tags.sys_tags()
+            ]
+            assert tag in supported_tags, (
+                f"would build wheel with unsupported tag {tag}"
+            )
+        return tag
+
+    def run(self):
+        build_scripts = self.reinitialize_command("build_scripts")
+        build_scripts.executable = "python"
+        build_scripts.force = True
+
+        build_ext = self.reinitialize_command("build_ext")
+        build_ext.inplace = False
+
+        if not self.skip_build:
+            self.run_command("build")
+
+        install = self.reinitialize_command("install", reinit_subcommands=True)
+        install.root = self.bdist_dir
+        install.compile = False
+        install.skip_build = self.skip_build
+        install.warn_dir = False
+
+        # A wheel without setuptools scripts is more cross-platform.
+        # Use the (undocumented) `no_ep` option to setuptools'
+        # install_scripts command to avoid creating entry point scripts.
+        install_scripts = self.reinitialize_command("install_scripts")
+        install_scripts.no_ep = True
+
+        # Use a custom scheme for the archive, because we have to decide
+        # at installation time which scheme to use.
+        for key in ("headers", "scripts", "data", "purelib", "platlib"):
+            setattr(install, "install_" + key, os.path.join(self.data_dir, key))
+
+        basedir_observed = ""
+
+        if os.name == "nt":
+            # win32 barfs if any of these are ''; could be '.'?
+            # (distutils.command.install:change_roots bug)
+            basedir_observed = os.path.normpath(os.path.join(self.data_dir, ".."))
+            self.install_libbase = self.install_lib = basedir_observed
+
+        setattr(
+            install,
+            "install_purelib" if self.root_is_pure else "install_platlib",
+            basedir_observed,
+        )
+
+        log.info(f"installing to {self.bdist_dir}")
+
+        self.run_command("install")
+
+        impl_tag, abi_tag, plat_tag = self.get_tag()
+        archive_basename = f"{self.wheel_dist_name}-{impl_tag}-{abi_tag}-{plat_tag}"
+        if not self.relative:
+            archive_root = self.bdist_dir
+        else:
+            archive_root = os.path.join(
+                self.bdist_dir, self._ensure_relative(install.install_base)
+            )
+
+        self.set_undefined_options("install_egg_info", ("target", "egginfo_dir"))
+        distinfo_dirname = (
+            f"{safer_name(self.distribution.get_name())}-"
+            f"{safer_version(self.distribution.get_version())}.dist-info"
+        )
+        distinfo_dir = os.path.join(self.bdist_dir, distinfo_dirname)
+        if self.dist_info_dir:
+            # Use the given dist-info directly.
+            log.debug(f"reusing {self.dist_info_dir}")
+            shutil.copytree(self.dist_info_dir, distinfo_dir)
+            # Egg info is still generated, so remove it now to avoid it getting
+            # copied into the wheel.
+            _shutil.rmtree(self.egginfo_dir)
+        else:
+            # Convert the generated egg-info into dist-info.
+            self.egg2dist(self.egginfo_dir, distinfo_dir)
+
+        self.write_wheelfile(distinfo_dir)
+
+        # Make the archive
+        if not os.path.exists(self.dist_dir):
+            os.makedirs(self.dist_dir)
+
+        wheel_path = os.path.join(self.dist_dir, archive_basename + ".whl")
+        with WheelFile(wheel_path, "w", self._zip_compression()) as wf:
+            wf.write_files(archive_root)
+
+        # Add to 'Distribution.dist_files' so that the "upload" command works
+        getattr(self.distribution, "dist_files", []).append((
+            "bdist_wheel",
+            f"{sys.version_info.major}.{sys.version_info.minor}",
+            wheel_path,
+        ))
+
+        if not self.keep_temp:
+            log.info(f"removing {self.bdist_dir}")
+            if not self.dry_run:
+                _shutil.rmtree(self.bdist_dir)
+
+    def write_wheelfile(
+        self, wheelfile_base: str, generator: str = f"setuptools ({__version__})"
+    ) -> None:
+        from email.message import Message
+
+        msg = Message()
+        msg["Wheel-Version"] = "1.0"  # of the spec
+        msg["Generator"] = generator
+        msg["Root-Is-Purelib"] = str(self.root_is_pure).lower()
+        if self.build_number is not None:
+            msg["Build"] = self.build_number
+
+        # Doesn't work for bdist_wininst
+        impl_tag, abi_tag, plat_tag = self.get_tag()
+        for impl in impl_tag.split("."):
+            for abi in abi_tag.split("."):
+                for plat in plat_tag.split("."):
+                    msg["Tag"] = "-".join((impl, abi, plat))
+
+        wheelfile_path = os.path.join(wheelfile_base, "WHEEL")
+        log.info(f"creating {wheelfile_path}")
+        with open(wheelfile_path, "wb") as f:
+            BytesGenerator(f, maxheaderlen=0).flatten(msg)
+
+    def _ensure_relative(self, path: str) -> str:
+        # copied from dir_util, deleted
+        drive, path = os.path.splitdrive(path)
+        if path[0:1] == os.sep:
+            path = drive + path[1:]
+        return path
+
+    @property
+    def license_paths(self) -> Iterable[str]:
+        if setuptools_major_version >= 57:
+            # Setuptools has resolved any patterns to actual file names
+            return self.distribution.metadata.license_files or ()
+
+        files = set[str]()
+        metadata = self.distribution.get_option_dict("metadata")
+        if setuptools_major_version >= 42:
+            # Setuptools recognizes the license_files option but does not do globbing
+            patterns = cast(Sequence[str], self.distribution.metadata.license_files)
+        else:
+            # Prior to those, wheel is entirely responsible for handling license files
+            if "license_files" in metadata:
+                patterns = metadata["license_files"][1].split()
+            else:
+                patterns = ()
+
+        if "license_file" in metadata:
+            warnings.warn(
+                'The "license_file" option is deprecated. Use "license_files" instead.',
+                DeprecationWarning,
+                stacklevel=2,
+            )
+            files.add(metadata["license_file"][1])
+
+        if not files and not patterns and not isinstance(patterns, list):
+            patterns = ("LICEN[CS]E*", "COPYING*", "NOTICE*", "AUTHORS*")
+
+        for pattern in patterns:
+            for path in iglob(pattern):
+                if path.endswith("~"):
+                    log.debug(
+                        f'ignoring license file "{path}" as it looks like a backup'
+                    )
+                    continue
+
+                if path not in files and os.path.isfile(path):
+                    log.info(
+                        f'adding license file "{path}" (matched pattern "{pattern}")'
+                    )
+                    files.add(path)
+
+        return files
+
+    def egg2dist(self, egginfo_path: str, distinfo_path: str) -> None:
+        """Convert an .egg-info directory into a .dist-info directory"""
+
+        def adios(p: str) -> None:
+            """Appropriately delete directory, file or link."""
+            if os.path.exists(p) and not os.path.islink(p) and os.path.isdir(p):
+                _shutil.rmtree(p)
+            elif os.path.exists(p):
+                os.unlink(p)
+
+        adios(distinfo_path)
+
+        if not os.path.exists(egginfo_path):
+            # There is no egg-info. This is probably because the egg-info
+            # file/directory is not named matching the distribution name used
+            # to name the archive file. Check for this case and report
+            # accordingly.
+            import glob
+
+            pat = os.path.join(os.path.dirname(egginfo_path), "*.egg-info")
+            possible = glob.glob(pat)
+            err = f"Egg metadata expected at {egginfo_path} but not found"
+            if possible:
+                alt = os.path.basename(possible[0])
+                err += f" ({alt} found - possible misnamed archive file?)"
+
+            raise ValueError(err)
+
+        # .egg-info is a directory
+        pkginfo_path = os.path.join(egginfo_path, "PKG-INFO")
+
+        # ignore common egg metadata that is useless to wheel
+        shutil.copytree(
+            egginfo_path,
+            distinfo_path,
+            ignore=lambda x, y: {
+                "PKG-INFO",
+                "requires.txt",
+                "SOURCES.txt",
+                "not-zip-safe",
+            },
+        )
+
+        # delete dependency_links if it is only whitespace
+        dependency_links_path = os.path.join(distinfo_path, "dependency_links.txt")
+        with open(dependency_links_path, encoding="utf-8") as dependency_links_file:
+            dependency_links = dependency_links_file.read().strip()
+        if not dependency_links:
+            adios(dependency_links_path)
+
+        metadata_path = os.path.join(distinfo_path, "METADATA")
+        shutil.copy(pkginfo_path, metadata_path)
+
+        licenses_folder_path = os.path.join(distinfo_path, "licenses")
+        for license_path in self.license_paths:
+            safe_path = _safe_license_file(license_path)
+            dist_info_license_path = os.path.join(licenses_folder_path, safe_path)
+            os.makedirs(os.path.dirname(dist_info_license_path), exist_ok=True)
+            shutil.copy(license_path, dist_info_license_path)
+
+        adios(egginfo_path)
+
+    def _zip_compression(self) -> int:
+        if (
+            isinstance(self.compression, int)
+            and self.compression in self.supported_compressions.values()
+        ):
+            return self.compression
+
+        compression = self.supported_compressions.get(str(self.compression))
+        if compression is not None:
+            return compression
+
+        raise ValueError(f"Unsupported compression: {self.compression!r}")
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/build.py b/.venv/lib/python3.12/site-packages/setuptools/command/build.py
new file mode 100644
index 0000000000000000000000000000000000000000..54cbb8d2e7bf41de4e187613718879f2a4c3d8d3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/build.py
@@ -0,0 +1,135 @@
+from __future__ import annotations
+
+from typing import Protocol
+
+from ..dist import Distribution
+
+from distutils.command.build import build as _build
+
+_ORIGINAL_SUBCOMMANDS = {"build_py", "build_clib", "build_ext", "build_scripts"}
+
+
+class build(_build):
+    distribution: Distribution  # override distutils.dist.Distribution with setuptools.dist.Distribution
+
+    # copy to avoid sharing the object with parent class
+    sub_commands = _build.sub_commands[:]
+
+
+class SubCommand(Protocol):
+    """In order to support editable installations (see :pep:`660`) all
+    build subcommands **SHOULD** implement this protocol. They also **MUST** inherit
+    from ``setuptools.Command``.
+
+    When creating an :pep:`editable wheel <660>`, ``setuptools`` will try to evaluate
+    custom ``build`` subcommands using the following procedure:
+
+    1. ``setuptools`` will set the ``editable_mode`` attribute to ``True``
+    2. ``setuptools`` will execute the ``run()`` command.
+
+       .. important::
+          Subcommands **SHOULD** take advantage of ``editable_mode=True`` to adequate
+          its behaviour or perform optimisations.
+
+          For example, if a subcommand doesn't need to generate an extra file and
+          all it does is to copy a source file into the build directory,
+          ``run()`` **SHOULD** simply "early return".
+
+          Similarly, if the subcommand creates files that would be placed alongside
+          Python files in the final distribution, during an editable install
+          the command **SHOULD** generate these files "in place" (i.e. write them to
+          the original source directory, instead of using the build directory).
+          Note that ``get_output_mapping()`` should reflect that and include mappings
+          for "in place" builds accordingly.
+
+    3. ``setuptools`` use any knowledge it can derive from the return values of
+       ``get_outputs()`` and ``get_output_mapping()`` to create an editable wheel.
+       When relevant ``setuptools`` **MAY** attempt to use file links based on the value
+       of ``get_output_mapping()``. Alternatively, ``setuptools`` **MAY** attempt to use
+       :doc:`import hooks <python:reference/import>` to redirect any attempt to import
+       to the directory with the original source code and other files built in place.
+
+    Please note that custom sub-commands **SHOULD NOT** rely on ``run()`` being
+    executed (or not) to provide correct return values for ``get_outputs()``,
+    ``get_output_mapping()`` or ``get_source_files()``. The ``get_*`` methods should
+    work independently of ``run()``.
+    """
+
+    editable_mode: bool = False
+    """Boolean flag that will be set to ``True`` when setuptools is used for an
+    editable installation (see :pep:`660`).
+    Implementations **SHOULD** explicitly set the default value of this attribute to
+    ``False``.
+    When subcommands run, they can use this flag to perform optimizations or change
+    their behaviour accordingly.
+    """
+
+    build_lib: str
+    """String representing the directory where the build artifacts should be stored,
+    e.g. ``build/lib``.
+    For example, if a distribution wants to provide a Python module named ``pkg.mod``,
+    then a corresponding file should be written to ``{build_lib}/package/module.py``.
+    A way of thinking about this is that the files saved under ``build_lib``
+    would be eventually copied to one of the directories in :obj:`site.PREFIXES`
+    upon installation.
+
+    A command that produces platform-independent files (e.g. compiling text templates
+    into Python functions), **CAN** initialize ``build_lib`` by copying its value from
+    the ``build_py`` command. On the other hand, a command that produces
+    platform-specific files **CAN** initialize ``build_lib`` by copying its value from
+    the ``build_ext`` command. In general this is done inside the ``finalize_options``
+    method with the help of the ``set_undefined_options`` command::
+
+        def finalize_options(self):
+            self.set_undefined_options("build_py", ("build_lib", "build_lib"))
+            ...
+    """
+
+    def initialize_options(self) -> None:
+        """(Required by the original :class:`setuptools.Command` interface)"""
+        ...
+
+    def finalize_options(self) -> None:
+        """(Required by the original :class:`setuptools.Command` interface)"""
+        ...
+
+    def run(self) -> None:
+        """(Required by the original :class:`setuptools.Command` interface)"""
+        ...
+
+    def get_source_files(self) -> list[str]:
+        """
+        Return a list of all files that are used by the command to create the expected
+        outputs.
+        For example, if your build command transpiles Java files into Python, you should
+        list here all the Java files.
+        The primary purpose of this function is to help populating the ``sdist``
+        with all the files necessary to build the distribution.
+        All files should be strings relative to the project root directory.
+        """
+        ...
+
+    def get_outputs(self) -> list[str]:
+        """
+        Return a list of files intended for distribution as they would have been
+        produced by the build.
+        These files should be strings in the form of
+        ``"{build_lib}/destination/file/path"``.
+
+        .. note::
+           The return value of ``get_output()`` should include all files used as keys
+           in ``get_output_mapping()`` plus files that are generated during the build
+           and don't correspond to any source file already present in the project.
+        """
+        ...
+
+    def get_output_mapping(self) -> dict[str, str]:
+        """
+        Return a mapping between destination files as they would be produced by the
+        build (dict keys) into the respective existing (source) files (dict values).
+        Existing (source) files should be represented as strings relative to the project
+        root directory.
+        Destination files should be strings in the form of
+        ``"{build_lib}/destination/file/path"``.
+        """
+        ...
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/build_clib.py b/.venv/lib/python3.12/site-packages/setuptools/command/build_clib.py
new file mode 100644
index 0000000000000000000000000000000000000000..f376f4ce4d2afc4a58f1fa0e85624136edc93835
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/build_clib.py
@@ -0,0 +1,103 @@
+from ..dist import Distribution
+from ..modified import newer_pairwise_group
+
+import distutils.command.build_clib as orig
+from distutils import log
+from distutils.errors import DistutilsSetupError
+
+
+class build_clib(orig.build_clib):
+    """
+    Override the default build_clib behaviour to do the following:
+
+    1. Implement a rudimentary timestamp-based dependency system
+       so 'compile()' doesn't run every time.
+    2. Add more keys to the 'build_info' dictionary:
+        * obj_deps - specify dependencies for each object compiled.
+                     this should be a dictionary mapping a key
+                     with the source filename to a list of
+                     dependencies. Use an empty string for global
+                     dependencies.
+        * cflags   - specify a list of additional flags to pass to
+                     the compiler.
+    """
+
+    distribution: Distribution  # override distutils.dist.Distribution with setuptools.dist.Distribution
+
+    def build_libraries(self, libraries) -> None:
+        for lib_name, build_info in libraries:
+            sources = build_info.get('sources')
+            if sources is None or not isinstance(sources, (list, tuple)):
+                raise DistutilsSetupError(
+                    f"in 'libraries' option (library '{lib_name}'), "
+                    "'sources' must be present and must be "
+                    "a list of source filenames"
+                )
+            sources = sorted(list(sources))
+
+            log.info("building '%s' library", lib_name)
+
+            # Make sure everything is the correct type.
+            # obj_deps should be a dictionary of keys as sources
+            # and a list/tuple of files that are its dependencies.
+            obj_deps = build_info.get('obj_deps', dict())
+            if not isinstance(obj_deps, dict):
+                raise DistutilsSetupError(
+                    f"in 'libraries' option (library '{lib_name}'), "
+                    "'obj_deps' must be a dictionary of "
+                    "type 'source: list'"
+                )
+            dependencies = []
+
+            # Get the global dependencies that are specified by the '' key.
+            # These will go into every source's dependency list.
+            global_deps = obj_deps.get('', list())
+            if not isinstance(global_deps, (list, tuple)):
+                raise DistutilsSetupError(
+                    f"in 'libraries' option (library '{lib_name}'), "
+                    "'obj_deps' must be a dictionary of "
+                    "type 'source: list'"
+                )
+
+            # Build the list to be used by newer_pairwise_group
+            # each source will be auto-added to its dependencies.
+            for source in sources:
+                src_deps = [source]
+                src_deps.extend(global_deps)
+                extra_deps = obj_deps.get(source, list())
+                if not isinstance(extra_deps, (list, tuple)):
+                    raise DistutilsSetupError(
+                        f"in 'libraries' option (library '{lib_name}'), "
+                        "'obj_deps' must be a dictionary of "
+                        "type 'source: list'"
+                    )
+                src_deps.extend(extra_deps)
+                dependencies.append(src_deps)
+
+            expected_objects = self.compiler.object_filenames(
+                sources,
+                output_dir=self.build_temp,
+            )
+
+            if newer_pairwise_group(dependencies, expected_objects) != ([], []):
+                # First, compile the source code to object files in the library
+                # directory.  (This should probably change to putting object
+                # files in a temporary build directory.)
+                macros = build_info.get('macros')
+                include_dirs = build_info.get('include_dirs')
+                cflags = build_info.get('cflags')
+                self.compiler.compile(
+                    sources,
+                    output_dir=self.build_temp,
+                    macros=macros,
+                    include_dirs=include_dirs,
+                    extra_postargs=cflags,
+                    debug=self.debug,
+                )
+
+            # Now "link" the object files together into a static library.
+            # (On Unix at least, this isn't really linking -- it just
+            # builds an archive.  Whatever.)
+            self.compiler.create_static_lib(
+                expected_objects, lib_name, output_dir=self.build_clib, debug=self.debug
+            )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/build_ext.py b/.venv/lib/python3.12/site-packages/setuptools/command/build_ext.py
new file mode 100644
index 0000000000000000000000000000000000000000..be833a379c71c3a6b1a85a89f8e9703c66d79824
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/build_ext.py
@@ -0,0 +1,469 @@
+from __future__ import annotations
+
+import itertools
+import os
+import sys
+from collections.abc import Iterator
+from importlib.machinery import EXTENSION_SUFFIXES
+from importlib.util import cache_from_source as _compiled_file_name
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+from setuptools.dist import Distribution
+from setuptools.errors import BaseError
+from setuptools.extension import Extension, Library
+
+from distutils import log
+from distutils.ccompiler import new_compiler
+from distutils.sysconfig import customize_compiler, get_config_var
+
+if TYPE_CHECKING:
+    # Cython not installed on CI tests, causing _build_ext to be `Any`
+    from distutils.command.build_ext import build_ext as _build_ext
+else:
+    try:
+        # Attempt to use Cython for building extensions, if available
+        from Cython.Distutils.build_ext import build_ext as _build_ext
+
+        # Additionally, assert that the compiler module will load
+        # also. Ref #1229.
+        __import__('Cython.Compiler.Main')
+    except ImportError:
+        from distutils.command.build_ext import build_ext as _build_ext
+
+# make sure _config_vars is initialized
+get_config_var("LDSHARED")
+# Not publicly exposed in typeshed distutils stubs, but this is done on purpose
+# See https://github.com/pypa/setuptools/pull/4228#issuecomment-1959856400
+from distutils.sysconfig import _config_vars as _CONFIG_VARS  # noqa: E402
+
+
+def _customize_compiler_for_shlib(compiler):
+    if sys.platform == "darwin":
+        # building .dylib requires additional compiler flags on OSX; here we
+        # temporarily substitute the pyconfig.h variables so that distutils'
+        # 'customize_compiler' uses them before we build the shared libraries.
+        tmp = _CONFIG_VARS.copy()
+        try:
+            # XXX Help!  I don't have any idea whether these are right...
+            _CONFIG_VARS['LDSHARED'] = (
+                "gcc -Wl,-x -dynamiclib -undefined dynamic_lookup"
+            )
+            _CONFIG_VARS['CCSHARED'] = " -dynamiclib"
+            _CONFIG_VARS['SO'] = ".dylib"
+            customize_compiler(compiler)
+        finally:
+            _CONFIG_VARS.clear()
+            _CONFIG_VARS.update(tmp)
+    else:
+        customize_compiler(compiler)
+
+
+have_rtld = False
+use_stubs = False
+libtype = 'shared'
+
+if sys.platform == "darwin":
+    use_stubs = True
+elif os.name != 'nt':
+    try:
+        import dl  # type: ignore[import-not-found] # https://github.com/python/mypy/issues/13002
+
+        use_stubs = have_rtld = hasattr(dl, 'RTLD_NOW')
+    except ImportError:
+        pass
+
+
+def if_dl(s):
+    return s if have_rtld else ''
+
+
+def get_abi3_suffix():
+    """Return the file extension for an abi3-compliant Extension()"""
+    for suffix in EXTENSION_SUFFIXES:
+        if '.abi3' in suffix:  # Unix
+            return suffix
+        elif suffix == '.pyd':  # Windows
+            return suffix
+    return None
+
+
+class build_ext(_build_ext):
+    distribution: Distribution  # override distutils.dist.Distribution with setuptools.dist.Distribution
+    editable_mode = False
+    inplace = False
+
+    def run(self):
+        """Build extensions in build directory, then copy if --inplace"""
+        old_inplace, self.inplace = self.inplace, False
+        _build_ext.run(self)
+        self.inplace = old_inplace
+        if old_inplace:
+            self.copy_extensions_to_source()
+
+    def _get_inplace_equivalent(self, build_py, ext: Extension) -> tuple[str, str]:
+        fullname = self.get_ext_fullname(ext.name)
+        filename = self.get_ext_filename(fullname)
+        modpath = fullname.split('.')
+        package = '.'.join(modpath[:-1])
+        package_dir = build_py.get_package_dir(package)
+        inplace_file = os.path.join(package_dir, os.path.basename(filename))
+        regular_file = os.path.join(self.build_lib, filename)
+        return (inplace_file, regular_file)
+
+    def copy_extensions_to_source(self) -> None:
+        build_py = self.get_finalized_command('build_py')
+        for ext in self.extensions:
+            inplace_file, regular_file = self._get_inplace_equivalent(build_py, ext)
+
+            # Always copy, even if source is older than destination, to ensure
+            # that the right extensions for the current Python/platform are
+            # used.
+            if os.path.exists(regular_file) or not ext.optional:
+                self.copy_file(regular_file, inplace_file, level=self.verbose)
+
+            if ext._needs_stub:
+                inplace_stub = self._get_equivalent_stub(ext, inplace_file)
+                self._write_stub_file(inplace_stub, ext, compile=True)
+                # Always compile stub and remove the original (leave the cache behind)
+                # (this behaviour was observed in previous iterations of the code)
+
+    def _get_equivalent_stub(self, ext: Extension, output_file: str) -> str:
+        dir_ = os.path.dirname(output_file)
+        _, _, name = ext.name.rpartition(".")
+        return f"{os.path.join(dir_, name)}.py"
+
+    def _get_output_mapping(self) -> Iterator[tuple[str, str]]:
+        if not self.inplace:
+            return
+
+        build_py = self.get_finalized_command('build_py')
+        opt = self.get_finalized_command('install_lib').optimize or ""
+
+        for ext in self.extensions:
+            inplace_file, regular_file = self._get_inplace_equivalent(build_py, ext)
+            yield (regular_file, inplace_file)
+
+            if ext._needs_stub:
+                # This version of `build_ext` always builds artifacts in another dir,
+                # when "inplace=True" is given it just copies them back.
+                # This is done in the `copy_extensions_to_source` function, which
+                # always compile stub files via `_compile_and_remove_stub`.
+                # At the end of the process, a `.pyc` stub file is created without the
+                # corresponding `.py`.
+
+                inplace_stub = self._get_equivalent_stub(ext, inplace_file)
+                regular_stub = self._get_equivalent_stub(ext, regular_file)
+                inplace_cache = _compiled_file_name(inplace_stub, optimization=opt)
+                output_cache = _compiled_file_name(regular_stub, optimization=opt)
+                yield (output_cache, inplace_cache)
+
+    def get_ext_filename(self, fullname: str) -> str:
+        so_ext = os.getenv('SETUPTOOLS_EXT_SUFFIX')
+        if so_ext:
+            filename = os.path.join(*fullname.split('.')) + so_ext
+        else:
+            filename = _build_ext.get_ext_filename(self, fullname)
+            ext_suffix = get_config_var('EXT_SUFFIX')
+            if not isinstance(ext_suffix, str):
+                raise OSError(
+                    "Configuration variable EXT_SUFFIX not found for this platform "
+                    "and environment variable SETUPTOOLS_EXT_SUFFIX is missing"
+                )
+            so_ext = ext_suffix
+
+        if fullname in self.ext_map:
+            ext = self.ext_map[fullname]
+            abi3_suffix = get_abi3_suffix()
+            if ext.py_limited_api and abi3_suffix:  # Use abi3
+                filename = filename[: -len(so_ext)] + abi3_suffix
+            if isinstance(ext, Library):
+                fn, ext = os.path.splitext(filename)
+                return self.shlib_compiler.library_filename(fn, libtype)
+            elif use_stubs and ext._links_to_dynamic:
+                d, fn = os.path.split(filename)
+                return os.path.join(d, 'dl-' + fn)
+        return filename
+
+    def initialize_options(self):
+        _build_ext.initialize_options(self)
+        self.shlib_compiler = None
+        self.shlibs = []
+        self.ext_map = {}
+        self.editable_mode = False
+
+    def finalize_options(self) -> None:
+        _build_ext.finalize_options(self)
+        self.extensions = self.extensions or []
+        self.check_extensions_list(self.extensions)
+        self.shlibs = [ext for ext in self.extensions if isinstance(ext, Library)]
+        if self.shlibs:
+            self.setup_shlib_compiler()
+        for ext in self.extensions:
+            ext._full_name = self.get_ext_fullname(ext.name)
+        for ext in self.extensions:
+            fullname = ext._full_name
+            self.ext_map[fullname] = ext
+
+            # distutils 3.1 will also ask for module names
+            # XXX what to do with conflicts?
+            self.ext_map[fullname.split('.')[-1]] = ext
+
+            ltd = self.shlibs and self.links_to_dynamic(ext) or False
+            ns = ltd and use_stubs and not isinstance(ext, Library)
+            ext._links_to_dynamic = ltd
+            ext._needs_stub = ns
+            filename = ext._file_name = self.get_ext_filename(fullname)
+            libdir = os.path.dirname(os.path.join(self.build_lib, filename))
+            if ltd and libdir not in ext.library_dirs:
+                ext.library_dirs.append(libdir)
+            if ltd and use_stubs and os.curdir not in ext.runtime_library_dirs:
+                ext.runtime_library_dirs.append(os.curdir)
+
+        if self.editable_mode:
+            self.inplace = True
+
+    def setup_shlib_compiler(self):
+        compiler = self.shlib_compiler = new_compiler(
+            compiler=self.compiler, dry_run=self.dry_run, force=self.force
+        )
+        _customize_compiler_for_shlib(compiler)
+
+        if self.include_dirs is not None:
+            compiler.set_include_dirs(self.include_dirs)
+        if self.define is not None:
+            # 'define' option is a list of (name,value) tuples
+            for name, value in self.define:
+                compiler.define_macro(name, value)
+        if self.undef is not None:
+            for macro in self.undef:
+                compiler.undefine_macro(macro)
+        if self.libraries is not None:
+            compiler.set_libraries(self.libraries)
+        if self.library_dirs is not None:
+            compiler.set_library_dirs(self.library_dirs)
+        if self.rpath is not None:
+            compiler.set_runtime_library_dirs(self.rpath)
+        if self.link_objects is not None:
+            compiler.set_link_objects(self.link_objects)
+
+        # hack so distutils' build_extension() builds a library instead
+        compiler.link_shared_object = link_shared_object.__get__(compiler)  # type: ignore[method-assign]
+
+    def get_export_symbols(self, ext):
+        if isinstance(ext, Library):
+            return ext.export_symbols
+        return _build_ext.get_export_symbols(self, ext)
+
+    def build_extension(self, ext) -> None:
+        ext._convert_pyx_sources_to_lang()
+        _compiler = self.compiler
+        try:
+            if isinstance(ext, Library):
+                self.compiler = self.shlib_compiler
+            _build_ext.build_extension(self, ext)
+            if ext._needs_stub:
+                build_lib = self.get_finalized_command('build_py').build_lib
+                self.write_stub(build_lib, ext)
+        finally:
+            self.compiler = _compiler
+
+    def links_to_dynamic(self, ext):
+        """Return true if 'ext' links to a dynamic lib in the same package"""
+        # XXX this should check to ensure the lib is actually being built
+        # XXX as dynamic, and not just using a locally-found version or a
+        # XXX static-compiled version
+        libnames = dict.fromkeys([lib._full_name for lib in self.shlibs])
+        pkg = '.'.join(ext._full_name.split('.')[:-1] + [''])
+        return any(pkg + libname in libnames for libname in ext.libraries)
+
+    def get_source_files(self) -> list[str]:
+        return [*_build_ext.get_source_files(self), *self._get_internal_depends()]
+
+    def _get_internal_depends(self) -> Iterator[str]:
+        """Yield ``ext.depends`` that are contained by the project directory"""
+        project_root = Path(self.distribution.src_root or os.curdir).resolve()
+        depends = (dep for ext in self.extensions for dep in ext.depends)
+
+        def skip(orig_path: str, reason: str) -> None:
+            log.info(
+                "dependency %s won't be automatically "
+                "included in the manifest: the path %s",
+                orig_path,
+                reason,
+            )
+
+        for dep in depends:
+            path = Path(dep)
+
+            if path.is_absolute():
+                skip(dep, "must be relative")
+                continue
+
+            if ".." in path.parts:
+                skip(dep, "can't have `..` segments")
+                continue
+
+            try:
+                resolved = (project_root / path).resolve(strict=True)
+            except OSError:
+                skip(dep, "doesn't exist")
+                continue
+
+            try:
+                resolved.relative_to(project_root)
+            except ValueError:
+                skip(dep, "must be inside the project root")
+                continue
+
+            yield path.as_posix()
+
+    def get_outputs(self) -> list[str]:
+        if self.inplace:
+            return list(self.get_output_mapping().keys())
+        return sorted(_build_ext.get_outputs(self) + self.__get_stubs_outputs())
+
+    def get_output_mapping(self) -> dict[str, str]:
+        """See :class:`setuptools.commands.build.SubCommand`"""
+        mapping = self._get_output_mapping()
+        return dict(sorted(mapping, key=lambda x: x[0]))
+
+    def __get_stubs_outputs(self):
+        # assemble the base name for each extension that needs a stub
+        ns_ext_bases = (
+            os.path.join(self.build_lib, *ext._full_name.split('.'))
+            for ext in self.extensions
+            if ext._needs_stub
+        )
+        # pair each base with the extension
+        pairs = itertools.product(ns_ext_bases, self.__get_output_extensions())
+        return list(base + fnext for base, fnext in pairs)
+
+    def __get_output_extensions(self):
+        yield '.py'
+        yield '.pyc'
+        if self.get_finalized_command('build_py').optimize:
+            yield '.pyo'
+
+    def write_stub(self, output_dir, ext, compile=False) -> None:
+        stub_file = os.path.join(output_dir, *ext._full_name.split('.')) + '.py'
+        self._write_stub_file(stub_file, ext, compile)
+
+    def _write_stub_file(self, stub_file: str, ext: Extension, compile=False):
+        log.info("writing stub loader for %s to %s", ext._full_name, stub_file)
+        if compile and os.path.exists(stub_file):
+            raise BaseError(stub_file + " already exists! Please delete.")
+        if not self.dry_run:
+            with open(stub_file, 'w', encoding="utf-8") as f:
+                content = '\n'.join([
+                    "def __bootstrap__():",
+                    "   global __bootstrap__, __file__, __loader__",
+                    "   import sys, os, pkg_resources, importlib.util" + if_dl(", dl"),
+                    "   __file__ = pkg_resources.resource_filename"
+                    f"(__name__,{os.path.basename(ext._file_name)!r})",
+                    "   del __bootstrap__",
+                    "   if '__loader__' in globals():",
+                    "       del __loader__",
+                    if_dl("   old_flags = sys.getdlopenflags()"),
+                    "   old_dir = os.getcwd()",
+                    "   try:",
+                    "     os.chdir(os.path.dirname(__file__))",
+                    if_dl("     sys.setdlopenflags(dl.RTLD_NOW)"),
+                    "     spec = importlib.util.spec_from_file_location(",
+                    "                __name__, __file__)",
+                    "     mod = importlib.util.module_from_spec(spec)",
+                    "     spec.loader.exec_module(mod)",
+                    "   finally:",
+                    if_dl("     sys.setdlopenflags(old_flags)"),
+                    "     os.chdir(old_dir)",
+                    "__bootstrap__()",
+                    "",  # terminal \n
+                ])
+                f.write(content)
+        if compile:
+            self._compile_and_remove_stub(stub_file)
+
+    def _compile_and_remove_stub(self, stub_file: str):
+        from distutils.util import byte_compile
+
+        byte_compile([stub_file], optimize=0, force=True, dry_run=self.dry_run)
+        optimize = self.get_finalized_command('install_lib').optimize
+        if optimize > 0:
+            byte_compile(
+                [stub_file],
+                optimize=optimize,
+                force=True,
+                dry_run=self.dry_run,
+            )
+        if os.path.exists(stub_file) and not self.dry_run:
+            os.unlink(stub_file)
+
+
+if use_stubs or os.name == 'nt':
+    # Build shared libraries
+    #
+    def link_shared_object(
+        self,
+        objects,
+        output_libname,
+        output_dir=None,
+        libraries=None,
+        library_dirs=None,
+        runtime_library_dirs=None,
+        export_symbols=None,
+        debug: bool = False,
+        extra_preargs=None,
+        extra_postargs=None,
+        build_temp=None,
+        target_lang=None,
+    ) -> None:
+        self.link(
+            self.SHARED_LIBRARY,
+            objects,
+            output_libname,
+            output_dir,
+            libraries,
+            library_dirs,
+            runtime_library_dirs,
+            export_symbols,
+            debug,
+            extra_preargs,
+            extra_postargs,
+            build_temp,
+            target_lang,
+        )
+
+else:
+    # Build static libraries everywhere else
+    libtype = 'static'
+
+    def link_shared_object(
+        self,
+        objects,
+        output_libname,
+        output_dir=None,
+        libraries=None,
+        library_dirs=None,
+        runtime_library_dirs=None,
+        export_symbols=None,
+        debug: bool = False,
+        extra_preargs=None,
+        extra_postargs=None,
+        build_temp=None,
+        target_lang=None,
+    ) -> None:
+        # XXX we need to either disallow these attrs on Library instances,
+        # or warn/abort here if set, or something...
+        # libraries=None, library_dirs=None, runtime_library_dirs=None,
+        # export_symbols=None, extra_preargs=None, extra_postargs=None,
+        # build_temp=None
+
+        assert output_dir is None  # distutils build_ext doesn't pass this
+        output_dir, filename = os.path.split(output_libname)
+        basename, _ext = os.path.splitext(filename)
+        if self.library_filename("x").startswith('lib'):
+            # strip 'lib' prefix; this is kludgy if some platform uses
+            # a different prefix
+            basename = basename[3:]
+
+        self.create_static_lib(objects, basename, output_dir, debug, target_lang)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/build_py.py b/.venv/lib/python3.12/site-packages/setuptools/command/build_py.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f6fcb7cdcb39a61007acbb399c84125cce93c9f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/build_py.py
@@ -0,0 +1,400 @@
+from __future__ import annotations
+
+import fnmatch
+import itertools
+import os
+import stat
+import textwrap
+from collections.abc import Iterable, Iterator
+from functools import partial
+from glob import glob
+from pathlib import Path
+
+from more_itertools import unique_everseen
+
+from .._path import StrPath, StrPathT
+from ..dist import Distribution
+from ..warnings import SetuptoolsDeprecationWarning
+
+import distutils.command.build_py as orig
+import distutils.errors
+from distutils.util import convert_path
+
+_IMPLICIT_DATA_FILES = ('*.pyi', 'py.typed')
+
+
+def make_writable(target) -> None:
+    os.chmod(target, os.stat(target).st_mode | stat.S_IWRITE)
+
+
+class build_py(orig.build_py):
+    """Enhanced 'build_py' command that includes data files with packages
+
+    The data files are specified via a 'package_data' argument to 'setup()'.
+    See 'setuptools.dist.Distribution' for more details.
+
+    Also, this version of the 'build_py' command allows you to specify both
+    'py_modules' and 'packages' in the same setup operation.
+    """
+
+    distribution: Distribution  # override distutils.dist.Distribution with setuptools.dist.Distribution
+    editable_mode: bool = False
+    existing_egg_info_dir: StrPath | None = None  #: Private API, internal use only.
+
+    def finalize_options(self):
+        orig.build_py.finalize_options(self)
+        self.package_data = self.distribution.package_data
+        self.exclude_package_data = self.distribution.exclude_package_data or {}
+        if 'data_files' in self.__dict__:
+            del self.__dict__['data_files']
+
+    def copy_file(  # type: ignore[override] # No overload, no bytes support
+        self,
+        infile: StrPath,
+        outfile: StrPathT,
+        preserve_mode: bool = True,
+        preserve_times: bool = True,
+        link: str | None = None,
+        level: object = 1,
+    ) -> tuple[StrPathT | str, bool]:
+        # Overwrite base class to allow using links
+        if link:
+            infile = str(Path(infile).resolve())
+            outfile = str(Path(outfile).resolve())  # type: ignore[assignment] # Re-assigning a str when outfile is StrPath is ok
+        return super().copy_file(  # pyright: ignore[reportReturnType] # pypa/distutils#309
+            infile, outfile, preserve_mode, preserve_times, link, level
+        )
+
+    def run(self) -> None:
+        """Build modules, packages, and copy data files to build directory"""
+        if not (self.py_modules or self.packages) or self.editable_mode:
+            return
+
+        if self.py_modules:
+            self.build_modules()
+
+        if self.packages:
+            self.build_packages()
+            self.build_package_data()
+
+        # Only compile actual .py files, using our base class' idea of what our
+        # output files are.
+        self.byte_compile(orig.build_py.get_outputs(self, include_bytecode=False))
+
+    def __getattr__(self, attr: str):
+        "lazily compute data files"
+        if attr == 'data_files':
+            self.data_files = self._get_data_files()
+            return self.data_files
+        return orig.build_py.__getattr__(self, attr)
+
+    def _get_data_files(self):
+        """Generate list of '(package,src_dir,build_dir,filenames)' tuples"""
+        self.analyze_manifest()
+        return list(map(self._get_pkg_data_files, self.packages or ()))
+
+    def get_data_files_without_manifest(self):
+        """
+        Generate list of ``(package,src_dir,build_dir,filenames)`` tuples,
+        but without triggering any attempt to analyze or build the manifest.
+        """
+        # Prevent eventual errors from unset `manifest_files`
+        # (that would otherwise be set by `analyze_manifest`)
+        self.__dict__.setdefault('manifest_files', {})
+        return list(map(self._get_pkg_data_files, self.packages or ()))
+
+    def _get_pkg_data_files(self, package):
+        # Locate package source directory
+        src_dir = self.get_package_dir(package)
+
+        # Compute package build directory
+        build_dir = os.path.join(*([self.build_lib] + package.split('.')))
+
+        # Strip directory from globbed filenames
+        filenames = [
+            os.path.relpath(file, src_dir)
+            for file in self.find_data_files(package, src_dir)
+        ]
+        return package, src_dir, build_dir, filenames
+
+    def find_data_files(self, package, src_dir):
+        """Return filenames for package's data files in 'src_dir'"""
+        patterns = self._get_platform_patterns(
+            self.package_data,
+            package,
+            src_dir,
+            extra_patterns=_IMPLICIT_DATA_FILES,
+        )
+        globs_expanded = map(partial(glob, recursive=True), patterns)
+        # flatten the expanded globs into an iterable of matches
+        globs_matches = itertools.chain.from_iterable(globs_expanded)
+        glob_files = filter(os.path.isfile, globs_matches)
+        files = itertools.chain(
+            self.manifest_files.get(package, []),
+            glob_files,
+        )
+        return self.exclude_data_files(package, src_dir, files)
+
+    def get_outputs(self, include_bytecode: bool = True) -> list[str]:  # type: ignore[override] # Using a real boolean instead of 0|1
+        """See :class:`setuptools.commands.build.SubCommand`"""
+        if self.editable_mode:
+            return list(self.get_output_mapping().keys())
+        return super().get_outputs(include_bytecode)
+
+    def get_output_mapping(self) -> dict[str, str]:
+        """See :class:`setuptools.commands.build.SubCommand`"""
+        mapping = itertools.chain(
+            self._get_package_data_output_mapping(),
+            self._get_module_mapping(),
+        )
+        return dict(sorted(mapping, key=lambda x: x[0]))
+
+    def _get_module_mapping(self) -> Iterator[tuple[str, str]]:
+        """Iterate over all modules producing (dest, src) pairs."""
+        for package, module, module_file in self.find_all_modules():
+            package = package.split('.')
+            filename = self.get_module_outfile(self.build_lib, package, module)
+            yield (filename, module_file)
+
+    def _get_package_data_output_mapping(self) -> Iterator[tuple[str, str]]:
+        """Iterate over package data producing (dest, src) pairs."""
+        for package, src_dir, build_dir, filenames in self.data_files:
+            for filename in filenames:
+                target = os.path.join(build_dir, filename)
+                srcfile = os.path.join(src_dir, filename)
+                yield (target, srcfile)
+
+    def build_package_data(self) -> None:
+        """Copy data files into build directory"""
+        for target, srcfile in self._get_package_data_output_mapping():
+            self.mkpath(os.path.dirname(target))
+            _outf, _copied = self.copy_file(srcfile, target)
+            make_writable(target)
+
+    def analyze_manifest(self) -> None:
+        self.manifest_files: dict[str, list[str]] = {}
+        if not self.distribution.include_package_data:
+            return
+        src_dirs: dict[str, str] = {}
+        for package in self.packages or ():
+            # Locate package source directory
+            src_dirs[assert_relative(self.get_package_dir(package))] = package
+
+        if (
+            self.existing_egg_info_dir
+            and Path(self.existing_egg_info_dir, "SOURCES.txt").exists()
+        ):
+            egg_info_dir = self.existing_egg_info_dir
+            manifest = Path(egg_info_dir, "SOURCES.txt")
+            files = manifest.read_text(encoding="utf-8").splitlines()
+        else:
+            self.run_command('egg_info')
+            ei_cmd = self.get_finalized_command('egg_info')
+            egg_info_dir = ei_cmd.egg_info
+            files = ei_cmd.filelist.files
+
+        check = _IncludePackageDataAbuse()
+        for path in self._filter_build_files(files, egg_info_dir):
+            d, f = os.path.split(assert_relative(path))
+            prev = None
+            oldf = f
+            while d and d != prev and d not in src_dirs:
+                prev = d
+                d, df = os.path.split(d)
+                f = os.path.join(df, f)
+            if d in src_dirs:
+                if f == oldf:
+                    if check.is_module(f):
+                        continue  # it's a module, not data
+                else:
+                    importable = check.importable_subpackage(src_dirs[d], f)
+                    if importable:
+                        check.warn(importable)
+                self.manifest_files.setdefault(src_dirs[d], []).append(path)
+
+    def _filter_build_files(
+        self, files: Iterable[str], egg_info: StrPath
+    ) -> Iterator[str]:
+        """
+        ``build_meta`` may try to create egg_info outside of the project directory,
+        and this can be problematic for certain plugins (reported in issue #3500).
+
+        Extensions might also include between their sources files created on the
+        ``build_lib`` and ``build_temp`` directories.
+
+        This function should filter this case of invalid files out.
+        """
+        build = self.get_finalized_command("build")
+        build_dirs = (egg_info, self.build_lib, build.build_temp, build.build_base)
+        norm_dirs = [os.path.normpath(p) for p in build_dirs if p]
+
+        for file in files:
+            norm_path = os.path.normpath(file)
+            if not os.path.isabs(file) or all(d not in norm_path for d in norm_dirs):
+                yield file
+
+    def get_data_files(self) -> None:
+        pass  # Lazily compute data files in _get_data_files() function.
+
+    def check_package(self, package, package_dir):
+        """Check namespace packages' __init__ for declare_namespace"""
+        try:
+            return self.packages_checked[package]
+        except KeyError:
+            pass
+
+        init_py = orig.build_py.check_package(self, package, package_dir)
+        self.packages_checked[package] = init_py
+
+        if not init_py or not self.distribution.namespace_packages:
+            return init_py
+
+        for pkg in self.distribution.namespace_packages:
+            if pkg == package or pkg.startswith(package + '.'):
+                break
+        else:
+            return init_py
+
+        with open(init_py, 'rb') as f:
+            contents = f.read()
+        if b'declare_namespace' not in contents:
+            raise distutils.errors.DistutilsError(
+                f"Namespace package problem: {package} is a namespace package, but "
+                "its\n__init__.py does not call declare_namespace()! Please "
+                'fix it.\n(See the setuptools manual under '
+                '"Namespace Packages" for details.)\n"'
+            )
+        return init_py
+
+    def initialize_options(self):
+        self.packages_checked = {}
+        orig.build_py.initialize_options(self)
+        self.editable_mode = False
+        self.existing_egg_info_dir = None
+
+    def get_package_dir(self, package):
+        res = orig.build_py.get_package_dir(self, package)
+        if self.distribution.src_root is not None:
+            return os.path.join(self.distribution.src_root, res)
+        return res
+
+    def exclude_data_files(self, package, src_dir, files):
+        """Filter filenames for package's data files in 'src_dir'"""
+        files = list(files)
+        patterns = self._get_platform_patterns(
+            self.exclude_package_data,
+            package,
+            src_dir,
+        )
+        match_groups = (fnmatch.filter(files, pattern) for pattern in patterns)
+        # flatten the groups of matches into an iterable of matches
+        matches = itertools.chain.from_iterable(match_groups)
+        bad = set(matches)
+        keepers = (fn for fn in files if fn not in bad)
+        # ditch dupes
+        return list(unique_everseen(keepers))
+
+    @staticmethod
+    def _get_platform_patterns(spec, package, src_dir, extra_patterns=()):
+        """
+        yield platform-specific path patterns (suitable for glob
+        or fn_match) from a glob-based spec (such as
+        self.package_data or self.exclude_package_data)
+        matching package in src_dir.
+        """
+        raw_patterns = itertools.chain(
+            extra_patterns,
+            spec.get('', []),
+            spec.get(package, []),
+        )
+        return (
+            # Each pattern has to be converted to a platform-specific path
+            os.path.join(src_dir, convert_path(pattern))
+            for pattern in raw_patterns
+        )
+
+
+def assert_relative(path):
+    if not os.path.isabs(path):
+        return path
+    from distutils.errors import DistutilsSetupError
+
+    msg = (
+        textwrap.dedent(
+            """
+        Error: setup script specifies an absolute path:
+
+            %s
+
+        setup() arguments must *always* be /-separated paths relative to the
+        setup.py directory, *never* absolute paths.
+        """
+        ).lstrip()
+        % path
+    )
+    raise DistutilsSetupError(msg)
+
+
+class _IncludePackageDataAbuse:
+    """Inform users that package or module is included as 'data file'"""
+
+    class _Warning(SetuptoolsDeprecationWarning):
+        _SUMMARY = """
+        Package {importable!r} is absent from the `packages` configuration.
+        """
+
+        _DETAILS = """
+        ############################
+        # Package would be ignored #
+        ############################
+        Python recognizes {importable!r} as an importable package[^1],
+        but it is absent from setuptools' `packages` configuration.
+
+        This leads to an ambiguous overall configuration. If you want to distribute this
+        package, please make sure that {importable!r} is explicitly added
+        to the `packages` configuration field.
+
+        Alternatively, you can also rely on setuptools' discovery methods
+        (for example by using `find_namespace_packages(...)`/`find_namespace:`
+        instead of `find_packages(...)`/`find:`).
+
+        You can read more about "package discovery" on setuptools documentation page:
+
+        - https://setuptools.pypa.io/en/latest/userguide/package_discovery.html
+
+        If you don't want {importable!r} to be distributed and are
+        already explicitly excluding {importable!r} via
+        `find_namespace_packages(...)/find_namespace` or `find_packages(...)/find`,
+        you can try to use `exclude_package_data`, or `include-package-data=False` in
+        combination with a more fine grained `package-data` configuration.
+
+        You can read more about "package data files" on setuptools documentation page:
+
+        - https://setuptools.pypa.io/en/latest/userguide/datafiles.html
+
+
+        [^1]: For Python, any directory (with suitable naming) can be imported,
+              even if it does not contain any `.py` files.
+              On the other hand, currently there is no concept of package data
+              directory, all directories are treated like packages.
+        """
+        # _DUE_DATE: still not defined as this is particularly controversial.
+        # Warning initially introduced in May 2022. See issue #3340 for discussion.
+
+    def __init__(self):
+        self._already_warned = set()
+
+    def is_module(self, file):
+        return file.endswith(".py") and file[: -len(".py")].isidentifier()
+
+    def importable_subpackage(self, parent, file):
+        pkg = Path(file).parent
+        parts = list(itertools.takewhile(str.isidentifier, pkg.parts))
+        if parts:
+            return ".".join([parent, *parts])
+        return None
+
+    def warn(self, importable):
+        if importable not in self._already_warned:
+            self._Warning.emit(importable=importable)
+            self._already_warned.add(importable)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/develop.py b/.venv/lib/python3.12/site-packages/setuptools/command/develop.py
new file mode 100644
index 0000000000000000000000000000000000000000..7eee29d491f72d8b0869493013a781d88c0aa3cd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/develop.py
@@ -0,0 +1,195 @@
+import glob
+import os
+
+import setuptools
+from setuptools import _normalization, _path, namespaces
+from setuptools.command.easy_install import easy_install
+
+from ..unicode_utils import _read_utf8_with_fallback
+
+from distutils import log
+from distutils.errors import DistutilsOptionError
+from distutils.util import convert_path
+
+
+class develop(namespaces.DevelopInstaller, easy_install):
+    """Set up package for development"""
+
+    description = "install package in 'development mode'"
+
+    user_options = easy_install.user_options + [
+        ("uninstall", "u", "Uninstall this source package"),
+        ("egg-path=", None, "Set the path to be used in the .egg-link file"),
+    ]
+
+    boolean_options = easy_install.boolean_options + ['uninstall']
+
+    command_consumes_arguments = False  # override base
+
+    def run(self):
+        if self.uninstall:
+            self.multi_version = True
+            self.uninstall_link()
+            self.uninstall_namespaces()
+        else:
+            self.install_for_development()
+        self.warn_deprecated_options()
+
+    def initialize_options(self):
+        self.uninstall = None
+        self.egg_path = None
+        easy_install.initialize_options(self)
+        self.setup_path = None
+        self.always_copy_from = '.'  # always copy eggs installed in curdir
+
+    def finalize_options(self) -> None:
+        import pkg_resources
+
+        ei = self.get_finalized_command("egg_info")
+        self.args = [ei.egg_name]
+
+        easy_install.finalize_options(self)
+        self.expand_basedirs()
+        self.expand_dirs()
+        # pick up setup-dir .egg files only: no .egg-info
+        self.package_index.scan(glob.glob('*.egg'))
+
+        egg_link_fn = (
+            _normalization.filename_component_broken(ei.egg_name) + '.egg-link'
+        )
+        self.egg_link = os.path.join(self.install_dir, egg_link_fn)
+        self.egg_base = ei.egg_base
+        if self.egg_path is None:
+            self.egg_path = os.path.abspath(ei.egg_base)
+
+        target = _path.normpath(self.egg_base)
+        egg_path = _path.normpath(os.path.join(self.install_dir, self.egg_path))
+        if egg_path != target:
+            raise DistutilsOptionError(
+                "--egg-path must be a relative path from the install"
+                " directory to " + target
+            )
+
+        # Make a distribution for the package's source
+        self.dist = pkg_resources.Distribution(
+            target,
+            pkg_resources.PathMetadata(target, os.path.abspath(ei.egg_info)),
+            project_name=ei.egg_name,
+        )
+
+        self.setup_path = self._resolve_setup_path(
+            self.egg_base,
+            self.install_dir,
+            self.egg_path,
+        )
+
+    @staticmethod
+    def _resolve_setup_path(egg_base, install_dir, egg_path):
+        """
+        Generate a path from egg_base back to '.' where the
+        setup script resides and ensure that path points to the
+        setup path from $install_dir/$egg_path.
+        """
+        path_to_setup = egg_base.replace(os.sep, '/').rstrip('/')
+        if path_to_setup != os.curdir:
+            path_to_setup = '../' * (path_to_setup.count('/') + 1)
+        resolved = _path.normpath(os.path.join(install_dir, egg_path, path_to_setup))
+        curdir = _path.normpath(os.curdir)
+        if resolved != curdir:
+            raise DistutilsOptionError(
+                "Can't get a consistent path to setup script from"
+                " installation directory",
+                resolved,
+                curdir,
+            )
+        return path_to_setup
+
+    def install_for_development(self) -> None:
+        self.run_command('egg_info')
+
+        # Build extensions in-place
+        self.reinitialize_command('build_ext', inplace=True)
+        self.run_command('build_ext')
+
+        if setuptools.bootstrap_install_from:
+            self.easy_install(setuptools.bootstrap_install_from)
+            setuptools.bootstrap_install_from = None
+
+        self.install_namespaces()
+
+        # create an .egg-link in the installation dir, pointing to our egg
+        log.info("Creating %s (link to %s)", self.egg_link, self.egg_base)
+        if not self.dry_run:
+            with open(self.egg_link, "w", encoding="utf-8") as f:
+                f.write(self.egg_path + "\n" + self.setup_path)
+        # postprocess the installed distro, fixing up .pth, installing scripts,
+        # and handling requirements
+        self.process_distribution(None, self.dist, not self.no_deps)
+
+    def uninstall_link(self) -> None:
+        if os.path.exists(self.egg_link):
+            log.info("Removing %s (link to %s)", self.egg_link, self.egg_base)
+
+            contents = [
+                line.rstrip()
+                for line in _read_utf8_with_fallback(self.egg_link).splitlines()
+            ]
+
+            if contents not in ([self.egg_path], [self.egg_path, self.setup_path]):
+                log.warn("Link points to %s: uninstall aborted", contents)
+                return
+            if not self.dry_run:
+                os.unlink(self.egg_link)
+        if not self.dry_run:
+            self.update_pth(self.dist)  # remove any .pth link to us
+        if self.distribution.scripts:
+            # XXX should also check for entry point scripts!
+            log.warn("Note: you must uninstall or replace scripts manually!")
+
+    def install_egg_scripts(self, dist):
+        if dist is not self.dist:
+            # Installing a dependency, so fall back to normal behavior
+            return easy_install.install_egg_scripts(self, dist)
+
+        # create wrapper scripts in the script dir, pointing to dist.scripts
+
+        # new-style...
+        self.install_wrapper_scripts(dist)
+
+        # ...and old-style
+        for script_name in self.distribution.scripts or []:
+            script_path = os.path.abspath(convert_path(script_name))
+            script_name = os.path.basename(script_path)
+            script_text = _read_utf8_with_fallback(script_path)
+            self.install_script(dist, script_name, script_text, script_path)
+
+        return None
+
+    def install_wrapper_scripts(self, dist):
+        dist = VersionlessRequirement(dist)
+        return easy_install.install_wrapper_scripts(self, dist)
+
+
+class VersionlessRequirement:
+    """
+    Adapt a pkg_resources.Distribution to simply return the project
+    name as the 'requirement' so that scripts will work across
+    multiple versions.
+
+    >>> from pkg_resources import Distribution
+    >>> dist = Distribution(project_name='foo', version='1.0')
+    >>> str(dist.as_requirement())
+    'foo==1.0'
+    >>> adapted_dist = VersionlessRequirement(dist)
+    >>> str(adapted_dist.as_requirement())
+    'foo'
+    """
+
+    def __init__(self, dist) -> None:
+        self.__dist = dist
+
+    def __getattr__(self, name: str):
+        return getattr(self.__dist, name)
+
+    def as_requirement(self):
+        return self.project_name
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/dist_info.py b/.venv/lib/python3.12/site-packages/setuptools/command/dist_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..dca01ff0ce355dfd375512e4a06b05b909ee41b6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/dist_info.py
@@ -0,0 +1,103 @@
+"""
+Create a dist_info directory
+As defined in the wheel specification
+"""
+
+import os
+import shutil
+from contextlib import contextmanager
+from pathlib import Path
+from typing import cast
+
+from .. import _normalization
+from .._shutil import rmdir as _rm
+from .egg_info import egg_info as egg_info_cls
+
+from distutils import log
+from distutils.core import Command
+
+
+class dist_info(Command):
+    """
+    This command is private and reserved for internal use of setuptools,
+    users should rely on ``setuptools.build_meta`` APIs.
+    """
+
+    description = "DO NOT CALL DIRECTLY, INTERNAL ONLY: create .dist-info directory"
+
+    user_options = [
+        (
+            'output-dir=',
+            'o',
+            "directory inside of which the .dist-info will be"
+            "created [default: top of the source tree]",
+        ),
+        ('tag-date', 'd', "Add date stamp (e.g. 20050528) to version number"),
+        ('tag-build=', 'b', "Specify explicit tag to add to version number"),
+        ('no-date', 'D', "Don't include date stamp [default]"),
+        ('keep-egg-info', None, "*TRANSITIONAL* will be removed in the future"),
+    ]
+
+    boolean_options = ['tag-date', 'keep-egg-info']
+    negative_opt = {'no-date': 'tag-date'}
+
+    def initialize_options(self):
+        self.output_dir = None
+        self.name = None
+        self.dist_info_dir = None
+        self.tag_date = None
+        self.tag_build = None
+        self.keep_egg_info = False
+
+    def finalize_options(self) -> None:
+        dist = self.distribution
+        project_dir = dist.src_root or os.curdir
+        self.output_dir = Path(self.output_dir or project_dir)
+
+        egg_info = cast(egg_info_cls, self.reinitialize_command("egg_info"))
+        egg_info.egg_base = str(self.output_dir)
+
+        if self.tag_date:
+            egg_info.tag_date = self.tag_date
+        else:
+            self.tag_date = egg_info.tag_date
+
+        if self.tag_build:
+            egg_info.tag_build = self.tag_build
+        else:
+            self.tag_build = egg_info.tag_build
+
+        egg_info.finalize_options()
+        self.egg_info = egg_info
+
+        name = _normalization.safer_name(dist.get_name())
+        version = _normalization.safer_best_effort_version(dist.get_version())
+        self.name = f"{name}-{version}"
+        self.dist_info_dir = os.path.join(self.output_dir, f"{self.name}.dist-info")
+
+    @contextmanager
+    def _maybe_bkp_dir(self, dir_path: str, requires_bkp: bool):
+        if requires_bkp:
+            bkp_name = f"{dir_path}.__bkp__"
+            _rm(bkp_name, ignore_errors=True)
+            shutil.copytree(dir_path, bkp_name, dirs_exist_ok=True, symlinks=True)
+            try:
+                yield
+            finally:
+                _rm(dir_path, ignore_errors=True)
+                shutil.move(bkp_name, dir_path)
+        else:
+            yield
+
+    def run(self) -> None:
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+        self.egg_info.run()
+        egg_info_dir = self.egg_info.egg_info
+        assert os.path.isdir(egg_info_dir), ".egg-info dir should have been created"
+
+        log.info(f"creating '{os.path.abspath(self.dist_info_dir)}'")
+        bdist_wheel = self.get_finalized_command('bdist_wheel')
+
+        # TODO: if bdist_wheel if merged into setuptools, just add "keep_egg_info" there
+        with self._maybe_bkp_dir(egg_info_dir, self.keep_egg_info):
+            bdist_wheel.egg2dist(egg_info_dir, self.dist_info_dir)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/easy_install.py b/.venv/lib/python3.12/site-packages/setuptools/command/easy_install.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb1b4c1fcc21dbebf23b5669913a59aaf3ce104a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/easy_install.py
@@ -0,0 +1,2365 @@
+"""
+Easy Install
+------------
+
+A tool for doing automatic download/extract/build of distutils-based Python
+packages.  For detailed documentation, see the accompanying EasyInstall.txt
+file, or visit the `EasyInstall home page`__.
+
+__ https://setuptools.pypa.io/en/latest/deprecated/easy_install.html
+
+"""
+
+from __future__ import annotations
+
+import configparser
+import contextlib
+import io
+import os
+import random
+import re
+import shlex
+import shutil
+import site
+import stat
+import struct
+import subprocess
+import sys
+import sysconfig
+import tempfile
+import textwrap
+import warnings
+import zipfile
+import zipimport
+from collections.abc import Iterable
+from glob import glob
+from sysconfig import get_path
+from typing import TYPE_CHECKING, NoReturn, TypedDict
+
+from jaraco.text import yield_lines
+
+import pkg_resources
+from pkg_resources import (
+    DEVELOP_DIST,
+    Distribution,
+    DistributionNotFound,
+    EggMetadata,
+    Environment,
+    PathMetadata,
+    Requirement,
+    VersionConflict,
+    WorkingSet,
+    find_distributions,
+    get_distribution,
+    normalize_path,
+    resource_string,
+)
+from setuptools import Command
+from setuptools.archive_util import unpack_archive
+from setuptools.command import bdist_egg, egg_info, setopt
+from setuptools.package_index import URL_SCHEME, PackageIndex, parse_requirement_arg
+from setuptools.sandbox import run_setup
+from setuptools.warnings import SetuptoolsDeprecationWarning, SetuptoolsWarning
+from setuptools.wheel import Wheel
+
+from .._path import ensure_directory
+from .._shutil import attempt_chmod_verbose as chmod, rmtree as _rmtree
+from ..compat import py39, py312
+
+from distutils import dir_util, log
+from distutils.command import install
+from distutils.command.build_scripts import first_line_re
+from distutils.errors import (
+    DistutilsArgError,
+    DistutilsError,
+    DistutilsOptionError,
+    DistutilsPlatformError,
+)
+from distutils.util import convert_path, get_platform, subst_vars
+
+if TYPE_CHECKING:
+    from typing_extensions import Self
+
+# Turn on PEP440Warnings
+warnings.filterwarnings("default", category=pkg_resources.PEP440Warning)
+
+__all__ = [
+    'easy_install',
+    'PthDistributions',
+    'extract_wininst_cfg',
+    'get_exe_prefixes',
+]
+
+
+def is_64bit():
+    return struct.calcsize("P") == 8
+
+
+def _to_bytes(s):
+    return s.encode('utf8')
+
+
+def isascii(s):
+    try:
+        s.encode('ascii')
+    except UnicodeError:
+        return False
+    return True
+
+
+def _one_liner(text):
+    return textwrap.dedent(text).strip().replace('\n', '; ')
+
+
+class easy_install(Command):
+    """Manage a download/build/install process"""
+
+    description = "Find/get/install Python packages"
+    command_consumes_arguments = True
+
+    user_options = [
+        ('prefix=', None, "installation prefix"),
+        ("zip-ok", "z", "install package as a zipfile"),
+        ("multi-version", "m", "make apps have to require() a version"),
+        ("upgrade", "U", "force upgrade (searches PyPI for latest versions)"),
+        ("install-dir=", "d", "install package to DIR"),
+        ("script-dir=", "s", "install scripts to DIR"),
+        ("exclude-scripts", "x", "Don't install scripts"),
+        ("always-copy", "a", "Copy all needed packages to install dir"),
+        ("index-url=", "i", "base URL of Python Package Index"),
+        ("find-links=", "f", "additional URL(s) to search for packages"),
+        ("build-directory=", "b", "download/extract/build in DIR; keep the results"),
+        (
+            'optimize=',
+            'O',
+            'also compile with optimization: -O1 for "python -O", '
+            '-O2 for "python -OO", and -O0 to disable [default: -O0]',
+        ),
+        ('record=', None, "filename in which to record list of installed files"),
+        ('always-unzip', 'Z', "don't install as a zipfile, no matter what"),
+        ('site-dirs=', 'S', "list of directories where .pth files work"),
+        ('editable', 'e', "Install specified packages in editable form"),
+        ('no-deps', 'N', "don't install dependencies"),
+        ('allow-hosts=', 'H', "pattern(s) that hostnames must match"),
+        ('local-snapshots-ok', 'l', "allow building eggs from local checkouts"),
+        ('version', None, "print version information and exit"),
+        (
+            'no-find-links',
+            None,
+            "Don't load find-links defined in packages being installed",
+        ),
+        ('user', None, f"install in user site-package '{site.USER_SITE}'"),
+    ]
+    boolean_options = [
+        'zip-ok',
+        'multi-version',
+        'exclude-scripts',
+        'upgrade',
+        'always-copy',
+        'editable',
+        'no-deps',
+        'local-snapshots-ok',
+        'version',
+        'user',
+    ]
+
+    negative_opt = {'always-unzip': 'zip-ok'}
+    create_index = PackageIndex
+
+    def initialize_options(self):
+        EasyInstallDeprecationWarning.emit()
+
+        # the --user option seems to be an opt-in one,
+        # so the default should be False.
+        self.user = False
+        self.zip_ok = self.local_snapshots_ok = None
+        self.install_dir = self.script_dir = self.exclude_scripts = None
+        self.index_url = None
+        self.find_links = None
+        self.build_directory = None
+        self.args = None
+        self.optimize = self.record = None
+        self.upgrade = self.always_copy = self.multi_version = None
+        self.editable = self.no_deps = self.allow_hosts = None
+        self.root = self.prefix = self.no_report = None
+        self.version = None
+        self.install_purelib = None  # for pure module distributions
+        self.install_platlib = None  # non-pure (dists w/ extensions)
+        self.install_headers = None  # for C/C++ headers
+        self.install_lib = None  # set to either purelib or platlib
+        self.install_scripts = None
+        self.install_data = None
+        self.install_base = None
+        self.install_platbase = None
+        self.install_userbase = site.USER_BASE
+        self.install_usersite = site.USER_SITE
+        self.no_find_links = None
+
+        # Options not specifiable via command line
+        self.package_index = None
+        self.pth_file = self.always_copy_from = None
+        self.site_dirs = None
+        self.installed_projects = {}
+        # Always read easy_install options, even if we are subclassed, or have
+        # an independent instance created.  This ensures that defaults will
+        # always come from the standard configuration file(s)' "easy_install"
+        # section, even if this is a "develop" or "install" command, or some
+        # other embedding.
+        self._dry_run = None
+        self.verbose = self.distribution.verbose
+        self.distribution._set_command_options(
+            self, self.distribution.get_option_dict('easy_install')
+        )
+
+    def delete_blockers(self, blockers) -> None:
+        extant_blockers = (
+            filename
+            for filename in blockers
+            if os.path.exists(filename) or os.path.islink(filename)
+        )
+        list(map(self._delete_path, extant_blockers))
+
+    def _delete_path(self, path):
+        log.info("Deleting %s", path)
+        if self.dry_run:
+            return
+
+        is_tree = os.path.isdir(path) and not os.path.islink(path)
+        remover = _rmtree if is_tree else os.unlink
+        remover(path)
+
+    @staticmethod
+    def _render_version():
+        """
+        Render the Setuptools version and installation details, then exit.
+        """
+        ver = f'{sys.version_info.major}.{sys.version_info.minor}'
+        dist = get_distribution('setuptools')
+        print(f'setuptools {dist.version} from {dist.location} (Python {ver})')
+        raise SystemExit
+
+    def finalize_options(self) -> None:  # noqa: C901  # is too complex (25)  # FIXME
+        self.version and self._render_version()
+
+        py_version = sys.version.split()[0]
+
+        self.config_vars = dict(sysconfig.get_config_vars())
+
+        self.config_vars.update({
+            'dist_name': self.distribution.get_name(),
+            'dist_version': self.distribution.get_version(),
+            'dist_fullname': self.distribution.get_fullname(),
+            'py_version': py_version,
+            'py_version_short': f'{sys.version_info.major}.{sys.version_info.minor}',
+            'py_version_nodot': f'{sys.version_info.major}{sys.version_info.minor}',
+            'sys_prefix': self.config_vars['prefix'],
+            'sys_exec_prefix': self.config_vars['exec_prefix'],
+            # Only POSIX systems have abiflags
+            'abiflags': getattr(sys, 'abiflags', ''),
+            # Only python 3.9+ has platlibdir
+            'platlibdir': getattr(sys, 'platlibdir', 'lib'),
+        })
+        with contextlib.suppress(AttributeError):
+            # only for distutils outside stdlib
+            self.config_vars.update({
+                'implementation_lower': install._get_implementation().lower(),
+                'implementation': install._get_implementation(),
+            })
+
+        # pypa/distutils#113 Python 3.9 compat
+        self.config_vars.setdefault(
+            'py_version_nodot_plat',
+            getattr(sys, 'windir', '').replace('.', ''),
+        )
+
+        self.config_vars['userbase'] = self.install_userbase
+        self.config_vars['usersite'] = self.install_usersite
+        if self.user and not site.ENABLE_USER_SITE:
+            log.warn("WARNING: The user site-packages directory is disabled.")
+
+        self._fix_install_dir_for_user_site()
+
+        self.expand_basedirs()
+        self.expand_dirs()
+
+        self._expand(
+            'install_dir',
+            'script_dir',
+            'build_directory',
+            'site_dirs',
+        )
+        # If a non-default installation directory was specified, default the
+        # script directory to match it.
+        if self.script_dir is None:
+            self.script_dir = self.install_dir
+
+        if self.no_find_links is None:
+            self.no_find_links = False
+
+        # Let install_dir get set by install_lib command, which in turn
+        # gets its info from the install command, and takes into account
+        # --prefix and --home and all that other crud.
+        self.set_undefined_options('install_lib', ('install_dir', 'install_dir'))
+        # Likewise, set default script_dir from 'install_scripts.install_dir'
+        self.set_undefined_options('install_scripts', ('install_dir', 'script_dir'))
+
+        if self.user and self.install_purelib:
+            self.install_dir = self.install_purelib
+            self.script_dir = self.install_scripts
+        # default --record from the install command
+        self.set_undefined_options('install', ('record', 'record'))
+        self.all_site_dirs = get_site_dirs()
+        self.all_site_dirs.extend(self._process_site_dirs(self.site_dirs))
+
+        if not self.editable:
+            self.check_site_dir()
+        default_index = os.getenv("__EASYINSTALL_INDEX", "https://pypi.org/simple/")
+        # ^ Private API for testing purposes only
+        self.index_url = self.index_url or default_index
+        self.shadow_path = self.all_site_dirs[:]
+        for path_item in self.install_dir, normalize_path(self.script_dir):
+            if path_item not in self.shadow_path:
+                self.shadow_path.insert(0, path_item)
+
+        if self.allow_hosts is not None:
+            hosts = [s.strip() for s in self.allow_hosts.split(',')]
+        else:
+            hosts = ['*']
+        if self.package_index is None:
+            self.package_index = self.create_index(
+                self.index_url,
+                search_path=self.shadow_path,
+                hosts=hosts,
+            )
+        self.local_index = Environment(self.shadow_path + sys.path)
+
+        if self.find_links is not None:
+            if isinstance(self.find_links, str):
+                self.find_links = self.find_links.split()
+        else:
+            self.find_links = []
+        if self.local_snapshots_ok:
+            self.package_index.scan_egg_links(self.shadow_path + sys.path)
+        if not self.no_find_links:
+            self.package_index.add_find_links(self.find_links)
+        self.set_undefined_options('install_lib', ('optimize', 'optimize'))
+        self.optimize = self._validate_optimize(self.optimize)
+
+        if self.editable and not self.build_directory:
+            raise DistutilsArgError(
+                "Must specify a build directory (-b) when using --editable"
+            )
+        if not self.args:
+            raise DistutilsArgError(
+                "No urls, filenames, or requirements specified (see --help)"
+            )
+
+        self.outputs: list[str] = []
+
+    @staticmethod
+    def _process_site_dirs(site_dirs):
+        if site_dirs is None:
+            return
+
+        normpath = map(normalize_path, sys.path)
+        site_dirs = [os.path.expanduser(s.strip()) for s in site_dirs.split(',')]
+        for d in site_dirs:
+            if not os.path.isdir(d):
+                log.warn("%s (in --site-dirs) does not exist", d)
+            elif normalize_path(d) not in normpath:
+                raise DistutilsOptionError(d + " (in --site-dirs) is not on sys.path")
+            else:
+                yield normalize_path(d)
+
+    @staticmethod
+    def _validate_optimize(value):
+        try:
+            value = int(value)
+            if value not in range(3):
+                raise ValueError
+        except ValueError as e:
+            raise DistutilsOptionError("--optimize must be 0, 1, or 2") from e
+
+        return value
+
+    def _fix_install_dir_for_user_site(self):
+        """
+        Fix the install_dir if "--user" was used.
+        """
+        if not self.user:
+            return
+
+        self.create_home_path()
+        if self.install_userbase is None:
+            msg = "User base directory is not specified"
+            raise DistutilsPlatformError(msg)
+        self.install_base = self.install_platbase = self.install_userbase
+        scheme_name = f'{os.name}_user'
+        self.select_scheme(scheme_name)
+
+    def _expand_attrs(self, attrs):
+        for attr in attrs:
+            val = getattr(self, attr)
+            if val is not None:
+                if os.name == 'posix' or os.name == 'nt':
+                    val = os.path.expanduser(val)
+                val = subst_vars(val, self.config_vars)
+                setattr(self, attr, val)
+
+    def expand_basedirs(self) -> None:
+        """Calls `os.path.expanduser` on install_base, install_platbase and
+        root."""
+        self._expand_attrs(['install_base', 'install_platbase', 'root'])
+
+    def expand_dirs(self) -> None:
+        """Calls `os.path.expanduser` on install dirs."""
+        dirs = [
+            'install_purelib',
+            'install_platlib',
+            'install_lib',
+            'install_headers',
+            'install_scripts',
+            'install_data',
+        ]
+        self._expand_attrs(dirs)
+
+    def run(self, show_deprecation: bool = True) -> None:
+        if show_deprecation:
+            self.announce(
+                "WARNING: The easy_install command is deprecated "
+                "and will be removed in a future version.",
+                log.WARN,
+            )
+        if self.verbose != self.distribution.verbose:
+            log.set_verbosity(self.verbose)
+        try:
+            for spec in self.args:
+                self.easy_install(spec, not self.no_deps)
+            if self.record:
+                outputs = self.outputs
+                if self.root:  # strip any package prefix
+                    root_len = len(self.root)
+                    for counter in range(len(outputs)):
+                        outputs[counter] = outputs[counter][root_len:]
+                from distutils import file_util
+
+                self.execute(
+                    file_util.write_file,
+                    (self.record, outputs),
+                    f"writing list of installed files to '{self.record}'",
+                )
+            self.warn_deprecated_options()
+        finally:
+            log.set_verbosity(self.distribution.verbose)
+
+    def pseudo_tempname(self):
+        """Return a pseudo-tempname base in the install directory.
+        This code is intentionally naive; if a malicious party can write to
+        the target directory you're already in deep doodoo.
+        """
+        try:
+            pid = os.getpid()
+        except Exception:
+            pid = random.randint(0, sys.maxsize)
+        return os.path.join(self.install_dir, f"test-easy-install-{pid}")
+
+    def warn_deprecated_options(self) -> None:
+        pass
+
+    def check_site_dir(self) -> None:  # is too complex (12)  # FIXME
+        """Verify that self.install_dir is .pth-capable dir, if needed"""
+
+        instdir = normalize_path(self.install_dir)
+        pth_file = os.path.join(instdir, 'easy-install.pth')
+
+        if not os.path.exists(instdir):
+            try:
+                os.makedirs(instdir)
+            except OSError:
+                self.cant_write_to_target()
+
+        # Is it a configured, PYTHONPATH, implicit, or explicit site dir?
+        is_site_dir = instdir in self.all_site_dirs
+
+        if not is_site_dir and not self.multi_version:
+            # No?  Then directly test whether it does .pth file processing
+            is_site_dir = self.check_pth_processing()
+        else:
+            # make sure we can write to target dir
+            testfile = self.pseudo_tempname() + '.write-test'
+            test_exists = os.path.exists(testfile)
+            try:
+                if test_exists:
+                    os.unlink(testfile)
+                open(testfile, 'wb').close()
+                os.unlink(testfile)
+            except OSError:
+                self.cant_write_to_target()
+
+        if not is_site_dir and not self.multi_version:
+            # Can't install non-multi to non-site dir with easy_install
+            pythonpath = os.environ.get('PYTHONPATH', '')
+            log.warn(self.__no_default_msg, self.install_dir, pythonpath)
+
+        if is_site_dir:
+            if self.pth_file is None:
+                self.pth_file = PthDistributions(pth_file, self.all_site_dirs)
+        else:
+            self.pth_file = None
+
+        if self.multi_version and not os.path.exists(pth_file):
+            self.pth_file = None  # don't create a .pth file
+        self.install_dir = instdir
+
+    __cant_write_msg = textwrap.dedent(
+        """
+        can't create or remove files in install directory
+
+        The following error occurred while trying to add or remove files in the
+        installation directory:
+
+            %s
+
+        The installation directory you specified (via --install-dir, --prefix, or
+        the distutils default setting) was:
+
+            %s
+        """
+    ).lstrip()
+
+    __not_exists_id = textwrap.dedent(
+        """
+        This directory does not currently exist.  Please create it and try again, or
+        choose a different installation directory (using the -d or --install-dir
+        option).
+        """
+    ).lstrip()
+
+    __access_msg = textwrap.dedent(
+        """
+        Perhaps your account does not have write access to this directory?  If the
+        installation directory is a system-owned directory, you may need to sign in
+        as the administrator or "root" account.  If you do not have administrative
+        access to this machine, you may wish to choose a different installation
+        directory, preferably one that is listed in your PYTHONPATH environment
+        variable.
+
+        For information on other options, you may wish to consult the
+        documentation at:
+
+          https://setuptools.pypa.io/en/latest/deprecated/easy_install.html
+
+        Please make the appropriate changes for your system and try again.
+        """
+    ).lstrip()
+
+    def cant_write_to_target(self) -> NoReturn:
+        msg = self.__cant_write_msg % (
+            sys.exc_info()[1],
+            self.install_dir,
+        )
+
+        if not os.path.exists(self.install_dir):
+            msg += '\n' + self.__not_exists_id
+        else:
+            msg += '\n' + self.__access_msg
+        raise DistutilsError(msg)
+
+    def check_pth_processing(self):  # noqa: C901
+        """Empirically verify whether .pth files are supported in inst. dir"""
+        instdir = self.install_dir
+        log.info("Checking .pth file support in %s", instdir)
+        pth_file = self.pseudo_tempname() + ".pth"
+        ok_file = pth_file + '.ok'
+        ok_exists = os.path.exists(ok_file)
+        tmpl = (
+            _one_liner(
+                """
+            import os
+            f = open({ok_file!r}, 'w', encoding="utf-8")
+            f.write('OK')
+            f.close()
+            """
+            )
+            + '\n'
+        )
+        try:
+            if ok_exists:
+                os.unlink(ok_file)
+            dirname = os.path.dirname(ok_file)
+            os.makedirs(dirname, exist_ok=True)
+            f = open(pth_file, 'w', encoding=py312.PTH_ENCODING)
+            # ^-- Python<3.13 require encoding="locale" instead of "utf-8",
+            #     see python/cpython#77102.
+        except OSError:
+            self.cant_write_to_target()
+        else:
+            try:
+                f.write(tmpl.format(**locals()))
+                f.close()
+                f = None
+                executable = sys.executable
+                if os.name == 'nt':
+                    dirname, basename = os.path.split(executable)
+                    alt = os.path.join(dirname, 'pythonw.exe')
+                    use_alt = basename.lower() == 'python.exe' and os.path.exists(alt)
+                    if use_alt:
+                        # use pythonw.exe to avoid opening a console window
+                        executable = alt
+
+                from distutils.spawn import spawn
+
+                spawn([executable, '-E', '-c', 'pass'], 0)
+
+                if os.path.exists(ok_file):
+                    log.info("TEST PASSED: %s appears to support .pth files", instdir)
+                    return True
+            finally:
+                if f:
+                    f.close()
+                if os.path.exists(ok_file):
+                    os.unlink(ok_file)
+                if os.path.exists(pth_file):
+                    os.unlink(pth_file)
+        if not self.multi_version:
+            log.warn("TEST FAILED: %s does NOT support .pth files", instdir)
+        return False
+
+    def install_egg_scripts(self, dist) -> None:
+        """Write all the scripts for `dist`, unless scripts are excluded"""
+        if not self.exclude_scripts and dist.metadata_isdir('scripts'):
+            for script_name in dist.metadata_listdir('scripts'):
+                if dist.metadata_isdir('scripts/' + script_name):
+                    # The "script" is a directory, likely a Python 3
+                    # __pycache__ directory, so skip it.
+                    continue
+                self.install_script(
+                    dist, script_name, dist.get_metadata('scripts/' + script_name)
+                )
+        self.install_wrapper_scripts(dist)
+
+    def add_output(self, path) -> None:
+        if os.path.isdir(path):
+            for base, dirs, files in os.walk(path):
+                for filename in files:
+                    self.outputs.append(os.path.join(base, filename))
+        else:
+            self.outputs.append(path)
+
+    def not_editable(self, spec) -> None:
+        if self.editable:
+            raise DistutilsArgError(
+                f"Invalid argument {spec!r}: you can't use filenames or URLs "
+                "with --editable (except via the --find-links option)."
+            )
+
+    def check_editable(self, spec) -> None:
+        if not self.editable:
+            return
+
+        if os.path.exists(os.path.join(self.build_directory, spec.key)):
+            raise DistutilsArgError(
+                f"{spec.key!r} already exists in {self.build_directory}; can't do a checkout there"
+            )
+
+    @contextlib.contextmanager
+    def _tmpdir(self):
+        tmpdir = tempfile.mkdtemp(prefix="easy_install-")
+        try:
+            # cast to str as workaround for #709 and #710 and #712
+            yield str(tmpdir)
+        finally:
+            os.path.exists(tmpdir) and _rmtree(tmpdir)
+
+    def easy_install(self, spec, deps: bool = False) -> Distribution | None:
+        with self._tmpdir() as tmpdir:
+            if not isinstance(spec, Requirement):
+                if URL_SCHEME(spec):
+                    # It's a url, download it to tmpdir and process
+                    self.not_editable(spec)
+                    dl = self.package_index.download(spec, tmpdir)
+                    return self.install_item(None, dl, tmpdir, deps, True)
+
+                elif os.path.exists(spec):
+                    # Existing file or directory, just process it directly
+                    self.not_editable(spec)
+                    return self.install_item(None, spec, tmpdir, deps, True)
+                else:
+                    spec = parse_requirement_arg(spec)
+
+            self.check_editable(spec)
+            dist = self.package_index.fetch_distribution(
+                spec,
+                tmpdir,
+                self.upgrade,
+                self.editable,
+                not self.always_copy,
+                self.local_index,
+            )
+            if dist is None:
+                msg = f"Could not find suitable distribution for {spec!r}"
+                if self.always_copy:
+                    msg += " (--always-copy skips system and development eggs)"
+                raise DistutilsError(msg)
+            elif dist.precedence == DEVELOP_DIST:
+                # .egg-info dists don't need installing, just process deps
+                self.process_distribution(spec, dist, deps, "Using")
+                return dist
+            else:
+                return self.install_item(spec, dist.location, tmpdir, deps)
+
+    def install_item(
+        self, spec, download, tmpdir, deps, install_needed: bool = False
+    ) -> Distribution | None:
+        # Installation is also needed if file in tmpdir or is not an egg
+        install_needed = install_needed or bool(self.always_copy)
+        install_needed = install_needed or os.path.dirname(download) == tmpdir
+        install_needed = install_needed or not download.endswith('.egg')
+        install_needed = install_needed or (
+            self.always_copy_from is not None
+            and os.path.dirname(normalize_path(download))
+            == normalize_path(self.always_copy_from)
+        )
+
+        if spec and not install_needed:
+            # at this point, we know it's a local .egg, we just don't know if
+            # it's already installed.
+            for dist in self.local_index[spec.project_name]:
+                if dist.location == download:
+                    break
+            else:
+                install_needed = True  # it's not in the local index
+
+        log.info("Processing %s", os.path.basename(download))
+
+        if install_needed:
+            dists = self.install_eggs(spec, download, tmpdir)
+            for dist in dists:
+                self.process_distribution(spec, dist, deps)
+        else:
+            dists = [self.egg_distribution(download)]
+            self.process_distribution(spec, dists[0], deps, "Using")
+
+        if spec is not None:
+            for dist in dists:
+                if dist in spec:
+                    return dist
+        return None
+
+    def select_scheme(self, name):
+        try:
+            install._select_scheme(self, name)
+        except AttributeError:
+            # stdlib distutils
+            install.install.select_scheme(self, name.replace('posix', 'unix'))
+
+    # FIXME: 'easy_install.process_distribution' is too complex (12)
+    def process_distribution(  # noqa: C901
+        self,
+        requirement,
+        dist,
+        deps: bool = True,
+        *info,
+    ) -> None:
+        self.update_pth(dist)
+        self.package_index.add(dist)
+        if dist in self.local_index[dist.key]:
+            self.local_index.remove(dist)
+        self.local_index.add(dist)
+        self.install_egg_scripts(dist)
+        self.installed_projects[dist.key] = dist
+        log.info(self.installation_report(requirement, dist, *info))
+        if dist.has_metadata('dependency_links.txt') and not self.no_find_links:
+            self.package_index.add_find_links(
+                dist.get_metadata_lines('dependency_links.txt')
+            )
+        if not deps and not self.always_copy:
+            return
+        elif requirement is not None and dist.key != requirement.key:
+            log.warn("Skipping dependencies for %s", dist)
+            return  # XXX this is not the distribution we were looking for
+        elif requirement is None or dist not in requirement:
+            # if we wound up with a different version, resolve what we've got
+            distreq = dist.as_requirement()
+            requirement = Requirement(str(distreq))
+        log.info("Processing dependencies for %s", requirement)
+        try:
+            distros = WorkingSet([]).resolve(
+                [requirement], self.local_index, self.easy_install
+            )
+        except DistributionNotFound as e:
+            raise DistutilsError(str(e)) from e
+        except VersionConflict as e:
+            raise DistutilsError(e.report()) from e
+        if self.always_copy or self.always_copy_from:
+            # Force all the relevant distros to be copied or activated
+            for dist in distros:
+                if dist.key not in self.installed_projects:
+                    self.easy_install(dist.as_requirement())
+        log.info("Finished processing dependencies for %s", requirement)
+
+    def should_unzip(self, dist) -> bool:
+        if self.zip_ok is not None:
+            return not self.zip_ok
+        if dist.has_metadata('not-zip-safe'):
+            return True
+        if not dist.has_metadata('zip-safe'):
+            return True
+        return False
+
+    def maybe_move(self, spec, dist_filename, setup_base):
+        dst = os.path.join(self.build_directory, spec.key)
+        if os.path.exists(dst):
+            msg = "%r already exists in %s; build directory %s will not be kept"
+            log.warn(msg, spec.key, self.build_directory, setup_base)
+            return setup_base
+        if os.path.isdir(dist_filename):
+            setup_base = dist_filename
+        else:
+            if os.path.dirname(dist_filename) == setup_base:
+                os.unlink(dist_filename)  # get it out of the tmp dir
+            contents = os.listdir(setup_base)
+            if len(contents) == 1:
+                dist_filename = os.path.join(setup_base, contents[0])
+                if os.path.isdir(dist_filename):
+                    # if the only thing there is a directory, move it instead
+                    setup_base = dist_filename
+        ensure_directory(dst)
+        shutil.move(setup_base, dst)
+        return dst
+
+    def install_wrapper_scripts(self, dist) -> None:
+        if self.exclude_scripts:
+            return
+        for args in ScriptWriter.best().get_args(dist):
+            self.write_script(*args)
+
+    def install_script(self, dist, script_name, script_text, dev_path=None) -> None:
+        """Generate a legacy script wrapper and install it"""
+        spec = str(dist.as_requirement())
+        is_script = is_python_script(script_text, script_name)
+
+        if is_script:
+            body = self._load_template(dev_path) % locals()
+            script_text = ScriptWriter.get_header(script_text) + body
+        self.write_script(script_name, _to_bytes(script_text), 'b')
+
+    @staticmethod
+    def _load_template(dev_path):
+        """
+        There are a couple of template scripts in the package. This
+        function loads one of them and prepares it for use.
+        """
+        # See https://github.com/pypa/setuptools/issues/134 for info
+        # on script file naming and downstream issues with SVR4
+        name = 'script.tmpl'
+        if dev_path:
+            name = name.replace('.tmpl', ' (dev).tmpl')
+
+        raw_bytes = resource_string('setuptools', name)
+        return raw_bytes.decode('utf-8')
+
+    def write_script(self, script_name, contents, mode: str = "t", blockers=()) -> None:
+        """Write an executable file to the scripts directory"""
+        self.delete_blockers(  # clean up old .py/.pyw w/o a script
+            [os.path.join(self.script_dir, x) for x in blockers]
+        )
+        log.info("Installing %s script to %s", script_name, self.script_dir)
+        target = os.path.join(self.script_dir, script_name)
+        self.add_output(target)
+
+        if self.dry_run:
+            return
+
+        mask = current_umask()
+        ensure_directory(target)
+        if os.path.exists(target):
+            os.unlink(target)
+
+        encoding = None if "b" in mode else "utf-8"
+        with open(target, "w" + mode, encoding=encoding) as f:
+            f.write(contents)
+        chmod(target, 0o777 - mask)
+
+    def install_eggs(self, spec, dist_filename, tmpdir) -> list[Distribution]:
+        # .egg dirs or files are already built, so just return them
+        installer_map = {
+            '.egg': self.install_egg,
+            '.exe': self.install_exe,
+            '.whl': self.install_wheel,
+        }
+        try:
+            install_dist = installer_map[dist_filename.lower()[-4:]]
+        except KeyError:
+            pass
+        else:
+            return [install_dist(dist_filename, tmpdir)]
+
+        # Anything else, try to extract and build
+        setup_base = tmpdir
+        if os.path.isfile(dist_filename) and not dist_filename.endswith('.py'):
+            unpack_archive(dist_filename, tmpdir, self.unpack_progress)
+        elif os.path.isdir(dist_filename):
+            setup_base = os.path.abspath(dist_filename)
+
+        if (
+            setup_base.startswith(tmpdir)  # something we downloaded
+            and self.build_directory
+            and spec is not None
+        ):
+            setup_base = self.maybe_move(spec, dist_filename, setup_base)
+
+        # Find the setup.py file
+        setup_script = os.path.join(setup_base, 'setup.py')
+
+        if not os.path.exists(setup_script):
+            setups = glob(os.path.join(setup_base, '*', 'setup.py'))
+            if not setups:
+                raise DistutilsError(
+                    f"Couldn't find a setup script in {os.path.abspath(dist_filename)}"
+                )
+            if len(setups) > 1:
+                raise DistutilsError(
+                    f"Multiple setup scripts in {os.path.abspath(dist_filename)}"
+                )
+            setup_script = setups[0]
+
+        # Now run it, and return the result
+        if self.editable:
+            log.info(self.report_editable(spec, setup_script))
+            return []
+        else:
+            return self.build_and_install(setup_script, setup_base)
+
+    def egg_distribution(self, egg_path):
+        if os.path.isdir(egg_path):
+            metadata = PathMetadata(egg_path, os.path.join(egg_path, 'EGG-INFO'))
+        else:
+            metadata = EggMetadata(zipimport.zipimporter(egg_path))
+        return Distribution.from_filename(egg_path, metadata=metadata)
+
+    # FIXME: 'easy_install.install_egg' is too complex (11)
+    def install_egg(self, egg_path, tmpdir):
+        destination = os.path.join(
+            self.install_dir,
+            os.path.basename(egg_path),
+        )
+        destination = os.path.abspath(destination)
+        if not self.dry_run:
+            ensure_directory(destination)
+
+        dist = self.egg_distribution(egg_path)
+        if not (
+            os.path.exists(destination) and os.path.samefile(egg_path, destination)
+        ):
+            if os.path.isdir(destination) and not os.path.islink(destination):
+                dir_util.remove_tree(destination, dry_run=self.dry_run)
+            elif os.path.exists(destination):
+                self.execute(
+                    os.unlink,
+                    (destination,),
+                    "Removing " + destination,
+                )
+            try:
+                new_dist_is_zipped = False
+                if os.path.isdir(egg_path):
+                    if egg_path.startswith(tmpdir):
+                        f, m = shutil.move, "Moving"
+                    else:
+                        f, m = shutil.copytree, "Copying"
+                elif self.should_unzip(dist):
+                    self.mkpath(destination)
+                    f, m = self.unpack_and_compile, "Extracting"
+                else:
+                    new_dist_is_zipped = True
+                    if egg_path.startswith(tmpdir):
+                        f, m = shutil.move, "Moving"
+                    else:
+                        f, m = shutil.copy2, "Copying"
+                self.execute(
+                    f,
+                    (egg_path, destination),
+                    (m + " %s to %s")
+                    % (os.path.basename(egg_path), os.path.dirname(destination)),
+                )
+                update_dist_caches(
+                    destination,
+                    fix_zipimporter_caches=new_dist_is_zipped,
+                )
+            except Exception:
+                update_dist_caches(destination, fix_zipimporter_caches=False)
+                raise
+
+        self.add_output(destination)
+        return self.egg_distribution(destination)
+
+    def install_exe(self, dist_filename, tmpdir):
+        # See if it's valid, get data
+        cfg = extract_wininst_cfg(dist_filename)
+        if cfg is None:
+            raise DistutilsError(
+                f"{dist_filename} is not a valid distutils Windows .exe"
+            )
+        # Create a dummy distribution object until we build the real distro
+        dist = Distribution(
+            None,
+            project_name=cfg.get('metadata', 'name'),
+            version=cfg.get('metadata', 'version'),
+            platform=get_platform(),
+        )
+
+        # Convert the .exe to an unpacked egg
+        egg_path = os.path.join(tmpdir, dist.egg_name() + '.egg')
+        dist.location = egg_path
+        egg_tmp = egg_path + '.tmp'
+        _egg_info = os.path.join(egg_tmp, 'EGG-INFO')
+        pkg_inf = os.path.join(_egg_info, 'PKG-INFO')
+        ensure_directory(pkg_inf)  # make sure EGG-INFO dir exists
+        dist._provider = PathMetadata(egg_tmp, _egg_info)  # XXX
+        self.exe_to_egg(dist_filename, egg_tmp)
+
+        # Write EGG-INFO/PKG-INFO
+        if not os.path.exists(pkg_inf):
+            with open(pkg_inf, 'w', encoding="utf-8") as f:
+                f.write('Metadata-Version: 1.0\n')
+                for k, v in cfg.items('metadata'):
+                    if k != 'target_version':
+                        k = k.replace('_', '-').title()
+                        f.write(f'{k}: {v}\n')
+        script_dir = os.path.join(_egg_info, 'scripts')
+        # delete entry-point scripts to avoid duping
+        self.delete_blockers([
+            os.path.join(script_dir, args[0]) for args in ScriptWriter.get_args(dist)
+        ])
+        # Build .egg file from tmpdir
+        bdist_egg.make_zipfile(
+            egg_path,
+            egg_tmp,
+            verbose=self.verbose,
+            dry_run=self.dry_run,
+        )
+        # install the .egg
+        return self.install_egg(egg_path, tmpdir)
+
+    # FIXME: 'easy_install.exe_to_egg' is too complex (12)
+    def exe_to_egg(self, dist_filename, egg_tmp) -> None:  # noqa: C901
+        """Extract a bdist_wininst to the directories an egg would use"""
+        # Check for .pth file and set up prefix translations
+        prefixes = get_exe_prefixes(dist_filename)
+        to_compile = []
+        native_libs = []
+        top_level = set()
+
+        def process(src, dst):
+            s = src.lower()
+            for old, new in prefixes:
+                if s.startswith(old):
+                    src = new + src[len(old) :]
+                    parts = src.split('/')
+                    dst = os.path.join(egg_tmp, *parts)
+                    dl = dst.lower()
+                    if dl.endswith('.pyd') or dl.endswith('.dll'):
+                        parts[-1] = bdist_egg.strip_module(parts[-1])
+                        top_level.add([os.path.splitext(parts[0])[0]])
+                        native_libs.append(src)
+                    elif dl.endswith('.py') and old != 'SCRIPTS/':
+                        top_level.add([os.path.splitext(parts[0])[0]])
+                        to_compile.append(dst)
+                    return dst
+            if not src.endswith('.pth'):
+                log.warn("WARNING: can't process %s", src)
+            return None
+
+        # extract, tracking .pyd/.dll->native_libs and .py -> to_compile
+        unpack_archive(dist_filename, egg_tmp, process)
+        stubs = []
+        for res in native_libs:
+            if res.lower().endswith('.pyd'):  # create stubs for .pyd's
+                parts = res.split('/')
+                resource = parts[-1]
+                parts[-1] = bdist_egg.strip_module(parts[-1]) + '.py'
+                pyfile = os.path.join(egg_tmp, *parts)
+                to_compile.append(pyfile)
+                stubs.append(pyfile)
+                bdist_egg.write_stub(resource, pyfile)
+        self.byte_compile(to_compile)  # compile .py's
+        bdist_egg.write_safety_flag(
+            os.path.join(egg_tmp, 'EGG-INFO'), bdist_egg.analyze_egg(egg_tmp, stubs)
+        )  # write zip-safety flag
+
+        for name in 'top_level', 'native_libs':
+            if locals()[name]:
+                txt = os.path.join(egg_tmp, 'EGG-INFO', name + '.txt')
+                if not os.path.exists(txt):
+                    with open(txt, 'w', encoding="utf-8") as f:
+                        f.write('\n'.join(locals()[name]) + '\n')
+
+    def install_wheel(self, wheel_path, tmpdir):
+        wheel = Wheel(wheel_path)
+        assert wheel.is_compatible()
+        destination = os.path.join(self.install_dir, wheel.egg_name())
+        destination = os.path.abspath(destination)
+        if not self.dry_run:
+            ensure_directory(destination)
+        if os.path.isdir(destination) and not os.path.islink(destination):
+            dir_util.remove_tree(destination, dry_run=self.dry_run)
+        elif os.path.exists(destination):
+            self.execute(
+                os.unlink,
+                (destination,),
+                "Removing " + destination,
+            )
+        try:
+            self.execute(
+                wheel.install_as_egg,
+                (destination,),
+                (
+                    f"Installing {os.path.basename(wheel_path)} to {os.path.dirname(destination)}"
+                ),
+            )
+        finally:
+            update_dist_caches(destination, fix_zipimporter_caches=False)
+        self.add_output(destination)
+        return self.egg_distribution(destination)
+
+    __mv_warning = textwrap.dedent(
+        """
+        Because this distribution was installed --multi-version, before you can
+        import modules from this package in an application, you will need to
+        'import pkg_resources' and then use a 'require()' call similar to one of
+        these examples, in order to select the desired version:
+
+            pkg_resources.require("%(name)s")  # latest installed version
+            pkg_resources.require("%(name)s==%(version)s")  # this exact version
+            pkg_resources.require("%(name)s>=%(version)s")  # this version or higher
+        """
+    ).lstrip()
+
+    __id_warning = textwrap.dedent(
+        """
+        Note also that the installation directory must be on sys.path at runtime for
+        this to work.  (e.g. by being the application's script directory, by being on
+        PYTHONPATH, or by being added to sys.path by your code.)
+        """
+    )
+
+    def installation_report(self, req, dist, what: str = "Installed") -> str:
+        """Helpful installation message for display to package users"""
+        msg = "\n%(what)s %(eggloc)s%(extras)s"
+        if self.multi_version and not self.no_report:
+            msg += '\n' + self.__mv_warning
+            if self.install_dir not in map(normalize_path, sys.path):
+                msg += '\n' + self.__id_warning
+
+        eggloc = dist.location
+        name = dist.project_name
+        version = dist.version
+        extras = ''  # TODO: self.report_extras(req, dist)
+        return msg % locals()
+
+    __editable_msg = textwrap.dedent(
+        """
+        Extracted editable version of %(spec)s to %(dirname)s
+
+        If it uses setuptools in its setup script, you can activate it in
+        "development" mode by going to that directory and running::
+
+            %(python)s setup.py develop
+
+        See the setuptools documentation for the "develop" command for more info.
+        """
+    ).lstrip()
+
+    def report_editable(self, spec, setup_script):
+        dirname = os.path.dirname(setup_script)
+        python = sys.executable
+        return '\n' + self.__editable_msg % locals()
+
+    def run_setup(self, setup_script, setup_base, args) -> None:
+        sys.modules.setdefault('distutils.command.bdist_egg', bdist_egg)
+        sys.modules.setdefault('distutils.command.egg_info', egg_info)
+
+        args = list(args)
+        if self.verbose > 2:
+            v = 'v' * (self.verbose - 1)
+            args.insert(0, '-' + v)
+        elif self.verbose < 2:
+            args.insert(0, '-q')
+        if self.dry_run:
+            args.insert(0, '-n')
+        log.info("Running %s %s", setup_script[len(setup_base) + 1 :], ' '.join(args))
+        try:
+            run_setup(setup_script, args)
+        except SystemExit as v:
+            raise DistutilsError(f"Setup script exited with {v.args[0]}") from v
+
+    def build_and_install(self, setup_script, setup_base):
+        args = ['bdist_egg', '--dist-dir']
+
+        dist_dir = tempfile.mkdtemp(
+            prefix='egg-dist-tmp-', dir=os.path.dirname(setup_script)
+        )
+        try:
+            self._set_fetcher_options(os.path.dirname(setup_script))
+            args.append(dist_dir)
+
+            self.run_setup(setup_script, setup_base, args)
+            all_eggs = Environment([dist_dir])
+            eggs = [
+                self.install_egg(dist.location, setup_base)
+                for key in all_eggs
+                for dist in all_eggs[key]
+            ]
+            if not eggs and not self.dry_run:
+                log.warn("No eggs found in %s (setup script problem?)", dist_dir)
+            return eggs
+        finally:
+            _rmtree(dist_dir)
+            log.set_verbosity(self.verbose)  # restore our log verbosity
+
+    def _set_fetcher_options(self, base):
+        """
+        When easy_install is about to run bdist_egg on a source dist, that
+        source dist might have 'setup_requires' directives, requiring
+        additional fetching. Ensure the fetcher options given to easy_install
+        are available to that command as well.
+        """
+        # find the fetch options from easy_install and write them out
+        # to the setup.cfg file.
+        ei_opts = self.distribution.get_option_dict('easy_install').copy()
+        fetch_directives = (
+            'find_links',
+            'site_dirs',
+            'index_url',
+            'optimize',
+            'allow_hosts',
+        )
+        fetch_options = {}
+        for key, val in ei_opts.items():
+            if key not in fetch_directives:
+                continue
+            fetch_options[key] = val[1]
+        # create a settings dictionary suitable for `edit_config`
+        settings = dict(easy_install=fetch_options)
+        cfg_filename = os.path.join(base, 'setup.cfg')
+        setopt.edit_config(cfg_filename, settings)
+
+    def update_pth(self, dist) -> None:  # noqa: C901  # is too complex (11)  # FIXME
+        if self.pth_file is None:
+            return
+
+        for d in self.pth_file[dist.key]:  # drop old entries
+            if not self.multi_version and d.location == dist.location:
+                continue
+
+            log.info("Removing %s from easy-install.pth file", d)
+            self.pth_file.remove(d)
+            if d.location in self.shadow_path:
+                self.shadow_path.remove(d.location)
+
+        if not self.multi_version:
+            if dist.location in self.pth_file.paths:
+                log.info(
+                    "%s is already the active version in easy-install.pth",
+                    dist,
+                )
+            else:
+                log.info("Adding %s to easy-install.pth file", dist)
+                self.pth_file.add(dist)  # add new entry
+                if dist.location not in self.shadow_path:
+                    self.shadow_path.append(dist.location)
+
+        if self.dry_run:
+            return
+
+        self.pth_file.save()
+
+        if dist.key != 'setuptools':
+            return
+
+        # Ensure that setuptools itself never becomes unavailable!
+        # XXX should this check for latest version?
+        filename = os.path.join(self.install_dir, 'setuptools.pth')
+        if os.path.islink(filename):
+            os.unlink(filename)
+
+        with open(filename, 'wt', encoding=py312.PTH_ENCODING) as f:
+            # ^-- Python<3.13 require encoding="locale" instead of "utf-8",
+            #     see python/cpython#77102.
+            f.write(self.pth_file.make_relative(dist.location) + '\n')
+
+    def unpack_progress(self, src, dst):
+        # Progress filter for unpacking
+        log.debug("Unpacking %s to %s", src, dst)
+        return dst  # only unpack-and-compile skips files for dry run
+
+    def unpack_and_compile(self, egg_path, destination) -> None:
+        to_compile = []
+        to_chmod = []
+
+        def pf(src, dst):
+            if dst.endswith('.py') and not src.startswith('EGG-INFO/'):
+                to_compile.append(dst)
+            elif dst.endswith('.dll') or dst.endswith('.so'):
+                to_chmod.append(dst)
+            self.unpack_progress(src, dst)
+            return not self.dry_run and dst or None
+
+        unpack_archive(egg_path, destination, pf)
+        self.byte_compile(to_compile)
+        if not self.dry_run:
+            for f in to_chmod:
+                mode = ((os.stat(f)[stat.ST_MODE]) | 0o555) & 0o7755
+                chmod(f, mode)
+
+    def byte_compile(self, to_compile) -> None:
+        if sys.dont_write_bytecode:
+            return
+
+        from distutils.util import byte_compile
+
+        try:
+            # try to make the byte compile messages quieter
+            log.set_verbosity(self.verbose - 1)
+
+            byte_compile(to_compile, optimize=0, force=True, dry_run=self.dry_run)
+            if self.optimize:
+                byte_compile(
+                    to_compile,
+                    optimize=self.optimize,
+                    force=True,
+                    dry_run=self.dry_run,
+                )
+        finally:
+            log.set_verbosity(self.verbose)  # restore original verbosity
+
+    __no_default_msg = textwrap.dedent(
+        """
+        bad install directory or PYTHONPATH
+
+        You are attempting to install a package to a directory that is not
+        on PYTHONPATH and which Python does not read ".pth" files from.  The
+        installation directory you specified (via --install-dir, --prefix, or
+        the distutils default setting) was:
+
+            %s
+
+        and your PYTHONPATH environment variable currently contains:
+
+            %r
+
+        Here are some of your options for correcting the problem:
+
+        * You can choose a different installation directory, i.e., one that is
+          on PYTHONPATH or supports .pth files
+
+        * You can add the installation directory to the PYTHONPATH environment
+          variable.  (It must then also be on PYTHONPATH whenever you run
+          Python and want to use the package(s) you are installing.)
+
+        * You can set up the installation directory to support ".pth" files by
+          using one of the approaches described here:
+
+          https://setuptools.pypa.io/en/latest/deprecated/easy_install.html#custom-installation-locations
+
+
+        Please make the appropriate changes for your system and try again.
+        """
+    ).strip()
+
+    def create_home_path(self) -> None:
+        """Create directories under ~."""
+        if not self.user:
+            return
+        home = convert_path(os.path.expanduser("~"))
+        for path in only_strs(self.config_vars.values()):
+            if path.startswith(home) and not os.path.isdir(path):
+                self.debug_print(f"os.makedirs('{path}', 0o700)")
+                os.makedirs(path, 0o700)
+
+    INSTALL_SCHEMES = dict(
+        posix=dict(
+            install_dir='$base/lib/python$py_version_short/site-packages',
+            script_dir='$base/bin',
+        ),
+    )
+
+    DEFAULT_SCHEME = dict(
+        install_dir='$base/Lib/site-packages',
+        script_dir='$base/Scripts',
+    )
+
+    def _expand(self, *attrs):
+        config_vars = self.get_finalized_command('install').config_vars
+
+        if self.prefix:
+            # Set default install_dir/scripts from --prefix
+            config_vars = dict(config_vars)
+            config_vars['base'] = self.prefix
+            scheme = self.INSTALL_SCHEMES.get(os.name, self.DEFAULT_SCHEME)
+            for attr, val in scheme.items():
+                if getattr(self, attr, None) is None:
+                    setattr(self, attr, val)
+
+        from distutils.util import subst_vars
+
+        for attr in attrs:
+            val = getattr(self, attr)
+            if val is not None:
+                val = subst_vars(val, config_vars)
+                if os.name == 'posix':
+                    val = os.path.expanduser(val)
+                setattr(self, attr, val)
+
+
+def _pythonpath():
+    items = os.environ.get('PYTHONPATH', '').split(os.pathsep)
+    return filter(None, items)
+
+
+def get_site_dirs():
+    """
+    Return a list of 'site' dirs
+    """
+
+    sitedirs = []
+
+    # start with PYTHONPATH
+    sitedirs.extend(_pythonpath())
+
+    prefixes = [sys.prefix]
+    if sys.exec_prefix != sys.prefix:
+        prefixes.append(sys.exec_prefix)
+    for prefix in prefixes:
+        if not prefix:
+            continue
+
+        if sys.platform in ('os2emx', 'riscos'):
+            sitedirs.append(os.path.join(prefix, "Lib", "site-packages"))
+        elif os.sep == '/':
+            sitedirs.extend([
+                os.path.join(
+                    prefix,
+                    "lib",
+                    f"python{sys.version_info.major}.{sys.version_info.minor}",
+                    "site-packages",
+                ),
+                os.path.join(prefix, "lib", "site-python"),
+            ])
+        else:
+            sitedirs.extend([
+                prefix,
+                os.path.join(prefix, "lib", "site-packages"),
+            ])
+        if sys.platform != 'darwin':
+            continue
+
+        # for framework builds *only* we add the standard Apple
+        # locations. Currently only per-user, but /Library and
+        # /Network/Library could be added too
+        if 'Python.framework' not in prefix:
+            continue
+
+        home = os.environ.get('HOME')
+        if not home:
+            continue
+
+        home_sp = os.path.join(
+            home,
+            'Library',
+            'Python',
+            f'{sys.version_info.major}.{sys.version_info.minor}',
+            'site-packages',
+        )
+        sitedirs.append(home_sp)
+    lib_paths = get_path('purelib'), get_path('platlib')
+
+    sitedirs.extend(s for s in lib_paths if s not in sitedirs)
+
+    if site.ENABLE_USER_SITE:
+        sitedirs.append(site.USER_SITE)
+
+    with contextlib.suppress(AttributeError):
+        sitedirs.extend(site.getsitepackages())
+
+    return list(map(normalize_path, sitedirs))
+
+
+def expand_paths(inputs):  # noqa: C901  # is too complex (11)  # FIXME
+    """Yield sys.path directories that might contain "old-style" packages"""
+
+    seen = set()
+
+    for dirname in inputs:
+        dirname = normalize_path(dirname)
+        if dirname in seen:
+            continue
+
+        seen.add(dirname)
+        if not os.path.isdir(dirname):
+            continue
+
+        files = os.listdir(dirname)
+        yield dirname, files
+
+        for name in files:
+            if not name.endswith('.pth'):
+                # We only care about the .pth files
+                continue
+            if name in ('easy-install.pth', 'setuptools.pth'):
+                # Ignore .pth files that we control
+                continue
+
+            # Read the .pth file
+            content = _read_pth(os.path.join(dirname, name))
+            lines = list(yield_lines(content))
+
+            # Yield existing non-dupe, non-import directory lines from it
+            for line in lines:
+                if line.startswith("import"):
+                    continue
+
+                line = normalize_path(line.rstrip())
+                if line in seen:
+                    continue
+
+                seen.add(line)
+                if not os.path.isdir(line):
+                    continue
+
+                yield line, os.listdir(line)
+
+
+def extract_wininst_cfg(dist_filename):
+    """Extract configuration data from a bdist_wininst .exe
+
+    Returns a configparser.RawConfigParser, or None
+    """
+    f = open(dist_filename, 'rb')
+    try:
+        endrec = zipfile._EndRecData(f)
+        if endrec is None:
+            return None
+
+        prepended = (endrec[9] - endrec[5]) - endrec[6]
+        if prepended < 12:  # no wininst data here
+            return None
+        f.seek(prepended - 12)
+
+        tag, cfglen, _bmlen = struct.unpack("<iii", f.read(12))
+        if tag not in (0x1234567A, 0x1234567B):
+            return None  # not a valid tag
+
+        f.seek(prepended - (12 + cfglen))
+        init = {'version': '', 'target_version': ''}
+        cfg = configparser.RawConfigParser(init)
+        try:
+            part = f.read(cfglen)
+            # Read up to the first null byte.
+            config = part.split(b'\0', 1)[0]
+            # Now the config is in bytes, but for RawConfigParser, it should
+            #  be text, so decode it.
+            config = config.decode(sys.getfilesystemencoding())
+            cfg.read_file(io.StringIO(config))
+        except configparser.Error:
+            return None
+        if not cfg.has_section('metadata') or not cfg.has_section('Setup'):
+            return None
+        return cfg
+
+    finally:
+        f.close()
+
+
+def get_exe_prefixes(exe_filename):
+    """Get exe->egg path translations for a given .exe file"""
+
+    prefixes = [
+        ('PURELIB/', ''),
+        ('PLATLIB/pywin32_system32', ''),
+        ('PLATLIB/', ''),
+        ('SCRIPTS/', 'EGG-INFO/scripts/'),
+        ('DATA/lib/site-packages', ''),
+    ]
+    z = zipfile.ZipFile(exe_filename)
+    try:
+        for info in z.infolist():
+            name = info.filename
+            parts = name.split('/')
+            if len(parts) == 3 and parts[2] == 'PKG-INFO':
+                if parts[1].endswith('.egg-info'):
+                    prefixes.insert(0, ('/'.join(parts[:2]), 'EGG-INFO/'))
+                    break
+            if len(parts) != 2 or not name.endswith('.pth'):
+                continue
+            if name.endswith('-nspkg.pth'):
+                continue
+            if parts[0].upper() in ('PURELIB', 'PLATLIB'):
+                contents = z.read(name).decode()
+                for pth in yield_lines(contents):
+                    pth = pth.strip().replace('\\', '/')
+                    if not pth.startswith('import'):
+                        prefixes.append(((f'{parts[0]}/{pth}/'), ''))
+    finally:
+        z.close()
+    prefixes = [(x.lower(), y) for x, y in prefixes]
+    prefixes.sort()
+    prefixes.reverse()
+    return prefixes
+
+
+class PthDistributions(Environment):
+    """A .pth file with Distribution paths in it"""
+
+    def __init__(self, filename, sitedirs=()) -> None:
+        self.filename = filename
+        self.sitedirs = list(map(normalize_path, sitedirs))
+        self.basedir = normalize_path(os.path.dirname(self.filename))
+        self.paths, self.dirty = self._load()
+        # keep a copy if someone manually updates the paths attribute on the instance
+        self._init_paths = self.paths[:]
+        super().__init__([], None, None)
+        for path in yield_lines(self.paths):
+            list(map(self.add, find_distributions(path, True)))
+
+    def _load_raw(self):
+        paths = []
+        dirty = saw_import = False
+        seen = set(self.sitedirs)
+        content = _read_pth(self.filename)
+        for line in content.splitlines():
+            path = line.rstrip()
+            # still keep imports and empty/commented lines for formatting
+            paths.append(path)
+            if line.startswith(('import ', 'from ')):
+                saw_import = True
+                continue
+            stripped_path = path.strip()
+            if not stripped_path or stripped_path.startswith('#'):
+                continue
+            # skip non-existent paths, in case somebody deleted a package
+            # manually, and duplicate paths as well
+            normalized_path = normalize_path(os.path.join(self.basedir, path))
+            if normalized_path in seen or not os.path.exists(normalized_path):
+                log.debug("cleaned up dirty or duplicated %r", path)
+                dirty = True
+                paths.pop()
+                continue
+            seen.add(normalized_path)
+        # remove any trailing empty/blank line
+        while paths and not paths[-1].strip():
+            paths.pop()
+            dirty = True
+        return paths, dirty or (paths and saw_import)
+
+    def _load(self):
+        if os.path.isfile(self.filename):
+            return self._load_raw()
+        return [], False
+
+    def save(self) -> None:
+        """Write changed .pth file back to disk"""
+        # first reload the file
+        last_paths, last_dirty = self._load()
+        # and check that there are no difference with what we have.
+        # there can be difference if someone else has written to the file
+        # since we first loaded it.
+        # we don't want to lose the eventual new paths added since then.
+        for path in last_paths[:]:
+            if path not in self.paths:
+                self.paths.append(path)
+                log.info("detected new path %r", path)
+                last_dirty = True
+            else:
+                last_paths.remove(path)
+        # also, re-check that all paths are still valid before saving them
+        for path in self.paths[:]:
+            if path not in last_paths and not path.startswith((
+                'import ',
+                'from ',
+                '#',
+            )):
+                absolute_path = os.path.join(self.basedir, path)
+                if not os.path.exists(absolute_path):
+                    self.paths.remove(path)
+                    log.info("removing now non-existent path %r", path)
+                    last_dirty = True
+
+        self.dirty |= last_dirty or self.paths != self._init_paths
+        if not self.dirty:
+            return
+
+        rel_paths = list(map(self.make_relative, self.paths))
+        if rel_paths:
+            log.debug("Saving %s", self.filename)
+            lines = self._wrap_lines(rel_paths)
+            data = '\n'.join(lines) + '\n'
+            if os.path.islink(self.filename):
+                os.unlink(self.filename)
+            with open(self.filename, 'wt', encoding=py312.PTH_ENCODING) as f:
+                # ^-- Python<3.13 require encoding="locale" instead of "utf-8",
+                #     see python/cpython#77102.
+                f.write(data)
+        elif os.path.exists(self.filename):
+            log.debug("Deleting empty %s", self.filename)
+            os.unlink(self.filename)
+
+        self.dirty = False
+        self._init_paths[:] = self.paths[:]
+
+    @staticmethod
+    def _wrap_lines(lines):
+        return lines
+
+    def add(self, dist) -> None:
+        """Add `dist` to the distribution map"""
+        new_path = dist.location not in self.paths and (
+            dist.location not in self.sitedirs
+            or
+            # account for '.' being in PYTHONPATH
+            dist.location == os.getcwd()
+        )
+        if new_path:
+            self.paths.append(dist.location)
+            self.dirty = True
+        super().add(dist)
+
+    def remove(self, dist) -> None:
+        """Remove `dist` from the distribution map"""
+        while dist.location in self.paths:
+            self.paths.remove(dist.location)
+            self.dirty = True
+        super().remove(dist)
+
+    def make_relative(self, path):
+        npath, last = os.path.split(normalize_path(path))
+        baselen = len(self.basedir)
+        parts = [last]
+        sep = os.altsep == '/' and '/' or os.sep
+        while len(npath) >= baselen:
+            if npath == self.basedir:
+                parts.append(os.curdir)
+                parts.reverse()
+                return sep.join(parts)
+            npath, last = os.path.split(npath)
+            parts.append(last)
+        else:
+            return path
+
+
+class RewritePthDistributions(PthDistributions):
+    @classmethod
+    def _wrap_lines(cls, lines):
+        yield cls.prelude
+        yield from lines
+        yield cls.postlude
+
+    prelude = _one_liner(
+        """
+        import sys
+        sys.__plen = len(sys.path)
+        """
+    )
+    postlude = _one_liner(
+        """
+        import sys
+        new = sys.path[sys.__plen:]
+        del sys.path[sys.__plen:]
+        p = getattr(sys, '__egginsert', 0)
+        sys.path[p:p] = new
+        sys.__egginsert = p + len(new)
+        """
+    )
+
+
+if os.environ.get('SETUPTOOLS_SYS_PATH_TECHNIQUE', 'raw') == 'rewrite':
+    PthDistributions = RewritePthDistributions  # type: ignore[misc]  # Overwriting type
+
+
+def _first_line_re():
+    """
+    Return a regular expression based on first_line_re suitable for matching
+    strings.
+    """
+    if isinstance(first_line_re.pattern, str):
+        return first_line_re
+
+    # first_line_re in Python >=3.1.4 and >=3.2.1 is a bytes pattern.
+    return re.compile(first_line_re.pattern.decode())
+
+
+def update_dist_caches(dist_path, fix_zipimporter_caches):
+    """
+    Fix any globally cached `dist_path` related data
+
+    `dist_path` should be a path of a newly installed egg distribution (zipped
+    or unzipped).
+
+    sys.path_importer_cache contains finder objects that have been cached when
+    importing data from the original distribution. Any such finders need to be
+    cleared since the replacement distribution might be packaged differently,
+    e.g. a zipped egg distribution might get replaced with an unzipped egg
+    folder or vice versa. Having the old finders cached may then cause Python
+    to attempt loading modules from the replacement distribution using an
+    incorrect loader.
+
+    zipimport.zipimporter objects are Python loaders charged with importing
+    data packaged inside zip archives. If stale loaders referencing the
+    original distribution, are left behind, they can fail to load modules from
+    the replacement distribution. E.g. if an old zipimport.zipimporter instance
+    is used to load data from a new zipped egg archive, it may cause the
+    operation to attempt to locate the requested data in the wrong location -
+    one indicated by the original distribution's zip archive directory
+    information. Such an operation may then fail outright, e.g. report having
+    read a 'bad local file header', or even worse, it may fail silently &
+    return invalid data.
+
+    zipimport._zip_directory_cache contains cached zip archive directory
+    information for all existing zipimport.zipimporter instances and all such
+    instances connected to the same archive share the same cached directory
+    information.
+
+    If asked, and the underlying Python implementation allows it, we can fix
+    all existing zipimport.zipimporter instances instead of having to track
+    them down and remove them one by one, by updating their shared cached zip
+    archive directory information. This, of course, assumes that the
+    replacement distribution is packaged as a zipped egg.
+
+    If not asked to fix existing zipimport.zipimporter instances, we still do
+    our best to clear any remaining zipimport.zipimporter related cached data
+    that might somehow later get used when attempting to load data from the new
+    distribution and thus cause such load operations to fail. Note that when
+    tracking down such remaining stale data, we can not catch every conceivable
+    usage from here, and we clear only those that we know of and have found to
+    cause problems if left alive. Any remaining caches should be updated by
+    whomever is in charge of maintaining them, i.e. they should be ready to
+    handle us replacing their zip archives with new distributions at runtime.
+
+    """
+    # There are several other known sources of stale zipimport.zipimporter
+    # instances that we do not clear here, but might if ever given a reason to
+    # do so:
+    # * Global setuptools pkg_resources.working_set (a.k.a. 'master working
+    # set') may contain distributions which may in turn contain their
+    #   zipimport.zipimporter loaders.
+    # * Several zipimport.zipimporter loaders held by local variables further
+    #   up the function call stack when running the setuptools installation.
+    # * Already loaded modules may have their __loader__ attribute set to the
+    #   exact loader instance used when importing them. Python 3.4 docs state
+    #   that this information is intended mostly for introspection and so is
+    #   not expected to cause us problems.
+    normalized_path = normalize_path(dist_path)
+    _uncache(normalized_path, sys.path_importer_cache)
+    if fix_zipimporter_caches:
+        _replace_zip_directory_cache_data(normalized_path)
+    else:
+        # Here, even though we do not want to fix existing and now stale
+        # zipimporter cache information, we still want to remove it. Related to
+        # Python's zip archive directory information cache, we clear each of
+        # its stale entries in two phases:
+        #   1. Clear the entry so attempting to access zip archive information
+        #      via any existing stale zipimport.zipimporter instances fails.
+        #   2. Remove the entry from the cache so any newly constructed
+        #      zipimport.zipimporter instances do not end up using old stale
+        #      zip archive directory information.
+        # This whole stale data removal step does not seem strictly necessary,
+        # but has been left in because it was done before we started replacing
+        # the zip archive directory information cache content if possible, and
+        # there are no relevant unit tests that we can depend on to tell us if
+        # this is really needed.
+        _remove_and_clear_zip_directory_cache_data(normalized_path)
+
+
+def _collect_zipimporter_cache_entries(normalized_path, cache):
+    """
+    Return zipimporter cache entry keys related to a given normalized path.
+
+    Alternative path spellings (e.g. those using different character case or
+    those using alternative path separators) related to the same path are
+    included. Any sub-path entries are included as well, i.e. those
+    corresponding to zip archives embedded in other zip archives.
+
+    """
+    result = []
+    prefix_len = len(normalized_path)
+    for p in cache:
+        np = normalize_path(p)
+        if np.startswith(normalized_path) and np[prefix_len : prefix_len + 1] in (
+            os.sep,
+            '',
+        ):
+            result.append(p)
+    return result
+
+
+def _update_zipimporter_cache(normalized_path, cache, updater=None):
+    """
+    Update zipimporter cache data for a given normalized path.
+
+    Any sub-path entries are processed as well, i.e. those corresponding to zip
+    archives embedded in other zip archives.
+
+    Given updater is a callable taking a cache entry key and the original entry
+    (after already removing the entry from the cache), and expected to update
+    the entry and possibly return a new one to be inserted in its place.
+    Returning None indicates that the entry should not be replaced with a new
+    one. If no updater is given, the cache entries are simply removed without
+    any additional processing, the same as if the updater simply returned None.
+
+    """
+    for p in _collect_zipimporter_cache_entries(normalized_path, cache):
+        # N.B. pypy's custom zipimport._zip_directory_cache implementation does
+        # not support the complete dict interface:
+        # * Does not support item assignment, thus not allowing this function
+        #    to be used only for removing existing cache entries.
+        #  * Does not support the dict.pop() method, forcing us to use the
+        #    get/del patterns instead. For more detailed information see the
+        #    following links:
+        #      https://github.com/pypa/setuptools/issues/202#issuecomment-202913420
+        #      https://foss.heptapod.net/pypy/pypy/-/blob/144c4e65cb6accb8e592f3a7584ea38265d1873c/pypy/module/zipimport/interp_zipimport.py
+        old_entry = cache[p]
+        del cache[p]
+        new_entry = updater and updater(p, old_entry)
+        if new_entry is not None:
+            cache[p] = new_entry
+
+
+def _uncache(normalized_path, cache):
+    _update_zipimporter_cache(normalized_path, cache)
+
+
+def _remove_and_clear_zip_directory_cache_data(normalized_path):
+    def clear_and_remove_cached_zip_archive_directory_data(path, old_entry):
+        old_entry.clear()
+
+    _update_zipimporter_cache(
+        normalized_path,
+        zipimport._zip_directory_cache,
+        updater=clear_and_remove_cached_zip_archive_directory_data,
+    )
+
+
+# PyPy Python implementation does not allow directly writing to the
+# zipimport._zip_directory_cache and so prevents us from attempting to correct
+# its content. The best we can do there is clear the problematic cache content
+# and have PyPy repopulate it as needed. The downside is that if there are any
+# stale zipimport.zipimporter instances laying around, attempting to use them
+# will fail due to not having its zip archive directory information available
+# instead of being automatically corrected to use the new correct zip archive
+# directory information.
+if '__pypy__' in sys.builtin_module_names:
+    _replace_zip_directory_cache_data = _remove_and_clear_zip_directory_cache_data
+else:
+
+    def _replace_zip_directory_cache_data(normalized_path):
+        def replace_cached_zip_archive_directory_data(path, old_entry):
+            # N.B. In theory, we could load the zip directory information just
+            # once for all updated path spellings, and then copy it locally and
+            # update its contained path strings to contain the correct
+            # spelling, but that seems like a way too invasive move (this cache
+            # structure is not officially documented anywhere and could in
+            # theory change with new Python releases) for no significant
+            # benefit.
+            old_entry.clear()
+            zipimport.zipimporter(path)
+            old_entry.update(zipimport._zip_directory_cache[path])
+            return old_entry
+
+        _update_zipimporter_cache(
+            normalized_path,
+            zipimport._zip_directory_cache,
+            updater=replace_cached_zip_archive_directory_data,
+        )
+
+
+def is_python(text, filename='<string>'):
+    "Is this string a valid Python script?"
+    try:
+        compile(text, filename, 'exec')
+    except (SyntaxError, TypeError):
+        return False
+    else:
+        return True
+
+
+def is_sh(executable):
+    """Determine if the specified executable is a .sh (contains a #! line)"""
+    try:
+        with open(executable, encoding='latin-1') as fp:
+            magic = fp.read(2)
+    except OSError:
+        return executable
+    return magic == '#!'
+
+
+def nt_quote_arg(arg):
+    """Quote a command line argument according to Windows parsing rules"""
+    return subprocess.list2cmdline([arg])
+
+
+def is_python_script(script_text, filename):
+    """Is this text, as a whole, a Python script? (as opposed to shell/bat/etc."""
+    if filename.endswith('.py') or filename.endswith('.pyw'):
+        return True  # extension says it's Python
+    if is_python(script_text, filename):
+        return True  # it's syntactically valid Python
+    if script_text.startswith('#!'):
+        # It begins with a '#!' line, so check if 'python' is in it somewhere
+        return 'python' in script_text.splitlines()[0].lower()
+
+    return False  # Not any Python I can recognize
+
+
+class _SplitArgs(TypedDict, total=False):
+    comments: bool
+    posix: bool
+
+
+class CommandSpec(list):
+    """
+    A command spec for a #! header, specified as a list of arguments akin to
+    those passed to Popen.
+    """
+
+    options: list[str] = []
+    split_args = _SplitArgs()
+
+    @classmethod
+    def best(cls):
+        """
+        Choose the best CommandSpec class based on environmental conditions.
+        """
+        return cls
+
+    @classmethod
+    def _sys_executable(cls):
+        _default = os.path.normpath(sys.executable)
+        return os.environ.get('__PYVENV_LAUNCHER__', _default)
+
+    @classmethod
+    def from_param(cls, param: Self | str | Iterable[str] | None) -> Self:
+        """
+        Construct a CommandSpec from a parameter to build_scripts, which may
+        be None.
+        """
+        if isinstance(param, cls):
+            return param
+        if isinstance(param, str):
+            return cls.from_string(param)
+        if isinstance(param, Iterable):
+            return cls(param)
+        if param is None:
+            return cls.from_environment()
+        raise TypeError(f"Argument has an unsupported type {type(param)}")
+
+    @classmethod
+    def from_environment(cls):
+        return cls([cls._sys_executable()])
+
+    @classmethod
+    def from_string(cls, string: str) -> Self:
+        """
+        Construct a command spec from a simple string representing a command
+        line parseable by shlex.split.
+        """
+        items = shlex.split(string, **cls.split_args)
+        return cls(items)
+
+    def install_options(self, script_text: str):
+        self.options = shlex.split(self._extract_options(script_text))
+        cmdline = subprocess.list2cmdline(self)
+        if not isascii(cmdline):
+            self.options[:0] = ['-x']
+
+    @staticmethod
+    def _extract_options(orig_script):
+        """
+        Extract any options from the first line of the script.
+        """
+        first = (orig_script + '\n').splitlines()[0]
+        match = _first_line_re().match(first)
+        options = match.group(1) or '' if match else ''
+        return options.strip()
+
+    def as_header(self):
+        return self._render(self + list(self.options))
+
+    @staticmethod
+    def _strip_quotes(item):
+        _QUOTES = '"\''
+        for q in _QUOTES:
+            if item.startswith(q) and item.endswith(q):
+                return item[1:-1]
+        return item
+
+    @staticmethod
+    def _render(items):
+        cmdline = subprocess.list2cmdline(
+            CommandSpec._strip_quotes(item.strip()) for item in items
+        )
+        return '#!' + cmdline + '\n'
+
+
+# For pbr compat; will be removed in a future version.
+sys_executable = CommandSpec._sys_executable()
+
+
+class WindowsCommandSpec(CommandSpec):
+    split_args = _SplitArgs(posix=False)
+
+
+class ScriptWriter:
+    """
+    Encapsulates behavior around writing entry point scripts for console and
+    gui apps.
+    """
+
+    template = textwrap.dedent(
+        r"""
+        # EASY-INSTALL-ENTRY-SCRIPT: %(spec)r,%(group)r,%(name)r
+        import re
+        import sys
+
+        # for compatibility with easy_install; see #2198
+        __requires__ = %(spec)r
+
+        try:
+            from importlib.metadata import distribution
+        except ImportError:
+            try:
+                from importlib_metadata import distribution
+            except ImportError:
+                from pkg_resources import load_entry_point
+
+
+        def importlib_load_entry_point(spec, group, name):
+            dist_name, _, _ = spec.partition('==')
+            matches = (
+                entry_point
+                for entry_point in distribution(dist_name).entry_points
+                if entry_point.group == group and entry_point.name == name
+            )
+            return next(matches).load()
+
+
+        globals().setdefault('load_entry_point', importlib_load_entry_point)
+
+
+        if __name__ == '__main__':
+            sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
+            sys.exit(load_entry_point(%(spec)r, %(group)r, %(name)r)())
+        """
+    ).lstrip()
+
+    command_spec_class = CommandSpec
+
+    @classmethod
+    def get_args(cls, dist, header=None):
+        """
+        Yield write_script() argument tuples for a distribution's
+        console_scripts and gui_scripts entry points.
+        """
+        if header is None:
+            header = cls.get_header()
+        spec = str(dist.as_requirement())
+        for type_ in 'console', 'gui':
+            group = type_ + '_scripts'
+            for name in dist.get_entry_map(group).keys():
+                cls._ensure_safe_name(name)
+                script_text = cls.template % locals()
+                args = cls._get_script_args(type_, name, header, script_text)
+                yield from args
+
+    @staticmethod
+    def _ensure_safe_name(name):
+        """
+        Prevent paths in *_scripts entry point names.
+        """
+        has_path_sep = re.search(r'[\\/]', name)
+        if has_path_sep:
+            raise ValueError("Path separators not allowed in script names")
+
+    @classmethod
+    def best(cls):
+        """
+        Select the best ScriptWriter for this environment.
+        """
+        if sys.platform == 'win32' or (os.name == 'java' and os._name == 'nt'):
+            return WindowsScriptWriter.best()
+        else:
+            return cls
+
+    @classmethod
+    def _get_script_args(cls, type_, name, header, script_text):
+        # Simply write the stub with no extension.
+        yield (name, header + script_text)
+
+    @classmethod
+    def get_header(
+        cls,
+        script_text: str = "",
+        executable: str | CommandSpec | Iterable[str] | None = None,
+    ) -> str:
+        """Create a #! line, getting options (if any) from script_text"""
+        cmd = cls.command_spec_class.best().from_param(executable)
+        cmd.install_options(script_text)
+        return cmd.as_header()
+
+
+class WindowsScriptWriter(ScriptWriter):
+    command_spec_class = WindowsCommandSpec
+
+    @classmethod
+    def best(cls):
+        """
+        Select the best ScriptWriter suitable for Windows
+        """
+        writer_lookup = dict(
+            executable=WindowsExecutableLauncherWriter,
+            natural=cls,
+        )
+        # for compatibility, use the executable launcher by default
+        launcher = os.environ.get('SETUPTOOLS_LAUNCHER', 'executable')
+        return writer_lookup[launcher]
+
+    @classmethod
+    def _get_script_args(cls, type_, name, header, script_text):
+        "For Windows, add a .py extension"
+        ext = dict(console='.pya', gui='.pyw')[type_]
+        if ext not in os.environ['PATHEXT'].lower().split(';'):
+            msg = (
+                "{ext} not listed in PATHEXT; scripts will not be "
+                "recognized as executables."
+            ).format(**locals())
+            SetuptoolsWarning.emit(msg)
+        old = ['.pya', '.py', '-script.py', '.pyc', '.pyo', '.pyw', '.exe']
+        old.remove(ext)
+        header = cls._adjust_header(type_, header)
+        blockers = [name + x for x in old]
+        yield name + ext, header + script_text, 't', blockers
+
+    @classmethod
+    def _adjust_header(cls, type_, orig_header):
+        """
+        Make sure 'pythonw' is used for gui and 'python' is used for
+        console (regardless of what sys.executable is).
+        """
+        pattern = 'pythonw.exe'
+        repl = 'python.exe'
+        if type_ == 'gui':
+            pattern, repl = repl, pattern
+        pattern_ob = re.compile(re.escape(pattern), re.IGNORECASE)
+        new_header = pattern_ob.sub(string=orig_header, repl=repl)
+        return new_header if cls._use_header(new_header) else orig_header
+
+    @staticmethod
+    def _use_header(new_header):
+        """
+        Should _adjust_header use the replaced header?
+
+        On non-windows systems, always use. On
+        Windows systems, only use the replaced header if it resolves
+        to an executable on the system.
+        """
+        clean_header = new_header[2:-1].strip('"')
+        return sys.platform != 'win32' or shutil.which(clean_header)
+
+
+class WindowsExecutableLauncherWriter(WindowsScriptWriter):
+    @classmethod
+    def _get_script_args(cls, type_, name, header, script_text):
+        """
+        For Windows, add a .py extension and an .exe launcher
+        """
+        if type_ == 'gui':
+            launcher_type = 'gui'
+            ext = '-script.pyw'
+            old = ['.pyw']
+        else:
+            launcher_type = 'cli'
+            ext = '-script.py'
+            old = ['.py', '.pyc', '.pyo']
+        hdr = cls._adjust_header(type_, header)
+        blockers = [name + x for x in old]
+        yield (name + ext, hdr + script_text, 't', blockers)
+        yield (
+            name + '.exe',
+            get_win_launcher(launcher_type),
+            'b',  # write in binary mode
+        )
+        if not is_64bit():
+            # install a manifest for the launcher to prevent Windows
+            # from detecting it as an installer (which it will for
+            #  launchers like easy_install.exe). Consider only
+            #  adding a manifest for launchers detected as installers.
+            #  See Distribute #143 for details.
+            m_name = name + '.exe.manifest'
+            yield (m_name, load_launcher_manifest(name), 't')
+
+
+def get_win_launcher(type):
+    """
+    Load the Windows launcher (executable) suitable for launching a script.
+
+    `type` should be either 'cli' or 'gui'
+
+    Returns the executable as a byte string.
+    """
+    launcher_fn = f'{type}.exe'
+    if is_64bit():
+        if get_platform() == "win-arm64":
+            launcher_fn = launcher_fn.replace(".", "-arm64.")
+        else:
+            launcher_fn = launcher_fn.replace(".", "-64.")
+    else:
+        launcher_fn = launcher_fn.replace(".", "-32.")
+    return resource_string('setuptools', launcher_fn)
+
+
+def load_launcher_manifest(name):
+    manifest = pkg_resources.resource_string(__name__, 'launcher manifest.xml')
+    return manifest.decode('utf-8') % vars()
+
+
+def current_umask():
+    tmp = os.umask(0o022)
+    os.umask(tmp)
+    return tmp
+
+
+def only_strs(values):
+    """
+    Exclude non-str values. Ref #3063.
+    """
+    return filter(lambda val: isinstance(val, str), values)
+
+
+def _read_pth(fullname: str) -> str:
+    # Python<3.13 require encoding="locale" instead of "utf-8", see python/cpython#77102
+    # In the case old versions of setuptools are producing `pth` files with
+    # different encodings that might be problematic... So we fallback to "locale".
+
+    try:
+        with open(fullname, encoding=py312.PTH_ENCODING) as f:
+            return f.read()
+    except UnicodeDecodeError:  # pragma: no cover
+        # This error may only happen for Python >= 3.13
+        # TODO: Possible deprecation warnings to be added in the future:
+        #       ``.pth file {fullname!r} is not UTF-8.``
+        #       Your environment contain {fullname!r} that cannot be read as UTF-8.
+        #       This is likely to have been produced with an old version of setuptools.
+        #       Please be mindful that this is deprecated and in the future, non-utf8
+        #       .pth files may cause setuptools to fail.
+        with open(fullname, encoding=py39.LOCALE_ENCODING) as f:
+            return f.read()
+
+
+class EasyInstallDeprecationWarning(SetuptoolsDeprecationWarning):
+    _SUMMARY = "easy_install command is deprecated."
+    _DETAILS = """
+    Please avoid running ``setup.py`` and ``easy_install``.
+    Instead, use pypa/build, pypa/installer or other
+    standards-based tools.
+    """
+    _SEE_URL = "https://github.com/pypa/setuptools/issues/917"
+    # _DUE_DATE not defined yet
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/editable_wheel.py b/.venv/lib/python3.12/site-packages/setuptools/command/editable_wheel.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a544ec258196616a4242b679c20b061c1c4010d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/editable_wheel.py
@@ -0,0 +1,925 @@
+"""
+Create a wheel that, when installed, will make the source package 'editable'
+(add it to the interpreter's path, including metadata) per PEP 660. Replaces
+'setup.py develop'.
+
+.. note::
+   One of the mechanisms briefly mentioned in PEP 660 to implement editable installs is
+   to create a separated directory inside ``build`` and use a .pth file to point to that
+   directory. In the context of this file such directory is referred as
+   *auxiliary build directory* or ``auxiliary_dir``.
+"""
+
+from __future__ import annotations
+
+import io
+import logging
+import os
+import shutil
+import traceback
+from collections.abc import Iterable, Iterator, Mapping
+from contextlib import suppress
+from enum import Enum
+from inspect import cleandoc
+from itertools import chain, starmap
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from types import TracebackType
+from typing import TYPE_CHECKING, Protocol, TypeVar, cast
+
+from .. import Command, _normalization, _path, _shutil, errors, namespaces
+from .._path import StrPath
+from ..compat import py312
+from ..discovery import find_package_path
+from ..dist import Distribution
+from ..warnings import InformationOnly, SetuptoolsDeprecationWarning, SetuptoolsWarning
+from .build import build as build_cls
+from .build_py import build_py as build_py_cls
+from .dist_info import dist_info as dist_info_cls
+from .egg_info import egg_info as egg_info_cls
+from .install import install as install_cls
+from .install_scripts import install_scripts as install_scripts_cls
+
+if TYPE_CHECKING:
+    from typing_extensions import Self
+
+    from .._vendor.wheel.wheelfile import WheelFile
+
+_P = TypeVar("_P", bound=StrPath)
+_logger = logging.getLogger(__name__)
+
+
+class _EditableMode(Enum):
+    """
+    Possible editable installation modes:
+    `lenient` (new files automatically added to the package - DEFAULT);
+    `strict` (requires a new installation when files are added/removed); or
+    `compat` (attempts to emulate `python setup.py develop` - DEPRECATED).
+    """
+
+    STRICT = "strict"
+    LENIENT = "lenient"
+    COMPAT = "compat"  # TODO: Remove `compat` after Dec/2022.
+
+    @classmethod
+    def convert(cls, mode: str | None) -> _EditableMode:
+        if not mode:
+            return _EditableMode.LENIENT  # default
+
+        _mode = mode.upper()
+        if _mode not in _EditableMode.__members__:
+            raise errors.OptionError(f"Invalid editable mode: {mode!r}. Try: 'strict'.")
+
+        if _mode == "COMPAT":
+            SetuptoolsDeprecationWarning.emit(
+                "Compat editable installs",
+                """
+                The 'compat' editable mode is transitional and will be removed
+                in future versions of `setuptools`.
+                Please adapt your code accordingly to use either the 'strict' or the
+                'lenient' modes.
+                """,
+                see_docs="userguide/development_mode.html",
+                # TODO: define due_date
+                # There is a series of shortcomings with the available editable install
+                # methods, and they are very controversial. This is something that still
+                # needs work.
+                # Moreover, `pip` is still hiding this warning, so users are not aware.
+            )
+
+        return _EditableMode[_mode]
+
+
+_STRICT_WARNING = """
+New or renamed files may not be automatically picked up without a new installation.
+"""
+
+_LENIENT_WARNING = """
+Options like `package-data`, `include/exclude-package-data` or
+`packages.find.exclude/include` may have no effect.
+"""
+
+
+class editable_wheel(Command):
+    """Build 'editable' wheel for development.
+    This command is private and reserved for internal use of setuptools,
+    users should rely on ``setuptools.build_meta`` APIs.
+    """
+
+    description = "DO NOT CALL DIRECTLY, INTERNAL ONLY: create PEP 660 editable wheel"
+
+    user_options = [
+        ("dist-dir=", "d", "directory to put final built distributions in"),
+        ("dist-info-dir=", "I", "path to a pre-build .dist-info directory"),
+        ("mode=", None, cleandoc(_EditableMode.__doc__ or "")),
+    ]
+
+    def initialize_options(self):
+        self.dist_dir = None
+        self.dist_info_dir = None
+        self.project_dir = None
+        self.mode = None
+
+    def finalize_options(self) -> None:
+        dist = self.distribution
+        self.project_dir = dist.src_root or os.curdir
+        self.package_dir = dist.package_dir or {}
+        self.dist_dir = Path(self.dist_dir or os.path.join(self.project_dir, "dist"))
+
+    def run(self) -> None:
+        try:
+            self.dist_dir.mkdir(exist_ok=True)
+            self._ensure_dist_info()
+
+            # Add missing dist_info files
+            self.reinitialize_command("bdist_wheel")
+            bdist_wheel = self.get_finalized_command("bdist_wheel")
+            bdist_wheel.write_wheelfile(self.dist_info_dir)
+
+            self._create_wheel_file(bdist_wheel)
+        except Exception:
+            traceback.print_exc()
+            project = self.distribution.name or self.distribution.get_name()
+            _DebuggingTips.emit(project=project)
+            raise
+
+    def _ensure_dist_info(self):
+        if self.dist_info_dir is None:
+            dist_info = cast(dist_info_cls, self.reinitialize_command("dist_info"))
+            dist_info.output_dir = self.dist_dir
+            dist_info.ensure_finalized()
+            dist_info.run()
+            self.dist_info_dir = dist_info.dist_info_dir
+        else:
+            assert str(self.dist_info_dir).endswith(".dist-info")
+            assert Path(self.dist_info_dir, "METADATA").exists()
+
+    def _install_namespaces(self, installation_dir, pth_prefix):
+        # XXX: Only required to support the deprecated namespace practice
+        dist = self.distribution
+        if not dist.namespace_packages:
+            return
+
+        src_root = Path(self.project_dir, self.package_dir.get("", ".")).resolve()
+        installer = _NamespaceInstaller(dist, installation_dir, pth_prefix, src_root)
+        installer.install_namespaces()
+
+    def _find_egg_info_dir(self) -> str | None:
+        parent_dir = Path(self.dist_info_dir).parent if self.dist_info_dir else Path()
+        candidates = map(str, parent_dir.glob("*.egg-info"))
+        return next(candidates, None)
+
+    def _configure_build(
+        self, name: str, unpacked_wheel: StrPath, build_lib: StrPath, tmp_dir: StrPath
+    ):
+        """Configure commands to behave in the following ways:
+
+        - Build commands can write to ``build_lib`` if they really want to...
+          (but this folder is expected to be ignored and modules are expected to live
+          in the project directory...)
+        - Binary extensions should be built in-place (editable_mode = True)
+        - Data/header/script files are not part of the "editable" specification
+          so they are written directly to the unpacked_wheel directory.
+        """
+        # Non-editable files (data, headers, scripts) are written directly to the
+        # unpacked_wheel
+
+        dist = self.distribution
+        wheel = str(unpacked_wheel)
+        build_lib = str(build_lib)
+        data = str(Path(unpacked_wheel, f"{name}.data", "data"))
+        headers = str(Path(unpacked_wheel, f"{name}.data", "headers"))
+        scripts = str(Path(unpacked_wheel, f"{name}.data", "scripts"))
+
+        # egg-info may be generated again to create a manifest (used for package data)
+        egg_info = cast(
+            egg_info_cls, dist.reinitialize_command("egg_info", reinit_subcommands=True)
+        )
+        egg_info.egg_base = str(tmp_dir)
+        egg_info.ignore_egg_info_in_manifest = True
+
+        build = cast(
+            build_cls, dist.reinitialize_command("build", reinit_subcommands=True)
+        )
+        install = cast(
+            install_cls, dist.reinitialize_command("install", reinit_subcommands=True)
+        )
+
+        build.build_platlib = build.build_purelib = build.build_lib = build_lib
+        install.install_purelib = install.install_platlib = install.install_lib = wheel
+        install.install_scripts = build.build_scripts = scripts
+        install.install_headers = headers
+        install.install_data = data
+
+        install_scripts = cast(
+            install_scripts_cls, dist.get_command_obj("install_scripts")
+        )
+        install_scripts.no_ep = True
+
+        build.build_temp = str(tmp_dir)
+
+        build_py = cast(build_py_cls, dist.get_command_obj("build_py"))
+        build_py.compile = False
+        build_py.existing_egg_info_dir = self._find_egg_info_dir()
+
+        self._set_editable_mode()
+
+        build.ensure_finalized()
+        install.ensure_finalized()
+
+    def _set_editable_mode(self):
+        """Set the ``editable_mode`` flag in the build sub-commands"""
+        dist = self.distribution
+        build = dist.get_command_obj("build")
+        for cmd_name in build.get_sub_commands():
+            cmd = dist.get_command_obj(cmd_name)
+            if hasattr(cmd, "editable_mode"):
+                cmd.editable_mode = True
+            elif hasattr(cmd, "inplace"):
+                cmd.inplace = True  # backward compatibility with distutils
+
+    def _collect_build_outputs(self) -> tuple[list[str], dict[str, str]]:
+        files: list[str] = []
+        mapping: dict[str, str] = {}
+        build = self.get_finalized_command("build")
+
+        for cmd_name in build.get_sub_commands():
+            cmd = self.get_finalized_command(cmd_name)
+            if hasattr(cmd, "get_outputs"):
+                files.extend(cmd.get_outputs() or [])
+            if hasattr(cmd, "get_output_mapping"):
+                mapping.update(cmd.get_output_mapping() or {})
+
+        return files, mapping
+
+    def _run_build_commands(
+        self,
+        dist_name: str,
+        unpacked_wheel: StrPath,
+        build_lib: StrPath,
+        tmp_dir: StrPath,
+    ) -> tuple[list[str], dict[str, str]]:
+        self._configure_build(dist_name, unpacked_wheel, build_lib, tmp_dir)
+        self._run_build_subcommands()
+        files, mapping = self._collect_build_outputs()
+        self._run_install("headers")
+        self._run_install("scripts")
+        self._run_install("data")
+        return files, mapping
+
+    def _run_build_subcommands(self) -> None:
+        """
+        Issue #3501 indicates that some plugins/customizations might rely on:
+
+        1. ``build_py`` not running
+        2. ``build_py`` always copying files to ``build_lib``
+
+        However both these assumptions may be false in editable_wheel.
+        This method implements a temporary workaround to support the ecosystem
+        while the implementations catch up.
+        """
+        # TODO: Once plugins/customizations had the chance to catch up, replace
+        #       `self._run_build_subcommands()` with `self.run_command("build")`.
+        #       Also remove _safely_run, TestCustomBuildPy. Suggested date: Aug/2023.
+        build = self.get_finalized_command("build")
+        for name in build.get_sub_commands():
+            cmd = self.get_finalized_command(name)
+            if name == "build_py" and type(cmd) is not build_py_cls:
+                self._safely_run(name)
+            else:
+                self.run_command(name)
+
+    def _safely_run(self, cmd_name: str):
+        try:
+            return self.run_command(cmd_name)
+        except Exception:
+            SetuptoolsDeprecationWarning.emit(
+                "Customization incompatible with editable install",
+                f"""
+                {traceback.format_exc()}
+
+                If you are seeing this warning it is very likely that a setuptools
+                plugin or customization overrides the `{cmd_name}` command, without
+                taking into consideration how editable installs run build steps
+                starting from setuptools v64.0.0.
+
+                Plugin authors and developers relying on custom build steps are
+                encouraged to update their `{cmd_name}` implementation considering the
+                information about editable installs in
+                https://setuptools.pypa.io/en/latest/userguide/extension.html.
+
+                For the time being `setuptools` will silence this error and ignore
+                the faulty command, but this behavior will change in future versions.
+                """,
+                # TODO: define due_date
+                # There is a series of shortcomings with the available editable install
+                # methods, and they are very controversial. This is something that still
+                # needs work.
+            )
+
+    def _create_wheel_file(self, bdist_wheel):
+        from wheel.wheelfile import WheelFile
+
+        dist_info = self.get_finalized_command("dist_info")
+        dist_name = dist_info.name
+        tag = "-".join(bdist_wheel.get_tag())
+        build_tag = "0.editable"  # According to PEP 427 needs to start with digit
+        archive_name = f"{dist_name}-{build_tag}-{tag}.whl"
+        wheel_path = Path(self.dist_dir, archive_name)
+        if wheel_path.exists():
+            wheel_path.unlink()
+
+        unpacked_wheel = TemporaryDirectory(suffix=archive_name)
+        build_lib = TemporaryDirectory(suffix=".build-lib")
+        build_tmp = TemporaryDirectory(suffix=".build-temp")
+
+        with unpacked_wheel as unpacked, build_lib as lib, build_tmp as tmp:
+            unpacked_dist_info = Path(unpacked, Path(self.dist_info_dir).name)
+            shutil.copytree(self.dist_info_dir, unpacked_dist_info)
+            self._install_namespaces(unpacked, dist_name)
+            files, mapping = self._run_build_commands(dist_name, unpacked, lib, tmp)
+            strategy = self._select_strategy(dist_name, tag, lib)
+            with strategy, WheelFile(wheel_path, "w") as wheel_obj:
+                strategy(wheel_obj, files, mapping)
+                wheel_obj.write_files(unpacked)
+
+        return wheel_path
+
+    def _run_install(self, category: str):
+        has_category = getattr(self.distribution, f"has_{category}", None)
+        if has_category and has_category():
+            _logger.info(f"Installing {category} as non editable")
+            self.run_command(f"install_{category}")
+
+    def _select_strategy(
+        self,
+        name: str,
+        tag: str,
+        build_lib: StrPath,
+    ) -> EditableStrategy:
+        """Decides which strategy to use to implement an editable installation."""
+        build_name = f"__editable__.{name}-{tag}"
+        project_dir = Path(self.project_dir)
+        mode = _EditableMode.convert(self.mode)
+
+        if mode is _EditableMode.STRICT:
+            auxiliary_dir = _empty_dir(Path(self.project_dir, "build", build_name))
+            return _LinkTree(self.distribution, name, auxiliary_dir, build_lib)
+
+        packages = _find_packages(self.distribution)
+        has_simple_layout = _simple_layout(packages, self.package_dir, project_dir)
+        is_compat_mode = mode is _EditableMode.COMPAT
+        if set(self.package_dir) == {""} and has_simple_layout or is_compat_mode:
+            # src-layout(ish) is relatively safe for a simple pth file
+            src_dir = self.package_dir.get("", ".")
+            return _StaticPth(self.distribution, name, [Path(project_dir, src_dir)])
+
+        # Use a MetaPathFinder to avoid adding accidental top-level packages/modules
+        return _TopLevelFinder(self.distribution, name)
+
+
+class EditableStrategy(Protocol):
+    def __call__(
+        self, wheel: WheelFile, files: list[str], mapping: Mapping[str, str]
+    ) -> object: ...
+    def __enter__(self) -> Self: ...
+    def __exit__(
+        self,
+        _exc_type: type[BaseException] | None,
+        _exc_value: BaseException | None,
+        _traceback: TracebackType | None,
+    ) -> object: ...
+
+
+class _StaticPth:
+    def __init__(self, dist: Distribution, name: str, path_entries: list[Path]) -> None:
+        self.dist = dist
+        self.name = name
+        self.path_entries = path_entries
+
+    def __call__(self, wheel: WheelFile, files: list[str], mapping: Mapping[str, str]):
+        entries = "\n".join(str(p.resolve()) for p in self.path_entries)
+        contents = _encode_pth(f"{entries}\n")
+        wheel.writestr(f"__editable__.{self.name}.pth", contents)
+
+    def __enter__(self) -> Self:
+        msg = f"""
+        Editable install will be performed using .pth file to extend `sys.path` with:
+        {list(map(os.fspath, self.path_entries))!r}
+        """
+        _logger.warning(msg + _LENIENT_WARNING)
+        return self
+
+    def __exit__(
+        self,
+        _exc_type: object,
+        _exc_value: object,
+        _traceback: object,
+    ) -> None:
+        pass
+
+
+class _LinkTree(_StaticPth):
+    """
+    Creates a ``.pth`` file that points to a link tree in the ``auxiliary_dir``.
+
+    This strategy will only link files (not dirs), so it can be implemented in
+    any OS, even if that means using hardlinks instead of symlinks.
+
+    By collocating ``auxiliary_dir`` and the original source code, limitations
+    with hardlinks should be avoided.
+    """
+
+    def __init__(
+        self,
+        dist: Distribution,
+        name: str,
+        auxiliary_dir: StrPath,
+        build_lib: StrPath,
+    ) -> None:
+        self.auxiliary_dir = Path(auxiliary_dir)
+        self.build_lib = Path(build_lib).resolve()
+        self._file = dist.get_command_obj("build_py").copy_file
+        super().__init__(dist, name, [self.auxiliary_dir])
+
+    def __call__(self, wheel: WheelFile, files: list[str], mapping: Mapping[str, str]):
+        self._create_links(files, mapping)
+        super().__call__(wheel, files, mapping)
+
+    def _normalize_output(self, file: str) -> str | None:
+        # Files relative to build_lib will be normalized to None
+        with suppress(ValueError):
+            path = Path(file).resolve().relative_to(self.build_lib)
+            return str(path).replace(os.sep, '/')
+        return None
+
+    def _create_file(self, relative_output: str, src_file: str, link=None):
+        dest = self.auxiliary_dir / relative_output
+        if not dest.parent.is_dir():
+            dest.parent.mkdir(parents=True)
+        self._file(src_file, dest, link=link)
+
+    def _create_links(self, outputs, output_mapping: Mapping[str, str]):
+        self.auxiliary_dir.mkdir(parents=True, exist_ok=True)
+        link_type = "sym" if _can_symlink_files(self.auxiliary_dir) else "hard"
+        normalised = ((self._normalize_output(k), v) for k, v in output_mapping.items())
+        # remove files that are not relative to build_lib
+        mappings = {k: v for k, v in normalised if k is not None}
+
+        for output in outputs:
+            relative = self._normalize_output(output)
+            if relative and relative not in mappings:
+                self._create_file(relative, output)
+
+        for relative, src in mappings.items():
+            self._create_file(relative, src, link=link_type)
+
+    def __enter__(self) -> Self:
+        msg = "Strict editable install will be performed using a link tree.\n"
+        _logger.warning(msg + _STRICT_WARNING)
+        return self
+
+    def __exit__(
+        self,
+        _exc_type: object,
+        _exc_value: object,
+        _traceback: object,
+    ) -> None:
+        msg = f"""\n
+        Strict editable installation performed using the auxiliary directory:
+            {self.auxiliary_dir}
+
+        Please be careful to not remove this directory, otherwise you might not be able
+        to import/use your package.
+        """
+        InformationOnly.emit("Editable installation.", msg)
+
+
+class _TopLevelFinder:
+    def __init__(self, dist: Distribution, name: str) -> None:
+        self.dist = dist
+        self.name = name
+
+    def template_vars(self) -> tuple[str, str, dict[str, str], dict[str, list[str]]]:
+        src_root = self.dist.src_root or os.curdir
+        top_level = chain(_find_packages(self.dist), _find_top_level_modules(self.dist))
+        package_dir = self.dist.package_dir or {}
+        roots = _find_package_roots(top_level, package_dir, src_root)
+
+        namespaces_ = dict(
+            chain(
+                _find_namespaces(self.dist.packages or [], roots),
+                ((ns, []) for ns in _find_virtual_namespaces(roots)),
+            )
+        )
+
+        legacy_namespaces = {
+            pkg: find_package_path(pkg, roots, self.dist.src_root or "")
+            for pkg in self.dist.namespace_packages or []
+        }
+
+        mapping = {**roots, **legacy_namespaces}
+        # ^-- We need to explicitly add the legacy_namespaces to the mapping to be
+        #     able to import their modules even if another package sharing the same
+        #     namespace is installed in a conventional (non-editable) way.
+
+        name = f"__editable__.{self.name}.finder"
+        finder = _normalization.safe_identifier(name)
+        return finder, name, mapping, namespaces_
+
+    def get_implementation(self) -> Iterator[tuple[str, bytes]]:
+        finder, name, mapping, namespaces_ = self.template_vars()
+
+        content = bytes(_finder_template(name, mapping, namespaces_), "utf-8")
+        yield (f"{finder}.py", content)
+
+        content = _encode_pth(f"import {finder}; {finder}.install()")
+        yield (f"__editable__.{self.name}.pth", content)
+
+    def __call__(self, wheel: WheelFile, files: list[str], mapping: Mapping[str, str]):
+        for file, content in self.get_implementation():
+            wheel.writestr(file, content)
+
+    def __enter__(self) -> Self:
+        msg = "Editable install will be performed using a meta path finder.\n"
+        _logger.warning(msg + _LENIENT_WARNING)
+        return self
+
+    def __exit__(
+        self,
+        _exc_type: object,
+        _exc_value: object,
+        _traceback: object,
+    ) -> None:
+        msg = """\n
+        Please be careful with folders in your working directory with the same
+        name as your package as they may take precedence during imports.
+        """
+        InformationOnly.emit("Editable installation.", msg)
+
+
+def _encode_pth(content: str) -> bytes:
+    """
+    Prior to Python 3.13 (see https://github.com/python/cpython/issues/77102),
+    .pth files are always read with 'locale' encoding, the recommendation
+    from the cpython core developers is to write them as ``open(path, "w")``
+    and ignore warnings (see python/cpython#77102, pypa/setuptools#3937).
+    This function tries to simulate this behavior without having to create an
+    actual file, in a way that supports a range of active Python versions.
+    (There seems to be some variety in the way different version of Python handle
+    ``encoding=None``, not all of them use ``locale.getpreferredencoding(False)``
+    or ``locale.getencoding()``).
+    """
+    with io.BytesIO() as buffer:
+        wrapper = io.TextIOWrapper(buffer, encoding=py312.PTH_ENCODING)
+        # TODO: Python 3.13 replace the whole function with `bytes(content, "utf-8")`
+        wrapper.write(content)
+        wrapper.flush()
+        buffer.seek(0)
+        return buffer.read()
+
+
+def _can_symlink_files(base_dir: Path) -> bool:
+    with TemporaryDirectory(dir=str(base_dir.resolve())) as tmp:
+        path1, path2 = Path(tmp, "file1.txt"), Path(tmp, "file2.txt")
+        path1.write_text("file1", encoding="utf-8")
+        with suppress(AttributeError, NotImplementedError, OSError):
+            os.symlink(path1, path2)
+            if path2.is_symlink() and path2.read_text(encoding="utf-8") == "file1":
+                return True
+
+        try:
+            os.link(path1, path2)  # Ensure hard links can be created
+        except Exception as ex:
+            msg = (
+                "File system does not seem to support either symlinks or hard links. "
+                "Strict editable installs require one of them to be supported."
+            )
+            raise LinksNotSupported(msg) from ex
+        return False
+
+
+def _simple_layout(
+    packages: Iterable[str], package_dir: dict[str, str], project_dir: StrPath
+) -> bool:
+    """Return ``True`` if:
+    - all packages are contained by the same parent directory, **and**
+    - all packages become importable if the parent directory is added to ``sys.path``.
+
+    >>> _simple_layout(['a'], {"": "src"}, "/tmp/myproj")
+    True
+    >>> _simple_layout(['a', 'a.b'], {"": "src"}, "/tmp/myproj")
+    True
+    >>> _simple_layout(['a', 'a.b'], {}, "/tmp/myproj")
+    True
+    >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"": "src"}, "/tmp/myproj")
+    True
+    >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"a": "a", "b": "b"}, ".")
+    True
+    >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"a": "_a", "b": "_b"}, ".")
+    False
+    >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"a": "_a"}, "/tmp/myproj")
+    False
+    >>> _simple_layout(['a', 'a.a1', 'a.a1.a2', 'b'], {"a.a1.a2": "_a2"}, ".")
+    False
+    >>> _simple_layout(['a', 'a.b'], {"": "src", "a.b": "_ab"}, "/tmp/myproj")
+    False
+    >>> # Special cases, no packages yet:
+    >>> _simple_layout([], {"": "src"}, "/tmp/myproj")
+    True
+    >>> _simple_layout([], {"a": "_a", "": "src"}, "/tmp/myproj")
+    False
+    """
+    layout = {pkg: find_package_path(pkg, package_dir, project_dir) for pkg in packages}
+    if not layout:
+        return set(package_dir) in ({}, {""})
+    parent = os.path.commonpath(starmap(_parent_path, layout.items()))
+    return all(
+        _path.same_path(Path(parent, *key.split('.')), value)
+        for key, value in layout.items()
+    )
+
+
+def _parent_path(pkg, pkg_path):
+    """Infer the parent path containing a package, that if added to ``sys.path`` would
+    allow importing that package.
+    When ``pkg`` is directly mapped into a directory with a different name, return its
+    own path.
+    >>> _parent_path("a", "src/a")
+    'src'
+    >>> _parent_path("b", "src/c")
+    'src/c'
+    """
+    parent = pkg_path[: -len(pkg)] if pkg_path.endswith(pkg) else pkg_path
+    return parent.rstrip("/" + os.sep)
+
+
+def _find_packages(dist: Distribution) -> Iterator[str]:
+    yield from iter(dist.packages or [])
+
+    py_modules = dist.py_modules or []
+    nested_modules = [mod for mod in py_modules if "." in mod]
+    if dist.ext_package:
+        yield dist.ext_package
+    else:
+        ext_modules = dist.ext_modules or []
+        nested_modules += [x.name for x in ext_modules if "." in x.name]
+
+    for module in nested_modules:
+        package, _, _ = module.rpartition(".")
+        yield package
+
+
+def _find_top_level_modules(dist: Distribution) -> Iterator[str]:
+    py_modules = dist.py_modules or []
+    yield from (mod for mod in py_modules if "." not in mod)
+
+    if not dist.ext_package:
+        ext_modules = dist.ext_modules or []
+        yield from (x.name for x in ext_modules if "." not in x.name)
+
+
+def _find_package_roots(
+    packages: Iterable[str],
+    package_dir: Mapping[str, str],
+    src_root: StrPath,
+) -> dict[str, str]:
+    pkg_roots: dict[str, str] = {
+        pkg: _absolute_root(find_package_path(pkg, package_dir, src_root))
+        for pkg in sorted(packages)
+    }
+
+    return _remove_nested(pkg_roots)
+
+
+def _absolute_root(path: StrPath) -> str:
+    """Works for packages and top-level modules"""
+    path_ = Path(path)
+    parent = path_.parent
+
+    if path_.exists():
+        return str(path_.resolve())
+    else:
+        return str(parent.resolve() / path_.name)
+
+
+def _find_virtual_namespaces(pkg_roots: dict[str, str]) -> Iterator[str]:
+    """By carefully designing ``package_dir``, it is possible to implement the logical
+    structure of PEP 420 in a package without the corresponding directories.
+
+    Moreover a parent package can be purposefully/accidentally skipped in the discovery
+    phase (e.g. ``find_packages(include=["mypkg.*"])``, when ``mypkg.foo`` is included
+    by ``mypkg`` itself is not).
+    We consider this case to also be a virtual namespace (ignoring the original
+    directory) to emulate a non-editable installation.
+
+    This function will try to find these kinds of namespaces.
+    """
+    for pkg in pkg_roots:
+        if "." not in pkg:
+            continue
+        parts = pkg.split(".")
+        for i in range(len(parts) - 1, 0, -1):
+            partial_name = ".".join(parts[:i])
+            path = Path(find_package_path(partial_name, pkg_roots, ""))
+            if not path.exists() or partial_name not in pkg_roots:
+                # partial_name not in pkg_roots ==> purposefully/accidentally skipped
+                yield partial_name
+
+
+def _find_namespaces(
+    packages: list[str], pkg_roots: dict[str, str]
+) -> Iterator[tuple[str, list[str]]]:
+    for pkg in packages:
+        path = find_package_path(pkg, pkg_roots, "")
+        if Path(path).exists() and not Path(path, "__init__.py").exists():
+            yield (pkg, [path])
+
+
+def _remove_nested(pkg_roots: dict[str, str]) -> dict[str, str]:
+    output = dict(pkg_roots.copy())
+
+    for pkg, path in reversed(list(pkg_roots.items())):
+        if any(
+            pkg != other and _is_nested(pkg, path, other, other_path)
+            for other, other_path in pkg_roots.items()
+        ):
+            output.pop(pkg)
+
+    return output
+
+
+def _is_nested(pkg: str, pkg_path: str, parent: str, parent_path: str) -> bool:
+    """
+    Return ``True`` if ``pkg`` is nested inside ``parent`` both logically and in the
+    file system.
+    >>> _is_nested("a.b", "path/a/b", "a", "path/a")
+    True
+    >>> _is_nested("a.b", "path/a/b", "a", "otherpath/a")
+    False
+    >>> _is_nested("a.b", "path/a/b", "c", "path/c")
+    False
+    >>> _is_nested("a.a", "path/a/a", "a", "path/a")
+    True
+    >>> _is_nested("b.a", "path/b/a", "a", "path/a")
+    False
+    """
+    norm_pkg_path = _path.normpath(pkg_path)
+    rest = pkg.replace(parent, "", 1).strip(".").split(".")
+    return pkg.startswith(parent) and norm_pkg_path == _path.normpath(
+        Path(parent_path, *rest)
+    )
+
+
+def _empty_dir(dir_: _P) -> _P:
+    """Create a directory ensured to be empty. Existing files may be removed."""
+    _shutil.rmtree(dir_, ignore_errors=True)
+    os.makedirs(dir_)
+    return dir_
+
+
+class _NamespaceInstaller(namespaces.Installer):
+    def __init__(self, distribution, installation_dir, editable_name, src_root) -> None:
+        self.distribution = distribution
+        self.src_root = src_root
+        self.installation_dir = installation_dir
+        self.editable_name = editable_name
+        self.outputs: list[str] = []
+        self.dry_run = False
+
+    def _get_nspkg_file(self):
+        """Installation target."""
+        return os.path.join(self.installation_dir, self.editable_name + self.nspkg_ext)
+
+    def _get_root(self):
+        """Where the modules/packages should be loaded from."""
+        return repr(str(self.src_root))
+
+
+_FINDER_TEMPLATE = """\
+from __future__ import annotations
+import sys
+from importlib.machinery import ModuleSpec, PathFinder
+from importlib.machinery import all_suffixes as module_suffixes
+from importlib.util import spec_from_file_location
+from itertools import chain
+from pathlib import Path
+
+MAPPING: dict[str, str] = {mapping!r}
+NAMESPACES: dict[str, list[str]] = {namespaces!r}
+PATH_PLACEHOLDER = {name!r} + ".__path_hook__"
+
+
+class _EditableFinder:  # MetaPathFinder
+    @classmethod
+    def find_spec(cls, fullname: str, path=None, target=None) -> ModuleSpec | None:  # type: ignore
+        # Top-level packages and modules (we know these exist in the FS)
+        if fullname in MAPPING:
+            pkg_path = MAPPING[fullname]
+            return cls._find_spec(fullname, Path(pkg_path))
+
+        # Handle immediate children modules (required for namespaces to work)
+        # To avoid problems with case sensitivity in the file system we delegate
+        # to the importlib.machinery implementation.
+        parent, _, child = fullname.rpartition(".")
+        if parent and parent in MAPPING:
+            return PathFinder.find_spec(fullname, path=[MAPPING[parent]])
+
+        # Other levels of nesting should be handled automatically by importlib
+        # using the parent path.
+        return None
+
+    @classmethod
+    def _find_spec(cls, fullname: str, candidate_path: Path) -> ModuleSpec | None:
+        init = candidate_path / "__init__.py"
+        candidates = (candidate_path.with_suffix(x) for x in module_suffixes())
+        for candidate in chain([init], candidates):
+            if candidate.exists():
+                return spec_from_file_location(fullname, candidate)
+        return None
+
+
+class _EditableNamespaceFinder:  # PathEntryFinder
+    @classmethod
+    def _path_hook(cls, path) -> type[_EditableNamespaceFinder]:
+        if path == PATH_PLACEHOLDER:
+            return cls
+        raise ImportError
+
+    @classmethod
+    def _paths(cls, fullname: str) -> list[str]:
+        paths = NAMESPACES[fullname]
+        if not paths and fullname in MAPPING:
+            paths = [MAPPING[fullname]]
+        # Always add placeholder, for 2 reasons:
+        # 1. __path__ cannot be empty for the spec to be considered namespace.
+        # 2. In the case of nested namespaces, we need to force
+        #    import machinery to query _EditableNamespaceFinder again.
+        return [*paths, PATH_PLACEHOLDER]
+
+    @classmethod
+    def find_spec(cls, fullname: str, target=None) -> ModuleSpec | None:  # type: ignore
+        if fullname in NAMESPACES:
+            spec = ModuleSpec(fullname, None, is_package=True)
+            spec.submodule_search_locations = cls._paths(fullname)
+            return spec
+        return None
+
+    @classmethod
+    def find_module(cls, _fullname) -> None:
+        return None
+
+
+def install():
+    if not any(finder == _EditableFinder for finder in sys.meta_path):
+        sys.meta_path.append(_EditableFinder)
+
+    if not NAMESPACES:
+        return
+
+    if not any(hook == _EditableNamespaceFinder._path_hook for hook in sys.path_hooks):
+        # PathEntryFinder is needed to create NamespaceSpec without private APIS
+        sys.path_hooks.append(_EditableNamespaceFinder._path_hook)
+    if PATH_PLACEHOLDER not in sys.path:
+        sys.path.append(PATH_PLACEHOLDER)  # Used just to trigger the path hook
+"""
+
+
+def _finder_template(
+    name: str, mapping: Mapping[str, str], namespaces: dict[str, list[str]]
+) -> str:
+    """Create a string containing the code for the``MetaPathFinder`` and
+    ``PathEntryFinder``.
+    """
+    mapping = dict(sorted(mapping.items(), key=lambda p: p[0]))
+    return _FINDER_TEMPLATE.format(name=name, mapping=mapping, namespaces=namespaces)
+
+
+class LinksNotSupported(errors.FileError):
+    """File system does not seem to support either symlinks or hard links."""
+
+
+class _DebuggingTips(SetuptoolsWarning):
+    _SUMMARY = "Problem in editable installation."
+    _DETAILS = """
+    An error happened while installing `{project}` in editable mode.
+
+    The following steps are recommended to help debug this problem:
+
+    - Try to install the project normally, without using the editable mode.
+      Does the error still persist?
+      (If it does, try fixing the problem before attempting the editable mode).
+    - If you are using binary extensions, make sure you have all OS-level
+      dependencies installed (e.g. compilers, toolchains, binary libraries, ...).
+    - Try the latest version of setuptools (maybe the error was already fixed).
+    - If you (or your project dependencies) are using any setuptools extension
+      or customization, make sure they support the editable mode.
+
+    After following the steps above, if the problem still persists and
+    you think this is related to how setuptools handles editable installations,
+    please submit a reproducible example
+    (see https://stackoverflow.com/help/minimal-reproducible-example) to:
+
+        https://github.com/pypa/setuptools/issues
+    """
+    _SEE_DOCS = "userguide/development_mode.html"
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/egg_info.py b/.venv/lib/python3.12/site-packages/setuptools/command/egg_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..f77631168fcdb60e14ddd757e18b4c301473aa82
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/egg_info.py
@@ -0,0 +1,720 @@
+"""setuptools.command.egg_info
+
+Create a distribution's .egg-info directory and contents"""
+
+import functools
+import os
+import re
+import sys
+import time
+from collections.abc import Callable
+
+import packaging
+import packaging.requirements
+import packaging.version
+
+import setuptools.unicode_utils as unicode_utils
+from setuptools import Command
+from setuptools.command import bdist_egg
+from setuptools.command.sdist import sdist, walk_revctrl
+from setuptools.command.setopt import edit_config
+from setuptools.glob import glob
+
+from .. import _entry_points, _normalization
+from .._importlib import metadata
+from ..warnings import SetuptoolsDeprecationWarning
+from . import _requirestxt
+
+import distutils.errors
+import distutils.filelist
+from distutils import log
+from distutils.errors import DistutilsInternalError
+from distutils.filelist import FileList as _FileList
+from distutils.util import convert_path
+
+PY_MAJOR = f'{sys.version_info.major}.{sys.version_info.minor}'
+
+
+def translate_pattern(glob):  # noqa: C901  # is too complex (14)  # FIXME
+    """
+    Translate a file path glob like '*.txt' in to a regular expression.
+    This differs from fnmatch.translate which allows wildcards to match
+    directory separators. It also knows about '**/' which matches any number of
+    directories.
+    """
+    pat = ''
+
+    # This will split on '/' within [character classes]. This is deliberate.
+    chunks = glob.split(os.path.sep)
+
+    sep = re.escape(os.sep)
+    valid_char = f'[^{sep}]'
+
+    for c, chunk in enumerate(chunks):
+        last_chunk = c == len(chunks) - 1
+
+        # Chunks that are a literal ** are globstars. They match anything.
+        if chunk == '**':
+            if last_chunk:
+                # Match anything if this is the last component
+                pat += '.*'
+            else:
+                # Match '(name/)*'
+                pat += f'(?:{valid_char}+{sep})*'
+            continue  # Break here as the whole path component has been handled
+
+        # Find any special characters in the remainder
+        i = 0
+        chunk_len = len(chunk)
+        while i < chunk_len:
+            char = chunk[i]
+            if char == '*':
+                # Match any number of name characters
+                pat += valid_char + '*'
+            elif char == '?':
+                # Match a name character
+                pat += valid_char
+            elif char == '[':
+                # Character class
+                inner_i = i + 1
+                # Skip initial !/] chars
+                if inner_i < chunk_len and chunk[inner_i] == '!':
+                    inner_i = inner_i + 1
+                if inner_i < chunk_len and chunk[inner_i] == ']':
+                    inner_i = inner_i + 1
+
+                # Loop till the closing ] is found
+                while inner_i < chunk_len and chunk[inner_i] != ']':
+                    inner_i = inner_i + 1
+
+                if inner_i >= chunk_len:
+                    # Got to the end of the string without finding a closing ]
+                    # Do not treat this as a matching group, but as a literal [
+                    pat += re.escape(char)
+                else:
+                    # Grab the insides of the [brackets]
+                    inner = chunk[i + 1 : inner_i]
+                    char_class = ''
+
+                    # Class negation
+                    if inner[0] == '!':
+                        char_class = '^'
+                        inner = inner[1:]
+
+                    char_class += re.escape(inner)
+                    pat += f'[{char_class}]'
+
+                    # Skip to the end ]
+                    i = inner_i
+            else:
+                pat += re.escape(char)
+            i += 1
+
+        # Join each chunk with the dir separator
+        if not last_chunk:
+            pat += sep
+
+    pat += r'\Z'
+    return re.compile(pat, flags=re.MULTILINE | re.DOTALL)
+
+
+class InfoCommon:
+    tag_build = None
+    tag_date = None
+
+    @property
+    def name(self):
+        return _normalization.safe_name(self.distribution.get_name())
+
+    def tagged_version(self):
+        tagged = self._maybe_tag(self.distribution.get_version())
+        return _normalization.safe_version(tagged)
+
+    def _maybe_tag(self, version):
+        """
+        egg_info may be called more than once for a distribution,
+        in which case the version string already contains all tags.
+        """
+        return (
+            version
+            if self.vtags and self._already_tagged(version)
+            else version + self.vtags
+        )
+
+    def _already_tagged(self, version: str) -> bool:
+        # Depending on their format, tags may change with version normalization.
+        # So in addition the regular tags, we have to search for the normalized ones.
+        return version.endswith(self.vtags) or version.endswith(self._safe_tags())
+
+    def _safe_tags(self) -> str:
+        # To implement this we can rely on `safe_version` pretending to be version 0
+        # followed by tags. Then we simply discard the starting 0 (fake version number)
+        try:
+            return _normalization.safe_version(f"0{self.vtags}")[1:]
+        except packaging.version.InvalidVersion:
+            return _normalization.safe_name(self.vtags.replace(' ', '.'))
+
+    def tags(self) -> str:
+        version = ''
+        if self.tag_build:
+            version += self.tag_build
+        if self.tag_date:
+            version += time.strftime("%Y%m%d")
+        return version
+
+    vtags = property(tags)
+
+
+class egg_info(InfoCommon, Command):
+    description = "create a distribution's .egg-info directory"
+
+    user_options = [
+        (
+            'egg-base=',
+            'e',
+            "directory containing .egg-info directories"
+            " [default: top of the source tree]",
+        ),
+        ('tag-date', 'd', "Add date stamp (e.g. 20050528) to version number"),
+        ('tag-build=', 'b', "Specify explicit tag to add to version number"),
+        ('no-date', 'D', "Don't include date stamp [default]"),
+    ]
+
+    boolean_options = ['tag-date']
+    negative_opt = {
+        'no-date': 'tag-date',
+    }
+
+    def initialize_options(self):
+        self.egg_base = None
+        self.egg_name = None
+        self.egg_info = None
+        self.egg_version = None
+        self.ignore_egg_info_in_manifest = False
+
+    ####################################
+    # allow the 'tag_svn_revision' to be detected and
+    # set, supporting sdists built on older Setuptools.
+    @property
+    def tag_svn_revision(self) -> None:
+        pass
+
+    @tag_svn_revision.setter
+    def tag_svn_revision(self, value):
+        pass
+
+    ####################################
+
+    def save_version_info(self, filename) -> None:
+        """
+        Materialize the value of date into the
+        build tag. Install build keys in a deterministic order
+        to avoid arbitrary reordering on subsequent builds.
+        """
+        # follow the order these keys would have been added
+        # when PYTHONHASHSEED=0
+        egg_info = dict(tag_build=self.tags(), tag_date=0)
+        edit_config(filename, dict(egg_info=egg_info))
+
+    def finalize_options(self) -> None:
+        # Note: we need to capture the current value returned
+        # by `self.tagged_version()`, so we can later update
+        # `self.distribution.metadata.version` without
+        # repercussions.
+        self.egg_name = self.name
+        self.egg_version = self.tagged_version()
+        parsed_version = packaging.version.Version(self.egg_version)
+
+        try:
+            is_version = isinstance(parsed_version, packaging.version.Version)
+            spec = "%s==%s" if is_version else "%s===%s"
+            packaging.requirements.Requirement(spec % (self.egg_name, self.egg_version))
+        except ValueError as e:
+            raise distutils.errors.DistutilsOptionError(
+                f"Invalid distribution name or version syntax: {self.egg_name}-{self.egg_version}"
+            ) from e
+
+        if self.egg_base is None:
+            dirs = self.distribution.package_dir
+            self.egg_base = (dirs or {}).get('', os.curdir)
+
+        self.ensure_dirname('egg_base')
+        self.egg_info = _normalization.filename_component(self.egg_name) + '.egg-info'
+        if self.egg_base != os.curdir:
+            self.egg_info = os.path.join(self.egg_base, self.egg_info)
+
+        # Set package version for the benefit of dumber commands
+        # (e.g. sdist, bdist_wininst, etc.)
+        #
+        self.distribution.metadata.version = self.egg_version
+
+    def _get_egg_basename(self, py_version=PY_MAJOR, platform=None):
+        """Compute filename of the output egg. Private API."""
+        return _egg_basename(self.egg_name, self.egg_version, py_version, platform)
+
+    def write_or_delete_file(self, what, filename, data, force: bool = False) -> None:
+        """Write `data` to `filename` or delete if empty
+
+        If `data` is non-empty, this routine is the same as ``write_file()``.
+        If `data` is empty but not ``None``, this is the same as calling
+        ``delete_file(filename)`.  If `data` is ``None``, then this is a no-op
+        unless `filename` exists, in which case a warning is issued about the
+        orphaned file (if `force` is false), or deleted (if `force` is true).
+        """
+        if data:
+            self.write_file(what, filename, data)
+        elif os.path.exists(filename):
+            if data is None and not force:
+                log.warn("%s not set in setup(), but %s exists", what, filename)
+                return
+            else:
+                self.delete_file(filename)
+
+    def write_file(self, what, filename, data) -> None:
+        """Write `data` to `filename` (if not a dry run) after announcing it
+
+        `what` is used in a log message to identify what is being written
+        to the file.
+        """
+        log.info("writing %s to %s", what, filename)
+        data = data.encode("utf-8")
+        if not self.dry_run:
+            f = open(filename, 'wb')
+            f.write(data)
+            f.close()
+
+    def delete_file(self, filename) -> None:
+        """Delete `filename` (if not a dry run) after announcing it"""
+        log.info("deleting %s", filename)
+        if not self.dry_run:
+            os.unlink(filename)
+
+    def run(self) -> None:
+        # Pre-load to avoid iterating over entry-points while an empty .egg-info
+        # exists in sys.path. See pypa/pyproject-hooks#206
+        writers = list(metadata.entry_points(group='egg_info.writers'))
+
+        self.mkpath(self.egg_info)
+        try:
+            os.utime(self.egg_info, None)
+        except OSError as e:
+            msg = f"Cannot update time stamp of directory '{self.egg_info}'"
+            raise distutils.errors.DistutilsFileError(msg) from e
+        for ep in writers:
+            writer = ep.load()
+            writer(self, ep.name, os.path.join(self.egg_info, ep.name))
+
+        # Get rid of native_libs.txt if it was put there by older bdist_egg
+        nl = os.path.join(self.egg_info, "native_libs.txt")
+        if os.path.exists(nl):
+            self.delete_file(nl)
+
+        self.find_sources()
+
+    def find_sources(self) -> None:
+        """Generate SOURCES.txt manifest file"""
+        manifest_filename = os.path.join(self.egg_info, "SOURCES.txt")
+        mm = manifest_maker(self.distribution)
+        mm.ignore_egg_info_dir = self.ignore_egg_info_in_manifest
+        mm.manifest = manifest_filename
+        mm.run()
+        self.filelist = mm.filelist
+
+
+class FileList(_FileList):
+    # Implementations of the various MANIFEST.in commands
+
+    def __init__(
+        self, warn=None, debug_print=None, ignore_egg_info_dir: bool = False
+    ) -> None:
+        super().__init__(warn, debug_print)
+        self.ignore_egg_info_dir = ignore_egg_info_dir
+
+    def process_template_line(self, line) -> None:
+        # Parse the line: split it up, make sure the right number of words
+        # is there, and return the relevant words.  'action' is always
+        # defined: it's the first word of the line.  Which of the other
+        # three are defined depends on the action; it'll be either
+        # patterns, (dir and patterns), or (dir_pattern).
+        (action, patterns, dir, dir_pattern) = self._parse_template_line(line)
+
+        action_map: dict[str, Callable] = {
+            'include': self.include,
+            'exclude': self.exclude,
+            'global-include': self.global_include,
+            'global-exclude': self.global_exclude,
+            'recursive-include': functools.partial(
+                self.recursive_include,
+                dir,
+            ),
+            'recursive-exclude': functools.partial(
+                self.recursive_exclude,
+                dir,
+            ),
+            'graft': self.graft,
+            'prune': self.prune,
+        }
+        log_map = {
+            'include': "warning: no files found matching '%s'",
+            'exclude': ("warning: no previously-included files found matching '%s'"),
+            'global-include': (
+                "warning: no files found matching '%s' anywhere in distribution"
+            ),
+            'global-exclude': (
+                "warning: no previously-included files matching "
+                "'%s' found anywhere in distribution"
+            ),
+            'recursive-include': (
+                "warning: no files found matching '%s' under directory '%s'"
+            ),
+            'recursive-exclude': (
+                "warning: no previously-included files matching "
+                "'%s' found under directory '%s'"
+            ),
+            'graft': "warning: no directories found matching '%s'",
+            'prune': "no previously-included directories found matching '%s'",
+        }
+
+        try:
+            process_action = action_map[action]
+        except KeyError:
+            msg = f"Invalid MANIFEST.in: unknown action {action!r} in {line!r}"
+            raise DistutilsInternalError(msg) from None
+
+        # OK, now we know that the action is valid and we have the
+        # right number of words on the line for that action -- so we
+        # can proceed with minimal error-checking.
+
+        action_is_recursive = action.startswith('recursive-')
+        if action in {'graft', 'prune'}:
+            patterns = [dir_pattern]
+        extra_log_args = (dir,) if action_is_recursive else ()
+        log_tmpl = log_map[action]
+
+        self.debug_print(
+            ' '.join(
+                [action] + ([dir] if action_is_recursive else []) + patterns,
+            )
+        )
+        for pattern in patterns:
+            if not process_action(pattern):
+                log.warn(log_tmpl, pattern, *extra_log_args)
+
+    def _remove_files(self, predicate):
+        """
+        Remove all files from the file list that match the predicate.
+        Return True if any matching files were removed
+        """
+        found = False
+        for i in range(len(self.files) - 1, -1, -1):
+            if predicate(self.files[i]):
+                self.debug_print(" removing " + self.files[i])
+                del self.files[i]
+                found = True
+        return found
+
+    def include(self, pattern):
+        """Include files that match 'pattern'."""
+        found = [f for f in glob(pattern) if not os.path.isdir(f)]
+        self.extend(found)
+        return bool(found)
+
+    def exclude(self, pattern):
+        """Exclude files that match 'pattern'."""
+        match = translate_pattern(pattern)
+        return self._remove_files(match.match)
+
+    def recursive_include(self, dir, pattern):
+        """
+        Include all files anywhere in 'dir/' that match the pattern.
+        """
+        full_pattern = os.path.join(dir, '**', pattern)
+        found = [f for f in glob(full_pattern, recursive=True) if not os.path.isdir(f)]
+        self.extend(found)
+        return bool(found)
+
+    def recursive_exclude(self, dir, pattern):
+        """
+        Exclude any file anywhere in 'dir/' that match the pattern.
+        """
+        match = translate_pattern(os.path.join(dir, '**', pattern))
+        return self._remove_files(match.match)
+
+    def graft(self, dir):
+        """Include all files from 'dir/'."""
+        found = [
+            item
+            for match_dir in glob(dir)
+            for item in distutils.filelist.findall(match_dir)
+        ]
+        self.extend(found)
+        return bool(found)
+
+    def prune(self, dir):
+        """Filter out files from 'dir/'."""
+        match = translate_pattern(os.path.join(dir, '**'))
+        return self._remove_files(match.match)
+
+    def global_include(self, pattern):
+        """
+        Include all files anywhere in the current directory that match the
+        pattern. This is very inefficient on large file trees.
+        """
+        if self.allfiles is None:
+            self.findall()
+        match = translate_pattern(os.path.join('**', pattern))
+        found = [f for f in self.allfiles if match.match(f)]
+        self.extend(found)
+        return bool(found)
+
+    def global_exclude(self, pattern):
+        """
+        Exclude all files anywhere that match the pattern.
+        """
+        match = translate_pattern(os.path.join('**', pattern))
+        return self._remove_files(match.match)
+
+    def append(self, item) -> None:
+        if item.endswith('\r'):  # Fix older sdists built on Windows
+            item = item[:-1]
+        path = convert_path(item)
+
+        if self._safe_path(path):
+            self.files.append(path)
+
+    def extend(self, paths) -> None:
+        self.files.extend(filter(self._safe_path, paths))
+
+    def _repair(self):
+        """
+        Replace self.files with only safe paths
+
+        Because some owners of FileList manipulate the underlying
+        ``files`` attribute directly, this method must be called to
+        repair those paths.
+        """
+        self.files = list(filter(self._safe_path, self.files))
+
+    def _safe_path(self, path):
+        enc_warn = "'%s' not %s encodable -- skipping"
+
+        # To avoid accidental trans-codings errors, first to unicode
+        u_path = unicode_utils.filesys_decode(path)
+        if u_path is None:
+            log.warn(f"'{path}' in unexpected encoding -- skipping")
+            return False
+
+        # Must ensure utf-8 encodability
+        utf8_path = unicode_utils.try_encode(u_path, "utf-8")
+        if utf8_path is None:
+            log.warn(enc_warn, path, 'utf-8')
+            return False
+
+        try:
+            # ignore egg-info paths
+            is_egg_info = ".egg-info" in u_path or b".egg-info" in utf8_path
+            if self.ignore_egg_info_dir and is_egg_info:
+                return False
+            # accept is either way checks out
+            if os.path.exists(u_path) or os.path.exists(utf8_path):
+                return True
+        # this will catch any encode errors decoding u_path
+        except UnicodeEncodeError:
+            log.warn(enc_warn, path, sys.getfilesystemencoding())
+
+
+class manifest_maker(sdist):
+    template = "MANIFEST.in"
+
+    def initialize_options(self) -> None:
+        self.use_defaults = True
+        self.prune = True
+        self.manifest_only = True
+        self.force_manifest = True
+        self.ignore_egg_info_dir = False
+
+    def finalize_options(self) -> None:
+        pass
+
+    def run(self) -> None:
+        self.filelist = FileList(ignore_egg_info_dir=self.ignore_egg_info_dir)
+        if not os.path.exists(self.manifest):
+            self.write_manifest()  # it must exist so it'll get in the list
+        self.add_defaults()
+        if os.path.exists(self.template):
+            self.read_template()
+        self.add_license_files()
+        self._add_referenced_files()
+        self.prune_file_list()
+        self.filelist.sort()
+        self.filelist.remove_duplicates()
+        self.write_manifest()
+
+    def _manifest_normalize(self, path):
+        path = unicode_utils.filesys_decode(path)
+        return path.replace(os.sep, '/')
+
+    def write_manifest(self) -> None:
+        """
+        Write the file list in 'self.filelist' to the manifest file
+        named by 'self.manifest'.
+        """
+        self.filelist._repair()
+
+        # Now _repairs should encodability, but not unicode
+        files = [self._manifest_normalize(f) for f in self.filelist.files]
+        msg = f"writing manifest file '{self.manifest}'"
+        self.execute(write_file, (self.manifest, files), msg)
+
+    def warn(self, msg) -> None:
+        if not self._should_suppress_warning(msg):
+            sdist.warn(self, msg)
+
+    @staticmethod
+    def _should_suppress_warning(msg):
+        """
+        suppress missing-file warnings from sdist
+        """
+        return re.match(r"standard file .*not found", msg)
+
+    def add_defaults(self) -> None:
+        sdist.add_defaults(self)
+        self.filelist.append(self.template)
+        self.filelist.append(self.manifest)
+        rcfiles = list(walk_revctrl())
+        if rcfiles:
+            self.filelist.extend(rcfiles)
+        elif os.path.exists(self.manifest):
+            self.read_manifest()
+
+        if os.path.exists("setup.py"):
+            # setup.py should be included by default, even if it's not
+            # the script called to create the sdist
+            self.filelist.append("setup.py")
+
+        ei_cmd = self.get_finalized_command('egg_info')
+        self.filelist.graft(ei_cmd.egg_info)
+
+    def add_license_files(self) -> None:
+        license_files = self.distribution.metadata.license_files or []
+        for lf in license_files:
+            log.info("adding license file '%s'", lf)
+        self.filelist.extend(license_files)
+
+    def _add_referenced_files(self):
+        """Add files referenced by the config (e.g. `file:` directive) to filelist"""
+        referenced = getattr(self.distribution, '_referenced_files', [])
+        # ^-- fallback if dist comes from distutils or is a custom class
+        for rf in referenced:
+            log.debug("adding file referenced by config '%s'", rf)
+        self.filelist.extend(referenced)
+
+    def _safe_data_files(self, build_py):
+        """
+        The parent class implementation of this method
+        (``sdist``) will try to include data files, which
+        might cause recursion problems when
+        ``include_package_data=True``.
+
+        Therefore, avoid triggering any attempt of
+        analyzing/building the manifest again.
+        """
+        if hasattr(build_py, 'get_data_files_without_manifest'):
+            return build_py.get_data_files_without_manifest()
+
+        SetuptoolsDeprecationWarning.emit(
+            "`build_py` command does not inherit from setuptools' `build_py`.",
+            """
+            Custom 'build_py' does not implement 'get_data_files_without_manifest'.
+            Please extend command classes from setuptools instead of distutils.
+            """,
+            see_url="https://peps.python.org/pep-0632/",
+            # due_date not defined yet, old projects might still do it?
+        )
+        return build_py.get_data_files()
+
+
+def write_file(filename, contents) -> None:
+    """Create a file with the specified name and write 'contents' (a
+    sequence of strings without line terminators) to it.
+    """
+    contents = "\n".join(contents)
+
+    # assuming the contents has been vetted for utf-8 encoding
+    contents = contents.encode("utf-8")
+
+    with open(filename, "wb") as f:  # always write POSIX-style manifest
+        f.write(contents)
+
+
+def write_pkg_info(cmd, basename, filename) -> None:
+    log.info("writing %s", filename)
+    if not cmd.dry_run:
+        metadata = cmd.distribution.metadata
+        metadata.version, oldver = cmd.egg_version, metadata.version
+        metadata.name, oldname = cmd.egg_name, metadata.name
+
+        try:
+            # write unescaped data to PKG-INFO, so older pkg_resources
+            # can still parse it
+            metadata.write_pkg_info(cmd.egg_info)
+        finally:
+            metadata.name, metadata.version = oldname, oldver
+
+        safe = getattr(cmd.distribution, 'zip_safe', None)
+
+        bdist_egg.write_safety_flag(cmd.egg_info, safe)
+
+
+def warn_depends_obsolete(cmd, basename, filename) -> None:
+    """
+    Unused: left to avoid errors when updating (from source) from <= 67.8.
+    Old installations have a .dist-info directory with the entry-point
+    ``depends.txt = setuptools.command.egg_info:warn_depends_obsolete``.
+    This may trigger errors when running the first egg_info in build_meta.
+    TODO: Remove this function in a version sufficiently > 68.
+    """
+
+
+# Export API used in entry_points
+write_requirements = _requirestxt.write_requirements
+write_setup_requirements = _requirestxt.write_setup_requirements
+
+
+def write_toplevel_names(cmd, basename, filename) -> None:
+    pkgs = dict.fromkeys([
+        k.split('.', 1)[0] for k in cmd.distribution.iter_distribution_names()
+    ])
+    cmd.write_file("top-level names", filename, '\n'.join(sorted(pkgs)) + '\n')
+
+
+def overwrite_arg(cmd, basename, filename) -> None:
+    write_arg(cmd, basename, filename, True)
+
+
+def write_arg(cmd, basename, filename, force: bool = False) -> None:
+    argname = os.path.splitext(basename)[0]
+    value = getattr(cmd.distribution, argname, None)
+    if value is not None:
+        value = '\n'.join(value) + '\n'
+    cmd.write_or_delete_file(argname, filename, value, force)
+
+
+def write_entries(cmd, basename, filename) -> None:
+    eps = _entry_points.load(cmd.distribution.entry_points)
+    defn = _entry_points.render(eps)
+    cmd.write_or_delete_file('entry points', filename, defn, True)
+
+
+def _egg_basename(egg_name, egg_version, py_version=None, platform=None):
+    """Compute filename of the output egg. Private API."""
+    name = _normalization.filename_component(egg_name)
+    version = _normalization.filename_component(egg_version)
+    egg = f"{name}-{version}-py{py_version or PY_MAJOR}"
+    if platform:
+        egg += f"-{platform}"
+    return egg
+
+
+class EggInfoDeprecationWarning(SetuptoolsDeprecationWarning):
+    """Deprecated behavior warning for EggInfo, bypassing suppression."""
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/install.py b/.venv/lib/python3.12/site-packages/setuptools/command/install.py
new file mode 100644
index 0000000000000000000000000000000000000000..15ef36468889d55718711edbe9c4e061c48ee6e8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/install.py
@@ -0,0 +1,183 @@
+from __future__ import annotations
+
+import glob
+import inspect
+import platform
+from collections.abc import Callable
+from typing import TYPE_CHECKING, Any, ClassVar, cast
+
+import setuptools
+
+from ..dist import Distribution
+from ..warnings import SetuptoolsDeprecationWarning, SetuptoolsWarning
+from .bdist_egg import bdist_egg as bdist_egg_cls
+
+import distutils.command.install as orig
+from distutils.errors import DistutilsArgError
+
+if TYPE_CHECKING:
+    # This is only used for a type-cast, don't import at runtime or it'll cause deprecation warnings
+    from .easy_install import easy_install as easy_install_cls
+else:
+    easy_install_cls = None
+
+
+def __getattr__(name: str):  # pragma: no cover
+    if name == "_install":
+        SetuptoolsDeprecationWarning.emit(
+            "`setuptools.command._install` was an internal implementation detail "
+            + "that was left in for numpy<1.9 support.",
+            due_date=(2025, 5, 2),  # Originally added on 2024-11-01
+        )
+        return orig.install
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+class install(orig.install):
+    """Use easy_install to install the package, w/dependencies"""
+
+    distribution: Distribution  # override distutils.dist.Distribution with setuptools.dist.Distribution
+
+    user_options = orig.install.user_options + [
+        ('old-and-unmanageable', None, "Try not to use this!"),
+        (
+            'single-version-externally-managed',
+            None,
+            "used by system package builders to create 'flat' eggs",
+        ),
+    ]
+    boolean_options = orig.install.boolean_options + [
+        'old-and-unmanageable',
+        'single-version-externally-managed',
+    ]
+    # Type the same as distutils.command.install.install.sub_commands
+    # Must keep the second tuple item potentially None due to invariance
+    new_commands: ClassVar[list[tuple[str, Callable[[Any], bool] | None]]] = [
+        ('install_egg_info', lambda self: True),
+        ('install_scripts', lambda self: True),
+    ]
+    _nc = dict(new_commands)
+
+    def initialize_options(self):
+        SetuptoolsDeprecationWarning.emit(
+            "setup.py install is deprecated.",
+            """
+            Please avoid running ``setup.py`` directly.
+            Instead, use pypa/build, pypa/installer or other
+            standards-based tools.
+            """,
+            see_url="https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html",
+            # TODO: Document how to bootstrap setuptools without install
+            #       (e.g. by unzipping the wheel file)
+            #       and then add a due_date to this warning.
+        )
+
+        super().initialize_options()
+        self.old_and_unmanageable = None
+        self.single_version_externally_managed = None
+
+    def finalize_options(self) -> None:
+        super().finalize_options()
+        if self.root:
+            self.single_version_externally_managed = True
+        elif self.single_version_externally_managed:
+            if not self.root and not self.record:
+                raise DistutilsArgError(
+                    "You must specify --record or --root when building system packages"
+                )
+
+    def handle_extra_path(self):
+        if self.root or self.single_version_externally_managed:
+            # explicit backward-compatibility mode, allow extra_path to work
+            return orig.install.handle_extra_path(self)
+
+        # Ignore extra_path when installing an egg (or being run by another
+        # command without --root or --single-version-externally-managed
+        self.path_file = None
+        self.extra_dirs = ''
+        return None
+
+    def run(self):
+        # Explicit request for old-style install?  Just do it
+        if self.old_and_unmanageable or self.single_version_externally_managed:
+            return super().run()
+
+        if not self._called_from_setup(inspect.currentframe()):
+            # Run in backward-compatibility mode to support bdist_* commands.
+            super().run()
+        else:
+            self.do_egg_install()
+
+        return None
+
+    @staticmethod
+    def _called_from_setup(run_frame):
+        """
+        Attempt to detect whether run() was called from setup() or by another
+        command.  If called by setup(), the parent caller will be the
+        'run_command' method in 'distutils.dist', and *its* caller will be
+        the 'run_commands' method.  If called any other way, the
+        immediate caller *might* be 'run_command', but it won't have been
+        called by 'run_commands'. Return True in that case or if a call stack
+        is unavailable. Return False otherwise.
+        """
+        if run_frame is None:
+            msg = "Call stack not available. bdist_* commands may fail."
+            SetuptoolsWarning.emit(msg)
+            if platform.python_implementation() == 'IronPython':
+                msg = "For best results, pass -X:Frames to enable call stack."
+                SetuptoolsWarning.emit(msg)
+            return True
+
+        frames = inspect.getouterframes(run_frame)
+        for frame in frames[2:4]:
+            (caller,) = frame[:1]
+            info = inspect.getframeinfo(caller)
+            caller_module = caller.f_globals.get('__name__', '')
+
+            if caller_module == "setuptools.dist" and info.function == "run_command":
+                # Starting from v61.0.0 setuptools overwrites dist.run_command
+                continue
+
+            return caller_module == 'distutils.dist' and info.function == 'run_commands'
+
+        return False
+
+    def do_egg_install(self) -> None:
+        easy_install = self.distribution.get_command_class('easy_install')
+
+        cmd = cast(
+            # We'd want to cast easy_install as type[easy_install_cls] but a bug in
+            # mypy makes it think easy_install() returns a Command on Python 3.12+
+            # https://github.com/python/mypy/issues/18088
+            easy_install_cls,
+            easy_install(  # type: ignore[call-arg]
+                self.distribution,
+                args="x",
+                root=self.root,
+                record=self.record,
+            ),
+        )
+        cmd.ensure_finalized()  # finalize before bdist_egg munges install cmd
+        cmd.always_copy_from = '.'  # make sure local-dir eggs get installed
+
+        # pick up setup-dir .egg files only: no .egg-info
+        cmd.package_index.scan(glob.glob('*.egg'))
+
+        self.run_command('bdist_egg')
+        bdist_egg = cast(bdist_egg_cls, self.distribution.get_command_obj('bdist_egg'))
+        args = [bdist_egg.egg_output]
+
+        if setuptools.bootstrap_install_from:
+            # Bootstrap self-installation of setuptools
+            args.insert(0, setuptools.bootstrap_install_from)
+
+        cmd.args = args
+        cmd.run(show_deprecation=False)
+        setuptools.bootstrap_install_from = None
+
+
+# XXX Python 3.1 doesn't see _nc if this is inside the class
+install.sub_commands = [
+    cmd for cmd in orig.install.sub_commands if cmd[0] not in install._nc
+] + install.new_commands
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/install_egg_info.py b/.venv/lib/python3.12/site-packages/setuptools/command/install_egg_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..44f22ccf51cc3fb1662b326c3150845356d77fc3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/install_egg_info.py
@@ -0,0 +1,58 @@
+import os
+
+from setuptools import Command, namespaces
+from setuptools.archive_util import unpack_archive
+
+from .._path import ensure_directory
+
+from distutils import dir_util, log
+
+
+class install_egg_info(namespaces.Installer, Command):
+    """Install an .egg-info directory for the package"""
+
+    description = "Install an .egg-info directory for the package"
+
+    user_options = [
+        ('install-dir=', 'd', "directory to install to"),
+    ]
+
+    def initialize_options(self):
+        self.install_dir = None
+
+    def finalize_options(self) -> None:
+        self.set_undefined_options('install_lib', ('install_dir', 'install_dir'))
+        ei_cmd = self.get_finalized_command("egg_info")
+        basename = f"{ei_cmd._get_egg_basename()}.egg-info"
+        self.source = ei_cmd.egg_info
+        self.target = os.path.join(self.install_dir, basename)
+        self.outputs: list[str] = []
+
+    def run(self) -> None:
+        self.run_command('egg_info')
+        if os.path.isdir(self.target) and not os.path.islink(self.target):
+            dir_util.remove_tree(self.target, dry_run=self.dry_run)
+        elif os.path.exists(self.target):
+            self.execute(os.unlink, (self.target,), "Removing " + self.target)
+        if not self.dry_run:
+            ensure_directory(self.target)
+        self.execute(self.copytree, (), f"Copying {self.source} to {self.target}")
+        self.install_namespaces()
+
+    def get_outputs(self):
+        return self.outputs
+
+    def copytree(self) -> None:
+        # Copy the .egg-info tree to site-packages
+        def skimmer(src, dst):
+            # filter out source-control directories; note that 'src' is always
+            # a '/'-separated path, regardless of platform.  'dst' is a
+            # platform-specific path.
+            for skip in '.svn/', 'CVS/':
+                if src.startswith(skip) or '/' + skip in src:
+                    return None
+            self.outputs.append(dst)
+            log.debug("Copying %s to %s", src, dst)
+            return dst
+
+        unpack_archive(self.source, self.target, skimmer)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/install_lib.py b/.venv/lib/python3.12/site-packages/setuptools/command/install_lib.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e1e07271028386e138585cac7619ab0338017cc
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/install_lib.py
@@ -0,0 +1,137 @@
+from __future__ import annotations
+
+import os
+import sys
+from itertools import product, starmap
+
+from .._path import StrPath
+from ..dist import Distribution
+
+import distutils.command.install_lib as orig
+
+
+class install_lib(orig.install_lib):
+    """Don't add compiled flags to filenames of non-Python files"""
+
+    distribution: Distribution  # override distutils.dist.Distribution with setuptools.dist.Distribution
+
+    def run(self) -> None:
+        self.build()
+        outfiles = self.install()
+        if outfiles is not None:
+            # always compile, in case we have any extension stubs to deal with
+            self.byte_compile(outfiles)
+
+    def get_exclusions(self):
+        """
+        Return a collections.Sized collections.Container of paths to be
+        excluded for single_version_externally_managed installations.
+        """
+        all_packages = (
+            pkg
+            for ns_pkg in self._get_SVEM_NSPs()
+            for pkg in self._all_packages(ns_pkg)
+        )
+
+        excl_specs = product(all_packages, self._gen_exclusion_paths())
+        return set(starmap(self._exclude_pkg_path, excl_specs))
+
+    def _exclude_pkg_path(self, pkg, exclusion_path):
+        """
+        Given a package name and exclusion path within that package,
+        compute the full exclusion path.
+        """
+        parts = pkg.split('.') + [exclusion_path]
+        return os.path.join(self.install_dir, *parts)
+
+    @staticmethod
+    def _all_packages(pkg_name):
+        """
+        >>> list(install_lib._all_packages('foo.bar.baz'))
+        ['foo.bar.baz', 'foo.bar', 'foo']
+        """
+        while pkg_name:
+            yield pkg_name
+            pkg_name, _sep, _child = pkg_name.rpartition('.')
+
+    def _get_SVEM_NSPs(self):
+        """
+        Get namespace packages (list) but only for
+        single_version_externally_managed installations and empty otherwise.
+        """
+        # TODO: is it necessary to short-circuit here? i.e. what's the cost
+        # if get_finalized_command is called even when namespace_packages is
+        # False?
+        if not self.distribution.namespace_packages:
+            return []
+
+        install_cmd = self.get_finalized_command('install')
+        svem = install_cmd.single_version_externally_managed
+
+        return self.distribution.namespace_packages if svem else []
+
+    @staticmethod
+    def _gen_exclusion_paths():
+        """
+        Generate file paths to be excluded for namespace packages (bytecode
+        cache files).
+        """
+        # always exclude the package module itself
+        yield '__init__.py'
+
+        yield '__init__.pyc'
+        yield '__init__.pyo'
+
+        if not hasattr(sys, 'implementation'):
+            return
+
+        base = os.path.join('__pycache__', '__init__.' + sys.implementation.cache_tag)
+        yield base + '.pyc'
+        yield base + '.pyo'
+        yield base + '.opt-1.pyc'
+        yield base + '.opt-2.pyc'
+
+    def copy_tree(
+        self,
+        infile: StrPath,
+        outfile: str,
+        # override: Using actual booleans
+        preserve_mode: bool = True,  # type: ignore[override]
+        preserve_times: bool = True,  # type: ignore[override]
+        preserve_symlinks: bool = False,  # type: ignore[override]
+        level: object = 1,
+    ) -> list[str]:
+        assert preserve_mode
+        assert preserve_times
+        assert not preserve_symlinks
+        exclude = self.get_exclusions()
+
+        if not exclude:
+            return orig.install_lib.copy_tree(self, infile, outfile)
+
+        # Exclude namespace package __init__.py* files from the output
+
+        from setuptools.archive_util import unpack_directory
+
+        from distutils import log
+
+        outfiles: list[str] = []
+
+        def pf(src: str, dst: str):
+            if dst in exclude:
+                log.warn("Skipping installation of %s (namespace package)", dst)
+                return False
+
+            log.info("copying %s -> %s", src, os.path.dirname(dst))
+            outfiles.append(dst)
+            return dst
+
+        unpack_directory(infile, outfile, pf)
+        return outfiles
+
+    def get_outputs(self):
+        outputs = orig.install_lib.get_outputs(self)
+        exclude = self.get_exclusions()
+        if exclude:
+            return [f for f in outputs if f not in exclude]
+        return outputs
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/install_scripts.py b/.venv/lib/python3.12/site-packages/setuptools/command/install_scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..4401cf693d2642abce449dee238c037038633fec
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/install_scripts.py
@@ -0,0 +1,73 @@
+from __future__ import annotations
+
+import os
+import sys
+
+from .._path import ensure_directory
+from ..dist import Distribution
+
+import distutils.command.install_scripts as orig
+from distutils import log
+
+
+class install_scripts(orig.install_scripts):
+    """Do normal script install, plus any egg_info wrapper scripts"""
+
+    distribution: Distribution  # override distutils.dist.Distribution with setuptools.dist.Distribution
+
+    def initialize_options(self) -> None:
+        orig.install_scripts.initialize_options(self)
+        self.no_ep = False
+
+    def run(self) -> None:
+        self.run_command("egg_info")
+        if self.distribution.scripts:
+            orig.install_scripts.run(self)  # run first to set up self.outfiles
+        else:
+            self.outfiles: list[str] = []
+        if self.no_ep:
+            # don't install entry point scripts into .egg file!
+            return
+        self._install_ep_scripts()
+
+    def _install_ep_scripts(self):
+        # Delay import side-effects
+        from pkg_resources import Distribution, PathMetadata
+
+        from . import easy_install as ei
+
+        ei_cmd = self.get_finalized_command("egg_info")
+        dist = Distribution(
+            ei_cmd.egg_base,
+            PathMetadata(ei_cmd.egg_base, ei_cmd.egg_info),
+            ei_cmd.egg_name,
+            ei_cmd.egg_version,
+        )
+        bs_cmd = self.get_finalized_command('build_scripts')
+        exec_param = getattr(bs_cmd, 'executable', None)
+        writer = ei.ScriptWriter
+        if exec_param == sys.executable:
+            # In case the path to the Python executable contains a space, wrap
+            # it so it's not split up.
+            exec_param = [exec_param]
+        # resolve the writer to the environment
+        writer = writer.best()
+        cmd = writer.command_spec_class.best().from_param(exec_param)
+        for args in writer.get_args(dist, cmd.as_header()):
+            self.write_script(*args)
+
+    def write_script(self, script_name, contents, mode: str = "t", *ignored) -> None:
+        """Write an executable file to the scripts directory"""
+        from setuptools.command.easy_install import chmod, current_umask
+
+        log.info("Installing %s script to %s", script_name, self.install_dir)
+        target = os.path.join(self.install_dir, script_name)
+        self.outfiles.append(target)
+
+        encoding = None if "b" in mode else "utf-8"
+        mask = current_umask()
+        if not self.dry_run:
+            ensure_directory(target)
+            with open(target, "w" + mode, encoding=encoding) as f:
+                f.write(contents)
+            chmod(target, 0o777 - mask)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/launcher manifest.xml b/.venv/lib/python3.12/site-packages/setuptools/command/launcher manifest.xml
new file mode 100644
index 0000000000000000000000000000000000000000..5972a96d8ded85cc14147ffc1400ec67c3b5a578
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/launcher manifest.xml	
@@ -0,0 +1,15 @@
+<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
+<assembly xmlns="urn:schemas-microsoft-com:asm.v1" manifestVersion="1.0">
+    <assemblyIdentity version="1.0.0.0"
+                      processorArchitecture="X86"
+                      name="%(name)s"
+                      type="win32"/>
+    <!-- Identify the application security requirements. -->
+    <trustInfo xmlns="urn:schemas-microsoft-com:asm.v3">
+        <security>
+            <requestedPrivileges>
+                <requestedExecutionLevel level="asInvoker" uiAccess="false"/>
+            </requestedPrivileges>
+        </security>
+    </trustInfo>
+</assembly>
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/rotate.py b/.venv/lib/python3.12/site-packages/setuptools/command/rotate.py
new file mode 100644
index 0000000000000000000000000000000000000000..acdce07baaf3e4fc87552c39d7ee6e0271fe2a72
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/rotate.py
@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+import os
+from typing import ClassVar
+
+from .. import Command, _shutil
+
+from distutils import log
+from distutils.errors import DistutilsOptionError
+from distutils.util import convert_path
+
+
+class rotate(Command):
+    """Delete older distributions"""
+
+    description = "delete older distributions, keeping N newest files"
+    user_options = [
+        ('match=', 'm', "patterns to match (required)"),
+        ('dist-dir=', 'd', "directory where the distributions are"),
+        ('keep=', 'k', "number of matching distributions to keep"),
+    ]
+
+    boolean_options: ClassVar[list[str]] = []
+
+    def initialize_options(self):
+        self.match = None
+        self.dist_dir = None
+        self.keep = None
+
+    def finalize_options(self) -> None:
+        if self.match is None:
+            raise DistutilsOptionError(
+                "Must specify one or more (comma-separated) match patterns "
+                "(e.g. '.zip' or '.egg')"
+            )
+        if self.keep is None:
+            raise DistutilsOptionError("Must specify number of files to keep")
+        try:
+            self.keep = int(self.keep)
+        except ValueError as e:
+            raise DistutilsOptionError("--keep must be an integer") from e
+        if isinstance(self.match, str):
+            self.match = [convert_path(p.strip()) for p in self.match.split(',')]
+        self.set_undefined_options('bdist', ('dist_dir', 'dist_dir'))
+
+    def run(self) -> None:
+        self.run_command("egg_info")
+        from glob import glob
+
+        for pattern in self.match:
+            pattern = self.distribution.get_name() + '*' + pattern
+            files = glob(os.path.join(self.dist_dir, pattern))
+            files = [(os.path.getmtime(f), f) for f in files]
+            files.sort()
+            files.reverse()
+
+            log.info("%d file(s) matching %s", len(files), pattern)
+            files = files[self.keep :]
+            for t, f in files:
+                log.info("Deleting %s", f)
+                if not self.dry_run:
+                    if os.path.isdir(f):
+                        _shutil.rmtree(f)
+                    else:
+                        os.unlink(f)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/saveopts.py b/.venv/lib/python3.12/site-packages/setuptools/command/saveopts.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a2cbce6e2aad396f4a5ee9788d53387237df96d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/saveopts.py
@@ -0,0 +1,21 @@
+from setuptools.command.setopt import edit_config, option_base
+
+
+class saveopts(option_base):
+    """Save command-line options to a file"""
+
+    description = "save supplied options to setup.cfg or other config file"
+
+    def run(self) -> None:
+        dist = self.distribution
+        settings: dict[str, dict[str, str]] = {}
+
+        for cmd in dist.command_options:
+            if cmd == 'saveopts':
+                continue  # don't save our own options!
+
+            for opt, (src, val) in dist.get_option_dict(cmd).items():
+                if src == "command line":
+                    settings.setdefault(cmd, {})[opt] = val
+
+        edit_config(self.filename, settings, self.dry_run)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/sdist.py b/.venv/lib/python3.12/site-packages/setuptools/command/sdist.py
new file mode 100644
index 0000000000000000000000000000000000000000..9631cf3114778a8865917d02878525fc0127a253
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/sdist.py
@@ -0,0 +1,217 @@
+from __future__ import annotations
+
+import contextlib
+import os
+import re
+from itertools import chain
+from typing import ClassVar
+
+from .._importlib import metadata
+from ..dist import Distribution
+from .build import _ORIGINAL_SUBCOMMANDS
+
+import distutils.command.sdist as orig
+from distutils import log
+
+_default_revctrl = list
+
+
+def walk_revctrl(dirname=''):
+    """Find all files under revision control"""
+    for ep in metadata.entry_points(group='setuptools.file_finders'):
+        yield from ep.load()(dirname)
+
+
+class sdist(orig.sdist):
+    """Smart sdist that finds anything supported by revision control"""
+
+    user_options = [
+        ('formats=', None, "formats for source distribution (comma-separated list)"),
+        (
+            'keep-temp',
+            'k',
+            "keep the distribution tree around after creating " + "archive file(s)",
+        ),
+        (
+            'dist-dir=',
+            'd',
+            "directory to put the source distribution archive(s) in [default: dist]",
+        ),
+        (
+            'owner=',
+            'u',
+            "Owner name used when creating a tar file [default: current user]",
+        ),
+        (
+            'group=',
+            'g',
+            "Group name used when creating a tar file [default: current group]",
+        ),
+    ]
+
+    distribution: Distribution  # override distutils.dist.Distribution with setuptools.dist.Distribution
+    negative_opt: ClassVar[dict[str, str]] = {}
+
+    README_EXTENSIONS = ['', '.rst', '.txt', '.md']
+    READMES = tuple(f'README{ext}' for ext in README_EXTENSIONS)
+
+    def run(self) -> None:
+        self.run_command('egg_info')
+        ei_cmd = self.get_finalized_command('egg_info')
+        self.filelist = ei_cmd.filelist
+        self.filelist.append(os.path.join(ei_cmd.egg_info, 'SOURCES.txt'))
+        self.check_readme()
+
+        # Run sub commands
+        for cmd_name in self.get_sub_commands():
+            self.run_command(cmd_name)
+
+        self.make_distribution()
+
+        dist_files = getattr(self.distribution, 'dist_files', [])
+        for file in self.archive_files:
+            data = ('sdist', '', file)
+            if data not in dist_files:
+                dist_files.append(data)
+
+    def initialize_options(self) -> None:
+        orig.sdist.initialize_options(self)
+
+    def make_distribution(self) -> None:
+        """
+        Workaround for #516
+        """
+        with self._remove_os_link():
+            orig.sdist.make_distribution(self)
+
+    @staticmethod
+    @contextlib.contextmanager
+    def _remove_os_link():
+        """
+        In a context, remove and restore os.link if it exists
+        """
+
+        class NoValue:
+            pass
+
+        orig_val = getattr(os, 'link', NoValue)
+        try:
+            del os.link
+        except Exception:
+            pass
+        try:
+            yield
+        finally:
+            if orig_val is not NoValue:
+                os.link = orig_val
+
+    def add_defaults(self) -> None:
+        super().add_defaults()
+        self._add_defaults_build_sub_commands()
+
+    def _add_defaults_optional(self):
+        super()._add_defaults_optional()
+        if os.path.isfile('pyproject.toml'):
+            self.filelist.append('pyproject.toml')
+
+    def _add_defaults_python(self):
+        """getting python files"""
+        if self.distribution.has_pure_modules():
+            build_py = self.get_finalized_command('build_py')
+            self.filelist.extend(build_py.get_source_files())
+            self._add_data_files(self._safe_data_files(build_py))
+
+    def _add_defaults_build_sub_commands(self):
+        build = self.get_finalized_command("build")
+        missing_cmds = set(build.get_sub_commands()) - _ORIGINAL_SUBCOMMANDS
+        # ^-- the original built-in sub-commands are already handled by default.
+        cmds = (self.get_finalized_command(c) for c in missing_cmds)
+        files = (c.get_source_files() for c in cmds if hasattr(c, "get_source_files"))
+        self.filelist.extend(chain.from_iterable(files))
+
+    def _safe_data_files(self, build_py):
+        """
+        Since the ``sdist`` class is also used to compute the MANIFEST
+        (via :obj:`setuptools.command.egg_info.manifest_maker`),
+        there might be recursion problems when trying to obtain the list of
+        data_files and ``include_package_data=True`` (which in turn depends on
+        the files included in the MANIFEST).
+
+        To avoid that, ``manifest_maker`` should be able to overwrite this
+        method and avoid recursive attempts to build/analyze the MANIFEST.
+        """
+        return build_py.data_files
+
+    def _add_data_files(self, data_files):
+        """
+        Add data files as found in build_py.data_files.
+        """
+        self.filelist.extend(
+            os.path.join(src_dir, name)
+            for _, src_dir, _, filenames in data_files
+            for name in filenames
+        )
+
+    def _add_defaults_data_files(self):
+        try:
+            super()._add_defaults_data_files()
+        except TypeError:
+            log.warn("data_files contains unexpected objects")
+
+    def prune_file_list(self) -> None:
+        super().prune_file_list()
+        # Prevent accidental inclusion of test-related cache dirs at the project root
+        sep = re.escape(os.sep)
+        self.filelist.exclude_pattern(r"^(\.tox|\.nox|\.venv)" + sep, is_regex=True)
+
+    def check_readme(self) -> None:
+        for f in self.READMES:
+            if os.path.exists(f):
+                return
+        else:
+            self.warn(
+                "standard file not found: should have one of " + ', '.join(self.READMES)
+            )
+
+    def make_release_tree(self, base_dir, files) -> None:
+        orig.sdist.make_release_tree(self, base_dir, files)
+
+        # Save any egg_info command line options used to create this sdist
+        dest = os.path.join(base_dir, 'setup.cfg')
+        if hasattr(os, 'link') and os.path.exists(dest):
+            # unlink and re-copy, since it might be hard-linked, and
+            # we don't want to change the source version
+            os.unlink(dest)
+            self.copy_file('setup.cfg', dest)
+
+        self.get_finalized_command('egg_info').save_version_info(dest)
+
+    def _manifest_is_not_generated(self):
+        # check for special comment used in 2.7.1 and higher
+        if not os.path.isfile(self.manifest):
+            return False
+
+        with open(self.manifest, 'rb') as fp:
+            first_line = fp.readline()
+        return first_line != b'# file GENERATED by distutils, do NOT edit\n'
+
+    def read_manifest(self):
+        """Read the manifest file (named by 'self.manifest') and use it to
+        fill in 'self.filelist', the list of files to include in the source
+        distribution.
+        """
+        log.info("reading manifest file '%s'", self.manifest)
+        manifest = open(self.manifest, 'rb')
+        for bytes_line in manifest:
+            # The manifest must contain UTF-8. See #303.
+            try:
+                line = bytes_line.decode('UTF-8')
+            except UnicodeDecodeError:
+                log.warn(f"{line!r} not UTF-8 decodable -- skipping")
+                continue
+            # ignore comments and blank lines
+            line = line.strip()
+            if line.startswith('#') or not line:
+                continue
+            self.filelist.append(line)
+        manifest.close()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/setopt.py b/.venv/lib/python3.12/site-packages/setuptools/command/setopt.py
new file mode 100644
index 0000000000000000000000000000000000000000..678a0593d6739db316bd4008860a613b59892f1f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/setopt.py
@@ -0,0 +1,141 @@
+import configparser
+import os
+
+from .. import Command
+from ..unicode_utils import _cfg_read_utf8_with_fallback
+
+import distutils
+from distutils import log
+from distutils.errors import DistutilsOptionError
+from distutils.util import convert_path
+
+__all__ = ['config_file', 'edit_config', 'option_base', 'setopt']
+
+
+def config_file(kind="local"):
+    """Get the filename of the distutils, local, global, or per-user config
+
+    `kind` must be one of "local", "global", or "user"
+    """
+    if kind == 'local':
+        return 'setup.cfg'
+    if kind == 'global':
+        return os.path.join(os.path.dirname(distutils.__file__), 'distutils.cfg')
+    if kind == 'user':
+        dot = os.name == 'posix' and '.' or ''
+        return os.path.expanduser(convert_path(f"~/{dot}pydistutils.cfg"))
+    raise ValueError("config_file() type must be 'local', 'global', or 'user'", kind)
+
+
+def edit_config(filename, settings, dry_run=False):
+    """Edit a configuration file to include `settings`
+
+    `settings` is a dictionary of dictionaries or ``None`` values, keyed by
+    command/section name.  A ``None`` value means to delete the entire section,
+    while a dictionary lists settings to be changed or deleted in that section.
+    A setting of ``None`` means to delete that setting.
+    """
+    log.debug("Reading configuration from %s", filename)
+    opts = configparser.RawConfigParser()
+    opts.optionxform = lambda optionstr: optionstr  # type: ignore[method-assign] # overriding method
+    _cfg_read_utf8_with_fallback(opts, filename)
+
+    for section, options in settings.items():
+        if options is None:
+            log.info("Deleting section [%s] from %s", section, filename)
+            opts.remove_section(section)
+        else:
+            if not opts.has_section(section):
+                log.debug("Adding new section [%s] to %s", section, filename)
+                opts.add_section(section)
+            for option, value in options.items():
+                if value is None:
+                    log.debug("Deleting %s.%s from %s", section, option, filename)
+                    opts.remove_option(section, option)
+                    if not opts.options(section):
+                        log.info(
+                            "Deleting empty [%s] section from %s", section, filename
+                        )
+                        opts.remove_section(section)
+                else:
+                    log.debug(
+                        "Setting %s.%s to %r in %s", section, option, value, filename
+                    )
+                    opts.set(section, option, value)
+
+    log.info("Writing %s", filename)
+    if not dry_run:
+        with open(filename, 'w', encoding="utf-8") as f:
+            opts.write(f)
+
+
+class option_base(Command):
+    """Abstract base class for commands that mess with config files"""
+
+    user_options = [
+        ('global-config', 'g', "save options to the site-wide distutils.cfg file"),
+        ('user-config', 'u', "save options to the current user's pydistutils.cfg file"),
+        ('filename=', 'f', "configuration file to use (default=setup.cfg)"),
+    ]
+
+    boolean_options = [
+        'global-config',
+        'user-config',
+    ]
+
+    def initialize_options(self):
+        self.global_config = None
+        self.user_config = None
+        self.filename = None
+
+    def finalize_options(self):
+        filenames = []
+        if self.global_config:
+            filenames.append(config_file('global'))
+        if self.user_config:
+            filenames.append(config_file('user'))
+        if self.filename is not None:
+            filenames.append(self.filename)
+        if not filenames:
+            filenames.append(config_file('local'))
+        if len(filenames) > 1:
+            raise DistutilsOptionError(
+                "Must specify only one configuration file option", filenames
+            )
+        (self.filename,) = filenames
+
+
+class setopt(option_base):
+    """Save command-line options to a file"""
+
+    description = "set an option in setup.cfg or another config file"
+
+    user_options = [
+        ('command=', 'c', 'command to set an option for'),
+        ('option=', 'o', 'option to set'),
+        ('set-value=', 's', 'value of the option'),
+        ('remove', 'r', 'remove (unset) the value'),
+    ] + option_base.user_options
+
+    boolean_options = option_base.boolean_options + ['remove']
+
+    def initialize_options(self):
+        option_base.initialize_options(self)
+        self.command = None
+        self.option = None
+        self.set_value = None
+        self.remove = None
+
+    def finalize_options(self) -> None:
+        option_base.finalize_options(self)
+        if self.command is None or self.option is None:
+            raise DistutilsOptionError("Must specify --command *and* --option")
+        if self.set_value is None and not self.remove:
+            raise DistutilsOptionError("Must specify --set-value or --remove")
+
+    def run(self) -> None:
+        edit_config(
+            self.filename,
+            {self.command: {self.option.replace('-', '_'): self.set_value}},
+            self.dry_run,
+        )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/command/test.py b/.venv/lib/python3.12/site-packages/setuptools/command/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..341b11a20eb2cce98cdeeebea9ba2de0282d5d38
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/command/test.py
@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+from setuptools import Command
+from setuptools.warnings import SetuptoolsDeprecationWarning
+
+
+# Would restrict to Literal["test"], but mypy doesn't support it: https://github.com/python/mypy/issues/8203
+def __getattr__(name: str) -> type[_test]:
+    if name == 'test':
+        SetuptoolsDeprecationWarning.emit(
+            "The test command is disabled and references to it are deprecated.",
+            "Please remove any references to `setuptools.command.test` in all "
+            "supported versions of the affected package.",
+            due_date=(2024, 11, 15),
+            stacklevel=2,
+        )
+        return _test
+    raise AttributeError(name)
+
+
+class _test(Command):
+    """
+    Stub to warn when test command is referenced or used.
+    """
+
+    description = "stub for old test command (do not use)"
+
+    user_options = [
+        ('test-module=', 'm', "Run 'test_suite' in specified module"),
+        (
+            'test-suite=',
+            's',
+            "Run single test, case or suite (e.g. 'module.test_suite')",
+        ),
+        ('test-runner=', 'r', "Test runner to use"),
+    ]
+
+    def initialize_options(self):
+        pass
+
+    def finalize_options(self):
+        pass
+
+    def run(self):
+        raise RuntimeError("Support for the test command was removed in Setuptools 72")
diff --git a/.venv/lib/python3.12/site-packages/setuptools/compat/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/compat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/compat/__pycache__/py39.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/compat/__pycache__/py39.cpython-312.pyc
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index 0000000000000000000000000000000000000000..b23002b387a6e8d9e0e3e8fa80377f2a4f70736e
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/compat/py310.py b/.venv/lib/python3.12/site-packages/setuptools/compat/py310.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3912f8e02a57a5b9ba738673a21a596e94c1122
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/compat/py310.py
@@ -0,0 +1,9 @@
+import sys
+
+__all__ = ['tomllib']
+
+
+if sys.version_info >= (3, 11):
+    import tomllib
+else:  # pragma: no cover
+    import tomli as tomllib
diff --git a/.venv/lib/python3.12/site-packages/setuptools/compat/py311.py b/.venv/lib/python3.12/site-packages/setuptools/compat/py311.py
new file mode 100644
index 0000000000000000000000000000000000000000..52b58af32a28817deeaf592cc795f311434e39e7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/compat/py311.py
@@ -0,0 +1,27 @@
+from __future__ import annotations
+
+import shutil
+import sys
+from typing import TYPE_CHECKING, Any, Callable
+
+if TYPE_CHECKING:
+    from _typeshed import ExcInfo, StrOrBytesPath
+    from typing_extensions import TypeAlias
+
+# Same as shutil._OnExcCallback from typeshed
+_OnExcCallback: TypeAlias = Callable[[Callable[..., Any], str, BaseException], object]
+
+
+def shutil_rmtree(
+    path: StrOrBytesPath,
+    ignore_errors: bool = False,
+    onexc: _OnExcCallback | None = None,
+) -> None:
+    if sys.version_info >= (3, 12):
+        return shutil.rmtree(path, ignore_errors, onexc=onexc)
+
+    def _handler(fn: Callable[..., Any], path: str, excinfo: ExcInfo) -> None:
+        if onexc:
+            onexc(fn, path, excinfo[1])
+
+    return shutil.rmtree(path, ignore_errors, onerror=_handler)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/compat/py312.py b/.venv/lib/python3.12/site-packages/setuptools/compat/py312.py
new file mode 100644
index 0000000000000000000000000000000000000000..b20c5f697a861a680c4a747baf9ca9c901e72978
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/compat/py312.py
@@ -0,0 +1,13 @@
+from __future__ import annotations
+
+import sys
+
+if sys.version_info >= (3, 12, 4):
+    # Python 3.13 should support `.pth` files encoded in UTF-8
+    # See discussion in https://github.com/python/cpython/issues/77102
+    PTH_ENCODING: str | None = "utf-8"
+else:
+    from .py39 import LOCALE_ENCODING
+
+    # PTH_ENCODING = "locale"
+    PTH_ENCODING = LOCALE_ENCODING
diff --git a/.venv/lib/python3.12/site-packages/setuptools/compat/py39.py b/.venv/lib/python3.12/site-packages/setuptools/compat/py39.py
new file mode 100644
index 0000000000000000000000000000000000000000..04a4abe5a9e61596f97dc3d1b0b3da93a558158f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/compat/py39.py
@@ -0,0 +1,9 @@
+import sys
+
+# Explicitly use the ``"locale"`` encoding in versions that support it,
+# otherwise just rely on the implicit handling of ``encoding=None``.
+# Since all platforms that support ``EncodingWarning`` also support
+# ``encoding="locale"``, this can be used to suppress the warning.
+# However, please try to use UTF-8 when possible
+# (.pth files are the notorious exception: python/cpython#77102, pypa/setuptools#3937).
+LOCALE_ENCODING = "locale" if sys.version_info >= (3, 10) else None
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/NOTICE b/.venv/lib/python3.12/site-packages/setuptools/config/NOTICE
new file mode 100644
index 0000000000000000000000000000000000000000..01864511b0f52a6bf944f70b5e15b75364791935
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/NOTICE
@@ -0,0 +1,10 @@
+The following files include code from opensource projects
+(either as direct copies or modified versions):
+
+- `setuptools.schema.json`, `distutils.schema.json`:
+    - project: `validate-pyproject` - licensed under MPL-2.0
+      (https://github.com/abravalheri/validate-pyproject):
+
+      This Source Code Form is subject to the terms of the Mozilla Public
+      License, v. 2.0. If a copy of the MPL was not distributed with this file,
+      You can obtain one at https://mozilla.org/MPL/2.0/.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcc7d008d6c9b3ab2610f2a1b05c4c1eeb5e1416
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/__init__.py
@@ -0,0 +1,43 @@
+"""For backward compatibility, expose main functions from
+``setuptools.config.setupcfg``
+"""
+
+from functools import wraps
+from typing import Callable, TypeVar, cast
+
+from ..warnings import SetuptoolsDeprecationWarning
+from . import setupcfg
+
+Fn = TypeVar("Fn", bound=Callable)
+
+__all__ = ('parse_configuration', 'read_configuration')
+
+
+def _deprecation_notice(fn: Fn) -> Fn:
+    @wraps(fn)
+    def _wrapper(*args, **kwargs):
+        SetuptoolsDeprecationWarning.emit(
+            "Deprecated API usage.",
+            f"""
+            As setuptools moves its configuration towards `pyproject.toml`,
+            `{__name__}.{fn.__name__}` became deprecated.
+
+            For the time being, you can use the `{setupcfg.__name__}` module
+            to access a backward compatible API, but this module is provisional
+            and might be removed in the future.
+
+            To read project metadata, consider using
+            ``build.util.project_wheel_metadata`` (https://pypi.org/project/build/).
+            For simple scenarios, you can also try parsing the file directly
+            with the help of ``configparser``.
+            """,
+            # due_date not defined yet, because the community still heavily relies on it
+            # Warning introduced in 24 Mar 2022
+        )
+        return fn(*args, **kwargs)
+
+    return cast(Fn, _wrapper)
+
+
+read_configuration = _deprecation_notice(setupcfg.read_configuration)
+parse_configuration = _deprecation_notice(setupcfg.parse_configuration)
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/_apply_pyprojecttoml.py b/.venv/lib/python3.12/site-packages/setuptools/config/_apply_pyprojecttoml.py
new file mode 100644
index 0000000000000000000000000000000000000000..9088bc1383c87d3ed62a5a4dc44481ffafa1e35e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/_apply_pyprojecttoml.py
@@ -0,0 +1,526 @@
+"""Translation layer between pyproject config and setuptools distribution and
+metadata objects.
+
+The distribution and metadata objects are modeled after (an old version of)
+core metadata, therefore configs in the format specified for ``pyproject.toml``
+need to be processed before being applied.
+
+**PRIVATE MODULE**: API reserved for setuptools internal usage only.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from collections.abc import Mapping
+from email.headerregistry import Address
+from functools import partial, reduce
+from inspect import cleandoc
+from itertools import chain
+from types import MappingProxyType
+from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union
+
+from .. import _static
+from .._path import StrPath
+from ..errors import InvalidConfigError, RemovedConfigError
+from ..extension import Extension
+from ..warnings import SetuptoolsDeprecationWarning, SetuptoolsWarning
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+    from setuptools._importlib import metadata
+    from setuptools.dist import Distribution
+
+    from distutils.dist import _OptionsList  # Comes from typeshed
+
+
+EMPTY: Mapping = MappingProxyType({})  # Immutable dict-like
+_ProjectReadmeValue: TypeAlias = Union[str, dict[str, str]]
+_Correspondence: TypeAlias = Callable[["Distribution", Any, Union[StrPath, None]], None]
+_T = TypeVar("_T")
+
+_logger = logging.getLogger(__name__)
+
+
+def apply(dist: Distribution, config: dict, filename: StrPath) -> Distribution:
+    """Apply configuration dict read with :func:`read_configuration`"""
+
+    if not config:
+        return dist  # short-circuit unrelated pyproject.toml file
+
+    root_dir = os.path.dirname(filename) or "."
+
+    _apply_project_table(dist, config, root_dir)
+    _apply_tool_table(dist, config, filename)
+
+    current_directory = os.getcwd()
+    os.chdir(root_dir)
+    try:
+        dist._finalize_requires()
+        dist._finalize_license_expression()
+        dist._finalize_license_files()
+    finally:
+        os.chdir(current_directory)
+
+    return dist
+
+
+def _apply_project_table(dist: Distribution, config: dict, root_dir: StrPath):
+    orig_config = config.get("project", {})
+    if not orig_config:
+        return  # short-circuit
+
+    project_table = {k: _static.attempt_conversion(v) for k, v in orig_config.items()}
+    _handle_missing_dynamic(dist, project_table)
+    _unify_entry_points(project_table)
+
+    for field, value in project_table.items():
+        norm_key = json_compatible_key(field)
+        corresp = PYPROJECT_CORRESPONDENCE.get(norm_key, norm_key)
+        if callable(corresp):
+            corresp(dist, value, root_dir)
+        else:
+            _set_config(dist, corresp, value)
+
+
+def _apply_tool_table(dist: Distribution, config: dict, filename: StrPath):
+    tool_table = config.get("tool", {}).get("setuptools", {})
+    if not tool_table:
+        return  # short-circuit
+
+    if "license-files" in tool_table:
+        if "license-files" in config.get("project", {}):
+            # https://github.com/pypa/setuptools/pull/4837#discussion_r2004983349
+            raise InvalidConfigError(
+                "'project.license-files' is defined already. "
+                "Remove 'tool.setuptools.license-files'."
+            )
+
+        pypa_guides = "guides/writing-pyproject-toml/#license-files"
+        SetuptoolsDeprecationWarning.emit(
+            "'tool.setuptools.license-files' is deprecated in favor of "
+            "'project.license-files' (available on setuptools>=77.0.0).",
+            see_url=f"https://packaging.python.org/en/latest/{pypa_guides}",
+            due_date=(2026, 2, 18),  # Warning introduced on 2025-02-18
+        )
+
+    for field, value in tool_table.items():
+        norm_key = json_compatible_key(field)
+
+        if norm_key in TOOL_TABLE_REMOVALS:
+            suggestion = cleandoc(TOOL_TABLE_REMOVALS[norm_key])
+            msg = f"""
+            The parameter `tool.setuptools.{field}` was long deprecated
+            and has been removed from `pyproject.toml`.
+            """
+            raise RemovedConfigError("\n".join([cleandoc(msg), suggestion]))
+
+        norm_key = TOOL_TABLE_RENAMES.get(norm_key, norm_key)
+        corresp = TOOL_TABLE_CORRESPONDENCE.get(norm_key, norm_key)
+        if callable(corresp):
+            corresp(dist, value)
+        else:
+            _set_config(dist, corresp, value)
+
+    _copy_command_options(config, dist, filename)
+
+
+def _handle_missing_dynamic(dist: Distribution, project_table: dict):
+    """Be temporarily forgiving with ``dynamic`` fields not listed in ``dynamic``"""
+    dynamic = set(project_table.get("dynamic", []))
+    for field, getter in _PREVIOUSLY_DEFINED.items():
+        if not (field in project_table or field in dynamic):
+            value = getter(dist)
+            if value:
+                _MissingDynamic.emit(field=field, value=value)
+                project_table[field] = _RESET_PREVIOUSLY_DEFINED.get(field)
+
+
+def json_compatible_key(key: str) -> str:
+    """As defined in :pep:`566#json-compatible-metadata`"""
+    return key.lower().replace("-", "_")
+
+
+def _set_config(dist: Distribution, field: str, value: Any):
+    val = _PREPROCESS.get(field, _noop)(dist, value)
+    setter = getattr(dist.metadata, f"set_{field}", None)
+    if setter:
+        setter(val)
+    elif hasattr(dist.metadata, field) or field in SETUPTOOLS_PATCHES:
+        setattr(dist.metadata, field, val)
+    else:
+        setattr(dist, field, val)
+
+
+_CONTENT_TYPES = {
+    ".md": "text/markdown",
+    ".rst": "text/x-rst",
+    ".txt": "text/plain",
+}
+
+
+def _guess_content_type(file: str) -> str | None:
+    _, ext = os.path.splitext(file.lower())
+    if not ext:
+        return None
+
+    if ext in _CONTENT_TYPES:
+        return _static.Str(_CONTENT_TYPES[ext])
+
+    valid = ", ".join(f"{k} ({v})" for k, v in _CONTENT_TYPES.items())
+    msg = f"only the following file extensions are recognized: {valid}."
+    raise ValueError(f"Undefined content type for {file}, {msg}")
+
+
+def _long_description(
+    dist: Distribution, val: _ProjectReadmeValue, root_dir: StrPath | None
+):
+    from setuptools.config import expand
+
+    file: str | tuple[()]
+    if isinstance(val, str):
+        file = val
+        text = expand.read_files(file, root_dir)
+        ctype = _guess_content_type(file)
+    else:
+        file = val.get("file") or ()
+        text = val.get("text") or expand.read_files(file, root_dir)
+        ctype = val["content-type"]
+
+    # XXX: Is it completely safe to assume static?
+    _set_config(dist, "long_description", _static.Str(text))
+
+    if ctype:
+        _set_config(dist, "long_description_content_type", _static.Str(ctype))
+
+    if file:
+        dist._referenced_files.add(file)
+
+
+def _license(dist: Distribution, val: str | dict, root_dir: StrPath | None):
+    from setuptools.config import expand
+
+    if isinstance(val, str):
+        if getattr(dist.metadata, "license", None):
+            SetuptoolsWarning.emit("`license` overwritten by `pyproject.toml`")
+            dist.metadata.license = None
+        _set_config(dist, "license_expression", _static.Str(val))
+    else:
+        pypa_guides = "guides/writing-pyproject-toml/#license"
+        SetuptoolsDeprecationWarning.emit(
+            "`project.license` as a TOML table is deprecated",
+            "Please use a simple string containing a SPDX expression for "
+            "`project.license`. You can also use `project.license-files`. "
+            "(Both options available on setuptools>=77.0.0).",
+            see_url=f"https://packaging.python.org/en/latest/{pypa_guides}",
+            due_date=(2026, 2, 18),  # Introduced on 2025-02-18
+        )
+        if "file" in val:
+            # XXX: Is it completely safe to assume static?
+            value = expand.read_files([val["file"]], root_dir)
+            _set_config(dist, "license", _static.Str(value))
+            dist._referenced_files.add(val["file"])
+        else:
+            _set_config(dist, "license", _static.Str(val["text"]))
+
+
+def _people(dist: Distribution, val: list[dict], _root_dir: StrPath | None, kind: str):
+    field = []
+    email_field = []
+    for person in val:
+        if "name" not in person:
+            email_field.append(person["email"])
+        elif "email" not in person:
+            field.append(person["name"])
+        else:
+            addr = Address(display_name=person["name"], addr_spec=person["email"])
+            email_field.append(str(addr))
+
+    if field:
+        _set_config(dist, kind, _static.Str(", ".join(field)))
+    if email_field:
+        _set_config(dist, f"{kind}_email", _static.Str(", ".join(email_field)))
+
+
+def _project_urls(dist: Distribution, val: dict, _root_dir: StrPath | None):
+    _set_config(dist, "project_urls", val)
+
+
+def _python_requires(dist: Distribution, val: str, _root_dir: StrPath | None):
+    _set_config(dist, "python_requires", _static.SpecifierSet(val))
+
+
+def _dependencies(dist: Distribution, val: list, _root_dir: StrPath | None):
+    if getattr(dist, "install_requires", []):
+        msg = "`install_requires` overwritten in `pyproject.toml` (dependencies)"
+        SetuptoolsWarning.emit(msg)
+    dist.install_requires = val
+
+
+def _optional_dependencies(dist: Distribution, val: dict, _root_dir: StrPath | None):
+    if getattr(dist, "extras_require", None):
+        msg = "`extras_require` overwritten in `pyproject.toml` (optional-dependencies)"
+        SetuptoolsWarning.emit(msg)
+    dist.extras_require = val
+
+
+def _ext_modules(dist: Distribution, val: list[dict]) -> list[Extension]:
+    existing = dist.ext_modules or []
+    args = ({k.replace("-", "_"): v for k, v in x.items()} for x in val)
+    new = [Extension(**kw) for kw in args]
+    return [*existing, *new]
+
+
+def _noop(_dist: Distribution, val: _T) -> _T:
+    return val
+
+
+def _identity(val: _T) -> _T:
+    return val
+
+
+def _unify_entry_points(project_table: dict):
+    project = project_table
+    given = project.pop("entry-points", project.pop("entry_points", {}))
+    entry_points = dict(given)  # Avoid problems with static
+    renaming = {"scripts": "console_scripts", "gui_scripts": "gui_scripts"}
+    for key, value in list(project.items()):  # eager to allow modifications
+        norm_key = json_compatible_key(key)
+        if norm_key in renaming:
+            # Don't skip even if value is empty (reason: reset missing `dynamic`)
+            entry_points[renaming[norm_key]] = project.pop(key)
+
+    if entry_points:
+        project["entry-points"] = {
+            name: [f"{k} = {v}" for k, v in group.items()]
+            for name, group in entry_points.items()
+            if group  # now we can skip empty groups
+        }
+        # Sometimes this will set `project["entry-points"] = {}`, and that is
+        # intentional (for resetting configurations that are missing `dynamic`).
+
+
+def _copy_command_options(pyproject: dict, dist: Distribution, filename: StrPath):
+    tool_table = pyproject.get("tool", {})
+    cmdclass = tool_table.get("setuptools", {}).get("cmdclass", {})
+    valid_options = _valid_command_options(cmdclass)
+
+    cmd_opts = dist.command_options
+    for cmd, config in pyproject.get("tool", {}).get("distutils", {}).items():
+        cmd = json_compatible_key(cmd)
+        valid = valid_options.get(cmd, set())
+        cmd_opts.setdefault(cmd, {})
+        for key, value in config.items():
+            key = json_compatible_key(key)
+            cmd_opts[cmd][key] = (str(filename), value)
+            if key not in valid:
+                # To avoid removing options that are specified dynamically we
+                # just log a warn...
+                _logger.warning(f"Command option {cmd}.{key} is not defined")
+
+
+def _valid_command_options(cmdclass: Mapping = EMPTY) -> dict[str, set[str]]:
+    from setuptools.dist import Distribution
+
+    from .._importlib import metadata
+
+    valid_options = {"global": _normalise_cmd_options(Distribution.global_options)}
+
+    unloaded_entry_points = metadata.entry_points(group='distutils.commands')
+    loaded_entry_points = (_load_ep(ep) for ep in unloaded_entry_points)
+    entry_points = (ep for ep in loaded_entry_points if ep)
+    for cmd, cmd_class in chain(entry_points, cmdclass.items()):
+        opts = valid_options.get(cmd, set())
+        opts = opts | _normalise_cmd_options(getattr(cmd_class, "user_options", []))
+        valid_options[cmd] = opts
+
+    return valid_options
+
+
+def _load_ep(ep: metadata.EntryPoint) -> tuple[str, type] | None:
+    if ep.value.startswith("wheel.bdist_wheel"):
+        # Ignore deprecated entrypoint from wheel and avoid warning pypa/wheel#631
+        # TODO: remove check when `bdist_wheel` has been fully removed from pypa/wheel
+        return None
+
+    # Ignore all the errors
+    try:
+        return (ep.name, ep.load())
+    except Exception as ex:
+        msg = f"{ex.__class__.__name__} while trying to load entry-point {ep.name}"
+        _logger.warning(f"{msg}: {ex}")
+        return None
+
+
+def _normalise_cmd_option_key(name: str) -> str:
+    return json_compatible_key(name).strip("_=")
+
+
+def _normalise_cmd_options(desc: _OptionsList) -> set[str]:
+    return {_normalise_cmd_option_key(fancy_option[0]) for fancy_option in desc}
+
+
+def _get_previous_entrypoints(dist: Distribution) -> dict[str, list]:
+    ignore = ("console_scripts", "gui_scripts")
+    value = getattr(dist, "entry_points", None) or {}
+    return {k: v for k, v in value.items() if k not in ignore}
+
+
+def _get_previous_scripts(dist: Distribution) -> list | None:
+    value = getattr(dist, "entry_points", None) or {}
+    return value.get("console_scripts")
+
+
+def _get_previous_gui_scripts(dist: Distribution) -> list | None:
+    value = getattr(dist, "entry_points", None) or {}
+    return value.get("gui_scripts")
+
+
+def _set_static_list_metadata(attr: str, dist: Distribution, val: list) -> None:
+    """Apply distutils metadata validation but preserve "static" behaviour"""
+    meta = dist.metadata
+    setter, getter = getattr(meta, f"set_{attr}"), getattr(meta, f"get_{attr}")
+    setter(val)
+    setattr(meta, attr, _static.List(getter()))
+
+
+def _attrgetter(attr):
+    """
+    Similar to ``operator.attrgetter`` but returns None if ``attr`` is not found
+    >>> from types import SimpleNamespace
+    >>> obj = SimpleNamespace(a=42, b=SimpleNamespace(c=13))
+    >>> _attrgetter("a")(obj)
+    42
+    >>> _attrgetter("b.c")(obj)
+    13
+    >>> _attrgetter("d")(obj) is None
+    True
+    """
+    return partial(reduce, lambda acc, x: getattr(acc, x, None), attr.split("."))
+
+
+def _some_attrgetter(*items):
+    """
+    Return the first "truth-y" attribute or None
+    >>> from types import SimpleNamespace
+    >>> obj = SimpleNamespace(a=42, b=SimpleNamespace(c=13))
+    >>> _some_attrgetter("d", "a", "b.c")(obj)
+    42
+    >>> _some_attrgetter("d", "e", "b.c", "a")(obj)
+    13
+    >>> _some_attrgetter("d", "e", "f")(obj) is None
+    True
+    """
+
+    def _acessor(obj):
+        values = (_attrgetter(i)(obj) for i in items)
+        return next((i for i in values if i is not None), None)
+
+    return _acessor
+
+
+PYPROJECT_CORRESPONDENCE: dict[str, _Correspondence] = {
+    "readme": _long_description,
+    "license": _license,
+    "authors": partial(_people, kind="author"),
+    "maintainers": partial(_people, kind="maintainer"),
+    "urls": _project_urls,
+    "dependencies": _dependencies,
+    "optional_dependencies": _optional_dependencies,
+    "requires_python": _python_requires,
+}
+
+TOOL_TABLE_RENAMES = {"script_files": "scripts"}
+TOOL_TABLE_REMOVALS = {
+    "namespace_packages": """
+        Please migrate to implicit native namespaces instead.
+        See https://packaging.python.org/en/latest/guides/packaging-namespace-packages/.
+        """,
+}
+TOOL_TABLE_CORRESPONDENCE = {
+    # Fields with corresponding core metadata need to be marked as static:
+    "obsoletes": partial(_set_static_list_metadata, "obsoletes"),
+    "provides": partial(_set_static_list_metadata, "provides"),
+    "platforms": partial(_set_static_list_metadata, "platforms"),
+}
+
+SETUPTOOLS_PATCHES = {
+    "long_description_content_type",
+    "project_urls",
+    "provides_extras",
+    "license_file",
+    "license_files",
+    "license_expression",
+}
+
+_PREPROCESS = {
+    "ext_modules": _ext_modules,
+}
+
+_PREVIOUSLY_DEFINED = {
+    "name": _attrgetter("metadata.name"),
+    "version": _attrgetter("metadata.version"),
+    "description": _attrgetter("metadata.description"),
+    "readme": _attrgetter("metadata.long_description"),
+    "requires-python": _some_attrgetter("python_requires", "metadata.python_requires"),
+    "license": _some_attrgetter("metadata.license_expression", "metadata.license"),
+    # XXX: `license-file` is currently not considered in the context of `dynamic`.
+    #      See TestPresetField.test_license_files_exempt_from_dynamic
+    "authors": _some_attrgetter("metadata.author", "metadata.author_email"),
+    "maintainers": _some_attrgetter("metadata.maintainer", "metadata.maintainer_email"),
+    "keywords": _attrgetter("metadata.keywords"),
+    "classifiers": _attrgetter("metadata.classifiers"),
+    "urls": _attrgetter("metadata.project_urls"),
+    "entry-points": _get_previous_entrypoints,
+    "scripts": _get_previous_scripts,
+    "gui-scripts": _get_previous_gui_scripts,
+    "dependencies": _attrgetter("install_requires"),
+    "optional-dependencies": _attrgetter("extras_require"),
+}
+
+
+_RESET_PREVIOUSLY_DEFINED: dict = {
+    # Fix improper setting: given in `setup.py`, but not listed in `dynamic`
+    # Use "immutable" data structures to avoid in-place modification.
+    # dict: pyproject name => value to which reset
+    "license": "",
+    # XXX: `license-file` is currently not considered in the context of `dynamic`.
+    #      See TestPresetField.test_license_files_exempt_from_dynamic
+    "authors": _static.EMPTY_LIST,
+    "maintainers": _static.EMPTY_LIST,
+    "keywords": _static.EMPTY_LIST,
+    "classifiers": _static.EMPTY_LIST,
+    "urls": _static.EMPTY_DICT,
+    "entry-points": _static.EMPTY_DICT,
+    "scripts": _static.EMPTY_DICT,
+    "gui-scripts": _static.EMPTY_DICT,
+    "dependencies": _static.EMPTY_LIST,
+    "optional-dependencies": _static.EMPTY_DICT,
+}
+
+
+class _MissingDynamic(SetuptoolsWarning):
+    _SUMMARY = "`{field}` defined outside of `pyproject.toml` is ignored."
+
+    _DETAILS = """
+    The following seems to be defined outside of `pyproject.toml`:
+
+    `{field} = {value!r}`
+
+    According to the spec (see the link below), however, setuptools CANNOT
+    consider this value unless `{field}` is listed as `dynamic`.
+
+    https://packaging.python.org/en/latest/specifications/pyproject-toml/#declaring-project-metadata-the-project-table
+
+    To prevent this problem, you can list `{field}` under `dynamic` or alternatively
+    remove the `[project]` table from your file and rely entirely on other means of
+    configuration.
+    """
+    # TODO: Consider removing this check in the future?
+    #       There is a trade-off here between improving "debug-ability" and the cost
+    #       of running/testing/maintaining these unnecessary checks...
+
+    @classmethod
+    def details(cls, field: str, value: Any) -> str:
+        return cls._DETAILS.format(field=field, value=value)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/NOTICE b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/NOTICE
new file mode 100644
index 0000000000000000000000000000000000000000..ac5464d88c6f071c18d8eceb1e81d2550573599b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/NOTICE
@@ -0,0 +1,438 @@
+The code contained in this directory was automatically generated using the
+following command:
+
+    python -m validate_pyproject.pre_compile --output-dir=setuptools/config/_validate_pyproject --enable-plugins setuptools distutils --very-verbose -t setuptools=setuptools/config/setuptools.schema.json -t distutils=setuptools/config/distutils.schema.json
+
+Please avoid changing it manually.
+
+
+You can report issues or suggest changes directly to `validate-pyproject`
+(or to the relevant plugin repository)
+
+- https://github.com/abravalheri/validate-pyproject/issues
+
+
+***
+
+The following files include code from opensource projects
+(either as direct copies or modified versions):
+
+- `fastjsonschema_exceptions.py`:
+    - project: `fastjsonschema` - licensed under BSD-3-Clause
+      (https://github.com/horejsek/python-fastjsonschema)
+- `extra_validations.py` and `format.py`, `error_reporting.py`:
+    - project: `validate-pyproject` - licensed under MPL-2.0
+      (https://github.com/abravalheri/validate-pyproject)
+
+
+Additionally the following files are automatically generated by tools provided
+by the same projects:
+
+- `__init__.py`
+- `fastjsonschema_validations.py`
+
+The relevant copyright notes and licenses are included below.
+
+
+***
+
+`fastjsonschema`
+================
+
+Copyright (c) 2018, Michal Horejsek
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+  Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  Redistributions in binary form must reproduce the above copyright notice, this
+  list of conditions and the following disclaimer in the documentation and/or
+  other materials provided with the distribution.
+
+  Neither the name of the {organization} nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+
+***
+
+`validate-pyproject`
+====================
+
+Mozilla Public License, version 2.0
+
+1. Definitions
+
+1.1. "Contributor"
+
+     means each individual or legal entity that creates, contributes to the
+     creation of, or owns Covered Software.
+
+1.2. "Contributor Version"
+
+     means the combination of the Contributions of others (if any) used by a
+     Contributor and that particular Contributor's Contribution.
+
+1.3. "Contribution"
+
+     means Covered Software of a particular Contributor.
+
+1.4. "Covered Software"
+
+     means Source Code Form to which the initial Contributor has attached the
+     notice in Exhibit A, the Executable Form of such Source Code Form, and
+     Modifications of such Source Code Form, in each case including portions
+     thereof.
+
+1.5. "Incompatible With Secondary Licenses"
+     means
+
+     a. that the initial Contributor has attached the notice described in
+        Exhibit B to the Covered Software; or
+
+     b. that the Covered Software was made available under the terms of
+        version 1.1 or earlier of the License, but not also under the terms of
+        a Secondary License.
+
+1.6. "Executable Form"
+
+     means any form of the work other than Source Code Form.
+
+1.7. "Larger Work"
+
+     means a work that combines Covered Software with other material, in a
+     separate file or files, that is not Covered Software.
+
+1.8. "License"
+
+     means this document.
+
+1.9. "Licensable"
+
+     means having the right to grant, to the maximum extent possible, whether
+     at the time of the initial grant or subsequently, any and all of the
+     rights conveyed by this License.
+
+1.10. "Modifications"
+
+     means any of the following:
+
+     a. any file in Source Code Form that results from an addition to,
+        deletion from, or modification of the contents of Covered Software; or
+
+     b. any new file in Source Code Form that contains any Covered Software.
+
+1.11. "Patent Claims" of a Contributor
+
+      means any patent claim(s), including without limitation, method,
+      process, and apparatus claims, in any patent Licensable by such
+      Contributor that would be infringed, but for the grant of the License,
+      by the making, using, selling, offering for sale, having made, import,
+      or transfer of either its Contributions or its Contributor Version.
+
+1.12. "Secondary License"
+
+      means either the GNU General Public License, Version 2.0, the GNU Lesser
+      General Public License, Version 2.1, the GNU Affero General Public
+      License, Version 3.0, or any later versions of those licenses.
+
+1.13. "Source Code Form"
+
+      means the form of the work preferred for making modifications.
+
+1.14. "You" (or "Your")
+
+      means an individual or a legal entity exercising rights under this
+      License. For legal entities, "You" includes any entity that controls, is
+      controlled by, or is under common control with You. For purposes of this
+      definition, "control" means (a) the power, direct or indirect, to cause
+      the direction or management of such entity, whether by contract or
+      otherwise, or (b) ownership of more than fifty percent (50%) of the
+      outstanding shares or beneficial ownership of such entity.
+
+
+2. License Grants and Conditions
+
+2.1. Grants
+
+     Each Contributor hereby grants You a world-wide, royalty-free,
+     non-exclusive license:
+
+     a. under intellectual property rights (other than patent or trademark)
+        Licensable by such Contributor to use, reproduce, make available,
+        modify, display, perform, distribute, and otherwise exploit its
+        Contributions, either on an unmodified basis, with Modifications, or
+        as part of a Larger Work; and
+
+     b. under Patent Claims of such Contributor to make, use, sell, offer for
+        sale, have made, import, and otherwise transfer either its
+        Contributions or its Contributor Version.
+
+2.2. Effective Date
+
+     The licenses granted in Section 2.1 with respect to any Contribution
+     become effective for each Contribution on the date the Contributor first
+     distributes such Contribution.
+
+2.3. Limitations on Grant Scope
+
+     The licenses granted in this Section 2 are the only rights granted under
+     this License. No additional rights or licenses will be implied from the
+     distribution or licensing of Covered Software under this License.
+     Notwithstanding Section 2.1(b) above, no patent license is granted by a
+     Contributor:
+
+     a. for any code that a Contributor has removed from Covered Software; or
+
+     b. for infringements caused by: (i) Your and any other third party's
+        modifications of Covered Software, or (ii) the combination of its
+        Contributions with other software (except as part of its Contributor
+        Version); or
+
+     c. under Patent Claims infringed by Covered Software in the absence of
+        its Contributions.
+
+     This License does not grant any rights in the trademarks, service marks,
+     or logos of any Contributor (except as may be necessary to comply with
+     the notice requirements in Section 3.4).
+
+2.4. Subsequent Licenses
+
+     No Contributor makes additional grants as a result of Your choice to
+     distribute the Covered Software under a subsequent version of this
+     License (see Section 10.2) or under the terms of a Secondary License (if
+     permitted under the terms of Section 3.3).
+
+2.5. Representation
+
+     Each Contributor represents that the Contributor believes its
+     Contributions are its original creation(s) or it has sufficient rights to
+     grant the rights to its Contributions conveyed by this License.
+
+2.6. Fair Use
+
+     This License is not intended to limit any rights You have under
+     applicable copyright doctrines of fair use, fair dealing, or other
+     equivalents.
+
+2.7. Conditions
+
+     Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted in
+     Section 2.1.
+
+
+3. Responsibilities
+
+3.1. Distribution of Source Form
+
+     All distribution of Covered Software in Source Code Form, including any
+     Modifications that You create or to which You contribute, must be under
+     the terms of this License. You must inform recipients that the Source
+     Code Form of the Covered Software is governed by the terms of this
+     License, and how they can obtain a copy of this License. You may not
+     attempt to alter or restrict the recipients' rights in the Source Code
+     Form.
+
+3.2. Distribution of Executable Form
+
+     If You distribute Covered Software in Executable Form then:
+
+     a. such Covered Software must also be made available in Source Code Form,
+        as described in Section 3.1, and You must inform recipients of the
+        Executable Form how they can obtain a copy of such Source Code Form by
+        reasonable means in a timely manner, at a charge no more than the cost
+        of distribution to the recipient; and
+
+     b. You may distribute such Executable Form under the terms of this
+        License, or sublicense it under different terms, provided that the
+        license for the Executable Form does not attempt to limit or alter the
+        recipients' rights in the Source Code Form under this License.
+
+3.3. Distribution of a Larger Work
+
+     You may create and distribute a Larger Work under terms of Your choice,
+     provided that You also comply with the requirements of this License for
+     the Covered Software. If the Larger Work is a combination of Covered
+     Software with a work governed by one or more Secondary Licenses, and the
+     Covered Software is not Incompatible With Secondary Licenses, this
+     License permits You to additionally distribute such Covered Software
+     under the terms of such Secondary License(s), so that the recipient of
+     the Larger Work may, at their option, further distribute the Covered
+     Software under the terms of either this License or such Secondary
+     License(s).
+
+3.4. Notices
+
+     You may not remove or alter the substance of any license notices
+     (including copyright notices, patent notices, disclaimers of warranty, or
+     limitations of liability) contained within the Source Code Form of the
+     Covered Software, except that You may alter any license notices to the
+     extent required to remedy known factual inaccuracies.
+
+3.5. Application of Additional Terms
+
+     You may choose to offer, and to charge a fee for, warranty, support,
+     indemnity or liability obligations to one or more recipients of Covered
+     Software. However, You may do so only on Your own behalf, and not on
+     behalf of any Contributor. You must make it absolutely clear that any
+     such warranty, support, indemnity, or liability obligation is offered by
+     You alone, and You hereby agree to indemnify every Contributor for any
+     liability incurred by such Contributor as a result of warranty, support,
+     indemnity or liability terms You offer. You may include additional
+     disclaimers of warranty and limitations of liability specific to any
+     jurisdiction.
+
+4. Inability to Comply Due to Statute or Regulation
+
+   If it is impossible for You to comply with any of the terms of this License
+   with respect to some or all of the Covered Software due to statute,
+   judicial order, or regulation then You must: (a) comply with the terms of
+   this License to the maximum extent possible; and (b) describe the
+   limitations and the code they affect. Such description must be placed in a
+   text file included with all distributions of the Covered Software under
+   this License. Except to the extent prohibited by statute or regulation,
+   such description must be sufficiently detailed for a recipient of ordinary
+   skill to be able to understand it.
+
+5. Termination
+
+5.1. The rights granted under this License will terminate automatically if You
+     fail to comply with any of its terms. However, if You become compliant,
+     then the rights granted under this License from a particular Contributor
+     are reinstated (a) provisionally, unless and until such Contributor
+     explicitly and finally terminates Your grants, and (b) on an ongoing
+     basis, if such Contributor fails to notify You of the non-compliance by
+     some reasonable means prior to 60 days after You have come back into
+     compliance. Moreover, Your grants from a particular Contributor are
+     reinstated on an ongoing basis if such Contributor notifies You of the
+     non-compliance by some reasonable means, this is the first time You have
+     received notice of non-compliance with this License from such
+     Contributor, and You become compliant prior to 30 days after Your receipt
+     of the notice.
+
+5.2. If You initiate litigation against any entity by asserting a patent
+     infringement claim (excluding declaratory judgment actions,
+     counter-claims, and cross-claims) alleging that a Contributor Version
+     directly or indirectly infringes any patent, then the rights granted to
+     You by any and all Contributors for the Covered Software under Section
+     2.1 of this License shall terminate.
+
+5.3. In the event of termination under Sections 5.1 or 5.2 above, all end user
+     license agreements (excluding distributors and resellers) which have been
+     validly granted by You or Your distributors under this License prior to
+     termination shall survive termination.
+
+6. Disclaimer of Warranty
+
+   Covered Software is provided under this License on an "as is" basis,
+   without warranty of any kind, either expressed, implied, or statutory,
+   including, without limitation, warranties that the Covered Software is free
+   of defects, merchantable, fit for a particular purpose or non-infringing.
+   The entire risk as to the quality and performance of the Covered Software
+   is with You. Should any Covered Software prove defective in any respect,
+   You (not any Contributor) assume the cost of any necessary servicing,
+   repair, or correction. This disclaimer of warranty constitutes an essential
+   part of this License. No use of  any Covered Software is authorized under
+   this License except under this disclaimer.
+
+7. Limitation of Liability
+
+   Under no circumstances and under no legal theory, whether tort (including
+   negligence), contract, or otherwise, shall any Contributor, or anyone who
+   distributes Covered Software as permitted above, be liable to You for any
+   direct, indirect, special, incidental, or consequential damages of any
+   character including, without limitation, damages for lost profits, loss of
+   goodwill, work stoppage, computer failure or malfunction, or any and all
+   other commercial damages or losses, even if such party shall have been
+   informed of the possibility of such damages. This limitation of liability
+   shall not apply to liability for death or personal injury resulting from
+   such party's negligence to the extent applicable law prohibits such
+   limitation. Some jurisdictions do not allow the exclusion or limitation of
+   incidental or consequential damages, so this exclusion and limitation may
+   not apply to You.
+
+8. Litigation
+
+   Any litigation relating to this License may be brought only in the courts
+   of a jurisdiction where the defendant maintains its principal place of
+   business and such litigation shall be governed by laws of that
+   jurisdiction, without reference to its conflict-of-law provisions. Nothing
+   in this Section shall prevent a party's ability to bring cross-claims or
+   counter-claims.
+
+9. Miscellaneous
+
+   This License represents the complete agreement concerning the subject
+   matter hereof. If any provision of this License is held to be
+   unenforceable, such provision shall be reformed only to the extent
+   necessary to make it enforceable. Any law or regulation which provides that
+   the language of a contract shall be construed against the drafter shall not
+   be used to construe this License against a Contributor.
+
+
+10. Versions of the License
+
+10.1. New Versions
+
+      Mozilla Foundation is the license steward. Except as provided in Section
+      10.3, no one other than the license steward has the right to modify or
+      publish new versions of this License. Each version will be given a
+      distinguishing version number.
+
+10.2. Effect of New Versions
+
+      You may distribute the Covered Software under the terms of the version
+      of the License under which You originally received the Covered Software,
+      or under the terms of any subsequent version published by the license
+      steward.
+
+10.3. Modified Versions
+
+      If you create software not governed by this License, and you want to
+      create a new license for such software, you may create and use a
+      modified version of this License if you rename the license and remove
+      any references to the name of the license steward (except to note that
+      such modified license differs from this License).
+
+10.4. Distributing Source Code Form that is Incompatible With Secondary
+      Licenses If You choose to distribute Source Code Form that is
+      Incompatible With Secondary Licenses under the terms of this version of
+      the License, the notice described in Exhibit B of this License must be
+      attached.
+
+Exhibit A - Source Code Form License Notice
+
+      This Source Code Form is subject to the
+      terms of the Mozilla Public License, v.
+      2.0. If a copy of the MPL was not
+      distributed with this file, You can
+      obtain one at
+      https://mozilla.org/MPL/2.0/.
+
+If it is not possible or desirable to put the notice in a particular file,
+then You may include the notice in a location (such as a LICENSE file in a
+relevant directory) where a recipient would be likely to look for such a
+notice.
+
+You may add additional accurate notices of copyright ownership.
+
+Exhibit B - "Incompatible With Secondary Licenses" Notice
+
+      This Source Code Form is "Incompatible
+      With Secondary Licenses", as defined by
+      the Mozilla Public License, v. 2.0.
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f612bd51c4ac1d272318ed6fb96e4aeb57e3e27
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/__init__.py
@@ -0,0 +1,34 @@
+from functools import reduce
+from typing import Any, Callable, Dict
+
+from . import formats
+from .error_reporting import detailed_errors, ValidationError
+from .extra_validations import EXTRA_VALIDATIONS
+from .fastjsonschema_exceptions import JsonSchemaException, JsonSchemaValueException
+from .fastjsonschema_validations import validate as _validate
+
+__all__ = [
+    "validate",
+    "FORMAT_FUNCTIONS",
+    "EXTRA_VALIDATIONS",
+    "ValidationError",
+    "JsonSchemaException",
+    "JsonSchemaValueException",
+]
+
+
+FORMAT_FUNCTIONS: Dict[str, Callable[[str], bool]] = {
+    fn.__name__.replace("_", "-"): fn
+    for fn in formats.__dict__.values()
+    if callable(fn) and not fn.__name__.startswith("_")
+}
+
+
+def validate(data: Any) -> bool:
+    """Validate the given ``data`` object using JSON Schema
+    This function raises ``ValidationError`` if ``data`` is invalid.
+    """
+    with detailed_errors():
+        _validate(data, custom_formats=FORMAT_FUNCTIONS)
+        reduce(lambda acc, fn: fn(acc), EXTRA_VALIDATIONS, data)
+    return True
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/__pycache__/fastjsonschema_validations.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/__pycache__/fastjsonschema_validations.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..b740eaabf15602a8780676e9e9759321144c5893
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/__pycache__/fastjsonschema_validations.cpython-312.pyc
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9fa30f2e8dc28794119de603c6d204647a967a49635997163f5b248c3c18dfe
+size 233678
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/__pycache__/formats.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/__pycache__/formats.cpython-312.pyc
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/error_reporting.py b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/error_reporting.py
new file mode 100644
index 0000000000000000000000000000000000000000..3591231c09f56494eb46119f9794053439b3fe8c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/error_reporting.py
@@ -0,0 +1,336 @@
+import io
+import json
+import logging
+import os
+import re
+import typing
+from contextlib import contextmanager
+from textwrap import indent, wrap
+from typing import Any, Dict, Generator, Iterator, List, Optional, Sequence, Union
+
+from .fastjsonschema_exceptions import JsonSchemaValueException
+
+if typing.TYPE_CHECKING:
+    import sys
+
+    if sys.version_info < (3, 11):
+        from typing_extensions import Self
+    else:
+        from typing import Self
+
+_logger = logging.getLogger(__name__)
+
+_MESSAGE_REPLACEMENTS = {
+    "must be named by propertyName definition": "keys must be named by",
+    "one of contains definition": "at least one item that matches",
+    " same as const definition:": "",
+    "only specified items": "only items matching the definition",
+}
+
+_SKIP_DETAILS = (
+    "must not be empty",
+    "is always invalid",
+    "must not be there",
+)
+
+_NEED_DETAILS = {"anyOf", "oneOf", "allOf", "contains", "propertyNames", "not", "items"}
+
+_CAMEL_CASE_SPLITTER = re.compile(r"\W+|([A-Z][^A-Z\W]*)")
+_IDENTIFIER = re.compile(r"^[\w_]+$", re.I)
+
+_TOML_JARGON = {
+    "object": "table",
+    "property": "key",
+    "properties": "keys",
+    "property names": "keys",
+}
+
+_FORMATS_HELP = """
+For more details about `format` see
+https://validate-pyproject.readthedocs.io/en/latest/api/validate_pyproject.formats.html
+"""
+
+
+class ValidationError(JsonSchemaValueException):
+    """Report violations of a given JSON schema.
+
+    This class extends :exc:`~fastjsonschema.JsonSchemaValueException`
+    by adding the following properties:
+
+    - ``summary``: an improved version of the ``JsonSchemaValueException`` error message
+      with only the necessary information)
+
+    - ``details``: more contextual information about the error like the failing schema
+      itself and the value that violates the schema.
+
+    Depending on the level of the verbosity of the ``logging`` configuration
+    the exception message will be only ``summary`` (default) or a combination of
+    ``summary`` and ``details`` (when the logging level is set to :obj:`logging.DEBUG`).
+    """
+
+    summary = ""
+    details = ""
+    _original_message = ""
+
+    @classmethod
+    def _from_jsonschema(cls, ex: JsonSchemaValueException) -> "Self":
+        formatter = _ErrorFormatting(ex)
+        obj = cls(str(formatter), ex.value, formatter.name, ex.definition, ex.rule)
+        debug_code = os.getenv("JSONSCHEMA_DEBUG_CODE_GENERATION", "false").lower()
+        if debug_code != "false":  # pragma: no cover
+            obj.__cause__, obj.__traceback__ = ex.__cause__, ex.__traceback__
+        obj._original_message = ex.message
+        obj.summary = formatter.summary
+        obj.details = formatter.details
+        return obj
+
+
+@contextmanager
+def detailed_errors() -> Generator[None, None, None]:
+    try:
+        yield
+    except JsonSchemaValueException as ex:
+        raise ValidationError._from_jsonschema(ex) from None
+
+
+class _ErrorFormatting:
+    def __init__(self, ex: JsonSchemaValueException):
+        self.ex = ex
+        self.name = f"`{self._simplify_name(ex.name)}`"
+        self._original_message: str = self.ex.message.replace(ex.name, self.name)
+        self._summary = ""
+        self._details = ""
+
+    def __str__(self) -> str:
+        if _logger.getEffectiveLevel() <= logging.DEBUG and self.details:
+            return f"{self.summary}\n\n{self.details}"
+
+        return self.summary
+
+    @property
+    def summary(self) -> str:
+        if not self._summary:
+            self._summary = self._expand_summary()
+
+        return self._summary
+
+    @property
+    def details(self) -> str:
+        if not self._details:
+            self._details = self._expand_details()
+
+        return self._details
+
+    @staticmethod
+    def _simplify_name(name: str) -> str:
+        x = len("data.")
+        return name[x:] if name.startswith("data.") else name
+
+    def _expand_summary(self) -> str:
+        msg = self._original_message
+
+        for bad, repl in _MESSAGE_REPLACEMENTS.items():
+            msg = msg.replace(bad, repl)
+
+        if any(substring in msg for substring in _SKIP_DETAILS):
+            return msg
+
+        schema = self.ex.rule_definition
+        if self.ex.rule in _NEED_DETAILS and schema:
+            summary = _SummaryWriter(_TOML_JARGON)
+            return f"{msg}:\n\n{indent(summary(schema), '    ')}"
+
+        return msg
+
+    def _expand_details(self) -> str:
+        optional = []
+        definition = self.ex.definition or {}
+        desc_lines = definition.pop("$$description", [])
+        desc = definition.pop("description", None) or " ".join(desc_lines)
+        if desc:
+            description = "\n".join(
+                wrap(
+                    desc,
+                    width=80,
+                    initial_indent="    ",
+                    subsequent_indent="    ",
+                    break_long_words=False,
+                )
+            )
+            optional.append(f"DESCRIPTION:\n{description}")
+        schema = json.dumps(definition, indent=4)
+        value = json.dumps(self.ex.value, indent=4)
+        defaults = [
+            f"GIVEN VALUE:\n{indent(value, '    ')}",
+            f"OFFENDING RULE: {self.ex.rule!r}",
+            f"DEFINITION:\n{indent(schema, '    ')}",
+        ]
+        msg = "\n\n".join(optional + defaults)
+        epilog = f"\n{_FORMATS_HELP}" if "format" in msg.lower() else ""
+        return msg + epilog
+
+
+class _SummaryWriter:
+    _IGNORE = frozenset(("description", "default", "title", "examples"))
+
+    def __init__(self, jargon: Optional[Dict[str, str]] = None):
+        self.jargon: Dict[str, str] = jargon or {}
+        # Clarify confusing terms
+        self._terms = {
+            "anyOf": "at least one of the following",
+            "oneOf": "exactly one of the following",
+            "allOf": "all of the following",
+            "not": "(*NOT* the following)",
+            "prefixItems": f"{self._jargon('items')} (in order)",
+            "items": "items",
+            "contains": "contains at least one of",
+            "propertyNames": (
+                f"non-predefined acceptable {self._jargon('property names')}"
+            ),
+            "patternProperties": f"{self._jargon('properties')} named via pattern",
+            "const": "predefined value",
+            "enum": "one of",
+        }
+        # Attributes that indicate that the definition is easy and can be done
+        # inline (e.g. string and number)
+        self._guess_inline_defs = [
+            "enum",
+            "const",
+            "maxLength",
+            "minLength",
+            "pattern",
+            "format",
+            "minimum",
+            "maximum",
+            "exclusiveMinimum",
+            "exclusiveMaximum",
+            "multipleOf",
+        ]
+
+    def _jargon(self, term: Union[str, List[str]]) -> Union[str, List[str]]:
+        if isinstance(term, list):
+            return [self.jargon.get(t, t) for t in term]
+        return self.jargon.get(term, term)
+
+    def __call__(
+        self,
+        schema: Union[dict, List[dict]],
+        prefix: str = "",
+        *,
+        _path: Sequence[str] = (),
+    ) -> str:
+        if isinstance(schema, list):
+            return self._handle_list(schema, prefix, _path)
+
+        filtered = self._filter_unecessary(schema, _path)
+        simple = self._handle_simple_dict(filtered, _path)
+        if simple:
+            return f"{prefix}{simple}"
+
+        child_prefix = self._child_prefix(prefix, "  ")
+        item_prefix = self._child_prefix(prefix, "- ")
+        indent = len(prefix) * " "
+        with io.StringIO() as buffer:
+            for i, (key, value) in enumerate(filtered.items()):
+                child_path = [*_path, key]
+                line_prefix = prefix if i == 0 else indent
+                buffer.write(f"{line_prefix}{self._label(child_path)}:")
+                # ^  just the first item should receive the complete prefix
+                if isinstance(value, dict):
+                    filtered = self._filter_unecessary(value, child_path)
+                    simple = self._handle_simple_dict(filtered, child_path)
+                    buffer.write(
+                        f" {simple}"
+                        if simple
+                        else f"\n{self(value, child_prefix, _path=child_path)}"
+                    )
+                elif isinstance(value, list) and (
+                    key != "type" or self._is_property(child_path)
+                ):
+                    children = self._handle_list(value, item_prefix, child_path)
+                    sep = " " if children.startswith("[") else "\n"
+                    buffer.write(f"{sep}{children}")
+                else:
+                    buffer.write(f" {self._value(value, child_path)}\n")
+            return buffer.getvalue()
+
+    def _is_unecessary(self, path: Sequence[str]) -> bool:
+        if self._is_property(path) or not path:  # empty path => instruction @ root
+            return False
+        key = path[-1]
+        return any(key.startswith(k) for k in "$_") or key in self._IGNORE
+
+    def _filter_unecessary(
+        self, schema: Dict[str, Any], path: Sequence[str]
+    ) -> Dict[str, Any]:
+        return {
+            key: value
+            for key, value in schema.items()
+            if not self._is_unecessary([*path, key])
+        }
+
+    def _handle_simple_dict(self, value: dict, path: Sequence[str]) -> Optional[str]:
+        inline = any(p in value for p in self._guess_inline_defs)
+        simple = not any(isinstance(v, (list, dict)) for v in value.values())
+        if inline or simple:
+            return f"{{{', '.join(self._inline_attrs(value, path))}}}\n"
+        return None
+
+    def _handle_list(
+        self, schemas: list, prefix: str = "", path: Sequence[str] = ()
+    ) -> str:
+        if self._is_unecessary(path):
+            return ""
+
+        repr_ = repr(schemas)
+        if all(not isinstance(e, (dict, list)) for e in schemas) and len(repr_) < 60:
+            return f"{repr_}\n"
+
+        item_prefix = self._child_prefix(prefix, "- ")
+        return "".join(
+            self(v, item_prefix, _path=[*path, f"[{i}]"]) for i, v in enumerate(schemas)
+        )
+
+    def _is_property(self, path: Sequence[str]) -> bool:
+        """Check if the given path can correspond to an arbitrarily named property"""
+        counter = 0
+        for key in path[-2::-1]:
+            if key not in {"properties", "patternProperties"}:
+                break
+            counter += 1
+
+        # If the counter if even, the path correspond to a JSON Schema keyword
+        # otherwise it can be any arbitrary string naming a property
+        return counter % 2 == 1
+
+    def _label(self, path: Sequence[str]) -> str:
+        *parents, key = path
+        if not self._is_property(path):
+            norm_key = _separate_terms(key)
+            return self._terms.get(key) or " ".join(self._jargon(norm_key))
+
+        if parents[-1] == "patternProperties":
+            return f"(regex {key!r})"
+        return repr(key)  # property name
+
+    def _value(self, value: Any, path: Sequence[str]) -> str:
+        if path[-1] == "type" and not self._is_property(path):
+            type_ = self._jargon(value)
+            return f"[{', '.join(type_)}]" if isinstance(type_, list) else type_
+        return repr(value)
+
+    def _inline_attrs(self, schema: dict, path: Sequence[str]) -> Iterator[str]:
+        for key, value in schema.items():
+            child_path = [*path, key]
+            yield f"{self._label(child_path)}: {self._value(value, child_path)}"
+
+    def _child_prefix(self, parent_prefix: str, child_prefix: str) -> str:
+        return len(parent_prefix) * " " + child_prefix
+
+
+def _separate_terms(word: str) -> List[str]:
+    """
+    >>> _separate_terms("FooBar-foo")
+    ['foo', 'bar', 'foo']
+    """
+    return [w.lower() for w in _CAMEL_CASE_SPLITTER.split(word) if w]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/extra_validations.py b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/extra_validations.py
new file mode 100644
index 0000000000000000000000000000000000000000..789411d0ff397041f87a834e634bac8488024326
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/extra_validations.py
@@ -0,0 +1,82 @@
+"""The purpose of this module is implement PEP 621 validations that are
+difficult to express as a JSON Schema (or that are not supported by the current
+JSON Schema library).
+"""
+
+from inspect import cleandoc
+from typing import Mapping, TypeVar
+
+from .error_reporting import ValidationError
+
+T = TypeVar("T", bound=Mapping)
+
+
+class RedefiningStaticFieldAsDynamic(ValidationError):
+    _DESC = """According to PEP 621:
+
+    Build back-ends MUST raise an error if the metadata specifies a field
+    statically as well as being listed in dynamic.
+    """
+    __doc__ = _DESC
+    _URL = (
+        "https://packaging.python.org/en/latest/specifications/"
+        "pyproject-toml/#dynamic"
+    )
+
+
+class IncludedDependencyGroupMustExist(ValidationError):
+    _DESC = """An included dependency group must exist and must not be cyclic.
+    """
+    __doc__ = _DESC
+    _URL = "https://peps.python.org/pep-0735/"
+
+
+def validate_project_dynamic(pyproject: T) -> T:
+    project_table = pyproject.get("project", {})
+    dynamic = project_table.get("dynamic", [])
+
+    for field in dynamic:
+        if field in project_table:
+            raise RedefiningStaticFieldAsDynamic(
+                message=f"You cannot provide a value for `project.{field}` and "
+                "list it under `project.dynamic` at the same time",
+                value={
+                    field: project_table[field],
+                    "...": " # ...",
+                    "dynamic": dynamic,
+                },
+                name=f"data.project.{field}",
+                definition={
+                    "description": cleandoc(RedefiningStaticFieldAsDynamic._DESC),
+                    "see": RedefiningStaticFieldAsDynamic._URL,
+                },
+                rule="PEP 621",
+            )
+
+    return pyproject
+
+
+def validate_include_depenency(pyproject: T) -> T:
+    dependency_groups = pyproject.get("dependency-groups", {})
+    for key, value in dependency_groups.items():
+        for each in value:
+            if (
+                isinstance(each, dict)
+                and (include_group := each.get("include-group"))
+                and include_group not in dependency_groups
+            ):
+                raise IncludedDependencyGroupMustExist(
+                    message=f"The included dependency group {include_group} doesn't exist",
+                    value=each,
+                    name=f"data.dependency_groups.{key}",
+                    definition={
+                        "description": cleandoc(IncludedDependencyGroupMustExist._DESC),
+                        "see": IncludedDependencyGroupMustExist._URL,
+                    },
+                    rule="PEP 735",
+                )
+    # TODO: check for `include-group` cycles (can be conditional to graphlib)
+    return pyproject
+
+
+EXTRA_VALIDATIONS = (validate_project_dynamic, validate_include_depenency)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/fastjsonschema_exceptions.py b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/fastjsonschema_exceptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2dddd6a106f021a4723c1e8f5953ccc09e55e1f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/fastjsonschema_exceptions.py
@@ -0,0 +1,51 @@
+import re
+
+
+SPLIT_RE = re.compile(r'[\.\[\]]+')
+
+
+class JsonSchemaException(ValueError):
+    """
+    Base exception of ``fastjsonschema`` library.
+    """
+
+
+class JsonSchemaValueException(JsonSchemaException):
+    """
+    Exception raised by validation function. Available properties:
+
+     * ``message`` containing human-readable information what is wrong (e.g. ``data.property[index] must be smaller than or equal to 42``),
+     * invalid ``value`` (e.g. ``60``),
+     * ``name`` of a path in the data structure (e.g. ``data.property[index]``),
+     * ``path`` as an array in the data structure (e.g. ``['data', 'property', 'index']``),
+     * the whole ``definition`` which the ``value`` has to fulfil (e.g. ``{'type': 'number', 'maximum': 42}``),
+     * ``rule`` which the ``value`` is breaking (e.g. ``maximum``)
+     * and ``rule_definition`` (e.g. ``42``).
+
+    .. versionchanged:: 2.14.0
+        Added all extra properties.
+    """
+
+    def __init__(self, message, value=None, name=None, definition=None, rule=None):
+        super().__init__(message)
+        self.message = message
+        self.value = value
+        self.name = name
+        self.definition = definition
+        self.rule = rule
+
+    @property
+    def path(self):
+        return [item for item in SPLIT_RE.split(self.name) if item != '']
+
+    @property
+    def rule_definition(self):
+        if not self.rule or not self.definition:
+            return None
+        return self.definition.get(self.rule)
+
+
+class JsonSchemaDefinitionException(JsonSchemaException):
+    """
+    Exception raised by generator of validation function.
+    """
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/fastjsonschema_validations.py b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/fastjsonschema_validations.py
new file mode 100644
index 0000000000000000000000000000000000000000..c69368a83ff1bdb89cf4853e00429f0125ca3dce
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/fastjsonschema_validations.py
@@ -0,0 +1,1412 @@
+# noqa
+# ruff: noqa
+# flake8: noqa
+# pylint: skip-file
+# mypy: ignore-errors
+# yapf: disable
+# pylama:skip=1
+
+
+# *** PLEASE DO NOT MODIFY DIRECTLY: Automatically generated code *** 
+
+
+VERSION = "2.20.0"
+from decimal import Decimal
+import re
+from .fastjsonschema_exceptions import JsonSchemaValueException
+
+
+REGEX_PATTERNS = {
+    '^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$': re.compile('^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])\\Z'),
+    '^.*$': re.compile('^.*$'),
+    '.+': re.compile('.+'),
+    '^.+$': re.compile('^.+$'),
+    'idn-email_re_pattern': re.compile('^[^@]+@[^@]+\\.[^@]+\\Z')
+}
+
+NoneType = type(None)
+
+def validate(data, custom_formats={}, name_prefix=None):
+    validate_https___packaging_python_org_en_latest_specifications_declaring_build_dependencies(data, custom_formats, (name_prefix or "data") + "")
+    return data
+
+def validate_https___packaging_python_org_en_latest_specifications_declaring_build_dependencies(data, custom_formats={}, name_prefix=None):
+    if not isinstance(data, (dict)):
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " must be object", value=data, name="" + (name_prefix or "data") + "", definition={'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://packaging.python.org/en/latest/specifications/declaring-build-dependencies/', 'title': 'Data structure for ``pyproject.toml`` files', '$$description': ['File format containing build-time configurations for the Python ecosystem. ', ':pep:`517` initially defined a build-system independent format for source trees', 'which was complemented by :pep:`518` to provide a way of specifying dependencies ', 'for building Python projects.', 'Please notice the ``project`` table (as initially defined in  :pep:`621`) is not included', 'in this schema and should be considered separately.'], 'type': 'object', 'additionalProperties': False, 'properties': {'build-system': {'type': 'object', 'description': 'Table used to store build-related data', 'additionalProperties': False, 'properties': {'requires': {'type': 'array', '$$description': ['List of dependencies in the :pep:`508` format required to execute the build', 'system. Please notice that the resulting dependency graph', '**MUST NOT contain cycles**'], 'items': {'type': 'string'}}, 'build-backend': {'type': 'string', 'description': 'Python object that will be used to perform the build according to :pep:`517`', 'format': 'pep517-backend-reference'}, 'backend-path': {'type': 'array', '$$description': ['List of directories to be prepended to ``sys.path`` when loading the', 'back-end, and running its hooks'], 'items': {'type': 'string', '$comment': 'Should be a path (TODO: enforce it with format?)'}}}, 'required': ['requires']}, 'project': {'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://packaging.python.org/en/latest/specifications/pyproject-toml/', 'title': 'Package metadata stored in the ``project`` table', '$$description': ['Data structure for the **project** table inside ``pyproject.toml``', '(as initially defined in :pep:`621`)'], 'type': 'object', 'properties': {'name': {'type': 'string', 'description': 'The name (primary identifier) of the project. MUST be statically defined.', 'format': 'pep508-identifier'}, 'version': {'type': 'string', 'description': 'The version of the project as supported by :pep:`440`.', 'format': 'pep440'}, 'description': {'type': 'string', '$$description': ['The `summary description of the project', '<https://packaging.python.org/specifications/core-metadata/#summary>`_']}, 'readme': {'$$description': ['`Full/detailed description of the project in the form of a README', '<https://peps.python.org/pep-0621/#readme>`_', "with meaning similar to the one defined in `core metadata's Description", '<https://packaging.python.org/specifications/core-metadata/#description>`_'], 'oneOf': [{'type': 'string', '$$description': ['Relative path to a text file (UTF-8) containing the full description', 'of the project. If the file path ends in case-insensitive ``.md`` or', '``.rst`` suffixes, then the content-type is respectively', '``text/markdown`` or ``text/x-rst``']}, {'type': 'object', 'allOf': [{'anyOf': [{'properties': {'file': {'type': 'string', '$$description': ['Relative path to a text file containing the full description', 'of the project.']}}, 'required': ['file']}, {'properties': {'text': {'type': 'string', 'description': 'Full text describing the project.'}}, 'required': ['text']}]}, {'properties': {'content-type': {'type': 'string', '$$description': ['Content-type (:rfc:`1341`) of the full description', '(e.g. ``text/markdown``). The ``charset`` parameter is assumed', 'UTF-8 when not present.'], '$comment': 'TODO: add regex pattern or format?'}}, 'required': ['content-type']}]}]}, 'requires-python': {'type': 'string', 'format': 'pep508-versionspec', '$$description': ['`The Python version requirements of the project', '<https://packaging.python.org/specifications/core-metadata/#requires-python>`_.']}, 'license': {'description': '`Project license <https://peps.python.org/pep-0621/#license>`_.', 'oneOf': [{'type': 'string', 'description': 'An SPDX license identifier', 'format': 'SPDX'}, {'type': 'object', 'properties': {'file': {'type': 'string', '$$description': ['Relative path to the file (UTF-8) which contains the license for the', 'project.']}}, 'required': ['file']}, {'type': 'object', 'properties': {'text': {'type': 'string', '$$description': ['The license of the project whose meaning is that of the', '`License field from the core metadata', '<https://packaging.python.org/specifications/core-metadata/#license>`_.']}}, 'required': ['text']}]}, 'license-files': {'description': 'Paths or globs to paths of license files', 'type': 'array', 'items': {'type': 'string'}}, 'authors': {'type': 'array', 'items': {'$ref': '#/definitions/author'}, '$$description': ["The people or organizations considered to be the 'authors' of the project.", 'The exact meaning is open to interpretation (e.g. original or primary authors,', 'current maintainers, or owners of the package).']}, 'maintainers': {'type': 'array', 'items': {'$ref': '#/definitions/author'}, '$$description': ["The people or organizations considered to be the 'maintainers' of the project.", 'Similarly to ``authors``, the exact meaning is open to interpretation.']}, 'keywords': {'type': 'array', 'items': {'type': 'string'}, 'description': 'List of keywords to assist searching for the distribution in a larger catalog.'}, 'classifiers': {'type': 'array', 'items': {'type': 'string', 'format': 'trove-classifier', 'description': '`PyPI classifier <https://pypi.org/classifiers/>`_.'}, '$$description': ['`Trove classifiers <https://pypi.org/classifiers/>`_', 'which apply to the project.']}, 'urls': {'type': 'object', 'description': 'URLs associated with the project in the form ``label => value``.', 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', 'format': 'url'}}}, 'scripts': {'$ref': '#/definitions/entry-point-group', '$$description': ['Instruct the installer to create command-line wrappers for the given', '`entry points <https://packaging.python.org/specifications/entry-points/>`_.']}, 'gui-scripts': {'$ref': '#/definitions/entry-point-group', '$$description': ['Instruct the installer to create GUI wrappers for the given', '`entry points <https://packaging.python.org/specifications/entry-points/>`_.', 'The difference between ``scripts`` and ``gui-scripts`` is only relevant in', 'Windows.']}, 'entry-points': {'$$description': ['Instruct the installer to expose the given modules/functions via', '``entry-point`` discovery mechanism (useful for plugins).', 'More information available in the `Python packaging guide', '<https://packaging.python.org/specifications/entry-points/>`_.'], 'propertyNames': {'format': 'python-entrypoint-group'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'$ref': '#/definitions/entry-point-group'}}}, 'dependencies': {'type': 'array', 'description': 'Project (mandatory) dependencies.', 'items': {'$ref': '#/definitions/dependency'}}, 'optional-dependencies': {'type': 'object', 'description': 'Optional dependency for the project', 'propertyNames': {'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'array', 'items': {'$ref': '#/definitions/dependency'}}}}, 'dynamic': {'type': 'array', '$$description': ['Specifies which fields are intentionally unspecified and expected to be', 'dynamically provided by build tools'], 'items': {'enum': ['version', 'description', 'readme', 'requires-python', 'license', 'license-files', 'authors', 'maintainers', 'keywords', 'classifiers', 'urls', 'scripts', 'gui-scripts', 'entry-points', 'dependencies', 'optional-dependencies']}}}, 'required': ['name'], 'additionalProperties': False, 'allOf': [{'if': {'not': {'required': ['dynamic'], 'properties': {'dynamic': {'contains': {'const': 'version'}, '$$description': ['version is listed in ``dynamic``']}}}, '$$comment': ['According to :pep:`621`:', '    If the core metadata specification lists a field as "Required", then', '    the metadata MUST specify the field statically or list it in dynamic', 'In turn, `core metadata`_ defines:', '    The required fields are: Metadata-Version, Name, Version.', '    All the other fields are optional.', 'Since ``Metadata-Version`` is defined by the build back-end, ``name`` and', '``version`` are the only mandatory information in ``pyproject.toml``.', '.. _core metadata: https://packaging.python.org/specifications/core-metadata/']}, 'then': {'required': ['version'], '$$description': ['version should be statically defined in the ``version`` field']}}, {'if': {'required': ['license-files']}, 'then': {'properties': {'license': {'type': 'string'}}}}], 'definitions': {'author': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, 'entry-point-group': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, 'dependency': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}}, 'tool': {'type': 'object', 'properties': {'distutils': {'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://setuptools.pypa.io/en/latest/deprecated/distutils/configfile.html', 'title': '``tool.distutils`` table', '$$description': ['**EXPERIMENTAL** (NOT OFFICIALLY SUPPORTED): Use ``tool.distutils``', 'subtables to configure arguments for ``distutils`` commands.', 'Originally, ``distutils`` allowed developers to configure arguments for', '``setup.py`` commands via `distutils configuration files', '<https://setuptools.pypa.io/en/latest/deprecated/distutils/configfile.html>`_.', 'See also `the old Python docs <https://docs.python.org/3.11/install/>_`.'], 'type': 'object', 'properties': {'global': {'type': 'object', 'description': 'Global options applied to all ``distutils`` commands'}}, 'patternProperties': {'.+': {'type': 'object'}}, '$comment': 'TODO: Is there a practical way of making this schema more specific?'}, 'setuptools': {'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html', 'title': '``tool.setuptools`` table', '$$description': ['``setuptools``-specific configurations that can be set by users that require', 'customization.', 'These configurations are completely optional and probably can be skipped when', 'creating simple packages. They are equivalent to some of the `Keywords', '<https://setuptools.pypa.io/en/latest/references/keywords.html>`_', 'used by the ``setup.py`` file, and can be set via the ``tool.setuptools`` table.', 'It considers only ``setuptools`` `parameters', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#setuptools-specific-configuration>`_', 'that are not covered by :pep:`621`; and intentionally excludes ``dependency_links``', 'and ``setup_requires`` (incompatible with modern workflows/standards).'], 'type': 'object', 'additionalProperties': False, 'properties': {'platforms': {'type': 'array', 'items': {'type': 'string'}}, 'provides': {'$$description': ['Package and virtual package names contained within this package', '**(not supported by pip)**'], 'type': 'array', 'items': {'type': 'string', 'format': 'pep508-identifier'}}, 'obsoletes': {'$$description': ['Packages which this package renders obsolete', '**(not supported by pip)**'], 'type': 'array', 'items': {'type': 'string', 'format': 'pep508-identifier'}}, 'zip-safe': {'$$description': ['Whether the project can be safely installed and run from a zip file.', '**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and', '``setup.py install`` in the context of ``eggs`` (**DEPRECATED**).'], 'type': 'boolean'}, 'script-files': {'$$description': ['Legacy way of defining scripts (entry-points are preferred).', 'Equivalent to the ``script`` keyword in ``setup.py``', '(it was renamed to avoid confusion with entry-point based ``project.scripts``', 'defined in :pep:`621`).', '**DISCOURAGED**: generic script wrappers are tricky and may not work properly.', 'Whenever possible, please use ``project.scripts`` instead.'], 'type': 'array', 'items': {'type': 'string'}, '$comment': 'TODO: is this field deprecated/should be removed?'}, 'eager-resources': {'$$description': ['Resources that should be extracted together, if any of them is needed,', 'or if any C extensions included in the project are imported.', '**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and', '``setup.py install`` in the context of ``eggs`` (**DEPRECATED**).'], 'type': 'array', 'items': {'type': 'string'}}, 'packages': {'$$description': ['Packages that should be included in the distribution.', 'It can be given either as a list of package identifiers', 'or as a ``dict``-like structure with a single key ``find``', 'which corresponds to a dynamic call to', '``setuptools.config.expand.find_packages`` function.', 'The ``find`` key is associated with a nested ``dict``-like structure that can', 'contain ``where``, ``include``, ``exclude`` and ``namespaces`` keys,', 'mimicking the keyword arguments of the associated function.'], 'oneOf': [{'title': 'Array of Python package identifiers', 'type': 'array', 'items': {'$ref': '#/definitions/package-name'}}, {'$ref': '#/definitions/find-directive'}]}, 'package-dir': {'$$description': [':class:`dict`-like structure mapping from package names to directories where their', 'code can be found.', 'The empty string (as key) means that all packages are contained inside', 'the given directory will be included in the distribution.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'const': ''}, {'$ref': '#/definitions/package-name'}]}, 'patternProperties': {'^.*$': {'type': 'string'}}}, 'package-data': {'$$description': ['Mapping from package names to lists of glob patterns.', 'Usually this option is not needed when using ``include-package-data = true``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'include-package-data': {'$$description': ['Automatically include any data files inside the package directories', 'that are specified by ``MANIFEST.in``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'boolean'}, 'exclude-package-data': {'$$description': ['Mapping from package names to lists of glob patterns that should be excluded', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'namespace-packages': {'type': 'array', 'items': {'type': 'string', 'format': 'python-module-name-relaxed'}, '$comment': 'https://setuptools.pypa.io/en/latest/userguide/package_discovery.html', 'description': '**DEPRECATED**: use implicit namespaces instead (:pep:`420`).'}, 'py-modules': {'description': 'Modules that setuptools will manipulate', 'type': 'array', 'items': {'type': 'string', 'format': 'python-module-name-relaxed'}, '$comment': 'TODO: clarify the relationship with ``packages``'}, 'ext-modules': {'description': 'Extension modules to be compiled by setuptools', 'type': 'array', 'items': {'$ref': '#/definitions/ext-module'}}, 'data-files': {'$$description': ['``dict``-like structure where each key represents a directory and', 'the value is a list of glob patterns that should be installed in them.', '**DISCOURAGED**: please notice this might not work as expected with wheels.', 'Whenever possible, consider using data files inside the package directories', '(or create a new namespace package that only contains data files).', 'See `data files support', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'cmdclass': {'$$description': ['Mapping of distutils-style command names to ``setuptools.Command`` subclasses', 'which in turn should be represented by strings with a qualified class name', '(i.e., "dotted" form with module), e.g.::\n\n', '    cmdclass = {mycmd = "pkg.subpkg.module.CommandClass"}\n\n', 'The command class should be a directly defined at the top-level of the', 'containing module (no class nesting).'], 'type': 'object', 'patternProperties': {'^.*$': {'type': 'string', 'format': 'python-qualified-identifier'}}}, 'license-files': {'type': 'array', 'items': {'type': 'string'}, '$$description': ['**PROVISIONAL**: list of glob patterns for all license files being distributed.', '(likely to become standard with :pep:`639`).', "By default: ``['LICEN[CS]E*', 'COPYING*', 'NOTICE*', 'AUTHORS*']``"], '$comment': 'TODO: revise if PEP 639 is accepted. Probably ``project.license-files``?'}, 'dynamic': {'type': 'object', 'description': 'Instructions for loading :pep:`621`-related metadata dynamically', 'additionalProperties': False, 'properties': {'version': {'$$description': ['A version dynamically loaded via either the ``attr:`` or ``file:``', 'directives. Please make sure the given file or attribute respects :pep:`440`.', 'Also ensure to set ``project.dynamic`` accordingly.'], 'oneOf': [{'$ref': '#/definitions/attr-directive'}, {'$ref': '#/definitions/file-directive'}]}, 'classifiers': {'$ref': '#/definitions/file-directive'}, 'description': {'$ref': '#/definitions/file-directive'}, 'entry-points': {'$ref': '#/definitions/file-directive'}, 'dependencies': {'$ref': '#/definitions/file-directive-for-dependencies'}, 'optional-dependencies': {'type': 'object', 'propertyNames': {'type': 'string', 'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'.+': {'$ref': '#/definitions/file-directive-for-dependencies'}}}, 'readme': {'type': 'object', 'anyOf': [{'$ref': '#/definitions/file-directive'}, {'type': 'object', 'properties': {'content-type': {'type': 'string'}, 'file': {'$ref': '#/definitions/file-directive/properties/file'}}, 'additionalProperties': False}], 'required': ['file']}}}}, 'definitions': {'package-name': {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}, 'ext-module': {'$id': '#/definitions/ext-module', 'title': 'Extension module', 'description': 'Parameters to construct a :class:`setuptools.Extension` object', 'type': 'object', 'required': ['name', 'sources'], 'additionalProperties': False, 'properties': {'name': {'type': 'string', 'format': 'python-module-name-relaxed'}, 'sources': {'type': 'array', 'items': {'type': 'string'}}, 'include-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'define-macros': {'type': 'array', 'items': {'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}}, 'undef-macros': {'type': 'array', 'items': {'type': 'string'}}, 'library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'libraries': {'type': 'array', 'items': {'type': 'string'}}, 'runtime-library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'extra-objects': {'type': 'array', 'items': {'type': 'string'}}, 'extra-compile-args': {'type': 'array', 'items': {'type': 'string'}}, 'extra-link-args': {'type': 'array', 'items': {'type': 'string'}}, 'export-symbols': {'type': 'array', 'items': {'type': 'string'}}, 'swig-opts': {'type': 'array', 'items': {'type': 'string'}}, 'depends': {'type': 'array', 'items': {'type': 'string'}}, 'language': {'type': 'string'}, 'optional': {'type': 'boolean'}, 'py-limited-api': {'type': 'boolean'}}}, 'file-directive': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'file-directive-for-dependencies': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}, 'attr-directive': {'title': "'attr:' directive", '$id': '#/definitions/attr-directive', '$$description': ['Value is read from a module attribute. Supports callables and iterables;', 'unsupported types are cast via ``str()``'], 'type': 'object', 'additionalProperties': False, 'properties': {'attr': {'type': 'string', 'format': 'python-qualified-identifier'}}, 'required': ['attr']}, 'find-directive': {'$id': '#/definitions/find-directive', 'title': "'find:' directive", 'type': 'object', 'additionalProperties': False, 'properties': {'find': {'type': 'object', '$$description': ['Dynamic `package discovery', '<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_.'], 'additionalProperties': False, 'properties': {'where': {'description': 'Directories to be searched for packages (Unix-style relative path)', 'type': 'array', 'items': {'type': 'string'}}, 'exclude': {'type': 'array', '$$description': ['Exclude packages that match the values listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'include': {'type': 'array', '$$description': ['Restrict the found packages to just the ones listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'namespaces': {'type': 'boolean', '$$description': ['When ``True``, directories without a ``__init__.py`` file will also', 'be scanned for :pep:`420`-style implicit namespaces']}}}}}}}}}, 'dependency-groups': {'type': 'object', 'description': 'Dependency groups following PEP 735', 'additionalProperties': False, 'patternProperties': {'^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$': {'type': 'array', 'items': {'oneOf': [{'type': 'string', 'description': 'Python package specifiers following PEP 508', 'format': 'pep508'}, {'type': 'object', 'additionalProperties': False, 'properties': {'include-group': {'description': 'Another dependency group to include in this one', 'type': 'string', 'pattern': '^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$'}}}]}}}}}, 'project': {'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://packaging.python.org/en/latest/specifications/pyproject-toml/', 'title': 'Package metadata stored in the ``project`` table', '$$description': ['Data structure for the **project** table inside ``pyproject.toml``', '(as initially defined in :pep:`621`)'], 'type': 'object', 'properties': {'name': {'type': 'string', 'description': 'The name (primary identifier) of the project. MUST be statically defined.', 'format': 'pep508-identifier'}, 'version': {'type': 'string', 'description': 'The version of the project as supported by :pep:`440`.', 'format': 'pep440'}, 'description': {'type': 'string', '$$description': ['The `summary description of the project', '<https://packaging.python.org/specifications/core-metadata/#summary>`_']}, 'readme': {'$$description': ['`Full/detailed description of the project in the form of a README', '<https://peps.python.org/pep-0621/#readme>`_', "with meaning similar to the one defined in `core metadata's Description", '<https://packaging.python.org/specifications/core-metadata/#description>`_'], 'oneOf': [{'type': 'string', '$$description': ['Relative path to a text file (UTF-8) containing the full description', 'of the project. If the file path ends in case-insensitive ``.md`` or', '``.rst`` suffixes, then the content-type is respectively', '``text/markdown`` or ``text/x-rst``']}, {'type': 'object', 'allOf': [{'anyOf': [{'properties': {'file': {'type': 'string', '$$description': ['Relative path to a text file containing the full description', 'of the project.']}}, 'required': ['file']}, {'properties': {'text': {'type': 'string', 'description': 'Full text describing the project.'}}, 'required': ['text']}]}, {'properties': {'content-type': {'type': 'string', '$$description': ['Content-type (:rfc:`1341`) of the full description', '(e.g. ``text/markdown``). The ``charset`` parameter is assumed', 'UTF-8 when not present.'], '$comment': 'TODO: add regex pattern or format?'}}, 'required': ['content-type']}]}]}, 'requires-python': {'type': 'string', 'format': 'pep508-versionspec', '$$description': ['`The Python version requirements of the project', '<https://packaging.python.org/specifications/core-metadata/#requires-python>`_.']}, 'license': {'description': '`Project license <https://peps.python.org/pep-0621/#license>`_.', 'oneOf': [{'type': 'string', 'description': 'An SPDX license identifier', 'format': 'SPDX'}, {'type': 'object', 'properties': {'file': {'type': 'string', '$$description': ['Relative path to the file (UTF-8) which contains the license for the', 'project.']}}, 'required': ['file']}, {'type': 'object', 'properties': {'text': {'type': 'string', '$$description': ['The license of the project whose meaning is that of the', '`License field from the core metadata', '<https://packaging.python.org/specifications/core-metadata/#license>`_.']}}, 'required': ['text']}]}, 'license-files': {'description': 'Paths or globs to paths of license files', 'type': 'array', 'items': {'type': 'string'}}, 'authors': {'type': 'array', 'items': {'$ref': '#/definitions/author'}, '$$description': ["The people or organizations considered to be the 'authors' of the project.", 'The exact meaning is open to interpretation (e.g. original or primary authors,', 'current maintainers, or owners of the package).']}, 'maintainers': {'type': 'array', 'items': {'$ref': '#/definitions/author'}, '$$description': ["The people or organizations considered to be the 'maintainers' of the project.", 'Similarly to ``authors``, the exact meaning is open to interpretation.']}, 'keywords': {'type': 'array', 'items': {'type': 'string'}, 'description': 'List of keywords to assist searching for the distribution in a larger catalog.'}, 'classifiers': {'type': 'array', 'items': {'type': 'string', 'format': 'trove-classifier', 'description': '`PyPI classifier <https://pypi.org/classifiers/>`_.'}, '$$description': ['`Trove classifiers <https://pypi.org/classifiers/>`_', 'which apply to the project.']}, 'urls': {'type': 'object', 'description': 'URLs associated with the project in the form ``label => value``.', 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', 'format': 'url'}}}, 'scripts': {'$ref': '#/definitions/entry-point-group', '$$description': ['Instruct the installer to create command-line wrappers for the given', '`entry points <https://packaging.python.org/specifications/entry-points/>`_.']}, 'gui-scripts': {'$ref': '#/definitions/entry-point-group', '$$description': ['Instruct the installer to create GUI wrappers for the given', '`entry points <https://packaging.python.org/specifications/entry-points/>`_.', 'The difference between ``scripts`` and ``gui-scripts`` is only relevant in', 'Windows.']}, 'entry-points': {'$$description': ['Instruct the installer to expose the given modules/functions via', '``entry-point`` discovery mechanism (useful for plugins).', 'More information available in the `Python packaging guide', '<https://packaging.python.org/specifications/entry-points/>`_.'], 'propertyNames': {'format': 'python-entrypoint-group'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'$ref': '#/definitions/entry-point-group'}}}, 'dependencies': {'type': 'array', 'description': 'Project (mandatory) dependencies.', 'items': {'$ref': '#/definitions/dependency'}}, 'optional-dependencies': {'type': 'object', 'description': 'Optional dependency for the project', 'propertyNames': {'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'array', 'items': {'$ref': '#/definitions/dependency'}}}}, 'dynamic': {'type': 'array', '$$description': ['Specifies which fields are intentionally unspecified and expected to be', 'dynamically provided by build tools'], 'items': {'enum': ['version', 'description', 'readme', 'requires-python', 'license', 'license-files', 'authors', 'maintainers', 'keywords', 'classifiers', 'urls', 'scripts', 'gui-scripts', 'entry-points', 'dependencies', 'optional-dependencies']}}}, 'required': ['name'], 'additionalProperties': False, 'allOf': [{'if': {'not': {'required': ['dynamic'], 'properties': {'dynamic': {'contains': {'const': 'version'}, '$$description': ['version is listed in ``dynamic``']}}}, '$$comment': ['According to :pep:`621`:', '    If the core metadata specification lists a field as "Required", then', '    the metadata MUST specify the field statically or list it in dynamic', 'In turn, `core metadata`_ defines:', '    The required fields are: Metadata-Version, Name, Version.', '    All the other fields are optional.', 'Since ``Metadata-Version`` is defined by the build back-end, ``name`` and', '``version`` are the only mandatory information in ``pyproject.toml``.', '.. _core metadata: https://packaging.python.org/specifications/core-metadata/']}, 'then': {'required': ['version'], '$$description': ['version should be statically defined in the ``version`` field']}}, {'if': {'required': ['license-files']}, 'then': {'properties': {'license': {'type': 'string'}}}}], 'definitions': {'author': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, 'entry-point-group': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, 'dependency': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}}}, rule='type')
+    data_is_dict = isinstance(data, dict)
+    if data_is_dict:
+        data_keys = set(data.keys())
+        if "build-system" in data_keys:
+            data_keys.remove("build-system")
+            data__buildsystem = data["build-system"]
+            if not isinstance(data__buildsystem, (dict)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".build-system must be object", value=data__buildsystem, name="" + (name_prefix or "data") + ".build-system", definition={'type': 'object', 'description': 'Table used to store build-related data', 'additionalProperties': False, 'properties': {'requires': {'type': 'array', '$$description': ['List of dependencies in the :pep:`508` format required to execute the build', 'system. Please notice that the resulting dependency graph', '**MUST NOT contain cycles**'], 'items': {'type': 'string'}}, 'build-backend': {'type': 'string', 'description': 'Python object that will be used to perform the build according to :pep:`517`', 'format': 'pep517-backend-reference'}, 'backend-path': {'type': 'array', '$$description': ['List of directories to be prepended to ``sys.path`` when loading the', 'back-end, and running its hooks'], 'items': {'type': 'string', '$comment': 'Should be a path (TODO: enforce it with format?)'}}}, 'required': ['requires']}, rule='type')
+            data__buildsystem_is_dict = isinstance(data__buildsystem, dict)
+            if data__buildsystem_is_dict:
+                data__buildsystem__missing_keys = set(['requires']) - data__buildsystem.keys()
+                if data__buildsystem__missing_keys:
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".build-system must contain " + (str(sorted(data__buildsystem__missing_keys)) + " properties"), value=data__buildsystem, name="" + (name_prefix or "data") + ".build-system", definition={'type': 'object', 'description': 'Table used to store build-related data', 'additionalProperties': False, 'properties': {'requires': {'type': 'array', '$$description': ['List of dependencies in the :pep:`508` format required to execute the build', 'system. Please notice that the resulting dependency graph', '**MUST NOT contain cycles**'], 'items': {'type': 'string'}}, 'build-backend': {'type': 'string', 'description': 'Python object that will be used to perform the build according to :pep:`517`', 'format': 'pep517-backend-reference'}, 'backend-path': {'type': 'array', '$$description': ['List of directories to be prepended to ``sys.path`` when loading the', 'back-end, and running its hooks'], 'items': {'type': 'string', '$comment': 'Should be a path (TODO: enforce it with format?)'}}}, 'required': ['requires']}, rule='required')
+                data__buildsystem_keys = set(data__buildsystem.keys())
+                if "requires" in data__buildsystem_keys:
+                    data__buildsystem_keys.remove("requires")
+                    data__buildsystem__requires = data__buildsystem["requires"]
+                    if not isinstance(data__buildsystem__requires, (list, tuple)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".build-system.requires must be array", value=data__buildsystem__requires, name="" + (name_prefix or "data") + ".build-system.requires", definition={'type': 'array', '$$description': ['List of dependencies in the :pep:`508` format required to execute the build', 'system. Please notice that the resulting dependency graph', '**MUST NOT contain cycles**'], 'items': {'type': 'string'}}, rule='type')
+                    data__buildsystem__requires_is_list = isinstance(data__buildsystem__requires, (list, tuple))
+                    if data__buildsystem__requires_is_list:
+                        data__buildsystem__requires_len = len(data__buildsystem__requires)
+                        for data__buildsystem__requires_x, data__buildsystem__requires_item in enumerate(data__buildsystem__requires):
+                            if not isinstance(data__buildsystem__requires_item, (str)):
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".build-system.requires[{data__buildsystem__requires_x}]".format(**locals()) + " must be string", value=data__buildsystem__requires_item, name="" + (name_prefix or "data") + ".build-system.requires[{data__buildsystem__requires_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+                if "build-backend" in data__buildsystem_keys:
+                    data__buildsystem_keys.remove("build-backend")
+                    data__buildsystem__buildbackend = data__buildsystem["build-backend"]
+                    if not isinstance(data__buildsystem__buildbackend, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".build-system.build-backend must be string", value=data__buildsystem__buildbackend, name="" + (name_prefix or "data") + ".build-system.build-backend", definition={'type': 'string', 'description': 'Python object that will be used to perform the build according to :pep:`517`', 'format': 'pep517-backend-reference'}, rule='type')
+                    if isinstance(data__buildsystem__buildbackend, str):
+                        if not custom_formats["pep517-backend-reference"](data__buildsystem__buildbackend):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".build-system.build-backend must be pep517-backend-reference", value=data__buildsystem__buildbackend, name="" + (name_prefix or "data") + ".build-system.build-backend", definition={'type': 'string', 'description': 'Python object that will be used to perform the build according to :pep:`517`', 'format': 'pep517-backend-reference'}, rule='format')
+                if "backend-path" in data__buildsystem_keys:
+                    data__buildsystem_keys.remove("backend-path")
+                    data__buildsystem__backendpath = data__buildsystem["backend-path"]
+                    if not isinstance(data__buildsystem__backendpath, (list, tuple)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".build-system.backend-path must be array", value=data__buildsystem__backendpath, name="" + (name_prefix or "data") + ".build-system.backend-path", definition={'type': 'array', '$$description': ['List of directories to be prepended to ``sys.path`` when loading the', 'back-end, and running its hooks'], 'items': {'type': 'string', '$comment': 'Should be a path (TODO: enforce it with format?)'}}, rule='type')
+                    data__buildsystem__backendpath_is_list = isinstance(data__buildsystem__backendpath, (list, tuple))
+                    if data__buildsystem__backendpath_is_list:
+                        data__buildsystem__backendpath_len = len(data__buildsystem__backendpath)
+                        for data__buildsystem__backendpath_x, data__buildsystem__backendpath_item in enumerate(data__buildsystem__backendpath):
+                            if not isinstance(data__buildsystem__backendpath_item, (str)):
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".build-system.backend-path[{data__buildsystem__backendpath_x}]".format(**locals()) + " must be string", value=data__buildsystem__backendpath_item, name="" + (name_prefix or "data") + ".build-system.backend-path[{data__buildsystem__backendpath_x}]".format(**locals()) + "", definition={'type': 'string', '$comment': 'Should be a path (TODO: enforce it with format?)'}, rule='type')
+                if data__buildsystem_keys:
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".build-system must not contain "+str(data__buildsystem_keys)+" properties", value=data__buildsystem, name="" + (name_prefix or "data") + ".build-system", definition={'type': 'object', 'description': 'Table used to store build-related data', 'additionalProperties': False, 'properties': {'requires': {'type': 'array', '$$description': ['List of dependencies in the :pep:`508` format required to execute the build', 'system. Please notice that the resulting dependency graph', '**MUST NOT contain cycles**'], 'items': {'type': 'string'}}, 'build-backend': {'type': 'string', 'description': 'Python object that will be used to perform the build according to :pep:`517`', 'format': 'pep517-backend-reference'}, 'backend-path': {'type': 'array', '$$description': ['List of directories to be prepended to ``sys.path`` when loading the', 'back-end, and running its hooks'], 'items': {'type': 'string', '$comment': 'Should be a path (TODO: enforce it with format?)'}}}, 'required': ['requires']}, rule='additionalProperties')
+        if "project" in data_keys:
+            data_keys.remove("project")
+            data__project = data["project"]
+            validate_https___packaging_python_org_en_latest_specifications_pyproject_toml(data__project, custom_formats, (name_prefix or "data") + ".project")
+        if "tool" in data_keys:
+            data_keys.remove("tool")
+            data__tool = data["tool"]
+            if not isinstance(data__tool, (dict)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".tool must be object", value=data__tool, name="" + (name_prefix or "data") + ".tool", definition={'type': 'object', 'properties': {'distutils': {'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://setuptools.pypa.io/en/latest/deprecated/distutils/configfile.html', 'title': '``tool.distutils`` table', '$$description': ['**EXPERIMENTAL** (NOT OFFICIALLY SUPPORTED): Use ``tool.distutils``', 'subtables to configure arguments for ``distutils`` commands.', 'Originally, ``distutils`` allowed developers to configure arguments for', '``setup.py`` commands via `distutils configuration files', '<https://setuptools.pypa.io/en/latest/deprecated/distutils/configfile.html>`_.', 'See also `the old Python docs <https://docs.python.org/3.11/install/>_`.'], 'type': 'object', 'properties': {'global': {'type': 'object', 'description': 'Global options applied to all ``distutils`` commands'}}, 'patternProperties': {'.+': {'type': 'object'}}, '$comment': 'TODO: Is there a practical way of making this schema more specific?'}, 'setuptools': {'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html', 'title': '``tool.setuptools`` table', '$$description': ['``setuptools``-specific configurations that can be set by users that require', 'customization.', 'These configurations are completely optional and probably can be skipped when', 'creating simple packages. They are equivalent to some of the `Keywords', '<https://setuptools.pypa.io/en/latest/references/keywords.html>`_', 'used by the ``setup.py`` file, and can be set via the ``tool.setuptools`` table.', 'It considers only ``setuptools`` `parameters', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#setuptools-specific-configuration>`_', 'that are not covered by :pep:`621`; and intentionally excludes ``dependency_links``', 'and ``setup_requires`` (incompatible with modern workflows/standards).'], 'type': 'object', 'additionalProperties': False, 'properties': {'platforms': {'type': 'array', 'items': {'type': 'string'}}, 'provides': {'$$description': ['Package and virtual package names contained within this package', '**(not supported by pip)**'], 'type': 'array', 'items': {'type': 'string', 'format': 'pep508-identifier'}}, 'obsoletes': {'$$description': ['Packages which this package renders obsolete', '**(not supported by pip)**'], 'type': 'array', 'items': {'type': 'string', 'format': 'pep508-identifier'}}, 'zip-safe': {'$$description': ['Whether the project can be safely installed and run from a zip file.', '**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and', '``setup.py install`` in the context of ``eggs`` (**DEPRECATED**).'], 'type': 'boolean'}, 'script-files': {'$$description': ['Legacy way of defining scripts (entry-points are preferred).', 'Equivalent to the ``script`` keyword in ``setup.py``', '(it was renamed to avoid confusion with entry-point based ``project.scripts``', 'defined in :pep:`621`).', '**DISCOURAGED**: generic script wrappers are tricky and may not work properly.', 'Whenever possible, please use ``project.scripts`` instead.'], 'type': 'array', 'items': {'type': 'string'}, '$comment': 'TODO: is this field deprecated/should be removed?'}, 'eager-resources': {'$$description': ['Resources that should be extracted together, if any of them is needed,', 'or if any C extensions included in the project are imported.', '**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and', '``setup.py install`` in the context of ``eggs`` (**DEPRECATED**).'], 'type': 'array', 'items': {'type': 'string'}}, 'packages': {'$$description': ['Packages that should be included in the distribution.', 'It can be given either as a list of package identifiers', 'or as a ``dict``-like structure with a single key ``find``', 'which corresponds to a dynamic call to', '``setuptools.config.expand.find_packages`` function.', 'The ``find`` key is associated with a nested ``dict``-like structure that can', 'contain ``where``, ``include``, ``exclude`` and ``namespaces`` keys,', 'mimicking the keyword arguments of the associated function.'], 'oneOf': [{'title': 'Array of Python package identifiers', 'type': 'array', 'items': {'$ref': '#/definitions/package-name'}}, {'$ref': '#/definitions/find-directive'}]}, 'package-dir': {'$$description': [':class:`dict`-like structure mapping from package names to directories where their', 'code can be found.', 'The empty string (as key) means that all packages are contained inside', 'the given directory will be included in the distribution.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'const': ''}, {'$ref': '#/definitions/package-name'}]}, 'patternProperties': {'^.*$': {'type': 'string'}}}, 'package-data': {'$$description': ['Mapping from package names to lists of glob patterns.', 'Usually this option is not needed when using ``include-package-data = true``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'include-package-data': {'$$description': ['Automatically include any data files inside the package directories', 'that are specified by ``MANIFEST.in``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'boolean'}, 'exclude-package-data': {'$$description': ['Mapping from package names to lists of glob patterns that should be excluded', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'namespace-packages': {'type': 'array', 'items': {'type': 'string', 'format': 'python-module-name-relaxed'}, '$comment': 'https://setuptools.pypa.io/en/latest/userguide/package_discovery.html', 'description': '**DEPRECATED**: use implicit namespaces instead (:pep:`420`).'}, 'py-modules': {'description': 'Modules that setuptools will manipulate', 'type': 'array', 'items': {'type': 'string', 'format': 'python-module-name-relaxed'}, '$comment': 'TODO: clarify the relationship with ``packages``'}, 'ext-modules': {'description': 'Extension modules to be compiled by setuptools', 'type': 'array', 'items': {'$ref': '#/definitions/ext-module'}}, 'data-files': {'$$description': ['``dict``-like structure where each key represents a directory and', 'the value is a list of glob patterns that should be installed in them.', '**DISCOURAGED**: please notice this might not work as expected with wheels.', 'Whenever possible, consider using data files inside the package directories', '(or create a new namespace package that only contains data files).', 'See `data files support', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'cmdclass': {'$$description': ['Mapping of distutils-style command names to ``setuptools.Command`` subclasses', 'which in turn should be represented by strings with a qualified class name', '(i.e., "dotted" form with module), e.g.::\n\n', '    cmdclass = {mycmd = "pkg.subpkg.module.CommandClass"}\n\n', 'The command class should be a directly defined at the top-level of the', 'containing module (no class nesting).'], 'type': 'object', 'patternProperties': {'^.*$': {'type': 'string', 'format': 'python-qualified-identifier'}}}, 'license-files': {'type': 'array', 'items': {'type': 'string'}, '$$description': ['**PROVISIONAL**: list of glob patterns for all license files being distributed.', '(likely to become standard with :pep:`639`).', "By default: ``['LICEN[CS]E*', 'COPYING*', 'NOTICE*', 'AUTHORS*']``"], '$comment': 'TODO: revise if PEP 639 is accepted. Probably ``project.license-files``?'}, 'dynamic': {'type': 'object', 'description': 'Instructions for loading :pep:`621`-related metadata dynamically', 'additionalProperties': False, 'properties': {'version': {'$$description': ['A version dynamically loaded via either the ``attr:`` or ``file:``', 'directives. Please make sure the given file or attribute respects :pep:`440`.', 'Also ensure to set ``project.dynamic`` accordingly.'], 'oneOf': [{'$ref': '#/definitions/attr-directive'}, {'$ref': '#/definitions/file-directive'}]}, 'classifiers': {'$ref': '#/definitions/file-directive'}, 'description': {'$ref': '#/definitions/file-directive'}, 'entry-points': {'$ref': '#/definitions/file-directive'}, 'dependencies': {'$ref': '#/definitions/file-directive-for-dependencies'}, 'optional-dependencies': {'type': 'object', 'propertyNames': {'type': 'string', 'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'.+': {'$ref': '#/definitions/file-directive-for-dependencies'}}}, 'readme': {'type': 'object', 'anyOf': [{'$ref': '#/definitions/file-directive'}, {'type': 'object', 'properties': {'content-type': {'type': 'string'}, 'file': {'$ref': '#/definitions/file-directive/properties/file'}}, 'additionalProperties': False}], 'required': ['file']}}}}, 'definitions': {'package-name': {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}, 'ext-module': {'$id': '#/definitions/ext-module', 'title': 'Extension module', 'description': 'Parameters to construct a :class:`setuptools.Extension` object', 'type': 'object', 'required': ['name', 'sources'], 'additionalProperties': False, 'properties': {'name': {'type': 'string', 'format': 'python-module-name-relaxed'}, 'sources': {'type': 'array', 'items': {'type': 'string'}}, 'include-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'define-macros': {'type': 'array', 'items': {'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}}, 'undef-macros': {'type': 'array', 'items': {'type': 'string'}}, 'library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'libraries': {'type': 'array', 'items': {'type': 'string'}}, 'runtime-library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'extra-objects': {'type': 'array', 'items': {'type': 'string'}}, 'extra-compile-args': {'type': 'array', 'items': {'type': 'string'}}, 'extra-link-args': {'type': 'array', 'items': {'type': 'string'}}, 'export-symbols': {'type': 'array', 'items': {'type': 'string'}}, 'swig-opts': {'type': 'array', 'items': {'type': 'string'}}, 'depends': {'type': 'array', 'items': {'type': 'string'}}, 'language': {'type': 'string'}, 'optional': {'type': 'boolean'}, 'py-limited-api': {'type': 'boolean'}}}, 'file-directive': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'file-directive-for-dependencies': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}, 'attr-directive': {'title': "'attr:' directive", '$id': '#/definitions/attr-directive', '$$description': ['Value is read from a module attribute. Supports callables and iterables;', 'unsupported types are cast via ``str()``'], 'type': 'object', 'additionalProperties': False, 'properties': {'attr': {'type': 'string', 'format': 'python-qualified-identifier'}}, 'required': ['attr']}, 'find-directive': {'$id': '#/definitions/find-directive', 'title': "'find:' directive", 'type': 'object', 'additionalProperties': False, 'properties': {'find': {'type': 'object', '$$description': ['Dynamic `package discovery', '<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_.'], 'additionalProperties': False, 'properties': {'where': {'description': 'Directories to be searched for packages (Unix-style relative path)', 'type': 'array', 'items': {'type': 'string'}}, 'exclude': {'type': 'array', '$$description': ['Exclude packages that match the values listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'include': {'type': 'array', '$$description': ['Restrict the found packages to just the ones listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'namespaces': {'type': 'boolean', '$$description': ['When ``True``, directories without a ``__init__.py`` file will also', 'be scanned for :pep:`420`-style implicit namespaces']}}}}}}}}}, rule='type')
+            data__tool_is_dict = isinstance(data__tool, dict)
+            if data__tool_is_dict:
+                data__tool_keys = set(data__tool.keys())
+                if "distutils" in data__tool_keys:
+                    data__tool_keys.remove("distutils")
+                    data__tool__distutils = data__tool["distutils"]
+                    validate_https___setuptools_pypa_io_en_latest_deprecated_distutils_configfile_html(data__tool__distutils, custom_formats, (name_prefix or "data") + ".tool.distutils")
+                if "setuptools" in data__tool_keys:
+                    data__tool_keys.remove("setuptools")
+                    data__tool__setuptools = data__tool["setuptools"]
+                    validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html(data__tool__setuptools, custom_formats, (name_prefix or "data") + ".tool.setuptools")
+        if "dependency-groups" in data_keys:
+            data_keys.remove("dependency-groups")
+            data__dependencygroups = data["dependency-groups"]
+            if not isinstance(data__dependencygroups, (dict)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".dependency-groups must be object", value=data__dependencygroups, name="" + (name_prefix or "data") + ".dependency-groups", definition={'type': 'object', 'description': 'Dependency groups following PEP 735', 'additionalProperties': False, 'patternProperties': {'^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$': {'type': 'array', 'items': {'oneOf': [{'type': 'string', 'description': 'Python package specifiers following PEP 508', 'format': 'pep508'}, {'type': 'object', 'additionalProperties': False, 'properties': {'include-group': {'description': 'Another dependency group to include in this one', 'type': 'string', 'pattern': '^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$'}}}]}}}}, rule='type')
+            data__dependencygroups_is_dict = isinstance(data__dependencygroups, dict)
+            if data__dependencygroups_is_dict:
+                data__dependencygroups_keys = set(data__dependencygroups.keys())
+                for data__dependencygroups_key, data__dependencygroups_val in data__dependencygroups.items():
+                    if REGEX_PATTERNS['^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$'].search(data__dependencygroups_key):
+                        if data__dependencygroups_key in data__dependencygroups_keys:
+                            data__dependencygroups_keys.remove(data__dependencygroups_key)
+                        if not isinstance(data__dependencygroups_val, (list, tuple)):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}".format(**locals()) + " must be array", value=data__dependencygroups_val, name="" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}".format(**locals()) + "", definition={'type': 'array', 'items': {'oneOf': [{'type': 'string', 'description': 'Python package specifiers following PEP 508', 'format': 'pep508'}, {'type': 'object', 'additionalProperties': False, 'properties': {'include-group': {'description': 'Another dependency group to include in this one', 'type': 'string', 'pattern': '^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$'}}}]}}, rule='type')
+                        data__dependencygroups_val_is_list = isinstance(data__dependencygroups_val, (list, tuple))
+                        if data__dependencygroups_val_is_list:
+                            data__dependencygroups_val_len = len(data__dependencygroups_val)
+                            for data__dependencygroups_val_x, data__dependencygroups_val_item in enumerate(data__dependencygroups_val):
+                                data__dependencygroups_val_item_one_of_count1 = 0
+                                if data__dependencygroups_val_item_one_of_count1 < 2:
+                                    try:
+                                        if not isinstance(data__dependencygroups_val_item, (str)):
+                                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}]".format(**locals()) + " must be string", value=data__dependencygroups_val_item, name="" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}]".format(**locals()) + "", definition={'type': 'string', 'description': 'Python package specifiers following PEP 508', 'format': 'pep508'}, rule='type')
+                                        if isinstance(data__dependencygroups_val_item, str):
+                                            if not custom_formats["pep508"](data__dependencygroups_val_item):
+                                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}]".format(**locals()) + " must be pep508", value=data__dependencygroups_val_item, name="" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}]".format(**locals()) + "", definition={'type': 'string', 'description': 'Python package specifiers following PEP 508', 'format': 'pep508'}, rule='format')
+                                        data__dependencygroups_val_item_one_of_count1 += 1
+                                    except JsonSchemaValueException: pass
+                                if data__dependencygroups_val_item_one_of_count1 < 2:
+                                    try:
+                                        if not isinstance(data__dependencygroups_val_item, (dict)):
+                                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}]".format(**locals()) + " must be object", value=data__dependencygroups_val_item, name="" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}]".format(**locals()) + "", definition={'type': 'object', 'additionalProperties': False, 'properties': {'include-group': {'description': 'Another dependency group to include in this one', 'type': 'string', 'pattern': '^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$'}}}, rule='type')
+                                        data__dependencygroups_val_item_is_dict = isinstance(data__dependencygroups_val_item, dict)
+                                        if data__dependencygroups_val_item_is_dict:
+                                            data__dependencygroups_val_item_keys = set(data__dependencygroups_val_item.keys())
+                                            if "include-group" in data__dependencygroups_val_item_keys:
+                                                data__dependencygroups_val_item_keys.remove("include-group")
+                                                data__dependencygroups_val_item__includegroup = data__dependencygroups_val_item["include-group"]
+                                                if not isinstance(data__dependencygroups_val_item__includegroup, (str)):
+                                                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}].include-group".format(**locals()) + " must be string", value=data__dependencygroups_val_item__includegroup, name="" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}].include-group".format(**locals()) + "", definition={'description': 'Another dependency group to include in this one', 'type': 'string', 'pattern': '^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$'}, rule='type')
+                                                if isinstance(data__dependencygroups_val_item__includegroup, str):
+                                                    if not REGEX_PATTERNS['^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$'].search(data__dependencygroups_val_item__includegroup):
+                                                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}].include-group".format(**locals()) + " must match pattern ^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$", value=data__dependencygroups_val_item__includegroup, name="" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}].include-group".format(**locals()) + "", definition={'description': 'Another dependency group to include in this one', 'type': 'string', 'pattern': '^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$'}, rule='pattern')
+                                            if data__dependencygroups_val_item_keys:
+                                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}]".format(**locals()) + " must not contain "+str(data__dependencygroups_val_item_keys)+" properties", value=data__dependencygroups_val_item, name="" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}]".format(**locals()) + "", definition={'type': 'object', 'additionalProperties': False, 'properties': {'include-group': {'description': 'Another dependency group to include in this one', 'type': 'string', 'pattern': '^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$'}}}, rule='additionalProperties')
+                                        data__dependencygroups_val_item_one_of_count1 += 1
+                                    except JsonSchemaValueException: pass
+                                if data__dependencygroups_val_item_one_of_count1 != 1:
+                                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}]".format(**locals()) + " must be valid exactly by one definition" + (" (" + str(data__dependencygroups_val_item_one_of_count1) + " matches found)"), value=data__dependencygroups_val_item, name="" + (name_prefix or "data") + ".dependency-groups.{data__dependencygroups_key}[{data__dependencygroups_val_x}]".format(**locals()) + "", definition={'oneOf': [{'type': 'string', 'description': 'Python package specifiers following PEP 508', 'format': 'pep508'}, {'type': 'object', 'additionalProperties': False, 'properties': {'include-group': {'description': 'Another dependency group to include in this one', 'type': 'string', 'pattern': '^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$'}}}]}, rule='oneOf')
+                if data__dependencygroups_keys:
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".dependency-groups must not contain "+str(data__dependencygroups_keys)+" properties", value=data__dependencygroups, name="" + (name_prefix or "data") + ".dependency-groups", definition={'type': 'object', 'description': 'Dependency groups following PEP 735', 'additionalProperties': False, 'patternProperties': {'^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$': {'type': 'array', 'items': {'oneOf': [{'type': 'string', 'description': 'Python package specifiers following PEP 508', 'format': 'pep508'}, {'type': 'object', 'additionalProperties': False, 'properties': {'include-group': {'description': 'Another dependency group to include in this one', 'type': 'string', 'pattern': '^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$'}}}]}}}}, rule='additionalProperties')
+        if data_keys:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must not contain "+str(data_keys)+" properties", value=data, name="" + (name_prefix or "data") + "", definition={'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://packaging.python.org/en/latest/specifications/declaring-build-dependencies/', 'title': 'Data structure for ``pyproject.toml`` files', '$$description': ['File format containing build-time configurations for the Python ecosystem. ', ':pep:`517` initially defined a build-system independent format for source trees', 'which was complemented by :pep:`518` to provide a way of specifying dependencies ', 'for building Python projects.', 'Please notice the ``project`` table (as initially defined in  :pep:`621`) is not included', 'in this schema and should be considered separately.'], 'type': 'object', 'additionalProperties': False, 'properties': {'build-system': {'type': 'object', 'description': 'Table used to store build-related data', 'additionalProperties': False, 'properties': {'requires': {'type': 'array', '$$description': ['List of dependencies in the :pep:`508` format required to execute the build', 'system. Please notice that the resulting dependency graph', '**MUST NOT contain cycles**'], 'items': {'type': 'string'}}, 'build-backend': {'type': 'string', 'description': 'Python object that will be used to perform the build according to :pep:`517`', 'format': 'pep517-backend-reference'}, 'backend-path': {'type': 'array', '$$description': ['List of directories to be prepended to ``sys.path`` when loading the', 'back-end, and running its hooks'], 'items': {'type': 'string', '$comment': 'Should be a path (TODO: enforce it with format?)'}}}, 'required': ['requires']}, 'project': {'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://packaging.python.org/en/latest/specifications/pyproject-toml/', 'title': 'Package metadata stored in the ``project`` table', '$$description': ['Data structure for the **project** table inside ``pyproject.toml``', '(as initially defined in :pep:`621`)'], 'type': 'object', 'properties': {'name': {'type': 'string', 'description': 'The name (primary identifier) of the project. MUST be statically defined.', 'format': 'pep508-identifier'}, 'version': {'type': 'string', 'description': 'The version of the project as supported by :pep:`440`.', 'format': 'pep440'}, 'description': {'type': 'string', '$$description': ['The `summary description of the project', '<https://packaging.python.org/specifications/core-metadata/#summary>`_']}, 'readme': {'$$description': ['`Full/detailed description of the project in the form of a README', '<https://peps.python.org/pep-0621/#readme>`_', "with meaning similar to the one defined in `core metadata's Description", '<https://packaging.python.org/specifications/core-metadata/#description>`_'], 'oneOf': [{'type': 'string', '$$description': ['Relative path to a text file (UTF-8) containing the full description', 'of the project. If the file path ends in case-insensitive ``.md`` or', '``.rst`` suffixes, then the content-type is respectively', '``text/markdown`` or ``text/x-rst``']}, {'type': 'object', 'allOf': [{'anyOf': [{'properties': {'file': {'type': 'string', '$$description': ['Relative path to a text file containing the full description', 'of the project.']}}, 'required': ['file']}, {'properties': {'text': {'type': 'string', 'description': 'Full text describing the project.'}}, 'required': ['text']}]}, {'properties': {'content-type': {'type': 'string', '$$description': ['Content-type (:rfc:`1341`) of the full description', '(e.g. ``text/markdown``). The ``charset`` parameter is assumed', 'UTF-8 when not present.'], '$comment': 'TODO: add regex pattern or format?'}}, 'required': ['content-type']}]}]}, 'requires-python': {'type': 'string', 'format': 'pep508-versionspec', '$$description': ['`The Python version requirements of the project', '<https://packaging.python.org/specifications/core-metadata/#requires-python>`_.']}, 'license': {'description': '`Project license <https://peps.python.org/pep-0621/#license>`_.', 'oneOf': [{'type': 'string', 'description': 'An SPDX license identifier', 'format': 'SPDX'}, {'type': 'object', 'properties': {'file': {'type': 'string', '$$description': ['Relative path to the file (UTF-8) which contains the license for the', 'project.']}}, 'required': ['file']}, {'type': 'object', 'properties': {'text': {'type': 'string', '$$description': ['The license of the project whose meaning is that of the', '`License field from the core metadata', '<https://packaging.python.org/specifications/core-metadata/#license>`_.']}}, 'required': ['text']}]}, 'license-files': {'description': 'Paths or globs to paths of license files', 'type': 'array', 'items': {'type': 'string'}}, 'authors': {'type': 'array', 'items': {'$ref': '#/definitions/author'}, '$$description': ["The people or organizations considered to be the 'authors' of the project.", 'The exact meaning is open to interpretation (e.g. original or primary authors,', 'current maintainers, or owners of the package).']}, 'maintainers': {'type': 'array', 'items': {'$ref': '#/definitions/author'}, '$$description': ["The people or organizations considered to be the 'maintainers' of the project.", 'Similarly to ``authors``, the exact meaning is open to interpretation.']}, 'keywords': {'type': 'array', 'items': {'type': 'string'}, 'description': 'List of keywords to assist searching for the distribution in a larger catalog.'}, 'classifiers': {'type': 'array', 'items': {'type': 'string', 'format': 'trove-classifier', 'description': '`PyPI classifier <https://pypi.org/classifiers/>`_.'}, '$$description': ['`Trove classifiers <https://pypi.org/classifiers/>`_', 'which apply to the project.']}, 'urls': {'type': 'object', 'description': 'URLs associated with the project in the form ``label => value``.', 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', 'format': 'url'}}}, 'scripts': {'$ref': '#/definitions/entry-point-group', '$$description': ['Instruct the installer to create command-line wrappers for the given', '`entry points <https://packaging.python.org/specifications/entry-points/>`_.']}, 'gui-scripts': {'$ref': '#/definitions/entry-point-group', '$$description': ['Instruct the installer to create GUI wrappers for the given', '`entry points <https://packaging.python.org/specifications/entry-points/>`_.', 'The difference between ``scripts`` and ``gui-scripts`` is only relevant in', 'Windows.']}, 'entry-points': {'$$description': ['Instruct the installer to expose the given modules/functions via', '``entry-point`` discovery mechanism (useful for plugins).', 'More information available in the `Python packaging guide', '<https://packaging.python.org/specifications/entry-points/>`_.'], 'propertyNames': {'format': 'python-entrypoint-group'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'$ref': '#/definitions/entry-point-group'}}}, 'dependencies': {'type': 'array', 'description': 'Project (mandatory) dependencies.', 'items': {'$ref': '#/definitions/dependency'}}, 'optional-dependencies': {'type': 'object', 'description': 'Optional dependency for the project', 'propertyNames': {'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'array', 'items': {'$ref': '#/definitions/dependency'}}}}, 'dynamic': {'type': 'array', '$$description': ['Specifies which fields are intentionally unspecified and expected to be', 'dynamically provided by build tools'], 'items': {'enum': ['version', 'description', 'readme', 'requires-python', 'license', 'license-files', 'authors', 'maintainers', 'keywords', 'classifiers', 'urls', 'scripts', 'gui-scripts', 'entry-points', 'dependencies', 'optional-dependencies']}}}, 'required': ['name'], 'additionalProperties': False, 'allOf': [{'if': {'not': {'required': ['dynamic'], 'properties': {'dynamic': {'contains': {'const': 'version'}, '$$description': ['version is listed in ``dynamic``']}}}, '$$comment': ['According to :pep:`621`:', '    If the core metadata specification lists a field as "Required", then', '    the metadata MUST specify the field statically or list it in dynamic', 'In turn, `core metadata`_ defines:', '    The required fields are: Metadata-Version, Name, Version.', '    All the other fields are optional.', 'Since ``Metadata-Version`` is defined by the build back-end, ``name`` and', '``version`` are the only mandatory information in ``pyproject.toml``.', '.. _core metadata: https://packaging.python.org/specifications/core-metadata/']}, 'then': {'required': ['version'], '$$description': ['version should be statically defined in the ``version`` field']}}, {'if': {'required': ['license-files']}, 'then': {'properties': {'license': {'type': 'string'}}}}], 'definitions': {'author': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, 'entry-point-group': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, 'dependency': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}}, 'tool': {'type': 'object', 'properties': {'distutils': {'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://setuptools.pypa.io/en/latest/deprecated/distutils/configfile.html', 'title': '``tool.distutils`` table', '$$description': ['**EXPERIMENTAL** (NOT OFFICIALLY SUPPORTED): Use ``tool.distutils``', 'subtables to configure arguments for ``distutils`` commands.', 'Originally, ``distutils`` allowed developers to configure arguments for', '``setup.py`` commands via `distutils configuration files', '<https://setuptools.pypa.io/en/latest/deprecated/distutils/configfile.html>`_.', 'See also `the old Python docs <https://docs.python.org/3.11/install/>_`.'], 'type': 'object', 'properties': {'global': {'type': 'object', 'description': 'Global options applied to all ``distutils`` commands'}}, 'patternProperties': {'.+': {'type': 'object'}}, '$comment': 'TODO: Is there a practical way of making this schema more specific?'}, 'setuptools': {'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html', 'title': '``tool.setuptools`` table', '$$description': ['``setuptools``-specific configurations that can be set by users that require', 'customization.', 'These configurations are completely optional and probably can be skipped when', 'creating simple packages. They are equivalent to some of the `Keywords', '<https://setuptools.pypa.io/en/latest/references/keywords.html>`_', 'used by the ``setup.py`` file, and can be set via the ``tool.setuptools`` table.', 'It considers only ``setuptools`` `parameters', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#setuptools-specific-configuration>`_', 'that are not covered by :pep:`621`; and intentionally excludes ``dependency_links``', 'and ``setup_requires`` (incompatible with modern workflows/standards).'], 'type': 'object', 'additionalProperties': False, 'properties': {'platforms': {'type': 'array', 'items': {'type': 'string'}}, 'provides': {'$$description': ['Package and virtual package names contained within this package', '**(not supported by pip)**'], 'type': 'array', 'items': {'type': 'string', 'format': 'pep508-identifier'}}, 'obsoletes': {'$$description': ['Packages which this package renders obsolete', '**(not supported by pip)**'], 'type': 'array', 'items': {'type': 'string', 'format': 'pep508-identifier'}}, 'zip-safe': {'$$description': ['Whether the project can be safely installed and run from a zip file.', '**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and', '``setup.py install`` in the context of ``eggs`` (**DEPRECATED**).'], 'type': 'boolean'}, 'script-files': {'$$description': ['Legacy way of defining scripts (entry-points are preferred).', 'Equivalent to the ``script`` keyword in ``setup.py``', '(it was renamed to avoid confusion with entry-point based ``project.scripts``', 'defined in :pep:`621`).', '**DISCOURAGED**: generic script wrappers are tricky and may not work properly.', 'Whenever possible, please use ``project.scripts`` instead.'], 'type': 'array', 'items': {'type': 'string'}, '$comment': 'TODO: is this field deprecated/should be removed?'}, 'eager-resources': {'$$description': ['Resources that should be extracted together, if any of them is needed,', 'or if any C extensions included in the project are imported.', '**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and', '``setup.py install`` in the context of ``eggs`` (**DEPRECATED**).'], 'type': 'array', 'items': {'type': 'string'}}, 'packages': {'$$description': ['Packages that should be included in the distribution.', 'It can be given either as a list of package identifiers', 'or as a ``dict``-like structure with a single key ``find``', 'which corresponds to a dynamic call to', '``setuptools.config.expand.find_packages`` function.', 'The ``find`` key is associated with a nested ``dict``-like structure that can', 'contain ``where``, ``include``, ``exclude`` and ``namespaces`` keys,', 'mimicking the keyword arguments of the associated function.'], 'oneOf': [{'title': 'Array of Python package identifiers', 'type': 'array', 'items': {'$ref': '#/definitions/package-name'}}, {'$ref': '#/definitions/find-directive'}]}, 'package-dir': {'$$description': [':class:`dict`-like structure mapping from package names to directories where their', 'code can be found.', 'The empty string (as key) means that all packages are contained inside', 'the given directory will be included in the distribution.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'const': ''}, {'$ref': '#/definitions/package-name'}]}, 'patternProperties': {'^.*$': {'type': 'string'}}}, 'package-data': {'$$description': ['Mapping from package names to lists of glob patterns.', 'Usually this option is not needed when using ``include-package-data = true``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'include-package-data': {'$$description': ['Automatically include any data files inside the package directories', 'that are specified by ``MANIFEST.in``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'boolean'}, 'exclude-package-data': {'$$description': ['Mapping from package names to lists of glob patterns that should be excluded', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'namespace-packages': {'type': 'array', 'items': {'type': 'string', 'format': 'python-module-name-relaxed'}, '$comment': 'https://setuptools.pypa.io/en/latest/userguide/package_discovery.html', 'description': '**DEPRECATED**: use implicit namespaces instead (:pep:`420`).'}, 'py-modules': {'description': 'Modules that setuptools will manipulate', 'type': 'array', 'items': {'type': 'string', 'format': 'python-module-name-relaxed'}, '$comment': 'TODO: clarify the relationship with ``packages``'}, 'ext-modules': {'description': 'Extension modules to be compiled by setuptools', 'type': 'array', 'items': {'$ref': '#/definitions/ext-module'}}, 'data-files': {'$$description': ['``dict``-like structure where each key represents a directory and', 'the value is a list of glob patterns that should be installed in them.', '**DISCOURAGED**: please notice this might not work as expected with wheels.', 'Whenever possible, consider using data files inside the package directories', '(or create a new namespace package that only contains data files).', 'See `data files support', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'cmdclass': {'$$description': ['Mapping of distutils-style command names to ``setuptools.Command`` subclasses', 'which in turn should be represented by strings with a qualified class name', '(i.e., "dotted" form with module), e.g.::\n\n', '    cmdclass = {mycmd = "pkg.subpkg.module.CommandClass"}\n\n', 'The command class should be a directly defined at the top-level of the', 'containing module (no class nesting).'], 'type': 'object', 'patternProperties': {'^.*$': {'type': 'string', 'format': 'python-qualified-identifier'}}}, 'license-files': {'type': 'array', 'items': {'type': 'string'}, '$$description': ['**PROVISIONAL**: list of glob patterns for all license files being distributed.', '(likely to become standard with :pep:`639`).', "By default: ``['LICEN[CS]E*', 'COPYING*', 'NOTICE*', 'AUTHORS*']``"], '$comment': 'TODO: revise if PEP 639 is accepted. Probably ``project.license-files``?'}, 'dynamic': {'type': 'object', 'description': 'Instructions for loading :pep:`621`-related metadata dynamically', 'additionalProperties': False, 'properties': {'version': {'$$description': ['A version dynamically loaded via either the ``attr:`` or ``file:``', 'directives. Please make sure the given file or attribute respects :pep:`440`.', 'Also ensure to set ``project.dynamic`` accordingly.'], 'oneOf': [{'$ref': '#/definitions/attr-directive'}, {'$ref': '#/definitions/file-directive'}]}, 'classifiers': {'$ref': '#/definitions/file-directive'}, 'description': {'$ref': '#/definitions/file-directive'}, 'entry-points': {'$ref': '#/definitions/file-directive'}, 'dependencies': {'$ref': '#/definitions/file-directive-for-dependencies'}, 'optional-dependencies': {'type': 'object', 'propertyNames': {'type': 'string', 'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'.+': {'$ref': '#/definitions/file-directive-for-dependencies'}}}, 'readme': {'type': 'object', 'anyOf': [{'$ref': '#/definitions/file-directive'}, {'type': 'object', 'properties': {'content-type': {'type': 'string'}, 'file': {'$ref': '#/definitions/file-directive/properties/file'}}, 'additionalProperties': False}], 'required': ['file']}}}}, 'definitions': {'package-name': {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}, 'ext-module': {'$id': '#/definitions/ext-module', 'title': 'Extension module', 'description': 'Parameters to construct a :class:`setuptools.Extension` object', 'type': 'object', 'required': ['name', 'sources'], 'additionalProperties': False, 'properties': {'name': {'type': 'string', 'format': 'python-module-name-relaxed'}, 'sources': {'type': 'array', 'items': {'type': 'string'}}, 'include-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'define-macros': {'type': 'array', 'items': {'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}}, 'undef-macros': {'type': 'array', 'items': {'type': 'string'}}, 'library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'libraries': {'type': 'array', 'items': {'type': 'string'}}, 'runtime-library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'extra-objects': {'type': 'array', 'items': {'type': 'string'}}, 'extra-compile-args': {'type': 'array', 'items': {'type': 'string'}}, 'extra-link-args': {'type': 'array', 'items': {'type': 'string'}}, 'export-symbols': {'type': 'array', 'items': {'type': 'string'}}, 'swig-opts': {'type': 'array', 'items': {'type': 'string'}}, 'depends': {'type': 'array', 'items': {'type': 'string'}}, 'language': {'type': 'string'}, 'optional': {'type': 'boolean'}, 'py-limited-api': {'type': 'boolean'}}}, 'file-directive': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'file-directive-for-dependencies': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}, 'attr-directive': {'title': "'attr:' directive", '$id': '#/definitions/attr-directive', '$$description': ['Value is read from a module attribute. Supports callables and iterables;', 'unsupported types are cast via ``str()``'], 'type': 'object', 'additionalProperties': False, 'properties': {'attr': {'type': 'string', 'format': 'python-qualified-identifier'}}, 'required': ['attr']}, 'find-directive': {'$id': '#/definitions/find-directive', 'title': "'find:' directive", 'type': 'object', 'additionalProperties': False, 'properties': {'find': {'type': 'object', '$$description': ['Dynamic `package discovery', '<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_.'], 'additionalProperties': False, 'properties': {'where': {'description': 'Directories to be searched for packages (Unix-style relative path)', 'type': 'array', 'items': {'type': 'string'}}, 'exclude': {'type': 'array', '$$description': ['Exclude packages that match the values listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'include': {'type': 'array', '$$description': ['Restrict the found packages to just the ones listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'namespaces': {'type': 'boolean', '$$description': ['When ``True``, directories without a ``__init__.py`` file will also', 'be scanned for :pep:`420`-style implicit namespaces']}}}}}}}}}, 'dependency-groups': {'type': 'object', 'description': 'Dependency groups following PEP 735', 'additionalProperties': False, 'patternProperties': {'^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$': {'type': 'array', 'items': {'oneOf': [{'type': 'string', 'description': 'Python package specifiers following PEP 508', 'format': 'pep508'}, {'type': 'object', 'additionalProperties': False, 'properties': {'include-group': {'description': 'Another dependency group to include in this one', 'type': 'string', 'pattern': '^([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9._-]*[a-zA-Z0-9])$'}}}]}}}}}, 'project': {'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://packaging.python.org/en/latest/specifications/pyproject-toml/', 'title': 'Package metadata stored in the ``project`` table', '$$description': ['Data structure for the **project** table inside ``pyproject.toml``', '(as initially defined in :pep:`621`)'], 'type': 'object', 'properties': {'name': {'type': 'string', 'description': 'The name (primary identifier) of the project. MUST be statically defined.', 'format': 'pep508-identifier'}, 'version': {'type': 'string', 'description': 'The version of the project as supported by :pep:`440`.', 'format': 'pep440'}, 'description': {'type': 'string', '$$description': ['The `summary description of the project', '<https://packaging.python.org/specifications/core-metadata/#summary>`_']}, 'readme': {'$$description': ['`Full/detailed description of the project in the form of a README', '<https://peps.python.org/pep-0621/#readme>`_', "with meaning similar to the one defined in `core metadata's Description", '<https://packaging.python.org/specifications/core-metadata/#description>`_'], 'oneOf': [{'type': 'string', '$$description': ['Relative path to a text file (UTF-8) containing the full description', 'of the project. If the file path ends in case-insensitive ``.md`` or', '``.rst`` suffixes, then the content-type is respectively', '``text/markdown`` or ``text/x-rst``']}, {'type': 'object', 'allOf': [{'anyOf': [{'properties': {'file': {'type': 'string', '$$description': ['Relative path to a text file containing the full description', 'of the project.']}}, 'required': ['file']}, {'properties': {'text': {'type': 'string', 'description': 'Full text describing the project.'}}, 'required': ['text']}]}, {'properties': {'content-type': {'type': 'string', '$$description': ['Content-type (:rfc:`1341`) of the full description', '(e.g. ``text/markdown``). The ``charset`` parameter is assumed', 'UTF-8 when not present.'], '$comment': 'TODO: add regex pattern or format?'}}, 'required': ['content-type']}]}]}, 'requires-python': {'type': 'string', 'format': 'pep508-versionspec', '$$description': ['`The Python version requirements of the project', '<https://packaging.python.org/specifications/core-metadata/#requires-python>`_.']}, 'license': {'description': '`Project license <https://peps.python.org/pep-0621/#license>`_.', 'oneOf': [{'type': 'string', 'description': 'An SPDX license identifier', 'format': 'SPDX'}, {'type': 'object', 'properties': {'file': {'type': 'string', '$$description': ['Relative path to the file (UTF-8) which contains the license for the', 'project.']}}, 'required': ['file']}, {'type': 'object', 'properties': {'text': {'type': 'string', '$$description': ['The license of the project whose meaning is that of the', '`License field from the core metadata', '<https://packaging.python.org/specifications/core-metadata/#license>`_.']}}, 'required': ['text']}]}, 'license-files': {'description': 'Paths or globs to paths of license files', 'type': 'array', 'items': {'type': 'string'}}, 'authors': {'type': 'array', 'items': {'$ref': '#/definitions/author'}, '$$description': ["The people or organizations considered to be the 'authors' of the project.", 'The exact meaning is open to interpretation (e.g. original or primary authors,', 'current maintainers, or owners of the package).']}, 'maintainers': {'type': 'array', 'items': {'$ref': '#/definitions/author'}, '$$description': ["The people or organizations considered to be the 'maintainers' of the project.", 'Similarly to ``authors``, the exact meaning is open to interpretation.']}, 'keywords': {'type': 'array', 'items': {'type': 'string'}, 'description': 'List of keywords to assist searching for the distribution in a larger catalog.'}, 'classifiers': {'type': 'array', 'items': {'type': 'string', 'format': 'trove-classifier', 'description': '`PyPI classifier <https://pypi.org/classifiers/>`_.'}, '$$description': ['`Trove classifiers <https://pypi.org/classifiers/>`_', 'which apply to the project.']}, 'urls': {'type': 'object', 'description': 'URLs associated with the project in the form ``label => value``.', 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', 'format': 'url'}}}, 'scripts': {'$ref': '#/definitions/entry-point-group', '$$description': ['Instruct the installer to create command-line wrappers for the given', '`entry points <https://packaging.python.org/specifications/entry-points/>`_.']}, 'gui-scripts': {'$ref': '#/definitions/entry-point-group', '$$description': ['Instruct the installer to create GUI wrappers for the given', '`entry points <https://packaging.python.org/specifications/entry-points/>`_.', 'The difference between ``scripts`` and ``gui-scripts`` is only relevant in', 'Windows.']}, 'entry-points': {'$$description': ['Instruct the installer to expose the given modules/functions via', '``entry-point`` discovery mechanism (useful for plugins).', 'More information available in the `Python packaging guide', '<https://packaging.python.org/specifications/entry-points/>`_.'], 'propertyNames': {'format': 'python-entrypoint-group'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'$ref': '#/definitions/entry-point-group'}}}, 'dependencies': {'type': 'array', 'description': 'Project (mandatory) dependencies.', 'items': {'$ref': '#/definitions/dependency'}}, 'optional-dependencies': {'type': 'object', 'description': 'Optional dependency for the project', 'propertyNames': {'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'array', 'items': {'$ref': '#/definitions/dependency'}}}}, 'dynamic': {'type': 'array', '$$description': ['Specifies which fields are intentionally unspecified and expected to be', 'dynamically provided by build tools'], 'items': {'enum': ['version', 'description', 'readme', 'requires-python', 'license', 'license-files', 'authors', 'maintainers', 'keywords', 'classifiers', 'urls', 'scripts', 'gui-scripts', 'entry-points', 'dependencies', 'optional-dependencies']}}}, 'required': ['name'], 'additionalProperties': False, 'allOf': [{'if': {'not': {'required': ['dynamic'], 'properties': {'dynamic': {'contains': {'const': 'version'}, '$$description': ['version is listed in ``dynamic``']}}}, '$$comment': ['According to :pep:`621`:', '    If the core metadata specification lists a field as "Required", then', '    the metadata MUST specify the field statically or list it in dynamic', 'In turn, `core metadata`_ defines:', '    The required fields are: Metadata-Version, Name, Version.', '    All the other fields are optional.', 'Since ``Metadata-Version`` is defined by the build back-end, ``name`` and', '``version`` are the only mandatory information in ``pyproject.toml``.', '.. _core metadata: https://packaging.python.org/specifications/core-metadata/']}, 'then': {'required': ['version'], '$$description': ['version should be statically defined in the ``version`` field']}}, {'if': {'required': ['license-files']}, 'then': {'properties': {'license': {'type': 'string'}}}}], 'definitions': {'author': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, 'entry-point-group': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, 'dependency': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}}}, rule='additionalProperties')
+    return data
+
+def validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html(data, custom_formats={}, name_prefix=None):
+    if not isinstance(data, (dict)):
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " must be object", value=data, name="" + (name_prefix or "data") + "", definition={'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html', 'title': '``tool.setuptools`` table', '$$description': ['``setuptools``-specific configurations that can be set by users that require', 'customization.', 'These configurations are completely optional and probably can be skipped when', 'creating simple packages. They are equivalent to some of the `Keywords', '<https://setuptools.pypa.io/en/latest/references/keywords.html>`_', 'used by the ``setup.py`` file, and can be set via the ``tool.setuptools`` table.', 'It considers only ``setuptools`` `parameters', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#setuptools-specific-configuration>`_', 'that are not covered by :pep:`621`; and intentionally excludes ``dependency_links``', 'and ``setup_requires`` (incompatible with modern workflows/standards).'], 'type': 'object', 'additionalProperties': False, 'properties': {'platforms': {'type': 'array', 'items': {'type': 'string'}}, 'provides': {'$$description': ['Package and virtual package names contained within this package', '**(not supported by pip)**'], 'type': 'array', 'items': {'type': 'string', 'format': 'pep508-identifier'}}, 'obsoletes': {'$$description': ['Packages which this package renders obsolete', '**(not supported by pip)**'], 'type': 'array', 'items': {'type': 'string', 'format': 'pep508-identifier'}}, 'zip-safe': {'$$description': ['Whether the project can be safely installed and run from a zip file.', '**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and', '``setup.py install`` in the context of ``eggs`` (**DEPRECATED**).'], 'type': 'boolean'}, 'script-files': {'$$description': ['Legacy way of defining scripts (entry-points are preferred).', 'Equivalent to the ``script`` keyword in ``setup.py``', '(it was renamed to avoid confusion with entry-point based ``project.scripts``', 'defined in :pep:`621`).', '**DISCOURAGED**: generic script wrappers are tricky and may not work properly.', 'Whenever possible, please use ``project.scripts`` instead.'], 'type': 'array', 'items': {'type': 'string'}, '$comment': 'TODO: is this field deprecated/should be removed?'}, 'eager-resources': {'$$description': ['Resources that should be extracted together, if any of them is needed,', 'or if any C extensions included in the project are imported.', '**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and', '``setup.py install`` in the context of ``eggs`` (**DEPRECATED**).'], 'type': 'array', 'items': {'type': 'string'}}, 'packages': {'$$description': ['Packages that should be included in the distribution.', 'It can be given either as a list of package identifiers', 'or as a ``dict``-like structure with a single key ``find``', 'which corresponds to a dynamic call to', '``setuptools.config.expand.find_packages`` function.', 'The ``find`` key is associated with a nested ``dict``-like structure that can', 'contain ``where``, ``include``, ``exclude`` and ``namespaces`` keys,', 'mimicking the keyword arguments of the associated function.'], 'oneOf': [{'title': 'Array of Python package identifiers', 'type': 'array', 'items': {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}}, {'$id': '#/definitions/find-directive', 'title': "'find:' directive", 'type': 'object', 'additionalProperties': False, 'properties': {'find': {'type': 'object', '$$description': ['Dynamic `package discovery', '<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_.'], 'additionalProperties': False, 'properties': {'where': {'description': 'Directories to be searched for packages (Unix-style relative path)', 'type': 'array', 'items': {'type': 'string'}}, 'exclude': {'type': 'array', '$$description': ['Exclude packages that match the values listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'include': {'type': 'array', '$$description': ['Restrict the found packages to just the ones listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'namespaces': {'type': 'boolean', '$$description': ['When ``True``, directories without a ``__init__.py`` file will also', 'be scanned for :pep:`420`-style implicit namespaces']}}}}}]}, 'package-dir': {'$$description': [':class:`dict`-like structure mapping from package names to directories where their', 'code can be found.', 'The empty string (as key) means that all packages are contained inside', 'the given directory will be included in the distribution.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'const': ''}, {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}]}, 'patternProperties': {'^.*$': {'type': 'string'}}}, 'package-data': {'$$description': ['Mapping from package names to lists of glob patterns.', 'Usually this option is not needed when using ``include-package-data = true``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'include-package-data': {'$$description': ['Automatically include any data files inside the package directories', 'that are specified by ``MANIFEST.in``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'boolean'}, 'exclude-package-data': {'$$description': ['Mapping from package names to lists of glob patterns that should be excluded', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'namespace-packages': {'type': 'array', 'items': {'type': 'string', 'format': 'python-module-name-relaxed'}, '$comment': 'https://setuptools.pypa.io/en/latest/userguide/package_discovery.html', 'description': '**DEPRECATED**: use implicit namespaces instead (:pep:`420`).'}, 'py-modules': {'description': 'Modules that setuptools will manipulate', 'type': 'array', 'items': {'type': 'string', 'format': 'python-module-name-relaxed'}, '$comment': 'TODO: clarify the relationship with ``packages``'}, 'ext-modules': {'description': 'Extension modules to be compiled by setuptools', 'type': 'array', 'items': {'$id': '#/definitions/ext-module', 'title': 'Extension module', 'description': 'Parameters to construct a :class:`setuptools.Extension` object', 'type': 'object', 'required': ['name', 'sources'], 'additionalProperties': False, 'properties': {'name': {'type': 'string', 'format': 'python-module-name-relaxed'}, 'sources': {'type': 'array', 'items': {'type': 'string'}}, 'include-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'define-macros': {'type': 'array', 'items': {'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}}, 'undef-macros': {'type': 'array', 'items': {'type': 'string'}}, 'library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'libraries': {'type': 'array', 'items': {'type': 'string'}}, 'runtime-library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'extra-objects': {'type': 'array', 'items': {'type': 'string'}}, 'extra-compile-args': {'type': 'array', 'items': {'type': 'string'}}, 'extra-link-args': {'type': 'array', 'items': {'type': 'string'}}, 'export-symbols': {'type': 'array', 'items': {'type': 'string'}}, 'swig-opts': {'type': 'array', 'items': {'type': 'string'}}, 'depends': {'type': 'array', 'items': {'type': 'string'}}, 'language': {'type': 'string'}, 'optional': {'type': 'boolean'}, 'py-limited-api': {'type': 'boolean'}}}}, 'data-files': {'$$description': ['``dict``-like structure where each key represents a directory and', 'the value is a list of glob patterns that should be installed in them.', '**DISCOURAGED**: please notice this might not work as expected with wheels.', 'Whenever possible, consider using data files inside the package directories', '(or create a new namespace package that only contains data files).', 'See `data files support', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'cmdclass': {'$$description': ['Mapping of distutils-style command names to ``setuptools.Command`` subclasses', 'which in turn should be represented by strings with a qualified class name', '(i.e., "dotted" form with module), e.g.::\n\n', '    cmdclass = {mycmd = "pkg.subpkg.module.CommandClass"}\n\n', 'The command class should be a directly defined at the top-level of the', 'containing module (no class nesting).'], 'type': 'object', 'patternProperties': {'^.*$': {'type': 'string', 'format': 'python-qualified-identifier'}}}, 'license-files': {'type': 'array', 'items': {'type': 'string'}, '$$description': ['**PROVISIONAL**: list of glob patterns for all license files being distributed.', '(likely to become standard with :pep:`639`).', "By default: ``['LICEN[CS]E*', 'COPYING*', 'NOTICE*', 'AUTHORS*']``"], '$comment': 'TODO: revise if PEP 639 is accepted. Probably ``project.license-files``?'}, 'dynamic': {'type': 'object', 'description': 'Instructions for loading :pep:`621`-related metadata dynamically', 'additionalProperties': False, 'properties': {'version': {'$$description': ['A version dynamically loaded via either the ``attr:`` or ``file:``', 'directives. Please make sure the given file or attribute respects :pep:`440`.', 'Also ensure to set ``project.dynamic`` accordingly.'], 'oneOf': [{'title': "'attr:' directive", '$id': '#/definitions/attr-directive', '$$description': ['Value is read from a module attribute. Supports callables and iterables;', 'unsupported types are cast via ``str()``'], 'type': 'object', 'additionalProperties': False, 'properties': {'attr': {'type': 'string', 'format': 'python-qualified-identifier'}}, 'required': ['attr']}, {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}]}, 'classifiers': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'description': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'entry-points': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'dependencies': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}, 'optional-dependencies': {'type': 'object', 'propertyNames': {'type': 'string', 'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'.+': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}}}, 'readme': {'type': 'object', 'anyOf': [{'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, {'type': 'object', 'properties': {'content-type': {'type': 'string'}, 'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'additionalProperties': False}], 'required': ['file']}}}}, 'definitions': {'package-name': {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}, 'ext-module': {'$id': '#/definitions/ext-module', 'title': 'Extension module', 'description': 'Parameters to construct a :class:`setuptools.Extension` object', 'type': 'object', 'required': ['name', 'sources'], 'additionalProperties': False, 'properties': {'name': {'type': 'string', 'format': 'python-module-name-relaxed'}, 'sources': {'type': 'array', 'items': {'type': 'string'}}, 'include-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'define-macros': {'type': 'array', 'items': {'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}}, 'undef-macros': {'type': 'array', 'items': {'type': 'string'}}, 'library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'libraries': {'type': 'array', 'items': {'type': 'string'}}, 'runtime-library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'extra-objects': {'type': 'array', 'items': {'type': 'string'}}, 'extra-compile-args': {'type': 'array', 'items': {'type': 'string'}}, 'extra-link-args': {'type': 'array', 'items': {'type': 'string'}}, 'export-symbols': {'type': 'array', 'items': {'type': 'string'}}, 'swig-opts': {'type': 'array', 'items': {'type': 'string'}}, 'depends': {'type': 'array', 'items': {'type': 'string'}}, 'language': {'type': 'string'}, 'optional': {'type': 'boolean'}, 'py-limited-api': {'type': 'boolean'}}}, 'file-directive': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'file-directive-for-dependencies': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}]}, 'attr-directive': {'title': "'attr:' directive", '$id': '#/definitions/attr-directive', '$$description': ['Value is read from a module attribute. Supports callables and iterables;', 'unsupported types are cast via ``str()``'], 'type': 'object', 'additionalProperties': False, 'properties': {'attr': {'type': 'string', 'format': 'python-qualified-identifier'}}, 'required': ['attr']}, 'find-directive': {'$id': '#/definitions/find-directive', 'title': "'find:' directive", 'type': 'object', 'additionalProperties': False, 'properties': {'find': {'type': 'object', '$$description': ['Dynamic `package discovery', '<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_.'], 'additionalProperties': False, 'properties': {'where': {'description': 'Directories to be searched for packages (Unix-style relative path)', 'type': 'array', 'items': {'type': 'string'}}, 'exclude': {'type': 'array', '$$description': ['Exclude packages that match the values listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'include': {'type': 'array', '$$description': ['Restrict the found packages to just the ones listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'namespaces': {'type': 'boolean', '$$description': ['When ``True``, directories without a ``__init__.py`` file will also', 'be scanned for :pep:`420`-style implicit namespaces']}}}}}}}, rule='type')
+    data_is_dict = isinstance(data, dict)
+    if data_is_dict:
+        data_keys = set(data.keys())
+        if "platforms" in data_keys:
+            data_keys.remove("platforms")
+            data__platforms = data["platforms"]
+            if not isinstance(data__platforms, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".platforms must be array", value=data__platforms, name="" + (name_prefix or "data") + ".platforms", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__platforms_is_list = isinstance(data__platforms, (list, tuple))
+            if data__platforms_is_list:
+                data__platforms_len = len(data__platforms)
+                for data__platforms_x, data__platforms_item in enumerate(data__platforms):
+                    if not isinstance(data__platforms_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".platforms[{data__platforms_x}]".format(**locals()) + " must be string", value=data__platforms_item, name="" + (name_prefix or "data") + ".platforms[{data__platforms_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "provides" in data_keys:
+            data_keys.remove("provides")
+            data__provides = data["provides"]
+            if not isinstance(data__provides, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".provides must be array", value=data__provides, name="" + (name_prefix or "data") + ".provides", definition={'$$description': ['Package and virtual package names contained within this package', '**(not supported by pip)**'], 'type': 'array', 'items': {'type': 'string', 'format': 'pep508-identifier'}}, rule='type')
+            data__provides_is_list = isinstance(data__provides, (list, tuple))
+            if data__provides_is_list:
+                data__provides_len = len(data__provides)
+                for data__provides_x, data__provides_item in enumerate(data__provides):
+                    if not isinstance(data__provides_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".provides[{data__provides_x}]".format(**locals()) + " must be string", value=data__provides_item, name="" + (name_prefix or "data") + ".provides[{data__provides_x}]".format(**locals()) + "", definition={'type': 'string', 'format': 'pep508-identifier'}, rule='type')
+                    if isinstance(data__provides_item, str):
+                        if not custom_formats["pep508-identifier"](data__provides_item):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".provides[{data__provides_x}]".format(**locals()) + " must be pep508-identifier", value=data__provides_item, name="" + (name_prefix or "data") + ".provides[{data__provides_x}]".format(**locals()) + "", definition={'type': 'string', 'format': 'pep508-identifier'}, rule='format')
+        if "obsoletes" in data_keys:
+            data_keys.remove("obsoletes")
+            data__obsoletes = data["obsoletes"]
+            if not isinstance(data__obsoletes, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".obsoletes must be array", value=data__obsoletes, name="" + (name_prefix or "data") + ".obsoletes", definition={'$$description': ['Packages which this package renders obsolete', '**(not supported by pip)**'], 'type': 'array', 'items': {'type': 'string', 'format': 'pep508-identifier'}}, rule='type')
+            data__obsoletes_is_list = isinstance(data__obsoletes, (list, tuple))
+            if data__obsoletes_is_list:
+                data__obsoletes_len = len(data__obsoletes)
+                for data__obsoletes_x, data__obsoletes_item in enumerate(data__obsoletes):
+                    if not isinstance(data__obsoletes_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".obsoletes[{data__obsoletes_x}]".format(**locals()) + " must be string", value=data__obsoletes_item, name="" + (name_prefix or "data") + ".obsoletes[{data__obsoletes_x}]".format(**locals()) + "", definition={'type': 'string', 'format': 'pep508-identifier'}, rule='type')
+                    if isinstance(data__obsoletes_item, str):
+                        if not custom_formats["pep508-identifier"](data__obsoletes_item):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".obsoletes[{data__obsoletes_x}]".format(**locals()) + " must be pep508-identifier", value=data__obsoletes_item, name="" + (name_prefix or "data") + ".obsoletes[{data__obsoletes_x}]".format(**locals()) + "", definition={'type': 'string', 'format': 'pep508-identifier'}, rule='format')
+        if "zip-safe" in data_keys:
+            data_keys.remove("zip-safe")
+            data__zipsafe = data["zip-safe"]
+            if not isinstance(data__zipsafe, (bool)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".zip-safe must be boolean", value=data__zipsafe, name="" + (name_prefix or "data") + ".zip-safe", definition={'$$description': ['Whether the project can be safely installed and run from a zip file.', '**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and', '``setup.py install`` in the context of ``eggs`` (**DEPRECATED**).'], 'type': 'boolean'}, rule='type')
+        if "script-files" in data_keys:
+            data_keys.remove("script-files")
+            data__scriptfiles = data["script-files"]
+            if not isinstance(data__scriptfiles, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".script-files must be array", value=data__scriptfiles, name="" + (name_prefix or "data") + ".script-files", definition={'$$description': ['Legacy way of defining scripts (entry-points are preferred).', 'Equivalent to the ``script`` keyword in ``setup.py``', '(it was renamed to avoid confusion with entry-point based ``project.scripts``', 'defined in :pep:`621`).', '**DISCOURAGED**: generic script wrappers are tricky and may not work properly.', 'Whenever possible, please use ``project.scripts`` instead.'], 'type': 'array', 'items': {'type': 'string'}, '$comment': 'TODO: is this field deprecated/should be removed?'}, rule='type')
+            data__scriptfiles_is_list = isinstance(data__scriptfiles, (list, tuple))
+            if data__scriptfiles_is_list:
+                data__scriptfiles_len = len(data__scriptfiles)
+                for data__scriptfiles_x, data__scriptfiles_item in enumerate(data__scriptfiles):
+                    if not isinstance(data__scriptfiles_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".script-files[{data__scriptfiles_x}]".format(**locals()) + " must be string", value=data__scriptfiles_item, name="" + (name_prefix or "data") + ".script-files[{data__scriptfiles_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "eager-resources" in data_keys:
+            data_keys.remove("eager-resources")
+            data__eagerresources = data["eager-resources"]
+            if not isinstance(data__eagerresources, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".eager-resources must be array", value=data__eagerresources, name="" + (name_prefix or "data") + ".eager-resources", definition={'$$description': ['Resources that should be extracted together, if any of them is needed,', 'or if any C extensions included in the project are imported.', '**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and', '``setup.py install`` in the context of ``eggs`` (**DEPRECATED**).'], 'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__eagerresources_is_list = isinstance(data__eagerresources, (list, tuple))
+            if data__eagerresources_is_list:
+                data__eagerresources_len = len(data__eagerresources)
+                for data__eagerresources_x, data__eagerresources_item in enumerate(data__eagerresources):
+                    if not isinstance(data__eagerresources_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".eager-resources[{data__eagerresources_x}]".format(**locals()) + " must be string", value=data__eagerresources_item, name="" + (name_prefix or "data") + ".eager-resources[{data__eagerresources_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "packages" in data_keys:
+            data_keys.remove("packages")
+            data__packages = data["packages"]
+            data__packages_one_of_count2 = 0
+            if data__packages_one_of_count2 < 2:
+                try:
+                    if not isinstance(data__packages, (list, tuple)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".packages must be array", value=data__packages, name="" + (name_prefix or "data") + ".packages", definition={'title': 'Array of Python package identifiers', 'type': 'array', 'items': {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}}, rule='type')
+                    data__packages_is_list = isinstance(data__packages, (list, tuple))
+                    if data__packages_is_list:
+                        data__packages_len = len(data__packages)
+                        for data__packages_x, data__packages_item in enumerate(data__packages):
+                            validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_package_name(data__packages_item, custom_formats, (name_prefix or "data") + ".packages[{data__packages_x}]".format(**locals()))
+                    data__packages_one_of_count2 += 1
+                except JsonSchemaValueException: pass
+            if data__packages_one_of_count2 < 2:
+                try:
+                    validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_find_directive(data__packages, custom_formats, (name_prefix or "data") + ".packages")
+                    data__packages_one_of_count2 += 1
+                except JsonSchemaValueException: pass
+            if data__packages_one_of_count2 != 1:
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".packages must be valid exactly by one definition" + (" (" + str(data__packages_one_of_count2) + " matches found)"), value=data__packages, name="" + (name_prefix or "data") + ".packages", definition={'$$description': ['Packages that should be included in the distribution.', 'It can be given either as a list of package identifiers', 'or as a ``dict``-like structure with a single key ``find``', 'which corresponds to a dynamic call to', '``setuptools.config.expand.find_packages`` function.', 'The ``find`` key is associated with a nested ``dict``-like structure that can', 'contain ``where``, ``include``, ``exclude`` and ``namespaces`` keys,', 'mimicking the keyword arguments of the associated function.'], 'oneOf': [{'title': 'Array of Python package identifiers', 'type': 'array', 'items': {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}}, {'$id': '#/definitions/find-directive', 'title': "'find:' directive", 'type': 'object', 'additionalProperties': False, 'properties': {'find': {'type': 'object', '$$description': ['Dynamic `package discovery', '<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_.'], 'additionalProperties': False, 'properties': {'where': {'description': 'Directories to be searched for packages (Unix-style relative path)', 'type': 'array', 'items': {'type': 'string'}}, 'exclude': {'type': 'array', '$$description': ['Exclude packages that match the values listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'include': {'type': 'array', '$$description': ['Restrict the found packages to just the ones listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'namespaces': {'type': 'boolean', '$$description': ['When ``True``, directories without a ``__init__.py`` file will also', 'be scanned for :pep:`420`-style implicit namespaces']}}}}}]}, rule='oneOf')
+        if "package-dir" in data_keys:
+            data_keys.remove("package-dir")
+            data__packagedir = data["package-dir"]
+            if not isinstance(data__packagedir, (dict)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-dir must be object", value=data__packagedir, name="" + (name_prefix or "data") + ".package-dir", definition={'$$description': [':class:`dict`-like structure mapping from package names to directories where their', 'code can be found.', 'The empty string (as key) means that all packages are contained inside', 'the given directory will be included in the distribution.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'const': ''}, {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}]}, 'patternProperties': {'^.*$': {'type': 'string'}}}, rule='type')
+            data__packagedir_is_dict = isinstance(data__packagedir, dict)
+            if data__packagedir_is_dict:
+                data__packagedir_keys = set(data__packagedir.keys())
+                for data__packagedir_key, data__packagedir_val in data__packagedir.items():
+                    if REGEX_PATTERNS['^.*$'].search(data__packagedir_key):
+                        if data__packagedir_key in data__packagedir_keys:
+                            data__packagedir_keys.remove(data__packagedir_key)
+                        if not isinstance(data__packagedir_val, (str)):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-dir.{data__packagedir_key}".format(**locals()) + " must be string", value=data__packagedir_val, name="" + (name_prefix or "data") + ".package-dir.{data__packagedir_key}".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+                if data__packagedir_keys:
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-dir must not contain "+str(data__packagedir_keys)+" properties", value=data__packagedir, name="" + (name_prefix or "data") + ".package-dir", definition={'$$description': [':class:`dict`-like structure mapping from package names to directories where their', 'code can be found.', 'The empty string (as key) means that all packages are contained inside', 'the given directory will be included in the distribution.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'const': ''}, {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}]}, 'patternProperties': {'^.*$': {'type': 'string'}}}, rule='additionalProperties')
+                data__packagedir_len = len(data__packagedir)
+                if data__packagedir_len != 0:
+                    data__packagedir_property_names = True
+                    for data__packagedir_key in data__packagedir:
+                        try:
+                            data__packagedir_key_any_of_count3 = 0
+                            if not data__packagedir_key_any_of_count3:
+                                try:
+                                    if data__packagedir_key != "":
+                                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-dir must be same as const definition: ", value=data__packagedir_key, name="" + (name_prefix or "data") + ".package-dir", definition={'const': ''}, rule='const')
+                                    data__packagedir_key_any_of_count3 += 1
+                                except JsonSchemaValueException: pass
+                            if not data__packagedir_key_any_of_count3:
+                                try:
+                                    validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_package_name(data__packagedir_key, custom_formats, (name_prefix or "data") + ".package-dir")
+                                    data__packagedir_key_any_of_count3 += 1
+                                except JsonSchemaValueException: pass
+                            if not data__packagedir_key_any_of_count3:
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-dir cannot be validated by any definition", value=data__packagedir_key, name="" + (name_prefix or "data") + ".package-dir", definition={'anyOf': [{'const': ''}, {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}]}, rule='anyOf')
+                        except JsonSchemaValueException:
+                            data__packagedir_property_names = False
+                    if not data__packagedir_property_names:
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-dir must be named by propertyName definition", value=data__packagedir, name="" + (name_prefix or "data") + ".package-dir", definition={'$$description': [':class:`dict`-like structure mapping from package names to directories where their', 'code can be found.', 'The empty string (as key) means that all packages are contained inside', 'the given directory will be included in the distribution.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'const': ''}, {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}]}, 'patternProperties': {'^.*$': {'type': 'string'}}}, rule='propertyNames')
+        if "package-data" in data_keys:
+            data_keys.remove("package-data")
+            data__packagedata = data["package-data"]
+            if not isinstance(data__packagedata, (dict)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-data must be object", value=data__packagedata, name="" + (name_prefix or "data") + ".package-data", definition={'$$description': ['Mapping from package names to lists of glob patterns.', 'Usually this option is not needed when using ``include-package-data = true``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, rule='type')
+            data__packagedata_is_dict = isinstance(data__packagedata, dict)
+            if data__packagedata_is_dict:
+                data__packagedata_keys = set(data__packagedata.keys())
+                for data__packagedata_key, data__packagedata_val in data__packagedata.items():
+                    if REGEX_PATTERNS['^.*$'].search(data__packagedata_key):
+                        if data__packagedata_key in data__packagedata_keys:
+                            data__packagedata_keys.remove(data__packagedata_key)
+                        if not isinstance(data__packagedata_val, (list, tuple)):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-data.{data__packagedata_key}".format(**locals()) + " must be array", value=data__packagedata_val, name="" + (name_prefix or "data") + ".package-data.{data__packagedata_key}".format(**locals()) + "", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+                        data__packagedata_val_is_list = isinstance(data__packagedata_val, (list, tuple))
+                        if data__packagedata_val_is_list:
+                            data__packagedata_val_len = len(data__packagedata_val)
+                            for data__packagedata_val_x, data__packagedata_val_item in enumerate(data__packagedata_val):
+                                if not isinstance(data__packagedata_val_item, (str)):
+                                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-data.{data__packagedata_key}[{data__packagedata_val_x}]".format(**locals()) + " must be string", value=data__packagedata_val_item, name="" + (name_prefix or "data") + ".package-data.{data__packagedata_key}[{data__packagedata_val_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+                if data__packagedata_keys:
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-data must not contain "+str(data__packagedata_keys)+" properties", value=data__packagedata, name="" + (name_prefix or "data") + ".package-data", definition={'$$description': ['Mapping from package names to lists of glob patterns.', 'Usually this option is not needed when using ``include-package-data = true``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, rule='additionalProperties')
+                data__packagedata_len = len(data__packagedata)
+                if data__packagedata_len != 0:
+                    data__packagedata_property_names = True
+                    for data__packagedata_key in data__packagedata:
+                        try:
+                            data__packagedata_key_any_of_count4 = 0
+                            if not data__packagedata_key_any_of_count4:
+                                try:
+                                    if not isinstance(data__packagedata_key, (str)):
+                                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-data must be string", value=data__packagedata_key, name="" + (name_prefix or "data") + ".package-data", definition={'type': 'string', 'format': 'python-module-name'}, rule='type')
+                                    if isinstance(data__packagedata_key, str):
+                                        if not custom_formats["python-module-name"](data__packagedata_key):
+                                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-data must be python-module-name", value=data__packagedata_key, name="" + (name_prefix or "data") + ".package-data", definition={'type': 'string', 'format': 'python-module-name'}, rule='format')
+                                    data__packagedata_key_any_of_count4 += 1
+                                except JsonSchemaValueException: pass
+                            if not data__packagedata_key_any_of_count4:
+                                try:
+                                    if data__packagedata_key != "*":
+                                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-data must be same as const definition: *", value=data__packagedata_key, name="" + (name_prefix or "data") + ".package-data", definition={'const': '*'}, rule='const')
+                                    data__packagedata_key_any_of_count4 += 1
+                                except JsonSchemaValueException: pass
+                            if not data__packagedata_key_any_of_count4:
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-data cannot be validated by any definition", value=data__packagedata_key, name="" + (name_prefix or "data") + ".package-data", definition={'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, rule='anyOf')
+                        except JsonSchemaValueException:
+                            data__packagedata_property_names = False
+                    if not data__packagedata_property_names:
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".package-data must be named by propertyName definition", value=data__packagedata, name="" + (name_prefix or "data") + ".package-data", definition={'$$description': ['Mapping from package names to lists of glob patterns.', 'Usually this option is not needed when using ``include-package-data = true``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, rule='propertyNames')
+        if "include-package-data" in data_keys:
+            data_keys.remove("include-package-data")
+            data__includepackagedata = data["include-package-data"]
+            if not isinstance(data__includepackagedata, (bool)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".include-package-data must be boolean", value=data__includepackagedata, name="" + (name_prefix or "data") + ".include-package-data", definition={'$$description': ['Automatically include any data files inside the package directories', 'that are specified by ``MANIFEST.in``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'boolean'}, rule='type')
+        if "exclude-package-data" in data_keys:
+            data_keys.remove("exclude-package-data")
+            data__excludepackagedata = data["exclude-package-data"]
+            if not isinstance(data__excludepackagedata, (dict)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".exclude-package-data must be object", value=data__excludepackagedata, name="" + (name_prefix or "data") + ".exclude-package-data", definition={'$$description': ['Mapping from package names to lists of glob patterns that should be excluded', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, rule='type')
+            data__excludepackagedata_is_dict = isinstance(data__excludepackagedata, dict)
+            if data__excludepackagedata_is_dict:
+                data__excludepackagedata_keys = set(data__excludepackagedata.keys())
+                for data__excludepackagedata_key, data__excludepackagedata_val in data__excludepackagedata.items():
+                    if REGEX_PATTERNS['^.*$'].search(data__excludepackagedata_key):
+                        if data__excludepackagedata_key in data__excludepackagedata_keys:
+                            data__excludepackagedata_keys.remove(data__excludepackagedata_key)
+                        if not isinstance(data__excludepackagedata_val, (list, tuple)):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".exclude-package-data.{data__excludepackagedata_key}".format(**locals()) + " must be array", value=data__excludepackagedata_val, name="" + (name_prefix or "data") + ".exclude-package-data.{data__excludepackagedata_key}".format(**locals()) + "", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+                        data__excludepackagedata_val_is_list = isinstance(data__excludepackagedata_val, (list, tuple))
+                        if data__excludepackagedata_val_is_list:
+                            data__excludepackagedata_val_len = len(data__excludepackagedata_val)
+                            for data__excludepackagedata_val_x, data__excludepackagedata_val_item in enumerate(data__excludepackagedata_val):
+                                if not isinstance(data__excludepackagedata_val_item, (str)):
+                                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".exclude-package-data.{data__excludepackagedata_key}[{data__excludepackagedata_val_x}]".format(**locals()) + " must be string", value=data__excludepackagedata_val_item, name="" + (name_prefix or "data") + ".exclude-package-data.{data__excludepackagedata_key}[{data__excludepackagedata_val_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+                if data__excludepackagedata_keys:
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".exclude-package-data must not contain "+str(data__excludepackagedata_keys)+" properties", value=data__excludepackagedata, name="" + (name_prefix or "data") + ".exclude-package-data", definition={'$$description': ['Mapping from package names to lists of glob patterns that should be excluded', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, rule='additionalProperties')
+                data__excludepackagedata_len = len(data__excludepackagedata)
+                if data__excludepackagedata_len != 0:
+                    data__excludepackagedata_property_names = True
+                    for data__excludepackagedata_key in data__excludepackagedata:
+                        try:
+                            data__excludepackagedata_key_any_of_count5 = 0
+                            if not data__excludepackagedata_key_any_of_count5:
+                                try:
+                                    if not isinstance(data__excludepackagedata_key, (str)):
+                                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".exclude-package-data must be string", value=data__excludepackagedata_key, name="" + (name_prefix or "data") + ".exclude-package-data", definition={'type': 'string', 'format': 'python-module-name'}, rule='type')
+                                    if isinstance(data__excludepackagedata_key, str):
+                                        if not custom_formats["python-module-name"](data__excludepackagedata_key):
+                                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".exclude-package-data must be python-module-name", value=data__excludepackagedata_key, name="" + (name_prefix or "data") + ".exclude-package-data", definition={'type': 'string', 'format': 'python-module-name'}, rule='format')
+                                    data__excludepackagedata_key_any_of_count5 += 1
+                                except JsonSchemaValueException: pass
+                            if not data__excludepackagedata_key_any_of_count5:
+                                try:
+                                    if data__excludepackagedata_key != "*":
+                                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".exclude-package-data must be same as const definition: *", value=data__excludepackagedata_key, name="" + (name_prefix or "data") + ".exclude-package-data", definition={'const': '*'}, rule='const')
+                                    data__excludepackagedata_key_any_of_count5 += 1
+                                except JsonSchemaValueException: pass
+                            if not data__excludepackagedata_key_any_of_count5:
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".exclude-package-data cannot be validated by any definition", value=data__excludepackagedata_key, name="" + (name_prefix or "data") + ".exclude-package-data", definition={'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, rule='anyOf')
+                        except JsonSchemaValueException:
+                            data__excludepackagedata_property_names = False
+                    if not data__excludepackagedata_property_names:
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".exclude-package-data must be named by propertyName definition", value=data__excludepackagedata, name="" + (name_prefix or "data") + ".exclude-package-data", definition={'$$description': ['Mapping from package names to lists of glob patterns that should be excluded', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, rule='propertyNames')
+        if "namespace-packages" in data_keys:
+            data_keys.remove("namespace-packages")
+            data__namespacepackages = data["namespace-packages"]
+            if not isinstance(data__namespacepackages, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".namespace-packages must be array", value=data__namespacepackages, name="" + (name_prefix or "data") + ".namespace-packages", definition={'type': 'array', 'items': {'type': 'string', 'format': 'python-module-name-relaxed'}, '$comment': 'https://setuptools.pypa.io/en/latest/userguide/package_discovery.html', 'description': '**DEPRECATED**: use implicit namespaces instead (:pep:`420`).'}, rule='type')
+            data__namespacepackages_is_list = isinstance(data__namespacepackages, (list, tuple))
+            if data__namespacepackages_is_list:
+                data__namespacepackages_len = len(data__namespacepackages)
+                for data__namespacepackages_x, data__namespacepackages_item in enumerate(data__namespacepackages):
+                    if not isinstance(data__namespacepackages_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".namespace-packages[{data__namespacepackages_x}]".format(**locals()) + " must be string", value=data__namespacepackages_item, name="" + (name_prefix or "data") + ".namespace-packages[{data__namespacepackages_x}]".format(**locals()) + "", definition={'type': 'string', 'format': 'python-module-name-relaxed'}, rule='type')
+                    if isinstance(data__namespacepackages_item, str):
+                        if not custom_formats["python-module-name-relaxed"](data__namespacepackages_item):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".namespace-packages[{data__namespacepackages_x}]".format(**locals()) + " must be python-module-name-relaxed", value=data__namespacepackages_item, name="" + (name_prefix or "data") + ".namespace-packages[{data__namespacepackages_x}]".format(**locals()) + "", definition={'type': 'string', 'format': 'python-module-name-relaxed'}, rule='format')
+        if "py-modules" in data_keys:
+            data_keys.remove("py-modules")
+            data__pymodules = data["py-modules"]
+            if not isinstance(data__pymodules, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".py-modules must be array", value=data__pymodules, name="" + (name_prefix or "data") + ".py-modules", definition={'description': 'Modules that setuptools will manipulate', 'type': 'array', 'items': {'type': 'string', 'format': 'python-module-name-relaxed'}, '$comment': 'TODO: clarify the relationship with ``packages``'}, rule='type')
+            data__pymodules_is_list = isinstance(data__pymodules, (list, tuple))
+            if data__pymodules_is_list:
+                data__pymodules_len = len(data__pymodules)
+                for data__pymodules_x, data__pymodules_item in enumerate(data__pymodules):
+                    if not isinstance(data__pymodules_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".py-modules[{data__pymodules_x}]".format(**locals()) + " must be string", value=data__pymodules_item, name="" + (name_prefix or "data") + ".py-modules[{data__pymodules_x}]".format(**locals()) + "", definition={'type': 'string', 'format': 'python-module-name-relaxed'}, rule='type')
+                    if isinstance(data__pymodules_item, str):
+                        if not custom_formats["python-module-name-relaxed"](data__pymodules_item):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".py-modules[{data__pymodules_x}]".format(**locals()) + " must be python-module-name-relaxed", value=data__pymodules_item, name="" + (name_prefix or "data") + ".py-modules[{data__pymodules_x}]".format(**locals()) + "", definition={'type': 'string', 'format': 'python-module-name-relaxed'}, rule='format')
+        if "ext-modules" in data_keys:
+            data_keys.remove("ext-modules")
+            data__extmodules = data["ext-modules"]
+            if not isinstance(data__extmodules, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".ext-modules must be array", value=data__extmodules, name="" + (name_prefix or "data") + ".ext-modules", definition={'description': 'Extension modules to be compiled by setuptools', 'type': 'array', 'items': {'$id': '#/definitions/ext-module', 'title': 'Extension module', 'description': 'Parameters to construct a :class:`setuptools.Extension` object', 'type': 'object', 'required': ['name', 'sources'], 'additionalProperties': False, 'properties': {'name': {'type': 'string', 'format': 'python-module-name-relaxed'}, 'sources': {'type': 'array', 'items': {'type': 'string'}}, 'include-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'define-macros': {'type': 'array', 'items': {'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}}, 'undef-macros': {'type': 'array', 'items': {'type': 'string'}}, 'library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'libraries': {'type': 'array', 'items': {'type': 'string'}}, 'runtime-library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'extra-objects': {'type': 'array', 'items': {'type': 'string'}}, 'extra-compile-args': {'type': 'array', 'items': {'type': 'string'}}, 'extra-link-args': {'type': 'array', 'items': {'type': 'string'}}, 'export-symbols': {'type': 'array', 'items': {'type': 'string'}}, 'swig-opts': {'type': 'array', 'items': {'type': 'string'}}, 'depends': {'type': 'array', 'items': {'type': 'string'}}, 'language': {'type': 'string'}, 'optional': {'type': 'boolean'}, 'py-limited-api': {'type': 'boolean'}}}}, rule='type')
+            data__extmodules_is_list = isinstance(data__extmodules, (list, tuple))
+            if data__extmodules_is_list:
+                data__extmodules_len = len(data__extmodules)
+                for data__extmodules_x, data__extmodules_item in enumerate(data__extmodules):
+                    validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_ext_module(data__extmodules_item, custom_formats, (name_prefix or "data") + ".ext-modules[{data__extmodules_x}]".format(**locals()))
+        if "data-files" in data_keys:
+            data_keys.remove("data-files")
+            data__datafiles = data["data-files"]
+            if not isinstance(data__datafiles, (dict)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".data-files must be object", value=data__datafiles, name="" + (name_prefix or "data") + ".data-files", definition={'$$description': ['``dict``-like structure where each key represents a directory and', 'the value is a list of glob patterns that should be installed in them.', '**DISCOURAGED**: please notice this might not work as expected with wheels.', 'Whenever possible, consider using data files inside the package directories', '(or create a new namespace package that only contains data files).', 'See `data files support', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, rule='type')
+            data__datafiles_is_dict = isinstance(data__datafiles, dict)
+            if data__datafiles_is_dict:
+                data__datafiles_keys = set(data__datafiles.keys())
+                for data__datafiles_key, data__datafiles_val in data__datafiles.items():
+                    if REGEX_PATTERNS['^.*$'].search(data__datafiles_key):
+                        if data__datafiles_key in data__datafiles_keys:
+                            data__datafiles_keys.remove(data__datafiles_key)
+                        if not isinstance(data__datafiles_val, (list, tuple)):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".data-files.{data__datafiles_key}".format(**locals()) + " must be array", value=data__datafiles_val, name="" + (name_prefix or "data") + ".data-files.{data__datafiles_key}".format(**locals()) + "", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+                        data__datafiles_val_is_list = isinstance(data__datafiles_val, (list, tuple))
+                        if data__datafiles_val_is_list:
+                            data__datafiles_val_len = len(data__datafiles_val)
+                            for data__datafiles_val_x, data__datafiles_val_item in enumerate(data__datafiles_val):
+                                if not isinstance(data__datafiles_val_item, (str)):
+                                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".data-files.{data__datafiles_key}[{data__datafiles_val_x}]".format(**locals()) + " must be string", value=data__datafiles_val_item, name="" + (name_prefix or "data") + ".data-files.{data__datafiles_key}[{data__datafiles_val_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "cmdclass" in data_keys:
+            data_keys.remove("cmdclass")
+            data__cmdclass = data["cmdclass"]
+            if not isinstance(data__cmdclass, (dict)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".cmdclass must be object", value=data__cmdclass, name="" + (name_prefix or "data") + ".cmdclass", definition={'$$description': ['Mapping of distutils-style command names to ``setuptools.Command`` subclasses', 'which in turn should be represented by strings with a qualified class name', '(i.e., "dotted" form with module), e.g.::\n\n', '    cmdclass = {mycmd = "pkg.subpkg.module.CommandClass"}\n\n', 'The command class should be a directly defined at the top-level of the', 'containing module (no class nesting).'], 'type': 'object', 'patternProperties': {'^.*$': {'type': 'string', 'format': 'python-qualified-identifier'}}}, rule='type')
+            data__cmdclass_is_dict = isinstance(data__cmdclass, dict)
+            if data__cmdclass_is_dict:
+                data__cmdclass_keys = set(data__cmdclass.keys())
+                for data__cmdclass_key, data__cmdclass_val in data__cmdclass.items():
+                    if REGEX_PATTERNS['^.*$'].search(data__cmdclass_key):
+                        if data__cmdclass_key in data__cmdclass_keys:
+                            data__cmdclass_keys.remove(data__cmdclass_key)
+                        if not isinstance(data__cmdclass_val, (str)):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".cmdclass.{data__cmdclass_key}".format(**locals()) + " must be string", value=data__cmdclass_val, name="" + (name_prefix or "data") + ".cmdclass.{data__cmdclass_key}".format(**locals()) + "", definition={'type': 'string', 'format': 'python-qualified-identifier'}, rule='type')
+                        if isinstance(data__cmdclass_val, str):
+                            if not custom_formats["python-qualified-identifier"](data__cmdclass_val):
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".cmdclass.{data__cmdclass_key}".format(**locals()) + " must be python-qualified-identifier", value=data__cmdclass_val, name="" + (name_prefix or "data") + ".cmdclass.{data__cmdclass_key}".format(**locals()) + "", definition={'type': 'string', 'format': 'python-qualified-identifier'}, rule='format')
+        if "license-files" in data_keys:
+            data_keys.remove("license-files")
+            data__licensefiles = data["license-files"]
+            if not isinstance(data__licensefiles, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".license-files must be array", value=data__licensefiles, name="" + (name_prefix or "data") + ".license-files", definition={'type': 'array', 'items': {'type': 'string'}, '$$description': ['**PROVISIONAL**: list of glob patterns for all license files being distributed.', '(likely to become standard with :pep:`639`).', "By default: ``['LICEN[CS]E*', 'COPYING*', 'NOTICE*', 'AUTHORS*']``"], '$comment': 'TODO: revise if PEP 639 is accepted. Probably ``project.license-files``?'}, rule='type')
+            data__licensefiles_is_list = isinstance(data__licensefiles, (list, tuple))
+            if data__licensefiles_is_list:
+                data__licensefiles_len = len(data__licensefiles)
+                for data__licensefiles_x, data__licensefiles_item in enumerate(data__licensefiles):
+                    if not isinstance(data__licensefiles_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".license-files[{data__licensefiles_x}]".format(**locals()) + " must be string", value=data__licensefiles_item, name="" + (name_prefix or "data") + ".license-files[{data__licensefiles_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "dynamic" in data_keys:
+            data_keys.remove("dynamic")
+            data__dynamic = data["dynamic"]
+            if not isinstance(data__dynamic, (dict)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic must be object", value=data__dynamic, name="" + (name_prefix or "data") + ".dynamic", definition={'type': 'object', 'description': 'Instructions for loading :pep:`621`-related metadata dynamically', 'additionalProperties': False, 'properties': {'version': {'$$description': ['A version dynamically loaded via either the ``attr:`` or ``file:``', 'directives. Please make sure the given file or attribute respects :pep:`440`.', 'Also ensure to set ``project.dynamic`` accordingly.'], 'oneOf': [{'title': "'attr:' directive", '$id': '#/definitions/attr-directive', '$$description': ['Value is read from a module attribute. Supports callables and iterables;', 'unsupported types are cast via ``str()``'], 'type': 'object', 'additionalProperties': False, 'properties': {'attr': {'type': 'string', 'format': 'python-qualified-identifier'}}, 'required': ['attr']}, {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}]}, 'classifiers': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'description': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'entry-points': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'dependencies': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}, 'optional-dependencies': {'type': 'object', 'propertyNames': {'type': 'string', 'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'.+': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}}}, 'readme': {'type': 'object', 'anyOf': [{'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, {'type': 'object', 'properties': {'content-type': {'type': 'string'}, 'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'additionalProperties': False}], 'required': ['file']}}}, rule='type')
+            data__dynamic_is_dict = isinstance(data__dynamic, dict)
+            if data__dynamic_is_dict:
+                data__dynamic_keys = set(data__dynamic.keys())
+                if "version" in data__dynamic_keys:
+                    data__dynamic_keys.remove("version")
+                    data__dynamic__version = data__dynamic["version"]
+                    data__dynamic__version_one_of_count6 = 0
+                    if data__dynamic__version_one_of_count6 < 2:
+                        try:
+                            validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_attr_directive(data__dynamic__version, custom_formats, (name_prefix or "data") + ".dynamic.version")
+                            data__dynamic__version_one_of_count6 += 1
+                        except JsonSchemaValueException: pass
+                    if data__dynamic__version_one_of_count6 < 2:
+                        try:
+                            validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_file_directive(data__dynamic__version, custom_formats, (name_prefix or "data") + ".dynamic.version")
+                            data__dynamic__version_one_of_count6 += 1
+                        except JsonSchemaValueException: pass
+                    if data__dynamic__version_one_of_count6 != 1:
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic.version must be valid exactly by one definition" + (" (" + str(data__dynamic__version_one_of_count6) + " matches found)"), value=data__dynamic__version, name="" + (name_prefix or "data") + ".dynamic.version", definition={'$$description': ['A version dynamically loaded via either the ``attr:`` or ``file:``', 'directives. Please make sure the given file or attribute respects :pep:`440`.', 'Also ensure to set ``project.dynamic`` accordingly.'], 'oneOf': [{'title': "'attr:' directive", '$id': '#/definitions/attr-directive', '$$description': ['Value is read from a module attribute. Supports callables and iterables;', 'unsupported types are cast via ``str()``'], 'type': 'object', 'additionalProperties': False, 'properties': {'attr': {'type': 'string', 'format': 'python-qualified-identifier'}}, 'required': ['attr']}, {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}]}, rule='oneOf')
+                if "classifiers" in data__dynamic_keys:
+                    data__dynamic_keys.remove("classifiers")
+                    data__dynamic__classifiers = data__dynamic["classifiers"]
+                    validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_file_directive(data__dynamic__classifiers, custom_formats, (name_prefix or "data") + ".dynamic.classifiers")
+                if "description" in data__dynamic_keys:
+                    data__dynamic_keys.remove("description")
+                    data__dynamic__description = data__dynamic["description"]
+                    validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_file_directive(data__dynamic__description, custom_formats, (name_prefix or "data") + ".dynamic.description")
+                if "entry-points" in data__dynamic_keys:
+                    data__dynamic_keys.remove("entry-points")
+                    data__dynamic__entrypoints = data__dynamic["entry-points"]
+                    validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_file_directive(data__dynamic__entrypoints, custom_formats, (name_prefix or "data") + ".dynamic.entry-points")
+                if "dependencies" in data__dynamic_keys:
+                    data__dynamic_keys.remove("dependencies")
+                    data__dynamic__dependencies = data__dynamic["dependencies"]
+                    validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_file_directive_for_dependencies(data__dynamic__dependencies, custom_formats, (name_prefix or "data") + ".dynamic.dependencies")
+                if "optional-dependencies" in data__dynamic_keys:
+                    data__dynamic_keys.remove("optional-dependencies")
+                    data__dynamic__optionaldependencies = data__dynamic["optional-dependencies"]
+                    if not isinstance(data__dynamic__optionaldependencies, (dict)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic.optional-dependencies must be object", value=data__dynamic__optionaldependencies, name="" + (name_prefix or "data") + ".dynamic.optional-dependencies", definition={'type': 'object', 'propertyNames': {'type': 'string', 'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'.+': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}}}, rule='type')
+                    data__dynamic__optionaldependencies_is_dict = isinstance(data__dynamic__optionaldependencies, dict)
+                    if data__dynamic__optionaldependencies_is_dict:
+                        data__dynamic__optionaldependencies_keys = set(data__dynamic__optionaldependencies.keys())
+                        for data__dynamic__optionaldependencies_key, data__dynamic__optionaldependencies_val in data__dynamic__optionaldependencies.items():
+                            if REGEX_PATTERNS['.+'].search(data__dynamic__optionaldependencies_key):
+                                if data__dynamic__optionaldependencies_key in data__dynamic__optionaldependencies_keys:
+                                    data__dynamic__optionaldependencies_keys.remove(data__dynamic__optionaldependencies_key)
+                                validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_file_directive_for_dependencies(data__dynamic__optionaldependencies_val, custom_formats, (name_prefix or "data") + ".dynamic.optional-dependencies.{data__dynamic__optionaldependencies_key}".format(**locals()))
+                        if data__dynamic__optionaldependencies_keys:
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic.optional-dependencies must not contain "+str(data__dynamic__optionaldependencies_keys)+" properties", value=data__dynamic__optionaldependencies, name="" + (name_prefix or "data") + ".dynamic.optional-dependencies", definition={'type': 'object', 'propertyNames': {'type': 'string', 'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'.+': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}}}, rule='additionalProperties')
+                        data__dynamic__optionaldependencies_len = len(data__dynamic__optionaldependencies)
+                        if data__dynamic__optionaldependencies_len != 0:
+                            data__dynamic__optionaldependencies_property_names = True
+                            for data__dynamic__optionaldependencies_key in data__dynamic__optionaldependencies:
+                                try:
+                                    if not isinstance(data__dynamic__optionaldependencies_key, (str)):
+                                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic.optional-dependencies must be string", value=data__dynamic__optionaldependencies_key, name="" + (name_prefix or "data") + ".dynamic.optional-dependencies", definition={'type': 'string', 'format': 'pep508-identifier'}, rule='type')
+                                    if isinstance(data__dynamic__optionaldependencies_key, str):
+                                        if not custom_formats["pep508-identifier"](data__dynamic__optionaldependencies_key):
+                                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic.optional-dependencies must be pep508-identifier", value=data__dynamic__optionaldependencies_key, name="" + (name_prefix or "data") + ".dynamic.optional-dependencies", definition={'type': 'string', 'format': 'pep508-identifier'}, rule='format')
+                                except JsonSchemaValueException:
+                                    data__dynamic__optionaldependencies_property_names = False
+                            if not data__dynamic__optionaldependencies_property_names:
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic.optional-dependencies must be named by propertyName definition", value=data__dynamic__optionaldependencies, name="" + (name_prefix or "data") + ".dynamic.optional-dependencies", definition={'type': 'object', 'propertyNames': {'type': 'string', 'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'.+': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}}}, rule='propertyNames')
+                if "readme" in data__dynamic_keys:
+                    data__dynamic_keys.remove("readme")
+                    data__dynamic__readme = data__dynamic["readme"]
+                    if not isinstance(data__dynamic__readme, (dict)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic.readme must be object", value=data__dynamic__readme, name="" + (name_prefix or "data") + ".dynamic.readme", definition={'type': 'object', 'anyOf': [{'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, {'type': 'object', 'properties': {'content-type': {'type': 'string'}, 'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'additionalProperties': False}], 'required': ['file']}, rule='type')
+                    data__dynamic__readme_any_of_count7 = 0
+                    if not data__dynamic__readme_any_of_count7:
+                        try:
+                            validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_file_directive(data__dynamic__readme, custom_formats, (name_prefix or "data") + ".dynamic.readme")
+                            data__dynamic__readme_any_of_count7 += 1
+                        except JsonSchemaValueException: pass
+                    if not data__dynamic__readme_any_of_count7:
+                        try:
+                            if not isinstance(data__dynamic__readme, (dict)):
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic.readme must be object", value=data__dynamic__readme, name="" + (name_prefix or "data") + ".dynamic.readme", definition={'type': 'object', 'properties': {'content-type': {'type': 'string'}, 'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'additionalProperties': False}, rule='type')
+                            data__dynamic__readme_is_dict = isinstance(data__dynamic__readme, dict)
+                            if data__dynamic__readme_is_dict:
+                                data__dynamic__readme_keys = set(data__dynamic__readme.keys())
+                                if "content-type" in data__dynamic__readme_keys:
+                                    data__dynamic__readme_keys.remove("content-type")
+                                    data__dynamic__readme__contenttype = data__dynamic__readme["content-type"]
+                                    if not isinstance(data__dynamic__readme__contenttype, (str)):
+                                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic.readme.content-type must be string", value=data__dynamic__readme__contenttype, name="" + (name_prefix or "data") + ".dynamic.readme.content-type", definition={'type': 'string'}, rule='type')
+                                if "file" in data__dynamic__readme_keys:
+                                    data__dynamic__readme_keys.remove("file")
+                                    data__dynamic__readme__file = data__dynamic__readme["file"]
+                                    validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_file_directive_properties_file(data__dynamic__readme__file, custom_formats, (name_prefix or "data") + ".dynamic.readme.file")
+                                if data__dynamic__readme_keys:
+                                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic.readme must not contain "+str(data__dynamic__readme_keys)+" properties", value=data__dynamic__readme, name="" + (name_prefix or "data") + ".dynamic.readme", definition={'type': 'object', 'properties': {'content-type': {'type': 'string'}, 'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'additionalProperties': False}, rule='additionalProperties')
+                            data__dynamic__readme_any_of_count7 += 1
+                        except JsonSchemaValueException: pass
+                    if not data__dynamic__readme_any_of_count7:
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic.readme cannot be validated by any definition", value=data__dynamic__readme, name="" + (name_prefix or "data") + ".dynamic.readme", definition={'type': 'object', 'anyOf': [{'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, {'type': 'object', 'properties': {'content-type': {'type': 'string'}, 'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'additionalProperties': False}], 'required': ['file']}, rule='anyOf')
+                    data__dynamic__readme_is_dict = isinstance(data__dynamic__readme, dict)
+                    if data__dynamic__readme_is_dict:
+                        data__dynamic__readme__missing_keys = set(['file']) - data__dynamic__readme.keys()
+                        if data__dynamic__readme__missing_keys:
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic.readme must contain " + (str(sorted(data__dynamic__readme__missing_keys)) + " properties"), value=data__dynamic__readme, name="" + (name_prefix or "data") + ".dynamic.readme", definition={'type': 'object', 'anyOf': [{'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, {'type': 'object', 'properties': {'content-type': {'type': 'string'}, 'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'additionalProperties': False}], 'required': ['file']}, rule='required')
+                if data__dynamic_keys:
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic must not contain "+str(data__dynamic_keys)+" properties", value=data__dynamic, name="" + (name_prefix or "data") + ".dynamic", definition={'type': 'object', 'description': 'Instructions for loading :pep:`621`-related metadata dynamically', 'additionalProperties': False, 'properties': {'version': {'$$description': ['A version dynamically loaded via either the ``attr:`` or ``file:``', 'directives. Please make sure the given file or attribute respects :pep:`440`.', 'Also ensure to set ``project.dynamic`` accordingly.'], 'oneOf': [{'title': "'attr:' directive", '$id': '#/definitions/attr-directive', '$$description': ['Value is read from a module attribute. Supports callables and iterables;', 'unsupported types are cast via ``str()``'], 'type': 'object', 'additionalProperties': False, 'properties': {'attr': {'type': 'string', 'format': 'python-qualified-identifier'}}, 'required': ['attr']}, {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}]}, 'classifiers': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'description': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'entry-points': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'dependencies': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}, 'optional-dependencies': {'type': 'object', 'propertyNames': {'type': 'string', 'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'.+': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}}}, 'readme': {'type': 'object', 'anyOf': [{'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, {'type': 'object', 'properties': {'content-type': {'type': 'string'}, 'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'additionalProperties': False}], 'required': ['file']}}}, rule='additionalProperties')
+        if data_keys:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must not contain "+str(data_keys)+" properties", value=data, name="" + (name_prefix or "data") + "", definition={'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html', 'title': '``tool.setuptools`` table', '$$description': ['``setuptools``-specific configurations that can be set by users that require', 'customization.', 'These configurations are completely optional and probably can be skipped when', 'creating simple packages. They are equivalent to some of the `Keywords', '<https://setuptools.pypa.io/en/latest/references/keywords.html>`_', 'used by the ``setup.py`` file, and can be set via the ``tool.setuptools`` table.', 'It considers only ``setuptools`` `parameters', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#setuptools-specific-configuration>`_', 'that are not covered by :pep:`621`; and intentionally excludes ``dependency_links``', 'and ``setup_requires`` (incompatible with modern workflows/standards).'], 'type': 'object', 'additionalProperties': False, 'properties': {'platforms': {'type': 'array', 'items': {'type': 'string'}}, 'provides': {'$$description': ['Package and virtual package names contained within this package', '**(not supported by pip)**'], 'type': 'array', 'items': {'type': 'string', 'format': 'pep508-identifier'}}, 'obsoletes': {'$$description': ['Packages which this package renders obsolete', '**(not supported by pip)**'], 'type': 'array', 'items': {'type': 'string', 'format': 'pep508-identifier'}}, 'zip-safe': {'$$description': ['Whether the project can be safely installed and run from a zip file.', '**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and', '``setup.py install`` in the context of ``eggs`` (**DEPRECATED**).'], 'type': 'boolean'}, 'script-files': {'$$description': ['Legacy way of defining scripts (entry-points are preferred).', 'Equivalent to the ``script`` keyword in ``setup.py``', '(it was renamed to avoid confusion with entry-point based ``project.scripts``', 'defined in :pep:`621`).', '**DISCOURAGED**: generic script wrappers are tricky and may not work properly.', 'Whenever possible, please use ``project.scripts`` instead.'], 'type': 'array', 'items': {'type': 'string'}, '$comment': 'TODO: is this field deprecated/should be removed?'}, 'eager-resources': {'$$description': ['Resources that should be extracted together, if any of them is needed,', 'or if any C extensions included in the project are imported.', '**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and', '``setup.py install`` in the context of ``eggs`` (**DEPRECATED**).'], 'type': 'array', 'items': {'type': 'string'}}, 'packages': {'$$description': ['Packages that should be included in the distribution.', 'It can be given either as a list of package identifiers', 'or as a ``dict``-like structure with a single key ``find``', 'which corresponds to a dynamic call to', '``setuptools.config.expand.find_packages`` function.', 'The ``find`` key is associated with a nested ``dict``-like structure that can', 'contain ``where``, ``include``, ``exclude`` and ``namespaces`` keys,', 'mimicking the keyword arguments of the associated function.'], 'oneOf': [{'title': 'Array of Python package identifiers', 'type': 'array', 'items': {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}}, {'$id': '#/definitions/find-directive', 'title': "'find:' directive", 'type': 'object', 'additionalProperties': False, 'properties': {'find': {'type': 'object', '$$description': ['Dynamic `package discovery', '<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_.'], 'additionalProperties': False, 'properties': {'where': {'description': 'Directories to be searched for packages (Unix-style relative path)', 'type': 'array', 'items': {'type': 'string'}}, 'exclude': {'type': 'array', '$$description': ['Exclude packages that match the values listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'include': {'type': 'array', '$$description': ['Restrict the found packages to just the ones listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'namespaces': {'type': 'boolean', '$$description': ['When ``True``, directories without a ``__init__.py`` file will also', 'be scanned for :pep:`420`-style implicit namespaces']}}}}}]}, 'package-dir': {'$$description': [':class:`dict`-like structure mapping from package names to directories where their', 'code can be found.', 'The empty string (as key) means that all packages are contained inside', 'the given directory will be included in the distribution.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'const': ''}, {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}]}, 'patternProperties': {'^.*$': {'type': 'string'}}}, 'package-data': {'$$description': ['Mapping from package names to lists of glob patterns.', 'Usually this option is not needed when using ``include-package-data = true``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'include-package-data': {'$$description': ['Automatically include any data files inside the package directories', 'that are specified by ``MANIFEST.in``', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'boolean'}, 'exclude-package-data': {'$$description': ['Mapping from package names to lists of glob patterns that should be excluded', 'For more information on how to include data files, check ``setuptools`` `docs', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'additionalProperties': False, 'propertyNames': {'anyOf': [{'type': 'string', 'format': 'python-module-name'}, {'const': '*'}]}, 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'namespace-packages': {'type': 'array', 'items': {'type': 'string', 'format': 'python-module-name-relaxed'}, '$comment': 'https://setuptools.pypa.io/en/latest/userguide/package_discovery.html', 'description': '**DEPRECATED**: use implicit namespaces instead (:pep:`420`).'}, 'py-modules': {'description': 'Modules that setuptools will manipulate', 'type': 'array', 'items': {'type': 'string', 'format': 'python-module-name-relaxed'}, '$comment': 'TODO: clarify the relationship with ``packages``'}, 'ext-modules': {'description': 'Extension modules to be compiled by setuptools', 'type': 'array', 'items': {'$id': '#/definitions/ext-module', 'title': 'Extension module', 'description': 'Parameters to construct a :class:`setuptools.Extension` object', 'type': 'object', 'required': ['name', 'sources'], 'additionalProperties': False, 'properties': {'name': {'type': 'string', 'format': 'python-module-name-relaxed'}, 'sources': {'type': 'array', 'items': {'type': 'string'}}, 'include-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'define-macros': {'type': 'array', 'items': {'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}}, 'undef-macros': {'type': 'array', 'items': {'type': 'string'}}, 'library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'libraries': {'type': 'array', 'items': {'type': 'string'}}, 'runtime-library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'extra-objects': {'type': 'array', 'items': {'type': 'string'}}, 'extra-compile-args': {'type': 'array', 'items': {'type': 'string'}}, 'extra-link-args': {'type': 'array', 'items': {'type': 'string'}}, 'export-symbols': {'type': 'array', 'items': {'type': 'string'}}, 'swig-opts': {'type': 'array', 'items': {'type': 'string'}}, 'depends': {'type': 'array', 'items': {'type': 'string'}}, 'language': {'type': 'string'}, 'optional': {'type': 'boolean'}, 'py-limited-api': {'type': 'boolean'}}}}, 'data-files': {'$$description': ['``dict``-like structure where each key represents a directory and', 'the value is a list of glob patterns that should be installed in them.', '**DISCOURAGED**: please notice this might not work as expected with wheels.', 'Whenever possible, consider using data files inside the package directories', '(or create a new namespace package that only contains data files).', 'See `data files support', '<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_.'], 'type': 'object', 'patternProperties': {'^.*$': {'type': 'array', 'items': {'type': 'string'}}}}, 'cmdclass': {'$$description': ['Mapping of distutils-style command names to ``setuptools.Command`` subclasses', 'which in turn should be represented by strings with a qualified class name', '(i.e., "dotted" form with module), e.g.::\n\n', '    cmdclass = {mycmd = "pkg.subpkg.module.CommandClass"}\n\n', 'The command class should be a directly defined at the top-level of the', 'containing module (no class nesting).'], 'type': 'object', 'patternProperties': {'^.*$': {'type': 'string', 'format': 'python-qualified-identifier'}}}, 'license-files': {'type': 'array', 'items': {'type': 'string'}, '$$description': ['**PROVISIONAL**: list of glob patterns for all license files being distributed.', '(likely to become standard with :pep:`639`).', "By default: ``['LICEN[CS]E*', 'COPYING*', 'NOTICE*', 'AUTHORS*']``"], '$comment': 'TODO: revise if PEP 639 is accepted. Probably ``project.license-files``?'}, 'dynamic': {'type': 'object', 'description': 'Instructions for loading :pep:`621`-related metadata dynamically', 'additionalProperties': False, 'properties': {'version': {'$$description': ['A version dynamically loaded via either the ``attr:`` or ``file:``', 'directives. Please make sure the given file or attribute respects :pep:`440`.', 'Also ensure to set ``project.dynamic`` accordingly.'], 'oneOf': [{'title': "'attr:' directive", '$id': '#/definitions/attr-directive', '$$description': ['Value is read from a module attribute. Supports callables and iterables;', 'unsupported types are cast via ``str()``'], 'type': 'object', 'additionalProperties': False, 'properties': {'attr': {'type': 'string', 'format': 'python-qualified-identifier'}}, 'required': ['attr']}, {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}]}, 'classifiers': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'description': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'entry-points': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'dependencies': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}, 'optional-dependencies': {'type': 'object', 'propertyNames': {'type': 'string', 'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'.+': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$ref': '#/definitions/file-directive'}]}}}, 'readme': {'type': 'object', 'anyOf': [{'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, {'type': 'object', 'properties': {'content-type': {'type': 'string'}, 'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'additionalProperties': False}], 'required': ['file']}}}}, 'definitions': {'package-name': {'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}, 'ext-module': {'$id': '#/definitions/ext-module', 'title': 'Extension module', 'description': 'Parameters to construct a :class:`setuptools.Extension` object', 'type': 'object', 'required': ['name', 'sources'], 'additionalProperties': False, 'properties': {'name': {'type': 'string', 'format': 'python-module-name-relaxed'}, 'sources': {'type': 'array', 'items': {'type': 'string'}}, 'include-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'define-macros': {'type': 'array', 'items': {'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}}, 'undef-macros': {'type': 'array', 'items': {'type': 'string'}}, 'library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'libraries': {'type': 'array', 'items': {'type': 'string'}}, 'runtime-library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'extra-objects': {'type': 'array', 'items': {'type': 'string'}}, 'extra-compile-args': {'type': 'array', 'items': {'type': 'string'}}, 'extra-link-args': {'type': 'array', 'items': {'type': 'string'}}, 'export-symbols': {'type': 'array', 'items': {'type': 'string'}}, 'swig-opts': {'type': 'array', 'items': {'type': 'string'}}, 'depends': {'type': 'array', 'items': {'type': 'string'}}, 'language': {'type': 'string'}, 'optional': {'type': 'boolean'}, 'py-limited-api': {'type': 'boolean'}}}, 'file-directive': {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, 'file-directive-for-dependencies': {'title': "'file:' directive for dependencies", 'allOf': [{'$$description': ['**BETA**: subset of the ``requirements.txt`` format', 'without ``pip`` flags and options', '(one :pep:`508`-compliant string per line,', 'lines that are blank or start with ``#`` are excluded).', 'See `dynamic metadata', '<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_.']}, {'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}]}, 'attr-directive': {'title': "'attr:' directive", '$id': '#/definitions/attr-directive', '$$description': ['Value is read from a module attribute. Supports callables and iterables;', 'unsupported types are cast via ``str()``'], 'type': 'object', 'additionalProperties': False, 'properties': {'attr': {'type': 'string', 'format': 'python-qualified-identifier'}}, 'required': ['attr']}, 'find-directive': {'$id': '#/definitions/find-directive', 'title': "'find:' directive", 'type': 'object', 'additionalProperties': False, 'properties': {'find': {'type': 'object', '$$description': ['Dynamic `package discovery', '<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_.'], 'additionalProperties': False, 'properties': {'where': {'description': 'Directories to be searched for packages (Unix-style relative path)', 'type': 'array', 'items': {'type': 'string'}}, 'exclude': {'type': 'array', '$$description': ['Exclude packages that match the values listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'include': {'type': 'array', '$$description': ['Restrict the found packages to just the ones listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'namespaces': {'type': 'boolean', '$$description': ['When ``True``, directories without a ``__init__.py`` file will also', 'be scanned for :pep:`420`-style implicit namespaces']}}}}}}}, rule='additionalProperties')
+    return data
+
+def validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_file_directive_properties_file(data, custom_formats={}, name_prefix=None):
+    data_one_of_count8 = 0
+    if data_one_of_count8 < 2:
+        try:
+            if not isinstance(data, (str)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + " must be string", value=data, name="" + (name_prefix or "data") + "", definition={'type': 'string'}, rule='type')
+            data_one_of_count8 += 1
+        except JsonSchemaValueException: pass
+    if data_one_of_count8 < 2:
+        try:
+            if not isinstance(data, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + " must be array", value=data, name="" + (name_prefix or "data") + "", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data_is_list = isinstance(data, (list, tuple))
+            if data_is_list:
+                data_len = len(data)
+                for data_x, data_item in enumerate(data):
+                    if not isinstance(data_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + "[{data_x}]".format(**locals()) + " must be string", value=data_item, name="" + (name_prefix or "data") + "[{data_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+            data_one_of_count8 += 1
+        except JsonSchemaValueException: pass
+    if data_one_of_count8 != 1:
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " must be valid exactly by one definition" + (" (" + str(data_one_of_count8) + " matches found)"), value=data, name="" + (name_prefix or "data") + "", definition={'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}, rule='oneOf')
+    return data
+
+def validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_file_directive_for_dependencies(data, custom_formats={}, name_prefix=None):
+    validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_file_directive(data, custom_formats, (name_prefix or "data") + "")
+    return data
+
+def validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_file_directive(data, custom_formats={}, name_prefix=None):
+    if not isinstance(data, (dict)):
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " must be object", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, rule='type')
+    data_is_dict = isinstance(data, dict)
+    if data_is_dict:
+        data__missing_keys = set(['file']) - data.keys()
+        if data__missing_keys:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must contain " + (str(sorted(data__missing_keys)) + " properties"), value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, rule='required')
+        data_keys = set(data.keys())
+        if "file" in data_keys:
+            data_keys.remove("file")
+            data__file = data["file"]
+            data__file_one_of_count9 = 0
+            if data__file_one_of_count9 < 2:
+                try:
+                    if not isinstance(data__file, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".file must be string", value=data__file, name="" + (name_prefix or "data") + ".file", definition={'type': 'string'}, rule='type')
+                    data__file_one_of_count9 += 1
+                except JsonSchemaValueException: pass
+            if data__file_one_of_count9 < 2:
+                try:
+                    if not isinstance(data__file, (list, tuple)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".file must be array", value=data__file, name="" + (name_prefix or "data") + ".file", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+                    data__file_is_list = isinstance(data__file, (list, tuple))
+                    if data__file_is_list:
+                        data__file_len = len(data__file)
+                        for data__file_x, data__file_item in enumerate(data__file):
+                            if not isinstance(data__file_item, (str)):
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".file[{data__file_x}]".format(**locals()) + " must be string", value=data__file_item, name="" + (name_prefix or "data") + ".file[{data__file_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+                    data__file_one_of_count9 += 1
+                except JsonSchemaValueException: pass
+            if data__file_one_of_count9 != 1:
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".file must be valid exactly by one definition" + (" (" + str(data__file_one_of_count9) + " matches found)"), value=data__file, name="" + (name_prefix or "data") + ".file", definition={'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}, rule='oneOf')
+        if data_keys:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must not contain "+str(data_keys)+" properties", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/file-directive', 'title': "'file:' directive", 'description': 'Value is read from a file (or list of files and then concatenated)', 'type': 'object', 'additionalProperties': False, 'properties': {'file': {'oneOf': [{'type': 'string'}, {'type': 'array', 'items': {'type': 'string'}}]}}, 'required': ['file']}, rule='additionalProperties')
+    return data
+
+def validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_attr_directive(data, custom_formats={}, name_prefix=None):
+    if not isinstance(data, (dict)):
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " must be object", value=data, name="" + (name_prefix or "data") + "", definition={'title': "'attr:' directive", '$id': '#/definitions/attr-directive', '$$description': ['Value is read from a module attribute. Supports callables and iterables;', 'unsupported types are cast via ``str()``'], 'type': 'object', 'additionalProperties': False, 'properties': {'attr': {'type': 'string', 'format': 'python-qualified-identifier'}}, 'required': ['attr']}, rule='type')
+    data_is_dict = isinstance(data, dict)
+    if data_is_dict:
+        data__missing_keys = set(['attr']) - data.keys()
+        if data__missing_keys:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must contain " + (str(sorted(data__missing_keys)) + " properties"), value=data, name="" + (name_prefix or "data") + "", definition={'title': "'attr:' directive", '$id': '#/definitions/attr-directive', '$$description': ['Value is read from a module attribute. Supports callables and iterables;', 'unsupported types are cast via ``str()``'], 'type': 'object', 'additionalProperties': False, 'properties': {'attr': {'type': 'string', 'format': 'python-qualified-identifier'}}, 'required': ['attr']}, rule='required')
+        data_keys = set(data.keys())
+        if "attr" in data_keys:
+            data_keys.remove("attr")
+            data__attr = data["attr"]
+            if not isinstance(data__attr, (str)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".attr must be string", value=data__attr, name="" + (name_prefix or "data") + ".attr", definition={'type': 'string', 'format': 'python-qualified-identifier'}, rule='type')
+            if isinstance(data__attr, str):
+                if not custom_formats["python-qualified-identifier"](data__attr):
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".attr must be python-qualified-identifier", value=data__attr, name="" + (name_prefix or "data") + ".attr", definition={'type': 'string', 'format': 'python-qualified-identifier'}, rule='format')
+        if data_keys:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must not contain "+str(data_keys)+" properties", value=data, name="" + (name_prefix or "data") + "", definition={'title': "'attr:' directive", '$id': '#/definitions/attr-directive', '$$description': ['Value is read from a module attribute. Supports callables and iterables;', 'unsupported types are cast via ``str()``'], 'type': 'object', 'additionalProperties': False, 'properties': {'attr': {'type': 'string', 'format': 'python-qualified-identifier'}}, 'required': ['attr']}, rule='additionalProperties')
+    return data
+
+def validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_ext_module(data, custom_formats={}, name_prefix=None):
+    if not isinstance(data, (dict)):
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " must be object", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/ext-module', 'title': 'Extension module', 'description': 'Parameters to construct a :class:`setuptools.Extension` object', 'type': 'object', 'required': ['name', 'sources'], 'additionalProperties': False, 'properties': {'name': {'type': 'string', 'format': 'python-module-name-relaxed'}, 'sources': {'type': 'array', 'items': {'type': 'string'}}, 'include-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'define-macros': {'type': 'array', 'items': {'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}}, 'undef-macros': {'type': 'array', 'items': {'type': 'string'}}, 'library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'libraries': {'type': 'array', 'items': {'type': 'string'}}, 'runtime-library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'extra-objects': {'type': 'array', 'items': {'type': 'string'}}, 'extra-compile-args': {'type': 'array', 'items': {'type': 'string'}}, 'extra-link-args': {'type': 'array', 'items': {'type': 'string'}}, 'export-symbols': {'type': 'array', 'items': {'type': 'string'}}, 'swig-opts': {'type': 'array', 'items': {'type': 'string'}}, 'depends': {'type': 'array', 'items': {'type': 'string'}}, 'language': {'type': 'string'}, 'optional': {'type': 'boolean'}, 'py-limited-api': {'type': 'boolean'}}}, rule='type')
+    data_is_dict = isinstance(data, dict)
+    if data_is_dict:
+        data__missing_keys = set(['name', 'sources']) - data.keys()
+        if data__missing_keys:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must contain " + (str(sorted(data__missing_keys)) + " properties"), value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/ext-module', 'title': 'Extension module', 'description': 'Parameters to construct a :class:`setuptools.Extension` object', 'type': 'object', 'required': ['name', 'sources'], 'additionalProperties': False, 'properties': {'name': {'type': 'string', 'format': 'python-module-name-relaxed'}, 'sources': {'type': 'array', 'items': {'type': 'string'}}, 'include-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'define-macros': {'type': 'array', 'items': {'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}}, 'undef-macros': {'type': 'array', 'items': {'type': 'string'}}, 'library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'libraries': {'type': 'array', 'items': {'type': 'string'}}, 'runtime-library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'extra-objects': {'type': 'array', 'items': {'type': 'string'}}, 'extra-compile-args': {'type': 'array', 'items': {'type': 'string'}}, 'extra-link-args': {'type': 'array', 'items': {'type': 'string'}}, 'export-symbols': {'type': 'array', 'items': {'type': 'string'}}, 'swig-opts': {'type': 'array', 'items': {'type': 'string'}}, 'depends': {'type': 'array', 'items': {'type': 'string'}}, 'language': {'type': 'string'}, 'optional': {'type': 'boolean'}, 'py-limited-api': {'type': 'boolean'}}}, rule='required')
+        data_keys = set(data.keys())
+        if "name" in data_keys:
+            data_keys.remove("name")
+            data__name = data["name"]
+            if not isinstance(data__name, (str)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".name must be string", value=data__name, name="" + (name_prefix or "data") + ".name", definition={'type': 'string', 'format': 'python-module-name-relaxed'}, rule='type')
+            if isinstance(data__name, str):
+                if not custom_formats["python-module-name-relaxed"](data__name):
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".name must be python-module-name-relaxed", value=data__name, name="" + (name_prefix or "data") + ".name", definition={'type': 'string', 'format': 'python-module-name-relaxed'}, rule='format')
+        if "sources" in data_keys:
+            data_keys.remove("sources")
+            data__sources = data["sources"]
+            if not isinstance(data__sources, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".sources must be array", value=data__sources, name="" + (name_prefix or "data") + ".sources", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__sources_is_list = isinstance(data__sources, (list, tuple))
+            if data__sources_is_list:
+                data__sources_len = len(data__sources)
+                for data__sources_x, data__sources_item in enumerate(data__sources):
+                    if not isinstance(data__sources_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".sources[{data__sources_x}]".format(**locals()) + " must be string", value=data__sources_item, name="" + (name_prefix or "data") + ".sources[{data__sources_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "include-dirs" in data_keys:
+            data_keys.remove("include-dirs")
+            data__includedirs = data["include-dirs"]
+            if not isinstance(data__includedirs, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".include-dirs must be array", value=data__includedirs, name="" + (name_prefix or "data") + ".include-dirs", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__includedirs_is_list = isinstance(data__includedirs, (list, tuple))
+            if data__includedirs_is_list:
+                data__includedirs_len = len(data__includedirs)
+                for data__includedirs_x, data__includedirs_item in enumerate(data__includedirs):
+                    if not isinstance(data__includedirs_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".include-dirs[{data__includedirs_x}]".format(**locals()) + " must be string", value=data__includedirs_item, name="" + (name_prefix or "data") + ".include-dirs[{data__includedirs_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "define-macros" in data_keys:
+            data_keys.remove("define-macros")
+            data__definemacros = data["define-macros"]
+            if not isinstance(data__definemacros, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".define-macros must be array", value=data__definemacros, name="" + (name_prefix or "data") + ".define-macros", definition={'type': 'array', 'items': {'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}}, rule='type')
+            data__definemacros_is_list = isinstance(data__definemacros, (list, tuple))
+            if data__definemacros_is_list:
+                data__definemacros_len = len(data__definemacros)
+                for data__definemacros_x, data__definemacros_item in enumerate(data__definemacros):
+                    if not isinstance(data__definemacros_item, (list, tuple)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".define-macros[{data__definemacros_x}]".format(**locals()) + " must be array", value=data__definemacros_item, name="" + (name_prefix or "data") + ".define-macros[{data__definemacros_x}]".format(**locals()) + "", definition={'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}, rule='type')
+                    data__definemacros_item_is_list = isinstance(data__definemacros_item, (list, tuple))
+                    if data__definemacros_item_is_list:
+                        data__definemacros_item_len = len(data__definemacros_item)
+                        if data__definemacros_item_len > 0:
+                            data__definemacros_item__0 = data__definemacros_item[0]
+                            if not isinstance(data__definemacros_item__0, (str)):
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".define-macros[{data__definemacros_x}][0]".format(**locals()) + " must be string", value=data__definemacros_item__0, name="" + (name_prefix or "data") + ".define-macros[{data__definemacros_x}][0]".format(**locals()) + "", definition={'description': 'macro name', 'type': 'string'}, rule='type')
+                        if data__definemacros_item_len > 1:
+                            data__definemacros_item__1 = data__definemacros_item[1]
+                            data__definemacros_item__1_one_of_count10 = 0
+                            if data__definemacros_item__1_one_of_count10 < 2:
+                                try:
+                                    if not isinstance(data__definemacros_item__1, (str)):
+                                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".define-macros[{data__definemacros_x}][1]".format(**locals()) + " must be string", value=data__definemacros_item__1, name="" + (name_prefix or "data") + ".define-macros[{data__definemacros_x}][1]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+                                    data__definemacros_item__1_one_of_count10 += 1
+                                except JsonSchemaValueException: pass
+                            if data__definemacros_item__1_one_of_count10 < 2:
+                                try:
+                                    if not isinstance(data__definemacros_item__1, (NoneType)):
+                                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".define-macros[{data__definemacros_x}][1]".format(**locals()) + " must be null", value=data__definemacros_item__1, name="" + (name_prefix or "data") + ".define-macros[{data__definemacros_x}][1]".format(**locals()) + "", definition={'type': 'null'}, rule='type')
+                                    data__definemacros_item__1_one_of_count10 += 1
+                                except JsonSchemaValueException: pass
+                            if data__definemacros_item__1_one_of_count10 != 1:
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".define-macros[{data__definemacros_x}][1]".format(**locals()) + " must be valid exactly by one definition" + (" (" + str(data__definemacros_item__1_one_of_count10) + " matches found)"), value=data__definemacros_item__1, name="" + (name_prefix or "data") + ".define-macros[{data__definemacros_x}][1]".format(**locals()) + "", definition={'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}, rule='oneOf')
+                        if data__definemacros_item_len > 2:
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".define-macros[{data__definemacros_x}]".format(**locals()) + " must contain only specified items", value=data__definemacros_item, name="" + (name_prefix or "data") + ".define-macros[{data__definemacros_x}]".format(**locals()) + "", definition={'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}, rule='items')
+        if "undef-macros" in data_keys:
+            data_keys.remove("undef-macros")
+            data__undefmacros = data["undef-macros"]
+            if not isinstance(data__undefmacros, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".undef-macros must be array", value=data__undefmacros, name="" + (name_prefix or "data") + ".undef-macros", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__undefmacros_is_list = isinstance(data__undefmacros, (list, tuple))
+            if data__undefmacros_is_list:
+                data__undefmacros_len = len(data__undefmacros)
+                for data__undefmacros_x, data__undefmacros_item in enumerate(data__undefmacros):
+                    if not isinstance(data__undefmacros_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".undef-macros[{data__undefmacros_x}]".format(**locals()) + " must be string", value=data__undefmacros_item, name="" + (name_prefix or "data") + ".undef-macros[{data__undefmacros_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "library-dirs" in data_keys:
+            data_keys.remove("library-dirs")
+            data__librarydirs = data["library-dirs"]
+            if not isinstance(data__librarydirs, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".library-dirs must be array", value=data__librarydirs, name="" + (name_prefix or "data") + ".library-dirs", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__librarydirs_is_list = isinstance(data__librarydirs, (list, tuple))
+            if data__librarydirs_is_list:
+                data__librarydirs_len = len(data__librarydirs)
+                for data__librarydirs_x, data__librarydirs_item in enumerate(data__librarydirs):
+                    if not isinstance(data__librarydirs_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".library-dirs[{data__librarydirs_x}]".format(**locals()) + " must be string", value=data__librarydirs_item, name="" + (name_prefix or "data") + ".library-dirs[{data__librarydirs_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "libraries" in data_keys:
+            data_keys.remove("libraries")
+            data__libraries = data["libraries"]
+            if not isinstance(data__libraries, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".libraries must be array", value=data__libraries, name="" + (name_prefix or "data") + ".libraries", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__libraries_is_list = isinstance(data__libraries, (list, tuple))
+            if data__libraries_is_list:
+                data__libraries_len = len(data__libraries)
+                for data__libraries_x, data__libraries_item in enumerate(data__libraries):
+                    if not isinstance(data__libraries_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".libraries[{data__libraries_x}]".format(**locals()) + " must be string", value=data__libraries_item, name="" + (name_prefix or "data") + ".libraries[{data__libraries_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "runtime-library-dirs" in data_keys:
+            data_keys.remove("runtime-library-dirs")
+            data__runtimelibrarydirs = data["runtime-library-dirs"]
+            if not isinstance(data__runtimelibrarydirs, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".runtime-library-dirs must be array", value=data__runtimelibrarydirs, name="" + (name_prefix or "data") + ".runtime-library-dirs", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__runtimelibrarydirs_is_list = isinstance(data__runtimelibrarydirs, (list, tuple))
+            if data__runtimelibrarydirs_is_list:
+                data__runtimelibrarydirs_len = len(data__runtimelibrarydirs)
+                for data__runtimelibrarydirs_x, data__runtimelibrarydirs_item in enumerate(data__runtimelibrarydirs):
+                    if not isinstance(data__runtimelibrarydirs_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".runtime-library-dirs[{data__runtimelibrarydirs_x}]".format(**locals()) + " must be string", value=data__runtimelibrarydirs_item, name="" + (name_prefix or "data") + ".runtime-library-dirs[{data__runtimelibrarydirs_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "extra-objects" in data_keys:
+            data_keys.remove("extra-objects")
+            data__extraobjects = data["extra-objects"]
+            if not isinstance(data__extraobjects, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".extra-objects must be array", value=data__extraobjects, name="" + (name_prefix or "data") + ".extra-objects", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__extraobjects_is_list = isinstance(data__extraobjects, (list, tuple))
+            if data__extraobjects_is_list:
+                data__extraobjects_len = len(data__extraobjects)
+                for data__extraobjects_x, data__extraobjects_item in enumerate(data__extraobjects):
+                    if not isinstance(data__extraobjects_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".extra-objects[{data__extraobjects_x}]".format(**locals()) + " must be string", value=data__extraobjects_item, name="" + (name_prefix or "data") + ".extra-objects[{data__extraobjects_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "extra-compile-args" in data_keys:
+            data_keys.remove("extra-compile-args")
+            data__extracompileargs = data["extra-compile-args"]
+            if not isinstance(data__extracompileargs, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".extra-compile-args must be array", value=data__extracompileargs, name="" + (name_prefix or "data") + ".extra-compile-args", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__extracompileargs_is_list = isinstance(data__extracompileargs, (list, tuple))
+            if data__extracompileargs_is_list:
+                data__extracompileargs_len = len(data__extracompileargs)
+                for data__extracompileargs_x, data__extracompileargs_item in enumerate(data__extracompileargs):
+                    if not isinstance(data__extracompileargs_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".extra-compile-args[{data__extracompileargs_x}]".format(**locals()) + " must be string", value=data__extracompileargs_item, name="" + (name_prefix or "data") + ".extra-compile-args[{data__extracompileargs_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "extra-link-args" in data_keys:
+            data_keys.remove("extra-link-args")
+            data__extralinkargs = data["extra-link-args"]
+            if not isinstance(data__extralinkargs, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".extra-link-args must be array", value=data__extralinkargs, name="" + (name_prefix or "data") + ".extra-link-args", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__extralinkargs_is_list = isinstance(data__extralinkargs, (list, tuple))
+            if data__extralinkargs_is_list:
+                data__extralinkargs_len = len(data__extralinkargs)
+                for data__extralinkargs_x, data__extralinkargs_item in enumerate(data__extralinkargs):
+                    if not isinstance(data__extralinkargs_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".extra-link-args[{data__extralinkargs_x}]".format(**locals()) + " must be string", value=data__extralinkargs_item, name="" + (name_prefix or "data") + ".extra-link-args[{data__extralinkargs_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "export-symbols" in data_keys:
+            data_keys.remove("export-symbols")
+            data__exportsymbols = data["export-symbols"]
+            if not isinstance(data__exportsymbols, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".export-symbols must be array", value=data__exportsymbols, name="" + (name_prefix or "data") + ".export-symbols", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__exportsymbols_is_list = isinstance(data__exportsymbols, (list, tuple))
+            if data__exportsymbols_is_list:
+                data__exportsymbols_len = len(data__exportsymbols)
+                for data__exportsymbols_x, data__exportsymbols_item in enumerate(data__exportsymbols):
+                    if not isinstance(data__exportsymbols_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".export-symbols[{data__exportsymbols_x}]".format(**locals()) + " must be string", value=data__exportsymbols_item, name="" + (name_prefix or "data") + ".export-symbols[{data__exportsymbols_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "swig-opts" in data_keys:
+            data_keys.remove("swig-opts")
+            data__swigopts = data["swig-opts"]
+            if not isinstance(data__swigopts, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".swig-opts must be array", value=data__swigopts, name="" + (name_prefix or "data") + ".swig-opts", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__swigopts_is_list = isinstance(data__swigopts, (list, tuple))
+            if data__swigopts_is_list:
+                data__swigopts_len = len(data__swigopts)
+                for data__swigopts_x, data__swigopts_item in enumerate(data__swigopts):
+                    if not isinstance(data__swigopts_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".swig-opts[{data__swigopts_x}]".format(**locals()) + " must be string", value=data__swigopts_item, name="" + (name_prefix or "data") + ".swig-opts[{data__swigopts_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "depends" in data_keys:
+            data_keys.remove("depends")
+            data__depends = data["depends"]
+            if not isinstance(data__depends, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".depends must be array", value=data__depends, name="" + (name_prefix or "data") + ".depends", definition={'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__depends_is_list = isinstance(data__depends, (list, tuple))
+            if data__depends_is_list:
+                data__depends_len = len(data__depends)
+                for data__depends_x, data__depends_item in enumerate(data__depends):
+                    if not isinstance(data__depends_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".depends[{data__depends_x}]".format(**locals()) + " must be string", value=data__depends_item, name="" + (name_prefix or "data") + ".depends[{data__depends_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "language" in data_keys:
+            data_keys.remove("language")
+            data__language = data["language"]
+            if not isinstance(data__language, (str)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".language must be string", value=data__language, name="" + (name_prefix or "data") + ".language", definition={'type': 'string'}, rule='type')
+        if "optional" in data_keys:
+            data_keys.remove("optional")
+            data__optional = data["optional"]
+            if not isinstance(data__optional, (bool)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".optional must be boolean", value=data__optional, name="" + (name_prefix or "data") + ".optional", definition={'type': 'boolean'}, rule='type')
+        if "py-limited-api" in data_keys:
+            data_keys.remove("py-limited-api")
+            data__pylimitedapi = data["py-limited-api"]
+            if not isinstance(data__pylimitedapi, (bool)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".py-limited-api must be boolean", value=data__pylimitedapi, name="" + (name_prefix or "data") + ".py-limited-api", definition={'type': 'boolean'}, rule='type')
+        if data_keys:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must not contain "+str(data_keys)+" properties", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/ext-module', 'title': 'Extension module', 'description': 'Parameters to construct a :class:`setuptools.Extension` object', 'type': 'object', 'required': ['name', 'sources'], 'additionalProperties': False, 'properties': {'name': {'type': 'string', 'format': 'python-module-name-relaxed'}, 'sources': {'type': 'array', 'items': {'type': 'string'}}, 'include-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'define-macros': {'type': 'array', 'items': {'type': 'array', 'items': [{'description': 'macro name', 'type': 'string'}, {'description': 'macro value', 'oneOf': [{'type': 'string'}, {'type': 'null'}]}], 'additionalItems': False}}, 'undef-macros': {'type': 'array', 'items': {'type': 'string'}}, 'library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'libraries': {'type': 'array', 'items': {'type': 'string'}}, 'runtime-library-dirs': {'type': 'array', 'items': {'type': 'string'}}, 'extra-objects': {'type': 'array', 'items': {'type': 'string'}}, 'extra-compile-args': {'type': 'array', 'items': {'type': 'string'}}, 'extra-link-args': {'type': 'array', 'items': {'type': 'string'}}, 'export-symbols': {'type': 'array', 'items': {'type': 'string'}}, 'swig-opts': {'type': 'array', 'items': {'type': 'string'}}, 'depends': {'type': 'array', 'items': {'type': 'string'}}, 'language': {'type': 'string'}, 'optional': {'type': 'boolean'}, 'py-limited-api': {'type': 'boolean'}}}, rule='additionalProperties')
+    return data
+
+def validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_find_directive(data, custom_formats={}, name_prefix=None):
+    if not isinstance(data, (dict)):
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " must be object", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/find-directive', 'title': "'find:' directive", 'type': 'object', 'additionalProperties': False, 'properties': {'find': {'type': 'object', '$$description': ['Dynamic `package discovery', '<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_.'], 'additionalProperties': False, 'properties': {'where': {'description': 'Directories to be searched for packages (Unix-style relative path)', 'type': 'array', 'items': {'type': 'string'}}, 'exclude': {'type': 'array', '$$description': ['Exclude packages that match the values listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'include': {'type': 'array', '$$description': ['Restrict the found packages to just the ones listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'namespaces': {'type': 'boolean', '$$description': ['When ``True``, directories without a ``__init__.py`` file will also', 'be scanned for :pep:`420`-style implicit namespaces']}}}}}, rule='type')
+    data_is_dict = isinstance(data, dict)
+    if data_is_dict:
+        data_keys = set(data.keys())
+        if "find" in data_keys:
+            data_keys.remove("find")
+            data__find = data["find"]
+            if not isinstance(data__find, (dict)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".find must be object", value=data__find, name="" + (name_prefix or "data") + ".find", definition={'type': 'object', '$$description': ['Dynamic `package discovery', '<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_.'], 'additionalProperties': False, 'properties': {'where': {'description': 'Directories to be searched for packages (Unix-style relative path)', 'type': 'array', 'items': {'type': 'string'}}, 'exclude': {'type': 'array', '$$description': ['Exclude packages that match the values listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'include': {'type': 'array', '$$description': ['Restrict the found packages to just the ones listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'namespaces': {'type': 'boolean', '$$description': ['When ``True``, directories without a ``__init__.py`` file will also', 'be scanned for :pep:`420`-style implicit namespaces']}}}, rule='type')
+            data__find_is_dict = isinstance(data__find, dict)
+            if data__find_is_dict:
+                data__find_keys = set(data__find.keys())
+                if "where" in data__find_keys:
+                    data__find_keys.remove("where")
+                    data__find__where = data__find["where"]
+                    if not isinstance(data__find__where, (list, tuple)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".find.where must be array", value=data__find__where, name="" + (name_prefix or "data") + ".find.where", definition={'description': 'Directories to be searched for packages (Unix-style relative path)', 'type': 'array', 'items': {'type': 'string'}}, rule='type')
+                    data__find__where_is_list = isinstance(data__find__where, (list, tuple))
+                    if data__find__where_is_list:
+                        data__find__where_len = len(data__find__where)
+                        for data__find__where_x, data__find__where_item in enumerate(data__find__where):
+                            if not isinstance(data__find__where_item, (str)):
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".find.where[{data__find__where_x}]".format(**locals()) + " must be string", value=data__find__where_item, name="" + (name_prefix or "data") + ".find.where[{data__find__where_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+                if "exclude" in data__find_keys:
+                    data__find_keys.remove("exclude")
+                    data__find__exclude = data__find["exclude"]
+                    if not isinstance(data__find__exclude, (list, tuple)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".find.exclude must be array", value=data__find__exclude, name="" + (name_prefix or "data") + ".find.exclude", definition={'type': 'array', '$$description': ['Exclude packages that match the values listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, rule='type')
+                    data__find__exclude_is_list = isinstance(data__find__exclude, (list, tuple))
+                    if data__find__exclude_is_list:
+                        data__find__exclude_len = len(data__find__exclude)
+                        for data__find__exclude_x, data__find__exclude_item in enumerate(data__find__exclude):
+                            if not isinstance(data__find__exclude_item, (str)):
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".find.exclude[{data__find__exclude_x}]".format(**locals()) + " must be string", value=data__find__exclude_item, name="" + (name_prefix or "data") + ".find.exclude[{data__find__exclude_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+                if "include" in data__find_keys:
+                    data__find_keys.remove("include")
+                    data__find__include = data__find["include"]
+                    if not isinstance(data__find__include, (list, tuple)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".find.include must be array", value=data__find__include, name="" + (name_prefix or "data") + ".find.include", definition={'type': 'array', '$$description': ['Restrict the found packages to just the ones listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, rule='type')
+                    data__find__include_is_list = isinstance(data__find__include, (list, tuple))
+                    if data__find__include_is_list:
+                        data__find__include_len = len(data__find__include)
+                        for data__find__include_x, data__find__include_item in enumerate(data__find__include):
+                            if not isinstance(data__find__include_item, (str)):
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".find.include[{data__find__include_x}]".format(**locals()) + " must be string", value=data__find__include_item, name="" + (name_prefix or "data") + ".find.include[{data__find__include_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+                if "namespaces" in data__find_keys:
+                    data__find_keys.remove("namespaces")
+                    data__find__namespaces = data__find["namespaces"]
+                    if not isinstance(data__find__namespaces, (bool)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".find.namespaces must be boolean", value=data__find__namespaces, name="" + (name_prefix or "data") + ".find.namespaces", definition={'type': 'boolean', '$$description': ['When ``True``, directories without a ``__init__.py`` file will also', 'be scanned for :pep:`420`-style implicit namespaces']}, rule='type')
+                if data__find_keys:
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".find must not contain "+str(data__find_keys)+" properties", value=data__find, name="" + (name_prefix or "data") + ".find", definition={'type': 'object', '$$description': ['Dynamic `package discovery', '<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_.'], 'additionalProperties': False, 'properties': {'where': {'description': 'Directories to be searched for packages (Unix-style relative path)', 'type': 'array', 'items': {'type': 'string'}}, 'exclude': {'type': 'array', '$$description': ['Exclude packages that match the values listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'include': {'type': 'array', '$$description': ['Restrict the found packages to just the ones listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'namespaces': {'type': 'boolean', '$$description': ['When ``True``, directories without a ``__init__.py`` file will also', 'be scanned for :pep:`420`-style implicit namespaces']}}}, rule='additionalProperties')
+        if data_keys:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must not contain "+str(data_keys)+" properties", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/find-directive', 'title': "'find:' directive", 'type': 'object', 'additionalProperties': False, 'properties': {'find': {'type': 'object', '$$description': ['Dynamic `package discovery', '<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_.'], 'additionalProperties': False, 'properties': {'where': {'description': 'Directories to be searched for packages (Unix-style relative path)', 'type': 'array', 'items': {'type': 'string'}}, 'exclude': {'type': 'array', '$$description': ['Exclude packages that match the values listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'include': {'type': 'array', '$$description': ['Restrict the found packages to just the ones listed in this field.', "Can container shell-style wildcards (e.g. ``'pkg.*'``)"], 'items': {'type': 'string'}}, 'namespaces': {'type': 'boolean', '$$description': ['When ``True``, directories without a ``__init__.py`` file will also', 'be scanned for :pep:`420`-style implicit namespaces']}}}}}, rule='additionalProperties')
+    return data
+
+def validate_https___setuptools_pypa_io_en_latest_userguide_pyproject_config_html__definitions_package_name(data, custom_formats={}, name_prefix=None):
+    if not isinstance(data, (str)):
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " must be string", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}, rule='type')
+    data_any_of_count11 = 0
+    if not data_any_of_count11:
+        try:
+            if not isinstance(data, (str)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + " must be string", value=data, name="" + (name_prefix or "data") + "", definition={'type': 'string', 'format': 'python-module-name-relaxed'}, rule='type')
+            if isinstance(data, str):
+                if not custom_formats["python-module-name-relaxed"](data):
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + " must be python-module-name-relaxed", value=data, name="" + (name_prefix or "data") + "", definition={'type': 'string', 'format': 'python-module-name-relaxed'}, rule='format')
+            data_any_of_count11 += 1
+        except JsonSchemaValueException: pass
+    if not data_any_of_count11:
+        try:
+            if not isinstance(data, (str)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + " must be string", value=data, name="" + (name_prefix or "data") + "", definition={'type': 'string', 'format': 'pep561-stub-name'}, rule='type')
+            if isinstance(data, str):
+                if not custom_formats["pep561-stub-name"](data):
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + " must be pep561-stub-name", value=data, name="" + (name_prefix or "data") + "", definition={'type': 'string', 'format': 'pep561-stub-name'}, rule='format')
+            data_any_of_count11 += 1
+        except JsonSchemaValueException: pass
+    if not data_any_of_count11:
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " cannot be validated by any definition", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/package-name', 'title': 'Valid package name', 'description': 'Valid package name (importable or :pep:`561`).', 'type': 'string', 'anyOf': [{'type': 'string', 'format': 'python-module-name-relaxed'}, {'type': 'string', 'format': 'pep561-stub-name'}]}, rule='anyOf')
+    return data
+
+def validate_https___setuptools_pypa_io_en_latest_deprecated_distutils_configfile_html(data, custom_formats={}, name_prefix=None):
+    if not isinstance(data, (dict)):
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " must be object", value=data, name="" + (name_prefix or "data") + "", definition={'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://setuptools.pypa.io/en/latest/deprecated/distutils/configfile.html', 'title': '``tool.distutils`` table', '$$description': ['**EXPERIMENTAL** (NOT OFFICIALLY SUPPORTED): Use ``tool.distutils``', 'subtables to configure arguments for ``distutils`` commands.', 'Originally, ``distutils`` allowed developers to configure arguments for', '``setup.py`` commands via `distutils configuration files', '<https://setuptools.pypa.io/en/latest/deprecated/distutils/configfile.html>`_.', 'See also `the old Python docs <https://docs.python.org/3.11/install/>_`.'], 'type': 'object', 'properties': {'global': {'type': 'object', 'description': 'Global options applied to all ``distutils`` commands'}}, 'patternProperties': {'.+': {'type': 'object'}}, '$comment': 'TODO: Is there a practical way of making this schema more specific?'}, rule='type')
+    data_is_dict = isinstance(data, dict)
+    if data_is_dict:
+        data_keys = set(data.keys())
+        if "global" in data_keys:
+            data_keys.remove("global")
+            data__global = data["global"]
+            if not isinstance(data__global, (dict)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".global must be object", value=data__global, name="" + (name_prefix or "data") + ".global", definition={'type': 'object', 'description': 'Global options applied to all ``distutils`` commands'}, rule='type')
+        for data_key, data_val in data.items():
+            if REGEX_PATTERNS['.+'].search(data_key):
+                if data_key in data_keys:
+                    data_keys.remove(data_key)
+                if not isinstance(data_val, (dict)):
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".{data_key}".format(**locals()) + " must be object", value=data_val, name="" + (name_prefix or "data") + ".{data_key}".format(**locals()) + "", definition={'type': 'object'}, rule='type')
+    return data
+
+def validate_https___packaging_python_org_en_latest_specifications_pyproject_toml(data, custom_formats={}, name_prefix=None):
+    if not isinstance(data, (dict)):
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " must be object", value=data, name="" + (name_prefix or "data") + "", definition={'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://packaging.python.org/en/latest/specifications/pyproject-toml/', 'title': 'Package metadata stored in the ``project`` table', '$$description': ['Data structure for the **project** table inside ``pyproject.toml``', '(as initially defined in :pep:`621`)'], 'type': 'object', 'properties': {'name': {'type': 'string', 'description': 'The name (primary identifier) of the project. MUST be statically defined.', 'format': 'pep508-identifier'}, 'version': {'type': 'string', 'description': 'The version of the project as supported by :pep:`440`.', 'format': 'pep440'}, 'description': {'type': 'string', '$$description': ['The `summary description of the project', '<https://packaging.python.org/specifications/core-metadata/#summary>`_']}, 'readme': {'$$description': ['`Full/detailed description of the project in the form of a README', '<https://peps.python.org/pep-0621/#readme>`_', "with meaning similar to the one defined in `core metadata's Description", '<https://packaging.python.org/specifications/core-metadata/#description>`_'], 'oneOf': [{'type': 'string', '$$description': ['Relative path to a text file (UTF-8) containing the full description', 'of the project. If the file path ends in case-insensitive ``.md`` or', '``.rst`` suffixes, then the content-type is respectively', '``text/markdown`` or ``text/x-rst``']}, {'type': 'object', 'allOf': [{'anyOf': [{'properties': {'file': {'type': 'string', '$$description': ['Relative path to a text file containing the full description', 'of the project.']}}, 'required': ['file']}, {'properties': {'text': {'type': 'string', 'description': 'Full text describing the project.'}}, 'required': ['text']}]}, {'properties': {'content-type': {'type': 'string', '$$description': ['Content-type (:rfc:`1341`) of the full description', '(e.g. ``text/markdown``). The ``charset`` parameter is assumed', 'UTF-8 when not present.'], '$comment': 'TODO: add regex pattern or format?'}}, 'required': ['content-type']}]}]}, 'requires-python': {'type': 'string', 'format': 'pep508-versionspec', '$$description': ['`The Python version requirements of the project', '<https://packaging.python.org/specifications/core-metadata/#requires-python>`_.']}, 'license': {'description': '`Project license <https://peps.python.org/pep-0621/#license>`_.', 'oneOf': [{'type': 'string', 'description': 'An SPDX license identifier', 'format': 'SPDX'}, {'type': 'object', 'properties': {'file': {'type': 'string', '$$description': ['Relative path to the file (UTF-8) which contains the license for the', 'project.']}}, 'required': ['file']}, {'type': 'object', 'properties': {'text': {'type': 'string', '$$description': ['The license of the project whose meaning is that of the', '`License field from the core metadata', '<https://packaging.python.org/specifications/core-metadata/#license>`_.']}}, 'required': ['text']}]}, 'license-files': {'description': 'Paths or globs to paths of license files', 'type': 'array', 'items': {'type': 'string'}}, 'authors': {'type': 'array', 'items': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, '$$description': ["The people or organizations considered to be the 'authors' of the project.", 'The exact meaning is open to interpretation (e.g. original or primary authors,', 'current maintainers, or owners of the package).']}, 'maintainers': {'type': 'array', 'items': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, '$$description': ["The people or organizations considered to be the 'maintainers' of the project.", 'Similarly to ``authors``, the exact meaning is open to interpretation.']}, 'keywords': {'type': 'array', 'items': {'type': 'string'}, 'description': 'List of keywords to assist searching for the distribution in a larger catalog.'}, 'classifiers': {'type': 'array', 'items': {'type': 'string', 'format': 'trove-classifier', 'description': '`PyPI classifier <https://pypi.org/classifiers/>`_.'}, '$$description': ['`Trove classifiers <https://pypi.org/classifiers/>`_', 'which apply to the project.']}, 'urls': {'type': 'object', 'description': 'URLs associated with the project in the form ``label => value``.', 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', 'format': 'url'}}}, 'scripts': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, 'gui-scripts': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, 'entry-points': {'$$description': ['Instruct the installer to expose the given modules/functions via', '``entry-point`` discovery mechanism (useful for plugins).', 'More information available in the `Python packaging guide', '<https://packaging.python.org/specifications/entry-points/>`_.'], 'propertyNames': {'format': 'python-entrypoint-group'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}}}, 'dependencies': {'type': 'array', 'description': 'Project (mandatory) dependencies.', 'items': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}, 'optional-dependencies': {'type': 'object', 'description': 'Optional dependency for the project', 'propertyNames': {'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'array', 'items': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}}}, 'dynamic': {'type': 'array', '$$description': ['Specifies which fields are intentionally unspecified and expected to be', 'dynamically provided by build tools'], 'items': {'enum': ['version', 'description', 'readme', 'requires-python', 'license', 'license-files', 'authors', 'maintainers', 'keywords', 'classifiers', 'urls', 'scripts', 'gui-scripts', 'entry-points', 'dependencies', 'optional-dependencies']}}}, 'required': ['name'], 'additionalProperties': False, 'allOf': [{'if': {'not': {'required': ['dynamic'], 'properties': {'dynamic': {'contains': {'const': 'version'}, '$$description': ['version is listed in ``dynamic``']}}}, '$$comment': ['According to :pep:`621`:', '    If the core metadata specification lists a field as "Required", then', '    the metadata MUST specify the field statically or list it in dynamic', 'In turn, `core metadata`_ defines:', '    The required fields are: Metadata-Version, Name, Version.', '    All the other fields are optional.', 'Since ``Metadata-Version`` is defined by the build back-end, ``name`` and', '``version`` are the only mandatory information in ``pyproject.toml``.', '.. _core metadata: https://packaging.python.org/specifications/core-metadata/']}, 'then': {'required': ['version'], '$$description': ['version should be statically defined in the ``version`` field']}}, {'if': {'required': ['license-files']}, 'then': {'properties': {'license': {'type': 'string'}}}}], 'definitions': {'author': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, 'entry-point-group': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, 'dependency': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}}, rule='type')
+    try:
+        try:
+            data_is_dict = isinstance(data, dict)
+            if data_is_dict:
+                data__missing_keys = set(['dynamic']) - data.keys()
+                if data__missing_keys:
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + " must contain " + (str(sorted(data__missing_keys)) + " properties"), value=data, name="" + (name_prefix or "data") + "", definition={'required': ['dynamic'], 'properties': {'dynamic': {'contains': {'const': 'version'}, '$$description': ['version is listed in ``dynamic``']}}}, rule='required')
+                data_keys = set(data.keys())
+                if "dynamic" in data_keys:
+                    data_keys.remove("dynamic")
+                    data__dynamic = data["dynamic"]
+                    data__dynamic_is_list = isinstance(data__dynamic, (list, tuple))
+                    if data__dynamic_is_list:
+                        data__dynamic_contains = False
+                        for data__dynamic_key in data__dynamic:
+                            try:
+                                if data__dynamic_key != "version":
+                                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic must be same as const definition: version", value=data__dynamic_key, name="" + (name_prefix or "data") + ".dynamic", definition={'const': 'version'}, rule='const')
+                                data__dynamic_contains = True
+                                break
+                            except JsonSchemaValueException: pass
+                        if not data__dynamic_contains:
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic must contain one of contains definition", value=data__dynamic, name="" + (name_prefix or "data") + ".dynamic", definition={'contains': {'const': 'version'}, '$$description': ['version is listed in ``dynamic``']}, rule='contains')
+        except JsonSchemaValueException: pass
+        else:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must NOT match a disallowed definition", value=data, name="" + (name_prefix or "data") + "", definition={'not': {'required': ['dynamic'], 'properties': {'dynamic': {'contains': {'const': 'version'}, '$$description': ['version is listed in ``dynamic``']}}}, '$$comment': ['According to :pep:`621`:', '    If the core metadata specification lists a field as "Required", then', '    the metadata MUST specify the field statically or list it in dynamic', 'In turn, `core metadata`_ defines:', '    The required fields are: Metadata-Version, Name, Version.', '    All the other fields are optional.', 'Since ``Metadata-Version`` is defined by the build back-end, ``name`` and', '``version`` are the only mandatory information in ``pyproject.toml``.', '.. _core metadata: https://packaging.python.org/specifications/core-metadata/']}, rule='not')
+    except JsonSchemaValueException:
+        pass
+    else:
+        data_is_dict = isinstance(data, dict)
+        if data_is_dict:
+            data__missing_keys = set(['version']) - data.keys()
+            if data__missing_keys:
+                raise JsonSchemaValueException("" + (name_prefix or "data") + " must contain " + (str(sorted(data__missing_keys)) + " properties"), value=data, name="" + (name_prefix or "data") + "", definition={'required': ['version'], '$$description': ['version should be statically defined in the ``version`` field']}, rule='required')
+    try:
+        data_is_dict = isinstance(data, dict)
+        if data_is_dict:
+            data__missing_keys = set(['license-files']) - data.keys()
+            if data__missing_keys:
+                raise JsonSchemaValueException("" + (name_prefix or "data") + " must contain " + (str(sorted(data__missing_keys)) + " properties"), value=data, name="" + (name_prefix or "data") + "", definition={'required': ['license-files']}, rule='required')
+    except JsonSchemaValueException:
+        pass
+    else:
+        data_is_dict = isinstance(data, dict)
+        if data_is_dict:
+            data_keys = set(data.keys())
+            if "license" in data_keys:
+                data_keys.remove("license")
+                data__license = data["license"]
+                if not isinstance(data__license, (str)):
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".license must be string", value=data__license, name="" + (name_prefix or "data") + ".license", definition={'type': 'string'}, rule='type')
+    data_is_dict = isinstance(data, dict)
+    if data_is_dict:
+        data__missing_keys = set(['name']) - data.keys()
+        if data__missing_keys:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must contain " + (str(sorted(data__missing_keys)) + " properties"), value=data, name="" + (name_prefix or "data") + "", definition={'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://packaging.python.org/en/latest/specifications/pyproject-toml/', 'title': 'Package metadata stored in the ``project`` table', '$$description': ['Data structure for the **project** table inside ``pyproject.toml``', '(as initially defined in :pep:`621`)'], 'type': 'object', 'properties': {'name': {'type': 'string', 'description': 'The name (primary identifier) of the project. MUST be statically defined.', 'format': 'pep508-identifier'}, 'version': {'type': 'string', 'description': 'The version of the project as supported by :pep:`440`.', 'format': 'pep440'}, 'description': {'type': 'string', '$$description': ['The `summary description of the project', '<https://packaging.python.org/specifications/core-metadata/#summary>`_']}, 'readme': {'$$description': ['`Full/detailed description of the project in the form of a README', '<https://peps.python.org/pep-0621/#readme>`_', "with meaning similar to the one defined in `core metadata's Description", '<https://packaging.python.org/specifications/core-metadata/#description>`_'], 'oneOf': [{'type': 'string', '$$description': ['Relative path to a text file (UTF-8) containing the full description', 'of the project. If the file path ends in case-insensitive ``.md`` or', '``.rst`` suffixes, then the content-type is respectively', '``text/markdown`` or ``text/x-rst``']}, {'type': 'object', 'allOf': [{'anyOf': [{'properties': {'file': {'type': 'string', '$$description': ['Relative path to a text file containing the full description', 'of the project.']}}, 'required': ['file']}, {'properties': {'text': {'type': 'string', 'description': 'Full text describing the project.'}}, 'required': ['text']}]}, {'properties': {'content-type': {'type': 'string', '$$description': ['Content-type (:rfc:`1341`) of the full description', '(e.g. ``text/markdown``). The ``charset`` parameter is assumed', 'UTF-8 when not present.'], '$comment': 'TODO: add regex pattern or format?'}}, 'required': ['content-type']}]}]}, 'requires-python': {'type': 'string', 'format': 'pep508-versionspec', '$$description': ['`The Python version requirements of the project', '<https://packaging.python.org/specifications/core-metadata/#requires-python>`_.']}, 'license': {'description': '`Project license <https://peps.python.org/pep-0621/#license>`_.', 'oneOf': [{'type': 'string', 'description': 'An SPDX license identifier', 'format': 'SPDX'}, {'type': 'object', 'properties': {'file': {'type': 'string', '$$description': ['Relative path to the file (UTF-8) which contains the license for the', 'project.']}}, 'required': ['file']}, {'type': 'object', 'properties': {'text': {'type': 'string', '$$description': ['The license of the project whose meaning is that of the', '`License field from the core metadata', '<https://packaging.python.org/specifications/core-metadata/#license>`_.']}}, 'required': ['text']}]}, 'license-files': {'description': 'Paths or globs to paths of license files', 'type': 'array', 'items': {'type': 'string'}}, 'authors': {'type': 'array', 'items': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, '$$description': ["The people or organizations considered to be the 'authors' of the project.", 'The exact meaning is open to interpretation (e.g. original or primary authors,', 'current maintainers, or owners of the package).']}, 'maintainers': {'type': 'array', 'items': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, '$$description': ["The people or organizations considered to be the 'maintainers' of the project.", 'Similarly to ``authors``, the exact meaning is open to interpretation.']}, 'keywords': {'type': 'array', 'items': {'type': 'string'}, 'description': 'List of keywords to assist searching for the distribution in a larger catalog.'}, 'classifiers': {'type': 'array', 'items': {'type': 'string', 'format': 'trove-classifier', 'description': '`PyPI classifier <https://pypi.org/classifiers/>`_.'}, '$$description': ['`Trove classifiers <https://pypi.org/classifiers/>`_', 'which apply to the project.']}, 'urls': {'type': 'object', 'description': 'URLs associated with the project in the form ``label => value``.', 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', 'format': 'url'}}}, 'scripts': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, 'gui-scripts': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, 'entry-points': {'$$description': ['Instruct the installer to expose the given modules/functions via', '``entry-point`` discovery mechanism (useful for plugins).', 'More information available in the `Python packaging guide', '<https://packaging.python.org/specifications/entry-points/>`_.'], 'propertyNames': {'format': 'python-entrypoint-group'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}}}, 'dependencies': {'type': 'array', 'description': 'Project (mandatory) dependencies.', 'items': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}, 'optional-dependencies': {'type': 'object', 'description': 'Optional dependency for the project', 'propertyNames': {'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'array', 'items': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}}}, 'dynamic': {'type': 'array', '$$description': ['Specifies which fields are intentionally unspecified and expected to be', 'dynamically provided by build tools'], 'items': {'enum': ['version', 'description', 'readme', 'requires-python', 'license', 'license-files', 'authors', 'maintainers', 'keywords', 'classifiers', 'urls', 'scripts', 'gui-scripts', 'entry-points', 'dependencies', 'optional-dependencies']}}}, 'required': ['name'], 'additionalProperties': False, 'allOf': [{'if': {'not': {'required': ['dynamic'], 'properties': {'dynamic': {'contains': {'const': 'version'}, '$$description': ['version is listed in ``dynamic``']}}}, '$$comment': ['According to :pep:`621`:', '    If the core metadata specification lists a field as "Required", then', '    the metadata MUST specify the field statically or list it in dynamic', 'In turn, `core metadata`_ defines:', '    The required fields are: Metadata-Version, Name, Version.', '    All the other fields are optional.', 'Since ``Metadata-Version`` is defined by the build back-end, ``name`` and', '``version`` are the only mandatory information in ``pyproject.toml``.', '.. _core metadata: https://packaging.python.org/specifications/core-metadata/']}, 'then': {'required': ['version'], '$$description': ['version should be statically defined in the ``version`` field']}}, {'if': {'required': ['license-files']}, 'then': {'properties': {'license': {'type': 'string'}}}}], 'definitions': {'author': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, 'entry-point-group': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, 'dependency': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}}, rule='required')
+        data_keys = set(data.keys())
+        if "name" in data_keys:
+            data_keys.remove("name")
+            data__name = data["name"]
+            if not isinstance(data__name, (str)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".name must be string", value=data__name, name="" + (name_prefix or "data") + ".name", definition={'type': 'string', 'description': 'The name (primary identifier) of the project. MUST be statically defined.', 'format': 'pep508-identifier'}, rule='type')
+            if isinstance(data__name, str):
+                if not custom_formats["pep508-identifier"](data__name):
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".name must be pep508-identifier", value=data__name, name="" + (name_prefix or "data") + ".name", definition={'type': 'string', 'description': 'The name (primary identifier) of the project. MUST be statically defined.', 'format': 'pep508-identifier'}, rule='format')
+        if "version" in data_keys:
+            data_keys.remove("version")
+            data__version = data["version"]
+            if not isinstance(data__version, (str)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".version must be string", value=data__version, name="" + (name_prefix or "data") + ".version", definition={'type': 'string', 'description': 'The version of the project as supported by :pep:`440`.', 'format': 'pep440'}, rule='type')
+            if isinstance(data__version, str):
+                if not custom_formats["pep440"](data__version):
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".version must be pep440", value=data__version, name="" + (name_prefix or "data") + ".version", definition={'type': 'string', 'description': 'The version of the project as supported by :pep:`440`.', 'format': 'pep440'}, rule='format')
+        if "description" in data_keys:
+            data_keys.remove("description")
+            data__description = data["description"]
+            if not isinstance(data__description, (str)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".description must be string", value=data__description, name="" + (name_prefix or "data") + ".description", definition={'type': 'string', '$$description': ['The `summary description of the project', '<https://packaging.python.org/specifications/core-metadata/#summary>`_']}, rule='type')
+        if "readme" in data_keys:
+            data_keys.remove("readme")
+            data__readme = data["readme"]
+            data__readme_one_of_count12 = 0
+            if data__readme_one_of_count12 < 2:
+                try:
+                    if not isinstance(data__readme, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".readme must be string", value=data__readme, name="" + (name_prefix or "data") + ".readme", definition={'type': 'string', '$$description': ['Relative path to a text file (UTF-8) containing the full description', 'of the project. If the file path ends in case-insensitive ``.md`` or', '``.rst`` suffixes, then the content-type is respectively', '``text/markdown`` or ``text/x-rst``']}, rule='type')
+                    data__readme_one_of_count12 += 1
+                except JsonSchemaValueException: pass
+            if data__readme_one_of_count12 < 2:
+                try:
+                    if not isinstance(data__readme, (dict)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".readme must be object", value=data__readme, name="" + (name_prefix or "data") + ".readme", definition={'type': 'object', 'allOf': [{'anyOf': [{'properties': {'file': {'type': 'string', '$$description': ['Relative path to a text file containing the full description', 'of the project.']}}, 'required': ['file']}, {'properties': {'text': {'type': 'string', 'description': 'Full text describing the project.'}}, 'required': ['text']}]}, {'properties': {'content-type': {'type': 'string', '$$description': ['Content-type (:rfc:`1341`) of the full description', '(e.g. ``text/markdown``). The ``charset`` parameter is assumed', 'UTF-8 when not present.'], '$comment': 'TODO: add regex pattern or format?'}}, 'required': ['content-type']}]}, rule='type')
+                    data__readme_any_of_count13 = 0
+                    if not data__readme_any_of_count13:
+                        try:
+                            data__readme_is_dict = isinstance(data__readme, dict)
+                            if data__readme_is_dict:
+                                data__readme__missing_keys = set(['file']) - data__readme.keys()
+                                if data__readme__missing_keys:
+                                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".readme must contain " + (str(sorted(data__readme__missing_keys)) + " properties"), value=data__readme, name="" + (name_prefix or "data") + ".readme", definition={'properties': {'file': {'type': 'string', '$$description': ['Relative path to a text file containing the full description', 'of the project.']}}, 'required': ['file']}, rule='required')
+                                data__readme_keys = set(data__readme.keys())
+                                if "file" in data__readme_keys:
+                                    data__readme_keys.remove("file")
+                                    data__readme__file = data__readme["file"]
+                                    if not isinstance(data__readme__file, (str)):
+                                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".readme.file must be string", value=data__readme__file, name="" + (name_prefix or "data") + ".readme.file", definition={'type': 'string', '$$description': ['Relative path to a text file containing the full description', 'of the project.']}, rule='type')
+                            data__readme_any_of_count13 += 1
+                        except JsonSchemaValueException: pass
+                    if not data__readme_any_of_count13:
+                        try:
+                            data__readme_is_dict = isinstance(data__readme, dict)
+                            if data__readme_is_dict:
+                                data__readme__missing_keys = set(['text']) - data__readme.keys()
+                                if data__readme__missing_keys:
+                                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".readme must contain " + (str(sorted(data__readme__missing_keys)) + " properties"), value=data__readme, name="" + (name_prefix or "data") + ".readme", definition={'properties': {'text': {'type': 'string', 'description': 'Full text describing the project.'}}, 'required': ['text']}, rule='required')
+                                data__readme_keys = set(data__readme.keys())
+                                if "text" in data__readme_keys:
+                                    data__readme_keys.remove("text")
+                                    data__readme__text = data__readme["text"]
+                                    if not isinstance(data__readme__text, (str)):
+                                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".readme.text must be string", value=data__readme__text, name="" + (name_prefix or "data") + ".readme.text", definition={'type': 'string', 'description': 'Full text describing the project.'}, rule='type')
+                            data__readme_any_of_count13 += 1
+                        except JsonSchemaValueException: pass
+                    if not data__readme_any_of_count13:
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".readme cannot be validated by any definition", value=data__readme, name="" + (name_prefix or "data") + ".readme", definition={'anyOf': [{'properties': {'file': {'type': 'string', '$$description': ['Relative path to a text file containing the full description', 'of the project.']}}, 'required': ['file']}, {'properties': {'text': {'type': 'string', 'description': 'Full text describing the project.'}}, 'required': ['text']}]}, rule='anyOf')
+                    data__readme_is_dict = isinstance(data__readme, dict)
+                    if data__readme_is_dict:
+                        data__readme__missing_keys = set(['content-type']) - data__readme.keys()
+                        if data__readme__missing_keys:
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".readme must contain " + (str(sorted(data__readme__missing_keys)) + " properties"), value=data__readme, name="" + (name_prefix or "data") + ".readme", definition={'properties': {'content-type': {'type': 'string', '$$description': ['Content-type (:rfc:`1341`) of the full description', '(e.g. ``text/markdown``). The ``charset`` parameter is assumed', 'UTF-8 when not present.'], '$comment': 'TODO: add regex pattern or format?'}}, 'required': ['content-type']}, rule='required')
+                        data__readme_keys = set(data__readme.keys())
+                        if "content-type" in data__readme_keys:
+                            data__readme_keys.remove("content-type")
+                            data__readme__contenttype = data__readme["content-type"]
+                            if not isinstance(data__readme__contenttype, (str)):
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".readme.content-type must be string", value=data__readme__contenttype, name="" + (name_prefix or "data") + ".readme.content-type", definition={'type': 'string', '$$description': ['Content-type (:rfc:`1341`) of the full description', '(e.g. ``text/markdown``). The ``charset`` parameter is assumed', 'UTF-8 when not present.'], '$comment': 'TODO: add regex pattern or format?'}, rule='type')
+                    data__readme_one_of_count12 += 1
+                except JsonSchemaValueException: pass
+            if data__readme_one_of_count12 != 1:
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".readme must be valid exactly by one definition" + (" (" + str(data__readme_one_of_count12) + " matches found)"), value=data__readme, name="" + (name_prefix or "data") + ".readme", definition={'$$description': ['`Full/detailed description of the project in the form of a README', '<https://peps.python.org/pep-0621/#readme>`_', "with meaning similar to the one defined in `core metadata's Description", '<https://packaging.python.org/specifications/core-metadata/#description>`_'], 'oneOf': [{'type': 'string', '$$description': ['Relative path to a text file (UTF-8) containing the full description', 'of the project. If the file path ends in case-insensitive ``.md`` or', '``.rst`` suffixes, then the content-type is respectively', '``text/markdown`` or ``text/x-rst``']}, {'type': 'object', 'allOf': [{'anyOf': [{'properties': {'file': {'type': 'string', '$$description': ['Relative path to a text file containing the full description', 'of the project.']}}, 'required': ['file']}, {'properties': {'text': {'type': 'string', 'description': 'Full text describing the project.'}}, 'required': ['text']}]}, {'properties': {'content-type': {'type': 'string', '$$description': ['Content-type (:rfc:`1341`) of the full description', '(e.g. ``text/markdown``). The ``charset`` parameter is assumed', 'UTF-8 when not present.'], '$comment': 'TODO: add regex pattern or format?'}}, 'required': ['content-type']}]}]}, rule='oneOf')
+        if "requires-python" in data_keys:
+            data_keys.remove("requires-python")
+            data__requirespython = data["requires-python"]
+            if not isinstance(data__requirespython, (str)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".requires-python must be string", value=data__requirespython, name="" + (name_prefix or "data") + ".requires-python", definition={'type': 'string', 'format': 'pep508-versionspec', '$$description': ['`The Python version requirements of the project', '<https://packaging.python.org/specifications/core-metadata/#requires-python>`_.']}, rule='type')
+            if isinstance(data__requirespython, str):
+                if not custom_formats["pep508-versionspec"](data__requirespython):
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".requires-python must be pep508-versionspec", value=data__requirespython, name="" + (name_prefix or "data") + ".requires-python", definition={'type': 'string', 'format': 'pep508-versionspec', '$$description': ['`The Python version requirements of the project', '<https://packaging.python.org/specifications/core-metadata/#requires-python>`_.']}, rule='format')
+        if "license" in data_keys:
+            data_keys.remove("license")
+            data__license = data["license"]
+            data__license_one_of_count14 = 0
+            if data__license_one_of_count14 < 2:
+                try:
+                    if not isinstance(data__license, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".license must be string", value=data__license, name="" + (name_prefix or "data") + ".license", definition={'type': 'string', 'description': 'An SPDX license identifier', 'format': 'SPDX'}, rule='type')
+                    if isinstance(data__license, str):
+                        if not custom_formats["SPDX"](data__license):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".license must be SPDX", value=data__license, name="" + (name_prefix or "data") + ".license", definition={'type': 'string', 'description': 'An SPDX license identifier', 'format': 'SPDX'}, rule='format')
+                    data__license_one_of_count14 += 1
+                except JsonSchemaValueException: pass
+            if data__license_one_of_count14 < 2:
+                try:
+                    if not isinstance(data__license, (dict)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".license must be object", value=data__license, name="" + (name_prefix or "data") + ".license", definition={'type': 'object', 'properties': {'file': {'type': 'string', '$$description': ['Relative path to the file (UTF-8) which contains the license for the', 'project.']}}, 'required': ['file']}, rule='type')
+                    data__license_is_dict = isinstance(data__license, dict)
+                    if data__license_is_dict:
+                        data__license__missing_keys = set(['file']) - data__license.keys()
+                        if data__license__missing_keys:
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".license must contain " + (str(sorted(data__license__missing_keys)) + " properties"), value=data__license, name="" + (name_prefix or "data") + ".license", definition={'type': 'object', 'properties': {'file': {'type': 'string', '$$description': ['Relative path to the file (UTF-8) which contains the license for the', 'project.']}}, 'required': ['file']}, rule='required')
+                        data__license_keys = set(data__license.keys())
+                        if "file" in data__license_keys:
+                            data__license_keys.remove("file")
+                            data__license__file = data__license["file"]
+                            if not isinstance(data__license__file, (str)):
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".license.file must be string", value=data__license__file, name="" + (name_prefix or "data") + ".license.file", definition={'type': 'string', '$$description': ['Relative path to the file (UTF-8) which contains the license for the', 'project.']}, rule='type')
+                    data__license_one_of_count14 += 1
+                except JsonSchemaValueException: pass
+            if data__license_one_of_count14 < 2:
+                try:
+                    if not isinstance(data__license, (dict)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".license must be object", value=data__license, name="" + (name_prefix or "data") + ".license", definition={'type': 'object', 'properties': {'text': {'type': 'string', '$$description': ['The license of the project whose meaning is that of the', '`License field from the core metadata', '<https://packaging.python.org/specifications/core-metadata/#license>`_.']}}, 'required': ['text']}, rule='type')
+                    data__license_is_dict = isinstance(data__license, dict)
+                    if data__license_is_dict:
+                        data__license__missing_keys = set(['text']) - data__license.keys()
+                        if data__license__missing_keys:
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".license must contain " + (str(sorted(data__license__missing_keys)) + " properties"), value=data__license, name="" + (name_prefix or "data") + ".license", definition={'type': 'object', 'properties': {'text': {'type': 'string', '$$description': ['The license of the project whose meaning is that of the', '`License field from the core metadata', '<https://packaging.python.org/specifications/core-metadata/#license>`_.']}}, 'required': ['text']}, rule='required')
+                        data__license_keys = set(data__license.keys())
+                        if "text" in data__license_keys:
+                            data__license_keys.remove("text")
+                            data__license__text = data__license["text"]
+                            if not isinstance(data__license__text, (str)):
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".license.text must be string", value=data__license__text, name="" + (name_prefix or "data") + ".license.text", definition={'type': 'string', '$$description': ['The license of the project whose meaning is that of the', '`License field from the core metadata', '<https://packaging.python.org/specifications/core-metadata/#license>`_.']}, rule='type')
+                    data__license_one_of_count14 += 1
+                except JsonSchemaValueException: pass
+            if data__license_one_of_count14 != 1:
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".license must be valid exactly by one definition" + (" (" + str(data__license_one_of_count14) + " matches found)"), value=data__license, name="" + (name_prefix or "data") + ".license", definition={'description': '`Project license <https://peps.python.org/pep-0621/#license>`_.', 'oneOf': [{'type': 'string', 'description': 'An SPDX license identifier', 'format': 'SPDX'}, {'type': 'object', 'properties': {'file': {'type': 'string', '$$description': ['Relative path to the file (UTF-8) which contains the license for the', 'project.']}}, 'required': ['file']}, {'type': 'object', 'properties': {'text': {'type': 'string', '$$description': ['The license of the project whose meaning is that of the', '`License field from the core metadata', '<https://packaging.python.org/specifications/core-metadata/#license>`_.']}}, 'required': ['text']}]}, rule='oneOf')
+        if "license-files" in data_keys:
+            data_keys.remove("license-files")
+            data__licensefiles = data["license-files"]
+            if not isinstance(data__licensefiles, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".license-files must be array", value=data__licensefiles, name="" + (name_prefix or "data") + ".license-files", definition={'description': 'Paths or globs to paths of license files', 'type': 'array', 'items': {'type': 'string'}}, rule='type')
+            data__licensefiles_is_list = isinstance(data__licensefiles, (list, tuple))
+            if data__licensefiles_is_list:
+                data__licensefiles_len = len(data__licensefiles)
+                for data__licensefiles_x, data__licensefiles_item in enumerate(data__licensefiles):
+                    if not isinstance(data__licensefiles_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".license-files[{data__licensefiles_x}]".format(**locals()) + " must be string", value=data__licensefiles_item, name="" + (name_prefix or "data") + ".license-files[{data__licensefiles_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "authors" in data_keys:
+            data_keys.remove("authors")
+            data__authors = data["authors"]
+            if not isinstance(data__authors, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".authors must be array", value=data__authors, name="" + (name_prefix or "data") + ".authors", definition={'type': 'array', 'items': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, '$$description': ["The people or organizations considered to be the 'authors' of the project.", 'The exact meaning is open to interpretation (e.g. original or primary authors,', 'current maintainers, or owners of the package).']}, rule='type')
+            data__authors_is_list = isinstance(data__authors, (list, tuple))
+            if data__authors_is_list:
+                data__authors_len = len(data__authors)
+                for data__authors_x, data__authors_item in enumerate(data__authors):
+                    validate_https___packaging_python_org_en_latest_specifications_pyproject_toml___definitions_author(data__authors_item, custom_formats, (name_prefix or "data") + ".authors[{data__authors_x}]".format(**locals()))
+        if "maintainers" in data_keys:
+            data_keys.remove("maintainers")
+            data__maintainers = data["maintainers"]
+            if not isinstance(data__maintainers, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".maintainers must be array", value=data__maintainers, name="" + (name_prefix or "data") + ".maintainers", definition={'type': 'array', 'items': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, '$$description': ["The people or organizations considered to be the 'maintainers' of the project.", 'Similarly to ``authors``, the exact meaning is open to interpretation.']}, rule='type')
+            data__maintainers_is_list = isinstance(data__maintainers, (list, tuple))
+            if data__maintainers_is_list:
+                data__maintainers_len = len(data__maintainers)
+                for data__maintainers_x, data__maintainers_item in enumerate(data__maintainers):
+                    validate_https___packaging_python_org_en_latest_specifications_pyproject_toml___definitions_author(data__maintainers_item, custom_formats, (name_prefix or "data") + ".maintainers[{data__maintainers_x}]".format(**locals()))
+        if "keywords" in data_keys:
+            data_keys.remove("keywords")
+            data__keywords = data["keywords"]
+            if not isinstance(data__keywords, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".keywords must be array", value=data__keywords, name="" + (name_prefix or "data") + ".keywords", definition={'type': 'array', 'items': {'type': 'string'}, 'description': 'List of keywords to assist searching for the distribution in a larger catalog.'}, rule='type')
+            data__keywords_is_list = isinstance(data__keywords, (list, tuple))
+            if data__keywords_is_list:
+                data__keywords_len = len(data__keywords)
+                for data__keywords_x, data__keywords_item in enumerate(data__keywords):
+                    if not isinstance(data__keywords_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".keywords[{data__keywords_x}]".format(**locals()) + " must be string", value=data__keywords_item, name="" + (name_prefix or "data") + ".keywords[{data__keywords_x}]".format(**locals()) + "", definition={'type': 'string'}, rule='type')
+        if "classifiers" in data_keys:
+            data_keys.remove("classifiers")
+            data__classifiers = data["classifiers"]
+            if not isinstance(data__classifiers, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".classifiers must be array", value=data__classifiers, name="" + (name_prefix or "data") + ".classifiers", definition={'type': 'array', 'items': {'type': 'string', 'format': 'trove-classifier', 'description': '`PyPI classifier <https://pypi.org/classifiers/>`_.'}, '$$description': ['`Trove classifiers <https://pypi.org/classifiers/>`_', 'which apply to the project.']}, rule='type')
+            data__classifiers_is_list = isinstance(data__classifiers, (list, tuple))
+            if data__classifiers_is_list:
+                data__classifiers_len = len(data__classifiers)
+                for data__classifiers_x, data__classifiers_item in enumerate(data__classifiers):
+                    if not isinstance(data__classifiers_item, (str)):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".classifiers[{data__classifiers_x}]".format(**locals()) + " must be string", value=data__classifiers_item, name="" + (name_prefix or "data") + ".classifiers[{data__classifiers_x}]".format(**locals()) + "", definition={'type': 'string', 'format': 'trove-classifier', 'description': '`PyPI classifier <https://pypi.org/classifiers/>`_.'}, rule='type')
+                    if isinstance(data__classifiers_item, str):
+                        if not custom_formats["trove-classifier"](data__classifiers_item):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".classifiers[{data__classifiers_x}]".format(**locals()) + " must be trove-classifier", value=data__classifiers_item, name="" + (name_prefix or "data") + ".classifiers[{data__classifiers_x}]".format(**locals()) + "", definition={'type': 'string', 'format': 'trove-classifier', 'description': '`PyPI classifier <https://pypi.org/classifiers/>`_.'}, rule='format')
+        if "urls" in data_keys:
+            data_keys.remove("urls")
+            data__urls = data["urls"]
+            if not isinstance(data__urls, (dict)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".urls must be object", value=data__urls, name="" + (name_prefix or "data") + ".urls", definition={'type': 'object', 'description': 'URLs associated with the project in the form ``label => value``.', 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', 'format': 'url'}}}, rule='type')
+            data__urls_is_dict = isinstance(data__urls, dict)
+            if data__urls_is_dict:
+                data__urls_keys = set(data__urls.keys())
+                for data__urls_key, data__urls_val in data__urls.items():
+                    if REGEX_PATTERNS['^.+$'].search(data__urls_key):
+                        if data__urls_key in data__urls_keys:
+                            data__urls_keys.remove(data__urls_key)
+                        if not isinstance(data__urls_val, (str)):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".urls.{data__urls_key}".format(**locals()) + " must be string", value=data__urls_val, name="" + (name_prefix or "data") + ".urls.{data__urls_key}".format(**locals()) + "", definition={'type': 'string', 'format': 'url'}, rule='type')
+                        if isinstance(data__urls_val, str):
+                            if not custom_formats["url"](data__urls_val):
+                                raise JsonSchemaValueException("" + (name_prefix or "data") + ".urls.{data__urls_key}".format(**locals()) + " must be url", value=data__urls_val, name="" + (name_prefix or "data") + ".urls.{data__urls_key}".format(**locals()) + "", definition={'type': 'string', 'format': 'url'}, rule='format')
+                if data__urls_keys:
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".urls must not contain "+str(data__urls_keys)+" properties", value=data__urls, name="" + (name_prefix or "data") + ".urls", definition={'type': 'object', 'description': 'URLs associated with the project in the form ``label => value``.', 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', 'format': 'url'}}}, rule='additionalProperties')
+        if "scripts" in data_keys:
+            data_keys.remove("scripts")
+            data__scripts = data["scripts"]
+            validate_https___packaging_python_org_en_latest_specifications_pyproject_toml___definitions_entry_point_group(data__scripts, custom_formats, (name_prefix or "data") + ".scripts")
+        if "gui-scripts" in data_keys:
+            data_keys.remove("gui-scripts")
+            data__guiscripts = data["gui-scripts"]
+            validate_https___packaging_python_org_en_latest_specifications_pyproject_toml___definitions_entry_point_group(data__guiscripts, custom_formats, (name_prefix or "data") + ".gui-scripts")
+        if "entry-points" in data_keys:
+            data_keys.remove("entry-points")
+            data__entrypoints = data["entry-points"]
+            data__entrypoints_is_dict = isinstance(data__entrypoints, dict)
+            if data__entrypoints_is_dict:
+                data__entrypoints_keys = set(data__entrypoints.keys())
+                for data__entrypoints_key, data__entrypoints_val in data__entrypoints.items():
+                    if REGEX_PATTERNS['^.+$'].search(data__entrypoints_key):
+                        if data__entrypoints_key in data__entrypoints_keys:
+                            data__entrypoints_keys.remove(data__entrypoints_key)
+                        validate_https___packaging_python_org_en_latest_specifications_pyproject_toml___definitions_entry_point_group(data__entrypoints_val, custom_formats, (name_prefix or "data") + ".entry-points.{data__entrypoints_key}".format(**locals()))
+                if data__entrypoints_keys:
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".entry-points must not contain "+str(data__entrypoints_keys)+" properties", value=data__entrypoints, name="" + (name_prefix or "data") + ".entry-points", definition={'$$description': ['Instruct the installer to expose the given modules/functions via', '``entry-point`` discovery mechanism (useful for plugins).', 'More information available in the `Python packaging guide', '<https://packaging.python.org/specifications/entry-points/>`_.'], 'propertyNames': {'format': 'python-entrypoint-group'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}}}, rule='additionalProperties')
+                data__entrypoints_len = len(data__entrypoints)
+                if data__entrypoints_len != 0:
+                    data__entrypoints_property_names = True
+                    for data__entrypoints_key in data__entrypoints:
+                        try:
+                            if isinstance(data__entrypoints_key, str):
+                                if not custom_formats["python-entrypoint-group"](data__entrypoints_key):
+                                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".entry-points must be python-entrypoint-group", value=data__entrypoints_key, name="" + (name_prefix or "data") + ".entry-points", definition={'format': 'python-entrypoint-group'}, rule='format')
+                        except JsonSchemaValueException:
+                            data__entrypoints_property_names = False
+                    if not data__entrypoints_property_names:
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".entry-points must be named by propertyName definition", value=data__entrypoints, name="" + (name_prefix or "data") + ".entry-points", definition={'$$description': ['Instruct the installer to expose the given modules/functions via', '``entry-point`` discovery mechanism (useful for plugins).', 'More information available in the `Python packaging guide', '<https://packaging.python.org/specifications/entry-points/>`_.'], 'propertyNames': {'format': 'python-entrypoint-group'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}}}, rule='propertyNames')
+        if "dependencies" in data_keys:
+            data_keys.remove("dependencies")
+            data__dependencies = data["dependencies"]
+            if not isinstance(data__dependencies, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".dependencies must be array", value=data__dependencies, name="" + (name_prefix or "data") + ".dependencies", definition={'type': 'array', 'description': 'Project (mandatory) dependencies.', 'items': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}, rule='type')
+            data__dependencies_is_list = isinstance(data__dependencies, (list, tuple))
+            if data__dependencies_is_list:
+                data__dependencies_len = len(data__dependencies)
+                for data__dependencies_x, data__dependencies_item in enumerate(data__dependencies):
+                    validate_https___packaging_python_org_en_latest_specifications_pyproject_toml___definitions_dependency(data__dependencies_item, custom_formats, (name_prefix or "data") + ".dependencies[{data__dependencies_x}]".format(**locals()))
+        if "optional-dependencies" in data_keys:
+            data_keys.remove("optional-dependencies")
+            data__optionaldependencies = data["optional-dependencies"]
+            if not isinstance(data__optionaldependencies, (dict)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".optional-dependencies must be object", value=data__optionaldependencies, name="" + (name_prefix or "data") + ".optional-dependencies", definition={'type': 'object', 'description': 'Optional dependency for the project', 'propertyNames': {'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'array', 'items': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}}}, rule='type')
+            data__optionaldependencies_is_dict = isinstance(data__optionaldependencies, dict)
+            if data__optionaldependencies_is_dict:
+                data__optionaldependencies_keys = set(data__optionaldependencies.keys())
+                for data__optionaldependencies_key, data__optionaldependencies_val in data__optionaldependencies.items():
+                    if REGEX_PATTERNS['^.+$'].search(data__optionaldependencies_key):
+                        if data__optionaldependencies_key in data__optionaldependencies_keys:
+                            data__optionaldependencies_keys.remove(data__optionaldependencies_key)
+                        if not isinstance(data__optionaldependencies_val, (list, tuple)):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + ".optional-dependencies.{data__optionaldependencies_key}".format(**locals()) + " must be array", value=data__optionaldependencies_val, name="" + (name_prefix or "data") + ".optional-dependencies.{data__optionaldependencies_key}".format(**locals()) + "", definition={'type': 'array', 'items': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}, rule='type')
+                        data__optionaldependencies_val_is_list = isinstance(data__optionaldependencies_val, (list, tuple))
+                        if data__optionaldependencies_val_is_list:
+                            data__optionaldependencies_val_len = len(data__optionaldependencies_val)
+                            for data__optionaldependencies_val_x, data__optionaldependencies_val_item in enumerate(data__optionaldependencies_val):
+                                validate_https___packaging_python_org_en_latest_specifications_pyproject_toml___definitions_dependency(data__optionaldependencies_val_item, custom_formats, (name_prefix or "data") + ".optional-dependencies.{data__optionaldependencies_key}[{data__optionaldependencies_val_x}]".format(**locals()))
+                if data__optionaldependencies_keys:
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".optional-dependencies must not contain "+str(data__optionaldependencies_keys)+" properties", value=data__optionaldependencies, name="" + (name_prefix or "data") + ".optional-dependencies", definition={'type': 'object', 'description': 'Optional dependency for the project', 'propertyNames': {'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'array', 'items': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}}}, rule='additionalProperties')
+                data__optionaldependencies_len = len(data__optionaldependencies)
+                if data__optionaldependencies_len != 0:
+                    data__optionaldependencies_property_names = True
+                    for data__optionaldependencies_key in data__optionaldependencies:
+                        try:
+                            if isinstance(data__optionaldependencies_key, str):
+                                if not custom_formats["pep508-identifier"](data__optionaldependencies_key):
+                                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".optional-dependencies must be pep508-identifier", value=data__optionaldependencies_key, name="" + (name_prefix or "data") + ".optional-dependencies", definition={'format': 'pep508-identifier'}, rule='format')
+                        except JsonSchemaValueException:
+                            data__optionaldependencies_property_names = False
+                    if not data__optionaldependencies_property_names:
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".optional-dependencies must be named by propertyName definition", value=data__optionaldependencies, name="" + (name_prefix or "data") + ".optional-dependencies", definition={'type': 'object', 'description': 'Optional dependency for the project', 'propertyNames': {'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'array', 'items': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}}}, rule='propertyNames')
+        if "dynamic" in data_keys:
+            data_keys.remove("dynamic")
+            data__dynamic = data["dynamic"]
+            if not isinstance(data__dynamic, (list, tuple)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic must be array", value=data__dynamic, name="" + (name_prefix or "data") + ".dynamic", definition={'type': 'array', '$$description': ['Specifies which fields are intentionally unspecified and expected to be', 'dynamically provided by build tools'], 'items': {'enum': ['version', 'description', 'readme', 'requires-python', 'license', 'license-files', 'authors', 'maintainers', 'keywords', 'classifiers', 'urls', 'scripts', 'gui-scripts', 'entry-points', 'dependencies', 'optional-dependencies']}}, rule='type')
+            data__dynamic_is_list = isinstance(data__dynamic, (list, tuple))
+            if data__dynamic_is_list:
+                data__dynamic_len = len(data__dynamic)
+                for data__dynamic_x, data__dynamic_item in enumerate(data__dynamic):
+                    if data__dynamic_item not in ['version', 'description', 'readme', 'requires-python', 'license', 'license-files', 'authors', 'maintainers', 'keywords', 'classifiers', 'urls', 'scripts', 'gui-scripts', 'entry-points', 'dependencies', 'optional-dependencies']:
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".dynamic[{data__dynamic_x}]".format(**locals()) + " must be one of ['version', 'description', 'readme', 'requires-python', 'license', 'license-files', 'authors', 'maintainers', 'keywords', 'classifiers', 'urls', 'scripts', 'gui-scripts', 'entry-points', 'dependencies', 'optional-dependencies']", value=data__dynamic_item, name="" + (name_prefix or "data") + ".dynamic[{data__dynamic_x}]".format(**locals()) + "", definition={'enum': ['version', 'description', 'readme', 'requires-python', 'license', 'license-files', 'authors', 'maintainers', 'keywords', 'classifiers', 'urls', 'scripts', 'gui-scripts', 'entry-points', 'dependencies', 'optional-dependencies']}, rule='enum')
+        if data_keys:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must not contain "+str(data_keys)+" properties", value=data, name="" + (name_prefix or "data") + "", definition={'$schema': 'http://json-schema.org/draft-07/schema#', '$id': 'https://packaging.python.org/en/latest/specifications/pyproject-toml/', 'title': 'Package metadata stored in the ``project`` table', '$$description': ['Data structure for the **project** table inside ``pyproject.toml``', '(as initially defined in :pep:`621`)'], 'type': 'object', 'properties': {'name': {'type': 'string', 'description': 'The name (primary identifier) of the project. MUST be statically defined.', 'format': 'pep508-identifier'}, 'version': {'type': 'string', 'description': 'The version of the project as supported by :pep:`440`.', 'format': 'pep440'}, 'description': {'type': 'string', '$$description': ['The `summary description of the project', '<https://packaging.python.org/specifications/core-metadata/#summary>`_']}, 'readme': {'$$description': ['`Full/detailed description of the project in the form of a README', '<https://peps.python.org/pep-0621/#readme>`_', "with meaning similar to the one defined in `core metadata's Description", '<https://packaging.python.org/specifications/core-metadata/#description>`_'], 'oneOf': [{'type': 'string', '$$description': ['Relative path to a text file (UTF-8) containing the full description', 'of the project. If the file path ends in case-insensitive ``.md`` or', '``.rst`` suffixes, then the content-type is respectively', '``text/markdown`` or ``text/x-rst``']}, {'type': 'object', 'allOf': [{'anyOf': [{'properties': {'file': {'type': 'string', '$$description': ['Relative path to a text file containing the full description', 'of the project.']}}, 'required': ['file']}, {'properties': {'text': {'type': 'string', 'description': 'Full text describing the project.'}}, 'required': ['text']}]}, {'properties': {'content-type': {'type': 'string', '$$description': ['Content-type (:rfc:`1341`) of the full description', '(e.g. ``text/markdown``). The ``charset`` parameter is assumed', 'UTF-8 when not present.'], '$comment': 'TODO: add regex pattern or format?'}}, 'required': ['content-type']}]}]}, 'requires-python': {'type': 'string', 'format': 'pep508-versionspec', '$$description': ['`The Python version requirements of the project', '<https://packaging.python.org/specifications/core-metadata/#requires-python>`_.']}, 'license': {'description': '`Project license <https://peps.python.org/pep-0621/#license>`_.', 'oneOf': [{'type': 'string', 'description': 'An SPDX license identifier', 'format': 'SPDX'}, {'type': 'object', 'properties': {'file': {'type': 'string', '$$description': ['Relative path to the file (UTF-8) which contains the license for the', 'project.']}}, 'required': ['file']}, {'type': 'object', 'properties': {'text': {'type': 'string', '$$description': ['The license of the project whose meaning is that of the', '`License field from the core metadata', '<https://packaging.python.org/specifications/core-metadata/#license>`_.']}}, 'required': ['text']}]}, 'license-files': {'description': 'Paths or globs to paths of license files', 'type': 'array', 'items': {'type': 'string'}}, 'authors': {'type': 'array', 'items': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, '$$description': ["The people or organizations considered to be the 'authors' of the project.", 'The exact meaning is open to interpretation (e.g. original or primary authors,', 'current maintainers, or owners of the package).']}, 'maintainers': {'type': 'array', 'items': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, '$$description': ["The people or organizations considered to be the 'maintainers' of the project.", 'Similarly to ``authors``, the exact meaning is open to interpretation.']}, 'keywords': {'type': 'array', 'items': {'type': 'string'}, 'description': 'List of keywords to assist searching for the distribution in a larger catalog.'}, 'classifiers': {'type': 'array', 'items': {'type': 'string', 'format': 'trove-classifier', 'description': '`PyPI classifier <https://pypi.org/classifiers/>`_.'}, '$$description': ['`Trove classifiers <https://pypi.org/classifiers/>`_', 'which apply to the project.']}, 'urls': {'type': 'object', 'description': 'URLs associated with the project in the form ``label => value``.', 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', 'format': 'url'}}}, 'scripts': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, 'gui-scripts': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, 'entry-points': {'$$description': ['Instruct the installer to expose the given modules/functions via', '``entry-point`` discovery mechanism (useful for plugins).', 'More information available in the `Python packaging guide', '<https://packaging.python.org/specifications/entry-points/>`_.'], 'propertyNames': {'format': 'python-entrypoint-group'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}}}, 'dependencies': {'type': 'array', 'description': 'Project (mandatory) dependencies.', 'items': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}, 'optional-dependencies': {'type': 'object', 'description': 'Optional dependency for the project', 'propertyNames': {'format': 'pep508-identifier'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'array', 'items': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}}}, 'dynamic': {'type': 'array', '$$description': ['Specifies which fields are intentionally unspecified and expected to be', 'dynamically provided by build tools'], 'items': {'enum': ['version', 'description', 'readme', 'requires-python', 'license', 'license-files', 'authors', 'maintainers', 'keywords', 'classifiers', 'urls', 'scripts', 'gui-scripts', 'entry-points', 'dependencies', 'optional-dependencies']}}}, 'required': ['name'], 'additionalProperties': False, 'allOf': [{'if': {'not': {'required': ['dynamic'], 'properties': {'dynamic': {'contains': {'const': 'version'}, '$$description': ['version is listed in ``dynamic``']}}}, '$$comment': ['According to :pep:`621`:', '    If the core metadata specification lists a field as "Required", then', '    the metadata MUST specify the field statically or list it in dynamic', 'In turn, `core metadata`_ defines:', '    The required fields are: Metadata-Version, Name, Version.', '    All the other fields are optional.', 'Since ``Metadata-Version`` is defined by the build back-end, ``name`` and', '``version`` are the only mandatory information in ``pyproject.toml``.', '.. _core metadata: https://packaging.python.org/specifications/core-metadata/']}, 'then': {'required': ['version'], '$$description': ['version should be statically defined in the ``version`` field']}}, {'if': {'required': ['license-files']}, 'then': {'properties': {'license': {'type': 'string'}}}}], 'definitions': {'author': {'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, 'entry-point-group': {'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, 'dependency': {'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}}}, rule='additionalProperties')
+    return data
+
+def validate_https___packaging_python_org_en_latest_specifications_pyproject_toml___definitions_dependency(data, custom_formats={}, name_prefix=None):
+    if not isinstance(data, (str)):
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " must be string", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}, rule='type')
+    if isinstance(data, str):
+        if not custom_formats["pep508"](data):
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must be pep508", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/dependency', 'title': 'Dependency', 'type': 'string', 'description': 'Project dependency specification according to PEP 508', 'format': 'pep508'}, rule='format')
+    return data
+
+def validate_https___packaging_python_org_en_latest_specifications_pyproject_toml___definitions_entry_point_group(data, custom_formats={}, name_prefix=None):
+    if not isinstance(data, (dict)):
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " must be object", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, rule='type')
+    data_is_dict = isinstance(data, dict)
+    if data_is_dict:
+        data_keys = set(data.keys())
+        for data_key, data_val in data.items():
+            if REGEX_PATTERNS['^.+$'].search(data_key):
+                if data_key in data_keys:
+                    data_keys.remove(data_key)
+                if not isinstance(data_val, (str)):
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".{data_key}".format(**locals()) + " must be string", value=data_val, name="" + (name_prefix or "data") + ".{data_key}".format(**locals()) + "", definition={'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}, rule='type')
+                if isinstance(data_val, str):
+                    if not custom_formats["python-entrypoint-reference"](data_val):
+                        raise JsonSchemaValueException("" + (name_prefix or "data") + ".{data_key}".format(**locals()) + " must be python-entrypoint-reference", value=data_val, name="" + (name_prefix or "data") + ".{data_key}".format(**locals()) + "", definition={'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}, rule='format')
+        if data_keys:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must not contain "+str(data_keys)+" properties", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, rule='additionalProperties')
+        data_len = len(data)
+        if data_len != 0:
+            data_property_names = True
+            for data_key in data:
+                try:
+                    if isinstance(data_key, str):
+                        if not custom_formats["python-entrypoint-name"](data_key):
+                            raise JsonSchemaValueException("" + (name_prefix or "data") + " must be python-entrypoint-name", value=data_key, name="" + (name_prefix or "data") + "", definition={'format': 'python-entrypoint-name'}, rule='format')
+                except JsonSchemaValueException:
+                    data_property_names = False
+            if not data_property_names:
+                raise JsonSchemaValueException("" + (name_prefix or "data") + " must be named by propertyName definition", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/entry-point-group', 'title': 'Entry-points', 'type': 'object', '$$description': ['Entry-points are grouped together to indicate what sort of capabilities they', 'provide.', 'See the `packaging guides', '<https://packaging.python.org/specifications/entry-points/>`_', 'and `setuptools docs', '<https://setuptools.pypa.io/en/latest/userguide/entry_point.html>`_', 'for more information.'], 'propertyNames': {'format': 'python-entrypoint-name'}, 'additionalProperties': False, 'patternProperties': {'^.+$': {'type': 'string', '$$description': ['Reference to a Python object. It is either in the form', '``importable.module``, or ``importable.module:object.attr``.'], 'format': 'python-entrypoint-reference', '$comment': 'https://packaging.python.org/specifications/entry-points/'}}}, rule='propertyNames')
+    return data
+
+def validate_https___packaging_python_org_en_latest_specifications_pyproject_toml___definitions_author(data, custom_formats={}, name_prefix=None):
+    if not isinstance(data, (dict)):
+        raise JsonSchemaValueException("" + (name_prefix or "data") + " must be object", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, rule='type')
+    data_is_dict = isinstance(data, dict)
+    if data_is_dict:
+        data_keys = set(data.keys())
+        if "name" in data_keys:
+            data_keys.remove("name")
+            data__name = data["name"]
+            if not isinstance(data__name, (str)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".name must be string", value=data__name, name="" + (name_prefix or "data") + ".name", definition={'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, rule='type')
+        if "email" in data_keys:
+            data_keys.remove("email")
+            data__email = data["email"]
+            if not isinstance(data__email, (str)):
+                raise JsonSchemaValueException("" + (name_prefix or "data") + ".email must be string", value=data__email, name="" + (name_prefix or "data") + ".email", definition={'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}, rule='type')
+            if isinstance(data__email, str):
+                if not REGEX_PATTERNS["idn-email_re_pattern"].match(data__email):
+                    raise JsonSchemaValueException("" + (name_prefix or "data") + ".email must be idn-email", value=data__email, name="" + (name_prefix or "data") + ".email", definition={'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}, rule='format')
+        if data_keys:
+            raise JsonSchemaValueException("" + (name_prefix or "data") + " must not contain "+str(data_keys)+" properties", value=data, name="" + (name_prefix or "data") + "", definition={'$id': '#/definitions/author', 'title': 'Author or Maintainer', '$comment': 'https://peps.python.org/pep-0621/#authors-maintainers', 'type': 'object', 'additionalProperties': False, 'properties': {'name': {'type': 'string', '$$description': ['MUST be a valid email name, i.e. whatever can be put as a name, before an', 'email, in :rfc:`822`.']}, 'email': {'type': 'string', 'format': 'idn-email', 'description': 'MUST be a valid email address'}}}, rule='additionalProperties')
+    return data
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/formats.py b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/formats.py
new file mode 100644
index 0000000000000000000000000000000000000000..1cf4a465ef0156deef5bd4d1fc02350e40ff2fe5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/_validate_pyproject/formats.py
@@ -0,0 +1,402 @@
+"""
+The functions in this module are used to validate schemas with the
+`format JSON Schema keyword
+<https://json-schema.org/understanding-json-schema/reference/string#format>`_.
+
+The correspondence is given by replacing the ``_`` character in the name of the
+function with a ``-`` to obtain the format name and vice versa.
+"""
+
+import builtins
+import logging
+import os
+import re
+import string
+import typing
+from itertools import chain as _chain
+
+if typing.TYPE_CHECKING:
+    from typing_extensions import Literal
+
+_logger = logging.getLogger(__name__)
+
+# -------------------------------------------------------------------------------------
+# PEP 440
+
+VERSION_PATTERN = r"""
+    v?
+    (?:
+        (?:(?P<epoch>[0-9]+)!)?                           # epoch
+        (?P<release>[0-9]+(?:\.[0-9]+)*)                  # release segment
+        (?P<pre>                                          # pre-release
+            [-_\.]?
+            (?P<pre_l>alpha|a|beta|b|preview|pre|c|rc)
+            [-_\.]?
+            (?P<pre_n>[0-9]+)?
+        )?
+        (?P<post>                                         # post release
+            (?:-(?P<post_n1>[0-9]+))
+            |
+            (?:
+                [-_\.]?
+                (?P<post_l>post|rev|r)
+                [-_\.]?
+                (?P<post_n2>[0-9]+)?
+            )
+        )?
+        (?P<dev>                                          # dev release
+            [-_\.]?
+            (?P<dev_l>dev)
+            [-_\.]?
+            (?P<dev_n>[0-9]+)?
+        )?
+    )
+    (?:\+(?P<local>[a-z0-9]+(?:[-_\.][a-z0-9]+)*))?       # local version
+"""
+
+VERSION_REGEX = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.X | re.I)
+
+
+def pep440(version: str) -> bool:
+    """See :ref:`PyPA's version specification <pypa:version-specifiers>`
+    (initially introduced in :pep:`440`).
+    """
+    return VERSION_REGEX.match(version) is not None
+
+
+# -------------------------------------------------------------------------------------
+# PEP 508
+
+PEP508_IDENTIFIER_PATTERN = r"([A-Z0-9]|[A-Z0-9][A-Z0-9._-]*[A-Z0-9])"
+PEP508_IDENTIFIER_REGEX = re.compile(f"^{PEP508_IDENTIFIER_PATTERN}$", re.I)
+
+
+def pep508_identifier(name: str) -> bool:
+    """See :ref:`PyPA's name specification <pypa:name-format>`
+    (initially introduced in :pep:`508#names`).
+    """
+    return PEP508_IDENTIFIER_REGEX.match(name) is not None
+
+
+try:
+    try:
+        from packaging import requirements as _req
+    except ImportError:  # pragma: no cover
+        # let's try setuptools vendored version
+        from setuptools._vendor.packaging import (  # type: ignore[no-redef]
+            requirements as _req,
+        )
+
+    def pep508(value: str) -> bool:
+        """See :ref:`PyPA's dependency specifiers <pypa:dependency-specifiers>`
+        (initially introduced in :pep:`508`).
+        """
+        try:
+            _req.Requirement(value)
+            return True
+        except _req.InvalidRequirement:
+            return False
+
+except ImportError:  # pragma: no cover
+    _logger.warning(
+        "Could not find an installation of `packaging`. Requirements, dependencies and "
+        "versions might not be validated. "
+        "To enforce validation, please install `packaging`."
+    )
+
+    def pep508(value: str) -> bool:
+        return True
+
+
+def pep508_versionspec(value: str) -> bool:
+    """Expression that can be used to specify/lock versions (including ranges)
+    See ``versionspec`` in :ref:`PyPA's dependency specifiers
+    <pypa:dependency-specifiers>` (initially introduced in :pep:`508`).
+    """
+    if any(c in value for c in (";", "]", "@")):
+        # In PEP 508:
+        # conditional markers, extras and URL specs are not included in the
+        # versionspec
+        return False
+    # Let's pretend we have a dependency called `requirement` with the given
+    # version spec, then we can reuse the pep508 function for validation:
+    return pep508(f"requirement{value}")
+
+
+# -------------------------------------------------------------------------------------
+# PEP 517
+
+
+def pep517_backend_reference(value: str) -> bool:
+    """See PyPA's specification for defining build-backend references
+    introduced in :pep:`517#source-trees`.
+
+    This is similar to an entry-point reference (e.g., ``package.module:object``).
+    """
+    module, _, obj = value.partition(":")
+    identifiers = (i.strip() for i in _chain(module.split("."), obj.split(".")))
+    return all(python_identifier(i) for i in identifiers if i)
+
+
+# -------------------------------------------------------------------------------------
+# Classifiers - PEP 301
+
+
+def _download_classifiers() -> str:
+    import ssl
+    from email.message import Message
+    from urllib.request import urlopen
+
+    url = "https://pypi.org/pypi?:action=list_classifiers"
+    context = ssl.create_default_context()
+    with urlopen(url, context=context) as response:  # noqa: S310 (audit URLs)
+        headers = Message()
+        headers["content_type"] = response.getheader("content-type", "text/plain")
+        return response.read().decode(headers.get_param("charset", "utf-8"))  # type: ignore[no-any-return]
+
+
+class _TroveClassifier:
+    """The ``trove_classifiers`` package is the official way of validating classifiers,
+    however this package might not be always available.
+    As a workaround we can still download a list from PyPI.
+    We also don't want to be over strict about it, so simply skipping silently is an
+    option (classifiers will be validated anyway during the upload to PyPI).
+    """
+
+    downloaded: typing.Union[None, "Literal[False]", typing.Set[str]]
+    """
+    None => not cached yet
+    False => unavailable
+    set => cached values
+    """
+
+    def __init__(self) -> None:
+        self.downloaded = None
+        self._skip_download = False
+        self.__name__ = "trove_classifier"  # Emulate a public function
+
+    def _disable_download(self) -> None:
+        # This is a private API. Only setuptools has the consent of using it.
+        self._skip_download = True
+
+    def __call__(self, value: str) -> bool:
+        if self.downloaded is False or self._skip_download is True:
+            return True
+
+        if os.getenv("NO_NETWORK") or os.getenv("VALIDATE_PYPROJECT_NO_NETWORK"):
+            self.downloaded = False
+            msg = (
+                "Install ``trove-classifiers`` to ensure proper validation. "
+                "Skipping download of classifiers list from PyPI (NO_NETWORK)."
+            )
+            _logger.debug(msg)
+            return True
+
+        if self.downloaded is None:
+            msg = (
+                "Install ``trove-classifiers`` to ensure proper validation. "
+                "Meanwhile a list of classifiers will be downloaded from PyPI."
+            )
+            _logger.debug(msg)
+            try:
+                self.downloaded = set(_download_classifiers().splitlines())
+            except Exception:
+                self.downloaded = False
+                _logger.debug("Problem with download, skipping validation")
+                return True
+
+        return value in self.downloaded or value.lower().startswith("private ::")
+
+
+try:
+    from trove_classifiers import classifiers as _trove_classifiers
+
+    def trove_classifier(value: str) -> bool:
+        """See https://pypi.org/classifiers/"""
+        return value in _trove_classifiers or value.lower().startswith("private ::")
+
+except ImportError:  # pragma: no cover
+    trove_classifier = _TroveClassifier()
+
+
+# -------------------------------------------------------------------------------------
+# Stub packages - PEP 561
+
+
+def pep561_stub_name(value: str) -> bool:
+    """Name of a directory containing type stubs.
+    It must follow the name scheme ``<package>-stubs`` as defined in
+    :pep:`561#stub-only-packages`.
+    """
+    top, *children = value.split(".")
+    if not top.endswith("-stubs"):
+        return False
+    return python_module_name(".".join([top[: -len("-stubs")], *children]))
+
+
+# -------------------------------------------------------------------------------------
+# Non-PEP related
+
+
+def url(value: str) -> bool:
+    """Valid URL (validation uses :obj:`urllib.parse`).
+    For maximum compatibility please make sure to include a ``scheme`` prefix
+    in your URL (e.g. ``http://``).
+    """
+    from urllib.parse import urlparse
+
+    try:
+        parts = urlparse(value)
+        if not parts.scheme:
+            _logger.warning(
+                "For maximum compatibility please make sure to include a "
+                "`scheme` prefix in your URL (e.g. 'http://'). "
+                f"Given value: {value}"
+            )
+            if not (value.startswith("/") or value.startswith("\\") or "@" in value):
+                parts = urlparse(f"http://{value}")
+
+        return bool(parts.scheme and parts.netloc)
+    except Exception:
+        return False
+
+
+# https://packaging.python.org/specifications/entry-points/
+ENTRYPOINT_PATTERN = r"[^\[\s=]([^=]*[^\s=])?"
+ENTRYPOINT_REGEX = re.compile(f"^{ENTRYPOINT_PATTERN}$", re.I)
+RECOMMEDED_ENTRYPOINT_PATTERN = r"[\w.-]+"
+RECOMMEDED_ENTRYPOINT_REGEX = re.compile(f"^{RECOMMEDED_ENTRYPOINT_PATTERN}$", re.I)
+ENTRYPOINT_GROUP_PATTERN = r"\w+(\.\w+)*"
+ENTRYPOINT_GROUP_REGEX = re.compile(f"^{ENTRYPOINT_GROUP_PATTERN}$", re.I)
+
+
+def python_identifier(value: str) -> bool:
+    """Can be used as identifier in Python.
+    (Validation uses :obj:`str.isidentifier`).
+    """
+    return value.isidentifier()
+
+
+def python_qualified_identifier(value: str) -> bool:
+    """
+    Python "dotted identifier", i.e. a sequence of :obj:`python_identifier`
+    concatenated with ``"."`` (e.g.: ``package.module.submodule``).
+    """
+    if value.startswith(".") or value.endswith("."):
+        return False
+    return all(python_identifier(m) for m in value.split("."))
+
+
+def python_module_name(value: str) -> bool:
+    """Module name that can be used in an ``import``-statement in Python.
+    See :obj:`python_qualified_identifier`.
+    """
+    return python_qualified_identifier(value)
+
+
+def python_module_name_relaxed(value: str) -> bool:
+    """Similar to :obj:`python_module_name`, but relaxed to also accept
+    dash characters (``-``) and cover special cases like ``pip-run``.
+
+    It is recommended, however, that beginners avoid dash characters,
+    as they require advanced knowledge about Python internals.
+
+    The following are disallowed:
+
+    * names starting/ending in dashes,
+    * names ending in ``-stubs`` (potentially collide with :obj:`pep561_stub_name`).
+    """
+    if value.startswith("-") or value.endswith("-"):
+        return False
+    if value.endswith("-stubs"):
+        return False  # Avoid collision with PEP 561
+    return python_module_name(value.replace("-", "_"))
+
+
+def python_entrypoint_group(value: str) -> bool:
+    """See ``Data model > group`` in the :ref:`PyPA's entry-points specification
+    <pypa:entry-points>`.
+    """
+    return ENTRYPOINT_GROUP_REGEX.match(value) is not None
+
+
+def python_entrypoint_name(value: str) -> bool:
+    """See ``Data model > name`` in the :ref:`PyPA's entry-points specification
+    <pypa:entry-points>`.
+    """
+    if not ENTRYPOINT_REGEX.match(value):
+        return False
+    if not RECOMMEDED_ENTRYPOINT_REGEX.match(value):
+        msg = f"Entry point `{value}` does not follow recommended pattern: "
+        msg += RECOMMEDED_ENTRYPOINT_PATTERN
+        _logger.warning(msg)
+    return True
+
+
+def python_entrypoint_reference(value: str) -> bool:
+    """Reference to a Python object using in the format::
+
+        importable.module:object.attr
+
+    See ``Data model >object reference`` in the :ref:`PyPA's entry-points specification
+    <pypa:entry-points>`.
+    """
+    module, _, rest = value.partition(":")
+    if "[" in rest:
+        obj, _, extras_ = rest.partition("[")
+        if extras_.strip()[-1] != "]":
+            return False
+        extras = (x.strip() for x in extras_.strip(string.whitespace + "[]").split(","))
+        if not all(pep508_identifier(e) for e in extras):
+            return False
+        _logger.warning(f"`{value}` - using extras for entry points is not recommended")
+    else:
+        obj = rest
+
+    module_parts = module.split(".")
+    identifiers = _chain(module_parts, obj.split(".")) if rest else iter(module_parts)
+    return all(python_identifier(i.strip()) for i in identifiers)
+
+
+def uint8(value: builtins.int) -> bool:
+    r"""Unsigned 8-bit integer (:math:`0 \leq x < 2^8`)"""
+    return 0 <= value < 2**8
+
+
+def uint16(value: builtins.int) -> bool:
+    r"""Unsigned 16-bit integer (:math:`0 \leq x < 2^{16}`)"""
+    return 0 <= value < 2**16
+
+
+def uint(value: builtins.int) -> bool:
+    r"""Unsigned 64-bit integer (:math:`0 \leq x < 2^{64}`)"""
+    return 0 <= value < 2**64
+
+
+def int(value: builtins.int) -> bool:
+    r"""Signed 64-bit integer (:math:`-2^{63} \leq x < 2^{63}`)"""
+    return -(2**63) <= value < 2**63
+
+
+try:
+    from packaging import licenses as _licenses
+
+    def SPDX(value: str) -> bool:
+        """See :ref:`PyPA's License-Expression specification
+        <pypa:core-metadata-license-expression>` (added in :pep:`639`).
+        """
+        try:
+            _licenses.canonicalize_license_expression(value)
+            return True
+        except _licenses.InvalidLicenseExpression:
+            return False
+
+except ImportError:  # pragma: no cover
+    _logger.warning(
+        "Could not find an up-to-date installation of `packaging`. "
+        "License expressions might not be validated. "
+        "To enforce validation, please install `packaging>=24.2`."
+    )
+
+    def SPDX(value: str) -> bool:
+        return True
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/distutils.schema.json b/.venv/lib/python3.12/site-packages/setuptools/config/distutils.schema.json
new file mode 100644
index 0000000000000000000000000000000000000000..93cd2e868acdaf41bf8b1f77026e84c02a5af3f8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/distutils.schema.json
@@ -0,0 +1,26 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+
+  "$id": "https://setuptools.pypa.io/en/latest/deprecated/distutils/configfile.html",
+  "title": "``tool.distutils`` table",
+  "$$description": [
+    "**EXPERIMENTAL** (NOT OFFICIALLY SUPPORTED): Use ``tool.distutils``",
+    "subtables to configure arguments for ``distutils`` commands.",
+    "Originally, ``distutils`` allowed developers to configure arguments for",
+    "``setup.py`` commands via `distutils configuration files",
+    "<https://setuptools.pypa.io/en/latest/deprecated/distutils/configfile.html>`_.",
+    "See also `the old Python docs <https://docs.python.org/3.11/install/>_`."
+  ],
+
+  "type": "object",
+  "properties": {
+    "global": {
+      "type": "object",
+      "description": "Global options applied to all ``distutils`` commands"
+    }
+  },
+  "patternProperties": {
+    ".+": {"type": "object"}
+  },
+  "$comment": "TODO: Is there a practical way of making this schema more specific?"
+}
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/expand.py b/.venv/lib/python3.12/site-packages/setuptools/config/expand.py
new file mode 100644
index 0000000000000000000000000000000000000000..531f965013031a8c5159c8b8505c38cfb0a4302f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/expand.py
@@ -0,0 +1,452 @@
+"""Utility functions to expand configuration directives or special values
+(such glob patterns).
+
+We can split the process of interpreting configuration files into 2 steps:
+
+1. The parsing the file contents from strings to value objects
+   that can be understand by Python (for example a string with a comma
+   separated list of keywords into an actual Python list of strings).
+
+2. The expansion (or post-processing) of these values according to the
+   semantics ``setuptools`` assign to them (for example a configuration field
+   with the ``file:`` directive should be expanded from a list of file paths to
+   a single string with the contents of those files concatenated)
+
+This module focus on the second step, and therefore allow sharing the expansion
+functions among several configuration file formats.
+
+**PRIVATE MODULE**: API reserved for setuptools internal usage only.
+"""
+
+from __future__ import annotations
+
+import ast
+import importlib
+import os
+import pathlib
+import sys
+from collections.abc import Iterable, Iterator, Mapping
+from configparser import ConfigParser
+from glob import iglob
+from importlib.machinery import ModuleSpec, all_suffixes
+from itertools import chain
+from pathlib import Path
+from types import ModuleType, TracebackType
+from typing import TYPE_CHECKING, Any, Callable, TypeVar
+
+from .. import _static
+from .._path import StrPath, same_path as _same_path
+from ..discovery import find_package_path
+from ..warnings import SetuptoolsWarning
+
+from distutils.errors import DistutilsOptionError
+
+if TYPE_CHECKING:
+    from typing_extensions import Self
+
+    from setuptools.dist import Distribution
+
+_K = TypeVar("_K")
+_V_co = TypeVar("_V_co", covariant=True)
+
+
+class StaticModule:
+    """Proxy to a module object that avoids executing arbitrary code."""
+
+    def __init__(self, name: str, spec: ModuleSpec) -> None:
+        module = ast.parse(pathlib.Path(spec.origin).read_bytes())  # type: ignore[arg-type] # Let it raise an error on None
+        vars(self).update(locals())
+        del self.self
+
+    def _find_assignments(self) -> Iterator[tuple[ast.AST, ast.AST]]:
+        for statement in self.module.body:
+            if isinstance(statement, ast.Assign):
+                yield from ((target, statement.value) for target in statement.targets)
+            elif isinstance(statement, ast.AnnAssign) and statement.value:
+                yield (statement.target, statement.value)
+
+    def __getattr__(self, attr: str):
+        """Attempt to load an attribute "statically", via :func:`ast.literal_eval`."""
+        try:
+            return next(
+                ast.literal_eval(value)
+                for target, value in self._find_assignments()
+                if isinstance(target, ast.Name) and target.id == attr
+            )
+        except Exception as e:
+            raise AttributeError(f"{self.name} has no attribute {attr}") from e
+
+
+def glob_relative(
+    patterns: Iterable[str], root_dir: StrPath | None = None
+) -> list[str]:
+    """Expand the list of glob patterns, but preserving relative paths.
+
+    :param list[str] patterns: List of glob patterns
+    :param str root_dir: Path to which globs should be relative
+                         (current directory by default)
+    :rtype: list
+    """
+    glob_characters = {'*', '?', '[', ']', '{', '}'}
+    expanded_values = []
+    root_dir = root_dir or os.getcwd()
+    for value in patterns:
+        # Has globby characters?
+        if any(char in value for char in glob_characters):
+            # then expand the glob pattern while keeping paths *relative*:
+            glob_path = os.path.abspath(os.path.join(root_dir, value))
+            expanded_values.extend(
+                sorted(
+                    os.path.relpath(path, root_dir).replace(os.sep, "/")
+                    for path in iglob(glob_path, recursive=True)
+                )
+            )
+
+        else:
+            # take the value as-is
+            path = os.path.relpath(value, root_dir).replace(os.sep, "/")
+            expanded_values.append(path)
+
+    return expanded_values
+
+
+def read_files(
+    filepaths: StrPath | Iterable[StrPath], root_dir: StrPath | None = None
+) -> str:
+    """Return the content of the files concatenated using ``\n`` as str
+
+    This function is sandboxed and won't reach anything outside ``root_dir``
+
+    (By default ``root_dir`` is the current directory).
+    """
+    from more_itertools import always_iterable
+
+    root_dir = os.path.abspath(root_dir or os.getcwd())
+    _filepaths = (os.path.join(root_dir, path) for path in always_iterable(filepaths))
+    return '\n'.join(
+        _read_file(path)
+        for path in _filter_existing_files(_filepaths)
+        if _assert_local(path, root_dir)
+    )
+
+
+def _filter_existing_files(filepaths: Iterable[StrPath]) -> Iterator[StrPath]:
+    for path in filepaths:
+        if os.path.isfile(path):
+            yield path
+        else:
+            SetuptoolsWarning.emit(f"File {path!r} cannot be found")
+
+
+def _read_file(filepath: bytes | StrPath) -> str:
+    with open(filepath, encoding='utf-8') as f:
+        return f.read()
+
+
+def _assert_local(filepath: StrPath, root_dir: str):
+    if Path(os.path.abspath(root_dir)) not in Path(os.path.abspath(filepath)).parents:
+        msg = f"Cannot access {filepath!r} (or anything outside {root_dir!r})"
+        raise DistutilsOptionError(msg)
+
+    return True
+
+
+def read_attr(
+    attr_desc: str,
+    package_dir: Mapping[str, str] | None = None,
+    root_dir: StrPath | None = None,
+) -> Any:
+    """Reads the value of an attribute from a module.
+
+    This function will try to read the attributed statically first
+    (via :func:`ast.literal_eval`), and only evaluate the module if it fails.
+
+    Examples:
+        read_attr("package.attr")
+        read_attr("package.module.attr")
+
+    :param str attr_desc: Dot-separated string describing how to reach the
+        attribute (see examples above)
+    :param dict[str, str] package_dir: Mapping of package names to their
+        location in disk (represented by paths relative to ``root_dir``).
+    :param str root_dir: Path to directory containing all the packages in
+        ``package_dir`` (current directory by default).
+    :rtype: str
+    """
+    root_dir = root_dir or os.getcwd()
+    attrs_path = attr_desc.strip().split('.')
+    attr_name = attrs_path.pop()
+    module_name = '.'.join(attrs_path)
+    module_name = module_name or '__init__'
+    path = _find_module(module_name, package_dir, root_dir)
+    spec = _find_spec(module_name, path)
+
+    try:
+        value = getattr(StaticModule(module_name, spec), attr_name)
+        # XXX: Is marking as static contents coming from modules too optimistic?
+        return _static.attempt_conversion(value)
+    except Exception:
+        # fallback to evaluate module
+        module = _load_spec(spec, module_name)
+        return getattr(module, attr_name)
+
+
+def _find_spec(module_name: str, module_path: StrPath | None) -> ModuleSpec:
+    spec = importlib.util.spec_from_file_location(module_name, module_path)
+    spec = spec or importlib.util.find_spec(module_name)
+
+    if spec is None:
+        raise ModuleNotFoundError(module_name)
+
+    return spec
+
+
+def _load_spec(spec: ModuleSpec, module_name: str) -> ModuleType:
+    name = getattr(spec, "__name__", module_name)
+    if name in sys.modules:
+        return sys.modules[name]
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[name] = module  # cache (it also ensures `==` works on loaded items)
+    assert spec.loader is not None
+    spec.loader.exec_module(module)
+    return module
+
+
+def _find_module(
+    module_name: str, package_dir: Mapping[str, str] | None, root_dir: StrPath
+) -> str | None:
+    """Find the path to the module named ``module_name``,
+    considering the ``package_dir`` in the build configuration and ``root_dir``.
+
+    >>> tmp = getfixture('tmpdir')
+    >>> _ = tmp.ensure("a/b/c.py")
+    >>> _ = tmp.ensure("a/b/d/__init__.py")
+    >>> r = lambda x: x.replace(str(tmp), "tmp").replace(os.sep, "/")
+    >>> r(_find_module("a.b.c", None, tmp))
+    'tmp/a/b/c.py'
+    >>> r(_find_module("f.g.h", {"": "1", "f": "2", "f.g": "3", "f.g.h": "a/b/d"}, tmp))
+    'tmp/a/b/d/__init__.py'
+    """
+    path_start = find_package_path(module_name, package_dir or {}, root_dir)
+    candidates = chain.from_iterable(
+        (f"{path_start}{ext}", os.path.join(path_start, f"__init__{ext}"))
+        for ext in all_suffixes()
+    )
+    return next((x for x in candidates if os.path.isfile(x)), None)
+
+
+def resolve_class(
+    qualified_class_name: str,
+    package_dir: Mapping[str, str] | None = None,
+    root_dir: StrPath | None = None,
+) -> Callable:
+    """Given a qualified class name, return the associated class object"""
+    root_dir = root_dir or os.getcwd()
+    idx = qualified_class_name.rfind('.')
+    class_name = qualified_class_name[idx + 1 :]
+    pkg_name = qualified_class_name[:idx]
+
+    path = _find_module(pkg_name, package_dir, root_dir)
+    module = _load_spec(_find_spec(pkg_name, path), pkg_name)
+    return getattr(module, class_name)
+
+
+def cmdclass(
+    values: dict[str, str],
+    package_dir: Mapping[str, str] | None = None,
+    root_dir: StrPath | None = None,
+) -> dict[str, Callable]:
+    """Given a dictionary mapping command names to strings for qualified class
+    names, apply :func:`resolve_class` to the dict values.
+    """
+    return {k: resolve_class(v, package_dir, root_dir) for k, v in values.items()}
+
+
+def find_packages(
+    *,
+    namespaces=True,
+    fill_package_dir: dict[str, str] | None = None,
+    root_dir: StrPath | None = None,
+    **kwargs,
+) -> list[str]:
+    """Works similarly to :func:`setuptools.find_packages`, but with all
+    arguments given as keyword arguments. Moreover, ``where`` can be given
+    as a list (the results will be simply concatenated).
+
+    When the additional keyword argument ``namespaces`` is ``True``, it will
+    behave like :func:`setuptools.find_namespace_packages`` (i.e. include
+    implicit namespaces as per :pep:`420`).
+
+    The ``where`` argument will be considered relative to ``root_dir`` (or the current
+    working directory when ``root_dir`` is not given).
+
+    If the ``fill_package_dir`` argument is passed, this function will consider it as a
+    similar data structure to the ``package_dir`` configuration parameter add fill-in
+    any missing package location.
+
+    :rtype: list
+    """
+    from more_itertools import always_iterable, unique_everseen
+
+    from setuptools.discovery import construct_package_dir
+
+    # check "not namespaces" first due to python/mypy#6232
+    if not namespaces:
+        from setuptools.discovery import PackageFinder
+    else:
+        from setuptools.discovery import PEP420PackageFinder as PackageFinder
+
+    root_dir = root_dir or os.curdir
+    where = kwargs.pop('where', ['.'])
+    packages: list[str] = []
+    fill_package_dir = {} if fill_package_dir is None else fill_package_dir
+    search = list(unique_everseen(always_iterable(where)))
+
+    if len(search) == 1 and all(not _same_path(search[0], x) for x in (".", root_dir)):
+        fill_package_dir.setdefault("", search[0])
+
+    for path in search:
+        package_path = _nest_path(root_dir, path)
+        pkgs = PackageFinder.find(package_path, **kwargs)
+        packages.extend(pkgs)
+        if pkgs and not (
+            fill_package_dir.get("") == path or os.path.samefile(package_path, root_dir)
+        ):
+            fill_package_dir.update(construct_package_dir(pkgs, path))
+
+    return packages
+
+
+def _nest_path(parent: StrPath, path: StrPath) -> str:
+    path = parent if path in {".", ""} else os.path.join(parent, path)
+    return os.path.normpath(path)
+
+
+def version(value: Callable | Iterable[str | int] | str) -> str:
+    """When getting the version directly from an attribute,
+    it should be normalised to string.
+    """
+    _value = value() if callable(value) else value
+
+    if isinstance(_value, str):
+        return _value
+    if hasattr(_value, '__iter__'):
+        return '.'.join(map(str, _value))
+    return f'{_value}'
+
+
+def canonic_package_data(package_data: dict) -> dict:
+    if "*" in package_data:
+        package_data[""] = package_data.pop("*")
+    return package_data
+
+
+def canonic_data_files(
+    data_files: list | dict, root_dir: StrPath | None = None
+) -> list[tuple[str, list[str]]]:
+    """For compatibility with ``setup.py``, ``data_files`` should be a list
+    of pairs instead of a dict.
+
+    This function also expands glob patterns.
+    """
+    if isinstance(data_files, list):
+        return data_files
+
+    return [
+        (dest, glob_relative(patterns, root_dir))
+        for dest, patterns in data_files.items()
+    ]
+
+
+def entry_points(
+    text: str, text_source: str = "entry-points"
+) -> dict[str, dict[str, str]]:
+    """Given the contents of entry-points file,
+    process it into a 2-level dictionary (``dict[str, dict[str, str]]``).
+    The first level keys are entry-point groups, the second level keys are
+    entry-point names, and the second level values are references to objects
+    (that correspond to the entry-point value).
+    """
+    # Using undocumented behaviour, see python/typeshed#12700
+    parser = ConfigParser(default_section=None, delimiters=("=",))  # type: ignore[call-overload]
+    parser.optionxform = str  # case sensitive
+    parser.read_string(text, text_source)
+    groups = {k: dict(v.items()) for k, v in parser.items()}
+    groups.pop(parser.default_section, None)
+    return groups
+
+
+class EnsurePackagesDiscovered:
+    """Some expand functions require all the packages to already be discovered before
+    they run, e.g. :func:`read_attr`, :func:`resolve_class`, :func:`cmdclass`.
+
+    Therefore in some cases we will need to run autodiscovery during the evaluation of
+    the configuration. However, it is better to postpone calling package discovery as
+    much as possible, because some parameters can influence it (e.g. ``package_dir``),
+    and those might not have been processed yet.
+    """
+
+    def __init__(self, distribution: Distribution) -> None:
+        self._dist = distribution
+        self._called = False
+
+    def __call__(self):
+        """Trigger the automatic package discovery, if it is still necessary."""
+        if not self._called:
+            self._called = True
+            self._dist.set_defaults(name=False)  # Skip name, we can still be parsing
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ):
+        if self._called:
+            self._dist.set_defaults.analyse_name()  # Now we can set a default name
+
+    def _get_package_dir(self) -> Mapping[str, str]:
+        self()
+        pkg_dir = self._dist.package_dir
+        return {} if pkg_dir is None else pkg_dir
+
+    @property
+    def package_dir(self) -> Mapping[str, str]:
+        """Proxy to ``package_dir`` that may trigger auto-discovery when used."""
+        return LazyMappingProxy(self._get_package_dir)
+
+
+class LazyMappingProxy(Mapping[_K, _V_co]):
+    """Mapping proxy that delays resolving the target object, until really needed.
+
+    >>> def obtain_mapping():
+    ...     print("Running expensive function!")
+    ...     return {"key": "value", "other key": "other value"}
+    >>> mapping = LazyMappingProxy(obtain_mapping)
+    >>> mapping["key"]
+    Running expensive function!
+    'value'
+    >>> mapping["other key"]
+    'other value'
+    """
+
+    def __init__(self, obtain_mapping_value: Callable[[], Mapping[_K, _V_co]]) -> None:
+        self._obtain = obtain_mapping_value
+        self._value: Mapping[_K, _V_co] | None = None
+
+    def _target(self) -> Mapping[_K, _V_co]:
+        if self._value is None:
+            self._value = self._obtain()
+        return self._value
+
+    def __getitem__(self, key: _K) -> _V_co:
+        return self._target()[key]
+
+    def __len__(self) -> int:
+        return len(self._target())
+
+    def __iter__(self) -> Iterator[_K]:
+        return iter(self._target())
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/pyprojecttoml.py b/.venv/lib/python3.12/site-packages/setuptools/config/pyprojecttoml.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd6c5968c820435a0a8a82ecfacbe94abd7e8d3d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/pyprojecttoml.py
@@ -0,0 +1,468 @@
+"""
+Load setuptools configuration from ``pyproject.toml`` files.
+
+**PRIVATE MODULE**: API reserved for setuptools internal usage only.
+
+To read project metadata, consider using
+``build.util.project_wheel_metadata`` (https://pypi.org/project/build/).
+For simple scenarios, you can also try parsing the file directly
+with the help of ``tomllib`` or ``tomli``.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from collections.abc import Mapping
+from contextlib import contextmanager
+from functools import partial
+from types import TracebackType
+from typing import TYPE_CHECKING, Any, Callable
+
+from .._path import StrPath
+from ..errors import FileError, InvalidConfigError
+from ..warnings import SetuptoolsWarning
+from . import expand as _expand
+from ._apply_pyprojecttoml import _PREVIOUSLY_DEFINED, _MissingDynamic, apply as _apply
+
+if TYPE_CHECKING:
+    from typing_extensions import Self
+
+    from setuptools.dist import Distribution
+
+_logger = logging.getLogger(__name__)
+
+
+def load_file(filepath: StrPath) -> dict:
+    from ..compat.py310 import tomllib
+
+    with open(filepath, "rb") as file:
+        return tomllib.load(file)
+
+
+def validate(config: dict, filepath: StrPath) -> bool:
+    from . import _validate_pyproject as validator
+
+    trove_classifier = validator.FORMAT_FUNCTIONS.get("trove-classifier")
+    if hasattr(trove_classifier, "_disable_download"):
+        # Improve reproducibility by default. See abravalheri/validate-pyproject#31
+        trove_classifier._disable_download()  # type: ignore[union-attr]
+
+    try:
+        return validator.validate(config)
+    except validator.ValidationError as ex:
+        summary = f"configuration error: {ex.summary}"
+        if ex.name.strip("`") != "project":
+            # Probably it is just a field missing/misnamed, not worthy the verbosity...
+            _logger.debug(summary)
+            _logger.debug(ex.details)
+
+        error = f"invalid pyproject.toml config: {ex.name}."
+        raise ValueError(f"{error}\n{summary}") from None
+
+
+def apply_configuration(
+    dist: Distribution,
+    filepath: StrPath,
+    ignore_option_errors: bool = False,
+) -> Distribution:
+    """Apply the configuration from a ``pyproject.toml`` file into an existing
+    distribution object.
+    """
+    config = read_configuration(filepath, True, ignore_option_errors, dist)
+    return _apply(dist, config, filepath)
+
+
+def read_configuration(
+    filepath: StrPath,
+    expand: bool = True,
+    ignore_option_errors: bool = False,
+    dist: Distribution | None = None,
+) -> dict[str, Any]:
+    """Read given configuration file and returns options from it as a dict.
+
+    :param str|unicode filepath: Path to configuration file in the ``pyproject.toml``
+        format.
+
+    :param bool expand: Whether to expand directives and other computed values
+        (i.e. post-process the given configuration)
+
+    :param bool ignore_option_errors: Whether to silently ignore
+        options, values of which could not be resolved (e.g. due to exceptions
+        in directives such as file:, attr:, etc.).
+        If False exceptions are propagated as expected.
+
+    :param Distribution|None: Distribution object to which the configuration refers.
+        If not given a dummy object will be created and discarded after the
+        configuration is read. This is used for auto-discovery of packages and in the
+        case a dynamic configuration (e.g. ``attr`` or ``cmdclass``) is expanded.
+        When ``expand=False`` this object is simply ignored.
+
+    :rtype: dict
+    """
+    filepath = os.path.abspath(filepath)
+
+    if not os.path.isfile(filepath):
+        raise FileError(f"Configuration file {filepath!r} does not exist.")
+
+    asdict = load_file(filepath) or {}
+    project_table = asdict.get("project", {})
+    tool_table = asdict.get("tool", {})
+    setuptools_table = tool_table.get("setuptools", {})
+    if not asdict or not (project_table or setuptools_table):
+        return {}  # User is not using pyproject to configure setuptools
+
+    if "setuptools" in asdict.get("tools", {}):
+        # let the user know they probably have a typo in their metadata
+        _ToolsTypoInMetadata.emit()
+
+    if "distutils" in tool_table:
+        _ExperimentalConfiguration.emit(subject="[tool.distutils]")
+
+    # There is an overall sense in the community that making include_package_data=True
+    # the default would be an improvement.
+    # `ini2toml` backfills include_package_data=False when nothing is explicitly given,
+    # therefore setting a default here is backwards compatible.
+    if dist and dist.include_package_data is not None:
+        setuptools_table.setdefault("include-package-data", dist.include_package_data)
+    else:
+        setuptools_table.setdefault("include-package-data", True)
+    # Persist changes:
+    asdict["tool"] = tool_table
+    tool_table["setuptools"] = setuptools_table
+
+    if "ext-modules" in setuptools_table:
+        _ExperimentalConfiguration.emit(subject="[tool.setuptools.ext-modules]")
+
+    with _ignore_errors(ignore_option_errors):
+        # Don't complain about unrelated errors (e.g. tools not using the "tool" table)
+        subset = {"project": project_table, "tool": {"setuptools": setuptools_table}}
+        validate(subset, filepath)
+
+    if expand:
+        root_dir = os.path.dirname(filepath)
+        return expand_configuration(asdict, root_dir, ignore_option_errors, dist)
+
+    return asdict
+
+
+def expand_configuration(
+    config: dict,
+    root_dir: StrPath | None = None,
+    ignore_option_errors: bool = False,
+    dist: Distribution | None = None,
+) -> dict:
+    """Given a configuration with unresolved fields (e.g. dynamic, cmdclass, ...)
+    find their final values.
+
+    :param dict config: Dict containing the configuration for the distribution
+    :param str root_dir: Top-level directory for the distribution/project
+        (the same directory where ``pyproject.toml`` is place)
+    :param bool ignore_option_errors: see :func:`read_configuration`
+    :param Distribution|None: Distribution object to which the configuration refers.
+        If not given a dummy object will be created and discarded after the
+        configuration is read. Used in the case a dynamic configuration
+        (e.g. ``attr`` or ``cmdclass``).
+
+    :rtype: dict
+    """
+    return _ConfigExpander(config, root_dir, ignore_option_errors, dist).expand()
+
+
+class _ConfigExpander:
+    def __init__(
+        self,
+        config: dict,
+        root_dir: StrPath | None = None,
+        ignore_option_errors: bool = False,
+        dist: Distribution | None = None,
+    ) -> None:
+        self.config = config
+        self.root_dir = root_dir or os.getcwd()
+        self.project_cfg = config.get("project", {})
+        self.dynamic = self.project_cfg.get("dynamic", [])
+        self.setuptools_cfg = config.get("tool", {}).get("setuptools", {})
+        self.dynamic_cfg = self.setuptools_cfg.get("dynamic", {})
+        self.ignore_option_errors = ignore_option_errors
+        self._dist = dist
+        self._referenced_files = set[str]()
+
+    def _ensure_dist(self) -> Distribution:
+        from setuptools.dist import Distribution
+
+        attrs = {"src_root": self.root_dir, "name": self.project_cfg.get("name", None)}
+        return self._dist or Distribution(attrs)
+
+    def _process_field(self, container: dict, field: str, fn: Callable):
+        if field in container:
+            with _ignore_errors(self.ignore_option_errors):
+                container[field] = fn(container[field])
+
+    def _canonic_package_data(self, field="package-data"):
+        package_data = self.setuptools_cfg.get(field, {})
+        return _expand.canonic_package_data(package_data)
+
+    def expand(self):
+        self._expand_packages()
+        self._canonic_package_data()
+        self._canonic_package_data("exclude-package-data")
+
+        # A distribution object is required for discovering the correct package_dir
+        dist = self._ensure_dist()
+        ctx = _EnsurePackagesDiscovered(dist, self.project_cfg, self.setuptools_cfg)
+        with ctx as ensure_discovered:
+            package_dir = ensure_discovered.package_dir
+            self._expand_data_files()
+            self._expand_cmdclass(package_dir)
+            self._expand_all_dynamic(dist, package_dir)
+
+        dist._referenced_files.update(self._referenced_files)
+        return self.config
+
+    def _expand_packages(self):
+        packages = self.setuptools_cfg.get("packages")
+        if packages is None or isinstance(packages, (list, tuple)):
+            return
+
+        find = packages.get("find")
+        if isinstance(find, dict):
+            find["root_dir"] = self.root_dir
+            find["fill_package_dir"] = self.setuptools_cfg.setdefault("package-dir", {})
+            with _ignore_errors(self.ignore_option_errors):
+                self.setuptools_cfg["packages"] = _expand.find_packages(**find)
+
+    def _expand_data_files(self):
+        data_files = partial(_expand.canonic_data_files, root_dir=self.root_dir)
+        self._process_field(self.setuptools_cfg, "data-files", data_files)
+
+    def _expand_cmdclass(self, package_dir: Mapping[str, str]):
+        root_dir = self.root_dir
+        cmdclass = partial(_expand.cmdclass, package_dir=package_dir, root_dir=root_dir)
+        self._process_field(self.setuptools_cfg, "cmdclass", cmdclass)
+
+    def _expand_all_dynamic(self, dist: Distribution, package_dir: Mapping[str, str]):
+        special = (  # need special handling
+            "version",
+            "readme",
+            "entry-points",
+            "scripts",
+            "gui-scripts",
+            "classifiers",
+            "dependencies",
+            "optional-dependencies",
+        )
+        # `_obtain` functions are assumed to raise appropriate exceptions/warnings.
+        obtained_dynamic = {
+            field: self._obtain(dist, field, package_dir)
+            for field in self.dynamic
+            if field not in special
+        }
+        obtained_dynamic.update(
+            self._obtain_entry_points(dist, package_dir) or {},
+            version=self._obtain_version(dist, package_dir),
+            readme=self._obtain_readme(dist),
+            classifiers=self._obtain_classifiers(dist),
+            dependencies=self._obtain_dependencies(dist),
+            optional_dependencies=self._obtain_optional_dependencies(dist),
+        )
+        # `None` indicates there is nothing in `tool.setuptools.dynamic` but the value
+        # might have already been set by setup.py/extensions, so avoid overwriting.
+        updates = {k: v for k, v in obtained_dynamic.items() if v is not None}
+        self.project_cfg.update(updates)
+
+    def _ensure_previously_set(self, dist: Distribution, field: str):
+        previous = _PREVIOUSLY_DEFINED[field](dist)
+        if previous is None and not self.ignore_option_errors:
+            msg = (
+                f"No configuration found for dynamic {field!r}.\n"
+                "Some dynamic fields need to be specified via `tool.setuptools.dynamic`"
+                "\nothers must be specified via the equivalent attribute in `setup.py`."
+            )
+            raise InvalidConfigError(msg)
+
+    def _expand_directive(
+        self, specifier: str, directive, package_dir: Mapping[str, str]
+    ):
+        from more_itertools import always_iterable
+
+        with _ignore_errors(self.ignore_option_errors):
+            root_dir = self.root_dir
+            if "file" in directive:
+                self._referenced_files.update(always_iterable(directive["file"]))
+                return _expand.read_files(directive["file"], root_dir)
+            if "attr" in directive:
+                return _expand.read_attr(directive["attr"], package_dir, root_dir)
+            raise ValueError(f"invalid `{specifier}`: {directive!r}")
+        return None
+
+    def _obtain(self, dist: Distribution, field: str, package_dir: Mapping[str, str]):
+        if field in self.dynamic_cfg:
+            return self._expand_directive(
+                f"tool.setuptools.dynamic.{field}",
+                self.dynamic_cfg[field],
+                package_dir,
+            )
+        self._ensure_previously_set(dist, field)
+        return None
+
+    def _obtain_version(self, dist: Distribution, package_dir: Mapping[str, str]):
+        # Since plugins can set version, let's silently skip if it cannot be obtained
+        if "version" in self.dynamic and "version" in self.dynamic_cfg:
+            return _expand.version(
+                # We already do an early check for the presence of "version"
+                self._obtain(dist, "version", package_dir)  # pyright: ignore[reportArgumentType]
+            )
+        return None
+
+    def _obtain_readme(self, dist: Distribution) -> dict[str, str] | None:
+        if "readme" not in self.dynamic:
+            return None
+
+        dynamic_cfg = self.dynamic_cfg
+        if "readme" in dynamic_cfg:
+            return {
+                # We already do an early check for the presence of "readme"
+                "text": self._obtain(dist, "readme", {}),
+                "content-type": dynamic_cfg["readme"].get("content-type", "text/x-rst"),
+            }  # pyright: ignore[reportReturnType]
+
+        self._ensure_previously_set(dist, "readme")
+        return None
+
+    def _obtain_entry_points(
+        self, dist: Distribution, package_dir: Mapping[str, str]
+    ) -> dict[str, dict[str, Any]] | None:
+        fields = ("entry-points", "scripts", "gui-scripts")
+        if not any(field in self.dynamic for field in fields):
+            return None
+
+        text = self._obtain(dist, "entry-points", package_dir)
+        if text is None:
+            return None
+
+        groups = _expand.entry_points(text)
+        # Any is str | dict[str, str], but causes variance issues
+        expanded: dict[str, dict[str, Any]] = {"entry-points": groups}
+
+        def _set_scripts(field: str, group: str):
+            if group in groups:
+                value = groups.pop(group)
+                if field not in self.dynamic:
+                    raise InvalidConfigError(_MissingDynamic.details(field, value))
+                expanded[field] = value
+
+        _set_scripts("scripts", "console_scripts")
+        _set_scripts("gui-scripts", "gui_scripts")
+
+        return expanded
+
+    def _obtain_classifiers(self, dist: Distribution):
+        if "classifiers" in self.dynamic:
+            value = self._obtain(dist, "classifiers", {})
+            if value:
+                return value.splitlines()
+        return None
+
+    def _obtain_dependencies(self, dist: Distribution):
+        if "dependencies" in self.dynamic:
+            value = self._obtain(dist, "dependencies", {})
+            if value:
+                return _parse_requirements_list(value)
+        return None
+
+    def _obtain_optional_dependencies(self, dist: Distribution):
+        if "optional-dependencies" not in self.dynamic:
+            return None
+        if "optional-dependencies" in self.dynamic_cfg:
+            optional_dependencies_map = self.dynamic_cfg["optional-dependencies"]
+            assert isinstance(optional_dependencies_map, dict)
+            return {
+                group: _parse_requirements_list(
+                    self._expand_directive(
+                        f"tool.setuptools.dynamic.optional-dependencies.{group}",
+                        directive,
+                        {},
+                    )
+                )
+                for group, directive in optional_dependencies_map.items()
+            }
+        self._ensure_previously_set(dist, "optional-dependencies")
+        return None
+
+
+def _parse_requirements_list(value):
+    return [
+        line
+        for line in value.splitlines()
+        if line.strip() and not line.strip().startswith("#")
+    ]
+
+
+@contextmanager
+def _ignore_errors(ignore_option_errors: bool):
+    if not ignore_option_errors:
+        yield
+        return
+
+    try:
+        yield
+    except Exception as ex:
+        _logger.debug(f"ignored error: {ex.__class__.__name__} - {ex}")
+
+
+class _EnsurePackagesDiscovered(_expand.EnsurePackagesDiscovered):
+    def __init__(
+        self, distribution: Distribution, project_cfg: dict, setuptools_cfg: dict
+    ) -> None:
+        super().__init__(distribution)
+        self._project_cfg = project_cfg
+        self._setuptools_cfg = setuptools_cfg
+
+    def __enter__(self) -> Self:
+        """When entering the context, the values of ``packages``, ``py_modules`` and
+        ``package_dir`` that are missing in ``dist`` are copied from ``setuptools_cfg``.
+        """
+        dist, cfg = self._dist, self._setuptools_cfg
+        package_dir: dict[str, str] = cfg.setdefault("package-dir", {})
+        package_dir.update(dist.package_dir or {})
+        dist.package_dir = package_dir  # needs to be the same object
+
+        dist.set_defaults._ignore_ext_modules()  # pyproject.toml-specific behaviour
+
+        # Set `name`, `py_modules` and `packages` in dist to short-circuit
+        # auto-discovery, but avoid overwriting empty lists purposefully set by users.
+        if dist.metadata.name is None:
+            dist.metadata.name = self._project_cfg.get("name")
+        if dist.py_modules is None:
+            dist.py_modules = cfg.get("py-modules")
+        if dist.packages is None:
+            dist.packages = cfg.get("packages")
+
+        return super().__enter__()
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        """When exiting the context, if values of ``packages``, ``py_modules`` and
+        ``package_dir`` are missing in ``setuptools_cfg``, copy from ``dist``.
+        """
+        # If anything was discovered set them back, so they count in the final config.
+        self._setuptools_cfg.setdefault("packages", self._dist.packages)
+        self._setuptools_cfg.setdefault("py-modules", self._dist.py_modules)
+        return super().__exit__(exc_type, exc_value, traceback)
+
+
+class _ExperimentalConfiguration(SetuptoolsWarning):
+    _SUMMARY = (
+        "`{subject}` in `pyproject.toml` is still *experimental* "
+        "and likely to change in future releases."
+    )
+
+
+class _ToolsTypoInMetadata(SetuptoolsWarning):
+    _SUMMARY = (
+        "Ignoring [tools.setuptools] in pyproject.toml, did you mean [tool.setuptools]?"
+    )
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/setupcfg.py b/.venv/lib/python3.12/site-packages/setuptools/config/setupcfg.py
new file mode 100644
index 0000000000000000000000000000000000000000..633aa9d45d086dd0290a2c38bee632ed7f9a63a4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/setupcfg.py
@@ -0,0 +1,780 @@
+"""
+Load setuptools configuration from ``setup.cfg`` files.
+
+**API will be made private in the future**
+
+To read project metadata, consider using
+``build.util.project_wheel_metadata`` (https://pypi.org/project/build/).
+For simple scenarios, you can also try parsing the file directly
+with the help of ``configparser``.
+"""
+
+from __future__ import annotations
+
+import contextlib
+import functools
+import os
+from collections import defaultdict
+from collections.abc import Iterable, Iterator
+from functools import partial, wraps
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, Generic, TypeVar, cast
+
+from packaging.markers import default_environment as marker_env
+from packaging.requirements import InvalidRequirement, Requirement
+from packaging.version import InvalidVersion, Version
+
+from .. import _static
+from .._path import StrPath
+from ..errors import FileError, OptionError
+from ..warnings import SetuptoolsDeprecationWarning
+from . import expand
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+    from setuptools.dist import Distribution
+
+    from distutils.dist import DistributionMetadata
+
+SingleCommandOptions: TypeAlias = dict[str, tuple[str, Any]]
+"""Dict that associate the name of the options of a particular command to a
+tuple. The first element of the tuple indicates the origin of the option value
+(e.g. the name of the configuration file where it was read from),
+while the second element of the tuple is the option value itself
+"""
+AllCommandOptions: TypeAlias = dict[str, SingleCommandOptions]
+"""cmd name => its options"""
+Target = TypeVar("Target", "Distribution", "DistributionMetadata")
+
+
+def read_configuration(
+    filepath: StrPath, find_others: bool = False, ignore_option_errors: bool = False
+) -> dict:
+    """Read given configuration file and returns options from it as a dict.
+
+    :param str|unicode filepath: Path to configuration file
+        to get options from.
+
+    :param bool find_others: Whether to search for other configuration files
+        which could be on in various places.
+
+    :param bool ignore_option_errors: Whether to silently ignore
+        options, values of which could not be resolved (e.g. due to exceptions
+        in directives such as file:, attr:, etc.).
+        If False exceptions are propagated as expected.
+
+    :rtype: dict
+    """
+    from setuptools.dist import Distribution
+
+    dist = Distribution()
+    filenames = dist.find_config_files() if find_others else []
+    handlers = _apply(dist, filepath, filenames, ignore_option_errors)
+    return configuration_to_dict(handlers)
+
+
+def apply_configuration(dist: Distribution, filepath: StrPath) -> Distribution:
+    """Apply the configuration from a ``setup.cfg`` file into an existing
+    distribution object.
+    """
+    _apply(dist, filepath)
+    dist._finalize_requires()
+    return dist
+
+
+def _apply(
+    dist: Distribution,
+    filepath: StrPath,
+    other_files: Iterable[StrPath] = (),
+    ignore_option_errors: bool = False,
+) -> tuple[ConfigMetadataHandler, ConfigOptionsHandler]:
+    """Read configuration from ``filepath`` and applies to the ``dist`` object."""
+    from setuptools.dist import _Distribution
+
+    filepath = os.path.abspath(filepath)
+
+    if not os.path.isfile(filepath):
+        raise FileError(f'Configuration file {filepath} does not exist.')
+
+    current_directory = os.getcwd()
+    os.chdir(os.path.dirname(filepath))
+    filenames = [*other_files, filepath]
+
+    try:
+        # TODO: Temporary cast until mypy 1.12 is released with upstream fixes from typeshed
+        _Distribution.parse_config_files(dist, filenames=cast(list[str], filenames))
+        handlers = parse_configuration(
+            dist, dist.command_options, ignore_option_errors=ignore_option_errors
+        )
+        dist._finalize_license_files()
+    finally:
+        os.chdir(current_directory)
+
+    return handlers
+
+
+def _get_option(target_obj: Distribution | DistributionMetadata, key: str):
+    """
+    Given a target object and option key, get that option from
+    the target object, either through a get_{key} method or
+    from an attribute directly.
+    """
+    getter_name = f'get_{key}'
+    by_attribute = functools.partial(getattr, target_obj, key)
+    getter = getattr(target_obj, getter_name, by_attribute)
+    return getter()
+
+
+def configuration_to_dict(
+    handlers: Iterable[
+        ConfigHandler[Distribution] | ConfigHandler[DistributionMetadata]
+    ],
+) -> dict:
+    """Returns configuration data gathered by given handlers as a dict.
+
+    :param Iterable[ConfigHandler] handlers: Handlers list,
+        usually from parse_configuration()
+
+    :rtype: dict
+    """
+    config_dict: dict = defaultdict(dict)
+
+    for handler in handlers:
+        for option in handler.set_options:
+            value = _get_option(handler.target_obj, option)
+            config_dict[handler.section_prefix][option] = value
+
+    return config_dict
+
+
+def parse_configuration(
+    distribution: Distribution,
+    command_options: AllCommandOptions,
+    ignore_option_errors: bool = False,
+) -> tuple[ConfigMetadataHandler, ConfigOptionsHandler]:
+    """Performs additional parsing of configuration options
+    for a distribution.
+
+    Returns a list of used option handlers.
+
+    :param Distribution distribution:
+    :param dict command_options:
+    :param bool ignore_option_errors: Whether to silently ignore
+        options, values of which could not be resolved (e.g. due to exceptions
+        in directives such as file:, attr:, etc.).
+        If False exceptions are propagated as expected.
+    :rtype: list
+    """
+    with expand.EnsurePackagesDiscovered(distribution) as ensure_discovered:
+        options = ConfigOptionsHandler(
+            distribution,
+            command_options,
+            ignore_option_errors,
+            ensure_discovered,
+        )
+
+        options.parse()
+        if not distribution.package_dir:
+            distribution.package_dir = options.package_dir  # Filled by `find_packages`
+
+        meta = ConfigMetadataHandler(
+            distribution.metadata,
+            command_options,
+            ignore_option_errors,
+            ensure_discovered,
+            distribution.package_dir,
+            distribution.src_root,
+        )
+        meta.parse()
+        distribution._referenced_files.update(
+            options._referenced_files, meta._referenced_files
+        )
+
+    return meta, options
+
+
+def _warn_accidental_env_marker_misconfig(label: str, orig_value: str, parsed: list):
+    """Because users sometimes misinterpret this configuration:
+
+    [options.extras_require]
+    foo = bar;python_version<"4"
+
+    It looks like one requirement with an environment marker
+    but because there is no newline, it's parsed as two requirements
+    with a semicolon as separator.
+
+    Therefore, if:
+        * input string does not contain a newline AND
+        * parsed result contains two requirements AND
+        * parsing of the two parts from the result ("<first>;<second>")
+        leads in a valid Requirement with a valid marker
+    a UserWarning is shown to inform the user about the possible problem.
+    """
+    if "\n" in orig_value or len(parsed) != 2:
+        return
+
+    markers = marker_env().keys()
+
+    try:
+        req = Requirement(parsed[1])
+        if req.name in markers:
+            _AmbiguousMarker.emit(field=label, req=parsed[1])
+    except InvalidRequirement as ex:
+        if any(parsed[1].startswith(marker) for marker in markers):
+            msg = _AmbiguousMarker.message(field=label, req=parsed[1])
+            raise InvalidRequirement(msg) from ex
+
+
+class ConfigHandler(Generic[Target]):
+    """Handles metadata supplied in configuration files."""
+
+    section_prefix: str
+    """Prefix for config sections handled by this handler.
+    Must be provided by class heirs.
+
+    """
+
+    aliases: ClassVar[dict[str, str]] = {}
+    """Options aliases.
+    For compatibility with various packages. E.g.: d2to1 and pbr.
+    Note: `-` in keys is replaced with `_` by config parser.
+
+    """
+
+    def __init__(
+        self,
+        target_obj: Target,
+        options: AllCommandOptions,
+        ignore_option_errors,
+        ensure_discovered: expand.EnsurePackagesDiscovered,
+    ) -> None:
+        self.ignore_option_errors = ignore_option_errors
+        self.target_obj: Target = target_obj
+        self.sections = dict(self._section_options(options))
+        self.set_options: list[str] = []
+        self.ensure_discovered = ensure_discovered
+        self._referenced_files = set[str]()
+        """After parsing configurations, this property will enumerate
+        all files referenced by the "file:" directive. Private API for setuptools only.
+        """
+
+    @classmethod
+    def _section_options(
+        cls, options: AllCommandOptions
+    ) -> Iterator[tuple[str, SingleCommandOptions]]:
+        for full_name, value in options.items():
+            pre, _sep, name = full_name.partition(cls.section_prefix)
+            if pre:
+                continue
+            yield name.lstrip('.'), value
+
+    @property
+    def parsers(self):
+        """Metadata item name to parser function mapping."""
+        raise NotImplementedError(
+            f'{self.__class__.__name__} must provide .parsers property'
+        )
+
+    def __setitem__(self, option_name, value) -> None:
+        target_obj = self.target_obj
+
+        # Translate alias into real name.
+        option_name = self.aliases.get(option_name, option_name)
+
+        try:
+            current_value = getattr(target_obj, option_name)
+        except AttributeError as e:
+            raise KeyError(option_name) from e
+
+        if current_value:
+            # Already inhabited. Skipping.
+            return
+
+        try:
+            parsed = self.parsers.get(option_name, lambda x: x)(value)
+        except (Exception,) * self.ignore_option_errors:
+            return
+
+        simple_setter = functools.partial(target_obj.__setattr__, option_name)
+        setter = getattr(target_obj, f"set_{option_name}", simple_setter)
+        setter(parsed)
+
+        self.set_options.append(option_name)
+
+    @classmethod
+    def _parse_list(cls, value, separator=','):
+        """Represents value as a list.
+
+        Value is split either by separator (defaults to comma) or by lines.
+
+        :param value:
+        :param separator: List items separator character.
+        :rtype: list
+        """
+        if isinstance(value, list):  # _get_parser_compound case
+            return value
+
+        if '\n' in value:
+            value = value.splitlines()
+        else:
+            value = value.split(separator)
+
+        return [chunk.strip() for chunk in value if chunk.strip()]
+
+    @classmethod
+    def _parse_dict(cls, value):
+        """Represents value as a dict.
+
+        :param value:
+        :rtype: dict
+        """
+        separator = '='
+        result = {}
+        for line in cls._parse_list(value):
+            key, sep, val = line.partition(separator)
+            if sep != separator:
+                raise OptionError(f"Unable to parse option value to dict: {value}")
+            result[key.strip()] = val.strip()
+
+        return result
+
+    @classmethod
+    def _parse_bool(cls, value):
+        """Represents value as boolean.
+
+        :param value:
+        :rtype: bool
+        """
+        value = value.lower()
+        return value in ('1', 'true', 'yes')
+
+    @classmethod
+    def _exclude_files_parser(cls, key):
+        """Returns a parser function to make sure field inputs
+        are not files.
+
+        Parses a value after getting the key so error messages are
+        more informative.
+
+        :param key:
+        :rtype: callable
+        """
+
+        def parser(value):
+            exclude_directive = 'file:'
+            if value.startswith(exclude_directive):
+                raise ValueError(
+                    f'Only strings are accepted for the {key} field, '
+                    'files are not accepted'
+                )
+            return _static.Str(value)
+
+        return parser
+
+    def _parse_file(self, value, root_dir: StrPath | None):
+        """Represents value as a string, allowing including text
+        from nearest files using `file:` directive.
+
+        Directive is sandboxed and won't reach anything outside
+        directory with setup.py.
+
+        Examples:
+            file: README.rst, CHANGELOG.md, src/file.txt
+
+        :param str value:
+        :rtype: str
+        """
+        include_directive = 'file:'
+
+        if not isinstance(value, str):
+            return value
+
+        if not value.startswith(include_directive):
+            return _static.Str(value)
+
+        spec = value[len(include_directive) :]
+        filepaths = [path.strip() for path in spec.split(',')]
+        self._referenced_files.update(filepaths)
+        # XXX: Is marking as static contents coming from files too optimistic?
+        return _static.Str(expand.read_files(filepaths, root_dir))
+
+    def _parse_attr(self, value, package_dir, root_dir: StrPath):
+        """Represents value as a module attribute.
+
+        Examples:
+            attr: package.attr
+            attr: package.module.attr
+
+        :param str value:
+        :rtype: str
+        """
+        attr_directive = 'attr:'
+        if not value.startswith(attr_directive):
+            return _static.Str(value)
+
+        attr_desc = value.replace(attr_directive, '')
+
+        # Make sure package_dir is populated correctly, so `attr:` directives can work
+        package_dir.update(self.ensure_discovered.package_dir)
+        return expand.read_attr(attr_desc, package_dir, root_dir)
+
+    @classmethod
+    def _get_parser_compound(cls, *parse_methods):
+        """Returns parser function to represents value as a list.
+
+        Parses a value applying given methods one after another.
+
+        :param parse_methods:
+        :rtype: callable
+        """
+
+        def parse(value):
+            parsed = value
+
+            for method in parse_methods:
+                parsed = method(parsed)
+
+            return parsed
+
+        return parse
+
+    @classmethod
+    def _parse_section_to_dict_with_key(cls, section_options, values_parser):
+        """Parses section options into a dictionary.
+
+        Applies a given parser to each option in a section.
+
+        :param dict section_options:
+        :param callable values_parser: function with 2 args corresponding to key, value
+        :rtype: dict
+        """
+        value = {}
+        for key, (_, val) in section_options.items():
+            value[key] = values_parser(key, val)
+        return value
+
+    @classmethod
+    def _parse_section_to_dict(cls, section_options, values_parser=None):
+        """Parses section options into a dictionary.
+
+        Optionally applies a given parser to each value.
+
+        :param dict section_options:
+        :param callable values_parser: function with 1 arg corresponding to option value
+        :rtype: dict
+        """
+        parser = (lambda _, v: values_parser(v)) if values_parser else (lambda _, v: v)
+        return cls._parse_section_to_dict_with_key(section_options, parser)
+
+    def parse_section(self, section_options) -> None:
+        """Parses configuration file section.
+
+        :param dict section_options:
+        """
+        for name, (_, value) in section_options.items():
+            with contextlib.suppress(KeyError):
+                # Keep silent for a new option may appear anytime.
+                self[name] = value
+
+    def parse(self) -> None:
+        """Parses configuration file items from one
+        or more related sections.
+
+        """
+        for section_name, section_options in self.sections.items():
+            method_postfix = ''
+            if section_name:  # [section.option] variant
+                method_postfix = f"_{section_name}"
+
+            section_parser_method: Callable | None = getattr(
+                self,
+                # Dots in section names are translated into dunderscores.
+                f'parse_section{method_postfix}'.replace('.', '__'),
+                None,
+            )
+
+            if section_parser_method is None:
+                raise OptionError(
+                    "Unsupported distribution option section: "
+                    f"[{self.section_prefix}.{section_name}]"
+                )
+
+            section_parser_method(section_options)
+
+    def _deprecated_config_handler(self, func, msg, **kw):
+        """this function will wrap around parameters that are deprecated
+
+        :param msg: deprecation message
+        :param func: function to be wrapped around
+        """
+
+        @wraps(func)
+        def config_handler(*args, **kwargs):
+            kw.setdefault("stacklevel", 2)
+            _DeprecatedConfig.emit("Deprecated config in `setup.cfg`", msg, **kw)
+            return func(*args, **kwargs)
+
+        return config_handler
+
+
+class ConfigMetadataHandler(ConfigHandler["DistributionMetadata"]):
+    section_prefix = 'metadata'
+
+    aliases = {
+        'home_page': 'url',
+        'summary': 'description',
+        'classifier': 'classifiers',
+        'platform': 'platforms',
+    }
+
+    strict_mode = False
+    """We need to keep it loose, to be partially compatible with
+    `pbr` and `d2to1` packages which also uses `metadata` section.
+
+    """
+
+    def __init__(
+        self,
+        target_obj: DistributionMetadata,
+        options: AllCommandOptions,
+        ignore_option_errors: bool,
+        ensure_discovered: expand.EnsurePackagesDiscovered,
+        package_dir: dict | None = None,
+        root_dir: StrPath | None = os.curdir,
+    ) -> None:
+        super().__init__(target_obj, options, ignore_option_errors, ensure_discovered)
+        self.package_dir = package_dir
+        self.root_dir = root_dir
+
+    @property
+    def parsers(self):
+        """Metadata item name to parser function mapping."""
+        parse_list_static = self._get_parser_compound(self._parse_list, _static.List)
+        parse_dict_static = self._get_parser_compound(self._parse_dict, _static.Dict)
+        parse_file = partial(self._parse_file, root_dir=self.root_dir)
+        exclude_files_parser = self._exclude_files_parser
+
+        return {
+            'author': _static.Str,
+            'author_email': _static.Str,
+            'maintainer': _static.Str,
+            'maintainer_email': _static.Str,
+            'platforms': parse_list_static,
+            'keywords': parse_list_static,
+            'provides': parse_list_static,
+            'obsoletes': parse_list_static,
+            'classifiers': self._get_parser_compound(parse_file, parse_list_static),
+            'license': exclude_files_parser('license'),
+            'license_files': parse_list_static,
+            'description': parse_file,
+            'long_description': parse_file,
+            'long_description_content_type': _static.Str,
+            'version': self._parse_version,  # Cannot be marked as dynamic
+            'url': _static.Str,
+            'project_urls': parse_dict_static,
+        }
+
+    def _parse_version(self, value):
+        """Parses `version` option value.
+
+        :param value:
+        :rtype: str
+
+        """
+        version = self._parse_file(value, self.root_dir)
+
+        if version != value:
+            version = version.strip()
+            # Be strict about versions loaded from file because it's easy to
+            # accidentally include newlines and other unintended content
+            try:
+                Version(version)
+            except InvalidVersion as e:
+                raise OptionError(
+                    f'Version loaded from {value} does not '
+                    f'comply with PEP 440: {version}'
+                ) from e
+
+            return version
+
+        return expand.version(self._parse_attr(value, self.package_dir, self.root_dir))
+
+
+class ConfigOptionsHandler(ConfigHandler["Distribution"]):
+    section_prefix = 'options'
+
+    def __init__(
+        self,
+        target_obj: Distribution,
+        options: AllCommandOptions,
+        ignore_option_errors: bool,
+        ensure_discovered: expand.EnsurePackagesDiscovered,
+    ) -> None:
+        super().__init__(target_obj, options, ignore_option_errors, ensure_discovered)
+        self.root_dir = target_obj.src_root
+        self.package_dir: dict[str, str] = {}  # To be filled by `find_packages`
+
+    @classmethod
+    def _parse_list_semicolon(cls, value):
+        return cls._parse_list(value, separator=';')
+
+    def _parse_file_in_root(self, value):
+        return self._parse_file(value, root_dir=self.root_dir)
+
+    def _parse_requirements_list(self, label: str, value: str):
+        # Parse a requirements list, either by reading in a `file:`, or a list.
+        parsed = self._parse_list_semicolon(self._parse_file_in_root(value))
+        _warn_accidental_env_marker_misconfig(label, value, parsed)
+        # Filter it to only include lines that are not comments. `parse_list`
+        # will have stripped each line and filtered out empties.
+        return _static.List(line for line in parsed if not line.startswith("#"))
+        # ^-- Use `_static.List` to mark a non-`Dynamic` Core Metadata
+
+    @property
+    def parsers(self):
+        """Metadata item name to parser function mapping."""
+        parse_list = self._parse_list
+        parse_bool = self._parse_bool
+        parse_cmdclass = self._parse_cmdclass
+
+        return {
+            'zip_safe': parse_bool,
+            'include_package_data': parse_bool,
+            'package_dir': self._parse_dict,
+            'scripts': parse_list,
+            'eager_resources': parse_list,
+            'dependency_links': parse_list,
+            'namespace_packages': self._deprecated_config_handler(
+                parse_list,
+                "The namespace_packages parameter is deprecated, "
+                "consider using implicit namespaces instead (PEP 420).",
+                # TODO: define due date, see setuptools.dist:check_nsp.
+            ),
+            'install_requires': partial(  # Core Metadata
+                self._parse_requirements_list, "install_requires"
+            ),
+            'setup_requires': self._parse_list_semicolon,
+            'packages': self._parse_packages,
+            'entry_points': self._parse_file_in_root,
+            'py_modules': parse_list,
+            'python_requires': _static.SpecifierSet,  # Core Metadata
+            'cmdclass': parse_cmdclass,
+        }
+
+    def _parse_cmdclass(self, value):
+        package_dir = self.ensure_discovered.package_dir
+        return expand.cmdclass(self._parse_dict(value), package_dir, self.root_dir)
+
+    def _parse_packages(self, value):
+        """Parses `packages` option value.
+
+        :param value:
+        :rtype: list
+        """
+        find_directives = ['find:', 'find_namespace:']
+        trimmed_value = value.strip()
+
+        if trimmed_value not in find_directives:
+            return self._parse_list(value)
+
+        # Read function arguments from a dedicated section.
+        find_kwargs = self.parse_section_packages__find(
+            self.sections.get('packages.find', {})
+        )
+
+        find_kwargs.update(
+            namespaces=(trimmed_value == find_directives[1]),
+            root_dir=self.root_dir,
+            fill_package_dir=self.package_dir,
+        )
+
+        return expand.find_packages(**find_kwargs)
+
+    def parse_section_packages__find(self, section_options):
+        """Parses `packages.find` configuration file section.
+
+        To be used in conjunction with _parse_packages().
+
+        :param dict section_options:
+        """
+        section_data = self._parse_section_to_dict(section_options, self._parse_list)
+
+        valid_keys = ['where', 'include', 'exclude']
+        find_kwargs = {k: v for k, v in section_data.items() if k in valid_keys and v}
+
+        where = find_kwargs.get('where')
+        if where is not None:
+            find_kwargs['where'] = where[0]  # cast list to single val
+
+        return find_kwargs
+
+    def parse_section_entry_points(self, section_options) -> None:
+        """Parses `entry_points` configuration file section.
+
+        :param dict section_options:
+        """
+        parsed = self._parse_section_to_dict(section_options, self._parse_list)
+        self['entry_points'] = parsed
+
+    def _parse_package_data(self, section_options):
+        package_data = self._parse_section_to_dict(section_options, self._parse_list)
+        return expand.canonic_package_data(package_data)
+
+    def parse_section_package_data(self, section_options) -> None:
+        """Parses `package_data` configuration file section.
+
+        :param dict section_options:
+        """
+        self['package_data'] = self._parse_package_data(section_options)
+
+    def parse_section_exclude_package_data(self, section_options) -> None:
+        """Parses `exclude_package_data` configuration file section.
+
+        :param dict section_options:
+        """
+        self['exclude_package_data'] = self._parse_package_data(section_options)
+
+    def parse_section_extras_require(self, section_options) -> None:  # Core Metadata
+        """Parses `extras_require` configuration file section.
+
+        :param dict section_options:
+        """
+        parsed = self._parse_section_to_dict_with_key(
+            section_options,
+            lambda k, v: self._parse_requirements_list(f"extras_require[{k}]", v),
+        )
+
+        self['extras_require'] = _static.Dict(parsed)
+        # ^-- Use `_static.Dict` to mark a non-`Dynamic` Core Metadata
+
+    def parse_section_data_files(self, section_options) -> None:
+        """Parses `data_files` configuration file section.
+
+        :param dict section_options:
+        """
+        parsed = self._parse_section_to_dict(section_options, self._parse_list)
+        self['data_files'] = expand.canonic_data_files(parsed, self.root_dir)
+
+
+class _AmbiguousMarker(SetuptoolsDeprecationWarning):
+    _SUMMARY = "Ambiguous requirement marker."
+    _DETAILS = """
+    One of the parsed requirements in `{field}` looks like a valid environment marker:
+
+        {req!r}
+
+    Please make sure that the configuration file is correct.
+    You can use dangling lines to avoid this problem.
+    """
+    _SEE_DOCS = "userguide/declarative_config.html#opt-2"
+    # TODO: should we include due_date here? Initially introduced in 6 Aug 2022.
+    # Does this make sense with latest version of packaging?
+
+    @classmethod
+    def message(cls, **kw):
+        docs = f"https://setuptools.pypa.io/en/latest/{cls._SEE_DOCS}"
+        return cls._format(cls._SUMMARY, cls._DETAILS, see_url=docs, format_args=kw)
+
+
+class _DeprecatedConfig(SetuptoolsDeprecationWarning):
+    _SEE_DOCS = "userguide/declarative_config.html"
diff --git a/.venv/lib/python3.12/site-packages/setuptools/config/setuptools.schema.json b/.venv/lib/python3.12/site-packages/setuptools/config/setuptools.schema.json
new file mode 100644
index 0000000000000000000000000000000000000000..ec887b35736e4ede6265e95e3a9e2520b43cc40f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/config/setuptools.schema.json
@@ -0,0 +1,433 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+
+  "$id": "https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html",
+  "title": "``tool.setuptools`` table",
+  "$$description": [
+    "``setuptools``-specific configurations that can be set by users that require",
+    "customization.",
+    "These configurations are completely optional and probably can be skipped when",
+    "creating simple packages. They are equivalent to some of the `Keywords",
+    "<https://setuptools.pypa.io/en/latest/references/keywords.html>`_",
+    "used by the ``setup.py`` file, and can be set via the ``tool.setuptools`` table.",
+    "It considers only ``setuptools`` `parameters",
+    "<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#setuptools-specific-configuration>`_",
+    "that are not covered by :pep:`621`; and intentionally excludes ``dependency_links``",
+    "and ``setup_requires`` (incompatible with modern workflows/standards)."
+  ],
+
+  "type": "object",
+  "additionalProperties": false,
+  "properties": {
+    "platforms": {
+      "type": "array",
+      "items": {"type": "string"}
+    },
+    "provides": {
+      "$$description": [
+        "Package and virtual package names contained within this package",
+        "**(not supported by pip)**"
+      ],
+      "type": "array",
+      "items": {"type": "string", "format": "pep508-identifier"}
+    },
+    "obsoletes": {
+      "$$description": [
+        "Packages which this package renders obsolete",
+        "**(not supported by pip)**"
+      ],
+      "type": "array",
+      "items": {"type": "string", "format": "pep508-identifier"}
+    },
+    "zip-safe": {
+      "$$description": [
+        "Whether the project can be safely installed and run from a zip file.",
+        "**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and",
+        "``setup.py install`` in the context of ``eggs`` (**DEPRECATED**)."
+      ],
+      "type": "boolean"
+    },
+    "script-files": {
+      "$$description": [
+        "Legacy way of defining scripts (entry-points are preferred).",
+        "Equivalent to the ``script`` keyword in ``setup.py``",
+        "(it was renamed to avoid confusion with entry-point based ``project.scripts``",
+        "defined in :pep:`621`).",
+        "**DISCOURAGED**: generic script wrappers are tricky and may not work properly.",
+        "Whenever possible, please use ``project.scripts`` instead."
+      ],
+      "type": "array",
+      "items": {"type": "string"},
+      "$comment": "TODO: is this field deprecated/should be removed?"
+    },
+    "eager-resources": {
+      "$$description": [
+        "Resources that should be extracted together, if any of them is needed,",
+        "or if any C extensions included in the project are imported.",
+        "**OBSOLETE**: only relevant for ``pkg_resources``, ``easy_install`` and",
+        "``setup.py install`` in the context of ``eggs`` (**DEPRECATED**)."
+      ],
+      "type": "array",
+      "items": {"type": "string"}
+    },
+    "packages": {
+      "$$description": [
+        "Packages that should be included in the distribution.",
+        "It can be given either as a list of package identifiers",
+        "or as a ``dict``-like structure with a single key ``find``",
+        "which corresponds to a dynamic call to",
+        "``setuptools.config.expand.find_packages`` function.",
+        "The ``find`` key is associated with a nested ``dict``-like structure that can",
+        "contain ``where``, ``include``, ``exclude`` and ``namespaces`` keys,",
+        "mimicking the keyword arguments of the associated function."
+      ],
+      "oneOf": [
+        {
+          "title": "Array of Python package identifiers",
+          "type": "array",
+          "items": {"$ref": "#/definitions/package-name"}
+        },
+        {"$ref": "#/definitions/find-directive"}
+      ]
+    },
+    "package-dir": {
+      "$$description": [
+        ":class:`dict`-like structure mapping from package names to directories where their",
+        "code can be found.",
+        "The empty string (as key) means that all packages are contained inside",
+        "the given directory will be included in the distribution."
+      ],
+      "type": "object",
+      "additionalProperties": false,
+      "propertyNames": {
+        "anyOf": [{"const": ""}, {"$ref": "#/definitions/package-name"}]
+      },
+      "patternProperties": {
+        "^.*$": {"type": "string" }
+      }
+    },
+    "package-data": {
+      "$$description": [
+        "Mapping from package names to lists of glob patterns.",
+        "Usually this option is not needed when using ``include-package-data = true``",
+        "For more information on how to include data files, check ``setuptools`` `docs",
+        "<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_."
+      ],
+      "type": "object",
+      "additionalProperties": false,
+      "propertyNames": {
+        "anyOf": [{"type": "string", "format": "python-module-name"}, {"const": "*"}]
+      },
+      "patternProperties": {
+        "^.*$": {"type": "array", "items": {"type": "string"}}
+      }
+    },
+    "include-package-data": {
+      "$$description": [
+        "Automatically include any data files inside the package directories",
+        "that are specified by ``MANIFEST.in``",
+        "For more information on how to include data files, check ``setuptools`` `docs",
+        "<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_."
+      ],
+      "type": "boolean"
+    },
+    "exclude-package-data": {
+      "$$description": [
+        "Mapping from package names to lists of glob patterns that should be excluded",
+        "For more information on how to include data files, check ``setuptools`` `docs",
+        "<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_."
+      ],
+      "type": "object",
+      "additionalProperties": false,
+      "propertyNames": {
+        "anyOf": [{"type": "string", "format": "python-module-name"}, {"const": "*"}]
+      },
+      "patternProperties": {
+          "^.*$": {"type": "array", "items": {"type": "string"}}
+      }
+    },
+    "namespace-packages": {
+      "type": "array",
+      "items": {"type": "string", "format": "python-module-name-relaxed"},
+      "$comment": "https://setuptools.pypa.io/en/latest/userguide/package_discovery.html",
+      "description": "**DEPRECATED**: use implicit namespaces instead (:pep:`420`)."
+    },
+    "py-modules": {
+      "description": "Modules that setuptools will manipulate",
+      "type": "array",
+      "items": {"type": "string", "format": "python-module-name-relaxed"},
+      "$comment": "TODO: clarify the relationship with ``packages``"
+    },
+    "ext-modules": {
+      "description": "Extension modules to be compiled by setuptools",
+      "type": "array",
+      "items": {"$ref": "#/definitions/ext-module"}
+    },
+    "data-files": {
+      "$$description": [
+        "``dict``-like structure where each key represents a directory and",
+        "the value is a list of glob patterns that should be installed in them.",
+        "**DISCOURAGED**: please notice this might not work as expected with wheels.",
+        "Whenever possible, consider using data files inside the package directories",
+        "(or create a new namespace package that only contains data files).",
+        "See `data files support",
+        "<https://setuptools.pypa.io/en/latest/userguide/datafiles.html>`_."
+      ],
+      "type": "object",
+      "patternProperties": {
+          "^.*$": {"type": "array", "items": {"type": "string"}}
+      }
+    },
+    "cmdclass": {
+      "$$description": [
+        "Mapping of distutils-style command names to ``setuptools.Command`` subclasses",
+        "which in turn should be represented by strings with a qualified class name",
+        "(i.e., \"dotted\" form with module), e.g.::\n\n",
+        "    cmdclass = {mycmd = \"pkg.subpkg.module.CommandClass\"}\n\n",
+        "The command class should be a directly defined at the top-level of the",
+        "containing module (no class nesting)."
+      ],
+      "type": "object",
+      "patternProperties": {
+          "^.*$": {"type": "string", "format": "python-qualified-identifier"}
+      }
+    },
+    "license-files": {
+      "type": "array",
+      "items": {"type": "string"},
+      "$$description": [
+        "**PROVISIONAL**: list of glob patterns for all license files being distributed.",
+        "(likely to become standard with :pep:`639`).",
+        "By default: ``['LICEN[CS]E*', 'COPYING*', 'NOTICE*', 'AUTHORS*']``"
+      ],
+      "$comment": "TODO: revise if PEP 639 is accepted. Probably ``project.license-files``?"
+    },
+    "dynamic": {
+      "type": "object",
+      "description": "Instructions for loading :pep:`621`-related metadata dynamically",
+      "additionalProperties": false,
+      "properties": {
+        "version": {
+          "$$description": [
+            "A version dynamically loaded via either the ``attr:`` or ``file:``",
+            "directives. Please make sure the given file or attribute respects :pep:`440`.",
+            "Also ensure to set ``project.dynamic`` accordingly."
+          ],
+          "oneOf": [
+            {"$ref": "#/definitions/attr-directive"},
+            {"$ref": "#/definitions/file-directive"}
+          ]
+        },
+        "classifiers": {"$ref": "#/definitions/file-directive"},
+        "description": {"$ref": "#/definitions/file-directive"},
+        "entry-points": {"$ref": "#/definitions/file-directive"},
+        "dependencies": {"$ref": "#/definitions/file-directive-for-dependencies"},
+        "optional-dependencies": {
+          "type": "object",
+          "propertyNames": {"type": "string", "format": "pep508-identifier"},
+          "additionalProperties": false,
+          "patternProperties": {
+            ".+": {"$ref": "#/definitions/file-directive-for-dependencies"}
+          }
+        },
+        "readme": {
+          "type": "object",
+          "anyOf": [
+            {"$ref": "#/definitions/file-directive"},
+            {
+              "type": "object",
+              "properties": {
+                "content-type": {"type": "string"},
+                "file": { "$ref": "#/definitions/file-directive/properties/file" }
+              },
+              "additionalProperties": false}
+          ],
+          "required": ["file"]
+        }
+      }
+    }
+  },
+
+  "definitions": {
+    "package-name": {
+      "$id": "#/definitions/package-name",
+      "title": "Valid package name",
+      "description": "Valid package name (importable or :pep:`561`).",
+      "type": "string",
+      "anyOf": [
+        {"type": "string", "format": "python-module-name-relaxed"},
+        {"type": "string", "format": "pep561-stub-name"}
+      ]
+    },
+    "ext-module": {
+      "$id": "#/definitions/ext-module",
+      "title": "Extension module",
+      "description": "Parameters to construct a :class:`setuptools.Extension` object",
+      "type": "object",
+      "required": ["name", "sources"],
+      "additionalProperties": false,
+      "properties": {
+        "name": {
+          "type": "string",
+          "format": "python-module-name-relaxed"
+        },
+        "sources": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "include-dirs":{
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "define-macros": {
+          "type": "array",
+          "items": {
+            "type": "array",
+            "items": [
+              {"description": "macro name", "type": "string"},
+              {"description": "macro value", "oneOf": [{"type": "string"}, {"type": "null"}]}
+            ],
+            "additionalItems": false
+          }
+        },
+        "undef-macros": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "library-dirs": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "libraries": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "runtime-library-dirs": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "extra-objects": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "extra-compile-args": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "extra-link-args": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "export-symbols": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "swig-opts": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "depends": {
+          "type": "array",
+          "items": {"type": "string"}
+        },
+        "language": {"type": "string"},
+        "optional": {"type": "boolean"},
+        "py-limited-api": {"type": "boolean"}
+      }
+    },
+    "file-directive": {
+      "$id": "#/definitions/file-directive",
+      "title": "'file:' directive",
+      "description":
+        "Value is read from a file (or list of files and then concatenated)",
+      "type": "object",
+      "additionalProperties": false,
+      "properties": {
+        "file": {
+          "oneOf": [
+            {"type": "string"},
+            {"type": "array", "items": {"type": "string"}}
+          ]
+        }
+      },
+      "required": ["file"]
+    },
+    "file-directive-for-dependencies": {
+      "title": "'file:' directive for dependencies",
+      "allOf": [
+        {
+          "$$description": [
+            "**BETA**: subset of the ``requirements.txt`` format",
+            "without ``pip`` flags and options",
+            "(one :pep:`508`-compliant string per line,",
+            "lines that are blank or start with ``#`` are excluded).",
+            "See `dynamic metadata",
+            "<https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata>`_."
+          ]
+        },
+        {"$ref": "#/definitions/file-directive"}
+      ]
+    },
+    "attr-directive": {
+      "title": "'attr:' directive",
+      "$id": "#/definitions/attr-directive",
+      "$$description": [
+        "Value is read from a module attribute. Supports callables and iterables;",
+        "unsupported types are cast via ``str()``"
+      ],
+      "type": "object",
+      "additionalProperties": false,
+      "properties": {
+        "attr": {"type": "string", "format": "python-qualified-identifier"}
+      },
+      "required": ["attr"]
+    },
+    "find-directive": {
+      "$id": "#/definitions/find-directive",
+      "title": "'find:' directive",
+      "type": "object",
+      "additionalProperties": false,
+      "properties": {
+        "find": {
+          "type": "object",
+          "$$description": [
+            "Dynamic `package discovery",
+            "<https://setuptools.pypa.io/en/latest/userguide/package_discovery.html>`_."
+          ],
+          "additionalProperties": false,
+          "properties": {
+            "where": {
+              "description":
+                "Directories to be searched for packages (Unix-style relative path)",
+              "type": "array",
+              "items": {"type": "string"}
+            },
+            "exclude": {
+              "type": "array",
+              "$$description": [
+                "Exclude packages that match the values listed in this field.",
+                "Can container shell-style wildcards (e.g. ``'pkg.*'``)"
+              ],
+              "items": {"type": "string"}
+            },
+            "include": {
+              "type": "array",
+              "$$description": [
+                "Restrict the found packages to just the ones listed in this field.",
+                "Can container shell-style wildcards (e.g. ``'pkg.*'``)"
+              ],
+              "items": {"type": "string"}
+            },
+            "namespaces": {
+              "type": "boolean",
+              "$$description": [
+                "When ``True``, directories without a ``__init__.py`` file will also",
+                "be scanned for :pep:`420`-style implicit namespaces"
+              ]
+            }
+          }
+        }
+      }
+    }
+  }
+}
diff --git a/.venv/lib/python3.12/site-packages/setuptools/depends.py b/.venv/lib/python3.12/site-packages/setuptools/depends.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5223b79561c36d9b6c45ead78288098e1cb0f1d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/depends.py
@@ -0,0 +1,185 @@
+from __future__ import annotations
+
+import contextlib
+import dis
+import marshal
+import sys
+from types import CodeType
+from typing import Any, Literal, TypeVar
+
+from packaging.version import Version
+
+from . import _imp
+from ._imp import PY_COMPILED, PY_FROZEN, PY_SOURCE, find_module
+
+_T = TypeVar("_T")
+
+__all__ = ['Require', 'find_module']
+
+
+class Require:
+    """A prerequisite to building or installing a distribution"""
+
+    def __init__(
+        self,
+        name,
+        requested_version,
+        module,
+        homepage: str = '',
+        attribute=None,
+        format=None,
+    ) -> None:
+        if format is None and requested_version is not None:
+            format = Version
+
+        if format is not None:
+            requested_version = format(requested_version)
+            if attribute is None:
+                attribute = '__version__'
+
+        self.__dict__.update(locals())
+        del self.self
+
+    def full_name(self):
+        """Return full package/distribution name, w/version"""
+        if self.requested_version is not None:
+            return f'{self.name}-{self.requested_version}'
+        return self.name
+
+    def version_ok(self, version):
+        """Is 'version' sufficiently up-to-date?"""
+        return (
+            self.attribute is None
+            or self.format is None
+            or str(version) != "unknown"
+            and self.format(version) >= self.requested_version
+        )
+
+    def get_version(
+        self, paths=None, default: _T | Literal["unknown"] = "unknown"
+    ) -> _T | Literal["unknown"] | None | Any:
+        """Get version number of installed module, 'None', or 'default'
+
+        Search 'paths' for module.  If not found, return 'None'.  If found,
+        return the extracted version attribute, or 'default' if no version
+        attribute was specified, or the value cannot be determined without
+        importing the module.  The version is formatted according to the
+        requirement's version format (if any), unless it is 'None' or the
+        supplied 'default'.
+        """
+
+        if self.attribute is None:
+            try:
+                f, _p, _i = find_module(self.module, paths)
+            except ImportError:
+                return None
+            if f:
+                f.close()
+            return default
+
+        v = get_module_constant(self.module, self.attribute, default, paths)
+
+        if v is not None and v is not default and self.format is not None:
+            return self.format(v)
+
+        return v
+
+    def is_present(self, paths=None):
+        """Return true if dependency is present on 'paths'"""
+        return self.get_version(paths) is not None
+
+    def is_current(self, paths=None):
+        """Return true if dependency is present and up-to-date on 'paths'"""
+        version = self.get_version(paths)
+        if version is None:
+            return False
+        return self.version_ok(str(version))
+
+
+def maybe_close(f):
+    @contextlib.contextmanager
+    def empty():
+        yield
+        return
+
+    if not f:
+        return empty()
+
+    return contextlib.closing(f)
+
+
+# Some objects are not available on some platforms.
+# XXX it'd be better to test assertions about bytecode instead.
+if not sys.platform.startswith('java') and sys.platform != 'cli':
+
+    def get_module_constant(
+        module, symbol, default: _T | int = -1, paths=None
+    ) -> _T | int | None | Any:
+        """Find 'module' by searching 'paths', and extract 'symbol'
+
+        Return 'None' if 'module' does not exist on 'paths', or it does not define
+        'symbol'.  If the module defines 'symbol' as a constant, return the
+        constant.  Otherwise, return 'default'."""
+
+        try:
+            f, path, (_suffix, _mode, kind) = info = find_module(module, paths)
+        except ImportError:
+            # Module doesn't exist
+            return None
+
+        with maybe_close(f):
+            if kind == PY_COMPILED:
+                f.read(8)  # skip magic & date
+                code = marshal.load(f)
+            elif kind == PY_FROZEN:
+                code = _imp.get_frozen_object(module, paths)
+            elif kind == PY_SOURCE:
+                code = compile(f.read(), path, 'exec')
+            else:
+                # Not something we can parse; we'll have to import it.  :(
+                imported = _imp.get_module(module, paths, info)
+                return getattr(imported, symbol, None)
+
+        return extract_constant(code, symbol, default)
+
+    def extract_constant(
+        code: CodeType, symbol: str, default: _T | int = -1
+    ) -> _T | int | None | Any:
+        """Extract the constant value of 'symbol' from 'code'
+
+        If the name 'symbol' is bound to a constant value by the Python code
+        object 'code', return that value.  If 'symbol' is bound to an expression,
+        return 'default'.  Otherwise, return 'None'.
+
+        Return value is based on the first assignment to 'symbol'.  'symbol' must
+        be a global, or at least a non-"fast" local in the code block.  That is,
+        only 'STORE_NAME' and 'STORE_GLOBAL' opcodes are checked, and 'symbol'
+        must be present in 'code.co_names'.
+        """
+        if symbol not in code.co_names:
+            # name's not there, can't possibly be an assignment
+            return None
+
+        name_idx = list(code.co_names).index(symbol)
+
+        STORE_NAME = dis.opmap['STORE_NAME']
+        STORE_GLOBAL = dis.opmap['STORE_GLOBAL']
+        LOAD_CONST = dis.opmap['LOAD_CONST']
+
+        const = default
+
+        for byte_code in dis.Bytecode(code):
+            op = byte_code.opcode
+            arg = byte_code.arg
+
+            if op == LOAD_CONST:
+                assert arg is not None
+                const = code.co_consts[arg]
+            elif arg == name_idx and (op == STORE_NAME or op == STORE_GLOBAL):
+                return const
+            else:
+                const = default
+
+        return None
+
+    __all__ += ['get_module_constant', 'extract_constant']
diff --git a/.venv/lib/python3.12/site-packages/setuptools/discovery.py b/.venv/lib/python3.12/site-packages/setuptools/discovery.py
new file mode 100644
index 0000000000000000000000000000000000000000..c88839918562bad12f1a2e72309f1eacfe23349c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/discovery.py
@@ -0,0 +1,614 @@
+"""Automatic discovery of Python modules and packages (for inclusion in the
+distribution) and other config values.
+
+For the purposes of this module, the following nomenclature is used:
+
+- "src-layout": a directory representing a Python project that contains a "src"
+  folder. Everything under the "src" folder is meant to be included in the
+  distribution when packaging the project. Example::
+
+    .
+    ├── tox.ini
+    ├── pyproject.toml
+    └── src/
+        └── mypkg/
+            ├── __init__.py
+            ├── mymodule.py
+            └── my_data_file.txt
+
+- "flat-layout": a Python project that does not use "src-layout" but instead
+  have a directory under the project root for each package::
+
+    .
+    ├── tox.ini
+    ├── pyproject.toml
+    └── mypkg/
+        ├── __init__.py
+        ├── mymodule.py
+        └── my_data_file.txt
+
+- "single-module": a project that contains a single Python script direct under
+  the project root (no directory used)::
+
+    .
+    ├── tox.ini
+    ├── pyproject.toml
+    └── mymodule.py
+
+"""
+
+from __future__ import annotations
+
+import itertools
+import os
+from collections.abc import Iterable, Iterator, Mapping
+from fnmatch import fnmatchcase
+from glob import glob
+from pathlib import Path
+from typing import TYPE_CHECKING, ClassVar
+
+import _distutils_hack.override  # noqa: F401
+
+from ._path import StrPath
+
+from distutils import log
+from distutils.util import convert_path
+
+if TYPE_CHECKING:
+    from setuptools import Distribution
+
+chain_iter = itertools.chain.from_iterable
+
+
+def _valid_name(path: StrPath) -> bool:
+    # Ignore invalid names that cannot be imported directly
+    return os.path.basename(path).isidentifier()
+
+
+class _Filter:
+    """
+    Given a list of patterns, create a callable that will be true only if
+    the input matches at least one of the patterns.
+    """
+
+    def __init__(self, *patterns: str) -> None:
+        self._patterns = dict.fromkeys(patterns)
+
+    def __call__(self, item: str) -> bool:
+        return any(fnmatchcase(item, pat) for pat in self._patterns)
+
+    def __contains__(self, item: str) -> bool:
+        return item in self._patterns
+
+
+class _Finder:
+    """Base class that exposes functionality for module/package finders"""
+
+    ALWAYS_EXCLUDE: ClassVar[tuple[str, ...]] = ()
+    DEFAULT_EXCLUDE: ClassVar[tuple[str, ...]] = ()
+
+    @classmethod
+    def find(
+        cls,
+        where: StrPath = '.',
+        exclude: Iterable[str] = (),
+        include: Iterable[str] = ('*',),
+    ) -> list[str]:
+        """Return a list of all Python items (packages or modules, depending on
+        the finder implementation) found within directory 'where'.
+
+        'where' is the root directory which will be searched.
+        It should be supplied as a "cross-platform" (i.e. URL-style) path;
+        it will be converted to the appropriate local path syntax.
+
+        'exclude' is a sequence of names to exclude; '*' can be used
+        as a wildcard in the names.
+        When finding packages, 'foo.*' will exclude all subpackages of 'foo'
+        (but not 'foo' itself).
+
+        'include' is a sequence of names to include.
+        If it's specified, only the named items will be included.
+        If it's not specified, all found items will be included.
+        'include' can contain shell style wildcard patterns just like
+        'exclude'.
+        """
+
+        exclude = exclude or cls.DEFAULT_EXCLUDE
+        return list(
+            cls._find_iter(
+                convert_path(str(where)),
+                _Filter(*cls.ALWAYS_EXCLUDE, *exclude),
+                _Filter(*include),
+            )
+        )
+
+    @classmethod
+    def _find_iter(
+        cls, where: StrPath, exclude: _Filter, include: _Filter
+    ) -> Iterator[str]:
+        raise NotImplementedError
+
+
+class PackageFinder(_Finder):
+    """
+    Generate a list of all Python packages found within a directory
+    """
+
+    ALWAYS_EXCLUDE = ("ez_setup", "*__pycache__")
+
+    @classmethod
+    def _find_iter(
+        cls, where: StrPath, exclude: _Filter, include: _Filter
+    ) -> Iterator[str]:
+        """
+        All the packages found in 'where' that pass the 'include' filter, but
+        not the 'exclude' filter.
+        """
+        for root, dirs, files in os.walk(str(where), followlinks=True):
+            # Copy dirs to iterate over it, then empty dirs.
+            all_dirs = dirs[:]
+            dirs[:] = []
+
+            for dir in all_dirs:
+                full_path = os.path.join(root, dir)
+                rel_path = os.path.relpath(full_path, where)
+                package = rel_path.replace(os.path.sep, '.')
+
+                # Skip directory trees that are not valid packages
+                if '.' in dir or not cls._looks_like_package(full_path, package):
+                    continue
+
+                # Should this package be included?
+                if include(package) and not exclude(package):
+                    yield package
+
+                # Early pruning if there is nothing else to be scanned
+                if f"{package}*" in exclude or f"{package}.*" in exclude:
+                    continue
+
+                # Keep searching subdirectories, as there may be more packages
+                # down there, even if the parent was excluded.
+                dirs.append(dir)
+
+    @staticmethod
+    def _looks_like_package(path: StrPath, _package_name: str) -> bool:
+        """Does a directory look like a package?"""
+        return os.path.isfile(os.path.join(path, '__init__.py'))
+
+
+class PEP420PackageFinder(PackageFinder):
+    @staticmethod
+    def _looks_like_package(_path: StrPath, _package_name: str) -> bool:
+        return True
+
+
+class ModuleFinder(_Finder):
+    """Find isolated Python modules.
+    This function will **not** recurse subdirectories.
+    """
+
+    @classmethod
+    def _find_iter(
+        cls, where: StrPath, exclude: _Filter, include: _Filter
+    ) -> Iterator[str]:
+        for file in glob(os.path.join(where, "*.py")):
+            module, _ext = os.path.splitext(os.path.basename(file))
+
+            if not cls._looks_like_module(module):
+                continue
+
+            if include(module) and not exclude(module):
+                yield module
+
+    _looks_like_module = staticmethod(_valid_name)
+
+
+# We have to be extra careful in the case of flat layout to not include files
+# and directories not meant for distribution (e.g. tool-related)
+
+
+class FlatLayoutPackageFinder(PEP420PackageFinder):
+    _EXCLUDE = (
+        "ci",
+        "bin",
+        "debian",
+        "doc",
+        "docs",
+        "documentation",
+        "manpages",
+        "news",
+        "newsfragments",
+        "changelog",
+        "test",
+        "tests",
+        "unit_test",
+        "unit_tests",
+        "example",
+        "examples",
+        "scripts",
+        "tools",
+        "util",
+        "utils",
+        "python",
+        "build",
+        "dist",
+        "venv",
+        "env",
+        "requirements",
+        # ---- Task runners / Build tools ----
+        "tasks",  # invoke
+        "fabfile",  # fabric
+        "site_scons",  # SCons
+        # ---- Other tools ----
+        "benchmark",
+        "benchmarks",
+        "exercise",
+        "exercises",
+        "htmlcov",  # Coverage.py
+        # ---- Hidden directories/Private packages ----
+        "[._]*",
+    )
+
+    DEFAULT_EXCLUDE = tuple(chain_iter((p, f"{p}.*") for p in _EXCLUDE))
+    """Reserved package names"""
+
+    @staticmethod
+    def _looks_like_package(_path: StrPath, package_name: str) -> bool:
+        names = package_name.split('.')
+        # Consider PEP 561
+        root_pkg_is_valid = names[0].isidentifier() or names[0].endswith("-stubs")
+        return root_pkg_is_valid and all(name.isidentifier() for name in names[1:])
+
+
+class FlatLayoutModuleFinder(ModuleFinder):
+    DEFAULT_EXCLUDE = (
+        "setup",
+        "conftest",
+        "test",
+        "tests",
+        "example",
+        "examples",
+        "build",
+        # ---- Task runners ----
+        "toxfile",
+        "noxfile",
+        "pavement",
+        "dodo",
+        "tasks",
+        "fabfile",
+        # ---- Other tools ----
+        "[Ss][Cc]onstruct",  # SCons
+        "conanfile",  # Connan: C/C++ build tool
+        "manage",  # Django
+        "benchmark",
+        "benchmarks",
+        "exercise",
+        "exercises",
+        # ---- Hidden files/Private modules ----
+        "[._]*",
+    )
+    """Reserved top-level module names"""
+
+
+def _find_packages_within(root_pkg: str, pkg_dir: StrPath) -> list[str]:
+    nested = PEP420PackageFinder.find(pkg_dir)
+    return [root_pkg] + [".".join((root_pkg, n)) for n in nested]
+
+
+class ConfigDiscovery:
+    """Fill-in metadata and options that can be automatically derived
+    (from other metadata/options, the file system or conventions)
+    """
+
+    def __init__(self, distribution: Distribution) -> None:
+        self.dist = distribution
+        self._called = False
+        self._disabled = False
+        self._skip_ext_modules = False
+
+    def _disable(self):
+        """Internal API to disable automatic discovery"""
+        self._disabled = True
+
+    def _ignore_ext_modules(self):
+        """Internal API to disregard ext_modules.
+
+        Normally auto-discovery would not be triggered if ``ext_modules`` are set
+        (this is done for backward compatibility with existing packages relying on
+        ``setup.py`` or ``setup.cfg``). However, ``setuptools`` can call this function
+        to ignore given ``ext_modules`` and proceed with the auto-discovery if
+        ``packages`` and ``py_modules`` are not given (e.g. when using pyproject.toml
+        metadata).
+        """
+        self._skip_ext_modules = True
+
+    @property
+    def _root_dir(self) -> StrPath:
+        # The best is to wait until `src_root` is set in dist, before using _root_dir.
+        return self.dist.src_root or os.curdir
+
+    @property
+    def _package_dir(self) -> dict[str, str]:
+        if self.dist.package_dir is None:
+            return {}
+        return self.dist.package_dir
+
+    def __call__(
+        self, force: bool = False, name: bool = True, ignore_ext_modules: bool = False
+    ):
+        """Automatically discover missing configuration fields
+        and modifies the given ``distribution`` object in-place.
+
+        Note that by default this will only have an effect the first time the
+        ``ConfigDiscovery`` object is called.
+
+        To repeatedly invoke automatic discovery (e.g. when the project
+        directory changes), please use ``force=True`` (or create a new
+        ``ConfigDiscovery`` instance).
+        """
+        if force is False and (self._called or self._disabled):
+            # Avoid overhead of multiple calls
+            return
+
+        self._analyse_package_layout(ignore_ext_modules)
+        if name:
+            self.analyse_name()  # depends on ``packages`` and ``py_modules``
+
+        self._called = True
+
+    def _explicitly_specified(self, ignore_ext_modules: bool) -> bool:
+        """``True`` if the user has specified some form of package/module listing"""
+        ignore_ext_modules = ignore_ext_modules or self._skip_ext_modules
+        ext_modules = not (self.dist.ext_modules is None or ignore_ext_modules)
+        return (
+            self.dist.packages is not None
+            or self.dist.py_modules is not None
+            or ext_modules
+            or hasattr(self.dist, "configuration")
+            and self.dist.configuration
+            # ^ Some projects use numpy.distutils.misc_util.Configuration
+        )
+
+    def _analyse_package_layout(self, ignore_ext_modules: bool) -> bool:
+        if self._explicitly_specified(ignore_ext_modules):
+            # For backward compatibility, just try to find modules/packages
+            # when nothing is given
+            return True
+
+        log.debug(
+            "No `packages` or `py_modules` configuration, performing "
+            "automatic discovery."
+        )
+
+        return (
+            self._analyse_explicit_layout()
+            or self._analyse_src_layout()
+            # flat-layout is the trickiest for discovery so it should be last
+            or self._analyse_flat_layout()
+        )
+
+    def _analyse_explicit_layout(self) -> bool:
+        """The user can explicitly give a package layout via ``package_dir``"""
+        package_dir = self._package_dir.copy()  # don't modify directly
+        package_dir.pop("", None)  # This falls under the "src-layout" umbrella
+        root_dir = self._root_dir
+
+        if not package_dir:
+            return False
+
+        log.debug(f"`explicit-layout` detected -- analysing {package_dir}")
+        pkgs = chain_iter(
+            _find_packages_within(pkg, os.path.join(root_dir, parent_dir))
+            for pkg, parent_dir in package_dir.items()
+        )
+        self.dist.packages = list(pkgs)
+        log.debug(f"discovered packages -- {self.dist.packages}")
+        return True
+
+    def _analyse_src_layout(self) -> bool:
+        """Try to find all packages or modules under the ``src`` directory
+        (or anything pointed by ``package_dir[""]``).
+
+        The "src-layout" is relatively safe for automatic discovery.
+        We assume that everything within is meant to be included in the
+        distribution.
+
+        If ``package_dir[""]`` is not given, but the ``src`` directory exists,
+        this function will set ``package_dir[""] = "src"``.
+        """
+        package_dir = self._package_dir
+        src_dir = os.path.join(self._root_dir, package_dir.get("", "src"))
+        if not os.path.isdir(src_dir):
+            return False
+
+        log.debug(f"`src-layout` detected -- analysing {src_dir}")
+        package_dir.setdefault("", os.path.basename(src_dir))
+        self.dist.package_dir = package_dir  # persist eventual modifications
+        self.dist.packages = PEP420PackageFinder.find(src_dir)
+        self.dist.py_modules = ModuleFinder.find(src_dir)
+        log.debug(f"discovered packages -- {self.dist.packages}")
+        log.debug(f"discovered py_modules -- {self.dist.py_modules}")
+        return True
+
+    def _analyse_flat_layout(self) -> bool:
+        """Try to find all packages and modules under the project root.
+
+        Since the ``flat-layout`` is more dangerous in terms of accidentally including
+        extra files/directories, this function is more conservative and will raise an
+        error if multiple packages or modules are found.
+
+        This assumes that multi-package dists are uncommon and refuse to support that
+        use case in order to be able to prevent unintended errors.
+        """
+        log.debug(f"`flat-layout` detected -- analysing {self._root_dir}")
+        return self._analyse_flat_packages() or self._analyse_flat_modules()
+
+    def _analyse_flat_packages(self) -> bool:
+        self.dist.packages = FlatLayoutPackageFinder.find(self._root_dir)
+        top_level = remove_nested_packages(remove_stubs(self.dist.packages))
+        log.debug(f"discovered packages -- {self.dist.packages}")
+        self._ensure_no_accidental_inclusion(top_level, "packages")
+        return bool(top_level)
+
+    def _analyse_flat_modules(self) -> bool:
+        self.dist.py_modules = FlatLayoutModuleFinder.find(self._root_dir)
+        log.debug(f"discovered py_modules -- {self.dist.py_modules}")
+        self._ensure_no_accidental_inclusion(self.dist.py_modules, "modules")
+        return bool(self.dist.py_modules)
+
+    def _ensure_no_accidental_inclusion(self, detected: list[str], kind: str):
+        if len(detected) > 1:
+            from inspect import cleandoc
+
+            from setuptools.errors import PackageDiscoveryError
+
+            msg = f"""Multiple top-level {kind} discovered in a flat-layout: {detected}.
+
+            To avoid accidental inclusion of unwanted files or directories,
+            setuptools will not proceed with this build.
+
+            If you are trying to create a single distribution with multiple {kind}
+            on purpose, you should not rely on automatic discovery.
+            Instead, consider the following options:
+
+            1. set up custom discovery (`find` directive with `include` or `exclude`)
+            2. use a `src-layout`
+            3. explicitly set `py_modules` or `packages` with a list of names
+
+            To find more information, look for "package discovery" on setuptools docs.
+            """
+            raise PackageDiscoveryError(cleandoc(msg))
+
+    def analyse_name(self) -> None:
+        """The packages/modules are the essential contribution of the author.
+        Therefore the name of the distribution can be derived from them.
+        """
+        if self.dist.metadata.name or self.dist.name:
+            # get_name() is not reliable (can return "UNKNOWN")
+            return
+
+        log.debug("No `name` configuration, performing automatic discovery")
+
+        name = (
+            self._find_name_single_package_or_module()
+            or self._find_name_from_packages()
+        )
+        if name:
+            self.dist.metadata.name = name
+
+    def _find_name_single_package_or_module(self) -> str | None:
+        """Exactly one module or package"""
+        for field in ('packages', 'py_modules'):
+            items = getattr(self.dist, field, None) or []
+            if items and len(items) == 1:
+                log.debug(f"Single module/package detected, name: {items[0]}")
+                return items[0]
+
+        return None
+
+    def _find_name_from_packages(self) -> str | None:
+        """Try to find the root package that is not a PEP 420 namespace"""
+        if not self.dist.packages:
+            return None
+
+        packages = remove_stubs(sorted(self.dist.packages, key=len))
+        package_dir = self.dist.package_dir or {}
+
+        parent_pkg = find_parent_package(packages, package_dir, self._root_dir)
+        if parent_pkg:
+            log.debug(f"Common parent package detected, name: {parent_pkg}")
+            return parent_pkg
+
+        log.warn("No parent package detected, impossible to derive `name`")
+        return None
+
+
+def remove_nested_packages(packages: list[str]) -> list[str]:
+    """Remove nested packages from a list of packages.
+
+    >>> remove_nested_packages(["a", "a.b1", "a.b2", "a.b1.c1"])
+    ['a']
+    >>> remove_nested_packages(["a", "b", "c.d", "c.d.e.f", "g.h", "a.a1"])
+    ['a', 'b', 'c.d', 'g.h']
+    """
+    pkgs = sorted(packages, key=len)
+    top_level = pkgs[:]
+    size = len(pkgs)
+    for i, name in enumerate(reversed(pkgs)):
+        if any(name.startswith(f"{other}.") for other in top_level):
+            top_level.pop(size - i - 1)
+
+    return top_level
+
+
+def remove_stubs(packages: list[str]) -> list[str]:
+    """Remove type stubs (:pep:`561`) from a list of packages.
+
+    >>> remove_stubs(["a", "a.b", "a-stubs", "a-stubs.b.c", "b", "c-stubs"])
+    ['a', 'a.b', 'b']
+    """
+    return [pkg for pkg in packages if not pkg.split(".")[0].endswith("-stubs")]
+
+
+def find_parent_package(
+    packages: list[str], package_dir: Mapping[str, str], root_dir: StrPath
+) -> str | None:
+    """Find the parent package that is not a namespace."""
+    packages = sorted(packages, key=len)
+    common_ancestors = []
+    for i, name in enumerate(packages):
+        if not all(n.startswith(f"{name}.") for n in packages[i + 1 :]):
+            # Since packages are sorted by length, this condition is able
+            # to find a list of all common ancestors.
+            # When there is divergence (e.g. multiple root packages)
+            # the list will be empty
+            break
+        common_ancestors.append(name)
+
+    for name in common_ancestors:
+        pkg_path = find_package_path(name, package_dir, root_dir)
+        init = os.path.join(pkg_path, "__init__.py")
+        if os.path.isfile(init):
+            return name
+
+    return None
+
+
+def find_package_path(
+    name: str, package_dir: Mapping[str, str], root_dir: StrPath
+) -> str:
+    """Given a package name, return the path where it should be found on
+    disk, considering the ``package_dir`` option.
+
+    >>> path = find_package_path("my.pkg", {"": "root/is/nested"}, ".")
+    >>> path.replace(os.sep, "/")
+    './root/is/nested/my/pkg'
+
+    >>> path = find_package_path("my.pkg", {"my": "root/is/nested"}, ".")
+    >>> path.replace(os.sep, "/")
+    './root/is/nested/pkg'
+
+    >>> path = find_package_path("my.pkg", {"my.pkg": "root/is/nested"}, ".")
+    >>> path.replace(os.sep, "/")
+    './root/is/nested'
+
+    >>> path = find_package_path("other.pkg", {"my.pkg": "root/is/nested"}, ".")
+    >>> path.replace(os.sep, "/")
+    './other/pkg'
+    """
+    parts = name.split(".")
+    for i in range(len(parts), 0, -1):
+        # Look backwards, the most specific package_dir first
+        partial_name = ".".join(parts[:i])
+        if partial_name in package_dir:
+            parent = package_dir[partial_name]
+            return os.path.join(root_dir, parent, *parts[i:])
+
+    parent = package_dir.get("") or ""
+    return os.path.join(root_dir, *parent.split("/"), *parts)
+
+
+def construct_package_dir(packages: list[str], package_path: StrPath) -> dict[str, str]:
+    parent_pkgs = remove_nested_packages(packages)
+    prefix = Path(package_path).parts
+    return {pkg: "/".join([*prefix, *pkg.split(".")]) for pkg in parent_pkgs}
diff --git a/.venv/lib/python3.12/site-packages/setuptools/dist.py b/.venv/lib/python3.12/site-packages/setuptools/dist.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d972cc49bda84edee28c90be172b17107f00b07
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/dist.py
@@ -0,0 +1,1121 @@
+from __future__ import annotations
+
+import functools
+import io
+import itertools
+import numbers
+import os
+import re
+import sys
+from collections.abc import Iterable, Iterator, MutableMapping, Sequence
+from glob import glob
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Union
+
+from more_itertools import partition, unique_everseen
+from packaging.markers import InvalidMarker, Marker
+from packaging.specifiers import InvalidSpecifier, SpecifierSet
+from packaging.version import Version
+
+from . import (
+    _entry_points,
+    _reqs,
+    _static,
+    command as _,  # noqa: F401 # imported for side-effects
+)
+from ._importlib import metadata
+from ._normalization import _canonicalize_license_expression
+from ._path import StrPath
+from ._reqs import _StrOrIter
+from .config import pyprojecttoml, setupcfg
+from .discovery import ConfigDiscovery
+from .errors import InvalidConfigError
+from .monkey import get_unpatched
+from .warnings import InformationOnly, SetuptoolsDeprecationWarning
+
+import distutils.cmd
+import distutils.command
+import distutils.core
+import distutils.dist
+import distutils.log
+from distutils.debug import DEBUG
+from distutils.errors import DistutilsOptionError, DistutilsSetupError
+from distutils.fancy_getopt import translate_longopt
+from distutils.util import strtobool
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+    from pkg_resources import Distribution as _pkg_resources_Distribution
+
+
+__all__ = ['Distribution']
+
+_sequence = tuple, list
+"""
+:meta private:
+
+Supported iterable types that are known to be:
+- ordered (which `set` isn't)
+- not match a str (which `Sequence[str]` does)
+- not imply a nested type (like `dict`)
+for use with `isinstance`.
+"""
+_Sequence: TypeAlias = Union[tuple[str, ...], list[str]]
+# This is how stringifying _Sequence would look in Python 3.10
+_sequence_type_repr = "tuple[str, ...] | list[str]"
+_OrderedStrSequence: TypeAlias = Union[str, dict[str, Any], Sequence[str]]
+"""
+:meta private:
+Avoid single-use iterable. Disallow sets.
+A poor approximation of an OrderedSequence (dict doesn't match a Sequence).
+"""
+
+
+def __getattr__(name: str) -> Any:  # pragma: no cover
+    if name == "sequence":
+        SetuptoolsDeprecationWarning.emit(
+            "`setuptools.dist.sequence` is an internal implementation detail.",
+            "Please define your own `sequence = tuple, list` instead.",
+            due_date=(2025, 8, 28),  # Originally added on 2024-08-27
+        )
+        return _sequence
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+def check_importable(dist, attr, value):
+    try:
+        ep = metadata.EntryPoint(value=value, name=None, group=None)
+        assert not ep.extras
+    except (TypeError, ValueError, AttributeError, AssertionError) as e:
+        raise DistutilsSetupError(
+            f"{attr!r} must be importable 'module:attrs' string (got {value!r})"
+        ) from e
+
+
+def assert_string_list(dist, attr: str, value: _Sequence) -> None:
+    """Verify that value is a string list"""
+    try:
+        # verify that value is a list or tuple to exclude unordered
+        # or single-use iterables
+        assert isinstance(value, _sequence)
+        # verify that elements of value are strings
+        assert ''.join(value) != value
+    except (TypeError, ValueError, AttributeError, AssertionError) as e:
+        raise DistutilsSetupError(
+            f"{attr!r} must be of type <{_sequence_type_repr}> (got {value!r})"
+        ) from e
+
+
+def check_nsp(dist, attr, value):
+    """Verify that namespace packages are valid"""
+    ns_packages = value
+    assert_string_list(dist, attr, ns_packages)
+    for nsp in ns_packages:
+        if not dist.has_contents_for(nsp):
+            raise DistutilsSetupError(
+                f"Distribution contains no modules or packages for namespace package {nsp!r}"
+            )
+        parent, _sep, _child = nsp.rpartition('.')
+        if parent and parent not in ns_packages:
+            distutils.log.warn(
+                "WARNING: %r is declared as a package namespace, but %r"
+                " is not: please correct this in setup.py",
+                nsp,
+                parent,
+            )
+        SetuptoolsDeprecationWarning.emit(
+            "The namespace_packages parameter is deprecated.",
+            "Please replace its usage with implicit namespaces (PEP 420).",
+            see_docs="references/keywords.html#keyword-namespace-packages",
+            # TODO: define due_date, it may break old packages that are no longer
+            # maintained (e.g. sphinxcontrib extensions) when installed from source.
+            # Warning officially introduced in May 2022, however the deprecation
+            # was mentioned much earlier in the docs (May 2020, see #2149).
+        )
+
+
+def check_extras(dist, attr, value):
+    """Verify that extras_require mapping is valid"""
+    try:
+        list(itertools.starmap(_check_extra, value.items()))
+    except (TypeError, ValueError, AttributeError) as e:
+        raise DistutilsSetupError(
+            "'extras_require' must be a dictionary whose values are "
+            "strings or lists of strings containing valid project/version "
+            "requirement specifiers."
+        ) from e
+
+
+def _check_extra(extra, reqs):
+    _name, _sep, marker = extra.partition(':')
+    try:
+        _check_marker(marker)
+    except InvalidMarker:
+        msg = f"Invalid environment marker: {marker} ({extra!r})"
+        raise DistutilsSetupError(msg) from None
+    list(_reqs.parse(reqs))
+
+
+def _check_marker(marker):
+    if not marker:
+        return
+    m = Marker(marker)
+    m.evaluate()
+
+
+def assert_bool(dist, attr, value):
+    """Verify that value is True, False, 0, or 1"""
+    if bool(value) != value:
+        raise DistutilsSetupError(f"{attr!r} must be a boolean value (got {value!r})")
+
+
+def invalid_unless_false(dist, attr, value):
+    if not value:
+        DistDeprecationWarning.emit(f"{attr} is ignored.")
+        # TODO: should there be a `due_date` here?
+        return
+    raise DistutilsSetupError(f"{attr} is invalid.")
+
+
+def check_requirements(dist, attr: str, value: _OrderedStrSequence) -> None:
+    """Verify that install_requires is a valid requirements list"""
+    try:
+        list(_reqs.parse(value))
+        if isinstance(value, set):
+            raise TypeError("Unordered types are not allowed")
+    except (TypeError, ValueError) as error:
+        msg = (
+            f"{attr!r} must be a string or iterable of strings "
+            f"containing valid project/version requirement specifiers; {error}"
+        )
+        raise DistutilsSetupError(msg) from error
+
+
+def check_specifier(dist, attr, value):
+    """Verify that value is a valid version specifier"""
+    try:
+        SpecifierSet(value)
+    except (InvalidSpecifier, AttributeError) as error:
+        msg = f"{attr!r} must be a string containing valid version specifiers; {error}"
+        raise DistutilsSetupError(msg) from error
+
+
+def check_entry_points(dist, attr, value):
+    """Verify that entry_points map is parseable"""
+    try:
+        _entry_points.load(value)
+    except Exception as e:
+        raise DistutilsSetupError(e) from e
+
+
+def check_package_data(dist, attr, value):
+    """Verify that value is a dictionary of package names to glob lists"""
+    if not isinstance(value, dict):
+        raise DistutilsSetupError(
+            f"{attr!r} must be a dictionary mapping package names to lists of "
+            "string wildcard patterns"
+        )
+    for k, v in value.items():
+        if not isinstance(k, str):
+            raise DistutilsSetupError(
+                f"keys of {attr!r} dict must be strings (got {k!r})"
+            )
+        assert_string_list(dist, f'values of {attr!r} dict', v)
+
+
+def check_packages(dist, attr, value):
+    for pkgname in value:
+        if not re.match(r'\w+(\.\w+)*', pkgname):
+            distutils.log.warn(
+                "WARNING: %r not a valid package name; please use only "
+                ".-separated package names in setup.py",
+                pkgname,
+            )
+
+
+if TYPE_CHECKING:
+    # Work around a mypy issue where type[T] can't be used as a base: https://github.com/python/mypy/issues/10962
+    from distutils.core import Distribution as _Distribution
+else:
+    _Distribution = get_unpatched(distutils.core.Distribution)
+
+
+class Distribution(_Distribution):
+    """Distribution with support for tests and package data
+
+    This is an enhanced version of 'distutils.dist.Distribution' that
+    effectively adds the following new optional keyword arguments to 'setup()':
+
+     'install_requires' -- a string or sequence of strings specifying project
+        versions that the distribution requires when installed, in the format
+        used by 'pkg_resources.require()'.  They will be installed
+        automatically when the package is installed.  If you wish to use
+        packages that are not available in PyPI, or want to give your users an
+        alternate download location, you can add a 'find_links' option to the
+        '[easy_install]' section of your project's 'setup.cfg' file, and then
+        setuptools will scan the listed web pages for links that satisfy the
+        requirements.
+
+     'extras_require' -- a dictionary mapping names of optional "extras" to the
+        additional requirement(s) that using those extras incurs. For example,
+        this::
+
+            extras_require = dict(reST = ["docutils>=0.3", "reSTedit"])
+
+        indicates that the distribution can optionally provide an extra
+        capability called "reST", but it can only be used if docutils and
+        reSTedit are installed.  If the user installs your package using
+        EasyInstall and requests one of your extras, the corresponding
+        additional requirements will be installed if needed.
+
+     'package_data' -- a dictionary mapping package names to lists of filenames
+        or globs to use to find data files contained in the named packages.
+        If the dictionary has filenames or globs listed under '""' (the empty
+        string), those names will be searched for in every package, in addition
+        to any names for the specific package.  Data files found using these
+        names/globs will be installed along with the package, in the same
+        location as the package.  Note that globs are allowed to reference
+        the contents of non-package subdirectories, as long as you use '/' as
+        a path separator.  (Globs are automatically converted to
+        platform-specific paths at runtime.)
+
+    In addition to these new keywords, this class also has several new methods
+    for manipulating the distribution's contents.  For example, the 'include()'
+    and 'exclude()' methods can be thought of as in-place add and subtract
+    commands that add or remove packages, modules, extensions, and so on from
+    the distribution.
+    """
+
+    _DISTUTILS_UNSUPPORTED_METADATA = {
+        'long_description_content_type': lambda: None,
+        'project_urls': dict,
+        'provides_extras': dict,  # behaves like an ordered set
+        'license_expression': lambda: None,
+        'license_file': lambda: None,
+        'license_files': lambda: None,
+        'install_requires': list,
+        'extras_require': dict,
+    }
+
+    # Used by build_py, editable_wheel and install_lib commands for legacy namespaces
+    namespace_packages: list[str]  #: :meta private: DEPRECATED
+
+    # Any: Dynamic assignment results in Incompatible types in assignment
+    def __init__(self, attrs: MutableMapping[str, Any] | None = None) -> None:
+        have_package_data = hasattr(self, "package_data")
+        if not have_package_data:
+            self.package_data: dict[str, list[str]] = {}
+        attrs = attrs or {}
+        self.dist_files: list[tuple[str, str, str]] = []
+        self.include_package_data: bool | None = None
+        self.exclude_package_data: dict[str, list[str]] | None = None
+        # Filter-out setuptools' specific options.
+        self.src_root: str | None = attrs.pop("src_root", None)
+        self.dependency_links: list[str] = attrs.pop('dependency_links', [])
+        self.setup_requires: list[str] = attrs.pop('setup_requires', [])
+        for ep in metadata.entry_points(group='distutils.setup_keywords'):
+            vars(self).setdefault(ep.name, None)
+
+        metadata_only = set(self._DISTUTILS_UNSUPPORTED_METADATA)
+        metadata_only -= {"install_requires", "extras_require"}
+        dist_attrs = {k: v for k, v in attrs.items() if k not in metadata_only}
+        _Distribution.__init__(self, dist_attrs)
+
+        # Private API (setuptools-use only, not restricted to Distribution)
+        # Stores files that are referenced by the configuration and need to be in the
+        # sdist (e.g. `version = file: VERSION.txt`)
+        self._referenced_files = set[str]()
+
+        self.set_defaults = ConfigDiscovery(self)
+
+        self._set_metadata_defaults(attrs)
+
+        self.metadata.version = self._normalize_version(self.metadata.version)
+        self._finalize_requires()
+
+    def _validate_metadata(self):
+        required = {"name"}
+        provided = {
+            key
+            for key in vars(self.metadata)
+            if getattr(self.metadata, key, None) is not None
+        }
+        missing = required - provided
+
+        if missing:
+            msg = f"Required package metadata is missing: {missing}"
+            raise DistutilsSetupError(msg)
+
+    def _set_metadata_defaults(self, attrs):
+        """
+        Fill-in missing metadata fields not supported by distutils.
+        Some fields may have been set by other tools (e.g. pbr).
+        Those fields (vars(self.metadata)) take precedence to
+        supplied attrs.
+        """
+        for option, default in self._DISTUTILS_UNSUPPORTED_METADATA.items():
+            vars(self.metadata).setdefault(option, attrs.get(option, default()))
+
+    @staticmethod
+    def _normalize_version(version):
+        from . import sic
+
+        if isinstance(version, numbers.Number):
+            # Some people apparently take "version number" too literally :)
+            version = str(version)
+        elif isinstance(version, sic) or version is None:
+            return version
+
+        normalized = str(Version(version))
+        if version != normalized:
+            InformationOnly.emit(f"Normalizing '{version}' to '{normalized}'")
+            return normalized
+        return version
+
+    def _finalize_requires(self):
+        """
+        Set `metadata.python_requires` and fix environment markers
+        in `install_requires` and `extras_require`.
+        """
+        if getattr(self, 'python_requires', None):
+            self.metadata.python_requires = self.python_requires
+
+        self._normalize_requires()
+        self.metadata.install_requires = self.install_requires
+        self.metadata.extras_require = self.extras_require
+
+        if self.extras_require:
+            for extra in self.extras_require.keys():
+                # Setuptools allows a weird "<name>:<env markers> syntax for extras
+                extra = extra.split(':')[0]
+                if extra:
+                    self.metadata.provides_extras.setdefault(extra)
+
+    def _normalize_requires(self):
+        """Make sure requirement-related attributes exist and are normalized"""
+        install_requires = getattr(self, "install_requires", None) or []
+        extras_require = getattr(self, "extras_require", None) or {}
+
+        # Preserve the "static"-ness of values parsed from config files
+        list_ = _static.List if _static.is_static(install_requires) else list
+        self.install_requires = list_(map(str, _reqs.parse(install_requires)))
+
+        dict_ = _static.Dict if _static.is_static(extras_require) else dict
+        self.extras_require = dict_(
+            (k, list(map(str, _reqs.parse(v or [])))) for k, v in extras_require.items()
+        )
+
+    def _finalize_license_expression(self) -> None:
+        """
+        Normalize license and license_expression.
+        >>> dist = Distribution({"license_expression": _static.Str("mit aNd  gpl-3.0-OR-later")})
+        >>> _static.is_static(dist.metadata.license_expression)
+        True
+        >>> dist._finalize_license_expression()
+        >>> _static.is_static(dist.metadata.license_expression)  # preserve "static-ness"
+        True
+        >>> print(dist.metadata.license_expression)
+        MIT AND GPL-3.0-or-later
+        """
+        classifiers = self.metadata.get_classifiers()
+        license_classifiers = [cl for cl in classifiers if cl.startswith("License :: ")]
+
+        license_expr = self.metadata.license_expression
+        if license_expr:
+            str_ = _static.Str if _static.is_static(license_expr) else str
+            normalized = str_(_canonicalize_license_expression(license_expr))
+            if license_expr != normalized:
+                InformationOnly.emit(f"Normalizing '{license_expr}' to '{normalized}'")
+                self.metadata.license_expression = normalized
+            if license_classifiers:
+                raise InvalidConfigError(
+                    "License classifiers have been superseded by license expressions "
+                    "(see https://peps.python.org/pep-0639/). Please remove:\n\n"
+                    + "\n".join(license_classifiers),
+                )
+        elif license_classifiers:
+            pypa_guides = "guides/writing-pyproject-toml/#license"
+            SetuptoolsDeprecationWarning.emit(
+                "License classifiers are deprecated.",
+                "Please consider removing the following classifiers in favor of a "
+                "SPDX license expression:\n\n" + "\n".join(license_classifiers),
+                see_url=f"https://packaging.python.org/en/latest/{pypa_guides}",
+                # Warning introduced on 2025-02-17
+                # TODO: Should we add a due date? It may affect old/unmaintained
+                #       packages in the ecosystem and cause problems...
+            )
+
+    def _finalize_license_files(self) -> None:
+        """Compute names of all license files which should be included."""
+        license_files: list[str] | None = self.metadata.license_files
+        patterns = license_files or []
+
+        license_file: str | None = self.metadata.license_file
+        if license_file and license_file not in patterns:
+            patterns.append(license_file)
+
+        if license_files is None and license_file is None:
+            # Default patterns match the ones wheel uses
+            # See https://wheel.readthedocs.io/en/stable/user_guide.html
+            # -> 'Including license files in the generated wheel file'
+            patterns = ['LICEN[CS]E*', 'COPYING*', 'NOTICE*', 'AUTHORS*']
+            files = self._expand_patterns(patterns, enforce_match=False)
+        else:  # Patterns explicitly given by the user
+            files = self._expand_patterns(patterns, enforce_match=True)
+
+        self.metadata.license_files = list(unique_everseen(files))
+
+    @classmethod
+    def _expand_patterns(
+        cls, patterns: list[str], enforce_match: bool = True
+    ) -> Iterator[str]:
+        """
+        >>> list(Distribution._expand_patterns(['LICENSE']))
+        ['LICENSE']
+        >>> list(Distribution._expand_patterns(['pyproject.toml', 'LIC*']))
+        ['pyproject.toml', 'LICENSE']
+        >>> list(Distribution._expand_patterns(['setuptools/**/pyprojecttoml.py']))
+        ['setuptools/config/pyprojecttoml.py']
+        """
+        return (
+            path.replace(os.sep, "/")
+            for pattern in patterns
+            for path in sorted(cls._find_pattern(pattern, enforce_match))
+            if not path.endswith('~') and os.path.isfile(path)
+        )
+
+    @staticmethod
+    def _find_pattern(pattern: str, enforce_match: bool = True) -> list[str]:
+        r"""
+        >>> Distribution._find_pattern("LICENSE")
+        ['LICENSE']
+        >>> Distribution._find_pattern("/LICENSE.MIT")
+        Traceback (most recent call last):
+        ...
+        setuptools.errors.InvalidConfigError: Pattern '/LICENSE.MIT' should be relative...
+        >>> Distribution._find_pattern("../LICENSE.MIT")
+        Traceback (most recent call last):
+        ...
+        setuptools.warnings.SetuptoolsDeprecationWarning: ...Pattern '../LICENSE.MIT' cannot contain '..'...
+        >>> Distribution._find_pattern("LICEN{CSE*")
+        Traceback (most recent call last):
+        ...
+        setuptools.warnings.SetuptoolsDeprecationWarning: ...Pattern 'LICEN{CSE*' contains invalid characters...
+        """
+        pypa_guides = "specifications/glob-patterns/"
+        if ".." in pattern:
+            SetuptoolsDeprecationWarning.emit(
+                f"Pattern {pattern!r} cannot contain '..'",
+                """
+                Please ensure the files specified are contained by the root
+                of the Python package (normally marked by `pyproject.toml`).
+                """,
+                see_url=f"https://packaging.python.org/en/latest/{pypa_guides}",
+                due_date=(2026, 3, 20),  # Introduced in 2025-03-20
+                # Replace with InvalidConfigError after deprecation
+            )
+        if pattern.startswith((os.sep, "/")) or ":\\" in pattern:
+            raise InvalidConfigError(
+                f"Pattern {pattern!r} should be relative and must not start with '/'"
+            )
+        if re.match(r'^[\w\-\.\/\*\?\[\]]+$', pattern) is None:
+            SetuptoolsDeprecationWarning.emit(
+                "Please provide a valid glob pattern.",
+                "Pattern {pattern!r} contains invalid characters.",
+                pattern=pattern,
+                see_url=f"https://packaging.python.org/en/latest/{pypa_guides}",
+                due_date=(2026, 3, 20),  # Introduced in 2025-02-20
+            )
+
+        found = glob(pattern, recursive=True)
+
+        if enforce_match and not found:
+            SetuptoolsDeprecationWarning.emit(
+                "Cannot find any files for the given pattern.",
+                "Pattern {pattern!r} did not match any files.",
+                pattern=pattern,
+                due_date=(2026, 3, 20),  # Introduced in 2025-02-20
+                # PEP 639 requires us to error, but as a transition period
+                # we will only issue a warning to give people time to prepare.
+                # After the transition, this should raise an InvalidConfigError.
+            )
+        return found
+
+    # FIXME: 'Distribution._parse_config_files' is too complex (14)
+    def _parse_config_files(self, filenames=None):  # noqa: C901
+        """
+        Adapted from distutils.dist.Distribution.parse_config_files,
+        this method provides the same functionality in subtly-improved
+        ways.
+        """
+        from configparser import ConfigParser
+
+        # Ignore install directory options if we have a venv
+        ignore_options = (
+            []
+            if sys.prefix == sys.base_prefix
+            else [
+                'install-base',
+                'install-platbase',
+                'install-lib',
+                'install-platlib',
+                'install-purelib',
+                'install-headers',
+                'install-scripts',
+                'install-data',
+                'prefix',
+                'exec-prefix',
+                'home',
+                'user',
+                'root',
+            ]
+        )
+
+        ignore_options = frozenset(ignore_options)
+
+        if filenames is None:
+            filenames = self.find_config_files()
+
+        if DEBUG:
+            self.announce("Distribution.parse_config_files():")
+
+        parser = ConfigParser()
+        parser.optionxform = str
+        for filename in filenames:
+            with open(filename, encoding='utf-8') as reader:
+                if DEBUG:
+                    self.announce("  reading {filename}".format(**locals()))
+                parser.read_file(reader)
+            for section in parser.sections():
+                options = parser.options(section)
+                opt_dict = self.get_option_dict(section)
+
+                for opt in options:
+                    if opt == '__name__' or opt in ignore_options:
+                        continue
+
+                    val = parser.get(section, opt)
+                    opt = self._enforce_underscore(opt, section)
+                    opt = self._enforce_option_lowercase(opt, section)
+                    opt_dict[opt] = (filename, val)
+
+            # Make the ConfigParser forget everything (so we retain
+            # the original filenames that options come from)
+            parser.__init__()
+
+        if 'global' not in self.command_options:
+            return
+
+        # If there was a "global" section in the config file, use it
+        # to set Distribution options.
+
+        for opt, (src, val) in self.command_options['global'].items():
+            alias = self.negative_opt.get(opt)
+            if alias:
+                val = not strtobool(val)
+            elif opt in ('verbose', 'dry_run'):  # ugh!
+                val = strtobool(val)
+
+            try:
+                setattr(self, alias or opt, val)
+            except ValueError as e:
+                raise DistutilsOptionError(e) from e
+
+    def _enforce_underscore(self, opt: str, section: str) -> str:
+        if "-" not in opt or self._skip_setupcfg_normalization(section):
+            return opt
+
+        underscore_opt = opt.replace('-', '_')
+        affected = f"(Affected: {self.metadata.name})." if self.metadata.name else ""
+        SetuptoolsDeprecationWarning.emit(
+            f"Invalid dash-separated key {opt!r} in {section!r} (setup.cfg), "
+            f"please use the underscore name {underscore_opt!r} instead.",
+            f"""
+            Usage of dash-separated {opt!r} will not be supported in future
+            versions. Please use the underscore name {underscore_opt!r} instead.
+            {affected}
+            """,
+            see_docs="userguide/declarative_config.html",
+            due_date=(2026, 3, 3),
+            # Warning initially introduced in 3 Mar 2021
+        )
+        return underscore_opt
+
+    def _enforce_option_lowercase(self, opt: str, section: str) -> str:
+        if opt.islower() or self._skip_setupcfg_normalization(section):
+            return opt
+
+        lowercase_opt = opt.lower()
+        affected = f"(Affected: {self.metadata.name})." if self.metadata.name else ""
+        SetuptoolsDeprecationWarning.emit(
+            f"Invalid uppercase key {opt!r} in {section!r} (setup.cfg), "
+            f"please use lowercase {lowercase_opt!r} instead.",
+            f"""
+            Usage of uppercase key {opt!r} in {section!r} will not be supported in
+            future versions. Please use lowercase {lowercase_opt!r} instead.
+            {affected}
+            """,
+            see_docs="userguide/declarative_config.html",
+            due_date=(2026, 3, 3),
+            # Warning initially introduced in 6 Mar 2021
+        )
+        return lowercase_opt
+
+    def _skip_setupcfg_normalization(self, section: str) -> bool:
+        skip = (
+            'options.extras_require',
+            'options.data_files',
+            'options.entry_points',
+            'options.package_data',
+            'options.exclude_package_data',
+        )
+        return section in skip or not self._is_setuptools_section(section)
+
+    def _is_setuptools_section(self, section: str) -> bool:
+        return (
+            section == "metadata"
+            or section.startswith("options")
+            or section in _setuptools_commands()
+        )
+
+    # FIXME: 'Distribution._set_command_options' is too complex (14)
+    def _set_command_options(self, command_obj, option_dict=None):  # noqa: C901
+        """
+        Set the options for 'command_obj' from 'option_dict'.  Basically
+        this means copying elements of a dictionary ('option_dict') to
+        attributes of an instance ('command').
+
+        'command_obj' must be a Command instance.  If 'option_dict' is not
+        supplied, uses the standard option dictionary for this command
+        (from 'self.command_options').
+
+        (Adopted from distutils.dist.Distribution._set_command_options)
+        """
+        command_name = command_obj.get_command_name()
+        if option_dict is None:
+            option_dict = self.get_option_dict(command_name)
+
+        if DEBUG:
+            self.announce(f"  setting options for '{command_name}' command:")
+        for option, (source, value) in option_dict.items():
+            if DEBUG:
+                self.announce(f"    {option} = {value} (from {source})")
+            try:
+                bool_opts = [translate_longopt(o) for o in command_obj.boolean_options]
+            except AttributeError:
+                bool_opts = []
+            try:
+                neg_opt = command_obj.negative_opt
+            except AttributeError:
+                neg_opt = {}
+
+            try:
+                is_string = isinstance(value, str)
+                if option in neg_opt and is_string:
+                    setattr(command_obj, neg_opt[option], not strtobool(value))
+                elif option in bool_opts and is_string:
+                    setattr(command_obj, option, strtobool(value))
+                elif hasattr(command_obj, option):
+                    setattr(command_obj, option, value)
+                else:
+                    raise DistutilsOptionError(
+                        f"error in {source}: command '{command_name}' has no such option '{option}'"
+                    )
+            except ValueError as e:
+                raise DistutilsOptionError(e) from e
+
+    def _get_project_config_files(self, filenames: Iterable[StrPath] | None):
+        """Add default file and split between INI and TOML"""
+        tomlfiles = []
+        standard_project_metadata = Path(self.src_root or os.curdir, "pyproject.toml")
+        if filenames is not None:
+            parts = partition(lambda f: Path(f).suffix == ".toml", filenames)
+            filenames = list(parts[0])  # 1st element => predicate is False
+            tomlfiles = list(parts[1])  # 2nd element => predicate is True
+        elif standard_project_metadata.exists():
+            tomlfiles = [standard_project_metadata]
+        return filenames, tomlfiles
+
+    def parse_config_files(
+        self,
+        filenames: Iterable[StrPath] | None = None,
+        ignore_option_errors: bool = False,
+    ) -> None:
+        """Parses configuration files from various levels
+        and loads configuration.
+        """
+        inifiles, tomlfiles = self._get_project_config_files(filenames)
+
+        self._parse_config_files(filenames=inifiles)
+
+        setupcfg.parse_configuration(
+            self, self.command_options, ignore_option_errors=ignore_option_errors
+        )
+        for filename in tomlfiles:
+            pyprojecttoml.apply_configuration(self, filename, ignore_option_errors)
+
+        self._finalize_requires()
+        self._finalize_license_expression()
+        self._finalize_license_files()
+
+    def fetch_build_eggs(
+        self, requires: _StrOrIter
+    ) -> list[_pkg_resources_Distribution]:
+        """Resolve pre-setup requirements"""
+        from .installer import _fetch_build_eggs
+
+        return _fetch_build_eggs(self, requires)
+
+    def finalize_options(self) -> None:
+        """
+        Allow plugins to apply arbitrary operations to the
+        distribution. Each hook may optionally define a 'order'
+        to influence the order of execution. Smaller numbers
+        go first and the default is 0.
+        """
+        group = 'setuptools.finalize_distribution_options'
+
+        def by_order(hook):
+            return getattr(hook, 'order', 0)
+
+        defined = metadata.entry_points(group=group)
+        filtered = itertools.filterfalse(self._removed, defined)
+        loaded = map(lambda e: e.load(), filtered)
+        for ep in sorted(loaded, key=by_order):
+            ep(self)
+
+    @staticmethod
+    def _removed(ep):
+        """
+        When removing an entry point, if metadata is loaded
+        from an older version of Setuptools, that removed
+        entry point will attempt to be loaded and will fail.
+        See #2765 for more details.
+        """
+        removed = {
+            # removed 2021-09-05
+            '2to3_doctests',
+        }
+        return ep.name in removed
+
+    def _finalize_setup_keywords(self):
+        for ep in metadata.entry_points(group='distutils.setup_keywords'):
+            value = getattr(self, ep.name, None)
+            if value is not None:
+                ep.load()(self, ep.name, value)
+
+    def get_egg_cache_dir(self):
+        from . import windows_support
+
+        egg_cache_dir = os.path.join(os.curdir, '.eggs')
+        if not os.path.exists(egg_cache_dir):
+            os.mkdir(egg_cache_dir)
+            windows_support.hide_file(egg_cache_dir)
+            readme_txt_filename = os.path.join(egg_cache_dir, 'README.txt')
+            with open(readme_txt_filename, 'w', encoding="utf-8") as f:
+                f.write(
+                    'This directory contains eggs that were downloaded '
+                    'by setuptools to build, test, and run plug-ins.\n\n'
+                )
+                f.write(
+                    'This directory caches those eggs to prevent '
+                    'repeated downloads.\n\n'
+                )
+                f.write('However, it is safe to delete this directory.\n\n')
+
+        return egg_cache_dir
+
+    def fetch_build_egg(self, req):
+        """Fetch an egg needed for building"""
+        from .installer import fetch_build_egg
+
+        return fetch_build_egg(self, req)
+
+    def get_command_class(self, command: str) -> type[distutils.cmd.Command]:  # type: ignore[override] # Not doing complex overrides yet
+        """Pluggable version of get_command_class()"""
+        if command in self.cmdclass:
+            return self.cmdclass[command]
+
+        # Special case bdist_wheel so it's never loaded from "wheel"
+        if command == 'bdist_wheel':
+            from .command.bdist_wheel import bdist_wheel
+
+            return bdist_wheel
+
+        eps = metadata.entry_points(group='distutils.commands', name=command)
+        for ep in eps:
+            self.cmdclass[command] = cmdclass = ep.load()
+            return cmdclass
+        else:
+            return _Distribution.get_command_class(self, command)
+
+    def print_commands(self):
+        for ep in metadata.entry_points(group='distutils.commands'):
+            if ep.name not in self.cmdclass:
+                cmdclass = ep.load()
+                self.cmdclass[ep.name] = cmdclass
+        return _Distribution.print_commands(self)
+
+    def get_command_list(self):
+        for ep in metadata.entry_points(group='distutils.commands'):
+            if ep.name not in self.cmdclass:
+                cmdclass = ep.load()
+                self.cmdclass[ep.name] = cmdclass
+        return _Distribution.get_command_list(self)
+
+    def include(self, **attrs) -> None:
+        """Add items to distribution that are named in keyword arguments
+
+        For example, 'dist.include(py_modules=["x"])' would add 'x' to
+        the distribution's 'py_modules' attribute, if it was not already
+        there.
+
+        Currently, this method only supports inclusion for attributes that are
+        lists or tuples.  If you need to add support for adding to other
+        attributes in this or a subclass, you can add an '_include_X' method,
+        where 'X' is the name of the attribute.  The method will be called with
+        the value passed to 'include()'.  So, 'dist.include(foo={"bar":"baz"})'
+        will try to call 'dist._include_foo({"bar":"baz"})', which can then
+        handle whatever special inclusion logic is needed.
+        """
+        for k, v in attrs.items():
+            include = getattr(self, '_include_' + k, None)
+            if include:
+                include(v)
+            else:
+                self._include_misc(k, v)
+
+    def exclude_package(self, package: str) -> None:
+        """Remove packages, modules, and extensions in named package"""
+
+        pfx = package + '.'
+        if self.packages:
+            self.packages = [
+                p for p in self.packages if p != package and not p.startswith(pfx)
+            ]
+
+        if self.py_modules:
+            self.py_modules = [
+                p for p in self.py_modules if p != package and not p.startswith(pfx)
+            ]
+
+        if self.ext_modules:
+            self.ext_modules = [
+                p
+                for p in self.ext_modules
+                if p.name != package and not p.name.startswith(pfx)
+            ]
+
+    def has_contents_for(self, package: str) -> bool:
+        """Return true if 'exclude_package(package)' would do something"""
+
+        pfx = package + '.'
+
+        for p in self.iter_distribution_names():
+            if p == package or p.startswith(pfx):
+                return True
+
+        return False
+
+    def _exclude_misc(self, name: str, value: _Sequence) -> None:
+        """Handle 'exclude()' for list/tuple attrs without a special handler"""
+        if not isinstance(value, _sequence):
+            raise DistutilsSetupError(
+                f"{name}: setting must be of type <{_sequence_type_repr}> (got {value!r})"
+            )
+        try:
+            old = getattr(self, name)
+        except AttributeError as e:
+            raise DistutilsSetupError(f"{name}: No such distribution setting") from e
+        if old is not None and not isinstance(old, _sequence):
+            raise DistutilsSetupError(
+                name + ": this setting cannot be changed via include/exclude"
+            )
+        elif old:
+            setattr(self, name, [item for item in old if item not in value])
+
+    def _include_misc(self, name: str, value: _Sequence) -> None:
+        """Handle 'include()' for list/tuple attrs without a special handler"""
+
+        if not isinstance(value, _sequence):
+            raise DistutilsSetupError(
+                f"{name}: setting must be of type <{_sequence_type_repr}> (got {value!r})"
+            )
+        try:
+            old = getattr(self, name)
+        except AttributeError as e:
+            raise DistutilsSetupError(f"{name}: No such distribution setting") from e
+        if old is None:
+            setattr(self, name, value)
+        elif not isinstance(old, _sequence):
+            raise DistutilsSetupError(
+                name + ": this setting cannot be changed via include/exclude"
+            )
+        else:
+            new = [item for item in value if item not in old]
+            setattr(self, name, list(old) + new)
+
+    def exclude(self, **attrs) -> None:
+        """Remove items from distribution that are named in keyword arguments
+
+        For example, 'dist.exclude(py_modules=["x"])' would remove 'x' from
+        the distribution's 'py_modules' attribute.  Excluding packages uses
+        the 'exclude_package()' method, so all of the package's contained
+        packages, modules, and extensions are also excluded.
+
+        Currently, this method only supports exclusion from attributes that are
+        lists or tuples.  If you need to add support for excluding from other
+        attributes in this or a subclass, you can add an '_exclude_X' method,
+        where 'X' is the name of the attribute.  The method will be called with
+        the value passed to 'exclude()'.  So, 'dist.exclude(foo={"bar":"baz"})'
+        will try to call 'dist._exclude_foo({"bar":"baz"})', which can then
+        handle whatever special exclusion logic is needed.
+        """
+        for k, v in attrs.items():
+            exclude = getattr(self, '_exclude_' + k, None)
+            if exclude:
+                exclude(v)
+            else:
+                self._exclude_misc(k, v)
+
+    def _exclude_packages(self, packages: _Sequence) -> None:
+        if not isinstance(packages, _sequence):
+            raise DistutilsSetupError(
+                f"packages: setting must be of type <{_sequence_type_repr}> (got {packages!r})"
+            )
+        list(map(self.exclude_package, packages))
+
+    def _parse_command_opts(self, parser, args):
+        # Remove --with-X/--without-X options when processing command args
+        self.global_options = self.__class__.global_options
+        self.negative_opt = self.__class__.negative_opt
+
+        # First, expand any aliases
+        command = args[0]
+        aliases = self.get_option_dict('aliases')
+        while command in aliases:
+            _src, alias = aliases[command]
+            del aliases[command]  # ensure each alias can expand only once!
+            import shlex
+
+            args[:1] = shlex.split(alias, True)
+            command = args[0]
+
+        nargs = _Distribution._parse_command_opts(self, parser, args)
+
+        # Handle commands that want to consume all remaining arguments
+        cmd_class = self.get_command_class(command)
+        if getattr(cmd_class, 'command_consumes_arguments', None):
+            self.get_option_dict(command)['args'] = ("command line", nargs)
+            if nargs is not None:
+                return []
+
+        return nargs
+
+    def get_cmdline_options(self) -> dict[str, dict[str, str | None]]:
+        """Return a '{cmd: {opt:val}}' map of all command-line options
+
+        Option names are all long, but do not include the leading '--', and
+        contain dashes rather than underscores.  If the option doesn't take
+        an argument (e.g. '--quiet'), the 'val' is 'None'.
+
+        Note that options provided by config files are intentionally excluded.
+        """
+
+        d: dict[str, dict[str, str | None]] = {}
+
+        for cmd, opts in self.command_options.items():
+            val: str | None
+            for opt, (src, val) in opts.items():
+                if src != "command line":
+                    continue
+
+                opt = opt.replace('_', '-')
+
+                if val == 0:
+                    cmdobj = self.get_command_obj(cmd)
+                    neg_opt = self.negative_opt.copy()
+                    neg_opt.update(getattr(cmdobj, 'negative_opt', {}))
+                    for neg, pos in neg_opt.items():
+                        if pos == opt:
+                            opt = neg
+                            val = None
+                            break
+                    else:
+                        raise AssertionError("Shouldn't be able to get here")
+
+                elif val == 1:
+                    val = None
+
+                d.setdefault(cmd, {})[opt] = val
+
+        return d
+
+    def iter_distribution_names(self):
+        """Yield all packages, modules, and extension names in distribution"""
+
+        yield from self.packages or ()
+
+        yield from self.py_modules or ()
+
+        for ext in self.ext_modules or ():
+            if isinstance(ext, tuple):
+                name, _buildinfo = ext
+            else:
+                name = ext.name
+            if name.endswith('module'):
+                name = name[:-6]
+            yield name
+
+    def handle_display_options(self, option_order):
+        """If there were any non-global "display-only" options
+        (--help-commands or the metadata display options) on the command
+        line, display the requested info and return true; else return
+        false.
+        """
+        import sys
+
+        if self.help_commands:
+            return _Distribution.handle_display_options(self, option_order)
+
+        # Stdout may be StringIO (e.g. in tests)
+        if not isinstance(sys.stdout, io.TextIOWrapper):
+            return _Distribution.handle_display_options(self, option_order)
+
+        # Don't wrap stdout if utf-8 is already the encoding. Provides
+        #  workaround for #334.
+        if sys.stdout.encoding.lower() in ('utf-8', 'utf8'):
+            return _Distribution.handle_display_options(self, option_order)
+
+        # Print metadata in UTF-8 no matter the platform
+        encoding = sys.stdout.encoding
+        sys.stdout.reconfigure(encoding='utf-8')
+        try:
+            return _Distribution.handle_display_options(self, option_order)
+        finally:
+            sys.stdout.reconfigure(encoding=encoding)
+
+    def run_command(self, command) -> None:
+        self.set_defaults()
+        # Postpone defaults until all explicit configuration is considered
+        # (setup() args, config files, command line and plugins)
+
+        super().run_command(command)
+
+
+@functools.cache
+def _setuptools_commands() -> set[str]:
+    try:
+        # Use older API for importlib.metadata compatibility
+        entry_points = metadata.distribution('setuptools').entry_points
+        eps: Iterable[str] = (ep.name for ep in entry_points)
+    except metadata.PackageNotFoundError:
+        # during bootstrapping, distribution doesn't exist
+        eps = []
+    return {*distutils.command.__all__, *eps}
+
+
+class DistDeprecationWarning(SetuptoolsDeprecationWarning):
+    """Class for warning about deprecations in dist in
+    setuptools. Not ignored by default, unlike DeprecationWarning."""
diff --git a/.venv/lib/python3.12/site-packages/setuptools/errors.py b/.venv/lib/python3.12/site-packages/setuptools/errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..990ecbf4e2f18eb188addc9e0466152a20193a90
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/errors.py
@@ -0,0 +1,67 @@
+"""setuptools.errors
+
+Provides exceptions used by setuptools modules.
+"""
+
+from __future__ import annotations
+
+from distutils import errors as _distutils_errors
+
+# Re-export errors from distutils to facilitate the migration to PEP632
+
+ByteCompileError = _distutils_errors.DistutilsByteCompileError
+CCompilerError = _distutils_errors.CCompilerError
+ClassError = _distutils_errors.DistutilsClassError
+CompileError = _distutils_errors.CompileError
+ExecError = _distutils_errors.DistutilsExecError
+FileError = _distutils_errors.DistutilsFileError
+InternalError = _distutils_errors.DistutilsInternalError
+LibError = _distutils_errors.LibError
+LinkError = _distutils_errors.LinkError
+ModuleError = _distutils_errors.DistutilsModuleError
+OptionError = _distutils_errors.DistutilsOptionError
+PlatformError = _distutils_errors.DistutilsPlatformError
+PreprocessError = _distutils_errors.PreprocessError
+SetupError = _distutils_errors.DistutilsSetupError
+TemplateError = _distutils_errors.DistutilsTemplateError
+UnknownFileError = _distutils_errors.UnknownFileError
+
+# The root error class in the hierarchy
+BaseError = _distutils_errors.DistutilsError
+
+
+class InvalidConfigError(OptionError):  # type: ignore[valid-type, misc] # distutils imports are `Any` on python 3.12+
+    """Error used for invalid configurations."""
+
+
+class RemovedConfigError(OptionError):  # type: ignore[valid-type, misc] # distutils imports are `Any` on python 3.12+
+    """Error used for configurations that were deprecated and removed."""
+
+
+class RemovedCommandError(BaseError, RuntimeError):  # type: ignore[valid-type, misc] # distutils imports are `Any` on python 3.12+
+    """Error used for commands that have been removed in setuptools.
+
+    Since ``setuptools`` is built on ``distutils``, simply removing a command
+    from ``setuptools`` will make the behavior fall back to ``distutils``; this
+    error is raised if a command exists in ``distutils`` but has been actively
+    removed in ``setuptools``.
+    """
+
+
+class PackageDiscoveryError(BaseError, RuntimeError):  # type: ignore[valid-type, misc] # distutils imports are `Any` on python 3.12+
+    """Impossible to perform automatic discovery of packages and/or modules.
+
+    The current project layout or given discovery options can lead to problems when
+    scanning the project directory.
+
+    Setuptools might also refuse to complete auto-discovery if an error prone condition
+    is detected (e.g. when a project is organised as a flat-layout but contains
+    multiple directories that can be taken as top-level packages inside a single
+    distribution [*]_). In these situations the users are encouraged to be explicit
+    about which packages to include or to make the discovery parameters more specific.
+
+    .. [*] Since multi-package distributions are uncommon it is very likely that the
+       developers did not intend for all the directories to be packaged, and are just
+       leaving auxiliary code in the repository top-level, such as maintenance-related
+       scripts.
+    """
diff --git a/.venv/lib/python3.12/site-packages/setuptools/extension.py b/.venv/lib/python3.12/site-packages/setuptools/extension.py
new file mode 100644
index 0000000000000000000000000000000000000000..76e03d9d6bdcdbd72e443e90c85d34429c22c261
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/extension.py
@@ -0,0 +1,177 @@
+from __future__ import annotations
+
+import functools
+import re
+from typing import TYPE_CHECKING
+
+from setuptools._path import StrPath
+
+from .monkey import get_unpatched
+
+import distutils.core
+import distutils.errors
+import distutils.extension
+
+
+def _have_cython():
+    """
+    Return True if Cython can be imported.
+    """
+    cython_impl = 'Cython.Distutils.build_ext'
+    try:
+        # from (cython_impl) import build_ext
+        __import__(cython_impl, fromlist=['build_ext']).build_ext
+    except Exception:
+        return False
+    return True
+
+
+# for compatibility
+have_pyrex = _have_cython
+if TYPE_CHECKING:
+    # Work around a mypy issue where type[T] can't be used as a base: https://github.com/python/mypy/issues/10962
+    from distutils.core import Extension as _Extension
+else:
+    _Extension = get_unpatched(distutils.core.Extension)
+
+
+class Extension(_Extension):
+    """
+    Describes a single extension module.
+
+    This means that all source files will be compiled into a single binary file
+    ``<module path>.<suffix>`` (with ``<module path>`` derived from ``name`` and
+    ``<suffix>`` defined by one of the values in
+    ``importlib.machinery.EXTENSION_SUFFIXES``).
+
+    In the case ``.pyx`` files are passed as ``sources and`` ``Cython`` is **not**
+    installed in the build environment, ``setuptools`` may also try to look for the
+    equivalent ``.cpp`` or ``.c`` files.
+
+    :arg str name:
+      the full name of the extension, including any packages -- ie.
+      *not* a filename or pathname, but Python dotted name
+
+    :arg list[str|os.PathLike[str]] sources:
+      list of source filenames, relative to the distribution root
+      (where the setup script lives), in Unix form (slash-separated)
+      for portability.  Source files may be C, C++, SWIG (.i),
+      platform-specific resource files, or whatever else is recognized
+      by the "build_ext" command as source for a Python extension.
+
+    :keyword list[str] include_dirs:
+      list of directories to search for C/C++ header files (in Unix
+      form for portability)
+
+    :keyword list[tuple[str, str|None]] define_macros:
+      list of macros to define; each macro is defined using a 2-tuple:
+      the first item corresponding to the name of the macro and the second
+      item either a string with its value or None to
+      define it without a particular value (equivalent of "#define
+      FOO" in source or -DFOO on Unix C compiler command line)
+
+    :keyword list[str] undef_macros:
+      list of macros to undefine explicitly
+
+    :keyword list[str] library_dirs:
+      list of directories to search for C/C++ libraries at link time
+
+    :keyword list[str] libraries:
+      list of library names (not filenames or paths) to link against
+
+    :keyword list[str] runtime_library_dirs:
+      list of directories to search for C/C++ libraries at run time
+      (for shared extensions, this is when the extension is loaded).
+      Setting this will cause an exception during build on Windows
+      platforms.
+
+    :keyword list[str] extra_objects:
+      list of extra files to link with (eg. object files not implied
+      by 'sources', static library that must be explicitly specified,
+      binary resource files, etc.)
+
+    :keyword list[str] extra_compile_args:
+      any extra platform- and compiler-specific information to use
+      when compiling the source files in 'sources'.  For platforms and
+      compilers where "command line" makes sense, this is typically a
+      list of command-line arguments, but for other platforms it could
+      be anything.
+
+    :keyword list[str] extra_link_args:
+      any extra platform- and compiler-specific information to use
+      when linking object files together to create the extension (or
+      to create a new static Python interpreter).  Similar
+      interpretation as for 'extra_compile_args'.
+
+    :keyword list[str] export_symbols:
+      list of symbols to be exported from a shared extension.  Not
+      used on all platforms, and not generally necessary for Python
+      extensions, which typically export exactly one symbol: "init" +
+      extension_name.
+
+    :keyword list[str] swig_opts:
+      any extra options to pass to SWIG if a source file has the .i
+      extension.
+
+    :keyword list[str] depends:
+      list of files that the extension depends on
+
+    :keyword str language:
+      extension language (i.e. "c", "c++", "objc"). Will be detected
+      from the source extensions if not provided.
+
+    :keyword bool optional:
+      specifies that a build failure in the extension should not abort the
+      build process, but simply not install the failing extension.
+
+    :keyword bool py_limited_api:
+      opt-in flag for the usage of :doc:`Python's limited API <python:c-api/stable>`.
+
+    :raises setuptools.errors.PlatformError: if ``runtime_library_dirs`` is
+      specified on Windows. (since v63)
+    """
+
+    # These 4 are set and used in setuptools/command/build_ext.py
+    # The lack of a default value and risk of `AttributeError` is purposeful
+    # to avoid people forgetting to call finalize_options if they modify the extension list.
+    # See example/rationale in https://github.com/pypa/setuptools/issues/4529.
+    _full_name: str  #: Private API, internal use only.
+    _links_to_dynamic: bool  #: Private API, internal use only.
+    _needs_stub: bool  #: Private API, internal use only.
+    _file_name: str  #: Private API, internal use only.
+
+    def __init__(
+        self,
+        name: str,
+        sources: list[StrPath],
+        *args,
+        py_limited_api: bool = False,
+        **kw,
+    ) -> None:
+        # The *args is needed for compatibility as calls may use positional
+        # arguments. py_limited_api may be set only via keyword.
+        self.py_limited_api = py_limited_api
+        super().__init__(
+            name,
+            sources,  # type: ignore[arg-type] # Vendored version of setuptools supports PathLike
+            *args,
+            **kw,
+        )
+
+    def _convert_pyx_sources_to_lang(self):
+        """
+        Replace sources with .pyx extensions to sources with the target
+        language extension. This mechanism allows language authors to supply
+        pre-converted sources but to prefer the .pyx sources.
+        """
+        if _have_cython():
+            # the build has Cython, so allow it to compile the .pyx files
+            return
+        lang = self.language or ''
+        target_ext = '.cpp' if lang.lower() == 'c++' else '.c'
+        sub = functools.partial(re.sub, '.pyx$', target_ext)
+        self.sources = list(map(sub, self.sources))
+
+
+class Library(Extension):
+    """Just like a regular Extension, but built as a library instead"""
diff --git a/.venv/lib/python3.12/site-packages/setuptools/glob.py b/.venv/lib/python3.12/site-packages/setuptools/glob.py
new file mode 100644
index 0000000000000000000000000000000000000000..1dfff2cd50ff87b8cef9d936f1fc9d4a2478b136
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/glob.py
@@ -0,0 +1,185 @@
+"""
+Filename globbing utility. Mostly a copy of `glob` from Python 3.5.
+
+Changes include:
+ * `yield from` and PEP3102 `*` removed.
+ * Hidden files are not ignored.
+"""
+
+from __future__ import annotations
+
+import fnmatch
+import os
+import re
+from collections.abc import Iterable, Iterator
+from typing import TYPE_CHECKING, AnyStr, overload
+
+if TYPE_CHECKING:
+    from _typeshed import BytesPath, StrOrBytesPath, StrPath
+
+__all__ = ["glob", "iglob", "escape"]
+
+
+def glob(pathname: AnyStr, recursive: bool = False) -> list[AnyStr]:
+    """Return a list of paths matching a pathname pattern.
+
+    The pattern may contain simple shell-style wildcards a la
+    fnmatch. However, unlike fnmatch, filenames starting with a
+    dot are special cases that are not matched by '*' and '?'
+    patterns.
+
+    If recursive is true, the pattern '**' will match any files and
+    zero or more directories and subdirectories.
+    """
+    return list(iglob(pathname, recursive=recursive))
+
+
+def iglob(pathname: AnyStr, recursive: bool = False) -> Iterator[AnyStr]:
+    """Return an iterator which yields the paths matching a pathname pattern.
+
+    The pattern may contain simple shell-style wildcards a la
+    fnmatch. However, unlike fnmatch, filenames starting with a
+    dot are special cases that are not matched by '*' and '?'
+    patterns.
+
+    If recursive is true, the pattern '**' will match any files and
+    zero or more directories and subdirectories.
+    """
+    it = _iglob(pathname, recursive)
+    if recursive and _isrecursive(pathname):
+        s = next(it)  # skip empty string
+        assert not s
+    return it
+
+
+def _iglob(pathname: AnyStr, recursive: bool) -> Iterator[AnyStr]:
+    dirname, basename = os.path.split(pathname)
+    glob_in_dir = glob2 if recursive and _isrecursive(basename) else glob1
+
+    if not has_magic(pathname):
+        if basename:
+            if os.path.lexists(pathname):
+                yield pathname
+        else:
+            # Patterns ending with a slash should match only directories
+            if os.path.isdir(dirname):
+                yield pathname
+        return
+
+    if not dirname:
+        yield from glob_in_dir(dirname, basename)
+        return
+    # `os.path.split()` returns the argument itself as a dirname if it is a
+    # drive or UNC path.  Prevent an infinite recursion if a drive or UNC path
+    # contains magic characters (i.e. r'\\?\C:').
+    if dirname != pathname and has_magic(dirname):
+        dirs: Iterable[AnyStr] = _iglob(dirname, recursive)
+    else:
+        dirs = [dirname]
+    if not has_magic(basename):
+        glob_in_dir = glob0
+    for dirname in dirs:
+        for name in glob_in_dir(dirname, basename):
+            yield os.path.join(dirname, name)
+
+
+# These 2 helper functions non-recursively glob inside a literal directory.
+# They return a list of basenames. `glob1` accepts a pattern while `glob0`
+# takes a literal basename (so it only has to check for its existence).
+
+
+@overload
+def glob1(dirname: StrPath, pattern: str) -> list[str]: ...
+@overload
+def glob1(dirname: BytesPath, pattern: bytes) -> list[bytes]: ...
+def glob1(dirname: StrOrBytesPath, pattern: str | bytes) -> list[str] | list[bytes]:
+    if not dirname:
+        if isinstance(pattern, bytes):
+            dirname = os.curdir.encode('ASCII')
+        else:
+            dirname = os.curdir
+    try:
+        names = os.listdir(dirname)
+    except OSError:
+        return []
+    # mypy false-positives: str or bytes type possibility is always kept in sync
+    return fnmatch.filter(names, pattern)  # type: ignore[type-var, return-value]
+
+
+def glob0(dirname, basename):
+    if not basename:
+        # `os.path.split()` returns an empty basename for paths ending with a
+        # directory separator.  'q*x/' should match only directories.
+        if os.path.isdir(dirname):
+            return [basename]
+    else:
+        if os.path.lexists(os.path.join(dirname, basename)):
+            return [basename]
+    return []
+
+
+# This helper function recursively yields relative pathnames inside a literal
+# directory.
+
+
+@overload
+def glob2(dirname: StrPath, pattern: str) -> Iterator[str]: ...
+@overload
+def glob2(dirname: BytesPath, pattern: bytes) -> Iterator[bytes]: ...
+def glob2(dirname: StrOrBytesPath, pattern: str | bytes) -> Iterator[str | bytes]:
+    assert _isrecursive(pattern)
+    yield pattern[:0]
+    yield from _rlistdir(dirname)
+
+
+# Recursively yields relative pathnames inside a literal directory.
+@overload
+def _rlistdir(dirname: StrPath) -> Iterator[str]: ...
+@overload
+def _rlistdir(dirname: BytesPath) -> Iterator[bytes]: ...
+def _rlistdir(dirname: StrOrBytesPath) -> Iterator[str | bytes]:
+    if not dirname:
+        if isinstance(dirname, bytes):
+            dirname = os.curdir.encode('ASCII')
+        else:
+            dirname = os.curdir
+    try:
+        names = os.listdir(dirname)
+    except OSError:
+        return
+    for x in names:
+        yield x
+        # mypy false-positives: str or bytes type possibility is always kept in sync
+        path = os.path.join(dirname, x) if dirname else x  # type: ignore[arg-type]
+        for y in _rlistdir(path):
+            yield os.path.join(x, y)  # type: ignore[arg-type]
+
+
+magic_check = re.compile('([*?[])')
+magic_check_bytes = re.compile(b'([*?[])')
+
+
+def has_magic(s: str | bytes) -> bool:
+    if isinstance(s, bytes):
+        return magic_check_bytes.search(s) is not None
+    else:
+        return magic_check.search(s) is not None
+
+
+def _isrecursive(pattern: str | bytes) -> bool:
+    if isinstance(pattern, bytes):
+        return pattern == b'**'
+    else:
+        return pattern == '**'
+
+
+def escape(pathname):
+    """Escape all special characters."""
+    # Escaping is done by wrapping any of "*?[" between square brackets.
+    # Metacharacters do not work in the drive part and shouldn't be escaped.
+    drive, pathname = os.path.splitdrive(pathname)
+    if isinstance(pathname, bytes):
+        pathname = magic_check_bytes.sub(rb'[\1]', pathname)
+    else:
+        pathname = magic_check.sub(r'[\1]', pathname)
+    return drive + pathname
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/installer.py b/.venv/lib/python3.12/site-packages/setuptools/installer.py
new file mode 100644
index 0000000000000000000000000000000000000000..64bc2def078bab5e4fb6aba26981e7b73bfc37a5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/installer.py
@@ -0,0 +1,150 @@
+from __future__ import annotations
+
+import glob
+import os
+import subprocess
+import sys
+import tempfile
+from functools import partial
+
+from pkg_resources import Distribution
+
+from . import _reqs
+from ._reqs import _StrOrIter
+from .warnings import SetuptoolsDeprecationWarning
+from .wheel import Wheel
+
+from distutils import log
+from distutils.errors import DistutilsError
+
+
+def _fixup_find_links(find_links):
+    """Ensure find-links option end-up being a list of strings."""
+    if isinstance(find_links, str):
+        return find_links.split()
+    assert isinstance(find_links, (tuple, list))
+    return find_links
+
+
+def fetch_build_egg(dist, req):
+    """Fetch an egg needed for building.
+
+    Use pip/wheel to fetch/build a wheel."""
+    _DeprecatedInstaller.emit()
+    _warn_wheel_not_available(dist)
+    return _fetch_build_egg_no_warn(dist, req)
+
+
+def _fetch_build_eggs(dist, requires: _StrOrIter) -> list[Distribution]:
+    import pkg_resources  # Delay import to avoid unnecessary side-effects
+
+    _DeprecatedInstaller.emit(stacklevel=3)
+    _warn_wheel_not_available(dist)
+
+    resolved_dists = pkg_resources.working_set.resolve(
+        _reqs.parse(requires, pkg_resources.Requirement),  # required for compatibility
+        installer=partial(_fetch_build_egg_no_warn, dist),  # avoid warning twice
+        replace_conflicting=True,
+    )
+    for dist in resolved_dists:
+        pkg_resources.working_set.add(dist, replace=True)
+    return resolved_dists
+
+
+def _fetch_build_egg_no_warn(dist, req):  # noqa: C901  # is too complex (16)  # FIXME
+    import pkg_resources  # Delay import to avoid unnecessary side-effects
+
+    # Ignore environment markers; if supplied, it is required.
+    req = strip_marker(req)
+    # Take easy_install options into account, but do not override relevant
+    # pip environment variables (like PIP_INDEX_URL or PIP_QUIET); they'll
+    # take precedence.
+    opts = dist.get_option_dict('easy_install')
+    if 'allow_hosts' in opts:
+        raise DistutilsError(
+            'the `allow-hosts` option is not supported '
+            'when using pip to install requirements.'
+        )
+    quiet = 'PIP_QUIET' not in os.environ and 'PIP_VERBOSE' not in os.environ
+    if 'PIP_INDEX_URL' in os.environ:
+        index_url = None
+    elif 'index_url' in opts:
+        index_url = opts['index_url'][1]
+    else:
+        index_url = None
+    find_links = (
+        _fixup_find_links(opts['find_links'][1])[:] if 'find_links' in opts else []
+    )
+    if dist.dependency_links:
+        find_links.extend(dist.dependency_links)
+    eggs_dir = os.path.realpath(dist.get_egg_cache_dir())
+    environment = pkg_resources.Environment()
+    for egg_dist in pkg_resources.find_distributions(eggs_dir):
+        if egg_dist in req and environment.can_add(egg_dist):
+            return egg_dist
+    with tempfile.TemporaryDirectory() as tmpdir:
+        cmd = [
+            sys.executable,
+            '-m',
+            'pip',
+            '--disable-pip-version-check',
+            'wheel',
+            '--no-deps',
+            '-w',
+            tmpdir,
+        ]
+        if quiet:
+            cmd.append('--quiet')
+        if index_url is not None:
+            cmd.extend(('--index-url', index_url))
+        for link in find_links or []:
+            cmd.extend(('--find-links', link))
+        # If requirement is a PEP 508 direct URL, directly pass
+        # the URL to pip, as `req @ url` does not work on the
+        # command line.
+        cmd.append(req.url or str(req))
+        try:
+            subprocess.check_call(cmd)
+        except subprocess.CalledProcessError as e:
+            raise DistutilsError(str(e)) from e
+        wheel = Wheel(glob.glob(os.path.join(tmpdir, '*.whl'))[0])
+        dist_location = os.path.join(eggs_dir, wheel.egg_name())
+        wheel.install_as_egg(dist_location)
+        dist_metadata = pkg_resources.PathMetadata(
+            dist_location, os.path.join(dist_location, 'EGG-INFO')
+        )
+        return pkg_resources.Distribution.from_filename(
+            dist_location, metadata=dist_metadata
+        )
+
+
+def strip_marker(req):
+    """
+    Return a new requirement without the environment marker to avoid
+    calling pip with something like `babel; extra == "i18n"`, which
+    would always be ignored.
+    """
+    import pkg_resources  # Delay import to avoid unnecessary side-effects
+
+    # create a copy to avoid mutating the input
+    req = pkg_resources.Requirement.parse(str(req))
+    req.marker = None
+    return req
+
+
+def _warn_wheel_not_available(dist):
+    import pkg_resources  # Delay import to avoid unnecessary side-effects
+
+    try:
+        pkg_resources.get_distribution('wheel')
+    except pkg_resources.DistributionNotFound:
+        dist.announce('WARNING: The wheel package is not available.', log.WARN)
+
+
+class _DeprecatedInstaller(SetuptoolsDeprecationWarning):
+    _SUMMARY = "setuptools.installer and fetch_build_eggs are deprecated."
+    _DETAILS = """
+    Requirements should be satisfied by a PEP 517 installer.
+    If you are using pip, you can try `pip install --use-pep517`.
+    """
+    # _DUE_DATE not decided yet
diff --git a/.venv/lib/python3.12/site-packages/setuptools/launch.py b/.venv/lib/python3.12/site-packages/setuptools/launch.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d162647d55777d7afa1bf1e44a6c200a3f82419
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/launch.py
@@ -0,0 +1,36 @@
+"""
+Launch the Python script on the command line after
+setuptools is bootstrapped via import.
+"""
+
+# Note that setuptools gets imported implicitly by the
+# invocation of this script using python -m setuptools.launch
+
+import sys
+import tokenize
+
+
+def run() -> None:
+    """
+    Run the script in sys.argv[1] as if it had
+    been invoked naturally.
+    """
+    __builtins__
+    script_name = sys.argv[1]
+    namespace = dict(
+        __file__=script_name,
+        __name__='__main__',
+        __doc__=None,
+    )
+    sys.argv[:] = sys.argv[1:]
+
+    open_ = getattr(tokenize, 'open', open)
+    with open_(script_name) as fid:
+        script = fid.read()
+    norm_script = script.replace('\\r\\n', '\\n')
+    code = compile(norm_script, script_name, 'exec')
+    exec(code, namespace)
+
+
+if __name__ == '__main__':
+    run()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/logging.py b/.venv/lib/python3.12/site-packages/setuptools/logging.py
new file mode 100644
index 0000000000000000000000000000000000000000..532da899f7dc02f9fea9a44c429086b98fe043d8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/logging.py
@@ -0,0 +1,40 @@
+import inspect
+import logging
+import sys
+
+from . import monkey
+
+import distutils.log
+
+
+def _not_warning(record):
+    return record.levelno < logging.WARNING
+
+
+def configure() -> None:
+    """
+    Configure logging to emit warning and above to stderr
+    and everything else to stdout. This behavior is provided
+    for compatibility with distutils.log but may change in
+    the future.
+    """
+    err_handler = logging.StreamHandler()
+    err_handler.setLevel(logging.WARNING)
+    out_handler = logging.StreamHandler(sys.stdout)
+    out_handler.addFilter(_not_warning)
+    handlers = err_handler, out_handler
+    logging.basicConfig(
+        format="{message}", style='{', handlers=handlers, level=logging.DEBUG
+    )
+    if inspect.ismodule(distutils.dist.log):
+        monkey.patch_func(set_threshold, distutils.log, 'set_threshold')
+        # For some reason `distutils.log` module is getting cached in `distutils.dist`
+        # and then loaded again when patched,
+        # implying: id(distutils.log) != id(distutils.dist.log).
+        # Make sure the same module object is used everywhere:
+        distutils.dist.log = distutils.log
+
+
+def set_threshold(level: int) -> int:
+    logging.root.setLevel(level * 10)
+    return set_threshold.unpatched(level)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/modified.py b/.venv/lib/python3.12/site-packages/setuptools/modified.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ba02fab68734e1e96fd50d7c4b6ffb1442717fb
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/modified.py
@@ -0,0 +1,18 @@
+try:
+    # Ensure a DistutilsError raised by these methods is the same as distutils.errors.DistutilsError
+    from distutils._modified import (
+        newer,
+        newer_group,
+        newer_pairwise,
+        newer_pairwise_group,
+    )
+except ImportError:
+    # fallback for SETUPTOOLS_USE_DISTUTILS=stdlib, because _modified never existed in stdlib
+    from ._distutils._modified import (
+        newer,
+        newer_group,
+        newer_pairwise,
+        newer_pairwise_group,
+    )
+
+__all__ = ['newer', 'newer_pairwise', 'newer_group', 'newer_pairwise_group']
diff --git a/.venv/lib/python3.12/site-packages/setuptools/monkey.py b/.venv/lib/python3.12/site-packages/setuptools/monkey.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ad1abac295c1df613942b8896edf089a103ae1f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/monkey.py
@@ -0,0 +1,126 @@
+"""
+Monkey patching of distutils.
+"""
+
+from __future__ import annotations
+
+import inspect
+import platform
+import sys
+import types
+from typing import TypeVar, cast, overload
+
+import distutils.filelist
+
+_T = TypeVar("_T")
+_UnpatchT = TypeVar("_UnpatchT", type, types.FunctionType)
+
+
+__all__: list[str] = []
+"""
+Everything is private. Contact the project team
+if you think you need this functionality.
+"""
+
+
+def _get_mro(cls):
+    """
+    Returns the bases classes for cls sorted by the MRO.
+
+    Works around an issue on Jython where inspect.getmro will not return all
+    base classes if multiple classes share the same name. Instead, this
+    function will return a tuple containing the class itself, and the contents
+    of cls.__bases__. See https://github.com/pypa/setuptools/issues/1024.
+    """
+    if platform.python_implementation() == "Jython":
+        return (cls,) + cls.__bases__
+    return inspect.getmro(cls)
+
+
+@overload
+def get_unpatched(item: _UnpatchT) -> _UnpatchT: ...
+@overload
+def get_unpatched(item: object) -> None: ...
+def get_unpatched(
+    item: type | types.FunctionType | object,
+) -> type | types.FunctionType | None:
+    if isinstance(item, type):
+        return get_unpatched_class(item)
+    if isinstance(item, types.FunctionType):
+        return get_unpatched_function(item)
+    return None
+
+
+def get_unpatched_class(cls: type[_T]) -> type[_T]:
+    """Protect against re-patching the distutils if reloaded
+
+    Also ensures that no other distutils extension monkeypatched the distutils
+    first.
+    """
+    external_bases = (
+        cast(type[_T], cls)
+        for cls in _get_mro(cls)
+        if not cls.__module__.startswith('setuptools')
+    )
+    base = next(external_bases)
+    if not base.__module__.startswith('distutils'):
+        msg = f"distutils has already been patched by {cls!r}"
+        raise AssertionError(msg)
+    return base
+
+
+def patch_all():
+    import setuptools
+
+    # we can't patch distutils.cmd, alas
+    distutils.core.Command = setuptools.Command  # type: ignore[misc,assignment] # monkeypatching
+
+    _patch_distribution_metadata()
+
+    # Install Distribution throughout the distutils
+    for module in distutils.dist, distutils.core, distutils.cmd:
+        module.Distribution = setuptools.dist.Distribution
+
+    # Install the patched Extension
+    distutils.core.Extension = setuptools.extension.Extension  # type: ignore[misc,assignment] # monkeypatching
+    distutils.extension.Extension = setuptools.extension.Extension  # type: ignore[misc,assignment] # monkeypatching
+    if 'distutils.command.build_ext' in sys.modules:
+        sys.modules[
+            'distutils.command.build_ext'
+        ].Extension = setuptools.extension.Extension
+
+
+def _patch_distribution_metadata():
+    from . import _core_metadata
+
+    """Patch write_pkg_file and read_pkg_file for higher metadata standards"""
+    for attr in (
+        'write_pkg_info',
+        'write_pkg_file',
+        'read_pkg_file',
+        'get_metadata_version',
+        'get_fullname',
+    ):
+        new_val = getattr(_core_metadata, attr)
+        setattr(distutils.dist.DistributionMetadata, attr, new_val)
+
+
+def patch_func(replacement, target_mod, func_name):
+    """
+    Patch func_name in target_mod with replacement
+
+    Important - original must be resolved by name to avoid
+    patching an already patched function.
+    """
+    original = getattr(target_mod, func_name)
+
+    # set the 'unpatched' attribute on the replacement to
+    # point to the original.
+    vars(replacement).setdefault('unpatched', original)
+
+    # replace the function in the original module
+    setattr(target_mod, func_name, replacement)
+
+
+def get_unpatched_function(candidate):
+    return candidate.unpatched
diff --git a/.venv/lib/python3.12/site-packages/setuptools/msvc.py b/.venv/lib/python3.12/site-packages/setuptools/msvc.py
new file mode 100644
index 0000000000000000000000000000000000000000..313a781ae095bbb20874795e5b87396790f0b9c9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/msvc.py
@@ -0,0 +1,1536 @@
+"""
+Environment info about Microsoft Compilers.
+
+>>> getfixture('windows_only')
+>>> ei = EnvironmentInfo('amd64')
+"""
+
+from __future__ import annotations
+
+import contextlib
+import itertools
+import json
+import os
+import os.path
+import platform
+from typing import TYPE_CHECKING, TypedDict
+
+from more_itertools import unique_everseen
+
+import distutils.errors
+
+if TYPE_CHECKING:
+    from typing_extensions import LiteralString, NotRequired
+
+# https://github.com/python/mypy/issues/8166
+if not TYPE_CHECKING and platform.system() == 'Windows':
+    import winreg
+    from os import environ
+else:
+    # Mock winreg and environ so the module can be imported on this platform.
+
+    class winreg:
+        HKEY_USERS = None
+        HKEY_CURRENT_USER = None
+        HKEY_LOCAL_MACHINE = None
+        HKEY_CLASSES_ROOT = None
+
+    environ: dict[str, str] = dict()
+
+
+class PlatformInfo:
+    """
+    Current and Target Architectures information.
+
+    Parameters
+    ----------
+    arch: str
+        Target architecture.
+    """
+
+    current_cpu = environ.get('processor_architecture', '').lower()
+
+    def __init__(self, arch) -> None:
+        self.arch = arch.lower().replace('x64', 'amd64')
+
+    @property
+    def target_cpu(self):
+        """
+        Return Target CPU architecture.
+
+        Return
+        ------
+        str
+            Target CPU
+        """
+        return self.arch[self.arch.find('_') + 1 :]
+
+    def target_is_x86(self):
+        """
+        Return True if target CPU is x86 32 bits..
+
+        Return
+        ------
+        bool
+            CPU is x86 32 bits
+        """
+        return self.target_cpu == 'x86'
+
+    def current_is_x86(self):
+        """
+        Return True if current CPU is x86 32 bits..
+
+        Return
+        ------
+        bool
+            CPU is x86 32 bits
+        """
+        return self.current_cpu == 'x86'
+
+    def current_dir(self, hidex86=False, x64=False) -> str:
+        """
+        Current platform specific subfolder.
+
+        Parameters
+        ----------
+        hidex86: bool
+            return '' and not '\x86' if architecture is x86.
+        x64: bool
+            return '\x64' and not '\amd64' if architecture is amd64.
+
+        Return
+        ------
+        str
+            subfolder: '\target', or '' (see hidex86 parameter)
+        """
+        return (
+            ''
+            if (self.current_cpu == 'x86' and hidex86)
+            else r'\x64'
+            if (self.current_cpu == 'amd64' and x64)
+            else rf'\{self.current_cpu}'
+        )
+
+    def target_dir(self, hidex86=False, x64=False) -> str:
+        r"""
+        Target platform specific subfolder.
+
+        Parameters
+        ----------
+        hidex86: bool
+            return '' and not '\x86' if architecture is x86.
+        x64: bool
+            return '\x64' and not '\amd64' if architecture is amd64.
+
+        Return
+        ------
+        str
+            subfolder: '\current', or '' (see hidex86 parameter)
+        """
+        return (
+            ''
+            if (self.target_cpu == 'x86' and hidex86)
+            else r'\x64'
+            if (self.target_cpu == 'amd64' and x64)
+            else rf'\{self.target_cpu}'
+        )
+
+    def cross_dir(self, forcex86=False):
+        r"""
+        Cross platform specific subfolder.
+
+        Parameters
+        ----------
+        forcex86: bool
+            Use 'x86' as current architecture even if current architecture is
+            not x86.
+
+        Return
+        ------
+        str
+            subfolder: '' if target architecture is current architecture,
+            '\current_target' if not.
+        """
+        current = 'x86' if forcex86 else self.current_cpu
+        return (
+            ''
+            if self.target_cpu == current
+            else self.target_dir().replace('\\', f'\\{current}_')
+        )
+
+
+class RegistryInfo:
+    """
+    Microsoft Visual Studio related registry information.
+
+    Parameters
+    ----------
+    platform_info: PlatformInfo
+        "PlatformInfo" instance.
+    """
+
+    HKEYS = (
+        winreg.HKEY_USERS,
+        winreg.HKEY_CURRENT_USER,
+        winreg.HKEY_LOCAL_MACHINE,
+        winreg.HKEY_CLASSES_ROOT,
+    )
+
+    def __init__(self, platform_info) -> None:
+        self.pi = platform_info
+
+    @property
+    def visualstudio(self) -> str:
+        """
+        Microsoft Visual Studio root registry key.
+
+        Return
+        ------
+        str
+            Registry key
+        """
+        return 'VisualStudio'
+
+    @property
+    def sxs(self):
+        """
+        Microsoft Visual Studio SxS registry key.
+
+        Return
+        ------
+        str
+            Registry key
+        """
+        return os.path.join(self.visualstudio, 'SxS')
+
+    @property
+    def vc(self):
+        """
+        Microsoft Visual C++ VC7 registry key.
+
+        Return
+        ------
+        str
+            Registry key
+        """
+        return os.path.join(self.sxs, 'VC7')
+
+    @property
+    def vs(self):
+        """
+        Microsoft Visual Studio VS7 registry key.
+
+        Return
+        ------
+        str
+            Registry key
+        """
+        return os.path.join(self.sxs, 'VS7')
+
+    @property
+    def vc_for_python(self) -> str:
+        """
+        Microsoft Visual C++ for Python registry key.
+
+        Return
+        ------
+        str
+            Registry key
+        """
+        return r'DevDiv\VCForPython'
+
+    @property
+    def microsoft_sdk(self) -> str:
+        """
+        Microsoft SDK registry key.
+
+        Return
+        ------
+        str
+            Registry key
+        """
+        return 'Microsoft SDKs'
+
+    @property
+    def windows_sdk(self):
+        """
+        Microsoft Windows/Platform SDK registry key.
+
+        Return
+        ------
+        str
+            Registry key
+        """
+        return os.path.join(self.microsoft_sdk, 'Windows')
+
+    @property
+    def netfx_sdk(self):
+        """
+        Microsoft .NET Framework SDK registry key.
+
+        Return
+        ------
+        str
+            Registry key
+        """
+        return os.path.join(self.microsoft_sdk, 'NETFXSDK')
+
+    @property
+    def windows_kits_roots(self) -> str:
+        """
+        Microsoft Windows Kits Roots registry key.
+
+        Return
+        ------
+        str
+            Registry key
+        """
+        return r'Windows Kits\Installed Roots'
+
+    def microsoft(self, key, x86=False):
+        """
+        Return key in Microsoft software registry.
+
+        Parameters
+        ----------
+        key: str
+            Registry key path where look.
+        x86: str
+            Force x86 software registry.
+
+        Return
+        ------
+        str
+            Registry key
+        """
+        node64 = '' if self.pi.current_is_x86() or x86 else 'Wow6432Node'
+        return os.path.join('Software', node64, 'Microsoft', key)
+
+    def lookup(self, key, name):
+        """
+        Look for values in registry in Microsoft software registry.
+
+        Parameters
+        ----------
+        key: str
+            Registry key path where look.
+        name: str
+            Value name to find.
+
+        Return
+        ------
+        str
+            value
+        """
+        key_read = winreg.KEY_READ
+        openkey = winreg.OpenKey
+        closekey = winreg.CloseKey
+        ms = self.microsoft
+        for hkey in self.HKEYS:
+            bkey = None
+            try:
+                bkey = openkey(hkey, ms(key), 0, key_read)
+            except OSError:
+                if not self.pi.current_is_x86():
+                    try:
+                        bkey = openkey(hkey, ms(key, True), 0, key_read)
+                    except OSError:
+                        continue
+                else:
+                    continue
+            try:
+                return winreg.QueryValueEx(bkey, name)[0]
+            except OSError:
+                pass
+            finally:
+                if bkey:
+                    closekey(bkey)
+        return None
+
+
+class SystemInfo:
+    """
+    Microsoft Windows and Visual Studio related system information.
+
+    Parameters
+    ----------
+    registry_info: RegistryInfo
+        "RegistryInfo" instance.
+    vc_ver: float
+        Required Microsoft Visual C++ version.
+    """
+
+    # Variables and properties in this class use originals CamelCase variables
+    # names from Microsoft source files for more easy comparison.
+    WinDir = environ.get('WinDir', '')
+    ProgramFiles = environ.get('ProgramFiles', '')
+    ProgramFilesx86 = environ.get('ProgramFiles(x86)', ProgramFiles)
+
+    def __init__(self, registry_info, vc_ver=None) -> None:
+        self.ri = registry_info
+        self.pi = self.ri.pi
+
+        self.known_vs_paths = self.find_programdata_vs_vers()
+
+        # Except for VS15+, VC version is aligned with VS version
+        self.vs_ver = self.vc_ver = vc_ver or self._find_latest_available_vs_ver()
+
+    def _find_latest_available_vs_ver(self):
+        """
+        Find the latest VC version
+
+        Return
+        ------
+        float
+            version
+        """
+        reg_vc_vers = self.find_reg_vs_vers()
+
+        if not (reg_vc_vers or self.known_vs_paths):
+            raise distutils.errors.DistutilsPlatformError(
+                'No Microsoft Visual C++ version found'
+            )
+
+        vc_vers = set(reg_vc_vers)
+        vc_vers.update(self.known_vs_paths)
+        return sorted(vc_vers)[-1]
+
+    def find_reg_vs_vers(self):
+        """
+        Find Microsoft Visual Studio versions available in registry.
+
+        Return
+        ------
+        list of float
+            Versions
+        """
+        ms = self.ri.microsoft
+        vckeys = (self.ri.vc, self.ri.vc_for_python, self.ri.vs)
+        vs_vers = []
+        for hkey, key in itertools.product(self.ri.HKEYS, vckeys):
+            try:
+                bkey = winreg.OpenKey(hkey, ms(key), 0, winreg.KEY_READ)
+            except OSError:
+                continue
+            with bkey:
+                subkeys, values, _ = winreg.QueryInfoKey(bkey)
+                for i in range(values):
+                    with contextlib.suppress(ValueError):
+                        ver = float(winreg.EnumValue(bkey, i)[0])
+                        if ver not in vs_vers:
+                            vs_vers.append(ver)
+                for i in range(subkeys):
+                    with contextlib.suppress(ValueError):
+                        ver = float(winreg.EnumKey(bkey, i))
+                        if ver not in vs_vers:
+                            vs_vers.append(ver)
+        return sorted(vs_vers)
+
+    def find_programdata_vs_vers(self) -> dict[float, str]:
+        r"""
+        Find Visual studio 2017+ versions from information in
+        "C:\ProgramData\Microsoft\VisualStudio\Packages\_Instances".
+
+        Return
+        ------
+        dict
+            float version as key, path as value.
+        """
+        vs_versions: dict[float, str] = {}
+        instances_dir = r'C:\ProgramData\Microsoft\VisualStudio\Packages\_Instances'
+
+        try:
+            hashed_names = os.listdir(instances_dir)
+
+        except OSError:
+            # Directory not exists with all Visual Studio versions
+            return vs_versions
+
+        for name in hashed_names:
+            try:
+                # Get VS installation path from "state.json" file
+                state_path = os.path.join(instances_dir, name, 'state.json')
+                with open(state_path, 'rt', encoding='utf-8') as state_file:
+                    state = json.load(state_file)
+                vs_path = state['installationPath']
+
+                # Raises OSError if this VS installation does not contain VC
+                os.listdir(os.path.join(vs_path, r'VC\Tools\MSVC'))
+
+                # Store version and path
+                vs_versions[self._as_float_version(state['installationVersion'])] = (
+                    vs_path
+                )
+
+            except (OSError, KeyError):
+                # Skip if "state.json" file is missing or bad format
+                continue
+
+        return vs_versions
+
+    @staticmethod
+    def _as_float_version(version):
+        """
+        Return a string version as a simplified float version (major.minor)
+
+        Parameters
+        ----------
+        version: str
+            Version.
+
+        Return
+        ------
+        float
+            version
+        """
+        return float('.'.join(version.split('.')[:2]))
+
+    @property
+    def VSInstallDir(self):
+        """
+        Microsoft Visual Studio directory.
+
+        Return
+        ------
+        str
+            path
+        """
+        # Default path
+        default = os.path.join(
+            self.ProgramFilesx86, f'Microsoft Visual Studio {self.vs_ver:0.1f}'
+        )
+
+        # Try to get path from registry, if fail use default path
+        return self.ri.lookup(self.ri.vs, f'{self.vs_ver:0.1f}') or default
+
+    @property
+    def VCInstallDir(self):
+        """
+        Microsoft Visual C++ directory.
+
+        Return
+        ------
+        str
+            path
+        """
+        path = self._guess_vc() or self._guess_vc_legacy()
+
+        if not os.path.isdir(path):
+            msg = 'Microsoft Visual C++ directory not found'
+            raise distutils.errors.DistutilsPlatformError(msg)
+
+        return path
+
+    def _guess_vc(self):
+        """
+        Locate Visual C++ for VS2017+.
+
+        Return
+        ------
+        str
+            path
+        """
+        if self.vs_ver <= 14.0:
+            return ''
+
+        try:
+            # First search in known VS paths
+            vs_dir = self.known_vs_paths[self.vs_ver]
+        except KeyError:
+            # Else, search with path from registry
+            vs_dir = self.VSInstallDir
+
+        guess_vc = os.path.join(vs_dir, r'VC\Tools\MSVC')
+
+        # Subdir with VC exact version as name
+        try:
+            # Update the VC version with real one instead of VS version
+            vc_ver = os.listdir(guess_vc)[-1]
+            self.vc_ver = self._as_float_version(vc_ver)
+            return os.path.join(guess_vc, vc_ver)
+        except (OSError, IndexError):
+            return ''
+
+    def _guess_vc_legacy(self):
+        """
+        Locate Visual C++ for versions prior to 2017.
+
+        Return
+        ------
+        str
+            path
+        """
+        default = os.path.join(
+            self.ProgramFilesx86,
+            rf'Microsoft Visual Studio {self.vs_ver:0.1f}\VC',
+        )
+
+        # Try to get "VC++ for Python" path from registry as default path
+        reg_path = os.path.join(self.ri.vc_for_python, f'{self.vs_ver:0.1f}')
+        python_vc = self.ri.lookup(reg_path, 'installdir')
+        default_vc = os.path.join(python_vc, 'VC') if python_vc else default
+
+        # Try to get path from registry, if fail use default path
+        return self.ri.lookup(self.ri.vc, f'{self.vs_ver:0.1f}') or default_vc
+
+    @property
+    def WindowsSdkVersion(self) -> tuple[LiteralString, ...]:
+        """
+        Microsoft Windows SDK versions for specified MSVC++ version.
+
+        Return
+        ------
+        tuple of str
+            versions
+        """
+        if self.vs_ver <= 9.0:
+            return '7.0', '6.1', '6.0a'
+        elif self.vs_ver == 10.0:
+            return '7.1', '7.0a'
+        elif self.vs_ver == 11.0:
+            return '8.0', '8.0a'
+        elif self.vs_ver == 12.0:
+            return '8.1', '8.1a'
+        elif self.vs_ver >= 14.0:
+            return '10.0', '8.1'
+        return ()
+
+    @property
+    def WindowsSdkLastVersion(self):
+        """
+        Microsoft Windows SDK last version.
+
+        Return
+        ------
+        str
+            version
+        """
+        return self._use_last_dir_name(os.path.join(self.WindowsSdkDir, 'lib'))
+
+    @property
+    def WindowsSdkDir(self) -> str | None:  # noqa: C901  # is too complex (12)  # FIXME
+        """
+        Microsoft Windows SDK directory.
+
+        Return
+        ------
+        str
+            path
+        """
+        sdkdir: str | None = ''
+        for ver in self.WindowsSdkVersion:
+            # Try to get it from registry
+            loc = os.path.join(self.ri.windows_sdk, f'v{ver}')
+            sdkdir = self.ri.lookup(loc, 'installationfolder')
+            if sdkdir:
+                break
+        if not sdkdir or not os.path.isdir(sdkdir):
+            # Try to get "VC++ for Python" version from registry
+            path = os.path.join(self.ri.vc_for_python, f'{self.vc_ver:0.1f}')
+            install_base = self.ri.lookup(path, 'installdir')
+            if install_base:
+                sdkdir = os.path.join(install_base, 'WinSDK')
+        if not sdkdir or not os.path.isdir(sdkdir):
+            # If fail, use default new path
+            for ver in self.WindowsSdkVersion:
+                intver = ver[: ver.rfind('.')]
+                path = rf'Microsoft SDKs\Windows Kits\{intver}'
+                d = os.path.join(self.ProgramFiles, path)
+                if os.path.isdir(d):
+                    sdkdir = d
+        if not sdkdir or not os.path.isdir(sdkdir):
+            # If fail, use default old path
+            for ver in self.WindowsSdkVersion:
+                path = rf'Microsoft SDKs\Windows\v{ver}'
+                d = os.path.join(self.ProgramFiles, path)
+                if os.path.isdir(d):
+                    sdkdir = d
+        if not sdkdir:
+            # If fail, use Platform SDK
+            sdkdir = os.path.join(self.VCInstallDir, 'PlatformSDK')
+        return sdkdir
+
+    @property
+    def WindowsSDKExecutablePath(self):
+        """
+        Microsoft Windows SDK executable directory.
+
+        Return
+        ------
+        str
+            path
+        """
+        # Find WinSDK NetFx Tools registry dir name
+        if self.vs_ver <= 11.0:
+            netfxver = 35
+            arch = ''
+        else:
+            netfxver = 40
+            hidex86 = True if self.vs_ver <= 12.0 else False
+            arch = self.pi.current_dir(x64=True, hidex86=hidex86).replace('\\', '-')
+        fx = f'WinSDK-NetFx{netfxver}Tools{arch}'
+
+        # list all possibles registry paths
+        regpaths = []
+        if self.vs_ver >= 14.0:
+            for ver in self.NetFxSdkVersion:
+                regpaths += [os.path.join(self.ri.netfx_sdk, ver, fx)]
+
+        for ver in self.WindowsSdkVersion:
+            regpaths += [os.path.join(self.ri.windows_sdk, f'v{ver}A', fx)]
+
+        # Return installation folder from the more recent path
+        for path in regpaths:
+            execpath = self.ri.lookup(path, 'installationfolder')
+            if execpath:
+                return execpath
+
+        return None
+
+    @property
+    def FSharpInstallDir(self):
+        """
+        Microsoft Visual F# directory.
+
+        Return
+        ------
+        str
+            path
+        """
+        path = os.path.join(self.ri.visualstudio, rf'{self.vs_ver:0.1f}\Setup\F#')
+        return self.ri.lookup(path, 'productdir') or ''
+
+    @property
+    def UniversalCRTSdkDir(self):
+        """
+        Microsoft Universal CRT SDK directory.
+
+        Return
+        ------
+        str
+            path
+        """
+        # Set Kit Roots versions for specified MSVC++ version
+        vers = ('10', '81') if self.vs_ver >= 14.0 else ()
+
+        # Find path of the more recent Kit
+        for ver in vers:
+            sdkdir = self.ri.lookup(self.ri.windows_kits_roots, f'kitsroot{ver}')
+            if sdkdir:
+                return sdkdir or ''
+
+        return None
+
+    @property
+    def UniversalCRTSdkLastVersion(self):
+        """
+        Microsoft Universal C Runtime SDK last version.
+
+        Return
+        ------
+        str
+            version
+        """
+        return self._use_last_dir_name(os.path.join(self.UniversalCRTSdkDir, 'lib'))
+
+    @property
+    def NetFxSdkVersion(self):
+        """
+        Microsoft .NET Framework SDK versions.
+
+        Return
+        ------
+        tuple of str
+            versions
+        """
+        # Set FxSdk versions for specified VS version
+        return (
+            ('4.7.2', '4.7.1', '4.7', '4.6.2', '4.6.1', '4.6', '4.5.2', '4.5.1', '4.5')
+            if self.vs_ver >= 14.0
+            else ()
+        )
+
+    @property
+    def NetFxSdkDir(self):
+        """
+        Microsoft .NET Framework SDK directory.
+
+        Return
+        ------
+        str
+            path
+        """
+        sdkdir = ''
+        for ver in self.NetFxSdkVersion:
+            loc = os.path.join(self.ri.netfx_sdk, ver)
+            sdkdir = self.ri.lookup(loc, 'kitsinstallationfolder')
+            if sdkdir:
+                break
+        return sdkdir
+
+    @property
+    def FrameworkDir32(self):
+        """
+        Microsoft .NET Framework 32bit directory.
+
+        Return
+        ------
+        str
+            path
+        """
+        # Default path
+        guess_fw = os.path.join(self.WinDir, r'Microsoft.NET\Framework')
+
+        # Try to get path from registry, if fail use default path
+        return self.ri.lookup(self.ri.vc, 'frameworkdir32') or guess_fw
+
+    @property
+    def FrameworkDir64(self):
+        """
+        Microsoft .NET Framework 64bit directory.
+
+        Return
+        ------
+        str
+            path
+        """
+        # Default path
+        guess_fw = os.path.join(self.WinDir, r'Microsoft.NET\Framework64')
+
+        # Try to get path from registry, if fail use default path
+        return self.ri.lookup(self.ri.vc, 'frameworkdir64') or guess_fw
+
+    @property
+    def FrameworkVersion32(self) -> tuple[str, ...]:
+        """
+        Microsoft .NET Framework 32bit versions.
+
+        Return
+        ------
+        tuple of str
+            versions
+        """
+        return self._find_dot_net_versions(32)
+
+    @property
+    def FrameworkVersion64(self) -> tuple[str, ...]:
+        """
+        Microsoft .NET Framework 64bit versions.
+
+        Return
+        ------
+        tuple of str
+            versions
+        """
+        return self._find_dot_net_versions(64)
+
+    def _find_dot_net_versions(self, bits) -> tuple[str, ...]:
+        """
+        Find Microsoft .NET Framework versions.
+
+        Parameters
+        ----------
+        bits: int
+            Platform number of bits: 32 or 64.
+
+        Return
+        ------
+        tuple of str
+            versions
+        """
+        # Find actual .NET version in registry
+        reg_ver = self.ri.lookup(self.ri.vc, f'frameworkver{bits}')
+        dot_net_dir = getattr(self, f'FrameworkDir{bits}')
+        ver = reg_ver or self._use_last_dir_name(dot_net_dir, 'v') or ''
+
+        # Set .NET versions for specified MSVC++ version
+        if self.vs_ver >= 12.0:
+            return ver, 'v4.0'
+        elif self.vs_ver >= 10.0:
+            return 'v4.0.30319' if ver.lower()[:2] != 'v4' else ver, 'v3.5'
+        elif self.vs_ver == 9.0:
+            return 'v3.5', 'v2.0.50727'
+        elif self.vs_ver == 8.0:
+            return 'v3.0', 'v2.0.50727'
+        return ()
+
+    @staticmethod
+    def _use_last_dir_name(path, prefix=''):
+        """
+        Return name of the last dir in path or '' if no dir found.
+
+        Parameters
+        ----------
+        path: str
+            Use dirs in this path
+        prefix: str
+            Use only dirs starting by this prefix
+
+        Return
+        ------
+        str
+            name
+        """
+        matching_dirs = (
+            dir_name
+            for dir_name in reversed(os.listdir(path))
+            if os.path.isdir(os.path.join(path, dir_name))
+            and dir_name.startswith(prefix)
+        )
+        return next(matching_dirs, None) or ''
+
+
+class _EnvironmentDict(TypedDict):
+    include: str
+    lib: str
+    libpath: str
+    path: str
+    py_vcruntime_redist: NotRequired[str | None]
+
+
+class EnvironmentInfo:
+    """
+    Return environment variables for specified Microsoft Visual C++ version
+    and platform : Lib, Include, Path and libpath.
+
+    This function is compatible with Microsoft Visual C++ 9.0 to 14.X.
+
+    Script created by analysing Microsoft environment configuration files like
+    "vcvars[...].bat", "SetEnv.Cmd", "vcbuildtools.bat", ...
+
+    Parameters
+    ----------
+    arch: str
+        Target architecture.
+    vc_ver: float
+        Required Microsoft Visual C++ version. If not set, autodetect the last
+        version.
+    vc_min_ver: float
+        Minimum Microsoft Visual C++ version.
+    """
+
+    # Variables and properties in this class use originals CamelCase variables
+    # names from Microsoft source files for more easy comparison.
+
+    def __init__(self, arch, vc_ver=None, vc_min_ver=0) -> None:
+        self.pi = PlatformInfo(arch)
+        self.ri = RegistryInfo(self.pi)
+        self.si = SystemInfo(self.ri, vc_ver)
+
+        if self.vc_ver < vc_min_ver:
+            err = 'No suitable Microsoft Visual C++ version found'
+            raise distutils.errors.DistutilsPlatformError(err)
+
+    @property
+    def vs_ver(self):
+        """
+        Microsoft Visual Studio.
+
+        Return
+        ------
+        float
+            version
+        """
+        return self.si.vs_ver
+
+    @property
+    def vc_ver(self):
+        """
+        Microsoft Visual C++ version.
+
+        Return
+        ------
+        float
+            version
+        """
+        return self.si.vc_ver
+
+    @property
+    def VSTools(self):
+        """
+        Microsoft Visual Studio Tools.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        paths = [r'Common7\IDE', r'Common7\Tools']
+
+        if self.vs_ver >= 14.0:
+            arch_subdir = self.pi.current_dir(hidex86=True, x64=True)
+            paths += [r'Common7\IDE\CommonExtensions\Microsoft\TestWindow']
+            paths += [r'Team Tools\Performance Tools']
+            paths += [rf'Team Tools\Performance Tools{arch_subdir}']
+
+        return [os.path.join(self.si.VSInstallDir, path) for path in paths]
+
+    @property
+    def VCIncludes(self):
+        """
+        Microsoft Visual C++ & Microsoft Foundation Class Includes.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        return [
+            os.path.join(self.si.VCInstallDir, 'Include'),
+            os.path.join(self.si.VCInstallDir, r'ATLMFC\Include'),
+        ]
+
+    @property
+    def VCLibraries(self):
+        """
+        Microsoft Visual C++ & Microsoft Foundation Class Libraries.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        if self.vs_ver >= 15.0:
+            arch_subdir = self.pi.target_dir(x64=True)
+        else:
+            arch_subdir = self.pi.target_dir(hidex86=True)
+        paths = [f'Lib{arch_subdir}', rf'ATLMFC\Lib{arch_subdir}']
+
+        if self.vs_ver >= 14.0:
+            paths += [rf'Lib\store{arch_subdir}']
+
+        return [os.path.join(self.si.VCInstallDir, path) for path in paths]
+
+    @property
+    def VCStoreRefs(self):
+        """
+        Microsoft Visual C++ store references Libraries.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        if self.vs_ver < 14.0:
+            return []
+        return [os.path.join(self.si.VCInstallDir, r'Lib\store\references')]
+
+    @property
+    def VCTools(self):
+        """
+        Microsoft Visual C++ Tools.
+
+        Return
+        ------
+        list of str
+            paths
+
+        When host CPU is ARM, the tools should be found for ARM.
+
+        >>> getfixture('windows_only')
+        >>> mp = getfixture('monkeypatch')
+        >>> mp.setattr(PlatformInfo, 'current_cpu', 'arm64')
+        >>> ei = EnvironmentInfo(arch='irrelevant')
+        >>> paths = ei.VCTools
+        >>> any('HostARM64' in path for path in paths)
+        True
+        """
+        si = self.si
+        tools = [os.path.join(si.VCInstallDir, 'VCPackages')]
+
+        forcex86 = True if self.vs_ver <= 10.0 else False
+        arch_subdir = self.pi.cross_dir(forcex86)
+        if arch_subdir:
+            tools += [os.path.join(si.VCInstallDir, f'Bin{arch_subdir}')]
+
+        if self.vs_ver == 14.0:
+            path = f'Bin{self.pi.current_dir(hidex86=True)}'
+            tools += [os.path.join(si.VCInstallDir, path)]
+
+        elif self.vs_ver >= 15.0:
+            host_id = self.pi.current_cpu.replace('amd64', 'x64').upper()
+            host_dir = os.path.join('bin', f'Host{host_id}%s')
+            tools += [
+                os.path.join(si.VCInstallDir, host_dir % self.pi.target_dir(x64=True))
+            ]
+
+            if self.pi.current_cpu != self.pi.target_cpu:
+                tools += [
+                    os.path.join(
+                        si.VCInstallDir, host_dir % self.pi.current_dir(x64=True)
+                    )
+                ]
+
+        else:
+            tools += [os.path.join(si.VCInstallDir, 'Bin')]
+
+        return tools
+
+    @property
+    def OSLibraries(self):
+        """
+        Microsoft Windows SDK Libraries.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        if self.vs_ver <= 10.0:
+            arch_subdir = self.pi.target_dir(hidex86=True, x64=True)
+            return [os.path.join(self.si.WindowsSdkDir, f'Lib{arch_subdir}')]
+
+        else:
+            arch_subdir = self.pi.target_dir(x64=True)
+            lib = os.path.join(self.si.WindowsSdkDir, 'lib')
+            libver = self._sdk_subdir
+            return [os.path.join(lib, f'{libver}um{arch_subdir}')]
+
+    @property
+    def OSIncludes(self):
+        """
+        Microsoft Windows SDK Include.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        include = os.path.join(self.si.WindowsSdkDir, 'include')
+
+        if self.vs_ver <= 10.0:
+            return [include, os.path.join(include, 'gl')]
+
+        else:
+            if self.vs_ver >= 14.0:
+                sdkver = self._sdk_subdir
+            else:
+                sdkver = ''
+            return [
+                os.path.join(include, f'{sdkver}shared'),
+                os.path.join(include, f'{sdkver}um'),
+                os.path.join(include, f'{sdkver}winrt'),
+            ]
+
+    @property
+    def OSLibpath(self):
+        """
+        Microsoft Windows SDK Libraries Paths.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        ref = os.path.join(self.si.WindowsSdkDir, 'References')
+        libpath = []
+
+        if self.vs_ver <= 9.0:
+            libpath += self.OSLibraries
+
+        if self.vs_ver >= 11.0:
+            libpath += [os.path.join(ref, r'CommonConfiguration\Neutral')]
+
+        if self.vs_ver >= 14.0:
+            libpath += [
+                ref,
+                os.path.join(self.si.WindowsSdkDir, 'UnionMetadata'),
+                os.path.join(ref, 'Windows.Foundation.UniversalApiContract', '1.0.0.0'),
+                os.path.join(ref, 'Windows.Foundation.FoundationContract', '1.0.0.0'),
+                os.path.join(
+                    ref, 'Windows.Networking.Connectivity.WwanContract', '1.0.0.0'
+                ),
+                os.path.join(
+                    self.si.WindowsSdkDir,
+                    'ExtensionSDKs',
+                    'Microsoft.VCLibs',
+                    f'{self.vs_ver:0.1f}',
+                    'References',
+                    'CommonConfiguration',
+                    'neutral',
+                ),
+            ]
+        return libpath
+
+    @property
+    def SdkTools(self):
+        """
+        Microsoft Windows SDK Tools.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        return list(self._sdk_tools())
+
+    def _sdk_tools(self):
+        """
+        Microsoft Windows SDK Tools paths generator.
+
+        Return
+        ------
+        generator of str
+            paths
+        """
+        if self.vs_ver < 15.0:
+            bin_dir = 'Bin' if self.vs_ver <= 11.0 else r'Bin\x86'
+            yield os.path.join(self.si.WindowsSdkDir, bin_dir)
+
+        if not self.pi.current_is_x86():
+            arch_subdir = self.pi.current_dir(x64=True)
+            path = f'Bin{arch_subdir}'
+            yield os.path.join(self.si.WindowsSdkDir, path)
+
+        if self.vs_ver in (10.0, 11.0):
+            if self.pi.target_is_x86():
+                arch_subdir = ''
+            else:
+                arch_subdir = self.pi.current_dir(hidex86=True, x64=True)
+            path = rf'Bin\NETFX 4.0 Tools{arch_subdir}'
+            yield os.path.join(self.si.WindowsSdkDir, path)
+
+        elif self.vs_ver >= 15.0:
+            path = os.path.join(self.si.WindowsSdkDir, 'Bin')
+            arch_subdir = self.pi.current_dir(x64=True)
+            sdkver = self.si.WindowsSdkLastVersion
+            yield os.path.join(path, f'{sdkver}{arch_subdir}')
+
+        if self.si.WindowsSDKExecutablePath:
+            yield self.si.WindowsSDKExecutablePath
+
+    @property
+    def _sdk_subdir(self):
+        """
+        Microsoft Windows SDK version subdir.
+
+        Return
+        ------
+        str
+            subdir
+        """
+        ucrtver = self.si.WindowsSdkLastVersion
+        return (f'{ucrtver}\\') if ucrtver else ''
+
+    @property
+    def SdkSetup(self):
+        """
+        Microsoft Windows SDK Setup.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        if self.vs_ver > 9.0:
+            return []
+
+        return [os.path.join(self.si.WindowsSdkDir, 'Setup')]
+
+    @property
+    def FxTools(self):
+        """
+        Microsoft .NET Framework Tools.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        pi = self.pi
+        si = self.si
+
+        if self.vs_ver <= 10.0:
+            include32 = True
+            include64 = not pi.target_is_x86() and not pi.current_is_x86()
+        else:
+            include32 = pi.target_is_x86() or pi.current_is_x86()
+            include64 = pi.current_cpu == 'amd64' or pi.target_cpu == 'amd64'
+
+        tools = []
+        if include32:
+            tools += [
+                os.path.join(si.FrameworkDir32, ver) for ver in si.FrameworkVersion32
+            ]
+        if include64:
+            tools += [
+                os.path.join(si.FrameworkDir64, ver) for ver in si.FrameworkVersion64
+            ]
+        return tools
+
+    @property
+    def NetFxSDKLibraries(self):
+        """
+        Microsoft .Net Framework SDK Libraries.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        if self.vs_ver < 14.0 or not self.si.NetFxSdkDir:
+            return []
+
+        arch_subdir = self.pi.target_dir(x64=True)
+        return [os.path.join(self.si.NetFxSdkDir, rf'lib\um{arch_subdir}')]
+
+    @property
+    def NetFxSDKIncludes(self):
+        """
+        Microsoft .Net Framework SDK Includes.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        if self.vs_ver < 14.0 or not self.si.NetFxSdkDir:
+            return []
+
+        return [os.path.join(self.si.NetFxSdkDir, r'include\um')]
+
+    @property
+    def VsTDb(self):
+        """
+        Microsoft Visual Studio Team System Database.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        return [os.path.join(self.si.VSInstallDir, r'VSTSDB\Deploy')]
+
+    @property
+    def MSBuild(self):
+        """
+        Microsoft Build Engine.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        if self.vs_ver < 12.0:
+            return []
+        elif self.vs_ver < 15.0:
+            base_path = self.si.ProgramFilesx86
+            arch_subdir = self.pi.current_dir(hidex86=True)
+        else:
+            base_path = self.si.VSInstallDir
+            arch_subdir = ''
+
+        path = rf'MSBuild\{self.vs_ver:0.1f}\bin{arch_subdir}'
+        build = [os.path.join(base_path, path)]
+
+        if self.vs_ver >= 15.0:
+            # Add Roslyn C# & Visual Basic Compiler
+            build += [os.path.join(base_path, path, 'Roslyn')]
+
+        return build
+
+    @property
+    def HTMLHelpWorkshop(self):
+        """
+        Microsoft HTML Help Workshop.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        if self.vs_ver < 11.0:
+            return []
+
+        return [os.path.join(self.si.ProgramFilesx86, 'HTML Help Workshop')]
+
+    @property
+    def UCRTLibraries(self):
+        """
+        Microsoft Universal C Runtime SDK Libraries.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        if self.vs_ver < 14.0:
+            return []
+
+        arch_subdir = self.pi.target_dir(x64=True)
+        lib = os.path.join(self.si.UniversalCRTSdkDir, 'lib')
+        ucrtver = self._ucrt_subdir
+        return [os.path.join(lib, f'{ucrtver}ucrt{arch_subdir}')]
+
+    @property
+    def UCRTIncludes(self):
+        """
+        Microsoft Universal C Runtime SDK Include.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        if self.vs_ver < 14.0:
+            return []
+
+        include = os.path.join(self.si.UniversalCRTSdkDir, 'include')
+        return [os.path.join(include, f'{self._ucrt_subdir}ucrt')]
+
+    @property
+    def _ucrt_subdir(self):
+        """
+        Microsoft Universal C Runtime SDK version subdir.
+
+        Return
+        ------
+        str
+            subdir
+        """
+        ucrtver = self.si.UniversalCRTSdkLastVersion
+        return (f'{ucrtver}\\') if ucrtver else ''
+
+    @property
+    def FSharp(self):
+        """
+        Microsoft Visual F#.
+
+        Return
+        ------
+        list of str
+            paths
+        """
+        if 11.0 > self.vs_ver > 12.0:
+            return []
+
+        return [self.si.FSharpInstallDir]
+
+    @property
+    def VCRuntimeRedist(self) -> str | None:
+        """
+        Microsoft Visual C++ runtime redistributable dll.
+
+        Returns the first suitable path found or None.
+        """
+        vcruntime = f'vcruntime{self.vc_ver}0.dll'
+        arch_subdir = self.pi.target_dir(x64=True).strip('\\')
+
+        # Installation prefixes candidates
+        prefixes = []
+        tools_path = self.si.VCInstallDir
+        redist_path = os.path.dirname(tools_path.replace(r'\Tools', r'\Redist'))
+        if os.path.isdir(redist_path):
+            # Redist version may not be exactly the same as tools
+            redist_path = os.path.join(redist_path, os.listdir(redist_path)[-1])
+            prefixes += [redist_path, os.path.join(redist_path, 'onecore')]
+
+        prefixes += [os.path.join(tools_path, 'redist')]  # VS14 legacy path
+
+        # CRT directory
+        crt_dirs = (
+            f'Microsoft.VC{self.vc_ver * 10}.CRT',
+            # Sometime store in directory with VS version instead of VC
+            f'Microsoft.VC{int(self.vs_ver) * 10}.CRT',
+        )
+
+        # vcruntime path
+        candidate_paths = (
+            os.path.join(prefix, arch_subdir, crt_dir, vcruntime)
+            for (prefix, crt_dir) in itertools.product(prefixes, crt_dirs)
+        )
+        return next(filter(os.path.isfile, candidate_paths), None)  # type: ignore[arg-type] #python/mypy#12682
+
+    def return_env(self, exists: bool = True) -> _EnvironmentDict:
+        """
+        Return environment dict.
+
+        Parameters
+        ----------
+        exists: bool
+            It True, only return existing paths.
+
+        Return
+        ------
+        dict
+            environment
+        """
+        env = _EnvironmentDict(
+            include=self._build_paths(
+                'include',
+                [
+                    self.VCIncludes,
+                    self.OSIncludes,
+                    self.UCRTIncludes,
+                    self.NetFxSDKIncludes,
+                ],
+                exists,
+            ),
+            lib=self._build_paths(
+                'lib',
+                [
+                    self.VCLibraries,
+                    self.OSLibraries,
+                    self.FxTools,
+                    self.UCRTLibraries,
+                    self.NetFxSDKLibraries,
+                ],
+                exists,
+            ),
+            libpath=self._build_paths(
+                'libpath',
+                [self.VCLibraries, self.FxTools, self.VCStoreRefs, self.OSLibpath],
+                exists,
+            ),
+            path=self._build_paths(
+                'path',
+                [
+                    self.VCTools,
+                    self.VSTools,
+                    self.VsTDb,
+                    self.SdkTools,
+                    self.SdkSetup,
+                    self.FxTools,
+                    self.MSBuild,
+                    self.HTMLHelpWorkshop,
+                    self.FSharp,
+                ],
+                exists,
+            ),
+        )
+        if self.vs_ver >= 14 and self.VCRuntimeRedist:
+            env['py_vcruntime_redist'] = self.VCRuntimeRedist
+        return env
+
+    def _build_paths(self, name, spec_path_lists, exists):
+        """
+        Given an environment variable name and specified paths,
+        return a pathsep-separated string of paths containing
+        unique, extant, directories from those paths and from
+        the environment variable. Raise an error if no paths
+        are resolved.
+
+        Parameters
+        ----------
+        name: str
+            Environment variable name
+        spec_path_lists: list of str
+            Paths
+        exists: bool
+            It True, only return existing paths.
+
+        Return
+        ------
+        str
+            Pathsep-separated paths
+        """
+        # flatten spec_path_lists
+        spec_paths = itertools.chain.from_iterable(spec_path_lists)
+        env_paths = environ.get(name, '').split(os.pathsep)
+        paths = itertools.chain(spec_paths, env_paths)
+        extant_paths = list(filter(os.path.isdir, paths)) if exists else paths
+        if not extant_paths:
+            msg = f"{name.upper()} environment variable is empty"
+            raise distutils.errors.DistutilsPlatformError(msg)
+        unique_paths = unique_everseen(extant_paths)
+        return os.pathsep.join(unique_paths)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/namespaces.py b/.venv/lib/python3.12/site-packages/setuptools/namespaces.py
new file mode 100644
index 0000000000000000000000000000000000000000..85ea2ebd654c480b8c19d1715b3772c4bcfd812e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/namespaces.py
@@ -0,0 +1,106 @@
+import itertools
+import os
+
+from .compat import py312
+
+from distutils import log
+
+flatten = itertools.chain.from_iterable
+
+
+class Installer:
+    nspkg_ext = '-nspkg.pth'
+
+    def install_namespaces(self) -> None:
+        nsp = self._get_all_ns_packages()
+        if not nsp:
+            return
+        filename = self._get_nspkg_file()
+        self.outputs.append(filename)
+        log.info("Installing %s", filename)
+        lines = map(self._gen_nspkg_line, nsp)
+
+        if self.dry_run:
+            # always generate the lines, even in dry run
+            list(lines)
+            return
+
+        with open(filename, 'wt', encoding=py312.PTH_ENCODING) as f:
+            # Python<3.13 requires encoding="locale" instead of "utf-8"
+            # See: python/cpython#77102
+            f.writelines(lines)
+
+    def uninstall_namespaces(self) -> None:
+        filename = self._get_nspkg_file()
+        if not os.path.exists(filename):
+            return
+        log.info("Removing %s", filename)
+        os.remove(filename)
+
+    def _get_nspkg_file(self):
+        filename, _ = os.path.splitext(self._get_target())
+        return filename + self.nspkg_ext
+
+    def _get_target(self):
+        return self.target
+
+    _nspkg_tmpl = (
+        "import sys, types, os",
+        "p = os.path.join(%(root)s, *%(pth)r)",
+        "importlib = __import__('importlib.util')",
+        "__import__('importlib.machinery')",
+        (
+            "m = "
+            "sys.modules.setdefault(%(pkg)r, "
+            "importlib.util.module_from_spec("
+            "importlib.machinery.PathFinder.find_spec(%(pkg)r, "
+            "[os.path.dirname(p)])))"
+        ),
+        ("m = m or sys.modules.setdefault(%(pkg)r, types.ModuleType(%(pkg)r))"),
+        "mp = (m or []) and m.__dict__.setdefault('__path__',[])",
+        "(p not in mp) and mp.append(p)",
+    )
+    "lines for the namespace installer"
+
+    _nspkg_tmpl_multi = ('m and setattr(sys.modules[%(parent)r], %(child)r, m)',)
+    "additional line(s) when a parent package is indicated"
+
+    def _get_root(self):
+        return "sys._getframe(1).f_locals['sitedir']"
+
+    def _gen_nspkg_line(self, pkg):
+        pth = tuple(pkg.split('.'))
+        root = self._get_root()
+        tmpl_lines = self._nspkg_tmpl
+        parent, sep, child = pkg.rpartition('.')
+        if parent:
+            tmpl_lines += self._nspkg_tmpl_multi
+        return ';'.join(tmpl_lines) % locals() + '\n'
+
+    def _get_all_ns_packages(self):
+        """Return sorted list of all package namespaces"""
+        pkgs = self.distribution.namespace_packages or []
+        return sorted(set(flatten(map(self._pkg_names, pkgs))))
+
+    @staticmethod
+    def _pkg_names(pkg):
+        """
+        Given a namespace package, yield the components of that
+        package.
+
+        >>> names = Installer._pkg_names('a.b.c')
+        >>> set(names) == set(['a', 'a.b', 'a.b.c'])
+        True
+        """
+        parts = pkg.split('.')
+        while parts:
+            yield '.'.join(parts)
+            parts.pop()
+
+
+class DevelopInstaller(Installer):
+    def _get_root(self):
+        return repr(str(self.egg_path))
+
+    def _get_target(self):
+        return self.egg_link
diff --git a/.venv/lib/python3.12/site-packages/setuptools/package_index.py b/.venv/lib/python3.12/site-packages/setuptools/package_index.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a6abebcda4f62c91e0843a17ed4716f8fd3b1d9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/package_index.py
@@ -0,0 +1,1137 @@
+"""PyPI and direct package downloading."""
+
+from __future__ import annotations
+
+import base64
+import configparser
+import hashlib
+import html
+import http.client
+import io
+import itertools
+import os
+import re
+import shutil
+import socket
+import subprocess
+import sys
+import urllib.error
+import urllib.parse
+import urllib.request
+from fnmatch import translate
+from functools import wraps
+from typing import NamedTuple
+
+from more_itertools import unique_everseen
+
+import setuptools
+from pkg_resources import (
+    BINARY_DIST,
+    CHECKOUT_DIST,
+    DEVELOP_DIST,
+    EGG_DIST,
+    SOURCE_DIST,
+    Distribution,
+    Environment,
+    Requirement,
+    find_distributions,
+    normalize_path,
+    parse_version,
+    safe_name,
+    safe_version,
+    to_filename,
+)
+from setuptools.wheel import Wheel
+
+from .unicode_utils import _cfg_read_utf8_with_fallback, _read_utf8_with_fallback
+
+from distutils import log
+from distutils.errors import DistutilsError
+
+EGG_FRAGMENT = re.compile(r'^egg=([-A-Za-z0-9_.+!]+)$')
+HREF = re.compile(r"""href\s*=\s*['"]?([^'"> ]+)""", re.I)
+PYPI_MD5 = re.compile(
+    r'<a href="([^"#]+)">([^<]+)</a>\n\s+\(<a (?:title="MD5 hash"\n\s+)'
+    r'href="[^?]+\?:action=show_md5&amp;digest=([0-9a-f]{32})">md5</a>\)'
+)
+URL_SCHEME = re.compile('([-+.a-z0-9]{2,}):', re.I).match
+EXTENSIONS = ".tar.gz .tar.bz2 .tar .zip .tgz".split()
+
+__all__ = [
+    'PackageIndex',
+    'distros_for_url',
+    'parse_bdist_wininst',
+    'interpret_distro_name',
+]
+
+_SOCKET_TIMEOUT = 15
+
+user_agent = f"setuptools/{setuptools.__version__} Python-urllib/{sys.version_info.major}.{sys.version_info.minor}"
+
+
+def parse_requirement_arg(spec):
+    try:
+        return Requirement.parse(spec)
+    except ValueError as e:
+        raise DistutilsError(
+            f"Not a URL, existing file, or requirement spec: {spec!r}"
+        ) from e
+
+
+def parse_bdist_wininst(name):
+    """Return (base,pyversion) or (None,None) for possible .exe name"""
+
+    lower = name.lower()
+    base, py_ver, plat = None, None, None
+
+    if lower.endswith('.exe'):
+        if lower.endswith('.win32.exe'):
+            base = name[:-10]
+            plat = 'win32'
+        elif lower.startswith('.win32-py', -16):
+            py_ver = name[-7:-4]
+            base = name[:-16]
+            plat = 'win32'
+        elif lower.endswith('.win-amd64.exe'):
+            base = name[:-14]
+            plat = 'win-amd64'
+        elif lower.startswith('.win-amd64-py', -20):
+            py_ver = name[-7:-4]
+            base = name[:-20]
+            plat = 'win-amd64'
+    return base, py_ver, plat
+
+
+def egg_info_for_url(url):
+    parts = urllib.parse.urlparse(url)
+    _scheme, server, path, _parameters, _query, fragment = parts
+    base = urllib.parse.unquote(path.split('/')[-1])
+    if server == 'sourceforge.net' and base == 'download':  # XXX Yuck
+        base = urllib.parse.unquote(path.split('/')[-2])
+    if '#' in base:
+        base, fragment = base.split('#', 1)
+    return base, fragment
+
+
+def distros_for_url(url, metadata=None):
+    """Yield egg or source distribution objects that might be found at a URL"""
+    base, fragment = egg_info_for_url(url)
+    yield from distros_for_location(url, base, metadata)
+    if fragment:
+        match = EGG_FRAGMENT.match(fragment)
+        if match:
+            yield from interpret_distro_name(
+                url, match.group(1), metadata, precedence=CHECKOUT_DIST
+            )
+
+
+def distros_for_location(location, basename, metadata=None):
+    """Yield egg or source distribution objects based on basename"""
+    if basename.endswith('.egg.zip'):
+        basename = basename[:-4]  # strip the .zip
+    if basename.endswith('.egg') and '-' in basename:
+        # only one, unambiguous interpretation
+        return [Distribution.from_location(location, basename, metadata)]
+    if basename.endswith('.whl') and '-' in basename:
+        wheel = Wheel(basename)
+        if not wheel.is_compatible():
+            return []
+        return [
+            Distribution(
+                location=location,
+                project_name=wheel.project_name,
+                version=wheel.version,
+                # Increase priority over eggs.
+                precedence=EGG_DIST + 1,
+            )
+        ]
+    if basename.endswith('.exe'):
+        win_base, py_ver, platform = parse_bdist_wininst(basename)
+        if win_base is not None:
+            return interpret_distro_name(
+                location, win_base, metadata, py_ver, BINARY_DIST, platform
+            )
+    # Try source distro extensions (.zip, .tgz, etc.)
+    #
+    for ext in EXTENSIONS:
+        if basename.endswith(ext):
+            basename = basename[: -len(ext)]
+            return interpret_distro_name(location, basename, metadata)
+    return []  # no extension matched
+
+
+def distros_for_filename(filename, metadata=None):
+    """Yield possible egg or source distribution objects based on a filename"""
+    return distros_for_location(
+        normalize_path(filename), os.path.basename(filename), metadata
+    )
+
+
+def interpret_distro_name(
+    location, basename, metadata, py_version=None, precedence=SOURCE_DIST, platform=None
+):
+    """Generate the interpretation of a source distro name
+
+    Note: if `location` is a filesystem filename, you should call
+    ``pkg_resources.normalize_path()`` on it before passing it to this
+    routine!
+    """
+
+    parts = basename.split('-')
+    if not py_version and any(re.match(r'py\d\.\d$', p) for p in parts[2:]):
+        # it is a bdist_dumb, not an sdist -- bail out
+        return
+
+    # find the pivot (p) that splits the name from the version.
+    # infer the version as the first item that has a digit.
+    for p in range(len(parts)):
+        if parts[p][:1].isdigit():
+            break
+    else:
+        p = len(parts)
+
+    yield Distribution(
+        location,
+        metadata,
+        '-'.join(parts[:p]),
+        '-'.join(parts[p:]),
+        py_version=py_version,
+        precedence=precedence,
+        platform=platform,
+    )
+
+
+def unique_values(func):
+    """
+    Wrap a function returning an iterable such that the resulting iterable
+    only ever yields unique items.
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        return unique_everseen(func(*args, **kwargs))
+
+    return wrapper
+
+
+REL = re.compile(r"""<([^>]*\srel\s{0,10}=\s{0,10}['"]?([^'" >]+)[^>]*)>""", re.I)
+"""
+Regex for an HTML tag with 'rel="val"' attributes.
+"""
+
+
+@unique_values
+def find_external_links(url, page):
+    """Find rel="homepage" and rel="download" links in `page`, yielding URLs"""
+
+    for match in REL.finditer(page):
+        tag, rel = match.groups()
+        rels = set(map(str.strip, rel.lower().split(',')))
+        if 'homepage' in rels or 'download' in rels:
+            for match in HREF.finditer(tag):
+                yield urllib.parse.urljoin(url, htmldecode(match.group(1)))
+
+    for tag in ("<th>Home Page", "<th>Download URL"):
+        pos = page.find(tag)
+        if pos != -1:
+            match = HREF.search(page, pos)
+            if match:
+                yield urllib.parse.urljoin(url, htmldecode(match.group(1)))
+
+
+class ContentChecker:
+    """
+    A null content checker that defines the interface for checking content
+    """
+
+    def feed(self, block):
+        """
+        Feed a block of data to the hash.
+        """
+        return
+
+    def is_valid(self):
+        """
+        Check the hash. Return False if validation fails.
+        """
+        return True
+
+    def report(self, reporter, template):
+        """
+        Call reporter with information about the checker (hash name)
+        substituted into the template.
+        """
+        return
+
+
+class HashChecker(ContentChecker):
+    pattern = re.compile(
+        r'(?P<hash_name>sha1|sha224|sha384|sha256|sha512|md5)='
+        r'(?P<expected>[a-f0-9]+)'
+    )
+
+    def __init__(self, hash_name, expected) -> None:
+        self.hash_name = hash_name
+        self.hash = hashlib.new(hash_name)
+        self.expected = expected
+
+    @classmethod
+    def from_url(cls, url):
+        "Construct a (possibly null) ContentChecker from a URL"
+        fragment = urllib.parse.urlparse(url)[-1]
+        if not fragment:
+            return ContentChecker()
+        match = cls.pattern.search(fragment)
+        if not match:
+            return ContentChecker()
+        return cls(**match.groupdict())
+
+    def feed(self, block):
+        self.hash.update(block)
+
+    def is_valid(self):
+        return self.hash.hexdigest() == self.expected
+
+    def report(self, reporter, template):
+        msg = template % self.hash_name
+        return reporter(msg)
+
+
+class PackageIndex(Environment):
+    """A distribution index that scans web pages for download URLs"""
+
+    def __init__(
+        self,
+        index_url: str = "https://pypi.org/simple/",
+        hosts=('*',),
+        ca_bundle=None,
+        verify_ssl: bool = True,
+        *args,
+        **kw,
+    ) -> None:
+        super().__init__(*args, **kw)
+        self.index_url = index_url + "/"[: not index_url.endswith('/')]
+        self.scanned_urls: dict = {}
+        self.fetched_urls: dict = {}
+        self.package_pages: dict = {}
+        self.allows = re.compile('|'.join(map(translate, hosts))).match
+        self.to_scan: list = []
+        self.opener = urllib.request.urlopen
+
+    def add(self, dist):
+        # ignore invalid versions
+        try:
+            parse_version(dist.version)
+        except Exception:
+            return None
+        return super().add(dist)
+
+    # FIXME: 'PackageIndex.process_url' is too complex (14)
+    def process_url(self, url, retrieve: bool = False) -> None:  # noqa: C901
+        """Evaluate a URL as a possible download, and maybe retrieve it"""
+        if url in self.scanned_urls and not retrieve:
+            return
+        self.scanned_urls[url] = True
+        if not URL_SCHEME(url):
+            self.process_filename(url)
+            return
+        else:
+            dists = list(distros_for_url(url))
+            if dists:
+                if not self.url_ok(url):
+                    return
+                self.debug("Found link: %s", url)
+
+        if dists or not retrieve or url in self.fetched_urls:
+            list(map(self.add, dists))
+            return  # don't need the actual page
+
+        if not self.url_ok(url):
+            self.fetched_urls[url] = True
+            return
+
+        self.info("Reading %s", url)
+        self.fetched_urls[url] = True  # prevent multiple fetch attempts
+        tmpl = "Download error on %s: %%s -- Some packages may not be found!"
+        f = self.open_url(url, tmpl % url)
+        if f is None:
+            return
+        if isinstance(f, urllib.error.HTTPError) and f.code == 401:
+            self.info(f"Authentication error: {f.msg}")
+        self.fetched_urls[f.url] = True
+        if 'html' not in f.headers.get('content-type', '').lower():
+            f.close()  # not html, we can't process it
+            return
+
+        base = f.url  # handle redirects
+        page = f.read()
+        if not isinstance(page, str):
+            # In Python 3 and got bytes but want str.
+            if isinstance(f, urllib.error.HTTPError):
+                # Errors have no charset, assume latin1:
+                charset = 'latin-1'
+            else:
+                charset = f.headers.get_param('charset') or 'latin-1'
+            page = page.decode(charset, "ignore")
+        f.close()
+        for match in HREF.finditer(page):
+            link = urllib.parse.urljoin(base, htmldecode(match.group(1)))
+            self.process_url(link)
+        if url.startswith(self.index_url) and getattr(f, 'code', None) != 404:
+            page = self.process_index(url, page)
+
+    def process_filename(self, fn, nested: bool = False) -> None:
+        # process filenames or directories
+        if not os.path.exists(fn):
+            self.warn("Not found: %s", fn)
+            return
+
+        if os.path.isdir(fn) and not nested:
+            path = os.path.realpath(fn)
+            for item in os.listdir(path):
+                self.process_filename(os.path.join(path, item), True)
+
+        dists = distros_for_filename(fn)
+        if dists:
+            self.debug("Found: %s", fn)
+            list(map(self.add, dists))
+
+    def url_ok(self, url, fatal: bool = False) -> bool:
+        s = URL_SCHEME(url)
+        is_file = s and s.group(1).lower() == 'file'
+        if is_file or self.allows(urllib.parse.urlparse(url)[1]):
+            return True
+        msg = (
+            "\nNote: Bypassing %s (disallowed host; see "
+            "https://setuptools.pypa.io/en/latest/deprecated/"
+            "easy_install.html#restricting-downloads-with-allow-hosts for details).\n"
+        )
+        if fatal:
+            raise DistutilsError(msg % url)
+        else:
+            self.warn(msg, url)
+            return False
+
+    def scan_egg_links(self, search_path) -> None:
+        dirs = filter(os.path.isdir, search_path)
+        egg_links = (
+            (path, entry)
+            for path in dirs
+            for entry in os.listdir(path)
+            if entry.endswith('.egg-link')
+        )
+        list(itertools.starmap(self.scan_egg_link, egg_links))
+
+    def scan_egg_link(self, path, entry) -> None:
+        content = _read_utf8_with_fallback(os.path.join(path, entry))
+        # filter non-empty lines
+        lines = list(filter(None, map(str.strip, content.splitlines())))
+
+        if len(lines) != 2:
+            # format is not recognized; punt
+            return
+
+        egg_path, _setup_path = lines
+
+        for dist in find_distributions(os.path.join(path, egg_path)):
+            dist.location = os.path.join(path, *lines)
+            dist.precedence = SOURCE_DIST
+            self.add(dist)
+
+    def _scan(self, link):
+        # Process a URL to see if it's for a package page
+        NO_MATCH_SENTINEL = None, None
+        if not link.startswith(self.index_url):
+            return NO_MATCH_SENTINEL
+
+        parts = list(map(urllib.parse.unquote, link[len(self.index_url) :].split('/')))
+        if len(parts) != 2 or '#' in parts[1]:
+            return NO_MATCH_SENTINEL
+
+        # it's a package page, sanitize and index it
+        pkg = safe_name(parts[0])
+        ver = safe_version(parts[1])
+        self.package_pages.setdefault(pkg.lower(), {})[link] = True
+        return to_filename(pkg), to_filename(ver)
+
+    def process_index(self, url, page):
+        """Process the contents of a PyPI page"""
+
+        # process an index page into the package-page index
+        for match in HREF.finditer(page):
+            try:
+                self._scan(urllib.parse.urljoin(url, htmldecode(match.group(1))))
+            except ValueError:
+                pass
+
+        pkg, ver = self._scan(url)  # ensure this page is in the page index
+        if not pkg:
+            return ""  # no sense double-scanning non-package pages
+
+        # process individual package page
+        for new_url in find_external_links(url, page):
+            # Process the found URL
+            base, frag = egg_info_for_url(new_url)
+            if base.endswith('.py') and not frag:
+                if ver:
+                    new_url += f'#egg={pkg}-{ver}'
+                else:
+                    self.need_version_info(url)
+            self.scan_url(new_url)
+
+        return PYPI_MD5.sub(
+            lambda m: '<a href="{}#md5={}">{}</a>'.format(*m.group(1, 3, 2)), page
+        )
+
+    def need_version_info(self, url) -> None:
+        self.scan_all(
+            "Page at %s links to .py file(s) without version info; an index "
+            "scan is required.",
+            url,
+        )
+
+    def scan_all(self, msg=None, *args) -> None:
+        if self.index_url not in self.fetched_urls:
+            if msg:
+                self.warn(msg, *args)
+            self.info("Scanning index of all packages (this may take a while)")
+        self.scan_url(self.index_url)
+
+    def find_packages(self, requirement) -> None:
+        self.scan_url(self.index_url + requirement.unsafe_name + '/')
+
+        if not self.package_pages.get(requirement.key):
+            # Fall back to safe version of the name
+            self.scan_url(self.index_url + requirement.project_name + '/')
+
+        if not self.package_pages.get(requirement.key):
+            # We couldn't find the target package, so search the index page too
+            self.not_found_in_index(requirement)
+
+        for url in list(self.package_pages.get(requirement.key, ())):
+            # scan each page that might be related to the desired package
+            self.scan_url(url)
+
+    def obtain(self, requirement, installer=None):
+        self.prescan()
+        self.find_packages(requirement)
+        for dist in self[requirement.key]:
+            if dist in requirement:
+                return dist
+            self.debug("%s does not match %s", requirement, dist)
+        return super().obtain(requirement, installer)
+
+    def check_hash(self, checker, filename, tfp) -> None:
+        """
+        checker is a ContentChecker
+        """
+        checker.report(self.debug, f"Validating %s checksum for {filename}")
+        if not checker.is_valid():
+            tfp.close()
+            os.unlink(filename)
+            raise DistutilsError(
+                f"{checker.hash.name} validation failed for {os.path.basename(filename)}; "
+                "possible download problem?"
+            )
+
+    def add_find_links(self, urls) -> None:
+        """Add `urls` to the list that will be prescanned for searches"""
+        for url in urls:
+            if (
+                self.to_scan is None  # if we have already "gone online"
+                or not URL_SCHEME(url)  # or it's a local file/directory
+                or url.startswith('file:')
+                or list(distros_for_url(url))  # or a direct package link
+            ):
+                # then go ahead and process it now
+                self.scan_url(url)
+            else:
+                # otherwise, defer retrieval till later
+                self.to_scan.append(url)
+
+    def prescan(self):
+        """Scan urls scheduled for prescanning (e.g. --find-links)"""
+        if self.to_scan:
+            list(map(self.scan_url, self.to_scan))
+        self.to_scan = None  # from now on, go ahead and process immediately
+
+    def not_found_in_index(self, requirement) -> None:
+        if self[requirement.key]:  # we've seen at least one distro
+            meth, msg = self.info, "Couldn't retrieve index page for %r"
+        else:  # no distros seen for this name, might be misspelled
+            meth, msg = self.warn, "Couldn't find index page for %r (maybe misspelled?)"
+        meth(msg, requirement.unsafe_name)
+        self.scan_all()
+
+    def download(self, spec, tmpdir):
+        """Locate and/or download `spec` to `tmpdir`, returning a local path
+
+        `spec` may be a ``Requirement`` object, or a string containing a URL,
+        an existing local filename, or a project/version requirement spec
+        (i.e. the string form of a ``Requirement`` object).  If it is the URL
+        of a .py file with an unambiguous ``#egg=name-version`` tag (i.e., one
+        that escapes ``-`` as ``_`` throughout), a trivial ``setup.py`` is
+        automatically created alongside the downloaded file.
+
+        If `spec` is a ``Requirement`` object or a string containing a
+        project/version requirement spec, this method returns the location of
+        a matching distribution (possibly after downloading it to `tmpdir`).
+        If `spec` is a locally existing file or directory name, it is simply
+        returned unchanged.  If `spec` is a URL, it is downloaded to a subpath
+        of `tmpdir`, and the local filename is returned.  Various errors may be
+        raised if a problem occurs during downloading.
+        """
+        if not isinstance(spec, Requirement):
+            scheme = URL_SCHEME(spec)
+            if scheme:
+                # It's a url, download it to tmpdir
+                found = self._download_url(spec, tmpdir)
+                base, fragment = egg_info_for_url(spec)
+                if base.endswith('.py'):
+                    found = self.gen_setup(found, fragment, tmpdir)
+                return found
+            elif os.path.exists(spec):
+                # Existing file or directory, just return it
+                return spec
+            else:
+                spec = parse_requirement_arg(spec)
+        return getattr(self.fetch_distribution(spec, tmpdir), 'location', None)
+
+    def fetch_distribution(  # noqa: C901  # is too complex (14)  # FIXME
+        self,
+        requirement,
+        tmpdir,
+        force_scan: bool = False,
+        source: bool = False,
+        develop_ok: bool = False,
+        local_index=None,
+    ) -> Distribution | None:
+        """Obtain a distribution suitable for fulfilling `requirement`
+
+        `requirement` must be a ``pkg_resources.Requirement`` instance.
+        If necessary, or if the `force_scan` flag is set, the requirement is
+        searched for in the (online) package index as well as the locally
+        installed packages.  If a distribution matching `requirement` is found,
+        the returned distribution's ``location`` is the value you would have
+        gotten from calling the ``download()`` method with the matching
+        distribution's URL or filename.  If no matching distribution is found,
+        ``None`` is returned.
+
+        If the `source` flag is set, only source distributions and source
+        checkout links will be considered.  Unless the `develop_ok` flag is
+        set, development and system eggs (i.e., those using the ``.egg-info``
+        format) will be ignored.
+        """
+        # process a Requirement
+        self.info("Searching for %s", requirement)
+        skipped = set()
+        dist = None
+
+        def find(req, env: Environment | None = None):
+            if env is None:
+                env = self
+            # Find a matching distribution; may be called more than once
+
+            for dist in env[req.key]:
+                if dist.precedence == DEVELOP_DIST and not develop_ok:
+                    if dist not in skipped:
+                        self.warn(
+                            "Skipping development or system egg: %s",
+                            dist,
+                        )
+                        skipped.add(dist)
+                    continue
+
+                test = dist in req and (dist.precedence <= SOURCE_DIST or not source)
+                if test:
+                    loc = self.download(dist.location, tmpdir)
+                    dist.download_location = loc
+                    if os.path.exists(dist.download_location):
+                        return dist
+
+            return None
+
+        if force_scan:
+            self.prescan()
+            self.find_packages(requirement)
+            dist = find(requirement)
+
+        if not dist and local_index is not None:
+            dist = find(requirement, local_index)
+
+        if dist is None:
+            if self.to_scan is not None:
+                self.prescan()
+            dist = find(requirement)
+
+        if dist is None and not force_scan:
+            self.find_packages(requirement)
+            dist = find(requirement)
+
+        if dist is None:
+            self.warn(
+                "No local packages or working download links found for %s%s",
+                (source and "a source distribution of " or ""),
+                requirement,
+            )
+            return None
+        else:
+            self.info("Best match: %s", dist)
+            return dist.clone(location=dist.download_location)
+
+    def fetch(
+        self, requirement, tmpdir, force_scan: bool = False, source: bool = False
+    ) -> str | None:
+        """Obtain a file suitable for fulfilling `requirement`
+
+        DEPRECATED; use the ``fetch_distribution()`` method now instead.  For
+        backward compatibility, this routine is identical but returns the
+        ``location`` of the downloaded distribution instead of a distribution
+        object.
+        """
+        dist = self.fetch_distribution(requirement, tmpdir, force_scan, source)
+        if dist is not None:
+            return dist.location
+        return None
+
+    def gen_setup(self, filename, fragment, tmpdir):
+        match = EGG_FRAGMENT.match(fragment)
+        dists = (
+            match
+            and [
+                d
+                for d in interpret_distro_name(filename, match.group(1), None)
+                if d.version
+            ]
+            or []
+        )
+
+        if len(dists) == 1:  # unambiguous ``#egg`` fragment
+            basename = os.path.basename(filename)
+
+            # Make sure the file has been downloaded to the temp dir.
+            if os.path.dirname(filename) != tmpdir:
+                dst = os.path.join(tmpdir, basename)
+                if not (os.path.exists(dst) and os.path.samefile(filename, dst)):
+                    shutil.copy2(filename, dst)
+                    filename = dst
+
+            with open(os.path.join(tmpdir, 'setup.py'), 'w', encoding="utf-8") as file:
+                file.write(
+                    "from setuptools import setup\n"
+                    f"setup(name={dists[0].project_name!r}, version={dists[0].version!r}, py_modules=[{os.path.splitext(basename)[0]!r}])\n"
+                )
+            return filename
+
+        elif match:
+            raise DistutilsError(
+                f"Can't unambiguously interpret project/version identifier {fragment!r}; "
+                "any dashes in the name or version should be escaped using "
+                f"underscores. {dists!r}"
+            )
+        else:
+            raise DistutilsError(
+                "Can't process plain .py files without an '#egg=name-version'"
+                " suffix to enable automatic setup script generation."
+            )
+
+    dl_blocksize = 8192
+
+    def _download_to(self, url, filename):
+        self.info("Downloading %s", url)
+        # Download the file
+        fp = None
+        try:
+            checker = HashChecker.from_url(url)
+            fp = self.open_url(url)
+            if isinstance(fp, urllib.error.HTTPError):
+                raise DistutilsError(f"Can't download {url}: {fp.code} {fp.msg}")
+            headers = fp.info()
+            blocknum = 0
+            bs = self.dl_blocksize
+            size = -1
+            if "content-length" in headers:
+                # Some servers return multiple Content-Length headers :(
+                sizes = headers.get_all('Content-Length')
+                size = max(map(int, sizes))
+                self.reporthook(url, filename, blocknum, bs, size)
+            with open(filename, 'wb') as tfp:
+                while True:
+                    block = fp.read(bs)
+                    if block:
+                        checker.feed(block)
+                        tfp.write(block)
+                        blocknum += 1
+                        self.reporthook(url, filename, blocknum, bs, size)
+                    else:
+                        break
+                self.check_hash(checker, filename, tfp)
+            return headers
+        finally:
+            if fp:
+                fp.close()
+
+    def reporthook(self, url, filename, blocknum, blksize, size) -> None:
+        pass  # no-op
+
+    # FIXME:
+    def open_url(self, url, warning=None):  # noqa: C901  # is too complex (12)
+        if url.startswith('file:'):
+            return local_open(url)
+        try:
+            return open_with_auth(url, self.opener)
+        except (ValueError, http.client.InvalidURL) as v:
+            msg = ' '.join([str(arg) for arg in v.args])
+            if warning:
+                self.warn(warning, msg)
+            else:
+                raise DistutilsError(f'{url} {msg}') from v
+        except urllib.error.HTTPError as v:
+            return v
+        except urllib.error.URLError as v:
+            if warning:
+                self.warn(warning, v.reason)
+            else:
+                raise DistutilsError(f"Download error for {url}: {v.reason}") from v
+        except http.client.BadStatusLine as v:
+            if warning:
+                self.warn(warning, v.line)
+            else:
+                raise DistutilsError(
+                    f'{url} returned a bad status line. The server might be '
+                    f'down, {v.line}'
+                ) from v
+        except (http.client.HTTPException, OSError) as v:
+            if warning:
+                self.warn(warning, v)
+            else:
+                raise DistutilsError(f"Download error for {url}: {v}") from v
+
+    def _download_url(self, url, tmpdir):
+        # Determine download filename
+        #
+        name, _fragment = egg_info_for_url(url)
+        if name:
+            while '..' in name:
+                name = name.replace('..', '.').replace('\\', '_')
+        else:
+            name = "__downloaded__"  # default if URL has no path contents
+
+        if name.endswith('.egg.zip'):
+            name = name[:-4]  # strip the extra .zip before download
+
+        filename = os.path.join(tmpdir, name)
+
+        return self._download_vcs(url, filename) or self._download_other(url, filename)
+
+    @staticmethod
+    def _resolve_vcs(url):
+        """
+        >>> rvcs = PackageIndex._resolve_vcs
+        >>> rvcs('git+http://foo/bar')
+        'git'
+        >>> rvcs('hg+https://foo/bar')
+        'hg'
+        >>> rvcs('git:myhost')
+        'git'
+        >>> rvcs('hg:myhost')
+        >>> rvcs('http://foo/bar')
+        """
+        scheme = urllib.parse.urlsplit(url).scheme
+        pre, sep, _post = scheme.partition('+')
+        # svn and git have their own protocol; hg does not
+        allowed = set(['svn', 'git'] + ['hg'] * bool(sep))
+        return next(iter({pre} & allowed), None)
+
+    def _download_vcs(self, url, spec_filename):
+        vcs = self._resolve_vcs(url)
+        if not vcs:
+            return None
+        if vcs == 'svn':
+            raise DistutilsError(
+                f"Invalid config, SVN download is not supported: {url}"
+            )
+
+        filename, _, _ = spec_filename.partition('#')
+        url, rev = self._vcs_split_rev_from_url(url)
+
+        self.info(f"Doing {vcs} clone from {url} to {filename}")
+        subprocess.check_call([vcs, 'clone', '--quiet', url, filename])
+
+        co_commands = dict(
+            git=[vcs, '-C', filename, 'checkout', '--quiet', rev],
+            hg=[vcs, '--cwd', filename, 'up', '-C', '-r', rev, '-q'],
+        )
+        if rev is not None:
+            self.info(f"Checking out {rev}")
+            subprocess.check_call(co_commands[vcs])
+
+        return filename
+
+    def _download_other(self, url, filename):
+        scheme = urllib.parse.urlsplit(url).scheme
+        if scheme == 'file':  # pragma: no cover
+            return urllib.request.url2pathname(urllib.parse.urlparse(url).path)
+        # raise error if not allowed
+        self.url_ok(url, True)
+        return self._attempt_download(url, filename)
+
+    def scan_url(self, url) -> None:
+        self.process_url(url, True)
+
+    def _attempt_download(self, url, filename):
+        headers = self._download_to(url, filename)
+        if 'html' in headers.get('content-type', '').lower():
+            return self._invalid_download_html(url, headers, filename)
+        else:
+            return filename
+
+    def _invalid_download_html(self, url, headers, filename):
+        os.unlink(filename)
+        raise DistutilsError(f"Unexpected HTML page found at {url}")
+
+    @staticmethod
+    def _vcs_split_rev_from_url(url):
+        """
+        Given a possible VCS URL, return a clean URL and resolved revision if any.
+
+        >>> vsrfu = PackageIndex._vcs_split_rev_from_url
+        >>> vsrfu('git+https://github.com/pypa/setuptools@v69.0.0#egg-info=setuptools')
+        ('https://github.com/pypa/setuptools', 'v69.0.0')
+        >>> vsrfu('git+https://github.com/pypa/setuptools#egg-info=setuptools')
+        ('https://github.com/pypa/setuptools', None)
+        >>> vsrfu('http://foo/bar')
+        ('http://foo/bar', None)
+        """
+        parts = urllib.parse.urlsplit(url)
+
+        clean_scheme = parts.scheme.split('+', 1)[-1]
+
+        # Some fragment identification fails
+        no_fragment_path, _, _ = parts.path.partition('#')
+
+        pre, sep, post = no_fragment_path.rpartition('@')
+        clean_path, rev = (pre, post) if sep else (post, None)
+
+        resolved = parts._replace(
+            scheme=clean_scheme,
+            path=clean_path,
+            # discard the fragment
+            fragment='',
+        ).geturl()
+
+        return resolved, rev
+
+    def debug(self, msg, *args) -> None:
+        log.debug(msg, *args)
+
+    def info(self, msg, *args) -> None:
+        log.info(msg, *args)
+
+    def warn(self, msg, *args) -> None:
+        log.warn(msg, *args)
+
+
+# This pattern matches a character entity reference (a decimal numeric
+# references, a hexadecimal numeric reference, or a named reference).
+entity_sub = re.compile(r'&(#(\d+|x[\da-fA-F]+)|[\w.:-]+);?').sub
+
+
+def decode_entity(match):
+    what = match.group(0)
+    return html.unescape(what)
+
+
+def htmldecode(text):
+    """
+    Decode HTML entities in the given text.
+
+    >>> htmldecode(
+    ...     'https://../package_name-0.1.2.tar.gz'
+    ...     '?tokena=A&amp;tokenb=B">package_name-0.1.2.tar.gz')
+    'https://../package_name-0.1.2.tar.gz?tokena=A&tokenb=B">package_name-0.1.2.tar.gz'
+    """
+    return entity_sub(decode_entity, text)
+
+
+def socket_timeout(timeout=15):
+    def _socket_timeout(func):
+        def _socket_timeout(*args, **kwargs):
+            old_timeout = socket.getdefaulttimeout()
+            socket.setdefaulttimeout(timeout)
+            try:
+                return func(*args, **kwargs)
+            finally:
+                socket.setdefaulttimeout(old_timeout)
+
+        return _socket_timeout
+
+    return _socket_timeout
+
+
+def _encode_auth(auth):
+    """
+    Encode auth from a URL suitable for an HTTP header.
+    >>> str(_encode_auth('username%3Apassword'))
+    'dXNlcm5hbWU6cGFzc3dvcmQ='
+
+    Long auth strings should not cause a newline to be inserted.
+    >>> long_auth = 'username:' + 'password'*10
+    >>> chr(10) in str(_encode_auth(long_auth))
+    False
+    """
+    auth_s = urllib.parse.unquote(auth)
+    # convert to bytes
+    auth_bytes = auth_s.encode()
+    encoded_bytes = base64.b64encode(auth_bytes)
+    # convert back to a string
+    encoded = encoded_bytes.decode()
+    # strip the trailing carriage return
+    return encoded.replace('\n', '')
+
+
+class Credential(NamedTuple):
+    """
+    A username/password pair.
+
+    Displayed separated by `:`.
+    >>> str(Credential('username', 'password'))
+    'username:password'
+    """
+
+    username: str
+    password: str
+
+    def __str__(self) -> str:
+        return f'{self.username}:{self.password}'
+
+
+class PyPIConfig(configparser.RawConfigParser):
+    def __init__(self):
+        """
+        Load from ~/.pypirc
+        """
+        defaults = dict.fromkeys(['username', 'password', 'repository'], '')
+        super().__init__(defaults)
+
+        rc = os.path.join(os.path.expanduser('~'), '.pypirc')
+        if os.path.exists(rc):
+            _cfg_read_utf8_with_fallback(self, rc)
+
+    @property
+    def creds_by_repository(self):
+        sections_with_repositories = [
+            section
+            for section in self.sections()
+            if self.get(section, 'repository').strip()
+        ]
+
+        return dict(map(self._get_repo_cred, sections_with_repositories))
+
+    def _get_repo_cred(self, section):
+        repo = self.get(section, 'repository').strip()
+        return repo, Credential(
+            self.get(section, 'username').strip(),
+            self.get(section, 'password').strip(),
+        )
+
+    def find_credential(self, url):
+        """
+        If the URL indicated appears to be a repository defined in this
+        config, return the credential for that repository.
+        """
+        for repository, cred in self.creds_by_repository.items():
+            if url.startswith(repository):
+                return cred
+        return None
+
+
+def open_with_auth(url, opener=urllib.request.urlopen):
+    """Open a urllib2 request, handling HTTP authentication"""
+
+    parsed = urllib.parse.urlparse(url)
+    scheme, netloc, path, params, query, frag = parsed
+
+    # Double scheme does not raise on macOS as revealed by a
+    # failing test. We would expect "nonnumeric port". Refs #20.
+    if netloc.endswith(':'):
+        raise http.client.InvalidURL("nonnumeric port: ''")
+
+    if scheme in ('http', 'https'):
+        auth, address = _splituser(netloc)
+    else:
+        auth, address = (None, None)
+
+    if not auth:
+        cred = PyPIConfig().find_credential(url)
+        if cred:
+            auth = str(cred)
+            info = cred.username, url
+            log.info('Authenticating as %s for %s (from .pypirc)', *info)
+
+    if auth:
+        auth = "Basic " + _encode_auth(auth)
+        parts = scheme, address, path, params, query, frag
+        new_url = urllib.parse.urlunparse(parts)
+        request = urllib.request.Request(new_url)
+        request.add_header("Authorization", auth)
+    else:
+        request = urllib.request.Request(url)
+
+    request.add_header('User-Agent', user_agent)
+    fp = opener(request)
+
+    if auth:
+        # Put authentication info back into request URL if same host,
+        # so that links found on the page will work
+        s2, h2, path2, param2, query2, frag2 = urllib.parse.urlparse(fp.url)
+        if s2 == scheme and h2 == address:
+            parts = s2, netloc, path2, param2, query2, frag2
+            fp.url = urllib.parse.urlunparse(parts)
+
+    return fp
+
+
+# copy of urllib.parse._splituser from Python 3.8
+# See https://github.com/python/cpython/issues/80072.
+def _splituser(host):
+    """splituser('user[:passwd]@host[:port]')
+    --> 'user[:passwd]', 'host[:port]'."""
+    user, delim, host = host.rpartition('@')
+    return (user if delim else None), host
+
+
+# adding a timeout to avoid freezing package_index
+open_with_auth = socket_timeout(_SOCKET_TIMEOUT)(open_with_auth)
+
+
+def fix_sf_url(url):
+    return url  # backward compatibility
+
+
+def local_open(url):
+    """Read a local path, with special support for directories"""
+    _scheme, _server, path, _param, _query, _frag = urllib.parse.urlparse(url)
+    filename = urllib.request.url2pathname(path)
+    if os.path.isfile(filename):
+        return urllib.request.urlopen(url)
+    elif path.endswith('/') and os.path.isdir(filename):
+        files = []
+        for f in os.listdir(filename):
+            filepath = os.path.join(filename, f)
+            if f == 'index.html':
+                body = _read_utf8_with_fallback(filepath)
+                break
+            elif os.path.isdir(filepath):
+                f += '/'
+            files.append(f'<a href="{f}">{f}</a>')
+        else:
+            tmpl = "<html><head><title>{url}</title></head><body>{files}</body></html>"
+            body = tmpl.format(url=url, files='\n'.join(files))
+        status, message = 200, "OK"
+    else:
+        status, message, body = 404, "Path not found", "Not found"
+
+    headers = {'content-type': 'text/html'}
+    body_stream = io.StringIO(body)
+    return urllib.error.HTTPError(url, status, message, headers, body_stream)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/sandbox.py b/.venv/lib/python3.12/site-packages/setuptools/sandbox.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d84242d667c7df6a20aa56eabce6ac34ddc4a7e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/sandbox.py
@@ -0,0 +1,536 @@
+from __future__ import annotations
+
+import builtins
+import contextlib
+import functools
+import itertools
+import operator
+import os
+import pickle
+import re
+import sys
+import tempfile
+import textwrap
+from types import TracebackType
+from typing import TYPE_CHECKING, Any, ClassVar
+
+import pkg_resources
+from pkg_resources import working_set
+
+from distutils.errors import DistutilsError
+
+if TYPE_CHECKING:
+    import os as _os
+elif sys.platform.startswith('java'):
+    import org.python.modules.posix.PosixModule as _os  # pyright: ignore[reportMissingImports]
+else:
+    _os = sys.modules[os.name]
+_open = open
+
+
+if TYPE_CHECKING:
+    from typing_extensions import Self
+
+__all__ = [
+    "AbstractSandbox",
+    "DirectorySandbox",
+    "SandboxViolation",
+    "run_setup",
+]
+
+
+def _execfile(filename, globals, locals=None):
+    """
+    Python 3 implementation of execfile.
+    """
+    mode = 'rb'
+    with open(filename, mode) as stream:
+        script = stream.read()
+    if locals is None:
+        locals = globals
+    code = compile(script, filename, 'exec')
+    exec(code, globals, locals)
+
+
+@contextlib.contextmanager
+def save_argv(repl=None):
+    saved = sys.argv[:]
+    if repl is not None:
+        sys.argv[:] = repl
+    try:
+        yield saved
+    finally:
+        sys.argv[:] = saved
+
+
+@contextlib.contextmanager
+def save_path():
+    saved = sys.path[:]
+    try:
+        yield saved
+    finally:
+        sys.path[:] = saved
+
+
+@contextlib.contextmanager
+def override_temp(replacement):
+    """
+    Monkey-patch tempfile.tempdir with replacement, ensuring it exists
+    """
+    os.makedirs(replacement, exist_ok=True)
+
+    saved = tempfile.tempdir
+
+    tempfile.tempdir = replacement
+
+    try:
+        yield
+    finally:
+        tempfile.tempdir = saved
+
+
+@contextlib.contextmanager
+def pushd(target):
+    saved = os.getcwd()
+    os.chdir(target)
+    try:
+        yield saved
+    finally:
+        os.chdir(saved)
+
+
+class UnpickleableException(Exception):
+    """
+    An exception representing another Exception that could not be pickled.
+    """
+
+    @staticmethod
+    def dump(type, exc):
+        """
+        Always return a dumped (pickled) type and exc. If exc can't be pickled,
+        wrap it in UnpickleableException first.
+        """
+        try:
+            return pickle.dumps(type), pickle.dumps(exc)
+        except Exception:
+            # get UnpickleableException inside the sandbox
+            from setuptools.sandbox import UnpickleableException as cls
+
+            return cls.dump(cls, cls(repr(exc)))
+
+
+class ExceptionSaver:
+    """
+    A Context Manager that will save an exception, serialize, and restore it
+    later.
+    """
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        type: type[BaseException] | None,
+        exc: BaseException | None,
+        tb: TracebackType | None,
+    ) -> bool:
+        if not exc:
+            return False
+
+        # dump the exception
+        self._saved = UnpickleableException.dump(type, exc)
+        self._tb = tb
+
+        # suppress the exception
+        return True
+
+    def resume(self):
+        "restore and re-raise any exception"
+
+        if '_saved' not in vars(self):
+            return
+
+        _type, exc = map(pickle.loads, self._saved)
+        raise exc.with_traceback(self._tb)
+
+
+@contextlib.contextmanager
+def save_modules():
+    """
+    Context in which imported modules are saved.
+
+    Translates exceptions internal to the context into the equivalent exception
+    outside the context.
+    """
+    saved = sys.modules.copy()
+    with ExceptionSaver() as saved_exc:
+        yield saved
+
+    sys.modules.update(saved)
+    # remove any modules imported since
+    del_modules = (
+        mod_name
+        for mod_name in sys.modules
+        if mod_name not in saved
+        # exclude any encodings modules. See #285
+        and not mod_name.startswith('encodings.')
+    )
+    _clear_modules(del_modules)
+
+    saved_exc.resume()
+
+
+def _clear_modules(module_names):
+    for mod_name in list(module_names):
+        del sys.modules[mod_name]
+
+
+@contextlib.contextmanager
+def save_pkg_resources_state():
+    saved = pkg_resources.__getstate__()
+    try:
+        yield saved
+    finally:
+        pkg_resources.__setstate__(saved)
+
+
+@contextlib.contextmanager
+def setup_context(setup_dir):
+    temp_dir = os.path.join(setup_dir, 'temp')
+    with save_pkg_resources_state():
+        with save_modules():
+            with save_path():
+                hide_setuptools()
+                with save_argv():
+                    with override_temp(temp_dir):
+                        with pushd(setup_dir):
+                            # ensure setuptools commands are available
+                            __import__('setuptools')
+                            yield
+
+
+_MODULES_TO_HIDE = {
+    'setuptools',
+    'distutils',
+    'pkg_resources',
+    'Cython',
+    '_distutils_hack',
+}
+
+
+def _needs_hiding(mod_name):
+    """
+    >>> _needs_hiding('setuptools')
+    True
+    >>> _needs_hiding('pkg_resources')
+    True
+    >>> _needs_hiding('setuptools_plugin')
+    False
+    >>> _needs_hiding('setuptools.__init__')
+    True
+    >>> _needs_hiding('distutils')
+    True
+    >>> _needs_hiding('os')
+    False
+    >>> _needs_hiding('Cython')
+    True
+    """
+    base_module = mod_name.split('.', 1)[0]
+    return base_module in _MODULES_TO_HIDE
+
+
+def hide_setuptools():
+    """
+    Remove references to setuptools' modules from sys.modules to allow the
+    invocation to import the most appropriate setuptools. This technique is
+    necessary to avoid issues such as #315 where setuptools upgrading itself
+    would fail to find a function declared in the metadata.
+    """
+    _distutils_hack = sys.modules.get('_distutils_hack', None)
+    if _distutils_hack is not None:
+        _distutils_hack._remove_shim()
+
+    modules = filter(_needs_hiding, sys.modules)
+    _clear_modules(modules)
+
+
+def run_setup(setup_script, args):
+    """Run a distutils setup script, sandboxed in its directory"""
+    setup_dir = os.path.abspath(os.path.dirname(setup_script))
+    with setup_context(setup_dir):
+        try:
+            sys.argv[:] = [setup_script] + list(args)
+            sys.path.insert(0, setup_dir)
+            # reset to include setup dir, w/clean callback list
+            working_set.__init__()
+            working_set.callbacks.append(lambda dist: dist.activate())
+
+            with DirectorySandbox(setup_dir):
+                ns = dict(__file__=setup_script, __name__='__main__')
+                _execfile(setup_script, ns)
+        except SystemExit as v:
+            if v.args and v.args[0]:
+                raise
+            # Normal exit, just return
+
+
+class AbstractSandbox:
+    """Wrap 'os' module and 'open()' builtin for virtualizing setup scripts"""
+
+    _active = False
+
+    def __init__(self) -> None:
+        self._attrs = [
+            name
+            for name in dir(_os)
+            if not name.startswith('_') and hasattr(self, name)
+        ]
+
+    def _copy(self, source):
+        for name in self._attrs:
+            setattr(os, name, getattr(source, name))
+
+    def __enter__(self) -> None:
+        self._copy(self)
+        builtins.open = self._open
+        self._active = True
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ):
+        self._active = False
+        builtins.open = _open
+        self._copy(_os)
+
+    def run(self, func):
+        """Run 'func' under os sandboxing"""
+        with self:
+            return func()
+
+    def _mk_dual_path_wrapper(name: str):  # type: ignore[misc] # https://github.com/pypa/setuptools/pull/4099
+        original = getattr(_os, name)
+
+        def wrap(self, src, dst, *args, **kw):
+            if self._active:
+                src, dst = self._remap_pair(name, src, dst, *args, **kw)
+            return original(src, dst, *args, **kw)
+
+        return wrap
+
+    for __name in ["rename", "link", "symlink"]:
+        if hasattr(_os, __name):
+            locals()[__name] = _mk_dual_path_wrapper(__name)
+
+    def _mk_single_path_wrapper(name: str, original=None):  # type: ignore[misc] # https://github.com/pypa/setuptools/pull/4099
+        original = original or getattr(_os, name)
+
+        def wrap(self, path, *args, **kw):
+            if self._active:
+                path = self._remap_input(name, path, *args, **kw)
+            return original(path, *args, **kw)
+
+        return wrap
+
+    _open = _mk_single_path_wrapper('open', _open)
+    for __name in [
+        "stat",
+        "listdir",
+        "chdir",
+        "open",
+        "chmod",
+        "chown",
+        "mkdir",
+        "remove",
+        "unlink",
+        "rmdir",
+        "utime",
+        "lchown",
+        "chroot",
+        "lstat",
+        "startfile",
+        "mkfifo",
+        "mknod",
+        "pathconf",
+        "access",
+    ]:
+        if hasattr(_os, __name):
+            locals()[__name] = _mk_single_path_wrapper(__name)
+
+    def _mk_single_with_return(name: str):  # type: ignore[misc] # https://github.com/pypa/setuptools/pull/4099
+        original = getattr(_os, name)
+
+        def wrap(self, path, *args, **kw):
+            if self._active:
+                path = self._remap_input(name, path, *args, **kw)
+                return self._remap_output(name, original(path, *args, **kw))
+            return original(path, *args, **kw)
+
+        return wrap
+
+    for __name in ['readlink', 'tempnam']:
+        if hasattr(_os, __name):
+            locals()[__name] = _mk_single_with_return(__name)
+
+    def _mk_query(name: str):  # type: ignore[misc] # https://github.com/pypa/setuptools/pull/4099
+        original = getattr(_os, name)
+
+        def wrap(self, *args, **kw):
+            retval = original(*args, **kw)
+            if self._active:
+                return self._remap_output(name, retval)
+            return retval
+
+        return wrap
+
+    for __name in ['getcwd', 'tmpnam']:
+        if hasattr(_os, __name):
+            locals()[__name] = _mk_query(__name)
+
+    def _validate_path(self, path):
+        """Called to remap or validate any path, whether input or output"""
+        return path
+
+    def _remap_input(self, operation, path, *args, **kw):
+        """Called for path inputs"""
+        return self._validate_path(path)
+
+    def _remap_output(self, operation, path):
+        """Called for path outputs"""
+        return self._validate_path(path)
+
+    def _remap_pair(self, operation, src, dst, *args, **kw):
+        """Called for path pairs like rename, link, and symlink operations"""
+        return (
+            self._remap_input(operation + '-from', src, *args, **kw),
+            self._remap_input(operation + '-to', dst, *args, **kw),
+        )
+
+    if TYPE_CHECKING:
+        # This is a catch-all for all the dynamically created attributes.
+        # This isn't public API anyway
+        def __getattribute__(self, name: str) -> Any: ...
+
+
+if hasattr(os, 'devnull'):
+    _EXCEPTIONS = [os.devnull]
+else:
+    _EXCEPTIONS = []
+
+
+class DirectorySandbox(AbstractSandbox):
+    """Restrict operations to a single subdirectory - pseudo-chroot"""
+
+    write_ops: ClassVar[dict[str, None]] = dict.fromkeys([
+        "open",
+        "chmod",
+        "chown",
+        "mkdir",
+        "remove",
+        "unlink",
+        "rmdir",
+        "utime",
+        "lchown",
+        "chroot",
+        "mkfifo",
+        "mknod",
+        "tempnam",
+    ])
+
+    _exception_patterns: list[str | re.Pattern] = []
+    "exempt writing to paths that match the pattern"
+
+    def __init__(self, sandbox, exceptions=_EXCEPTIONS) -> None:
+        self._sandbox = os.path.normcase(os.path.realpath(sandbox))
+        self._prefix = os.path.join(self._sandbox, '')
+        self._exceptions = [
+            os.path.normcase(os.path.realpath(path)) for path in exceptions
+        ]
+        AbstractSandbox.__init__(self)
+
+    def _violation(self, operation, *args, **kw):
+        from setuptools.sandbox import SandboxViolation
+
+        raise SandboxViolation(operation, args, kw)
+
+    def _open(self, path, mode='r', *args, **kw):
+        if mode not in ('r', 'rt', 'rb', 'rU', 'U') and not self._ok(path):
+            self._violation("open", path, mode, *args, **kw)
+        return _open(path, mode, *args, **kw)
+
+    def tmpnam(self) -> None:
+        self._violation("tmpnam")
+
+    def _ok(self, path):
+        active = self._active
+        try:
+            self._active = False
+            realpath = os.path.normcase(os.path.realpath(path))
+            return (
+                self._exempted(realpath)
+                or realpath == self._sandbox
+                or realpath.startswith(self._prefix)
+            )
+        finally:
+            self._active = active
+
+    def _exempted(self, filepath):
+        start_matches = (
+            filepath.startswith(exception) for exception in self._exceptions
+        )
+        pattern_matches = (
+            re.match(pattern, filepath) for pattern in self._exception_patterns
+        )
+        candidates = itertools.chain(start_matches, pattern_matches)
+        return any(candidates)
+
+    def _remap_input(self, operation, path, *args, **kw):
+        """Called for path inputs"""
+        if operation in self.write_ops and not self._ok(path):
+            self._violation(operation, os.path.realpath(path), *args, **kw)
+        return path
+
+    def _remap_pair(self, operation, src, dst, *args, **kw):
+        """Called for path pairs like rename, link, and symlink operations"""
+        if not self._ok(src) or not self._ok(dst):
+            self._violation(operation, src, dst, *args, **kw)
+        return (src, dst)
+
+    def open(self, file, flags, mode: int = 0o777, *args, **kw) -> int:
+        """Called for low-level os.open()"""
+        if flags & WRITE_FLAGS and not self._ok(file):
+            self._violation("os.open", file, flags, mode, *args, **kw)
+        return _os.open(file, flags, mode, *args, **kw)
+
+
+WRITE_FLAGS = functools.reduce(
+    operator.or_,
+    [
+        getattr(_os, a, 0)
+        for a in "O_WRONLY O_RDWR O_APPEND O_CREAT O_TRUNC O_TEMPORARY".split()
+    ],
+)
+
+
+class SandboxViolation(DistutilsError):
+    """A setup script attempted to modify the filesystem outside the sandbox"""
+
+    tmpl = textwrap.dedent(
+        """
+        SandboxViolation: {cmd}{args!r} {kwargs}
+
+        The package setup script has attempted to modify files on your system
+        that are not within the EasyInstall build area, and has been aborted.
+
+        This package cannot be safely installed by EasyInstall, and may not
+        support alternate installation locations even if you run its setup
+        script by hand.  Please inform the package's author and the EasyInstall
+        maintainers to find out if a fix or workaround is available.
+        """
+    ).lstrip()
+
+    def __str__(self) -> str:
+        cmd, args, kwargs = self.args
+        return self.tmpl.format(**locals())
diff --git a/.venv/lib/python3.12/site-packages/setuptools/script (dev).tmpl b/.venv/lib/python3.12/site-packages/setuptools/script (dev).tmpl
new file mode 100644
index 0000000000000000000000000000000000000000..39a24b04888e79df51e2237577b303a2f901be63
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/script (dev).tmpl	
@@ -0,0 +1,6 @@
+# EASY-INSTALL-DEV-SCRIPT: %(spec)r,%(script_name)r
+__requires__ = %(spec)r
+__import__('pkg_resources').require(%(spec)r)
+__file__ = %(dev_path)r
+with open(__file__) as f:
+    exec(compile(f.read(), __file__, 'exec'))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/script.tmpl b/.venv/lib/python3.12/site-packages/setuptools/script.tmpl
new file mode 100644
index 0000000000000000000000000000000000000000..ff5efbcab3b58063dd84787181c26a95fb663d94
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/script.tmpl
@@ -0,0 +1,3 @@
+# EASY-INSTALL-SCRIPT: %(spec)r,%(script_name)r
+__requires__ = %(spec)r
+__import__('pkg_resources').run_script(%(spec)r, %(script_name)r)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb70bfb7115a2a94a8b942b31cafc3a550f0c005
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/__init__.py
@@ -0,0 +1,13 @@
+import locale
+import sys
+
+import pytest
+
+__all__ = ['fail_on_ascii']
+
+if sys.version_info >= (3, 11):
+    locale_encoding = locale.getencoding()
+else:
+    locale_encoding = locale.getpreferredencoding(False)
+is_ascii = locale_encoding == 'ANSI_X3.4-1968'
+fail_on_ascii = pytest.mark.xfail(is_ascii, reason="Test fails in this locale")
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--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/compat/py39.py
@@ -0,0 +1,3 @@
+from jaraco.test.cpython import from_test_support, try_import
+
+os_helper = try_import('os_helper') or from_test_support('can_symlink')
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/config/downloads/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/tests/config/downloads/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..00a16423f448e7773f5e6dc9b365efe80f40a778
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/config/downloads/__init__.py
@@ -0,0 +1,59 @@
+from __future__ import annotations
+
+import re
+import time
+from pathlib import Path
+from urllib.error import HTTPError
+from urllib.request import urlopen
+
+__all__ = ["DOWNLOAD_DIR", "retrieve_file", "output_file", "urls_from_file"]
+
+
+NAME_REMOVE = ("http://", "https://", "github.com/", "/raw/")
+DOWNLOAD_DIR = Path(__file__).parent
+
+
+# ----------------------------------------------------------------------
+# Please update ./preload.py accordingly when modifying this file
+# ----------------------------------------------------------------------
+
+
+def output_file(url: str, download_dir: Path = DOWNLOAD_DIR) -> Path:
+    file_name = url.strip()
+    for part in NAME_REMOVE:
+        file_name = file_name.replace(part, '').strip().strip('/:').strip()
+    return Path(download_dir, re.sub(r"[^\-_\.\w\d]+", "_", file_name))
+
+
+def retrieve_file(url: str, download_dir: Path = DOWNLOAD_DIR, wait: float = 5) -> Path:
+    path = output_file(url, download_dir)
+    if path.exists():
+        print(f"Skipping {url} (already exists: {path})")
+    else:
+        download_dir.mkdir(exist_ok=True, parents=True)
+        print(f"Downloading {url} to {path}")
+        try:
+            download(url, path)
+        except HTTPError:
+            time.sleep(wait)  # wait a few seconds and try again.
+            download(url, path)
+    return path
+
+
+def urls_from_file(list_file: Path) -> list[str]:
+    """``list_file`` should be a text file where each line corresponds to a URL to
+    download.
+    """
+    print(f"file: {list_file}")
+    content = list_file.read_text(encoding="utf-8")
+    return [url for url in content.splitlines() if not url.startswith("#")]
+
+
+def download(url: str, dest: Path):
+    with urlopen(url) as f:
+        data = f.read()
+
+    with open(dest, "wb") as f:
+        f.write(data)
+
+    assert Path(dest).exists()
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/config/downloads/__pycache__/preload.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/tests/config/downloads/__pycache__/preload.cpython-312.pyc
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/config/downloads/preload.py b/.venv/lib/python3.12/site-packages/setuptools/tests/config/downloads/preload.py
new file mode 100644
index 0000000000000000000000000000000000000000..8eeb5dd75d3dcb375cee5acaf11ad385084bff5a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/config/downloads/preload.py
@@ -0,0 +1,18 @@
+"""This file can be used to preload files needed for testing.
+
+For example you can use::
+
+    cd setuptools/tests/config
+    python -m downloads.preload setupcfg_examples.txt
+
+to make sure the `setup.cfg` examples are downloaded before starting the tests.
+"""
+
+import sys
+from pathlib import Path
+
+from . import retrieve_file, urls_from_file
+
+if __name__ == "__main__":
+    urls = urls_from_file(Path(sys.argv[1]))
+    list(map(retrieve_file, urls))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/config/setupcfg_examples.txt b/.venv/lib/python3.12/site-packages/setuptools/tests/config/setupcfg_examples.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6aab887ff1fe631d97f1abea90a8448040746a12
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/config/setupcfg_examples.txt
@@ -0,0 +1,22 @@
+# ====================================================================
+# Some popular packages that use setup.cfg (and others not so popular)
+# Reference: https://hugovk.github.io/top-pypi-packages/
+# ====================================================================
+https://github.com/pypa/setuptools/raw/52c990172fec37766b3566679724aa8bf70ae06d/setup.cfg
+https://github.com/pypa/wheel/raw/0acd203cd896afec7f715aa2ff5980a403459a3b/setup.cfg
+https://github.com/python/importlib_metadata/raw/2f05392ca980952a6960d82b2f2d2ea10aa53239/setup.cfg
+https://github.com/jaraco/skeleton/raw/d9008b5c510cd6969127a6a2ab6f832edddef296/setup.cfg
+https://github.com/jaraco/zipp/raw/700d3a96390e970b6b962823bfea78b4f7e1c537/setup.cfg
+https://github.com/pallets/jinja/raw/7d72eb7fefb7dce065193967f31f805180508448/setup.cfg
+https://github.com/tkem/cachetools/raw/2fd87a94b8d3861d80e9e4236cd480bfdd21c90d/setup.cfg
+https://github.com/aio-libs/aiohttp/raw/5e0e6b7080f2408d5f1dd544c0e1cf88378b7b10/setup.cfg
+https://github.com/pallets/flask/raw/9486b6cf57bd6a8a261f67091aca8ca78eeec1e3/setup.cfg
+https://github.com/pallets/click/raw/6411f425fae545f42795665af4162006b36c5e4a/setup.cfg
+https://github.com/sqlalchemy/sqlalchemy/raw/533f5718904b620be8d63f2474229945d6f8ba5d/setup.cfg
+https://github.com/pytest-dev/pluggy/raw/461ef63291d13589c4e21aa182cd1529257e9a0a/setup.cfg
+https://github.com/pytest-dev/pytest/raw/c7be96dae487edbd2f55b561b31b68afac1dabe6/setup.cfg
+https://github.com/platformdirs/platformdirs/raw/7b7852128dd6f07511b618d6edea35046bd0c6ff/setup.cfg
+https://github.com/pandas-dev/pandas/raw/bc17343f934a33dc231c8c74be95d8365537c376/setup.cfg
+https://github.com/django/django/raw/4e249d11a6e56ca8feb4b055b681cec457ef3a3d/setup.cfg
+https://github.com/pyscaffold/pyscaffold/raw/de7aa5dc059fbd04307419c667cc4961bc9df4b8/setup.cfg
+https://github.com/pypa/virtualenv/raw/f92eda6e3da26a4d28c2663ffb85c4960bdb990c/setup.cfg
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_apply_pyprojecttoml.py b/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_apply_pyprojecttoml.py
new file mode 100644
index 0000000000000000000000000000000000000000..489fd98e26710e74d66204b6f51c2523586aaa04
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_apply_pyprojecttoml.py
@@ -0,0 +1,772 @@
+"""Make sure that applying the configuration from pyproject.toml is equivalent to
+applying a similar configuration from setup.cfg
+
+To run these tests offline, please have a look on ``./downloads/preload.py``
+"""
+
+from __future__ import annotations
+
+import io
+import re
+import tarfile
+from inspect import cleandoc
+from pathlib import Path
+from unittest.mock import Mock
+
+import pytest
+from ini2toml.api import LiteTranslator
+from packaging.metadata import Metadata
+
+import setuptools  # noqa: F401 # ensure monkey patch to metadata
+from setuptools._static import is_static
+from setuptools.command.egg_info import write_requirements
+from setuptools.config import expand, pyprojecttoml, setupcfg
+from setuptools.config._apply_pyprojecttoml import _MissingDynamic, _some_attrgetter
+from setuptools.dist import Distribution
+from setuptools.errors import InvalidConfigError, RemovedConfigError
+from setuptools.warnings import InformationOnly, SetuptoolsDeprecationWarning
+
+from .downloads import retrieve_file, urls_from_file
+
+HERE = Path(__file__).parent
+EXAMPLES_FILE = "setupcfg_examples.txt"
+
+
+def makedist(path, **attrs):
+    return Distribution({"src_root": path, **attrs})
+
+
+def _mock_expand_patterns(patterns, *_, **__):
+    """
+    Allow comparing the given patterns for 2 dist objects.
+    We need to strip special chars to avoid errors when validating.
+    """
+    return [re.sub("[^a-z0-9]+", "", p, flags=re.I) or "empty" for p in patterns]
+
+
+@pytest.mark.parametrize("url", urls_from_file(HERE / EXAMPLES_FILE))
+@pytest.mark.filterwarnings("ignore")
+@pytest.mark.uses_network
+def test_apply_pyproject_equivalent_to_setupcfg(url, monkeypatch, tmp_path):
+    monkeypatch.setattr(expand, "read_attr", Mock(return_value="0.0.1"))
+    monkeypatch.setattr(
+        Distribution, "_expand_patterns", Mock(side_effect=_mock_expand_patterns)
+    )
+    setupcfg_example = retrieve_file(url)
+    pyproject_example = Path(tmp_path, "pyproject.toml")
+    setupcfg_text = setupcfg_example.read_text(encoding="utf-8")
+    toml_config = LiteTranslator().translate(setupcfg_text, "setup.cfg")
+    pyproject_example.write_text(toml_config, encoding="utf-8")
+
+    dist_toml = pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject_example)
+    dist_cfg = setupcfg.apply_configuration(makedist(tmp_path), setupcfg_example)
+
+    pkg_info_toml = core_metadata(dist_toml)
+    pkg_info_cfg = core_metadata(dist_cfg)
+    assert pkg_info_toml == pkg_info_cfg
+
+    if any(getattr(d, "license_files", None) for d in (dist_toml, dist_cfg)):
+        assert set(dist_toml.license_files) == set(dist_cfg.license_files)
+
+    if any(getattr(d, "entry_points", None) for d in (dist_toml, dist_cfg)):
+        print(dist_cfg.entry_points)
+        ep_toml = {
+            (k, *sorted(i.replace(" ", "") for i in v))
+            for k, v in dist_toml.entry_points.items()
+        }
+        ep_cfg = {
+            (k, *sorted(i.replace(" ", "") for i in v))
+            for k, v in dist_cfg.entry_points.items()
+        }
+        assert ep_toml == ep_cfg
+
+    if any(getattr(d, "package_data", None) for d in (dist_toml, dist_cfg)):
+        pkg_data_toml = {(k, *sorted(v)) for k, v in dist_toml.package_data.items()}
+        pkg_data_cfg = {(k, *sorted(v)) for k, v in dist_cfg.package_data.items()}
+        assert pkg_data_toml == pkg_data_cfg
+
+    if any(getattr(d, "data_files", None) for d in (dist_toml, dist_cfg)):
+        data_files_toml = {(k, *sorted(v)) for k, v in dist_toml.data_files}
+        data_files_cfg = {(k, *sorted(v)) for k, v in dist_cfg.data_files}
+        assert data_files_toml == data_files_cfg
+
+    assert set(dist_toml.install_requires) == set(dist_cfg.install_requires)
+    if any(getattr(d, "extras_require", None) for d in (dist_toml, dist_cfg)):
+        extra_req_toml = {(k, *sorted(v)) for k, v in dist_toml.extras_require.items()}
+        extra_req_cfg = {(k, *sorted(v)) for k, v in dist_cfg.extras_require.items()}
+        assert extra_req_toml == extra_req_cfg
+
+
+PEP621_EXAMPLE = """\
+[project]
+name = "spam"
+version = "2020.0.0"
+description = "Lovely Spam! Wonderful Spam!"
+readme = "README.rst"
+requires-python = ">=3.8"
+license-files = ["LICENSE.txt"]  # Updated to be PEP 639 compliant
+keywords = ["egg", "bacon", "sausage", "tomatoes", "Lobster Thermidor"]
+authors = [
+  {email = "hi@pradyunsg.me"},
+  {name = "Tzu-Ping Chung"}
+]
+maintainers = [
+  {name = "Brett Cannon", email = "brett@python.org"},
+  {name = "John X. Ãørçeč", email = "john@utf8.org"},
+  {name = "Γαμα קּ 東", email = "gama@utf8.org"},
+]
+classifiers = [
+  "Development Status :: 4 - Beta",
+  "Programming Language :: Python"
+]
+
+dependencies = [
+  "httpx",
+  "gidgethub[httpx]>4.0.0",
+  "django>2.1; os_name != 'nt'",
+  "django>2.0; os_name == 'nt'"
+]
+
+[project.optional-dependencies]
+test = [
+  "pytest < 5.0.0",
+  "pytest-cov[all]"
+]
+
+[project.urls]
+homepage = "http://example.com"
+documentation = "http://readthedocs.org"
+repository = "http://github.com"
+changelog = "http://github.com/me/spam/blob/master/CHANGELOG.md"
+
+[project.scripts]
+spam-cli = "spam:main_cli"
+
+[project.gui-scripts]
+spam-gui = "spam:main_gui"
+
+[project.entry-points."spam.magical"]
+tomatoes = "spam:main_tomatoes"
+"""
+
+PEP621_INTERNATIONAL_EMAIL_EXAMPLE = """\
+[project]
+name = "spam"
+version = "2020.0.0"
+authors = [
+  {email = "hi@pradyunsg.me"},
+  {name = "Tzu-Ping Chung"}
+]
+maintainers = [
+  {name = "Степан Бандера", email = "криївка@оун-упа.укр"},
+]
+"""
+
+PEP621_EXAMPLE_SCRIPT = """
+def main_cli(): pass
+def main_gui(): pass
+def main_tomatoes(): pass
+"""
+
+PEP639_LICENSE_TEXT = """\
+[project]
+name = "spam"
+version = "2020.0.0"
+authors = [
+  {email = "hi@pradyunsg.me"},
+  {name = "Tzu-Ping Chung"}
+]
+license = {text = "MIT"}
+"""
+
+PEP639_LICENSE_EXPRESSION = """\
+[project]
+name = "spam"
+version = "2020.0.0"
+authors = [
+  {email = "hi@pradyunsg.me"},
+  {name = "Tzu-Ping Chung"}
+]
+license = "mit or apache-2.0"  # should be normalized in metadata
+classifiers = [
+    "Development Status :: 5 - Production/Stable",
+    "Programming Language :: Python",
+]
+"""
+
+
+def _pep621_example_project(
+    tmp_path,
+    readme="README.rst",
+    pyproject_text=PEP621_EXAMPLE,
+):
+    pyproject = tmp_path / "pyproject.toml"
+    text = pyproject_text
+    replacements = {'readme = "README.rst"': f'readme = "{readme}"'}
+    for orig, subst in replacements.items():
+        text = text.replace(orig, subst)
+    pyproject.write_text(text, encoding="utf-8")
+
+    (tmp_path / readme).write_text("hello world", encoding="utf-8")
+    (tmp_path / "LICENSE.txt").write_text("--- LICENSE stub ---", encoding="utf-8")
+    (tmp_path / "spam.py").write_text(PEP621_EXAMPLE_SCRIPT, encoding="utf-8")
+    return pyproject
+
+
+def test_pep621_example(tmp_path):
+    """Make sure the example in PEP 621 works"""
+    pyproject = _pep621_example_project(tmp_path)
+    dist = pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+    assert set(dist.metadata.license_files) == {"LICENSE.txt"}
+
+
+@pytest.mark.parametrize(
+    ("readme", "ctype"),
+    [
+        ("Readme.txt", "text/plain"),
+        ("readme.md", "text/markdown"),
+        ("text.rst", "text/x-rst"),
+    ],
+)
+def test_readme_content_type(tmp_path, readme, ctype):
+    pyproject = _pep621_example_project(tmp_path, readme)
+    dist = pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+    assert dist.metadata.long_description_content_type == ctype
+
+
+def test_undefined_content_type(tmp_path):
+    pyproject = _pep621_example_project(tmp_path, "README.tex")
+    with pytest.raises(ValueError, match="Undefined content type for README.tex"):
+        pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+
+
+def test_no_explicit_content_type_for_missing_extension(tmp_path):
+    pyproject = _pep621_example_project(tmp_path, "README")
+    dist = pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+    assert dist.metadata.long_description_content_type is None
+
+
+@pytest.mark.parametrize(
+    ("pyproject_text", "expected_maintainers_meta_value"),
+    (
+        pytest.param(
+            PEP621_EXAMPLE,
+            (
+                'Brett Cannon <brett@python.org>, "John X. Ãørçeč" <john@utf8.org>, '
+                'Γαμα קּ 東 <gama@utf8.org>'
+            ),
+            id='non-international-emails',
+        ),
+        pytest.param(
+            PEP621_INTERNATIONAL_EMAIL_EXAMPLE,
+            'Степан Бандера <криївка@оун-упа.укр>',
+            marks=pytest.mark.xfail(
+                reason="CPython's `email.headerregistry.Address` only supports "
+                'RFC 5322, as of Nov 10, 2022 and latest Python 3.11.0',
+                strict=True,
+            ),
+            id='international-email',
+        ),
+    ),
+)
+def test_utf8_maintainer_in_metadata(  # issue-3663
+    expected_maintainers_meta_value,
+    pyproject_text,
+    tmp_path,
+):
+    pyproject = _pep621_example_project(
+        tmp_path,
+        "README",
+        pyproject_text=pyproject_text,
+    )
+    dist = pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+    assert dist.metadata.maintainer_email == expected_maintainers_meta_value
+    pkg_file = tmp_path / "PKG-FILE"
+    with open(pkg_file, "w", encoding="utf-8") as fh:
+        dist.metadata.write_pkg_file(fh)
+    content = pkg_file.read_text(encoding="utf-8")
+    assert f"Maintainer-email: {expected_maintainers_meta_value}" in content
+
+
+@pytest.mark.parametrize(
+    (
+        'pyproject_text',
+        'license',
+        'license_expression',
+        'content_str',
+        'not_content_str',
+    ),
+    (
+        pytest.param(
+            PEP639_LICENSE_TEXT,
+            'MIT',
+            None,
+            'License: MIT',
+            'License-Expression: ',
+            id='license-text',
+            marks=[
+                pytest.mark.filterwarnings(
+                    "ignore:.project.license. as a TOML table is deprecated",
+                )
+            ],
+        ),
+        pytest.param(
+            PEP639_LICENSE_EXPRESSION,
+            None,
+            'MIT OR Apache-2.0',
+            'License-Expression: MIT OR Apache-2.0',
+            'License: ',
+            id='license-expression',
+        ),
+    ),
+)
+def test_license_in_metadata(
+    license,
+    license_expression,
+    content_str,
+    not_content_str,
+    pyproject_text,
+    tmp_path,
+):
+    pyproject = _pep621_example_project(
+        tmp_path,
+        "README",
+        pyproject_text=pyproject_text,
+    )
+    dist = pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+    assert dist.metadata.license == license
+    assert dist.metadata.license_expression == license_expression
+    pkg_file = tmp_path / "PKG-FILE"
+    with open(pkg_file, "w", encoding="utf-8") as fh:
+        dist.metadata.write_pkg_file(fh)
+    content = pkg_file.read_text(encoding="utf-8")
+    assert "Metadata-Version: 2.4" in content
+    assert content_str in content
+    assert not_content_str not in content
+
+
+def test_license_classifier_with_license_expression(tmp_path):
+    text = PEP639_LICENSE_EXPRESSION.rsplit("\n", 2)[0]
+    pyproject = _pep621_example_project(
+        tmp_path,
+        "README",
+        f"{text}\n    \"License :: OSI Approved :: MIT License\"\n]",
+    )
+    msg = "License classifiers have been superseded by license expressions"
+    with pytest.raises(InvalidConfigError, match=msg) as exc:
+        pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+
+    assert "License :: OSI Approved :: MIT License" in str(exc.value)
+
+
+def test_license_classifier_without_license_expression(tmp_path):
+    text = """\
+    [project]
+    name = "spam"
+    version = "2020.0.0"
+    license = {text = "mit or apache-2.0"}
+    classifiers = ["License :: OSI Approved :: MIT License"]
+    """
+    pyproject = _pep621_example_project(tmp_path, "README", text)
+
+    msg1 = "License classifiers are deprecated(?:.|\n)*MIT License"
+    msg2 = ".project.license. as a TOML table is deprecated"
+    with (
+        pytest.warns(SetuptoolsDeprecationWarning, match=msg1),
+        pytest.warns(SetuptoolsDeprecationWarning, match=msg2),
+    ):
+        dist = pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+
+    # Check license classifier is still included
+    assert dist.metadata.get_classifiers() == ["License :: OSI Approved :: MIT License"]
+
+
+class TestLicenseFiles:
+    def base_pyproject(
+        self,
+        tmp_path,
+        additional_text="",
+        license_toml='license = {file = "LICENSE.txt"}\n',
+    ):
+        text = PEP639_LICENSE_EXPRESSION
+
+        # Sanity-check
+        assert 'license = "mit or apache-2.0"' in text
+        assert 'license-files' not in text
+        assert "[tool.setuptools]" not in text
+
+        text = re.sub(
+            r"(license = .*)\n",
+            license_toml,
+            text,
+            count=1,
+        )
+        assert license_toml in text  # sanity check
+        text = f"{text}\n{additional_text}\n"
+        pyproject = _pep621_example_project(tmp_path, "README", pyproject_text=text)
+        return pyproject
+
+    def base_pyproject_license_pep639(self, tmp_path, additional_text=""):
+        return self.base_pyproject(
+            tmp_path,
+            additional_text=additional_text,
+            license_toml='license = "licenseref-Proprietary"'
+            '\nlicense-files = ["_FILE*"]\n',
+        )
+
+    def test_both_license_and_license_files_defined(self, tmp_path):
+        setuptools_config = '[tool.setuptools]\nlicense-files = ["_FILE*"]'
+        pyproject = self.base_pyproject(tmp_path, setuptools_config)
+
+        (tmp_path / "_FILE.txt").touch()
+        (tmp_path / "_FILE.rst").touch()
+
+        # Would normally match the `license_files` patterns, but we want to exclude it
+        # by being explicit. On the other hand, contents should be added to `license`
+        license = tmp_path / "LICENSE.txt"
+        license.write_text("LicenseRef-Proprietary\n", encoding="utf-8")
+
+        msg1 = "'tool.setuptools.license-files' is deprecated in favor of 'project.license-files'"
+        msg2 = ".project.license. as a TOML table is deprecated"
+        with (
+            pytest.warns(SetuptoolsDeprecationWarning, match=msg1),
+            pytest.warns(SetuptoolsDeprecationWarning, match=msg2),
+        ):
+            dist = pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+        assert set(dist.metadata.license_files) == {"_FILE.rst", "_FILE.txt"}
+        assert dist.metadata.license == "LicenseRef-Proprietary\n"
+
+    def test_both_license_and_license_files_defined_pep639(self, tmp_path):
+        # Set license and license-files
+        pyproject = self.base_pyproject_license_pep639(tmp_path)
+
+        (tmp_path / "_FILE.txt").touch()
+        (tmp_path / "_FILE.rst").touch()
+
+        msg = "Normalizing.*LicenseRef"
+        with pytest.warns(InformationOnly, match=msg):
+            dist = pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+
+        assert set(dist.metadata.license_files) == {"_FILE.rst", "_FILE.txt"}
+        assert dist.metadata.license is None
+        assert dist.metadata.license_expression == "LicenseRef-Proprietary"
+
+    def test_license_files_defined_twice(self, tmp_path):
+        # Set project.license-files and tools.setuptools.license-files
+        setuptools_config = '[tool.setuptools]\nlicense-files = ["_FILE*"]'
+        pyproject = self.base_pyproject_license_pep639(tmp_path, setuptools_config)
+
+        msg = "'project.license-files' is defined already. Remove 'tool.setuptools.license-files'"
+        with pytest.raises(InvalidConfigError, match=msg):
+            pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+
+    def test_default_patterns(self, tmp_path):
+        setuptools_config = '[tool.setuptools]\nzip-safe = false'
+        # ^ used just to trigger section validation
+        pyproject = self.base_pyproject(tmp_path, setuptools_config, license_toml="")
+
+        license_files = "LICENCE-a.html COPYING-abc.txt AUTHORS-xyz NOTICE,def".split()
+
+        for fname in license_files:
+            (tmp_path / fname).write_text(f"{fname}\n", encoding="utf-8")
+
+        dist = pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+
+        assert (tmp_path / "LICENSE.txt").exists()  # from base example
+        assert set(dist.metadata.license_files) == {*license_files, "LICENSE.txt"}
+
+    def test_missing_patterns(self, tmp_path):
+        pyproject = self.base_pyproject_license_pep639(tmp_path)
+        assert list(tmp_path.glob("_FILE*")) == []  # sanity check
+
+        msg1 = "Cannot find any files for the given pattern.*"
+        msg2 = "Normalizing 'licenseref-Proprietary' to 'LicenseRef-Proprietary'"
+        with (
+            pytest.warns(SetuptoolsDeprecationWarning, match=msg1),
+            pytest.warns(InformationOnly, match=msg2),
+        ):
+            pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+
+    def test_deprecated_file_expands_to_text(self, tmp_path):
+        """Make sure the old example with ``license = {text = ...}`` works"""
+
+        assert 'license-files = ["LICENSE.txt"]' in PEP621_EXAMPLE  # sanity check
+        text = PEP621_EXAMPLE.replace(
+            'license-files = ["LICENSE.txt"]',
+            'license = {file = "LICENSE.txt"}',
+        )
+        pyproject = _pep621_example_project(tmp_path, pyproject_text=text)
+
+        msg = ".project.license. as a TOML table is deprecated"
+        with pytest.warns(SetuptoolsDeprecationWarning, match=msg):
+            dist = pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+
+        assert dist.metadata.license == "--- LICENSE stub ---"
+        assert set(dist.metadata.license_files) == {"LICENSE.txt"}  # auto-filled
+
+
+class TestPyModules:
+    # https://github.com/pypa/setuptools/issues/4316
+
+    def dist(self, name):
+        toml_config = f"""
+        [project]
+        name = "test"
+        version = "42.0"
+        [tool.setuptools]
+        py-modules = [{name!r}]
+        """
+        pyproject = Path("pyproject.toml")
+        pyproject.write_text(cleandoc(toml_config), encoding="utf-8")
+        return pyprojecttoml.apply_configuration(Distribution({}), pyproject)
+
+    @pytest.mark.parametrize("module", ["pip-run", "abc-d.λ-xyz-e"])
+    def test_valid_module_name(self, tmp_path, monkeypatch, module):
+        monkeypatch.chdir(tmp_path)
+        assert module in self.dist(module).py_modules
+
+    @pytest.mark.parametrize("module", ["pip run", "-pip-run", "pip-run-stubs"])
+    def test_invalid_module_name(self, tmp_path, monkeypatch, module):
+        monkeypatch.chdir(tmp_path)
+        with pytest.raises(ValueError, match="py-modules"):
+            self.dist(module).py_modules
+
+
+class TestExtModules:
+    def test_pyproject_sets_attribute(self, tmp_path, monkeypatch):
+        monkeypatch.chdir(tmp_path)
+        pyproject = Path("pyproject.toml")
+        toml_config = """
+        [project]
+        name = "test"
+        version = "42.0"
+        [tool.setuptools]
+        ext-modules = [
+          {name = "my.ext", sources = ["hello.c", "world.c"]}
+        ]
+        """
+        pyproject.write_text(cleandoc(toml_config), encoding="utf-8")
+        with pytest.warns(pyprojecttoml._ExperimentalConfiguration):
+            dist = pyprojecttoml.apply_configuration(Distribution({}), pyproject)
+        assert len(dist.ext_modules) == 1
+        assert dist.ext_modules[0].name == "my.ext"
+        assert set(dist.ext_modules[0].sources) == {"hello.c", "world.c"}
+
+
+class TestDeprecatedFields:
+    def test_namespace_packages(self, tmp_path):
+        pyproject = tmp_path / "pyproject.toml"
+        config = """
+        [project]
+        name = "myproj"
+        version = "42"
+        [tool.setuptools]
+        namespace-packages = ["myproj.pkg"]
+        """
+        pyproject.write_text(cleandoc(config), encoding="utf-8")
+        with pytest.raises(RemovedConfigError, match="namespace-packages"):
+            pyprojecttoml.apply_configuration(makedist(tmp_path), pyproject)
+
+
+class TestPresetField:
+    def pyproject(self, tmp_path, dynamic, extra_content=""):
+        content = f"[project]\nname = 'proj'\ndynamic = {dynamic!r}\n"
+        if "version" not in dynamic:
+            content += "version = '42'\n"
+        file = tmp_path / "pyproject.toml"
+        file.write_text(content + extra_content, encoding="utf-8")
+        return file
+
+    @pytest.mark.parametrize(
+        ("attr", "field", "value"),
+        [
+            ("license_expression", "license", "MIT"),
+            pytest.param(
+                *("license", "license", "Not SPDX"),
+                marks=[pytest.mark.filterwarnings("ignore:.*license. overwritten")],
+            ),
+            ("classifiers", "classifiers", ["Private :: Classifier"]),
+            ("entry_points", "scripts", {"console_scripts": ["foobar=foobar:main"]}),
+            ("entry_points", "gui-scripts", {"gui_scripts": ["bazquux=bazquux:main"]}),
+            pytest.param(
+                *("install_requires", "dependencies", ["six"]),
+                marks=[
+                    pytest.mark.filterwarnings("ignore:.*install_requires. overwritten")
+                ],
+            ),
+        ],
+    )
+    def test_not_listed_in_dynamic(self, tmp_path, attr, field, value):
+        """Setuptools cannot set a field if not listed in ``dynamic``"""
+        pyproject = self.pyproject(tmp_path, [])
+        dist = makedist(tmp_path, **{attr: value})
+        msg = re.compile(f"defined outside of `pyproject.toml`:.*{field}", re.S)
+        with pytest.warns(_MissingDynamic, match=msg):
+            dist = pyprojecttoml.apply_configuration(dist, pyproject)
+
+        dist_value = _some_attrgetter(f"metadata.{attr}", attr)(dist)
+        assert not dist_value
+
+    @pytest.mark.parametrize(
+        ("attr", "field", "value"),
+        [
+            ("license_expression", "license", "MIT"),
+            ("install_requires", "dependencies", []),
+            ("extras_require", "optional-dependencies", {}),
+            ("install_requires", "dependencies", ["six"]),
+            ("classifiers", "classifiers", ["Private :: Classifier"]),
+        ],
+    )
+    def test_listed_in_dynamic(self, tmp_path, attr, field, value):
+        pyproject = self.pyproject(tmp_path, [field])
+        dist = makedist(tmp_path, **{attr: value})
+        dist = pyprojecttoml.apply_configuration(dist, pyproject)
+        dist_value = _some_attrgetter(f"metadata.{attr}", attr)(dist)
+        assert dist_value == value
+
+    def test_license_files_exempt_from_dynamic(self, monkeypatch, tmp_path):
+        """
+        license-file is currently not considered in the context of dynamic.
+        As per 2025-02-19, https://packaging.python.org/en/latest/specifications/pyproject-toml/#license-files
+        allows setuptools to fill-in `license-files` the way it sees fit:
+
+        > If the license-files key is not defined, tools can decide how to handle license files.
+        > For example they can choose not to include any files or use their own
+        > logic to discover the appropriate files in the distribution.
+
+        Using license_files from setup.py to fill-in the value is in accordance
+        with this rule.
+        """
+        monkeypatch.chdir(tmp_path)
+        pyproject = self.pyproject(tmp_path, [])
+        dist = makedist(tmp_path, license_files=["LIC*"])
+        (tmp_path / "LIC1").write_text("42", encoding="utf-8")
+        dist = pyprojecttoml.apply_configuration(dist, pyproject)
+        assert dist.metadata.license_files == ["LIC1"]
+
+    def test_warning_overwritten_dependencies(self, tmp_path):
+        src = "[project]\nname='pkg'\nversion='0.1'\ndependencies=['click']\n"
+        pyproject = tmp_path / "pyproject.toml"
+        pyproject.write_text(src, encoding="utf-8")
+        dist = makedist(tmp_path, install_requires=["wheel"])
+        with pytest.warns(match="`install_requires` overwritten"):
+            dist = pyprojecttoml.apply_configuration(dist, pyproject)
+        assert "wheel" not in dist.install_requires
+
+    def test_optional_dependencies_dont_remove_env_markers(self, tmp_path):
+        """
+        Internally setuptools converts dependencies with markers to "extras".
+        If ``install_requires`` is given by ``setup.py``, we have to ensure that
+        applying ``optional-dependencies`` does not overwrite the mandatory
+        dependencies with markers (see #3204).
+        """
+        # If setuptools replace its internal mechanism that uses `requires.txt`
+        # this test has to be rewritten to adapt accordingly
+        extra = "\n[project.optional-dependencies]\nfoo = ['bar>1']\n"
+        pyproject = self.pyproject(tmp_path, ["dependencies"], extra)
+        install_req = ['importlib-resources (>=3.0.0) ; python_version < "3.7"']
+        dist = makedist(tmp_path, install_requires=install_req)
+        dist = pyprojecttoml.apply_configuration(dist, pyproject)
+        assert "foo" in dist.extras_require
+        egg_info = dist.get_command_obj("egg_info")
+        write_requirements(egg_info, tmp_path, tmp_path / "requires.txt")
+        reqs = (tmp_path / "requires.txt").read_text(encoding="utf-8")
+        assert "importlib-resources" in reqs
+        assert "bar" in reqs
+        assert ':python_version < "3.7"' in reqs
+
+    @pytest.mark.parametrize(
+        ("field", "group"),
+        [("scripts", "console_scripts"), ("gui-scripts", "gui_scripts")],
+    )
+    @pytest.mark.filterwarnings("error")
+    def test_scripts_dont_require_dynamic_entry_points(self, tmp_path, field, group):
+        # Issue 3862
+        pyproject = self.pyproject(tmp_path, [field])
+        dist = makedist(tmp_path, entry_points={group: ["foobar=foobar:main"]})
+        dist = pyprojecttoml.apply_configuration(dist, pyproject)
+        assert group in dist.entry_points
+
+
+class TestMeta:
+    def test_example_file_in_sdist(self, setuptools_sdist):
+        """Meta test to ensure tests can run from sdist"""
+        with tarfile.open(setuptools_sdist) as tar:
+            assert any(name.endswith(EXAMPLES_FILE) for name in tar.getnames())
+
+
+class TestInteropCommandLineParsing:
+    def test_version(self, tmp_path, monkeypatch, capsys):
+        # See pypa/setuptools#4047
+        # This test can be removed once the CLI interface of setup.py is removed
+        monkeypatch.chdir(tmp_path)
+        toml_config = """
+        [project]
+        name = "test"
+        version = "42.0"
+        """
+        pyproject = Path(tmp_path, "pyproject.toml")
+        pyproject.write_text(cleandoc(toml_config), encoding="utf-8")
+        opts = {"script_args": ["--version"]}
+        dist = pyprojecttoml.apply_configuration(Distribution(opts), pyproject)
+        dist.parse_command_line()  # <-- there should be no exception here.
+        captured = capsys.readouterr()
+        assert "42.0" in captured.out
+
+
+class TestStaticConfig:
+    def test_mark_static_fields(self, tmp_path, monkeypatch):
+        monkeypatch.chdir(tmp_path)
+        toml_config = """
+        [project]
+        name = "test"
+        version = "42.0"
+        dependencies = ["hello"]
+        keywords = ["world"]
+        classifiers = ["private :: hello world"]
+        [tool.setuptools]
+        obsoletes = ["abcd"]
+        provides = ["abcd"]
+        platforms = ["abcd"]
+        """
+        pyproject = Path(tmp_path, "pyproject.toml")
+        pyproject.write_text(cleandoc(toml_config), encoding="utf-8")
+        dist = pyprojecttoml.apply_configuration(Distribution({}), pyproject)
+        assert is_static(dist.install_requires)
+        assert is_static(dist.metadata.keywords)
+        assert is_static(dist.metadata.classifiers)
+        assert is_static(dist.metadata.obsoletes)
+        assert is_static(dist.metadata.provides)
+        assert is_static(dist.metadata.platforms)
+
+
+# --- Auxiliary Functions ---
+
+
+def core_metadata(dist) -> str:
+    with io.StringIO() as buffer:
+        dist.metadata.write_pkg_file(buffer)
+        pkg_file_txt = buffer.getvalue()
+
+    # Make sure core metadata is valid
+    Metadata.from_email(pkg_file_txt, validate=True)  # can raise exceptions
+
+    skip_prefixes: tuple[str, ...] = ()
+    skip_lines = set()
+    # ---- DIFF NORMALISATION ----
+    # PEP 621 is very particular about author/maintainer metadata conversion, so skip
+    skip_prefixes += ("Author:", "Author-email:", "Maintainer:", "Maintainer-email:")
+    # May be redundant with Home-page
+    skip_prefixes += ("Project-URL: Homepage,", "Home-page:")
+    # May be missing in original (relying on default) but backfilled in the TOML
+    skip_prefixes += ("Description-Content-Type:",)
+    # Remove empty lines
+    skip_lines.add("")
+
+    result = []
+    for line in pkg_file_txt.splitlines():
+        if line.startswith(skip_prefixes) or line in skip_lines:
+            continue
+        result.append(line + "\n")
+
+    return "".join(result)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_expand.py b/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_expand.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5710ec63d7d9d4ed7b709203bb2fc4b512f2093
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_expand.py
@@ -0,0 +1,247 @@
+import os
+import sys
+from pathlib import Path
+
+import pytest
+
+from setuptools._static import is_static
+from setuptools.config import expand
+from setuptools.discovery import find_package_path
+
+from distutils.errors import DistutilsOptionError
+
+
+def write_files(files, root_dir):
+    for file, content in files.items():
+        path = root_dir / file
+        path.parent.mkdir(exist_ok=True, parents=True)
+        path.write_text(content, encoding="utf-8")
+
+
+def test_glob_relative(tmp_path, monkeypatch):
+    files = {
+        "dir1/dir2/dir3/file1.txt",
+        "dir1/dir2/file2.txt",
+        "dir1/file3.txt",
+        "a.ini",
+        "b.ini",
+        "dir1/c.ini",
+        "dir1/dir2/a.ini",
+    }
+
+    write_files({k: "" for k in files}, tmp_path)
+    patterns = ["**/*.txt", "[ab].*", "**/[ac].ini"]
+    monkeypatch.chdir(tmp_path)
+    assert set(expand.glob_relative(patterns)) == files
+    # Make sure the same APIs work outside cwd
+    assert set(expand.glob_relative(patterns, tmp_path)) == files
+
+
+def test_read_files(tmp_path, monkeypatch):
+    dir_ = tmp_path / "dir_"
+    (tmp_path / "_dir").mkdir(exist_ok=True)
+    (tmp_path / "a.txt").touch()
+    files = {"a.txt": "a", "dir1/b.txt": "b", "dir1/dir2/c.txt": "c"}
+    write_files(files, dir_)
+
+    secrets = Path(str(dir_) + "secrets")
+    secrets.mkdir(exist_ok=True)
+    write_files({"secrets.txt": "secret keys"}, secrets)
+
+    with monkeypatch.context() as m:
+        m.chdir(dir_)
+        assert expand.read_files(list(files)) == "a\nb\nc"
+
+        cannot_access_msg = r"Cannot access '.*\.\..a\.txt'"
+        with pytest.raises(DistutilsOptionError, match=cannot_access_msg):
+            expand.read_files(["../a.txt"])
+
+        cannot_access_secrets_msg = r"Cannot access '.*secrets\.txt'"
+        with pytest.raises(DistutilsOptionError, match=cannot_access_secrets_msg):
+            expand.read_files(["../dir_secrets/secrets.txt"])
+
+    # Make sure the same APIs work outside cwd
+    assert expand.read_files(list(files), dir_) == "a\nb\nc"
+    with pytest.raises(DistutilsOptionError, match=cannot_access_msg):
+        expand.read_files(["../a.txt"], dir_)
+
+
+class TestReadAttr:
+    @pytest.mark.parametrize(
+        "example",
+        [
+            # No cookie means UTF-8:
+            b"__version__ = '\xc3\xa9'\nraise SystemExit(1)\n",
+            # If a cookie is present, honor it:
+            b"# -*- coding: utf-8 -*-\n__version__ = '\xc3\xa9'\nraise SystemExit(1)\n",
+            b"# -*- coding: latin1 -*-\n__version__ = '\xe9'\nraise SystemExit(1)\n",
+        ],
+    )
+    def test_read_attr_encoding_cookie(self, example, tmp_path):
+        (tmp_path / "mod.py").write_bytes(example)
+        assert expand.read_attr('mod.__version__', root_dir=tmp_path) == 'é'
+
+    def test_read_attr(self, tmp_path, monkeypatch):
+        files = {
+            "pkg/__init__.py": "",
+            "pkg/sub/__init__.py": "VERSION = '0.1.1'",
+            "pkg/sub/mod.py": (
+                "VALUES = {'a': 0, 'b': {42}, 'c': (0, 1, 1)}\nraise SystemExit(1)"
+            ),
+        }
+        write_files(files, tmp_path)
+
+        with monkeypatch.context() as m:
+            m.chdir(tmp_path)
+            # Make sure it can read the attr statically without evaluating the module
+            version = expand.read_attr('pkg.sub.VERSION')
+            values = expand.read_attr('lib.mod.VALUES', {'lib': 'pkg/sub'})
+
+        assert version == '0.1.1'
+        assert is_static(values)
+
+        assert values['a'] == 0
+        assert values['b'] == {42}
+        assert is_static(values)
+
+        # Make sure the same APIs work outside cwd
+        assert expand.read_attr('pkg.sub.VERSION', root_dir=tmp_path) == '0.1.1'
+        values = expand.read_attr('lib.mod.VALUES', {'lib': 'pkg/sub'}, tmp_path)
+        assert values['c'] == (0, 1, 1)
+
+    @pytest.mark.parametrize(
+        "example",
+        [
+            "VERSION: str\nVERSION = '0.1.1'\nraise SystemExit(1)\n",
+            "VERSION: str = '0.1.1'\nraise SystemExit(1)\n",
+        ],
+    )
+    def test_read_annotated_attr(self, tmp_path, example):
+        files = {
+            "pkg/__init__.py": "",
+            "pkg/sub/__init__.py": example,
+        }
+        write_files(files, tmp_path)
+        # Make sure this attribute can be read statically
+        version = expand.read_attr('pkg.sub.VERSION', root_dir=tmp_path)
+        assert version == '0.1.1'
+        assert is_static(version)
+
+    @pytest.mark.parametrize(
+        "example",
+        [
+            "VERSION = (lambda: '0.1.1')()\n",
+            "def fn(): return '0.1.1'\nVERSION = fn()\n",
+            "VERSION: str = (lambda: '0.1.1')()\n",
+        ],
+    )
+    def test_read_dynamic_attr(self, tmp_path, monkeypatch, example):
+        files = {
+            "pkg/__init__.py": "",
+            "pkg/sub/__init__.py": example,
+        }
+        write_files(files, tmp_path)
+        monkeypatch.chdir(tmp_path)
+        version = expand.read_attr('pkg.sub.VERSION')
+        assert version == '0.1.1'
+        assert not is_static(version)
+
+    def test_import_order(self, tmp_path):
+        """
+        Sometimes the import machinery will import the parent package of a nested
+        module, which triggers side-effects and might create problems (see issue #3176)
+
+        ``read_attr`` should bypass these limitations by resolving modules statically
+        (via ast.literal_eval).
+        """
+        files = {
+            "src/pkg/__init__.py": "from .main import func\nfrom .about import version",
+            "src/pkg/main.py": "import super_complicated_dep\ndef func(): return 42",
+            "src/pkg/about.py": "version = '42'",
+        }
+        write_files(files, tmp_path)
+        attr_desc = "pkg.about.version"
+        package_dir = {"": "src"}
+        # `import super_complicated_dep` should not run, otherwise the build fails
+        assert expand.read_attr(attr_desc, package_dir, tmp_path) == "42"
+
+
+@pytest.mark.parametrize(
+    ("package_dir", "file", "module", "return_value"),
+    [
+        ({"": "src"}, "src/pkg/main.py", "pkg.main", 42),
+        ({"pkg": "lib"}, "lib/main.py", "pkg.main", 13),
+        ({}, "single_module.py", "single_module", 70),
+        ({}, "flat_layout/pkg.py", "flat_layout.pkg", 836),
+    ],
+)
+def test_resolve_class(monkeypatch, tmp_path, package_dir, file, module, return_value):
+    monkeypatch.setattr(sys, "modules", {})  # reproducibility
+    files = {file: f"class Custom:\n    def testing(self): return {return_value}"}
+    write_files(files, tmp_path)
+    cls = expand.resolve_class(f"{module}.Custom", package_dir, tmp_path)
+    assert cls().testing() == return_value
+
+
+@pytest.mark.parametrize(
+    ("args", "pkgs"),
+    [
+        ({"where": ["."], "namespaces": False}, {"pkg", "other"}),
+        ({"where": [".", "dir1"], "namespaces": False}, {"pkg", "other", "dir2"}),
+        ({"namespaces": True}, {"pkg", "other", "dir1", "dir1.dir2"}),
+        ({}, {"pkg", "other", "dir1", "dir1.dir2"}),  # default value for `namespaces`
+    ],
+)
+def test_find_packages(tmp_path, args, pkgs):
+    files = {
+        "pkg/__init__.py",
+        "other/__init__.py",
+        "dir1/dir2/__init__.py",
+    }
+    write_files({k: "" for k in files}, tmp_path)
+
+    package_dir = {}
+    kwargs = {"root_dir": tmp_path, "fill_package_dir": package_dir, **args}
+    where = kwargs.get("where", ["."])
+    assert set(expand.find_packages(**kwargs)) == pkgs
+    for pkg in pkgs:
+        pkg_path = find_package_path(pkg, package_dir, tmp_path)
+        assert os.path.exists(pkg_path)
+
+    # Make sure the same APIs work outside cwd
+    where = [
+        str((tmp_path / p).resolve()).replace(os.sep, "/")  # ensure posix-style paths
+        for p in args.pop("where", ["."])
+    ]
+
+    assert set(expand.find_packages(where=where, **args)) == pkgs
+
+
+@pytest.mark.parametrize(
+    ("files", "where", "expected_package_dir"),
+    [
+        (["pkg1/__init__.py", "pkg1/other.py"], ["."], {}),
+        (["pkg1/__init__.py", "pkg2/__init__.py"], ["."], {}),
+        (["src/pkg1/__init__.py", "src/pkg1/other.py"], ["src"], {"": "src"}),
+        (["src/pkg1/__init__.py", "src/pkg2/__init__.py"], ["src"], {"": "src"}),
+        (
+            ["src1/pkg1/__init__.py", "src2/pkg2/__init__.py"],
+            ["src1", "src2"],
+            {"pkg1": "src1/pkg1", "pkg2": "src2/pkg2"},
+        ),
+        (
+            ["src/pkg1/__init__.py", "pkg2/__init__.py"],
+            ["src", "."],
+            {"pkg1": "src/pkg1"},
+        ),
+    ],
+)
+def test_fill_package_dir(tmp_path, files, where, expected_package_dir):
+    write_files({k: "" for k in files}, tmp_path)
+    pkg_dir = {}
+    kwargs = {"root_dir": tmp_path, "fill_package_dir": pkg_dir, "namespaces": False}
+    pkgs = expand.find_packages(where=where, **kwargs)
+    assert set(pkg_dir.items()) == set(expected_package_dir.items())
+    for pkg in pkgs:
+        pkg_path = find_package_path(pkg, pkg_dir, tmp_path)
+        assert os.path.exists(pkg_path)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_pyprojecttoml.py b/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_pyprojecttoml.py
new file mode 100644
index 0000000000000000000000000000000000000000..db40fcd23da137dfb2f4ddde7ffb722193ee83e2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_pyprojecttoml.py
@@ -0,0 +1,396 @@
+import re
+from configparser import ConfigParser
+from inspect import cleandoc
+
+import jaraco.path
+import pytest
+import tomli_w
+from path import Path
+
+import setuptools  # noqa: F401 # force distutils.core to be patched
+from setuptools.config.pyprojecttoml import (
+    _ToolsTypoInMetadata,
+    apply_configuration,
+    expand_configuration,
+    read_configuration,
+    validate,
+)
+from setuptools.dist import Distribution
+from setuptools.errors import OptionError
+
+import distutils.core
+
+EXAMPLE = """
+[project]
+name = "myproj"
+keywords = ["some", "key", "words"]
+dynamic = ["version", "readme"]
+requires-python = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+dependencies = [
+    'importlib-metadata>=0.12;python_version<"3.8"',
+    'importlib-resources>=1.0;python_version<"3.7"',
+    'pathlib2>=2.3.3,<3;python_version < "3.4" and sys.platform != "win32"',
+]
+
+[project.optional-dependencies]
+docs = [
+    "sphinx>=3",
+    "sphinx-argparse>=0.2.5",
+    "sphinx-rtd-theme>=0.4.3",
+]
+testing = [
+    "pytest>=1",
+    "coverage>=3,<5",
+]
+
+[project.scripts]
+exec = "pkg.__main__:exec"
+
+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools]
+package-dir = {"" = "src"}
+zip-safe = true
+platforms = ["any"]
+
+[tool.setuptools.packages.find]
+where = ["src"]
+
+[tool.setuptools.cmdclass]
+sdist = "pkg.mod.CustomSdist"
+
+[tool.setuptools.dynamic.version]
+attr = "pkg.__version__.VERSION"
+
+[tool.setuptools.dynamic.readme]
+file = ["README.md"]
+content-type = "text/markdown"
+
+[tool.setuptools.package-data]
+"*" = ["*.txt"]
+
+[tool.setuptools.data-files]
+"data" = ["_files/*.txt"]
+
+[tool.distutils.sdist]
+formats = "gztar"
+
+[tool.distutils.bdist_wheel]
+universal = true
+"""
+
+
+def create_example(path, pkg_root):
+    files = {
+        "pyproject.toml": EXAMPLE,
+        "README.md": "hello world",
+        "_files": {
+            "file.txt": "",
+        },
+    }
+    packages = {
+        "pkg": {
+            "__init__.py": "",
+            "mod.py": "class CustomSdist: pass",
+            "__version__.py": "VERSION = (3, 10)",
+            "__main__.py": "def exec(): print('hello')",
+        },
+    }
+
+    assert pkg_root  # Meta-test: cannot be empty string.
+
+    if pkg_root == ".":
+        files = {**files, **packages}
+        # skip other files: flat-layout will raise error for multi-package dist
+    else:
+        # Use this opportunity to ensure namespaces are discovered
+        files[pkg_root] = {**packages, "other": {"nested": {"__init__.py": ""}}}
+
+    jaraco.path.build(files, prefix=path)
+
+
+def verify_example(config, path, pkg_root):
+    pyproject = path / "pyproject.toml"
+    pyproject.write_text(tomli_w.dumps(config), encoding="utf-8")
+    expanded = expand_configuration(config, path)
+    expanded_project = expanded["project"]
+    assert read_configuration(pyproject, expand=True) == expanded
+    assert expanded_project["version"] == "3.10"
+    assert expanded_project["readme"]["text"] == "hello world"
+    assert "packages" in expanded["tool"]["setuptools"]
+    if pkg_root == ".":
+        # Auto-discovery will raise error for multi-package dist
+        assert set(expanded["tool"]["setuptools"]["packages"]) == {"pkg"}
+    else:
+        assert set(expanded["tool"]["setuptools"]["packages"]) == {
+            "pkg",
+            "other",
+            "other.nested",
+        }
+    assert expanded["tool"]["setuptools"]["include-package-data"] is True
+    assert "" in expanded["tool"]["setuptools"]["package-data"]
+    assert "*" not in expanded["tool"]["setuptools"]["package-data"]
+    assert expanded["tool"]["setuptools"]["data-files"] == [
+        ("data", ["_files/file.txt"])
+    ]
+
+
+def test_read_configuration(tmp_path):
+    create_example(tmp_path, "src")
+    pyproject = tmp_path / "pyproject.toml"
+
+    config = read_configuration(pyproject, expand=False)
+    assert config["project"].get("version") is None
+    assert config["project"].get("readme") is None
+
+    verify_example(config, tmp_path, "src")
+
+
+@pytest.mark.parametrize(
+    ("pkg_root", "opts"),
+    [
+        (".", {}),
+        ("src", {}),
+        ("lib", {"packages": {"find": {"where": ["lib"]}}}),
+    ],
+)
+def test_discovered_package_dir_with_attr_directive_in_config(tmp_path, pkg_root, opts):
+    create_example(tmp_path, pkg_root)
+
+    pyproject = tmp_path / "pyproject.toml"
+
+    config = read_configuration(pyproject, expand=False)
+    assert config["project"].get("version") is None
+    assert config["project"].get("readme") is None
+    config["tool"]["setuptools"].pop("packages", None)
+    config["tool"]["setuptools"].pop("package-dir", None)
+
+    config["tool"]["setuptools"].update(opts)
+    verify_example(config, tmp_path, pkg_root)
+
+
+ENTRY_POINTS = {
+    "console_scripts": {"a": "mod.a:func"},
+    "gui_scripts": {"b": "mod.b:func"},
+    "other": {"c": "mod.c:func [extra]"},
+}
+
+
+class TestEntryPoints:
+    def write_entry_points(self, tmp_path):
+        entry_points = ConfigParser()
+        entry_points.read_dict(ENTRY_POINTS)
+        with open(tmp_path / "entry-points.txt", "w", encoding="utf-8") as f:
+            entry_points.write(f)
+
+    def pyproject(self, dynamic=None):
+        project = {"dynamic": dynamic or ["scripts", "gui-scripts", "entry-points"]}
+        tool = {"dynamic": {"entry-points": {"file": "entry-points.txt"}}}
+        return {"project": project, "tool": {"setuptools": tool}}
+
+    def test_all_listed_in_dynamic(self, tmp_path):
+        self.write_entry_points(tmp_path)
+        expanded = expand_configuration(self.pyproject(), tmp_path)
+        expanded_project = expanded["project"]
+        assert len(expanded_project["scripts"]) == 1
+        assert expanded_project["scripts"]["a"] == "mod.a:func"
+        assert len(expanded_project["gui-scripts"]) == 1
+        assert expanded_project["gui-scripts"]["b"] == "mod.b:func"
+        assert len(expanded_project["entry-points"]) == 1
+        assert expanded_project["entry-points"]["other"]["c"] == "mod.c:func [extra]"
+
+    @pytest.mark.parametrize("missing_dynamic", ("scripts", "gui-scripts"))
+    def test_scripts_not_listed_in_dynamic(self, tmp_path, missing_dynamic):
+        self.write_entry_points(tmp_path)
+        dynamic = {"scripts", "gui-scripts", "entry-points"} - {missing_dynamic}
+
+        msg = f"defined outside of `pyproject.toml`:.*{missing_dynamic}"
+        with pytest.raises(OptionError, match=re.compile(msg, re.S)):
+            expand_configuration(self.pyproject(dynamic), tmp_path)
+
+
+class TestClassifiers:
+    def test_dynamic(self, tmp_path):
+        # Let's create a project example that has dynamic classifiers
+        # coming from a txt file.
+        create_example(tmp_path, "src")
+        classifiers = cleandoc(
+            """
+            Framework :: Flask
+            Programming Language :: Haskell
+            """
+        )
+        (tmp_path / "classifiers.txt").write_text(classifiers, encoding="utf-8")
+
+        pyproject = tmp_path / "pyproject.toml"
+        config = read_configuration(pyproject, expand=False)
+        dynamic = config["project"]["dynamic"]
+        config["project"]["dynamic"] = list({*dynamic, "classifiers"})
+        dynamic_config = config["tool"]["setuptools"]["dynamic"]
+        dynamic_config["classifiers"] = {"file": "classifiers.txt"}
+
+        # When the configuration is expanded,
+        # each line of the file should be an different classifier.
+        validate(config, pyproject)
+        expanded = expand_configuration(config, tmp_path)
+
+        assert set(expanded["project"]["classifiers"]) == {
+            "Framework :: Flask",
+            "Programming Language :: Haskell",
+        }
+
+    def test_dynamic_without_config(self, tmp_path):
+        config = """
+        [project]
+        name = "myproj"
+        version = '42'
+        dynamic = ["classifiers"]
+        """
+
+        pyproject = tmp_path / "pyproject.toml"
+        pyproject.write_text(cleandoc(config), encoding="utf-8")
+        with pytest.raises(OptionError, match="No configuration .* .classifiers."):
+            read_configuration(pyproject)
+
+    def test_dynamic_readme_from_setup_script_args(self, tmp_path):
+        config = """
+        [project]
+        name = "myproj"
+        version = '42'
+        dynamic = ["readme"]
+        """
+        pyproject = tmp_path / "pyproject.toml"
+        pyproject.write_text(cleandoc(config), encoding="utf-8")
+        dist = Distribution(attrs={"long_description": "42"})
+        # No error should occur because of missing `readme`
+        dist = apply_configuration(dist, pyproject)
+        assert dist.metadata.long_description == "42"
+
+    def test_dynamic_without_file(self, tmp_path):
+        config = """
+        [project]
+        name = "myproj"
+        version = '42'
+        dynamic = ["classifiers"]
+
+        [tool.setuptools.dynamic]
+        classifiers = {file = ["classifiers.txt"]}
+        """
+
+        pyproject = tmp_path / "pyproject.toml"
+        pyproject.write_text(cleandoc(config), encoding="utf-8")
+        with pytest.warns(UserWarning, match="File .*classifiers.txt. cannot be found"):
+            expanded = read_configuration(pyproject)
+        assert "classifiers" not in expanded["project"]
+
+
+@pytest.mark.parametrize(
+    "example",
+    (
+        """
+        [project]
+        name = "myproj"
+        version = "1.2"
+
+        [my-tool.that-disrespect.pep518]
+        value = 42
+        """,
+    ),
+)
+def test_ignore_unrelated_config(tmp_path, example):
+    pyproject = tmp_path / "pyproject.toml"
+    pyproject.write_text(cleandoc(example), encoding="utf-8")
+
+    # Make sure no error is raised due to 3rd party configs in pyproject.toml
+    assert read_configuration(pyproject) is not None
+
+
+@pytest.mark.parametrize(
+    ("example", "error_msg"),
+    [
+        (
+            """
+            [project]
+            name = "myproj"
+            version = "1.2"
+            requires = ['pywin32; platform_system=="Windows"' ]
+            """,
+            "configuration error: .project. must not contain ..requires.. properties",
+        ),
+    ],
+)
+def test_invalid_example(tmp_path, example, error_msg):
+    pyproject = tmp_path / "pyproject.toml"
+    pyproject.write_text(cleandoc(example), encoding="utf-8")
+
+    pattern = re.compile(f"invalid pyproject.toml.*{error_msg}.*", re.M | re.S)
+    with pytest.raises(ValueError, match=pattern):
+        read_configuration(pyproject)
+
+
+@pytest.mark.parametrize("config", ("", "[tool.something]\nvalue = 42"))
+def test_empty(tmp_path, config):
+    pyproject = tmp_path / "pyproject.toml"
+    pyproject.write_text(config, encoding="utf-8")
+
+    # Make sure no error is raised
+    assert read_configuration(pyproject) == {}
+
+
+@pytest.mark.parametrize("config", ("[project]\nname = 'myproj'\nversion='42'\n",))
+def test_include_package_data_by_default(tmp_path, config):
+    """Builds with ``pyproject.toml`` should consider ``include-package-data=True`` as
+    default.
+    """
+    pyproject = tmp_path / "pyproject.toml"
+    pyproject.write_text(config, encoding="utf-8")
+
+    config = read_configuration(pyproject)
+    assert config["tool"]["setuptools"]["include-package-data"] is True
+
+
+def test_include_package_data_in_setuppy(tmp_path):
+    """Builds with ``pyproject.toml`` should consider ``include_package_data`` set in
+    ``setup.py``.
+
+    See https://github.com/pypa/setuptools/issues/3197#issuecomment-1079023889
+    """
+    files = {
+        "pyproject.toml": "[project]\nname = 'myproj'\nversion='42'\n",
+        "setup.py": "__import__('setuptools').setup(include_package_data=False)",
+    }
+    jaraco.path.build(files, prefix=tmp_path)
+
+    with Path(tmp_path):
+        dist = distutils.core.run_setup("setup.py", {}, stop_after="config")
+
+    assert dist.get_name() == "myproj"
+    assert dist.get_version() == "42"
+    assert dist.include_package_data is False
+
+
+def test_warn_tools_typo(tmp_path):
+    """Test that the common ``tools.setuptools`` typo in ``pyproject.toml`` issues a warning
+
+    See https://github.com/pypa/setuptools/issues/4150
+    """
+    config = """
+    [build-system]
+    requires = ["setuptools"]
+    build-backend = "setuptools.build_meta"
+
+    [project]
+    name = "myproj"
+    version = '42'
+
+    [tools.setuptools]
+    packages = ["package"]
+    """
+
+    pyproject = tmp_path / "pyproject.toml"
+    pyproject.write_text(cleandoc(config), encoding="utf-8")
+
+    with pytest.warns(_ToolsTypoInMetadata):
+        read_configuration(pyproject)
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_pyprojecttoml_dynamic_deps.py b/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_pyprojecttoml_dynamic_deps.py
new file mode 100644
index 0000000000000000000000000000000000000000..e42f28ffaaf5270a74fe9f03885b75bb91858030
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_pyprojecttoml_dynamic_deps.py
@@ -0,0 +1,109 @@
+from inspect import cleandoc
+
+import pytest
+from jaraco import path
+
+from setuptools.config.pyprojecttoml import apply_configuration
+from setuptools.dist import Distribution
+from setuptools.warnings import SetuptoolsWarning
+
+
+def test_dynamic_dependencies(tmp_path):
+    files = {
+        "requirements.txt": "six\n  # comment\n",
+        "pyproject.toml": cleandoc(
+            """
+            [project]
+            name = "myproj"
+            version = "1.0"
+            dynamic = ["dependencies"]
+
+            [build-system]
+            requires = ["setuptools", "wheel"]
+            build-backend = "setuptools.build_meta"
+
+            [tool.setuptools.dynamic.dependencies]
+            file = ["requirements.txt"]
+            """
+        ),
+    }
+    path.build(files, prefix=tmp_path)
+    dist = Distribution()
+    dist = apply_configuration(dist, tmp_path / "pyproject.toml")
+    assert dist.install_requires == ["six"]
+
+
+def test_dynamic_optional_dependencies(tmp_path):
+    files = {
+        "requirements-docs.txt": "sphinx\n  # comment\n",
+        "pyproject.toml": cleandoc(
+            """
+            [project]
+            name = "myproj"
+            version = "1.0"
+            dynamic = ["optional-dependencies"]
+
+            [tool.setuptools.dynamic.optional-dependencies.docs]
+            file = ["requirements-docs.txt"]
+
+            [build-system]
+            requires = ["setuptools", "wheel"]
+            build-backend = "setuptools.build_meta"
+            """
+        ),
+    }
+    path.build(files, prefix=tmp_path)
+    dist = Distribution()
+    dist = apply_configuration(dist, tmp_path / "pyproject.toml")
+    assert dist.extras_require == {"docs": ["sphinx"]}
+
+
+def test_mixed_dynamic_optional_dependencies(tmp_path):
+    """
+    Test that if PEP 621 was loosened to allow mixing of dynamic and static
+    configurations in the case of fields containing sub-fields (groups),
+    things would work out.
+    """
+    files = {
+        "requirements-images.txt": "pillow~=42.0\n  # comment\n",
+        "pyproject.toml": cleandoc(
+            """
+            [project]
+            name = "myproj"
+            version = "1.0"
+            dynamic = ["optional-dependencies"]
+
+            [project.optional-dependencies]
+            docs = ["sphinx"]
+
+            [tool.setuptools.dynamic.optional-dependencies.images]
+            file = ["requirements-images.txt"]
+            """
+        ),
+    }
+
+    path.build(files, prefix=tmp_path)
+    pyproject = tmp_path / "pyproject.toml"
+    with pytest.raises(ValueError, match="project.optional-dependencies"):
+        apply_configuration(Distribution(), pyproject)
+
+
+def test_mixed_extras_require_optional_dependencies(tmp_path):
+    files = {
+        "pyproject.toml": cleandoc(
+            """
+            [project]
+            name = "myproj"
+            version = "1.0"
+            optional-dependencies.docs = ["sphinx"]
+            """
+        ),
+    }
+
+    path.build(files, prefix=tmp_path)
+    pyproject = tmp_path / "pyproject.toml"
+
+    with pytest.warns(SetuptoolsWarning, match=".extras_require. overwritten"):
+        dist = Distribution({"extras_require": {"hello": ["world"]}})
+        dist = apply_configuration(dist, pyproject)
+        assert dist.extras_require == {"docs": ["sphinx"]}
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_setupcfg.py b/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_setupcfg.py
new file mode 100644
index 0000000000000000000000000000000000000000..61af99044742b46ec99ad1d38f34f576908f3e68
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/config/test_setupcfg.py
@@ -0,0 +1,980 @@
+import configparser
+import contextlib
+import inspect
+import re
+from pathlib import Path
+from unittest.mock import Mock, patch
+
+import pytest
+from packaging.requirements import InvalidRequirement
+
+from setuptools.config.setupcfg import ConfigHandler, Target, read_configuration
+from setuptools.dist import Distribution, _Distribution
+from setuptools.warnings import SetuptoolsDeprecationWarning
+
+from ..textwrap import DALS
+
+from distutils.errors import DistutilsFileError, DistutilsOptionError
+
+
+class ErrConfigHandler(ConfigHandler[Target]):
+    """Erroneous handler. Fails to implement required methods."""
+
+    section_prefix = "**err**"
+
+
+def make_package_dir(name, base_dir, ns=False):
+    dir_package = base_dir
+    for dir_name in name.split('/'):
+        dir_package = dir_package.mkdir(dir_name)
+    init_file = None
+    if not ns:
+        init_file = dir_package.join('__init__.py')
+        init_file.write('')
+    return dir_package, init_file
+
+
+def fake_env(
+    tmpdir, setup_cfg, setup_py=None, encoding='ascii', package_path='fake_package'
+):
+    if setup_py is None:
+        setup_py = 'from setuptools import setup\nsetup()\n'
+
+    tmpdir.join('setup.py').write(setup_py)
+    config = tmpdir.join('setup.cfg')
+    config.write(setup_cfg.encode(encoding), mode='wb')
+
+    package_dir, init_file = make_package_dir(package_path, tmpdir)
+
+    init_file.write(
+        'VERSION = (1, 2, 3)\n'
+        '\n'
+        'VERSION_MAJOR = 1'
+        '\n'
+        'def get_version():\n'
+        '    return [3, 4, 5, "dev"]\n'
+        '\n'
+    )
+
+    return package_dir, config
+
+
+@contextlib.contextmanager
+def get_dist(tmpdir, kwargs_initial=None, parse=True):
+    kwargs_initial = kwargs_initial or {}
+
+    with tmpdir.as_cwd():
+        dist = Distribution(kwargs_initial)
+        dist.script_name = 'setup.py'
+        parse and dist.parse_config_files()
+
+        yield dist
+
+
+def test_parsers_implemented():
+    with pytest.raises(NotImplementedError):
+        handler = ErrConfigHandler(None, {}, False, Mock())
+        handler.parsers
+
+
+class TestConfigurationReader:
+    def test_basic(self, tmpdir):
+        _, config = fake_env(
+            tmpdir,
+            '[metadata]\n'
+            'version = 10.1.1\n'
+            'keywords = one, two\n'
+            '\n'
+            '[options]\n'
+            'scripts = bin/a.py, bin/b.py\n',
+        )
+        config_dict = read_configuration(str(config))
+        assert config_dict['metadata']['version'] == '10.1.1'
+        assert config_dict['metadata']['keywords'] == ['one', 'two']
+        assert config_dict['options']['scripts'] == ['bin/a.py', 'bin/b.py']
+
+    def test_no_config(self, tmpdir):
+        with pytest.raises(DistutilsFileError):
+            read_configuration(str(tmpdir.join('setup.cfg')))
+
+    def test_ignore_errors(self, tmpdir):
+        _, config = fake_env(
+            tmpdir,
+            '[metadata]\nversion = attr: none.VERSION\nkeywords = one, two\n',
+        )
+        with pytest.raises(ImportError):
+            read_configuration(str(config))
+
+        config_dict = read_configuration(str(config), ignore_option_errors=True)
+
+        assert config_dict['metadata']['keywords'] == ['one', 'two']
+        assert 'version' not in config_dict['metadata']
+
+        config.remove()
+
+
+class TestMetadata:
+    def test_basic(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[metadata]\n'
+            'version = 10.1.1\n'
+            'description = Some description\n'
+            'long_description_content_type = text/something\n'
+            'long_description = file: README\n'
+            'name = fake_name\n'
+            'keywords = one, two\n'
+            'provides = package, package.sub\n'
+            'license = otherlic\n'
+            'download_url = http://test.test.com/test/\n'
+            'maintainer_email = test@test.com\n',
+        )
+
+        tmpdir.join('README').write('readme contents\nline2')
+
+        meta_initial = {
+            # This will be used so `otherlic` won't replace it.
+            'license': 'BSD 3-Clause License',
+        }
+
+        with get_dist(tmpdir, meta_initial) as dist:
+            metadata = dist.metadata
+
+            assert metadata.version == '10.1.1'
+            assert metadata.description == 'Some description'
+            assert metadata.long_description_content_type == 'text/something'
+            assert metadata.long_description == 'readme contents\nline2'
+            assert metadata.provides == ['package', 'package.sub']
+            assert metadata.license == 'BSD 3-Clause License'
+            assert metadata.name == 'fake_name'
+            assert metadata.keywords == ['one', 'two']
+            assert metadata.download_url == 'http://test.test.com/test/'
+            assert metadata.maintainer_email == 'test@test.com'
+
+    def test_license_cfg(self, tmpdir):
+        fake_env(
+            tmpdir,
+            DALS(
+                """
+            [metadata]
+            name=foo
+            version=0.0.1
+            license=Apache 2.0
+            """
+            ),
+        )
+
+        with get_dist(tmpdir) as dist:
+            metadata = dist.metadata
+
+            assert metadata.name == "foo"
+            assert metadata.version == "0.0.1"
+            assert metadata.license == "Apache 2.0"
+
+    def test_file_mixed(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[metadata]\nlong_description = file: README.rst, CHANGES.rst\n\n',
+        )
+
+        tmpdir.join('README.rst').write('readme contents\nline2')
+        tmpdir.join('CHANGES.rst').write('changelog contents\nand stuff')
+
+        with get_dist(tmpdir) as dist:
+            assert dist.metadata.long_description == (
+                'readme contents\nline2\nchangelog contents\nand stuff'
+            )
+
+    def test_file_sandboxed(self, tmpdir):
+        tmpdir.ensure("README")
+        project = tmpdir.join('depth1', 'depth2')
+        project.ensure(dir=True)
+        fake_env(project, '[metadata]\nlong_description = file: ../../README\n')
+
+        with get_dist(project, parse=False) as dist:
+            with pytest.raises(DistutilsOptionError):
+                dist.parse_config_files()  # file: out of sandbox
+
+    def test_aliases(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[metadata]\n'
+            'author_email = test@test.com\n'
+            'home_page = http://test.test.com/test/\n'
+            'summary = Short summary\n'
+            'platform = a, b\n'
+            'classifier =\n'
+            '  Framework :: Django\n'
+            '  Programming Language :: Python :: 3.5\n',
+        )
+
+        with get_dist(tmpdir) as dist:
+            metadata = dist.metadata
+            assert metadata.author_email == 'test@test.com'
+            assert metadata.url == 'http://test.test.com/test/'
+            assert metadata.description == 'Short summary'
+            assert metadata.platforms == ['a', 'b']
+            assert metadata.classifiers == [
+                'Framework :: Django',
+                'Programming Language :: Python :: 3.5',
+            ]
+
+    def test_multiline(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[metadata]\n'
+            'name = fake_name\n'
+            'keywords =\n'
+            '  one\n'
+            '  two\n'
+            'classifiers =\n'
+            '  Framework :: Django\n'
+            '  Programming Language :: Python :: 3.5\n',
+        )
+        with get_dist(tmpdir) as dist:
+            metadata = dist.metadata
+            assert metadata.keywords == ['one', 'two']
+            assert metadata.classifiers == [
+                'Framework :: Django',
+                'Programming Language :: Python :: 3.5',
+            ]
+
+    def test_dict(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[metadata]\n'
+            'project_urls =\n'
+            '  Link One = https://example.com/one/\n'
+            '  Link Two = https://example.com/two/\n',
+        )
+        with get_dist(tmpdir) as dist:
+            metadata = dist.metadata
+            assert metadata.project_urls == {
+                'Link One': 'https://example.com/one/',
+                'Link Two': 'https://example.com/two/',
+            }
+
+    def test_version(self, tmpdir):
+        package_dir, config = fake_env(
+            tmpdir, '[metadata]\nversion = attr: fake_package.VERSION\n'
+        )
+
+        sub_a = package_dir.mkdir('subpkg_a')
+        sub_a.join('__init__.py').write('')
+        sub_a.join('mod.py').write('VERSION = (2016, 11, 26)')
+
+        sub_b = package_dir.mkdir('subpkg_b')
+        sub_b.join('__init__.py').write('')
+        sub_b.join('mod.py').write(
+            'import third_party_module\nVERSION = (2016, 11, 26)'
+        )
+
+        with get_dist(tmpdir) as dist:
+            assert dist.metadata.version == '1.2.3'
+
+        config.write('[metadata]\nversion = attr: fake_package.get_version\n')
+        with get_dist(tmpdir) as dist:
+            assert dist.metadata.version == '3.4.5.dev'
+
+        config.write('[metadata]\nversion = attr: fake_package.VERSION_MAJOR\n')
+        with get_dist(tmpdir) as dist:
+            assert dist.metadata.version == '1'
+
+        config.write('[metadata]\nversion = attr: fake_package.subpkg_a.mod.VERSION\n')
+        with get_dist(tmpdir) as dist:
+            assert dist.metadata.version == '2016.11.26'
+
+        config.write('[metadata]\nversion = attr: fake_package.subpkg_b.mod.VERSION\n')
+        with get_dist(tmpdir) as dist:
+            assert dist.metadata.version == '2016.11.26'
+
+    def test_version_file(self, tmpdir):
+        fake_env(tmpdir, '[metadata]\nversion = file: fake_package/version.txt\n')
+        tmpdir.join('fake_package', 'version.txt').write('1.2.3\n')
+
+        with get_dist(tmpdir) as dist:
+            assert dist.metadata.version == '1.2.3'
+
+        tmpdir.join('fake_package', 'version.txt').write('1.2.3\n4.5.6\n')
+        with pytest.raises(DistutilsOptionError):
+            with get_dist(tmpdir) as dist:
+                dist.metadata.version
+
+    def test_version_with_package_dir_simple(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[metadata]\n'
+            'version = attr: fake_package_simple.VERSION\n'
+            '[options]\n'
+            'package_dir =\n'
+            '    = src\n',
+            package_path='src/fake_package_simple',
+        )
+
+        with get_dist(tmpdir) as dist:
+            assert dist.metadata.version == '1.2.3'
+
+    def test_version_with_package_dir_rename(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[metadata]\n'
+            'version = attr: fake_package_rename.VERSION\n'
+            '[options]\n'
+            'package_dir =\n'
+            '    fake_package_rename = fake_dir\n',
+            package_path='fake_dir',
+        )
+
+        with get_dist(tmpdir) as dist:
+            assert dist.metadata.version == '1.2.3'
+
+    def test_version_with_package_dir_complex(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[metadata]\n'
+            'version = attr: fake_package_complex.VERSION\n'
+            '[options]\n'
+            'package_dir =\n'
+            '    fake_package_complex = src/fake_dir\n',
+            package_path='src/fake_dir',
+        )
+
+        with get_dist(tmpdir) as dist:
+            assert dist.metadata.version == '1.2.3'
+
+    def test_unknown_meta_item(self, tmpdir):
+        fake_env(tmpdir, '[metadata]\nname = fake_name\nunknown = some\n')
+        with get_dist(tmpdir, parse=False) as dist:
+            dist.parse_config_files()  # Skip unknown.
+
+    def test_usupported_section(self, tmpdir):
+        fake_env(tmpdir, '[metadata.some]\nkey = val\n')
+        with get_dist(tmpdir, parse=False) as dist:
+            with pytest.raises(DistutilsOptionError):
+                dist.parse_config_files()
+
+    def test_classifiers(self, tmpdir):
+        expected = set([
+            'Framework :: Django',
+            'Programming Language :: Python :: 3',
+            'Programming Language :: Python :: 3.5',
+        ])
+
+        # From file.
+        _, config = fake_env(tmpdir, '[metadata]\nclassifiers = file: classifiers\n')
+
+        tmpdir.join('classifiers').write(
+            'Framework :: Django\n'
+            'Programming Language :: Python :: 3\n'
+            'Programming Language :: Python :: 3.5\n'
+        )
+
+        with get_dist(tmpdir) as dist:
+            assert set(dist.metadata.classifiers) == expected
+
+        # From list notation
+        config.write(
+            '[metadata]\n'
+            'classifiers =\n'
+            '    Framework :: Django\n'
+            '    Programming Language :: Python :: 3\n'
+            '    Programming Language :: Python :: 3.5\n'
+        )
+        with get_dist(tmpdir) as dist:
+            assert set(dist.metadata.classifiers) == expected
+
+    def test_interpolation(self, tmpdir):
+        fake_env(tmpdir, '[metadata]\ndescription = %(message)s\n')
+        with pytest.raises(configparser.InterpolationMissingOptionError):
+            with get_dist(tmpdir):
+                pass
+
+    def test_non_ascii_1(self, tmpdir):
+        fake_env(tmpdir, '[metadata]\ndescription = éàïôñ\n', encoding='utf-8')
+        with get_dist(tmpdir):
+            pass
+
+    def test_non_ascii_3(self, tmpdir):
+        fake_env(tmpdir, '\n# -*- coding: invalid\n')
+        with get_dist(tmpdir):
+            pass
+
+    def test_non_ascii_4(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '# -*- coding: utf-8\n[metadata]\ndescription = éàïôñ\n',
+            encoding='utf-8',
+        )
+        with get_dist(tmpdir) as dist:
+            assert dist.metadata.description == 'éàïôñ'
+
+    def test_not_utf8(self, tmpdir):
+        """
+        Config files encoded not in UTF-8 will fail
+        """
+        fake_env(
+            tmpdir,
+            '# vim: set fileencoding=iso-8859-15 :\n[metadata]\ndescription = éàïôñ\n',
+            encoding='iso-8859-15',
+        )
+        with pytest.raises(UnicodeDecodeError):
+            with get_dist(tmpdir):
+                pass
+
+    @pytest.mark.parametrize(
+        ("error_msg", "config", "invalid"),
+        [
+            (
+                "Invalid dash-separated key 'author-email' in 'metadata' (setup.cfg)",
+                DALS(
+                    """
+                    [metadata]
+                    author-email = test@test.com
+                    maintainer_email = foo@foo.com
+                    """
+                ),
+                {"author-email": "test@test.com"},
+            ),
+            (
+                "Invalid uppercase key 'Name' in 'metadata' (setup.cfg)",
+                DALS(
+                    """
+                    [metadata]
+                    Name = foo
+                    description = Some description
+                    """
+                ),
+                {"Name": "foo"},
+            ),
+        ],
+    )
+    def test_invalid_options_previously_deprecated(
+        self, tmpdir, error_msg, config, invalid
+    ):
+        # This test and related methods can be removed when no longer needed.
+        # Deprecation postponed due to push-back from the community in
+        # https://github.com/pypa/setuptools/issues/4910
+        fake_env(tmpdir, config)
+        with pytest.warns(SetuptoolsDeprecationWarning, match=re.escape(error_msg)):
+            dist = get_dist(tmpdir).__enter__()
+
+        tmpdir.join('setup.cfg').remove()
+
+        for field, value in invalid.items():
+            attr = field.replace("-", "_").lower()
+            assert getattr(dist.metadata, attr) == value
+
+
+class TestOptions:
+    def test_basic(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[options]\n'
+            'zip_safe = True\n'
+            'include_package_data = yes\n'
+            'package_dir = b=c, =src\n'
+            'packages = pack_a, pack_b.subpack\n'
+            'namespace_packages = pack1, pack2\n'
+            'scripts = bin/one.py, bin/two.py\n'
+            'eager_resources = bin/one.py, bin/two.py\n'
+            'install_requires = docutils>=0.3; pack ==1.1, ==1.3; hey\n'
+            'setup_requires = docutils>=0.3; spack ==1.1, ==1.3; there\n'
+            'dependency_links = http://some.com/here/1, '
+            'http://some.com/there/2\n'
+            'python_requires = >=1.0, !=2.8\n'
+            'py_modules = module1, module2\n',
+        )
+        deprec = pytest.warns(SetuptoolsDeprecationWarning, match="namespace_packages")
+        with deprec, get_dist(tmpdir) as dist:
+            assert dist.zip_safe
+            assert dist.include_package_data
+            assert dist.package_dir == {'': 'src', 'b': 'c'}
+            assert dist.packages == ['pack_a', 'pack_b.subpack']
+            assert dist.namespace_packages == ['pack1', 'pack2']
+            assert dist.scripts == ['bin/one.py', 'bin/two.py']
+            assert dist.dependency_links == ([
+                'http://some.com/here/1',
+                'http://some.com/there/2',
+            ])
+            assert dist.install_requires == ([
+                'docutils>=0.3',
+                'pack==1.1,==1.3',
+                'hey',
+            ])
+            assert dist.setup_requires == ([
+                'docutils>=0.3',
+                'spack ==1.1, ==1.3',
+                'there',
+            ])
+            assert dist.python_requires == '>=1.0, !=2.8'
+            assert dist.py_modules == ['module1', 'module2']
+
+    def test_multiline(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[options]\n'
+            'package_dir = \n'
+            '  b=c\n'
+            '  =src\n'
+            'packages = \n'
+            '  pack_a\n'
+            '  pack_b.subpack\n'
+            'namespace_packages = \n'
+            '  pack1\n'
+            '  pack2\n'
+            'scripts = \n'
+            '  bin/one.py\n'
+            '  bin/two.py\n'
+            'eager_resources = \n'
+            '  bin/one.py\n'
+            '  bin/two.py\n'
+            'install_requires = \n'
+            '  docutils>=0.3\n'
+            '  pack ==1.1, ==1.3\n'
+            '  hey\n'
+            'setup_requires = \n'
+            '  docutils>=0.3\n'
+            '  spack ==1.1, ==1.3\n'
+            '  there\n'
+            'dependency_links = \n'
+            '  http://some.com/here/1\n'
+            '  http://some.com/there/2\n',
+        )
+        deprec = pytest.warns(SetuptoolsDeprecationWarning, match="namespace_packages")
+        with deprec, get_dist(tmpdir) as dist:
+            assert dist.package_dir == {'': 'src', 'b': 'c'}
+            assert dist.packages == ['pack_a', 'pack_b.subpack']
+            assert dist.namespace_packages == ['pack1', 'pack2']
+            assert dist.scripts == ['bin/one.py', 'bin/two.py']
+            assert dist.dependency_links == ([
+                'http://some.com/here/1',
+                'http://some.com/there/2',
+            ])
+            assert dist.install_requires == ([
+                'docutils>=0.3',
+                'pack==1.1,==1.3',
+                'hey',
+            ])
+            assert dist.setup_requires == ([
+                'docutils>=0.3',
+                'spack ==1.1, ==1.3',
+                'there',
+            ])
+
+    def test_package_dir_fail(self, tmpdir):
+        fake_env(tmpdir, '[options]\npackage_dir = a b\n')
+        with get_dist(tmpdir, parse=False) as dist:
+            with pytest.raises(DistutilsOptionError):
+                dist.parse_config_files()
+
+    def test_package_data(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[options.package_data]\n'
+            '* = *.txt, *.rst\n'
+            'hello = *.msg\n'
+            '\n'
+            '[options.exclude_package_data]\n'
+            '* = fake1.txt, fake2.txt\n'
+            'hello = *.dat\n',
+        )
+
+        with get_dist(tmpdir) as dist:
+            assert dist.package_data == {
+                '': ['*.txt', '*.rst'],
+                'hello': ['*.msg'],
+            }
+            assert dist.exclude_package_data == {
+                '': ['fake1.txt', 'fake2.txt'],
+                'hello': ['*.dat'],
+            }
+
+    def test_packages(self, tmpdir):
+        fake_env(tmpdir, '[options]\npackages = find:\n')
+
+        with get_dist(tmpdir) as dist:
+            assert dist.packages == ['fake_package']
+
+    def test_find_directive(self, tmpdir):
+        dir_package, config = fake_env(tmpdir, '[options]\npackages = find:\n')
+
+        make_package_dir('sub_one', dir_package)
+        make_package_dir('sub_two', dir_package)
+
+        with get_dist(tmpdir) as dist:
+            assert set(dist.packages) == set([
+                'fake_package',
+                'fake_package.sub_two',
+                'fake_package.sub_one',
+            ])
+
+        config.write(
+            '[options]\n'
+            'packages = find:\n'
+            '\n'
+            '[options.packages.find]\n'
+            'where = .\n'
+            'include =\n'
+            '    fake_package.sub_one\n'
+            '    two\n'
+        )
+        with get_dist(tmpdir) as dist:
+            assert dist.packages == ['fake_package.sub_one']
+
+        config.write(
+            '[options]\n'
+            'packages = find:\n'
+            '\n'
+            '[options.packages.find]\n'
+            'exclude =\n'
+            '    fake_package.sub_one\n'
+        )
+        with get_dist(tmpdir) as dist:
+            assert set(dist.packages) == set(['fake_package', 'fake_package.sub_two'])
+
+    def test_find_namespace_directive(self, tmpdir):
+        dir_package, config = fake_env(
+            tmpdir, '[options]\npackages = find_namespace:\n'
+        )
+
+        make_package_dir('sub_one', dir_package)
+        make_package_dir('sub_two', dir_package, ns=True)
+
+        with get_dist(tmpdir) as dist:
+            assert set(dist.packages) == {
+                'fake_package',
+                'fake_package.sub_two',
+                'fake_package.sub_one',
+            }
+
+        config.write(
+            '[options]\n'
+            'packages = find_namespace:\n'
+            '\n'
+            '[options.packages.find]\n'
+            'where = .\n'
+            'include =\n'
+            '    fake_package.sub_one\n'
+            '    two\n'
+        )
+        with get_dist(tmpdir) as dist:
+            assert dist.packages == ['fake_package.sub_one']
+
+        config.write(
+            '[options]\n'
+            'packages = find_namespace:\n'
+            '\n'
+            '[options.packages.find]\n'
+            'exclude =\n'
+            '    fake_package.sub_one\n'
+        )
+        with get_dist(tmpdir) as dist:
+            assert set(dist.packages) == {'fake_package', 'fake_package.sub_two'}
+
+    def test_extras_require(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[options.extras_require]\n'
+            'pdf = ReportLab>=1.2; RXP\n'
+            'rest = \n'
+            '  docutils>=0.3\n'
+            '  pack ==1.1, ==1.3\n',
+        )
+
+        with get_dist(tmpdir) as dist:
+            assert dist.extras_require == {
+                'pdf': ['ReportLab>=1.2', 'RXP'],
+                'rest': ['docutils>=0.3', 'pack==1.1,==1.3'],
+            }
+            assert set(dist.metadata.provides_extras) == {'pdf', 'rest'}
+
+    @pytest.mark.parametrize(
+        "config",
+        [
+            "[options.extras_require]\nfoo = bar;python_version<'3'",
+            "[options.extras_require]\nfoo = bar;os_name=='linux'",
+            "[options.extras_require]\nfoo = bar;python_version<'3'\n",
+            "[options.extras_require]\nfoo = bar;os_name=='linux'\n",
+            "[options]\ninstall_requires = bar;python_version<'3'",
+            "[options]\ninstall_requires = bar;os_name=='linux'",
+            "[options]\ninstall_requires = bar;python_version<'3'\n",
+            "[options]\ninstall_requires = bar;os_name=='linux'\n",
+        ],
+    )
+    def test_raises_accidental_env_marker_misconfig(self, config, tmpdir):
+        fake_env(tmpdir, config)
+        match = (
+            r"One of the parsed requirements in `(install_requires|extras_require.+)` "
+            "looks like a valid environment marker.*"
+        )
+        with pytest.raises(InvalidRequirement, match=match):
+            with get_dist(tmpdir) as _:
+                pass
+
+    @pytest.mark.parametrize(
+        "config",
+        [
+            "[options.extras_require]\nfoo = bar;python_version<3",
+            "[options.extras_require]\nfoo = bar;python_version<3\n",
+            "[options]\ninstall_requires = bar;python_version<3",
+            "[options]\ninstall_requires = bar;python_version<3\n",
+        ],
+    )
+    def test_warn_accidental_env_marker_misconfig(self, config, tmpdir):
+        fake_env(tmpdir, config)
+        match = (
+            r"One of the parsed requirements in `(install_requires|extras_require.+)` "
+            "looks like a valid environment marker.*"
+        )
+        with pytest.warns(SetuptoolsDeprecationWarning, match=match):
+            with get_dist(tmpdir) as _:
+                pass
+
+    @pytest.mark.parametrize(
+        "config",
+        [
+            "[options.extras_require]\nfoo =\n    bar;python_version<'3'",
+            "[options.extras_require]\nfoo = bar;baz\nboo = xxx;yyy",
+            "[options.extras_require]\nfoo =\n    bar;python_version<'3'\n",
+            "[options.extras_require]\nfoo = bar;baz\nboo = xxx;yyy\n",
+            "[options.extras_require]\nfoo =\n    bar\n    python_version<3\n",
+            "[options]\ninstall_requires =\n    bar;python_version<'3'",
+            "[options]\ninstall_requires = bar;baz\nboo = xxx;yyy",
+            "[options]\ninstall_requires =\n    bar;python_version<'3'\n",
+            "[options]\ninstall_requires = bar;baz\nboo = xxx;yyy\n",
+            "[options]\ninstall_requires =\n    bar\n    python_version<3\n",
+        ],
+    )
+    @pytest.mark.filterwarnings("error::setuptools.SetuptoolsDeprecationWarning")
+    def test_nowarn_accidental_env_marker_misconfig(self, config, tmpdir, recwarn):
+        fake_env(tmpdir, config)
+        num_warnings = len(recwarn)
+        with get_dist(tmpdir) as _:
+            pass
+        # The examples are valid, no warnings shown
+        assert len(recwarn) == num_warnings
+
+    def test_dash_preserved_extras_require(self, tmpdir):
+        fake_env(tmpdir, '[options.extras_require]\nfoo-a = foo\nfoo_b = test\n')
+
+        with get_dist(tmpdir) as dist:
+            assert dist.extras_require == {'foo-a': ['foo'], 'foo_b': ['test']}
+
+    def test_entry_points(self, tmpdir):
+        _, config = fake_env(
+            tmpdir,
+            '[options.entry_points]\n'
+            'group1 = point1 = pack.module:func, '
+            '.point2 = pack.module2:func_rest [rest]\n'
+            'group2 = point3 = pack.module:func2\n',
+        )
+
+        with get_dist(tmpdir) as dist:
+            assert dist.entry_points == {
+                'group1': [
+                    'point1 = pack.module:func',
+                    '.point2 = pack.module2:func_rest [rest]',
+                ],
+                'group2': ['point3 = pack.module:func2'],
+            }
+
+        expected = (
+            '[blogtool.parsers]\n'
+            '.rst = some.nested.module:SomeClass.some_classmethod[reST]\n'
+        )
+
+        tmpdir.join('entry_points').write(expected)
+
+        # From file.
+        config.write('[options]\nentry_points = file: entry_points\n')
+
+        with get_dist(tmpdir) as dist:
+            assert dist.entry_points == expected
+
+    def test_case_sensitive_entry_points(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[options.entry_points]\n'
+            'GROUP1 = point1 = pack.module:func, '
+            '.point2 = pack.module2:func_rest [rest]\n'
+            'group2 = point3 = pack.module:func2\n',
+        )
+
+        with get_dist(tmpdir) as dist:
+            assert dist.entry_points == {
+                'GROUP1': [
+                    'point1 = pack.module:func',
+                    '.point2 = pack.module2:func_rest [rest]',
+                ],
+                'group2': ['point3 = pack.module:func2'],
+            }
+
+    def test_data_files(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[options.data_files]\n'
+            'cfg =\n'
+            '      a/b.conf\n'
+            '      c/d.conf\n'
+            'data = e/f.dat, g/h.dat\n',
+        )
+
+        with get_dist(tmpdir) as dist:
+            expected = [
+                ('cfg', ['a/b.conf', 'c/d.conf']),
+                ('data', ['e/f.dat', 'g/h.dat']),
+            ]
+            assert sorted(dist.data_files) == sorted(expected)
+
+    def test_data_files_globby(self, tmpdir):
+        fake_env(
+            tmpdir,
+            '[options.data_files]\n'
+            'cfg =\n'
+            '      a/b.conf\n'
+            '      c/d.conf\n'
+            'data = *.dat\n'
+            'icons = \n'
+            '      *.ico\n'
+            'audio = \n'
+            '      *.wav\n'
+            '      sounds.db\n',
+        )
+
+        # Create dummy files for glob()'s sake:
+        tmpdir.join('a.dat').write('')
+        tmpdir.join('b.dat').write('')
+        tmpdir.join('c.dat').write('')
+        tmpdir.join('a.ico').write('')
+        tmpdir.join('b.ico').write('')
+        tmpdir.join('c.ico').write('')
+        tmpdir.join('beep.wav').write('')
+        tmpdir.join('boop.wav').write('')
+        tmpdir.join('sounds.db').write('')
+
+        with get_dist(tmpdir) as dist:
+            expected = [
+                ('cfg', ['a/b.conf', 'c/d.conf']),
+                ('data', ['a.dat', 'b.dat', 'c.dat']),
+                ('icons', ['a.ico', 'b.ico', 'c.ico']),
+                ('audio', ['beep.wav', 'boop.wav', 'sounds.db']),
+            ]
+            assert sorted(dist.data_files) == sorted(expected)
+
+    def test_python_requires_simple(self, tmpdir):
+        fake_env(
+            tmpdir,
+            DALS(
+                """
+            [options]
+            python_requires=>=2.7
+            """
+            ),
+        )
+        with get_dist(tmpdir) as dist:
+            dist.parse_config_files()
+
+    def test_python_requires_compound(self, tmpdir):
+        fake_env(
+            tmpdir,
+            DALS(
+                """
+            [options]
+            python_requires=>=2.7,!=3.0.*
+            """
+            ),
+        )
+        with get_dist(tmpdir) as dist:
+            dist.parse_config_files()
+
+    def test_python_requires_invalid(self, tmpdir):
+        fake_env(
+            tmpdir,
+            DALS(
+                """
+            [options]
+            python_requires=invalid
+            """
+            ),
+        )
+        with pytest.raises(Exception):
+            with get_dist(tmpdir) as dist:
+                dist.parse_config_files()
+
+    def test_cmdclass(self, tmpdir):
+        module_path = Path(tmpdir, "src/custom_build.py")  # auto discovery for src
+        module_path.parent.mkdir(parents=True, exist_ok=True)
+        module_path.write_text(
+            "from distutils.core import Command\nclass CustomCmd(Command): pass\n",
+            encoding="utf-8",
+        )
+
+        setup_cfg = """
+            [options]
+            cmdclass =
+                customcmd = custom_build.CustomCmd
+        """
+        fake_env(tmpdir, inspect.cleandoc(setup_cfg))
+
+        with get_dist(tmpdir) as dist:
+            cmdclass = dist.cmdclass['customcmd']
+            assert cmdclass.__name__ == "CustomCmd"
+            assert cmdclass.__module__ == "custom_build"
+            assert module_path.samefile(inspect.getfile(cmdclass))
+
+    def test_requirements_file(self, tmpdir):
+        fake_env(
+            tmpdir,
+            DALS(
+                """
+            [options]
+            install_requires = file:requirements.txt
+            [options.extras_require]
+            colors = file:requirements-extra.txt
+            """
+            ),
+        )
+
+        tmpdir.join('requirements.txt').write('\ndocutils>=0.3\n\n')
+        tmpdir.join('requirements-extra.txt').write('colorama')
+
+        with get_dist(tmpdir) as dist:
+            assert dist.install_requires == ['docutils>=0.3']
+            assert dist.extras_require == {'colors': ['colorama']}
+
+
+saved_dist_init = _Distribution.__init__
+
+
+class TestExternalSetters:
+    # During creation of the setuptools Distribution() object, we call
+    # the init of the parent distutils Distribution object via
+    # _Distribution.__init__ ().
+    #
+    # It's possible distutils calls out to various keyword
+    # implementations (i.e. distutils.setup_keywords entry points)
+    # that may set a range of variables.
+    #
+    # This wraps distutil's Distribution.__init__ and simulates
+    # pbr or something else setting these values.
+    def _fake_distribution_init(self, dist, attrs):
+        saved_dist_init(dist, attrs)
+        # see self._DISTUTILS_UNSUPPORTED_METADATA
+        dist.metadata.long_description_content_type = 'text/something'
+        # Test overwrite setup() args
+        dist.metadata.project_urls = {
+            'Link One': 'https://example.com/one/',
+            'Link Two': 'https://example.com/two/',
+        }
+
+    @patch.object(_Distribution, '__init__', autospec=True)
+    def test_external_setters(self, mock_parent_init, tmpdir):
+        mock_parent_init.side_effect = self._fake_distribution_init
+
+        dist = Distribution(attrs={'project_urls': {'will_be': 'ignored'}})
+
+        assert dist.metadata.long_description_content_type == 'text/something'
+        assert dist.metadata.project_urls == {
+            'Link One': 'https://example.com/one/',
+            'Link Two': 'https://example.com/two/',
+        }
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/contexts.py b/.venv/lib/python3.12/site-packages/setuptools/tests/contexts.py
new file mode 100644
index 0000000000000000000000000000000000000000..97cceea0e7060c6e3568956e1ba20893301da387
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/contexts.py
@@ -0,0 +1,145 @@
+import contextlib
+import io
+import os
+import shutil
+import site
+import sys
+import tempfile
+
+from filelock import FileLock
+
+
+@contextlib.contextmanager
+def tempdir(cd=lambda dir: None, **kwargs):
+    temp_dir = tempfile.mkdtemp(**kwargs)
+    orig_dir = os.getcwd()
+    try:
+        cd(temp_dir)
+        yield temp_dir
+    finally:
+        cd(orig_dir)
+        shutil.rmtree(temp_dir)
+
+
+@contextlib.contextmanager
+def environment(**replacements):
+    """
+    In a context, patch the environment with replacements. Pass None values
+    to clear the values.
+    """
+    saved = dict((key, os.environ[key]) for key in replacements if key in os.environ)
+
+    # remove values that are null
+    remove = (key for (key, value) in replacements.items() if value is None)
+    for key in list(remove):
+        os.environ.pop(key, None)
+        replacements.pop(key)
+
+    os.environ.update(replacements)
+
+    try:
+        yield saved
+    finally:
+        for key in replacements:
+            os.environ.pop(key, None)
+        os.environ.update(saved)
+
+
+@contextlib.contextmanager
+def quiet():
+    """
+    Redirect stdout/stderr to StringIO objects to prevent console output from
+    distutils commands.
+    """
+
+    old_stdout = sys.stdout
+    old_stderr = sys.stderr
+    new_stdout = sys.stdout = io.StringIO()
+    new_stderr = sys.stderr = io.StringIO()
+    try:
+        yield new_stdout, new_stderr
+    finally:
+        new_stdout.seek(0)
+        new_stderr.seek(0)
+        sys.stdout = old_stdout
+        sys.stderr = old_stderr
+
+
+@contextlib.contextmanager
+def save_user_site_setting():
+    saved = site.ENABLE_USER_SITE
+    try:
+        yield saved
+    finally:
+        site.ENABLE_USER_SITE = saved
+
+
+@contextlib.contextmanager
+def save_pkg_resources_state():
+    import pkg_resources
+
+    pr_state = pkg_resources.__getstate__()
+    # also save sys.path
+    sys_path = sys.path[:]
+    try:
+        yield pr_state, sys_path
+    finally:
+        sys.path[:] = sys_path
+        pkg_resources.__setstate__(pr_state)
+
+
+@contextlib.contextmanager
+def suppress_exceptions(*excs):
+    try:
+        yield
+    except excs:
+        pass
+
+
+def multiproc(request):
+    """
+    Return True if running under xdist and multiple
+    workers are used.
+    """
+    try:
+        worker_id = request.getfixturevalue('worker_id')
+    except Exception:
+        return False
+    return worker_id != 'master'
+
+
+@contextlib.contextmanager
+def session_locked_tmp_dir(request, tmp_path_factory, name):
+    """Uses a file lock to guarantee only one worker can access a temp dir"""
+    # get the temp directory shared by all workers
+    base = tmp_path_factory.getbasetemp()
+    shared_dir = base.parent if multiproc(request) else base
+
+    locked_dir = shared_dir / name
+    with FileLock(locked_dir.with_suffix(".lock")):
+        # ^-- prevent multiple workers to access the directory at once
+        locked_dir.mkdir(exist_ok=True, parents=True)
+        yield locked_dir
+
+
+@contextlib.contextmanager
+def save_paths():
+    """Make sure ``sys.path``, ``sys.meta_path`` and ``sys.path_hooks`` are preserved"""
+    prev = sys.path[:], sys.meta_path[:], sys.path_hooks[:]
+
+    try:
+        yield
+    finally:
+        sys.path, sys.meta_path, sys.path_hooks = prev
+
+
+@contextlib.contextmanager
+def save_sys_modules():
+    """Make sure initial ``sys.modules`` is preserved"""
+    prev_modules = sys.modules
+
+    try:
+        sys.modules = sys.modules.copy()
+        yield
+    finally:
+        sys.modules = prev_modules
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/environment.py b/.venv/lib/python3.12/site-packages/setuptools/tests/environment.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed5499ef7d73762d033a4877bfe586d6c0b82235
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/environment.py
@@ -0,0 +1,95 @@
+import os
+import subprocess
+import sys
+import unicodedata
+from subprocess import PIPE as _PIPE, Popen as _Popen
+
+import jaraco.envs
+
+
+class VirtualEnv(jaraco.envs.VirtualEnv):
+    name = '.env'
+    # Some version of PyPy will import distutils on startup, implicitly
+    # importing setuptools, and thus leading to BackendInvalid errors
+    # when upgrading Setuptools. Bypass this behavior by avoiding the
+    # early availability and need to upgrade.
+    create_opts = ['--no-setuptools']
+
+    def run(self, cmd, *args, **kwargs):
+        cmd = [self.exe(cmd[0])] + cmd[1:]
+        kwargs = {"cwd": self.root, "encoding": "utf-8", **kwargs}  # Allow overriding
+        # In some environments (eg. downstream distro packaging), where:
+        # - tox isn't used to run tests and
+        # - PYTHONPATH is set to point to a specific setuptools codebase and
+        # - no custom env is explicitly set by a test
+        # PYTHONPATH will leak into the spawned processes.
+        # In that case tests look for module in the wrong place (on PYTHONPATH).
+        # Unless the test sets its own special env, pass a copy of the existing
+        # environment with removed PYTHONPATH to the subprocesses.
+        if "env" not in kwargs:
+            env = dict(os.environ)
+            if "PYTHONPATH" in env:
+                del env["PYTHONPATH"]
+            kwargs["env"] = env
+        return subprocess.check_output(cmd, *args, **kwargs)
+
+
+def _which_dirs(cmd):
+    result = set()
+    for path in os.environ.get('PATH', '').split(os.pathsep):
+        filename = os.path.join(path, cmd)
+        if os.access(filename, os.X_OK):
+            result.add(path)
+    return result
+
+
+def run_setup_py(cmd, pypath=None, path=None, data_stream=0, env=None):
+    """
+    Execution command for tests, separate from those used by the
+    code directly to prevent accidental behavior issues
+    """
+    if env is None:
+        env = dict()
+        for envname in os.environ:
+            env[envname] = os.environ[envname]
+
+    # override the python path if needed
+    if pypath is not None:
+        env["PYTHONPATH"] = pypath
+
+    # override the execution path if needed
+    if path is not None:
+        env["PATH"] = path
+    if not env.get("PATH", ""):
+        env["PATH"] = _which_dirs("tar").union(_which_dirs("gzip"))
+        env["PATH"] = os.pathsep.join(env["PATH"])
+
+    cmd = [sys.executable, "setup.py"] + list(cmd)
+
+    # https://bugs.python.org/issue8557
+    shell = sys.platform == 'win32'
+
+    try:
+        proc = _Popen(
+            cmd,
+            stdout=_PIPE,
+            stderr=_PIPE,
+            shell=shell,
+            env=env,
+            encoding="utf-8",
+        )
+
+        if isinstance(data_stream, tuple):
+            data_stream = slice(*data_stream)
+        data = proc.communicate()[data_stream]
+    except OSError:
+        return 1, ''
+
+    # decode the console string if needed
+    if hasattr(data, "decode"):
+        # use the default encoding
+        data = data.decode()
+        data = unicodedata.normalize('NFC', data)
+
+    # communicate calls wait()
+    return proc.returncode, data
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/fixtures.py b/.venv/lib/python3.12/site-packages/setuptools/tests/fixtures.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5472984b5690572285a84575f6c2d598f06dd11
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/fixtures.py
@@ -0,0 +1,157 @@
+import contextlib
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+import path
+import pytest
+
+from . import contexts, environment
+
+
+@pytest.fixture
+def user_override(monkeypatch):
+    """
+    Override site.USER_BASE and site.USER_SITE with temporary directories in
+    a context.
+    """
+    with contexts.tempdir() as user_base:
+        monkeypatch.setattr('site.USER_BASE', user_base)
+        with contexts.tempdir() as user_site:
+            monkeypatch.setattr('site.USER_SITE', user_site)
+            with contexts.save_user_site_setting():
+                yield
+
+
+@pytest.fixture
+def tmpdir_cwd(tmpdir):
+    with tmpdir.as_cwd() as orig:
+        yield orig
+
+
+@pytest.fixture(autouse=True, scope="session")
+def workaround_xdist_376(request):
+    """
+    Workaround pytest-dev/pytest-xdist#376
+
+    ``pytest-xdist`` tends to inject '' into ``sys.path``,
+    which may break certain isolation expectations.
+    Remove the entry so the import
+    machinery behaves the same irrespective of xdist.
+    """
+    if not request.config.pluginmanager.has_plugin('xdist'):
+        return
+
+    with contextlib.suppress(ValueError):
+        sys.path.remove('')
+
+
+@pytest.fixture
+def sample_project(tmp_path):
+    """
+    Clone the 'sampleproject' and return a path to it.
+    """
+    cmd = ['git', 'clone', 'https://github.com/pypa/sampleproject']
+    try:
+        subprocess.check_call(cmd, cwd=str(tmp_path))
+    except Exception:
+        pytest.skip("Unable to clone sampleproject")
+    return tmp_path / 'sampleproject'
+
+
+# sdist and wheel artifacts should be stable across a round of tests
+# so we can build them once per session and use the files as "readonly"
+
+# In the case of setuptools, building the wheel without sdist may cause
+# it to contain the `build` directory, and therefore create situations with
+# `setuptools/build/lib/build/lib/...`. To avoid that, build both artifacts at once.
+
+
+def _build_distributions(tmp_path_factory, request):
+    with contexts.session_locked_tmp_dir(
+        request, tmp_path_factory, "dist_build"
+    ) as tmp:  # pragma: no cover
+        sdist = next(tmp.glob("*.tar.gz"), None)
+        wheel = next(tmp.glob("*.whl"), None)
+        if sdist and wheel:
+            return (sdist, wheel)
+
+        # Sanity check: should not create recursive setuptools/build/lib/build/lib/...
+        assert not Path(request.config.rootdir, "build/lib/build").exists()
+
+        subprocess.check_output([
+            sys.executable,
+            "-m",
+            "build",
+            "--outdir",
+            str(tmp),
+            str(request.config.rootdir),
+        ])
+
+        # Sanity check: should not create recursive setuptools/build/lib/build/lib/...
+        assert not Path(request.config.rootdir, "build/lib/build").exists()
+
+        return next(tmp.glob("*.tar.gz")), next(tmp.glob("*.whl"))
+
+
+@pytest.fixture(scope="session")
+def setuptools_sdist(tmp_path_factory, request):
+    prebuilt = os.getenv("PRE_BUILT_SETUPTOOLS_SDIST")
+    if prebuilt and os.path.exists(prebuilt):  # pragma: no cover
+        return Path(prebuilt).resolve()
+
+    sdist, _ = _build_distributions(tmp_path_factory, request)
+    return sdist
+
+
+@pytest.fixture(scope="session")
+def setuptools_wheel(tmp_path_factory, request):
+    prebuilt = os.getenv("PRE_BUILT_SETUPTOOLS_WHEEL")
+    if prebuilt and os.path.exists(prebuilt):  # pragma: no cover
+        return Path(prebuilt).resolve()
+
+    _, wheel = _build_distributions(tmp_path_factory, request)
+    return wheel
+
+
+@pytest.fixture
+def venv(tmp_path, setuptools_wheel):
+    """Virtual env with the version of setuptools under test installed"""
+    env = environment.VirtualEnv()
+    env.root = path.Path(tmp_path / 'venv')
+    env.create_opts = ['--no-setuptools', '--wheel=bundle']
+    # TODO: Use `--no-wheel` when setuptools implements its own bdist_wheel
+    env.req = str(setuptools_wheel)
+    # In some environments (eg. downstream distro packaging),
+    # where tox isn't used to run tests and PYTHONPATH is set to point to
+    # a specific setuptools codebase, PYTHONPATH will leak into the spawned
+    # processes.
+    # env.create() should install the just created setuptools
+    # wheel, but it doesn't if it finds another existing matching setuptools
+    # installation present on PYTHONPATH:
+    # `setuptools is already installed with the same version as the provided
+    # wheel. Use --force-reinstall to force an installation of the wheel.`
+    # This prevents leaking PYTHONPATH to the created environment.
+    with contexts.environment(PYTHONPATH=None):
+        return env.create()
+
+
+@pytest.fixture
+def venv_without_setuptools(tmp_path):
+    """Virtual env without any version of setuptools installed"""
+    env = environment.VirtualEnv()
+    env.root = path.Path(tmp_path / 'venv_without_setuptools')
+    env.create_opts = ['--no-setuptools', '--no-wheel']
+    env.ensure_env()
+    return env
+
+
+@pytest.fixture
+def bare_venv(tmp_path):
+    """Virtual env without any common packages installed"""
+    env = environment.VirtualEnv()
+    env.root = path.Path(tmp_path / 'bare_venv')
+    env.create_opts = ['--no-setuptools', '--no-pip', '--no-wheel', '--no-seed']
+    env.ensure_env()
+    return env
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/indexes/test_links_priority/external.html b/.venv/lib/python3.12/site-packages/setuptools/tests/indexes/test_links_priority/external.html
new file mode 100644
index 0000000000000000000000000000000000000000..92e4702f634dfb37a404bec3103b76f6afcaa917
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/indexes/test_links_priority/external.html
@@ -0,0 +1,3 @@
+<html><body>
+<a href="/foobar-0.1.tar.gz#md5=1__bad_md5___">bad old link</a>
+</body></html>
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/indexes/test_links_priority/simple/foobar/index.html b/.venv/lib/python3.12/site-packages/setuptools/tests/indexes/test_links_priority/simple/foobar/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..fefb028bd3ee7d45a414d6e96a7b2a21ffd7eda7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/indexes/test_links_priority/simple/foobar/index.html
@@ -0,0 +1,4 @@
+<html><body>
+<a href="/foobar-0.1.tar.gz#md5=0_correct_md5">foobar-0.1.tar.gz</a><br/>
+<a href="../../external.html" rel="homepage">external homepage</a><br/>
+</body></html>
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/integration/__init__.py b/.venv/lib/python3.12/site-packages/setuptools/tests/integration/__init__.py
new file mode 100644
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/integration/__pycache__/test_pip_install_sdist.cpython-312.pyc b/.venv/lib/python3.12/site-packages/setuptools/tests/integration/__pycache__/test_pip_install_sdist.cpython-312.pyc
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diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/integration/helpers.py b/.venv/lib/python3.12/site-packages/setuptools/tests/integration/helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..77b196e029b6d2eeb25f0b84a4f7630f65201189
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/integration/helpers.py
@@ -0,0 +1,77 @@
+"""Reusable functions and classes for different types of integration tests.
+
+For example ``Archive`` can be used to check the contents of distribution built
+with setuptools, and ``run`` will always try to be as verbose as possible to
+facilitate debugging.
+"""
+
+import os
+import subprocess
+import tarfile
+from pathlib import Path
+from zipfile import ZipFile
+
+
+def run(cmd, env=None):
+    r = subprocess.run(
+        cmd,
+        capture_output=True,
+        text=True,
+        encoding="utf-8",
+        env={**os.environ, **(env or {})},
+        # ^-- allow overwriting instead of discarding the current env
+    )
+
+    out = r.stdout + "\n" + r.stderr
+    # pytest omits stdout/err by default, if the test fails they help debugging
+    print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
+    print(f"Command: {cmd}\nreturn code: {r.returncode}\n\n{out}")
+
+    if r.returncode == 0:
+        return out
+    raise subprocess.CalledProcessError(r.returncode, cmd, r.stdout, r.stderr)
+
+
+class Archive:
+    """Compatibility layer for ZipFile/Info and TarFile/Info"""
+
+    def __init__(self, filename):
+        self._filename = filename
+        if filename.endswith("tar.gz"):
+            self._obj = tarfile.open(filename, "r:gz")
+        elif filename.endswith("zip"):
+            self._obj = ZipFile(filename)
+        else:
+            raise ValueError(f"{filename} doesn't seem to be a zip or tar.gz")
+
+    def __iter__(self):
+        if hasattr(self._obj, "infolist"):
+            return iter(self._obj.infolist())
+        return iter(self._obj)
+
+    def get_name(self, zip_or_tar_info):
+        if hasattr(zip_or_tar_info, "filename"):
+            return zip_or_tar_info.filename
+        return zip_or_tar_info.name
+
+    def get_content(self, zip_or_tar_info):
+        if hasattr(self._obj, "extractfile"):
+            content = self._obj.extractfile(zip_or_tar_info)
+            if content is None:
+                msg = f"Invalid {zip_or_tar_info.name} in {self._filename}"
+                raise ValueError(msg)
+            return str(content.read(), "utf-8")
+        return str(self._obj.read(zip_or_tar_info), "utf-8")
+
+
+def get_sdist_members(sdist_path):
+    with tarfile.open(sdist_path, "r:gz") as tar:
+        files = [Path(f) for f in tar.getnames()]
+    # remove root folder
+    relative_files = ("/".join(f.parts[1:]) for f in files)
+    return {f for f in relative_files if f}
+
+
+def get_wheel_members(wheel_path):
+    with ZipFile(wheel_path) as zipfile:
+        return set(zipfile.namelist())
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/integration/test_pip_install_sdist.py b/.venv/lib/python3.12/site-packages/setuptools/tests/integration/test_pip_install_sdist.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e84f218323e6ad67adfbce07cea5a16e91a311c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/integration/test_pip_install_sdist.py
@@ -0,0 +1,223 @@
+# https://github.com/python/mypy/issues/16936
+# mypy: disable-error-code="has-type"
+"""Integration tests for setuptools that focus on building packages via pip.
+
+The idea behind these tests is not to exhaustively check all the possible
+combinations of packages, operating systems, supporting libraries, etc, but
+rather check a limited number of popular packages and how they interact with
+the exposed public API. This way if any change in API is introduced, we hope to
+identify backward compatibility problems before publishing a release.
+
+The number of tested packages is purposefully kept small, to minimise duration
+and the associated maintenance cost (changes in the way these packages define
+their build process may require changes in the tests).
+"""
+
+import json
+import os
+import shutil
+import sys
+from enum import Enum
+from glob import glob
+from hashlib import md5
+from urllib.request import urlopen
+
+import pytest
+from packaging.requirements import Requirement
+
+from .helpers import Archive, run
+
+pytestmark = pytest.mark.integration
+
+
+(LATEST,) = Enum("v", "LATEST")  # type: ignore[misc] # https://github.com/python/mypy/issues/16936
+"""Default version to be checked"""
+# There are positive and negative aspects of checking the latest version of the
+# packages.
+# The main positive aspect is that the latest version might have already
+# removed the use of APIs deprecated in previous releases of setuptools.
+
+
+# Packages to be tested:
+# (Please notice the test environment cannot support EVERY library required for
+# compiling binary extensions. In Ubuntu/Debian nomenclature, we only assume
+# that `build-essential`, `gfortran` and `libopenblas-dev` are installed,
+# due to their relevance to the numerical/scientific programming ecosystem)
+EXAMPLES = [
+    ("pip", LATEST),  # just in case...
+    ("pytest", LATEST),  # uses setuptools_scm
+    ("mypy", LATEST),  # custom build_py + ext_modules
+    # --- Popular packages: https://hugovk.github.io/top-pypi-packages/ ---
+    ("botocore", LATEST),
+    ("kiwisolver", LATEST),  # build_ext
+    ("brotli", LATEST),  # not in the list but used by urllib3
+    ("pyyaml", LATEST),  # cython + custom build_ext + custom distclass
+    ("charset-normalizer", LATEST),  # uses mypyc, used by aiohttp
+    ("protobuf", LATEST),
+    # ("requests", LATEST),  # XXX: https://github.com/psf/requests/pull/6920
+    ("celery", LATEST),
+    # When adding packages to this list, make sure they expose a `__version__`
+    # attribute, or modify the tests below
+]
+
+
+# Some packages have "optional" dependencies that modify their build behaviour
+# and are not listed in pyproject.toml, others still use `setup_requires`
+EXTRA_BUILD_DEPS = {
+    "pyyaml": ("Cython<3.0",),  # constraint to avoid errors
+    "charset-normalizer": ("mypy>=1.4.1",),  # no pyproject.toml available
+}
+
+EXTRA_ENV_VARS = {
+    "pyyaml": {"PYYAML_FORCE_CYTHON": "1"},
+    "charset-normalizer": {"CHARSET_NORMALIZER_USE_MYPYC": "1"},
+}
+
+IMPORT_NAME = {
+    "pyyaml": "yaml",
+    "protobuf": "google.protobuf",
+}
+
+
+VIRTUALENV = (sys.executable, "-m", "virtualenv")
+
+
+# By default, pip will try to build packages in isolation (PEP 517), which
+# means it will download the previous stable version of setuptools.
+# `pip` flags can avoid that (the version of setuptools under test
+# should be the one to be used)
+INSTALL_OPTIONS = (
+    "--ignore-installed",
+    "--no-build-isolation",
+    # Omit "--no-binary :all:" the sdist is supplied directly.
+    # Allows dependencies as wheels.
+)
+# The downside of `--no-build-isolation` is that pip will not download build
+# dependencies. The test script will have to also handle that.
+
+
+@pytest.fixture
+def venv_python(tmp_path):
+    run([*VIRTUALENV, str(tmp_path / ".venv")])
+    possible_path = (str(p.parent) for p in tmp_path.glob(".venv/*/python*"))
+    return shutil.which("python", path=os.pathsep.join(possible_path))
+
+
+@pytest.fixture(autouse=True)
+def _prepare(tmp_path, venv_python, monkeypatch):
+    download_path = os.getenv("DOWNLOAD_PATH", str(tmp_path))
+    os.makedirs(download_path, exist_ok=True)
+
+    # Environment vars used for building some of the packages
+    monkeypatch.setenv("USE_MYPYC", "1")
+
+    yield
+
+    # Let's provide the maximum amount of information possible in the case
+    # it is necessary to debug the tests directly from the CI logs.
+    print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
+    print("Temporary directory:")
+    map(print, tmp_path.glob("*"))
+    print("Virtual environment:")
+    run([venv_python, "-m", "pip", "freeze"])
+
+
+@pytest.mark.parametrize(("package", "version"), EXAMPLES)
+@pytest.mark.uses_network
+def test_install_sdist(package, version, tmp_path, venv_python, setuptools_wheel):
+    venv_pip = (venv_python, "-m", "pip")
+    sdist = retrieve_sdist(package, version, tmp_path)
+    deps = build_deps(package, sdist)
+    if deps:
+        print("~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
+        print("Dependencies:", deps)
+        run([*venv_pip, "install", *deps])
+
+    # Use a virtualenv to simulate PEP 517 isolation
+    # but install fresh setuptools wheel to ensure the version under development
+    env = EXTRA_ENV_VARS.get(package, {})
+    run([*venv_pip, "install", "--force-reinstall", setuptools_wheel])
+    run([*venv_pip, "install", *INSTALL_OPTIONS, sdist], env)
+
+    # Execute a simple script to make sure the package was installed correctly
+    pkg = IMPORT_NAME.get(package, package).replace("-", "_")
+    script = f"import {pkg}; print(getattr({pkg}, '__version__', 0))"
+    run([venv_python, "-c", script])
+
+
+# ---- Helper Functions ----
+
+
+def retrieve_sdist(package, version, tmp_path):
+    """Either use cached sdist file or download it from PyPI"""
+    # `pip download` cannot be used due to
+    # https://github.com/pypa/pip/issues/1884
+    # https://discuss.python.org/t/pep-625-file-name-of-a-source-distribution/4686
+    # We have to find the correct distribution file and download it
+    download_path = os.getenv("DOWNLOAD_PATH", str(tmp_path))
+    dist = retrieve_pypi_sdist_metadata(package, version)
+
+    # Remove old files to prevent cache to grow indefinitely
+    for file in glob(os.path.join(download_path, f"{package}*")):
+        if dist["filename"] != file:
+            os.unlink(file)
+
+    dist_file = os.path.join(download_path, dist["filename"])
+    if not os.path.exists(dist_file):
+        download(dist["url"], dist_file, dist["md5_digest"])
+    return dist_file
+
+
+def retrieve_pypi_sdist_metadata(package, version):
+    # https://warehouse.pypa.io/api-reference/json.html
+    id_ = package if version is LATEST else f"{package}/{version}"
+    with urlopen(f"https://pypi.org/pypi/{id_}/json") as f:
+        metadata = json.load(f)
+
+    if metadata["info"]["yanked"]:
+        raise ValueError(f"Release for {package} {version} was yanked")
+
+    version = metadata["info"]["version"]
+    release = metadata["releases"][version] if version is LATEST else metadata["urls"]
+    (sdist,) = filter(lambda d: d["packagetype"] == "sdist", release)
+    return sdist
+
+
+def download(url, dest, md5_digest):
+    with urlopen(url) as f:
+        data = f.read()
+
+    assert md5(data).hexdigest() == md5_digest
+
+    with open(dest, "wb") as f:
+        f.write(data)
+
+    assert os.path.exists(dest)
+
+
+def build_deps(package, sdist_file):
+    """Find out what are the build dependencies for a package.
+
+    "Manually" install them, since pip will not install build
+    deps with `--no-build-isolation`.
+    """
+    # delay importing, since pytest discovery phase may hit this file from a
+    # testenv without tomli
+    from setuptools.compat.py310 import tomllib
+
+    archive = Archive(sdist_file)
+    info = tomllib.loads(_read_pyproject(archive))
+    deps = info.get("build-system", {}).get("requires", [])
+    deps += EXTRA_BUILD_DEPS.get(package, [])
+    # Remove setuptools from requirements (and deduplicate)
+    requirements = {Requirement(d).name: d for d in deps}
+    return [v for k, v in requirements.items() if k != "setuptools"]
+
+
+def _read_pyproject(archive):
+    contents = (
+        archive.get_content(member)
+        for member in archive
+        if os.path.basename(archive.get_name(member)) == "pyproject.toml"
+    )
+    return next(contents, "")
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/mod_with_constant.py b/.venv/lib/python3.12/site-packages/setuptools/tests/mod_with_constant.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef755dd1c7a8d1f116fe51f1b43315057f03379d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/mod_with_constant.py
@@ -0,0 +1 @@
+value = 'three, sir!'
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/namespaces.py b/.venv/lib/python3.12/site-packages/setuptools/tests/namespaces.py
new file mode 100644
index 0000000000000000000000000000000000000000..248db98f97951aeeee0222131417e73074cc72d2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/namespaces.py
@@ -0,0 +1,90 @@
+import ast
+import json
+import textwrap
+from pathlib import Path
+
+
+def iter_namespace_pkgs(namespace):
+    parts = namespace.split(".")
+    for i in range(len(parts)):
+        yield ".".join(parts[: i + 1])
+
+
+def build_namespace_package(tmpdir, name, version="1.0", impl="pkg_resources"):
+    src_dir = tmpdir / name
+    src_dir.mkdir()
+    setup_py = src_dir / 'setup.py'
+    namespace, _, rest = name.rpartition('.')
+    namespaces = list(iter_namespace_pkgs(namespace))
+    setup_args = {
+        "name": name,
+        "version": version,
+        "packages": namespaces,
+    }
+
+    if impl == "pkg_resources":
+        tmpl = '__import__("pkg_resources").declare_namespace(__name__)'
+        setup_args["namespace_packages"] = namespaces
+    elif impl == "pkgutil":
+        tmpl = '__path__ = __import__("pkgutil").extend_path(__path__, __name__)'
+    else:
+        raise ValueError(f"Cannot recognise {impl=} when creating namespaces")
+
+    args = json.dumps(setup_args, indent=4)
+    assert ast.literal_eval(args)  # ensure it is valid Python
+
+    script = textwrap.dedent(
+        """\
+        import setuptools
+        args = {args}
+        setuptools.setup(**args)
+        """
+    ).format(args=args)
+    setup_py.write_text(script, encoding='utf-8')
+
+    ns_pkg_dir = Path(src_dir, namespace.replace(".", "/"))
+    ns_pkg_dir.mkdir(parents=True)
+
+    for ns in namespaces:
+        pkg_init = src_dir / ns.replace(".", "/") / '__init__.py'
+        pkg_init.write_text(tmpl, encoding='utf-8')
+
+    pkg_mod = ns_pkg_dir / (rest + '.py')
+    some_functionality = 'name = {rest!r}'.format(**locals())
+    pkg_mod.write_text(some_functionality, encoding='utf-8')
+    return src_dir
+
+
+def build_pep420_namespace_package(tmpdir, name):
+    src_dir = tmpdir / name
+    src_dir.mkdir()
+    pyproject = src_dir / "pyproject.toml"
+    namespace, _, rest = name.rpartition(".")
+    script = f"""\
+        [build-system]
+        requires = ["setuptools"]
+        build-backend = "setuptools.build_meta"
+
+        [project]
+        name = "{name}"
+        version = "3.14159"
+        """
+    pyproject.write_text(textwrap.dedent(script), encoding='utf-8')
+    ns_pkg_dir = Path(src_dir, namespace.replace(".", "/"))
+    ns_pkg_dir.mkdir(parents=True)
+    pkg_mod = ns_pkg_dir / (rest + ".py")
+    some_functionality = f"name = {rest!r}"
+    pkg_mod.write_text(some_functionality, encoding='utf-8')
+    return src_dir
+
+
+def make_site_dir(target):
+    """
+    Add a sitecustomize.py module in target to cause
+    target to be added to site dirs such that .pth files
+    are processed there.
+    """
+    sc = target / 'sitecustomize.py'
+    target_str = str(target)
+    tmpl = '__import__("site").addsitedir({target_str!r})'
+    sc.write_text(tmpl.format(**locals()), encoding='utf-8')
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/script-with-bom.py b/.venv/lib/python3.12/site-packages/setuptools/tests/script-with-bom.py
new file mode 100644
index 0000000000000000000000000000000000000000..c074d263c45bcaebe32fdba328d975c73d1ad5ca
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/script-with-bom.py
@@ -0,0 +1 @@
+result = 'passed'
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/server.py b/.venv/lib/python3.12/site-packages/setuptools/tests/server.py
new file mode 100644
index 0000000000000000000000000000000000000000..623a49a550f161f117d3af2173f91a2af260181d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/server.py
@@ -0,0 +1,86 @@
+"""Basic http server for tests to simulate PyPI or custom indexes"""
+
+import http.server
+import os
+import threading
+import time
+import urllib.parse
+import urllib.request
+
+
+class IndexServer(http.server.HTTPServer):
+    """Basic single-threaded http server simulating a package index
+
+    You can use this server in unittest like this::
+        s = IndexServer()
+        s.start()
+        index_url = s.base_url() + 'mytestindex'
+        # do some test requests to the index
+        # The index files should be located in setuptools/tests/indexes
+        s.stop()
+    """
+
+    def __init__(
+        self,
+        server_address=('', 0),
+        RequestHandlerClass=http.server.SimpleHTTPRequestHandler,
+    ):
+        http.server.HTTPServer.__init__(self, server_address, RequestHandlerClass)
+        self._run = True
+
+    def start(self):
+        self.thread = threading.Thread(target=self.serve_forever)
+        self.thread.start()
+
+    def stop(self):
+        "Stop the server"
+
+        # Let the server finish the last request and wait for a new one.
+        time.sleep(0.1)
+
+        self.shutdown()
+        self.thread.join()
+        self.socket.close()
+
+    def base_url(self):
+        port = self.server_port
+        return f'http://127.0.0.1:{port}/setuptools/tests/indexes/'
+
+
+class RequestRecorder(http.server.BaseHTTPRequestHandler):
+    def do_GET(self):
+        requests = vars(self.server).setdefault('requests', [])
+        requests.append(self)
+        self.send_response(200, 'OK')
+
+
+class MockServer(http.server.HTTPServer, threading.Thread):
+    """
+    A simple HTTP Server that records the requests made to it.
+    """
+
+    def __init__(self, server_address=('', 0), RequestHandlerClass=RequestRecorder):
+        http.server.HTTPServer.__init__(self, server_address, RequestHandlerClass)
+        threading.Thread.__init__(self)
+        self.daemon = True
+        self.requests = []
+
+    def run(self):
+        self.serve_forever()
+
+    @property
+    def netloc(self):
+        return f'localhost:{self.server_port}'
+
+    @property
+    def url(self):
+        return f'http://{self.netloc}/'
+
+
+def path_to_url(path, authority=None):
+    """Convert a path to a file: URL."""
+    path = os.path.normpath(os.path.abspath(path))
+    base = 'file:'
+    if authority is not None:
+        base += '//' + authority
+    return urllib.parse.urljoin(base, urllib.request.pathname2url(path))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/test_archive_util.py b/.venv/lib/python3.12/site-packages/setuptools/tests/test_archive_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3efc62889994fa68bc9170e8a0e403f48a204e1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/test_archive_util.py
@@ -0,0 +1,36 @@
+import io
+import tarfile
+
+import pytest
+
+from setuptools import archive_util
+
+
+@pytest.fixture
+def tarfile_with_unicode(tmpdir):
+    """
+    Create a tarfile containing only a file whose name is
+    a zero byte file called testimäge.png.
+    """
+    tarobj = io.BytesIO()
+
+    with tarfile.open(fileobj=tarobj, mode="w:gz") as tgz:
+        data = b""
+
+        filename = "testimäge.png"
+
+        t = tarfile.TarInfo(filename)
+        t.size = len(data)
+
+        tgz.addfile(t, io.BytesIO(data))
+
+    target = tmpdir / 'unicode-pkg-1.0.tar.gz'
+    with open(str(target), mode='wb') as tf:
+        tf.write(tarobj.getvalue())
+    return str(target)
+
+
+@pytest.mark.xfail(reason="#710 and #712")
+def test_unicode_files(tarfile_with_unicode, tmpdir):
+    target = tmpdir / 'out'
+    archive_util.unpack_archive(tarfile_with_unicode, str(target))
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/test_bdist_deprecations.py b/.venv/lib/python3.12/site-packages/setuptools/tests/test_bdist_deprecations.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9d67b06161a2b36e7b15fab09f797c5c15575c2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/test_bdist_deprecations.py
@@ -0,0 +1,28 @@
+"""develop tests"""
+
+import sys
+from unittest import mock
+
+import pytest
+
+from setuptools import SetuptoolsDeprecationWarning
+from setuptools.dist import Distribution
+
+
+@pytest.mark.skipif(sys.platform == 'win32', reason='non-Windows only')
+@pytest.mark.xfail(reason="bdist_rpm is long deprecated, should we remove it? #1988")
+@mock.patch('distutils.command.bdist_rpm.bdist_rpm')
+def test_bdist_rpm_warning(distutils_cmd, tmpdir_cwd):
+    dist = Distribution(
+        dict(
+            script_name='setup.py',
+            script_args=['bdist_rpm'],
+            name='foo',
+            py_modules=['hi'],
+        )
+    )
+    dist.parse_command_line()
+    with pytest.warns(SetuptoolsDeprecationWarning):
+        dist.run_commands()
+
+    distutils_cmd.run.assert_called_once()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/test_bdist_egg.py b/.venv/lib/python3.12/site-packages/setuptools/tests/test_bdist_egg.py
new file mode 100644
index 0000000000000000000000000000000000000000..036167dd951e70ad543775529d5ce3f6d6544c71
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/test_bdist_egg.py
@@ -0,0 +1,73 @@
+"""develop tests"""
+
+import os
+import re
+import zipfile
+
+import pytest
+
+from setuptools.dist import Distribution
+
+from . import contexts
+
+SETUP_PY = """\
+from setuptools import setup
+
+setup(py_modules=['hi'])
+"""
+
+
+@pytest.fixture
+def setup_context(tmpdir):
+    with (tmpdir / 'setup.py').open('w') as f:
+        f.write(SETUP_PY)
+    with (tmpdir / 'hi.py').open('w') as f:
+        f.write('1\n')
+    with tmpdir.as_cwd():
+        yield tmpdir
+
+
+class Test:
+    @pytest.mark.usefixtures("user_override")
+    @pytest.mark.usefixtures("setup_context")
+    def test_bdist_egg(self):
+        dist = Distribution(
+            dict(
+                script_name='setup.py',
+                script_args=['bdist_egg'],
+                name='foo',
+                py_modules=['hi'],
+            )
+        )
+        os.makedirs(os.path.join('build', 'src'))
+        with contexts.quiet():
+            dist.parse_command_line()
+            dist.run_commands()
+
+        # let's see if we got our egg link at the right place
+        [content] = os.listdir('dist')
+        assert re.match(r'foo-0.0.0-py[23].\d+.egg$', content)
+
+    @pytest.mark.xfail(
+        os.environ.get('PYTHONDONTWRITEBYTECODE', False),
+        reason="Byte code disabled",
+    )
+    @pytest.mark.usefixtures("user_override")
+    @pytest.mark.usefixtures("setup_context")
+    def test_exclude_source_files(self):
+        dist = Distribution(
+            dict(
+                script_name='setup.py',
+                script_args=['bdist_egg', '--exclude-source-files'],
+                py_modules=['hi'],
+            )
+        )
+        with contexts.quiet():
+            dist.parse_command_line()
+            dist.run_commands()
+        [dist_name] = os.listdir('dist')
+        dist_filename = os.path.join('dist', dist_name)
+        zip = zipfile.ZipFile(dist_filename)
+        names = list(zi.filename for zi in zip.filelist)
+        assert 'hi.pyc' in names
+        assert 'hi.py' not in names
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/test_bdist_wheel.py b/.venv/lib/python3.12/site-packages/setuptools/tests/test_bdist_wheel.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ab4e9cfc6cf0d930b3ceeececcce10cfe91a687
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/test_bdist_wheel.py
@@ -0,0 +1,708 @@
+from __future__ import annotations
+
+import builtins
+import importlib
+import os.path
+import platform
+import shutil
+import stat
+import struct
+import sys
+import sysconfig
+from contextlib import suppress
+from inspect import cleandoc
+from zipfile import ZipFile
+
+import jaraco.path
+import pytest
+from packaging import tags
+
+import setuptools
+from setuptools.command.bdist_wheel import bdist_wheel, get_abi_tag
+from setuptools.dist import Distribution
+from setuptools.warnings import SetuptoolsDeprecationWarning
+
+from distutils.core import run_setup
+
+DEFAULT_FILES = {
+    "dummy_dist-1.0.dist-info/top_level.txt",
+    "dummy_dist-1.0.dist-info/METADATA",
+    "dummy_dist-1.0.dist-info/WHEEL",
+    "dummy_dist-1.0.dist-info/RECORD",
+}
+DEFAULT_LICENSE_FILES = {
+    "LICENSE",
+    "LICENSE.txt",
+    "LICENCE",
+    "LICENCE.txt",
+    "COPYING",
+    "COPYING.md",
+    "NOTICE",
+    "NOTICE.rst",
+    "AUTHORS",
+    "AUTHORS.txt",
+}
+OTHER_IGNORED_FILES = {
+    "LICENSE~",
+    "AUTHORS~",
+}
+SETUPPY_EXAMPLE = """\
+from setuptools import setup
+
+setup(
+    name='dummy_dist',
+    version='1.0',
+)
+"""
+
+
+EXAMPLES = {
+    "dummy-dist": {
+        "setup.py": SETUPPY_EXAMPLE,
+        "licenses_dir": {"DUMMYFILE": ""},
+        **dict.fromkeys(DEFAULT_LICENSE_FILES | OTHER_IGNORED_FILES, ""),
+    },
+    "simple-dist": {
+        "setup.py": cleandoc(
+            """
+            from setuptools import setup
+
+            setup(
+                name="simple.dist",
+                version="0.1",
+                description="A testing distribution \N{SNOWMAN}",
+                extras_require={"voting": ["beaglevote"]},
+            )
+            """
+        ),
+        "simpledist": "",
+    },
+    "complex-dist": {
+        "setup.py": cleandoc(
+            """
+            from setuptools import setup
+
+            setup(
+                name="complex-dist",
+                version="0.1",
+                description="Another testing distribution \N{SNOWMAN}",
+                long_description="Another testing distribution \N{SNOWMAN}",
+                author="Illustrious Author",
+                author_email="illustrious@example.org",
+                url="http://example.org/exemplary",
+                packages=["complexdist"],
+                setup_requires=["setuptools"],
+                install_requires=["quux", "splort"],
+                extras_require={"simple": ["simple.dist"]},
+                entry_points={
+                    "console_scripts": [
+                        "complex-dist=complexdist:main",
+                        "complex-dist2=complexdist:main",
+                    ],
+                },
+            )
+            """
+        ),
+        "complexdist": {"__init__.py": "def main(): return"},
+    },
+    "headers-dist": {
+        "setup.py": cleandoc(
+            """
+            from setuptools import setup
+
+            setup(
+                name="headers.dist",
+                version="0.1",
+                description="A distribution with headers",
+                headers=["header.h"],
+            )
+            """
+        ),
+        "headersdist.py": "",
+        "header.h": "",
+    },
+    "commasinfilenames-dist": {
+        "setup.py": cleandoc(
+            """
+            from setuptools import setup
+
+            setup(
+                name="testrepo",
+                version="0.1",
+                packages=["mypackage"],
+                description="A test package with commas in file names",
+                include_package_data=True,
+                package_data={"mypackage.data": ["*"]},
+            )
+            """
+        ),
+        "mypackage": {
+            "__init__.py": "",
+            "data": {"__init__.py": "", "1,2,3.txt": ""},
+        },
+        "testrepo-0.1.0": {
+            "mypackage": {"__init__.py": ""},
+        },
+    },
+    "unicode-dist": {
+        "setup.py": cleandoc(
+            """
+            from setuptools import setup
+
+            setup(
+                name="unicode.dist",
+                version="0.1",
+                description="A testing distribution \N{SNOWMAN}",
+                packages=["unicodedist"],
+                zip_safe=True,
+            )
+            """
+        ),
+        "unicodedist": {"__init__.py": "", "åäö_日本語.py": ""},
+    },
+    "utf8-metadata-dist": {
+        "setup.cfg": cleandoc(
+            """
+            [metadata]
+            name = utf8-metadata-dist
+            version = 42
+            author_email = "John X. Ãørçeč" <john@utf8.org>, Γαμα קּ 東 <gama@utf8.org>
+            long_description = file: README.rst
+            """
+        ),
+        "README.rst": "UTF-8 描述 説明",
+    },
+    "licenses-dist": {
+        "setup.cfg": cleandoc(
+            """
+            [metadata]
+            name = licenses-dist
+            version = 1.0
+            license_files = **/LICENSE
+            """
+        ),
+        "LICENSE": "",
+        "src": {
+            "vendor": {"LICENSE": ""},
+        },
+    },
+}
+
+
+if sys.platform != "win32":
+    # ABI3 extensions don't really work on Windows
+    EXAMPLES["abi3extension-dist"] = {
+        "setup.py": cleandoc(
+            """
+            from setuptools import Extension, setup
+
+            setup(
+                name="extension.dist",
+                version="0.1",
+                description="A testing distribution \N{SNOWMAN}",
+                ext_modules=[
+                    Extension(
+                        name="extension", sources=["extension.c"], py_limited_api=True
+                    )
+                ],
+            )
+            """
+        ),
+        "setup.cfg": "[bdist_wheel]\npy_limited_api=cp32",
+        "extension.c": "#define Py_LIMITED_API 0x03020000\n#include <Python.h>",
+    }
+
+
+def bdist_wheel_cmd(**kwargs):
+    """Run command in the same process so that it is easier to collect coverage"""
+    dist_obj = (
+        run_setup("setup.py", stop_after="init")
+        if os.path.exists("setup.py")
+        else Distribution({"script_name": "%%build_meta%%"})
+    )
+    dist_obj.parse_config_files()
+    cmd = bdist_wheel(dist_obj)
+    for attr, value in kwargs.items():
+        setattr(cmd, attr, value)
+    cmd.finalize_options()
+    return cmd
+
+
+def mkexample(tmp_path_factory, name):
+    basedir = tmp_path_factory.mktemp(name)
+    jaraco.path.build(EXAMPLES[name], prefix=str(basedir))
+    return basedir
+
+
+@pytest.fixture(scope="session")
+def wheel_paths(tmp_path_factory):
+    build_base = tmp_path_factory.mktemp("build")
+    dist_dir = tmp_path_factory.mktemp("dist")
+    for name in EXAMPLES:
+        example_dir = mkexample(tmp_path_factory, name)
+        build_dir = build_base / name
+        with jaraco.path.DirectoryStack().context(example_dir):
+            bdist_wheel_cmd(bdist_dir=str(build_dir), dist_dir=str(dist_dir)).run()
+
+    return sorted(str(fname) for fname in dist_dir.glob("*.whl"))
+
+
+@pytest.fixture
+def dummy_dist(tmp_path_factory):
+    return mkexample(tmp_path_factory, "dummy-dist")
+
+
+@pytest.fixture
+def licenses_dist(tmp_path_factory):
+    return mkexample(tmp_path_factory, "licenses-dist")
+
+
+def test_no_scripts(wheel_paths):
+    """Make sure entry point scripts are not generated."""
+    path = next(path for path in wheel_paths if "complex_dist" in path)
+    for entry in ZipFile(path).infolist():
+        assert ".data/scripts/" not in entry.filename
+
+
+def test_unicode_record(wheel_paths):
+    path = next(path for path in wheel_paths if "unicode_dist" in path)
+    with ZipFile(path) as zf:
+        record = zf.read("unicode_dist-0.1.dist-info/RECORD")
+
+    assert "åäö_日本語.py".encode() in record
+
+
+UTF8_PKG_INFO = """\
+Metadata-Version: 2.1
+Name: helloworld
+Version: 42
+Author-email: "John X. Ãørçeč" <john@utf8.org>, Γαμα קּ 東 <gama@utf8.org>
+
+
+UTF-8 描述 説明
+"""
+
+
+def test_preserve_unicode_metadata(monkeypatch, tmp_path):
+    monkeypatch.chdir(tmp_path)
+    egginfo = tmp_path / "dummy_dist.egg-info"
+    distinfo = tmp_path / "dummy_dist.dist-info"
+
+    egginfo.mkdir()
+    (egginfo / "PKG-INFO").write_text(UTF8_PKG_INFO, encoding="utf-8")
+    (egginfo / "dependency_links.txt").touch()
+
+    class simpler_bdist_wheel(bdist_wheel):
+        """Avoid messing with setuptools/distutils internals"""
+
+        def __init__(self):
+            pass
+
+        @property
+        def license_paths(self):
+            return []
+
+    cmd_obj = simpler_bdist_wheel()
+    cmd_obj.egg2dist(egginfo, distinfo)
+
+    metadata = (distinfo / "METADATA").read_text(encoding="utf-8")
+    assert 'Author-email: "John X. Ãørçeč"' in metadata
+    assert "Γαμα קּ 東 " in metadata
+    assert "UTF-8 描述 説明" in metadata
+
+
+def test_licenses_default(dummy_dist, monkeypatch, tmp_path):
+    monkeypatch.chdir(dummy_dist)
+    bdist_wheel_cmd(bdist_dir=str(tmp_path)).run()
+    with ZipFile("dist/dummy_dist-1.0-py3-none-any.whl") as wf:
+        license_files = {
+            "dummy_dist-1.0.dist-info/licenses/" + fname
+            for fname in DEFAULT_LICENSE_FILES
+        }
+        assert set(wf.namelist()) == DEFAULT_FILES | license_files
+
+
+def test_licenses_deprecated(dummy_dist, monkeypatch, tmp_path):
+    dummy_dist.joinpath("setup.cfg").write_text(
+        "[metadata]\nlicense_file=licenses_dir/DUMMYFILE", encoding="utf-8"
+    )
+    monkeypatch.chdir(dummy_dist)
+
+    bdist_wheel_cmd(bdist_dir=str(tmp_path)).run()
+
+    with ZipFile("dist/dummy_dist-1.0-py3-none-any.whl") as wf:
+        license_files = {"dummy_dist-1.0.dist-info/licenses/licenses_dir/DUMMYFILE"}
+        assert set(wf.namelist()) == DEFAULT_FILES | license_files
+
+
+@pytest.mark.parametrize(
+    ("config_file", "config"),
+    [
+        ("setup.cfg", "[metadata]\nlicense_files=licenses_dir/*\n  LICENSE"),
+        ("setup.cfg", "[metadata]\nlicense_files=licenses_dir/*, LICENSE"),
+        (
+            "setup.py",
+            SETUPPY_EXAMPLE.replace(
+                ")", "  license_files=['licenses_dir/DUMMYFILE', 'LICENSE'])"
+            ),
+        ),
+    ],
+)
+def test_licenses_override(dummy_dist, monkeypatch, tmp_path, config_file, config):
+    dummy_dist.joinpath(config_file).write_text(config, encoding="utf-8")
+    monkeypatch.chdir(dummy_dist)
+    bdist_wheel_cmd(bdist_dir=str(tmp_path)).run()
+    with ZipFile("dist/dummy_dist-1.0-py3-none-any.whl") as wf:
+        license_files = {
+            "dummy_dist-1.0.dist-info/licenses/" + fname
+            for fname in {"licenses_dir/DUMMYFILE", "LICENSE"}
+        }
+        assert set(wf.namelist()) == DEFAULT_FILES | license_files
+        metadata = wf.read("dummy_dist-1.0.dist-info/METADATA").decode("utf8")
+        assert "License-File: licenses_dir/DUMMYFILE" in metadata
+        assert "License-File: LICENSE" in metadata
+
+
+def test_licenses_preserve_folder_structure(licenses_dist, monkeypatch, tmp_path):
+    monkeypatch.chdir(licenses_dist)
+    bdist_wheel_cmd(bdist_dir=str(tmp_path)).run()
+    print(os.listdir("dist"))
+    with ZipFile("dist/licenses_dist-1.0-py3-none-any.whl") as wf:
+        default_files = {name.replace("dummy_", "licenses_") for name in DEFAULT_FILES}
+        license_files = {
+            "licenses_dist-1.0.dist-info/licenses/LICENSE",
+            "licenses_dist-1.0.dist-info/licenses/src/vendor/LICENSE",
+        }
+        assert set(wf.namelist()) == default_files | license_files
+        metadata = wf.read("licenses_dist-1.0.dist-info/METADATA").decode("utf8")
+        assert "License-File: src/vendor/LICENSE" in metadata
+        assert "License-File: LICENSE" in metadata
+
+
+def test_licenses_disabled(dummy_dist, monkeypatch, tmp_path):
+    dummy_dist.joinpath("setup.cfg").write_text(
+        "[metadata]\nlicense_files=\n", encoding="utf-8"
+    )
+    monkeypatch.chdir(dummy_dist)
+    bdist_wheel_cmd(bdist_dir=str(tmp_path)).run()
+    with ZipFile("dist/dummy_dist-1.0-py3-none-any.whl") as wf:
+        assert set(wf.namelist()) == DEFAULT_FILES
+
+
+def test_build_number(dummy_dist, monkeypatch, tmp_path):
+    monkeypatch.chdir(dummy_dist)
+    bdist_wheel_cmd(bdist_dir=str(tmp_path), build_number="2").run()
+    with ZipFile("dist/dummy_dist-1.0-2-py3-none-any.whl") as wf:
+        filenames = set(wf.namelist())
+        assert "dummy_dist-1.0.dist-info/RECORD" in filenames
+        assert "dummy_dist-1.0.dist-info/METADATA" in filenames
+
+
+def test_universal_deprecated(dummy_dist, monkeypatch, tmp_path):
+    monkeypatch.chdir(dummy_dist)
+    with pytest.warns(SetuptoolsDeprecationWarning, match=".*universal is deprecated"):
+        bdist_wheel_cmd(bdist_dir=str(tmp_path), universal=True).run()
+
+    # For now we still respect the option
+    assert os.path.exists("dist/dummy_dist-1.0-py2.py3-none-any.whl")
+
+
+EXTENSION_EXAMPLE = """\
+#include <Python.h>
+
+static PyMethodDef methods[] = {
+  { NULL, NULL, 0, NULL }
+};
+
+static struct PyModuleDef module_def = {
+  PyModuleDef_HEAD_INIT,
+  "extension",
+  "Dummy extension module",
+  -1,
+  methods
+};
+
+PyMODINIT_FUNC PyInit_extension(void) {
+  return PyModule_Create(&module_def);
+}
+"""
+EXTENSION_SETUPPY = """\
+from __future__ import annotations
+
+from setuptools import Extension, setup
+
+setup(
+    name="extension.dist",
+    version="0.1",
+    description="A testing distribution \N{SNOWMAN}",
+    ext_modules=[Extension(name="extension", sources=["extension.c"])],
+)
+"""
+
+
+@pytest.mark.filterwarnings(
+    "once:Config variable '.*' is unset.*, Python ABI tag may be incorrect"
+)
+def test_limited_abi(monkeypatch, tmp_path, tmp_path_factory):
+    """Test that building a binary wheel with the limited ABI works."""
+    source_dir = tmp_path_factory.mktemp("extension_dist")
+    (source_dir / "setup.py").write_text(EXTENSION_SETUPPY, encoding="utf-8")
+    (source_dir / "extension.c").write_text(EXTENSION_EXAMPLE, encoding="utf-8")
+    build_dir = tmp_path.joinpath("build")
+    dist_dir = tmp_path.joinpath("dist")
+    monkeypatch.chdir(source_dir)
+    bdist_wheel_cmd(bdist_dir=str(build_dir), dist_dir=str(dist_dir)).run()
+
+
+def test_build_from_readonly_tree(dummy_dist, monkeypatch, tmp_path):
+    basedir = str(tmp_path.joinpath("dummy"))
+    shutil.copytree(str(dummy_dist), basedir)
+    monkeypatch.chdir(basedir)
+
+    # Make the tree read-only
+    for root, _dirs, files in os.walk(basedir):
+        for fname in files:
+            os.chmod(os.path.join(root, fname), stat.S_IREAD)
+
+    bdist_wheel_cmd().run()
+
+
+@pytest.mark.parametrize(
+    ("option", "compress_type"),
+    list(bdist_wheel.supported_compressions.items()),
+    ids=list(bdist_wheel.supported_compressions),
+)
+def test_compression(dummy_dist, monkeypatch, tmp_path, option, compress_type):
+    monkeypatch.chdir(dummy_dist)
+    bdist_wheel_cmd(bdist_dir=str(tmp_path), compression=option).run()
+    with ZipFile("dist/dummy_dist-1.0-py3-none-any.whl") as wf:
+        filenames = set(wf.namelist())
+        assert "dummy_dist-1.0.dist-info/RECORD" in filenames
+        assert "dummy_dist-1.0.dist-info/METADATA" in filenames
+        for zinfo in wf.filelist:
+            assert zinfo.compress_type == compress_type
+
+
+def test_wheelfile_line_endings(wheel_paths):
+    for path in wheel_paths:
+        with ZipFile(path) as wf:
+            wheelfile = next(fn for fn in wf.filelist if fn.filename.endswith("WHEEL"))
+            wheelfile_contents = wf.read(wheelfile)
+            assert b"\r" not in wheelfile_contents
+
+
+def test_unix_epoch_timestamps(dummy_dist, monkeypatch, tmp_path):
+    monkeypatch.setenv("SOURCE_DATE_EPOCH", "0")
+    monkeypatch.chdir(dummy_dist)
+    bdist_wheel_cmd(bdist_dir=str(tmp_path), build_number="2a").run()
+    with ZipFile("dist/dummy_dist-1.0-2a-py3-none-any.whl") as wf:
+        for zinfo in wf.filelist:
+            assert zinfo.date_time >= (1980, 1, 1, 0, 0, 0)  # min epoch is used
+
+
+def test_get_abi_tag_windows(monkeypatch):
+    monkeypatch.setattr(tags, "interpreter_name", lambda: "cp")
+    monkeypatch.setattr(sysconfig, "get_config_var", lambda x: "cp313-win_amd64")
+    assert get_abi_tag() == "cp313"
+    monkeypatch.setattr(sys, "gettotalrefcount", lambda: 1, False)
+    assert get_abi_tag() == "cp313d"
+    monkeypatch.setattr(sysconfig, "get_config_var", lambda x: "cp313t-win_amd64")
+    assert get_abi_tag() == "cp313td"
+    monkeypatch.delattr(sys, "gettotalrefcount")
+    assert get_abi_tag() == "cp313t"
+
+
+def test_get_abi_tag_pypy_old(monkeypatch):
+    monkeypatch.setattr(tags, "interpreter_name", lambda: "pp")
+    monkeypatch.setattr(sysconfig, "get_config_var", lambda x: "pypy36-pp73")
+    assert get_abi_tag() == "pypy36_pp73"
+
+
+def test_get_abi_tag_pypy_new(monkeypatch):
+    monkeypatch.setattr(sysconfig, "get_config_var", lambda x: "pypy37-pp73-darwin")
+    monkeypatch.setattr(tags, "interpreter_name", lambda: "pp")
+    assert get_abi_tag() == "pypy37_pp73"
+
+
+def test_get_abi_tag_graalpy(monkeypatch):
+    monkeypatch.setattr(
+        sysconfig, "get_config_var", lambda x: "graalpy231-310-native-x86_64-linux"
+    )
+    monkeypatch.setattr(tags, "interpreter_name", lambda: "graalpy")
+    assert get_abi_tag() == "graalpy231_310_native"
+
+
+def test_get_abi_tag_fallback(monkeypatch):
+    monkeypatch.setattr(sysconfig, "get_config_var", lambda x: "unknown-python-310")
+    monkeypatch.setattr(tags, "interpreter_name", lambda: "unknown-python")
+    assert get_abi_tag() == "unknown_python_310"
+
+
+def test_platform_with_space(dummy_dist, monkeypatch):
+    """Ensure building on platforms with a space in the name succeed."""
+    monkeypatch.chdir(dummy_dist)
+    bdist_wheel_cmd(plat_name="isilon onefs").run()
+
+
+def test_data_dir_with_tag_build(monkeypatch, tmp_path):
+    """
+    Setuptools allow authors to set PEP 440's local version segments
+    using ``egg_info.tag_build``. This should be reflected not only in the
+    ``.whl`` file name, but also in the ``.dist-info`` and ``.data`` dirs.
+    See pypa/setuptools#3997.
+    """
+    monkeypatch.chdir(tmp_path)
+    files = {
+        "setup.py": """
+            from setuptools import setup
+            setup(headers=["hello.h"])
+            """,
+        "setup.cfg": """
+            [metadata]
+            name = test
+            version = 1.0
+
+            [options.data_files]
+            hello/world = file.txt
+
+            [egg_info]
+            tag_build = +what
+            tag_date = 0
+            """,
+        "file.txt": "",
+        "hello.h": "",
+    }
+    for file, content in files.items():
+        with open(file, "w", encoding="utf-8") as fh:
+            fh.write(cleandoc(content))
+
+    bdist_wheel_cmd().run()
+
+    # Ensure .whl, .dist-info and .data contain the local segment
+    wheel_path = "dist/test-1.0+what-py3-none-any.whl"
+    assert os.path.exists(wheel_path)
+    entries = set(ZipFile(wheel_path).namelist())
+    for expected in (
+        "test-1.0+what.data/headers/hello.h",
+        "test-1.0+what.data/data/hello/world/file.txt",
+        "test-1.0+what.dist-info/METADATA",
+        "test-1.0+what.dist-info/WHEEL",
+    ):
+        assert expected in entries
+
+    for not_expected in (
+        "test.data/headers/hello.h",
+        "test-1.0.data/data/hello/world/file.txt",
+        "test.dist-info/METADATA",
+        "test-1.0.dist-info/WHEEL",
+    ):
+        assert not_expected not in entries
+
+
+@pytest.mark.parametrize(
+    ("reported", "expected"),
+    [("linux-x86_64", "linux_i686"), ("linux-aarch64", "linux_armv7l")],
+)
+@pytest.mark.skipif(
+    platform.system() != "Linux", reason="Only makes sense to test on Linux"
+)
+def test_platform_linux32(reported, expected, monkeypatch):
+    monkeypatch.setattr(struct, "calcsize", lambda x: 4)
+    dist = setuptools.Distribution()
+    cmd = bdist_wheel(dist)
+    cmd.plat_name = reported
+    cmd.root_is_pure = False
+    _, _, actual = cmd.get_tag()
+    assert actual == expected
+
+
+def test_no_ctypes(monkeypatch) -> None:
+    def _fake_import(name: str, *args, **kwargs):
+        if name == "ctypes":
+            raise ModuleNotFoundError(f"No module named {name}")
+
+        return importlib.__import__(name, *args, **kwargs)
+
+    with suppress(KeyError):
+        monkeypatch.delitem(sys.modules, "wheel.macosx_libfile")
+
+    # Install an importer shim that refuses to load ctypes
+    monkeypatch.setattr(builtins, "__import__", _fake_import)
+    with pytest.raises(ModuleNotFoundError, match="No module named ctypes"):
+        import wheel.macosx_libfile  # noqa: F401
+
+    # Unload and reimport the bdist_wheel command module to make sure it won't try to
+    # import ctypes
+    monkeypatch.delitem(sys.modules, "setuptools.command.bdist_wheel")
+
+    import setuptools.command.bdist_wheel  # noqa: F401
+
+
+def test_dist_info_provided(dummy_dist, monkeypatch, tmp_path):
+    monkeypatch.chdir(dummy_dist)
+    distinfo = tmp_path / "dummy_dist.dist-info"
+
+    distinfo.mkdir()
+    (distinfo / "METADATA").write_text("name: helloworld", encoding="utf-8")
+
+    # We don't control the metadata. According to PEP-517, "The hook MAY also
+    # create other files inside this directory, and a build frontend MUST
+    # preserve".
+    (distinfo / "FOO").write_text("bar", encoding="utf-8")
+
+    bdist_wheel_cmd(bdist_dir=str(tmp_path), dist_info_dir=str(distinfo)).run()
+    expected = {
+        "dummy_dist-1.0.dist-info/FOO",
+        "dummy_dist-1.0.dist-info/RECORD",
+    }
+    with ZipFile("dist/dummy_dist-1.0-py3-none-any.whl") as wf:
+        files_found = set(wf.namelist())
+    # Check that all expected files are there.
+    assert expected - files_found == set()
+    # Make sure there is no accidental egg-info bleeding into the wheel.
+    assert not [path for path in files_found if 'egg-info' in str(path)]
+
+
+def test_allow_grace_period_parent_directory_license(monkeypatch, tmp_path):
+    # Motivation: https://github.com/pypa/setuptools/issues/4892
+    # TODO: Remove this test after deprecation period is over
+    files = {
+        "LICENSE.txt": "parent license",  # <---- the license files are outside
+        "NOTICE.txt": "parent notice",
+        "python": {
+            "pyproject.toml": cleandoc(
+                """
+                [project]
+                name = "test-proj"
+                dynamic = ["version"]      # <---- testing dynamic will not break
+                [tool.setuptools.dynamic]
+                version.file = "VERSION"
+                """
+            ),
+            "setup.cfg": cleandoc(
+                """
+                [metadata]
+                license_files =
+                  ../LICENSE.txt
+                  ../NOTICE.txt
+                """
+            ),
+            "VERSION": "42",
+        },
+    }
+    jaraco.path.build(files, prefix=str(tmp_path))
+    monkeypatch.chdir(tmp_path / "python")
+    msg = "Pattern '../.*.txt' cannot contain '..'"
+    with pytest.warns(SetuptoolsDeprecationWarning, match=msg):
+        bdist_wheel_cmd().run()
+    with ZipFile("dist/test_proj-42-py3-none-any.whl") as wf:
+        files_found = set(wf.namelist())
+        expected_files = {
+            "test_proj-42.dist-info/licenses/LICENSE.txt",
+            "test_proj-42.dist-info/licenses/NOTICE.txt",
+        }
+        assert expected_files <= files_found
+
+        metadata = wf.read("test_proj-42.dist-info/METADATA").decode("utf8")
+        assert "License-File: LICENSE.txt" in metadata
+        assert "License-File: NOTICE.txt" in metadata
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/test_build.py b/.venv/lib/python3.12/site-packages/setuptools/tests/test_build.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0f1d9dcf21bafe9dc82a76d373b366ddeecfcec
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/test_build.py
@@ -0,0 +1,33 @@
+from setuptools import Command
+from setuptools.command.build import build
+from setuptools.dist import Distribution
+
+
+def test_distribution_gives_setuptools_build_obj(tmpdir_cwd):
+    """
+    Check that the setuptools Distribution uses the
+    setuptools specific build object.
+    """
+
+    dist = Distribution(
+        dict(
+            script_name='setup.py',
+            script_args=['build'],
+            packages=[],
+            package_data={'': ['path/*']},
+        )
+    )
+    assert isinstance(dist.get_command_obj("build"), build)
+
+
+class Subcommand(Command):
+    """Dummy command to be used in tests"""
+
+    def initialize_options(self):
+        pass
+
+    def finalize_options(self):
+        pass
+
+    def run(self):
+        raise NotImplementedError("just to check if the command runs")
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/test_build_clib.py b/.venv/lib/python3.12/site-packages/setuptools/tests/test_build_clib.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5315df4f6599cd376e628c0d74cb14129cd89b8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/test_build_clib.py
@@ -0,0 +1,84 @@
+import random
+from unittest import mock
+
+import pytest
+
+from setuptools.command.build_clib import build_clib
+from setuptools.dist import Distribution
+
+from distutils.errors import DistutilsSetupError
+
+
+class TestBuildCLib:
+    @mock.patch('setuptools.command.build_clib.newer_pairwise_group')
+    def test_build_libraries(self, mock_newer):
+        dist = Distribution()
+        cmd = build_clib(dist)
+
+        # this will be a long section, just making sure all
+        # exceptions are properly raised
+        libs = [('example', {'sources': 'broken.c'})]
+        with pytest.raises(DistutilsSetupError):
+            cmd.build_libraries(libs)
+
+        obj_deps = 'some_string'
+        libs = [('example', {'sources': ['source.c'], 'obj_deps': obj_deps})]
+        with pytest.raises(DistutilsSetupError):
+            cmd.build_libraries(libs)
+
+        obj_deps = {'': ''}
+        libs = [('example', {'sources': ['source.c'], 'obj_deps': obj_deps})]
+        with pytest.raises(DistutilsSetupError):
+            cmd.build_libraries(libs)
+
+        obj_deps = {'source.c': ''}
+        libs = [('example', {'sources': ['source.c'], 'obj_deps': obj_deps})]
+        with pytest.raises(DistutilsSetupError):
+            cmd.build_libraries(libs)
+
+        # with that out of the way, let's see if the crude dependency
+        # system works
+        cmd.compiler = mock.MagicMock(spec=cmd.compiler)
+        mock_newer.return_value = ([], [])
+
+        obj_deps = {'': ('global.h',), 'example.c': ('example.h',)}
+        libs = [('example', {'sources': ['example.c'], 'obj_deps': obj_deps})]
+
+        cmd.build_libraries(libs)
+        assert [['example.c', 'global.h', 'example.h']] in mock_newer.call_args[0]
+        assert not cmd.compiler.compile.called
+        assert cmd.compiler.create_static_lib.call_count == 1
+
+        # reset the call numbers so we can test again
+        cmd.compiler.reset_mock()
+
+        mock_newer.return_value = ''  # anything as long as it's not ([],[])
+        cmd.build_libraries(libs)
+        assert cmd.compiler.compile.call_count == 1
+        assert cmd.compiler.create_static_lib.call_count == 1
+
+    @mock.patch('setuptools.command.build_clib.newer_pairwise_group')
+    def test_build_libraries_reproducible(self, mock_newer):
+        dist = Distribution()
+        cmd = build_clib(dist)
+
+        # with that out of the way, let's see if the crude dependency
+        # system works
+        cmd.compiler = mock.MagicMock(spec=cmd.compiler)
+        mock_newer.return_value = ([], [])
+
+        original_sources = ['a-example.c', 'example.c']
+        sources = original_sources
+
+        obj_deps = {'': ('global.h',), 'example.c': ('example.h',)}
+        libs = [('example', {'sources': sources, 'obj_deps': obj_deps})]
+
+        cmd.build_libraries(libs)
+        computed_call_args = mock_newer.call_args[0]
+
+        while sources == original_sources:
+            sources = random.sample(original_sources, len(original_sources))
+        libs = [('example', {'sources': sources, 'obj_deps': obj_deps})]
+
+        cmd.build_libraries(libs)
+        assert computed_call_args == mock_newer.call_args[0]
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/test_build_ext.py b/.venv/lib/python3.12/site-packages/setuptools/tests/test_build_ext.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7b60ac32fcd6628cf96396a7e5d8fdb50f67c19
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/test_build_ext.py
@@ -0,0 +1,293 @@
+from __future__ import annotations
+
+import os
+import sys
+from importlib.util import cache_from_source as _compiled_file_name
+
+import pytest
+from jaraco import path
+
+from setuptools.command.build_ext import build_ext, get_abi3_suffix
+from setuptools.dist import Distribution
+from setuptools.errors import CompileError
+from setuptools.extension import Extension
+
+from . import environment
+from .textwrap import DALS
+
+import distutils.command.build_ext as orig
+from distutils.sysconfig import get_config_var
+
+IS_PYPY = '__pypy__' in sys.builtin_module_names
+
+
+class TestBuildExt:
+    def test_get_ext_filename(self):
+        """
+        Setuptools needs to give back the same
+        result as distutils, even if the fullname
+        is not in ext_map.
+        """
+        dist = Distribution()
+        cmd = build_ext(dist)
+        cmd.ext_map['foo/bar'] = ''
+        res = cmd.get_ext_filename('foo')
+        wanted = orig.build_ext.get_ext_filename(cmd, 'foo')
+        assert res == wanted
+
+    def test_abi3_filename(self):
+        """
+        Filename needs to be loadable by several versions
+        of Python 3 if 'is_abi3' is truthy on Extension()
+        """
+        print(get_abi3_suffix())
+
+        extension = Extension('spam.eggs', ['eggs.c'], py_limited_api=True)
+        dist = Distribution(dict(ext_modules=[extension]))
+        cmd = build_ext(dist)
+        cmd.finalize_options()
+        assert 'spam.eggs' in cmd.ext_map
+        res = cmd.get_ext_filename('spam.eggs')
+
+        if not get_abi3_suffix():
+            assert res.endswith(get_config_var('EXT_SUFFIX'))
+        elif sys.platform == 'win32':
+            assert res.endswith('eggs.pyd')
+        else:
+            assert 'abi3' in res
+
+    def test_ext_suffix_override(self):
+        """
+        SETUPTOOLS_EXT_SUFFIX variable always overrides
+        default extension options.
+        """
+        dist = Distribution()
+        cmd = build_ext(dist)
+        cmd.ext_map['for_abi3'] = ext = Extension(
+            'for_abi3',
+            ['s.c'],
+            # Override shouldn't affect abi3 modules
+            py_limited_api=True,
+        )
+        # Mock value needed to pass tests
+        ext._links_to_dynamic = False
+
+        if not IS_PYPY:
+            expect = cmd.get_ext_filename('for_abi3')
+        else:
+            # PyPy builds do not use ABI3 tag, so they will
+            # also get the overridden suffix.
+            expect = 'for_abi3.test-suffix'
+
+        try:
+            os.environ['SETUPTOOLS_EXT_SUFFIX'] = '.test-suffix'
+            res = cmd.get_ext_filename('normal')
+            assert 'normal.test-suffix' == res
+            res = cmd.get_ext_filename('for_abi3')
+            assert expect == res
+        finally:
+            del os.environ['SETUPTOOLS_EXT_SUFFIX']
+
+    def dist_with_example(self):
+        files = {
+            "src": {"mypkg": {"subpkg": {"ext2.c": ""}}},
+            "c-extensions": {"ext1": {"main.c": ""}},
+        }
+
+        ext1 = Extension("mypkg.ext1", ["c-extensions/ext1/main.c"])
+        ext2 = Extension("mypkg.subpkg.ext2", ["src/mypkg/subpkg/ext2.c"])
+        ext3 = Extension("ext3", ["c-extension/ext3.c"])
+
+        path.build(files)
+        return Distribution({
+            "script_name": "%test%",
+            "ext_modules": [ext1, ext2, ext3],
+            "package_dir": {"": "src"},
+        })
+
+    def test_get_outputs(self, tmpdir_cwd, monkeypatch):
+        monkeypatch.setenv('SETUPTOOLS_EXT_SUFFIX', '.mp3')  # make test OS-independent
+        monkeypatch.setattr('setuptools.command.build_ext.use_stubs', False)
+        dist = self.dist_with_example()
+
+        # Regular build: get_outputs not empty, but get_output_mappings is empty
+        build_ext = dist.get_command_obj("build_ext")
+        build_ext.editable_mode = False
+        build_ext.ensure_finalized()
+        build_lib = build_ext.build_lib.replace(os.sep, "/")
+        outputs = [x.replace(os.sep, "/") for x in build_ext.get_outputs()]
+        assert outputs == [
+            f"{build_lib}/ext3.mp3",
+            f"{build_lib}/mypkg/ext1.mp3",
+            f"{build_lib}/mypkg/subpkg/ext2.mp3",
+        ]
+        assert build_ext.get_output_mapping() == {}
+
+        # Editable build: get_output_mappings should contain everything in get_outputs
+        dist.reinitialize_command("build_ext")
+        build_ext.editable_mode = True
+        build_ext.ensure_finalized()
+        mapping = {
+            k.replace(os.sep, "/"): v.replace(os.sep, "/")
+            for k, v in build_ext.get_output_mapping().items()
+        }
+        assert mapping == {
+            f"{build_lib}/ext3.mp3": "src/ext3.mp3",
+            f"{build_lib}/mypkg/ext1.mp3": "src/mypkg/ext1.mp3",
+            f"{build_lib}/mypkg/subpkg/ext2.mp3": "src/mypkg/subpkg/ext2.mp3",
+        }
+
+    def test_get_output_mapping_with_stub(self, tmpdir_cwd, monkeypatch):
+        monkeypatch.setenv('SETUPTOOLS_EXT_SUFFIX', '.mp3')  # make test OS-independent
+        monkeypatch.setattr('setuptools.command.build_ext.use_stubs', True)
+        dist = self.dist_with_example()
+
+        # Editable build should create compiled stubs (.pyc files only, no .py)
+        build_ext = dist.get_command_obj("build_ext")
+        build_ext.editable_mode = True
+        build_ext.ensure_finalized()
+        for ext in build_ext.extensions:
+            monkeypatch.setattr(ext, "_needs_stub", True)
+
+        build_lib = build_ext.build_lib.replace(os.sep, "/")
+        mapping = {
+            k.replace(os.sep, "/"): v.replace(os.sep, "/")
+            for k, v in build_ext.get_output_mapping().items()
+        }
+
+        def C(file):
+            """Make it possible to do comparisons and tests in a OS-independent way"""
+            return _compiled_file_name(file).replace(os.sep, "/")
+
+        assert mapping == {
+            C(f"{build_lib}/ext3.py"): C("src/ext3.py"),
+            f"{build_lib}/ext3.mp3": "src/ext3.mp3",
+            C(f"{build_lib}/mypkg/ext1.py"): C("src/mypkg/ext1.py"),
+            f"{build_lib}/mypkg/ext1.mp3": "src/mypkg/ext1.mp3",
+            C(f"{build_lib}/mypkg/subpkg/ext2.py"): C("src/mypkg/subpkg/ext2.py"),
+            f"{build_lib}/mypkg/subpkg/ext2.mp3": "src/mypkg/subpkg/ext2.mp3",
+        }
+
+        # Ensure only the compiled stubs are present not the raw .py stub
+        assert f"{build_lib}/mypkg/ext1.py" not in mapping
+        assert f"{build_lib}/mypkg/subpkg/ext2.py" not in mapping
+
+        # Visualize what the cached stub files look like
+        example_stub = C(f"{build_lib}/mypkg/ext1.py")
+        assert example_stub in mapping
+        assert example_stub.startswith(f"{build_lib}/mypkg/__pycache__/ext1")
+        assert example_stub.endswith(".pyc")
+
+
+class TestBuildExtInplace:
+    def get_build_ext_cmd(self, optional: bool, **opts) -> build_ext:
+        files: dict[str, str | dict[str, dict[str, str]]] = {
+            "eggs.c": "#include missingheader.h\n",
+            ".build": {"lib": {}, "tmp": {}},
+        }
+        path.build(files)
+        extension = Extension('spam.eggs', ['eggs.c'], optional=optional)
+        dist = Distribution(dict(ext_modules=[extension]))
+        dist.script_name = 'setup.py'
+        cmd = build_ext(dist)
+        vars(cmd).update(build_lib=".build/lib", build_temp=".build/tmp", **opts)
+        cmd.ensure_finalized()
+        return cmd
+
+    def get_log_messages(self, caplog, capsys):
+        """
+        Historically, distutils "logged" by printing to sys.std*.
+        Later versions adopted the logging framework. Grab
+        messages regardless of how they were captured.
+        """
+        std = capsys.readouterr()
+        return std.out.splitlines() + std.err.splitlines() + caplog.messages
+
+    def test_optional(self, tmpdir_cwd, caplog, capsys):
+        """
+        If optional extensions fail to build, setuptools should show the error
+        in the logs but not fail to build
+        """
+        cmd = self.get_build_ext_cmd(optional=True, inplace=True)
+        cmd.run()
+        assert any(
+            'build_ext: building extension "spam.eggs" failed'
+            for msg in self.get_log_messages(caplog, capsys)
+        )
+        # No compile error exception should be raised
+
+    def test_non_optional(self, tmpdir_cwd):
+        # Non-optional extensions should raise an exception
+        cmd = self.get_build_ext_cmd(optional=False, inplace=True)
+        with pytest.raises(CompileError):
+            cmd.run()
+
+
+def test_build_ext_config_handling(tmpdir_cwd):
+    files = {
+        'setup.py': DALS(
+            """
+            from setuptools import Extension, setup
+            setup(
+                name='foo',
+                version='0.0.0',
+                ext_modules=[Extension('foo', ['foo.c'])],
+            )
+            """
+        ),
+        'foo.c': DALS(
+            """
+            #include "Python.h"
+
+            #if PY_MAJOR_VERSION >= 3
+
+            static struct PyModuleDef moduledef = {
+                    PyModuleDef_HEAD_INIT,
+                    "foo",
+                    NULL,
+                    0,
+                    NULL,
+                    NULL,
+                    NULL,
+                    NULL,
+                    NULL
+            };
+
+            #define INITERROR return NULL
+
+            PyMODINIT_FUNC PyInit_foo(void)
+
+            #else
+
+            #define INITERROR return
+
+            void initfoo(void)
+
+            #endif
+            {
+            #if PY_MAJOR_VERSION >= 3
+                PyObject *module = PyModule_Create(&moduledef);
+            #else
+                PyObject *module = Py_InitModule("extension", NULL);
+            #endif
+                if (module == NULL)
+                    INITERROR;
+            #if PY_MAJOR_VERSION >= 3
+                return module;
+            #endif
+            }
+            """
+        ),
+        'setup.cfg': DALS(
+            """
+            [build]
+            build_base = foo_build
+            """
+        ),
+    }
+    path.build(files)
+    code, (stdout, stderr) = environment.run_setup_py(
+        cmd=['build'],
+        data_stream=(0, 2),
+    )
+    assert code == 0, f'\nSTDOUT:\n{stdout}\nSTDERR:\n{stderr}'
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/test_build_meta.py b/.venv/lib/python3.12/site-packages/setuptools/tests/test_build_meta.py
new file mode 100644
index 0000000000000000000000000000000000000000..624bba862eaed942802e68ee37427438c7d389ad
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/test_build_meta.py
@@ -0,0 +1,983 @@
+import contextlib
+import importlib
+import os
+import re
+import shutil
+import signal
+import sys
+import tarfile
+import warnings
+from concurrent import futures
+from pathlib import Path
+from typing import Any, Callable
+from zipfile import ZipFile
+
+import pytest
+from jaraco import path
+from packaging.requirements import Requirement
+
+from setuptools.warnings import SetuptoolsDeprecationWarning
+
+from .textwrap import DALS
+
+SETUP_SCRIPT_STUB = "__import__('setuptools').setup()"
+
+
+TIMEOUT = int(os.getenv("TIMEOUT_BACKEND_TEST", "180"))  # in seconds
+IS_PYPY = '__pypy__' in sys.builtin_module_names
+
+
+pytestmark = pytest.mark.skipif(
+    sys.platform == "win32" and IS_PYPY,
+    reason="The combination of PyPy + Windows + pytest-xdist + ProcessPoolExecutor "
+    "is flaky and problematic",
+)
+
+
+class BuildBackendBase:
+    def __init__(self, cwd='.', env=None, backend_name='setuptools.build_meta'):
+        self.cwd = cwd
+        self.env = env or {}
+        self.backend_name = backend_name
+
+
+class BuildBackend(BuildBackendBase):
+    """PEP 517 Build Backend"""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.pool = futures.ProcessPoolExecutor(max_workers=1)
+
+    def __getattr__(self, name: str) -> Callable[..., Any]:
+        """Handles arbitrary function invocations on the build backend."""
+
+        def method(*args, **kw):
+            root = os.path.abspath(self.cwd)
+            caller = BuildBackendCaller(root, self.env, self.backend_name)
+            pid = None
+            try:
+                pid = self.pool.submit(os.getpid).result(TIMEOUT)
+                return self.pool.submit(caller, name, *args, **kw).result(TIMEOUT)
+            except futures.TimeoutError:
+                self.pool.shutdown(wait=False)  # doesn't stop already running processes
+                self._kill(pid)
+                pytest.xfail(f"Backend did not respond before timeout ({TIMEOUT} s)")
+            except (futures.process.BrokenProcessPool, MemoryError, OSError):
+                if IS_PYPY:
+                    pytest.xfail("PyPy frequently fails tests with ProcessPoolExector")
+                raise
+
+        return method
+
+    def _kill(self, pid):
+        if pid is None:
+            return
+        with contextlib.suppress(ProcessLookupError, OSError):
+            os.kill(pid, signal.SIGTERM if os.name == "nt" else signal.SIGKILL)
+
+
+class BuildBackendCaller(BuildBackendBase):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        (self.backend_name, _, self.backend_obj) = self.backend_name.partition(':')
+
+    def __call__(self, name, *args, **kw):
+        """Handles arbitrary function invocations on the build backend."""
+        os.chdir(self.cwd)
+        os.environ.update(self.env)
+        mod = importlib.import_module(self.backend_name)
+
+        if self.backend_obj:
+            backend = getattr(mod, self.backend_obj)
+        else:
+            backend = mod
+
+        return getattr(backend, name)(*args, **kw)
+
+
+defns = [
+    {  # simple setup.py script
+        'setup.py': DALS(
+            """
+            __import__('setuptools').setup(
+                name='foo',
+                version='0.0.0',
+                py_modules=['hello'],
+                setup_requires=['six'],
+            )
+            """
+        ),
+        'hello.py': DALS(
+            """
+            def run():
+                print('hello')
+            """
+        ),
+    },
+    {  # setup.py that relies on __name__
+        'setup.py': DALS(
+            """
+            assert __name__ == '__main__'
+            __import__('setuptools').setup(
+                name='foo',
+                version='0.0.0',
+                py_modules=['hello'],
+                setup_requires=['six'],
+            )
+            """
+        ),
+        'hello.py': DALS(
+            """
+            def run():
+                print('hello')
+            """
+        ),
+    },
+    {  # setup.py script that runs arbitrary code
+        'setup.py': DALS(
+            """
+            variable = True
+            def function():
+                return variable
+            assert variable
+            __import__('setuptools').setup(
+                name='foo',
+                version='0.0.0',
+                py_modules=['hello'],
+                setup_requires=['six'],
+            )
+            """
+        ),
+        'hello.py': DALS(
+            """
+            def run():
+                print('hello')
+            """
+        ),
+    },
+    {  # setup.py script that constructs temp files to be included in the distribution
+        'setup.py': DALS(
+            """
+            # Some packages construct files on the fly, include them in the package,
+            # and immediately remove them after `setup()` (e.g. pybind11==2.9.1).
+            # Therefore, we cannot use `distutils.core.run_setup(..., stop_after=...)`
+            # to obtain a distribution object first, and then run the distutils
+            # commands later, because these files will be removed in the meantime.
+
+            with open('world.py', 'w', encoding="utf-8") as f:
+                f.write('x = 42')
+
+            try:
+                __import__('setuptools').setup(
+                    name='foo',
+                    version='0.0.0',
+                    py_modules=['world'],
+                    setup_requires=['six'],
+                )
+            finally:
+                # Some packages will clean temporary files
+                __import__('os').unlink('world.py')
+            """
+        ),
+    },
+    {  # setup.cfg only
+        'setup.cfg': DALS(
+            """
+        [metadata]
+        name = foo
+        version = 0.0.0
+
+        [options]
+        py_modules=hello
+        setup_requires=six
+        """
+        ),
+        'hello.py': DALS(
+            """
+        def run():
+            print('hello')
+        """
+        ),
+    },
+    {  # setup.cfg and setup.py
+        'setup.cfg': DALS(
+            """
+        [metadata]
+        name = foo
+        version = 0.0.0
+
+        [options]
+        py_modules=hello
+        setup_requires=six
+        """
+        ),
+        'setup.py': "__import__('setuptools').setup()",
+        'hello.py': DALS(
+            """
+        def run():
+            print('hello')
+        """
+        ),
+    },
+]
+
+
+class TestBuildMetaBackend:
+    backend_name = 'setuptools.build_meta'
+
+    def get_build_backend(self):
+        return BuildBackend(backend_name=self.backend_name)
+
+    @pytest.fixture(params=defns)
+    def build_backend(self, tmpdir, request):
+        path.build(request.param, prefix=str(tmpdir))
+        with tmpdir.as_cwd():
+            yield self.get_build_backend()
+
+    def test_get_requires_for_build_wheel(self, build_backend):
+        actual = build_backend.get_requires_for_build_wheel()
+        expected = ['six']
+        assert sorted(actual) == sorted(expected)
+
+    def test_get_requires_for_build_sdist(self, build_backend):
+        actual = build_backend.get_requires_for_build_sdist()
+        expected = ['six']
+        assert sorted(actual) == sorted(expected)
+
+    def test_build_wheel(self, build_backend):
+        dist_dir = os.path.abspath('pip-wheel')
+        os.makedirs(dist_dir)
+        wheel_name = build_backend.build_wheel(dist_dir)
+
+        wheel_file = os.path.join(dist_dir, wheel_name)
+        assert os.path.isfile(wheel_file)
+
+        # Temporary files should be removed
+        assert not os.path.isfile('world.py')
+
+        with ZipFile(wheel_file) as zipfile:
+            wheel_contents = set(zipfile.namelist())
+
+        # Each one of the examples have a single module
+        # that should be included in the distribution
+        python_scripts = (f for f in wheel_contents if f.endswith('.py'))
+        modules = [f for f in python_scripts if not f.endswith('setup.py')]
+        assert len(modules) == 1
+
+    @pytest.mark.parametrize('build_type', ('wheel', 'sdist'))
+    def test_build_with_existing_file_present(self, build_type, tmpdir_cwd):
+        # Building a sdist/wheel should still succeed if there's
+        # already a sdist/wheel in the destination directory.
+        files = {
+            'setup.py': "from setuptools import setup\nsetup()",
+            'VERSION': "0.0.1",
+            'setup.cfg': DALS(
+                """
+                [metadata]
+                name = foo
+                version = file: VERSION
+                """
+            ),
+            'pyproject.toml': DALS(
+                """
+                [build-system]
+                requires = ["setuptools", "wheel"]
+                build-backend = "setuptools.build_meta"
+                """
+            ),
+        }
+
+        path.build(files)
+
+        dist_dir = os.path.abspath('preexisting-' + build_type)
+
+        build_backend = self.get_build_backend()
+        build_method = getattr(build_backend, 'build_' + build_type)
+
+        # Build a first sdist/wheel.
+        # Note: this also check the destination directory is
+        # successfully created if it does not exist already.
+        first_result = build_method(dist_dir)
+
+        # Change version.
+        with open("VERSION", "wt", encoding="utf-8") as version_file:
+            version_file.write("0.0.2")
+
+        # Build a *second* sdist/wheel.
+        second_result = build_method(dist_dir)
+
+        assert os.path.isfile(os.path.join(dist_dir, first_result))
+        assert first_result != second_result
+
+        # And if rebuilding the exact same sdist/wheel?
+        open(os.path.join(dist_dir, second_result), 'wb').close()
+        third_result = build_method(dist_dir)
+        assert third_result == second_result
+        assert os.path.getsize(os.path.join(dist_dir, third_result)) > 0
+
+    @pytest.mark.parametrize("setup_script", [None, SETUP_SCRIPT_STUB])
+    def test_build_with_pyproject_config(self, tmpdir, setup_script):
+        files = {
+            'pyproject.toml': DALS(
+                """
+                [build-system]
+                requires = ["setuptools", "wheel"]
+                build-backend = "setuptools.build_meta"
+
+                [project]
+                name = "foo"
+                license = {text = "MIT"}
+                description = "This is a Python package"
+                dynamic = ["version", "readme"]
+                classifiers = [
+                    "Development Status :: 5 - Production/Stable",
+                    "Intended Audience :: Developers"
+                ]
+                urls = {Homepage = "http://github.com"}
+                dependencies = [
+                    "appdirs",
+                ]
+
+                [project.optional-dependencies]
+                all = [
+                    "tomli>=1",
+                    "pyscaffold>=4,<5",
+                    'importlib; python_version == "2.6"',
+                ]
+
+                [project.scripts]
+                foo = "foo.cli:main"
+
+                [tool.setuptools]
+                zip-safe = false
+                package-dir = {"" = "src"}
+                packages = {find = {where = ["src"]}}
+                license-files = ["LICENSE*"]
+
+                [tool.setuptools.dynamic]
+                version = {attr = "foo.__version__"}
+                readme = {file = "README.rst"}
+
+                [tool.distutils.sdist]
+                formats = "gztar"
+                """
+            ),
+            "MANIFEST.in": DALS(
+                """
+                global-include *.py *.txt
+                global-exclude *.py[cod]
+                """
+            ),
+            "README.rst": "This is a ``README``",
+            "LICENSE.txt": "---- placeholder MIT license ----",
+            "src": {
+                "foo": {
+                    "__init__.py": "__version__ = '0.1'",
+                    "__init__.pyi": "__version__: str",
+                    "cli.py": "def main(): print('hello world')",
+                    "data.txt": "def main(): print('hello world')",
+                    "py.typed": "",
+                }
+            },
+        }
+        if setup_script:
+            files["setup.py"] = setup_script
+
+        build_backend = self.get_build_backend()
+        with tmpdir.as_cwd():
+            path.build(files)
+            msgs = [
+                "'tool.setuptools.license-files' is deprecated in favor of 'project.license-files'",
+                "`project.license` as a TOML table is deprecated",
+            ]
+            with warnings.catch_warnings():
+                for msg in msgs:
+                    warnings.filterwarnings("ignore", msg, SetuptoolsDeprecationWarning)
+                sdist_path = build_backend.build_sdist("temp")
+                wheel_file = build_backend.build_wheel("temp")
+
+        with tarfile.open(os.path.join(tmpdir, "temp", sdist_path)) as tar:
+            sdist_contents = set(tar.getnames())
+
+        with ZipFile(os.path.join(tmpdir, "temp", wheel_file)) as zipfile:
+            wheel_contents = set(zipfile.namelist())
+            metadata = str(zipfile.read("foo-0.1.dist-info/METADATA"), "utf-8")
+            license = str(
+                zipfile.read("foo-0.1.dist-info/licenses/LICENSE.txt"), "utf-8"
+            )
+            epoints = str(zipfile.read("foo-0.1.dist-info/entry_points.txt"), "utf-8")
+
+        assert sdist_contents - {"foo-0.1/setup.py"} == {
+            'foo-0.1',
+            'foo-0.1/LICENSE.txt',
+            'foo-0.1/MANIFEST.in',
+            'foo-0.1/PKG-INFO',
+            'foo-0.1/README.rst',
+            'foo-0.1/pyproject.toml',
+            'foo-0.1/setup.cfg',
+            'foo-0.1/src',
+            'foo-0.1/src/foo',
+            'foo-0.1/src/foo/__init__.py',
+            'foo-0.1/src/foo/__init__.pyi',
+            'foo-0.1/src/foo/cli.py',
+            'foo-0.1/src/foo/data.txt',
+            'foo-0.1/src/foo/py.typed',
+            'foo-0.1/src/foo.egg-info',
+            'foo-0.1/src/foo.egg-info/PKG-INFO',
+            'foo-0.1/src/foo.egg-info/SOURCES.txt',
+            'foo-0.1/src/foo.egg-info/dependency_links.txt',
+            'foo-0.1/src/foo.egg-info/entry_points.txt',
+            'foo-0.1/src/foo.egg-info/requires.txt',
+            'foo-0.1/src/foo.egg-info/top_level.txt',
+            'foo-0.1/src/foo.egg-info/not-zip-safe',
+        }
+        assert wheel_contents == {
+            "foo/__init__.py",
+            "foo/__init__.pyi",  # include type information by default
+            "foo/cli.py",
+            "foo/data.txt",  # include_package_data defaults to True
+            "foo/py.typed",  # include type information by default
+            "foo-0.1.dist-info/licenses/LICENSE.txt",
+            "foo-0.1.dist-info/METADATA",
+            "foo-0.1.dist-info/WHEEL",
+            "foo-0.1.dist-info/entry_points.txt",
+            "foo-0.1.dist-info/top_level.txt",
+            "foo-0.1.dist-info/RECORD",
+        }
+        assert license == "---- placeholder MIT license ----"
+
+        for line in (
+            "Summary: This is a Python package",
+            "License: MIT",
+            "License-File: LICENSE.txt",
+            "Classifier: Intended Audience :: Developers",
+            "Requires-Dist: appdirs",
+            "Requires-Dist: " + str(Requirement('tomli>=1 ; extra == "all"')),
+            "Requires-Dist: "
+            + str(Requirement('importlib; python_version=="2.6" and extra =="all"')),
+        ):
+            assert line in metadata, (line, metadata)
+
+        assert metadata.strip().endswith("This is a ``README``")
+        assert epoints.strip() == "[console_scripts]\nfoo = foo.cli:main"
+
+    def test_static_metadata_in_pyproject_config(self, tmpdir):
+        # Make sure static metadata in pyproject.toml is not overwritten by setup.py
+        # as required by PEP 621
+        files = {
+            'pyproject.toml': DALS(
+                """
+                [build-system]
+                requires = ["setuptools", "wheel"]
+                build-backend = "setuptools.build_meta"
+
+                [project]
+                name = "foo"
+                description = "This is a Python package"
+                version = "42"
+                dependencies = ["six"]
+                """
+            ),
+            'hello.py': DALS(
+                """
+                def run():
+                    print('hello')
+                """
+            ),
+            'setup.py': DALS(
+                """
+                __import__('setuptools').setup(
+                    name='bar',
+                    version='13',
+                )
+                """
+            ),
+        }
+        build_backend = self.get_build_backend()
+        with tmpdir.as_cwd():
+            path.build(files)
+            sdist_path = build_backend.build_sdist("temp")
+            wheel_file = build_backend.build_wheel("temp")
+
+        assert (tmpdir / "temp/foo-42.tar.gz").exists()
+        assert (tmpdir / "temp/foo-42-py3-none-any.whl").exists()
+        assert not (tmpdir / "temp/bar-13.tar.gz").exists()
+        assert not (tmpdir / "temp/bar-42.tar.gz").exists()
+        assert not (tmpdir / "temp/foo-13.tar.gz").exists()
+        assert not (tmpdir / "temp/bar-13-py3-none-any.whl").exists()
+        assert not (tmpdir / "temp/bar-42-py3-none-any.whl").exists()
+        assert not (tmpdir / "temp/foo-13-py3-none-any.whl").exists()
+
+        with tarfile.open(os.path.join(tmpdir, "temp", sdist_path)) as tar:
+            pkg_info = str(tar.extractfile('foo-42/PKG-INFO').read(), "utf-8")
+            members = tar.getnames()
+            assert "bar-13/PKG-INFO" not in members
+
+        with ZipFile(os.path.join(tmpdir, "temp", wheel_file)) as zipfile:
+            metadata = str(zipfile.read("foo-42.dist-info/METADATA"), "utf-8")
+            members = zipfile.namelist()
+            assert "bar-13.dist-info/METADATA" not in members
+
+        for file in pkg_info, metadata:
+            for line in ("Name: foo", "Version: 42"):
+                assert line in file
+            for line in ("Name: bar", "Version: 13"):
+                assert line not in file
+
+    def test_build_sdist(self, build_backend):
+        dist_dir = os.path.abspath('pip-sdist')
+        os.makedirs(dist_dir)
+        sdist_name = build_backend.build_sdist(dist_dir)
+
+        assert os.path.isfile(os.path.join(dist_dir, sdist_name))
+
+    def test_prepare_metadata_for_build_wheel(self, build_backend):
+        dist_dir = os.path.abspath('pip-dist-info')
+        os.makedirs(dist_dir)
+
+        dist_info = build_backend.prepare_metadata_for_build_wheel(dist_dir)
+
+        assert os.path.isfile(os.path.join(dist_dir, dist_info, 'METADATA'))
+
+    def test_prepare_metadata_inplace(self, build_backend):
+        """
+        Some users might pass metadata_directory pre-populated with `.tox` or `.venv`.
+        See issue #3523.
+        """
+        for pre_existing in [
+            ".tox/python/lib/python3.10/site-packages/attrs-22.1.0.dist-info",
+            ".tox/python/lib/python3.10/site-packages/autocommand-2.2.1.dist-info",
+            ".nox/python/lib/python3.10/site-packages/build-0.8.0.dist-info",
+            ".venv/python3.10/site-packages/click-8.1.3.dist-info",
+            "venv/python3.10/site-packages/distlib-0.3.5.dist-info",
+            "env/python3.10/site-packages/docutils-0.19.dist-info",
+        ]:
+            os.makedirs(pre_existing, exist_ok=True)
+        dist_info = build_backend.prepare_metadata_for_build_wheel(".")
+        assert os.path.isfile(os.path.join(dist_info, 'METADATA'))
+
+    def test_build_sdist_explicit_dist(self, build_backend):
+        # explicitly specifying the dist folder should work
+        # the folder sdist_directory and the ``--dist-dir`` can be the same
+        dist_dir = os.path.abspath('dist')
+        sdist_name = build_backend.build_sdist(dist_dir)
+        assert os.path.isfile(os.path.join(dist_dir, sdist_name))
+
+    def test_build_sdist_version_change(self, build_backend):
+        sdist_into_directory = os.path.abspath("out_sdist")
+        os.makedirs(sdist_into_directory)
+
+        sdist_name = build_backend.build_sdist(sdist_into_directory)
+        assert os.path.isfile(os.path.join(sdist_into_directory, sdist_name))
+
+        # if the setup.py changes subsequent call of the build meta
+        # should still succeed, given the
+        # sdist_directory the frontend specifies is empty
+        setup_loc = os.path.abspath("setup.py")
+        if not os.path.exists(setup_loc):
+            setup_loc = os.path.abspath("setup.cfg")
+
+        with open(setup_loc, 'rt', encoding="utf-8") as file_handler:
+            content = file_handler.read()
+        with open(setup_loc, 'wt', encoding="utf-8") as file_handler:
+            file_handler.write(content.replace("version='0.0.0'", "version='0.0.1'"))
+
+        shutil.rmtree(sdist_into_directory)
+        os.makedirs(sdist_into_directory)
+
+        sdist_name = build_backend.build_sdist("out_sdist")
+        assert os.path.isfile(os.path.join(os.path.abspath("out_sdist"), sdist_name))
+
+    def test_build_sdist_pyproject_toml_exists(self, tmpdir_cwd):
+        files = {
+            'setup.py': DALS(
+                """
+                __import__('setuptools').setup(
+                    name='foo',
+                    version='0.0.0',
+                    py_modules=['hello']
+                )"""
+            ),
+            'hello.py': '',
+            'pyproject.toml': DALS(
+                """
+                [build-system]
+                requires = ["setuptools", "wheel"]
+                build-backend = "setuptools.build_meta"
+                """
+            ),
+        }
+        path.build(files)
+        build_backend = self.get_build_backend()
+        targz_path = build_backend.build_sdist("temp")
+        with tarfile.open(os.path.join("temp", targz_path)) as tar:
+            assert any('pyproject.toml' in name for name in tar.getnames())
+
+    def test_build_sdist_setup_py_exists(self, tmpdir_cwd):
+        # If build_sdist is called from a script other than setup.py,
+        # ensure setup.py is included
+        path.build(defns[0])
+
+        build_backend = self.get_build_backend()
+        targz_path = build_backend.build_sdist("temp")
+        with tarfile.open(os.path.join("temp", targz_path)) as tar:
+            assert any('setup.py' in name for name in tar.getnames())
+
+    def test_build_sdist_setup_py_manifest_excluded(self, tmpdir_cwd):
+        # Ensure that MANIFEST.in can exclude setup.py
+        files = {
+            'setup.py': DALS(
+                """
+        __import__('setuptools').setup(
+            name='foo',
+            version='0.0.0',
+            py_modules=['hello']
+        )"""
+            ),
+            'hello.py': '',
+            'MANIFEST.in': DALS(
+                """
+        exclude setup.py
+        """
+            ),
+        }
+
+        path.build(files)
+
+        build_backend = self.get_build_backend()
+        targz_path = build_backend.build_sdist("temp")
+        with tarfile.open(os.path.join("temp", targz_path)) as tar:
+            assert not any('setup.py' in name for name in tar.getnames())
+
+    def test_build_sdist_builds_targz_even_if_zip_indicated(self, tmpdir_cwd):
+        files = {
+            'setup.py': DALS(
+                """
+                __import__('setuptools').setup(
+                    name='foo',
+                    version='0.0.0',
+                    py_modules=['hello']
+                )"""
+            ),
+            'hello.py': '',
+            'setup.cfg': DALS(
+                """
+                [sdist]
+                formats=zip
+                """
+            ),
+        }
+
+        path.build(files)
+
+        build_backend = self.get_build_backend()
+        build_backend.build_sdist("temp")
+
+    _relative_path_import_files = {
+        'setup.py': DALS(
+            """
+            __import__('setuptools').setup(
+                name='foo',
+                version=__import__('hello').__version__,
+                py_modules=['hello']
+            )"""
+        ),
+        'hello.py': '__version__ = "0.0.0"',
+        'setup.cfg': DALS(
+            """
+            [sdist]
+            formats=zip
+            """
+        ),
+    }
+
+    def test_build_sdist_relative_path_import(self, tmpdir_cwd):
+        path.build(self._relative_path_import_files)
+        build_backend = self.get_build_backend()
+        with pytest.raises(ImportError, match="^No module named 'hello'$"):
+            build_backend.build_sdist("temp")
+
+    _simple_pyproject_example = {
+        "pyproject.toml": DALS(
+            """
+            [project]
+            name = "proj"
+            version = "42"
+            """
+        ),
+        "src": {"proj": {"__init__.py": ""}},
+    }
+
+    def _assert_link_tree(self, parent_dir):
+        """All files in the directory should be either links or hard links"""
+        files = list(Path(parent_dir).glob("**/*"))
+        assert files  # Should not be empty
+        for file in files:
+            assert file.is_symlink() or os.stat(file).st_nlink > 0
+
+    def test_editable_without_config_settings(self, tmpdir_cwd):
+        """
+        Sanity check to ensure tests with --mode=strict are different from the ones
+        without --mode.
+
+        --mode=strict should create a local directory with a package tree.
+        The directory should not get created otherwise.
+        """
+        path.build(self._simple_pyproject_example)
+        build_backend = self.get_build_backend()
+        assert not Path("build").exists()
+        build_backend.build_editable("temp")
+        assert not Path("build").exists()
+
+    def test_build_wheel_inplace(self, tmpdir_cwd):
+        config_settings = {"--build-option": ["build_ext", "--inplace"]}
+        path.build(self._simple_pyproject_example)
+        build_backend = self.get_build_backend()
+        assert not Path("build").exists()
+        Path("build").mkdir()
+        build_backend.prepare_metadata_for_build_wheel("build", config_settings)
+        build_backend.build_wheel("build", config_settings)
+        assert Path("build/proj-42-py3-none-any.whl").exists()
+
+    @pytest.mark.parametrize("config_settings", [{"editable-mode": "strict"}])
+    def test_editable_with_config_settings(self, tmpdir_cwd, config_settings):
+        path.build({**self._simple_pyproject_example, '_meta': {}})
+        assert not Path("build").exists()
+        build_backend = self.get_build_backend()
+        build_backend.prepare_metadata_for_build_editable("_meta", config_settings)
+        build_backend.build_editable("temp", config_settings, "_meta")
+        self._assert_link_tree(next(Path("build").glob("__editable__.*")))
+
+    @pytest.mark.parametrize(
+        ("setup_literal", "requirements"),
+        [
+            ("'foo'", ['foo']),
+            ("['foo']", ['foo']),
+            (r"'foo\n'", ['foo']),
+            (r"'foo\n\n'", ['foo']),
+            ("['foo', 'bar']", ['foo', 'bar']),
+            (r"'# Has a comment line\nfoo'", ['foo']),
+            (r"'foo # Has an inline comment'", ['foo']),
+            (r"'foo \\\n >=3.0'", ['foo>=3.0']),
+            (r"'foo\nbar'", ['foo', 'bar']),
+            (r"'foo\nbar\n'", ['foo', 'bar']),
+            (r"['foo\n', 'bar\n']", ['foo', 'bar']),
+        ],
+    )
+    @pytest.mark.parametrize('use_wheel', [True, False])
+    def test_setup_requires(self, setup_literal, requirements, use_wheel, tmpdir_cwd):
+        files = {
+            'setup.py': DALS(
+                """
+                from setuptools import setup
+
+                setup(
+                    name="qux",
+                    version="0.0.0",
+                    py_modules=["hello"],
+                    setup_requires={setup_literal},
+                )
+            """
+            ).format(setup_literal=setup_literal),
+            'hello.py': DALS(
+                """
+            def run():
+                print('hello')
+            """
+            ),
+        }
+
+        path.build(files)
+
+        build_backend = self.get_build_backend()
+
+        if use_wheel:
+            get_requires = build_backend.get_requires_for_build_wheel
+        else:
+            get_requires = build_backend.get_requires_for_build_sdist
+
+        # Ensure that the build requirements are properly parsed
+        expected = sorted(requirements)
+        actual = get_requires()
+
+        assert expected == sorted(actual)
+
+    def test_setup_requires_with_auto_discovery(self, tmpdir_cwd):
+        # Make sure patches introduced to retrieve setup_requires don't accidentally
+        # activate auto-discovery and cause problems due to the incomplete set of
+        # attributes passed to MinimalDistribution
+        files = {
+            'pyproject.toml': DALS(
+                """
+                [project]
+                name = "proj"
+                version = "42"
+            """
+            ),
+            "setup.py": DALS(
+                """
+                __import__('setuptools').setup(
+                    setup_requires=["foo"],
+                    py_modules = ["hello", "world"]
+                )
+            """
+            ),
+            'hello.py': "'hello'",
+            'world.py': "'world'",
+        }
+        path.build(files)
+        build_backend = self.get_build_backend()
+        setup_requires = build_backend.get_requires_for_build_wheel()
+        assert setup_requires == ["foo"]
+
+    def test_dont_install_setup_requires(self, tmpdir_cwd):
+        files = {
+            'setup.py': DALS(
+                """
+                        from setuptools import setup
+
+                        setup(
+                            name="qux",
+                            version="0.0.0",
+                            py_modules=["hello"],
+                            setup_requires=["does-not-exist >99"],
+                        )
+                    """
+            ),
+            'hello.py': DALS(
+                """
+                    def run():
+                        print('hello')
+                    """
+            ),
+        }
+
+        path.build(files)
+
+        build_backend = self.get_build_backend()
+
+        dist_dir = os.path.abspath('pip-dist-info')
+        os.makedirs(dist_dir)
+
+        # does-not-exist can't be satisfied, so if it attempts to install
+        # setup_requires, it will fail.
+        build_backend.prepare_metadata_for_build_wheel(dist_dir)
+
+    _sys_argv_0_passthrough = {
+        'setup.py': DALS(
+            """
+            import os
+            import sys
+
+            __import__('setuptools').setup(
+                name='foo',
+                version='0.0.0',
+            )
+
+            sys_argv = os.path.abspath(sys.argv[0])
+            file_path = os.path.abspath('setup.py')
+            assert sys_argv == file_path
+            """
+        )
+    }
+
+    def test_sys_argv_passthrough(self, tmpdir_cwd):
+        path.build(self._sys_argv_0_passthrough)
+        build_backend = self.get_build_backend()
+        with pytest.raises(AssertionError):
+            build_backend.build_sdist("temp")
+
+    _setup_py_file_abspath = {
+        'setup.py': DALS(
+            """
+            import os
+            assert os.path.isabs(__file__)
+            __import__('setuptools').setup(
+                name='foo',
+                version='0.0.0',
+                py_modules=['hello'],
+                setup_requires=['six'],
+            )
+            """
+        )
+    }
+
+    def test_setup_py_file_abspath(self, tmpdir_cwd):
+        path.build(self._setup_py_file_abspath)
+        build_backend = self.get_build_backend()
+        build_backend.build_sdist("temp")
+
+    @pytest.mark.parametrize('build_hook', ('build_sdist', 'build_wheel'))
+    def test_build_with_empty_setuppy(self, build_backend, build_hook):
+        files = {'setup.py': ''}
+        path.build(files)
+
+        msg = re.escape('No distribution was found.')
+        with pytest.raises(ValueError, match=msg):
+            getattr(build_backend, build_hook)("temp")
+
+
+class TestBuildMetaLegacyBackend(TestBuildMetaBackend):
+    backend_name = 'setuptools.build_meta:__legacy__'
+
+    # build_meta_legacy-specific tests
+    def test_build_sdist_relative_path_import(self, tmpdir_cwd):
+        # This must fail in build_meta, but must pass in build_meta_legacy
+        path.build(self._relative_path_import_files)
+
+        build_backend = self.get_build_backend()
+        build_backend.build_sdist("temp")
+
+    def test_sys_argv_passthrough(self, tmpdir_cwd):
+        path.build(self._sys_argv_0_passthrough)
+
+        build_backend = self.get_build_backend()
+        build_backend.build_sdist("temp")
+
+
+def test_legacy_editable_install(venv, tmpdir, tmpdir_cwd):
+    pyproject = """
+    [build-system]
+    requires = ["setuptools"]
+    build-backend = "setuptools.build_meta"
+    [project]
+    name = "myproj"
+    version = "42"
+    """
+    path.build({"pyproject.toml": DALS(pyproject), "mymod.py": ""})
+
+    # First: sanity check
+    cmd = ["pip", "install", "--no-build-isolation", "-e", "."]
+    output = venv.run(cmd, cwd=tmpdir).lower()
+    assert "running setup.py develop for myproj" not in output
+    assert "created wheel for myproj" in output
+
+    # Then: real test
+    env = {**os.environ, "SETUPTOOLS_ENABLE_FEATURES": "legacy-editable"}
+    cmd = ["pip", "install", "--no-build-isolation", "-e", "."]
+    output = venv.run(cmd, cwd=tmpdir, env=env).lower()
+    assert "running setup.py develop for myproj" in output
+
+
+@pytest.mark.filterwarnings("ignore::setuptools.SetuptoolsDeprecationWarning")
+def test_sys_exit_0_in_setuppy(monkeypatch, tmp_path):
+    """Setuptools should be resilient to setup.py with ``sys.exit(0)`` (#3973)."""
+    monkeypatch.chdir(tmp_path)
+    setuppy = """
+        import sys, setuptools
+        setuptools.setup(name='foo', version='0.0.0')
+        sys.exit(0)
+        """
+    (tmp_path / "setup.py").write_text(DALS(setuppy), encoding="utf-8")
+    backend = BuildBackend(backend_name="setuptools.build_meta")
+    assert backend.get_requires_for_build_wheel() == []
+
+
+def test_system_exit_in_setuppy(monkeypatch, tmp_path):
+    monkeypatch.chdir(tmp_path)
+    setuppy = "import sys; sys.exit('some error')"
+    (tmp_path / "setup.py").write_text(setuppy, encoding="utf-8")
+    with pytest.raises(SystemExit, match="some error"):
+        backend = BuildBackend(backend_name="setuptools.build_meta")
+        backend.get_requires_for_build_wheel()
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/test_build_py.py b/.venv/lib/python3.12/site-packages/setuptools/tests/test_build_py.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e3a660833933717310de66978771c4e48b5b914
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/test_build_py.py
@@ -0,0 +1,480 @@
+import os
+import shutil
+import stat
+import warnings
+from pathlib import Path
+from unittest.mock import Mock
+
+import jaraco.path
+import pytest
+
+from setuptools import SetuptoolsDeprecationWarning
+from setuptools.dist import Distribution
+
+from .textwrap import DALS
+
+
+def test_directories_in_package_data_glob(tmpdir_cwd):
+    """
+    Directories matching the glob in package_data should
+    not be included in the package data.
+
+    Regression test for #261.
+    """
+    dist = Distribution(
+        dict(
+            script_name='setup.py',
+            script_args=['build_py'],
+            packages=[''],
+            package_data={'': ['path/*']},
+        )
+    )
+    os.makedirs('path/subpath')
+    dist.parse_command_line()
+    dist.run_commands()
+
+
+def test_recursive_in_package_data_glob(tmpdir_cwd):
+    """
+    Files matching recursive globs (**) in package_data should
+    be included in the package data.
+
+    #1806
+    """
+    dist = Distribution(
+        dict(
+            script_name='setup.py',
+            script_args=['build_py'],
+            packages=[''],
+            package_data={'': ['path/**/data']},
+        )
+    )
+    os.makedirs('path/subpath/subsubpath')
+    open('path/subpath/subsubpath/data', 'wb').close()
+
+    dist.parse_command_line()
+    dist.run_commands()
+
+    assert stat.S_ISREG(os.stat('build/lib/path/subpath/subsubpath/data').st_mode), (
+        "File is not included"
+    )
+
+
+def test_read_only(tmpdir_cwd):
+    """
+    Ensure read-only flag is not preserved in copy
+    for package modules and package data, as that
+    causes problems with deleting read-only files on
+    Windows.
+
+    #1451
+    """
+    dist = Distribution(
+        dict(
+            script_name='setup.py',
+            script_args=['build_py'],
+            packages=['pkg'],
+            package_data={'pkg': ['data.dat']},
+        )
+    )
+    os.makedirs('pkg')
+    open('pkg/__init__.py', 'wb').close()
+    open('pkg/data.dat', 'wb').close()
+    os.chmod('pkg/__init__.py', stat.S_IREAD)
+    os.chmod('pkg/data.dat', stat.S_IREAD)
+    dist.parse_command_line()
+    dist.run_commands()
+    shutil.rmtree('build')
+
+
+@pytest.mark.xfail(
+    'platform.system() == "Windows"',
+    reason="On Windows, files do not have executable bits",
+    raises=AssertionError,
+    strict=True,
+)
+def test_executable_data(tmpdir_cwd):
+    """
+    Ensure executable bit is preserved in copy for
+    package data, as users rely on it for scripts.
+
+    #2041
+    """
+    dist = Distribution(
+        dict(
+            script_name='setup.py',
+            script_args=['build_py'],
+            packages=['pkg'],
+            package_data={'pkg': ['run-me']},
+        )
+    )
+    os.makedirs('pkg')
+    open('pkg/__init__.py', 'wb').close()
+    open('pkg/run-me', 'wb').close()
+    os.chmod('pkg/run-me', 0o700)
+
+    dist.parse_command_line()
+    dist.run_commands()
+
+    assert os.stat('build/lib/pkg/run-me').st_mode & stat.S_IEXEC, (
+        "Script is not executable"
+    )
+
+
+EXAMPLE_WITH_MANIFEST = {
+    "setup.cfg": DALS(
+        """
+        [metadata]
+        name = mypkg
+        version = 42
+
+        [options]
+        include_package_data = True
+        packages = find:
+
+        [options.packages.find]
+        exclude = *.tests*
+        """
+    ),
+    "mypkg": {
+        "__init__.py": "",
+        "resource_file.txt": "",
+        "tests": {
+            "__init__.py": "",
+            "test_mypkg.py": "",
+            "test_file.txt": "",
+        },
+    },
+    "MANIFEST.in": DALS(
+        """
+        global-include *.py *.txt
+        global-exclude *.py[cod]
+        prune dist
+        prune build
+        prune *.egg-info
+        """
+    ),
+}
+
+
+def test_excluded_subpackages(tmpdir_cwd):
+    jaraco.path.build(EXAMPLE_WITH_MANIFEST)
+    dist = Distribution({"script_name": "%PEP 517%"})
+    dist.parse_config_files()
+
+    build_py = dist.get_command_obj("build_py")
+
+    msg = r"Python recognizes 'mypkg\.tests' as an importable package"
+    with pytest.warns(SetuptoolsDeprecationWarning, match=msg):
+        # TODO: To fix #3260 we need some transition period to deprecate the
+        # existing behavior of `include_package_data`. After the transition, we
+        # should remove the warning and fix the behavior.
+
+        if os.getenv("SETUPTOOLS_USE_DISTUTILS") == "stdlib":
+            # pytest.warns reset the warning filter temporarily
+            # https://github.com/pytest-dev/pytest/issues/4011#issuecomment-423494810
+            warnings.filterwarnings(
+                "ignore",
+                "'encoding' argument not specified",
+                module="distutils.text_file",
+                # This warning is already fixed in pypa/distutils but not in stdlib
+            )
+
+        build_py.finalize_options()
+        build_py.run()
+
+    build_dir = Path(dist.get_command_obj("build_py").build_lib)
+    assert (build_dir / "mypkg/__init__.py").exists()
+    assert (build_dir / "mypkg/resource_file.txt").exists()
+
+    # Setuptools is configured to ignore `mypkg.tests`, therefore the following
+    # files/dirs should not be included in the distribution.
+    for f in [
+        "mypkg/tests/__init__.py",
+        "mypkg/tests/test_mypkg.py",
+        "mypkg/tests/test_file.txt",
+        "mypkg/tests",
+    ]:
+        with pytest.raises(AssertionError):
+            # TODO: Enforce the following assertion once #3260 is fixed
+            # (remove context manager and the following xfail).
+            assert not (build_dir / f).exists()
+
+    pytest.xfail("#3260")
+
+
+@pytest.mark.filterwarnings("ignore::setuptools.SetuptoolsDeprecationWarning")
+def test_existing_egg_info(tmpdir_cwd, monkeypatch):
+    """When provided with the ``existing_egg_info_dir`` attribute, build_py should not
+    attempt to run egg_info again.
+    """
+    # == Pre-condition ==
+    # Generate an egg-info dir
+    jaraco.path.build(EXAMPLE_WITH_MANIFEST)
+    dist = Distribution({"script_name": "%PEP 517%"})
+    dist.parse_config_files()
+    assert dist.include_package_data
+
+    egg_info = dist.get_command_obj("egg_info")
+    dist.run_command("egg_info")
+    egg_info_dir = next(Path(egg_info.egg_base).glob("*.egg-info"))
+    assert egg_info_dir.is_dir()
+
+    # == Setup ==
+    build_py = dist.get_command_obj("build_py")
+    build_py.finalize_options()
+    egg_info = dist.get_command_obj("egg_info")
+    egg_info_run = Mock(side_effect=egg_info.run)
+    monkeypatch.setattr(egg_info, "run", egg_info_run)
+
+    # == Remove caches ==
+    # egg_info is called when build_py looks for data_files, which gets cached.
+    # We need to ensure it is not cached yet, otherwise it may impact on the tests
+    build_py.__dict__.pop('data_files', None)
+    dist.reinitialize_command(egg_info)
+
+    # == Sanity check ==
+    # Ensure that if existing_egg_info is not given, build_py attempts to run egg_info
+    build_py.existing_egg_info_dir = None
+    build_py.run()
+    egg_info_run.assert_called()
+
+    # == Remove caches ==
+    egg_info_run.reset_mock()
+    build_py.__dict__.pop('data_files', None)
+    dist.reinitialize_command(egg_info)
+
+    # == Actual test ==
+    # Ensure that if existing_egg_info_dir is given, egg_info doesn't run
+    build_py.existing_egg_info_dir = egg_info_dir
+    build_py.run()
+    egg_info_run.assert_not_called()
+    assert build_py.data_files
+
+    # Make sure the list of outputs is actually OK
+    outputs = map(lambda x: x.replace(os.sep, "/"), build_py.get_outputs())
+    assert outputs
+    example = str(Path(build_py.build_lib, "mypkg/__init__.py")).replace(os.sep, "/")
+    assert example in outputs
+
+
+EXAMPLE_ARBITRARY_MAPPING = {
+    "pyproject.toml": DALS(
+        """
+        [project]
+        name = "mypkg"
+        version = "42"
+
+        [tool.setuptools]
+        packages = ["mypkg", "mypkg.sub1", "mypkg.sub2", "mypkg.sub2.nested"]
+
+        [tool.setuptools.package-dir]
+        "" = "src"
+        "mypkg.sub2" = "src/mypkg/_sub2"
+        "mypkg.sub2.nested" = "other"
+        """
+    ),
+    "src": {
+        "mypkg": {
+            "__init__.py": "",
+            "resource_file.txt": "",
+            "sub1": {
+                "__init__.py": "",
+                "mod1.py": "",
+            },
+            "_sub2": {
+                "mod2.py": "",
+            },
+        },
+    },
+    "other": {
+        "__init__.py": "",
+        "mod3.py": "",
+    },
+    "MANIFEST.in": DALS(
+        """
+        global-include *.py *.txt
+        global-exclude *.py[cod]
+        """
+    ),
+}
+
+
+def test_get_outputs(tmpdir_cwd):
+    jaraco.path.build(EXAMPLE_ARBITRARY_MAPPING)
+    dist = Distribution({"script_name": "%test%"})
+    dist.parse_config_files()
+
+    build_py = dist.get_command_obj("build_py")
+    build_py.editable_mode = True
+    build_py.ensure_finalized()
+    build_lib = build_py.build_lib.replace(os.sep, "/")
+    outputs = {x.replace(os.sep, "/") for x in build_py.get_outputs()}
+    assert outputs == {
+        f"{build_lib}/mypkg/__init__.py",
+        f"{build_lib}/mypkg/resource_file.txt",
+        f"{build_lib}/mypkg/sub1/__init__.py",
+        f"{build_lib}/mypkg/sub1/mod1.py",
+        f"{build_lib}/mypkg/sub2/mod2.py",
+        f"{build_lib}/mypkg/sub2/nested/__init__.py",
+        f"{build_lib}/mypkg/sub2/nested/mod3.py",
+    }
+    mapping = {
+        k.replace(os.sep, "/"): v.replace(os.sep, "/")
+        for k, v in build_py.get_output_mapping().items()
+    }
+    assert mapping == {
+        f"{build_lib}/mypkg/__init__.py": "src/mypkg/__init__.py",
+        f"{build_lib}/mypkg/resource_file.txt": "src/mypkg/resource_file.txt",
+        f"{build_lib}/mypkg/sub1/__init__.py": "src/mypkg/sub1/__init__.py",
+        f"{build_lib}/mypkg/sub1/mod1.py": "src/mypkg/sub1/mod1.py",
+        f"{build_lib}/mypkg/sub2/mod2.py": "src/mypkg/_sub2/mod2.py",
+        f"{build_lib}/mypkg/sub2/nested/__init__.py": "other/__init__.py",
+        f"{build_lib}/mypkg/sub2/nested/mod3.py": "other/mod3.py",
+    }
+
+
+class TestTypeInfoFiles:
+    PYPROJECTS = {
+        "default_pyproject": DALS(
+            """
+            [project]
+            name = "foo"
+            version = "1"
+            """
+        ),
+        "dont_include_package_data": DALS(
+            """
+            [project]
+            name = "foo"
+            version = "1"
+
+            [tool.setuptools]
+            include-package-data = false
+            """
+        ),
+        "exclude_type_info": DALS(
+            """
+            [project]
+            name = "foo"
+            version = "1"
+
+            [tool.setuptools]
+            include-package-data = false
+
+            [tool.setuptools.exclude-package-data]
+            "*" = ["py.typed", "*.pyi"]
+            """
+        ),
+    }
+
+    EXAMPLES = {
+        "simple_namespace": {
+            "directory_structure": {
+                "foo": {
+                    "bar.pyi": "",
+                    "py.typed": "",
+                    "__init__.py": "",
+                }
+            },
+            "expected_type_files": {"foo/bar.pyi", "foo/py.typed"},
+        },
+        "nested_inside_namespace": {
+            "directory_structure": {
+                "foo": {
+                    "bar": {
+                        "py.typed": "",
+                        "mod.pyi": "",
+                    }
+                }
+            },
+            "expected_type_files": {"foo/bar/mod.pyi", "foo/bar/py.typed"},
+        },
+        "namespace_nested_inside_regular": {
+            "directory_structure": {
+                "foo": {
+                    "namespace": {
+                        "foo.pyi": "",
+                    },
+                    "__init__.pyi": "",
+                    "py.typed": "",
+                }
+            },
+            "expected_type_files": {
+                "foo/namespace/foo.pyi",
+                "foo/__init__.pyi",
+                "foo/py.typed",
+            },
+        },
+    }
+
+    @pytest.mark.parametrize(
+        "pyproject",
+        [
+            "default_pyproject",
+            pytest.param(
+                "dont_include_package_data",
+                marks=pytest.mark.xfail(reason="pypa/setuptools#4350"),
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("example", EXAMPLES.keys())
+    def test_type_files_included_by_default(self, tmpdir_cwd, pyproject, example):
+        structure = {
+            **self.EXAMPLES[example]["directory_structure"],
+            "pyproject.toml": self.PYPROJECTS[pyproject],
+        }
+        expected_type_files = self.EXAMPLES[example]["expected_type_files"]
+        jaraco.path.build(structure)
+
+        build_py = get_finalized_build_py()
+        outputs = get_outputs(build_py)
+        assert expected_type_files <= outputs
+
+    @pytest.mark.parametrize("pyproject", ["exclude_type_info"])
+    @pytest.mark.parametrize("example", EXAMPLES.keys())
+    def test_type_files_can_be_excluded(self, tmpdir_cwd, pyproject, example):
+        structure = {
+            **self.EXAMPLES[example]["directory_structure"],
+            "pyproject.toml": self.PYPROJECTS[pyproject],
+        }
+        expected_type_files = self.EXAMPLES[example]["expected_type_files"]
+        jaraco.path.build(structure)
+
+        build_py = get_finalized_build_py()
+        outputs = get_outputs(build_py)
+        assert expected_type_files.isdisjoint(outputs)
+
+    def test_stub_only_package(self, tmpdir_cwd):
+        structure = {
+            "pyproject.toml": DALS(
+                """
+                [project]
+                name = "foo-stubs"
+                version = "1"
+                """
+            ),
+            "foo-stubs": {"__init__.pyi": "", "bar.pyi": ""},
+        }
+        expected_type_files = {"foo-stubs/__init__.pyi", "foo-stubs/bar.pyi"}
+        jaraco.path.build(structure)
+
+        build_py = get_finalized_build_py()
+        outputs = get_outputs(build_py)
+        assert expected_type_files <= outputs
+
+
+def get_finalized_build_py(script_name="%build_py-test%"):
+    dist = Distribution({"script_name": script_name})
+    dist.parse_config_files()
+    build_py = dist.get_command_obj("build_py")
+    build_py.finalize_options()
+    return build_py
+
+
+def get_outputs(build_py):
+    build_dir = Path(build_py.build_lib)
+    return {
+        os.path.relpath(x, build_dir).replace(os.sep, "/")
+        for x in build_py.get_outputs()
+    }
diff --git a/.venv/lib/python3.12/site-packages/setuptools/tests/test_config_discovery.py b/.venv/lib/python3.12/site-packages/setuptools/tests/test_config_discovery.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5df8203cdb6f9129a65d0c503c4f51e21315b1f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/setuptools/tests/test_config_discovery.py
@@ -0,0 +1,647 @@
+import os
+import sys
+from configparser import ConfigParser
+from itertools import product
+from typing import cast
+
+import jaraco.path
+import pytest
+from path import Path
+
+import setuptools  # noqa: F401 # force distutils.core to be patched
+from setuptools.command.sdist import sdist
+from setuptools.discovery import find_package_path, find_parent_package
+from setuptools.dist import Distribution
+from setuptools.errors import PackageDiscoveryError
+
+from .contexts import quiet
+from .integration.helpers import get_sdist_members, get_wheel_members, run
+from .textwrap import DALS
+
+import distutils.core
+
+
+class TestFindParentPackage:
+    def test_single_package(self, tmp_path):
+        # find_parent_package should find a non-namespace parent package
+        (tmp_path / "src/namespace/pkg/nested").mkdir(exist_ok=True, parents=True)
+        (tmp_path / "src/namespace/pkg/nested/__init__.py").touch()
+        (tmp_path / "src/namespace/pkg/__init__.py").touch()
+        packages = ["namespace", "namespace.pkg", "namespace.pkg.nested"]
+        assert find_parent_package(packages, {"": "src"}, tmp_path) == "namespace.pkg"
+
+    def test_multiple_toplevel(self, tmp_path):
+        # find_parent_package should return null if the given list of packages does not
+        # have a single parent package
+        multiple = ["pkg", "pkg1", "pkg2"]
+        for name in multiple:
+            (tmp_path / f"src/{name}").mkdir(exist_ok=True, parents=True)
+            (tmp_path / f"src/{name}/__init__.py").touch()
+        assert find_parent_package(multiple, {"": "src"}, tmp_path) is None
+
+
+class TestDiscoverPackagesAndPyModules:
+    """Make sure discovered values for ``packages`` and ``py_modules`` work
+    similarly to explicit configuration for the simple scenarios.
+    """
+
+    OPTIONS = {
+        # Different options according to the circumstance being tested
+        "explicit-src": {"package_dir": {"": "src"}, "packages": ["pkg"]},
+        "variation-lib": {
+            "package_dir": {"": "lib"},  # variation of the source-layout
+        },
+        "explicit-flat": {"packages": ["pkg"]},
+        "explicit-single_module": {"py_modules": ["pkg"]},
+        "explicit-namespace": {"packages": ["ns", "ns.pkg"]},
+        "automatic-src": {},
+        "automatic-flat": {},
+        "automatic-single_module": {},
+        "automatic-namespace": {},
+    }
+    FILES = {
+        "src": ["src/pkg/__init__.py", "src/pkg/main.py"],
+        "lib": ["lib/pkg/__init__.py", "lib/pkg/main.py"],
+        "flat": ["pkg/__init__.py", "pkg/main.py"],
+        "single_module": ["pkg.py"],
+        "namespace": ["ns/pkg/__init__.py"],
+    }
+
+    def _get_info(self, circumstance):
+        _, _, layout = circumstance.partition("-")
+        files = self.FILES[layout]
+        options = self.OPTIONS[circumstance]
+        return files, options
+
+    @pytest.mark.parametrize("circumstance", OPTIONS.keys())
+    def test_sdist_filelist(self, tmp_path, circumstance):
+        files, options = self._get_info(circumstance)
+        _populate_project_dir(tmp_path, files, options)
+
+        _, cmd = _run_sdist_programatically(tmp_path, options)
+
+        manifest = [f.replace(os.sep, "/") for f in cmd.filelist.files]
+        for file in files:
+            assert any(f.endswith(file) for f in manifest)
+
+    @pytest.mark.parametrize("circumstance", OPTIONS.keys())
+    def test_project(self, tmp_path, circumstance):
+        files, options = self._get_info(circumstance)
+        _populate_project_dir(tmp_path, files, options)
+
+        # Simulate a pre-existing `build` directory
+        (tmp_path / "build").mkdir()
+        (tmp_path / "build/lib").mkdir()
+        (tmp_path / "build/bdist.linux-x86_64").mkdir()
+        (tmp_path / "build/bdist.linux-x86_64/file.py").touch()
+        (tmp_path / "build/lib/__init__.py").touch()
+        (tmp_path / "build/lib/file.py").touch()
+        (tmp_path / "dist").mkdir()
+        (tmp_path / "dist/file.py").touch()
+
+        _run_build(tmp_path)
+
+        sdist_files = get_sdist_members(next(tmp_path.glob("dist/*.tar.gz")))
+        print("~~~~~ sdist_members ~~~~~")
+        print('\n'.join(sdist_files))
+        assert sdist_files >= set(files)
+
+        wheel_files = get_wheel_members(next(tmp_path.glob("dist/*.whl")))
+        print("~~~~~ wheel_members ~~~~~")
+        print('\n'.join(wheel_files))
+        orig_files = {f.replace("src/", "").replace("lib/", "") for f in files}
+        assert wheel_files >= orig_files
+
+        # Make sure build files are not included by mistake
+        for file in wheel_files:
+            assert "build" not in files
+            assert "dist" not in files
+
+    PURPOSEFULLY_EMPY = {
+        "setup.cfg": DALS(
+            """
+            [metadata]
+            name = myproj
+            version = 0.0.0
+
+            [options]
+            {param} =
+            """
+        ),
+        "setup.py": DALS(
+            """
+            __import__('setuptools').setup(
+                name="myproj",
+                version="0.0.0",
+                {param}=[]
+            )
+            """
+        ),
+        "pyproject.toml": DALS(
+            """
+            [build-system]
+            requires = []
+            build-backend = 'setuptools.build_meta'
+
+            [project]
+            name = "myproj"
+            version = "0.0.0"
+
+            [tool.setuptools]
+            {param} = []
+            """
+        ),
+        "template-pyproject.toml": DALS(
+            """
+            [build-system]
+            requires = []
+            build-backend = 'setuptools.build_meta'
+            """
+        ),
+    }
+
+    @pytest.mark.parametrize(
+        ("config_file", "param", "circumstance"),
+        product(
+            ["setup.cfg", "setup.py", "pyproject.toml"],
+            ["packages", "py_modules"],
+            FILES.keys(),
+        ),
+    )
+    def test_purposefully_empty(self, tmp_path, config_file, param, circumstance):
+        files = self.FILES[circumstance] + ["mod.py", "other.py", "src/pkg/__init__.py"]
+        _populate_project_dir(tmp_path, files, {})
+
+        if config_file == "pyproject.toml":
+            template_param = param.replace("_", "-")
+        else:
+            # Make sure build works with or without setup.cfg
+            pyproject = self.PURPOSEFULLY_EMPY["template-pyproject.toml"]
+            (tmp_path / "pyproject.toml").write_text(pyproject, encoding="utf-8")
+            template_param = param
+
+        config = self.PURPOSEFULLY_EMPY[config_file].format(param=template_param)
+        (tmp_path / config_file).write_text(config, encoding="utf-8")
+
+        dist = _get_dist(tmp_path, {})
+        # When either parameter package or py_modules is an empty list,
+        # then there should be no discovery
+        assert getattr(dist, param) == []
+        other = {"py_modules": "packages", "packages": "py_modules"}[param]
+        assert getattr(dist, other) is None
+
+    @pytest.mark.parametrize(
+        ("extra_files", "pkgs"),
+        [
+            (["venv/bin/simulate_venv"], {"pkg"}),
+            (["pkg-stubs/__init__.pyi"], {"pkg", "pkg-stubs"}),
+            (["other-stubs/__init__.pyi"], {"pkg", "other-stubs"}),
+            (
+                # Type stubs can also be namespaced
+                ["namespace-stubs/pkg/__init__.pyi"],
+                {"pkg", "namespace-stubs", "namespace-stubs.pkg"},
+            ),
+            (
+                # Just the top-level package can have `-stubs`, ignore nested ones
+                ["namespace-stubs/pkg-stubs/__init__.pyi"],
+                {"pkg", "namespace-stubs"},
+            ),
+            (["_hidden/file.py"], {"pkg"}),
+            (["news/finalize.py"], {"pkg"}),
+        ],
+    )
+    def test_flat_layout_with_extra_files(self, tmp_path, extra_files, pkgs):
+        files = self.FILES["flat"] + extra_files
+        _populate_project_dir(tmp_path, files, {})
+        dist = _get_dist(tmp_path, {})
+        assert set(dist.packages) == pkgs
+
+    @pytest.mark.parametrize(
+        "extra_files",
+        [
+            ["other/__init__.py"],
+            ["other/finalize.py"],
+        ],
+    )
+    def test_flat_layout_with_dangerous_extra_files(self, tmp_path, extra_files):
+        files = self.FILES["flat"] + extra_files
+        _populate_project_dir(tmp_path, files, {})
+        with pytest.raises(PackageDiscoveryError, match="multiple (packages|modules)"):
+            _get_dist(tmp_path, {})
+
+    def test_flat_layout_with_single_module(self, tmp_path):
+        files = self.FILES["single_module"] + ["invalid-module-name.py"]
+        _populate_project_dir(tmp_path, files, {})
+        dist = _get_dist(tmp_path, {})
+        assert set(dist.py_modules) == {"pkg"}
+
+    def test_flat_layout_with_multiple_modules(self, tmp_path):
+        files = self.FILES["single_module"] + ["valid_module_name.py"]
+        _populate_project_dir(tmp_path, files, {})
+        with pytest.raises(PackageDiscoveryError, match="multiple (packages|modules)"):
+            _get_dist(tmp_path, {})
+
+    def test_py_modules_when_wheel_dir_is_cwd(self, tmp_path):
+        """Regression for issue 3692"""
+        from setuptools import build_meta
+
+        pyproject = '[project]\nname = "test"\nversion = "1"'
+        (tmp_path / "pyproject.toml").write_text(DALS(pyproject), encoding="utf-8")
+        (tmp_path / "foo.py").touch()
+        with jaraco.path.DirectoryStack().context(tmp_path):
+            build_meta.build_wheel(".")
+        # Ensure py_modules are found
+        wheel_files = get_wheel_members(next(tmp_path.glob("*.whl")))
+        assert "foo.py" in wheel_files
+
+
+class TestNoConfig:
+    DEFAULT_VERSION = "0.0.0"  # Default version given by setuptools
+
+    EXAMPLES = {
+        "pkg1": ["src/pkg1.py"],
+        "pkg2": ["src/pkg2/__init__.py"],
+        "pkg3": ["src/pkg3/__init__.py", "src/pkg3-stubs/__init__.py"],
+        "pkg4": ["pkg4/__init__.py", "pkg4-stubs/__init__.py"],
+        "ns.nested.pkg1": ["src/ns/nested/pkg1/__init__.py"],
+        "ns.nested.pkg2": ["ns/nested/pkg2/__init__.py"],
+    }
+
+    @pytest.mark.parametrize("example", EXAMPLES.keys())
+    def test_discover_name(self, tmp_path, example):
+        _populate_project_dir(tmp_path, self.EXAMPLES[example], {})
+        dist = _get_dist(tmp_path, {})
+        assert dist.get_name() == example
+
+    def test_build_with_discovered_name(self, tmp_path):
+        files = ["src/ns/nested/pkg/__init__.py"]
+        _populate_project_dir(tmp_path, files, {})
+        _run_build(tmp_path, "--sdist")
+        # Expected distribution file
+        dist_file = tmp_path / f"dist/ns_nested_pkg-{self.DEFAULT_VERSION}.tar.gz"
+        assert dist_file.is_file()
+
+
+class TestWithAttrDirective:
+    @pytest.mark.parametrize(
+        ("folder", "opts"),
+        [
+            ("src", {}),
+            ("lib", {"packages": "find:", "packages.find": {"where": "lib"}}),
+        ],
+    )
+    def test_setupcfg_metadata(self, tmp_path, folder, opts):
+        files = [f"{folder}/pkg/__init__.py", "setup.cfg"]
+        _populate_project_dir(tmp_path, files, opts)
+
+        config = (tmp_path / "setup.cfg").read_text(encoding="utf-8")
+        overwrite = {
+            folder: {"pkg": {"__init__.py": "version = 42"}},
+            "setup.cfg": "[metadata]\nversion = attr: pkg.version\n" + config,
+        }
+        jaraco.path.build(overwrite, prefix=tmp_path)
+
+        dist = _get_dist(tmp_path, {})
+        assert dist.get_name() == "pkg"
+        assert dist.get_version() == "42"
+        assert dist.package_dir
+        package_path = find_package_path("pkg", dist.package_dir, tmp_path)
+        assert os.path.exists(package_path)
+        assert folder in Path(package_path).parts()
+
+        _run_build(tmp_path, "--sdist")
+        dist_file = tmp_path / "dist/pkg-42.tar.gz"
+        assert dist_file.is_file()
+
+    def test_pyproject_metadata(self, tmp_path):
+        _populate_project_dir(tmp_path, ["src/pkg/__init__.py"], {})
+
+        overwrite = {
+            "src": {"pkg": {"__init__.py": "version = 42"}},
+            "pyproject.toml": (
+                "[project]\nname = 'pkg'\ndynamic = ['version']\n"
+                "[tool.setuptools.dynamic]\nversion = {attr = 'pkg.version'}\n"
+            ),
+        }
+        jaraco.path.build(overwrite, prefix=tmp_path)
+
+        dist = _get_dist(tmp_path, {})
+        assert dist.get_version() == "42"
+        assert dist.package_dir == {"": "src"}
+
+
+class TestWithCExtension:
+    def _simulate_package_with_extension(self, tmp_path):
+        # This example is based on: https://github.com/nucleic/kiwi/tree/1.4.0
+        files = [
+            "benchmarks/file.py",
+            "docs/Makefile",
+            "docs/requirements.txt",
+            "docs/source/conf.py",
+            "proj/header.h",
+            "proj/file.py",
+            "py/proj.cpp",
+            "py/other.cpp",
+            "py/file.py",
+            "py/py.typed",
+            "py/tests/test_proj.py",
+            "README.rst",
+        ]
+        _populate_project_dir(tmp_path, files, {})
+
+        setup_script = """
+            from setuptools import Extension, setup
+
+            ext_modules = [
+                Extension(
+                    "proj",
+                    ["py/proj.cpp", "py/other.cpp"],
+                    include_dirs=["."],
+                    language="c++",
+                ),
+            ]
+            setup(ext_modules=ext_modules)
+        """
+        (tmp_path / "setup.py").write_text(DALS(setup_script), encoding="utf-8")
+
+    def test_skip_discovery_with_setupcfg_metadata(self, tmp_path):
+        """Ensure that auto-discovery is not triggered when the project is based on
+        C-extensions only, for backward compatibility.
+        """
+        self._simulate_package_with_extension(tmp_path)
+
+        pyproject = """
+            [build-system]
+            requires = []
+            build-backend = 'setuptools.build_meta'
+        """
+        (tmp_path / "pyproject.toml").write_text(DALS(pyproject), encoding="utf-8")
+
+        setupcfg = """
+            [metadata]
+            name = proj
+            version = 42
+        """
+        (tmp_path / "setup.cfg").write_text(DALS(setupcfg), encoding="utf-8")
+
+        dist = _get_dist(tmp_path, {})
+        assert dist.get_name() == "proj"
+        assert dist.get_version() == "42"
+        assert dist.py_modules is None
+        assert dist.packages is None
+        assert len(dist.ext_modules) == 1
+        assert dist.ext_modules[0].name == "proj"
+
+    def test_dont_skip_discovery_with_pyproject_metadata(self, tmp_path):
+        """When opting-in to pyproject.toml metadata, auto-discovery will be active if
+        the package lists C-extensions, but does not configure py-modules or packages.
+
+        This way we ensure users with complex package layouts that would lead to the
+        discovery of multiple top-level modules/packages see errors and are forced to
+        explicitly set ``packages`` or ``py-modules``.
+        """
+        self._simulate_package_with_extension(tmp_path)
+
+        pyproject = """
+            [project]
+            name = 'proj'
+            version = '42'
+        """
+        (tmp_path / "pyproject.toml").write_text(DALS(pyproject), encoding="utf-8")
+        with pytest.raises(PackageDiscoveryError, match="multiple (packages|modules)"):
+            _get_dist(tmp_path, {})
+
+
+class TestWithPackageData:
+    def _simulate_package_with_data_files(self, tmp_path, src_root):
+        files = [
+            f"{src_root}/proj/__init__.py",
+            f"{src_root}/proj/file1.txt",
+            f"{src_root}/proj/nested/file2.txt",
+        ]
+        _populate_project_dir(tmp_path, files, {})
+
+        manifest = """
+            global-include *.py *.txt
+        """
+        (tmp_path / "MANIFEST.in").write_text(DALS(manifest), encoding="utf-8")
+
+    EXAMPLE_SETUPCFG = """
+    [metadata]
+    name = proj
+    version = 42
+
+    [options]
+    include_package_data = True
+    """
+    EXAMPLE_PYPROJECT = """
+    [project]
+    name = "proj"
+    version = "42"
+    """
+
+    PYPROJECT_PACKAGE_DIR = """
+    [tool.setuptools]
+    package-dir = {"" = "src"}
+    """
+
+    @pytest.mark.parametrize(
+        ("src_root", "files"),
+        [
+            (".", {"setup.cfg": DALS(EXAMPLE_SETUPCFG)}),
+            (".", {"pyproject.toml": DALS(EXAMPLE_PYPROJECT)}),
+            ("src", {"setup.cfg": DALS(EXAMPLE_SETUPCFG)}),
+            ("src", {"pyproject.toml": DALS(EXAMPLE_PYPROJECT)}),
+            (
+                "src",
+                {
+                    "setup.cfg": DALS(EXAMPLE_SETUPCFG)
+                    + DALS(
+                        """
+                        packages = find:
+                        package_dir =
+                            =src
+
+                        [options.packages.find]
+                        where = src
+                        """
+                    )
+                },
+            ),
+            (
+                "src",
+                {
+                    "pyproject.toml": DALS(EXAMPLE_PYPROJECT)
+                    + DALS(
+                        """
+                        [tool.setuptools]
+                        package-dir = {"" = "src"}
+                        """
+                    )
+                },
+            ),
+        ],
+    )
+    def test_include_package_data(self, tmp_path, src_root, files):
+        """
+        Make sure auto-discovery does not affect package include_package_data.
+        See issue #3196.
+        """
+        jaraco.path.build(files, prefix=str(tmp_path))
+        self._simulate_package_with_data_files(tmp_path, src_root)
+
+        expected = {
+            os.path.normpath(f"{src_root}/proj/file1.txt").replace(os.sep, "/"),
+            os.path.normpath(f"{src_root}/proj/nested/file2.txt").replace(os.sep, "/"),
+        }
+
+        _run_build(tmp_path)
+
+        sdist_files = get_sdist_members(next(tmp_path.glob("dist/*.tar.gz")))
+        print("~~~~~ sdist_members ~~~~~")
+        print('\n'.join(sdist_files))
+        assert sdist_files >= expected
+
+        wheel_files = get_wheel_members(next(tmp_path.glob("dist/*.whl")))
+        print("~~~~~ wheel_members ~~~~~")
+        print('\n'.join(wheel_files))
+        orig_files = {f.replace("src/", "").replace("lib/", "") for f in expected}
+        assert wheel_files >= orig_files
+
+
+def test_compatible_with_numpy_configuration(tmp_path):
+    files = [
+        "dir1/__init__.py",
+        "dir2/__init__.py",
+        "file.py",
+    ]
+    _populate_project_dir(tmp_path, files, {})
+    dist = Distribution({})
+    dist.configuration = object()
+    dist.set_defaults()
+    assert dist.py_modules is None
+    assert dist.packages is None
+
+
+def test_name_discovery_doesnt_break_cli(tmpdir_cwd):
+    jaraco.path.build({"pkg.py": ""})
+    dist = Distribution({})
+    dist.script_args = ["--name"]
+    dist.set_defaults()
+    dist.parse_command_line()  # <-- no exception should be raised here.
+    assert dist.get_name() == "pkg"
+
+
+def test_preserve_explicit_name_with_dynamic_version(tmpdir_cwd, monkeypatch):
+    """According to #3545 it seems that ``name`` discovery is running,
+    even when the project already explicitly sets it.
+    This seems to be related to parsing of dynamic versions (via ``attr`` directive),
+    which requires the auto-discovery of ``package_dir``.
+    """
+    files = {
+        "src": {
+            "pkg": {"__init__.py": "__version__ = 42\n"},
+        },
+        "pyproject.toml": DALS(
+            """
+            [project]
+            name = "myproj"  # purposefully different from package name
+            dynamic = ["version"]
+            [tool.setuptools.dynamic]
+            version = {"attr" = "pkg.__version__"}
+            """
+        ),
+    }
+    jaraco.path.build(files)
+    dist = Distribution({})
+    orig_analyse_name = dist.set_defaults.analyse_name
+
+    def spy_analyse_name():
+        # We can check if name discovery was triggered by ensuring the original
+        # name remains instead of the package name.
+        orig_analyse_name()
+        assert dist.get_name() == "myproj"
+
+    monkeypatch.setattr(dist.set_defaults, "analyse_name", spy_analyse_name)
+    dist.parse_config_files()
+    assert dist.get_version() == "42"
+    assert set(dist.packages) == {"pkg"}
+
+
+def _populate_project_dir(root, files, options):
+    # NOTE: Currently pypa/build will refuse to build the project if no
+    # `pyproject.toml` or `setup.py` is found. So it is impossible to do
+    # completely "config-less" projects.
+    basic = {
+        "setup.py": "import setuptools\nsetuptools.setup()",
+        "README.md": "# Example Package",
+        "LICENSE": "Copyright (c) 2018",
+    }
+    jaraco.path.build(basic, prefix=root)
+    _write_setupcfg(root, options)
+    paths = (root / f for f in files)
+    for path in paths:
+        path.parent.mkdir(exist_ok=True, parents=True)
+        path.touch()
+
+
+def _write_setupcfg(root, options):
+    if not options:
+        print("~~~~~ **NO** setup.cfg ~~~~~")
+        return
+    setupcfg = ConfigParser()
+    setupcfg.add_section("options")
+    for key, value in options.items():
+        if key == "packages.find":
+            setupcfg.add_section(f"options.{key}")
+            setupcfg[f"options.{key}"].update(value)
+        elif isinstance(value, list):
+            setupcfg["options"][key] = ", ".join(value)
+        elif isinstance(value, dict):
+            str_value = "\n".join(f"\t{k} = {v}" for k, v in value.items())
+            setupcfg["options"][key] = "\n" + str_value
+        else:
+            setupcfg["options"][key] = str(value)
+    with open(root / "setup.cfg", "w", encoding="utf-8") as f:
+        setupcfg.write(f)
+    print("~~~~~ setup.cfg ~~~~~")
+    print((root / "setup.cfg").read_text(encoding="utf-8"))
+
+
+def _run_build(path, *flags):
+    cmd = [sys.executable, "-m", "build", "--no-isolation", *flags, str(path)]
+    return run(cmd, env={'DISTUTILS_DEBUG': ''})
+
+
+def _get_dist(dist_path, attrs):
+    root = "/".join(os.path.split(dist_path))  # POSIX-style
+
+    script = dist_path / 'setup.py'
+    if script.exists():
+        with Path(dist_path):
+            dist = cast(
+                Distribution,
+                distutils.core.run_setup("setup.py", {}, stop_after="init"),
+            )
+    else:
+        dist = Distribution(attrs)
+
+    dist.src_root = root
+    dist.script_name = "setup.py"
+    with Path(dist_path):
+        dist.parse_config_files()
+
+    dist.set_defaults()
+    return dist
+
+
+def _run_sdist_programatically(dist_path, attrs):
+    dist = _get_dist(dist_path, attrs)
+    cmd = sdist(dist)
+    cmd.ensure_finalized()
+    assert cmd.distribution.packages or cmd.distribution.py_modules
+
+    with quiet(), Path(dist_path):
+        cmd.run()
+
+    return dist, cmd